From befc69e05a733c490ba3f193be99c9dbc97f637e Mon Sep 17 00:00:00 2001
From: Naren Dasan <naren@narendasan.com>
Date: Mon, 12 Sep 2022 17:41:32 -0700
Subject: [PATCH] chore: Documentation and release version

Signed-off-by: Naren Dasan <naren@narendasan.com>
Signed-off-by: Naren Dasan <narens@nvidia.com>
---
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diff --git a/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html b/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html
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 <span id="exhale-define-macros-8h-1a18d295a837ac71add5578860b55e5502"></span><h1>Define STR<a class="headerlink" href="#define-str" title="Permalink to this headline">¶</a></h1>
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 <span id="exhale-define-macros-8h-1a31398a6d4d27e28817afb0f0139e909e"></span><h1>Define TORCH_TENSORRT_MAJOR_VERSION<a class="headerlink" href="#define-torch-tensorrt-major-version" title="Permalink to this headline">¶</a></h1>
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 <span id="exhale-define-macros-8h-1abd1465eb38256d3f22cc1426b23d516b"></span><h1>Define TORCHTRT_API<a class="headerlink" href="#define-torchtrt-api" title="Permalink to this headline">¶</a></h1>
 <ul class="simple">
@@ -366,25 +366,25 @@ <h2>Define Documentation<a class="headerlink" href="#define-documentation" title
 
 
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@@ -392,11 +392,11 @@ <h2>Define Documentation<a class="headerlink" href="#define-documentation" title
 
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-    
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       <div>
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+
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
          <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
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@@ -433,9 +433,9 @@ <h2>Define Documentation<a class="headerlink" href="#define-documentation" title
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-     
 
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+
 
   <script type="text/javascript" src="../_static/js/vendor/popper.min.js"></script>
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@@ -446,7 +446,7 @@ <h2>Define Documentation<a class="headerlink" href="#define-documentation" title
       jQuery(function () {
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 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
@@ -277,8 +277,8 @@
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   <section id="define-xstr">
 <span id="exhale-define-macros-8h-1abe87b341f562fd1cf40b7672e4d759da"></span><h1>Define XSTR<a class="headerlink" href="#define-xstr" title="Permalink to this headline">¶</a></h1>
 <ul class="simple">
@@ -366,25 +366,25 @@ <h2>Define Documentation<a class="headerlink" href="#define-documentation" title
 
 
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+
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
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@@ -433,9 +433,9 @@ <h2>Define Documentation<a class="headerlink" href="#define-documentation" title
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+
+
 
   <script type="text/javascript" src="../_static/js/vendor/popper.min.js"></script>
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+          project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC,
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index 68d8e60492..69c328af13 100644
--- a/docs/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html
+++ b/docs/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html
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   <meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
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@@ -36,11 +36,11 @@
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 <span id="exhale-define-macros-8h-1adad592a7b1b7eed529cdf6acd584c883"></span><h1>Define TORCH_TENSORRT_VERSION<a class="headerlink" href="#define-torch-tensorrt-version" title="Permalink to this headline">¶</a></h1>
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 <span id="dir-cpp"></span><h1>Directory cpp<a class="headerlink" href="#directory-cpp" title="Permalink to this headline">¶</a></h1>
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 <span id="exhale-enum-torch-tensorrt-8h-1a3fbe5d72e4fc624dbd038853079620eb"></span><h1>Enum EngineCapability<a class="headerlink" href="#enum-enginecapability" title="Permalink to this headline">¶</a></h1>
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 <dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt16EngineCapabilityE">
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+<span class="target" id="torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">EngineCapability</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt16EngineCapabilityE" title="Permalink to this definition">¶</a><br /></dt>
 <dd><p>Emum for selecting engine capability </p>
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+<span class="target" id="dummy_1a3fbe5d72e4fc624dbd038853079620eba543001d95e8c88682d65526cf5cd3646"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kSTANDARD</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt16EngineCapability9kSTANDARDE" title="Permalink to this definition">¶</a><br /></dt>
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+<span class="target" id="dummy_1a3fbe5d72e4fc624dbd038853079620ebab6a4114091b4977e7072461ec3a956fc"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kDLA_STANDALONE</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt16EngineCapability15kDLA_STANDALONEE" title="Permalink to this definition">¶</a><br /></dt>
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 <span id="exhale-function-ptq-8h-1a6186e305f47c1d94b6130ef6c7f7e178"></span><h1>Template Function torch_tensorrt::ptq::make_int8_calibrator<a class="headerlink" href="#template-function-torch-tensorrt-ptq-make-int8-calibrator" title="Permalink to this headline">¶</a></h1>
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 <h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
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 <dt class="sig sig-object cpp" id="_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8EntropyCalibrator2</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DataLoader</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="ptq_8h_1a83ff2be7e0b80bc7434de415861dc039"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE" title="torch_tensorrt::ptq::Int8Calibrator"><span class="n"><span class="pre">Int8Calibrator</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::DataLoader"><span class="n"><span class="pre">DataLoader</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">make_int8_calibrator</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::DataLoader"><span class="n"><span class="pre">DataLoader</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dataloader</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">use_cache</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="Permalink to this definition">¶</a><br /></dt>
+<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8EntropyCalibrator2</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DataLoader</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE" title="torch_tensorrt::ptq::Int8Calibrator"><span class="n"><span class="pre">Int8Calibrator</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::DataLoader"><span class="n"><span class="pre">DataLoader</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">make_int8_calibrator</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::DataLoader"><span class="n"><span class="pre">DataLoader</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dataloader</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">use_cache</span></span><span class="sig-paren">)</span><br /></dt>
 <dd><p>A factory to build a post training quantization calibrator from a torch dataloader. </p>
 <p>Creates a calibrator to use for post training quantization. By default the returned calibrator uses TensorRT Entropy v2 algorithm to perform calibration. This is recommended for feed forward networks. You can override the algorithm selection (such as to use the MinMax Calibrator recomended for NLP tasks) by calling make_int8_calibrator with the calibrator class as a template parameter.</p>
 <p>e.g. <code class="docutils literal notranslate"><span class="pre">torch_tensorrt::ptq::make_int8_calibrator&lt;nvinfer1::IInt8MinMaxCalibrator&gt;(std::move(calibration_dataloader),</span> <span class="pre">calibration_cache_file,</span> <span class="pre">use_cache);</span></code> </p>
@@ -393,15 +387,6 @@ <h2>Function Documentation<a class="headerlink" href="#function-documentation" t
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index 44379b92e2..d83f45f049 100644
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@@ -199,7 +197,7 @@
 
 
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@@ -229,29 +227,22 @@
 
               <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
 <ul>
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-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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@@ -259,15 +250,20 @@
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 <p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
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+<ul>
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+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
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+</ul>
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@@ -317,8 +313,6 @@
       </li>
 
 
-          <li><a href="torch_tensort_cpp.html">Torch-TensorRT C++ API</a> &gt;</li>
-
       <li>Template Function torch_tensorrt::ptq::make_int8_cache_calibrator</li>
 
 
@@ -360,7 +354,7 @@
 <h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
 <dl class="cpp function">
 <dt class="sig sig-object cpp" id="_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE">
-<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8EntropyCalibrator2</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE" title="torch_tensorrt::ptq::Int8CacheCalibrator"><span class="n"><span class="pre">Int8CacheCalibrator</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE" title="torch_tensorrt::ptq::make_int8_cache_calibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">make_int8_cache_calibrator</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
+<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8EntropyCalibrator2</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="ptq_8h_1a226e3c83379d1012cde8578c1c86b16c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE" title="torch_tensorrt::ptq::Int8CacheCalibrator"><span class="n"><span class="pre">Int8CacheCalibrator</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.html#_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE" title="torch_tensorrt::ptq::make_int8_cache_calibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">make_int8_cache_calibrator</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span><span class="sig-paren">)</span><br /></dt>
 <dd><p>A factory to build a post training quantization calibrator from a torch dataloader that only uses the calibration cache. </p>
 <p>Creates a calibrator to use for post training quantization which reads from a previously created calibration cache, therefore you can have a calibration cache generating program that requires a dataloader and a dataset, then save the cache to use later in a different program that needs to calibrate from scratch and not have the dataset dependency. However, the network should also be recalibrated if its structure changes, or the input data set changes, and it is the responsibility of the application to ensure this.</p>
 <p>By default the returned calibrator uses TensorRT Entropy v2 algorithm to perform calibration. This is recommended for feed forward networks You can override the algorithm selection (such as to use the MinMax Calibrator recomended for NLP tasks) by calling make_int8_calibrator with the calibrator class as a template parameter.</p>
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 <span id="exhale-function-torch-tensorrt-8h-1a5b405fd3bf3c8fc2e2a54cbbab979797"></span><h1>Function torch_tensorrt::torchscript::check_method_operator_support<a class="headerlink" href="#function-torch-tensorrt-torchscript-check-method-operator-support" title="Permalink to this headline">¶</a></h1>
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   <section id="struct-input">
 <span id="exhale-struct-structtorch-tensorrt-1-1input"></span><h1>Struct Input<a class="headerlink" href="#struct-input" title="Permalink to this headline">¶</a></h1>
 <ul class="simple">
@@ -379,7 +379,7 @@ <h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title
 
 <dl class="cpp function">
 <dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE12TensorFormat">
-<span class="target" id="structtorch__tensorrt_1_1Input_1a0328d70f3daf18b425d459689be2591a"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<span class="target" id="structtorch__tensorrt_1_1Input_1a0328d70f3daf18b425d459689be2591a"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
 <dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from vector, optional arguments allow the user to configure expected input shape tensor format. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -393,7 +393,7 @@ <h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title
 
 <dl class="cpp function">
 <dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataType12TensorFormat">
-<span class="target" id="structtorch__tensorrt_1_1Input_1af8b7efdf0e96b4221742a658b214738b"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<span class="target" id="structtorch__tensorrt_1_1Input_1af8b7efdf0e96b4221742a658b214738b"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
 <dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from vector, optional arguments allow the user to configure expected input shape tensor format. </p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -408,7 +408,7 @@ <h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title
 
 <dl class="cpp function">
 <dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE12TensorFormat">
-<span class="target" id="structtorch__tensorrt_1_1Input_1a70d54a3caadadab68faad230ff302f23"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<span class="target" id="structtorch__tensorrt_1_1Input_1a70d54a3caadadab68faad230ff302f23"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
 <dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from c10::ArrayRef (the type produced by tensor.sizes()), vector, optional arguments allow the user to configure expected input shape tensor format dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -422,7 +422,7 @@ <h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title
 
 <dl class="cpp function">
 <dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">
-<span class="target" id="structtorch__tensorrt_1_1Input_1a7069ee5a6db2f840423a923d08d08c6f"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<span class="target" id="structtorch__tensorrt_1_1Input_1a7069ee5a6db2f840423a923d08d08c6f"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
 <dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from c10::ArrayRef (the type produced by tensor.sizes()), vector, optional arguments allow the user to configure expected input shape tensor format. </p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -437,7 +437,7 @@ <h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title
 
 <dl class="cpp function">
 <dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE12TensorFormat">
-<span class="target" id="structtorch__tensorrt_1_1Input_1a6f305b4f28ce0a7c76d39ed61dc9a964"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<span class="target" id="structtorch__tensorrt_1_1Input_1a6f305b4f28ce0a7c76d39ed61dc9a964"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
 <dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -453,7 +453,7 @@ <h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title
 
 <dl class="cpp function">
 <dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataType12TensorFormat">
-<span class="target" id="structtorch__tensorrt_1_1Input_1a93370fc5773212005972056184a527a5"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<span class="target" id="structtorch__tensorrt_1_1Input_1a93370fc5773212005972056184a527a5"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
 <dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for a dynamic input size from vectors for minimum shape, optimal shape, and max shape supported sizes optional arguments allow the user to configure expected input shape tensor format. </p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -470,7 +470,7 @@ <h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title
 
 <dl class="cpp function">
 <dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE12TensorFormat">
-<span class="target" id="structtorch__tensorrt_1_1Input_1abda696b9f2f689af6f3577e51647ad6f"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<span class="target" id="structtorch__tensorrt_1_1Input_1abda696b9f2f689af6f3577e51647ad6f"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
 <dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -486,7 +486,7 @@ <h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title
 
 <dl class="cpp function">
 <dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">
-<span class="target" id="structtorch__tensorrt_1_1Input_1a59eb3e2d3455017d19c9b498eefbb35e"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<span class="target" id="structtorch__tensorrt_1_1Input_1a59eb3e2d3455017d19c9b498eefbb35e"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
 <dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. </p>
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 <span id="exhale-struct-structtorch-tensorrt-1-1torchscript-1-1compilespec"></span><h1>Struct CompileSpec<a class="headerlink" href="#struct-compilespec" title="Permalink to this headline">¶</a></h1>
 <ul class="simple">
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-<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a83ce8e55ab1e1830a440513fbabae043"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">set</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">enabled_precisions</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="p"><span class="pre">{</span></span><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value6kFloatE" title="torch_tensorrt::DataType::kFloat"><span class="n"><span class="pre">kFloat</span></span></a><span class="p"><span class="pre">}</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec18enabled_precisionsE" title="Permalink to this definition">¶</a><br /></dt>
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a83ce8e55ab1e1830a440513fbabae043"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">set</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">enabled_precisions</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="p"><span class="pre">{</span></span><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kFloat</span></span><span class="p"><span class="pre">}</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec18enabled_precisionsE" title="Permalink to this definition">¶</a><br /></dt>
 <dd><p>The set of precisions TensorRT is allowed to use for kernels during compilation. </p>
 </dd></dl>
 
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html">Function torch_tensorrt::logging::get_is_colored_output_on</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html">Function torch_tensorrt::logging::get_logging_prefix</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html">Function torch_tensorrt::logging::get_reportable_log_level</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html">Function torch_tensorrt::logging::log</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html">Function torch_tensorrt::logging::set_is_colored_output_on</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1af8f3443813315af7901903d25dd495cc.html">Function torch_tensorrt::logging::set_logging_prefix</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1af8f3443813315af7901903d25dd495cc.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html">Function torch_tensorrt::logging::set_reportable_log_level</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.html">Template Function torch_tensorrt::ptq::make_int8_cache_calibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.html">Template Function torch_tensorrt::ptq::make_int8_calibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html">Function torch_tensorrt::set_device</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html">Function torch_tensorrt::torchscript::check_method_operator_support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html">Function torch_tensorrt::torchscript::compile</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html">Function torch_tensorrt::torchscript::convert_method_to_trt_engine</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.html">Function torch_tensorrt::torchscript::embed_engine_in_new_module</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="defines">
+<h2>Defines<a class="headerlink" href="#defines" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a18d295a837ac71add5578860b55e5502.html">Define STR</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a18d295a837ac71add5578860b55e5502.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html">Define TORCH_TENSORRT_MAJOR_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html">Define TORCH_TENSORRT_MINOR_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html">Define TORCH_TENSORRT_PATCH_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html">Define TORCH_TENSORRT_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html">Define TORCHTRT_API</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html">Define TORCHTRT_HIDDEN</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html">Define XSTR</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+</section>
+
+
+             </article>
+
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+    <p>
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+          <div class="pytorch-right-menu" id="pytorch-right-menu">
+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
-               <li class="toctree-l3">
-                <a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#base-type">
-                 Base Type
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-              </ul>
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-             <li class="toctree-l2">
-              <a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#class-documentation">
-               Class Documentation
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-           <li class="toctree-l1">
-            <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html">
-             Class TensorFormat
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-              <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#class-documentation">
-               Class Documentation
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-        </section>
-        <section id="enums">
-         <h2 id="enums">
-          Enums
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-           ¶
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-         <div class="toctree-wrapper compound">
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-            <a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html">
-             Enum EngineCapability
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#enum-documentation">
-               Enum Documentation
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-             Enum Level
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-               Enum Documentation
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-        </section>
-        <section id="functions">
-         <h2 id="functions">
-          Functions
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-           ¶
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-            <a class="reference internal" href="function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html">
-             Function torch_tensorrt::dump_build_info
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html#function-documentation">
-               Function Documentation
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-            <a class="reference internal" href="function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html">
-             Function torch_tensorrt::get_build_info
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html#function-documentation">
-               Function Documentation
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-             </li>
-            </ul>
-           </li>
-          </ul>
-         </div>
-         <div class="toctree-wrapper compound">
-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html">
-             Function torch_tensorrt::logging::get_is_colored_output_on
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html#function-documentation">
-               Function Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
-          </ul>
-         </div>
-         <div class="toctree-wrapper compound">
-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html">
-             Function torch_tensorrt::logging::get_logging_prefix
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html#function-documentation">
-               Function Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
-          </ul>
-         </div>
-         <div class="toctree-wrapper compound">
-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html">
-             Function torch_tensorrt::logging::get_reportable_log_level
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html#function-documentation">
-               Function Documentation
-              </a>
-             </li>
-            </ul>
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-          </ul>
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-         <div class="toctree-wrapper compound">
-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html">
-             Function torch_tensorrt::logging::log
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html#function-documentation">
-               Function Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
-          </ul>
-         </div>
-         <div class="toctree-wrapper compound">
-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html">
-             Function torch_tensorrt::logging::set_is_colored_output_on
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html#function-documentation">
-               Function Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
-          </ul>
-         </div>
-         <div class="toctree-wrapper compound">
-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="function_logging_8h_1af8f3443813315af7901903d25dd495cc.html">
-             Function torch_tensorrt::logging::set_logging_prefix
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_logging_8h_1af8f3443813315af7901903d25dd495cc.html#function-documentation">
-               Function Documentation
-              </a>
-             </li>
-            </ul>
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-          </ul>
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-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html">
-             Function torch_tensorrt::logging::set_reportable_log_level
-            </a>
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-             <li class="toctree-l2">
-              <a class="reference internal" href="function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html#function-documentation">
-               Function Documentation
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-            <a class="reference internal" href="function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.html">
-             Template Function torch_tensorrt::ptq::make_int8_cache_calibrator
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.html#function-documentation">
-               Function Documentation
-              </a>
-             </li>
-            </ul>
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-          </ul>
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-         <div class="toctree-wrapper compound">
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-           <li class="toctree-l1">
-            <a class="reference internal" href="function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.html">
-             Template Function torch_tensorrt::ptq::make_int8_calibrator
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.html#function-documentation">
-               Function Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
-          </ul>
-         </div>
-         <div class="toctree-wrapper compound">
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-           <li class="toctree-l1">
-            <a class="reference internal" href="function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html">
-             Function torch_tensorrt::set_device
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html#function-documentation">
-               Function Documentation
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-             </li>
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-         <div class="toctree-wrapper compound">
-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html">
-             Function torch_tensorrt::torchscript::check_method_operator_support
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html#function-documentation">
-               Function Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
-          </ul>
-         </div>
-         <div class="toctree-wrapper compound">
-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html">
-             Function torch_tensorrt::torchscript::compile
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html#function-documentation">
-               Function Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
-          </ul>
-         </div>
-         <div class="toctree-wrapper compound">
-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html">
-             Function torch_tensorrt::torchscript::convert_method_to_trt_engine
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html#function-documentation">
-               Function Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
-          </ul>
-         </div>
-         <div class="toctree-wrapper compound">
-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.html">
-             Function torch_tensorrt::torchscript::embed_engine_in_new_module
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.html#function-documentation">
-               Function Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
-          </ul>
-         </div>
-        </section>
-        <section id="defines">
-         <h2 id="defines">
-          Defines
-          <a class="headerlink" href="#defines" title="Permalink to this headline">
-           ¶
-          </a>
-         </h2>
-         <div class="toctree-wrapper compound">
-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="define_macros_8h_1a18d295a837ac71add5578860b55e5502.html">
-             Define STR
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="define_macros_8h_1a18d295a837ac71add5578860b55e5502.html#define-documentation">
-               Define Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
-          </ul>
-         </div>
-         <div class="toctree-wrapper compound">
-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html">
-             Define TORCH_TENSORRT_MAJOR_VERSION
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html#define-documentation">
-               Define Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
-          </ul>
-         </div>
-         <div class="toctree-wrapper compound">
+<li><a class="reference internal" href="#">Full API</a><ul>
+<li><a class="reference internal" href="#namespaces">Namespaces</a><ul>
+</ul>
+</li>
+<li><a class="reference internal" href="#classes-and-structs">Classes and Structs</a><ul>
+</ul>
+</li>
+<li><a class="reference internal" href="#enums">Enums</a><ul>
+</ul>
+</li>
+<li><a class="reference internal" href="#functions">Functions</a><ul>
+</ul>
+</li>
+<li><a class="reference internal" href="#defines">Defines</a><ul>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+            </div>
+          </div>
+        </div>
+      </section>
+    </div>
+
+
+
+
+
+
+
+       <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
+         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
+         <script src="../_static/collapsible-lists/js/CollapsibleLists.compressed.js"></script>
+         <script src="../_static/collapsible-lists/js/apply-collapsible-lists.js"></script>
+         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+
+
+
+
+  <script type="text/javascript" src="../_static/js/vendor/popper.min.js"></script>
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+          <h2>Docs</h2>
+          <p>Access comprehensive developer documentation for PyTorch</p>
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+          <p>Get in-depth tutorials for beginners and advanced developers</p>
+          <a class="with-right-arrow" href="https://pytorch.org/tutorials">View Tutorials</a>
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+          <h2>Resources</h2>
+          <p>Find development resources and get your questions answered</p>
+          <a class="with-right-arrow" href="https://pytorch.org/resources">View Resources</a>
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+
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+        <div class="footer-links-col">
           <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html">
-             Define TORCH_TENSORRT_MINOR_VERSION
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html#define-documentation">
-               Define Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
+            <li class="list-title"><a href="https://pytorch.org/">PyTorch</a></li>
+            <li><a href="https://pytorch.org/get-started">Get Started</a></li>
+            <li><a href="https://pytorch.org/features">Features</a></li>
+            <li><a href="https://pytorch.org/ecosystem">Ecosystem</a></li>
+            <li><a href="https://pytorch.org/blog/">Blog</a></li>
+            <li><a href="https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md">Contributing</a></li>
           </ul>
-         </div>
-         <div class="toctree-wrapper compound">
+        </div>
+
+        <div class="footer-links-col">
           <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html">
-             Define TORCH_TENSORRT_PATCH_VERSION
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html#define-documentation">
-               Define Documentation
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-             </li>
-            </ul>
-           </li>
+            <li class="list-title"><a href="https://pytorch.org/resources">Resources</a></li>
+            <li><a href="https://pytorch.org/tutorials">Tutorials</a></li>
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+            <li><a href="https://discuss.pytorch.org" target="_blank">Discuss</a></li>
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-           <li class="toctree-l1">
-            <a class="reference internal" href="define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html">
-             Define TORCH_TENSORRT_VERSION
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html#define-documentation">
-               Define Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
+            <li class="list-title">Stay up to date</li>
+            <li><a href="https://www.facebook.com/pytorch" target="_blank">Facebook</a></li>
+            <li><a href="https://twitter.com/pytorch" target="_blank">Twitter</a></li>
+            <li><a href="https://www.youtube.com/pytorch" target="_blank">YouTube</a></li>
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-           <li class="toctree-l1">
-            <a class="reference internal" href="define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html">
-             Define TORCHTRT_API
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html#define-documentation">
-               Define Documentation
-              </a>
-             </li>
-            </ul>
-           </li>
+            <li class="list-title">PyTorch Podcasts</li>
+            <li><a href="https://open.spotify.com/show/6UzHKeiy368jKfQMKKvJY5" target="_blank">Spotify</a></li>
+            <li><a href="https://podcasts.apple.com/us/podcast/pytorch-developer-podcast/id1566080008" target="_blank">Apple</a></li>
+            <li><a href="https://www.google.com/podcasts?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9PQjVGa0lsOA%3D%3D" target="_blank">Google</a></li>
+            <li><a href="https://music.amazon.com/podcasts/7a4e6f0e-26c2-49e9-a478-41bd244197d0/PyTorch-Developer-Podcast?" target="_blank">Amazon</a></li>
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-         <div class="toctree-wrapper compound">
-          <ul>
-           <li class="toctree-l1">
-            <a class="reference internal" href="define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html">
-             Define TORCHTRT_HIDDEN
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html#define-documentation">
-               Define Documentation
-              </a>
-             </li>
-            </ul>
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-          </ul>
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-         <div class="toctree-wrapper compound">
+        </div>
+
+        <div class="privacy-policy">
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-           <li class="toctree-l1">
-            <a class="reference internal" href="define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html">
-             Define XSTR
-            </a>
-            <ul>
-             <li class="toctree-l2">
-              <a class="reference internal" href="define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html#define-documentation">
-               Define Documentation
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+            <li class="privacy-policy-links"><a href="https://www.linuxfoundation.org/terms/" target="_blank">Terms</a></li>
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+            <li class="privacy-policy-links"><a href="https://www.linuxfoundation.org/privacy-policy/" target="_blank">Privacy</a></li>
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+        </div>
+        <div class="copyright">
+        <p>© Copyright The Linux Foundation. The PyTorch Foundation is a project of The Linux Foundation.
+          For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see
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+          project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC,
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+              <a href="https://pytorch.org/torchx/">TorchX</a>
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+        $(this).closest("a").addClass("has-code");
+      });
+    })
   </script>
- </body>
+</body>
 </html>
\ No newline at end of file
diff --git a/docs/_cpp_api/unabridged_orphan.html b/docs/_cpp_api/unabridged_orphan.html
index 45490933de..362eeede77 100644
--- a/docs/_cpp_api/unabridged_orphan.html
+++ b/docs/_cpp_api/unabridged_orphan.html
@@ -9,22 +9,22 @@
   <meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
-  
+
   <title>Full API &mdash; Torch-TensorRT master documentation</title>
-  
 
-  
-  
-  
-  
 
-  
 
-  
-  
-    
 
-  
+
+
+
+
+
+
+
+
+
+
 
   <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!-- <link rel="stylesheet" href="../_static/pygments.css" type="text/css" /> -->
@@ -34,11 +34,11 @@
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
   <!-- Google Analytics -->
-  
+
   <!-- End Google Analytics -->
-  
 
-  
+
+
   <script src="../_static/js/modernizr.min.js"></script>
 
   <!-- Preload the theme fonts -->
@@ -178,9 +178,9 @@
 
 <body class="pytorch-body">
 
-   
 
-    
+
+
 
     <div class="table-of-contents-link-wrapper">
       <span>Table of Contents</span>
@@ -191,21 +191,21 @@
       <div class="pytorch-side-scroll">
         <div class="pytorch-menu pytorch-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
           <div class="pytorch-left-menu-search">
-            
 
-            
-              
-              
+
+
+
+
                 <div class="version">
-                  master (1.2.0a0+450c869)
+                  master (1.2.0)
                 </div>
-              
-            
 
-            
 
 
-  
+
+
+
+
 
 
 <div role="search">
@@ -216,15 +216,15 @@
   </form>
 </div>
 
-            
+
           </div>
 
-          
-            
-            
-              
-            
-            
+
+
+
+
+
+
               <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
@@ -275,8 +275,8 @@
 <li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
 </ul>
 
-            
-          
+
+
         </div>
       </div>
     </nav>
@@ -284,7 +284,7 @@
     <div class="pytorch-container">
       <div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
         <div class="pytorch-breadcrumbs-wrapper">
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+
 
 
 
@@ -303,30 +303,30 @@
 <div role="navigation" aria-label="breadcrumbs navigation">
 
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@@ -483,28 +479,28 @@ <h1>Source code for torch_tensorrt._Device</h1><div class="highlight"><pre>
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   <h1>Source code for torch_tensorrt.fx.lower</h1><div class="highlight"><pre>
 <span></span><span class="kn">import</span> <span class="nn">dataclasses</span> <span class="k">as</span> <span class="nn">dc</span>
 <span class="kn">import</span> <span class="nn">logging</span>
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 <span></span><span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
 <span class="kn">from</span> <span class="nn">torch_tensorrt._C</span> <span class="kn">import</span> <span class="p">(</span>
@@ -560,28 +556,28 @@ <h1>Source code for torch_tensorrt.logging</h1><div class="highlight"><pre>
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 <span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span>
 <span class="kn">import</span> <span class="nn">torch</span>
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 <span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Set</span>
 <span class="kn">import</span> <span class="nn">torch</span>
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 <span class="kn">import</span> <span class="nn">torch</span>
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 <li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notebooks</span></p>
-<ul class="current">
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Torch-TensorRT Getting Started - CitriNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic-shapes.html">Torch-TensorRT - Using Dynamic Shapes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="EfficientNet-example.html">Torch-TensorRT Getting Started - EfficientNet-B0</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Hugging-Face-BERT.html">Masked Language Modeling (MLM) with Hugging Face BERT Transformer</a></li>
-<li class="toctree-l1"><a class="reference internal" href="lenet-getting-started.html">Torch-TensorRT Getting Started - LeNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Resnet50-example.html">Torch-TensorRT Getting Started - ResNet 50</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ssd-object-detection-demo.html">Object Detection with Torch-TensorRT (SSD)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="vgg-qat.html">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
 <ul>
@@ -259,6 +250,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../py_api/logging.html">torch_tensorrt.logging</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
 <ul>
@@ -268,6 +260,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
 </ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
 <p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
@@ -618,7 +614,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="2acf632451c2482b8eee3d1e461fb6c5" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_citrinet/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="198e4deb2921411abba2a37241da72ab" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png" /></p>
 <section id="Torch-TensorRT-Getting-Started---CitriNet">
 <h1>Torch-TensorRT Getting Started - CitriNet<a class="headerlink" href="#Torch-TensorRT-Getting-Started---CitriNet" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -1422,7 +1418,7 @@ <h3>FP16 (half precision)<a class="headerlink" href="#FP16-(half-precision)" tit
 </section>
 <section id="What’s-next">
 <h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to this headline">¶</a></h3>
-<p>Now it’s time to try Torch-TensorRT on your own model. Fill out issues at <a class="reference external" href="https://github.com/pytorch/TensorRT">https://github.com/pytorch/TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
+<p>Now it’s time to try Torch-TensorRT on your own model. Fill out issues at <a class="reference external" href="https://github.com/NVIDIA/Torch-TensorRT">https://github.com/NVIDIA/Torch-TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
@@ -1441,15 +1437,6 @@ <h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to
             </div>
             <footer>
 
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@@ -1586,19 +1573,43 @@ <h2>Resources</h2>
           </ul>
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+            <li><a href="https://www.google.com/podcasts?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9PQjVGa0lsOA%3D%3D" target="_blank">Google</a></li>
+            <li><a href="https://music.amazon.com/podcasts/7a4e6f0e-26c2-49e9-a478-41bd244197d0/PyTorch-Developer-Podcast?" target="_blank">Amazon</a></li>
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+          </ul>
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+        <div class="copyright">
+        <p>© Copyright The Linux Foundation. The PyTorch Foundation is a project of The Linux Foundation.
+          For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see
+          <a href="www.linuxfoundation.org/policies/">www.linuxfoundation.org/policies/</a>. The PyTorch Foundation supports the PyTorch open source
+          project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC,
+          please see <a href="www.lfprojects.org/policies/">www.lfprojects.org/policies/</a>.</p>
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diff --git a/docs/_notebooks/CitriNet-example.ipynb b/docs/_notebooks/CitriNet-example.ipynb
index b9d615d5f1..0573af0176 100644
--- a/docs/_notebooks/CitriNet-example.ipynb
+++ b/docs/_notebooks/CitriNet-example.ipynb
@@ -26,7 +26,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_citrinet/nvidia_logo.png\" width=\"90px\">\n",
+    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
     "\n",
     "# Torch-TensorRT Getting Started - CitriNet"
    ]
@@ -929,7 +929,7 @@
     "In this notebook, we have walked through the complete process of optimizing the Citrinet model with Torch-TensorRT. On an A100 GPU, with Torch-TensorRT, we observe a speedup of ~**2.4X** with FP32, and ~**2.9X** with FP16 at batchsize of 128.\n",
     "\n",
     "### What's next\n",
-    "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+    "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
    ]
   },
   {
@@ -956,7 +956,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/_notebooks/EfficientNet-example.html b/docs/_notebooks/EfficientNet-example.html
index a1e660ee42..62900356fa 100644
--- a/docs/_notebooks/EfficientNet-example.html
+++ b/docs/_notebooks/EfficientNet-example.html
@@ -33,8 +33,6 @@
   <link rel="stylesheet" href="../_static/collapsible-lists/css/tree_view.css" type="text/css" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="Masked Language Modeling (MLM) with Hugging Face BERT Transformer" href="Hugging-Face-BERT.html" />
-    <link rel="prev" title="Torch-TensorRT - Using Dynamic Shapes" href="dynamic-shapes.html" />
   <!-- Google Analytics -->
 
   <!-- End Google Analytics -->
@@ -199,7 +197,7 @@
 
 
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0)
                 </div>
 
 
@@ -229,29 +227,22 @@
 
               <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_cpp_api.html">Getting Started with C++</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/torchtrtc.html">torchtrtc</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notebooks</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="CitriNet-example.html">Torch-TensorRT Getting Started - CitriNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic-shapes.html">Torch-TensorRT - Using Dynamic Shapes</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Torch-TensorRT Getting Started - EfficientNet-B0</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Hugging-Face-BERT.html">Masked Language Modeling (MLM) with Hugging Face BERT Transformer</a></li>
-<li class="toctree-l1"><a class="reference internal" href="lenet-getting-started.html">Torch-TensorRT Getting Started - LeNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Resnet50-example.html">Torch-TensorRT Getting Started - ResNet 50</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ssd-object-detection-demo.html">Object Detection with Torch-TensorRT (SSD)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="vgg-qat.html">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
 <ul>
@@ -259,6 +250,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../py_api/logging.html">torch_tensorrt.logging</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
 <ul>
@@ -268,6 +260,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
 </ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
 <p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
@@ -618,7 +614,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="783b2cf2fc5d4e06a7736325633fdd88" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_efficientnet/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="219a0350e192474794f36336bd8c82ef" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png" /></p>
 <section id="Torch-TensorRT-Getting-Started---EfficientNet-B0">
 <h1>Torch-TensorRT Getting Started - EfficientNet-B0<a class="headerlink" href="#Torch-TensorRT-Getting-Started---EfficientNet-B0" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -1132,7 +1128,7 @@ <h3>FP16 (half precision)<a class="headerlink" href="#FP16-(half-precision)" tit
 </section>
 <section id="What’s-next">
 <h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to this headline">¶</a></h3>
-<p>Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at <a class="reference external" href="https://github.com/pytorch/TensorRT">https://github.com/pytorch/TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
+<p>Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at <a class="reference external" href="https://github.com/NVIDIA/Torch-TensorRT">https://github.com/NVIDIA/Torch-TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
@@ -1151,15 +1147,6 @@ <h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to
             </div>
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-
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-        <a href="dynamic-shapes.html" class="btn btn-neutral" title="Torch-TensorRT - Using Dynamic Shapes" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
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@@ -1296,19 +1283,43 @@ <h2>Resources</h2>
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+            <li><a href="https://www.google.com/podcasts?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9PQjVGa0lsOA%3D%3D" target="_blank">Google</a></li>
+            <li><a href="https://music.amazon.com/podcasts/7a4e6f0e-26c2-49e9-a478-41bd244197d0/PyTorch-Developer-Podcast?" target="_blank">Amazon</a></li>
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+        <div class="copyright">
+        <p>© Copyright The Linux Foundation. The PyTorch Foundation is a project of The Linux Foundation.
+          For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see
+          <a href="www.linuxfoundation.org/policies/">www.linuxfoundation.org/policies/</a>. The PyTorch Foundation supports the PyTorch open source
+          project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC,
+          please see <a href="www.lfprojects.org/policies/">www.lfprojects.org/policies/</a>.</p>
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diff --git a/docs/_notebooks/EfficientNet-example.ipynb b/docs/_notebooks/EfficientNet-example.ipynb
index 518254c3ab..31a3dad874 100644
--- a/docs/_notebooks/EfficientNet-example.ipynb
+++ b/docs/_notebooks/EfficientNet-example.ipynb
@@ -26,7 +26,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_efficientnet/nvidia_logo.png\" width=\"90px\">\n",
+    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
     "\n",
     "# Torch-TensorRT Getting Started - EfficientNet-B0"
    ]
@@ -658,7 +658,7 @@
     "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for EfficientNet-B0 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of **1.35x** with FP32, and **3.13x** with FP16 on an NVIDIA 3090 GPU. These acceleration numbers will vary from GPU to GPU(as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute cards for maximum acceleration.\n",
     "\n",
     "### What's next\n",
-    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
    ]
   },
   {
@@ -685,7 +685,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/_notebooks/Hugging-Face-BERT.html b/docs/_notebooks/Hugging-Face-BERT.html
index aa4756eb56..6a6b51f0fb 100644
--- a/docs/_notebooks/Hugging-Face-BERT.html
+++ b/docs/_notebooks/Hugging-Face-BERT.html
@@ -33,8 +33,6 @@
   <link rel="stylesheet" href="../_static/collapsible-lists/css/tree_view.css" type="text/css" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="Torch-TensorRT Getting Started - LeNet" href="lenet-getting-started.html" />
-    <link rel="prev" title="Torch-TensorRT Getting Started - EfficientNet-B0" href="EfficientNet-example.html" />
   <!-- Google Analytics -->
 
   <!-- End Google Analytics -->
@@ -199,7 +197,7 @@
 
 
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0)
                 </div>
 
 
@@ -229,29 +227,22 @@
 
               <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_cpp_api.html">Getting Started with C++</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/torchtrtc.html">torchtrtc</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notebooks</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="CitriNet-example.html">Torch-TensorRT Getting Started - CitriNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic-shapes.html">Torch-TensorRT - Using Dynamic Shapes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="EfficientNet-example.html">Torch-TensorRT Getting Started - EfficientNet-B0</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Masked Language Modeling (MLM) with Hugging Face BERT Transformer</a></li>
-<li class="toctree-l1"><a class="reference internal" href="lenet-getting-started.html">Torch-TensorRT Getting Started - LeNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Resnet50-example.html">Torch-TensorRT Getting Started - ResNet 50</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ssd-object-detection-demo.html">Object Detection with Torch-TensorRT (SSD)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="vgg-qat.html">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
 <ul>
@@ -259,6 +250,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../py_api/logging.html">torch_tensorrt.logging</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
 <ul>
@@ -268,6 +260,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
 </ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
 <p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
@@ -618,7 +614,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="2e8a7944f7a54c9a93c9656fc33a0ab3" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_hf_bert/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="05794857f0be4549aac84aed85b14705" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png" /></p>
 <section id="Masked-Language-Modeling-(MLM)-with-Hugging-Face-BERT-Transformer">
 <h1>Masked Language Modeling (MLM) with Hugging Face BERT Transformer<a class="headerlink" href="#Masked-Language-Modeling-(MLM)-with-Hugging-Face-BERT-Transformer" title="Permalink to this headline">¶</a></h1>
 <section id="Learning-objectives">
@@ -1055,7 +1051,7 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 <p>Scripted (GPU): 1.0x Traced (GPU): 1.62x Torch-TensorRT (FP32): 2.14x Torch-TensorRT (FP16): 3.15x</p>
 <section id="What’s-next">
 <h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to this headline">¶</a></h3>
-<p>Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at <a class="reference external" href="https://github.com/pytorch/TensorRT">https://github.com/pytorch/TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
+<p>Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at <a class="reference external" href="https://github.com/NVIDIA/Torch-TensorRT">https://github.com/NVIDIA/Torch-TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
@@ -1074,15 +1070,6 @@ <h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to
             </div>
             <footer>
 
-    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
-
-        <a href="lenet-getting-started.html" class="btn btn-neutral float-right" title="Torch-TensorRT Getting Started - LeNet" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
-
-
-        <a href="EfficientNet-example.html" class="btn btn-neutral" title="Torch-TensorRT Getting Started - EfficientNet-B0" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
-
-    </div>
-
 
 
 
@@ -1213,19 +1200,43 @@ <h2>Resources</h2>
           </ul>
         </div>
 
-        <div class="footer-links-col follow-us-col">
+        <div class="footer-links-col">
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-            <li class="list-title">Stay Connected</li>
+            <li class="list-title">Stay up to date</li>
+            <li><a href="https://www.facebook.com/pytorch" target="_blank">Facebook</a></li>
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+            <li><a href="https://www.youtube.com/pytorch" target="_blank">YouTube</a></li>
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-            <a href="https://www.youtube.com/pytorch" target="_blank" class="youtube"></a>
           </div>
+
+        <div class="footer-links-col">
+          <ul>
+            <li class="list-title">PyTorch Podcasts</li>
+            <li><a href="https://open.spotify.com/show/6UzHKeiy368jKfQMKKvJY5" target="_blank">Spotify</a></li>
+            <li><a href="https://podcasts.apple.com/us/podcast/pytorch-developer-podcast/id1566080008" target="_blank">Apple</a></li>
+            <li><a href="https://www.google.com/podcasts?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9PQjVGa0lsOA%3D%3D" target="_blank">Google</a></li>
+            <li><a href="https://music.amazon.com/podcasts/7a4e6f0e-26c2-49e9-a478-41bd244197d0/PyTorch-Developer-Podcast?" target="_blank">Amazon</a></li>
+          </ul>
+         </div>
         </div>
+
+        <div class="privacy-policy">
+          <ul>
+            <li class="privacy-policy-links"><a href="https://www.linuxfoundation.org/terms/" target="_blank">Terms</a></li>
+            <li class="privacy-policy-links">|</li>
+            <li class="privacy-policy-links"><a href="https://www.linuxfoundation.org/privacy-policy/" target="_blank">Privacy</a></li>
+          </ul>
+        </div>
+        <div class="copyright">
+        <p>© Copyright The Linux Foundation. The PyTorch Foundation is a project of The Linux Foundation.
+          For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see
+          <a href="www.linuxfoundation.org/policies/">www.linuxfoundation.org/policies/</a>. The PyTorch Foundation supports the PyTorch open source
+          project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC,
+          please see <a href="www.lfprojects.org/policies/">www.lfprojects.org/policies/</a>.</p>
       </div>
-    </div>
+     </div>
+
   </footer>
 
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diff --git a/docs/_notebooks/Hugging-Face-BERT.ipynb b/docs/_notebooks/Hugging-Face-BERT.ipynb
index 81034d8e38..9b027b473e 100644
--- a/docs/_notebooks/Hugging-Face-BERT.ipynb
+++ b/docs/_notebooks/Hugging-Face-BERT.ipynb
@@ -28,8 +28,7 @@
    "id": "d0a97ac5",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_hf_bert/nvidia_logo.png\" width=\"90px\">\n",
-    "\n",
+    "<img src=\"https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png\" style=\"width: 90px; float: right;\">\n",
     "\n",
     "# Masked Language Modeling (MLM) with Hugging Face BERT Transformer"
    ]
@@ -679,7 +678,7 @@
     "Torch-TensorRT (FP16): 3.15x\n",
     "\n",
     "### What's next\n",
-    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT."
+    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT."
    ]
   },
   {
@@ -707,7 +706,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/_notebooks/Resnet50-example.html b/docs/_notebooks/Resnet50-example.html
index 7436bf3a19..232a1e38d9 100644
--- a/docs/_notebooks/Resnet50-example.html
+++ b/docs/_notebooks/Resnet50-example.html
@@ -33,8 +33,6 @@
   <link rel="stylesheet" href="../_static/collapsible-lists/css/tree_view.css" type="text/css" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="Object Detection with Torch-TensorRT (SSD)" href="ssd-object-detection-demo.html" />
-    <link rel="prev" title="Torch-TensorRT Getting Started - LeNet" href="lenet-getting-started.html" />
   <!-- Google Analytics -->
 
   <!-- End Google Analytics -->
@@ -199,7 +197,7 @@
 
 
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0)
                 </div>
 
 
@@ -229,29 +227,22 @@
 
               <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_cpp_api.html">Getting Started with C++</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/torchtrtc.html">torchtrtc</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notebooks</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="CitriNet-example.html">Torch-TensorRT Getting Started - CitriNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic-shapes.html">Torch-TensorRT - Using Dynamic Shapes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="EfficientNet-example.html">Torch-TensorRT Getting Started - EfficientNet-B0</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Hugging-Face-BERT.html">Masked Language Modeling (MLM) with Hugging Face BERT Transformer</a></li>
-<li class="toctree-l1"><a class="reference internal" href="lenet-getting-started.html">Torch-TensorRT Getting Started - LeNet</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Torch-TensorRT Getting Started - ResNet 50</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ssd-object-detection-demo.html">Object Detection with Torch-TensorRT (SSD)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="vgg-qat.html">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
 <ul>
@@ -259,6 +250,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../py_api/logging.html">torch_tensorrt.logging</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
 <ul>
@@ -268,6 +260,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
 </ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
 <p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
@@ -618,7 +614,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="fdd5160d4757402ea245c7fbb8dc0f03" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png" /></p>
+<p><img alt="72eea9f56ee749e080dacfcd4ab2364a" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png" /></p>
 <section id="Torch-TensorRT-Getting-Started---ResNet-50">
 <h1>Torch-TensorRT Getting Started - ResNet 50<a class="headerlink" href="#Torch-TensorRT-Getting-Started---ResNet-50" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -1351,7 +1347,7 @@ <h3>FP16 (half precision)<a class="headerlink" href="#FP16-(half-precision)" tit
 </section>
 <section id="What’s-next">
 <h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to this headline">¶</a></h3>
-<p>Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at <a class="reference external" href="https://github.com/pytorch/TensorRT">https://github.com/pytorch/TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
+<p>Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at <a class="reference external" href="https://github.com/NVIDIA/Torch-TensorRT">https://github.com/NVIDIA/Torch-TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
 </section>
 </section>
 </section>
@@ -1362,15 +1358,6 @@ <h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to
             </div>
             <footer>
 
-    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
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-        <a href="ssd-object-detection-demo.html" class="btn btn-neutral float-right" title="Object Detection with Torch-TensorRT (SSD)" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
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-        <a href="lenet-getting-started.html" class="btn btn-neutral" title="Torch-TensorRT Getting Started - LeNet" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
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@@ -1507,19 +1494,43 @@ <h2>Resources</h2>
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+            <li><a href="https://www.facebook.com/pytorch" target="_blank">Facebook</a></li>
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+          <ul>
+            <li class="list-title">PyTorch Podcasts</li>
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+            <li><a href="https://www.google.com/podcasts?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9PQjVGa0lsOA%3D%3D" target="_blank">Google</a></li>
+            <li><a href="https://music.amazon.com/podcasts/7a4e6f0e-26c2-49e9-a478-41bd244197d0/PyTorch-Developer-Podcast?" target="_blank">Amazon</a></li>
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+          For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see
+          <a href="www.linuxfoundation.org/policies/">www.linuxfoundation.org/policies/</a>. The PyTorch Foundation supports the PyTorch open source
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+          please see <a href="www.lfprojects.org/policies/">www.lfprojects.org/policies/</a>.</p>
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diff --git a/docs/_notebooks/Resnet50-example.ipynb b/docs/_notebooks/Resnet50-example.ipynb
index f75a2b0e64..f020662c73 100644
--- a/docs/_notebooks/Resnet50-example.ipynb
+++ b/docs/_notebooks/Resnet50-example.ipynb
@@ -897,7 +897,7 @@
     "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for EfficientNet-B0 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of **1.84x** with FP32, and **5.2x** with FP16 on an NVIDIA 3090 GPU. These acceleration numbers will vary from GPU to GPU(as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute cards for maximum acceleration.\n",
     "\n",
     "### What's next\n",
-    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
    ]
   }
  ],
diff --git a/docs/_notebooks/dynamic-shapes.html b/docs/_notebooks/dynamic-shapes.html
index cd596f0bf8..6cc136aedf 100644
--- a/docs/_notebooks/dynamic-shapes.html
+++ b/docs/_notebooks/dynamic-shapes.html
@@ -33,8 +33,6 @@
   <link rel="stylesheet" href="../_static/collapsible-lists/css/tree_view.css" type="text/css" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="Torch-TensorRT Getting Started - EfficientNet-B0" href="EfficientNet-example.html" />
-    <link rel="prev" title="Torch-TensorRT Getting Started - CitriNet" href="CitriNet-example.html" />
   <!-- Google Analytics -->
 
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@@ -199,7 +197,7 @@
 
 
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0)
                 </div>
 
 
@@ -229,29 +227,22 @@
 
               <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_cpp_api.html">Getting Started with C++</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/torchtrtc.html">torchtrtc</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notebooks</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="CitriNet-example.html">Torch-TensorRT Getting Started - CitriNet</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Torch-TensorRT - Using Dynamic Shapes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="EfficientNet-example.html">Torch-TensorRT Getting Started - EfficientNet-B0</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Hugging-Face-BERT.html">Masked Language Modeling (MLM) with Hugging Face BERT Transformer</a></li>
-<li class="toctree-l1"><a class="reference internal" href="lenet-getting-started.html">Torch-TensorRT Getting Started - LeNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Resnet50-example.html">Torch-TensorRT Getting Started - ResNet 50</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ssd-object-detection-demo.html">Object Detection with Torch-TensorRT (SSD)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="vgg-qat.html">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
 <ul>
@@ -259,6 +250,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../py_api/logging.html">torch_tensorrt.logging</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
 <ul>
@@ -268,6 +260,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
 </ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
 <p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
@@ -601,7 +597,7 @@
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2022 NVIDIA Corporation. All Rights Reserved.
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2020 NVIDIA Corporation. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);
 # you may not use this file except in compliance with the License.
@@ -618,30 +614,13 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="195ac3794a674b1a8c327ebde46a3be4" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_dynamic_shapes/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Torch-TensorRT---Using-Dynamic-Shapes">
 <h1>Torch-TensorRT - Using Dynamic Shapes<a class="headerlink" href="#Torch-TensorRT---Using-Dynamic-Shapes" title="Permalink to this headline">¶</a></h1>
-<p>Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA’s TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch’s Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into a module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extension and compiles modules that integrate into
-the JIT runtime seamlessly. After compilation, using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT’s suite of configurations at compile-time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.</p>
-<p>We highly encourage users to run this notebook using our NVIDIA’s <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">PyTorch container</a>. It comes packaged with a host of NVIDIA libraries and optimizations to widely used third-party libraries. In addition, this container is tested and updated on a monthly cadence!</p>
-<p>This notebook has the following sections: 1. <a class="reference external" href="#1">TL;DR Explanation</a> 2. <a class="reference external" href="#2">Setting up the model</a> 3. <a class="reference external" href="#3">Working with Dynamic shapes in Torch TRT</a></p>
-<section id="TL;DR-Explanation">
-<h2>TL;DR Explanation<a class="headerlink" href="#TL;DR-Explanation" title="Permalink to this headline">¶</a></h2>
-<p>Making use of Dynamic Shaped Tensors in Torch TensorRT is quite simple. Let’s say you are using the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.compile(...)</span></code> function to compile a torchscript module.</p>
-<p>One of the <code class="docutils literal notranslate"><span class="pre">args</span></code> in this function in this function is <code class="docutils literal notranslate"><span class="pre">input</span></code>: which defines an input to a module in terms of expected shape, data type and tensor format: <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.Input</span></code>.</p>
-<p>For the purposes of this walkthrough we just need three <code class="docutils literal notranslate"><span class="pre">kwargs</span></code>: <code class="docutils literal notranslate"><span class="pre">min_shape</span></code>, <code class="docutils literal notranslate"><span class="pre">opt_shape</span></code> and <code class="docutils literal notranslate"><span class="pre">max_shape</span></code>.</p>
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>...
-torch_tensorrt.Input(
-        min_shape=(1, 224, 224, 3),
-        opt_shape=(1, 512, 512, 3),
-        max_shape=(1, 1024, 1024, 3),
-        dtype=torch.int32
-        format=torch.channel_last
-    )
-...
-</pre></div>
-</div>
-<p>In this example, we are going to use a simple ResNet model to demonstrate the use of the API. We will be using different batch sizes in the example, but you can use the same method to alter any of the dimensions of the tensor.</p>
+<p>Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA’s TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch’s Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extention and compiles modules that integrate into
+the JIT runtime seamlessly. After compilation using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT’s suite of configurations at compile time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.</p>
+<p>We highly encorage users to use our NVIDIA’s <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">PyTorch container</a> to run this notebook. It comes packaged with a host of NVIDIA libraries and optimizations to widely used third party libraries. This container is tested and updated on a monthly cadence!</p>
+<p>This notebook has the following sections: 1. <a class="reference external" href="#1">TL;DR Explanation</a> 1. <a class="reference external" href="#2">Setting up the model</a> 1. <a class="reference external" href="#3">Working with Dynamic shapes in Torch TRT</a></p>
+<p>torch_tensorrt.Input( min_shape=(1, 224, 224, 3), opt_shape=(1, 512, 512, 3), max_shape=(1, 1024, 1024, 3), dtype=torch.int32 format=torch.channel_last ) … ``` In this example, we are going to use a simple ResNet model to demonstrate the use of the API. We will be using different batch sizes in the example, but you can use the same method to alter any of the dimensions of the tensor.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
@@ -743,7 +722,7 @@ <h2>TL;DR Explanation<a class="headerlink" href="#TL;DR-Explanation" title="Perm
 <span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
 </pre></div></div>
 </div>
-</section>
+<hr class="docutils" />
 <section id="Setting-up-the-model">
 <h2>Setting up the model<a class="headerlink" href="#Setting-up-the-model" title="Permalink to this headline">¶</a></h2>
 <p>In this section, we will: * Get sample data. * Download model from torch hub. * Build simple utility functions</p>
@@ -1144,7 +1123,7 @@ <h3>Build simple utility functions<a class="headerlink" href="#Build-simple-util
 </pre></div>
 </div>
 </div>
-<p>Let’s test our util functions on the model we have set up, starting with simple predictions.</p>
+<p>Let’s test our util functions on the model we have set up, starting with simple predictions</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
@@ -1292,12 +1271,13 @@ <h2>Benchmarking with Torch-TRT (without dynamic shapes)<a class="headerlink" hr
 </div>
 <p>With the baseline ready, we can proceed to the section working discussing dynamic shapes!</p>
 </section>
+<hr class="docutils" />
 <section id="Working-with-Dynamic-shapes-in-Torch-TRT">
 <h2>Working with Dynamic shapes in Torch TRT<a class="headerlink" href="#Working-with-Dynamic-shapes-in-Torch-TRT" title="Permalink to this headline">¶</a></h2>
-<p>Enabling “Dynamic Shaped” tensors to be used is essentially enabling the ability to defer defining the shape of tensors until run-time. Torch TensorRT simply leverages TensorRT’s Dynamic shape support. You can read more about TensorRT’s implementation in the <a class="reference external" href="https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#work_dynamic_shapes">TensorRT Documentation</a>.</p>
-<p>To make use of dynamic shapes, you need to provide three shapes: * <code class="docutils literal notranslate"><span class="pre">min_shape</span></code>: The minimum size of the tensor considered for optimizations. * <code class="docutils literal notranslate"><span class="pre">opt_shape</span></code>: The optimizations will be done in an effort to maximize performance for this shape. * <code class="docutils literal notranslate"><span class="pre">max_shape</span></code>: The maximum size of the tensor considered for optimizations.</p>
-<p>Generally, users can expect the best performance within the specified ranges. Performance for other shapes maybe be lower for other shapes (depending on the model ops and GPU used)</p>
-<p>In the following example, we will showcase varying batch sizes, which is the zeroth dimension of our input tensors. As Convolution operations require that the channel dimension be a build-time constant, we won’t be changing the sizes of other channels in this example, but for models which contain ops conducive to changes in other channels, this functionality can be freely used.</p>
+<p>Enabling “Dynamic Shaped” tensors to be used is essentially enabling the ability to defer defining the shape of tensors until runetime. Torch TensorRT simply leverages TensorRT’s Dynamic shape support. You can read more about TensorRT’s implementation in the <a class="reference external" href="https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#work_dynamic_shapes">TensorRT Documentation</a>.</p>
+<p>To make use of dynamic shapes, you need to provide three shapes: * <code class="docutils literal notranslate"><span class="pre">min_shape</span></code>: The minimum size of the tensor considered for optimizations. * <code class="docutils literal notranslate"><span class="pre">opt_shape</span></code>: The optimizations will be done with an effort to maximize performance for this shape. * <code class="docutils literal notranslate"><span class="pre">min_shape</span></code>: The maximum size of the tensor considered for optimizations.</p>
+<p>Generally, users can expect best performance within the specified ranges. Performance for other shapes may be be lower for other shapes (depending on the model ops and GPU used)</p>
+<p>In the following example, we will showcase varing batch size, which is the zeroth dimension of our input tensors. As Convolution operations require that the channel dimension be a build-time constant, we won’t be changing sizes of other channels in this example, but for models which contain ops conducive to changes in other channels, this functionality can be freely used.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
 </pre></div>
@@ -1456,15 +1436,6 @@ <h2>What’s Next?<a class="headerlink" href="#What’s-Next?" title="Permalink
             </div>
             <footer>
 
-    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
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@@ -1494,7 +1465,6 @@ <h2>What’s Next?<a class="headerlink" href="#What’s-Next?" title="Permalink
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
 <li><a class="reference internal" href="#">Torch-TensorRT - Using Dynamic Shapes</a><ul>
-<li><a class="reference internal" href="#TL;DR-Explanation">TL;DR Explanation</a></li>
 <li><a class="reference internal" href="#Setting-up-the-model">Setting up the model</a><ul>
 <li><a class="reference internal" href="#Getting-sample-data">Getting sample data</a></li>
 <li><a class="reference internal" href="#Download-model-from-torch-hub.">Download model from torch hub.</a></li>
@@ -1600,19 +1570,43 @@ <h2>Resources</h2>
           </ul>
         </div>
 
-        <div class="footer-links-col follow-us-col">
+        <div class="footer-links-col">
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-            <li class="list-title">Stay Connected</li>
+            <li class="list-title">Stay up to date</li>
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+            <li class="list-title">PyTorch Podcasts</li>
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+            <li><a href="https://podcasts.apple.com/us/podcast/pytorch-developer-podcast/id1566080008" target="_blank">Apple</a></li>
+            <li><a href="https://www.google.com/podcasts?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9PQjVGa0lsOA%3D%3D" target="_blank">Google</a></li>
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+          </ul>
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+        <div class="copyright">
+        <p>© Copyright The Linux Foundation. The PyTorch Foundation is a project of The Linux Foundation.
+          For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see
+          <a href="www.linuxfoundation.org/policies/">www.linuxfoundation.org/policies/</a>. The PyTorch Foundation supports the PyTorch open source
+          project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC,
+          please see <a href="www.lfprojects.org/policies/">www.lfprojects.org/policies/</a>.</p>
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-    </div>
+     </div>
+
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diff --git a/docs/_notebooks/dynamic-shapes.ipynb b/docs/_notebooks/dynamic-shapes.ipynb
index 5738f13521..a0ceaab576 100644
--- a/docs/_notebooks/dynamic-shapes.ipynb
+++ b/docs/_notebooks/dynamic-shapes.ipynb
@@ -7,7 +7,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Copyright 2022 NVIDIA Corporation. All Rights Reserved.\n",
+    "# Copyright 2020 NVIDIA Corporation. All Rights Reserved.\n",
     "#\n",
     "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
     "# you may not use this file except in compliance with the License.\n",
@@ -28,8 +28,6 @@
    "id": "18ee9d62",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_dynamic_shapes/nvidia_logo.png\" width=\"90px\">\n",
-    "\n",
     "# Torch-TensorRT - Using Dynamic Shapes"
    ]
   },
@@ -38,14 +36,14 @@
    "id": "73703695",
    "metadata": {},
    "source": [
-    "Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch's Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into a module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extension and compiles modules that integrate into the JIT runtime seamlessly. After compilation, using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT's suite of configurations at compile-time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.\n",
+    "Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch's Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extention and compiles modules that integrate into the JIT runtime seamlessly. After compilation using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT's suite of configurations at compile time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.\n",
     "\n",
-    "We highly encourage users to run this notebook using our NVIDIA's [PyTorch container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch). It comes packaged with a host of NVIDIA libraries and optimizations to widely used third-party libraries. In addition, this container is tested and updated on a monthly cadence!\n",
+    "We highly encorage users to use our NVIDIA's [PyTorch container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) to run this notebook. It comes packaged with a host of NVIDIA libraries and optimizations to widely used third party libraries. This container is tested and updated on a monthly cadence!\n",
     "\n",
     "This notebook has the following sections:\n",
     "1. [TL;DR Explanation](#1)\n",
-    "2. [Setting up the model](#2)\n",
-    "3. [Working with Dynamic shapes in Torch TRT](#3)"
+    "1. [Setting up the model](#2)\n",
+    "1. [Working with Dynamic shapes in Torch TRT](#3)"
    ]
   },
   {
@@ -53,7 +51,6 @@
    "id": "1603028d",
    "metadata": {},
    "source": [
-    "<a id=\"1\"></a>\n",
     "---\n",
     "## TL;DR Explanation\n",
     "\n",
@@ -190,7 +187,6 @@
    "id": "0ed1b4f7",
    "metadata": {},
    "source": [
-    "<a id=\"2\"></a>\n",
     "---\n",
     "## Setting up the model\n",
     "\n",
@@ -637,7 +633,7 @@
    "id": "21402d53",
    "metadata": {},
    "source": [
-    "Let's test our util functions on the model we have set up, starting with simple predictions."
+    "Let's test our util functions on the model we have set up, starting with simple predictions"
    ]
   },
   {
@@ -822,22 +818,21 @@
    "id": "bd5746da",
    "metadata": {},
    "source": [
-    "<a id=\"3\"></a>\n",
     "---\n",
     "## Working with Dynamic shapes in Torch TRT\n",
-    " \n",
-    "Enabling \"Dynamic Shaped\" tensors to be used is essentially enabling the ability to defer defining the shape of tensors until run-time. Torch TensorRT simply leverages TensorRT's Dynamic shape support. You can read more about TensorRT's implementation in the [TensorRT Documentation](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#work_dynamic_shapes).\n",
-    " \n",
+    "\n",
+    "Enabling \"Dynamic Shaped\" tensors to be used is essentially enabling the ability to defer defining the shape of tensors until runetime. Torch TensorRT simply leverages TensorRT's Dynamic shape support. You can read more about TensorRT's implementation in the [TensorRT Documentation](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#work_dynamic_shapes).\n",
+    "\n",
     "#### How can you use this feature?\n",
-    " \n",
+    "\n",
     "To make use of dynamic shapes, you need to provide three shapes:\n",
     "* `min_shape`: The minimum size of the tensor considered for optimizations.\n",
-    "* `opt_shape`: The optimizations will be done in an effort to maximize performance for this shape.\n",
-    "* `max_shape`: The maximum size of the tensor considered for optimizations.\n",
-    " \n",
-    "Generally, users can expect the best performance within the specified ranges. Performance for other shapes maybe be lower for other shapes (depending on the model ops and GPU used)\n",
-    " \n",
-    "In the following example, we will showcase varying batch sizes, which is the zeroth dimension of our input tensors. As Convolution operations require that the channel dimension be a build-time constant, we won't be changing the sizes of other channels in this example, but for models which contain ops conducive to changes in other channels, this functionality can be freely used."
+    "* `opt_shape`: The optimizations will be done with an effort to maximize performance for this shape.\n",
+    "* `min_shape`: The maximum size of the tensor considered for optimizations.\n",
+    "\n",
+    "Generally, users can expect best performance within the specified ranges. Performance for other shapes may be be lower for other shapes (depending on the model ops and GPU used)\n",
+    "\n",
+    "In the following example, we will showcase varing batch size, which is the zeroth dimension of our input tensors. As Convolution operations require that the channel dimension be a build-time constant, we won't be changing sizes of other channels in this example, but for models which contain ops conducive to changes in other channels, this functionality can be freely used."
    ]
   },
   {
diff --git a/docs/_notebooks/lenet-getting-started.html b/docs/_notebooks/lenet-getting-started.html
index a4bd00722d..9d4a4102c2 100644
--- a/docs/_notebooks/lenet-getting-started.html
+++ b/docs/_notebooks/lenet-getting-started.html
@@ -33,8 +33,6 @@
   <link rel="stylesheet" href="../_static/collapsible-lists/css/tree_view.css" type="text/css" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="Torch-TensorRT Getting Started - ResNet 50" href="Resnet50-example.html" />
-    <link rel="prev" title="Masked Language Modeling (MLM) with Hugging Face BERT Transformer" href="Hugging-Face-BERT.html" />
   <!-- Google Analytics -->
 
   <!-- End Google Analytics -->
@@ -199,7 +197,7 @@
 
 
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0)
                 </div>
 
 
@@ -229,29 +227,22 @@
 
               <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_cpp_api.html">Getting Started with C++</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/torchtrtc.html">torchtrtc</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notebooks</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="CitriNet-example.html">Torch-TensorRT Getting Started - CitriNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic-shapes.html">Torch-TensorRT - Using Dynamic Shapes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="EfficientNet-example.html">Torch-TensorRT Getting Started - EfficientNet-B0</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Hugging-Face-BERT.html">Masked Language Modeling (MLM) with Hugging Face BERT Transformer</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Torch-TensorRT Getting Started - LeNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Resnet50-example.html">Torch-TensorRT Getting Started - ResNet 50</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ssd-object-detection-demo.html">Object Detection with Torch-TensorRT (SSD)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="vgg-qat.html">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
 <ul>
@@ -259,6 +250,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../py_api/logging.html">torch_tensorrt.logging</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
 <ul>
@@ -268,6 +260,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
 </ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
 <p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
@@ -601,7 +597,7 @@
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2019-22 NVIDIA Corporation. All Rights Reserved.
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2019 NVIDIA Corporation. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);
 # you may not use this file except in compliance with the License.
@@ -618,7 +614,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="e35ce4ca215c4513810fb9d533909cc9" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_lenet/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="6e5fd25e8d6b4ce79aa5fcb073d51b40" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png" /></p>
 <section id="Torch-TensorRT-Getting-Started---LeNet">
 <h1>Torch-TensorRT Getting Started - LeNet<a class="headerlink" href="#Torch-TensorRT-Getting-Started---LeNet" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -1190,7 +1186,7 @@ <h2>Conclusion<a class="headerlink" href="#Conclusion" title="Permalink to this
 <p>In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT and test the performance impact of the optimization.</p>
 <section id="What’s-next">
 <h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to this headline">¶</a></h3>
-<p>Now it’s time to try Torch-TensorRT on your own model. Fill out issues at <a class="reference external" href="https://github.com/pytorch/TensorRT">https://github.com/pytorch/TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
+<p>Now it’s time to try Torch-TensorRT on your own model. Fill out issues at <a class="reference external" href="https://github.com/NVIDIA/Torch-TensorRT">https://github.com/NVIDIA/Torch-TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
 </section>
 </section>
 </section>
@@ -1201,15 +1197,6 @@ <h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to
             </div>
             <footer>
 
-    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
-
-        <a href="Resnet50-example.html" class="btn btn-neutral float-right" title="Torch-TensorRT Getting Started - ResNet 50" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
-
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-        <a href="Hugging-Face-BERT.html" class="btn btn-neutral" title="Masked Language Modeling (MLM) with Hugging Face BERT Transformer" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
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-
 
 
 
@@ -1351,19 +1338,43 @@ <h2>Resources</h2>
           </ul>
         </div>
 
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+        <div class="footer-links-col">
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+
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+          <ul>
+            <li class="list-title">PyTorch Podcasts</li>
+            <li><a href="https://open.spotify.com/show/6UzHKeiy368jKfQMKKvJY5" target="_blank">Spotify</a></li>
+            <li><a href="https://podcasts.apple.com/us/podcast/pytorch-developer-podcast/id1566080008" target="_blank">Apple</a></li>
+            <li><a href="https://www.google.com/podcasts?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9PQjVGa0lsOA%3D%3D" target="_blank">Google</a></li>
+            <li><a href="https://music.amazon.com/podcasts/7a4e6f0e-26c2-49e9-a478-41bd244197d0/PyTorch-Developer-Podcast?" target="_blank">Amazon</a></li>
+          </ul>
+         </div>
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+            <li class="privacy-policy-links">|</li>
+            <li class="privacy-policy-links"><a href="https://www.linuxfoundation.org/privacy-policy/" target="_blank">Privacy</a></li>
+          </ul>
+        </div>
+        <div class="copyright">
+        <p>© Copyright The Linux Foundation. The PyTorch Foundation is a project of The Linux Foundation.
+          For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see
+          <a href="www.linuxfoundation.org/policies/">www.linuxfoundation.org/policies/</a>. The PyTorch Foundation supports the PyTorch open source
+          project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC,
+          please see <a href="www.lfprojects.org/policies/">www.lfprojects.org/policies/</a>.</p>
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diff --git a/docs/_notebooks/lenet-getting-started.ipynb b/docs/_notebooks/lenet-getting-started.ipynb
index 144d47813b..2db954946d 100644
--- a/docs/_notebooks/lenet-getting-started.ipynb
+++ b/docs/_notebooks/lenet-getting-started.ipynb
@@ -6,7 +6,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Copyright 2019-22 NVIDIA Corporation. All Rights Reserved.\n",
+    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
     "#\n",
     "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
     "# you may not use this file except in compliance with the License.\n",
@@ -26,8 +26,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_lenet/nvidia_logo.png\" width=\"90px\">\n",
-    "\n",
+    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
     "\n",
     "# Torch-TensorRT Getting Started - LeNet"
    ]
@@ -691,7 +690,7 @@
     "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT and test the performance impact of the optimization.\n",
     "\n",
     "### What's next\n",
-    "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+    "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
    ]
   }
  ],
@@ -711,7 +710,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/_notebooks/ssd-object-detection-demo.html b/docs/_notebooks/ssd-object-detection-demo.html
index 3c7bab1761..9ad9fef7cb 100644
--- a/docs/_notebooks/ssd-object-detection-demo.html
+++ b/docs/_notebooks/ssd-object-detection-demo.html
@@ -33,8 +33,6 @@
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@@ -199,7 +197,7 @@
 
 
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0)
                 </div>
 
 
@@ -229,29 +227,22 @@
 
               <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_cpp_api.html">Getting Started with C++</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/torchtrtc.html">torchtrtc</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notebooks</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="CitriNet-example.html">Torch-TensorRT Getting Started - CitriNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic-shapes.html">Torch-TensorRT - Using Dynamic Shapes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="EfficientNet-example.html">Torch-TensorRT Getting Started - EfficientNet-B0</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Hugging-Face-BERT.html">Masked Language Modeling (MLM) with Hugging Face BERT Transformer</a></li>
-<li class="toctree-l1"><a class="reference internal" href="lenet-getting-started.html">Torch-TensorRT Getting Started - LeNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Resnet50-example.html">Torch-TensorRT Getting Started - ResNet 50</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Object Detection with Torch-TensorRT (SSD)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="vgg-qat.html">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
 <ul>
@@ -259,6 +250,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../py_api/logging.html">torch_tensorrt.logging</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
 <ul>
@@ -268,6 +260,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
 </ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
 <p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
@@ -618,7 +614,7 @@
 </pre></div>
 </div>
 </div>
-<p><img alt="aa77691fd7754170ac652a03d651d0b9" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_ssd/nvidia_logo.png" style="width: 90px;" /></p>
+<p><img alt="0651fd225e2f45ecadc9b43b60a4e5ff" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_ssd_notebook.png" /></p>
 <section id="Object-Detection-with-Torch-TensorRT-(SSD)">
 <h1>Object Detection with Torch-TensorRT (SSD)<a class="headerlink" href="#Object-Detection-with-Torch-TensorRT-(SSD)" title="Permalink to this headline">¶</a></h1>
 <hr class="docutils" />
@@ -770,8 +766,8 @@ <h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this head
 <h3>Single Shot MultiBox Detector model for object detection<a class="headerlink" href="#Single-Shot-MultiBox-Detector-model-for-object-detection" title="Permalink to this headline">¶</a></h3>
 <table class="docutils align-default">
 <colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
+<col style="width: 38%" />
+<col style="width: 62%" />
 </colgroup>
 <thead>
 <tr class="row-odd"><th class="head"><p>_</p></th>
@@ -779,8 +775,8 @@ <h3>Single Shot MultiBox Detector model for object detection<a class="headerlink
 </tr>
 </thead>
 <tbody>
-<tr class="row-even"><td><p><img alt="alt" src="https://pytorch.org/assets/images/ssd.png" /></p></td>
-<td><p><img alt="alt" src="https://pytorch.org/assets/images/ssd.png" /></p></td>
+<tr class="row-even"><td><p><img alt="alt" src="https://pytorch.org/assets/images/ssd_diagram.png" /></p></td>
+<td><p><img alt="image1" src="https://pytorch.org/assets/images/ssd.png" /></p></td>
 </tr>
 </tbody>
 </table>
@@ -1316,15 +1312,6 @@ <h3>References<a class="headerlink" href="#References" title="Permalink to this
             </div>
             <footer>
 
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@@ -1468,19 +1455,43 @@ <h2>Resources</h2>
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+            <li><a href="https://www.google.com/podcasts?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9PQjVGa0lsOA%3D%3D" target="_blank">Google</a></li>
+            <li><a href="https://music.amazon.com/podcasts/7a4e6f0e-26c2-49e9-a478-41bd244197d0/PyTorch-Developer-Podcast?" target="_blank">Amazon</a></li>
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diff --git a/docs/_notebooks/ssd-object-detection-demo.ipynb b/docs/_notebooks/ssd-object-detection-demo.ipynb
index b7ae8dc2e8..f2a0bd8063 100644
--- a/docs/_notebooks/ssd-object-detection-demo.ipynb
+++ b/docs/_notebooks/ssd-object-detection-demo.ipynb
@@ -26,7 +26,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_ssd/nvidia_logo.png\" width=\"90px\">\n",
+    "<img src=\"https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_ssd_notebook.png\" style=\"width: 90px; float: right;\">\n",
     "\n",
     "# Object Detection with Torch-TensorRT (SSD)"
    ]
@@ -935,7 +935,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/_notebooks/vgg-qat.html b/docs/_notebooks/vgg-qat.html
index 79f0099a15..71940b30cb 100644
--- a/docs/_notebooks/vgg-qat.html
+++ b/docs/_notebooks/vgg-qat.html
@@ -33,8 +33,6 @@
   <link rel="stylesheet" href="../_static/collapsible-lists/css/tree_view.css" type="text/css" />
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@@ -199,7 +197,7 @@
 
 
                 <div class="version">
-                  master (1.2.0a0+ffedb78)
+                  master (1.2.0)
                 </div>
 
 
@@ -229,29 +227,22 @@
 
               <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_cpp_api.html">Getting Started with C++</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/torchtrtc.html">torchtrtc</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notebooks</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="CitriNet-example.html">Torch-TensorRT Getting Started - CitriNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic-shapes.html">Torch-TensorRT - Using Dynamic Shapes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="EfficientNet-example.html">Torch-TensorRT Getting Started - EfficientNet-B0</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Hugging-Face-BERT.html">Masked Language Modeling (MLM) with Hugging Face BERT Transformer</a></li>
-<li class="toctree-l1"><a class="reference internal" href="lenet-getting-started.html">Torch-TensorRT Getting Started - LeNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Resnet50-example.html">Torch-TensorRT Getting Started - ResNet 50</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ssd-object-detection-demo.html">Object Detection with Torch-TensorRT (SSD)</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
 <ul>
@@ -259,6 +250,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../py_api/logging.html">torch_tensorrt.logging</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
 </ul>
 <p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
 <ul>
@@ -268,6 +260,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
 </ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
 <p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
@@ -597,28 +593,6 @@
   background: rgba(66, 165, 245, 0.2);
 }
 </style>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2020-2022 NVIDIA Corporation. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an &quot;AS IS&quot; BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-</pre></div>
-</div>
-</div>
-<p><img alt="ea58b17a1b2146f390fb14f89011e298" class="no-scaled-link" src="http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_qat_vgg/nvidia_logo.png" style="width: 90px;" /></p>
 <section id="Deploying-Quantization-Aware-Trained-models-in-INT8-using-Torch-TensorRT">
 <h1>Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT<a class="headerlink" href="#Deploying-Quantization-Aware-Trained-models-in-INT8-using-Torch-TensorRT" title="Permalink to this headline">¶</a></h1>
 <section id="Overview">
@@ -683,7 +657,7 @@ <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this head
 2.1.0
 </pre></div></div>
 </div>
-<p>## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows <img alt="0f5a0c4917974040a2d4b6035bb1c98a" src="https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png" /></p>
+<p>## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows <img alt="beac8a15065243e699379551998f212b" src="https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png" /></p>
 <p>## 3. Training a baseline VGG16 model We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your <code class="docutils literal notranslate"><span class="pre">data</span></code> directory. Data preprocessing is performed using <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> transforms.</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
@@ -1634,15 +1608,6 @@ <h3>Performance benchmarking<a class="headerlink" href="#Performance-benchmarkin
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-    </div>
-
 
 
 
@@ -1772,19 +1737,43 @@ <h2>Resources</h2>
           </ul>
         </div>
 
-        <div class="footer-links-col follow-us-col">
+        <div class="footer-links-col">
           <ul>
-            <li class="list-title">Stay Connected</li>
+            <li class="list-title">Stay up to date</li>
+            <li><a href="https://www.facebook.com/pytorch" target="_blank">Facebook</a></li>
+            <li><a href="https://twitter.com/pytorch" target="_blank">Twitter</a></li>
+            <li><a href="https://www.youtube.com/pytorch" target="_blank">YouTube</a></li>
+            <li><a href="https://www.linkedin.com/company/pytorch" target="_blank">LinkedIn</a></li>
           </ul>
-
-          <div class="footer-social-icons">
-            <a href="https://www.facebook.com/pytorch" target="_blank" class="facebook"></a>
-            <a href="https://twitter.com/pytorch" target="_blank" class="twitter"></a>
-            <a href="https://www.youtube.com/pytorch" target="_blank" class="youtube"></a>
           </div>
+
+        <div class="footer-links-col">
+          <ul>
+            <li class="list-title">PyTorch Podcasts</li>
+            <li><a href="https://open.spotify.com/show/6UzHKeiy368jKfQMKKvJY5" target="_blank">Spotify</a></li>
+            <li><a href="https://podcasts.apple.com/us/podcast/pytorch-developer-podcast/id1566080008" target="_blank">Apple</a></li>
+            <li><a href="https://www.google.com/podcasts?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9PQjVGa0lsOA%3D%3D" target="_blank">Google</a></li>
+            <li><a href="https://music.amazon.com/podcasts/7a4e6f0e-26c2-49e9-a478-41bd244197d0/PyTorch-Developer-Podcast?" target="_blank">Amazon</a></li>
+          </ul>
+         </div>
+        </div>
+
+        <div class="privacy-policy">
+          <ul>
+            <li class="privacy-policy-links"><a href="https://www.linuxfoundation.org/terms/" target="_blank">Terms</a></li>
+            <li class="privacy-policy-links">|</li>
+            <li class="privacy-policy-links"><a href="https://www.linuxfoundation.org/privacy-policy/" target="_blank">Privacy</a></li>
+          </ul>
         </div>
+        <div class="copyright">
+        <p>© Copyright The Linux Foundation. The PyTorch Foundation is a project of The Linux Foundation.
+          For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see
+          <a href="www.linuxfoundation.org/policies/">www.linuxfoundation.org/policies/</a>. The PyTorch Foundation supports the PyTorch open source
+          project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC,
+          please see <a href="www.lfprojects.org/policies/">www.lfprojects.org/policies/</a>.</p>
       </div>
-    </div>
+     </div>
+
   </footer>
 
   <div class="cookie-banner-wrapper">
diff --git a/docs/_notebooks/vgg-qat.ipynb b/docs/_notebooks/vgg-qat.ipynb
index 5888950378..cca771ad92 100644
--- a/docs/_notebooks/vgg-qat.ipynb
+++ b/docs/_notebooks/vgg-qat.ipynb
@@ -1,35 +1,10 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a054a8df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Copyright 2020-2022 NVIDIA Corporation. All Rights Reserved.\n",
-    "#\n",
-    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-    "# you may not use this file except in compliance with the License.\n",
-    "# You may obtain a copy of the License at\n",
-    "#\n",
-    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
-    "#\n",
-    "# Unless required by applicable law or agreed to in writing, software\n",
-    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-    "# See the License for the specific language governing permissions and\n",
-    "# limitations under the License.\n",
-    "# =============================================================================="
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "8033b2eb",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_qat_vgg/nvidia_logo.png\" width=\"90px\">\n",
-    "\n",
     "# Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT"
    ]
   },
@@ -60,7 +35,7 @@
    "source": [
     "<a id=\"1\"></a>\n",
     "##  1. Requirements\n",
-    "Please install the <a href=\"https://github.com/pytorch/TensorRT/tree/master/examples/int8/training/vgg16#prequisites\">required dependencies</a> and import these libraries accordingly"
+    "Please install the <a href=\"https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/int8/training/vgg16#prequisites\">required dependencies</a> and import these libraries accordingly"
    ]
   },
   {
@@ -1028,7 +1003,7 @@
     "%quant_weight : Tensor = aten::fake_quantize_per_channel_affine(%394, %640, %641, %637, %638, %639)\n",
     "%input.2 : Tensor = aten::_convolution(%quant_input, %quant_weight, %395, %687, %688, %689, %643, %690, %642, %643, %643, %644, %644)\n",
     "```\n",
-    "`aten::fake_quantize_per_*_affine` is converted into `QuantizeLayer` + `DequantizeLayer` in Torch-TensorRT internally. Please refer to <a href=\"https://github.com/pytorch/TensorRT/blob/master/core/conversion/converters/impl/quantization.cpp\">quantization op converters</a> in Torch-TensorRT."
+    "`aten::fake_quantize_per_*_affine` is converted into `QuantizeLayer` + `DequantizeLayer` in Torch-TensorRT internally. Please refer to <a href=\"https://github.com/NVIDIA/Torch-TensorRT/blob/master/core/conversion/converters/impl/quantization.cpp\">quantization op converters</a> in Torch-TensorRT."
    ]
   },
   {
@@ -1193,8 +1168,8 @@
     "##  9. References\n",
     "* <a href=\"https://arxiv.org/pdf/1409.1556.pdf\">Very Deep Convolution Networks for large scale Image Recognition</a>\n",
     "* <a href=\"https://developer.nvidia.com/blog/achieving-fp32-accuracy-for-int8-inference-using-quantization-aware-training-with-tensorrt/\">Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT</a>\n",
-    "* <a href=\"https://github.com/pytorch/TensorRT/tree/master/examples/int8/training/vgg16#quantization-aware-fine-tuning-for-trying-out-qat-workflows\">QAT workflow for VGG16</a>\n",
-    "* <a href=\"https://github.com/pytorch/TensorRT/tree/master/examples/int8/qat\">Deploying VGG QAT model in C++ using Torch-TensorRT</a>\n",
+    "* <a href=\"https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/int8/training/vgg16#quantization-aware-fine-tuning-for-trying-out-qat-workflows\">QAT workflow for VGG16</a>\n",
+    "* <a href=\"https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/int8/qat\">Deploying VGG QAT model in C++ using Torch-TensorRT</a>\n",
     "* <a href=\"https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization\">Pytorch-quantization toolkit from NVIDIA</a>\n",
     "* <a href=\"https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/userguide.html\">Pytorch quantization toolkit userguide</a>\n",
     "* <a href=\"https://arxiv.org/pdf/2004.09602.pdf\">Quantization basics</a>"
@@ -1217,7 +1192,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/_sources/_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.rst.txt b/docs/_sources/_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.rst.txt
index b0fa677071..caa9ad6226 100644
--- a/docs/_sources/_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.rst.txt
+++ b/docs/_sources/_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.rst.txt
@@ -11,4 +11,3 @@ Function Documentation
 
 
 .. doxygenfunction:: torch_tensorrt::ptq::make_int8_calibrator(DataLoader, const std::string&, bool)
-   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/_sources/_cpp_api/function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.rst.txt b/docs/_sources/_cpp_api/function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.rst.txt
index 9312e7b01f..477c2655ae 100644
--- a/docs/_sources/_cpp_api/function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.rst.txt
+++ b/docs/_sources/_cpp_api/function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.rst.txt
@@ -11,4 +11,3 @@ Function Documentation
 
 
 .. doxygenfunction:: torch_tensorrt::ptq::make_int8_cache_calibrator(const std::string&)
-   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/_sources/_notebooks/CitriNet-example.ipynb.txt b/docs/_sources/_notebooks/CitriNet-example.ipynb.txt
index b9d615d5f1..0573af0176 100644
--- a/docs/_sources/_notebooks/CitriNet-example.ipynb.txt
+++ b/docs/_sources/_notebooks/CitriNet-example.ipynb.txt
@@ -26,7 +26,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_citrinet/nvidia_logo.png\" width=\"90px\">\n",
+    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
     "\n",
     "# Torch-TensorRT Getting Started - CitriNet"
    ]
@@ -929,7 +929,7 @@
     "In this notebook, we have walked through the complete process of optimizing the Citrinet model with Torch-TensorRT. On an A100 GPU, with Torch-TensorRT, we observe a speedup of ~**2.4X** with FP32, and ~**2.9X** with FP16 at batchsize of 128.\n",
     "\n",
     "### What's next\n",
-    "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+    "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
    ]
   },
   {
@@ -956,7 +956,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/_sources/_notebooks/EfficientNet-example.ipynb.txt b/docs/_sources/_notebooks/EfficientNet-example.ipynb.txt
index 518254c3ab..31a3dad874 100644
--- a/docs/_sources/_notebooks/EfficientNet-example.ipynb.txt
+++ b/docs/_sources/_notebooks/EfficientNet-example.ipynb.txt
@@ -26,7 +26,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_efficientnet/nvidia_logo.png\" width=\"90px\">\n",
+    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
     "\n",
     "# Torch-TensorRT Getting Started - EfficientNet-B0"
    ]
@@ -658,7 +658,7 @@
     "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for EfficientNet-B0 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of **1.35x** with FP32, and **3.13x** with FP16 on an NVIDIA 3090 GPU. These acceleration numbers will vary from GPU to GPU(as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute cards for maximum acceleration.\n",
     "\n",
     "### What's next\n",
-    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
    ]
   },
   {
@@ -685,7 +685,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/_sources/_notebooks/Hugging-Face-BERT.ipynb.txt b/docs/_sources/_notebooks/Hugging-Face-BERT.ipynb.txt
index 81034d8e38..9b027b473e 100644
--- a/docs/_sources/_notebooks/Hugging-Face-BERT.ipynb.txt
+++ b/docs/_sources/_notebooks/Hugging-Face-BERT.ipynb.txt
@@ -28,8 +28,7 @@
    "id": "d0a97ac5",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_hf_bert/nvidia_logo.png\" width=\"90px\">\n",
-    "\n",
+    "<img src=\"https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png\" style=\"width: 90px; float: right;\">\n",
     "\n",
     "# Masked Language Modeling (MLM) with Hugging Face BERT Transformer"
    ]
@@ -679,7 +678,7 @@
     "Torch-TensorRT (FP16): 3.15x\n",
     "\n",
     "### What's next\n",
-    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT."
+    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT."
    ]
   },
   {
@@ -707,7 +706,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/_sources/_notebooks/Resnet50-example.ipynb.txt b/docs/_sources/_notebooks/Resnet50-example.ipynb.txt
index f75a2b0e64..f020662c73 100644
--- a/docs/_sources/_notebooks/Resnet50-example.ipynb.txt
+++ b/docs/_sources/_notebooks/Resnet50-example.ipynb.txt
@@ -897,7 +897,7 @@
     "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for EfficientNet-B0 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of **1.84x** with FP32, and **5.2x** with FP16 on an NVIDIA 3090 GPU. These acceleration numbers will vary from GPU to GPU(as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute cards for maximum acceleration.\n",
     "\n",
     "### What's next\n",
-    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
    ]
   }
  ],
diff --git a/docs/_sources/_notebooks/dynamic-shapes.ipynb.txt b/docs/_sources/_notebooks/dynamic-shapes.ipynb.txt
index 5738f13521..a0ceaab576 100644
--- a/docs/_sources/_notebooks/dynamic-shapes.ipynb.txt
+++ b/docs/_sources/_notebooks/dynamic-shapes.ipynb.txt
@@ -7,7 +7,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Copyright 2022 NVIDIA Corporation. All Rights Reserved.\n",
+    "# Copyright 2020 NVIDIA Corporation. All Rights Reserved.\n",
     "#\n",
     "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
     "# you may not use this file except in compliance with the License.\n",
@@ -28,8 +28,6 @@
    "id": "18ee9d62",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_dynamic_shapes/nvidia_logo.png\" width=\"90px\">\n",
-    "\n",
     "# Torch-TensorRT - Using Dynamic Shapes"
    ]
   },
@@ -38,14 +36,14 @@
    "id": "73703695",
    "metadata": {},
    "source": [
-    "Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch's Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into a module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extension and compiles modules that integrate into the JIT runtime seamlessly. After compilation, using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT's suite of configurations at compile-time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.\n",
+    "Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch's Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extention and compiles modules that integrate into the JIT runtime seamlessly. After compilation using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT's suite of configurations at compile time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.\n",
     "\n",
-    "We highly encourage users to run this notebook using our NVIDIA's [PyTorch container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch). It comes packaged with a host of NVIDIA libraries and optimizations to widely used third-party libraries. In addition, this container is tested and updated on a monthly cadence!\n",
+    "We highly encorage users to use our NVIDIA's [PyTorch container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) to run this notebook. It comes packaged with a host of NVIDIA libraries and optimizations to widely used third party libraries. This container is tested and updated on a monthly cadence!\n",
     "\n",
     "This notebook has the following sections:\n",
     "1. [TL;DR Explanation](#1)\n",
-    "2. [Setting up the model](#2)\n",
-    "3. [Working with Dynamic shapes in Torch TRT](#3)"
+    "1. [Setting up the model](#2)\n",
+    "1. [Working with Dynamic shapes in Torch TRT](#3)"
    ]
   },
   {
@@ -53,7 +51,6 @@
    "id": "1603028d",
    "metadata": {},
    "source": [
-    "<a id=\"1\"></a>\n",
     "---\n",
     "## TL;DR Explanation\n",
     "\n",
@@ -190,7 +187,6 @@
    "id": "0ed1b4f7",
    "metadata": {},
    "source": [
-    "<a id=\"2\"></a>\n",
     "---\n",
     "## Setting up the model\n",
     "\n",
@@ -637,7 +633,7 @@
    "id": "21402d53",
    "metadata": {},
    "source": [
-    "Let's test our util functions on the model we have set up, starting with simple predictions."
+    "Let's test our util functions on the model we have set up, starting with simple predictions"
    ]
   },
   {
@@ -822,22 +818,21 @@
    "id": "bd5746da",
    "metadata": {},
    "source": [
-    "<a id=\"3\"></a>\n",
     "---\n",
     "## Working with Dynamic shapes in Torch TRT\n",
-    " \n",
-    "Enabling \"Dynamic Shaped\" tensors to be used is essentially enabling the ability to defer defining the shape of tensors until run-time. Torch TensorRT simply leverages TensorRT's Dynamic shape support. You can read more about TensorRT's implementation in the [TensorRT Documentation](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#work_dynamic_shapes).\n",
-    " \n",
+    "\n",
+    "Enabling \"Dynamic Shaped\" tensors to be used is essentially enabling the ability to defer defining the shape of tensors until runetime. Torch TensorRT simply leverages TensorRT's Dynamic shape support. You can read more about TensorRT's implementation in the [TensorRT Documentation](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#work_dynamic_shapes).\n",
+    "\n",
     "#### How can you use this feature?\n",
-    " \n",
+    "\n",
     "To make use of dynamic shapes, you need to provide three shapes:\n",
     "* `min_shape`: The minimum size of the tensor considered for optimizations.\n",
-    "* `opt_shape`: The optimizations will be done in an effort to maximize performance for this shape.\n",
-    "* `max_shape`: The maximum size of the tensor considered for optimizations.\n",
-    " \n",
-    "Generally, users can expect the best performance within the specified ranges. Performance for other shapes maybe be lower for other shapes (depending on the model ops and GPU used)\n",
-    " \n",
-    "In the following example, we will showcase varying batch sizes, which is the zeroth dimension of our input tensors. As Convolution operations require that the channel dimension be a build-time constant, we won't be changing the sizes of other channels in this example, but for models which contain ops conducive to changes in other channels, this functionality can be freely used."
+    "* `opt_shape`: The optimizations will be done with an effort to maximize performance for this shape.\n",
+    "* `min_shape`: The maximum size of the tensor considered for optimizations.\n",
+    "\n",
+    "Generally, users can expect best performance within the specified ranges. Performance for other shapes may be be lower for other shapes (depending on the model ops and GPU used)\n",
+    "\n",
+    "In the following example, we will showcase varing batch size, which is the zeroth dimension of our input tensors. As Convolution operations require that the channel dimension be a build-time constant, we won't be changing sizes of other channels in this example, but for models which contain ops conducive to changes in other channels, this functionality can be freely used."
    ]
   },
   {
diff --git a/docs/_sources/_notebooks/lenet-getting-started.ipynb.txt b/docs/_sources/_notebooks/lenet-getting-started.ipynb.txt
index 144d47813b..2db954946d 100644
--- a/docs/_sources/_notebooks/lenet-getting-started.ipynb.txt
+++ b/docs/_sources/_notebooks/lenet-getting-started.ipynb.txt
@@ -6,7 +6,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Copyright 2019-22 NVIDIA Corporation. All Rights Reserved.\n",
+    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
     "#\n",
     "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
     "# you may not use this file except in compliance with the License.\n",
@@ -26,8 +26,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_lenet/nvidia_logo.png\" width=\"90px\">\n",
-    "\n",
+    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
     "\n",
     "# Torch-TensorRT Getting Started - LeNet"
    ]
@@ -691,7 +690,7 @@
     "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT and test the performance impact of the optimization.\n",
     "\n",
     "### What's next\n",
-    "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/pytorch/TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+    "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
    ]
   }
  ],
@@ -711,7 +710,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/_sources/_notebooks/ssd-object-detection-demo.ipynb.txt b/docs/_sources/_notebooks/ssd-object-detection-demo.ipynb.txt
index b7ae8dc2e8..f2a0bd8063 100644
--- a/docs/_sources/_notebooks/ssd-object-detection-demo.ipynb.txt
+++ b/docs/_sources/_notebooks/ssd-object-detection-demo.ipynb.txt
@@ -26,7 +26,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_ssd/nvidia_logo.png\" width=\"90px\">\n",
+    "<img src=\"https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_ssd_notebook.png\" style=\"width: 90px; float: right;\">\n",
     "\n",
     "# Object Detection with Torch-TensorRT (SSD)"
    ]
@@ -935,7 +935,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/_sources/_notebooks/vgg-qat.ipynb.txt b/docs/_sources/_notebooks/vgg-qat.ipynb.txt
index 5888950378..cca771ad92 100644
--- a/docs/_sources/_notebooks/vgg-qat.ipynb.txt
+++ b/docs/_sources/_notebooks/vgg-qat.ipynb.txt
@@ -1,35 +1,10 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a054a8df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Copyright 2020-2022 NVIDIA Corporation. All Rights Reserved.\n",
-    "#\n",
-    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-    "# you may not use this file except in compliance with the License.\n",
-    "# You may obtain a copy of the License at\n",
-    "#\n",
-    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
-    "#\n",
-    "# Unless required by applicable law or agreed to in writing, software\n",
-    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-    "# See the License for the specific language governing permissions and\n",
-    "# limitations under the License.\n",
-    "# =============================================================================="
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "8033b2eb",
    "metadata": {},
    "source": [
-    "<img src=\"http://developer.download.nvidia.com/notebooks/dlsw-notebooks/tensorrt_torchtrt_qat_vgg/nvidia_logo.png\" width=\"90px\">\n",
-    "\n",
     "# Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT"
    ]
   },
@@ -60,7 +35,7 @@
    "source": [
     "<a id=\"1\"></a>\n",
     "##  1. Requirements\n",
-    "Please install the <a href=\"https://github.com/pytorch/TensorRT/tree/master/examples/int8/training/vgg16#prequisites\">required dependencies</a> and import these libraries accordingly"
+    "Please install the <a href=\"https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/int8/training/vgg16#prequisites\">required dependencies</a> and import these libraries accordingly"
    ]
   },
   {
@@ -1028,7 +1003,7 @@
     "%quant_weight : Tensor = aten::fake_quantize_per_channel_affine(%394, %640, %641, %637, %638, %639)\n",
     "%input.2 : Tensor = aten::_convolution(%quant_input, %quant_weight, %395, %687, %688, %689, %643, %690, %642, %643, %643, %644, %644)\n",
     "```\n",
-    "`aten::fake_quantize_per_*_affine` is converted into `QuantizeLayer` + `DequantizeLayer` in Torch-TensorRT internally. Please refer to <a href=\"https://github.com/pytorch/TensorRT/blob/master/core/conversion/converters/impl/quantization.cpp\">quantization op converters</a> in Torch-TensorRT."
+    "`aten::fake_quantize_per_*_affine` is converted into `QuantizeLayer` + `DequantizeLayer` in Torch-TensorRT internally. Please refer to <a href=\"https://github.com/NVIDIA/Torch-TensorRT/blob/master/core/conversion/converters/impl/quantization.cpp\">quantization op converters</a> in Torch-TensorRT."
    ]
   },
   {
@@ -1193,8 +1168,8 @@
     "##  9. References\n",
     "* <a href=\"https://arxiv.org/pdf/1409.1556.pdf\">Very Deep Convolution Networks for large scale Image Recognition</a>\n",
     "* <a href=\"https://developer.nvidia.com/blog/achieving-fp32-accuracy-for-int8-inference-using-quantization-aware-training-with-tensorrt/\">Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT</a>\n",
-    "* <a href=\"https://github.com/pytorch/TensorRT/tree/master/examples/int8/training/vgg16#quantization-aware-fine-tuning-for-trying-out-qat-workflows\">QAT workflow for VGG16</a>\n",
-    "* <a href=\"https://github.com/pytorch/TensorRT/tree/master/examples/int8/qat\">Deploying VGG QAT model in C++ using Torch-TensorRT</a>\n",
+    "* <a href=\"https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/int8/training/vgg16#quantization-aware-fine-tuning-for-trying-out-qat-workflows\">QAT workflow for VGG16</a>\n",
+    "* <a href=\"https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/int8/qat\">Deploying VGG QAT model in C++ using Torch-TensorRT</a>\n",
     "* <a href=\"https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization\">Pytorch-quantization toolkit from NVIDIA</a>\n",
     "* <a href=\"https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/userguide.html\">Pytorch quantization toolkit userguide</a>\n",
     "* <a href=\"https://arxiv.org/pdf/2004.09602.pdf\">Quantization basics</a>"
@@ -1217,7 +1192,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/_sources/getting_started/getting_started_with_windows.rst.txt b/docs/_sources/getting_started/getting_started_with_windows.rst.txt
index d5d3394855..04eba960e0 100644
--- a/docs/_sources/getting_started/getting_started_with_windows.rst.txt
+++ b/docs/_sources/getting_started/getting_started_with_windows.rst.txt
@@ -59,7 +59,7 @@ Build configuration
             }
           ]
         }
-      ]  
+      ]
     }
 
 
diff --git a/docs/_sources/indices/supported_ops.rst.txt b/docs/_sources/indices/supported_ops.rst.txt
index 2ce7b34ae7..0367c02710 100644
--- a/docs/_sources/indices/supported_ops.rst.txt
+++ b/docs/_sources/indices/supported_ops.rst.txt
@@ -23,6 +23,8 @@ Operators Currently Supported Through Converters
 - aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
 - aten::add.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
 - aten::add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> (Tensor(a!))
+- aten::argmax(Tensor self, int dim, bool keepdim=False) -> (Tensor)
+- aten::argmin(Tensor self, int dim, bool keepdim=False) -> (Tensor)
 - aten::asin(Tensor self) -> (Tensor)
 - aten::asinh(Tensor self) -> (Tensor)
 - aten::atan(Tensor self) -> (Tensor)
@@ -31,6 +33,7 @@ Operators Currently Supported Through Converters
 - aten::avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=[0, 0], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> (Tensor)
 - aten::avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=[], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> (Tensor)
 - aten::batch_norm(Tensor input, Tensor? gamma, Tensor? beta, Tensor? mean, Tensor? var, bool training, float momentum, float eps, bool cudnn_enabled) -> (Tensor)
+- aten::bitwise_not(Tensor self) -> (Tensor)
 - aten::bmm(Tensor self, Tensor mat2) -> (Tensor)
 - aten::cat(Tensor[] tensors, int dim=0) -> (Tensor)
 - aten::ceil(Tensor self) -> (Tensor)
@@ -90,6 +93,7 @@ Operators Currently Supported Through Converters
 - aten::mean(Tensor self, *, int? dtype=None) -> (Tensor)
 - aten::mean.dim(Tensor self, int[] dim, bool keepdim=False, *, int? dtype=None) -> (Tensor)
 - aten::min(Tensor self) -> (Tensor)
+- aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
 - aten::min.other(Tensor self, Tensor other) -> (Tensor)
 - aten::mul.Scalar(Tensor self, Scalar other) -> (Tensor)
 - aten::mul.Tensor(Tensor self, Tensor other) -> (Tensor)
@@ -113,6 +117,7 @@ Operators Currently Supported Through Converters
 - aten::relu(Tensor input) -> (Tensor)
 - aten::relu_(Tensor(a!) self) -> (Tensor(a!))
 - aten::repeat(Tensor self, int[] repeats) -> (Tensor)
+- aten::repeat_interleave.self_int(Tensor self, int repeats, int? dim=None, *, int? output_size=None) -> (Tensor)
 - aten::replication_pad1d(Tensor self, int[2] padding) -> (Tensor)
 - aten::replication_pad2d(Tensor self, int[4] padding) -> (Tensor)
 - aten::replication_pad3d(Tensor self, int[6] padding) -> (Tensor)
@@ -120,6 +125,8 @@ Operators Currently Supported Through Converters
 - aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> (Tensor)
 - aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
 - aten::rsub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
+- aten::scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> (Tensor)
+- aten::scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> (Tensor)
 - aten::select.int(Tensor(a) self, int dim, int index) -> (Tensor(a))
 - aten::sigmoid(Tensor input) -> (Tensor)
 - aten::sigmoid_(Tensor(a!) self) -> (Tensor(a!))
@@ -129,8 +136,10 @@ Operators Currently Supported Through Converters
 - aten::softmax.int(Tensor self, int dim, int? dtype=None) -> (Tensor)
 - aten::split(Tensor self, int[] split_sizes, int dim=0) -> (Tensor[])
 - aten::split.Tensor(Tensor(a) self, int split_size, int dim=0) -> (Tensor[])
+- aten::split.sizes(Tensor(a -> *) self, int[] split_size, int dim=0) -> (Tensor[])
 - aten::split_with_sizes(Tensor(a) self, int[] split_sizes, int dim=0) -> (Tensor[])
 - aten::sqrt(Tensor self) -> (Tensor)
+- aten::square(Tensor self) -> (Tensor)
 - aten::squeeze.dim(Tensor(a) self, int dim) -> (Tensor(a))
 - aten::stack(Tensor[] tensors, int dim=0) -> (Tensor)
 - aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
@@ -179,6 +188,7 @@ Operators Currently Supported Through Evaluators
 - aten::Int.int(int a) -> int
 - aten::__and__(int a, int b) -> (bool)
 - aten::__and__.bool(bool a, bool b) -> (bool)
+- aten::__derive_index(int idx, int start, int step) -> int
 - aten::__getitem__.t(t[](a) list, int idx) -> (t(*))
 - aten::__is__(t1 self, t2 obj) -> bool
 - aten::__isnot__(t1 self, t2 obj) -> bool
@@ -258,6 +268,7 @@ Operators Currently Supported Through Evaluators
 - aten::sub.float(float a, float b) -> (float)
 - aten::sub.int(int a, int b) -> (int)
 - aten::tensor(t[] data, *, int? dtype=None, Device? device=None, bool requires_grad=False) -> (Tensor)
+- prim::TupleIndex(Any tup, int i) -> (Any)
 - prim::dtype(Tensor a) -> (int)
 - prim::max.bool(bool a, bool b) -> (bool)
 - prim::max.float(float a, float b) -> (bool)
diff --git a/docs/_sources/tutorials/getting_started_with_fx_path.rst.txt b/docs/_sources/tutorials/getting_started_with_fx_path.rst.txt
index a81894bb81..3e8b8733ad 100644
--- a/docs/_sources/tutorials/getting_started_with_fx_path.rst.txt
+++ b/docs/_sources/tutorials/getting_started_with_fx_path.rst.txt
@@ -22,7 +22,7 @@ Converting a PyTorch Model to TensorRT Engine
 In general, users are welcome to use the ``compile()`` to finish the conversion from a model to tensorRT engine. It is a
 wrapper API that consists of the major steps needed to finish this converison. Please refer to an example usage in ``lower_example.py`` file under ``examples/fx``.
 
-.. code-block:: shell
+.. code-block:: python
 
     def compile(
         module: nn.Module,
@@ -37,7 +37,7 @@ wrapper API that consists of the major steps needed to finish this converison. P
         cuda_graph_batch_size=-1,
         dynamic_batch=True,
     ) -> nn.Module:
-    
+
         """
         Takes in original module, input and lowering setting, run lowering workflow to turn module
         into lowered module, or so called TRTModule.
@@ -64,7 +64,7 @@ Users can refer to ``fx2trt_example.py`` file in ``examples/fx``.
 * **Step 1: Trace the model with acc_tracer**
 Acc_tracer is a tracer inheritated from FX tracer. It comes with args normalizer to convert all args to kwargs and pass to TRT converters.
 
-.. code-block:: shell
+.. code-block:: python
 
    import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
 
@@ -92,10 +92,10 @@ Explicit batch is the default mode and it must be set for dynamic shape. For mos
 
 For examples of the last path, if we have a 3D tensor t shaped as (batch, sequence, dimension), operations such as torch.transpose(0, 2). If any of these three are not satisfied, we’ll need to specify InputTensorSpec as inputs with dynamic range.
 
-c
+.. code-block:: python
 
-    import deeplearning.trt.fx2trt.converter.converters
-    from torch.fx.experimental.fx2trt.fx2trt import InputTensorSpec, TRTInterpreter
+    import torch_tensorrt.fx.converters
+    from torch_tensorrt.fx import InputTensorSpec, TRTInterpreter
 
     # InputTensorSpec is a dataclass we use to store input information.
     # There're two ways we can build input_specs.
@@ -169,7 +169,7 @@ RuntimeError: Conversion of function xxx not currently supported!
 * **Step 3: Run the model**
 One way is using TRTModule, which is basically a PyTorch nn.Module.
 
-.. code-block:: shell
+.. code-block:: python
 
     from torch_tensorrt.fx import TRTModule
     mod = TRTModule(
@@ -192,13 +192,13 @@ Acc tracer is a custom FX symbolic tracer. It does a couple more things compare
 1. there’re many ops that do similar things in PyTorch ops and builtin ops such like torch.add, builtin.add and torch.Tensor.add. Using acc tracer, we normalize these three ops to a single acc_ops.add. This helps reduce the number of converters we need to write.
 2. acc ops only have kwargs which makes writing converter easier as we don’t need to add additional logic to find arguments in args and kwargs.
 
-FX2TRT
+Torch-TensorRT FX Frontend
 --------
-After symbolic tracing, we have the graph representation of a PyTorch model. fx2trt leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).
+After symbolic tracing, we have the graph representation of a PyTorch model. The Torch-TensorRT FX Frontend leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).
 
 Below is an example of a converter function. The decorator is used to register this converter function with the corresponding node. In this example, we register this converter to a fx node whose target is acc_ops.sigmoid.
 
-.. code-block:: shell
+.. code-block:: python
 
     @tensorrt_converter(acc_ops.sigmoid)
     def acc_ops_sigmoid(network, target, args, kwargs, name):
diff --git a/docs/_sources/tutorials/installation.rst.txt b/docs/_sources/tutorials/installation.rst.txt
index 4c4905db96..949fa2ddc9 100644
--- a/docs/_sources/tutorials/installation.rst.txt
+++ b/docs/_sources/tutorials/installation.rst.txt
@@ -25,14 +25,14 @@ You can install the python package using
 
 .. code-block:: sh
 
-    pip3 install torch-tensorrt -f https://github.com/pytorch/TensorRT/releases
+    pip3 install torch-tensorrt -f https://github.com/NVIDIA/Torch-TensorRT/releases
 
 .. _bin-dist:
 
 C++ Binary Distribution
 ------------------------
 
-Precompiled tarballs for releases are provided here: https://github.com/pytorch/TensorRT/releases
+Precompiled tarballs for releases are provided here: https://github.com/NVIDIA/Torch-TensorRT/releases
 
 .. _compile-from-source:
 
diff --git a/docs/_sources/tutorials/notebooks.rst.txt b/docs/_sources/tutorials/notebooks.rst.txt
index df903fc353..24279113ab 100644
--- a/docs/_sources/tutorials/notebooks.rst.txt
+++ b/docs/_sources/tutorials/notebooks.rst.txt
@@ -125,10 +125,10 @@ Accelerate Deep Learning Models using Quantization in Torch-TensorRT
 Model Quantization is a popular way of optimization which reduces the size of models thereby
 accelerating inference, also opening up the possibilities of deployments on devices with lower
 computation power such as Jetson. Simply put, quantization is a process of mapping input values
- from a larger set to output values in a smaller set. In this notebook, we illustrate the workflow
- that you can adopt while quantizing a deep learning model in Torch-TensorRT. The notebook takes
- you through an example of Mobilenetv2 for a classification task on a subset of Imagenet Dataset
- called Imagenette which has 10 classes.
+from a larger set to output values in a smaller set. In this notebook, we illustrate the workflow
+that you can adopt while quantizing a deep learning model in Torch-TensorRT. The notebook takes
+you through an example of Mobilenetv2 for a classification task on a subset of Imagenet Dataset
+called Imagenette which has 10 classes.
 
 * `Accelerate Deep Learning Models using Quantization in Torch-TensorRT <https://github.com/pytorch/TensorRT/blob/master/notebooks/qat-ptq-workflow.ipynb>`_
 
diff --git a/docs/_sources/tutorials/torchtrtc.rst.txt b/docs/_sources/tutorials/torchtrtc.rst.txt
index dc9e4b6768..b841c891e5 100644
--- a/docs/_sources/tutorials/torchtrtc.rst.txt
+++ b/docs/_sources/tutorials/torchtrtc.rst.txt
@@ -92,12 +92,10 @@ to standard TorchScript. Load with ``torch.jit.load()`` and run like you would r
                                           used to select kernels
         --workspace-size=[workspace_size] Maximum size of workspace given to
                                           TensorRT
-        --atol=[atol]                     Absolute tolerance threshold for acceptable
-                                          numerical deviation from standard torchscript
-                                          output (default 1e-8)
-        --rtol=[rtol]                     Relative tolerance threshold for acceptable
-                                          numerical deviation from standard torchscript
-                                          output  (default 1e-5)
+        -t[threshold],
+        --threshold=[threshold]           Maximum acceptable numerical deviation
+                                          from standard torchscript output
+                                          (default 2e-5)
         --no-threshold-check              Skip checking threshold compliance
         --truncate-long-double,
         --truncate, --truncate-64bit      Truncate weights that are provided in
diff --git a/docs/_static/css/theme.css b/docs/_static/css/theme.css
index e4a55c2ac9..335a5b8346 100644
--- a/docs/_static/css/theme.css
+++ b/docs/_static/css/theme.css
@@ -9947,6 +9947,7 @@ pre {
     padding: 5rem 0;
   }
 }
+
 .site-footer p {
   color: #ffffff;
 }
@@ -9955,6 +9956,7 @@ pre {
   padding-left: 0;
   margin-bottom: 0;
 }
+
 .site-footer ul li {
   font-size: 1.125rem;
   line-height: 2rem;
@@ -9975,6 +9977,44 @@ pre {
   }
 }
 
+.site-footer .privacy-policy {
+    background: #000000;
+    display: flex;
+    border-bottom: 1px solid white;
+    padding-bottom: 10px;
+}
+
+.site-footer .privacy-policy-links {
+    background: #000000;
+    display: flex;
+    padding-top: 1rem;
+    padding-right: 1rem;
+    display: inline-flex;
+    color: white;
+}
+
+.site-footer .footer-links-wrapper {
+    display: flex;
+    flex-wrap: wrap;
+    border-bottom: 1px solid white;
+    padding-bottom: 1rem;
+}
+
+.site-footer .copyright {
+    padding-top: 1rem;
+    padding-right: 1rem;
+    display: inline-flex;
+    color: white;
+}
+
+.site-footer .copyright p {
+    color: white;
+}
+
+.site-footer .copyright a {
+    color: red;
+}
+
 .docs-tutorials-resources {
   background-color: #262626;
   color: #ffffff;
diff --git a/docs/_static/js/theme.js b/docs/_static/js/theme.js
index 798a8fe3c8..ceee8f062c 100644
--- a/docs/_static/js/theme.js
+++ b/docs/_static/js/theme.js
@@ -1124,4 +1124,5 @@ $(window).scroll(function () {
   });
 });
 
+
 },{"jquery":"jquery"}]},{},[1,2,3,4,5,6,7,8,9,10,"pytorch-sphinx-theme"]);
diff --git a/docs/cli/torchtrtc.html b/docs/cli/torchtrtc.html
index 1dd6a961bc..07e7926245 100644
--- a/docs/cli/torchtrtc.html
+++ b/docs/cli/torchtrtc.html
@@ -9,22 +9,22 @@
   <meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
-  
+
   <title>torchtrtc &mdash; Torch-TensorRT master documentation</title>
-  
 
-  
-  
-  
-  
 
-  
 
-  
-  
-    
 
-  
+
+
+
+
+
+
+
+
+
+
 
   <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!-- <link rel="stylesheet" href="../_static/pygments.css" type="text/css" /> -->
@@ -36,11 +36,11 @@
     <link rel="next" title="System Overview" href="../contributors/system_overview.html" />
     <link rel="prev" title="Define XSTR" href="../_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html" />
   <!-- Google Analytics -->
-  
+
   <!-- End Google Analytics -->
-  
 
-  
+
+
   <script src="../_static/js/modernizr.min.js"></script>
 
   <!-- Preload the theme fonts -->
@@ -180,9 +180,9 @@
 
 <body class="pytorch-body">
 
-   
 
-    
+
+
 
     <div class="table-of-contents-link-wrapper">
       <span>Table of Contents</span>
@@ -193,21 +193,21 @@
       <div class="pytorch-side-scroll">
         <div class="pytorch-menu pytorch-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
           <div class="pytorch-left-menu-search">
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 <span id="partitioning"></span><h1>Partitioning Phase<a class="headerlink" href="#partitioning-phase" title="Permalink to this headline">¶</a></h1>
 <p>The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
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 <span id="execution"></span><h1>Runtime Phase<a class="headerlink" href="#runtime-phase" title="Permalink to this headline">¶</a></h1>
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 <span id="getting-started-cpp"></span><h1>Using Torch-TensorRT in  C++<a class="headerlink" href="#using-torch-tensorrt-in-c" title="Permalink to this headline">¶</a></h1>
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 <span id="getting-started-with-python-api"></span><h1>Using Torch-TensorRT in Python<a class="headerlink" href="#using-torch-tensorrt-in-python" title="Permalink to this headline">¶</a></h1>
 <p>The Torch-TensorRT Python API supports a number of unique usecases compared to the CLI and C++ APIs which solely support TorchScript compilation.</p>
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 <span id="getting-started-windows"></span><h1>Building Torch-TensorRT on Windows<a class="headerlink" href="#building-torch-tensorrt-on-windows" title="Permalink to this headline">¶</a></h1>
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   <section id="operators-supported">
 <span id="supported-ops"></span><h1>Operators Supported<a class="headerlink" href="#operators-supported" title="Permalink to this headline">¶</a></h1>
 <section id="operators-currently-supported-through-converters">
@@ -364,7 +364,9 @@ <h2>Operators Currently Supported Through Converters<a class="headerlink" href="
 <li><p>aten::adaptive_max_pool3d(Tensor self, int[3] output_size) -&gt; (Tensor, Tensor)</p></li>
 <li><p>aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -&gt; (Tensor)</p></li>
 <li><p>aten::add.Tensor(Tensor self, Tensor other, Scalar alpha=1) -&gt; (Tensor)</p></li>
-<li><p>aten::<a href="#id37"><span class="problematic" id="id38">add_</span></a>.Tensor(Tensor(a!) self, Tensor other, <a href="#id1"><span class="problematic" id="id2">*</span></a>, Scalar alpha=1) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::<a href="#id41"><span class="problematic" id="id42">add_</span></a>.Tensor(Tensor(a!) self, Tensor other, <a href="#id1"><span class="problematic" id="id2">*</span></a>, Scalar alpha=1) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::argmax(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor)</p></li>
+<li><p>aten::argmin(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor)</p></li>
 <li><p>aten::asin(Tensor self) -&gt; (Tensor)</p></li>
 <li><p>aten::asinh(Tensor self) -&gt; (Tensor)</p></li>
 <li><p>aten::atan(Tensor self) -&gt; (Tensor)</p></li>
@@ -373,6 +375,7 @@ <h2>Operators Currently Supported Through Converters<a class="headerlink" href="
 <li><p>aten::avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=[0, 0], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -&gt; (Tensor)</p></li>
 <li><p>aten::avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=[], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -&gt; (Tensor)</p></li>
 <li><p>aten::batch_norm(Tensor input, Tensor? gamma, Tensor? beta, Tensor? mean, Tensor? var, bool training, float momentum, float eps, bool cudnn_enabled) -&gt; (Tensor)</p></li>
+<li><p>aten::bitwise_not(Tensor self) -&gt; (Tensor)</p></li>
 <li><p>aten::bmm(Tensor self, Tensor mat2) -&gt; (Tensor)</p></li>
 <li><p>aten::cat(Tensor[] tensors, int dim=0) -&gt; (Tensor)</p></li>
 <li><p>aten::ceil(Tensor self) -&gt; (Tensor)</p></li>
@@ -386,8 +389,8 @@ <h2>Operators Currently Supported Through Converters<a class="headerlink" href="
 <li><p>aten::div.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
 <li><p>aten::div.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
 <li><p>aten::div.Tensor_mode(Tensor self, Tensor other, <a href="#id5"><span class="problematic" id="id6">*</span></a>, str? rounding_mode) -&gt; (Tensor)</p></li>
-<li><p>aten::<a href="#id39"><span class="problematic" id="id40">div_</span></a>.Scalar(Tensor(a!) self, Scalar other) -&gt; (Tensor(a!))</p></li>
-<li><p>aten::<a href="#id41"><span class="problematic" id="id42">div_</span></a>.Tensor(Tensor(a!) self, Tensor other) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::<a href="#id43"><span class="problematic" id="id44">div_</span></a>.Scalar(Tensor(a!) self, Scalar other) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::<a href="#id45"><span class="problematic" id="id46">div_</span></a>.Tensor(Tensor(a!) self, Tensor other) -&gt; (Tensor(a!))</p></li>
 <li><p>aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -&gt; (Tensor)</p></li>
 <li><p>aten::embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -&gt; (Tensor)</p></li>
 <li><p>aten::eq.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
@@ -432,10 +435,11 @@ <h2>Operators Currently Supported Through Converters<a class="headerlink" href="
 <li><p>aten::mean(Tensor self, <a href="#id9"><span class="problematic" id="id10">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
 <li><p>aten::mean.dim(Tensor self, int[] dim, bool keepdim=False, <a href="#id11"><span class="problematic" id="id12">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
 <li><p>aten::min(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::min.dim(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor values, Tensor indices)</p></li>
 <li><p>aten::min.other(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
 <li><p>aten::mul.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
 <li><p>aten::mul.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
-<li><p>aten::<a href="#id43"><span class="problematic" id="id44">mul_</span></a>.Tensor(Tensor(a!) self, Tensor other) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::<a href="#id47"><span class="problematic" id="id48">mul_</span></a>.Tensor(Tensor(a!) self, Tensor other) -&gt; (Tensor(a!))</p></li>
 <li><p>aten::narrow(Tensor(a) self, int dim, int start, int length) -&gt; (Tensor(a))</p></li>
 <li><p>aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, int length) -&gt; (Tensor(a))</p></li>
 <li><p>aten::ne.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
@@ -455,6 +459,7 @@ <h2>Operators Currently Supported Through Converters<a class="headerlink" href="
 <li><p>aten::relu(Tensor input) -&gt; (Tensor)</p></li>
 <li><p>aten::relu_(Tensor(a!) self) -&gt; (Tensor(a!))</p></li>
 <li><p>aten::repeat(Tensor self, int[] repeats) -&gt; (Tensor)</p></li>
+<li><p>aten::repeat_interleave.self_int(Tensor self, int repeats, int? dim=None, <a href="#id17"><span class="problematic" id="id18">*</span></a>, int? output_size=None) -&gt; (Tensor)</p></li>
 <li><p>aten::replication_pad1d(Tensor self, int[2] padding) -&gt; (Tensor)</p></li>
 <li><p>aten::replication_pad2d(Tensor self, int[4] padding) -&gt; (Tensor)</p></li>
 <li><p>aten::replication_pad3d(Tensor self, int[6] padding) -&gt; (Tensor)</p></li>
@@ -462,6 +467,8 @@ <h2>Operators Currently Supported Through Converters<a class="headerlink" href="
 <li><p>aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -&gt; (Tensor)</p></li>
 <li><p>aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -&gt; (Tensor)</p></li>
 <li><p>aten::rsub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -&gt; (Tensor)</p></li>
+<li><p>aten::scatter.src(Tensor self, int dim, Tensor index, Tensor src) -&gt; (Tensor)</p></li>
+<li><p>aten::scatter.value(Tensor self, int dim, Tensor index, Scalar value) -&gt; (Tensor)</p></li>
 <li><p>aten::select.int(Tensor(a) self, int dim, int index) -&gt; (Tensor(a))</p></li>
 <li><p>aten::sigmoid(Tensor input) -&gt; (Tensor)</p></li>
 <li><p>aten::sigmoid_(Tensor(a!) self) -&gt; (Tensor(a!))</p></li>
@@ -471,15 +478,17 @@ <h2>Operators Currently Supported Through Converters<a class="headerlink" href="
 <li><p>aten::softmax.int(Tensor self, int dim, int? dtype=None) -&gt; (Tensor)</p></li>
 <li><p>aten::split(Tensor self, int[] split_sizes, int dim=0) -&gt; (Tensor[])</p></li>
 <li><p>aten::split.Tensor(Tensor(a) self, int split_size, int dim=0) -&gt; (Tensor[])</p></li>
+<li><p>aten::split.sizes(Tensor(a -&gt; <a href="#id19"><span class="problematic" id="id20">*</span></a>) self, int[] split_size, int dim=0) -&gt; (Tensor[])</p></li>
 <li><p>aten::split_with_sizes(Tensor(a) self, int[] split_sizes, int dim=0) -&gt; (Tensor[])</p></li>
 <li><p>aten::sqrt(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::square(Tensor self) -&gt; (Tensor)</p></li>
 <li><p>aten::squeeze.dim(Tensor(a) self, int dim) -&gt; (Tensor(a))</p></li>
 <li><p>aten::stack(Tensor[] tensors, int dim=0) -&gt; (Tensor)</p></li>
 <li><p>aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -&gt; (Tensor)</p></li>
 <li><p>aten::sub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -&gt; (Tensor)</p></li>
-<li><p>aten::<a href="#id45"><span class="problematic" id="id46">sub_</span></a>.Tensor(Tensor(a!) self, Tensor other, <a href="#id17"><span class="problematic" id="id18">*</span></a>, Scalar alpha=1) -&gt; (Tensor(a!))</p></li>
-<li><p>aten::sum(Tensor self, <a href="#id19"><span class="problematic" id="id20">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
-<li><p>aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, <a href="#id21"><span class="problematic" id="id22">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::<a href="#id49"><span class="problematic" id="id50">sub_</span></a>.Tensor(Tensor(a!) self, Tensor other, <a href="#id21"><span class="problematic" id="id22">*</span></a>, Scalar alpha=1) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::sum(Tensor self, <a href="#id23"><span class="problematic" id="id24">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, <a href="#id25"><span class="problematic" id="id26">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
 <li><p>aten::t(Tensor self) -&gt; (Tensor)</p></li>
 <li><p>aten::tan(Tensor self) -&gt; (Tensor)</p></li>
 <li><p>aten::tanh(Tensor input) -&gt; (Tensor)</p></li>
@@ -490,7 +499,7 @@ <h2>Operators Currently Supported Through Converters<a class="headerlink" href="
 <li><p>aten::to.prim_Device(Tensor(a) self, Device? device, int? dtype=None, bool non_blocking=False, bool copy=False) -&gt; (Tensor(a|b))</p></li>
 <li><p>aten::topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -&gt; (Tensor values, Tensor indices)</p></li>
 <li><p>aten::transpose.int(Tensor(a) self, int dim0, int dim1) -&gt; (Tensor(a))</p></li>
-<li><p>aten::unbind.int(Tensor(a -&gt; <a href="#id23"><span class="problematic" id="id24">*</span></a>) self, int dim=0) -&gt; (Tensor[])</p></li>
+<li><p>aten::unbind.int(Tensor(a -&gt; <a href="#id27"><span class="problematic" id="id28">*</span></a>) self, int dim=0) -&gt; (Tensor[])</p></li>
 <li><p>aten::unsqueeze(Tensor(a) self, int dim) -&gt; (Tensor(a))</p></li>
 <li><p>aten::upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -&gt; (Tensor)</p></li>
 <li><p>aten::upsample_bilinear2d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -&gt; (Tensor)</p></li>
@@ -522,6 +531,7 @@ <h2>Operators Currently Supported Through Evaluators<a class="headerlink" href="
 <li><p>aten::Int.int(int a) -&gt; int</p></li>
 <li><p>aten::__and__(int a, int b) -&gt; (bool)</p></li>
 <li><p>aten::__and__.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::__derive_index(int idx, int start, int step) -&gt; int</p></li>
 <li><p>aten::__getitem__.t(t[](a) list, int idx) -&gt; (t(*))</p></li>
 <li><p>aten::__is__(t1 self, t2 obj) -&gt; bool</p></li>
 <li><p>aten::__isnot__(t1 self, t2 obj) -&gt; bool</p></li>
@@ -533,24 +543,24 @@ <h2>Operators Currently Supported Through Evaluators<a class="headerlink" href="
 <li><p>aten::add.float(float a, float b) -&gt; (float)</p></li>
 <li><p>aten::add.int(int a, int b) -&gt; (int)</p></li>
 <li><p>aten::add.str(str a, str b) -&gt; (str)</p></li>
-<li><p>aten::<a href="#id47"><span class="problematic" id="id48">add_</span></a>.t(t[](a!) self, t[] b) -&gt; (t[])</p></li>
-<li><p>aten::append.t(t[](a!) self, t(c -&gt; <a href="#id25"><span class="problematic" id="id26">*</span></a>) el) -&gt; (t[](a!))</p></li>
+<li><p>aten::<a href="#id51"><span class="problematic" id="id52">add_</span></a>.t(t[](a!) self, t[] b) -&gt; (t[])</p></li>
+<li><p>aten::append.t(t[](a!) self, t(c -&gt; <a href="#id29"><span class="problematic" id="id30">*</span></a>) el) -&gt; (t[](a!))</p></li>
 <li><dl class="simple">
-<dt>aten::arange(Scalar end, <a href="#id27"><span class="problematic" id="id28">*</span></a>, int? dtype=None, int? layout=None,</dt><dd><p>Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
+<dt>aten::arange(Scalar end, <a href="#id31"><span class="problematic" id="id32">*</span></a>, int? dtype=None, int? layout=None,</dt><dd><p>Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
 </dd>
 </dl>
 </li>
 <li><dl class="simple">
-<dt>aten::arange.start(Scalar start, Scalar end, <a href="#id29"><span class="problematic" id="id30">*</span></a>, ScalarType? dtype=None,</dt><dd><p>Layout? layout=None, Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
+<dt>aten::arange.start(Scalar start, Scalar end, <a href="#id33"><span class="problematic" id="id34">*</span></a>, ScalarType? dtype=None,</dt><dd><p>Layout? layout=None, Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
 </dd>
 </dl>
 </li>
 <li><dl class="simple">
-<dt>aten::arange.start_step(Scalar start, Scalar end, Scalar step, <a href="#id31"><span class="problematic" id="id32">*</span></a>, ScalarType? dtype=None,</dt><dd><p>Layout? layout=None, Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
+<dt>aten::arange.start_step(Scalar start, Scalar end, Scalar step, <a href="#id35"><span class="problematic" id="id36">*</span></a>, ScalarType? dtype=None,</dt><dd><p>Layout? layout=None, Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
 </dd>
 </dl>
 </li>
-<li><p>aten::clone(Tensor self, <a href="#id33"><span class="problematic" id="id34">*</span></a>, int? memory_format=None) -&gt; (Tensor)</p></li>
+<li><p>aten::clone(Tensor self, <a href="#id37"><span class="problematic" id="id38">*</span></a>, int? memory_format=None) -&gt; (Tensor)</p></li>
 <li><p>aten::copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -&gt; (Tensor(a!))</p></li>
 <li><p>aten::dim(Tensor self) -&gt; int</p></li>
 <li><p>aten::div.float(float a, float b) -&gt; (float)</p></li>
@@ -609,7 +619,8 @@ <h2>Operators Currently Supported Through Evaluators<a class="headerlink" href="
 <li><p>aten::sqrt.int(int a) -&gt; (float)</p></li>
 <li><p>aten::sub.float(float a, float b) -&gt; (float)</p></li>
 <li><p>aten::sub.int(int a, int b) -&gt; (int)</p></li>
-<li><p>aten::tensor(t[] data, <a href="#id35"><span class="problematic" id="id36">*</span></a>, int? dtype=None, Device? device=None, bool requires_grad=False) -&gt; (Tensor)</p></li>
+<li><p>aten::tensor(t[] data, <a href="#id39"><span class="problematic" id="id40">*</span></a>, int? dtype=None, Device? device=None, bool requires_grad=False) -&gt; (Tensor)</p></li>
+<li><p>prim::TupleIndex(Any tup, int i) -&gt; (Any)</p></li>
 <li><p>prim::dtype(Tensor a) -&gt; (int)</p></li>
 <li><p>prim::max.bool(bool a, bool b) -&gt; (bool)</p></li>
 <li><p>prim::max.float(float a, float b) -&gt; (bool)</p></li>
@@ -630,23 +641,23 @@ <h2>Operators Currently Supported Through Evaluators<a class="headerlink" href="
 
 
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 <dl class="py function">
 <dt class="sig sig-object py" id="torch_tensorrt.ts.compile">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_signature=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_full_compilation=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_block_size=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_ops=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_modules=[]</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.jit._script.ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.compile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_signature=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=&lt;torch_tensorrt._Device.Device</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_full_compilation=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_block_size=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_ops=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_modules=[]</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.jit._script.ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.compile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compile a TorchScript module for NVIDIA GPUs using TensorRT</p>
 <p>Takes a existing TorchScript module and a set of settings to configure the compiler
 and will convert methods to JIT Graphs which call equivalent TensorRT engines</p>
@@ -434,7 +434,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch_tensorrt.ts.convert_method_to_trt_engine">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">convert_method_to_trt_engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name:</span> <span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#convert_method_to_trt_engine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.convert_method_to_trt_engine" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">convert_method_to_trt_engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name:</span> <span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=&lt;torch_tensorrt._Device.Device</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#convert_method_to_trt_engine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.convert_method_to_trt_engine" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a TorchScript module method to a serialized TensorRT engine</p>
 <p>Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</p>
 <dl class="field-list simple">
@@ -533,7 +533,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch_tensorrt.ts.embed_engine_in_new_module">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">embed_engine_in_new_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">serialized_engine</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bytes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.jit._script.ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#embed_engine_in_new_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.embed_engine_in_new_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">embed_engine_in_new_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">serialized_engine:</span> <span class="pre">bytes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=&lt;torch_tensorrt._Device.Device</span> <span class="pre">object&gt;</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.jit._script.ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#embed_engine_in_new_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.embed_engine_in_new_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Takes a pre-built serialized TensorRT engine and embeds it within a TorchScript module</p>
 <p>Takes a pre-built serialied TensorRT engine (as bytes) and embeds it within a TorchScript module.
 Registers the forward method to execute the TensorRT engine with the function signature:</p>
@@ -567,7 +567,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch_tensorrt.ts.TensorRTCompileSpec">
-<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">TensorRTCompileSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_signature=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">&lt;torch.ScriptClass</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fc1ca3ce630&gt;</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compile_spec.html#TensorRTCompileSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.TensorRTCompileSpec" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">TensorRTCompileSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_signature=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=&lt;torch_tensorrt._Device.Device</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">&lt;torch.ScriptClass</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f6a8d162db0&gt;</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compile_spec.html#TensorRTCompileSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.TensorRTCompileSpec" title="Permalink to this definition">¶</a></dt>
 <dd><p>Utility to create a formated spec dictionary for using the PyTorch TensorRT backend</p>
 <dl class="field-list simple">
 <dt class="field-odd">Keyword Arguments</dt>
@@ -614,25 +614,25 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 
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@@ -667,12 +667,12 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
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@@ -477,19 +477,43 @@ <h2>Resources</h2>
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-        <div class="footer-links-col follow-us-col">
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-            
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-        
+
       <li><span class="section-number">3. </span>Paragraph Level Markup</li>
-    
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+
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 <pre class="literal-block"># parsed-literal test
-curl -O <a class="reference external" href="http://someurl/release">http://someurl/release</a>-master (1.2.0a0+450c869).tar-gz</pre>
+curl -O <a class="reference external" href="http://someurl/release">http://someurl/release</a>-master (1.2.0).tar-gz</pre>
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+  </script>
 
   <!-- Begin Footer -->
 
@@ -1087,19 +1087,43 @@ <h2>Resources</h2>
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         </div>
 
-        <div class="footer-links-col follow-us-col">
+        <div class="footer-links-col">
           <ul>
-            <li class="list-title">Stay Connected</li>
+            <li class="list-title">Stay up to date</li>
+            <li><a href="https://www.facebook.com/pytorch" target="_blank">Facebook</a></li>
+            <li><a href="https://twitter.com/pytorch" target="_blank">Twitter</a></li>
+            <li><a href="https://www.youtube.com/pytorch" target="_blank">YouTube</a></li>
+            <li><a href="https://www.linkedin.com/company/pytorch" target="_blank">LinkedIn</a></li>
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+          <ul>
+            <li class="list-title">PyTorch Podcasts</li>
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+            <li><a href="https://podcasts.apple.com/us/podcast/pytorch-developer-podcast/id1566080008" target="_blank">Apple</a></li>
+            <li><a href="https://www.google.com/podcasts?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9PQjVGa0lsOA%3D%3D" target="_blank">Google</a></li>
+            <li><a href="https://music.amazon.com/podcasts/7a4e6f0e-26c2-49e9-a478-41bd244197d0/PyTorch-Developer-Podcast?" target="_blank">Amazon</a></li>
+          </ul>
+         </div>
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+
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+          <ul>
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+            <li class="privacy-policy-links">|</li>
+            <li class="privacy-policy-links"><a href="https://www.linuxfoundation.org/privacy-policy/" target="_blank">Privacy</a></li>
+          </ul>
+        </div>
+        <div class="copyright">
+        <p>© Copyright The Linux Foundation. The PyTorch Foundation is a project of The Linux Foundation.
+          For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see
+          <a href="www.linuxfoundation.org/policies/">www.linuxfoundation.org/policies/</a>. The PyTorch Foundation supports the PyTorch open source
+          project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC,
+          please see <a href="www.lfprojects.org/policies/">www.lfprojects.org/policies/</a>.</p>
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+
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diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html b/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
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-  
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-  
 
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+
 
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+                  master (1.2.0)
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+
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+
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+
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+
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+
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 <div class="contents topic" id="table-of-contents">
@@ -911,16 +911,16 @@ <h3><a class="toc-backref" href="#id31"><span class="section-number">4.2.2. </sp
 
 
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             <footer>
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-  
 
 
-  
 
-     
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         </div>
 
-        <div class="footer-links-col follow-us-col">
+        <div class="footer-links-col">
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-            <li class="list-title">Stay Connected</li>
+            <li class="list-title">Stay up to date</li>
+            <li><a href="https://www.facebook.com/pytorch" target="_blank">Facebook</a></li>
+            <li><a href="https://twitter.com/pytorch" target="_blank">Twitter</a></li>
+            <li><a href="https://www.youtube.com/pytorch" target="_blank">YouTube</a></li>
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+            <li class="list-title">PyTorch Podcasts</li>
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+            <li><a href="https://podcasts.apple.com/us/podcast/pytorch-developer-podcast/id1566080008" target="_blank">Apple</a></li>
+            <li><a href="https://www.google.com/podcasts?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9PQjVGa0lsOA%3D%3D" target="_blank">Google</a></li>
+            <li><a href="https://music.amazon.com/podcasts/7a4e6f0e-26c2-49e9-a478-41bd244197d0/PyTorch-Developer-Podcast?" target="_blank">Amazon</a></li>
+          </ul>
+         </div>
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+
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+            <li class="privacy-policy-links"><a href="https://www.linuxfoundation.org/privacy-policy/" target="_blank">Privacy</a></li>
+          </ul>
+        </div>
+        <div class="copyright">
+        <p>© Copyright The Linux Foundation. The PyTorch Foundation is a project of The Linux Foundation.
+          For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see
+          <a href="www.linuxfoundation.org/policies/">www.linuxfoundation.org/policies/</a>. The PyTorch Foundation supports the PyTorch open source
+          project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC,
+          please see <a href="www.lfprojects.org/policies/">www.lfprojects.org/policies/</a>.</p>
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diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/long.html b/docs/src/pytorch-sphinx-theme/docs/demo/long.html
index d687335baf..588c89495d 100644
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-  
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-   
 
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-<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
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-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notebooks</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/CitriNet-example.html">Torch-TensorRT Getting Started - CitriNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/dynamic-shapes.html">Torch-TensorRT - Using Dynamic Shapes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/EfficientNet-example.html">Torch-TensorRT Getting Started - EfficientNet-B0</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/Hugging-Face-BERT.html">Masked Language Modeling (MLM) with Hugging Face BERT Transformer</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/lenet-getting-started.html">Torch-TensorRT Getting Started - LeNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/Resnet50-example.html">Torch-TensorRT Getting Started - ResNet 50</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/ssd-object-detection-demo.html">Object Detection with Torch-TensorRT (SSD)</a></li>
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-</ul>
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../contributors/writing_converters.html">Writing Converters</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
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-<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
-</ul>
-
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+          <li class="md-nav__item">
+           <a class="md-nav__link" href="torchtrtc.html">
+            torchtrtc
+           </a>
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+          <li class="md-nav__item">
+           <a class="md-nav__link" href="use_from_pytorch.html">
+            Using Torch-TensorRT Directly From PyTorch
+           </a>
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+          <li class="md-nav__item">
+           <a class="md-nav__link" href="runtime.html">
+            Deploying Torch-TensorRT Programs
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+            DLA
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+           <a class="md-nav__link" href="../_notebooks/CitriNet-example.html">
+            Torch-TensorRT Getting Started - CitriNet
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/dynamic-shapes.html">
+            Torch-TensorRT - Using Dynamic Shapes
+           </a>
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+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/EfficientNet-example.html">
+            Torch-TensorRT Getting Started - EfficientNet-B0
+           </a>
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+           <a class="md-nav__link" href="../_notebooks/Hugging-Face-BERT.html">
+            Masked Language Modeling (MLM) with Hugging Face BERT Transformer
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/lenet-getting-started.html">
+            Torch-TensorRT Getting Started - LeNet
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/Resnet50-example.html">
+            Torch-TensorRT Getting Started - ResNet 50
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/ssd-object-detection-demo.html">
+            Object Detection with Torch-TensorRT (SSD)
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/vgg-qat.html">
+            Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT
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+            torch_tensorrt.ptq
+           </a>
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+            <span class="caption-text">
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+           <a class="md-nav__link" href="../_cpp_api/torch_tensort_cpp.html">
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+           </a>
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+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt.html">
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+           </a>
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+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__logging.html">
+            Namespace torch_tensorrt::logging
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">
+            Namespace torch_tensorrt::torchscript
+           </a>
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+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">
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+           <a class="md-nav__link" href="../indices/supported_ops.html">
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+            Getting Started with C++
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+            <ul class="md-nav__list">
+             <li class="md-nav__item">
+              <a class="md-nav__link" href="#using-torch-tensorrt-in-c">
+               Using Torch-TensorRT in C++
+              </a>
+              <nav class="md-nav">
+               <ul class="md-nav__list">
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc">
+                  [Torch-TensorRT Quickstart] Compiling TorchScript Modules with
+                  <code class="docutils literal notranslate">
+                   <span class="pre">
+                    torchtrtc
+                   </span>
+                  </code>
+                 </a>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#working-with-torchscript-in-c">
+                  Working with TorchScript in C++
+                 </a>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#compiling-with-torch-tensorrt-in-c">
+                  Compiling with Torch-TensorRT in C++
+                 </a>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#under-the-hood">
+                  Under The Hood
+                 </a>
+                </li>
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+                 <a class="md-nav__link" href="#working-with-unsupported-operators">
+                  Working with Unsupported Operators
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+                    <a class="md-nav__link" href="#registering-custom-converters">
+                     Registering Custom Converters
+                    </a>
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-
-  <section id="getting-started-with-c">
-<span id="getting-started"></span><h1>Getting Started with C++<a class="headerlink" href="#getting-started-with-c" title="Permalink to this headline">¶</a></h1>
-<p>If you haven’t already, acquire a tarball of the library by following the instructions in <a class="reference internal" href="installation.html#installation"><span class="std std-ref">Installation</span></a></p>
-<section id="using-torch-tensorrt-in-c">
-<h2>Using Torch-TensorRT in C++<a class="headerlink" href="#using-torch-tensorrt-in-c" title="Permalink to this headline">¶</a></h2>
-<p>Torch-TensorRT C++ API accepts TorchScript modules (generated either from <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.jit.trace</span></code>) as an input and returns
+     </div>
+     <div class="md-content">
+      <article class="md-content__inner md-typeset" role="main">
+       <span id="getting-started">
+       </span>
+       <h1 id="tutorials-getting-started-with-cpp-api--page-root">
+        Getting Started with C++
+        <a class="headerlink" href="#tutorials-getting-started-with-cpp-api--page-root" title="Permalink to this headline">
+         ¶
+        </a>
+       </h1>
+       <p>
+        If you haven’t already, acquire a tarball of the library by following the instructions in
+        <a class="reference internal" href="installation.html#installation">
+         <span class="std std-ref">
+          Installation
+         </span>
+        </a>
+       </p>
+       <h2 id="using-torch-tensorrt-in-c">
+        Using Torch-TensorRT in C++
+        <a class="headerlink" href="#using-torch-tensorrt-in-c" title="Permalink to this headline">
+         ¶
+        </a>
+       </h2>
+       <p>
+        Torch-TensorRT C++ API accepts TorchScript modules (generated either from
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch.jit.script
+         </span>
+        </code>
+        or
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch.jit.trace
+         </span>
+        </code>
+        ) as an input and returns
 a Torchscript module (optimized using TensorRT). This requires users to use Pytorch (in python) to generate torchscript modules beforehand.
-Please refer to <a class="reference external" href="https://nvidia.github.io/Torch-TensorRT/tutorials/creating_torchscript_module_in_python.html">Creating TorchScript modules in Python</a> section to generate torchscript graphs.</p>
-<section id="torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc">
-<span id="torch-tensorrt-quickstart"></span><h3>[Torch-TensorRT Quickstart] Compiling TorchScript Modules with <code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code><a class="headerlink" href="#torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc" title="Permalink to this headline">¶</a></h3>
-<p>An easy way to get started with Torch-TensorRT and to check if your model can be supported without extra work is to run it through
-<code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code>, which supports almost all features of the compiler from the command line including post training quantization
+Please refer to
+        <a class="reference external" href="https://nvidia.github.io/Torch-TensorRT/tutorials/creating_torchscript_module_in_python.html">
+         Creating TorchScript modules in Python
+        </a>
+        section to generate torchscript graphs.
+       </p>
+       <span id="torch-tensorrt-quickstart">
+       </span>
+       <h3 id="torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc">
+        [Torch-TensorRT Quickstart] Compiling TorchScript Modules with
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torchtrtc
+         </span>
+        </code>
+        <a class="headerlink" href="#torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        An easy way to get started with Torch-TensorRT and to check if your model can be supported without extra work is to run it through
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torchtrtc
+         </span>
+        </code>
+        , which supports almost all features of the compiler from the command line including post training quantization
 (given a previously created calibration cache). For example we can compile our lenet model by setting our preferred operating
-precision and input size. This new TorchScript file can be loaded into Python (note: you need to <code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">torch_tensorrt</span></code> before loading
-these compiled modules because the compiler extends the PyTorch the deserializer and runtime to execute compiled modules).</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>❯ torchtrtc -p f16 lenet_scripted.ts trt_lenet_scripted.ts <span class="s2">&quot;(1,1,32,32)&quot;</span>
+precision and input size. This new TorchScript file can be loaded into Python (note: you need to
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          import
+         </span>
+         <span class="pre">
+          torch_tensorrt
+         </span>
+        </code>
+        before loading
+these compiled modules because the compiler extends the PyTorch the deserializer and runtime to execute compiled modules).
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>❯ torchtrtc -p f16 lenet_scripted.ts trt_lenet_scripted.ts <span class="s2">"(1,1,32,32)"</span>
 
 ❯ python3
 Python <span class="m">3</span>.6.9 <span class="o">(</span>default, Apr <span class="m">18</span> <span class="m">2020</span>, <span class="m">01</span>:56:04<span class="o">)</span>
 <span class="o">[</span>GCC <span class="m">8</span>.4.0<span class="o">]</span> on linux
-Type <span class="s2">&quot;help&quot;</span>, <span class="s2">&quot;copyright&quot;</span>, <span class="s2">&quot;credits&quot;</span> or <span class="s2">&quot;license&quot;</span> <span class="k">for</span> more information.
+Type <span class="s2">"help"</span>, <span class="s2">"copyright"</span>, <span class="s2">"credits"</span> or <span class="s2">"license"</span> <span class="k">for</span> more information.
 &gt;&gt;&gt; import torch
 &gt;&gt;&gt; import torch_tensorrt
 &gt;&gt;&gt; <span class="nv">ts_model</span> <span class="o">=</span> torch.jit.load<span class="o">(</span>“trt_lenet_scripted.ts”<span class="o">)</span>
 &gt;&gt;&gt; ts_model<span class="o">(</span>torch.randn<span class="o">((</span><span class="m">1</span>,1,32,32<span class="o">))</span>.to<span class="o">(</span>“cuda”<span class="o">)</span>.half<span class="o">())</span>
-</pre></div>
-</div>
-<p>You can learn more about <code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code> usage here: <a class="reference internal" href="torchtrtc.html#torchtrtc"><span class="std std-ref">torchtrtc</span></a></p>
-</section>
-<section id="working-with-torchscript-in-c">
-<span id="ts-in-cc"></span><h3>Working with TorchScript in C++<a class="headerlink" href="#working-with-torchscript-in-c" title="Permalink to this headline">¶</a></h3>
-<p>If we are developing an application to deploy with C++, we can save either our traced or scripted module using <code class="docutils literal notranslate"><span class="pre">torch.jit.save</span></code>
-which will serialize the TorchScript code, weights and other information into a package. This is also where our dependency on Python ends.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">torch_script_module</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;lenet.jit.pt&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>From here we can now load our TorchScript module in C++</p>
-<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;torch/script.h&gt;</span><span class="c1"> // One-stop header.</span><span class="cp"></span>
-
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span><span class="cp"></span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;memory&gt;</span><span class="cp"></span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
-<span class="w">    </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="w"> </span><span class="k">module</span><span class="p">;</span><span class="w"></span>
-<span class="w">    </span><span class="k">try</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
-<span class="w">        </span><span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
-<span class="w">        </span><span class="k">module</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;&lt;PATH TO SAVED TS MOD&gt;&quot;</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="p">}</span><span class="w"></span>
-<span class="w">    </span><span class="k">catch</span><span class="w"> </span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&amp;</span><span class="w"> </span><span class="n">e</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
-<span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">cerr</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;error loading the model</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span><span class="w"></span>
-<span class="w">        </span><span class="k">return</span><span class="w"> </span><span class="mi">-1</span><span class="p">;</span><span class="w"></span>
-<span class="w">    </span><span class="p">}</span><span class="w"></span>
-
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;ok</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span><span class="w"></span>
-</pre></div>
-</div>
-<p>You can do full training and inference in C++ with PyTorch / LibTorch if you would like, you can even define your modules in C++ and
-have access to the same powerful tensor library that backs PyTorch. (For more information: <a class="reference external" href="https://pytorch.org/cppdocs/">https://pytorch.org/cppdocs/</a>).
-For instance we can do inference with our LeNet module like this:</p>
-<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span><span class="w"></span>
-<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">});</span><span class="w"></span>
-<span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span><span class="w"></span>
-</pre></div>
-</div>
-<p>and to run on the GPU:</p>
-<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span><span class="w"></span>
-<span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span><span class="w"></span>
-<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span><span class="w"></span>
-<span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span><span class="w"></span>
-</pre></div>
-</div>
-<p>As you can see it is pretty similar to the Python API. When you call the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method, you invoke the PyTorch JIT compiler, which will optimize and run your TorchScript code.</p>
-</section>
-<section id="compiling-with-torch-tensorrt-in-c">
-<span id="compile-cpp"></span><h3>Compiling with Torch-TensorRT in C++<a class="headerlink" href="#compiling-with-torch-tensorrt-in-c" title="Permalink to this headline">¶</a></h3>
-<p>We are also at the point were we can compile and optimize our module with Torch-TensorRT, but instead of in a JIT fashion we must do it ahead-of-time (AOT) i.e. before we start doing actual inference work
-since it takes a bit of time to optimize the module, it would not make sense to do this every time you run the module or even the first time you run it.</p>
-<p>With our module loaded, we can feed it into the Torch-TensorRT compiler. When we do so we must provide some information on the expected input size and also configure any additional settings.</p>
-<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span><span class="cp"></span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span><span class="cp"></span>
-<span class="p">...</span><span class="w"></span>
-
-<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span><span class="w"></span>
-
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">});</span><span class="w"></span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">trt_mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">{{</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()}});</span><span class="w"></span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">trt_mod</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">in</span><span class="p">});</span><span class="w"></span>
-</pre></div>
-</div>
-<p>Thats it! Now the graph runs primarily not with the JIT compiler but using TensorRT (though we execute the graph using the JIT runtime).</p>
-<p>We can also set settings like operating precision to run in FP16.</p>
-<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span><span class="cp"></span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span><span class="cp"></span>
-<span class="p">...</span><span class="w"></span>
-
-<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span><span class="w"></span>
-
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">}).</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">input_sizes</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">({</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()});</span><span class="w"></span>
-<span class="w">    </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="w"> </span><span class="nf">info</span><span class="p">(</span><span class="n">input_sizes</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="n">info</span><span class="p">.</span><span class="n">enable_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">trt_mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span><span class="w"> </span><span class="n">info</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">trt_mod</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">in</span><span class="p">});</span><span class="w"></span>
-</pre></div>
-</div>
-<p>And now we are running the module in FP16 precision. You can then save the module to load later.</p>
-<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">trt_mod</span><span class="p">.</span><span class="n">save</span><span class="p">(</span><span class="s">&quot;&lt;PATH TO SAVED TRT/TS MOD&gt;&quot;</span><span class="p">)</span><span class="w"></span>
-</pre></div>
-</div>
-<p>Torch-TensorRT compiled TorchScript modules are loaded in the same way as normal TorchScript module. Make sure your deployment application is linked against <code class="docutils literal notranslate"><span class="pre">libtorchtrt.so</span></code></p>
-<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span><span class="cp"></span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span><span class="cp"></span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
-<span class="w">    </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="w"> </span><span class="k">module</span><span class="p">;</span><span class="w"></span>
-<span class="w">    </span><span class="k">try</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
-<span class="w">        </span><span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
-<span class="w">        </span><span class="k">module</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;&lt;PATH TO SAVED TRT/TS MOD&gt;&quot;</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="p">}</span><span class="w"></span>
-<span class="w">    </span><span class="k">catch</span><span class="w"> </span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&amp;</span><span class="w"> </span><span class="n">e</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
-<span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">cerr</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;error loading the model</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span><span class="w"></span>
-<span class="w">        </span><span class="k">return</span><span class="w"> </span><span class="mi">-1</span><span class="p">;</span><span class="w"></span>
-<span class="w">    </span><span class="p">}</span><span class="w"></span>
-
-<span class="w">    </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span><span class="w"></span>
-
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;ok</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span><span class="w"></span>
-<span class="p">}</span><span class="w"></span>
-</pre></div>
-</div>
-<p>If you want to save the engine produced by Torch-TensorRT to use in a TensorRT application you can use the <code class="docutils literal notranslate"><span class="pre">ConvertGraphToTRTEngine</span></code> API.</p>
-<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span><span class="cp"></span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span><span class="cp"></span>
-<span class="p">...</span><span class="w"></span>
-
-<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span><span class="w"></span>
-
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">}).</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">input_sizes</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">({</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()});</span><span class="w"></span>
-<span class="w">    </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="w"> </span><span class="nf">info</span><span class="p">(</span><span class="n">input_sizes</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="n">info</span><span class="p">.</span><span class="n">enabled_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">trt_mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">ConvertGraphToTRTEngine</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;forward&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">info</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ofstream</span><span class="w"> </span><span class="nf">out</span><span class="p">(</span><span class="s">&quot;/tmp/engine_converted_from_jit.trt&quot;</span><span class="p">);</span><span class="w"></span>
-<span class="w">    </span><span class="n">out</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">engine</span><span class="p">;</span><span class="w"></span>
-<span class="w">    </span><span class="n">out</span><span class="p">.</span><span class="n">close</span><span class="p">();</span><span class="w"></span>
-</pre></div>
-</div>
-</section>
-<section id="under-the-hood">
-<span id="id1"></span><h3>Under The Hood<a class="headerlink" href="#under-the-hood" title="Permalink to this headline">¶</a></h3>
-<p>When a module is provided to Torch-TensorRT, the compiler starts by mapping a graph like you saw above to a graph like this:</p>
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%input.2 : Tensor):
+</pre>
+        </div>
+       </div>
+       <p>
+        You can learn more about
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torchtrtc
+         </span>
+        </code>
+        usage here:
+        <a class="reference internal" href="torchtrtc.html#torchtrtc">
+         <span class="std std-ref">
+          torchtrtc
+         </span>
+        </a>
+       </p>
+       <span id="ts-in-cc">
+       </span>
+       <h3 id="working-with-torchscript-in-c">
+        Working with TorchScript in C++
+        <a class="headerlink" href="#working-with-torchscript-in-c" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        If we are developing an application to deploy with C++, we can save either our traced or scripted module using
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch.jit.save
+         </span>
+        </code>
+        which will serialize the TorchScript code, weights and other information into a package. This is also where our dependency on Python ends.
+       </p>
+       <div class="highlight-python notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="n">torch_script_module</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"lenet.jit.pt"</span><span class="p">)</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        From here we can now load our TorchScript module in C++
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="cp">#include</span> <span class="cpf">&lt;torch/script.h&gt; // One-stop header.</span><span class="cp"></span>
+
+<span class="cp">#include</span> <span class="cpf">&lt;iostream&gt;</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">&lt;memory&gt;</span><span class="cp"></span>
+
+<span class="kt">int</span> <span class="nf">main</span><span class="p">(</span><span class="kt">int</span> <span class="n">argc</span><span class="p">,</span> <span class="k">const</span> <span class="kt">char</span><span class="o">*</span> <span class="n">argv</span><span class="p">[])</span> <span class="p">{</span>
+    <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span> <span class="k">module</span><span class="p">;</span>
+    <span class="k">try</span> <span class="p">{</span>
+        <span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
+        <span class="k">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="s">"&lt;PATH TO SAVED TS MOD&gt;"</span><span class="p">);</span>
+    <span class="p">}</span>
+    <span class="k">catch</span> <span class="p">(</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&amp;</span> <span class="n">e</span><span class="p">)</span> <span class="p">{</span>
+        <span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o">&lt;&lt;</span> <span class="s">"error loading the model</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span>
+        <span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="p">;</span>
+    <span class="p">}</span>
+
+    <span class="n">std</span><span class="o">::</span><span class="n">cout</span> <span class="o">&lt;&lt;</span> <span class="s">"ok</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        You can do full training and inference in C++ with PyTorch / LibTorch if you would like, you can even define your modules in C++ and
+have access to the same powerful tensor library that backs PyTorch. (For more information:
+        <a class="reference external" href="https://pytorch.org/cppdocs/">
+         https://pytorch.org/cppdocs/
+        </a>
+        ).
+For instance we can do inference with our LeNet module like this:
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">in</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">});</span>
+<span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        and to run on the GPU:
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+<span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">in</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+<span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        As you can see it is pretty similar to the Python API. When you call the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          forward
+         </span>
+        </code>
+        method, you invoke the PyTorch JIT compiler, which will optimize and run your TorchScript code.
+       </p>
+       <span id="compile-cpp">
+       </span>
+       <h3 id="compiling-with-torch-tensorrt-in-c">
+        Compiling with Torch-TensorRT in C++
+        <a class="headerlink" href="#compiling-with-torch-tensorrt-in-c" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        We are also at the point were we can compile and optimize our module with Torch-TensorRT, but instead of in a JIT fashion we must do it ahead-of-time (AOT) i.e. before we start doing actual inference work
+since it takes a bit of time to optimize the module, it would not make sense to do this every time you run the module or even the first time you run it.
+       </p>
+       <p>
+        With our module loaded, we can feed it into the Torch-TensorRT compiler. When we do so we must provide some information on the expected input size and also configure any additional settings.
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
+<span class="p">...</span>
+
+    <span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+    <span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+
+    <span class="k">auto</span> <span class="n">in</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">});</span>
+    <span class="k">auto</span> <span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">{{</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()}});</span>
+    <span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">trt_mod</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">in</span><span class="p">});</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        Thats it! Now the graph runs primarily not with the JIT compiler but using TensorRT (though we execute the graph using the JIT runtime).
+       </p>
+       <p>
+        We can also set settings like operating precision to run in FP16.
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
+<span class="p">...</span>
+
+    <span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+    <span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+
+    <span class="k">auto</span> <span class="n">in</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">}).</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
+    <span class="k">auto</span> <span class="n">input_sizes</span> <span class="o">=</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">({</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()});</span>
+    <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span> <span class="n">info</span><span class="p">(</span><span class="n">input_sizes</span><span class="p">);</span>
+    <span class="n">info</span><span class="p">.</span><span class="n">enable_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
+    <span class="k">auto</span> <span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">info</span><span class="p">);</span>
+    <span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">trt_mod</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">in</span><span class="p">});</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        And now we are running the module in FP16 precision. You can then save the module to load later.
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="n">trt_mod</span><span class="p">.</span><span class="n">save</span><span class="p">(</span><span class="s">"&lt;PATH TO SAVED TRT/TS MOD&gt;"</span><span class="p">)</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        Torch-TensorRT compiled TorchScript modules are loaded in the same way as normal TorchScript module. Make sure your deployment application is linked against
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          libtorchtrt.so
+         </span>
+        </code>
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
+
+<span class="kt">int</span> <span class="nf">main</span><span class="p">(</span><span class="kt">int</span> <span class="n">argc</span><span class="p">,</span> <span class="k">const</span> <span class="kt">char</span><span class="o">*</span> <span class="n">argv</span><span class="p">[])</span> <span class="p">{</span>
+    <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span> <span class="k">module</span><span class="p">;</span>
+    <span class="k">try</span> <span class="p">{</span>
+        <span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
+        <span class="k">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="s">"&lt;PATH TO SAVED TRT/TS MOD&gt;"</span><span class="p">);</span>
+    <span class="p">}</span>
+    <span class="k">catch</span> <span class="p">(</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&amp;</span> <span class="n">e</span><span class="p">)</span> <span class="p">{</span>
+        <span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o">&lt;&lt;</span> <span class="s">"error loading the model</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span>
+        <span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="p">;</span>
+    <span class="p">}</span>
+
+    <span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">in</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+    <span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span>
+
+    <span class="n">std</span><span class="o">::</span><span class="n">cout</span> <span class="o">&lt;&lt;</span> <span class="s">"ok</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span>
+<span class="p">}</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        If you want to save the engine produced by Torch-TensorRT to use in a TensorRT application you can use the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          ConvertGraphToTRTEngine
+         </span>
+        </code>
+        API.
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
+<span class="p">...</span>
+
+    <span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+    <span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+
+    <span class="k">auto</span> <span class="n">in</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">}).</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
+    <span class="k">auto</span> <span class="n">input_sizes</span> <span class="o">=</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">({</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()});</span>
+    <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span> <span class="n">info</span><span class="p">(</span><span class="n">input_sizes</span><span class="p">);</span>
+    <span class="n">info</span><span class="p">.</span><span class="n">enabled_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
+    <span class="k">auto</span> <span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">ConvertGraphToTRTEngine</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="s">"forward"</span><span class="p">,</span> <span class="n">info</span><span class="p">);</span>
+    <span class="n">std</span><span class="o">::</span><span class="n">ofstream</span> <span class="n">out</span><span class="p">(</span><span class="s">"/tmp/engine_converted_from_jit.trt"</span><span class="p">);</span>
+    <span class="n">out</span> <span class="o">&lt;&lt;</span> <span class="n">engine</span><span class="p">;</span>
+    <span class="n">out</span><span class="p">.</span><span class="n">close</span><span class="p">();</span>
+</pre>
+        </div>
+       </div>
+       <span id="id1">
+       </span>
+       <h3 id="under-the-hood">
+        Under The Hood
+        <a class="headerlink" href="#under-the-hood" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        When a module is provided to Torch-TensorRT, the compiler starts by mapping a graph like you saw above to a graph like this:
+       </p>
+       <div class="highlight-none notranslate">
+        <div class="highlight">
+         <pre><span></span>graph(%input.2 : Tensor):
     %2 : Float(84, 10) = prim::Constant[value=&lt;Tensor&gt;]()
     %3 : Float(120, 84) = prim::Constant[value=&lt;Tensor&gt;]()
     %4 : Float(576, 120) = prim::Constant[value=&lt;Tensor&gt;]()
@@ -543,395 +966,315 @@ <h2>Using Torch-TensorRT in C++<a class="headerlink" href="#using-torch-tensorrt
     %37 : Tensor = aten::add_(%36, %35, %11)
     return (%37)
 (CompileGraph)
-</pre></div>
-</div>
-<p>The graph has now been transformed from a collection of modules, each managing their own parameters into a single graph with the parameters inlined
+</pre>
+        </div>
+       </div>
+       <p>
+        The graph has now been transformed from a collection of modules, each managing their own parameters into a single graph with the parameters inlined
 into the graph and all of the operations laid out. Torch-TensorRT has also executed a number of optimizations and mappings to make the graph easier to translate to TensorRT.
-From here the compiler can assemble the TensorRT engine by following the dataflow through the graph.</p>
-<p>When the graph construction phase is complete, Torch-TensorRT produces a serialized TensorRT engine. From here depending on the API, this engine is returned
+From here the compiler can assemble the TensorRT engine by following the dataflow through the graph.
+       </p>
+       <p>
+        When the graph construction phase is complete, Torch-TensorRT produces a serialized TensorRT engine. From here depending on the API, this engine is returned
 to the user or moves into the graph construction phase. Here Torch-TensorRT creates a JIT Module to execute the TensorRT engine which will be instantiated and managed
-by the Torch-TensorRT runtime.</p>
-<p>Here is the graph that you get back after compilation is complete:</p>
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%self_1 : __torch__.lenet, %input_0 : Tensor):
-    %1 : ...trt.Engine = prim::GetAttr[name=&quot;lenet&quot;](%self_1)
+by the Torch-TensorRT runtime.
+       </p>
+       <p>
+        Here is the graph that you get back after compilation is complete:
+       </p>
+       <div class="highlight-none notranslate">
+        <div class="highlight">
+         <pre><span></span>graph(%self_1 : __torch__.lenet, %input_0 : Tensor):
+    %1 : ...trt.Engine = prim::GetAttr[name="lenet"](%self_1)
     %3 : Tensor[] = prim::ListConstruct(%input_0)
     %4 : Tensor[] = trt::execute_engine(%3, %1)
     %5 : Tensor = prim::ListUnpack(%4)
     return (%5)
-</pre></div>
-</div>
-<p>You can see the call where the engine is executed, after extracting the attribute containing the engine and constructing a list of inputs, then returns the tensors back to the user.</p>
-</section>
-<section id="working-with-unsupported-operators">
-<span id="unsupported-ops"></span><h3>Working with Unsupported Operators<a class="headerlink" href="#working-with-unsupported-operators" title="Permalink to this headline">¶</a></h3>
-<p>Torch-TensorRT is a new library and the PyTorch operator library is quite large, so there will be ops that aren’t supported natively by the compiler. You can either use the composition techinques
-shown above to make modules are fully Torch-TensorRT supported and ones that are not and stitch the modules together in the deployment application or you can register converters for missing ops.</p>
-<blockquote>
-<div><p>You can check support without going through the full compilation pipleine using the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt::CheckMethodOperatorSupport(const</span> <span class="pre">torch::jit::Module&amp;</span> <span class="pre">module,</span> <span class="pre">std::string</span> <span class="pre">method_name)</span></code> api
-to see what operators are not supported. <code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code> automatically checks modules with this method before starting compilation and will print out a list of operators that are not supported.</p>
-</div></blockquote>
-<section id="registering-custom-converters">
-<span id="custom-converters"></span><h4>Registering Custom Converters<a class="headerlink" href="#registering-custom-converters" title="Permalink to this headline">¶</a></h4>
-<p>Operations are mapped to TensorRT through the use of modular converters, a function that takes a node from a the JIT graph and produces an equivalent layer or subgraph in TensorRT.
-Torch-TensorRT ships with a library of these converters stored in a registry, that will be executed depending on the node being parsed. For instance a <code class="docutils literal notranslate"><span class="pre">aten::relu(%input0.4)</span></code> instruction will trigger
+</pre>
+        </div>
+       </div>
+       <p>
+        You can see the call where the engine is executed, after extracting the attribute containing the engine and constructing a list of inputs, then returns the tensors back to the user.
+       </p>
+       <span id="unsupported-ops">
+       </span>
+       <h3 id="working-with-unsupported-operators">
+        Working with Unsupported Operators
+        <a class="headerlink" href="#working-with-unsupported-operators" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        Torch-TensorRT is a new library and the PyTorch operator library is quite large, so there will be ops that aren’t supported natively by the compiler. You can either use the composition techinques
+shown above to make modules are fully Torch-TensorRT supported and ones that are not and stitch the modules together in the deployment application or you can register converters for missing ops.
+       </p>
+       <blockquote>
+        <div>
+         <p>
+          You can check support without going through the full compilation pipleine using the
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            torch_tensorrt::CheckMethodOperatorSupport(const
+           </span>
+           <span class="pre">
+            torch::jit::Module&amp;
+           </span>
+           <span class="pre">
+            module,
+           </span>
+           <span class="pre">
+            std::string
+           </span>
+           <span class="pre">
+            method_name)
+           </span>
+          </code>
+          api
+to see what operators are not supported.
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            torchtrtc
+           </span>
+          </code>
+          automatically checks modules with this method before starting compilation and will print out a list of operators that are not supported.
+         </p>
+        </div>
+       </blockquote>
+       <span id="custom-converters">
+       </span>
+       <h4 id="registering-custom-converters">
+        Registering Custom Converters
+        <a class="headerlink" href="#registering-custom-converters" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        Operations are mapped to TensorRT through the use of modular converters, a function that takes a node from a the JIT graph and produces an equivalent layer or subgraph in TensorRT.
+Torch-TensorRT ships with a library of these converters stored in a registry, that will be executed depending on the node being parsed. For instance a
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          aten::relu(%input0.4)
+         </span>
+        </code>
+        instruction will trigger
 the relu converter to be run on it, producing an activation layer in the TensorRT graph. But since this library is not exhaustive you may need to write your own to get Torch-TensorRT
-to support your module.</p>
-<p>Shipped with the Torch-TensorRT distribution are the internal core API headers. You can therefore access the converter registry and add a converter for the op you need.</p>
-<p>For example, if we try to compile a graph with a build of Torch-TensorRT that doesn’t support the flatten operation (<code class="docutils literal notranslate"><span class="pre">aten::flatten</span></code>) you may see this error:</p>
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>terminate called after throwing an instance of &#39;torch_tensorrt::Error&#39;
+to support your module.
+       </p>
+       <p>
+        Shipped with the Torch-TensorRT distribution are the internal core API headers. You can therefore access the converter registry and add a converter for the op you need.
+       </p>
+       <p>
+        For example, if we try to compile a graph with a build of Torch-TensorRT that doesn’t support the flatten operation (
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          aten::flatten
+         </span>
+        </code>
+        ) you may see this error:
+       </p>
+       <div class="highlight-none notranslate">
+        <div class="highlight">
+         <pre><span></span>terminate called after throwing an instance of 'torch_tensorrt::Error'
 what():  [enforce fail at core/conversion/conversion.cpp:109] Expected converter to be true but got false
 Unable to convert node: %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0 (conversion.AddLayer)
 Schema: aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -&gt; (Tensor)
 Converter for aten::flatten requested, but no such converter was found.
 If you need a converter for this operator, you can try implementing one yourself
 or request a converter: https://www.github.com/NVIDIA/Torch-TensorRT/issues
-</pre></div>
-</div>
-<p>We can register a converter for this operator in our application. All of the tools required to build a converter can be imported by including <code class="docutils literal notranslate"><span class="pre">torch_tensorrt/core/conversion/converters/converters.h</span></code>.
-We start by creating an instance of the self-registering class <code class="docutils literal notranslate"><span class="pre">torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns()</span></code> which will register converters
-in the global converter registry, associating a function schema like <code class="docutils literal notranslate"><span class="pre">aten::flatten.using_ints(Tensor</span> <span class="pre">self,</span> <span class="pre">int</span> <span class="pre">start_dim=0,</span> <span class="pre">int</span> <span class="pre">end_dim=-1)</span> <span class="pre">-&gt;</span> <span class="pre">(Tensor)</span></code> with a lambda that
-will take the state of the conversion, the node/operation in question to convert and all of the inputs to the node and produces as a side effect a new layer in the TensorRT network.
-Arguments are passed as a vector of inspectable unions of TensorRT <code class="docutils literal notranslate"><span class="pre">ITensors</span></code> and Torch <code class="docutils literal notranslate"><span class="pre">IValues</span></code> in the order arguments are listed in the schema.</p>
-<p>Below is a implementation of a <code class="docutils literal notranslate"><span class="pre">aten::flatten</span></code> converter that we can use in our application. You have full access to the Torch and TensorRT libraries in the converter implementation. So
-for example we can quickly get the output size by just running the operation in PyTorch instead of implementing the full calculation outself like we do below for this flatten converter.</p>
-<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span><span class="cp"></span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span><span class="cp"></span>
-<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/core/conversion/converters/converters.h&quot;</span><span class="cp"></span>
-
-<span class="k">static</span><span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">flatten_converter</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">converters</span><span class="o">::</span><span class="n">RegisterNodeConversionPatterns</span><span class="p">()</span><span class="w"></span>
-<span class="w">    </span><span class="p">.</span><span class="n">pattern</span><span class="p">({</span><span class="w"></span>
-<span class="w">        </span><span class="s">&quot;aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -&gt; (Tensor)&quot;</span><span class="p">,</span><span class="w"></span>
-<span class="w">        </span><span class="p">[](</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">ConversionCtx</span><span class="o">*</span><span class="w"> </span><span class="n">ctx</span><span class="p">,</span><span class="w"></span>
-<span class="w">           </span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Node</span><span class="o">*</span><span class="w"> </span><span class="n">n</span><span class="p">,</span><span class="w"></span>
-<span class="w">           </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">converters</span><span class="o">::</span><span class="n">args</span><span class="o">&amp;</span><span class="w"> </span><span class="n">args</span><span class="p">)</span><span class="w"> </span><span class="o">-&gt;</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
-<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">ITensor</span><span class="p">();</span><span class="w"></span>
-<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">start_dim</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">unwrapToInt</span><span class="p">();</span><span class="w"></span>
-<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">end_dim</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">2</span><span class="p">].</span><span class="n">unwrapToInt</span><span class="p">();</span><span class="w"></span>
-<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">in_shape</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">toVec</span><span class="p">(</span><span class="n">in</span><span class="o">-&gt;</span><span class="n">getDimensions</span><span class="p">());</span><span class="w"></span>
-<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">out_shape</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">flatten</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">rand</span><span class="p">(</span><span class="n">in_shape</span><span class="p">),</span><span class="w"> </span><span class="n">start_dim</span><span class="p">,</span><span class="w"> </span><span class="n">end_dim</span><span class="p">).</span><span class="n">sizes</span><span class="p">();</span><span class="w"></span>
-
-<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">shuffle</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ctx</span><span class="o">-&gt;</span><span class="n">net</span><span class="o">-&gt;</span><span class="n">addShuffle</span><span class="p">(</span><span class="o">*</span><span class="n">in</span><span class="p">);</span><span class="w"></span>
-<span class="w">            </span><span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">setReshapeDimensions</span><span class="p">(</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">toDims</span><span class="p">(</span><span class="n">out_shape</span><span class="p">));</span><span class="w"></span>
-<span class="w">            </span><span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">setName</span><span class="p">(</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">node_info</span><span class="p">(</span><span class="n">n</span><span class="p">).</span><span class="n">c_str</span><span class="p">());</span><span class="w"></span>
-
-<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">out_tensor</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ctx</span><span class="o">-&gt;</span><span class="n">AssociateValueAndTensor</span><span class="p">(</span><span class="n">n</span><span class="o">-&gt;</span><span class="n">outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">getOutput</span><span class="p">(</span><span class="mi">0</span><span class="p">));</span><span class="w"></span>
-<span class="w">            </span><span class="k">return</span><span class="w"> </span><span class="nb">true</span><span class="p">;</span><span class="w"></span>
-<span class="w">        </span><span class="p">}</span><span class="w"></span>
-<span class="w">    </span><span class="p">});</span><span class="w"></span>
-
-<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
-<span class="w">    </span><span class="p">...</span><span class="w"></span>
-</pre></div>
-</div>
-<p>To use this converter in Python, it is recommended to use PyTorch’s <a class="reference external" href="https://pytorch.org/tutorials/advanced/cpp_extension.html#custom-c-and-cuda-extensions">C++ / CUDA Extention</a>
-template to wrap your library of converters into a <code class="docutils literal notranslate"><span class="pre">.so</span></code> that you can load with <code class="docutils literal notranslate"><span class="pre">ctypes.CDLL()</span></code> in your Python application.</p>
-<p>You can find more information on all the details of writing converters in the contributors documentation (<a class="reference internal" href="../contributors/writing_converters.html#writing-converters"><span class="std std-ref">Writing Converters</span></a>).
-If you find yourself with a large library of converter implementations, do consider upstreaming them, PRs are welcome and it would be great for the community to benefit as well.</p>
-</section>
-</section>
-</section>
-</section>
-
-
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-              <ul>
-<li><a class="reference internal" href="#">Getting Started with C++</a><ul>
-<li><a class="reference internal" href="#using-torch-tensorrt-in-c">Using Torch-TensorRT in C++</a><ul>
-<li><a class="reference internal" href="#torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc">[Torch-TensorRT Quickstart] Compiling TorchScript Modules with <code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code></a></li>
-<li><a class="reference internal" href="#working-with-torchscript-in-c">Working with TorchScript in C++</a></li>
-<li><a class="reference internal" href="#compiling-with-torch-tensorrt-in-c">Compiling with Torch-TensorRT in C++</a></li>
-<li><a class="reference internal" href="#under-the-hood">Under The Hood</a></li>
-<li><a class="reference internal" href="#working-with-unsupported-operators">Working with Unsupported Operators</a><ul>
-<li><a class="reference internal" href="#registering-custom-converters">Registering Custom Converters</a></li>
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+       </div>
+       <p>
+        We can register a converter for this operator in our application. All of the tools required to build a converter can be imported by including
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt/core/conversion/converters/converters.h
+         </span>
+        </code>
+        .
+We start by creating an instance of the self-registering class
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns()
+         </span>
+        </code>
+        which will register converters
+in the global converter registry, associating a function schema like
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          aten::flatten.using_ints(Tensor
+         </span>
+         <span class="pre">
+          self,
+         </span>
+         <span class="pre">
+          int
+         </span>
+         <span class="pre">
+          start_dim=0,
+         </span>
+         <span class="pre">
+          int
+         </span>
+         <span class="pre">
+          end_dim=-1)
+         </span>
+         <span class="pre">
+          -&gt;
+         </span>
+         <span class="pre">
+          (Tensor)
+         </span>
+        </code>
+        with a lambda that
+will take the state of the conversion, the node/operation in question to convert and all of the inputs to the node and produces as a side effect a new layer in the TensorRT network.
+Arguments are passed as a vector of inspectable unions of TensorRT
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          ITensors
+         </span>
+        </code>
+        and Torch
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          IValues
+         </span>
+        </code>
+        in the order arguments are listed in the schema.
+       </p>
+       <p>
+        Below is a implementation of a
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          aten::flatten
+         </span>
+        </code>
+        converter that we can use in our application. You have full access to the Torch and TensorRT libraries in the converter implementation. So
+for example we can quickly get the output size by just running the operation in PyTorch instead of implementing the full calculation outself like we do below for this flatten converter.
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/core/conversion/converters/converters.h"</span><span class="cp"></span>
+
+<span class="k">static</span> <span class="k">auto</span> <span class="n">flatten_converter</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">converters</span><span class="o">::</span><span class="n">RegisterNodeConversionPatterns</span><span class="p">()</span>
+    <span class="p">.</span><span class="n">pattern</span><span class="p">({</span>
+        <span class="s">"aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -&gt; (Tensor)"</span><span class="p">,</span>
+        <span class="p">[](</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">ConversionCtx</span><span class="o">*</span> <span class="n">ctx</span><span class="p">,</span>
+           <span class="k">const</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Node</span><span class="o">*</span> <span class="n">n</span><span class="p">,</span>
+           <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">converters</span><span class="o">::</span><span class="n">args</span><span class="o">&amp;</span> <span class="n">args</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kt">bool</span> <span class="p">{</span>
+            <span class="k">auto</span> <span class="n">in</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">ITensor</span><span class="p">();</span>
+            <span class="k">auto</span> <span class="n">start_dim</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">unwrapToInt</span><span class="p">();</span>
+            <span class="k">auto</span> <span class="n">end_dim</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">2</span><span class="p">].</span><span class="n">unwrapToInt</span><span class="p">();</span>
+            <span class="k">auto</span> <span class="n">in_shape</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">toVec</span><span class="p">(</span><span class="n">in</span><span class="o">-&gt;</span><span class="n">getDimensions</span><span class="p">());</span>
+            <span class="k">auto</span> <span class="n">out_shape</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">flatten</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">rand</span><span class="p">(</span><span class="n">in_shape</span><span class="p">),</span> <span class="n">start_dim</span><span class="p">,</span> <span class="n">end_dim</span><span class="p">).</span><span class="n">sizes</span><span class="p">();</span>
+
+            <span class="k">auto</span> <span class="n">shuffle</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">-&gt;</span><span class="n">net</span><span class="o">-&gt;</span><span class="n">addShuffle</span><span class="p">(</span><span class="o">*</span><span class="n">in</span><span class="p">);</span>
+            <span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">setReshapeDimensions</span><span class="p">(</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">toDims</span><span class="p">(</span><span class="n">out_shape</span><span class="p">));</span>
+            <span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">setName</span><span class="p">(</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">node_info</span><span class="p">(</span><span class="n">n</span><span class="p">).</span><span class="n">c_str</span><span class="p">());</span>
+
+            <span class="k">auto</span> <span class="n">out_tensor</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">-&gt;</span><span class="n">AssociateValueAndTensor</span><span class="p">(</span><span class="n">n</span><span class="o">-&gt;</span><span class="n">outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">getOutput</span><span class="p">(</span><span class="mi">0</span><span class="p">));</span>
+            <span class="k">return</span> <span class="nb">true</span><span class="p">;</span>
+        <span class="p">}</span>
+    <span class="p">});</span>
+
+<span class="kt">int</span> <span class="nf">main</span><span class="p">()</span> <span class="p">{</span>
+    <span class="p">...</span>
+</pre>
         </div>
-      </div>
+       </div>
+       <p>
+        To use this converter in Python, it is recommended to use PyTorch’s
+        <a class="reference external" href="https://pytorch.org/tutorials/advanced/cpp_extension.html#custom-c-and-cuda-extensions">
+         C++ / CUDA Extention
+        </a>
+        template to wrap your library of converters into a
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          .so
+         </span>
+        </code>
+        that you can load with
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          ctypes.CDLL()
+         </span>
+        </code>
+        in your Python application.
+       </p>
+       <p>
+        You can find more information on all the details of writing converters in the contributors documentation (
+        <a class="reference internal" href="../contributors/writing_converters.html#writing-converters">
+         <span class="std std-ref">
+          Writing Converters
+         </span>
+        </a>
+        ).
+If you find yourself with a large library of converter implementations, do consider upstreaming them, PRs are welcome and it would be great for the community to benefit as well.
+       </p>
+      </article>
+     </div>
     </div>
+   </main>
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diff --git a/docs/tutorials/getting_started_with_fx_path.html b/docs/tutorials/getting_started_with_fx_path.html
index 9a9850b549..225e2deb03 100644
--- a/docs/tutorials/getting_started_with_fx_path.html
+++ b/docs/tutorials/getting_started_with_fx_path.html
@@ -9,22 +9,22 @@
   <meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
-  
+
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-  
 
-  
-  
-  
-  
 
-  
 
-  
-  
-    
 
-  
+
+
+
+
+
+
+
+
+
+
 
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@@ -180,9 +180,9 @@
 
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-   
 
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+
+
 
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@@ -193,21 +193,21 @@
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-                  master (1.2.0a0+450c869)
+                  master (1.2.0)
                 </div>
-              
-            
 
-            
 
 
-  
+
+
+
+
 
 
 <div role="search">
@@ -218,15 +218,15 @@
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 </div>
 
-            
+
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-          
-            
-            
-              
-            
-            
+
+
+
+
+
+
               <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
@@ -277,8 +277,8 @@
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+
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@@ -286,7 +286,7 @@
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+
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-    
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+
             <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
              <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
-              
+
   <section id="torch-tensorrt-fx-frontend-user-guide">
 <span id="getting-started-with-fx"></span><h1>Torch-TensorRT (FX Frontend) User Guide<a class="headerlink" href="#torch-tensorrt-fx-frontend-user-guide" title="Permalink to this headline">¶</a></h1>
 <p>Torch-TensorRT (FX Frontend) is a tool that can convert a PyTorch model through <code class="docutils literal notranslate"><span class="pre">torch.fx</span></code> to an
@@ -364,39 +364,39 @@
 <h2>Converting a PyTorch Model to TensorRT Engine<a class="headerlink" href="#converting-a-pytorch-model-to-tensorrt-engine" title="Permalink to this headline">¶</a></h2>
 <p>In general, users are welcome to use the <code class="docutils literal notranslate"><span class="pre">compile()</span></code> to finish the conversion from a model to tensorRT engine. It is a
 wrapper API that consists of the major steps needed to finish this converison. Please refer to an example usage in <code class="docutils literal notranslate"><span class="pre">lower_example.py</span></code> file under <code class="docutils literal notranslate"><span class="pre">examples/fx</span></code>.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>def compile<span class="o">(</span>
-    module: nn.Module,
-    input,
-    <span class="nv">max_batch_size</span><span class="o">=</span><span class="m">2048</span>,
-    <span class="nv">max_workspace_size</span><span class="o">=</span><span class="m">33554432</span>,
-    <span class="nv">explicit_batch_dimension</span><span class="o">=</span>False,
-    <span class="nv">lower_precision</span><span class="o">=</span>LowerPrecision.FP16,
-    <span class="nv">verbose_log</span><span class="o">=</span>False,
-    <span class="nv">timing_cache_prefix</span><span class="o">=</span><span class="s2">&quot;&quot;</span>,
-    <span class="nv">save_timing_cache</span><span class="o">=</span>False,
-    <span class="nv">cuda_graph_batch_size</span><span class="o">=</span>-1,
-    <span class="nv">dynamic_batch</span><span class="o">=</span>True,
-<span class="o">)</span> -&gt; nn.Module:
-
-    <span class="s2">&quot;&quot;&quot;</span>
-<span class="s2">    Takes in original module, input and lowering setting, run lowering workflow to turn module</span>
-<span class="s2">    into lowered module, or so called TRTModule.</span>
-
-<span class="s2">    Args:</span>
-<span class="s2">        module: Original module for lowering.</span>
-<span class="s2">        input: Input for module.</span>
-<span class="s2">        max_batch_size: Maximum batch size (must be &gt;= 1 to be set, 0 means not set)</span>
-<span class="s2">        max_workspace_size: Maximum size of workspace given to TensorRT.</span>
-<span class="s2">        explicit_batch_dimension: Use explicit batch dimension in TensorRT if set True, otherwise use implicit batch dimension.</span>
-<span class="s2">        lower_precision: lower_precision config given to TRTModule.</span>
-<span class="s2">        verbose_log: Enable verbose log for TensorRT if set True.</span>
-<span class="s2">        timing_cache_prefix: Timing cache file name for timing cache used by fx2trt.</span>
-<span class="s2">        save_timing_cache: Update timing cache with current timing cache data if set to True.</span>
-<span class="s2">        cuda_graph_batch_size: Cuda graph batch size, default to be -1.</span>
-<span class="s2">        dynamic_batch: batch dimension (dim=0) is dynamic.</span>
-<span class="s2">    Returns:</span>
-<span class="s2">        A torch.nn.Module lowered by TensorRT.</span>
-<span class="s2">    &quot;&quot;&quot;</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span>
+    <span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
+    <span class="nb">input</span><span class="p">,</span>
+    <span class="n">max_batch_size</span><span class="o">=</span><span class="mi">2048</span><span class="p">,</span>
+    <span class="n">max_workspace_size</span><span class="o">=</span><span class="mi">33554432</span><span class="p">,</span>
+    <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">lower_precision</span><span class="o">=</span><span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span><span class="p">,</span>
+    <span class="n">verbose_log</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">timing_cache_prefix</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
+    <span class="n">save_timing_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">cuda_graph_batch_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">dynamic_batch</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
+
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Takes in original module, input and lowering setting, run lowering workflow to turn module</span>
+<span class="sd">    into lowered module, or so called TRTModule.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        module: Original module for lowering.</span>
+<span class="sd">        input: Input for module.</span>
+<span class="sd">        max_batch_size: Maximum batch size (must be &gt;= 1 to be set, 0 means not set)</span>
+<span class="sd">        max_workspace_size: Maximum size of workspace given to TensorRT.</span>
+<span class="sd">        explicit_batch_dimension: Use explicit batch dimension in TensorRT if set True, otherwise use implicit batch dimension.</span>
+<span class="sd">        lower_precision: lower_precision config given to TRTModule.</span>
+<span class="sd">        verbose_log: Enable verbose log for TensorRT if set True.</span>
+<span class="sd">        timing_cache_prefix: Timing cache file name for timing cache used by fx2trt.</span>
+<span class="sd">        save_timing_cache: Update timing cache with current timing cache data if set to True.</span>
+<span class="sd">        cuda_graph_batch_size: Cuda graph batch size, default to be -1.</span>
+<span class="sd">        dynamic_batch: batch dimension (dim=0) is dynamic.</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        A torch.nn.Module lowered by TensorRT.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
 </pre></div>
 </div>
 <p>In this section, we will go through an example to illustrate the major steps that fx path uses.
@@ -405,16 +405,16 @@ <h2>Converting a PyTorch Model to TensorRT Engine<a class="headerlink" href="#co
 <li><p><strong>Step 1: Trace the model with acc_tracer</strong></p></li>
 </ul>
 <p>Acc_tracer is a tracer inheritated from FX tracer. It comes with args normalizer to convert all args to kwargs and pass to TRT converters.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt.fx.tracer.acc_tracer.acc_tracer</span> <span class="k">as</span> <span class="nn">acc_tracer</span>
 
 <span class="c1"># Build the model which needs to be a PyTorch nn.Module.</span>
-<span class="nv">my_pytorch_model</span> <span class="o">=</span> build_model<span class="o">()</span>
+<span class="n">my_pytorch_model</span> <span class="o">=</span> <span class="n">build_model</span><span class="p">()</span>
 
 <span class="c1"># Prepare inputs to the model. Inputs have to be a List of Tensors</span>
-<span class="nv">inputs</span> <span class="o">=</span> <span class="o">[</span>Tensor, Tensor, ...<span class="o">]</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span>
 
 <span class="c1"># Trace the model with acc_tracer.</span>
-<span class="nv">acc_mod</span> <span class="o">=</span> acc_tracer.trace<span class="o">(</span>my_pytorch_model, inputs<span class="o">)</span>
+<span class="n">acc_mod</span> <span class="o">=</span> <span class="n">acc_tracer</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">my_pytorch_model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
 </pre></div>
 </div>
 <p><em>Common Errors:</em></p>
@@ -429,83 +429,73 @@ <h2>Converting a PyTorch Model to TensorRT Engine<a class="headerlink" href="#co
 2. Inputs, outputs and activations have batch dimension as the major dimension.
 3. All the operators in the model do not modify batch dimension (permute, transpose, split, etc.) or compute over batch dimension (sum, softmax, etc.).</p>
 <p>For examples of the last path, if we have a 3D tensor t shaped as (batch, sequence, dimension), operations such as torch.transpose(0, 2). If any of these three are not satisfied, we’ll need to specify InputTensorSpec as inputs with dynamic range.</p>
-<p>c</p>
-<blockquote>
-<div><p>import deeplearning.trt.fx2trt.converter.converters
-from torch.fx.experimental.fx2trt.fx2trt import InputTensorSpec, TRTInterpreter</p>
-<p># InputTensorSpec is a dataclass we use to store input information.
-# There’re two ways we can build input_specs.
-# Option 1, build it manually.
-input_specs = [</p>
-<blockquote>
-<div><p>InputTensorSpec(shape=(1, 2, 3), dtype=torch.float32),
-InputTensorSpec(shape=(1, 4, 5), dtype=torch.float32),</p>
-</div></blockquote>
-<p>]
-# Option 2, build it using sample_inputs where user provide a sample
-inputs = [
-torch.rand((1,2,3), dtype=torch.float32),
-torch.rand((1,4,5), dtype=torch.float32),
-]
-input_specs = InputTensorSpec.from_tensors(inputs)</p>
-<p># IMPORTANT: If dynamic shape is needed, we need to build it slightly differently.
-input_specs = [</p>
-<blockquote>
-<div><dl>
-<dt>InputTensorSpec(</dt><dd><p>shape=(-1, 2, 3),
-dtype=torch.float32,
-# Currently we only support one set of dynamic range. User may set other dimensions but it is not promised to work for any models
-# (min_shape, optimize_target_shape, max_shape)
-# For more information refer to fx/input_tensor_spec.py
-shape_ranges = [</p>
-<blockquote>
-<div><p>((1, 2, 3), (4, 2, 3), (100, 2, 3)),</p>
-</div></blockquote>
-<p>],</p>
-</dd>
-</dl>
-<p>),
-InputTensorSpec(shape=(1, 4, 5), dtype=torch.float32),</p>
-</div></blockquote>
-<p>]</p>
-<p># Build a TRT interpreter. Set explicit_batch_dimension accordingly.
-interpreter = TRTInterpreter(</p>
-<blockquote>
-<div><p>acc_mod, input_specs, explicit_batch_dimension=True/False</p>
-</div></blockquote>
-<p>)</p>
-<p># The output of TRTInterpreter run() is wrapped as TRTInterpreterResult.
-# The TRTInterpreterResult contains required parameter to build TRTModule,
-# and other informational output from TRTInterpreter run.
-class TRTInterpreterResult(NamedTuple):</p>
-<blockquote>
-<div><p>engine: Any
-input_names: Sequence[str]
-output_names: Sequence[str]
-serialized_cache: bytearray</p>
-</div></blockquote>
-<p>#max_batch_size: set accordingly for maximum batch size you will use.
-#max_workspace_size: set to the maximum size we can afford for temporary buffer
-#lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).
-#sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity
-#force_fp32_output: force output to be fp32
-#strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric #reasons.
-#algorithm_selector: set up algorithm selection for certain layer
-#timing_cache: enable timing cache for TensorRT
-#profiling_verbosity: TensorRT logging level
-trt_interpreter_result = interpreter.run(</p>
-<blockquote>
-<div><p>max_batch_size=64,
-max_workspace_size=1 &lt;&lt; 25,
-sparse_weights=False,
-force_fp32_output=False,
-strict_type_constraints=False,
-algorithm_selector=None,
-timing_cache=None,
-profiling_verbosity=None,</p>
-</div></blockquote>
-<p>)</p>
-</div></blockquote>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt.fx.converters</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.fx</span> <span class="kn">import</span> <span class="n">InputTensorSpec</span><span class="p">,</span> <span class="n">TRTInterpreter</span>
+
+<span class="c1"># InputTensorSpec is a dataclass we use to store input information.</span>
+<span class="c1"># There&#39;re two ways we can build input_specs.</span>
+<span class="c1"># Option 1, build it manually.</span>
+<span class="n">input_specs</span> <span class="o">=</span> <span class="p">[</span>
+  <span class="n">InputTensorSpec</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
+  <span class="n">InputTensorSpec</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
+<span class="p">]</span>
+<span class="c1"># Option 2, build it using sample_inputs where user provide a sample</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
+<span class="p">]</span>
+<span class="n">input_specs</span> <span class="o">=</span> <span class="n">InputTensorSpec</span><span class="o">.</span><span class="n">from_tensors</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+
+<span class="c1"># IMPORTANT: If dynamic shape is needed, we need to build it slightly differently.</span>
+<span class="n">input_specs</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">InputTensorSpec</span><span class="p">(</span>
+        <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
+        <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
+        <span class="c1"># Currently we only support one set of dynamic range. User may set other dimensions but it is not promised to work for any models</span>
+        <span class="c1"># (min_shape, optimize_target_shape, max_shape)</span>
+        <span class="c1"># For more information refer to fx/input_tensor_spec.py</span>
+        <span class="n">shape_ranges</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)),</span>
+        <span class="p">],</span>
+    <span class="p">),</span>
+    <span class="n">InputTensorSpec</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
+<span class="p">]</span>
+
+<span class="c1"># Build a TRT interpreter. Set explicit_batch_dimension accordingly.</span>
+<span class="n">interpreter</span> <span class="o">=</span> <span class="n">TRTInterpreter</span><span class="p">(</span>
+    <span class="n">acc_mod</span><span class="p">,</span> <span class="n">input_specs</span><span class="p">,</span> <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="kc">True</span><span class="o">/</span><span class="kc">False</span>
+<span class="p">)</span>
+
+<span class="c1"># The output of TRTInterpreter run() is wrapped as TRTInterpreterResult.</span>
+<span class="c1"># The TRTInterpreterResult contains required parameter to build TRTModule,</span>
+<span class="c1"># and other informational output from TRTInterpreter run.</span>
+<span class="k">class</span> <span class="nc">TRTInterpreterResult</span><span class="p">(</span><span class="n">NamedTuple</span><span class="p">):</span>
+    <span class="n">engine</span><span class="p">:</span> <span class="n">Any</span>
+    <span class="n">input_names</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
+    <span class="n">output_names</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
+    <span class="n">serialized_cache</span><span class="p">:</span> <span class="nb">bytearray</span>
+
+<span class="c1">#max_batch_size: set accordingly for maximum batch size you will use.</span>
+<span class="c1">#max_workspace_size: set to the maximum size we can afford for temporary buffer</span>
+<span class="c1">#lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).</span>
+<span class="c1">#sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity</span>
+<span class="c1">#force_fp32_output: force output to be fp32</span>
+<span class="c1">#strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric #reasons.</span>
+<span class="c1">#algorithm_selector: set up algorithm selection for certain layer</span>
+<span class="c1">#timing_cache: enable timing cache for TensorRT</span>
+<span class="c1">#profiling_verbosity: TensorRT logging level</span>
+<span class="n">trt_interpreter_result</span> <span class="o">=</span> <span class="n">interpreter</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+    <span class="n">max_batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
+    <span class="n">max_workspace_size</span><span class="o">=</span><span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="mi">25</span><span class="p">,</span>
+    <span class="n">sparse_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">force_fp32_output</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">strict_type_constraints</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">algorithm_selector</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">timing_cache</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">profiling_verbosity</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
 <p><em>Common Errors:</em></p>
 <p>RuntimeError: Conversion of function xxx not currently supported!
 - This means we don’t have the support for this xxx operator. Please refer to section “How to add a missing op” below for further instructions.</p>
@@ -513,16 +503,16 @@ <h2>Converting a PyTorch Model to TensorRT Engine<a class="headerlink" href="#co
 <li><p><strong>Step 3: Run the model</strong></p></li>
 </ul>
 <p>One way is using TRTModule, which is basically a PyTorch nn.Module.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>from torch_tensorrt.fx import TRTModule
-<span class="nv">mod</span> <span class="o">=</span> TRTModule<span class="o">(</span>
-    trt_interpreter_result.engine,
-    trt_interpreter_result.input_names,
-    trt_interpreter_result.output_names<span class="o">)</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch_tensorrt.fx</span> <span class="kn">import</span> <span class="n">TRTModule</span>
+<span class="n">mod</span> <span class="o">=</span> <span class="n">TRTModule</span><span class="p">(</span>
+    <span class="n">trt_interpreter_result</span><span class="o">.</span><span class="n">engine</span><span class="p">,</span>
+    <span class="n">trt_interpreter_result</span><span class="o">.</span><span class="n">input_names</span><span class="p">,</span>
+    <span class="n">trt_interpreter_result</span><span class="o">.</span><span class="n">output_names</span><span class="p">)</span>
 <span class="c1"># Just like all other PyTorch modules</span>
-<span class="nv">outputs</span> <span class="o">=</span> mod<span class="o">(</span>*inputs<span class="o">)</span>
-torch.save<span class="o">(</span>mod, <span class="s2">&quot;trt.pt&quot;</span><span class="o">)</span>
-<span class="nv">reload_trt_mod</span> <span class="o">=</span> torch.load<span class="o">(</span><span class="s2">&quot;trt.pt&quot;</span><span class="o">)</span>
-<span class="nv">reload_model_output</span> <span class="o">=</span> reload_trt_mod<span class="o">(</span>*inputs<span class="o">)</span>
+<span class="n">outputs</span> <span class="o">=</span> <span class="n">mod</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="s2">&quot;trt.pt&quot;</span><span class="p">)</span>
+<span class="n">reload_trt_mod</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;trt.pt&quot;</span><span class="p">)</span>
+<span class="n">reload_model_output</span> <span class="o">=</span> <span class="n">reload_trt_mod</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>So far, we give a detailed explanation of major steps in convterting a PyTorch model into TensorRT engine. Users are welcome to refer to the source code for some parameters explanations. In the converting scheme, there are two important actions in it. One is acc tracer which helps us to convert a PyTorch model to acc graph. The other is FX path converter which helps to convert the acc graph’s operation to corresponding TensorRT operation and build up the TensoRT engine for it.</p>
@@ -535,26 +525,26 @@ <h2>Acc Tracer<a class="headerlink" href="#acc-tracer" title="Permalink to this
 <li><p>acc ops only have kwargs which makes writing converter easier as we don’t need to add additional logic to find arguments in args and kwargs.</p></li>
 </ol>
 </section>
-<section id="fx2trt">
-<h2>FX2TRT<a class="headerlink" href="#fx2trt" title="Permalink to this headline">¶</a></h2>
-<p>After symbolic tracing, we have the graph representation of a PyTorch model. fx2trt leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).</p>
+<section id="torch-tensorrt-fx-frontend">
+<h2>Torch-TensorRT FX Frontend<a class="headerlink" href="#torch-tensorrt-fx-frontend" title="Permalink to this headline">¶</a></h2>
+<p>After symbolic tracing, we have the graph representation of a PyTorch model. The Torch-TensorRT FX Frontend leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).</p>
 <p>Below is an example of a converter function. The decorator is used to register this converter function with the corresponding node. In this example, we register this converter to a fx node whose target is acc_ops.sigmoid.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>@tensorrt_converter<span class="o">(</span>acc_ops.sigmoid<span class="o">)</span>
-def acc_ops_sigmoid<span class="o">(</span>network, target, args, kwargs, name<span class="o">)</span>:
-    <span class="s2">&quot;&quot;&quot;</span>
-<span class="s2">    network: TensorRT network. We&#39;ll be adding layers to it.</span>
-
-<span class="s2">    The rest arguments are attributes of fx node.</span>
-<span class="s2">    &quot;&quot;&quot;</span>
-    <span class="nv">input_val</span> <span class="o">=</span> kwargs<span class="o">[</span><span class="s1">&#39;input&#39;</span><span class="o">]</span>
-
-    <span class="k">if</span> not isinstance<span class="o">(</span>input_val, trt.tensorrt.ITensor<span class="o">)</span>:
-        raise RuntimeError<span class="o">(</span>f<span class="s1">&#39;Sigmoid received input {input_val} that is not part &#39;</span>
-                        <span class="s1">&#39;of the TensorRT region!&#39;</span><span class="o">)</span>
-
-    <span class="nv">layer</span> <span class="o">=</span> network.add_activation<span class="o">(</span><span class="nv">input</span><span class="o">=</span>input_val, <span class="nv">type</span><span class="o">=</span>trt.ActivationType.SIGMOID<span class="o">)</span>
-    layer.name <span class="o">=</span> name
-    <span class="k">return</span> layer.get_output<span class="o">(</span><span class="m">0</span><span class="o">)</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@tensorrt_converter</span><span class="p">(</span><span class="n">acc_ops</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">acc_ops_sigmoid</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    network: TensorRT network. We&#39;ll be adding layers to it.</span>
+
+<span class="sd">    The rest arguments are attributes of fx node.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">input_val</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_val</span><span class="p">,</span> <span class="n">trt</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Sigmoid received input </span><span class="si">{</span><span class="n">input_val</span><span class="si">}</span><span class="s1"> that is not part &#39;</span>
+                        <span class="s1">&#39;of the TensorRT region!&#39;</span><span class="p">)</span>
+
+    <span class="n">layer</span> <span class="o">=</span> <span class="n">network</span><span class="o">.</span><span class="n">add_activation</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">input_val</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">ActivationType</span><span class="o">.</span><span class="n">SIGMOID</span><span class="p">)</span>
+    <span class="n">layer</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
+    <span class="k">return</span> <span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
 </pre></div>
 </div>
 <section id="how-to-add-a-missing-op">
@@ -659,25 +649,25 @@ <h3>How to Add a Missing Op<a class="headerlink" href="#how-to-add-a-missing-op"
 
 
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+
 
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     <p>
@@ -685,11 +675,11 @@ <h3>How to Add a Missing Op<a class="headerlink" href="#how-to-add-a-missing-op"
 
     </p>
   </div>
-    
+
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         Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
       </div>
-     
+
 
 </footer>
 
@@ -703,7 +693,7 @@ <h3>How to Add a Missing Op<a class="headerlink" href="#how-to-add-a-missing-op"
 <li><a class="reference internal" href="#">Torch-TensorRT (FX Frontend) User Guide</a><ul>
 <li><a class="reference internal" href="#converting-a-pytorch-model-to-tensorrt-engine">Converting a PyTorch Model to TensorRT Engine</a></li>
 <li><a class="reference internal" href="#acc-tracer">Acc Tracer</a></li>
-<li><a class="reference internal" href="#fx2trt">FX2TRT</a><ul>
+<li><a class="reference internal" href="#torch-tensorrt-fx-frontend">Torch-TensorRT FX Frontend</a><ul>
 <li><a class="reference internal" href="#how-to-add-a-missing-op">How to Add a Missing Op</a></li>
 </ul>
 </li>
@@ -717,12 +707,12 @@ <h3>How to Add a Missing Op<a class="headerlink" href="#how-to-add-a-missing-op"
       </section>
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index 5ed3f75ec0..e063850389 100644
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@@ -1,363 +1,532 @@
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+            torch_tensorrt.ptq
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+           </a>
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+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">
+            Namespace torch_tensorrt::torchscript
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">
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-  <section id="using-torch-tensorrt-in-python">
-<span id="getting-started-with-python-api"></span><h1>Using Torch-TensorRT in Python<a class="headerlink" href="#using-torch-tensorrt-in-python" title="Permalink to this headline">¶</a></h1>
-<p>Torch-TensorRT Python API accepts a <code class="docutils literal notranslate"><span class="pre">`torch.nn.Module</span></code> as an input. Under the hood, it uses <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code> to convert the input module into a
-TorchScript module. To compile your input <code class="docutils literal notranslate"><span class="pre">`torch.nn.Module</span></code> with Torch-TensorRT, all you need to do is provide the module and inputs
+     </div>
+     <div class="md-content">
+      <article class="md-content__inner md-typeset" role="main">
+       <span id="getting-started-with-python-api">
+       </span>
+       <h1 id="tutorials-getting-started-with-python-api--page-root">
+        Using Torch-TensorRT in Python
+        <a class="headerlink" href="#tutorials-getting-started-with-python-api--page-root" title="Permalink to this headline">
+         ¶
+        </a>
+       </h1>
+       <p>
+        Torch-TensorRT Python API accepts a
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          `torch.nn.Module
+         </span>
+        </code>
+        as an input. Under the hood, it uses
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch.jit.script
+         </span>
+        </code>
+        to convert the input module into a
+TorchScript module. To compile your input
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          `torch.nn.Module
+         </span>
+        </code>
+        with Torch-TensorRT, all you need to do is provide the module and inputs
 to Torch-TensorRT and you will be returned an optimized TorchScript module to run or add into another PyTorch module. Inputs
-is a list of <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.Input</span></code> classes which define input’s shape, datatype and memory format. You can also specify settings such as
+is a list of
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt.Input
+         </span>
+        </code>
+        classes which define input’s shape, datatype and memory format. You can also specify settings such as
 operating precision for the engine or target device. After compilation you can save the module just like any other module
-to load in a deployment application. In order to load a TensorRT/TorchScript module, make sure you first import <code class="docutils literal notranslate"><span class="pre">torch_tensorrt</span></code>.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+to load in a deployment application. In order to load a TensorRT/TorchScript module, make sure you first import
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt
+         </span>
+        </code>
+        .
+       </p>
+       <div class="highlight-python notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt</span>
 
 <span class="o">...</span>
 
@@ -373,312 +542,113 @@
 
 <span class="n">trt_ts_module</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions</span><span class="p">)</span>
 
-<span class="n">input_data</span> <span class="o">=</span> <span class="n">input_data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
+<span class="n">input_data</span> <span class="o">=</span> <span class="n">input_data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
 <span class="n">result</span> <span class="o">=</span> <span class="n">trt_ts_module</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span>
-<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">trt_ts_module</span><span class="p">,</span> <span class="s2">&quot;trt_ts_module.ts&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Deployment application</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">trt_ts_module</span><span class="p">,</span> <span class="s2">"trt_ts_module.ts"</span><span class="p">)</span>
+</pre>
+        </div>
+       </div>
+       <div class="highlight-python notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="c1"># Deployment application</span>
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch_tensorrt</span>
 
-<span class="n">trt_ts_module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;trt_ts_module.ts&quot;</span><span class="p">)</span>
-<span class="n">input_data</span> <span class="o">=</span> <span class="n">input_data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
+<span class="n">trt_ts_module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"trt_ts_module.ts"</span><span class="p">)</span>
+<span class="n">input_data</span> <span class="o">=</span> <span class="n">input_data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
 <span class="n">result</span> <span class="o">=</span> <span class="n">trt_ts_module</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Torch-TensorRT python API also provides <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.ts.compile</span></code> which accepts a TorchScript module as input.
-The torchscript module can be obtained via scripting or tracing (refer to <span class="xref std std-ref">creating_torchscript_module_in_python</span>). <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.ts.compile</span></code> accepts a Torchscript module
-and a list of <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.Input</span></code> classes.</p>
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+       </div>
+       <p>
+        Torch-TensorRT python API also provides
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt.ts.compile
+         </span>
+        </code>
+        which accepts a TorchScript module as input.
+The torchscript module can be obtained via scripting or tracing (refer to
+        <span class="xref std std-ref">
+         creating_torchscript_module_in_python
+        </span>
+        ).
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt.ts.compile
+         </span>
+        </code>
+        accepts a Torchscript module
+and a list of
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt.Input
+         </span>
+        </code>
+        classes.
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+           </label>
+           <a class="md-nav__link md-nav__link--active" href="#">
+            Installation
+           </a>
+           <nav class="md-nav md-nav--secondary">
+            <label class="md-nav__title" for="__toc">
+             Contents
+            </label>
+            <ul class="md-nav__list" data-md-scrollfix="">
+             <li class="md-nav__item">
+              <a class="md-nav__link" href="#tutorials-installation--page-root">
+               Installation
+              </a>
+              <nav class="md-nav">
+               <ul class="md-nav__list">
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#precompiled-binaries">
+                  Precompiled Binaries
+                 </a>
+                 <nav class="md-nav">
+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#dependencies">
+                     Dependencies
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#python-package">
+                     Python Package
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#c-binary-distribution">
+                     C++ Binary Distribution
+                    </a>
+                   </li>
+                  </ul>
+                 </nav>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#compiling-from-source">
+                  Compiling From Source
+                 </a>
+                 <nav class="md-nav">
+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#dependencies-for-compilation">
+                     Dependencies for Compilation
+                    </a>
+                    <nav class="md-nav">
+                     <ul class="md-nav__list">
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#choosing-the-right-abi">
+                        Choosing the Right ABI
+                       </a>
+                      </li>
+                     </ul>
+                    </nav>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#building-using-cudnn-tensorrt-tarball-distributions">
+                     <strong>
+                      Building using cuDNN &amp; TensorRT tarball distributions
+                     </strong>
+                    </a>
+                    <nav class="md-nav">
+                     <ul class="md-nav__list">
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#release-build">
+                        Release Build
+                       </a>
+                      </li>
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#debug-build">
+                        Debug Build
+                       </a>
+                      </li>
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#pre-cxx11-abi-build">
+                        Pre CXX11 ABI Build
+                       </a>
+                      </li>
+                     </ul>
+                    </nav>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#building-using-locally-installed-cudnn-tensorrt">
+                     <strong>
+                      Building using locally installed cuDNN &amp; TensorRT
+                     </strong>
+                    </a>
+                    <nav class="md-nav">
+                     <ul class="md-nav__list">
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#id6">
+                        Release Build
+                       </a>
+                      </li>
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#build-from-local-debug">
+                        Debug Build
+                       </a>
+                      </li>
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#id8">
+                        Pre CXX11 ABI Build
+                       </a>
+                      </li>
+                     </ul>
+                    </nav>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#building-the-python-package">
+                     <strong>
+                      Building the Python package
+                     </strong>
+                    </a>
+                    <nav class="md-nav">
+                     <ul class="md-nav__list">
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#id9">
+                        Debug Build
+                       </a>
+                      </li>
+                     </ul>
+                    </nav>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#building-natively-on-aarch64-jetson">
+                     <strong>
+                      Building Natively on aarch64 (Jetson)
+                     </strong>
+                    </a>
+                    <nav class="md-nav">
+                     <ul class="md-nav__list">
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#prerequisites">
+                        Prerequisites
+                       </a>
+                      </li>
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#enviorment-setup">
+                        Enviorment Setup
+                       </a>
+                      </li>
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#compile-c-library-and-compiler-cli">
+                        Compile C++ Library and Compiler CLI
+                       </a>
+                      </li>
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#compile-python-api">
+                        Compile Python API
+                       </a>
+                      </li>
+                     </ul>
+                    </nav>
+                   </li>
+                  </ul>
+                 </nav>
+                </li>
+               </ul>
+              </nav>
+             </li>
+             <li class="md-nav__item">
+              <a class="md-nav__extra_link" href="../_sources/tutorials/installation.rst.txt">
+               Show Source
+              </a>
+             </li>
+            </ul>
+           </nav>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="getting_started_with_cpp_api.html">
+            Getting Started with C++
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="getting_started_with_python_api.html">
+            Using Torch-TensorRT in Python
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="creating_torchscript_module_in_python.html">
+            Creating a TorchScript Module
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="creating_torchscript_module_in_python.html#working-with-torchscript-in-python">
+            Working with TorchScript in Python
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">
+            Saving TorchScript Module to Disk
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="ptq.html">
+            Post Training Quantization (PTQ)
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="torchtrtc.html">
+            torchtrtc
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="use_from_pytorch.html">
+            Using Torch-TensorRT Directly From PyTorch
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="runtime.html">
+            Deploying Torch-TensorRT Programs
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="using_dla.html">
+            DLA
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             Notebooks
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/CitriNet-example.html">
+            Torch-TensorRT Getting Started - CitriNet
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/dynamic-shapes.html">
+            Torch-TensorRT - Using Dynamic Shapes
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/EfficientNet-example.html">
+            Torch-TensorRT Getting Started - EfficientNet-B0
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/Hugging-Face-BERT.html">
+            Masked Language Modeling (MLM) with Hugging Face BERT Transformer
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/lenet-getting-started.html">
+            Torch-TensorRT Getting Started - LeNet
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/Resnet50-example.html">
+            Torch-TensorRT Getting Started - ResNet 50
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/ssd-object-detection-demo.html">
+            Object Detection with Torch-TensorRT (SSD)
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/vgg-qat.html">
+            Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             Python API Documenation
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../py_api/torch_tensorrt.html">
+            torch_tensorrt
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../py_api/logging.html">
+            torch_tensorrt.logging
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../py_api/ptq.html">
+            torch_tensorrt.ptq
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../py_api/ts.html">
+            torch_tensorrt.ts
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             C++ API Documenation
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/torch_tensort_cpp.html">
+            Torch-TensorRT C++ API
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt.html">
+            Namespace torch_tensorrt
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__logging.html">
+            Namespace torch_tensorrt::logging
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">
+            Namespace torch_tensorrt::torchscript
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">
+            Namespace torch_tensorrt::ptq
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             Contributor Documentation
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../contributors/system_overview.html">
+            System Overview
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../contributors/writing_converters.html">
+            Writing Converters
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../contributors/useful_links.html">
+            Useful Links for Torch-TensorRT Development
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             Indices
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../indices/supported_ops.html">
+            Operators Supported
+           </a>
+          </li>
+         </ul>
+        </nav>
+       </div>
+      </div>
+     </div>
+     <div class="md-sidebar md-sidebar--secondary" data-md-component="toc">
+      <div class="md-sidebar__scrollwrap">
+       <div class="md-sidebar__inner">
+        <nav class="md-nav md-nav--secondary">
+         <label class="md-nav__title" for="__toc">
+          Contents
+         </label>
+         <ul class="md-nav__list" data-md-scrollfix="">
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="#tutorials-installation--page-root">
+            Installation
+           </a>
+           <nav class="md-nav">
+            <ul class="md-nav__list">
+             <li class="md-nav__item">
+              <a class="md-nav__link" href="#precompiled-binaries">
+               Precompiled Binaries
+              </a>
+              <nav class="md-nav">
+               <ul class="md-nav__list">
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#dependencies">
+                  Dependencies
+                 </a>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#python-package">
+                  Python Package
+                 </a>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#c-binary-distribution">
+                  C++ Binary Distribution
+                 </a>
+                </li>
+               </ul>
+              </nav>
+             </li>
+             <li class="md-nav__item">
+              <a class="md-nav__link" href="#compiling-from-source">
+               Compiling From Source
+              </a>
+              <nav class="md-nav">
+               <ul class="md-nav__list">
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#dependencies-for-compilation">
+                  Dependencies for Compilation
+                 </a>
+                 <nav class="md-nav">
+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#choosing-the-right-abi">
+                     Choosing the Right ABI
+                    </a>
+                   </li>
+                  </ul>
+                 </nav>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#building-using-cudnn-tensorrt-tarball-distributions">
+                  <strong>
+                   Building using cuDNN &amp; TensorRT tarball distributions
+                  </strong>
+                 </a>
+                 <nav class="md-nav">
+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#release-build">
+                     Release Build
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#debug-build">
+                     Debug Build
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#pre-cxx11-abi-build">
+                     Pre CXX11 ABI Build
+                    </a>
+                   </li>
+                  </ul>
+                 </nav>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#building-using-locally-installed-cudnn-tensorrt">
+                  <strong>
+                   Building using locally installed cuDNN &amp; TensorRT
+                  </strong>
+                 </a>
+                 <nav class="md-nav">
+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#id6">
+                     Release Build
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#build-from-local-debug">
+                     Debug Build
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#id8">
+                     Pre CXX11 ABI Build
+                    </a>
+                   </li>
+                  </ul>
+                 </nav>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#building-the-python-package">
+                  <strong>
+                   Building the Python package
+                  </strong>
+                 </a>
+                 <nav class="md-nav">
+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#id9">
+                     Debug Build
+                    </a>
+                   </li>
+                  </ul>
+                 </nav>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#building-natively-on-aarch64-jetson">
+                  <strong>
+                   Building Natively on aarch64 (Jetson)
+                  </strong>
+                 </a>
+                 <nav class="md-nav">
+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#prerequisites">
+                     Prerequisites
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#enviorment-setup">
+                     Enviorment Setup
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#compile-c-library-and-compiler-cli">
+                     Compile C++ Library and Compiler CLI
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#compile-python-api">
+                     Compile Python API
+                    </a>
+                   </li>
+                  </ul>
+                 </nav>
+                </li>
+               </ul>
+              </nav>
+             </li>
+            </ul>
+           </nav>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__extra_link" href="../_sources/tutorials/installation.rst.txt">
+            Show Source
+           </a>
+          </li>
+          <li class="md-nav__item" id="searchbox">
+          </li>
+         </ul>
+        </nav>
+       </div>
+      </div>
+     </div>
+     <div class="md-content">
+      <article class="md-content__inner md-typeset" role="main">
+       <span id="id1">
+       </span>
+       <h1 id="tutorials-installation--page-root">
+        Installation
+        <a class="headerlink" href="#tutorials-installation--page-root" title="Permalink to this headline">
+         ¶
+        </a>
+       </h1>
+       <h2 id="precompiled-binaries">
+        Precompiled Binaries
+        <a class="headerlink" href="#precompiled-binaries" title="Permalink to this headline">
+         ¶
+        </a>
+       </h2>
+       <h3 id="dependencies">
+        Dependencies
+        <a class="headerlink" href="#dependencies" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        You need to have either PyTorch or LibTorch installed based on if you are using Python or C++
+and you must have CUDA, cuDNN and TensorRT installed.
+       </p>
+       <blockquote>
+        <div>
+         <ul class="simple">
           <li>
-            <a href="https://pytorch.org/ecosystem">Ecosystem</a>
+           <p>
+            <a class="reference external" href="https://www.pytorch.org">
+             https://www.pytorch.org
+            </a>
+           </p>
           </li>
-
           <li>
-            <a href="https://pytorch.org/mobile">Mobile</a>
+           <p>
+            <a class="reference external" href="https://developer.nvidia.com/cuda">
+             https://developer.nvidia.com/cuda
+            </a>
+           </p>
           </li>
-
           <li>
-            <a href="https://pytorch.org/blog/">Blog</a>
+           <p>
+            <a class="reference external" href="https://developer.nvidia.com/cudnn">
+             https://developer.nvidia.com/cudnn
+            </a>
+           </p>
           </li>
-
           <li>
-            <a href="https://pytorch.org/tutorials">Tutorials</a>
+           <p>
+            <a class="reference external" href="https://developer.nvidia.com/tensorrt">
+             https://developer.nvidia.com/tensorrt
+            </a>
+           </p>
           </li>
-
+         </ul>
+        </div>
+       </blockquote>
+       <h3 id="python-package">
+        Python Package
+        <a class="headerlink" href="#python-package" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        You can install the python package using
+       </p>
+       <div class="highlight-sh notranslate">
+        <div class="highlight">
+         <pre><span></span>pip3 install torch-tensorrt -f https://github.com/NVIDIA/Torch-TensorRT/releases
+</pre>
+        </div>
+       </div>
+       <span id="bin-dist">
+       </span>
+       <h3 id="c-binary-distribution">
+        C++ Binary Distribution
+        <a class="headerlink" href="#c-binary-distribution" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        Precompiled tarballs for releases are provided here:
+        <a class="reference external" href="https://github.com/NVIDIA/Torch-TensorRT/releases">
+         https://github.com/NVIDIA/Torch-TensorRT/releases
+        </a>
+       </p>
+       <span id="compile-from-source">
+       </span>
+       <h2 id="compiling-from-source">
+        Compiling From Source
+        <a class="headerlink" href="#compiling-from-source" title="Permalink to this headline">
+         ¶
+        </a>
+       </h2>
+       <span id="installing-deps">
+       </span>
+       <h3 id="dependencies-for-compilation">
+        Dependencies for Compilation
+        <a class="headerlink" href="#dependencies-for-compilation" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        Torch-TensorRT is built with Bazel, so begin by installing it.
+       </p>
+       <blockquote>
+        <div>
+         <ul class="simple">
           <li>
-            <div id="resourcesDropdownButton" data-toggle="resources-dropdown" class="resources-dropdown">
-              <a class="resource-option with-down-orange-arrow">
-                Docs
-              </a>
-              <div class="resources-dropdown-menu">
-                <a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/docs/stable/index.html">
-                  <span class="dropdown-title">PyTorch</span>
-                  <p></p>
-                </a>
-                <a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/audio/stable/index.html">
-                  <span class="dropdown-title">torchaudio</span>
-                  <p></p>
-                </a>
-                <a class="doc-dropdown-option nav-dropdown-item" href="https://pytorch.org/text/stable/index.html">
-                  <span class="dropdown-title">torchtext</span>
-                  <p></p>
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-                  <span class="dropdown-title">PyTorch on XLA Devices</span>
-                  <p></p>
-                </a>
-            </div>
+           <p>
+            The easiest way is to install bazelisk using the method of your choosing
+            <a class="reference external" href="https://github.com/bazelbuild/bazelisk">
+             https://github.com/bazelbuild/bazelisk
+            </a>
+           </p>
           </li>
-
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-              <a class="resource-option with-down-arrow">
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-                  <span class="dropdown-title">Models (Beta)</span>
-                  <p>Discover, publish, and reuse pre-trained models</p>
-                </a>
-              </div>
-            </div>
+           <p>
+            Otherwise you can use the following instructions to install binaries
+            <a class="reference external" href="https://docs.bazel.build/versions/master/install.html">
+             https://docs.bazel.build/versions/master/install.html
+            </a>
+           </p>
           </li>
-
           <li>
-            <a href="https://github.com/pytorch/pytorch">GitHub</a>
+           <p>
+            Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions
+           </p>
           </li>
-        </ul>
-      </div>
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-<li class="toctree-l1"><a class="reference internal" href="getting_started_with_cpp_api.html">Getting Started with C++</a></li>
-<li class="toctree-l1"><a class="reference internal" href="getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
-<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
-<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
-<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ptq.html">Post Training Quantization (PTQ)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="torchtrtc.html">torchtrtc</a></li>
-<li class="toctree-l1"><a class="reference internal" href="use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="runtime.html">Deploying Torch-TensorRT Programs</a></li>
-<li class="toctree-l1"><a class="reference internal" href="using_dla.html">DLA</a></li>
-<li class="toctree-l1"><a class="reference internal" href="serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Notebooks</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/CitriNet-example.html">Torch-TensorRT Getting Started - CitriNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/dynamic-shapes.html">Torch-TensorRT - Using Dynamic Shapes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/EfficientNet-example.html">Torch-TensorRT Getting Started - EfficientNet-B0</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/Hugging-Face-BERT.html">Masked Language Modeling (MLM) with Hugging Face BERT Transformer</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/lenet-getting-started.html">Torch-TensorRT Getting Started - LeNet</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/Resnet50-example.html">Torch-TensorRT Getting Started - ResNet 50</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/ssd-object-detection-demo.html">Object Detection with Torch-TensorRT (SSD)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_notebooks/vgg-qat.html">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../py_api/logging.html">torch_tensorrt.logging</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../py_api/ts.html">torch_tensorrt.ts</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../contributors/writing_converters.html">Writing Converters</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
-</ul>
-<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
-</ul>
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-
-  <section id="installation">
-<span id="id1"></span><h1>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h1>
-<section id="precompiled-binaries">
-<h2>Precompiled Binaries<a class="headerlink" href="#precompiled-binaries" title="Permalink to this headline">¶</a></h2>
-<section id="dependencies">
-<h3>Dependencies<a class="headerlink" href="#dependencies" title="Permalink to this headline">¶</a></h3>
-<p>You need to have either PyTorch or LibTorch installed based on if you are using Python or C++
-and you must have CUDA, cuDNN and TensorRT installed.</p>
-<blockquote>
-<div><ul class="simple">
-<li><p><a class="reference external" href="https://www.pytorch.org">https://www.pytorch.org</a></p></li>
-<li><p><a class="reference external" href="https://developer.nvidia.com/cuda">https://developer.nvidia.com/cuda</a></p></li>
-<li><p><a class="reference external" href="https://developer.nvidia.com/cudnn">https://developer.nvidia.com/cudnn</a></p></li>
-<li><p><a class="reference external" href="https://developer.nvidia.com/tensorrt">https://developer.nvidia.com/tensorrt</a></p></li>
-</ul>
-</div></blockquote>
-</section>
-<section id="python-package">
-<h3>Python Package<a class="headerlink" href="#python-package" title="Permalink to this headline">¶</a></h3>
-<p>You can install the python package using</p>
-<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>pip3 install torch-tensorrt -f https://github.com/pytorch/TensorRT/releases
-</pre></div>
-</div>
-</section>
-<section id="c-binary-distribution">
-<span id="bin-dist"></span><h3>C++ Binary Distribution<a class="headerlink" href="#c-binary-distribution" title="Permalink to this headline">¶</a></h3>
-<p>Precompiled tarballs for releases are provided here: <a class="reference external" href="https://github.com/pytorch/TensorRT/releases">https://github.com/pytorch/TensorRT/releases</a></p>
-</section>
-</section>
-<section id="compiling-from-source">
-<span id="compile-from-source"></span><h2>Compiling From Source<a class="headerlink" href="#compiling-from-source" title="Permalink to this headline">¶</a></h2>
-<section id="dependencies-for-compilation">
-<span id="installing-deps"></span><h3>Dependencies for Compilation<a class="headerlink" href="#dependencies-for-compilation" title="Permalink to this headline">¶</a></h3>
-<p>Torch-TensorRT is built with Bazel, so begin by installing it.</p>
-<blockquote>
-<div><ul class="simple">
-<li><p>The easiest way is to install bazelisk using the method of your choosing <a class="reference external" href="https://github.com/bazelbuild/bazelisk">https://github.com/bazelbuild/bazelisk</a></p></li>
-<li><p>Otherwise you can use the following instructions to install binaries <a class="reference external" href="https://docs.bazel.build/versions/master/install.html">https://docs.bazel.build/versions/master/install.html</a></p></li>
-<li><p>Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions</p></li>
-</ul>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">BAZEL_VERSION</span><span class="o">=</span><span class="k">$(</span>cat &lt;PATH_TO_TORCHTRT_ROOT&gt;/.bazelversion<span class="k">)</span>
+         </ul>
+         <div class="highlight-shell notranslate">
+          <div class="highlight">
+           <pre><span></span><span class="nb">export</span> <span class="nv">BAZEL_VERSION</span><span class="o">=</span><span class="k">$(</span>cat &lt;PATH_TO_TORCHTRT_ROOT&gt;/.bazelversion<span class="k">)</span>
 mkdir bazel
 <span class="nb">cd</span> bazel
 curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/<span class="nv">$BAZEL_VERSION</span>/bazel-<span class="nv">$BAZEL_VERSION</span>-dist.zip
 unzip bazel-<span class="nv">$BAZEL_VERSION</span>-dist.zip
 bash ./compile.sh
 cp output/bazel /usr/local/bin/
-</pre></div>
-</div>
-</div></blockquote>
-<p>You will also need to have CUDA installed on the system (or if running in a container, the system must have
-the CUDA driver installed and the container must have CUDA)</p>
-<p>The correct LibTorch version will be pulled down for you by bazel.</p>
-<blockquote>
-<div><p>NOTE: For best compatability with official PyTorch, use torch==1.10.0+cuda113, TensorRT 8.0 and cuDNN 8.2 for CUDA 11.3 however Torch-TensorRT itself supports
+</pre>
+          </div>
+         </div>
+        </div>
+       </blockquote>
+       <p>
+        You will also need to have CUDA installed on the system (or if running in a container, the system must have
+the CUDA driver installed and the container must have CUDA)
+       </p>
+       <p>
+        The correct LibTorch version will be pulled down for you by bazel.
+       </p>
+       <blockquote>
+        <div>
+         <p>
+          NOTE: For best compatability with official PyTorch, use torch==1.10.0+cuda113, TensorRT 8.0 and cuDNN 8.2 for CUDA 11.3 however Torch-TensorRT itself supports
 TensorRT and cuDNN for other CUDA versions for usecases such as using NVIDIA compiled distributions of PyTorch that use other versions of CUDA
-e.g. aarch64 or custom compiled version of PyTorch.</p>
-</div></blockquote>
-<section id="choosing-the-right-abi">
-<span id="abis"></span><h4>Choosing the Right ABI<a class="headerlink" href="#choosing-the-right-abi" title="Permalink to this headline">¶</a></h4>
-<p>Likely the most complicated thing about compiling Torch-TensorRT is selecting the correct ABI. There are two options
+e.g. aarch64 or custom compiled version of PyTorch.
+         </p>
+        </div>
+       </blockquote>
+       <span id="abis">
+       </span>
+       <h4 id="choosing-the-right-abi">
+        Choosing the Right ABI
+        <a class="headerlink" href="#choosing-the-right-abi" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        Likely the most complicated thing about compiling Torch-TensorRT is selecting the correct ABI. There are two options
 which are incompatible with each other, pre-cxx11-abi and the cxx11-abi. The complexity comes from the fact that while
 the most popular distribution of PyTorch (wheels downloaded from pytorch.org/pypi directly) use the pre-cxx11-abi, most
 other distributions you might encounter (e.g. ones from NVIDIA - NGC containers, and builds for Jetson as well as certain
 libtorch builds and likely if you build PyTorch from source) use the cxx11-abi. It is important you compile Torch-TensorRT
 using the correct ABI to function properly. Below is a table with general pairings of PyTorch distribution sources and the
-recommended commands:</p>
-<table class="docutils align-default">
-<colgroup>
-<col style="width: 33%" />
-<col style="width: 31%" />
-<col style="width: 36%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>PyTorch Source</p></th>
-<th class="head"><p>Recommended C++ Compilation Command</p></th>
-<th class="head"><p>Recommended Python Compilation Command</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>PyTorch whl file from PyTorch.org</p></td>
-<td><p>bazel build //:libtorchtrt -c opt –config pre_cxx11_abi</p></td>
-<td><p>python3 setup.py bdist_wheel</p></td>
-</tr>
-<tr class="row-odd"><td><p>libtorch-shared-with-deps-<a href="#id2"><span class="problematic" id="id3">*</span></a>.zip from PyTorch.org</p></td>
-<td><p>bazel build //:libtorchtrt -c opt –config pre_cxx11_abi</p></td>
-<td><p>python3 setup.py bdist_wheel</p></td>
-</tr>
-<tr class="row-even"><td><p>libtorch-cxx11-abi-shared-with-deps-<a href="#id4"><span class="problematic" id="id5">*</span></a>.zip from PyTorch.org</p></td>
-<td><p>bazel build //:libtorchtrt -c opt</p></td>
-<td><p>python3 setup.py bdist_wheel –use-cxx11-abi</p></td>
-</tr>
-<tr class="row-odd"><td><p>PyTorch preinstalled in an NGC container</p></td>
-<td><p>bazel build //:libtorchtrt -c opt</p></td>
-<td><p>python3 setup.py bdist_wheel –use-cxx11-abi</p></td>
-</tr>
-<tr class="row-even"><td><p>PyTorch from the NVIDIA Forums for Jetson</p></td>
-<td><p>bazel build //:libtorchtrt -c opt</p></td>
-<td><p>python3 setup.py bdist_wheel –jetpack-version 4.6 –use-cxx11-abi</p></td>
-</tr>
-<tr class="row-odd"><td><p>PyTorch built from Source</p></td>
-<td><p>bazel build //:libtorchtrt -c opt</p></td>
-<td><p>python3 setup.py bdist_wheel –use-cxx11-abi</p></td>
-</tr>
-</tbody>
-</table>
-<blockquote>
-<div><p>NOTE: For all of the above cases you must correctly declare the source of PyTorch you intend to use in your WORKSPACE file for both Python and C++ builds. See below for more information</p>
-</div></blockquote>
-<p>You then have two compilation options:</p>
-</section>
-</section>
-<section id="building-using-cudnn-tensorrt-tarball-distributions">
-<span id="build-from-archive"></span><h3><strong>Building using cuDNN &amp; TensorRT tarball distributions</strong><a class="headerlink" href="#building-using-cudnn-tensorrt-tarball-distributions" title="Permalink to this headline">¶</a></h3>
-<blockquote>
-<div><p>This is recommended so as to build Torch-TensorRT hermetically and insures any compilation errors are not caused by version issues</p>
-<p>Make sure when running Torch-TensorRT that these versions of the libraries are prioritized in your <code class="docutils literal notranslate"><span class="pre">$LD_LIBRARY_PATH</span></code></p>
-</div></blockquote>
-<dl class="simple">
-<dt>You need to download the tarball distributions of TensorRT and cuDNN from the NVIDIA website.</dt><dd><ul class="simple">
-<li><p><a class="reference external" href="https://developer.nvidia.com/cudnn">https://developer.nvidia.com/cudnn</a></p></li>
-<li><p><a class="reference external" href="https://developer.nvidia.com/tensorrt">https://developer.nvidia.com/tensorrt</a></p></li>
-</ul>
-</dd>
-</dl>
-<p>Place these files in a directory (the directories <code class="docutils literal notranslate"><span class="pre">third_party/distdir/[x86_64-linux-gnu</span> <span class="pre">|</span> <span class="pre">aarch64-linux-gnu]</span></code> exist for this purpose)</p>
-<p>Then compile referencing the directory with the tarballs</p>
-<blockquote>
-<div><p>If you get errors regarding the packages, check their sha256 hashes and make sure they match the ones listed in <code class="docutils literal notranslate"><span class="pre">WORKSPACE</span></code></p>
-</div></blockquote>
-<section id="release-build">
-<h4>Release Build<a class="headerlink" href="#release-build" title="Permalink to this headline">¶</a></h4>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt -c opt --distdir third_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
-</pre></div>
-</div>
-<p>A tarball with the include files and library can then be found in <code class="docutils literal notranslate"><span class="pre">bazel-bin</span></code></p>
-</section>
-<section id="debug-build">
-<span id="build-from-archive-debug"></span><h4>Debug Build<a class="headerlink" href="#debug-build" title="Permalink to this headline">¶</a></h4>
-<p>To build with debug symbols use the following command</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt -c dbg --distdir third_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
-</pre></div>
-</div>
-<p>A tarball with the include files and library can then be found in <code class="docutils literal notranslate"><span class="pre">bazel-bin</span></code></p>
-</section>
-<section id="pre-cxx11-abi-build">
-<h4>Pre CXX11 ABI Build<a class="headerlink" href="#pre-cxx11-abi-build" title="Permalink to this headline">¶</a></h4>
-<p>To build using the pre-CXX11 ABI use the <code class="docutils literal notranslate"><span class="pre">pre_cxx11_abi</span></code> config</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt --config pre_cxx11_abi -c <span class="o">[</span>dbg/opt<span class="o">]</span> --distdir third_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
-</pre></div>
-</div>
-<p>A tarball with the include files and library can then be found in <code class="docutils literal notranslate"><span class="pre">bazel-bin</span></code></p>
-</section>
-</section>
-<section id="building-using-locally-installed-cudnn-tensorrt">
-<span id="build-from-local"></span><h3><strong>Building using locally installed cuDNN &amp; TensorRT</strong><a class="headerlink" href="#building-using-locally-installed-cudnn-tensorrt" title="Permalink to this headline">¶</a></h3>
-<blockquote>
-<div><p>If you encounter bugs and you compiled using this method please disclose that you used local sources in the issue (an ldd dump would be nice too)</p>
-</div></blockquote>
-<p>Install TensorRT, CUDA and cuDNN on the system before starting to compile.</p>
-<p>In WORKSPACE comment out:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Downloaded distributions to use with --distdir</span>
+recommended commands:
+       </p>
+       <table>
+        <colgroup>
+         <col style="width: 33%"/>
+         <col style="width: 31%"/>
+         <col style="width: 36%"/>
+        </colgroup>
+        <thead>
+         <tr class="row-odd">
+          <th class="head">
+           <p>
+            PyTorch Source
+           </p>
+          </th>
+          <th class="head">
+           <p>
+            Recommended C++ Compilation Command
+           </p>
+          </th>
+          <th class="head">
+           <p>
+            Recommended Python Compilation Command
+           </p>
+          </th>
+         </tr>
+        </thead>
+        <tbody>
+         <tr class="row-even">
+          <td>
+           <p>
+            PyTorch whl file from PyTorch.org
+           </p>
+          </td>
+          <td>
+           <p>
+            bazel build //:libtorchtrt -c opt –config pre_cxx11_abi
+           </p>
+          </td>
+          <td>
+           <p>
+            python3 setup.py bdist_wheel
+           </p>
+          </td>
+         </tr>
+         <tr class="row-odd">
+          <td>
+           <p>
+            libtorch-shared-with-deps-
+            <a href="#id2">
+             <span class="problematic" id="id3">
+              *
+             </span>
+            </a>
+            .zip from PyTorch.org
+           </p>
+          </td>
+          <td>
+           <p>
+            bazel build //:libtorchtrt -c opt –config pre_cxx11_abi
+           </p>
+          </td>
+          <td>
+           <p>
+            python3 setup.py bdist_wheel
+           </p>
+          </td>
+         </tr>
+         <tr class="row-even">
+          <td>
+           <p>
+            libtorch-cxx11-abi-shared-with-deps-
+            <a href="#id4">
+             <span class="problematic" id="id5">
+              *
+             </span>
+            </a>
+            .zip from PyTorch.org
+           </p>
+          </td>
+          <td>
+           <p>
+            bazel build //:libtorchtrt -c opt
+           </p>
+          </td>
+          <td>
+           <p>
+            python3 setup.py bdist_wheel –use-cxx11-abi
+           </p>
+          </td>
+         </tr>
+         <tr class="row-odd">
+          <td>
+           <p>
+            PyTorch preinstalled in an NGC container
+           </p>
+          </td>
+          <td>
+           <p>
+            bazel build //:libtorchtrt -c opt
+           </p>
+          </td>
+          <td>
+           <p>
+            python3 setup.py bdist_wheel –use-cxx11-abi
+           </p>
+          </td>
+         </tr>
+         <tr class="row-even">
+          <td>
+           <p>
+            PyTorch from the NVIDIA Forums for Jetson
+           </p>
+          </td>
+          <td>
+           <p>
+            bazel build //:libtorchtrt -c opt
+           </p>
+          </td>
+          <td>
+           <p>
+            python3 setup.py bdist_wheel –jetpack-version 4.6 –use-cxx11-abi
+           </p>
+          </td>
+         </tr>
+         <tr class="row-odd">
+          <td>
+           <p>
+            PyTorch built from Source
+           </p>
+          </td>
+          <td>
+           <p>
+            bazel build //:libtorchtrt -c opt
+           </p>
+          </td>
+          <td>
+           <p>
+            python3 setup.py bdist_wheel –use-cxx11-abi
+           </p>
+          </td>
+         </tr>
+        </tbody>
+       </table>
+       <blockquote>
+        <div>
+         <p>
+          NOTE: For all of the above cases you must correctly declare the source of PyTorch you intend to use in your WORKSPACE file for both Python and C++ builds. See below for more information
+         </p>
+        </div>
+       </blockquote>
+       <p>
+        You then have two compilation options:
+       </p>
+       <span id="build-from-archive">
+       </span>
+       <h3 id="building-using-cudnn-tensorrt-tarball-distributions">
+        <strong>
+         Building using cuDNN &amp; TensorRT tarball distributions
+        </strong>
+        <a class="headerlink" href="#building-using-cudnn-tensorrt-tarball-distributions" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <blockquote>
+        <div>
+         <p>
+          This is recommended so as to build Torch-TensorRT hermetically and insures any compilation errors are not caused by version issues
+         </p>
+         <p>
+          Make sure when running Torch-TensorRT that these versions of the libraries are prioritized in your
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            $LD_LIBRARY_PATH
+           </span>
+          </code>
+         </p>
+        </div>
+       </blockquote>
+       <dl class="simple">
+        <dt>
+         You need to download the tarball distributions of TensorRT and cuDNN from the NVIDIA website.
+        </dt>
+        <dd>
+         <ul class="simple">
+          <li>
+           <p>
+            <a class="reference external" href="https://developer.nvidia.com/cudnn">
+             https://developer.nvidia.com/cudnn
+            </a>
+           </p>
+          </li>
+          <li>
+           <p>
+            <a class="reference external" href="https://developer.nvidia.com/tensorrt">
+             https://developer.nvidia.com/tensorrt
+            </a>
+           </p>
+          </li>
+         </ul>
+        </dd>
+       </dl>
+       <p>
+        Place these files in a directory (the directories
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          third_party/distdir/[x86_64-linux-gnu
+         </span>
+         <span class="pre">
+          |
+         </span>
+         <span class="pre">
+          aarch64-linux-gnu]
+         </span>
+        </code>
+        exist for this purpose)
+       </p>
+       <p>
+        Then compile referencing the directory with the tarballs
+       </p>
+       <blockquote>
+        <div>
+         <p>
+          If you get errors regarding the packages, check their sha256 hashes and make sure they match the ones listed in
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            WORKSPACE
+           </span>
+          </code>
+         </p>
+        </div>
+       </blockquote>
+       <h4 id="release-build">
+        Release Build
+        <a class="headerlink" href="#release-build" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt -c opt --distdir third_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        A tarball with the include files and library can then be found in
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          bazel-bin
+         </span>
+        </code>
+       </p>
+       <span id="build-from-archive-debug">
+       </span>
+       <h4 id="debug-build">
+        Debug Build
+        <a class="headerlink" href="#debug-build" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        To build with debug symbols use the following command
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt -c dbg --distdir third_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        A tarball with the include files and library can then be found in
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          bazel-bin
+         </span>
+        </code>
+       </p>
+       <h4 id="pre-cxx11-abi-build">
+        Pre CXX11 ABI Build
+        <a class="headerlink" href="#pre-cxx11-abi-build" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        To build using the pre-CXX11 ABI use the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          pre_cxx11_abi
+         </span>
+        </code>
+        config
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt --config pre_cxx11_abi -c <span class="o">[</span>dbg/opt<span class="o">]</span> --distdir third_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        A tarball with the include files and library can then be found in
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          bazel-bin
+         </span>
+        </code>
+       </p>
+       <span id="build-from-local">
+       </span>
+       <h3 id="building-using-locally-installed-cudnn-tensorrt">
+        <strong>
+         Building using locally installed cuDNN &amp; TensorRT
+        </strong>
+        <a class="headerlink" href="#building-using-locally-installed-cudnn-tensorrt" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <blockquote>
+        <div>
+         <p>
+          If you encounter bugs and you compiled using this method please disclose that you used local sources in the issue (an ldd dump would be nice too)
+         </p>
+        </div>
+       </blockquote>
+       <p>
+        Install TensorRT, CUDA and cuDNN on the system before starting to compile.
+       </p>
+       <p>
+        In WORKSPACE comment out:
+       </p>
+       <div class="highlight-python notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="c1"># Downloaded distributions to use with --distdir</span>
 <span class="n">http_archive</span><span class="p">(</span>
-    <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;cudnn&quot;</span><span class="p">,</span>
-    <span class="n">urls</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;&lt;URL&gt;&quot;</span><span class="p">,],</span>
+    <span class="n">name</span> <span class="o">=</span> <span class="s2">"cudnn"</span><span class="p">,</span>
+    <span class="n">urls</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"&lt;URL&gt;"</span><span class="p">,],</span>
 
-    <span class="n">build_file</span> <span class="o">=</span> <span class="s2">&quot;@//third_party/cudnn/archive:BUILD&quot;</span><span class="p">,</span>
-    <span class="n">sha256</span> <span class="o">=</span> <span class="s2">&quot;&lt;TAR SHA256&gt;&quot;</span><span class="p">,</span>
-    <span class="n">strip_prefix</span> <span class="o">=</span> <span class="s2">&quot;cuda&quot;</span>
+    <span class="n">build_file</span> <span class="o">=</span> <span class="s2">"@//third_party/cudnn/archive:BUILD"</span><span class="p">,</span>
+    <span class="n">sha256</span> <span class="o">=</span> <span class="s2">"&lt;TAR SHA256&gt;"</span><span class="p">,</span>
+    <span class="n">strip_prefix</span> <span class="o">=</span> <span class="s2">"cuda"</span>
 <span class="p">)</span>
 
 <span class="n">http_archive</span><span class="p">(</span>
-    <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;tensorrt&quot;</span><span class="p">,</span>
-    <span class="n">urls</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;&lt;URL&gt;&quot;</span><span class="p">,],</span>
+    <span class="n">name</span> <span class="o">=</span> <span class="s2">"tensorrt"</span><span class="p">,</span>
+    <span class="n">urls</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"&lt;URL&gt;"</span><span class="p">,],</span>
 
-    <span class="n">build_file</span> <span class="o">=</span> <span class="s2">&quot;@//third_party/tensorrt/archive:BUILD&quot;</span><span class="p">,</span>
-    <span class="n">sha256</span> <span class="o">=</span> <span class="s2">&quot;&lt;TAR SHA256&gt;&quot;</span><span class="p">,</span>
-    <span class="n">strip_prefix</span> <span class="o">=</span> <span class="s2">&quot;TensorRT-&lt;VERSION&gt;&quot;</span>
+    <span class="n">build_file</span> <span class="o">=</span> <span class="s2">"@//third_party/tensorrt/archive:BUILD"</span><span class="p">,</span>
+    <span class="n">sha256</span> <span class="o">=</span> <span class="s2">"&lt;TAR SHA256&gt;"</span><span class="p">,</span>
+    <span class="n">strip_prefix</span> <span class="o">=</span> <span class="s2">"TensorRT-&lt;VERSION&gt;"</span>
 <span class="p">)</span>
-</pre></div>
-</div>
-<p>and uncomment</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Locally installed dependencies</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        and uncomment
+       </p>
+       <div class="highlight-python notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="c1"># Locally installed dependencies</span>
 <span class="n">new_local_repository</span><span class="p">(</span>
-    <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;cudnn&quot;</span><span class="p">,</span>
-    <span class="n">path</span> <span class="o">=</span> <span class="s2">&quot;/usr/&quot;</span><span class="p">,</span>
-    <span class="n">build_file</span> <span class="o">=</span> <span class="s2">&quot;@//third_party/cudnn/local:BUILD&quot;</span>
+    <span class="n">name</span> <span class="o">=</span> <span class="s2">"cudnn"</span><span class="p">,</span>
+    <span class="n">path</span> <span class="o">=</span> <span class="s2">"/usr/"</span><span class="p">,</span>
+    <span class="n">build_file</span> <span class="o">=</span> <span class="s2">"@//third_party/cudnn/local:BUILD"</span>
 <span class="p">)</span>
 
 <span class="n">new_local_repository</span><span class="p">(</span>
-<span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;tensorrt&quot;</span><span class="p">,</span>
-<span class="n">path</span> <span class="o">=</span> <span class="s2">&quot;/usr/&quot;</span><span class="p">,</span>
-<span class="n">build_file</span> <span class="o">=</span> <span class="s2">&quot;@//third_party/tensorrt/local:BUILD&quot;</span>
+<span class="n">name</span> <span class="o">=</span> <span class="s2">"tensorrt"</span><span class="p">,</span>
+<span class="n">path</span> <span class="o">=</span> <span class="s2">"/usr/"</span><span class="p">,</span>
+<span class="n">build_file</span> <span class="o">=</span> <span class="s2">"@//third_party/tensorrt/local:BUILD"</span>
 <span class="p">)</span>
-</pre></div>
-</div>
-<section id="id6">
-<h4>Release Build<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h4>
-<p>Compile using:</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt -c opt
-</pre></div>
-</div>
-<p>A tarball with the include files and library can then be found in <code class="docutils literal notranslate"><span class="pre">bazel-bin</span></code></p>
-</section>
-<section id="build-from-local-debug">
-<span id="id7"></span><h4>Debug Build<a class="headerlink" href="#build-from-local-debug" title="Permalink to this headline">¶</a></h4>
-<p>To build with debug symbols use the following command</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt -c dbg
-</pre></div>
-</div>
-<p>A tarball with the include files and library can then be found in <code class="docutils literal notranslate"><span class="pre">bazel-bin</span></code></p>
-</section>
-<section id="id8">
-<h4>Pre CXX11 ABI Build<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h4>
-<p>To build using the pre-CXX11 ABI use the <code class="docutils literal notranslate"><span class="pre">pre_cxx11_abi</span></code> config</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt --config pre_cxx11_abi -c <span class="o">[</span>dbg/opt<span class="o">]</span>
-</pre></div>
-</div>
-</section>
-</section>
-<section id="building-the-python-package">
-<h3><strong>Building the Python package</strong><a class="headerlink" href="#building-the-python-package" title="Permalink to this headline">¶</a></h3>
-<p>Begin by installing <code class="docutils literal notranslate"><span class="pre">ninja</span></code></p>
-<p>You can build the Python package using <code class="docutils literal notranslate"><span class="pre">setup.py</span></code> (this will also build the correct version of <code class="docutils literal notranslate"><span class="pre">libtorchtrt.so</span></code>)</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>python3 setup.py <span class="o">[</span>install/bdist_wheel<span class="o">]</span>
-</pre></div>
-</div>
-<section id="id9">
-<h4>Debug Build<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h4>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>python3 setup.py develop <span class="o">[</span>--user<span class="o">]</span>
-</pre></div>
-</div>
-<p>This also compiles a debug build of <code class="docutils literal notranslate"><span class="pre">libtorchtrt.so</span></code></p>
-</section>
-</section>
-<section id="building-natively-on-aarch64-jetson">
-<h3><strong>Building Natively on aarch64 (Jetson)</strong><a class="headerlink" href="#building-natively-on-aarch64-jetson" title="Permalink to this headline">¶</a></h3>
-<section id="prerequisites">
-<h4>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h4>
-<p>Install or compile a build of PyTorch/LibTorch for aarch64</p>
-<p>NVIDIA hosts builds the latest release branch for Jetson here:</p>
-<blockquote>
-<div><p><a class="reference external" href="https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048">https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048</a></p>
-</div></blockquote>
-</section>
-<section id="enviorment-setup">
-<h4>Enviorment Setup<a class="headerlink" href="#enviorment-setup" title="Permalink to this headline">¶</a></h4>
-<p>To build natively on aarch64-linux-gnu platform, configure the <code class="docutils literal notranslate"><span class="pre">WORKSPACE</span></code> with local available dependencies.</p>
-<ol class="arabic simple">
-<li><p>Disable the rules with <code class="docutils literal notranslate"><span class="pre">http_archive</span></code> for x86_64 by commenting the following rules:</p></li>
-</ol>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="c1">#http_archive(</span>
-<span class="c1">#    name = &quot;libtorch&quot;,</span>
-<span class="c1">#    build_file = &quot;@//third_party/libtorch:BUILD&quot;,</span>
-<span class="c1">#    strip_prefix = &quot;libtorch&quot;,</span>
-<span class="c1">#    urls = [&quot;https://download.pytorch.org/libtorch/cu102/libtorch-cxx11-abi-shared-with-deps-1.5.1.zip&quot;],</span>
-<span class="c1">#    sha256 = &quot;cf0691493d05062fe3239cf76773bae4c5124f4b039050dbdd291c652af3ab2a&quot;</span>
+</pre>
+        </div>
+       </div>
+       <h4 id="id6">
+        Release Build
+        <a class="headerlink" href="#id6" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        Compile using:
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt -c opt
+</pre>
+        </div>
+       </div>
+       <p>
+        A tarball with the include files and library can then be found in
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          bazel-bin
+         </span>
+        </code>
+       </p>
+       <span id="id7">
+       </span>
+       <h4 id="build-from-local-debug">
+        Debug Build
+        <a class="headerlink" href="#build-from-local-debug" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        To build with debug symbols use the following command
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt -c dbg
+</pre>
+        </div>
+       </div>
+       <p>
+        A tarball with the include files and library can then be found in
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          bazel-bin
+         </span>
+        </code>
+       </p>
+       <h4 id="id8">
+        Pre CXX11 ABI Build
+        <a class="headerlink" href="#id8" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        To build using the pre-CXX11 ABI use the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          pre_cxx11_abi
+         </span>
+        </code>
+        config
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt --config pre_cxx11_abi -c <span class="o">[</span>dbg/opt<span class="o">]</span>
+</pre>
+        </div>
+       </div>
+       <h3 id="building-the-python-package">
+        <strong>
+         Building the Python package
+        </strong>
+        <a class="headerlink" href="#building-the-python-package" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        Begin by installing
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          ninja
+         </span>
+        </code>
+       </p>
+       <p>
+        You can build the Python package using
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          setup.py
+         </span>
+        </code>
+        (this will also build the correct version of
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          libtorchtrt.so
+         </span>
+        </code>
+        )
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>python3 setup.py <span class="o">[</span>install/bdist_wheel<span class="o">]</span>
+</pre>
+        </div>
+       </div>
+       <h4 id="id9">
+        Debug Build
+        <a class="headerlink" href="#id9" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>python3 setup.py develop <span class="o">[</span>--user<span class="o">]</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        This also compiles a debug build of
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          libtorchtrt.so
+         </span>
+        </code>
+       </p>
+       <h3 id="building-natively-on-aarch64-jetson">
+        <strong>
+         Building Natively on aarch64 (Jetson)
+        </strong>
+        <a class="headerlink" href="#building-natively-on-aarch64-jetson" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <h4 id="prerequisites">
+        Prerequisites
+        <a class="headerlink" href="#prerequisites" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        Install or compile a build of PyTorch/LibTorch for aarch64
+       </p>
+       <p>
+        NVIDIA hosts builds the latest release branch for Jetson here:
+       </p>
+       <blockquote>
+        <div>
+         <p>
+          <a class="reference external" href="https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048">
+           https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048
+          </a>
+         </p>
+        </div>
+       </blockquote>
+       <h4 id="enviorment-setup">
+        Enviorment Setup
+        <a class="headerlink" href="#enviorment-setup" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        To build natively on aarch64-linux-gnu platform, configure the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          WORKSPACE
+         </span>
+        </code>
+        with local available dependencies.
+       </p>
+       <ol class="arabic simple">
+        <li>
+         <p>
+          Disable the rules with
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            http_archive
+           </span>
+          </code>
+          for x86_64 by commenting the following rules:
+         </p>
+        </li>
+       </ol>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="c1">#http_archive(</span>
+<span class="c1">#    name = "libtorch",</span>
+<span class="c1">#    build_file = "@//third_party/libtorch:BUILD",</span>
+<span class="c1">#    strip_prefix = "libtorch",</span>
+<span class="c1">#    urls = ["https://download.pytorch.org/libtorch/cu102/libtorch-cxx11-abi-shared-with-deps-1.5.1.zip"],</span>
+<span class="c1">#    sha256 = "cf0691493d05062fe3239cf76773bae4c5124f4b039050dbdd291c652af3ab2a"</span>
 <span class="c1">#)</span>
 
 <span class="c1">#http_archive(</span>
-<span class="c1">#    name = &quot;libtorch_pre_cxx11_abi&quot;,</span>
-<span class="c1">#    build_file = &quot;@//third_party/libtorch:BUILD&quot;,</span>
-<span class="c1">#    strip_prefix = &quot;libtorch&quot;,</span>
-<span class="c1">#    sha256 = &quot;818977576572eadaf62c80434a25afe44dbaa32ebda3a0919e389dcbe74f8656&quot;,</span>
-<span class="c1">#    urls = [&quot;https://download.pytorch.org/libtorch/cu102/libtorch-shared-with-deps-1.5.1.zip&quot;],</span>
+<span class="c1">#    name = "libtorch_pre_cxx11_abi",</span>
+<span class="c1">#    build_file = "@//third_party/libtorch:BUILD",</span>
+<span class="c1">#    strip_prefix = "libtorch",</span>
+<span class="c1">#    sha256 = "818977576572eadaf62c80434a25afe44dbaa32ebda3a0919e389dcbe74f8656",</span>
+<span class="c1">#    urls = ["https://download.pytorch.org/libtorch/cu102/libtorch-shared-with-deps-1.5.1.zip"],</span>
 <span class="c1">#)</span>
 
 <span class="c1"># Download these tarballs manually from the NVIDIA website</span>
 <span class="c1"># Either place them in the distdir directory in third_party and use the --distdir flag</span>
-<span class="c1"># or modify the urls to &quot;file:///&lt;PATH TO TARBALL&gt;/&lt;TARBALL NAME&gt;.tar.gz</span>
+<span class="c1"># or modify the urls to "file:///&lt;PATH TO TARBALL&gt;/&lt;TARBALL NAME&gt;.tar.gz</span>
 
 <span class="c1">#http_archive(</span>
-<span class="c1">#    name = &quot;cudnn&quot;,</span>
-<span class="c1">#    urls = [&quot;https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.0.1.13/10.2_20200626/cudnn-10.2-linux-x64-v8.0.1.13.tgz&quot;],</span>
-<span class="c1">#    build_file = &quot;@//third_party/cudnn/archive:BUILD&quot;,</span>
-<span class="c1">#    sha256 = &quot;0c106ec84f199a0fbcf1199010166986da732f9b0907768c9ac5ea5b120772db&quot;,</span>
-<span class="c1">#    strip_prefix = &quot;cuda&quot;</span>
+<span class="c1">#    name = "cudnn",</span>
+<span class="c1">#    urls = ["https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.0.1.13/10.2_20200626/cudnn-10.2-linux-x64-v8.0.1.13.tgz"],</span>
+<span class="c1">#    build_file = "@//third_party/cudnn/archive:BUILD",</span>
+<span class="c1">#    sha256 = "0c106ec84f199a0fbcf1199010166986da732f9b0907768c9ac5ea5b120772db",</span>
+<span class="c1">#    strip_prefix = "cuda"</span>
 <span class="c1">#)</span>
 
 <span class="c1">#http_archive(</span>
-<span class="c1">#    name = &quot;tensorrt&quot;,</span>
-<span class="c1">#    urls = [&quot;https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/7.1/tars/TensorRT-7.1.3.4.Ubuntu-18.04.x86_64-gnu.cuda-10.2.cudnn8.0.tar.gz&quot;],</span>
-<span class="c1">#    build_file = &quot;@//third_party/tensorrt/archive:BUILD&quot;,</span>
-<span class="c1">#    sha256 = &quot;9205bed204e2ae7aafd2e01cce0f21309e281e18d5bfd7172ef8541771539d41&quot;,</span>
-<span class="c1">#    strip_prefix = &quot;TensorRT-7.1.3.4&quot;</span>
+<span class="c1">#    name = "tensorrt",</span>
+<span class="c1">#    urls = ["https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/7.1/tars/TensorRT-7.1.3.4.Ubuntu-18.04.x86_64-gnu.cuda-10.2.cudnn8.0.tar.gz"],</span>
+<span class="c1">#    build_file = "@//third_party/tensorrt/archive:BUILD",</span>
+<span class="c1">#    sha256 = "9205bed204e2ae7aafd2e01cce0f21309e281e18d5bfd7172ef8541771539d41",</span>
+<span class="c1">#    strip_prefix = "TensorRT-7.1.3.4"</span>
 <span class="c1">#)</span>
 
 NOTE: You may also need to configure the CUDA version to <span class="m">10</span>.2 by setting the path <span class="k">for</span> the cuda new_local_repository
-</pre></div>
-</div>
-<ol class="arabic" start="2">
-<li><p>Configure the correct paths to directory roots containing local dependencies in the <code class="docutils literal notranslate"><span class="pre">new_local_repository</span></code> rules:</p>
-<blockquote>
-<div><p>NOTE: If you installed PyTorch using a pip package, the correct path is the path to the root of the python torch package.
-In the case that you installed with <code class="docutils literal notranslate"><span class="pre">sudo</span> <span class="pre">pip</span> <span class="pre">install</span></code> this will be <code class="docutils literal notranslate"><span class="pre">/usr/local/lib/python3.6/dist-packages/torch</span></code>.
-In the case you installed with <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">--user</span></code> this will be <code class="docutils literal notranslate"><span class="pre">$HOME/.local/lib/python3.6/site-packages/torch</span></code>.</p>
-</div></blockquote>
-</li>
-</ol>
-<p>In the case you are using NVIDIA compiled pip packages, set the path for both libtorch sources to the same path. This is because unlike
+</pre>
+        </div>
+       </div>
+       <ol class="arabic" start="2">
+        <li>
+         <p>
+          Configure the correct paths to directory roots containing local dependencies in the
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            new_local_repository
+           </span>
+          </code>
+          rules:
+         </p>
+         <blockquote>
+          <div>
+           <p>
+            NOTE: If you installed PyTorch using a pip package, the correct path is the path to the root of the python torch package.
+In the case that you installed with
+            <code class="docutils literal notranslate">
+             <span class="pre">
+              sudo
+             </span>
+             <span class="pre">
+              pip
+             </span>
+             <span class="pre">
+              install
+             </span>
+            </code>
+            this will be
+            <code class="docutils literal notranslate">
+             <span class="pre">
+              /usr/local/lib/python3.6/dist-packages/torch
+             </span>
+            </code>
+            .
+In the case you installed with
+            <code class="docutils literal notranslate">
+             <span class="pre">
+              pip
+             </span>
+             <span class="pre">
+              install
+             </span>
+             <span class="pre">
+              --user
+             </span>
+            </code>
+            this will be
+            <code class="docutils literal notranslate">
+             <span class="pre">
+              $HOME/.local/lib/python3.6/site-packages/torch
+             </span>
+            </code>
+            .
+           </p>
+          </div>
+         </blockquote>
+        </li>
+       </ol>
+       <p>
+        In the case you are using NVIDIA compiled pip packages, set the path for both libtorch sources to the same path. This is because unlike
 PyTorch on x86_64, NVIDIA aarch64 PyTorch uses the CXX11-ABI. If you compiled for source using the pre_cxx11_abi and only would like to
-use that library, set the paths to the same path but when you compile make sure to add the flag <code class="docutils literal notranslate"><span class="pre">--config=pre_cxx11_abi</span></code></p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>new_local_repository<span class="o">(</span>
-    <span class="nv">name</span> <span class="o">=</span> <span class="s2">&quot;libtorch&quot;</span>,
-    <span class="nv">path</span> <span class="o">=</span> <span class="s2">&quot;/usr/local/lib/python3.6/dist-packages/torch&quot;</span>,
-    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">&quot;third_party/libtorch/BUILD&quot;</span>
+use that library, set the paths to the same path but when you compile make sure to add the flag
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          --config=pre_cxx11_abi
+         </span>
+        </code>
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>new_local_repository<span class="o">(</span>
+    <span class="nv">name</span> <span class="o">=</span> <span class="s2">"libtorch"</span>,
+    <span class="nv">path</span> <span class="o">=</span> <span class="s2">"/usr/local/lib/python3.6/dist-packages/torch"</span>,
+    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">"third_party/libtorch/BUILD"</span>
 <span class="o">)</span>
 
 new_local_repository<span class="o">(</span>
-    <span class="nv">name</span> <span class="o">=</span> <span class="s2">&quot;libtorch_pre_cxx11_abi&quot;</span>,
-    <span class="nv">path</span> <span class="o">=</span> <span class="s2">&quot;/usr/local/lib/python3.6/dist-packages/torch&quot;</span>,
-    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">&quot;third_party/libtorch/BUILD&quot;</span>
+    <span class="nv">name</span> <span class="o">=</span> <span class="s2">"libtorch_pre_cxx11_abi"</span>,
+    <span class="nv">path</span> <span class="o">=</span> <span class="s2">"/usr/local/lib/python3.6/dist-packages/torch"</span>,
+    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">"third_party/libtorch/BUILD"</span>
 <span class="o">)</span>
 
 new_local_repository<span class="o">(</span>
-    <span class="nv">name</span> <span class="o">=</span> <span class="s2">&quot;cudnn&quot;</span>,
-    <span class="nv">path</span> <span class="o">=</span> <span class="s2">&quot;/usr/&quot;</span>,
-    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">&quot;@//third_party/cudnn/local:BUILD&quot;</span>
+    <span class="nv">name</span> <span class="o">=</span> <span class="s2">"cudnn"</span>,
+    <span class="nv">path</span> <span class="o">=</span> <span class="s2">"/usr/"</span>,
+    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">"@//third_party/cudnn/local:BUILD"</span>
 <span class="o">)</span>
 
 new_local_repository<span class="o">(</span>
-    <span class="nv">name</span> <span class="o">=</span> <span class="s2">&quot;tensorrt&quot;</span>,
-    <span class="nv">path</span> <span class="o">=</span> <span class="s2">&quot;/usr/&quot;</span>,
-    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">&quot;@//third_party/tensorrt/local:BUILD&quot;</span>
+    <span class="nv">name</span> <span class="o">=</span> <span class="s2">"tensorrt"</span>,
+    <span class="nv">path</span> <span class="o">=</span> <span class="s2">"/usr/"</span>,
+    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">"@//third_party/tensorrt/local:BUILD"</span>
 <span class="o">)</span>
-</pre></div>
-</div>
-</section>
-<section id="compile-c-library-and-compiler-cli">
-<h4>Compile C++ Library and Compiler CLI<a class="headerlink" href="#compile-c-library-and-compiler-cli" title="Permalink to this headline">¶</a></h4>
-<blockquote>
-<div><p>NOTE: Due to shifting dependency locations between Jetpack 4.5 and 4.6 there is a now a flag to inform bazel of the Jetpack version</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>--platforms //toolchains:jetpack_4.x
-</pre></div>
-</div>
-</div></blockquote>
-<p>Compile Torch-TensorRT library using bazel command:</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt --platforms //toolchains:jetpack_4.6
-</pre></div>
-</div>
-</section>
-<section id="compile-python-api">
-<h4>Compile Python API<a class="headerlink" href="#compile-python-api" title="Permalink to this headline">¶</a></h4>
-<blockquote>
-<div><p>NOTE: Due to shifting dependencies locations between Jetpack 4.5 and Jetpack 4.6 there is now a flag for <code class="docutils literal notranslate"><span class="pre">setup.py</span></code> which sets the jetpack version (default: 4.6)</p>
-</div></blockquote>
-<p>Compile the Python API using the following command from the <code class="docutils literal notranslate"><span class="pre">//py</span></code> directory:</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>python3 setup.py install --use-cxx11-abi
-</pre></div>
-</div>
-<p>If you have a build of PyTorch that uses Pre-CXX11 ABI drop the <code class="docutils literal notranslate"><span class="pre">--use-cxx11-abi</span></code> flag</p>
-<p>If you are building for Jetpack 4.5 add the <code class="docutils literal notranslate"><span class="pre">--jetpack-version</span> <span class="pre">4.5</span></code> flag</p>
-</section>
-</section>
-</section>
-</section>
-
-
-             </article>
-
-            </div>
-            <footer>
-
-    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
-
-        <a href="getting_started_with_cpp_api.html" class="btn btn-neutral float-right" title="Getting Started with C++" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
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-        <a href="../index.html" class="btn btn-neutral" title="Torch-TensorRT" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
-
-    </div>
-
-
-
-
-    <hr>
-
-
-
-  <div role="contentinfo">
-    <p>
-        &copy; Copyright 2021, NVIDIA Corporation.
-
-    </p>
-  </div>
-
-      <div>
-        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
-      </div>
-
-
-</footer>
-
-          </div>
+</pre>
         </div>
-
-        <div class="pytorch-content-right" id="pytorch-content-right">
-          <div class="pytorch-right-menu" id="pytorch-right-menu">
-            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
-              <ul>
-<li><a class="reference internal" href="#">Installation</a><ul>
-<li><a class="reference internal" href="#precompiled-binaries">Precompiled Binaries</a><ul>
-<li><a class="reference internal" href="#dependencies">Dependencies</a></li>
-<li><a class="reference internal" href="#python-package">Python Package</a></li>
-<li><a class="reference internal" href="#c-binary-distribution">C++ Binary Distribution</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#compiling-from-source">Compiling From Source</a><ul>
-<li><a class="reference internal" href="#dependencies-for-compilation">Dependencies for Compilation</a><ul>
-<li><a class="reference internal" href="#choosing-the-right-abi">Choosing the Right ABI</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#building-using-cudnn-tensorrt-tarball-distributions"><strong>Building using cuDNN &amp; TensorRT tarball distributions</strong></a><ul>
-<li><a class="reference internal" href="#release-build">Release Build</a></li>
-<li><a class="reference internal" href="#debug-build">Debug Build</a></li>
-<li><a class="reference internal" href="#pre-cxx11-abi-build">Pre CXX11 ABI Build</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#building-using-locally-installed-cudnn-tensorrt"><strong>Building using locally installed cuDNN &amp; TensorRT</strong></a><ul>
-<li><a class="reference internal" href="#id6">Release Build</a></li>
-<li><a class="reference internal" href="#build-from-local-debug">Debug Build</a></li>
-<li><a class="reference internal" href="#id8">Pre CXX11 ABI Build</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#building-the-python-package"><strong>Building the Python package</strong></a><ul>
-<li><a class="reference internal" href="#id9">Debug Build</a></li>
-</ul>
-</li>
-<li><a class="reference internal" href="#building-natively-on-aarch64-jetson"><strong>Building Natively on aarch64 (Jetson)</strong></a><ul>
-<li><a class="reference internal" href="#prerequisites">Prerequisites</a></li>
-<li><a class="reference internal" href="#enviorment-setup">Enviorment Setup</a></li>
-<li><a class="reference internal" href="#compile-c-library-and-compiler-cli">Compile C++ Library and Compiler CLI</a></li>
-<li><a class="reference internal" href="#compile-python-api">Compile Python API</a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-
-            </div>
+       </div>
+       <h4 id="compile-c-library-and-compiler-cli">
+        Compile C++ Library and Compiler CLI
+        <a class="headerlink" href="#compile-c-library-and-compiler-cli" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <blockquote>
+        <div>
+         <p>
+          NOTE: Due to shifting dependency locations between Jetpack 4.5 and 4.6 there is a now a flag to inform bazel of the Jetpack version
+         </p>
+         <div class="highlight-shell notranslate">
+          <div class="highlight">
+           <pre><span></span>--platforms //toolchains:jetpack_4.x
+</pre>
           </div>
+         </div>
         </div>
-      </section>
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-
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-       <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
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+       </blockquote>
+       <p>
+        Compile Torch-TensorRT library using bazel command:
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt --platforms //toolchains:jetpack_4.6
+</pre>
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+       </div>
+       <h4 id="compile-python-api">
+        Compile Python API
+        <a class="headerlink" href="#compile-python-api" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <blockquote>
+        <div>
+         <p>
+          NOTE: Due to shifting dependencies locations between Jetpack 4.5 and Jetpack 4.6 there is now a flag for
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            setup.py
+           </span>
+          </code>
+          which sets the jetpack version (default: 4.6)
+         </p>
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-          <h2>Resources</h2>
-          <p>Find development resources and get your questions answered</p>
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+       <p>
+        Compile the Python API using the following command from the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          //py
+         </span>
+        </code>
+        directory:
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>python3 setup.py install --use-cxx11-abi
+</pre>
         </div>
-      </div>
+       </div>
+       <p>
+        If you have a build of PyTorch that uses Pre-CXX11 ABI drop the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          --use-cxx11-abi
+         </span>
+        </code>
+        flag
+       </p>
+       <p>
+        If you are building for Jetpack 4.5 add the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          --jetpack-version
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+         <span class="pre">
+          4.5
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+      <a href="https://github.com/bashtage/sphinx-material/">
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diff --git a/docs/tutorials/notebooks.html b/docs/tutorials/notebooks.html
index 579691b172..f1a3883d41 100644
--- a/docs/tutorials/notebooks.html
+++ b/docs/tutorials/notebooks.html
@@ -9,22 +9,22 @@
   <meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
-  
+
   <title>Example notebooks &mdash; Torch-TensorRT master documentation</title>
-  
 
-  
-  
-  
-  
 
-  
 
-  
-  
-    
 
-  
+
+
+
+
+
+
+
+
+
+
 
   <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
   <!-- <link rel="stylesheet" href="../_static/pygments.css" type="text/css" /> -->
@@ -36,11 +36,11 @@
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     <link rel="prev" title="DLA" href="using_dla.html" />
   <!-- Google Analytics -->
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@@ -180,9 +180,9 @@
 
 <body class="pytorch-body">
 
-   
 
-    
+
+
 
     <div class="table-of-contents-link-wrapper">
       <span>Table of Contents</span>
@@ -193,21 +193,21 @@
       <div class="pytorch-side-scroll">
         <div class="pytorch-menu pytorch-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
           <div class="pytorch-left-menu-search">
-            
 
-            
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-              
+
+
+
+
                 <div class="version">
-                  master (1.2.0a0+450c869)
+                  master (1.2.0)
                 </div>
-              
-            
 
-            
 
 
-  
+
+
+
+
 
 
 <div role="search">
@@ -218,15 +218,15 @@
   </form>
 </div>
 
-            
+
           </div>
 
-          
-            
-            
-              
-            
-            
+
+
+
+
+
+
               <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
@@ -277,8 +277,8 @@
 <li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
 </ul>
 
-            
-          
+
+
         </div>
       </div>
     </nav>
@@ -286,7 +286,7 @@
     <div class="pytorch-container">
       <div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
         <div class="pytorch-breadcrumbs-wrapper">
-          
+
 
 
 
@@ -305,30 +305,30 @@
 <div role="navigation" aria-label="breadcrumbs navigation">
 
   <ul class="pytorch-breadcrumbs">
-    
+
       <li>
         <a href="../index.html">
-          
+
             Docs
-          
+
         </a> &gt;
       </li>
 
-        
+
       <li>Example notebooks</li>
-    
-    
+
+
       <li class="pytorch-breadcrumbs-aside">
-        
-            
+
+
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-          
-        
+
+
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-    
+
   </ul>
 
-  
+
 </div>
         </div>
 
@@ -340,13 +340,13 @@
       <section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
         <div class="pytorch-content-left">
 
-        
-          
+
+
           <div class="rst-content">
-          
+
             <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
              <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
-              
+
   <section id="example-notebooks">
 <span id="notebooks"></span><h1>Example notebooks<a class="headerlink" href="#example-notebooks" title="Permalink to this headline">¶</a></h1>
 <p>There exists a number of notebooks which cover specific using specific features and models
@@ -453,13 +453,11 @@ <h3>Compiling LeNet with Torch-TensorRT<a class="headerlink" href="#compiling-le
 <h3>Accelerate Deep Learning Models using Quantization in Torch-TensorRT<a class="headerlink" href="#accelerate-deep-learning-models-using-quantization-in-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
 <p>Model Quantization is a popular way of optimization which reduces the size of models thereby
 accelerating inference, also opening up the possibilities of deployments on devices with lower
-computation power such as Jetson. Simply put, quantization is a process of mapping input values</p>
-<blockquote>
-<div><p>from a larger set to output values in a smaller set. In this notebook, we illustrate the workflow
+computation power such as Jetson. Simply put, quantization is a process of mapping input values
+from a larger set to output values in a smaller set. In this notebook, we illustrate the workflow
 that you can adopt while quantizing a deep learning model in Torch-TensorRT. The notebook takes
 you through an example of Mobilenetv2 for a classification task on a subset of Imagenet Dataset
 called Imagenette which has 10 classes.</p>
-</div></blockquote>
 <ul class="simple">
 <li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/qat-ptq-workflow.ipynb">Accelerate Deep Learning Models using Quantization in Torch-TensorRT</a></p></li>
 </ul>
@@ -488,25 +486,25 @@ <h3>Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT<a c
 
 
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@@ -514,11 +512,11 @@ <h3>Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT<a c
 
     </p>
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@@ -555,12 +553,12 @@ <h3>Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT<a c
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+
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@@ -569,9 +567,9 @@ <h3>Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT<a c
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+
 
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 <span id="ptq"></span><h1>Post Training Quantization (PTQ)<a class="headerlink" href="#post-training-quantization-ptq" title="Permalink to this headline">¶</a></h1>
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 <span id="serving-torch-tensorrt-with-triton"></span><h1>Serving a Torch-TensorRT model with Triton<a class="headerlink" href="#serving-a-torch-tensorrt-model-with-triton" title="Permalink to this headline">¶</a></h1>
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-
-  <section id="torchtrtc">
-<span id="id1"></span><h1>torchtrtc<a class="headerlink" href="#torchtrtc" title="Permalink to this headline">¶</a></h1>
-<p><code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code> is a CLI application for using the Torch-TensorRT compiler. It serves as an easy way to compile a
+     </div>
+     <div class="md-content">
+      <article class="md-content__inner md-typeset" role="main">
+       <span id="id1">
+       </span>
+       <h1 id="tutorials-torchtrtc--page-root">
+        torchtrtc
+        <a class="headerlink" href="#tutorials-torchtrtc--page-root" title="Permalink to this headline">
+         ¶
+        </a>
+       </h1>
+       <p>
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torchtrtc
+         </span>
+        </code>
+        is a CLI application for using the Torch-TensorRT compiler. It serves as an easy way to compile a
 TorchScript Module with Torch-TensorRT from the command-line to quickly check support or as part of
 a deployment pipeline. All basic features of the compiler are supported including post training
 quantization (though you must already have a calibration cache file to use the PTQ feature). The compiler can
 output two formats, either a TorchScript program with the TensorRT engine embedded or
-the TensorRT engine itself as a PLAN file.</p>
-<p>All that is required to run the program after compilation is for C++ linking against <code class="docutils literal notranslate"><span class="pre">libtorchtrt.so</span></code>
-or in Python importing the torch_tensorrt package. All other aspects of using compiled modules are identical
-to standard TorchScript. Load with <code class="docutils literal notranslate"><span class="pre">torch.jit.load()</span></code> and run like you would run any other module.</p>
-<div class="highlight-txt notranslate"><div class="highlight"><pre><span></span>torchtrtc [input_file_path] [output_file_path]
+the TensorRT engine itself as a PLAN file.
+       </p>
+       <p>
+        All that is required to run the program after compilation is for C++ linking against
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          libtorchtrt.so
+         </span>
+        </code>
+        or in Python importing the torch_tensorrt package. All other aspects of using compiled modules are identical
+to standard TorchScript. Load with
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch.jit.load()
+         </span>
+        </code>
+        and run like you would run any other module.
+       </p>
+       <div class="highlight-txt notranslate">
+        <div class="highlight">
+         <pre><span></span>torchtrtc [input_file_path] [output_file_path]
   [input_specs...] {OPTIONS}
 
   torchtrtc is a compiler for TorchScript, it will compile and optimize
@@ -436,12 +593,10 @@
                                       used to select kernels
     --workspace-size=[workspace_size] Maximum size of workspace given to
                                       TensorRT
-    --atol=[atol]                     Absolute tolerance threshold for acceptable
-                                      numerical deviation from standard torchscript
-                                      output (default 1e-8)
-    --rtol=[rtol]                     Relative tolerance threshold for acceptable
-                                      numerical deviation from standard torchscript
-                                      output  (default 1e-5)
+    -t[threshold],
+    --threshold=[threshold]           Maximum acceptable numerical deviation
+                                      from standard torchscript output
+                                      (default 2e-5)
     --no-threshold-check              Skip checking threshold compliance
     --truncate-long-double,
     --truncate, --truncate-64bit      Truncate weights that are provided in
@@ -457,310 +612,89 @@
     input_specs...                    Specs for inputs to engine, can either
                                       be a single size or a range defined by
                                       Min, Optimal, Max sizes, e.g.
-                                      &quot;(N,..,C,H,W)&quot;
-                                      &quot;[(MIN_N,..,MIN_C,MIN_H,MIN_W);(OPT_N,..,OPT_C,OPT_H,OPT_W);(MAX_N,..,MAX_C,MAX_H,MAX_W)]&quot;.
+                                      "(N,..,C,H,W)"
+                                      "[(MIN_N,..,MIN_C,MIN_H,MIN_W);(OPT_N,..,OPT_C,OPT_H,OPT_W);(MAX_N,..,MAX_C,MAX_H,MAX_W)]".
                                       Data Type and format can be specified by
-                                      adding an &quot;@&quot; followed by dtype and &quot;%&quot;
+                                      adding an "@" followed by dtype and "%"
                                       followed by format to the end of the
-                                      shape spec. e.g. &quot;(3, 3, 32,
-                                      32)@f16%NHWC&quot;
-    &quot;--&quot; can be used to terminate flag options and force all following
+                                      shape spec. e.g. "(3, 3, 32,
+                                      32)@f16%NHWC"
+    "--" can be used to terminate flag options and force all following
     arguments to be treated as positional options
-</pre></div>
-</div>
-<p>e.g.</p>
-<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts <span class="s2">&quot;[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous&quot;</span> -p f16
-</pre></div>
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+       <p>
+        e.g.
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+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts <span class="s2">"[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous"</span> -p f16
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diff --git a/docs/tutorials/use_from_pytorch.html b/docs/tutorials/use_from_pytorch.html
index 2fb4e8e3ae..0e39215811 100644
--- a/docs/tutorials/use_from_pytorch.html
+++ b/docs/tutorials/use_from_pytorch.html
@@ -9,22 +9,22 @@
   <meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
-  
+
   <title>Using Torch-TensorRT Directly From PyTorch &mdash; Torch-TensorRT master documentation</title>
-  
 
-  
-  
-  
-  
 
-  
 
-  
-  
-    
 
-  
+
+
+
+
+
+
+
+
+
+
 
   <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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@@ -36,11 +36,11 @@
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+
+
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@@ -180,9 +180,9 @@
 
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-   
 
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+
+
 
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@@ -193,21 +193,21 @@
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+
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+
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-                  master (1.2.0a0+450c869)
+                  master (1.2.0)
                 </div>
-              
-            
 
-            
 
 
-  
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+
+
+
 
 
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@@ -218,15 +218,15 @@
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+
+
+
+
+
+
               <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
@@ -277,8 +277,8 @@
 <li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
 </ul>
 
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 <span id="using-dla"></span><h1>DLA<a class="headerlink" href="#dla" title="Permalink to this headline">¶</a></h1>
 <p><code class="docutils literal notranslate"><span class="pre">DLA</span></code> NVIDIA Deep Learning Accelerator is a fixed-function accelerator engine targeted for deep learning operations. DLA is designed to do full hardware acceleration of convolutional neural networks. DLA supports various layers such as convolution, deconvolution, fully-connected, activation, pooling, batch normalization, etc. <code class="docutils literal notranslate"><span class="pre">torch_tensorrt</span></code> supports compilation of TorchScript Module and deployment pipeline on the DLA hardware available on NVIDIA embedded platforms.</p>
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diff --git a/docs/v1.2.0/_cpp_api/classtorch__tensorrt_1_1DataType.html b/docs/v1.2.0/_cpp_api/classtorch__tensorrt_1_1DataType.html
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+
+  <section id="class-datatype">
+<span id="exhale-class-classtorch-tensorrt-1-1datatype"></span><h1>Class DataType<a class="headerlink" href="#class-datatype" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="class-documentation">
+<h2>Class Documentation<a class="headerlink" href="#class-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp class">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataTypeE">
+<span class="target" id="classtorch__tensorrt_1_1DataType"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">DataType</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataTypeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Supported Data Types that can be used with TensorRT engines</p>
+<p>This class is compatable with c10::DataTypes (but will check for TRT support) so there should not be a reason that you need to use this type explictly. </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-types">Public Types</p>
+<dl class="cpp enum">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Value</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Underlying enum class to support the <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a> Class</p>
+<p>In the case that you need to use the <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a> class itself, interface using this enum vs. normal instatination</p>
+<p>ex. <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">torch_tensorrt::DataType</span></a> type = <a class="reference internal" href="#classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684a45ceda04c1ab50695a4a6aeaeae99817"><span class="std std-ref">DataType::kFloat</span></a>; </p>
+<p><em>Values:</em></p>
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value6kFloatE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684a45ceda04c1ab50695a4a6aeaeae99817"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kFloat</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value6kFloatE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>FP32. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value5kHalfE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684a3733a4b256b56596c5c52de99a196179"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kHalf</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value5kHalfE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>FP16. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value5kCharE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684ab97bba9140703786c7aa08baea3ab276"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kChar</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value5kCharE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>INT8. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value4kIntE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684aa331051c24898e3701b925251d85c7c1"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kInt</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value4kIntE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>INT. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value5kBoolE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684a67f7e5fb797f4da732c5e217cbda4353"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kBool</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value5kBoolE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Bool. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value8kUnknownE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684a140934e6b67dfe841b32eb73bea55358"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kUnknown</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value8kUnknownE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Sentinel value. </p>
+</dd></dl>
+
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType8DataTypeEv">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1aeaf580c5f2d949bfe358e47b3824de85"></span><span class="sig-name descname"><span class="n"><span class="pre">DataType</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType8DataTypeEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Data Type object. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType8DataTypeE5Value">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a2804fe4287453fd5b5e35f44a382dcdd"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DataType</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataType5ValueE" title="torch_tensorrt::DataType::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType8DataTypeE5Value" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p><a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a> constructor from enum. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType8DataTypeEN3c1010ScalarTypeE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a7cb2fc58fcea525b8c07388134cb1b37"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DataType</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ScalarType</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType8DataTypeEN3c1010ScalarTypeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Data Type object from torch type enums. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt8DataTypecv5ValueEv">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a09bc3c1870d9c469de12095a98e8caa4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataType5ValueE" title="torch_tensorrt::DataType::Value"><span class="n"><span class="pre">Value</span></span></a></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt8DataTypecv5ValueEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the enum value of the <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a> object. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Value </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataTypecvbEv">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1aba0ed97193a870cbddf50c7bc02dd9fb"></span><span class="k"><span class="pre">explicit</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">delete</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataTypecvbEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt8DataTypeeqE8DataType">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1ae8b45ac54760deb077d6dfc4acbd2e2b"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt8DataTypeeqE8DataType" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt8DataTypeeqEN8DataType5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a1232a15ad160b309db10a5ea8dd4e26c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataType5ValueE" title="torch_tensorrt::DataType::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt8DataTypeeqEN8DataType5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt8DataTypeneE8DataType">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a3fb2cbdb14408da52fa9bb0e0840d7e4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">!=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt8DataTypeneE8DataType" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt8DataTypeneEN8DataType5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a25dd020fd88ec93b421c990045c1aba4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">!=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataType5ValueE" title="torch_tensorrt::DataType::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt8DataTypeneEN8DataType5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+</dd></dl>
+
+</section>
+</section>
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diff --git a/docs/v1.2.0/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html b/docs/v1.2.0/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html
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+  <section id="class-device-devicetype">
+<span id="exhale-class-classtorch-tensorrt-1-1device-1-1devicetype"></span><h1>Class Device::DeviceType<a class="headerlink" href="#class-device-devicetype" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="nested-relationships">
+<h2>Nested Relationships<a class="headerlink" href="#nested-relationships" title="Permalink to this headline">¶</a></h2>
+<p>This class is a nested type of <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#exhale-struct-structtorch-tensorrt-1-1device"><span class="std std-ref">Struct Device</span></a>.</p>
+</section>
+<section id="class-documentation">
+<h2>Class Documentation<a class="headerlink" href="#class-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp class">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceTypeE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6DeviceE" title="torch_tensorrt::Device"><span class="n"><span class="pre">Device</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceTypeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Supported <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Types that can be used with TensorRT engines</p>
+<p>This class is compatable with c10::DeviceTypes (but will check for TRT support) but the only applicable value is at::kCUDA, which maps to <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"><span class="std std-ref">DeviceType::kGPU</span></a></p>
+<p>To use the <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a> class itself, interface using the enum vs. normal instatination</p>
+<p>ex. torch_tensorrt::DeviceType type = <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"><span class="std std-ref">DeviceType::kGPU</span></a>; </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-types">Public Types</p>
+<dl class="cpp enum">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Value</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Underlying enum class to support the <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a> Class</p>
+<p>In the case that you need to use the <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a> class itself, interface using this enum vs. normal instatination</p>
+<p>ex. torch_tensorrt::DeviceType type = <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"><span class="std std-ref">DeviceType::kGPU</span></a>; </p>
+<p><em>Values:</em></p>
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kGPUE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kGPU</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kGPUE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Target GPU to run engine. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kDLAE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350aa13fb3ec20096f0d0c205b67ca05c98e"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kDLA</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kDLAE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Target DLA to run engine. </p>
+</dd></dl>
+
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEv">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1afe573ee91b95b81be8a0f1503a00ca45"></span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Type object. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeE5Value">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a5a7e6c4eb98321bd53869a8ec08e2a2c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE" title="torch_tensorrt::Device::DeviceType::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeE5Value" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Type object from internal enum. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEN3c1010DeviceTypeE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a499573dd56df8fa1479dcec95dfda711"></span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">DeviceType</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEN3c1010DeviceTypeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Type object from torch device enums Note: The only valid value is torch::kCUDA (torch::kCPU is not supported) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt6Device10DeviceTypecv5ValueEv">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a4afa0b396b7a076e37054754a367f2f4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE" title="torch_tensorrt::Device::DeviceType::Value"><span class="n"><span class="pre">Value</span></span></a></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt6Device10DeviceTypecv5ValueEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the internal value from the <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> object. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Value </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceTypecvbEv">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a1e78ae4780df8bd258ee8703a22d9a4d"></span><span class="k"><span class="pre">explicit</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">delete</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceTypecvbEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt6Device10DeviceTypeeqE10DeviceType">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a0177ee9788edabb89a8cb64234d2a60c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt6Device10DeviceTypeE" title="torch_tensorrt::Device::DeviceType"><span class="n"><span class="pre">DeviceType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt6Device10DeviceTypeeqE10DeviceType" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparison operator for <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt6Device10DeviceTypeneE10DeviceType">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a0d8973b436226f17597d94f4e8f63336"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">!=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt6Device10DeviceTypeE" title="torch_tensorrt::Device::DeviceType"><span class="n"><span class="pre">DeviceType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt6Device10DeviceTypeneE10DeviceType" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparison operator for <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+</dd></dl>
+
+</section>
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+<li><a class="reference internal" href="#">Class Device::DeviceType</a><ul>
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diff --git a/docs/v1.2.0/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html b/docs/v1.2.0/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html
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+  <section id="class-tensorformat">
+<span id="exhale-class-classtorch-tensorrt-1-1tensorformat"></span><h1>Class TensorFormat<a class="headerlink" href="#class-tensorformat" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
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+<dl class="cpp class">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormatE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">TensorFormat</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormatE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p><a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> is an enum class which defines the memeory layout used to store Tensor Data. </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-types">Public Types</p>
+<dl class="cpp enum">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a5f9c2efc6450a9a6a536fb597762c1d5"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Value</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Underlying enum class to support the <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> Class</p>
+<p>In the case that you need to use the <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> class itself, interface using this enum vs. normal instatination</p>
+<p>ex. <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">torch_tensorrt::TensorFormat</span></a> type = <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat_1a5f9c2efc6450a9a6a536fb597762c1d5abe49335174da5bd374439929a7313a8e"><span class="std std-ref">TensorFormat::kContiguous</span></a>; </p>
+<p><em>Values:</em></p>
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a5f9c2efc6450a9a6a536fb597762c1d5abe49335174da5bd374439929a7313a8e"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kContiguous</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Contiguous / NCHW / Linear. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat5Value13kChannelsLastE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a5f9c2efc6450a9a6a536fb597762c1d5a4a290b531327a2bdd79cd8a220764bc5"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kChannelsLast</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat5Value13kChannelsLastE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Channel Last / NHWC. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat5Value8kUnknownE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a5f9c2efc6450a9a6a536fb597762c1d5a39873c22f833d49326284997a7354886"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kUnknown</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat5Value8kUnknownE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Sentinel value. </p>
+</dd></dl>
+
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatEv">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1ae6c5fe5a84ce30238e7b34b11f747194"></span><span class="sig-name descname"><span class="n"><span class="pre">TensorFormat</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> object. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatE5Value">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a6c68a24cbf07126e9a7232ec101f6173"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">TensorFormat</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormat5ValueE" title="torch_tensorrt::TensorFormat::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatE5Value" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p><a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> constructor from enum. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatEN2at12MemoryFormatE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1aec707cf4f1cc30ef51959b598fdeb4ef"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">TensorFormat</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">at</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">MemoryFormat</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatEN2at12MemoryFormatE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> object from torch type enums. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt12TensorFormatcv5ValueEv">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a7a6bc4e6c832af328b038486e7c8edd4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormat5ValueE" title="torch_tensorrt::TensorFormat::Value"><span class="n"><span class="pre">Value</span></span></a></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt12TensorFormatcv5ValueEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the enum value of the <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> object. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Value </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormatcvbEv">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a2acd96cd0b987a998eac9d6dbcd64513"></span><span class="k"><span class="pre">explicit</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">delete</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormatcvbEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt12TensorFormateqE12TensorFormat">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a0bf675d9440ba073bdd2e0fb4dd4ee02"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt12TensorFormateqE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt12TensorFormateqEN12TensorFormat5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a37a19654f18bdc6f1277b8d3403e4142"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormat5ValueE" title="torch_tensorrt::TensorFormat::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt12TensorFormateqEN12TensorFormat5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt12TensorFormatneE12TensorFormat">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1ad2e6f3e072ffc909bb3020f6ad4c373e"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">!=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt12TensorFormatneE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt12TensorFormatneEN12TensorFormat5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a7d87b14aa1585bb442e40e547677862d"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">!=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormat5ValueE" title="torch_tensorrt::TensorFormat::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt12TensorFormatneEN12TensorFormat5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+</dd></dl>
+
+</section>
+</section>
+
+
+             </article>
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+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
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+
+  <section id="template-class-int8cachecalibrator">
+<span id="exhale-class-classtorch-tensorrt-1-1ptq-1-1int8cachecalibrator"></span><h1>Template Class Int8CacheCalibrator<a class="headerlink" href="#template-class-int8cachecalibrator" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">File ptq.h</span></a></p></li>
+</ul>
+<section id="inheritance-relationships">
+<h2>Inheritance Relationships<a class="headerlink" href="#inheritance-relationships" title="Permalink to this headline">¶</a></h2>
+<section id="base-type">
+<h3>Base Type<a class="headerlink" href="#base-type" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">private</span> <span class="pre">Algorithm</span></code></p></li>
+</ul>
+</section>
+</section>
+<section id="class-documentation">
+<h2>Class Documentation<a class="headerlink" href="#class-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp class">
+<dt class="sig sig-object cpp" id="_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE">
+<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">Int8CacheCalibrator</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">private</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE" title="torch_tensorrt::ptq::Int8CacheCalibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><a class="headerlink" href="#_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Generic <a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#classtorch__tensorrt_1_1ptq_1_1Int8Calibrator"><span class="std std-ref">Int8Calibrator</span></a> implementation based on a specified TensorRT calibration algorithm that only reads from a calibration file. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Template Parameters</dt>
+<dd class="field-odd"><p><strong>Algorithm</strong> – class nvinfer1::IInt8Calibrator (Default: nvinfer1::IInt8EntropyCalibrator2) - Algorithm to use </p>
+</dd>
+</dl>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator19Int8CacheCalibratorERKNSt6stringE">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator_1acdad3ba79f02daaf858fce843f80d504"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Int8CacheCalibrator</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator19Int8CacheCalibratorERKNSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Int 8 Cache Calibrator object. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>cache_file_path</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt3ptq19Int8CacheCalibrator12getBatchSizeEv">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator_1ab118606e734d860a0689fabb8e08447c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getBatchSize</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt3ptq19Int8CacheCalibrator12getBatchSizeEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the Batch Size for the next batch (always 1 due to issues with TRT and explicit batch) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>int </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator8getBatchEA_PvA_PKci">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator_1a63ffe6457b752cf3405990158f2fac96"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getBatch</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">bindings</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">]</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">char</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">names</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">]</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">nbBindings</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator8getBatchEA_PvA_PKci" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the next Batch. </p>
+<p>Not used always returns false</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>bindings</strong> – void*[] - An array of binding pointers (fed in from TensorRT calibrator), these buffers should be filed with batch data for each input </p></li>
+<li><p><strong>names</strong> – const char*[] - Names of bindings </p></li>
+<li><p><strong>nbBindings</strong> – int - Number of bindings </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator20readCalibrationCacheER6size_t">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator_1ad4b4808923d96f3cfe2e7616c1825cf0"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="sig-name descname"><span class="n"><span class="pre">readCalibrationCache</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">length</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator20readCalibrationCacheER6size_t" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Read calibration cache. </p>
+<p>How to read from the calibration cache, only enabled if use_cache is set</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>length</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>const void* - Pointer to cache data </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator21writeCalibrationCacheEPKv6size_t">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator_1a1a31513eea8017a31852afc9e49eddd9"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">writeCalibrationCache</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">cache</span></span>, <span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">length</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator21writeCalibrationCacheEPKv6size_t" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Write calibration cache. </p>
+<p>Write a the calibration cache provided by TensorRT to a specified file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>cache</strong> – const void* - cache data </p></li>
+<li><p><strong>length</strong> – size_t - length of cache </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibratorcvPN8nvinfer115IInt8CalibratorEEv">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator_1ae51df6619de0f02dc6dc877ba147700a"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8Calibrator</span></span><span class="p"><span class="pre">*</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibratorcvPN8nvinfer115IInt8CalibratorEEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>operator to cast to nvinfer1::IInt8Calibrator* </p>
+<p>Convience function to convert to a IInt8Calibrator* to easily be assigned to the ptq_calibrator field in CompileSpec</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>nvinfer1::IInt8Calibrator* </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+</dd></dl>
+
+</section>
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diff --git a/docs/v1.2.0/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html b/docs/v1.2.0/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html
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@@ -0,0 +1,798 @@
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+        <div class="pytorch-content-left">
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+
+            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+
+  <section id="template-class-int8calibrator">
+<span id="exhale-class-classtorch-tensorrt-1-1ptq-1-1int8calibrator"></span><h1>Template Class Int8Calibrator<a class="headerlink" href="#template-class-int8calibrator" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">File ptq.h</span></a></p></li>
+</ul>
+<section id="inheritance-relationships">
+<h2>Inheritance Relationships<a class="headerlink" href="#inheritance-relationships" title="Permalink to this headline">¶</a></h2>
+<section id="base-type">
+<h3>Base Type<a class="headerlink" href="#base-type" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">private</span> <span class="pre">Algorithm</span></code></p></li>
+</ul>
+</section>
+</section>
+<section id="class-documentation">
+<h2>Class Documentation<a class="headerlink" href="#class-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp class">
+<dt class="sig sig-object cpp" id="_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE">
+<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DataLoaderUniquePtr</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">Int8Calibrator</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">private</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE" title="torch_tensorrt::ptq::Int8Calibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><a class="headerlink" href="#_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Generic <a class="reference internal" href="#classtorch__tensorrt_1_1ptq_1_1Int8Calibrator"><span class="std std-ref">Int8Calibrator</span></a> implementation based on a specified TensorRT calibration algorithm and a LibTorch DataLoader. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Template Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>Algorithm</strong> – class nvinfer1::IInt8Calibrator (Default: nvinfer1::IInt8EntropyCalibrator2) - Algorithm to use </p></li>
+<li><p><strong>DataLoaderUniquePtr</strong> – std::unique_ptr&lt;torch::data::DataLoader&gt; - DataLoader type </p></li>
+</ul>
+</dd>
+</dl>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq14Int8Calibrator14Int8CalibratorE19DataLoaderUniquePtrRKNSt6stringEb">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator_1a7cd5d19c509ef27870a6d52e4932884e"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Int8Calibrator</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE" title="torch_tensorrt::ptq::Int8Calibrator::DataLoaderUniquePtr"><span class="n"><span class="pre">DataLoaderUniquePtr</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dataloader</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">use_cache</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator14Int8CalibratorE19DataLoaderUniquePtrRKNSt6stringEb" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#classtorch__tensorrt_1_1ptq_1_1Int8Calibrator"><span class="std std-ref">Int8Calibrator</span></a> object. </p>
+<p>Using the provided DataLoader, construct a calibrator that can be used for PTQ with Torch-TensorRT</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataloader</strong> – std::unqiue_ptr&lt;torch::data::DataLoader&gt; - A unique pointer to the DataLoader, should be what is returned from the make_data_loader factory </p></li>
+<li><p><strong>cache_file_path</strong> – const std::string&amp; - A path to store / find the calibration cache </p></li>
+<li><p><strong>use_cache</strong> – : bool - Whether to use the cache (if it exists) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt3ptq14Int8Calibrator12getBatchSizeEv">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator_1a2fb5b5bb33808075d54adfdbc5727573"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getBatchSize</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt3ptq14Int8Calibrator12getBatchSizeEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the Batch Size for the next batch (always 1 due to issues with TRT and explicit batch) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>int </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq14Int8Calibrator8getBatchEA_PvA_PKci">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator_1a52b0ffc4230d610181c7c47fc0faaf23"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getBatch</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">bindings</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">]</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">char</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">names</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">]</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">nbBindings</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator8getBatchEA_PvA_PKci" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the next Batch. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>bindings</strong> – void*[] - An array of binding pointers (fed in from TensorRT calibrator), these buffers should be filed with batch data for each input </p></li>
+<li><p><strong>names</strong> – const char*[] - Names of bindings </p></li>
+<li><p><strong>nbBindings</strong> – int - Number of bindings </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true - There is a new batch for the calibrator to consume </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false - There is not a new batch for the calibrator to consume </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq14Int8Calibrator20readCalibrationCacheER6size_t">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator_1adb666718fcc006b8e4057625469de6a8"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="sig-name descname"><span class="n"><span class="pre">readCalibrationCache</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">length</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator20readCalibrationCacheER6size_t" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Read calibration cache. </p>
+<p>How to read from the calibration cache, only enabled if use_cache is set</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>length</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>const void* - Pointer to cache data </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq14Int8Calibrator21writeCalibrationCacheEPKv6size_t">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator_1ae8756c27ffa527d05ba32da03561a417"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">writeCalibrationCache</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">cache</span></span>, <span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">length</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator21writeCalibrationCacheEPKv6size_t" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Write calibration cache. </p>
+<p>Write a the calibration cache provided by TensorRT to a specified file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>cache</strong> – const void* - cache data </p></li>
+<li><p><strong>length</strong> – size_t - length of cache </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq14Int8CalibratorcvPN8nvinfer115IInt8CalibratorEEv">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator_1aaf70be340fa11c8cc95295d2692c913d"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8Calibrator</span></span><span class="p"><span class="pre">*</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq14Int8CalibratorcvPN8nvinfer115IInt8CalibratorEEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>operator to cast to nvinfer1::IInt8Calibrator* </p>
+<p>Convience function to convert to a IInt8Calibrator* to easily be assigned to the ptq_calibrator field in CompileSpec</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>nvinfer1::IInt8Calibrator* </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+</dd></dl>
+
+</section>
+</section>
+
+
+             </article>
+
+            </div>
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+  <section id="define-str">
+<span id="exhale-define-macros-8h-1a18d295a837ac71add5578860b55e5502"></span><h1>Define STR<a class="headerlink" href="#define-str" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros-h"><span class="std std-ref">File macros.h</span></a></p></li>
+</ul>
+<section id="define-documentation">
+<h2>Define Documentation<a class="headerlink" href="#define-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp macro">
+<dt class="sig sig-object cpp" id="c.STR">
+<span class="target" id="macros_8h_1a18d295a837ac71add5578860b55e5502"></span><span class="sig-name descname"><span class="n"><span class="pre">STR</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">x</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.STR" title="Permalink to this definition">¶</a><br /></dt>
+<dd></dd></dl>
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diff --git a/docs/v1.2.0/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html b/docs/v1.2.0/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html
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@@ -0,0 +1,683 @@
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+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros-h"><span class="std std-ref">File macros.h</span></a></p></li>
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+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros-h"><span class="std std-ref">File macros.h</span></a></p></li>
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+  <section id="define-xstr">
+<span id="exhale-define-macros-8h-1abe87b341f562fd1cf40b7672e4d759da"></span><h1>Define XSTR<a class="headerlink" href="#define-xstr" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros-h"><span class="std std-ref">File macros.h</span></a></p></li>
+</ul>
+<section id="define-documentation">
+<h2>Define Documentation<a class="headerlink" href="#define-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp macro">
+<dt class="sig sig-object cpp" id="c.XSTR">
+<span class="target" id="macros_8h_1abe87b341f562fd1cf40b7672e4d759da"></span><span class="sig-name descname"><span class="n"><span class="pre">XSTR</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">x</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.XSTR" title="Permalink to this definition">¶</a><br /></dt>
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diff --git a/docs/v1.2.0/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html b/docs/v1.2.0/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html
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@@ -0,0 +1,683 @@
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<h2>Define Documentation<a class="headerlink" href="#define-documentation" title="Permalink to this headline">¶</a></h2>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
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+
+  <section id="enum-level">
+<span id="exhale-enum-logging-8h-1a130f65408ad8cbaee060f05e8db69558"></span><h1>Enum Level<a class="headerlink" href="#enum-level" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a></p></li>
+</ul>
+<section id="enum-documentation">
+<h2>Enum Documentation<a class="headerlink" href="#enum-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp enum">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5LevelE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">logging</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">Level</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5LevelE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Emum for setting message severity </p>
+<p><em>Values:</em></p>
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5Level15kINTERNAL_ERRORE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558a9cb66846a4b6da3a54a00dc1471f49db"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kINTERNAL_ERROR</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5Level15kINTERNAL_ERRORE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Only print messages for internal errors. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5Level6kERRORE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558a4e69ac7a513b3ec45e04be00b614bb1c"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kERROR</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5Level6kERRORE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Print all internal errors and errors (default) </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5Level8kWARNINGE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558a6cfdce2a1b702d1c93b210dfa2e510c8"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kWARNING</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5Level8kWARNINGE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Print warnings and errors. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5Level5kINFOE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558a762a21c4c39c9214fdc85008587096bc"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kINFO</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5Level5kINFOE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Print all info, warnings and errors. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5Level6kDEBUGE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558a9c330d1bdbebdb7506897806426959d1"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kDEBUG</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5Level6kDEBUGE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Print all debug info, info, warnings and errors. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5Level6kGRAPHE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558a97098a2a97f4840a7d50b285703ff929"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kGRAPH</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5Level6kGRAPHE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Print everything including the intermediate graphs of the lowering phase. </p>
+</dd></dl>
+
+</dd></dl>
+
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@@ -0,0 +1,700 @@
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diff --git a/docs/v1.2.0/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.html b/docs/v1.2.0/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.html
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+
+  <section id="file-logging-h">
+<span id="file-cpp-include-torch-tensorrt-logging-h"></span><h1>File logging.h<a class="headerlink" href="#file-logging-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="dir_cpp_include_torch_tensorrt.html#dir-cpp-include-torch-tensorrt"><span class="std std-ref">Parent directory</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt</span></code>)</p>
+<div class="contents local topic" id="contents">
+<p class="topic-title">Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#definition-cpp-include-torch-tensorrt-logging-h" id="id1">Definition (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/logging.h</span></code>)</a></p></li>
+<li><p><a class="reference internal" href="#includes" id="id2">Includes</a></p></li>
+<li><p><a class="reference internal" href="#included-by" id="id3">Included By</a></p></li>
+<li><p><a class="reference internal" href="#namespaces" id="id4">Namespaces</a></p></li>
+<li><p><a class="reference internal" href="#enums" id="id5">Enums</a></p></li>
+<li><p><a class="reference internal" href="#functions" id="id6">Functions</a></p></li>
+</ul>
+</div>
+<section id="definition-cpp-include-torch-tensorrt-logging-h">
+<h2>Definition (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/logging.h</span></code>)<a class="headerlink" href="#definition-cpp-include-torch-tensorrt-logging-h" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="program_listing_file_cpp_include_torch_tensorrt_logging.h.html">Program Listing for File logging.h</a></li>
+</ul>
+</div>
+</section>
+<section id="includes">
+<h2>Includes<a class="headerlink" href="#includes" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">string</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt/macros.h</span></code> (<a class="reference internal" href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros-h"><span class="std std-ref">File macros.h</span></a>)</p></li>
+</ul>
+</section>
+<section id="included-by">
+<h2>Included By<a class="headerlink" href="#included-by" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">File ptq.h</span></a></p></li>
+</ul>
+</section>
+<section id="namespaces">
+<h2>Namespaces<a class="headerlink" href="#namespaces" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="namespace_torch_tensorrt.html#namespace-torch-tensorrt"><span class="std std-ref">Namespace torch_tensorrt</span></a></p></li>
+<li><p><a class="reference internal" href="namespace_torch_tensorrt__logging.html#namespace-torch-tensorrt-logging"><span class="std std-ref">Namespace torch_tensorrt::logging</span></a></p></li>
+</ul>
+</section>
+<section id="enums">
+<h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#exhale-enum-logging-8h-1a130f65408ad8cbaee060f05e8db69558"><span class="std std-ref">Enum Level</span></a></p></li>
+</ul>
+</section>
+<section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html#exhale-function-logging-8h-1a56e110feaaba2c3fd44bd201fd21a76a"><span class="std std-ref">Function torch_tensorrt::logging::get_is_colored_output_on</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html#exhale-function-logging-8h-1a0593f776f469c20469e2f729fc7861a3"><span class="std std-ref">Function torch_tensorrt::logging::get_logging_prefix</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html#exhale-function-logging-8h-1a0c012cb374addd90eb1f42eaec570650"><span class="std std-ref">Function torch_tensorrt::logging::get_reportable_log_level</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html#exhale-function-logging-8h-1ac46ac0901cb97e3ae6e93b45f24e90b8"><span class="std std-ref">Function torch_tensorrt::logging::log</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html#exhale-function-logging-8h-1ad2efd47b6c3689e58ccc595680579ae5"><span class="std std-ref">Function torch_tensorrt::logging::set_is_colored_output_on</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1af8f3443813315af7901903d25dd495cc.html#exhale-function-logging-8h-1af8f3443813315af7901903d25dd495cc"><span class="std std-ref">Function torch_tensorrt::logging::set_logging_prefix</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html#exhale-function-logging-8h-1a7cb50492421ea9de4e3db895819df6f2"><span class="std std-ref">Function torch_tensorrt::logging::set_reportable_log_level</span></a></p></li>
+</ul>
+</section>
+</section>
+
+
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+<li><a class="reference internal" href="#">File logging.h</a><ul>
+<li><a class="reference internal" href="#definition-cpp-include-torch-tensorrt-logging-h">Definition (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/logging.h</span></code>)</a></li>
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diff --git a/docs/v1.2.0/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.html b/docs/v1.2.0/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.html
new file mode 100644
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@@ -0,0 +1,708 @@
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diff --git a/docs/v1.2.0/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.html b/docs/v1.2.0/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.html
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
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+  <section id="file-ptq-h">
+<span id="file-cpp-include-torch-tensorrt-ptq-h"></span><h1>File ptq.h<a class="headerlink" href="#file-ptq-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="dir_cpp_include_torch_tensorrt.html#dir-cpp-include-torch-tensorrt"><span class="std std-ref">Parent directory</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt</span></code>)</p>
+<div class="contents local topic" id="contents">
+<p class="topic-title">Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#definition-cpp-include-torch-tensorrt-ptq-h" id="id1">Definition (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/ptq.h</span></code>)</a></p></li>
+<li><p><a class="reference internal" href="#includes" id="id2">Includes</a></p></li>
+<li><p><a class="reference internal" href="#namespaces" id="id3">Namespaces</a></p></li>
+<li><p><a class="reference internal" href="#classes" id="id4">Classes</a></p></li>
+<li><p><a class="reference internal" href="#functions" id="id5">Functions</a></p></li>
+</ul>
+</div>
+<section id="definition-cpp-include-torch-tensorrt-ptq-h">
+<h2>Definition (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/ptq.h</span></code>)<a class="headerlink" href="#definition-cpp-include-torch-tensorrt-ptq-h" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="program_listing_file_cpp_include_torch_tensorrt_ptq.h.html">Program Listing for File ptq.h</a></li>
+</ul>
+</div>
+</section>
+<section id="includes">
+<h2>Includes<a class="headerlink" href="#includes" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">NvInfer.h</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">fstream</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">iostream</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">iterator</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">memory</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sstream</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">string</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch/torch.h</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt/logging.h</span></code> (<a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a>)</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt/macros.h</span></code> (<a class="reference internal" href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros-h"><span class="std std-ref">File macros.h</span></a>)</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">vector</span></code></p></li>
+</ul>
+</section>
+<section id="namespaces">
+<h2>Namespaces<a class="headerlink" href="#namespaces" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="namespace_torch_tensorrt.html#namespace-torch-tensorrt"><span class="std std-ref">Namespace torch_tensorrt</span></a></p></li>
+<li><p><a class="reference internal" href="namespace_torch_tensorrt__ptq.html#namespace-torch-tensorrt-ptq"><span class="std std-ref">Namespace torch_tensorrt::ptq</span></a></p></li>
+</ul>
+</section>
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+<h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#exhale-class-classtorch-tensorrt-1-1ptq-1-1int8cachecalibrator"><span class="std std-ref">Template Class Int8CacheCalibrator</span></a></p></li>
+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#exhale-class-classtorch-tensorrt-1-1ptq-1-1int8calibrator"><span class="std std-ref">Template Class Int8Calibrator</span></a></p></li>
+</ul>
+</section>
+<section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.html#exhale-function-ptq-8h-1a226e3c83379d1012cde8578c1c86b16c"><span class="std std-ref">Template Function torch_tensorrt::ptq::make_int8_cache_calibrator</span></a></p></li>
+<li><p><a class="reference internal" href="function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html#exhale-function-ptq-8h-1a6186e305f47c1d94b6130ef6c7f7e178"><span class="std std-ref">Template Function torch_tensorrt::ptq::make_int8_calibrator</span></a></p></li>
+</ul>
+</section>
+</section>
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diff --git a/docs/v1.2.0/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.html b/docs/v1.2.0/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.html
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+  <section id="file-torch-tensorrt-h">
+<span id="file-cpp-include-torch-tensorrt-torch-tensorrt-h"></span><h1>File torch_tensorrt.h<a class="headerlink" href="#file-torch-tensorrt-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="dir_cpp_include_torch_tensorrt.html#dir-cpp-include-torch-tensorrt"><span class="std std-ref">Parent directory</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt</span></code>)</p>
+<div class="contents local topic" id="contents">
+<p class="topic-title">Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#definition-cpp-include-torch-tensorrt-torch-tensorrt-h" id="id1">Definition (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/torch_tensorrt.h</span></code>)</a></p></li>
+<li><p><a class="reference internal" href="#includes" id="id2">Includes</a></p></li>
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+<li><p><a class="reference internal" href="#functions" id="id6">Functions</a></p></li>
+</ul>
+</div>
+<section id="definition-cpp-include-torch-tensorrt-torch-tensorrt-h">
+<h2>Definition (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/torch_tensorrt.h</span></code>)<a class="headerlink" href="#definition-cpp-include-torch-tensorrt-torch-tensorrt-h" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.html">Program Listing for File torch_tensorrt.h</a></li>
+</ul>
+</div>
+</section>
+<section id="includes">
+<h2>Includes<a class="headerlink" href="#includes" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">cuda_runtime.h</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">iostream</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">memory</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">set</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">string</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch/custom_class.h</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt/macros.h</span></code> (<a class="reference internal" href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros-h"><span class="std std-ref">File macros.h</span></a>)</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">vector</span></code></p></li>
+</ul>
+</section>
+<section id="namespaces">
+<h2>Namespaces<a class="headerlink" href="#namespaces" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="namespace_torch_tensorrt.html#namespace-torch-tensorrt"><span class="std std-ref">Namespace torch_tensorrt</span></a></p></li>
+<li><p><a class="reference internal" href="namespace_torch_tensorrt__torchscript.html#namespace-torch-tensorrt-torchscript"><span class="std std-ref">Namespace torch_tensorrt::torchscript</span></a></p></li>
+</ul>
+</section>
+<section id="classes">
+<h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#exhale-struct-structtorch-tensorrt-1-1device"><span class="std std-ref">Struct Device</span></a></p></li>
+<li><p><a class="reference internal" href="structtorch__tensorrt_1_1GraphInputs.html#exhale-struct-structtorch-tensorrt-1-1graphinputs"><span class="std std-ref">Struct GraphInputs</span></a></p></li>
+<li><p><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#exhale-struct-structtorch-tensorrt-1-1input"><span class="std std-ref">Struct Input</span></a></p></li>
+<li><p><a class="reference internal" href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#exhale-struct-structtorch-tensorrt-1-1torchscript-1-1compilespec"><span class="std std-ref">Struct CompileSpec</span></a></p></li>
+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#exhale-class-classtorch-tensorrt-1-1datatype"><span class="std std-ref">Class DataType</span></a></p></li>
+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#exhale-class-classtorch-tensorrt-1-1device-1-1devicetype"><span class="std std-ref">Class Device::DeviceType</span></a></p></li>
+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#exhale-class-classtorch-tensorrt-1-1tensorformat"><span class="std std-ref">Class TensorFormat</span></a></p></li>
+</ul>
+</section>
+<section id="enums">
+<h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#exhale-enum-torch-tensorrt-8h-1a3fbe5d72e4fc624dbd038853079620eb"><span class="std std-ref">Enum EngineCapability</span></a></p></li>
+</ul>
+</section>
+<section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html#exhale-function-torch-tensorrt-8h-1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1"><span class="std std-ref">Function torch_tensorrt::dump_build_info</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html#exhale-function-torch-tensorrt-8h-1ac4ab8313ae72c2c899ea31548b528528"><span class="std std-ref">Function torch_tensorrt::get_build_info</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html#exhale-function-torch-tensorrt-8h-1ad1acd06eaeaffbbcf6e7ebf426891384"><span class="std std-ref">Function torch_tensorrt::set_device</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html#exhale-function-torch-tensorrt-8h-1a5b405fd3bf3c8fc2e2a54cbbab979797"><span class="std std-ref">Function torch_tensorrt::torchscript::check_method_operator_support</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html#exhale-function-torch-tensorrt-8h-1a6e19490a08fb1553c9dd347a5ae79db9"><span class="std std-ref">Function torch_tensorrt::torchscript::compile</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html#exhale-function-torch-tensorrt-8h-1ae8d56472106eeef37fbe51ff7f40c9b2"><span class="std std-ref">Function torch_tensorrt::torchscript::convert_method_to_trt_engine</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.html#exhale-function-torch-tensorrt-8h-1a710df824a7718b440e4bc17bf9693cef"><span class="std std-ref">Function torch_tensorrt::torchscript::embed_engine_in_new_module</span></a></p></li>
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diff --git a/docs/v1.2.0/_cpp_api/file_view_hierarchy.html b/docs/v1.2.0/_cpp_api/file_view_hierarchy.html
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diff --git a/docs/v1.2.0/_cpp_api/function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html b/docs/v1.2.0/_cpp_api/function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html
new file mode 100644
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@@ -0,0 +1,683 @@
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+<span id="exhale-function-logging-8h-1a0c012cb374addd90eb1f42eaec570650"></span><h1>Function torch_tensorrt::logging::get_reportable_log_level<a class="headerlink" href="#function-torch-tensorrt-logging-get-reportable-log-level" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a></p></li>
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+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging24get_reportable_log_levelEv">
+<span class="target" id="logging_8h_1a0c012cb374addd90eb1f42eaec570650"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5LevelE" title="torch_tensorrt::logging::Level"><span class="n"><span class="pre">Level</span></span></a><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">logging</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">get_reportable_log_level</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging24get_reportable_log_levelEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the current reportable log level. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>TORCHTRT_API get_reportable_log_level </p>
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diff --git a/docs/v1.2.0/_cpp_api/function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html b/docs/v1.2.0/_cpp_api/function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html
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+<span id="exhale-function-logging-8h-1a56e110feaaba2c3fd44bd201fd21a76a"></span><h1>Function torch_tensorrt::logging::get_is_colored_output_on<a class="headerlink" href="#function-torch-tensorrt-logging-get-is-colored-output-on" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a></p></li>
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+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
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+<span class="target" id="logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">logging</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">get_is_colored_output_on</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging24get_is_colored_output_onEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Is colored output enabled? </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>TORCHTRT_API get_is_colored_output_on </p>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
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+<span id="exhale-function-logging-8h-1a7cb50492421ea9de4e3db895819df6f2"></span><h1>Function torch_tensorrt::logging::set_reportable_log_level<a class="headerlink" href="#function-torch-tensorrt-logging-set-reportable-log-level" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a></p></li>
+</ul>
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+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging24set_reportable_log_levelE5Level">
+<span class="target" id="logging_8h_1a7cb50492421ea9de4e3db895819df6f2"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">logging</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">set_reportable_log_level</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5LevelE" title="torch_tensorrt::logging::Level"><span class="n"><span class="pre">Level</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">lvl</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging24set_reportable_log_levelE5Level" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Sets the level that logging information needs to be to be added to the log. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>lvl</strong> – torch_tensorrt::logging::Level - Level that messages need to be at or above to be added to the log </p>
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+  <section id="function-torch-tensorrt-logging-log">
+<span id="exhale-function-logging-8h-1ac46ac0901cb97e3ae6e93b45f24e90b8"></span><h1>Function torch_tensorrt::logging::log<a class="headerlink" href="#function-torch-tensorrt-logging-log" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging3logE5LevelNSt6stringE">
+<span class="target" id="logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">logging</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">log</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5LevelE" title="torch_tensorrt::logging::Level"><span class="n"><span class="pre">Level</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">lvl</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">msg</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging3logE5LevelNSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Adds a message to the global log. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>lvl</strong> – torch_tensorrt::logging::Level - Severity of the message </p></li>
+<li><p><strong>msg</strong> – std::string - Message to be logged </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</section>
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+<span id="exhale-function-logging-8h-1ad2efd47b6c3689e58ccc595680579ae5"></span><h1>Function torch_tensorrt::logging::set_is_colored_output_on<a class="headerlink" href="#function-torch-tensorrt-logging-set-is-colored-output-on" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a></p></li>
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+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
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+<span class="target" id="logging_8h_1ad2efd47b6c3689e58ccc595680579ae5"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">logging</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">set_is_colored_output_on</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">colored_output_on</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging24set_is_colored_output_onEb" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Sets if logging prefix will be colored (helpful when debugging but not always supported by terminal) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>colored_output_on</strong> – bool - If the output will be colored or not </p>
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diff --git a/docs/v1.2.0/_cpp_api/function_logging_8h_1af8f3443813315af7901903d25dd495cc.html b/docs/v1.2.0/_cpp_api/function_logging_8h_1af8f3443813315af7901903d25dd495cc.html
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+
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+<span id="exhale-function-logging-8h-1af8f3443813315af7901903d25dd495cc"></span><h1>Function torch_tensorrt::logging::set_logging_prefix<a class="headerlink" href="#function-torch-tensorrt-logging-set-logging-prefix" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a></p></li>
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+  <section id="template-function-torch-tensorrt-ptq-make-int8-cache-calibrator">
+<span id="exhale-function-ptq-8h-1a226e3c83379d1012cde8578c1c86b16c"></span><h1>Template Function torch_tensorrt::ptq::make_int8_cache_calibrator<a class="headerlink" href="#template-function-torch-tensorrt-ptq-make-int8-cache-calibrator" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">File ptq.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE">
+<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8EntropyCalibrator2</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="ptq_8h_1a226e3c83379d1012cde8578c1c86b16c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE" title="torch_tensorrt::ptq::Int8CacheCalibrator"><span class="n"><span class="pre">Int8CacheCalibrator</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE" title="torch_tensorrt::ptq::make_int8_cache_calibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">make_int8_cache_calibrator</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>A factory to build a post training quantization calibrator from a torch dataloader that only uses the calibration cache. </p>
+<p>Creates a calibrator to use for post training quantization which reads from a previously created calibration cache, therefore you can have a calibration cache generating program that requires a dataloader and a dataset, then save the cache to use later in a different program that needs to calibrate from scratch and not have the dataset dependency. However, the network should also be recalibrated if its structure changes, or the input data set changes, and it is the responsibility of the application to ensure this.</p>
+<p>By default the returned calibrator uses TensorRT Entropy v2 algorithm to perform calibration. This is recommended for feed forward networks You can override the algorithm selection (such as to use the MinMax Calibrator recomended for NLP tasks) by calling make_int8_calibrator with the calibrator class as a template parameter.</p>
+<p>e.g. torch_tensorrt::ptq::make_int8_cache_calibrator&lt;nvinfer1::IInt8MinMaxCalibrator&gt;(calibration_cache_file); </p>
+<dl class="field-list simple">
+<dt class="field-odd">Template Parameters</dt>
+<dd class="field-odd"><p><strong>Algorithm</strong> – class nvinfer1::IInt8Calibrator (Default: nvinfer1::IInt8EntropyCalibrator2) - Algorithm to use </p>
+</dd>
+<dt class="field-even">Parameters</dt>
+<dd class="field-even"><p><strong>cache_file_path</strong> – const std::string&amp; - Path to read/write calibration cache </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Int8CacheCalibrator&lt;Algorithm&gt; </p>
+</dd>
+</dl>
+</dd></dl>
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+  <section id="template-function-torch-tensorrt-ptq-make-int8-calibrator">
+<span id="exhale-function-ptq-8h-1a6186e305f47c1d94b6130ef6c7f7e178"></span><h1>Template Function torch_tensorrt::ptq::make_int8_calibrator<a class="headerlink" href="#template-function-torch-tensorrt-ptq-make-int8-calibrator" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">File ptq.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb">
+<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8EntropyCalibrator2</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DataLoader</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE" title="torch_tensorrt::ptq::Int8Calibrator"><span class="n"><span class="pre">Int8Calibrator</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::DataLoader"><span class="n"><span class="pre">DataLoader</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">make_int8_calibrator</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::DataLoader"><span class="n"><span class="pre">DataLoader</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dataloader</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">use_cache</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>A factory to build a post training quantization calibrator from a torch dataloader. </p>
+<p>Creates a calibrator to use for post training quantization. By default the returned calibrator uses TensorRT Entropy v2 algorithm to perform calibration. This is recommended for feed forward networks. You can override the algorithm selection (such as to use the MinMax Calibrator recomended for NLP tasks) by calling make_int8_calibrator with the calibrator class as a template parameter.</p>
+<p>e.g. <code class="docutils literal notranslate"><span class="pre">torch_tensorrt::ptq::make_int8_calibrator&lt;nvinfer1::IInt8MinMaxCalibrator&gt;(std::move(calibration_dataloader),</span> <span class="pre">calibration_cache_file,</span> <span class="pre">use_cache);</span></code> </p>
+<dl class="field-list simple">
+<dt class="field-odd">Template Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>Algorithm</strong> – class nvinfer1::IInt8Calibrator (Default: nvinfer1::IInt8EntropyCalibrator2) - Algorithm to use </p></li>
+<li><p><strong>DataLoader</strong> – std::unique_ptr&lt;torch::data::DataLoader&gt; - DataLoader type </p></li>
+</ul>
+</dd>
+<dt class="field-even">Parameters</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>dataloader</strong> – std::unique_ptr&lt;torch::data::DataLoader&gt; - DataLoader containing data </p></li>
+<li><p><strong>cache_file_path</strong> – const std::string&amp; - Path to read/write calibration cache </p></li>
+<li><p><strong>use_cache</strong> – bool - use calibration cache </p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Int8Calibrator&lt;Algorithm, DataLoader&gt; </p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
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diff --git a/docs/v1.2.0/_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.html b/docs/v1.2.0/_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.html
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@@ -0,0 +1,691 @@
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+  <section id="template-function-torch-tensorrt-ptq-make-int8-calibrator">
+<span id="exhale-function-ptq-8h-1a83ff2be7e0b80bc7434de415861dc039"></span><h1>Template Function torch_tensorrt::ptq::make_int8_calibrator<a class="headerlink" href="#template-function-torch-tensorrt-ptq-make-int8-calibrator" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">File ptq.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb">
+<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8EntropyCalibrator2</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DataLoader</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE" title="torch_tensorrt::ptq::Int8Calibrator"><span class="n"><span class="pre">Int8Calibrator</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::DataLoader"><span class="n"><span class="pre">DataLoader</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">make_int8_calibrator</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::DataLoader"><span class="n"><span class="pre">DataLoader</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dataloader</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">use_cache</span></span><span class="sig-paren">)</span><br /></dt>
+<dd><p>A factory to build a post training quantization calibrator from a torch dataloader. </p>
+<p>Creates a calibrator to use for post training quantization. By default the returned calibrator uses TensorRT Entropy v2 algorithm to perform calibration. This is recommended for feed forward networks. You can override the algorithm selection (such as to use the MinMax Calibrator recomended for NLP tasks) by calling make_int8_calibrator with the calibrator class as a template parameter.</p>
+<p>e.g. <code class="docutils literal notranslate"><span class="pre">torch_tensorrt::ptq::make_int8_calibrator&lt;nvinfer1::IInt8MinMaxCalibrator&gt;(std::move(calibration_dataloader),</span> <span class="pre">calibration_cache_file,</span> <span class="pre">use_cache);</span></code> </p>
+<dl class="field-list simple">
+<dt class="field-odd">Template Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>Algorithm</strong> – class nvinfer1::IInt8Calibrator (Default: nvinfer1::IInt8EntropyCalibrator2) - Algorithm to use </p></li>
+<li><p><strong>DataLoader</strong> – std::unique_ptr&lt;torch::data::DataLoader&gt; - DataLoader type </p></li>
+</ul>
+</dd>
+<dt class="field-even">Parameters</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>dataloader</strong> – std::unique_ptr&lt;torch::data::DataLoader&gt; - DataLoader containing data </p></li>
+<li><p><strong>cache_file_path</strong> – const std::string&amp; - Path to read/write calibration cache </p></li>
+<li><p><strong>use_cache</strong> – bool - use calibration cache </p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Int8Calibrator&lt;Algorithm, DataLoader&gt; </p>
+</dd>
+</dl>
+</dd></dl>
+
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+  <section id="template-function-torch-tensorrt-ptq-make-int8-cache-calibrator">
+<span id="exhale-function-ptq-8h-1a9835f6e605dce1abf442a55b64d6dffa"></span><h1>Template Function torch_tensorrt::ptq::make_int8_cache_calibrator<a class="headerlink" href="#template-function-torch-tensorrt-ptq-make-int8-cache-calibrator" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">File ptq.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE">
+<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8EntropyCalibrator2</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="ptq_8h_1a226e3c83379d1012cde8578c1c86b16c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE" title="torch_tensorrt::ptq::Int8CacheCalibrator"><span class="n"><span class="pre">Int8CacheCalibrator</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.html#_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE" title="torch_tensorrt::ptq::make_int8_cache_calibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">make_int8_cache_calibrator</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span><span class="sig-paren">)</span><br /></dt>
+<dd><p>A factory to build a post training quantization calibrator from a torch dataloader that only uses the calibration cache. </p>
+<p>Creates a calibrator to use for post training quantization which reads from a previously created calibration cache, therefore you can have a calibration cache generating program that requires a dataloader and a dataset, then save the cache to use later in a different program that needs to calibrate from scratch and not have the dataset dependency. However, the network should also be recalibrated if its structure changes, or the input data set changes, and it is the responsibility of the application to ensure this.</p>
+<p>By default the returned calibrator uses TensorRT Entropy v2 algorithm to perform calibration. This is recommended for feed forward networks You can override the algorithm selection (such as to use the MinMax Calibrator recomended for NLP tasks) by calling make_int8_calibrator with the calibrator class as a template parameter.</p>
+<p>e.g. torch_tensorrt::ptq::make_int8_cache_calibrator&lt;nvinfer1::IInt8MinMaxCalibrator&gt;(calibration_cache_file); </p>
+<dl class="field-list simple">
+<dt class="field-odd">Template Parameters</dt>
+<dd class="field-odd"><p><strong>Algorithm</strong> – class nvinfer1::IInt8Calibrator (Default: nvinfer1::IInt8EntropyCalibrator2) - Algorithm to use </p>
+</dd>
+<dt class="field-even">Parameters</dt>
+<dd class="field-even"><p><strong>cache_file_path</strong> – const std::string&amp; - Path to read/write calibration cache </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Int8CacheCalibrator&lt;Algorithm&gt; </p>
+</dd>
+</dl>
+</dd></dl>
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diff --git a/docs/v1.2.0/_cpp_api/function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html b/docs/v1.2.0/_cpp_api/function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html
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@@ -0,0 +1,698 @@
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+          <li><a href="torch_tensort_cpp.html">Torch-TensorRT C++ API</a> &gt;</li>
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+      <li>Function torch_tensorrt::torchscript::check_method_operator_support</li>
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+  <section id="function-torch-tensorrt-torchscript-check-method-operator-support">
+<span id="exhale-function-torch-tensorrt-8h-1a5b405fd3bf3c8fc2e2a54cbbab979797"></span><h1>Function torch_tensorrt::torchscript::check_method_operator_support<a class="headerlink" href="#function-torch-tensorrt-torchscript-check-method-operator-support" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
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+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript29check_method_operator_supportERKN5torch3jit6ModuleENSt6stringE">
+<span class="target" id="torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">torchscript</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">check_method_operator_support</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">jit</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Module</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">module</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">method_name</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript29check_method_operator_supportERKN5torch3jit6ModuleENSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Check to see if a module is fully supported by the compiler. </p>
+<p>
+Takes a module and a method name and checks if the method graph contains purely convertable operators</p>
+<p>Will print out a list of unsupported operators if the graph is unsupported</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> – torch::jit::script::Module - Existing TorchScript module </p></li>
+<li><p><strong>method_name</strong> – std::string - Name of method to compile</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>bool: Method is supported by Torch-TensorRT.TorchScript </p>
+</dd>
+</dl>
+</dd></dl>
+
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+
+
+
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
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+  <title>Function torch_tensorrt::torchscript::compile &mdash; Torch-TensorRT master documentation</title>
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+  <section id="function-torch-tensorrt-torchscript-compile">
+<span id="exhale-function-torch-tensorrt-8h-1a6e19490a08fb1553c9dd347a5ae79db9"></span><h1>Function torch_tensorrt::torchscript::compile<a class="headerlink" href="#function-torch-tensorrt-torchscript-compile" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript7compileERKN5torch3jit6ModuleE11CompileSpec">
+<span class="target" id="torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">jit</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Module</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">torchscript</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">compile</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">jit</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Module</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">module</span></span>, <a class="reference internal" href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpecE" title="torch_tensorrt::torchscript::CompileSpec"><span class="n"><span class="pre">CompileSpec</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">info</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript7compileERKN5torch3jit6ModuleE11CompileSpec" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Compile a TorchScript module for NVIDIA GPUs using TensorRT. </p>
+<p>
+Takes a existing TorchScript module and a set of settings to configure the compiler and will convert methods to JIT Graphs which call equivalent TensorRT engines</p>
+<p>Converts specifically the forward method of a TorchScript Module</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> – torch::jit::Module - Existing TorchScript module </p></li>
+<li><p><strong>info</strong> – torch_tensorrt::CompileSpec - Compilation settings</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>: A new module trageting a TensorRT engine </p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</section>
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diff --git a/docs/v1.2.0/_cpp_api/function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.html b/docs/v1.2.0/_cpp_api/function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.html
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@@ -0,0 +1,702 @@
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+      <li>Function torch_tensorrt::torchscript::embed_engine_in_new_module</li>
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+
+  <section id="function-torch-tensorrt-torchscript-embed-engine-in-new-module">
+<span id="exhale-function-torch-tensorrt-8h-1a710df824a7718b440e4bc17bf9693cef"></span><h1>Function torch_tensorrt::torchscript::embed_engine_in_new_module<a class="headerlink" href="#function-torch-tensorrt-torchscript-embed-engine-in-new-module" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript26embed_engine_in_new_moduleERKNSt6stringE6Device">
+<span class="target" id="torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">jit</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Module</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">torchscript</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">embed_engine_in_new_module</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">engine</span></span>, <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6DeviceE" title="torch_tensorrt::Device"><span class="n"><span class="pre">Device</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">device</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript26embed_engine_in_new_moduleERKNSt6stringE6Device" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Take a previously created TensorRT engine and embed it in in a TorchScript module. </p>
+<p>
+Takes a pre-built serialized TensorRT engine and embeds it in a TorchScript module. Registers execution of the engine as the forward method of the module Forward is defined as: forward(Tensor[]) -&gt; Tensor[]</p>
+<p>TensorRT bindings must have names with the following format:<ul class="simple">
+<li><p>[symbol].[index in input / output array] ex.</p></li>
+<li><p>[x.0, x.1, x.2] -&gt; [y.0]</p></li>
+</ul>
+</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>engine</strong> – std::string - Pre-built serialized TensorRT engine </p></li>
+<li><p><strong>device</strong> – CompileSepc::Device - <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> information</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>: A new module targeting a TensorRT engine </p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</section>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<span id="exhale-function-torch-tensorrt-8h-1ac4ab8313ae72c2c899ea31548b528528"></span><h1>Function torch_tensorrt::get_build_info<a class="headerlink" href="#function-torch-tensorrt-get-build-info" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
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+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt14get_build_infoEv">
+<span class="target" id="torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">get_build_info</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt14get_build_infoEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the build information for the library including the dependency versions. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>std::string </p>
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diff --git a/docs/v1.2.0/_cpp_api/function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html b/docs/v1.2.0/_cpp_api/function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html
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+<span id="exhale-function-torch-tensorrt-8h-1ad1acd06eaeaffbbcf6e7ebf426891384"></span><h1>Function torch_tensorrt::set_device<a class="headerlink" href="#function-torch-tensorrt-set-device" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
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+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt10set_deviceEKi">
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+<dd><p>Set gpu device id. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>gpu_id</strong> – Sets gpu id using cudaSetDevice </p>
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diff --git a/docs/v1.2.0/_cpp_api/function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html b/docs/v1.2.0/_cpp_api/function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html
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@@ -0,0 +1,684 @@
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
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+<span id="exhale-function-torch-tensorrt-8h-1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1"></span><h1>Function torch_tensorrt::dump_build_info<a class="headerlink" href="#function-torch-tensorrt-dump-build-info" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
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+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt15dump_build_infoEv">
+<span class="target" id="torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">dump_build_info</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt15dump_build_infoEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Dump the version information for Torch-TensorRT including base libtorch and TensorRT versions to stdout. </p>
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+  <section id="function-torch-tensorrt-torchscript-convert-method-to-trt-engine">
+<span id="exhale-function-torch-tensorrt-8h-1ae8d56472106eeef37fbe51ff7f40c9b2"></span><h1>Function torch_tensorrt::torchscript::convert_method_to_trt_engine<a class="headerlink" href="#function-torch-tensorrt-torchscript-convert-method-to-trt-engine" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript28convert_method_to_trt_engineERKN5torch3jit6ModuleENSt6stringE11CompileSpec">
+<span class="target" id="torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">torchscript</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">convert_method_to_trt_engine</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">jit</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Module</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">module</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">method_name</span></span>, <a class="reference internal" href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpecE" title="torch_tensorrt::torchscript::CompileSpec"><span class="n"><span class="pre">CompileSpec</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">info</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript28convert_method_to_trt_engineERKN5torch3jit6ModuleENSt6stringE11CompileSpec" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Compile a TorchScript method for NVIDIA GPUs using TensorRT. </p>
+<p>
+Takes a existing TorchScript module and a set of settings to configure the compiler and will convert selected method to a serialized TensorRT engine which can be run with TensorRT</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> – torch::jit::Module - Existing TorchScript module </p></li>
+<li><p><strong>method_name</strong> – std::string - Name of method to compile </p></li>
+<li><p><strong>info</strong> – torch_tensorrt::CompileSpec - Compilation settings</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
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+
+  <section id="namespace-torch-tensorrt-logging">
+<span id="id1"></span><h1>Namespace torch_tensorrt::logging<a class="headerlink" href="#namespace-torch-tensorrt-logging" title="Permalink to this headline">¶</a></h1>
+<div class="contents local topic" id="contents">
+<p class="topic-title">Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#enums" id="id2">Enums</a></p></li>
+<li><p><a class="reference internal" href="#functions" id="id3">Functions</a></p></li>
+</ul>
+</div>
+<section id="enums">
+<h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#exhale-enum-logging-8h-1a130f65408ad8cbaee060f05e8db69558"><span class="std std-ref">Enum Level</span></a></p></li>
+</ul>
+</section>
+<section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html#exhale-function-logging-8h-1a56e110feaaba2c3fd44bd201fd21a76a"><span class="std std-ref">Function torch_tensorrt::logging::get_is_colored_output_on</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html#exhale-function-logging-8h-1a0593f776f469c20469e2f729fc7861a3"><span class="std std-ref">Function torch_tensorrt::logging::get_logging_prefix</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html#exhale-function-logging-8h-1a0c012cb374addd90eb1f42eaec570650"><span class="std std-ref">Function torch_tensorrt::logging::get_reportable_log_level</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html#exhale-function-logging-8h-1ac46ac0901cb97e3ae6e93b45f24e90b8"><span class="std std-ref">Function torch_tensorrt::logging::log</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html#exhale-function-logging-8h-1ad2efd47b6c3689e58ccc595680579ae5"><span class="std std-ref">Function torch_tensorrt::logging::set_is_colored_output_on</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1af8f3443813315af7901903d25dd495cc.html#exhale-function-logging-8h-1af8f3443813315af7901903d25dd495cc"><span class="std std-ref">Function torch_tensorrt::logging::set_logging_prefix</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html#exhale-function-logging-8h-1a7cb50492421ea9de4e3db895819df6f2"><span class="std std-ref">Function torch_tensorrt::logging::set_reportable_log_level</span></a></p></li>
+</ul>
+</section>
+</section>
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diff --git a/docs/v1.2.0/_cpp_api/namespace_torch_tensorrt__ptq.html b/docs/v1.2.0/_cpp_api/namespace_torch_tensorrt__ptq.html
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html#exhale-function-torch-tensorrt-8h-1a6e19490a08fb1553c9dd347a5ae79db9"><span class="std std-ref">Function torch_tensorrt::torchscript::compile</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html#exhale-function-torch-tensorrt-8h-1ae8d56472106eeef37fbe51ff7f40c9b2"><span class="std std-ref">Function torch_tensorrt::torchscript::convert_method_to_trt_engine</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.html#exhale-function-torch-tensorrt-8h-1a710df824a7718b440e4bc17bf9693cef"><span class="std std-ref">Function torch_tensorrt::torchscript::embed_engine_in_new_module</span></a></p></li>
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diff --git a/docs/v1.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.html b/docs/v1.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.html
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+
+  <section id="program-listing-for-file-logging-h">
+<span id="program-listing-file-cpp-include-torch-tensorrt-logging-h"></span><h1>Program Listing for File logging.h<a class="headerlink" href="#program-listing-for-file-logging-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">Return to documentation for file</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/logging.h</span></code>)</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cm">/*</span>
+<span class="cm"> * Copyright (c) NVIDIA Corporation.</span>
+<span class="cm"> * All rights reserved.</span>
+<span class="cm"> *</span>
+<span class="cm"> * This library is licensed under the BSD-style license found in the</span>
+<span class="cm"> * LICENSE file in the root directory of this source tree.</span>
+<span class="cm"> */</span><span class="w"></span>
+<span class="cp">#pragma once</span>
+
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;string&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/macros.h&quot;</span><span class="cp"></span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torch_tensorrt</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">logging</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="k">enum</span><span class="w"> </span><span class="nc">Level</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">  </span><span class="n">kINTERNAL_ERROR</span><span class="p">,</span><span class="w"></span>
+<span class="w">  </span><span class="n">kERROR</span><span class="p">,</span><span class="w"></span>
+<span class="w">  </span><span class="n">kWARNING</span><span class="p">,</span><span class="w"></span>
+<span class="w">  </span><span class="n">kINFO</span><span class="p">,</span><span class="w"></span>
+<span class="w">  </span><span class="n">kDEBUG</span><span class="p">,</span><span class="w"></span>
+<span class="w">  </span><span class="n">kGRAPH</span><span class="p">,</span><span class="w"></span>
+<span class="p">};</span><span class="w"></span>
+
+<span class="c1">// Are these ones necessary for the user?</span>
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">get_logging_prefix</span><span class="p">();</span><span class="w"></span>
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">set_logging_prefix</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">prefix</span><span class="p">);</span><span class="w"></span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="w"> </span><span class="n">lvl</span><span class="p">);</span><span class="w"></span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">set_is_colored_output_on</span><span class="p">(</span><span class="kt">bool</span><span class="w"> </span><span class="n">colored_output_on</span><span class="p">);</span><span class="w"></span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">Level</span><span class="w"> </span><span class="n">get_reportable_log_level</span><span class="p">();</span><span class="w"></span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="n">get_is_colored_output_on</span><span class="p">();</span><span class="w"></span>
+
+<span class="c1">// Dont know if we want this?</span>
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">log</span><span class="p">(</span><span class="n">Level</span><span class="w"> </span><span class="n">lvl</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">msg</span><span class="p">);</span><span class="w"></span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace logging</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torch_tensorrt</span>
+</pre></div>
+</div>
+</section>
+
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diff --git a/docs/v1.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.html b/docs/v1.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.html
new file mode 100644
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+++ b/docs/v1.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.html
@@ -0,0 +1,697 @@
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+
+  <section id="program-listing-for-file-macros-h">
+<span id="program-listing-file-cpp-include-torch-tensorrt-macros-h"></span><h1>Program Listing for File macros.h<a class="headerlink" href="#program-listing-for-file-macros-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros-h"><span class="std std-ref">Return to documentation for file</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/macros.h</span></code>)</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cm">/*</span>
+<span class="cm"> * Copyright (c) NVIDIA Corporation.</span>
+<span class="cm"> * All rights reserved.</span>
+<span class="cm"> *</span>
+<span class="cm"> * This library is licensed under the BSD-style license found in the</span>
+<span class="cm"> * LICENSE file in the root directory of this source tree.</span>
+<span class="cm"> */</span><span class="w"></span>
+<span class="cp">#pragma once</span>
+
+<span class="cp">#if defined(USE_CMAKE_GENERATED_EXPORT_HEADER)</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;torch_tensorrt_export.h&gt;</span><span class="cp"></span>
+<span class="cp">#else</span>
+<span class="cp">#if defined(__GNUC__)</span>
+<span class="cp">#define TORCHTRT_API __attribute__((__visibility__(&quot;default&quot;)))</span>
+<span class="cp">#define TORCHTRT_HIDDEN __attribute__((__visibility__(&quot;hidden&quot;)))</span>
+<span class="cp">#else</span>
+<span class="cp">#define TORCHTRT_API</span>
+<span class="cp">#define TORCHTRT_HIDDEN</span>
+<span class="cp">#endif </span><span class="c1">// defined(__GNUC__)</span>
+<span class="cp">#endif </span><span class="c1">// defined(USE_CMAKE_GENERATED_EXPORT_HEADER)</span>
+
+<span class="c1">// Does this need to be gaurded or something?</span>
+<span class="cp">#define XSTR(x) #x</span>
+<span class="cp">#define STR(x) XSTR(x)</span>
+
+<span class="cp">#define TORCH_TENSORRT_MAJOR_VERSION 1</span>
+<span class="cp">#define TORCH_TENSORRT_MINOR_VERSION 2</span>
+<span class="cp">#define TORCH_TENSORRT_PATCH_VERSION 0</span>
+<span class="cp">#define TORCH_TENSORRT_VERSION      \</span>
+<span class="cp">  STR(TORCH_TENSORRT_MAJOR_VERSION) \</span>
+<span class="cp">  &quot;.&quot; STR(TORCH_TENSORRT_MINOR_VERSION) &quot;.&quot; STR(TORCH_TENSORRT_PATCH_VERSION)</span>
+
+<span class="c1">// Setup namespace aliases for ease of use</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torch_tensorrt</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torchscript</span><span class="w"> </span><span class="p">{}</span><span class="w"></span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">ts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nn">torchscript</span><span class="p">;</span><span class="w"></span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torch_tensorrt</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torchtrt</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nn">torch_tensorrt</span><span class="p">;</span><span class="w"></span>
+</pre></div>
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diff --git a/docs/v1.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.html b/docs/v1.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.html
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+
+  <section id="program-listing-for-file-ptq-h">
+<span id="program-listing-file-cpp-include-torch-tensorrt-ptq-h"></span><h1>Program Listing for File ptq.h<a class="headerlink" href="#program-listing-for-file-ptq-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">Return to documentation for file</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/ptq.h</span></code>)</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cm">/*</span>
+<span class="cm"> * Copyright (c) NVIDIA Corporation.</span>
+<span class="cm"> * All rights reserved.</span>
+<span class="cm"> *</span>
+<span class="cm"> * This library is licensed under the BSD-style license found in the</span>
+<span class="cm"> * LICENSE file in the root directory of this source tree.</span>
+<span class="cm"> */</span><span class="w"></span>
+<span class="cp">#pragma once</span>
+
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;fstream&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iterator&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;memory&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;sstream&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;string&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;vector&gt;</span><span class="cp"></span>
+
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;NvInfer.h&quot;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/torch.h&quot;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/logging.h&quot;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/macros.h&quot;</span><span class="cp"></span>
+
+<span class="cp">#ifndef DOXYGEN_SHOULD_SKIP_THIS</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">nvinfer1</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="k">class</span><span class="w"> </span><span class="nc">IInt8Calibrator</span><span class="p">;</span><span class="w"></span>
+<span class="k">class</span><span class="w"> </span><span class="nc">IInt8EntropyCalibrator2</span><span class="p">;</span><span class="w"></span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace nvinfer1</span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torch_tensorrt</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">ptq</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="n">get_batch_impl</span><span class="p">(</span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">bindings</span><span class="p">[],</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">names</span><span class="p">[],</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">nbBindings</span><span class="p">,</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&amp;</span><span class="w"> </span><span class="n">data</span><span class="p">);</span><span class="w"></span>
+<span class="p">}</span><span class="w"></span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torch_tensorrt</span>
+<span class="cp">#endif </span><span class="c1">// DOXYGEN_SHOULD_SKIP_THIS</span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torch_tensorrt</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">ptq</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+
+<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">Algorithm</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="w"> </span><span class="nc">DataLoaderUniquePtr</span><span class="o">&gt;</span><span class="w"></span>
+<span class="k">class</span><span class="w"> </span><span class="nc">Int8Calibrator</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Algorithm</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="n">DataLoader</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">typename</span><span class="w"> </span><span class="nc">DataLoaderUniquePtr</span><span class="o">::</span><span class="n">element_type</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="n">Batch</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">typename</span><span class="w"> </span><span class="nc">DataLoader</span><span class="o">::</span><span class="n">super</span><span class="o">::</span><span class="n">BatchType</span><span class="p">;</span><span class="w"></span>
+
+<span class="w"> </span><span class="k">public</span><span class="o">:</span><span class="w"></span>
+<span class="w">  </span><span class="n">Int8Calibrator</span><span class="p">(</span><span class="n">DataLoaderUniquePtr</span><span class="w"> </span><span class="n">dataloader</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">cache_file_path</span><span class="p">,</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="n">use_cache</span><span class="p">)</span><span class="w"></span>
+<span class="w">      </span><span class="o">:</span><span class="w"> </span><span class="n">dataloader_</span><span class="p">(</span><span class="n">dataloader</span><span class="p">.</span><span class="n">get</span><span class="p">()),</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">(</span><span class="n">cache_file_path</span><span class="p">),</span><span class="w"> </span><span class="n">use_cache_</span><span class="p">(</span><span class="n">use_cache</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="n">batch</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="o">*</span><span class="n">dataloader_</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">      </span><span class="n">batched_data_</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="n">batch</span><span class="p">.</span><span class="n">data</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="p">}</span><span class="w"></span>
+<span class="w">    </span><span class="n">it_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">batched_data_</span><span class="p">.</span><span class="n">begin</span><span class="p">();</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">int</span><span class="w"> </span><span class="n">getBatchSize</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="c1">// HACK: Torch-TensorRT only uses explict batch sizing, INT8 Calibrator does not</span>
+<span class="w">    </span><span class="c1">// work when reporting the batch size here and having explicity batching.</span>
+<span class="w">    </span><span class="c1">// So we just report batch size 1 (warnings will still be printed out).</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="c1">// return static_cast&lt;int&gt;(dataloader_-&gt;options().batch_size);</span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">getBatch</span><span class="p">(</span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">bindings</span><span class="p">[],</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">names</span><span class="p">[],</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">nbBindings</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">it_</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">batched_data_</span><span class="p">.</span><span class="n">end</span><span class="p">())</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">status</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">get_batch_impl</span><span class="p">(</span><span class="n">bindings</span><span class="p">,</span><span class="w"> </span><span class="n">names</span><span class="p">,</span><span class="w"> </span><span class="n">nbBindings</span><span class="p">,</span><span class="w"> </span><span class="o">*</span><span class="n">it_</span><span class="p">);</span><span class="w"></span>
+<span class="w">      </span><span class="n">it_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">++</span><span class="n">it_</span><span class="p">;</span><span class="w"></span>
+<span class="w">      </span><span class="k">return</span><span class="w"> </span><span class="n">status</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="p">}</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">      </span><span class="c1">// Reset iterator if incase calibrator is going to be used again</span>
+<span class="w">      </span><span class="n">it_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">batched_data_</span><span class="p">.</span><span class="n">begin</span><span class="p">();</span><span class="w"></span>
+<span class="w">      </span><span class="k">return</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="p">}</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+
+<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">readCalibrationCache</span><span class="p">(</span><span class="kt">size_t</span><span class="o">&amp;</span><span class="w"> </span><span class="n">length</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">use_cache_</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">stringstream</span><span class="w"> </span><span class="n">ss</span><span class="p">;</span><span class="w"></span>
+<span class="w">      </span><span class="n">ss</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;Reading Calibration Cache from &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">;</span><span class="w"></span>
+<span class="w">      </span><span class="n">logging</span><span class="o">::</span><span class="n">log</span><span class="p">(</span><span class="n">logging</span><span class="o">::</span><span class="n">Level</span><span class="o">::</span><span class="n">kINFO</span><span class="p">,</span><span class="w"> </span><span class="n">ss</span><span class="p">.</span><span class="n">str</span><span class="p">());</span><span class="w"></span>
+
+<span class="w">      </span><span class="n">cache_</span><span class="p">.</span><span class="n">clear</span><span class="p">();</span><span class="w"></span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">ifstream</span><span class="w"> </span><span class="nf">input</span><span class="p">(</span><span class="n">cache_file_path_</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ios</span><span class="o">::</span><span class="n">binary</span><span class="p">);</span><span class="w"></span>
+<span class="w">      </span><span class="n">input</span><span class="w"> </span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">noskipws</span><span class="p">;</span><span class="w"></span>
+<span class="w">      </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">input</span><span class="p">.</span><span class="n">good</span><span class="p">())</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">copy</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">istream_iterator</span><span class="o">&lt;</span><span class="kt">char</span><span class="o">&gt;</span><span class="p">(</span><span class="n">input</span><span class="p">),</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">istream_iterator</span><span class="o">&lt;</span><span class="kt">char</span><span class="o">&gt;</span><span class="p">(),</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">back_inserter</span><span class="p">(</span><span class="n">cache_</span><span class="p">));</span><span class="w"></span>
+<span class="w">        </span><span class="n">logging</span><span class="o">::</span><span class="n">log</span><span class="p">(</span><span class="n">logging</span><span class="o">::</span><span class="n">Level</span><span class="o">::</span><span class="n">kDEBUG</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;Cache read&quot;</span><span class="p">);</span><span class="w"></span>
+<span class="w">      </span><span class="p">}</span><span class="w"></span>
+<span class="w">      </span><span class="n">length</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cache_</span><span class="p">.</span><span class="n">size</span><span class="p">();</span><span class="w"></span>
+<span class="w">      </span><span class="k">return</span><span class="w"> </span><span class="n">length</span><span class="w"> </span><span class="o">?</span><span class="w"> </span><span class="n">cache_</span><span class="p">.</span><span class="n">data</span><span class="p">()</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="k">nullptr</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="p">}</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="k">nullptr</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="n">writeCalibrationCache</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">cache</span><span class="p">,</span><span class="w"> </span><span class="kt">size_t</span><span class="w"> </span><span class="n">length</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ofstream</span><span class="w"> </span><span class="nf">cache_file</span><span class="p">(</span><span class="n">cache_file_path_</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ios</span><span class="o">::</span><span class="n">binary</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="n">cache_file</span><span class="p">.</span><span class="n">write</span><span class="p">(</span><span class="k">reinterpret_cast</span><span class="o">&lt;</span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*&gt;</span><span class="p">(</span><span class="n">cache</span><span class="p">),</span><span class="w"> </span><span class="n">length</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">stringstream</span><span class="w"> </span><span class="n">ss</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="n">ss</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;Saved Calibration Cache to &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="n">logging</span><span class="o">::</span><span class="n">log</span><span class="p">(</span><span class="n">logging</span><span class="o">::</span><span class="n">Level</span><span class="o">::</span><span class="n">kINFO</span><span class="p">,</span><span class="w"> </span><span class="n">ss</span><span class="p">.</span><span class="n">str</span><span class="p">());</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+
+<span class="w">  </span><span class="k">operator</span><span class="w"> </span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8Calibrator</span><span class="o">*</span><span class="p">()</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="k">reinterpret_cast</span><span class="o">&lt;</span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8Calibrator</span><span class="o">*&gt;</span><span class="p">(</span><span class="k">this</span><span class="p">);</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+
+<span class="w"> </span><span class="k">private</span><span class="o">:</span><span class="w"></span>
+<span class="w">  </span><span class="n">DataLoader</span><span class="o">*</span><span class="w"> </span><span class="n">dataloader_</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="kt">size_t</span><span class="w"> </span><span class="n">cache_size_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">use_cache_</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">char</span><span class="o">&gt;</span><span class="w"> </span><span class="n">cache_</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&gt;</span><span class="w"> </span><span class="n">batched_data_</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&gt;::</span><span class="n">iterator</span><span class="w"> </span><span class="n">it_</span><span class="p">;</span><span class="w"></span>
+<span class="p">};</span><span class="w"></span>
+
+<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">Algorithm</span><span class="o">&gt;</span><span class="w"></span>
+<span class="k">class</span><span class="w"> </span><span class="nc">Int8CacheCalibrator</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Algorithm</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w"> </span><span class="k">public</span><span class="o">:</span><span class="w"></span>
+<span class="w">  </span><span class="n">Int8CacheCalibrator</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">cache_file_path</span><span class="p">)</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">(</span><span class="n">cache_file_path</span><span class="p">)</span><span class="w"> </span><span class="p">{}</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">int</span><span class="w"> </span><span class="n">getBatchSize</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="c1">// HACK: Torch-TensorRT only uses explict batch sizing, INT8 Calibrator does not</span>
+<span class="w">    </span><span class="c1">// work when reporting the batch size here and having explicity batching.</span>
+<span class="w">    </span><span class="c1">// So we just report batch size 1 (warnings will still be printed out).</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">getBatch</span><span class="p">(</span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">bindings</span><span class="p">[],</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">names</span><span class="p">[],</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">nbBindings</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+
+<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">readCalibrationCache</span><span class="p">(</span><span class="kt">size_t</span><span class="o">&amp;</span><span class="w"> </span><span class="n">length</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">stringstream</span><span class="w"> </span><span class="n">ss</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="n">ss</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;Reading Calibration Cache from &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="n">logging</span><span class="o">::</span><span class="n">log</span><span class="p">(</span><span class="n">logging</span><span class="o">::</span><span class="n">Level</span><span class="o">::</span><span class="n">kINFO</span><span class="p">,</span><span class="w"> </span><span class="n">ss</span><span class="p">.</span><span class="n">str</span><span class="p">());</span><span class="w"></span>
+
+<span class="w">    </span><span class="n">cache_</span><span class="p">.</span><span class="n">clear</span><span class="p">();</span><span class="w"></span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ifstream</span><span class="w"> </span><span class="nf">input</span><span class="p">(</span><span class="n">cache_file_path_</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ios</span><span class="o">::</span><span class="n">binary</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="n">input</span><span class="w"> </span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">noskipws</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">input</span><span class="p">.</span><span class="n">good</span><span class="p">())</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">copy</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">istream_iterator</span><span class="o">&lt;</span><span class="kt">char</span><span class="o">&gt;</span><span class="p">(</span><span class="n">input</span><span class="p">),</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">istream_iterator</span><span class="o">&lt;</span><span class="kt">char</span><span class="o">&gt;</span><span class="p">(),</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">back_inserter</span><span class="p">(</span><span class="n">cache_</span><span class="p">));</span><span class="w"></span>
+<span class="w">      </span><span class="n">logging</span><span class="o">::</span><span class="n">log</span><span class="p">(</span><span class="n">logging</span><span class="o">::</span><span class="n">Level</span><span class="o">::</span><span class="n">kDEBUG</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;Cache read&quot;</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="p">}</span><span class="w"></span>
+<span class="w">    </span><span class="n">length</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cache_</span><span class="p">.</span><span class="n">size</span><span class="p">();</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">length</span><span class="w"> </span><span class="o">?</span><span class="w"> </span><span class="n">cache_</span><span class="p">.</span><span class="n">data</span><span class="p">()</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="k">nullptr</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="n">writeCalibrationCache</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">cache</span><span class="p">,</span><span class="w"> </span><span class="kt">size_t</span><span class="w"> </span><span class="n">length</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ofstream</span><span class="w"> </span><span class="nf">cache_file</span><span class="p">(</span><span class="n">cache_file_path_</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ios</span><span class="o">::</span><span class="n">binary</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="n">cache_file</span><span class="p">.</span><span class="n">write</span><span class="p">(</span><span class="k">reinterpret_cast</span><span class="o">&lt;</span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*&gt;</span><span class="p">(</span><span class="n">cache</span><span class="p">),</span><span class="w"> </span><span class="n">length</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">stringstream</span><span class="w"> </span><span class="n">ss</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="n">ss</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;Saved Calibration Cache to &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="n">logging</span><span class="o">::</span><span class="n">log</span><span class="p">(</span><span class="n">logging</span><span class="o">::</span><span class="n">Level</span><span class="o">::</span><span class="n">kINFO</span><span class="p">,</span><span class="w"> </span><span class="n">ss</span><span class="p">.</span><span class="n">str</span><span class="p">());</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+
+<span class="w">  </span><span class="k">operator</span><span class="w"> </span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8Calibrator</span><span class="o">*</span><span class="p">()</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="k">reinterpret_cast</span><span class="o">&lt;</span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8Calibrator</span><span class="o">*&gt;</span><span class="p">(</span><span class="k">this</span><span class="p">);</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+
+<span class="w"> </span><span class="k">private</span><span class="o">:</span><span class="w"></span>
+<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="kt">size_t</span><span class="w"> </span><span class="n">cache_size_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">char</span><span class="o">&gt;</span><span class="w"> </span><span class="n">cache_</span><span class="p">;</span><span class="w"></span>
+<span class="p">};</span><span class="w"></span>
+
+<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">Algorithm</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8EntropyCalibrator2</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="w"> </span><span class="nc">DataLoader</span><span class="o">&gt;</span><span class="w"></span>
+<span class="kr">inline</span><span class="w"> </span><span class="n">Int8Calibrator</span><span class="o">&lt;</span><span class="n">Algorithm</span><span class="p">,</span><span class="w"> </span><span class="n">DataLoader</span><span class="o">&gt;</span><span class="w"> </span><span class="n">make_int8_calibrator</span><span class="p">(</span><span class="w"></span>
+<span class="w">    </span><span class="n">DataLoader</span><span class="w"> </span><span class="n">dataloader</span><span class="p">,</span><span class="w"></span>
+<span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">cache_file_path</span><span class="p">,</span><span class="w"></span>
+<span class="w">    </span><span class="kt">bool</span><span class="w"> </span><span class="n">use_cache</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="n">Int8Calibrator</span><span class="o">&lt;</span><span class="n">Algorithm</span><span class="p">,</span><span class="w"> </span><span class="n">DataLoader</span><span class="o">&gt;</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">move</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span><span class="w"> </span><span class="n">cache_file_path</span><span class="p">,</span><span class="w"> </span><span class="n">use_cache</span><span class="p">);</span><span class="w"></span>
+<span class="p">}</span><span class="w"></span>
+
+<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">Algorithm</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8EntropyCalibrator2</span><span class="o">&gt;</span><span class="w"></span>
+<span class="kr">inline</span><span class="w"> </span><span class="n">Int8CacheCalibrator</span><span class="o">&lt;</span><span class="n">Algorithm</span><span class="o">&gt;</span><span class="w"> </span><span class="n">make_int8_cache_calibrator</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">cache_file_path</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="n">Int8CacheCalibrator</span><span class="o">&lt;</span><span class="n">Algorithm</span><span class="o">&gt;</span><span class="p">(</span><span class="n">cache_file_path</span><span class="p">);</span><span class="w"></span>
+<span class="p">}</span><span class="w"></span>
+
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace ptq</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torch_tensorrt</span>
+</pre></div>
+</div>
+</section>
+
+
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diff --git a/docs/v1.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.html b/docs/v1.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.html
new file mode 100644
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
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+
+  <section id="program-listing-for-file-torch-tensorrt-h">
+<span id="program-listing-file-cpp-include-torch-tensorrt-torch-tensorrt-h"></span><h1>Program Listing for File torch_tensorrt.h<a class="headerlink" href="#program-listing-for-file-torch-tensorrt-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">Return to documentation for file</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/torch_tensorrt.h</span></code>)</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cm">/*</span>
+<span class="cm"> * Copyright (c) NVIDIA Corporation.</span>
+<span class="cm"> * All rights reserved.</span>
+<span class="cm"> *</span>
+<span class="cm"> * This library is licensed under the BSD-style license found in the</span>
+<span class="cm"> * LICENSE file in the root directory of this source tree.</span>
+<span class="cm"> */</span><span class="w"></span>
+
+<span class="cp">#pragma once</span>
+
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cuda_runtime.h&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;memory&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;set&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;string&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;vector&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/custom_class.h&quot;</span><span class="cp"></span>
+
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/macros.h&quot;</span><span class="cp"></span>
+
+<span class="c1">// Just include the .h?</span>
+<span class="cp">#ifndef DOXYGEN_SHOULD_SKIP_THIS</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torch</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">jit</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="k">struct</span><span class="w"> </span><span class="nc">Graph</span><span class="p">;</span><span class="w"></span>
+<span class="k">struct</span><span class="w"> </span><span class="nc">Module</span><span class="p">;</span><span class="w"></span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace jit</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torch</span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">c10</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="k">enum</span><span class="w"> </span><span class="k">class</span><span class="w"> </span><span class="nc">DeviceType</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="kt">int8_t</span><span class="p">;</span><span class="w"></span>
+<span class="k">enum</span><span class="w"> </span><span class="k">class</span><span class="w"> </span><span class="nc">ScalarType</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="kt">int8_t</span><span class="p">;</span><span class="w"></span>
+<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">class</span><span class="o">&gt;</span><span class="w"></span>
+<span class="k">class</span><span class="w"> </span><span class="nc">ArrayRef</span><span class="p">;</span><span class="w"></span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace c10</span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">nvinfer1</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="k">class</span><span class="w"> </span><span class="nc">IInt8Calibrator</span><span class="p">;</span><span class="w"></span>
+<span class="p">}</span><span class="w"></span>
+<span class="cp">#endif </span><span class="c1">// DOXYGEN_SHOULD_SKIP_THIS</span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torch_tensorrt</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="k">class</span><span class="w"> </span><span class="nc">DataType</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w"> </span><span class="k">public</span><span class="o">:</span><span class="w"></span>
+<span class="w">  </span><span class="k">enum</span><span class="w"> </span><span class="nc">Value</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="kt">int8_t</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="n">kFloat</span><span class="p">,</span><span class="w"></span>
+<span class="w">    </span><span class="n">kHalf</span><span class="p">,</span><span class="w"></span>
+<span class="w">    </span><span class="n">kChar</span><span class="p">,</span><span class="w"></span>
+<span class="w">    </span><span class="n">kInt</span><span class="p">,</span><span class="w"></span>
+<span class="w">    </span><span class="n">kBool</span><span class="p">,</span><span class="w"></span>
+<span class="w">    </span><span class="n">kUnknown</span><span class="w"></span>
+<span class="w">  </span><span class="p">};</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">DataType</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">default</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="n">DataType</span><span class="p">(</span><span class="n">Value</span><span class="w"> </span><span class="n">t</span><span class="p">)</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">value</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="w"> </span><span class="p">{}</span><span class="w"></span>
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">DataType</span><span class="p">(</span><span class="n">c10</span><span class="o">::</span><span class="n">ScalarType</span><span class="w"> </span><span class="n">t</span><span class="p">);</span><span class="w"></span>
+<span class="w">  </span><span class="k">operator</span><span class="w"> </span><span class="n">Value</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+<span class="w">  </span><span class="k">explicit</span><span class="w"> </span><span class="k">operator</span><span class="w"> </span><span class="kt">bool</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">delete</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">==</span><span class="p">(</span><span class="n">DataType</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">other</span><span class="p">.</span><span class="n">value</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">==</span><span class="p">(</span><span class="n">DataType</span><span class="o">::</span><span class="n">Value</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">other</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">!=</span><span class="p">(</span><span class="n">DataType</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">other</span><span class="p">.</span><span class="n">value</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">!=</span><span class="p">(</span><span class="n">DataType</span><span class="o">::</span><span class="n">Value</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">other</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+
+<span class="w"> </span><span class="k">private</span><span class="o">:</span><span class="w"></span>
+<span class="w">  </span><span class="k">friend</span><span class="w"> </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ostream</span><span class="o">&amp;</span><span class="w"> </span><span class="k">operator</span><span class="o">&lt;&lt;</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">ostream</span><span class="o">&amp;</span><span class="w"> </span><span class="n">os</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">DataType</span><span class="o">&amp;</span><span class="w"> </span><span class="n">dtype</span><span class="p">);</span><span class="w"></span>
+<span class="w">  </span><span class="n">Value</span><span class="w"> </span><span class="n">value</span><span class="p">;</span><span class="w"></span>
+<span class="p">};</span><span class="w"></span>
+
+<span class="k">struct</span><span class="w"> </span><span class="nc">Device</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">DeviceType</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">   </span><span class="k">public</span><span class="o">:</span><span class="w"></span>
+<span class="w">    </span><span class="k">enum</span><span class="w"> </span><span class="nc">Value</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="kt">int8_t</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">      </span><span class="n">kGPU</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">kDLA</span><span class="p">,</span><span class="w"></span>
+<span class="w">    </span><span class="p">};</span><span class="w"></span>
+
+<span class="w">    </span><span class="n">DeviceType</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">default</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="k">constexpr</span><span class="w"> </span><span class="n">DeviceType</span><span class="p">(</span><span class="n">Value</span><span class="w"> </span><span class="n">t</span><span class="p">)</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">value</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="w"> </span><span class="p">{}</span><span class="w"></span>
+<span class="w">    </span><span class="n">DeviceType</span><span class="p">(</span><span class="n">c10</span><span class="o">::</span><span class="n">DeviceType</span><span class="w"> </span><span class="n">t</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="k">operator</span><span class="w"> </span><span class="n">Value</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">      </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="p">}</span><span class="w"></span>
+<span class="w">    </span><span class="k">explicit</span><span class="w"> </span><span class="k">operator</span><span class="w"> </span><span class="kt">bool</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">delete</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">==</span><span class="p">(</span><span class="n">DeviceType</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">      </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">other</span><span class="p">.</span><span class="n">value</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="p">}</span><span class="w"></span>
+<span class="w">    </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">!=</span><span class="p">(</span><span class="n">DeviceType</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">      </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">other</span><span class="p">.</span><span class="n">value</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="p">}</span><span class="w"></span>
+
+<span class="w">   </span><span class="k">private</span><span class="o">:</span><span class="w"></span>
+<span class="w">    </span><span class="n">Value</span><span class="w"> </span><span class="n">value</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">};</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">DeviceType</span><span class="w"> </span><span class="n">device_type</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="cm">/*</span>
+<span class="cm">   * Target gpu id</span>
+<span class="cm">   */</span><span class="w"></span>
+<span class="w">  </span><span class="kt">int64_t</span><span class="w"> </span><span class="n">gpu_id</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="cm">/*</span>
+<span class="cm">   * When using DLA core on NVIDIA AGX platforms gpu_id should be set as Xavier device</span>
+<span class="cm">   */</span><span class="w"></span>
+<span class="w">  </span><span class="kt">int64_t</span><span class="w"> </span><span class="n">dla_core</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">Device</span><span class="p">()</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">device_type</span><span class="p">(</span><span class="n">DeviceType</span><span class="o">::</span><span class="n">kGPU</span><span class="p">),</span><span class="w"> </span><span class="n">gpu_id</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span><span class="w"> </span><span class="n">dla_core</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="p">(</span><span class="nb">false</span><span class="p">)</span><span class="w"> </span><span class="p">{}</span><span class="w"></span>
+<span class="p">};</span><span class="w"></span>
+
+<span class="k">enum</span><span class="w"> </span><span class="k">class</span><span class="w"> </span><span class="nc">EngineCapability</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="kt">int8_t</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">  </span><span class="n">kSTANDARD</span><span class="p">,</span><span class="w"></span>
+<span class="w">  </span><span class="n">kSAFETY</span><span class="p">,</span><span class="w"></span>
+<span class="w">  </span><span class="n">kDLA_STANDALONE</span><span class="p">,</span><span class="w"></span>
+<span class="p">};</span><span class="w"></span>
+
+<span class="k">class</span><span class="w"> </span><span class="nc">TensorFormat</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w"> </span><span class="k">public</span><span class="o">:</span><span class="w"></span>
+<span class="w">  </span><span class="k">enum</span><span class="w"> </span><span class="nc">Value</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="kt">int8_t</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="n">kContiguous</span><span class="p">,</span><span class="w"></span>
+<span class="w">    </span><span class="n">kChannelsLast</span><span class="p">,</span><span class="w"></span>
+<span class="w">    </span><span class="n">kUnknown</span><span class="p">,</span><span class="w"></span>
+<span class="w">  </span><span class="p">};</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">TensorFormat</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">default</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="n">TensorFormat</span><span class="p">(</span><span class="n">Value</span><span class="w"> </span><span class="n">t</span><span class="p">)</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">value</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="w"> </span><span class="p">{}</span><span class="w"></span>
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">TensorFormat</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">MemoryFormat</span><span class="w"> </span><span class="n">t</span><span class="p">);</span><span class="w"></span>
+<span class="w">  </span><span class="k">operator</span><span class="w"> </span><span class="n">Value</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+<span class="w">  </span><span class="k">explicit</span><span class="w"> </span><span class="k">operator</span><span class="w"> </span><span class="kt">bool</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">delete</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">==</span><span class="p">(</span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">other</span><span class="p">.</span><span class="n">value</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">==</span><span class="p">(</span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">Value</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">other</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">!=</span><span class="p">(</span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">other</span><span class="p">.</span><span class="n">value</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">!=</span><span class="p">(</span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">Value</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">other</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+
+<span class="w"> </span><span class="k">private</span><span class="o">:</span><span class="w"></span>
+<span class="w">  </span><span class="k">friend</span><span class="w"> </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ostream</span><span class="o">&amp;</span><span class="w"> </span><span class="k">operator</span><span class="o">&lt;&lt;</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">ostream</span><span class="o">&amp;</span><span class="w"> </span><span class="n">os</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">&amp;</span><span class="w"> </span><span class="n">format</span><span class="p">);</span><span class="w"></span>
+<span class="w">  </span><span class="n">Value</span><span class="w"> </span><span class="n">value</span><span class="p">;</span><span class="w"></span>
+<span class="p">};</span><span class="w"></span>
+
+<span class="k">struct</span><span class="w"> </span><span class="nc">Input</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">CustomClassHolder</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">Input</span><span class="p">()</span><span class="w"> </span><span class="p">{}</span><span class="w"></span>
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">Input</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">,</span><span class="w"> </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">,</span><span class="w"> </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">,</span><span class="w"> </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">,</span><span class="w"> </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">,</span><span class="w"> </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">,</span><span class="w"> </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span><span class="w"></span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span><span class="w"></span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span><span class="w"></span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span><span class="w"></span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">,</span><span class="w"></span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">tensor</span><span class="p">);</span><span class="w"></span>
+
+<span class="w"> </span><span class="k">private</span><span class="o">:</span><span class="w"></span>
+<span class="w">  </span><span class="k">friend</span><span class="w"> </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ostream</span><span class="o">&amp;</span><span class="w"> </span><span class="k">operator</span><span class="o">&lt;&lt;</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">ostream</span><span class="o">&amp;</span><span class="w"> </span><span class="n">os</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">Input</span><span class="o">&amp;</span><span class="w"> </span><span class="n">input</span><span class="p">);</span><span class="w"></span>
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">input_is_dynamic</span><span class="p">;</span><span class="w"></span>
+<span class="p">};</span><span class="w"></span>
+
+<span class="k">struct</span><span class="w"> </span><span class="nc">GraphInputs</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">  </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">IValue</span><span class="w"> </span><span class="n">input_signature</span><span class="p">;</span><span class="w"> </span><span class="c1">// nested Input, full input spec</span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">Input</span><span class="o">&gt;</span><span class="w"> </span><span class="n">inputs</span><span class="p">;</span><span class="w"> </span><span class="c1">// flatten input spec</span>
+<span class="p">};</span><span class="w"></span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">get_build_info</span><span class="p">();</span><span class="w"></span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">dump_build_info</span><span class="p">();</span><span class="w"></span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">set_device</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">gpu_id</span><span class="p">);</span><span class="w"></span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torchscript</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="k">struct</span><span class="w"> </span><span class="nc">CompileSpec</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">CompileSpec</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">fixed_sizes</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">CompileSpec</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">fixed_sizes</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">CompileSpec</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">Input</span><span class="o">&gt;</span><span class="w"> </span><span class="n">inputs</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">CompileSpec</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">IValue</span><span class="w"> </span><span class="n">input_signature</span><span class="p">);</span><span class="w"></span>
+<span class="w">  </span><span class="c1">// Defaults should reflect TensorRT defaults for BuilderConfig</span>
+
+<span class="w">  </span><span class="n">GraphInputs</span><span class="w"> </span><span class="n">graph_inputs</span><span class="p">;</span><span class="w"></span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">set</span><span class="o">&lt;</span><span class="n">DataType</span><span class="o">&gt;</span><span class="w"> </span><span class="n">enabled_precisions</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="n">DataType</span><span class="o">::</span><span class="n">kFloat</span><span class="p">};</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">disable_tf32</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">sparse_weights</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">refit</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">debug</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">truncate_long_and_double</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">Device</span><span class="w"> </span><span class="n">device</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">EngineCapability</span><span class="w"> </span><span class="n">capability</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">EngineCapability</span><span class="o">::</span><span class="n">kSTANDARD</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">uint64_t</span><span class="w"> </span><span class="n">num_avg_timing_iters</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">uint64_t</span><span class="w"> </span><span class="n">workspace_size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">uint64_t</span><span class="w"> </span><span class="n">dla_sram_size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1048576</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">uint64_t</span><span class="w"> </span><span class="n">dla_local_dram_size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1073741824</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">uint64_t</span><span class="w"> </span><span class="n">dla_global_dram_size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">536870912</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8Calibrator</span><span class="o">*</span><span class="w"> </span><span class="n">ptq_calibrator</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">nullptr</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">require_full_compilation</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="kt">uint64_t</span><span class="w"> </span><span class="n">min_block_size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">3</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&gt;</span><span class="w"> </span><span class="n">torch_executed_ops</span><span class="p">;</span><span class="w"></span>
+
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&gt;</span><span class="w"> </span><span class="n">torch_executed_modules</span><span class="p">;</span><span class="w"></span>
+<span class="p">};</span><span class="w"></span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="n">check_method_operator_support</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="o">&amp;</span><span class="w"> </span><span class="k">module</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">method_name</span><span class="p">);</span><span class="w"></span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="w"> </span><span class="n">compile</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="o">&amp;</span><span class="w"> </span><span class="k">module</span><span class="p">,</span><span class="w"> </span><span class="n">CompileSpec</span><span class="w"> </span><span class="n">info</span><span class="p">);</span><span class="w"></span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">convert_method_to_trt_engine</span><span class="p">(</span><span class="w"></span>
+<span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="o">&amp;</span><span class="w"> </span><span class="k">module</span><span class="p">,</span><span class="w"></span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">method_name</span><span class="p">,</span><span class="w"></span>
+<span class="w">    </span><span class="n">CompileSpec</span><span class="w"> </span><span class="n">info</span><span class="p">);</span><span class="w"></span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="w"> </span><span class="n">embed_engine_in_new_module</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">engine</span><span class="p">,</span><span class="w"> </span><span class="n">Device</span><span class="w"> </span><span class="n">device</span><span class="p">);</span><span class="w"></span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torchscript</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torch_tensorrt</span>
+</pre></div>
+</div>
+</section>
+
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diff --git a/docs/v1.2.0/_cpp_api/structtorch__tensorrt_1_1Device.html b/docs/v1.2.0/_cpp_api/structtorch__tensorrt_1_1Device.html
new file mode 100644
index 0000000000..59a0881709
--- /dev/null
+++ b/docs/v1.2.0/_cpp_api/structtorch__tensorrt_1_1Device.html
@@ -0,0 +1,840 @@
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+
+  <section id="struct-device">
+<span id="exhale-struct-structtorch-tensorrt-1-1device"></span><h1>Struct Device<a class="headerlink" href="#struct-device" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="nested-relationships">
+<h2>Nested Relationships<a class="headerlink" href="#nested-relationships" title="Permalink to this headline">¶</a></h2>
+<section id="nested-types">
+<h3>Nested Types<a class="headerlink" href="#nested-types" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#exhale-class-classtorch-tensorrt-1-1device-1-1devicetype"><span class="std std-ref">Class Device::DeviceType</span></a></p></li>
+</ul>
+</section>
+</section>
+<section id="struct-documentation">
+<h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp struct">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6DeviceE">
+<span class="target" id="structtorch__tensorrt_1_1Device"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">Device</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6DeviceE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Setting data structure for Target device. </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device6DeviceEv">
+<span class="target" id="structtorch__tensorrt_1_1Device_1afb5a5a4833237e1a106094c95cafbdf8"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Device</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device6DeviceEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Constructor for <a class="reference internal" href="#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> structure </p>
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device11device_typeE">
+<span class="target" id="structtorch__tensorrt_1_1Device_1abaa77ed18f126bf6cae9305b6660c9d6"></span><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceTypeE" title="torch_tensorrt::Device::DeviceType"><span class="n"><span class="pre">DeviceType</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">device_type</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device11device_typeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Setting data structure for device This struct will hold Target device related parameters such as device_type, gpu_id, dla_core. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device6gpu_idE">
+<span class="target" id="structtorch__tensorrt_1_1Device_1a5498d9e53e3e6598e1ea0b4158ae9105"></span><span class="n"><span class="pre">int64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gpu_id</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device6gpu_idE" title="Permalink to this definition">¶</a><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device8dla_coreE">
+<span class="target" id="structtorch__tensorrt_1_1Device_1ae1d7bcca2367c3eff77a725c5be8d549"></span><span class="n"><span class="pre">int64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dla_core</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device8dla_coreE" title="Permalink to this definition">¶</a><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device18allow_gpu_fallbackE">
+<span class="target" id="structtorch__tensorrt_1_1Device_1a0ccaae1d02a1078175fd3a91e74fc398"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">allow_gpu_fallback</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device18allow_gpu_fallbackE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>(Only used when targeting DLA (device)) Lets engine run layers on GPU if they are not supported on DLA </p>
+</dd></dl>
+
+</div>
+<dl class="cpp class">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceTypeE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><br /></dt>
+<dd><p>Supported <a class="reference internal" href="#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Types that can be used with TensorRT engines</p>
+<p>This class is compatable with c10::DeviceTypes (but will check for TRT support) but the only applicable value is at::kCUDA, which maps to <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"><span class="std std-ref">DeviceType::kGPU</span></a></p>
+<p>To use the <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a> class itself, interface using the enum vs. normal instatination</p>
+<p>ex. torch_tensorrt::DeviceType type = <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"><span class="std std-ref">DeviceType::kGPU</span></a>; </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-types">Public Types</p>
+<dl class="cpp enum">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Value</span></span></span><br /></dt>
+<dd><p>Underlying enum class to support the <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a> Class</p>
+<p>In the case that you need to use the <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a> class itself, interface using this enum vs. normal instatination</p>
+<p>ex. torch_tensorrt::DeviceType type = <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"><span class="std std-ref">DeviceType::kGPU</span></a>; </p>
+<p><em>Values:</em></p>
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kGPUE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kGPU</span></span></span><br /></dt>
+<dd><p>Target GPU to run engine. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kDLAE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350aa13fb3ec20096f0d0c205b67ca05c98e"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kDLA</span></span></span><br /></dt>
+<dd><p>Target DLA to run engine. </p>
+</dd></dl>
+
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEv">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1afe573ee91b95b81be8a0f1503a00ca45"></span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Type object. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeE5Value">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a5a7e6c4eb98321bd53869a8ec08e2a2c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE" title="torch_tensorrt::Device::DeviceType::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Type object from internal enum. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEN3c1010DeviceTypeE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a499573dd56df8fa1479dcec95dfda711"></span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">DeviceType</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Type object from torch device enums Note: The only valid value is torch::kCUDA (torch::kCPU is not supported) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt6Device10DeviceTypecv5ValueEv">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a4afa0b396b7a076e37054754a367f2f4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE" title="torch_tensorrt::Device::DeviceType::Value"><span class="n"><span class="pre">Value</span></span></a></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><br /></dt>
+<dd><p>Get the internal value from the <a class="reference internal" href="#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> object. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Value </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceTypecvbEv">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a1e78ae4780df8bd258ee8703a22d9a4d"></span><span class="k"><span class="pre">explicit</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">delete</span></span><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt6Device10DeviceTypeeqE10DeviceType">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a0177ee9788edabb89a8cb64234d2a60c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceTypeE" title="torch_tensorrt::Device::DeviceType"><span class="n"><span class="pre">DeviceType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><br /></dt>
+<dd><p>Comparison operator for <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt6Device10DeviceTypeneE10DeviceType">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a0d8973b436226f17597d94f4e8f63336"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">!=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceTypeE" title="torch_tensorrt::Device::DeviceType"><span class="n"><span class="pre">DeviceType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><br /></dt>
+<dd><p>Comparison operator for <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
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+</dd></dl>
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diff --git a/docs/v1.2.0/_cpp_api/structtorch__tensorrt_1_1GraphInputs.html b/docs/v1.2.0/_cpp_api/structtorch__tensorrt_1_1GraphInputs.html
new file mode 100644
index 0000000000..7f59d7979e
--- /dev/null
+++ b/docs/v1.2.0/_cpp_api/structtorch__tensorrt_1_1GraphInputs.html
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+  <section id="struct-graphinputs">
+<span id="exhale-struct-structtorch-tensorrt-1-1graphinputs"></span><h1>Struct GraphInputs<a class="headerlink" href="#struct-graphinputs" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
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+<section id="struct-documentation">
+<h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp struct">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11GraphInputsE">
+<span class="target" id="structtorch__tensorrt_1_1GraphInputs"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">GraphInputs</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11GraphInputsE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>A struct to hold complex inputs. </p>
+<p>This struct can either hold a complex inputs of shape or a flattened one, </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
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+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11GraphInputs15input_signatureE">
+<span class="target" id="structtorch__tensorrt_1_1GraphInputs_1ad5a2d79fb0ab7afd2a4d0a5f0e200ac7"></span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">jit</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IValue</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">input_signature</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11GraphInputs15input_signatureE" title="Permalink to this definition">¶</a><br /></dt>
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+<span class="target" id="structtorch__tensorrt_1_1GraphInputs_1a3d2a9ff85bc319c29793b2177085cb0f"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5InputE" title="torch_tensorrt::Input"><span class="n"><span class="pre">Input</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">inputs</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11GraphInputs6inputsE" title="Permalink to this definition">¶</a><br /></dt>
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+        <div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper">
+          Shortcuts
+        </div>
+      </div>
+
+      <section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
+        <div class="pytorch-content-left">
+
+
+
+          <div class="rst-content">
+
+            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+
+  <section id="struct-input">
+<span id="exhale-struct-structtorch-tensorrt-1-1input"></span><h1>Struct Input<a class="headerlink" href="#struct-input" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="inheritance-relationships">
+<h2>Inheritance Relationships<a class="headerlink" href="#inheritance-relationships" title="Permalink to this headline">¶</a></h2>
+<section id="base-type">
+<h3>Base Type<a class="headerlink" href="#base-type" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">public</span> <span class="pre">CustomClassHolder</span></code></p></li>
+</ul>
+</section>
+</section>
+<section id="struct-documentation">
+<h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp struct">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5InputE">
+<span class="target" id="structtorch__tensorrt_1_1Input"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><span class="n"><span class="pre">CustomClassHolder</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5InputE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>A struct to hold an input range (used by TensorRT Optimization profile) </p>
+<p>This struct can either hold a single vector representing an input shape, signifying a static input shape or a set of three input shapes representing the min, optiminal and max input shapes allowed for the engine. </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEv">
+<span class="target" id="structtorch__tensorrt_1_1Input_1ae217f6c7c13be354e03d3f9b5b6b5a5e"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a0328d70f3daf18b425d459689be2591a"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from vector, optional arguments allow the user to configure expected input shape tensor format. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataType12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1af8b7efdf0e96b4221742a658b214738b"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from vector, optional arguments allow the user to configure expected input shape tensor format. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
+<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a70d54a3caadadab68faad230ff302f23"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from c10::ArrayRef (the type produced by tensor.sizes()), vector, optional arguments allow the user to configure expected input shape tensor format dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a7069ee5a6db2f840423a923d08d08c6f"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from c10::ArrayRef (the type produced by tensor.sizes()), vector, optional arguments allow the user to configure expected input shape tensor format. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
+<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a6f305b4f28ce0a7c76d39ed61dc9a964"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
+<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
+<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataType12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a93370fc5773212005972056184a527a5"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for a dynamic input size from vectors for minimum shape, optimal shape, and max shape supported sizes optional arguments allow the user to configure expected input shape tensor format. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
+<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
+<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
+<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1abda696b9f2f689af6f3577e51647ad6f"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
+<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
+<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a59eb3e2d3455017d19c9b498eefbb35e"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kContiguous</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
+<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
+<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
+<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN2at6TensorE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a3b472eb488183b47f470507860b49f1c"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">at</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Tensor</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">tensor</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN2at6TensorE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object using a torch tensor as an example The tensor’s shape, type and layout inform the spec’s values. </p>
+<p>Note: You cannot set dynamic shape through this method, you must use an alternative constructor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>tensor</strong> – Reference tensor to set shape, type and layout </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input9min_shapeE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1af95ee167b1e25e1a599a8fc67ffea9ac"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">min_shape</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input9min_shapeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Minimum acceptable input size into the engine. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input9opt_shapeE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a63666d30decf34115f642e75f0cf8256"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">opt_shape</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input9opt_shapeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Optimal input size into the engine (size optimized for given kernels accept any size in min max range) </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input9max_shapeE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1ad3dfdcaaa9126dcb8dfa08dec085d589"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">max_shape</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input9max_shapeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Maximum acceptable input size into the engine. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5shapeE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a26997c30b04c8013a5afadb181b70377"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">shape</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5shapeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p><a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> shape to be fed to TensorRT, in the event of a dynamic shape, -1’s will hold the place of variable dimensions </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5dtypeE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a05d520340d486851d9e8a4797611895e"></span><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dtype</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5dtypeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Expected data type for the input. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input6formatE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1ad544c1a1584d2f45bccf0e4963b8d292"></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">format</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input6formatE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Expected tensor format for the input. </p>
+</dd></dl>
+
+</div>
+</dd></dl>
+
+</section>
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diff --git a/docs/v1.2.0/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html b/docs/v1.2.0/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html
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+
+  <section id="struct-compilespec">
+<span id="exhale-struct-structtorch-tensorrt-1-1torchscript-1-1compilespec"></span><h1>Struct CompileSpec<a class="headerlink" href="#struct-compilespec" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="struct-documentation">
+<h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp struct">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpecE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">torchscript</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">CompileSpec</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpecE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Settings data structure for Torch-TensorRT TorchScript compilation </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorINSt6vectorI7int64_tEEEE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a18fd2eb916e1f5358ae536e6cc064377"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">CompileSpec</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">fixed_sizes</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorINSt6vectorI7int64_tEEEE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Compile Spec object Convienence constructor to set fixed input size from vectors describing size of input tensors. Each entry in the vector represents a input and should be provided in call order. </p>
+<p>This constructor should be use as a convience in the case that all inputs are static sized and you are okay with default input dtype and formats (FP32 for FP32 and INT8 weights, FP16 for FP16 weights, contiguous)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>fixed_sizes</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorIN3c108ArrayRefI7int64_tEEEE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a594ca70c02cf2be7fb0f198ca15a0e73"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">CompileSpec</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">fixed_sizes</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorIN3c108ArrayRefI7int64_tEEEE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Compile Spec object Convienence constructor to set fixed input size from c10::ArrayRef’s (the output of tensor.sizes()) describing size of input tensors. Each entry in the vector represents a input and should be provided in call order. </p>
+<p>This constructor should be use as a convience in the case that all inputs are static sized and you are okay with default input dtype and formats (FP32 for FP32 and INT8 weights, FP16 for FP16 weights, contiguous)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>fixed_sizes</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorI5InputEE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a9599633a5a3e893a6e63dc12980e5be8"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">CompileSpec</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5InputE" title="torch_tensorrt::Input"><span class="n"><span class="pre">Input</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">inputs</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorI5InputEE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Compile Spec object from input ranges. Each entry in the vector represents a input and should be provided in call order. </p>
+<p>Use this constructor to define inputs with dynamic shape, specific input types or tensor formats</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>inputs</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecEN5torch3jit6IValueE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1af8550292118cd8dd139edebe33b99f8c"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">CompileSpec</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">jit</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IValue</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">input_signature</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecEN5torch3jit6IValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Compile Spec object from IValue which represents the nesting of input tensors for a module. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>input_signature</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec12graph_inputsE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a72536728d26e36c8b9d64dd7711fea1f"></span><a class="reference internal" href="structtorch__tensorrt_1_1GraphInputs.html#_CPPv4N14torch_tensorrt11GraphInputsE" title="torch_tensorrt::GraphInputs"><span class="n"><span class="pre">GraphInputs</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">graph_inputs</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec12graph_inputsE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Specifications for inputs to the engine, can store a IValue which has stored complex <a class="reference internal" href="structtorch__tensorrt_1_1Input.html#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> or a flatened <a class="reference internal" href="structtorch__tensorrt_1_1Input.html#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a>. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec18enabled_precisionsE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a83ce8e55ab1e1830a440513fbabae043"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">set</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">enabled_precisions</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="p"><span class="pre">{</span></span><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">kFloat</span></span><span class="p"><span class="pre">}</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec18enabled_precisionsE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>The set of precisions TensorRT is allowed to use for kernels during compilation. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec12disable_tf32E">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1ac1d3b92892fbeee1a77c6ec89b451fb2"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">disable_tf32</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec12disable_tf32E" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Prevent Float32 layers from using TF32 data format</p>
+<p>TF32 computes inner products by rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas. This is the behavior of FP32 layers by default. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec14sparse_weightsE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a5b4ed74b243c63a708c7bcb8e48ab307"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">sparse_weights</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14sparse_weightsE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Enable sparsity for weights of conv and FC layers </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec5refitE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a5aa4e3201f756a2a952f01f08902cd7d"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">refit</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec5refitE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Build a refitable engine </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec5debugE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a973775a29be52d24983b02073c4e1592"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">debug</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec5debugE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Build a debugable engine </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec24truncate_long_and_doubleE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1ad47a41aa623a797c0212a157a4091a16"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">truncate_long_and_double</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec24truncate_long_and_doubleE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Truncate long/double type to int/float type </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec6deviceE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a7eec2ecd57b3debd73239a7d0d552ffd"></span><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6DeviceE" title="torch_tensorrt::Device"><span class="n"><span class="pre">Device</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">device</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec6deviceE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Target <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec10capabilityE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1aa9f0624c0b2a01589bdf67343435b517"></span><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapabilityE" title="torch_tensorrt::EngineCapability"><span class="n"><span class="pre">EngineCapability</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">capability</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapabilityE" title="torch_tensorrt::EngineCapability"><span class="n"><span class="pre">EngineCapability</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapability9kSTANDARDE" title="torch_tensorrt::EngineCapability::kSTANDARD"><span class="n"><span class="pre">kSTANDARD</span></span></a><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec10capabilityE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Sets the restrictions for the engine (CUDA Safety) </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec20num_avg_timing_itersE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a0b08c3de8acd85c6c7d277984aadd63d"></span><span class="n"><span class="pre">uint64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">num_avg_timing_iters</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">1</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec20num_avg_timing_itersE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Number of averaging timing iterations used to select kernels </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec14workspace_sizeE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a2dee309060f0bf4052c9b734d91feef1"></span><span class="n"><span class="pre">uint64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">workspace_size</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14workspace_sizeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Maximum size of workspace given to TensorRT </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec13dla_sram_sizeE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1ab895c21f1ab78b2b2790b6664ea1d0b7"></span><span class="n"><span class="pre">uint64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dla_sram_size</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">1048576</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec13dla_sram_sizeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Fast software managed RAM used by DLA to communicate within a layer. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec19dla_local_dram_sizeE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a7f98e1909d484589228fce66945b218a"></span><span class="n"><span class="pre">uint64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dla_local_dram_size</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">1073741824</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec19dla_local_dram_sizeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Host RAM used by DLA to share intermediate tensor data across operations </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec20dla_global_dram_sizeE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a3cdc173909775362b3e41efa844b6c89"></span><span class="n"><span class="pre">uint64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dla_global_dram_size</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">536870912</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec20dla_global_dram_sizeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>host RAM used by DLA to store weights and metadata for execution </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec14ptq_calibratorE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a4d4a1651c2a1516d96374dedd566f96b"></span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8Calibrator</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="sig-name descname"><span class="n"><span class="pre">ptq_calibrator</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">nullptr</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14ptq_calibratorE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Calibration dataloaders for each input for post training quantizatiom </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec24require_full_compilationE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a2b1e0e8adbbaf0192e6d30502f867a85"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">require_full_compilation</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec24require_full_compilationE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Require the full module be compiled to TensorRT instead of potentially running unsupported operations in PyTorch </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec14min_block_sizeE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1ad75441730fd7776726a7baed34d8d2bb"></span><span class="n"><span class="pre">uint64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">min_block_size</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">3</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14min_block_sizeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Minimum number of contiguous supported operators to compile a subgraph to TensorRT </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec18torch_executed_opsE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a3c60fb30768398ff1c75b25f5969ebc6"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">torch_executed_ops</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec18torch_executed_opsE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>List of aten operators that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec22torch_executed_modulesE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a878095a83d812f67e3b6f27085abc43b"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">torch_executed_modules</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec22torch_executed_modulesE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True </p>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+
+  <section id="torch-tensorrt-c-api">
+<h1>Torch-TensorRT C++ API<a class="headerlink" href="#torch-tensorrt-c-api" title="Permalink to this headline">¶</a></h1>
+<section id="class-hierarchy">
+<h2>Class Hierarchy<a class="headerlink" href="#class-hierarchy" title="Permalink to this headline">¶</a></h2>
+<ul class="treeView" id="class-treeView">
+  <li>
+    <ul class="collapsibleList">
+      <li class="lastChild">Namespace <a href="namespace_torch_tensorrt.html#namespace-torch-tensorrt">torch_tensorrt</a><ul><li>Namespace <a href="namespace_torch_tensorrt__logging.html#namespace-torch-tensorrt-logging">torch_tensorrt::logging</a><ul><li class="lastChild">Enum <a href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#exhale-enum-logging-8h-1a130f65408ad8cbaee060f05e8db69558">Level</a></li></ul></li><li>Namespace <a href="namespace_torch_tensorrt__ptq.html#namespace-torch-tensorrt-ptq">torch_tensorrt::ptq</a><ul><li>Template Class <a href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#exhale-class-classtorch-tensorrt-1-1ptq-1-1Int8CacheCalibrator">Int8CacheCalibrator</a></li><li class="lastChild">Template Class <a href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#exhale-class-classtorch-tensorrt-1-1ptq-1-1Int8Calibrator">Int8Calibrator</a></li></ul></li><li>Namespace <a href="namespace_torch_tensorrt__torchscript.html#namespace-torch-tensorrt-torchscript">torch_tensorrt::torchscript</a><ul><li class="lastChild">Struct <a href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#exhale-struct-structtorch-tensorrt-1-1torchscript-1-1CompileSpec">CompileSpec</a></li></ul></li><li>Struct <a href="structtorch__tensorrt_1_1Device.html#exhale-struct-structtorch-tensorrt-1-1Device">Device</a><ul><li class="lastChild">Class <a href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#exhale-class-classtorch-tensorrt-1-1Device-1-1DeviceType">Device::DeviceType</a></li></ul></li><li>Struct <a href="structtorch__tensorrt_1_1GraphInputs.html#exhale-struct-structtorch-tensorrt-1-1GraphInputs">GraphInputs</a></li><li>Struct <a href="structtorch__tensorrt_1_1Input.html#exhale-struct-structtorch-tensorrt-1-1Input">Input</a></li><li>Class <a href="classtorch__tensorrt_1_1DataType.html#exhale-class-classtorch-tensorrt-1-1DataType">DataType</a></li><li>Class <a href="classtorch__tensorrt_1_1TensorFormat.html#exhale-class-classtorch-tensorrt-1-1TensorFormat">TensorFormat</a></li><li class="lastChild">Enum <a href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#exhale-enum-torch-tensorrt-8h-1a3fbe5d72e4fc624dbd038853079620eb">EngineCapability</a></li></ul></li>
+    </ul>
+  </li><!-- only tree view element -->
+</ul><!-- /treeView class-treeView --></section>
+<section id="file-hierarchy">
+<h2>File Hierarchy<a class="headerlink" href="#file-hierarchy" title="Permalink to this headline">¶</a></h2>
+<ul class="treeView" id="file-treeView">
+  <li>
+    <ul class="collapsibleList">
+      <li class="lastChild">Directory <a href="dir_cpp.html#dir-cpp">cpp</a><ul><li class="lastChild">Directory <a href="dir_cpp_include.html#dir-cpp-include">include</a><ul><li class="lastChild">Directory <a href="dir_cpp_include_torch_tensorrt.html#dir-cpp-include-torch-tensorrt">torch_tensorrt</a><ul><li>File <a href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging.h">logging.h</a></li><li>File <a href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros.h">macros.h</a></li><li>File <a href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq.h">ptq.h</a></li><li class="lastChild">File <a href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt.h">torch_tensorrt.h</a></li></ul></li></ul></li></ul></li>
+    </ul>
+  </li><!-- only tree view element -->
+</ul><!-- /treeView file-treeView --></section>
+<section id="full-api">
+<h2>Full API<a class="headerlink" href="#full-api" title="Permalink to this headline">¶</a></h2>
+<section id="namespaces">
+<h3>Namespaces<a class="headerlink" href="#namespaces" title="Permalink to this headline">¶</a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt.html">Namespace torch_tensorrt</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt.html#namespaces">Namespaces</a></li>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt.html#classes">Classes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt.html#enums">Enums</a></li>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt.html#functions">Functions</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt__logging.html#enums">Enums</a></li>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt__logging.html#functions">Functions</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt__ptq.html#classes">Classes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt__ptq.html#functions">Functions</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt__torchscript.html#classes">Classes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt__torchscript.html#functions">Functions</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="classes-and-structs">
+<h3>Classes and Structs<a class="headerlink" href="#classes-and-structs" title="Permalink to this headline">¶</a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="structtorch__tensorrt_1_1Device.html">Struct Device</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#nested-relationships">Nested Relationships</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#nested-types">Nested Types</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#struct-documentation">Struct Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="structtorch__tensorrt_1_1GraphInputs.html">Struct GraphInputs</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1GraphInputs.html#struct-documentation">Struct Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="structtorch__tensorrt_1_1Input.html">Struct Input</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#inheritance-relationships">Inheritance Relationships</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#base-type">Base Type</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#struct-documentation">Struct Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html">Struct CompileSpec</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#struct-documentation">Struct Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html">Class DataType</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#class-documentation">Class Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html">Class Device::DeviceType</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#nested-relationships">Nested Relationships</a></li>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#class-documentation">Class Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html">Template Class Int8CacheCalibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#inheritance-relationships">Inheritance Relationships</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#base-type">Base Type</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#class-documentation">Class Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html">Template Class Int8Calibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#inheritance-relationships">Inheritance Relationships</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#base-type">Base Type</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#class-documentation">Class Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html">Class TensorFormat</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#class-documentation">Class Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="enums">
+<h3>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html">Enum EngineCapability</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#enum-documentation">Enum Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html">Enum Level</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#enum-documentation">Enum Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="functions">
+<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html">Function torch_tensorrt::dump_build_info</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html">Function torch_tensorrt::get_build_info</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html">Function torch_tensorrt::logging::get_is_colored_output_on</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html">Function torch_tensorrt::logging::get_logging_prefix</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html">Function torch_tensorrt::logging::get_reportable_log_level</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html">Function torch_tensorrt::logging::log</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html">Function torch_tensorrt::logging::set_is_colored_output_on</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1af8f3443813315af7901903d25dd495cc.html">Function torch_tensorrt::logging::set_logging_prefix</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1af8f3443813315af7901903d25dd495cc.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html">Function torch_tensorrt::logging::set_reportable_log_level</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.html">Template Function torch_tensorrt::ptq::make_int8_cache_calibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html">Template Function torch_tensorrt::ptq::make_int8_calibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html">Function torch_tensorrt::set_device</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html">Function torch_tensorrt::torchscript::check_method_operator_support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html">Function torch_tensorrt::torchscript::compile</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html">Function torch_tensorrt::torchscript::convert_method_to_trt_engine</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.html">Function torch_tensorrt::torchscript::embed_engine_in_new_module</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="defines">
+<h3>Defines<a class="headerlink" href="#defines" title="Permalink to this headline">¶</a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a18d295a837ac71add5578860b55e5502.html">Define STR</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a18d295a837ac71add5578860b55e5502.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html">Define TORCH_TENSORRT_MAJOR_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html">Define TORCH_TENSORRT_MINOR_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html">Define TORCH_TENSORRT_PATCH_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html">Define TORCH_TENSORRT_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html">Define TORCHTRT_API</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html">Define TORCHTRT_HIDDEN</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html">Define XSTR</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
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diff --git a/docs/v1.2.0/_cpp_api/unabridged_api.html b/docs/v1.2.0/_cpp_api/unabridged_api.html
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
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+  <section id="full-api">
+<h1>Full API<a class="headerlink" href="#full-api" title="Permalink to this headline">¶</a></h1>
+<section id="namespaces">
+<h2>Namespaces<a class="headerlink" href="#namespaces" title="Permalink to this headline">¶</a></h2>
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+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt__ptq.html#functions">Functions</a></li>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt__torchscript.html#classes">Classes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt__torchscript.html#functions">Functions</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="classes-and-structs">
+<h2>Classes and Structs<a class="headerlink" href="#classes-and-structs" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="structtorch__tensorrt_1_1Device.html">Struct Device</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#nested-relationships">Nested Relationships</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#nested-types">Nested Types</a></li>
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+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="structtorch__tensorrt_1_1Input.html">Struct Input</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#inheritance-relationships">Inheritance Relationships</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#base-type">Base Type</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#struct-documentation">Struct Documentation</a></li>
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+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html">Struct CompileSpec</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#struct-documentation">Struct Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html">Class DataType</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#class-documentation">Class Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html">Class Device::DeviceType</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#nested-relationships">Nested Relationships</a></li>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#class-documentation">Class Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html">Template Class Int8CacheCalibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#inheritance-relationships">Inheritance Relationships</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#base-type">Base Type</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#class-documentation">Class Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html">Template Class Int8Calibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#inheritance-relationships">Inheritance Relationships</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#base-type">Base Type</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#class-documentation">Class Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html">Class TensorFormat</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#class-documentation">Class Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="enums">
+<h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html">Enum EngineCapability</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#enum-documentation">Enum Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html">Enum Level</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#enum-documentation">Enum Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html">Function torch_tensorrt::dump_build_info</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html">Function torch_tensorrt::get_build_info</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html">Function torch_tensorrt::logging::get_is_colored_output_on</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html">Function torch_tensorrt::logging::get_logging_prefix</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html">Function torch_tensorrt::logging::get_reportable_log_level</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html">Function torch_tensorrt::logging::log</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html">Function torch_tensorrt::logging::set_is_colored_output_on</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1af8f3443813315af7901903d25dd495cc.html">Function torch_tensorrt::logging::set_logging_prefix</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1af8f3443813315af7901903d25dd495cc.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html">Function torch_tensorrt::logging::set_reportable_log_level</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.html">Template Function torch_tensorrt::ptq::make_int8_cache_calibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.html">Template Function torch_tensorrt::ptq::make_int8_calibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html">Function torch_tensorrt::set_device</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html">Function torch_tensorrt::torchscript::check_method_operator_support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html">Function torch_tensorrt::torchscript::compile</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html">Function torch_tensorrt::torchscript::convert_method_to_trt_engine</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.html">Function torch_tensorrt::torchscript::embed_engine_in_new_module</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="defines">
+<h2>Defines<a class="headerlink" href="#defines" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a18d295a837ac71add5578860b55e5502.html">Define STR</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a18d295a837ac71add5578860b55e5502.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html">Define TORCH_TENSORRT_MAJOR_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html">Define TORCH_TENSORRT_MINOR_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html">Define TORCH_TENSORRT_PATCH_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html">Define TORCH_TENSORRT_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html">Define TORCHTRT_API</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html">Define TORCHTRT_HIDDEN</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html">Define XSTR</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
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+
+  <h1>Source code for torch_tensorrt._Device</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_enums</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">logging</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_C</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+
+<div class="viewcode-block" id="Device"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Device">[docs]</a><span class="k">class</span> <span class="nc">Device</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Defines a device that can be used to specify target devices for engines</span>
+
+<span class="sd">    Attributes:</span>
+<span class="sd">        device_type (torch_tensorrt.DeviceType): Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.</span>
+<span class="sd">        gpu_id (int): Device ID for target GPU</span>
+<span class="sd">        dla_core (int): Core ID for target DLA core</span>
+<span class="sd">        allow_gpu_fallback (bool): Whether falling back to GPU if DLA cannot support an op should be allowed</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">device_type</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1">#: (torch_tensorrt.DeviceType): Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.</span>
+    <span class="n">gpu_id</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>  <span class="c1">#: (int) Device ID for target GPU</span>
+    <span class="n">dla_core</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>  <span class="c1">#: (int) Core ID for target DLA core</span>
+    <span class="n">allow_gpu_fallback</span> <span class="o">=</span> <span class="kc">False</span>  <span class="c1">#: (bool) Whether falling back to GPU if DLA cannot support an op should be allowed</span>
+
+<div class="viewcode-block" id="Device.__init__"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Device.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;__init__ Method for torch_tensorrt.Device</span>
+
+<span class="sd">        Device accepts one of a few construction patterns</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            spec (str): String with device spec e.g. &quot;dla:0&quot; for dla, core_id 0</span>
+
+<span class="sd">        Keyword Arguments:</span>
+<span class="sd">            gpu_id (int): ID of target GPU (will get overrided if dla_core is specified to the GPU managing DLA). If specified, no positional arguments should be provided</span>
+<span class="sd">            dla_core (int): ID of target DLA core. If specified, no positional arguments should be provided.</span>
+<span class="sd">            allow_gpu_fallback (bool): Allow TensorRT to schedule operations on GPU if they are not supported on DLA (ignored if device type is not DLA)</span>
+
+<span class="sd">        Examples:</span>
+<span class="sd">            - Device(&quot;gpu:1&quot;)</span>
+<span class="sd">            - Device(&quot;cuda:1&quot;)</span>
+<span class="sd">            - Device(&quot;dla:0&quot;, allow_gpu_fallback=True)</span>
+<span class="sd">            - Device(gpu_id=0, dla_core=0, allow_gpu_fallback=True)</span>
+<span class="sd">            - Device(dla_core=0, allow_gpu_fallback=True)</span>
+<span class="sd">            - Device(gpu_id=1)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="s2">&quot;When specifying Device through positional argument, argument must be str&quot;</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device_type</span><span class="p">,</span> <span class="nb">id</span><span class="p">)</span> <span class="o">=</span> <span class="n">Device</span><span class="o">.</span><span class="n">_parse_device_str</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_type</span> <span class="o">==</span> <span class="n">_enums</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="nb">id</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">dla_core</span> <span class="o">=</span> <span class="nb">id</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="mi">0</span>
+                    <span class="n">logging</span><span class="o">.</span><span class="n">log</span><span class="p">(</span>
+                        <span class="n">logging</span><span class="o">.</span><span class="n">Level</span><span class="o">.</span><span class="n">Warning</span><span class="p">,</span>
+                        <span class="s2">&quot;Setting GPU id to 0 for device because device 0 manages DLA on Xavier&quot;</span><span class="p">,</span>
+                    <span class="p">)</span>
+
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s2">&quot;gpu_id&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="ow">or</span> <span class="s2">&quot;dla_core&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="s2">&quot;dla_core&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">device_type</span> <span class="o">=</span> <span class="n">_enums</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">DLA</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">dla_core</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dla_core&quot;</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="s2">&quot;gpu_id&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;gpu_id&quot;</span><span class="p">]</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="mi">0</span>
+                        <span class="n">logging</span><span class="o">.</span><span class="n">log</span><span class="p">(</span>
+                            <span class="n">logging</span><span class="o">.</span><span class="n">Level</span><span class="o">.</span><span class="n">Warning</span><span class="p">,</span>
+                            <span class="s2">&quot;Setting GPU id to 0 for device because device 0 manages DLA on Xavier&quot;</span><span class="p">,</span>
+                        <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;gpu_id&quot;</span><span class="p">]</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">device_type</span> <span class="o">=</span> <span class="n">_enums</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Either gpu_id or dla_core or both must be defined if no string with device specs is provided as an arg&quot;</span>
+                <span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Unexpected number of positional arguments for class Device </span><span class="se">\n</span><span class="s2">    Found </span><span class="si">{}</span><span class="s2"> arguments, expected either zero or a single positional arguments&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="s2">&quot;allow_gpu_fallback&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;allow_gpu_fallback&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;allow_gpu_fallback must be a bool&quot;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">allow_gpu_fallback</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;allow_gpu_fallback&quot;</span><span class="p">]</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="s2">&quot;Device(type=</span><span class="si">{}</span><span class="s2">, gpu_id=</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device_type</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_type</span> <span class="o">==</span> <span class="n">_enums</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span>
+            <span class="k">else</span> <span class="s2">&quot;, dla_core=</span><span class="si">{}</span><span class="s2">, allow_gpu_fallback=</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">dla_core</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">allow_gpu_fallback</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_to_internal</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_C</span><span class="o">.</span><span class="n">Device</span><span class="p">:</span>
+        <span class="n">internal_dev</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">Device</span><span class="p">()</span>
+        <span class="n">internal_dev</span><span class="o">.</span><span class="n">device_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_type</span>
+        <span class="n">internal_dev</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span>
+        <span class="n">internal_dev</span><span class="o">.</span><span class="n">dla_core</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dla_core</span>
+        <span class="n">internal_dev</span><span class="o">.</span><span class="n">allow_gpu_fallback</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">allow_gpu_fallback</span>
+        <span class="k">return</span> <span class="n">internal_dev</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">_from_torch_device</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">torch_dev</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">torch_dev</span><span class="o">.</span><span class="n">type</span> <span class="o">!=</span> <span class="s2">&quot;cuda&quot;</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Torch Device specs must have type &quot;cuda&quot;&#39;</span><span class="p">)</span>
+        <span class="n">gpu_id</span> <span class="o">=</span> <span class="n">torch_dev</span><span class="o">.</span><span class="n">index</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">gpu_id</span><span class="o">=</span><span class="n">gpu_id</span><span class="p">)</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">_current_device</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">dev</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_current_device</span><span class="p">()</span>
+        <span class="k">except</span> <span class="ne">RuntimeError</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">,</span> <span class="s2">&quot;Cannot get current device&quot;</span><span class="p">)</span>
+            <span class="k">return</span> <span class="kc">None</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">gpu_id</span><span class="o">=</span><span class="n">dev</span><span class="o">.</span><span class="n">gpu_id</span><span class="p">)</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_parse_device_str</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
+        <span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+        <span class="n">spec</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;:&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">spec</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;gpu&quot;</span> <span class="ow">or</span> <span class="n">spec</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;cuda&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="n">_enums</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">spec</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="k">elif</span> <span class="n">spec</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;dla&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="n">_enums</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">DLA</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">spec</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span></div>
+</pre></div>
+
+             </article>
+
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+
+  <h1>Source code for torch_tensorrt._Input</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_enums</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_C</span>
+
+
+<div class="viewcode-block" id="Input"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input">[docs]</a><span class="k">class</span> <span class="nc">Input</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Defines an input to a module in terms of expected shape, data type and tensor format.</span>
+
+<span class="sd">    Attributes:</span>
+<span class="sd">        shape_mode (torch_tensorrt.Input._ShapeMode): Is input statically or dynamically shaped</span>
+<span class="sd">        shape (Tuple or Dict): Either a single Tuple or a dict of tuples defining the input shape.</span>
+<span class="sd">            Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form</span>
+<span class="sd">            ``{</span>
+<span class="sd">                &quot;min_shape&quot;: Tuple,</span>
+<span class="sd">                &quot;opt_shape&quot;: Tuple,</span>
+<span class="sd">                &quot;max_shape&quot;: Tuple</span>
+<span class="sd">            }``</span>
+<span class="sd">        dtype (torch_tensorrt.dtype): The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)</span>
+<span class="sd">        format (torch_tensorrt.TensorFormat): The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">class</span> <span class="nc">_ShapeMode</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
+        <span class="n">STATIC</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">DYNAMIC</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="n">shape_mode</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1">#: (torch_tensorrt.Input._ShapeMode): Is input statically or dynamically shaped</span>
+    <span class="n">shape</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1">#: (Tuple or Dict): Either a single Tuple or a dict of tuples defining the input shape. Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form ``{ &quot;min_shape&quot;: Tuple, &quot;opt_shape&quot;: Tuple, &quot;max_shape&quot;: Tuple }``</span>
+    <span class="n">dtype</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">unknown</span>
+    <span class="p">)</span>  <span class="c1">#: The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)</span>
+    <span class="n">_explicit_set_dtype</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="nb">format</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="o">.</span><span class="n">contiguous</span>
+    <span class="p">)</span>  <span class="c1">#: The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</span>
+
+<div class="viewcode-block" id="Input.__init__"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;__init__ Method for torch_tensorrt.Input</span>
+
+<span class="sd">        Input accepts one of a few construction patterns</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            shape (Tuple or List, optional): Static shape of input tensor</span>
+
+<span class="sd">        Keyword Arguments:</span>
+<span class="sd">            shape (Tuple or List, optional): Static shape of input tensor</span>
+<span class="sd">            min_shape (Tuple or List, optional): Min size of input tensor&#39;s shape range</span>
+<span class="sd">                Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input&#39;s shape_mode to DYNAMIC</span>
+<span class="sd">            opt_shape (Tuple or List, optional): Opt size of input tensor&#39;s shape range</span>
+<span class="sd">                Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input&#39;s shape_mode to DYNAMIC</span>
+<span class="sd">            max_shape (Tuple or List, optional): Max size of input tensor&#39;s shape range</span>
+<span class="sd">                Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input&#39;s shape_mode to DYNAMIC</span>
+<span class="sd">            dtype (torch.dtype or torch_tensorrt.dtype): Expected data type for input tensor (default: torch_tensorrt.dtype.float32)</span>
+<span class="sd">            format (torch.memory_format or torch_tensorrt.TensorFormat): The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</span>
+
+<span class="sd">        Examples:</span>
+<span class="sd">            - Input([1,3,32,32], dtype=torch.float32, format=torch.channel_last)</span>
+<span class="sd">            - Input(shape=(1,3,32,32), dtype=torch_tensorrt.dtype.int32, format=torch_tensorrt.TensorFormat.NCHW)</span>
+<span class="sd">            - Input(min_shape=(1,3,32,32), opt_shape=[2,3,32,32], max_shape=(3,3,32,32)) #Implicitly dtype=torch_tensorrt.dtype.float32, format=torch_tensorrt.TensorFormat.NCHW</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                    <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+                <span class="p">)</span>
+            <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">k</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">]):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Found that both shape (as a positional argument), and one or more of min_shape, opt_shape, max_shape were specified</span><span class="se">\n</span><span class="s2">class Input expects that only either shape or all three of min_shape, opt_shape, max_shape are defined&quot;</span>
+                <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span>
+
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="s2">&quot;shape&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="p">(</span>
+                <span class="nb">all</span><span class="p">(</span><span class="n">k</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">])</span>
+            <span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Missing required arguments for class Input</span><span class="se">\n</span><span class="s2">Either shape or all three of min_shape, opt_shape, max_shape must be defined&quot;</span>
+                <span class="p">)</span>
+            <span class="k">elif</span> <span class="p">(</span><span class="s2">&quot;shape&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span>
+                <span class="n">k</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">]</span>
+            <span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Found that both shape, and one or more of min_shape, opt_shape, max_shape were specified</span><span class="se">\n</span><span class="s2">class Input expects that only either shape or all three of min_shape, opt_shape, max_shape are defined&quot;</span>
+                <span class="p">)</span>
+
+            <span class="k">if</span> <span class="s2">&quot;shape&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">]):</span>
+                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">]))</span>
+                    <span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">])</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]):</span>
+                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]))</span>
+                        <span class="o">+</span> <span class="s2">&quot; for min_shape&quot;</span>
+                    <span class="p">)</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]):</span>
+                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]))</span>
+                        <span class="o">+</span> <span class="s2">&quot; for opt_shape&quot;</span>
+                    <span class="p">)</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]):</span>
+                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]))</span>
+                        <span class="o">+</span> <span class="s2">&quot; for max_shape&quot;</span>
+                    <span class="p">)</span>
+
+                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">{</span>
+                    <span class="s2">&quot;min_shape&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]),</span>
+                    <span class="s2">&quot;opt_shape&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]),</span>
+                    <span class="s2">&quot;max_shape&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]),</span>
+                <span class="p">}</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Unexpected number of positional arguments for class Input </span><span class="se">\n</span><span class="s2">    Found </span><span class="si">{}</span><span class="s2"> arguments, expected either zero or a single positional arguments&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="s2">&quot;dtype&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_parse_dtype</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dtype&quot;</span><span class="p">])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_explicit_set_dtype</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">if</span> <span class="s2">&quot;format&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">format</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_parse_format</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;format&quot;</span><span class="p">])</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span><span class="p">:</span>
+            <span class="k">return</span> <span class="s2">&quot;Input(shape=</span><span class="si">{}</span><span class="s2">, dtype=</span><span class="si">{}</span><span class="s2">, format=</span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">format</span><span class="p">)</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span><span class="p">:</span>
+            <span class="k">return</span> <span class="s2">&quot;Input(min_shape=</span><span class="si">{}</span><span class="s2">, opt_shape=</span><span class="si">{}</span><span class="s2">, max_shape=</span><span class="si">{}</span><span class="s2">, dtype=</span><span class="si">{}</span><span class="s2">, format=</span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">],</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">],</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">],</span>
+                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span>
+                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">format</span><span class="p">),</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Unknown input shape mode&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_to_internal</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_C</span><span class="o">.</span><span class="n">Input</span><span class="p">:</span>
+        <span class="n">internal_in</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">Input</span><span class="p">()</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]):</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                    <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]))</span>
+                    <span class="o">+</span> <span class="s2">&quot; for min_shape&quot;</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">internal_in</span><span class="o">.</span><span class="n">min</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]):</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                    <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]))</span>
+                    <span class="o">+</span> <span class="s2">&quot; for opt_shape&quot;</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">internal_in</span><span class="o">.</span><span class="n">opt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]):</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                    <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]))</span>
+                    <span class="o">+</span> <span class="s2">&quot; for max_shape&quot;</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">internal_in</span><span class="o">.</span><span class="n">max</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]</span>
+            <span class="n">internal_in</span><span class="o">.</span><span class="n">input_is_dynamic</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                    <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+                    <span class="o">+</span> <span class="s2">&quot; for shape&quot;</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">internal_in</span><span class="o">.</span><span class="n">opt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span>
+            <span class="n">internal_in</span><span class="o">.</span><span class="n">input_is_dynamic</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">unknown</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_explicit_set_dtype</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_explicit_set_dtype</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="n">internal_in</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_parse_dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">internal_in</span><span class="o">.</span><span class="n">_explicit_set_dtype</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_explicit_set_dtype</span>
+        <span class="n">internal_in</span><span class="o">.</span><span class="n">format</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_parse_format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">format</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">internal_in</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_supported_input_size_type</span><span class="p">(</span><span class="n">input_size</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_parse_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">int32</span>
+            <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">half</span>
+            <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span>
+            <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">bool</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">bool</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="s2">&quot;Provided an unsupported data type as an input data type (support: bool, int32, half, float), got: &quot;</span>
+                    <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
+                <span class="p">)</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">dtype</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;Input data type needs to be specified with a torch.dtype or a torch_tensorrt.dtype, got: &quot;</span>
+                <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dtype</span><span class="p">))</span>
+            <span class="p">)</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_parse_format</span><span class="p">(</span><span class="nb">format</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">format</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">memory_format</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">format</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="o">.</span><span class="n">contiguous</span>
+            <span class="k">elif</span> <span class="nb">format</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="o">.</span><span class="n">channels_last</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Provided an unsupported tensor format (support: NHCW/contiguous_format, NHWC/channel_last)&quot;</span>
+                <span class="p">)</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">format</span><span class="p">,</span> <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="p">):</span>
+            <span class="k">return</span> <span class="nb">format</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;Tensor format needs to be specified with either torch.memory_format or torch_tensorrt.TensorFormat&quot;</span>
+            <span class="p">)</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">_from_tensor</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">t</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span><span class="p">),</span>
+                <span class="n">t</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">),</span>
+            <span class="p">]</span>
+        <span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Tensor does not have a supported contiguous memory format, supported formats are contiguous or channel_last&quot;</span>
+            <span class="p">)</span>
+        <span class="n">frmt</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span>
+            <span class="k">if</span> <span class="n">t</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span><span class="p">)</span>
+            <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">t</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">t</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="n">frmt</span><span class="p">)</span></div>
+</pre></div>
+
+             </article>
+
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+
+  <h1>Source code for torch_tensorrt._compile</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_enums</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt.ts</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">logging</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.fx</span>
+<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
+
+<span class="kn">import</span> <span class="nn">torch_tensorrt.fx</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.fx.utils</span> <span class="kn">import</span> <span class="n">LowerPrecision</span>
+
+
+<span class="k">class</span> <span class="nc">_IRType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Enum to set the minimum required logging level to print a message to stdout&quot;&quot;&quot;</span>
+
+    <span class="n">ts</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">fx</span> <span class="o">=</span> <span class="mi">1</span>
+
+
+<span class="k">class</span> <span class="nc">_ModuleType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Enum to set the minimum required logging level to print a message to stdout&quot;&quot;&quot;</span>
+
+    <span class="n">nn</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">ts</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="n">fx</span> <span class="o">=</span> <span class="mi">2</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_module_type</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_ModuleType</span><span class="p">:</span>
+    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span>
+        <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptFunction</span><span class="p">]</span>
+    <span class="p">):</span>
+        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ts</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">fx</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Module is an unknown format&quot;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_get_target_ir</span><span class="p">(</span><span class="n">module_type</span><span class="p">:</span> <span class="n">_ModuleType</span><span class="p">,</span> <span class="n">ir</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_IRType</span><span class="p">:</span>
+    <span class="n">module_is_tsable</span> <span class="o">=</span> <span class="nb">any</span><span class="p">([</span><span class="n">module_type</span> <span class="o">==</span> <span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">,</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ts</span><span class="p">]])</span>
+    <span class="n">module_is_fxable</span> <span class="o">=</span> <span class="nb">any</span><span class="p">([</span><span class="n">module_type</span> <span class="o">==</span> <span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">,</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">fx</span><span class="p">]])</span>
+
+    <span class="n">ir_targets_torchscript</span> <span class="o">=</span> <span class="nb">any</span><span class="p">([</span><span class="n">ir</span> <span class="o">==</span> <span class="n">opt</span> <span class="k">for</span> <span class="n">opt</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;torchscript&quot;</span><span class="p">,</span> <span class="s2">&quot;ts&quot;</span><span class="p">]])</span>
+    <span class="n">ir_targets_fx</span> <span class="o">=</span> <span class="n">ir</span> <span class="o">==</span> <span class="s2">&quot;fx&quot;</span>
+
+    <span class="k">if</span> <span class="n">module_is_tsable</span> <span class="ow">and</span> <span class="n">ir_targets_torchscript</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span>
+    <span class="k">elif</span> <span class="n">module_is_fxable</span> <span class="ow">and</span> <span class="n">ir_targets_fx</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">fx</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">ir</span> <span class="o">==</span> <span class="s2">&quot;default&quot;</span><span class="p">:</span>
+            <span class="c1"># Options are listed in order of preference</span>
+            <span class="k">if</span> <span class="n">module_is_tsable</span><span class="p">:</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">log</span><span class="p">(</span>
+                    <span class="n">logging</span><span class="o">.</span><span class="n">Level</span><span class="o">.</span><span class="n">Info</span><span class="p">,</span> <span class="s2">&quot;ir was set to default, using TorchScript as ir&quot;</span>
+                <span class="p">)</span>
+                <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span>
+            <span class="k">elif</span> <span class="n">module_is_fxable</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Was given a torch.fx.GraphModule, fx is not currently supported by Torch-TensorRT&quot;</span>
+                <span class="p">)</span>
+                <span class="c1"># logging.log(logging.Level.Info, &quot;ir was set to default, using TorchScript as fx&quot;)</span>
+                <span class="c1"># return _IRType.fx</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Module was provided with in an unsupported format&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unknown ir was requested&quot;</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="compile"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.compile">[docs]</a><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span>
+    <span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
+    <span class="n">ir</span><span class="o">=</span><span class="s2">&quot;default&quot;</span><span class="p">,</span>
+    <span class="n">inputs</span><span class="o">=</span><span class="p">[],</span>
+    <span class="n">enabled_precisions</span><span class="o">=</span><span class="nb">set</span><span class="p">([</span><span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span><span class="p">]),</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Compile a PyTorch module for NVIDIA GPUs using TensorRT</span>
+
+<span class="sd">    Takes a existing PyTorch module and a set of settings to configure the compiler</span>
+<span class="sd">    and using the path specified in ``ir`` lower and compile the module to TensorRT</span>
+<span class="sd">    returning a PyTorch Module back</span>
+
+<span class="sd">    Converts specifically the forward method of a Module</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        module (Union(torch.nn.Module,torch.jit.ScriptModule): Source module</span>
+
+<span class="sd">    Keyword Arguments:</span>
+<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
+<span class="sd">            to select device type. ::</span>
+
+<span class="sd">                input=[</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
+<span class="sd">                ]</span>
+
+<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
+<span class="sd">        ir (str): The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</span>
+<span class="sd">        **kwargs: Additional settings for the specific requested strategy (See submodules for more info)</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.nn.Module: Compiled Module, when run it will execute via TensorRT</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">module_type</span> <span class="o">=</span> <span class="n">_parse_module_type</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
+    <span class="n">target_ir</span> <span class="o">=</span> <span class="n">_get_target_ir</span><span class="p">(</span><span class="n">module_type</span><span class="p">,</span> <span class="n">ir</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span><span class="p">:</span>
+        <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">module</span>
+        <span class="k">if</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">log</span><span class="p">(</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">Level</span><span class="o">.</span><span class="n">Info</span><span class="p">,</span>
+                <span class="s2">&quot;Module was provided as a torch.nn.Module, trying to script the module with torch.jit.script. In the event of a failure please preconvert your module to TorchScript&quot;</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ts</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
+            <span class="n">ts_mod</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+        <span class="p">)</span>
+    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">fx</span><span class="p">:</span>
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">float16</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
+            <span class="ow">or</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">half</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
+        <span class="p">):</span>
+            <span class="n">lower_precision</span> <span class="o">=</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span>
+        <span class="k">elif</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">float32</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
+            <span class="ow">or</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
+        <span class="p">):</span>
+            <span class="n">lower_precision</span> <span class="o">=</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP32</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Precision </span><span class="si">{</span><span class="n">enabled_precisions</span><span class="si">}</span><span class="s2"> not supported on FX&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
+            <span class="n">module</span><span class="p">,</span>
+            <span class="n">inputs</span><span class="p">,</span>
+            <span class="n">lower_precision</span><span class="o">=</span><span class="n">lower_precision</span><span class="p">,</span>
+            <span class="n">max_batch_size</span><span class="o">=</span><span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
+            <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">dynamic_batch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Module is an unknown format or the ir requested is unknown&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="convert_method_to_trt_engine"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.convert_method_to_trt_engine">[docs]</a><span class="k">def</span> <span class="nf">convert_method_to_trt_engine</span><span class="p">(</span>
+    <span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
+    <span class="n">method_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">ir</span><span class="o">=</span><span class="s2">&quot;default&quot;</span><span class="p">,</span>
+    <span class="n">inputs</span><span class="o">=</span><span class="p">[],</span>
+    <span class="n">enabled_precisions</span><span class="o">=</span><span class="nb">set</span><span class="p">([</span><span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span><span class="p">]),</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Convert a TorchScript module method to a serialized TensorRT engine</span>
+
+<span class="sd">    Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        module (Union(torch.nn.Module,torch.jit.ScriptModule): Source module</span>
+
+<span class="sd">    Keyword Arguments:</span>
+<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
+<span class="sd">            to select device type. ::</span>
+
+<span class="sd">                input=[</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
+<span class="sd">                ]</span>
+
+<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
+<span class="sd">        ir (str): The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</span>
+<span class="sd">        **kwargs: Additional settings for the specific requested strategy (See submodules for more info)</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        bytes: Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">module_type</span> <span class="o">=</span> <span class="n">_parse_module_type</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
+    <span class="n">target_ir</span> <span class="o">=</span> <span class="n">_get_target_ir</span><span class="p">(</span><span class="n">module_type</span><span class="p">,</span> <span class="n">ir</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span><span class="p">:</span>
+        <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">module</span>
+        <span class="k">if</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">:</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">log</span><span class="p">(</span>
+                <span class="n">logging</span><span class="o">.</span><span class="n">Level</span><span class="o">.</span><span class="n">Info</span><span class="p">,</span>
+                <span class="s2">&quot;Module was provided as a torch.nn.Module, trying to script the module with torch.jit.script. In the event of a failure please preconvert your module to TorchScript&quot;</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ts</span><span class="o">.</span><span class="n">convert_method_to_trt_engine</span><span class="p">(</span>
+            <span class="n">ts_mod</span><span class="p">,</span>
+            <span class="n">method_name</span><span class="p">,</span>
+            <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span>
+            <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">fx</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;fx is currently not supported&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Module is an unknown format or the ir requested is unknown&quot;</span><span class="p">)</span></div>
+</pre></div>
+
+             </article>
+
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+</ul>
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+
+  <h1>Source code for torch_tensorrt._util</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">__version__</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_C</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+
+
+<div class="viewcode-block" id="dump_build_info"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.dump_build_info">[docs]</a><span class="k">def</span> <span class="nf">dump_build_info</span><span class="p">():</span>
+    <span class="sd">&quot;&quot;&quot;Prints build information about the torch_tensorrt distribution to stdout&quot;&quot;&quot;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">get_build_info</span><span class="p">())</span></div>
+
+
+<div class="viewcode-block" id="get_build_info"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.get_build_info">[docs]</a><span class="k">def</span> <span class="nf">get_build_info</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Returns a string containing the build information of torch_tensorrt distribution</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        str: String containing the build information for torch_tensorrt distribution</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">build_info</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">get_build_info</span><span class="p">()</span>
+    <span class="n">build_info</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="s2">&quot;Torch-TensorRT Version: &quot;</span>
+        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">__version__</span><span class="p">)</span>
+        <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="o">+</span> <span class="s2">&quot;Using PyTorch Version: &quot;</span>
+        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
+        <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="o">+</span> <span class="n">build_info</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">build_info</span></div>
+
+
+<div class="viewcode-block" id="set_device"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.set_device">[docs]</a><span class="k">def</span> <span class="nf">set_device</span><span class="p">(</span><span class="n">gpu_id</span><span class="p">):</span>
+    <span class="n">_C</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="n">gpu_id</span><span class="p">)</span></div>
+</pre></div>
+
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diff --git a/docs/v1.2.0/_modules/torch_tensorrt/fx/fx2trt.html b/docs/v1.2.0/_modules/torch_tensorrt/fx/fx2trt.html
new file mode 100644
index 0000000000..99468887bf
--- /dev/null
+++ b/docs/v1.2.0/_modules/torch_tensorrt/fx/fx2trt.html
@@ -0,0 +1,1019 @@
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+
+  <h1>Source code for torch_tensorrt.fx.fx2trt</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">NamedTuple</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+<span class="c1"># @manual=//deeplearning/trt/python:py_tensorrt</span>
+<span class="kn">import</span> <span class="nn">tensorrt</span> <span class="k">as</span> <span class="nn">trt</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.fx</span>
+<span class="kn">from</span> <span class="nn">torch.fx.node</span> <span class="kn">import</span> <span class="n">_get_qualified_name</span>
+<span class="kn">from</span> <span class="nn">torch.fx.passes.shape_prop</span> <span class="kn">import</span> <span class="n">TensorMetadata</span>
+
+<span class="kn">from</span> <span class="nn">.converter_registry</span> <span class="kn">import</span> <span class="n">CONVERTERS</span>
+<span class="kn">from</span> <span class="nn">.input_tensor_spec</span> <span class="kn">import</span> <span class="n">InputTensorSpec</span>
+<span class="kn">from</span> <span class="nn">.observer</span> <span class="kn">import</span> <span class="n">Observer</span>
+<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">get_dynamic_dims</span><span class="p">,</span> <span class="n">LowerPrecision</span><span class="p">,</span> <span class="n">torch_dtype_to_trt</span>
+
+<span class="n">_LOGGER</span><span class="p">:</span> <span class="n">logging</span><span class="o">.</span><span class="n">Logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+<span class="n">TRT_INTERPRETER_CALL_PRE_OBSERVER</span><span class="p">:</span> <span class="n">Observer</span><span class="p">[</span>
+    <span class="n">Callable</span><span class="p">[[</span><span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">],</span> <span class="kc">None</span><span class="p">]</span>
+<span class="p">]</span> <span class="o">=</span> <span class="n">Observer</span><span class="p">(</span><span class="s2">&quot;TRT_INTERPRETER_CALL_PRE_OBSERVER&quot;</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="TRTInterpreterResult"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.TRTInterpreterResult">[docs]</a><span class="k">class</span> <span class="nc">TRTInterpreterResult</span><span class="p">(</span><span class="n">NamedTuple</span><span class="p">):</span>
+    <span class="n">engine</span><span class="p">:</span> <span class="n">Any</span>
+    <span class="n">input_names</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
+    <span class="n">output_names</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
+    <span class="n">serialized_cache</span><span class="p">:</span> <span class="nb">bytearray</span></div>
+
+
+<div class="viewcode-block" id="TRTInterpreter"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.TRTInterpreter">[docs]</a><span class="k">class</span> <span class="nc">TRTInterpreter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">Interpreter</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
+        <span class="n">input_specs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">InputTensorSpec</span><span class="p">],</span>
+        <span class="n">explicit_batch_dimension</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">explicit_precision</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">logger_level</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">logger</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="p">(</span><span class="n">logger_level</span> <span class="ow">or</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="o">.</span><span class="n">WARNING</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">builder</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Builder</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="p">)</span>
+
+        <span class="n">flag</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="n">explicit_batch_dimension</span><span class="p">:</span>
+            <span class="n">EXPLICIT_BATCH</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="p">(</span><span class="nb">int</span><span class="p">)(</span>
+                <span class="n">trt</span><span class="o">.</span><span class="n">NetworkDefinitionCreationFlag</span><span class="o">.</span><span class="n">EXPLICIT_BATCH</span>
+            <span class="p">)</span>
+            <span class="n">flag</span> <span class="o">|=</span> <span class="n">EXPLICIT_BATCH</span>
+
+        <span class="k">if</span> <span class="n">explicit_precision</span><span class="p">:</span>
+            <span class="n">EXPLICIT_PRECISION</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="p">(</span><span class="nb">int</span><span class="p">)(</span>
+                <span class="n">trt</span><span class="o">.</span><span class="n">NetworkDefinitionCreationFlag</span><span class="o">.</span><span class="n">EXPLICIT_PRECISION</span>
+            <span class="p">)</span>
+            <span class="n">flag</span> <span class="o">|=</span> <span class="n">EXPLICIT_PRECISION</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">network</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">create_network</span><span class="p">(</span><span class="n">flag</span><span class="p">)</span>
+
+        <span class="n">missing_ops</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">validate_conversion</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">missing_ops</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;Interpretation will fail due to missing operations </span><span class="se">\n</span><span class="s2">&quot;</span>
+                <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">missing_ops</span><span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_specs</span> <span class="o">=</span> <span class="n">input_specs</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_specs_iter</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">validate_input_specs</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_input_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_output_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_itensor_to_tensor_meta</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span>
+            <span class="n">trt</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">,</span> <span class="n">TensorMetadata</span>
+        <span class="p">]</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">validate_input_specs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">shape</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">shape_ranges</span><span class="p">,</span> <span class="n">has_batch_dim</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_specs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
+                <span class="k">assert</span> <span class="p">(</span>
+                    <span class="n">has_batch_dim</span>
+                <span class="p">),</span> <span class="s2">&quot;It&#39;s required to specify batch dimension when it&#39;s explicit in TensorRT network.&quot;</span>
+
+            <span class="n">dynamic_dims</span> <span class="o">=</span> <span class="n">get_dynamic_dims</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">dynamic_dims</span><span class="p">):</span>
+                <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">,</span> <span class="p">(</span>
+                    <span class="s2">&quot;Can&#39;t have dynamic dim when &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;batch dim is implicit, got </span><span class="si">{</span><span class="n">shape</span><span class="si">}</span><span class="s2">.&quot;</span>
+                <span class="p">)</span>
+                <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span>
+                    <span class="n">shape_ranges</span>
+                <span class="p">),</span> <span class="s2">&quot;shape_ranges must be provided when shape has dynamic dim.&quot;</span>
+
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">:</span>
+                    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">),</span> <span class="p">(</span>
+                        <span class="s2">&quot;Number of optimization &quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot;profiles </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">)</span><span class="si">}</span><span class="s2"> doesn&#39;t match with the number of shape_range&quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">)</span><span class="si">}</span><span class="s2"> provided.&quot;</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span> <span class="o">=</span> <span class="p">[</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">create_optimization_profile</span><span class="p">()</span>
+                        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">))</span>
+                    <span class="p">]</span>
+
+                <span class="k">for</span> <span class="n">shape_range</span> <span class="ow">in</span> <span class="n">shape_ranges</span><span class="p">:</span>
+                    <span class="k">assert</span> <span class="p">(</span>
+                        <span class="nb">len</span><span class="p">(</span><span class="n">shape_range</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span>
+                    <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;Expect three elements in shape_range, got </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">shape_range</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+                    <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">shape_range</span><span class="p">),</span> <span class="p">(</span>
+                        <span class="s2">&quot;Expect elements in shape_range&quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">shape_range</span><span class="si">}</span><span class="s2"> have the same number of dimension as the provided shape </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+                    <span class="p">)</span>
+
+                    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)):</span>
+                        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">dynamic_dims</span><span class="p">:</span>
+                            <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span>
+                                <span class="n">shape_range</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">shape_range</span><span class="p">[</span><span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">]</span>
+                                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+                            <span class="p">),</span> <span class="p">(</span>
+                                <span class="s2">&quot;Expect dynamic dim&quot;</span>
+                                <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2"> to have incremental value for shapes in shape_range </span><span class="si">{</span><span class="n">shape_range</span><span class="si">}</span><span class="s2">.&quot;</span>
+                            <span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">shape_range</span><span class="p">),</span> <span class="p">(</span>
+                                <span class="sa">f</span><span class="s2">&quot;Expect non dynamic dim </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2"> to be the same&quot;</span>
+                                <span class="sa">f</span><span class="s2">&quot; for all shapes in shape_range </span><span class="si">{</span><span class="n">shape_range</span><span class="si">}</span><span class="s2">.&quot;</span>
+                            <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">assert</span> <span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span>
+                <span class="p">),</span> <span class="s2">&quot;shape_ranges are provided for input that doesn&#39;t have dynamic dim.&quot;</span>
+
+    <span class="k">def</span> <span class="nf">validate_conversion</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">missing_converter</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;call_function&quot;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">):</span>
+                <span class="n">missing_converter</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">op</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">_get_qualified_name</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;call_method&quot;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">):</span>
+                <span class="n">missing_converter</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">op</span><span class="si">}</span><span class="s2"> torch.Tensor.</span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;call_module&quot;</span><span class="p">:</span>
+                <span class="n">submod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fetch_attr</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
+                <span class="n">submod_type</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">submod</span><span class="p">,</span> <span class="s2">&quot;_base_class_origin&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">submod</span><span class="p">))</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">submod_type</span><span class="p">):</span>
+                    <span class="n">missing_converter</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">op</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">typename</span><span class="p">(</span><span class="n">submod_type</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">missing_converter</span>
+
+    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">max_batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
+        <span class="n">max_workspace_size</span><span class="o">=</span><span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="mi">25</span><span class="p">,</span>
+        <span class="n">lower_precision</span><span class="o">=</span><span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span><span class="p">,</span>
+        <span class="n">sparse_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">force_fp32_output</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">strict_type_constraints</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">algorithm_selector</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">timing_cache</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">profiling_verbosity</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">tactic_sources</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TRTInterpreterResult</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Build TensorRT engine with some configs.</span>
+<span class="sd">        Args:</span>
+<span class="sd">            max_batch_size: set accordingly for maximum batch size you will use.</span>
+<span class="sd">            max_workspace_size: set to the maximum size we can afford for temporary buffer</span>
+<span class="sd">            lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).</span>
+<span class="sd">            sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity</span>
+<span class="sd">            force_fp32_output: force output to be fp32</span>
+<span class="sd">            strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric reasons.</span>
+<span class="sd">            algorithm_selector: set up algorithm selection for certain layer</span>
+<span class="sd">            timing_cache: enable timing cache for TensorRT</span>
+<span class="sd">            profiling_verbosity: TensorRT logging level</span>
+<span class="sd">        Return:</span>
+<span class="sd">            TRTInterpreterResult</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">TRT_INTERPRETER_CALL_PRE_OBSERVER</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">)</span>
+
+        <span class="c1"># For float outputs, we set their dtype to fp16 only if lower_precision == LowerPrecision.FP16 and</span>
+        <span class="c1"># force_fp32_output=False.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_fp16</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="ow">not</span> <span class="n">force_fp32_output</span> <span class="ow">and</span> <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span>
+        <span class="p">)</span>
+
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">INT8</span>
+            <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">platform_has_fast_int8</span>
+        <span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Current platform doesn&#39;t support fast native int8!&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span>
+            <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">platform_has_fast_fp16</span>
+        <span class="p">):</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Current platform doesn&#39;t support fast native fp16!&quot;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_specs_iter</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">run_module_start_time</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+        <span class="n">_LOGGER</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Run Module elapsed time: </span><span class="si">{</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span> <span class="o">-</span> <span class="n">run_module_start_time</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="n">build_engine_start_time</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">max_batch_size</span> <span class="o">=</span> <span class="n">max_batch_size</span>
+        <span class="n">builder_config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">create_builder_config</span><span class="p">()</span>
+        <span class="n">builder_config</span><span class="o">.</span><span class="n">max_workspace_size</span> <span class="o">=</span> <span class="n">max_workspace_size</span>
+
+        <span class="n">cache</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">timing_cache</span><span class="p">:</span>
+            <span class="n">cache_file</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">timing_cache</span><span class="p">)</span>
+            <span class="n">cache</span> <span class="o">=</span> <span class="n">builder_config</span><span class="o">.</span><span class="n">create_timing_cache</span><span class="p">(</span><span class="n">cache_file</span><span class="o">.</span><span class="n">tobytes</span><span class="p">())</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">cache</span> <span class="o">=</span> <span class="n">builder_config</span><span class="o">.</span><span class="n">create_timing_cache</span><span class="p">(</span><span class="sa">b</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+        <span class="n">builder_config</span><span class="o">.</span><span class="n">set_timing_cache</span><span class="p">(</span><span class="n">cache</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">trt</span><span class="o">.</span><span class="n">__version__</span> <span class="o">&gt;=</span> <span class="s2">&quot;8.2&quot;</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">profiling_verbosity</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="n">profiling_verbosity</span>
+                <span class="k">if</span> <span class="n">profiling_verbosity</span>
+                <span class="k">else</span> <span class="n">trt</span><span class="o">.</span><span class="n">ProfilingVerbosity</span><span class="o">.</span><span class="n">LAYER_NAMES_ONLY</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">FP16</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">INT8</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">INT8</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">sparse_weights</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">SPARSE_WEIGHTS</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">strict_type_constraints</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">STRICT_TYPES</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">optimization_profile</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">:</span>
+                <span class="n">builder_config</span><span class="o">.</span><span class="n">add_optimization_profile</span><span class="p">(</span><span class="n">optimization_profile</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">algorithm_selector</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">DISABLE_TIMING_CACHE</span><span class="p">)</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">algorithm_selector</span> <span class="o">=</span> <span class="n">algorithm_selector</span>
+
+        <span class="k">if</span> <span class="n">tactic_sources</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_tactic_sources</span><span class="p">(</span><span class="n">tactic_sources</span><span class="o">=</span><span class="n">tactic_sources</span><span class="p">)</span>
+
+        <span class="n">engine</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">build_engine</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> <span class="n">builder_config</span><span class="p">)</span>
+        <span class="k">assert</span> <span class="n">engine</span>
+
+        <span class="n">serialized_cache</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="nb">bytearray</span><span class="p">(</span><span class="n">cache</span><span class="o">.</span><span class="n">serialize</span><span class="p">())</span>
+            <span class="k">if</span> <span class="n">builder_config</span><span class="o">.</span><span class="n">get_timing_cache</span><span class="p">()</span>
+            <span class="k">else</span> <span class="nb">bytearray</span><span class="p">()</span>
+        <span class="p">)</span>
+        <span class="n">_LOGGER</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Build TRT engine elapsed time: </span><span class="si">{</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span> <span class="o">-</span> <span class="n">build_engine_start_time</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">TRTInterpreterResult</span><span class="p">(</span>
+            <span class="n">engine</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_input_names</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_output_names</span><span class="p">,</span> <span class="n">serialized_cache</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">run_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+        <span class="c1"># add &quot;_itensor_to_tensor_meta&quot;</span>
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">n</span><span class="o">.</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;_itensor_to_tensor_meta&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_itensor_to_tensor_meta</span>
+        <span class="n">n</span><span class="o">.</span><span class="n">kwargs</span> <span class="o">=</span> <span class="n">kwargs</span>
+
+        <span class="c1"># run the node</span>
+        <span class="n">trt_node</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">run_node</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+
+        <span class="c1"># remove &quot;_itensor_to_tensor_meta&quot;</span>
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">n</span><span class="o">.</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;_itensor_to_tensor_meta&quot;</span><span class="p">]</span>
+        <span class="n">n</span><span class="o">.</span><span class="n">kwargs</span> <span class="o">=</span> <span class="n">kwargs</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">trt_node</span><span class="p">,</span> <span class="n">trt</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_itensor_to_tensor_meta</span><span class="p">[</span><span class="n">trt_node</span><span class="p">]</span> <span class="o">=</span> <span class="n">n</span><span class="o">.</span><span class="n">meta</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;tensor_meta&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">trt_node</span>
+
+    <span class="k">def</span> <span class="nf">placeholder</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_input_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
+        <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">shape_ranges</span><span class="p">,</span> <span class="n">has_batch_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_specs</span><span class="p">[</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">input_specs_iter</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_specs_iter</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">has_batch_dim</span><span class="p">:</span>
+                <span class="n">shape</span> <span class="o">=</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">shape_range</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">):</span>
+                <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_shape</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="o">*</span><span class="n">shape_range</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">add_input</span><span class="p">(</span>
+            <span class="n">name</span><span class="o">=</span><span class="n">target</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch_dtype_to_trt</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">call_module</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
+        <span class="n">submod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fetch_attr</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
+        <span class="n">submod_type</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">submod</span><span class="p">,</span> <span class="s2">&quot;_base_class_origin&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">submod</span><span class="p">))</span>
+        <span class="n">converter</span> <span class="o">=</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">submod_type</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">converter</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Conversion of module of type </span><span class="si">{</span><span class="n">submod_type</span><span class="si">}</span><span class="s2"> not currently supported!&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+        <span class="k">return</span> <span class="n">converter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> <span class="n">submod</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">call_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">converter</span> <span class="o">=</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">converter</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Conversion of function </span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">typename</span><span class="p">(</span><span class="n">target</span><span class="p">)</span><span class="si">}</span><span class="s2"> not currently supported!&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+        <span class="k">return</span> <span class="n">converter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">call_method</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
+        <span class="n">converter</span> <span class="o">=</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">converter</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Conversion of method </span><span class="si">{</span><span class="n">target</span><span class="si">}</span><span class="s2"> not currently supported!&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+        <span class="k">return</span> <span class="n">converter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
+        <span class="n">outputs</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">)</span> <span class="k">else</span> <span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">trt</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">)</span> <span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">outputs</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;TensorRT requires all outputs to be Tensor!&quot;</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">output</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">outputs</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">any</span><span class="p">(</span>
+                <span class="n">op_name</span> <span class="ow">in</span> <span class="n">output</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">op_name</span> <span class="ow">in</span> <span class="p">(</span>
+                    <span class="s2">&quot;eq&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;gt&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;lt&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;or&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;xor&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;and&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;not&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;ne&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;isinf&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;any&quot;</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="p">):</span>
+                <span class="n">output_bool</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">output_bool</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="n">name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;output</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="n">output</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">output_bool</span><span class="p">:</span>
+                <span class="n">output</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">bool</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_fp16</span> <span class="ow">and</span> <span class="n">output</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">trt</span><span class="o">.</span><span class="n">float32</span><span class="p">:</span>
+                <span class="n">output</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">float16</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_output_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">name</span><span class="p">)</span></div>
+</pre></div>
+
+             </article>
+
+            </div>
+            <footer>
+
+
+
+
+    <hr>
+
+
+
+  <div role="contentinfo">
+    <p>
+        &copy; Copyright 2022, NVIDIA Corporation.
+
+    </p>
+  </div>
+
+      <div>
+        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
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+
+  <h1>Source code for torch_tensorrt.fx.input_tensor_spec</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">NamedTuple</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">.types</span> <span class="kn">import</span> <span class="n">Shape</span><span class="p">,</span> <span class="n">ShapeRange</span>
+<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">get_dynamic_dims</span>
+
+
+<span class="k">def</span> <span class="nf">generate_input_specs</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">lower_setting</span><span class="p">,</span> <span class="n">additional_inputs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="c1"># dynamic_batch is TRT only flag.</span>
+    <span class="k">if</span> <span class="p">(</span>
+        <span class="ow">not</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">explicit_batch_dimension</span>
+        <span class="ow">or</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">dynamic_batch</span> <span class="ow">is</span> <span class="kc">False</span>
+    <span class="p">):</span>
+        <span class="k">return</span> <span class="n">InputTensorSpec</span><span class="o">.</span><span class="n">from_tensors</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+
+    <span class="c1"># If we don&#39;t have additional inputs, we assume the first dimension</span>
+    <span class="c1"># is the dynamic batch dimension. Otherwise, we use the additional</span>
+    <span class="c1"># inputs to determine the batch dimension.</span>
+    <span class="k">if</span> <span class="n">additional_inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">InputTensorSpec</span><span class="o">.</span><span class="n">from_tensors_with_dynamic_batch_size</span><span class="p">(</span>
+            <span class="n">inputs</span><span class="p">,</span>
+            <span class="p">(</span>
+                <span class="mi">0</span><span class="p">,</span>
+                <span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
+                <span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">lower_setting</span><span class="o">.</span><span class="n">opt_profile_replica</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">batch_dims</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">additional_inputs</span><span class="p">):</span>
+            <span class="n">found_batch_dim</span> <span class="o">=</span> <span class="kc">False</span>
+
+            <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">values</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">j</span><span class="o">.</span><span class="n">shape</span><span class="p">)):</span>
+                <span class="k">if</span> <span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">values</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                    <span class="k">assert</span> <span class="p">(</span>
+                        <span class="n">found_batch_dim</span> <span class="ow">is</span> <span class="kc">False</span>
+                    <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;We&#39;ve already found a batch dim, </span><span class="si">{</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">j</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">.&quot;</span>
+                    <span class="n">batch_dims</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
+                    <span class="n">found_batch_dim</span> <span class="o">=</span> <span class="kc">True</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">found_batch_dim</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Failed to find batch dimension because shapes are the same, </span><span class="si">{</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">InputTensorSpec</span><span class="o">.</span><span class="n">from_tensors_with_dynamic_batch_size</span><span class="p">(</span>
+            <span class="n">inputs</span><span class="p">,</span>
+            <span class="p">(</span>
+                <span class="mi">0</span><span class="p">,</span>
+                <span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
+                <span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">lower_setting</span><span class="o">.</span><span class="n">opt_profile_replica</span><span class="p">,</span>
+            <span class="n">batch_dims</span><span class="p">,</span>
+        <span class="p">)</span>
+
+
+<div class="viewcode-block" id="InputTensorSpec"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.InputTensorSpec">[docs]</a><span class="k">class</span> <span class="nc">InputTensorSpec</span><span class="p">(</span><span class="n">NamedTuple</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    This class contains the information of a input tensor.</span>
+
+<span class="sd">    shape: shape of the tensor.</span>
+
+<span class="sd">    dtype: dtyep of the tensor.</span>
+
+<span class="sd">    device: device of the tensor. This is only used to generate inputs to the given model</span>
+<span class="sd">        in order to run shape prop. For TensorRT engine, inputs have to be on cuda device.</span>
+
+<span class="sd">    shape_ranges: If dynamic shape is needed (shape has dimensions of -1), then this field</span>
+<span class="sd">        has to be provided (default is empty list). Every shape_range is a tuple of three</span>
+<span class="sd">        tuples ((min_input_shape), (optimized_input_shape), (max_input_shape)). Each shape_range</span>
+<span class="sd">        is used to populate a TensorRT optimization profile.</span>
+<span class="sd">        e.g. If the input shape varies from (1, 224) to (100, 224) and we want to optimize</span>
+<span class="sd">        for (25, 224) because it&#39;s the most common input shape, then we set shape_ranges to</span>
+<span class="sd">        ((1, 224), (25, 225), (100, 224)).</span>
+
+<span class="sd">    has_batch_dim: Whether the shape includes batch dimension. Batch dimension has to be provided</span>
+<span class="sd">        if the engine want to run with dynamic shape.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">shape</span><span class="p">:</span> <span class="n">Shape</span>
+    <span class="n">dtype</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span>
+    <span class="n">device</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
+    <span class="n">shape_ranges</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">ShapeRange</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">has_batch_dim</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_tensor</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">tensor</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;InputTensorSpec&quot;</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Produce an InputTenosrSpec named tuple which contains the</span>
+<span class="sd">        information of the given PyTorch tensor.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            tensor (torch.Tensor): A PyTorch tensor.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            An InputTensorSpec named tuple.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_tensors</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">tensors</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="s2">&quot;InputTensorSpec&quot;</span><span class="p">]:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Produce a list of InputTenosrSpec named tuples which contain</span>
+<span class="sd">        the information of all the given PyTorch tensors.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            tensors (Iterable[torch.Tensor]): A list of PyTorch tensors.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            A list of InputTensorSpec named tuples.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">))</span>
+        <span class="k">return</span> <span class="p">[</span><span class="bp">cls</span><span class="o">.</span><span class="n">from_tensor</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">]</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_tensors_with_dynamic_batch_size</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">tensors</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
+        <span class="n">batch_size_range</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
+        <span class="n">opt_profile_replica</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">batch_dims</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="s2">&quot;InputTensorSpec&quot;</span><span class="p">]:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Produce a list of InputTenosrSpec named tuples which would contain</span>
+<span class="sd">        the information of all the given PyTorch tensors. The produced input</span>
+<span class="sd">        tensor specs will treat all tensors&#39; first dimension as batch dimension</span>
+<span class="sd">        and mark them as dynmaic.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            tensors (Sequence[torch.Tensor]): A list of PyTorch tensors.</span>
+<span class="sd">            batch_size_range (Tuple[int, int, int]): The first integer indicates</span>
+<span class="sd">                the smallest batch size allowed. The second integer indiceates</span>
+<span class="sd">                the batch size that we&#39;ll optimize for. The third integer indicates</span>
+<span class="sd">                the largest batch size allowed.</span>
+<span class="sd">            opt_profile_replica (int): If dynamic shape is enabled, each execution</span>
+<span class="sd">                context requires a different optimization profile. This arg determines</span>
+<span class="sd">                how many optimization profile replicas we want to produce.</span>
+<span class="sd">            batch_dims (Optional[List[int]]): The batch dim might not be the leading dim</span>
+<span class="sd">                and allow user to specify the batch dims using this arg. Default we treat</span>
+<span class="sd">                dim 0 as the batch dim.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            A list of InputTensorSpec named tuples with dynamic ranges.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">batch_dims</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">batch_dims</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span>
+
+        <span class="n">input_specs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">tensors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">batch_dims</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tensors</span><span class="p">):</span>
+            <span class="n">batch_dim</span> <span class="o">=</span> <span class="n">batch_dims</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="k">assert</span> <span class="n">batch_size</span> <span class="o">==</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span>
+                <span class="n">batch_dim</span>
+            <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;The </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">th tensor (shape: </span><span class="si">{</span><span class="n">tensor</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">) doesn&#39;t have the correct batch size: </span><span class="si">{</span><span class="n">batch_size</span><span class="si">}</span><span class="s2">.&quot;</span>
+            <span class="n">shape</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+            <span class="n">shape</span><span class="p">[</span><span class="n">batch_dim</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+            <span class="n">shape_ranges</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">ShapeRange</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">batch_dim</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">bs</span><span class="p">]</span> <span class="o">+</span> <span class="n">shape</span><span class="p">[</span><span class="n">batch_dim</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:])</span> <span class="k">for</span> <span class="n">bs</span> <span class="ow">in</span> <span class="n">batch_size_range</span><span class="p">)]</span> <span class="o">*</span> <span class="n">opt_profile_replica</span>  <span class="c1"># type: ignore[list-item]</span>
+            <span class="n">input_specs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="bp">cls</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">),</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">shape_ranges</span><span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">input_specs</span>
+
+    <span class="k">def</span> <span class="nf">to_random_tensor</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">get_dynamic_dims</span><span class="p">(</span><span class="n">shape</span><span class="p">)):</span>
+            <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape_ranges</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
+        <span class="k">elif</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_batch_dim</span><span class="p">:</span>
+            <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">+</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">create_inputs_from_specs</span><span class="p">(</span><span class="n">input_specs</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="s2">&quot;InputTensorSpec&quot;</span><span class="p">]):</span>
+        <span class="n">inputs</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">spec</span> <span class="ow">in</span> <span class="n">input_specs</span><span class="p">:</span>
+            <span class="n">inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">spec</span><span class="o">.</span><span class="n">to_random_tensor</span><span class="p">())</span>
+
+        <span class="k">return</span> <span class="n">inputs</span></div>
+</pre></div>
+
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diff --git a/docs/v1.2.0/_modules/torch_tensorrt/fx/lower.html b/docs/v1.2.0/_modules/torch_tensorrt/fx/lower.html
new file mode 100644
index 0000000000..b3ebfbf1cb
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+
+  <h1>Source code for torch_tensorrt.fx.lower</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">dataclasses</span> <span class="k">as</span> <span class="nn">dc</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span>
+
+<span class="c1"># @manual=//deeplearning/trt/python:py_tensorrt</span>
+<span class="kn">import</span> <span class="nn">tensorrt</span> <span class="k">as</span> <span class="nn">trt</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.fx</span> <span class="k">as</span> <span class="nn">fx</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="kn">from</span> <span class="nn">torch.fx.passes.splitter_base</span> <span class="kn">import</span> <span class="n">SplitResult</span>
+
+<span class="kn">from</span> <span class="nn">.fx2trt</span> <span class="kn">import</span> <span class="n">TRTInterpreter</span><span class="p">,</span> <span class="n">TRTInterpreterResult</span>
+<span class="kn">from</span> <span class="nn">.lower_setting</span> <span class="kn">import</span> <span class="n">LowerSetting</span>
+<span class="kn">from</span> <span class="nn">.passes.lower_pass_manager_builder</span> <span class="kn">import</span> <span class="n">LowerPassManagerBuilder</span>
+<span class="kn">from</span> <span class="nn">.passes.pass_utils</span> <span class="kn">import</span> <span class="n">decorate_method</span><span class="p">,</span> <span class="n">PassFunc</span><span class="p">,</span> <span class="n">validate_inference</span>
+<span class="kn">from</span> <span class="nn">.tools.timing_cache_utils</span> <span class="kn">import</span> <span class="n">TimingCacheManager</span>
+<span class="kn">from</span> <span class="nn">.tools.trt_splitter</span> <span class="kn">import</span> <span class="n">TRTSplitter</span><span class="p">,</span> <span class="n">TRTSplitterSetting</span>
+
+<span class="kn">from</span> <span class="nn">.tracer.acc_tracer</span> <span class="kn">import</span> <span class="n">acc_tracer</span>
+<span class="kn">from</span> <span class="nn">.trt_module</span> <span class="kn">import</span> <span class="n">TRTModule</span>
+<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">LowerPrecision</span>
+
+<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+<span class="n">Input</span> <span class="o">=</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span>
+
+
+<div class="viewcode-block" id="compile"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.compile">[docs]</a><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span>
+    <span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
+    <span class="nb">input</span><span class="p">,</span>
+    <span class="n">max_batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2048</span><span class="p">,</span>
+    <span class="n">max_workspace_size</span><span class="o">=</span><span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="mi">25</span><span class="p">,</span>
+    <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">lower_precision</span><span class="o">=</span><span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span><span class="p">,</span>
+    <span class="n">verbose_log</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">timing_cache_prefix</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
+    <span class="n">save_timing_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">cuda_graph_batch_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">dynamic_batch</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Takes in original module, input and lowering setting, run lowering workflow to turn module</span>
+<span class="sd">    into lowered module, or so called TRTModule.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        module: Original module for lowering.</span>
+<span class="sd">        input: Input for module.</span>
+<span class="sd">        max_batch_size: Maximum batch size (must be &gt;= 1 to be set, 0 means not set)</span>
+<span class="sd">        max_workspace_size: Maximum size of workspace given to TensorRT.</span>
+<span class="sd">        explicit_batch_dimension: Use explicit batch dimension in TensorRT if set True, otherwise use implicit batch dimension.</span>
+<span class="sd">        lower_precision: lower_precision config given to TRTModule.</span>
+<span class="sd">        verbose_log: Enable verbose log for TensorRT if set True.</span>
+<span class="sd">        timing_cache_prefix: Timing cache file name for timing cache used by fx2trt.</span>
+<span class="sd">        save_timing_cache: Update timing cache with current timing cache data if set to True.</span>
+<span class="sd">        cuda_graph_batch_size: Cuda graph batch size, default to be -1.</span>
+<span class="sd">        dynamic_batch: batch dimension (dim=0) is dynamic.</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        A torch.nn.Module lowered by TensorRT.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">lower_setting</span> <span class="o">=</span> <span class="n">LowerSetting</span><span class="p">(</span>
+        <span class="n">max_batch_size</span><span class="o">=</span><span class="n">max_batch_size</span><span class="p">,</span>
+        <span class="n">max_workspace_size</span><span class="o">=</span><span class="n">max_workspace_size</span><span class="p">,</span>
+        <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="n">explicit_batch_dimension</span><span class="p">,</span>
+        <span class="n">lower_precision</span><span class="o">=</span><span class="n">lower_precision</span><span class="p">,</span>
+        <span class="n">verbose_log</span><span class="o">=</span><span class="n">verbose_log</span><span class="p">,</span>
+        <span class="n">timing_cache_prefix</span><span class="o">=</span><span class="n">timing_cache_prefix</span><span class="p">,</span>
+        <span class="n">save_timing_cache</span><span class="o">=</span><span class="n">save_timing_cache</span><span class="p">,</span>
+        <span class="n">cuda_graph_batch_size</span><span class="o">=</span><span class="n">cuda_graph_batch_size</span><span class="p">,</span>
+        <span class="n">dynamic_batch</span><span class="o">=</span><span class="n">dynamic_batch</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">lowerer</span> <span class="o">=</span> <span class="n">Lowerer</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">lower_setting</span><span class="o">=</span><span class="n">lower_setting</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">lowerer</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="nb">input</span><span class="p">)</span></div>
+
+
+<span class="nd">@dc</span><span class="o">.</span><span class="n">dataclass</span>
+<span class="k">class</span> <span class="nc">LowerTrtInterpreter</span><span class="p">:</span>
+    <span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span>
+    <span class="n">timing_cache_manager</span><span class="p">:</span> <span class="n">TimingCacheManager</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">create</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">lower_setting</span><span class="p">):</span>
+        <span class="n">timing_cache_manager</span> <span class="o">=</span> <span class="n">TimingCacheManager</span><span class="p">(</span>
+            <span class="n">lower_setting</span><span class="o">.</span><span class="n">timing_cache_prefix</span><span class="p">,</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">save_timing_cache</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">LowerTrtInterpreter</span><span class="p">(</span><span class="n">lower_setting</span><span class="p">,</span> <span class="n">timing_cache_manager</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mod</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">split_name</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TRTInterpreterResult</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">input_specs</span><span class="p">,</span> <span class="s2">&quot;Can&#39;t find input specs for lowering!&quot;</span>
+        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;split_name=</span><span class="si">{</span><span class="n">split_name</span><span class="si">}</span><span class="s2">, input_specs=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">input_specs</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Prepare algorithm selector and timing_cache for TRTInterpreter</span>
+        <span class="n">algo_selector</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">algo_selector</span><span class="p">:</span>
+            <span class="n">algo_selector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">algo_selector</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">split_name</span><span class="si">}</span><span class="s2">.json&quot;</span><span class="p">)</span>
+        <span class="n">cache_data</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">timing_cache_manager</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">cache_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">timing_cache_manager</span><span class="o">.</span><span class="n">get_timing_cache_trt</span><span class="p">(</span><span class="n">split_name</span><span class="p">)</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Cannot load timing cache for </span><span class="si">{</span><span class="n">split_name</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+                <span class="n">cache_data</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="n">interpreter</span> <span class="o">=</span> <span class="n">TRTInterpreter</span><span class="p">(</span>
+            <span class="n">mod</span><span class="p">,</span>
+            <span class="n">input_specs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">input_specs</span><span class="p">,</span>
+            <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">explicit_batch_dimension</span><span class="p">,</span>
+            <span class="n">explicit_precision</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">explicit_precision</span><span class="p">,</span>
+            <span class="n">logger_level</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="o">.</span><span class="n">VERBOSE</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">verbose_log</span>
+            <span class="k">else</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="o">.</span><span class="n">WARNING</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">interp_result</span><span class="p">:</span> <span class="n">TRTInterpreterResult</span> <span class="o">=</span> <span class="n">interpreter</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+            <span class="n">max_batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
+            <span class="n">max_workspace_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">max_workspace_size</span><span class="p">,</span>
+            <span class="n">lower_precision</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">lower_precision</span><span class="p">,</span>
+            <span class="n">strict_type_constraints</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">strict_type_constraints</span><span class="p">,</span>
+            <span class="n">algorithm_selector</span><span class="o">=</span><span class="n">algo_selector</span><span class="p">,</span>
+            <span class="n">timing_cache</span><span class="o">=</span><span class="n">cache_data</span><span class="p">,</span>
+            <span class="n">profiling_verbosity</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">ProfilingVerbosity</span><span class="o">.</span><span class="n">DETAILED</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">verbose_profile</span>
+            <span class="k">else</span> <span class="n">trt</span><span class="o">.</span><span class="n">ProfilingVerbosity</span><span class="o">.</span><span class="n">LAYER_NAMES_ONLY</span><span class="p">,</span>
+            <span class="n">tactic_sources</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">tactic_sources</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Update timing cache file if needed</span>
+        <span class="n">timing_cache</span> <span class="o">=</span> <span class="n">interp_result</span><span class="o">.</span><span class="n">serialized_cache</span>
+        <span class="k">if</span> <span class="n">timing_cache</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">timing_cache_manager</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">timing_cache_manager</span><span class="o">.</span><span class="n">update_timing_cache</span><span class="p">(</span><span class="n">split_name</span><span class="p">,</span> <span class="n">timing_cache</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">interp_result</span>
+
+
+<span class="k">def</span> <span class="nf">default_split_function</span><span class="p">(</span>
+    <span class="n">model</span><span class="p">:</span> <span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Input</span><span class="p">,</span> <span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">SplitResult</span><span class="p">:</span>
+    <span class="n">splitter_setting</span> <span class="o">=</span> <span class="n">TRTSplitterSetting</span><span class="p">()</span>
+    <span class="n">splitter_setting</span><span class="o">.</span><span class="n">use_implicit_batch_dim</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">explicit_batch_dimension</span>
+    <span class="n">splitter_setting</span><span class="o">.</span><span class="n">min_acc_module_size</span> <span class="o">=</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">min_acc_module_size</span>
+    <span class="n">splitter</span> <span class="o">=</span> <span class="n">TRTSplitter</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">settings</span><span class="o">=</span><span class="n">splitter_setting</span><span class="p">)</span>
+    <span class="n">splitter</span><span class="o">.</span><span class="n">node_support_preview</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">splitter</span><span class="o">.</span><span class="n">generate_split_results</span><span class="p">()</span>
+
+
+<span class="k">def</span> <span class="nf">create_lower_trt_interpreter</span><span class="p">(</span><span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">LowerTrtInterpreter</span><span class="p">:</span>
+    <span class="k">return</span> <span class="n">LowerTrtInterpreter</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">lower_setting</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">default_lower_pass</span><span class="p">(</span>
+    <span class="n">create_trt_interpreter</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">LowerSetting</span><span class="p">],</span> <span class="n">LowerTrtInterpreter</span><span class="p">],</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">PassFunc</span><span class="p">:</span>
+    <span class="k">def</span> <span class="nf">lower_pass</span><span class="p">(</span>
+        <span class="n">mod</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="nb">input</span><span class="p">:</span> <span class="n">Input</span><span class="p">,</span> <span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span><span class="p">,</span> <span class="n">module_name</span><span class="p">:</span> <span class="nb">str</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create a module transformation pass which lowers an `fx.GraphModule` into a</span>
+<span class="sd">        `TRTModule`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">interpreter</span> <span class="o">=</span> <span class="n">create_trt_interpreter</span><span class="p">(</span><span class="n">lower_setting</span><span class="p">)</span>
+        <span class="n">interp_res</span><span class="p">:</span> <span class="n">TRTInterpreterResult</span> <span class="o">=</span> <span class="n">interpreter</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">module_name</span><span class="p">)</span>
+        <span class="n">trt_module</span> <span class="o">=</span> <span class="n">TRTModule</span><span class="p">(</span>
+            <span class="n">engine</span><span class="o">=</span><span class="n">interp_res</span><span class="o">.</span><span class="n">engine</span><span class="p">,</span>
+            <span class="n">input_names</span><span class="o">=</span><span class="n">interp_res</span><span class="o">.</span><span class="n">input_names</span><span class="p">,</span>
+            <span class="n">output_names</span><span class="o">=</span><span class="n">interp_res</span><span class="o">.</span><span class="n">output_names</span><span class="p">,</span>
+            <span class="n">cuda_graph_batch_size</span><span class="o">=</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">cuda_graph_batch_size</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">trt_module</span>
+
+    <span class="k">return</span> <span class="n">lower_pass</span>
+
+
+<span class="nd">@dc</span><span class="o">.</span><span class="n">dataclass</span><span class="p">(</span><span class="n">frozen</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">class</span> <span class="nc">Lowerer</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Lowers a module using fx2trt.</span>
+
+<span class="sd">    This is a composable class to facilitate fx2trt. A normal fx2trt process</span>
+<span class="sd">    composes of the following passes to transform an `fx.GraphModule`:</span>
+
+<span class="sd">        1. trace - use torch.fx to trace the module so we can get the graph</span>
+<span class="sd">            representation of the model.</span>
+<span class="sd">        2. split - the graph module is split into several submodules,</span>
+<span class="sd">            running either via TensorRT, or via regular CUDA.</span>
+
+<span class="sd">    For each split that need to run via TRT, the following passes are</span>
+<span class="sd">    invoked:</span>
+
+<span class="sd">        3. `TRTInterpreter` - build the TRT engine for the submodule that</span>
+<span class="sd">            can be supported through `TRTInterpreter`.</span>
+<span class="sd">        4. Wraps the executable TRT engine into `TRTModule`, which is an `nn.Module`.</span>
+<span class="sd">        5. The converted submodule is then set back onto the top-level module</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">lower_pass_manager_builder</span><span class="p">:</span> <span class="n">LowerPassManagerBuilder</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">create</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span><span class="p">,</span>
+        <span class="n">interpreter_builder</span><span class="p">:</span> <span class="n">Callable</span> <span class="o">=</span> <span class="n">create_lower_trt_interpreter</span><span class="p">,</span>
+        <span class="n">split_func</span><span class="p">:</span> <span class="n">Callable</span> <span class="o">=</span> <span class="n">default_split_function</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;Lowerer&quot;</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;Instantiate a `Lowerer` instance.&quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span>
+            <span class="n">lower_pass_manager_builder</span><span class="o">=</span><span class="n">LowerPassManagerBuilder</span><span class="p">(</span>
+                <span class="n">lower_setting</span><span class="o">=</span><span class="n">lower_setting</span><span class="p">,</span>
+                <span class="n">trace_func</span><span class="o">=</span><span class="k">lambda</span> <span class="n">module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">acc_tracer</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span>
+                    <span class="n">module</span><span class="p">,</span>
+                    <span class="n">inputs</span><span class="p">,</span>  <span class="c1"># type: ignore[arg-type]</span>
+                    <span class="n">ast_rewriter_allow_list</span><span class="o">=</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">ast_rewriter_allow_list</span><span class="p">,</span>
+                    <span class="n">leaf_module_list</span><span class="o">=</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">leaf_module_list</span><span class="p">,</span>
+                <span class="p">),</span>
+                <span class="n">split_func</span><span class="o">=</span><span class="n">split_func</span><span class="p">,</span>
+                <span class="n">lower_func</span><span class="o">=</span><span class="n">default_lower_pass</span><span class="p">(</span><span class="n">interpreter_builder</span><span class="p">),</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
+        <span class="n">inputs</span><span class="p">:</span> <span class="n">Input</span><span class="p">,</span>
+        <span class="n">additional_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Input</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
+        <span class="n">lower_setting</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_pass_manager_builder</span><span class="o">.</span><span class="n">lower_setting</span>
+        <span class="n">atol</span> <span class="o">=</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">correctness_atol</span>
+        <span class="n">rtol</span> <span class="o">=</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">correctness_rtol</span>
+
+        <span class="nd">@validate_inference</span><span class="p">(</span><span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">)</span>
+        <span class="k">def</span> <span class="nf">do_lower</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Input</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
+            <span class="n">module</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+            <span class="k">if</span> <span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">lower_pass_manager_builder</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">lower_precision</span>
+                <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span>
+            <span class="p">):</span>
+                <span class="n">module</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
+                <span class="n">inputs</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span>
+                    <span class="n">x</span><span class="o">.</span><span class="n">half</span><span class="p">()</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span> <span class="k">else</span> <span class="n">x</span>
+                    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">inputs</span>
+                <span class="p">)</span>
+            <span class="n">pm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_pass_manager_builder</span><span class="o">.</span><span class="n">build_trt_lower_pipeline</span><span class="p">(</span>
+                <span class="n">inputs</span><span class="p">,</span> <span class="n">additional_inputs</span>
+            <span class="p">)</span>
+            <span class="n">lower_result</span> <span class="o">=</span> <span class="n">pm</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">lower_result</span>
+
+        <span class="k">return</span> <span class="n">do_lower</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
+</pre></div>
+
+             </article>
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+
+  <h1>Source code for torch_tensorrt.fx.trt_module</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Sequence</span>
+
+<span class="c1"># @manual=//deeplearning/trt/python:py_tensorrt</span>
+<span class="kn">import</span> <span class="nn">tensorrt</span> <span class="k">as</span> <span class="nn">trt</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">torch_dtype_from_trt</span>
+
+
+<div class="viewcode-block" id="TRTModule"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.TRTModule">[docs]</a><span class="k">class</span> <span class="nc">TRTModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">input_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cuda_graph_batch_size</span><span class="o">=-</span><span class="mi">1</span>
+    <span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">TRTModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_register_state_dict_hook</span><span class="p">(</span><span class="n">TRTModule</span><span class="o">.</span><span class="n">_on_state_dict</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="o">=</span> <span class="n">engine</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span> <span class="o">=</span> <span class="n">input_names</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span> <span class="o">=</span> <span class="n">output_names</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">cuda_graph_batch_size</span> <span class="o">=</span> <span class="n">cuda_graph_batch_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">initialized</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="n">engine</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_initialize</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">_initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">initialized</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">context</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">create_execution_context</span><span class="p">()</span>
+
+        <span class="c1"># Indices of inputs/outputs in the trt engine bindings, in the order</span>
+        <span class="c1"># as they are in the original PyTorch model.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_binding_indices_in_order</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_index</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_binding_indices_in_order</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_index</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span>
+        <span class="p">]</span>
+        <span class="n">primary_input_outputs</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+        <span class="n">primary_input_outputs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_binding_indices_in_order</span><span class="p">)</span>
+        <span class="n">primary_input_outputs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_binding_indices_in_order</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_binding_indices_in_order</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_names</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">num_bindings</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">num_optimization_profiles</span>
+        <span class="p">):</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">primary_input_outputs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_binding_indices_in_order</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_name</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+
+        <span class="k">assert</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">num_bindings</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">num_optimization_profiles</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span>
+            <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_names</span><span class="p">)</span>
+            <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">)</span>
+            <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_names</span><span class="p">)</span>
+        <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_dtypes</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">torch_dtype_from_trt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_dtype</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>
+            <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_binding_indices_in_order</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_shapes</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_shape</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>
+            <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_binding_indices_in_order</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_dtypes</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">torch_dtype_from_trt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_dtype</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>
+            <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_binding_indices_in_order</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_shapes</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_shape</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span>
+            <span class="k">else</span> <span class="nb">tuple</span><span class="p">()</span>
+            <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_binding_indices_in_order</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_dtypes</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">torch_dtype_from_trt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_dtype</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>
+            <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_binding_indices_in_order</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_shapes</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_shape</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span>
+            <span class="k">else</span> <span class="nb">tuple</span><span class="p">()</span>
+            <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_binding_indices_in_order</span>
+        <span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">_check_initialized</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">initialized</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;TRTModule is not initialized.&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_on_state_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">local_metadata</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_check_initialized</span><span class="p">()</span>
+        <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;engine&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">bytearray</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">serialize</span><span class="p">())</span>
+        <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;input_names&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span>
+        <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;output_names&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span>
+        <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;cuda_graph_batch_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cuda_graph_batch_size</span>
+
+    <span class="k">def</span> <span class="nf">_load_from_state_dict</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">state_dict</span><span class="p">,</span>
+        <span class="n">prefix</span><span class="p">,</span>
+        <span class="n">local_metadata</span><span class="p">,</span>
+        <span class="n">strict</span><span class="p">,</span>
+        <span class="n">missing_keys</span><span class="p">,</span>
+        <span class="n">unexpected_keys</span><span class="p">,</span>
+        <span class="n">error_msgs</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="n">engine_bytes</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;engine&quot;</span><span class="p">]</span>
+
+        <span class="n">logger</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="p">()</span>
+        <span class="n">runtime</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Runtime</span><span class="p">(</span><span class="n">logger</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="o">=</span> <span class="n">runtime</span><span class="o">.</span><span class="n">deserialize_cuda_engine</span><span class="p">(</span><span class="n">engine_bytes</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;input_names&quot;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;output_names&quot;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_initialize</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">__getstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="n">state</span><span class="p">[</span><span class="s2">&quot;engine&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">bytearray</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">serialize</span><span class="p">())</span>
+        <span class="n">state</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;context&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">state</span>
+
+    <span class="k">def</span> <span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
+        <span class="n">logger</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="p">()</span>
+        <span class="n">runtime</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Runtime</span><span class="p">(</span><span class="n">logger</span><span class="p">)</span>
+        <span class="n">state</span><span class="p">[</span><span class="s2">&quot;engine&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">runtime</span><span class="o">.</span><span class="n">deserialize_cuda_engine</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s2">&quot;engine&quot;</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">context</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">create_execution_context</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">inputs</span><span class="p">):</span>
+        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;TRTModule:Forward&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_check_initialized</span><span class="p">()</span>
+
+            <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;TRTModule:ProcessInputs&quot;</span><span class="p">):</span>
+                <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span>
+                <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;Wrong number of inputs, expect </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_names</span><span class="p">)</span><span class="si">}</span><span class="s2"> get </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span>
+
+                <span class="c1"># This is only used when the trt engine is using implicit batch dim.</span>
+                <span class="n">batch_size</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">contiguous_inputs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">]</span>
+                <span class="n">bindings</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_names</span><span class="p">)</span>
+                    <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">)</span>
+                    <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_names</span><span class="p">)</span>
+                <span class="p">)</span>
+
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">input_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_names</span><span class="p">):</span>
+                    <span class="k">assert</span> <span class="n">inputs</span><span class="p">[</span>
+                        <span class="n">i</span>
+                    <span class="p">]</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">th input(</span><span class="si">{</span><span class="n">input_name</span><span class="si">}</span><span class="s2">) is not on cuda device.&quot;</span>
+                    <span class="k">assert</span> <span class="p">(</span>
+                        <span class="n">inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_dtypes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;Dtype mismatch for </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">th input(</span><span class="si">{</span><span class="n">input_name</span><span class="si">}</span><span class="s2">). Expect </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">input_dtypes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s2">, got </span><span class="si">{</span><span class="n">inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="si">}</span><span class="s2">.&quot;</span>
+
+                    <span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_binding_indices_in_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="n">bindings</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span>
+
+                    <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">set_binding_shape</span><span class="p">(</span>
+                            <span class="n">idx</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+                        <span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="k">assert</span> <span class="n">inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_shapes</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="p">(</span>
+                            <span class="sa">f</span><span class="s2">&quot;Shape mismatch for </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">th input(</span><span class="si">{</span><span class="n">input_name</span><span class="si">}</span><span class="s2">). &quot;</span>
+                            <span class="sa">f</span><span class="s2">&quot;Expect </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">input_shapes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s2">, got </span><span class="si">{</span><span class="n">inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">1</span><span class="p">:]</span><span class="si">}</span><span class="s2">.&quot;</span>
+                        <span class="p">)</span>
+
+            <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;TRTModule:ProcessOutputs&quot;</span><span class="p">):</span>
+                <span class="c1"># create output tensors</span>
+                <span class="n">outputs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_binding_indices_in_order</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
+                        <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">batch_size</span><span class="p">,)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_shapes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">get_binding_shape</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>
+
+                    <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span>  <span class="c1"># type: ignore[call-overload]</span>
+                        <span class="n">size</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span>
+                        <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_dtypes</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
+                        <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_device</span><span class="p">(),</span>
+                    <span class="p">)</span>
+                    <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
+                    <span class="n">bindings</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span>
+
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_binding_indices_in_order</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
+                        <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">batch_size</span><span class="p">,)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_shapes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">get_binding_shape</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>
+
+                    <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span>  <span class="c1"># type: ignore[call-overload]</span>
+                        <span class="n">size</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span>
+                        <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_dtypes</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
+                        <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_device</span><span class="p">(),</span>
+                    <span class="p">)</span>
+                    <span class="n">bindings</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span>
+
+            <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;TRTModule:TensorRTRuntime&quot;</span><span class="p">):</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">execute_async</span><span class="p">(</span>
+                        <span class="n">batch_size</span><span class="p">,</span> <span class="n">bindings</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span><span class="o">.</span><span class="n">cuda_stream</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">execute_async_v2</span><span class="p">(</span>
+                        <span class="n">bindings</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span><span class="o">.</span><span class="n">cuda_stream</span>
+                    <span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">enable_profiling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">profiler</span><span class="p">:</span> <span class="s2">&quot;trt.IProfiler&quot;</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Enable TensorRT profiling. After calling this function, TensorRT will report</span>
+<span class="sd">        time spent on each layer in stdout for each forward run.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_check_initialized</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">profiler</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">profiler</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Profiler</span><span class="p">()</span> <span class="k">if</span> <span class="n">profiler</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">profiler</span>
+
+    <span class="k">def</span> <span class="nf">disable_profiling</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Disable TensorRT profiling.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_check_initialized</span><span class="p">()</span>
+
+        <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
+        <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">context</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">context</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">create_execution_context</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">get_layer_info</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Get layer info of the engine. Only support for TRT &gt; 8.2.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">inspector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">create_engine_inspector</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">inspector</span><span class="o">.</span><span class="n">get_engine_information</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">LayerInformationFormat</span><span class="o">.</span><span class="n">JSON</span><span class="p">)</span></div>
+</pre></div>
+
+             </article>
+
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+
+  <h1>Source code for torch_tensorrt.logging</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._C</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">_get_logging_prefix</span><span class="p">,</span>
+    <span class="n">_set_logging_prefix</span><span class="p">,</span>
+    <span class="n">_get_reportable_log_level</span><span class="p">,</span>
+    <span class="n">_set_reportable_log_level</span><span class="p">,</span>
+    <span class="n">_get_is_colored_output_on</span><span class="p">,</span>
+    <span class="n">_set_is_colored_output_on</span><span class="p">,</span>
+    <span class="n">_log</span><span class="p">,</span>
+    <span class="n">LogLevel</span><span class="p">,</span>
+<span class="p">)</span>
+
+
+<div class="viewcode-block" id="Level"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.Level">[docs]</a><span class="k">class</span> <span class="nc">Level</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Enum to set the minimum required logging level to print a message to stdout&quot;&quot;&quot;</span>
+
+    <span class="n">InternalError</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">INTERNAL_ERROR</span>
+    <span class="n">Error</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">ERROR</span>
+    <span class="ne">Warning</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">WARNING</span>
+    <span class="n">Info</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">INFO</span>
+    <span class="n">Debug</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">DEBUG</span>
+    <span class="n">Graph</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">GRAPH</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_to_internal_level</span><span class="p">(</span><span class="n">external</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">LogLevel</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">external</span> <span class="o">==</span> <span class="n">Level</span><span class="o">.</span><span class="n">InternalError</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">INTERNAL_ERROR</span>
+        <span class="k">if</span> <span class="n">external</span> <span class="o">==</span> <span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">ERROR</span>
+        <span class="k">if</span> <span class="n">external</span> <span class="o">==</span> <span class="n">Level</span><span class="o">.</span><span class="n">Warning</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">WARNING</span>
+        <span class="k">if</span> <span class="n">external</span> <span class="o">==</span> <span class="n">Level</span><span class="o">.</span><span class="n">Info</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">INFO</span>
+        <span class="k">if</span> <span class="n">external</span> <span class="o">==</span> <span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">DEBUG</span>
+        <span class="k">if</span> <span class="n">external</span> <span class="o">==</span> <span class="n">Level</span><span class="o">.</span><span class="n">Graph</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">GRAPH</span></div>
+
+
+<div class="viewcode-block" id="get_logging_prefix"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.get_logging_prefix">[docs]</a><span class="k">def</span> <span class="nf">get_logging_prefix</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Get the prefix set for logging messages</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        str: Prefix used for logger</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">_get_logging_prefix</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="set_logging_prefix"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.set_logging_prefix">[docs]</a><span class="k">def</span> <span class="nf">set_logging_prefix</span><span class="p">(</span><span class="n">prefix</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Set the prefix used when logging messages</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        prefix (str): Prefix to use for logging messages</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">_set_logging_prefix</span><span class="p">(</span><span class="n">prefix</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="get_reportable_log_level"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.get_reportable_log_level">[docs]</a><span class="k">def</span> <span class="nf">get_reportable_log_level</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">Level</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Get the level required for a message to be printed in the log</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch_tensorrt.logging.Level: The enum representing the level required to print</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">Level</span><span class="p">(</span><span class="n">_get_reportable_log_level</span><span class="p">())</span></div>
+
+
+<div class="viewcode-block" id="set_reportable_log_level"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.set_reportable_log_level">[docs]</a><span class="k">def</span> <span class="nf">set_reportable_log_level</span><span class="p">(</span><span class="n">level</span><span class="p">:</span> <span class="n">Level</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Set the level required for a message to be printed to the log</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        level (torch_tensorrt.logging.Level): The enum representing the level required to print</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">_set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">_to_internal_level</span><span class="p">(</span><span class="n">level</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="get_is_colored_output_on"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.get_is_colored_output_on">[docs]</a><span class="k">def</span> <span class="nf">get_is_colored_output_on</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Get if colored output is enabled for logging</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        bool: If colored output is one</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">_get_is_colored_output_on</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="set_is_colored_output_on"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.set_is_colored_output_on">[docs]</a><span class="k">def</span> <span class="nf">set_is_colored_output_on</span><span class="p">(</span><span class="n">colored_output_on</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Enable or disable color in the log output</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        colored_output_on (bool): If colored output should be enabled or not</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">_set_is_colored_output_on</span><span class="p">(</span><span class="n">colored_output_on</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="log"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.log">[docs]</a><span class="k">def</span> <span class="nf">log</span><span class="p">(</span><span class="n">level</span><span class="p">:</span> <span class="n">Level</span><span class="p">,</span> <span class="n">msg</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Add a new message to the log</span>
+
+<span class="sd">    Adds a new message to the log at a specified level. The message</span>
+<span class="sd">    will only get printed out if Level &gt; reportable_log_level</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        level (torch_tensorrt.logging.Level): Severity of the message</span>
+<span class="sd">        msg (str): Actual message text</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">_log</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">_to_internal_level</span><span class="p">(</span><span class="n">level</span><span class="p">),</span> <span class="n">msg</span><span class="p">)</span>
+
+    <span class="n">InternalError</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">INTERNAL_ERROR</span>
+    <span class="n">Error</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">ERROR</span>
+    <span class="ne">Warning</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">WARNING</span>
+    <span class="n">Info</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">INFO</span>
+    <span class="n">Debug</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">DEBUG</span>
+    <span class="n">Graph</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">GRAPH</span></div>
+
+
+<div class="viewcode-block" id="internal_errors"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.internal_errors">[docs]</a><span class="k">class</span> <span class="nc">internal_errors</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Context-manager to limit displayed log messages to just internal errors</span>
+
+<span class="sd">    Example::</span>
+
+<span class="sd">    with torch_tensorrt.logging.internal_errors():</span>
+<span class="sd">        outputs = model_torchtrt(inputs)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span> <span class="o">=</span> <span class="n">get_reportable_log_level</span><span class="p">()</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">InternalError</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">,</span> <span class="n">exc_tb</span><span class="p">):</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="errors"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.errors">[docs]</a><span class="k">class</span> <span class="nc">errors</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Context-manager to limit displayed log messages to just errors and above</span>
+
+<span class="sd">    Example::</span>
+
+<span class="sd">    with torch_tensorrt.logging.errors():</span>
+<span class="sd">        outputs = model_torchtrt(inputs)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span> <span class="o">=</span> <span class="n">get_reportable_log_level</span><span class="p">()</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">,</span> <span class="n">exc_tb</span><span class="p">):</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="warnings"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.warnings">[docs]</a><span class="k">class</span> <span class="nc">warnings</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Context-manager to limit displayed log messages to just warnings and above</span>
+
+<span class="sd">    Example::</span>
+
+<span class="sd">    with torch_tensorrt.logging.warnings():</span>
+<span class="sd">        model_trt = torch_tensorrt.compile(model, **spec)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span> <span class="o">=</span> <span class="n">get_reportable_log_level</span><span class="p">()</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Warning</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">,</span> <span class="n">exc_tb</span><span class="p">):</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="info"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.info">[docs]</a><span class="k">class</span> <span class="nc">info</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Context-manager to display all info and greater severity messages</span>
+
+<span class="sd">    Example::</span>
+
+<span class="sd">    with torch_tensorrt.logging.info():</span>
+<span class="sd">        model_trt = torch_tensorrt.compile(model, **spec)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span> <span class="o">=</span> <span class="n">get_reportable_log_level</span><span class="p">()</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Info</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">,</span> <span class="n">exc_tb</span><span class="p">):</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="debug"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.debug">[docs]</a><span class="k">class</span> <span class="nc">debug</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Context-manager to display full debug information through the logger</span>
+
+<span class="sd">    Example::</span>
+
+<span class="sd">    with torch_tensorrt.logging.debug():</span>
+<span class="sd">        model_trt = torch_tensorrt.compile(model, **spec)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span> <span class="o">=</span> <span class="n">get_reportable_log_level</span><span class="p">()</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">,</span> <span class="n">exc_tb</span><span class="p">):</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="graphs"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.graphs">[docs]</a><span class="k">class</span> <span class="nc">graphs</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Context-manager to display the results of intermediate lowering passes</span>
+<span class="sd">    as well as full debug information through the logger</span>
+
+<span class="sd">    Example::</span>
+
+<span class="sd">    with torch_tensorrt.logging.graphs():</span>
+<span class="sd">        model_trt = torch_tensorrt.compile(model, **spec)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span> <span class="o">=</span> <span class="n">get_reportable_log_level</span><span class="p">()</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Graph</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">,</span> <span class="n">exc_tb</span><span class="p">):</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span><span class="p">)</span></div>
+</pre></div>
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diff --git a/docs/v1.2.0/_modules/torch_tensorrt/ptq.html b/docs/v1.2.0/_modules/torch_tensorrt/ptq.html
new file mode 100644
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+
+  <h1>Source code for torch_tensorrt.ptq</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">os</span>
+
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_C</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._version</span> <span class="kn">import</span> <span class="n">__version__</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.logging</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">types</span> <span class="kn">import</span> <span class="n">FunctionType</span>
+<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
+
+
+<div class="viewcode-block" id="CalibrationAlgo"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo">[docs]</a><span class="k">class</span> <span class="nc">CalibrationAlgo</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
+    <span class="n">ENTROPY_CALIBRATION</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION</span>
+    <span class="n">ENTROPY_CALIBRATION_2</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span>
+    <span class="n">LEGACY_CALIBRATION</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">LEGACY_CALIBRATION</span>
+    <span class="n">MINMAX_CALIBRATION</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">MINMAX_CALIBRATION</span></div>
+
+
+<div class="viewcode-block" id="get_cache_mode_batch"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.get_cache_mode_batch">[docs]</a><span class="k">def</span> <span class="nf">get_cache_mode_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">return</span> <span class="kc">None</span></div>
+
+
+<div class="viewcode-block" id="get_batch_size"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.get_batch_size">[docs]</a><span class="k">def</span> <span class="nf">get_batch_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">return</span> <span class="mi">1</span></div>
+
+
+<div class="viewcode-block" id="get_batch"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.get_batch">[docs]</a><span class="k">def</span> <span class="nf">get_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">names</span><span class="p">):</span>
+    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_batch_idx</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">&gt;</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
+    <span class="n">batch</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dataset_iterator</span><span class="p">)</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">current_batch_idx</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span>
+    <span class="n">inputs_gpu</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">:</span>
+            <span class="n">inputs_gpu</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">example</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">())</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">inputs_gpu</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">())</span>
+    <span class="k">return</span> <span class="n">inputs_gpu</span></div>
+
+
+<div class="viewcode-block" id="read_calibration_cache"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.read_calibration_cache">[docs]</a><span class="k">def</span> <span class="nf">read_calibration_cache</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_cache</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span><span class="p">):</span>
+            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="sa">b</span><span class="s2">&quot;&quot;</span></div>
+
+
+<div class="viewcode-block" id="write_calibration_cache"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.write_calibration_cache">[docs]</a><span class="k">def</span> <span class="nf">write_calibration_cache</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cache</span><span class="p">):</span>
+    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span><span class="p">:</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+            <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">cache</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="sa">b</span><span class="s2">&quot;&quot;</span></div>
+
+
+<span class="c1"># deepcopy (which involves pickling) is performed on the compile_spec internally during compilation.</span>
+<span class="c1"># We register this __reduce__ function for pickler to identity the calibrator object returned by DataLoaderCalibrator during deepcopy.</span>
+<span class="c1"># This should be the object&#39;s local name relative to the module https://docs.python.org/3/library/pickle.html#object.__reduce__</span>
+<span class="k">def</span> <span class="nf">__reduce__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
+
+
+<div class="viewcode-block" id="DataLoaderCalibrator"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator">[docs]</a><span class="k">class</span> <span class="nc">DataLoaderCalibrator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces</span>
+<span class="sd">    data which is passed during calibration.</span>
+<span class="sd">    Args:</span>
+<span class="sd">        dataloader: an instance of pytorch dataloader which iterates through a given dataset.</span>
+<span class="sd">        algo_type: choice of calibration algorithm.</span>
+<span class="sd">        cache_file: path to cache file.</span>
+<span class="sd">        use_cache: flag which enables usage of pre-existing cache.</span>
+<span class="sd">        device: device on which calibration data is copied to.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="DataLoaderCalibrator.__init__"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">pass</span></div>
+
+    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">dataloader</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">algo_type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;algo_type&quot;</span><span class="p">,</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">)</span>
+        <span class="n">cache_file</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;cache_file&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">use_cache</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;use_cache&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+        <span class="n">device</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda:0&quot;</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataloader</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">):</span>
+            <span class="n">log</span><span class="p">(</span>
+                <span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">,</span>
+                <span class="s2">&quot;Dataloader : </span><span class="si">{}</span><span class="s2"> is not a valid instance of torch.utils.data.DataLoader&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="n">dataloader</span>
+                <span class="p">),</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">cache_file</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">use_cache</span><span class="p">:</span>
+                <span class="n">log</span><span class="p">(</span>
+                    <span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">,</span>
+                    <span class="s2">&quot;Using existing cache_file </span><span class="si">{}</span><span class="s2"> for calibration&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cache_file</span><span class="p">),</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">log</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">,</span> <span class="s2">&quot;Overwriting existing calibration cache file.&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">use_cache</span><span class="p">:</span>
+                <span class="n">log</span><span class="p">(</span>
+                    <span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">,</span>
+                    <span class="s2">&quot;Input cache file is None but use_cache is set to True in INT8 mode.&quot;</span><span class="p">,</span>
+                <span class="p">)</span>
+
+        <span class="c1"># Define attributes and member functions for the calibrator class</span>
+        <span class="n">attribute_mapping</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;data_loader&quot;</span><span class="p">:</span> <span class="n">dataloader</span><span class="p">,</span>
+            <span class="s2">&quot;current_batch_idx&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+            <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="n">dataloader</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span>
+            <span class="s2">&quot;dataset_iterator&quot;</span><span class="p">:</span> <span class="nb">iter</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span>
+            <span class="s2">&quot;cache_file&quot;</span><span class="p">:</span> <span class="n">cache_file</span><span class="p">,</span>
+            <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span>
+            <span class="s2">&quot;use_cache&quot;</span><span class="p">:</span> <span class="n">use_cache</span><span class="p">,</span>
+            <span class="s2">&quot;get_batch_size&quot;</span><span class="p">:</span> <span class="n">get_batch_size</span><span class="p">,</span>
+            <span class="s2">&quot;get_batch&quot;</span><span class="p">:</span> <span class="n">get_cache_mode_batch</span> <span class="k">if</span> <span class="n">use_cache</span> <span class="k">else</span> <span class="n">get_batch</span><span class="p">,</span>
+            <span class="s2">&quot;read_calibration_cache&quot;</span><span class="p">:</span> <span class="n">read_calibration_cache</span><span class="p">,</span>
+            <span class="s2">&quot;write_calibration_cache&quot;</span><span class="p">:</span> <span class="n">write_calibration_cache</span><span class="p">,</span>
+            <span class="s2">&quot;__reduce__&quot;</span><span class="p">:</span> <span class="n">__reduce__</span><span class="p">,</span>  <span class="c1"># used when you deepcopy the DataLoaderCalibrator object</span>
+        <span class="p">}</span>
+
+        <span class="c1"># Using type metaclass to construct calibrator class based on algorithm type</span>
+        <span class="k">if</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;Int8EntropyCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8EntropyCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+        <span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;Int8EntropyCalibrator2&quot;</span><span class="p">,</span>
+                <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8EntropyCalibrator2</span><span class="p">,),</span>
+                <span class="n">attribute_mapping</span><span class="p">,</span>
+            <span class="p">)()</span>
+        <span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">LEGACY_CALIBRATION</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;Int8LegacyCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8LegacyCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+        <span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">MINMAX_CALIBRATION</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;Int8MinMaxCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8MinMaxCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">log</span><span class="p">(</span>
+                <span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">,</span>
+                <span class="s2">&quot;Invalid calibration algorithm type. Please select among ENTROPY_CALIBRATION, ENTROPY_CALIBRATION, LEGACY_CALIBRATION or MINMAX_CALIBRATION&quot;</span><span class="p">,</span>
+            <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="CacheCalibrator"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator">[docs]</a><span class="k">class</span> <span class="nc">CacheCalibrator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Constructs a calibrator class in TensorRT which directly uses pre-existing cache file for calibration.</span>
+<span class="sd">    Args:</span>
+<span class="sd">        cache_file: path to cache file.</span>
+<span class="sd">        algo_type: choice of calibration algorithm.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="CacheCalibrator.__init__"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">pass</span></div>
+
+    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">cache_file</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">algo_type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;algo_type&quot;</span><span class="p">,</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">cache_file</span><span class="p">):</span>
+            <span class="n">log</span><span class="p">(</span>
+                <span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">,</span>
+                <span class="s2">&quot;Using existing cache_file </span><span class="si">{}</span><span class="s2"> for calibration&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cache_file</span><span class="p">),</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">log</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">,</span> <span class="s2">&quot;Invalid calibration cache file.&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Define attributes and member functions for the calibrator class</span>
+        <span class="n">attribute_mapping</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;use_cache&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+            <span class="s2">&quot;cache_file&quot;</span><span class="p">:</span> <span class="n">cache_file</span><span class="p">,</span>
+            <span class="s2">&quot;get_batch_size&quot;</span><span class="p">:</span> <span class="n">get_batch_size</span><span class="p">,</span>
+            <span class="s2">&quot;get_batch&quot;</span><span class="p">:</span> <span class="n">get_cache_mode_batch</span><span class="p">,</span>
+            <span class="s2">&quot;read_calibration_cache&quot;</span><span class="p">:</span> <span class="n">read_calibration_cache</span><span class="p">,</span>
+            <span class="s2">&quot;write_calibration_cache&quot;</span><span class="p">:</span> <span class="n">write_calibration_cache</span><span class="p">,</span>
+        <span class="p">}</span>
+        <span class="c1"># Using type metaclass to construct calibrator class based on algorithm type</span>
+        <span class="k">if</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;DataLoaderCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8EntropyCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+        <span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;DataLoaderCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8MinMaxCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+        <span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">LEGACY_CALIBRATION</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;DataLoaderCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8LegacyCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+        <span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">MINMAX_CALIBRATION</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;DataLoaderCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8MinMaxCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">log</span><span class="p">(</span>
+                <span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">,</span>
+                <span class="s2">&quot;Invalid calibration algorithm type. Please select among ENTROPY_CALIBRATION, ENTROPY_CALIBRATION, LEGACY_CALIBRATION or MINMAX_CALIBRATION&quot;</span><span class="p">,</span>
+            <span class="p">)</span></div>
+</pre></div>
+
+             </article>
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+
+  <h1>Source code for torch_tensorrt.ts._compile_spec</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Set</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_C</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt._C.ts</span> <span class="k">as</span> <span class="nn">_ts_C</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_enums</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._Input</span> <span class="kn">import</span> <span class="n">Input</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._Device</span> <span class="kn">import</span> <span class="n">Device</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.logging</span> <span class="kn">import</span> <span class="n">Level</span><span class="p">,</span> <span class="n">log</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
+
+
+<span class="k">def</span> <span class="nf">_internal_input_to_torch_class_input</span><span class="p">(</span><span class="n">i</span><span class="p">:</span> <span class="n">_C</span><span class="o">.</span><span class="n">Input</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">_Input</span><span class="p">:</span>
+    <span class="n">clone</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">_Input</span><span class="p">()</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_min</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">min</span><span class="p">)</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_opt</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">opt</span><span class="p">)</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_max</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">max</span><span class="p">)</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_dtype</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_format</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">format</span><span class="p">)</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_input_is_dynamic</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">input_is_dynamic</span><span class="p">)</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_explicit_set_dtype</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">_explicit_set_dtype</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">clone</span>
+
+
+<span class="k">def</span> <span class="nf">_supported_input_size_type</span><span class="p">(</span><span class="n">input_size</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+            <span class="s2">&quot;Input sizes for inputs are required to be a List, tuple or torch.Size or a Dict of three sizes (min, opt, max), found type: &quot;</span>
+            <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">input_size</span><span class="p">))</span>
+        <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_input_ranges</span><span class="p">(</span><span class="n">input_sizes</span><span class="p">:</span> <span class="n">List</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">:</span>
+
+    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span>
+        <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">input_sizes</span>
+    <span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span>
+            <span class="s2">&quot;An input size must either be a static size or a range of three sizes (min, opt, max) as Dict&quot;</span>
+        <span class="p">)</span>
+
+    <span class="n">parsed_input_sizes</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">input_sizes</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">all</span><span class="p">(</span><span class="n">k</span> <span class="ow">in</span> <span class="n">i</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min&quot;</span><span class="p">,</span> <span class="s2">&quot;opt&quot;</span><span class="p">,</span> <span class="s2">&quot;min&quot;</span><span class="p">]):</span>
+                <span class="n">parsed_input_sizes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                    <span class="n">Input</span><span class="p">(</span>
+                        <span class="n">min_shape</span><span class="o">=</span><span class="n">i</span><span class="p">[</span><span class="s2">&quot;min&quot;</span><span class="p">],</span> <span class="n">opt_shape</span><span class="o">=</span><span class="n">i</span><span class="p">[</span><span class="s2">&quot;opt&quot;</span><span class="p">],</span> <span class="n">max_shape</span><span class="o">=</span><span class="n">i</span><span class="p">[</span><span class="s2">&quot;max&quot;</span><span class="p">]</span>
+                    <span class="p">)</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">()</span>
+                <span class="p">)</span>
+
+            <span class="k">elif</span> <span class="s2">&quot;opt&quot;</span> <span class="ow">in</span> <span class="n">i</span><span class="p">:</span>
+                <span class="n">parsed_input_sizes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">i</span><span class="p">[</span><span class="s2">&quot;opt&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">())</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span>
+                    <span class="s2">&quot;An input size must either be a static size or a range of three sizes (min, opt, max) as Dict&quot;</span>
+                <span class="p">)</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">parsed_input_sizes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">())</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+            <span class="n">parsed_input_sizes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">())</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">):</span>
+            <span class="n">parsed_input_sizes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">())</span>
+
+    <span class="k">return</span> <span class="n">parsed_input_sizes</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_op_precision</span><span class="p">(</span><span class="n">precision</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">precision</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">precision</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">int8</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">int8</span>
+        <span class="k">elif</span> <span class="n">precision</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">half</span>
+        <span class="k">elif</span> <span class="n">precision</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;Provided an unsupported dtype as operating precision (support: int8, half, float), got: &quot;</span>
+                <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">precision</span><span class="p">)</span>
+            <span class="p">)</span>
+
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">precision</span><span class="p">,</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">precision</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+            <span class="s2">&quot;Op precision type needs to be specified with a torch.dtype or a torch_tensorrt.dtype, got: &quot;</span>
+            <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">precision</span><span class="p">))</span>
+        <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_enabled_precisions</span><span class="p">(</span><span class="n">precisions</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Set</span><span class="p">:</span>
+    <span class="n">parsed_precisions</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+    <span class="k">if</span> <span class="nb">any</span><span class="p">([</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">precisions</span><span class="p">,</span> <span class="nb">type</span><span class="p">)</span> <span class="k">for</span> <span class="nb">type</span> <span class="ow">in</span> <span class="p">[</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="nb">set</span><span class="p">]]):</span>
+        <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">precisions</span><span class="p">:</span>
+            <span class="n">parsed_precisions</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_parse_op_precision</span><span class="p">(</span><span class="n">p</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">parsed_precisions</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_parse_op_precision</span><span class="p">(</span><span class="n">precisions</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">parsed_precisions</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_device_type</span><span class="p">(</span><span class="n">device</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_enums</span><span class="o">.</span><span class="n">DeviceType</span><span class="p">:</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;cuda&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">gpu</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Got a device type other than GPU or DLA (type: &quot;</span>
+                <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">device</span><span class="o">.</span><span class="n">type</span><span class="p">)</span>
+                <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+            <span class="p">)</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">_enums</span><span class="o">.</span><span class="n">DeviceType</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">device</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;gpu&quot;</span> <span class="ow">or</span> <span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;GPU&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">gpu</span>
+        <span class="k">elif</span> <span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;dla&quot;</span> <span class="ow">or</span> <span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;DLA&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">dla</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Got a device type other than GPU or DLA (type: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+            <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+            <span class="s2">&quot;Device specification must be of type torch.device, string or torch_tensorrt.DeviceType, but got: &quot;</span>
+            <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">device</span><span class="p">))</span>
+        <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_device</span><span class="p">(</span><span class="n">device_info</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_C</span><span class="o">.</span><span class="n">Device</span><span class="p">:</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device_info</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+        <span class="n">info</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">Device</span><span class="p">()</span>
+        <span class="k">if</span> <span class="s2">&quot;device_type&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">device_info</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s2">&quot;Device type is required parameter&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;device_type&quot;</span><span class="p">],</span> <span class="n">_enums</span><span class="o">.</span><span class="n">DeviceType</span><span class="p">)</span>
+            <span class="n">info</span><span class="o">.</span><span class="n">device_type</span> <span class="o">=</span> <span class="n">_parse_device_type</span><span class="p">(</span><span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;device_type&quot;</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="s2">&quot;gpu_id&quot;</span> <span class="ow">in</span> <span class="n">device_info</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;gpu_id&quot;</span><span class="p">],</span> <span class="nb">int</span><span class="p">)</span>
+            <span class="n">info</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;gpu_id&quot;</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="s2">&quot;dla_core&quot;</span> <span class="ow">in</span> <span class="n">device_info</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;dla_core&quot;</span><span class="p">],</span> <span class="nb">int</span><span class="p">)</span>
+            <span class="n">info</span><span class="o">.</span><span class="n">dla_core</span> <span class="o">=</span> <span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;dla_core&quot;</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="s2">&quot;allow_gpu_fallback&quot;</span> <span class="ow">in</span> <span class="n">device_info</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;allow_gpu_fallback&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">)</span>
+            <span class="n">info</span><span class="o">.</span><span class="n">allow_gpu_fallback</span> <span class="o">=</span> <span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;allow_gpu_fallback&quot;</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">info</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device_info</span><span class="p">,</span> <span class="n">Device</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">device_info</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">()</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device_info</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">(</span><span class="n">Device</span><span class="o">.</span><span class="n">_from_torch_device</span><span class="p">(</span><span class="n">device_info</span><span class="p">))</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">()</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="s2">&quot;Unsupported data for device specification. Expected either a dict, torch_tensorrt.Device or torch.Device&quot;</span>
+        <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_torch_fallback</span><span class="p">(</span><span class="n">fallback_info</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">_ts_C</span><span class="o">.</span><span class="n">TorchFallback</span><span class="p">:</span>
+    <span class="n">info</span> <span class="o">=</span> <span class="n">_ts_C</span><span class="o">.</span><span class="n">TorchFallback</span><span class="p">()</span>
+    <span class="k">if</span> <span class="s2">&quot;enabled&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">fallback_info</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s2">&quot;Enabled is required parameter&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;enabled&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">enabled</span> <span class="o">=</span> <span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;enabled&quot;</span><span class="p">]</span>
+    <span class="k">if</span> <span class="s2">&quot;min_block_size&quot;</span> <span class="ow">in</span> <span class="n">fallback_info</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;min_block_size&quot;</span><span class="p">],</span> <span class="nb">int</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">min_block_size</span> <span class="o">=</span> <span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;min_block_size&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;forced_fallback_ops&quot;</span> <span class="ow">in</span> <span class="n">fallback_info</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;forced_fallback_ops&quot;</span><span class="p">],</span> <span class="nb">list</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">forced_fallback_operators</span> <span class="o">=</span> <span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;forced_fallback_ops&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;forced_fallback_modules&quot;</span> <span class="ow">in</span> <span class="n">fallback_info</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;forced_fallback_modules&quot;</span><span class="p">],</span> <span class="nb">list</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">forced_fallback_modules</span> <span class="o">=</span> <span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;forced_fallback_modules&quot;</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">info</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_input_signature</span><span class="p">(</span><span class="n">input_signature</span><span class="p">:</span> <span class="n">Any</span><span class="p">):</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_signature</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+        <span class="n">input_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">input_signature</span><span class="p">:</span>
+            <span class="nb">input</span> <span class="o">=</span> <span class="n">_parse_input_signature</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
+            <span class="n">input_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">input_list</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_signature</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">input_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">input_signature</span><span class="p">:</span>
+            <span class="nb">input</span> <span class="o">=</span> <span class="n">_parse_input_signature</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
+            <span class="n">input_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">input_list</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_signature</span><span class="p">,</span> <span class="n">Input</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span>
+        <span class="n">input_signature</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span>
+    <span class="p">):</span>
+        <span class="n">i</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">Input</span><span class="o">.</span><span class="n">_from_tensor</span><span class="p">(</span><span class="n">input_signature</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_signature</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span>
+            <span class="k">else</span> <span class="n">input_signature</span>
+        <span class="p">)</span>
+        <span class="n">clone</span> <span class="o">=</span> <span class="n">_internal_input_to_torch_class_input</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">())</span>
+        <span class="k">return</span> <span class="n">clone</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span>
+            <span class="s2">&quot;Input signature contains an unsupported type </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="nb">type</span><span class="p">(</span><span class="n">input_signature</span><span class="p">)</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_compile_spec</span><span class="p">(</span><span class="n">compile_spec_</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">_ts_C</span><span class="o">.</span><span class="n">CompileSpec</span><span class="p">:</span>
+    <span class="c1"># TODO: Use deepcopy to support partial compilation of collections</span>
+    <span class="n">compile_spec</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">compile_spec_</span><span class="p">)</span>
+    <span class="n">info</span> <span class="o">=</span> <span class="n">_ts_C</span><span class="o">.</span><span class="n">CompileSpec</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;inputs&quot;</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">Input</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;inputs&quot;</span><span class="p">]</span>
+            <span class="p">]</span>
+        <span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span>
+                <span class="s2">&quot;Input specs should be either torch_tensorrt.Input or torch.Tensor, found types: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="p">[</span><span class="nb">type</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;inputs&quot;</span><span class="p">]]</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">Input</span><span class="o">.</span><span class="n">_from_tensor</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">i</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;inputs&quot;</span><span class="p">]</span>
+        <span class="p">]</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">]</span>
+
+    <span class="k">elif</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;input_signature&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">log</span><span class="p">(</span>
+            <span class="n">Level</span><span class="o">.</span><span class="n">Warning</span><span class="p">,</span>
+            <span class="s2">&quot;Input signature parsing is an experimental feature, behavior and APIs may change&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">signature</span> <span class="o">=</span> <span class="n">_parse_input_signature</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;input_signature&quot;</span><span class="p">])</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">input_signature</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">InputSignature</span><span class="p">(</span><span class="n">signature</span><span class="p">)</span>  <span class="c1"># py_object</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;torch_fallback&quot;</span><span class="p">][</span><span class="s2">&quot;enabled&quot;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Grouped inputs currently requires partial compilation to be enabled, this restriction will be relaxed in a future release&quot;</span>
+            <span class="p">)</span>
+
+        <span class="n">log</span><span class="p">(</span>
+            <span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">,</span>
+            <span class="s2">&quot;Grouped inputs currently requires additional settings to enable the feature&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">log</span><span class="p">(</span>
+            <span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">,</span>
+            <span class="sd">&quot;&quot;&quot;Adding the following ops to torch_executed_ops:</span>
+<span class="sd">    - aten::__getitem__</span>
+<span class="sd">    - prim::ListConstruct</span>
+<span class="sd">    - prim::ListUnpack</span>
+<span class="sd">    - prim::TupleIndex</span>
+<span class="sd">    - prim::TupleConstruct</span>
+<span class="sd">    - prim::TupleUnpack</span>
+<span class="sd">&quot;&quot;&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;torch_fallback&quot;</span><span class="p">][</span><span class="s2">&quot;forced_fallback_ops&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+            <span class="s2">&quot;aten::__getitem__&quot;</span>
+        <span class="p">)</span>
+        <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;torch_fallback&quot;</span><span class="p">][</span><span class="s2">&quot;forced_fallback_ops&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+            <span class="s2">&quot;prim::ListConstruct&quot;</span>
+        <span class="p">)</span>
+        <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;torch_fallback&quot;</span><span class="p">][</span><span class="s2">&quot;forced_fallback_ops&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;prim::ListUnpack&quot;</span><span class="p">)</span>
+        <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;torch_fallback&quot;</span><span class="p">][</span><span class="s2">&quot;forced_fallback_ops&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;prim::TupleIndex&quot;</span><span class="p">)</span>
+        <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;torch_fallback&quot;</span><span class="p">][</span><span class="s2">&quot;forced_fallback_ops&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+            <span class="s2">&quot;prim::TupleConstruct&quot;</span>
+        <span class="p">)</span>
+        <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;torch_fallback&quot;</span><span class="p">][</span><span class="s2">&quot;forced_fallback_ops&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+            <span class="s2">&quot;prim::TupleUnpack&quot;</span>
+        <span class="p">)</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span>
+            <span class="s1">&#39;Module input definitions are requried to compile module. Provide a list of torch_tensorrt.Input keyed to &quot;inputs&quot; in the compile spec&#39;</span>
+        <span class="p">)</span>
+
+    <span class="k">if</span> <span class="s2">&quot;enabled_precisions&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">enabled_precisions</span> <span class="o">=</span> <span class="n">_parse_enabled_precisions</span><span class="p">(</span>
+            <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;enabled_precisions&quot;</span><span class="p">]</span>
+        <span class="p">)</span>
+
+    <span class="k">if</span> <span class="s2">&quot;calibrator&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span> <span class="ow">and</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;calibrator&quot;</span><span class="p">]:</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">ptq_calibrator</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;calibrator&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;sparse_weights&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;sparse_weights&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">sparse_weights</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;sparse_weights&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;disable_tf32&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;disable_tf32&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">disable_tf32</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;disable_tf32&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;refit&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;refit&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">refit</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;refit&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;debug&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;debug&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">debug</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;debug&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;device&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">_parse_device</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;device&quot;</span><span class="p">])</span>
+
+    <span class="k">if</span> <span class="s2">&quot;capability&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;capability&quot;</span><span class="p">],</span> <span class="n">_enums</span><span class="o">.</span><span class="n">EngineCapability</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">capability</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;capability&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;num_avg_timing_iters&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">int</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">num_avg_timing_iters</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;workspace_size&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;workspace_size&quot;</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">int</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">workspace_size</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;workspace_size&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;dla_sram_size&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;dla_sram_size&quot;</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">int</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">dla_sram_size</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;dla_sram_size&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;dla_local_dram_size&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;dla_local_dram_size&quot;</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">int</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">dla_local_dram_size</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;dla_local_dram_size&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;dla_global_dram_size&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;dla_global_dram_size&quot;</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">int</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">dla_global_dram_size</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;dla_global_dram_size&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;truncate_long_and_double&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">bool</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">truncate_long_and_double</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;torch_fallback&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">torch_fallback</span> <span class="o">=</span> <span class="n">_parse_torch_fallback</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;torch_fallback&quot;</span><span class="p">])</span>
+
+    <span class="n">log</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">info</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">info</span>
+
+
+<div class="viewcode-block" id="TensorRTCompileSpec"><a class="viewcode-back" href="../../../py_api/ts.html#torch_tensorrt.ts.TensorRTCompileSpec">[docs]</a><span class="k">def</span> <span class="nf">TensorRTCompileSpec</span><span class="p">(</span>
+    <span class="n">inputs</span><span class="o">=</span><span class="p">[],</span>
+    <span class="n">input_signature</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">device</span><span class="o">=</span><span class="n">Device</span><span class="o">.</span><span class="n">_current_device</span><span class="p">(),</span>
+    <span class="n">disable_tf32</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">sparse_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">enabled_precisions</span><span class="o">=</span><span class="nb">set</span><span class="p">(),</span>
+    <span class="n">refit</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">capability</span><span class="o">=</span><span class="n">_enums</span><span class="o">.</span><span class="n">EngineCapability</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
+    <span class="n">num_avg_timing_iters</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">workspace_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">dla_sram_size</span><span class="o">=</span><span class="mi">1048576</span><span class="p">,</span>
+    <span class="n">dla_local_dram_size</span><span class="o">=</span><span class="mi">1073741824</span><span class="p">,</span>
+    <span class="n">dla_global_dram_size</span><span class="o">=</span><span class="mi">536870912</span><span class="p">,</span>
+    <span class="n">truncate_long_and_double</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">calibrator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">CompileSpec</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Utility to create a formated spec dictionary for using the PyTorch TensorRT backend</span>
+
+<span class="sd">    Keyword Args:</span>
+<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
+<span class="sd">            to select device type. ::</span>
+
+<span class="sd">                input=[</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
+<span class="sd">                ]</span>
+
+<span class="sd">        device (Union(torch_tensorrt.Device, torch.device, dict)): Target device for TensorRT engines to run on ::</span>
+
+<span class="sd">            device=torch_tensorrt.Device(&quot;dla:1&quot;, allow_gpu_fallback=True)</span>
+
+<span class="sd">        disable_tf32 (bool): Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
+<span class="sd">        sparse_weights (bool): Enable sparsity for convolution and fully connected layers.</span>
+<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
+<span class="sd">        refit (bool): Enable refitting</span>
+<span class="sd">        debug (bool): Enable debuggable engine</span>
+<span class="sd">        capability (torch_tensorrt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+<span class="sd">        num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels</span>
+<span class="sd">        workspace_size (int): Maximum size of workspace given to TensorRT</span>
+<span class="sd">        truncate_long_and_double (bool): Truncate weights provided in int64 or double (float64) to int32 and float32</span>
+<span class="sd">        calibrator (Union(torch_tensorrt._C.IInt8Calibrator, tensorrt.IInt8Calibrator)): Calibrator object which will provide data to the PTQ system for INT8 Calibration</span>
+
+<span class="sd">      Returns:</span>
+<span class="sd">        torch.classes.tensorrt.CompileSpec: List of methods and formated spec objects to be provided to ``torch._C._jit_to_tensorrt``</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">compile_spec</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="s2">&quot;inputs&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">,</span>
+        <span class="c1"># &quot;input_signature&quot;: input_signature,</span>
+        <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span>
+        <span class="s2">&quot;disable_tf32&quot;</span><span class="p">:</span> <span class="n">disable_tf32</span><span class="p">,</span>  <span class="c1"># Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
+        <span class="s2">&quot;sparse_weights&quot;</span><span class="p">:</span> <span class="n">sparse_weights</span><span class="p">,</span>  <span class="c1"># Enable sparsity for convolution and fully connected layers.</span>
+        <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="n">enabled_precisions</span><span class="p">,</span>  <span class="c1"># Enabling FP16 kernels</span>
+        <span class="s2">&quot;refit&quot;</span><span class="p">:</span> <span class="n">refit</span><span class="p">,</span>  <span class="c1"># enable refit</span>
+        <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="n">debug</span><span class="p">,</span>  <span class="c1"># enable debuggable engine</span>
+        <span class="s2">&quot;capability&quot;</span><span class="p">:</span> <span class="n">capability</span><span class="p">,</span>  <span class="c1"># Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+        <span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">:</span> <span class="n">num_avg_timing_iters</span><span class="p">,</span>  <span class="c1"># Number of averaging timing iterations used to select kernels</span>
+        <span class="s2">&quot;workspace_size&quot;</span><span class="p">:</span> <span class="n">workspace_size</span><span class="p">,</span>  <span class="c1"># Maximum size of workspace given to TensorRT</span>
+        <span class="s2">&quot;dla_sram_size&quot;</span><span class="p">:</span> <span class="n">dla_sram_size</span><span class="p">,</span>  <span class="c1"># Fast software managed RAM used by DLA to communicate within a layer.</span>
+        <span class="s2">&quot;dla_local_dram_size&quot;</span><span class="p">:</span> <span class="n">dla_local_dram_size</span><span class="p">,</span>  <span class="c1"># Host RAM used by DLA to share intermediate tensor data across operations</span>
+        <span class="s2">&quot;dla_global_dram_size&quot;</span><span class="p">:</span> <span class="n">dla_global_dram_size</span><span class="p">,</span>  <span class="c1"># Host RAM used by DLA to store weights and metadata for execution</span>
+        <span class="s2">&quot;calibrator&quot;</span><span class="p">:</span> <span class="n">calibrator</span><span class="p">,</span>
+        <span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="n">truncate_long_and_double</span><span class="p">,</span>
+    <span class="p">}</span>
+
+    <span class="n">parsed_spec</span> <span class="o">=</span> <span class="n">_parse_compile_spec</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">)</span>
+
+    <span class="n">backend_spec</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">CompileSpec</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">input_signature</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="s2">&quot;Input signature parsing is not currently supported in the TorchScript backend integration&quot;</span>
+        <span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">parsed_spec</span><span class="o">.</span><span class="n">inputs</span><span class="p">:</span>
+        <span class="n">clone</span> <span class="o">=</span> <span class="n">_internal_input_to_torch_class_input</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+        <span class="n">backend_spec</span><span class="o">.</span><span class="n">_append_input</span><span class="p">(</span><span class="n">clone</span><span class="p">)</span>
+
+    <span class="n">d</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">_Device</span><span class="p">()</span>
+    <span class="n">d</span><span class="o">.</span><span class="n">_set_device_type</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">device_type</span><span class="p">))</span>
+    <span class="n">d</span><span class="o">.</span><span class="n">_set_gpu_id</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">gpu_id</span><span class="p">)</span>
+    <span class="n">d</span><span class="o">.</span><span class="n">_set_dla_core</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">dla_core</span><span class="p">)</span>
+    <span class="n">d</span><span class="o">.</span><span class="n">_set_allow_gpu_fallback</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">allow_gpu_fallback</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">parsed_spec</span><span class="o">.</span><span class="n">torch_fallback</span><span class="o">.</span><span class="n">enabled</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+            <span class="s2">&quot;Partial module compilation is not currently supported via the PyTorch TensorRT backend. If you need partial compilation, use torch_tensorrt.compile&quot;</span>
+        <span class="p">)</span>
+
+    <span class="n">torch_fallback</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">_TorchFallback</span><span class="p">()</span>
+    <span class="n">torch_fallback</span><span class="o">.</span><span class="n">_set_enabled</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">torch_fallback</span><span class="o">.</span><span class="n">enabled</span><span class="p">)</span>
+    <span class="n">torch_fallback</span><span class="o">.</span><span class="n">_set_min_block_size</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">torch_fallback</span><span class="o">.</span><span class="n">min_block_size</span><span class="p">)</span>
+    <span class="n">torch_fallback</span><span class="o">.</span><span class="n">_set_forced_fallback_operators</span><span class="p">(</span>
+        <span class="n">parsed_spec</span><span class="o">.</span><span class="n">torch_fallback</span><span class="o">.</span><span class="n">forced_fallback_operators</span>
+    <span class="p">)</span>
+    <span class="n">torch_fallback</span><span class="o">.</span><span class="n">_set_forced_fallback_modules</span><span class="p">(</span>
+        <span class="n">parsed_spec</span><span class="o">.</span><span class="n">torch_fallback</span><span class="o">.</span><span class="n">forced_fallback_modules</span>
+    <span class="p">)</span>
+
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_device</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_torch_fallback</span><span class="p">(</span><span class="n">torch_fallback</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_precisions</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">parsed_spec</span><span class="o">.</span><span class="n">enabled_precisions</span><span class="p">])</span>
+
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_disable_tf32</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">disable_tf32</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_refit</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">refit</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_debug</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">debug</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_refit</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">refit</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_capability</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">capability</span><span class="p">))</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_num_avg_timing_iters</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">num_avg_timing_iters</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_workspace_size</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">workspace_size</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_dla_sram_size</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">dla_sram_size</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_dla_local_dram_size</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">dla_local_dram_size</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_dla_global_dram_size</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">dla_global_dram_size</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_truncate_long_and_double</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">truncate_long_and_double</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_ptq_calibrator</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">_get_calibrator_handle</span><span class="p">())</span>
+
+    <span class="k">return</span> <span class="n">backend_spec</span></div>
+</pre></div>
+
+             </article>
+
+            </div>
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+
+  <h1>Source code for torch_tensorrt.ts._compiler</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+
+<span class="kn">import</span> <span class="nn">torch_tensorrt._C.ts</span> <span class="k">as</span> <span class="nn">_C</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_enums</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.ts._compile_spec</span> <span class="kn">import</span> <span class="n">_parse_compile_spec</span><span class="p">,</span> <span class="n">_parse_device</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._Device</span> <span class="kn">import</span> <span class="n">Device</span>
+<span class="kn">from</span> <span class="nn">types</span> <span class="kn">import</span> <span class="n">FunctionType</span>
+
+
+<div class="viewcode-block" id="compile"><a class="viewcode-back" href="../../../py_api/ts.html#torch_tensorrt.ts.compile">[docs]</a><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span>
+    <span class="n">module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">,</span>
+    <span class="n">inputs</span><span class="o">=</span><span class="p">[],</span>
+    <span class="n">input_signature</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">device</span><span class="o">=</span><span class="n">Device</span><span class="o">.</span><span class="n">_current_device</span><span class="p">(),</span>
+    <span class="n">disable_tf32</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">sparse_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">enabled_precisions</span><span class="o">=</span><span class="nb">set</span><span class="p">(),</span>
+    <span class="n">refit</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">capability</span><span class="o">=</span><span class="n">_enums</span><span class="o">.</span><span class="n">EngineCapability</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
+    <span class="n">num_avg_timing_iters</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">workspace_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">dla_sram_size</span><span class="o">=</span><span class="mi">1048576</span><span class="p">,</span>
+    <span class="n">dla_local_dram_size</span><span class="o">=</span><span class="mi">1073741824</span><span class="p">,</span>
+    <span class="n">dla_global_dram_size</span><span class="o">=</span><span class="mi">536870912</span><span class="p">,</span>
+    <span class="n">calibrator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">truncate_long_and_double</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">require_full_compilation</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">min_block_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+    <span class="n">torch_executed_ops</span><span class="o">=</span><span class="p">[],</span>
+    <span class="n">torch_executed_modules</span><span class="o">=</span><span class="p">[],</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Compile a TorchScript module for NVIDIA GPUs using TensorRT</span>
+
+<span class="sd">    Takes a existing TorchScript module and a set of settings to configure the compiler</span>
+<span class="sd">    and will convert methods to JIT Graphs which call equivalent TensorRT engines</span>
+
+<span class="sd">    Converts specifically the forward method of a TorchScript Module</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        module (torch.jit.ScriptModule): Source module, a result of tracing or scripting a PyTorch</span>
+<span class="sd">            ``torch.nn.Module``</span>
+
+<span class="sd">    Keyword Arguments:</span>
+<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
+<span class="sd">            to select device type. ::</span>
+
+<span class="sd">                input=[</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
+<span class="sd">                ]</span>
+
+<span class="sd">        input_signature Union(List, Tuple, torch_tensorrt.Input, torch.Tensor): A formatted collection of input specifications for the module. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum to select device type. **This API should be considered beta-level stable and may change in the future** ::</span>
+
+<span class="sd">                input_signature=([</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                ], torch.randn((1, 3, 224, 244))) # Use an example tensor and let torch_tensorrt infer settings for input #3</span>
+<span class="sd">        device (Union(torch_tensorrt.Device, torch.device, dict)): Target device for TensorRT engines to run on ::</span>
+
+<span class="sd">            device=torch_tensorrt.Device(&quot;dla:1&quot;, allow_gpu_fallback=True)</span>
+
+<span class="sd">        disable_tf32 (bool): Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
+<span class="sd">        sparse_weights (bool): Enable sparsity for convolution and fully connected layers.</span>
+<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
+<span class="sd">        refit (bool): Enable refitting</span>
+<span class="sd">        debug (bool): Enable debuggable engine</span>
+<span class="sd">        capability (torch_tensorrt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+<span class="sd">        num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels</span>
+<span class="sd">        workspace_size (int): Maximum size of workspace given to TensorRT</span>
+<span class="sd">        dla_sram_size (int): Fast software managed RAM used by DLA to communicate within a layer.</span>
+<span class="sd">        dla_local_dram_size (int): Host RAM used by DLA to share intermediate tensor data across operations</span>
+<span class="sd">        dla_global_dram_size (int): Host RAM used by DLA to store weights and metadata for execution</span>
+<span class="sd">        truncate_long_and_double (bool): Truncate weights provided in int64 or double (float64) to int32 and float32</span>
+<span class="sd">        calibrator (Union(torch_tensorrt._C.IInt8Calibrator, tensorrt.IInt8Calibrator)): Calibrator object which will provide data to the PTQ system for INT8 Calibration</span>
+<span class="sd">        require_full_compilation (bool): Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</span>
+<span class="sd">        min_block_size (int): The minimum number of contiguous TensorRT convertable operations in order to run a set of operations in TensorRT</span>
+<span class="sd">        torch_executed_ops (List[str]): List of aten operators that must be run in PyTorch. An error will be thrown if this list is not empty but ``require_full_compilation`` is True</span>
+<span class="sd">        torch_executed_modules (List[str]): List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but ``require_full_compilation`` is True</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.jit.ScriptModule: Compiled TorchScript Module, when run it will execute via TensorRT</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptFunction</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+            <span class="s2">&quot;torch.jit.ScriptFunction currently is not directly supported, wrap the function in a module to compile&quot;</span>
+        <span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">require_full_compilation</span> <span class="ow">and</span> <span class="p">(</span>
+        <span class="nb">len</span><span class="p">(</span><span class="n">torch_executed_modules</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">torch_executed_ops</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
+    <span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;require_full_compilation is enabled however the list of modules and ops to run in torch is not empty. Found: torch_executed_ops: </span><span class="si">{</span><span class="n">torch_executed_ops</span><span class="si">}</span><span class="s2">, torch_executed_modules: </span><span class="si">{</span><span class="n">torch_executed_modules</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+
+    <span class="n">spec</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="s2">&quot;inputs&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">,</span>
+        <span class="s2">&quot;input_signature&quot;</span><span class="p">:</span> <span class="n">input_signature</span><span class="p">,</span>
+        <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span>
+        <span class="s2">&quot;disable_tf32&quot;</span><span class="p">:</span> <span class="n">disable_tf32</span><span class="p">,</span>  <span class="c1"># Force FP32 layers to use traditional as FP32 format</span>
+        <span class="s2">&quot;sparse_weights&quot;</span><span class="p">:</span> <span class="n">sparse_weights</span><span class="p">,</span>  <span class="c1"># Enable sparsity for convolution and fully connected layers.</span>
+        <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="n">enabled_precisions</span><span class="p">,</span>  <span class="c1"># Enabling FP16 kernels</span>
+        <span class="s2">&quot;refit&quot;</span><span class="p">:</span> <span class="n">refit</span><span class="p">,</span>  <span class="c1"># enable refit</span>
+        <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="n">debug</span><span class="p">,</span>  <span class="c1"># enable debuggable engine</span>
+        <span class="s2">&quot;capability&quot;</span><span class="p">:</span> <span class="n">capability</span><span class="p">,</span>  <span class="c1"># Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+        <span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">:</span> <span class="n">num_avg_timing_iters</span><span class="p">,</span>  <span class="c1"># Number of averaging timing iterations used to select kernels</span>
+        <span class="s2">&quot;workspace_size&quot;</span><span class="p">:</span> <span class="n">workspace_size</span><span class="p">,</span>  <span class="c1"># Maximum size of workspace given to TensorRT</span>
+        <span class="s2">&quot;calibrator&quot;</span><span class="p">:</span> <span class="n">calibrator</span><span class="p">,</span>
+        <span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="n">truncate_long_and_double</span><span class="p">,</span>
+        <span class="s2">&quot;torch_fallback&quot;</span><span class="p">:</span> <span class="p">{</span>
+            <span class="s2">&quot;enabled&quot;</span><span class="p">:</span> <span class="ow">not</span> <span class="n">require_full_compilation</span><span class="p">,</span>
+            <span class="s2">&quot;forced_fallback_ops&quot;</span><span class="p">:</span> <span class="n">torch_executed_ops</span><span class="p">,</span>
+            <span class="s2">&quot;forced_fallback_modules&quot;</span><span class="p">:</span> <span class="n">torch_executed_modules</span><span class="p">,</span>
+            <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="n">min_block_size</span><span class="p">,</span>
+        <span class="p">},</span>
+    <span class="p">}</span>
+
+    <span class="n">compiled_cpp_mod</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">compile_graph</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">_c</span><span class="p">,</span> <span class="n">_parse_compile_spec</span><span class="p">(</span><span class="n">spec</span><span class="p">))</span>
+    <span class="n">compiled_module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">_recursive</span><span class="o">.</span><span class="n">wrap_cpp_module</span><span class="p">(</span><span class="n">compiled_cpp_mod</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">compiled_module</span></div>
+
+
+<div class="viewcode-block" id="convert_method_to_trt_engine"><a class="viewcode-back" href="../../../py_api/ts.html#torch_tensorrt.ts.convert_method_to_trt_engine">[docs]</a><span class="k">def</span> <span class="nf">convert_method_to_trt_engine</span><span class="p">(</span>
+    <span class="n">module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">,</span>
+    <span class="n">method_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">inputs</span><span class="o">=</span><span class="p">[],</span>
+    <span class="n">device</span><span class="o">=</span><span class="n">Device</span><span class="o">.</span><span class="n">_current_device</span><span class="p">(),</span>
+    <span class="n">disable_tf32</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">sparse_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">enabled_precisions</span><span class="o">=</span><span class="nb">set</span><span class="p">(),</span>
+    <span class="n">refit</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">capability</span><span class="o">=</span><span class="n">_enums</span><span class="o">.</span><span class="n">EngineCapability</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
+    <span class="n">num_avg_timing_iters</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">workspace_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">dla_sram_size</span><span class="o">=</span><span class="mi">1048576</span><span class="p">,</span>
+    <span class="n">dla_local_dram_size</span><span class="o">=</span><span class="mi">1073741824</span><span class="p">,</span>
+    <span class="n">dla_global_dram_size</span><span class="o">=</span><span class="mi">536870912</span><span class="p">,</span>
+    <span class="n">truncate_long_and_double</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">calibrator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Convert a TorchScript module method to a serialized TensorRT engine</span>
+
+<span class="sd">    Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        module (torch.jit.ScriptModule): Source module, a result of tracing or scripting a PyTorch</span>
+<span class="sd">            ``torch.nn.Module``</span>
+<span class="sd">        method_name (str): Name of method to convert</span>
+
+<span class="sd">    Keyword Args:</span>
+<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
+<span class="sd">            to select device type. ::</span>
+
+<span class="sd">                input=[</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
+<span class="sd">                ]</span>
+
+<span class="sd">        input_signature Union(List, Tuple, torch_tensorrt.Input, torch.Tensor): A formatted collection of input specifications for the module. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum to select device type. **This API should be considered beta-level stable and may change in the future** ::</span>
+
+<span class="sd">                input_signature=([</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                ], torch.randn((1, 3, 224, 244))) # Use an example tensor and let torch_tensorrt infer settings for input #3</span>
+
+<span class="sd">        device (Union(torch_tensorrt.Device, torch.device, dict)): Target device for TensorRT engines to run on ::</span>
+
+<span class="sd">            device=torch_tensorrt.Device(&quot;dla:1&quot;, allow_gpu_fallback=True)</span>
+
+<span class="sd">        disable_tf32 (bool): Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
+<span class="sd">        sparse_weights (bool): Enable sparsity for convolution and fully connected layers.</span>
+<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
+<span class="sd">        refit (bool): Enable refitting</span>
+<span class="sd">        debug (bool): Enable debuggable engine</span>
+<span class="sd">        capability (torch_tensorrt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+<span class="sd">        num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels</span>
+<span class="sd">        workspace_size (int): Maximum size of workspace given to TensorRT</span>
+<span class="sd">        dla_sram_size (int): Fast software managed RAM used by DLA to communicate within a layer.</span>
+<span class="sd">        dla_local_dram_size (int): Host RAM used by DLA to share intermediate tensor data across operations</span>
+<span class="sd">        dla_global_dram_size (int): Host RAM used by DLA to store weights and metadata for execution</span>
+<span class="sd">        truncate_long_and_double (bool): Truncate weights provided in int64 or double (float64) to int32 and float32</span>
+<span class="sd">        calibrator (Union(torch_tensorrt._C.IInt8Calibrator, tensorrt.IInt8Calibrator)): Calibrator object which will provide data to the PTQ system for INT8 Calibration</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        bytes: Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptFunction</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+            <span class="s2">&quot;torch.jit.ScriptFunctions currently are not directly supported, wrap the function in a module to compile&quot;</span>
+        <span class="p">)</span>
+
+    <span class="n">compile_spec</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="s2">&quot;inputs&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">,</span>
+        <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span>
+        <span class="s2">&quot;disable_tf32&quot;</span><span class="p">:</span> <span class="n">disable_tf32</span><span class="p">,</span>  <span class="c1"># Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
+        <span class="s2">&quot;sparse_weights&quot;</span><span class="p">:</span> <span class="n">sparse_weights</span><span class="p">,</span>  <span class="c1"># Enable sparsity for convolution and fully connected layers.</span>
+        <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="n">enabled_precisions</span><span class="p">,</span>  <span class="c1"># Enabling FP16 kernels</span>
+        <span class="s2">&quot;refit&quot;</span><span class="p">:</span> <span class="n">refit</span><span class="p">,</span>  <span class="c1"># enable refit</span>
+        <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="n">debug</span><span class="p">,</span>  <span class="c1"># enable debuggable engine</span>
+        <span class="s2">&quot;capability&quot;</span><span class="p">:</span> <span class="n">capability</span><span class="p">,</span>  <span class="c1"># Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+        <span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">:</span> <span class="n">num_avg_timing_iters</span><span class="p">,</span>  <span class="c1"># Number of averaging timing iterations used to select kernels</span>
+        <span class="s2">&quot;workspace_size&quot;</span><span class="p">:</span> <span class="n">workspace_size</span><span class="p">,</span>  <span class="c1"># Maximum size of workspace given to TensorRT</span>
+        <span class="s2">&quot;calibrator&quot;</span><span class="p">:</span> <span class="n">calibrator</span><span class="p">,</span>
+        <span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="n">truncate_long_and_double</span><span class="p">,</span>
+    <span class="p">}</span>
+
+    <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">convert_graph_to_trt_engine</span><span class="p">(</span>
+        <span class="n">module</span><span class="o">.</span><span class="n">_c</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="n">_parse_compile_spec</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">)</span>
+    <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="embed_engine_in_new_module"><a class="viewcode-back" href="../../../py_api/ts.html#torch_tensorrt.ts.embed_engine_in_new_module">[docs]</a><span class="k">def</span> <span class="nf">embed_engine_in_new_module</span><span class="p">(</span>
+    <span class="n">serialized_engine</span><span class="p">:</span> <span class="nb">bytes</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">Device</span><span class="o">.</span><span class="n">_current_device</span><span class="p">()</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Takes a pre-built serialized TensorRT engine and embeds it within a TorchScript module</span>
+
+<span class="sd">    Takes a pre-built serialied TensorRT engine (as bytes) and embeds it within a TorchScript module.</span>
+<span class="sd">    Registers the forward method to execute the TensorRT engine with the function signature:</span>
+
+<span class="sd">        forward(Tensor[]) -&gt; Tensor[]</span>
+
+<span class="sd">    TensorRT bindings must have names with the following format:</span>
+<span class="sd">      - [symbol].[index in input / output array]</span>
+<span class="sd">      ex.</span>
+<span class="sd">      - [x.0, x.1, x.2] -&gt; [y.0]</span>
+
+<span class="sd">    Module can be save with engine embedded with torch.jit.save and moved / loaded according to torch_tensorrt portability rules</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        serialized_engine (bytes): Serialized TensorRT engine from either torch_tensorrt or TensorRT APIs</span>
+
+<span class="sd">    Keyword Arguments:</span>
+<span class="sd">        device (Union(torch_tensorrt.Device, torch.device, dict)): Target device to run engine on. Must be compatible with engine provided. Default: Current active device</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.jit.ScriptModule: New TorchScript module with engine embedded</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">cpp_mod</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">embed_engine_in_new_module</span><span class="p">(</span><span class="n">serialized_engine</span><span class="p">,</span> <span class="n">_parse_device</span><span class="p">(</span><span class="n">device</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">_recursive</span><span class="o">.</span><span class="n">wrap_cpp_module</span><span class="p">(</span><span class="n">cpp_mod</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="check_method_op_support"><a class="viewcode-back" href="../../../py_api/ts.html#torch_tensorrt.ts.check_method_op_support">[docs]</a><span class="k">def</span> <span class="nf">check_method_op_support</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">,</span> <span class="n">method_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Checks to see if a method is fully supported by torch_tensorrt</span>
+
+<span class="sd">    Checks if a method of a TorchScript module can be compiled by torch_tensorrt, if not, a list of operators</span>
+<span class="sd">    that are not supported are printed out and the function returns false, else true.</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        module (torch.jit.ScriptModule): Source module, a result of tracing or scripting a PyTorch</span>
+<span class="sd">            ``torch.nn.Module``</span>
+<span class="sd">        method_name (str): Name of method to check</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        bool: True if supported Method</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">check_method_op_support</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">_c</span><span class="p">,</span> <span class="n">method_name</span><span class="p">)</span></div>
+</pre></div>
+
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
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+
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+.prompt .copybtn {
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+</style>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2019 NVIDIA Corporation. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an &quot;AS IS&quot; BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+</pre></div>
+</div>
+</div>
+<p><img alt="198e4deb2921411abba2a37241da72ab" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png" /></p>
+<section id="Torch-TensorRT-Getting-Started---CitriNet">
+<h1>Torch-TensorRT Getting Started - CitriNet<a class="headerlink" href="#Torch-TensorRT-Getting-Started---CitriNet" title="Permalink to this headline">¶</a></h1>
+<section id="Overview">
+<h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference external" href="https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#citrinet">Citrinet</a> is an acoustic model used for the speech to text recognition task. It is a version of <a class="reference external" href="https://arxiv.org/pdf/1910.10261.pdf">QuartzNet</a> that extends <a class="reference external" href="https://arxiv.org/pdf/2005.03191.pdf">ContextNet</a>, utilizing subword encoding (via Word Piece tokenization) and Squeeze-and-Excitation(SE) mechanism and are therefore smaller than QuartzNet models.</p>
+<p>CitriNet models take in audio segments and transcribe them to letter, byte pair, or word piece sequences.</p>
+<p><img alt="alt" class="no-scaled-link" src="https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/_images/jasper_vertical.png" style="width: 50%;" /></p>
+<section id="Learning-objectives">
+<h3>Learning objectives<a class="headerlink" href="#Learning-objectives" title="Permalink to this headline">¶</a></h3>
+<p>This notebook demonstrates the steps for optimizing a pretrained CitriNet model with Torch-TensorRT, and running it to test the speedup obtained.</p>
+</section>
+</section>
+<section id="Content">
+<h2>Content<a class="headerlink" href="#Content" title="Permalink to this headline">¶</a></h2>
+<ol class="arabic simple">
+<li><p><a class="reference external" href="#1">Requirements</a></p></li>
+<li><p><a class="reference external" href="#2">Download Citrinet model</a></p></li>
+<li><p><a class="reference external" href="#3">Create Torch-TensorRT modules</a></p></li>
+<li><p><a class="reference external" href="#4">Benchmark Torch-TensorRT models</a></p></li>
+<li><p><a class="reference external" href="#5">Conclusion</a></p></li>
+</ol>
+<p>## 1. Requirements</p>
+<p>Follow the steps in <a class="reference external" href="README.md">README</a> to prepare a Docker container, within which you can run this notebook. This notebook assumes that you are within a Jupyter environment in a docker container with Torch-TensorRT installed, such as an NGC monthly release of <code class="docutils literal notranslate"><span class="pre">nvcr.io/nvidia/pytorch:&lt;yy.mm&gt;-py3</span></code> (where <code class="docutils literal notranslate"><span class="pre">yy</span></code> indicates the last two numbers of a calendar year, and <code class="docutils literal notranslate"><span class="pre">mm</span></code> indicates the month in two-digit numerical form)</p>
+<p>Now that you are in the docker, the next step is to install the required dependencies.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Install dependencies
+!pip install wget
+!apt-get update &amp;&amp; DEBIAN_FRONTEND=noninteractive  apt-get install -y libsndfile1 ffmpeg
+!pip install Cython
+
+## Install NeMo
+!pip install nemo_toolkit[all]==1.5.1
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
+Requirement already satisfied: wget in /opt/conda/lib/python3.8/site-packages (3.2)
+<span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
+Hit:1 http://security.ubuntu.com/ubuntu focal-security InRelease
+Hit:2 http://archive.ubuntu.com/ubuntu focal InRelease
+Hit:3 http://archive.ubuntu.com/ubuntu focal-updates InRelease
+Hit:4 http://archive.ubuntu.com/ubuntu focal-backports InRelease
+Reading package lists... Done
+Reading package lists... Done
+Building dependency tree
+Reading state information... Done
+libsndfile1 is already the newest version (1.0.28-7ubuntu0.1).
+ffmpeg is already the newest version (7:4.2.4-1ubuntu0.1).
+0 upgraded, 0 newly installed, 0 to remove and 22 not upgraded.
+Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
+Requirement already satisfied: Cython in /opt/conda/lib/python3.8/site-packages (0.29.28)
+<span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
+Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
+Requirement already satisfied: nemo_toolkit[all]==1.5.1 in /opt/conda/lib/python3.8/site-packages (1.5.1)
+Requirement already satisfied: numpy&gt;=1.18.2 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.22.3)
+Requirement already satisfied: onnx&gt;=1.7.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.10.1)
+Requirement already satisfied: python-dateutil in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.8.2)
+Requirement already satisfied: tqdm&gt;=4.41.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.63.0)
+Requirement already satisfied: sentencepiece&lt;1.0.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.1.96)
+Requirement already satisfied: wget in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (3.2)
+Requirement already satisfied: numba in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.53.1)
+Requirement already satisfied: torch in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.12.0a0+2c916ef)
+Requirement already satisfied: unidecode in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.3.4)
+Requirement already satisfied: frozendict in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.3.2)
+Requirement already satisfied: wrapt in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.14.0)
+Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.24.2)
+Requirement already satisfied: ruamel.yaml in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.17.21)
+Requirement already satisfied: pesq in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.0.3)
+Requirement already satisfied: torchvision in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.13.0a0)
+Requirement already satisfied: gdown in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.4.0)
+Requirement already satisfied: editdistance in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.6.0)
+Requirement already satisfied: boto3 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.21.45)
+Requirement already satisfied: isort[requirements]&lt;5 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.3.21)
+Requirement already satisfied: hydra-core&gt;=1.1.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.1.2)
+Requirement already satisfied: youtokentome&gt;=1.0.5 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.0.6)
+Requirement already satisfied: pytorch-lightning&gt;=1.5.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.6.1)
+Requirement already satisfied: jieba in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.42.1)
+Requirement already satisfied: fasttext in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.9.2)
+Requirement already satisfied: soundfile in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.10.3.post1)
+Requirement already satisfied: kaldiio in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.17.2)
+Requirement already satisfied: pangu in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.0.6.1)
+Requirement already satisfied: kaldi-python-io in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.2.2)
+Requirement already satisfied: parameterized in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.8.1)
+Requirement already satisfied: h5py in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (3.6.0)
+Requirement already satisfied: rapidfuzz in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.0.10)
+Requirement already satisfied: marshmallow in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (3.15.0)
+Requirement already satisfied: opencc in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.1.3)
+Requirement already satisfied: braceexpand in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.1.7)
+Requirement already satisfied: omegaconf&gt;=2.1.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.1.2)
+Requirement already satisfied: sphinx in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.4.0)
+Requirement already satisfied: pillow in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (9.0.0)
+Requirement already satisfied: wordninja==2.0.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.0.0)
+Requirement already satisfied: torch-stft in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.1.4)
+Requirement already satisfied: sox in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.4.1)
+Requirement already satisfied: librosa in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.9.1)
+Requirement already satisfied: regex in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2022.3.15)
+Requirement already satisfied: sacrebleu[ja] in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.0.0)
+Requirement already satisfied: black==19.10b0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (19.10b0)
+Requirement already satisfied: pydub in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.25.1)
+Requirement already satisfied: sphinxcontrib-bibtex in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.4.2)
+Requirement already satisfied: inflect in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (5.5.2)
+Requirement already satisfied: pyannote.core in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.4)
+Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (21.3)
+Requirement already satisfied: kaldi-io in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.9.4)
+Requirement already satisfied: pyannote.metrics in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (3.2)
+Requirement already satisfied: g2p-en in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.1.0)
+Requirement already satisfied: matplotlib in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (3.5.1)
+Requirement already satisfied: torchmetrics&gt;=0.4.1rc0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.8.0)
+Requirement already satisfied: nltk&gt;=3.6.5 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (3.7)
+Requirement already satisfied: pyyaml&lt;6 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (5.4.1)
+Requirement already satisfied: scipy in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.6.3)
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+Requirement already satisfied: pytest in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (6.2.5)
+Requirement already satisfied: pandas in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.3.5)
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+Requirement already satisfied: transformers&gt;=4.0.1 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.18.0)
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+Requirement already satisfied: appdirs in /opt/conda/lib/python3.8/site-packages (from black==19.10b0-&gt;nemo_toolkit[all]==1.5.1) (1.4.4)
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+Requirement already satisfied: pathspec&lt;1,&gt;=0.6 in /opt/conda/lib/python3.8/site-packages (from black==19.10b0-&gt;nemo_toolkit[all]==1.5.1) (0.9.0)
+Requirement already satisfied: click&gt;=6.5 in /opt/conda/lib/python3.8/site-packages (from black==19.10b0-&gt;nemo_toolkit[all]==1.5.1) (8.0.4)
+Requirement already satisfied: toml&gt;=0.9.4 in /opt/conda/lib/python3.8/site-packages (from black==19.10b0-&gt;nemo_toolkit[all]==1.5.1) (0.10.2)
+Requirement already satisfied: antlr4-python3-runtime==4.8 in /opt/conda/lib/python3.8/site-packages (from hydra-core&gt;=1.1.0-&gt;nemo_toolkit[all]==1.5.1) (4.8)
+Requirement already satisfied: importlib-resources&lt;5.3 in /opt/conda/lib/python3.8/site-packages (from hydra-core&gt;=1.1.0-&gt;nemo_toolkit[all]==1.5.1) (5.2.3)
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+Requirement already satisfied: kiwisolver&gt;=1.0.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib-&gt;nemo_toolkit[all]==1.5.1) (1.4.0)
+Requirement already satisfied: pyparsing&gt;=2.2.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib-&gt;nemo_toolkit[all]==1.5.1) (3.0.7)
+Requirement already satisfied: cycler&gt;=0.10 in /opt/conda/lib/python3.8/site-packages (from matplotlib-&gt;nemo_toolkit[all]==1.5.1) (0.11.0)
+Requirement already satisfied: joblib in /opt/conda/lib/python3.8/site-packages (from nltk&gt;=3.6.5-&gt;nemo_toolkit[all]==1.5.1) (1.1.0)
+Requirement already satisfied: typing-extensions&gt;=3.6.2.1 in /opt/conda/lib/python3.8/site-packages (from onnx&gt;=1.7.0-&gt;nemo_toolkit[all]==1.5.1) (4.1.1)
+Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from onnx&gt;=1.7.0-&gt;nemo_toolkit[all]==1.5.1) (1.16.0)
+Requirement already satisfied: protobuf&gt;=3.12.2 in /opt/conda/lib/python3.8/site-packages (from onnx&gt;=1.7.0-&gt;nemo_toolkit[all]==1.5.1) (3.19.4)
+Requirement already satisfied: pyDeprecate&lt;0.4.0,&gt;=0.3.1 in /opt/conda/lib/python3.8/site-packages (from pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (0.3.2)
+Requirement already satisfied: tensorboard&gt;=2.2.0 in /opt/conda/lib/python3.8/site-packages (from pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (2.8.0)
+Requirement already satisfied: fsspec[http]!=2021.06.0,&gt;=2021.05.0 in /opt/conda/lib/python3.8/site-packages (from pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (2022.2.0)
+Requirement already satisfied: requests in /opt/conda/lib/python3.8/site-packages (from fsspec[http]!=2021.06.0,&gt;=2021.05.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (2.27.1)
+Requirement already satisfied: aiohttp in /opt/conda/lib/python3.8/site-packages (from fsspec[http]!=2021.06.0,&gt;=2021.05.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (3.8.1)
+Requirement already satisfied: werkzeug&gt;=0.11.15 in /opt/conda/lib/python3.8/site-packages (from tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (2.0.3)
+Requirement already satisfied: markdown&gt;=2.6.8 in /opt/conda/lib/python3.8/site-packages (from tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (3.3.6)
+Requirement already satisfied: setuptools&gt;=41.0.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (59.5.0)
+Requirement already satisfied: google-auth-oauthlib&lt;0.5,&gt;=0.4.1 in /opt/conda/lib/python3.8/site-packages (from tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (0.4.6)
+Requirement already satisfied: google-auth&lt;3,&gt;=1.6.3 in /opt/conda/lib/python3.8/site-packages (from tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (2.6.2)
+Requirement already satisfied: wheel&gt;=0.26 in /opt/conda/lib/python3.8/site-packages (from tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (0.37.1)
+Requirement already satisfied: grpcio&gt;=1.24.3 in /opt/conda/lib/python3.8/site-packages (from tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (1.44.0)
+Requirement already satisfied: tensorboard-data-server&lt;0.7.0,&gt;=0.6.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (0.6.1)
+Requirement already satisfied: tensorboard-plugin-wit&gt;=1.6.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (1.8.1)
+Requirement already satisfied: absl-py&gt;=0.4 in /opt/conda/lib/python3.8/site-packages (from tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (1.0.0)
+Requirement already satisfied: cachetools&lt;6.0,&gt;=2.0.0 in /opt/conda/lib/python3.8/site-packages (from google-auth&lt;3,&gt;=1.6.3-&gt;tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (5.0.0)
+Requirement already satisfied: pyasn1-modules&gt;=0.2.1 in /opt/conda/lib/python3.8/site-packages (from google-auth&lt;3,&gt;=1.6.3-&gt;tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (0.2.8)
+Requirement already satisfied: rsa&lt;5,&gt;=3.1.4 in /opt/conda/lib/python3.8/site-packages (from google-auth&lt;3,&gt;=1.6.3-&gt;tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (4.8)
+Requirement already satisfied: requests-oauthlib&gt;=0.7.0 in /opt/conda/lib/python3.8/site-packages (from google-auth-oauthlib&lt;0.5,&gt;=0.4.1-&gt;tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (1.3.1)
+Requirement already satisfied: importlib-metadata&gt;=4.4 in /opt/conda/lib/python3.8/site-packages (from markdown&gt;=2.6.8-&gt;tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (4.11.3)
+Requirement already satisfied: pyasn1&lt;0.5.0,&gt;=0.4.6 in /opt/conda/lib/python3.8/site-packages (from pyasn1-modules&gt;=0.2.1-&gt;google-auth&lt;3,&gt;=1.6.3-&gt;tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (0.4.8)
+Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.8/site-packages (from requests-&gt;fsspec[http]!=2021.06.0,&gt;=2021.05.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (2.0.12)
+Requirement already satisfied: certifi&gt;=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests-&gt;fsspec[http]!=2021.06.0,&gt;=2021.05.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (2021.10.8)
+Requirement already satisfied: idna&lt;4,&gt;=2.5 in /opt/conda/lib/python3.8/site-packages (from requests-&gt;fsspec[http]!=2021.06.0,&gt;=2021.05.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (3.3)
+Requirement already satisfied: urllib3&lt;1.27,&gt;=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests-&gt;fsspec[http]!=2021.06.0,&gt;=2021.05.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (1.26.8)
+Requirement already satisfied: oauthlib&gt;=3.0.0 in /opt/conda/lib/python3.8/site-packages (from requests-oauthlib&gt;=0.7.0-&gt;google-auth-oauthlib&lt;0.5,&gt;=0.4.1-&gt;tensorboard&gt;=2.2.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (3.2.0)
+Requirement already satisfied: huggingface-hub&lt;1.0,&gt;=0.1.0 in /opt/conda/lib/python3.8/site-packages (from transformers&gt;=4.0.1-&gt;nemo_toolkit[all]==1.5.1) (0.5.1)
+Requirement already satisfied: tokenizers!=0.11.3,&lt;0.13,&gt;=0.11.1 in /opt/conda/lib/python3.8/site-packages (from transformers&gt;=4.0.1-&gt;nemo_toolkit[all]==1.5.1) (0.12.1)
+Requirement already satisfied: filelock in /opt/conda/lib/python3.8/site-packages (from transformers&gt;=4.0.1-&gt;nemo_toolkit[all]==1.5.1) (3.6.0)
+Requirement already satisfied: frozenlist&gt;=1.1.1 in /opt/conda/lib/python3.8/site-packages (from aiohttp-&gt;fsspec[http]!=2021.06.0,&gt;=2021.05.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (1.3.0)
+Requirement already satisfied: yarl&lt;2.0,&gt;=1.0 in /opt/conda/lib/python3.8/site-packages (from aiohttp-&gt;fsspec[http]!=2021.06.0,&gt;=2021.05.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (1.7.2)
+Requirement already satisfied: async-timeout&lt;5.0,&gt;=4.0.0a3 in /opt/conda/lib/python3.8/site-packages (from aiohttp-&gt;fsspec[http]!=2021.06.0,&gt;=2021.05.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (4.0.2)
+Requirement already satisfied: multidict&lt;7.0,&gt;=4.5 in /opt/conda/lib/python3.8/site-packages (from aiohttp-&gt;fsspec[http]!=2021.06.0,&gt;=2021.05.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (6.0.2)
+Requirement already satisfied: aiosignal&gt;=1.1.2 in /opt/conda/lib/python3.8/site-packages (from aiohttp-&gt;fsspec[http]!=2021.06.0,&gt;=2021.05.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (1.2.0)
+Requirement already satisfied: s3transfer&lt;0.6.0,&gt;=0.5.0 in /opt/conda/lib/python3.8/site-packages (from boto3-&gt;nemo_toolkit[all]==1.5.1) (0.5.2)
+Requirement already satisfied: botocore&lt;1.25.0,&gt;=1.24.45 in /opt/conda/lib/python3.8/site-packages (from boto3-&gt;nemo_toolkit[all]==1.5.1) (1.24.45)
+Requirement already satisfied: jmespath&lt;2.0.0,&gt;=0.7.1 in /opt/conda/lib/python3.8/site-packages (from boto3-&gt;nemo_toolkit[all]==1.5.1) (1.0.0)
+Requirement already satisfied: pybind11&gt;=2.2 in /opt/conda/lib/python3.8/site-packages (from fasttext-&gt;nemo_toolkit[all]==1.5.1) (2.9.1)
+Requirement already satisfied: distance&gt;=0.1.3 in /opt/conda/lib/python3.8/site-packages (from g2p-en-&gt;nemo_toolkit[all]==1.5.1) (0.1.3)
+Requirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.8/site-packages (from gdown-&gt;nemo_toolkit[all]==1.5.1) (4.10.0)
+Requirement already satisfied: soupsieve&gt;1.2 in /opt/conda/lib/python3.8/site-packages (from beautifulsoup4-&gt;gdown-&gt;nemo_toolkit[all]==1.5.1) (2.3.1)
+Requirement already satisfied: ipython-genutils~=0.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.2.0)
+Requirement already satisfied: ipython&gt;=4.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (8.1.1)
+Requirement already satisfied: ipykernel&gt;=4.5.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (6.9.2)
+Requirement already satisfied: jupyterlab-widgets&gt;=1.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (1.1.0)
+Requirement already satisfied: widgetsnbextension~=3.6.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (3.6.0)
+Requirement already satisfied: traitlets&gt;=4.3.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (5.1.1)
+Requirement already satisfied: nbformat&gt;=4.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (5.2.0)
+Requirement already satisfied: jupyter-client&lt;8.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (7.1.2)
+Requirement already satisfied: psutil in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (5.9.0)
+Requirement already satisfied: tornado&lt;7.0,&gt;=4.2 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (6.1)
+Requirement already satisfied: debugpy&lt;2.0,&gt;=1.0.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (1.5.1)
+Requirement already satisfied: nest-asyncio in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (1.5.4)
+Requirement already satisfied: matplotlib-inline&lt;0.2.0,&gt;=0.1.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.1.3)
+Requirement already satisfied: pickleshare in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.7.5)
+Requirement already satisfied: decorator in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (5.1.1)
+Requirement already satisfied: pygments&gt;=2.4.0 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (2.11.2)
+Requirement already satisfied: stack-data in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.2.0)
+Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,&lt;3.1.0,&gt;=2.0.0 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (3.0.27)
+Requirement already satisfied: backcall in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.2.0)
+Requirement already satisfied: jedi&gt;=0.16 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.18.1)
+Requirement already satisfied: pexpect&gt;4.3 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (4.8.0)
+Requirement already satisfied: parso&lt;0.9.0,&gt;=0.8.0 in /opt/conda/lib/python3.8/site-packages (from jedi&gt;=0.16-&gt;ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.8.3)
+Requirement already satisfied: entrypoints in /opt/conda/lib/python3.8/site-packages (from jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.4)
+Requirement already satisfied: pyzmq&gt;=13 in /opt/conda/lib/python3.8/site-packages (from jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (22.3.0)
+Requirement already satisfied: jupyter-core&gt;=4.6.0 in /opt/conda/lib/python3.8/site-packages (from jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (4.9.2)
+Requirement already satisfied: jsonschema!=2.5.0,&gt;=2.4 in /opt/conda/lib/python3.8/site-packages (from nbformat&gt;=4.2.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (4.4.0)
+Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,&gt;=0.14.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,&gt;=2.4-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.18.1)
+Requirement already satisfied: ptyprocess&gt;=0.5 in /opt/conda/lib/python3.8/site-packages (from pexpect&gt;4.3-&gt;ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.7.0)
+Requirement already satisfied: wcwidth in /opt/conda/lib/python3.8/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,&lt;3.1.0,&gt;=2.0.0-&gt;ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.2.5)
+Requirement already satisfied: notebook&gt;=4.4.1 in /opt/conda/lib/python3.8/site-packages (from widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (6.4.1)
+Requirement already satisfied: Send2Trash&gt;=1.5.0 in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (1.8.0)
+Requirement already satisfied: prometheus-client in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.13.1)
+Requirement already satisfied: terminado&gt;=0.8.3 in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.13.3)
+Requirement already satisfied: jinja2 in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (3.0.3)
+Requirement already satisfied: nbconvert in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (6.4.4)
+Requirement already satisfied: argon2-cffi in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (21.3.0)
+Requirement already satisfied: argon2-cffi-bindings in /opt/conda/lib/python3.8/site-packages (from argon2-cffi-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (21.2.0)
+Requirement already satisfied: cffi&gt;=1.0.1 in /opt/conda/lib/python3.8/site-packages (from argon2-cffi-bindings-&gt;argon2-cffi-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (1.15.0)
+Requirement already satisfied: pycparser in /opt/conda/lib/python3.8/site-packages (from cffi&gt;=1.0.1-&gt;argon2-cffi-bindings-&gt;argon2-cffi-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (2.21)
+Requirement already satisfied: MarkupSafe&gt;=2.0 in /opt/conda/lib/python3.8/site-packages (from jinja2-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (2.1.1)
+Requirement already satisfied: resampy&gt;=0.2.2 in /opt/conda/lib/python3.8/site-packages (from librosa-&gt;nemo_toolkit[all]==1.5.1) (0.2.2)
+Requirement already satisfied: pooch&gt;=1.0 in /opt/conda/lib/python3.8/site-packages (from librosa-&gt;nemo_toolkit[all]==1.5.1) (1.6.0)
+Requirement already satisfied: audioread&gt;=2.1.5 in /opt/conda/lib/python3.8/site-packages (from librosa-&gt;nemo_toolkit[all]==1.5.1) (2.1.9)
+Requirement already satisfied: llvmlite&lt;0.37,&gt;=0.36.0rc1 in /opt/conda/lib/python3.8/site-packages (from numba-&gt;nemo_toolkit[all]==1.5.1) (0.36.0)
+Requirement already satisfied: threadpoolctl&gt;=2.0.0 in /opt/conda/lib/python3.8/site-packages (from scikit-learn-&gt;nemo_toolkit[all]==1.5.1) (3.1.0)
+Requirement already satisfied: defusedxml in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.7.1)
+Requirement already satisfied: nbclient&lt;0.6.0,&gt;=0.5.0 in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.5.13)
+Requirement already satisfied: bleach in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (4.1.0)
+Requirement already satisfied: mistune&lt;2,&gt;=0.8.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.8.4)
+Requirement already satisfied: pandocfilters&gt;=1.4.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (1.5.0)
+Requirement already satisfied: testpath in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.6.0)
+Requirement already satisfied: jupyterlab-pygments in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.1.2)
+Requirement already satisfied: webencodings in /opt/conda/lib/python3.8/site-packages (from bleach-&gt;nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.5.1)
+Requirement already satisfied: pytz&gt;=2017.3 in /opt/conda/lib/python3.8/site-packages (from pandas-&gt;nemo_toolkit[all]==1.5.1) (2021.3)
+Requirement already satisfied: pip in /opt/conda/lib/python3.8/site-packages (from pip-api-&gt;isort[requirements]&lt;5-&gt;nemo_toolkit[all]==1.5.1) (21.2.4)
+Requirement already satisfied: yarg in /opt/conda/lib/python3.8/site-packages (from pipreqs-&gt;isort[requirements]&lt;5-&gt;nemo_toolkit[all]==1.5.1) (0.1.9)
+Requirement already satisfied: docopt in /opt/conda/lib/python3.8/site-packages (from pipreqs-&gt;isort[requirements]&lt;5-&gt;nemo_toolkit[all]==1.5.1) (0.6.2)
+Requirement already satisfied: simplejson&gt;=3.8.1 in /opt/conda/lib/python3.8/site-packages (from pyannote.core-&gt;nemo_toolkit[all]==1.5.1) (3.17.6)
+Requirement already satisfied: sortedcontainers&gt;=2.0.4 in /opt/conda/lib/python3.8/site-packages (from pyannote.core-&gt;nemo_toolkit[all]==1.5.1) (2.4.0)
+Requirement already satisfied: tabulate&gt;=0.7.7 in /opt/conda/lib/python3.8/site-packages (from pyannote.metrics-&gt;nemo_toolkit[all]==1.5.1) (0.8.9)
+Requirement already satisfied: pyannote.database&gt;=4.0.1 in /opt/conda/lib/python3.8/site-packages (from pyannote.metrics-&gt;nemo_toolkit[all]==1.5.1) (4.1.3)
+Requirement already satisfied: sympy&gt;=1.1 in /opt/conda/lib/python3.8/site-packages (from pyannote.metrics-&gt;nemo_toolkit[all]==1.5.1) (1.10.1)
+Requirement already satisfied: typer[all]&gt;=0.2.1 in /opt/conda/lib/python3.8/site-packages (from pyannote.database&gt;=4.0.1-&gt;pyannote.metrics-&gt;nemo_toolkit[all]==1.5.1) (0.4.0)
+Requirement already satisfied: mpmath&gt;=0.19 in /opt/conda/lib/python3.8/site-packages (from sympy&gt;=1.1-&gt;pyannote.metrics-&gt;nemo_toolkit[all]==1.5.1) (1.2.1)
+Requirement already satisfied: colorama&lt;0.5.0,&gt;=0.4.3 in /opt/conda/lib/python3.8/site-packages (from typer[all]&gt;=0.2.1-&gt;pyannote.database&gt;=4.0.1-&gt;pyannote.metrics-&gt;nemo_toolkit[all]==1.5.1) (0.4.4)
+Requirement already satisfied: shellingham&lt;2.0.0,&gt;=1.3.0 in /opt/conda/lib/python3.8/site-packages (from typer[all]&gt;=0.2.1-&gt;pyannote.database&gt;=4.0.1-&gt;pyannote.metrics-&gt;nemo_toolkit[all]==1.5.1) (1.4.0)
+Requirement already satisfied: py&gt;=1.8.2 in /opt/conda/lib/python3.8/site-packages (from pytest-&gt;nemo_toolkit[all]==1.5.1) (1.11.0)
+Requirement already satisfied: iniconfig in /opt/conda/lib/python3.8/site-packages (from pytest-&gt;nemo_toolkit[all]==1.5.1) (1.1.1)
+Requirement already satisfied: pluggy&lt;2.0,&gt;=0.12 in /opt/conda/lib/python3.8/site-packages (from pytest-&gt;nemo_toolkit[all]==1.5.1) (1.0.0)
+Requirement already satisfied: jarowinkler&lt;1.1.0,&gt;=1.0.2 in /opt/conda/lib/python3.8/site-packages (from rapidfuzz-&gt;nemo_toolkit[all]==1.5.1) (1.0.2)
+Requirement already satisfied: PySocks!=1.5.7,&gt;=1.5.6 in /opt/conda/lib/python3.8/site-packages (from requests-&gt;fsspec[http]!=2021.06.0,&gt;=2021.05.0-&gt;pytorch-lightning&gt;=1.5.0-&gt;nemo_toolkit[all]==1.5.1) (1.7.1)
+Requirement already satisfied: ruamel.yaml.clib&gt;=0.2.6 in /opt/conda/lib/python3.8/site-packages (from ruamel.yaml-&gt;nemo_toolkit[all]==1.5.1) (0.2.6)
+Requirement already satisfied: portalocker in /opt/conda/lib/python3.8/site-packages (from sacrebleu[ja]-&gt;nemo_toolkit[all]==1.5.1) (2.4.0)
+Requirement already satisfied: ipadic&lt;2.0,&gt;=1.0 in /opt/conda/lib/python3.8/site-packages (from sacrebleu[ja]-&gt;nemo_toolkit[all]==1.5.1) (1.0.0)
+Requirement already satisfied: mecab-python3==1.0.3 in /opt/conda/lib/python3.8/site-packages (from sacrebleu[ja]-&gt;nemo_toolkit[all]==1.5.1) (1.0.3)
+Requirement already satisfied: sphinxcontrib-htmlhelp&gt;=2.0.0 in /opt/conda/lib/python3.8/site-packages (from sphinx-&gt;nemo_toolkit[all]==1.5.1) (2.0.0)
+Requirement already satisfied: alabaster&lt;0.8,&gt;=0.7 in /opt/conda/lib/python3.8/site-packages (from sphinx-&gt;nemo_toolkit[all]==1.5.1) (0.7.12)
+Requirement already satisfied: babel&gt;=1.3 in /opt/conda/lib/python3.8/site-packages (from sphinx-&gt;nemo_toolkit[all]==1.5.1) (2.9.1)
+Requirement already satisfied: sphinxcontrib-serializinghtml&gt;=1.1.5 in /opt/conda/lib/python3.8/site-packages (from sphinx-&gt;nemo_toolkit[all]==1.5.1) (1.1.5)
+Requirement already satisfied: sphinxcontrib-devhelp in /opt/conda/lib/python3.8/site-packages (from sphinx-&gt;nemo_toolkit[all]==1.5.1) (1.0.2)
+Requirement already satisfied: sphinxcontrib-jsmath in /opt/conda/lib/python3.8/site-packages (from sphinx-&gt;nemo_toolkit[all]==1.5.1) (1.0.1)
+Requirement already satisfied: sphinxcontrib-qthelp in /opt/conda/lib/python3.8/site-packages (from sphinx-&gt;nemo_toolkit[all]==1.5.1) (1.0.3)
+Requirement already satisfied: snowballstemmer&gt;=1.1 in /opt/conda/lib/python3.8/site-packages (from sphinx-&gt;nemo_toolkit[all]==1.5.1) (2.2.0)
+Requirement already satisfied: imagesize in /opt/conda/lib/python3.8/site-packages (from sphinx-&gt;nemo_toolkit[all]==1.5.1) (1.3.0)
+Requirement already satisfied: sphinxcontrib-applehelp in /opt/conda/lib/python3.8/site-packages (from sphinx-&gt;nemo_toolkit[all]==1.5.1) (1.0.2)
+Requirement already satisfied: docutils&lt;0.18,&gt;=0.14 in /opt/conda/lib/python3.8/site-packages (from sphinx-&gt;nemo_toolkit[all]==1.5.1) (0.17.1)
+Requirement already satisfied: pybtex-docutils&gt;=1.0.0 in /opt/conda/lib/python3.8/site-packages (from sphinxcontrib-bibtex-&gt;nemo_toolkit[all]==1.5.1) (1.0.1)
+Requirement already satisfied: pybtex&gt;=0.24 in /opt/conda/lib/python3.8/site-packages (from sphinxcontrib-bibtex-&gt;nemo_toolkit[all]==1.5.1) (0.24.0)
+Requirement already satisfied: latexcodec&gt;=1.0.4 in /opt/conda/lib/python3.8/site-packages (from pybtex&gt;=0.24-&gt;sphinxcontrib-bibtex-&gt;nemo_toolkit[all]==1.5.1) (2.0.1)
+Requirement already satisfied: pure-eval in /opt/conda/lib/python3.8/site-packages (from stack-data-&gt;ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.2.2)
+Requirement already satisfied: asttokens in /opt/conda/lib/python3.8/site-packages (from stack-data-&gt;ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (2.0.5)
+Requirement already satisfied: executing in /opt/conda/lib/python3.8/site-packages (from stack-data-&gt;ipython&gt;=4.0.0-&gt;ipywidgets-&gt;nemo_toolkit[all]==1.5.1) (0.8.3)
+Requirement already satisfied: pathtools in /opt/conda/lib/python3.8/site-packages (from wandb-&gt;nemo_toolkit[all]==1.5.1) (0.1.2)
+Requirement already satisfied: setproctitle in /opt/conda/lib/python3.8/site-packages (from wandb-&gt;nemo_toolkit[all]==1.5.1) (1.2.3)
+Requirement already satisfied: GitPython&gt;=1.0.0 in /opt/conda/lib/python3.8/site-packages (from wandb-&gt;nemo_toolkit[all]==1.5.1) (3.1.27)
+Requirement already satisfied: sentry-sdk&gt;=1.0.0 in /opt/conda/lib/python3.8/site-packages (from wandb-&gt;nemo_toolkit[all]==1.5.1) (1.5.10)
+Requirement already satisfied: shortuuid&gt;=0.5.0 in /opt/conda/lib/python3.8/site-packages (from wandb-&gt;nemo_toolkit[all]==1.5.1) (1.0.8)
+Requirement already satisfied: docker-pycreds&gt;=0.4.0 in /opt/conda/lib/python3.8/site-packages (from wandb-&gt;nemo_toolkit[all]==1.5.1) (0.4.0)
+Requirement already satisfied: promise&lt;3,&gt;=2.0 in /opt/conda/lib/python3.8/site-packages (from wandb-&gt;nemo_toolkit[all]==1.5.1) (2.3)
+Requirement already satisfied: gitdb&lt;5,&gt;=4.0.1 in /opt/conda/lib/python3.8/site-packages (from GitPython&gt;=1.0.0-&gt;wandb-&gt;nemo_toolkit[all]==1.5.1) (4.0.9)
+Requirement already satisfied: smmap&lt;6,&gt;=3.0.1 in /opt/conda/lib/python3.8/site-packages (from gitdb&lt;5,&gt;=4.0.1-&gt;GitPython&gt;=1.0.0-&gt;wandb-&gt;nemo_toolkit[all]==1.5.1) (5.0.0)
+<span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
+</pre></div></div>
+</div>
+<p>## 2. Download Citrinet model</p>
+<p>Next, we download a pretrained Nemo Citrinet model and convert it to a Torchscript module:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import nemo
+import torch
+
+import nemo.collections.asr as nemo_asr
+from nemo.core import typecheck
+typecheck.set_typecheck_enabled(False)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>variant = &#39;stt_en_citrinet_256&#39;
+
+print(f&quot;Downloading and saving {variant}...&quot;)
+asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=variant)
+asr_model.export(f&quot;{variant}.ts&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Downloading and saving stt_en_citrinet_256...
+[NeMo I 2022-04-21 23:12:45 cloud:56] Found existing object /root/.cache/torch/NeMo/NeMo_1.5.1/stt_en_citrinet_256/91a9cc5850784b2065e8a0aa3d526fd9/stt_en_citrinet_256.nemo.
+[NeMo I 2022-04-21 23:12:45 cloud:62] Re-using file from: /root/.cache/torch/NeMo/NeMo_1.5.1/stt_en_citrinet_256/91a9cc5850784b2065e8a0aa3d526fd9/stt_en_citrinet_256.nemo
+[NeMo I 2022-04-21 23:12:45 common:728] Instantiating model from pre-trained checkpoint
+[NeMo I 2022-04-21 23:12:46 mixins:146] Tokenizer SentencePieceTokenizer initialized with 1024 tokens
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+[NeMo W 2022-04-21 23:12:47 modelPT:130] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
+    Train config :
+    manifest_filepath: null
+    sample_rate: 16000
+    batch_size: 32
+    trim_silence: true
+    max_duration: 16.7
+    shuffle: true
+    is_tarred: false
+    tarred_audio_filepaths: null
+    use_start_end_token: false
+
+[NeMo W 2022-04-21 23:12:47 modelPT:137] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
+    Validation config :
+    manifest_filepath: null
+    sample_rate: 16000
+    batch_size: 32
+    shuffle: false
+    use_start_end_token: false
+
+[NeMo W 2022-04-21 23:12:47 modelPT:143] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
+    Test config :
+    manifest_filepath: null
+    sample_rate: 16000
+    batch_size: 32
+    shuffle: false
+    use_start_end_token: false
+
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[NeMo I 2022-04-21 23:12:47 features:265] PADDING: 16
+[NeMo I 2022-04-21 23:12:47 features:282] STFT using torch
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+[NeMo W 2022-04-21 23:12:47 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/preprocessing/features.py:315: FutureWarning: Pass sr=16000, n_fft=512 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+      librosa.filters.mel(sample_rate, self.n_fft, n_mels=nfilt, fmin=lowfreq, fmax=highfreq), dtype=torch.float
+
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[NeMo I 2022-04-21 23:12:49 save_restore_connector:149] Model EncDecCTCModelBPE was successfully restored from /root/.cache/torch/NeMo/NeMo_1.5.1/stt_en_citrinet_256/91a9cc5850784b2065e8a0aa3d526fd9/stt_en_citrinet_256.nemo.
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+[NeMo W 2022-04-21 23:12:49 export_utils:198] Swapped 0 modules
+[NeMo W 2022-04-21 23:12:49 conv_asr:73] Turned off 235 masked convolutions
+[NeMo W 2022-04-21 23:12:49 export_utils:198] Swapped 0 modules
+[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/jit/_trace.py:916: UserWarning: `optimize` is deprecated and has no effect. Use `with torch.jit.optimized_execution() instead
+      warnings.warn(
+
+[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:668: LightningDeprecationWarning: The `LightningModule.model_size` property was deprecated in v1.5 and will be removed in v1.7. Please use the `pytorch_lightning.utilities.memory.get_model_size_mb`.
+      if hasattr(mod, name):
+
+[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:669: LightningDeprecationWarning: The `LightningModule.model_size` property was deprecated in v1.5 and will be removed in v1.7. Please use the `pytorch_lightning.utilities.memory.get_model_size_mb`.
+      item = getattr(mod, name)
+
+[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:668: LightningDeprecationWarning: `LightningModule.use_amp` was deprecated in v1.6 and will be removed in v1.8. Please use `Trainer.amp_backend`.
+      if hasattr(mod, name):
+
+[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:669: LightningDeprecationWarning: `LightningModule.use_amp` was deprecated in v1.6 and will be removed in v1.8. Please use `Trainer.amp_backend`.
+      item = getattr(mod, name)
+
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+([&#39;stt_en_citrinet_256.ts&#39;],
+ [&#39;nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE exported to ONNX&#39;])
+</pre></div></div>
+</div>
+<section id="Benchmark-utility">
+<h3>Benchmark utility<a class="headerlink" href="#Benchmark-utility" title="Permalink to this headline">¶</a></h3>
+<p>Let us define a helper benchmarking function, then benchmark the original Pytorch model.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from __future__ import print_function
+from __future__ import absolute_import
+from __future__ import division
+
+import argparse
+import timeit
+import numpy as np
+import torch
+import torch_tensorrt as trtorch
+import torch.backends.cudnn as cudnn
+
+def benchmark(model, input_tensor, num_loops, model_name, batch_size):
+    def timeGraph(model, input_tensor, num_loops):
+        print(&quot;Warm up ...&quot;)
+        with torch.no_grad():
+            for _ in range(20):
+                features = model(input_tensor)
+
+        torch.cuda.synchronize()
+        print(&quot;Start timing ...&quot;)
+        timings = []
+        with torch.no_grad():
+            for i in range(num_loops):
+                start_time = timeit.default_timer()
+                features = model(input_tensor)
+                torch.cuda.synchronize()
+                end_time = timeit.default_timer()
+                timings.append(end_time - start_time)
+                # print(&quot;Iteration {}: {:.6f} s&quot;.format(i, end_time - start_time))
+        return timings
+    def printStats(graphName, timings, batch_size):
+        times = np.array(timings)
+        steps = len(times)
+        speeds = batch_size / times
+        time_mean = np.mean(times)
+        time_med = np.median(times)
+        time_99th = np.percentile(times, 99)
+        time_std = np.std(times, ddof=0)
+        speed_mean = np.mean(speeds)
+        speed_med = np.median(speeds)
+        msg = (&quot;\n%s =================================\n&quot;
+                &quot;batch size=%d, num iterations=%d\n&quot;
+                &quot;  Median samples/s: %.1f, mean: %.1f\n&quot;
+                &quot;  Median latency (s): %.6f, mean: %.6f, 99th_p: %.6f, std_dev: %.6f\n&quot;
+                ) % (graphName,
+                    batch_size, steps,
+                    speed_med, speed_mean,
+                    time_med, time_mean, time_99th, time_std)
+        print(msg)
+    timings = timeGraph(model, input_tensor, num_loops)
+    printStats(model_name, timings, batch_size)
+
+precisions_str = &#39;fp32&#39; # Precision (default=fp32, fp16)
+variant = &#39;stt_en_citrinet_256&#39; # Nemo Citrinet variant
+batch_sizes = [1, 8, 32, 128] # Batch sizes (default=1,8,32,128)
+trt = False # If True, infer with Torch-TensorRT engine. Else, infer with Pytorch model.
+precision = torch.float32 if precisions_str ==&#39;fp32&#39; else torch.float16
+
+for batch_size in batch_sizes:
+    if trt:
+        model_name = f&quot;{variant}_bs{batch_size}_{precision}.torch-tensorrt&quot;
+    else:
+        model_name = f&quot;{variant}.ts&quot;
+
+    print(f&quot;Loading model: {model_name}&quot;)
+    # Load traced model to CPU first
+    model = torch.jit.load(model_name).cuda()
+    cudnn.benchmark = True
+    # Create random input tensor of certain size
+    torch.manual_seed(12345)
+    input_shape=(batch_size, 80, 1488)
+    input_tensor = torch.randn(input_shape).cuda()
+
+    # Timing graph inference
+    benchmark(model, input_tensor, 50, model_name, batch_size)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Loading model: stt_en_citrinet_256.ts
+Warm up ...
+Start timing ...
+
+stt_en_citrinet_256.ts =================================
+batch size=1, num iterations=50
+  Median samples/s: 102.0, mean: 102.0
+  Median latency (s): 0.009802, mean: 0.009803, 99th_p: 0.009836, std_dev: 0.000014
+
+Loading model: stt_en_citrinet_256.ts
+Warm up ...
+Start timing ...
+
+stt_en_citrinet_256.ts =================================
+batch size=8, num iterations=50
+  Median samples/s: 429.1, mean: 429.1
+  Median latency (s): 0.018642, mean: 0.018643, 99th_p: 0.018670, std_dev: 0.000014
+
+Loading model: stt_en_citrinet_256.ts
+Warm up ...
+Start timing ...
+
+stt_en_citrinet_256.ts =================================
+batch size=32, num iterations=50
+  Median samples/s: 551.3, mean: 551.2
+  Median latency (s): 0.058047, mean: 0.058053, 99th_p: 0.058375, std_dev: 0.000106
+
+Loading model: stt_en_citrinet_256.ts
+Warm up ...
+Start timing ...
+
+stt_en_citrinet_256.ts =================================
+batch size=128, num iterations=50
+  Median samples/s: 594.1, mean: 594.1
+  Median latency (s): 0.215434, mean: 0.215446, 99th_p: 0.215806, std_dev: 0.000116
+
+</pre></div></div>
+</div>
+<p>Confirming the GPU we are using here:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!nvidia-smi
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Thu Apr 21 23:13:32 2022
++-----------------------------------------------------------------------------+
+| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |
+|-------------------------------+----------------------+----------------------+
+| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+|                               |                      |               MIG M. |
+|===============================+======================+======================|
+|   0  NVIDIA TITAN V      On   | 00000000:17:00.0 Off |                  N/A |
+| 38%   55C    P2    42W / 250W |   2462MiB / 12288MiB |      0%      Default |
+|                               |                      |                  N/A |
++-------------------------------+----------------------+----------------------+
+|   1  NVIDIA TITAN V      On   | 00000000:65:00.0 Off |                  N/A |
+| 28%   39C    P8    26W / 250W |    112MiB / 12288MiB |      0%      Default |
+|                               |                      |                  N/A |
++-------------------------------+----------------------+----------------------+
+
++-----------------------------------------------------------------------------+
+| Processes:                                                                  |
+|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
+|        ID   ID                                                   Usage      |
+|=============================================================================|
+|    0   N/A  N/A      3909      G                                       4MiB |
+|    0   N/A  N/A      6047      C                                    2453MiB |
+|    1   N/A  N/A      3909      G                                      39MiB |
+|    1   N/A  N/A      4161      G                                      67MiB |
++-----------------------------------------------------------------------------+
+</pre></div></div>
+</div>
+<p>## 3. Create Torch-TensorRT modules</p>
+<p>In this step, we optimize the Citrinet Torchscript module with Torch-TensorRT with various precisions and batch sizes.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch
+import torch.nn as nn
+import torch_tensorrt as torchtrt
+import argparse
+
+variant = &quot;stt_en_citrinet_256&quot;
+precisions = [torch.float, torch.half]
+batch_sizes = [1,8,32,128]
+
+model = torch.jit.load(f&quot;{variant}.ts&quot;)
+
+for precision in precisions:
+    for batch_size in batch_sizes:
+        compile_settings = {
+            &quot;inputs&quot;: [torchtrt.Input(shape=[batch_size, 80, 1488])],
+            &quot;enabled_precisions&quot;: {precision},
+            &quot;workspace_size&quot;: 2000000000,
+            &quot;truncate_long_and_double&quot;: True,
+        }
+        print(f&quot;Generating Torchscript-TensorRT module for batchsize {batch_size} precision {precision}&quot;)
+        trt_ts_module = torchtrt.compile(model, **compile_settings)
+        torch.jit.save(trt_ts_module, f&quot;{variant}_bs{batch_size}_{precision}.torch-tensorrt&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Generating Torchscript-TensorRT module for batchsize 1 precision torch.float32
+Generating Torchscript-TensorRT module for batchsize 8 precision torch.float32
+Generating Torchscript-TensorRT module for batchsize 32 precision torch.float32
+Generating Torchscript-TensorRT module for batchsize 128 precision torch.float32
+Generating Torchscript-TensorRT module for batchsize 1 precision torch.float16
+Generating Torchscript-TensorRT module for batchsize 8 precision torch.float16
+Generating Torchscript-TensorRT module for batchsize 32 precision torch.float16
+Generating Torchscript-TensorRT module for batchsize 128 precision torch.float16
+</pre></div></div>
+</div>
+<p>## 4. Benchmark Torch-TensorRT models</p>
+<p>Finally, we are ready to benchmark the Torch-TensorRT optimized Citrinet models.</p>
+</section>
+<section id="FP32-(single-precision)">
+<h3>FP32 (single precision)<a class="headerlink" href="#FP32-(single-precision)" title="Permalink to this headline">¶</a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>precisions_str = &#39;fp32&#39; # Precision (default=fp32, fp16)
+batch_sizes = [1, 8, 32, 128] # Batch sizes (default=1,8,32,128)
+precision = torch.float32 if precisions_str ==&#39;fp32&#39; else torch.float16
+trt = True
+
+for batch_size in batch_sizes:
+    if trt:
+        model_name = f&quot;{variant}_bs{batch_size}_{precision}.torch-tensorrt&quot;
+    else:
+        model_name = f&quot;{variant}.ts&quot;
+
+    print(f&quot;Loading model: {model_name}&quot;)
+    # Load traced model to CPU first
+    model = torch.jit.load(model_name).cuda()
+    cudnn.benchmark = True
+    # Create random input tensor of certain size
+    torch.manual_seed(12345)
+    input_shape=(batch_size, 80, 1488)
+    input_tensor = torch.randn(input_shape).cuda()
+
+    # Timing graph inference
+    benchmark(model, input_tensor, 50, model_name, batch_size)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Loading model: stt_en_citrinet_256_bs1_torch.float32.torch-tensorrt
+Warm up ...
+Start timing ...
+
+stt_en_citrinet_256_bs1_torch.float32.torch-tensorrt =================================
+batch size=1, num iterations=50
+  Median samples/s: 242.2, mean: 218.0
+  Median latency (s): 0.004128, mean: 0.004825, 99th_p: 0.008071, std_dev: 0.001270
+
+Loading model: stt_en_citrinet_256_bs8_torch.float32.torch-tensorrt
+Warm up ...
+Start timing ...
+
+stt_en_citrinet_256_bs8_torch.float32.torch-tensorrt =================================
+batch size=8, num iterations=50
+  Median samples/s: 729.9, mean: 709.0
+  Median latency (s): 0.010961, mean: 0.011388, 99th_p: 0.016114, std_dev: 0.001256
+
+Loading model: stt_en_citrinet_256_bs32_torch.float32.torch-tensorrt
+Warm up ...
+Start timing ...
+
+stt_en_citrinet_256_bs32_torch.float32.torch-tensorrt =================================
+batch size=32, num iterations=50
+  Median samples/s: 955.6, mean: 953.4
+  Median latency (s): 0.033488, mean: 0.033572, 99th_p: 0.035722, std_dev: 0.000545
+
+Loading model: stt_en_citrinet_256_bs128_torch.float32.torch-tensorrt
+Warm up ...
+Start timing ...
+
+stt_en_citrinet_256_bs128_torch.float32.torch-tensorrt =================================
+batch size=128, num iterations=50
+  Median samples/s: 1065.8, mean: 1069.4
+  Median latency (s): 0.120097, mean: 0.119708, 99th_p: 0.121618, std_dev: 0.001260
+
+</pre></div></div>
+</div>
+</section>
+<section id="FP16-(half-precision)">
+<h3>FP16 (half precision)<a class="headerlink" href="#FP16-(half-precision)" title="Permalink to this headline">¶</a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>precisions_str = &#39;fp16&#39; # Precision (default=fp32, fp16)
+batch_sizes = [1, 8, 32, 128] # Batch sizes (default=1,8,32,128)
+precision = torch.float32 if precisions_str ==&#39;fp32&#39; else torch.float16
+
+for batch_size in batch_sizes:
+    if trt:
+        model_name = f&quot;{variant}_bs{batch_size}_{precision}.torch-tensorrt&quot;
+    else:
+        model_name = f&quot;{variant}.ts&quot;
+
+    print(f&quot;Loading model: {model_name}&quot;)
+    # Load traced model to CPU first
+    model = torch.jit.load(model_name).cuda()
+    cudnn.benchmark = True
+    # Create random input tensor of certain size
+    torch.manual_seed(12345)
+    input_shape=(batch_size, 80, 1488)
+    input_tensor = torch.randn(input_shape).cuda()
+
+    # Timing graph inference
+    benchmark(model, input_tensor, 50, model_name, batch_size)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Loading model: stt_en_citrinet_256_bs1_torch.float16.torch-tensorrt
+Warm up ...
+Start timing ...
+
+stt_en_citrinet_256_bs1_torch.float16.torch-tensorrt =================================
+batch size=1, num iterations=50
+  Median samples/s: 288.9, mean: 272.9
+  Median latency (s): 0.003462, mean: 0.003774, 99th_p: 0.006846, std_dev: 0.000820
+
+Loading model: stt_en_citrinet_256_bs8_torch.float16.torch-tensorrt
+Warm up ...
+Start timing ...
+
+stt_en_citrinet_256_bs8_torch.float16.torch-tensorrt =================================
+batch size=8, num iterations=50
+  Median samples/s: 1201.0, mean: 1190.9
+  Median latency (s): 0.006661, mean: 0.006733, 99th_p: 0.008453, std_dev: 0.000368
+
+Loading model: stt_en_citrinet_256_bs32_torch.float16.torch-tensorrt
+Warm up ...
+Start timing ...
+
+stt_en_citrinet_256_bs32_torch.float16.torch-tensorrt =================================
+batch size=32, num iterations=50
+  Median samples/s: 1538.2, mean: 1516.4
+  Median latency (s): 0.020804, mean: 0.021143, 99th_p: 0.024492, std_dev: 0.000973
+
+Loading model: stt_en_citrinet_256_bs128_torch.float16.torch-tensorrt
+Warm up ...
+Start timing ...
+
+stt_en_citrinet_256_bs128_torch.float16.torch-tensorrt =================================
+batch size=128, num iterations=50
+  Median samples/s: 1792.0, mean: 1777.0
+  Median latency (s): 0.071428, mean: 0.072057, 99th_p: 0.076796, std_dev: 0.001351
+
+</pre></div></div>
+</div>
+<p>## 5. Conclusion</p>
+<p>In this notebook, we have walked through the complete process of optimizing the Citrinet model with Torch-TensorRT. On an A100 GPU, with Torch-TensorRT, we observe a speedup of ~<strong>2.4X</strong> with FP32, and ~<strong>2.9X</strong> with FP16 at batchsize of 128.</p>
+</section>
+<section id="What’s-next">
+<h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to this headline">¶</a></h3>
+<p>Now it’s time to try Torch-TensorRT on your own model. Fill out issues at <a class="reference external" href="https://github.com/NVIDIA/Torch-TensorRT">https://github.com/NVIDIA/Torch-TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+</section>
+
+
+             </article>
+
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+
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+
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+  <div role="contentinfo">
+    <p>
+        &copy; Copyright 2021, NVIDIA Corporation.
+
+    </p>
+  </div>
+
+      <div>
+        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+      </div>
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+</footer>
+
+          </div>
+        </div>
+
+        <div class="pytorch-content-right" id="pytorch-content-right">
+          <div class="pytorch-right-menu" id="pytorch-right-menu">
+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
+              <ul>
+<li><a class="reference internal" href="#">Torch-TensorRT Getting Started - CitriNet</a><ul>
+<li><a class="reference internal" href="#Overview">Overview</a><ul>
+<li><a class="reference internal" href="#Learning-objectives">Learning objectives</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#Content">Content</a><ul>
+<li><a class="reference internal" href="#Benchmark-utility">Benchmark utility</a></li>
+<li><a class="reference internal" href="#FP32-(single-precision)">FP32 (single precision)</a></li>
+<li><a class="reference internal" href="#FP16-(half-precision)">FP16 (half precision)</a></li>
+<li><a class="reference internal" href="#What’s-next">What’s next</a></li>
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+            <li><a href="https://music.amazon.com/podcasts/7a4e6f0e-26c2-49e9-a478-41bd244197d0/PyTorch-Developer-Podcast?" target="_blank">Amazon</a></li>
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diff --git a/docs/v1.2.0/_notebooks/CitriNet-example.ipynb b/docs/v1.2.0/_notebooks/CitriNet-example.ipynb
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index 0000000000..0573af0176
--- /dev/null
+++ b/docs/v1.2.0/_notebooks/CitriNet-example.ipynb
@@ -0,0 +1,964 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
+    "\n",
+    "# Torch-TensorRT Getting Started - CitriNet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "[Citrinet](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#citrinet) is an acoustic model used for the speech to text recognition task. It is a version of [QuartzNet](https://arxiv.org/pdf/1910.10261.pdf) that extends [ContextNet](https://arxiv.org/pdf/2005.03191.pdf), utilizing subword encoding (via Word Piece tokenization) and Squeeze-and-Excitation(SE) mechanism and are therefore smaller than QuartzNet models.\n",
+    "\n",
+    "CitriNet models take in audio segments and transcribe them to letter, byte pair, or word piece sequences. \n",
+    "\n",
+    "<img src=\"https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/_images/jasper_vertical.png\" alt=\"alt\" width=\"50%\"/>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning objectives\n",
+    "\n",
+    "This notebook demonstrates the steps for optimizing a pretrained CitriNet model with Torch-TensorRT, and running it to test the speedup obtained.\n",
+    "\n",
+    "## Content\n",
+    "1. [Requirements](#1)\n",
+    "1. [Download Citrinet model](#2)\n",
+    "1. [Create Torch-TensorRT modules](#3)\n",
+    "1. [Benchmark Torch-TensorRT models](#4)\n",
+    "1. [Conclusion](#5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"1\"></a>\n",
+    "## 1. Requirements\n",
+    "\n",
+    "Follow the steps in [README](README.md) to prepare a Docker container, within which you can run this notebook. \n",
+    "This notebook assumes that you are within a Jupyter environment in a docker container with Torch-TensorRT installed, such as an NGC monthly release of `nvcr.io/nvidia/pytorch:<yy.mm>-py3` (where `yy` indicates the last two numbers of a calendar year, and `mm` indicates the month in two-digit numerical form)\n",
+    "\n",
+    "Now that you are in the docker, the next step is to install the required dependencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Requirement already satisfied: wget in /opt/conda/lib/python3.8/site-packages (3.2)\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
+      "Hit:1 http://security.ubuntu.com/ubuntu focal-security InRelease\n",
+      "Hit:2 http://archive.ubuntu.com/ubuntu focal InRelease\n",
+      "Hit:3 http://archive.ubuntu.com/ubuntu focal-updates InRelease\n",
+      "Hit:4 http://archive.ubuntu.com/ubuntu focal-backports InRelease\n",
+      "Reading package lists... Done\n",
+      "Reading package lists... Done\n",
+      "Building dependency tree       \n",
+      "Reading state information... Done\n",
+      "libsndfile1 is already the newest version (1.0.28-7ubuntu0.1).\n",
+      "ffmpeg is already the newest version (7:4.2.4-1ubuntu0.1).\n",
+      "0 upgraded, 0 newly installed, 0 to remove and 22 not upgraded.\n",
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Requirement already satisfied: Cython in /opt/conda/lib/python3.8/site-packages (0.29.28)\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Requirement already satisfied: nemo_toolkit[all]==1.5.1 in /opt/conda/lib/python3.8/site-packages (1.5.1)\n",
+      "Requirement already satisfied: numpy>=1.18.2 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.22.3)\n",
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+      "Requirement already satisfied: sentencepiece<1.0.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.1.96)\n",
+      "Requirement already satisfied: wget in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (3.2)\n",
+      "Requirement already satisfied: numba in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.53.1)\n",
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+      "Requirement already satisfied: isort[requirements]<5 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.3.21)\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: appdirs in /opt/conda/lib/python3.8/site-packages (from black==19.10b0->nemo_toolkit[all]==1.5.1) (1.4.4)\n",
+      "Requirement already satisfied: typed-ast>=1.4.0 in /opt/conda/lib/python3.8/site-packages (from black==19.10b0->nemo_toolkit[all]==1.5.1) (1.5.3)\n",
+      "Requirement already satisfied: pathspec<1,>=0.6 in /opt/conda/lib/python3.8/site-packages (from black==19.10b0->nemo_toolkit[all]==1.5.1) (0.9.0)\n",
+      "Requirement already satisfied: click>=6.5 in /opt/conda/lib/python3.8/site-packages (from black==19.10b0->nemo_toolkit[all]==1.5.1) (8.0.4)\n",
+      "Requirement already satisfied: toml>=0.9.4 in /opt/conda/lib/python3.8/site-packages (from black==19.10b0->nemo_toolkit[all]==1.5.1) (0.10.2)\n",
+      "Requirement already satisfied: antlr4-python3-runtime==4.8 in /opt/conda/lib/python3.8/site-packages (from hydra-core>=1.1.0->nemo_toolkit[all]==1.5.1) (4.8)\n",
+      "Requirement already satisfied: importlib-resources<5.3 in /opt/conda/lib/python3.8/site-packages (from hydra-core>=1.1.0->nemo_toolkit[all]==1.5.1) (5.2.3)\n",
+      "Requirement already satisfied: zipp>=3.1.0 in /opt/conda/lib/python3.8/site-packages (from importlib-resources<5.3->hydra-core>=1.1.0->nemo_toolkit[all]==1.5.1) (3.7.0)\n",
+      "Requirement already satisfied: pip-api in /opt/conda/lib/python3.8/site-packages (from isort[requirements]<5->nemo_toolkit[all]==1.5.1) (0.0.29)\n",
+      "Requirement already satisfied: pipreqs in /opt/conda/lib/python3.8/site-packages (from isort[requirements]<5->nemo_toolkit[all]==1.5.1) (0.4.11)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nemo_toolkit[all]==1.5.1) (4.31.2)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nemo_toolkit[all]==1.5.1) (1.4.0)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nemo_toolkit[all]==1.5.1) (3.0.7)\n",
+      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nemo_toolkit[all]==1.5.1) (0.11.0)\n",
+      "Requirement already satisfied: joblib in /opt/conda/lib/python3.8/site-packages (from nltk>=3.6.5->nemo_toolkit[all]==1.5.1) (1.1.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.2.1 in /opt/conda/lib/python3.8/site-packages (from onnx>=1.7.0->nemo_toolkit[all]==1.5.1) (4.1.1)\n",
+      "Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from onnx>=1.7.0->nemo_toolkit[all]==1.5.1) (1.16.0)\n",
+      "Requirement already satisfied: protobuf>=3.12.2 in /opt/conda/lib/python3.8/site-packages (from onnx>=1.7.0->nemo_toolkit[all]==1.5.1) (3.19.4)\n",
+      "Requirement already satisfied: pyDeprecate<0.4.0,>=0.3.1 in /opt/conda/lib/python3.8/site-packages (from pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (0.3.2)\n",
+      "Requirement already satisfied: tensorboard>=2.2.0 in /opt/conda/lib/python3.8/site-packages (from pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2.8.0)\n",
+      "Requirement already satisfied: fsspec[http]!=2021.06.0,>=2021.05.0 in /opt/conda/lib/python3.8/site-packages (from pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2022.2.0)\n",
+      "Requirement already satisfied: requests in /opt/conda/lib/python3.8/site-packages (from fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2.27.1)\n",
+      "Requirement already satisfied: aiohttp in /opt/conda/lib/python3.8/site-packages (from fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (3.8.1)\n",
+      "Requirement already satisfied: werkzeug>=0.11.15 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2.0.3)\n",
+      "Requirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (3.3.6)\n",
+      "Requirement already satisfied: setuptools>=41.0.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (59.5.0)\n",
+      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (0.4.6)\n",
+      "Requirement already satisfied: google-auth<3,>=1.6.3 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2.6.2)\n",
+      "Requirement already satisfied: wheel>=0.26 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (0.37.1)\n",
+      "Requirement already satisfied: grpcio>=1.24.3 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.44.0)\n",
+      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (0.6.1)\n",
+      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.8.1)\n",
+      "Requirement already satisfied: absl-py>=0.4 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.0.0)\n",
+      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (5.0.0)\n",
+      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (0.2.8)\n",
+      "Requirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (4.8)\n",
+      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.8/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.3.1)\n",
+      "Requirement already satisfied: importlib-metadata>=4.4 in /opt/conda/lib/python3.8/site-packages (from markdown>=2.6.8->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (4.11.3)\n",
+      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (0.4.8)\n",
+      "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.8/site-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2.0.12)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2021.10.8)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (3.3)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.26.8)\n",
+      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (3.2.0)\n",
+      "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /opt/conda/lib/python3.8/site-packages (from transformers>=4.0.1->nemo_toolkit[all]==1.5.1) (0.5.1)\n",
+      "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /opt/conda/lib/python3.8/site-packages (from transformers>=4.0.1->nemo_toolkit[all]==1.5.1) (0.12.1)\n",
+      "Requirement already satisfied: filelock in /opt/conda/lib/python3.8/site-packages (from transformers>=4.0.1->nemo_toolkit[all]==1.5.1) (3.6.0)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.8/site-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.3.0)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.8/site-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.7.2)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/conda/lib/python3.8/site-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (4.0.2)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.8/site-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (6.0.2)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.8/site-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.2.0)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: s3transfer<0.6.0,>=0.5.0 in /opt/conda/lib/python3.8/site-packages (from boto3->nemo_toolkit[all]==1.5.1) (0.5.2)\n",
+      "Requirement already satisfied: botocore<1.25.0,>=1.24.45 in /opt/conda/lib/python3.8/site-packages (from boto3->nemo_toolkit[all]==1.5.1) (1.24.45)\n",
+      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.8/site-packages (from boto3->nemo_toolkit[all]==1.5.1) (1.0.0)\n",
+      "Requirement already satisfied: pybind11>=2.2 in /opt/conda/lib/python3.8/site-packages (from fasttext->nemo_toolkit[all]==1.5.1) (2.9.1)\n",
+      "Requirement already satisfied: distance>=0.1.3 in /opt/conda/lib/python3.8/site-packages (from g2p-en->nemo_toolkit[all]==1.5.1) (0.1.3)\n",
+      "Requirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.8/site-packages (from gdown->nemo_toolkit[all]==1.5.1) (4.10.0)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.8/site-packages (from beautifulsoup4->gdown->nemo_toolkit[all]==1.5.1) (2.3.1)\n",
+      "Requirement already satisfied: ipython-genutils~=0.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (0.2.0)\n",
+      "Requirement already satisfied: ipython>=4.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (8.1.1)\n",
+      "Requirement already satisfied: ipykernel>=4.5.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (6.9.2)\n",
+      "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (1.1.0)\n",
+      "Requirement already satisfied: widgetsnbextension~=3.6.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (3.6.0)\n",
+      "Requirement already satisfied: traitlets>=4.3.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (5.1.1)\n",
+      "Requirement already satisfied: nbformat>=4.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (5.2.0)\n",
+      "Requirement already satisfied: jupyter-client<8.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (7.1.2)\n",
+      "Requirement already satisfied: psutil in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (5.9.0)\n",
+      "Requirement already satisfied: tornado<7.0,>=4.2 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (6.1)\n",
+      "Requirement already satisfied: debugpy<2.0,>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (1.5.1)\n",
+      "Requirement already satisfied: nest-asyncio in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (1.5.4)\n",
+      "Requirement already satisfied: matplotlib-inline<0.2.0,>=0.1.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (0.1.3)\n",
+      "Requirement already satisfied: pickleshare in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.7.5)\n",
+      "Requirement already satisfied: decorator in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (5.1.1)\n",
+      "Requirement already satisfied: pygments>=2.4.0 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (2.11.2)\n",
+      "Requirement already satisfied: stack-data in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.2.0)\n",
+      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (3.0.27)\n",
+      "Requirement already satisfied: backcall in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.2.0)\n",
+      "Requirement already satisfied: jedi>=0.16 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.18.1)\n",
+      "Requirement already satisfied: pexpect>4.3 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (4.8.0)\n",
+      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /opt/conda/lib/python3.8/site-packages (from jedi>=0.16->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.8.3)\n",
+      "Requirement already satisfied: entrypoints in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (0.4)\n",
+      "Requirement already satisfied: pyzmq>=13 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (22.3.0)\n",
+      "Requirement already satisfied: jupyter-core>=4.6.0 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (4.9.2)\n",
+      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /opt/conda/lib/python3.8/site-packages (from nbformat>=4.2.0->ipywidgets->nemo_toolkit[all]==1.5.1) (4.4.0)\n",
+      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.18.1)\n",
+      "Requirement already satisfied: ptyprocess>=0.5 in /opt/conda/lib/python3.8/site-packages (from pexpect>4.3->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.7.0)\n",
+      "Requirement already satisfied: wcwidth in /opt/conda/lib/python3.8/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.2.5)\n",
+      "Requirement already satisfied: notebook>=4.4.1 in /opt/conda/lib/python3.8/site-packages (from widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (6.4.1)\n",
+      "Requirement already satisfied: Send2Trash>=1.5.0 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (1.8.0)\n",
+      "Requirement already satisfied: prometheus-client in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.13.1)\n",
+      "Requirement already satisfied: terminado>=0.8.3 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.13.3)\n",
+      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (3.0.3)\n",
+      "Requirement already satisfied: nbconvert in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (6.4.4)\n",
+      "Requirement already satisfied: argon2-cffi in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (21.3.0)\n",
+      "Requirement already satisfied: argon2-cffi-bindings in /opt/conda/lib/python3.8/site-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (21.2.0)\n",
+      "Requirement already satisfied: cffi>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (1.15.0)\n",
+      "Requirement already satisfied: pycparser in /opt/conda/lib/python3.8/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (2.21)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.8/site-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (2.1.1)\n",
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+      "Requirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in /opt/conda/lib/python3.8/site-packages (from numba->nemo_toolkit[all]==1.5.1) (0.36.0)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from scikit-learn->nemo_toolkit[all]==1.5.1) (3.1.0)\n",
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+      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.8/site-packages (from pandas->nemo_toolkit[all]==1.5.1) (2021.3)\n",
+      "Requirement already satisfied: pip in /opt/conda/lib/python3.8/site-packages (from pip-api->isort[requirements]<5->nemo_toolkit[all]==1.5.1) (21.2.4)\n",
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+      "Requirement already satisfied: pluggy<2.0,>=0.12 in /opt/conda/lib/python3.8/site-packages (from pytest->nemo_toolkit[all]==1.5.1) (1.0.0)\n",
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+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /opt/conda/lib/python3.8/site-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.7.1)\n",
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+      "Requirement already satisfied: mecab-python3==1.0.3 in /opt/conda/lib/python3.8/site-packages (from sacrebleu[ja]->nemo_toolkit[all]==1.5.1) (1.0.3)\n",
+      "Requirement already satisfied: sphinxcontrib-htmlhelp>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (2.0.0)\n",
+      "Requirement already satisfied: alabaster<0.8,>=0.7 in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (0.7.12)\n",
+      "Requirement already satisfied: babel>=1.3 in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (2.9.1)\n",
+      "Requirement already satisfied: sphinxcontrib-serializinghtml>=1.1.5 in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (1.1.5)\n",
+      "Requirement already satisfied: sphinxcontrib-devhelp in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (1.0.2)\n",
+      "Requirement already satisfied: sphinxcontrib-jsmath in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (1.0.1)\n",
+      "Requirement already satisfied: sphinxcontrib-qthelp in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (1.0.3)\n",
+      "Requirement already satisfied: snowballstemmer>=1.1 in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (2.2.0)\n",
+      "Requirement already satisfied: imagesize in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (1.3.0)\n",
+      "Requirement already satisfied: sphinxcontrib-applehelp in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (1.0.2)\n",
+      "Requirement already satisfied: docutils<0.18,>=0.14 in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (0.17.1)\n",
+      "Requirement already satisfied: pybtex-docutils>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from sphinxcontrib-bibtex->nemo_toolkit[all]==1.5.1) (1.0.1)\n",
+      "Requirement already satisfied: pybtex>=0.24 in /opt/conda/lib/python3.8/site-packages (from sphinxcontrib-bibtex->nemo_toolkit[all]==1.5.1) (0.24.0)\n",
+      "Requirement already satisfied: latexcodec>=1.0.4 in /opt/conda/lib/python3.8/site-packages (from pybtex>=0.24->sphinxcontrib-bibtex->nemo_toolkit[all]==1.5.1) (2.0.1)\n",
+      "Requirement already satisfied: pure-eval in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.2.2)\n",
+      "Requirement already satisfied: asttokens in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (2.0.5)\n",
+      "Requirement already satisfied: executing in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.8.3)\n",
+      "Requirement already satisfied: pathtools in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (0.1.2)\n",
+      "Requirement already satisfied: setproctitle in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (1.2.3)\n",
+      "Requirement already satisfied: GitPython>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (3.1.27)\n",
+      "Requirement already satisfied: sentry-sdk>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (1.5.10)\n",
+      "Requirement already satisfied: shortuuid>=0.5.0 in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (1.0.8)\n",
+      "Requirement already satisfied: docker-pycreds>=0.4.0 in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (0.4.0)\n",
+      "Requirement already satisfied: promise<3,>=2.0 in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (2.3)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gitdb<5,>=4.0.1 in /opt/conda/lib/python3.8/site-packages (from GitPython>=1.0.0->wandb->nemo_toolkit[all]==1.5.1) (4.0.9)\n",
+      "Requirement already satisfied: smmap<6,>=3.0.1 in /opt/conda/lib/python3.8/site-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb->nemo_toolkit[all]==1.5.1) (5.0.0)\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Install dependencies\n",
+    "!pip install wget\n",
+    "!apt-get update && DEBIAN_FRONTEND=noninteractive  apt-get install -y libsndfile1 ffmpeg\n",
+    "!pip install Cython\n",
+    "\n",
+    "## Install NeMo\n",
+    "!pip install nemo_toolkit[all]==1.5.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"2\"></a>\n",
+    "## 2. Download Citrinet model\n",
+    "\n",
+    "Next, we download a pretrained Nemo Citrinet model and convert it to a Torchscript module:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nemo\n",
+    "import torch\n",
+    "\n",
+    "import nemo.collections.asr as nemo_asr\n",
+    "from nemo.core import typecheck\n",
+    "typecheck.set_typecheck_enabled(False) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading and saving stt_en_citrinet_256...\n",
+      "[NeMo I 2022-04-21 23:12:45 cloud:56] Found existing object /root/.cache/torch/NeMo/NeMo_1.5.1/stt_en_citrinet_256/91a9cc5850784b2065e8a0aa3d526fd9/stt_en_citrinet_256.nemo.\n",
+      "[NeMo I 2022-04-21 23:12:45 cloud:62] Re-using file from: /root/.cache/torch/NeMo/NeMo_1.5.1/stt_en_citrinet_256/91a9cc5850784b2065e8a0aa3d526fd9/stt_en_citrinet_256.nemo\n",
+      "[NeMo I 2022-04-21 23:12:45 common:728] Instantiating model from pre-trained checkpoint\n",
+      "[NeMo I 2022-04-21 23:12:46 mixins:146] Tokenizer SentencePieceTokenizer initialized with 1024 tokens\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[NeMo W 2022-04-21 23:12:47 modelPT:130] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n",
+      "    Train config : \n",
+      "    manifest_filepath: null\n",
+      "    sample_rate: 16000\n",
+      "    batch_size: 32\n",
+      "    trim_silence: true\n",
+      "    max_duration: 16.7\n",
+      "    shuffle: true\n",
+      "    is_tarred: false\n",
+      "    tarred_audio_filepaths: null\n",
+      "    use_start_end_token: false\n",
+      "    \n",
+      "[NeMo W 2022-04-21 23:12:47 modelPT:137] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). \n",
+      "    Validation config : \n",
+      "    manifest_filepath: null\n",
+      "    sample_rate: 16000\n",
+      "    batch_size: 32\n",
+      "    shuffle: false\n",
+      "    use_start_end_token: false\n",
+      "    \n",
+      "[NeMo W 2022-04-21 23:12:47 modelPT:143] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).\n",
+      "    Test config : \n",
+      "    manifest_filepath: null\n",
+      "    sample_rate: 16000\n",
+      "    batch_size: 32\n",
+      "    shuffle: false\n",
+      "    use_start_end_token: false\n",
+      "    \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[NeMo I 2022-04-21 23:12:47 features:265] PADDING: 16\n",
+      "[NeMo I 2022-04-21 23:12:47 features:282] STFT using torch\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[NeMo W 2022-04-21 23:12:47 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/preprocessing/features.py:315: FutureWarning: Pass sr=16000, n_fft=512 as keyword args. From version 0.10 passing these as positional arguments will result in an error\n",
+      "      librosa.filters.mel(sample_rate, self.n_fft, n_mels=nfilt, fmin=lowfreq, fmax=highfreq), dtype=torch.float\n",
+      "    \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[NeMo I 2022-04-21 23:12:49 save_restore_connector:149] Model EncDecCTCModelBPE was successfully restored from /root/.cache/torch/NeMo/NeMo_1.5.1/stt_en_citrinet_256/91a9cc5850784b2065e8a0aa3d526fd9/stt_en_citrinet_256.nemo.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[NeMo W 2022-04-21 23:12:49 export_utils:198] Swapped 0 modules\n",
+      "[NeMo W 2022-04-21 23:12:49 conv_asr:73] Turned off 235 masked convolutions\n",
+      "[NeMo W 2022-04-21 23:12:49 export_utils:198] Swapped 0 modules\n",
+      "[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/jit/_trace.py:916: UserWarning: `optimize` is deprecated and has no effect. Use `with torch.jit.optimized_execution() instead\n",
+      "      warnings.warn(\n",
+      "    \n",
+      "[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:668: LightningDeprecationWarning: The `LightningModule.model_size` property was deprecated in v1.5 and will be removed in v1.7. Please use the `pytorch_lightning.utilities.memory.get_model_size_mb`.\n",
+      "      if hasattr(mod, name):\n",
+      "    \n",
+      "[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:669: LightningDeprecationWarning: The `LightningModule.model_size` property was deprecated in v1.5 and will be removed in v1.7. Please use the `pytorch_lightning.utilities.memory.get_model_size_mb`.\n",
+      "      item = getattr(mod, name)\n",
+      "    \n",
+      "[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:668: LightningDeprecationWarning: `LightningModule.use_amp` was deprecated in v1.6 and will be removed in v1.8. Please use `Trainer.amp_backend`.\n",
+      "      if hasattr(mod, name):\n",
+      "    \n",
+      "[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:669: LightningDeprecationWarning: `LightningModule.use_amp` was deprecated in v1.6 and will be removed in v1.8. Please use `Trainer.amp_backend`.\n",
+      "      item = getattr(mod, name)\n",
+      "    \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(['stt_en_citrinet_256.ts'],\n",
+       " ['nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE exported to ONNX'])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variant = 'stt_en_citrinet_256'\n",
+    "\n",
+    "print(f\"Downloading and saving {variant}...\")\n",
+    "asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=variant)\n",
+    "asr_model.export(f\"{variant}.ts\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Benchmark utility\n",
+    "\n",
+    "Let us define a helper benchmarking function, then benchmark the original Pytorch model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model: stt_en_citrinet_256.ts\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256.ts =================================\n",
+      "batch size=1, num iterations=50\n",
+      "  Median samples/s: 102.0, mean: 102.0\n",
+      "  Median latency (s): 0.009802, mean: 0.009803, 99th_p: 0.009836, std_dev: 0.000014\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256.ts\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256.ts =================================\n",
+      "batch size=8, num iterations=50\n",
+      "  Median samples/s: 429.1, mean: 429.1\n",
+      "  Median latency (s): 0.018642, mean: 0.018643, 99th_p: 0.018670, std_dev: 0.000014\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256.ts\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256.ts =================================\n",
+      "batch size=32, num iterations=50\n",
+      "  Median samples/s: 551.3, mean: 551.2\n",
+      "  Median latency (s): 0.058047, mean: 0.058053, 99th_p: 0.058375, std_dev: 0.000106\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256.ts\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256.ts =================================\n",
+      "batch size=128, num iterations=50\n",
+      "  Median samples/s: 594.1, mean: 594.1\n",
+      "  Median latency (s): 0.215434, mean: 0.215446, 99th_p: 0.215806, std_dev: 0.000116\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import print_function\n",
+    "from __future__ import absolute_import\n",
+    "from __future__ import division\n",
+    "\n",
+    "import argparse\n",
+    "import timeit\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch_tensorrt as trtorch\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "\n",
+    "def benchmark(model, input_tensor, num_loops, model_name, batch_size):\n",
+    "    def timeGraph(model, input_tensor, num_loops):\n",
+    "        print(\"Warm up ...\")\n",
+    "        with torch.no_grad():\n",
+    "            for _ in range(20):\n",
+    "                features = model(input_tensor)\n",
+    "\n",
+    "        torch.cuda.synchronize()\n",
+    "        print(\"Start timing ...\")\n",
+    "        timings = []\n",
+    "        with torch.no_grad():\n",
+    "            for i in range(num_loops):\n",
+    "                start_time = timeit.default_timer()\n",
+    "                features = model(input_tensor)\n",
+    "                torch.cuda.synchronize()\n",
+    "                end_time = timeit.default_timer()\n",
+    "                timings.append(end_time - start_time)\n",
+    "                # print(\"Iteration {}: {:.6f} s\".format(i, end_time - start_time))\n",
+    "        return timings\n",
+    "    def printStats(graphName, timings, batch_size):\n",
+    "        times = np.array(timings)\n",
+    "        steps = len(times)\n",
+    "        speeds = batch_size / times\n",
+    "        time_mean = np.mean(times)\n",
+    "        time_med = np.median(times)\n",
+    "        time_99th = np.percentile(times, 99)\n",
+    "        time_std = np.std(times, ddof=0)\n",
+    "        speed_mean = np.mean(speeds)\n",
+    "        speed_med = np.median(speeds)\n",
+    "        msg = (\"\\n%s =================================\\n\"\n",
+    "                \"batch size=%d, num iterations=%d\\n\"\n",
+    "                \"  Median samples/s: %.1f, mean: %.1f\\n\"\n",
+    "                \"  Median latency (s): %.6f, mean: %.6f, 99th_p: %.6f, std_dev: %.6f\\n\"\n",
+    "                ) % (graphName,\n",
+    "                    batch_size, steps,\n",
+    "                    speed_med, speed_mean,\n",
+    "                    time_med, time_mean, time_99th, time_std)\n",
+    "        print(msg)\n",
+    "    timings = timeGraph(model, input_tensor, num_loops)\n",
+    "    printStats(model_name, timings, batch_size)\n",
+    "\n",
+    "precisions_str = 'fp32' # Precision (default=fp32, fp16)\n",
+    "variant = 'stt_en_citrinet_256' # Nemo Citrinet variant\n",
+    "batch_sizes = [1, 8, 32, 128] # Batch sizes (default=1,8,32,128)\n",
+    "trt = False # If True, infer with Torch-TensorRT engine. Else, infer with Pytorch model.\n",
+    "precision = torch.float32 if precisions_str =='fp32' else torch.float16\n",
+    "\n",
+    "for batch_size in batch_sizes:\n",
+    "    if trt:\n",
+    "        model_name = f\"{variant}_bs{batch_size}_{precision}.torch-tensorrt\"\n",
+    "    else:\n",
+    "        model_name = f\"{variant}.ts\"\n",
+    "\n",
+    "    print(f\"Loading model: {model_name}\") \n",
+    "    # Load traced model to CPU first\n",
+    "    model = torch.jit.load(model_name).cuda()\n",
+    "    cudnn.benchmark = True\n",
+    "    # Create random input tensor of certain size\n",
+    "    torch.manual_seed(12345)\n",
+    "    input_shape=(batch_size, 80, 1488)\n",
+    "    input_tensor = torch.randn(input_shape).cuda()\n",
+    "\n",
+    "    # Timing graph inference\n",
+    "    benchmark(model, input_tensor, 50, model_name, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Confirming the GPU we are using here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Thu Apr 21 23:13:32 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA TITAN V      On   | 00000000:17:00.0 Off |                  N/A |\n",
+      "| 38%   55C    P2    42W / 250W |   2462MiB / 12288MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   1  NVIDIA TITAN V      On   | 00000000:65:00.0 Off |                  N/A |\n",
+      "| 28%   39C    P8    26W / 250W |    112MiB / 12288MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|    0   N/A  N/A      3909      G                                       4MiB |\n",
+      "|    0   N/A  N/A      6047      C                                    2453MiB |\n",
+      "|    1   N/A  N/A      3909      G                                      39MiB |\n",
+      "|    1   N/A  N/A      4161      G                                      67MiB |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"3\"></a>\n",
+    "## 3. Create Torch-TensorRT modules\n",
+    "\n",
+    "In this step, we optimize the Citrinet Torchscript module with Torch-TensorRT with various precisions and batch sizes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating Torchscript-TensorRT module for batchsize 1 precision torch.float32\n",
+      "Generating Torchscript-TensorRT module for batchsize 8 precision torch.float32\n",
+      "Generating Torchscript-TensorRT module for batchsize 32 precision torch.float32\n",
+      "Generating Torchscript-TensorRT module for batchsize 128 precision torch.float32\n",
+      "Generating Torchscript-TensorRT module for batchsize 1 precision torch.float16\n",
+      "Generating Torchscript-TensorRT module for batchsize 8 precision torch.float16\n",
+      "Generating Torchscript-TensorRT module for batchsize 32 precision torch.float16\n",
+      "Generating Torchscript-TensorRT module for batchsize 128 precision torch.float16\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch_tensorrt as torchtrt\n",
+    "import argparse\n",
+    "\n",
+    "variant = \"stt_en_citrinet_256\"\n",
+    "precisions = [torch.float, torch.half]\n",
+    "batch_sizes = [1,8,32,128]\n",
+    "\n",
+    "model = torch.jit.load(f\"{variant}.ts\")\n",
+    "\n",
+    "for precision in precisions:\n",
+    "    for batch_size in batch_sizes:\n",
+    "        compile_settings = {\n",
+    "            \"inputs\": [torchtrt.Input(shape=[batch_size, 80, 1488])],\n",
+    "            \"enabled_precisions\": {precision},\n",
+    "            \"workspace_size\": 2000000000,\n",
+    "            \"truncate_long_and_double\": True,\n",
+    "        }\n",
+    "        print(f\"Generating Torchscript-TensorRT module for batchsize {batch_size} precision {precision}\")\n",
+    "        trt_ts_module = torchtrt.compile(model, **compile_settings)\n",
+    "        torch.jit.save(trt_ts_module, f\"{variant}_bs{batch_size}_{precision}.torch-tensorrt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"4\"></a>\n",
+    "## 4. Benchmark Torch-TensorRT models\n",
+    "\n",
+    "Finally, we are ready to benchmark the Torch-TensorRT optimized Citrinet models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FP32 (single precision)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model: stt_en_citrinet_256_bs1_torch.float32.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs1_torch.float32.torch-tensorrt =================================\n",
+      "batch size=1, num iterations=50\n",
+      "  Median samples/s: 242.2, mean: 218.0\n",
+      "  Median latency (s): 0.004128, mean: 0.004825, 99th_p: 0.008071, std_dev: 0.001270\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256_bs8_torch.float32.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs8_torch.float32.torch-tensorrt =================================\n",
+      "batch size=8, num iterations=50\n",
+      "  Median samples/s: 729.9, mean: 709.0\n",
+      "  Median latency (s): 0.010961, mean: 0.011388, 99th_p: 0.016114, std_dev: 0.001256\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256_bs32_torch.float32.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs32_torch.float32.torch-tensorrt =================================\n",
+      "batch size=32, num iterations=50\n",
+      "  Median samples/s: 955.6, mean: 953.4\n",
+      "  Median latency (s): 0.033488, mean: 0.033572, 99th_p: 0.035722, std_dev: 0.000545\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256_bs128_torch.float32.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs128_torch.float32.torch-tensorrt =================================\n",
+      "batch size=128, num iterations=50\n",
+      "  Median samples/s: 1065.8, mean: 1069.4\n",
+      "  Median latency (s): 0.120097, mean: 0.119708, 99th_p: 0.121618, std_dev: 0.001260\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "precisions_str = 'fp32' # Precision (default=fp32, fp16)\n",
+    "batch_sizes = [1, 8, 32, 128] # Batch sizes (default=1,8,32,128)\n",
+    "precision = torch.float32 if precisions_str =='fp32' else torch.float16\n",
+    "trt = True\n",
+    "\n",
+    "for batch_size in batch_sizes:\n",
+    "    if trt:\n",
+    "        model_name = f\"{variant}_bs{batch_size}_{precision}.torch-tensorrt\"\n",
+    "    else:\n",
+    "        model_name = f\"{variant}.ts\"\n",
+    "\n",
+    "    print(f\"Loading model: {model_name}\") \n",
+    "    # Load traced model to CPU first\n",
+    "    model = torch.jit.load(model_name).cuda()\n",
+    "    cudnn.benchmark = True\n",
+    "    # Create random input tensor of certain size\n",
+    "    torch.manual_seed(12345)\n",
+    "    input_shape=(batch_size, 80, 1488)\n",
+    "    input_tensor = torch.randn(input_shape).cuda()\n",
+    "\n",
+    "    # Timing graph inference\n",
+    "    benchmark(model, input_tensor, 50, model_name, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FP16 (half precision)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model: stt_en_citrinet_256_bs1_torch.float16.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs1_torch.float16.torch-tensorrt =================================\n",
+      "batch size=1, num iterations=50\n",
+      "  Median samples/s: 288.9, mean: 272.9\n",
+      "  Median latency (s): 0.003462, mean: 0.003774, 99th_p: 0.006846, std_dev: 0.000820\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256_bs8_torch.float16.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs8_torch.float16.torch-tensorrt =================================\n",
+      "batch size=8, num iterations=50\n",
+      "  Median samples/s: 1201.0, mean: 1190.9\n",
+      "  Median latency (s): 0.006661, mean: 0.006733, 99th_p: 0.008453, std_dev: 0.000368\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256_bs32_torch.float16.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs32_torch.float16.torch-tensorrt =================================\n",
+      "batch size=32, num iterations=50\n",
+      "  Median samples/s: 1538.2, mean: 1516.4\n",
+      "  Median latency (s): 0.020804, mean: 0.021143, 99th_p: 0.024492, std_dev: 0.000973\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256_bs128_torch.float16.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs128_torch.float16.torch-tensorrt =================================\n",
+      "batch size=128, num iterations=50\n",
+      "  Median samples/s: 1792.0, mean: 1777.0\n",
+      "  Median latency (s): 0.071428, mean: 0.072057, 99th_p: 0.076796, std_dev: 0.001351\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "precisions_str = 'fp16' # Precision (default=fp32, fp16)\n",
+    "batch_sizes = [1, 8, 32, 128] # Batch sizes (default=1,8,32,128)\n",
+    "precision = torch.float32 if precisions_str =='fp32' else torch.float16\n",
+    "\n",
+    "for batch_size in batch_sizes:\n",
+    "    if trt:\n",
+    "        model_name = f\"{variant}_bs{batch_size}_{precision}.torch-tensorrt\"\n",
+    "    else:\n",
+    "        model_name = f\"{variant}.ts\"\n",
+    "\n",
+    "    print(f\"Loading model: {model_name}\") \n",
+    "    # Load traced model to CPU first\n",
+    "    model = torch.jit.load(model_name).cuda()\n",
+    "    cudnn.benchmark = True\n",
+    "    # Create random input tensor of certain size\n",
+    "    torch.manual_seed(12345)\n",
+    "    input_shape=(batch_size, 80, 1488)\n",
+    "    input_tensor = torch.randn(input_shape).cuda()\n",
+    "\n",
+    "    # Timing graph inference\n",
+    "    benchmark(model, input_tensor, 50, model_name, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"5\"></a>\n",
+    "## 5. Conclusion\n",
+    "\n",
+    "In this notebook, we have walked through the complete process of optimizing the Citrinet model with Torch-TensorRT. On an A100 GPU, with Torch-TensorRT, we observe a speedup of ~**2.4X** with FP32, and ~**2.9X** with FP16 at batchsize of 128.\n",
+    "\n",
+    "### What's next\n",
+    "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/v1.2.0/_notebooks/EfficientNet-example.html b/docs/v1.2.0/_notebooks/EfficientNet-example.html
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+
+
+
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/writing_converters.html">Writing Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
+</ul>
+
+
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+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2019 NVIDIA Corporation. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an &quot;AS IS&quot; BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+</pre></div>
+</div>
+</div>
+<p><img alt="219a0350e192474794f36336bd8c82ef" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png" /></p>
+<section id="Torch-TensorRT-Getting-Started---EfficientNet-B0">
+<h1>Torch-TensorRT Getting Started - EfficientNet-B0<a class="headerlink" href="#Torch-TensorRT-Getting-Started---EfficientNet-B0" title="Permalink to this headline">¶</a></h1>
+<section id="Overview">
+<h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline">¶</a></h2>
+<p>In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the
+best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch’s JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new
+world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.</p>
+<p>When deploying on NVIDIA GPUs TensorRT, NVIDIA’s Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA’s Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that
+perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.</p>
+<p>Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch.</p>
+<section id="Learning-objectives">
+<h3>Learning objectives<a class="headerlink" href="#Learning-objectives" title="Permalink to this headline">¶</a></h3>
+<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained EfficientNet network, and running it to test the speedup obtained.</p>
+</section>
+</section>
+<section id="Content">
+<h2>Content<a class="headerlink" href="#Content" title="Permalink to this headline">¶</a></h2>
+<ol class="arabic simple">
+<li><p><a class="reference external" href="#1">Requirements</a></p></li>
+<li><p><a class="reference external" href="#2">EfficientNet Overview</a></p></li>
+<li><p><a class="reference external" href="#3">Running the model without optimizations</a></p></li>
+<li><p><a class="reference external" href="#4">Accelerating with Torch-TensorRT</a></p></li>
+<li><p><a class="reference external" href="#5">Conclusion</a></p></li>
+</ol>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!pip install timm==0.4.12
+!nvidia-smi
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
+Collecting timm==0.4.12
+  Downloading timm-0.4.12-py3-none-any.whl (376 kB)
+     |████████████████████████████████| 376 kB 11.9 MB/s eta 0:00:01
+Requirement already satisfied: torch&gt;=1.4 in /opt/conda/lib/python3.8/site-packages (from timm==0.4.12) (1.11.0a0+bfe5ad2)
+Requirement already satisfied: torchvision in /opt/conda/lib/python3.8/site-packages (from timm==0.4.12) (0.12.0a0)
+Requirement already satisfied: typing_extensions in /opt/conda/lib/python3.8/site-packages (from torch&gt;=1.4-&gt;timm==0.4.12) (4.0.1)
+Requirement already satisfied: pillow!=8.3.*,&gt;=5.3.0 in /opt/conda/lib/python3.8/site-packages (from torchvision-&gt;timm==0.4.12) (8.2.0)
+Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from torchvision-&gt;timm==0.4.12) (1.22.0)
+Installing collected packages: timm
+Successfully installed timm-0.4.12
+<span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
+Fri Feb  4 21:29:36 2022
++-----------------------------------------------------------------------------+
+| NVIDIA-SMI 510.39.01    Driver Version: 510.39.01    CUDA Version: 11.6     |
+|-------------------------------+----------------------+----------------------+
+| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+|                               |                      |               MIG M. |
+|===============================+======================+======================|
+|   0  NVIDIA GeForce ...  On   | 00000000:65:00.0 Off |                  N/A |
+| 30%   28C    P8    11W / 350W |      0MiB / 24576MiB |      0%      Default |
+|                               |                      |                  N/A |
++-------------------------------+----------------------+----------------------+
+
++-----------------------------------------------------------------------------+
+| Processes:                                                                  |
+|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
+|        ID   ID                                                   Usage      |
+|=============================================================================|
+|  No running processes found                                                 |
++-----------------------------------------------------------------------------+
+</pre></div></div>
+</div>
+<p>## 1. Requirements</p>
+<p>NVIDIA’s NGC provides PyTorch Docker Container which contains PyTorch and Torch-TensorRT. We can make use of <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">latest pytorch</a> container to run this notebook.</p>
+<p>Otherwise, you can follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container yourself, within which you can run this demo notebook.</p>
+<p>## 2. EfficientNet Overview</p>
+<p>PyTorch has a model repository called <code class="docutils literal notranslate"><span class="pre">timm</span></code>, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet.</p>
+<section id="Model-Description">
+<h3>Model Description<a class="headerlink" href="#Model-Description" title="Permalink to this headline">¶</a></h3>
+<p>This model is based on the <a class="reference external" href="https://arxiv.org/abs/1905.11946">EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks</a> paper.</p>
+<p><img alt="alt" class="no-scaled-link" src="https://1.bp.blogspot.com/-Cdtb97FtgdA/XO3BHsB7oEI/AAAAAAAAEKE/bmtkonwgs8cmWyI5esVo8wJPnhPLQ5bGQCLcBGAs/s1600/image4.png" style="width: 100%;" /></p>
+<p>## 3. Running the model without optimizations</p>
+<p>PyTorch has a model repository called <code class="docutils literal notranslate"><span class="pre">timm</span></code>, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch
+import torch_tensorrt
+import timm
+import time
+import numpy as np
+import torch.backends.cudnn as cudnn
+from timm.data import resolve_data_config
+from timm.data.transforms_factory import create_transform
+import json
+
+efficientnet_b0_model = timm.create_model(&#39;efficientnet_b0&#39;,pretrained=True)
+model = efficientnet_b0_model.eval().to(&quot;cuda&quot;)
+</pre></div>
+</div>
+</div>
+<p>With our model loaded, let’s proceed to downloading some images!</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!mkdir -p ./data
+!wget  -O ./data/img0.JPG &quot;https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&amp;width=630&quot;
+!wget  -O ./data/img1.JPG &quot;https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg&quot;
+!wget  -O ./data/img2.JPG &quot;https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg&quot;
+!wget  -O ./data/img3.JPG &quot;https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg&quot;
+
+!wget  -O ./data/imagenet_class_index.json &quot;https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json&quot;
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+--2022-02-04 21:30:07--  https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&amp;width=630
+Resolving d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)... 18.65.227.127, 18.65.227.37, 18.65.227.99, ...
+Connecting to d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)|18.65.227.127|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 24112 (24K) [image/jpeg]
+Saving to: ‘./data/img0.JPG’
+
+./data/img0.JPG     100%[===================&gt;]  23.55K  --.-KB/s    in 0.004s
+
+2022-02-04 21:30:07 (6.40 MB/s) - ‘./data/img0.JPG’ saved [24112/24112]
+
+--2022-02-04 21:30:07--  https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg
+Resolving www.hakaimagazine.com (www.hakaimagazine.com)... 164.92.73.117
+Connecting to www.hakaimagazine.com (www.hakaimagazine.com)|164.92.73.117|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 452718 (442K) [image/jpeg]
+Saving to: ‘./data/img1.JPG’
+
+./data/img1.JPG     100%[===================&gt;] 442.11K  --.-KB/s    in 0.06s
+
+2022-02-04 21:30:07 (6.83 MB/s) - ‘./data/img1.JPG’ saved [452718/452718]
+
+--2022-02-04 21:30:08--  https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg
+Resolving www.artis.nl (www.artis.nl)... 94.75.225.20
+Connecting to www.artis.nl (www.artis.nl)|94.75.225.20|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 361413 (353K) [image/jpeg]
+Saving to: ‘./data/img2.JPG’
+
+./data/img2.JPG     100%[===================&gt;] 352.94K   246KB/s    in 1.4s
+
+2022-02-04 21:30:10 (246 KB/s) - ‘./data/img2.JPG’ saved [361413/361413]
+
+--2022-02-04 21:30:10--  https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg
+Resolving www.familyhandyman.com (www.familyhandyman.com)... 104.18.202.107, 104.18.201.107, 2606:4700::6812:ca6b, ...
+Connecting to www.familyhandyman.com (www.familyhandyman.com)|104.18.202.107|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 90994 (89K) [image/jpeg]
+Saving to: ‘./data/img3.JPG’
+
+./data/img3.JPG     100%[===================&gt;]  88.86K  --.-KB/s    in 0.006s
+
+2022-02-04 21:30:10 (14.4 MB/s) - ‘./data/img3.JPG’ saved [90994/90994]
+
+--2022-02-04 21:30:11--  https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json
+Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.133.45
+Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.133.45|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 35363 (35K) [application/octet-stream]
+Saving to: ‘./data/imagenet_class_index.json’
+
+./data/imagenet_cla 100%[===================&gt;]  34.53K  --.-KB/s    in 0.07s
+
+2022-02-04 21:30:11 (474 KB/s) - ‘./data/imagenet_class_index.json’ saved [35363/35363]
+
+</pre></div></div>
+</div>
+<p>All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape <code class="docutils literal notranslate"><span class="pre">(3</span> <span class="pre">x</span> <span class="pre">H</span> <span class="pre">x</span> <span class="pre">W)</span></code>, where <code class="docutils literal notranslate"><span class="pre">H</span></code> and <code class="docutils literal notranslate"><span class="pre">W</span></code> are expected to be at least <code class="docutils literal notranslate"><span class="pre">224</span></code>. The images have to be loaded in to a range of <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1]</span></code> and then normalized using <code class="docutils literal notranslate"><span class="pre">mean</span> <span class="pre">=</span> <span class="pre">[0.485,</span> <span class="pre">0.456,</span> <span class="pre">0.406]</span></code> and <code class="docutils literal notranslate"><span class="pre">std</span> <span class="pre">=</span> <span class="pre">[0.229,</span> <span class="pre">0.224,</span> <span class="pre">0.225]</span></code>.</p>
+<p>Here’s a sample execution.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from PIL import Image
+from torchvision import transforms
+import matplotlib.pyplot as plt
+
+fig, axes = plt.subplots(nrows=2, ncols=2)
+
+for i in range(4):
+    img_path = &#39;./data/img%d.JPG&#39;%i
+    img = Image.open(img_path)
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    input_tensor = preprocess(img)
+    plt.subplot(2,2,i+1)
+    plt.imshow(img)
+    plt.axis(&#39;off&#39;)
+
+# loading labels
+with open(&quot;./data/imagenet_class_index.json&quot;) as json_file:
+    d = json.load(json_file)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/_notebooks_EfficientNet-example_12_0.png" src="../_images/_notebooks_EfficientNet-example_12_0.png" />
+</div>
+</div>
+<p>Throughout this tutorial, we will be making use of some utility functions; <code class="docutils literal notranslate"><span class="pre">efficientnet_preprocess</span></code> for preprocessing input images, <code class="docutils literal notranslate"><span class="pre">predict</span></code> to use the model for prediction and <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> to benchmark the inference. You do not need to understand/go through these utilities to make use of Torch TensorRT, but are welecomed to do so if you choose.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>cudnn.benchmark = True
+
+def efficientnet_preprocess():
+    config = resolve_data_config({}, model=model)
+    transform = create_transform(**config)
+    return transform
+
+# decode the results into ([predicted class, description], probability)
+def predict(img_path, model):
+    img = Image.open(img_path)
+    preprocess = efficientnet_preprocess()
+    input_tensor = preprocess(img)
+    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+
+    # move the input and model to GPU for speed if available
+    if torch.cuda.is_available():
+        input_batch = input_batch.to(&#39;cuda&#39;)
+        model.to(&#39;cuda&#39;)
+
+    with torch.no_grad():
+        output = model(input_batch)
+        # Tensor of shape 1000, with confidence scores over Imagenet&#39;s 1000 classes
+        sm_output = torch.nn.functional.softmax(output[0], dim=0)
+
+    ind = torch.argmax(sm_output)
+    return d[str(ind.item())], sm_output[ind] #([predicted class, description], probability)
+
+def benchmark(model, input_shape=(1024, 1, 224, 224), dtype=&#39;fp32&#39;, nwarmup=50, nruns=10000):
+    input_data = torch.randn(input_shape)
+    input_data = input_data.to(&quot;cuda&quot;)
+    if dtype==&#39;fp16&#39;:
+        input_data = input_data.half()
+
+    print(&quot;Warm up ...&quot;)
+    with torch.no_grad():
+        for _ in range(nwarmup):
+            features = model(input_data)
+    torch.cuda.synchronize()
+    print(&quot;Start timing ...&quot;)
+    timings = []
+    with torch.no_grad():
+        for i in range(1, nruns+1):
+            start_time = time.time()
+            features = model(input_data)
+            torch.cuda.synchronize()
+            end_time = time.time()
+            timings.append(end_time - start_time)
+            if i%10==0:
+                print(&#39;Iteration %d/%d, avg batch time %.2f ms&#39;%(i, nruns, np.mean(timings)*1000))
+
+    print(&quot;Input shape:&quot;, input_data.size())
+    print(&quot;Output features size:&quot;, features.size())
+    print(&#39;Average throughput: %.2f images/second&#39;%(input_shape[0]/np.mean(timings)))
+</pre></div>
+</div>
+</div>
+<p>With the model downloaded and the util functions written, let’s just quickly see some predictions, and benchmark the model in its current un-optimized state.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>for i in range(4):
+    img_path = &#39;./data/img%d.JPG&#39;%i
+    img = Image.open(img_path)
+
+    pred, prob = predict(img_path, efficientnet_b0_model)
+    print(&#39;{} - Predicted: {}, Probablility: {}&#39;.format(img_path, pred, prob))
+
+    plt.subplot(2,2,i+1)
+    plt.imshow(img);
+    plt.axis(&#39;off&#39;);
+    plt.title(pred[1])
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/opt/conda/lib/python3.8/site-packages/torchvision/transforms/transforms.py:321: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum.
+  warnings.warn(
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+./data/img0.JPG - Predicted: [&#39;n02109961&#39;, &#39;Eskimo_dog&#39;], Probablility: 0.3987298309803009
+./data/img1.JPG - Predicted: [&#39;n01537544&#39;, &#39;indigo_bunting&#39;], Probablility: 0.23344755172729492
+./data/img2.JPG - Predicted: [&#39;n02481823&#39;, &#39;chimpanzee&#39;], Probablility: 0.9695423245429993
+./data/img3.JPG - Predicted: [&#39;n01739381&#39;, &#39;vine_snake&#39;], Probablility: 0.227739155292511
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/_notebooks_EfficientNet-example_16_2.png" src="../_images/_notebooks_EfficientNet-example_16_2.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Model benchmark without Torch-TensorRT
+benchmark(model, input_shape=(128, 3, 224, 224), nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, avg batch time 37.62 ms
+Iteration 20/100, avg batch time 37.66 ms
+Iteration 30/100, avg batch time 37.65 ms
+Iteration 40/100, avg batch time 37.66 ms
+Iteration 50/100, avg batch time 37.70 ms
+Iteration 60/100, avg batch time 37.70 ms
+Iteration 70/100, avg batch time 37.70 ms
+Iteration 80/100, avg batch time 37.71 ms
+Iteration 90/100, avg batch time 37.72 ms
+Iteration 100/100, avg batch time 37.72 ms
+Input shape: torch.Size([128, 3, 224, 224])
+Output features size: torch.Size([128, 1000])
+Average throughput: 3393.46 images/second
+</pre></div></div>
+</div>
+<p>## 4. Accelerating with Torch-TensorRT</p>
+<p>Onwards to the next step, accelerating with Torch TensorRT. In these examples we showcase the results for FP32 (single precision) and FP16 (half precision). We do not demonstrat specific tuning, just showcase the simplicity of usage. If you want to learn more about the possible customizations, visit our <a class="reference external" href="https://nvidia.github.io/Torch-TensorRT/">documentation</a>.</p>
+</section>
+<section id="FP32-(single-precision)">
+<h3>FP32 (single precision)<a class="headerlink" href="#FP32-(single-precision)" title="Permalink to this headline">¶</a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># The compiled module will have precision as specified by &quot;op_precision&quot;.
+# Here, it will have FP32 precision.
+trt_model_fp32 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.float32)],
+    enabled_precisions = torch.float32, # Run with FP32
+    workspace_size = 1 &lt;&lt; 22
+)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Obtain the average time taken by a batch of input
+benchmark(trt_model_fp32, input_shape=(128, 3, 224, 224), nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, avg batch time 27.86 ms
+Iteration 20/100, avg batch time 27.71 ms
+Iteration 30/100, avg batch time 27.99 ms
+Iteration 40/100, avg batch time 27.95 ms
+Iteration 50/100, avg batch time 27.89 ms
+Iteration 60/100, avg batch time 27.85 ms
+Iteration 70/100, avg batch time 28.00 ms
+Iteration 80/100, avg batch time 27.97 ms
+Iteration 90/100, avg batch time 27.95 ms
+Iteration 100/100, avg batch time 27.92 ms
+Input shape: torch.Size([128, 3, 224, 224])
+Output features size: torch.Size([128, 1000])
+Average throughput: 4584.06 images/second
+</pre></div></div>
+</div>
+</section>
+<section id="FP16-(half-precision)">
+<h3>FP16 (half precision)<a class="headerlink" href="#FP16-(half-precision)" title="Permalink to this headline">¶</a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># The compiled module will have precision as specified by &quot;op_precision&quot;.
+# Here, it will have FP16 precision.
+trt_model_fp16 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.half)],
+    enabled_precisions = {torch.half}, # Run with FP32
+    workspace_size = 1 &lt;&lt; 22
+)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float
+The compiler is going to use the user setting Float16
+This conflict may cause an error at runtime due to partial compilation being enabled and therefore
+compatibility with PyTorch&#39;s data type convention is required.
+If you do indeed see errors at runtime either:
+- Remove the dtype spec for x.1
+- Disable partial compilation by setting require_full_compilation to True
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT] - Mean converter disregards dtype
+WARNING: [Torch-TensorRT TorchScript Conversion Context] - Tensor DataType is determined at build time for tensors not marked as input or output.
+WARNING: [Torch-TensorRT TorchScript Conversion Context] - Tensor DataType is determined at build time for tensors not marked as input or output.
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Obtain the average time taken by a batch of input
+benchmark(trt_model_fp16, input_shape=(128, 3, 224, 224), dtype=&#39;fp16&#39;, nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, avg batch time 12.05 ms
+Iteration 20/100, avg batch time 12.56 ms
+Iteration 30/100, avg batch time 12.39 ms
+Iteration 40/100, avg batch time 12.34 ms
+Iteration 50/100, avg batch time 12.33 ms
+Iteration 60/100, avg batch time 12.32 ms
+Iteration 70/100, avg batch time 12.30 ms
+Iteration 80/100, avg batch time 12.28 ms
+Iteration 90/100, avg batch time 12.35 ms
+Iteration 100/100, avg batch time 12.35 ms
+Input shape: torch.Size([128, 3, 224, 224])
+Output features size: torch.Size([128, 1000])
+Average throughput: 10362.23 images/second
+</pre></div></div>
+</div>
+<p>## 5. Conclusion</p>
+<p>In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for EfficientNet-B0 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of <strong>1.35x</strong> with FP32, and <strong>3.13x</strong> with FP16 on an NVIDIA 3090 GPU. These acceleration numbers will vary from GPU to GPU(as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute
+cards for maximum acceleration.</p>
+</section>
+<section id="What’s-next">
+<h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to this headline">¶</a></h3>
+<p>Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at <a class="reference external" href="https://github.com/NVIDIA/Torch-TensorRT">https://github.com/NVIDIA/Torch-TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+</section>
+
+
+             </article>
+
+            </div>
+            <footer>
+
+
+
+
+    <hr>
+
+
+
+  <div role="contentinfo">
+    <p>
+        &copy; Copyright 2021, NVIDIA Corporation.
+
+    </p>
+  </div>
+
+      <div>
+        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+      </div>
+
+
+</footer>
+
+          </div>
+        </div>
+
+        <div class="pytorch-content-right" id="pytorch-content-right">
+          <div class="pytorch-right-menu" id="pytorch-right-menu">
+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
+              <ul>
+<li><a class="reference internal" href="#">Torch-TensorRT Getting Started - EfficientNet-B0</a><ul>
+<li><a class="reference internal" href="#Overview">Overview</a><ul>
+<li><a class="reference internal" href="#Learning-objectives">Learning objectives</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#Content">Content</a><ul>
+<li><a class="reference internal" href="#Model-Description">Model Description</a></li>
+<li><a class="reference internal" href="#FP32-(single-precision)">FP32 (single precision)</a></li>
+<li><a class="reference internal" href="#FP16-(half-precision)">FP16 (half precision)</a></li>
+<li><a class="reference internal" href="#What’s-next">What’s next</a></li>
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diff --git a/docs/v1.2.0/_notebooks/EfficientNet-example.ipynb b/docs/v1.2.0/_notebooks/EfficientNet-example.ipynb
new file mode 100644
index 0000000000..31a3dad874
--- /dev/null
+++ b/docs/v1.2.0/_notebooks/EfficientNet-example.ipynb
@@ -0,0 +1,693 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
+    "\n",
+    "# Torch-TensorRT Getting Started - EfficientNet-B0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch's JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.\n",
+    "\n",
+    "When deploying on NVIDIA GPUs TensorRT, NVIDIA's Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA's Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.\n",
+    "\n",
+    "Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning objectives\n",
+    "\n",
+    "This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained EfficientNet network, and running it to test the speedup obtained.\n",
+    "\n",
+    "## Content\n",
+    "1. [Requirements](#1)\n",
+    "1. [EfficientNet Overview](#2)\n",
+    "1. [Running the model without optimizations](#3)\n",
+    "1. [Accelerating with Torch-TensorRT](#4)\n",
+    "1. [Conclusion](#5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Collecting timm==0.4.12\n",
+      "  Downloading timm-0.4.12-py3-none-any.whl (376 kB)\n",
+      "\u001b[K     |████████████████████████████████| 376 kB 11.9 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: torch>=1.4 in /opt/conda/lib/python3.8/site-packages (from timm==0.4.12) (1.11.0a0+bfe5ad2)\n",
+      "Requirement already satisfied: torchvision in /opt/conda/lib/python3.8/site-packages (from timm==0.4.12) (0.12.0a0)\n",
+      "Requirement already satisfied: typing_extensions in /opt/conda/lib/python3.8/site-packages (from torch>=1.4->timm==0.4.12) (4.0.1)\n",
+      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.8/site-packages (from torchvision->timm==0.4.12) (8.2.0)\n",
+      "Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from torchvision->timm==0.4.12) (1.22.0)\n",
+      "Installing collected packages: timm\n",
+      "Successfully installed timm-0.4.12\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
+      "Fri Feb  4 21:29:36 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 510.39.01    Driver Version: 510.39.01    CUDA Version: 11.6     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA GeForce ...  On   | 00000000:65:00.0 Off |                  N/A |\n",
+      "| 30%   28C    P8    11W / 350W |      0MiB / 24576MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|  No running processes found                                                 |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install timm==0.4.12\n",
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"1\"></a>\n",
+    "## 1. Requirements\n",
+    "\n",
+    "NVIDIA's NGC provides PyTorch Docker Container which contains PyTorch and Torch-TensorRT. We can make use of [latest pytorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) container to run this notebook.\n",
+    "\n",
+    "Otherwise, you can follow the steps in `notebooks/README` to prepare a Docker container yourself, within which you can run this demo notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"2\"></a>\n",
+    "## 2. EfficientNet Overview\n",
+    "\n",
+    "\n",
+    "PyTorch has a model repository called `timm`, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet.\n",
+    "\n",
+    "### Model Description\n",
+    "\n",
+    "This model is based on the [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) paper.\n",
+    "\n",
+    "<img src=\"https://1.bp.blogspot.com/-Cdtb97FtgdA/XO3BHsB7oEI/AAAAAAAAEKE/bmtkonwgs8cmWyI5esVo8wJPnhPLQ5bGQCLcBGAs/s1600/image4.png\" alt=\"alt\" width=\"100%\"/>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"3\"></a>\n",
+    "## 3. Running the model without optimizations\n",
+    "\n",
+    "\n",
+    "PyTorch has a model repository called `timm`, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch_tensorrt\n",
+    "import timm\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "from timm.data import resolve_data_config\n",
+    "from timm.data.transforms_factory import create_transform\n",
+    "import json \n",
+    "\n",
+    "efficientnet_b0_model = timm.create_model('efficientnet_b0',pretrained=True)\n",
+    "model = efficientnet_b0_model.eval().to(\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With our model loaded, let's proceed to downloading some images!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2022-02-04 21:30:07--  https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\n",
+      "Resolving d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)... 18.65.227.127, 18.65.227.37, 18.65.227.99, ...\n",
+      "Connecting to d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)|18.65.227.127|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 24112 (24K) [image/jpeg]\n",
+      "Saving to: ‘./data/img0.JPG’\n",
+      "\n",
+      "./data/img0.JPG     100%[===================>]  23.55K  --.-KB/s    in 0.004s  \n",
+      "\n",
+      "2022-02-04 21:30:07 (6.40 MB/s) - ‘./data/img0.JPG’ saved [24112/24112]\n",
+      "\n",
+      "--2022-02-04 21:30:07--  https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\n",
+      "Resolving www.hakaimagazine.com (www.hakaimagazine.com)... 164.92.73.117\n",
+      "Connecting to www.hakaimagazine.com (www.hakaimagazine.com)|164.92.73.117|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 452718 (442K) [image/jpeg]\n",
+      "Saving to: ‘./data/img1.JPG’\n",
+      "\n",
+      "./data/img1.JPG     100%[===================>] 442.11K  --.-KB/s    in 0.06s   \n",
+      "\n",
+      "2022-02-04 21:30:07 (6.83 MB/s) - ‘./data/img1.JPG’ saved [452718/452718]\n",
+      "\n",
+      "--2022-02-04 21:30:08--  https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\n",
+      "Resolving www.artis.nl (www.artis.nl)... 94.75.225.20\n",
+      "Connecting to www.artis.nl (www.artis.nl)|94.75.225.20|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 361413 (353K) [image/jpeg]\n",
+      "Saving to: ‘./data/img2.JPG’\n",
+      "\n",
+      "./data/img2.JPG     100%[===================>] 352.94K   246KB/s    in 1.4s    \n",
+      "\n",
+      "2022-02-04 21:30:10 (246 KB/s) - ‘./data/img2.JPG’ saved [361413/361413]\n",
+      "\n",
+      "--2022-02-04 21:30:10--  https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\n",
+      "Resolving www.familyhandyman.com (www.familyhandyman.com)... 104.18.202.107, 104.18.201.107, 2606:4700::6812:ca6b, ...\n",
+      "Connecting to www.familyhandyman.com (www.familyhandyman.com)|104.18.202.107|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 90994 (89K) [image/jpeg]\n",
+      "Saving to: ‘./data/img3.JPG’\n",
+      "\n",
+      "./data/img3.JPG     100%[===================>]  88.86K  --.-KB/s    in 0.006s  \n",
+      "\n",
+      "2022-02-04 21:30:10 (14.4 MB/s) - ‘./data/img3.JPG’ saved [90994/90994]\n",
+      "\n",
+      "--2022-02-04 21:30:11--  https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\n",
+      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.133.45\n",
+      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.133.45|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 35363 (35K) [application/octet-stream]\n",
+      "Saving to: ‘./data/imagenet_class_index.json’\n",
+      "\n",
+      "./data/imagenet_cla 100%[===================>]  34.53K  --.-KB/s    in 0.07s   \n",
+      "\n",
+      "2022-02-04 21:30:11 (474 KB/s) - ‘./data/imagenet_class_index.json’ saved [35363/35363]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!mkdir -p ./data\n",
+    "!wget  -O ./data/img0.JPG \"https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\"\n",
+    "!wget  -O ./data/img1.JPG \"https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\"\n",
+    "!wget  -O ./data/img2.JPG \"https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\"\n",
+    "!wget  -O ./data/img3.JPG \"https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\"\n",
+    "\n",
+    "!wget  -O ./data/imagenet_class_index.json \"https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All pre-trained models expect input images normalized in the same way,\n",
+    "i.e. mini-batches of 3-channel RGB images of shape `(3 x H x W)`, where `H` and `W` are expected to be at least `224`.\n",
+    "The images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]`\n",
+    "and `std = [0.229, 0.224, 0.225]`.\n",
+    "\n",
+    "Here's a sample execution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "from torchvision import transforms\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
+    "\n",
+    "for i in range(4):\n",
+    "    img_path = './data/img%d.JPG'%i\n",
+    "    img = Image.open(img_path)\n",
+    "    preprocess = transforms.Compose([\n",
+    "        transforms.Resize(256),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
+    "    ])\n",
+    "    input_tensor = preprocess(img)      \n",
+    "    plt.subplot(2,2,i+1)\n",
+    "    plt.imshow(img)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "# loading labels\n",
+    "with open(\"./data/imagenet_class_index.json\") as json_file: \n",
+    "    d = json.load(json_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Throughout this tutorial, we will be making use of some utility functions; `efficientnet_preprocess` for preprocessing input images, `predict` to use the model for prediction and `benchmark` to benchmark the inference. You do not need to understand/go through these utilities to make use of Torch TensorRT, but are welecomed to do so if you choose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cudnn.benchmark = True\n",
+    "\n",
+    "def efficientnet_preprocess():\n",
+    "    config = resolve_data_config({}, model=model)\n",
+    "    transform = create_transform(**config)\n",
+    "    return transform\n",
+    "\n",
+    "# decode the results into ([predicted class, description], probability)\n",
+    "def predict(img_path, model):\n",
+    "    img = Image.open(img_path)\n",
+    "    preprocess = efficientnet_preprocess()\n",
+    "    input_tensor = preprocess(img)\n",
+    "    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model\n",
+    "    \n",
+    "    # move the input and model to GPU for speed if available\n",
+    "    if torch.cuda.is_available():\n",
+    "        input_batch = input_batch.to('cuda')\n",
+    "        model.to('cuda')\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        output = model(input_batch)\n",
+    "        # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes\n",
+    "        sm_output = torch.nn.functional.softmax(output[0], dim=0)\n",
+    "        \n",
+    "    ind = torch.argmax(sm_output)\n",
+    "    return d[str(ind.item())], sm_output[ind] #([predicted class, description], probability)\n",
+    "\n",
+    "def benchmark(model, input_shape=(1024, 1, 224, 224), dtype='fp32', nwarmup=50, nruns=10000):\n",
+    "    input_data = torch.randn(input_shape)\n",
+    "    input_data = input_data.to(\"cuda\")\n",
+    "    if dtype=='fp16':\n",
+    "        input_data = input_data.half()\n",
+    "        \n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(nwarmup):\n",
+    "            features = model(input_data)\n",
+    "    torch.cuda.synchronize()\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(1, nruns+1):\n",
+    "            start_time = time.time()\n",
+    "            features = model(input_data)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = time.time()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            if i%10==0:\n",
+    "                print('Iteration %d/%d, avg batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
+    "\n",
+    "    print(\"Input shape:\", input_data.size())\n",
+    "    print(\"Output features size:\", features.size())\n",
+    "    print('Average throughput: %.2f images/second'%(input_shape[0]/np.mean(timings)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the model downloaded and the util functions written, let's just quickly see some predictions, and benchmark the model in its current un-optimized state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.8/site-packages/torchvision/transforms/transforms.py:321: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./data/img0.JPG - Predicted: ['n02109961', 'Eskimo_dog'], Probablility: 0.3987298309803009\n",
+      "./data/img1.JPG - Predicted: ['n01537544', 'indigo_bunting'], Probablility: 0.23344755172729492\n",
+      "./data/img2.JPG - Predicted: ['n02481823', 'chimpanzee'], Probablility: 0.9695423245429993\n",
+      "./data/img3.JPG - Predicted: ['n01739381', 'vine_snake'], Probablility: 0.227739155292511\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(4):\n",
+    "    img_path = './data/img%d.JPG'%i\n",
+    "    img = Image.open(img_path)\n",
+    "    \n",
+    "    pred, prob = predict(img_path, efficientnet_b0_model)\n",
+    "    print('{} - Predicted: {}, Probablility: {}'.format(img_path, pred, prob))\n",
+    "\n",
+    "    plt.subplot(2,2,i+1)\n",
+    "    plt.imshow(img);\n",
+    "    plt.axis('off');\n",
+    "    plt.title(pred[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, avg batch time 37.62 ms\n",
+      "Iteration 20/100, avg batch time 37.66 ms\n",
+      "Iteration 30/100, avg batch time 37.65 ms\n",
+      "Iteration 40/100, avg batch time 37.66 ms\n",
+      "Iteration 50/100, avg batch time 37.70 ms\n",
+      "Iteration 60/100, avg batch time 37.70 ms\n",
+      "Iteration 70/100, avg batch time 37.70 ms\n",
+      "Iteration 80/100, avg batch time 37.71 ms\n",
+      "Iteration 90/100, avg batch time 37.72 ms\n",
+      "Iteration 100/100, avg batch time 37.72 ms\n",
+      "Input shape: torch.Size([128, 3, 224, 224])\n",
+      "Output features size: torch.Size([128, 1000])\n",
+      "Average throughput: 3393.46 images/second\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Model benchmark without Torch-TensorRT\n",
+    "benchmark(model, input_shape=(128, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"4\"></a>\n",
+    "## 4. Accelerating with Torch-TensorRT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Onwards to the next step, accelerating with Torch TensorRT. In these examples we showcase the results for FP32 (single precision) and FP16 (half precision). We do not demonstrat specific tuning, just showcase the simplicity of usage. If you want to learn more about the possible customizations, visit our [documentation](https://nvidia.github.io/Torch-TensorRT/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FP32 (single precision)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The compiled module will have precision as specified by \"op_precision\".\n",
+    "# Here, it will have FP32 precision.\n",
+    "trt_model_fp32 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.float32)],\n",
+    "    enabled_precisions = torch.float32, # Run with FP32\n",
+    "    workspace_size = 1 << 22\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, avg batch time 27.86 ms\n",
+      "Iteration 20/100, avg batch time 27.71 ms\n",
+      "Iteration 30/100, avg batch time 27.99 ms\n",
+      "Iteration 40/100, avg batch time 27.95 ms\n",
+      "Iteration 50/100, avg batch time 27.89 ms\n",
+      "Iteration 60/100, avg batch time 27.85 ms\n",
+      "Iteration 70/100, avg batch time 28.00 ms\n",
+      "Iteration 80/100, avg batch time 27.97 ms\n",
+      "Iteration 90/100, avg batch time 27.95 ms\n",
+      "Iteration 100/100, avg batch time 27.92 ms\n",
+      "Input shape: torch.Size([128, 3, 224, 224])\n",
+      "Output features size: torch.Size([128, 1000])\n",
+      "Average throughput: 4584.06 images/second\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Obtain the average time taken by a batch of input\n",
+    "benchmark(trt_model_fp32, input_shape=(128, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FP16 (half precision)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
+      "The compiler is going to use the user setting Float16\n",
+      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
+      "compatibility with PyTorch's data type convention is required.\n",
+      "If you do indeed see errors at runtime either:\n",
+      "- Remove the dtype spec for x.1\n",
+      "- Disable partial compilation by setting require_full_compilation to True\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT TorchScript Conversion Context] - Tensor DataType is determined at build time for tensors not marked as input or output.\n",
+      "WARNING: [Torch-TensorRT TorchScript Conversion Context] - Tensor DataType is determined at build time for tensors not marked as input or output.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The compiled module will have precision as specified by \"op_precision\".\n",
+    "# Here, it will have FP16 precision.\n",
+    "trt_model_fp16 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.half)],\n",
+    "    enabled_precisions = {torch.half}, # Run with FP32\n",
+    "    workspace_size = 1 << 22\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, avg batch time 12.05 ms\n",
+      "Iteration 20/100, avg batch time 12.56 ms\n",
+      "Iteration 30/100, avg batch time 12.39 ms\n",
+      "Iteration 40/100, avg batch time 12.34 ms\n",
+      "Iteration 50/100, avg batch time 12.33 ms\n",
+      "Iteration 60/100, avg batch time 12.32 ms\n",
+      "Iteration 70/100, avg batch time 12.30 ms\n",
+      "Iteration 80/100, avg batch time 12.28 ms\n",
+      "Iteration 90/100, avg batch time 12.35 ms\n",
+      "Iteration 100/100, avg batch time 12.35 ms\n",
+      "Input shape: torch.Size([128, 3, 224, 224])\n",
+      "Output features size: torch.Size([128, 1000])\n",
+      "Average throughput: 10362.23 images/second\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Obtain the average time taken by a batch of input\n",
+    "benchmark(trt_model_fp16, input_shape=(128, 3, 224, 224), dtype='fp16', nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"5\"></a>\n",
+    "## 5. Conclusion\n",
+    "\n",
+    "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for EfficientNet-B0 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of **1.35x** with FP32, and **3.13x** with FP16 on an NVIDIA 3090 GPU. These acceleration numbers will vary from GPU to GPU(as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute cards for maximum acceleration.\n",
+    "\n",
+    "### What's next\n",
+    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/v1.2.0/_notebooks/Hugging-Face-BERT.html b/docs/v1.2.0/_notebooks/Hugging-Face-BERT.html
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+</style>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2022 NVIDIA Corporation. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an &quot;AS IS&quot; BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+</pre></div>
+</div>
+</div>
+<p><img alt="05794857f0be4549aac84aed85b14705" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png" /></p>
+<section id="Masked-Language-Modeling-(MLM)-with-Hugging-Face-BERT-Transformer">
+<h1>Masked Language Modeling (MLM) with Hugging Face BERT Transformer<a class="headerlink" href="#Masked-Language-Modeling-(MLM)-with-Hugging-Face-BERT-Transformer" title="Permalink to this headline">¶</a></h1>
+<section id="Learning-objectives">
+<h2>Learning objectives<a class="headerlink" href="#Learning-objectives" title="Permalink to this headline">¶</a></h2>
+<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained BERT transformer from Hugging Face, and running it to test the speedup obtained.</p>
+</section>
+<section id="Contents">
+<h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this headline">¶</a></h2>
+<ol class="arabic simple">
+<li><p><a class="reference external" href="#1">Requirements</a></p></li>
+<li><p><a class="reference external" href="#2">BERT Overview</a></p></li>
+<li><p><a class="reference external" href="#3">Creating TorchScript modules</a></p></li>
+<li><p><a class="reference external" href="#4">Compiling with Torch-TensorRT</a></p></li>
+<li><p><a class="reference external" href="#5">Benchmarking</a></p></li>
+<li><p><a class="reference external" href="#6">Conclusion</a></p></li>
+</ol>
+<p>## 1. Requirements</p>
+<p>NVIDIA’s NGC provides a PyTorch Docker Container which contains PyTorch and Torch-TensorRT. Starting with version <code class="docutils literal notranslate"><span class="pre">22.05-py3</span></code>, we can make use of <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">latest pytorch</a> container to run this notebook.</p>
+<p>Otherwise, you can follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container yourself, within which you can run this demo notebook.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!pip install transformers
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
+Requirement already satisfied: transformers in /opt/conda/lib/python3.8/site-packages (4.18.0)
+Requirement already satisfied: tqdm&gt;=4.27 in /opt/conda/lib/python3.8/site-packages (from transformers) (4.63.0)
+Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.8/site-packages (from transformers) (2022.3.15)
+Requirement already satisfied: huggingface-hub&lt;1.0,&gt;=0.1.0 in /opt/conda/lib/python3.8/site-packages (from transformers) (0.5.1)
+Requirement already satisfied: tokenizers!=0.11.3,&lt;0.13,&gt;=0.11.1 in /opt/conda/lib/python3.8/site-packages (from transformers) (0.12.1)
+Requirement already satisfied: numpy&gt;=1.17 in /opt/conda/lib/python3.8/site-packages (from transformers) (1.22.3)
+Requirement already satisfied: sacremoses in /opt/conda/lib/python3.8/site-packages (from transformers) (0.0.49)
+Requirement already satisfied: requests in /opt/conda/lib/python3.8/site-packages (from transformers) (2.27.1)
+Requirement already satisfied: pyyaml&gt;=5.1 in /opt/conda/lib/python3.8/site-packages (from transformers) (6.0)
+Requirement already satisfied: filelock in /opt/conda/lib/python3.8/site-packages (from transformers) (3.6.0)
+Requirement already satisfied: packaging&gt;=20.0 in /opt/conda/lib/python3.8/site-packages (from transformers) (21.3)
+Requirement already satisfied: typing-extensions&gt;=3.7.4.3 in /opt/conda/lib/python3.8/site-packages (from huggingface-hub&lt;1.0,&gt;=0.1.0-&gt;transformers) (4.1.1)
+Requirement already satisfied: pyparsing!=3.0.5,&gt;=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging&gt;=20.0-&gt;transformers) (3.0.7)
+Requirement already satisfied: urllib3&lt;1.27,&gt;=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests-&gt;transformers) (1.26.8)
+Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.8/site-packages (from requests-&gt;transformers) (2.0.12)
+Requirement already satisfied: certifi&gt;=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests-&gt;transformers) (2021.10.8)
+Requirement already satisfied: idna&lt;4,&gt;=2.5 in /opt/conda/lib/python3.8/site-packages (from requests-&gt;transformers) (3.3)
+Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from sacremoses-&gt;transformers) (1.16.0)
+Requirement already satisfied: click in /opt/conda/lib/python3.8/site-packages (from sacremoses-&gt;transformers) (8.0.4)
+Requirement already satisfied: joblib in /opt/conda/lib/python3.8/site-packages (from sacremoses-&gt;transformers) (1.1.0)
+<span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from transformers import BertTokenizer, BertForMaskedLM
+import torch
+import timeit
+import numpy as np
+import torch_tensorrt
+import torch.backends.cudnn as cudnn
+</pre></div>
+</div>
+</div>
+<p>## 2. BERT Overview</p>
+<p>Transformers comprise a class of deep learning algorithms employing self-attention; broadly speaking, the models learn large matrices of numbers, each element of which denotes how important one component of input data is to another. Since their introduction in 2017, transformers have enjoyed widespread adoption, particularly in natural language processing, but also in computer vision problems. This is largely because they are easier to parallelize than the sequence models which attention
+mechanisms were originally designed to augment.</p>
+<p>Hugging Face is a company that maintains a huge respository of pre-trained transformer models. The company also provides tools for integrating those models into PyTorch code and running inference with them.</p>
+<p>One of the most popular transformer models is BERT (Bidirectional Encoder Representations from Transformers). First developed at Google and released in 2018, it has become the backbone of Google’s search engine and a standard benchmark for NLP experiments. BERT was originally trained for next sentence prediction and masked language modeling (MLM), which aims to predict hidden words in sentences. In this notebook, we will use Hugging Face’s <code class="docutils literal notranslate"><span class="pre">bert-base-uncased</span></code> model (BERT’s smallest and
+simplest form, which does not employ text capitalization) for MLM.</p>
+<p>## 3. Creating TorchScript modules</p>
+<p>First, create a pretrained BERT tokenizer from the <code class="docutils literal notranslate"><span class="pre">bert-base-uncased</span></code> model</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>enc = BertTokenizer.from_pretrained(&#39;bert-base-uncased&#39;)
+</pre></div>
+</div>
+</div>
+<p>Create dummy inputs to generate a traced TorchScript model later</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>batch_size = 4
+
+batched_indexed_tokens = [[101, 64]*64]*batch_size
+batched_segment_ids = [[0, 1]*64]*batch_size
+batched_attention_masks = [[1, 1]*64]*batch_size
+
+tokens_tensor = torch.tensor(batched_indexed_tokens)
+segments_tensor = torch.tensor(batched_segment_ids)
+attention_masks_tensor = torch.tensor(batched_attention_masks)
+</pre></div>
+</div>
+</div>
+<p>Obtain a BERT masked language model from Hugging Face in the (scripted) TorchScript, then use the dummy inputs to trace it</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>mlm_model_ts = BertForMaskedLM.from_pretrained(&#39;bert-base-uncased&#39;, torchscript=True)
+traced_mlm_model = torch.jit.trace(mlm_model_ts, [tokens_tensor, segments_tensor, attention_masks_tensor])
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: [&#39;cls.seq_relationship.bias&#39;, &#39;cls.seq_relationship.weight&#39;]
+- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
+- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
+</pre></div></div>
+</div>
+<p>Define 4 masked sentences, with 1 word in each sentence hidden from the model. Fluent English speakers will probably be able to guess the masked words, but just in case, they are <code class="docutils literal notranslate"><span class="pre">'capital'</span></code>, <code class="docutils literal notranslate"><span class="pre">'language'</span></code>, <code class="docutils literal notranslate"><span class="pre">'innings'</span></code>, and <code class="docutils literal notranslate"><span class="pre">'mathematics'</span></code>.</p>
+<p>Also create a list containing the position of the masked word within each sentence. Given Python’s 0-based indexing convention, the numbers are each higher by 1 than might be expected. This is because the token at index 0 in each sentence is a beginning-of-sentence token, denoted <code class="docutils literal notranslate"><span class="pre">[CLS]</span></code> when entered explicitly.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>masked_sentences = [&#39;Paris is the [MASK] of France.&#39;,
+                    &#39;The primary [MASK] of the United States is English.&#39;,
+                    &#39;A baseball game consists of at least nine [MASK].&#39;,
+                    &#39;Topology is a branch of [MASK] concerned with the properties of geometric objects that remain unchanged under continuous transformations.&#39;]
+pos_masks = [4, 3, 9, 6]
+</pre></div>
+</div>
+</div>
+<p>Pass the masked sentences into the (scripted) TorchScript MLM model and verify that the unmasked sentences yield the expected results.</p>
+<p>Because the sentences are of different lengths, we must specify the <code class="docutils literal notranslate"><span class="pre">padding</span></code> argument in calling our encoder/tokenizer. There are several possible padding strategies, but we’ll use <code class="docutils literal notranslate"><span class="pre">'max_length'</span></code> padding with <code class="docutils literal notranslate"><span class="pre">max_length=128</span></code>. Later, when we compile an optimized version of the model with Torch-TensorRT, the optimized model will expect inputs of length 128, hence our choice of padding strategy and length here.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>encoded_inputs = enc(masked_sentences, return_tensors=&#39;pt&#39;, padding=&#39;max_length&#39;, max_length=128)
+outputs = mlm_model_ts(**encoded_inputs)
+most_likely_token_ids = [torch.argmax(outputs[0][i, pos, :]) for i, pos in enumerate(pos_masks)]
+unmasked_tokens = enc.decode(most_likely_token_ids).split(&#39; &#39;)
+unmasked_sentences = [masked_sentences[i].replace(&#39;[MASK]&#39;, token) for i, token in enumerate(unmasked_tokens)]
+for sentence in unmasked_sentences:
+    print(sentence)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Paris is the capital of France.
+The primary language of the United States is English.
+A baseball game consists of at least nine innings.
+Topology is a branch of mathematics concerned with the properties of geometric objects that remain unchanged under continuous transformations.
+</pre></div></div>
+</div>
+<p>Pass the masked sentences into the traced MLM model and verify that the unmasked sentences yield the expected results.</p>
+<p>Note the difference in how the <code class="docutils literal notranslate"><span class="pre">encoded_inputs</span></code> are passed into the model in the following cell compared to the previous one. If you examine <code class="docutils literal notranslate"><span class="pre">encoded_inputs</span></code>, you’ll find that it’s a dictionary with 3 keys, <code class="docutils literal notranslate"><span class="pre">'input_ids'</span></code>, <code class="docutils literal notranslate"><span class="pre">'token_type_ids'</span></code>, and <code class="docutils literal notranslate"><span class="pre">'attention_mask'</span></code>, each with a PyTorch tensor as an associated value. The traced model will accept <code class="docutils literal notranslate"><span class="pre">**encoded_inputs</span></code> as an input, but the Torch-TensorRT-optimized model (to be defined later) will not.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>encoded_inputs = enc(masked_sentences, return_tensors=&#39;pt&#39;, padding=&#39;max_length&#39;, max_length=128)
+outputs = traced_mlm_model(encoded_inputs[&#39;input_ids&#39;], encoded_inputs[&#39;token_type_ids&#39;], encoded_inputs[&#39;attention_mask&#39;])
+most_likely_token_ids = [torch.argmax(outputs[0][i, pos, :]) for i, pos in enumerate(pos_masks)]
+unmasked_tokens = enc.decode(most_likely_token_ids).split(&#39; &#39;)
+unmasked_sentences = [masked_sentences[i].replace(&#39;[MASK]&#39;, token) for i, token in enumerate(unmasked_tokens)]
+for sentence in unmasked_sentences:
+    print(sentence)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Paris is the capital of France.
+The primary language of the United States is English.
+A baseball game consists of at least nine innings.
+Topology is a branch of mathematics concerned with the properties of geometric objects that remain unchanged under continuous transformations.
+</pre></div></div>
+</div>
+<p>## 4. Compiling with Torch-TensorRT</p>
+<p>Change the logging level to avoid long printouts</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>new_level = torch_tensorrt.logging.Level.Error
+torch_tensorrt.logging.set_reportable_log_level(new_level)
+</pre></div>
+</div>
+</div>
+<p>Compile the model</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>trt_model = torch_tensorrt.compile(traced_mlm_model,
+    inputs= [torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32),  # input_ids
+             torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32),  # token_type_ids
+             torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32)], # attention_mask
+    enabled_precisions= {torch.float32}, # Run with 32-bit precision
+    workspace_size=2000000000,
+    truncate_long_and_double=True
+)
+</pre></div>
+</div>
+</div>
+<p>Pass the masked sentences into the compiled model and verify that the unmasked sentences yield the expected results.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>enc_inputs = enc(masked_sentences, return_tensors=&#39;pt&#39;, padding=&#39;max_length&#39;, max_length=128)
+enc_inputs = {k: v.type(torch.int32).cuda() for k, v in enc_inputs.items()}
+output_trt = trt_model(enc_inputs[&#39;input_ids&#39;], enc_inputs[&#39;token_type_ids&#39;], enc_inputs[&#39;attention_mask&#39;])
+most_likely_token_ids_trt = [torch.argmax(output_trt[i, pos, :]) for i, pos in enumerate(pos_masks)]
+unmasked_tokens_trt = enc.decode(most_likely_token_ids_trt).split(&#39; &#39;)
+unmasked_sentences_trt = [masked_sentences[i].replace(&#39;[MASK]&#39;, token) for i, token in enumerate(unmasked_tokens_trt)]
+for sentence in unmasked_sentences_trt:
+    print(sentence)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Paris is the capital of France.
+The primary language of the United States is English.
+A baseball game consists of at least nine innings.
+Topology is a branch of mathematics concerned with the properties of geometric objects that remain unchanged under continuous transformations.
+</pre></div></div>
+</div>
+<p>Compile the model again, this time with 16-bit precision</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>trt_model_fp16 = torch_tensorrt.compile(traced_mlm_model,
+    inputs= [torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32),  # input_ids
+             torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32),  # token_type_ids
+             torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32)], # attention_mask
+    enabled_precisions= {torch.half}, # Run with 16-bit precision
+    workspace_size=2000000000,
+    truncate_long_and_double=True
+)
+</pre></div>
+</div>
+</div>
+<p>## 5. Benchmarking</p>
+<p>In developing this notebook, we conducted our benchmarking on a single NVIDIA A100 GPU. Your results may differ from those shown, particularly on a different GPU.</p>
+<p>This function passes the inputs into the model and runs inference <code class="docutils literal notranslate"><span class="pre">num_loops</span></code> times, then returns a list of length containing the amount of time in seconds that each instance of inference took.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def timeGraph(model, input_tensor1, input_tensor2, input_tensor3, num_loops=50):
+    print(&quot;Warm up ...&quot;)
+    with torch.no_grad():
+        for _ in range(20):
+            features = model(input_tensor1, input_tensor2, input_tensor3)
+
+    torch.cuda.synchronize()
+
+    print(&quot;Start timing ...&quot;)
+    timings = []
+    with torch.no_grad():
+        for i in range(num_loops):
+            start_time = timeit.default_timer()
+            features = model(input_tensor1, input_tensor2, input_tensor3)
+            torch.cuda.synchronize()
+            end_time = timeit.default_timer()
+            timings.append(end_time - start_time)
+            # print(&quot;Iteration {}: {:.6f} s&quot;.format(i, end_time - start_time))
+
+    return timings
+</pre></div>
+</div>
+</div>
+<p>This function prints the number of input batches the model is able to process each second and summary statistics of the model’s latency.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def printStats(graphName, timings, batch_size):
+    times = np.array(timings)
+    steps = len(times)
+    speeds = batch_size / times
+    time_mean = np.mean(times)
+    time_med = np.median(times)
+    time_99th = np.percentile(times, 99)
+    time_std = np.std(times, ddof=0)
+    speed_mean = np.mean(speeds)
+    speed_med = np.median(speeds)
+
+    msg = (&quot;\n%s =================================\n&quot;
+            &quot;batch size=%d, num iterations=%d\n&quot;
+            &quot;  Median text batches/second: %.1f, mean: %.1f\n&quot;
+            &quot;  Median latency: %.6f, mean: %.6f, 99th_p: %.6f, std_dev: %.6f\n&quot;
+            ) % (graphName,
+                batch_size, steps,
+                speed_med, speed_mean,
+                time_med, time_mean, time_99th, time_std)
+    print(msg)
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>cudnn.benchmark = True
+</pre></div>
+</div>
+</div>
+<p>Benchmark the (scripted) TorchScript model on GPU</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>timings = timeGraph(mlm_model_ts.cuda(), enc_inputs[&#39;input_ids&#39;], enc_inputs[&#39;token_type_ids&#39;], enc_inputs[&#39;attention_mask&#39;])
+
+printStats(&quot;BERT&quot;, timings, batch_size)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+
+BERT =================================
+batch size=4, num iterations=50
+  Median text batches/second: 599.1, mean: 597.6
+  Median latency: 0.006677, mean: 0.006693, 99th_p: 0.006943, std_dev: 0.000059
+
+</pre></div></div>
+</div>
+<p>Benchmark the traced model on GPU</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>timings = timeGraph(traced_mlm_model.cuda(), enc_inputs[&#39;input_ids&#39;], enc_inputs[&#39;token_type_ids&#39;], enc_inputs[&#39;attention_mask&#39;])
+
+printStats(&quot;BERT&quot;, timings, batch_size)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+
+BERT =================================
+batch size=4, num iterations=50
+  Median text batches/second: 951.2, mean: 951.0
+  Median latency: 0.004205, mean: 0.004206, 99th_p: 0.004256, std_dev: 0.000015
+
+</pre></div></div>
+</div>
+<p>Benchmark the compiled FP32 model on GPU</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>timings = timeGraph(trt_model, enc_inputs[&#39;input_ids&#39;], enc_inputs[&#39;token_type_ids&#39;], enc_inputs[&#39;attention_mask&#39;])
+
+printStats(&quot;BERT&quot;, timings, batch_size)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+
+BERT =================================
+batch size=4, num iterations=50
+  Median text batches/second: 1216.9, mean: 1216.4
+  Median latency: 0.003287, mean: 0.003289, 99th_p: 0.003317, std_dev: 0.000007
+
+</pre></div></div>
+</div>
+<p>Benchmark the compiled FP16 model on GPU</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>timings = timeGraph(trt_model_fp16, enc_inputs[&#39;input_ids&#39;], enc_inputs[&#39;token_type_ids&#39;], enc_inputs[&#39;attention_mask&#39;])
+
+printStats(&quot;BERT&quot;, timings, batch_size)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+
+BERT =================================
+batch size=4, num iterations=50
+  Median text batches/second: 1776.7, mean: 1771.1
+  Median latency: 0.002251, mean: 0.002259, 99th_p: 0.002305, std_dev: 0.000015
+
+</pre></div></div>
+</div>
+<p>## 6. Conclusion</p>
+<p>In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for Masked Language Modeling with Hugging Face’s <code class="docutils literal notranslate"><span class="pre">bert-base-uncased</span></code> transformer and testing the performance impact of the optimization. With Torch-TensorRT on an NVIDIA A100 GPU, we observe the speedups indicated below. These acceleration numbers will vary from GPU to GPU (as well as implementation to implementation based on the ops used) and we encorage you to try out latest
+generation of Data center compute cards for maximum acceleration.</p>
+<p>Scripted (GPU): 1.0x Traced (GPU): 1.62x Torch-TensorRT (FP32): 2.14x Torch-TensorRT (FP16): 3.15x</p>
+<section id="What’s-next">
+<h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to this headline">¶</a></h3>
+<p>Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at <a class="reference external" href="https://github.com/NVIDIA/Torch-TensorRT">https://github.com/NVIDIA/Torch-TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+</section>
+
+
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+
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+
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+      <div>
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+
+          <li class="resources-mobile-menu-title">
+            Resources
+          </li>
+
+          <ul class="resources-mobile-menu-items">
+            <li>
+              <a href="https://pytorch.org/resources">Developer Resources</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/features">About</a>
+            </li>
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+            <li>
+              <a href="https://pytorch.org/hub">Models (Beta)</a>
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+              <a href="https://pytorch.org/#community-module">Community</a>
+            </li>
+
+            <li>
+              <a href="https://discuss.pytorch.org/">Forums</a>
+            </li>
+          </ul>
+
+          <li>
+            <a href="https://github.com/pytorch/pytorch">Github</a>
+          </li>
+        </ul>
+      </div>
+    </div>
+  </div>
+
+  <!-- End Mobile Menu -->
+
+  <script type="text/javascript" src="../_static/js/vendor/anchor.min.js"></script>
+
+  <script type="text/javascript">
+    $(document).ready(function() {
+      mobileMenu.bind();
+      mobileTOC.bind();
+      pytorchAnchors.bind();
+      sideMenus.bind();
+      scrollToAnchor.bind();
+      highlightNavigation.bind();
+      mainMenuDropdown.bind();
+      filterTags.bind();
+
+      // Add class to links that have code blocks, since we cannot create links in code blocks
+      $("article.pytorch-article a span.pre").each(function(e) {
+        $(this).closest("a").addClass("has-code");
+      });
+    })
+  </script>
+</body>
+</html>
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diff --git a/docs/v1.2.0/_notebooks/Hugging-Face-BERT.ipynb b/docs/v1.2.0/_notebooks/Hugging-Face-BERT.ipynb
new file mode 100644
index 0000000000..9b027b473e
--- /dev/null
+++ b/docs/v1.2.0/_notebooks/Hugging-Face-BERT.ipynb
@@ -0,0 +1,714 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9369b63c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2022 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0a97ac5",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png\" style=\"width: 90px; float: right;\">\n",
+    "\n",
+    "# Masked Language Modeling (MLM) with Hugging Face BERT Transformer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83f47edb",
+   "metadata": {},
+   "source": [
+    "## Learning objectives\n",
+    "\n",
+    "This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained BERT transformer from Hugging Face, and running it to test the speedup obtained.\n",
+    "\n",
+    "## Contents\n",
+    "1. [Requirements](#1)\n",
+    "2. [BERT Overview](#2)\n",
+    "3. [Creating TorchScript modules](#3)\n",
+    "4. [Compiling with Torch-TensorRT](#4)\n",
+    "5. [Benchmarking](#5)\n",
+    "6. [Conclusion](#6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "596fa151",
+   "metadata": {},
+   "source": [
+    "<a id=\"1\"></a>\n",
+    "## 1. Requirements\n",
+    "\n",
+    "NVIDIA's NGC provides a PyTorch Docker Container which contains PyTorch and Torch-TensorRT. Starting with version `22.05-py3`, we can make use of [latest pytorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) container to run this notebook.\n",
+    "\n",
+    "Otherwise, you can follow the steps in `notebooks/README` to prepare a Docker container yourself, within which you can run this demo notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "58e687d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Requirement already satisfied: transformers in /opt/conda/lib/python3.8/site-packages (4.18.0)\n",
+      "Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.8/site-packages (from transformers) (4.63.0)\n",
+      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.8/site-packages (from transformers) (2022.3.15)\n",
+      "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /opt/conda/lib/python3.8/site-packages (from transformers) (0.5.1)\n",
+      "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /opt/conda/lib/python3.8/site-packages (from transformers) (0.12.1)\n",
+      "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.8/site-packages (from transformers) (1.22.3)\n",
+      "Requirement already satisfied: sacremoses in /opt/conda/lib/python3.8/site-packages (from transformers) (0.0.49)\n",
+      "Requirement already satisfied: requests in /opt/conda/lib/python3.8/site-packages (from transformers) (2.27.1)\n",
+      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.8/site-packages (from transformers) (6.0)\n",
+      "Requirement already satisfied: filelock in /opt/conda/lib/python3.8/site-packages (from transformers) (3.6.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.8/site-packages (from transformers) (21.3)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.8/site-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\n",
+      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging>=20.0->transformers) (3.0.7)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests->transformers) (1.26.8)\n",
+      "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.8/site-packages (from requests->transformers) (2.0.12)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests->transformers) (2021.10.8)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests->transformers) (3.3)\n",
+      "Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from sacremoses->transformers) (1.16.0)\n",
+      "Requirement already satisfied: click in /opt/conda/lib/python3.8/site-packages (from sacremoses->transformers) (8.0.4)\n",
+      "Requirement already satisfied: joblib in /opt/conda/lib/python3.8/site-packages (from sacremoses->transformers) (1.1.0)\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1104c4f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import BertTokenizer, BertForMaskedLM\n",
+    "import torch\n",
+    "import timeit\n",
+    "import numpy as np\n",
+    "import torch_tensorrt\n",
+    "import torch.backends.cudnn as cudnn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acf67a5e",
+   "metadata": {},
+   "source": [
+    "<a id=\"2\"></a>\n",
+    "## 2. BERT Overview\n",
+    "\n",
+    "Transformers comprise a class of deep learning algorithms employing self-attention; broadly speaking, the models learn large matrices of numbers, each element of which denotes how important one component of input data is to another. Since their introduction in 2017, transformers have enjoyed widespread adoption, particularly in natural language processing, but also in computer vision problems. This is largely because they are easier to parallelize than the sequence models which attention mechanisms were originally designed to augment. \n",
+    "\n",
+    "Hugging Face is a company that maintains a huge respository of pre-trained transformer models. The company also provides tools for integrating those models into PyTorch code and running inference with them. \n",
+    "\n",
+    "One of the most popular transformer models is BERT (Bidirectional Encoder Representations from Transformers). First developed at Google and released in 2018, it has become the backbone of Google's search engine and a standard benchmark for NLP experiments. BERT was originally trained for next sentence prediction and masked language modeling (MLM), which aims to predict hidden words in sentences. In this notebook, we will use Hugging Face's `bert-base-uncased` model (BERT's smallest and simplest form, which does not employ text capitalization) for MLM."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19e711c0",
+   "metadata": {},
+   "source": [
+    "<a id=\"3\"></a>\n",
+    "## 3. Creating TorchScript modules  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81d4c6f6",
+   "metadata": {},
+   "source": [
+    "First, create a pretrained BERT tokenizer from the `bert-base-uncased` model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c7c8721e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enc = BertTokenizer.from_pretrained('bert-base-uncased')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7c1c679",
+   "metadata": {},
+   "source": [
+    "Create dummy inputs to generate a traced TorchScript model later"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c3827087",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 4\n",
+    "\n",
+    "batched_indexed_tokens = [[101, 64]*64]*batch_size\n",
+    "batched_segment_ids = [[0, 1]*64]*batch_size\n",
+    "batched_attention_masks = [[1, 1]*64]*batch_size\n",
+    "\n",
+    "tokens_tensor = torch.tensor(batched_indexed_tokens)\n",
+    "segments_tensor = torch.tensor(batched_segment_ids)\n",
+    "attention_masks_tensor = torch.tensor(batched_attention_masks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e31b27f",
+   "metadata": {},
+   "source": [
+    "Obtain a BERT masked language model from Hugging Face in the (scripted) TorchScript, then use the dummy inputs to trace it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a3cd5a35",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
+      "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+     ]
+    }
+   ],
+   "source": [
+    "mlm_model_ts = BertForMaskedLM.from_pretrained('bert-base-uncased', torchscript=True)\n",
+    "traced_mlm_model = torch.jit.trace(mlm_model_ts, [tokens_tensor, segments_tensor, attention_masks_tensor])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8d2217a",
+   "metadata": {},
+   "source": [
+    "Define 4 masked sentences, with 1 word in each sentence hidden from the model. Fluent English speakers will probably be able to guess the masked words, but just in case, they are `'capital'`, `'language'`, `'innings'`, and `'mathematics'`.\n",
+    "\n",
+    "Also create a list containing the position of the masked word within each sentence. Given Python's 0-based indexing convention, the numbers are each higher by 1 than might be expected. This is because the token at index 0 in each sentence is a beginning-of-sentence token, denoted `[CLS]` when entered explicitly. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4d1af982",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "masked_sentences = ['Paris is the [MASK] of France.', \n",
+    "                    'The primary [MASK] of the United States is English.', \n",
+    "                    'A baseball game consists of at least nine [MASK].', \n",
+    "                    'Topology is a branch of [MASK] concerned with the properties of geometric objects that remain unchanged under continuous transformations.']\n",
+    "pos_masks = [4, 3, 9, 6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d89b4c8",
+   "metadata": {},
+   "source": [
+    "Pass the masked sentences into the (scripted) TorchScript MLM model and verify that the unmasked sentences yield the expected results.  \n",
+    "\n",
+    "Because the sentences are of different lengths, we must specify the `padding` argument in calling our encoder/tokenizer. There are several possible padding strategies, but we'll use `'max_length'` padding with `max_length=128`. Later, when we compile an optimized version of the model with Torch-TensorRT, the optimized model will expect inputs of length 128, hence our choice of padding strategy and length here. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d2d7546b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Paris is the capital of France.\n",
+      "The primary language of the United States is English.\n",
+      "A baseball game consists of at least nine innings.\n",
+      "Topology is a branch of mathematics concerned with the properties of geometric objects that remain unchanged under continuous transformations.\n"
+     ]
+    }
+   ],
+   "source": [
+    "encoded_inputs = enc(masked_sentences, return_tensors='pt', padding='max_length', max_length=128)\n",
+    "outputs = mlm_model_ts(**encoded_inputs)\n",
+    "most_likely_token_ids = [torch.argmax(outputs[0][i, pos, :]) for i, pos in enumerate(pos_masks)]\n",
+    "unmasked_tokens = enc.decode(most_likely_token_ids).split(' ')\n",
+    "unmasked_sentences = [masked_sentences[i].replace('[MASK]', token) for i, token in enumerate(unmasked_tokens)]\n",
+    "for sentence in unmasked_sentences:\n",
+    "    print(sentence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0b423ff",
+   "metadata": {},
+   "source": [
+    "Pass the masked sentences into the traced MLM model and verify that the unmasked sentences yield the expected results. \n",
+    "\n",
+    "Note the difference in how the `encoded_inputs` are passed into the model in the following cell compared to the previous one. If you examine `encoded_inputs`, you'll find that it's a dictionary with 3 keys, `'input_ids'`, `'token_type_ids'`, and `'attention_mask'`, each with a PyTorch tensor as an associated value. The traced model will accept `**encoded_inputs` as an input, but the Torch-TensorRT-optimized model (to be defined later) will not. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "683a4a73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Paris is the capital of France.\n",
+      "The primary language of the United States is English.\n",
+      "A baseball game consists of at least nine innings.\n",
+      "Topology is a branch of mathematics concerned with the properties of geometric objects that remain unchanged under continuous transformations.\n"
+     ]
+    }
+   ],
+   "source": [
+    "encoded_inputs = enc(masked_sentences, return_tensors='pt', padding='max_length', max_length=128)\n",
+    "outputs = traced_mlm_model(encoded_inputs['input_ids'], encoded_inputs['token_type_ids'], encoded_inputs['attention_mask'])\n",
+    "most_likely_token_ids = [torch.argmax(outputs[0][i, pos, :]) for i, pos in enumerate(pos_masks)]\n",
+    "unmasked_tokens = enc.decode(most_likely_token_ids).split(' ')\n",
+    "unmasked_sentences = [masked_sentences[i].replace('[MASK]', token) for i, token in enumerate(unmasked_tokens)]\n",
+    "for sentence in unmasked_sentences:\n",
+    "    print(sentence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a31b545",
+   "metadata": {},
+   "source": [
+    "<a id=\"4\"></a>\n",
+    "## 4. Compiling with Torch-TensorRT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "413d8b4f",
+   "metadata": {},
+   "source": [
+    "Change the logging level to avoid long printouts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "42862893",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_level = torch_tensorrt.logging.Level.Error\n",
+    "torch_tensorrt.logging.set_reportable_log_level(new_level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "121d6d59",
+   "metadata": {},
+   "source": [
+    "Compile the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "eab90150",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trt_model = torch_tensorrt.compile(traced_mlm_model, \n",
+    "    inputs= [torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32),  # input_ids\n",
+    "             torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32),  # token_type_ids\n",
+    "             torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32)], # attention_mask\n",
+    "    enabled_precisions= {torch.float32}, # Run with 32-bit precision\n",
+    "    workspace_size=2000000000,\n",
+    "    truncate_long_and_double=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a96751ce",
+   "metadata": {},
+   "source": [
+    "Pass the masked sentences into the compiled model and verify that the unmasked sentences yield the expected results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "097ea381",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Paris is the capital of France.\n",
+      "The primary language of the United States is English.\n",
+      "A baseball game consists of at least nine innings.\n",
+      "Topology is a branch of mathematics concerned with the properties of geometric objects that remain unchanged under continuous transformations.\n"
+     ]
+    }
+   ],
+   "source": [
+    "enc_inputs = enc(masked_sentences, return_tensors='pt', padding='max_length', max_length=128)\n",
+    "enc_inputs = {k: v.type(torch.int32).cuda() for k, v in enc_inputs.items()}\n",
+    "output_trt = trt_model(enc_inputs['input_ids'], enc_inputs['token_type_ids'], enc_inputs['attention_mask'])\n",
+    "most_likely_token_ids_trt = [torch.argmax(output_trt[i, pos, :]) for i, pos in enumerate(pos_masks)] \n",
+    "unmasked_tokens_trt = enc.decode(most_likely_token_ids_trt).split(' ')\n",
+    "unmasked_sentences_trt = [masked_sentences[i].replace('[MASK]', token) for i, token in enumerate(unmasked_tokens_trt)]\n",
+    "for sentence in unmasked_sentences_trt:\n",
+    "    print(sentence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a398271d",
+   "metadata": {},
+   "source": [
+    "Compile the model again, this time with 16-bit precision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a063dee2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trt_model_fp16 = torch_tensorrt.compile(traced_mlm_model, \n",
+    "    inputs= [torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32),  # input_ids\n",
+    "             torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32),  # token_type_ids\n",
+    "             torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32)], # attention_mask\n",
+    "    enabled_precisions= {torch.half}, # Run with 16-bit precision\n",
+    "    workspace_size=2000000000,\n",
+    "    truncate_long_and_double=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a926334a",
+   "metadata": {},
+   "source": [
+    "<a id=\"5\"></a>\n",
+    "## 5. Benchmarking\n",
+    "\n",
+    "In developing this notebook, we conducted our benchmarking on a single NVIDIA A100 GPU. Your results may differ from those shown, particularly on a different GPU."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "976c6fb9",
+   "metadata": {},
+   "source": [
+    "This function passes the inputs into the model and runs inference `num_loops` times, then returns a list of length containing the amount of time in seconds that each instance of inference took."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b72a091e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def timeGraph(model, input_tensor1, input_tensor2, input_tensor3, num_loops=50):\n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(20):\n",
+    "            features = model(input_tensor1, input_tensor2, input_tensor3)\n",
+    "\n",
+    "    torch.cuda.synchronize()\n",
+    "\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(num_loops):\n",
+    "            start_time = timeit.default_timer()\n",
+    "            features = model(input_tensor1, input_tensor2, input_tensor3)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = timeit.default_timer()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            # print(\"Iteration {}: {:.6f} s\".format(i, end_time - start_time))\n",
+    "\n",
+    "    return timings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b44dcf8",
+   "metadata": {},
+   "source": [
+    "This function prints the number of input batches the model is able to process each second and summary statistics of the model's latency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2ef71ab7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def printStats(graphName, timings, batch_size):\n",
+    "    times = np.array(timings)\n",
+    "    steps = len(times)\n",
+    "    speeds = batch_size / times\n",
+    "    time_mean = np.mean(times)\n",
+    "    time_med = np.median(times)\n",
+    "    time_99th = np.percentile(times, 99)\n",
+    "    time_std = np.std(times, ddof=0)\n",
+    "    speed_mean = np.mean(speeds)\n",
+    "    speed_med = np.median(speeds)\n",
+    "\n",
+    "    msg = (\"\\n%s =================================\\n\"\n",
+    "            \"batch size=%d, num iterations=%d\\n\"\n",
+    "            \"  Median text batches/second: %.1f, mean: %.1f\\n\"\n",
+    "            \"  Median latency: %.6f, mean: %.6f, 99th_p: %.6f, std_dev: %.6f\\n\"\n",
+    "            ) % (graphName,\n",
+    "                batch_size, steps,\n",
+    "                speed_med, speed_mean,\n",
+    "                time_med, time_mean, time_99th, time_std)\n",
+    "    print(msg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "afe97b9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cudnn.benchmark = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eba98b24",
+   "metadata": {},
+   "source": [
+    "Benchmark the (scripted) TorchScript model on GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "bab5fa8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "BERT =================================\n",
+      "batch size=4, num iterations=50\n",
+      "  Median text batches/second: 599.1, mean: 597.6\n",
+      "  Median latency: 0.006677, mean: 0.006693, 99th_p: 0.006943, std_dev: 0.000059\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "timings = timeGraph(mlm_model_ts.cuda(), enc_inputs['input_ids'], enc_inputs['token_type_ids'], enc_inputs['attention_mask'])\n",
+    "\n",
+    "printStats(\"BERT\", timings, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc79c452",
+   "metadata": {},
+   "source": [
+    "Benchmark the traced model on GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "5c0bd8e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "BERT =================================\n",
+      "batch size=4, num iterations=50\n",
+      "  Median text batches/second: 951.2, mean: 951.0\n",
+      "  Median latency: 0.004205, mean: 0.004206, 99th_p: 0.004256, std_dev: 0.000015\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "timings = timeGraph(traced_mlm_model.cuda(), enc_inputs['input_ids'], enc_inputs['token_type_ids'], enc_inputs['attention_mask'])\n",
+    "\n",
+    "printStats(\"BERT\", timings, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41db22a1",
+   "metadata": {},
+   "source": [
+    "Benchmark the compiled FP32 model on GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ade7b508",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "BERT =================================\n",
+      "batch size=4, num iterations=50\n",
+      "  Median text batches/second: 1216.9, mean: 1216.4\n",
+      "  Median latency: 0.003287, mean: 0.003289, 99th_p: 0.003317, std_dev: 0.000007\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "timings = timeGraph(trt_model, enc_inputs['input_ids'], enc_inputs['token_type_ids'], enc_inputs['attention_mask'])\n",
+    "\n",
+    "printStats(\"BERT\", timings, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57b696de",
+   "metadata": {},
+   "source": [
+    "Benchmark the compiled FP16 model on GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f61b83fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "BERT =================================\n",
+      "batch size=4, num iterations=50\n",
+      "  Median text batches/second: 1776.7, mean: 1771.1\n",
+      "  Median latency: 0.002251, mean: 0.002259, 99th_p: 0.002305, std_dev: 0.000015\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "timings = timeGraph(trt_model_fp16, enc_inputs['input_ids'], enc_inputs['token_type_ids'], enc_inputs['attention_mask'])\n",
+    "\n",
+    "printStats(\"BERT\", timings, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43f67ba3",
+   "metadata": {},
+   "source": [
+    "<a id=\"6\"></a>\n",
+    "## 6. Conclusion\n",
+    "\n",
+    "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for Masked Language Modeling with Hugging Face's `bert-base-uncased` transformer and testing the performance impact of the optimization. With Torch-TensorRT on an NVIDIA A100 GPU, we observe the speedups indicated below. These acceleration numbers will vary from GPU to GPU (as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute cards for maximum acceleration.\n",
+    "\n",
+    "Scripted (GPU): 1.0x\n",
+    "Traced (GPU): 1.62x\n",
+    "Torch-TensorRT (FP32): 2.14x\n",
+    "Torch-TensorRT (FP16): 3.15x\n",
+    "\n",
+    "### What's next\n",
+    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ebd152d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/v1.2.0/_notebooks/Resnet50-example.html b/docs/v1.2.0/_notebooks/Resnet50-example.html
new file mode 100644
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+++ b/docs/v1.2.0/_notebooks/Resnet50-example.html
@@ -0,0 +1,1674 @@
+
+
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
+<ul>
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+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/writing_converters.html">Writing Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
+</ul>
+
+
+
+        </div>
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+            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
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+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2019 NVIDIA Corporation. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an &quot;AS IS&quot; BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+</pre></div>
+</div>
+</div>
+<p><img alt="72eea9f56ee749e080dacfcd4ab2364a" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png" /></p>
+<section id="Torch-TensorRT-Getting-Started---ResNet-50">
+<h1>Torch-TensorRT Getting Started - ResNet 50<a class="headerlink" href="#Torch-TensorRT-Getting-Started---ResNet-50" title="Permalink to this headline">¶</a></h1>
+<section id="Overview">
+<h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline">¶</a></h2>
+<p>In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the
+best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch’s JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new
+world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.</p>
+<p>When deploying on NVIDIA GPUs TensorRT, NVIDIA’s Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA’s Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that
+perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.</p>
+<p>Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch.</p>
+<section id="Learning-objectives">
+<h3>Learning objectives<a class="headerlink" href="#Learning-objectives" title="Permalink to this headline">¶</a></h3>
+<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained ResNet-50 network, and running it to test the speedup obtained.</p>
+</section>
+</section>
+<section id="Content">
+<h2>Content<a class="headerlink" href="#Content" title="Permalink to this headline">¶</a></h2>
+<ol class="arabic simple">
+<li><p><a class="reference external" href="#1">Requirements</a></p></li>
+<li><p><a class="reference external" href="#2">ResNet-50 Overview</a></p></li>
+<li><p><a class="reference external" href="#3">Running the model without optimizations</a></p></li>
+<li><p><a class="reference external" href="#4">Accelerating with Torch-TensorRT</a></p></li>
+<li><p><a class="reference external" href="#5">Conclusion</a></p></li>
+</ol>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!nvidia-smi
+!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Tue Feb  8 19:16:53 2022
++-----------------------------------------------------------------------------+
+| NVIDIA-SMI 510.39.01    Driver Version: 510.39.01    CUDA Version: 11.6     |
+|-------------------------------+----------------------+----------------------+
+| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+|                               |                      |               MIG M. |
+|===============================+======================+======================|
+|   0  NVIDIA GeForce ...  On   | 00000000:65:00.0 Off |                  N/A |
+| 30%   29C    P8    15W / 350W |      0MiB / 24576MiB |      0%      Default |
+|                               |                      |                  N/A |
++-------------------------------+----------------------+----------------------+
+
++-----------------------------------------------------------------------------+
+| Processes:                                                                  |
+|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
+|        ID   ID                                                   Usage      |
+|=============================================================================|
+|  No running processes found                                                 |
++-----------------------------------------------------------------------------+
+Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
+Collecting ipywidgets
+  Downloading ipywidgets-7.6.5-py2.py3-none-any.whl (121 kB)
+     |████████████████████████████████| 121 kB 5.3 MB/s eta 0:00:01
+Requirement already satisfied: ipython-genutils~=0.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (0.2.0)
+Requirement already satisfied: ipykernel&gt;=4.5.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (6.7.0)
+Requirement already satisfied: nbformat&gt;=4.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.3)
+Requirement already satisfied: ipython&gt;=4.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (7.31.0)
+Collecting jupyterlab-widgets&gt;=1.0.0
+  Downloading jupyterlab_widgets-1.0.2-py3-none-any.whl (243 kB)
+     |████████████████████████████████| 243 kB 2.6 MB/s eta 0:00:01
+Collecting widgetsnbextension~=3.5.0
+  Downloading widgetsnbextension-3.5.2-py2.py3-none-any.whl (1.6 MB)
+     |████████████████████████████████| 1.6 MB 3.5 MB/s eta 0:00:01
+Requirement already satisfied: traitlets&gt;=4.3.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.1)
+Requirement already satisfied: jupyter-client&lt;8.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (7.1.2)
+Requirement already satisfied: tornado&lt;7.0,&gt;=4.2 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (6.1)
+Requirement already satisfied: nest-asyncio in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (1.5.4)
+Requirement already satisfied: debugpy&lt;2.0,&gt;=1.0.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (1.5.1)
+Requirement already satisfied: matplotlib-inline&lt;0.2.0,&gt;=0.1.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (0.1.3)
+Requirement already satisfied: pexpect&gt;4.3 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (4.8.0)
+Requirement already satisfied: setuptools&gt;=18.5 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (59.5.0)
+Requirement already satisfied: jedi&gt;=0.16 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (0.18.1)
+Requirement already satisfied: decorator in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (5.1.0)
+Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,&lt;3.1.0,&gt;=2.0.0 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (3.0.24)
+Requirement already satisfied: backcall in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (0.2.0)
+Requirement already satisfied: pickleshare in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (0.7.5)
+Requirement already satisfied: pygments in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (2.11.1)
+Requirement already satisfied: parso&lt;0.9.0,&gt;=0.8.0 in /opt/conda/lib/python3.8/site-packages (from jedi&gt;=0.16-&gt;ipython&gt;=4.0.0-&gt;ipywidgets) (0.8.3)
+Requirement already satisfied: entrypoints in /opt/conda/lib/python3.8/site-packages (from jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (0.3)
+Requirement already satisfied: pyzmq&gt;=13 in /opt/conda/lib/python3.8/site-packages (from jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (22.3.0)
+Requirement already satisfied: python-dateutil&gt;=2.1 in /opt/conda/lib/python3.8/site-packages (from jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (2.8.2)
+Requirement already satisfied: jupyter-core&gt;=4.6.0 in /opt/conda/lib/python3.8/site-packages (from jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (4.9.1)
+Requirement already satisfied: jsonschema!=2.5.0,&gt;=2.4 in /opt/conda/lib/python3.8/site-packages (from nbformat&gt;=4.2.0-&gt;ipywidgets) (4.4.0)
+Requirement already satisfied: attrs&gt;=17.4.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,&gt;=2.4-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets) (21.4.0)
+Requirement already satisfied: importlib-resources&gt;=1.4.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,&gt;=2.4-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets) (5.4.0)
+Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,&gt;=0.14.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,&gt;=2.4-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets) (0.18.1)
+Requirement already satisfied: zipp&gt;=3.1.0 in /opt/conda/lib/python3.8/site-packages (from importlib-resources&gt;=1.4.0-&gt;jsonschema!=2.5.0,&gt;=2.4-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets) (3.7.0)
+Requirement already satisfied: ptyprocess&gt;=0.5 in /opt/conda/lib/python3.8/site-packages (from pexpect&gt;4.3-&gt;ipython&gt;=4.0.0-&gt;ipywidgets) (0.7.0)
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+Requirement already satisfied: six&gt;=1.5 in /opt/conda/lib/python3.8/site-packages (from python-dateutil&gt;=2.1-&gt;jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (1.16.0)
+Requirement already satisfied: notebook&gt;=4.4.1 in /opt/conda/lib/python3.8/site-packages (from widgetsnbextension~=3.5.0-&gt;ipywidgets) (6.4.1)
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+Requirement already satisfied: jinja2 in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (3.0.3)
+Requirement already satisfied: argon2-cffi in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (21.3.0)
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+Requirement already satisfied: terminado&gt;=0.8.3 in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (0.12.1)
+Requirement already satisfied: nbconvert in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (6.4.0)
+Requirement already satisfied: argon2-cffi-bindings in /opt/conda/lib/python3.8/site-packages (from argon2-cffi-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (21.2.0)
+Requirement already satisfied: cffi&gt;=1.0.1 in /opt/conda/lib/python3.8/site-packages (from argon2-cffi-bindings-&gt;argon2-cffi-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (1.15.0)
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+Requirement already satisfied: MarkupSafe&gt;=2.0 in /opt/conda/lib/python3.8/site-packages (from jinja2-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (2.0.1)
+Requirement already satisfied: jupyterlab-pygments in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (0.1.2)
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+Requirement already satisfied: bleach in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (4.1.0)
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+Requirement already satisfied: testpath in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (0.5.0)
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+Requirement already satisfied: mistune&lt;2,&gt;=0.8.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (0.8.4)
+Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from bleach-&gt;nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (21.3)
+Requirement already satisfied: webencodings in /opt/conda/lib/python3.8/site-packages (from bleach-&gt;nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (0.5.1)
+Requirement already satisfied: pyparsing!=3.0.5,&gt;=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging-&gt;bleach-&gt;nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (3.0.6)
+Installing collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets
+Successfully installed ipywidgets-7.6.5 jupyterlab-widgets-1.0.2 widgetsnbextension-3.5.2
+<span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
+</pre></div></div>
+</div>
+<p>## 1. Requirements</p>
+<p>NVIDIA’s NGC provides PyTorch Docker Container which contains PyTorch and Torch-TensorRT. We can make use of <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">latest pytorch</a> container to run this notebook.</p>
+<p>Otherwise, you can follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container yourself, within which you can run this demo notebook.</p>
+<p>## 2. ResNet-50 Overview</p>
+<p>PyTorch has a model repository called the PyTorch Hub, which is a source for high quality implementations of common models. We can get our ResNet-50 model from there pretrained on ImageNet.</p>
+<section id="Model-Description">
+<h3>Model Description<a class="headerlink" href="#Model-Description" title="Permalink to this headline">¶</a></h3>
+<p>This ResNet-50 model is based on the <a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf">Deep Residual Learning for Image Recognition</a> paper, which describes ResNet as “a method for detecting objects in images using a single deep neural network”. The input size is fixed to 32x32.</p>
+<p><img alt="alt" class="no-scaled-link" src="https://pytorch.org/assets/images/resnet.png" style="width: 50%;" /></p>
+<p>## 3. Running the model without optimizations</p>
+<p>PyTorch has a model repository called <code class="docutils literal notranslate"><span class="pre">timm</span></code>, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch
+import torchvision
+
+torch.hub._validate_not_a_forked_repo=lambda a,b,c: True
+
+resnet50_model = torch.hub.load(&#39;pytorch/vision:v0.10.0&#39;, &#39;resnet50&#39;, pretrained=True)
+resnet50_model.eval()
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Downloading: &#34;https://github.com/pytorch/vision/archive/v0.10.0.zip&#34; to /root/.cache/torch/hub/v0.10.0.zip
+Downloading: &#34;https://download.pytorch.org/models/resnet50-0676ba61.pth&#34; to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "a7036a6122874340b14aaef7b8319260", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+ResNet(
+  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
+  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu): ReLU(inplace=True)
+  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
+  (layer1): Sequential(
+    (0): Bottleneck(
+      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+      (downsample): Sequential(
+        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      )
+    )
+    (1): Bottleneck(
+      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (2): Bottleneck(
+      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+  )
+  (layer2): Sequential(
+    (0): Bottleneck(
+      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+      (downsample): Sequential(
+        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
+        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      )
+    )
+    (1): Bottleneck(
+      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (2): Bottleneck(
+      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (3): Bottleneck(
+      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+  )
+  (layer3): Sequential(
+    (0): Bottleneck(
+      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+      (downsample): Sequential(
+        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
+        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      )
+    )
+    (1): Bottleneck(
+      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (2): Bottleneck(
+      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (3): Bottleneck(
+      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (4): Bottleneck(
+      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (5): Bottleneck(
+      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+  )
+  (layer4): Sequential(
+    (0): Bottleneck(
+      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+      (downsample): Sequential(
+        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
+        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      )
+    )
+    (1): Bottleneck(
+      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (2): Bottleneck(
+      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+  )
+  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
+  (fc): Linear(in_features=2048, out_features=1000, bias=True)
+)
+</pre></div></div>
+</div>
+<p>With our model loaded, let’s proceed to downloading some images!</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!mkdir -p ./data
+!wget  -O ./data/img0.JPG &quot;https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&amp;width=630&quot;
+!wget  -O ./data/img1.JPG &quot;https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg&quot;
+!wget  -O ./data/img2.JPG &quot;https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg&quot;
+!wget  -O ./data/img3.JPG &quot;https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg&quot;
+
+!wget  -O ./data/imagenet_class_index.json &quot;https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json&quot;
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+--2022-02-08 19:17:19--  https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&amp;width=630
+Resolving d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)... 18.65.227.99, 18.65.227.37, 18.65.227.223, ...
+Connecting to d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)|18.65.227.99|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 24112 (24K) [image/jpeg]
+Saving to: ‘./data/img0.JPG’
+
+./data/img0.JPG     100%[===================&gt;]  23.55K  --.-KB/s    in 0.002s
+
+2022-02-08 19:17:19 (14.6 MB/s) - ‘./data/img0.JPG’ saved [24112/24112]
+
+--2022-02-08 19:17:19--  https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg
+Resolving www.hakaimagazine.com (www.hakaimagazine.com)... 164.92.73.117
+Connecting to www.hakaimagazine.com (www.hakaimagazine.com)|164.92.73.117|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 452718 (442K) [image/jpeg]
+Saving to: ‘./data/img1.JPG’
+
+./data/img1.JPG     100%[===================&gt;] 442.11K  2.40MB/s    in 0.2s
+
+2022-02-08 19:17:19 (2.40 MB/s) - ‘./data/img1.JPG’ saved [452718/452718]
+
+--2022-02-08 19:17:20--  https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg
+Resolving www.artis.nl (www.artis.nl)... 94.75.225.20
+Connecting to www.artis.nl (www.artis.nl)|94.75.225.20|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 361413 (353K) [image/jpeg]
+Saving to: ‘./data/img2.JPG’
+
+./data/img2.JPG     100%[===================&gt;] 352.94K   366KB/s    in 1.0s
+
+2022-02-08 19:17:24 (366 KB/s) - ‘./data/img2.JPG’ saved [361413/361413]
+
+--2022-02-08 19:17:24--  https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg
+Resolving www.familyhandyman.com (www.familyhandyman.com)... 104.18.201.107, 104.18.202.107, 2606:4700::6812:ca6b, ...
+Connecting to www.familyhandyman.com (www.familyhandyman.com)|104.18.201.107|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 94328 (92K) [image/jpeg]
+Saving to: ‘./data/img3.JPG’
+
+./data/img3.JPG     100%[===================&gt;]  92.12K  --.-KB/s    in 0.005s
+
+2022-02-08 19:17:25 (16.8 MB/s) - ‘./data/img3.JPG’ saved [94328/94328]
+
+--2022-02-08 19:17:25--  https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json
+Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.41.222
+Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.41.222|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 35363 (35K) [application/octet-stream]
+Saving to: ‘./data/imagenet_class_index.json’
+
+./data/imagenet_cla 100%[===================&gt;]  34.53K  --.-KB/s    in 0.07s
+
+2022-02-08 19:17:26 (481 KB/s) - ‘./data/imagenet_class_index.json’ saved [35363/35363]
+
+</pre></div></div>
+</div>
+<p>All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape <code class="docutils literal notranslate"><span class="pre">(3</span> <span class="pre">x</span> <span class="pre">H</span> <span class="pre">x</span> <span class="pre">W)</span></code>, where <code class="docutils literal notranslate"><span class="pre">H</span></code> and <code class="docutils literal notranslate"><span class="pre">W</span></code> are expected to be at least <code class="docutils literal notranslate"><span class="pre">224</span></code>. The images have to be loaded in to a range of <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1]</span></code> and then normalized using <code class="docutils literal notranslate"><span class="pre">mean</span> <span class="pre">=</span> <span class="pre">[0.485,</span> <span class="pre">0.456,</span> <span class="pre">0.406]</span></code> and <code class="docutils literal notranslate"><span class="pre">std</span> <span class="pre">=</span> <span class="pre">[0.229,</span> <span class="pre">0.224,</span> <span class="pre">0.225]</span></code>.</p>
+<p>Here’s a sample execution.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from PIL import Image
+from torchvision import transforms
+import matplotlib.pyplot as plt
+import json
+
+fig, axes = plt.subplots(nrows=2, ncols=2)
+
+for i in range(4):
+    img_path = &#39;./data/img%d.JPG&#39;%i
+    img = Image.open(img_path)
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    input_tensor = preprocess(img)
+    plt.subplot(2,2,i+1)
+    plt.imshow(img)
+    plt.axis(&#39;off&#39;)
+
+# loading labels
+with open(&quot;./data/imagenet_class_index.json&quot;) as json_file:
+    d = json.load(json_file)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/_notebooks_Resnet50-example_12_0.png" src="../_images/_notebooks_Resnet50-example_12_0.png" />
+</div>
+</div>
+<p>Throughout this tutorial, we will be making use of some utility functions; <code class="docutils literal notranslate"><span class="pre">rn50_preprocess</span></code> for preprocessing input images, <code class="docutils literal notranslate"><span class="pre">predict</span></code> to use the model for prediction and <code class="docutils literal notranslate"><span class="pre">benchmark</span></code> to benchmark the inference. You do not need to understand/go through these utilities to make use of Torch TensorRT, but are welecomed to do so if you choose.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import numpy as np
+import time
+import torch.backends.cudnn as cudnn
+cudnn.benchmark = True
+
+def rn50_preprocess():
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    return preprocess
+
+# decode the results into ([predicted class, description], probability)
+def predict(img_path, model):
+    img = Image.open(img_path)
+    preprocess = rn50_preprocess()
+    input_tensor = preprocess(img)
+    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+
+    # move the input and model to GPU for speed if available
+    if torch.cuda.is_available():
+        input_batch = input_batch.to(&#39;cuda&#39;)
+        model.to(&#39;cuda&#39;)
+
+    with torch.no_grad():
+        output = model(input_batch)
+        # Tensor of shape 1000, with confidence scores over Imagenet&#39;s 1000 classes
+        sm_output = torch.nn.functional.softmax(output[0], dim=0)
+
+    ind = torch.argmax(sm_output)
+    return d[str(ind.item())], sm_output[ind] #([predicted class, description], probability)
+
+def benchmark(model, input_shape=(1024, 1, 224, 224), dtype=&#39;fp32&#39;, nwarmup=50, nruns=10000):
+    input_data = torch.randn(input_shape)
+    input_data = input_data.to(&quot;cuda&quot;)
+    if dtype==&#39;fp16&#39;:
+        input_data = input_data.half()
+
+    print(&quot;Warm up ...&quot;)
+    with torch.no_grad():
+        for _ in range(nwarmup):
+            features = model(input_data)
+    torch.cuda.synchronize()
+    print(&quot;Start timing ...&quot;)
+    timings = []
+    with torch.no_grad():
+        for i in range(1, nruns+1):
+            start_time = time.time()
+            features = model(input_data)
+            torch.cuda.synchronize()
+            end_time = time.time()
+            timings.append(end_time - start_time)
+            if i%10==0:
+                print(&#39;Iteration %d/%d, ave batch time %.2f ms&#39;%(i, nruns, np.mean(timings)*1000))
+
+    print(&quot;Input shape:&quot;, input_data.size())
+    print(&quot;Output features size:&quot;, features.size())
+    print(&#39;Average batch time: %.2f ms&#39;%(np.mean(timings)*1000))
+</pre></div>
+</div>
+</div>
+<p>With the model downloaded and the util functions written, let’s just quickly see some predictions, and benchmark the model in its current un-optimized state.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>for i in range(4):
+    img_path = &#39;./data/img%d.JPG&#39;%i
+    img = Image.open(img_path)
+
+    pred, prob = predict(img_path, resnet50_model)
+    print(&#39;{} - Predicted: {}, Probablility: {}&#39;.format(img_path, pred, prob))
+
+    plt.subplot(2,2,i+1)
+    plt.imshow(img);
+    plt.axis(&#39;off&#39;);
+    plt.title(pred[1])
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+./data/img0.JPG - Predicted: [&#39;n02110185&#39;, &#39;Siberian_husky&#39;], Probablility: 0.49785590171813965
+./data/img1.JPG - Predicted: [&#39;n01820546&#39;, &#39;lorikeet&#39;], Probablility: 0.6445754766464233
+./data/img2.JPG - Predicted: [&#39;n02481823&#39;, &#39;chimpanzee&#39;], Probablility: 0.9899807572364807
+./data/img3.JPG - Predicted: [&#39;n01749939&#39;, &#39;green_mamba&#39;], Probablility: 0.39485082030296326
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/_notebooks_Resnet50-example_16_1.png" src="../_images/_notebooks_Resnet50-example_16_1.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Model benchmark without Torch-TensorRT
+model = resnet50_model.eval().to(&quot;cuda&quot;)
+benchmark(model, input_shape=(128, 3, 224, 224), nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, ave batch time 75.34 ms
+Iteration 20/100, ave batch time 75.33 ms
+Iteration 30/100, ave batch time 75.35 ms
+Iteration 40/100, ave batch time 75.37 ms
+Iteration 50/100, ave batch time 75.38 ms
+Iteration 60/100, ave batch time 75.38 ms
+Iteration 70/100, ave batch time 75.39 ms
+Iteration 80/100, ave batch time 75.39 ms
+Iteration 90/100, ave batch time 75.40 ms
+Iteration 100/100, ave batch time 75.41 ms
+Input shape: torch.Size([128, 3, 224, 224])
+Output features size: torch.Size([128, 1000])
+Average batch time: 75.41 ms
+</pre></div></div>
+</div>
+<p>## 4. Accelerating with Torch-TensorRT</p>
+<p>Onwards to the next step, accelerating with Torch TensorRT. In these examples we showcase the results for FP32 (single precision) and FP16 (half precision). We do not demonstrat specific tuning, just showcase the simplicity of usage. If you want to learn more about the possible customizations, visit our <a class="reference external" href="https://nvidia.github.io/Torch-TensorRT/">documentation</a>.</p>
+</section>
+<section id="FP32-(single-precision)">
+<h3>FP32 (single precision)<a class="headerlink" href="#FP32-(single-precision)" title="Permalink to this headline">¶</a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch_tensorrt
+
+# The compiled module will have precision as specified by &quot;op_precision&quot;.
+# Here, it will have FP32 precision.
+trt_model_fp32 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.float32)],
+    enabled_precisions = torch.float32, # Run with FP32
+    workspace_size = 1 &lt;&lt; 22
+)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Obtain the average time taken by a batch of input
+benchmark(trt_model_fp32, input_shape=(128, 3, 224, 224), nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, ave batch time 41.02 ms
+Iteration 20/100, ave batch time 41.12 ms
+Iteration 30/100, ave batch time 41.22 ms
+Iteration 40/100, ave batch time 41.14 ms
+Iteration 50/100, ave batch time 41.20 ms
+Iteration 60/100, ave batch time 41.20 ms
+Iteration 70/100, ave batch time 41.19 ms
+Iteration 80/100, ave batch time 41.23 ms
+Iteration 90/100, ave batch time 41.20 ms
+Iteration 100/100, ave batch time 41.21 ms
+Input shape: torch.Size([128, 3, 224, 224])
+Output features size: torch.Size([128, 1000])
+Average batch time: 41.21 ms
+</pre></div></div>
+</div>
+</section>
+<section id="FP16-(half-precision)">
+<h3>FP16 (half precision)<a class="headerlink" href="#FP16-(half-precision)" title="Permalink to this headline">¶</a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch_tensorrt
+
+# The compiled module will have precision as specified by &quot;op_precision&quot;.
+# Here, it will have FP16 precision.
+trt_model_fp16 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.half)],
+    enabled_precisions = {torch.half}, # Run with FP32
+    workspace_size = 1 &lt;&lt; 22
+)
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float
+The compiler is going to use the user setting Float16
+This conflict may cause an error at runtime due to partial compilation being enabled and therefore
+compatibility with PyTorch&#39;s data type convention is required.
+If you do indeed see errors at runtime either:
+- Remove the dtype spec for x.1
+- Disable partial compilation by setting require_full_compilation to True
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Obtain the average time taken by a batch of input
+benchmark(trt_model_fp16, input_shape=(128, 3, 224, 224), dtype=&#39;fp16&#39;, nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, ave batch time 14.48 ms
+Iteration 20/100, ave batch time 14.58 ms
+Iteration 30/100, ave batch time 14.72 ms
+Iteration 40/100, ave batch time 14.73 ms
+Iteration 50/100, ave batch time 14.70 ms
+Iteration 60/100, ave batch time 14.79 ms
+Iteration 70/100, ave batch time 14.73 ms
+Iteration 80/100, ave batch time 14.69 ms
+Iteration 90/100, ave batch time 14.68 ms
+Iteration 100/100, ave batch time 14.69 ms
+Input shape: torch.Size([128, 3, 224, 224])
+Output features size: torch.Size([128, 1000])
+Average batch time: 14.69 ms
+</pre></div></div>
+</div>
+<p>## 5. Conclusion</p>
+<p>In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for EfficientNet-B0 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of <strong>1.84x</strong> with FP32, and <strong>5.2x</strong> with FP16 on an NVIDIA 3090 GPU. These acceleration numbers will vary from GPU to GPU(as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute
+cards for maximum acceleration.</p>
+</section>
+<section id="What’s-next">
+<h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to this headline">¶</a></h3>
+<p>Now it’s time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at <a class="reference external" href="https://github.com/NVIDIA/Torch-TensorRT">https://github.com/NVIDIA/Torch-TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
+</section>
+</section>
+</section>
+
+
+             </article>
+
+            </div>
+            <footer>
+
+
+
+
+    <hr>
+
+
+
+  <div role="contentinfo">
+    <p>
+        &copy; Copyright 2021, NVIDIA Corporation.
+
+    </p>
+  </div>
+
+      <div>
+        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+      </div>
+
+
+</footer>
+
+          </div>
+        </div>
+
+        <div class="pytorch-content-right" id="pytorch-content-right">
+          <div class="pytorch-right-menu" id="pytorch-right-menu">
+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
+              <ul>
+<li><a class="reference internal" href="#">Torch-TensorRT Getting Started - ResNet 50</a><ul>
+<li><a class="reference internal" href="#Overview">Overview</a><ul>
+<li><a class="reference internal" href="#Learning-objectives">Learning objectives</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#Content">Content</a><ul>
+<li><a class="reference internal" href="#Model-Description">Model Description</a></li>
+<li><a class="reference internal" href="#FP32-(single-precision)">FP32 (single precision)</a></li>
+<li><a class="reference internal" href="#FP16-(half-precision)">FP16 (half precision)</a></li>
+<li><a class="reference internal" href="#What’s-next">What’s next</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+            </div>
+          </div>
+        </div>
+      </section>
+    </div>
+
+
+
+
+
+
+
+       <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
+         <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
+         <script src="../_static/jquery.js"></script>
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diff --git a/docs/v1.2.0/_notebooks/Resnet50-example.ipynb b/docs/v1.2.0/_notebooks/Resnet50-example.ipynb
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--- /dev/null
+++ b/docs/v1.2.0/_notebooks/Resnet50-example.ipynb
@@ -0,0 +1,925 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png\" style=\"width: 90px; float: right;\">\n",
+    "\n",
+    "# Torch-TensorRT Getting Started - ResNet 50"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch's JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.\n",
+    "\n",
+    "When deploying on NVIDIA GPUs TensorRT, NVIDIA's Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA's Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.\n",
+    "\n",
+    "Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning objectives\n",
+    "\n",
+    "This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained ResNet-50 network, and running it to test the speedup obtained.\n",
+    "\n",
+    "## Content\n",
+    "1. [Requirements](#1)\n",
+    "1. [ResNet-50 Overview](#2)\n",
+    "1. [Running the model without optimizations](#3)\n",
+    "1. [Accelerating with Torch-TensorRT](#4)\n",
+    "1. [Conclusion](#5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tue Feb  8 19:16:53 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 510.39.01    Driver Version: 510.39.01    CUDA Version: 11.6     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA GeForce ...  On   | 00000000:65:00.0 Off |                  N/A |\n",
+      "| 30%   29C    P8    15W / 350W |      0MiB / 24576MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|  No running processes found                                                 |\n",
+      "+-----------------------------------------------------------------------------+\n",
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Collecting ipywidgets\n",
+      "  Downloading ipywidgets-7.6.5-py2.py3-none-any.whl (121 kB)\n",
+      "\u001b[K     |████████████████████████████████| 121 kB 5.3 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: ipython-genutils~=0.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (0.2.0)\n",
+      "Requirement already satisfied: ipykernel>=4.5.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (6.7.0)\n",
+      "Requirement already satisfied: nbformat>=4.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.3)\n",
+      "Requirement already satisfied: ipython>=4.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (7.31.0)\n",
+      "Collecting jupyterlab-widgets>=1.0.0\n",
+      "  Downloading jupyterlab_widgets-1.0.2-py3-none-any.whl (243 kB)\n",
+      "\u001b[K     |████████████████████████████████| 243 kB 2.6 MB/s eta 0:00:01\n",
+      "\u001b[?25hCollecting widgetsnbextension~=3.5.0\n",
+      "  Downloading widgetsnbextension-3.5.2-py2.py3-none-any.whl (1.6 MB)\n",
+      "\u001b[K     |████████████████████████████████| 1.6 MB 3.5 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: traitlets>=4.3.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.1)\n",
+      "Requirement already satisfied: jupyter-client<8.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets) (7.1.2)\n",
+      "Requirement already satisfied: tornado<7.0,>=4.2 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets) (6.1)\n",
+      "Requirement already satisfied: nest-asyncio in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets) (1.5.4)\n",
+      "Requirement already satisfied: debugpy<2.0,>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets) (1.5.1)\n",
+      "Requirement already satisfied: matplotlib-inline<0.2.0,>=0.1.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets) (0.1.3)\n",
+      "Requirement already satisfied: pexpect>4.3 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (4.8.0)\n",
+      "Requirement already satisfied: setuptools>=18.5 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (59.5.0)\n",
+      "Requirement already satisfied: jedi>=0.16 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (0.18.1)\n",
+      "Requirement already satisfied: decorator in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (5.1.0)\n",
+      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (3.0.24)\n",
+      "Requirement already satisfied: backcall in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (0.2.0)\n",
+      "Requirement already satisfied: pickleshare in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (0.7.5)\n",
+      "Requirement already satisfied: pygments in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (2.11.1)\n",
+      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /opt/conda/lib/python3.8/site-packages (from jedi>=0.16->ipython>=4.0.0->ipywidgets) (0.8.3)\n",
+      "Requirement already satisfied: entrypoints in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (0.3)\n",
+      "Requirement already satisfied: pyzmq>=13 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (22.3.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (2.8.2)\n",
+      "Requirement already satisfied: jupyter-core>=4.6.0 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (4.9.1)\n",
+      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /opt/conda/lib/python3.8/site-packages (from nbformat>=4.2.0->ipywidgets) (4.4.0)\n",
+      "Requirement already satisfied: attrs>=17.4.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets) (21.4.0)\n",
+      "Requirement already satisfied: importlib-resources>=1.4.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets) (5.4.0)\n",
+      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets) (0.18.1)\n",
+      "Requirement already satisfied: zipp>=3.1.0 in /opt/conda/lib/python3.8/site-packages (from importlib-resources>=1.4.0->jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets) (3.7.0)\n",
+      "Requirement already satisfied: ptyprocess>=0.5 in /opt/conda/lib/python3.8/site-packages (from pexpect>4.3->ipython>=4.0.0->ipywidgets) (0.7.0)\n",
+      "Requirement already satisfied: wcwidth in /opt/conda/lib/python3.8/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=4.0.0->ipywidgets) (0.2.5)\n",
+      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.8/site-packages (from python-dateutil>=2.1->jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (1.16.0)\n",
+      "Requirement already satisfied: notebook>=4.4.1 in /opt/conda/lib/python3.8/site-packages (from widgetsnbextension~=3.5.0->ipywidgets) (6.4.1)\n",
+      "Requirement already satisfied: Send2Trash>=1.5.0 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (1.8.0)\n",
+      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (3.0.3)\n",
+      "Requirement already satisfied: argon2-cffi in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (21.3.0)\n",
+      "Requirement already satisfied: prometheus-client in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.13.0)\n",
+      "Requirement already satisfied: terminado>=0.8.3 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.12.1)\n",
+      "Requirement already satisfied: nbconvert in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (6.4.0)\n",
+      "Requirement already satisfied: argon2-cffi-bindings in /opt/conda/lib/python3.8/site-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (21.2.0)\n",
+      "Requirement already satisfied: cffi>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (1.15.0)\n",
+      "Requirement already satisfied: pycparser in /opt/conda/lib/python3.8/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (2.21)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.8/site-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (2.0.1)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: jupyterlab-pygments in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.1.2)\n",
+      "Requirement already satisfied: defusedxml in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.7.1)\n",
+      "Requirement already satisfied: bleach in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (4.1.0)\n",
+      "Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.5.10)\n",
+      "Requirement already satisfied: testpath in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.5.0)\n",
+      "Requirement already satisfied: pandocfilters>=1.4.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (1.5.0)\n",
+      "Requirement already satisfied: mistune<2,>=0.8.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.8.4)\n",
+      "Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (21.3)\n",
+      "Requirement already satisfied: webencodings in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.5.1)\n",
+      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging->bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (3.0.6)\n",
+      "Installing collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets\n",
+      "Successfully installed ipywidgets-7.6.5 jupyterlab-widgets-1.0.2 widgetsnbextension-3.5.2\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi\n",
+    "!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"1\"></a>\n",
+    "## 1. Requirements\n",
+    "\n",
+    "NVIDIA's NGC provides PyTorch Docker Container which contains PyTorch and Torch-TensorRT. We can make use of [latest pytorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) container to run this notebook.\n",
+    "\n",
+    "Otherwise, you can follow the steps in `notebooks/README` to prepare a Docker container yourself, within which you can run this demo notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"2\"></a>\n",
+    "## 2. ResNet-50 Overview\n",
+    "\n",
+    "\n",
+    "PyTorch has a model repository called the PyTorch Hub, which is a source for high quality implementations of common models. We can get our ResNet-50 model from there pretrained on ImageNet.\n",
+    "\n",
+    "### Model Description\n",
+    "\n",
+    "This ResNet-50 model is based on the [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf) paper, which describes ResNet as “a method for detecting objects in images using a single deep neural network\". The input size is fixed to 32x32.\n",
+    "\n",
+    "<img src=\"https://pytorch.org/assets/images/resnet.png\" alt=\"alt\" width=\"50%\"/>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"3\"></a>\n",
+    "## 3. Running the model without optimizations\n",
+    "\n",
+    "\n",
+    "PyTorch has a model repository called `timm`, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: \"https://github.com/pytorch/vision/archive/v0.10.0.zip\" to /root/.cache/torch/hub/v0.10.0.zip\n",
+      "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7036a6122874340b14aaef7b8319260",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0.00/97.8M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ResNet(\n",
+       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  (relu): ReLU(inplace=True)\n",
+       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+       "  (layer1): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer2): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (3): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer3): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (3): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (4): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (5): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer4): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+       "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torchvision\n",
+    "\n",
+    "torch.hub._validate_not_a_forked_repo=lambda a,b,c: True\n",
+    "\n",
+    "resnet50_model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)\n",
+    "resnet50_model.eval()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With our model loaded, let's proceed to downloading some images!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2022-02-08 19:17:19--  https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\n",
+      "Resolving d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)... 18.65.227.99, 18.65.227.37, 18.65.227.223, ...\n",
+      "Connecting to d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)|18.65.227.99|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 24112 (24K) [image/jpeg]\n",
+      "Saving to: ‘./data/img0.JPG’\n",
+      "\n",
+      "./data/img0.JPG     100%[===================>]  23.55K  --.-KB/s    in 0.002s  \n",
+      "\n",
+      "2022-02-08 19:17:19 (14.6 MB/s) - ‘./data/img0.JPG’ saved [24112/24112]\n",
+      "\n",
+      "--2022-02-08 19:17:19--  https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\n",
+      "Resolving www.hakaimagazine.com (www.hakaimagazine.com)... 164.92.73.117\n",
+      "Connecting to www.hakaimagazine.com (www.hakaimagazine.com)|164.92.73.117|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 452718 (442K) [image/jpeg]\n",
+      "Saving to: ‘./data/img1.JPG’\n",
+      "\n",
+      "./data/img1.JPG     100%[===================>] 442.11K  2.40MB/s    in 0.2s    \n",
+      "\n",
+      "2022-02-08 19:17:19 (2.40 MB/s) - ‘./data/img1.JPG’ saved [452718/452718]\n",
+      "\n",
+      "--2022-02-08 19:17:20--  https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\n",
+      "Resolving www.artis.nl (www.artis.nl)... 94.75.225.20\n",
+      "Connecting to www.artis.nl (www.artis.nl)|94.75.225.20|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 361413 (353K) [image/jpeg]\n",
+      "Saving to: ‘./data/img2.JPG’\n",
+      "\n",
+      "./data/img2.JPG     100%[===================>] 352.94K   366KB/s    in 1.0s    \n",
+      "\n",
+      "2022-02-08 19:17:24 (366 KB/s) - ‘./data/img2.JPG’ saved [361413/361413]\n",
+      "\n",
+      "--2022-02-08 19:17:24--  https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\n",
+      "Resolving www.familyhandyman.com (www.familyhandyman.com)... 104.18.201.107, 104.18.202.107, 2606:4700::6812:ca6b, ...\n",
+      "Connecting to www.familyhandyman.com (www.familyhandyman.com)|104.18.201.107|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 94328 (92K) [image/jpeg]\n",
+      "Saving to: ‘./data/img3.JPG’\n",
+      "\n",
+      "./data/img3.JPG     100%[===================>]  92.12K  --.-KB/s    in 0.005s  \n",
+      "\n",
+      "2022-02-08 19:17:25 (16.8 MB/s) - ‘./data/img3.JPG’ saved [94328/94328]\n",
+      "\n",
+      "--2022-02-08 19:17:25--  https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\n",
+      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.41.222\n",
+      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.41.222|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 35363 (35K) [application/octet-stream]\n",
+      "Saving to: ‘./data/imagenet_class_index.json’\n",
+      "\n",
+      "./data/imagenet_cla 100%[===================>]  34.53K  --.-KB/s    in 0.07s   \n",
+      "\n",
+      "2022-02-08 19:17:26 (481 KB/s) - ‘./data/imagenet_class_index.json’ saved [35363/35363]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!mkdir -p ./data\n",
+    "!wget  -O ./data/img0.JPG \"https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\"\n",
+    "!wget  -O ./data/img1.JPG \"https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\"\n",
+    "!wget  -O ./data/img2.JPG \"https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\"\n",
+    "!wget  -O ./data/img3.JPG \"https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\"\n",
+    "\n",
+    "!wget  -O ./data/imagenet_class_index.json \"https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All pre-trained models expect input images normalized in the same way,\n",
+    "i.e. mini-batches of 3-channel RGB images of shape `(3 x H x W)`, where `H` and `W` are expected to be at least `224`.\n",
+    "The images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]`\n",
+    "and `std = [0.229, 0.224, 0.225]`.\n",
+    "\n",
+    "Here's a sample execution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YbNll9STPmo2VGCXSpUgF2WSOph4DVXy7O15DoHDc1aAFFCbiqFzh5nSLg6zL7jTGFAU5AlMuWA3nXGjtcnXliE4wsXcx7l2N58KeI5YYo7DqAsfw/bJSLXQWJP24q20LdiGqWsx4UKR+J/cU359+znq+YIyVUHUlJThpRjiOa04rgOp11p7XWDhOjaHT5TGldrRszmfz0lpTihJRmorF2Up7EaJemyrvBecGhnDidelyORhpI9WE10tp68olLAgbbUNlfd6HCvikRKgm0NVqD12WaBft5vV8otG8tVuXbHymOl+4OjYXbGtoUwihUNp6VjQnp6Go7uPbI4wC4iSs2sWraLznRxha7wIV2MEwGs5YLEqKogShybIZu7t7nJyMwQiOxxOuX7/BpfMXWMwzLl2+wHNXL3D2zCpBI+ihNJqd+4d8+P4dvvyVl4mTOheyF/s8u7dGvZpkaA1lc5I+IUVXv2u9pTe2AhYfHCJLBzKW+Qg3oUQ1MY3wZKvOkue1N7qah14NIByANyU752mDtDpad9ROXuNrCLhIw4oIWFblmbWXnvwdpFsofJ6SWs1Ro4K3HVdDd0mXWi9GtilMtUBMzAq/f/gy741XuDvWxHlGK4CFDhlnObNFgNY9ZouEvtjkz1055mcv3iKQmdO3esit2asFXlkx44ole91Is/41Bavu5QP9RQW4xq2D9lojrBrRNq+ujgl8NkGvTvBd4KNPhfssMFUOWE2lvffqleb68Clj7zGldqzHlAlsQnOtDYg6GMKeV4OvFPU6EoYhcRwThJ6+W/DJ85yicEpsd420o9WqCEpDWXWXLU3Vgym9jsf63lq/V+MGAdbvVEpnFLN8wetbS+fNQD1Mlt41UAKfdKcZKWajxaTLaaCqEa51rTfyi5O/Vko3eSux3tQBEMoGluzsnXByMmZl0KHXgsU8Yz6f0Y1Dzj93hcODY44P9nnpykU+/xMvcP/eHv/gH/1T1rY3+erPfpVz5y9wcjzk1s0bZJM5rXbCiy89Zxl0xdap3rduz6qX3QIHjbRGT1BpqqNqsdL3rJ9AiIaE5VieVzfJapLb+wkcSPr8IMKBpPHfO6G/mpS1gcYDr3Hn2TvIKrWo191CXR/pJngN9A0EtYf829TgX51i1Ql2Ia1DxJtwBqZSq1TAZSSjPOa7e2f46Djkjd0xk/GCJArIhCA0kAaGeWHQiykLJAeZ4v/+/bN8/6DLf/7y+3TisZ1bPhuJse/qua/9UVTSrH9v4xccp/4Q9rxKTYAGoXxzO5yulCwOzD1DXpY83IhoqCBtSzeoV9WgwgO8F2ywGGZtT8K90sPTHMDjYrqOwmvnfqSCkNCAyTIKk6NCa0EPfLivqFUJBlAqIAqtdwJCYpCUxgYDqDyn1Noa0qq0jxY8i0JDXtjMWl48lk48WjLaGevV4BmDlAShrCLaPJMwxqAL48JpS4qGasH7xtqQZQuI0nkE2Ny9jcnoFodaTeHusyTGOuAVGp8D2BoZLGsKjaEUhsPhjLff/oDeoMvWap9erDBlwTxfsNLrIGWPyWzG0eEeV7e3iOOQYjZjc32F2eiYj/Z32bt7g/XtCwwnCy5sDPilX/oFtrfWSdsxsXI8wy35VVBHk7YZO3l9Ap8nULuA548l1AY146L/PGd1beBZbKXXq0iCceKqcOBsZ6HnahWhdH3oOdpS1mIhlwJ4rL3XMVNTqyashrH2PrDMUDvw9X3m+WytEnAPcXDfUCG4PrZivHCU18Gem79erEYKSg3DMbx/Y4gINfPb/1+ufbckUy+RRc+QGU0ic/7j1WPmh9cZiBb/OvkiE5Fi4pDCwNfvdjmevMDf++I11tJjaNTTqj5qacpUTNLXt8Dzbss2/dvVemM3SKt+acJp1RJOv+u9EyRWzeQXqAqYG1faOsrq+xq863pYbbVTQzT0vA8rjyefrtd5gt21IRCATaQSRaFLx1i7LQnP7FyxAQ4QeLYsBMoISgGBFBXza65KGuNAU6GKgrKs+b43btE43z9HuCg25dmtA0rcL59CUbv0iODYraFWobgkOkJ4sdLU53kApmmPdfrhpWlRsyfRGADSlCAk2gju7w75/W99j6PDQ37xa1+k305QlGSFIVABMlAMj47o9Hq0woD/7p/9c175/E/wE194jVs3bnC4v4PWmps3PuTihT3+yt/6W2wMuuzcvclKP2VzY2D5RQNQpdeBY2VNYdyLOA8eIVw++SetGCgrJl9xUCt+VkzIZ7Rziymmag9bvJHFTvZKMBf2O0cRHYt1i3XFjH1j0niSHeP2o+e19diAhlZV+OtqEVk0dfDOYLakq62ehmNjnvNSM1rcNaaOANo/nHP91oSVwQoXzp3jmeef4923ND91519z7f63eO/wCkkwZVv1efvWTcTKJuNrb/ILv7zFv5hdRecCI9uYcs47xzH/xz/+HP/rL79NNx0tkaGa02q3GFAvCP4thDtTeAnFzTfpvRS84cx/V8No86hpqJIqG0t1RZPv++tqplsx7sYyVi1yolZcPao8noQ3+AlpmUNQiduhraDb6UG55AfSMwL/XtL73tYTWguX40A47zgv8hj7XekHnlDIQKJLoDHYfcPadvNigj2u3L1rxXj9HpZVgDGyamIvClbv2LimoSWp71Uxl0YbPaBpd9N86Vo7wbQRHAynfOt7H/DJjZv84s99kfV+i0hBnhVMpxNKbcjmGZ1Oizs3b/D7v/3bnAxPSDptPvroI373d7+OknB8uM/6+iYr6ytonfF7v/3bfPVnfprtzQ0SF9RSDXFHxn27aGz+Ya3rd5JN7HiCijHe79agwOlSDbXRzLsUiRqIHOOt12/j9LDGAZ7wNM0toJ5zagfc7rz6CO4RtrgETLKh0rBepjVvNR7o7QVOsG4a+qgZKjW7tezOM0k32YRXLQh3Zn2uERKzKNjfOebgOzd5KYoY//G3WRQFN3/5ddaSFf7nf+ev8uZ3f59/8eY+OyczevM9vnd4yMnN6zy72uZCdp/B4GVOTIo+mdmsd6Xg+gH8P751mf/qK+8TBXNniPXP1g1ANEugXAGiEbhQpWrB8N4UjqRX/r/VClPdX1RtUUkp1bf1zK+nca3YwX/y48DZbTBNBVuJ+QGbrD8W0HXOFLUVVRiUNBjp120Lhj42woJuk/WBf0GJndQSD7q2UaWsG0DjBqqyng6lEbXNqoGAsmFkEMJU10tjlkS8JdR09be90uCqov4tGoO45idmqZv98333amqf5SXNUcW+vJZLsD+a8403P+SjT67z/DPnWO0mhFJa391CM1/Mabd6tBPFdDFiOhnz9ltvs3NwTPpHbzBdTFhkMxbzGbOTE9rdDiJq89233iNE8Mxzz9BJ0woErDuZ03cDRrt+E3aBs4uRbUNZL/Gnh8GPdTGidDhqgcsYkEJXi7Ifvd66rZqGiqaCQDieKrT1k3XHMKYC8iVx1S/W7t96TIjGteDVU7Umt/JAdYu8/abE38/d3wm6Vh/afGGo7+a8AnTtBuUNQH5xkQjk927ypd/+Q9LhDpO9EbNWi/sXz5D31wnTguNbnxDv3uFXLvf51fc6DDnhb/9sm2k246eSl/mN6AqtMqMoSuRKyImJ0SdzQPKt4xV+9f2L/OWXriHcAqcr10PvpeAr7t/OL15Uf+NJkSNz/rhfOKlQyC5GfmadjlhtNBJeKl6SgYTEbqTogy7AWfCrFscjljl93+Xy2NQLHhwrNinrQQNOJ+o+e1CtW9MJFkIg61zo1deefTUh0hok7MFHuTCJpc81y6jDYRsVbF7UNEc2G9QfrthQ4zvn1F7driF7WtBqMIuG9V+4FbzQgsks52g44Y+/831OxjO2N1Z45tJ5umkLhHUzWywWSKlIWhGLxYw7O/f5nW98k/aZS3z+lZ8kL0qEFkQq5MzlZyi1JGn3EFGKiju0kgAZd20eh1Ov79vF92Xtp1tnjrPs7skLA66ZlLAM04GUz0rndbh2saknlMKzTx/Z5YDO+bU2xXtvaRG4nByNRc2ySVE9C6gNYUI29Mj22caYxrBzxxxhsAPx1CJvGsPZi8MVAfbA5sDKq7pcBwsjkGXJ5T/4Ptsne7Bxhdalgna6xuWXXuOTL/0FVJoSBQEj+X9g5/230eGXua6njI7us722zdvlBRYzRdgTbAUFkyBilmcsAg3YnB6/eus8l9I9XrmwTxDIpXrZKCHPQhts0+iqvRoiAl4hVi9wtd+txQvXh8avMP56L1M0Jq9YcrqryVjD39qCq4+OkwhRWJc3HLh/ikfPY1Mv+NVHGid+Czd5XZ09g/XFu8AADdHb0NBVL7GLJj+sVxlOnfiwmlWwv3SvT71WnPpw6hq/jtaAu4zc9qmGUtc313Lpyup2JZJZrvnk1j7f/u67HJ8cMhoPuXzpHC8+e4mNlT6htIEh3i1usLJClhd8cO0Ov/eH3ybTCWvblzg43McYw9aZs5w7v4ESgg+3z7C7u8/WhUvMZgXjecbb71znmQvbRPJUOz74KktCKFCFeD9pTFf4SeoHmABh6lgk6ZTXfuFpSkl1Kk/n+Wp8OKkbi84rYMnmIAwI6SZcrTaop7FlVxUvM1Tqs1IYAvd0r/sFGu3eEI4barBKleAlKj+vlvrUuWdVeOQWg9mU+RvfRj93gWQ0JV69xIIS8d3f4dv5jHxtjTSV3H3vGm/d7TINtyiSi9zVBXfDlF4kWC3HtOcjZkjCIqMddBGRZIGGMGVawj/9+DmeOTOjLcdWVPfAaFvtVK/Z961A2Ph280DtPROMa78G1zFeRmjey/2uhq9vH7/4+UPm1DUCKKsHCGGjQEtZQC5PXfNgeTzeCw1tlhFOrSCw+qyGka05WCReLyUqNlhBwAN46lvDuOd9+pQ3zbEmHnZmwz/TU4T6L2pezHLnPKpUz9AVC9I+isn4Hz+AoNAlo1HGZLrgxt0Drt24w5079zC6YD49YTDosrU2YKXXIpSG6WxGFEUUeUlmBLv39/nw2idc++QOWrWYTA6YjI4YnxwTt1IGgzY/9YWfYNDr0mm1uHV7l1e/+Brff+cTbly7SVYUVnVQBdrXDWCc5FAKnyNC1AwX56r3aabaH/NS81azNDZq4BUoUYOv734tHLM1tRjvPVNqX17HobzXQwWyPsTAVDNFiHocV6EVriuUqUd99cHU53ojYLUkOsJTTREBOMnEM3PrfVKDybKLk6Fstzhab9P75BbrnZiwyGCtw/Gzqwj5Eff/6Nc4GilOxori7F+mGyhanVWKg2PkYoGJEwoCBhR0VMFdo5kYQdJpoVHElCxkwN3FgH954zX+9nPfRFBU+mbjfYxrOYB69tcMFVFSq3sqKu/ucZoMlJUeV4hGG7nr6kXSyTHemGeWPRWsd4IfM/YZRSHQ4Qzd0USmS6vdeeSYezx+un5VrqJEqvo2xIQGUxUNVmAa14jlJqvIYSUa1Pf7VJ7lG7iuxtKnB5cAls6qr3z40x5Y2Kp+t4MhcDcxaEqsG1imYTpZcP/+AW++9SG/9wdvECcps3zC6mqf6fiYo4N9VgY9Ll/cJk0iyjyjUIpFWTAezjEaFmXBZDyi3+3wk59/hWuf3ORwb4fNrVXSJGBldZXXXnqRFy5eQCrJlcsX6Q/WeOnZi+zdO2HaP+Y/+jM/SSSN1YX7Nm+8jxfcjF8MRQ28Uj6ZEWkAtQvXsutQgKhE/+YU9+DmdZA+eKDEs19T6YcFzbFvyYQfZabycrGrsOPU1JKU9aJQDXWbtYPUaoamt0EFnS422/9XGZL8ABXUoCaM8+l3XjmNnB/oAmTA7H/2N/jk//QPCRYTWvs3uV0MuPM//Sucyec8e38PjOCa7PKv9vcZXfk8CQWTOGRqIlZGhygzZ8qMTCnuD55HLOZkSQfVCgnDmBRBnEhuzSO+f/AiL6++jbeo13PKq2Ga6pO6XeqoSerrPFAvhejWc9z3iahUMqK61p5lgbypVfQ2UqDhsiaWfHVV3kKUM8rBAYvkR8x0qSrvxCVTH2mu0t541XTT8sdxh/ylAirLrz1klu/5KexziTU/9ITaCdwhND53QiO2pbLmN6gNVO/hv/ecwTEhDCWCRV4ymWUcHg25v3PAt998l2984w12d/cIQ8WZs+dotyQhObt3btiQXZMxHh3R7SREgUTnBTPmHI9Hjj2XbK6scHZthXbaIlCKL/3EC5yMv0KUxBwcnRDGKWv9FmkEhYbFdM6gm7LWTXj9c8/xyz/zKqu9FG940afIgVkSE5rO46L6/qHCw4958Yu6Mk4ag8oDwfu72n61s0oKbIY5pxi149Ht6OHu6JmTdrkPcPeugdughSDAg7RAGEHwkPaT1SxoQIxb6Cr965KtwTNE/7fzzqjcm6yU6ff+owJo8D65puKVwiL4lUuM/+v/Ce/t7tE/uEt85x4agQpj4gsvcGH/+6xzxEfpIb+ZlwyFQAeKIrP67gPZIj35mHnnImb3DgUxuruFWECqIJYFl1qCQay4MbnC+fYO3ehexcz9u9kgEKdSEALMabVD1aNUYIhx0Xqmbrvm0laxOt8g9UK5TOiEbSxv0KieUYkQS3UQZQxHiozDh9TRlscHuk1XMD9BK4x0XStANEQv3wbNMbj0mmJZ1KgNa59OtcQjPlfPaOCIqJYue0/pY+epRR3LTHwYoeVI3h1HA9N5wfBkxp07uwyHM/aHE27fvcf+zn2OD4945+23yXRBb6VPf7XF8z/xKu12l+OjA1hMyRcLxsdDtjbWODo+5PBgj7NnN1mEAePplNwY9g+PGQy6rPa7tMOAQCkEgqQTsdpdozCag3s7/F//L3+fF154jv/i7/x1FouM+TRjY3ud8bhkdaXNhbM9axMwyy5tvlRBAK59jAEjhU3Us2Ruf8KKcOkPqwVbIKQLzmmM1yo/c20VroIWtMTlOqBmoHiJ0wKsdPexRi9HFkQd61RLFrJiXsKcHqX1SugBt9YanBKxhKH2irH/CpfCxYOF8AuNqVmxv2flu2ks4QhWumQrA3a5itAF2mik0GTnz9L91rcxa8/zRXGZ320PWOiQMAkx0xHz4Q5Z3EHGWwxFh0gawiQkHn9Mv9fi4uKIC+khz/Qm/MSZKaKn2Ly8x/gk5O6dmGzu2b1szG+32FAbvqyxWrt3sovmkq2oBh37S1iJrfKld0RpyUh/6lmIJg+ulybwWQfr1rbTJUAe9x859B6TeqGCxeplT0vfPjKkpr4NDwJ37ek8B7rBLE8/71N47EOLz5lgH91wdG5UQhiB0II6yto4v3HDdFGwyAuOT0YMTybki4yD/UO+8Udv8P1334coREtY294GIbhz/Rrjk2Nm8wX9wSorcczGmTNcuHIVESiEKZieHHB8cEC+mJPEIbdufEKrlXDvzm3OnjvDdDwhLwobDo0kmkQUhUZEXudo/QaNsS5Ozz17kT//Sz/L+x9+RF7YpEOXL55DCMn33/6Qr3zlBUJlV3Lv/1m1aUPdo0Q9SP3C8kM2949d8TpV5UJ5EbWaATwhcAoCu8pWoqxnh9I4DukmZWnqMV/fx+14ggUEm/VL1J4K1YB3umX/Z9OuIU5JHNVArXvMg4cXyZZhFwcgPgVjo1SLhXWLMqZ217JSm8H6nmogAKPRBibtGKEUwcH3eGF9wOv9lJuLiMOpQIspetBlPb3D1vAGl/qKi9036cfHtM9KyuhZtrbHdDbfI+1mtLua1dUOa501dJbz0su/wPvv9bj1SUlRONe5SsdLDZiNNJC1K55rj6V/ar9b0+jDiuw2BnPlqNBovVpOMY1VFddOzdasJeDiU+wcj43pLlXiITP0f8g523y/psX40072lmGvq8RY3WSel8zmGcfHY0bjKTfv7HJ4PCUvS3YPDsjyHK1hb+eQ6XjC7s59xuMjRif7CFMwPLzHbDokSrtcvvIs09EJW2cv0O90MeWM7bNbHA9HhJHg6tULbJzd5PatW9y5eYPdO3fZuXubOApZWVtlMZuiywXHRwccHuwTpy1OToZkWUa/v8Z8VvDGd9/hJz//OXrthEhaAUo7AEmTiF/+pa/xtZ//CnEcoNqSJI3RSJ45d4Zupxkw6iP3Ti2QDeOQMU7U43Rk+ZOKwC6KXtQqIRf1SvOdLBN1LdOQxKqJa/ySJSviXE1s47ODnbZviDqgAlONR+8dapYmtjvHLCsBvN62UoEZ6smwJA77qgsrmjsQP82SLeB6Ebt0l9ssw/b60o0Ug5Gwf2GVC5M5O5liOJkShAU/u/VvOXv133EmvcH2mRntRKAKQVHkbJ+FOBUU+R1UaEgSkJlBlFCOh5Sxoiwk6H/Jq69e5eqV13jrO13292yD2kVR1jkbGhBbLVHG92tD8qhaq+6TKljK81qvujHLGCIaY6EiZs72wSlS6IljKQy/ceM3+Nv8708POOAxgq5fKSs1gKlXG/8ila/tQ4GyQfkb1nLf6V4V01z5/Llg0FqQFSWT6ZwsK8izgvFoyiLLOTwckmtNWRSURcFsvmBv74jj4zH37+8xm8/4+MYtToYnpElAu9MlTlrIOKCVtmh12syzGWknoVumRC1odSIO9+7RXd2k21/h5HCHD9/+Fq1On9loH6MEvcEq1979Pv1+l97WOqtrKwhKpuMR4+GQxWyC1AXDoyFxFJKmKQd799lJYsaTKUGUsHPvHkeHR2xszTh78TL39/a59vF1nr1ykUE7AYnNqgbYPwyt2G6triT0WpHd+VeCMorS67xOtX1dlg04NWOozzkNUk9GcXkuKrA0VQCEDXZojM8lAG6YdGocxuuAG4IwIJyKob7eu9svnyUaF9bMuXnctrlu+Nkuj3xj/L4WHpRNdYY/x6VkqdQKdf3rz00gEdQ7Q/iautmFETC+8grXb2/xvwl/jlj8Jv/LL/43nDu/Q3cA2QwWC0ErhTgxXP9EMJkaytLQTSRJakhbIUWRkSjItWFv/4AkbaGyGeOTd+it3OMXf+m/4M3vTPjoPY02sjLO+7b0QRA+oaWXNHxmsqZm3AeSuBMeii8yEFWAWR1OfGrHkMYvMJwmtR+cvMcnh2/yqPKYma4tVf5ZTB1NI5anabVmNVcQt7r5UFzv36yNZ/gGb1DQQpAtcj75+DZvvPku337zHW7d2aEUEbnOGE6OMGXJxsYGV5+9SpykxHFEmc05ORpy9+5dDJrxaEi33+fln/wcaadNFARkWcF0MuVgb5f5+JiynKNNyXw+IoliiiBABCFnLz/DbDZFGrjw7Isc7N0nm08J45jB2irT6YxOq0XSSrn87BUClyvhcH+fxXQGBkIVMi8N0+EYISAKI452DzjcP+J4OGR/b4/h4TGLRUHcajPodxiOpuzuHSLMCv12anNWONFYCeGs4M7pXViHPmWkFXsbYqwXw2pxtKlPs5OtNjLgjfJPpCHNpgWVlU2hjkh06oHKfVdUJNczRB/x5flV7XPrPQtOQbWonNLqCD4HnhVxEO4vF1EjaWbdasiMxoFeddTULNwDrcbu29e8EMdshX+vBvj6laPmsdU1QKX/pfrWMsLjtZ/iHxytcSX6B/yNv/b/IewVTKaCIFCEWtPtAYUlQFsphErSGmjMrESMYToJWFuTzIdz0tBACMfjnNWeJBKa+cl9iuTXefWVLTqtS7z9XUlZBhWj9Ab4umZ1G1YkTVARNKt+W2a9tlE9WJcUayPktI0Yh7XKZmmhO10aKif391ayxtfOf/kh59ry2FI7Pqx+otFYgkerAioANjWzAn/PhvOHMeQlHI1m/PbvfoPf/q3f5xvf+CZFPmE2GwGaMGqztnWGsxevcOn5q1y+cp5OJ6bX6dFKUspswY1bt7nw3AXGozHHxyekrZTVtVU63TZCSO7euU9RZuzfu818OCZsJahQsZjNCMIQLRVnzp2nFIK1M2eQQlCanPPPP8N0PCIJJLc++ZjpyRFJkrJ9/iIXLpyjKAqOjw6598nHTCcTwjhhsL6OigJmkymT8ZgkSRgeHfPB997i0nPPkSYp42DE+PiI4fEJK/0+02wOUrB/eECZd1lZXbW7Rsg6YQ3Q2JHi1IpHk7k+qGKoT6p7qAKJJxFxgSp3hAdLb1PwASJeTPWGmQYrrLIfuDaTbmLKJYlXVOQCaESf2XO8sc3u5uDZricnNZQ0mWgzMste7rxuaBiYTelyF9QuYzXYmuWOXQJ/U4ezN76uzhGW9fphktPjH3/QYm/4r/nSa/+U6cJwMYJIGcbzElMKyhm0kpjjYUaSGIQWrG8p9ndKxkeQLWYY3SZqhZT3croJtFTG/o5ic73HYjbnqPwe/Y0Tzq3u0fmZV/nmNyIWWYRo5KCoMrk1GthK+35BopEAyPcPVd8Zp0s3CPKTjHn7Pt32ZYJJsrzaQUPqE1W/6NKGwwdRTJEXrAabbKxsPHLoPTZD2rJcVH/8THPUTwB3XSN4C78qC6BA8HtvXOM7737C7s5dovV1Xv/5r2G04WBvjygMSbst4jhga2ud3qDHfDpBUVIsFixaqc12FsDR3V1GowkgCRSgc4TOmU7mfHLtfSbjGUJqolaICgXT8TF7d+6Sdrt0eivMRiN0EHLu/DlanZhWFDCfzpgNT/jg7e9z/+Z12p0OvQsDXnz5ZaSUlGXG8OSYg/t3mUwmbJ6/xOrZLVbPbjIdjjg5OiKfZ7S7XW5+9BGmtEaYoiiYTSZMxxOm0xlJegapAjpJytFwhApjwiCkncYI1Uy8blgscsIwRDrjW7Xx4ZKbjBOqmgPUneM8qNxcFcuT+Akq0m1dA6oymiG8V80pUdL7A7pp7idcpQKoJDGxdKzWG7rgHxoJbIRmyYXJo7FnYd6LxhiMtATDG5eFqPvJs/FKzykagF3xuYrq1ZZ2/z6mkYuWWtbxi4KtC1XbWKNgwMc3zvLxje8xePWfMzMDvvfRLqXSlBIWE0Eaw1pfMFca2ob5BERo2Ns3dDci9nZzZjPD0f6YbkfRTSU3P5BcPV8SFwJuGFZW+4hxiNpuU2xrusU7fPG1i3zjjQ2b0EoImjF6zaxjlarBlWaiIeH+Ni5KsI4ZECSLNXJzzFH4LudWf4riuKTUpW+E6n5+oTZaM/rwHjwTs739Mvf/4T/lxmHCTz136ZFj7zFFpC2r+6V4MGrM9/0PNHz54gaTZ79Fafj9b3/I1//4PTbPrnCpfZ47d+9SaMNiMefspYsopYhixZXL50liRaQUYRAwm89RUjAajQA43D9k5849tDYcHB7SWxtw5swZkjhmni1ASLLFgm6vy/HhIfPZlOO9HYpsQpHHzOZzDg8PWNvaZjGfsbExgLIgyzMW8xmT8QhjDEVZsFjMGQx6rK6scGt0y+qV8wX5fIIQ0Or22d7aYDQ84ca1a4yOTpjNDTKIuHfnNt3BgDBNIMDurEFBURRgDEoquv0Bk9mcTjuwkWYycCuzwWjBNNOU2ZT1ICBxuzPrajI6vWRDovAZMl0XWEOdsamjETZS7VMMtT+2RQu/k7QBr8OlZqRLPkSVPC7wG0VCTR6rHXUd82/IFoAD2dM6RqfKsAa2ppbXA7VrWPeQ5eP+dqbWDFCrAHzUpzEPssCl7Tj9O1GDkbsTni36u2vHqoWAhV7je9+5wdtv/GP+3i9vcfFcCnKX63cisjInm0K/A6U0TBYFaRvmBjolLEaGtJ2ztqE4zkvSNcP0umZ+oAiF4d2vS157uUURlZj3FSocUB6EpLsRer1DmA+40hV8dFy7htWJbryax6rBTpFa+1YN1wWBbDYxoZIURtDNn0GYe5wkH7B97nOc3JuhC91I2WAIhaJFwPxoyMnqHuurZ/jg5lv8t//i23RiSTG+x998xNj7keh0gQZb+GGuqT96IDCiRAu4vTfl99+8QW89JMsy7t67zvDwmNmsYP3MFlGouHBum3YnIYlCYrfVzXA4ZLGYcXg4YTweMZvNGJ+MGA1PSNMWk+EQpSSLlRWm0yndXodLly4gjKFczFEYrr33DvPxkMJIOv1VuivrdAd9jDB02ylKQGk0Os9Jo5goimi1WsznU2aTMbPJiDDcotNp026nhEnM7M6YyckQrUGGIecunCUMBJ98+AnFzi4mbTEcDgnzgqsvXaIscgKl6XZSssWU4+EJaM1gdUAripFSUpRAVuvohuM57374CfNsxhdefp6L22sVGNSsSNTd1fjGHqvdxZf66AkEXa9awGetarojembvZTbPOj3DdAxQG1EFObj/HY412rHZWg09hWl84zWMS/65QjeCcSyke1nam8k8AakzPYMP9PFbWFXPqLwVBPV2OfU13nhXvb7LZ1BrJLxyQ5CP1zCjd9GjAya3f4V9OWHzwg7IfSSwkIaP9wR39gSdLkQSjDJ0JQz6gsVtuzB0OpDsKC693OGdb07odiQb2wauZaSbLdgUyOMAM40odtrIiYBixnPRCocy4IgFmMIterLOWyE8xbOt41rPHbHSol8mfS9JAUEgKfMCIUK6xTmmh7e5n32flc3z6GGEngm6cUwrz5hPxyxKwb3ZITfMNxGHL7CYR1z63AptCiZh/six95h0uj+YvS6d0ViSHnblshpKMJoU/Kt//XvkxQgxTxDM0GXO/bt36HbXuHj5Aqu9Fiu9NoaC6WTGycmEsiyYTEZobQgCRRRFTCYTsmzObDqiyDKy2ZRFFHF8dIRSijPBFkkS0e12uHWwz9HOHcZHB+iiYHX7EoP1beJ2CyMEg0GXIBDoIufk6JCTo0P2dnaRUpKVBbrI0WXBaHjCwcE+WhdcuHCOja1Ndj++znw8ZjgcoYKItJ1y5sI5ikJjypLjw2OkiukMurQ6HdbWVomVIgyl3R2iLNFSssgyjArQoqTdajHPcoyxjPXXf/N3+fv/r39EkqT8lb/yF/mv/sd/o9oO5oE2N/WP75fTZ4pHHH8SSuXi5dibBR4nqlYuQV7qF7W3TBXBJBCi3mC1NtLwgEO+1yfWygmvylhmk7VZy9S5sKHBeGkwabNk4PLn+QxXHnC9a5OH+ipbWXWZrvyGfS2orvedbyqyL4A7731MPtshTltM71/i/tERu7de4tkv/BFJb46KDWECsxHkC4HWkjDQDLVgnIGaSEwmaAlJT2uiWUG00uHoRNKZ5SgBo0lCMusSLFpMRprOokAVfVpiQj6+y1dbF/lOa5N74j7G2JwKNmmPN29WtNS1uleV1Hr2OsDCIFDMs4I//J3f5LlnXmHj/HkSeR49yTicntBrC85vpxzcm3EnF5gggRBYbXHz6wfk999g6+xZfv6nf5rhcMR4Pnzk2HuMwRFNmuqOn7Kw1Xuk8UAk2oPFiTla8Gu/9QZ3jg7ZPrfKycmQ+WzK0c4h2Tzn6hcuY7IpQgcIYjK3r9rB4THD4QlCGKI4IAoDgkDSaiUUWYfhyTHZbIZUAqEkk/EEpRS6KFjf3CRMQorFnLsfvc98kXPmyoucuXiFVn9AksaUZUZ/0CUvFhyfzNnb2eHk8IgyzxCAkorWyjpr29skrTZlntvVNZRcfvZZPnrrTcYn+0xPjtGFIYpSOu0W2SJjdHLMbD5HRoL1zW0uXrzAYKVLEkVEQcTw5AQB9PoDlAwIgpAoVESBoJO2OTgeUZaG1dU10rTD1vYan3/1RZQXL13D23nu95GzQSEVcLgfjRVFtYvsqUTXJ6zUjgoNdYIzfpkKlZxetuGDWUUjVaAsaqbsTeX4e7v7GG/0sfcy0sFABaaVd24FxJ69CtPQs9LkJ2JJteCBeclnvTK01fX2XNaI0t7bBU34/BoWXQ3eTcwDuxfWNYr50T7FYsy5jTMcHoz53GrKvfcvMooPMduf0N8Y0Y5h0DOMDgR6ZuhtGkYngnkJWWDQuWY2TRgVkrFRaBlyWG5zGYWe54gy5qSMSFRAoEPm0xItp4h2iAk6tCYHfDHXvN/vc41j2z7GB5fURq7KYImHXt0Y875dXSJ6ITnzzFX+5a/+E7pBDxUHhJcFul3y0y9cQKtz5MUVEHb/tkSmTPduwKTF9cmMNDmhi8SEC175/OuPHHuPL+GNaK7fNWN64NzmwIeqcx9yU8Bw+/4Jv/MH32JlYxUlJHs7OxRlTtqJ+U//9v+I9a11DvcPuXvvDovFDIPdXWE+nzOfLwhCiZAwm06JwpAwDAkju/ttURTEcYTWBTrPONjd4eTwkOloxPHREXc+fpf5ImPz7BXWz1/kyovP0+51yeczyiKj2+2gAml3cihLTk5OKIsCIwRpu03aSml3O4xHYzrtlCSJicKQrQtn6a6vM75+h707t9m9f5+1tR6ttE2SpsTtFptntxgOZ0RJQqvTY3V1hSgIiIKQPC84Ojomjlus9nruvRRxIGlFgtZ6n6woWP/5L/K1L75Ep53S66TgggKkEdXOHH7/LV8aRMzl0BXV5NZaPDwM/gkoAsvuKmND5WJkWB6CDsyo2V4l8os64YmhVjtUm6wKU4N29eBaZVEH6HgnNLBb0Zg6AU0FHDT6pQbEitD5ulY1NA13Kc+fPQNsMGt3j2pfOK9kcAxZUzNgz8qDXNEVBdNuj+noY9TgKhfEgvKtc8y/s6Dz5T3K/hyZZER7XaKNKcWiRMuA3sqc27cDdFszywv6Zo18PyCchhRG8Vsf9fmLHQimBiVK5hJEoRGEULbQBMQpLOYFmRa8bDTZoMMtM0aLEuGCuKuWcuHejTfDA7QRdXpNXH9cufQsvS88S/Fexnj3iNGtMZe/nHLxxVe5fftD1sVVhNFEQUJX9Hjl8udJwy3eeu/3uHf7HZ7/ouH82k85FcfDy2PX6VYDYont1n9XzeOZQ3Xe8l0MkOeG/+6//30KYUgSyc3r161P7Lmz/JmvfZkz66tkRU6Z5dy6XXL77j3arZTFPLebVmJQUqKkZF4ULOZzgiCg2+2SJAmjo2MQEqkEh3u76CJndHzIR6VmMhpT5CWbl57n/HMvMFhZp5W26CQxJg5I04QoDjg8OiRbLFgsFrTabQDyxdRtrx7T7fXJs4zpbIpSEq01SRzR6vaR0S7ZdMLBzg7D89tEkf1+sLpC2m6hohPanS5hGNPvD5DGECjF5tYWRVHSbnWwG4EqgkARhIF9H2FIw4AkVKx0IsD6NWsE0ouVBu8iig9drXZCaDIqrxkzFnCfRCMaOC4kmywVl/S/Dvus2SvVpGx6MTRZhHefk43x7NmoJyFNSiFq2b/WOjq/2Sr9aU1rq0f6Y82YQH+tPU83VBBWdDbabxHuYwkdBLmON9U9rJFNe/cqYceHRlQbeApj2OiEHLnv7sqAT4Z3eCFJWA8LinGf6NsxZVujVYacr7MnNdHVXTobc+azE7KsoBjnhMLALCAaZyyyPQ5251y/p7g/XfD3vnCOdDbH6JBQKVAKNS9RwRbZcEaU5EzLCe+OIi6FqxTtgnssGn3jSmX4tOzXRwcuyQ8NTxGB5PXLL/PNb/4WLTmjvxLwc3/pNfYne7YdgV5nnVh30UXBjVv3WcwyzicJzz3/K+RMqSL/HlEeO+guDc1Gewio9j/7LGoFMLx/7TZvfv9d+hsDJtMx+/v32dza5M//2a9xdmOAKQ1FltFOY86fP8d0OmM6GRO0I46OhwRhSBhEHO0fMJtPkUrSbrcRrZD5bE62mBN1O8StFmEYMzk5YTGdMp1O2b54kSDucvXFF1g7s4U0hu2tNVrtGLA+sJPJmEAqpJCsbW3S6kwpsozJWFHmGXEco5Tk5PiY3koHoSwrVmHA1eeeY/fGXbr9LqOTA8ajEe1Oi8UiJwgj4iSl2xtwfDRiNpsTqYReu4XA0GtppFSkaUqaJBhjiKUkErIyQNp8CcsM1nrciGqTSTQEos7BIIVGG0HpBqTdH825GDnWpTH1LsFPUBHCOxtZ8V/gtvV2sKkbulPwxKFBK70k7hcqvGqiAdr+Dh6A/aVN4PSRUU7UXwpGER6c7YOa4b84sdkAQrvvjT6lwquB1IOqzz5mwVs3GKCu9b9uMSgbagW7p5hBkHH+TIvknQPm7cv8q2zMKOrzph7yUhQi22ukJkfMY0opOUoS5uUB4laffKckzBJWe4asoyENyIsFB1rTjRLObrSZ3g94dzTl//a9Q/4X59sE0lCaCKVDEIpimgMKoQ0r7ZyMjPd3Si72WxTrIXvYcDK/oDQjCJd7xYNwDbz2tUue37zE4K/9AidHH7Hy+grd1RVu3H+PLXWVlfQSg6TDrRu3ONg/4GjvA1Qw5vLrnyeIu9zf+4j54og46j9y7P0IvBeag4CKMYjGtz+oGEAXgn/2z36NolwQRnDn7l22tjb5a3/5L3LhzCYBJXNdMFvMyLKMMAhYW1uj1+0AhsGgy71797h39z4CQakNMlC0uz067Q6hChkeD9nu9VgZDAiUYtrtkHTarG1s0Bv0KbKSwdoKYRzRShOEhPl8gS5LxpMJRZ6jlMIYQxiGmFYKaUKchghtyPIMFVpVxtHhEWEYEgQBoZRM51OiNCHtdYnimCLPmIxHTKYTiqLgzJltVgZ9nr0aUxYl89mctcEAhWE2m9FqpyRJYndaloI4aGwQI2hM+WUu0BQlm196a7mPpPKBkMr5NRqBjXrSoqmNeIKKqH585qkmI/CBDl7311Q71Hpf97dotGvVfo1IpVofYc/XlmV7Pa5FPsuOJNYNb2nPPuE5mY3PrBZMV5ZVIk4mMdYP2D663hzK6nIFhqKWXhxLtuTbApCVdPzG4tqqGYxlyuqs4vn1Fh8ND7gsFboVM1InvJ9OiFsKOQ8pyoKWLogikFFAwD7j/IQkyUnKgvZUgJozac3Jdcp0done3QFfXBU801vnuwcL/t93FvzdcwoZzhGBscnkS43JIie+p2zICOYnHIaSraOU0WDBXOraWUr4kdtYLR3rt29vrBrIYHdBcYvm+sULdD5nKJOM8aSkyCcMkpdYHE64V+xz5/q7LGZ3WT1/lu7G84BEm4L11Qvsn7zBevjFR468HwHoNunsg7JocxV6AILd6BIYbly/y+99/Q944QsvYzBsnTnPL/2Zn+PSmS1iockWOdPpjEWes8gW7O7u0un22NpcIwwkwpQMuglRoDg6GVOeGPorKwRhTJK2OP/MM4ymUwIJeTanvbrCmXNnGI9GTifbYjZdoE1JGkdgSqIoJFsUDIcT5vMZUiqGw2OyLMNv2y6kQChlF4s4IowjkiQhSSPSNCUMQ6aTKWmrTdrt0FlZZX1zCyFhkc0py5IoitjcWGdjtUen06csDaaEosiJ04SEFO237xHQCiOUoMG6HtbyP6DLxPI1wqkiwE5M1QCRJ1GtK52sXo0/IR3ggAVIWY0/z2TBmpxEA/VE9T21SsGV6lbu+mWNmlMnVABgXJt7UGjMCB9Z5Zh2M0esfbZNpV6Z+ip8MfgkNvZAlY6eGopMrUGp6ldijJcDLORqX19KxumQtStnWXn/kAujOW/tTojPrWEGE2R7HzkrCDJYzBTdJKLQGSQQRZJpviDuhcymOaPckLQgY47cC5ClJMcwEBm/eDbinUnEv8ki/noaks2HmHaMCCKINEUhSUtBKTWLecTiZA95ts+5do8b8by5pFT9vSRHGNPob9vWgTDksSCPJmStI4piiMkMJ9N9ImWYHxTkw4/Zv3uLMJ1w9uWXUXHLefkYhEv1mCQtsmL8yLH3I/PTbW5xDlT6Q6ih9uGEyVAaza/+239P0m8RJglp2mF7a4tLZzZIAmsAW+QFUipaSYtOe0GcJEwnUyaTmJVeh27aIlYB+YWzxOkhUkpa7S537t1FCMnW9hlWVvq045jRaMTh8RGz+Yw4juj1OkymE5QsKfOSvZ17BJFkb3cXYxRFqcmzBUIYptMpWmuUUsRJUol7URwhtCGKIlbXVuh0u4RhgDCadprS6XV5/tVX2Dp/kc2tLfJ8ThgF9HpdVgd9zp/ZoN9p0Uo7SBmiS5jPZggEYRyCMeT5gnarSxSHf4IeqhWGtcbL9Z0x1VY9nv1aA9yndtyPddHCbb3uhe+G2quybjuPBsv4oHLzmk0QrU4tHVQqBjuRpTtglr6DimXhoc8xzEqv7heAhjrD65UrpkmDUmNVBpVfqicwFjQdP22I2P75pjq3FKC8D29lb5H2nYWNdNNGYyjBRfEVZOw9e5Hu8YTzwzGHWvM7f/Qx0Zbk4i8k9LZ26ZQzilyymHdIkylZNgIlCVuSnTKjNZCEGg6mAVL1SNKEbJAymc5YjGEDzedXYV8kfC9IeTmbkk+OkCsB2ayDNF1MFpGEKavRhF+/dpPudJ8rwSXa5/qcyIzaT6/RdtQSxOkw9txo5vEOs+SIEAlKYjJJUZ4QZwMm+zvs7Nxk++I67a3n7R10bWrUWoCQxOEVjsbvPXLsPV7Q9YOIOqPPw3x4zfK718Up/g+Pp3zru+/xwsufYzyfsCpKnn/2Iv22ZZyLPGeeZ9YGLOwuuUJKoigijmPKUlMUBePxkHwxY321T6fdYTyZc/H8RfK8ICsWrK30ee7KZaaTKXuHB+zs7mKMIc8zDvf3OTo8soxPBshQkqRt4ih1DHtBq9Wi1W6hNURRRBgGSCWI477dN0trWu2Efr9Lp9tHGI0pC7JFxmBlwOb2Nuvrm3S6HaYzq67o9bpcPH+OjdUV0jhGSoUUISYQhIFCa40UEEcxGEMSBFXi7AeSYTcXvdrr/cEvl3QP/sNDpBBz+oInp9iE4ZIqSbbR7jPVQukZbt1M9j0LMSOghd01Vla5ce23Eh8K5q+tGGW1lDXavgG4D5NG6mtqycX3nXbzqmKzwuOMqwOWfdWCtA+rMA6IauzOxjuozhqBCNDCqhe0sQY4UbkPuucKw6ijCV46z+YHn/DFUcGdC5f47ltv0jbnSf/sJtng+4TRFNkeIkxC3ArReorUBj0NGE+7IBShVkR3P0dvLmlFBbqniTuGe9Oc+6OQpFXwT3aP+KutiC+0VhCLFiYEMsEi10gj6CcDrpzf5p/c/4SfuX2XFV0iLvaw2ces3lxX6iS/wEjXZF6vbVsomg6Yx3MWxRwl4GB8HYDhnYjJzodceuUnCNPUqVtcDmRTmUIRRqNkhOFHHBzx0PKQefsDL3Hs6ne//ib9jW2yfI4pM1YGLZLAIE1JkZfMswXzfMZ8kVNqKIoSKWWl4wQoS402Je00ptXqUgJ3ij1Goyk+FPKFZ67QayWsdTtsb66x0m9zeHzMbDZHCUlv0CfPS0ajMcPjIwKprIfDyQlBFBElA+KkRa/bIwhChDB0OilBIJESRKmJk5A0jVEywJQFpdCUheDs2W16/QGrq2sESjJftJhNp2ysrrO1ukIripBKolSA3/I8CEKOj09QgSIOQlpxQhpEjVDez9YvS/71P/B043IyOEgSPrXjk1X81uu1Jtflv/MqAuHDTJuuVwACmUjyckYUdNwRW3wyFBCVMwFeMez9aB3Y1UERDe+BpqqiQsRmqm7RqE+9bbwvwlhjmveCsIuJM5zhI9xM9Qzj9NlWJ6X4aOff8uyZv1hn8AOnXLARbM1t4Euh2d9ucealc6y8c53Pj94hf/kV9u/eonjvEsMzKTI8Ieq8DatHdtcMGaDLkjgtGO1KVg5+ip5WKJMynw+ZjYasDgSd1JB2F0zKjLuHmjyN+PtHBVG4wusRpGIVnURI2hTTCUXY5VeeeZ37ccR7szs8fzIk2QkItxMb8FJ5EtTKhno5831mdcZxtMJY3iYvpmQLSVkcEAcDklaf7S8+i5GhdZ304E1R3drepUSYgH763CPH3uMHXWMQsiG5NL76wXNVMJ4s+PXf+Dqr5zc4OTxk0B+wsb5GuxWCKcnLHKGEdb8yJcPRiNmioJXGZIsF85lhYTRlFBKFEWWpKXWJVCFRGGJ0ye7ePs9cOU+33aKdxkRxRJZlPPfMFWbzOVlWcnh4zCTPmcwWTCYzbt28xfDkkPliisEazqIoIopCgjAgjkN6nQ6tVgJogkDSbSUARFGIznOKPKcsAsqi5MzmBoOVVVqtFtpoOu2ErNWi2+7QTltEUUStv/MbQhrSJEaqgCSM6Max3Z69nrkPbeWlHTnc70pIWtLlekd9lgIlvI7ydGTPk1QqsDElCKtoqF/QQVK1A2xtAUcYpGgx0iNCOlVkWxX6W9HSGtrs/w/ZTty5d9Ws1H72br4gEabecW9Z54tTpvv7+pwDXo1QiyLCMz27E6VzExSUwqDcYhCqAeP8Ojvjt9juvFrpKev7N5Qi/tWE5OBLz7C9t8OFj3dI73yTb599mShbcHgzwRQxw/sv8OqXW0zkNVqJIs9CZqMUWUj0ccYEzXqqbb4CmTLeGzLpQX8DVjpzopU5O0ddht0V/lWhiUSLl+MVGA9J9XXM0W0Ua6gw4z85+zz/5304Sqaom/uc65+D1Iec1Gy03jvOvlWgFOv9dQarz3Nz9gllPkGXiuPR+0RBiJaa1a1zmNway+pwRYPVfyugBKPcOCgJ5Y94N2BfrBHA7ZWG+YzzsnmS4drHt/ngww+4YMbs3P4YVMyLLz7DdDoh73Qdu9AYrZES0nZK2o2YzeacHB+TZwvarYQszzA6ZJEVaJGRtmM63S5ZfhulJL1u2wJmEJHlBVIpeu027U4HjKTXG3A8HHE0HDOf3ieOUwZrG2idMToZksYJaZpY41igaKcx6yt9kjhGSkGShKRRaKeoEOTZnMlkQhgEhGFEGEZ0Oh2iKAKsnjoJYlppilQBQigb59/IFaocmw+kopMmFnBdEy4ZRj5jWbLIN66v3ebd5KfeSeKHfcaPT3Esh3qc+tQvNcurWwC8eC8RIqYMdhtGNFPdo+lnaxrXLakTHOP0Envl+oVXw3l26lUgNSO2GOmB3NTuucJUAE2DRTvZxJ20vLAaFxJhF9CIQbzJzujbtOI+aXDBRR02WLOgYbyz4yBrxez+yk+z+S9/m+5wSjD8mN3jdxAbV9htbxP31jBHa7SLAePxEVJrWkmfPC8g1gi9YJRltClpY+gmKaPxlN2hILkc0Vqdce7iEb05nIxjflOlrMxmXJ5eBwFRp4tUHfTwBu0s42+uX+T/ebRDxgHqnUPOvL5WE4lGvwghacUJm2uXWOufY14U7Ozf5K75XXKhOT7aQZYFffMCgUiJ4gCdObUMfjES+J2BbZ8W1e4en2ZefsxM14svf5JiNz78+u9/l/WNVUxZIAUMD/d4+9vf4erFMxRFDthUh14007oky6bkeY5SArRiNpuRxjGT+ZzJbEY3iijyAmOg1WqhVEAcxYRhSKvVptAlSikQhsC5RfVVSBgGtFptJuMZO3u7xCqhLAOMHtLudFhZWSGOYzbW1ul2UnrtDmkYowKBlBCFoTVKGMPC7fBgvQ4MaWpdvgROOV9qpBJIaVMz+ggm4YwmSkmrOjGGVhQSBfX+BL7F/zTcs5lf90FkXb5zLbA+OcUIkEZjhPJHgEZggZfMPIAJn+zHYFDE0lDqnEBG1NBrHKh6tknFZB22OgJqMG4bd6orG1nKTG38qnLcNrwZ7L20+6CdJqLufV3pKR1LrhZhu6hoqJ7h6ymlYGvlp9m5/8+4N/wmV1ZaQB8Pz/56G8Ulq/fRwKwT0/ra68S/8zZbKuRKd4XZ4oBv7Ay5sfk8pQxJojaqGFGYgtlwn07SYjEzlFHIwgimZc5ELxhITS8J6ZuIg3sltw6he1Gyed6Q9k7YH3X4d0XEfyY3aBW7LDII4wgMBPMFV/uavzQ4y68mCTevf0Lr9ojehTZenSCFpN9ZYWv9Mq14ldHJEfeuv8fh/nXGbUOxOmQ6HmMWIy7Gv0hkVjGLEpPpJUNm5dvr2twY7+yusCoGP64eLI8FdH2HQEMP5b/wIk/TciZOX20n/Wiy4Nr12yTtmNHwgNl4jKJkNp1QasN8kdmQy8ppH3ycuBSG6XSMLksCJdHETOcLZvOMTl8gpKQo58RJzGDQZ3VlQBKnSGX1o8a9hFBugkroyA5RVPLcs1fZ3Nrk2kcfc3B4wAGC6WTK1nZIK0npd9us9nqkcUyoAlQgkdWWAdgUjwiSJLW+gca4wI0A3ECOwsi+k6jZq/1lUEoRBAFKCKIgIFY1UDyoy20cMMsTt9FbDzmO06k3DTx1OKU5baR7AkuVjaARCCHcJmnVqBXVxkd4XbpA0DUtJpwQskkFsE2QBOeKVRvQ7AjV9eYvpuqS6pgFMrf5jPBQYVVJwoF1re5oihreEaypz63TnttrTX2Jy+eLAS000kAan6Eddphm9zkcfpOV/i+4d7a5NqQxVUBJc6E1GA4ubbL2E5eJvn8HNZyx1Q74j0PNB/vf5V7rC5wEKTIMKMZ2Z5WDoyGdNARato4yYaI1C5GT5YaugI0opCvb3Lme8+Fhxsb5FTb6XfYPSn47KvmVvE8cHVMWNmQ41yXR9Igvxau8u3/IW9029z7ap7vdIoxDVnobbG1eQRFTTEfsHl7j/r3r3L99g/v3r3P2F89Q5oLF9Jjz0VeJWLHv5xdLr4+nrFUMzbngdeqioY56SHnM6oU6o1G1Vj5krj5wyBkRPvzkLiKCuBUjVQ+dzRgeH3L//n2Oj064cvYMSRgxLzKUUjYwQWvyLKMoCvJsgZSK0XhCEMVkRYk2EEcJYRShRxMGgx6DXoetzXWSOCGOwqpGdmsWgxbOIV0FgGR10CeNI6QpyS6d5cXnnmU0GtHtdmm3W2yurdJOYgK3INQLjG54DQibmEaFCAFFWVIWpT1XCMIoZJFl1oIscHRFooQgVIIgECQqJKioz8MA9+FtfPo0U51hTh0X9QR2RwyS0vi38fvEPnkAXOWZrTfScWoZgXZ+sZWngL2idisTIOQaU32LjtjEJrTRFWD6LdorN1Dq+H7PYZtqBSOEc7/zTNcBr6mSOFZ3qe9TOSpVPed9af1f1a4Y/h2q53uWXFYLjgGUkZztfon3D/4tu9xGJu/Qj16p4PX06Kji45z70eGrl1kfTYk+3iUnZO3cWV7ev8Pmvbd4L1plp71CHsUUWUYnSRnOZoj5IavdHkJEZKrFSEIuc0pZUCLpCMGVtM9BHnD7u3O6mwYVx9xYTbkxvc5Fs0YcSERpCJgyzwISdZ+/sdrlrT/+I4ZrbU52cr76Cz+L1Irp4S5H+zuMxyfcvv0BRTGhtbbClbOvkbdOmIz3OBt8nph1C6ZGV31oP+jqd9WsWC8RO4Z8zoX/gGHAPh+9wSW+/oylNJCbgN6gjTZTpEjob64hAzDakC8WLOYLQqmqQIT5bOZcw8YcHx+T5zkyiCm0YTKbs8gyAhVQliWTyRxdGlpxxLmtTVb6PdIkJA4CKkd4xzS1LjHGUBjH0AWgIy6cO8N0MWcxyMmyjDRNicOQMFBEYVCHcILVOxsL5FmWMRyNaKctOmstABZZRpZlgFU7RJFlukbbd8PgANcmuYkDVU+ExnM+c1L4qoiHTChfmoDrjpjay1Nj0MaqgZ489UJzMXR6U6EAUfkfN3dvqNkw7uUDSjOnICMwAQ1TJD6/LlVLCWrY8uoC2Ty78kjwWd48KNtTXEIcp5LwwE614NUZyewx43ivWXqqcMuoVz74BceyM3tGr/0c7P4Ow8WIYPgd0hVBHD6P1gojNbEImBXeYm8wosQFUVMqycFPv8j5eUF5f0hxdEiv1AStlDTIuHv0IZ8EK+wIKGVMJ47IS8P+cAQSummfPEnJdcDcFGy2FHkQkBrBRhrSayfslzmZANNK+Wh9QHTvPhGGVnvDBitNj9nTkvODbf5Xr7/G//Y7f8xbb39Mf3CWTrHLweiAo5N9Sjlh5exF2oNnQCq++40PufXWPV7cbJE+t+VUehq/JX0lQRiXZ++USGjX8Notjx+1eqGqC/UkrWPUH35eQwmBEJCX8MmtO9bVShqiKOTkeIEUgnTQQ4YBaZpa30qn71RBQOn0sXme2x10Q+3SO2ryLGdlYJOTh1FCmsSsDrqs9rt0WilJGFbGKAEYpWyDygCDITAQSE2hFZGU5GWJlFbEL+OYOI4JlHLvqiudK0BZGkpdUuQF09mMyXhCp9VCCK8aMZalF7kF5jwDBFEcE6iAUAUkQUAUhkRKVbtz+FI7fNf/PlrEqd3la6L8qLNFbUAxvj+F82hwnfWEAS6AD6m1bSGo86xWsEbtZ+t3qfQ+uPa8WCVMyzFdMaBKz2hq0bt5j8rqT3Nh9CKKRghFzVp9XUz9PLdo4xmyC112HNs9xbOwajP4hhxS0x/TON8b5rRjb0oozvRf4sP9b5PKEQejP+LS6m0OdZfRaMSZwZ+tckVo9zyvI0ZIiijg7tdeZvPffZvpzgHTbELZTog6q7wclmyWu9xWMZ+UEceyg1QpSRwxzTLm5YxsOCWKu6TtDrtFwFRAr9smKXIGSciZKGGsZ8x1RDEcc6s8QRQlg6xEqS0mWUEETMd7XFnZ4O9eucB/sz/h3/37P+DqVkZnXbBy6Sxhq4tQlpkWuWJ/X/PeH73DV//LX8QYiU30fsqoWhGoWrSspRD8ytlgQg8vP4IsY58m2NaldnyxP8NxQWk07XbK/n5Bls8IAsU8y8gWU/YPD8mKkjBSDtwkKggRQhKGIb1ej9F4wnA0RmtYzEta7ZjxZEzabtHrpKx0u6yvDuh2WrSikKDJZvBzq56MCgidq1YRKLKyJAgs+/SbPlpXrprV2ltY4NLGsMgzirJwrm/rRIGiKAxxGBAFirIsKbRmMptSlppet0caxaRhRCsObPy5ofIzbRbx4KGlFq9E5VMvKXQ1/V20ac3BTHUPUfmY+qxTFcQv7cj4ZBQjdGOTHJajJRscVDRA1n/2kNySKxzrIV3Ts/eo+hsLwrKeqF615rnm6RSmtbAOlWduJcE0dwY2S2qKJR4rfB3txPfX1FhtquTkTa2s1yGXjgVv9V7lndtvMgpKEq25M/qIiTb01M+gdSOdj/ALSW1EBEOeRuz8uZ9k67e+SXAvQ81zCCdkSYe1WBMXBSvjO9xTHT5RA4bBClEQEsYJC10wN3P2DuYM1tYoVI/ZBDY3zpKhaYuSQGmCw7ski5xcdDD6hOH0Hl2RUWrDraMZfdNGdFpcvHCRvxTc49dPRtwdtvjpL14hVPWGYiaXvPm77/DNf/+b/PW/+SW6Z89ilWaGOuih6fvs3rfpElhRlrpXEOUjx97j25jSD9Tq6KMB93QpDNzbHzIZj1DKGpyUkkgkcaQ4PDxiNrN5FpI4Qhtc6kZJHLfQzMjLkqIsieOYPLeKb6Ui4jikk8Rs9PvW3zeJaMURkZL1/lWwPAtZBlAlBEpCGEiiQFKEmiy3inxtNJR+vlh9l5TS5aK1RrA0TcEIoihCGMuWwzBESokUVrfYarU4ODwkTUKSKCBxgFuJvqda19axXrQ+tcVPY6R4UGdndc+nxJPGCu6lbmsI/EE9+uNX7O4Qp1rQGOotG5w0UA1mN7n8giQEgU4oxD4lJdIEjTFjVz//jIqlNvRB9fTVLqOZqdtZVPhV8dVa1eN1qLVx2p8jqolf73mmTQOyhX9N7bwQasgurYIAYwRB2KWTdCmCE2bZgqwwmEJx/sxz1aJc77XmXqoxX4yBIgm590tfYv1b79F55yP0eIYJW4SRZNCN6UjDYDRmkO9yx2TstzYYkxIFMYFIUDJjPj8mNyVqfZu9wxHdOGShc8TshI2VPicr6yTX3yYbZeSyB5MFSpX0oi6f3Nqh09vgfrnCYHCBP9e+yx+MFrz7xh1e+cnzCGEoF3B4/y733/n3fPUnz3H55WeQQVD5Jxs/TvAqBfueTRuHHUum8tO2w0PyKbPvRxiR9mnF18/1W65h72jIdDLi8HCP2WxKFMXIQBGImP5gwN7uHifDIZ1W6hLMCIyGvCgYTycUuiRttQiChNlsAQjW1zYYdFPOba6zvrpCO45Io5BEBY3B7Q0mDdGdUxjsvlKACOyGl1EQsCgLcl1SFJaxetC1obqCIAhsFrAgxBtpyqKsWLI3BEohaacpan2NOAyJgoBAWsD1Aqs+VZfl5mwc/EEEtDrVTZ4GgpqHnUadREcASIF59KL+Y1yWCYF9P+lYphX1l9+/MdH8AixtwvsZM9rG5k72Hgt2CNWM2Xuh2O3arfjq9X92oZRYVyPhdjGwi55PhlOzLQ+eGuHyK9i3abBf48G59mbwGceMMwxJz4gx+C3bG4I0a4NN7kxPGC00aSAITAslk0p/78+tF2TjFrH62WUYsPflV5gNWrTf+j7s7aEHPVpCEkYxq/GCVqbYFjPu736fvWiN/fWrnLQGhGmb6WKKETA/3ke1BsyFIM0mtKKARV5QiJJ06woDUo6LKVlp2JQGJSNWVi7wO29e42uf/ypvThYk7U2+crbkg/KE2x/dY3WzTxiUnItjfvav/Vk62+dcv/jUpX6JqxRpVKuhlyLcWlNL86Lhu/0fUL3wWYugFo2nk5zd/T1yPSdOErQuSZKQQMXs7w3pdLusrq3R7fVRUiFVQLnImC0WTKZTptMZWkOWl2isv+7WxiYXtrfodVNW+h3i2PrdRkFQxdoj/JD77KKyr3OoBIG02e/LwKCNtjkftKZ0GcCMDvDO5tYhXdot0p3BLstzDIZYhQggcVuph1KhGghgJ+9yedi6+si19rQo7RM9CwsK2qkQqgneWBSlZ1pI5zz/6QPsx7cY15B1Swo8a/cKBJy9QNdJaYQzRekxO9l9tIJpMKaVtZ0/rDNQGVMlBvNAKitjm9cRN1QYuMQ17jrvAyuhBsQQdGkQ2i+9pn4Pp/et4cGngLS8t1YIG5qeyTQA1+K8W37CkFYIRSFoRQnk7QbfrhegemgI625X6fidKkrA6KVnyda7rP/OH6HHhxCAHCToJKQVaCKlSOWUDTNkPHuPgyPBvk456W4y7W0yB4zOkQWIMIRAosM2i8mY0WzMMAnZFpvcXYw4mE/ZbA1AatbXC77+wUdcef513ji4zX6ouTxQjDoSEUIQRWi5T2frgm13U9aLic/LW40V/066mjO1jbX2JLHjRuKztD2s/NiAri9GwGiW0UojkjRmNp0QBAFBoFgsNCvra4RhSH/QRwpJnhcUZUFeZIxGQ46ODlnkC7KF3Za83+mwvbnJxuoandQq7ZMoIg5CkiiyARRAJTafRqqH4YmoP9QLvdXZhUISSgCJDtwW5tSM1xv9pBDkZclsoZHKgrQQirLMKbRGCo1SgV1UpKwfe0ptU4upP7g0875WopFj9qZiz95IJh4AXLuy1xuGV+5Pjx5fP8bFIVVDP7rk0VAZxAx1msMSoSVGGgQx8dwwVffJWpDJAbGOK9az1EPuo92Lq3TfNHMCuO+NrnZtMK4OWngvBMNoPuWDyTu83v9itQ2Pk81q9oWd+gq/U7F2MFtWTLtePPwC6jpZaNvv+YJesEZ/4xU+vPU9MLn1IadxbmXgoxYDmzlqfb2wvquLjU12f/nnWf3kY4rDGZ28QK1twOiYMtckrRSlDbGAaHFMe3GXyehjjj9MGK1eIvvcC8wmGhVIwnaf2fEh2XRGQE7c7/HBvfv0Ox2OwphxeUxsFBEZ/Y5kf+c6F1cGXD/aZVYGXMpD0tBg5mMWK30qt7DK5mGNlM33rX1CGseqpqh9qv2k+TQnoscWHGHrbqpcnT/4CntWqWG8yCnLHCEF48mYTqdD4TKHtdotJpMJRVEym00pF4KTyQnD8Zij4UmVy1YqydkzZ7h8/jxrK6vOlUsQBSFRENKKbJ6C6ukNsJWV2ICz6jbKkvKzoYIQ9ZFKWBV2/7GqBVStNRaAFJKsKClcGLPVsym0MeS6RAjre9xUqZ5uywpIH6FYfSTbpZ4oBihNrZvVRtj3FjhxySzdSNBM8l0nZHnSis/a5fMaVG3b7N9T+lMjtOtTRT+5RKIHHBT3OFZ7bHN+qf9NpRP3R+11ppqUwgErLj+CPSZPTWzvtNQKUm5NdojDN3m59Xpj3HoFAmhRIis9pMsJTIGNI9OVHruGEA/JurqfUBEb3c+jxQKCDxBSE2ivrzTLE0b4xOrOX8Kd0zC34Vlv1u2x88qr7Beajffe59xUEwcR8zxjXpbIqISkjVg7Q6QixOo6SWeDdaORg3XmWcj7b+8yH0+Q+ZQgCNFCkeUFQTumnxraE82RCimCiKK1Dse7JEJyfABb/VX2J0dcO5ZcDkLaHCPSlo3/EF4K8cBZey5Ug8PUenJRtbpXKVQhLz8Q8B4L6Pr9tWypGdWj6uLFMIlhmhnu7+7brdILQyCU7VclGHQ3yOZzWmnXXmcyZtMZo+GQRVHQSlq0khZJEpEmKSsrK/RaLZIkRkmFknaXhiSJCMMH6VkFJq7aQljtXgVGS4D7qNL013gAIusV0EAgJUoqhKi3zhNyOStSICU+HcsPU8ypxzfZbT22PNI4m7q7qBIajWfB1VyqxFotaoatfmjf4B+DIgzLmxj6yK86p5fFDBeGXTcVPuWoJCBVq6xpxaG8x0ItiMoIv8G7ZTz1hKzHTx2w4D0J/Cef/wq89GHcVjm2li8PXuStg++wlZxnVa5VvNqgLRiYGg7sPZaXxOZTvXubPVdWT/SQoohoqx7zbESiYgfWdapIf55ftKie7DXVTknl9dQGSiEwYcjO515A3log7x5wYkbMB13iV65QIiiVokg6lEGAQKGMzXFw96MDfvPf/PfsT+Ern3+ZV197EVGUzMZD2q0YdXaDZHKLNA/ZOSkpckWwcoZsOmYgjzgY5Wy14WRR8O6dfc4NWvTf3SG6NKDox9RudfrUG7oEOU6aaG5Iatfl2s+Zavl7dPkRqhf81H30BC0x7B6Mmc1mzGYT9vZ26Q76jMcnmNIwGU/QZUm/2+PC2bN00pRcQjCZkqoAKRVxnNBppbRaLZI4Jols1JdSEoEgjkOSMLTDYkncXi51mr6a3Eq/CJ56q6XrTr9frRN44CoBNpJOu3TaphbHBFa9ECjVYBafzmofXupVevk60XDnt9ND412OnFLRuOtE/Z7aMTOfjuUUCX5yituu2xq5apCrzZT1nmGw3INN3gqCVA5o58cMwx1WuVBBrgcpOwRquKu2Rq9AvXTg3wAsoytpQ/pEOsCF9CLX0o/41s4f86UzX2ZVDhBl7YDmQdP+p+u+q4xqVB1mn2nBVhq7gY+HbO9u0UvOsT96m6DTx4a/1r1tXd9kVetK3K6WEfdWDZWJW7IpA8lur0d70eODbEhurnGht0Zt9JPVuM8Xmg+/+zE7Nz/hmQspg9GY+fQW2xdf4d6ORImIla0BZhAxX7uKwLA2z7lzaxfmISYdUOYT2nFOVgb0ggKF4cbxjM1Wh607GelCk2/E9XZ/3vuk0bLWt9IDaz0q/Lvafm7KpQ8vP9J8ugYerutwSFYYzTw3gEYbq6c1ooUBxsMhQgVIpXj++ed5+cUXiclZhCGagFIXBEFEmraIw8DmJlCKUFr9qUQQRyFpbLeyeVip8PEh7dVQEoDxanZ3+ulrHrj/8pfNpUe4NvEg7xXxNqw5qM73uGs+pTM/O/hZ1lUaKqAvjXcv8pjb9CStNVkG4zartC9uAK2bb/RkFLtPX22OqkV9g1TWYOWWWnC7JtRjQ1aAJoxACE1fnmevfJ88mBEVSXWq8EZKBMJopDGUjtE2s4vVvyv6ZAEXq/oB6+ESy5Bz6Rk+Lm7xb6/9c3754p9jPbzopBDP0erINWvw8f3nXdOsDcGfWzNXx62NAGk1ySvtS9w7eJs47ldG4EYrOhuFb6fGW7gh4QG81oeKSmIQccSsyJkXx6y9uFmPMue6J4RidDDmwze+x2g4ZjgakXRafOWVC7zwhS+BDIjTfTorEbIfU4gcYRQG+N7wQ945fptndZeN+Av0eyuYIiMwEp0XdKOMSGlGxrCbhawfaeTomPjiOnlUNNYWX3+shxTLOveHye+eDT+qPBbQlc1+aawGpavI6aTXftAVpWIyW1DqDK1LwjCiLDS6LMkWM1qDFV753Ku8/uqrpJGkLDRBlNDtBTZqK4yQUtp9yYzdnA5pXbHiMCKNApsgp1qp3PPN6Tp/WrHX18akJcmiFiF/AAZ5oBZCoEvjNAq1VVu5XYX97R51S//dQ59xuuZO/+oIbGV99e9T6xmX1x7pKlu6742bF7UW5MkCXKDquKW2FSCMxMiSRTInnsbgPEuqxncBDxY4LBjaCLKAXnmGo/IOm+KqBelKS2uZaxPGPch7Mm2EcbpYVy9nsNSOoToPMoSBy+kV7mbXGc4qTaoDVutlgfcbrYSVhsENHD9tgrRpfAM+WsZgaMUbBCIgibssR2j5AUx1ByFs4FDtlOwbuill1dfooM39/B6L7ojO1nO2/iiQUOaSwzvHfPid72NMjhAlnXbBKz/1OhsXLlp3S6PZ2Fpx47GskMZg+NatN7h9cMQ02uWCKXhGfpVO0ELoguECUhWSyoJuPmEsOtyaSM6mHbL3T0gvdGDgJU7ntrck9brPPgq0YvinQsUfUR4P062sgI1KNDqnOqeyegIGsqwgSgI6nTZZltlMW2XBaDhCCsmrL7/Mz37lp+mmKabMEFIRKEEQRnYbG5cGUbosUUoYwtAm+I6CsAH2D1mdGmPEToS61R5YtXySEGMeaORHEOUHn0eTPbtPnt24JDnWy+Ez3vBRK+updzZmWcsn3UTxeSJK4wXdUw/1IH1qGTD1eHyyStNy2mQ1QkAu0HLCtJORjvrVZLa+1bIhZoKnbQZDLPvI4h5ZNCMuWw6MTQWWXioSDpCNEFZKEPWzq5YXNqdFDdX1mOwGHaQpGHTOkGHdDEu03/uCZcNnw0BWJfLxe6f5d7J38AuwVbnY65QIuLRyiXaQklf3rCeLcIZBD9I2MsgZ3cyycck7PACU+YBP3rxG2JnyzEsX8DIeCCaHC9795ofc//gD2n2FiFK2zm9x5aUvEbT7VsVlCuodnDUQOJC0Y/nLF77IpPw6M53x3ug6ImjzQvvnYC4JA8HdwxPSpM1KBMFiyiJMuV0knI8Djj+ekKwq0ostSrFojJnGWG+qpIRzE6sa/dMn7WN19nmAhXmGZB5eJaUErVZEEAZsbW0SRiGj8Qmj0Yif/Mkv8/Nf+Sr9dogUhVUZSFm5kymlXICB3YI8UpJWEtNNUpIgsBnyH2XhF5ahavGnI20/NPYIL+ay5ErmAfd0VT5NtfCZ6me8KuPRdTaNdqi4iQfl5hWNtfOH0zH/eBRjGvFbwkWOCW8AMqSLVUw+Z9I9rnxXgXqyLemipL2fDFgNXmRc3nWeEe4czzgdqAj3HJ+G1E7ZRlCvmyACy34tA/a/DVob+uEmW4OLjIoxu8WQ37/1azVr9RIJNTAY7ILquar1A16GX29Us/t/1X17rvMF1meZ/d7lg7Z1r7XX9fiQ1SRyHNc3HH5h0fmAm394g5XtkrMvbiCjFJDoMuXOxyP++Nd+n+//0RugDHcPx2xfWuGZ1z+HSrtoXaK1lQyNMdbrx7iFw9jdY7TRvLj+PH/jpb9At9XnxfMX+bnXv8zWMyGH8YK5CBj0u4znY3YWESpMaImSkJy7OgGhuHVzzL3vHmMWbrv3pfzHkmp7p+qzP+7UVp/iR/lYQFebumMr4G04Z/vvq4xVxlYkDKz+rN/v0uv3abUTjCk5c2aLr3z5y6RRZEmGtqKFClSlu0VCqCRpGNJJInrtFq04QXl6uyztLP2cJj268eMFSB/G+zCAMTQFr4c85yHFVyeQAiWsKsEnOBdY8PVRTKL6+fQV4fSrPerJD7tLqQ3NbAS+70pqQ5pwbaWgCoUO1KNJ9o9zET6s0/N+v7mjqc2L7fk6UucsekPXYRoV+Qy1pgJjb/gsi5LDk0Ni02KhRh5vTz3Xiq1+3Al/UiNhjr1nxW/dGKihURjD1eQ5ZvmUUkk+2r9DJ+g1RF+nkmisjB5S/X5ptoO1e2+3YXmlevNQancCXsiYaCRol/7+7n7GJvf0Ok8tmjKUb1dvkHYj2fS4/5191i4H9C8MEFKgS8VimHB0/YR7H3zMtVuHMMr46MNDvvJzr3H1tVctGTCFW5DKagGwgR8lNkGNY+nG1mm7fYb/6Lk/z9ee+3kCGZNxzFH6q+StAtNus7G+gihnHC2gRCKKAiFK9kRIEodc2xlz7Y19inEPYayfsqiMzKcXE6hdA+HB7Znq8nhcxtwKbYTxMqmrXnPlrbqhqrgU0G2nxKFg2u9x5swWmJJ+u0MShRitKR1T8PlzBaCkIFKKJIwIZK1/q+7dEAsehg/m1PGmb/GDk+ZBZmdO3fhR19bf19onJXB6Ztd2xum1GoSybqPPWJpMdsnaLKrx0TjB/vIiISyJxNJ3lBSValM7OdF7MgThE4m6DvCcIciJ+AKBXiwQcYBBkU5XmQ4OyVfnBCctFvEJhDHRLPLModIbzGVClsQkChbmkJg2VNZ9avWBZSEVaFd7mDn/Wns7U6lGhfdqQVcpH1eDFQZ5l810CxMfcanzqmc3eD9T03hWLegbzJJxzRZTxXLLqg7+3yIIuLXa4vlM8G5inEdGbTirs5U1zZIPzgJdJuy9eUzQH9I527PBQPOURHSYjncwMuDbb1/j5PCI1bWU/+Rv/Qyr5886wqOpQ+DdhDZ2i3ghXBi/yynRnPD9eBUhSrQpOBnvMrsz5ePFv+HZzV9Cxqu045TZcMiNwwnbqx0SBKbdZbQY0+62ef/+MVrusXV5g/7GCCMLC+017XVuerVZ1uLvjzgiTTv94KfBRe083aiMFLQjRWACttZWCZVkY3WFQbdLGkeAqaKzVGDTLkopSYOANFQ0/QV/GIz605bGuvInuFYQKMWiyDFaV9FddsKYyqPBM+A/aTGP+Aw0JowtPjm5wJGfB1QdTfHRf3rCgNepTJpZ1TzwyjBgXh5wJI5pxWuEWcq0NaS1ojFlyVycsAhAlQEUYEqDNBELc0wQSEazBWkS0+2UjEe1SsGllEE0dMIWO4TbLNLWw3od2LoIt6I3DWk+2PS55ApTccJakBEHHSrLphNTvPa2AkaopChtakbruXtzM3LtnucNvoWSXC/GnDEt7njPDf8CeN2uqOrt8d8XrVP2v7egEDtsPbNBGq9TTCLMfMTR+B6zWYYoDygK+OrrHZ778k/SP3fG5ZI2VsQ3Gl0FMQv3v3K2Gqfi8BILEk3JIh9x//AG9/ZvUCwKonSV9U7J+bNTFuYMRwcRYRDST9vc3t9jTUVkxdjmeYgjVjZX2VkIzN6C1bUOWh4vgWtF4KuX9eqjRyPCY4tIq0TSylDgmBaV40rFGn0oqjAQSNChopOkhKuKtZUVQqWQwg0EA0GgKjEtVIo4stS/qY99GKjz0CPu+Ckm/MB5ohrL1d+nB9YPC7zeTUxKp4T3SW/wCgX7IN+ej3rAA3poc6rLK9Zsmn/WL+KJg2NEpTaUfpZjma80csljAyxDryfrk1Zc4If2IiP1IJCKhE1WdMQ422NUzNBzyUTdJ4p6iCBAuIQupcgwam7Hm14Qa0GZTInLgPb165QrX2Uqm5PUYFxyG0yt2rJKAS+223nhg49tCp6arfocCQkRs/GCNhp0iRB2HPm8DXau+RBXgRSm6mPfi5p6YcfXi+ohtp0cm59EMZvHC5J2wDyy7+Hj5Rq1s3c3loFKJFo9w2K/4ODw93jta1eJg1Vi2szVmDv3dphMDugOBPH6CqmEi89s0z9zDq39zrvOZ9e490PgfcklgsPxfYo5rK72CIMOmoLRfJeP777Hwc4tWnFC2OnT7nbIygNePffzBNEKLTGkFaaMFy0m04gglkR6wuUrHYxYMJvMKHWCISZUc3K1oOo04/W8finzo8rX8UfMdCsMqKz7rmqNYIQq8YsP63CTQErchoyCKFAEwu4FVhY5Stn0h2WpUUjiMKQVBQ0XnPoZp2ryqUTsYVuSV5c1UFj7ufmwl/1TED0pQLhsXUVhiCOrKPWqMGvc8rbdR9DqRsUMTgWAd9muNX2+6IZOykOxT08nhXT+pO7GhqXrlwz3NIfck1bquC04JbEISNUKCQMKM2G62OeovM5I3iaN2sTZglmckhUZulhgAFVqUtOhJbcJ1DoHScT524fcvrBK0bR2u4fo6phXIfjvHNvEZgPzqgBpas7qBcmV6AxMM4SsRerSGGaHU1przoPCuZD5a7TRVRL8pqrPqySoQNgzEf9bcLMreOHI8O56Ha1YSzoGlQtMOUMnLUTwEq32NjduT7h//2Ne/uLryDxiPl+wP7zJOLvBqNhn++o2gWxx953rvPjSgP5gC1Mx1iZRq5UkHuxOshN+45u/TjGf02612Ny8yGhyQrE4pNdps3Z+hU6SEhjBdvdZDvItijJDGZvuNY4mRPGE1Z7EbBmkShAix9Cm3fN1KPDZ2SzZ87ggq/rYJUhgU4N+Oqw+vuAIJxY9cNg0gKx5qvskrMCAlAIT2O1TvO+hwCeFEgTC0HJb4tTuJn+K6j7gImaq+tonPuT+/wNgjcAao6QQVl/tt+dRtmsehq/LzPa0KGct1AVWR+5FILN0xfLVTZZf7SrrRI+KHZta54tYBvAnsVi/2EZbuPm9LF8IhBSEpkM/7NINL3CQv8tU7GKyMTOdIVH0wy3irEccrdAONvB5vPII7gQ5G3eH7J3tUkpT8UJwbW38bhE10Pp+qPSjDi3nkxFRJ0aYECWcGk8oIqlYOMIhgbRUHI+OaK2lCOFS3RgoZwVSaWQUVukcrWSuXTYzr5ekKUBTu0NpCqXYjUs2piU7bZsVrZwZVBTYUP1DxSJVTORzZHmXe7fvcff2DQ7vXeO1s69S5CP2Du4zntznuHuDMjKUuye0OgFnt7fZeuUqwbzAiIDZZJe0vQLY3WHrzGmaTOfcOrjFH3znmxTzKSIAM52S3fmQza0Oa9trdNqGRCg2k0u04jMIEXImWgdT4HXxRng/a+PUFHY+2F0x/Oio55gR2g2SSnyvc+k6H5TmQv6w8njUC6butqZj+dIAh0oX4g0a7pAVW5VCSInUoIvc+eb54WyIw7D2TKAG80e5L/2gvcOa34vGv9XHBov8zOURjPR0FWWlVijshGuAvh0YzVs6EGwea+h7K+MMXmVQn3OqGtbGbaxhxbIWUd3XM56mrtc3icayqWVVzJPFdgWW7UtRJ5TxKq5KTPaN5CFIhKzGn2PCmHmSckFeRRrJPJzRkimx2gZdc0SBoEwidtcMmzeGjDYCZlFEqWxIQ6vsYEzBRE4aKhrnjmXqvva2CqFnzMsdtnpXYJqBkcxu3OSb795Edu/yhS99GXUy5dgErJ1/hlCF4H1atWZxtI/qSwhsStFSlGhj4+Ok63tLeCS6hPFYIJMCsBsCBJFEiJL9nuK5G2OO4w6LQEJomJ9MufnxCavFgM3PbbHW7pCNbvPJO7/LIk8oipJ33/02YSLorIZsnz9DMJxzv/0JM1HS75yBsM3xvROU2eX+0ZhPbtxBhS1QAd02XFl5ntW1MyAk/+4bv8bt/V3iWHDhQpd+L0HEXeb5HmmroCMlZ5ILdONnkDKs5gNIpIzcOK+pg6n0xo1R7ZC2ERzt0lc2sEa4seIHj1d2/6gNaX74+IyUfjtpj8GVuNqI6PBgYzMlgZHK6qa0ptQlCFWl3pMCwjC0jeFmjHH3abIVX2yDfza4fBh0NJng0l1+wC1P38s0bqYb15duogceAVx9jTCUlIBs6Oke/uxKi+t+KazvaTNL2jIAN/Y5o6E3rhKSi8qXVQsP0A3xFgfSDYPpE1WMn0reEk/FfIXwQQP16T6RjBACsRCcDV4lFCkCQavUHOV7vHPyh7x+9jXSWRtRWA+U+WjOeHzCzr1jLuwIuLxKZAStRZcX7i0oyoL9swnXL2ZInwBJKiIVIGRIqCLQmpIA3b3EvLxDNkzodNYZDifcPrzHaGh49xsfciHtcHx4wMlMc/7Zl+mt9kjbHWRZYnRJZ2UbrQv0eEEYdpmNx5SLGUk6sBuglrntax2ACGH/mBvHc+7d+oSJDNjeaPHScz1MT3J7q8XZayfce2aNeQDBSsKFc5pg1uInrj7HeDxDr7T40k9/kY/2NN+/dh+hPuDcSy9gTACiZKN7lTVzhVwX3Avu0SkizPouxYmmbRIuXOyxWEC+KEkixTi7xezgBkGQsL4ZsXbuHFpltNKUWX5CKId0gxCAy2uvkagNjFF4N0AN1jaEchKGC/H2hMsYlgJf8MAKdeRhQxKkguJapAS0KRGfsg3vY3IZkzXIUv8WpyxRDfbuRCp7HGNckg9BaTwsgJISvw+ZfQ4PzHdh6hxPP/zOuA1gevDWPxzLdeefvqZakJa+EEgl7Q4TFauyQFC6DH0Nu9ZDyunlQCwdau5q6zeY1LisV03FiWncyhgL2jifZfcy9quGI/8Tqmfwslgl1zim7wUzH6Hn837ZawRCS7aDFwlJa2FEhdy5f4/f/eg36eqQ58NtomALspxwMuXO967xvQ9vcX7jDBuR4PzWBpOD64Sbl1g5e4a42+PMah8pbEL+2XTKYj4nK0r2D49YZDPybEF32/DBH75P3F5je/2Y9z+6aQfGaotgQ/Lrb7zD+c0EE0R8eP07iFuavDSIICYKIgKpkCokX9h37iQJSoI2u7QUHB4ckrQSjqcLJhmgAj748C56NmE+nnHltcuIu5rv/cYHXH3l8whOaH/YYqgLttcjnt04S7x2kTu3b5KVhmvvv4P4eIdy0GdyWPLcz71CoXxgg7BkCrglrnGiF6SF4nCxw1ZynpXuBt1Jn3xyTDpYYY9jlA4xYoERmnayQV4MEZSEekGntwY6w4iIxWxEELSxhrcSYaS1x3ibhYVfmwazgkDn52vquWOo3fqoZD4/GeuZ493y7G7SD5v1y+WxgG7pLMKVH7E7LvE2UpYjrhrqB9su1phj3GeE1a1Vr++CBh6gnQ1DnRcnxNJxL/Q1rmlWwB9pWP+bSa0fUDkswfLDnUSaxyoDSCWyNNQq7p20MTb4o9RWgV0a6qi1h9/XHnBDYknmb15gKnVDPSxqr5Fm+kL/vTb1ZiXGUWFvWa9Z7hNavHSEV2+JalIuh6c3+9VgipJQdRtNa9Blyctbr6GEZLp7m/uvnCWSh7TLFlF/jWfPrrL+hRcoJyUr7VXykwn7wynH4Q7rapv8aI+ja2+TmznkY4zO0GiMLohbMZqS/dkuF9QWZy+vIFXJdHKbF35izSa6V9t89c98vvJEqBNL2sQ2Bh9FphGl5s73dvj3v/ldtF6Q5wvOnGnzyleeo301pdNt0zYdhLJS0vYzCSe7exzuHHPz+JB5ueBgcsJgcsLNoyHD4wOunF1nfFASvZ7yh7/zWwznU/TOPrN7x/zsXc1bzx6g46vot24iX71AKcrKkH4cHDEpb4Nc59rJtxnIDVQYIBAMOucw7XMAKB0xyY+ZiZJ5MUcXJ7RUi4HapC/OEqV9NIaJPmFXXGOuj2nRsWxW1Dkqql4XZmnW+3SN9k9nYPRqUqHd9Y1ADwe6RtReG9bXWVUL9qPKYzKkuU43orL4e/9CCTQ3BPQg5MHRszmljVMzSIS0Gzj6BkAE1D6BsNSe1H6HSw8RHuBMrWY2zfDFRhHNWz6i8U4fbiwcj2K3/iSz9J1w64UiSBLyLCfXBk1JBAghyUttrdPUi4Be6lTReHfTyDFhv/NhpcapHJZc6xyw+rFU+ls5v1LtWEk11oyP1jPVNkO1HvRJKroCW7uVji+ikjasYalxhTZ2TzjtnePrb6WUPLf2Mt+490e8d3+MCk+42nmJZ1t/lSgc0EoVs9mMw51b7B3fJb15h+wbb3D4j/81K9M5rV/+HMHPfRklByBdULCwEuMnBzcZFjm99iqoEG00rV6v8fSiohMuULgx5y0ESwkChQkEBRHrAQxeOsfnv/oSUbeDkT5ZjXaSjdUyt1dh65lNjNFMjkbcu7ZHKzbsz4/ZHWdcv3mLlWTKQa65M9xn9XKf8y+dQ5wckZozfDJ8h4Puq0TxgDtvf4/La32Kc20MBVES8/H0m7SSAfN8gZ4eIlqbJEkH8OMQMIJEbpCEW2gzYRwcslMWbKgeq+3P4xSYSKCvOggBx5MbtLtnQQuMCJDO89xLM0uOz1C5TC4BqxsPtV9ulV7I1a9phLYs2JMWIU6jQF0ej3rBq0lEwwdSeGi14oW2SwWeefrIZhdJ54MVq8FtjQsWPGbzBUVhSOIY9/Y0Hfjd4xq9RuMbe25z599Gqlr3tVj+7lOLWf4llkG1OqtxUOAGuF+chO3OVhQzQ5JnGcLgwFZjtDVweOnBTh9nqDRezeLVB6JWy/oloDJsOgbr2Z3PROUNcZ7BVnX2zLZWI9Sh2wJjZPX9k1YqZ7oGqxWNrG6+LPs2N81d7l/jMhAYwf3Dt5lQslEEPNv/82x1fpbZFO7e22Ex2cGUJ7TaAc+8dJb0W98h+/7HhEqwuLrK+JkLrIcJOGZqb69BKD4Yf8D56Dz4nWor5XstDtcumH4BdVpMZxvwK+3KUNFLI87/3Z9B9AfOBdZKV9olvTF4QHHjy3X+xsYq7UEXjGI+FCTfu8FifsS9Mudzn79A9/wG7Y0W0eyQ45U7jPM2ShbMP/yIxeCYyWSHfHcDc66NMZLFPCORXeKgzcH8iJW4hymmzt+YemA1WKUULXoiJU9y2qqD9tF1RmDcrhm94CylnKHlCGUG1Yj2rNff1Af+uGlkB0G1/To1iBiwagU3VExzjHhdhEUx4/a5+7TY+McDuo1seFpWGoJK9q0nqXD/O32JGyzSWdNtjlfX+S7Zq9aGyWyK1jN6vR6RB14HJB5wK9j0DdQA1ib9936sTXboDZLC+BQW9bUs/e3uUbF1UTHBB4tZ+uR1q+DngyAQAiUDFhRkRU6oBOQapRRa2S3YDRJVqW6WI1/MAx3tl+G63UtT77nl1Rv1fepG8vXzuyw3J4HRXr1gjxdPIOhCrWiymrhaOhLG+2Q3IXd5NfUbVQoBUkakrT4XWq9x9fJfIJ8EjI/3uXbjDZAz0k7CynqXMNpCO4Pc/L/8q8ivvUY2H6NffIb1+P/H3n8925bcd57YJ81y2x5/Xd1b3hcKhjAE2WSz2a2WRpppSd0RHTOhGD3qRaE/Qs96UISe+k0zCulBIYVmRqPRqN2wp5tsohsECRBAVQFlrnfHn+2WzUw9ZK619rl161aRmCqQwfMDTt1z9l42V65v/n7fn0vx6bRrXQuEf5GPVo94WV/pVnQXANf/3z+cNEspipI2ntStPXvpLBLJxv6C6anl1qtjlIp8WJT1YG0CgPf7t3OgdUEJctMQZY85vfUGs08+ZDK8xO7VB1z/mmL8nECJM45O3yeVz9G8foVH7h6TU83g4AzOTsl2E3585wFvfvN6eFccmdihsXMmeoC0p54W7ZdEWlBrn4Mv/CPZ4iqizBGZpC8c1PLvio3BK8T1MUYEIFx/N8RagkX7d/cOrOeb9Yts64/qrcSwjWvnUX+8HoWeLl9O9IJnrVvwD4DrENZPJEOrRfYhSV2gTUcs+odiEdjQD03piNEwY5AOcM7RNA1IiYwiCBlTgr5oyJNg2mmj3ff9A21X9W7sQp68bLVoFwy1tVXMv6QiPPAetNZB3q0BXn8djrbWkAOctSHt0nPXjRNUVUMkLXGkUY0jUhBFGuksBjyXJyTiGQ+3C3UJGn97jk6b7TyRT4yHEzjrze420/5cCIcPK/G1Z91fpBbxXx1pQ6Q8wIYSmq1V1SoCIhQQb9MB17InhFREKmVjtItKNqhNxP49zeGjXyLMnNFIsndtExnt+B2cC8XerS9irsC+8RL9gwjx2fj3on2sDoGRJS4atXYizkosDUq2LXYcD1dHfHD2S97ZfIuBjBG2pZUcKRGTgyNKbTm6MqEJFEmfGtFDjbcqWy3a0XW/FQ5jwMw0xcFjtra3ODn9mDd/XzEZDDAHNbd+dpPr33uLSO+BHFNs/ZzBf/IuN040VitGr15mRwYbTXgtPI4i4niT1WLOg+MZL14adk5kf9r2pVx/hxwqSkEPw7E6OO4BFajlFtKK4PsSa1quou+b0o8hrC9W5yG4U97CdfWMsFj/EJDh3r5i0O1iwNZM1V6rX7u5YBa16bDiHEC1TgFYVBWHJ/tsbWyQrwRZmlDkOWnmixILI5BKIxGhqaTo3pHuXVpD3w5mWuUar0G3PJ5Z02o6LVbQAXBQLuhrPYj2Lzp+pOU619C2hzfRm+ngOzE474CsrOV0UTA/O+Pg4X02NzbJEs3mxpTxcAxNjTSW4WRC4SrSLG3n5KfgtzMVA29raEv7uVCYvL8yt7aTC3HhNmi6YQTC93Rhe+ulYP+6ScvQ9RX4RA/ECMoatHDY0O5cS41SCTpKwSqKvMaWcO/khKPjj8iXj6jkPq+88BpxfNkvbM56AOtyuB19MsSa/taaEiFrrH+WluNyxtLMqBLfTKdqHLfvvc/mzpCt0dWwOAiOiyX/7P1/ww/iH7M7vsb2YMJ2OmYSD7hq4HTT8Prum9w6PAGn6YMFu9I44fpa4IX1agwtBWeTAcpZ/tWPPiaJP+Kb397h5OAhk8dbXHvjXbTawjqI9IhETThRv2R4+Tk2N9+kKE4ZJmDc5W4+ZVHEzDbMHuWczR36Uk+Otc+pc5zTKxmhNFFQanyRKA8faxEEApwKioTs41AEPcj68RcQFgGB8NXSHF2YoB+oHlxbpSX0D4FwjXRjJjtn5tPky+kcIXsHVfev9JFr7UsalC9aUlD2qgZSgDF+kJSEZWn55PZjiga0bKjynHsP7vHSiy+hleP55y6zMR37ypaCTmvxcb+91tuhE3T1ldux8iZIO8T9I3HhGtuY4PXr7sOme9Oi5zjbkKvuAzreFQ+ArcZtrKdSamOxxjIZDmjyBdIZtLDMT06xteXgcEm+XDIcpuwZGA9iBmlo+/2UhbXl94QQvTZqg+MsrOQEYG2jElqxHYcLbdQCOB9Fcs6jf95y++siSvoaF62ybytLGiWcnpTMlxX7j86IbMl4OiUdDKhMQ748ZrZYMj855vjWhyhd8s5vfZPJ1phSP2KQaqJ4GGKwW+3Iv5p0i2ygbUTbDj1QBh3ghpENz+1nj35ErBNWtgYsH9/9E4rBHQzPs8EV5nXBx/NH/P9+8QcICbUrOFve4bhQ3I00TdUQO8HvXn8LtfuIWlYkbHSg1caLC9qSq2u0V6flA1JQ6lMWy0Pu3h1w89YdvvX3lywWBRv6BuKF14l0Gu5IYJ1gZ+cdHpV/jNAPOCkOiNQZLhdk6XcRXMcCg3hIUdfs33lELCWRzMLJ115QwriIFuoCISlaKJZ9dbFezaMN4hdr7zXCL4Ydh9gtK2s44VoL2LdI6v06/XctsnSUhnOAd/A/6SN6Ur6c2gst59hqYAKE9JNMhmtvv5fITw0V+Nq4zjlq5xgPhlx//hWEhOFgg+Fkg+HmJpPREO0aJqMhcQDbc5llgaZgbXg6afvehT+7cRX9WtB93gLquXjn/jyfWtPcmubIWmKg68+4Xru2BTgi39/J4rg8vs7bL14L49dv2webie7Ipl1InvRoCbri2OAQzqLDXk3Yto1caPnb9jj9i0fgdMPENEGDsOf3+esmP/vzYwrjWB2fsL23wXC0ReRy7ty8yaOHtzk4O2GYanY2YwbDhGwwIMLx6JOHfHhnn8t7Y/7+f/j7TLYv8d//dz/kdOe/5zde/A/o2q671jpoE3/DQNIzNN3iDN1DcKLPAXROcH3yHB88/jPK/AimrxBvn1Erwbw84p/f+qd8cusWp6bCjhzDNCKNI8pFQdk4dJPxxvg637z6LvF4wrJektmE/fIOe9mNtQQd11lAndLRdX2wCAW5u8PKHpGs3mFrb8B3vnPE9otHjO0bJMPX8VDi1ua+YZBd4Vr8CpW+42tGy2vUTc1SfIS0ZwzEa5SyZpwnxGJFqiRaZK3uGC4uBKp3Oq6f/a0jXmJBrJeAX9+vt848XSA8d9vxs6FgTedMc2vPwXFOc+w4/PA8W8t8jQoi3Lc/z2dD65eaBtxer/+QjmPoAaN3VPWg2xsQDhDKcXUn49r2C8jW+SUENgCRFK7XoNfPR3dA1kelJfDPf3p+fM9v34v9DHwRT/zVAuOnN3z6Aazo79+xDmRtosS6k3AdwLtR+ozrWruWdjGxa7+3lxS8v/1l+m99hf7eYbZuJXRg/OSXf03kX/7Tf4WQsLe9wccffsjmdsKrL2zz4itbvP3Nd4mShDhJQap+ATUGOfu3PPjkLr/3W7/P7x5tIY6OcTcuc3/8O4zUAOFsGC8HTnY91dpWMr6l+rqjznXj663A1iry0PLC9BUune5QVUdY4dCjBDs7YxCPmAwd29uG7WbKL9wxq8YgjOF3zQbTnRe49tw7xCrDCUEjahpriURKvip5YG9xefg80F9v50Rr55Zz6MRRRI9x5RwWI+a/aFgs9tm4WrGZvcEgeQPngknv+nnsRZKorzNQY7LBQxpb0+gNFkWJs3PK6ufIxSYJBZuDiNxA7JK1oj/n55RDhHoiraLWAmNoobSOHF0argfl0PM4AK3oABhh2iWRdtlxnaPN9THs3TXI1vyDDsHaL3ugdk+MxLp86d2AXTeAQcQTf4cPe+sgJFAIwsCokFQh20pzCAGqNXefBPfPELGO7M/e8ukbPsWCf+rh1m/1sy7KrQdmtbv1q1Nf/wCckJyrvP+pY4tzC9pny/lx6hIucN4KEfgGmWvabjeJAzo714OxpxzWAPmvmfzu71xlY7JB01gGkyG7l7bJBqOgVzmaLnrDrXGu8Prf+01eeO0Kv/14i607P8UNDX/HPOYnv/N7nKRJRy145cmFHo8hOWH9VXRtfYV2/Nbpqu5DJPDO9Nt8PHuPJiqoVofEwz0EgoEb8OaVN5noXX4D+Hd3f8JvD69yRe/irvq6EM5Zz99bjTE+F+u5zZf4b97/b/nuqxk78Y7HCtdqqX2lsWggqOMjbNMwr+ak1Qtce/V5fvynf04TL8miN+k7sfa+/tZSbBWCun4VmW8w2HiIkksqW4ItqKqC+OEp9SBinlckcUwsh7R+E//O9eZ8L0/ESYs2Mr+d0cGh1jnVXEcTdlSh8IkMLiyC/YrR3k+7iHT25BOvWdiuw7OWbnBPOO0+LV9aj7R1E7Ubj3ZVQmJduJmn4FIXpBLMByGsH0QsEl+WztMTf1HtqgUe8Zk/7XbnKBKxdgv0hIEMa65c09jb6/q8a3NrP34hCZNE9Ly0FP5RqvD7Zx4xPPh2//Xz9/fWarXh7GG+9AxZy8X3x5ASlBahspu/Wl9xr+eArQ2pyn/N5Jvf+x4vvfU6r339LZ578XniwTDw70Hzs677YU2jF1IxuHqd27c/wEYCcfkF5OEB1/7ov8AvgK22CB6GTAdGbTBY+6JbXD+fAiCIAMbW2c4Re2P4IrP8hMf5e4h4ilYjhJRkyYBGO07MMc+P3+A/ff5/zNX0EuLyKwgrsdbX7LUYbLliNV8hnG8P9dbe2/zXP/7nPCwOAsb1mh1AlCY0Sc68XDBfHBCXcCV6ifF4QpTm7G1sI0jW0sPbhbhduAU+rEAiMFTlFquTV5CMSaIBQw0DdY1s+g2ce5eTU4e2MVEy9vuJsD/QEpP9HF6jzBC9dRDmqN8kAGFY7LrY5nUwDG19bBfJAH0OJv7JOHAh3kq0Cg79dfXnDed0Gvc5sCq+jFTO92a+zIoQ3jssAqhoEWhn1wOZXgOTPorB114QtqcTPH3SrnCfBtzPg9/1GN4n5Wlj0Ff6+qzxedrRAvO5Pg/+gvLUVkBPOec6vWDPbff0u+z802ExNCEWep3TBTDO4qzsj+/wFAM+uqKjG9rjhIy137z219GddiEX8tXLl6LpfhrEgpZEeMlFDxJPbfT4hM0q1kDsr3q+f5fMwZqW3K7Qv5I8/QDngry/oPShSuevtT3ek9ryk9utWzHymSr4hVzIhTwpX15yRJsEsoafbbHf/h1tw6rabQOnteZI8sWE1/f5LH31q5L1c396AZBija39VflO0d+3Wz/o+hUEjmo9Ku3J7/0Xa4dttX4h1oJtwzbnON3wcQimP3cdImTfuScrpl3IhVzIs+RLcqStv7herZW6B6O+yUVPkTgcqt0v/LQUepukdr7O5f8Q2mO42i98oBbwP5t26HmffvO/iHK+fi3PJBoC6LXhLuthZee36+kD1zog1ugdKdecYWunkFJ8phXioKvT21ovF3IhF/LF5EuhF5Tuyei2CPC6J3i9io93xpxXCVuuse1k76z9LIz7Ky3rXOmXepJniji3OFnrQjWpdv8nK5atH3rNgx82Xj+WC46eZ2XfXMiFXMh5+VI03UgL6ga896/Vdtvg4labFd1//d/04Ny+2WvaVKv5/qra7V+2nc9fRNarlH3KqRaiB74oGH8uh70W+vXESdaO0X60dl2uj8RYp0Q84xBooHAf1vYFQtqynC1PbZ1AXai6F3IhX1i+HNAFmvXAWBeCO7owJv9x3yjFb+RBud3nqXEGtJkgzyr08izp8PwLK2efdZ4veP41yrR3Hn7BU39qQ/GprwQ+29x0sPsZ1+UCp946yUIY1BqN212sBKxZz7xZoygCrxAaneAQF5zuhVzIX0C+tOQIRyjpKx2EXOceKJ8sOQFdrB8OIdeqDD1hHnca6V/aaR407C/Etf6KgMunAfdXl/6I68WCPu+yBKDWFrJWOX1aRlsb6udsO96hMIyx57cN1INfKD+7J9SFXMiF9PKlNqaEYIau9ZDpc6o99HpYXScQnoIda7jgHWi9w607z1/iOv8ijEIfMvWE6f6U384nMj95INZG4C8vbUbOueIz6w68c9sKnlzh2v2fnjjjzl2jcyEpAl96EttrvBfRuRdyIX8x+XI03eBgcUJ0xYHbTKv2ZRbBjFXCdSmIXfrCkxxhu5/7NOj9ahe5doJf8UhPBCx0Ytc2ar8TYq3p7l9aBG1PrycjDz5PwuOhrbfalqhzEKpyii7lui+pvbb/ehyyw4cIXsiFXMgXki9J033SeeY/b9Nl29Llnps9X8X9nNn/1HfZH9vaz85O+6xr+gw9+quRAFDQ3l+vof6luhY/je9tvWOfcf5+515T7r5+WkxwF17W672thutTMV1IdL0A3Qu5kC8qX5qm2763UrSc7HqtdddzuK365zjPT0KnebWhr5/S6Frw+B8kaPeznVYQru2JrcS5358G6euFfOg19o5W+RxZX4E6imBtfLpU4CfL53y+rGP2k1TCsxYmIb2a3oWZuc8OObuQC7mQT8uXU8RcrDO3rqMFHHQtUnxR7ae/3H2tSl8jQLqwg2gddOsgd54P/ixZD6Z4yhmf3PpztxKf8fsXlfM1k55+aR6wuwKA63rpeU63W3j+Ytew3r3C/71WHm896qL9Yx2oRc8HX/C6F3IhX1y+lII3F3IhF3IhF/J0+dJKO17IhVzIhVzIp+UCdC/kQi7kQr5CuQDdC7mQC7mQr1AuQPdCLuRCLuQrlAvQvZALuZAL+QrlAnQv5EIu5EK+QrkA3Qu5kAu5kK9QLkD3Qi7kQi7kK5QL0L2QC7mQC/kK5QJ0L+RCLuRCvkK5AN0LuZALuZCvUC5A90Iu5EIu5CuUC9C9kAu5kAv5CuUCdC/kQi7kQr5CuQDdC7mQC7mQr1AuQPdCLuRCLuQrlAvQvZALuZAL+QrlAnQv5EIu5EK+QrkA3Qu5kAu5kK9QvpxuwBdyIX9N5Hv/8HknQmdN0XXYtOFvEEL5H6lQMkIojZQKKaXfvu1k7SSC0ETU+XafInRSdc43Z7W2AWzX0NPRYG3TdXX+9HX4rfo/RfjOH883Em2/suFzB5iwnwNhcc7iMGF7h7UGh8U5g3MG6xqsNVhrEZZzLVC7wwvRXVffV9Gda0oqhEPIJ/fr93WibbGqkFIjhEapCCH8mLb3JoRbG3vR3vq5xquf7u0ouhMKoVHtj1LhR3bNXH1TW4l0Yu1ZSJwNJ3G+9a1vvu1wGPycsGGcz99f/6T8//4f/8cfP7NV6wXoXsjfaFl/eX0XagCHlBKEBOkBQkqNFBqxBrg9IAhkizah83XfOblto+xQSuOcQTgPjgKFw4Ewz7hCDwQCEQBl7ahCemAFWqPVd25WtMDrUcXinMQ6g7MWKcE5QQsf/hBurbN238H7s8aq6/XtngCeJ/5u93M43woc3w7cX6fDWouUFmsFUkqklGuLydpi1J5aPA1wObdwdedbA/zPuranigCw4cm5sJR+agP6Pt79Yuk4D8pPkwvQvZC/0eJfkjVgC9qkkCr8eI1MCAVSIvA/IHCi1ZlEt7cT/bE8EAj/tluHcwKHwGI9WFrh/3Xty8s5ba4FDCHOX1u7nYcC22lwzoGUQdMV/m+/lYFw5U5afx3CghUeB3FYJ3HC9GvEk+Pk3KdAuL/gAMB4zdHftevuxeFv0eG6exFOgAUnHdY6pLTBGqBbSNrxc+19u6dfRw/CYh0DPwXOUq5d0NpV96AZ/nZ+TB147dybFd04P3nvDvsF0dzLBeheyN9ocR3gytYWRiqFUEGjRQXTVyKExEmJFa3WKcIrK5EtAHfg0GtdXhMWOAt2HWMlSCTOqXMA0Zrk/vcebFvQ6DQ3HKC6O1n/1wNVq1EHPVaIQCmAdRIpBda2x/TLgQdofw1PAtyTIPYpDdK1O9Kqzx5sPb72Wnq3GPjrk9hAlzzrOZ3XZJ++ADz9AP14PrnPE2PWft2B63lAbhe+ln5qF7Vz1Mcz7qGVC9C9kL/Z4ryuivSg6vnaVqsVnlek5W8DhyuF/1nTQAODu/bC+rdQBvARznlt2XmAt9bijEN0JGgLKq2G216gCIAv/Clbbe0JjdRhgwkfjoPEYQKQttRHuAd/BdigvYlgQgvRani2WyzOo8inNcynUQniCQBy69fbMgVuDZvD4uC61YgnNMdudDvAfZIH76+vR87+u3UrwRMG/vye6mjR1npuyB/B9Zp6sFtaWwaCFSNku73r+N+n0OGfkgvQvZC/0dJii0AghUIK7f9FdoAohUQIhQtg25nIrQba8qmiff16kO4o14AyMujWxhqskDgjwNYBlPvr6uliGRxKEhkcS05YnHB+HzgPkM527721zlO9ztMLQohgvoPF+sXFghPKQ7NwnjJxTQfi7tzxnybiib8cYg171nTFAGY98Lfg1YKtDXyz/1zgZFjjHHgapd9n3aknOipFdIvaZ4OfCAtS2PwcUPfPqtN6O168vZv2nm33uT+CDUzS56u6F6B7IX+jpaUNvJbrvem9o6x3mPWarneatRqnFLIHX6F6cHIigLkH5JYuDF+CNUhRewrA0AFP60Q6TyuE62u14vBvSwkIIbA+7GDNBPb3FlTJTjv0jiqHFDpwqf7c0imsIWwLFu/ww9nAHHcQzDpInQO9LhSg19rbP70mGBYjJE7IoDtKnOs1ag+8/hqxPeXTRYQ8BUw7M/8zkPY8ELYWytq+a8/M/7OmcdMr6b07zZ0D3DBqTz330+SZoPtP/sn/weEEVV1xfHLG/sEhx2dHyETx1qsv8+rzL7EzmRKJCFM3nJ6e8tGtW/z4/V/w4c27zGYryspQG4MxBuccZs16UGFyaylRSgR2yvNfsp1fa5PfITHOYWyDbQyNsThrsdb5z/2y7c2mMIYuDKZ/8HZtrfKmomzNOan8Cu3arYKjREiGgxFvvvM1NqdT/vbf+l3Oliuy4RDXGKIowmG5tL3DqigRtuHg6JD9g0MePXzIvfu3ee/996nqCicVLlJYrbHSeX7PWpx13bmxBtdYXFPjGotxDmFdoMucd9oI7/eWQuCEpA1Dsu00cF7jsGG2eH5uzQgOGlnrw7HdBOr9137eORAWgfIT0XlnkA2TvPfqurD2h+O2NqSwCCf9dHX9Vqydozp99MVn65cgqtVuuygFFSIXRKAYesagnYuyAz0PwH5/D8RS9MDSRhSIdfQBpLMYazAiOGqEwFqHs3YNNwI4SAlt6JR48hX3L3+HOUH7ap14Uoow5i3vawPPC8bhzePgxJLWeieg8M/aR0bZ3lnofIiZfQqd0N2jCyFzCD+XWbvkjmMIIBs0WT8/wyg7D2zWeWefDPPX4IFdBnB+Gt+8nnLg+eF+iegg1HXDGs7nj2WfBOXzd9guB+Fbtwbv68Dr6LXxZ8szQdfWNRaoqgrnLEor0sGQrZ0pV69c5bkrV9nb2CSLMrCWxWLGdDxiPMwYDjN+8eEdDo7OkFVDLSSN8bGC1vpV2ViLwE84ZQUqcFla+gcjpEAL4QFZahASh8VaidWWumkwjcEYi3LeG2vCqtoOugmPwwmHcfKJMJneTJQIdKRJ0hTnHLt7e+xdvsp4POHS3mW+8fVvsrd7GR3FHnSEREUKWzfESrJcLUBEOOe4FGVcvfoc0W98j4ODx3zw8S/5+KMPufvoPqfLBY0UNM6RlwVNXYPwmoR3yIAQ3qMupPSLVff8RT9pWk1BCHASpEOsza4O/DqHTMARQfe5V2Rcx6+BC6ay8y8e4IS/Jr8IgbMB0Akg1I2jDJBqe5BCdVqK5wq96R3UC++Q+nVLiFDoAFcoD6pS4sKYCiF6aoE1DVcIlNIoGUA3ON88vaA6LRe8+elcNzm9mz5otjYApmufSQCUDuTbmODuWL1m3ALGp51c+HcoKBf9nuBjVL0SYtprFdbPOylwVoWTNB5k2kVWSH8fTwzhU03qXj30M6JVnALgdtpxGz7XLUwBBJ0PI+sjN+gWgPXznudtaRnb7vtz19ZaLO0R1+Zme3LveFzXdHvFYm3wO+A9N4PF2m7PkGeCbl1Xfn6YhjiKmUwmjCZTrl7d5crOJXY2t9ne3CKLUgQwGg1Ik5g4joijlEGS8cEnt3n4+Ih8VVI2II3ACOEdCc5PGisExvrQGyUFTjmUkGg8mEgEWgqUklgk1nmtQAmo8BqztV7rlWFi24AkJgy6dX6ltK5zOQRT0R8bPCC9+fqbvPr6W7z99ltcvXodkCRJjJSSs9MF8/kSpRRWSCKtURIWRckyzzHGUDcNWZrQNJAXS7LBgG997Td46/WvcXp2wmq15N7BA37yi/e4+/ghK0N48Ux42AEgpcTHdNIuyrRLdbdQCK/99ppO//Sf/NzPNX+/SvQT1Mgw0brwwn7CeaCWHQXndep2LG0IDwpjt3Z1HeB3J6b/1LVWnOCLaAVftvTUgudMpVRI5UHHiWBGCq8AiEAl+HhSjWwD76X/ly7KQfiIBxQErVOE4HpvNguENeAinG3jaQN4uTXOUrS0xVoMLt4EbzWr8/rckzfXPx//CGW3vRQ+kkKiMDhkCN3q6WHntV7nr7u9FwF4yuHJ2NXz4lpStF2wvVIdlIQwLtZ/56eeCRanCCaqvz9CVEkXftdabZ+aOr3+CWvabhvudW5Y3Hm07LUahFgbz3ae9jfif1sHaQjqBuFaPx91nwm6VV2DMSAUSRKTZBlpmnL10mV2t3YYD8cMsiFJmgIQRQotdSD8JSrSpFlKkt3iwf19ZvMVdd1gjKABGuuwWHAWE8wIg8QhPYkuQBjnTSxr1zzKIAIIKysRwjsFjBVg/PF8GKRA2uA0wANua7KEBRelFHGk2d7Z5dU33uA7v/E9XnrhZYxtqFY5i8WSoqqCp1ehI01dNzTWMRikICTVasVildPUNUppjo8NVVUyTBOGoxHGNJR1TVM3TAZDvvHKG9y4dJWb9+7w4Z2bfHznNvPFAussCodwntqwzgazvn0R2oU2aEUixDsG7cO1plfQSjtHS5gzCojaV0+IzmCyzoXtWx3ZX4PnJK0Pawoah5AOYbxfnE4bdsGcfEIbgzDhWzN3na98Wt7TVy+tZtsCrpDKUzYeXfw9BueONzBkANvIa7kBeFvQbjlYhMRzmO2CKTsAQ+Izv6xCWt0vuEFhkGHRJVxCqw2uZ4I512ZHrQOuoNXW/J8ixA23c6jVGp23jAhapwvxx0IFnlj0dBLr0QJy7ZkFTf1Zg3vOxBedht6PSdB8bTDiRB+Tu+6APO84exrgnjspnQbhpKfvntB423tqR6xd8NoQu35MwzwV3WxmXb/tDBfh40N6Cu3Z8kzQlVFE3VQI4dDRgDjJGA3GTEYjxoMhaZKidQSR9pqQyMiALWc8Z2UdTgqEkkRacf/hPrPZgrJsEI3AGYO14IzpAvmc9eaWcQKFpUGAaOhi+gJ31r7ISnktpV/9BdbKkIhj2veGFrGs9YAjpUbpiNdef5U3X3udV195nc3NbUzjODw8xDnLarmiqBuauqZoGpx1pGkKQmBsQ6QikiShrmuOjo5oGuu1AxyDQUZZlKzKitEg9hPQNBydnYKEsqrYzCZ85/V32JlO+emHH7JYzClXeeBYLQKJoueYPVi2U991k7VlV0Uw3VsFzbUmfTuzwu9CgCJEZApPDNju1einpleOA78uPOzYAEYuOF0kniboNOuOlnSBgwuLnguB+CHF8wnF5Ncmcj3jTGoEOtwP+BduPSVYIpSnEZSSAXB1oBja1NagoXYvZ3hZgznvnPXPSAis9Fqyk95OsNYDvNe8e8++z959kkZo/4Y+K63l/vvTAgF4A1yEtS/AxDnzXKDCc2xTiNuFGGjjdxFhQWFt1pw/X3de6Cy1MNoQIgfC8tBpkc6Ghbm7HOkViqci7LMmj8M5bz04IXFOduPYOulaaqO9hhZk+wXh08C7Ln0MdeDs2+3FU4fhU/JM0E2TDIyhrkuEsygpkUqilSZSGq00Uqv+KWuQiSYxKaPxmN2dmsI0FFVFbWqQDh0pzs5WVEWFqAWqMdQOGpw3W4LG66z1ky8MhnVeiVXST3avmQikjIKm4ENLtI6ojZ8Mpig8neC85jAej4jihHQwZGNzi52dXb729te4evkqWkVY09BUBYtVjnWWfLXi9GxGnucgJEprzpAhO9RrOJGOQSmKPKdqGmyYkLOzM3CO0WTCapixOfWLlZKCs8WS2ji0kCgp+PrLb/Lqczf4yS/f5/2PPmI+m2GdQEUxQnhrQwrB5sYGw9EI1ziOjg4wTd1pLk4I/14I/6b4hy/6yd29rJ4v9g44Fzjd7s06pzdZ17tgWs1XEVJPpeteqpYn9taFw7q1VEw49zK1FEPP8f56pU09lcIDrp9zMoBtr90JIRFaobQHWimkX/C1RqoILUNYmWgZweAYdn4EnfD+Cw8CwXmqJMJ4sLYWlHSdid1ql9Zanz0maXGPXtt6+vj14+2vX64BQhuL2odKtc6f8FytP1EXmSoCBy8IYIbX4Nvvn+RE166pZ0dl9yNE8Kt0eNpeRw/IXXQG3pIT1oFa02Cfcsf9uLS/OoywCOcd7dbaNY29nX9h73PDGBSITlFrFzJBTzU8IeJTvzxTPh90rQ3AZRHO0dQ1dVODc6j2FbVNeHFt8IZKkiRhOBwwmQzY2dlkUS6omgpwKKmYzxaoQlBWIVrBWE8PWOudSmHCWevnrfc9OIxUKOeII69ntau1UorxZIM33voa2WDA/Qf3efDgIaenJ1Sm8cUtZMzu3lXeeedtXrh+ndFwjBOKsqpppKUqCxaLJas8p25qTGNRUUwsBIvlkuXxGcJZ6rpmMByRDYaMhqBlgrWWpqrJy4pVsaBpDFma0VhDVRas8pzpKGNjMmY6GhLHCdYYtBQ0xpIlQ77++ttsjsd8fO8+R0eHLGdzsJpv/8Z3eev1N3jt5VdZzub88Z/8e/7sz/+Uw8cPvBsa12kUreHeroN+0aLXWixYKQL37RBdXGcL092v3qERUlVbs1NikUJgVatVi/784DVZGzi8c9pCr966oIbLLzZHv1RZB7m+MAu0QNCFbEmfOKGFQomWw40QKkJIDYFSCFZzCLfqwfOcI0x4R7GUPsPNSYVSLQDSO5nwC1pLMbXo1oVpdR+1x3ZPvPdB33b+HesZ4DY1GFqw8+cN4WdOrmmh4bidOttblD78gB61xJPGdbC+UH5shF90vDXQ0x3rIXnrencfprfmn/hsBbfbs6ULcCZEhphO42213fMLVquprqkcT43NJSwI8txl9DbiF1Mingm6Wkl0FBPb4NUUYEzNarWkKAvquiKpK1Qgvp3z6qi1DVY4nBZESUQ2SplsjNhYjSmqHGsNUjnUXCGKElk2qNrQNIbGCYQxfcokDmN8MLmyDqUcDo2SDin8a62k4vkXX+Fv/52/ywvPv8jLL72CE5Kbt27yySefcPv2bWazGZcuX+LGtatMp1vgLGVZczZfMhpkHhgXC2azs8CnSQwedGkaVnnOcrXCNBVKCKbTKdb4AiI45518tmG5OKM2NSqKPSdrDVVZooRgKbwHOFIKhSXSEcbUCCCKE5Ik4+o3LvPyC6d8ePsTHj18wP/8P/iP+OY3v8W//oN/wb/8Z/+UWGnefu11nIQf/tuc07NTz8m2piV4/aJdD3G4EJvjPw8UjiQUXlmbKh5j/fTpHJAW6ySq05D9BqpVVWzQaoNZ6tWY8Lddf0tsoEYC8OK+6Bz9UqWtpeBjWoM2Fj73fK6nFHo6oY0mCE63kMWGUAFI2vsS50ClzfLyjiTrFxzhAddKhRY+JLJHl/ZfjZI+KgdpAu610Sn+POcWtKcMaguV63jS0k8hbgchDF11r7BKi242tVqeCg6y4FxboybWr7rVqrvU6i7RpAfW1hmMCBAm2gvr457X6044XHcP/in15r0/p1hbG4LF4CwO76Q2tuXbFdbZoP235t3aHO1vhw5OO01EdJ89ZSI9oTF/tjwTdE1TAw6tI3Sk0VqBg8ViwcnpMZMsJYoUaeIPY43BWkdZ1+RlQVlXGCxSCeIkYjhMGU8HVLbGSVCRIs4jilVFsawo6xrVNN7JRgiHsR4AGgcGixYCYSyNaZBodBJz9bnr/N7f/j3+5I9/wH/1//ov+O53vsfv/d7fY2tzl+u/+zz5bM6du7epbcNqWfDg4SPKskRIwSAbsFotefR4n7KqWC6XaKUZDkcoqVktV5yeHLFa5pRlhcAipKQoCgaZNzWd8VrycjkPHLhEWEtT1xht0dpRVSVCOAZZhowiojgF5ygrQ1XUUNWkccRysWCaDfjWa2+SffM3eOetrzFMUv7+3/ufMJls8H/7v/5n/Py9n/LNb/4G165fZ5EvacoG0/JY3WSBNmxOBPLXAdI6lHBI6zVO69p6AO0+YT9HZypjvONOKhlKvbRONv+vEfhY4nY/t6ZRObpJ22rF3Uv6V6Kac+v4ajlnrxV6Z62n06RSnlprQVZIVJsq3CVQtE4oOnOhBYvuI9YcUsJH5hCcd53pam3nn+i5QxUiXFz/Zj/xhnsnXYCzp2iDTzri2hjtDthcu29b0Kd1yhLAqz2o7KHnCfXTtdpqd0wZ8CzUrehGnDApgsNMCEQ3c9tF5PxNiPZ8Yl29cF2UhOg+J3wexluYEPFkOh1dhuP0zroedNfHrud223O0Vks3qf9S8kzQLYolUiiU1iRxjJaaxjQslgv2jxTjQUqSxmg1RAiHqS1V01DUFas89yZ5vqJsfJyvjgXZUDMyCQhLnEiKPGaZVKxUyWpVUJYllRAgLE3jg7G9xuQH0hqLFQbTQIXgyt5lvva1b/BP/8v/ko9v3cIIWM7OKMuCr3/z+wyHA7SSzBcrzuZznPPxsXmeMxoNiaKIYpUTxTHz1QqcZTQZIZxiVRacnpyyynNWZUFVV2gpSCKNszAYDjDWUBZLVOBylVDkZY5z1idc4NDRlFgPUUJwdHDAZDxiMpkyGGSMN7dY5QVVVbBcLoh0hKUmiyNeufEi0jlOj485PTrCVQ2bkym3b33CD3/wh7z+1jvk129w89ZNbGXb4IA1I7LVQEKwOXTB+O0rYMN25wLEnZ/MNtRfbYTngFUbLC9BiT6ioQ3X89wZGGc74PXah9f0Ws1GOesjWJ+MtP81iJSi18i6xAYPHlJ62kqqkBosAigJFdKB+0wxKfvMM9dqUQ4CweO5yTZszL/6ICxCSSRRiF1v6YV+0WzfbSlFqPcKfdFa0fGsvXNI0K90/XHafzvga7VeWuANERciXFuwbHwkgyR488IesptZnSYcFh7abD78eLZHCat0d852kRHd4uJaHO6vq1OZz4Ow18W8o7mLIT+3CLm1bQ3WCqyQXQo0+NuT3aJm17ZftzJEd77WwdYqEG0I218Ge58Juk1TIYQiVhKtFZHU4ByLsuTg9Jjt6ZjN6YRhmiAl1E1DWTeUZcWyWHG6XLBYLVmtlpR1jqFCxY5kqHAyJk4USdIQ6ZhIaLSULIVAygofVCZorMGZEJsYzOjGGISAwXDIa6++zr/+gz/g1u07aK2J44h5XvCjH/8ZOh5w9co1pBAcHR+zKEuEEBjTcOXKZdIkYbnKcY0B6xgOBjAYYB3M5wuKsqSoK2rbkGiFqxXGVDSNQgKJjojTjNVyQV3WWNOwXM6oqoqqqUmSFIGnaDY3t4kjzXgyociXzOYLhBRUdU0UJ0RxzMCNmC9X5IsVgyTio08+IUszZrMzzmanPHr4CB3FTDe3qJuGg/19bly5xunpKUeHhz4j7/ycW7OGepoBPN61YNxppP18hO678Go4C07ipEAjfOJASOlwOIwFY51/Xu1EddI7gVz/0rSZcn8FmAWAwBsGIG1BVeITHVSr3fY1dLti2118byhy3tZbAFr3kWuP74Lh7xr8H22SgOtMYSl9EW2vANs+K9K1Wq/swMGJ9jvRnbPVWp+CGeeljYgg5HCtGx9CdgvGOjsrggZ8roQiijaJBwQujINcX7jWNVYRtNgeewOWBn+BCHOlA+Ewbmv311oQrlvCXTe2nxXz7a/ZYGyNsA4lbFhcWItS6bbuLZM2fTosMn6sWw26jWhRXfTIF6m50MozQXcwGFHVBcbVgEUpQWP8lMqLilWRU5cVpq4RWmOspWkayqpmmRcs8iWL1ZJlsaSochrjQ790ZBmqGBNZoihCixhp24kLMlfgSqyooRGIoNkaazHh5ddC8sILL/Po3l0ePHrMcDggyzLidEA6GjEYjDg6OWRjMkFHUSDxHU3dcOnyHlmScHo2Q0rFapUHIJdoJSlXS0zQzuumZrFckC9zqiL3CRHKZ64Za8D5oO75ck7dNFRNTV7m1HVNYyxKx6xWK/b3H3Pj2jV0FLE7vUK+XDI/m6GiCKFyBoOMJMtACJRW1HnB+x+8z+ZkzNliztnpGdPpNleuvsBgsklTlcRKs7ExZXtrl9V8jjGGpmloo0BaL3HP27ambkghps8yM62JKVwwv1oNxnX+BSttp+PYYITi+sSKrv6pwwPy+XihTvMFF2KMv/A8/dKkd+JAq7m0INinBYvus96x5mN1pVBdCGOrvXWpIaK3OsLZuvO2VMz5i3Eh21GEhWrdtPVZcJbzXvjexF9nHMK+HRPRUxxtQsunuV/Ra730z6o/5loJyZAZ6ULGYR/jqzqNvxXZEQdtYSBvxXZYLELqcktl0Wudn6VGnou5dW2MeQugT1IBfuFyzoewImxvqYRr69Pj+2fVF/lpF1LZj2uniLTH//R1PUueCbqbO5dYrRYUxQpnTfACGp+xY0ItzjBJlVBo4dt9FKUH3VVesipyVnlOUeWUTUFtS4SCOFagI5SyCGNxRnYTTWr/8FQhKMqaupHUCKQJLUWED/+6cukS7//sZwyGQ8ajMZPpBhub20w3tkgGGVpFHJ2dIqWPULDOMRmPsXXF8UnF3t4eRV5ydjZnOBqQRhEP9x9jm4Y0iThbnDGfnTA/O8Vai1KSNIkZZymTyYTxeAwItNJMxhMiKSmLAlN7rdeamtXiBFxNohXHJ8ckiyXD4ZDJeIQej8mLFeViSVMVWCE6KsUYy2Rjk7IqyVcFr7z1LulgRL5ccsNaGtuwmM9RSvL66zA7O+Hs5MinqyJC1lhwsD3hjVa0BmSY6CG4u7XmEMG4DHPQCdEl27S8pYAuF94KXzhFWokVFuXaF1d6Z12vP7TKCr3O/esVzwB0rx0AQsoQmijPAW7749N9dcfnStcXummdUz7d2Ycs+UWwBZPWUec9+211Kv9u2U4D6wzsVrttnT1rBXHOv+PnR7PlNNuv3HkEDd+0KdkuXGtIDw6LUGuRCNdq78LPGbGm/XqzoHMutjw/qPA622DxGED7xVz4+Gd/GoUT1hfsEcrXFml507Vb8PMpxNLiA9BaP0V7f/3C13PSPUB6+ksQouLCV6L7aQG1XyCFU74+xTmaZo0K4dML569cZWxzc5skHTCfn2LrgqqqvAOrjZXVkQ8KR6KCuWWso6wqVkVJUZTkZcmqKCjqktrWGFd7T7AUiEhiG0mcOmwtaTMi4yQmjhPiIkIvc/JViRRQSxEy0xSDwZB8tUInKbvDCVsbO2xubTIajtnY3PK586bm9OyEk6NDirpmY2OTxjTMFzUvv/IytjGcnZ6SpglN1XD74SOMM5R5QWNqHj96SFmWpEkMSGKlGAwGTCebxEnCYDDEWOfpgShmNBwTpwPi45jlculBR/gQlcVyjhAOaywb4ylZcoPJdIMkTakGFSdHhz6hxDmWyxUKgZIeGuNsSLnKSeKMq5cvU5sGiWS1mSNNw3Q65e6dT6jLnNVqgbMC5RyNbY0wP1lUpwX4ySLDC2WlV4f7aSeQDowME9v1k7Ofr2Fr0RlgaCXAypChIzs+rtWyO7NWCHqHyK9b2pc7OH+k6OZ378jqgRcZed5yLfOyM0NbbtTvRZuF1ufzt2DZ88HebDfnzWjEued2LhOt/a1z8vSm7Xo92vPaoGiZjPaRhJC2T2/rz+6Lt3dAvZZg0N4ZwlMKCO3jmhGBI/WftckyTiiEc4y3X+fS9d9gvDHFlScc3v+ARwcfY5pVAOQQmujaEMU+4+3p+u7as4Pu+vw4tvUhWu09LHVOYlzjFwHbJll1y1tH9bSj4HncNqtwbcES4jPB9VcG3WwwJIpSpFQs5icsF3OqqgIBUeQB1wkfz2msozaGsq5YlQV5UXjQXeUUZUHZ1FgMSIkWEcJJbGNxTqGlItKSJLYooWisIUkr4iImSWJmesFqmaOrUBxHa567epV4MGI83WSysc10c5vpZEQcJ6yWS06Pjzg+OWZVFmitieKExXLBcDjktVdfoywKTs7mDEdjqqrg3v17lEXJcnHGYrkABEkc8fprr3vNXkqasqKsarIsYzQckCUxMkmxztGUJUVR4NwYIWF7y5CmKY1rOD46oa4qH1ZW1yxWc/YPDyirkuFowmg0wU43WSxmOOeooor7Dx5QFwXD4YAkSTk+OcKZhutXr/Pa2++yubnJtvD560Wx5Pu/9Tuczc4oihyhBHXlY6Jb/c2/KK7XMAUeUIMbW4pWlxBB2+lylp7QBMKr7hyq1YTw2pGSDicDSDs6rVbQphoHEA+Ux1+F2gvQ3rXtONoWcD0IS2+Khjhc0RXGkaGsY6gC1qbt0Zv8bQZab76Cf5kDdeOsP28XuO8B4zxwug5Q3JMg2Vbrco4u6aXdpz3KmvOspdgIzwdChua5yAZ/3HU46vE5zKauf1xYOFA4oUjGu2zuvsyl6+8wGU2xzlIXC+4/fEhzsOLN8YvMasGfPMr5xm/8p3xnKDk5vMPZ7C5nJw85Pr7DqjhFqYg4VsyXx2AKpKvWtOXzch4AQ4RHlzovQy3eNXi2PrEdqZFOdpqqn+Nyba6LdkT7ok4hRtfaTwPrOs3zefJM0BVCESearBnQ1BWr1ZLGGu/NVRonBdb4MnW1MdRVTV6ULHNvEq9WK1arnKKpaZxFKIeWConGOYWnH705IKXzhXLiBIslqRPiOCZLEnSkiWLFallQlzWRjhlkGVVRsbW1x+6Va2RZhqsLjvYf8/HNj1ksFlggywbUxiBUhDOW8WjCwdGxpwrShJOTY+7evct8PqcucpTW7F25xje+/i7f+fa3uLR3GaUjVnmBVJKz2YzDxwc8fPSQIi/BGbIkxsWa7b1ttra22NnZJk1ShsMxKtI0dcXJyQmf3LrD+++9x61PPuHe/Qes8pIrQjEcDplOJzTOcnp6ShzHbGxMefhgyf7+I8q8JE5imqbhzu2bHBwd8N3v/haTzW2KPMeYhheu3eDS5SvsHzxmlee+kEpLBwjRqTnrSU2tudZOyxYkO49z0Ix6OAmmYwDlBhHANWiC9Hn1zrX56D6+UoaIiJ7C+KtBMLSZc14j6iMRfJZa0F6lAtU2pexrNXjtrg35WjueawHX+AUtfHfO6UUActdW+FqvSeBTv1s31TrofioSwT2pXYlzANB9I1prpk2MEd1z6nXJFqF6E7slFixtGUtA+IJP0nm6IBnu8cqbv4+avsTm9h6LVcVhUxIPB5SyYv/gI148fED+8Z9w4+W3+GV+yL/+4H2u3niJN7ff5Y3nvsc8XxAJGCnBJPVKzfHqEZ/c/RF/+Ef/F0x19Klnd37RdoHC6aM5hGvjdc8tQxgrw+iqri5y6/htF0UPwG1SUA/uXsFoTYZzp//Cs/nZBW+qGhXSfJXSaJ2QxL55nURQVTV5WVKUFToSrMqCVV6wylcsliuWyxVlEWJIJaAkkYpRMvYV8513XiHACYuOZdCs/bGTJKIyMToRxIkgSSNWiwJn/AuRpQlCJeTLBU25oljOuXf/Hnfv3CHJMqbTDZq6QmpFU1dsXbuOEIIo0qRJyv7+Y+7cu8PZ7IxYKS5fv8G3vvMd3v36u2ghOTw64ujxe4ynU6rGx9qOhkOGgyHvvvsuTWOYn50iEMRpwnAyRThHWRQUq4L5yT0QktFoSBrFvP3G63ztrTe5dfs2P/jBH/PwwQPu37+LqWsuX7nCdDIhL0pOj4+QSjMajcmylKODfQ4OHntrovTheEWZ8/qb7yKAPM/J4ojNjS2UjMAWmMbPBP+SnM8Uav1bXinqYxDE2otpXeuI8y8oov09HMTi62q0jgzwYWUiBLGHSW+Fz+AMLBwWh0J4HvivQMiYT8m1PrdBhljkrp5uWwSnL/2oOkAOhcyF7ECuB0bf4rxd6DqTvgPdNgkBekdUeFbSa7xWVIETbWOfP0PzDT8txK7zmGBak6KnBtYccO3zJ2jd4RBri3H7sNs45rDoBg1fKMX0+vfZev47/OTuIeXRfXjvI6yKKbd3GPGI4vgQcecB4tEnPLp3G/fjH3JgYg6muzy+85DHO1dIpOGNl17ByAF70w1G0y3QCZM05XvvvspsfsaP/uQ/6yia88+vVxp8FaH1+NvwXERwHbb3jvF1WZDBudfW6w7qhmsBV3bccs8Tt3yz4Auj7BPy7JCxuqRpoK4bn7KqI9J0gDEN1sJ8ueLo9IxISJJ0wKoomOcrlqsVy9WSoqioGoOTnp+MkohEx+AkTRMKXAuf6y2lr/iVJJF3YmhJZDUpGp1AHEmiSKOjiKbwNRzKMudscYgQijSJqcqSR48f0ThLLCVVXdM0NeNkxOWr1xhPJkRak+qIw/3H3PzkJkcnh+g45u23v8Zv/9ZvszGdcHZyyuzsjOVszmy+4OGDuxwcHtAYQ5okXL58lRdfeYVsMKSuG4bDEQ8fPCRKEt5/72c8enAP6WA0TInimJ29K1y6dJXJxhZRpLl2aY9/9L/8h/z7P/kRP/zRD7l99y6L1Yqd3UsMBwMiKTk9O0WhODk9YmNrh8ViycGjh9S1J75v3/6Ek5Mj0jQjTVLSJKOocoyxFHXdaVhtDdK26pj/O4CBC4474TrNwAXKoTdhPT8rQlIFSF9824LPrPKTT0kPviqYdeJpmpgAaYM53xbA+TWLNQalw4vVUgodga098AqNFr6oTR+D6ljPIOvSTp05d7/AWk3i8LcLWll4Br64C2Ghat2b4bl1YXvteey5Z+M13jXqZw0IOg2t/TZQPK1DqXNxtpfr/Da2+2mvr20V72N2PQ4p9O43mY1e46c//RjXLGlqh8tXxNMhy6M76J1NDh4dk+Q1Q0DWNUenSxYbV5lEI9K7HxH/8gfUWvAv/w1w+RXklTfYuv4ir17a4J0rz7HXpPzmt/8x9+/8KY8O/qzjzlkHUdcWhWrjMkJMuPPX3XKyQthugTSuoa8Hoda03LBPuzCGRbFtrS6EQdK2TfrLzblnt+sRgqrKyfMcZwxS+Ow0nKOuK2bzwtdfsDAa1BhnyfOcfLmiyAvq2uDwKbJJosmShFgmNI2hpgLh6+YK7R+2VAqtRGgMCInyXJGOPY2E9JzuwlSUZUFTOmanxzR140Ou0gHG+mPa2iAHgsEwY2Nzh73dXYbZgMl4zEcff8Sj/UPmixkIydtvv833v/dd5qdHzE6OqcqS+/fvc3i4z6OHD3hw/y5lXYJURDri7r3b/OQnf8J0Y5PtvaskUczZ2Yy7dz4iywaczRecnRwxSGK2t7cwBo4ODhmNhjz3wksIfQXpLN/99reRSvGDH/wxB0fHFFXF1uYWk8mYzc0pQjiKcsBiOWd3d5f5bEZ+eozWkjTLKFYLytWKE+fY3NhCJJqqqRDCJyzYAKTtWyVCeVIbOFbhWofDGs8nCLxXoATC90K4zhSzodOFE77Eo6cWvFFsAnDIMNH7LCoPIm3BrL4c5K9XjG1QtvEp2622z3kAbiMYRKvpEcYgtA13LQ9rHaHqEJ/WhPwYtDVrn+RRzxWdwadZOyQG32Gid3q1x1qLCmn/s3ZKf5kynNn2n4YNTBcJHPYNgNtqz62m246Hcu0CEzLStl7iRF/iwa33UYlh8tw2pZXs7DxHJuCsOGRXxWxee4nZ0WWOftRQyYriG8+z8dbfwv7gj0hv/YQIB8+9RjaeYOenbJU/4ocPf8rd51/nZ3fuMc5Sdnev0Exehv0fBypnXQs/P8TnKoWJ4JlfR0cRymsSigk5gZC1L3gkdBhg1dExhJEWa+ewbeaaaHn6tcN/AT/FM0FXBa92UxdYY0PgMyAk1lmKsuZM5kQqoqw87TBfLFgsFhRlgXUNSkkiLYhCYXMlNE3jQnX+UIBcBE+99NWbOgCOhc/YUb72QmMsRdFQV/5lSeKE+WxGYy3YhkhpitxX8NrY2EAryWS8wfVrz5FFmtVqxcnxCUdHR6wWM+qq4qUXX+J73/4uSTbEOcEnn3zEL977Obc++YjZ7Ji6rnx8n5RUjQHhY3JHwxFSx5ycfUBZl2ipWK2W3L9/x4e1SUVd58xmp9y6fRulIybjEVdv3+LGiy/z6iuvUjWG568/z+HRMb94/30ePXpEnhcsV14jLiqDs47JeJOT4wMuX9ojXy1xxpJon0FXlBXWWUaDjJ2NS36u2LYgej9dOmbA2jVXjThHGfh3t1XP+gB9KxxatDSE39LSlsn0cOqcr10sgznXJcEGK1uKDpqCLtIylr9esVisa3Cuxrm4hbFwZZ736xMG2pfdF3FxTnTj0SUxIFlTPDvpKYjQ9iY0i3RrANc62vyIhgw30a6Dtj+maxc02zk8uwgDERFpiYoGpNmUNBnQOEeSZNSmoVzMyKsFq+UR1CtsRz21ziOLwYaaHf7T1mJqt3Iiphw+z2NrGU4rfuutTaaqRrmCjyJJXq34/tYG16Ypm5MrmGbKj69tk43HqMuX+XBmyS/9L3jp1RdJrWD/pXcoxJSHj28x/OinXHZnRHafo49/xrHe4e7yFfaOb6NpQ9rkWnzCedV+LcqYPkyPLpqirT/S+TTwcenW1cHi08F6a/nsvvBpFwpI+2YFZWIt4sF9AfX32ZquE11l/LosvLklVChYo4jTDCcilrWjNgswNbP5jMVqQdPUvvSi0uhIkkYpWkY93xLSL4USXalE4XyAvsUXGlFKIbTDCkncxESJIUpKVOSjEVI96Eo8zlc5xrrAERtOT0/YvbTHxmTq60FUNa5xnJwec3R6zGK55OrlK7z26mtUZcnHH/6S/UePeO+9n3Dzkw85Pj2jKCtMqHomhV840ijGaI01llXRdouoiZPU12AwDXVVU5YljTFdPQrrII5j7j96xMPHjzDGcmlvj/Fkyo3nnuNg/zE3b51y6/ZNLuUrNje3aawjTmKMaRhPNzk82CeOY1Skqaoi0Dy+DF8cJyAUkdbeMg4LY4eh3YIvuvc7MAPBnGo/OLeu0/ZIsy249lMaB1jn40uF895sK3y4mutAJGzrfPNER4vz4q+EphtuAmcN53nM89u0Xv+ubkJbfrANkwLWedGnaTw21FWwYSXqnW6eklnnBmwL3ghP2eCo/cXSaW8OpIoYjy+xsX2DrZ0bPH/jVaJ0EytSJmlGMsj45HTJhpNsDjPyJkdQU8wPeP+jP+XjD/+Yk/1PkK4OdMJaV4h2gjwhzeAyD1czkg34zrWUr0cL9vfnLBclm+KQq8MENaw5mN9itZXzwDV87c3vUSUjFk3C5rhksJjz/N/9nzHYGNMUC+bLmo+GG6zSa7x7opGzE3Z39liYmqPhnNnD+09chWgfzBOfrX3X6fJtfC+dNbM+30W7IArTGQMizN++gH2Yr6JXZdYdnXTP8NPj9aQ8m9M1jXce6BhETt00aO1XiTgZkA18/KpwjrycUy3nzGczyrLAYVAqJo6Vj7uNEpTS2KpBOokSCqssCBtifP2aaq1ENITYP5BCo3weBZFyKFVibcHZfEayNUIrDbbGoCirio3plIeP93E6RilNXVe+l5oxHB4dcf/RA8qqYjKaMB2POT18zJ2PPmC1WnB4eMC9Bw+YLxbM85xlUdBYhxKKKJKUTUOuKrSQxEqjtHcOqZDVJqSiLAqsbWgaS9nUvn5EiJetGm/GmtsfU+YLnr/xAltbu2xdvsJ0OmE4yKjrmkcH+1R1zWg8wYSmnkWxYjCecHx8SKQ1p6cnvo1S7DPj4tjzx5PpFFM35EUe+pn10s0HIeiCyR3eDBMEjj2YTiGhouupRgu6rQc3HNH5Ty3W87TgNQZEaLgpOi3DAiI4z5zriwv+WsWt/9v3HKOlW9r0Z+crrrWxuXJNu2k5YCds4MjpeV8bKl217alsyyuKUNjGa1DrVQV7kT65gjamVwXTVjGebrJ76RXefP27JJPrlEZQN5JjLPunFZvG8jCr+fCjA64hceOUXy5L6ga+Pc7Q2XU2XtjiH7zxd3lw78f84R/931me3PR9yQIQtbh0LnwMxSoasnIl192E1SrlX54eUa9qmtzgGsNVtcfB/Ye8+eJrLH75kCvX3+DjW3cYDIf8eSypJxvIyykH7pTm9vvohweMbt/lzePHiKrhjnPspJvoKGYiar4xGPAjrShLHzbmXK/fdkDaXaN/eJ2z68lH7c09ED65Z32vdtF0+Fq8UlgEkT9WV8bSD4pr/0WsrU1dnuYz5ZmgW5YlcRyhdYTSCcassKbxPaHimHQwRDpNXVWUxZK8qFiucpqmQSrQCqJIkMYJUZTgrKNpbChF6I1UKR1K+QnmrG+/Y1r9y4XV38lw0/7HCf/yirAU6VC13xf1dmxOpzgU1kJtLdPJlIf373P/8SOKsgRncbamrgo+/viXLBczVqsVx0fHnM3nLIqSqrEMhhOklDShBVAifS0IYx2VMMRWoLXqCrzUVekLvxhHVRssgsFgTJLElFVFvlqxKkpwYMxDmrrm+OSIjdNjJtNtnDHEkbcglqsch2U8HNM4X3ZvYzom394hjTSLxZk3fKxjNBwyGg5x0rKxvYGToGcztBCg/MJgQucLhJ9bxvrFwIf8hWydsHS3vJ6UijbsqdMaQqpwy9V6S60lE9oJHDQIWpPOT0TpPFXRmq3qi6gFX7qs6e5ripMHQnEuztO6PskB6JxU/vcQWCdbkxPaYjQuZKQ524JuT/CEnekuYE382hfqL1iH0im7V17n+kvfpdY7RKMhxyLj6ChHKsXduuL+Sc5vWsHN2PHLMmG7MmxNBvxbU0Ad85oQfOhiZouc1GmWeUO9/Tbf/3v/O37+h/9n7tz+IUBIE+cJEPFpEyu7QiUSsTRYKZFFykBGIAqIapbLE9Qo4een+1zevsoDW2OaM9zpbX62fESaJFwTI6qV4uj+Q2ZK8IvZMZvZmHvVjPHWFe6RM5kOuDLZQVpIpKLEnrfYAgXSj9b6r/1s7KJFIKiubeLFeh5itzUO01kh3qGmvbOtpY86LTkksYgQ3eFa1eLZ8kzQnc0XbG1u+D5icYppah+CBURakUQxiKhbxYuqoqprHA6tJDIClUiSWBNJr4kaY7E0IRXQILVEKoGw0Ji1QO6gZTgnsJY1j6pPP05jnzgxHo1YLH3IlxEQRZq9S5c5W+RESUIcxTx4eJ/9gwOKsmB+dkYUScbDjMXijMXpCfv7+yyXS5ZlSVkbyrpGSsVoMGK5WpCmMYPhCFNX1GVJU9ckcYIPsxYoDY2FWAgqaylrT0lo6btNOOuwjSHNYkxjKBuDXeW4w32KumK+XHL5SoPUCrMy5KsZOopYrlZY41sEJVFGvprz+suvsJyf0VQFOEjimMl4SpKmxJOMOM0Ybyu29i6zPZ2wu7dHmsZ+okoR2sAImqqmKHKWqwWr+YK8KLAmgASCsqzIF3OqPMeZhrJqqOrw/AJlYpzzE6jVbB2hEpbtyzgKD0iCwA2H98HSm2W/TjkPtOHfzmHmuVtxjkoQa/u2O/rPpGyLwojOTLdBu6WrxGYg8LMiaM2u1ZTWjt9FJDiHUCnjS6+jr36d6WCX9+cF18ZDHtw/REeKN0bb/H8f3uOhTHm7qPn48pRfRhFbwNcnY95XObke8K5M2Eojblc1W2nG1zc2+PnJPsel5eXxNV76jX/M4/3b5Pn+2l3KcK2u0+pi25A0BUYkPDhY4vKKSGnSWGDqisFQkWhBFhtwh4iHj8lwJE7xzbNT0sZhiPgQi4linLW8e+k6WmhO6oI9EhrtGFLiFjmzKCfPT3iifcZnSE+JdPz82prWWtTOWdomlC073D1XQpw1dLWHhWjDKluNt01ykU9Qcp8/p58JukfHx6RJTJolaK3RUUzdVNRNTeosUgpab6Axhqr2aruKIUoESaKIY43Uwn/f1F5TdBYhDEL5CmNKSZrKBeeA8auRDJX/nI97bIu51LWhaRxnxYLrVyRXr13lzl3fKlqmknQw5NqV57hkavbPzkgjxYP9fU4XMxbzBaau0DpjmKYsl3PKsqCsKsrG0FhL4yzSGTYGGVf2dqjLAbcePCaJIuIoQhjLIPElLeu6IlUaJySVMahIUxQ5br7E5gWpsFy+9gJ7meRnNz9htZgT66irblQVNWdyhgCK5QxkRF3XGFPTmIbhcELdNCQ4kkhw9caLJHHEzTwnTVKcdago5sqVq0it2JhMeOP1N9ifzaiKiuvXrvP8tWvcuP4c440NojjqlABjDXVdsZjPWcxOKaoSH5VSU1cG6xynZ4ecHh2yOD1lPl9wOjtjPvehgBYHyqKj0CAUn2FYlhVlWeGsQycRyWBIlg3QUUSUJKRp5p2MxpAvlp87Qb98aTPoRMvc4V/APiXkvFIqOvO2fZk7qHS9o9HhQdb/iNAgscHH74LoKmCIPjvMSSwK8MDsEGxsvEB87Vv85Myx2rfU7LNbLrk1jDi0Oe8mY/7fCj4aJLxYR6SXRhxPNb+ZbvFWbfh3riEebfKOiNhsBD+o5uzEGS/LiF+cHrLMMq7ECWVZkG9d5Vu/+Y/5oz/4J4BZy1704yHDopC5ChFP0E6SJQkrU9LUS1a58EWqTEQdDahKR2VKzLLAIcltTapiTl3BeDxgNTvkuXhMHI9BaaRMePvSS8hoxDCJKeoZo6hi/6M/pjYrP9YuRE+05S/PPcnO7Rd+TPdNC7jOhogMEVK0xTpcrmnLnSPNdcdzmL4k5pqzDdG3pf+VHWn7h8cMBynbatM7Z6RE6QhTlzSBJ5XCOyGsaTDOoiJFFEVEiSTJfEaZEoraGkxjcLZGSAOqQiqH1EAoSmhtW7czhOoI0Q0u1pvBddNQNw1FXlE3hijWTCdTTF1icESR8u2kDYyHY47P5pyenTKbLSjKUPfWxBwfH+K7R5TUpqa2jadR4pRi6XhuqNlpDvnodEWqIYozhINLV3bZ2tpBaUmxmCGcIC9K5osZw8kUJwRH8iFVfch2arnuHtGsFNoYUq0wQpNlMVif/ukB0DKfz0gGE6QQngqQkqosyLIhzgqM9XUBmrqiKHOSJENrhY4jXn3xBR4fnzIcThhsbFDYO9QDRzIcMZlO2du7zOXLlxgMh72zyEFd1+TLJXXlky2MleR5TlUVCAdlXTA7PebxowecnZxycnrK4fEJs/kCJx1RLBlkGq0ItIqlrgxFUaLimK3tPba2d5lOt5hsbDGdbJJmQ4yDsqiYz04/d4J+2bKeJdY7y85/78IGbWWwvsTf+v59eJVPi7ed4+x8Nlm773nesY3dtQGYo3jIZO8NJjuv86PHllFd8iifUecV48jyi+1rfH9wmQ0MH8eOvzve5dK85L+TZ2yWEc/VNQ+ujKjdioktuTwTPN6LuTaYMl6VNIuK5tIQcXjMxKV8QMFz0wn2xW9x7YN3ePzwJ+BUV/K4zToEQVws0eNdSieZTjbJFESuQVrBqpgjIk2iFKPxFGMrkiihsTW1MWwMNijzUyJb88611xjrCRWC+8sDdkfbvKKHzBtDNtQ0Tc3hgw9oVgfhGtaeCz0U9t/1dULWt1p/NuePsA7QXnret9+qfT4tbdRu2BZ+t6JdBFogfrY8E3SPT07YnGSkiSbLUhS+v5lSGinBmtqT+6bG2QYhHTIK4WGp9mZxHINT1K72HJe0SGUR0hJFChWBbQw+LVEEZ4znCi0GLQIkB61XIhgNh7z64qtcvXqNRw8eMh4MOJ3VTEcjkIplUbAqamaLOVVd+5KLpsY50JFPvqiKHCkFxtREWhNrSzYa49CMBxOWcUxlNDZJ2BolbO3uoaRkc2uT6WSDpjGojR2ssxyfHJENR2QjX/DGrEqsFZwtTrlfJOQNTLevoHQIdRGCYrUgXy2RQtBUFUVZE2XOA25jqGyF1r5ZZl4sUcMxR6e+8JAUkuFwDM6wt3uJX3xyk+HGJienc5xyKCSlraiaylesUpIo9kWE0tibQ9ZamiZmNEjJ8yJEW1h2dwVN40t5lkVBvrPNxuaU06Mjjk7OGD9+zMnZKdYZ0ixikMVo5WsXWGNpal+8Zbp1ma3NPSbjDcabm2xu7jEaTtAq8gtnVVKU+edO0C9f+iwyu8a9OkEf72lDFn7gVy0ghVozY9ttw9/OZzytZ4GtrXVBLNZ6uqelI6yrsM4xGOxgN17h1jLm8PQ+qqq5YwWiaphOhvx8usGLtSPTln81kSRGolcNP9obkWSCiaiYnMFdk3MpqrkSC8RozD9fHCNP5/x9kfFwc8IPHu/zH46HlLOK8UTC8YyFNVy/8Q0eP/gZXXZbqwAF7TwWGh2lxMmAs9MDrm1sEEtoyoqd8QiDZVWXmKYk0wqpGjAV42zEIBowTickKkYLTRaP0Uhem2wziFKWecFoMqaRNQ/3H3E6uwfOO9Zd25k5AFvL7QY89GV3RKuhPplm3hYaakuPEurGiJA0Efj61nEY2tJ3Lew7rlbStqzvoL/Vir9AjC58DuguVznz+YI0URgzJNYREoijGK21V76dxVpfzEZriSFCa59EEccJWsXUtW3nLT4R1CCUQEU+vdLgzVnhY51oWZU2t6Qtbqy1ZjKdsjmNUU6yXC5Jsoy6LNhL9pBSkw4y8qpikZ+RVxV1WbDKl1S5dzZYa/ykKA2R8gVMsnQAKmZraxchJPlqRZSmjMdTLsUJgywLGXkxk/GYycYGdVWTxjFIQTYYYYzxTscoJUJyucxZzM6ItC+GYpuG5WoB1hInKcdKYqxFSf84y6pGr1a+20SkobIYY1mVORvxhHy1RKQp0voY5DhKGGYps9WS2aogGky4tDskHaRc2d3jbLVERApT1zw+OGRV1KRxwiCKcc4yGqSoKKKoSt9+RgiyLCUdxCgpaeoSk2VUwyFRu4AOBqhYk44HNE3FYBAzzmIi7aegcQ22MQyyEePpJYajDQbDCZONTcajDdIk88/bNNR17fnNX7NY63vt9eFcBmMNoguiByGlB+IQa++rjbUaVqAkvB1Oy8O2mmsbqrSu0fZ/mg6ofT89QZTsEG2/yy/PFJFt2M8LXtrcYV6veNUUzIYOuTPmrXiPW5xi3JzXok2eiyQPoxXjpua52vJoJ+b/uTzAnp3yHxdb3MlW/Ky6yR4jnN3jD1YfsECQu4R/S46+t+B6HLOVKQbPvUE22qNYHiBD+UYrI6J4zMbWNTavf4NltsuDk/sM1AMWszM2k4hYRUTGJ92MdYKOBwjhUGLIxnQP6yxpkqGkpMgLSDNsmjKoHdJatJJsX9plURxz8+M/ZH//A6SrOlwLumYYvrWylGE1a+vqdnHVHYfbj38vrlstbduqyD+g4CDz+zxB269RSOsu1RC161y3IDxLngm6q6KkyHOKPCKKHI2OiFSMjjMipZACH9LUVIAl0golNFHkiGIVstfANn7CBqXLm6aRQkWqM9WM9SuqaG84hC5ZvAYihO/AkKaCQTJhPJgghebg6JjGwTBNGA4G5LVlscrBOfI8ZzGfk69Wflikz8Qqq5pYK6SMaGqDUpqhSoiUYjrdpJ54LnU8GofrN0jp414HozFSKCJlsHWFsQZnGoZp6ttxa8Xm1gb2xHWONq0kdVUiI4m0UDW1LwvZ1Ejn06xRito0PqNOx7jKpzBb68tPbmxu4Jxjd/cysVYM0oTTsxMOTk6wQnN86qszXb28w3Q8ZHc0oUFw+PCQWx/fReuY6WSK0IosTYjjyNeESFKUklza22JrZ5Mk9mX5okjiXIpcLRkz8gV0BNTG4CTUTc0g0WwMEyLlE2na4jFpNiIbbjIaTMiGI4ajIekg6SgR01jqquxCpn6d0pr9Leia0H8PFArP0QkbKo05CcrhnOpaendpw5x/rdvqXe05vEbdmp8hlVd4KqINIXPxNveqCY9+8TFiWXHoGjYmO/wkqXk93sDcyDiexnw9b1hlCw53IjJRUjQr/mSww784u0lU5PxDe43o9Dab+T2G+QmRu0YcNXzj+Ba70SbVICcqH/BCPGU5cczK+7y79RofsOStQjHMG15/++9xvDqmaBrGwylbWzeYbN/AxCNWThObikNr+eRwn6s6I2tq4romjjSREDgjkJVD6cQrNQgaDcuqQQrDcLyLVgmmriGJGA2GKCG59+A9PvjlH9CYOXKtM8mnArHculZ7Pp35af5Z7zhbd4KuWydyjZLwz6XlZj0ut/E5IoCreCIkrVsROrrjWfLskLGioqxKpJLEcQw4alMhTLQ2kQzG1L48Y5KA0KjI+NKPQtMY317HYUAYhLJIDTrWRFrhTA+yPi40pPlJkNIPgBSCJE7ASRql0TJGRxFlXmIaQ+0EjanY3LxMdTynaRoWqyV1VVDVvjOvkjYUnxaURYnKUogEURqHAuURSRwjnWOYZJSRoSxXjAYjnDGsyoLr12/4zqxNwSBNQ0KCgEDO18aXfhwMhjTWcXi4z2Q4pChrZmfHxHGM0NrzyEXl+VpT+8VGSt/yBryDLkmZz0+ZTjdxUpAXBaNsBM73J0vTjAcffsDZMmcynmAFlE3NydmCNEkxtmI+X/D46MhzqFXDfeVrVwyygY8LHo+4tLvDSy/e4IXnrzEcxkDIEsRRlAVIi9Yh1jrNSAcDsjJH15JBqskGMXGkfLU/61O4ZRSRpRFJGpEkMVEk0ZFAxwKpBE5KpIhwTfW5E/TLFm/Whx9rME3j66yGlhq2SwX2ZR+NA6e8F1t2fdLWQNetcY2uT6VugbV9QdvOBE5YX8uiGfG4yCjziubhXeTmJps3XuHaaIfDqGFntMd76oxVdkI8eZHKCWaP7rGth7zaxPxI/4LB8fsM8hrHjL0Yvn2yT7WY4fQROYbN1RkTdcR8eJ/tqmJvlLMqcq6amok4RDUz7g12iOucd1/7Hr//yjuc5iWZSjmdFcRpQm0rZnXFe0cPeSAtxfNXuXXzZ8zPZkx0wkApNpIUJRVZptBSY6xDao2Wmigek8W+ZOyiOuVoeQ8WDdGJ4vjoY44Pb+FcTTugnmb0IkO4YvulN//Xi9msWRznQLN7OmvcbOf+DJ/323WHWOuQIQI4t87PPpy7Jb3bBfZXDBnLQ1bVYDRhc2sP5yDPV9RN5Z0tQNPUNMagdESKzzpC+ephjXHUlW8w2diQ7S0kkYxRKkEIHTKdHMpX2/X8l5RoKZHK4qQvkmLwTSmVkKFXGyRJ7AvjRDFGCo5mOYcnp5RlgZCKOMrQusRaQ2MEUinv/NMRKklxzvn0YyV8QfEopW5qiDTOGuIoYjmbMRhPeOml6yRJQqwlly5d8yZ5FLFaFcRaY0KzxbquuP/gEWkS8c7bX+PmzY+p64KdvUucnBxjm5o4jhkkGU1T+u4SqqBqfHF423iTs64rhJQsV0u2traJksQT9hJG4xG3Htzh7v6+L4ATuOqFlKhQNSuOI4oypypLauNjbrEOjPePO2MYZik7u9tsbG1hLKzy0gNoFFFbQ2Mb3wGjMUghQ1foCKmVz6cQDrr6DSI0b/QzuCxXCCURWoIGqzw3L5SmMbZvLcT4cyfplytt1IyPLrC2wdYCExIiOoeuUv3LGEoCOuc7BcsuI+3ca8ynTdo1ESJEOIBzGXcWGQ9OjymcRQ7HVOMh1+qKD8Ux188U1daSZgN28hmRnvELazg8/YTCJizY49uiYvPgBDMvEOaUPIvR+ZLYgs4MZ1XB2AoSWXF4OicWGqEds/KMqUh4tFqxo2IeylOu7l5mZQx3lzOfgekqVKoppWUm4MwY5qll98qY/JMPKaoZj/IckUQ0WlI1ORuDEc1yQVnXjCZTIpmgdMpwMMZgufXwx9y7/SOq8tSzr7Kt6mVAKHxHDa+AOWGw7bOgDc8KAOtDT8JItxxmy+fKboETbZ3nFoBdH6fbAbMLC6jzONXHYbf0w1pES3CgtY/a+2A/X8uFzwPdoqKy3qTe2d5DKc1iteTk5MCnuhY5ta8hSBSnKG2pTY2xFbbxpnxVNFS1r7drhGdppYpQVtE0Ctv4ViU41bV28XUFVRhcf6PGOJJ4QCxSkiglTVLOFgWj8dj3IcsLjk9nWGvQcUxxfMyqKJBSUpWVf3FClEWSZejQ6E9FMYNIYxqDjQXD8cSHlUiHtI6rL77IYDiiKks2J2M2t7ZI04yjw0M2hwO0SnC24eT0hPF45ON5Ldy/f5/5YsGVvSvcf3CHe7dvMRyNSAZDhMWn65JyNjsm0pF/+fBp07EQ5HlDGic0xlLmBePBiFHmm2D+/PF9Hh0e4qxBK0lerJjNNNgmpGKfsgzlNaWKQno0JKnvIbdcLdmoN8mGGUdHR6RpwjCL2NoYo4SgrCpfXKhpKOuaqq5wznPqWTYgTiKoJYlShAYr+KwtH9rnrKUxNuSz+AiUsikRucbiMx1r02CM461Ll77QRP2ypE1c8G2MLdCEYKkG1b6sQmEda22IQAjZl60MDt4QzdunmbJOL7QigzHsY52dVZyuUg7vHaKFZTLeJE0T1JWXGZmau3bO9Rsv893xJkeHn5DnUy6JnEIckZyckpaGqjpCp4qdvKJwgoGWGGdIdcQoSrHOoLRmHGWs6oJIx8Qq4mi+9LVOWLKqG9LxlFhHLDLNmXFcpqGJYpamZi4FBkfuHLUyXJMJ5vYR9w5u05QLJoMJDsjLApWmXfKMxFNRIvYOq5v3fsbjxx+QLx54vTEUf5ddFEHrvAq+HWHoQsE6dPRZfq3e2dE2Tqztu5ay0619Yu0YwZoR7dEFsUyZ6AmjJCM3hquXXuPB4W0OFnfXtO0WXF33LP2T7lONP0+eCbqLvGCxKrFCkg3GDIcjBqMJQjpOjg7JFwvffFHF6MjHjVIrmrKhqRrquqEqGl9VzFoqalTkG/4JFVYSA00JpsZXhwcQisY6RCORWhKpAUSGemWopSXNIsraEGkNtsAZQ1PXzJYLVqslq3yFaWqcczRNqP8bhWI61pGlKYM0pakKkkhjjaFqKp8EohQ7e7tk2dAXrVaSoix57vp1dnd2SJKEJEoRTpBkGUdHxzhTs7OzS5IkOCSTTUjHUx4/vMfNTz5hMt1gMBixKpaYusYYi3SNL7ZiLEiNUBbjfDufqirRceQph6rEAKUx5FWFNRU3799nsVgQxZpBNkDqGGcanDUsFkufmIIvXLfKVz45UQXNTSq0Ur4YUexDziaTMVJKyrrBGHz4i/FArZRAK4Ux3quvpfL98LRGqxitpQ8ZEw5q5+OxTYnQESrS1HWNKHJsvcJgMdaF4kUGPs3UfeXShor57LwaKxSY3jMtrMBKiwhlGxUqVKSKaNOiXWiT0em2n+tMCfy3dZRNxMFxzsw2VLtXmFjD2bUpe+WMX744wTnHJM35o1nOe7d+xLZMWKorvJ0JNlfWO2BxuCoiQmIVREnGss49/aUUZWVIkpRZvkCiiKSiyHOUUljnKwZK45ifHmPTlJM6J7oRkd78kCvPXWFjPGKoFImWnBVLbs5O+OX9u/zy/Z9hZhYayXSU0tQFm/GAYTqisYbRYEwyHKKTlPnikP2P3idfPgZf/n4NDF0/dkHzbEuQStfXoWhrdfTdgp+wJjrNcy1y4Vw3atH5OsOBvHUiBNrFjM0GO8UWz2VXef6Nd3jj9d/jdH7I4+Vt/vzWD/n43s+ZFYcdaWzXFtb+eL+iI22+KjiZLZjNl1SNYSOK0HFEY3ZomoblckFtDINYE0UR1uK76lqBqS11aSmLhqoxlMbSALFVCKUwoXuns2AqsLUjEtLXLEWAUxjjHTc4g3AJOhYM0wF11bBarVgWhqooyIsVVVlgmxIlQuiIEig0oqlRSqFVhDENcRyhhPKxrlr5QH7hH7KKtC/a7jyvKZMYUxteeuklNra2yYuSu/c+5Otfe5fdvV2qyofKSSnY3NxCOMeDB3f52fsf8Npb7/Lqa28wHo748x//KVEcMdH+JSryFdI2YCvi6QStYmarJYenp6jIv/TOmE5LbZoGrSLKuibWGq09p16VNVo3xEJRW8vpyQnGOfIiRwoZQvZSUBoVabT0L1ljKsq65PDomOnWlKOTI4bDhOEgRmoVzO2GvKyoK08f1Y2hqkryIsc01teYVdrXOFZQm8oX9mksVZljxSpUrPKNK72iYnHSt7P2HRqiz52gX770kQbWWoxtoNW7nPVFzJ1DWIuTXsOK2vdd2dC8UILQ3jILxG4fv7ue8usAi7MN1lrqxvHw3gmHc4HSGduRYmc45e5ZzkuTLeThkqUVxOaIg+qUF4uaDWoKcx9ZpFzWmlPpUE6h4iGiqYmVxShHVVVU5ZyTconQEXYJg2Tbp+zbAq0UpWn65qM44khTy4aiqLl37xOaFwV3D2Pk8TG1NRw2DbFUfLJ/j5N7t3DLhk0ZsTPeYBwNiAYbDIRmkI7QaUwymGCsYf/xTUSVs7F1lc2965R15cuHK4EThrxYYm2FaQrqusCZEucaREhBd21djxZrW0uiizqQPf46WE+KaNPWz8fv0j8TJ0hUym52FXEkuPPzW1z55jXeeuG32NnaY3O6xw33Jt954++zv3jMBzd/yPu3f8zH939OXi9pbB0KOYUIii+g6z67c0RecXR8xsNHjzk6PWYyGZOlGVk6ZDTcIM1OqesZQvmKYDhH09RUpc9qqitLVVmKqqYwDY0D4xRN0Lx8QpvDNd4U8WFkvnOv0AorFUongMBZyXSyQRYn2DS0BlqeUhYFdV15jSuUXxTWMMyGmGWORBBlA8rCTzQp8Ykd1oCzaKlRWoMxnJ2deVNDSnQUIUzD5ctX2dzeRUjFxx/9nB/80R8QS8crr7zJcLIJQpENB2gdc//Wh/z8/Z/zpz/+MTu7e0yGA3Z29/jau1/n1q3bOODxw/uUdYkpS+p8gVKKF65dJ82GWOOYlwUmpqvENcgGWGMQ1pHECVW5YjyZ0FQl8+UCZxuMkVgHDT5UTwBlaANfpwaplQ+NUoo4SamSGOMclXOoLOqyDherOZHWDLIErHeQzhZn1GWNMVAUJaaqiJXEOoVScs109qUbGxu6JkiBsbVvZd806Dj2hZOk9KU4HUiVfO4E/bJlvRuDtRZpLcKzz/6FD86wnusLmhFBJ3MWgabtigtPe7kD2DrrOWPrq8+5KgISRlsDGqtRWcTx5oRDu8/h1jVunt5HnD6mzp7j20nGsR5T1AXDSNPgiIUm0ZoKKCPBfPaQ1XKfsphjbdM5ddqC6eXgGoPtV7G2pqpypLEI63z6sjW42iGlZqA0i9WCx7c+wWSPKFzhwwGdQGJwizl6dsq1JEMKzU42xVpNpDKGgxGoiHi4wbff/CZZNKJuLEmWYWqf4CSkQiiJkwKNIdKOSDhWVcFZPmOxnFM1K+bzYx4f3eTw9C7l8ohqeUjdLFF+xfOO+Ta+n7Wyii4QBm0xkXPP2yt70vnC5RLFcxsvcGPwArdvf8zmcIs3336b4vgRj+qaOJsgtCKKEqZyj++/8R/x/Tf/p4ioYVHNeXh8jw9v/YwPbv6YO4cfUJn6c+fcM0HXGMPh8Yy7dx/y6PlHbEzGyK1tEAIdRQxHY28+4svWVXVBkS+8d77x9QaqxlLVjqp2NEJjhaYyCgtoKZDCoRxEQKUMroEkS6mdoKodpqwZZAM2RiOSKMbXYnCczeaYpmY2P6OqSrIkpSpKmqZiY2uLVVExWyxRnvRBS4lWviNuWyhaR6l3BjpBWRQ+rAQYjSY+lCpO2N3d9ZXW4oTtvSsURc1//f/5r3jlxZ/z7jd/kzgbMphs8Od//mf823/zL7h55za7u1cZDkZIqamqkul0iytXKo4Ojtja3OQXv3yfIl9inMEZw8nZGePxhM2tHRqcj2KwNrzsCqskq9XSh41Zy3K5JIojBoyo6xKofbU1Z2mqCi0lZQj499024q7MZGMbhNMUdUHWjDg4OMQ1FQdHB2xNNxlkPoRMK19HdJBFvm1S3dDUNVjjLQnZVoEDKTXKWgy+5KcWGUiJ1DFCarTWpHFKFMUgBI0JqeA6/twJ+tWIB1yD8fVAnC92Y1yfU99prsbrVuvhYs664Aj6LOeZ16RN65g0PjTt4HjG/ZOKaFyTDbYYzWYUuwk3SslkPmNvtk/S1IjyFFcXPJ8mrHRE3hhOmophNCLOFCdHN3n88AHO5LQanJQeYNrQNOdgsbwPG7tw+XXU0QPk2dzXQA4ByKapESH8byQk5WpOvTxBSEcsBJEUKCEp6wobZ2RKkcQpZdP4aJVkwKKyGCu4Ptrhl3cXZBuwPVDc/MUBIkowWkOUoZsK4WBnZwOnDJlSjMSIK1uXyTcNOZJdA6/b0i/csuL0+DYf3v1Tjh+/x8HD93D1wvd7eyIKwXWZLYGWaD1deEpTodlINnn3lW+zMZxy+5Nfcve9W9z9+B6/8+3f5flXv8buc9c5O5pxfHjIyeExZdmQJCmDjSmz0wVvvPMGe9uXuXbjKt95/ruY37f89MMf8Kfv/eBzZ9vn1NOF1bLg4OSY46MDTo42fNfeJEEKR5pmDIdDyjynrgryfEVZ197r7Xz7nTiCqva6vZYxOI1pvCexEQKl/IwQ0pAkCQIoCsNkY4dJHPsan9YyGkwYJBG2qTlczijLiuViSV366ISj4yMkgmQwIsmGzJY5tmmIopimsURaUxa+ueQ4SYljTTYYUKxyhG0YD316apb6AH6tY65du0ocJyit0HHKdDLl1dde5yc/+ff8uz/5Ae998B7f+d5vc/PmhFsf/4J7D+4yHI546+2vsbN3mWzgeWGRpuxJX+/g4PABSZqxWC0BxWA4xFQlVV0zXy6praUsPT0QxTGRiqmtI0kSX9chtFqZzedo5ev65tUKKSRN09A0NU5ItNQMRkMP3tZ6DjbLyAYDX5wnStBCYfKag/KIk8MTppMJ4/EYKQTDQcp4kHAkRGhpowFHLH1dDC2hDhWbfD0XQd14r39oOIYQsY9pdRJrHFb54vXCiOB4Us+cfl+F9DV0fQSDMb64uHT4uRo60Gql/bbGkybmXDcJ67twBJBriwqFM2BsgzE1pq2vbAzGWMpFw/Z4kyQbcCQtcm9CsSx5eLTktd2c72d7nIk5iZRYpWgMJDpCRJpVFDNvTjh99AlNOesWgnWXXUvf4RS+nKFl8eDniOEYhptkq5IYA1WJUK0zKnT0cA0aSxRqYoNFOcdASxYIsuGAeVHQGIOOEo7nK05vnTBfWbY3r/Hg8KeMr1xH3wdpK+6ZIYtBRtZI8qrkuY1dylRTNw07VcFukvHW9jYfFacUCHZ0wiUBjw1YnZDqIenmlL+19TZbI8Of3/p3/Pjn/5LHt/4EVx55usF2lYA7psE52f2N87HW33jpu/yD7/8nvHDtbT76+M9QK8290zvsp8dcuXoD63yy1mR7m7wo+NnBAU4PuL69i04ThsOKs/1H2HpKkiqWpydopbgs9vhH3/9ffe6cezbo4mt+FoUvyl3kK/LVwi/3ThBrRZpm1HVFvlj51ic6YTCMaaqasqyRSnitBh/n6ITysXVSEAmJFL4jRZZodNCgRsMxWscY67v5DpKIKIqJIk1e1zRlQ1WWnJyeYkwTqnE1xOmAYZx4IG5qojgGBEI5ynxFXdckMkZGEZGOENLzl7777iZN5R1+jWnYvrTDZHMTITQqiomUZvfSFb717e8jhOP+3ZvkywVHjx9w5XpCEidcvXKDV157izfe/jqD0ZTaND5MrW7I84IkiSnKkqqq2Nz0HYOFNZi6ZJWvmK9W1I2P9Ih0RDIYsJjPiXSCQNA03uE3n88ocg/MOom6JJVIa6SUvsB7iCPWkQ+vc9ZgKoeNI6RQxEITIRgNxhhryFdn7BcHFEVJMkhZFjnzlSKOJNPRmKKsaGpLGkWhbZMCY2gaSR15J1tZNSCNd6IJhTAKI6GpLJXz5TFdiIWVUqG/YIjNlynW+t5ZMmSdmbajgxC+prPxGUsG03UJttaCab3qIfNMWt/AUvgqeOseG2Prri6yDck0xjiWxmKVI3cNXBoTC58BOjZnlKuUvek2l8cxpTUUVcNpVaNqgxwPqJb3ONr/COfqtcI7a17/JzKrfMEaBxjqm39G9Ny7rKZT6sWcLBYI06CVb9NEcD4rCc4YVDhBYxoqBFVjaBZLnJPcfvCQ5emS41WNibfY2bnCfpFzUqwY4qiiiKVOyHRFNTvE7F7meGPMSaaJTclwLthS8G8w/PjslMtxxNtpyqJZMtcx9xqLyB3v2IhNFfFJZVjGCS/d+Nu8fO03+eWdP+Lf/OF/TrH/IYSSjMpHfNFWxyXwwQ5HIiLevf4tdtPrpMmIy9vXKfYP+Ojkpzx3+Sovv/4GiY44u3eHKMto8hlNviJvKu5WFW+/+xYvvfEyTVmSZBnpaEBdG1+m1DXUy8Xnzrlnt2B31nsPbU8Um8ZQ5Cvf1UHg42mFD51IkpTBIME5SVnkHB0fQ1WjI987S0e+Uq51IKQgiSUYB1YgtCSOYgaDEUXl6+IK5UOMzpY5kSx93y1rMQga60OY8nzpec2m9mXPraOsCqwz+ELuEldXNE2N0oLRYNhnQjkf65skiY+okL5sYRJHKCWp8gKEZrVYMpxsMN7c4tXX3mRzc5MH926Rz2eMxiMePrzP9s4eb731DZ5/6WU2tnY5OT1lPjtBK8ndO7dZzefs7O2g4wQnFZGKGWUDhLPUjWa+mFM2FVL5zhBaa6qiBASL5Zy9QRY4Qf9CNI1BabBl4cskGouQMVFIW07SNMQ1SpI0VInTPjmibGqgIc9XSKmJkoRssIlz3uF5cniAsQ3DNGGQJRzN516LaxqyKCbWmiSOqAYpWeyLueOcLxbvLEQNg2yENoamAeEqmkjQ1H5xoPE8s/z1ZwF3mq61odyodF0vOUxbc8Cbq221SizBD+E1KWu9Fi+EYb2Pmj++X/CsbTrQxRqwjlRHyDjCKMnIRDR5yRtZhs12yZRiZmo2RIQyhqFOMFpRxorTxV3OHn8Qrqltmtj3nHNOYp2v8SqkROmMOB4yGEwZjrcYTbbZ2X2ReLLHe3duc3DvJsXsAF02aGFD6xpLY2uEUlRlSRrFnM3OSKTk5OSUk6NT6lXJ8dkCE49IrlxnY3cHN455/YXXuV8b5iryjknjMDohF4IqUhR1wVYWkzp4QM1DrRiOE35zFHN3/4A/dlM2h1N2tMLahlg6TpclhyPJQsDZ0YpXhgk3xiPeevnvMJk+x7/45/8nTu79O9omksKKgEshnTus78/vvcALl9+hmM0pkkPEYsUnP/8pjx485MaVF9CxJh6OWa0KyqIhHW/wD/7jf4QejDB5SRRH5KslOp5isdSNZePSJZqqQFjDYJB97px7Juha5zDOhnKDvtC1MQ3OOeraoVQceD1JnCYkyZAsHfvmkcsFy2JJWRekSJS1KN22MvBmaRLHUAuMlaRJTJZkSAlJrGhsRd2UFMUC5xSDJAXnMI2lsTCaTFmtVpyenSKlIhukDCdjXyMhkrilQAjti9oEemMwHBElsXcqhdApEbJOyiL3kRc4rl65RpYOkEoxHI7JhmO/6NQ1cZzx3PMvsrW9RV3kNE3N9Rde9vV3R1OiJKPIV6SRZHRpj/l8zqW9SyTPP4+SsHlvhwcPHtE0JaZJiSK/sEipwRriQcKqyImyEfN8RRrFISzJV/RfLpYMB2OKlefvJBBFPsQpjVOixD/0OkRlaKU9/6o80AuhyZIYoRSNaVgt52TOEMUJDYba1pzOzqic4c5+Tpoo4jhGC4GWkY/VjSLG2YBhFjNMY+Io8oVbnKRx1i+oWUmaDrzzr3ZoVaJlFEDLtxGS6tdPL3SarvD1FJy1IU3Hd9HA+GSVVlPyipNDOgmuwTmJCCnE6ynBQE81BnrBhlKP0rvpQMNSVFQDQVV6a+3t0Q02tGRuC5x17C9n4IQvYqQ0L3/zb3Hp+BoHu9dxtqGpS8qqQKsELTVC4kuXDrf83E2HjIdTBsMNEBHG+fZbjXXsL5bsXX2Lld7BnZ2yOnpINT+gXhxxcnCf1ekB5XLuu5AYQzFfghKYSOGyDKkyop0dTBLTbKTkk4x0OuFHj35BMdxmsnWVLE6pa8tRtoGYjHgjS7i5f8DpMOVkusvLg5jp/gN+Vs/5odtm47nrmNWCPz6d8dZE8414zE9dzXwo2ahrZF3ibMy9gyULKYmsYSN7md/5vf8t/+y/OSU/eq/veh0iFrrsMefYGGyyNb3M1nQH4SwDUVNXDVcvvcxv/+3/EciI5WLBfLZgdjZHRxF1A5eev8FgPMKZmuXhQ+LxBjJJEaGH3vLsiMM7nyCl4tKbX3vmnHsm6LpgDpZ5wXyxpChzmmaI1pKyzFGqCi1eHGk6YDAeM8qmOGN9eFakEbJByQYrLUgTiqtopFJkqUCmA0xtiHVGXha+B5hOMXVDmmQ4ITCNz14qK4MJWWTj0Yj5YMDm1gYKQVUbHwMrFbZyPqIigK2U2nPIxiFUhBQNWZrgHKRxSpnnWKlBKTYmU1548QWmW9vMz2bM52cIKYmTFKUimqaExhHrhHQ6CI4khcP6BaGu0UqhZEJT10gcOtLkRc50POaVl17mzq3blKs5whXMFy0AhTRTY2iqkqJYUeU5sdY+5TQ8k7IovRavPEhnaYqOJJPRhEE2RKsY6wx1XVJWVUjYMdi6pHaWWmhUFCF0TJZmpGnqnWYSmqrBCoFzinnuwwSXZY0QOY1pKOqSLPJ94rIkYTzIGMQJkY59ooRSOOFrTSjtw8niJCGNU2IVoXVMorV3XprGp3n/msVZgxMKpEVY6cuCWhdqglgMDQ4fCucjdPBhb75zngddKXHWhowq8AkS3RmwIdPNWd9e3YVW4KmMibUkTQdIlRBnEadNQSZTNmXMolohtV80q9rAtWvceOnrjF+OKZuaoRAUxtM+DgvKOzxN49Aq8gF7TYlUEYd5gXE+MmdZ5szrFUsreDSf46TmXmPRKmNWCSKjKErDg9sPaESDzCKII6KXXySKNSsqkt0rjLausGET7t5/H72doK4OeOX6a9z75BMepCuKXcdrl6/zYpHwn9/8MU2yw0fj63zr6tscP3zAL80x+0XCxqVtXrIVHzQF+8byv97aIs1WvKdhnsJvJQMenM15aCXvJmMWkaZxlsg5toXkrDKMN1/nm7/7v+GP/9v/PS4/wDqQXUqDT6iIZMzu5CrSgDG+hdbRwSGHBye88+73uPLKm6yWS0Rh2N7d4cr15/7/zP3Zj25plt6H/d5hj98Y8xkzTw5VlZWVNfRQ3RRJsSmx1TQtSCJswTDMGxn0haEL+68w4EvbuvCNL3wnSDYEQYJsmKAgqdXsgeyusWvK8cwn5vimPb2jL94dJ7PaZqVgWipuIDMjT5yMjO/E/tZe71rP83uIUrK9ueb5z3/E3uE+IcD12SnZpqXa2+NsvebZZ5/y5OOPUEIwX+7xe19yz31J0U1/67qe9WpL03TM5wbvJX03ELxLy5woUVVBnhdkeY73AaUlSqcX65zBRkMUSVwvdEEkxwwtWgsyXROAPM/o+hatA1IX7MbcMRDYztD0LXmW4Z1nt9mQa8VysWS93mKjJfhAJhNIx7ce4x1K5PiQFkFhtJ9W5YSyrhn6DmMdg7FUdcFib58P3v8G+wdHrNebcT6apcy3GCmKSJQaKeLovFLookgSFCFTbqfQqWu2lqbdYaylKMrxlBB4cO8+v/d7v8ePfvA9ut0N1vQEBOVswbp5hbep++u7NlmBmSWQkErJGzpTbLYNzhlyrZhUJXVVU09nTGb7LBb7TIqcIlNsthtevnpK0/f4eJtmAfhIVLcUrGRuUUJS5iWN6ZhNJjTecNUk56H3ljwrcM6y3myQMgWWZlpTZAW5yih0inXKlCaXyQCT6dRVCZW0rJlSyWYsFIGUF/frvuLIbw5BIuUIHJepc0VGbqNyBLcxL+PICoG4LbxBJLnAFzkMr7veW0auf51AcMvtlZlk5z3DpuW9Nw9Z5CWDNQx4bjabcZ6avm7rHH2mQAd6ASHmPDOGQpZkeUxpJCHi0BgpaIeBQoAVmnZwaJExaIn3gZ2AT/uBlWnoW0vT9ywKiM0AwvD89BP67RnywQFZiMQ8UL/xEHVwj+n8hLtVRhsCl6bH1nN++/2v8+TZY87anj85/4zfeP/b3LMdPx7O2dot+u4dfr/+Gj9yW742zfhf3Nnjs3nB/2X9Kd9ZHHFX1bwvS/7Di6esc80PpORvL07YXjzjuYGX0fO7h0f8sem40AX3kdh+YCEVtRKcGccQBW/e+22efO0PePHD/wgZHb/kXRCCt4/f462jr9G3A1K1eNvxo3/6F0wW9zEmYHvDvUePkDKdXtr1DeurV5R1zersOT/7/p8y+Ixv/s7fwKqM/vIaoSKHD++x/+Ae0+kMa4Yvvee+RL2Qbo5+MFyv1qzWG5aLBVVZ4Bz0w4C3G3ReMi9rlEwb7hgcwbmUjusMTbfBBocQgiwrEbEgzyUh5MSoyfIpxlhsiPgQiU5hBzMWmTwtJ3wguMDl6grrLZNcY52lbQe0ztHOoVTGbD7FDJa23SHEqD8EtMroh7Rtnc4mZFLjhKDpB/K8oJ5M+fa3f4Nvf+c7CCGxLhkwdtttQlkqSd+nYMK8LNBKp+5USax3KCGx1uCsxdoBZxxaKiyKzWYDwHRSU1YVH3zwDR4+fMg/+7M/5enTT7m6vETkBfV8wTBYsqJivV4xqSfJDSaTJTqMkrBENY3MZjPKIs1wq3KSnHaTCcu9EwiW6fKQoqx48vQzjDW4UZssZCob1gyjxAbm0wnBOnRIc8LD+R7OO9Ztw3q3pjM9GZJJWTNYi7WOzligRZJ02tkIgcnQI1FOpbWOkOOR/PP3QCCdon7dV/zC30IIqWu9JY/d7sIjyBBGB5IljhpPwoh4FKM8S34esPp50U1QcjHaVj+HyEekiiyrktnekrxQLKoJpa9o221iZWhFRNIMDus815sNf7474/fvvEPjBcUQcUGwNpY8i4QgGJBYCVaXrIeBC+eYZSVb12FNx1X0fNKtmU1K5gqG0OD7az787KdEHIfBoCqoqoOk5a4K7j98i20MrI6WhJMH/I8ffoeDMucn65c83a55Wx8QspLr7VO88ny4Peffuf8+f3q64p8OW358+Yz/zYP3+eT5Ez62A5/Ynm/s7fPv4bmxAyYM6IMZf83XXIUBJXpOiop/ePcO389WRFGhhOa3RcY/axuM1PxriwWn0pNFyRSDHwaUqvjqB/8WLz/9J8TNZ5/L/WIa333w5u/wla/8LlVRUxYlZy+uufv2B/ytv/8PMH3LfDFjspxj2gFnTNK024FmfcHp9QU/e/yEqGeojz9GRMfq+pJXL55xdXXO+cUlAejalt//d//nv/Ke+5KiOyaZWsf19YqLiyv2FnvJdz6GL/ZdjzSeampTFphPR2xrUrKDs5G+G+jtgM5KhmFHnGqybI4UE4g1Tevoe0NvEjvTmNRFuxjwdpuy04JDS0mz29J3DRsRGboOpRV1WXKwXKTZE3Bxc4kWKgF2gqfIciyWoQ/kWYaSgsEOWOeZzafsLfd55513+eCbHyCEoBu5tnleUFYVfT+wWa/p+54sz6mqGkGKVI9SYYaePMvxztL3KVXDGpvAMFKlTT+C3W6Hs4bZfMbR4RG//3f/Hn/0h/81f3b9R1jrkkpB5nTtDq01CInzjrqu6fpmjAlnlN/JpG9WivlsRlnPmE6nLObLNM9SCq1z9g/u0A89F5fndMMwzo8FWgR8BGt6skzTDpbgTLI363FJikCGmKRlwRGkwBqT5rbjks6N0G/jB4aQlhbiNlGWtEb+YpDgrVcgjlv/X/d1i+mLXyi8o+cUEW5f50jNlZ9rEgLJHjxuhdNMO1FZUpQ3SRYpxS2eZQQQfj5gpFCGYbBMspKTxT7OeC7alma9JgabcJJy/PMLEa5v+NMPP+Q39u/ycDLljIiXOUbByiUljiw8SmrsMKDygq8Vcz6+uuaJcKhaYVDbagAAeDZJREFUEXrDVw/2+d7zn3HhHYUqyM0Vk2OFOV+xjZ7FG++wWl0ziAb25+QP3+KkyGhKz6wsMM2aH708p3Etd3xggmW2ueAt0yOl4j43uOe/YN/s6HLBcb6k3jUs8oKH04rnfc9gV7xdH/BnuzN+FDb82ZXn35/d5T9bPeef+is+jI7/5d4J97aeTvYoJfit5Qw/FSBLLIJJF3hsWr6RFfi8guC5y9vcf/f3eP7954TgYCy9R5P7fPXRb1NWU8x2jYwON/Qsl0v27pywu7ogeI+MkXyUppoYaLqBjz98TNsL7r/xVX7+8cf8Z//5f8q0LJkUJcfHx9zsWp6enhFITsAvu371eAEgCrwPrDZbzs6v2FvO0VpQ5DlaZuRZRRjp98E6vLAYM4xRPgqtSkLQrNcrsixy984jpvVh0s8FgVJpXisBbx390I9bXjEmDBiGoYex+G+2K6xJdt/lfMZilpZcfd8hYmS32WL7IcWjhziqESRKFylCWylcjGhkYgB0hvkbC777u38NN/Sst9sRlKNAwvnZGTFGhq5PbATniFIgBQlVVxR0Q8+kmqAENLuGokyjkVxnyDwfs+Qks+kkFVMS5OfO8SFf+frX+f4Pv8dm2xCkQEtNkRfYosB7n1xlNiX5Wu9ApvGD95HddkuRZbRdT1bMECLDDAOZ88nWHBPk5uDwTnL5mBH8g8Qj8dYiVTam1HqsCwTvED5ZiO8u9pOe1Fl8CHRDj/MWNzIUJIzzzNv5f0p6FjGOaQujE+u1uCq8dtqJz++wX/t1mxb7+cMgaXZVHGVhIj00iWnRJsfCOoYrj0VbwKg7FqSHTnrRo6LgCxnrrw0L0mLsDusNlSrQ2hNjQ8wzYpSp03SJXRKFIG92XL18xv/5h3/MP/z6tzmoKgSRbFowN4ZXPnKvnPDjm1Puz/ZYO8eHu0veP9njcHPNXw7X3F3M8V3Pm1PBbnXD6csVhZpxhwW/MM8IZUaWK95+9CYfDVdM5vuc6IKpg+pizVFoCOGM3dUpNgwUUrFVBXrXsq9TCviy7DHXl7zpe1jlnMxXtOfXzAhstjPqxRFvTo8wXnBpe76elzwUBVkQOF0zzQINgZvWcGPm/N/choeV4d8vK+Z9i88HSjXh7qTElY7eRvKYAZJDOeFb3/77vPjFP4btCxLVsODvfOff5uTgLuuXT7l59Yzz01O6zvA3/83/KX2zxduBTEna3ZYsS7jXpmlYHt/jO793l5ubcz760ffZ31silUZFxR/83X+Tr37rG3zvz/+Y/+A/+D+ybRpmi/0vvd++ZKYbRz9qwBrDzW7F9Tr59PeWexRaI2OJKDRFkfLhvbOJgxAcuc6o8ppMTdCyRcsKbwX49Fbc2g6ahtRLqFGzG9ntNlgXkErjvcUOPcYarBmwxgIisXgj7LY7iqpASZUKtrPoLKPICzZNQ1GUVGVJ9AGrDJnWRB8RWmAHw+HhIf/K3/gbVFXBeujpjGG1WuGsoZpMGEz6/3oXQMiESrRJnua8Qxc5IoLtB/IsI8SIjgmgriaKoW1TKKOUBFciKkmI6YGy2Wy5f/8+X3//m/z5X/wFzhqMt/jgMdYhBAgkbdOglaK3Jr3BQ6S3PVIUGGtpu5aybBjMBNUCZY0gMgSHYEJZVdRlhR0G1rtdUjWIgrqeY53FWctsMkPVirbtaLsmzZO1YplXbOQGLyWyqGitwg5NcgYSET5pI+XnblduwxdHRfpfqa2fH9n/u8RV//d9fQ4aF2MeaiSQwknjOGpBhtctrohq5CePXS23lu3bbnZ8qIgR0BIDfKHg3v4/k5nBUmcDkyLDi8DVds3NdpUIUMS0kB3RkoMb0FoyPXvJJjr+g6bl2/ffZLacUsWMd5dLQtOy8S33pOAn22sOspL72vLD65dU2YT39JT/8vQpN1bxMO6xufyUprmiCRvulEuK+QJk5GFRc1eWDLam7h2HZy8J6wa5PmNlLZ2NXF9cYqzlIkaeouichzpjPqupl4dEmZFVOcobSger9ZZp8MjrnGy2h997wGdZyWEpqSYTjgtPMIJjDLmW7AmYWY8nUqiBKiie7LZUlPxRu+JhGfkDodgjGZuyXvBJ01FmBcd3H7H/4De4+tkrYgzcXdzn3fvv0V1dcvHpp4QAZT3jzhvHiOhp19eEvqWYLxj7R6SWTGYT2s5i+g4hIsv9fd4SirPzS66vr7m5POXHf75ldXXJd3/nu1xeXTOp5196z32pTjdGUJlAVxDkgHENTbsizyVqskCrlOigpSbGkOAoJiU3FEXBfD7lsNujNw197zBDy+Mn18ymC6qqhiAIIgnOBRoRPbNZSdP27HY7tk3D0A+vVQFSBGZVyXRSo7XG+4BWCjN0dG2L1BlCJUeUkjrpiaVAqWx0eCkIge12iw+Rb3zwHd566212uy3DkCLWi5HwtVmt04wZQdO2FEVBZ4YRQZlkRFqqxCRwgZ3fUk9n9MMGM3bpjrQUm04m9MOQmAZs6c2CelJz5/iIv/W3/3U++uQzLs5eEUVSKCghRsmewzlHjCnKPUaPAJTUKXVApG7TWstudYWcOaaTKaZvcLZHj9Ks5WyWFnzOs9ltsCEB48tqivOOXd9xdLCPQiazRt9SlYnmf1DPcMHT7Hb4kOLnB5sAN7dzf+Lt0Tnwmj7yurqOcSaM//5X6/Cv8fql0MIxGy2+julJPFcRAolAmCzoUd4GdMPnr/MWk5P+eznObePnT6L0u1+DcNKvy9CxW71i+vBdjNQMWhOFex113/tAEDAvcza9YeJaJmcXbJqep7s1H2nNV4/e4D9+9gu+ObvDj82Wvz474U+3p7RFwd8ul/zh+hV9zPhr2ZQXuxu2XYc2kqmDKw/T4NjvW8yQ9MTTZkUMCnH5ivVuS9sO9Fdrrq+usV2HHyzejovQ2weITK9eZ4pP64piUjM52uPg5Jj6+A6mKHFlQR4ycufpOsfaRfJaIaoaPznkWVYRdWBSKg7KGUJ6BJ5v5JZD7dnLHR2A6Fj1lp0vuPaKLZavlBP2igzlBMJovv7Nv8d/89EfUdiO9+9/k/3ZAaXM0V9R4AJ3vvoBeVWwPn3J+uI0LXnHU2mE16fkvtnRNS1KZuwd3yUrp/Qu4lxg8A63WrG5WZOrnEIXzMrJl95zXzJeSH+Y1SRjb69gNs9RZQRlcb7HuAKtMlTI8dZguTVPpDe3EJqyyDnYX2B8z263pjMdugh0doULPUVekmU1Ep04DcZjjE0MXpfevIMZiM4gyFL45Hjzt22DEIK2cylOvTcUdXrR1lnyIseHgHMBKcJrKZOPKXsszwvu3r9HXuRkQ3J22WGg6Tp2bZNsxsag8owoEsDdD4mgXxUVTdeRFQUxRsqyZLNeJWlbNyCUxHhLXeQEXeGFwI4PjqzMEdEzqWvyLKOuC5SSdENPVZWvH14xBowxCAQm2FG65LDWsJjN6LsWPxZ3o/sk3O52BGfRWhOCZ7fbEKLAIdFFTW5SKoVxBmM6ohBUZY1CMFjP4d4e1lvatqHru/SgEop5MUHojHZoac0w1tkIzuOM+QI4Bj53EYwfi9t/Bj4PDPyXpuyOwJTx4/SPEYDjQYxAmFtbrxgHJnGcXUtJIIxT3yTsizEQRPo96b/5AnXsdfUFQUBJR9dd8v0Xz4m9g2EgeoMmkitNpmUa/RjHJMvQ3tPuGo58YLve8N7enGZ1icwL3EFLYxqudh2Zb1JaQ+E5aK9wQTBlS312TuxbDoKi2KyxTcMyRMrYk19dsdnd8GLXYXc9V5fXOBtA54lsV8wJxRzpHQwd0ZmU/mEDwTnwDmsC1li2N1tuXp1zPX3G9fEx9dE+1cE+i/0l9cEhqy7SApVX1MZQ9o5TY2llQJaaUC94Ts5OBKpKM62mZDU8i5aT0nKczdFl5FloecwOEe4wj4GDokJUkjcefEC5vM9hu+J3P/hX2d87pLtZYXerlFw+7MimNUpJLl4+Z7k8pChrAKTW5PWUEEAVBcuqwlpLNZkyXx5weHKXi9MzijJnc33NrE8BsPPJdByr/errS2zAEZXDZJazXE7Y36+YzXOmexOW0yWZqLAuYJ2hbXfkOsc4Q9e3I79AJ/uuzijLjMFnoD0+SJQqgAyCxrrULbbdQAwCY9NCLcsLlJRoIn2zJQaPHRKYvAkeHwKm77DWQPQIKdlfLlmtNwidOFAheCKCvu+QOiPLMpq2TV2xVCitqKqM6/P0daazaUqacB5XJlJT8ImHUJVVArpkOX3fE30ky7JkDviCHtg4ixKaUiiE0mS6wJgenamUdDHiE1c3KyKRly9fsdmsidGPryVt9gUihVcKyTD0ZEpizAB4BBlVWUCMeG9xdiC4Aj2ZMpgu6XytQUhFWc5ASrw1yTqcaVrTQ3RgBrIsR2cFvTFse818Nmez3dG0DcImy2okyaJ0XhC9S0kXzqF9pJQSa1zq/mQcifq3qau3ReavEJ8CnxelX+P1OSTt8xnsbT5BEJ7bGBYh5OvJtAgxuR0jIzUrwX/CF3K4IiNtTdzacz+/xBdmvSkkb0cYtpTVCTqAsBLjB/rBjCwHRak1nTVEF5kVJbumpcoU2SpgLge+tdinOb/ivbxgr9zx2bDmTjUjZpfUmwsKIcl0zd7pS0o/UIoMdjvk9pKmtzzvHZcXF2zalhg1kgy7uAN5Ri5zYlHggoEQ0NFTW0vb9/i+xfctOIscElEuJSEnvsR2u2O725G9eMnhnSPCozcRjWFVlqhJReYnZEGzNVs23iEzyTSWFL7jqr9GyMhMw/50jyu55iYMxLqgmJ1wXUSa3LE/VWTS8cwXnNJypAJ7ewsO7n2T+csfUWUFw26HdRZVlezOznj54c+5KJ+wurriFz/5CW+/8xV0pnB1TV5URJVRzubkMzDdwESNrktjcN5RTiuef/QRu92GB2885F5wnL588cvmmH/O9aWOtDwTVJVmNitYLGrmswl7e3uc7N9Bi5q2dbTdlqFrXhdAF6EoCm7xakoKpvUUlQtctBgbsX2JNRnd0NF1G/rB0nfdeJwPaKGJSuKDQ4qI8w4RAirPQESaNs0dtZDIUZ5TTaZAImRlWcau7ZJX3ju6YaCUKfU2AakT4Pzs/JSXp+dkRYYfBvrd5nWY5L17Dzg7OxuP5Q5nky650JpsnJFO65LBpDdXVVdIneMj1HWdEhfQFGVBWeQ0TUuz2XD37h3W2y3zSYXxlpvVGu9H5YP1KV05BpAS4QMmetquZVKWo4IhMVnryZRMK3IlURK8M3TtDiWgNxbjDZnKkSojz3MCkTwvMNaQS0VvBwafHlZCauqRL0xw1EVB1zap046BQim8FJghoHVOFiLB2tFi7dBC4G8ts1KO6c7wutDejhxCQIWEFPzlRIVfz3XrJAPSzHa0kkeSsy5ya5YIIElJGSEiPESZeOdy/BqBz7W5EY8Q8XX3e3uJ/y//IoSjWX9EMT/meuuJw4COnlwoRAwMw4ALPuX4SUnT7ABBKTPW12smWiLcil27pSozrtDUpmFSVNxEzbDbEqTgQmZ0mzXeG7ZRYrctu80NuIhWJbY+Rs8Tm9mojCAUOTATmoZImdfU3rOLhlZZfF4ipjP0YHCmJ/Q9wg7Q7RBu4DYHTiqJc4HTVy/pZU43PSJ2a/ZtzwGCzgquEcRCMmHCTIM3FiEVe7kik1Bax7VpkViKoUI0lk/dc1bLCSeHd5jLyKdix03TsMyWBFly/857yKunXD17gSkajh99hTe/+gj7lsFFSbe6Yj2eZq9ePufw6JDFwRFRSGxvyKdz9OgLkEoiEXjv6DY7ri8vePHqlD/7kz/lN77zHeo8Y29/STX5FxwvQEQJRZYpijKnqiqqqmIynTBfLqmzJWaAq+tzrvqWttliXUCX9WsNa4yKTEnKqgbjGKxmtzW0O8utZzx4x267oW1bhEikr6JMTITBCIwzzKeTFHOuNFEEnEkPBSXS3KwoCkqtEMFTFgVxzOsarE2ErBhxxtCbnlwrlBR07ZbPPvmUNx6+QbvZ0PctXdtxefoyzS37jkIJRF6QaY0xPWVWUNUV3jpO7p4g8wKpHUpp0rhYkGcKIRRlnmGdo2sbrLU8+fQTiqJgubfP0fExz5+/ZHFwyPe/9wOGYUiW4OBv3eJkQuK8xVqbZGwhUOaaTAq8czhnyLIalWkCCXa+221QMrEpIiC0SA/ErqXvWrTWY3CmwwVH2w8gFVlZj5IykZyESpJlGmf6xMdwlqgiSmfUxHSUHIuQHOfPSEXUkjjaqyPx864ujh0iqUip8OsvuOmS42LrFuM44gDDOAqQ8FrDKwRB+DSvDeo1SNvLiJQBH2X6OtGPI4WYPBMiLengFj0zKhxee1Qlvrti+/zPme29jcgOaNY3bPoGbw1agBJgbD9yM5J78WaTkrBVUXC+ukrJ1K3j2nSJUaIHzo2hHXoymUA8XdOCd+xUhpQFy8O3mU4XbF1kZwZyZ8mspSOio0DqyEak8VTIMvZnM/CBo6LGmI5z12CMQzqP6na4dkuYzZF9B9aAHYjRE4WBIFmdPcNlGW/8zX+DOKlYu4F106CVYCEnlIUgOI8vFIuypNAZlcxo7UCe55yokoXK02nVDsxiz77MuXQZ2xLuzuZsEDwJWx7sH/PNv/Fvc3dyjDWBarlkcXwPKVIiuFnuU+8fYIeBVx9/ineRrKxQeUk3DFiT9PdCCprNBmstSiraZsuf/OEf8otPPqMY8QRlkaO1Ji++pKTy30GnKyToLMmY6nLCpJpR5BV5XlJXM6oqLXTaZsdms8Y4NyZAaLRKXnupMqL1SLHH0G+RMZBnin4wtG3HetfgvWdSVeOyK0sFPs+TJEbUr7WOInp2ux1VUTKtazItabsW5yxCRPq+wTgHMsO6pB22pk+FyFnW6xXz6ZRMa7abLR9++AsevfU2jx7e5+byEqQgqyY8ffIYpU45OT6ins1RWYYSgroomFQlLk++/c3VNW3bppSGQrPb9jhnsdZgjOHs/Iy2aajqGfdOTvjGN7+FyjKurq5Y7O9z58EDpovZ6MkXY4coUSTb6XqzGjWvHmcHmuBRIlLojMxHfBC4qChksqDmWcZgDFmWYUa9dDYGblrvkHa05pYVw2DItafrWvKiHheSQEhLuyzLRu10TAYI5/BhwMnA3qRCKIhZQ7QpJbdF0IqEIbw9qsex2ogQkN6jrEMBmfDIf0ki2BMS8XbBF5FBvjbtvxZgxPj6r7T0/WIW2vj5MbvrVvVz2+lLeZvvdft7bz/+4pw30nendMMV9fH7lIcPMOeXRHdDNwz0Q4d3nmwcc7SDwQUSrD8YpPPMtEZ7SR0ylJQpRD4GJmTkUYMQzOdT5osTZgcHzKdzms0Nl9sbht0W4S0DHq8iCEWVl9B1BK0o8wK05nnwSBk50ZJ3732V4vKK62bNttsQsgyhC6SzME33rOx2uGYNpgfviN6xe/mCj/7xf8Z7//Y/oPyN32XV3rA8e8HMtHgpmZc1ZZWjhCCXqehVVUkpUiKwcwEnJfOqZKIVZbPhs+0N+d6SE1nzqtggtGQ/K9nLCoa2p2t6jnWBKnIkAe09sshwZUU1W3C13nL6/IK94/tMDnO0Lggh3bfESN809EOP0skUtVwuWa9WvPvGu3zwnW+x2Jvw+MOf8k//5C/4t/69//WvvOe+VKcrlSLPS2bTJfPZIWU5R5ER/BiZLpN3fDJbkt1c0pn16/8+8UlNgpFbSWcceTah7yUuDOx2A6vVjq7rmNYTqqLAWkMIjq5tcUNPJuHocJ+qrhPTwfZcFxk36w1d37PeGtwwMJvVKJXRdT3Oj5HaPrw+3EUpiT7Qd0lT68ZAzel0SlEWvPv1r3Hnzgl/8of/hMVizsnvfJdXLy94+uQx69VPEzS8rtlfzNBSstlu2O52rLe78U2YijCCJONqO0Cyv3/E+x98i6+8+xVm8wnX19d0/cDR8TFvvvM2n3z2bJwNF5SFxRjojUGQEoHNkObVUmm8NZRC4gEnI9YnDGTXG7SqX8NnUupykhwNbsC7niAUNkSCG7DeUVZT8rLGeIvynr5rUEozneb0g0FLUDJLJhMREB6Uc5Q4tI5UKrKcV6hZTgwepzRbF7joDBvjb1OwklFDiPQanCPXiqkIVCKOqMFf7xVDkgJ+UeIWEK9HVilJNi0Ib40TUoyt8G1hTjHWI7diJH6NLe1toSaOyoXIGJ742ksxys9yZF6z3LvP/sEdjo8ekX37r/OLzz6jefEpu8sz+t0WNdqJZ1VGpnOm0ylSJ6OKzIoxlSLhSoWQ2ChxKiNWM3ReYJ0j6prTzYar63MWMRCFZC+rKQfHhTEEoRDOsptM8PmMIHIO6op9FxBlRjSGs+01f9quiT4yUZq9suBytyPWU2QU5N4wDA1+tiSbzhFDg+9bYrslDD1mfcNP/pP/K4t2xfaD7xD371A2V8xtxwfe8D6aLIIWkmmZUkokis5Y2hDYqyfJjB3gxvTE6Njb7bD6Gj8pOZotWG2uWcfIspyyd3SHoqxStFIMENN+aGgb1jfXPH32lDqvuPfWI/L5nLyaJGPPGJpgjUFJxXw+Z7eDkzsnfPvr7/HeV9/n8N4dvE+nkK7vvvSe+9VFV6RuZz7bZ1bvU2ULclkRvaRvB3LRkuXpxsx10n12XYcP6egbraXrBvreEqOkyCvW6y39MLAZYTLeOQ73D3E+HcOJgTxX7M8X1PWEST3BWsOuablZXbNarzHDgLdDmq1KQT6p0pHZebyzWB/ZNQ1FlWRlhNGCqdJTcrvdJLF5mUEMmN7w8sUps7rgzXfe5NWLV2w3Ow4P93nj0dvsdjsef/IJn3zyEZ989CFd09INHf2o2dUqcWyLvGC+2OP+vQf81m+8wztvf4XpcsF2t+Hy+oxnL9LM+r1vvM9bb7/Lrml59fIFP/jhX7DdbNJr1xrlAqhA03bjQsKiQ0zzXZuSelWeY30aKYQsYJzHBkE+AoLyXGPskNI2hKDbbRmcT3CeEJCqJ4RAnlV0Q0JfWmcTaTM9bhF4JnUJAygnEU5Q6QqyQJVHjiY506pAKEUf4LpzyG1Dd7kiuHFxJkf4SxBoBNM642iSMSsVVfbrj+sRr7vtEd0ox2QRcatY+Dy6O4VJpllukoGNUTGCMRom8RnG2ppsz/ELs+uxsAtB+vpaocp9Dg7f4WD5kLtHb3F4/CZVvUAIyVUz8NWvHfLq3ntcXt/Q7a6Ruyv2hKcHOmdZljnLyYRd12L7nv35DKlrrpqWVdfTRU0+P6BznuH6nJvdhpYNcbfF+IYmOrquIViDUhJVTdjTJVnXcrNrkGVNCD3XKnITPNJGltWEOycPOd11tK5jg6PSEya6xniPUWCtg6xESIEXgSossTcXkJeIriOajjBY1v+v/xw5dIT33qeRsBssZ8OKP7x4SYXh3eWUr072eGeyx8F0RpVn5HlG8JHeGno8pVQsVQbOc3X5gn19j3vlgvnxe9zNLXuzmhgUeZWKrvDJAr+7vuby8pyuaZjPFzhnaTY7Zm2fOvZRtd02Kew2ywsQkuA8cbB87avvsH+YuAw+eHbbHX3bf+k996UQ87KsqPOKXI3fRBBYE9htWtwARTUkj70IlOWEup4zDB1Dn1xMTdfjY8rw2jVpI97sthQ5LGY1oUpbQW89mRRkOmO52KMoJyAU66aj7xqub64Y2i3BOZzpE3RFSxSC6Rib3nUJMNMPlqZtkVmOlpJcSZK3QRMwbJoGrRUH+weUVcVHn37MdD7ha1/7Csd37yJ0xtPHT1htNlxf33B8dMLv/e2/xe//G7+PGUz6IbQt6/WKEAJ1PaEqqxSxXtYIKdltVrSbaz76+Y/Zdg3z+ZK9w0PKomS+2B+3ug3Pnz1laBuGrmEySWYFSHlou6ZFCUWIFucGtNIM1qFznbpqUhCosYY8z7EIBhfpjcGHmLJ0kKM5zGNMj5A6xasjybUiAkVRst6sRxSmZjKZ0I96ZKUkRVZgMcmNZh1FJtmrJhwsag72ZqhcsR0satNhpOS8aTDbnijka8OAjJBrwf685u7BjP15xbz69RfdmKJJ0jx3TE+4xSPcTl8T0AZem3lvs8fGZhc+192K12qH9PvHz46SOYHQBeX0mJM773Hn5G0OD95gsbhDpiuEULRuYN1bNsZxNQS2IXITFU21wFVzxN4xJofOeZoouDAD/c0VYuOQUVLsNDYE5Laja3Z0RcFktaXPNbkS+KIg367QdodwKfjxuJhx1p6yFTBIwQ5HVpdoUaBUyRt1zdnZK0xW0yvF1WDZxg3V5IilXHC9vmSnKmShWRYF6+sr/GRKnEtqCV1zQ+8C4mgKpkV4h2g3uPU5YbMh/Df/iCpa+ve/SpxXBGMZYkkv4C/aNd+/vuYrxZT/0YN3eGexT6YFWZajNExQBBdw1hFRvHPvbQ6Wx0ityPqesshRMuUtZnk+GgsTonZoW0SI3Lt3n3lRM3Q9zWbL1dkVSzLKmSTK0egkFVIKrDXpPTObol3BbtdwfvaC1c2K7WbH7F90kSZEit9WagSVBwgWjHc4ERhag97tqOoahKLICyaTOSGShusyQ8gC7wPtKAkTQrC3V2Osw1iBJqU65ALINJPJlOlin8EY2mbLMAwMQ4cZOiIC48NofEjQ88O9OYOLXN1cE0lHgW3TYJ1nGAzCO6rlAiUExiUG8K5p8QEQkovzM4Zh4L/6R+dUVcnJ0TFVXfPGoze4urjk+dOnPHnyMZ9+8jNypdnfP6SYzMjrmsX+XuJLGMt2u+L64hWbzYZVs0uJv0pSTebMFkt0VTGdzdhtdjRNg3OOFy9fcnl1TV1XSVWhNINJ/OJu6CHGkXLWpqXhKGsKIRKDR4s0e3Qx0vQDZR0gOiIS5yxSaYIcOzWpyFXOYA1RKYwz5FkNUpKFhHlsmw0qS9CiuqwSuMinxAchEsIw2IAbUvqBVJos1+RFiUORF54itxR6jLaJ7nVViiFQFprFfMLR4YKj/QnzSf3fvTr+93R5nzpUKQQiiM+/3yhG1nLq1lN7K8Z7zMOomrldForb7ncszMmhJ5MsTlbU+3d4+OAD3nrzOxwfPWI+OcSN38PgHL0X9M6zHXqEFLTGsusNaxPYbm4QZofZbtldvOCTi1esrSFO93Cq4rCz1FlGXxbYyw2hKCHXzJdzpt7wIKt5uWs5u17jzl7QdxvsdEIIkUoqFvsPeDibjBJBwcXmhk2UmKImaM1L68kOjplnigPvebW6opcTjNkxLXLmQrH2A15Hdr2nqGssYJXEqpx8WWD6DkSG7je4ZoXMlggZoSiJuw3ue99H7k2JX3mLqAvYWUQLWIXy6c/z5W7Dsqg4nNRoEZlkWYq1mkyZTQ+YTPbIVUZQiugD1XxKJhvaZkc52UdIgbOG0A+EALKsyLxnryhYzOesLlc8f/yCcHaJKmtCpgkENustrm9QPThvOH/2govzc3z0XJ++ZLAd282WN998gyL/F7QBpxFXHJ1Q6SYKAaIPxOiILsm5vDNJvpQpqqpMVl3hCVGTEwjCoF1PXQiKomRwA2dnA8FFTLdDhIALjrIssRF0UeBDAtp0w4AxPUQwxhCDZ1JXlJnmYG9BiILV5oYsy9hutjRNm6JlYqTte0SR0fcdeZ6nbtCmqJkQI6ZPnIfV6pqynPD82XPefust1psNeVlwfHICQnBzc83zx0+4bi74+V/+gE3b0lvHtMhpxpyoXGtyrQlCstzb486DN6inU/KiQkrBdr2hyAqmsxmm71ivblivV3z44c8QwpONiaxmMHRdg86y0VfgCSGMDrTRKaVSmm9vDLMypUV0fc+2aZlOEvjDegdYqqqmtw6tC4Ty4EViXUid2MQidXFlUWGsZ+g7vA/kWY6SSSKjdArWCS4Bd5zx2MFiekPbW1AKaw3eufSXTwwHGQVBpFKkYpI8TeqC2WzCbDqjKn/9Eeze+7FojuSwmDrbcDtrhdHLMbrNYgSRuLjItPR8XWS57XLTHCEKwfzobX73t/8t3nnjWxwsD9FCMYTIzjlaE5DBsus6ghDgA93lOTfX13TNmub0BY+ffMTjx5+wajdQ1Mz3jtCLAyblFHt5hS8rLqf7lHsH7AXPYd+y2mzYygwTenb9NacSBg86Ro7mNSJTXERQWY7dXfLR2WeJ6aEL9ucL3jp4m9Vqy+AC59fnbAudjtux4ETnPKxmXLuGNjZswgQ9mTHPMjaXF5jFDJdXLPKM1eUZrpxSLvbJJYl5PdsnypwQDXoyx/U7xHqL3d3AX/yAyb0Zclqz8wOYSOk8d4qCd5aHlCrDktKEG+OYFCXTsmZ//yFBlngp+MuPHrMboCwl7x8oCtkxbFboYs4wGELTYJodIXqazYbrswvyomC6mCKznBCTfDLLUnqMs562bRk2a4osAym4OD/l8aefMnQdRSGxwbBrbvgnf/wJm92G/+3/7v/0K++5L9U3+JDA4X7MwIpBjJBsg3MDuRAIlSDcVVGTa0WWaQYfqKuK6XSO94HLy1dsVtd0fU/XCXY7S9c2ODOQqzRWqOuaqp5junZcODU0bYOzKXAx04rFdMJilv7adkMSaHvHepMWcoNNGWQoSXSWqAVDP6SOXaeEYiUEzgd6m1gMmcpYXV/R7Dacnp4hhGC1uubm5pq6qhiMoZ5NKCYVXkB/fs75i+fstglIE0mR7Y/eeovF/iF929IPPTYEysqxmC8IPiQFgXVUteT6ZoX3ntlsxtX5K1Rd4QMMJpHAfAw4n7LpQvAj7T/BViAtY8LIeNBZQZml4EqpBJOiwpGSPuhbHJLlwSH9kEYULnoGMyT7ttaJvCYkeZFjjMGZDmM6yrrGDoau76jqCWVe0JsUWdJ0lvObFQ7HdJgxBM+m61nvGtouJQ24LxYqItH7MUIljUWGLwcy/fd+heBH62cgRgU+/fkKkey+6TeRRhCCpEqQach7C/cR8vO5b7oEMUoWi/v8wd/4ByyXDwh9z6tX54nt4Qx972iuVzw+/xRrAmFoybzg8ZNP2DYrtqsNzeoS53tkFIg8J+qMJnr0MCCl5qia4HuDN5e4l0+5KhXn9QwtQDvL/nSJmh7wYnMFfQ/dlpeTKi3KlGI5KTguTrhqN4hqSdCaq3bL2XZNJGM2nXLfT7herdjljhB6XhYJ2nR4dIewW7NF4vMSU84o7xb0bkfMNH1eM330Fruuw0RHuVhgdi1BVsjJArob1NAghCZmc6IuiNtL5j/8FPnb77NrLKofKLzhaDJP9245pdQFKsuZFhlVOcPqOT8/7ZkvM27anlUsyaYZV03D01fnmOGCQuUs74GxA+31Dduba3Rdcv7ylCc//QVvfuUrLI9PWBxpDlcdqp4wnU2pZhNC01KWNb7vyIsshXRqzXQxgTDw3nvv8PjpZ3z/Lz5h07b4f1FH2vjcJvjI0Bka2dHnBu87+n6XMp6yjKHTQAK5aJWjtGY6y5gtD5hUM+xgsWag2ba0g2N107Dbbl9bB73UTOspVT0lArvdjma3wRmLcw7jLCIGFrM5Rwf75FnGatvQdgNaSbp+oO16nEvW4YhAS4USoFUqVkpndF2yvRZ5xjBYnLFM6xohIpNJTd/3PH78GVVZsr9cMpRVmt/udggh6LuOwVoOjk9AyvE1bdOWOARsSBSqzXaD95HZfI41HucC+weHRB9YLJf0g2Wz3XJzdcW0rrmK8XUUknXJ9quFZjsYhn6EIt8yDmQaHeRZ8TqtwAwDRVXjSQ9HF9OfgQ2OEJKl+PLyirIo6YEsWkywtF1HlhcEH5nVFYtJpJWCXdclTW+WpDO79oZ+6KjqKVoqnPXsnGO923B2dcl0PkPlOa0LXG1bhj5J9WK4hX4nMdXQG7brhlWtsUOb6Gu/5ivEAMGP4xOV3Gi3fIUvGCdeJ/+qdPKT8lZxGyDc4ivTJaIiKyreefNv8tOPT/HyFb1xxCDZ7Qae2Y62HziuKsqu57Td0vcbsiAojGV7dsEgAjHPwUZkljGpa0ResdAVze6Gpl/TZim5ei8rKboNlzuLK1tcXpDFwHOXRlxonYJXt2u2Z5eYeg71lMDAej5PVtfJjOOipPvFK7ZZRixytt2WrlCUb77Bicw5e/kRXmqcitzs1hwdH6O6hrUUNJOayfSI4uWHtHlGO51xNMlouyf4eo9hsqCqW3oTkUoQzRqnM4IWxDJD6gxdlnQvV7gPHyMXJfeKklwq9vKa+/MD0rlIIoRCZlPE7D5PL7dsrm84e3XORgma7Qp7ccVs/4CZvEYOlzx48A66rMiyAqk1SMnu6ppXz56yWt3wSCuq2ZTZfI43gZv1Fh8C1hr63RZrB4pphes6rl9ecn72ima7wQfDfDnl3eIdzi+2fPzpp7x89eJL77kvmemmWe7QDWw2O/ARnQlcGNLIQCa8nXMDsQuE6KknS7SSTGcz5oslmSowysAVbFvHarVlNaYyVGVB23ryrGRaTxkGR9u1mCF1r8YZrHMQApO6QijNrhswmzYNt5UcM61GuRYiRUJrjRotvj6kpFczmGTPRaB0RlFofPAEH5iOcrTNes3Zq1NO7pww/fZ3uHv/Ps+fPmWxWLBerWi6pCfe7W7GYMicsq5RUnCzXjMMHW3bpFmy2LHcWyZ8pDFE76mnE7bbLT/68Y/QRcn65gYpoChLCGCswfR9io0XJqXtjkCREGPavIoUXu5GK6p1EaRD9D1CRDb9gNpLTj6lNP1gUUoy9C1ST0FpVCZRAUJwOGvQUtJZyyRPMPg8QNc15GVNXU3I85KubWibBiUVw+AQUnF1vaXvdjj1gnw6IaicXYyYkGa4t7SxGMHHSNsOvHp1TjAtdalx/tcPMSeIhCYNyeIrZUCE0dIr1eti+xr/KFLBDSEpFtJ75JbHe8te8OwvHoFYsNltcQouzlt67VBdgsW00fFkc8mbkwnaR4bzCy5Mj7ctOpeIrqfYNnSlpA8RszVk2Yb9ySP2JhNod3TDBm8sF1VBNp+T64rDvODudMnN1SXPLy9xeU6c1Fxmivm9+0yFoPKKPeDTm5dszBpdzmi9IRQFByd3mJhIu77kOot4VWJ0T1xW3Hn0Ho1vWdmOYVLzynoe3H+EHRxNFhlU5OFv/DY3py9YK822mrD/3vtcrncEnZNNFcOLC0I9Q+8fYc2AaHviYKGo0VWO3K4pbzrKWc69vORodoAMEu8sVZ7T9i3brKIPkvOfPOFlFBjpCTc77j84pnA9w3QOwaJcS5GVHNy5QzWbopQkKwuUErx68oRPP/mYWT1lvpecZPnYgDRdT9M0DDi2q2tEDFSTmnWz5tnTJzx+8in9bsXJ4R5D37JcLPid3/lr/Gu///f46c9+8qW33K8sukqQ5BW9pWt6MqUpkERhidrhpQSd4k2MMVgfiFFS1VOKIicTwGhmOL9csdm0dG3S1CkRk0wpeParikwLmrbDDD3D0OGDZ3AOYy2Zkqgspx08N+trlEp6yEylN24+Rqoba5IGUgiUFEmzah15VeGsJXiXlnA6GR2sT2OLsqqw/cDPf/ozDg4PaD9t2N874PjeHbSSnNw5Zr1eUeQlwTrOz89xPrlTgguUVYEQEmcdN+s11lkqOWHX7hBCs39wSFWVXFxeIoXkxbOnGDPQNTu0UtRFwbZJzh5rB0QEJctUaIHbBc6tpVnKHCVIYx5nUEphokFpibWebbOlyPNx657RdDuqoqBvGrJiQoyWoijwQeNDIHqLM9AL8N4hCWR5Qd/tyMZYHqMVzpqUzTZu45VQDL2jNwOrTYeoaqgKQqaJpJ/R6/198PSd53zo2axWaBHxxv3zbr3/Aa8k+wpRvHbOKW4jfNLnX3e5Am6jgoUYTQ+EUcMrkPLWgSfJ1YyPP/6IItecrm+Yl0tunj1lpyR75YQ7zvNye8MvLgIZkaW0NO2awVucCwibopkKoAjytcb3bHUBOiNTOUezOWzWPN9tkuSyqmnFjqbt2D8+4cHeHkpkqMHyUey49oJMZjRKIoeWo85QrBv8dGA7qXkca4SqyOqS49mbhPMLttlAyAIrIttqwuHeXeanT9hmmpDnvOwd94/nmGef4hYHbLzgjb05P9ruGHRBsZxThYG2nBKLktJ7+sFSnRzgzi8QeYFa7cjMwELnTBdLBtux7ycsdcFeVlDrkhAck1yzPzvAFYf89GYgoCkKhbcBYxv86iKFzgbHQimO6ilHB0uWd+9QVAXeOvCebtfw9LOnvDq/5M637lNOysR4yTPq2YR6UnJ+fs6UPYqyIAsaYqAZWi62F7TlwPmrU/b2JlxfXXN+ec0//kf/Fe997QN+7+/+3S+9476UvRBCwBqH6Q2utmRokA4YUuelEy80uIi3A34X8UJQ9QNa9jSt5fGzF1xe3UCwCcHYD6jgIViKLCXNnl9esmt76lxhrWXTtJhhoMoV08kcIRTOdCiZEoEjSeQ8DAlfGMcM1zTrdBR5iYiCLFMJ0BJTEZA6QhytwlEkWhQJUNL2O+KlR6uMi/NTPvz4F7z7zjvcuX+X/f092qYhliWL6ZTL6yuyvMIFi/eesiyoyop+sHiXZrCz6ZLF/t6YNOxo24bHn35G3/dsNtcEH1FSMJvNcZs1w9AzmKTwSI2uR8Rbi22SX0mdpCsxpuVaFGDGwpurHCEiXbNDiCkCS15OEDGBc7CGGBUyy/DRUxQlfd8jlEQqhYiRTOnRoBIJHjbbFUVRIiLorMRbO84wI/v7J1xeX+GjQ8oioSZjfM0uuHV43bq6/Bh02ZqeaA3R/fo73fhXPooxEKL4XEQ/LsSSlTdxGVJHzOvuN35hdh0jCKVZXe+4vrpgVhWsr9dc84KoFG0/MIhzjhZ7VFqh2w7rLZchoOdTpl2P7hvW1tLlGq0ztJIcZTVd17FtdpBpZFFzFQPZZMZk/wjdG0rn2RG4WU7ojEFqzVx4lrs1s8tXdHlJNquJQ8/L+Zz8zfsUWcXdYoZ7/FNsGxF7NU4JznQkf/Q2x0TOH/8YHyJiX7JuJIs3vsKd1QterJ7j1ANCn3Nvf8Fzs+W69bzxxtscqS2XlWNbR37r4CH/rOmwWcVhdszL8xWmmiCvb/BZhTAWqyPrwTEpCma2oNhYDu/UqAjzPONwskddFFi1x0tf0QaLefmck4kmMw4dGq4/ecpalzQXa777r/4WVVWwf+eEvK4TJtWYlJ7cDaw3SV4qtWIwPe1ui5AQZEBm0FxfUkxzJnvLtLRutlxvVlx0p9ja8e5vfkDzvMG4kre+/jXm3/sJH374M77727/7pffcl6YBO+voup7eDoRQIYTAxwElQyq4OoE9VMjBJVuqMZa+7fA2cHW94/z8DBmSCLvpOoZhQIQEqzla1OyGgYubFVVRcLPdsdlt6duWXGnKYkYQMm3VXaKPhRipqppd25IJsHYghIAPyUWVLEIR6yx1meNHmY/WEiUFIniyIiMWIzyn2SXso5QpjaEu+bN/9mcMw0CMgd1uy717d5nUJXmmuNaKO3fu4HziRkwmEy4vL5kvFsTtjpOTY4JLyRAyLzh/dZqOYG3D5fkpN6sbjk9OeHV6SlWWyUUUA9b0iakgzevCx+jmEviksSXRzCAQnCUryteWWz9aFqM3NLvtCC5vESqlZIQoMW5LnS1TQbeWPMsJ3oyOuoCxFi01IVhc8Ay9S59zHi8gz4r0dcyAkIK79x5yev6coBROSWwMaTl1uzy77XVDHFkFIR3NPwd4/ZqvdIoQpAfJaxbaOJaChH6MyJGMlrTqUccxuBJuHWi3IZdKZ9STQ4gdLzdXzBd7nBuDK0sezGDb7jhzA1oo3lweslrfcNGsCAE0AYxBh0gYBqJzOB84mwTy6ZRa1Uyzilk55dN+Q5MlZ18xqZk7T3H+Cms63N4hGYGrrmM1XyAfvsFMZEy3K876lmF/gXWeXe6xwlC++3VqqQirS85FjxcVViuyacWDD34TE+Dy8jFuEOyqJe/s3aPpNrRhy4vW8duP7tE//wTDlHvDnJfbn3CgTvjAlzhzTVUGptNH/MbJG6yUxkXQB3sED5U6oNu1SQOsNUe5Yxg65jHnrZNDtBDUmcaKjJ+/vOayXXGz2TLVsDaBt5dTVmvLbrbHR23g7skDhE70P12WANi2xbY7XN+TZ4Jvfvs97t/fYzop2K5uEELR9R1CS/q+QSiBMQOy3ZFXJUIL+tBSTCf87e/8AW8cvsF/8h//R3z0yce8/f57/L1/53/Cf/of/t+5vNx+6R33peoF5xLf1ntPwBNiSnZVWQLahNFvrnREyRxrA1KkOW/X9ZydXxCsIXjHZrvDDIa+a5AxouuK3gbOrm8QMdJ7R9O1aKDISqq6JkjF0Pc02w1Spkyw6XRKiIHoLE5L+q4jSgVKE28h5ELgRwi7eG3BFLRt8k+rLAHNnXM0TctiuWQxnXNurljfrMdodcnpqzP29/b56KOPePDwPqYfuF7dMKknrDcJN6mznPl8QZYVFFkCc0wP0nLpxauXfPr4Uwqdk5U5N6sbvLcMXUsxWjWbrsOMHbKIEWNTtpOSKa5cyMRqzXUxvqaYYsNjwFlDXk3GyG0gJvuvc56maShKR1mmmbUjcQZM25JXk8SrKGR6mCFwpO461ZwA4yJJkFQsxhkGO1AWFWVR09ueIq/Iqgm9+1yKEEOC23yhhCFCHBMXJFHrJLnyv0zg+nVc4nZW+9p2Nj4qhPilRVr8Qk8shGDcvyHVra53/PUoEaKkazuikgQHG2EwQ09vOnI5Yb7cZ7h6yWAdz9uGyWLBMlgwPTfe0s1miBgpvKUyhsb19GXBIJLsS+Wavbxkv+/Z9gNIi900vCgKsuMDiqxGDwPi9BWXJ8cE4RC2YVAZcVZRxiXeZsRcEczAWqfXoPOc+3v7LPsNbZ3jxcCq6bmpa46WNdWpZSe2lNHzamj54Lc+4Acf/TEqqzjuppj1GrMLbK4f82bb89lPv8+H2WMePTjgneUed9+6y8ODG+rC0sQj1GyLbzpkloMU5KVEjYGuLq+57AMfyEguHTYqfnph+Nn1jjKfcG9vSq4U56eP+WeXl1xdvmKIgmu95NuHMzanL+H+HlmuESEwdBuGZoPtepQKHJ4sKCqPDJroLH3bYIeegGd1eUEUlmAH1pcd5aQiiMiinvMP//7/Ch0if/SHf8jp6RUqdvyTf/Rf8Jt//W/w9//dv0/s1Zfec1+q043W46NDkKJ0pIxILcl1Ih35aDEBMhHJtKKQKS7cW0ez6zFdR5UpHp9dY4eBvu+wZkAKyLIl1+sNzhgyJXEhkEmN0hpdpCOds4aubbHWUhYZQSSCVj+GULphwAeQOsMOHURPpstkZ43gvEsyqxiIPrDrB4osUJaOosghU3R9ix0qnLdIKZhUJRGRjA431/zxn/4xe4sF9WRCUVWc3L1L9IHBWHSuWF9eM5nUVPUMHwNaK2J0DNbw/Nkz1jc3GDvgY6DIC5RQNE1DVmQorRm6lmHoCN5/Tqki4j0ooUblfYo9FzHNdQkOosS4Hik1skwjAoRMeMzg8SJiraDMa4SSuMGQ5wVD3xLEWGAjIFQqyKMeO/V1aeZNiAxSjeL/SLfdEqxjNl1QZAU9AzIv8MHgY0TElA92e+QWY7oAMVG3UqqHAKX+peDpJrUCCS42dq1RCnyMfwXKmB4hrxN9R+davOXvjjFFCQ2UE4DL1Q3Hy0N+3lyxnC+YNC2vJhk703O3mrMKWy61ZNd1zLRmYTNuwkAoCpARN4AZLEVVI3oLtUIqyW53w8fjz73OchaN43GZ4yfJ8DA4yzSDxf6SA+tpVUEIDSbLOZ8tkA/epJwtedMFPn3yl2Br1OEhynhOfUG+d8SjquKT858T8hmSHDm03Hn3PVzf8PTiF/DGHYTrWYqcqSn59MNTZt2Un3/0E546KNotWZFzciTYnZ5xFg27t0659wd/i+/eecipmOLye/z4fI2VGtG9SEkdYUtQEzIpuewsT8+eYnbPWHOXy+m3oJpRB0N/c8VPXr1ivbvkJmYoHdnLZry7zCi2L1DFgsXyAXlVELzB9hv6ZsVmtWV9vcKajn7YUugp1XQfNRL1tts1zWadnKDOgIB2s2VSV7xx9AarlxdMFzOePXvBbDLBGPjxhx/RdS1/7a//TR48+vqX3nNfQhkDH31aGmjQWURnEZUrdKaTrzo4fIh4AlEJCjVBjfOw3XaL1pKb1TrlwQeHHYa0fKonNIOh7Ycxrh1QMh2FY0AJhekahJC0fYeKEY9EC0XTdFjXE2IKs0RluBCwXUeRa/IspzM2Wf1MRCUjVyJdMS5EYiDXioCmbTsyldgJ08qhtOLmOqVAXF+eY50n+sDpy1ccHh1ztbomU2nGZ4xhtlhSFCVN21KXJW3f0VtLXnb0Q8JJOjdaaPMKmeVEIsPgqESGMQPRpUWfUJro/ec8WpmoUmGMp08ISYV3Ay64kQtsCD1kWqN0hoCEZXRuVD54vHHYoRsLRsDtNiids7GWST3BWQ8xgbcnVYUfC6d3hooyGTJ8T4gRNxgatuRFTlFW5DpLQKHXReiLYTbiC3IqCZlAoMbP/PolY+mhM34vQaQbxad/xltQwjiUvk01jiMm7HaGe9sZR1Kg5bTaQ/iKob+gPCrJLi3rYcVBvkBudjg/cNHvqMopehiIwbHb7nDTimo+I7+5wbdrWqWwi3nSUkvBQcxog+FKeaLtkCSaWL634P5guHQ9IZNYY9lk0MwLimrKo9kB5uPHfKw7/ABCS4T2XGrFyVd/AxktZ68+oZ1NiPkcOyiaCIdqyXZaouyK8/Oey/0Fv31c8eLmJe94zXG/TzALfvyDv6C5vmF2vWHfB967/yb333nEt7/5Dd557xuUWcazV8+4WW/Y/eWH3DkfmD16xPe05lkVUd6zzQq+VUS+PvTMl3soJJUP4FdsN1te+j3K0jK/uUR2F/zR6TUxm7LJFshqQs7AfrXgTogslOZ4Pmc2n6EleNNi+oZms+b0xSlXV9dst9fstg1f+9q3ubvYo5xMaDYB79JMX0qJj6C0IsbAfLbk6vQVJlgeP/2Mpy/PefL0KbPZhNOzFbtdy8XlJe+++3N+/3/2LxDBntJeI3kORSHRWTJC6Eynbo4wZglZovBkKh0TtALn3AglD1g3UOaSi6GnH4aELsyylElmhrQxFyBDUhZ4wLaJJeqtRQlBlMlF1lvL0HVoBUiNjxItJcPQpfeHSvwAIRwhCKIWqTDfHsuJSCFH1mlMqRa5JlOSSV0zDAN5kZHlGqUEzhisdzSbNbumYdM+5ulnj3nnnbew1rHcWyAKhY+By8tzqumEZy9eMTQN220zJhlHlFRkmUx4wywtxIZuwJiBtmmwzhDGtAghxHi090iVj1v0FPqZ5Rkg0Log2oFIRAHODsmaGxxlNUkLQiEQQo60N59sq84RAesMZfl5HhSElMXmHS5LiRQ6y9MD0VpCCGNBTYu7MVAcnRfUZYU0Dc6710yIyNjxSka3lkzULSlTt5tCx/5/KZP/f71uHw63TJrbB8EX84pTIvDt7x+zjOP4YIly5DF8/gixg0G4HYv5kifbFUdonhY1m7ri5PqK9eaK1cEeRimObcCsdlwsSqzUxGA5OTgiuJ6WQPAGj8fqDF9XzLMpoVsTQqQPsDUDH8oVZV5wf3aMuN7xGRuMVCm12XZc9StOHr3F0dUrOuEI2tKtzjgr5shu4M29ffZiRtNsyUsojGO7seTTgm/s7/GXT79PPJqyPy+Ydpb7jcQ8ueC//uFT/tajr6GfvkA+ecVsVvOdb3yNb37jGzx8cJ8Hj97CBYMdDMezOXOVM51UNLuG/NVjTnzk9ydT8jsPkW/e50BnHLjfZDopUBJmyz2csfzgo2f8H/7xH3L2F3/MbzxY8kRIWufJ7BaCo2o37N+7z9GwYonl/sk+b7x9j6ou8KbH7tb0zZbN6oZnT55wtd5gvWMx3ef4/kMOju+QFUUaaxhHpnNChCAl3qd4q+XRESpXvHz8FOsjD+7cY1rVdH3D4f6cz5485kcffsZPPn3C//5L7rlf3ekqQVFpqlqTV5KsFGS5Is81Ut7OTAVRBJSSlOWEMqshZtjeIESg63vqssANDus8gQTGcd7T9W2ypAqBHeeAOgq6Zpc29jEtCRKkRdH3Hd6lAipkmWaEUmK9S8sbrZLUzBps8COQO0XlBB/w40bdhUDfD2RaUVWSSVXivUmZSDLFm7sYyZXCFQLTOlSmuTw/5eXpGdY6JpNETNs/2Gc6m+JMT9sNvDo/Z71as91sWK3XKbo9OLI8Z1LVuBjQMeBtmpt2XZPUDUMyRYhEvR7nibdzc/9aqRBHB9QtI8EFB0oS7KiaCB4jJOV0npINiCmtQY+6ZGfT7DuM9FfvGUyKi49jZ+2cR8qQlpNEBjOQ1zUxOJRUSUNth9QFNjtmkymF3jKMuMw4do5Rjl1uEK/XVaj06ygJ8suBz/+DXLeEG3H7wPj8En/1o8jrznc8P/H6E+MvRRlpTENWTmluztkrphTrNV0cmBeaHAGbG/qqZBMli8mEcrVhmGqE0GyGgcnBAQfG0zc3qEnGWkReeYuKgZPZHvPdwDNa2hw8HhMM0nW8fXjE3goufYMsAtH2nO8C52LHfF5wz0o+++wxfl5DtOiiRq0lk6MFR/sL1mcXPLVb7HxBlkfuNBcU/Q0TbdmzCz6zgXuLR3zvR/81WYh875NP6J+csa8Fd/dnDH3Lj378fT799Bcc/uSQYjLl/a99g6HpCMFx6gNCqJSkMvQonbG/WaX3SlWxnkz41re+gxksn/z8OV3b848/vmQjSvqjEz7tLT+4biCr6a0hHNwhUxO+cXTM/e6Ce3Lg29/6Osf375Bl0K6vGLZrumbHbrPl+vqKs6sVOqt4eP+AveMTyukMIQWTxRylNeubFd3QY70nUGL6nvOXT9G64PLikq7dkmWCw8MFUS55+eols9mMXdNwvVp96e32q3W6uaSa5tTTkrLOycuMolDoLHWKMYVhoaIiL0pmswV1vsA7Rd/doLOUEFzkY1R42yOQlHkiv8cQUEqPfn2Hkpqu3ybvfhxTWPMsRco4i/eB6AxZOSUKnbovEdIsl8TejD5iZSpSZGlu6ULEOEcMfrT9ajKdMfQGrRVVVWH7HsYgQOMspdbEsmRoWiKStm3RKqVHDF3PJx99xMHhIZ9+9CGL5QLrHbumBc8oN4o0uy1KjdyCEHEhoRV7Y4nO4Z3H2B5n/eia02l+O27OI3JkA4ysLiEIPlAVVVqa+YAIHhVT9x68I0Q3duc2xYVHj/cOoXX6OlqhpMQrgTMDkpQkLERKo4g+oKQkUxIpk/vHh4AzyUTho0MplZjFwac8OWs5nu3RB0tvhtdSqnR3jEkMt78gJVHI2xL8a78if3V2+8+/fvn7VXwR7Zi+VgJitm1PzGYp/UDnXNWKh/kdrnZrzpXi6K13mV5c0mi41hIv4GBRIy9XXEnPusxohKCYVTysS/pmy8obApogJWtsssO3kr7bEKYK7WG3veHnZUe1v+Stvmb98innc01QEYopfaFQywVv1DPWrseFjl2z4pN+RTRzjpTh0XLK44s/pw4TTuQ97mT3ODr+FleXF/zs1Y/wQ0/QObN1w/b5OVetZV5lHO3NQESMMUwmNUTo+47Tiwt+8fNf8MH7H1Bmiq7teOONt7nZrOm6gZcX5/CXP+bh/TdQSnKwv+C/+E8+5qMPPyEEh5rv8dnkTfTefZazkvPrCxbHR9TVhO3umreLigc68LbYssgMbx3vc3CyRzWpGZobzHaNsyYBucqavb19Tq/WTCdzHj56xHRvLy3lB0+Mkno6S3r5kUuyWt3QrFd89pMfM13ucXhyyP/jH/8/OX35ismkRmlNZ3qur6/puwElvpwn8iuL7i3ApqpLyjJ/DY2RWYKzZLw28qT8pGpGXcwgKJptj9IKrQKQZGdt25FnGSKmohp8wEeHNTbNyVRiGXRdCp/M8zwl2brUySoiWVmS5VlaAkmJ6XqsTbHgLqbjbamSSUCQgOIp6DGMDU3KW1OqRCmN6QcmdZWCHW0qIlpKMqWwSr3uOJ1ztF2fkiicBWC9WuGdpdnuaJod7dCjVE5eZDAex7M8f92JD2YgV2lR6LwnOIMzJv1ZRMiVREqNC4mbIEYzRJbd5s2lGaOxJvnBSYoBJUH6CFqOMKJA33ZUVUVEMBhDIVIRdTZ9PaUcwXlEpmnbljiqHrz3adFZliigGHkM3trEhPB2nNiKdNrxHjsMiEwyr6YQAyak0c4XG8Egb+ejknjL2P2lnvLXcynkyMNNetx/3nckbhvhW+vyCMQRI/QmXRKQyAz6rsMGA85zvlsxySpKAS9DQwiSd5cz5NBzbnc0MknxHh4d0qxXbIcGyhQ5ZaYTqumUvZvrlBNIoO1aPq4ixeGCN2zF8OqUVxOLrWscnn7YMN8/YuHv0dgbhthTigKzHfhF2yDKnPfvvwkvL/nxdEPUA2Um+Nb+e9RWcNJZamHZXK35Lz/8bziZT3l3uke+u2Y/K9Bnp2x+8QJipJ4U1FWG9w4oaLuOtutQmaQsS6qiom07fvKTHzGZzHh49y5t1/Hhhx8RhWS12VJXJdvdmqIo+MEPnvLwwRvcufuQk7sP+HDVcvnyBulroq4Ikxk5OYfC87fu3eHtwrHQEXvzilz01JOcEF0CBp2f0Ww3RME4ckzY0lxnzKczlkeHiV/vbWo28iItd0Wk71uEFlSTin/6x79ARccvPvuMew8eMJ3NeOuNmqIouHvvDsYP/PGf/Qmrze619f1XXV+iXpAopSiLgrq6LbzpGBqEAKmQEaSQFHmNkhl5XmAN5EVBnmm0EljjUkKvTBv43g54b2iGJkWQ6AzvLN7Dbpdi1dW45e6G7vMYGwlapRgaIRTRWKzpU9LtmBSRlhvJDixjmoOqLMOFSKY1fhT/KKnSjFMK+r5n2zSEAFmRQRSYYUAJgZYCkSmMNWzNKiXnqiTXMqZDROh2m5Syq1QCuKelJ0pprEuCbEKOFDKFRYaA9yPsOoQRFXibJJCG+FKmIb61aamWFxlK6VGHm+LBhdZEN4w/aJ/swyGlCafIoERLstYjxUBWlmhdIGM6FVgsPniEjLRdQ5kXCCTWGoSI1HmOlCotHGWaDSunMEMPUmGdJZNJ/jX0HS4aEDLFdcd0yvDjAkrGOLoF45i8wBcJMb/265e+ky/wFn7l7/9iBz9+LKTA+p5icY/dekt1co/l5Skvu5bj5THzi2cI0fEiSA5O7rB83uGVox86XswL5nfvwrOnBBNYuZYndkAVJftHB8xfnHKd9ZhKQ/AMtmGyXDDTb1LtLgl2R17MyBCcbzfEqeJgdp/DVc+r9oatXENWk+s5izIjTmv2r3dkwTJT+3z28hn1bMlvfuX3+fMf/hkXzWdIHdD6iIPZjPerCnN5TWwbfueDtxFSJidcbwje0vYDzqe8QuUD6/Ul8+mUqixpmob95R6nlys++uxlCio4OCTGDffu3WdWV0gl+O7v/DbTyZzrqxsyJbixgqArbFkwnR+geofKCx5VkW8eTzlmh12d8fTyJdIbDveXNKsNTXCcPn7C1eUlUiZ9/tB3nF9f0TUNgpDUVNueWOboPCfLMoaupes72maD94YoFccnx/zxf/vfYoPjk6efMZ3u89d/93eZTwtWq2t+/NMnWOdYLOaUxZczon9l0fU20Lee4CBTJXlWkumkzUWo8UgbEWi0KEeBffKmV1WBznOUzmjajrbt8T6gC5UWxM6PEGhF13VpYRMjmVLp6Ckl1hikUmn2GDxaFbiQPhdCSMyHGNFSp5EcoHSOdxZJ6mhjhOh9gsNIMepY0/KjH35ZW2pHfSxC4oxBjBZYIQJ93xFiREtHpjJk9KN5IXU6ozk0HVWCJ0qVlAPOJfsuCaChpUSPHaKPKWH2ttCmDXpAqQwvDVIKiqLA+RQBo7R63RzGAFlWYIcOFyIhSkqVEXWK8UmyrTEefHy9wQ6UVZK1ERRK5zjnyFRy5TiZNNi5Sq8/aE2uc1AZUkQ6Y1AymSfk+HNy1iIR5FqjXUJvRlKWJaO+mBHwnStJWWUUVYHKFIFff9G9hY0nQUL8lQ+CNJm+LbTyl757IQRSSKRQiOhxtiM4w43bMDc917nimbTcFZpd33ChFatuxZ3JBH/+krNJxrZbc5U3vHE0wZxe46ucmEm8jpjMUy3nVNYxxA6Vg1JwvWu5LKZM99/h682atlnztH2BqwqErtmTmvvH73D54nvUYcc8z5llFeetobp7xHvVb3H29FNe9o6d/RRxFvHLe9ydHyFFzzvHR9ytFtBsOZCRF9sNzbolWIsZLJN5zXw6wQyCYbBorVku51hr6U3PYJJdPsbA1fUVnz45Z7lYEIPjerXiYO+Aptnx1qMHXJy+5MXT52w2Hc4Y7j44obmxlJO7qIP7gKI0W+oq5927C8ruHNee8eLjn/Ps+SnH+8skVV3vaLuGJ89e8tlnnzL0A7NpzaTKWG93SKGYTecJ63qzoqwq9o/28d7SNluasYla7h/y0cc/5/rijOViQTEp+OGPf4IULS9Pn/InTz7m7PwiNTVRsDdffI4D/RXXryy6zkVWq4bzix0nRwOL5QTyZBoQUiBlkqyIUWJhrCWTPVIlSVSeJ6pPyg0ac6aiRwmNM44YAtaktNsQPHmeo1Wa1TprU/yxTxjJPC8QIpGzNArnTMqx1zp1rCGRunSeeALOBYL3aakTwms8ZaZS0bfO47xHCckwOCKCTKfPDSNusreOQmuc64mk7lCJREmSQqYHxygZUkAmIUaFJ6YQAm/hVnaER+i09fYuvXbnPASPzlPUiNYZUaa5rYgpdicrCqR3SKlQKkuSN6WI0aOVIs8rnO3ROvEmhJRoIkWmyXWWvq+iIgabpGRaoaPGhEAg4BxkKvnoUzijJ40O0kNAqjT6kEpTVkl1EAHjLEVZ46yjHwYqPWVWVGxD6na0UriYjDJJcqiYVQV7i5rjgxnzSf0vRdG9fYrdRux8YVP2S9dfpY1JkcYrQvB54q8YR0AYCt1T1wUzJRgKzVQEgmvocshCgZYWiaERnvpgHzUkIti0LFn1LdUbd9kbfMo/ywTr3ZpdqZnslbxlcgoj+FlzRp9lCDWg7Y7j47e5OZUo9xkz2XJQTFnmB6z3S75y8DtMf2bQfsJnVwO7ix8QReAre3eZVnt0l88o9MBRtcdbywNOJhMuV5BLmAdH9IYyy4kH+zztDLPpBKN7JlWBJFDkOXVlyKuK1WrFrumQI6d5GAyz6ZTzqzVN2xCxaKFZLJYsFkvu3b2HVpq95SFDb5hMZggcP3/yhJd2yraLHB/cg3KOnC8o3UDcrAiu4fzZZ3z66Wccntznqx+8z+GD+0TncdbTDAYTIlEpglK0NmAd3Ln7BvcevY0qatp2QKks7WqGju1mQ7PdUZQlKstxxpOrHOd6Lp68RIvAanXOH/3RC6SE1WqDtZ69vX1i8Aj15ff0rx4vxIC3juv1msurNctFRV5Mk/RJyoS3i/q17tI7zxA6hExov6IomE7nWHdKJNK0DWQagsF5k0YKwQEBrdRrM0MEUCl+/BbcrbMMRIJqayUwfYrQIKTQRhcdiITm895iQ3LB3ZK5IiKNJILHukA7GHIlEqMWsN6Ny5/kXEsgkxHXePumjCmqu9A6AVJ0cmrdZmHJEFGSlDgQ4+tkX0aDgHeRLMuxowPMe08IDq1Th6lVQkQikz46bcsqdJ4njqcQaKXJixrjEqYyr2qUSInLSnhiNGiVURYFWVkiSCGF+KQoUFLggyDLCkwMCGlx3qG1xvlAlqVMOVRSGCilU75ZnpOTpGAhRkTXIWIgL3Kk1jTNmqqqmOqca9fifGIsh5iSc8ssYzYtub8/5c27h8zq6l+SVdptcxu/+C/jx7dTEPF6ni7H2biQYiSM3Z5UBFKq9L5AYM0NTkypbM4TOXCSJ47ARwwsphPe9hmddzzr1mzrguN6yrEs+bQ5Y206rqPn4XyPBw08Hc5w/pqgp/TMWNx9i2zjqW8uQF5QS7hbvstHvqN8dI93rixic0M/7PHDZ0+xT39BWSi+u3ibV6eXWFpwG1Se0XVrvjJ/g53fcXe/YlksuVvXSN+zJwJLAr5bp6O2CNw92qcqCpqu48Z0OOfIixohJDfPdmSlJc+zZJzyjvXNiocP7rO3f8jP/ux7TCYlSufszw/5re98l+XhPg/u3WG7veSjTz7i6eMn7C2PMcNArwrk3gmZqui7HXuLAwYTyTNwRJpux2q9YjaZ861vfZN7775Fnhdcnr7i4vyCi8srVptt4ugOHdNqwny65Ktf+zrLoxOKakpWJSmsGQaGvme7XhO8T6RDO1DojOlsxtfee59PP/6Ir3/1PcwwsN1uGILl6dPnnJ6fM5gOqSqUzr/0fvvVOl0tyCcSnQUG17JrdlSTHJ1VY4DfKHKPqU1wzqYuTUq0LimLgkk9RQiVrMTB4azDjtv1NDYISDWOKmJ4HZzog38dmaIyjRRgbI8Qkq6xeOdQuX49S0uAaTmaNTyEtKjTUhNcknwRw+tuMMZkcVYqdW7epeItBfgQMH2HHI0ImJAeKwJciBSFpJAKaSXWpAKpRl2kZBw3hDFdQIDOctwwJIB3jAzWkmXZqHQlDfGVRumcoduSZRVeZTibxivEiBy76RBDGnnIYsx6Cug6LbBECoJjWk+Y1hVeJsVCqRXVdELf90QhkJkikhFiIL9dMBKIAawZKLN04hACqrJOtl6tEEKyV5bJeYYYZ+siEc1iUjGURY4UPT4GvB8B30QyAdNcs6hKlmXFvKoIv/492nil8U0KvpcEEX+54L4uyLy+327Tgm/xp0n7PXa6EgoN9aQgiyVLViyrGt8ZqmgJec+cfVx3QzZpCZlnvjik2EGlLUZb8sqznE0ZSs1ijFGSdYkuD/ik65ktpnx1+nV215+RVXf45HrF1r1CaMXX9u+yDJIXuzXBrSlzSR1rXBy4s9hn214wFDPuTRe8Oz/mKK+I6hjre8Sw47w5pVtfUxlHPZ1RZJpV0/Ls1SmQEnHvP3yAmHtM6MAbohd85b0HnJ5viS4wmZQ0TUsgcHm14qNPnjKdptzD+8cP+Jv/yr/CZFIzX84oC82LZys2Nzd88P7XWe7v0bYdjYHdjWXtO3QxYVYWONMlC7yIdL2lbQ1vPnrE3YcPmM7n7G5uOH91yieffsrLs1e8ePmCruuZVDVvPXiT7/7mX+ONdx8x399DqowYPVKk9/v12Rm7piErc05fvuTq1Svefe/rHN25w89/8kP29+ecX5xxdLDPwzfu8tmTx9y7d0JZV5yenVFVBVn2L2gDLmvNYlmy3CsoagHSJ8KXB3QCr8ioIYxC95A6VW8DJYK6rOnznKIo0gJFqjS8Hnqij/hRo5vlOd45BDLlf43FJNM5MQSKskxZXYC1FmdNWr75dDQPwcE4ughi7CBH8frtsizPM6IP6XgsBdY6gghIodPWOqaIGZ2NIxOpUtEpcjqT1BREXrNUwxhCKNU4Y47pv5FjYkMMyaGXaUWuJCLTo8sldX6ZzrG2QwiJHkPvhhHoI1VaYFobxze3GmVajlyXCMHruaq1FqTC2w4bPFVVcXiwj1aa3nqiFNRVwbSajEYJQdQJOo+3iFik3Dk8WJfCKLOMup5Q5BmzyQQhVYpKIjkRDxaSy5vr9PPwyXihVZIHqgi51HTWjLdEeg3BJW1v27Xs2pb0vPyXo9N9ff2S/vb/4xPjqenzYvv5x7fLz/HXxtOW7ltcVvH28g5ZvkdfR94bPPX0kMu1Z14XvI9mUh7xdBO4qQIHk0dMzIq6PuSTqy1GBd7cu89Bs0RlBT+9aehiw6rb4KcHPCofcbHtCH5Ai0AuFcZ2nBwf02wEvSzxMlConL1qyvFixsRU+DhQa8FJmdN117j2FX13Ru9bgjdor1hfKq5fXlOWOduRD6KzgvW25e7dgsXRnJ+8+Cg5+qSgypYs3/0G12dXKOcokDx/fkap4Wj/kN224avvvsMf/J2/QxTQm5bnP/iM7W5HmWmqquTps6d88ulnbJstSmX0i4f0HZzzU+oq5+DoLrMo6F59zO70JfPJlEdfeZfp3hJ8YH1xydnLV6w2a5q2Yxgc00nN3cNjvvtb3+Vr33ifgzvHKJ1hjSVEzzAMrC4vuD59RcwUnsB8sQcx8PMP/5JXr55CSDmMLgSads29k2POT1+RFSWLWY13e+x2W2T88nv6VxfdScZ8UbG/X7NYlEwWNfPlnDwrCdETg0xHeikJPh09fQxJfyoG8rxEKUFdlXifBOjW9Ijx6J38+ILgU6dog0MJSQgSNaLzsiIfVQmp+/XOpnGBVK91rSEBA9Lya+y4JaSuOUYEikzluJhGCN6nMUGIpAgi0kw4U5EgoCwLrEvFvdaKuijpuwEISNLyMIxvQmTAu7S0UmqUDsWA0qlQZlonfoKKSbI1qjU+f4Omrb71kUwGAiNjYTRJiHHjnxUFgzEEn/LkBJE4OvikVNi+gSjZny9Zzqapi1RpFpznJYNz5FmGi1CVFdaD6TRaenwUKJWhIsQYqPOcQipm9ZQsz6mrCc6lSHipJEUWU5DfZoPS4MaThfMeEQLzYkLv7Njtk9QNg2e7brkAhA+s1xX6y3lL/wNcAVC/NGJIP8UvPBLEreGD18X1dafL+DMaT1C33F0hBM61hN6j5wdkfmB6uOREvUe7atlUBkTGvpriVxYXG5zQhMmCN8tDLpqOrLC4sGXjOh7s36G/blhOYSYLikwwqwqOju4w6Ttys8EJQ1UVHJVzvjq/w3SzxJUST8N+VXNvusBst8hty9nFOXa35vGwwdiWEJO0MtlhAj5qRCaxHWgTsJ3BhwS2ci7QdQMH945RrzJcMEQEje9pRaBfHNDe3KBLwWT/ANUZ3njwJt/9je+wN58yX07487/4PtY4Htx/wMM7D9GF4od/+X3OL6/I8oIsz7m+uWarlsjZHWIcWG0ueXDnmKV3RL/DmoY337rP0d0jdK7YXl9zdXHGerNi12xwzrF3sMfDk31+8zvf4oNvfp3l4T55UeBMT7db0ex2rG9u6DYNfdcRtKK7WdH3La/On/PDH34Pa/3/u70za5Ikua7z5x4ea2bWvnRX790DDDaCEERBJCiCRpleKJF/QH+Iv0Lvkt70IJnM9AiRIClAwACcpdEzPdNdS9deucfq7nq4nlk1JISmGcAhzFC3J7snqyIja/G4cf3cc89hdTAgy1LmVcnlxZS9nV22NrY4Ojmm1+uRpYbp1NL9IySif+mqTzNDvydGgiuDgpXBKuubOxgM0/lYlP+18BUdgmPKcxEVrkqDxhFFiixNRFMgSDA6J2pW1jk8QplaYIkKwRY9SjQEWouzVpKd90RRqC4ioZrpSAe7G0cUSyK31glk4SxK60DRUjS2w3joOkeioQMU0tBK8kRseLouJD9D27UMBgOGkyltY0lMcKQIYjDWeryXKlgpTxRF13J/UYTRGm+twBQQknNEF7i+GoEcrHNExHITU0K81zoK8ItejjZLQzKIr/iOLO2LyaYTc8w4kXMYE5GiiZQRNoIFkxoiJ00uH7Bep6CravKsR6eF4qeVI89SgUxaS61ber0eeZHTVBUzoLYpK4MV5vM5SZTivMIBk9kE72V02wbIQpqGMLUzfN0yvJqiTfRLKVlffCwSKNc3OxZMBXVND1scHRKuXjBPbiTb6/DEpkXbKf31DYrEkG9tcmd1m2I2pFItRVJIA6qecU5DkmesZH02Zw2r7YypL8mzgkG+yrt3n7I9GzF2FVnmGRR9Hva2WI9zzsoJnarpbEmuFbmas1WXzLqK0eiQo9mQ/aak60qsb5Yj8dZZPBbvXXhY0eqgIyoMvta0nVvCXM6Jseu8rCmqmNQUtM2CBeRIsx5x1qfIV0ks+PiIr+3t8Sd/8PuMhpfYrsHWDQ/v7tBZx9npERtfepc0S6jqEm1iNjY3uTw/ZTS8hGiANgW2mlLS0OQJ2caAXlQzeLjL3qN7xGlMO59ydfqG0eiCsprRNC06isiTmHffeYdnz56yvrGGiRVtOWV4dc7p8RtODo8YDickSUZvbZ3ZbM50MuLTz15y+OaIOzuPiE1E25Zoozg8OWY+nXJ8dsbD+3scvjnkcP8zIhOx0ssw5lcdjog0SSJlf54X5L2Mfr9PojK6zjGtx3jZsQtdSknSJVJ0Tce8HNOUnjg2bKyv8mr/DZExuNYG+laY+/eetquJtApuB5o0k/E7rTXetzjElFGkGjV4K5MmCL/V2+vPi/mhDcNdljgyotylZKFFkaazHh0ZamuFUtZaih7gZPxVjBoNjbUY58iSGNs0aAXGiBW8KBFotHK0nbjmRlrjjcIFse9Iaaz2YcLML0n4TdeFalYucuesTJ+hcK7FKzBG2AqxSdFakqVGzmW7BeNYdGpd26CzDBPYHCaKaDpHnBi0jsjjIMZtHTqKiSNHnGW0zpGmooWhjYGuIU8MJjb0ewVxnFI3LbP5XOx7BqtkvT56OGI619jWygWpIzG4jDRX1ZSei/G2wzqHcoLdV7ajLGuwogW8gIz+WUMRph6ET7zk3d6EGYQhtkyq19zcRXMtwA3cSNQhbGSJ/AznpiQ+I/OW7b0dtptNrqopPonYeTDg/qTmuBxT+pY8Tbn3YMDDquGymhDFUrRsxykP6oyrasa0vKSaH3Nx9ZLLrqZsauq2pGsrvG3w3sq15MS52+FwvluKGjnvhfqJD8LtNrh+25CIFWmuWMt3GR5cSSPaA86TJgng0DZmkK8yrUdhACFiZ2WDROV4PSMeXrL3pcd85dkzskQzNZpHT9+hms24OD8hi6FfxMzqEe+/eMX52QnjWcVweEE5n8rw0uQCm+S0cQ9zWsnY8/07bKxnbN+9Q9bPqeZTZsMrhhcn1PVMDGA7se1aX11jZ2uH/mAAwHw6ZHY14fTNMa8PDji/HGK9YnUjw3jPtKyYTMfUdUWSpDy8d5/d3S3eHB/w4uMXFHnBcDThg48/kdHhfsHoYk7TzOlvbpCkv2IjLTIRSZKQZ336xSpZ0sfEhtTkZFnOvJ7gXQdKGlAKaJXDaycYZTVjMqnRQfbROUdiDLVt0Ij2aJgQQClpmHsnjaIowA/eOqkqvRc8VGtx+nUO77qAfyYi4ag1rhMzRhUqS+usDDhoI5WokqpSK03TNMRJSt3WdM7StB6NQBBxbFDao70OCy2liiocXu782oQmkcV2oCJxBFYAnccbQxwnKO8w4YcjIuuiwaW9R+lItubaC2c5OBZ4J0I8nXOSRKMInODTtrMynOIt2ossZZoV2NYSFRG9rEBHskswoTpZ8E+7rsU7MHkf27UkJqUxLc41KAVZ3mM2afHaoOOUpnWkWUzRS5nPp1TjK6xdIU5ier1iCdXUTU1R9KlaEX23SjOrShKv6Lwi0IxlV+Acznqw4LvfhE6aSE6Kyy94taCQhRSqRUtBaR8s16+ZDEKb1KGZdo3vLl6PIEN0ruXq8jOmoyPik5zR7j16+YBYG3RsOIvP0U1H1jXEOJp5xauTGbruaH2LtQ3n9ZzXbY3GhmrUhaGYpWSPPPyNh7sWecJbgQ+Cyhzeowjn8aKO5q1dJmCFYWNlh997+of8tH2P5y8+EAtyPJPxFbar2N3ZIjPFEpqJ8KzGinZc0rMt3/7db7C5vkLbyui4dZ4P3vsRzlnSOIfIMGtrfvC//gezSSlUMSWJLEk0Vd3irEyVDgYFa3lObEtWfMPmyjbZoCcMiZMzTo+POB9eMJ7NqNpWbNQV7G5tkiYxrm0ppyMmwzFnJxccHZ9weHpG6yBJUrxWdLZjc3ODj5//jNl8JhO5RcZ0dMVsMiKKQOOYTkpOT6co79nb3WB1dUBTl0wnU1b0r5h0kyRh0F9jbbBFnq1jdLqUYIwiEdi2gdKlQmdXe/BaeJkuYERJalBBVMR2HTpCBhC8AyvW3EYrNB4fGaKAdaZZiqiVKlpn8X6xffdEKDpvcV1HkffAeaI4oWlq8J4oku68UgaNwoRJKGmaSVJru5YkSZjNZhAZ2rYVdwklbsJKOUA4vmmSkKUpnZWKQJsIEwlsYQOsEAUOsAtiOYmJsbYRAR0rMIe6oa7ltQhhKy8XtNYKHZnQBJSLWpgWHQ7RUu06JXY4ztF5L5KXoWo2YbzYetBWbnxGCe83imJM5Om8lUahiYk7sezxXoRvbOfoD9ZxXYMjou46TN1Q9HL6/QHj8YjZeEhSiMlfnvfoOksXbn6b/S1GUYT1sGk3OLk8DuaTfqlQJipvXsaC/xGcxn/quE6SgHLXCxmBixbVr9JcP24kWKUFz70JSQDXesJhvXlncVS0ruZgfyS6DYj0pvwX9CrCkIZBnEwiFTR8AQHxQpHiQ5Uq74ZfPl/8e+NPgAycdyHJurA9lPUtDEeHc600gRVEUcTdzcd8/dm3qYee5y8+oKoqsYWKYyKt6JoW12o0Maku2C0ecF8VPPnyHTbXB0xHl3RtQxobbDPn4aPHRApefvycSTnl+M0ZhweHuE6ztbXD5tYOz188p+1qmqaiKivK2qGjnF6/z3rsuNcfsLsxoFjtY+KE4fkp+4cHnJ6eMpqOuRgOuZzUOKfY3Nxgc3UAOOaTCaOzU4ajCSdnlxxfDBlOw+4tSZiXJbNyxqBXsHvnLnGccnJ+wgcfvs+/+6M/ZDS6YjicMivnSzroaDRha61PmhtWV1fxRIwm07euuV+adPv9NQbFOlncJ9YZyhnquUVbkWMU5SgXKgB5TaTDr9w5olh0eGOlieMocEQ7vAPrWlHt10oaOVoWrYz/ikNEhEy4tVpBKwsmMYbOtigT49tWtiBdK1tnLTisVbKF9p1QmnDy2ihOUKF69J3MWkfGLDUOGhtgDxSJRswumy64KSjZOrSyMJ0DtApuwwYTSdJz3tM2DUkcVLvQeIL1i4oAG2QXCRWgRStNFBqSUj0LwhgFE0hxkJCvSyuP64QH7bzFa49u6rBTEF0GFORJClqhjQ6yjE6GL1DMqxlJUlD0e6iZJHvbddJoxGCSmM56olgEtq33rPZ7rK1vMp9NSZKYuq6ZdlaclZOUq/EVnXWsbWxLIu5a5r0VXDmlrmusb9FG6Gtay+9kqaf4zxoyS7jgYMvzheNFjEIyrQr8XLQD7T7HXtCLqphFQg6wz0KTYcHbdgK/+UWhIi3c64ZdECrTXoEKtkZ6kcDD1xdoh/7Gz877a9ofyK6JMJG4qGIXfoeC38pNwHtR3rNIdbuQ73Qo0qTgwc5D1le3KPoFg16P6XRKHMesr/Ypspgkiri/+oiEjDsrd9ldvcuD+w8YDHrMZ1MGg1VcW7O/f4BGsfco4vTygtOzE95//iHD8ZiVlTUePnrMaHzJJ5+8YD6bhDVmKLKcNIbIKHaLmDsrBU/u32X77i5JnlPNZ5ydnrL/5pir0ZjT80vOhyOqtmVjsMqgyIi0TGJenJ0wHF4wn3dcjkqqWthSURSRZRkKxfMXLzg4+JR3v/wuf/Cvv8v3v/992nLGyckxDx7s0XaKv/voI4p8hqdiNK64Gs7Z3XpMVU5BObJi5a0r7pcm3V6Sk+oUTQROY1sopxVt1dG6ail07YUkK1WAtBdk62UicZ1twITml3M+CNh4tBEalYkSQpsJpRRJFAlvNeixNlFEhUAPeLvUYtBKLhNx+pWLODIRtm6lEedkO60CBhpFHms7kiShcTIQ0FQVRmvazstNgGtKUBxFGGNEzDiKaDpD5CyRgtaJURHh4nOwpKsppTEmwURgMbLb0zroHGjZ7csdRdTylQyaKBS2a0iSDG2MOESAMDpsR5wV2K4RQfTOCp4YWB8e+d6EPmeEwRFpiKMweOLwKqP1wjTQtiMyKWliaNoGp8W6pfUVRW9AU82AiDjJaFpL2Vh6eUyvP0AhzZThcEQbcKEIxdnFCU3TsLKyDloza+bUbU3dlOR5Qi9LGPQSktSQJIok+k1gL1zHsgGqQ7JULjTQrpPpTVz3/98IvFYeu3H2sOP3cl4IuV19riq+OYnsEShGLY4P1EZuJl0f6H5cJ2PBZkNTzLllleuXlW74vJOdqPVuWTODNHd70Tq2dHz26gVn50d474WeiKfrUuraMxpesbK+zW7+gPube6z0+1TzKeVsSNs6PptMuLe9xaOHjzk9O+avfvC/eXN8DEqxd/8R5vSU2WzEq9ef0DYNk/E05BSB4Tpa1tc2KHorbKQFX33yhAf39yj6OVU54+D1az58/hHnl1cMJ3POh2PGUxFvitaQPkgsmrij0YSr4YTprGTegFUCnRZFQdXW5HnO2kqfn43G/NXf/g3j0ZhvfO1rmAhefvqcy6tznj35Ov/2e9/j4OiI//uTn3B4fMr+2YhHDx27d+7zev8V0/mvWOkKDUYaXi5skTttUTR0rqSjQ2sxLXSRNBI8MqjgQ+VmYo3ynn4vxSiYt8HWheA04Dw6NmF7pUWMJY6J4xijFW3XSQNJaYEiIuHGOifJzeMDE2IhfyhKT0qBNolYK8cpXVuFVS522R6FMoa2rsI4pyRwHUWkcSyaDklM5sSzTG4OEXUNJjJgGyItCW0h5SgKX2C0MBxc2xJFGq0TsdDxHhv4ElrrpRPxomMuNxFJwLGJltW794JBa7xoVDgrWds5THDvtV2LbRuauiYxBov4zHnriWKBS6wXFbckjulsR2xlCtB5S9M0mDijbUXxbWVljdlsirUteZ4yK2c4D/1eQRrLhjfNUpGptDII4m3HbCauqlrBzsYOUZKgrkCrjo21PlsbA9YGOSuDHlnydvzrnzokcUqFK7nuptCjC0iDusZJuX5IEl48v8nbXfbYwusXlfM1ngo3C/3wPv560MjrxWcWUIJ8rXbR3/PXRp9yrgWM4yDof9hQ4fobSVegqe66eeYDcwE5l/eK1f4O3/3mn7Bd3OGj5x/y8uXP5TrSEbaztG1HEmfM65bdLGW7t0KeZ1wOh5R1iTGa+WTOt//Ft8iSjJ9/+FOOTk/ZPzohz3pkec7B61c0XctkMqZparpWiqE8EmcY7zxJnJGlPQbFgC8/eMDe3g5ZL6dtGg5fv+a9937My5evGE2nWBVRVQ3eOVb6fWFLuVZ2ZMpTVhWTyYxZ1WKJUJEmH+QMVleom4aDg31Gowu++c1/wf/54Q/56OcviFTMowd3Ob+85PT8lKPjC+7fe8zjR4/5l9/+NhsH+/zs/Y94dfCG7Z0d9u494vBo/61r7pdbsFsh+uPEA6r1Dtc6vG1xvkIZh06NUEycRovXBNpHQXdVBZEWS1YkxInBT2VIgoBNRSYRWpQxYS3JNmzRgW+7LljWEGhXPuzLRUTGmCxgoB4T+JPGxKA0JvIyHhsZ2jpU4Dg6J80o5YSyZt31tk3Ea8Trax54qULDlA5yWdfESULdCm9RxcJR1gjXFxVR9PtkWYyta+LEgIrDgEHLeHQlWKmRbatTQifTaSRfq4cszUTByYubhPWeNMuXY7WRk6mypmmJuibwlztxj3C5uC0HVwilRXEtDxi2846mlVFhG6fUdS084tB4MUZTl1OKdJ0iy8OWE5Ikx9pWBHJMirUdXdfJyPXC8y7SzGdjWtuysrJGFMVsrGzgfItzJXvbm9y7u87OxhpFkcna+E0K7wP90S3hnJt/locRGm9LvUcXoIQlyBAS7g0IYPHXMnEv3nJR5crLbWiGaU/Ab1me17OoeEOJYa9vBNe0LznO+y7c6KXi9Qhu753F+S5UuzcaaUrWPCi2Bpv87rvf4uHuM9ZX1vm7D39M55xMJTpFYy19k5KkBbubW/TyjNFkJgMPcykA9u5u8eknz7FWKuLz4ZjIxDx++oTz81MuLy+om1quP9tJk1V1mEiTxIbV1Q12tnZY6a/x6MFj7t7bJilSWtdwenTEez/9KT/+2QdcDUUSMklz0jABuzLoUaQpXddyfn5OliZUVU1ZN9SdQ0cGo0Vft+1kruDs+JTxZML9+32ePHxEXZXs3t3BFBleJyiVM5lN+Pjlp+wfHlL0Cu7c3ePf/P53+PFPfsKnrw9490vvsL6x+9Zl9kuT7n/+T//l19rp+Iu/+HWe7TZu49cRn59A8wvWjCI00QgJdnGsX6ZAv6iOQyPqGm4IjbgblLPleRdJV30uRUOAzBRSc6vggil9NSXN1sV5lBynljrR7jrpski61wyHhfKdc/Y6CXsRPFriw34xPWjo5i0/+eHf8tnmK169fs35+Qmz+VzYSV1Hkq6zkuf80b/6Fttbq1yeX9FWJfuvj7h//y7zSc3B60+XRdWr/QNGs4r19TX+8gc/oK7naOVpW8u8qskyoSuORhOU79HLCzY31tnd3ubp42fs7GwTZym2bTg+esXz5y/48KMXnJxd0jlLmhV4D03bUKwUbK2tMsgT2nrG1XAG3tN1shtx2mAiQ5zlmLSH6zpoHU8ePmZlbZ3L4TlZFtE2DeV8zHh4zt0799ja2mM4GlKWc7SW3smnn7zg61//HZ49e8ZwOOTs/JL79x68dcX9ZoFqt3EbX3iESlG8ipaJU+pXaV7pBbMBJfv+JS6wwHUDL8MvGmw3Ob7h/wNWKTWq2EYtcrRevOGNjpr36gYqsaCBLYpl6T8EfA4fKld5pfBxPcI3XzxwXUjE19CGU/I9ehYGm4pIGerS8snHn5EeXnJ6fkakUp4+ekJblRwcf8bThw/4zu98hdVexNXFMUXRo5/FfOnhHqPxkGo2o+gVTCYT3hweMCvnZGkiMKDWxEnO6qDHweERVW2xzlGVQRLUKnZ373D/7h6PHj5ia2cdnUY43/Hpy5c8//nHvDo6ZTQrWVvfEGlRrUnSlEgbelnGoCjYXh/Q1CmnZ2fMyhLbeiIjWs/GxPRWN4jXdtlZLdjpRbz//k/48c9+RK9fcPzmkDzvcefONvsHL0XGQGuaxhKblKLXwyP6uYeHB6R5QVs3zKcVw4vJW1fcbdK9jd/yuJntFpWoNKZuVsAS13jt9fE3n4ePLulxSHZTnz/H5477Bc04D1gVEn5QBRIqolu8q7w2MEAWz4WTG3DcG0l3WWW76wQLMkgUiA5LHkVETKxSkjTl97/7x6R5j/OTC3pxxs8/eo8igz/6vW8SR3Bxds54POXOrsHbhvOLE6bzEtDMzq8o5yVFvsrG1n3G0xHnl6dU1ZzYJGgVc//+Y+LkjOl8zGg8Jo5jtrZ22draYXd3l16/EKXBDk4Oj/jwo+d8dnTGeDLBJBl5VlAkOU3TkCYp2WpCnqWkaYL3oJVB6xjnykBjNcSxoVhZx6xs0hXrzOKE46t9jk4OefnJx/T7PZSK+MpXHnJ4+JrZXGRpT88vA8tObq5N05GnGXt7O0zPxygUTx485cnTd9664m6T7m38locPAxFBcB233NrLFl7JBGQ4Vv52QpW8Tq1LvHXRF2CBx4YGGbBs1iofIToeC8z2xqkWH/KgQ6/Ce8R1w4UqOcACKmC/+GipmbAY5100zqR5tmDoBTNStaiYZUDJhxJaoVDWUE86Xl18hv3L/8nThw/pD+4xWLvL1dUZf/h73yBSlquzc5SK2drYoSgGnF+cc3JyTBQlvPP0MUolbG3tsba5zdlwxI9++h5XM8u3nv0uiVH0Bgk//dmHrK2v0rRzVldylIqIY0Oaipbt5dWQJEmYTEa8fPWa129OOD65oCwr1je30bH8NNbX1un3ByIuZeSbm85K5uWMaVkJ0wfRaDFJTrK6gU16WJNz2mns/hnnF5fc23vAV9/5Ei2OD3/+PvP5jDhJSeIeedqK5omSxn2aSoN9PJ5SV5Z7O4/5xte+wYNnT9664m6T7m38VsciJ6pFYvx7n/uHcZPBED6yrFhdoHXBwu5ShWy6GJJ4W/yDwjjAEn5R8aqgveFEIH5J2wx/JLmqJT1NitzFqC+4hUAU1z29BetIBaNN7z1dZ/n+D/6Kzz59yZ/96X8kigx372zh2znHR5dsr29Qd56N3R32Xx/w0c9fsLm+wZcfP2T/7Jg3pxecjc7pH6/T76/ynW9/i8fjKRcXp7x4/gGnJ/s4h6j9OUuWxWRpQRRp5mXJp5/to6MI23VcDa84vbzi6OyS4XAqei1XQ7ZUTB7ngPixJbEB1zGcjJhNRkxnM9Fj0QIrKAtR1qdVhnkH1bzCaS02SoN1mukVRW+Nsp1SpH3Oji/Y3lkn0oqNjdWgK+KoKidsnyjBuZi9nUf8+//wZ+w9fcLq9vZbf8e3Sfc2fqvD61+cWtWiQ4ZaFqxwA84NeOrimMXRUgBLAlQqVMD+5hlCFazU8mR/H2JYvIWISC3wYH8N74Yk2S00m8O7L5KzNM/8kizhPFgvQv4BGkaqZQKWvfg2hN9jTAydCLSXVU05HdM1M1wHw8spsYF8r8erFy/Yf3PCxsZdMZ18eJ83l+f84Ec/pmpacB9jTMLaxiZ3drc5fHNCax2TyQRnFXFi6FQk+i5ZinagTMRwPGHoZ3ivaLuWeV1yfjni8mKM85AVPbK8TxKnpElKkma0bYftWrquZjyZUE7ndF1HluUkaYZHoU2KU4bKKq4mJZfjGqci1tZSIOZP//zPefTsIT/867/hm1/7HVb7fT7+9GN8YSjLkulsRtM2dJ2nqR15FvHVJ1/ie9/7Y979xldZ29ymKAZvXXO3Sfc2fqsjMFQl8f2CQlTdSKqfjwUdS31+8mzBMFiezC7PdH0+HSCHX4zpwqKRpwINUybNFpjuAoJYVLHAUi7VORew2wU0IRKmYXZ0WTGjbtxACCcLk3ddJ+4tcSSuubPpmPFoxPadRxx9ckyeJewf7PPik5dYr+i6iKdPn3B6esxf/+g9us6xd2eTsiwpS8/hmxOGw0vSLGVeVhRpzMZan5m1lLUnSlNMrEi9GArMywpjUrxX4nlYVYwnc6qqJsky4iRla2uXvZ1dVgd9EVMq52gtRgfzeUlZVuA9/V4iiVAb0TrxIqN6MRlxXLVk/XVeW9gsBpTtlP/+3/4rXWV5cP8+jx8/4Oj0iJOTI7T2VGUJSmOtQuuEr375q3z3D77D42eP6A8GJMET8m2h/jFbntu4jdu4jdv49YR++yG3cRu3cRu38euK26R7G7dxG7fxBcZt0r2N27iN2/gC4zbp3sZt3MZtfIFxm3Rv4zZu4za+wLhNurdxG7dxG19g/D8GDVx+dznetwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "from torchvision import transforms\n",
+    "import matplotlib.pyplot as plt\n",
+    "import json \n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
+    "\n",
+    "for i in range(4):\n",
+    "    img_path = './data/img%d.JPG'%i\n",
+    "    img = Image.open(img_path)\n",
+    "    preprocess = transforms.Compose([\n",
+    "        transforms.Resize(256),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
+    "    ])\n",
+    "    input_tensor = preprocess(img)      \n",
+    "    plt.subplot(2,2,i+1)\n",
+    "    plt.imshow(img)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "# loading labels    \n",
+    "with open(\"./data/imagenet_class_index.json\") as json_file: \n",
+    "    d = json.load(json_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Throughout this tutorial, we will be making use of some utility functions; `rn50_preprocess` for preprocessing input images, `predict` to use the model for prediction and `benchmark` to benchmark the inference. You do not need to understand/go through these utilities to make use of Torch TensorRT, but are welecomed to do so if you choose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import time\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "cudnn.benchmark = True\n",
+    "\n",
+    "def rn50_preprocess():\n",
+    "    preprocess = transforms.Compose([\n",
+    "        transforms.Resize(256),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
+    "    ])\n",
+    "    return preprocess\n",
+    "\n",
+    "# decode the results into ([predicted class, description], probability)\n",
+    "def predict(img_path, model):\n",
+    "    img = Image.open(img_path)\n",
+    "    preprocess = rn50_preprocess()\n",
+    "    input_tensor = preprocess(img)\n",
+    "    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model\n",
+    "    \n",
+    "    # move the input and model to GPU for speed if available\n",
+    "    if torch.cuda.is_available():\n",
+    "        input_batch = input_batch.to('cuda')\n",
+    "        model.to('cuda')\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        output = model(input_batch)\n",
+    "        # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes\n",
+    "        sm_output = torch.nn.functional.softmax(output[0], dim=0)\n",
+    "        \n",
+    "    ind = torch.argmax(sm_output)\n",
+    "    return d[str(ind.item())], sm_output[ind] #([predicted class, description], probability)\n",
+    "\n",
+    "def benchmark(model, input_shape=(1024, 1, 224, 224), dtype='fp32', nwarmup=50, nruns=10000):\n",
+    "    input_data = torch.randn(input_shape)\n",
+    "    input_data = input_data.to(\"cuda\")\n",
+    "    if dtype=='fp16':\n",
+    "        input_data = input_data.half()\n",
+    "        \n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(nwarmup):\n",
+    "            features = model(input_data)\n",
+    "    torch.cuda.synchronize()\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(1, nruns+1):\n",
+    "            start_time = time.time()\n",
+    "            features = model(input_data)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = time.time()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            if i%10==0:\n",
+    "                print('Iteration %d/%d, ave batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
+    "\n",
+    "    print(\"Input shape:\", input_data.size())\n",
+    "    print(\"Output features size:\", features.size())\n",
+    "    print('Average batch time: %.2f ms'%(np.mean(timings)*1000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the model downloaded and the util functions written, let's just quickly see some predictions, and benchmark the model in its current un-optimized state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./data/img0.JPG - Predicted: ['n02110185', 'Siberian_husky'], Probablility: 0.49785590171813965\n",
+      "./data/img1.JPG - Predicted: ['n01820546', 'lorikeet'], Probablility: 0.6445754766464233\n",
+      "./data/img2.JPG - Predicted: ['n02481823', 'chimpanzee'], Probablility: 0.9899807572364807\n",
+      "./data/img3.JPG - Predicted: ['n01749939', 'green_mamba'], Probablility: 0.39485082030296326\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(4):\n",
+    "    img_path = './data/img%d.JPG'%i\n",
+    "    img = Image.open(img_path)\n",
+    "    \n",
+    "    pred, prob = predict(img_path, resnet50_model)\n",
+    "    print('{} - Predicted: {}, Probablility: {}'.format(img_path, pred, prob))\n",
+    "\n",
+    "    plt.subplot(2,2,i+1)\n",
+    "    plt.imshow(img);\n",
+    "    plt.axis('off');\n",
+    "    plt.title(pred[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 75.34 ms\n",
+      "Iteration 20/100, ave batch time 75.33 ms\n",
+      "Iteration 30/100, ave batch time 75.35 ms\n",
+      "Iteration 40/100, ave batch time 75.37 ms\n",
+      "Iteration 50/100, ave batch time 75.38 ms\n",
+      "Iteration 60/100, ave batch time 75.38 ms\n",
+      "Iteration 70/100, ave batch time 75.39 ms\n",
+      "Iteration 80/100, ave batch time 75.39 ms\n",
+      "Iteration 90/100, ave batch time 75.40 ms\n",
+      "Iteration 100/100, ave batch time 75.41 ms\n",
+      "Input shape: torch.Size([128, 3, 224, 224])\n",
+      "Output features size: torch.Size([128, 1000])\n",
+      "Average batch time: 75.41 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Model benchmark without Torch-TensorRT\n",
+    "model = resnet50_model.eval().to(\"cuda\")\n",
+    "benchmark(model, input_shape=(128, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"4\"></a>\n",
+    "## 4. Accelerating with Torch-TensorRT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Onwards to the next step, accelerating with Torch TensorRT. In these examples we showcase the results for FP32 (single precision) and FP16 (half precision). We do not demonstrat specific tuning, just showcase the simplicity of usage. If you want to learn more about the possible customizations, visit our [documentation](https://nvidia.github.io/Torch-TensorRT/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FP32 (single precision)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch_tensorrt\n",
+    "\n",
+    "# The compiled module will have precision as specified by \"op_precision\".\n",
+    "# Here, it will have FP32 precision.\n",
+    "trt_model_fp32 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.float32)],\n",
+    "    enabled_precisions = torch.float32, # Run with FP32\n",
+    "    workspace_size = 1 << 22\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 41.02 ms\n",
+      "Iteration 20/100, ave batch time 41.12 ms\n",
+      "Iteration 30/100, ave batch time 41.22 ms\n",
+      "Iteration 40/100, ave batch time 41.14 ms\n",
+      "Iteration 50/100, ave batch time 41.20 ms\n",
+      "Iteration 60/100, ave batch time 41.20 ms\n",
+      "Iteration 70/100, ave batch time 41.19 ms\n",
+      "Iteration 80/100, ave batch time 41.23 ms\n",
+      "Iteration 90/100, ave batch time 41.20 ms\n",
+      "Iteration 100/100, ave batch time 41.21 ms\n",
+      "Input shape: torch.Size([128, 3, 224, 224])\n",
+      "Output features size: torch.Size([128, 1000])\n",
+      "Average batch time: 41.21 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Obtain the average time taken by a batch of input\n",
+    "benchmark(trt_model_fp32, input_shape=(128, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FP16 (half precision)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
+      "The compiler is going to use the user setting Float16\n",
+      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
+      "compatibility with PyTorch's data type convention is required.\n",
+      "If you do indeed see errors at runtime either:\n",
+      "- Remove the dtype spec for x.1\n",
+      "- Disable partial compilation by setting require_full_compilation to True\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch_tensorrt\n",
+    "\n",
+    "# The compiled module will have precision as specified by \"op_precision\".\n",
+    "# Here, it will have FP16 precision.\n",
+    "trt_model_fp16 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.half)],\n",
+    "    enabled_precisions = {torch.half}, # Run with FP32\n",
+    "    workspace_size = 1 << 22\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 14.48 ms\n",
+      "Iteration 20/100, ave batch time 14.58 ms\n",
+      "Iteration 30/100, ave batch time 14.72 ms\n",
+      "Iteration 40/100, ave batch time 14.73 ms\n",
+      "Iteration 50/100, ave batch time 14.70 ms\n",
+      "Iteration 60/100, ave batch time 14.79 ms\n",
+      "Iteration 70/100, ave batch time 14.73 ms\n",
+      "Iteration 80/100, ave batch time 14.69 ms\n",
+      "Iteration 90/100, ave batch time 14.68 ms\n",
+      "Iteration 100/100, ave batch time 14.69 ms\n",
+      "Input shape: torch.Size([128, 3, 224, 224])\n",
+      "Output features size: torch.Size([128, 1000])\n",
+      "Average batch time: 14.69 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Obtain the average time taken by a batch of input\n",
+    "benchmark(trt_model_fp16, input_shape=(128, 3, 224, 224), dtype='fp16', nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"5\"></a>\n",
+    "## 5. Conclusion\n",
+    "\n",
+    "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for EfficientNet-B0 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of **1.84x** with FP32, and **5.2x** with FP16 on an NVIDIA 3090 GPU. These acceleration numbers will vary from GPU to GPU(as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute cards for maximum acceleration.\n",
+    "\n",
+    "### What's next\n",
+    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
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+    div.nbinput.container div.input_area,
+    div.nboutput.container div.output_area {
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+
+/* override MathJax center alignment in output cells */
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+    text-align: left !important;
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+
+/* override sphinx.ext.imgmath center alignment in output cells */
+div.nboutput.container div.math p {
+    text-align: left;
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+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-default-inverse-fg { color: #FFFFFF; }
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+
+.ansi-bold { font-weight: bold; }
+.ansi-underline { text-decoration: underline; }
+
+
+div.nbinput.container div.input_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight] > pre,
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+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
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+}
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+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
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+div.nboutput.container div.output_area > div[class^='highlight']{
+    overflow-y: hidden;
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+
+/* hide copybtn icon on prompts (needed for 'sphinx_copybutton') */
+.prompt .copybtn {
+    display: none;
+}
+
+/* Some additional styling taken form the Jupyter notebook CSS */
+div.rendered_html table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
+div.rendered_html thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
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+div.rendered_html th {
+  font-weight: bold;
+}
+div.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+div.rendered_html tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+</style>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2020 NVIDIA Corporation. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an &quot;AS IS&quot; BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+</pre></div>
+</div>
+</div>
+<section id="Torch-TensorRT---Using-Dynamic-Shapes">
+<h1>Torch-TensorRT - Using Dynamic Shapes<a class="headerlink" href="#Torch-TensorRT---Using-Dynamic-Shapes" title="Permalink to this headline">¶</a></h1>
+<p>Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA’s TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch’s Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extention and compiles modules that integrate into
+the JIT runtime seamlessly. After compilation using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT’s suite of configurations at compile time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.</p>
+<p>We highly encorage users to use our NVIDIA’s <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">PyTorch container</a> to run this notebook. It comes packaged with a host of NVIDIA libraries and optimizations to widely used third party libraries. This container is tested and updated on a monthly cadence!</p>
+<p>This notebook has the following sections: 1. <a class="reference external" href="#1">TL;DR Explanation</a> 1. <a class="reference external" href="#2">Setting up the model</a> 1. <a class="reference external" href="#3">Working with Dynamic shapes in Torch TRT</a></p>
+<p>torch_tensorrt.Input( min_shape=(1, 224, 224, 3), opt_shape=(1, 512, 512, 3), max_shape=(1, 1024, 1024, 3), dtype=torch.int32 format=torch.channel_last ) … ``` In this example, we are going to use a simple ResNet model to demonstrate the use of the API. We will be using different batch sizes in the example, but you can use the same method to alter any of the dimensions of the tensor.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!nvidia-smi
+!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Mon May  2 20:40:30 2022
++-----------------------------------------------------------------------------+
+| NVIDIA-SMI 470.57.02    Driver Version: 470.57.02    CUDA Version: 11.6     |
+|-------------------------------+----------------------+----------------------+
+| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+|                               |                      |               MIG M. |
+|===============================+======================+======================|
+|   0  NVIDIA Graphics...  On   | 00000000:01:00.0 Off |                    0 |
+| 41%   51C    P0    62W / 200W |      0MiB / 47681MiB |      0%      Default |
+|                               |                      |             Disabled |
++-------------------------------+----------------------+----------------------+
+
++-----------------------------------------------------------------------------+
+| Processes:                                                                  |
+|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
+|        ID   ID                                                   Usage      |
+|=============================================================================|
+|  No running processes found                                                 |
++-----------------------------------------------------------------------------+
+Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
+Requirement already satisfied: ipywidgets in /opt/conda/lib/python3.8/site-packages (7.7.0)
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+Requirement already satisfied: widgetsnbextension~=3.6.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (3.6.0)
+Requirement already satisfied: ipython&gt;=4.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (8.2.0)
+Requirement already satisfied: jupyterlab-widgets&gt;=1.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (1.1.0)
+Requirement already satisfied: psutil in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (5.9.0)
+Requirement already satisfied: tornado&gt;=6.1 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (6.1)
+Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (21.3)
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+Requirement already satisfied: matplotlib-inline&gt;=0.1 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (0.1.3)
+Requirement already satisfied: jupyter-client&gt;=6.1.12 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (7.2.2)
+Requirement already satisfied: debugpy&gt;=1.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (1.6.0)
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+Requirement already satisfied: pygments&gt;=2.4.0 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (2.11.2)
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+Requirement already satisfied: pyzmq&gt;=22.3 in /opt/conda/lib/python3.8/site-packages (from jupyter-client&gt;=6.1.12-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (22.3.0)
+Requirement already satisfied: entrypoints in /opt/conda/lib/python3.8/site-packages (from jupyter-client&gt;=6.1.12-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (0.4)
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+Requirement already satisfied: attrs&gt;=17.4.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema&gt;=2.6-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets) (21.4.0)
+Requirement already satisfied: importlib-resources&gt;=1.4.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema&gt;=2.6-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets) (5.7.0)
+Requirement already satisfied: zipp&gt;=3.1.0 in /opt/conda/lib/python3.8/site-packages (from importlib-resources&gt;=1.4.0-&gt;jsonschema&gt;=2.6-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets) (3.8.0)
+Requirement already satisfied: ptyprocess&gt;=0.5 in /opt/conda/lib/python3.8/site-packages (from pexpect&gt;4.3-&gt;ipython&gt;=4.0.0-&gt;ipywidgets) (0.7.0)
+Requirement already satisfied: wcwidth in /opt/conda/lib/python3.8/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,&lt;3.1.0,&gt;=2.0.0-&gt;ipython&gt;=4.0.0-&gt;ipywidgets) (0.2.5)
+Requirement already satisfied: six&gt;=1.5 in /opt/conda/lib/python3.8/site-packages (from python-dateutil&gt;=2.8.2-&gt;jupyter-client&gt;=6.1.12-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (1.16.0)
+Requirement already satisfied: notebook&gt;=4.4.1 in /opt/conda/lib/python3.8/site-packages (from widgetsnbextension~=3.6.0-&gt;ipywidgets) (6.4.1)
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+Requirement already satisfied: prometheus-client in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets) (0.14.1)
+Requirement already satisfied: Send2Trash&gt;=1.5.0 in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets) (1.8.0)
+Requirement already satisfied: argon2-cffi in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets) (21.3.0)
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+Requirement already satisfied: MarkupSafe&gt;=2.0 in /opt/conda/lib/python3.8/site-packages (from jinja2-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.6.0-&gt;ipywidgets) (2.1.1)
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+Requirement already satisfied: pyparsing!=3.0.5,&gt;=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (3.0.8)
+Requirement already satisfied: asttokens in /opt/conda/lib/python3.8/site-packages (from stack-data-&gt;ipython&gt;=4.0.0-&gt;ipywidgets) (2.0.5)
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+Requirement already satisfied: executing in /opt/conda/lib/python3.8/site-packages (from stack-data-&gt;ipython&gt;=4.0.0-&gt;ipywidgets) (0.8.3)
+<span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
+</pre></div></div>
+</div>
+<hr class="docutils" />
+<section id="Setting-up-the-model">
+<h2>Setting up the model<a class="headerlink" href="#Setting-up-the-model" title="Permalink to this headline">¶</a></h2>
+<p>In this section, we will: * Get sample data. * Download model from torch hub. * Build simple utility functions</p>
+<section id="Getting-sample-data">
+<h3>Getting sample data<a class="headerlink" href="#Getting-sample-data" title="Permalink to this headline">¶</a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!mkdir -p ./data
+!wget  -O ./data/img0.JPG &quot;https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&amp;width=630&quot;
+!wget  -O ./data/img1.JPG &quot;https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg&quot;
+!wget  -O ./data/img2.JPG &quot;https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg&quot;
+!wget  -O ./data/img3.JPG &quot;https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg&quot;
+
+!wget  -O ./data/imagenet_class_index.json &quot;https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json&quot;
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+--2022-05-02 20:40:33--  https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&amp;width=630
+Resolving d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)... 18.65.227.37, 18.65.227.99, 18.65.227.223, ...
+Connecting to d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)|18.65.227.37|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 24112 (24K) [image/jpeg]
+Saving to: ‘./data/img0.JPG’
+
+./data/img0.JPG     100%[===================&gt;]  23.55K  --.-KB/s    in 0.005s
+
+2022-05-02 20:40:33 (4.69 MB/s) - ‘./data/img0.JPG’ saved [24112/24112]
+
+--2022-05-02 20:40:34--  https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg
+Resolving www.hakaimagazine.com (www.hakaimagazine.com)... 164.92.73.117
+Connecting to www.hakaimagazine.com (www.hakaimagazine.com)|164.92.73.117|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 452718 (442K) [image/jpeg]
+Saving to: ‘./data/img1.JPG’
+
+./data/img1.JPG     100%[===================&gt;] 442.11K  --.-KB/s    in 0.02s
+
+2022-05-02 20:40:34 (26.2 MB/s) - ‘./data/img1.JPG’ saved [452718/452718]
+
+--2022-05-02 20:40:34--  https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg
+Resolving www.artis.nl (www.artis.nl)... 94.75.225.20
+Connecting to www.artis.nl (www.artis.nl)|94.75.225.20|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 361413 (353K) [image/jpeg]
+Saving to: ‘./data/img2.JPG’
+
+./data/img2.JPG     100%[===================&gt;] 352.94K   608KB/s    in 0.6s
+
+2022-05-02 20:40:36 (608 KB/s) - ‘./data/img2.JPG’ saved [361413/361413]
+
+--2022-05-02 20:40:37--  https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg
+Resolving www.familyhandyman.com (www.familyhandyman.com)... 104.18.201.107, 104.18.202.107, 2606:4700::6812:c96b, ...
+Connecting to www.familyhandyman.com (www.familyhandyman.com)|104.18.201.107|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 90994 (89K) [image/jpeg]
+Saving to: ‘./data/img3.JPG’
+
+./data/img3.JPG     100%[===================&gt;]  88.86K  --.-KB/s    in 0.006s
+
+2022-05-02 20:40:37 (15.4 MB/s) - ‘./data/img3.JPG’ saved [90994/90994]
+
+--2022-05-02 20:40:37--  https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json
+Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.33.238
+Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.33.238|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: 35363 (35K) [application/octet-stream]
+Saving to: ‘./data/imagenet_class_index.json’
+
+./data/imagenet_cla 100%[===================&gt;]  34.53K  --.-KB/s    in 0.07s
+
+2022-05-02 20:40:38 (489 KB/s) - ‘./data/imagenet_class_index.json’ saved [35363/35363]
+
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># visualizing the downloaded images
+
+from PIL import Image
+from torchvision import transforms
+import matplotlib.pyplot as plt
+import json
+
+fig, axes = plt.subplots(nrows=2, ncols=2)
+
+for i in range(4):
+    img_path = &#39;./data/img%d.JPG&#39;%i
+    img = Image.open(img_path)
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    input_tensor = preprocess(img)
+    plt.subplot(2,2,i+1)
+    plt.imshow(img)
+    plt.axis(&#39;off&#39;)
+
+# loading labels
+with open(&quot;./data/imagenet_class_index.json&quot;) as json_file:
+    d = json.load(json_file)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/_notebooks_dynamic-shapes_8_0.png" src="../_images/_notebooks_dynamic-shapes_8_0.png" />
+</div>
+</div>
+</section>
+<section id="Download-model-from-torch-hub.">
+<h3>Download model from torch hub.<a class="headerlink" href="#Download-model-from-torch-hub." title="Permalink to this headline">¶</a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch
+
+torch.hub._validate_not_a_forked_repo=lambda a,b,c: True
+
+resnet50_model = torch.hub.load(&#39;pytorch/vision:v0.10.0&#39;, &#39;resnet50&#39;, pretrained=True)
+resnet50_model.eval()
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Using cache found in /root/.cache/torch/hub/pytorch_vision_v0.10.0
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+ResNet(
+  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
+  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (relu): ReLU(inplace=True)
+  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
+  (layer1): Sequential(
+    (0): Bottleneck(
+      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+      (downsample): Sequential(
+        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      )
+    )
+    (1): Bottleneck(
+      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (2): Bottleneck(
+      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+  )
+  (layer2): Sequential(
+    (0): Bottleneck(
+      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+      (downsample): Sequential(
+        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
+        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      )
+    )
+    (1): Bottleneck(
+      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (2): Bottleneck(
+      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (3): Bottleneck(
+      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+  )
+  (layer3): Sequential(
+    (0): Bottleneck(
+      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+      (downsample): Sequential(
+        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
+        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      )
+    )
+    (1): Bottleneck(
+      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (2): Bottleneck(
+      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (3): Bottleneck(
+      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (4): Bottleneck(
+      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (5): Bottleneck(
+      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+  )
+  (layer4): Sequential(
+    (0): Bottleneck(
+      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+      (downsample): Sequential(
+        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
+        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      )
+    )
+    (1): Bottleneck(
+      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+    (2): Bottleneck(
+      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
+      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
+      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+      (relu): ReLU(inplace=True)
+    )
+  )
+  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
+  (fc): Linear(in_features=2048, out_features=1000, bias=True)
+)
+</pre></div></div>
+</div>
+</section>
+<section id="Build-simple-utility-functions">
+<h3>Build simple utility functions<a class="headerlink" href="#Build-simple-utility-functions" title="Permalink to this headline">¶</a></h3>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import numpy as np
+import time
+import torch.backends.cudnn as cudnn
+cudnn.benchmark = True
+
+def rn50_preprocess():
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    return preprocess
+
+# decode the results into ([predicted class, description], probability)
+def predict(img_path, model):
+    img = Image.open(img_path)
+    preprocess = rn50_preprocess()
+    input_tensor = preprocess(img)
+    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+
+    # move the input and model to GPU for speed if available
+    if torch.cuda.is_available():
+        input_batch = input_batch.to(&#39;cuda&#39;)
+        model.to(&#39;cuda&#39;)
+
+    with torch.no_grad():
+        output = model(input_batch)
+        # Tensor of shape 1000, with confidence scores over Imagenet&#39;s 1000 classes
+        sm_output = torch.nn.functional.softmax(output[0], dim=0)
+
+    ind = torch.argmax(sm_output)
+    return d[str(ind.item())], sm_output[ind] #([predicted class, description], probability)
+
+# benchmarking models
+def benchmark(model, input_shape=(1024, 1, 224, 224), dtype=&#39;fp32&#39;, nwarmup=50, nruns=10000):
+    input_data = torch.randn(input_shape)
+    input_data = input_data.to(&quot;cuda&quot;)
+    if dtype==&#39;fp16&#39;:
+        input_data = input_data.half()
+
+    print(&quot;Warm up ...&quot;)
+    with torch.no_grad():
+        for _ in range(nwarmup):
+            features = model(input_data)
+    torch.cuda.synchronize()
+    print(&quot;Start timing ...&quot;)
+    timings = []
+    with torch.no_grad():
+        for i in range(1, nruns+1):
+            start_time = time.time()
+            features = model(input_data)
+            torch.cuda.synchronize()
+            end_time = time.time()
+            timings.append(end_time - start_time)
+            if i%10==0:
+                print(&#39;Iteration %d/%d, ave batch time %.2f ms&#39;%(i, nruns, np.mean(timings)*1000))
+                print(&#39;Images processed per second=&#39;, int(1000*input_shape[0]/(np.mean(timings)*1000)))
+    print(&quot;Input shape:&quot;, input_data.size())
+    print(&quot;Output features size:&quot;, features.size())
+    print(&#39;Average batch time: %.2f ms&#39;%(np.mean(timings)*1000))
+</pre></div>
+</div>
+</div>
+<p>Let’s test our util functions on the model we have set up, starting with simple predictions</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>for i in range(4):
+    img_path = &#39;./data/img%d.JPG&#39;%i
+    img = Image.open(img_path)
+
+    pred, prob = predict(img_path, resnet50_model)
+    print(&#39;{} - Predicted: {}, Probablility: {}&#39;.format(img_path, pred, prob))
+
+    plt.subplot(2,2,i+1)
+    plt.imshow(img);
+    plt.axis(&#39;off&#39;);
+    plt.title(pred[1])
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+./data/img0.JPG - Predicted: [&#39;n02110185&#39;, &#39;Siberian_husky&#39;], Probablility: 0.49788108468055725
+./data/img1.JPG - Predicted: [&#39;n01820546&#39;, &#39;lorikeet&#39;], Probablility: 0.6442285180091858
+./data/img2.JPG - Predicted: [&#39;n02481823&#39;, &#39;chimpanzee&#39;], Probablility: 0.9899841547012329
+./data/img3.JPG - Predicted: [&#39;n01749939&#39;, &#39;green_mamba&#39;], Probablility: 0.45675724744796753
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/_notebooks_dynamic-shapes_14_1.png" src="../_images/_notebooks_dynamic-shapes_14_1.png" />
+</div>
+</div>
+<p>Onwards, to benchmarking.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Model benchmark without Torch-TensorRT
+model = resnet50_model.eval().to(&quot;cuda&quot;)
+benchmark(model, input_shape=(16, 3, 224, 224), nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, ave batch time 10.01 ms
+Images processed per second= 1598
+Iteration 20/100, ave batch time 10.01 ms
+Images processed per second= 1598
+Iteration 30/100, ave batch time 10.21 ms
+Images processed per second= 1566
+Iteration 40/100, ave batch time 10.33 ms
+Images processed per second= 1549
+Iteration 50/100, ave batch time 10.31 ms
+Images processed per second= 1552
+Iteration 60/100, ave batch time 10.25 ms
+Images processed per second= 1560
+Iteration 70/100, ave batch time 10.20 ms
+Images processed per second= 1568
+Iteration 80/100, ave batch time 10.18 ms
+Images processed per second= 1572
+Iteration 90/100, ave batch time 10.16 ms
+Images processed per second= 1574
+Iteration 100/100, ave batch time 10.15 ms
+Images processed per second= 1575
+Input shape: torch.Size([16, 3, 224, 224])
+Output features size: torch.Size([16, 1000])
+Average batch time: 10.15 ms
+</pre></div></div>
+</div>
+</section>
+</section>
+<hr class="docutils" />
+<section id="Benchmarking-with-Torch-TRT-(without-dynamic-shapes)">
+<h2>Benchmarking with Torch-TRT (without dynamic shapes)<a class="headerlink" href="#Benchmarking-with-Torch-TRT-(without-dynamic-shapes)" title="Permalink to this headline">¶</a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch_tensorrt
+
+trt_model_without_ds = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((32, 3, 224, 224), dtype=torch.float32)],
+    enabled_precisions = torch.float32, # Run with FP32
+    workspace_size = 1 &lt;&lt; 33
+)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>benchmark(trt_model_without_ds, input_shape=(32, 3, 224, 224), nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, ave batch time 6.10 ms
+Images processed per second= 5242
+Iteration 20/100, ave batch time 6.12 ms
+Images processed per second= 5231
+Iteration 30/100, ave batch time 6.14 ms
+Images processed per second= 5215
+Iteration 40/100, ave batch time 6.14 ms
+Images processed per second= 5207
+Iteration 50/100, ave batch time 6.15 ms
+Images processed per second= 5202
+Iteration 60/100, ave batch time 6.28 ms
+Images processed per second= 5094
+Iteration 70/100, ave batch time 6.26 ms
+Images processed per second= 5110
+Iteration 80/100, ave batch time 6.25 ms
+Images processed per second= 5118
+Iteration 90/100, ave batch time 6.25 ms
+Images processed per second= 5115
+Iteration 100/100, ave batch time 6.40 ms
+Images processed per second= 5002
+Input shape: torch.Size([32, 3, 224, 224])
+Output features size: torch.Size([32, 1000])
+Average batch time: 6.40 ms
+</pre></div></div>
+</div>
+<p>With the baseline ready, we can proceed to the section working discussing dynamic shapes!</p>
+</section>
+<hr class="docutils" />
+<section id="Working-with-Dynamic-shapes-in-Torch-TRT">
+<h2>Working with Dynamic shapes in Torch TRT<a class="headerlink" href="#Working-with-Dynamic-shapes-in-Torch-TRT" title="Permalink to this headline">¶</a></h2>
+<p>Enabling “Dynamic Shaped” tensors to be used is essentially enabling the ability to defer defining the shape of tensors until runetime. Torch TensorRT simply leverages TensorRT’s Dynamic shape support. You can read more about TensorRT’s implementation in the <a class="reference external" href="https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#work_dynamic_shapes">TensorRT Documentation</a>.</p>
+<p>To make use of dynamic shapes, you need to provide three shapes: * <code class="docutils literal notranslate"><span class="pre">min_shape</span></code>: The minimum size of the tensor considered for optimizations. * <code class="docutils literal notranslate"><span class="pre">opt_shape</span></code>: The optimizations will be done with an effort to maximize performance for this shape. * <code class="docutils literal notranslate"><span class="pre">min_shape</span></code>: The maximum size of the tensor considered for optimizations.</p>
+<p>Generally, users can expect best performance within the specified ranges. Performance for other shapes may be be lower for other shapes (depending on the model ops and GPU used)</p>
+<p>In the following example, we will showcase varing batch size, which is the zeroth dimension of our input tensors. As Convolution operations require that the channel dimension be a build-time constant, we won’t be changing sizes of other channels in this example, but for models which contain ops conducive to changes in other channels, this functionality can be freely used.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># The compiled module will have precision as specified by &quot;op_precision&quot;.
+# Here, it will have FP32 precision.
+trt_model_with_ds = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input(
+        min_shape=(16, 3, 224, 224),
+        opt_shape=(32, 3, 224, 224),
+        max_shape=(64, 3, 224, 224),
+        dtype=torch.float32)],
+    enabled_precisions = torch.float32, # Run with FP32
+    workspace_size = 1 &lt;&lt; 33
+)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>benchmark(trt_model_with_ds, input_shape=(16, 3, 224, 224), nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, ave batch time 3.88 ms
+Images processed per second= 4122
+Iteration 20/100, ave batch time 3.89 ms
+Images processed per second= 4116
+Iteration 30/100, ave batch time 3.88 ms
+Images processed per second= 4123
+Iteration 40/100, ave batch time 3.86 ms
+Images processed per second= 4142
+Iteration 50/100, ave batch time 3.85 ms
+Images processed per second= 4156
+Iteration 60/100, ave batch time 3.84 ms
+Images processed per second= 4166
+Iteration 70/100, ave batch time 3.84 ms
+Images processed per second= 4170
+Iteration 80/100, ave batch time 3.83 ms
+Images processed per second= 4172
+Iteration 90/100, ave batch time 3.83 ms
+Images processed per second= 4176
+Iteration 100/100, ave batch time 3.83 ms
+Images processed per second= 4178
+Input shape: torch.Size([16, 3, 224, 224])
+Output features size: torch.Size([16, 1000])
+Average batch time: 3.83 ms
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>benchmark(trt_model_with_ds, input_shape=(32, 3, 224, 224), nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, ave batch time 6.71 ms
+Images processed per second= 4767
+Iteration 20/100, ave batch time 6.48 ms
+Images processed per second= 4935
+Iteration 30/100, ave batch time 6.39 ms
+Images processed per second= 5005
+Iteration 40/100, ave batch time 6.38 ms
+Images processed per second= 5014
+Iteration 50/100, ave batch time 6.38 ms
+Images processed per second= 5016
+Iteration 60/100, ave batch time 6.37 ms
+Images processed per second= 5020
+Iteration 70/100, ave batch time 6.37 ms
+Images processed per second= 5024
+Iteration 80/100, ave batch time 6.37 ms
+Images processed per second= 5027
+Iteration 90/100, ave batch time 6.37 ms
+Images processed per second= 5026
+Iteration 100/100, ave batch time 6.38 ms
+Images processed per second= 5018
+Input shape: torch.Size([32, 3, 224, 224])
+Output features size: torch.Size([32, 1000])
+Average batch time: 6.38 ms
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>benchmark(trt_model_with_ds, input_shape=(64, 3, 224, 224), nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, ave batch time 12.31 ms
+Images processed per second= 5197
+Iteration 20/100, ave batch time 12.42 ms
+Images processed per second= 5153
+Iteration 30/100, ave batch time 12.85 ms
+Images processed per second= 4980
+Iteration 40/100, ave batch time 12.71 ms
+Images processed per second= 5033
+Iteration 50/100, ave batch time 12.67 ms
+Images processed per second= 5052
+Iteration 60/100, ave batch time 12.63 ms
+Images processed per second= 5067
+Iteration 70/100, ave batch time 12.58 ms
+Images processed per second= 5088
+Iteration 80/100, ave batch time 12.56 ms
+Images processed per second= 5096
+Iteration 90/100, ave batch time 12.55 ms
+Images processed per second= 5100
+Iteration 100/100, ave batch time 12.57 ms
+Images processed per second= 5091
+Input shape: torch.Size([64, 3, 224, 224])
+Output features size: torch.Size([64, 1000])
+Average batch time: 12.57 ms
+</pre></div></div>
+</div>
+</section>
+<section id="What’s-Next?">
+<h2>What’s Next?<a class="headerlink" href="#What’s-Next?" title="Permalink to this headline">¶</a></h2>
+<p>Check out the <a class="reference external" href="https://developer.nvidia.com/tensorrt-getting-started">TensorRT Getting started page</a> for more tutorials, or visit the Torch-TensorRT <a class="reference external" href="https://nvidia.github.io/Torch-TensorRT/">documentation</a> for more information!</p>
+</section>
+</section>
+
+
+             </article>
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+
+    </p>
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+
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+              <ul>
+<li><a class="reference internal" href="#">Torch-TensorRT - Using Dynamic Shapes</a><ul>
+<li><a class="reference internal" href="#Setting-up-the-model">Setting up the model</a><ul>
+<li><a class="reference internal" href="#Getting-sample-data">Getting sample data</a></li>
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+</ul>
+</li>
+<li><a class="reference internal" href="#Benchmarking-with-Torch-TRT-(without-dynamic-shapes)">Benchmarking with Torch-TRT (without dynamic shapes)</a></li>
+<li><a class="reference internal" href="#Working-with-Dynamic-shapes-in-Torch-TRT">Working with Dynamic shapes in Torch TRT</a></li>
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+            <li><a href="https://music.amazon.com/podcasts/7a4e6f0e-26c2-49e9-a478-41bd244197d0/PyTorch-Developer-Podcast?" target="_blank">Amazon</a></li>
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diff --git a/docs/v1.2.0/_notebooks/dynamic-shapes.ipynb b/docs/v1.2.0/_notebooks/dynamic-shapes.ipynb
new file mode 100644
index 0000000000..a0ceaab576
--- /dev/null
+++ b/docs/v1.2.0/_notebooks/dynamic-shapes.ipynb
@@ -0,0 +1,1023 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "332a2ed8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2020 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18ee9d62",
+   "metadata": {},
+   "source": [
+    "# Torch-TensorRT - Using Dynamic Shapes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73703695",
+   "metadata": {},
+   "source": [
+    "Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch's Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extention and compiles modules that integrate into the JIT runtime seamlessly. After compilation using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT's suite of configurations at compile time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.\n",
+    "\n",
+    "We highly encorage users to use our NVIDIA's [PyTorch container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) to run this notebook. It comes packaged with a host of NVIDIA libraries and optimizations to widely used third party libraries. This container is tested and updated on a monthly cadence!\n",
+    "\n",
+    "This notebook has the following sections:\n",
+    "1. [TL;DR Explanation](#1)\n",
+    "1. [Setting up the model](#2)\n",
+    "1. [Working with Dynamic shapes in Torch TRT](#3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1603028d",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## TL;DR Explanation\n",
+    "\n",
+    "Making use of Dynamic Shaped Tensors in Torch TensorRT is quite simple. Let's say you are using the `torch_tensorrt.compile(...)` function to compile a torchscript module.\n",
+    "\n",
+    "One of the `args` in this function in this function is `input`: which defines an input to a module in terms of expected shape, data type and tensor format: `torch_tensorrt.Input`. \n",
+    "\n",
+    "For the purposes of this walkthrough we just need three `kwargs`: `min_shape`, `opt_shape` and `max_shape`. \n",
+    "```\n",
+    "...\n",
+    "torch_tensorrt.Input(\n",
+    "        min_shape=(1, 224, 224, 3),\n",
+    "        opt_shape=(1, 512, 512, 3),\n",
+    "        max_shape=(1, 1024, 1024, 3),\n",
+    "        dtype=torch.int32\n",
+    "        format=torch.channel_last\n",
+    "    )\n",
+    "...\n",
+    "```\n",
+    "In this example, we are going to use a simple ResNet model to demonstrate the use of the API. We will be using different batch sizes in the example, but you can use the same method to alter any of the dimensions of the tensor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "db3493d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mon May  2 20:40:30 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 470.57.02    Driver Version: 470.57.02    CUDA Version: 11.6     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA Graphics...  On   | 00000000:01:00.0 Off |                    0 |\n",
+      "| 41%   51C    P0    62W / 200W |      0MiB / 47681MiB |      0%      Default |\n",
+      "|                               |                      |             Disabled |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|  No running processes found                                                 |\n",
+      "+-----------------------------------------------------------------------------+\n",
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging->ipykernel>=4.5.1->ipywidgets) (3.0.8)\n",
+      "Requirement already satisfied: asttokens in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets) (2.0.5)\n",
+      "Requirement already satisfied: pure-eval in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets) (0.2.2)\n",
+      "Requirement already satisfied: executing in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets) (0.8.3)\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi\n",
+    "!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ed1b4f7",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Setting up the model\n",
+    "\n",
+    "In this section, we will:\n",
+    "* Get sample data.\n",
+    "* Download model from torch hub.\n",
+    "* Build simple utility functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5934504f",
+   "metadata": {},
+   "source": [
+    "### Getting sample data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e46814cc",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2022-05-02 20:40:33--  https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\n",
+      "Resolving d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)... 18.65.227.37, 18.65.227.99, 18.65.227.223, ...\n",
+      "Connecting to d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)|18.65.227.37|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 24112 (24K) [image/jpeg]\n",
+      "Saving to: ‘./data/img0.JPG’\n",
+      "\n",
+      "./data/img0.JPG     100%[===================>]  23.55K  --.-KB/s    in 0.005s  \n",
+      "\n",
+      "2022-05-02 20:40:33 (4.69 MB/s) - ‘./data/img0.JPG’ saved [24112/24112]\n",
+      "\n",
+      "--2022-05-02 20:40:34--  https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\n",
+      "Resolving www.hakaimagazine.com (www.hakaimagazine.com)... 164.92.73.117\n",
+      "Connecting to www.hakaimagazine.com (www.hakaimagazine.com)|164.92.73.117|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 452718 (442K) [image/jpeg]\n",
+      "Saving to: ‘./data/img1.JPG’\n",
+      "\n",
+      "./data/img1.JPG     100%[===================>] 442.11K  --.-KB/s    in 0.02s   \n",
+      "\n",
+      "2022-05-02 20:40:34 (26.2 MB/s) - ‘./data/img1.JPG’ saved [452718/452718]\n",
+      "\n",
+      "--2022-05-02 20:40:34--  https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\n",
+      "Resolving www.artis.nl (www.artis.nl)... 94.75.225.20\n",
+      "Connecting to www.artis.nl (www.artis.nl)|94.75.225.20|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 361413 (353K) [image/jpeg]\n",
+      "Saving to: ‘./data/img2.JPG’\n",
+      "\n",
+      "./data/img2.JPG     100%[===================>] 352.94K   608KB/s    in 0.6s    \n",
+      "\n",
+      "2022-05-02 20:40:36 (608 KB/s) - ‘./data/img2.JPG’ saved [361413/361413]\n",
+      "\n",
+      "--2022-05-02 20:40:37--  https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\n",
+      "Resolving www.familyhandyman.com (www.familyhandyman.com)... 104.18.201.107, 104.18.202.107, 2606:4700::6812:c96b, ...\n",
+      "Connecting to www.familyhandyman.com (www.familyhandyman.com)|104.18.201.107|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 90994 (89K) [image/jpeg]\n",
+      "Saving to: ‘./data/img3.JPG’\n",
+      "\n",
+      "./data/img3.JPG     100%[===================>]  88.86K  --.-KB/s    in 0.006s  \n",
+      "\n",
+      "2022-05-02 20:40:37 (15.4 MB/s) - ‘./data/img3.JPG’ saved [90994/90994]\n",
+      "\n",
+      "--2022-05-02 20:40:37--  https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\n",
+      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.33.238\n",
+      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.33.238|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 35363 (35K) [application/octet-stream]\n",
+      "Saving to: ‘./data/imagenet_class_index.json’\n",
+      "\n",
+      "./data/imagenet_cla 100%[===================>]  34.53K  --.-KB/s    in 0.07s   \n",
+      "\n",
+      "2022-05-02 20:40:38 (489 KB/s) - ‘./data/imagenet_class_index.json’ saved [35363/35363]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!mkdir -p ./data\n",
+    "!wget  -O ./data/img0.JPG \"https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\"\n",
+    "!wget  -O ./data/img1.JPG \"https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\"\n",
+    "!wget  -O ./data/img2.JPG \"https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\"\n",
+    "!wget  -O ./data/img3.JPG \"https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\"\n",
+    "\n",
+    "!wget  -O ./data/imagenet_class_index.json \"https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7392a8ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualizing the downloaded images\n",
+    "\n",
+    "from PIL import Image\n",
+    "from torchvision import transforms\n",
+    "import matplotlib.pyplot as plt\n",
+    "import json \n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
+    "\n",
+    "for i in range(4):\n",
+    "    img_path = './data/img%d.JPG'%i\n",
+    "    img = Image.open(img_path)\n",
+    "    preprocess = transforms.Compose([\n",
+    "        transforms.Resize(256),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
+    "    ])\n",
+    "    input_tensor = preprocess(img)      \n",
+    "    plt.subplot(2,2,i+1)\n",
+    "    plt.imshow(img)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "# loading labels    \n",
+    "with open(\"./data/imagenet_class_index.json\") as json_file: \n",
+    "    d = json.load(json_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1374edab",
+   "metadata": {},
+   "source": [
+    "### Download model from torch hub."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "659fa798",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using cache found in /root/.cache/torch/hub/pytorch_vision_v0.10.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ResNet(\n",
+       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  (relu): ReLU(inplace=True)\n",
+       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+       "  (layer1): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer2): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (3): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer3): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (3): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (4): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (5): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer4): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+       "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "\n",
+    "torch.hub._validate_not_a_forked_repo=lambda a,b,c: True\n",
+    "\n",
+    "resnet50_model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)\n",
+    "resnet50_model.eval()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a67e8db",
+   "metadata": {},
+   "source": [
+    "### Build simple utility functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "60286396",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import time\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "cudnn.benchmark = True\n",
+    "\n",
+    "def rn50_preprocess():\n",
+    "    preprocess = transforms.Compose([\n",
+    "        transforms.Resize(256),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
+    "    ])\n",
+    "    return preprocess\n",
+    "\n",
+    "# decode the results into ([predicted class, description], probability)\n",
+    "def predict(img_path, model):\n",
+    "    img = Image.open(img_path)\n",
+    "    preprocess = rn50_preprocess()\n",
+    "    input_tensor = preprocess(img)\n",
+    "    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model\n",
+    "    \n",
+    "    # move the input and model to GPU for speed if available\n",
+    "    if torch.cuda.is_available():\n",
+    "        input_batch = input_batch.to('cuda')\n",
+    "        model.to('cuda')\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        output = model(input_batch)\n",
+    "        # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes\n",
+    "        sm_output = torch.nn.functional.softmax(output[0], dim=0)\n",
+    "        \n",
+    "    ind = torch.argmax(sm_output)\n",
+    "    return d[str(ind.item())], sm_output[ind] #([predicted class, description], probability)\n",
+    "\n",
+    "# benchmarking models\n",
+    "def benchmark(model, input_shape=(1024, 1, 224, 224), dtype='fp32', nwarmup=50, nruns=10000):\n",
+    "    input_data = torch.randn(input_shape)\n",
+    "    input_data = input_data.to(\"cuda\")\n",
+    "    if dtype=='fp16':\n",
+    "        input_data = input_data.half()\n",
+    "        \n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(nwarmup):\n",
+    "            features = model(input_data)\n",
+    "    torch.cuda.synchronize()\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(1, nruns+1):\n",
+    "            start_time = time.time()\n",
+    "            features = model(input_data)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = time.time()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            if i%10==0:\n",
+    "                print('Iteration %d/%d, ave batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
+    "                print('Images processed per second=', int(1000*input_shape[0]/(np.mean(timings)*1000)))\n",
+    "    print(\"Input shape:\", input_data.size())\n",
+    "    print(\"Output features size:\", features.size())\n",
+    "    print('Average batch time: %.2f ms'%(np.mean(timings)*1000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21402d53",
+   "metadata": {},
+   "source": [
+    "Let's test our util functions on the model we have set up, starting with simple predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bb3e4a0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./data/img0.JPG - Predicted: ['n02110185', 'Siberian_husky'], Probablility: 0.49788108468055725\n",
+      "./data/img1.JPG - Predicted: ['n01820546', 'lorikeet'], Probablility: 0.6442285180091858\n",
+      "./data/img2.JPG - Predicted: ['n02481823', 'chimpanzee'], Probablility: 0.9899841547012329\n",
+      "./data/img3.JPG - Predicted: ['n01749939', 'green_mamba'], Probablility: 0.45675724744796753\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(4):\n",
+    "    img_path = './data/img%d.JPG'%i\n",
+    "    img = Image.open(img_path)\n",
+    "    \n",
+    "    pred, prob = predict(img_path, resnet50_model)\n",
+    "    print('{} - Predicted: {}, Probablility: {}'.format(img_path, pred, prob))\n",
+    "\n",
+    "    plt.subplot(2,2,i+1)\n",
+    "    plt.imshow(img);\n",
+    "    plt.axis('off');\n",
+    "    plt.title(pred[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fc7f347",
+   "metadata": {},
+   "source": [
+    "Onwards, to benchmarking."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f182b433",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 10.01 ms\n",
+      "Images processed per second= 1598\n",
+      "Iteration 20/100, ave batch time 10.01 ms\n",
+      "Images processed per second= 1598\n",
+      "Iteration 30/100, ave batch time 10.21 ms\n",
+      "Images processed per second= 1566\n",
+      "Iteration 40/100, ave batch time 10.33 ms\n",
+      "Images processed per second= 1549\n",
+      "Iteration 50/100, ave batch time 10.31 ms\n",
+      "Images processed per second= 1552\n",
+      "Iteration 60/100, ave batch time 10.25 ms\n",
+      "Images processed per second= 1560\n",
+      "Iteration 70/100, ave batch time 10.20 ms\n",
+      "Images processed per second= 1568\n",
+      "Iteration 80/100, ave batch time 10.18 ms\n",
+      "Images processed per second= 1572\n",
+      "Iteration 90/100, ave batch time 10.16 ms\n",
+      "Images processed per second= 1574\n",
+      "Iteration 100/100, ave batch time 10.15 ms\n",
+      "Images processed per second= 1575\n",
+      "Input shape: torch.Size([16, 3, 224, 224])\n",
+      "Output features size: torch.Size([16, 1000])\n",
+      "Average batch time: 10.15 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Model benchmark without Torch-TensorRT\n",
+    "model = resnet50_model.eval().to(\"cuda\")\n",
+    "benchmark(model, input_shape=(16, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21e56cdf",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Benchmarking with Torch-TRT (without dynamic shapes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8c9ed780",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch_tensorrt\n",
+    "\n",
+    "trt_model_without_ds = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((32, 3, 224, 224), dtype=torch.float32)],\n",
+    "    enabled_precisions = torch.float32, # Run with FP32\n",
+    "    workspace_size = 1 << 33\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "58a4ba94",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 6.10 ms\n",
+      "Images processed per second= 5242\n",
+      "Iteration 20/100, ave batch time 6.12 ms\n",
+      "Images processed per second= 5231\n",
+      "Iteration 30/100, ave batch time 6.14 ms\n",
+      "Images processed per second= 5215\n",
+      "Iteration 40/100, ave batch time 6.14 ms\n",
+      "Images processed per second= 5207\n",
+      "Iteration 50/100, ave batch time 6.15 ms\n",
+      "Images processed per second= 5202\n",
+      "Iteration 60/100, ave batch time 6.28 ms\n",
+      "Images processed per second= 5094\n",
+      "Iteration 70/100, ave batch time 6.26 ms\n",
+      "Images processed per second= 5110\n",
+      "Iteration 80/100, ave batch time 6.25 ms\n",
+      "Images processed per second= 5118\n",
+      "Iteration 90/100, ave batch time 6.25 ms\n",
+      "Images processed per second= 5115\n",
+      "Iteration 100/100, ave batch time 6.40 ms\n",
+      "Images processed per second= 5002\n",
+      "Input shape: torch.Size([32, 3, 224, 224])\n",
+      "Output features size: torch.Size([32, 1000])\n",
+      "Average batch time: 6.40 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_model_without_ds, input_shape=(32, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd233b85",
+   "metadata": {},
+   "source": [
+    "With the baseline ready, we can proceed to the section working discussing dynamic shapes!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd5746da",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Working with Dynamic shapes in Torch TRT\n",
+    "\n",
+    "Enabling \"Dynamic Shaped\" tensors to be used is essentially enabling the ability to defer defining the shape of tensors until runetime. Torch TensorRT simply leverages TensorRT's Dynamic shape support. You can read more about TensorRT's implementation in the [TensorRT Documentation](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#work_dynamic_shapes).\n",
+    "\n",
+    "#### How can you use this feature?\n",
+    "\n",
+    "To make use of dynamic shapes, you need to provide three shapes:\n",
+    "* `min_shape`: The minimum size of the tensor considered for optimizations.\n",
+    "* `opt_shape`: The optimizations will be done with an effort to maximize performance for this shape.\n",
+    "* `min_shape`: The maximum size of the tensor considered for optimizations.\n",
+    "\n",
+    "Generally, users can expect best performance within the specified ranges. Performance for other shapes may be be lower for other shapes (depending on the model ops and GPU used)\n",
+    "\n",
+    "In the following example, we will showcase varing batch size, which is the zeroth dimension of our input tensors. As Convolution operations require that the channel dimension be a build-time constant, we won't be changing sizes of other channels in this example, but for models which contain ops conducive to changes in other channels, this functionality can be freely used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9a04007d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The compiled module will have precision as specified by \"op_precision\".\n",
+    "# Here, it will have FP32 precision.\n",
+    "trt_model_with_ds = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input(\n",
+    "        min_shape=(16, 3, 224, 224),\n",
+    "        opt_shape=(32, 3, 224, 224),\n",
+    "        max_shape=(64, 3, 224, 224),\n",
+    "        dtype=torch.float32)],\n",
+    "    enabled_precisions = torch.float32, # Run with FP32\n",
+    "    workspace_size = 1 << 33\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f0babc85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 3.88 ms\n",
+      "Images processed per second= 4122\n",
+      "Iteration 20/100, ave batch time 3.89 ms\n",
+      "Images processed per second= 4116\n",
+      "Iteration 30/100, ave batch time 3.88 ms\n",
+      "Images processed per second= 4123\n",
+      "Iteration 40/100, ave batch time 3.86 ms\n",
+      "Images processed per second= 4142\n",
+      "Iteration 50/100, ave batch time 3.85 ms\n",
+      "Images processed per second= 4156\n",
+      "Iteration 60/100, ave batch time 3.84 ms\n",
+      "Images processed per second= 4166\n",
+      "Iteration 70/100, ave batch time 3.84 ms\n",
+      "Images processed per second= 4170\n",
+      "Iteration 80/100, ave batch time 3.83 ms\n",
+      "Images processed per second= 4172\n",
+      "Iteration 90/100, ave batch time 3.83 ms\n",
+      "Images processed per second= 4176\n",
+      "Iteration 100/100, ave batch time 3.83 ms\n",
+      "Images processed per second= 4178\n",
+      "Input shape: torch.Size([16, 3, 224, 224])\n",
+      "Output features size: torch.Size([16, 1000])\n",
+      "Average batch time: 3.83 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_model_with_ds, input_shape=(16, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e86a3541",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 6.71 ms\n",
+      "Images processed per second= 4767\n",
+      "Iteration 20/100, ave batch time 6.48 ms\n",
+      "Images processed per second= 4935\n",
+      "Iteration 30/100, ave batch time 6.39 ms\n",
+      "Images processed per second= 5005\n",
+      "Iteration 40/100, ave batch time 6.38 ms\n",
+      "Images processed per second= 5014\n",
+      "Iteration 50/100, ave batch time 6.38 ms\n",
+      "Images processed per second= 5016\n",
+      "Iteration 60/100, ave batch time 6.37 ms\n",
+      "Images processed per second= 5020\n",
+      "Iteration 70/100, ave batch time 6.37 ms\n",
+      "Images processed per second= 5024\n",
+      "Iteration 80/100, ave batch time 6.37 ms\n",
+      "Images processed per second= 5027\n",
+      "Iteration 90/100, ave batch time 6.37 ms\n",
+      "Images processed per second= 5026\n",
+      "Iteration 100/100, ave batch time 6.38 ms\n",
+      "Images processed per second= 5018\n",
+      "Input shape: torch.Size([32, 3, 224, 224])\n",
+      "Output features size: torch.Size([32, 1000])\n",
+      "Average batch time: 6.38 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_model_with_ds, input_shape=(32, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "35800f63",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 12.31 ms\n",
+      "Images processed per second= 5197\n",
+      "Iteration 20/100, ave batch time 12.42 ms\n",
+      "Images processed per second= 5153\n",
+      "Iteration 30/100, ave batch time 12.85 ms\n",
+      "Images processed per second= 4980\n",
+      "Iteration 40/100, ave batch time 12.71 ms\n",
+      "Images processed per second= 5033\n",
+      "Iteration 50/100, ave batch time 12.67 ms\n",
+      "Images processed per second= 5052\n",
+      "Iteration 60/100, ave batch time 12.63 ms\n",
+      "Images processed per second= 5067\n",
+      "Iteration 70/100, ave batch time 12.58 ms\n",
+      "Images processed per second= 5088\n",
+      "Iteration 80/100, ave batch time 12.56 ms\n",
+      "Images processed per second= 5096\n",
+      "Iteration 90/100, ave batch time 12.55 ms\n",
+      "Images processed per second= 5100\n",
+      "Iteration 100/100, ave batch time 12.57 ms\n",
+      "Images processed per second= 5091\n",
+      "Input shape: torch.Size([64, 3, 224, 224])\n",
+      "Output features size: torch.Size([64, 1000])\n",
+      "Average batch time: 12.57 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_model_with_ds, input_shape=(64, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f87a75bd",
+   "metadata": {},
+   "source": [
+    "## What's Next?\n",
+    "\n",
+    "Check out the [TensorRT Getting started page](https://developer.nvidia.com/tensorrt-getting-started) for more tutorials, or visit the Torch-TensorRT [documentation](https://nvidia.github.io/Torch-TensorRT/) for more information!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/v1.2.0/_notebooks/lenet-getting-started.html b/docs/v1.2.0/_notebooks/lenet-getting-started.html
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+
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/writing_converters.html">Writing Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2019 NVIDIA Corporation. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an &quot;AS IS&quot; BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+</pre></div>
+</div>
+</div>
+<p><img alt="6e5fd25e8d6b4ce79aa5fcb073d51b40" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png" /></p>
+<section id="Torch-TensorRT-Getting-Started---LeNet">
+<h1>Torch-TensorRT Getting Started - LeNet<a class="headerlink" href="#Torch-TensorRT-Getting-Started---LeNet" title="Permalink to this headline">¶</a></h1>
+<section id="Overview">
+<h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline">¶</a></h2>
+<p>In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the
+best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch’s JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new
+world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.</p>
+<p>When deploying on NVIDIA GPUs TensorRT, NVIDIA’s Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA’s Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that
+perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.</p>
+<p>Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch.</p>
+<section id="Learning-objectives">
+<h3>Learning objectives<a class="headerlink" href="#Learning-objectives" title="Permalink to this headline">¶</a></h3>
+<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a simple LeNet network.</p>
+</section>
+</section>
+<section id="Content">
+<h2>Content<a class="headerlink" href="#Content" title="Permalink to this headline">¶</a></h2>
+<ol class="arabic simple">
+<li><p><a class="reference external" href="#1">Requirements</a></p></li>
+<li><p><a class="reference external" href="#2">Creating TorchScript modules</a></p></li>
+<li><p><a class="reference external" href="#3">Compiling with Torch-TensorRT</a></p></li>
+</ol>
+<p>## 1. Requirements</p>
+<p>Follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container, within which you can run this notebook.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org
+!nvidia-smi
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
+Collecting ipywidgets
+  Downloading ipywidgets-7.6.5-py2.py3-none-any.whl (121 kB)
+     |████████████████████████████████| 121 kB 12.7 MB/s eta 0:00:01
+Requirement already satisfied: traitlets&gt;=4.3.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.1)
+Collecting jupyterlab-widgets&gt;=1.0.0
+  Downloading jupyterlab_widgets-1.0.2-py3-none-any.whl (243 kB)
+     |████████████████████████████████| 243 kB 115.0 MB/s eta 0:00:01
+Requirement already satisfied: ipython-genutils~=0.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (0.2.0)
+Requirement already satisfied: nbformat&gt;=4.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.3)
+Requirement already satisfied: ipykernel&gt;=4.5.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (6.7.0)
+Requirement already satisfied: ipython&gt;=4.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (7.31.0)
+Collecting widgetsnbextension~=3.5.0
+  Downloading widgetsnbextension-3.5.2-py2.py3-none-any.whl (1.6 MB)
+     |████████████████████████████████| 1.6 MB 122.5 MB/s eta 0:00:01
+Requirement already satisfied: tornado&lt;7.0,&gt;=4.2 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (6.1)
+Requirement already satisfied: debugpy&lt;2.0,&gt;=1.0.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (1.5.1)
+Requirement already satisfied: jupyter-client&lt;8.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (7.1.2)
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+Requirement already satisfied: setuptools&gt;=18.5 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (59.5.0)
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+Requirement already satisfied: jedi&gt;=0.16 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (0.18.1)
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+Requirement already satisfied: pyzmq&gt;=13 in /opt/conda/lib/python3.8/site-packages (from jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (22.3.0)
+Requirement already satisfied: entrypoints in /opt/conda/lib/python3.8/site-packages (from jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (0.3)
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+Requirement already satisfied: attrs&gt;=17.4.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,&gt;=2.4-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets) (21.4.0)
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+Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,&gt;=0.14.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,&gt;=2.4-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets) (0.18.1)
+Requirement already satisfied: zipp&gt;=3.1.0 in /opt/conda/lib/python3.8/site-packages (from importlib-resources&gt;=1.4.0-&gt;jsonschema!=2.5.0,&gt;=2.4-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets) (3.7.0)
+Requirement already satisfied: ptyprocess&gt;=0.5 in /opt/conda/lib/python3.8/site-packages (from pexpect&gt;4.3-&gt;ipython&gt;=4.0.0-&gt;ipywidgets) (0.7.0)
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+Requirement already satisfied: notebook&gt;=4.4.1 in /opt/conda/lib/python3.8/site-packages (from widgetsnbextension~=3.5.0-&gt;ipywidgets) (6.4.1)
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+Requirement already satisfied: Send2Trash&gt;=1.5.0 in /opt/conda/lib/python3.8/site-packages (from notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (1.8.0)
+Requirement already satisfied: argon2-cffi-bindings in /opt/conda/lib/python3.8/site-packages (from argon2-cffi-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (21.2.0)
+Requirement already satisfied: cffi&gt;=1.0.1 in /opt/conda/lib/python3.8/site-packages (from argon2-cffi-bindings-&gt;argon2-cffi-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (1.15.0)
+Requirement already satisfied: pycparser in /opt/conda/lib/python3.8/site-packages (from cffi&gt;=1.0.1-&gt;argon2-cffi-bindings-&gt;argon2-cffi-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (2.21)
+Requirement already satisfied: MarkupSafe&gt;=2.0 in /opt/conda/lib/python3.8/site-packages (from jinja2-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (2.0.1)
+Requirement already satisfied: testpath in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (0.5.0)
+Requirement already satisfied: defusedxml in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (0.7.1)
+Requirement already satisfied: jupyterlab-pygments in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (0.1.2)
+Requirement already satisfied: nbclient&lt;0.6.0,&gt;=0.5.0 in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (0.5.10)
+Requirement already satisfied: bleach in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (4.1.0)
+Requirement already satisfied: pandocfilters&gt;=1.4.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (1.5.0)
+Requirement already satisfied: mistune&lt;2,&gt;=0.8.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (0.8.4)
+Requirement already satisfied: webencodings in /opt/conda/lib/python3.8/site-packages (from bleach-&gt;nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (0.5.1)
+Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from bleach-&gt;nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (21.3)
+Requirement already satisfied: pyparsing!=3.0.5,&gt;=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging-&gt;bleach-&gt;nbconvert-&gt;notebook&gt;=4.4.1-&gt;widgetsnbextension~=3.5.0-&gt;ipywidgets) (3.0.6)
+Installing collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets
+Successfully installed ipywidgets-7.6.5 jupyterlab-widgets-1.0.2 widgetsnbextension-3.5.2
+<span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
+Thu Feb 10 22:01:27 2022
++-----------------------------------------------------------------------------+
+| NVIDIA-SMI 510.39.01    Driver Version: 510.39.01    CUDA Version: 11.6     |
+|-------------------------------+----------------------+----------------------+
+| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+|                               |                      |               MIG M. |
+|===============================+======================+======================|
+|   0  NVIDIA GeForce ...  On   | 00000000:09:00.0 Off |                  N/A |
+|  0%   42C    P8    20W / 320W |      0MiB / 10240MiB |      0%      Default |
+|                               |                      |                  N/A |
++-------------------------------+----------------------+----------------------+
+
++-----------------------------------------------------------------------------+
+| Processes:                                                                  |
+|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
+|        ID   ID                                                   Usage      |
+|=============================================================================|
+|  No running processes found                                                 |
++-----------------------------------------------------------------------------+
+</pre></div></div>
+</div>
+<p>## 2. Creating TorchScript modules</p>
+<p>Here we create two submodules for a feature extractor and a classifier and stitch them together in a single LeNet module. In this case this is overkill but modules give us granular control over our program including where we decide to optimize and where we don’t. It is also the unit that the TorchScript compiler operates on. So you can decide to only convert/optimize the feature extractor and leave the classifier in standard PyTorch or you can convert the whole thing. When compiling your module
+to TorchScript, there are two paths: Tracing and Scripting.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch
+from torch import nn
+import torch.nn.functional as F
+
+class LeNetFeatExtractor(nn.Module):
+    def __init__(self):
+        super(LeNetFeatExtractor, self).__init__()
+        self.conv1 = nn.Conv2d(1, 128, 3)
+        self.conv2 = nn.Conv2d(128, 16, 3)
+
+    def forward(self, x):
+        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
+        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
+        return x
+
+class LeNetClassifier(nn.Module):
+    def __init__(self):
+        super(LeNetClassifier, self).__init__()
+        self.fc1 = nn.Linear(16 * 6 * 6, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = torch.flatten(x,1)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+class LeNet(nn.Module):
+    def __init__(self):
+        super(LeNet, self).__init__()
+        self.feat = LeNetFeatExtractor()
+        self.classifer = LeNetClassifier()
+
+    def forward(self, x):
+        x = self.feat(x)
+        x = self.classifer(x)
+        return x
+<br/></pre></div>
+</div>
+</div>
+<p>Let us define a helper function to benchmark a model.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import time
+import numpy as np
+
+import torch.backends.cudnn as cudnn
+cudnn.benchmark = True
+
+def benchmark(model, input_shape=(1024, 1, 32, 32), dtype=&#39;fp32&#39;, nwarmup=50, nruns=1000):
+    input_data = torch.randn(input_shape)
+    input_data = input_data.to(&quot;cuda&quot;)
+    if dtype==&#39;fp16&#39;:
+        input_data = input_data.half()
+
+    print(&quot;Warm up ...&quot;)
+    with torch.no_grad():
+        for _ in range(nwarmup):
+            features = model(input_data)
+    torch.cuda.synchronize()
+    print(&quot;Start timing ...&quot;)
+    timings = []
+    with torch.no_grad():
+        for i in range(1, nruns+1):
+            start_time = time.time()
+            features = model(input_data)
+            torch.cuda.synchronize()
+            end_time = time.time()
+            timings.append(end_time - start_time)
+            if i%100==0:
+                print(&#39;Iteration %d/%d, ave batch time %.2f ms&#39;%(i, nruns, np.mean(timings)*1000))
+
+    print(&quot;Input shape:&quot;, input_data.size())
+    print(&quot;Output features size:&quot;, features.size())
+
+    print(&#39;Average batch time: %.2f ms&#39;%(np.mean(timings)*1000))
+</pre></div>
+</div>
+</div>
+<section id="PyTorch-model">
+<h3>PyTorch model<a class="headerlink" href="#PyTorch-model" title="Permalink to this headline">¶</a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>model = LeNet()
+model.to(&quot;cuda&quot;).eval()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+LeNet(
+  (feat): LeNetFeatExtractor(
+    (conv1): Conv2d(1, 128, kernel_size=(3, 3), stride=(1, 1))
+    (conv2): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1))
+  )
+  (classifer): LeNetClassifier(
+    (fc1): Linear(in_features=576, out_features=120, bias=True)
+    (fc2): Linear(in_features=120, out_features=84, bias=True)
+    (fc3): Linear(in_features=84, out_features=10, bias=True)
+  )
+)
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>benchmark(model)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 100/1000, ave batch time 5.56 ms
+Iteration 200/1000, ave batch time 5.56 ms
+Iteration 300/1000, ave batch time 5.56 ms
+Iteration 400/1000, ave batch time 5.56 ms
+Iteration 500/1000, ave batch time 5.56 ms
+Iteration 600/1000, ave batch time 5.56 ms
+Iteration 700/1000, ave batch time 5.56 ms
+Iteration 800/1000, ave batch time 5.56 ms
+Iteration 900/1000, ave batch time 5.56 ms
+Iteration 1000/1000, ave batch time 5.56 ms
+Input shape: torch.Size([1024, 1, 32, 32])
+Output features size: torch.Size([1024, 10])
+Average batch time: 5.56 ms
+</pre></div></div>
+</div>
+<p>When compiling your module to TorchScript, there are two paths: Tracing and Scripting.</p>
+</section>
+<section id="Tracing">
+<h3>Tracing<a class="headerlink" href="#Tracing" title="Permalink to this headline">¶</a></h3>
+<p>Tracing follows the path of execution when the module is called and records what happens. This recording is what the TorchScript IR will describe. To trace an instance of our LeNet module, we can call torch.jit.trace with an example input.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>traced_model = torch.jit.trace(model, torch.empty([1,1,32,32]).to(&quot;cuda&quot;))
+traced_model
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+LeNet(
+  original_name=LeNet
+  (feat): LeNetFeatExtractor(
+    original_name=LeNetFeatExtractor
+    (conv1): Conv2d(original_name=Conv2d)
+    (conv2): Conv2d(original_name=Conv2d)
+  )
+  (classifer): LeNetClassifier(
+    original_name=LeNetClassifier
+    (fc1): Linear(original_name=Linear)
+    (fc2): Linear(original_name=Linear)
+    (fc3): Linear(original_name=Linear)
+  )
+)
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>benchmark(traced_model)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 100/1000, ave batch time 5.56 ms
+Iteration 200/1000, ave batch time 5.56 ms
+Iteration 300/1000, ave batch time 5.56 ms
+Iteration 400/1000, ave batch time 5.56 ms
+Iteration 500/1000, ave batch time 5.56 ms
+Iteration 600/1000, ave batch time 5.56 ms
+Iteration 700/1000, ave batch time 5.56 ms
+Iteration 800/1000, ave batch time 5.56 ms
+Iteration 900/1000, ave batch time 5.56 ms
+Iteration 1000/1000, ave batch time 5.56 ms
+Input shape: torch.Size([1024, 1, 32, 32])
+Output features size: torch.Size([1024, 10])
+Average batch time: 5.56 ms
+</pre></div></div>
+</div>
+</section>
+<section id="Scripting">
+<h3>Scripting<a class="headerlink" href="#Scripting" title="Permalink to this headline">¶</a></h3>
+<p>Scripting actually inspects your code with a compiler and generates an equivalent TorchScript program. The difference is that since tracing simply follows the execution of your module, it cannot pick up control flow for instance, it will only follow the code path that a particular input triggers. By working from the Python code, the compiler can include these components. We can run the script compiler on our LeNet module by calling torch.jit.script.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>model = LeNet().to(&quot;cuda&quot;).eval()
+script_model = torch.jit.script(model)
+<br/></pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>script_model
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+RecursiveScriptModule(
+  original_name=LeNet
+  (feat): RecursiveScriptModule(
+    original_name=LeNetFeatExtractor
+    (conv1): RecursiveScriptModule(original_name=Conv2d)
+    (conv2): RecursiveScriptModule(original_name=Conv2d)
+  )
+  (classifer): RecursiveScriptModule(
+    original_name=LeNetClassifier
+    (fc1): RecursiveScriptModule(original_name=Linear)
+    (fc2): RecursiveScriptModule(original_name=Linear)
+    (fc3): RecursiveScriptModule(original_name=Linear)
+  )
+)
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>benchmark(script_model)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 100/1000, ave batch time 5.56 ms
+Iteration 200/1000, ave batch time 5.56 ms
+Iteration 300/1000, ave batch time 5.56 ms
+Iteration 400/1000, ave batch time 5.56 ms
+Iteration 500/1000, ave batch time 5.56 ms
+Iteration 600/1000, ave batch time 5.56 ms
+Iteration 700/1000, ave batch time 5.56 ms
+Iteration 800/1000, ave batch time 5.56 ms
+Iteration 900/1000, ave batch time 5.56 ms
+Iteration 1000/1000, ave batch time 5.56 ms
+Input shape: torch.Size([1024, 1, 32, 32])
+Output features size: torch.Size([1024, 10])
+Average batch time: 5.56 ms
+</pre></div></div>
+</div>
+<p>## 3. Compiling with Torch-TensorRT</p>
+</section>
+<section id="TorchScript-traced-model">
+<h3>TorchScript traced model<a class="headerlink" href="#TorchScript-traced-model" title="Permalink to this headline">¶</a></h3>
+<p>First, we compile the TorchScript traced model with Torch-TensorRT. Notice the performance impact.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch_tensorrt
+
+# We use a batch-size of 1024, and half precision
+trt_ts_module = torch_tensorrt.compile(traced_model, inputs=[torch_tensorrt.Input(
+            min_shape=[1024, 1, 32, 32],
+            opt_shape=[1024, 1, 33, 33],
+            max_shape=[1024, 1, 34, 34],
+            dtype=torch.half
+            )],
+            enabled_precisions = {torch.half})
+
+input_data = torch.randn((1024, 1, 32, 32))
+input_data = input_data.half().to(&quot;cuda&quot;)
+
+input_data = input_data.half()
+result = trt_ts_module(input_data)
+torch.jit.save(trt_ts_module, &quot;trt_ts_module.ts&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float
+The compiler is going to use the user setting Float16
+This conflict may cause an error at runtime due to partial compilation being enabled and therefore
+compatibility with PyTorch&#39;s data type convention is required.
+If you do indeed see errors at runtime either:
+- Remove the dtype spec for x.1
+- Disable partial compilation by setting require_full_compilation to True
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+WARNING: [Torch-TensorRT TorchScript Conversion Context] - Max value of this profile is not valid
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>benchmark(trt_ts_module, input_shape=(1024, 1, 32, 32), dtype=&quot;fp16&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 100/1000, ave batch time 1.41 ms
+Iteration 200/1000, ave batch time 1.40 ms
+Iteration 300/1000, ave batch time 1.40 ms
+Iteration 400/1000, ave batch time 1.39 ms
+Iteration 500/1000, ave batch time 1.40 ms
+Iteration 600/1000, ave batch time 1.40 ms
+Iteration 700/1000, ave batch time 1.40 ms
+Iteration 800/1000, ave batch time 1.40 ms
+Iteration 900/1000, ave batch time 1.40 ms
+Iteration 1000/1000, ave batch time 1.40 ms
+Input shape: torch.Size([1024, 1, 32, 32])
+Output features size: torch.Size([1024, 10])
+Average batch time: 1.40 ms
+</pre></div></div>
+</div>
+</section>
+<section id="TorchScript-script-model">
+<h3>TorchScript script model<a class="headerlink" href="#TorchScript-script-model" title="Permalink to this headline">¶</a></h3>
+<p>Next, we compile the TorchScript script model with Torch-TensorRT. Notice the performance impact.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch_tensorrt
+
+trt_script_module = torch_tensorrt.compile(script_model, inputs = [torch_tensorrt.Input(
+            min_shape=[1024, 1, 32, 32],
+            opt_shape=[1024, 1, 33, 33],
+            max_shape=[1024, 1, 34, 34],
+            dtype=torch.half
+            )],
+            enabled_precisions={torch.half})
+
+input_data = torch.randn((1024, 1, 32, 32))
+input_data = input_data.half().to(&quot;cuda&quot;)
+
+input_data = input_data.half()
+result = trt_script_module(input_data)
+torch.jit.save(trt_script_module, &quot;trt_script_module.ts&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float
+The compiler is going to use the user setting Float16
+This conflict may cause an error at runtime due to partial compilation being enabled and therefore
+compatibility with PyTorch&#39;s data type convention is required.
+If you do indeed see errors at runtime either:
+- Remove the dtype spec for x.1
+- Disable partial compilation by setting require_full_compilation to True
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+WARNING: [Torch-TensorRT TorchScript Conversion Context] - Max value of this profile is not valid
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>benchmark(trt_script_module, input_shape=(1024, 1, 32, 32), dtype=&quot;fp16&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 100/1000, ave batch time 1.43 ms
+Iteration 200/1000, ave batch time 1.41 ms
+Iteration 300/1000, ave batch time 1.40 ms
+Iteration 400/1000, ave batch time 1.42 ms
+Iteration 500/1000, ave batch time 1.42 ms
+Iteration 600/1000, ave batch time 1.41 ms
+Iteration 700/1000, ave batch time 1.41 ms
+Iteration 800/1000, ave batch time 1.40 ms
+Iteration 900/1000, ave batch time 1.40 ms
+Iteration 1000/1000, ave batch time 1.40 ms
+Input shape: torch.Size([1024, 1, 32, 32])
+Output features size: torch.Size([1024, 10])
+Average batch time: 1.40 ms
+</pre></div></div>
+</div>
+</section>
+</section>
+<section id="Conclusion">
+<h2>Conclusion<a class="headerlink" href="#Conclusion" title="Permalink to this headline">¶</a></h2>
+<p>In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT and test the performance impact of the optimization.</p>
+<section id="What’s-next">
+<h3>What’s next<a class="headerlink" href="#What’s-next" title="Permalink to this headline">¶</a></h3>
+<p>Now it’s time to try Torch-TensorRT on your own model. Fill out issues at <a class="reference external" href="https://github.com/NVIDIA/Torch-TensorRT">https://github.com/NVIDIA/Torch-TensorRT</a>. Your involvement will help future development of Torch-TensorRT.</p>
+</section>
+</section>
+</section>
+
+
+             </article>
+
+            </div>
+            <footer>
+
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+
+
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+  <div role="contentinfo">
+    <p>
+        &copy; Copyright 2021, NVIDIA Corporation.
+
+    </p>
+  </div>
+
+      <div>
+        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+      </div>
+
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+        </div>
+
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+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
+              <ul>
+<li><a class="reference internal" href="#">Torch-TensorRT Getting Started - LeNet</a><ul>
+<li><a class="reference internal" href="#Overview">Overview</a><ul>
+<li><a class="reference internal" href="#Learning-objectives">Learning objectives</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#Content">Content</a><ul>
+<li><a class="reference internal" href="#PyTorch-model">PyTorch model</a></li>
+<li><a class="reference internal" href="#Tracing">Tracing</a></li>
+<li><a class="reference internal" href="#Scripting">Scripting</a></li>
+<li><a class="reference internal" href="#TorchScript-traced-model">TorchScript traced model</a></li>
+<li><a class="reference internal" href="#TorchScript-script-model">TorchScript script model</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#Conclusion">Conclusion</a><ul>
+<li><a class="reference internal" href="#What’s-next">What’s next</a></li>
+</ul>
+</li>
+</ul>
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diff --git a/docs/v1.2.0/_notebooks/lenet-getting-started.ipynb b/docs/v1.2.0/_notebooks/lenet-getting-started.ipynb
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--- /dev/null
+++ b/docs/v1.2.0/_notebooks/lenet-getting-started.ipynb
@@ -0,0 +1,718 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
+    "\n",
+    "# Torch-TensorRT Getting Started - LeNet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch's JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.\n",
+    "\n",
+    "When deploying on NVIDIA GPUs TensorRT, NVIDIA's Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA's Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.\n",
+    "\n",
+    "Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning objectives\n",
+    "\n",
+    "This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a simple LeNet network. \n",
+    "\n",
+    "## Content\n",
+    "1. [Requirements](#1)\n",
+    "1. [Creating TorchScript modules](#2)\n",
+    "1. [Compiling with Torch-TensorRT](#3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"1\"></a>\n",
+    "## 1. Requirements\n",
+    "\n",
+    "Follow the steps in `notebooks/README` to prepare a Docker container, within which you can run this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Collecting ipywidgets\n",
+      "  Downloading ipywidgets-7.6.5-py2.py3-none-any.whl (121 kB)\n",
+      "\u001b[K     |████████████████████████████████| 121 kB 12.7 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: traitlets>=4.3.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.1)\n",
+      "Collecting jupyterlab-widgets>=1.0.0\n",
+      "  Downloading jupyterlab_widgets-1.0.2-py3-none-any.whl (243 kB)\n",
+      "\u001b[K     |████████████████████████████████| 243 kB 115.0 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: ipython-genutils~=0.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (0.2.0)\n",
+      "Requirement already satisfied: nbformat>=4.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.3)\n",
+      "Requirement already satisfied: ipykernel>=4.5.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (6.7.0)\n",
+      "Requirement already satisfied: ipython>=4.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (7.31.0)\n",
+      "Collecting widgetsnbextension~=3.5.0\n",
+      "  Downloading widgetsnbextension-3.5.2-py2.py3-none-any.whl (1.6 MB)\n",
+      "\u001b[K     |████████████████████████████████| 1.6 MB 122.5 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: tornado<7.0,>=4.2 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets) (6.1)\n",
+      "Requirement already satisfied: debugpy<2.0,>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets) (1.5.1)\n",
+      "Requirement already satisfied: jupyter-client<8.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets) (7.1.2)\n",
+      "Requirement already satisfied: nest-asyncio in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets) (1.5.4)\n",
+      "Requirement already satisfied: matplotlib-inline<0.2.0,>=0.1.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets) (0.1.3)\n",
+      "Requirement already satisfied: setuptools>=18.5 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (59.5.0)\n",
+      "Requirement already satisfied: backcall in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (0.2.0)\n",
+      "Requirement already satisfied: pexpect>4.3 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (4.8.0)\n",
+      "Requirement already satisfied: pickleshare in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (0.7.5)\n",
+      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (3.0.24)\n",
+      "Requirement already satisfied: jedi>=0.16 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (0.18.1)\n",
+      "Requirement already satisfied: decorator in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (5.1.0)\n",
+      "Requirement already satisfied: pygments in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (2.11.1)\n",
+      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /opt/conda/lib/python3.8/site-packages (from jedi>=0.16->ipython>=4.0.0->ipywidgets) (0.8.3)\n",
+      "Requirement already satisfied: pyzmq>=13 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (22.3.0)\n",
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+      "Requirement already satisfied: jupyter-core>=4.6.0 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (4.9.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (2.8.2)\n",
+      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /opt/conda/lib/python3.8/site-packages (from nbformat>=4.2.0->ipywidgets) (4.4.0)\n",
+      "Requirement already satisfied: attrs>=17.4.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets) (21.4.0)\n",
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+      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets) (0.18.1)\n",
+      "Requirement already satisfied: zipp>=3.1.0 in /opt/conda/lib/python3.8/site-packages (from importlib-resources>=1.4.0->jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets) (3.7.0)\n",
+      "Requirement already satisfied: ptyprocess>=0.5 in /opt/conda/lib/python3.8/site-packages (from pexpect>4.3->ipython>=4.0.0->ipywidgets) (0.7.0)\n",
+      "Requirement already satisfied: wcwidth in /opt/conda/lib/python3.8/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=4.0.0->ipywidgets) (0.2.5)\n",
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+      "Requirement already satisfied: terminado>=0.8.3 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.12.1)\n",
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+      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (3.0.3)\n",
+      "Requirement already satisfied: argon2-cffi in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (21.3.0)\n",
+      "Requirement already satisfied: nbconvert in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (6.4.0)\n",
+      "Requirement already satisfied: Send2Trash>=1.5.0 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (1.8.0)\n",
+      "Requirement already satisfied: argon2-cffi-bindings in /opt/conda/lib/python3.8/site-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (21.2.0)\n",
+      "Requirement already satisfied: cffi>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (1.15.0)\n",
+      "Requirement already satisfied: pycparser in /opt/conda/lib/python3.8/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (2.21)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.8/site-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (2.0.1)\n",
+      "Requirement already satisfied: testpath in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.5.0)\n",
+      "Requirement already satisfied: defusedxml in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.7.1)\n",
+      "Requirement already satisfied: jupyterlab-pygments in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.1.2)\n",
+      "Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.5.10)\n",
+      "Requirement already satisfied: bleach in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (4.1.0)\n",
+      "Requirement already satisfied: pandocfilters>=1.4.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (1.5.0)\n",
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+      "Requirement already satisfied: webencodings in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.5.1)\n",
+      "Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (21.3)\n",
+      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging->bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (3.0.6)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Installing collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets\n",
+      "Successfully installed ipywidgets-7.6.5 jupyterlab-widgets-1.0.2 widgetsnbextension-3.5.2\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
+      "Thu Feb 10 22:01:27 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 510.39.01    Driver Version: 510.39.01    CUDA Version: 11.6     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA GeForce ...  On   | 00000000:09:00.0 Off |                  N/A |\n",
+      "|  0%   42C    P8    20W / 320W |      0MiB / 10240MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|  No running processes found                                                 |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org\n",
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"2\"></a>\n",
+    "## 2. Creating TorchScript modules\n",
+    "\n",
+    "Here we create two submodules for a feature extractor and a classifier and stitch them together in a single LeNet module. In this case this is overkill but modules give us granular control over our program including where we decide to optimize and where we don't. It is also the unit that the TorchScript compiler operates on. So you can decide to only convert/optimize the feature extractor and leave the classifier in standard PyTorch or you can convert the whole thing. When compiling your module to TorchScript, there are two paths: Tracing and Scripting.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch \n",
+    "from torch import nn\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "class LeNetFeatExtractor(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(LeNetFeatExtractor, self).__init__()\n",
+    "        self.conv1 = nn.Conv2d(1, 128, 3)\n",
+    "        self.conv2 = nn.Conv2d(128, 16, 3)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n",
+    "        x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n",
+    "        return x\n",
+    "\n",
+    "class LeNetClassifier(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(LeNetClassifier, self).__init__()\n",
+    "        self.fc1 = nn.Linear(16 * 6 * 6, 120)\n",
+    "        self.fc2 = nn.Linear(120, 84)\n",
+    "        self.fc3 = nn.Linear(84, 10)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = torch.flatten(x,1)\n",
+    "        x = F.relu(self.fc1(x))\n",
+    "        x = F.relu(self.fc2(x))\n",
+    "        x = self.fc3(x)\n",
+    "        return x\n",
+    "\n",
+    "class LeNet(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(LeNet, self).__init__()\n",
+    "        self.feat = LeNetFeatExtractor()\n",
+    "        self.classifer = LeNetClassifier()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.feat(x)\n",
+    "        x = self.classifer(x)\n",
+    "        return x\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us define a helper function to benchmark a model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "cudnn.benchmark = True\n",
+    "\n",
+    "def benchmark(model, input_shape=(1024, 1, 32, 32), dtype='fp32', nwarmup=50, nruns=1000):\n",
+    "    input_data = torch.randn(input_shape)\n",
+    "    input_data = input_data.to(\"cuda\")\n",
+    "    if dtype=='fp16':\n",
+    "        input_data = input_data.half()\n",
+    "        \n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(nwarmup):\n",
+    "            features = model(input_data)\n",
+    "    torch.cuda.synchronize()\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(1, nruns+1):\n",
+    "            start_time = time.time()\n",
+    "            features = model(input_data)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = time.time()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            if i%100==0:\n",
+    "                print('Iteration %d/%d, ave batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
+    "\n",
+    "    print(\"Input shape:\", input_data.size())\n",
+    "    print(\"Output features size:\", features.size())\n",
+    "    \n",
+    "    print('Average batch time: %.2f ms'%(np.mean(timings)*1000))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PyTorch model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LeNet(\n",
+       "  (feat): LeNetFeatExtractor(\n",
+       "    (conv1): Conv2d(1, 128, kernel_size=(3, 3), stride=(1, 1))\n",
+       "    (conv2): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1))\n",
+       "  )\n",
+       "  (classifer): LeNetClassifier(\n",
+       "    (fc1): Linear(in_features=576, out_features=120, bias=True)\n",
+       "    (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
+       "    (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LeNet()\n",
+    "model.to(\"cuda\").eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, ave batch time 5.56 ms\n",
+      "Iteration 200/1000, ave batch time 5.56 ms\n",
+      "Iteration 300/1000, ave batch time 5.56 ms\n",
+      "Iteration 400/1000, ave batch time 5.56 ms\n",
+      "Iteration 500/1000, ave batch time 5.56 ms\n",
+      "Iteration 600/1000, ave batch time 5.56 ms\n",
+      "Iteration 700/1000, ave batch time 5.56 ms\n",
+      "Iteration 800/1000, ave batch time 5.56 ms\n",
+      "Iteration 900/1000, ave batch time 5.56 ms\n",
+      "Iteration 1000/1000, ave batch time 5.56 ms\n",
+      "Input shape: torch.Size([1024, 1, 32, 32])\n",
+      "Output features size: torch.Size([1024, 10])\n",
+      "Average batch time: 5.56 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When compiling your module to TorchScript, there are two paths: Tracing and Scripting.  \n",
+    " \n",
+    "### Tracing\n",
+    "\n",
+    "Tracing follows the path of execution when the module is called and records what happens. This recording is what the TorchScript IR will describe. To trace an instance of our LeNet module, we can call torch.jit.trace  with an example input. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LeNet(\n",
+       "  original_name=LeNet\n",
+       "  (feat): LeNetFeatExtractor(\n",
+       "    original_name=LeNetFeatExtractor\n",
+       "    (conv1): Conv2d(original_name=Conv2d)\n",
+       "    (conv2): Conv2d(original_name=Conv2d)\n",
+       "  )\n",
+       "  (classifer): LeNetClassifier(\n",
+       "    original_name=LeNetClassifier\n",
+       "    (fc1): Linear(original_name=Linear)\n",
+       "    (fc2): Linear(original_name=Linear)\n",
+       "    (fc3): Linear(original_name=Linear)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "traced_model = torch.jit.trace(model, torch.empty([1,1,32,32]).to(\"cuda\"))\n",
+    "traced_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, ave batch time 5.56 ms\n",
+      "Iteration 200/1000, ave batch time 5.56 ms\n",
+      "Iteration 300/1000, ave batch time 5.56 ms\n",
+      "Iteration 400/1000, ave batch time 5.56 ms\n",
+      "Iteration 500/1000, ave batch time 5.56 ms\n",
+      "Iteration 600/1000, ave batch time 5.56 ms\n",
+      "Iteration 700/1000, ave batch time 5.56 ms\n",
+      "Iteration 800/1000, ave batch time 5.56 ms\n",
+      "Iteration 900/1000, ave batch time 5.56 ms\n",
+      "Iteration 1000/1000, ave batch time 5.56 ms\n",
+      "Input shape: torch.Size([1024, 1, 32, 32])\n",
+      "Output features size: torch.Size([1024, 10])\n",
+      "Average batch time: 5.56 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(traced_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scripting\n",
+    "\n",
+    "Scripting actually inspects your code with a compiler and  generates an equivalent TorchScript program. The difference is that since tracing simply follows the execution of your module, it cannot pick up control flow for instance, it will only follow the code path that a particular input triggers. By working from the Python code, the compiler can include these components. We can run the script compiler on our LeNet  module by calling torch.jit.script.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = LeNet().to(\"cuda\").eval()\n",
+    "script_model = torch.jit.script(model)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RecursiveScriptModule(\n",
+       "  original_name=LeNet\n",
+       "  (feat): RecursiveScriptModule(\n",
+       "    original_name=LeNetFeatExtractor\n",
+       "    (conv1): RecursiveScriptModule(original_name=Conv2d)\n",
+       "    (conv2): RecursiveScriptModule(original_name=Conv2d)\n",
+       "  )\n",
+       "  (classifer): RecursiveScriptModule(\n",
+       "    original_name=LeNetClassifier\n",
+       "    (fc1): RecursiveScriptModule(original_name=Linear)\n",
+       "    (fc2): RecursiveScriptModule(original_name=Linear)\n",
+       "    (fc3): RecursiveScriptModule(original_name=Linear)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "script_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, ave batch time 5.56 ms\n",
+      "Iteration 200/1000, ave batch time 5.56 ms\n",
+      "Iteration 300/1000, ave batch time 5.56 ms\n",
+      "Iteration 400/1000, ave batch time 5.56 ms\n",
+      "Iteration 500/1000, ave batch time 5.56 ms\n",
+      "Iteration 600/1000, ave batch time 5.56 ms\n",
+      "Iteration 700/1000, ave batch time 5.56 ms\n",
+      "Iteration 800/1000, ave batch time 5.56 ms\n",
+      "Iteration 900/1000, ave batch time 5.56 ms\n",
+      "Iteration 1000/1000, ave batch time 5.56 ms\n",
+      "Input shape: torch.Size([1024, 1, 32, 32])\n",
+      "Output features size: torch.Size([1024, 10])\n",
+      "Average batch time: 5.56 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(script_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"3\"></a>\n",
+    "## 3. Compiling with Torch-TensorRT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TorchScript traced model\n",
+    "\n",
+    "First, we compile the TorchScript traced model with Torch-TensorRT. Notice the performance impact."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
+      "The compiler is going to use the user setting Float16\n",
+      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
+      "compatibility with PyTorch's data type convention is required.\n",
+      "If you do indeed see errors at runtime either:\n",
+      "- Remove the dtype spec for x.1\n",
+      "- Disable partial compilation by setting require_full_compilation to True\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT TorchScript Conversion Context] - Max value of this profile is not valid\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch_tensorrt\n",
+    "\n",
+    "# We use a batch-size of 1024, and half precision\n",
+    "trt_ts_module = torch_tensorrt.compile(traced_model, inputs=[torch_tensorrt.Input(\n",
+    "            min_shape=[1024, 1, 32, 32],\n",
+    "            opt_shape=[1024, 1, 33, 33],\n",
+    "            max_shape=[1024, 1, 34, 34],\n",
+    "            dtype=torch.half\n",
+    "            )], \n",
+    "            enabled_precisions = {torch.half})\n",
+    "\n",
+    "input_data = torch.randn((1024, 1, 32, 32))\n",
+    "input_data = input_data.half().to(\"cuda\")\n",
+    "\n",
+    "input_data = input_data.half()\n",
+    "result = trt_ts_module(input_data)\n",
+    "torch.jit.save(trt_ts_module, \"trt_ts_module.ts\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, ave batch time 1.41 ms\n",
+      "Iteration 200/1000, ave batch time 1.40 ms\n",
+      "Iteration 300/1000, ave batch time 1.40 ms\n",
+      "Iteration 400/1000, ave batch time 1.39 ms\n",
+      "Iteration 500/1000, ave batch time 1.40 ms\n",
+      "Iteration 600/1000, ave batch time 1.40 ms\n",
+      "Iteration 700/1000, ave batch time 1.40 ms\n",
+      "Iteration 800/1000, ave batch time 1.40 ms\n",
+      "Iteration 900/1000, ave batch time 1.40 ms\n",
+      "Iteration 1000/1000, ave batch time 1.40 ms\n",
+      "Input shape: torch.Size([1024, 1, 32, 32])\n",
+      "Output features size: torch.Size([1024, 10])\n",
+      "Average batch time: 1.40 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_ts_module, input_shape=(1024, 1, 32, 32), dtype=\"fp16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TorchScript script model\n",
+    "\n",
+    "Next, we compile the TorchScript script model with Torch-TensorRT. Notice the performance impact."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
+      "The compiler is going to use the user setting Float16\n",
+      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
+      "compatibility with PyTorch's data type convention is required.\n",
+      "If you do indeed see errors at runtime either:\n",
+      "- Remove the dtype spec for x.1\n",
+      "- Disable partial compilation by setting require_full_compilation to True\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT TorchScript Conversion Context] - Max value of this profile is not valid\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch_tensorrt\n",
+    "\n",
+    "trt_script_module = torch_tensorrt.compile(script_model, inputs = [torch_tensorrt.Input(\n",
+    "            min_shape=[1024, 1, 32, 32],\n",
+    "            opt_shape=[1024, 1, 33, 33],\n",
+    "            max_shape=[1024, 1, 34, 34],\n",
+    "            dtype=torch.half\n",
+    "            )],\n",
+    "            enabled_precisions={torch.half})\n",
+    "\n",
+    "input_data = torch.randn((1024, 1, 32, 32))\n",
+    "input_data = input_data.half().to(\"cuda\")\n",
+    "\n",
+    "input_data = input_data.half()\n",
+    "result = trt_script_module(input_data)\n",
+    "torch.jit.save(trt_script_module, \"trt_script_module.ts\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, ave batch time 1.43 ms\n",
+      "Iteration 200/1000, ave batch time 1.41 ms\n",
+      "Iteration 300/1000, ave batch time 1.40 ms\n",
+      "Iteration 400/1000, ave batch time 1.42 ms\n",
+      "Iteration 500/1000, ave batch time 1.42 ms\n",
+      "Iteration 600/1000, ave batch time 1.41 ms\n",
+      "Iteration 700/1000, ave batch time 1.41 ms\n",
+      "Iteration 800/1000, ave batch time 1.40 ms\n",
+      "Iteration 900/1000, ave batch time 1.40 ms\n",
+      "Iteration 1000/1000, ave batch time 1.40 ms\n",
+      "Input shape: torch.Size([1024, 1, 32, 32])\n",
+      "Output features size: torch.Size([1024, 10])\n",
+      "Average batch time: 1.40 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_script_module, input_shape=(1024, 1, 32, 32), dtype=\"fp16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT and test the performance impact of the optimization.\n",
+    "\n",
+    "### What's next\n",
+    "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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@@ -0,0 +1,1635 @@
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
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+      </li>
+
+  </ul>
+
+
+</div>
+        </div>
+
+        <div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper">
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+        </div>
+      </div>
+
+      <section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
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+
+
+
+          <div class="rst-content">
+
+            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
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+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Copyright 2020 NVIDIA Corporation. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an &quot;AS IS&quot; BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+</pre></div>
+</div>
+</div>
+<p><img alt="0651fd225e2f45ecadc9b43b60a4e5ff" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_ssd_notebook.png" /></p>
+<section id="Object-Detection-with-Torch-TensorRT-(SSD)">
+<h1>Object Detection with Torch-TensorRT (SSD)<a class="headerlink" href="#Object-Detection-with-Torch-TensorRT-(SSD)" title="Permalink to this headline">¶</a></h1>
+<hr class="docutils" />
+<section id="Overview">
+<h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline">¶</a></h2>
+<p>In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the
+best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch’s JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new
+world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.</p>
+<p>When deploying on NVIDIA GPUs TensorRT, NVIDIA’s Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA’s Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that
+perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.</p>
+<p>Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch.</p>
+<section id="Learning-objectives">
+<h3>Learning objectives<a class="headerlink" href="#Learning-objectives" title="Permalink to this headline">¶</a></h3>
+<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained SSD network, and running it to test the speedup obtained.</p>
+</section>
+</section>
+<section id="Contents">
+<h2>Contents<a class="headerlink" href="#Contents" title="Permalink to this headline">¶</a></h2>
+<ol class="arabic simple">
+<li><p><a class="reference external" href="#1">Requirements</a></p></li>
+<li><p><a class="reference external" href="#2">SSD Overview</a></p></li>
+<li><p><a class="reference external" href="#3">Creating TorchScript modules</a></p></li>
+<li><p><a class="reference external" href="#4">Compiling with Torch-TensorRT</a></p></li>
+<li><p><a class="reference external" href="#5">Running Inference</a></p></li>
+<li><p><a class="reference external" href="#6">Measuring Speedup</a></p></li>
+<li><p><a class="reference external" href="#7">Conclusion</a></p></li>
+</ol>
+<hr class="docutils" />
+<p>## 1. Requirements</p>
+<p>Follow the steps in <code class="docutils literal notranslate"><span class="pre">notebooks/README</span></code> to prepare a Docker container, within which you can run this demo notebook.</p>
+<p>In addition to that, run the following cell to obtain additional libraries specific to this demo.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!pip install scikit-image==0.19.1
+!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
+Collecting scikit-image==0.19.1
+  Downloading scikit_image-0.19.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.8 MB)
+     |████████████████████████████████| 13.8 MB 8.8 MB/s eta 0:00:01
+Requirement already satisfied: networkx&gt;=2.2 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.19.1) (2.6.3)
+Collecting tifffile&gt;=2019.7.26
+  Downloading tifffile-2022.3.16-py3-none-any.whl (179 kB)
+     |████████████████████████████████| 179 kB 110.1 MB/s eta 0:00:01
+Requirement already satisfied: packaging&gt;=20.0 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.19.1) (21.3)
+Requirement already satisfied: scipy&gt;=1.4.1 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.19.1) (1.6.3)
+Requirement already satisfied: numpy&gt;=1.17.0 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.19.1) (1.22.2)
+Collecting imageio&gt;=2.4.1
+  Downloading imageio-2.16.1-py3-none-any.whl (3.3 MB)
+     |████████████████████████████████| 3.3 MB 42.3 MB/s eta 0:00:01
+Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,&gt;=6.1.0 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.19.1) (9.0.0)
+Collecting PyWavelets&gt;=1.1.1
+  Downloading PyWavelets-1.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.9 MB)
+     |████████████████████████████████| 6.9 MB 61.3 MB/s eta 0:00:01
+Requirement already satisfied: pyparsing!=3.0.5,&gt;=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging&gt;=20.0-&gt;scikit-image==0.19.1) (3.0.7)
+Installing collected packages: tifffile, PyWavelets, imageio, scikit-image
+Successfully installed PyWavelets-1.3.0 imageio-2.16.1 scikit-image-0.19.1 tifffile-2022.3.16
+<span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
+Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
+Collecting ipywidgets
+  Downloading ipywidgets-7.7.0-py2.py3-none-any.whl (123 kB)
+     |████████████████████████████████| 123 kB 12.1 MB/s eta 0:00:01
+Requirement already satisfied: ipykernel&gt;=4.5.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (6.9.0)
+Collecting jupyterlab-widgets&gt;=1.0.0
+  Downloading jupyterlab_widgets-1.1.0-py3-none-any.whl (245 kB)
+     |████████████████████████████████| 245 kB 60.6 MB/s eta 0:00:01
+Requirement already satisfied: nbformat&gt;=4.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.3)
+Requirement already satisfied: ipython-genutils~=0.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (0.2.0)
+Requirement already satisfied: ipython&gt;=4.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (8.0.1)
+Requirement already satisfied: traitlets&gt;=4.3.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.1)
+Collecting widgetsnbextension~=3.6.0
+  Downloading widgetsnbextension-3.6.0-py2.py3-none-any.whl (1.6 MB)
+     |████████████████████████████████| 1.6 MB 35.2 MB/s eta 0:00:01
+Requirement already satisfied: nest-asyncio in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (1.5.4)
+Requirement already satisfied: jupyter-client&lt;8.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (7.1.2)
+Requirement already satisfied: tornado&lt;7.0,&gt;=4.2 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (6.1)
+Requirement already satisfied: matplotlib-inline&lt;0.2.0,&gt;=0.1.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (0.1.3)
+Requirement already satisfied: debugpy&lt;2.0,&gt;=1.0.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel&gt;=4.5.1-&gt;ipywidgets) (1.5.1)
+Requirement already satisfied: stack-data in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (0.1.4)
+Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,&lt;3.1.0,&gt;=2.0.0 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (3.0.26)
+Requirement already satisfied: pickleshare in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (0.7.5)
+Requirement already satisfied: pygments in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (2.11.2)
+Requirement already satisfied: pexpect&gt;4.3 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (4.8.0)
+Requirement already satisfied: decorator in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (5.1.1)
+Requirement already satisfied: backcall in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (0.2.0)
+Requirement already satisfied: black in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (22.1.0)
+Requirement already satisfied: setuptools&gt;=18.5 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (59.5.0)
+Requirement already satisfied: jedi&gt;=0.16 in /opt/conda/lib/python3.8/site-packages (from ipython&gt;=4.0.0-&gt;ipywidgets) (0.18.1)
+Requirement already satisfied: parso&lt;0.9.0,&gt;=0.8.0 in /opt/conda/lib/python3.8/site-packages (from jedi&gt;=0.16-&gt;ipython&gt;=4.0.0-&gt;ipywidgets) (0.8.3)
+Requirement already satisfied: entrypoints in /opt/conda/lib/python3.8/site-packages (from jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (0.3)
+Requirement already satisfied: pyzmq&gt;=13 in /opt/conda/lib/python3.8/site-packages (from jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (22.3.0)
+Requirement already satisfied: jupyter-core&gt;=4.6.0 in /opt/conda/lib/python3.8/site-packages (from jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (4.9.1)
+Requirement already satisfied: python-dateutil&gt;=2.1 in /opt/conda/lib/python3.8/site-packages (from jupyter-client&lt;8.0-&gt;ipykernel&gt;=4.5.1-&gt;ipywidgets) (2.8.2)
+Requirement already satisfied: jsonschema!=2.5.0,&gt;=2.4 in /opt/conda/lib/python3.8/site-packages (from nbformat&gt;=4.2.0-&gt;ipywidgets) (4.4.0)
+Requirement already satisfied: attrs&gt;=17.4.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,&gt;=2.4-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets) (21.4.0)
+Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,&gt;=0.14.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,&gt;=2.4-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets) (0.18.1)
+Requirement already satisfied: importlib-resources&gt;=1.4.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,&gt;=2.4-&gt;nbformat&gt;=4.2.0-&gt;ipywidgets) (5.4.0)
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+Requirement already satisfied: executing in /opt/conda/lib/python3.8/site-packages (from stack-data-&gt;ipython&gt;=4.0.0-&gt;ipywidgets) (0.8.2)
+Requirement already satisfied: pure-eval in /opt/conda/lib/python3.8/site-packages (from stack-data-&gt;ipython&gt;=4.0.0-&gt;ipywidgets) (0.2.2)
+Requirement already satisfied: asttokens in /opt/conda/lib/python3.8/site-packages (from stack-data-&gt;ipython&gt;=4.0.0-&gt;ipywidgets) (2.0.5)
+Installing collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets
+Successfully installed ipywidgets-7.7.0 jupyterlab-widgets-1.1.0 widgetsnbextension-3.6.0
+<span class="ansi-yellow-fg">WARNING: Running pip as the &#39;root&#39; user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</span>
+</pre></div></div>
+</div>
+<hr class="docutils" />
+<p>## 2. SSD</p>
+<section id="Single-Shot-MultiBox-Detector-model-for-object-detection">
+<h3>Single Shot MultiBox Detector model for object detection<a class="headerlink" href="#Single-Shot-MultiBox-Detector-model-for-object-detection" title="Permalink to this headline">¶</a></h3>
+<table class="docutils align-default">
+<colgroup>
+<col style="width: 38%" />
+<col style="width: 62%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>_</p></th>
+<th class="head"><p>_</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p><img alt="alt" src="https://pytorch.org/assets/images/ssd_diagram.png" /></p></td>
+<td><p><img alt="image1" src="https://pytorch.org/assets/images/ssd.png" /></p></td>
+</tr>
+</tbody>
+</table>
+<p>PyTorch has a model repository called the PyTorch Hub, which is a source for high quality implementations of common models. We can get our SSD model pretrained on <a class="reference external" href="https://cocodataset.org/#home">COCO</a> from there.</p>
+</section>
+<section id="Model-Description">
+<h3>Model Description<a class="headerlink" href="#Model-Description" title="Permalink to this headline">¶</a></h3>
+<p>This SSD300 model is based on the <a class="reference external" href="https://arxiv.org/abs/1512.02325">SSD: Single Shot MultiBox Detector</a> paper, which describes SSD as “a method for detecting objects in images using a single deep neural network”. The input size is fixed to 300x300.</p>
+<p>The main difference between this model and the one described in the paper is in the backbone. Specifically, the VGG model is obsolete and is replaced by the ResNet-50 model.</p>
+<p>From the <a class="reference external" href="https://arxiv.org/abs/1611.10012">Speed/accuracy trade-offs for modern convolutional object detectors</a> paper, the following enhancements were made to the backbone: * The conv5_x, avgpool, fc and softmax layers were removed from the original classification model. * All strides in conv4_x are set to 1x1.</p>
+<p>The backbone is followed by 5 additional convolutional layers. In addition to the convolutional layers, we attached 6 detection heads: * The first detection head is attached to the last conv4_x layer. * The other five detection heads are attached to the corresponding 5 additional layers.</p>
+<p>Detector heads are similar to the ones referenced in the paper, however, they are enhanced by additional BatchNorm layers after each convolution.</p>
+<p>More information about this SSD model is available at Nvidia’s “DeepLearningExamples” Github <a class="reference external" href="https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD">here</a>.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch
+torch.hub._validate_not_a_forked_repo=lambda a,b,c: True
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># List of available models in PyTorch Hub from Nvidia/DeepLearningExamples
+torch.hub.list(&#39;NVIDIA/DeepLearningExamples:torchhub&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Downloading: &#34;https://github.com/NVIDIA/DeepLearningExamples/archive/torchhub.zip&#34; to /root/.cache/torch/hub/torchhub.zip
+/root/.cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub/PyTorch/Classification/ConvNets/image_classification/models/efficientnet.py:17: UserWarning: pytorch_quantization module not found, quantization will not be available
+  warnings.warn(
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[&#39;nvidia_convnets_processing_utils&#39;,
+ &#39;nvidia_efficientnet&#39;,
+ &#39;nvidia_efficientnet_b0&#39;,
+ &#39;nvidia_efficientnet_b4&#39;,
+ &#39;nvidia_efficientnet_widese_b0&#39;,
+ &#39;nvidia_efficientnet_widese_b4&#39;,
+ &#39;nvidia_resneXt&#39;,
+ &#39;nvidia_resnet50&#39;,
+ &#39;nvidia_resnext101_32x4d&#39;,
+ &#39;nvidia_se_resnext101_32x4d&#39;,
+ &#39;nvidia_ssd&#39;,
+ &#39;nvidia_ssd_processing_utils&#39;,
+ &#39;nvidia_tacotron2&#39;,
+ &#39;nvidia_tts_utils&#39;,
+ &#39;nvidia_waveglow&#39;]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># load SSD model pretrained on COCO from Torch Hub
+precision = &#39;fp32&#39;
+ssd300 = torch.hub.load(&#39;NVIDIA/DeepLearningExamples:torchhub&#39;, &#39;nvidia_ssd&#39;, model_math=precision);
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Using cache found in /root/.cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub
+Downloading: &#34;https://download.pytorch.org/models/resnet50-0676ba61.pth&#34; to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "1c7cf3e1635d4a2b9c8731b7fc9ce724", "version_major": 2, "version_minor": 0}</script></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Downloading checkpoint from https://api.ngc.nvidia.com/v2/models/nvidia/ssd_pyt_ckpt_amp/versions/20.06.0/files/nvidia_ssdpyt_amp_200703.pt
+</pre></div></div>
+</div>
+<p>Setting <code class="docutils literal notranslate"><span class="pre">precision=&quot;fp16&quot;</span></code> will load a checkpoint trained with mixed precision into architecture enabling execution on Tensor Cores. Handling mixed precision data requires the Apex library.</p>
+</section>
+<section id="Sample-Inference">
+<h3>Sample Inference<a class="headerlink" href="#Sample-Inference" title="Permalink to this headline">¶</a></h3>
+<p>We can now run inference on the model. This is demonstrated below using sample images from the COCO 2017 Validation set.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Sample images from the COCO validation set
+uris = [
+    &#39;http://images.cocodataset.org/val2017/000000397133.jpg&#39;,
+    &#39;http://images.cocodataset.org/val2017/000000037777.jpg&#39;,
+    &#39;http://images.cocodataset.org/val2017/000000252219.jpg&#39;
+]
+
+# For convenient and comprehensive formatting of input and output of the model, load a set of utility methods.
+utils = torch.hub.load(&#39;NVIDIA/DeepLearningExamples:torchhub&#39;, &#39;nvidia_ssd_processing_utils&#39;)
+
+# Format images to comply with the network input
+inputs = [utils.prepare_input(uri) for uri in uris]
+tensor = utils.prepare_tensor(inputs, False)
+
+# The model was trained on COCO dataset, which we need to access in order to
+# translate class IDs into object names.
+classes_to_labels = utils.get_coco_object_dictionary()
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+Using cache found in /root/.cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Downloading COCO annotations.
+Downloading finished.
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Next, we run object detection
+model = ssd300.eval().to(&quot;cuda&quot;)
+detections_batch = model(tensor)
+
+# By default, raw output from SSD network per input image contains 8732 boxes with
+# localization and class probability distribution.
+# Let’s filter this output to only get reasonable detections (confidence&gt;40%) in a more comprehensive format.
+results_per_input = utils.decode_results(detections_batch)
+best_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Visualize-results">
+<h3>Visualize results<a class="headerlink" href="#Visualize-results" title="Permalink to this headline">¶</a></h3>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from matplotlib import pyplot as plt
+import matplotlib.patches as patches
+
+# The utility plots the images and predicted bounding boxes (with confidence scores).
+def plot_results(best_results):
+    for image_idx in range(len(best_results)):
+        fig, ax = plt.subplots(1)
+        # Show original, denormalized image...
+        image = inputs[image_idx] / 2 + 0.5
+        ax.imshow(image)
+        # ...with detections
+        bboxes, classes, confidences = best_results[image_idx]
+        for idx in range(len(bboxes)):
+            left, bot, right, top = bboxes[idx]
+            x, y, w, h = [val * 300 for val in [left, bot, right - left, top - bot]]
+            rect = patches.Rectangle((x, y), w, h, linewidth=1, edgecolor=&#39;r&#39;, facecolor=&#39;none&#39;)
+            ax.add_patch(rect)
+            ax.text(x, y, &quot;{} {:.0f}%&quot;.format(classes_to_labels[classes[idx] - 1], confidences[idx]*100), bbox=dict(facecolor=&#39;white&#39;, alpha=0.5))
+    plt.show()
+<br/></pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Visualize results without Torch-TensorRT
+plot_results(best_results_per_input)
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/_notebooks_ssd-object-detection-demo_18_0.png" src="../_images/_notebooks_ssd-object-detection-demo_18_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/_notebooks_ssd-object-detection-demo_18_1.png" src="../_images/_notebooks_ssd-object-detection-demo_18_1.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/_notebooks_ssd-object-detection-demo_18_2.png" src="../_images/_notebooks_ssd-object-detection-demo_18_2.png" />
+</div>
+</div>
+</section>
+<section id="Benchmark-utility">
+<h3>Benchmark utility<a class="headerlink" href="#Benchmark-utility" title="Permalink to this headline">¶</a></h3>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import time
+import numpy as np
+
+import torch.backends.cudnn as cudnn
+cudnn.benchmark = True
+
+# Helper function to benchmark the model
+def benchmark(model, input_shape=(1024, 1, 32, 32), dtype=&#39;fp32&#39;, nwarmup=50, nruns=1000):
+    input_data = torch.randn(input_shape)
+    input_data = input_data.to(&quot;cuda&quot;)
+    if dtype==&#39;fp16&#39;:
+        input_data = input_data.half()
+
+    print(&quot;Warm up ...&quot;)
+    with torch.no_grad():
+        for _ in range(nwarmup):
+            features = model(input_data)
+    torch.cuda.synchronize()
+    print(&quot;Start timing ...&quot;)
+    timings = []
+    with torch.no_grad():
+        for i in range(1, nruns+1):
+            start_time = time.time()
+            pred_loc, pred_label  = model(input_data)
+            torch.cuda.synchronize()
+            end_time = time.time()
+            timings.append(end_time - start_time)
+            if i%10==0:
+                print(&#39;Iteration %d/%d, avg batch time %.2f ms&#39;%(i, nruns, np.mean(timings)*1000))
+
+    print(&quot;Input shape:&quot;, input_data.size())
+    print(&quot;Output location prediction size:&quot;, pred_loc.size())
+    print(&quot;Output label prediction size:&quot;, pred_label.size())
+    print(&#39;Average batch time: %.2f ms&#39;%(np.mean(timings)*1000))
+<br/></pre></div>
+</div>
+</div>
+<p>We check how well the model performs <strong>before</strong> we use Torch-TensorRT</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Model benchmark without Torch-TensorRT
+model = ssd300.eval().to(&quot;cuda&quot;)
+benchmark(model, input_shape=(128, 3, 300, 300), nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, avg batch time 165.38 ms
+Iteration 20/100, avg batch time 165.99 ms
+Iteration 30/100, avg batch time 166.04 ms
+Iteration 40/100, avg batch time 166.32 ms
+Iteration 50/100, avg batch time 166.49 ms
+Iteration 60/100, avg batch time 166.74 ms
+Iteration 70/100, avg batch time 166.88 ms
+Iteration 80/100, avg batch time 167.05 ms
+Iteration 90/100, avg batch time 167.18 ms
+Iteration 100/100, avg batch time 167.31 ms
+Input shape: torch.Size([128, 3, 300, 300])
+Output location prediction size: torch.Size([128, 4, 8732])
+Output label prediction size: torch.Size([128, 81, 8732])
+Average batch time: 167.31 ms
+</pre></div></div>
+</div>
+<hr class="docutils" />
+<p>## 3. Creating TorchScript modules</p>
+<p>To compile with Torch-TensorRT, the model must first be in <strong>TorchScript</strong>. TorchScript is a programming language included in PyTorch which removes the Python dependency normal PyTorch models have. This conversion is done via a JIT compiler which given a PyTorch Module will generate an equivalent TorchScript Module. There are two paths that can be used to generate TorchScript: <strong>Tracing</strong> and <strong>Scripting</strong>. - Tracing follows execution of PyTorch generating ops in TorchScript corresponding to
+what it sees. - Scripting does an analysis of the Python code and generates TorchScript, this allows the resulting graph to include control flow which tracing cannot do.</p>
+<p>Tracing however due to its simplicity is more likely to compile successfully with Torch-TensorRT (though both systems are supported).</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>model = ssd300.eval().to(&quot;cuda&quot;)
+traced_model = torch.jit.trace(model, [torch.randn((1,3,300,300)).to(&quot;cuda&quot;)])
+</pre></div>
+</div>
+</div>
+<p>If required, we can also save this model and use it independently of Python.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># This is just an example, and not required for the purposes of this demo
+torch.jit.save(traced_model, &quot;ssd_300_traced.jit.pt&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Obtain the average time taken by a batch of input with Torchscript compiled modules
+benchmark(traced_model, input_shape=(128, 3, 300, 300), nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, avg batch time 165.53 ms
+Iteration 20/100, avg batch time 166.19 ms
+Iteration 30/100, avg batch time 166.12 ms
+Iteration 40/100, avg batch time 166.16 ms
+Iteration 50/100, avg batch time 166.24 ms
+Iteration 60/100, avg batch time 166.33 ms
+Iteration 70/100, avg batch time 166.43 ms
+Iteration 80/100, avg batch time 166.44 ms
+Iteration 90/100, avg batch time 166.53 ms
+Iteration 100/100, avg batch time 166.59 ms
+Input shape: torch.Size([128, 3, 300, 300])
+Output location prediction size: torch.Size([128, 4, 8732])
+Output label prediction size: torch.Size([128, 81, 8732])
+Average batch time: 166.59 ms
+</pre></div></div>
+</div>
+<hr class="docutils" />
+<p>## 4. Compiling with Torch-TensorRT TorchScript modules behave just like normal PyTorch modules and are intercompatible. From TorchScript we can now compile a TensorRT based module. This module will still be implemented in TorchScript but all the computation will be done in TensorRT.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch_tensorrt
+
+# The compiled module will have precision as specified by &quot;op_precision&quot;.
+# Here, it will have FP16 precision.
+trt_model = torch_tensorrt.compile(traced_model,
+    inputs= [torch_tensorrt.Input((3, 3, 300, 300), dtype=torch.half)],
+    enabled_precisions= {torch.half}, # Run with FP16
+    workspace_size= 1 &lt;&lt; 20
+)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING: [Torch-TensorRT] - For input x, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float
+The compiler is going to use the user setting Float16
+This conflict may cause an error at runtime due to partial compilation being enabled and therefore
+compatibility with PyTorch&#39;s data type convention is required.
+If you do indeed see errors at runtime either:
+- Remove the dtype spec for x
+- Disable partial compilation by setting require_full_compilation to True
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size
+WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size
+WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size
+WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size
+WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size
+WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size
+</pre></div></div>
+</div>
+<hr class="docutils" />
+<p>## 5. Running Inference</p>
+<p>Next, we run object detection</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># using a Torch-TensorRT module is exactly the same as how we usually do inference in PyTorch i.e. model(inputs)
+detections_batch = trt_model(tensor.to(torch.half)) # convert the input to half precision
+
+# By default, raw output from SSD network per input image contains 8732 boxes with
+# localization and class probability distribution.
+# Let’s filter this output to only get reasonable detections (confidence&gt;40%) in a more comprehensive format.
+results_per_input = utils.decode_results(detections_batch)
+best_results_per_input_trt = [utils.pick_best(results, 0.40) for results in results_per_input]
+</pre></div>
+</div>
+</div>
+<p>Now, let’s visualize our predictions!</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Visualize results with Torch-TensorRT
+plot_results(best_results_per_input_trt)
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/_notebooks_ssd-object-detection-demo_35_0.png" src="../_images/_notebooks_ssd-object-detection-demo_35_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/_notebooks_ssd-object-detection-demo_35_1.png" src="../_images/_notebooks_ssd-object-detection-demo_35_1.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/_notebooks_ssd-object-detection-demo_35_2.png" src="../_images/_notebooks_ssd-object-detection-demo_35_2.png" />
+</div>
+</div>
+<p>We get similar results as before!</p>
+</section>
+</section>
+<hr class="docutils" />
+<section id="6.-Measuring-Speedup">
+<h2>6. Measuring Speedup<a class="headerlink" href="#6.-Measuring-Speedup" title="Permalink to this headline">¶</a></h2>
+<p>We can run the benchmark function again to see the speedup gained! Compare this result with the same batch-size of input in the case without Torch-TensorRT above.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>batch_size = 128
+
+# Recompiling with batch_size we use for evaluating performance
+trt_model = torch_tensorrt.compile(traced_model,
+    inputs = [torch_tensorrt.Input((batch_size, 3, 300, 300), dtype=torch.half)],
+    enabled_precisions= {torch.half}, # Run with FP16
+    workspace_size= 1 &lt;&lt; 20
+)
+
+benchmark(trt_model, input_shape=(batch_size, 3, 300, 300), dtype=&#39;fp16&#39;, nruns=100)
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING: [Torch-TensorRT] - For input x, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float
+The compiler is going to use the user setting Float16
+This conflict may cause an error at runtime due to partial compilation being enabled and therefore
+compatibility with PyTorch&#39;s data type convention is required.
+If you do indeed see errors at runtime either:
+- Remove the dtype spec for x
+- Disable partial compilation by setting require_full_compilation to True
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size
+WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size
+WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size
+WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size
+WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size
+WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 10/100, avg batch time 45.88 ms
+Iteration 20/100, avg batch time 46.20 ms
+Iteration 30/100, avg batch time 46.10 ms
+Iteration 40/100, avg batch time 46.19 ms
+Iteration 50/100, avg batch time 46.15 ms
+Iteration 60/100, avg batch time 46.17 ms
+Iteration 70/100, avg batch time 46.19 ms
+Iteration 80/100, avg batch time 46.20 ms
+Iteration 90/100, avg batch time 46.22 ms
+Iteration 100/100, avg batch time 46.21 ms
+Input shape: torch.Size([128, 3, 300, 300])
+Output location prediction size: torch.Size([128, 4, 8732])
+Output label prediction size: torch.Size([128, 81, 8732])
+Average batch time: 46.21 ms
+</pre></div></div>
+</div>
+</section>
+<hr class="docutils" />
+<section id="7.-Conclusion">
+<h2>7. Conclusion<a class="headerlink" href="#7.-Conclusion" title="Permalink to this headline">¶</a></h2>
+<p>In this notebook, we have walked through the complete process of compiling a TorchScript SSD300 model with Torch-TensorRT, and tested the performance impact of the optimization. We find that using the Torch-TensorRT compiled model, we gain significant speedup in inference without any noticeable drop in performance!</p>
+<section id="Details">
+<h3>Details<a class="headerlink" href="#Details" title="Permalink to this headline">¶</a></h3>
+<p>For detailed information on model input and output, training recipies, inference and performance visit: <a class="reference external" href="https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD">github</a> and/or <a class="reference external" href="https://ngc.nvidia.com/catalog/model-scripts/nvidia:ssd_for_pytorch">NGC</a></p>
+</section>
+<section id="References">
+<h3>References<a class="headerlink" href="#References" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><p><a class="reference external" href="https://arxiv.org/abs/1512.02325">SSD: Single Shot MultiBox Detector</a> paper</p></li>
+<li><p><a class="reference external" href="https://arxiv.org/abs/1611.10012">Speed/accuracy trade-offs for modern convolutional object detectors</a> paper</p></li>
+<li><p><a class="reference external" href="https://ngc.nvidia.com/catalog/model-scripts/nvidia:ssd_for_pytorch">SSD on NGC</a></p></li>
+<li><p><a class="reference external" href="https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD">SSD on github</a></p></li>
+</ul>
+</section>
+</section>
+</section>
+
+
+             </article>
+
+            </div>
+            <footer>
+
+
+
+
+    <hr>
+
+
+
+  <div role="contentinfo">
+    <p>
+        &copy; Copyright 2021, NVIDIA Corporation.
+
+    </p>
+  </div>
+
+      <div>
+        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+      </div>
+
+
+</footer>
+
+          </div>
+        </div>
+
+        <div class="pytorch-content-right" id="pytorch-content-right">
+          <div class="pytorch-right-menu" id="pytorch-right-menu">
+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
+              <ul>
+<li><a class="reference internal" href="#">Object Detection with Torch-TensorRT (SSD)</a><ul>
+<li><a class="reference internal" href="#Overview">Overview</a><ul>
+<li><a class="reference internal" href="#Learning-objectives">Learning objectives</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#Contents">Contents</a><ul>
+<li><a class="reference internal" href="#Single-Shot-MultiBox-Detector-model-for-object-detection">Single Shot MultiBox Detector model for object detection</a></li>
+<li><a class="reference internal" href="#Model-Description">Model Description</a></li>
+<li><a class="reference internal" href="#Sample-Inference">Sample Inference</a></li>
+<li><a class="reference internal" href="#Visualize-results">Visualize results</a></li>
+<li><a class="reference internal" href="#Benchmark-utility">Benchmark utility</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#6.-Measuring-Speedup">6. Measuring Speedup</a></li>
+<li><a class="reference internal" href="#7.-Conclusion">7. Conclusion</a><ul>
+<li><a class="reference internal" href="#Details">Details</a></li>
+<li><a class="reference internal" href="#References">References</a></li>
+</ul>
+</li>
+</ul>
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+            <a href="https://pytorch.org/ecosystem">Ecosystem</a>
+          </li>
+
+          <li>
+            <a href="https://pytorch.org/mobile">Mobile</a>
+          </li>
+
+          <li>
+            <a href="https://pytorch.org/hub">PyTorch Hub</a>
+          </li>
+
+          <li>
+            <a href="https://pytorch.org/blog/">Blog</a>
+          </li>
+
+          <li>
+            <a href="https://pytorch.org/tutorials">Tutorials</a>
+          </li>
+
+          <li class="resources-mobile-menu-title">
+            Docs
+          </li>
+
+          <ul class="resources-mobile-menu-items">
+            <li>
+              <a href="https://pytorch.org/docs/stable/index.html">PyTorch</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/audio/stable/index.html">torchaudio</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/text/stable/index.html">torchtext</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/vision/stable/index.html">torchvision</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/serve/">TorchServe</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/torchx/">TorchX</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/xla">PyTorch on XLA Devices</a>
+            </li>
+          </ul>
+
+          <li class="resources-mobile-menu-title">
+            Resources
+          </li>
+
+          <ul class="resources-mobile-menu-items">
+            <li>
+              <a href="https://pytorch.org/resources">Developer Resources</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/features">About</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/hub">Models (Beta)</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/#community-module">Community</a>
+            </li>
+
+            <li>
+              <a href="https://discuss.pytorch.org/">Forums</a>
+            </li>
+          </ul>
+
+          <li>
+            <a href="https://github.com/pytorch/pytorch">Github</a>
+          </li>
+        </ul>
+      </div>
+    </div>
+  </div>
+
+  <!-- End Mobile Menu -->
+
+  <script type="text/javascript" src="../_static/js/vendor/anchor.min.js"></script>
+
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+    $(document).ready(function() {
+      mobileMenu.bind();
+      mobileTOC.bind();
+      pytorchAnchors.bind();
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+      scrollToAnchor.bind();
+      highlightNavigation.bind();
+      mainMenuDropdown.bind();
+      filterTags.bind();
+
+      // Add class to links that have code blocks, since we cannot create links in code blocks
+      $("article.pytorch-article a span.pre").each(function(e) {
+        $(this).closest("a").addClass("has-code");
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+    })
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diff --git a/docs/v1.2.0/_notebooks/ssd-object-detection-demo.ipynb b/docs/v1.2.0/_notebooks/ssd-object-detection-demo.ipynb
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index 0000000000..f2a0bd8063
--- /dev/null
+++ b/docs/v1.2.0/_notebooks/ssd-object-detection-demo.ipynb
@@ -0,0 +1,943 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2020 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_ssd_notebook.png\" style=\"width: 90px; float: right;\">\n",
+    "\n",
+    "# Object Detection with Torch-TensorRT (SSD)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Overview\n",
+    "\n",
+    "In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch's JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.\n",
+    "\n",
+    "When deploying on NVIDIA GPUs TensorRT, NVIDIA's Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA's Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.\n",
+    "\n",
+    "Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning objectives\n",
+    "\n",
+    "This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained SSD network, and running it to test the speedup obtained.\n",
+    "\n",
+    "## Contents\n",
+    "1. [Requirements](#1)\n",
+    "2. [SSD Overview](#2)\n",
+    "3. [Creating TorchScript modules](#3)\n",
+    "4. [Compiling with Torch-TensorRT](#4)\n",
+    "5. [Running Inference](#5)\n",
+    "6. [Measuring Speedup](#6)\n",
+    "7. [Conclusion](#7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<a id=\"1\"></a>\n",
+    "## 1. Requirements\n",
+    "\n",
+    "Follow the steps in `notebooks/README` to prepare a Docker container, within which you can run this demo notebook.\n",
+    "\n",
+    "In addition to that, run the following cell to obtain additional libraries specific to this demo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Collecting scikit-image==0.19.1\n",
+      "  Downloading scikit_image-0.19.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.8 MB)\n",
+      "\u001b[K     |████████████████████████████████| 13.8 MB 8.8 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: networkx>=2.2 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.19.1) (2.6.3)\n",
+      "Collecting tifffile>=2019.7.26\n",
+      "  Downloading tifffile-2022.3.16-py3-none-any.whl (179 kB)\n",
+      "\u001b[K     |████████████████████████████████| 179 kB 110.1 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.19.1) (21.3)\n",
+      "Requirement already satisfied: scipy>=1.4.1 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.19.1) (1.6.3)\n",
+      "Requirement already satisfied: numpy>=1.17.0 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.19.1) (1.22.2)\n",
+      "Collecting imageio>=2.4.1\n",
+      "  Downloading imageio-2.16.1-py3-none-any.whl (3.3 MB)\n",
+      "\u001b[K     |████████████████████████████████| 3.3 MB 42.3 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.19.1) (9.0.0)\n",
+      "Collecting PyWavelets>=1.1.1\n",
+      "  Downloading PyWavelets-1.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.9 MB)\n",
+      "\u001b[K     |████████████████████████████████| 6.9 MB 61.3 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging>=20.0->scikit-image==0.19.1) (3.0.7)\n",
+      "Installing collected packages: tifffile, PyWavelets, imageio, scikit-image\n",
+      "Successfully installed PyWavelets-1.3.0 imageio-2.16.1 scikit-image-0.19.1 tifffile-2022.3.16\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Collecting ipywidgets\n",
+      "  Downloading ipywidgets-7.7.0-py2.py3-none-any.whl (123 kB)\n",
+      "\u001b[K     |████████████████████████████████| 123 kB 12.1 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: ipykernel>=4.5.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (6.9.0)\n",
+      "Collecting jupyterlab-widgets>=1.0.0\n",
+      "  Downloading jupyterlab_widgets-1.1.0-py3-none-any.whl (245 kB)\n",
+      "\u001b[K     |████████████████████████████████| 245 kB 60.6 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: nbformat>=4.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.3)\n",
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+      "Requirement already satisfied: traitlets>=4.3.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.1)\n",
+      "Collecting widgetsnbextension~=3.6.0\n",
+      "  Downloading widgetsnbextension-3.6.0-py2.py3-none-any.whl (1.6 MB)\n",
+      "\u001b[K     |████████████████████████████████| 1.6 MB 35.2 MB/s eta 0:00:01\n",
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+      "Requirement already satisfied: prometheus-client in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.13.1)\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "Requirement already satisfied: webencodings in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.5.1)\n",
+      "Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (21.3)\n",
+      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging->bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (3.0.7)\n",
+      "Requirement already satisfied: executing in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets) (0.8.2)\n",
+      "Requirement already satisfied: pure-eval in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets) (0.2.2)\n",
+      "Requirement already satisfied: asttokens in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets) (2.0.5)\n",
+      "Installing collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets\n",
+      "Successfully installed ipywidgets-7.7.0 jupyterlab-widgets-1.1.0 widgetsnbextension-3.6.0\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install scikit-image==0.19.1\n",
+    "!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<a id=\"2\"></a>\n",
+    "## 2. SSD\n",
+    "\n",
+    "### Single Shot MultiBox Detector model for object detection\n",
+    "\n",
+    "_ | _\n",
+    "- | -\n",
+    "![alt](https://pytorch.org/assets/images/ssd_diagram.png) | ![alt](https://pytorch.org/assets/images/ssd.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PyTorch has a model repository called the PyTorch Hub, which is a source for high quality implementations of common models. We can get our SSD model pretrained on [COCO](https://cocodataset.org/#home) from there.\n",
+    "\n",
+    "### Model Description\n",
+    "\n",
+    "This SSD300 model is based on the\n",
+    "[SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) paper, which\n",
+    "describes SSD as “a method for detecting objects in images using a single deep neural network\".\n",
+    "The input size is fixed to 300x300.\n",
+    "\n",
+    "The main difference between this model and the one described in the paper is in the backbone.\n",
+    "Specifically, the VGG model is obsolete and is replaced by the ResNet-50 model.\n",
+    "\n",
+    "From the\n",
+    "[Speed/accuracy trade-offs for modern convolutional object detectors](https://arxiv.org/abs/1611.10012)\n",
+    "paper, the following enhancements were made to the backbone:\n",
+    "*   The conv5_x, avgpool, fc and softmax layers were removed from the original classification model.\n",
+    "*   All strides in conv4_x are set to 1x1.\n",
+    "\n",
+    "The backbone is followed by 5 additional convolutional layers.\n",
+    "In addition to the convolutional layers, we attached 6 detection heads:\n",
+    "*   The first detection head is attached to the last conv4_x layer.\n",
+    "*   The other five detection heads are attached to the corresponding 5 additional layers.\n",
+    "\n",
+    "Detector heads are similar to the ones referenced in the paper, however,\n",
+    "they are enhanced by additional BatchNorm layers after each convolution.\n",
+    "\n",
+    "More information about this SSD model is available at Nvidia's \"DeepLearningExamples\" Github [here](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "torch.hub._validate_not_a_forked_repo=lambda a,b,c: True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: \"https://github.com/NVIDIA/DeepLearningExamples/archive/torchhub.zip\" to /root/.cache/torch/hub/torchhub.zip\n",
+      "/root/.cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub/PyTorch/Classification/ConvNets/image_classification/models/efficientnet.py:17: UserWarning: pytorch_quantization module not found, quantization will not be available\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['nvidia_convnets_processing_utils',\n",
+       " 'nvidia_efficientnet',\n",
+       " 'nvidia_efficientnet_b0',\n",
+       " 'nvidia_efficientnet_b4',\n",
+       " 'nvidia_efficientnet_widese_b0',\n",
+       " 'nvidia_efficientnet_widese_b4',\n",
+       " 'nvidia_resneXt',\n",
+       " 'nvidia_resnet50',\n",
+       " 'nvidia_resnext101_32x4d',\n",
+       " 'nvidia_se_resnext101_32x4d',\n",
+       " 'nvidia_ssd',\n",
+       " 'nvidia_ssd_processing_utils',\n",
+       " 'nvidia_tacotron2',\n",
+       " 'nvidia_tts_utils',\n",
+       " 'nvidia_waveglow']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# List of available models in PyTorch Hub from Nvidia/DeepLearningExamples\n",
+    "torch.hub.list('NVIDIA/DeepLearningExamples:torchhub')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using cache found in /root/.cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub\n",
+      "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c7cf3e1635d4a2b9c8731b7fc9ce724",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0.00/97.8M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading checkpoint from https://api.ngc.nvidia.com/v2/models/nvidia/ssd_pyt_ckpt_amp/versions/20.06.0/files/nvidia_ssdpyt_amp_200703.pt\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load SSD model pretrained on COCO from Torch Hub\n",
+    "precision = 'fp32'\n",
+    "ssd300 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_ssd', model_math=precision);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setting `precision=\"fp16\"` will load a checkpoint trained with mixed precision \n",
+    "into architecture enabling execution on Tensor Cores. Handling mixed precision data requires the Apex library."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sample Inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now run inference on the model. This is demonstrated below using sample images from the COCO 2017 Validation set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using cache found in /root/.cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading COCO annotations.\n",
+      "Downloading finished.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sample images from the COCO validation set\n",
+    "uris = [\n",
+    "    'http://images.cocodataset.org/val2017/000000397133.jpg',\n",
+    "    'http://images.cocodataset.org/val2017/000000037777.jpg',\n",
+    "    'http://images.cocodataset.org/val2017/000000252219.jpg'\n",
+    "]\n",
+    "\n",
+    "# For convenient and comprehensive formatting of input and output of the model, load a set of utility methods.\n",
+    "utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_ssd_processing_utils')\n",
+    "\n",
+    "# Format images to comply with the network input\n",
+    "inputs = [utils.prepare_input(uri) for uri in uris]\n",
+    "tensor = utils.prepare_tensor(inputs, False)\n",
+    "\n",
+    "# The model was trained on COCO dataset, which we need to access in order to\n",
+    "# translate class IDs into object names. \n",
+    "classes_to_labels = utils.get_coco_object_dictionary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Next, we run object detection\n",
+    "model = ssd300.eval().to(\"cuda\")\n",
+    "detections_batch = model(tensor)\n",
+    "\n",
+    "# By default, raw output from SSD network per input image contains 8732 boxes with \n",
+    "# localization and class probability distribution. \n",
+    "# Let’s filter this output to only get reasonable detections (confidence>40%) in a more comprehensive format.\n",
+    "results_per_input = utils.decode_results(detections_batch)\n",
+    "best_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import matplotlib.patches as patches\n",
+    "\n",
+    "# The utility plots the images and predicted bounding boxes (with confidence scores).\n",
+    "def plot_results(best_results):\n",
+    "    for image_idx in range(len(best_results)):\n",
+    "        fig, ax = plt.subplots(1)\n",
+    "        # Show original, denormalized image...\n",
+    "        image = inputs[image_idx] / 2 + 0.5\n",
+    "        ax.imshow(image)\n",
+    "        # ...with detections\n",
+    "        bboxes, classes, confidences = best_results[image_idx]\n",
+    "        for idx in range(len(bboxes)):\n",
+    "            left, bot, right, top = bboxes[idx]\n",
+    "            x, y, w, h = [val * 300 for val in [left, bot, right - left, top - bot]]\n",
+    "            rect = patches.Rectangle((x, y), w, h, linewidth=1, edgecolor='r', facecolor='none')\n",
+    "            ax.add_patch(rect)\n",
+    "            ax.text(x, y, \"{} {:.0f}%\".format(classes_to_labels[classes[idx] - 1], confidences[idx]*100), bbox=dict(facecolor='white', alpha=0.5))\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize results without Torch-TensorRT\n",
+    "plot_results(best_results_per_input)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Benchmark utility"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "cudnn.benchmark = True\n",
+    "\n",
+    "# Helper function to benchmark the model\n",
+    "def benchmark(model, input_shape=(1024, 1, 32, 32), dtype='fp32', nwarmup=50, nruns=1000):\n",
+    "    input_data = torch.randn(input_shape)\n",
+    "    input_data = input_data.to(\"cuda\")\n",
+    "    if dtype=='fp16':\n",
+    "        input_data = input_data.half()\n",
+    "        \n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(nwarmup):\n",
+    "            features = model(input_data)\n",
+    "    torch.cuda.synchronize()\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(1, nruns+1):\n",
+    "            start_time = time.time()\n",
+    "            pred_loc, pred_label  = model(input_data)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = time.time()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            if i%10==0:\n",
+    "                print('Iteration %d/%d, avg batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
+    "\n",
+    "    print(\"Input shape:\", input_data.size())\n",
+    "    print(\"Output location prediction size:\", pred_loc.size())\n",
+    "    print(\"Output label prediction size:\", pred_label.size())\n",
+    "    print('Average batch time: %.2f ms'%(np.mean(timings)*1000))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We check how well the model performs **before** we use Torch-TensorRT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, avg batch time 165.38 ms\n",
+      "Iteration 20/100, avg batch time 165.99 ms\n",
+      "Iteration 30/100, avg batch time 166.04 ms\n",
+      "Iteration 40/100, avg batch time 166.32 ms\n",
+      "Iteration 50/100, avg batch time 166.49 ms\n",
+      "Iteration 60/100, avg batch time 166.74 ms\n",
+      "Iteration 70/100, avg batch time 166.88 ms\n",
+      "Iteration 80/100, avg batch time 167.05 ms\n",
+      "Iteration 90/100, avg batch time 167.18 ms\n",
+      "Iteration 100/100, avg batch time 167.31 ms\n",
+      "Input shape: torch.Size([128, 3, 300, 300])\n",
+      "Output location prediction size: torch.Size([128, 4, 8732])\n",
+      "Output label prediction size: torch.Size([128, 81, 8732])\n",
+      "Average batch time: 167.31 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Model benchmark without Torch-TensorRT\n",
+    "model = ssd300.eval().to(\"cuda\")\n",
+    "benchmark(model, input_shape=(128, 3, 300, 300), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<a id=\"3\"></a>\n",
+    "## 3. Creating TorchScript modules  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To compile with Torch-TensorRT, the model must first be in **TorchScript**. TorchScript is a programming language included in PyTorch which removes the Python dependency normal PyTorch models have. This conversion is done via a JIT compiler which given a PyTorch Module will generate an equivalent TorchScript Module. There are two paths that can be used to generate TorchScript: **Tracing** and **Scripting**. <br>\n",
+    "- Tracing follows execution of PyTorch generating ops in TorchScript corresponding to what it sees. <br>\n",
+    "- Scripting does an analysis of the Python code and generates TorchScript, this allows the resulting graph to include control flow which tracing cannot do. \n",
+    "\n",
+    "Tracing however due to its simplicity is more likely to compile successfully with Torch-TensorRT (though both systems are supported)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ssd300.eval().to(\"cuda\")\n",
+    "traced_model = torch.jit.trace(model, [torch.randn((1,3,300,300)).to(\"cuda\")])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If required, we can also save this model and use it independently of Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is just an example, and not required for the purposes of this demo\n",
+    "torch.jit.save(traced_model, \"ssd_300_traced.jit.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, avg batch time 165.53 ms\n",
+      "Iteration 20/100, avg batch time 166.19 ms\n",
+      "Iteration 30/100, avg batch time 166.12 ms\n",
+      "Iteration 40/100, avg batch time 166.16 ms\n",
+      "Iteration 50/100, avg batch time 166.24 ms\n",
+      "Iteration 60/100, avg batch time 166.33 ms\n",
+      "Iteration 70/100, avg batch time 166.43 ms\n",
+      "Iteration 80/100, avg batch time 166.44 ms\n",
+      "Iteration 90/100, avg batch time 166.53 ms\n",
+      "Iteration 100/100, avg batch time 166.59 ms\n",
+      "Input shape: torch.Size([128, 3, 300, 300])\n",
+      "Output location prediction size: torch.Size([128, 4, 8732])\n",
+      "Output label prediction size: torch.Size([128, 81, 8732])\n",
+      "Average batch time: 166.59 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Obtain the average time taken by a batch of input with Torchscript compiled modules\n",
+    "benchmark(traced_model, input_shape=(128, 3, 300, 300), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<a id=\"4\"></a>\n",
+    "## 4. Compiling with Torch-TensorRT\n",
+    "TorchScript modules behave just like normal PyTorch modules and are intercompatible. From TorchScript we can now compile a TensorRT based module. This module will still be implemented in TorchScript but all the computation will be done in TensorRT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - For input x, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
+      "The compiler is going to use the user setting Float16\n",
+      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
+      "compatibility with PyTorch's data type convention is required.\n",
+      "If you do indeed see errors at runtime either:\n",
+      "- Remove the dtype spec for x\n",
+      "- Disable partial compilation by setting require_full_compilation to True\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch_tensorrt\n",
+    "\n",
+    "# The compiled module will have precision as specified by \"op_precision\".\n",
+    "# Here, it will have FP16 precision.\n",
+    "trt_model = torch_tensorrt.compile(traced_model, \n",
+    "    inputs= [torch_tensorrt.Input((3, 3, 300, 300), dtype=torch.half)],\n",
+    "    enabled_precisions= {torch.half}, # Run with FP16\n",
+    "    workspace_size= 1 << 20\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<a id=\"5\"></a>\n",
+    "## 5. Running Inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we run object detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using a Torch-TensorRT module is exactly the same as how we usually do inference in PyTorch i.e. model(inputs)\n",
+    "detections_batch = trt_model(tensor.to(torch.half)) # convert the input to half precision\n",
+    "\n",
+    "# By default, raw output from SSD network per input image contains 8732 boxes with \n",
+    "# localization and class probability distribution. \n",
+    "# Let’s filter this output to only get reasonable detections (confidence>40%) in a more comprehensive format.\n",
+    "results_per_input = utils.decode_results(detections_batch)\n",
+    "best_results_per_input_trt = [utils.pick_best(results, 0.40) for results in results_per_input]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's visualize our predictions!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize results with Torch-TensorRT\n",
+    "plot_results(best_results_per_input_trt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We get similar results as before!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 6. Measuring Speedup\n",
+    "We can run the benchmark function again to see the speedup gained! Compare this result with the same batch-size of input in the case without Torch-TensorRT above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - For input x, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
+      "The compiler is going to use the user setting Float16\n",
+      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
+      "compatibility with PyTorch's data type convention is required.\n",
+      "If you do indeed see errors at runtime either:\n",
+      "- Remove the dtype spec for x\n",
+      "- Disable partial compilation by setting require_full_compilation to True\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, avg batch time 45.88 ms\n",
+      "Iteration 20/100, avg batch time 46.20 ms\n",
+      "Iteration 30/100, avg batch time 46.10 ms\n",
+      "Iteration 40/100, avg batch time 46.19 ms\n",
+      "Iteration 50/100, avg batch time 46.15 ms\n",
+      "Iteration 60/100, avg batch time 46.17 ms\n",
+      "Iteration 70/100, avg batch time 46.19 ms\n",
+      "Iteration 80/100, avg batch time 46.20 ms\n",
+      "Iteration 90/100, avg batch time 46.22 ms\n",
+      "Iteration 100/100, avg batch time 46.21 ms\n",
+      "Input shape: torch.Size([128, 3, 300, 300])\n",
+      "Output location prediction size: torch.Size([128, 4, 8732])\n",
+      "Output label prediction size: torch.Size([128, 81, 8732])\n",
+      "Average batch time: 46.21 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "batch_size = 128\n",
+    "\n",
+    "# Recompiling with batch_size we use for evaluating performance\n",
+    "trt_model = torch_tensorrt.compile(traced_model,\n",
+    "    inputs = [torch_tensorrt.Input((batch_size, 3, 300, 300), dtype=torch.half)],\n",
+    "    enabled_precisions= {torch.half}, # Run with FP16\n",
+    "    workspace_size= 1 << 20\n",
+    ")\n",
+    "\n",
+    "benchmark(trt_model, input_shape=(batch_size, 3, 300, 300), dtype='fp16', nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 7. Conclusion\n",
+    "\n",
+    "In this notebook, we have walked through the complete process of compiling a TorchScript SSD300 model with Torch-TensorRT, and tested the performance impact of the optimization. We find that using the Torch-TensorRT compiled model, we gain significant speedup in inference without any noticeable drop in performance!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Details\n",
+    "For detailed information on model input and output,\n",
+    "training recipies, inference and performance visit:\n",
+    "[github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD)\n",
+    "and/or [NGC](https://ngc.nvidia.com/catalog/model-scripts/nvidia:ssd_for_pytorch)\n",
+    "\n",
+    "### References\n",
+    "\n",
+    " - [SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) paper\n",
+    " - [Speed/accuracy trade-offs for modern convolutional object detectors](https://arxiv.org/abs/1611.10012) paper\n",
+    " - [SSD on NGC](https://ngc.nvidia.com/catalog/model-scripts/nvidia:ssd_for_pytorch)\n",
+    " - [SSD on github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/v1.2.0/_notebooks/vgg-qat.html b/docs/v1.2.0/_notebooks/vgg-qat.html
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+<section id="Deploying-Quantization-Aware-Trained-models-in-INT8-using-Torch-TensorRT">
+<h1>Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT<a class="headerlink" href="#Deploying-Quantization-Aware-Trained-models-in-INT8-using-Torch-TensorRT" title="Permalink to this headline">¶</a></h1>
+<section id="Overview">
+<h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline">¶</a></h2>
+<p>Quantization Aware training (QAT) simulates quantization during training by quantizing weights and activation layers. This will help to reduce the loss in accuracy when we convert the network trained in FP32 to INT8 for faster inference. QAT introduces additional nodes in the graph which will be used to learn the dynamic ranges of weights and activation layers. In this notebook, we illustrate the following steps from training to inference of a QAT model in Torch-TensorRT.</p>
+<ol class="arabic simple">
+<li><p><a class="reference external" href="#1">Requirements</a></p></li>
+<li><p><a class="reference external" href="#2">VGG16 Overview</a></p></li>
+<li><p><a class="reference external" href="#3">Training a baseline VGG16 model</a></p></li>
+<li><p><a class="reference external" href="#4">Apply Quantization</a></p></li>
+<li><p><a class="reference external" href="#5">Model calibration</a></p></li>
+<li><p><a class="reference external" href="#6">Quantization Aware training</a></p></li>
+<li><p><a class="reference external" href="#7">Export to Torchscript</a></p></li>
+<li><p><a class="reference external" href="#8">Inference using Torch-TensorRT</a></p></li>
+<li><p><a class="reference external" href="#8">References</a></p></li>
+</ol>
+<p>## 1. Requirements Please install the required dependencies and import these libraries accordingly</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torch.utils.data as data
+import torchvision.transforms as transforms
+import torchvision.datasets as datasets
+import torch_tensorrt
+
+from torch.utils.tensorboard import SummaryWriter
+
+import pytorch_quantization
+from pytorch_quantization import nn as quant_nn
+from pytorch_quantization import quant_modules
+from pytorch_quantization.tensor_quant import QuantDescriptor
+from pytorch_quantization import calib
+from tqdm import tqdm
+
+print(pytorch_quantization.__version__)
+
+import os
+import sys
+sys.path.insert(0, &quot;../examples/int8/training/vgg16&quot;)
+from vgg16 import vgg16
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+2.1.0
+</pre></div></div>
+</div>
+<p>## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows <img alt="beac8a15065243e699379551998f212b" src="https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png" /></p>
+<p>## 3. Training a baseline VGG16 model We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your <code class="docutils literal notranslate"><span class="pre">data</span></code> directory. Data preprocessing is performed using <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> transforms.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>classes = (&#39;plane&#39;, &#39;car&#39;, &#39;bird&#39;, &#39;cat&#39;, &#39;deer&#39;, &#39;dog&#39;, &#39;frog&#39;, &#39;horse&#39;, &#39;ship&#39;, &#39;truck&#39;)
+
+# ========== Define Training dataset and dataloaders =============#
+training_dataset = datasets.CIFAR10(root=&#39;./data&#39;,
+                                        train=True,
+                                        download=True,
+                                        transform=transforms.Compose([
+                                            transforms.RandomCrop(32, padding=4),
+                                            transforms.RandomHorizontalFlip(),
+                                            transforms.ToTensor(),
+                                            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+                                        ]))
+
+training_dataloader = torch.utils.data.DataLoader(training_dataset,
+                                                      batch_size=32,
+                                                      shuffle=True,
+                                                      num_workers=2)
+
+# ========== Define Testing dataset and dataloaders =============#
+testing_dataset = datasets.CIFAR10(root=&#39;./data&#39;,
+                                   train=False,
+                                   download=True,
+                                   transform=transforms.Compose([
+                                       transforms.ToTensor(),
+                                       transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+                                   ]))
+
+testing_dataloader = torch.utils.data.DataLoader(testing_dataset,
+                                                 batch_size=16,
+                                                 shuffle=False,
+                                                 num_workers=2)
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Files already downloaded and verified
+Files already downloaded and verified
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def train(model, dataloader, crit, opt, epoch):
+#     global writer
+    model.train()
+    running_loss = 0.0
+    for batch, (data, labels) in enumerate(dataloader):
+        data, labels = data.cuda(), labels.cuda(non_blocking=True)
+        opt.zero_grad()
+        out = model(data)
+        loss = crit(out, labels)
+        loss.backward()
+        opt.step()
+
+        running_loss += loss.item()
+        if batch % 500 == 499:
+            print(&quot;Batch: [%5d | %5d] loss: %.3f&quot; % (batch + 1, len(dataloader), running_loss / 100))
+            running_loss = 0.0
+
+def test(model, dataloader, crit, epoch):
+    global writer
+    global classes
+    total = 0
+    correct = 0
+    loss = 0.0
+    class_probs = []
+    class_preds = []
+    model.eval()
+    with torch.no_grad():
+        for data, labels in dataloader:
+            data, labels = data.cuda(), labels.cuda(non_blocking=True)
+            out = model(data)
+            loss += crit(out, labels)
+            preds = torch.max(out, 1)[1]
+            class_probs.append([F.softmax(i, dim=0) for i in out])
+            class_preds.append(preds)
+            total += labels.size(0)
+            correct += (preds == labels).sum().item()
+
+    test_probs = torch.cat([torch.stack(batch) for batch in class_probs])
+    test_preds = torch.cat(class_preds)
+
+    return loss / total, correct / total
+
+def save_checkpoint(state, ckpt_path=&quot;checkpoint.pth&quot;):
+    torch.save(state, ckpt_path)
+    print(&quot;Checkpoint saved&quot;)
+</pre></div>
+</div>
+</div>
+<p><em>Define the VGG model that we are going to perfom QAT on.</em></p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># CIFAR 10 has 10 classes
+model = vgg16(num_classes=len(classes), init_weights=False)
+model = model.cuda()
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Declare Learning rate
+lr = 0.1
+state = {}
+state[&quot;lr&quot;] = lr
+
+# Use cross entropy loss for classification and SGD optimizer
+crit = nn.CrossEntropyLoss()
+opt = optim.SGD(model.parameters(), lr=state[&quot;lr&quot;], momentum=0.9, weight_decay=1e-4)
+
+
+# Adjust learning rate based on epoch number
+def adjust_lr(optimizer, epoch):
+    global state
+    new_lr = lr * (0.5**(epoch // 12)) if state[&quot;lr&quot;] &gt; 1e-7 else state[&quot;lr&quot;]
+    if new_lr != state[&quot;lr&quot;]:
+        state[&quot;lr&quot;] = new_lr
+        print(&quot;Updating learning rate: {}&quot;.format(state[&quot;lr&quot;]))
+        for param_group in optimizer.param_groups:
+            param_group[&quot;lr&quot;] = state[&quot;lr&quot;]
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Train the model for 25 epochs to get ~80% accuracy.
+num_epochs=25
+for epoch in range(num_epochs):
+    adjust_lr(opt, epoch)
+    print(&#39;Epoch: [%5d / %5d] LR: %f&#39; % (epoch + 1, num_epochs, state[&quot;lr&quot;]))
+
+    train(model, training_dataloader, crit, opt, epoch)
+    test_loss, test_acc = test(model, testing_dataloader, crit, epoch)
+
+    print(&quot;Test Loss: {:.5f} Test Acc: {:.2f}%&quot;.format(test_loss, 100 * test_acc))
+
+save_checkpoint({&#39;epoch&#39;: epoch + 1,
+                 &#39;model_state_dict&#39;: model.state_dict(),
+                 &#39;acc&#39;: test_acc,
+                 &#39;opt_state_dict&#39;: opt.state_dict(),
+                 &#39;state&#39;: state},
+                ckpt_path=&quot;vgg16_base_ckpt&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Epoch: [    1 /    25] LR: 0.100000
+Batch: [  500 |  1563] loss: 13.288
+Batch: [ 1000 |  1563] loss: 11.345
+Batch: [ 1500 |  1563] loss: 11.008
+Test Loss: 0.13388 Test Acc: 13.23%
+Epoch: [    2 /    25] LR: 0.100000
+Batch: [  500 |  1563] loss: 10.742
+Batch: [ 1000 |  1563] loss: 10.311
+Batch: [ 1500 |  1563] loss: 10.141
+Test Loss: 0.11888 Test Acc: 23.96%
+Epoch: [    3 /    25] LR: 0.100000
+Batch: [  500 |  1563] loss: 9.877
+Batch: [ 1000 |  1563] loss: 9.821
+Batch: [ 1500 |  1563] loss: 9.818
+Test Loss: 0.11879 Test Acc: 24.68%
+Epoch: [    4 /    25] LR: 0.100000
+Batch: [  500 |  1563] loss: 9.677
+Batch: [ 1000 |  1563] loss: 9.613
+Batch: [ 1500 |  1563] loss: 9.504
+Test Loss: 0.11499 Test Acc: 23.68%
+Epoch: [    5 /    25] LR: 0.100000
+Batch: [  500 |  1563] loss: 9.560
+Batch: [ 1000 |  1563] loss: 9.536
+Batch: [ 1500 |  1563] loss: 9.309
+Test Loss: 0.10990 Test Acc: 27.84%
+Epoch: [    6 /    25] LR: 0.100000
+Batch: [  500 |  1563] loss: 9.254
+Batch: [ 1000 |  1563] loss: 9.234
+Batch: [ 1500 |  1563] loss: 9.188
+Test Loss: 0.11594 Test Acc: 23.29%
+Epoch: [    7 /    25] LR: 0.100000
+Batch: [  500 |  1563] loss: 9.141
+Batch: [ 1000 |  1563] loss: 9.110
+Batch: [ 1500 |  1563] loss: 9.013
+Test Loss: 0.10732 Test Acc: 29.24%
+Epoch: [    8 /    25] LR: 0.100000
+Batch: [  500 |  1563] loss: 9.120
+Batch: [ 1000 |  1563] loss: 9.086
+Batch: [ 1500 |  1563] loss: 8.948
+Test Loss: 0.10732 Test Acc: 27.24%
+Epoch: [    9 /    25] LR: 0.100000
+Batch: [  500 |  1563] loss: 8.941
+Batch: [ 1000 |  1563] loss: 8.997
+Batch: [ 1500 |  1563] loss: 9.028
+Test Loss: 0.11299 Test Acc: 25.52%
+Epoch: [   10 /    25] LR: 0.100000
+Batch: [  500 |  1563] loss: 8.927
+Batch: [ 1000 |  1563] loss: 8.837
+Batch: [ 1500 |  1563] loss: 8.860
+Test Loss: 0.10130 Test Acc: 34.61%
+Epoch: [   11 /    25] LR: 0.100000
+Batch: [  500 |  1563] loss: 8.953
+Batch: [ 1000 |  1563] loss: 8.738
+Batch: [ 1500 |  1563] loss: 8.724
+Test Loss: 0.10018 Test Acc: 32.27%
+Epoch: [   12 /    25] LR: 0.100000
+Batch: [  500 |  1563] loss: 8.721
+Batch: [ 1000 |  1563] loss: 8.716
+Batch: [ 1500 |  1563] loss: 8.701
+Test Loss: 0.10070 Test Acc: 29.57%
+Updating learning rate: 0.05
+Epoch: [   13 /    25] LR: 0.050000
+Batch: [  500 |  1563] loss: 7.944
+Batch: [ 1000 |  1563] loss: 7.649
+Batch: [ 1500 |  1563] loss: 7.511
+Test Loss: 0.08555 Test Acc: 44.62%
+Epoch: [   14 /    25] LR: 0.050000
+Batch: [  500 |  1563] loss: 7.057
+Batch: [ 1000 |  1563] loss: 6.944
+Batch: [ 1500 |  1563] loss: 6.687
+Test Loss: 0.08331 Test Acc: 52.27%
+Epoch: [   15 /    25] LR: 0.050000
+Batch: [  500 |  1563] loss: 6.470
+Batch: [ 1000 |  1563] loss: 6.439
+Batch: [ 1500 |  1563] loss: 6.126
+Test Loss: 0.07266 Test Acc: 58.02%
+Epoch: [   16 /    25] LR: 0.050000
+Batch: [  500 |  1563] loss: 5.834
+Batch: [ 1000 |  1563] loss: 5.801
+Batch: [ 1500 |  1563] loss: 5.622
+Test Loss: 0.06340 Test Acc: 65.17%
+Epoch: [   17 /    25] LR: 0.050000
+Batch: [  500 |  1563] loss: 5.459
+Batch: [ 1000 |  1563] loss: 5.442
+Batch: [ 1500 |  1563] loss: 5.314
+Test Loss: 0.05945 Test Acc: 67.22%
+Epoch: [   18 /    25] LR: 0.050000
+Batch: [  500 |  1563] loss: 5.071
+Batch: [ 1000 |  1563] loss: 5.145
+Batch: [ 1500 |  1563] loss: 5.063
+Test Loss: 0.06567 Test Acc: 64.46%
+Epoch: [   19 /    25] LR: 0.050000
+Batch: [  500 |  1563] loss: 4.796
+Batch: [ 1000 |  1563] loss: 4.781
+Batch: [ 1500 |  1563] loss: 4.732
+Test Loss: 0.05374 Test Acc: 71.87%
+Epoch: [   20 /    25] LR: 0.050000
+Batch: [  500 |  1563] loss: 4.568
+Batch: [ 1000 |  1563] loss: 4.564
+Batch: [ 1500 |  1563] loss: 4.484
+Test Loss: 0.05311 Test Acc: 71.12%
+Epoch: [   21 /    25] LR: 0.050000
+Batch: [  500 |  1563] loss: 4.385
+Batch: [ 1000 |  1563] loss: 4.302
+Batch: [ 1500 |  1563] loss: 4.285
+Test Loss: 0.05080 Test Acc: 74.29%
+Epoch: [   22 /    25] LR: 0.050000
+Batch: [  500 |  1563] loss: 4.069
+Batch: [ 1000 |  1563] loss: 4.105
+Batch: [ 1500 |  1563] loss: 4.096
+Test Loss: 0.04807 Test Acc: 75.20%
+Epoch: [   23 /    25] LR: 0.050000
+Batch: [  500 |  1563] loss: 3.959
+Batch: [ 1000 |  1563] loss: 3.898
+Batch: [ 1500 |  1563] loss: 3.916
+Test Loss: 0.04743 Test Acc: 75.81%
+Epoch: [   24 /    25] LR: 0.050000
+Batch: [  500 |  1563] loss: 3.738
+Batch: [ 1000 |  1563] loss: 3.847
+Batch: [ 1500 |  1563] loss: 3.797
+Test Loss: 0.04609 Test Acc: 76.42%
+Updating learning rate: 0.025
+Epoch: [   25 /    25] LR: 0.025000
+Batch: [  500 |  1563] loss: 2.952
+Batch: [ 1000 |  1563] loss: 2.906
+Batch: [ 1500 |  1563] loss: 2.735
+Test Loss: 0.03466 Test Acc: 82.00%
+Checkpoint saved
+</pre></div></div>
+</div>
+<p>## 4. Apply Quantization</p>
+<p><code class="docutils literal notranslate"><span class="pre">quant_modules.initialize()</span></code> will ensure quantized version of modules will be called instead of original modules. For example, when you define a model with convolution, linear, pooling layers, <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code>, <code class="docutils literal notranslate"><span class="pre">QuantLinear</span></code> and <code class="docutils literal notranslate"><span class="pre">QuantPooling</span></code> will be called. <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code> basically wraps quantizer nodes around inputs and weights of regular <code class="docutils literal notranslate"><span class="pre">Conv2d</span></code>. Please refer to all the quantized modules in pytorch-quantization toolkit for more information. A <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code> is represented in
+<code class="docutils literal notranslate"><span class="pre">pytorch-quantization</span></code> toolkit as follows.</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>def forward(self, input):
+        # the actual quantization happens in the next level of the class hierarchy
+        quant_input, quant_weight = self._quant(input)
+
+        if self.padding_mode == &#39;circular&#39;:
+            expanded_padding = ((self.padding[1] + 1) // 2, self.padding[1] // 2,
+                                (self.padding[0] + 1) // 2, self.padding[0] // 2)
+            output = F.conv2d(F.pad(quant_input, expanded_padding, mode=&#39;circular&#39;),
+                              quant_weight, self.bias, self.stride,
+                              _pair(0), self.dilation, self.groups)
+        else:
+            output = F.conv2d(quant_input, quant_weight, self.bias, self.stride, self.padding, self.dilation,
+                              self.groups)
+
+        return output
+</pre></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>quant_modules.initialize()
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># All the regular conv, FC layers will be converted to their quantozed counterparts due to quant_modules.initialize()
+qat_model = vgg16(num_classes=len(classes), init_weights=False)
+qat_model = qat_model.cuda()
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># vgg16_base_ckpt is the checkpoint generated from Step 3 : Training a baseline VGG16 model.
+ckpt = torch.load(&quot;./vgg16_base_ckpt&quot;)
+modified_state_dict={}
+for key, val in ckpt[&quot;model_state_dict&quot;].items():
+    # Remove &#39;module.&#39; from the key names
+    if key.startswith(&#39;module&#39;):
+        modified_state_dict[key[7:]] = val
+    else:
+        modified_state_dict[key] = val
+
+# Load the pre-trained checkpoint
+qat_model.load_state_dict(modified_state_dict)
+opt.load_state_dict(ckpt[&quot;opt_state_dict&quot;])
+</pre></div>
+</div>
+</div>
+<p>## 5. Model Calibration</p>
+<p>The quantizer nodes introduced in the model around desired layers capture the dynamic range (min_value, max_value) that is observed by the layer. Calibration is the process of computing the dynamic range of these layers by passing calibration data, which is usually a subset of training or validation data. There are different ways of calibration: <code class="docutils literal notranslate"><span class="pre">max</span></code>, <code class="docutils literal notranslate"><span class="pre">histogram</span></code> and <code class="docutils literal notranslate"><span class="pre">entropy</span></code>. We use <code class="docutils literal notranslate"><span class="pre">max</span></code> calibration technique as it is simple and effective.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>def compute_amax(model, **kwargs):
+    # Load calib result
+    for name, module in model.named_modules():
+        if isinstance(module, quant_nn.TensorQuantizer):
+            if module._calibrator is not None:
+                if isinstance(module._calibrator, calib.MaxCalibrator):
+                    module.load_calib_amax()
+                else:
+                    module.load_calib_amax(**kwargs)
+            print(F&quot;{name:40}: {module}&quot;)
+    model.cuda()
+
+def collect_stats(model, data_loader, num_batches):
+    &quot;&quot;&quot;Feed data to the network and collect statistics&quot;&quot;&quot;
+    # Enable calibrators
+    for name, module in model.named_modules():
+        if isinstance(module, quant_nn.TensorQuantizer):
+            if module._calibrator is not None:
+                module.disable_quant()
+                module.enable_calib()
+            else:
+                module.disable()
+
+    # Feed data to the network for collecting stats
+    for i, (image, _) in tqdm(enumerate(data_loader), total=num_batches):
+        model(image.cuda())
+        if i &gt;= num_batches:
+            break
+
+    # Disable calibrators
+    for name, module in model.named_modules():
+        if isinstance(module, quant_nn.TensorQuantizer):
+            if module._calibrator is not None:
+                module.enable_quant()
+                module.disable_calib()
+            else:
+                module.enable()
+
+def calibrate_model(model, model_name, data_loader, num_calib_batch, calibrator, hist_percentile, out_dir):
+    &quot;&quot;&quot;
+        Feed data to the network and calibrate.
+        Arguments:
+            model: classification model
+            model_name: name to use when creating state files
+            data_loader: calibration data set
+            num_calib_batch: amount of calibration passes to perform
+            calibrator: type of calibration to use (max/histogram)
+            hist_percentile: percentiles to be used for historgram calibration
+            out_dir: dir to save state files in
+    &quot;&quot;&quot;
+
+    if num_calib_batch &gt; 0:
+        print(&quot;Calibrating model&quot;)
+        with torch.no_grad():
+            collect_stats(model, data_loader, num_calib_batch)
+
+        if not calibrator == &quot;histogram&quot;:
+            compute_amax(model, method=&quot;max&quot;)
+            calib_output = os.path.join(
+                out_dir,
+                F&quot;{model_name}-max-{num_calib_batch*data_loader.batch_size}.pth&quot;)
+            torch.save(model.state_dict(), calib_output)
+        else:
+            for percentile in hist_percentile:
+                print(F&quot;{percentile} percentile calibration&quot;)
+                compute_amax(model, method=&quot;percentile&quot;)
+                calib_output = os.path.join(
+                    out_dir,
+                    F&quot;{model_name}-percentile-{percentile}-{num_calib_batch*data_loader.batch_size}.pth&quot;)
+                torch.save(model.state_dict(), calib_output)
+
+            for method in [&quot;mse&quot;, &quot;entropy&quot;]:
+                print(F&quot;{method} calibration&quot;)
+                compute_amax(model, method=method)
+                calib_output = os.path.join(
+                    out_dir,
+                    F&quot;{model_name}-{method}-{num_calib_batch*data_loader.batch_size}.pth&quot;)
+                torch.save(model.state_dict(), calib_output)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>#Calibrate the model using max calibration technique.
+with torch.no_grad():
+    calibrate_model(
+        model=qat_model,
+        model_name=&quot;vgg16&quot;,
+        data_loader=training_dataloader,
+        num_calib_batch=32,
+        calibrator=&quot;max&quot;,
+        hist_percentile=[99.9, 99.99, 99.999, 99.9999],
+        out_dir=&quot;./&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Calibrating model
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+100%|███████████████████████████████████████████████████████| 32/32 [00:00&lt;00:00, 96.04it/s]
+WARNING: Logging before flag parsing goes to stderr.
+W1109 04:01:43.512364 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.513354 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.514046 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.514638 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.515270 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.515859 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.516441 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.517009 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.517600 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.518167 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.518752 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.519333 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.519911 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.520473 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.521038 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.521596 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.522170 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.522742 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.523360 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.523957 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.524581 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.525059 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.525366 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.525675 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.525962 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.526257 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.526566 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.526885 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.527188 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.527489 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.527792 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.528097 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.528387 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator
+W1109 04:01:43.528834 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.529163 139704147265344 tensor_quantizer.py:238] Call .cuda() if running on GPU after loading calibrated amax.
+W1109 04:01:43.532748 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([64, 1, 1, 1]).
+W1109 04:01:43.533468 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.534033 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([64, 1, 1, 1]).
+W1109 04:01:43.534684 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.535320 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([128, 1, 1, 1]).
+W1109 04:01:43.535983 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.536569 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([128, 1, 1, 1]).
+W1109 04:01:43.537248 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.537833 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([256, 1, 1, 1]).
+W1109 04:01:43.538480 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.539074 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([256, 1, 1, 1]).
+W1109 04:01:43.539724 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.540307 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([256, 1, 1, 1]).
+W1109 04:01:43.540952 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.541534 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).
+W1109 04:01:43.542075 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.542596 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).
+W1109 04:01:43.543248 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.543719 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).
+W1109 04:01:43.544424 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.544952 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).
+W1109 04:01:43.545530 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.546114 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).
+W1109 04:01:43.546713 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.547292 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).
+W1109 04:01:43.547902 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.548453 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.549015 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([4096, 1]).
+W1109 04:01:43.549665 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.550436 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([4096, 1]).
+W1109 04:01:43.551925 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).
+W1109 04:01:43.553105 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([10, 1]).
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+features.0._input_quantizer             : TensorQuantizer(8bit narrow fake per-tensor amax=2.7537 calibrator=MaxCalibrator scale=1.0 quant)
+features.0._weight_quantizer            : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0263, 2.7454](64) calibrator=MaxCalibrator scale=1.0 quant)
+features.3._input_quantizer             : TensorQuantizer(8bit narrow fake per-tensor amax=27.5676 calibrator=MaxCalibrator scale=1.0 quant)
+features.3._weight_quantizer            : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0169, 1.8204](64) calibrator=MaxCalibrator scale=1.0 quant)
+features.7._input_quantizer             : TensorQuantizer(8bit narrow fake per-tensor amax=15.2002 calibrator=MaxCalibrator scale=1.0 quant)
+features.7._weight_quantizer            : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0493, 1.3207](128) calibrator=MaxCalibrator scale=1.0 quant)
+features.10._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=7.7376 calibrator=MaxCalibrator scale=1.0 quant)
+features.10._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0163, 0.9624](128) calibrator=MaxCalibrator scale=1.0 quant)
+features.14._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=8.8351 calibrator=MaxCalibrator scale=1.0 quant)
+features.14._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0622, 0.8791](256) calibrator=MaxCalibrator scale=1.0 quant)
+features.17._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=12.5746 calibrator=MaxCalibrator scale=1.0 quant)
+features.17._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0505, 0.5117](256) calibrator=MaxCalibrator scale=1.0 quant)
+features.20._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=9.7203 calibrator=MaxCalibrator scale=1.0 quant)
+features.20._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0296, 0.5335](256) calibrator=MaxCalibrator scale=1.0 quant)
+features.24._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=8.9367 calibrator=MaxCalibrator scale=1.0 quant)
+features.24._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0220, 0.3763](512) calibrator=MaxCalibrator scale=1.0 quant)
+features.27._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=6.6539 calibrator=MaxCalibrator scale=1.0 quant)
+features.27._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0151, 0.1777](512) calibrator=MaxCalibrator scale=1.0 quant)
+features.30._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=3.7099 calibrator=MaxCalibrator scale=1.0 quant)
+features.30._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0087, 0.1906](512) calibrator=MaxCalibrator scale=1.0 quant)
+features.34._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=4.0491 calibrator=MaxCalibrator scale=1.0 quant)
+features.34._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0106, 0.1971](512) calibrator=MaxCalibrator scale=1.0 quant)
+features.37._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=2.1531 calibrator=MaxCalibrator scale=1.0 quant)
+features.37._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0070, 0.2305](512) calibrator=MaxCalibrator scale=1.0 quant)
+features.40._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=3.3631 calibrator=MaxCalibrator scale=1.0 quant)
+features.40._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0023, 0.4726](512) calibrator=MaxCalibrator scale=1.0 quant)
+avgpool._input_quantizer                : TensorQuantizer(8bit narrow fake per-tensor amax=5.3550 calibrator=MaxCalibrator scale=1.0 quant)
+classifier.0._input_quantizer           : TensorQuantizer(8bit narrow fake per-tensor amax=5.3550 calibrator=MaxCalibrator scale=1.0 quant)
+classifier.0._weight_quantizer          : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0026, 0.5320](4096) calibrator=MaxCalibrator scale=1.0 quant)
+classifier.3._input_quantizer           : TensorQuantizer(8bit narrow fake per-tensor amax=6.6733 calibrator=MaxCalibrator scale=1.0 quant)
+classifier.3._weight_quantizer          : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0018, 0.5172](4096) calibrator=MaxCalibrator scale=1.0 quant)
+classifier.6._input_quantizer           : TensorQuantizer(8bit narrow fake per-tensor amax=9.4352 calibrator=MaxCalibrator scale=1.0 quant)
+classifier.6._weight_quantizer          : TensorQuantizer(8bit narrow fake axis=0 amax=[0.3877, 0.5620](10) calibrator=MaxCalibrator scale=1.0 quant)
+</pre></div></div>
+</div>
+<p>## 6. Quantization Aware Training</p>
+<p>In this phase, we finetune the model weights and leave the quantizer node values frozen. The dynamic ranges for each layer obtained from the calibration are kept constant while the weights of the model are finetuned to be close to the accuracy of original FP32 model (model without quantizer nodes) is preserved. Usually the finetuning of QAT model should be quick compared to the full training of the original model. Use QAT to fine-tune for around 10% of the original training schedule with an
+annealing learning-rate. Please refer to Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT for detailed recommendations. For this VGG model, it is enough to finetune for 1 epoch to get acceptable accuracy. During finetuning with QAT, the quantization is applied as a composition of <code class="docutils literal notranslate"><span class="pre">max</span></code>, <code class="docutils literal notranslate"><span class="pre">clamp</span></code>, <code class="docutils literal notranslate"><span class="pre">round</span></code> and <code class="docutils literal notranslate"><span class="pre">mul</span></code> ops.</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span># amax is absolute maximum value for an input
+# The upper bound for integer quantization (127 for int8)
+max_bound = torch.tensor((2.0**(num_bits - 1 + int(unsigned))) - 1.0, device=amax.device)
+scale = max_bound / amax
+outputs = torch.clamp((inputs * scale).round_(), min_bound, max_bound)
+</pre></div>
+</div>
+<p>tensor_quant function in <code class="docutils literal notranslate"><span class="pre">pytorch_quantization</span></code> toolkit is responsible for the above tensor quantization. Usually, per channel quantization is recommended for weights, while per tensor quantization is recommended for activations in a network. During inference, we use <code class="docutils literal notranslate"><span class="pre">torch.fake_quantize_per_tensor_affine</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.fake_quantize_per_channel_affine</span></code> to perform quantization as this is easier to convert into corresponding TensorRT operators. Please refer to next sections for more details on
+how these operators are exported in torchscript and converted in Torch-TensorRT.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Finetune the QAT model for 1 epoch
+num_epochs=1
+for epoch in range(num_epochs):
+    adjust_lr(opt, epoch)
+    print(&#39;Epoch: [%5d / %5d] LR: %f&#39; % (epoch + 1, num_epochs, state[&quot;lr&quot;]))
+
+    train(qat_model, training_dataloader, crit, opt, epoch)
+    test_loss, test_acc = test(qat_model, testing_dataloader, crit, epoch)
+
+    print(&quot;Test Loss: {:.5f} Test Acc: {:.2f}%&quot;.format(test_loss, 100 * test_acc))
+
+save_checkpoint({&#39;epoch&#39;: epoch + 1,
+                 &#39;model_state_dict&#39;: qat_model.state_dict(),
+                 &#39;acc&#39;: test_acc,
+                 &#39;opt_state_dict&#39;: opt.state_dict(),
+                 &#39;state&#39;: state},
+                ckpt_path=&quot;vgg16_qat_ckpt&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Updating learning rate: 0.1
+Epoch: [    1 /     1] LR: 0.100000
+Batch: [  500 |  1563] loss: 2.635
+Batch: [ 1000 |  1563] loss: 2.655
+Batch: [ 1500 |  1563] loss: 2.646
+Test Loss: 0.03291 Test Acc: 82.98%
+Checkpoint saved
+</pre></div></div>
+</div>
+<p>## 7. Export to Torchscript Export the model to Torch script. Trace the model and convert it into torchscript for deployment. To learn more about Torchscript, please refer to <a class="reference external" href="https://pytorch.org/docs/stable/jit.html">https://pytorch.org/docs/stable/jit.html</a>. Setting <code class="docutils literal notranslate"><span class="pre">quant_nn.TensorQuantizer.use_fb_fake_quant</span> <span class="pre">=</span> <span class="pre">True</span></code> enables the QAT model to use <code class="docutils literal notranslate"><span class="pre">torch.fake_quantize_per_tensor_affine</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.fake_quantize_per_channel_affine</span></code> operators instead of <code class="docutils literal notranslate"><span class="pre">tensor_quant</span></code> function to export quantization operators. In torchscript, they
+are represented as <code class="docutils literal notranslate"><span class="pre">aten::fake_quantize_per_tensor_affine</span></code> and <code class="docutils literal notranslate"><span class="pre">aten::fake_quantize_per_channel_affine</span></code>.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>quant_nn.TensorQuantizer.use_fb_fake_quant = True
+with torch.no_grad():
+    data = iter(testing_dataloader)
+    images, _ = data.next()
+    jit_model = torch.jit.trace(qat_model, images.to(&quot;cuda&quot;))
+    torch.jit.save(jit_model, &quot;trained_vgg16_qat.jit.pt&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+E1109 04:02:37.101168 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.102248 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.107194 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.107625 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.115269 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.115740 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.117969 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.118358 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.126382 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.126834 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.128674 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.129518 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.135453 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.135936 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.137858 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.138366 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.145539 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.146053 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.147871 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.148353 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.154252 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.154685 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.156558 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.157159 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.163197 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.163676 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.165549 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.165991 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.173305 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.173926 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.176034 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.176697 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.182843 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.183426 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.185377 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.185962 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.191966 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.192424 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.194325 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.194817 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.201988 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.202665 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.204763 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.205461 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.211393 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.211987 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.213899 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.214450 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.220892 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.221533 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.223519 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.224037 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.233809 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.234434 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.238212 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.239042 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.241022 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.241654 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.247820 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.248445 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.250366 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.250959 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.257248 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.257854 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.259968 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.260660 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+W1109 04:02:37.268160 139704147265344 tensor_quantizer.py:280] Use Pytorch&#39;s native experimental fake quantization.
+/opt/conda/lib/python3.8/site-packages/pytorch_quantization/nn/modules/tensor_quantizer.py:285: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can&#39;t record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+  inputs, amax.item() / bound, 0,
+/opt/conda/lib/python3.8/site-packages/pytorch_quantization/nn/modules/tensor_quantizer.py:291: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can&#39;t record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
+  quant_dim = list(amax.shape).index(list(amax_sequeeze.shape)[0])
+E1109 04:02:37.329273 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.330212 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.332529 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.333365 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.339547 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.340248 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.342257 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.342890 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.350619 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.351372 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.353470 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.354121 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.360090 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.360806 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.362803 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.363274 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.370369 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.371057 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.373071 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.373766 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.379890 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.380538 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.382532 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.383128 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.389077 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.389760 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.391815 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.392399 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.399809 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.400472 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.402399 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.402939 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.408818 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.409424 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.411513 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.412097 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.418537 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.419128 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.421343 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.421946 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.429382 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.430156 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.432259 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.433079 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.439297 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.440027 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.442149 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.442826 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.449377 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.449968 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.452122 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.452754 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.462532 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.463295 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.466963 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.467725 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.469692 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.470336 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.476204 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.476738 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.478809 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.479375 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.485666 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.486219 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.488416 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+E1109 04:02:37.488986 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn&#39;t use scale explicitly!
+</pre></div></div>
+</div>
+<p>## 8. Inference using Torch-TensorRT In this phase, we run the exported torchscript graph of VGG QAT using Torch-TensorRT. Torch-TensorRT is a Pytorch-TensorRT compiler which converts Torchscript graphs into TensorRT. TensorRT 8.0 supports inference of quantization aware trained models and introduces new APIs; <code class="docutils literal notranslate"><span class="pre">QuantizeLayer</span></code> and <code class="docutils literal notranslate"><span class="pre">DequantizeLayer</span></code>. We can observe the entire VGG QAT graph quantization nodes from the debug log of Torch-TensorRT. To enable debug logging, you can set
+<code class="docutils literal notranslate"><span class="pre">torch_tensorrt.logging.set_reportable_log_level(torch_tensorrt.logging.Level.Debug)</span></code>. For example, <code class="docutils literal notranslate"><span class="pre">QuantConv2d</span></code> layer from <code class="docutils literal notranslate"><span class="pre">pytorch_quantization</span></code> toolkit is represented as follows in Torchscript</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%quant_input : Tensor = aten::fake_quantize_per_tensor_affine(%x, %636, %637, %638, %639)
+%quant_weight : Tensor = aten::fake_quantize_per_channel_affine(%394, %640, %641, %637, %638, %639)
+%input.2 : Tensor = aten::_convolution(%quant_input, %quant_weight, %395, %687, %688, %689, %643, %690, %642, %643, %643, %644, %644)
+</pre></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">aten::fake_quantize_per_*_affine</span></code> is converted into <code class="docutils literal notranslate"><span class="pre">QuantizeLayer</span></code> + <code class="docutils literal notranslate"><span class="pre">DequantizeLayer</span></code> in Torch-TensorRT internally. Please refer to quantization op converters in Torch-TensorRT.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>qat_model = torch.jit.load(&quot;trained_vgg16_qat.jit.pt&quot;).eval()
+
+compile_spec = {&quot;inputs&quot;: [torch_tensorrt.Input([16, 3, 32, 32])],
+                &quot;enabled_precisions&quot;: torch.int8,
+                }
+trt_mod = torch_tensorrt.compile(qat_model, **compile_spec)
+
+test_loss, test_acc = test(trt_mod, testing_dataloader, crit, 0)
+print(&quot;VGG QAT accuracy using TensorRT: {:.2f}%&quot;.format(100 * test_acc))
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+WARNING: [Torch-TensorRT] - Cannot infer input type from calcuations in graph for input x.2. Assuming it is Float32. If not, specify input type explicity
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter
+WARNING: [Torch-TensorRT TorchScript Conversion Context] - Detected invalid timing cache, setup a local cache instead
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+VGG QAT accuracy using TensorRT: 82.97%
+</pre></div></div>
+</div>
+<section id="Performance-benchmarking">
+<h3>Performance benchmarking<a class="headerlink" href="#Performance-benchmarking" title="Permalink to this headline">¶</a></h3>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import time
+import numpy as np
+
+import torch.backends.cudnn as cudnn
+cudnn.benchmark = True
+
+# Helper function to benchmark the model
+def benchmark(model, input_shape=(1024, 1, 32, 32), dtype=&#39;fp32&#39;, nwarmup=50, nruns=1000):
+    input_data = torch.randn(input_shape)
+    input_data = input_data.to(&quot;cuda&quot;)
+    if dtype==&#39;fp16&#39;:
+        input_data = input_data.half()
+
+    print(&quot;Warm up ...&quot;)
+    with torch.no_grad():
+        for _ in range(nwarmup):
+            features = model(input_data)
+    torch.cuda.synchronize()
+    print(&quot;Start timing ...&quot;)
+    timings = []
+    with torch.no_grad():
+        for i in range(1, nruns+1):
+            start_time = time.time()
+            output = model(input_data)
+            torch.cuda.synchronize()
+            end_time = time.time()
+            timings.append(end_time - start_time)
+            if i%100==0:
+                print(&#39;Iteration %d/%d, avg batch time %.2f ms&#39;%(i, nruns, np.mean(timings)*1000))
+
+    print(&quot;Input shape:&quot;, input_data.size())
+    print(&quot;Output shape:&quot;, output.shape)
+    print(&#39;Average batch time: %.2f ms&#39;%(np.mean(timings)*1000))
+<br/></pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>benchmark(jit_model, input_shape=(16, 3, 32, 32))
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 100/1000, avg batch time 4.83 ms
+Iteration 200/1000, avg batch time 4.83 ms
+Iteration 300/1000, avg batch time 4.83 ms
+Iteration 400/1000, avg batch time 4.83 ms
+Iteration 500/1000, avg batch time 4.83 ms
+Iteration 600/1000, avg batch time 4.83 ms
+Iteration 700/1000, avg batch time 4.83 ms
+Iteration 800/1000, avg batch time 4.83 ms
+Iteration 900/1000, avg batch time 4.83 ms
+Iteration 1000/1000, avg batch time 4.83 ms
+Input shape: torch.Size([16, 3, 32, 32])
+Output shape: torch.Size([16, 10])
+Average batch time: 4.83 ms
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>benchmark(trt_mod, input_shape=(16, 3, 32, 32))
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Warm up ...
+Start timing ...
+Iteration 100/1000, avg batch time 1.87 ms
+Iteration 200/1000, avg batch time 1.84 ms
+Iteration 300/1000, avg batch time 1.85 ms
+Iteration 400/1000, avg batch time 1.83 ms
+Iteration 500/1000, avg batch time 1.82 ms
+Iteration 600/1000, avg batch time 1.81 ms
+Iteration 700/1000, avg batch time 1.81 ms
+Iteration 800/1000, avg batch time 1.80 ms
+Iteration 900/1000, avg batch time 1.80 ms
+Iteration 1000/1000, avg batch time 1.79 ms
+Input shape: torch.Size([16, 3, 32, 32])
+Output shape: torch.Size([16, 10])
+Average batch time: 1.79 ms
+</pre></div></div>
+</div>
+<p>## 9. References * Very Deep Convolution Networks for large scale Image Recognition * Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT * QAT workflow for VGG16 * Deploying VGG QAT model in C++ using Torch-TensorRT * Pytorch-quantization toolkit from NVIDIA * Pytorch quantization toolkit userguide * Quantization basics</p>
+</section>
+</section>
+</section>
+
+
+             </article>
+
+            </div>
+            <footer>
+
+
+
+
+    <hr>
+
+
+
+  <div role="contentinfo">
+    <p>
+        &copy; Copyright 2021, NVIDIA Corporation.
+
+    </p>
+  </div>
+
+      <div>
+        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+      </div>
+
+
+</footer>
+
+          </div>
+        </div>
+
+        <div class="pytorch-content-right" id="pytorch-content-right">
+          <div class="pytorch-right-menu" id="pytorch-right-menu">
+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
+              <ul>
+<li><a class="reference internal" href="#">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a><ul>
+<li><a class="reference internal" href="#Overview">Overview</a><ul>
+<li><a class="reference internal" href="#Performance-benchmarking">Performance benchmarking</a></li>
+</ul>
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diff --git a/docs/v1.2.0/_notebooks/vgg-qat.ipynb b/docs/v1.2.0/_notebooks/vgg-qat.ipynb
new file mode 100644
index 0000000000..cca771ad92
--- /dev/null
+++ b/docs/v1.2.0/_notebooks/vgg-qat.ipynb
@@ -0,0 +1,1200 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8033b2eb",
+   "metadata": {},
+   "source": [
+    "# Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69ec3ca7",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "Quantization Aware training (QAT) simulates quantization during training by quantizing weights and activation layers. This will help to reduce the loss in accuracy when we convert the network trained in FP32 to INT8 for faster inference. QAT introduces additional nodes in the graph which will be used to learn the dynamic ranges of weights and activation layers. In this notebook, we illustrate the following steps from training to inference of a QAT model in Torch-TensorRT.\n",
+    "\n",
+    "1. [Requirements](#1)\n",
+    "2. [VGG16 Overview](#2)\n",
+    "3. [Training a baseline VGG16 model](#3)\n",
+    "4. [Apply Quantization](#4)\n",
+    "5. [Model calibration](#5)\n",
+    "6. [Quantization Aware training](#6)\n",
+    "7. [Export to Torchscript](#7)\n",
+    "8. [Inference using Torch-TensorRT](#8)\n",
+    "8. [References](#8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79655ea8",
+   "metadata": {},
+   "source": [
+    "<a id=\"1\"></a>\n",
+    "##  1. Requirements\n",
+    "Please install the <a href=\"https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/int8/training/vgg16#prequisites\">required dependencies</a> and import these libraries accordingly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14a72941",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6493e915",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch.optim as optim\n",
+    "import torch.utils.data as data\n",
+    "import torchvision.transforms as transforms\n",
+    "import torchvision.datasets as datasets\n",
+    "import torch_tensorrt\n",
+    "\n",
+    "from torch.utils.tensorboard import SummaryWriter\n",
+    "\n",
+    "import pytorch_quantization\n",
+    "from pytorch_quantization import nn as quant_nn\n",
+    "from pytorch_quantization import quant_modules\n",
+    "from pytorch_quantization.tensor_quant import QuantDescriptor\n",
+    "from pytorch_quantization import calib\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "print(pytorch_quantization.__version__)\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "sys.path.insert(0, \"../examples/int8/training/vgg16\")\n",
+    "from vgg16 import vgg16\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4de5060a",
+   "metadata": {},
+   "source": [
+    "<a id=\"2\"></a>\n",
+    "##  2. VGG16 Overview\n",
+    "### Very Deep Convolutional Networks for Large-Scale Image Recognition\n",
+    "VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows\n",
+    "<img src=\"https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png\">\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a5afc49",
+   "metadata": {},
+   "source": [
+    "<a id=\"3\"></a>\n",
+    "##  3. Training a baseline VGG16 model\n",
+    "We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your `data` directory. Data preprocessing is performed using `torchvision` transforms. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5d2c4c45",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Files already downloaded and verified\n",
+      "Files already downloaded and verified\n"
+     ]
+    }
+   ],
+   "source": [
+    "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
+    "\n",
+    "# ========== Define Training dataset and dataloaders =============#\n",
+    "training_dataset = datasets.CIFAR10(root='./data',\n",
+    "                                        train=True,\n",
+    "                                        download=True,\n",
+    "                                        transform=transforms.Compose([\n",
+    "                                            transforms.RandomCrop(32, padding=4),\n",
+    "                                            transforms.RandomHorizontalFlip(),\n",
+    "                                            transforms.ToTensor(),\n",
+    "                                            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
+    "                                        ]))\n",
+    "\n",
+    "training_dataloader = torch.utils.data.DataLoader(training_dataset,\n",
+    "                                                      batch_size=32,\n",
+    "                                                      shuffle=True,\n",
+    "                                                      num_workers=2)\n",
+    "\n",
+    "# ========== Define Testing dataset and dataloaders =============#\n",
+    "testing_dataset = datasets.CIFAR10(root='./data',\n",
+    "                                   train=False,\n",
+    "                                   download=True,\n",
+    "                                   transform=transforms.Compose([\n",
+    "                                       transforms.ToTensor(),\n",
+    "                                       transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
+    "                                   ]))\n",
+    "\n",
+    "testing_dataloader = torch.utils.data.DataLoader(testing_dataset,\n",
+    "                                                 batch_size=16,\n",
+    "                                                 shuffle=False,\n",
+    "                                                 num_workers=2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2bd092b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(model, dataloader, crit, opt, epoch):\n",
+    "#     global writer\n",
+    "    model.train()\n",
+    "    running_loss = 0.0\n",
+    "    for batch, (data, labels) in enumerate(dataloader):\n",
+    "        data, labels = data.cuda(), labels.cuda(non_blocking=True)\n",
+    "        opt.zero_grad()\n",
+    "        out = model(data)\n",
+    "        loss = crit(out, labels)\n",
+    "        loss.backward()\n",
+    "        opt.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "        if batch % 500 == 499:\n",
+    "            print(\"Batch: [%5d | %5d] loss: %.3f\" % (batch + 1, len(dataloader), running_loss / 100))\n",
+    "            running_loss = 0.0\n",
+    "        \n",
+    "def test(model, dataloader, crit, epoch):\n",
+    "    global writer\n",
+    "    global classes\n",
+    "    total = 0\n",
+    "    correct = 0\n",
+    "    loss = 0.0\n",
+    "    class_probs = []\n",
+    "    class_preds = []\n",
+    "    model.eval()\n",
+    "    with torch.no_grad():\n",
+    "        for data, labels in dataloader:\n",
+    "            data, labels = data.cuda(), labels.cuda(non_blocking=True)\n",
+    "            out = model(data)\n",
+    "            loss += crit(out, labels)\n",
+    "            preds = torch.max(out, 1)[1]\n",
+    "            class_probs.append([F.softmax(i, dim=0) for i in out])\n",
+    "            class_preds.append(preds)\n",
+    "            total += labels.size(0)\n",
+    "            correct += (preds == labels).sum().item()\n",
+    "\n",
+    "    test_probs = torch.cat([torch.stack(batch) for batch in class_probs])\n",
+    "    test_preds = torch.cat(class_preds)\n",
+    "\n",
+    "    return loss / total, correct / total\n",
+    "\n",
+    "def save_checkpoint(state, ckpt_path=\"checkpoint.pth\"):\n",
+    "    torch.save(state, ckpt_path)\n",
+    "    print(\"Checkpoint saved\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c80a86cc",
+   "metadata": {},
+   "source": [
+    "*Define the VGG model that we are going to perfom QAT on.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8c564b8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CIFAR 10 has 10 classes\n",
+    "model = vgg16(num_classes=len(classes), init_weights=False)\n",
+    "model = model.cuda()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "abc00452",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Declare Learning rate\n",
+    "lr = 0.1\n",
+    "state = {}\n",
+    "state[\"lr\"] = lr\n",
+    "\n",
+    "# Use cross entropy loss for classification and SGD optimizer\n",
+    "crit = nn.CrossEntropyLoss()\n",
+    "opt = optim.SGD(model.parameters(), lr=state[\"lr\"], momentum=0.9, weight_decay=1e-4)\n",
+    "\n",
+    "\n",
+    "# Adjust learning rate based on epoch number\n",
+    "def adjust_lr(optimizer, epoch):\n",
+    "    global state\n",
+    "    new_lr = lr * (0.5**(epoch // 12)) if state[\"lr\"] > 1e-7 else state[\"lr\"]\n",
+    "    if new_lr != state[\"lr\"]:\n",
+    "        state[\"lr\"] = new_lr\n",
+    "        print(\"Updating learning rate: {}\".format(state[\"lr\"]))\n",
+    "        for param_group in optimizer.param_groups:\n",
+    "            param_group[\"lr\"] = state[\"lr\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d80865a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: [    1 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 13.288\n",
+      "Batch: [ 1000 |  1563] loss: 11.345\n",
+      "Batch: [ 1500 |  1563] loss: 11.008\n",
+      "Test Loss: 0.13388 Test Acc: 13.23%\n",
+      "Epoch: [    2 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 10.742\n",
+      "Batch: [ 1000 |  1563] loss: 10.311\n",
+      "Batch: [ 1500 |  1563] loss: 10.141\n",
+      "Test Loss: 0.11888 Test Acc: 23.96%\n",
+      "Epoch: [    3 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 9.877\n",
+      "Batch: [ 1000 |  1563] loss: 9.821\n",
+      "Batch: [ 1500 |  1563] loss: 9.818\n",
+      "Test Loss: 0.11879 Test Acc: 24.68%\n",
+      "Epoch: [    4 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 9.677\n",
+      "Batch: [ 1000 |  1563] loss: 9.613\n",
+      "Batch: [ 1500 |  1563] loss: 9.504\n",
+      "Test Loss: 0.11499 Test Acc: 23.68%\n",
+      "Epoch: [    5 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 9.560\n",
+      "Batch: [ 1000 |  1563] loss: 9.536\n",
+      "Batch: [ 1500 |  1563] loss: 9.309\n",
+      "Test Loss: 0.10990 Test Acc: 27.84%\n",
+      "Epoch: [    6 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 9.254\n",
+      "Batch: [ 1000 |  1563] loss: 9.234\n",
+      "Batch: [ 1500 |  1563] loss: 9.188\n",
+      "Test Loss: 0.11594 Test Acc: 23.29%\n",
+      "Epoch: [    7 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 9.141\n",
+      "Batch: [ 1000 |  1563] loss: 9.110\n",
+      "Batch: [ 1500 |  1563] loss: 9.013\n",
+      "Test Loss: 0.10732 Test Acc: 29.24%\n",
+      "Epoch: [    8 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 9.120\n",
+      "Batch: [ 1000 |  1563] loss: 9.086\n",
+      "Batch: [ 1500 |  1563] loss: 8.948\n",
+      "Test Loss: 0.10732 Test Acc: 27.24%\n",
+      "Epoch: [    9 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 8.941\n",
+      "Batch: [ 1000 |  1563] loss: 8.997\n",
+      "Batch: [ 1500 |  1563] loss: 9.028\n",
+      "Test Loss: 0.11299 Test Acc: 25.52%\n",
+      "Epoch: [   10 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 8.927\n",
+      "Batch: [ 1000 |  1563] loss: 8.837\n",
+      "Batch: [ 1500 |  1563] loss: 8.860\n",
+      "Test Loss: 0.10130 Test Acc: 34.61%\n",
+      "Epoch: [   11 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 8.953\n",
+      "Batch: [ 1000 |  1563] loss: 8.738\n",
+      "Batch: [ 1500 |  1563] loss: 8.724\n",
+      "Test Loss: 0.10018 Test Acc: 32.27%\n",
+      "Epoch: [   12 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 8.721\n",
+      "Batch: [ 1000 |  1563] loss: 8.716\n",
+      "Batch: [ 1500 |  1563] loss: 8.701\n",
+      "Test Loss: 0.10070 Test Acc: 29.57%\n",
+      "Updating learning rate: 0.05\n",
+      "Epoch: [   13 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 7.944\n",
+      "Batch: [ 1000 |  1563] loss: 7.649\n",
+      "Batch: [ 1500 |  1563] loss: 7.511\n",
+      "Test Loss: 0.08555 Test Acc: 44.62%\n",
+      "Epoch: [   14 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 7.057\n",
+      "Batch: [ 1000 |  1563] loss: 6.944\n",
+      "Batch: [ 1500 |  1563] loss: 6.687\n",
+      "Test Loss: 0.08331 Test Acc: 52.27%\n",
+      "Epoch: [   15 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 6.470\n",
+      "Batch: [ 1000 |  1563] loss: 6.439\n",
+      "Batch: [ 1500 |  1563] loss: 6.126\n",
+      "Test Loss: 0.07266 Test Acc: 58.02%\n",
+      "Epoch: [   16 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 5.834\n",
+      "Batch: [ 1000 |  1563] loss: 5.801\n",
+      "Batch: [ 1500 |  1563] loss: 5.622\n",
+      "Test Loss: 0.06340 Test Acc: 65.17%\n",
+      "Epoch: [   17 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 5.459\n",
+      "Batch: [ 1000 |  1563] loss: 5.442\n",
+      "Batch: [ 1500 |  1563] loss: 5.314\n",
+      "Test Loss: 0.05945 Test Acc: 67.22%\n",
+      "Epoch: [   18 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 5.071\n",
+      "Batch: [ 1000 |  1563] loss: 5.145\n",
+      "Batch: [ 1500 |  1563] loss: 5.063\n",
+      "Test Loss: 0.06567 Test Acc: 64.46%\n",
+      "Epoch: [   19 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 4.796\n",
+      "Batch: [ 1000 |  1563] loss: 4.781\n",
+      "Batch: [ 1500 |  1563] loss: 4.732\n",
+      "Test Loss: 0.05374 Test Acc: 71.87%\n",
+      "Epoch: [   20 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 4.568\n",
+      "Batch: [ 1000 |  1563] loss: 4.564\n",
+      "Batch: [ 1500 |  1563] loss: 4.484\n",
+      "Test Loss: 0.05311 Test Acc: 71.12%\n",
+      "Epoch: [   21 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 4.385\n",
+      "Batch: [ 1000 |  1563] loss: 4.302\n",
+      "Batch: [ 1500 |  1563] loss: 4.285\n",
+      "Test Loss: 0.05080 Test Acc: 74.29%\n",
+      "Epoch: [   22 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 4.069\n",
+      "Batch: [ 1000 |  1563] loss: 4.105\n",
+      "Batch: [ 1500 |  1563] loss: 4.096\n",
+      "Test Loss: 0.04807 Test Acc: 75.20%\n",
+      "Epoch: [   23 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 3.959\n",
+      "Batch: [ 1000 |  1563] loss: 3.898\n",
+      "Batch: [ 1500 |  1563] loss: 3.916\n",
+      "Test Loss: 0.04743 Test Acc: 75.81%\n",
+      "Epoch: [   24 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 3.738\n",
+      "Batch: [ 1000 |  1563] loss: 3.847\n",
+      "Batch: [ 1500 |  1563] loss: 3.797\n",
+      "Test Loss: 0.04609 Test Acc: 76.42%\n",
+      "Updating learning rate: 0.025\n",
+      "Epoch: [   25 /    25] LR: 0.025000\n",
+      "Batch: [  500 |  1563] loss: 2.952\n",
+      "Batch: [ 1000 |  1563] loss: 2.906\n",
+      "Batch: [ 1500 |  1563] loss: 2.735\n",
+      "Test Loss: 0.03466 Test Acc: 82.00%\n",
+      "Checkpoint saved\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the model for 25 epochs to get ~80% accuracy.\n",
+    "num_epochs=25\n",
+    "for epoch in range(num_epochs):\n",
+    "    adjust_lr(opt, epoch)\n",
+    "    print('Epoch: [%5d / %5d] LR: %f' % (epoch + 1, num_epochs, state[\"lr\"]))\n",
+    "\n",
+    "    train(model, training_dataloader, crit, opt, epoch)\n",
+    "    test_loss, test_acc = test(model, testing_dataloader, crit, epoch)\n",
+    "\n",
+    "    print(\"Test Loss: {:.5f} Test Acc: {:.2f}%\".format(test_loss, 100 * test_acc))\n",
+    "    \n",
+    "save_checkpoint({'epoch': epoch + 1,\n",
+    "                 'model_state_dict': model.state_dict(),\n",
+    "                 'acc': test_acc,\n",
+    "                 'opt_state_dict': opt.state_dict(),\n",
+    "                 'state': state},\n",
+    "                ckpt_path=\"vgg16_base_ckpt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1044537",
+   "metadata": {},
+   "source": [
+    "<a id=\"4\"></a>\n",
+    "##  4. Apply Quantization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c33b7f4e",
+   "metadata": {},
+   "source": [
+    "`quant_modules.initialize()` will ensure quantized version of modules will be called instead of original modules. For example, when you define a model with convolution, linear, pooling layers, `QuantConv2d`, `QuantLinear` and `QuantPooling` will be called. `QuantConv2d` basically wraps quantizer nodes around inputs and weights of regular `Conv2d`. Please refer to all the <a href=\"https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization/pytorch_quantization/nn/modules\">quantized modules</a> in pytorch-quantization toolkit for more information. A `QuantConv2d` is represented in `pytorch-quantization` toolkit as follows.\n",
+    "\n",
+    "```\n",
+    "def forward(self, input):\n",
+    "        # the actual quantization happens in the next level of the class hierarchy\n",
+    "        quant_input, quant_weight = self._quant(input)\n",
+    "\n",
+    "        if self.padding_mode == 'circular':\n",
+    "            expanded_padding = ((self.padding[1] + 1) // 2, self.padding[1] // 2,\n",
+    "                                (self.padding[0] + 1) // 2, self.padding[0] // 2)\n",
+    "            output = F.conv2d(F.pad(quant_input, expanded_padding, mode='circular'),\n",
+    "                              quant_weight, self.bias, self.stride,\n",
+    "                              _pair(0), self.dilation, self.groups)\n",
+    "        else:\n",
+    "            output = F.conv2d(quant_input, quant_weight, self.bias, self.stride, self.padding, self.dilation,\n",
+    "                              self.groups)\n",
+    "\n",
+    "        return output\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "985dc59e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quant_modules.initialize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "164ce8cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# All the regular conv, FC layers will be converted to their quantozed counterparts due to quant_modules.initialize()\n",
+    "qat_model = vgg16(num_classes=len(classes), init_weights=False)\n",
+    "qat_model = qat_model.cuda()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8e5f7fb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# vgg16_base_ckpt is the checkpoint generated from Step 3 : Training a baseline VGG16 model.\n",
+    "ckpt = torch.load(\"./vgg16_base_ckpt\")\n",
+    "modified_state_dict={}\n",
+    "for key, val in ckpt[\"model_state_dict\"].items():\n",
+    "    # Remove 'module.' from the key names\n",
+    "    if key.startswith('module'):\n",
+    "        modified_state_dict[key[7:]] = val\n",
+    "    else:\n",
+    "        modified_state_dict[key] = val\n",
+    "\n",
+    "# Load the pre-trained checkpoint\n",
+    "qat_model.load_state_dict(modified_state_dict)\n",
+    "opt.load_state_dict(ckpt[\"opt_state_dict\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f8a74e8",
+   "metadata": {},
+   "source": [
+    "<a id=\"5\"></a>\n",
+    "##  5. Model Calibration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2a321f9",
+   "metadata": {},
+   "source": [
+    "The quantizer nodes introduced in the model around desired layers capture the dynamic range (min_value, max_value) that is observed by the layer. Calibration is the process of computing the dynamic range of these layers by passing calibration data, which is usually a subset of training or validation data. There are different ways of calibration: `max`, `histogram` and `entropy`. We use `max` calibration technique as it is simple and effective. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "039423dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_amax(model, **kwargs):\n",
+    "    # Load calib result\n",
+    "    for name, module in model.named_modules():\n",
+    "        if isinstance(module, quant_nn.TensorQuantizer):\n",
+    "            if module._calibrator is not None:\n",
+    "                if isinstance(module._calibrator, calib.MaxCalibrator):\n",
+    "                    module.load_calib_amax()\n",
+    "                else:\n",
+    "                    module.load_calib_amax(**kwargs)\n",
+    "            print(F\"{name:40}: {module}\")\n",
+    "    model.cuda()\n",
+    "\n",
+    "def collect_stats(model, data_loader, num_batches):\n",
+    "    \"\"\"Feed data to the network and collect statistics\"\"\"\n",
+    "    # Enable calibrators\n",
+    "    for name, module in model.named_modules():\n",
+    "        if isinstance(module, quant_nn.TensorQuantizer):\n",
+    "            if module._calibrator is not None:\n",
+    "                module.disable_quant()\n",
+    "                module.enable_calib()\n",
+    "            else:\n",
+    "                module.disable()\n",
+    "\n",
+    "    # Feed data to the network for collecting stats\n",
+    "    for i, (image, _) in tqdm(enumerate(data_loader), total=num_batches):\n",
+    "        model(image.cuda())\n",
+    "        if i >= num_batches:\n",
+    "            break\n",
+    "\n",
+    "    # Disable calibrators\n",
+    "    for name, module in model.named_modules():\n",
+    "        if isinstance(module, quant_nn.TensorQuantizer):\n",
+    "            if module._calibrator is not None:\n",
+    "                module.enable_quant()\n",
+    "                module.disable_calib()\n",
+    "            else:\n",
+    "                module.enable()\n",
+    "\n",
+    "def calibrate_model(model, model_name, data_loader, num_calib_batch, calibrator, hist_percentile, out_dir):\n",
+    "    \"\"\"\n",
+    "        Feed data to the network and calibrate.\n",
+    "        Arguments:\n",
+    "            model: classification model\n",
+    "            model_name: name to use when creating state files\n",
+    "            data_loader: calibration data set\n",
+    "            num_calib_batch: amount of calibration passes to perform\n",
+    "            calibrator: type of calibration to use (max/histogram)\n",
+    "            hist_percentile: percentiles to be used for historgram calibration\n",
+    "            out_dir: dir to save state files in\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if num_calib_batch > 0:\n",
+    "        print(\"Calibrating model\")\n",
+    "        with torch.no_grad():\n",
+    "            collect_stats(model, data_loader, num_calib_batch)\n",
+    "\n",
+    "        if not calibrator == \"histogram\":\n",
+    "            compute_amax(model, method=\"max\")\n",
+    "            calib_output = os.path.join(\n",
+    "                out_dir,\n",
+    "                F\"{model_name}-max-{num_calib_batch*data_loader.batch_size}.pth\")\n",
+    "            torch.save(model.state_dict(), calib_output)\n",
+    "        else:\n",
+    "            for percentile in hist_percentile:\n",
+    "                print(F\"{percentile} percentile calibration\")\n",
+    "                compute_amax(model, method=\"percentile\")\n",
+    "                calib_output = os.path.join(\n",
+    "                    out_dir,\n",
+    "                    F\"{model_name}-percentile-{percentile}-{num_calib_batch*data_loader.batch_size}.pth\")\n",
+    "                torch.save(model.state_dict(), calib_output)\n",
+    "\n",
+    "            for method in [\"mse\", \"entropy\"]:\n",
+    "                print(F\"{method} calibration\")\n",
+    "                compute_amax(model, method=method)\n",
+    "                calib_output = os.path.join(\n",
+    "                    out_dir,\n",
+    "                    F\"{model_name}-{method}-{num_calib_batch*data_loader.batch_size}.pth\")\n",
+    "                torch.save(model.state_dict(), calib_output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "78504a6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calibrating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████| 32/32 [00:00<00:00, 96.04it/s]\n",
+      "WARNING: Logging before flag parsing goes to stderr.\n",
+      "W1109 04:01:43.512364 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.513354 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.514046 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.514638 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.515270 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.515859 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.516441 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.517009 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.517600 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.518167 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.518752 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.519333 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.519911 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.520473 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.521038 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.521596 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.522170 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.522742 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.523360 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.523957 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.524581 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.525059 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.525366 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.525675 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.525962 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.526257 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.526566 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.526885 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.527188 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.527489 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.527792 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.528097 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.528387 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.528834 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.529163 139704147265344 tensor_quantizer.py:238] Call .cuda() if running on GPU after loading calibrated amax.\n",
+      "W1109 04:01:43.532748 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([64, 1, 1, 1]).\n",
+      "W1109 04:01:43.533468 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.534033 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([64, 1, 1, 1]).\n",
+      "W1109 04:01:43.534684 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.535320 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([128, 1, 1, 1]).\n",
+      "W1109 04:01:43.535983 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.536569 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([128, 1, 1, 1]).\n",
+      "W1109 04:01:43.537248 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.537833 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([256, 1, 1, 1]).\n",
+      "W1109 04:01:43.538480 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.539074 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([256, 1, 1, 1]).\n",
+      "W1109 04:01:43.539724 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.540307 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([256, 1, 1, 1]).\n",
+      "W1109 04:01:43.540952 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.541534 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).\n",
+      "W1109 04:01:43.542075 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.542596 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).\n",
+      "W1109 04:01:43.543248 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.543719 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).\n",
+      "W1109 04:01:43.544424 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.544952 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).\n",
+      "W1109 04:01:43.545530 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.546114 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).\n",
+      "W1109 04:01:43.546713 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.547292 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).\n",
+      "W1109 04:01:43.547902 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.548453 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.549015 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([4096, 1]).\n",
+      "W1109 04:01:43.549665 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.550436 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([4096, 1]).\n",
+      "W1109 04:01:43.551925 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.553105 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([10, 1]).\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "features.0._input_quantizer             : TensorQuantizer(8bit narrow fake per-tensor amax=2.7537 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.0._weight_quantizer            : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0263, 2.7454](64) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.3._input_quantizer             : TensorQuantizer(8bit narrow fake per-tensor amax=27.5676 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.3._weight_quantizer            : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0169, 1.8204](64) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.7._input_quantizer             : TensorQuantizer(8bit narrow fake per-tensor amax=15.2002 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.7._weight_quantizer            : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0493, 1.3207](128) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.10._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=7.7376 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.10._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0163, 0.9624](128) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.14._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=8.8351 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.14._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0622, 0.8791](256) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.17._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=12.5746 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.17._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0505, 0.5117](256) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.20._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=9.7203 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.20._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0296, 0.5335](256) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.24._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=8.9367 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.24._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0220, 0.3763](512) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.27._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=6.6539 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.27._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0151, 0.1777](512) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.30._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=3.7099 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.30._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0087, 0.1906](512) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.34._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=4.0491 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.34._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0106, 0.1971](512) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.37._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=2.1531 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.37._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0070, 0.2305](512) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.40._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=3.3631 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.40._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0023, 0.4726](512) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "avgpool._input_quantizer                : TensorQuantizer(8bit narrow fake per-tensor amax=5.3550 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "classifier.0._input_quantizer           : TensorQuantizer(8bit narrow fake per-tensor amax=5.3550 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "classifier.0._weight_quantizer          : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0026, 0.5320](4096) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "classifier.3._input_quantizer           : TensorQuantizer(8bit narrow fake per-tensor amax=6.6733 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "classifier.3._weight_quantizer          : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0018, 0.5172](4096) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "classifier.6._input_quantizer           : TensorQuantizer(8bit narrow fake per-tensor amax=9.4352 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "classifier.6._weight_quantizer          : TensorQuantizer(8bit narrow fake axis=0 amax=[0.3877, 0.5620](10) calibrator=MaxCalibrator scale=1.0 quant)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calibrate the model using max calibration technique.\n",
+    "with torch.no_grad():\n",
+    "    calibrate_model(\n",
+    "        model=qat_model,\n",
+    "        model_name=\"vgg16\",\n",
+    "        data_loader=training_dataloader,\n",
+    "        num_calib_batch=32,\n",
+    "        calibrator=\"max\",\n",
+    "        hist_percentile=[99.9, 99.99, 99.999, 99.9999],\n",
+    "        out_dir=\"./\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1aa0c109",
+   "metadata": {},
+   "source": [
+    "<a id=\"6\"></a>\n",
+    "##  6. Quantization Aware Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fe8ec11",
+   "metadata": {},
+   "source": [
+    "In this phase, we finetune the model weights and leave the quantizer node values frozen. The dynamic ranges for each layer obtained from the calibration are kept constant while the weights of the model are finetuned to be close to the accuracy of original FP32 model (model without quantizer nodes) is preserved. Usually the finetuning of QAT model should be quick compared to the full training of the original model. Use QAT to fine-tune for around 10% of the original training schedule with an annealing learning-rate. Please refer to <a href=\"https://developer.nvidia.com/blog/achieving-fp32-accuracy-for-int8-inference-using-quantization-aware-training-with-tensorrt/\">Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT</a> for detailed recommendations. For this VGG model, it is enough to finetune for 1 epoch to get acceptable accuracy. \n",
+    "During finetuning with QAT, the quantization is applied as a composition of `max`, `clamp`, `round` and `mul` ops. \n",
+    "```\n",
+    "# amax is absolute maximum value for an input\n",
+    "# The upper bound for integer quantization (127 for int8)\n",
+    "max_bound = torch.tensor((2.0**(num_bits - 1 + int(unsigned))) - 1.0, device=amax.device)\n",
+    "scale = max_bound / amax\n",
+    "outputs = torch.clamp((inputs * scale).round_(), min_bound, max_bound)\n",
+    "```\n",
+    "<a href=\"https://github.com/NVIDIA/TensorRT/blob/8.0.1/tools/pytorch-quantization/pytorch_quantization/tensor_quant.py\">tensor_quant function</a> in `pytorch_quantization` toolkit is responsible for the above tensor quantization. Usually, per channel quantization is recommended for weights, while per tensor quantization is recommended for activations in a network.\n",
+    "During inference, we use `torch.fake_quantize_per_tensor_affine` and `torch.fake_quantize_per_channel_affine` to perform quantization as this is easier to convert into corresponding TensorRT operators. Please refer to next sections for more details on how these operators are exported in torchscript and converted in Torch-TensorRT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1f28d228",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Updating learning rate: 0.1\n",
+      "Epoch: [    1 /     1] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 2.635\n",
+      "Batch: [ 1000 |  1563] loss: 2.655\n",
+      "Batch: [ 1500 |  1563] loss: 2.646\n",
+      "Test Loss: 0.03291 Test Acc: 82.98%\n",
+      "Checkpoint saved\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Finetune the QAT model for 1 epoch\n",
+    "num_epochs=1\n",
+    "for epoch in range(num_epochs):\n",
+    "    adjust_lr(opt, epoch)\n",
+    "    print('Epoch: [%5d / %5d] LR: %f' % (epoch + 1, num_epochs, state[\"lr\"]))\n",
+    "\n",
+    "    train(qat_model, training_dataloader, crit, opt, epoch)\n",
+    "    test_loss, test_acc = test(qat_model, testing_dataloader, crit, epoch)\n",
+    "\n",
+    "    print(\"Test Loss: {:.5f} Test Acc: {:.2f}%\".format(test_loss, 100 * test_acc))\n",
+    "    \n",
+    "save_checkpoint({'epoch': epoch + 1,\n",
+    "                 'model_state_dict': qat_model.state_dict(),\n",
+    "                 'acc': test_acc,\n",
+    "                 'opt_state_dict': opt.state_dict(),\n",
+    "                 'state': state},\n",
+    "                ckpt_path=\"vgg16_qat_ckpt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a4dcaa2",
+   "metadata": {},
+   "source": [
+    "<a id=\"7\"></a>\n",
+    "##  7. Export to Torchscript\n",
+    "Export the model to Torch script. Trace the model and convert it into torchscript for deployment. To learn more about Torchscript, please refer to https://pytorch.org/docs/stable/jit.html. Setting `quant_nn.TensorQuantizer.use_fb_fake_quant = True` enables the QAT model to use `torch.fake_quantize_per_tensor_affine` and `torch.fake_quantize_per_channel_affine` operators instead of `tensor_quant` function to export quantization operators. In torchscript, they are represented as `aten::fake_quantize_per_tensor_affine` and `aten::fake_quantize_per_channel_affine`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3d34f526",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E1109 04:02:37.101168 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.102248 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
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+      "E1109 04:02:37.247820 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.248445 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.250366 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.250959 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.257248 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.257854 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.259968 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.260660 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "W1109 04:02:37.268160 139704147265344 tensor_quantizer.py:280] Use Pytorch's native experimental fake quantization.\n",
+      "/opt/conda/lib/python3.8/site-packages/pytorch_quantization/nn/modules/tensor_quantizer.py:285: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+      "  inputs, amax.item() / bound, 0,\n",
+      "/opt/conda/lib/python3.8/site-packages/pytorch_quantization/nn/modules/tensor_quantizer.py:291: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+      "  quant_dim = list(amax.shape).index(list(amax_sequeeze.shape)[0])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E1109 04:02:37.329273 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.330212 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.332529 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.333365 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.339547 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.340248 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.342257 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.342890 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.350619 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.351372 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.353470 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.354121 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.360090 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.360806 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.362803 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.363274 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.370369 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.371057 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.373071 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.373766 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.379890 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.380538 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.382532 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.383128 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.389077 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.389760 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.391815 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.392399 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.399809 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.400472 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.402399 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.402939 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.408818 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.409424 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.411513 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.412097 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.418537 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.419128 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.421343 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.421946 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.429382 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.430156 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.432259 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.433079 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.439297 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.440027 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.442149 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.442826 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.449377 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.449968 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.452122 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.452754 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.462532 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.463295 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.466963 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.467725 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.469692 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.470336 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.476204 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.476738 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.478809 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.479375 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.485666 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.486219 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.488416 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.488986 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n"
+     ]
+    }
+   ],
+   "source": [
+    "quant_nn.TensorQuantizer.use_fb_fake_quant = True\n",
+    "with torch.no_grad():\n",
+    "    data = iter(testing_dataloader)\n",
+    "    images, _ = data.next()\n",
+    "    jit_model = torch.jit.trace(qat_model, images.to(\"cuda\"))\n",
+    "    torch.jit.save(jit_model, \"trained_vgg16_qat.jit.pt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7341418a",
+   "metadata": {},
+   "source": [
+    "<a id=\"8\"></a>\n",
+    "##  8. Inference using Torch-TensorRT\n",
+    "In this phase, we run the exported torchscript graph of VGG QAT using Torch-TensorRT. Torch-TensorRT is a Pytorch-TensorRT compiler which converts Torchscript graphs into TensorRT. TensorRT 8.0 supports inference of quantization aware trained models and introduces new APIs; `QuantizeLayer` and `DequantizeLayer`. We can observe the entire VGG QAT graph quantization nodes from the debug log of Torch-TensorRT. To enable debug logging, you can set `torch_tensorrt.logging.set_reportable_log_level(torch_tensorrt.logging.Level.Debug)`. For example, `QuantConv2d` layer from `pytorch_quantization` toolkit is represented as follows in Torchscript\n",
+    "```\n",
+    "%quant_input : Tensor = aten::fake_quantize_per_tensor_affine(%x, %636, %637, %638, %639)\n",
+    "%quant_weight : Tensor = aten::fake_quantize_per_channel_affine(%394, %640, %641, %637, %638, %639)\n",
+    "%input.2 : Tensor = aten::_convolution(%quant_input, %quant_weight, %395, %687, %688, %689, %643, %690, %642, %643, %643, %644, %644)\n",
+    "```\n",
+    "`aten::fake_quantize_per_*_affine` is converted into `QuantizeLayer` + `DequantizeLayer` in Torch-TensorRT internally. Please refer to <a href=\"https://github.com/NVIDIA/Torch-TensorRT/blob/master/core/conversion/converters/impl/quantization.cpp\">quantization op converters</a> in Torch-TensorRT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "aa7495e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - Cannot infer input type from calcuations in graph for input x.2. Assuming it is Float32. If not, specify input type explicity\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT TorchScript Conversion Context] - Detected invalid timing cache, setup a local cache instead\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VGG QAT accuracy using TensorRT: 82.97%\n"
+     ]
+    }
+   ],
+   "source": [
+    "qat_model = torch.jit.load(\"trained_vgg16_qat.jit.pt\").eval()\n",
+    "\n",
+    "compile_spec = {\"inputs\": [torch_tensorrt.Input([16, 3, 32, 32])],\n",
+    "                \"enabled_precisions\": torch.int8,\n",
+    "                }\n",
+    "trt_mod = torch_tensorrt.compile(qat_model, **compile_spec)\n",
+    "\n",
+    "test_loss, test_acc = test(trt_mod, testing_dataloader, crit, 0)\n",
+    "print(\"VGG QAT accuracy using TensorRT: {:.2f}%\".format(100 * test_acc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9df5a90e",
+   "metadata": {},
+   "source": [
+    "### Performance benchmarking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "9eb2cd2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "cudnn.benchmark = True\n",
+    "\n",
+    "# Helper function to benchmark the model\n",
+    "def benchmark(model, input_shape=(1024, 1, 32, 32), dtype='fp32', nwarmup=50, nruns=1000):\n",
+    "    input_data = torch.randn(input_shape)\n",
+    "    input_data = input_data.to(\"cuda\")\n",
+    "    if dtype=='fp16':\n",
+    "        input_data = input_data.half()\n",
+    "        \n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(nwarmup):\n",
+    "            features = model(input_data)\n",
+    "    torch.cuda.synchronize()\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(1, nruns+1):\n",
+    "            start_time = time.time()\n",
+    "            output = model(input_data)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = time.time()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            if i%100==0:\n",
+    "                print('Iteration %d/%d, avg batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
+    "\n",
+    "    print(\"Input shape:\", input_data.size())\n",
+    "    print(\"Output shape:\", output.shape)\n",
+    "    print('Average batch time: %.2f ms'%(np.mean(timings)*1000))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5c2514ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, avg batch time 4.83 ms\n",
+      "Iteration 200/1000, avg batch time 4.83 ms\n",
+      "Iteration 300/1000, avg batch time 4.83 ms\n",
+      "Iteration 400/1000, avg batch time 4.83 ms\n",
+      "Iteration 500/1000, avg batch time 4.83 ms\n",
+      "Iteration 600/1000, avg batch time 4.83 ms\n",
+      "Iteration 700/1000, avg batch time 4.83 ms\n",
+      "Iteration 800/1000, avg batch time 4.83 ms\n",
+      "Iteration 900/1000, avg batch time 4.83 ms\n",
+      "Iteration 1000/1000, avg batch time 4.83 ms\n",
+      "Input shape: torch.Size([16, 3, 32, 32])\n",
+      "Output shape: torch.Size([16, 10])\n",
+      "Average batch time: 4.83 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(jit_model, input_shape=(16, 3, 32, 32))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c5378ed6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, avg batch time 1.87 ms\n",
+      "Iteration 200/1000, avg batch time 1.84 ms\n",
+      "Iteration 300/1000, avg batch time 1.85 ms\n",
+      "Iteration 400/1000, avg batch time 1.83 ms\n",
+      "Iteration 500/1000, avg batch time 1.82 ms\n",
+      "Iteration 600/1000, avg batch time 1.81 ms\n",
+      "Iteration 700/1000, avg batch time 1.81 ms\n",
+      "Iteration 800/1000, avg batch time 1.80 ms\n",
+      "Iteration 900/1000, avg batch time 1.80 ms\n",
+      "Iteration 1000/1000, avg batch time 1.79 ms\n",
+      "Input shape: torch.Size([16, 3, 32, 32])\n",
+      "Output shape: torch.Size([16, 10])\n",
+      "Average batch time: 1.79 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_mod, input_shape=(16, 3, 32, 32))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6a5ec1c",
+   "metadata": {},
+   "source": [
+    "<a id=\"9\"></a>\n",
+    "##  9. References\n",
+    "* <a href=\"https://arxiv.org/pdf/1409.1556.pdf\">Very Deep Convolution Networks for large scale Image Recognition</a>\n",
+    "* <a href=\"https://developer.nvidia.com/blog/achieving-fp32-accuracy-for-int8-inference-using-quantization-aware-training-with-tensorrt/\">Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT</a>\n",
+    "* <a href=\"https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/int8/training/vgg16#quantization-aware-fine-tuning-for-trying-out-qat-workflows\">QAT workflow for VGG16</a>\n",
+    "* <a href=\"https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/int8/qat\">Deploying VGG QAT model in C++ using Torch-TensorRT</a>\n",
+    "* <a href=\"https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization\">Pytorch-quantization toolkit from NVIDIA</a>\n",
+    "* <a href=\"https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/userguide.html\">Pytorch quantization toolkit userguide</a>\n",
+    "* <a href=\"https://arxiv.org/pdf/2004.09602.pdf\">Quantization basics</a>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/v1.2.0/_sources/_cpp_api/class_view_hierarchy.rst.txt b/docs/v1.2.0/_sources/_cpp_api/class_view_hierarchy.rst.txt
new file mode 100644
index 0000000000..a75671077c
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/class_view_hierarchy.rst.txt
@@ -0,0 +1,18 @@
+
+Class Hierarchy
+---------------
+
+
+.. raw:: html
+
+   <ul class="treeView" id="class-treeView">
+     <li>
+       <ul class="collapsibleList">
+         <li class="lastChild">Namespace <a href="namespace_torch_tensorrt.html#namespace-torch-tensorrt">torch_tensorrt</a><ul><li>Namespace <a href="namespace_torch_tensorrt__logging.html#namespace-torch-tensorrt-logging">torch_tensorrt::logging</a><ul><li class="lastChild">Enum <a href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#exhale-enum-logging-8h-1a130f65408ad8cbaee060f05e8db69558">Level</a></li></ul></li><li>Namespace <a href="namespace_torch_tensorrt__ptq.html#namespace-torch-tensorrt-ptq">torch_tensorrt::ptq</a><ul><li>Template Class <a href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#exhale-class-classtorch-tensorrt-1-1ptq-1-1Int8CacheCalibrator">Int8CacheCalibrator</a></li><li class="lastChild">Template Class <a href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#exhale-class-classtorch-tensorrt-1-1ptq-1-1Int8Calibrator">Int8Calibrator</a></li></ul></li><li>Namespace <a href="namespace_torch_tensorrt__torchscript.html#namespace-torch-tensorrt-torchscript">torch_tensorrt::torchscript</a><ul><li class="lastChild">Struct <a href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#exhale-struct-structtorch-tensorrt-1-1torchscript-1-1CompileSpec">CompileSpec</a></li></ul></li><li>Struct <a href="structtorch__tensorrt_1_1Device.html#exhale-struct-structtorch-tensorrt-1-1Device">Device</a><ul><li class="lastChild">Class <a href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#exhale-class-classtorch-tensorrt-1-1Device-1-1DeviceType">Device::DeviceType</a></li></ul></li><li>Struct <a href="structtorch__tensorrt_1_1Input.html#exhale-struct-structtorch-tensorrt-1-1Input">Input</a></li><li>Class <a href="classtorch__tensorrt_1_1DataType.html#exhale-class-classtorch-tensorrt-1-1DataType">DataType</a></li><li>Class <a href="classtorch__tensorrt_1_1TensorFormat.html#exhale-class-classtorch-tensorrt-1-1TensorFormat">TensorFormat</a></li><li class="lastChild">Enum <a href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#exhale-enum-torch-tensorrt-8h-1a3fbe5d72e4fc624dbd038853079620eb">EngineCapability</a></li></ul></li>
+       </ul>
+     </li><!-- only tree view element -->
+   </ul><!-- /treeView class-treeView -->
+
+.. end raw html for treeView
+
+
diff --git a/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1DataType.rst.txt b/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1DataType.rst.txt
new file mode 100644
index 0000000000..6a40d653a7
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1DataType.rst.txt
@@ -0,0 +1,17 @@
+.. _exhale_class_classtorch__tensorrt_1_1DataType:
+
+Class DataType
+==============
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Class Documentation
+-------------------
+
+
+.. doxygenclass:: torch_tensorrt::DataType
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.rst.txt b/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.rst.txt
new file mode 100644
index 0000000000..5479dea0ee
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.rst.txt
@@ -0,0 +1,23 @@
+.. _exhale_class_classtorch__tensorrt_1_1Device_1_1DeviceType:
+
+Class Device::DeviceType
+========================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Nested Relationships
+--------------------
+
+This class is a nested type of :ref:`exhale_struct_structtorch__tensorrt_1_1Device`.
+
+
+Class Documentation
+-------------------
+
+
+.. doxygenclass:: torch_tensorrt::Device::DeviceType
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1TensorFormat.rst.txt b/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1TensorFormat.rst.txt
new file mode 100644
index 0000000000..3f2030e8f2
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1TensorFormat.rst.txt
@@ -0,0 +1,17 @@
+.. _exhale_class_classtorch__tensorrt_1_1TensorFormat:
+
+Class TensorFormat
+==================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Class Documentation
+-------------------
+
+
+.. doxygenclass:: torch_tensorrt::TensorFormat
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.rst.txt b/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.rst.txt
new file mode 100644
index 0000000000..7e76229f19
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.rst.txt
@@ -0,0 +1,26 @@
+.. _exhale_class_classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator:
+
+Template Class Int8CacheCalibrator
+==================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+
+Inheritance Relationships
+-------------------------
+
+Base Type
+*********
+
+- ``private Algorithm``
+
+
+Class Documentation
+-------------------
+
+
+.. doxygenclass:: torch_tensorrt::ptq::Int8CacheCalibrator
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.rst.txt b/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.rst.txt
new file mode 100644
index 0000000000..6ef15f8ff1
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.rst.txt
@@ -0,0 +1,26 @@
+.. _exhale_class_classtorch__tensorrt_1_1ptq_1_1Int8Calibrator:
+
+Template Class Int8Calibrator
+=============================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+
+Inheritance Relationships
+-------------------------
+
+Base Type
+*********
+
+- ``private Algorithm``
+
+
+Class Documentation
+-------------------
+
+
+.. doxygenclass:: torch_tensorrt::ptq::Int8Calibrator
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.rst.txt b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.rst.txt
new file mode 100644
index 0000000000..70809499b3
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1a18d295a837ac71add5578860b55e5502:
+
+Define STR
+==========
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: STR
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.rst.txt b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.rst.txt
new file mode 100644
index 0000000000..10850f77a2
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268:
+
+Define TORCH_TENSORRT_PATCH_VERSION
+===================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: TORCH_TENSORRT_PATCH_VERSION
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.rst.txt b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.rst.txt
new file mode 100644
index 0000000000..9a3d3c8aa8
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e:
+
+Define TORCH_TENSORRT_MAJOR_VERSION
+===================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: TORCH_TENSORRT_MAJOR_VERSION
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.rst.txt b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.rst.txt
new file mode 100644
index 0000000000..0c495bc60c
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827:
+
+Define TORCH_TENSORRT_MINOR_VERSION
+===================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: TORCH_TENSORRT_MINOR_VERSION
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.rst.txt b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.rst.txt
new file mode 100644
index 0000000000..40ad2fd99f
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b:
+
+Define TORCHTRT_API
+===================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: TORCHTRT_API
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.rst.txt b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.rst.txt
new file mode 100644
index 0000000000..75d62d3b65
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da:
+
+Define XSTR
+===========
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: XSTR
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.rst.txt b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.rst.txt
new file mode 100644
index 0000000000..262bd89acd
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1ad19939408f7be171a74a89928b36eb59:
+
+Define TORCHTRT_HIDDEN
+======================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: TORCHTRT_HIDDEN
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.rst.txt b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.rst.txt
new file mode 100644
index 0000000000..ea5d28d205
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883:
+
+Define TORCH_TENSORRT_VERSION
+=============================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: TORCH_TENSORRT_VERSION
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/dir_cpp.rst.txt b/docs/v1.2.0/_sources/_cpp_api/dir_cpp.rst.txt
new file mode 100644
index 0000000000..f09e39c518
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/dir_cpp.rst.txt
@@ -0,0 +1,16 @@
+.. _dir_cpp:
+
+
+Directory cpp
+=============
+
+
+*Directory path:* ``cpp``
+
+Subdirectories
+--------------
+
+- :ref:`dir_cpp_include`
+
+
+
diff --git a/docs/v1.2.0/_sources/_cpp_api/dir_cpp_include.rst.txt b/docs/v1.2.0/_sources/_cpp_api/dir_cpp_include.rst.txt
new file mode 100644
index 0000000000..e262b4a9af
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/dir_cpp_include.rst.txt
@@ -0,0 +1,20 @@
+.. _dir_cpp_include:
+
+
+Directory include
+=================
+
+
+|exhale_lsh| :ref:`Parent directory <dir_cpp>` (``cpp``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+*Directory path:* ``cpp/include``
+
+Subdirectories
+--------------
+
+- :ref:`dir_cpp_include_torch_tensorrt`
+
+
+
diff --git a/docs/v1.2.0/_sources/_cpp_api/dir_cpp_include_torch_tensorrt.rst.txt b/docs/v1.2.0/_sources/_cpp_api/dir_cpp_include_torch_tensorrt.rst.txt
new file mode 100644
index 0000000000..f1da041e12
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/dir_cpp_include_torch_tensorrt.rst.txt
@@ -0,0 +1,23 @@
+.. _dir_cpp_include_torch_tensorrt:
+
+
+Directory torch_tensorrt
+========================
+
+
+|exhale_lsh| :ref:`Parent directory <dir_cpp_include>` (``cpp/include``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+*Directory path:* ``cpp/include/torch_tensorrt``
+
+
+Files
+-----
+
+- :ref:`file_cpp_include_torch_tensorrt_logging.h`
+- :ref:`file_cpp_include_torch_tensorrt_macros.h`
+- :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+- :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
diff --git a/docs/v1.2.0/_sources/_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.rst.txt b/docs/v1.2.0/_sources/_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.rst.txt
new file mode 100644
index 0000000000..6a1c2671ae
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558:
+
+Enum Level
+==========
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Enum Documentation
+------------------
+
+
+.. doxygenenum:: torch_tensorrt::logging::Level
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.rst.txt b/docs/v1.2.0/_sources/_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.rst.txt
new file mode 100644
index 0000000000..a004103bfe
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb:
+
+Enum EngineCapability
+=====================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Enum Documentation
+------------------
+
+
+.. doxygenenum:: torch_tensorrt::EngineCapability
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.rst.txt b/docs/v1.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.rst.txt
new file mode 100644
index 0000000000..ea89d38038
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.rst.txt
@@ -0,0 +1,80 @@
+
+.. _file_cpp_include_torch_tensorrt_logging.h:
+
+File logging.h
+==============
+
+|exhale_lsh| :ref:`Parent directory <dir_cpp_include_torch_tensorrt>` (``cpp/include/torch_tensorrt``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+Definition (``cpp/include/torch_tensorrt/logging.h``)
+-----------------------------------------------------
+
+
+.. toctree::
+   :maxdepth: 1
+
+   program_listing_file_cpp_include_torch_tensorrt_logging.h.rst
+
+
+
+
+
+Includes
+--------
+
+
+- ``string``
+
+- ``torch_tensorrt/macros.h`` (:ref:`file_cpp_include_torch_tensorrt_macros.h`)
+
+
+
+Included By
+-----------
+
+
+- :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+
+
+
+Namespaces
+----------
+
+
+- :ref:`namespace_torch_tensorrt`
+
+- :ref:`namespace_torch_tensorrt__logging`
+
+
+Enums
+-----
+
+
+- :ref:`exhale_enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a`
+
+- :ref:`exhale_function_logging_8h_1a0593f776f469c20469e2f729fc7861a3`
+
+- :ref:`exhale_function_logging_8h_1a0c012cb374addd90eb1f42eaec570650`
+
+- :ref:`exhale_function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8`
+
+- :ref:`exhale_function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5`
+
+- :ref:`exhale_function_logging_8h_1af8f3443813315af7901903d25dd495cc`
+
+- :ref:`exhale_function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2`
+
diff --git a/docs/v1.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.rst.txt b/docs/v1.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.rst.txt
new file mode 100644
index 0000000000..61447e1ada
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.rst.txt
@@ -0,0 +1,71 @@
+
+.. _file_cpp_include_torch_tensorrt_macros.h:
+
+File macros.h
+=============
+
+|exhale_lsh| :ref:`Parent directory <dir_cpp_include_torch_tensorrt>` (``cpp/include/torch_tensorrt``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+Definition (``cpp/include/torch_tensorrt/macros.h``)
+----------------------------------------------------
+
+
+.. toctree::
+   :maxdepth: 1
+
+   program_listing_file_cpp_include_torch_tensorrt_macros.h.rst
+
+
+
+
+
+
+
+Included By
+-----------
+
+
+- :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+- :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+- :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+
+
+Namespaces
+----------
+
+
+- :ref:`namespace_torch_tensorrt`
+
+- :ref:`namespace_torch_tensorrt__torchscript`
+
+
+Defines
+-------
+
+
+- :ref:`exhale_define_macros_8h_1a18d295a837ac71add5578860b55e5502`
+
+- :ref:`exhale_define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e`
+
+- :ref:`exhale_define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827`
+
+- :ref:`exhale_define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268`
+
+- :ref:`exhale_define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883`
+
+- :ref:`exhale_define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b`
+
+- :ref:`exhale_define_macros_8h_1ad19939408f7be171a74a89928b36eb59`
+
+- :ref:`exhale_define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da`
+
diff --git a/docs/v1.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.rst.txt b/docs/v1.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.rst.txt
new file mode 100644
index 0000000000..34b64af6e0
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.rst.txt
@@ -0,0 +1,84 @@
+
+.. _file_cpp_include_torch_tensorrt_ptq.h:
+
+File ptq.h
+==========
+
+|exhale_lsh| :ref:`Parent directory <dir_cpp_include_torch_tensorrt>` (``cpp/include/torch_tensorrt``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+Definition (``cpp/include/torch_tensorrt/ptq.h``)
+-------------------------------------------------
+
+
+.. toctree::
+   :maxdepth: 1
+
+   program_listing_file_cpp_include_torch_tensorrt_ptq.h.rst
+
+
+
+
+
+Includes
+--------
+
+
+- ``NvInfer.h``
+
+- ``fstream``
+
+- ``iostream``
+
+- ``iterator``
+
+- ``memory``
+
+- ``sstream``
+
+- ``string``
+
+- ``torch/torch.h``
+
+- ``torch_tensorrt/logging.h`` (:ref:`file_cpp_include_torch_tensorrt_logging.h`)
+
+- ``torch_tensorrt/macros.h`` (:ref:`file_cpp_include_torch_tensorrt_macros.h`)
+
+- ``vector``
+
+
+
+
+
+
+Namespaces
+----------
+
+
+- :ref:`namespace_torch_tensorrt`
+
+- :ref:`namespace_torch_tensorrt__ptq`
+
+
+Classes
+-------
+
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1ptq_1_1Int8Calibrator`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c`
+
+- :ref:`exhale_function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178`
+
diff --git a/docs/v1.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt b/docs/v1.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
new file mode 100644
index 0000000000..e29afa26a3
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
@@ -0,0 +1,105 @@
+
+.. _file_cpp_include_torch_tensorrt_torch_tensorrt.h:
+
+File torch_tensorrt.h
+=====================
+
+|exhale_lsh| :ref:`Parent directory <dir_cpp_include_torch_tensorrt>` (``cpp/include/torch_tensorrt``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+Definition (``cpp/include/torch_tensorrt/torch_tensorrt.h``)
+------------------------------------------------------------
+
+
+.. toctree::
+   :maxdepth: 1
+
+   program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst
+
+
+
+
+
+Includes
+--------
+
+
+- ``cuda_runtime.h``
+
+- ``iostream``
+
+- ``memory``
+
+- ``set``
+
+- ``string``
+
+- ``torch/custom_class.h``
+
+- ``torch_tensorrt/macros.h`` (:ref:`file_cpp_include_torch_tensorrt_macros.h`)
+
+- ``vector``
+
+
+
+
+
+
+Namespaces
+----------
+
+
+- :ref:`namespace_torch_tensorrt`
+
+- :ref:`namespace_torch_tensorrt__torchscript`
+
+
+Classes
+-------
+
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1Device`
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1GraphInputs`
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1Input`
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1torchscript_1_1CompileSpec`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1DataType`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1Device_1_1DeviceType`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1TensorFormat`
+
+
+Enums
+-----
+
+
+- :ref:`exhale_enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef`
+
diff --git a/docs/v1.2.0/_sources/_cpp_api/file_view_hierarchy.rst.txt b/docs/v1.2.0/_sources/_cpp_api/file_view_hierarchy.rst.txt
new file mode 100644
index 0000000000..c47d5aa483
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/file_view_hierarchy.rst.txt
@@ -0,0 +1,18 @@
+
+File Hierarchy
+--------------
+
+
+.. raw:: html
+
+   <ul class="treeView" id="file-treeView">
+     <li>
+       <ul class="collapsibleList">
+         <li class="lastChild">Directory <a href="dir_cpp.html#dir-cpp">cpp</a><ul><li class="lastChild">Directory <a href="dir_cpp_include.html#dir-cpp-include">include</a><ul><li class="lastChild">Directory <a href="dir_cpp_include_torch_tensorrt.html#dir-cpp-include-torch-tensorrt">tensorrt</a><ul><li>File <a href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging.h">logging.h</a></li><li>File <a href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros.h">macros.h</a></li><li>File <a href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq.h">ptq.h</a></li><li class="lastChild">File <a href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt.h">torch_tensorrt.h</a></li></ul></li></ul></li></ul></li>
+       </ul>
+     </li><!-- only tree view element -->
+   </ul><!-- /treeView file-treeView -->
+
+.. end raw html for treeView
+
+
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.rst.txt
new file mode 100644
index 0000000000..8cab943a9a
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1a0593f776f469c20469e2f729fc7861a3:
+
+Function torch_tensorrt::logging::get_logging_prefix
+====================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::get_logging_prefix()
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.rst.txt
new file mode 100644
index 0000000000..55bb2bde5c
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1a0c012cb374addd90eb1f42eaec570650:
+
+Function torch_tensorrt::logging::get_reportable_log_level
+==========================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::get_reportable_log_level()
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.rst.txt
new file mode 100644
index 0000000000..e8b0ac046d
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a:
+
+Function torch_tensorrt::logging::get_is_colored_output_on
+==========================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::get_is_colored_output_on()
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.rst.txt
new file mode 100644
index 0000000000..95380eb157
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2:
+
+Function torch_tensorrt::logging::set_reportable_log_level
+==========================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::set_reportable_log_level(Level)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.rst.txt
new file mode 100644
index 0000000000..0ca83fe039
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8:
+
+Function torch_tensorrt::logging::log
+=====================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::log(Level, std::string)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.rst.txt
new file mode 100644
index 0000000000..86c2c7d61e
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5:
+
+Function torch_tensorrt::logging::set_is_colored_output_on
+==========================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::set_is_colored_output_on(bool)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1af8f3443813315af7901903d25dd495cc.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1af8f3443813315af7901903d25dd495cc.rst.txt
new file mode 100644
index 0000000000..9d0a42d2bd
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_logging_8h_1af8f3443813315af7901903d25dd495cc.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1af8f3443813315af7901903d25dd495cc:
+
+Function torch_tensorrt::logging::set_logging_prefix
+====================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::set_logging_prefix(std::string)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.rst.txt
new file mode 100644
index 0000000000..5d9188270f
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c:
+
+Template Function torch_tensorrt::ptq::make_int8_cache_calibrator
+=================================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::ptq::make_int8_cache_calibrator(const std::string&)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.rst.txt
new file mode 100644
index 0000000000..9551f48bb6
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178:
+
+Template Function torch_tensorrt::ptq::make_int8_calibrator
+===========================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::ptq::make_int8_calibrator(DataLoader, const std::string&, bool)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.rst.txt
new file mode 100644
index 0000000000..caa9ad6226
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.rst.txt
@@ -0,0 +1,13 @@
+.. _exhale_function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039:
+
+Template Function torch_tensorrt::ptq::make_int8_calibrator
+===========================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::ptq::make_int8_calibrator(DataLoader, const std::string&, bool)
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.rst.txt
new file mode 100644
index 0000000000..477c2655ae
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.rst.txt
@@ -0,0 +1,13 @@
+.. _exhale_function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa:
+
+Template Function torch_tensorrt::ptq::make_int8_cache_calibrator
+=================================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::ptq::make_int8_cache_calibrator(const std::string&)
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.rst.txt
new file mode 100644
index 0000000000..54f7c29df3
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797:
+
+Function torch_tensorrt::torchscript::check_method_operator_support
+===================================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::torchscript::check_method_operator_support(const torch::jit::Module&, std::string)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.rst.txt
new file mode 100644
index 0000000000..b80699688a
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9:
+
+Function torch_tensorrt::torchscript::compile
+=============================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::torchscript::compile(const torch::jit::Module&, CompileSpec)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.rst.txt
new file mode 100644
index 0000000000..a49fea74a1
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef:
+
+Function torch_tensorrt::torchscript::embed_engine_in_new_module
+================================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::torchscript::embed_engine_in_new_module(const std::string&, Device)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.rst.txt
new file mode 100644
index 0000000000..d551c4894d
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528:
+
+Function torch_tensorrt::get_build_info
+=======================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::get_build_info()
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.rst.txt
new file mode 100644
index 0000000000..e296177bfa
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384:
+
+Function torch_tensorrt::set_device
+===================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::set_device(const int)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.rst.txt
new file mode 100644
index 0000000000..06b03cbfd9
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1:
+
+Function torch_tensorrt::dump_build_info
+========================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::dump_build_info()
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.rst.txt b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.rst.txt
new file mode 100644
index 0000000000..2a1f0c89d0
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2:
+
+Function torch_tensorrt::torchscript::convert_method_to_trt_engine
+==================================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::torchscript::convert_method_to_trt_engine(const torch::jit::Module&, std::string, CompileSpec)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/namespace_torch_tensorrt.rst.txt b/docs/v1.2.0/_sources/_cpp_api/namespace_torch_tensorrt.rst.txt
new file mode 100644
index 0000000000..43a0afb20d
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/namespace_torch_tensorrt.rst.txt
@@ -0,0 +1,59 @@
+
+.. _namespace_torch_tensorrt:
+
+Namespace torch_tensorrt
+========================
+
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+
+
+
+
+Namespaces
+----------
+
+
+- :ref:`namespace_torch_tensorrt__logging`
+
+- :ref:`namespace_torch_tensorrt__ptq`
+
+- :ref:`namespace_torch_tensorrt__torchscript`
+
+
+Classes
+-------
+
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1Device`
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1GraphInputs`
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1Input`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1DataType`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1Device_1_1DeviceType`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1TensorFormat`
+
+
+Enums
+-----
+
+
+- :ref:`exhale_enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384`
diff --git a/docs/v1.2.0/_sources/_cpp_api/namespace_torch_tensorrt__logging.rst.txt b/docs/v1.2.0/_sources/_cpp_api/namespace_torch_tensorrt__logging.rst.txt
new file mode 100644
index 0000000000..49f946f937
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/namespace_torch_tensorrt__logging.rst.txt
@@ -0,0 +1,39 @@
+
+.. _namespace_torch_tensorrt__logging:
+
+Namespace torch_tensorrt::logging
+=================================
+
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+
+
+
+
+Enums
+-----
+
+
+- :ref:`exhale_enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a`
+
+- :ref:`exhale_function_logging_8h_1a0593f776f469c20469e2f729fc7861a3`
+
+- :ref:`exhale_function_logging_8h_1a0c012cb374addd90eb1f42eaec570650`
+
+- :ref:`exhale_function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8`
+
+- :ref:`exhale_function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5`
+
+- :ref:`exhale_function_logging_8h_1af8f3443813315af7901903d25dd495cc`
+
+- :ref:`exhale_function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2`
diff --git a/docs/v1.2.0/_sources/_cpp_api/namespace_torch_tensorrt__ptq.rst.txt b/docs/v1.2.0/_sources/_cpp_api/namespace_torch_tensorrt__ptq.rst.txt
new file mode 100644
index 0000000000..bdc39cb326
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/namespace_torch_tensorrt__ptq.rst.txt
@@ -0,0 +1,31 @@
+
+.. _namespace_torch_tensorrt__ptq:
+
+Namespace torch_tensorrt::ptq
+=============================
+
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+
+
+
+
+Classes
+-------
+
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1ptq_1_1Int8Calibrator`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c`
+
+- :ref:`exhale_function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178`
diff --git a/docs/v1.2.0/_sources/_cpp_api/namespace_torch_tensorrt__torchscript.rst.txt b/docs/v1.2.0/_sources/_cpp_api/namespace_torch_tensorrt__torchscript.rst.txt
new file mode 100644
index 0000000000..b56b3f738e
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/namespace_torch_tensorrt__torchscript.rst.txt
@@ -0,0 +1,33 @@
+
+.. _namespace_torch_tensorrt__torchscript:
+
+Namespace torch_tensorrt::torchscript
+=====================================
+
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+
+
+
+
+Classes
+-------
+
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1torchscript_1_1CompileSpec`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef`
diff --git a/docs/v1.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.rst.txt b/docs/v1.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.rst.txt
new file mode 100644
index 0000000000..af68cbb678
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.rst.txt
@@ -0,0 +1,51 @@
+
+.. _program_listing_file_cpp_include_torch_tensorrt_logging.h:
+
+Program Listing for File logging.h
+==================================
+
+|exhale_lsh| :ref:`Return to documentation for file <file_cpp_include_torch_tensorrt_logging.h>` (``cpp/include/torch_tensorrt/logging.h``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. code-block:: cpp
+
+   /*
+    * Copyright (c) NVIDIA Corporation.
+    * All rights reserved.
+    *
+    * This library is licensed under the BSD-style license found in the
+    * LICENSE file in the root directory of this source tree.
+    */
+   #pragma once
+
+   #include <string>
+   #include "torch_tensorrt/macros.h"
+
+   namespace torch_tensorrt {
+   namespace logging {
+   enum Level {
+     kINTERNAL_ERROR,
+     kERROR,
+     kWARNING,
+     kINFO,
+     kDEBUG,
+     kGRAPH,
+   };
+
+   // Are these ones necessary for the user?
+   TORCHTRT_API std::string get_logging_prefix();
+   TORCHTRT_API void set_logging_prefix(std::string prefix);
+
+   TORCHTRT_API void set_reportable_log_level(Level lvl);
+
+   TORCHTRT_API void set_is_colored_output_on(bool colored_output_on);
+
+   TORCHTRT_API Level get_reportable_log_level();
+
+   TORCHTRT_API bool get_is_colored_output_on();
+
+   // Dont know if we want this?
+   TORCHTRT_API void log(Level lvl, std::string msg);
+   } // namespace logging
+   } // namespace torch_tensorrt
diff --git a/docs/v1.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.rst.txt b/docs/v1.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.rst.txt
new file mode 100644
index 0000000000..66f0005450
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.rst.txt
@@ -0,0 +1,50 @@
+
+.. _program_listing_file_cpp_include_torch_tensorrt_macros.h:
+
+Program Listing for File macros.h
+=================================
+
+|exhale_lsh| :ref:`Return to documentation for file <file_cpp_include_torch_tensorrt_macros.h>` (``cpp/include/torch_tensorrt/macros.h``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. code-block:: cpp
+
+   /*
+    * Copyright (c) NVIDIA Corporation.
+    * All rights reserved.
+    *
+    * This library is licensed under the BSD-style license found in the
+    * LICENSE file in the root directory of this source tree.
+    */
+   #pragma once
+
+   #if defined(USE_CMAKE_GENERATED_EXPORT_HEADER)
+   #include <torch_tensorrt_export.h>
+   #else
+   #if defined(__GNUC__)
+   #define TORCHTRT_API __attribute__((__visibility__("default")))
+   #define TORCHTRT_HIDDEN __attribute__((__visibility__("hidden")))
+   #else
+   #define TORCHTRT_API
+   #define TORCHTRT_HIDDEN
+   #endif // defined(__GNUC__)
+   #endif // defined(USE_CMAKE_GENERATED_EXPORT_HEADER)
+
+   // Does this need to be gaurded or something?
+   #define XSTR(x) #x
+   #define STR(x) XSTR(x)
+
+   #define TORCH_TENSORRT_MAJOR_VERSION 1
+   #define TORCH_TENSORRT_MINOR_VERSION 2
+   #define TORCH_TENSORRT_PATCH_VERSION 0
+   #define TORCH_TENSORRT_VERSION      \
+     STR(TORCH_TENSORRT_MAJOR_VERSION) \
+     "." STR(TORCH_TENSORRT_MINOR_VERSION) "." STR(TORCH_TENSORRT_PATCH_VERSION)
+
+   // Setup namespace aliases for ease of use
+   namespace torch_tensorrt {
+   namespace torchscript {}
+   namespace ts = torchscript;
+   } // namespace torch_tensorrt
+   namespace torchtrt = torch_tensorrt;
diff --git a/docs/v1.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.rst.txt b/docs/v1.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.rst.txt
new file mode 100644
index 0000000000..65834c370b
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.rst.txt
@@ -0,0 +1,190 @@
+
+.. _program_listing_file_cpp_include_torch_tensorrt_ptq.h:
+
+Program Listing for File ptq.h
+==============================
+
+|exhale_lsh| :ref:`Return to documentation for file <file_cpp_include_torch_tensorrt_ptq.h>` (``cpp/include/torch_tensorrt/ptq.h``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. code-block:: cpp
+
+   /*
+    * Copyright (c) NVIDIA Corporation.
+    * All rights reserved.
+    *
+    * This library is licensed under the BSD-style license found in the
+    * LICENSE file in the root directory of this source tree.
+    */
+   #pragma once
+
+   #include <fstream>
+   #include <iostream>
+   #include <iterator>
+   #include <memory>
+   #include <sstream>
+   #include <string>
+   #include <vector>
+
+   #include "NvInfer.h"
+   #include "torch/torch.h"
+   #include "torch_tensorrt/logging.h"
+   #include "torch_tensorrt/macros.h"
+
+   #ifndef DOXYGEN_SHOULD_SKIP_THIS
+   namespace nvinfer1 {
+   class IInt8Calibrator;
+   class IInt8EntropyCalibrator2;
+   } // namespace nvinfer1
+
+   namespace torch_tensorrt {
+   namespace ptq {
+   TORCHTRT_API bool get_batch_impl(void* bindings[], const char* names[], int nbBindings, torch::Tensor& data);
+   }
+   } // namespace torch_tensorrt
+   #endif // DOXYGEN_SHOULD_SKIP_THIS
+
+   namespace torch_tensorrt {
+   namespace ptq {
+
+   template <typename Algorithm, typename DataLoaderUniquePtr>
+   class Int8Calibrator : Algorithm {
+     using DataLoader = typename DataLoaderUniquePtr::element_type;
+     using Batch = typename DataLoader::super::BatchType;
+
+    public:
+     Int8Calibrator(DataLoaderUniquePtr dataloader, const std::string& cache_file_path, bool use_cache)
+         : dataloader_(dataloader.get()), cache_file_path_(cache_file_path), use_cache_(use_cache) {
+       for (auto batch : *dataloader_) {
+         batched_data_.push_back(batch.data);
+       }
+       it_ = batched_data_.begin();
+     }
+
+     int getBatchSize() const noexcept override {
+       // HACK: Torch-TensorRT only uses explict batch sizing, INT8 Calibrator does not
+       // work when reporting the batch size here and having explicity batching.
+       // So we just report batch size 1 (warnings will still be printed out).
+       return 1;
+       // return static_cast<int>(dataloader_->options().batch_size);
+     }
+
+     bool getBatch(void* bindings[], const char* names[], int nbBindings) noexcept override {
+       if (it_ != batched_data_.end()) {
+         auto status = get_batch_impl(bindings, names, nbBindings, *it_);
+         it_ = ++it_;
+         return status;
+       } else {
+         // Reset iterator if incase calibrator is going to be used again
+         it_ = batched_data_.begin();
+         return false;
+       }
+     }
+
+     const void* readCalibrationCache(size_t& length) noexcept override {
+       if (use_cache_) {
+         std::stringstream ss;
+         ss << "Reading Calibration Cache from " << cache_file_path_;
+         logging::log(logging::Level::kINFO, ss.str());
+
+         cache_.clear();
+         std::ifstream input(cache_file_path_, std::ios::binary);
+         input >> std::noskipws;
+         if (input.good()) {
+           std::copy(std::istream_iterator<char>(input), std::istream_iterator<char>(), std::back_inserter(cache_));
+           logging::log(logging::Level::kDEBUG, "Cache read");
+         }
+         length = cache_.size();
+         return length ? cache_.data() : nullptr;
+       }
+       return nullptr;
+     }
+
+     void writeCalibrationCache(const void* cache, size_t length) noexcept override {
+       std::ofstream cache_file(cache_file_path_, std::ios::binary);
+       cache_file.write(reinterpret_cast<const char*>(cache), length);
+       std::stringstream ss;
+       ss << "Saved Calibration Cache to " << cache_file_path_;
+       logging::log(logging::Level::kINFO, ss.str());
+     }
+
+     operator nvinfer1::IInt8Calibrator*() {
+       return reinterpret_cast<nvinfer1::IInt8Calibrator*>(this);
+     }
+
+    private:
+     DataLoader* dataloader_;
+     const std::string& cache_file_path_;
+     size_t cache_size_ = 0;
+     bool use_cache_;
+     std::vector<char> cache_;
+     std::vector<torch::Tensor> batched_data_;
+     std::vector<torch::Tensor>::iterator it_;
+   };
+
+   template <typename Algorithm>
+   class Int8CacheCalibrator : Algorithm {
+    public:
+     Int8CacheCalibrator(const std::string& cache_file_path) : cache_file_path_(cache_file_path) {}
+
+     int getBatchSize() const noexcept override {
+       // HACK: Torch-TensorRT only uses explict batch sizing, INT8 Calibrator does not
+       // work when reporting the batch size here and having explicity batching.
+       // So we just report batch size 1 (warnings will still be printed out).
+       return 1;
+     }
+
+     bool getBatch(void* bindings[], const char* names[], int nbBindings) noexcept override {
+       return false;
+     }
+
+     const void* readCalibrationCache(size_t& length) noexcept override {
+       std::stringstream ss;
+       ss << "Reading Calibration Cache from " << cache_file_path_;
+       logging::log(logging::Level::kINFO, ss.str());
+
+       cache_.clear();
+       std::ifstream input(cache_file_path_, std::ios::binary);
+       input >> std::noskipws;
+       if (input.good()) {
+         std::copy(std::istream_iterator<char>(input), std::istream_iterator<char>(), std::back_inserter(cache_));
+         logging::log(logging::Level::kDEBUG, "Cache read");
+       }
+       length = cache_.size();
+       return length ? cache_.data() : nullptr;
+     }
+
+     void writeCalibrationCache(const void* cache, size_t length) noexcept override {
+       std::ofstream cache_file(cache_file_path_, std::ios::binary);
+       cache_file.write(reinterpret_cast<const char*>(cache), length);
+       std::stringstream ss;
+       ss << "Saved Calibration Cache to " << cache_file_path_;
+       logging::log(logging::Level::kINFO, ss.str());
+     }
+
+     operator nvinfer1::IInt8Calibrator*() {
+       return reinterpret_cast<nvinfer1::IInt8Calibrator*>(this);
+     }
+
+    private:
+     const std::string& cache_file_path_;
+     size_t cache_size_ = 0;
+     std::vector<char> cache_;
+   };
+
+   template <typename Algorithm = nvinfer1::IInt8EntropyCalibrator2, typename DataLoader>
+   inline Int8Calibrator<Algorithm, DataLoader> make_int8_calibrator(
+       DataLoader dataloader,
+       const std::string& cache_file_path,
+       bool use_cache) {
+     return Int8Calibrator<Algorithm, DataLoader>(std::move(dataloader), cache_file_path, use_cache);
+   }
+
+   template <typename Algorithm = nvinfer1::IInt8EntropyCalibrator2>
+   inline Int8CacheCalibrator<Algorithm> make_int8_cache_calibrator(const std::string& cache_file_path) {
+     return Int8CacheCalibrator<Algorithm>(cache_file_path);
+   }
+
+   } // namespace ptq
+   } // namespace torch_tensorrt
diff --git a/docs/v1.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt b/docs/v1.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
new file mode 100644
index 0000000000..6526f355aa
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
@@ -0,0 +1,294 @@
+
+.. _program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h:
+
+Program Listing for File torch_tensorrt.h
+=========================================
+
+|exhale_lsh| :ref:`Return to documentation for file <file_cpp_include_torch_tensorrt_torch_tensorrt.h>` (``cpp/include/torch_tensorrt/torch_tensorrt.h``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. code-block:: cpp
+
+   /*
+    * Copyright (c) NVIDIA Corporation.
+    * All rights reserved.
+    *
+    * This library is licensed under the BSD-style license found in the
+    * LICENSE file in the root directory of this source tree.
+    */
+
+   #pragma once
+
+   #include <cuda_runtime.h>
+   #include <iostream>
+   #include <memory>
+   #include <set>
+   #include <string>
+   #include <vector>
+   #include "torch/custom_class.h"
+
+   #include "torch_tensorrt/macros.h"
+
+   // Just include the .h?
+   #ifndef DOXYGEN_SHOULD_SKIP_THIS
+   namespace torch {
+   namespace jit {
+   struct Graph;
+   struct Module;
+   } // namespace jit
+   } // namespace torch
+
+   namespace c10 {
+   enum class DeviceType : int8_t;
+   enum class ScalarType : int8_t;
+   template <class>
+   class ArrayRef;
+   } // namespace c10
+
+   namespace nvinfer1 {
+   class IInt8Calibrator;
+   }
+   #endif // DOXYGEN_SHOULD_SKIP_THIS
+
+   namespace torch_tensorrt {
+   class DataType {
+    public:
+     enum Value : int8_t {
+       kFloat,
+       kHalf,
+       kChar,
+       kInt,
+       kBool,
+       kUnknown
+     };
+
+     DataType() = default;
+     constexpr DataType(Value t) : value(t) {}
+     TORCHTRT_API DataType(c10::ScalarType t);
+     operator Value() const {
+       return value;
+     }
+     explicit operator bool() = delete;
+     constexpr bool operator==(DataType other) const {
+       return value == other.value;
+     }
+     constexpr bool operator==(DataType::Value other) const {
+       return value == other;
+     }
+     constexpr bool operator!=(DataType other) const {
+       return value != other.value;
+     }
+     constexpr bool operator!=(DataType::Value other) const {
+       return value != other;
+     }
+
+    private:
+     friend TORCHTRT_API std::ostream& operator<<(std::ostream& os, const DataType& dtype);
+     Value value;
+   };
+
+   struct Device {
+     class DeviceType {
+      public:
+       enum Value : int8_t {
+         kGPU,
+         kDLA,
+       };
+
+       DeviceType() = default;
+       constexpr DeviceType(Value t) : value(t) {}
+       DeviceType(c10::DeviceType t);
+       operator Value() const {
+         return value;
+       }
+       explicit operator bool() = delete;
+       constexpr bool operator==(DeviceType other) const {
+         return value == other.value;
+       }
+       constexpr bool operator!=(DeviceType other) const {
+         return value != other.value;
+       }
+
+      private:
+       Value value;
+     };
+
+     DeviceType device_type;
+
+     /*
+      * Target gpu id
+      */
+     int64_t gpu_id;
+
+     /*
+      * When using DLA core on NVIDIA AGX platforms gpu_id should be set as Xavier device
+      */
+     int64_t dla_core;
+
+     bool allow_gpu_fallback;
+
+     Device() : device_type(DeviceType::kGPU), gpu_id(0), dla_core(0), allow_gpu_fallback(false) {}
+   };
+
+   enum class EngineCapability : int8_t {
+     kSTANDARD,
+     kSAFETY,
+     kDLA_STANDALONE,
+   };
+
+   class TensorFormat {
+    public:
+     enum Value : int8_t {
+       kContiguous,
+       kChannelsLast,
+       kUnknown,
+     };
+
+     TensorFormat() = default;
+     constexpr TensorFormat(Value t) : value(t) {}
+     TORCHTRT_API TensorFormat(at::MemoryFormat t);
+     operator Value() const {
+       return value;
+     }
+     explicit operator bool() = delete;
+     constexpr bool operator==(TensorFormat other) const {
+       return value == other.value;
+     }
+     constexpr bool operator==(TensorFormat::Value other) const {
+       return value == other;
+     }
+     constexpr bool operator!=(TensorFormat other) const {
+       return value != other.value;
+     }
+     constexpr bool operator!=(TensorFormat::Value other) const {
+       return value != other;
+     }
+
+    private:
+     friend TORCHTRT_API std::ostream& operator<<(std::ostream& os, const TensorFormat& format);
+     Value value;
+   };
+
+   struct Input : torch::CustomClassHolder {
+     std::vector<int64_t> min_shape;
+     std::vector<int64_t> opt_shape;
+     std::vector<int64_t> max_shape;
+     std::vector<int64_t> shape;
+     DataType dtype;
+     TensorFormat format;
+
+     Input() {}
+     TORCHTRT_API Input(std::vector<int64_t> shape, TensorFormat format = TensorFormat::kContiguous);
+
+     TORCHTRT_API Input(std::vector<int64_t> shape, DataType dtype, TensorFormat format = TensorFormat::kContiguous);
+
+     TORCHTRT_API Input(c10::ArrayRef<int64_t> shape, TensorFormat format = TensorFormat::kContiguous);
+
+     TORCHTRT_API Input(c10::ArrayRef<int64_t> shape, DataType dtype, TensorFormat format = TensorFormat::kContiguous);
+
+     TORCHTRT_API Input(
+         std::vector<int64_t> min_shape,
+         std::vector<int64_t> opt_shape,
+         std::vector<int64_t> max_shape,
+         TensorFormat format = TensorFormat::kContiguous);
+
+     TORCHTRT_API Input(
+         std::vector<int64_t> min_shape,
+         std::vector<int64_t> opt_shape,
+         std::vector<int64_t> max_shape,
+         DataType dtype,
+         TensorFormat format = TensorFormat::kContiguous);
+
+     TORCHTRT_API Input(
+         c10::ArrayRef<int64_t> min_shape,
+         c10::ArrayRef<int64_t> opt_shape,
+         c10::ArrayRef<int64_t> max_shape,
+         TensorFormat format = TensorFormat::kContiguous);
+
+     TORCHTRT_API Input(
+         c10::ArrayRef<int64_t> min_shape,
+         c10::ArrayRef<int64_t> opt_shape,
+         c10::ArrayRef<int64_t> max_shape,
+         DataType dtype,
+         TensorFormat format = TensorFormat::kContiguous);
+
+     TORCHTRT_API Input(at::Tensor tensor);
+
+    private:
+     friend TORCHTRT_API std::ostream& operator<<(std::ostream& os, const Input& input);
+     bool input_is_dynamic;
+   };
+
+   struct GraphInputs {
+     torch::jit::IValue input_signature; // nested Input, full input spec
+     std::vector<Input> inputs; // flatten input spec
+   };
+
+   TORCHTRT_API std::string get_build_info();
+
+   TORCHTRT_API void dump_build_info();
+
+   TORCHTRT_API void set_device(const int gpu_id);
+
+   namespace torchscript {
+   struct CompileSpec {
+     TORCHTRT_API CompileSpec(std::vector<std::vector<int64_t>> fixed_sizes);
+
+     TORCHTRT_API CompileSpec(std::vector<c10::ArrayRef<int64_t>> fixed_sizes);
+
+     TORCHTRT_API CompileSpec(std::vector<Input> inputs);
+
+     TORCHTRT_API CompileSpec(torch::jit::IValue input_signature);
+     // Defaults should reflect TensorRT defaults for BuilderConfig
+
+     GraphInputs graph_inputs;
+     std::set<DataType> enabled_precisions = {DataType::kFloat};
+
+     bool disable_tf32 = false;
+
+     bool sparse_weights = false;
+
+     bool refit = false;
+
+     bool debug = false;
+
+     bool truncate_long_and_double = false;
+
+     Device device;
+
+     EngineCapability capability = EngineCapability::kSTANDARD;
+
+     uint64_t num_avg_timing_iters = 1;
+
+     uint64_t workspace_size = 0;
+
+     uint64_t dla_sram_size = 1048576;
+
+     uint64_t dla_local_dram_size = 1073741824;
+
+     uint64_t dla_global_dram_size = 536870912;
+
+     nvinfer1::IInt8Calibrator* ptq_calibrator = nullptr;
+
+     bool require_full_compilation = false;
+
+     uint64_t min_block_size = 3;
+
+     std::vector<std::string> torch_executed_ops;
+
+     std::vector<std::string> torch_executed_modules;
+   };
+
+   TORCHTRT_API bool check_method_operator_support(const torch::jit::Module& module, std::string method_name);
+
+   TORCHTRT_API torch::jit::Module compile(const torch::jit::Module& module, CompileSpec info);
+
+   TORCHTRT_API std::string convert_method_to_trt_engine(
+       const torch::jit::Module& module,
+       std::string method_name,
+       CompileSpec info);
+
+   TORCHTRT_API torch::jit::Module embed_engine_in_new_module(const std::string& engine, Device device);
+   } // namespace torchscript
+   } // namespace torch_tensorrt
diff --git a/docs/v1.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1Device.rst.txt b/docs/v1.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1Device.rst.txt
new file mode 100644
index 0000000000..a090eaa324
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1Device.rst.txt
@@ -0,0 +1,27 @@
+.. _exhale_struct_structtorch__tensorrt_1_1Device:
+
+Struct Device
+=============
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Nested Relationships
+--------------------
+
+
+Nested Types
+************
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1Device_1_1DeviceType`
+
+
+Struct Documentation
+--------------------
+
+
+.. doxygenstruct:: torch_tensorrt::Device
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1GraphInputs.rst.txt b/docs/v1.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1GraphInputs.rst.txt
new file mode 100644
index 0000000000..60bc3285c6
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1GraphInputs.rst.txt
@@ -0,0 +1,17 @@
+.. _exhale_struct_structtorch__tensorrt_1_1GraphInputs:
+
+Struct GraphInputs
+==================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Struct Documentation
+--------------------
+
+
+.. doxygenstruct:: torch_tensorrt::GraphInputs
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1Input.rst.txt b/docs/v1.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1Input.rst.txt
new file mode 100644
index 0000000000..74c594cd94
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1Input.rst.txt
@@ -0,0 +1,26 @@
+.. _exhale_struct_structtorch__tensorrt_1_1Input:
+
+Struct Input
+============
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Inheritance Relationships
+-------------------------
+
+Base Type
+*********
+
+- ``public CustomClassHolder``
+
+
+Struct Documentation
+--------------------
+
+
+.. doxygenstruct:: torch_tensorrt::Input
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.rst.txt b/docs/v1.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.rst.txt
new file mode 100644
index 0000000000..31f2296658
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.rst.txt
@@ -0,0 +1,17 @@
+.. _exhale_struct_structtorch__tensorrt_1_1torchscript_1_1CompileSpec:
+
+Struct CompileSpec
+==================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Struct Documentation
+--------------------
+
+
+.. doxygenstruct:: torch_tensorrt::torchscript::CompileSpec
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/_cpp_api/torch_tensort_cpp.rst.txt b/docs/v1.2.0/_sources/_cpp_api/torch_tensort_cpp.rst.txt
new file mode 100644
index 0000000000..1b34d45a77
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/torch_tensort_cpp.rst.txt
@@ -0,0 +1,10 @@
+======================
+Torch-TensorRT C++ API
+======================
+
+.. include:: class_view_hierarchy.rst.include
+
+.. include:: file_view_hierarchy.rst.include
+
+.. include:: unabridged_api.rst.include
+
diff --git a/docs/v1.2.0/_sources/_cpp_api/unabridged_api.rst.txt b/docs/v1.2.0/_sources/_cpp_api/unabridged_api.rst.txt
new file mode 100644
index 0000000000..29689c465f
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/unabridged_api.rst.txt
@@ -0,0 +1,213 @@
+
+Full API
+--------
+
+Namespaces
+**********
+
+
+.. toctree::
+   :maxdepth: 5
+
+   namespace_torch_tensorrt.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   namespace_torch_tensorrt__logging.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   namespace_torch_tensorrt__ptq.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   namespace_torch_tensorrt__torchscript.rst
+
+Classes and Structs
+*******************
+
+
+.. toctree::
+   :maxdepth: 5
+
+   structtorch__tensorrt_1_1Device.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   structtorch__tensorrt_1_1Input.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   structtorch__tensorrt_1_1torchscript_1_1CompileSpec.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   classtorch__tensorrt_1_1DataType.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   classtorch__tensorrt_1_1Device_1_1DeviceType.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   classtorch__tensorrt_1_1TensorFormat.rst
+
+Enums
+*****
+
+
+.. toctree::
+   :maxdepth: 5
+
+   enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.rst
+
+Functions
+*********
+
+
+.. toctree::
+   :maxdepth: 5
+
+   function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_logging_8h_1af8f3443813315af7901903d25dd495cc.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.rst
+
+Defines
+*******
+
+
+.. toctree::
+   :maxdepth: 5
+
+   define_macros_8h_1a18d295a837ac71add5578860b55e5502.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   define_macros_8h_1ad19939408f7be171a74a89928b36eb59.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.rst
diff --git a/docs/v1.2.0/_sources/_cpp_api/unabridged_orphan.rst.txt b/docs/v1.2.0/_sources/_cpp_api/unabridged_orphan.rst.txt
new file mode 100644
index 0000000000..5789e581e8
--- /dev/null
+++ b/docs/v1.2.0/_sources/_cpp_api/unabridged_orphan.rst.txt
@@ -0,0 +1,48 @@
+:orphan:
+
+
+Full API
+========
+
+Directories
+***********
+
+
+.. toctree::
+   :maxdepth: 5
+
+   dir_cpp.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   dir_cpp_include.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   dir_cpp_include_torch_tensorrt.rst
+
+Files
+*****
+
+
+.. toctree::
+   :maxdepth: 5
+
+   file_cpp_include_torch_tensorrt_logging.h.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   file_cpp_include_torch_tensorrt_macros.h.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   file_cpp_include_torch_tensorrt_ptq.h.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst
diff --git a/docs/v1.2.0/_sources/_notebooks/CitriNet-example.ipynb.txt b/docs/v1.2.0/_sources/_notebooks/CitriNet-example.ipynb.txt
new file mode 100644
index 0000000000..0573af0176
--- /dev/null
+++ b/docs/v1.2.0/_sources/_notebooks/CitriNet-example.ipynb.txt
@@ -0,0 +1,964 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
+    "\n",
+    "# Torch-TensorRT Getting Started - CitriNet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "[Citrinet](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#citrinet) is an acoustic model used for the speech to text recognition task. It is a version of [QuartzNet](https://arxiv.org/pdf/1910.10261.pdf) that extends [ContextNet](https://arxiv.org/pdf/2005.03191.pdf), utilizing subword encoding (via Word Piece tokenization) and Squeeze-and-Excitation(SE) mechanism and are therefore smaller than QuartzNet models.\n",
+    "\n",
+    "CitriNet models take in audio segments and transcribe them to letter, byte pair, or word piece sequences. \n",
+    "\n",
+    "<img src=\"https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/_images/jasper_vertical.png\" alt=\"alt\" width=\"50%\"/>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning objectives\n",
+    "\n",
+    "This notebook demonstrates the steps for optimizing a pretrained CitriNet model with Torch-TensorRT, and running it to test the speedup obtained.\n",
+    "\n",
+    "## Content\n",
+    "1. [Requirements](#1)\n",
+    "1. [Download Citrinet model](#2)\n",
+    "1. [Create Torch-TensorRT modules](#3)\n",
+    "1. [Benchmark Torch-TensorRT models](#4)\n",
+    "1. [Conclusion](#5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"1\"></a>\n",
+    "## 1. Requirements\n",
+    "\n",
+    "Follow the steps in [README](README.md) to prepare a Docker container, within which you can run this notebook. \n",
+    "This notebook assumes that you are within a Jupyter environment in a docker container with Torch-TensorRT installed, such as an NGC monthly release of `nvcr.io/nvidia/pytorch:<yy.mm>-py3` (where `yy` indicates the last two numbers of a calendar year, and `mm` indicates the month in two-digit numerical form)\n",
+    "\n",
+    "Now that you are in the docker, the next step is to install the required dependencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Requirement already satisfied: wget in /opt/conda/lib/python3.8/site-packages (3.2)\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
+      "Hit:1 http://security.ubuntu.com/ubuntu focal-security InRelease\n",
+      "Hit:2 http://archive.ubuntu.com/ubuntu focal InRelease\n",
+      "Hit:3 http://archive.ubuntu.com/ubuntu focal-updates InRelease\n",
+      "Hit:4 http://archive.ubuntu.com/ubuntu focal-backports InRelease\n",
+      "Reading package lists... Done\n",
+      "Reading package lists... Done\n",
+      "Building dependency tree       \n",
+      "Reading state information... Done\n",
+      "libsndfile1 is already the newest version (1.0.28-7ubuntu0.1).\n",
+      "ffmpeg is already the newest version (7:4.2.4-1ubuntu0.1).\n",
+      "0 upgraded, 0 newly installed, 0 to remove and 22 not upgraded.\n",
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Requirement already satisfied: Cython in /opt/conda/lib/python3.8/site-packages (0.29.28)\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Requirement already satisfied: nemo_toolkit[all]==1.5.1 in /opt/conda/lib/python3.8/site-packages (1.5.1)\n",
+      "Requirement already satisfied: numpy>=1.18.2 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.22.3)\n",
+      "Requirement already satisfied: onnx>=1.7.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.10.1)\n",
+      "Requirement already satisfied: python-dateutil in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.8.2)\n",
+      "Requirement already satisfied: tqdm>=4.41.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.63.0)\n",
+      "Requirement already satisfied: sentencepiece<1.0.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.1.96)\n",
+      "Requirement already satisfied: wget in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (3.2)\n",
+      "Requirement already satisfied: numba in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.53.1)\n",
+      "Requirement already satisfied: torch in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.12.0a0+2c916ef)\n",
+      "Requirement already satisfied: unidecode in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.3.4)\n",
+      "Requirement already satisfied: frozendict in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.3.2)\n",
+      "Requirement already satisfied: wrapt in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.14.0)\n",
+      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.24.2)\n",
+      "Requirement already satisfied: ruamel.yaml in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.17.21)\n",
+      "Requirement already satisfied: pesq in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.0.3)\n",
+      "Requirement already satisfied: torchvision in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.13.0a0)\n",
+      "Requirement already satisfied: gdown in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.4.0)\n",
+      "Requirement already satisfied: editdistance in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.6.0)\n",
+      "Requirement already satisfied: boto3 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.21.45)\n",
+      "Requirement already satisfied: isort[requirements]<5 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.3.21)\n",
+      "Requirement already satisfied: hydra-core>=1.1.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.1.2)\n",
+      "Requirement already satisfied: youtokentome>=1.0.5 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.0.6)\n",
+      "Requirement already satisfied: pytorch-lightning>=1.5.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.6.1)\n",
+      "Requirement already satisfied: jieba in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.42.1)\n",
+      "Requirement already satisfied: fasttext in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.9.2)\n",
+      "Requirement already satisfied: soundfile in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.10.3.post1)\n",
+      "Requirement already satisfied: kaldiio in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.17.2)\n",
+      "Requirement already satisfied: pangu in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.0.6.1)\n",
+      "Requirement already satisfied: kaldi-python-io in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.2.2)\n",
+      "Requirement already satisfied: parameterized in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.8.1)\n",
+      "Requirement already satisfied: h5py in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (3.6.0)\n",
+      "Requirement already satisfied: rapidfuzz in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.0.10)\n",
+      "Requirement already satisfied: marshmallow in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (3.15.0)\n",
+      "Requirement already satisfied: opencc in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.1.3)\n",
+      "Requirement already satisfied: braceexpand in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.1.7)\n",
+      "Requirement already satisfied: omegaconf>=2.1.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.1.2)\n",
+      "Requirement already satisfied: sphinx in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.4.0)\n",
+      "Requirement already satisfied: pillow in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (9.0.0)\n",
+      "Requirement already satisfied: wordninja==2.0.0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.0.0)\n",
+      "Requirement already satisfied: torch-stft in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.1.4)\n",
+      "Requirement already satisfied: sox in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.4.1)\n",
+      "Requirement already satisfied: librosa in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.9.1)\n",
+      "Requirement already satisfied: regex in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2022.3.15)\n",
+      "Requirement already satisfied: sacrebleu[ja] in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.0.0)\n",
+      "Requirement already satisfied: black==19.10b0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (19.10b0)\n",
+      "Requirement already satisfied: pydub in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.25.1)\n",
+      "Requirement already satisfied: sphinxcontrib-bibtex in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.4.2)\n",
+      "Requirement already satisfied: inflect in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (5.5.2)\n",
+      "Requirement already satisfied: pyannote.core in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.4)\n",
+      "Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (21.3)\n",
+      "Requirement already satisfied: kaldi-io in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.9.4)\n",
+      "Requirement already satisfied: pyannote.metrics in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (3.2)\n",
+      "Requirement already satisfied: g2p-en in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.1.0)\n",
+      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (3.5.1)\n",
+      "Requirement already satisfied: torchmetrics>=0.4.1rc0 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.8.0)\n",
+      "Requirement already satisfied: nltk>=3.6.5 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (3.7)\n",
+      "Requirement already satisfied: pyyaml<6 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (5.4.1)\n",
+      "Requirement already satisfied: scipy in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.6.3)\n",
+      "Requirement already satisfied: ipywidgets in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (7.7.0)\n",
+      "Requirement already satisfied: pytest in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (6.2.5)\n",
+      "Requirement already satisfied: pandas in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (1.3.5)\n",
+      "Requirement already satisfied: pytest-runner in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (6.0.0)\n",
+      "Requirement already satisfied: transformers>=4.0.1 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (4.18.0)\n",
+      "Requirement already satisfied: sacremoses>=0.0.43 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.0.49)\n",
+      "Requirement already satisfied: pystoi in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.3.3)\n",
+      "Requirement already satisfied: attrdict in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (2.0.1)\n",
+      "Requirement already satisfied: webdataset<=0.1.62,>=0.1.48 in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.1.62)\n",
+      "Requirement already satisfied: wandb in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.12.15)\n",
+      "Requirement already satisfied: pypinyin in /opt/conda/lib/python3.8/site-packages (from nemo_toolkit[all]==1.5.1) (0.46.0)\n",
+      "Requirement already satisfied: attrs>=18.1.0 in /opt/conda/lib/python3.8/site-packages (from black==19.10b0->nemo_toolkit[all]==1.5.1) (21.4.0)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: appdirs in /opt/conda/lib/python3.8/site-packages (from black==19.10b0->nemo_toolkit[all]==1.5.1) (1.4.4)\n",
+      "Requirement already satisfied: typed-ast>=1.4.0 in /opt/conda/lib/python3.8/site-packages (from black==19.10b0->nemo_toolkit[all]==1.5.1) (1.5.3)\n",
+      "Requirement already satisfied: pathspec<1,>=0.6 in /opt/conda/lib/python3.8/site-packages (from black==19.10b0->nemo_toolkit[all]==1.5.1) (0.9.0)\n",
+      "Requirement already satisfied: click>=6.5 in /opt/conda/lib/python3.8/site-packages (from black==19.10b0->nemo_toolkit[all]==1.5.1) (8.0.4)\n",
+      "Requirement already satisfied: toml>=0.9.4 in /opt/conda/lib/python3.8/site-packages (from black==19.10b0->nemo_toolkit[all]==1.5.1) (0.10.2)\n",
+      "Requirement already satisfied: antlr4-python3-runtime==4.8 in /opt/conda/lib/python3.8/site-packages (from hydra-core>=1.1.0->nemo_toolkit[all]==1.5.1) (4.8)\n",
+      "Requirement already satisfied: importlib-resources<5.3 in /opt/conda/lib/python3.8/site-packages (from hydra-core>=1.1.0->nemo_toolkit[all]==1.5.1) (5.2.3)\n",
+      "Requirement already satisfied: zipp>=3.1.0 in /opt/conda/lib/python3.8/site-packages (from importlib-resources<5.3->hydra-core>=1.1.0->nemo_toolkit[all]==1.5.1) (3.7.0)\n",
+      "Requirement already satisfied: pip-api in /opt/conda/lib/python3.8/site-packages (from isort[requirements]<5->nemo_toolkit[all]==1.5.1) (0.0.29)\n",
+      "Requirement already satisfied: pipreqs in /opt/conda/lib/python3.8/site-packages (from isort[requirements]<5->nemo_toolkit[all]==1.5.1) (0.4.11)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nemo_toolkit[all]==1.5.1) (4.31.2)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nemo_toolkit[all]==1.5.1) (1.4.0)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nemo_toolkit[all]==1.5.1) (3.0.7)\n",
+      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nemo_toolkit[all]==1.5.1) (0.11.0)\n",
+      "Requirement already satisfied: joblib in /opt/conda/lib/python3.8/site-packages (from nltk>=3.6.5->nemo_toolkit[all]==1.5.1) (1.1.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.2.1 in /opt/conda/lib/python3.8/site-packages (from onnx>=1.7.0->nemo_toolkit[all]==1.5.1) (4.1.1)\n",
+      "Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from onnx>=1.7.0->nemo_toolkit[all]==1.5.1) (1.16.0)\n",
+      "Requirement already satisfied: protobuf>=3.12.2 in /opt/conda/lib/python3.8/site-packages (from onnx>=1.7.0->nemo_toolkit[all]==1.5.1) (3.19.4)\n",
+      "Requirement already satisfied: pyDeprecate<0.4.0,>=0.3.1 in /opt/conda/lib/python3.8/site-packages (from pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (0.3.2)\n",
+      "Requirement already satisfied: tensorboard>=2.2.0 in /opt/conda/lib/python3.8/site-packages (from pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2.8.0)\n",
+      "Requirement already satisfied: fsspec[http]!=2021.06.0,>=2021.05.0 in /opt/conda/lib/python3.8/site-packages (from pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2022.2.0)\n",
+      "Requirement already satisfied: requests in /opt/conda/lib/python3.8/site-packages (from fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2.27.1)\n",
+      "Requirement already satisfied: aiohttp in /opt/conda/lib/python3.8/site-packages (from fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (3.8.1)\n",
+      "Requirement already satisfied: werkzeug>=0.11.15 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2.0.3)\n",
+      "Requirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (3.3.6)\n",
+      "Requirement already satisfied: setuptools>=41.0.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (59.5.0)\n",
+      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (0.4.6)\n",
+      "Requirement already satisfied: google-auth<3,>=1.6.3 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2.6.2)\n",
+      "Requirement already satisfied: wheel>=0.26 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (0.37.1)\n",
+      "Requirement already satisfied: grpcio>=1.24.3 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.44.0)\n",
+      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (0.6.1)\n",
+      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.8.1)\n",
+      "Requirement already satisfied: absl-py>=0.4 in /opt/conda/lib/python3.8/site-packages (from tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.0.0)\n",
+      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (5.0.0)\n",
+      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (0.2.8)\n",
+      "Requirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (4.8)\n",
+      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.8/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.3.1)\n",
+      "Requirement already satisfied: importlib-metadata>=4.4 in /opt/conda/lib/python3.8/site-packages (from markdown>=2.6.8->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (4.11.3)\n",
+      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (0.4.8)\n",
+      "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.8/site-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2.0.12)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (2021.10.8)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (3.3)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.26.8)\n",
+      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (3.2.0)\n",
+      "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /opt/conda/lib/python3.8/site-packages (from transformers>=4.0.1->nemo_toolkit[all]==1.5.1) (0.5.1)\n",
+      "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /opt/conda/lib/python3.8/site-packages (from transformers>=4.0.1->nemo_toolkit[all]==1.5.1) (0.12.1)\n",
+      "Requirement already satisfied: filelock in /opt/conda/lib/python3.8/site-packages (from transformers>=4.0.1->nemo_toolkit[all]==1.5.1) (3.6.0)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.8/site-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.3.0)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.8/site-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.7.2)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/conda/lib/python3.8/site-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (4.0.2)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.8/site-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (6.0.2)\n",
+      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.8/site-packages (from aiohttp->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.2.0)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: s3transfer<0.6.0,>=0.5.0 in /opt/conda/lib/python3.8/site-packages (from boto3->nemo_toolkit[all]==1.5.1) (0.5.2)\n",
+      "Requirement already satisfied: botocore<1.25.0,>=1.24.45 in /opt/conda/lib/python3.8/site-packages (from boto3->nemo_toolkit[all]==1.5.1) (1.24.45)\n",
+      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.8/site-packages (from boto3->nemo_toolkit[all]==1.5.1) (1.0.0)\n",
+      "Requirement already satisfied: pybind11>=2.2 in /opt/conda/lib/python3.8/site-packages (from fasttext->nemo_toolkit[all]==1.5.1) (2.9.1)\n",
+      "Requirement already satisfied: distance>=0.1.3 in /opt/conda/lib/python3.8/site-packages (from g2p-en->nemo_toolkit[all]==1.5.1) (0.1.3)\n",
+      "Requirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.8/site-packages (from gdown->nemo_toolkit[all]==1.5.1) (4.10.0)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.8/site-packages (from beautifulsoup4->gdown->nemo_toolkit[all]==1.5.1) (2.3.1)\n",
+      "Requirement already satisfied: ipython-genutils~=0.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (0.2.0)\n",
+      "Requirement already satisfied: ipython>=4.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (8.1.1)\n",
+      "Requirement already satisfied: ipykernel>=4.5.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (6.9.2)\n",
+      "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (1.1.0)\n",
+      "Requirement already satisfied: widgetsnbextension~=3.6.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (3.6.0)\n",
+      "Requirement already satisfied: traitlets>=4.3.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (5.1.1)\n",
+      "Requirement already satisfied: nbformat>=4.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets->nemo_toolkit[all]==1.5.1) (5.2.0)\n",
+      "Requirement already satisfied: jupyter-client<8.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (7.1.2)\n",
+      "Requirement already satisfied: psutil in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (5.9.0)\n",
+      "Requirement already satisfied: tornado<7.0,>=4.2 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (6.1)\n",
+      "Requirement already satisfied: debugpy<2.0,>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (1.5.1)\n",
+      "Requirement already satisfied: nest-asyncio in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (1.5.4)\n",
+      "Requirement already satisfied: matplotlib-inline<0.2.0,>=0.1.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (0.1.3)\n",
+      "Requirement already satisfied: pickleshare in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.7.5)\n",
+      "Requirement already satisfied: decorator in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (5.1.1)\n",
+      "Requirement already satisfied: pygments>=2.4.0 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (2.11.2)\n",
+      "Requirement already satisfied: stack-data in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.2.0)\n",
+      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (3.0.27)\n",
+      "Requirement already satisfied: backcall in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.2.0)\n",
+      "Requirement already satisfied: jedi>=0.16 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.18.1)\n",
+      "Requirement already satisfied: pexpect>4.3 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (4.8.0)\n",
+      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /opt/conda/lib/python3.8/site-packages (from jedi>=0.16->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.8.3)\n",
+      "Requirement already satisfied: entrypoints in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (0.4)\n",
+      "Requirement already satisfied: pyzmq>=13 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (22.3.0)\n",
+      "Requirement already satisfied: jupyter-core>=4.6.0 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets->nemo_toolkit[all]==1.5.1) (4.9.2)\n",
+      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /opt/conda/lib/python3.8/site-packages (from nbformat>=4.2.0->ipywidgets->nemo_toolkit[all]==1.5.1) (4.4.0)\n",
+      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.18.1)\n",
+      "Requirement already satisfied: ptyprocess>=0.5 in /opt/conda/lib/python3.8/site-packages (from pexpect>4.3->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.7.0)\n",
+      "Requirement already satisfied: wcwidth in /opt/conda/lib/python3.8/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.2.5)\n",
+      "Requirement already satisfied: notebook>=4.4.1 in /opt/conda/lib/python3.8/site-packages (from widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (6.4.1)\n",
+      "Requirement already satisfied: Send2Trash>=1.5.0 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (1.8.0)\n",
+      "Requirement already satisfied: prometheus-client in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.13.1)\n",
+      "Requirement already satisfied: terminado>=0.8.3 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.13.3)\n",
+      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (3.0.3)\n",
+      "Requirement already satisfied: nbconvert in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (6.4.4)\n",
+      "Requirement already satisfied: argon2-cffi in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (21.3.0)\n",
+      "Requirement already satisfied: argon2-cffi-bindings in /opt/conda/lib/python3.8/site-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (21.2.0)\n",
+      "Requirement already satisfied: cffi>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (1.15.0)\n",
+      "Requirement already satisfied: pycparser in /opt/conda/lib/python3.8/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (2.21)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.8/site-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (2.1.1)\n",
+      "Requirement already satisfied: resampy>=0.2.2 in /opt/conda/lib/python3.8/site-packages (from librosa->nemo_toolkit[all]==1.5.1) (0.2.2)\n",
+      "Requirement already satisfied: pooch>=1.0 in /opt/conda/lib/python3.8/site-packages (from librosa->nemo_toolkit[all]==1.5.1) (1.6.0)\n",
+      "Requirement already satisfied: audioread>=2.1.5 in /opt/conda/lib/python3.8/site-packages (from librosa->nemo_toolkit[all]==1.5.1) (2.1.9)\n",
+      "Requirement already satisfied: llvmlite<0.37,>=0.36.0rc1 in /opt/conda/lib/python3.8/site-packages (from numba->nemo_toolkit[all]==1.5.1) (0.36.0)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from scikit-learn->nemo_toolkit[all]==1.5.1) (3.1.0)\n",
+      "Requirement already satisfied: defusedxml in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.7.1)\n",
+      "Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.5.13)\n",
+      "Requirement already satisfied: bleach in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (4.1.0)\n",
+      "Requirement already satisfied: mistune<2,>=0.8.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.8.4)\n",
+      "Requirement already satisfied: pandocfilters>=1.4.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (1.5.0)\n",
+      "Requirement already satisfied: testpath in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.6.0)\n",
+      "Requirement already satisfied: jupyterlab-pygments in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.1.2)\n",
+      "Requirement already satisfied: webencodings in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.5.1)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.8/site-packages (from pandas->nemo_toolkit[all]==1.5.1) (2021.3)\n",
+      "Requirement already satisfied: pip in /opt/conda/lib/python3.8/site-packages (from pip-api->isort[requirements]<5->nemo_toolkit[all]==1.5.1) (21.2.4)\n",
+      "Requirement already satisfied: yarg in /opt/conda/lib/python3.8/site-packages (from pipreqs->isort[requirements]<5->nemo_toolkit[all]==1.5.1) (0.1.9)\n",
+      "Requirement already satisfied: docopt in /opt/conda/lib/python3.8/site-packages (from pipreqs->isort[requirements]<5->nemo_toolkit[all]==1.5.1) (0.6.2)\n",
+      "Requirement already satisfied: simplejson>=3.8.1 in /opt/conda/lib/python3.8/site-packages (from pyannote.core->nemo_toolkit[all]==1.5.1) (3.17.6)\n",
+      "Requirement already satisfied: sortedcontainers>=2.0.4 in /opt/conda/lib/python3.8/site-packages (from pyannote.core->nemo_toolkit[all]==1.5.1) (2.4.0)\n",
+      "Requirement already satisfied: tabulate>=0.7.7 in /opt/conda/lib/python3.8/site-packages (from pyannote.metrics->nemo_toolkit[all]==1.5.1) (0.8.9)\n",
+      "Requirement already satisfied: pyannote.database>=4.0.1 in /opt/conda/lib/python3.8/site-packages (from pyannote.metrics->nemo_toolkit[all]==1.5.1) (4.1.3)\n",
+      "Requirement already satisfied: sympy>=1.1 in /opt/conda/lib/python3.8/site-packages (from pyannote.metrics->nemo_toolkit[all]==1.5.1) (1.10.1)\n",
+      "Requirement already satisfied: typer[all]>=0.2.1 in /opt/conda/lib/python3.8/site-packages (from pyannote.database>=4.0.1->pyannote.metrics->nemo_toolkit[all]==1.5.1) (0.4.0)\n",
+      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.8/site-packages (from sympy>=1.1->pyannote.metrics->nemo_toolkit[all]==1.5.1) (1.2.1)\n",
+      "Requirement already satisfied: colorama<0.5.0,>=0.4.3 in /opt/conda/lib/python3.8/site-packages (from typer[all]>=0.2.1->pyannote.database>=4.0.1->pyannote.metrics->nemo_toolkit[all]==1.5.1) (0.4.4)\n",
+      "Requirement already satisfied: shellingham<2.0.0,>=1.3.0 in /opt/conda/lib/python3.8/site-packages (from typer[all]>=0.2.1->pyannote.database>=4.0.1->pyannote.metrics->nemo_toolkit[all]==1.5.1) (1.4.0)\n",
+      "Requirement already satisfied: py>=1.8.2 in /opt/conda/lib/python3.8/site-packages (from pytest->nemo_toolkit[all]==1.5.1) (1.11.0)\n",
+      "Requirement already satisfied: iniconfig in /opt/conda/lib/python3.8/site-packages (from pytest->nemo_toolkit[all]==1.5.1) (1.1.1)\n",
+      "Requirement already satisfied: pluggy<2.0,>=0.12 in /opt/conda/lib/python3.8/site-packages (from pytest->nemo_toolkit[all]==1.5.1) (1.0.0)\n",
+      "Requirement already satisfied: jarowinkler<1.1.0,>=1.0.2 in /opt/conda/lib/python3.8/site-packages (from rapidfuzz->nemo_toolkit[all]==1.5.1) (1.0.2)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /opt/conda/lib/python3.8/site-packages (from requests->fsspec[http]!=2021.06.0,>=2021.05.0->pytorch-lightning>=1.5.0->nemo_toolkit[all]==1.5.1) (1.7.1)\n",
+      "Requirement already satisfied: ruamel.yaml.clib>=0.2.6 in /opt/conda/lib/python3.8/site-packages (from ruamel.yaml->nemo_toolkit[all]==1.5.1) (0.2.6)\n",
+      "Requirement already satisfied: portalocker in /opt/conda/lib/python3.8/site-packages (from sacrebleu[ja]->nemo_toolkit[all]==1.5.1) (2.4.0)\n",
+      "Requirement already satisfied: ipadic<2.0,>=1.0 in /opt/conda/lib/python3.8/site-packages (from sacrebleu[ja]->nemo_toolkit[all]==1.5.1) (1.0.0)\n",
+      "Requirement already satisfied: mecab-python3==1.0.3 in /opt/conda/lib/python3.8/site-packages (from sacrebleu[ja]->nemo_toolkit[all]==1.5.1) (1.0.3)\n",
+      "Requirement already satisfied: sphinxcontrib-htmlhelp>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (2.0.0)\n",
+      "Requirement already satisfied: alabaster<0.8,>=0.7 in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (0.7.12)\n",
+      "Requirement already satisfied: babel>=1.3 in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (2.9.1)\n",
+      "Requirement already satisfied: sphinxcontrib-serializinghtml>=1.1.5 in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (1.1.5)\n",
+      "Requirement already satisfied: sphinxcontrib-devhelp in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (1.0.2)\n",
+      "Requirement already satisfied: sphinxcontrib-jsmath in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (1.0.1)\n",
+      "Requirement already satisfied: sphinxcontrib-qthelp in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (1.0.3)\n",
+      "Requirement already satisfied: snowballstemmer>=1.1 in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (2.2.0)\n",
+      "Requirement already satisfied: imagesize in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (1.3.0)\n",
+      "Requirement already satisfied: sphinxcontrib-applehelp in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (1.0.2)\n",
+      "Requirement already satisfied: docutils<0.18,>=0.14 in /opt/conda/lib/python3.8/site-packages (from sphinx->nemo_toolkit[all]==1.5.1) (0.17.1)\n",
+      "Requirement already satisfied: pybtex-docutils>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from sphinxcontrib-bibtex->nemo_toolkit[all]==1.5.1) (1.0.1)\n",
+      "Requirement already satisfied: pybtex>=0.24 in /opt/conda/lib/python3.8/site-packages (from sphinxcontrib-bibtex->nemo_toolkit[all]==1.5.1) (0.24.0)\n",
+      "Requirement already satisfied: latexcodec>=1.0.4 in /opt/conda/lib/python3.8/site-packages (from pybtex>=0.24->sphinxcontrib-bibtex->nemo_toolkit[all]==1.5.1) (2.0.1)\n",
+      "Requirement already satisfied: pure-eval in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.2.2)\n",
+      "Requirement already satisfied: asttokens in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (2.0.5)\n",
+      "Requirement already satisfied: executing in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets->nemo_toolkit[all]==1.5.1) (0.8.3)\n",
+      "Requirement already satisfied: pathtools in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (0.1.2)\n",
+      "Requirement already satisfied: setproctitle in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (1.2.3)\n",
+      "Requirement already satisfied: GitPython>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (3.1.27)\n",
+      "Requirement already satisfied: sentry-sdk>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (1.5.10)\n",
+      "Requirement already satisfied: shortuuid>=0.5.0 in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (1.0.8)\n",
+      "Requirement already satisfied: docker-pycreds>=0.4.0 in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (0.4.0)\n",
+      "Requirement already satisfied: promise<3,>=2.0 in /opt/conda/lib/python3.8/site-packages (from wandb->nemo_toolkit[all]==1.5.1) (2.3)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gitdb<5,>=4.0.1 in /opt/conda/lib/python3.8/site-packages (from GitPython>=1.0.0->wandb->nemo_toolkit[all]==1.5.1) (4.0.9)\n",
+      "Requirement already satisfied: smmap<6,>=3.0.1 in /opt/conda/lib/python3.8/site-packages (from gitdb<5,>=4.0.1->GitPython>=1.0.0->wandb->nemo_toolkit[all]==1.5.1) (5.0.0)\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Install dependencies\n",
+    "!pip install wget\n",
+    "!apt-get update && DEBIAN_FRONTEND=noninteractive  apt-get install -y libsndfile1 ffmpeg\n",
+    "!pip install Cython\n",
+    "\n",
+    "## Install NeMo\n",
+    "!pip install nemo_toolkit[all]==1.5.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"2\"></a>\n",
+    "## 2. Download Citrinet model\n",
+    "\n",
+    "Next, we download a pretrained Nemo Citrinet model and convert it to a Torchscript module:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import nemo\n",
+    "import torch\n",
+    "\n",
+    "import nemo.collections.asr as nemo_asr\n",
+    "from nemo.core import typecheck\n",
+    "typecheck.set_typecheck_enabled(False) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading and saving stt_en_citrinet_256...\n",
+      "[NeMo I 2022-04-21 23:12:45 cloud:56] Found existing object /root/.cache/torch/NeMo/NeMo_1.5.1/stt_en_citrinet_256/91a9cc5850784b2065e8a0aa3d526fd9/stt_en_citrinet_256.nemo.\n",
+      "[NeMo I 2022-04-21 23:12:45 cloud:62] Re-using file from: /root/.cache/torch/NeMo/NeMo_1.5.1/stt_en_citrinet_256/91a9cc5850784b2065e8a0aa3d526fd9/stt_en_citrinet_256.nemo\n",
+      "[NeMo I 2022-04-21 23:12:45 common:728] Instantiating model from pre-trained checkpoint\n",
+      "[NeMo I 2022-04-21 23:12:46 mixins:146] Tokenizer SentencePieceTokenizer initialized with 1024 tokens\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[NeMo W 2022-04-21 23:12:47 modelPT:130] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n",
+      "    Train config : \n",
+      "    manifest_filepath: null\n",
+      "    sample_rate: 16000\n",
+      "    batch_size: 32\n",
+      "    trim_silence: true\n",
+      "    max_duration: 16.7\n",
+      "    shuffle: true\n",
+      "    is_tarred: false\n",
+      "    tarred_audio_filepaths: null\n",
+      "    use_start_end_token: false\n",
+      "    \n",
+      "[NeMo W 2022-04-21 23:12:47 modelPT:137] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). \n",
+      "    Validation config : \n",
+      "    manifest_filepath: null\n",
+      "    sample_rate: 16000\n",
+      "    batch_size: 32\n",
+      "    shuffle: false\n",
+      "    use_start_end_token: false\n",
+      "    \n",
+      "[NeMo W 2022-04-21 23:12:47 modelPT:143] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).\n",
+      "    Test config : \n",
+      "    manifest_filepath: null\n",
+      "    sample_rate: 16000\n",
+      "    batch_size: 32\n",
+      "    shuffle: false\n",
+      "    use_start_end_token: false\n",
+      "    \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[NeMo I 2022-04-21 23:12:47 features:265] PADDING: 16\n",
+      "[NeMo I 2022-04-21 23:12:47 features:282] STFT using torch\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[NeMo W 2022-04-21 23:12:47 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/nemo/collections/asr/parts/preprocessing/features.py:315: FutureWarning: Pass sr=16000, n_fft=512 as keyword args. From version 0.10 passing these as positional arguments will result in an error\n",
+      "      librosa.filters.mel(sample_rate, self.n_fft, n_mels=nfilt, fmin=lowfreq, fmax=highfreq), dtype=torch.float\n",
+      "    \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[NeMo I 2022-04-21 23:12:49 save_restore_connector:149] Model EncDecCTCModelBPE was successfully restored from /root/.cache/torch/NeMo/NeMo_1.5.1/stt_en_citrinet_256/91a9cc5850784b2065e8a0aa3d526fd9/stt_en_citrinet_256.nemo.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[NeMo W 2022-04-21 23:12:49 export_utils:198] Swapped 0 modules\n",
+      "[NeMo W 2022-04-21 23:12:49 conv_asr:73] Turned off 235 masked convolutions\n",
+      "[NeMo W 2022-04-21 23:12:49 export_utils:198] Swapped 0 modules\n",
+      "[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/jit/_trace.py:916: UserWarning: `optimize` is deprecated and has no effect. Use `with torch.jit.optimized_execution() instead\n",
+      "      warnings.warn(\n",
+      "    \n",
+      "[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:668: LightningDeprecationWarning: The `LightningModule.model_size` property was deprecated in v1.5 and will be removed in v1.7. Please use the `pytorch_lightning.utilities.memory.get_model_size_mb`.\n",
+      "      if hasattr(mod, name):\n",
+      "    \n",
+      "[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:669: LightningDeprecationWarning: The `LightningModule.model_size` property was deprecated in v1.5 and will be removed in v1.7. Please use the `pytorch_lightning.utilities.memory.get_model_size_mb`.\n",
+      "      item = getattr(mod, name)\n",
+      "    \n",
+      "[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:668: LightningDeprecationWarning: `LightningModule.use_amp` was deprecated in v1.6 and will be removed in v1.8. Please use `Trainer.amp_backend`.\n",
+      "      if hasattr(mod, name):\n",
+      "    \n",
+      "[NeMo W 2022-04-21 23:12:50 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_jit_internal.py:669: LightningDeprecationWarning: `LightningModule.use_amp` was deprecated in v1.6 and will be removed in v1.8. Please use `Trainer.amp_backend`.\n",
+      "      item = getattr(mod, name)\n",
+      "    \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(['stt_en_citrinet_256.ts'],\n",
+       " ['nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE exported to ONNX'])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variant = 'stt_en_citrinet_256'\n",
+    "\n",
+    "print(f\"Downloading and saving {variant}...\")\n",
+    "asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name=variant)\n",
+    "asr_model.export(f\"{variant}.ts\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Benchmark utility\n",
+    "\n",
+    "Let us define a helper benchmarking function, then benchmark the original Pytorch model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model: stt_en_citrinet_256.ts\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256.ts =================================\n",
+      "batch size=1, num iterations=50\n",
+      "  Median samples/s: 102.0, mean: 102.0\n",
+      "  Median latency (s): 0.009802, mean: 0.009803, 99th_p: 0.009836, std_dev: 0.000014\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256.ts\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256.ts =================================\n",
+      "batch size=8, num iterations=50\n",
+      "  Median samples/s: 429.1, mean: 429.1\n",
+      "  Median latency (s): 0.018642, mean: 0.018643, 99th_p: 0.018670, std_dev: 0.000014\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256.ts\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256.ts =================================\n",
+      "batch size=32, num iterations=50\n",
+      "  Median samples/s: 551.3, mean: 551.2\n",
+      "  Median latency (s): 0.058047, mean: 0.058053, 99th_p: 0.058375, std_dev: 0.000106\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256.ts\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256.ts =================================\n",
+      "batch size=128, num iterations=50\n",
+      "  Median samples/s: 594.1, mean: 594.1\n",
+      "  Median latency (s): 0.215434, mean: 0.215446, 99th_p: 0.215806, std_dev: 0.000116\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import print_function\n",
+    "from __future__ import absolute_import\n",
+    "from __future__ import division\n",
+    "\n",
+    "import argparse\n",
+    "import timeit\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch_tensorrt as trtorch\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "\n",
+    "def benchmark(model, input_tensor, num_loops, model_name, batch_size):\n",
+    "    def timeGraph(model, input_tensor, num_loops):\n",
+    "        print(\"Warm up ...\")\n",
+    "        with torch.no_grad():\n",
+    "            for _ in range(20):\n",
+    "                features = model(input_tensor)\n",
+    "\n",
+    "        torch.cuda.synchronize()\n",
+    "        print(\"Start timing ...\")\n",
+    "        timings = []\n",
+    "        with torch.no_grad():\n",
+    "            for i in range(num_loops):\n",
+    "                start_time = timeit.default_timer()\n",
+    "                features = model(input_tensor)\n",
+    "                torch.cuda.synchronize()\n",
+    "                end_time = timeit.default_timer()\n",
+    "                timings.append(end_time - start_time)\n",
+    "                # print(\"Iteration {}: {:.6f} s\".format(i, end_time - start_time))\n",
+    "        return timings\n",
+    "    def printStats(graphName, timings, batch_size):\n",
+    "        times = np.array(timings)\n",
+    "        steps = len(times)\n",
+    "        speeds = batch_size / times\n",
+    "        time_mean = np.mean(times)\n",
+    "        time_med = np.median(times)\n",
+    "        time_99th = np.percentile(times, 99)\n",
+    "        time_std = np.std(times, ddof=0)\n",
+    "        speed_mean = np.mean(speeds)\n",
+    "        speed_med = np.median(speeds)\n",
+    "        msg = (\"\\n%s =================================\\n\"\n",
+    "                \"batch size=%d, num iterations=%d\\n\"\n",
+    "                \"  Median samples/s: %.1f, mean: %.1f\\n\"\n",
+    "                \"  Median latency (s): %.6f, mean: %.6f, 99th_p: %.6f, std_dev: %.6f\\n\"\n",
+    "                ) % (graphName,\n",
+    "                    batch_size, steps,\n",
+    "                    speed_med, speed_mean,\n",
+    "                    time_med, time_mean, time_99th, time_std)\n",
+    "        print(msg)\n",
+    "    timings = timeGraph(model, input_tensor, num_loops)\n",
+    "    printStats(model_name, timings, batch_size)\n",
+    "\n",
+    "precisions_str = 'fp32' # Precision (default=fp32, fp16)\n",
+    "variant = 'stt_en_citrinet_256' # Nemo Citrinet variant\n",
+    "batch_sizes = [1, 8, 32, 128] # Batch sizes (default=1,8,32,128)\n",
+    "trt = False # If True, infer with Torch-TensorRT engine. Else, infer with Pytorch model.\n",
+    "precision = torch.float32 if precisions_str =='fp32' else torch.float16\n",
+    "\n",
+    "for batch_size in batch_sizes:\n",
+    "    if trt:\n",
+    "        model_name = f\"{variant}_bs{batch_size}_{precision}.torch-tensorrt\"\n",
+    "    else:\n",
+    "        model_name = f\"{variant}.ts\"\n",
+    "\n",
+    "    print(f\"Loading model: {model_name}\") \n",
+    "    # Load traced model to CPU first\n",
+    "    model = torch.jit.load(model_name).cuda()\n",
+    "    cudnn.benchmark = True\n",
+    "    # Create random input tensor of certain size\n",
+    "    torch.manual_seed(12345)\n",
+    "    input_shape=(batch_size, 80, 1488)\n",
+    "    input_tensor = torch.randn(input_shape).cuda()\n",
+    "\n",
+    "    # Timing graph inference\n",
+    "    benchmark(model, input_tensor, 50, model_name, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Confirming the GPU we are using here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Thu Apr 21 23:13:32 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA TITAN V      On   | 00000000:17:00.0 Off |                  N/A |\n",
+      "| 38%   55C    P2    42W / 250W |   2462MiB / 12288MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "|   1  NVIDIA TITAN V      On   | 00000000:65:00.0 Off |                  N/A |\n",
+      "| 28%   39C    P8    26W / 250W |    112MiB / 12288MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|    0   N/A  N/A      3909      G                                       4MiB |\n",
+      "|    0   N/A  N/A      6047      C                                    2453MiB |\n",
+      "|    1   N/A  N/A      3909      G                                      39MiB |\n",
+      "|    1   N/A  N/A      4161      G                                      67MiB |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"3\"></a>\n",
+    "## 3. Create Torch-TensorRT modules\n",
+    "\n",
+    "In this step, we optimize the Citrinet Torchscript module with Torch-TensorRT with various precisions and batch sizes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating Torchscript-TensorRT module for batchsize 1 precision torch.float32\n",
+      "Generating Torchscript-TensorRT module for batchsize 8 precision torch.float32\n",
+      "Generating Torchscript-TensorRT module for batchsize 32 precision torch.float32\n",
+      "Generating Torchscript-TensorRT module for batchsize 128 precision torch.float32\n",
+      "Generating Torchscript-TensorRT module for batchsize 1 precision torch.float16\n",
+      "Generating Torchscript-TensorRT module for batchsize 8 precision torch.float16\n",
+      "Generating Torchscript-TensorRT module for batchsize 32 precision torch.float16\n",
+      "Generating Torchscript-TensorRT module for batchsize 128 precision torch.float16\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch_tensorrt as torchtrt\n",
+    "import argparse\n",
+    "\n",
+    "variant = \"stt_en_citrinet_256\"\n",
+    "precisions = [torch.float, torch.half]\n",
+    "batch_sizes = [1,8,32,128]\n",
+    "\n",
+    "model = torch.jit.load(f\"{variant}.ts\")\n",
+    "\n",
+    "for precision in precisions:\n",
+    "    for batch_size in batch_sizes:\n",
+    "        compile_settings = {\n",
+    "            \"inputs\": [torchtrt.Input(shape=[batch_size, 80, 1488])],\n",
+    "            \"enabled_precisions\": {precision},\n",
+    "            \"workspace_size\": 2000000000,\n",
+    "            \"truncate_long_and_double\": True,\n",
+    "        }\n",
+    "        print(f\"Generating Torchscript-TensorRT module for batchsize {batch_size} precision {precision}\")\n",
+    "        trt_ts_module = torchtrt.compile(model, **compile_settings)\n",
+    "        torch.jit.save(trt_ts_module, f\"{variant}_bs{batch_size}_{precision}.torch-tensorrt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"4\"></a>\n",
+    "## 4. Benchmark Torch-TensorRT models\n",
+    "\n",
+    "Finally, we are ready to benchmark the Torch-TensorRT optimized Citrinet models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FP32 (single precision)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model: stt_en_citrinet_256_bs1_torch.float32.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs1_torch.float32.torch-tensorrt =================================\n",
+      "batch size=1, num iterations=50\n",
+      "  Median samples/s: 242.2, mean: 218.0\n",
+      "  Median latency (s): 0.004128, mean: 0.004825, 99th_p: 0.008071, std_dev: 0.001270\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256_bs8_torch.float32.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs8_torch.float32.torch-tensorrt =================================\n",
+      "batch size=8, num iterations=50\n",
+      "  Median samples/s: 729.9, mean: 709.0\n",
+      "  Median latency (s): 0.010961, mean: 0.011388, 99th_p: 0.016114, std_dev: 0.001256\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256_bs32_torch.float32.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs32_torch.float32.torch-tensorrt =================================\n",
+      "batch size=32, num iterations=50\n",
+      "  Median samples/s: 955.6, mean: 953.4\n",
+      "  Median latency (s): 0.033488, mean: 0.033572, 99th_p: 0.035722, std_dev: 0.000545\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256_bs128_torch.float32.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs128_torch.float32.torch-tensorrt =================================\n",
+      "batch size=128, num iterations=50\n",
+      "  Median samples/s: 1065.8, mean: 1069.4\n",
+      "  Median latency (s): 0.120097, mean: 0.119708, 99th_p: 0.121618, std_dev: 0.001260\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "precisions_str = 'fp32' # Precision (default=fp32, fp16)\n",
+    "batch_sizes = [1, 8, 32, 128] # Batch sizes (default=1,8,32,128)\n",
+    "precision = torch.float32 if precisions_str =='fp32' else torch.float16\n",
+    "trt = True\n",
+    "\n",
+    "for batch_size in batch_sizes:\n",
+    "    if trt:\n",
+    "        model_name = f\"{variant}_bs{batch_size}_{precision}.torch-tensorrt\"\n",
+    "    else:\n",
+    "        model_name = f\"{variant}.ts\"\n",
+    "\n",
+    "    print(f\"Loading model: {model_name}\") \n",
+    "    # Load traced model to CPU first\n",
+    "    model = torch.jit.load(model_name).cuda()\n",
+    "    cudnn.benchmark = True\n",
+    "    # Create random input tensor of certain size\n",
+    "    torch.manual_seed(12345)\n",
+    "    input_shape=(batch_size, 80, 1488)\n",
+    "    input_tensor = torch.randn(input_shape).cuda()\n",
+    "\n",
+    "    # Timing graph inference\n",
+    "    benchmark(model, input_tensor, 50, model_name, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FP16 (half precision)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading model: stt_en_citrinet_256_bs1_torch.float16.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs1_torch.float16.torch-tensorrt =================================\n",
+      "batch size=1, num iterations=50\n",
+      "  Median samples/s: 288.9, mean: 272.9\n",
+      "  Median latency (s): 0.003462, mean: 0.003774, 99th_p: 0.006846, std_dev: 0.000820\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256_bs8_torch.float16.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs8_torch.float16.torch-tensorrt =================================\n",
+      "batch size=8, num iterations=50\n",
+      "  Median samples/s: 1201.0, mean: 1190.9\n",
+      "  Median latency (s): 0.006661, mean: 0.006733, 99th_p: 0.008453, std_dev: 0.000368\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256_bs32_torch.float16.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs32_torch.float16.torch-tensorrt =================================\n",
+      "batch size=32, num iterations=50\n",
+      "  Median samples/s: 1538.2, mean: 1516.4\n",
+      "  Median latency (s): 0.020804, mean: 0.021143, 99th_p: 0.024492, std_dev: 0.000973\n",
+      "\n",
+      "Loading model: stt_en_citrinet_256_bs128_torch.float16.torch-tensorrt\n",
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "stt_en_citrinet_256_bs128_torch.float16.torch-tensorrt =================================\n",
+      "batch size=128, num iterations=50\n",
+      "  Median samples/s: 1792.0, mean: 1777.0\n",
+      "  Median latency (s): 0.071428, mean: 0.072057, 99th_p: 0.076796, std_dev: 0.001351\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "precisions_str = 'fp16' # Precision (default=fp32, fp16)\n",
+    "batch_sizes = [1, 8, 32, 128] # Batch sizes (default=1,8,32,128)\n",
+    "precision = torch.float32 if precisions_str =='fp32' else torch.float16\n",
+    "\n",
+    "for batch_size in batch_sizes:\n",
+    "    if trt:\n",
+    "        model_name = f\"{variant}_bs{batch_size}_{precision}.torch-tensorrt\"\n",
+    "    else:\n",
+    "        model_name = f\"{variant}.ts\"\n",
+    "\n",
+    "    print(f\"Loading model: {model_name}\") \n",
+    "    # Load traced model to CPU first\n",
+    "    model = torch.jit.load(model_name).cuda()\n",
+    "    cudnn.benchmark = True\n",
+    "    # Create random input tensor of certain size\n",
+    "    torch.manual_seed(12345)\n",
+    "    input_shape=(batch_size, 80, 1488)\n",
+    "    input_tensor = torch.randn(input_shape).cuda()\n",
+    "\n",
+    "    # Timing graph inference\n",
+    "    benchmark(model, input_tensor, 50, model_name, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"5\"></a>\n",
+    "## 5. Conclusion\n",
+    "\n",
+    "In this notebook, we have walked through the complete process of optimizing the Citrinet model with Torch-TensorRT. On an A100 GPU, with Torch-TensorRT, we observe a speedup of ~**2.4X** with FP32, and ~**2.9X** with FP16 at batchsize of 128.\n",
+    "\n",
+    "### What's next\n",
+    "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/v1.2.0/_sources/_notebooks/EfficientNet-example.ipynb.txt b/docs/v1.2.0/_sources/_notebooks/EfficientNet-example.ipynb.txt
new file mode 100644
index 0000000000..31a3dad874
--- /dev/null
+++ b/docs/v1.2.0/_sources/_notebooks/EfficientNet-example.ipynb.txt
@@ -0,0 +1,693 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
+    "\n",
+    "# Torch-TensorRT Getting Started - EfficientNet-B0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch's JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.\n",
+    "\n",
+    "When deploying on NVIDIA GPUs TensorRT, NVIDIA's Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA's Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.\n",
+    "\n",
+    "Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning objectives\n",
+    "\n",
+    "This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained EfficientNet network, and running it to test the speedup obtained.\n",
+    "\n",
+    "## Content\n",
+    "1. [Requirements](#1)\n",
+    "1. [EfficientNet Overview](#2)\n",
+    "1. [Running the model without optimizations](#3)\n",
+    "1. [Accelerating with Torch-TensorRT](#4)\n",
+    "1. [Conclusion](#5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Collecting timm==0.4.12\n",
+      "  Downloading timm-0.4.12-py3-none-any.whl (376 kB)\n",
+      "\u001b[K     |████████████████████████████████| 376 kB 11.9 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: torch>=1.4 in /opt/conda/lib/python3.8/site-packages (from timm==0.4.12) (1.11.0a0+bfe5ad2)\n",
+      "Requirement already satisfied: torchvision in /opt/conda/lib/python3.8/site-packages (from timm==0.4.12) (0.12.0a0)\n",
+      "Requirement already satisfied: typing_extensions in /opt/conda/lib/python3.8/site-packages (from torch>=1.4->timm==0.4.12) (4.0.1)\n",
+      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.8/site-packages (from torchvision->timm==0.4.12) (8.2.0)\n",
+      "Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from torchvision->timm==0.4.12) (1.22.0)\n",
+      "Installing collected packages: timm\n",
+      "Successfully installed timm-0.4.12\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
+      "Fri Feb  4 21:29:36 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 510.39.01    Driver Version: 510.39.01    CUDA Version: 11.6     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA GeForce ...  On   | 00000000:65:00.0 Off |                  N/A |\n",
+      "| 30%   28C    P8    11W / 350W |      0MiB / 24576MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|  No running processes found                                                 |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install timm==0.4.12\n",
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"1\"></a>\n",
+    "## 1. Requirements\n",
+    "\n",
+    "NVIDIA's NGC provides PyTorch Docker Container which contains PyTorch and Torch-TensorRT. We can make use of [latest pytorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) container to run this notebook.\n",
+    "\n",
+    "Otherwise, you can follow the steps in `notebooks/README` to prepare a Docker container yourself, within which you can run this demo notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"2\"></a>\n",
+    "## 2. EfficientNet Overview\n",
+    "\n",
+    "\n",
+    "PyTorch has a model repository called `timm`, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet.\n",
+    "\n",
+    "### Model Description\n",
+    "\n",
+    "This model is based on the [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) paper.\n",
+    "\n",
+    "<img src=\"https://1.bp.blogspot.com/-Cdtb97FtgdA/XO3BHsB7oEI/AAAAAAAAEKE/bmtkonwgs8cmWyI5esVo8wJPnhPLQ5bGQCLcBGAs/s1600/image4.png\" alt=\"alt\" width=\"100%\"/>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"3\"></a>\n",
+    "## 3. Running the model without optimizations\n",
+    "\n",
+    "\n",
+    "PyTorch has a model repository called `timm`, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch_tensorrt\n",
+    "import timm\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "from timm.data import resolve_data_config\n",
+    "from timm.data.transforms_factory import create_transform\n",
+    "import json \n",
+    "\n",
+    "efficientnet_b0_model = timm.create_model('efficientnet_b0',pretrained=True)\n",
+    "model = efficientnet_b0_model.eval().to(\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With our model loaded, let's proceed to downloading some images!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2022-02-04 21:30:07--  https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\n",
+      "Resolving d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)... 18.65.227.127, 18.65.227.37, 18.65.227.99, ...\n",
+      "Connecting to d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)|18.65.227.127|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 24112 (24K) [image/jpeg]\n",
+      "Saving to: ‘./data/img0.JPG’\n",
+      "\n",
+      "./data/img0.JPG     100%[===================>]  23.55K  --.-KB/s    in 0.004s  \n",
+      "\n",
+      "2022-02-04 21:30:07 (6.40 MB/s) - ‘./data/img0.JPG’ saved [24112/24112]\n",
+      "\n",
+      "--2022-02-04 21:30:07--  https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\n",
+      "Resolving www.hakaimagazine.com (www.hakaimagazine.com)... 164.92.73.117\n",
+      "Connecting to www.hakaimagazine.com (www.hakaimagazine.com)|164.92.73.117|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 452718 (442K) [image/jpeg]\n",
+      "Saving to: ‘./data/img1.JPG’\n",
+      "\n",
+      "./data/img1.JPG     100%[===================>] 442.11K  --.-KB/s    in 0.06s   \n",
+      "\n",
+      "2022-02-04 21:30:07 (6.83 MB/s) - ‘./data/img1.JPG’ saved [452718/452718]\n",
+      "\n",
+      "--2022-02-04 21:30:08--  https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\n",
+      "Resolving www.artis.nl (www.artis.nl)... 94.75.225.20\n",
+      "Connecting to www.artis.nl (www.artis.nl)|94.75.225.20|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 361413 (353K) [image/jpeg]\n",
+      "Saving to: ‘./data/img2.JPG’\n",
+      "\n",
+      "./data/img2.JPG     100%[===================>] 352.94K   246KB/s    in 1.4s    \n",
+      "\n",
+      "2022-02-04 21:30:10 (246 KB/s) - ‘./data/img2.JPG’ saved [361413/361413]\n",
+      "\n",
+      "--2022-02-04 21:30:10--  https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\n",
+      "Resolving www.familyhandyman.com (www.familyhandyman.com)... 104.18.202.107, 104.18.201.107, 2606:4700::6812:ca6b, ...\n",
+      "Connecting to www.familyhandyman.com (www.familyhandyman.com)|104.18.202.107|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 90994 (89K) [image/jpeg]\n",
+      "Saving to: ‘./data/img3.JPG’\n",
+      "\n",
+      "./data/img3.JPG     100%[===================>]  88.86K  --.-KB/s    in 0.006s  \n",
+      "\n",
+      "2022-02-04 21:30:10 (14.4 MB/s) - ‘./data/img3.JPG’ saved [90994/90994]\n",
+      "\n",
+      "--2022-02-04 21:30:11--  https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\n",
+      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.133.45\n",
+      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.133.45|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 35363 (35K) [application/octet-stream]\n",
+      "Saving to: ‘./data/imagenet_class_index.json’\n",
+      "\n",
+      "./data/imagenet_cla 100%[===================>]  34.53K  --.-KB/s    in 0.07s   \n",
+      "\n",
+      "2022-02-04 21:30:11 (474 KB/s) - ‘./data/imagenet_class_index.json’ saved [35363/35363]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!mkdir -p ./data\n",
+    "!wget  -O ./data/img0.JPG \"https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\"\n",
+    "!wget  -O ./data/img1.JPG \"https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\"\n",
+    "!wget  -O ./data/img2.JPG \"https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\"\n",
+    "!wget  -O ./data/img3.JPG \"https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\"\n",
+    "\n",
+    "!wget  -O ./data/imagenet_class_index.json \"https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All pre-trained models expect input images normalized in the same way,\n",
+    "i.e. mini-batches of 3-channel RGB images of shape `(3 x H x W)`, where `H` and `W` are expected to be at least `224`.\n",
+    "The images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]`\n",
+    "and `std = [0.229, 0.224, 0.225]`.\n",
+    "\n",
+    "Here's a sample execution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "from torchvision import transforms\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
+    "\n",
+    "for i in range(4):\n",
+    "    img_path = './data/img%d.JPG'%i\n",
+    "    img = Image.open(img_path)\n",
+    "    preprocess = transforms.Compose([\n",
+    "        transforms.Resize(256),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
+    "    ])\n",
+    "    input_tensor = preprocess(img)      \n",
+    "    plt.subplot(2,2,i+1)\n",
+    "    plt.imshow(img)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "# loading labels\n",
+    "with open(\"./data/imagenet_class_index.json\") as json_file: \n",
+    "    d = json.load(json_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Throughout this tutorial, we will be making use of some utility functions; `efficientnet_preprocess` for preprocessing input images, `predict` to use the model for prediction and `benchmark` to benchmark the inference. You do not need to understand/go through these utilities to make use of Torch TensorRT, but are welecomed to do so if you choose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cudnn.benchmark = True\n",
+    "\n",
+    "def efficientnet_preprocess():\n",
+    "    config = resolve_data_config({}, model=model)\n",
+    "    transform = create_transform(**config)\n",
+    "    return transform\n",
+    "\n",
+    "# decode the results into ([predicted class, description], probability)\n",
+    "def predict(img_path, model):\n",
+    "    img = Image.open(img_path)\n",
+    "    preprocess = efficientnet_preprocess()\n",
+    "    input_tensor = preprocess(img)\n",
+    "    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model\n",
+    "    \n",
+    "    # move the input and model to GPU for speed if available\n",
+    "    if torch.cuda.is_available():\n",
+    "        input_batch = input_batch.to('cuda')\n",
+    "        model.to('cuda')\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        output = model(input_batch)\n",
+    "        # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes\n",
+    "        sm_output = torch.nn.functional.softmax(output[0], dim=0)\n",
+    "        \n",
+    "    ind = torch.argmax(sm_output)\n",
+    "    return d[str(ind.item())], sm_output[ind] #([predicted class, description], probability)\n",
+    "\n",
+    "def benchmark(model, input_shape=(1024, 1, 224, 224), dtype='fp32', nwarmup=50, nruns=10000):\n",
+    "    input_data = torch.randn(input_shape)\n",
+    "    input_data = input_data.to(\"cuda\")\n",
+    "    if dtype=='fp16':\n",
+    "        input_data = input_data.half()\n",
+    "        \n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(nwarmup):\n",
+    "            features = model(input_data)\n",
+    "    torch.cuda.synchronize()\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(1, nruns+1):\n",
+    "            start_time = time.time()\n",
+    "            features = model(input_data)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = time.time()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            if i%10==0:\n",
+    "                print('Iteration %d/%d, avg batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
+    "\n",
+    "    print(\"Input shape:\", input_data.size())\n",
+    "    print(\"Output features size:\", features.size())\n",
+    "    print('Average throughput: %.2f images/second'%(input_shape[0]/np.mean(timings)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the model downloaded and the util functions written, let's just quickly see some predictions, and benchmark the model in its current un-optimized state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.8/site-packages/torchvision/transforms/transforms.py:321: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./data/img0.JPG - Predicted: ['n02109961', 'Eskimo_dog'], Probablility: 0.3987298309803009\n",
+      "./data/img1.JPG - Predicted: ['n01537544', 'indigo_bunting'], Probablility: 0.23344755172729492\n",
+      "./data/img2.JPG - Predicted: ['n02481823', 'chimpanzee'], Probablility: 0.9695423245429993\n",
+      "./data/img3.JPG - Predicted: ['n01739381', 'vine_snake'], Probablility: 0.227739155292511\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(4):\n",
+    "    img_path = './data/img%d.JPG'%i\n",
+    "    img = Image.open(img_path)\n",
+    "    \n",
+    "    pred, prob = predict(img_path, efficientnet_b0_model)\n",
+    "    print('{} - Predicted: {}, Probablility: {}'.format(img_path, pred, prob))\n",
+    "\n",
+    "    plt.subplot(2,2,i+1)\n",
+    "    plt.imshow(img);\n",
+    "    plt.axis('off');\n",
+    "    plt.title(pred[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, avg batch time 37.62 ms\n",
+      "Iteration 20/100, avg batch time 37.66 ms\n",
+      "Iteration 30/100, avg batch time 37.65 ms\n",
+      "Iteration 40/100, avg batch time 37.66 ms\n",
+      "Iteration 50/100, avg batch time 37.70 ms\n",
+      "Iteration 60/100, avg batch time 37.70 ms\n",
+      "Iteration 70/100, avg batch time 37.70 ms\n",
+      "Iteration 80/100, avg batch time 37.71 ms\n",
+      "Iteration 90/100, avg batch time 37.72 ms\n",
+      "Iteration 100/100, avg batch time 37.72 ms\n",
+      "Input shape: torch.Size([128, 3, 224, 224])\n",
+      "Output features size: torch.Size([128, 1000])\n",
+      "Average throughput: 3393.46 images/second\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Model benchmark without Torch-TensorRT\n",
+    "benchmark(model, input_shape=(128, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"4\"></a>\n",
+    "## 4. Accelerating with Torch-TensorRT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Onwards to the next step, accelerating with Torch TensorRT. In these examples we showcase the results for FP32 (single precision) and FP16 (half precision). We do not demonstrat specific tuning, just showcase the simplicity of usage. If you want to learn more about the possible customizations, visit our [documentation](https://nvidia.github.io/Torch-TensorRT/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FP32 (single precision)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The compiled module will have precision as specified by \"op_precision\".\n",
+    "# Here, it will have FP32 precision.\n",
+    "trt_model_fp32 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.float32)],\n",
+    "    enabled_precisions = torch.float32, # Run with FP32\n",
+    "    workspace_size = 1 << 22\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, avg batch time 27.86 ms\n",
+      "Iteration 20/100, avg batch time 27.71 ms\n",
+      "Iteration 30/100, avg batch time 27.99 ms\n",
+      "Iteration 40/100, avg batch time 27.95 ms\n",
+      "Iteration 50/100, avg batch time 27.89 ms\n",
+      "Iteration 60/100, avg batch time 27.85 ms\n",
+      "Iteration 70/100, avg batch time 28.00 ms\n",
+      "Iteration 80/100, avg batch time 27.97 ms\n",
+      "Iteration 90/100, avg batch time 27.95 ms\n",
+      "Iteration 100/100, avg batch time 27.92 ms\n",
+      "Input shape: torch.Size([128, 3, 224, 224])\n",
+      "Output features size: torch.Size([128, 1000])\n",
+      "Average throughput: 4584.06 images/second\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Obtain the average time taken by a batch of input\n",
+    "benchmark(trt_model_fp32, input_shape=(128, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FP16 (half precision)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
+      "The compiler is going to use the user setting Float16\n",
+      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
+      "compatibility with PyTorch's data type convention is required.\n",
+      "If you do indeed see errors at runtime either:\n",
+      "- Remove the dtype spec for x.1\n",
+      "- Disable partial compilation by setting require_full_compilation to True\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT] - Mean converter disregards dtype\n",
+      "WARNING: [Torch-TensorRT TorchScript Conversion Context] - Tensor DataType is determined at build time for tensors not marked as input or output.\n",
+      "WARNING: [Torch-TensorRT TorchScript Conversion Context] - Tensor DataType is determined at build time for tensors not marked as input or output.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The compiled module will have precision as specified by \"op_precision\".\n",
+    "# Here, it will have FP16 precision.\n",
+    "trt_model_fp16 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.half)],\n",
+    "    enabled_precisions = {torch.half}, # Run with FP32\n",
+    "    workspace_size = 1 << 22\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, avg batch time 12.05 ms\n",
+      "Iteration 20/100, avg batch time 12.56 ms\n",
+      "Iteration 30/100, avg batch time 12.39 ms\n",
+      "Iteration 40/100, avg batch time 12.34 ms\n",
+      "Iteration 50/100, avg batch time 12.33 ms\n",
+      "Iteration 60/100, avg batch time 12.32 ms\n",
+      "Iteration 70/100, avg batch time 12.30 ms\n",
+      "Iteration 80/100, avg batch time 12.28 ms\n",
+      "Iteration 90/100, avg batch time 12.35 ms\n",
+      "Iteration 100/100, avg batch time 12.35 ms\n",
+      "Input shape: torch.Size([128, 3, 224, 224])\n",
+      "Output features size: torch.Size([128, 1000])\n",
+      "Average throughput: 10362.23 images/second\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Obtain the average time taken by a batch of input\n",
+    "benchmark(trt_model_fp16, input_shape=(128, 3, 224, 224), dtype='fp16', nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"5\"></a>\n",
+    "## 5. Conclusion\n",
+    "\n",
+    "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for EfficientNet-B0 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of **1.35x** with FP32, and **3.13x** with FP16 on an NVIDIA 3090 GPU. These acceleration numbers will vary from GPU to GPU(as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute cards for maximum acceleration.\n",
+    "\n",
+    "### What's next\n",
+    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/v1.2.0/_sources/_notebooks/Hugging-Face-BERT.ipynb.txt b/docs/v1.2.0/_sources/_notebooks/Hugging-Face-BERT.ipynb.txt
new file mode 100644
index 0000000000..9b027b473e
--- /dev/null
+++ b/docs/v1.2.0/_sources/_notebooks/Hugging-Face-BERT.ipynb.txt
@@ -0,0 +1,714 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9369b63c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2022 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0a97ac5",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png\" style=\"width: 90px; float: right;\">\n",
+    "\n",
+    "# Masked Language Modeling (MLM) with Hugging Face BERT Transformer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83f47edb",
+   "metadata": {},
+   "source": [
+    "## Learning objectives\n",
+    "\n",
+    "This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained BERT transformer from Hugging Face, and running it to test the speedup obtained.\n",
+    "\n",
+    "## Contents\n",
+    "1. [Requirements](#1)\n",
+    "2. [BERT Overview](#2)\n",
+    "3. [Creating TorchScript modules](#3)\n",
+    "4. [Compiling with Torch-TensorRT](#4)\n",
+    "5. [Benchmarking](#5)\n",
+    "6. [Conclusion](#6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "596fa151",
+   "metadata": {},
+   "source": [
+    "<a id=\"1\"></a>\n",
+    "## 1. Requirements\n",
+    "\n",
+    "NVIDIA's NGC provides a PyTorch Docker Container which contains PyTorch and Torch-TensorRT. Starting with version `22.05-py3`, we can make use of [latest pytorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) container to run this notebook.\n",
+    "\n",
+    "Otherwise, you can follow the steps in `notebooks/README` to prepare a Docker container yourself, within which you can run this demo notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "58e687d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Requirement already satisfied: transformers in /opt/conda/lib/python3.8/site-packages (4.18.0)\n",
+      "Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.8/site-packages (from transformers) (4.63.0)\n",
+      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.8/site-packages (from transformers) (2022.3.15)\n",
+      "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /opt/conda/lib/python3.8/site-packages (from transformers) (0.5.1)\n",
+      "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /opt/conda/lib/python3.8/site-packages (from transformers) (0.12.1)\n",
+      "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.8/site-packages (from transformers) (1.22.3)\n",
+      "Requirement already satisfied: sacremoses in /opt/conda/lib/python3.8/site-packages (from transformers) (0.0.49)\n",
+      "Requirement already satisfied: requests in /opt/conda/lib/python3.8/site-packages (from transformers) (2.27.1)\n",
+      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.8/site-packages (from transformers) (6.0)\n",
+      "Requirement already satisfied: filelock in /opt/conda/lib/python3.8/site-packages (from transformers) (3.6.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.8/site-packages (from transformers) (21.3)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.8/site-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\n",
+      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging>=20.0->transformers) (3.0.7)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests->transformers) (1.26.8)\n",
+      "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.8/site-packages (from requests->transformers) (2.0.12)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests->transformers) (2021.10.8)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests->transformers) (3.3)\n",
+      "Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from sacremoses->transformers) (1.16.0)\n",
+      "Requirement already satisfied: click in /opt/conda/lib/python3.8/site-packages (from sacremoses->transformers) (8.0.4)\n",
+      "Requirement already satisfied: joblib in /opt/conda/lib/python3.8/site-packages (from sacremoses->transformers) (1.1.0)\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1104c4f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import BertTokenizer, BertForMaskedLM\n",
+    "import torch\n",
+    "import timeit\n",
+    "import numpy as np\n",
+    "import torch_tensorrt\n",
+    "import torch.backends.cudnn as cudnn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acf67a5e",
+   "metadata": {},
+   "source": [
+    "<a id=\"2\"></a>\n",
+    "## 2. BERT Overview\n",
+    "\n",
+    "Transformers comprise a class of deep learning algorithms employing self-attention; broadly speaking, the models learn large matrices of numbers, each element of which denotes how important one component of input data is to another. Since their introduction in 2017, transformers have enjoyed widespread adoption, particularly in natural language processing, but also in computer vision problems. This is largely because they are easier to parallelize than the sequence models which attention mechanisms were originally designed to augment. \n",
+    "\n",
+    "Hugging Face is a company that maintains a huge respository of pre-trained transformer models. The company also provides tools for integrating those models into PyTorch code and running inference with them. \n",
+    "\n",
+    "One of the most popular transformer models is BERT (Bidirectional Encoder Representations from Transformers). First developed at Google and released in 2018, it has become the backbone of Google's search engine and a standard benchmark for NLP experiments. BERT was originally trained for next sentence prediction and masked language modeling (MLM), which aims to predict hidden words in sentences. In this notebook, we will use Hugging Face's `bert-base-uncased` model (BERT's smallest and simplest form, which does not employ text capitalization) for MLM."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19e711c0",
+   "metadata": {},
+   "source": [
+    "<a id=\"3\"></a>\n",
+    "## 3. Creating TorchScript modules  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81d4c6f6",
+   "metadata": {},
+   "source": [
+    "First, create a pretrained BERT tokenizer from the `bert-base-uncased` model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c7c8721e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "enc = BertTokenizer.from_pretrained('bert-base-uncased')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7c1c679",
+   "metadata": {},
+   "source": [
+    "Create dummy inputs to generate a traced TorchScript model later"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c3827087",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 4\n",
+    "\n",
+    "batched_indexed_tokens = [[101, 64]*64]*batch_size\n",
+    "batched_segment_ids = [[0, 1]*64]*batch_size\n",
+    "batched_attention_masks = [[1, 1]*64]*batch_size\n",
+    "\n",
+    "tokens_tensor = torch.tensor(batched_indexed_tokens)\n",
+    "segments_tensor = torch.tensor(batched_segment_ids)\n",
+    "attention_masks_tensor = torch.tensor(batched_attention_masks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e31b27f",
+   "metadata": {},
+   "source": [
+    "Obtain a BERT masked language model from Hugging Face in the (scripted) TorchScript, then use the dummy inputs to trace it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a3cd5a35",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
+      "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+     ]
+    }
+   ],
+   "source": [
+    "mlm_model_ts = BertForMaskedLM.from_pretrained('bert-base-uncased', torchscript=True)\n",
+    "traced_mlm_model = torch.jit.trace(mlm_model_ts, [tokens_tensor, segments_tensor, attention_masks_tensor])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8d2217a",
+   "metadata": {},
+   "source": [
+    "Define 4 masked sentences, with 1 word in each sentence hidden from the model. Fluent English speakers will probably be able to guess the masked words, but just in case, they are `'capital'`, `'language'`, `'innings'`, and `'mathematics'`.\n",
+    "\n",
+    "Also create a list containing the position of the masked word within each sentence. Given Python's 0-based indexing convention, the numbers are each higher by 1 than might be expected. This is because the token at index 0 in each sentence is a beginning-of-sentence token, denoted `[CLS]` when entered explicitly. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4d1af982",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "masked_sentences = ['Paris is the [MASK] of France.', \n",
+    "                    'The primary [MASK] of the United States is English.', \n",
+    "                    'A baseball game consists of at least nine [MASK].', \n",
+    "                    'Topology is a branch of [MASK] concerned with the properties of geometric objects that remain unchanged under continuous transformations.']\n",
+    "pos_masks = [4, 3, 9, 6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d89b4c8",
+   "metadata": {},
+   "source": [
+    "Pass the masked sentences into the (scripted) TorchScript MLM model and verify that the unmasked sentences yield the expected results.  \n",
+    "\n",
+    "Because the sentences are of different lengths, we must specify the `padding` argument in calling our encoder/tokenizer. There are several possible padding strategies, but we'll use `'max_length'` padding with `max_length=128`. Later, when we compile an optimized version of the model with Torch-TensorRT, the optimized model will expect inputs of length 128, hence our choice of padding strategy and length here. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d2d7546b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Paris is the capital of France.\n",
+      "The primary language of the United States is English.\n",
+      "A baseball game consists of at least nine innings.\n",
+      "Topology is a branch of mathematics concerned with the properties of geometric objects that remain unchanged under continuous transformations.\n"
+     ]
+    }
+   ],
+   "source": [
+    "encoded_inputs = enc(masked_sentences, return_tensors='pt', padding='max_length', max_length=128)\n",
+    "outputs = mlm_model_ts(**encoded_inputs)\n",
+    "most_likely_token_ids = [torch.argmax(outputs[0][i, pos, :]) for i, pos in enumerate(pos_masks)]\n",
+    "unmasked_tokens = enc.decode(most_likely_token_ids).split(' ')\n",
+    "unmasked_sentences = [masked_sentences[i].replace('[MASK]', token) for i, token in enumerate(unmasked_tokens)]\n",
+    "for sentence in unmasked_sentences:\n",
+    "    print(sentence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0b423ff",
+   "metadata": {},
+   "source": [
+    "Pass the masked sentences into the traced MLM model and verify that the unmasked sentences yield the expected results. \n",
+    "\n",
+    "Note the difference in how the `encoded_inputs` are passed into the model in the following cell compared to the previous one. If you examine `encoded_inputs`, you'll find that it's a dictionary with 3 keys, `'input_ids'`, `'token_type_ids'`, and `'attention_mask'`, each with a PyTorch tensor as an associated value. The traced model will accept `**encoded_inputs` as an input, but the Torch-TensorRT-optimized model (to be defined later) will not. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "683a4a73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Paris is the capital of France.\n",
+      "The primary language of the United States is English.\n",
+      "A baseball game consists of at least nine innings.\n",
+      "Topology is a branch of mathematics concerned with the properties of geometric objects that remain unchanged under continuous transformations.\n"
+     ]
+    }
+   ],
+   "source": [
+    "encoded_inputs = enc(masked_sentences, return_tensors='pt', padding='max_length', max_length=128)\n",
+    "outputs = traced_mlm_model(encoded_inputs['input_ids'], encoded_inputs['token_type_ids'], encoded_inputs['attention_mask'])\n",
+    "most_likely_token_ids = [torch.argmax(outputs[0][i, pos, :]) for i, pos in enumerate(pos_masks)]\n",
+    "unmasked_tokens = enc.decode(most_likely_token_ids).split(' ')\n",
+    "unmasked_sentences = [masked_sentences[i].replace('[MASK]', token) for i, token in enumerate(unmasked_tokens)]\n",
+    "for sentence in unmasked_sentences:\n",
+    "    print(sentence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a31b545",
+   "metadata": {},
+   "source": [
+    "<a id=\"4\"></a>\n",
+    "## 4. Compiling with Torch-TensorRT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "413d8b4f",
+   "metadata": {},
+   "source": [
+    "Change the logging level to avoid long printouts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "42862893",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_level = torch_tensorrt.logging.Level.Error\n",
+    "torch_tensorrt.logging.set_reportable_log_level(new_level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "121d6d59",
+   "metadata": {},
+   "source": [
+    "Compile the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "eab90150",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trt_model = torch_tensorrt.compile(traced_mlm_model, \n",
+    "    inputs= [torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32),  # input_ids\n",
+    "             torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32),  # token_type_ids\n",
+    "             torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32)], # attention_mask\n",
+    "    enabled_precisions= {torch.float32}, # Run with 32-bit precision\n",
+    "    workspace_size=2000000000,\n",
+    "    truncate_long_and_double=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a96751ce",
+   "metadata": {},
+   "source": [
+    "Pass the masked sentences into the compiled model and verify that the unmasked sentences yield the expected results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "097ea381",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Paris is the capital of France.\n",
+      "The primary language of the United States is English.\n",
+      "A baseball game consists of at least nine innings.\n",
+      "Topology is a branch of mathematics concerned with the properties of geometric objects that remain unchanged under continuous transformations.\n"
+     ]
+    }
+   ],
+   "source": [
+    "enc_inputs = enc(masked_sentences, return_tensors='pt', padding='max_length', max_length=128)\n",
+    "enc_inputs = {k: v.type(torch.int32).cuda() for k, v in enc_inputs.items()}\n",
+    "output_trt = trt_model(enc_inputs['input_ids'], enc_inputs['token_type_ids'], enc_inputs['attention_mask'])\n",
+    "most_likely_token_ids_trt = [torch.argmax(output_trt[i, pos, :]) for i, pos in enumerate(pos_masks)] \n",
+    "unmasked_tokens_trt = enc.decode(most_likely_token_ids_trt).split(' ')\n",
+    "unmasked_sentences_trt = [masked_sentences[i].replace('[MASK]', token) for i, token in enumerate(unmasked_tokens_trt)]\n",
+    "for sentence in unmasked_sentences_trt:\n",
+    "    print(sentence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a398271d",
+   "metadata": {},
+   "source": [
+    "Compile the model again, this time with 16-bit precision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a063dee2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trt_model_fp16 = torch_tensorrt.compile(traced_mlm_model, \n",
+    "    inputs= [torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32),  # input_ids\n",
+    "             torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32),  # token_type_ids\n",
+    "             torch_tensorrt.Input(shape=[batch_size, 128], dtype=torch.int32)], # attention_mask\n",
+    "    enabled_precisions= {torch.half}, # Run with 16-bit precision\n",
+    "    workspace_size=2000000000,\n",
+    "    truncate_long_and_double=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a926334a",
+   "metadata": {},
+   "source": [
+    "<a id=\"5\"></a>\n",
+    "## 5. Benchmarking\n",
+    "\n",
+    "In developing this notebook, we conducted our benchmarking on a single NVIDIA A100 GPU. Your results may differ from those shown, particularly on a different GPU."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "976c6fb9",
+   "metadata": {},
+   "source": [
+    "This function passes the inputs into the model and runs inference `num_loops` times, then returns a list of length containing the amount of time in seconds that each instance of inference took."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b72a091e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def timeGraph(model, input_tensor1, input_tensor2, input_tensor3, num_loops=50):\n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(20):\n",
+    "            features = model(input_tensor1, input_tensor2, input_tensor3)\n",
+    "\n",
+    "    torch.cuda.synchronize()\n",
+    "\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(num_loops):\n",
+    "            start_time = timeit.default_timer()\n",
+    "            features = model(input_tensor1, input_tensor2, input_tensor3)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = timeit.default_timer()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            # print(\"Iteration {}: {:.6f} s\".format(i, end_time - start_time))\n",
+    "\n",
+    "    return timings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b44dcf8",
+   "metadata": {},
+   "source": [
+    "This function prints the number of input batches the model is able to process each second and summary statistics of the model's latency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2ef71ab7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def printStats(graphName, timings, batch_size):\n",
+    "    times = np.array(timings)\n",
+    "    steps = len(times)\n",
+    "    speeds = batch_size / times\n",
+    "    time_mean = np.mean(times)\n",
+    "    time_med = np.median(times)\n",
+    "    time_99th = np.percentile(times, 99)\n",
+    "    time_std = np.std(times, ddof=0)\n",
+    "    speed_mean = np.mean(speeds)\n",
+    "    speed_med = np.median(speeds)\n",
+    "\n",
+    "    msg = (\"\\n%s =================================\\n\"\n",
+    "            \"batch size=%d, num iterations=%d\\n\"\n",
+    "            \"  Median text batches/second: %.1f, mean: %.1f\\n\"\n",
+    "            \"  Median latency: %.6f, mean: %.6f, 99th_p: %.6f, std_dev: %.6f\\n\"\n",
+    "            ) % (graphName,\n",
+    "                batch_size, steps,\n",
+    "                speed_med, speed_mean,\n",
+    "                time_med, time_mean, time_99th, time_std)\n",
+    "    print(msg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "afe97b9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cudnn.benchmark = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eba98b24",
+   "metadata": {},
+   "source": [
+    "Benchmark the (scripted) TorchScript model on GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "bab5fa8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "BERT =================================\n",
+      "batch size=4, num iterations=50\n",
+      "  Median text batches/second: 599.1, mean: 597.6\n",
+      "  Median latency: 0.006677, mean: 0.006693, 99th_p: 0.006943, std_dev: 0.000059\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "timings = timeGraph(mlm_model_ts.cuda(), enc_inputs['input_ids'], enc_inputs['token_type_ids'], enc_inputs['attention_mask'])\n",
+    "\n",
+    "printStats(\"BERT\", timings, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc79c452",
+   "metadata": {},
+   "source": [
+    "Benchmark the traced model on GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "5c0bd8e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "BERT =================================\n",
+      "batch size=4, num iterations=50\n",
+      "  Median text batches/second: 951.2, mean: 951.0\n",
+      "  Median latency: 0.004205, mean: 0.004206, 99th_p: 0.004256, std_dev: 0.000015\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "timings = timeGraph(traced_mlm_model.cuda(), enc_inputs['input_ids'], enc_inputs['token_type_ids'], enc_inputs['attention_mask'])\n",
+    "\n",
+    "printStats(\"BERT\", timings, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41db22a1",
+   "metadata": {},
+   "source": [
+    "Benchmark the compiled FP32 model on GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ade7b508",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "BERT =================================\n",
+      "batch size=4, num iterations=50\n",
+      "  Median text batches/second: 1216.9, mean: 1216.4\n",
+      "  Median latency: 0.003287, mean: 0.003289, 99th_p: 0.003317, std_dev: 0.000007\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "timings = timeGraph(trt_model, enc_inputs['input_ids'], enc_inputs['token_type_ids'], enc_inputs['attention_mask'])\n",
+    "\n",
+    "printStats(\"BERT\", timings, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57b696de",
+   "metadata": {},
+   "source": [
+    "Benchmark the compiled FP16 model on GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f61b83fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "\n",
+      "BERT =================================\n",
+      "batch size=4, num iterations=50\n",
+      "  Median text batches/second: 1776.7, mean: 1771.1\n",
+      "  Median latency: 0.002251, mean: 0.002259, 99th_p: 0.002305, std_dev: 0.000015\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "timings = timeGraph(trt_model_fp16, enc_inputs['input_ids'], enc_inputs['token_type_ids'], enc_inputs['attention_mask'])\n",
+    "\n",
+    "printStats(\"BERT\", timings, batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43f67ba3",
+   "metadata": {},
+   "source": [
+    "<a id=\"6\"></a>\n",
+    "## 6. Conclusion\n",
+    "\n",
+    "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for Masked Language Modeling with Hugging Face's `bert-base-uncased` transformer and testing the performance impact of the optimization. With Torch-TensorRT on an NVIDIA A100 GPU, we observe the speedups indicated below. These acceleration numbers will vary from GPU to GPU (as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute cards for maximum acceleration.\n",
+    "\n",
+    "Scripted (GPU): 1.0x\n",
+    "Traced (GPU): 1.62x\n",
+    "Torch-TensorRT (FP32): 2.14x\n",
+    "Torch-TensorRT (FP16): 3.15x\n",
+    "\n",
+    "### What's next\n",
+    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ebd152d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/v1.2.0/_sources/_notebooks/Resnet50-example.ipynb.txt b/docs/v1.2.0/_sources/_notebooks/Resnet50-example.ipynb.txt
new file mode 100644
index 0000000000..f020662c73
--- /dev/null
+++ b/docs/v1.2.0/_sources/_notebooks/Resnet50-example.ipynb.txt
@@ -0,0 +1,925 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png\" style=\"width: 90px; float: right;\">\n",
+    "\n",
+    "# Torch-TensorRT Getting Started - ResNet 50"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch's JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.\n",
+    "\n",
+    "When deploying on NVIDIA GPUs TensorRT, NVIDIA's Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA's Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.\n",
+    "\n",
+    "Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning objectives\n",
+    "\n",
+    "This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained ResNet-50 network, and running it to test the speedup obtained.\n",
+    "\n",
+    "## Content\n",
+    "1. [Requirements](#1)\n",
+    "1. [ResNet-50 Overview](#2)\n",
+    "1. [Running the model without optimizations](#3)\n",
+    "1. [Accelerating with Torch-TensorRT](#4)\n",
+    "1. [Conclusion](#5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tue Feb  8 19:16:53 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 510.39.01    Driver Version: 510.39.01    CUDA Version: 11.6     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA GeForce ...  On   | 00000000:65:00.0 Off |                  N/A |\n",
+      "| 30%   29C    P8    15W / 350W |      0MiB / 24576MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|  No running processes found                                                 |\n",
+      "+-----------------------------------------------------------------------------+\n",
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Collecting ipywidgets\n",
+      "  Downloading ipywidgets-7.6.5-py2.py3-none-any.whl (121 kB)\n",
+      "\u001b[K     |████████████████████████████████| 121 kB 5.3 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: ipython-genutils~=0.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (0.2.0)\n",
+      "Requirement already satisfied: ipykernel>=4.5.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (6.7.0)\n",
+      "Requirement already satisfied: nbformat>=4.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.3)\n",
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+      "Collecting jupyterlab-widgets>=1.0.0\n",
+      "  Downloading jupyterlab_widgets-1.0.2-py3-none-any.whl (243 kB)\n",
+      "\u001b[K     |████████████████████████████████| 243 kB 2.6 MB/s eta 0:00:01\n",
+      "\u001b[?25hCollecting widgetsnbextension~=3.5.0\n",
+      "  Downloading widgetsnbextension-3.5.2-py2.py3-none-any.whl (1.6 MB)\n",
+      "\u001b[K     |████████████████████████████████| 1.6 MB 3.5 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: traitlets>=4.3.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.1)\n",
+      "Requirement already satisfied: jupyter-client<8.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets) (7.1.2)\n",
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+      "Requirement already satisfied: matplotlib-inline<0.2.0,>=0.1.0 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets) (0.1.3)\n",
+      "Requirement already satisfied: pexpect>4.3 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (4.8.0)\n",
+      "Requirement already satisfied: setuptools>=18.5 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (59.5.0)\n",
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+      "Requirement already satisfied: backcall in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (0.2.0)\n",
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+      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /opt/conda/lib/python3.8/site-packages (from jedi>=0.16->ipython>=4.0.0->ipywidgets) (0.8.3)\n",
+      "Requirement already satisfied: entrypoints in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (0.3)\n",
+      "Requirement already satisfied: pyzmq>=13 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (22.3.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (2.8.2)\n",
+      "Requirement already satisfied: jupyter-core>=4.6.0 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (4.9.1)\n",
+      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /opt/conda/lib/python3.8/site-packages (from nbformat>=4.2.0->ipywidgets) (4.4.0)\n",
+      "Requirement already satisfied: attrs>=17.4.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets) (21.4.0)\n",
+      "Requirement already satisfied: importlib-resources>=1.4.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets) (5.4.0)\n",
+      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets) (0.18.1)\n",
+      "Requirement already satisfied: zipp>=3.1.0 in /opt/conda/lib/python3.8/site-packages (from importlib-resources>=1.4.0->jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets) (3.7.0)\n",
+      "Requirement already satisfied: ptyprocess>=0.5 in /opt/conda/lib/python3.8/site-packages (from pexpect>4.3->ipython>=4.0.0->ipywidgets) (0.7.0)\n",
+      "Requirement already satisfied: wcwidth in /opt/conda/lib/python3.8/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=4.0.0->ipywidgets) (0.2.5)\n",
+      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.8/site-packages (from python-dateutil>=2.1->jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (1.16.0)\n",
+      "Requirement already satisfied: notebook>=4.4.1 in /opt/conda/lib/python3.8/site-packages (from widgetsnbextension~=3.5.0->ipywidgets) (6.4.1)\n",
+      "Requirement already satisfied: Send2Trash>=1.5.0 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (1.8.0)\n",
+      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (3.0.3)\n",
+      "Requirement already satisfied: argon2-cffi in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (21.3.0)\n",
+      "Requirement already satisfied: prometheus-client in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.13.0)\n",
+      "Requirement already satisfied: terminado>=0.8.3 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.12.1)\n",
+      "Requirement already satisfied: nbconvert in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (6.4.0)\n",
+      "Requirement already satisfied: argon2-cffi-bindings in /opt/conda/lib/python3.8/site-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (21.2.0)\n",
+      "Requirement already satisfied: cffi>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (1.15.0)\n",
+      "Requirement already satisfied: pycparser in /opt/conda/lib/python3.8/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (2.21)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.8/site-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (2.0.1)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: jupyterlab-pygments in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.1.2)\n",
+      "Requirement already satisfied: defusedxml in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.7.1)\n",
+      "Requirement already satisfied: bleach in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (4.1.0)\n",
+      "Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.5.10)\n",
+      "Requirement already satisfied: testpath in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.5.0)\n",
+      "Requirement already satisfied: pandocfilters>=1.4.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (1.5.0)\n",
+      "Requirement already satisfied: mistune<2,>=0.8.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.8.4)\n",
+      "Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (21.3)\n",
+      "Requirement already satisfied: webencodings in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.5.1)\n",
+      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging->bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (3.0.6)\n",
+      "Installing collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets\n",
+      "Successfully installed ipywidgets-7.6.5 jupyterlab-widgets-1.0.2 widgetsnbextension-3.5.2\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi\n",
+    "!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"1\"></a>\n",
+    "## 1. Requirements\n",
+    "\n",
+    "NVIDIA's NGC provides PyTorch Docker Container which contains PyTorch and Torch-TensorRT. We can make use of [latest pytorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) container to run this notebook.\n",
+    "\n",
+    "Otherwise, you can follow the steps in `notebooks/README` to prepare a Docker container yourself, within which you can run this demo notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"2\"></a>\n",
+    "## 2. ResNet-50 Overview\n",
+    "\n",
+    "\n",
+    "PyTorch has a model repository called the PyTorch Hub, which is a source for high quality implementations of common models. We can get our ResNet-50 model from there pretrained on ImageNet.\n",
+    "\n",
+    "### Model Description\n",
+    "\n",
+    "This ResNet-50 model is based on the [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf) paper, which describes ResNet as “a method for detecting objects in images using a single deep neural network\". The input size is fixed to 32x32.\n",
+    "\n",
+    "<img src=\"https://pytorch.org/assets/images/resnet.png\" alt=\"alt\" width=\"50%\"/>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"3\"></a>\n",
+    "## 3. Running the model without optimizations\n",
+    "\n",
+    "\n",
+    "PyTorch has a model repository called `timm`, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: \"https://github.com/pytorch/vision/archive/v0.10.0.zip\" to /root/.cache/torch/hub/v0.10.0.zip\n",
+      "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7036a6122874340b14aaef7b8319260",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0.00/97.8M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ResNet(\n",
+       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  (relu): ReLU(inplace=True)\n",
+       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+       "  (layer1): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer2): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (3): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer3): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (3): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (4): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (5): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer4): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+       "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torchvision\n",
+    "\n",
+    "torch.hub._validate_not_a_forked_repo=lambda a,b,c: True\n",
+    "\n",
+    "resnet50_model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)\n",
+    "resnet50_model.eval()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With our model loaded, let's proceed to downloading some images!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2022-02-08 19:17:19--  https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\n",
+      "Resolving d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)... 18.65.227.99, 18.65.227.37, 18.65.227.223, ...\n",
+      "Connecting to d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)|18.65.227.99|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 24112 (24K) [image/jpeg]\n",
+      "Saving to: ‘./data/img0.JPG’\n",
+      "\n",
+      "./data/img0.JPG     100%[===================>]  23.55K  --.-KB/s    in 0.002s  \n",
+      "\n",
+      "2022-02-08 19:17:19 (14.6 MB/s) - ‘./data/img0.JPG’ saved [24112/24112]\n",
+      "\n",
+      "--2022-02-08 19:17:19--  https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\n",
+      "Resolving www.hakaimagazine.com (www.hakaimagazine.com)... 164.92.73.117\n",
+      "Connecting to www.hakaimagazine.com (www.hakaimagazine.com)|164.92.73.117|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 452718 (442K) [image/jpeg]\n",
+      "Saving to: ‘./data/img1.JPG’\n",
+      "\n",
+      "./data/img1.JPG     100%[===================>] 442.11K  2.40MB/s    in 0.2s    \n",
+      "\n",
+      "2022-02-08 19:17:19 (2.40 MB/s) - ‘./data/img1.JPG’ saved [452718/452718]\n",
+      "\n",
+      "--2022-02-08 19:17:20--  https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\n",
+      "Resolving www.artis.nl (www.artis.nl)... 94.75.225.20\n",
+      "Connecting to www.artis.nl (www.artis.nl)|94.75.225.20|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 361413 (353K) [image/jpeg]\n",
+      "Saving to: ‘./data/img2.JPG’\n",
+      "\n",
+      "./data/img2.JPG     100%[===================>] 352.94K   366KB/s    in 1.0s    \n",
+      "\n",
+      "2022-02-08 19:17:24 (366 KB/s) - ‘./data/img2.JPG’ saved [361413/361413]\n",
+      "\n",
+      "--2022-02-08 19:17:24--  https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\n",
+      "Resolving www.familyhandyman.com (www.familyhandyman.com)... 104.18.201.107, 104.18.202.107, 2606:4700::6812:ca6b, ...\n",
+      "Connecting to www.familyhandyman.com (www.familyhandyman.com)|104.18.201.107|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 94328 (92K) [image/jpeg]\n",
+      "Saving to: ‘./data/img3.JPG’\n",
+      "\n",
+      "./data/img3.JPG     100%[===================>]  92.12K  --.-KB/s    in 0.005s  \n",
+      "\n",
+      "2022-02-08 19:17:25 (16.8 MB/s) - ‘./data/img3.JPG’ saved [94328/94328]\n",
+      "\n",
+      "--2022-02-08 19:17:25--  https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\n",
+      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.41.222\n",
+      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.41.222|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 35363 (35K) [application/octet-stream]\n",
+      "Saving to: ‘./data/imagenet_class_index.json’\n",
+      "\n",
+      "./data/imagenet_cla 100%[===================>]  34.53K  --.-KB/s    in 0.07s   \n",
+      "\n",
+      "2022-02-08 19:17:26 (481 KB/s) - ‘./data/imagenet_class_index.json’ saved [35363/35363]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!mkdir -p ./data\n",
+    "!wget  -O ./data/img0.JPG \"https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\"\n",
+    "!wget  -O ./data/img1.JPG \"https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\"\n",
+    "!wget  -O ./data/img2.JPG \"https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\"\n",
+    "!wget  -O ./data/img3.JPG \"https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\"\n",
+    "\n",
+    "!wget  -O ./data/imagenet_class_index.json \"https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All pre-trained models expect input images normalized in the same way,\n",
+    "i.e. mini-batches of 3-channel RGB images of shape `(3 x H x W)`, where `H` and `W` are expected to be at least `224`.\n",
+    "The images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]`\n",
+    "and `std = [0.229, 0.224, 0.225]`.\n",
+    "\n",
+    "Here's a sample execution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "from torchvision import transforms\n",
+    "import matplotlib.pyplot as plt\n",
+    "import json \n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
+    "\n",
+    "for i in range(4):\n",
+    "    img_path = './data/img%d.JPG'%i\n",
+    "    img = Image.open(img_path)\n",
+    "    preprocess = transforms.Compose([\n",
+    "        transforms.Resize(256),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
+    "    ])\n",
+    "    input_tensor = preprocess(img)      \n",
+    "    plt.subplot(2,2,i+1)\n",
+    "    plt.imshow(img)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "# loading labels    \n",
+    "with open(\"./data/imagenet_class_index.json\") as json_file: \n",
+    "    d = json.load(json_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Throughout this tutorial, we will be making use of some utility functions; `rn50_preprocess` for preprocessing input images, `predict` to use the model for prediction and `benchmark` to benchmark the inference. You do not need to understand/go through these utilities to make use of Torch TensorRT, but are welecomed to do so if you choose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import time\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "cudnn.benchmark = True\n",
+    "\n",
+    "def rn50_preprocess():\n",
+    "    preprocess = transforms.Compose([\n",
+    "        transforms.Resize(256),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
+    "    ])\n",
+    "    return preprocess\n",
+    "\n",
+    "# decode the results into ([predicted class, description], probability)\n",
+    "def predict(img_path, model):\n",
+    "    img = Image.open(img_path)\n",
+    "    preprocess = rn50_preprocess()\n",
+    "    input_tensor = preprocess(img)\n",
+    "    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model\n",
+    "    \n",
+    "    # move the input and model to GPU for speed if available\n",
+    "    if torch.cuda.is_available():\n",
+    "        input_batch = input_batch.to('cuda')\n",
+    "        model.to('cuda')\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        output = model(input_batch)\n",
+    "        # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes\n",
+    "        sm_output = torch.nn.functional.softmax(output[0], dim=0)\n",
+    "        \n",
+    "    ind = torch.argmax(sm_output)\n",
+    "    return d[str(ind.item())], sm_output[ind] #([predicted class, description], probability)\n",
+    "\n",
+    "def benchmark(model, input_shape=(1024, 1, 224, 224), dtype='fp32', nwarmup=50, nruns=10000):\n",
+    "    input_data = torch.randn(input_shape)\n",
+    "    input_data = input_data.to(\"cuda\")\n",
+    "    if dtype=='fp16':\n",
+    "        input_data = input_data.half()\n",
+    "        \n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(nwarmup):\n",
+    "            features = model(input_data)\n",
+    "    torch.cuda.synchronize()\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(1, nruns+1):\n",
+    "            start_time = time.time()\n",
+    "            features = model(input_data)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = time.time()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            if i%10==0:\n",
+    "                print('Iteration %d/%d, ave batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
+    "\n",
+    "    print(\"Input shape:\", input_data.size())\n",
+    "    print(\"Output features size:\", features.size())\n",
+    "    print('Average batch time: %.2f ms'%(np.mean(timings)*1000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the model downloaded and the util functions written, let's just quickly see some predictions, and benchmark the model in its current un-optimized state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./data/img0.JPG - Predicted: ['n02110185', 'Siberian_husky'], Probablility: 0.49785590171813965\n",
+      "./data/img1.JPG - Predicted: ['n01820546', 'lorikeet'], Probablility: 0.6445754766464233\n",
+      "./data/img2.JPG - Predicted: ['n02481823', 'chimpanzee'], Probablility: 0.9899807572364807\n",
+      "./data/img3.JPG - Predicted: ['n01749939', 'green_mamba'], Probablility: 0.39485082030296326\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(4):\n",
+    "    img_path = './data/img%d.JPG'%i\n",
+    "    img = Image.open(img_path)\n",
+    "    \n",
+    "    pred, prob = predict(img_path, resnet50_model)\n",
+    "    print('{} - Predicted: {}, Probablility: {}'.format(img_path, pred, prob))\n",
+    "\n",
+    "    plt.subplot(2,2,i+1)\n",
+    "    plt.imshow(img);\n",
+    "    plt.axis('off');\n",
+    "    plt.title(pred[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 75.34 ms\n",
+      "Iteration 20/100, ave batch time 75.33 ms\n",
+      "Iteration 30/100, ave batch time 75.35 ms\n",
+      "Iteration 40/100, ave batch time 75.37 ms\n",
+      "Iteration 50/100, ave batch time 75.38 ms\n",
+      "Iteration 60/100, ave batch time 75.38 ms\n",
+      "Iteration 70/100, ave batch time 75.39 ms\n",
+      "Iteration 80/100, ave batch time 75.39 ms\n",
+      "Iteration 90/100, ave batch time 75.40 ms\n",
+      "Iteration 100/100, ave batch time 75.41 ms\n",
+      "Input shape: torch.Size([128, 3, 224, 224])\n",
+      "Output features size: torch.Size([128, 1000])\n",
+      "Average batch time: 75.41 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Model benchmark without Torch-TensorRT\n",
+    "model = resnet50_model.eval().to(\"cuda\")\n",
+    "benchmark(model, input_shape=(128, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"4\"></a>\n",
+    "## 4. Accelerating with Torch-TensorRT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Onwards to the next step, accelerating with Torch TensorRT. In these examples we showcase the results for FP32 (single precision) and FP16 (half precision). We do not demonstrat specific tuning, just showcase the simplicity of usage. If you want to learn more about the possible customizations, visit our [documentation](https://nvidia.github.io/Torch-TensorRT/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FP32 (single precision)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch_tensorrt\n",
+    "\n",
+    "# The compiled module will have precision as specified by \"op_precision\".\n",
+    "# Here, it will have FP32 precision.\n",
+    "trt_model_fp32 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.float32)],\n",
+    "    enabled_precisions = torch.float32, # Run with FP32\n",
+    "    workspace_size = 1 << 22\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 41.02 ms\n",
+      "Iteration 20/100, ave batch time 41.12 ms\n",
+      "Iteration 30/100, ave batch time 41.22 ms\n",
+      "Iteration 40/100, ave batch time 41.14 ms\n",
+      "Iteration 50/100, ave batch time 41.20 ms\n",
+      "Iteration 60/100, ave batch time 41.20 ms\n",
+      "Iteration 70/100, ave batch time 41.19 ms\n",
+      "Iteration 80/100, ave batch time 41.23 ms\n",
+      "Iteration 90/100, ave batch time 41.20 ms\n",
+      "Iteration 100/100, ave batch time 41.21 ms\n",
+      "Input shape: torch.Size([128, 3, 224, 224])\n",
+      "Output features size: torch.Size([128, 1000])\n",
+      "Average batch time: 41.21 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Obtain the average time taken by a batch of input\n",
+    "benchmark(trt_model_fp32, input_shape=(128, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FP16 (half precision)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
+      "The compiler is going to use the user setting Float16\n",
+      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
+      "compatibility with PyTorch's data type convention is required.\n",
+      "If you do indeed see errors at runtime either:\n",
+      "- Remove the dtype spec for x.1\n",
+      "- Disable partial compilation by setting require_full_compilation to True\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch_tensorrt\n",
+    "\n",
+    "# The compiled module will have precision as specified by \"op_precision\".\n",
+    "# Here, it will have FP16 precision.\n",
+    "trt_model_fp16 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.half)],\n",
+    "    enabled_precisions = {torch.half}, # Run with FP32\n",
+    "    workspace_size = 1 << 22\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 14.48 ms\n",
+      "Iteration 20/100, ave batch time 14.58 ms\n",
+      "Iteration 30/100, ave batch time 14.72 ms\n",
+      "Iteration 40/100, ave batch time 14.73 ms\n",
+      "Iteration 50/100, ave batch time 14.70 ms\n",
+      "Iteration 60/100, ave batch time 14.79 ms\n",
+      "Iteration 70/100, ave batch time 14.73 ms\n",
+      "Iteration 80/100, ave batch time 14.69 ms\n",
+      "Iteration 90/100, ave batch time 14.68 ms\n",
+      "Iteration 100/100, ave batch time 14.69 ms\n",
+      "Input shape: torch.Size([128, 3, 224, 224])\n",
+      "Output features size: torch.Size([128, 1000])\n",
+      "Average batch time: 14.69 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Obtain the average time taken by a batch of input\n",
+    "benchmark(trt_model_fp16, input_shape=(128, 3, 224, 224), dtype='fp16', nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"5\"></a>\n",
+    "## 5. Conclusion\n",
+    "\n",
+    "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for EfficientNet-B0 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of **1.84x** with FP32, and **5.2x** with FP16 on an NVIDIA 3090 GPU. These acceleration numbers will vary from GPU to GPU(as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute cards for maximum acceleration.\n",
+    "\n",
+    "### What's next\n",
+    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/v1.2.0/_sources/_notebooks/dynamic-shapes.ipynb.txt b/docs/v1.2.0/_sources/_notebooks/dynamic-shapes.ipynb.txt
new file mode 100644
index 0000000000..a0ceaab576
--- /dev/null
+++ b/docs/v1.2.0/_sources/_notebooks/dynamic-shapes.ipynb.txt
@@ -0,0 +1,1023 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "332a2ed8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2020 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18ee9d62",
+   "metadata": {},
+   "source": [
+    "# Torch-TensorRT - Using Dynamic Shapes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73703695",
+   "metadata": {},
+   "source": [
+    "Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch's Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting a TensorRT engine. Torch-TensorRT operates as a PyTorch extention and compiles modules that integrate into the JIT runtime seamlessly. After compilation using the optimized graph should feel no different than running a TorchScript module. You also have access to TensorRT's suite of configurations at compile time, so you are able to specify operating precision (FP32/FP16/INT8) and other settings for your module.\n",
+    "\n",
+    "We highly encorage users to use our NVIDIA's [PyTorch container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) to run this notebook. It comes packaged with a host of NVIDIA libraries and optimizations to widely used third party libraries. This container is tested and updated on a monthly cadence!\n",
+    "\n",
+    "This notebook has the following sections:\n",
+    "1. [TL;DR Explanation](#1)\n",
+    "1. [Setting up the model](#2)\n",
+    "1. [Working with Dynamic shapes in Torch TRT](#3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1603028d",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## TL;DR Explanation\n",
+    "\n",
+    "Making use of Dynamic Shaped Tensors in Torch TensorRT is quite simple. Let's say you are using the `torch_tensorrt.compile(...)` function to compile a torchscript module.\n",
+    "\n",
+    "One of the `args` in this function in this function is `input`: which defines an input to a module in terms of expected shape, data type and tensor format: `torch_tensorrt.Input`. \n",
+    "\n",
+    "For the purposes of this walkthrough we just need three `kwargs`: `min_shape`, `opt_shape` and `max_shape`. \n",
+    "```\n",
+    "...\n",
+    "torch_tensorrt.Input(\n",
+    "        min_shape=(1, 224, 224, 3),\n",
+    "        opt_shape=(1, 512, 512, 3),\n",
+    "        max_shape=(1, 1024, 1024, 3),\n",
+    "        dtype=torch.int32\n",
+    "        format=torch.channel_last\n",
+    "    )\n",
+    "...\n",
+    "```\n",
+    "In this example, we are going to use a simple ResNet model to demonstrate the use of the API. We will be using different batch sizes in the example, but you can use the same method to alter any of the dimensions of the tensor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "db3493d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mon May  2 20:40:30 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 470.57.02    Driver Version: 470.57.02    CUDA Version: 11.6     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA Graphics...  On   | 00000000:01:00.0 Off |                    0 |\n",
+      "| 41%   51C    P0    62W / 200W |      0MiB / 47681MiB |      0%      Default |\n",
+      "|                               |                      |             Disabled |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|  No running processes found                                                 |\n",
+      "+-----------------------------------------------------------------------------+\n",
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
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+      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (3.1.1)\n",
+      "Requirement already satisfied: prometheus-client in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.14.1)\n",
+      "Requirement already satisfied: Send2Trash>=1.5.0 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.8.0)\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: defusedxml in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.7.1)\n",
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+      "Requirement already satisfied: webencodings in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.5.1)\n",
+      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging->ipykernel>=4.5.1->ipywidgets) (3.0.8)\n",
+      "Requirement already satisfied: asttokens in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets) (2.0.5)\n",
+      "Requirement already satisfied: pure-eval in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets) (0.2.2)\n",
+      "Requirement already satisfied: executing in /opt/conda/lib/python3.8/site-packages (from stack-data->ipython>=4.0.0->ipywidgets) (0.8.3)\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi\n",
+    "!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ed1b4f7",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Setting up the model\n",
+    "\n",
+    "In this section, we will:\n",
+    "* Get sample data.\n",
+    "* Download model from torch hub.\n",
+    "* Build simple utility functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5934504f",
+   "metadata": {},
+   "source": [
+    "### Getting sample data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e46814cc",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "Connecting to d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)|18.65.227.37|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 24112 (24K) [image/jpeg]\n",
+      "Saving to: ‘./data/img0.JPG’\n",
+      "\n",
+      "./data/img0.JPG     100%[===================>]  23.55K  --.-KB/s    in 0.005s  \n",
+      "\n",
+      "2022-05-02 20:40:33 (4.69 MB/s) - ‘./data/img0.JPG’ saved [24112/24112]\n",
+      "\n",
+      "--2022-05-02 20:40:34--  https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\n",
+      "Resolving www.hakaimagazine.com (www.hakaimagazine.com)... 164.92.73.117\n",
+      "Connecting to www.hakaimagazine.com (www.hakaimagazine.com)|164.92.73.117|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 452718 (442K) [image/jpeg]\n",
+      "Saving to: ‘./data/img1.JPG’\n",
+      "\n",
+      "./data/img1.JPG     100%[===================>] 442.11K  --.-KB/s    in 0.02s   \n",
+      "\n",
+      "2022-05-02 20:40:34 (26.2 MB/s) - ‘./data/img1.JPG’ saved [452718/452718]\n",
+      "\n",
+      "--2022-05-02 20:40:34--  https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\n",
+      "Resolving www.artis.nl (www.artis.nl)... 94.75.225.20\n",
+      "Connecting to www.artis.nl (www.artis.nl)|94.75.225.20|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 361413 (353K) [image/jpeg]\n",
+      "Saving to: ‘./data/img2.JPG’\n",
+      "\n",
+      "./data/img2.JPG     100%[===================>] 352.94K   608KB/s    in 0.6s    \n",
+      "\n",
+      "2022-05-02 20:40:36 (608 KB/s) - ‘./data/img2.JPG’ saved [361413/361413]\n",
+      "\n",
+      "--2022-05-02 20:40:37--  https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\n",
+      "Resolving www.familyhandyman.com (www.familyhandyman.com)... 104.18.201.107, 104.18.202.107, 2606:4700::6812:c96b, ...\n",
+      "Connecting to www.familyhandyman.com (www.familyhandyman.com)|104.18.201.107|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 90994 (89K) [image/jpeg]\n",
+      "Saving to: ‘./data/img3.JPG’\n",
+      "\n",
+      "./data/img3.JPG     100%[===================>]  88.86K  --.-KB/s    in 0.006s  \n",
+      "\n",
+      "2022-05-02 20:40:37 (15.4 MB/s) - ‘./data/img3.JPG’ saved [90994/90994]\n",
+      "\n",
+      "--2022-05-02 20:40:37--  https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\n",
+      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.33.238\n",
+      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.33.238|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 35363 (35K) [application/octet-stream]\n",
+      "Saving to: ‘./data/imagenet_class_index.json’\n",
+      "\n",
+      "./data/imagenet_cla 100%[===================>]  34.53K  --.-KB/s    in 0.07s   \n",
+      "\n",
+      "2022-05-02 20:40:38 (489 KB/s) - ‘./data/imagenet_class_index.json’ saved [35363/35363]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!mkdir -p ./data\n",
+    "!wget  -O ./data/img0.JPG \"https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\"\n",
+    "!wget  -O ./data/img1.JPG \"https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\"\n",
+    "!wget  -O ./data/img2.JPG \"https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\"\n",
+    "!wget  -O ./data/img3.JPG \"https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\"\n",
+    "\n",
+    "!wget  -O ./data/imagenet_class_index.json \"https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7392a8ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualizing the downloaded images\n",
+    "\n",
+    "from PIL import Image\n",
+    "from torchvision import transforms\n",
+    "import matplotlib.pyplot as plt\n",
+    "import json \n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
+    "\n",
+    "for i in range(4):\n",
+    "    img_path = './data/img%d.JPG'%i\n",
+    "    img = Image.open(img_path)\n",
+    "    preprocess = transforms.Compose([\n",
+    "        transforms.Resize(256),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
+    "    ])\n",
+    "    input_tensor = preprocess(img)      \n",
+    "    plt.subplot(2,2,i+1)\n",
+    "    plt.imshow(img)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "# loading labels    \n",
+    "with open(\"./data/imagenet_class_index.json\") as json_file: \n",
+    "    d = json.load(json_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1374edab",
+   "metadata": {},
+   "source": [
+    "### Download model from torch hub."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "659fa798",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using cache found in /root/.cache/torch/hub/pytorch_vision_v0.10.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ResNet(\n",
+       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  (relu): ReLU(inplace=True)\n",
+       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+       "  (layer1): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer2): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (3): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer3): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (3): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (4): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (5): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer4): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (relu): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+       "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "\n",
+    "torch.hub._validate_not_a_forked_repo=lambda a,b,c: True\n",
+    "\n",
+    "resnet50_model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)\n",
+    "resnet50_model.eval()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a67e8db",
+   "metadata": {},
+   "source": [
+    "### Build simple utility functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "60286396",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import time\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "cudnn.benchmark = True\n",
+    "\n",
+    "def rn50_preprocess():\n",
+    "    preprocess = transforms.Compose([\n",
+    "        transforms.Resize(256),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
+    "    ])\n",
+    "    return preprocess\n",
+    "\n",
+    "# decode the results into ([predicted class, description], probability)\n",
+    "def predict(img_path, model):\n",
+    "    img = Image.open(img_path)\n",
+    "    preprocess = rn50_preprocess()\n",
+    "    input_tensor = preprocess(img)\n",
+    "    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model\n",
+    "    \n",
+    "    # move the input and model to GPU for speed if available\n",
+    "    if torch.cuda.is_available():\n",
+    "        input_batch = input_batch.to('cuda')\n",
+    "        model.to('cuda')\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        output = model(input_batch)\n",
+    "        # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes\n",
+    "        sm_output = torch.nn.functional.softmax(output[0], dim=0)\n",
+    "        \n",
+    "    ind = torch.argmax(sm_output)\n",
+    "    return d[str(ind.item())], sm_output[ind] #([predicted class, description], probability)\n",
+    "\n",
+    "# benchmarking models\n",
+    "def benchmark(model, input_shape=(1024, 1, 224, 224), dtype='fp32', nwarmup=50, nruns=10000):\n",
+    "    input_data = torch.randn(input_shape)\n",
+    "    input_data = input_data.to(\"cuda\")\n",
+    "    if dtype=='fp16':\n",
+    "        input_data = input_data.half()\n",
+    "        \n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(nwarmup):\n",
+    "            features = model(input_data)\n",
+    "    torch.cuda.synchronize()\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(1, nruns+1):\n",
+    "            start_time = time.time()\n",
+    "            features = model(input_data)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = time.time()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            if i%10==0:\n",
+    "                print('Iteration %d/%d, ave batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
+    "                print('Images processed per second=', int(1000*input_shape[0]/(np.mean(timings)*1000)))\n",
+    "    print(\"Input shape:\", input_data.size())\n",
+    "    print(\"Output features size:\", features.size())\n",
+    "    print('Average batch time: %.2f ms'%(np.mean(timings)*1000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21402d53",
+   "metadata": {},
+   "source": [
+    "Let's test our util functions on the model we have set up, starting with simple predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bb3e4a0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./data/img0.JPG - Predicted: ['n02110185', 'Siberian_husky'], Probablility: 0.49788108468055725\n",
+      "./data/img1.JPG - Predicted: ['n01820546', 'lorikeet'], Probablility: 0.6442285180091858\n",
+      "./data/img2.JPG - Predicted: ['n02481823', 'chimpanzee'], Probablility: 0.9899841547012329\n",
+      "./data/img3.JPG - Predicted: ['n01749939', 'green_mamba'], Probablility: 0.45675724744796753\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(4):\n",
+    "    img_path = './data/img%d.JPG'%i\n",
+    "    img = Image.open(img_path)\n",
+    "    \n",
+    "    pred, prob = predict(img_path, resnet50_model)\n",
+    "    print('{} - Predicted: {}, Probablility: {}'.format(img_path, pred, prob))\n",
+    "\n",
+    "    plt.subplot(2,2,i+1)\n",
+    "    plt.imshow(img);\n",
+    "    plt.axis('off');\n",
+    "    plt.title(pred[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fc7f347",
+   "metadata": {},
+   "source": [
+    "Onwards, to benchmarking."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f182b433",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 10.01 ms\n",
+      "Images processed per second= 1598\n",
+      "Iteration 20/100, ave batch time 10.01 ms\n",
+      "Images processed per second= 1598\n",
+      "Iteration 30/100, ave batch time 10.21 ms\n",
+      "Images processed per second= 1566\n",
+      "Iteration 40/100, ave batch time 10.33 ms\n",
+      "Images processed per second= 1549\n",
+      "Iteration 50/100, ave batch time 10.31 ms\n",
+      "Images processed per second= 1552\n",
+      "Iteration 60/100, ave batch time 10.25 ms\n",
+      "Images processed per second= 1560\n",
+      "Iteration 70/100, ave batch time 10.20 ms\n",
+      "Images processed per second= 1568\n",
+      "Iteration 80/100, ave batch time 10.18 ms\n",
+      "Images processed per second= 1572\n",
+      "Iteration 90/100, ave batch time 10.16 ms\n",
+      "Images processed per second= 1574\n",
+      "Iteration 100/100, ave batch time 10.15 ms\n",
+      "Images processed per second= 1575\n",
+      "Input shape: torch.Size([16, 3, 224, 224])\n",
+      "Output features size: torch.Size([16, 1000])\n",
+      "Average batch time: 10.15 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Model benchmark without Torch-TensorRT\n",
+    "model = resnet50_model.eval().to(\"cuda\")\n",
+    "benchmark(model, input_shape=(16, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21e56cdf",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Benchmarking with Torch-TRT (without dynamic shapes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8c9ed780",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch_tensorrt\n",
+    "\n",
+    "trt_model_without_ds = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((32, 3, 224, 224), dtype=torch.float32)],\n",
+    "    enabled_precisions = torch.float32, # Run with FP32\n",
+    "    workspace_size = 1 << 33\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "58a4ba94",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 6.10 ms\n",
+      "Images processed per second= 5242\n",
+      "Iteration 20/100, ave batch time 6.12 ms\n",
+      "Images processed per second= 5231\n",
+      "Iteration 30/100, ave batch time 6.14 ms\n",
+      "Images processed per second= 5215\n",
+      "Iteration 40/100, ave batch time 6.14 ms\n",
+      "Images processed per second= 5207\n",
+      "Iteration 50/100, ave batch time 6.15 ms\n",
+      "Images processed per second= 5202\n",
+      "Iteration 60/100, ave batch time 6.28 ms\n",
+      "Images processed per second= 5094\n",
+      "Iteration 70/100, ave batch time 6.26 ms\n",
+      "Images processed per second= 5110\n",
+      "Iteration 80/100, ave batch time 6.25 ms\n",
+      "Images processed per second= 5118\n",
+      "Iteration 90/100, ave batch time 6.25 ms\n",
+      "Images processed per second= 5115\n",
+      "Iteration 100/100, ave batch time 6.40 ms\n",
+      "Images processed per second= 5002\n",
+      "Input shape: torch.Size([32, 3, 224, 224])\n",
+      "Output features size: torch.Size([32, 1000])\n",
+      "Average batch time: 6.40 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_model_without_ds, input_shape=(32, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd233b85",
+   "metadata": {},
+   "source": [
+    "With the baseline ready, we can proceed to the section working discussing dynamic shapes!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd5746da",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Working with Dynamic shapes in Torch TRT\n",
+    "\n",
+    "Enabling \"Dynamic Shaped\" tensors to be used is essentially enabling the ability to defer defining the shape of tensors until runetime. Torch TensorRT simply leverages TensorRT's Dynamic shape support. You can read more about TensorRT's implementation in the [TensorRT Documentation](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#work_dynamic_shapes).\n",
+    "\n",
+    "#### How can you use this feature?\n",
+    "\n",
+    "To make use of dynamic shapes, you need to provide three shapes:\n",
+    "* `min_shape`: The minimum size of the tensor considered for optimizations.\n",
+    "* `opt_shape`: The optimizations will be done with an effort to maximize performance for this shape.\n",
+    "* `min_shape`: The maximum size of the tensor considered for optimizations.\n",
+    "\n",
+    "Generally, users can expect best performance within the specified ranges. Performance for other shapes may be be lower for other shapes (depending on the model ops and GPU used)\n",
+    "\n",
+    "In the following example, we will showcase varing batch size, which is the zeroth dimension of our input tensors. As Convolution operations require that the channel dimension be a build-time constant, we won't be changing sizes of other channels in this example, but for models which contain ops conducive to changes in other channels, this functionality can be freely used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9a04007d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The compiled module will have precision as specified by \"op_precision\".\n",
+    "# Here, it will have FP32 precision.\n",
+    "trt_model_with_ds = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input(\n",
+    "        min_shape=(16, 3, 224, 224),\n",
+    "        opt_shape=(32, 3, 224, 224),\n",
+    "        max_shape=(64, 3, 224, 224),\n",
+    "        dtype=torch.float32)],\n",
+    "    enabled_precisions = torch.float32, # Run with FP32\n",
+    "    workspace_size = 1 << 33\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f0babc85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 3.88 ms\n",
+      "Images processed per second= 4122\n",
+      "Iteration 20/100, ave batch time 3.89 ms\n",
+      "Images processed per second= 4116\n",
+      "Iteration 30/100, ave batch time 3.88 ms\n",
+      "Images processed per second= 4123\n",
+      "Iteration 40/100, ave batch time 3.86 ms\n",
+      "Images processed per second= 4142\n",
+      "Iteration 50/100, ave batch time 3.85 ms\n",
+      "Images processed per second= 4156\n",
+      "Iteration 60/100, ave batch time 3.84 ms\n",
+      "Images processed per second= 4166\n",
+      "Iteration 70/100, ave batch time 3.84 ms\n",
+      "Images processed per second= 4170\n",
+      "Iteration 80/100, ave batch time 3.83 ms\n",
+      "Images processed per second= 4172\n",
+      "Iteration 90/100, ave batch time 3.83 ms\n",
+      "Images processed per second= 4176\n",
+      "Iteration 100/100, ave batch time 3.83 ms\n",
+      "Images processed per second= 4178\n",
+      "Input shape: torch.Size([16, 3, 224, 224])\n",
+      "Output features size: torch.Size([16, 1000])\n",
+      "Average batch time: 3.83 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_model_with_ds, input_shape=(16, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e86a3541",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 6.71 ms\n",
+      "Images processed per second= 4767\n",
+      "Iteration 20/100, ave batch time 6.48 ms\n",
+      "Images processed per second= 4935\n",
+      "Iteration 30/100, ave batch time 6.39 ms\n",
+      "Images processed per second= 5005\n",
+      "Iteration 40/100, ave batch time 6.38 ms\n",
+      "Images processed per second= 5014\n",
+      "Iteration 50/100, ave batch time 6.38 ms\n",
+      "Images processed per second= 5016\n",
+      "Iteration 60/100, ave batch time 6.37 ms\n",
+      "Images processed per second= 5020\n",
+      "Iteration 70/100, ave batch time 6.37 ms\n",
+      "Images processed per second= 5024\n",
+      "Iteration 80/100, ave batch time 6.37 ms\n",
+      "Images processed per second= 5027\n",
+      "Iteration 90/100, ave batch time 6.37 ms\n",
+      "Images processed per second= 5026\n",
+      "Iteration 100/100, ave batch time 6.38 ms\n",
+      "Images processed per second= 5018\n",
+      "Input shape: torch.Size([32, 3, 224, 224])\n",
+      "Output features size: torch.Size([32, 1000])\n",
+      "Average batch time: 6.38 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_model_with_ds, input_shape=(32, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "35800f63",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, ave batch time 12.31 ms\n",
+      "Images processed per second= 5197\n",
+      "Iteration 20/100, ave batch time 12.42 ms\n",
+      "Images processed per second= 5153\n",
+      "Iteration 30/100, ave batch time 12.85 ms\n",
+      "Images processed per second= 4980\n",
+      "Iteration 40/100, ave batch time 12.71 ms\n",
+      "Images processed per second= 5033\n",
+      "Iteration 50/100, ave batch time 12.67 ms\n",
+      "Images processed per second= 5052\n",
+      "Iteration 60/100, ave batch time 12.63 ms\n",
+      "Images processed per second= 5067\n",
+      "Iteration 70/100, ave batch time 12.58 ms\n",
+      "Images processed per second= 5088\n",
+      "Iteration 80/100, ave batch time 12.56 ms\n",
+      "Images processed per second= 5096\n",
+      "Iteration 90/100, ave batch time 12.55 ms\n",
+      "Images processed per second= 5100\n",
+      "Iteration 100/100, ave batch time 12.57 ms\n",
+      "Images processed per second= 5091\n",
+      "Input shape: torch.Size([64, 3, 224, 224])\n",
+      "Output features size: torch.Size([64, 1000])\n",
+      "Average batch time: 12.57 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_model_with_ds, input_shape=(64, 3, 224, 224), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f87a75bd",
+   "metadata": {},
+   "source": [
+    "## What's Next?\n",
+    "\n",
+    "Check out the [TensorRT Getting started page](https://developer.nvidia.com/tensorrt-getting-started) for more tutorials, or visit the Torch-TensorRT [documentation](https://nvidia.github.io/Torch-TensorRT/) for more information!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/v1.2.0/_sources/_notebooks/lenet-getting-started.ipynb.txt b/docs/v1.2.0/_sources/_notebooks/lenet-getting-started.ipynb.txt
new file mode 100644
index 0000000000..2db954946d
--- /dev/null
+++ b/docs/v1.2.0/_sources/_notebooks/lenet-getting-started.ipynb.txt
@@ -0,0 +1,718 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
+    "\n",
+    "# Torch-TensorRT Getting Started - LeNet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch's JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.\n",
+    "\n",
+    "When deploying on NVIDIA GPUs TensorRT, NVIDIA's Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA's Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.\n",
+    "\n",
+    "Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning objectives\n",
+    "\n",
+    "This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a simple LeNet network. \n",
+    "\n",
+    "## Content\n",
+    "1. [Requirements](#1)\n",
+    "1. [Creating TorchScript modules](#2)\n",
+    "1. [Compiling with Torch-TensorRT](#3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"1\"></a>\n",
+    "## 1. Requirements\n",
+    "\n",
+    "Follow the steps in `notebooks/README` to prepare a Docker container, within which you can run this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Collecting ipywidgets\n",
+      "  Downloading ipywidgets-7.6.5-py2.py3-none-any.whl (121 kB)\n",
+      "\u001b[K     |████████████████████████████████| 121 kB 12.7 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: traitlets>=4.3.1 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.1)\n",
+      "Collecting jupyterlab-widgets>=1.0.0\n",
+      "  Downloading jupyterlab_widgets-1.0.2-py3-none-any.whl (243 kB)\n",
+      "\u001b[K     |████████████████████████████████| 243 kB 115.0 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: ipython-genutils~=0.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (0.2.0)\n",
+      "Requirement already satisfied: nbformat>=4.2.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (5.1.3)\n",
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+      "Requirement already satisfied: ipython>=4.0.0 in /opt/conda/lib/python3.8/site-packages (from ipywidgets) (7.31.0)\n",
+      "Collecting widgetsnbextension~=3.5.0\n",
+      "  Downloading widgetsnbextension-3.5.2-py2.py3-none-any.whl (1.6 MB)\n",
+      "\u001b[K     |████████████████████████████████| 1.6 MB 122.5 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: tornado<7.0,>=4.2 in /opt/conda/lib/python3.8/site-packages (from ipykernel>=4.5.1->ipywidgets) (6.1)\n",
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+      "Requirement already satisfied: jedi>=0.16 in /opt/conda/lib/python3.8/site-packages (from ipython>=4.0.0->ipywidgets) (0.18.1)\n",
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+      "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=4.5.1->ipywidgets) (2.8.2)\n",
+      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /opt/conda/lib/python3.8/site-packages (from nbformat>=4.2.0->ipywidgets) (4.4.0)\n",
+      "Requirement already satisfied: attrs>=17.4.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets) (21.4.0)\n",
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+      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /opt/conda/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets) (0.18.1)\n",
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+      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (3.0.3)\n",
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+      "Requirement already satisfied: Send2Trash>=1.5.0 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (1.8.0)\n",
+      "Requirement already satisfied: argon2-cffi-bindings in /opt/conda/lib/python3.8/site-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (21.2.0)\n",
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+      "Requirement already satisfied: pandocfilters>=1.4.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (1.5.0)\n",
+      "Requirement already satisfied: mistune<2,>=0.8.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.8.4)\n",
+      "Requirement already satisfied: webencodings in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.5.1)\n",
+      "Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (21.3)\n",
+      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging->bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (3.0.6)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Installing collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets\n",
+      "Successfully installed ipywidgets-7.6.5 jupyterlab-widgets-1.0.2 widgetsnbextension-3.5.2\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
+      "Thu Feb 10 22:01:27 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 510.39.01    Driver Version: 510.39.01    CUDA Version: 11.6     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA GeForce ...  On   | 00000000:09:00.0 Off |                  N/A |\n",
+      "|  0%   42C    P8    20W / 320W |      0MiB / 10240MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|  No running processes found                                                 |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org\n",
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"2\"></a>\n",
+    "## 2. Creating TorchScript modules\n",
+    "\n",
+    "Here we create two submodules for a feature extractor and a classifier and stitch them together in a single LeNet module. In this case this is overkill but modules give us granular control over our program including where we decide to optimize and where we don't. It is also the unit that the TorchScript compiler operates on. So you can decide to only convert/optimize the feature extractor and leave the classifier in standard PyTorch or you can convert the whole thing. When compiling your module to TorchScript, there are two paths: Tracing and Scripting.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch \n",
+    "from torch import nn\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "class LeNetFeatExtractor(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(LeNetFeatExtractor, self).__init__()\n",
+    "        self.conv1 = nn.Conv2d(1, 128, 3)\n",
+    "        self.conv2 = nn.Conv2d(128, 16, 3)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n",
+    "        x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n",
+    "        return x\n",
+    "\n",
+    "class LeNetClassifier(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(LeNetClassifier, self).__init__()\n",
+    "        self.fc1 = nn.Linear(16 * 6 * 6, 120)\n",
+    "        self.fc2 = nn.Linear(120, 84)\n",
+    "        self.fc3 = nn.Linear(84, 10)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = torch.flatten(x,1)\n",
+    "        x = F.relu(self.fc1(x))\n",
+    "        x = F.relu(self.fc2(x))\n",
+    "        x = self.fc3(x)\n",
+    "        return x\n",
+    "\n",
+    "class LeNet(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(LeNet, self).__init__()\n",
+    "        self.feat = LeNetFeatExtractor()\n",
+    "        self.classifer = LeNetClassifier()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.feat(x)\n",
+    "        x = self.classifer(x)\n",
+    "        return x\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us define a helper function to benchmark a model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "cudnn.benchmark = True\n",
+    "\n",
+    "def benchmark(model, input_shape=(1024, 1, 32, 32), dtype='fp32', nwarmup=50, nruns=1000):\n",
+    "    input_data = torch.randn(input_shape)\n",
+    "    input_data = input_data.to(\"cuda\")\n",
+    "    if dtype=='fp16':\n",
+    "        input_data = input_data.half()\n",
+    "        \n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(nwarmup):\n",
+    "            features = model(input_data)\n",
+    "    torch.cuda.synchronize()\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(1, nruns+1):\n",
+    "            start_time = time.time()\n",
+    "            features = model(input_data)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = time.time()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            if i%100==0:\n",
+    "                print('Iteration %d/%d, ave batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
+    "\n",
+    "    print(\"Input shape:\", input_data.size())\n",
+    "    print(\"Output features size:\", features.size())\n",
+    "    \n",
+    "    print('Average batch time: %.2f ms'%(np.mean(timings)*1000))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PyTorch model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LeNet(\n",
+       "  (feat): LeNetFeatExtractor(\n",
+       "    (conv1): Conv2d(1, 128, kernel_size=(3, 3), stride=(1, 1))\n",
+       "    (conv2): Conv2d(128, 16, kernel_size=(3, 3), stride=(1, 1))\n",
+       "  )\n",
+       "  (classifer): LeNetClassifier(\n",
+       "    (fc1): Linear(in_features=576, out_features=120, bias=True)\n",
+       "    (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
+       "    (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LeNet()\n",
+    "model.to(\"cuda\").eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, ave batch time 5.56 ms\n",
+      "Iteration 200/1000, ave batch time 5.56 ms\n",
+      "Iteration 300/1000, ave batch time 5.56 ms\n",
+      "Iteration 400/1000, ave batch time 5.56 ms\n",
+      "Iteration 500/1000, ave batch time 5.56 ms\n",
+      "Iteration 600/1000, ave batch time 5.56 ms\n",
+      "Iteration 700/1000, ave batch time 5.56 ms\n",
+      "Iteration 800/1000, ave batch time 5.56 ms\n",
+      "Iteration 900/1000, ave batch time 5.56 ms\n",
+      "Iteration 1000/1000, ave batch time 5.56 ms\n",
+      "Input shape: torch.Size([1024, 1, 32, 32])\n",
+      "Output features size: torch.Size([1024, 10])\n",
+      "Average batch time: 5.56 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When compiling your module to TorchScript, there are two paths: Tracing and Scripting.  \n",
+    " \n",
+    "### Tracing\n",
+    "\n",
+    "Tracing follows the path of execution when the module is called and records what happens. This recording is what the TorchScript IR will describe. To trace an instance of our LeNet module, we can call torch.jit.trace  with an example input. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LeNet(\n",
+       "  original_name=LeNet\n",
+       "  (feat): LeNetFeatExtractor(\n",
+       "    original_name=LeNetFeatExtractor\n",
+       "    (conv1): Conv2d(original_name=Conv2d)\n",
+       "    (conv2): Conv2d(original_name=Conv2d)\n",
+       "  )\n",
+       "  (classifer): LeNetClassifier(\n",
+       "    original_name=LeNetClassifier\n",
+       "    (fc1): Linear(original_name=Linear)\n",
+       "    (fc2): Linear(original_name=Linear)\n",
+       "    (fc3): Linear(original_name=Linear)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "traced_model = torch.jit.trace(model, torch.empty([1,1,32,32]).to(\"cuda\"))\n",
+    "traced_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, ave batch time 5.56 ms\n",
+      "Iteration 200/1000, ave batch time 5.56 ms\n",
+      "Iteration 300/1000, ave batch time 5.56 ms\n",
+      "Iteration 400/1000, ave batch time 5.56 ms\n",
+      "Iteration 500/1000, ave batch time 5.56 ms\n",
+      "Iteration 600/1000, ave batch time 5.56 ms\n",
+      "Iteration 700/1000, ave batch time 5.56 ms\n",
+      "Iteration 800/1000, ave batch time 5.56 ms\n",
+      "Iteration 900/1000, ave batch time 5.56 ms\n",
+      "Iteration 1000/1000, ave batch time 5.56 ms\n",
+      "Input shape: torch.Size([1024, 1, 32, 32])\n",
+      "Output features size: torch.Size([1024, 10])\n",
+      "Average batch time: 5.56 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(traced_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scripting\n",
+    "\n",
+    "Scripting actually inspects your code with a compiler and  generates an equivalent TorchScript program. The difference is that since tracing simply follows the execution of your module, it cannot pick up control flow for instance, it will only follow the code path that a particular input triggers. By working from the Python code, the compiler can include these components. We can run the script compiler on our LeNet  module by calling torch.jit.script.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = LeNet().to(\"cuda\").eval()\n",
+    "script_model = torch.jit.script(model)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RecursiveScriptModule(\n",
+       "  original_name=LeNet\n",
+       "  (feat): RecursiveScriptModule(\n",
+       "    original_name=LeNetFeatExtractor\n",
+       "    (conv1): RecursiveScriptModule(original_name=Conv2d)\n",
+       "    (conv2): RecursiveScriptModule(original_name=Conv2d)\n",
+       "  )\n",
+       "  (classifer): RecursiveScriptModule(\n",
+       "    original_name=LeNetClassifier\n",
+       "    (fc1): RecursiveScriptModule(original_name=Linear)\n",
+       "    (fc2): RecursiveScriptModule(original_name=Linear)\n",
+       "    (fc3): RecursiveScriptModule(original_name=Linear)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "script_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, ave batch time 5.56 ms\n",
+      "Iteration 200/1000, ave batch time 5.56 ms\n",
+      "Iteration 300/1000, ave batch time 5.56 ms\n",
+      "Iteration 400/1000, ave batch time 5.56 ms\n",
+      "Iteration 500/1000, ave batch time 5.56 ms\n",
+      "Iteration 600/1000, ave batch time 5.56 ms\n",
+      "Iteration 700/1000, ave batch time 5.56 ms\n",
+      "Iteration 800/1000, ave batch time 5.56 ms\n",
+      "Iteration 900/1000, ave batch time 5.56 ms\n",
+      "Iteration 1000/1000, ave batch time 5.56 ms\n",
+      "Input shape: torch.Size([1024, 1, 32, 32])\n",
+      "Output features size: torch.Size([1024, 10])\n",
+      "Average batch time: 5.56 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(script_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"3\"></a>\n",
+    "## 3. Compiling with Torch-TensorRT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TorchScript traced model\n",
+    "\n",
+    "First, we compile the TorchScript traced model with Torch-TensorRT. Notice the performance impact."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
+      "The compiler is going to use the user setting Float16\n",
+      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
+      "compatibility with PyTorch's data type convention is required.\n",
+      "If you do indeed see errors at runtime either:\n",
+      "- Remove the dtype spec for x.1\n",
+      "- Disable partial compilation by setting require_full_compilation to True\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT TorchScript Conversion Context] - Max value of this profile is not valid\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch_tensorrt\n",
+    "\n",
+    "# We use a batch-size of 1024, and half precision\n",
+    "trt_ts_module = torch_tensorrt.compile(traced_model, inputs=[torch_tensorrt.Input(\n",
+    "            min_shape=[1024, 1, 32, 32],\n",
+    "            opt_shape=[1024, 1, 33, 33],\n",
+    "            max_shape=[1024, 1, 34, 34],\n",
+    "            dtype=torch.half\n",
+    "            )], \n",
+    "            enabled_precisions = {torch.half})\n",
+    "\n",
+    "input_data = torch.randn((1024, 1, 32, 32))\n",
+    "input_data = input_data.half().to(\"cuda\")\n",
+    "\n",
+    "input_data = input_data.half()\n",
+    "result = trt_ts_module(input_data)\n",
+    "torch.jit.save(trt_ts_module, \"trt_ts_module.ts\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, ave batch time 1.41 ms\n",
+      "Iteration 200/1000, ave batch time 1.40 ms\n",
+      "Iteration 300/1000, ave batch time 1.40 ms\n",
+      "Iteration 400/1000, ave batch time 1.39 ms\n",
+      "Iteration 500/1000, ave batch time 1.40 ms\n",
+      "Iteration 600/1000, ave batch time 1.40 ms\n",
+      "Iteration 700/1000, ave batch time 1.40 ms\n",
+      "Iteration 800/1000, ave batch time 1.40 ms\n",
+      "Iteration 900/1000, ave batch time 1.40 ms\n",
+      "Iteration 1000/1000, ave batch time 1.40 ms\n",
+      "Input shape: torch.Size([1024, 1, 32, 32])\n",
+      "Output features size: torch.Size([1024, 10])\n",
+      "Average batch time: 1.40 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_ts_module, input_shape=(1024, 1, 32, 32), dtype=\"fp16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TorchScript script model\n",
+    "\n",
+    "Next, we compile the TorchScript script model with Torch-TensorRT. Notice the performance impact."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
+      "The compiler is going to use the user setting Float16\n",
+      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
+      "compatibility with PyTorch's data type convention is required.\n",
+      "If you do indeed see errors at runtime either:\n",
+      "- Remove the dtype spec for x.1\n",
+      "- Disable partial compilation by setting require_full_compilation to True\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT TorchScript Conversion Context] - Max value of this profile is not valid\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch_tensorrt\n",
+    "\n",
+    "trt_script_module = torch_tensorrt.compile(script_model, inputs = [torch_tensorrt.Input(\n",
+    "            min_shape=[1024, 1, 32, 32],\n",
+    "            opt_shape=[1024, 1, 33, 33],\n",
+    "            max_shape=[1024, 1, 34, 34],\n",
+    "            dtype=torch.half\n",
+    "            )],\n",
+    "            enabled_precisions={torch.half})\n",
+    "\n",
+    "input_data = torch.randn((1024, 1, 32, 32))\n",
+    "input_data = input_data.half().to(\"cuda\")\n",
+    "\n",
+    "input_data = input_data.half()\n",
+    "result = trt_script_module(input_data)\n",
+    "torch.jit.save(trt_script_module, \"trt_script_module.ts\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, ave batch time 1.43 ms\n",
+      "Iteration 200/1000, ave batch time 1.41 ms\n",
+      "Iteration 300/1000, ave batch time 1.40 ms\n",
+      "Iteration 400/1000, ave batch time 1.42 ms\n",
+      "Iteration 500/1000, ave batch time 1.42 ms\n",
+      "Iteration 600/1000, ave batch time 1.41 ms\n",
+      "Iteration 700/1000, ave batch time 1.41 ms\n",
+      "Iteration 800/1000, ave batch time 1.40 ms\n",
+      "Iteration 900/1000, ave batch time 1.40 ms\n",
+      "Iteration 1000/1000, ave batch time 1.40 ms\n",
+      "Input shape: torch.Size([1024, 1, 32, 32])\n",
+      "Output features size: torch.Size([1024, 10])\n",
+      "Average batch time: 1.40 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_script_module, input_shape=(1024, 1, 32, 32), dtype=\"fp16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT and test the performance impact of the optimization.\n",
+    "\n",
+    "### What's next\n",
+    "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/v1.2.0/_sources/_notebooks/ssd-object-detection-demo.ipynb.txt b/docs/v1.2.0/_sources/_notebooks/ssd-object-detection-demo.ipynb.txt
new file mode 100644
index 0000000000..f2a0bd8063
--- /dev/null
+++ b/docs/v1.2.0/_sources/_notebooks/ssd-object-detection-demo.ipynb.txt
@@ -0,0 +1,943 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright 2020 NVIDIA Corporation. All Rights Reserved.\n",
+    "#\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
+    "# =============================================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_ssd_notebook.png\" style=\"width: 90px; float: right;\">\n",
+    "\n",
+    "# Object Detection with Torch-TensorRT (SSD)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Overview\n",
+    "\n",
+    "In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch's JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.\n",
+    "\n",
+    "When deploying on NVIDIA GPUs TensorRT, NVIDIA's Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA's Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.\n",
+    "\n",
+    "Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Learning objectives\n",
+    "\n",
+    "This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained SSD network, and running it to test the speedup obtained.\n",
+    "\n",
+    "## Contents\n",
+    "1. [Requirements](#1)\n",
+    "2. [SSD Overview](#2)\n",
+    "3. [Creating TorchScript modules](#3)\n",
+    "4. [Compiling with Torch-TensorRT](#4)\n",
+    "5. [Running Inference](#5)\n",
+    "6. [Measuring Speedup](#6)\n",
+    "7. [Conclusion](#7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<a id=\"1\"></a>\n",
+    "## 1. Requirements\n",
+    "\n",
+    "Follow the steps in `notebooks/README` to prepare a Docker container, within which you can run this demo notebook.\n",
+    "\n",
+    "In addition to that, run the following cell to obtain additional libraries specific to this demo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Collecting scikit-image==0.19.1\n",
+      "  Downloading scikit_image-0.19.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.8 MB)\n",
+      "\u001b[K     |████████████████████████████████| 13.8 MB 8.8 MB/s eta 0:00:01\n",
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+      "Collecting tifffile>=2019.7.26\n",
+      "  Downloading tifffile-2022.3.16-py3-none-any.whl (179 kB)\n",
+      "\u001b[K     |████████████████████████████████| 179 kB 110.1 MB/s eta 0:00:01\n",
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+      "Collecting imageio>=2.4.1\n",
+      "  Downloading imageio-2.16.1-py3-none-any.whl (3.3 MB)\n",
+      "\u001b[K     |████████████████████████████████| 3.3 MB 42.3 MB/s eta 0:00:01\n",
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+      "Collecting PyWavelets>=1.1.1\n",
+      "  Downloading PyWavelets-1.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.9 MB)\n",
+      "\u001b[K     |████████████████████████████████| 6.9 MB 61.3 MB/s eta 0:00:01\n",
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+      "Installing collected packages: tifffile, PyWavelets, imageio, scikit-image\n",
+      "Successfully installed PyWavelets-1.3.0 imageio-2.16.1 scikit-image-0.19.1 tifffile-2022.3.16\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Collecting ipywidgets\n",
+      "  Downloading ipywidgets-7.7.0-py2.py3-none-any.whl (123 kB)\n",
+      "\u001b[K     |████████████████████████████████| 123 kB 12.1 MB/s eta 0:00:01\n",
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+      "Collecting jupyterlab-widgets>=1.0.0\n",
+      "  Downloading jupyterlab_widgets-1.1.0-py3-none-any.whl (245 kB)\n",
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+      "Collecting widgetsnbextension~=3.6.0\n",
+      "  Downloading widgetsnbextension-3.6.0-py2.py3-none-any.whl (1.6 MB)\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "Installing collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets\n",
+      "Successfully installed ipywidgets-7.7.0 jupyterlab-widgets-1.1.0 widgetsnbextension-3.6.0\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install scikit-image==0.19.1\n",
+    "!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<a id=\"2\"></a>\n",
+    "## 2. SSD\n",
+    "\n",
+    "### Single Shot MultiBox Detector model for object detection\n",
+    "\n",
+    "_ | _\n",
+    "- | -\n",
+    "![alt](https://pytorch.org/assets/images/ssd_diagram.png) | ![alt](https://pytorch.org/assets/images/ssd.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PyTorch has a model repository called the PyTorch Hub, which is a source for high quality implementations of common models. We can get our SSD model pretrained on [COCO](https://cocodataset.org/#home) from there.\n",
+    "\n",
+    "### Model Description\n",
+    "\n",
+    "This SSD300 model is based on the\n",
+    "[SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) paper, which\n",
+    "describes SSD as “a method for detecting objects in images using a single deep neural network\".\n",
+    "The input size is fixed to 300x300.\n",
+    "\n",
+    "The main difference between this model and the one described in the paper is in the backbone.\n",
+    "Specifically, the VGG model is obsolete and is replaced by the ResNet-50 model.\n",
+    "\n",
+    "From the\n",
+    "[Speed/accuracy trade-offs for modern convolutional object detectors](https://arxiv.org/abs/1611.10012)\n",
+    "paper, the following enhancements were made to the backbone:\n",
+    "*   The conv5_x, avgpool, fc and softmax layers were removed from the original classification model.\n",
+    "*   All strides in conv4_x are set to 1x1.\n",
+    "\n",
+    "The backbone is followed by 5 additional convolutional layers.\n",
+    "In addition to the convolutional layers, we attached 6 detection heads:\n",
+    "*   The first detection head is attached to the last conv4_x layer.\n",
+    "*   The other five detection heads are attached to the corresponding 5 additional layers.\n",
+    "\n",
+    "Detector heads are similar to the ones referenced in the paper, however,\n",
+    "they are enhanced by additional BatchNorm layers after each convolution.\n",
+    "\n",
+    "More information about this SSD model is available at Nvidia's \"DeepLearningExamples\" Github [here](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "torch.hub._validate_not_a_forked_repo=lambda a,b,c: True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading: \"https://github.com/NVIDIA/DeepLearningExamples/archive/torchhub.zip\" to /root/.cache/torch/hub/torchhub.zip\n",
+      "/root/.cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub/PyTorch/Classification/ConvNets/image_classification/models/efficientnet.py:17: UserWarning: pytorch_quantization module not found, quantization will not be available\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['nvidia_convnets_processing_utils',\n",
+       " 'nvidia_efficientnet',\n",
+       " 'nvidia_efficientnet_b0',\n",
+       " 'nvidia_efficientnet_b4',\n",
+       " 'nvidia_efficientnet_widese_b0',\n",
+       " 'nvidia_efficientnet_widese_b4',\n",
+       " 'nvidia_resneXt',\n",
+       " 'nvidia_resnet50',\n",
+       " 'nvidia_resnext101_32x4d',\n",
+       " 'nvidia_se_resnext101_32x4d',\n",
+       " 'nvidia_ssd',\n",
+       " 'nvidia_ssd_processing_utils',\n",
+       " 'nvidia_tacotron2',\n",
+       " 'nvidia_tts_utils',\n",
+       " 'nvidia_waveglow']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# List of available models in PyTorch Hub from Nvidia/DeepLearningExamples\n",
+    "torch.hub.list('NVIDIA/DeepLearningExamples:torchhub')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using cache found in /root/.cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub\n",
+      "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c7cf3e1635d4a2b9c8731b7fc9ce724",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0.00/97.8M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading checkpoint from https://api.ngc.nvidia.com/v2/models/nvidia/ssd_pyt_ckpt_amp/versions/20.06.0/files/nvidia_ssdpyt_amp_200703.pt\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load SSD model pretrained on COCO from Torch Hub\n",
+    "precision = 'fp32'\n",
+    "ssd300 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_ssd', model_math=precision);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setting `precision=\"fp16\"` will load a checkpoint trained with mixed precision \n",
+    "into architecture enabling execution on Tensor Cores. Handling mixed precision data requires the Apex library."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sample Inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now run inference on the model. This is demonstrated below using sample images from the COCO 2017 Validation set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using cache found in /root/.cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading COCO annotations.\n",
+      "Downloading finished.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sample images from the COCO validation set\n",
+    "uris = [\n",
+    "    'http://images.cocodataset.org/val2017/000000397133.jpg',\n",
+    "    'http://images.cocodataset.org/val2017/000000037777.jpg',\n",
+    "    'http://images.cocodataset.org/val2017/000000252219.jpg'\n",
+    "]\n",
+    "\n",
+    "# For convenient and comprehensive formatting of input and output of the model, load a set of utility methods.\n",
+    "utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_ssd_processing_utils')\n",
+    "\n",
+    "# Format images to comply with the network input\n",
+    "inputs = [utils.prepare_input(uri) for uri in uris]\n",
+    "tensor = utils.prepare_tensor(inputs, False)\n",
+    "\n",
+    "# The model was trained on COCO dataset, which we need to access in order to\n",
+    "# translate class IDs into object names. \n",
+    "classes_to_labels = utils.get_coco_object_dictionary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Next, we run object detection\n",
+    "model = ssd300.eval().to(\"cuda\")\n",
+    "detections_batch = model(tensor)\n",
+    "\n",
+    "# By default, raw output from SSD network per input image contains 8732 boxes with \n",
+    "# localization and class probability distribution. \n",
+    "# Let’s filter this output to only get reasonable detections (confidence>40%) in a more comprehensive format.\n",
+    "results_per_input = utils.decode_results(detections_batch)\n",
+    "best_results_per_input = [utils.pick_best(results, 0.40) for results in results_per_input]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import matplotlib.patches as patches\n",
+    "\n",
+    "# The utility plots the images and predicted bounding boxes (with confidence scores).\n",
+    "def plot_results(best_results):\n",
+    "    for image_idx in range(len(best_results)):\n",
+    "        fig, ax = plt.subplots(1)\n",
+    "        # Show original, denormalized image...\n",
+    "        image = inputs[image_idx] / 2 + 0.5\n",
+    "        ax.imshow(image)\n",
+    "        # ...with detections\n",
+    "        bboxes, classes, confidences = best_results[image_idx]\n",
+    "        for idx in range(len(bboxes)):\n",
+    "            left, bot, right, top = bboxes[idx]\n",
+    "            x, y, w, h = [val * 300 for val in [left, bot, right - left, top - bot]]\n",
+    "            rect = patches.Rectangle((x, y), w, h, linewidth=1, edgecolor='r', facecolor='none')\n",
+    "            ax.add_patch(rect)\n",
+    "            ax.text(x, y, \"{} {:.0f}%\".format(classes_to_labels[classes[idx] - 1], confidences[idx]*100), bbox=dict(facecolor='white', alpha=0.5))\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize results without Torch-TensorRT\n",
+    "plot_results(best_results_per_input)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Benchmark utility"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "cudnn.benchmark = True\n",
+    "\n",
+    "# Helper function to benchmark the model\n",
+    "def benchmark(model, input_shape=(1024, 1, 32, 32), dtype='fp32', nwarmup=50, nruns=1000):\n",
+    "    input_data = torch.randn(input_shape)\n",
+    "    input_data = input_data.to(\"cuda\")\n",
+    "    if dtype=='fp16':\n",
+    "        input_data = input_data.half()\n",
+    "        \n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(nwarmup):\n",
+    "            features = model(input_data)\n",
+    "    torch.cuda.synchronize()\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(1, nruns+1):\n",
+    "            start_time = time.time()\n",
+    "            pred_loc, pred_label  = model(input_data)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = time.time()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            if i%10==0:\n",
+    "                print('Iteration %d/%d, avg batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
+    "\n",
+    "    print(\"Input shape:\", input_data.size())\n",
+    "    print(\"Output location prediction size:\", pred_loc.size())\n",
+    "    print(\"Output label prediction size:\", pred_label.size())\n",
+    "    print('Average batch time: %.2f ms'%(np.mean(timings)*1000))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We check how well the model performs **before** we use Torch-TensorRT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, avg batch time 165.38 ms\n",
+      "Iteration 20/100, avg batch time 165.99 ms\n",
+      "Iteration 30/100, avg batch time 166.04 ms\n",
+      "Iteration 40/100, avg batch time 166.32 ms\n",
+      "Iteration 50/100, avg batch time 166.49 ms\n",
+      "Iteration 60/100, avg batch time 166.74 ms\n",
+      "Iteration 70/100, avg batch time 166.88 ms\n",
+      "Iteration 80/100, avg batch time 167.05 ms\n",
+      "Iteration 90/100, avg batch time 167.18 ms\n",
+      "Iteration 100/100, avg batch time 167.31 ms\n",
+      "Input shape: torch.Size([128, 3, 300, 300])\n",
+      "Output location prediction size: torch.Size([128, 4, 8732])\n",
+      "Output label prediction size: torch.Size([128, 81, 8732])\n",
+      "Average batch time: 167.31 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Model benchmark without Torch-TensorRT\n",
+    "model = ssd300.eval().to(\"cuda\")\n",
+    "benchmark(model, input_shape=(128, 3, 300, 300), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<a id=\"3\"></a>\n",
+    "## 3. Creating TorchScript modules  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To compile with Torch-TensorRT, the model must first be in **TorchScript**. TorchScript is a programming language included in PyTorch which removes the Python dependency normal PyTorch models have. This conversion is done via a JIT compiler which given a PyTorch Module will generate an equivalent TorchScript Module. There are two paths that can be used to generate TorchScript: **Tracing** and **Scripting**. <br>\n",
+    "- Tracing follows execution of PyTorch generating ops in TorchScript corresponding to what it sees. <br>\n",
+    "- Scripting does an analysis of the Python code and generates TorchScript, this allows the resulting graph to include control flow which tracing cannot do. \n",
+    "\n",
+    "Tracing however due to its simplicity is more likely to compile successfully with Torch-TensorRT (though both systems are supported)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ssd300.eval().to(\"cuda\")\n",
+    "traced_model = torch.jit.trace(model, [torch.randn((1,3,300,300)).to(\"cuda\")])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If required, we can also save this model and use it independently of Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is just an example, and not required for the purposes of this demo\n",
+    "torch.jit.save(traced_model, \"ssd_300_traced.jit.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, avg batch time 165.53 ms\n",
+      "Iteration 20/100, avg batch time 166.19 ms\n",
+      "Iteration 30/100, avg batch time 166.12 ms\n",
+      "Iteration 40/100, avg batch time 166.16 ms\n",
+      "Iteration 50/100, avg batch time 166.24 ms\n",
+      "Iteration 60/100, avg batch time 166.33 ms\n",
+      "Iteration 70/100, avg batch time 166.43 ms\n",
+      "Iteration 80/100, avg batch time 166.44 ms\n",
+      "Iteration 90/100, avg batch time 166.53 ms\n",
+      "Iteration 100/100, avg batch time 166.59 ms\n",
+      "Input shape: torch.Size([128, 3, 300, 300])\n",
+      "Output location prediction size: torch.Size([128, 4, 8732])\n",
+      "Output label prediction size: torch.Size([128, 81, 8732])\n",
+      "Average batch time: 166.59 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Obtain the average time taken by a batch of input with Torchscript compiled modules\n",
+    "benchmark(traced_model, input_shape=(128, 3, 300, 300), nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<a id=\"4\"></a>\n",
+    "## 4. Compiling with Torch-TensorRT\n",
+    "TorchScript modules behave just like normal PyTorch modules and are intercompatible. From TorchScript we can now compile a TensorRT based module. This module will still be implemented in TorchScript but all the computation will be done in TensorRT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - For input x, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
+      "The compiler is going to use the user setting Float16\n",
+      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
+      "compatibility with PyTorch's data type convention is required.\n",
+      "If you do indeed see errors at runtime either:\n",
+      "- Remove the dtype spec for x\n",
+      "- Disable partial compilation by setting require_full_compilation to True\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch_tensorrt\n",
+    "\n",
+    "# The compiled module will have precision as specified by \"op_precision\".\n",
+    "# Here, it will have FP16 precision.\n",
+    "trt_model = torch_tensorrt.compile(traced_model, \n",
+    "    inputs= [torch_tensorrt.Input((3, 3, 300, 300), dtype=torch.half)],\n",
+    "    enabled_precisions= {torch.half}, # Run with FP16\n",
+    "    workspace_size= 1 << 20\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "<a id=\"5\"></a>\n",
+    "## 5. Running Inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we run object detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using a Torch-TensorRT module is exactly the same as how we usually do inference in PyTorch i.e. model(inputs)\n",
+    "detections_batch = trt_model(tensor.to(torch.half)) # convert the input to half precision\n",
+    "\n",
+    "# By default, raw output from SSD network per input image contains 8732 boxes with \n",
+    "# localization and class probability distribution. \n",
+    "# Let’s filter this output to only get reasonable detections (confidence>40%) in a more comprehensive format.\n",
+    "results_per_input = utils.decode_results(detections_batch)\n",
+    "best_results_per_input_trt = [utils.pick_best(results, 0.40) for results in results_per_input]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's visualize our predictions!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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VezaOneCGluVVr3wJ27ZNMT/dhs3NcEuFfJXG49FKo5SZQIzBohT3R0VIBJQXkiRBaYXSQR/0XDFClwkSgEtSORiz95UcXQMLXBQLbJWpJxRrFzOVjFU8tWgeD0caFjXXYMVQOugIGAmabpSqeMnGGBrALBUwx0e5rTJadZ+afLfqet94de+3zFO4SASM0hhtcN6TtDTH732Ao7/7J/TEImmKmZlGzU6RTPVQ8wvsfOGtpNsWEW8BQbQwPLPM4MQ5cNBanKK3YwnaGiMJWinKaJ7yLlB4rXXUlRC05gQAKUsbgV/VCKCi+IHqqwuQiduivKqAfpJDqPB5cw8EC4h1FqWjEkwFsUb5YBZ0iWqIYQ37evN5KoiBk/srIHYhckf4BkBVSCgApvO+vs3ZaM70HqM1HrBSksW1906C/iLf4PFHH+PAgUW8s1QUvaLeWgvGBHcT68ogpmIRiY5MFeIkSAQiCnwZfBii6OUrq8Il2uWFBGhAYCVTb9UPXEoEuMQ5ibbkpqvtBdhmAuIrqh7ukQZb5SP2FZeDahNiozqI6LFznxApf0Vx/Bjgo1OMXGScldppQhdQiwcNDqkhQzdvtlpHD1uNVh6MQhUOdfw42bAfbvUGxCI4RkmH0ZFjXP1t30iqE8r1DQ6/932sf/IBypPnKJ0jWegxvX8/2770jZwYDFg9t0yaKpLE0JmaZXFhjqUd2xAFZRlY2Pf/5V9SjEY472tqX7Px0oDDSiCKwFhPUS06bJmDC45V3FGDeERxz0eOcmOQsbi+wox1+Ob1jXm9aKt1TvFJNkPSEnDo4Vo01wVgFhHSsxltfO1QlKqAEOYtaFdw6PGHWD1zgoe6bVbWN5g/t0nXOVrFJhuq5KMf/mtWV44xeuYo+0ZZvdbiIXN93r95ggc++RE0ioXtS7z1bV8xZg4jMvTB9hud08LerUQCZ6MH6CXa5YMEtrLrTUz8Od0/phJNEPHNvqSi6w32qH4ujGV1NXHMR1u6RGVRXhYILRKtKKIni3jw4kLfyKTnX4UI4t/EmPCIr5AMW7iYLfNQv9hWrsnjRWO0xujg6SgKsBbxFu09vpNidu0mHw4wyysk+YiNez5I8bY3YpOUJ/7Tb5Df+wDKjcB7Whr86RVGZ1cZvfgWPvnEE3z0/R/BaB9Z1Q7ttmFx2zxvedtbuOW2W1BaU+Y53/dPv782SapKXxHFJ7yKcxT9GCKlquel5hQmqT+VKFC/ciNmorHWdV/ec3ZjyMeeOU1RlpGmNIOjmnttYsIn94uXMCfr58BZTLcXORobcL0o2p0OpXdxTwltJVgXfCLmKdmxbYnt23cwNTXF2voaDx86isIjo016LU2nPc9L7n4R9zxwmEfPrkSOIxCwXX6TG/btZNee3Ux3e/zkv/qXrG2sko0KlBislI3xhv0pWmMSMzFXzv2fIA5MUMexh91FFTgXvS98XvzKCm26mrkYs4MVQEnA7pG1l6hJrzdOjTSCc4qW4CtQcy9U1B8EXW/G0Brsf6WgbJDG2oGoXszq/Ztjr8jSFs6o6lMURmu0Dl5tIhZsiXIWBbSuuYI7fvrH2Dh5mqf+33dhH3wYWw7YeOJxhkfPkH36fowd4YwmvfZK5q+/EltahkfOhxiDIiMvckZZASJoNaLINetrK/znX3gnb3rLm/jiL3lzkF+1ipt4LM97gu/7Iw89wvL5Zaamp7juhuuYmp6OnNp4jpv3Ve/XVApOHG/sga2w7KLYMeaethCWiXlscKKN3wLB9biygtRnw3mHx3ofnfXUhbJ33L/WWkQ5siJHdEpuLe2o6QdHXlh67ZTKe7TeeyIooxCtUEajTcLc/CzKEM2FzZiIJpcZRDWtg5nwuUjp5YME6naR4U64AT8HQqiv3ypGVADfVLg1n1V59lUyOWMlFL6BaIOipXSBSBgBnB33GB/jLwD4yBm4i4xrYoxSb3ioRICKNW3oArYggUhr0RJ9G0ShxIFzweNRKVwrwas2M3v30dm9neyhx/DO4mzO4GP3krgCr4T5l97Fnm/7Wtrd6dpzrhhl+EcfAkLg1E233Mz2ndt58N4HWFldpT9Y5k//6I+45bab0NEPoZJBK6peadeHwyFnz55lOBxwzbXX4Alig2+q34GtIpNMHBvHA9Tvv3XNo0jgJXJml1SuNrmqretS6QJ8rf0XYsyAKLwLx4zWuKgoRAUR0BEQkHeVoOdxKLxKWbaGUemZzx3TqSFJNFlh6bVbKC+1/lgA8Z4yy9lYXYcCNjf6rC2vhhiCJuHw4+cgIA6wvl6Loiwu8u7EnXM5NJEweUpdsME/6++t55qs0eRJJhRvtfZXJs57wsRrFWzrYdkb4ChC5hUKT0s5xAZZMVCNSYAOmuKG0lAa79iIMhuLqhL87oWxDXjifWTy2koelLBZlTYoFTaoFgWlRXtBnKdlUjbPn+STP/ZvWf7wJ9GuwMzPYkvH8PjxwILOzbL9bW9memYbOumQqjaddo+p2TlcGZ2ktOa2227jO//Jd/EjP/lj7Nm/D6XaDPpD/vj3/yg4pkRAcWXJ2uoav/2bv1krDWsrgwKlBC1qQhnnx9oC3vunf8Zb3/RG/v7f+0YQ4fu+55/wti/+In71l3+Zn/vZn+WjH/7wWCRoiAbV9LgITatnz/DzP/CdiIcTzzzJn/7azzfWaSwSVBzI6rnT/Pw//bYAxIraB0IreOCjH2K0vkZiDIlJaCctpjodZrtdVk6d5P/90R/k7T/wffz7/+cHePbpJ2pEdd+9n+bLv/hNfMvXvI1f+NHvxSFsrK/xwz/8w3zHd3wbf/LHf0ivnWBE8Zv/+v9hY/k8glDkBdY6Wu02UzNT9Hpdtu/YgWlNR6KlQMx4P0kQTY3RJKmJooClLC6NBC4jTmCM5Z8T0OGzn/+sz2kAX7NVykhcA5zVxJ2IpiC4dyYGfGlr5DBmBS+h16h/NzXVW4ZWXyeT1P+CG/yW7wS/diSGjgq+dHU4Lc5y6Jd+g/xjn0SkQFopO97yBWTLQ4wvgzPR9BwL114bOAgErwLLaaOvP0JgfYG8cOzYtYs777qTY8/+Hkrg9InT7N+3k7XVdY4ePsrmxgZr6+v8l1/5Fb7sy78SY8bvpBUMB308kCQJrU4bEcGWJSoJrr+/+5538y9+8ie568Uv4ezZszz8wIO8933vq+c6xtchldnONVn2gFB8ZZmJU7fnymvZc/U1DRduGUtdDQ4AiCoBCf4WMT/Bx//6HhaWtmG0iQFTitKVeO/5rf/8i7zuLV/KrbffwaMP3scf/9Z/4eYf+lHW1lb512//SX7pXb+K7vW458FnAcUnPvphXvsFr+VVX/BF/Ou3/wSv+II38vinP8rOK65mZmFb4DJaLdJ2G210ND2CdTY4PilBdPAnCFPhalFXR9NglmVY2yEvci7VLiMkEJtUcnXFpvmGBFDJ2Z+jSAAXFx+aSGS8A6hke0HQEhbdb80+oxQZgb01AqLcxGhESYg8gwvlwyYyEGqkE6IXq/NN7mQ8yJrx9eOw3XGfARyUljGL6Akx594hXjAjBzs6gEWLQi0usvM1r+DZP/wYEozXKNPCJynkw2io9jgX18JViMkF+7goslHG1VdfDc4i2pCPRqytrHHvJ+9lfX0DEeG3fvO/cPz4cb70i9/Ma1/3BSwtbePdv/3bzExPc+7cOf7p9/8Iv/5f3sloNGR9fZ1XvuoNvPXLv5wPf+Av+Myn7+VH/tkP8eKXvJSPf/xjnD59ire99S388I/+c97zO+/mta97HW988xdx32c+w0/9yx9nNBiSpAm//Gu/Rq/XCwlVGtFzInD4kfv48B/9N77u+/8l/fVV3vNzb2dj5Tx7r72BZx68l3/4k/8RCBGNf/COf8fRJx9hemaGr/26r+epJ5/ixJHDvPud7yBJUr7z+3+YztQUSmlGwyHeQzYYIs6zfOYcU7NzeO/5wD1/xSte9Rp27dnNufVNpmbnGZQ5SRIyEq1nHlGBu/zwn/wOX/0DP1lvzcqUWv2r9kDg+oN+qmZoGsFOKgYd5UVOv9//P8REuIW6V5riIOPGbV75tv6P9HUxseCCYw3FXfzQOmiAfUV1Kk5caUY2RNYZSalccSsl1Njy4C+CBGjA93gMPgLtmKWL71mFIvvmPVuixqrPSoEU3Ui992OKjqAWF7j2276RB+97iuHRI5Qrm6w+9CSt/Tux3oTw3cE65ZlzJHPTNTUN7r9x00XZXhqsc7vTBiVYPKN8xLFnj7K6tkorTbnx5hvZsft7+KEf+AG+7du/kxfd+ULef8/7OXniON/6Iz/KNVdfwzOHj/D13/AN7Nl7BcfPneE//MxP8YVf/qW85i1fwT3vfz9f963fwZ69+7n5RS/jP/67f8VP/MzPsWvXdjY2Bjz44FN0pz7Oj/7g9/C93/9DHLzyKnbu2o4yCdFvCO0sHRW4NQ1oPEYpjHj++j2/zpU33sorv/RrefK+T/KZe/4MkZAVYvnUcb7iu3+QL/2W7+C3/sNP8Mgjj3Dby17BJz72Yd78NV/P3gMHUIAV0AJJt8OXfvO38ks//S/5g9/+Dbx3fNc//ykATp48iTaGb/76r2VtY4MXvu5t3PDyV3Hn3S/nd975C/zJe9/H933/D/Hu//qr3PnqN5C023VMkGesyJ5Uto5FwXq/RIShdPDo1DqIs3leIZyLt8sTCTSA5wIwqgDmguNbvjc17Bew3hWXISAusoYVghG8aFINFo3Gh+guCOyXEoSE0uV0xJEg5Eoj1teso685FiYtAc1BOD85xoZsW1svIrcg8bqAJxrvX11D3ByJDkhLgXcKcQUh5MfhUWBTei+5leLEcfJhzsYnHmD/3/96Trc6kA1pr/Y5/Zn72PvKV6DEUCqH9uAoApKrOBetIyuqOXv2fPB9Esf6xjpTvRQR6HRa7Ny9E53EgCXgqScfx5Y5e/buY2lpe73B/+Iv/oInn3icLC9YXj7H+bNnmZlbREToddssLc5T5rtotVvM71ikENBpwrY92xm5ktnFRa666Vac85zZKGAtB28ZlAVz2qE7CYkSlkzOeVWgsczLiONPPMgbv+cHmfV97rjlet7Tm2LKbjAqByxs285VuxaQfJ29e/exuro8scU8IeZCvNTKx4++78/5km/4Ju586cv45Ec+zLt/6ef5gX/2wzjnePrxx3jnr/w6J8+f59u+6RvZceVV7Nje48d+4sd5Zj1hby/ht379nXzZd/8L/usv/mtGmxvc/ZavZs+1+1Ex7sJVHo4VsVANoiEafBlG5lV0Idd0e23A4opLxw5cHopBuDgXfIFSMP5XZ1VRE4Bw8U4bfdcTVp2P0KSklu9EB+VdIjFTkBuHkFawV6LJihwjHlPJZA1FnUR5rX54fa4x7uYCNr9Xg2uoF4LiPJzzjfE2w4NFQGuDCBjRWDTeWXR8bdVLMNqw7QtehezZQ0rJyqc/jV07y8xdN4NSFPkmy+/5E1YffRSVOjpacDjKwTAqxwA0WiUkoknQfOLjn4iegcLs7AxJkoIHbRQ6sruVoq8/yMlLXydudR6eefopnn7qSX7zPb/DD/3zn2LX7n088ejjHDn0LFlW8MyRZ3nosSd44qlDDEcZDzz4OPc/+BiraxucPrPMmbPL5HnB8to6m4MB6xtrbA43GORDlCuZMRqKLIhF2QCXjXBlSb65jreWYrBOsbFGubGO9568v0HR76OVothcJe9vhLE6Xys3650V9RKVF+SnPvB+brrzJQiK215yN88+/RQisLR9O3e/7OXMzs4wMzvPlTfcypmjhzAE5WW/cPzKf/4F/vE/+R4e+PD72H/dLXzZd/0z7vntd1UScT2H9daockRU+yqOyss4DsM5R5omOOcon0MncPkggbpdxM46AcDSOMaFwNW0CzfZ8eo+JVv6usj9CEapLZMzBmqbpDjnoqbbXWJsl/p9sWdTIzxP49po7plk+8fjqf2PAO+CfV4hKO+xlBSuJBeLUyF6rxDHtmuuZeE1r8SoFDOyPPOu32PHF70evWsPohzF0Wd5/Ed/lsM//6s887t/xMPvejfLp07jJPjrK6U5fuwE933iXt7xC+/ggfvuxzqLx3P7nS+i1W4BAWhsWTLdmybLMqZnp5ldWqLV7ZEkCZ2paZQJdvGp6WmStE1/fYWjh59Gyox2S1DKoymx68v4jVW8Ldk8c5y1U8coRkPWz52GImPl3Fke/MRHWTl1mpPPPMXJQ4c5efgYxw4d5fDhw5w9cZIiyzl99Cgby8sUecb68nn27NvPpz70AYara9z/qU8wHPQZbfbJBgOcc+TDnCIrQ8ajqGBsdToUWYaOiFsRrDGJUszOL3D48cdQSnjy4YdY2rkLQXjJS1/G/ffdi3WOLM84+uSjLO3chXKO0sPJkyc4f/4cr3jZy/FlHrIZi1AU8bt3tTmyQjrOV6JA09qkoi5NKIoCJTGGQCkGg8ElIe7yEQeqtpWyNxV39bFLkv7xvd4T/SgbwNgArvriLX35wEAntWvoOKS4nmyT4gqL7ng0lefauMsqU1HtBdhQ5AcOvwHUE2OWeLhpaZDJYdaixZZJimbNKq+cMSl7Xv5iuttnWT+zTLprL6lKyLKcpS+8m5N/+Vf4cysUx46AaK76J9/CI//+XbTOnEFkwKk//XO077DtK7+YxWuuwNugoHXO8sEP/jUf//gn2Nzo470jSQ27du3ky77yK3jnL/w8CAyHOdlgSK83w9UvuJ5/8zNv57Y77uC2O15Eq92ht7AEwKvf/EXcd/9neMsb38juPXu55roXsGPPTrYtLaCVYnFhgYNXXsXZmSnSVsrBa68kLwq6vS6Li/McuGIvP/72n+bf/8zbKYuS6ekpfu4//SJp2uLY6WWeOnqKwpaIEjrTU1Sr09bC2972pfyXX/kVfuH+f8MV11zL1Ow8rf3Xk6+uQJLi910FRYE3Sfh0jhe+9OW855d/iSRN+Y5/9qN0Op3A9HnPV3/7P+J3f/Wd/HdrMUnKV/2D70REOHDgCu5+2Sv5kje/Aec9d77uLezcewCx63gUf/HuX+Yffft3oY3iC9/0Vn7o+/4RH/rvv8FrvvpbcBWnV+c1iJyj0ogKUaGIijqE4D4sCGma0mqlgGCMeU73ms9LBaL/0SazOzwv+3ou5ALkQu1+Rfrq31s7a1xXmRubf5/ldQVI8HzFlYYXv/CF3HLzzezZvROjNEdOneZX//Dj/NYHP8HNxZMkUz2O2ZRjS9fHJBZExLF1nA3lGnEbVvJ1xe/BBHIbexxehJth8j0EECV89Qv28DWveEFMEJqi7ADTbqFbKR5PmWUoHxJslPmI0coao3yIkDK3ewf98+c48b4Pkj/9LGyugulw5f/99/Bmmnf/5m/z+COPYG3BIOuTDUsGA0uSam686Uq+8mu+jquvuY5f/sX/yCtf+Wo2NwZ0Oy2+rb/GzHPEsn++WjE1xXtf8hruO9vHr5ykV2yQpgnDwRBfZPgYCJV2ukxNdXnq6Al+/Zd/me/4mf8cBZiYzNNbWDmG76+h0gSSBO8IzkMIaatF2m4Fc2xcy5bWWO8pJWVeCnbt2MmuPbvotNscO7vMJ59dx+Ub7PWbTC0u8uB5xcGe5nV338DDh1f46OHAeSGwW/rcuGc7O3ZsZ3pmhrf/1E/yld/w9/j27/sPPHzq2Zi/QMK+K3Okv8G0z3jNS27hpptv4rY7bse7kocffIh/8UM/+Gnv/Yu2ztVlxAlcBAFccMlzadsb99WeY1sorvdbbriQCwhWAIdJVAwF9WNAlUillaHwQkcrWj4uQNXflpx0Yz1GGFftIttg/1VEEMHKIxPjec7xQq0T8ECqFYIGybEjz5OPHaYsO5RFQduUWJPQTg3ttkHahlarRTLXo23aeGuZXdzBzNe+jTyzSJlhCkM5naKt8JVf/WUMsy8OzivFiGE2YHN9SKvdYd+V+5npzWLLkrn5Oe540Qu57zP3MdgYMGMtP3fFdaRa0WmlJFqHFNwEDzvrffC6dJV8Hd7LxWuc8zEiL8i44XicHje+FjxKDCQJC9u28Q33fzQq7BxetxhmI4rNYZCZZ7dTnD3B+RMn+J13vzuY50yLt/zD7yUEnDWWQAlIyAGASMzc5Op4D2U0onU47X3IHCRQlCWuot61Rn+8hhJzQBaRU82cwxWWbjtBS+WpElLISuRipdoS3gdFr6iQ2qmxzz2gjCJNEnQSck0mSUL5f4SJcGtrAkvTDXd8ARcARfNnFTDR9EKsKauavH6Ltl2hSCoWSpp5BSJvrxTOaBKtGeZho1Ra4rE5QsZ9b+VKtr5qZVGoFIbN9ydsLj/xftLoXoKSsIR2EnzRE59gOprrb72GwuYsn1ll+eQaO/cv0krBF57SavL+KnnfsMEIcTl5CUVZ0Cthw2u8zmi5BCdCOxHKRJGkHaY7U0zNtbniCoNWLgKKhyTEu+/YtZNXz78aXxTwu78XsuvohE63U4dmee+CsdOHyHvxus7UU83VJd2m/dj86iXoH8qipCws64OMzX6Qf13UkJetDjbtUtocEY0YIWmtsGf/Ab7/n/84SWeK9fYshaQoN6LlS7pa8ElKVpYMpcQlCWiDmAR0tJTEdRGlIA2emV4EdNAT2ChKiqjJmH6J6dWUULiwf/p5SEveSoIXZeEbCSkbhqZ6GwpjUYDKjD6+vizLgKSions4HF6w76p2+SCBWk5uHov/baWOTeo6cW31JQLjBaLCc+gSqkuIKaJNEtNeV88ad++VZuQc4koKWoRojmqBGyvVRF6VxUCgNmKPH0qdnqw5xopbqJFXQxzY+l5eocWT2wLvBElKvNK40vDAg89w+x038NBDR3jlq2/H25JDR47z9ANH6bYWuOHOHczOL6FbhvMnN1i6Ygb6fRa2LbLR15R+iO07zp1fRXvDaGWTQ6dWGS2fCvkDuzPMtDXeCJvLfforfUjatKNt2vnoIqwUacwrmCbB8qJzj0lDwtGQQFNqiiaNZaw187WuNGzu4LMPeVLQFs/Ajimei2m3qtTlQkkyWKWrLL3t26gzQXlYsBnWj/DeYvMcWxbMb1/EJ5qTrmBUzboEIMUFN2oIKcelfqanipR0MWNx5cEZhx/3hkMrRe40KBgWQpGXdFONUQqpOZ5G0JKMx+Cb1oGKQsR9JkowUSFY1T2oLDIXa5cJEpCGSa3Rar7vImLAxSSDWtT21OWrqkm6JAJoPDsyCUp5dAwPbqLfYG4Lk2/RKGeZNsLZ2otvnC9gknJXmyEmvFRqYvxjv4C4sE1E5hq6gVrEqZybquvDta7IKFxJIoK1RBOnJ0nb7NqzxONPPAGqRJTizKlN7nrdC9i/7wo+9Nf3Mr99ESmHPP3oEY4+ZZjuptx8xwyjwZBjR0+QD0bsObiThe3zlHvnmNq9wdmjliuu3I+eFlLT5Y9+/376/RGlC3EVVHkFgUQpxCkKLDjL7u403bZhOu3CYJ2niz7tToszgwLnHTpJqhkI7+bHeDRJErTS9AdD0sRQFAUtYyhtiaJRBITg5iwidItN/Po5KDKk00ZJm1a7jUlaKKPQKGwUScq8oN/vk7Y6KFH0Oj2yrIDo7xAA3Y25tZgOPLgyV9EmlTKv4gAkpl4Lm0z5kE698IEDLEXI8pyknaIM+FzV4O+cHXv8SYhwnQiLFlVvJwVx7zqUShAxOHfpuAG4XJBAtbd980CT/WteK5OsYaP56nyT5W+o1SssGq7wDeTQBNqqsoAQMsqMZfXaL1+EMmL7ljFR9qROxln3PTG4ppggk+OvzJbVe8WkoHUJsyYyEXVh34REn500weOx1uC9IR/2EUr27F3k93/7Hm5/0QvY3BygcVx11TaeeewMTz96mn0H57FliZgWew7uoL+xyfnVPkWicZ2c2R09jj6ecd99R3nt6xeC/F5Cph2+k4BzTPUM4oXZ+Rlsqeh2WqTt6A8gAlTJTT1dpdmfGtKspDVl2dxw3Do/Q95K6GerbNiYyUmqFGe6RsDtdhoy8viYVjvK29YFOd0T2Oww5YJg6RabTNkhy/11CmvJi5zhaBT8KmLqMx3rCaiYPaksS/rDIV4gL/KxxyjBizLk/xtHSrooHgQ332qfUtvsfSxcExB9cELTWlEGNgdHwWhUMDs3TaIbXqHEqMUqKxM+ioeCKB0Ug/WeUngV9BQ6himnpkXpAlG7VLs8kABwAWA0NeBbAf6iYgCMC/Y1KwJd+AxfXzuWOSUiDyHIcyZSMUHq4JmARMKiFj6wqCOSUO6qYvHrsbnJ5xI25QUIDQKrWiGS2vjf1INs0WHUolDFPYTrkrjwTjnKYsRws4+1JWna4pYXXkmWj1g7bBEypLS0bRuVDFk5XnLmxCmmkpzStWhPb2dnssb5E5to7SFXzM232Ll7AYXGGEV7OmF2MEuqDWIceUhPjG4l/Lv/8NNMz3ZITMKuP/5j/tuOvcx02sy0Wky3U1qlZ/t8Gz8q6S5OMTqzRjrdJhtZzlvHycEAEV1zR1Uy02D6NGFNvMO6UIzTeyErSpx1rG72UTrl5LNP8r77nuDUyjpzdoNiNGJjYwMLGGNigI2i8s2vvBpFgo0dAkvt8WRF4E4kaaHTNABhrA+ojQnKQgjcAYqOFkZOKB3MScn2pSXm5+dJ04Qza5s8fm5Ea3CWbR3Fssyw5jRS5ty/Y5bduxf5yBOnOZ8VCLBEnwNLcywsLNDptJmamQvIpSEu1IQhchpaQpKZSqnovX/OUOLLBwnIZNII36T2E5Rvqzzc+NL8TowFv1T56YvJ1pGVM6JoJWmt+AlUbMyqCJpSgutsLrHSrUhM7STgbeAixkwIzQy7UcgNj69YRhgjAufqDTWhKKRhhWiM3+MRFzLuhnx/GpVodhzYhStzSiGEFBOcU1xZ4L1Q5h7HCFtqrLWo0lNaQyk5SStlOBihxaK8I0k7OD/k8UeOkOeO1OaMSHnioaP0jJCrNgf3zjB3yxeRW8e2mRa9+Rne9PM/z2/sv55tO/dww/ZZ/v6LrmZ2Cooc7KkB0zfvYv3BY0zv28WZs0MeXlvl/cdPQjqFFYcoxczUNN1em7W1dTrtDu1WQlGUbA6GeDeitI6VtQFFAWcPH6XbXeDHPvSXfOqbfoAH730Qc/YxRrlw/NwaXiu609MkiWCUBhNCmRNtkCRQ8Y3hiKTVwqgU62E1yykRdLtLuzeL9uCLAmcLWt0p0l4vpBW1JQpDp6NZKRIyp9klm9x8x4u46tqr2bVrOx986Agf/tRJth3/MPv3TnGMa3i2SBHb5ytv3Mc3fuXLePSdn+S+U2egLNmpj/OSO27i1hfexp5d2/GiWVs5W8NMU4RUIphiRLfXQrTBaIMxhrwEW17uKcdjG7PzWw5uhdUJJWIEtiifBRVNBbTNUNKGvNG0OPhaGKgfpwQSY2KVlxCRVaXCCl5ZBk/AtCQ6jiEAtfeVlngrNY8DrmvbNT4jB+GrpJEqso4+cih10g0ZWwoqBybGXZuqvgCC1pa8zMFqSj/AkiKE+ndGzeDJ0T0HJIhPg4LNlHgraKux4nBecHZIJ+lhKUiSNibxpK0WJgWjU5zPsYWlGFrK1SGD4YDz656TT55guXiCNwHoFJUo0iJDn9okH2ScL8HlOb3tu2BoKI+dJVuaxfY1RrpkrgSl2LljO2vLq/SdpchLnB2SZUMWFhdZ2xiQmISizOJ8Okrr64zASdIisyPOHHuWqalO0MsYEwJsCAozbJg8K8HTUgieejo1WDxlpPghxbqOEqOvw7gq5yxbxr1TbSDReFEkCtI0ZXpmhiqEXKSkoxRJq00+ilYMrTm3uko2HDLTTmuxUlfVrr3DegmiQqUOqkSCqCeQwQaLLdg+O13Dg1JSh1hfqv2tkICIHAY2CFNZeu9fJCILwG8DVwCHga/y3q98lp7GcvAWvYBEQL/Ag47qQwGhGKRWaZA6J7juOIkVohgPPk5mI/13nDgjQa6sqtg65xCp/QCDTKgU4iyJDpxAs8hm5TRU14dsAKsI42vre5qYrooJ1w3RpeKKYsUZCddFtoFKDxLi6qMc7Q1aKZxyKJuOxR2vQILba+GIbHDIThvMdB5JqryH4JMOIh7tNc4V5Bl4JxRDgBznylg+26NnO8wtdJjb60FtJ0k0/AgkvpJPZ0mntjF9W5vBuT756hrPnjlN25ek87vQ5zewqxlXz7Tpzbc50xeG2ZDFxUU2N1fpdLsMByOyLGeYn6Gdmui+DUYnFMUQUSHVNgBao9IZFBaPBaVQxgTxTSnExGKnhGzAoQCrxRUlOE+WF3VNhbAEEqMyQ2GP2gfKeZQPnKApFdYTwqudoH0oxTYcjYJVwZZgNOXGOs7NkrvwfJEum3lBN20z0wUkjW4AIfOS0g4vGu9tGJMB0Y19NFhjh9mkm7To5wWp1rSShDIbsXLuPGX++c0n8Brv/bnG7x8E3ue9f7uI/GD8/QOftZe6kgrUIDwRO7+VqjZlewVGRbXT5DUT7blMhBV2FUHrcYmsZtLLmmX3jtIriqCCH2uJxw+qLQ4eqfFN3YdvAHd1ffNvq6WkYmYmsJ+m0mnU1GmL8qdZ+IM49irYCAHlBYlJR2KcYX1fBdgqBqSoRrCSsxalNS6mVgvVhVxEmJWbtWCLMvYYsi/10oJ0u4ZBTnl6SG9plt78LNnyGn6pTW/Ks6cNty+1mHKK95/a5Jm1AZtFRquV0h8WpIlm985dnF9dY6rbYzjcwHnI88oUqeoKvKUF6aVsW1oiK/NYk9BgkjSIAjoi+Jj2zfmg+Xdxr3jng+cegSpXJregCHQT0l3FBVREqy0FuhjRdyPOnj1L2mqxsLhAf3ONucEJem3BGE0hCcY7vNZsWkuRZWyb6WCsi+XTgsOTIyGRgMjCmhqUSvHiSAZrbGfI0s5Fjhw5R5ZZzp8/z+GnnkbjGGQj8ux/bVKRLwFeHb//KvB+PhckUNWBkyZsRBm8YYIZm8kaTQCvt1D/5rkt1yIN6hqXsLpOgmxVlc1q3lZlzkVprBiKcoT29kJxxbnxMRkjD4jMjjReVKBW/kV7/4WtyQXJeNxbFKdam8b56HXXQGLN3sQLiUqwPkcLKEli4IqnKAPQelzszlMne4kmMe89xhiMifoEVZnAJBYd0WTZKEyXApEUo4TNtSGtlke6CanPyTfWGPT7tAqHSwxXZ8Kn7j/CngMH6DtPu93h1Onz7Nm5yDDv0223GWUjZudmoXT0ej1MUZLlg+Bd6D2JCWtnRUisJ01CDgiMRiVJSMMWkadSOiYKDcSkrhewhXusEWg1Bw2OtUYGnoibhVbepzccMrd9ns2NNZ56akDr5AzLZ05w8/A051xOamb5kmt3MCwh9yMWN0o2BiU37JrlC/entFsJ9lkT0tz5IF4oJSiTcvu1V3JgMIf3BaycoCPzPPT0EfZsW+QFVx9gaX4OnQaxzjpHu9O6yL4K7W+LBDzw5xJCrP6T9/4dwA7v/cl4/hSw47P2UgNCVKDVXHI101VByep6maS8E3qDKCs3g4zqSkAXGf3WcaDQmpqqel+Z6ca2WJTgEKwrmaUgFMKusIibeJ8KcCuVYl1ssgbo6rpGNGFN+ScG1pirSiKNYyO8r54wisgFCKCZqNNaz32feZDde7dhC8f68jpXXLOP48fPs//KneA0pSuxRY6z0O22AthoT9I2ITuRUpFp0bXcHOYrZB4qIicg3uEp8EVCtunJsoJiMKTVnWG5n9FSGl+ATVaZ29fhajXDoCg5tz5ko7Ck7ZRRDIVtd9qMhgP8yKIEZmd7eIQ0CcDsnMVUSAChnQ8IVXk9ohOUDj4GeF8jgdKVNSdaT72ukHbQz3gJtQSqmP5qPet9ErVRVYIYXeZMk+NcTssYdJpQZCNOH36Knh/RSbvML23ny197G+KhoM9jn3ictZU+N+9fYmf3BlpTKb/334/gvEX5sk5zZoziK954J4kZMRwEc+fq6jLPPP1ONk8f45QpSNRBWlMddJIyt9hjY2ONS7W/LRJ4uff+uIhsB/5CRB5rnvTe+4ggLmgi8u3AtwPQmY2adagDcBoAMuFPjzSAoPpdP7ABL025ewtU1cilSis+qXOQuk5AQO8+8u8hiQQ4VyBoCleyiyEtHENUrbQbOwsJ4/RPka2sORGY9BCsSEnFFTQsAY2P+nstNsVnOk2SEsuQOarciM2IRN9AqkosM9NtHrj/CDfcuBvVEQo/4sThZY48eRiH44qr9nDy+BnKkeeK63Yz3MjI84K9VxxkaVuX0SinHfPftVtp/TytNaIVlRjqrcfmJTsXu6RX7CJvAavrnPZCutjl6YcPsWk22C7z5CsbZAPPyhUtzh0dQKnQLY3LC7pph421dbq9DhtrI1qdhPOrG0xN9yiKHIvDOUtSlQFHSMv1qAQExATERVCYBeYm1EawUPuABLFBITHxi0QORxqWGh89FSsRoWbOXCj1RnuKDI02mizP8LbE2ZJhYdnwij09YbB2ht//wz/GuJxhmUHh2bN7gc0V4U9/58+QtmV9vY/Zf5AMT+4sHaUoreUzH/sIo+Eaoywjy3IGwxF5nlOI4ujx0yzt2EOiEpI6kvDSovDfCgl474/HzzMi8nvAXcBpEdnlvT8pIruAM5e49x3AOwBkfvcYeiu2U0lQrlVycgU8VRM1qeiDSVEhAt5YGmjKBTIuDCQVta/GFY/hx2whUuMXiQjBqpSRU6i8T9JzZChwwRrhqzJjlRlwKwTL+F0l2nbre6pH+kvcM9HXGIl470hMErIgNQp8jsWQRkEPpbBlTpIa7n7FzXhXorRhenqGa67fgZediIOlnQssLGwjG+XsO7jEsUNnWD63zslnT/CZj52nN9vmymv3c+XVB/C+RCs9oYPIIxYIqc4sWMW5kyeYKQqOn8/YMZXy1AOH2H/1TmaMIh2M6KkesmA48vQpvNLB9TgVShuoYGJSOu0u6+t9slJw1jHKVikKi7ch7r5ykS2dRbJRqL7sAa1Cwk7va9HJ+uDZKFEnoKoVF4UjVloOfBWVtaYW7+KWqvaGVwqDA3EsS5dcDHObm+zaPcW1199EKo7W4l4++tjTSHGcIyPNB54dUvgSj2KPWG7fzOjNjvi1kw7TUdzRCUlXWxB8MsRTFpb773uQE88+zWC4GQqdlJYszyldSafdYTAasbG+ydraJoPBkMPPnOBS7W+MBESkByjv/Ub8/nrgx4E/AL4JeHv8/P3PoTfqzd0oVKlsgVMmcgJVopBwfbU49Spc0KJlwceqQGNOOCCHCc189TWw2JVyqeIfKpO91NyJB+/InQoOJT4G0Sgh2pyiIDzp2knzkTKOER+XGJN6bH5SOcIk8Mdrm3USlCM1SawRGIFdxp9bxYLCGtbXLSdPP8O2hQ7rKwNsZplf7OFbmoXpOTyO6fmZyJyNuPK6Axy4usQkgisDkhsMByAlqpHdJkyT1EhAJCjBTq4WXLPTsNoV5qZnKbJNbrjmBlaePcGq85wbjHjhdVcyaxzHj53AuGBpUTqh399g2+ICK6trbG5skrY0ttKfeCExCVXtgtSEmAVvS1SRIXjKCKjGJEgDqfoowoRqSQERJNH5x6MQbUBy1MXEyYn1qBZWgRZym+K7PbqpsG3bDrYtbmN+pstJO4U8c5ZUr9GXFqvteUodxt3JV1lePsO1111BMbWHjbZm6J/EKRNKyKswt0oJyyvnOP7sIWa6SYjBcI6lTgetWxRKs33ndg5efZAbbngBa2trnD/17CWH/rfhBHYAvxc3lwH+q/f+z0Tkk8C7ReRbgSPAV33WnoSwiYQ6MkqsqwMwKrnL2VDvrqLk8VaqbCu1vkYIAR6Vv3X0DAsY24O3hBScALGwhA9mP7xgVLDbWl+x0JXYELrTIriYZnpkm9xF5d+tQ2Rh7M/bMqQtq8QEVzE+DTGkjifw9TPrVmMwqeX9SgoVqmASITEaqYEx3npRBBlMZtMzM1xz415GmwWiVzl18jzPHj7DNTcdIJk3YZgVZ+QMXmu0TkAJWgfdR8+Y4NMQEZpSKqotJPjbV8eMwfSArKQ7O8P6+gqoFJ/nnN4ccWDHIttaHco85/jJDVaKMiA5pUm90Ot2QFm63TZZVjA9M8XK6mbt5psXOa4M7sStKnDJlnRLhxVP6aos0nGfVaqXSN0D/AZ9TavVCtWAbTl21orvVH36yjxYA7+AL3AkVGZcUQanNKlpoTwkSYoyFpOvk3QNqxa80mE9vGNEwgP33setN13Ljl7CYQetWMlYRKO8C35o3nL+7Bn6gz7OGhSQJorNfoFzwvb9VyGiSU2bdtJlU2+SlyWXan9jJOC9fwa49SLHzwNf8D/cn3O17IX3IdFuu4vE8ta1mSrm9PPWRYwOSLTfNopEhkWUMTKwIXS1o0EZSz8vw06IIdnelRCYOVIXFtpZW6v8KjEg+L87chFcXmDzgvKhj6B7O/DTC2Fjq2Cy9FqHEt8IJAkojTJJKJGtqCv8SO1THnanRO5m7MFQWUbiL6mofEXpAeXQJogCQdQZizgXOooIqJxOL3i5zW/vMrPQZmmpw/TsDBYJlMc5yszSagmJThAlJKlEX4PASpeeaMaSiAciDRXI8gApToI+5Ux/DWNm6VpBtztYErQWFmZnOL82JPOOA+0WD66vITrF2pKkZcii6LK2PqDb7lAUA1ypKPISSyj7XUUgeq9Io2Kw9CWdUU5mfEDocbKUUrXIp0RwNuYyiOKVcy5WGfIgGnFg9bj2YeAyXcgJIFU2H1AEdl07Cd6OXgEpRTHCVxWFbahT2DYJm6R4sWH+tKHIHZ2pHtNThn1zLY4sD6N4GgiOUIJrI3hmp+bIFuZBqloLMfOTV6g06GkCIQvBR8vnzl8S9i4Pj0ERfJKMN3q9wavEE1LxcmNXAgmOHpUQXxXzFOeDg4UoRGmsjiWh4nNyb4Off7eLL4OTUXDmMUQ0S9Iap2euMg266E0WHIkcXqtoCfS0jnyKqd1Xsb7rDeQSzE8uArHzIEWBH2XRHg2hWpBGTFCG2lThEw3R2w/tI/EPyEFcjB6Ix0K+OTWOJYocTpIExVcofTUpClT+9xBMh+urIz7wV5/BqDZJK+HFL7+Bh+49zPyCZnUl5467bmQ07HP40HFmphfI85zNtRHzC9OUVtB4Dl67n8VdM2jjQvhyNWmxbHkeq954B5SWzVaPfKpL8dSDDM0cSavLk0+s0ZrtoXo9DsxqjuQ5z25kIU5At0jTlGGWMxxmFEURRBMHw9EQpQh+CdEg473D+XEiU8qACLwNmXpFmTi8BgFhjCSVCCPnaKUpiVakpVBqQ7D4qDHH4Mf3VQAIwahaBZN5Qui0MQlaa9K0FTwWPagyx5iEUgxlzd2B1Qlueol2Z4qds238+cH4OcQCKBK4BieCNglKbB2qPMpyhqMcJIipiUmQCCdNorC1XR5IYELOB2KFH+9DhlejFEoryrKoQyqloj5a4Z2N14LXYH3IBz8h/0dtrhODT9v4skTRyBfgAxB6V6JFwNlYVyCy1DHteEsFLzAnnsw6SiUcnJ4iTwyreXBLdTETjRDFGZPiE8HruGEqhxQiu+0FyUDEBnFo5JCYbaeiPr7KRSBhI1ViQFCgeiQLpihjDLa0E1VoqwCZChmIKBYWZ3jDm1/McJhhXcr8wjS3vehasuGInbss/cGI6ekZtm/3zM53aLc1Tz16nI31PpJoEpeyurLO9j0LWJfVcwyEwhcS4uOh2ryGQbHJg8fO8/Kd2zC2x7nNDeYO7kGKjNZwgzKb5eFTZ9AmZTTaZH5mhqzIUVphiyCjJ2mLJGnRbieUtsCXfqLuIUC73QbAZBlaFZQ2Kv0qW3/FScl4J1WEx+NRxmCUxiQJRdKOOQsCoFUBvhK5pWr7hiQjDoml6yqZw5YWoxOsdSg8ykPqSrxOKRxANFeKwqqEc6OS3MKebW04NNYjBdMyoIK78/LKGoePnQJvsTYQr3a3w/YdO9EmDUlNXPD3sLbk6acPXRL8Lg8kAKBVxOhjpY3ETV8GNI9ojS/DgnkZs3RV3T4vBMuaG7PMdV2/KGe7KOh6ERwJYAMHEN1HqsirUZbRanWYYLnxtJTQchZlHWUWSl4vZyWDUYnXKj5PRSeb4FVmKzm9fr/oQ+59GGssYlnrM4yA0yGxhB1T9cp1tSqGgiOKE5bEExJ2VEiy3vBjpWDlPAQlaQvOnCy4+roDrK+vYsSza+8C1uaUOSGuXRQL27qIJHivuP2uWURZhplFrCbp6LjRmo5VkWp5X/sJaB3mNneWM8MRfuYgpt9namaWY0dOsGP7InNL2/jgs6d4dlBixNJOWmhtSNMWWV5QFEH5OByOmJubpT/YwKQJhbM46+JeCO/aipyAHg0QcdjSTlhLao7FR+t+45j3RLfjcK5UUTdCQGzVVvKRM2xGH1YcWVjbiLy9p93pBGuEc4hzqDKj8G0KJyivQvCReKxoBlYxGuX0ksouNtYHSXRfjnQjWDycR6IOoszL6KgVfmut8VictZSXfQBRJQKoSgXXfPFqQSKTZZJqFcbAoMf3+OA+H75vfU7kBprJf0DHhJLxnBhW+wNOn7YcPDCDFRsNFtG2TEGRj3CjIQU5IkI/L9m0UdMswYXWK1dzEOO88JVLCZFQSBxrY8A1f1+lqhpzA9J0XKrkA+fxymBaPuS7cwETVpvnQgQAYFhfXuae997D2RP7eOKRs4xGli/72tdz/ORpPv3xR3jp3TfywP1HKYoRe/ft4lWvu4OHHz7E+uoG3V6P3XumaRUdls+vMr+0RG8qwbtgc3dRRIuMAEmSIGlCWqb084L1kUcry8xsC46OmJ7rMPCKZwYjxHly7Zhu99BaMRrmDEZZCIySoNIsygJrPWUsieZcKLpRWkWB0I0+C8qNKEkpyHE4Uq1rpSUuhCDXmpc4vxpQJgTmaFdgdaDUipAXIbqO4CsXMQnBO2EZE0Sq2I9QAcjaklOnz7Jr5/4Yt2DpiQPdIY8p1ap977ViPRtx7vwys20hkSoXY+ACrHWkdZUpX+c9rMURH8qn1YQizALOeXrdmUuC3+WBBKCu3xdxac0N+HiM+lhAAKalsWVZb7hasKoun9CKR+21Byqf7y0Zvio2GyWc7pcMswC8dTKHCEQD68glwecFdjRklA+Z6U2TmSQkjaw1xdVg1MQwJl+68W5I44IxoqBpLoxj9E1/iGhSTZTDRDOlqjwsY6sSdNaxBd6ilKbV7nDnXXdx6uT7yM6ucPLkeR598CiHnjpFr2s5/PRppqdnKXb0OH9qnWNHDnP0mTU2h0P27hY6U3OcPDlgx+7dfN03v448d7XuxQNljLBJ0gSdJphOmzVxfPDIIUxL2J52WdWGXmbJE01mDEaExFuyPENFu36v12U0GgWZ21mM1pgY5ZmVBc6WQXdkAxatHJdsllP6ktK6sSt4JRrWCxLnvdpvrjI8+2ANUSYEZkmw1wdz4sWWM+ad0OPjAUk4rC0QHf1ObEnSSihVQoaEyqx1f5qN3DHKcoxztFKNyip3+gr7jN3ng/VpTOFnZmdIWu3g7KQUJjE458nynLd82dv42Ifex8XaZYIEqo1e/7wQQCLrX10TSl9FNAr1IgIV39y4H+qonRo4Q364sOABI3gJooBT05gECmuxZRAhNIAoSpVSSAeVtGGUoa0L5gUdOIoaCGrkFAZb6ThCqzajTAxxvLsqDgImJ8VNXl8jPqFtUoxWoFXQZsfNMbasNJJnKM3C4ja+6hveSHtqli/84tdR2hGLi9u57oYruPWu7Vyx9zqePXqew88cZzQasbA4z/LKBq2ZgvbsNK2uo9Xqsrl5jqXCo3WCd8OgsNIa59xEDLtF6BcF1pesZyUm0Wi3hlOGTz3yZM2xeKXQzuOMYpgVKB2y5Git6ZgkZMixllaa4kXTbnewpWVjcxPPMkAtDuSDYaDMNix/NQdhOwREoKriHhCyEwkk0c9AtEL7wDm4OlgrlB/banqtUHhFMEJMsSdNEgb9PnmRY52DogysO5q8EqMqHl88617x7LPHWLzyIC0j+CyYz30M5qp0VNMzM2zfuQOtwNog7pjE1MlVlVI4a1lbXcVozfU33MCl2uWBBETAmMjiQBSuCP81qCsSVlNCuWxRYyCYAHqRmJorfveN59RUVMbdVo/0HhFP0urRSgu0CDpqlL2PNQtNGzW3hPIb+NOafFQgicFJtPlFbmJi3JdKbEJ1yQRkjxWkE4jMU/m2TzohAV6YTkPWnYJxOqqqVRGRVXPegihMOsOozGlPT+Ncj7x0pJ0eN9/6Emzhuf6WOW676wWIgmxU8A++62tw3jDKSowqMAnc/ZoNFhbnyLJsQgGJjFODF0XO+nqfVish0QmlUhhCBBwCJIbCerR3OAdKDJkNWYOy/pBupxM1+g7lXPThJ2q+XWS7Hc4GBVu7HYA4WIjC8WqruBhbH5jFoMyrUpkJxAxRAZCrvIN1kphqbSpqHLnSioDgPFpUyJHoPZQlTiyLC4t0O93aM1Fpj8MEq5cLGKoqYDJSKWfPrZL3juGtDRnr4rYd9vtMT6cYbdi7fy+djtDfXA+u0q0W8wuL7N9/BZv9AVO9GTY3+xiTsLi0WCd9vVi7PJBABfwqpIOuWZ4Jbq3hWCNNzkAYl2JizAVEOVzVnIMnWB0aATwwBqp4ryBMKcXiwiLiNfgqIUPYMAkJTrdB9xj1M/J8iJGQ531UWzcm2foaxKt3UtEFEbkAAVR31+9bk5jqrJpAXhI1RPPt4EilKuem2hJwYf9KgvNU1V8oNV4pzjy29HF4mjy39XVBz1dijEdo4ZxncWmROLOIGpshjUpYWpwCYDjoc/78eTwlrVZKp92Gdqya60Mcvy0iFW4Fc2niNUVpsfjg7isGyhKtfCwGVek8woy5skTpAi0GnYQx5EVJnmdx7aNVQI2VehWwSqSu2FBmVIixAz4l70yhnMfWeQvD84RoCaj2xnjlQuCvt2gszoecBInRYUodIAWlaGxRgCsQLSEdmMBAt/j0409xa8eRlfNBeawAXDA/SoESjdZS0492u8X8tu1s276X+dkFrrv2IKOhIx8NWDlbMDs7xZGTVUzfhe3yQAJVc5VShSDz6HEkX7B9VxxCA8iaAlrgx2osP2YCxmz3uLq5NMSO0IEXhS9LXnfjAWaSLPguSBYyuHqD1/CyG3cwO9XiT9+/zGf66yyvJwxWl9EL+1BGgVeB7WtKJzSeCWOEdalWjXtigzXvpyHbhk083euiqCoA2wbyqh45pmAqJtS0diwyNM1sYy6iAh5pfFZzG91tVVN/3RipV7z87lsByDczzq0dY+30ydrpy9PCe0HpEP0Y1Cgha5GKpkC0RHFAoUWR6GB6dTHhS6IVvakeShmy0ZB8lDHbazE7FZDPqCwZIXhX1haEsDdcVOJKdAUL/iAFZSzrHXIp+FabqhaE+OCX4ZyNdqRxyjnnLMoLZSx7ZxFst4tWU4jdwCStwLI7hy9zDDrUJIguzFUWKZRilCScb81SkjJyYVzGK2xRkrRShMD1JImi02kzPd1l27ZtzC9tI88tnakORhueeuwBBpvrbKyu8dJXvowPfPCDl9xulxcSAIQxF+C9Q7QZWwgqTxE/VuSMobt5rMIIMpbPZXITV0AU7O1qTKV9wrPHj3Lk0Qd5/Ze8Fl/qeG8AluXzK0j/NPumMu6zI9bO9hmNBqTWBoudqMj+N4G3EZteH57AQJOczlYMsVW/Ub1jhQzEkSY66D5r4B+LA1W/lY28+l0hgzojbjx+cQ6iiQgqrmHsdx+GOL7HiyWRDgDf8ve+nl9/128wWFnHOU+32+Vlr3oxpRNsWbK6tkyn0+KJJx7h7JlDOG9JTApOsbi0i+mZBZzyrGUbCIrBaIDzlpXls5Q2Q4mi3Zpl146dvOGNr2PX9gUANnWbvNSgC3pospV1bDpAkpBl2IhCpQlKBG8txod0Ys5arAhF2g2BRUoHtY93sQRZtd9AOQdlGR3aPGIVGo1B4Y1g0BgTZPTClZTFiJZpMfTBEck1alYEB1bN+XSRqc4MxqR4a5Ak6EKUJ1qoQsqy2bk5pqan6E5Nk7RanF8+zaA/4vH+Ohur51lbPs8VBw5w9uxpDh48yF/fw0Xb5YME4qTWOQF9nGxrY2hnyA7ry6KR2VePZbtqAzZYch9DQysOodaY+zGCqIEzHvda+OCxDa4phjhVYEXj8TjlkAKeOJXxa/eep3/qLM4Z1vIRqU9JW73JsWxRHFVsSVNFccl2EXgfz5E0Mg9V4oJjbnY6KJorsWeik8knKiWENGZ+AkE855AatvRamebHLt1NDiJwPilegsfbS+++jX37tyPiyLICJYprr7sG7xXOerKsz6ljZ/i3P/1pVk4/GzzrJCHRKf/4u76Na2+4ntKXgU0XjXjHvZ98gJ/72XeRF5soUdx453V87/f+I6a2LWFiENcyLRbUFAtXb2fWpJw7c4anHn08yPsmAZPQnu6RJCmbm5ukrQ55WTBoGcSkjNDYrIgWCUVpC8ro66GNYEToJAkdkzAiiKdeLHmWYVyJtzlaHFqCXkYnBo9FqQDY3umQ2kwEL4GrSkooVI/5TpftGF77wpewMNcFrWi3W4Eb8Z75+cVQZ0EbrIeNzU1mZ6dYWV7j45/8JAcP7OHgdddy403Xc/rUGQ7s3XPJtb1skIBUMj5B4UMjU7CIBMCPSiNvKiWHr7Hy2PMvlm+qbf+BdaspZGRlK0SDh5ZXdBODNh6jHbaYRuaWMJIg8RnehVTekmg6pkM5ux/9pn9A2e5ReM2KJLjoey7OI0Yh1ocqQPgIlDbSUxVSR0VXzsqxhGi/Dv4AVRpyGwYZ2VIqhZRSeFfJxSFyDfHoJObfd2MZtvJ2s/F5zpVRjRLiACSQtDpgSlfmMCpXaRWUiRPadXA4rNPh0UIoXy4hr4L4AkgY7drNF7z0rs9pD3zTxQ5+3z++6LVfBvxE88D7DsP73lP/XN++g2J2npW+w6wMGXShNz1Pt9tl7fx5yqxAa8Uw69OPTkMl6+ikxdCGJKss7CPNwQ/W8Z0Z3PqAcjBEyhKnNUz12EhS8o0h4sGkiu7UFIPRgKwLKk2xJSCCxdMRoSzDellJcWJiKrPAWWnnmfZrXK83uXpuHz/1ygWuvWEfxaiP1Yajjx5naf8Sf33PJ7j6wJVccWA3q6sbrG1u4HAogXanxbYdO7niyqu5+cYb2LNnD9t37OT0ycs8dsCIYl51AwWoFGYENldERxgIslNTTJYqCWSVDRjw1kb7YehCiaBV+DMCWhQtZehomG0lbJ+d4qrds2ybKVHFkGJQcm5tlUOH1wM98R7rfZC3BW7Y0+P/2XszZVbSz3PO9jc5duw8JzZGnMpTVkcqJNHQYJ0NSMBZPFDGUFXxsdBmreBz0ewExMSpIDgfCoyqWHGoUmJ6QuIKFTkZpx0ffPgZrpjpMDvbpZNAN01IjcFohfaOmek2aNBGBauHDZlwfQK5tbW8HXBMkHstgA4bGIJmW6JYVmLQQpRnQ/CX8iEYyqOxKuhmPv2nfxmSbkb/dhfNn6qWp+HjH/4EP/GTP8VguALeBmTlPVJaxCtanS7tdivk1CNF6xbd3gy92SX2XnElu/fuodVpo9MEpTWJ9myMRtxw6BlaN19NtrnM7OI8xiiKYsj8xs4QRxA3k6sUzBbE6LhOniLv099YZ5CkmJbCzM2gp7pBQYkgSUJrZpq028NZR6Icvc4UmfJ4kxAdxynLkuFoxFSvRVkWeFcwqrMAR4WIeLQdsfvUY9w0NeIDHzjNnmvv4Pqb9nLi+DInzy1TFo6HnnmS0XCdZx4+wR0vvo6zwwEf+/CH2HdwP3Nz8xw5coylbTvYte8Aizv3YAl1EY4cfubS8Pc/D5T/5u3K7TP8p//r9TTrzSGgdFRmOV8DeRXX7V0VLRjk2ureik0N4nJwtTQxP4COSCF+wShIjaCxnDm7xvm1dU6cOkWZZSwkCbr05MWIc+fOMTc/gxJhptNiaWmGXqtDmmiybJPh+RXyfEhrcQ+WhNXzfZLpOYoiRNnlDkpbUjhP4Vz0PYCisOTWkpeQl45RUVAUBaPcYgFrPXlhKa1QlI68KMjynMKOKBFGhZB7RW4hLyy/+ZkNhI3gmiqNQCpvaaWaBE9HKxI8LXF0RUjEU7qSqY4h6aSkLUMrTZjutuh2E+amEmZ6mk7b0Om0SFKFSRXaZbS14AuFE4UxmtJ7bGmxRYj6C6KZ4GNQVR3pE1tQSFqUVrTSFEc7ZDb0Dmujia7IQSkG2RClc1zMJdm3awzVEE57NvJNkrSFThKSThrKyIllZ6tAtWcYJI5sc8C5/oBCtdDzbUTraP4LoeG1Rck7Uht0Ty4fghuASUhVLDJjkpjzryIMCkkMPgHnYzLTVgufpIgotNJY63HWk2cjlC1oKxi6Kiv2WDdUmDaPb7+VUxsnuO7Rj3H0bMYLb7mSJ48e4tTJMyzs3Mn55eM8fO+DbF9MeMd/+hWOHT9FgeXAgStZmFnkYyc/wRVXXUur06M3Pcfy2fMoKbn//vsuCX+XBRIQPG2KMVURhbNQjgoglgUTCSWWo0a7kkNdSAhIle7Je09pG15dzhEyTFfyqsW6gm7ao9vuUWhDJo6scEy1W1y5dy898Zw7e45COY6dOMnhR5e59c4r6XRStDIkph1YPa3IsxbLpWXXtgV6nTbFcMSpU0/xgitfTNrqBn9zrWK5qzACoxSmpgBjLiaCDOKCmUnpWB25Vio6stGIrN9nenqapD0VQpJ9HiLqMkdeKgZF8CP31pHnBXmRk5U5o1JRWENeCmtZyUZR0i8dg9yS5yVZ4SgLz2iY8+wwIz/jcUdLfFFSlOAkx0mJaFiShF67pNsVkrZj+4zGtDsorZhOHNNL02jlaRkwqsQ5Q0DhlRVCKK0jSVJ279nFLTe8iLPnzzMsRngszuVkoxyDsHvXdoo8o7ChGlDhCqwIjpTRoE85egZnLcO8JLeOvPC43EKZM1o5h9clvV4X6bSiVr/StQql8zEhTPTCVSqE31qLswXWpGglKJXEUGWPdR4jgTstnQ35JbwPLs1FgVMK6z0mpHFGS3iqdQ6dGFq9Fqu5gjTKUZHr9b4k7ySc7+zn0+Lwrsf7/+FP8cJrFvmWb/tats3Po9s3Mtpc449+98949uQxPAVzi4uYGEhXupKyyGlpw8ljx9ixfQdnziwzHI4uCX+XBRI4d26Fd/3K7wbK4SzWekQ0xkRXz5ALGtNKo/urZjAc0ul0albfeR/Z42DDVUoFxUkloypFNhrh0ZSFkCQaZQJV23tgB1ddtYdOO2V9fcDaYEBRWFzpmOn0yLxnfn6aLC+C6K4drVYIKLHKc+ixQ3zi2HFGwzwU7ShHfPyhx2l354J5UQxJopjqJLSUICbBJAm4kMDC6hD7HYqehBBUJwLK0O1OIcqjY6RzYtqgEzqdNcRCloMyhumpLsZoElNtrGBW897hCOG1U70EL5C2W2gfGKLEmGDaTBWpSRDrwJZIIvjofmytDR52XnBO4TyUrsAVhGSkZdj0NiZL0eJxVVL+ulCMQykfvBojh6Yjpt6+OMf1P3OQbJQzyDLysiAvCkaDEucLdu5eClxTVpLnlqKwWFeSZwNyG+LxnfVkowJrHf3BgGeePsmRw2sUoyFZNqDMCzbzgs3RIHj95wWF87Tmpxkkmlw8tmXQKTiVYqVAzj2LLJ/FesF5G8WhgKqLwoaCn16R2wLrwnpZI5hccL0ErzRatSiVZW3tLP31EsEzvWuJ82e76DTkmyhtzH9RKMgtLtEMt9+ESWH32Xu55sq9zE51mV+YBa155atfwQfe/wHay0LhDc6G2g+lC9GjWmA0WGfbtgU63bH151LtskAC8wszfMlXvRLvfUgIEXPVGa1QOgxRJGJSJRhtyLOMJOYgGL/gpC28DFqYWJHGs7y8ghLPzPQUznlsWTIcFqyvDDl/dhnvPdkop3BDuu0OeVYyPTPFtdftIE0Vo8yzsTlkcCgj1Qa8w1pL0plhfk/KxmjEejZilEFWKuwggFHuhmTWkzmFdSpUiXU2sO0exFp05AKUeMSVMXMRJCJ1IRDrLKjgnVjoBJN4EhvMKLrVItGaRMcswCoUvfS2RFXzWgZRKWm3SNMWxkMCdJI0OO4ImG6LdKoLymO0JlEmJKhQwdSVJiGTjTY6hNw6haSeRKk67XiSJCEARwcnGa2CTV75oOZMjMEbjVUh048ox/TcDDPeY/MMrYPOQBKDbpngFagS8KoW8SrnpvDMEF1nyxBwIzrkHrRZUBh75/A26F0sJaV3YKGwwsg6hllOkRU8duIsf/TRhzm3sUaPIceLnKyjGBUFXlqUUXmtk1C+bLrXZW6qi/WesgxxAloZkhbcvrSTa3ftYmm0k6lWl3xDc/L8Buv9WZKF7dzKbqRjMAqmEo/KN9nRS5ntao6dX+apo4c4/fQZDvQUS7sXaXfbDF3OQm+WpW1LtNMWBw/s45ljx9GxqlKwREtMpApz87OkrSQg3uewSV0WSEBE0KYFeJI0DZNqLcokGKXrBB2JDhvCOTBpu76/8pwKUYUBYJz3EYFEhZRzzM8vYG1wFEF5TLvNdMsxuzgX+xBwCmM8h546wdNPPYXWmna7hUlSYESv12Jx21QEhoB89l+zA1eGkNbSWrQ2rK9v0O10aLdSSkIKM4mKTUul5Pd469kcDELSiah08zbkRBgWJTb2WdqSLC8ZZJaMgl/5wAke38wCZTIFkiVRK185VLkoWkSjgoT8BOFFhyg20c6hbYl4G7z1KoSrhJSgRNVIUKFEJGVUKHvd9RlqeA7p7sJ1ILXU4o5Wmq6kaAVG+YCYvEOVFnE2zJ0CrTRJYjDKYZxDfElZjminhm6rixiNbrUxpofWkXswKdoY2u0WWgutdhedJBR5RidJkZgHoJW2Qgp2Y0ICVOfQ2mNUEEtCLYTAwivvaWu4Zf88BxdvYWNzjfW1NT7T7fPhj69xbDDAJo58OMSVFuVDIdLcw4YIw1FGYgxt0fhEc9XenXz9a2/jpbdehW4JiUnxLlh2nANlHIU3VK7VWoTNjU0eevhRJDGcmcm4ioR7T5yh09FMz3bZ3NhkRgxqOiQTycucqak2L7j2Ko6fPI9WmtKWVMVuOq2UbqtFp9WJ++xyDyUGTMz+qmJRz7IEqkQOInWdeSH4ZteJLaWR2bfiCiqfg2gDt9aCCFrpWEGwDgOJwBiM977StntXgRAhaadGGMcoOy+AwXmpPDpBNE5KPIEiTfWmsM6Sly76kYV7g6UhBix5waFI292Q3FTpqCgO4+8RNoiHmCbKYp0n1SXv+dh50Bne62hbiKbVyg9BKp+CiNwEvKoyLAULjHgHNociC4gzbQVqi8ZrPUE7glqiSpTiSfIhvVPPsHHFldiyhZQKqZ4JiKvs+j5QJjzK2pDIBYdyMckHJcbbkN7NlSQSQoA1GarcRJUZSlrRWlCibYFxJamE3I1GqZCQNstJlUKLJU1Cim2tNCbtoLTBFxaVpJilXXR7PRiukSQGa12sUhySimZFhrM5GxtrDPrrnF5bJy9GtHrzeGUYDQZkWU5KiEPI8pLRKMdKjiQJU2mLI4+f4MPzH2Lt1FFmZmZJ0g5eoNNu0zIGQaFTRavbRZmQGGc4zMkLmJ3uMjszx9LiNhaWZrGFJdWKx588RFvPMPvqBVIcd9+0h1Mnj7LOPHkx6VoiArYs+ciHPsiLX3o3tiyx9vOQY/B/ZhMJZaArdxQt0G2ldTCKszYGAVabveEbVIXNemp/+OjsClGedS4sdKWDoa4rzxYf+wrRBH2EeB02caRwlfLRqIpihsFIxLRS6yWqvqpkIg6JFY2UD2NWOspq3jdSdUe/9aoAKVDE+HAhJKAY5lkwx+kqWaojFKajkTylqXAKY6xcfav5wduIKyUoVnXQAVB7Z/pYkYgt5sn4Sh4KLyAGbzoBwUhAbHjAjCveiFRhtg20IvG/yo24LMM6SrAiiHWIy/B2iO7O4lUay4w1vBOjAxl4xFokZthxzuKzIebEIfziQexwgPE5dpDhhynzWrHx8DO4YR8/ykkKi7EOk2haPU061UJMgtVdhomitC1azqJ7HWZ6C0E5aB2602V6bpb5dHtA3MMBaZJiTcYH77uPh+7/JFOqy8KObczMzGKHfc6dPU1PpZzxmkL3mJ7uMj/VYte2efbu3QkdGFpFkgjTC7P0l9dAEpamEgrfptU1jDYL8jzEVRx66kmuv+mFdNodnA0Zp3Wi0SZlx44Zur02Fg1y2XMCErX6ZWBJpRGNhmwBVOIiWJpZcy6WXx9CLfraHfciYlHTXbbyiqtq1k/27WvXYUEuOF95zlXNucm0V83xTow1dDB+/pb3qTgIAVAhtXYr0bHUVuVQFYG9AqIJusDYi7F+1zHF9jH4SqoaCDEr8xjgGxi3er/qXm3qhCzhtIwxdFU9l8kkqXWrKzIHRx1fl08i+CtU1xiDxcRspcHUVrmVSRWRU727ja7lCJJ2YHoBmVrEtaYptI6RiJpl55EbXhlcffHRDwJUYcEXuLJAWY9XnsXNVfToFMNsiOQ+IK3IbLJgyHWfkd0Mpd9F8K2CNprT3VlG2Tq3LXa5/tYXcNU1V1GMhvzVX9zDQ48d5dHtN7M5vy2YbweO3rOOXavLbP/E40jSZb2n2LC7uV5ZlHMsHtjPrp37QGCUFzz65BGWV0+xc8/VZOJIex1G/RWu3DVNR2u0aXH9zbegjcFozY3XXc9jDz96IQBwmSCBCrjqdNWMgaQqhBmQgq+Pb21bi22Efpthn1yASKrWDLutEQJVNeI6R88YYTQ2dA2sMQin+cwKCWwtFLr13q3ft17THJMxhnZqYjLN4dar48c41Fhq8UDq7Ma112FFjaPoM45srPqoB1HNznjOIIgM2kQurYEAqmxKtfemUIWA192oGPlZZTxSuv4NKgRLeoOIGdvSpTGWyku05u6qfRPrBrvAEVDG6DsXg55USC2vLISUbgHZKRFsaoAU2iFewKew+sAa6eYGnR07GJWefDTEWxsSl+igrN7Y3MSXjpk0mEVPnz7L5tUvokuLgpxRXpIVOe2pHt2D1/J4f4G1gcJLD6+g0LApwukhmGN92DWN7S6gZtvs6J8BZ5luTzOVmuB7kKSc3ywZlW1ufeGLWRuscObYUWa6hrmpFq1WClGEbhkFzrJn966L7i+4TJCA98TEiHa8LyPQVVlxxi3Efys1DoetM/A3KF3wGQibsOnfbl2oJBwsBpHiRA++qoZdtXE9sdb9Fuo9Hre/qPmlOtYM0Nla4LTJfXxO/vs+OEwpBBEXagzEFFcBCGSMPOs5rICTmuoiUs9vIKc2fK8RlUwA1sT1jSYITlQQYAIUjRFAPObZijhrGa6BT6LgpgBr62CaChHV3Ih3DWRUzUlz/iTk8I9iDj44KaEDB4H1eJ2EefJBUaqkShoadT1Kxi7cIui8CHUDetOoNCHttkinuzEJrMWJ0J7uIa1WdKNWdLSnvW2JTUlwuotKV+mmHbpJStt0oDNP3vX40TJiyzBmFUvYCTgVEJDOfQh2yjLyIohKpWhSEUQZrO6xnA147wc/zsHd0zz10EMgcOedd3H3S+5imFtsXnJuY5O/et9HuGr/ZY4EyqLg3KnTzC/N8dGP3M/+fbvYd8U+EM8jDz7Frj1LTM/2SJRh+dwm3hVMz83gcRSZJWkLSSsJRTJxeFWiaOPJSXSXvNwMzh4uSN1GgSs1hRuRJilZniFakSQGbwPgh2zgFutDzvdgnoqKROdQGLyUIAk0kFClE3BMcgkXEwVgvIkvxRVUeohwPHpMakVLR4onoXhFrRMIV46pbtzmk60yGbmqU6qkrJU+YwL4awmjYiuCW22wHThCpuYK4MP5qjZBAHIfEog0xJBA8cdchzB+z3rYVaBU80Q9VzKuUelpXBefKZ4UT64c2JBxydfm5ipJJzXiCnEUjio3hRNApUAonKKNAaVjotrxHBRldQ94a7GiMCgK1SIRHxPfhozHIh7lBHEFHo03Cu/HhEsAphdR/Yxe/xTd/CyJH5K02iQqxHCI1yhRJNsP0JqdIyszVlbXQ4RibsnzEP/y9BNP8tSTT7O6sszZUycoh+tcql0WSCDPC44/e471jXU+8BcPsf/KJ/nWf/gtPPb4Yf7gv32YG27dx1u//HV02p4/+++f5tz5k7z5y17JmZPn+O1f+z1e+soX8dXf9Bbwmo9/7CGyYcaHP3gvK8sjRsOSr/i623nq8WW275hjMArZbBcWOtx42w08cO8hbr3jSk6fWsN7z+6dXfrrAzY2+ySqjSFW4437zWiDLfuQmJDRpuHSbHR0nY3yvzHj6d2KBJrtUuJCdZ+us9/G7MMidcrEMS8cgW5ryrLQSfxsBE5VQK1UTE6qxn3Ulpex3D7uY3wfOgn3q8oFthpOMOlW2fpqKG2++wSyEbx1476pbolxFH4SkfkKEH2Fk/zkOCtOEkBcQJQ66E9ExvELFcdXP050vZYQ+ETlHUqHvNFlRFw+ZhBSOiIFFfUNEogMWnBpC7EFZQH3P/AQOZoX33kbkiZBva3MeAy1trXAt+dJcvD2NDNnn6A918LbAqeE3DvaIlgFvtUmH61igNVhyYErrqGN5dSJ49x374PMLu7kuhtfwNNPHuHRBx/hwL65S+6xywIJlEXO+WPLbK45XvWaF7D7wDZG+ZD+5iZZdpbNzV5w4bRw5MhjnDl1hnb7lZR5zvXX7eX226+h0xGK3DHsb/Br73w3i9sMo0HCvv37WFyc4pf+8t30phJe/NIXcv70On/y2BPs3nsVe3cv8ZEPfYTp6TZPPH6YL3zDS/nExx7g1jtewOzCNgpfRGpcUhQlDz/wNI899AzXXr+PL/nyV5EXjioTslIKtA4Wic+Bxb+UMrPZxrkK/RgYpN7iURKoNnOQicOJCvgq4G3y+NX5mJudyjKgqZKruorVZtxFU4EZrKQJKNOI4KyuiZmSq5xeE/JJg92vFbYReJVuBH9FRBM5i8akTf5u9tGcNwm+Flvvn9An1Qhj/G71UKv3cA6VEF3TJeoiJGZvjkOPa+M90QAakJSzBaaruOnmG7jxlhtJTAB8hyBKxwCyxhz5FCGnyFconvwwRzbXuWp2H5K0EYKXsXYW7WC66LMw2uD8uWXWdcLy+gbF8mm6JqGwnq/75m+mNzPLrbffzDNP3MdMJ+VS7dIk6H9hc87xyU9/jG3be3zso/fyzNMnKW1JPrJoWhw/eh5rS0Q8O3cfZP8V+/jUhx/g4UeeYnnF8uCDh/nIB58MCSs6XWamOyhV0u0m7NjdY35uGzfcvB+tNddeczV54bj7VXfR6c6xY8cu8rxESQYObrn1Rl5w49W89BV3kGU5zhcEqHO1Puvrvv7V3HD9QbwvortyZfOO4gAXV15u1f5vtQRcrF2Ue5gg5lEX4BWRr9+CECrKXu/2yLpXlD8Cv1Ig0Vszhi2PkUv13LjZ6xqPBq/0GHAaBVJqEaIC5uqvmYsxUu/JtHFqfE/9WX2XxvvRuGfLsWqOJJaAq6DV+4ZilM/eB8EFXcdzKr6f1MQ7JmOJFZcU1KnPggOcQZuETq9bW6lEGvOAbHm2w5V9/NMP4U8doxiNgr4iFjQx2tTJRRbcgDnbxxRDsixnLReKdA4xKfuu2E+n2yYfWbq9Dvv376fVusxzDM4tzPN9P/aPSJMu19x0M4JBaccLX3Q1N964H5RFoRhmnm/4ltdinae0I2xZ0t+wIQ9A2kNUyXW3XMUP/svv4tzZFdbXh4wGI2bn5vjab/wynnnqLAevvZbW1Cw33XwNDz/0NPf8+cf4mm94K/3hBjPzJ9jsW+YXFonSNwqF8yXemVCmapTxCz/3p7z29bdw1fX7IoAWeB9DfsWijMKVCk9BlVlYSSX3BarnPIgqQ5GRiqrL2CIBY0TivY3fA5VOlYTqNSIgFj9R/GMsF0/I4BfgmYgkFODN2CcA8P5CgKo4kIqoeifYaLkJ1ZGqCNAIZKraWio+2zHJBVSQtAUglTSVG6HpaGloKhhrhNF85cpcGQ46ceO5aIofYyXHBOBXw6p+SVSa6pg4tloP50Jdy/DOrraMiLcIGnzUP5QFRmuUNyE2gJCbQrlgEm36YYhzMOrDySeR5UP44TrJ7A5QJaHcvWBdHvJpKJidTlnP2kxvttjINEhK4QrUVIt0qo0gtNttTNJidm6RvL+6dQPU7bMiARF5F/DFwBnv/U3x2ALw28AVwGHgq7z3KxJ2788CbwYGwDd77+/9bM8oi5JPf/Rpls+dZWrO4H3KwSunOXV8yMkTm2SjjLm5aYpywE23XMf+q3dz/0cOMdVpMb+wxNNPHmJubpr+6gy7du/g+NmcMtcc2LeX7TsX0KpLtzfizpfswPmSvQd2YJKUm2+5ittfdDXWW/JCuOPOF2F0wa23X82ZMytoZYKfP6HuoDaK62+6gmuu285NN1+PEk+3oyhKg9JQjASlu6SSo5Wvq/JCyCgTgBwgASmxZQo4jI4Vi0QmOIgLLQdSw0FZFTGt9AH1TROrV3V0ESRQnR9T29CbTN47IY+PHZk8gqgknFYNmbx6HoyBfKtusgLEib6bbLq/ADdM9FsDfgO4qz5ExQIo0nh92fJeDSQw+bYTz/GurIOGQoFSNVHJx8drJ7M4VcgpuLsHb72YzCWKDL5eOwJnUhaQD5GV07B8Gr+0Ew5eg91/NUV5htRqnPK1NcHj6XRatJLWOIeUgU7LsX22S6pDxOP80hzeW0yShByLl2ifCyfwK8DPAb/WOPaDwPu8928XkR+Mv38AeBNwTfx7MfAL8fM5W56V/OovvpuDVy0xNTNLnhd8+iOabrvFK157Ow8/fJQbb93BL/679/Lmt74EJQkf+ssH2b2nxcx8l0NPn+INb7ybe/78I9z+4iv4nf/6Afbu2cWOfS127bmWq67dw8//+9/D6IKDV+7gz/7kHr7xm97CYHOV02fW6E0t8MG//hQ33nQN+w9uZ2NjgyOHhtx617ZQittXJsWQIfpTH3uSVjLN8vnz5FnBbbce5Jmnz3DTbQc4/NQyS0sLbGyuM784TVF6ur0SZwVlCrRuBfu1txilKFyQu7270FS41a+h1n0pRWkdvk5AElsT2Gvq2GDR69ZU2hEuVBp/MQBpUsqmKKMkhkfroEmvKW4DEVRcQT2ecE11eUjTXZ1sfsJYUvURKQTgGpscx0rFpu6xervqeSKMRYuxwB/GsRUPNZpUykrnUKLHtSOR8azJeLJ9fJWqpJhHhcCvyjIU7/NIWH9UEKtKi+QjGK5Dvg5Y2HstescBioUlypNrYIIn6WiU0UvbKBHSxNBrKbYvtllZU2yYaRY7hmt3L9BLW9Fvpaz3TZZlF75kbJ8VCXjvPyAiV2w5/CXAq+P3XwXeT0ACXwL8mg+7+WMiMiciu7z3l853TFiIHbuX6Pb2cPMLd3Pi2Gne+8cPcNXVs+AK5udnmZlbQulpFue3sbq+AVYxMzXH+spZnnnyGP6NipW1TT7414+gVIpSlsX5PYz6Q9bObvLWL76bj37oIdZWzjI7M0ueb3LPPR8hGylefPetLM5N87YvezH/6id/jd5Uhy//6rdw+sxK9DUIy+es5+GHnqGdtnnPb/8pSko+9pEH+KZveTN/8ocf5c67buSJR07z8ldfx397z3v5pm/9Sg4fOoFG8eTjJ9i+Ywcey/GTp/nyr3gx/+037+H1b34Tr33zreSlY6seoWk+bJoKERXyKjRl6KiMqs0GwvhYJU9vlYUbiMLXvHDF2jfs8pVYsoWaK2WwognxG/WJsWkvXrf1ob66rjpfA2dTybeF6qvG3dK4v+qjuq6ep4rqulgstIngVBxWhT0uyiYhtqQqMhq4sHGeygq4BBhXlldj7iDeU+NfAe8d4kOeA3EaX+ZQFPh8iPjgBp60e7ilXUh3Hq1TUlEUAF6TdNP4bIfCMd9p0TUzHM8tG7aFdyNskcfkq0Kqk2A6LO0FfirN9jfVCexoAPYpYEf8vgd4tnHdsXjsAiQgIt8OfDvAtu07+bpveSujoWbHzh6zcwssLe2m12vRm51hf3cRpdvceuc1ZGUJHtpt2NzIWNi2l6Xt6/QHm+zfvxfrNknTGbLBkFOnz/PskeOsnjvIuXPnKV1ON53nzW+9nhfefoC//IuPctW1uyktrKzlnDyzSa83TZKM2Ld/G6dOrYEL/gFCTBfmPbv3bOPY0ZOUZcgB3+606HRnSVo9rrrmSlQr5DHAlmSDTU4dXecL3/BSVtc3+MiH7mffnn08/eRh7vvMp3j5a+5CYjgtLpbBvigv3OAgxeEKFwFDIj9wMXLWVAjCBOmTeN5JtOFX11TIJ/6odQvNjkNQEEbHsU7mhLxgwBd9meq/ymfBNyj6VhFBLrwWwu/6kc1rI80V1UAaDRY8IojmeAPeGOtTvIAuQup2VEwIY2NV4tiP8z7kZcQHPUCFc4lKVUJehUrXo/FYXyJlgdcCtgBbBH2ACJK2UdLCp1182sIrwaoE4zWiQvBZyLQFRRGSp6xtluReg3isFjJLFNlczMvpGGUjys9nAJH33otsXf3P6b53AO8AuP7GG/2Bg7tQKiSuWNo+T/sllmwAaUewNtTX+8qvvYOy8KRpj3/8A1+FJwVleP2bXoKYhBfepRFnyO0axchhDGRZSbtjGAxGaG3waNrdkBT6+3/kn5KkHlcKL7prmR27pvkn/79voSyHJGkPbTzOhVx/SEhTdmD/Tt7/54/wkrtvZnUlp9fbRq83wz/+3q+m3W3z//83f8iNd9zItS+4mdX1Ie32NMN8wMYgY3ahzd7923n6icPceferuPaGmzCJCcrBuMB1SrWaq26w4BJYSe89wyJSpZjqaoLlr5Vf1XfPBHWFSWATopxRUTl3YR/VOGho8+vcjuPzF3AbjdYUQCZPVMjpImc9jK0elZ+D24IsmhdXIkn8PvHaW/tviC8XO2fLGMdQKWyjC7Mfr0elTaxqGIaeIgcXEZFEfYuKTlHG+qBHiZyGVwpUG2U0brSJO3qEtHsG3W6h7To+KYMOt9BIEqtLKc2p1XWeOXmSFbMD3+2xPsg4vgY7pYUySfDotJZRNqQo80uuy98UCZyu2HwR2QWcicePA/sa1+2Nx56zaa35yD2HeOD+BxF9jr27r+HAFdu499NP8PV//w286xfuYf/BLscOrzM967j1thv49Cee5MBVu8iGGRvLA667bT93vuxm7v/Ukzz68FPccvN1PHvkKNrA7Xddx9OPrTAzH0KUi7wgz4dce/1VzMzNgy8prWJzc4Vt25bQuscTj53AJNTJPbwPkYj79i/xYz/9VZw/s8n0TI9e7xWcO7dBq9MDUfzbn/8OimLES+++i9JlGJ0wGPQR7TG6zUvuvp3N/jpL26fYf9UBup1O8AAzmlE+wjlVs7NVawYoiQgaYaP0NcVxNWVvAn6zSYOXr5BKcFMVXRW/2Ertt7QI3HNP/hUzkiNljjiLO/vIhex5df3ngBgaN1wCQzR0DBdcewkkgAdvkWyI3+wQLDTVNTJ5aX1Yxs+PcylZH9olU2Uf2x/R9RJjHRziQa8N0ZumMcwQ69L2MLtZwq4XAMSELRJEC+ehtDGi0kenKw0mwZsEO7cDlJCXHimGuFyBBWdLxATTrsMzzEvWByPy0uOUQpUWOxzg5tsYhODa4FHG0G61aaUtLtX+pkjgDwgZot8eP3+/cfy7ReS3CArBtc+mDwBA4LqbF/jN//pxrF1mcT7l99/zAFdft5unHn2SW27ewZvechf/7Pt+lUOHnuBL3/py3vnzT7CycoKsn3B+5Rg2GXLn3Tdx9sQy2eaQP/i932W47nGMWFho8eSjJzl1+gwzMz3yzPL6L76ZNO0gkjAa9vnrv/wUn/r4o+zbt8gdd9zAn//F+3n162+lThggRGVcwrNHTvLjP/Rb3HHnAbqtJe69/zN89/d8Lfuv3MHRQ+f467/4NAtLC7zuzbfS73t+7t+8jy/9ijv58/few5d++d08+MDTzMy28DZlatoyNb3KoWee5RWveXGMCo6mp9pEOJnfH+fZtIREnD5UTpqYzIlPfwFSkIoC+piy3Tl8w7uxpvoRuJu6ihkp+Ob/65+i8kHwn29Px0ep5hM/ZyQw5g4iIvJbTk5AZtUudoyJe8UXqMEmrjsd/PMnOx1/b0zXuOJzeHkZriLHH2NuqkOhQ+3AUA0r6BmSdgtt0vodFSG1V+EVP/uu36JPlfXIjf1JdKz2JFI7Z6F0QATKgOngVYk2beh0cfY4HkXpSkSCebJ0lo3hgKJ0/H/M/XeYZEd23gn/IuKa9OWr2vtGG6DR8BgMBna8tyRFiqIXZWhWXEpLSZREilxqVyIlLrkUvVYUzXCGw6HGe8sZzGDgPdBo78vbdNdExPdHxM3Mqm7M8BO1+/TF06iqzJs3r4k4cc57znlfkxsILeQZJaEZKpVQSrj0qPBVj0KwtLD4is/gb5Ii/HMcCDguhLgA/AJu8v+FEOJHgbPAd/vdP4VLD57ApQh/+Dsd399BduzawfU3HmW4YRkeHUHbZS6dX+H6I4Ynn3qZTpLRzbqMTWzj2ecvUB+NOHT9Hh791kUaow0C5bThV1tN2s0FlIxJ0gXSVBPHMQdu2M7XH3qMe++9jcvLa3z6Y1/ngTeV2HdoN2srHZSt8fd+4HU8+fjLfOgDnyTJm/76vVtnLVKGtNY6fOqzT/OqV+8lDhIefvgR3v2+13P58jQ7dk+RJwkz02dwPASWkyeOo/MLfPNrOa1lzde/+nUuXpxFUWPv3q186iPHGZ0IqNSq3PfaO327q+h1JQ7mpsGFNytZm9QYl88v0mkbV7orBvvgdLPFvHX6e8a35lrhEPureQQb8+n0y6B70m6ityCuNzyD6cMrnnz//+u+4Kp/DHzRhoOIwtnpn8DA31ccFBDrFv+rOiID1PWDHoToXZNg8CvEunsMxmRoLwlvrUUopwgtrHYCvCrwhVrOGFinHgNSYEyKDSS5dM1KgXVhn7aGPMsdbZxXuzI2R5gcVOx4DwPHb4hQWJOg85xS6ZUrBv8m2YHvfYW3XnuVfS3wE9/pmBs3YyCM4Kd/9vuxOkFryQOvv4+AMqqkGJnYzNhojb3XXcfQKHQ7Ge/+rjegTcIb3raDTZsjui2LNSFTW0dYW6nz+rc9wMzlGTrthCCI2Lp9lAdefxe333mEb3z1ZVaXBZ/52BO8LWqwe/cEqVnmkx87x94DVd76zgf42kNfRYjQoc3ODGBMzvzcMuOTEY8+fpzQJKhIcPylc2zbM8y5s7PkucNjThw/z8L8CiqQdNqC1eUWURgQKIHVgtOnT3PTTTsJQ4kxKe2WIApDOlkXIQzG9LGBfiMS1Go1KqHjTjS++E4YP3mLDji/wq1rROoNVVs8rJ5hsEWBknVewvr04cb96VW+FV2arN/TbRu9gFc0BK9kAMSV83b9DuuOt26u2+IarmYQ+y9dcTbrDKk/34ECpwJMtGZQr3HAKGw4lsASBEGfG8M6WhxhjSsmkv1SbVRRpQmgsNICQQ9vMNalHxWO9i0Q0Ol2QPnko5TooEQuXElyKfTyasaSpRnlUvlqNxO4RioGlxdbzE+v8eTjx7nr7htQSjA/12LfwSqXL61w6MadYEps2u5EHRGC/YckUgYYq7FGuzw7lrtecyN33X0TUlp27tvBIAHIu77nDQRBiamtkySdFU6faLJl6xClSpn3fd+D5JkgLkVIKzh44w5efPE8UJCIuLTSyHiDW27bxaGbdzNUq6IxrC40abVgfrrFrr0N3vKO+7l8sYmUJfZft50H33Qn+/Zv5blnTnDkpt1s2TGDfo3D81/9wCGq1RLT03OkWYaSkR97Ds2VHnhz16DJ8g65clLeLv+NrzzzRSwCXy0sWDdFBsG+wnkwxQcEBZmIWBdaQLFi+qMMbH3mXeENhL1yWv2NNzd3xTpjMxheXC0YGET7B44yEGNc7XxeybhsOBcPgLoiIe9mCNujSOjV/Q+el3V9Az2I0Prj4OtMhH8GQnovIBgoiy5Kq/371ngwEQe+CscPEEnXKRuFCo1BRRHCBqBCrArRWIRShEHoFLktdJOULLvGmYXSrMWH/uQLLCyfpxxZlhaW+Pxnn+Sf/vz382v//g/517/8U4xMBAgT4CSxu25hMV5mTEZgM4zOAcdya6whiAK0BqkyTK4II0dNXSmXqJarbL4XktTxFVTLE9hqx7HkSsHw2BjWXAShMVb3aMoqtZAwHOfAgbJTCLKK7TtyFuaXqNeGCSPJ0FCd62+M0cZ99oE33YrOJXsP7qLbzdi5dxe5zRAiAm0JQ4MSEa1u6vkTvDtr+7wExpNthGFEN81INVgPLvUmXw/Fv9rKV7zmDQcOpFII8p4hEFdMwGIr+BcLD0P5342AfpNT8V0D8/BqHsBVXvtO5uMKHHAAA7xaduGRT3yQhz/6ZyzNXOKfvf/LVIZGnFdnLZ/5vf/A8ce+ThiXeNfP/BKb9x0C4KkvfIyvfeAPAbj37/w4R+98FXmW8tu/9lssLi5y12vfwF0Pvg5j4b//tz/k7te/mZ37r1t3WsanFPuXKvqPpAjtgKKcvGcEZPG79wygd03FfZQqcHiOyYmjEKsNYRiCDSAIUCYiDGOECrzylbveJM034Ebrt2uigcgayfTMGY7csItHH3mWT3zyK8xMn+fjf/UFpi+u0lxrIYXBkGNJe2kpi3GD2WbO9VKu19r4eLkQHnHNNU6NRyln2XOT0mxnvf5woVICFTuyTwKUDNBmfQGPKBpSZJNAxYQRhKEEQiYmJwhjV9KJCMlN7lcTic4DsNBuZwgkWWbJUkXSTUnzjKQraLUzjHZEF4X7WMSSxvg2YkAgSVPtViLrHXfZG2W9ibp+K14coE0TAisk2hEn9Lr+io5ApxwknGZir8VY9N5zzkZ/gG5cr+3Vvr53bv3zNCYfWPKL4N7H1Rtddiv6/xj4uf7gAGw/dCM/8PO/xtDk5nUndOKxr7N46Rw//Qcf4+0/9a/55H/+FQA6ayt89f2/x4/9+p/yY7/+fr7y/t+hs7LEiZdeYM91B/iZ//3f88RDXwMLl8+dw2jD9t171o9j4Z5Przy4SLmCywpIXzVoDUIVk97dV3dHi8IvwHue2MKgWIRVIF1jUiUuEQSSUlhGBK6Jy+aarNslkq6SU4UuK5JlXS9Ue/XtmvAEpFLc//q7eObJ49x9/80cPHwdTz1+kvtfezPTM0uUSmUECilDrMm9Z+tZfXD5VyNML2Yr8rLGWKzR3lU1WKtc6CAcE622jhhC4IptZM+R67MWG+28jWITRcGKAIHCeuCwiN2FUP68ioYTz9Dr3UopXfOQLEpL/QQSwjUrFVDTxj4CwBOmOiPQA6WMcLU6RcfgK24DE9Sv3ALr2XTwLij+n103QNd9vMAF/O9LMxf503/1D9my7zCXT77IxI69vPuf/gphqcyl4y/wud//VdJum0pjhHf+7C9THx3nj/63H2XTngOce+FJbrjvTQxNbOar7/9dpJDE1Ro//B/+K3ma8Mn//CtcOv4CUine8GP/lN1H7+Cpz3+UY9/6ClnSZWn6AgfvepDX/8jPrI8dsGzZcx2ys7b+2oXgpW99hRsffBtCCLYfvJFua421xTnOPPsYe2++i3J9GATsufkuTjz1KBWlyNLUi6k4o/f5j/wl7/57P3oV78X27+9ADGU8tlBkdywD99f3OPSjl/5zMp6/wHWJuvLlooirkOYLVIQwhUq0Idc5uXGNT9YfI027BPZv1zvw//o2OTnKa990Nw++8VWuICaTvOntdxKGIfuvv54o9tbQ5hTAzEZGnt4K50kqe8h18SXCx67gH8T6bZAqbF37bs/d5YpJ+TfZBlf0wdd65+fPRcC6/a68Ni9bhqWdeCk2QPg2UyPYECNfLSSAgoqtnwXQG3Yt3H4xMPGvPI7jS3AGceHCGd7xT36JHdffzEf/07/m0U98kDvf9f18+nf+D/7Ov/lNqsOjPPfVz/ClP/q/eef/+ksA6Dzjx3/jA2Atv/OP38v3/9Jv0xibpNtyE/eRT3wQhOAf/c6HmT9/mj/5V/+Qn/r9jwEwfeoY/+D//iBBGPFbP/5O7nj79zI0sckZY/9kBU4JSQycM8DawixDE5t6l9IYn2JtYZa1hVka45t6+zXGp1hbnOfGO2/npaef4Ld+6d9w71vexvNPPs7mHbuojwxfxenqBUE9Z8cY3/0oC8/V79Jr3/bNW6KoMhzYpA/2CsJavLKT90iFkKhAgXbeXJ5naFHqhWzF0bIsI/5/oWz4f+rmdOkShC2B0M5VQpBrQVTqj0md5wipesis9NxsxdabRIOobvHehsk9aAh6tGADf/f+Kp6pX+nX9b4P7F8cs/h70KAMtgezwXj1PieKBpOBcyiMRO9YEshZ62aeuMKiLAQ2JxOelbc4Z7HREGzwra0HC41re+n3DVwlnhjE3fA+hBRg3HNoTGxix/U3g4AbH3wb3/rYn7Hvttcwe+YEf/LzP+4OYTS1kYneIa+/9429Z7T98E189Nf/DYdf8wYO3e2STudfeJI73u4SU+PbdjM8uZmFi2cB2H3TnZSqdQAmduxhZfbSwMT2nhSOebh/P+EKy79uu7oXFSjFj/30P2E10+RZzh/+6r/jB37qZ/jEB/6MteUl7rjvQY7cdsf6W+sNqbUQeL4FU2AWHnwVQejj9CI0G/S63LMSUnlvNHeaE1a7ZibrukgtBcWC9E8FIqkGnDbhPYEUG75y5H+NGAHL6lKXctmSphnGSOKyJlCKJMkol5yLHccxnW5GGAmstghpPUmt0++zxmvdIbDWdWo50R2HDRjtpLIsBqNdYYfx3y+F6klbAb243BbuMcVEhEGLL2DdhL/a1hdDpWekxAYjMlgluHHyu+9xAyg3AqkTaiohlDl7woTNoeZYUuW8rmGs6zf/zpvwq491g61n4K6M7686P2zf+PXKZ/tHBiyTO/fyY7/+Zx5YHzw2RHGlF/6/7Sf/DRdeeorjj36N3/9fvpcf/433bzzTdX8HYZ8gQ0jl3eaBcubijg12PHlD2xifZHV+uncqq/Mz1McnaYxNcubZxyju9tr8DLt27fLYnQQ0D3/p89xy9z2cP3WScrnMe37gh/ntX/m33HDbHX0cw9reGBECp4JlHagcBqJHFms9R0EvW3BF2AVI4bOHorcwSYTXHez5dBSYQikMiJQiQDnAEIHOc/I8x6hrnFRE5ym/9kt/wp59W1hbzWl3F9mzex8j43WeeeIljhzdTaPRYM+BUU4cX+Te+27iEx/7MuPjZS5caPP6N9/BX3/lJe65bz+PfvMkcWw4cnQ3M9MZW3Y0CMKc0ycXOLB/M5emZwnLMaWwQhBYyo0aWmukcJLhQRCDMH368N7qiK8UK1ZKH69JyxUkGKz3GorJYmwfSFvvCRSTY32Y06M8d34lUkpSIxi1be6tXeAckh8cn6XZaTApmnyoUyY17pHadRNn/flZv9K41cfLshUpxMLtsld+bvDzg673yuxlzr/wFNsP38SzX/kUO264hVfv3cxH1xZoH3+E6w/dRKJTXjxzkVcd2kIkDFvDFkcraxxvx1y8PM2tRw5y+PAhzj7xdbrzFzlw5Eae+/In2XP0TuYvnmFldpqxbbu4fOLq3Pm9MMYbS4e4+JdEkbYTXHfnfTz68Q9yw31v5uJLz1Kq1hgaHWffbXfwxf/2m3RWlxBCcfKJb/D6t74dgVNGbrdavPDUE/zIz/4cLz3xOAW/Q5am67zOXhuzj8kzA8JqL+XmrHNQ7Cc24i4b73+/NUy6gYKwhix3WTBQYFJnLJCUIkUUq95i43gQIE8zuNaZhTrdDq+57zBDjSlmFy+wMAvPP3OKIGxxz7038cLzLyCV5dOfmuG6Qwe5//4jfPGzX0OFbS6dhev2T/Kxv/oM23dG/O5v/TlhtMbP/Yuf5E//+BN83/e/ieefOcexl1/m/gdu4Iuf+wa3v+pmnn3qAguLl/nuv/tOjt58gMZow6PgHkApkHBre5V7boJu7Ne7MiwABva3V3m/73IXAGf/9fXeiPFVa85EaOJYMStiDpVaBJmkIQL+qFXlxlAQ6oREuYak/leI/s/BUxaix7YrZEHDNRAOXG3+D3o61gW9P/L8V/jM2BjJf/0PfPDyZQ5PTPAntx6k8tBnueFtb+Onf+3f8pkkITeGf3LnnXz35Vv57ZUF3vrUt7ht1jWcvueDH+SLi04Q9p27d/N/zb1MMlHjHz1ylr/84dcTSMl/f/MbeODJT/BHp5/isZlL/OIjfwXAY8uX+aEX/5r7O+fWneq/f+wJ/t03v8na8hK/8xPfzf7bXsM7/skvsP/2ezj+6Nf5zR99G2Fc4t0/84vsUBk7N4csf/8P8cc/831o4Pt+8Ec4NBJxYSlFK8sXPvJhHnj7uxBCsv+GI3zzy5/nP/zcz3D369/kjGJh8KX3QCReONXfX2sx2pUPO/xmgF5s470tnpbPMmRZ5hYq5RYEp6YlPQ6Q9/qqVOAYjGTBfuyB7zRNgWu8WEipgGeePsbOXQlbd25CiTpnTnYoVw3NVpsgKlGpBVx66mkOHTmMNlCuVFlcngPKnD09zcgwnDt1lsX5C1ixwOzlOcIwY/rSJV54+ixjUw0uXZhlbmaFerXKxfOLtNtLXDo7TdLRvOldr4ZcImwOSALf9OGkvQtvUvYFf7wL3+ON4+rAodg4ca7cow8kXwWALEKJwvdwK7ZmuiW4odGlY0ukVjAUJggCpFXoK8ISsYFnwJ2L8JLv/WBfXDEYBy5k4xGxUtBIuyzVRtj3H9/PPk/E+Z+s5cH6CrnI+eW/+70s5iEpiiRL+WRseNedb+NxkXJWJryc1Hj1HW/mF0aWaWWSFS35gImQNuQ1976D3XkZKyxfBb6KgDvfyzjwi/48brvzvXwF+Grvvrl+iF8A8u//SfLquCvRxYOiQvLWn/h5PLJBACjR5GIb3vfO1/KOd76FlVQzFeScOX3BSb5pxdu/7wccaYgxqDDiR372XxCXSkiv2WiFcPdTFDkEZ0yNLYA997s2lvyKuoqrGOriLVkoLxc0dH5xcvQlYC3KCnI/4a0FIaVjqPYpyiu1O9Zv14QRKJdLvObeV3Hw+m1oY8hSwcSmYcYmQl585hL3vm4fcRhz5OiNeAo3Xvemu4hKd3D+ZItqo8Sdd91MFEve/V2vo9VaIcna7N9/kPm5JnfefZDPfeFLiGA7YSlibmGNUjVgaGQzWhsaI/WeG2cLWioBReOHx2idFzAgelK46leb21djB+pF9wP7b5z4BaYpeobGYwgy6JFkTkjJiioxHq+QiwqvipooEaJF7AQ3bIpzF/vXMnAC/leBxfXLu3z8VQzAK15ccb7+Gnv/K65bgJWkRJxNQxoix2BYMjHByiK15jydJONynlCSIWEc8HI3YGq4hhWSlhWMCO8l9bgZ+zqIV9us90xcuGbWvVbc13Uejp+swuOjmyqwkIUk1lIVAeezkIxCx0L0MIT+jVzv9fW/xPaMrcXH89IXr/XA3sJjGLhxV7n/Bd7RZyayvbGhvHeRGSgoJo21zmhJ6QV73XjRee4xsqtv14QRyDLNgcPbmZja7Dj7VZcwqjK5qczUxHaqjQBhNd32GEHJiYQ+8IZXI2UO9xVy5Ya4FJNnmtAXT3TbGUEIKtB81w/eDwLyTKNUxHu/r0meJUAIMsRqEISO9EEUBbHWc1n2H956gP3KbMDV3iu2YoIIMTBwGAwd6AGCg5urcchRQhHHAZcJeDkfZnMe8tJCg731Ob6aTJAKi9XdfsXaYEjQPwlnVPCKO8J5Cb1CqL4FWre/P9Hea8UE2jU8zE/8wUfA2J4xsdaSWsulLGbVhEyGBikMC6szLCzNo40j4DTWYpMuQses2DKt7iLVkVGGIwgEaMoIBEYWoUvvhqzfBib74GTtdUuum8GFVXBhnbYBQgacbga0hWUszCirnPOpIrVOQTgQLlRyCstXfr3Y8EufY8GRxfRL14tqDn9/e2In6x5W7/4WWYZu0kVrTZomiMBJjBVGrZ1k2MjzT0jpFLCV5zDwx8myb28EromKwQvnpvm3P/9bLCzOIhXEccyv/conaa6t8F9+70t0O8t84+vH+PBffpVP/PdvYDSeLin0KdeAIAxJkozcaLpZRpKnyAi0sGR5TJIqugkYIrLcEJWqVOpj1IeGqVZjtLYgUoRyN0sI0dMOKFbjAh9w24Z6gg3ble+tXz2+3Wek7GMC7l+AksqBPN2UE0mZE91RfndxG1/Oh/l/lo7yQnscIwRCyQ1fVXgDov9P+skh8PTiivV04Vc5z0FsAxwJSvEWBcjo/rJCcElXWDUhLULmcsVaN6GzMI/WGeSCNNdgXDOMMRqTZejUsNrOQJTIpGBZe2/GDp5ScR0Dt1T0L7ifs8H5DrYA4Tbc/l69viFH0JGKhJCaMJSUYERKhkWCsEVbrleZusqTXGcU/OQsDH5RuluYIQE9CnNXzrqxUrD/U+AK4cIw6oeF/u08z929lv3jZlmGRXj+Amdu2q02xuS0O38LjsH/LzZrNeWSQoHTFyAgTVeRCLrdZUIRcuHcPO94x33Mzc56V0hjrWvKKFZOpQqJb+tVrlzaD2m8LkCAk30SSAnaGrS1SBECTtdAmMiPMbEOEARXF14YgSINNAjkfftiog0r0StsrtYBoF9bYIyrfhTWaRpORRkPNGZ5xkxwKS8hSDE26OnoBRZyOTBR3HAamOT0KASLCdL3dF/5/AaNmgLyDTQGhdNgBZxMSiByjJBcMFXU8gqlNEFLSyQDAhUTBRGlUsjK2gq5ydAixOQZs90SVgpyPE130SHZu4/FhW1wzYrXiuex8cR6K23xugUDsyYmR6KxnMxqBBl0rAFdIpAtLwEvvvMT3OAt9sq/VX9VttapF627aT0wVlx5POiVkktfZWh8pqiftdJgJO1Wm1zj1KWLJjK/eOWvDAlcG0agWqtx9OhNPPX4i2zesZ2Dh3YQhmv8wW//JQsLS/zmf3g/W3Zu57d+/aPcedcuDtywE2sFxjfBuJ72QidAY61ECeO0Aq3qlej2xodygZQSjpvNZccirE39+Ar7N9DQNwZXPP5BNxM24gC9ON8zURapm6ttGz/rWnXdwFXSXav0eeNt5TI/MXacv2p3+dPl/WRSYY3ygHOOEWFx0PUDrLdag8VRgwnrKa6KyXU1l3tgUq3ZiD/6jV9FdlYxYYw99k3+6NHTGz5XTBmfEhM5YmkGsTrNSG0YKRVDo8MIC512m5W1VbRxdSFWBbQbm8mlo8eyVg4YKXulLb3a7bQW+9LX+NPtj5BXR1y3Xi+U2fgEXRoOXApPyhxhXVbGzJ8n1h3qw0OsdhMoFgZj3AodR6yrSrUeR5IBq41dhEJ6VW03YV1BmmtDFlKC7ROT9gzbAN26eyKeUAYQQnqKMsiNQQjbU7uygNUGISRBqHrjz312AxC5YbsmjMDmLWN8/w+9jtm5jKgiCQP4l7/w90EkWJPTXOkyMTnG2fPT7Nm3G4FAyYiwlJN0csKgRI7G5F5LzkZYEoQPFyzWu/YGJQKs9fGoTZ1ltQIlAiSRi5eFm5TGGN87UACDTqzU9RU49FV6gOdqdQFQhBLuARcVbBtDiMHP9YA1947/4QpMip+ZWePPZnfxqBjHyBBDjlDGZZRtWPjr/eMUIUDxu7WAxgiFyl0TlumtmFd5QAVaKSVLB1/PkpJULr1Ed2QLv/i5D/Bvv+vtFBV6wrckKxFgMUiTsn/pabKVeRaXOoyObKZcHWX73v3MXL5ERgnbycm7CamQjDTqbN62lXZjE7PRGCezBpkxLq1mN5xTH43sYRkCC9ryi5/9U371xvfS2XkYG5RRFvSgFoE3qMYUz0cyKg2vqa6ypdTksdmYpdUlVjuW0S07WZxdQQjXjKNzjRQBw6Mjzvv0z1AYgbGaRMYsHXgDEzPHsSYjL/pIrHQdqUJhpcDmrJ+bhYdSAA/+mk2usd4YSCmRBoySuOkdYAwIpVxXaOBDSm9whHJBTPa3oRz//2JbXenwsz/1RwxNSH7+3/4g58/O8pEPPcrRW7bz5GOn+P4fvpfFpXlOHV/h8JEca0PW1pb5648+x133HuDiyTOUq4bte/cAGiE7CKtIE4sKc4IgIkus60EQThUolAKtFUqFWDIsCVJU0Eajc69fiaHb6fTiOrAYDUk3IahJsq4giDVKuuqsjXUBfZCvmHhXbsYYtL46JfRgXUFR/COEoJ1oXjc2Q96xnF+pkYVgi9oAtzv9P67WWCR6zDMGhRCv4Cu+QmbAGRSPml/xpv9O339fbp4nOHuMtdnL5Ebz3MsvU640OHHuDIF0XZ1CCZQEtKGiYhqiw+7JiLQrUbkhY90CufEm9X/2jJjbU/YyFWJ9/78Y4D4Q0jkCUhCrjIbKqBjB9lKbNG/TUZAL3cMWCo4B/EKBN9y5znuAb4EB9B6f9efivQgbqP64eIVN4HCWYgEqFh8L0AszFFZrChYDhPVdsP2FxWqNspCY7BW/65owAkIItu2q8No33kG5XOPRh5/k7MkLZOkMly+v8MXPxDz/7FlefPECQdzidW+4k699+Xne/8d/xejY23nkq2dpJR1+8p9tpVIpYXTII994hg998ONsmhrjhhv38KEPfpl/8BM/wKvuOUi5ovjjP/w0p0+eYmpynMWFJaq1SX7sH7+JLIfPfPwhtm6te1esGF/OfT535iKPPfw8t96+ny997hne+3fuYWrL2CuG0kKIXmnyxnBgY9PSxs8V+xReelEGO5NLHl4eYX8j8yBiRF+MY/AoV8F9/WTopaiE5IphsHFCWdzxfSGMkAKTe41G9wE/CEXPvTUiR3TabGvNUNZrtIRGCEm1XnF6esKVfAdhn01nuArDVUGytEjStRxfq5Dp1KU9N15L4RkU2Yzi2gZCNFGkV1FFyV3/+gbuZ2EnUy1JbcaNQynPzWbkgURmisAatMjASlcLYH0mxD8/XXh4tgBb3bPW2pCnGVYbTO6qBrGgQ8cLYNdZioHrEkXgWYQf/joGAFFjHQArZYBUypGJSNErUCrCgDRJ0MbS7b6yJ3BNZAeSbk6lPMmWHRNom7Fnz3YqcZlSpOistcEExHFMFFRoNbt0uwmbtjTYvHmcl569xI03Hea2V92GUgF5ZtHGxWYnjh1nevochw9fx8TkGLfeuQ1rHUg4c2mVL33xCySdJiYLaTZnXUeWFRw/Ns2F83Osra46odyC4lrAxNQQD3/tGEvzy3TbLZbmFtdN7KtlCwrHtefoD2AGRZfh+vxj3zA4cAmK/nIpBZsjeN3QLBfzmAyDYGP6R6z/d8U5WT9B/Aq3sXy1OEbBcjOILRT/13nPO3GH7K+6blFy3kY763D5/EW6SUJULlMqlSiFIbVyxFC9QmOoRrVaoVKusmt0iC0lRbubc7JpWdUZCNXvhR+osRcFuj7gLQ3+gGJwD4ZYA9eDzw70PuNCjsUs5vGViB2VDjLP/cRTyIIFWojez6L6zxbPtDiUl0JXSqFUgBMwKTAmgxGerWrdc1r/7ApjKqV0xyjsnO1jCq7ngx4ehnDguFSuyc4aetWDJrvGPYGxiRpvffetjI42kIRs2jrK2955DzbM2bZ7gRuObqedp4xNbOHQoZ1EUZlSZQhtQvYduB5jLDExH/nQ19m2q87uPfuJ4oh3vOeNpJlmaLTB/a+9mzMnVzl18jTf/XdvQueS6647TK3e4PZXHWRmfpYkkYyNlzh4cDelimZm/pLLJhQSYdYSRZJyOeTShWWqjZB+nxpXhAODW/9xu46xwdXfMQk5IxCErlYBCpDQjVljBV7UmqfXyvzZ3M1cUlW0EFgtrzTnA/Fv3wQVf3rQTnqXseDLG6wHsHpgwqwfqML6+nfvejo3tQhki2OGyLJgubKJ8thWbCsh6i4j0jaBsQQ6JdYRcV4h15bDO7awa9zSsVWSdpNn8wgtFNpKb4hhwEr2wf7CYxE+RHFwOuBZvWWIFboHMK67J736AmcUUiHRoeGmccP754YRMkJZS5ZbRwArlWOHEALZ8+zoGT0hwGiNUa6+wGlZ4oRqgwCrc4QFLZ1Yiy1GRm+F75+go23zYC+uIa7gqCjGmBQhAo2xigCLVGGvRsRYi0SitSEIA8y1niLMM0upHPD0Yxc5ctNOlpeXuOG2SVQYc+lSjV17NnPwyF4wBp25tuPrj27jt//rz2FMlzwNcKnCPZw5ucxQvc6m24e46bYDaNOhFFe598FDpN2QLItIUsF3f98baLXuoFyOKFcjJrYOE0QRaQ5HbtlBqaRYXFrFaO3SkWiM0Vw8v8jd9x/m0OGdfO2rbTZt3rTOdb/SCAicTo9w2vV+gS0qwYyxLCx3GB6OCJXoK8WoQaPicWLvLaQi43TeIJMWK0PH8cUrxPVXC1NEfxL1XNLB/da5Ld6HLfbx15dpg1Drh88gUm6NRkvF2vB+2jc1kM0VGk9+hkZzGqk0UaowbcP2XTtoNduUkzU2lzbx8Owax8f2sRJsx9oUYVIsnnKrB3bKwipsOE+xzpnRHsy0g/v13hdexNj2jEhi4bnWEL9xvsTZ3KDzEuNx6oBl760VBV1QsE+6zzo8oK9QTMEwLL1AywCmIJQamPhy3Tmtf0aD/Bb0ZM/B4QVFybIQwvEMWOtCLVVkH5znUatXuby6cPXxwTViBObnlnnkayexskl7dYFPfvwpSnHOrXce4ZvffIrv/Xtv5pY7r0fnAkGCsRq0opO1EcLVEQuvHLxt5yRCZgSqhNaWUCowIUMjw2gNI+Ml8gx27BnH2CpCROQ6Z1yMIIQg0zl7r9tJu7WGVJIsM55j0CHfk5uH2LdvF7VqjR27t1EuCxB9D+AKMhBAF5TeFPOobyiMMQQqIAwibJ75z/aPBQNsNNZlOUzi3XktAE2P8O9vtNn+/y2+FBlfuFJMeAZ+ckU44QyYQoqBykToUZO5eerAQRPXsVEV2UjJLzxHqTXNVKmGQrO42mTbSIORrdvotLok7Q6RsLx6+whfmm/SVTFpWAaZIXODRfXwlSs9H3eeFov0LDpF3O5ibNk3eAXGUZy8n2hawIoos5yV0foiVkjGhSDLixV/vYEvynmLUMAWWSBPGmqhB9Rpo8EYhHKT1BmIYH0Dqjdaxe/FONLaqU5p7URsjU/pqjDoQzbFOcl+GCmFRGiNNJosucbDATBcvHSMmek1Nm8Z5VX37uWTH/0mB7stvud7H+DCuUXuusfQSrOBKisDhBgjKXTkhSxqAkLHxS5xN1NlGK0Qwn3eWIO1LkvQQ3utUwqWgcBa6ceKJS6FGO34Ca0RBEGICCDVCaWKwxAGAb6eMaC/2mv6K4dzmn2Zp7AoJUjbXZayjNExpxJjxcBgG/i1MCrNTJMriSk4D17JC3ilzfamO9IYJ7O2bqkccJN7rnZxDiCUdBWWhevqHQU7aPysxQqDla4Iq5EvMk6L5TwlSmFubYWJ2iiVSoNFYXni7Bm25GMElQZ3xm3e+6ZxmlpxftlyoWU538w4v9rlbMvQEcrXgBRRv0PGrZf5Ej4cENJPUqkcS3BxPcVtsJbBbj4hBcaX3hK4ZjIpBNrfn35k1Dcc9O6CS08q4c5HYDHC9kqO8erTTuRQ9fgGezd18GdxVOGOWhiBwugU7EJBEPSvR7jKWSUlymcSjHB5r1zrb7tEXBNGII4Ddu3ahRQXue7Abh771gluuXU/jUaVhx46xmvfeAtppl2Rj42A3D+3vhvs5p4v+hiIrfpAjgWUv8/91VZ4F9PiXSvjkGuEi9sEzppLKVyRSI8JsM8AjO3z0A8aA2M9/FOslBTupyeEEII01cwvtcAq0twwNbU+xYOg191svCua5YqCT9oIg7Cqn/K6OiTR37yn4RBzizIGI/xqJiUFt4F7aRBUK7jxwCpJrvursUCh0Lio1dFkSSxWGWKTMNWdZuvCcXI6XNKa82urYC1L7TW+8NTTDG8aozI1zFI3Y3IkIoxjhgLNSAQHRmPqtRphEHNmucXj59b478e7PLuS+W8MfIGYRcsQa7o9wFIpkGHk8YA+l0NvwBS/S4uVrgTXSFdKKT2UL1UIaDd8pHt2kn4TmMX2SFOs0CgDRgTOQxWKOJBOPsxY9z0GbBQjRNDHNa54Rv7YXnIskLKnDWGQaAyd1JHS2swiKn0CWSVAycDhAdbQTVKeeOIpKuVXnurXhBEYGRvitW+6i27SpV6vc+urricuBWBjmmsLjI5tRmfCW3hXrbWReWdjA8/GrT/5+xPsamm5Iq51/dpFjtb3g/uuwr4ykOiZk6K4qDhO8dAGw4Te9xboP5LWmmWtaVFBRrk+4o0P9EMCdy7WCIRvONFF+68YmJwbR5O4yh/rkHR3fNMjEykQ6eIM6TsCPRNTGBAcrXV/KcNIhdSpwyiQRCpnTz7NkeQiDd1lOVmgVY9ZHa2TJF3QMDk6TKNaAauYmVthYmKCxugYlXoNIVyxbqfTYXl5lc2TU4xXYt5xY53bd1m++4NP0lQCjHKpPK2BovpmoLW7uHTEevtYhF2D90l6iyvALSg5UgVOj3JdLYUdGCsD4ZsQSCFp6JSRpWOkKxc4Zg2cOkN9eIKJRow0KWEQ0LGurblfr9AbSP3nb12VapZnvh1YuxZhCydePsn5c+fR5TpyLMZ0HQu3UoEfd66gKVCCQwcPcuH8aV5puyaMgJSKUqVCuVrCWkVjeJQgsJgcSqVNSGVBaLSJEDJx1MuwzhB8OwOwcbuys6//uyhWRe/2ebPhrOwAn7yS0oW9Beuv7TcXFUai6Ki78tyKIpCU4bGAQ+EYWddSiZ0/YqTt4V4FFmARWKMxhGTaqSUPeu/9WBd4JYPQm7VFh5rEaBBSrdPgu+Kzxcd8JtPlw8OBRdUitasDEAg2s8KbywvsDxPmljIunjlL2u2SWQNBRAnF2Ogw2zdPUY1jVltdto6MgrDIOCIuxWRJF1WqUCpVaHdSlltrlCtlyuUyuyYlP3HTBP/x2SZaSiDv4RvravwH7knfIIj11zZ4gYXl8wsAxuf2wWEKPhdkraWYvj3OIiFQVqKVZiRKef14zqePz/Clc3N8+bHn+exfP89P/sA7fDTrtQc3KDhdEY31rqNIARahAWS5pdvNEfXQLRzWE5tI6clHLKCpVcrs2b2L6cvneaXtmqgTsL0HCBR8gCZFIghkDDg3VQYJWEfOBPTd7gF9gEGD0EdWrzQQ/Rz81fcRvh/bHduPCTvQ1FMgxL6UUwjXvdWXp3Iy4kU8V+zbu0YRIkXkhE4aZcbH66ggQPeosPoeiyvEcZTSnU5Ku530V24Gzn1wkF8tLPArfs9gAAxc05X7+W8vmImL0MAC0uWji/2NFGAVNd3kXeNd9kcpMhJUwog4LhEIqMUxjUqZ+kidyS2TyDCiawSqWmZsapQdu3YwPjFeaKUSl0pEUcTI8DAra00sISGaJE05OlVmUzlCiMz5Yyp0eXP6HR6D/RcDN6h/jRs9JNvv6rNWOOVhqXzpuMs2FCXovXFDP8wTNkRpQagidABaWmRUQlTLLLWX+NxDXwepXFWHgHUFXuLK0yyKg5RXmsI6Ah5niA0YjRYGI139iMRpEigl0XlGICVRGHDy5AlardZVBoTbvqMREEL8P0KIWSHEcwOv/aIQ4qIQ4in/7y0D7/0LIcQJIcQxIcQbv9PxAdZWW8zPLPHSc2e4fG6W88cvcealiywuL5PLlNWVNWYuLWAzQ7edovOctJsiRH8CFjet+L2YkFeb7IMhRG/VtgOrgQBrTU8jQAiL0xNw7ZtYemwxjv7c1c0X52A9wanw4M26FCI4y13Ihvm4WyqXh3aAoo8dDW4AGrw2oSCOAiIVgiy666Q/nrj6xC+uqUC3fUrMaXi4jIcpVr++y9Cf9D1abEEBZQuTO6/E96gX4U0sNG8cbnLTqEQKQygd/0G9WiYMQlrNVRr1MrVyiTzNaHXbqHJAY6hBOY6plGKqpQrDQyNgBWEYoo0mLpeoxGXaqy2MCEk6CfVQcv1wyVfJ2X6tUigQsqCIU1jlKuus6Htx/clW1EH0J6MDNf0ioYvaAPe2Ku5h71kW/7mOVCs0uZAoBKEMsEagrEYJgxaG5dU2khwZxGA0/dTnlcVaVgivieHHkvdxjHWVmlo76TmRhc6js5YgClEegxK468hyw8unzjIxOv6K8+9vEg78EfBbwB9veP3XrbW/NviCEOIw8HeA64EtwBeEENfZb8doAHTaXT78gc/y6GPfYmrzdrIsBdvlTW97M/c+eDOf/shTvPD8C7zzPXfz0NefZ2KywtSmSV73ltvJsquX3G6s4rtaXf/GzbmMnkzEu5COlcXtG0VRvyJLqV7I4MIFSZ7lqCB0bu0GDYHi53qvw2746f/yvQqDxskKSRgEBCpYTxVlbT+bMHgfeis5V4k3Wb/Ke8PWP5fBuLS31vnDut56lHCAIoAwSBsyrjrc2TBE5RLVqpcsr8Ho2BiddptOu8ns3DxLS2tMTk4wOjxEiCAQlnIcMTU5wdjEGKVqhWZzDa01YVwiyzRjY2PMzCyQZTl5llEONLdtjvn6jKTTt0/0HXScEbDOSNFTadqw9erC+96f80wNRZquUKJyR/CTrMBjinBPCLRjpqHV7dLsdBxnQpF9EoI4DFCCAXrxwisrFqD140BI6epKAjWAQQw8X79P8VocRQgsSoVIpYjimDiOmRgdZnX1byFNbq39ayHEru+0n9/eCXzAWpsAp4UQJ4A7gG9+uw8NjzRYXe4yPzNP0mmzZ89+Rib2MLV1gjhWHL1pD/Ozl5BSkSU5WVc7+mZC5w5exd0fxAvW8f6z3mMoJlTPjbf9mWOt698vdAgd35tEScdBiK/g6ztU/dV4cPUvCEMHlWy/7ebnXXHeSimskARSYHpKNMJNRueWcIVrC/35vNHe+ZXCWoNfZvwxBq5fDBgoO2hk3P1UgSQvaNawIHJGJVSVIFYh4cgIxgparTW0zlhZWiQINhOEEZ3OWU4eO8VCrc78pQVUHLL/0H4OXX8D1XqdKI4ZDkKCIELKiDCSjI2PsbjUJojK0GrS6SS86fBm/uT5Fc7nuaNOt3h6MX9eQdj3cAT951RMvJ4Ai8eYsAjrOIFdJsjde2O0xwGKW9Jr53JpRVO0mTt+ChUGdLOMTDv1q6KOP1QBUoi+aGnB+UBxPuufVYFpWH/ORdixbgHrAzPO4PjXgjDCGgilwqa5l8u7+va3AQZ/UgjxA8BjwM9aa5eArcDDA/tc8K9dsQkhfhz4cYBKpcGhG65jbLKGsU2ybkQcKsLEkSU8/K0nQMG27VNMTmxl05Yy5WqZvOdg+JQXtjd4e4AdnkeOAqATvr9/8GT8P+tqCNwC7yaYCyd8aIFXfPExmLWyl5M22iCl6qXY7ODTtP34ThTvDyDXg58ZRNyFAKm8dJm/ykC69JyAV04xwZWDauMA866t0D48GLQYg8ccBMsQCAxW0+trLw6uteBAdYkwcOkspULiqIQxgigKSJOUPEsZGhtjcvNmzp4+x+LcAlrCvgP7uffB1zI2PkagAgIZEcSKcrmKCsogArQRDI2M0+3mSBnQaiccqAXcu7PB+08t+NhJOPDSn5m2LgPTj7u/nRH2Bry4Lm9flZSYXPeIWt1hnHFQ0oeD/kb5shSf1ej2KreFcKXGcRS5r1Jivfvfc7yKMeJeM8JVgGijXamB72GxPrOAVy2ygLSWpNslzzPvaUjSLOPc2QtMT88xNlp5xSv/HzUCvwP8sr9Vvwz8R+BH/v85gLX294HfBzh0+Eb7lnffSprdQq4hz3I6TcPwmMJgec/7HqCbpoxO1Hjru28jLBvXpWYFrmCo6wutDMJKB1phPcYjezF8Yb37/dre7SdHiBhjJMZ2kCLAWo0SCp0psix3x8YJPRapSuFz4hiBUhat/VT1D9PVjTvARuOMBLoA+RSWFFf3UIiO+rIfPymNsQSBWzkkYHyc2M39uaB7BX/9gWQHXMwBt36dtyQcDuAzHMbmIFRvoPc+M+AV9dxarGseEgJsIYfmVsLraxYdBFRrVebmVyhrSb02RJoqhodHKZVCWu0OYRSz/7rr0Llh67btTE5totYYot1sAi4NNju9zJbtJaw0xLEiyzVBGJDnWS8NNj/f4e69Q3zo5SWMMoTCkksQXhmpwDos9CbYwC3gCkisMOjW+jHgLIG2/VYfvAclhTOE0qdYDR4/yDTW5LS7Ca7GUVKY8DgKsVZiQomyAt2P0/rPzBZGzAAagSKQjvBECOkMQhGaWMcrIIxxQz2OepoSWZYRSYW1msbEGOONCsfOXbrqXPwfyg5Ya2estdo6NOwPcC4/wEVg+8Cu2/xr33ZbWVnj9KkLfPOhk1TKAWeOX+LkiZcJlOLkSxc5d/IiI8NVHv3mcdqdhHp9hOH6FAuzq6SdjMW5FUymaa4kYAStlQRru2RpQp5lpN0MISx57ioFTWbAageiGIkkQticMLBEQRlJgJQRKohQyqJU4BtIJIECjMAQYqUhLIS9hW/lVClI9/CkCFy2SXiDYQBh0FZgiSiozowtUk84wk6PCgZBAAS+N8ANEm1z2nmGEUWK6ir+/uCf1l7l7+KfAZuDyXsxMBbH1mQHDInwy5OvqhQmxaqgt+JKa5kQLSaHqgSqRK0xwvDImOMbkJJcG9aaLUbHxhkZnWRychu79xxm73WH2bZjByOjkw71RpNnGZ1uhxePvcjXH3qEJ598gcWlNeI4JstSQs8lqaSgmyUcmqiyqxwgyJFSI20/UyQLvsXiplg7cHOu5kI5b8etD/1waF09ivfWnIcoei67i9+gSC+3k4QszwiEIypVFspx7G7xQBx/BU6zwUMQPi9r/CImKIrh/Hd6a2ARxOUypXIZIYQLV4WgXCrxmlffhQz+J4cDQojN1trL/s93A0Xm4GPA+4UQ/wkHDO4HHvlOx8uznBeefYGHH57mnnv388XPP0WSLvHic6d4+dg0Y/UhnnryJV58+TTX7T/E9//YfQRByAf+5AuMjFR46KGH+F/+yQ/yu7/1Id71ngf58Ie+yO2vPsTlS4scPnSAr33xBd79vrv52tce5uD1e/nGQ8e46ZbtTF9osmvPENV6lYnJUZqrOUnWplwe4ejtWxDSoIISYRwRRRFJ0iFNDWiDECnTl5ZYuNRl98ERzp6aY9v2KaYvzTK1dYxKucLFC9N0OzmNkZi11Rajo6PMzS8xNFTi8qUF9h/ehQpyjC4sfzE0FUJ5jUCRoYTqqSenuSXVPjPhV32x0QYUPNqDK4z2Mai1jjGliIkp8AHbm/S2F1uzzoW2WGyeE+gcKwsySxBCcqDq2m7DKCIsVakNh0RJF6OhMTLG4sISQRhSa0jK5RiDJApjEIo07bC6ssL0xcvoNGd6dob60AhnL8wzv5CQJHDffbc5bGE1od1cI9eGrrVsLUW8++h2fvWbL2Fi6Twt31rd81x6QE3xv4FJ5C5gYOK5ldW1Srv7qweMtFi3v8MEhLY9eMEYiy4KxYzt32YDofQ4TA9m2WCgi2P3fvUVnP6n9RyS6+y6NxxSQqkU94rUpOcZrJTKNBcXWVj8WwCDQog/B+4HxoUQF3C6DvcLIW7yd/EM8A8ArLXPCyH+AngBV771E98pMwAOAOu0uySdFvMzM4yOxezdf4Qvf/4RRsbG2bF1iKFGxGtfd4CF+Q7CCqSICALLJz72l+zes5uTxy9Qq9X56hcfp9tZ5fOf/xxHbzzIpz/1RXKd8NgTIxC0GR2vMjyquPX23fzhw3/N3r2jfO4Tj3LXPft49okZnnz2a9z1mns4euv3IpVE03HgIgalFAuLTWqVkEqzwcc/9Agz86u8sX2YD/zZF3nbO+/iEx99hL//k29kqDbKX/zXb1EbzdmydYJjLx5jcmqCk8cX2bNnktOnL/J9P1ph+96dIFPXf29tb4XJc4MxsLjQpBQp6qNVlFUsL63RaXd7uXSwDuOQvsCpAJl6YOHASmOlFyDtxxAW5Uk7KA5ID9PwKjd42EuIACG0mySyzyZgEGyrhiRZl3JZooKIxlCNixcvUIoqgGBsYgyLpbnWpd0UdNMWSpZYW2mxZ+9mzl+Y4cL5BdrNDnEc0c1aGB3Q6UCaKVqtLnEUcnn6IhgIQoVNUhCG1+wb5gPP1DmTJgibI4RvlvEMwX38xeJBA//++tvj9nDhm7DGudvWTWrX/j0weRnEUdy9Mx70C5RCeUDa+GBACkGjUXPnIhVXUEBA3wDYvlGyuL6VIttUYFBuf9m7HoWlGvW5MYUQJEnK6dNneebxJyB6JfDob5Yd+N6rvPxfvs3+vwL8ync67uBmrGFqyyZGRlf48Ie+RKfd5fL5Wb7re97OJz/5KE89d4EjR6d44fmU4RH4s//yDb7nh+5i195NHD+2l1fffTMnj8/Qaq+iUNx440G0WKVcKfOWNx/ks595itmF88xOr3HwYJP9u3eza88ORsZ2IGWdheVVLlxYIs066NwShSCFcmquxrWtesyYsbEq549dYOGiISgZnn3qOQ4cKDMzO8Mzzz7PpUvzPPv0SVaWzhNXNc8/c4rbb99Lc3UryCZnTp/g8OFNpHmXTZvHMVr4blJJlmd02i26a23KpRgZKKTIiMqlHq4wVC8RqQhoo3IwUmGCFKUFRvruQuFBUcfN7lxUYzFCIq1fLa2EPMNGrjfeDWbXg2AD61t1fR8Efn9hIIwgN4igTNHUECnNcCXC5itADSsF5UgxObmZCxcuI02bixcWePTcGkmSMjo5hskVpdiJkORZg0vnV1nttsmFZf5ch4XlVcrlOkHQxeiYndvLbNo0SjmQLCysMjZWJk1T1/U5VuO2HTGXT0Aq0l5NwHiU09SGVAmXl7chPbr1waW0AA4tCLT3pDTY1Idg+KYcTQGFFDoNGu0AaiEI/F2MVEAUehJSowGnFBwqSSIkQqh+x6WbNfSBKrz3Jt1nPSN27nUdJMohMMJVMCIcPqGsJJKBw6B8bYcUAYvLM9yyfzsYy4kLhfO+frsmyoa3bZ/kTW+9nwdf/2qX9zSCPBHUR0IOH91bNF2xMLdIuVoHqYlLinvvv4mbjh6hUs+47U4YHX8nFy/MEEaScrnOWrNJo1Jl687tlCqKQEYYrYnLdaDC695ykFod/v4/fhdhKOi0cx54882UKzWE1EgknVYLo4uqQctaJ+PG+45QVoLPf/4p7r37Zo7ceAMXz3e494HryfOIo7ceYPvOKb7+5RcYHS/x0suLNLttbjx6Pe943yQmz9mzbzfdrqFWyjFWECrL6efmOHfyLLlNmJleRMkKO3ePcvjIdZRKXnQCkNZJUmnlCSiNRghLJTd0hQAkSjhwNEiXqZo2MSFL1OlSBqkwViNNhhECaVznnAk9l0HB6mn7RVAUde4WyHMCpcitGz4lKRgJLEJLZFAmDBRCKEZG68zOLLG6NMfs9CKXL64xuXmIMDCEpTKt1ZRzl1osrJ1kcX6Z+YUMoSJK5ZwwriIkpEmX1dUFTr6Ycvolze7919FsLjM8EmM0pElCXIq5b/MEnzh5EoQikG6Z1QJMLJBWYYWr3yjceJeQsX0DQJE2LrI0BoH3AgCM9RT1BZ+fwwKKUKywo0VruPDhm/JydkoowiAkKayNpG+MBmsF3M12/y/qD4qaBf9e/6drW9bG9ZQUbENRGJLnOY1yxPX7j3JLe5HJdI2/+OZjV51/14QRuHxpkf/4f3yI173xNprNFKzh0PVb+dT7n+ae1xzlU5/4Eps3T3Lfa29lZTXjGw89yaFDOwhlxMhomccfOc2+67YSRcPs3bed0y9Ps9hcZs91kxx7YYbDN+1gcaGDEhGbtwwhBMzPrXDoyCT1eoUkbVMqlbA2wNocQUirvUwYQbkce95497watRiRC6wKuPe+W7jnnqOoKGL77s1UKhX2XreHqBwhhOHV918PxsVFCEMYRtx4214naGEUKnQNLxIJVlIdCWhMDaEzTbVad1WJxhGkGqMRystNS42wFmkEWkkCm7M9W2Rb2GQpU8yqIVRnmU3ZIrbTolqtMTY+wppokeqAlVwwS50EQctojHD6izJ3A1Yr6NUNDPjLtsiI5Bljcc5F4YxGRQiq0qVzlQpRKvDcd4JGo87CdIdaLeKd77mXvddtJYwMS4srLMyv8PDvrqKWLWnbsHvPEHOzbQJZZmJzzPhYCcEQpaDEoYPb+MsPfYIzF6aZ2jRJ0h1Ca03e6SCHGlRKAcokSBUSujvOYh4gjcYKf1299LGfxAWtmxB9N1vgwVkXzxegrRSiVwXaa+v00KiSsj9RhRch9QZCSccKjIVAudVdSAG5uWo4MvhCkVY2Huw0Wvf4A4W/DqmcloZEI8ld1am1BEoyMjKMWcsod9qElWtckLTbzsnSFmfPP0PeGiIIFM92U/78jz7DtqlhLp1psTA7zS23NTl+bJaFmQU+8sxx1tYy7rxrJ1/83NNs3ryVH/mHb2BlOeevPvgYwxMJzz03xUNffo4Dhya4dKlLsznPL/67v48KFb/6Sx/iwvlp3vr2e/na1x7nbe++m507R/jC5x/lwQdfw+/+9h+z68Aw23be4R6A6fdzWyxaWlQkMbjYPa6UyKymVKuQ5hmYyGnTKwisezhO+irCSosKLMZICtpzZRXVMGbvtilGJkLCOAIdYrSglay5lcmvFsIYRztNRi1bZGvnHJtsh/FSjVEJE4uXybRBRpZovEyWdGnEESVhCYShk6RcTwcpNAuEnM4zlk2FFVkhRaKMxtjIdc55shFlXRgrlLt+Iy0ic8NnXEFZOhrtUCqsFohIYbSmVo9J0y61WkC1VmJoqEqns0YprLJv/zDX37DMyXPTGJlw3z23s7CwSq46NFcqrC1qTp1ssWPPCPXJLbzn+3+QP/rj91OurrK8ski9FDF/5gR/+eFP8RccoBNVQGcoP9krgaCDJLcBxjqviXUFW/1lvKByK1Z5V0hlfFTk0qOFAExv7nrvsNda4Q1JFARUyyWHC0iJlK4eIwwEgbaQO4O5rk5AwDqGEdE7u/7bVy0McbUbsdQ04qAvSmKd4ZpfbXN+dpFbtzRecf5dE0Ygy1OeeOwpDh1+M9/65jF27hnm0596gky3uHj+HPOzcwyNjqCCkMmpGp//1AJ3v+YwnXbA1CbF5FSVW27bQ3N5lYtnF6iPpjSbbZLsNBNTkrXVJQ4cmuKZZxY5d/4ycVznR//RAzz92DlOn5rlzW99FceeO80XPvUQm7aUqdUk27ftIs8XEYF03O3S5YWNtVgpHZmJBadhYLAGtM19d59FSYvWqeMisCHGFwRVIpci0hiyXJFrgda4+nKrWVvrkGhFEHfRpkvSNjSGFZVKjEaQZzlauhbWkWyefXKFemyplIcgiChXqpRrI5w49jJbxsZZXlxheKRON0tQKiQIoRwFrvc/KDHU7nAknyMPQuaIWc7qLAR15gNfM28tWlnHiuPpuZXRbFUpsz7DXFaGsjLoPPQNVQKlIEssShmaqx3qZcXnP/txTp7dy87JKUrVGpeXNI3GGpsmJHJqlNNnZtG5IkkMOl9hanKI3fu2c/zlOaxQjIwO0dh5mNxcYnV1jXMvnaHRadOoTJGTgi1hhKLlzyuKAtKiulIqP8982tBo7473l2Nhi6ZpX/BkgSKFa22fqcjtva5yr5e+k5I4joijEG0sqjA+IkIGgUs/C9XzLNdtRTLDew6ucxTfPWpASd/z4X0Q6fpURJqQJ10yY11a2RuxtWaHpdVFTs/OMWRfuYHomjACW7aO896/8y5e9ZqbmZ7tcviGfdTqW9m5536WpleZmCpx8IYdRFGZ2dmzLC2vsbzaZMfOHZw6cZ7jJ6bZtmOJV99zC6XSCN/61jHyTLBv704+8s2HuP2Og8RxjUZ9mN17dlCrKb74mcvs2D1BO8k5ceplwlKMsZrZ2SZf+fKjjIyVWWymGB31lInBu4O49I/xllmbxNUEWOXiyExjSJBKoE0OyhJoWFxr87mXprm4klIu1RgfKVOrlGnliau7yTXD5QpTFQVSM1wuEY0oRJSD6LoqSCHdfskM+/V5hkoBWKdXp+IyeaapVaps2baF1kqTwMLZE+fYc3Af9aEq1hpKoSuE0Wikgi6ayAp2i5S1bJojQZWvL0hWhq+nKyUi0xhVwhrhaMKtpqsU4MgrhyMoCUVHBcSVkDTrEHQsmVJoY1leWaPSqPHEy2f58NefY3QoRqoqNqqyc3gbuzaXmBobJYwqLK4uMdyokSYp0sBNRzZxx+2bKZcdTjR23R5OPzVLePYiZ489z/6pYXYMNdiTdlmiSmBxExx8Z6p/drZw3qEHBvby7AUqX7ztAD3psYICI+gp/RiHCxTdhD2eQuny+UoFVMolp3Dlv1tYlz7Nus67Er1u0cH1nvULvc8wZEnaq+Po9ZTgvZYsxXbbdNptllfbhEHgi6lgenaGi9MnqQEnL8y84vy7JoxAFEe84S13AoIf/8fvIsty7r7PIkRIEAjex+vQNifLLPc9eAuvff1tpFkGaF59z37e+70PYoSrLKsOl/mJf/pdWGOJYskdr76RqS1VLpxf4r7X30xcisgyzY037UdFigM37OHMqXPs2buTOC4xM3eRctzg8Uefo74SYkndhDfGrwgOJDR+ZXTCJDFJO+XFF0+wZcc4k5NDZKkmCsvoPMNklrNzy3zkL7/Gwd1D3LVnG2G1hA4jFtuaE09e4olvPUSlbKmOTaBqI1TrdW4/vINt42WybpfAdBgardFe7TIqZziUnsHkTYLKOCoMyDJNs7vGyMgoUbnM0GiD6YvTTI5UWFqG5flFhuo1kjShXCohjNN9bDebjgJLa2S5Spxb2s0Oh9Akyw8zs5oxb0NaW4+iY9cPgIWTcnOPbXhXWVOpRMxdXOULn/kcyhgmR4e5/tAhRrfvoL26xvnnLyOlZjVrs9ZMiaqKXUNw/XZFs22dgTE5jZpiYgqicJTGcJU4UugsoWsE5XLE1Mgof94psfqtR4iCJu2hMrFK2CNWeByXhow8npGqwIm0ejBwHTnpYOyNn4qeYt2pGOX99KD18h4bGnzcIQcYi6xnHdKeyt0aRwYjvQclFLk1rpQB+pmXV9gKTCCKIsdlUeCa9NuYBQabdsiTrutkEf3jGqGpmoTxaolLq/Ov+D3XhBFYXlzmz/7Lx8n0Gq++7zaOPX+Zubkl9u7bwezcElu2jbO63GVhdpU77zrM17/6DK+57zouXejS7bZ4/Vtvoz5c4vKlZV567hTj40Po3JLrLkZLZi6XmdxUYXF+ifnZFbpJzg037OHM6cuoIODwjYeRKmP24jJD9Qkq9YgH33gTn/jYI57F1edsvREQwnhFmOJBGc4en+bf//LvMjRe5/D1+1heXmHfnt1s3zbB8NgwJy+sstw8SSU+wLYR2LKpilCSVioYr+0iClt87s8/wfzMN1GB4HVve4A3/uBdBFLxB3/yKU6feom4XCJE0IgtnUCyutZlunWeTTv2gIChhmNM1mhKpWE2b9vMhTOn2b13F+fPXcRYjUE7jkCTo/Mc5UOMPNeM1KqsJivEYcza0hLd5irB3AI7alNMbT1AeUtEN6pydq7MS0GM8G63bjY59tICLx07xZc/8ylqQcrRPXu4+MRD7LnhJtK1ORYXpjlUj9l/3TjdapXHLi9TMxLVWWXfnr2MjDcYagxRroSUKhAEAVFcxZiUTqtDtVYmLgfUoxKdoW3Ml0fZlrUphSVAMkETJQJyNCV/Xk1RxsgYpV3KTwjV9wYKw4DoewPCudjCGp8iNAPVnL6Yd131YGEEnEchvSHQWvsyddPTBggkxFEIIvXH6/NB0Dva4N+41d9Yp9qMXaf41OszMW48Cp0RKOnbEpwHoa1mUobEyjii01fYrgkj0O2mLC+usLRymUe+UiWQZYbiOsefe4FAdZGTZX7/t95PFHcZH/+73HbbdtK24cXnn+PCpUXe930PYmwHLPzeb32I3bvqjE+MsrTQ5eL0LP/y536IP/y9D9Dq5EQm5LkXzvCe9z3I2TPzdBLBv/ql76G1Br/y8x/mLe88ypveeRudTsjCwhqjmyI/+d1DW1vt8NSjz/Olz3ydhfk1du3bwZbNY0xPrzAzd5pLiy2OHX+IbrNDrdFAhjE2y5AyJqpWOf38i1y/fwc//Y/+PiPDNerliJu3ldn11lt49e0HefzlS7z08klUYHnh+QscPbqL2csX0TZlfnGFWmOYWn2I2ugQ3azL7MWLbE4TGsNDiMAQKEGeOpafsYkJ5mZmmV9aoTE2xOzlORpDdXKT0026Ln4tRQSqwqVz51hbEuRJQrfTor08S4SkpSz/+Cd/in3X7WR+vsv8UotzN29l9lvPs7xpDwAfnhVMnD7N0Op5RjYNMSlhZKiCSdZ46VtfIVOS6YtzLC03aUjFyMg4B0dGWEks52amOfLqmxgdGyYIBXmW0FrLqdTqKJm7wigRENVKxDiAL6fE3LaD7Lq4TKkUEYuQHUHLpQKlpuQ9gVyEgHFEr55pB6Eo9BJB9gxAQRUmAKsihNYuRZlrAummifbpQ2kdPsQAmCiM+6kkKJODdB2DVmQYEzIx1GBsaAQxP09ocpKiQOuK9CD4vCFWKbS13pBIpO2T6AhjXNGclNg8gTztFQkp4QRLTK6phZpmkjhv7xW2a8IIhGFIpVKlVNlFpV7j1MnzPPv0k+y/bgutVkJleJJao0yuE3RuOHVijlffcyP7D+zltW+6k+nLMzQaNYaGysRRhedfOMZtt96GNQFxLBCR4OL5iywsznLz9Xdx6PBOzp1ssf/6Bgvzrix3eWkFG15mdn6CPNdcuDDL6VOn2HtgGK0zdO4q81qtZT7/ia+QtBfptg1nT2qW58/w1Ycepq0XwLpW46gS0WwuI1RAUIrQnQ66tczcrGH20mUynfNTP/ljbClvIlaCiYqhtllx3eRO9D37Sboply4sk+eabquNyVrE0lJShigIiEfGUTIi6aSkeU6Sduk012g0NEEYE0Qu3Tk+OsyJYy+zbetm4rIky1LMmiAKFGEQIKMIi2VkfITzp85QKkUkrTbdtTZBOWTz1BZsppkaG2HzhCDTllZzK3RzfvuxZwBYDUJWb3yAsfY8Y09/g/ljX6MkUobrQ+jMay3ajJGJBvVKmdFynW1bt9AZGufA/u1IDEanpNo6aXqhaK2uoYYkudZs3rqFII5odxKWOl26VhLWx8hUiTTP6HS7NGRAlDbJ4lJvUZUFI09vBS3qAIpf/YpehAwFnwCA0f14X/hjFTww/vNFhkDgqjxt7tiuO0nO7NISSWopy4gje6/j9XffRr0WOe0Jn9dflx0A1vU2eM9E+GxGj6GqyG6qfp+DRWBl0Cst722+07CV5GT2GjcCY+NDfN8Pv5EgCshNwuL8YUz+RvIkZa1lqI9UOXrbLSzNz7B3326+9Y2XmNhc57p8B1NTw7z83Bo791u2bR/lvd/9Bp58cpwjR25jdmaF0c4ID3/zFO95z/v42l9/HUPCmTMX+Gf//I188qPfYHVtDilhbraJDCpMTzfJdU61XqFWjzFa9Nq+pYBOOyVQVeJGRLmk+N6/90b+91/4TVbbM67asCQJowrdThcVBGRJF2MySnGFIJRMjUzQabf50pceQVLih37gnWzaupl6vYFShrK0KJWymkvickC31SUSKZnNCIKA1flZZFiia6DbSdEaGsPDhJFrHe02VylXKpTCCghBrVp2LQO5ZWJihNVWghUBEksUOImsUydOMnfxInEgqVZjLs9Ms3PTFJs3j5HqBnEpJDeGUIaUS5JyKeDBB2/loScdTUTwsd9F3/pGFvcdItl5A5tfepTZmTXaazlRICnHEcoolBXs2zRGpazopl26nVWm52bZM1wjS1OsLajYcoyF5UXD6OQmJ8iSuMq9xWaTrrVE1QZZVCHNcow1DIWSTc15TkY76YG4vg6g1+1ZzN6iNqAXk3vCUGsRsgASXfuu9lWXAlce4OZhf+WWRfWetajApYs379hPJOHOmxRBbnjdPTexf99OX3+AZyryZKjFgTYUCwk/2wV9zosCz7S9Ii4fosgAQqfx2G+ecu3va802S80mzWtdhiwIA+rjDYTRYOs0GiNIFRIEhb66a7pQ4iDapOw/tAWdw+GRUbCWA0dhaKhGbhRv/67beMd778WwijERINA6JZBl7n/DEbJcIqwmLMVs3bGZMJQYBTfeup2fGvoeymVHWTUyVmbfvj1gc+ckWqdAMTE5yvf8wDvoph2UjGiMDXHywjRprrF5SrJiyeKUMK6QpzlhEJOmHZK8SVirsNpcZWp8gk6+wGe+9CVeevFp3vK6u9mydRO33XE7Q8NDiLBGqRxQLYe89PwJpMoJlUv9DI0Mo6SmEkTEQQmd1qmUY7qdjlOoVYZWcwmtU+JyhbhSZsfOHawur2BVSFyWtFpdmt2Exfk5VhfmWV5YdCXEQiJswh03H2FiYpRumrNz/AhHbz3M4kK7RxUWhwHbxifYvd01jOazLxB+4iXyV72X1h2vZ/qWtzB57K9Br1JO22gvlbWyvIKUkqn6CLPdNhP7yhwcatBudsjbHQdalkuu5VoGhMNlhJToPCfPEpSNybVD7Y2IyMISlsTF30qwn2WOi90UzVimt3IKXwpcoPnFyPOzagBs8wl5X+4L2hhkWNDM94G5gqlIQK/HAKkoRwHHz57mzW+6n53795J02lQaDQIZkpschBxoF+cqaf+BTIVx9QkFo1CfyFbjgxCEsK40PCwhpXTEJT3vwVktGYUE+hrHBBDOaqdpipIdR86BJcsCgsABcwJFjmPYybVAGgEyx5iA0fEhPBs3xigEGVpHYL1goxRkxqBURCBcf3euBfXhOpBjUFhh2bVvEmscUUiepQS+PtvagnlIU63W2LRlnKgckeeGz37iIVqdaYwwWHKEVeRJhjVtVBCQa00Ql8jThLzVJOt2WJqfI4wCVBhw8sJF/viDH+WGQ/uZW1jk9jvuYNuO3QRxhfGxEZaXl7EY0jxDhRHKGLKkg1QaawWhtAiMo+PKU2QpBGtJOm2wllK5zOYtU6yurNBpdpxUXZ5i84y0tYbOEmqNKp21JjOXLxIrxU0Hb8SSYwnZvW8nI8MVhutVFmab6CynmXUp12LGxz1v3egwZmYB8dRnMFM7Wbvl9WR7b6Z6/ikmnvk0VZ3TGGqwe+tWDuzYQSYMNs/ZtqWBTru0sjarmSbTOSOjYxhtKFVrhIEi6Xaw2oIUSGsYiiQlJchVQCIVaWbIbA4SRnDUZwVzhIv/vSZDrx5gEFzrrbn9cYh1zVF55jwBa1A+ndif9p5URfTbwLFgdIpBcOz0GdKPfxEVhQQq5tUHNjM5dJMTrlFOucmK9QAjG2J2W/xfSVTg8YXiCoTspS/B9SI4msmCU1P0vAUpoRTFBJUqnL6GewekEDRKMYutnCAuAYY8z1yRTuZZf22GIUFYi7IBAkWaaXJpIHfFPIHGuVzSYIVL70h880cAxkZuJ6tRhK5wBOU0TcANGhUirCUWBkceoP2Cobw3AInpcu74ebKu4MMf/BiCFJM5zTqUA450lmB0RhCGmCyjVCohhKXTXAM0Jg/IM+duzrUV33xkhedfeJGHH32aWq3B7j0Huf1Vr0EoQTmOabW9vhyWPOmCzNA5JJ0uWZKStFtYnRGImDAMCaTBpB0Sk5FLxdYtmzh98iQ7dm6BPKW95KjcxqcmqA2Nsjw7R6QEBw5ez/DYBEmW0VCK1lqX5mJOqaxoDEesNdsIGdHJc/Z7T0A0qqj5NVRnheyrH8IMjZOM70BURhBxTCc3kCWEtsKFdpNarcbwUIM8y+imCVmaumy8kGityTLNSKnkCGWTLlIGJO2cer3BaDkilorlUpXpaIRWdw1HDxZSCXIC0yb3w9r6Jp9epWVRLVhkddxe/jXRe91IkJnzAItKQlef3/t4r4LPrequmErb3PVj2DUunn0BJSAM68wOBXTyhKEo9A2CEjA4UZQ+Y3Fvs4PnBbnPNmR5ThB4XgoKbsOCqNS4sRv0WaCNp8WrjQwjwvAV5981YQTOTTf56d9+hKgUARlKJIRCo2xIqAIiJSmHgkqoiKSgJRXTSYcD4xPu2QWCIBDEQhCCS8lEMYtLq8RxSK2sCK0mkAJUSCvJCFVAVUpqgSBSGqkUKlBeLDJHW0GSZ4ypClIWbqMl7bYR3RHGh2p88ZGvcur0k+Q69Y1PxrMQ+xXGGHSWEwrI2jkiUESlCsbkaJ2jlCIIPLMQAcutjL9+6GF0miLFpxn/8z9jfGKc4Ylh4rhM3BghiELCQNLptMkyy/SFyzSqVSqlgMW5JaIAbJaRUrSdSrLckBvB6MgoNkvpri6iTMLwcIOR0VGarTbDw3VuuuH1jA036LS71Cslcgw6tFyYX2SkXiVPDeVqGSkzWksZR44cAOCoSLlt+yaiqU083hriicUlktEpwigkCmNKlYhAOSe9myTOxTeaIIxotdpEUYCUIaVKlWpjmDRJsQiarRZDwyXSNKVSbRCGEbUKBDSxMuDytptZeukMZDlGJ1irmRQaY1z8KwcmfbEN6g/0VuBeY1GfMMTkGVK4qkmn9eKERfr1RIWylPWHEJiupitC7Moa3XYbISXjjYxGEBHgujlVoJycuaVneNYVDRXLvf/TFBkIW2hhFLv1wxuEEydVXqC098+4zwXVEja/xsMBFcBX15qsrUqECLE6xymo5G61tq4oIgACKxyBpJXY0+ddr7aUnu6poMDy6RoV+husCdBeQkxirSFEEGUp5VwTGPeAlXSNJFIEiDBjbz7L+KYdGCvQgJSW5VbOf/+rb7F66SLPPfJF2mkTYzNU4R56VNm1kRqMtmTWEiqFMq57LVABMi6RZTnGQKgEaZb24tAwDtBpwszl08xMn+bo7bfSWYvZ1BglzXKUzui2244aQGfMXL7Epslh0Bk2S1laaxGEIeVqiTwztDs57a6Lq7eNVykHgmioASoka66yuV5n5/atCCBWAlUKCIVltZOSq4yVpRZx6O7P8ZcvMjk5ThwGRH70/MvNdboty5cyxdkshYkJtzrJkFK5TiAThhplojDAAqvNNWqNGlEpwgqJCmKklJSrNYRUNIaGKZcrhHEZFcYEYYkgLqOikCjuoWO0hjcxU5lAS/dsQiNJVEDJbADBPBDYaxyCfpl+r0agmHfSud65du3bflIKRE9rYp3rXhQLWYNUglwYgp1HsKLEpOrwqq0j3HbzDSwuLVGOp8i07/xUsle71DvmgAcwSITb1yJ05+aYrvteRM8Y+GIl6RcgjCHXlkolxuj0FeffNWEEttQDfvHuEf7wuTW+vqrphCWsyL1Ig0QYjREBubKO0EIIhKEnFy39EzQFYYQHd4TRvVpxUcSEhRU2BggRQUgRRYoiD2xywk7GhEwRXvJZ4tI1XSE4PneOs3NzLKoIjHGobJ7SYxQ0BuvabZBKEQQOrDHWoHVOKCVJOwFrCJRTU5bSorE9oYvMM9hKa2ivrNFoRHRaq1RkjSTpknY6xHGJoaEac7PTTI5WMXlGp5OQpDnVWoOkm3Lm1Fl0bhkZG2Xb+Ai6u0pHQKlWpRyFmESze/suiGNGxkbIM0vSbEI5ZrW9gO2mHKgFXL68wmijQmwlC+cWiEoRnW6XW4FPTycca2Y8trxC+sZ/gByaRFmB1Jrq8BCSFKFcAVCepYyNT9AYHkEGIaVSiSCMQCrqjWHKcRmhJEFcptYYppvkVGoNhJXE1Qqd6UUSk6NkiBGSubG9GHMKKUsIm7FqFHGhiSL9JBGecFQyoLQEPYTOL70OhHbPWemuY3bWBmltjwYerGMHsEVY4LMKQmJsRJ6ntG59kHxkE9UzjxJVu2ibs2nzNlQoCGRAgCTt5yl7nonrTrRuHFmLMRmi0KAAQiGQQhFIiaN+F+RuGfTZDuPe89etjUYgiIOIju6+4vy7JoyA0Zrbt1hu2TTMHz/X4qOXDc+vgrUZRlUcmYLSmMwjveRYKRFGYnHc7G4bqLoqLKu33ragyRrsxLJFgqjvPTiYzWKFcSQc0OstB0kARHlCpg2MTmAvVTHdtvu80V7U07uiUiGUJ5+wljzPkYHEKkWoQvIsJ8m6SAthGKCERGddx19vHZgllEIFAeV6BWMS8lSwurhIpVp1rbS5IekkLMwtOGZcr1OYJglZlrB3zy6EMcShpL22QK1WJc8NJJpcGI7e9iqicoUcSTO1tFspW7ftZm7+PM8//iQ3Hr2LY4+8QJZqFuplKlVNHNeYns1JcndvP3l2BVUpE990J/mOPVjjDFqpXKJUqVEpBQidECnB2FhEpVJx7DtKUWu47rawVKYxPML87BxjExNMbtrE6mqTcrmCEIJQRkRxSDNzHQvaaoSVdMMSJsmwxhCgibGs+SVWF2NAWigAPHwMDf0wQNDrLiyac4ROe2570TkofWZBiMI170vQWXBiIAjyQJIj6WZO/MP44qLcaoqR1AcjBwFBScExUKhdGdMnoVUeHNTaZQecIrY/DylQQV+Mx+kluNbjIAz6vBBX2a4JI6DTlLMvnqHcCPi7B8Z43d4SXz7Z5YPnQi60c1IyrAnRymKUQKQlLLnrbrNF5dZAHbaf59+mPsJtAvriGhs3t6oHKnS4jwdZpASZ5xiTAyEmGkLkFzAyRNrcNQL5wg1rDdK6NI6b6CF5npG0mghjUVGMCmMkgkznWGuo1hsooVlZWkIqhbUGLRXlap122sXqHN3tYOIYLSFJEzrtFrOz0xw6dIDcStJsmaTToVwKyJM1bJ6TtzRRGDgNBaE4NzPLa+59gPLQOEsrK1QqZVQYMTRSY621ysNffpyLJ05TLQfYYIRyeYwH7r+d5mJOTsaWXVOEoXMxZWOYudlLVOcvMaqXWLATEIYYI9i7/zBKZMTKkCdt8rSNsZpWq0W3mzAyOkalWmXLtm0IC1u2b2N0fIIkzYiiElEU0mq3mRirE0nFcmLIjMEG7mkbocBKQhWyd0QyJHJS4/gEjFI9WjA3MVSPots/4v4kFAUVvQsb8lyjrCNsUUUtQQES6oFCIv9ZYa3PGjq1I9uTMytcezceMmHRg6HIxgFpBQhHcS5UgFB92b3iWtxE91oJxiCVa2eWvqDF4VOuKCnLUoQsu07KV9iuCSOw3En41nNPU1WSsbERtm+b4gf3b+ZHDoS0k5y5rMGJlYRHViK+ejHhZXIyA4F1N7Tn4g04Au6nn+CDOaAB1HWjlbBFTGZtz2PQ2pdpes1BhON8s2kHm2oEGhGEyKgByQpC51htkIHsrRxgXbowSRHaYPLENRalHUcUEkbu+ELRbrcZG6lSKsd0OimqFDM8OkK300apgKybEpdCKtUSIioTxyWStWXXsy4FcRjR7bapRBEYTWtlBSUEga8w0xjiWoN7XncPe/bsp9XOHEOtVCgV0u2mSJWzY+dWVLZIsxnwuve8gaeeOsuFuSX27d7KyZen6SQnmdo0BkDazQgFtI89zo179xLd+C4udnN0rrl8eZqD1+2iHEkW5xLqQ2NUyiWCQJGmCSoIGB5ukHbalMsVRsfGe4M4jsskScLExDgqDtG55cJCE43xbMiGNNcsrzUZazYZqo4wtNqk7Y2AUkV3g/St3H3XuT9ebP/hF3iBNY7AwxZEIqJA2Rzg6+PyYus9Z6PdpFXKIf9W9LhZtBc3tRIwTkDGrBt+/g/pz8MojHAZrH75gvchiu/zbELWaMIoRhrT04Mw1pBnOUKFhHHJS61ffbsmjMC8LfMH81PsWHiRqexFxmsVXn3L7ezasQXSlOGxER4Yr3JL3OEdm2L+5FSbD85ZrAlYR/Z4te1qqaCeBbf9d4oYrXjPWKzy8Z4sePYcY6wyGeQaqzMsBoIIGmPYpsS2VxFobJ77IpbQ0W53HQZgbI4y2hF2GJeuNGikDNwA07CyuIiIQkQ5ZvP+69iyaw9xnpPmGe12GxnHGKMJhSFUhqFamVazzTNPPM22nTupxIpYCZYXlsjThDgKETLAZJY9u7Zz9JbbfLydoa0hLpWQQpBklkxrlIkYmdzEhZePcfHMMb75lc9xz4NvZHWlTZpLbrnzIF/67MMst10Y1O10CIMIhCJbmGNzmDLbhZIwBErx0vPHePVr7uCm7dspxSFh5CoVlxfnKIWSOHDUaY1GgzzPyLpdyuU6UkpK5XJvArY7HS6vrnm9PUkuFbrd5dLMDKPDMWQlIjRzulBTckbbInsoem/BgH5oWLAwF7CRNQid+7BQ9O2EsOTWeOLPjVJ3TiXAuftOIwCpsCbr6V0KP3FtUcG4brMDxskZKiklVgqCICAMgl6KuPdP+DHs+RJciCX6dQtZjgpj5/3IazxFqGXAiamjnBvZx+aZF9h16mEuXfg4E8OjhFrTNRn7Jrez+8hRWvPz/PBdt/DluRKXVe6yBOtW/sGtB+2u9/gHPYTee4NuhOnFfIHyMZYUSK9yZLLMVWMZA0I5EEcEiMoQaE2QtDBSQyyQRa4mzxDCCWQYpVBWoU2OBKomJ5CSeOdudJ6ShwZVrdIIytz5ujeRr6yQryyA106olGto46i1TK59ua1heWERk+fcdNMR5253mkSh1z5AsGP3Hg7feCMj42MYI+l0UuJSCXBqtrMLy4yMVDEZRKWIjJRjLz/F7HKbW267hfOnm4yPlqmXJrjjzkP80R9+AIDKiKLTrBFHkkZ9mExFBDphKF8mMQlTU6M89cTTVCsxR244yLYdW1lZXkJJyNKEQArGJqcQUpIkCdVaHWsN3W5GuVolzzKsNSSZQYkChBMIJZlonadRismbKSpYQJX3oLPi6Su38grVj/8Hx0GR+i3GhHCgoDC5y1DhPluAi45DwLndUngq8qIE2eLIQAtmH7zxKYC+wnAAeEqwKwdmMf78+XsjYzyXgGMt7hsCC77jUZJq/7fsZxasMVjlvZYgeoXZd40YAbAYYciCmIs77mB1eCvXnfxr0nyVik2oJZaXT53g0cceZdu+/dQPbCVUWwjzEl11FcDjaiH+uhUA+g9gEKaxvcxCsZsBFyv6jzh2IQcCWqN77Z1uH4mNXKMTaQ6ZQQWWQEKXHGUMMgwIpEDqnEQKtlVK3DI1zMjIJszb3sfDj3yN6dmLCCuZmNzK1ORmHj/2ArWszfjYFNqskRvnrubdDJOlZN2EXDul4FIYkHQ6zM/NYk2OFAFZlrFlyw5uuOlmrAxptroIGRCGfoVRknY7IQwdWp/JLq3VhJW1FZJMc+D6PdTqFcIwZXHBGbjh0SH23n4r/CVs2X4rWZ5i85CJWx/k621DRs5IfYiouYA1hu07tkOekHa7zM1MUy4FrLXWqFUrTGyawlhot1sEQYTJNVmWMTI27opfDAiZYYi4YarBZ2YW0EHA8PwZbmyeYnw4ZKhRpTFUYrvJueRdX+1luopJvHEobGi38TbCYm2ONU6Buu84uk5BSTFNByXj+sPH9r7AC8jIoIcfXLH/xnG57kT649IWZKd+EF6hro1xuBNeMs8bIW1zRCAJwoC4/D9fhux/8iYQtoSWGdYKloa28fiN38XQ2iKNvEOcz7B3+gWmSgG7t01iuoZmGmBkjig8gSITsPGmXu21De8XdltQTGbfoCF8e6n/vCz03waUZpDCx23WFWRog9SAMYQYNg3VCHLLkoYs1UgjiaIypq6QXcN4uQRGcX5pEU6dIs8ESZoiMkF9dJjTx49x6ulnOLhzM61omcZQzfe8W5rLy6ysLLO6usLy8hppmjAyNsTy8hJLi8vEkWMlbrU6jI6PkxrjWmSTlHI5IggkUkGuc9ZWW1QrEWm3jdaWNM1YW1vFZIYXn3mWS3ffzaEbdoAwvPzCRSbGGkwfOw3A4dffT6ebcOHiAl9rxiwpg0Zw7PwcR3aNcXD/Xk6fOUHebTEyVMbkIUvzyyTdDjdcfz25AWtzarUaAsn0pYts2brDUWVJ93SMAbKcXZWUeiRoZR0OT79Iw3Ypxw3CIGNIBhzpXuLZxK16VnjJuGJy9nDjdcXCA0PBT1RrQed9Y+FB3l7l4cCx+kO4IPoosg+erVhnYDVKOnB5YMj3PdKBY/R/HWxwGliIeieABzO1+07fZyB9q3RBRhr6moJy4xrnGHSPpY0QfV67XErmRiZYlBbynZwZO8L9c89x9/Yyf9kZY01CFtATx8Qz/oD1pZm+1rrwutxb9DwA0f9baov1/HLS2l66VhapI+k/LJzSi5UOZUZbhIqwMnBiQSZDZF2sSTEioZTBdc0mmbKILKUURaQWFpNVbq1v5vl8DpVDO5U8szDPlvkZ4koFqS2BDBGdhIe+9HFa89NcEJo4jBkar1IfG6G5ssbq8grdTpfR0VGkR4vDMODixcsOCFSS1lqTpeVlnn3sUaJqjZHxTa7XvN1CyjI6T9G+6Mhog7BljLYYm/fYm2YunuL/+j//E9/3gz+CUJIsaxEEAc8990UAPv2ff5NcSfKD95NNBRgpEblitBHz+ONPs7JwgbGxIer1CmGkCALF5PbtrC4u0mq1kUFEtVZ2ZbgmZ3Jqklq95tmbHGGpFQHWJmyvSzaXA87Pr1BuzXNmcZlbdm2iGoWEoeK2Ws5n55x32EsLX3UrxERsLxTwrzqRVm0KsWIPFA/87I2tvkahsAYprAsnKI5liaKQyytdmnqWw3s3eXWiYhyyHqOi/32OqCRAGnxxUJFhsH4o+vAlz1wDkXCq17JoIBKOnRgpXTpbvvJCeE0YAYFAGS/WYAfmrAYlBUbkGFnjmxO38PLcMpcrNaSGGAlW9XO8nhLb1RP1H2oRmTl4wK2iFpC5IVaKnY2YQ2MR1TIsLy0ibUorydmaORJRJUHiUnVqMF2jAnRYQgRlJxiaNhHpKuQJQgXkKCZUihUGGytmrSUQguvLVV5cmuH+iSpnmgk5EfdMjPHouUuMHDyItYqwFJOnKTaCeGKY+bUW27oJWZKwML9ILF3P/dj4GNV6tZcSWlpYJvSdd1YLAqsZ7nbJjr3I7OQY6ohzF8MwQokcKZyCbpLlqLBOqVSmUg5ZlgIhS8gg6FWr3fPgPZw5f5E//4sP01m9wOW5aQBGdk3Rqk2xdMNdrhlLGyKT0bAJ+/dsZm1pDmNySlti72XkrLWaVKoVjNGUohAlFXmW0u12GRodpdluUi43XN+Hcq3PeSgZrlTYX+1wYqXKpY6iMr9ESxumoiorOXzruWOs5ZsB0L5mY130XbjQvTCBnhHoqf26PiQvOW4plIyMFxkpvIqeAcC1m/dEcIVTrAZBx2iyJCNViZvrUvYWqys81OKEBhYzZV2dAMawtrLiRnnupEyREmkytAzdiq9cWbPxXoPRBhNGBJU68xcuvOL8uyaMwJZ6mX947wHAuoon/7q1zsVRVmNIsSIgEIFfjSWIHCUcDfiALlcv3ysAJQOn6Kr6rpcUgkgaKoFgrF5mrCIwWYtcJzQXFJ2kzfSlS1y84Ixs1mnTWQuJKlVXo10AM0K6isOgis262M4KRidIGSJV7IAkZakrhU4MU1FMy2S0jOH60Ron2prdE2PMLbU4bdqMmhjp1YujKELGitJInTwN6S6s0NU5SZoSKkWSZdRq9Z6rGoYBrWbTEV1KSWoMoVZUKzETcYlQgn3yWS4sr7C2/3pqo2O0ajXK1RppnhGGESbPaa058c9SHDIxOYUMY7CGSnmMXbt2Uh0aIv7IF0kq0xA746tKFaJdhzEq9mGUIkBTSlu0RYtyOUYJ4fUIBJVKBawmz1O0LSGUK27qdlqAJU8SKrVhlxPvAXYC02kjpeLIsOHj01UWth6kceExTl5cJDSChcRyupnQrbpwQEtwoWY/EQD0Zm/PuS9eL/Adq51I6+DqaelL3OMX8KLAb0DT0cmF+6Y3IamEhuu2jhCUY4zxzWxy3QkMghT+3NyyZX3iQuucXGdgcgccp8k67KqHE/TwAgcm5lpjpKDV7rK0cI1zDI5VFT98xyhBENIDQ2zh+li0MNgErMyQgWMFVlpglUTj2Gicy+TBPOvaKbEOVDHGIehSOnkxbQRZlqNTjZI5mbG0U0u3k5GS0ek00XkTLXNMpjl36jwzwSyHbznsegwEjn/fCIQIoFTFrk5D3sXKEiKsI4RGktIIIraUQs6YDjozbKuVuNDRDEchXatQOmJoW5XV6SXGJLTWWozXhgniGK0NKghJ84SoViHJUiRQiWMWZmdJkg5ChrSbhtWVZYQQlEol2u0O1VqFoVqZUSTbbtxCUIpIzp9i6cSLnF1ZpLptN5Nbt1MdHiGuVKiVHU9Amue0VjrUShFRqUQYlei0miQdg9WS8dFx6nFMt+t72YF22qa0Nk844no9DAYpNLXAQmapVGpIacmyjCRJmZ+fp1oKkCanVHJcCKGQKGEoxTFSQJ6lIALwlOEiDqk16gRK8qqtq5SeX2bp+tsxpsmeM1/gr0+scGDf9VwaGSGxDgTrawjgO4kHswH0XHs8ttNbiI123BYipEjVuQo+wFoCX39gcD0AUkhM5lLASOXFTlz5eNdmGBEzOraFJG17enpnKPuzHvoFb37me1Voay1Wa3SWEUURXWtAZj2soKhpKeTHHDGqMwLdbgek4OSZc1Sjv4X4iBBiO/DHwJQ/69+31v6GEGIU+CCwCydK+t3W2iXh/PDfAN4CtIEfstY+8e2+Y25ukT/4vb9w5Y0of13O3zKpxsiEPBNgXPGMRKCEpZPn6CQjyzJKpTIuJ+yFJDzLSpammFx7QVGJKoo8rCZWkmqlxIHD+9h33U4qFU1XQGmsgdFdLsyuEVrLzNwC42Pj9LTucbCPwFWUGVEQkIYIFYMw5NYSCMv+bWOEq8scyMss1xWtTPGmnRX+4uQsr9syydjuScTIdu4ZXualbRO8dH6R2sgE3bU1Go1hhIEgjMhTN2Eipbh49izT05eRFqKoztpahyxLKZcqpElCmiVYGWO6XUZLIwxv2k4sNMunnkUFbViZY7ZaYblSodntMDk+zoJPNcqoTBCGnLtwhmeeeZxyuUKW5qT5IqsrS2zbtplGucrFmY5T0QFkoIjIyIxEK+HU8oQgT9o0Gg2qtQojwzUq5Sr1egPIMTonLkVUanUatTpp0iGOqmANndYalaCElAGBcrX2SihynRPGIft3jXH/VIdPz6d0hrahbYVarFla0ZzbfANmZcbPc1+s4yd6ZC2p8JJdeCmyYiK65A5SWKzJKIAhJYTjExCFDXFuvrGuaEwbiyVHCUkuXKrV2Rs3fkMadLotdNahFAWujlDgak96aKX1HoRvezbWf19IGAhfDp5iRE6tMY4Wsx6kNFiPXWitSbtdl+A2GmsDumlKKgOOvXyGTaPD/+NGAKei9bPW2ieEEHXgcSHE54EfAr5orf0/hRD/HPjnwM8Bb8ZJku8H7gR+x/98xW0mhf84rRyoKnxdNHgLF2CpgAVtXWVdD1UVjoXGWoPMpc/jCgoySSftXqT7LGiBMO6BS6tdj/tCxtSZk9z6zBJVs0RncYbmWpfVTpfJyQm2bNXsObiXTVObUEHYr94SjryEbhOTJYQKbFTCRFW0ThBWokyItgFTITzRanOovolGnPGSgX909DpOD48hW00mAkn8wJ0ceeIZTq8toeox7VYbYyyBUORCEcQhyyurlFBcunCRVmuNKAwBh2IHKiDPc9IsoRSEmHaL0SRiz4HrqdQUlTNnqLaXmCsFiKzD6uVLrAYx9dEJFkPBsBBgJgikYmFhhuc/93kWz56mMTnJcEnQ7qbkOqMUKoYbkwTakK42/XOCWPVLtw2KysoaOxo1RBwQhSUgJghq6DykVq8hsjZCWLpdTSDdsddWOzTqdZSKCYQkwBVktZOUXOe0VjJmFhZ47rmnOWXGEeE4SaXON4JNiKm96MkR5qubmFw6C7gJ6+S8i7nmAORBXG5w6xWe5XkvnBSAMNbF+z1sybndDjNwkzoQihRBoDxGJUDIwGkfFFWAwnmmFHqG3n13IZT7WwmBVNKpWFlnkNbW2ggbcvlyi507RpGEzmu20jEm4YxKwSpUPAdhDUtzcwzFEdFG/fqB7W+iSnwZuOx/XxNCvAhsBd4J3O93+2/AV3BG4J3AH1s3+x4WQgwLITb741x1MyqkNbTJ/+EBvZ7rZnrIrDUuUVu4eaJX7FGIgxW/iT6A0ztMPzC0OIoxSk7Tb1lbjjUN1kTI0hZMZIltwr3hEkYZGsMjNIaGoMgTe+FHKzNMp4UIJUOViKaukKgSAoPMBRkdzPIysgyvmhpnRlvGhyqMq4gzKmKHzTjZTEhnzrAz7fLEWhOztICsuhVRolBRjOwkWKVYWFqhFkYOZPNVgzp3HoKx0E27hIGik6WUFuYJjWH20YRNcYTIW6SRIssC1vKErN1i+eIFasMj1KpD5EnOSy++QLPZZGK4gT7//2Puz2Mty7L0Puy3hzPd8Y0xR2REZuVcU9bUXT25J5It0rZEwzRlQSRBCaRlyDAEyDYgAgYMCBIMDzT8jy3QkCVLoGxOTZOWOTabPVaxuqq6hqysnDMjYx7efKcz7MF/rH3ue5GVWd00KSEPEJkR9757373nnL32Wt/6vm/dYavIuHp1m4N7nof7OXlRkJvI088+yz/6B8eEuQSBdrXEzecoPCYKQWfha954902ee/F5Ll2+wHgyYTQaMxwOqIqMW2//gNzCaJAzGOUs54ZlG3j7nbvcf/SQe3cfc/f2Hvcen1BHT9ssWTWBWAa64xWPf/KPwae20OWUuz//Z8m1JlDjQ3ZmvkC/IKRdt6EVBwRQOW7dITqTVgMhaug6VNRnWoqkO0ut0+/TnqM8HmNkY1AyM2k+QJpipI3MkYzIXMIngo9KtmfrmzSVs0SUkbZzCJG8FBeqX/vHv8f/4E9NsbYHMVNw0QptNVluTgegagM+snfnAcMiw3X/klSESqnrwCvAN4DzZxb2A6RcAAkQt8+87E567GODAKhTgYPy65Mh/9HrjKkHefr2X0xAzFrrrTjDxOIMJJz+0T8cU7CIaeY7oKImaKGJag0uZqiU5mklAzaDc2cGV1qCyaUWcw0nRwe4bIKyhXQDfIeymu3z24xmh6jpOZ6uMm4/fox2cy48PeaDO7cYlGPefHDCe2/e46SwrOYnNPtDdDkkotm8cIn65hyUok3UWa3A08uQPcvFgtYFBoMh2kf0aokKloMB7BQti90p+03B8VGNHw94/94jHoU5J/tH5Hfv4lVOXlWMpxtcu3iO+uiAg8UJVDmT8YDZzjXObUwhSox++plLLBZIKw1YnJyQba4gjWkjwmze8I3f/jo/+N53+dqlcwwGY4q8YjQaMh4OOdk/IEYHOqPzjsWi5vDggIPDfearJcVojGs9G+cu0hrDKLMsa5jkOW2h0LffJz71Cl7LRhFjC+QYFTHJ2CWugUGF1pEyikuU1+qMjPjM0WMEviES0KFXFqo0zej0vowxSN2uZBJR9IFF11AbJ7gAIa3t3og0rHv9pxOR1wuLdPMmcpq09MRWPIC22Czy5//Cv0aRFxwe7mGtUOZV6hwY1RMg1dqL0OYZ2XDIo9s3KQvDxx1/6CCglBoBfwv492KMJ6f2TBBjjEr9mHzjo9/vLwJ/EYDJ7ukJifZD10baND2iGmPvDt8H77MOMT1kG9YnXwZRwnr8ex8slLyvmHMKlTdGAx6Cipi+XeQEaQ5eorqPgRgcSmdgIiqviEd7eFsSiqFE52SewbLjjcMVFy5f46u/8CfpQqT8p3+XV09mbB4v2KsKjo5O2NSOf3K4z59++hr/eLbByfEKZ3KWixWXn36GR3fewwVHULCqV2vagrGy23TeMxqPiD7i5wuujUaEyTkePb7FV4YTrr/yOU7euUen52x84ctU3/gud197FRs7Dt98h0cHh7z0+S+yUVmGuuXWBzeJbaCtAlU1xg+GvPKln+TurTs89dQFXnjxCq4RizeQUx4nW7gQUTbVz3Q47Tmez5m/VaMUYhaqhZorC0E8F9DgQxTFJhofNKoYEt2CqCN5VmBLi/LSbhsqRb54zMOupcvHaN0w9DOukPPQRrZDv+updSswRsMdNBEjAh6BLzlz4yV0H7RvED8ImzaYgOnnDMA6I/Bp5Bgxoo2hc5rQb1AqorXCOYe1hsVsTjadpE91uuhPI0syyYRTTgABbQIh5EQK8qwfXxYIvpMA4TyoBquinFslalVjDHXXsnNll71bb2Ns8bFr8cexKc4u2AwJAH81xvir6eGHSqmL6fmLwKP0+F3g6pmXX0mPPXHEGP9KjPFLMcYvMZimE8t6EZ/+SZLRPkquHznTZ03tG2JMc970em482iQk1qSva+XvQRODlh09CAtQhYixqg/VuAhey8UU/5KAQTEwYLXUfurcJdTu04SNq6i8RGU5KmZECsgML0xz7LymfuM16jv3uPyFL/DL58/z2tZlNr0lcy16Ffk3n7rAP20if/yLn+eXL10gp+X48R6b0w3yrEL5SGg7urphNBwLgOy7FC8NbdtS+o5P7+zw/Be/yI2tMTemO/zWnX3uff2bHLoTQhYx792m2Z7y/IUdNscVJ8pgj+Z899vfQn/zG0zufEC4fx9rFKXJ6NoWl+cMjWRd9+7ss7FhKTeuERPivHn1WY7G1wkqS7baLaGsUFHMQoLW6HxARDKF1nfEmJyflEbrgsyIe5DNcjAKa8SKy7UeYd4pBqUlRLBFiSkrlFsSjSYqg9djHhjLype4ZK+te/BMSQYTUKKzAII6xZZ6JqCwBR26bSCaFBaUWIOlgNITxogBrUJyg5JAobzcbyYEiDKFWsaXBWYHB3R1vbZBl4Ul/xHGH3LuUj0fk3kOPgnYjCJ2p0tWqw7jZujVCXZ+D7U8QgHGCDYWibjWMV82HFnNM1/5yY9d339gEEho/38KvB5j/Mtnnvq7wJ9Lf/9zwN858/ifVXL8JHD84/CA/lgvbDhzktSTf574+ZTe90F1/YGRmU+JTvwjjOF1dDjzRNJhR5WEGKq3eGZdLqxTOyAzWnwNbEbICvxgg1iNIS+JJhk9Go3WFaNLVygGBeraDTbKCn20wtw4x3/vucs055+mGA8gC7y6V/Ov/+QXuPTSMzz34g3O+UjbLvBtx2hzS9pkTcN8tcCHjiuXL6KUwShNnhlU7chWNXvO0d57SFlOmV66zJV8yt946z6vv7uPW0YeZTCul9ybzdFkXBmMUZMpF3XG54/fo2lbEUgp2CpyGqeJ001WJyJGMoXC+MBXfumLPPP5zwAQzr/ManIZnGhA8BE9rIjDIRgjizE3mMEAbJYs3hIIrnQi0ERslmHzQoI4goo3TQ0xUC/nlJnsup3riKFD+xadxFMzXbFnxnQmx52t5dc3xpmNQ3H6f9I1PvNv78SMw2iFNvLnw4muUkr4J+JrlW6hmOp8BcgYs+gjWhvOX7qELQphxCYq9Clx6SPudS3itQwlqsmYNjcFSsk9pkJHHlZot8KELondUsvSi4nN3tEhHzx8RByNPnbt/WHKgZ8G/gzwqlLqu+mxvwT8b4G/rpT6t4EPgP9Reu7vIe3Bd5AW4Z//Q/yOj1it8HG8//jEhVSnQaOPqtFTqEDT7/iqH96gPnxnyGuiEYKIIsk/4/q9e/wBkrGkUtRK01gL3khL0MxESGTzRG+tUWpFcIZ7P3yfbGjZzjo+cBmTbMju+at84xvf5ec+92Xenm5y9+3XeWrTc/z4MZXVLI5P0L4ltDV7R0eMN7a4/eYPmGYav2g4OTzk4OCIuumwVnNxa0JsWia2ZJQZ7LDgmfNX2G8WsL3NcG+Pw/09Op2TL5Y0XcPTkzH35jNc1jHNKybO8fRWwXfKAUUEMmhWc5besDPaYWdzwus/eIOnnrlM07Rce+EzvOpK+Ge/yf7WU8QIhgYfc8CAGcJ0C/YbwIGKlOMx9UL871KZjUoCm84HmrbD5rlkWEg513UtMTratqbrGsoyx/lAaBuicxB8uh8iKEetPDPv+ksrpkKkTYPT69qDeqe3WkyofiQ6J7eCRlqEWrpKvbOPUaIiDKnzoFSqNtfIPBAdIfoU0DQuQJ5eB0hJ1G9kMf6oojHtbsEn6bJRGKPXZfHGxiZXn7rK8dEJIXiMzYnKrJ2lQoiMJhNOVivazvP4wSM+7vjDdAd+54mz9eTxSx/x8xH4d/+g9/2RQ63/07/R6f9/hF55JgicPZEKQYKd4+k88q6LNNjT6332fdaz6TlFcxOY05M1zlJO06VLNaIm2oKIx9sM8qG8V1ejaIjBoLzwBa5UDcPgeP8b38BMzvH+eIr5+19n8tXP8eiDD9gxHXzmZZrXvkfcqdh/cJc7Hzygm59QYjm6dZ/x5oh80XIuqxhODI99YBkVVVFiTceGVWTGcbias2ta6nqD4DuyRQtPXeMLu7u8evNdZkcnjE+OOMw020PL5nTK2/szNnLFptHE4YTa5/iupTAaX1Ts64zr4wkXLmzz+N4eh48PGRc508tXeXxrBYBDo5TF2wyFRelAoEBvXiA+vkdM/WxTFWRVpOtarNE4L7iL62SKr+sEVBPsB0yW4buGGMTVablaMiwrog90q5rgOvoOgApC67Uqg+703umtv+i7AR9a/Gfvp6QeQXnBlPrbQYBkcQ6KoTezPdUgagUqRjKjUgs7Jnm5+Bb2bbu+bI1e0vY+Y3ly8fefERwRh+ALMXrBGdI9P5lMuXH9Ou/fvMne432KPBMBkdZrUlMIgcxatnd2uXf/LFb/5PGJYAwCp5uz+ojH1qtRcTq+6cwJU6w1nqL9r8AuiD6nn0bzRM1wNiBE0nQaqXnX90p/s8Rwagqh1KlIRMvAB7KSmHfgvexKQdRmURlUCBxQUWjFP353j0494qfOj/mOj3zu269SPXOFI+VZvvZN7gwzvvDgMfWlczy7NaH4zk2+bjzzw4dkZYFuO8ZVzYtf+RJ7r9/jONc8Wi447zLmB8dUkwk/u7PD3lQzPjzi+HlLU025cPMWb22VVDqwW3reG074tPP83v0P2N7Y4PpGwauPZvzE+chBdR7vGwpricuI0hknjCizkgsXdzGUNE2L92Kz5tNwPpc8F4ThKVRVZTIYi/OQUeJ0Q1BkWUnIC4Jz6NyifEypbgQf8F1Hn3VpKzbdKgQ0Clc3tAgG0ETJArQxopPQCoLGmbhG4RUy0VcCS/yRRfakcEehuoAxDpplkqMojLbp80haLt0jQ0CLc1HqGmltcMYRMetpRVoJCBmJ5GUuBqBKo6wV/8Ozx5p63H+sSPQyjs0aK4ajSSgnXoIZB/sndMHjgsKHIC7W1qyDnVaGX/mVX2EwHuNWM37zd373I5feJycI/MjRp2xn/y1pm05tIcIpWPPEdVWRt+sMRzwNFh+Zy/Qp4tkfSG+kpbUkXaNTg0mxJjcpBzQomxNtDrlkBihJ9XznQHc0TcdSLXl2nHF3rvnB/jGfvXqVX9/z/MLjfRb1IYeF5qcuXeQk00zyAXHZ8MNBzeZ+ZE8dU4we86nBgHxY0T54jBrAYrHkenBMn36Kjbrm4GTJYTXkcq24mzWM9vcoj5e8bWo23z7h7dmcerniJy5Ehp95iZ/O4d29e5yoghcmOc/YQx7uXiDemWGaOTFAyAtqNeDQrRgMB5QljMZjVJZJK773zlpr35P6DXFJjp1HxYCPEbynXS2oJmP0YMT8cB+TZwK8aNBWrYUyKllkWaXBR3zTUmQFTSO+CSa3GJMBYgprDKCC9P5jEN5/f4XFc+NMvX3mMoeeOJS6TkaDUYQ2JCKQlCKSUShQ4ohkdUaRFTgnz1lt0EQqVXCsLEEFgotoD9EHnBMw0vdS4n4ewvreXO92Zyrb5A2Qsk+VSgMyQ4wRFyJZPuTchYqd7YAeDNbEIcl+FE3bcffWXWyeURUfv9Q/wUGA03r/bG2ermORWVovuvX1kW5GjaMjQ+FZj6H+kSwg8uQTZ37naf2wdn3t8QCt1PrCoIzsMlkB0SFgkNS6OoDKKs6NItt14GGj2d2aMPclt+8f81PXzvOt2484F1tilvMb336TV168wu/cfJt2vqCJmqbtID/H/u13cMsZo5hxuzuiigozqykv7DDRBcWo4rndXR4tDqiXkQ09YHGyZBhrzqnASXBc3yq4m+WozR026Xi7U/hiwqCdkw8U54cF74w28Y/fT4FPowcjap3xcL5A5xWTTViuGrTWrFZ1snHvz31/euW8xeihbSQoBLHudn6Fnk4F2VcQvQfEI09r4dpLr1x2T2NESOO82I6v6jrxEALG2rQBaAgdhQ6Sl0SFToM2TmnBZ+6l/gY6k0sqpYgaquPbTOvHDMoF4eoVOt9RTibYvBD+fh7pmo5BUWEx5Lkl0+I8ZK3G1R27A0MVT9hDhsqoRG4LAYJ3IglYZ6Fn7r0f2fNUCjjgQ0fwHV27IssHaGA2X/Fg7xBsREfFpJBZhKdZq2a5WHDrvZt0viUvPvHOQqRrc9bBlSdOSjzzX6UUznUyLUCd6fWmaB+jTil570H44SjQ/2x84uE+oegfCjGKHDOdWI1YSymtTuXLwYLNIBZS7AV5r6gV3gdeW5Q8r5fcuHGN+x1sHTzgzuaY8r0P2Lm4yYHX7K5qDnPLt1+9w0nXcm8559nJLros2ChgcafGWMXF8ZTDxYpF57l6fgc1naDbmt0Ll2hN4IWuhmsTBmYTM9nlnfffe4RcogAAc4NJREFU5eTWm0wvT8EZfvJcSbt7gXffeod8e4vDd48oGo1VC2wGDYbw6IBFgG5YMD1/iUhk/3DBnXuPeOXlT7GcN8KtUFYWOv1plAUsu1FERS8iq5SGqxCIymC0wthSDFG7DmMzoelmlvXJw6y7BdoY2UGVSr14h0npsRiIerSKWCVd/RAVNg1xOQ3n8cyttEYKU56oCUqyj/j9b6Pmt1jGFqUNmdHUqxUja7mwu4PSGUoprM0IpIEySpNlGY1rmKkVY3fMxhtfozQldeOhlIBqrKXrmvW9FtcBqce30j/7WzK1oxUuBYEWu54wJExCHyImjVrT2qKUJbOZmLEkoVIPZtof0wj8xAQBRVyn36dACeuSXp2JoJLsnflSZyNqTKNEekQnJkOFeOZ9z2AMZ164ZnKpGACp+3RiLPbBSRFxShNtJr1cCwQDIUOZHJVFtAoELCaHZ4aa8wvL6mjO3vyIrx8s+LkLlq+Hli9vVbyyscU7G1t89fvf4zde+wBd5Pz8p57n4fY2Lx/W7C9P6C5scim7SPbZ53junfs0yvJ44Dn/+JAHAw1373Euh0cjy+X377PzR76EnlYs3/whvzpb8MV7kXPXX2BvkqO/+wNW5zdZ3rxJfTjnbtfxb11T2EHJ7HBBbFpmGlRR0Q13qBtPbGtef+Mttqqc3OYUWvH+nUcp+0GGvBipVxOKJjMUdESFZK2lZIBMjKBNTjacUO8/RGdKJuxYjY79yK+IjoE8y2S8V5C2oM1yuq7DSlsBrUV6HTG0rmbqNYdYjJfFZmJY3wsqBiKnmI/8Go9RBsknLUU9ZzQ0qFAJABcjLgTapuNw0RJCJ7MSvReWoBICmYqRnY0JmdVEo1HeY7qGgROLuTYA3tP5gA7ikXEKRrM+Z318ogclVaRrVhDGMt8SsD6d56SO1T75GgaPyQxEhfMBMbuV8Xja6B/LBfjkBIEYExPPrOtwYB3Dz3QBz7zoIwv909c+kW599M8+iQvIz0UlbaxezhzpR1on2zEAbVkPrLdicRSjJeKJJhc0t9Asq012po5Cz9jwS65MNHcfPuTntybcfOdd3o3v84Wnn+IfvH+fy0PDjRvPMD9/kafuPuaDKmcr38YWkcN8yLP39rlzfsiwtewu9ogvPsPL9x7xxuP7nJyf8OXVirvVismrv88HyyXu3Ih/rXiB1xcL4uqY7NEx7xrFZ1vH37r7mHMbY25Mc0Lc42FxmeNHBwxzxdGqZaOqOA7gXSAax9def59poejmHdeffZpZU6P72rsPtuuODnhjiLY8c30B0o2rFcVowuLRPfCF6OYJKJ2lm73FRys26MnV17UtxmZ0Ufr43geUzdFR4ZXYzwdTY1zEJqfRTIvFuihTT/GAuP48Gqec8BbaFZPQMB5UOB/F+yAKq7ANmtFkyqkWQQ4XA8vlkkGRMxmV5DajaVq0DRR1S9Ms0ZnMSwjBk2kl4LGXbCauM4Iz+ee6bNFkRv4oJZ2EfjoSSmFN6nqFiA8uVTjSjSDdp67rcE2NUoH2k+4sBBBSeq5UX1eyHu4pR1+np+PH8Aq0FgzBpTTy7HNPIsL9W/UX9smgIcPETvGB3vfNWiNuI7GPyhaMh5hBDGgfUSZQmyH/8cEm10LJjlpw0W6xnbXcuFCi/Yqfzkts1zF77w3+3KUNusND4tFNFvP7xNmCwbUb6KOGpq7Z2XIsH+3xVHcOTM7xKqBv3uQHTcvz1y7zaO+IbyrPK5s53779HpdfeoqJs8yH8BM3XsC1gaNuTrj5iFcPH/LcpV3mdcesbricwyM7oXnwCGU0NZpGGw7mTUKzVxx4+Htf+32+fOUSb+1vcfP48BTQimfqqCgYQDQaspJe0RYTCzN4kRHnmSUbVHSqFROWC0/TPboJPmAi0vOOEWssrRf3X5PlGGvIbM68jcSyRNEABatgWZAxsS154oU0AaLWkkyaXhAUz5SCGkxAB09x8zVGFTIYNdM4IiYYDJaNyRbPPP88XY+DetkgOudp2w4jY6gxuiXTGnTEFpbD2YKQhtegBeXvQcizrkUk3OSJKIVi1TpWIYIyQqRKitmza0IplQaPaKy1cr+mTMI5T/BimHpUNx+98PgEBQEVELT/7K589vioTODJd1g/F84Gjw+pxD7q6L0L1r6E/c3SPx9C4o8bjNHkeYbSRtqCuu9NylSYqC0BT7QZ2jseDofstSWRDXS3wnQOczCj6mDgHBMaRioyfHzEJRPYXs7I9AHbOqN6900GBorKku8f4ssBs4N7DHYqrHI8Opjx7O42zckjbOw4LgYc3Dsk39zm/Q8esb2YUz73Avff+T7bF57h8vPPcf/br6IoaFcnNFHz6OCEfFvzyIzYn93k2Cl8VmFNyZELqCyQh5ZF2KDNI6tM8e037/C4RQBRQEReZy6M9LUwxWDdoI0pE/De47qWPC+wg03a+X30uevoX/mzqL/5f4H5IZFa1mwEk1ncfEVbt+gsJ0QxkfFFCXnBOaU58IpWa6y3jP2c2HbpPkiLZn0pz6DwOqapQYbYnbD54D3yUUGIGhUi3ogLUOzg4vY5PvfC83gvIqAYAjptMspa2q7l9q0PqN0szUiMFK6DyZDQd5JURFuZKNSPTF9jAD19Uku00On5YHK8LUEbXOcxURH8GQar1sl/MbUw9WmrNnhH23Y0rWBnh81px+TDxycjCESk56sM/sOPw4/u4E9kAR+OCop/ntLhtD0YIa6bMesnVSoHxKFIWkpGCU8AgrANjQYkM1BRixDJBgIBVEHEQQy46MFrlM1ZOPm+MWiZWNQGsuiwNLBaUMSOUgUKrSiMpsgMgzwndx3b7xsuDkY8Owzk9+csY06MsOVrqtJQhRWrkyXHdY19/XVGqqV5dMTs3bfxdGx2jndjRrM64enCogYVj1TFQkWe3j3Hc/mEd4pcxptHWKpMZkIbyz+9tccin9ANJmt8XVRvWjCCYHvXDLj+EupbI9RJi0c0+S6IRVrUBltm6OMcNdwkbl0nbF1AnTxG65IYpEdelCVHhweoEuquRnnL0jXEczuEqmLPOZlIBHQq8KAzTNL16wfNxijmsfHMPaGTAlD5wOhkj43K4XzAxYCJspO2eLQuuHzhKhc2JzgX1yVmDOEMESiyf7DP8d4CrwSh99FQTMYELzJfFTVYs6aoK8AqmalIaokqktEuyfBEp/aj0pDnwjuQfiijoRi1ZCbiMGRZRtTSVhTxdGTpPLOmpcYyi6el2YePT0YQIIrCI/twu69/ro9+H5og0x8fW+/Dj7QZPuopBUT1oWRDpcw2rD9K/3hAbMbRBkwUYDD6M4MndOKcSqCIOt2BNhOLciujqgKJ2OI9QWmaqGiigQIW9SHatamNHtcOxhrAwPZJx//0cxd45vw2333/Ln/9nQP2g+cL166RnTxAqY7zwzE79oir4wpGFeqo5rlqk7snj3k509wlUlAxm26gleZnrj7PTHl+69338NevM9SRuQm0akA0Fq3gqPNgayKnPvYxdAnY14iKRhF9B9tX4fO/CL/9q8LxRxxwg/ciyy4yFBm+GslcyYufQr3/TXQc0+mAD4GsKASg8wEVHJnSBJ8TP/0KKiq63n8CiMoBYY1VKNUvJr3O9kh1tYpanKpCzYWDd8lVxzLNGHTJqFbFyCCf8PSNy5R5jlOnu2kIAWNFlJZZw3Q44YP79/FBhsl2PkBW4CvhEESjCZ2X1D1tapO8ZFUHVkSiDkiBoPGpLW2UIlMapZOU3QgwHZJq0dqMPIsYZcmyjMxmCe9WGGPQxlKOJ9RNwHzSg4DqudhKUPkP909VPLVa7kc0PXl8CC+As/yhs4/+6GvUmX/HD/2oOp05IExESbdEzZi4C2sSSvpcSqVuhJEFoQxKOfn8RlSLmEym0IReSmtF39B/N1PI6PXlHopW/FCS+USIGh1yTrqWv/1owOYv/wX+y3/yv2bhc6KKLKcTHi0WHOzNifs1lc6Ju1fIr16j8J582VIONjChRY8bpkXHP2q3MYXia03kgcp5/tPP8Naxx1gtWvWgiLGFUDD2SxwFTQjrTEBao1K7ogrQERUtsZ4Tn/85eO0bqMe3JQCGIAHTOVRmsNWALs+JIaKe+wL87t+V6ULep7hpwWqcayniAFVY2LxMvPEsdC41eiRb075DBy+uUXDaFdLyPXr3nrSUUAauPnif6fKIY9/Q+0tI5aIYlIYyG3D1yha51lh1ulxCiOvrb1VkZzoieIXvoSclE6sWzrOqWx7ducv53XNpYpCAlSerBh8VURthWJ4lX0WooqcgoJJRSBM6SiMlWNN1LFYrnIu4qClHE9kgVC+djtRNzUnd0MScfPgvJiD6b/4Qb+Un2jfyuIAmMvjhzI+fXfP9hT2jPJSaSoZWxvULf7RsOEVkP+Lz9C1BJYSh3vRUAEMZaxXP9JzXLZ8+Q4gBghawQyspFWBN5IgJQ0BHok8AkT2VQetsl5DnhPkjdFev1XLyVVu8UsxDwdazT7Pw27T+LVCwPFkSKeiQ1tBs9zLmxqeZ60LadVoWrI6gF55Lk8jzW5t8/3HDN+0A7aA8duRFySwqPBkRyWgMgdZpFAGrHF26aXX0kg3oTK5ViGKGaXLU0BI/999B/fpfo/fEizESYkA3kWw8YOXSEJmLzxC/8Iv47/2myGqJsptVFUopvAsUZQ7TDYw3+NAlL0nhCuh+XkA8Ledi37n4ENYUdWBz/oArR+/wIC7IsHQqrH0D0RprDTsbW+xMCppVwGub7gVAS9oul9Bz7cIWIQTxE4hevAeVJ2rLu3sH/OSz18kKu74HtE00ZC/+ADgZeht7J2JgkimGOPAOkyYq93wCMT8VT4N+9HkIcY0LgAQqFxVBGbL848lCfyg/gf82Dhn8mJ8COemPTtNdxSo4ESMQDEUTnywb0lEpsZNSRJQKp8+f7cbEjw4AQtYw4jsfk3vrmRvIak1rc1RXozuHOnNhzvxF3isxDddjqrVJHyN9F61lcIk1qVTIBBU3GpflMNxET85DPiZo0d5HbZNaLHBwfJff/vo/wNj9tLsoVHQMBwWqLNCXnyU+9TJeV4KSWyNzAZTGq0BXRB7MIsdN5GSlUdEQspwPFo4pns3eVSl1bSBShYZMBcZxhVVNOoWO3NdkKqBjQOPRwacgqIjb14jFcN0Skx67DPBEW4rgsRGUtcTP/zxmvCXnysvwkTxRsstyQHAOmw+JoSV6uRu0lvZy1JUQmYIAgzFlViquETjo623vyd5/k8ODh4RVR1RRxsMRyVQk15GmieycG7JYrEQz0TUE72SitJMuB8nau8oNl3Y2gYjRliKzaWF6bh0cMfMpawxenIojuChWYiSG5DpzSfdr51wad+9puxYdUss6eLx34vXgOjon92GWSRAOUQJL13X4zuO6TtyQP+b4ZGQCaWXGte5zvbXKzqIQY4UewQ8BpRU2s7StY932AVQMXLYN7zUF3pwBCfvf8+HKYf3vs5lBT1NOApSkHeh5A0EraIWwHMvT8mSdGaTMZF3C9O+XYoQyadFHcTZ64kMGuSlQmqAsutqArEAtT4iLQ2yu8a0nuo62WfDwwU3yDGpl0Gha5ykHI8z1F3GTp2RhxZaIpJFi3qIhGhQeVGS6eYmfuzLmztdfY06k3TrHg8URP/vylN850DzqxGBDh46BqnFGC9IdxVnIaId1GTVWdrf0vWN0BGXhwhXi5euom98jEsT9WbVok+NVQHVBtBaVRV+8Trz+CuGDb9B1DePxmHIwoDms6UJgYEvazS1C50BbxNcw4NEMrSL6BhVa+a462db1gTr2BYzQkQ8ePqTbf4zKJLgHpQTwVVK3B6945/33+C//5oyoc4yyVFWJSeQbqw25zaRssRlt3XBysC+7tOtE8pwSk99qv8Of/uM/je0ziKDwWp1OPVbJGzMxLxWA72jbFV1o0Vpsw3yiRIu/pMeb5HytxKB0fRv5SF3XtG3D4WLGVjn52NX3iQgCESRa6+TbdqatE0MnBKK+/g4hBQIxTQDOYAQRQ2Tkl+iYJbZYf3LhdKF/BFB4tg6Jpw9K5BVL8ZAGQ7bGEqZT8A7tOrw6AwgqfSaopDfrY0O/2LUWcHBNk02Ph1QW9O02nUnpYTJMPgRb4pUDdYwKLZ3vUJllNB4yX86IxYilGjCotlCDcwi+AlEPeip6Ordp5wkQTMlhPeIv/Ilf4bd++Ig3TvYJBO7mIx4/mPGLT1/h/31zxSo2GGMwNITouRMyIUwBIeY02QATLNZ4MlpWyuCVmICoaoN45TnU7Vc5Ld1kqKqPHayW0MxRepOoS/Kd69i3fxPXFXSJWuyVpUq++tlgIFbv2hC8THZWEVbGMe48KhbpzJ+tL3uvPzkHyrcQW7EJD44uof3Be3xqMRul+eDuB+wd7tMBBN87zWC0xvmw3rO8j5QWilG17ihppQhBgv/xyZC2q1O9rzBGrQFA8TJQacNTCTeIDBQo3+BcQ27FbJSItCpjxHuP95JF9+Y3RonVCQGRsnSOKquYH88/dv19QsoB2Y5VD+jEHpdT6xv2dLM83a2f4APIg1ilccGI9tp/eLF/BD7wkY2F01YQJHVb7NN7TR1BeSNdAW3Swj8dh0YKPlKbyV34ZPLRdxaMZAX9TtUbS6wtqPpdQRNURpycI44uwPYzgryPdlnUjktPv4i/+lnC01+imV4hyy2+TYaXJg3ITmmISoGUKINTOtXyT37wA37jd9/jFz79VWgq2Q2Liu+uImPf8NVLI6zOUWQMfcuObQhxiOtHOCiLV3CJFS/qu/yceo/N2KRhsRGiR0+2CNoSO8F4glJYa8lMgOUJ+uCxlIJW080e0ClDcJ62bRlUQ2xRkqmIHVo2tIKwEroy4KJGB5iGjs+YAyq1nk2eNoH+QgsbHxXJlGNEi3YeHaT/77wjxoDVCqPABUfUHh8dWrUoE8gzw9bGBmVZkuUZWW7JCkNeaGyZoYzYjykVk8+gTir3nj6twWoCDq18GrUuVmU98BWDlLNlCOiuZX5yzHI+E7wr+QXofq5G1603Q601+wf7PH68Byg6HwlZDmVF82PmMn5CggDEKLZVayANBUG05RrT2w3KRU0rSiWwjRCljZjoko91JRJWbU4Bvf7oedoJ/ZfH+p1XnZGdGpySnbkXaYQIPjiwhVxIo/DKSFqq08LWp+8Zka8DvbFmOP3dUhcIx8CmlmIfDPryJgGRkrsowQNMDuWIuP0UXHqWvdby1NXLMD0HZUU04vunVvPkb5AR+1kOKhIU4oATQyq/NMcs+a9+89f47DNX+OzFz6DICApmdsA/ffMBz1Wel8cDVFSsYo7qVuKln2Jw0AUqBjI/ZxQ6ct/RelHXqQDarYi714TJGQMOJ6aoeKnjadHf/i3oOmIXiO2RBDAFi8UCpRV5NAQVach4nI3ITcZQZRS0vKxn/HeLR/wbw3tsqiWhX0wEdAhpSKgRCbqRwFq+8zqXC7h4cYvt7W22tzbY2thgMhxRliVFUVIWI0pbMRkMmVQTNqspw2KAd54yLxhUFVVeUGUFZZZT2AKLAHNWWfJo0LGBNpAZyKxdZ4wmGZ6uMbAAJioy5SmNwXpH3i3wGlYnj5gf7QuXKESMKQheYZRBk6N1RmYF6xkORpw7v0tTL3DNCuc1bRvZ3T73sWvvE1EOABC9UIc5C46AzuxpXbeerpoyB61ZT25PKXeD5lGnZddJI6Ll+R436NPR9D7rUuJsPdDHjdMMICTARX61SXe3iEdij+oaexoIYuIJZCEhQIL8k2ilMSajiyQGScnDaY0IZz4b6fkUEEIgmgwXI48Pj/nyc9fRap8QA4333Lt1i5htoUYbMivxya/G+pelr0hU3Gke8df+zm/wJ3/qZ3nwWx/wyB0SNXygd7j1wQP+1Bde4f/xw3s8jmOc3ULRQTyd5oR1nDSeoqsY2yWtzokInqMBP71AzCfobp/oIVqpuyMKbRX+8Vvw+AO4+CxmNsdoRWYzfPA0TUtejanrGWZc0hRDgh3S+iVXVceL+YztuOLEl7y7HDDov6yPrOdUENGp+gpK0zx+wIPH+wwnJSE4bPJrtHlOZSU7y/OCq9eusbm5KVmg92jUWj1oc0sIyf5LKU7qloePH+J8l5pWhmjAKsvSd7z+9l3GGxfYyDV7bSCQpg2r00zXBU0Za5Sq0UaBgZ3REDU/gSgU6UjAFhnKGIxV6EyGjmilMLl0xmIIDIqCqrAsneLenVsfu/Q+EUHgdB2mWqbv1aa+uzjF9PthshJP9fkT5X56I68sYvGUUGJ19qaX1NwqIWWEdXB4snRQSthXxpg0Ils+R18SrDkBKuEAWhRcmFxqx7Sri2us6ObhTGraB6S1FPojcIoPnaAPj9FGaeZ1BxG2tWdYRfYXS3YvXuCkzuiyHNDCljujVHsiDtLDGIZv7r/BS4c3+LmrP8Gvvv/bxLCiqxyv+5KN117lS9sX+NV3InNVEoKCTIKAjpGdrmNlDY8Ys60WBGMFQFURHyMxL1HXPk34wa+jQurHR2EaahUxfkX3+u+hLr+I8wuqKI5B2hhc2zCaTGmXx5i8hCxDEzkXO66pmnud4qru+Ef1eVbumEuqD05RcBAt905QWuZUmkhWH5FnGSZKPJf0PeCCCG88cDJvOK5vgr4lZWlUa2vRzEiNbozFai2DRXSf2oPShkBNkRcoGtrac3/vkMn2FemcJIyr11SIVD2ilKENYG1JbRtcEzg8mqNHA9xsQTnJACEqZUXef/h1+ayS+1Agsuw8cwfD7fOMbMm9Dz7aWeiTUQ6kqClDRPuebjgNAB+q23vizrqOXps0nqn5Y2oS9tx+OC0LotRoH3rX04/Tqwdj75TTvyz9W2kiWkZmGyt1vUm+AlkOWSnBQGegc5SxYPTpZ+kDljpTAsSzOMTHnKN1aaPSedC0ASbDnP/+V15h5UrmW9uE8ZiiENRaWdaBZu133xOa1i7LFqVgUQT+P9/6Bn/8Zy7x8viSzBAIBbday/2gePTetxngUbogw6OSU04ZV2QsIU5xWcuJHpHbKMM7vMyWxNeoz/wMygwS0OolR/MBGyPRWLI3fw9190101+IQXkTwjq7tyHLFcFgRBxuoaoJxDefVCuWlXXenM9xVFT50xD7760uzVC722aM+PmKw3Ac6GTwak3dEAv16V+kQGlzsiEahM4XJFNEEnPG0qqHpFtT1MSezfY6OHzObnxCjLNCoogjNfKSrWzoXOV4cUgM1WpyBYyrzejBZy9A9pzKCj4x8i4ow2d1hsr3N+++9x/HeHtE5yiKnrApG4yGj0SjNQFRYbQk+0vlIGzXVdIfGpa7HxxyfjCAAoGRoqOoBLE5L5ycXaE/lPVvnn11YnAYHya9/5FdFoIuiCV+/qfrwTyiUMqchoC/nVT+gUiK573+vFiowNpdAoEUsoqxw7qVUSIHiifpfrXfn06zmdIGe4gd6/ZxKNwwYvNK8+vY9fvv9WzyMGTpmrE4OOb85QQWFcggm0GMS9CXVWb2DMN2id9xvjvhP/uY/5l/98pfI6vPkyqKHA763MFRZha0U1msGJgpWAzhleejGNDpnK3pOvKKN4n+vQiJ7uY64vYMeTOV0Jj/GiNhu+dDgj48w3/8nZN0CrwIu9b+1BrdaYcuCdrRBpywhtLythwy0xzq4E8cQHNZ1ZL0HBTKqSzYMKQ2jVWSP72G9mJ2maggfRRQkUExMhhwmkW/k37514AM6iqZCG+H6ByWmoELWEWo0IeA6j3OdEIei5+BkQRc76m6F0pDrwMgqCmCoNTo6hH4uMSvWSyLynqvVgudefJbN3e0ksYYyzykyQ5aLnkBFyPIcZWSKcpYL3bieLzna/xdwG/5v69AgHmr0tXJKf58IU33q9KHHftwOyoeCRf+Ss//+qCOlaiHtDM452UWUWFCf/vY+27Dgu4T6p0VPqvdj1m/28nl6M9K1fFQWuVhRebmrnigR+tJDnQaQtLNHY3h8dIIvM2GV6ciknHLQCS02qkjmHdqHZPbpUb18NaYpTCTUOohK8oPZfRb79/mlGzf4wYN32MgNDw5ew29ZvNPUqsP5QqiugIsFZJqgS276iYCX62GZcd3tQSl0OSAsY5LO9tcigtcQl6gffh2vIrnJ6UdqdV1Lt1oRhmPa6RZRR5wviUS+48Y4Y9FofDQQmiT/Po2pAg5LOo9XMM3wAfIzrlR9W6/HluScRCIOFQUDMCm69B89JlXhaWYZcUFwA6XlzLqIWNRpxaP9Yyb3HnK9eIEDrTg2HSGsOK8rZr4hV4rV+pYw+M6T6ZzQedrlivnshFJZYgyMq5LtyQSbRbrA2vFKJf8FnCPXipgXbF3c4uCTHgQiUkZj9Bl6Lqep78eVzE8AZ2cL3TMF7z/PcTZIJGprj9Z77zHeY7ROfgXy8+LqYoXjEFLQihay/LSLQW94mnCNnjXUmxHq3pSk1w9wBshMR29p1rcke4mqMTTOMYjS2pvUMwal5935iklYMXQzJixRVYU2FkOkMJpMZ2SFLOLOtWg0JisYbw55+fmnuPnmt3j2wiW+eOU69fKQv/uD23SjHYIfYqPGqoA3q/VnC9qilCNYAU3lK/ZAqpaMA0PXf6fgUaH3CpQ2mvOR9mTOaDCiUx2o1HXxnqaNDMeaYjClQ3PDnuCDZaYtrbKsghUGYujSzQSKkARnEaPl9OsYaepjYYIqhY8yfjw+0d4FhQzwyGMmyk8tQqEQ4loQFhNo3Hv7KaVPh+bGmHCG/m6MTPIBm9MJRiv80jEKlrkKHIaWZfCMVZaytECuNNo5lNUEY6mGI7IyqSvbFZNhwcZkiMZRd0FkzwibkAjROQZlxfHtB2zsDrly8Rpvffejb/tPRBAAqW+D7khTJNN67lP6D/3whxcI9Fs7fKi99rHHxz3fZ8ooYpKOipONmGHS39x9G/Ns3WmSZkBb1ndJjETvZLGbLO00fWALp39Ceo8fSQLS+/cprZKxar31elBpIMXqiM2jGVN3zNHMcS5UjJSDcpOZHdPGjOBkQeml1M1Ke/muNgOjJRN7cMzX3/1dXrq8iV9E/so336QrWtRglwvBkvkVeXAUwZGl7oDsmjkqOlA5YoR1inXQ6+ejQmWWXu8eg1/vpB4vA06VS24/leygabF23RLf5NzggAdcpPBLLhSeH7ZTFkHhVACVoZwmJkP/6D0Yi2jz5PkuOqq9fUnUOC3xBAfog7U8bpTCag0ZlJWlOXEURS6ef2mc2RP4UdKFnEJQYT0gGSKruMK1HUckvwM8m9pSR1hpxTLIzMNApAkBq6AsMopqQMRisgJX1yjfMqhyJtMxRnv0ohbC2noZqGRoGmmamvu3b1GW449dCp+YIBAJKK8gRU/l+x6qWEuvF1VfHyTm1dqRpV+MMY2D9inlxnxosZ/5+9pyKr231uvFF5UwyKwuUCoSfEuMJT5CplL7LyRaVgJdRLCUgM0oHQoRjYsTsQ4Z0SiUjsRYo60lugBWEVwKdlpSV9V/loh0KJQmGPE8NMoRKNHAeHXAZ597jnt37uCpuXLpOlevPMP1556lKku8C9RNhzYZbeNouw4XAkErutZzeHjEe/fe4+aDu7ijObku+aVf/AkO792DgaXdHBOyDG2HBHeTYZgDkYFrOJ9IOSoY0E3qXmiiFm/AmK5nf25VZ9HlAEdHcBbvI6bvVqhIxAv7zmjyTHbifu5D23mOj/d5+vb7THavktkZ10LGTQOlb3gYNSvV0bqOuo/LdBIYQPgHxZLcK4rHj7BK5iFAD7+cdp18KgG3N6Z8/kufRX8qZ+fCkL23HvL1X3uP2EGMFmVaSDMHepm0dwGT9epQ0fbHGHA+4lRkvlxwpAyd7ahs5LzxvKVKqqWm1XNi21CaQIg5g3ZFVJF2ucD5OuFPsHQebTLKvCCEVrCrKCWBTEZqsLmmcR0bm1t0tqDIC44/Zu19YoIAaU2rj9yhzwJ38cy/Y0o15WYR8EcTvGznKmUT8cPvBaev7/9xNuOIwtjSRq2ZWcak2e8xYqNLJo6iJ+9nyfXaddlTVApSER0sESNIvdeoUEhJ4I9RRssNpcK6xMAgLrqksoSASnx17SOhadkYRK4Mc65fO8/1K5s8f/UKqwYetS03Zw0//Par8pqYALL0epNEMkp5AQ5ROFUzGYKdjCizAcvFvohVgnRnQgLX2rrDBBHGXMlnjLUo07SaQyyEm4GT7+BrTFbgo0KFDBMi0OBMhnI52nhiV9NpIWGpTjIDlVsIgdB0BHqn5yCGM8dzXv3h19mcbvKp515i5jsuqQMWscQz5K6t0HRUmXyuTBWJlamI1siQ1NkMVR9B1mdTp3dHjBFrZZjI7u4O53Z3OdmfM2LAWz94jDs+4LNf3uHt371NowKtJ83AOL23jBEpfH8vF5kVNyUibYC869gZe+pOfA9fWwQmlafNGq6ajIfKY21ObAN5NyO2Hd1KYYKnPpkxKHKm0w3ysqKsKop8iIua42VLZnPZx4IhoGhdBsrj50cc7R1+7NL7xASB3hb6R0r/sxpiBafUwfRA/6KUhqkIhECuZShFHSCe3fE/fKwfOpODKxJyLVRSSLuxl7pdBw/eobR90uQkReonOhcxEpVBByXqNu8YxpbdqeLWI/DKII52UhJFpVEhk+iuZNJtFjUjDFVsKQ1cu77LjcvblIUMRX1w2PL+3iEfnKzY6xRNNsClgKLWAS5C9NC1EBxKCailMQJGso0mI3aKN253/PROTutS2wyxtQqxTXmyI3eaRZ4AOC/0Z0NPzVJElRFRKBNRqsX4iPYOU+bEQSV+eF1Lc3wgmVbnUn9d49uO4BtCEBqvuDpHdDA0zYrm1/5rTo4O+eKlHYiaUePJM8/CD+nuf8DeSnjy9vv/XyxgvSd2HS40xFWHWs2gOHsfSUlw9u8bkylKW+oQGCwD+3tzZgdH/OyL57l/KfLwm/fYuPIUPniZKRACxvSqUdnRjEnD7WIgMwYV4Hi25OpWxBUR38LEKFZYdBO4et7zaD9j0TnG0XF5bMmGQ0yZobIMpTRZXtDULUVRMhqPIDjyPEetOuGyxECZl6zqyCzmNLM9VL1kfPkyi4+ZRPYHBgGl1FXgvwDOp7P2V2KM/2el1P8G+AvA4/SjfynG+PfSa/4D4N9GisP/eYzxH/5Bv+f0eiQwaZ2Xn8kCepxgXXRBr7iT153+3Wip20P4qIXfI9N9SyjNnkNOIvSGp+C9GIJYY5JIQ2OzAm37wSfx9LNHhNXovbSSvAPXwuoB26tj1PEhOXBhc8C2DjTzR9y4dI2Xrz/Psm44Oj4UkC4EhFwbCK1ia2OH6cYAoxZMKkswmnfvH/D+o8C9JjAL0IUSTClep8neq89IYqqtBS4RL4SQ3HdiT61WBocGo1lhsCaj6XqIxAMd6A7T1DRvf5fvx0CZnHb0d/4hVinwDZGU2fgWryJoIcEYLeCcv3UPZ3Oa5YzQ1RL8jZHP7BylzRhv77CYryToIiPIVNfiQiAScCoS797h/faErJkxySu0czydw2Gcc6+TTMB97dcISou3gtYU1qAKQzEcoVRBP1ouhJhwVrV+TFsrArQQ6PAs5wsWy5Z3337I5Z/4FHfe2MMCXTgFoVU6r5kx+CAtw8Y5wWxiBBUx8wXXmyWv5QWrrmPUtbxUzPhnesjN/SVj7zDacL455opueIgmmByd5VTVlKABOiajIaFryKzh8Hi2xllU1HSu5q2373H/uCE7d1EwjfDxPIE/TCbggH8/xvj7Sqkx8G2l1D9Oz/2fYoz/h7M/rJR6CfjXgZeBS8CvKaWei/2kio85ZD2HdX0taf7Z4RasMb8EwUvEDSpVBDGh0hodFa2PeB9PA4Rs7+m3ye6m+rFXpp9Vh/xc4iHIWCzffzpBgDVkyqO9x1nStFpOOwkRMIYMz7Q5onlwC7v/gK+8/BTXPnONH777Hnv7D1kUY164sY3Wir/zO98Grem8w+Apleby9oTnn77ExQu7LJc1KMP9w5rfu3PAvaOaI29oixJ0KSYqVlhiCgMk1SWwbp8qLVqCKINA+o8q/XqPyfSZc6QYaEsTpY+uZ0ecn9/l6lPX2dkK1HfusT+9wDIUwO/C7jW0yTDaYLMcbQq8rfDG4K34IxRlhq1nzG/+3+mWh5ioyMcTfCcee0LyUdSrFepwn4CmGE4YjCYYY5gdHVEf7jOoBtjnXqH8o/9DzOZ5lPesNCznJ8y9pzmZEY4W8J/9+8Rf+Yu4LCNmGViLyi1m7wPia7/F1Mo05JAYpDGmYZ9K6uq8KHBBnCBCG9AOTGh5+/Uf8tzzX2T3j75A/raj3Z/jfSqBYgq86ZzHIGzJXFu8c1gCvt3jxfZ1vrJ1hdsuMtXH7A40z+cVdjWnDhGnIMsd56oRJ0WBxVLYRG82BShFllmKPCPPFLktZDKWtRzsH/Pq99/h13/z27RRk5cVShnxt/yY4w8zlfg+cD/9faaUeh24/GNe8q8C/68YYwO8r5R6B/gK8PUf81sQVXQg9t2BtIMbL6CRNkbsx1RauDpFcZ9srkK/ABLZgpSWkWiVqtduR3lSq+TiQkqV00dRfQNAds7gz7DIEnHFmJgAPlGmZ7HAZRrfLtDGgzEMH7/PM9zm0jMVV7/4Ba5fvc79WzW7WcVw+hw7ky1CbDHlhHvjN6gLR2cVUxX5uc9c4OlrW7z1zjt847Uf8MFBy2EwzF3A6YKgC2KZdApEvEoZUI+d+oiOVpxxY5CdGC/6BxVQuqPqWoIWRaSOEDsPeUAFg/IaFWpWS48KHt01DFxHZTUh77g4qIiZoz73KQDaG6/QKo3NcvHDM5bO9jZdEfKcYX3I7Df/ESrOyMoBUJKNRnBySGGh7UISHQWaxQJlLF3nqZcrds9fwBjN1uWr5CFy9Pgxg5N9NquWw0XD4aomdtCuHK5uyFsx0MjuvAu1Q9cOEyPKNmi1ovIBtEMpGRV2Gv9PFzAptVZJo68zTZlZymKDbbvJwcYRq6rFmowQIoPKChCa7ieVNipFmp2ZZNBlljMe5qiDx1x0HSYztA08bRp8CS4PFNZgbEGWldypSmYnC5qmwWYy7Uhp4a2EJlB3SzocykgG8J//7X/Ia999j4Y5XhlGq2tML11B5dXHrr5/LkxAKXUdeAX4BvDTwP9MKfVngW8h2cIhEiD+2ZmX3eHHB410yJTZ3tQhJqAs9K42ypNFMX9oiFhZyXgji9OnWkypkNZ52u1Tqhb7f0exrFJK9NwY2f36NDCGuNZzxxBQyVeuBwdVovtGZUUhGAMvbXU8XCgetjXR1wSvWbjAo4MO04DujumWb7C1c4FPf2GL1WxJvZgznQ7IspZL2xdo2wW+sOwdHvLDmzf521//Nnt1ECPNwsqCMgNQveZc2oURCZjKStaiozj3KBOISohB/QBLFUF3DSUN0+YEypx5sJiosFZh9FIYgli6bsDqZAaPDona41gQome1mrE5sRy/9y7jxYlcuq/9DVR0uPS7ASoDhIDROT5olsd7qPkxphhSpc5KvTxh49wF9h8/pF3VGDxZlgnrMnkV4DpmBwdMplMa72mdZ7toWP39v8roxnnGxmJLg44aqzW5tRSlmGr+2acjmS0o7ViMOI3ltTv3+dobcyy5XOMoAF4QKqosijzHOfEvTDpVrLXMVMFi/xC7DLy8fZ5vuUf4ThZ817YYLaPZY0r9QWjKznuheRiDtbnoDYwmi5IhtJ2jTpbgMQZawHXHaOWZzxdkeYa1hnq5IM8LDBprFNZ6CpNhnaMj8A+++S6PmDJ48TnMTEoEQ2CxOMFm/xJmESqlRsDfAv69GOOJUur/CvyHyB76HwL/R+Df+ud4v78I/EUAJjtp8Yo1FTG5DBHRSG/daDDek2clnW/ZHg5ZrRqWRgtXu20JocVoTYg6mU0H2eWUTvV8qvlT2q+IYrITSZ52pHpZLprIbiWtds6RZUXabaUNRgwEo3jjsKXzEJwn8442elwx5tb0KvdODlHv3+TTW4o/+osbbFzcZOmWfO/7b/Lc89f41HNP8erbr3L8aI/7s4639pa4YkC0BjRoNKoWwDCaGjonvgtGyUlBpTRfKKcqBlTnZHcLQXrlvkOHlhgiBZ7PPHWOzWGDUp6gpAWrlCLTmq7ThGLCp5//aZ6aLVjd+fvcfOsHLKqChxslARhVA5btjMmJpJiXDt/BGkNelhRlQZZl2CynKAo+uHWXo1krXZ8yokIGyrGYn9C1kWw4pRiNCK6BdoW1Bh+hHA4pioxMW4zJZbfVGmclA8O3NJ3iy59/iaoairuuzWibFpNIANeu3KBv0miriTqnufcArEfhCDGVir0AB/Hl0yr5VMaw7g553zv5lLz9ndtUOxUcyKC6QMBHcSsIMWIUiXeg1l2jgASbumk5ns0YZrlsQj5S100yDJFULkQZw2a0zCDQRmZqdm0LxLQhKfYPDrhwbgdtM/ZPlrzxwQNaFRlkGbvTXQ67Dtc1dMvZmkD1UccfKggopTIkAPzVGOOvAsQYH555/v8G/Nfpn3eBq2defiU99sQRY/wrwF8BKMZb8eo3/wZ12zAYjEHDsnNoY8nygcBS3lGVGbNVI1bLwxHNoubSeISLsGpabOiYDCs8mqysCF3HztYmg0HFbLng6GSGHW9xf+kIxtIpRdDJ8DMmaavzhNCRZ4oiq8m73QSqhSRFsBSLx5S338b5hjiPOBpyIiyPMMGh5zOoZ3jXEJoO3a1472HG+zd2KQdDCBHXPOK1757QNS3z+YpZu2RzVLFx+z7ueJ7SUg1KOhR9iRISbz2E5E+XPOsjCrQluI7oZSh7r5vpKx1jLArH9Kkxk1yGZ3QhMl91eB/IbEEToLAVVi2oTMcv//xP862NIWVlefOH3+Pi+QtEbdABmqXcWF/+6lfJjcVmGSGEpMcvyKzmqaee4ve++W3uP9ijXTaEzmOtIS8G6Fz0FYUNtEj93HYtNq/Y2pyyWsyJ0VFVQ5rOg/coAs51aG15++YdXv7M8yidkRkDdQ0oXCoHlGGdmRig6RpWixWZUkSE0GWUJO6pmlqXmFopPGmwLZ7ovbRWURw8OiY8nmG0IUSXMlDJPvv3EAKVTB0KCW8wBFxwjHJDlluWqxprM4ajXqMi+ETXtsQYyXINGlyQjaeqhhibY1TN4cExDx7s8/DwhKPa895r77E6mVFUI37huS0+9/wx//mv3+SdQ4ctK4rBqUX8h48/THdAAf8p8HqM8S+fefxiwgsA/iTwg/T3vwv8V0qpv4wAg88Cv/djf8dqhvre36dUgRDFMzD3+syA0kihNZjIRlRkaV6h6YIk/EHcYFQU80qvoENq//tKFK8RLaYgMbLpA8bYZBIiri7iACOlgbIaT4l/4UXql/+UGG0S8b7DaKgevc347/0XqDinMwZrPGVZcnFng/HmLt+69SbNYolW8vlMNWS6c5FXv/8Dnn3mU9SLAwZFwWSywfd+/zto1bCzO+Vwb4YJNVZ1rN2UokKHQF6WhCgec0rJdyGN2NJKFI3TjW1c29LUC2wmU5K0sUlhBhiDb2v2l47jlfDZXQgEMhn55TVZnrNqOg737tJ2nrywVKplYzDlqaee5YObNxnNVtSrFRvnh3LrxpzWiVbBGOHlr5qOptMMh1N+6Rd/ifde/x4f3LzJrA1U013pVNoMM6hwXYtvGk72lgxHQy5evMQbb7xF8J6yqsiyXKS5Sq5fcJ42dOxub7G1sUFuDS65SPkQZMoxcHR4IOmzsdQrjwdWqxprbAKYRQxmkkeFDxHXOSbTqbSEk8uQTnR2IXQGvHdok0xxlVr71BCfHHcO4J2TMhJwIfLo8JDHjweMJ1Mmk3FiTYrTU+cCTdeRW0tdtyjn5Xej0b3KFk9E8cPX3+ONdz5gd2eL/YMjQj3n+obm09uOf/OPv8LWhV2+9dYJJ4u7HB4f0Mw/fqn/YTKBnwb+DPCqUuq76bG/BPyPlVKfRwLoTeB/IuchvqaU+uvAD5HOwr/7B3UGrIadzVyELXEAStG0HTbLhKwSRPHnY8RFj82NaNCd0FX7FlTTRpyDYS43tdYix+p9AKKH1nvRqiTjRpWaYCpZNmssXbKasjFj0Izouo6ua7FFQdSKfFiyc2HCZDwl15rd8ZTp5oTxIGPZKd54y2Kcpe4aQggMhhOuXb8E0fLt3/sug0HG3dsPeOqnL2F1YDodM6rGvPHgPVarmqi8LF6dYW1OXor9dZ7nwmHPcspqgDGGqhpyfHJCllnKaogiYrRKg12FxrparVBKdpTVcokpS7SxaC2ERqUNCi1EIq25+f49Hjx6yNbuNk0z59LuBrPljOnGBpvb29x693XOX9ylqqYAOFeT5xnOe9quQ2sBsMDyve+/Tudazk8sn3n5BfaOl+zPVhyuOi7fuMHhcg4BRhvb1IsZbdPx7NOf4v133qNJ2v4H9+9TloUsPMQHcDQY8LnPvISOHU1d42MvNurIrGgiqrIU8g6KIi9Z1A3zxeI0KNJTfU1SUnqUQqjBzkEAi0n+AUKrjiFgbSHDUBJRTVqLafHLIkgMRAkGOnFUMmsotcF4T1gtmZzfIirLar5guapxQcqRxWKBMZq26SQryUQYJGWLDDL1KtDVc65d+xSczPl3vppRriLHxZTxqGCSL/h3/uQrfOb7u/zg7fv88J17fP//3yAQY/yd/rt96Ph7P+Y1/xHwH/1B790fmbVs72wnqq8Qh7xzKUVL0l3AR422OZm1GKOZzRd0XuyXQwgMczlBmLiWG/cU0L5LOFRGbJmspfNO2Fw+KQW7LmUfkboLuFjTKLlpsizH6IwutJyfbvCTn3mBjWnBILcMRxN0JgvqnTt7DMYTsqykCh6jLbYaMhpsMBhZvvvt14kx8tLLN9DGAjnaDHnr7dtMNze4eOkcNpfUWtxiJG3tmWlrnnvPawjidGuMJi9yWezJjKMnX1ZVBShC8KjNDVkYiYQVvFsvFEWkbh2bWzs8uvcA5WUC0KIJxNBRZDnnd3bYnPwEddNRt7LjDqsySQR04lZ4Vq7l/Q/u8ejxAdduXIeNbW6fnHB4Mmc2a6mXM26/9zYmzzh8eA/XNmgFX/nSlzl3focXXnyO1157E+c8bbOirZdk5ZBzl66wu7vNYDDgaH5CCBfJM4vWaVR8Wa3PVZlZ6kamGYd0j7SdI+bC/BRtUSAGIfYoL6Dw/OSEyXQKPvDWm68TFVTTiQDFPX7kZZOJinVrESU1vDVqPR8QokiWo2xGBM9yMaerl/zgO3sQYdU0jDa3iCZjrXgMgeBbVoslo80hPnopj3p3pOA4mdVYG6kmcGc2ZKvQPP3yc6jFI4gtz+5MeelPPEvgJY4ODnnxz7z2kevvE8EYrH3gnSOIISMIkicmniGirVn392MSy2gdZdiEHooluIaoFE3rKfI8uXulKitFZSL4IIw/HxzKKcpySlbmmNQGykNcyz4zFzl/7hK5tiilUTpirRaXHJsx2NiWcWGuo607tBPwbqlKnnvpZYJLlNcQWbU1t+/c5Utf+Qx/7Fd+jqZtmW5Oee3Vd5iOK1TsuHL5HHlu0dYm1LpntCkRLyWyErAWD1lrAcWIQUI34/q7aG3wQUoHndpT1trkdJSIf0aCqdTYQoterhpmJ47tac7td98noPExY3d3QFZaMhuoypJxgI3NCr77NTanU7TROCdOvYvlkuOTJavlEmLgYH+Pk5MTQlsTuxbjO0JbgyuYjEcstZCRBuWItnO8+e4HlKNNNrd22dt7TCRg84JqOGK8MWG8uUFd1xwfnzCoSsbVQEZ9I+i6TsDg7vYmnfP4GMispX54QN125FWRvAN6MlmSsKfa/eTokLZe0S1XKOeoveficEiRZ7hO3H6NlS6WxFK1DrgqdQGgb1GfAotKKboQqb1PHJQgMwyI3L17l+1z56mqijwv5dpGx97BAhUD9XLB8OIuxgRq5wkOUJat8QY7Lw/oHtzjKy8OubBVkXdH2DJHKUvwLb6bY2g/dv19IoJAORjz+Z/5ZXKl16QGuaeFH9AjtGctvzSsb2ih6iraxqG1wrmGLJFBIhHXdRhjyIxGa1jNFuRFxnRzE6Mty+WCGCNFluOjp6oKRJGpMdrSNCt++OoRz73wLFE7yuEQu7HDqluJ7bfXdKsGbRQNhnIwpG0avPPUqxV17Whbz2/8+u9z6dwOqMjJYsFwWHJ+e0BZSNqvjUhnnfPrOrJvMfnkB6CVpqwKTllqJHdamaKrjXgegHrivIk5hZQIWuk1eu29J8Qs+THCaDTgvXc/oNq4iHn0iKO7h1y6tMWlS7vcetjwwgs3GFUZOshnA9icDNOEG5VqW0fb7DIwGQ8ePUQpTVGUXL5wnSKTeQrBe+qmIc8Lss/cIPhAvWqEMNQ5Do9OuHxul81Rxd7+YxqniJ3n6OEe9WxBWeS89OwNcqPwTrKIIheeQi8my4yhLAvqpkEpmC8WaedO7TsSQagnm9KfJ8VqdkJzeEy9WmE3N6i9J89sahvr9U4vk4CSiDQBh/0IcR/jGrxFJT8HrWi6juilxb2Yz3Des7mzzerkhKPHjxgOh4zHY3Z2txkMKhGPBY8PjhBafFA8ff0Sqq4pfeCzN7Y494JmEGryQU5bT9HGEtCCj0SYbu9+7Pr7RAQBow3L1lDrFq1yUCmFV0BURCe98CwXdVpoJcXyPhKSR5vRKunSFaYY0viwjvaUJV0QZHacFwxHhjwzRBfo6KiqIQpoG1Fk1auOPBMiiYqR4By3b3/AM5+6jsqUCGtQlGVFVVT44PFFjtaayWQiyzNNMQ7O0bY1QYXkaZejtQHjUdpjlaHrfLqRIs4F7NBiraVtW7q2pcpLbJZRpOGcvcoiyzKcc1hjyKxFawGzALSW3rVK3YPgPXmeyzhtpfDI7uRcFL9AND6N8/rSlz7DD19/n+c+80X2d28RXMd7t47Z2BmxszFMNTQorTje2OA//t//7/7bu1kOzvz9u/8M/sb/8yN/bLa1RdN0LI9nhBgoq5LH+4dYW6CQOt0aJQxvH+m8T6ItORdRKeywlG4PEFvHYDrGOenQ2OQt4JxjMByxaprUWlQyKMQJYBuTRFwjHPq2c4wnW2Rtw+bmFsOhdIvmqyXWGjJraJoGpRSr2TGu7bCDksGgInpHUVqakyO+cLXkT//EFzi/scn8wR3cKpCVu9TukGgGBGNpfYdyEa0H+Kb4yPMEn5AgMKpymtmKrlKobrWu6foUc+0pWLcynhlRw/WTZnvgTxud2r59qifEIecjSkNpFL5ZUGSaycaUrm1kd4xibmGtpSgKQvBpuISUD0WR8wu/8JNMNipm8warpaeOcuRFJpN4lCazlphwBq20LNoQ6dpAnptE8JOenfMOYyxexgPgg6NzHuc9IQSyzOKKHGsMbStgm/dCppFdwWMiVNWAGCN5lmGtuM7WdS3nz2br86CtZbVaCeqNpKq+c2LmqWSIR/AdrYfOrRjlllu3HlDkQ2I0TMeGp6/uopzDlrmkvBH+s//gfyUALIo8z7HWojS4tqNtWwghgYSy45Zlj6oL0FYvFnTeYa18fpRMmLIm4QvO0fMYvPc0bYtJhLKAdCNQ8ruVtXRthzICTGbasDXYFIBUa07mizWV2igjKnAAI10F1Y/5kpuPVmmatsOw5Kkrl7j01FN849u/T+fEB0FrQwjgnSfLcpkBEHrzWlEY9tIVndSlFsVkY4NhFDxlsZiRWUvb1ihdsDiepwxX0XUNrmnJB2CUo54dEeoKoyNbuWPk93AzRbAFerRD7VqaZSAqBzoSXIfCY4rsdKbHRxyfiCAwrHIubUx4fzHDpPq+Z+/1Fzymxa31qXmCpMwx2UqndC7ZN5+CZ1LLh2T6gIIiz2hbIRbF4LE2I3pZ7EoFse9LDMEQIsEnliEaozRlWTAaDjA2SGmSePrBpdFYWb62gI46it8oolW3NgMViQ4BIq3YmGmtyawSb/r0DfMEDpaF7F4hBtbc9FQG9DuSzPgIdJ0T51ulMLr3yJPzURXFmhsvBDnxLQhBvrNYqVmyTPPSsz59zoDRDhcNwVuMibiAtPUSAcXoVLpUFS0igw5dpCwLnIssmyWDwYCmaanncwmiZZEWYyQrc8EujAReZYTD0Ae/qiyo64Y2BYSiKBiNhvQKTR882hppi44GQM/2lAEi1lqU0azqWrgSSha6T6zQHn8xiVQmQ9TFtPPKlSvsXDjPsm34/vdeTYNcInmeS+BQ0DkHRmOT3FywwyBzNBGcwvuA0QrnI4dHM/JRIVwPH3EhkOUlzgVQJt2rwjlwoaVezZkdHnFv7yHTnXM8/eynRNp9ckyrRYi1WjXUq5rFagUKbFkxqMZMti4xGA4+ympzfXwigoCxhleeOc/jN1qcb9Y2egFxUH1Ss51aJSkwoJAbnVQ9kG5uhYy4Rq2R8tIoCgLTsaT/WoHJcxTSghN3GLemkzapnooC/GKt7OYxpY0xRNH6+R6akF0vOC+ehMiCU1oRoqeXesbUthRgTqjIzjmMOeWur52Dwiml2Vqb3pd1UNCIL2DPCJKdOCFUUYJolgkA2GME0inoR2snD7yo1gNDlRYANnhPcOJrYEMg5EllF6JMEEJ89733ZHlO23XyGTt53XzerbOTnoBjy4K2rSmNTl4FKVglTrxJP6+1kUwCyLIMbSx5KmWUEtwhT/ReZSR9d04AwJiwE4jrz+edY1k3GKvRWupzH3tWqtiCrT0RU8FlMsOqWfHu2+9QTbeYbm5hixyTRpWFkExbE36V9hx6t2K5+5IvYYwQlMwnxFINhrRdJ90tF8nynNi25LlBFTJbIAaPPZxjjCIvh3zuK18lKzNaB4tFi5/NWNZLYmY4PDhk/9ERdStZlAsZBKimH7Czs8X58+c/dv19MoKANvyRr36a77x7jz09Aloh7WgBAPthjD1vH07JGHLENAQijWlKSLohtW+iEDyUUgwHJZmVVNYYg01AWu8bqNCJGRjpTuYoBJQTCa605PKsxEfZ/ULT0nQNVhsxG/Kyq/QDKXyqCYuiSK04aaGdLvbTcWpn/2+MWS/6LGUEzkmbK9MyfNTabC1xtnmWxqVFbJHL99caayx5np3OTkgBpZ+nIMi2LJSolPAjMiNBQCt0nuG6jkwJucr5QKYkzRX+wel7aa3PPKbwPuC8o121jNWYLM/Q1lDlGSbL8D5Q2L4FKp9VgqEAl2VZrDkHTSsdjP662yyjcx2D0ZAmdTcEWxFE3q1nRUA0MFsu6Xwgt0JD9zEm0VnKGJUSFWZawIZk0601RoMKMjasyA1dEAiw98PsO1DrLDX0gVYAW59k3CF6lNYsViuybBttNcY5YpBSz2aWrm3QSBDsgl+DvK0LZFWF9wEfA3cPFfmqIs9zMnKq85fY3GiYz2Ysm4hrOiYFTMqKAMxXH58KfCKCAEpx+alL/Bu/9GX+2u9+l/2ZosViYlwPHOqR3N5IqI8BsvP3nQNZwEar9U6arjXbm1N2RhWZkEEJ3lOVOXlusTrKLqQ1d27f59ath2zsbHDx0mXKYkxmNFWRYXWkLBRZoRhsjBkMB8z2HlMOyjQ3b4UPHmvETcanWj3EKOWHEcKPfK5TZaJSkuJqrSnLkn7GXAiBqqrWwWE0Gq1vUuG2+/XCDmkuX59ZaK0F70iLszfDBOERuDNMthACbduuF8N8Nlt/B2Ikz3Ja5zCZkHB8EEPLPPkFeu8xNmM8Fh+7EALz2Yyqsum7bRGBoihomybt0on74aUT0jQtqtRoY5Kpp8UYJeO1fRDkX2m6lG0IlqHxzsvoL5I+QKk0Tt4SfSAvCuqmpm4cLoJzHZmxZFqchG0KYCHRwnvXYEwacJPwiVxDbiJFblk0DTGmDkvilYQgrkTSItQyrq6nJKXMUjI9TecDW9vbLBYLopcSz0Uxb3BdRdO0LJdLjo5nPN7b58KVATLnoiLXmqDmfOv1d/nuN77O5d0ttsuSkFW8s3dIFw2h05Rxyc9+/gZhc0D0nrsPHnzs8vtEBIEItAq+8JOvcPlTV/nB6+/z97/xffYWDl1atOvExUcLlZgzwItSyTs+SMAIfVqWDmtEkLJaLOlMYDQZpboQ2WlUpMw1s9mc/eM5e3v7lGXGaDCkXtTsP9jn8OEJ7712C9e1NE3Du++8x2A0ZLJRcX5nJJRW4yhyizVC7+wXYQhBatYzH6qsKrSSefJSQ8pn7lt3wUuU69N355yIpGJKb52TtDe1SEUi3PMhIioEmtUKlboH0QuF2Adht4cQsErTNY0YWwZRTfbTmMq8ILd52p2ECWjznJjAvwg430k6HwXnCP1nCZIJDVPwiiHi2k5281UNJH1AkUsAco7oHEWeoWJYlwGRCJ2UN8YY6qYBpUSXn6XBKmkXFmwn0nYNzom/n9IqpexS3ixXtWRKWvAQrQ151gvKBBPxXjASqenlXChtMEHsJ5vWERZLVAo+axKX9B3xzhNtKk0TP6PrOqwxdMmYVBE5aVYsV0sUQUBeH9BWpaCs152n7a1dGhcQI27hMZg8J48Fr3z281zZ3RQDWyxZVvKc0ajoCSpitSVXCmM1bd2wuzH92PX3iQgCIXiic1gduLQ74tLO5/jCi8/ye99/j1ffvcP7R3vECFZFgkbq4Mwm9D4mICyeAQ3PpApEXOvJDexujqmKDOcDtsixWrNazHjm+vM8fvyYrvNMN6Y8frzP7Vu3yWyBNTmrxYKyKtAK5rNjLlzYZWd3yPbOFovZEcPxkKD6GlCCU5Zlqb5WhC6s+Q467XL9Atda44KYVep049hMUukss3RdR24kdTbGCNJeFGtMJATpJjgvmQMhUmUZlTXSpUg3qkq1a55lKK0psgLnHJ1zlEWxrnGNlT57lhW0bUteSDCIaadD9dyCAq30ExNxxfK6Jy1ZXNet0fgQAnme4b1MGvZBdBA6AZxdK8HEeUfddAmaS7V1yjbKsqQqCiJQFkVS1clO2zQNwaWhH86l4CHSYglAK5H0ZjmRSOecdJW0ZJI9mNzPnOyvjTYa37asmgZvaipboItCsKEzeI1CrYN+TOXcKS2dU7wgBhZtR9sFqkI2DZc6NE29Eh5Msh0vq4LpZMDB4Vzs3VTieXearXNbTEcFWS7DbLQSIZQxEmhiyvqa1rFcLlOH5qOPT0QQUChWi5pVVAzKkk53lBPDL/zsy/ziVz/F73zjDb7++m32mqW4ACdhkURc8QiAZK+klKDEaZUEIISWssopM0NmhImlfCD4wIO79zk+POK9D26SFQUnJ3NslpPlGfPlETaztKGlXsyYTqdsntvk/NVLaBOo2xM2N6dgDF3TkNmM4H3q8Ten1GXfE6DEkbbfeSHhEnmGcwGdiS1VPwjDOb8mTXnXiRWY1rStEJO8k90lM4bYtxaNkbZqWkbaZGS5BKQizzHp+ZBAtuFoJBwCIzhAXhQykitGIU0FCSR5macFr7BWY7WUAir6M+WZSt0OWVw6Pw12wTvBeQyYUvwCmqYl+E5KDttnLQFrFKvZHI3CZZlgIkrhVzVdaud2iQDVdW0iglkcgSLPCNbgvQCHMULsPPV8QURUiFGd+kxYY+iSP2AIMbEsTzEnbQ06sz0rC+868sGAumnpCVlKSSvbWkuWn5ZeWmmikUxEJzaijoq6bnn11df5wisv0TUtMTg2p1MGRSGBVCmOFwuKsqJbtDx4/xbLF1/Ctx06kwnQi2XDZDAhHxgxaA1AVATXpgBmUvcoYzKe9LjxRx6fiCBwcjLn1s1HXLy4QasCOstlQIZrUabgl3/yZZ49v8Hf+K3v8MCJE7APQg/tATabvrRKaXZP0wwhEDLNhXNbFGVJ10oNGpWSej1Ebr73AYu2ZZIPsLbAGg30Y5wU2p769WVFRtsusTbDt4FhoTBG6t2+lee9oyyFnOG9p0uMxVPjkrAOEM6JDXiMkVnniCFQJgCozxqKIie3GVmWuAzWrLEEa60ozZSi7TqZ35SAqM4lS3Clcd6xajsKpftqSoJL20EM6Ex27sY7WbhBWqS+aciyjG65lN9pUo8+yPfJjXQInHNUVUXXdZJGR2l/+baRHTVC3bTrbK0L8nOu7VCIE2/PjyiyglwbMmPoUteksFbaoQgt16eOhoE1iGqMtESNtetgGyLpXJ7iL337WafPqWIUJzvVswDTvAFAeTGBza1NZUdYk8GUOV0+ZTIyAQECrTHJ2SmuA77YkAn4+fxzz5AZjS1ytCmp247RaEyMHXfu7PHbv/stfupnv8jmhS1+4sJPkI80B7OHdLERVuOg4NHJMWZpuLi7QZZZcWR2MiDVe5fuM8kEu6772PX3iQgCy9WSf/Y7v8anLlzGLmdcuHSZeduycW6H3EC7Urz3eEEwGtV1oPQ6rV27tqieiiE7RJbq/hgD4+mIQW6EPpzlFLmmqCpiCOye22H/8CHL4zk2z6hX0kteLBboREgaDEa4zoFfoqPBrTTD4Zjd3QnVMBe2WSL6kC5623ZkmV0DbutuRpTdJ6SFZtMgSennS0uxrMrE55ddVNpsKomYFL4sxBo7EVN8EC77sCgkAASpaW1KSwWMlAAk2UOkbRqsFeafa1tUPMM5UAqsputatJbMoT8XKiH+KCNtrZi8GJVisUz1cvAMqird+4bFaiVuPYJC4ryT1qOS7220Xrs9WWsSEJejYiTLcmyWiS7fC3hn84zSGOpVg9Fa+Pfeo9A4AtGLCtRkhqap8QGWy6V0mtK1CMm4MsaAMTqBiSIZJyIdJFTyPoTVfIEdpmnWqWPTOnmt8Fk0bdsRUGRVse7U6IRZKSXtzxjkPigLyyC3wlgk8k9+47f5I3/0j7GxOWH+1i1ef/1dojbsXj6PD5b6nX2q4V2aGGnbQLeYMTvYozKaP/Gv/Awvv3CD5WIGWFarmrauKYo8dS/VWmr9UccnIghgM253BQ/u7qFDwD9+G20yeOehOAVpQ+MjXlmssZLeJs9QbUjAmkoLilOgKniqwnBhZ4K1PUkmoLHoGHHec//+vkT6bIC1ltF4JMwua0UwFDqIlkE1YGM6YXNzjFHJRWcorSsfkzgngXuESFlKDTYaFcTk7KuUIjc2sRGDOAfZRLQ5Q97RWmGs6AOyzKZp7RFlDHXbkFsrQYkkWEHsxrzvDVLDurXWsx6D63BOspKyqijKAu+E3ZiXpXjdte36dcvFksJYATW1ofNuXdPnlGQmXwe4zEp7res68kI0BMYYskqC2HC6kWjeEoB6tDykzoCoHiV49KQnRUwWXwGHYrWqKcsq7aqa1apJ7WBNnuVobSEivfdkVU4nLV9rFNcuX0a/9h71bE4xFKWhyaTV1DiPUwHjkuw3RlzbghH//tPlEwldh2sassEQ5xtUCgCFtmACZZ5kymsJvF/L2kNAplAROT5ZMNAabcVZ6K0fvsVv/NY3eOGF5/nCKy/xv/hf/nmWxx3WZAnsBAjklcVmlqywtE3NdDJi59w2JoeRqjg5nq8p4kppskIy0rNZy48sv38JS/hf+FDKYAYbhDTNx3ce14/tNnrdn8eHtQEoCBhFjElcI/ZL1oqaTNLOyMHhHs89c5XgmtSTBpUQ/CI37Gxt0nYzHu+taGrHZDIWwKytGVQDBsMNFvOGsiwoioKmcVjraeYNXbdiePEC4+Fw7WFXZLnQjs3pqDDvW9lVE3dAodIIMC0WUyGux5xF74FTELGrhUeO1rQu0LRCLx4MytR7Tig2kuL3nILlcknjJLVfLJZAIhIZ6XF7H4lRrW/0xWzJcrlgMBhgu0BWlGQ2E0FSn3ElrkLfIcgTb0EbIwFASVkmPXbZ4fuyJ4JoJRJiHmLEFCXHR0ci2+XUn9B1HdpoTJTWpfceW1q6tMOvOpeyq/TeLuLcEu+dtDa7bk3WEuVoZDLI+Ve++lke7s/43W98i1m9xGYGkxVSflYFnRfBmouCG+goClYVpD2gE5IQQsAoKHOT+BWe2q/Ecdmq5I+poEulQJSJxpnWxM5jfKSockxmMLFjnBsKE4mh4f1336a0hpee/xR2I1KWpbSbU8AUe/QkNGsTYa0LnKyWBCfEtaIS/oD3jrwoaJoWHT/pmYBSKCtTejCGLFdJJosIbJxHpd3Gd9J2Eh+7HhOQPnwIAWusyDyjp1227GxuMChzYtBEHynzgqZtCc6RlQVdUzOoRKChjUn0TsN4PCbLc+q6Eb+CKIHD2owYIctyXNsxrEaMBhWZzc7U/SRQKuJch9Ka8WRC27QCHBoLWSYkEu/pumbd7zcpo2i7TroeIQjxJakLQ4wUWcn88Jiu6xiPx8JHKEpCCKzaFpP66G3bpi6FdBGWiyXD4YCu7WQ3yXNIGVQ5GLC1tbUuXcKZjEIphVEyk9G1ndTRCZfQCYC1xqwnMRFPKc6BiLEmkVyEDhuUWvs5VOMRjetkHBmkLopJmQLkthB/iITY51G6FXUj16XPfrqukxJK9aQq8ZnoXODx4z3yMiPQomgIria6Duc7mlULEWa+oa2bJIeWYalBCYV9WA3Z3N1BFQU2t8IJWZxw6403CE0j4KE25FmRJMbSnsx1zoWLl5lsbjA/nnPv3l1+/me+wnM3LjEqMx7vHbL58Dbb9Yqn8px3ncErBbGjWS7Z29tnujGlqRtW9YrhYIjSMJufkBUZzaplOt7mZC7io9B58sxw7vwO2miGk00W8wXVcLBeTx91fCKCgCD6RTLSOBX/GK2xmSVHTqpzDhOlt5pbQ57b1Nry6LzvuUPb1Fit0cExmY6YHR8l1lxkOV+ilaJzLXW9YjiqKEeWvBxzPF/StC3j0YiyqlguhYddr+aMRkOm0ymLxZLp5ga+c2xvbpHl4rXvvRBwXOdwzjOZSpovJhCKpu7W4qe6bcmsXe98NhGIFosFeb9rKnFW1sZQWEuIgTwBZ95H7Ggs4KgWX0SfwEVpQ0lGUBYyvtt7j7Ul3gfKokAb0VIoLfVt150yFDvXifVav4NHCbgooV/36b7v3Lr1BwKAzldLBsMhRgli3uGSK1NHWRZrr4GiLLA2o2u7dSuuDYI71PM5g6H44Qnec8qXIP3sarVKYJdYc3kv3AmlNHv7B2szmtWyYbFa8fDhPnXruH3vHsZY5ouGzQvnGE43MUVFkRdoAgd7++zv77GYHeMWM7R3dK6l61Z0waPqlqAtuc1RQRHbmuXhATrdn0UxxJY5O+fOMd3aZWN7hwtPXUFZy1a3y+aVi8ycJ/hImVu2n7pId+08B13HL3/xK9x49yZHRydUgwFtB9YUNKuWPC9QaKpyQFEWlOWQGKDLvbgPOVg1NVVZMBqNGQ5Ha55JTzAL/6JGo/9NH1obkeCm/ro2mnop9FNB+aEqCsxgkHwE5IaxWeqFa5N2BamF2rbi3q3bDErDxuaYalChFNR1Q1SRhw8fce/+fZklZxSZNTzz7A3MvQPuP9oDhFn2eG+PuglsjEZ0ueb4eIXzHfrkhPl8hlZiSqrYxyjF5sYG0QeyLGc+X3JycsxwOBDwKEbyXHrzVZmnzy0ZUE8xLYqSzAo3oBoMhPkYxftOK0s0kaPjE8lG0kV2rbDefOfSYpUFYPM8AZON4BMRCtMHprjuoAwGAyHIJLdlawWcW9NeU0A+O5NRATHLBFNIQaAoS9B6be1lEi/CGkOeOBPOOyajEc57XNOtX18WRSL1GKqiJHQixjg8OGAymVCWJSFGCWBnSozhYHjmHhJgbjwco63m8OiYqnTY2SHNqubgeM54PEShmEwmjKopXTB0bWBW12gUNhsw2dhmNBhifEuZKQwZs0XNMjSo6Ci05vqlLUK0bH7xpxhnitiuhMwVNNu7WzgFi5WjdZblUUvdzMnyjK3xmDK2dKsFV6+8mAJuse4gbW1uslqtGG8MCSissmKdlq5TP5exF5E5H/j9b/8+z79wI50FAaT3Hu+LRBlSadt+iGb/5PGJCALGaCbTyVrcslqtmE4nKCQ110qvb8SubSgKUYq1bUuMHh0irpPsQIfI4uSIC+e2KQrF9vYmmc04Pj4WWm2eYcqcYAq+9rXfY3d7i1/8+Ve4enGH3e0NircK9g+OWLVLBmXJaCCClNnsiKKQ9LlrO4KP7O3NWMwazp3fZrqzyWy5ZGtzQ7gIwM65ncRAFXwiyzLaVtBmncg0PjhGw6F0E/IcbQ1FlklWpETPUC+WFOUAo2GabxET8OaTpNUaUdLluU2tSb1uMea5BFVjRQPgnV+78GpjE/IvHYb1rtx26wDclwN9RrBcLokhiEyapIMgod9azC/kOgacOx3Z3bVdastprBEAtSxFeqyVXpd1WWrbhhDY3NxcE6h6UlSWCT3ZJml0/5nr1YqyLEWUlRmmkylt01FWzxBjYFU3LOuaxWzOqnY83Dsgyw2di7TOczKbsbUprLqubbFa0awW3L/3kMXKc+/xMaNxxtUL2zxz6TIXr19hPluwMagY5oosU+hoGQ4r8Vt0gcGwZFUvE9CcJcswKQcPD+aJZCTZUFmWaB2pplPBMYIiatGptLGlXrUpcIu1WN+xeeWVz1OvlhhtJVNLWZtLLcG+NdhnbB91fEKCgMEaQ1HkuETcKAtph8XUc+26jjzPaYy02EDRNM2aKz8cjxJQFshshncdl5+6RmlziIrJQG6ck5Njdra2cM7zlS9/juADeVbw/nt3KauM7Y0pVTVgNjtmVddsb29KqyVq5rOa27fvMN4ccO2pyxR5Tl2vsFYzGFR0bSNlS7dkMp6sySo9gGmMTpRbL8o/rdgYjaT1lbwM1io+BD0v8pxyUEm9nUBAHwTk69qOrmlQkaQ9SBLcpubx431u3LhOLw7yaWEFHyjLgt6UU2tD27UihyWeCnaads1m6+nNi8VirSeo63oN+DnnyJNSUZiOgoBba6nrmpCCifcdmZWFp5NcWyy7HP1wGNd16+xjtWzXZUofTPqg1CZb7t47oalXjMYTBoMKZRQhuOQ9qGm7msmoYjosUVtT8qJksWyom5rDg0M2t3fYOzxge3siIibvk12Z4vad+zx4eMDu3Qd84bMv0MxrLl0+z/bFXfTlHaLz6KhQKiZL95jKVZ2sw4XanmcFs8VSlI5eKOY9xqO1lvNthU8h4GYrKlGtxQAHOV+Z1YQg2auYwjiqapBau6Rr4BI/QohcfbD8uOMTEQQABgMZk2SSuwp9/1sp5ssFmc1o2lZ01Ek1Vg6qtIOKP0BZlrRtx8lsQRY79h48olkuKYsSpRSDQUWR52yMRgwHGbs7Y7K8ZDGb8ejBMefOb6Ftzqru2NqsmG6MWSzmbG3sEqIYlLz4/LUk/VSMJ0NihKPjGdF7tre3UEQyW9E1Ld759c4Pcc3cU1qzrGupzReLNYOv2z8Qem1yKfKdOC7L+o94F1guaxrXMJmOxcPfyLnK0mISEYt4HsxmM1arFS546rphUFXUq9V6x+hNV7TR688Wuk7ISz7gejac92tUvPdO6HwacIJIrIWC3CW8JciEpCAU76jAe1ncy6UYe1RlKXVqDHjfJVq0fPaeTy9qTAkMPRZwcnwMwHAkrVxphfVy8yBZTCoVm3bF5sa28CsS6BuDA62phprxZMjWxpThaMylyxcAMVrJs4LWtRRlTpVnfOlzn2G5WDAeDzCZkd6+a+lahy1GMscNBz6pAxJJqCjFEaprOrq2ZWM8pHMOrS0xQD8UqK5rCbZ5QYgK30acFxXsrdu3ham6uYnNhBDVtW3SJNi1fkPs0Q1t0+ASW9IkynRRFmt690cdn4ggIIKNnmeTyCxti3MdeW7JShm6GZJpBhiUli5AaQtCDFTDHBQ0iyXWaC7tnmNre4MQNqiqcl3TeudomxbnFcZUBK+wtmRzV7GsPYNSMo2yKJkd1axqR1U0hBgYjkfMFgs2pxvMFwuOjhZJKJTRdS2+k568Tch5jF0yHZEbcFXLjpsndltXd7jGreXRxliK/NTaGhTLZQLB2o6iKJhujCjLXbxzxOpUieicaAdC8AyqHLAyGktpjk+OiRFMWTIZj4nIpGDnZUH7EJMgRi5GHxz6LkGMXuTEmcUFQeqddxh16vgT0iL2TrIcpdU6a1AabJGTKelS9LLtGOPa7ahuasq8TJ2BcOZ7OZSCznmMNbLbJe2FSXZqPvg12FlWpQSmzGK1lbZhZkWVqBXLRUz+CorM5uhc1JIqgZDKWKKGQSZZ2aXz52RBVxYbDdZm/7/2ziXGkjS7678vvnjHvfmoR5db/fJ0z4zHI7B7mhGyxMjIFsJ4LDGw8wovkNiABAsWg7zxFiRYICEkEJYMQljiYeEN4mEhsbDaQ2PGPT3j6fFMP6u6qrIy8z4j4ntExMfifJldHroK3Jqum1Lfv3SVN+Om8p773YgT5zvnf/4HhyPolCwbyLKcrjNkWcmgwDsrY8iVqGhba8jynLKec/fefaZp5Mb1Y8ZJIoRpHLlx8xrDMDCGiQSwvUNnKXVZ8PnPvkgISgRVlDgfHUbQAZ3JZ9r0hrOzc5q6ptu0tJ3h4GiG94FhGnjq5o2rnxgE4pz36ZI0MzhpqlFTgTMjTJaqLAiJUGCdH8nJCFPsiw8Tm82Gs5N7vPTcLa4fz5nUiJrUZfvpBcEgz3NypUiMkTvnwUz42sslN27dvGy1nc1rES0JOopyaKa6RhO4fngoJ/80MoQJrTIpiU2jJPWiTr21lrqU8V1D/CIvUzTxLgZAJBw556QZSQlhaK4bplHWQyVyl7F9T5pl4jmVkIicsbJv1ymm6+LsASVc90wxhUQuTuIAk0RINs57ylx0HYX/Lhp3WZ5ftL4QwkSeJjKoIxf1nPGi8zDyBEKiYsNRdhmNoCQkzdKMIVKYCckldTh/6PMfHR8LJ8APHF+7xrbdEGIOIE1TnPfkhTAlg07YtC25SgE5Z5IkoTeSF5BmpfySLxEAPzgmpTg6PhRSlIJp9Ix2pO87BjfQ1DMg0Bwfsl4uUdOIGUeckW2nixGbNFJBmmu8Ttmst7G1YGTwgwxw6frLXEdV1ehUc352Ttd3FHlOCIqmbjh7cMrgxR7nvEzfajuCSthutmSpyM9vtlsSrbl16ym8sYQY9ayWHfdPz0W1qe/oe8MXfuInGMOAdSPeTbzzg/dZLhePvPauhBNw1nL77XeFCjsOVKXQZo+vHXFQK8qiQjFxcDAXiaYL9eDYGTVNE227RT87R3/xOTbLtXzpjCRKCy03SnFPjIxeqgizphF2VVFAojg+OJAQO5bYiK2e6lLBd2JWVbEGPqETSHWGmgbqspC9PcIdz3SKHS1lkZNmWk7i/KHljozFVCfCppvkwqyyGh3rzAQYI8NvzCUBN44jk5JJOnmaxsz6xOH8UML4uK/XiXxm7z2TtSy3Lce5dE4m6YRWsgvPVEqihcBUFBnGGJpZxRR7B4BIvR0vL9gQQDhNCZuupYqlyGEYGcdAnoudrekjczM2/QyeLJU8iXOiALxcLjk6OmJwgyTEgPKDuzjrIqVXoqr8ohoxjuRlQe/c5czAvu+jCtIg6kUxh5Bq6Yh0g5QpGSaapkKpBBudZppJ9DINAyPQNA0h03hjxdmmKXVVMThP23UcHByQpRnG9IxhQuuMvjfMZg3OdDGCSjDWMXhHM6uxkYmZ6JSubRmNZ9u1LFdr5rMZWie02y1N07DtW7a9ENXyTBrSnB+EYQos1hvqsiRRiuVqyWbbMk6KpqkJOMZJc3Z+wvVbL9Jay2K9wlvLdNUlx7VOuPXUEQBDpEKenS558YUbHMxn1HXN6L3sSwOopBb+dp5J5940cXBQEwislisG5EJJdS6c+zQTtRwVRSomS6Kl/yCNzL4sy3DWEpQwri56/EUwI46R8nKSdb29ZANmqaKMmfKLiCNNdFTnkQs0xHmCYSTy92OyLZU7qumtjF4PozTIRGnwcRhwxsB2S6IuhFMDKk2xzjE4J++l08sEmY7RhLWWPM8xxtH2HUMYWS7OSZRk/3Xc6+dliY0X3IVY6UUyST10QU1Ihn8K02UyShqJZJ4fYcI7d8nzyHJJ8lrnSJVimEb8MMaGFo2PF2aiFPrOPRglkRhi845OErq+B6Q5J9HJpYZCWZUYa8iznHGccG7AeUsat3zGGA4PDiSBOA4EJfvuDMUxR0zjxGbTkmgRcbHWoFWCHzydMag0iapDCWYYIMvou47BDoyrDVmqJdmpFePkaNuOznSUsbyNUgxhZGBiAHo/YKyR3hLAA5vtlm67obdGZk0mCRtjsN5DmrJZLGkqEasRtqVG6xTXGk4enFHkMhDXDANpluOnEUmvBtpty3vv3WHSmjTP6UyHzq84WSjLMg4PDsizlKrM2K7XLB9YDg8art+4zjCMbHqD6S3z+THbdsPBYc04KvrekuUpxnr8MLDadMJA0xqVJljnWLdbqrJmigKZ0yjjoAfvqZv6w4466+mNwTihreZFQd/3cgIl0i6cpinD9GFSRinF/OAQ6xybdkuSCm31x27eoOs6tr25VBaSUDKn37Qkqfx/Y3pM11PWNWVd42LZ7+JOmecZ27ZlGAOzphEmWpGhVUrfdiRaUzcN67UkzJxzkeefS7Vl8GJ/mjJ5S5bKhF9vLWlR0o8dxsmchlXbxZkHKd5b6YzMM6L+Ln0vgz4JE4vVElAUZYnpDUmiqasSovMqy5K+78nznM57WtuTZoWUzqpKZL6HQBKnK6+3a1ItzhMCs2ZGUdcMw4gdJ5yxQnrSmrBa44eBMUr5Ns1M2sgvKjBa03lROxbCl5RUQ55z/3QhDn3wVFVN2xsWi3O50Uyw3LQ0dU2aJoQhkGQJvfOYtocQlYmyjCxJJNeRFzw4O6eaVWSJxjpDPWvYbg1lkYPOWLc9Z4slSQJNXbNYn5DnBaNSdNay2cr3OEyiQF1EqrDpLU1d473Dek/dNLRdiyaJ/I8C5x3DCF3npJtUp4Q0Y9N7tt0qNndNsaL20bgSToAQKIoMxcTp6RlhdHz2809z69ZNlotWuroymcP37vvv8Pbbd1mvWl767LPMDxuMtbTbluViQdd2WOfpu15GgEUJ7HGUfaJ0m4VL/vqsaej6rYh6JKIYoxCJK4KIT1RVzmAdxhgSLWF2lkmjiE5T7p8vZf8dm2OcsZydLyQplqaA3MWmaWQYjYwu7y12uaAqyni38Pi+x8cEXRJD4N55dJqDmggo1usN9UwuIoW04q5bGcZxIcwZYiPNFNmVUwhst1ua8jpn50uGQaTBlB8IiYwv2267GD2IYEbbdRhjUEcHsVITojPzdN0GjSIvcrIsJ0mlw29C2piHaWSz3Qj1N0yxHfjDzL+PYb2NfQGBQF4UBKXwUZno/umZZLaLIpYvo6pQmuKtjU5mK2uVRNKYTsiVlCdt2zL6kbKqWW3WTMayWbdUZclqvWI+b/CjDPccg2LTSRnNOXcpVhOQcl2iU9wwotTAaAf8esP86BDXd6KjOE20K0dZFPSmZ9F1JEnKettz594JeZlLOVtJj+sUhGNSRHGYsqwIYSRTqVDkp6ivMEmbd4ht7X3fotR0OTBlGId4vjiquiGEkaousc4yuR6lJUpt+5b0j4n1/nFcCScwTRJKplrhnOfdt97j+educef2CR/cfsDt2+9TViU/+cUvEEJgNs9J08B6c4YfjWjkWcvxwRzb9XSbTqi6g8N5h06TqL4jF4lO5C58dnaGH2ToIwqMtZHhN1CmJcYYirKgtxbTdZIEC1ruAkHR9X3srJM97zAOOOfJ04xxdBSF3PkCgApsN1sRwUhlWIixA2OwoBNyrckSzWq5EFLINJKmCW3bMp/NSZKUYexFrTaWfK2zeD/SNDXGinxWlmW4vicQolCnkt73ENC5JuhAGCfcJINRirrEOglplVIY02Ot4WL4S9/3XL92jeV6gzGe09NTDg4a2s0G6xxBJRJVRc3C2azGWEuRSxSCSi+5/XgJ3ZVSUVo8YYiiIBdbD2stfW9iZt3RGyPlLp3gvGW1XpLpFB+Tt1VV0fc9ZSXluG3bUpYFeVaQZTKDQeucaXQkaWBi5OBoJvMSrcN7mRkwjhOoEaU1nXUURSn9GiGQjROu3ZDmIjCLht50MvjggjKcl3SdwRkvJbnRCEkorximwPxgzmA7FCNHh3PqusT2nizT5HlGWVS0XY+LEczoJbJRYaTKs0hJl5Z1KsgrieiqPGeYZKiv9wPjEKjKkt4YlEpw1sgkrKteItRxfw4yhPHsdMFmdcJzzz+D9S1PP3MTpRQPHtyL+27ICykzpWrCesPRvCFNNcdHc5aLrVyU08AUBnSaYDvPFBIICjtatm1HUQgdNSjZ7+exOcg4j/Nj1PEX/X0/fdg957zHOodxjvPlEh1bj7NMTk6Ljd2OCj+MItgxOIyV+q4eJsxk6Y1l6HqOjuaMfc9msyVLRcFnGkcyXVCXNYvFkqZpotJQYLVcita/UqRJhrOOzXYjTqsoyFIRtDBG9tl1WvLgZIHSS5JUobKCCzXnoihYLVdRr0CUiZ139L0jUxmb9ZrFYskYYNYccO36dSl7xYSk5BMgKwoG79hst5SFMPemEGQAaJBcRm8s3gtJKagQBUYtKknwTshgfW8IAYosJ8tT2q7FDwO9seR5RtPUOOtZLJboNCOgKArR7HeuZxwn2k2PSYWo451HZxlJksWt0igDZoIQisYAy9MFaZpS1Tk60Xg3MAw9ISh623OtqXhq1pCXOa71rFY95ZCTpRqlArOjG7x17x6rUSTLJhKqMifTM55//qeYHx3yzjvf5ezBm6R65N79c+YHx3gv+aaJnmYGd++dROZnEdu0pcdAtjqKz7/4OX7s5g3e++A2b713B51JJ+J226J1R1mWLM/OuHbzBsvVKo7iE1FXY/pHXn9XwgkMw8CdOx+wXJ7h2h5vDUf1EYPrWC0W6EwLIai3TATyIsV6T5VnFKlCZdIslATRgwuh4oUXP8fJ/TNOT9/HDx3jlPGlP/Nlvv369zD9A/ISrBO6ZV7KCeecdN4J+UKB3BwoG0ngLVdbqiojIcWYjt44dAhx3t1E36+lFTfLabsW+l6EMZQiaJgfygCOIWZ760YaQ3KdYKynM5ajgwKNiI1u2zby7kcCnmEcWa23HM4bbHDMZwfcOL7Bet0yDkshHDkptW5WLXlRcuvajK+8/DI/uPY8r77xDbJCkRcVo/cY4zg9XaF1QpFn9Naz3TxgGAaapsTYNd57VBA2Z9+fU1SVnLxKCD2ds6zXG/K84HCWMY1w//6GqkhROiYwvWHWlCyXGw6Pb2CcSIo536MShfNGKjZZRllVGGfZtIambuh6hx+3JEHyL1lZsFquOTw8ZtluWW49SdLzpZf/FHWd8to3fh/vRJi1iOXCZn4NpomzxZaXXvpxUq159933SLXQ0AkykOT0ZINKEuo6lXJfSPAjrGzL4WxG6RL+XH7IzReOCVVFk2W0pyvudyOqPuIbqxVJptBFxab3TJPlmZBzdHgodltFnqe4qWTcWMq6YrHcMpvPOb19n8PDOdu2Zb2R2Rs3b14jSeJcCKX5wZ373H2wYdUuqQ4OGYZAmqccXRe9R0XC/DBQVRVlVQuvwzu0zkiu+nag73teffU1FIFXvvTT/Pwv/BxvvPpdXn/tu6BkT9v2LV235eZT1+m7jlUnsszHtUiRNXXJK19+mfPTE5579if5pb/8F/id//wq3/nO60zB8Pzzf5qf/qlXeOHZz/Dv/u1v8eZ332A+rxiGgaPjA9nDrba0bUuWZSiV8fM/9+d5+3sf8PwLN/nd332VRGe0nSFMCms6DuYzrs8rvvD55/jWm7d55845p8uOsqoZR1GZTQDjlgRGDg7mnJ8vo/hJoCwkkTb4EXRKbwybrSdBSDXDIFyJssgZJxGnbLuJcTB0fY/WW/RnZ5ydrVi1hjLXkGQ8WK5Jk5wETWsc//uNb3F34Wj7gdAOJKmUJsOkJMM9epSSbYxx4D2EZGKcMmbzQ0wr5TBUgh8VvR8JXogyUwA3KBINaVYRtEK7QFZUkGisV6A0Y8gpm4KgSiH7KEVnNsyaGfVcY3sjY9bzAh0UeZHgh0BRlDRpTZgGmSuA4sbNmyLvFgJFLmPRTx4sSBJF1RyBakkzTVMXHB8f0tkAE1xLjhmGgc16Q9M05HmK6VOYFM0sjzTfwNHxDOdGWjORlwWj6TnvB24by/1ioJgy9HJkpgJ5pTlJR3woOL52XdSch4GyyJgmxVvvfJs7916nbipefOFZilJzcnImn6spuHnjBnkh+/hEK55NnwY0168dsVwuyHIZJlM3c5aLNbNmxuL8jGEYOD4+lkhkVhMCeDfIeRWVnvNCVJmFVPVo2rB6HKf4SUEp9QBogdNd2/IQbrC353G4avbA1bPpqtnzQgjh/xpPfCWcAIBS6rUQwpd3bccF9vY8HlfNHrh6Nl01ex6FZNcG7LHHHrvF3gnsscenHFfJCfyzXRvwQ9jb83hcNXvg6tl01ez5SFyZnMAee+yxG1ylSGCPPfbYAXbuBJRSf0kp9aZS6vtKqa/vyIZ3lFLfUkp9Uyn1Wjx2TSn1X5VSfxR/Hn/CNvy6UupEKfXGQ8c+0gYl+MdxzV5XSr3yhOz5NaXUnbhO31RKffWh1/5etOdNpdQvfAL2PKeU+u9Kqe8opb6tlPrb8fhO1ugx9uxsjT42LrTbdvEANPAD4EUgB/4A+OIO7HgHuPFDx/4B8PX4/OvA3/+EbfhZ4BXgjf+XDcBXgf+E6JP8DPB7T8ieXwP+7kf87Rfjd1cAn4nfqf4R2/M08Ep8Pge+F993J2v0GHt2tkYf97HrSODPAt8PIbwVQnDAbwJf27FNF/ga8Bvx+W8Af+WTfLMQwv8Azv8/bfga8C+D4FXgSCn19BOw51H4GvCbIQQbQngb+D7y3f4o7bkbQvj9+HwD/CHwDDtao8fY8yh84mv0cbFrJ/AM8P5Dv9/m8Qv5SSEA/0Up9b+UUn8jHrsVQrgbn98Dbu3ArkfZsMt1+1sxvP71h7ZIT9QepdSPA18Cfo8rsEY/ZA9cgTX6k2DXTuCq4CshhFeAXwT+plLqZx9+MUg8t9MyylWwAfinwEvAy8Bd4B8+aQOUUjPg3wN/J4Swfvi1XazRR9iz8zX6k2LXTuAO8NxDvz8bjz1RhBDuxJ8nwG8hYdr9i/Ax/jx50nY9xoadrFsI4X4IYQwhTMA/58Nw9onYo5TKkAvuX4cQ/kM8vLM1+ih7dr1GHwe7dgL/E/icUuozSqkc+GXgt5+kAUqpRik1v3gO/EXgjWjHr8Q/+xXgPz5JuyIeZcNvA38tZsB/Blg9FBJ/YvihPfVfRdbpwp5fVkoVSqnPAJ8DvvEjfm8F/AvgD0MI/+ihl3ayRo+yZ5dr9LGx68wkksX9HpIt/dUdvP+LSNb2D4BvX9gAXAd+B/gj4L8B1z5hO/4NEj56ZL/41x9lA5Lx/idxzb4FfPkJ2fOv4vu9jpzUTz/0978a7XkT+MVPwJ6vIKH+68A34+Oru1qjx9izszX6uI89Y3CPPT7l2PV2YI899tgx9k5gjz0+5dg7gT32+JRj7wT22ONTjr0T2GOPTzn2TmCPPT7l2DuBPfb4lGPvBPbY41OO/wNuJyFtV0lNWQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize results with Torch-TensorRT\n",
+    "plot_results(best_results_per_input_trt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We get similar results as before!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 6. Measuring Speedup\n",
+    "We can run the benchmark function again to see the speedup gained! Compare this result with the same batch-size of input in the case without Torch-TensorRT above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - For input x, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
+      "The compiler is going to use the user setting Float16\n",
+      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
+      "compatibility with PyTorch's data type convention is required.\n",
+      "If you do indeed see errors at runtime either:\n",
+      "- Remove the dtype spec for x\n",
+      "- Disable partial compilation by setting require_full_compilation to True\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n",
+      "WARNING: [Torch-TensorRT] - There may be undefined behavior using dynamic shape and aten::size\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 10/100, avg batch time 45.88 ms\n",
+      "Iteration 20/100, avg batch time 46.20 ms\n",
+      "Iteration 30/100, avg batch time 46.10 ms\n",
+      "Iteration 40/100, avg batch time 46.19 ms\n",
+      "Iteration 50/100, avg batch time 46.15 ms\n",
+      "Iteration 60/100, avg batch time 46.17 ms\n",
+      "Iteration 70/100, avg batch time 46.19 ms\n",
+      "Iteration 80/100, avg batch time 46.20 ms\n",
+      "Iteration 90/100, avg batch time 46.22 ms\n",
+      "Iteration 100/100, avg batch time 46.21 ms\n",
+      "Input shape: torch.Size([128, 3, 300, 300])\n",
+      "Output location prediction size: torch.Size([128, 4, 8732])\n",
+      "Output label prediction size: torch.Size([128, 81, 8732])\n",
+      "Average batch time: 46.21 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "batch_size = 128\n",
+    "\n",
+    "# Recompiling with batch_size we use for evaluating performance\n",
+    "trt_model = torch_tensorrt.compile(traced_model,\n",
+    "    inputs = [torch_tensorrt.Input((batch_size, 3, 300, 300), dtype=torch.half)],\n",
+    "    enabled_precisions= {torch.half}, # Run with FP16\n",
+    "    workspace_size= 1 << 20\n",
+    ")\n",
+    "\n",
+    "benchmark(trt_model, input_shape=(batch_size, 3, 300, 300), dtype='fp16', nruns=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 7. Conclusion\n",
+    "\n",
+    "In this notebook, we have walked through the complete process of compiling a TorchScript SSD300 model with Torch-TensorRT, and tested the performance impact of the optimization. We find that using the Torch-TensorRT compiled model, we gain significant speedup in inference without any noticeable drop in performance!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Details\n",
+    "For detailed information on model input and output,\n",
+    "training recipies, inference and performance visit:\n",
+    "[github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD)\n",
+    "and/or [NGC](https://ngc.nvidia.com/catalog/model-scripts/nvidia:ssd_for_pytorch)\n",
+    "\n",
+    "### References\n",
+    "\n",
+    " - [SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) paper\n",
+    " - [Speed/accuracy trade-offs for modern convolutional object detectors](https://arxiv.org/abs/1611.10012) paper\n",
+    " - [SSD on NGC](https://ngc.nvidia.com/catalog/model-scripts/nvidia:ssd_for_pytorch)\n",
+    " - [SSD on github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/SSD)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/v1.2.0/_sources/_notebooks/vgg-qat.ipynb.txt b/docs/v1.2.0/_sources/_notebooks/vgg-qat.ipynb.txt
new file mode 100644
index 0000000000..cca771ad92
--- /dev/null
+++ b/docs/v1.2.0/_sources/_notebooks/vgg-qat.ipynb.txt
@@ -0,0 +1,1200 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8033b2eb",
+   "metadata": {},
+   "source": [
+    "# Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69ec3ca7",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "Quantization Aware training (QAT) simulates quantization during training by quantizing weights and activation layers. This will help to reduce the loss in accuracy when we convert the network trained in FP32 to INT8 for faster inference. QAT introduces additional nodes in the graph which will be used to learn the dynamic ranges of weights and activation layers. In this notebook, we illustrate the following steps from training to inference of a QAT model in Torch-TensorRT.\n",
+    "\n",
+    "1. [Requirements](#1)\n",
+    "2. [VGG16 Overview](#2)\n",
+    "3. [Training a baseline VGG16 model](#3)\n",
+    "4. [Apply Quantization](#4)\n",
+    "5. [Model calibration](#5)\n",
+    "6. [Quantization Aware training](#6)\n",
+    "7. [Export to Torchscript](#7)\n",
+    "8. [Inference using Torch-TensorRT](#8)\n",
+    "8. [References](#8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79655ea8",
+   "metadata": {},
+   "source": [
+    "<a id=\"1\"></a>\n",
+    "##  1. Requirements\n",
+    "Please install the <a href=\"https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/int8/training/vgg16#prequisites\">required dependencies</a> and import these libraries accordingly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14a72941",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6493e915",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch.optim as optim\n",
+    "import torch.utils.data as data\n",
+    "import torchvision.transforms as transforms\n",
+    "import torchvision.datasets as datasets\n",
+    "import torch_tensorrt\n",
+    "\n",
+    "from torch.utils.tensorboard import SummaryWriter\n",
+    "\n",
+    "import pytorch_quantization\n",
+    "from pytorch_quantization import nn as quant_nn\n",
+    "from pytorch_quantization import quant_modules\n",
+    "from pytorch_quantization.tensor_quant import QuantDescriptor\n",
+    "from pytorch_quantization import calib\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "print(pytorch_quantization.__version__)\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "sys.path.insert(0, \"../examples/int8/training/vgg16\")\n",
+    "from vgg16 import vgg16\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4de5060a",
+   "metadata": {},
+   "source": [
+    "<a id=\"2\"></a>\n",
+    "##  2. VGG16 Overview\n",
+    "### Very Deep Convolutional Networks for Large-Scale Image Recognition\n",
+    "VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows\n",
+    "<img src=\"https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png\">\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a5afc49",
+   "metadata": {},
+   "source": [
+    "<a id=\"3\"></a>\n",
+    "##  3. Training a baseline VGG16 model\n",
+    "We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your `data` directory. Data preprocessing is performed using `torchvision` transforms. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5d2c4c45",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Files already downloaded and verified\n",
+      "Files already downloaded and verified\n"
+     ]
+    }
+   ],
+   "source": [
+    "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
+    "\n",
+    "# ========== Define Training dataset and dataloaders =============#\n",
+    "training_dataset = datasets.CIFAR10(root='./data',\n",
+    "                                        train=True,\n",
+    "                                        download=True,\n",
+    "                                        transform=transforms.Compose([\n",
+    "                                            transforms.RandomCrop(32, padding=4),\n",
+    "                                            transforms.RandomHorizontalFlip(),\n",
+    "                                            transforms.ToTensor(),\n",
+    "                                            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
+    "                                        ]))\n",
+    "\n",
+    "training_dataloader = torch.utils.data.DataLoader(training_dataset,\n",
+    "                                                      batch_size=32,\n",
+    "                                                      shuffle=True,\n",
+    "                                                      num_workers=2)\n",
+    "\n",
+    "# ========== Define Testing dataset and dataloaders =============#\n",
+    "testing_dataset = datasets.CIFAR10(root='./data',\n",
+    "                                   train=False,\n",
+    "                                   download=True,\n",
+    "                                   transform=transforms.Compose([\n",
+    "                                       transforms.ToTensor(),\n",
+    "                                       transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n",
+    "                                   ]))\n",
+    "\n",
+    "testing_dataloader = torch.utils.data.DataLoader(testing_dataset,\n",
+    "                                                 batch_size=16,\n",
+    "                                                 shuffle=False,\n",
+    "                                                 num_workers=2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2bd092b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(model, dataloader, crit, opt, epoch):\n",
+    "#     global writer\n",
+    "    model.train()\n",
+    "    running_loss = 0.0\n",
+    "    for batch, (data, labels) in enumerate(dataloader):\n",
+    "        data, labels = data.cuda(), labels.cuda(non_blocking=True)\n",
+    "        opt.zero_grad()\n",
+    "        out = model(data)\n",
+    "        loss = crit(out, labels)\n",
+    "        loss.backward()\n",
+    "        opt.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "        if batch % 500 == 499:\n",
+    "            print(\"Batch: [%5d | %5d] loss: %.3f\" % (batch + 1, len(dataloader), running_loss / 100))\n",
+    "            running_loss = 0.0\n",
+    "        \n",
+    "def test(model, dataloader, crit, epoch):\n",
+    "    global writer\n",
+    "    global classes\n",
+    "    total = 0\n",
+    "    correct = 0\n",
+    "    loss = 0.0\n",
+    "    class_probs = []\n",
+    "    class_preds = []\n",
+    "    model.eval()\n",
+    "    with torch.no_grad():\n",
+    "        for data, labels in dataloader:\n",
+    "            data, labels = data.cuda(), labels.cuda(non_blocking=True)\n",
+    "            out = model(data)\n",
+    "            loss += crit(out, labels)\n",
+    "            preds = torch.max(out, 1)[1]\n",
+    "            class_probs.append([F.softmax(i, dim=0) for i in out])\n",
+    "            class_preds.append(preds)\n",
+    "            total += labels.size(0)\n",
+    "            correct += (preds == labels).sum().item()\n",
+    "\n",
+    "    test_probs = torch.cat([torch.stack(batch) for batch in class_probs])\n",
+    "    test_preds = torch.cat(class_preds)\n",
+    "\n",
+    "    return loss / total, correct / total\n",
+    "\n",
+    "def save_checkpoint(state, ckpt_path=\"checkpoint.pth\"):\n",
+    "    torch.save(state, ckpt_path)\n",
+    "    print(\"Checkpoint saved\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c80a86cc",
+   "metadata": {},
+   "source": [
+    "*Define the VGG model that we are going to perfom QAT on.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8c564b8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CIFAR 10 has 10 classes\n",
+    "model = vgg16(num_classes=len(classes), init_weights=False)\n",
+    "model = model.cuda()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "abc00452",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Declare Learning rate\n",
+    "lr = 0.1\n",
+    "state = {}\n",
+    "state[\"lr\"] = lr\n",
+    "\n",
+    "# Use cross entropy loss for classification and SGD optimizer\n",
+    "crit = nn.CrossEntropyLoss()\n",
+    "opt = optim.SGD(model.parameters(), lr=state[\"lr\"], momentum=0.9, weight_decay=1e-4)\n",
+    "\n",
+    "\n",
+    "# Adjust learning rate based on epoch number\n",
+    "def adjust_lr(optimizer, epoch):\n",
+    "    global state\n",
+    "    new_lr = lr * (0.5**(epoch // 12)) if state[\"lr\"] > 1e-7 else state[\"lr\"]\n",
+    "    if new_lr != state[\"lr\"]:\n",
+    "        state[\"lr\"] = new_lr\n",
+    "        print(\"Updating learning rate: {}\".format(state[\"lr\"]))\n",
+    "        for param_group in optimizer.param_groups:\n",
+    "            param_group[\"lr\"] = state[\"lr\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d80865a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: [    1 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 13.288\n",
+      "Batch: [ 1000 |  1563] loss: 11.345\n",
+      "Batch: [ 1500 |  1563] loss: 11.008\n",
+      "Test Loss: 0.13388 Test Acc: 13.23%\n",
+      "Epoch: [    2 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 10.742\n",
+      "Batch: [ 1000 |  1563] loss: 10.311\n",
+      "Batch: [ 1500 |  1563] loss: 10.141\n",
+      "Test Loss: 0.11888 Test Acc: 23.96%\n",
+      "Epoch: [    3 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 9.877\n",
+      "Batch: [ 1000 |  1563] loss: 9.821\n",
+      "Batch: [ 1500 |  1563] loss: 9.818\n",
+      "Test Loss: 0.11879 Test Acc: 24.68%\n",
+      "Epoch: [    4 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 9.677\n",
+      "Batch: [ 1000 |  1563] loss: 9.613\n",
+      "Batch: [ 1500 |  1563] loss: 9.504\n",
+      "Test Loss: 0.11499 Test Acc: 23.68%\n",
+      "Epoch: [    5 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 9.560\n",
+      "Batch: [ 1000 |  1563] loss: 9.536\n",
+      "Batch: [ 1500 |  1563] loss: 9.309\n",
+      "Test Loss: 0.10990 Test Acc: 27.84%\n",
+      "Epoch: [    6 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 9.254\n",
+      "Batch: [ 1000 |  1563] loss: 9.234\n",
+      "Batch: [ 1500 |  1563] loss: 9.188\n",
+      "Test Loss: 0.11594 Test Acc: 23.29%\n",
+      "Epoch: [    7 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 9.141\n",
+      "Batch: [ 1000 |  1563] loss: 9.110\n",
+      "Batch: [ 1500 |  1563] loss: 9.013\n",
+      "Test Loss: 0.10732 Test Acc: 29.24%\n",
+      "Epoch: [    8 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 9.120\n",
+      "Batch: [ 1000 |  1563] loss: 9.086\n",
+      "Batch: [ 1500 |  1563] loss: 8.948\n",
+      "Test Loss: 0.10732 Test Acc: 27.24%\n",
+      "Epoch: [    9 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 8.941\n",
+      "Batch: [ 1000 |  1563] loss: 8.997\n",
+      "Batch: [ 1500 |  1563] loss: 9.028\n",
+      "Test Loss: 0.11299 Test Acc: 25.52%\n",
+      "Epoch: [   10 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 8.927\n",
+      "Batch: [ 1000 |  1563] loss: 8.837\n",
+      "Batch: [ 1500 |  1563] loss: 8.860\n",
+      "Test Loss: 0.10130 Test Acc: 34.61%\n",
+      "Epoch: [   11 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 8.953\n",
+      "Batch: [ 1000 |  1563] loss: 8.738\n",
+      "Batch: [ 1500 |  1563] loss: 8.724\n",
+      "Test Loss: 0.10018 Test Acc: 32.27%\n",
+      "Epoch: [   12 /    25] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 8.721\n",
+      "Batch: [ 1000 |  1563] loss: 8.716\n",
+      "Batch: [ 1500 |  1563] loss: 8.701\n",
+      "Test Loss: 0.10070 Test Acc: 29.57%\n",
+      "Updating learning rate: 0.05\n",
+      "Epoch: [   13 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 7.944\n",
+      "Batch: [ 1000 |  1563] loss: 7.649\n",
+      "Batch: [ 1500 |  1563] loss: 7.511\n",
+      "Test Loss: 0.08555 Test Acc: 44.62%\n",
+      "Epoch: [   14 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 7.057\n",
+      "Batch: [ 1000 |  1563] loss: 6.944\n",
+      "Batch: [ 1500 |  1563] loss: 6.687\n",
+      "Test Loss: 0.08331 Test Acc: 52.27%\n",
+      "Epoch: [   15 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 6.470\n",
+      "Batch: [ 1000 |  1563] loss: 6.439\n",
+      "Batch: [ 1500 |  1563] loss: 6.126\n",
+      "Test Loss: 0.07266 Test Acc: 58.02%\n",
+      "Epoch: [   16 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 5.834\n",
+      "Batch: [ 1000 |  1563] loss: 5.801\n",
+      "Batch: [ 1500 |  1563] loss: 5.622\n",
+      "Test Loss: 0.06340 Test Acc: 65.17%\n",
+      "Epoch: [   17 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 5.459\n",
+      "Batch: [ 1000 |  1563] loss: 5.442\n",
+      "Batch: [ 1500 |  1563] loss: 5.314\n",
+      "Test Loss: 0.05945 Test Acc: 67.22%\n",
+      "Epoch: [   18 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 5.071\n",
+      "Batch: [ 1000 |  1563] loss: 5.145\n",
+      "Batch: [ 1500 |  1563] loss: 5.063\n",
+      "Test Loss: 0.06567 Test Acc: 64.46%\n",
+      "Epoch: [   19 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 4.796\n",
+      "Batch: [ 1000 |  1563] loss: 4.781\n",
+      "Batch: [ 1500 |  1563] loss: 4.732\n",
+      "Test Loss: 0.05374 Test Acc: 71.87%\n",
+      "Epoch: [   20 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 4.568\n",
+      "Batch: [ 1000 |  1563] loss: 4.564\n",
+      "Batch: [ 1500 |  1563] loss: 4.484\n",
+      "Test Loss: 0.05311 Test Acc: 71.12%\n",
+      "Epoch: [   21 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 4.385\n",
+      "Batch: [ 1000 |  1563] loss: 4.302\n",
+      "Batch: [ 1500 |  1563] loss: 4.285\n",
+      "Test Loss: 0.05080 Test Acc: 74.29%\n",
+      "Epoch: [   22 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 4.069\n",
+      "Batch: [ 1000 |  1563] loss: 4.105\n",
+      "Batch: [ 1500 |  1563] loss: 4.096\n",
+      "Test Loss: 0.04807 Test Acc: 75.20%\n",
+      "Epoch: [   23 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 3.959\n",
+      "Batch: [ 1000 |  1563] loss: 3.898\n",
+      "Batch: [ 1500 |  1563] loss: 3.916\n",
+      "Test Loss: 0.04743 Test Acc: 75.81%\n",
+      "Epoch: [   24 /    25] LR: 0.050000\n",
+      "Batch: [  500 |  1563] loss: 3.738\n",
+      "Batch: [ 1000 |  1563] loss: 3.847\n",
+      "Batch: [ 1500 |  1563] loss: 3.797\n",
+      "Test Loss: 0.04609 Test Acc: 76.42%\n",
+      "Updating learning rate: 0.025\n",
+      "Epoch: [   25 /    25] LR: 0.025000\n",
+      "Batch: [  500 |  1563] loss: 2.952\n",
+      "Batch: [ 1000 |  1563] loss: 2.906\n",
+      "Batch: [ 1500 |  1563] loss: 2.735\n",
+      "Test Loss: 0.03466 Test Acc: 82.00%\n",
+      "Checkpoint saved\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the model for 25 epochs to get ~80% accuracy.\n",
+    "num_epochs=25\n",
+    "for epoch in range(num_epochs):\n",
+    "    adjust_lr(opt, epoch)\n",
+    "    print('Epoch: [%5d / %5d] LR: %f' % (epoch + 1, num_epochs, state[\"lr\"]))\n",
+    "\n",
+    "    train(model, training_dataloader, crit, opt, epoch)\n",
+    "    test_loss, test_acc = test(model, testing_dataloader, crit, epoch)\n",
+    "\n",
+    "    print(\"Test Loss: {:.5f} Test Acc: {:.2f}%\".format(test_loss, 100 * test_acc))\n",
+    "    \n",
+    "save_checkpoint({'epoch': epoch + 1,\n",
+    "                 'model_state_dict': model.state_dict(),\n",
+    "                 'acc': test_acc,\n",
+    "                 'opt_state_dict': opt.state_dict(),\n",
+    "                 'state': state},\n",
+    "                ckpt_path=\"vgg16_base_ckpt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1044537",
+   "metadata": {},
+   "source": [
+    "<a id=\"4\"></a>\n",
+    "##  4. Apply Quantization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c33b7f4e",
+   "metadata": {},
+   "source": [
+    "`quant_modules.initialize()` will ensure quantized version of modules will be called instead of original modules. For example, when you define a model with convolution, linear, pooling layers, `QuantConv2d`, `QuantLinear` and `QuantPooling` will be called. `QuantConv2d` basically wraps quantizer nodes around inputs and weights of regular `Conv2d`. Please refer to all the <a href=\"https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization/pytorch_quantization/nn/modules\">quantized modules</a> in pytorch-quantization toolkit for more information. A `QuantConv2d` is represented in `pytorch-quantization` toolkit as follows.\n",
+    "\n",
+    "```\n",
+    "def forward(self, input):\n",
+    "        # the actual quantization happens in the next level of the class hierarchy\n",
+    "        quant_input, quant_weight = self._quant(input)\n",
+    "\n",
+    "        if self.padding_mode == 'circular':\n",
+    "            expanded_padding = ((self.padding[1] + 1) // 2, self.padding[1] // 2,\n",
+    "                                (self.padding[0] + 1) // 2, self.padding[0] // 2)\n",
+    "            output = F.conv2d(F.pad(quant_input, expanded_padding, mode='circular'),\n",
+    "                              quant_weight, self.bias, self.stride,\n",
+    "                              _pair(0), self.dilation, self.groups)\n",
+    "        else:\n",
+    "            output = F.conv2d(quant_input, quant_weight, self.bias, self.stride, self.padding, self.dilation,\n",
+    "                              self.groups)\n",
+    "\n",
+    "        return output\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "985dc59e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quant_modules.initialize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "164ce8cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# All the regular conv, FC layers will be converted to their quantozed counterparts due to quant_modules.initialize()\n",
+    "qat_model = vgg16(num_classes=len(classes), init_weights=False)\n",
+    "qat_model = qat_model.cuda()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8e5f7fb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# vgg16_base_ckpt is the checkpoint generated from Step 3 : Training a baseline VGG16 model.\n",
+    "ckpt = torch.load(\"./vgg16_base_ckpt\")\n",
+    "modified_state_dict={}\n",
+    "for key, val in ckpt[\"model_state_dict\"].items():\n",
+    "    # Remove 'module.' from the key names\n",
+    "    if key.startswith('module'):\n",
+    "        modified_state_dict[key[7:]] = val\n",
+    "    else:\n",
+    "        modified_state_dict[key] = val\n",
+    "\n",
+    "# Load the pre-trained checkpoint\n",
+    "qat_model.load_state_dict(modified_state_dict)\n",
+    "opt.load_state_dict(ckpt[\"opt_state_dict\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f8a74e8",
+   "metadata": {},
+   "source": [
+    "<a id=\"5\"></a>\n",
+    "##  5. Model Calibration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2a321f9",
+   "metadata": {},
+   "source": [
+    "The quantizer nodes introduced in the model around desired layers capture the dynamic range (min_value, max_value) that is observed by the layer. Calibration is the process of computing the dynamic range of these layers by passing calibration data, which is usually a subset of training or validation data. There are different ways of calibration: `max`, `histogram` and `entropy`. We use `max` calibration technique as it is simple and effective. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "039423dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_amax(model, **kwargs):\n",
+    "    # Load calib result\n",
+    "    for name, module in model.named_modules():\n",
+    "        if isinstance(module, quant_nn.TensorQuantizer):\n",
+    "            if module._calibrator is not None:\n",
+    "                if isinstance(module._calibrator, calib.MaxCalibrator):\n",
+    "                    module.load_calib_amax()\n",
+    "                else:\n",
+    "                    module.load_calib_amax(**kwargs)\n",
+    "            print(F\"{name:40}: {module}\")\n",
+    "    model.cuda()\n",
+    "\n",
+    "def collect_stats(model, data_loader, num_batches):\n",
+    "    \"\"\"Feed data to the network and collect statistics\"\"\"\n",
+    "    # Enable calibrators\n",
+    "    for name, module in model.named_modules():\n",
+    "        if isinstance(module, quant_nn.TensorQuantizer):\n",
+    "            if module._calibrator is not None:\n",
+    "                module.disable_quant()\n",
+    "                module.enable_calib()\n",
+    "            else:\n",
+    "                module.disable()\n",
+    "\n",
+    "    # Feed data to the network for collecting stats\n",
+    "    for i, (image, _) in tqdm(enumerate(data_loader), total=num_batches):\n",
+    "        model(image.cuda())\n",
+    "        if i >= num_batches:\n",
+    "            break\n",
+    "\n",
+    "    # Disable calibrators\n",
+    "    for name, module in model.named_modules():\n",
+    "        if isinstance(module, quant_nn.TensorQuantizer):\n",
+    "            if module._calibrator is not None:\n",
+    "                module.enable_quant()\n",
+    "                module.disable_calib()\n",
+    "            else:\n",
+    "                module.enable()\n",
+    "\n",
+    "def calibrate_model(model, model_name, data_loader, num_calib_batch, calibrator, hist_percentile, out_dir):\n",
+    "    \"\"\"\n",
+    "        Feed data to the network and calibrate.\n",
+    "        Arguments:\n",
+    "            model: classification model\n",
+    "            model_name: name to use when creating state files\n",
+    "            data_loader: calibration data set\n",
+    "            num_calib_batch: amount of calibration passes to perform\n",
+    "            calibrator: type of calibration to use (max/histogram)\n",
+    "            hist_percentile: percentiles to be used for historgram calibration\n",
+    "            out_dir: dir to save state files in\n",
+    "    \"\"\"\n",
+    "\n",
+    "    if num_calib_batch > 0:\n",
+    "        print(\"Calibrating model\")\n",
+    "        with torch.no_grad():\n",
+    "            collect_stats(model, data_loader, num_calib_batch)\n",
+    "\n",
+    "        if not calibrator == \"histogram\":\n",
+    "            compute_amax(model, method=\"max\")\n",
+    "            calib_output = os.path.join(\n",
+    "                out_dir,\n",
+    "                F\"{model_name}-max-{num_calib_batch*data_loader.batch_size}.pth\")\n",
+    "            torch.save(model.state_dict(), calib_output)\n",
+    "        else:\n",
+    "            for percentile in hist_percentile:\n",
+    "                print(F\"{percentile} percentile calibration\")\n",
+    "                compute_amax(model, method=\"percentile\")\n",
+    "                calib_output = os.path.join(\n",
+    "                    out_dir,\n",
+    "                    F\"{model_name}-percentile-{percentile}-{num_calib_batch*data_loader.batch_size}.pth\")\n",
+    "                torch.save(model.state_dict(), calib_output)\n",
+    "\n",
+    "            for method in [\"mse\", \"entropy\"]:\n",
+    "                print(F\"{method} calibration\")\n",
+    "                compute_amax(model, method=method)\n",
+    "                calib_output = os.path.join(\n",
+    "                    out_dir,\n",
+    "                    F\"{model_name}-{method}-{num_calib_batch*data_loader.batch_size}.pth\")\n",
+    "                torch.save(model.state_dict(), calib_output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "78504a6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calibrating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████| 32/32 [00:00<00:00, 96.04it/s]\n",
+      "WARNING: Logging before flag parsing goes to stderr.\n",
+      "W1109 04:01:43.512364 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.513354 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.514046 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.514638 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.515270 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.515859 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.516441 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.517009 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.517600 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.518167 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.518752 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.519333 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.519911 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.520473 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.521038 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.521596 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.522170 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.522742 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.523360 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.523957 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.524581 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.525059 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.525366 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.525675 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.525962 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.526257 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.526566 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.526885 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.527188 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.527489 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.527792 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.528097 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.528387 139704147265344 tensor_quantizer.py:173] Disable MaxCalibrator\n",
+      "W1109 04:01:43.528834 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.529163 139704147265344 tensor_quantizer.py:238] Call .cuda() if running on GPU after loading calibrated amax.\n",
+      "W1109 04:01:43.532748 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([64, 1, 1, 1]).\n",
+      "W1109 04:01:43.533468 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.534033 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([64, 1, 1, 1]).\n",
+      "W1109 04:01:43.534684 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.535320 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([128, 1, 1, 1]).\n",
+      "W1109 04:01:43.535983 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.536569 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([128, 1, 1, 1]).\n",
+      "W1109 04:01:43.537248 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.537833 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([256, 1, 1, 1]).\n",
+      "W1109 04:01:43.538480 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.539074 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([256, 1, 1, 1]).\n",
+      "W1109 04:01:43.539724 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.540307 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([256, 1, 1, 1]).\n",
+      "W1109 04:01:43.540952 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.541534 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).\n",
+      "W1109 04:01:43.542075 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.542596 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).\n",
+      "W1109 04:01:43.543248 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.543719 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).\n",
+      "W1109 04:01:43.544424 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.544952 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).\n",
+      "W1109 04:01:43.545530 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.546114 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).\n",
+      "W1109 04:01:43.546713 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.547292 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([512, 1, 1, 1]).\n",
+      "W1109 04:01:43.547902 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.548453 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.549015 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([4096, 1]).\n",
+      "W1109 04:01:43.549665 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.550436 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([4096, 1]).\n",
+      "W1109 04:01:43.551925 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([]).\n",
+      "W1109 04:01:43.553105 139704147265344 tensor_quantizer.py:237] Load calibrated amax, shape=torch.Size([10, 1]).\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "features.0._input_quantizer             : TensorQuantizer(8bit narrow fake per-tensor amax=2.7537 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.0._weight_quantizer            : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0263, 2.7454](64) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.3._input_quantizer             : TensorQuantizer(8bit narrow fake per-tensor amax=27.5676 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.3._weight_quantizer            : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0169, 1.8204](64) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.7._input_quantizer             : TensorQuantizer(8bit narrow fake per-tensor amax=15.2002 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.7._weight_quantizer            : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0493, 1.3207](128) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.10._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=7.7376 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.10._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0163, 0.9624](128) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.14._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=8.8351 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.14._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0622, 0.8791](256) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.17._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=12.5746 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.17._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0505, 0.5117](256) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.20._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=9.7203 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.20._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0296, 0.5335](256) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.24._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=8.9367 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.24._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0220, 0.3763](512) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.27._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=6.6539 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.27._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0151, 0.1777](512) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.30._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=3.7099 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.30._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0087, 0.1906](512) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.34._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=4.0491 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.34._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0106, 0.1971](512) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.37._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=2.1531 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.37._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0070, 0.2305](512) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.40._input_quantizer            : TensorQuantizer(8bit narrow fake per-tensor amax=3.3631 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "features.40._weight_quantizer           : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0023, 0.4726](512) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "avgpool._input_quantizer                : TensorQuantizer(8bit narrow fake per-tensor amax=5.3550 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "classifier.0._input_quantizer           : TensorQuantizer(8bit narrow fake per-tensor amax=5.3550 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "classifier.0._weight_quantizer          : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0026, 0.5320](4096) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "classifier.3._input_quantizer           : TensorQuantizer(8bit narrow fake per-tensor amax=6.6733 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "classifier.3._weight_quantizer          : TensorQuantizer(8bit narrow fake axis=0 amax=[0.0018, 0.5172](4096) calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "classifier.6._input_quantizer           : TensorQuantizer(8bit narrow fake per-tensor amax=9.4352 calibrator=MaxCalibrator scale=1.0 quant)\n",
+      "classifier.6._weight_quantizer          : TensorQuantizer(8bit narrow fake axis=0 amax=[0.3877, 0.5620](10) calibrator=MaxCalibrator scale=1.0 quant)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calibrate the model using max calibration technique.\n",
+    "with torch.no_grad():\n",
+    "    calibrate_model(\n",
+    "        model=qat_model,\n",
+    "        model_name=\"vgg16\",\n",
+    "        data_loader=training_dataloader,\n",
+    "        num_calib_batch=32,\n",
+    "        calibrator=\"max\",\n",
+    "        hist_percentile=[99.9, 99.99, 99.999, 99.9999],\n",
+    "        out_dir=\"./\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1aa0c109",
+   "metadata": {},
+   "source": [
+    "<a id=\"6\"></a>\n",
+    "##  6. Quantization Aware Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fe8ec11",
+   "metadata": {},
+   "source": [
+    "In this phase, we finetune the model weights and leave the quantizer node values frozen. The dynamic ranges for each layer obtained from the calibration are kept constant while the weights of the model are finetuned to be close to the accuracy of original FP32 model (model without quantizer nodes) is preserved. Usually the finetuning of QAT model should be quick compared to the full training of the original model. Use QAT to fine-tune for around 10% of the original training schedule with an annealing learning-rate. Please refer to <a href=\"https://developer.nvidia.com/blog/achieving-fp32-accuracy-for-int8-inference-using-quantization-aware-training-with-tensorrt/\">Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT</a> for detailed recommendations. For this VGG model, it is enough to finetune for 1 epoch to get acceptable accuracy. \n",
+    "During finetuning with QAT, the quantization is applied as a composition of `max`, `clamp`, `round` and `mul` ops. \n",
+    "```\n",
+    "# amax is absolute maximum value for an input\n",
+    "# The upper bound for integer quantization (127 for int8)\n",
+    "max_bound = torch.tensor((2.0**(num_bits - 1 + int(unsigned))) - 1.0, device=amax.device)\n",
+    "scale = max_bound / amax\n",
+    "outputs = torch.clamp((inputs * scale).round_(), min_bound, max_bound)\n",
+    "```\n",
+    "<a href=\"https://github.com/NVIDIA/TensorRT/blob/8.0.1/tools/pytorch-quantization/pytorch_quantization/tensor_quant.py\">tensor_quant function</a> in `pytorch_quantization` toolkit is responsible for the above tensor quantization. Usually, per channel quantization is recommended for weights, while per tensor quantization is recommended for activations in a network.\n",
+    "During inference, we use `torch.fake_quantize_per_tensor_affine` and `torch.fake_quantize_per_channel_affine` to perform quantization as this is easier to convert into corresponding TensorRT operators. Please refer to next sections for more details on how these operators are exported in torchscript and converted in Torch-TensorRT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1f28d228",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Updating learning rate: 0.1\n",
+      "Epoch: [    1 /     1] LR: 0.100000\n",
+      "Batch: [  500 |  1563] loss: 2.635\n",
+      "Batch: [ 1000 |  1563] loss: 2.655\n",
+      "Batch: [ 1500 |  1563] loss: 2.646\n",
+      "Test Loss: 0.03291 Test Acc: 82.98%\n",
+      "Checkpoint saved\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Finetune the QAT model for 1 epoch\n",
+    "num_epochs=1\n",
+    "for epoch in range(num_epochs):\n",
+    "    adjust_lr(opt, epoch)\n",
+    "    print('Epoch: [%5d / %5d] LR: %f' % (epoch + 1, num_epochs, state[\"lr\"]))\n",
+    "\n",
+    "    train(qat_model, training_dataloader, crit, opt, epoch)\n",
+    "    test_loss, test_acc = test(qat_model, testing_dataloader, crit, epoch)\n",
+    "\n",
+    "    print(\"Test Loss: {:.5f} Test Acc: {:.2f}%\".format(test_loss, 100 * test_acc))\n",
+    "    \n",
+    "save_checkpoint({'epoch': epoch + 1,\n",
+    "                 'model_state_dict': qat_model.state_dict(),\n",
+    "                 'acc': test_acc,\n",
+    "                 'opt_state_dict': opt.state_dict(),\n",
+    "                 'state': state},\n",
+    "                ckpt_path=\"vgg16_qat_ckpt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a4dcaa2",
+   "metadata": {},
+   "source": [
+    "<a id=\"7\"></a>\n",
+    "##  7. Export to Torchscript\n",
+    "Export the model to Torch script. Trace the model and convert it into torchscript for deployment. To learn more about Torchscript, please refer to https://pytorch.org/docs/stable/jit.html. Setting `quant_nn.TensorQuantizer.use_fb_fake_quant = True` enables the QAT model to use `torch.fake_quantize_per_tensor_affine` and `torch.fake_quantize_per_channel_affine` operators instead of `tensor_quant` function to export quantization operators. In torchscript, they are represented as `aten::fake_quantize_per_tensor_affine` and `aten::fake_quantize_per_channel_affine`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3d34f526",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E1109 04:02:37.101168 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.102248 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.107194 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
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+      "E1109 04:02:37.241022 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.241654 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.247820 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.248445 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.250366 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.250959 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.257248 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.257854 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.259968 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.260660 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "W1109 04:02:37.268160 139704147265344 tensor_quantizer.py:280] Use Pytorch's native experimental fake quantization.\n",
+      "/opt/conda/lib/python3.8/site-packages/pytorch_quantization/nn/modules/tensor_quantizer.py:285: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+      "  inputs, amax.item() / bound, 0,\n",
+      "/opt/conda/lib/python3.8/site-packages/pytorch_quantization/nn/modules/tensor_quantizer.py:291: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+      "  quant_dim = list(amax.shape).index(list(amax_sequeeze.shape)[0])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E1109 04:02:37.329273 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.330212 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
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+      "E1109 04:02:37.476738 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.478809 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.479375 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.485666 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.486219 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.488416 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n",
+      "E1109 04:02:37.488986 139704147265344 tensor_quantizer.py:120] Fake quantize mode doesn't use scale explicitly!\n"
+     ]
+    }
+   ],
+   "source": [
+    "quant_nn.TensorQuantizer.use_fb_fake_quant = True\n",
+    "with torch.no_grad():\n",
+    "    data = iter(testing_dataloader)\n",
+    "    images, _ = data.next()\n",
+    "    jit_model = torch.jit.trace(qat_model, images.to(\"cuda\"))\n",
+    "    torch.jit.save(jit_model, \"trained_vgg16_qat.jit.pt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7341418a",
+   "metadata": {},
+   "source": [
+    "<a id=\"8\"></a>\n",
+    "##  8. Inference using Torch-TensorRT\n",
+    "In this phase, we run the exported torchscript graph of VGG QAT using Torch-TensorRT. Torch-TensorRT is a Pytorch-TensorRT compiler which converts Torchscript graphs into TensorRT. TensorRT 8.0 supports inference of quantization aware trained models and introduces new APIs; `QuantizeLayer` and `DequantizeLayer`. We can observe the entire VGG QAT graph quantization nodes from the debug log of Torch-TensorRT. To enable debug logging, you can set `torch_tensorrt.logging.set_reportable_log_level(torch_tensorrt.logging.Level.Debug)`. For example, `QuantConv2d` layer from `pytorch_quantization` toolkit is represented as follows in Torchscript\n",
+    "```\n",
+    "%quant_input : Tensor = aten::fake_quantize_per_tensor_affine(%x, %636, %637, %638, %639)\n",
+    "%quant_weight : Tensor = aten::fake_quantize_per_channel_affine(%394, %640, %641, %637, %638, %639)\n",
+    "%input.2 : Tensor = aten::_convolution(%quant_input, %quant_weight, %395, %687, %688, %689, %643, %690, %642, %643, %643, %644, %644)\n",
+    "```\n",
+    "`aten::fake_quantize_per_*_affine` is converted into `QuantizeLayer` + `DequantizeLayer` in Torch-TensorRT internally. Please refer to <a href=\"https://github.com/NVIDIA/Torch-TensorRT/blob/master/core/conversion/converters/impl/quantization.cpp\">quantization op converters</a> in Torch-TensorRT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "aa7495e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: [Torch-TensorRT] - Cannot infer input type from calcuations in graph for input x.2. Assuming it is Float32. If not, specify input type explicity\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
+      "WARNING: [Torch-TensorRT TorchScript Conversion Context] - Detected invalid timing cache, setup a local cache instead\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VGG QAT accuracy using TensorRT: 82.97%\n"
+     ]
+    }
+   ],
+   "source": [
+    "qat_model = torch.jit.load(\"trained_vgg16_qat.jit.pt\").eval()\n",
+    "\n",
+    "compile_spec = {\"inputs\": [torch_tensorrt.Input([16, 3, 32, 32])],\n",
+    "                \"enabled_precisions\": torch.int8,\n",
+    "                }\n",
+    "trt_mod = torch_tensorrt.compile(qat_model, **compile_spec)\n",
+    "\n",
+    "test_loss, test_acc = test(trt_mod, testing_dataloader, crit, 0)\n",
+    "print(\"VGG QAT accuracy using TensorRT: {:.2f}%\".format(100 * test_acc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9df5a90e",
+   "metadata": {},
+   "source": [
+    "### Performance benchmarking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "9eb2cd2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "cudnn.benchmark = True\n",
+    "\n",
+    "# Helper function to benchmark the model\n",
+    "def benchmark(model, input_shape=(1024, 1, 32, 32), dtype='fp32', nwarmup=50, nruns=1000):\n",
+    "    input_data = torch.randn(input_shape)\n",
+    "    input_data = input_data.to(\"cuda\")\n",
+    "    if dtype=='fp16':\n",
+    "        input_data = input_data.half()\n",
+    "        \n",
+    "    print(\"Warm up ...\")\n",
+    "    with torch.no_grad():\n",
+    "        for _ in range(nwarmup):\n",
+    "            features = model(input_data)\n",
+    "    torch.cuda.synchronize()\n",
+    "    print(\"Start timing ...\")\n",
+    "    timings = []\n",
+    "    with torch.no_grad():\n",
+    "        for i in range(1, nruns+1):\n",
+    "            start_time = time.time()\n",
+    "            output = model(input_data)\n",
+    "            torch.cuda.synchronize()\n",
+    "            end_time = time.time()\n",
+    "            timings.append(end_time - start_time)\n",
+    "            if i%100==0:\n",
+    "                print('Iteration %d/%d, avg batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
+    "\n",
+    "    print(\"Input shape:\", input_data.size())\n",
+    "    print(\"Output shape:\", output.shape)\n",
+    "    print('Average batch time: %.2f ms'%(np.mean(timings)*1000))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5c2514ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, avg batch time 4.83 ms\n",
+      "Iteration 200/1000, avg batch time 4.83 ms\n",
+      "Iteration 300/1000, avg batch time 4.83 ms\n",
+      "Iteration 400/1000, avg batch time 4.83 ms\n",
+      "Iteration 500/1000, avg batch time 4.83 ms\n",
+      "Iteration 600/1000, avg batch time 4.83 ms\n",
+      "Iteration 700/1000, avg batch time 4.83 ms\n",
+      "Iteration 800/1000, avg batch time 4.83 ms\n",
+      "Iteration 900/1000, avg batch time 4.83 ms\n",
+      "Iteration 1000/1000, avg batch time 4.83 ms\n",
+      "Input shape: torch.Size([16, 3, 32, 32])\n",
+      "Output shape: torch.Size([16, 10])\n",
+      "Average batch time: 4.83 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(jit_model, input_shape=(16, 3, 32, 32))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c5378ed6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warm up ...\n",
+      "Start timing ...\n",
+      "Iteration 100/1000, avg batch time 1.87 ms\n",
+      "Iteration 200/1000, avg batch time 1.84 ms\n",
+      "Iteration 300/1000, avg batch time 1.85 ms\n",
+      "Iteration 400/1000, avg batch time 1.83 ms\n",
+      "Iteration 500/1000, avg batch time 1.82 ms\n",
+      "Iteration 600/1000, avg batch time 1.81 ms\n",
+      "Iteration 700/1000, avg batch time 1.81 ms\n",
+      "Iteration 800/1000, avg batch time 1.80 ms\n",
+      "Iteration 900/1000, avg batch time 1.80 ms\n",
+      "Iteration 1000/1000, avg batch time 1.79 ms\n",
+      "Input shape: torch.Size([16, 3, 32, 32])\n",
+      "Output shape: torch.Size([16, 10])\n",
+      "Average batch time: 1.79 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "benchmark(trt_mod, input_shape=(16, 3, 32, 32))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6a5ec1c",
+   "metadata": {},
+   "source": [
+    "<a id=\"9\"></a>\n",
+    "##  9. References\n",
+    "* <a href=\"https://arxiv.org/pdf/1409.1556.pdf\">Very Deep Convolution Networks for large scale Image Recognition</a>\n",
+    "* <a href=\"https://developer.nvidia.com/blog/achieving-fp32-accuracy-for-int8-inference-using-quantization-aware-training-with-tensorrt/\">Achieving FP32 Accuracy for INT8 Inference Using Quantization Aware Training with NVIDIA TensorRT</a>\n",
+    "* <a href=\"https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/int8/training/vgg16#quantization-aware-fine-tuning-for-trying-out-qat-workflows\">QAT workflow for VGG16</a>\n",
+    "* <a href=\"https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/int8/qat\">Deploying VGG QAT model in C++ using Torch-TensorRT</a>\n",
+    "* <a href=\"https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization\">Pytorch-quantization toolkit from NVIDIA</a>\n",
+    "* <a href=\"https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/userguide.html\">Pytorch quantization toolkit userguide</a>\n",
+    "* <a href=\"https://arxiv.org/pdf/2004.09602.pdf\">Quantization basics</a>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/v1.2.0/_sources/cli/torchtrtc.rst.txt b/docs/v1.2.0/_sources/cli/torchtrtc.rst.txt
new file mode 100644
index 0000000000..69292c491c
--- /dev/null
+++ b/docs/v1.2.0/_sources/cli/torchtrtc.rst.txt
@@ -0,0 +1,149 @@
+.. _torchtrtc:
+
+torchtrtc
+=================================
+
+``torchtrtc`` is a CLI application for using the Torch-TensorRT compiler. It serves as an easy way to compile a
+TorchScript Module with Torch-TensorRT from the command-line to quickly check support or as part of
+a deployment pipeline. All basic features of the compiler are supported including post training
+quantization (though you must already have a calibration cache file to use the PTQ feature). The compiler can
+output two formats, either a TorchScript program with the TensorRT engine embedded or
+the TensorRT engine itself as a PLAN file.
+
+All that is required to run the program after compilation is for C++ linking against ``libtorchtrt.so``
+or in Python importing the torch_tensorrt package. All other aspects of using compiled modules are identical
+to standard TorchScript. Load with ``torch.jit.load()`` and run like you would run any other module.
+
+.. code-block:: txt
+
+    torchtrtc [input_file_path] [output_file_path]
+      [input_specs...] {OPTIONS}
+
+      torchtrtc is a compiler for TorchScript, it will compile and optimize
+      TorchScript programs to run on NVIDIA GPUs using TensorRT
+
+    OPTIONS:
+
+        -h, --help                        Display this help menu
+        Verbiosity of the compiler
+          -v, --verbose                     Dumps debugging information about the
+                                            compilation process onto the console
+          -w, --warnings                    Disables warnings generated during
+                                            compilation onto the console (warnings
+                                            are on by default)
+          --i, --info                       Dumps info messages generated during
+                                            compilation onto the console
+        --build-debuggable-engine         Creates a debuggable engine
+        --allow-gpu-fallback              (Only used when targeting DLA
+                                          (device-type)) Lets engine run layers on
+                                          GPU if they are not supported on DLA
+        --require-full-compilation        Require that the model should be fully
+                                          compiled to TensorRT or throw an error
+        --check-method-support=[method_name]
+                                          Check the support for end to end
+                                          compilation of a specified method in the
+                                          TorchScript module
+        --disable-tf32                    Prevent Float32 layers from using the
+                                          TF32 data format
+        --sparse-weights                  Enable sparsity for weights of conv and
+                                          FC layers
+        -p[precision...],
+        --enable-precision=[precision...] (Repeatable) Enabling an operating
+                                          precision for kernels to use when
+                                          building the engine (Int8 requires a
+                                          calibration-cache argument) [ float |
+                                          float32 | f32 | fp32 | half | float16 |
+                                          f16 | fp16 | int8 | i8 | char ]
+                                          (default: float)
+        -d[type], --device-type=[type]    The type of device the engine should be
+                                          built for [ gpu | dla ] (default: gpu)
+        --gpu-id=[gpu_id]                 GPU id if running on multi-GPU platform
+                                          (defaults to 0)
+        --dla-core=[dla_core]             DLACore id if running on available DLA
+                                          (defaults to 0)
+        --engine-capability=[capability]  The type of device the engine should be
+                                          built for [ standard | safety |
+                                          dla_standalone ]
+        --calibration-cache-file=[file_path]
+                                          Path to calibration cache file to use
+                                          for post training quantization
+        --teo=[op_name...],
+        --torch-executed-op=[op_name...]  (Repeatable) Operator in the graph that
+                                          should always be run in PyTorch for
+                                          execution (partial compilation must be
+                                          enabled)
+        --tem=[module_name...],
+        --torch-executed-mod=[module_name...]
+                                          (Repeatable) Module that should always
+                                          be run in Pytorch for execution (partial
+                                          compilation must be enabled)
+        --mbs=[num_ops],
+        --min-block-size=[num_ops]        Minimum number of contiguous TensorRT
+                                          supported ops to compile a subgraph to
+                                          TensorRT
+        --embed-engine                    Whether to treat input file as a
+                                          serialized TensorRT engine and embed it
+                                          into a TorchScript module (device spec
+                                          must be provided)
+        --num-avg-timing-iters=[num_iters]
+                                          Number of averaging timing iterations
+                                          used to select kernels
+        --workspace-size=[workspace_size] Maximum size of workspace given to
+                                          TensorRT
+        --dla-sram-size=[dla_sram_size]   Fast software managed RAM used by DLA
+                                          to communicate within a layer.
+        --dla-local-dram-size=[dla_local_dram_size]  Host RAM used by DLA to share
+                                          intermediate tensor data across operations.
+        --dla-global-dram-size=[dla_global_dram_size] Host RAM used by DLA to store
+                                          weights and metadata for execution
+        --atol=[atol]                     Absolute tolerance threshold for acceptable
+                                          numerical deviation from standard torchscript
+                                          output (default 1e-8)
+        --rtol=[rtol]                     Relative tolerance threshold for acceptable
+                                          numerical deviation from standard torchscript
+                                          output  (default 1e-5)
+        --no-threshold-check              Skip checking threshold compliance
+        --truncate-long-double,
+        --truncate, --truncate-64bit      Truncate weights that are provided in
+                                          64bit to 32bit (Long, Double to Int,
+                                          Float)
+        --save-engine                     Instead of compiling a full a
+                                          TorchScript program, save the created
+                                          engine to the path specified as the
+                                          output path
+        --custom-torch-ops                (repeatable) Shared object/DLL containing custom torch operators
+        --custom-converters               (repeatable) Shared object/DLL containing custom converters
+        input_file_path                   Path to input TorchScript file
+        output_file_path                  Path for compiled TorchScript (or
+                                          TensorRT engine) file
+        input_specs...                    Specs for inputs to engine, can either
+                                          be a single size or a range defined by
+                                          Min, Optimal, Max sizes, e.g.
+                                          "(N,..,C,H,W)"
+                                          "[(MIN_N,..,MIN_C,MIN_H,MIN_W);(OPT_N,..,OPT_C,OPT_H,OPT_W);(MAX_N,..,MAX_C,MAX_H,MAX_W)]".
+                                          Data Type and format can be specified by
+                                          adding an "@" followed by dtype and "%"
+                                          followed by format to the end of the
+                                          shape spec. e.g. "(3, 3, 32,
+                                          32)@f16%NHWC"
+        "--" can be used to terminate flag options and force all following
+        arguments to be treated as positional options
+
+e.g.
+
+.. code-block:: shell
+
+    torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous" -p f16
+
+- To include a set of custom operators
+
+.. code-block:: shell
+
+    torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-torch-ops=<path to custom library .so file> "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous" -p f16
+
+
+- To include a set of custom converters
+
+.. code-block:: shell
+
+    torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-converters=<path to custom library .so file> "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous" -p f16
diff --git a/docs/v1.2.0/_sources/contributors/conversion.rst.txt b/docs/v1.2.0/_sources/contributors/conversion.rst.txt
new file mode 100644
index 0000000000..f19fc5eba8
--- /dev/null
+++ b/docs/v1.2.0/_sources/contributors/conversion.rst.txt
@@ -0,0 +1,53 @@
+.. _conversion:
+
+Conversion Phase
+==================
+
+Once the graph has be simplified to a form thats easy to convert, we then set up a conversion context
+to manage the construction of a TensorRT ``INetworkDefinition`` from the blocks nodes. The conversion context
+records the set of converted nodes, block inputs and outputs and other information about the conversion
+of the graph. This data is then used to help converters link together layers and also hold build time
+information like weights required to construct the engine. After the context is created, the block
+converter starts iterating through the list of nodes, for each node, the converter will look at its
+inputs and assemble an array of resources to pass to the converter. Inputs can be in a couple of states:
+
+*  The input is a block parameter
+
+   *  In this case the input should have already been stored in as an IValue in the
+      conversion context ``evaluated_value_map``. The conversion stage will add the IValue to the list of args for the
+      converter
+
+*  The input is an output of a node that has already been converted
+
+   *  In this case the ITensor of the output has added to the ``value_tensor_map``,
+      The conversion stage will add the ITensor to the list of args for the converter
+
+*  The input is from a node that produces a static value
+
+   *  There are nodes that produce static values, typically used to store parameters for operators, we need to
+      evaluate these nodes at conversion time to be able to convert a op. The conversion system will look for a node
+      evaluator in the evaluator registry and run it on the node. The IValue produced will be entered in the
+      conversion context ``evaluated_value_map`` and added to the list of args for the converter. If the node
+      to be evaluated takes inputs, the conversion stage will recursively resolve dependencies until the final
+      static value has been evaluated
+
+*  The input is from a node that has not been converted
+
+   *  Torch-TensorRT will error out here
+
+Node Evaluation
+-----------------
+There are some nodes that contain static data and are resources for operations. These can be evaluated at
+conversion time so that you can use those values when doing node conversion. In theory any node kind can have
+a conversion time evaluator as long as it produces a static IValue, This IValue will be stored in the conversion
+context so it can be consumed by any node that takes the evaluated node as an input. Common node types are
+``prim::Constant`` which emits a constant and ``prim::ListConstruct`` which makes lists.
+
+Node Converters
+----------------
+
+Node converters map JIT nodes to layers or subgraphs of layers. They then associate outputs from the JIT graph
+and the TRT graph together in the conversion context. This allows the conversion stage to assemble the inputs
+for the next node. There are some cases where a node produces an output that is not a Tensor but a static result
+from a calculation done on inputs which need to be converted first. In this case the converter may associate the outputs in
+the ``evaluated_value_map`` instead of the ``value_tensor_map``. For more information take a look at: :ref:`writing_converters`
diff --git a/docs/v1.2.0/_sources/contributors/lowering.rst.txt b/docs/v1.2.0/_sources/contributors/lowering.rst.txt
new file mode 100644
index 0000000000..956c2004e1
--- /dev/null
+++ b/docs/v1.2.0/_sources/contributors/lowering.rst.txt
@@ -0,0 +1,207 @@
+.. _lowering:
+
+Lowering Phase
+===============
+
+The lowering phase is made up out of passes which are operations which map a graph from a high level representation
+to a lower level one. Each pass does something specific for instance inlining method calls. The idea is to
+significantly reduce what the conversion phase needs to be able to handle when actually mapping to TensorRT.
+We aim for closer to 1->1 op conversion vs looking for applicable subgraphs, limiting the number of converters and
+reduce the scope of each converter.
+
+You can see the effects of each pass by setting the log level to ``Level::kGraph``
+
+Passes Used
+-------------
+
+EliminateCommonSubexpression
+***********************************
+
+    `torch/csrc/jit/passes/common_subexpression_elimination.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/common_subexpression_elimination.h>`_
+
+Removes common subexpressions in the graph
+
+
+
+Eliminate Dead Code
+**************************
+
+    `torch/csrc/jit/passes/dead_code_elimination.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/dead_code_elimination.h>`_
+
+Dead code elimination will check if a node has side effects and not delete it if it does.
+
+Eliminate Exeception Or Pass Pattern
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/exception_elimination.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/exception_elimination.cpp>`_
+
+A common pattern in scripted modules are dimension gaurds which will throw execptions if
+the input dimension is not what was expected.
+
+.. code-block:: none
+
+    %1013 : bool = aten::ne(%1012, %24) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py:248:11
+        = prim::If(%1013) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py:248:8
+        block0():
+            = prim::RaiseException(%23) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py:249:12
+        -> ()
+        block1():
+        -> ()
+
+Since we are resolving all of this at compile time and there are no execptions in the TensorRT graph, we just remove it.
+
+Eliminate Redundant Gaurds
+***************************************
+
+    `torch/csrc/jit/passes/guard_elimination.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/guard_elimination.h>`_
+
+Eliminate redundant guards for ops whose outputs are fully determined by their inputs i.e. if inputs to such ops are
+guarded we are allowed to remove a guard on ops' outputs
+
+Freeze Module
+***************************************
+
+    `torch/csrc/jit/passes/freeze_module.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/freeze_module.h>`_
+
+Freeze attributes and inline constants and modules. Propogates constants in the graph.
+
+Fuse AddMM Branches
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/fuse_addmm_branches.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/fuse_addmm_branches.cpp>`_
+
+A common pattern in scripted modules is tensors of different dimensions use different constructions for implementing linear layers. We fuse these
+different varients into a single one that will get caught by the Unpack AddMM pass.
+
+.. code-block:: none
+
+    %ret : Tensor = prim::If(%622)
+    block0():
+      %ret.1 : Tensor = aten::addmm(%self.fc.bias, %x9.1, %3677, %3, %3)
+      -> (%ret.1)
+    block1():
+      %output.1 : Tensor = aten::matmul(%x9.1, %3677)
+      %output0.1 : Tensor = aten::add_(%output.1, %self.fc.bias, %3)
+      -> (%output0.1)
+
+We fuse this set of blocks into a graph like this:
+
+.. code-block:: none
+
+    %ret : Tensor = aten::addmm(%self.fc.bias, %x9.1, %3677, %3, %3)
+
+Fuse Linear
+***************************************
+
+    `torch/csrc/jit/passes/fuse_linear.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/fuse_linear.h>`_
+
+Match the ``aten::linear`` pattern and fuse it into a single ``aten::linear``
+This pass fuse the addmm or matmul + add generated by JIT back to linear
+
+Fuse Flatten Linear
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/fuse_flatten_linear.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/fuse_flatten_linear.cpp>`_
+
+TensorRT implicity flattens input layers into fully connected layers when they are higher than 1D. So when there is a
+``aten::flatten`` -> ``aten::linear`` pattern we remove the ``aten::flatten``.
+
+Lower Graph
+***************************************
+
+    `torch/csrc/jit/passes/lower_graph.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/lower_graph.h>`_
+
+Given a graph with of a method which first argument is %self, lower it to a graph where
+all attributes accesses are replaced with explicit inputs of the graph
+(rather than results of prim::GetAttr executed on %self). Returns a tuple
+(graph, parameters) where the last module.parameters.size() inputs to the
+graph are the trainable parameters used in this method. The remaining inputs
+are the true inputs to the function.
+
+Lower Tuples
+***************************************
+
+    `torch/csrc/jit/passes/lower_tuples.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/lower_tuples.h>`_
+
+* ``LowerSimpleTuples``:
+
+Removes tuples where TupleConstruct and TupleUnpack are matched but leaves tuples in place across if statements, loops, and as inputs/outputs
+
+* ``LowerAllTuples``:
+
+Removes _all_ tuples and raises an error if some cannot be removed, this is used by ONNX to ensure there are not tuples before conversion, but will not work on graphs whose inputs contain tuples.
+
+Module Fallback
+*****************
+
+    `Torch-TensorRT/core/lowering/passes/module_fallback.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/module_fallback.cpp>`_
+
+Module fallback consists of two lowering passes that must be run as a pair. The first pass is run before freezing to place delimiters in the graph around modules
+that should run in PyTorch. The second pass marks nodes between these delimiters after freezing to signify they should run in PyTorch.
+
+* ``NotateModuleForFallback``
+
+Places delimiting nodes around module calls pre freezing to signify where in the graph nodes should run in PyTorch
+
+* ``MarkNodesForFallback``
+
+Looks for delimiters then marks all nodes between the delimiters to tell partitioning to run them in PyTorch
+
+Peephole Optimze
+***************************************
+
+    `torch/csrc/jit/passes/peephole_optimze.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/ppeephole_optimze.h>`_
+
+The intent for this optimization pass is to catch all of the small, easy to catch peephole optimizations you might be interested in doing.
+
+Right now, it does:
+    - Eliminate no-op 'expand' nodes
+    - Simply x.t().t() to x
+
+
+Remove Contiguous
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/remove_contiguous.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/remove_contiguous.cpp>`_
+
+Removes contiguous operators since we are doing TensorRT memory is already contiguous.
+
+
+Remove Dropout
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/remove_dropout.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/remove_dropout.cpp>`_
+
+Removes dropout operators since we are doing inference.
+
+Remove To
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/remove_to.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/remove_to.cpp>`_
+
+Removes ``aten::to`` operators that do casting, since TensorRT mangages it itself. It is important that this is one of the last passes run so that
+other passes have a change to move required cast operators out of the main namespace.
+
+Unpack AddMM
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/unpack_addmm.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/unpack_addmm.cpp>`_
+
+Unpacks ``aten::addmm`` into ``aten::matmul`` and ``aten::add_`` (with an additional ``trt::const``
+op to freeze the bias in the TensorRT graph). This lets us reuse the ``aten::matmul`` and ``aten::add_``
+converters instead of needing a dedicated converter.
+
+Unpack LogSoftmax
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/unpack_log_softmax.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/unpack_log_softmax.cpp>`_
+
+Unpacks ``aten::logsoftmax`` into ``aten::softmax`` and ``aten::log``. This lets us reuse the
+``aten::softmax`` and ``aten::log`` converters instead of needing a dedicated converter.
+
+Unroll Loops
+***************************************
+
+    `torch/csrc/jit/passes/loop_unrolling.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/loop_unrolling.h>`_
+
+Unrolls the operations of compatable loops (e.g. sufficently short) so that you only have to go through the loop once.
diff --git a/docs/v1.2.0/_sources/contributors/partitioning.rst.txt b/docs/v1.2.0/_sources/contributors/partitioning.rst.txt
new file mode 100644
index 0000000000..2d027c57f4
--- /dev/null
+++ b/docs/v1.2.0/_sources/contributors/partitioning.rst.txt
@@ -0,0 +1,93 @@
+.. _partitioning:
+
+Partitioning Phase
+====================
+
+The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
+Criteria for separation include: Lack of a converter, operator is explicitly set to run in PyTorch by the user or the node has a flag which tells partitioning to
+run in PyTorch by the module fallback passes.
+
+On a high level, Torch-TensorRT partitioning phase does the following:
+
+* Segmentation. Go through the set of operators in order and verify if there is converter for each operator. Then, roughly separate the graph into parts that Torch-TensorRT can support and parts Torch-TensorRT cannot.
+
+* Dependency Analysis. For every to be compiled operator there is a "complete dependency graph", which means that every input can to traced back to an input as Tensor or TensorList. Go through all segments after segmentation then do dependency analysis to ensure that there are only Tensor/TensorList inputs and outputs for TensorRT segments.
+
+* Shape Analysis. For each segments, figure out the input and outputs shapes starting from the provided input shape from the user. Shapes can be calculated by running the graphs with JIT.
+
+* Conversion. Every TensorRT segments will be converted to TensorRT engine. This part is done in compiler.cpp, but it's still a phase in our partitioning process.
+
+* Stitching. Stitch all TensorRT engines with PyTorch nodes altogether.
+
+Here are the brief description of these functions of each file:
+
+PartitonInfo.h/.cpp
+***********************************
+
+    `core/partitioning/PartitionInfo.h <https://github.com/pytorch/TensorRT/blob/master/core/partitioning/PartitionInfo.h>`_
+
+The automatic fallback APIs that is used for partitioning.
+
+
+SegmentedBlock.h/.cpp
+***********************************
+
+    `core/partitioning/SegmentedBlock.h <https://github.com/pytorch/TensorRT/blob/master/core/partitioning/SegmentedBlock.h>`_
+
+The main data structures that is used to maintain information for each segments after segmentation.
+
+
+shape_analysis.h/.cpp
+***********************************
+
+    `core/partitioning/shape_analysis.h <https://github.com/pytorch/TensorRT/blob/master/core/partitioning/shape_analysis.h>`_
+
+Code implementation to get the shapes for each segments by running them in JIT.
+
+
+partitioning.h/.cpp
+***********************************
+    `core/partitioning/partitioning.h <https://github.com/pytorch/TensorRT/blob/master/core/partitioning/partitioning.h>`_
+
+APIs and main code implementation for partitioning phase.
+
+Automatic Fallback
+====================
+
+To enable automatic fallback feature, you can set following attributes in Python:
+
+.. code-block:: none
+
+      import torch
+      import torch_tensorrt as torchtrt
+
+      ...
+      model = MyModel()
+      ts_model = torch.jit.script(model)
+      trt_model = torchtrt.ts.compile(model, **{
+        ...
+        "min_block_size" : 3,
+        "torch_executed_ops": ["aten::add"],
+        "torch_executed_modules": [],
+      })
+
+* enabled: By default automatic fallback will be off. It is enabled by setting it to True.
+* min_block_size: The minimum number of consecutive operations that must satisfy to be converted to TensorRT. For example, if it's set to 3, then there must be 3 consecutive supported operators then this segments will be converted.
+* forced_fallback_ops: A list of strings that will be the names of operations that the user explicitly want to be in PyTorch nodes.
+
+
+.. code-block:: none
+
+      #include "torch/script.h"
+      #include "torch_tensorrt/torch_tensorrt.h"
+
+      ...
+      auto in = torch::randn({1, 3, 224, 224}, {torch::kCUDA});
+
+      auto mod = torch::jit::load("trt_ts_module.ts");
+      auto input_sizes =  std::vector<torchtrt::InputRange>{{in.sizes()}};
+      torchtrt::ts::CompileSpec cfg(input_sizes);
+      cfg.min_block_size = 2;
+      cfg.torch_executed_ops.push_back("aten::relu");
+      auto trt_mod = torchtrt::ts::compile(mod, cfg);
+      auto out = trt_mod.forward({in});
diff --git a/docs/v1.2.0/_sources/contributors/phases.rst.txt b/docs/v1.2.0/_sources/contributors/phases.rst.txt
new file mode 100644
index 0000000000..e99af77faa
--- /dev/null
+++ b/docs/v1.2.0/_sources/contributors/phases.rst.txt
@@ -0,0 +1,46 @@
+Compiler Phases
+----------------
+
+.. toctree::
+    :caption: Compiler Phases
+    :maxdepth: 1
+    :hidden:
+
+    lowering
+    partitioning
+    conversion
+    runtime
+
+Lowering
+^^^^^^^^^^^
+:ref:`lowering`
+
+The lowering is made up of a set of passes (some from PyTorch and some specific to Torch-TensorRT)
+run over the graph IR to map the large PyTorch opset to a reduced opset that is easier to convert to
+TensorRT.
+
+Partitioning
+^^^^^^^^^^^^^
+:ref:`partitioning`
+
+The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
+Criteria for separation include: Lack of a converter, operator is explicitly set to run in PyTorch by the user or the node has a flag which tells partitioning to
+run in PyTorch by the module fallback passes.
+
+Conversion
+^^^^^^^^^^^
+:ref:`conversion`
+
+In the conversion phase we traverse the lowered graph and construct an equivalent TensorRT graph.
+The conversion phase is made up of three main components, a context to manage compile time data,
+a evaluator library which will execute operations that can be resolved at compile time and a converter
+library which maps an op from JIT to TensorRT.
+
+Compilation and Runtime
+^^^^^^^^^^^^^^^^^^^^^^^^
+:ref:`runtime`
+
+The final compilation phase constructs a TorchScript program to run the converted TensorRT engine. It
+takes a serialized engine and instantiates it within a engine manager, then the compiler will
+build out a JIT graph that references this engine and wraps it in a module to return to the user.
+When the user executes the module, the JIT program run in the JIT runtime extended by Torch-TensorRT with the data providied from the user.
diff --git a/docs/v1.2.0/_sources/contributors/runtime.rst.txt b/docs/v1.2.0/_sources/contributors/runtime.rst.txt
new file mode 100644
index 0000000000..23d83b6db2
--- /dev/null
+++ b/docs/v1.2.0/_sources/contributors/runtime.rst.txt
@@ -0,0 +1,85 @@
+.. _execution:
+
+Runtime Phase
+================
+
+The Runtime phase is responsible for constructing self standing TorchScript graphs with embedded TensorRT engines and serving as the runtime
+when these engines are called. The main interface accepts a serialized TensorRT engine. The execution phase
+will deserialize and wrap this engine in a class which maintains a execution context for each engine
+and some metadata about its inputs and outputs and is compatable with the TorchScript interpreter so that
+it can be moved around and used like other TorchScript IValues. The engine is run by providing it and inputs
+to the ``tensorrt::execute_engine`` operator which will take the engine and its inputs and return the results of engine exeuction.
+
+
+Background
+------------
+PyTorch JIT's runtime is based around a stack machine, all operators pop off arguments from the stack, pass them to
+some implementation of the operator then push results back onto the stack. The actual elements of the stack
+are ``torch::jit::IValues``, the same type we evaluate in the conversion phase (the realization of the abstract
+torch::jit::Value type).
+
+TensorRT Engine Executor Op
+----------------------------
+
+When the Torch-TensorRT is loaded, it registers an operator in the PyTorch JIT operator library called
+``trt::execute_engine(Tensor[] inputs, __torch__.torch.classes.tensorrt.Engine engine) -> Tensor[]`` which takes an
+instantiated engine and list of inputs. Compiled graphs store this engine in an attribute so that it is portable and serializable.
+When the op is called, an instnantiated engine and input tensors are popped off the runtime stack. These inputs are passed into a generic engine execution function which
+will run the tensors through the TensorRT engine and return new tensors as results. These tensors are pushed on to the
+stack so that the next op whatever it is can use it.
+
+Constructing the Resulting Graph
+-----------------------------------
+
+Once the engine is deserialized and instantiated, the compiler will construct a graph that will execute the engine when the module is called.
+Here is an example:
+
+.. code-block::
+
+    graph(%self_1 : __torch__.torchvision.models.resnet.___torch_mangle_4847.ResNet_trt,
+      %input_0 : Tensor):
+        %1 : __torch__.torch.classes.tensorrt.Engine = prim::GetAttr[name="__torch___torchvision_models_resnet____torch_mangle_4847_ResNet_trt_engine"](%self_1)
+        %3 : Tensor[] = prim::ListConstruct(%input_0)
+        %4 : Tensor[] = trt::execute_engine(%3, %1)
+        %5 : Tensor = prim::ListUnpack(%4)
+    return (%5)
+
+You can see the engine attribute in the graph and the ``trt::execute_engine`` op taking a list of input tensors and an engine in
+and produces a list of output tensors which is returned. When ``forward`` is called on the module this graph is executed, thereby
+running the TensorRT engine.
+
+In the case of multiple outputs, the compiled graph may repack output tensors into a Tuple to return back to the user.
+
+.. code-block::
+
+    graph(%self_1 : __torch__.PyTorch.Detection.SSD.src.model.SSD300_trt,
+      %input_0 : Tensor):
+        %1 : __torch__.torch.classes.tensorrt.Engine = prim::GetAttr[name="__torch___PyTorch_Detection_SSD_src_model_SSD300_trt_engine"](%self_1)
+        %3 : Tensor[] = prim::ListConstruct(%input_0)
+        %4 : Tensor[] = trt::execute_engine(%3, %1)
+        %5 : Tensor, %6 : Tensor = prim::ListUnpack(%4)
+        %7 : (Tensor, Tensor) = prim::TupleConstruct(%5, %6)
+    return (%7)
+
+Serialization and Deserialization
+----------------------------------
+
+Serialization and deserialization of TensorRT engines embedded in TorchScript graphs are handled by the holder class for the engine and TorchBind.
+When a TorchScript module is saved, the pickler will run serilization on the cuda engine and store the serialized engine in the zip file created.
+When deserializing, the depickler will call a constructor for the engine holder class with the serialized engine so that it can be set up again for
+execution.
+
+ABI Versioning and Serialization Format
+=========================================
+
+Torch-TensorRT programs are standard TorchScript with TensorRT engines as objects embedded in the graph. Therefore there is a serialization format
+for the TensorRT engines. The format for Torch-TensorRT serialized programs are versioned with an "ABI" version which tells the runtime about runtime compatibility.
+
+> Current ABI version is 3
+
+The format is a vector of serialized strings. They encode the following information
+
+* ABI Version for the program
+* Name of the TRT engine
+* Device information: Includes the target device the engine was built on, SM capability and other device information. This information is used at deserialization time to select the correct device to run the engine
+* Serialized TensorRT engine
diff --git a/docs/v1.2.0/_sources/contributors/system_overview.rst.txt b/docs/v1.2.0/_sources/contributors/system_overview.rst.txt
new file mode 100644
index 0000000000..d16697e3a3
--- /dev/null
+++ b/docs/v1.2.0/_sources/contributors/system_overview.rst.txt
@@ -0,0 +1,29 @@
+.. _system_overview:
+
+System Overview
+================
+
+Torch-TensorRT is primarily a C++ Library with a Python API planned. We use Bazel as our build system and target Linux x86_64 and
+Linux aarch64 (only natively) right now. The compiler we use is GCC 7.5.0 and the library is untested with compilers before that
+version so there may be compilation errors if you try to use an older compiler.
+
+The repository is structured into:
+
+* core: Main compiler source code
+* cpp: C++ API
+* tests: tests of the C++ API, the core and converters
+* py: Python API
+* notebooks: Example applications built with Torch-TensorRT
+* docs: Documentation
+* docsrc: Documentation Source
+* third_party: BUILD files for dependency libraries
+* toolchains: Toolchains for different platforms
+
+
+The C++ API is unstable and subject to change until the library matures, though most work is done under the hood in the core.
+
+The core has a couple major parts: The top level compiler interface which coordinates ingesting a module, lowering,
+converting and generating a new module and returning it back to the user. There are the three main phases of the
+compiler, the lowering phase, the conversion phase, and the execution phase.
+
+.. include:: phases.rst
diff --git a/docs/v1.2.0/_sources/contributors/useful_links.rst.txt b/docs/v1.2.0/_sources/contributors/useful_links.rst.txt
new file mode 100644
index 0000000000..d5903a16b0
--- /dev/null
+++ b/docs/v1.2.0/_sources/contributors/useful_links.rst.txt
@@ -0,0 +1,34 @@
+.. _useful_links:
+
+Useful Links for Torch-TensorRT Development
+==============================================
+
+TensorRT Available Layers and Expected Dimensions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* https://docs.nvidia.com/deeplearning/sdk/tensorrt-support-matrix/index.html#layers-matrix
+
+TensorRT C++ Documentation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* https://docs.nvidia.com/deeplearning/tensorrt/api/c_api/index.html
+
+TensorRT Python Documentation (Sometimes easier to read)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/python_api/index.html
+
+PyTorch Functional API
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* https://pytorch.org/docs/stable/nn.functional.html
+
+PyTorch native_ops
+^^^^^^^^^^^^^^^^^^^^^
+
+* https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml
+
+PyTorch IR Documentation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/OVERVIEW.md
diff --git a/docs/v1.2.0/_sources/contributors/writing_converters.rst.txt b/docs/v1.2.0/_sources/contributors/writing_converters.rst.txt
new file mode 100644
index 0000000000..990c4dc77d
--- /dev/null
+++ b/docs/v1.2.0/_sources/contributors/writing_converters.rst.txt
@@ -0,0 +1,129 @@
+.. _writing_converters:
+
+Writing Converters
+===================
+
+Background
+------------
+
+In the JIT IR, operations are represented as nodes in a graph. A node has inputs and outputs, represented by ``torch::jit::Values``
+which are typed abstract representation of data flowing into and out of a node. TensorRT represents its graph though the
+use of ``nvinfer1::ILayers`` and ``nvinfer1::ITensors`` which are its analogues to nodes and values. The goal of
+converters create new ILayers and subgraphs that do operation specified by the node and associate produced ITensors
+and Values together.
+
+Converters
+------------
+
+Converters should be functions which will use a list of inputs (either ``nvinfer1::ITensors`` or ``torch::jit::IValues``) to
+construct an equivalent layer to the LibTorch op.
+
+Converters can be registered using the ``RegisterNodeConversionPatterns`` helper class where you instantiate a
+RegisterNodeConversionPatterns object and call the pattern function on it (like below) which takes a string
+which describes the function schema of the op that will cause the converter to be run and a lambda or function
+which will do the actual conversion:
+
+    Note the pattern function can be chained
+
+.. code-block:: c++
+
+    auto acthardtanh TORCHTRT_UNUSED = RegisterNodeConversionPatterns()
+        .pattern({
+            "aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> (Tensor)",
+            [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+                auto in = args[0].ITensor();
+                auto min = args[1].unwrapToDouble();
+                auto max = args[2].unwrapToDouble();
+
+                auto new_layer = ctx->net->addActivation(*in, nvinfer1::ActivationType::kCLIP);
+                TORCHTRT_CHECK(new_layer, "Unable to create layer for aten::hardtanh");
+
+                new_layer->setAlpha(min);
+                new_layer->setBeta(max);
+
+                new_layer->setName(util::node_info(n).c_str());
+                auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], new_layer->getOutput(0));
+
+                LOG_DEBUG("Output shape: " << out_tensor->getDimensions());
+                return true;
+            }
+        });
+
+
+Converter Contract
+----------------------
+
+What is guaranteed to converters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+1. In the args there will be an entry for each node input value, either a ITensor or IValue
+2. Inputs will be provided in order according to the function schema
+
+Responsibilities of a converter
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+1.  Args must be guaranteed to be a type to unwrap the Arg union without checking, typically input tensor arguments can be expected to be ITensors
+2.  Any weights or static values must guaranteed to be valid until the end of conversion time
+
+    a. A helpful tool is the Weights helper class described below
+
+3.  Converters are expected to produce an IValue or ITensor for each output of a node. The compiler will check this and produce warnings if there are Values that don't have associated ITensors or IValues.
+4.  Outputs must be annotated
+
+    a.  There must be an association between a JIT nodes output values and the new TRT layers output tensors in the ``value_tensor_map`` in the conversion context
+
+5.  Name your layers
+
+    a.  Its much easier to debug when we can track which layers and nodes correspond with each other. The system we are currently using is to use the "node info" of the node as the name of the layer
+
+6.  Name your tensors
+
+    a.  Use the output value debug name as the name for the new ITensor (again for debugging)
+
+Conversion Context
+--------------------
+
+The conversion context maintains the state of conversion, it manages the Network Definition, two maps
+one that stores associations between Values and IValues (the evaluated_value_map) and one that stores
+associations between Values and ITensors, and any sort of memory that needs to live until the end of
+conversion. The main apis that you will interface with in converters is directly accessing the network
+definition to add layers ``ctx->net`` and data association functions ``ctx->AssociateValueAndTensor()``
+and ``ctx->AssociateValueAndIValue()``, which you will use to add layers to the TRT layers and log
+pairs of node outputs and static values or TensorRT layer outputs.
+
+Args
+-------
+
+Arguments provided to the converter are inspectable unions of ``nvinfer1::ITensors`` and ``torch::jit::IValues`` (i.e.
+abstract dataflow in the TensorRT graph and static values). You are guaranteed that you will have some
+argument for each input value for the node. They are provided in the order of the function schema.
+It can be expected that inputs (meaning the parameters that would be passed into the forward
+function of a module in PyTorch) will be ITensors but the Arg class also has mechanisms to inspect arguments safely
+before unwrapping if you are unsure. Args also have deep unwrap methods that let you get straight to the
+underlying data in an IValue if you know it's safe. You can also pass in a fallback value if there is a
+chance the IValue is None. IValues have been extended to be able to hold a wrapper around ITensors only in the case of TensorLists.
+You can get an ITensor from an IValue by a pattern similar to this: ``ivalue.toCustomClass<TensorContainer>()->tensor()``.
+You can tell if an IValue contains a Tensor or an ITensor by using ``ivalue.isTensor()`` or ``ivalue.isCustomClass()``.
+
+
+Weights
+--------------
+
+Weights are used during build time, so any weights need to be guaranteed to live until the end of the conversion phase.
+TensorRT also uses its own weights structure to hold the weights. There is a wrapper around this class available
+to converts which abstracts a lot of this.
+
+The weights wrapper class can accept either ``at::Tensors`` or singular values (right now). You also need to pass the
+conversion context when constructing these weights because internally the weights class will allocate memory managed
+by the conversion context to store a copy of the tensor data. This data gets freed when the conversion context
+destructor gets destroyed so converters don't really need to think about it.
+
+There is metadata generated from the shape of the input data which becomes useful in interfacing with TensorRT, such
+as number of input maps, number of output maps and kernel shape.
+
+Other advice
+--------------
+
+You have the benefit of the full aten library when dealing with weights and other static values. This means that you
+can do quite a bit of work during conversion time to produce efficient conversion. A good example is batch_norm
+converter where the converter does fusion of operations with PyTorch before creating the TensorRT layer.
diff --git a/docs/v1.2.0/_sources/getting_started/getting_started_with_cpp_api.rst.txt b/docs/v1.2.0/_sources/getting_started/getting_started_with_cpp_api.rst.txt
new file mode 100644
index 0000000000..7f7f60a669
--- /dev/null
+++ b/docs/v1.2.0/_sources/getting_started/getting_started_with_cpp_api.rst.txt
@@ -0,0 +1,338 @@
+.. _getting_started_cpp:
+
+Using Torch-TensorRT in  C++
+==============================
+
+If you haven't already, acquire a tarball of the library by following the instructions in :ref:`Installation`
+
+Using Torch-TensorRT in C++
+***************************
+Torch-TensorRT C++ API accepts TorchScript modules (generated either from ``torch.jit.script`` or ``torch.jit.trace``) as an input and returns
+a Torchscript module (optimized using TensorRT). This requires users to use Pytorch (in python) to generate torchscript modules beforehand.
+Please refer to `Creating TorchScript modules in Python <https://nvidia.github.io/Torch-TensorRT/tutorials/creating_torchscript_module_in_python.html>`_ section to generate torchscript graphs.
+
+
+.. _torch_tensorrt_quickstart:
+
+[Torch-TensorRT Quickstart] Compiling TorchScript Modules with ``torchtrtc``
+---------------------------------------------------------------------------------
+
+An easy way to get started with Torch-TensorRT and to check if your model can be supported without extra work is to run it through
+``torchtrtc``, which supports almost all features of the compiler from the command line including post training quantization
+(given a previously created calibration cache). For example we can compile our lenet model by setting our preferred operating
+precision and input size. This new TorchScript file can be loaded into Python (note: you need to ``import torch_tensorrt`` before loading
+these compiled modules because the compiler extends the PyTorch the deserializer and runtime to execute compiled modules).
+
+.. code-block:: shell
+
+    ❯ torchtrtc -p f16 lenet_scripted.ts trt_lenet_scripted.ts "(1,1,32,32)"
+
+    ❯ python3
+    Python 3.6.9 (default, Apr 18 2020, 01:56:04)
+    [GCC 8.4.0] on linux
+    Type "help", "copyright", "credits" or "license" for more information.
+    >>> import torch
+    >>> import torch_tensorrt
+    >>> ts_model = torch.jit.load(“trt_lenet_scripted.ts”)
+    >>> ts_model(torch.randn((1,1,32,32)).to(“cuda”).half())
+
+You can learn more about ``torchtrtc`` usage here: :ref:`torchtrtc`
+
+.. _ts_in_cc:
+
+Working with TorchScript in C++
+--------------------------------
+
+If we are developing an application to deploy with C++, we can save either our traced or scripted module using ``torch.jit.save``
+which will serialize the TorchScript code, weights and other information into a package. This is also where our dependency on Python ends.
+
+.. code-block:: python
+
+    torch_script_module.save("lenet.jit.pt")
+
+From here we can now load our TorchScript module in C++
+
+.. code-block:: c++
+
+    #include <torch/script.h> // One-stop header.
+
+    #include <iostream>
+    #include <memory>
+
+    int main(int argc, const char* argv[]) {
+        torch::jit::Module module;
+        try {
+            // Deserialize the ScriptModule from a file using torch::jit::load().
+            module = torch::jit::load("<PATH TO SAVED TS MOD>");
+        }
+        catch (const c10::Error& e) {
+            std::cerr << "error loading the model\n";
+            return -1;
+        }
+
+        std::cout << "ok\n";
+
+
+You can do full training and inference in C++ with PyTorch / LibTorch if you would like, you can even define your modules in C++ and
+have access to the same powerful tensor library that backs PyTorch. (For more information: https://pytorch.org/cppdocs/).
+For instance we can do inference with our LeNet module like this:
+
+.. code-block:: c++
+
+        mod.eval();
+        torch::Tensor in = torch::randn({1, 1, 32, 32});
+        auto out = mod.forward(in);
+
+and to run on the GPU:
+
+.. code-block:: c++
+
+        mod.eval();
+        mod.to(torch::kCUDA);
+        torch::Tensor in = torch::randn({1, 1, 32, 32}, torch::kCUDA);
+        auto out = mod.forward(in);
+
+As you can see it is pretty similar to the Python API. When you call the ``forward`` method, you invoke the PyTorch JIT compiler, which will optimize and run your TorchScript code.
+
+.. _compile_cpp:
+
+Compiling with Torch-TensorRT in C++
+-------------------------------------
+We are also at the point were we can compile and optimize our module with Torch-TensorRT, but instead of in a JIT fashion we must do it ahead-of-time (AOT) i.e. before we start doing actual inference work
+since it takes a bit of time to optimize the module, it would not make sense to do this every time you run the module or even the first time you run it.
+
+With our module loaded, we can feed it into the Torch-TensorRT compiler. When we do so we must provide some information on the expected input size and also configure any additional settings.
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+    ...
+
+        mod.to(at::kCUDA);
+        mod.eval();
+
+        auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA});
+        auto trt_mod = torch_tensorrt::CompileGraph(mod, std::vector<torch_tensorrt::CompileSpec::InputRange>{{in.sizes()}});
+        auto out = trt_mod.forward({in});
+
+Thats it! Now the graph runs primarily not with the JIT compiler but using TensorRT (though we execute the graph using the JIT runtime).
+
+We can also set settings like operating precision to run in FP16.
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+    ...
+
+        mod.to(at::kCUDA);
+        mod.eval();
+
+        auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA}).to(torch::kHALF);
+        auto input_sizes = std::vector<torch_tensorrt::CompileSpec::InputRange>({in.sizes()});
+        torch_tensorrt::CompileSpec info(input_sizes);
+        info.enable_precisions.insert(torch::kHALF);
+        auto trt_mod = torch_tensorrt::CompileGraph(mod, info);
+        auto out = trt_mod.forward({in});
+
+And now we are running the module in FP16 precision. You can then save the module to load later.
+
+.. code-block:: c++
+
+    trt_mod.save("<PATH TO SAVED TRT/TS MOD>")
+
+Torch-TensorRT compiled TorchScript modules are loaded in the same way as normal TorchScript module. Make sure your deployment application is linked against ``libtorchtrt.so``
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+
+    int main(int argc, const char* argv[]) {
+        torch::jit::Module module;
+        try {
+            // Deserialize the ScriptModule from a file using torch::jit::load().
+            module = torch::jit::load("<PATH TO SAVED TRT/TS MOD>");
+        }
+        catch (const c10::Error& e) {
+            std::cerr << "error loading the model\n";
+            return -1;
+        }
+
+        torch::Tensor in = torch::randn({1, 1, 32, 32}, torch::kCUDA);
+        auto out = mod.forward(in);
+
+        std::cout << "ok\n";
+    }
+
+If you want to save the engine produced by Torch-TensorRT to use in a TensorRT application you can use the ``ConvertGraphToTRTEngine`` API.
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+    ...
+
+        mod.to(at::kCUDA);
+        mod.eval();
+
+        auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA}).to(torch::kHALF);
+        auto input_sizes = std::vector<torch_tensorrt::CompileSpec::InputRange>({in.sizes()});
+        torch_tensorrt::CompileSpec info(input_sizes);
+        info.enabled_precisions.insert(torch::kHALF);
+        auto trt_mod = torch_tensorrt::ConvertGraphToTRTEngine(mod, "forward", info);
+        std::ofstream out("/tmp/engine_converted_from_jit.trt");
+        out << engine;
+        out.close();
+
+.. _under_the_hood:
+
+Under The Hood
+---------------
+
+When a module is provided to Torch-TensorRT, the compiler starts by mapping a graph like you saw above to a graph like this:
+
+.. code-block:: none
+
+    graph(%input.2 : Tensor):
+        %2 : Float(84, 10) = prim::Constant[value=<Tensor>]()
+        %3 : Float(120, 84) = prim::Constant[value=<Tensor>]()
+        %4 : Float(576, 120) = prim::Constant[value=<Tensor>]()
+        %5 : int = prim::Constant[value=-1]() # x.py:25:0
+        %6 : int[] = prim::Constant[value=annotate(List[int], [])]()
+        %7 : int[] = prim::Constant[value=[2, 2]]()
+        %8 : int[] = prim::Constant[value=[0, 0]]()
+        %9 : int[] = prim::Constant[value=[1, 1]]()
+        %10 : bool = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+        %11 : int = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+        %12 : bool = prim::Constant[value=0]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+        %self.classifer.fc3.bias : Float(10) = prim::Constant[value= 0.0464  0.0383  0.0678  0.0932  0.1045 -0.0805 -0.0435 -0.0818  0.0208 -0.0358 [ CUDAFloatType{10} ]]()
+        %self.classifer.fc2.bias : Float(84) = prim::Constant[value=<Tensor>]()
+        %self.classifer.fc1.bias : Float(120) = prim::Constant[value=<Tensor>]()
+        %self.feat.conv2.weight : Float(16, 6, 3, 3) = prim::Constant[value=<Tensor>]()
+        %self.feat.conv2.bias : Float(16) = prim::Constant[value=<Tensor>]()
+        %self.feat.conv1.weight : Float(6, 1, 3, 3) = prim::Constant[value=<Tensor>]()
+        %self.feat.conv1.bias : Float(6) = prim::Constant[value= 0.0530 -0.1691  0.2802  0.1502  0.1056 -0.1549 [ CUDAFloatType{6} ]]()
+        %input0.4 : Tensor = aten::_convolution(%input.2, %self.feat.conv1.weight, %self.feat.conv1.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+        %input0.5 : Tensor = aten::relu(%input0.4) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+        %input1.2 : Tensor = aten::max_pool2d(%input0.5, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+        %input0.6 : Tensor = aten::_convolution(%input1.2, %self.feat.conv2.weight, %self.feat.conv2.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+        %input2.1 : Tensor = aten::relu(%input0.6) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+        %x.1 : Tensor = aten::max_pool2d(%input2.1, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+        %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0
+        %27 : Tensor = aten::matmul(%input.1, %4)
+        %28 : Tensor = trt::const(%self.classifer.fc1.bias)
+        %29 : Tensor = aten::add_(%28, %27, %11)
+        %input0.2 : Tensor = aten::relu(%29) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+        %31 : Tensor = aten::matmul(%input0.2, %3)
+        %32 : Tensor = trt::const(%self.classifer.fc2.bias)
+        %33 : Tensor = aten::add_(%32, %31, %11)
+        %input1.1 : Tensor = aten::relu(%33) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+        %35 : Tensor = aten::matmul(%input1.1, %2)
+        %36 : Tensor = trt::const(%self.classifer.fc3.bias)
+        %37 : Tensor = aten::add_(%36, %35, %11)
+        return (%37)
+    (CompileGraph)
+
+The graph has now been transformed from a collection of modules, each managing their own parameters into a single graph with the parameters inlined
+into the graph and all of the operations laid out. Torch-TensorRT has also executed a number of optimizations and mappings to make the graph easier to translate to TensorRT.
+From here the compiler can assemble the TensorRT engine by following the dataflow through the graph.
+
+When the graph construction phase is complete, Torch-TensorRT produces a serialized TensorRT engine. From here depending on the API, this engine is returned
+to the user or moves into the graph construction phase. Here Torch-TensorRT creates a JIT Module to execute the TensorRT engine which will be instantiated and managed
+by the Torch-TensorRT runtime.
+
+Here is the graph that you get back after compilation is complete:
+
+.. code-block:: none
+
+    graph(%self_1 : __torch__.lenet, %input_0 : Tensor):
+        %1 : ...trt.Engine = prim::GetAttr[name="lenet"](%self_1)
+        %3 : Tensor[] = prim::ListConstruct(%input_0)
+        %4 : Tensor[] = trt::execute_engine(%3, %1)
+        %5 : Tensor = prim::ListUnpack(%4)
+        return (%5)
+
+
+You can see the call where the engine is executed, after extracting the attribute containing the engine and constructing a list of inputs, then returns the tensors back to the user.
+
+.. _unsupported_ops:
+
+Working with Unsupported Operators
+-----------------------------------
+
+Torch-TensorRT is a new library and the PyTorch operator library is quite large, so there will be ops that aren't supported natively by the compiler. You can either use the composition techinques
+shown above to make modules are fully Torch-TensorRT supported and ones that are not and stitch the modules together in the deployment application or you can register converters for missing ops.
+
+    You can check support without going through the full compilation pipleine using the ``torch_tensorrt::CheckMethodOperatorSupport(const torch::jit::Module& module, std::string method_name)`` api
+    to see what operators are not supported. ``torchtrtc`` automatically checks modules with this method before starting compilation and will print out a list of operators that are not supported.
+
+.. _custom_converters:
+
+Registering Custom Converters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Operations are mapped to TensorRT through the use of modular converters, a function that takes a node from a the JIT graph and produces an equivalent layer or subgraph in TensorRT.
+Torch-TensorRT ships with a library of these converters stored in a registry, that will be executed depending on the node being parsed. For instance a ``aten::relu(%input0.4)`` instruction will trigger
+the relu converter to be run on it, producing an activation layer in the TensorRT graph. But since this library is not exhaustive you may need to write your own to get Torch-TensorRT
+to support your module.
+
+Shipped with the Torch-TensorRT distribution are the internal core API headers. You can therefore access the converter registry and add a converter for the op you need.
+
+For example, if we try to compile a graph with a build of Torch-TensorRT that doesn't support the flatten operation (``aten::flatten``) you may see this error:
+
+.. code-block:: none
+
+    terminate called after throwing an instance of 'torch_tensorrt::Error'
+    what():  [enforce fail at core/conversion/conversion.cpp:109] Expected converter to be true but got false
+    Unable to convert node: %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0 (conversion.AddLayer)
+    Schema: aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)
+    Converter for aten::flatten requested, but no such converter was found.
+    If you need a converter for this operator, you can try implementing one yourself
+    or request a converter: https://www.github.com/NVIDIA/Torch-TensorRT/issues
+
+We can register a converter for this operator in our application. All of the tools required to build a converter can be imported by including ``torch_tensorrt/core/conversion/converters/converters.h``.
+We start by creating an instance of the self-registering class ``torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns()`` which will register converters
+in the global converter registry, associating a function schema like ``aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)`` with a lambda that
+will take the state of the conversion, the node/operation in question to convert and all of the inputs to the node and produces as a side effect a new layer in the TensorRT network.
+Arguments are passed as a vector of inspectable unions of TensorRT ``ITensors`` and Torch ``IValues`` in the order arguments are listed in the schema.
+
+Below is a implementation of a ``aten::flatten`` converter that we can use in our application. You have full access to the Torch and TensorRT libraries in the converter implementation. So
+for example we can quickly get the output size by just running the operation in PyTorch instead of implementing the full calculation outself like we do below for this flatten converter.
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+    #include "torch_tensorrt/core/conversion/converters/converters.h"
+
+    static auto flatten_converter = torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns()
+        .pattern({
+            "aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)",
+            [](torch_tensorrt::core::conversion::ConversionCtx* ctx,
+               const torch::jit::Node* n,
+               torch_tensorrt::core::conversion::converters::args& args) -> bool {
+                auto in = args[0].ITensor();
+                auto start_dim = args[1].unwrapToInt();
+                auto end_dim = args[2].unwrapToInt();
+                auto in_shape = torch_tensorrt::core::util::toVec(in->getDimensions());
+                auto out_shape = torch::flatten(torch::rand(in_shape), start_dim, end_dim).sizes();
+
+                auto shuffle = ctx->net->addShuffle(*in);
+                shuffle->setReshapeDimensions(torch_tensorrt::core::util::toDims(out_shape));
+                shuffle->setName(torch_tensorrt::core::util::node_info(n).c_str());
+
+                auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle->getOutput(0));
+                return true;
+            }
+        });
+
+    int main() {
+        ...
+
+To use this converter in Python, it is recommended to use PyTorch's `C++ / CUDA Extention <https://pytorch.org/tutorials/advanced/cpp_extension.html#custom-c-and-cuda-extensions>`_
+template to wrap your library of converters into a ``.so`` that you can load with ``ctypes.CDLL()`` in your Python application.
+
+You can find more information on all the details of writing converters in the contributors documentation (:ref:`writing_converters`).
+If you find yourself with a large library of converter implementations, do consider upstreaming them, PRs are welcome and it would be great for the community to benefit as well.
diff --git a/docs/v1.2.0/_sources/getting_started/getting_started_with_python_api.rst.txt b/docs/v1.2.0/_sources/getting_started/getting_started_with_python_api.rst.txt
new file mode 100644
index 0000000000..fece176156
--- /dev/null
+++ b/docs/v1.2.0/_sources/getting_started/getting_started_with_python_api.rst.txt
@@ -0,0 +1,58 @@
+.. _getting_started_with_python_api:
+
+Using Torch-TensorRT in Python
+*******************************
+
+The Torch-TensorRT Python API supports a number of unique usecases compared to the CLI and C++ APIs which solely support TorchScript compilation.
+
+Torch-TensorRT Python API can accept a ``torch.nn.Module``, ``torch.jit.ScriptModule``, or ``torch.fx.GraphModule`` as an input.
+Depending on what is provided one of the two frontends (TorchScript or FX) will be selected to compile the module. Provided the
+module type is supported, users may explicitly set which frontend they would like to use using the ``ir`` flag for ``compile``.
+If given a ``torch.nn.Module`` and the ``ir`` flag is set to either ``default`` or ``torchscript`` the module will be run through
+``torch.jit.script`` to convert the input module into a  TorchScript module.
+
+
+To compile your input ``torch.nn.Module`` with Torch-TensorRT, all you need to do is provide the module and inputs
+to Torch-TensorRT and you will be returned an optimized TorchScript module to run or add into another PyTorch module. Inputs
+is a list of ``torch_tensorrt.Input`` classes which define input's shape, datatype and memory format. You can also specify settings such as
+operating precision for the engine or target device. After compilation you can save the module just like any other module
+to load in a deployment application. In order to load a TensorRT/TorchScript module, make sure you first import ``torch_tensorrt``.
+
+.. code-block:: python
+
+    import torch_tensorrt
+
+    ...
+
+    model = MyModel().eval()  # torch module needs to be in eval (not training) mode
+
+    inputs = [
+        torch_tensorrt.Input(
+            min_shape=[1, 1, 16, 16],
+            opt_shape=[1, 1, 32, 32],
+            max_shape=[1, 1, 64, 64],
+            dtype=torch.half,
+        )
+    ]
+    enabled_precisions = {torch.float, torch.half}  # Run with fp16
+
+    trt_ts_module = torch_tensorrt.compile(
+        model, inputs=inputs, enabled_precisions=enabled_precisions
+    )
+
+    input_data = input_data.to("cuda").half()
+    result = trt_ts_module(input_data)
+    torch.jit.save(trt_ts_module, "trt_ts_module.ts")
+
+.. code-block:: python
+
+    # Deployment application
+    import torch
+    import torch_tensorrt
+
+    trt_ts_module = torch.jit.load("trt_ts_module.ts")
+    input_data = input_data.to("cuda").half()
+    result = trt_ts_module(input_data)
+
+Torch-TensorRT Python API also provides ``torch_tensorrt.ts.compile`` which accepts a TorchScript module as input and ``torch_tensorrt.fx.compile`` which accepts a FX GraphModule as input.
+
diff --git a/docs/v1.2.0/_sources/getting_started/getting_started_with_windows.rst.txt b/docs/v1.2.0/_sources/getting_started/getting_started_with_windows.rst.txt
new file mode 100644
index 0000000000..04eba960e0
--- /dev/null
+++ b/docs/v1.2.0/_sources/getting_started/getting_started_with_windows.rst.txt
@@ -0,0 +1,78 @@
+.. _getting_started_windows:
+
+Building Torch-TensorRT on Windows
+====================================
+
+Torch-TensorRT has community support for Windows platform using CMake
+
+Prerequisite:
+
+* Microsoft Visual Studio
+* LibTorch
+* TensorRT
+* CUDA
+* cuDNN
+
+
+Build configuration
+-------------------
+
+* Open Microsoft Visual Studio
+* Open Torch-TensorRT source code folder
+* Open Manage configurations -> Edit JSON to open CMakeSettings.json file.
+* Configure the CMake build configurations. Following is an example configuration:
+
+.. code-block:: none
+
+    {
+      "configurations": [
+        {
+          "name": "x64-Debug",
+          "generator": "Ninja",
+          "configurationType": "Debug",
+          "inheritEnvironments": [ "msvc_x64_x64" ],
+          "buildRoot": "${projectDir}\\out\\build\\${name}",
+          "installRoot": "${projectDir}\\out\\install\\${name}",
+          "cmakeCommandArgs": "-S . -B out",
+          "buildCommandArgs": "cmake --build out",
+          "ctestCommandArgs": "",
+          "variables": [
+            {
+              "name": "CMAKE_MODULE_PATH",
+              "value": "$PWD\cmake\Modules",
+              "type": "FILEPATH"
+            },
+            {
+              "name": "Torch_DIR",
+              "value": "<Path to libtorch>\share\cmake\Torch",
+              "type": "FILEPATH"
+            },
+            {
+              "name": "TensorRT_ROOT",
+              "value": "<Path to TensorRT directory>",
+              "type": "FILEPATH"
+            },
+            {
+              "name": "CMAKE_BUILD_TYPE",
+              "value": "Release",
+              "type": " STRING"
+            }
+          ]
+        }
+      ]
+    }
+
+
+Compilation
+-----------
+
+* Click Build -> Build All or directly press Ctrl + Shift + B
+
+Note: After successful compilation, the build artifacts will be present at buildRoot path configured.
+
+Installation
+------------
+
+* Build -> Install Torch-TensorRT
+
+Note: After successful installation, the artifacts will be present at installRoot.
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/getting_started/installation.rst.txt b/docs/v1.2.0/_sources/getting_started/installation.rst.txt
new file mode 100644
index 0000000000..937dd1dde9
--- /dev/null
+++ b/docs/v1.2.0/_sources/getting_started/installation.rst.txt
@@ -0,0 +1,362 @@
+.. _installation:
+
+Installation
+=============
+
+Precompiled Binaries
+*********************
+
+Dependencies
+---------------
+
+You need to have either PyTorch or LibTorch installed based on if you are using Python or C++
+and you must have CUDA, cuDNN and TensorRT installed.
+
+    * https://www.pytorch.org
+    * https://developer.nvidia.com/cuda
+    * https://developer.nvidia.com/cudnn
+    * https://developer.nvidia.com/tensorrt
+
+
+Python Package
+---------------
+
+You can install the python package using
+
+.. code-block:: sh
+
+    pip3 install torch-tensorrt -f https://github.com/pytorch/TensorRT/releases
+
+.. _bin-dist:
+
+C++ Binary Distribution
+------------------------
+
+Precompiled tarballs for releases are provided here: https://github.com/pytorch/TensorRT/releases
+
+.. _compile-from-source:
+
+Compiling From Source
+******************************************
+
+.. _installing-deps:
+
+Dependencies for Compilation
+-------------------------------
+
+Torch-TensorRT is built with Bazel, so begin by installing it.
+
+    * The easiest way is to install bazelisk using the method of your choosing https://github.com/bazelbuild/bazelisk
+    * Otherwise you can use the following instructions to install binaries https://docs.bazel.build/versions/master/install.html
+    * Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions
+
+    .. code-block:: shell
+
+        export BAZEL_VERSION=$(cat <PATH_TO_TORCHTRT_ROOT>/.bazelversion)
+        mkdir bazel
+        cd bazel
+        curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-dist.zip
+        unzip bazel-$BAZEL_VERSION-dist.zip
+        bash ./compile.sh
+        cp output/bazel /usr/local/bin/
+
+
+You will also need to have CUDA installed on the system (or if running in a container, the system must have
+the CUDA driver installed and the container must have CUDA)
+
+The correct LibTorch version will be pulled down for you by bazel.
+
+    NOTE: For best compatability with official PyTorch, use torch==1.10.0+cuda113, TensorRT 8.0 and cuDNN 8.2 for CUDA 11.3 however Torch-TensorRT itself supports
+    TensorRT and cuDNN for other CUDA versions for usecases such as using NVIDIA compiled distributions of PyTorch that use other versions of CUDA
+    e.g. aarch64 or custom compiled version of PyTorch.
+
+.. _abis:
+
+Choosing the Right ABI
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+Likely the most complicated thing about compiling Torch-TensorRT is selecting the correct ABI. There are two options
+which are incompatible with each other, pre-cxx11-abi and the cxx11-abi. The complexity comes from the fact that while
+the most popular distribution of PyTorch (wheels downloaded from pytorch.org/pypi directly) use the pre-cxx11-abi, most
+other distributions you might encounter (e.g. ones from NVIDIA - NGC containers, and builds for Jetson as well as certain
+libtorch builds and likely if you build PyTorch from source) use the cxx11-abi. It is important you compile Torch-TensorRT
+using the correct ABI to function properly. Below is a table with general pairings of PyTorch distribution sources and the
+recommended commands:
+
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| PyTorch Source                                              | Recommended C++ Compilation Command                      | Recommended Python Compilation Command                             |
++=============================================================+==========================================================+====================================================================+
+| PyTorch whl file from PyTorch.org                           | bazel build //:libtorchtrt -c opt --config pre_cxx11_abi | python3 setup.py bdist_wheel                                       |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| libtorch-shared-with-deps-*.zip from PyTorch.org            | bazel build //:libtorchtrt -c opt --config pre_cxx11_abi | python3 setup.py bdist_wheel                                       |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| libtorch-cxx11-abi-shared-with-deps-*.zip from PyTorch.org  | bazel build //:libtorchtrt -c opt                        | python3 setup.py bdist_wheel --use-cxx11-abi                       |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| PyTorch preinstalled in an NGC container                    | bazel build //:libtorchtrt -c opt                        | python3 setup.py bdist_wheel --use-cxx11-abi                       |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| PyTorch from the NVIDIA Forums for Jetson                   | bazel build //:libtorchtrt -c opt                        | python3 setup.py bdist_wheel --jetpack-version 4.6 --use-cxx11-abi |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| PyTorch built from Source                                   | bazel build //:libtorchtrt -c opt                        | python3 setup.py bdist_wheel --use-cxx11-abi                       |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+
+    NOTE: For all of the above cases you must correctly declare the source of PyTorch you intend to use in your WORKSPACE file for both Python and C++ builds. See below for more information
+
+You then have two compilation options:
+
+.. _build-from-archive:
+
+**Building using cuDNN & TensorRT tarball distributions**
+--------------------------------------------------------------
+
+    This is recommended so as to build Torch-TensorRT hermetically and insures any compilation errors are not caused by version issues
+
+    Make sure when running Torch-TensorRT that these versions of the libraries are prioritized in your ``$LD_LIBRARY_PATH``
+
+You need to download the tarball distributions of TensorRT and cuDNN from the NVIDIA website.
+    * https://developer.nvidia.com/cudnn
+    * https://developer.nvidia.com/tensorrt
+
+Place these files in a directory (the directories ``third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]`` exist for this purpose)
+
+Then compile referencing the directory with the tarballs
+
+    If you get errors regarding the packages, check their sha256 hashes and make sure they match the ones listed in ``WORKSPACE``
+
+Release Build
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt -c opt --distdir third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]
+
+A tarball with the include files and library can then be found in ``bazel-bin``
+
+.. _build-from-archive-debug:
+
+Debug Build
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+To build with debug symbols use the following command
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt -c dbg --distdir third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]
+
+A tarball with the include files and library can then be found in ``bazel-bin``
+
+Pre CXX11 ABI Build
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+To build using the pre-CXX11 ABI use the ``pre_cxx11_abi`` config
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt --config pre_cxx11_abi -c [dbg/opt] --distdir third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]
+
+A tarball with the include files and library can then be found in ``bazel-bin``
+
+.. _build-from-local:
+
+**Building using locally installed cuDNN & TensorRT**
+--------------------------------------------------------------
+
+    If you encounter bugs and you compiled using this method please disclose that you used local sources in the issue (an ldd dump would be nice too)
+
+Install TensorRT, CUDA and cuDNN on the system before starting to compile.
+
+In WORKSPACE comment out:
+
+.. code-block:: python
+
+    # Downloaded distributions to use with --distdir
+    http_archive(
+        name="cudnn",
+        urls=[
+            "<URL>",
+        ],
+        build_file="@//third_party/cudnn/archive:BUILD",
+        sha256="<TAR SHA256>",
+        strip_prefix="cuda",
+    )
+
+    http_archive(
+        name="tensorrt",
+        urls=[
+            "<URL>",
+        ],
+        build_file="@//third_party/tensorrt/archive:BUILD",
+        sha256="<TAR SHA256>",
+        strip_prefix="TensorRT-<VERSION>",
+    )
+
+and uncomment
+
+.. code-block:: python
+
+    # Locally installed dependencies
+    new_local_repository(
+        name="cudnn", path="/usr/", build_file="@//third_party/cudnn/local:BUILD"
+    )
+
+    new_local_repository(
+        name="tensorrt", path="/usr/", build_file="@//third_party/tensorrt/local:BUILD"
+    )
+
+Release Build
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+Compile using:
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt -c opt
+
+A tarball with the include files and library can then be found in ``bazel-bin``
+
+.. _build-from-local-debug:
+
+Debug Build
+^^^^^^^^^^^^
+
+To build with debug symbols use the following command
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt -c dbg
+
+
+A tarball with the include files and library can then be found in ``bazel-bin``
+
+Pre CXX11 ABI Build
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+To build using the pre-CXX11 ABI use the ``pre_cxx11_abi`` config
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt --config pre_cxx11_abi -c [dbg/opt]
+
+**Building with CMake**
+-----------------------
+
+It is possible to build the API libraries (in cpp/) and the torchtrtc executable using CMake instead of Bazel.
+Currently, the python API and the tests cannot be built with CMake.
+Begin by installing CMake.
+
+    * Latest releases of CMake and instructions on how to install are available for different platforms
+      [on their website](https://cmake.org/download/).
+
+A few useful CMake options include:
+
+    * CMake finders for TensorRT and cuDNN are provided in `cmake/Modules`. In order for CMake to use them, pass
+      `-DCMAKE_MODULE_PATH=cmake/Modules` when configuring the project with CMake.
+    * Libtorch provides its own CMake finder. In case CMake doesn't find it, pass the path to your install of
+      libtorch with `-DTorch_DIR=<path to libtorch>/share/cmake/Torch`
+    * If TensorRT is not found with the provided cmake finder, specify `-DTensorRT_ROOT=<path to TensorRT>`
+    * Finally, configure and build the project in a build directory of your choice with the following command
+      from the root of Torch-TensorRT project:
+
+    .. code-block:: shell
+
+        cmake -S. -B<build directory> \
+            [-DCMAKE_MODULE_PATH=cmake/Module] \
+            [-DTorch_DIR=<path to libtorch>/share/cmake/Torch] \
+            [-DTensorRT_ROOT=<path to TensorRT>] \
+            [-DCMAKE_BUILD_TYPE=Debug|Release]
+        cmake --build <build directory>
+
+**Building the Python package**
+--------------------------------
+
+Begin by installing ``ninja``
+
+You can build the Python package using ``setup.py`` (this will also build the correct version of ``libtorchtrt.so``)
+
+.. code-block:: shell
+
+    python3 setup.py [install/bdist_wheel]
+
+Debug Build
+^^^^^^^^^^^^
+
+.. code-block:: shell
+
+    python3 setup.py develop [--user]
+
+This also compiles a debug build of ``libtorchtrt.so``
+
+**Building Natively on aarch64 (Jetson)**
+-------------------------------------------
+
+Prerequisites
+^^^^^^^^^^^^^^
+
+Install or compile a build of PyTorch/LibTorch for aarch64
+
+NVIDIA hosts builds the latest release branch for Jetson here:
+
+    https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048
+
+
+Enviorment Setup
+^^^^^^^^^^^^^^^^^
+
+To build natively on aarch64-linux-gnu platform, configure the ``WORKSPACE`` with local available dependencies.
+
+1. Replace ``WORKSPACE`` with the corresponding WORKSPACE file in ``//toolchains/jp_workspaces``
+
+2. Configure the correct paths to directory roots containing local dependencies in the ``new_local_repository`` rules:
+
+    NOTE: If you installed PyTorch using a pip package, the correct path is the path to the root of the python torch package.
+    In the case that you installed with ``sudo pip install`` this will be ``/usr/local/lib/python3.8/dist-packages/torch``.
+    In the case you installed with ``pip install --user`` this will be ``$HOME/.local/lib/python3.8/site-packages/torch``.
+
+In the case you are using NVIDIA compiled pip packages, set the path for both libtorch sources to the same path. This is because unlike
+PyTorch on x86_64, NVIDIA aarch64 PyTorch uses the CXX11-ABI. If you compiled for source using the pre_cxx11_abi and only would like to
+use that library, set the paths to the same path but when you compile make sure to add the flag ``--config=pre_cxx11_abi``
+
+.. code-block:: shell
+
+    new_local_repository(
+        name = "libtorch",
+        path = "/usr/local/lib/python3.8/dist-packages/torch",
+        build_file = "third_party/libtorch/BUILD"
+    )
+
+    new_local_repository(
+        name = "libtorch_pre_cxx11_abi",
+        path = "/usr/local/lib/python3.8/dist-packages/torch",
+        build_file = "third_party/libtorch/BUILD"
+    )
+
+
+Compile C++ Library and Compiler CLI
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+    NOTE: Due to shifting dependency locations between Jetpack 4.5 and 4.6 there is a now a flag to inform bazel of the Jetpack version
+
+    .. code-block:: shell
+
+        --platforms //toolchains:jetpack_x.x
+
+
+Compile Torch-TensorRT library using bazel command:
+
+.. code-block:: shell
+
+   bazel build //:libtorchtrt --platforms //toolchains:jetpack_5.0
+
+Compile Python API
+^^^^^^^^^^^^^^^^^^^^
+
+    NOTE: Due to shifting dependencies locations between Jetpack 4.5 and newer Jetpack verisons there is now a flag for ``setup.py`` which sets the jetpack version (default: 5.0)
+
+Compile the Python API using the following command from the ``//py`` directory:
+
+.. code-block:: shell
+
+    python3 setup.py install --use-cxx11-abi
+
+If you have a build of PyTorch that uses Pre-CXX11 ABI drop the ``--use-cxx11-abi`` flag
+
+If you are building for Jetpack 4.5 add the ``--jetpack-version 5.0`` flag
diff --git a/docs/v1.2.0/_sources/index.rst.txt b/docs/v1.2.0/_sources/index.rst.txt
new file mode 100644
index 0000000000..c0de8dd400
--- /dev/null
+++ b/docs/v1.2.0/_sources/index.rst.txt
@@ -0,0 +1,139 @@
+.. Torch-TensorRT documentation master file, created by
+   sphinx-quickstart on Mon May  4 13:43:16 2020.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Torch-TensorRT
+==============
+Ahead-of-time compilation of TorchScript / PyTorch JIT for NVIDIA GPUs
+-----------------------------------------------------------------------
+Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA's TensorRT Deep Learning Optimizer and Runtime.
+Unlike PyTorch's Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your
+TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting
+a TensorRT engine. Torch-TensorRT operates as a PyTorch extention and compiles modules that integrate into the JIT runtime seamlessly.
+After compilation using the optimized graph should feel no different than running a TorchScript module.
+You also have access to TensorRT's suite of configurations at compile time, so you are able to specify
+operating precision (FP32/FP16/INT8) and other settings for your module.
+
+More Information / System Architecture:
+
+* `GTC 2020 Talk <https://developer.nvidia.com/gtc/2020/video/s21671>`_
+
+Getting Started
+----------------
+* :ref:`installation`
+* :ref:`getting_started_with_python_api`
+* :ref:`getting_started_cpp`
+
+.. toctree::
+   :caption: Getting Started
+   :maxdepth: 1
+   :hidden:
+
+   getting_started/installation
+   getting_started/getting_started_with_python_api
+   getting_started/getting_started_with_cpp_api
+
+
+Tutorials
+------------
+* :ref:`creating_a_ts_mod`
+* :ref:`getting_started_with_fx`
+* :ref:`ptq`
+* :ref:`runtime`
+* :ref:`serving_torch_tensorrt_with_triton`
+* :ref:`use_from_pytorch`
+* :ref:`using_dla`
+* :ref:`notebooks`
+
+.. toctree::
+   :caption: Tutorials
+   :maxdepth: 1
+   :hidden:
+
+   tutorials/creating_torchscript_module_in_python
+   tutorials/getting_started_with_fx_path
+   tutorials/ptq
+   tutorials/runtime
+   tutorials/serving_torch_tensorrt_with_triton
+   tutorials/use_from_pytorch
+   tutorials/using_dla
+   tutorials/notebooks
+
+Python API Documenation
+------------------------
+* :ref:`torch_tensorrt_py`
+* :ref:`torch_tensorrt_logging_py`
+* :ref:`torch_tensorrt_ptq_py`
+* :ref:`torch_tensorrt_ts_py`
+* :ref:`torch_tensorrt_fx_py`
+
+.. toctree::
+   :caption: Python API Documenation
+   :maxdepth: 0
+   :hidden:
+
+   py_api/torch_tensorrt
+   py_api/logging
+   py_api/ptq
+   py_api/ts
+   py_api/fx
+
+C++ API Documenation
+----------------------
+* :ref:`namespace_torch_tensorrt`
+* :ref:`namespace_torch_tensorrt__logging`
+* :ref:`namespace_torch_tensorrt__ptq`
+* :ref:`namespace_torch_tensorrt__torchscript`
+
+
+.. toctree::
+   :caption: C++ API Documenation
+   :maxdepth: 1
+   :hidden:
+
+   _cpp_api/torch_tensort_cpp
+   _cpp_api/namespace_torch_tensorrt
+   _cpp_api/namespace_torch_tensorrt__logging
+   _cpp_api/namespace_torch_tensorrt__torchscript
+   _cpp_api/namespace_torch_tensorrt__ptq
+
+CLI Documentation
+---------------------
+* :ref:`torchtrtc`
+
+.. toctree::
+   :caption: CLI Documenation
+   :maxdepth: 0
+   :hidden:
+
+   cli/torchtrtc
+
+
+Contributor Documentation
+--------------------------------
+* :ref:`system_overview`
+* :ref:`writing_converters`
+* :ref:`useful_links`
+
+.. toctree::
+   :caption: Contributor Documentation
+   :maxdepth: 1
+   :hidden:
+
+   contributors/system_overview
+   contributors/writing_converters
+   contributors/useful_links
+
+Indices
+----------------
+* :ref:`supported_ops`
+* :ref:`genindex`
+* :ref:`search`
+
+.. toctree::
+   :caption: Indices
+   :maxdepth: 1
+   :hidden:
+
+   indices/supported_ops
diff --git a/docs/v1.2.0/_sources/indices/supported_ops.rst.txt b/docs/v1.2.0/_sources/indices/supported_ops.rst.txt
new file mode 100644
index 0000000000..0367c02710
--- /dev/null
+++ b/docs/v1.2.0/_sources/indices/supported_ops.rst.txt
@@ -0,0 +1,285 @@
+
+.. _supported_ops:
+
+=================================
+Operators Supported
+=================================
+
+
+Operators Currently Supported Through Converters
+-------------------------------------------------
+
+- aten::_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> (Tensor)
+- aten::_convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> (Tensor)
+- aten::abs(Tensor self) -> (Tensor)
+- aten::acos(Tensor self) -> (Tensor)
+- aten::acosh(Tensor self) -> (Tensor)
+- aten::adaptive_avg_pool1d(Tensor self, int[1] output_size) -> (Tensor)
+- aten::adaptive_avg_pool2d(Tensor self, int[2] output_size) -> (Tensor)
+- aten::adaptive_avg_pool3d(Tensor self, int[3] output_size) -> (Tensor)
+- aten::adaptive_max_pool1d(Tensor self, int[2] output_size) -> (Tensor, Tensor)
+- aten::adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor)
+- aten::adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor)
+- aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
+- aten::add.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
+- aten::add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> (Tensor(a!))
+- aten::argmax(Tensor self, int dim, bool keepdim=False) -> (Tensor)
+- aten::argmin(Tensor self, int dim, bool keepdim=False) -> (Tensor)
+- aten::asin(Tensor self) -> (Tensor)
+- aten::asinh(Tensor self) -> (Tensor)
+- aten::atan(Tensor self) -> (Tensor)
+- aten::atanh(Tensor self) -> (Tensor)
+- aten::avg_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=[0], bool ceil_mode=False, bool count_include_pad=True) -> (Tensor)
+- aten::avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=[0, 0], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> (Tensor)
+- aten::avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=[], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> (Tensor)
+- aten::batch_norm(Tensor input, Tensor? gamma, Tensor? beta, Tensor? mean, Tensor? var, bool training, float momentum, float eps, bool cudnn_enabled) -> (Tensor)
+- aten::bitwise_not(Tensor self) -> (Tensor)
+- aten::bmm(Tensor self, Tensor mat2) -> (Tensor)
+- aten::cat(Tensor[] tensors, int dim=0) -> (Tensor)
+- aten::ceil(Tensor self) -> (Tensor)
+- aten::clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> (Tensor)
+- aten::clamp_max(Tensor self, Scalar max) -> (Tensor)
+- aten::clamp_min(Tensor self, Scalar min) -> (Tensor)
+- aten::constant_pad_nd(Tensor self, int[] pad, Scalar value=0) -> (Tensor)
+- aten::cos(Tensor self) -> (Tensor)
+- aten::cosh(Tensor self) -> (Tensor)
+- aten::cumsum(Tensor self, int dim, *, int? dtype=None) -> (Tensor)
+- aten::div.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::div.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> (Tensor)
+- aten::div_.Scalar(Tensor(a!) self, Scalar other) -> (Tensor(a!))
+- aten::div_.Tensor(Tensor(a!) self, Tensor other) -> (Tensor(a!))
+- aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> (Tensor)
+- aten::embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> (Tensor)
+- aten::eq.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::eq.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::erf(Tensor self) -> (Tensor)
+- aten::exp(Tensor self) -> (Tensor)
+- aten::expand(Tensor(a) self, int[] size, *, bool implicit=False) -> (Tensor(a))
+- aten::expand_as(Tensor(a) self, Tensor other) -> (Tensor(a))
+- aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> (Tensor)
+- aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> (Tensor)
+- aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)
+- aten::floor(Tensor self) -> (Tensor)
+- aten::floor_divide(Tensor self, Tensor other) -> (Tensor)
+- aten::floor_divide.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::ge.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::ge.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor)
+- aten::gt.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::gt.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> (Tensor)
+- aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> (Tensor(a!))
+- aten::index.Tensor(Tensor self, Tensor?[] indices) -> (Tensor)
+- aten::instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -> (Tensor)
+- aten::layer_norm(Tensor input, int[] normalized_shape, Tensor? gamma, Tensor? beta, float eps, bool cudnn_enabled) -> (Tensor)
+- aten::le.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::le.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -> (Tensor)
+- aten::leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> (Tensor(a!))
+- aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> (Tensor)
+- aten::log(Tensor self) -> (Tensor)
+- aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor)
+- aten::lt.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::lt.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -> (Tensor)
+- aten::matmul(Tensor self, Tensor other) -> (Tensor)
+- aten::max(Tensor self) -> (Tensor)
+- aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
+- aten::max.other(Tensor self, Tensor other) -> (Tensor)
+- aten::max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=[], int[1] dilation=[], bool ceil_mode=False) -> (Tensor)
+- aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=[0, 0], int[2] dilation=[1, 1], bool ceil_mode=False) -> (Tensor)
+- aten::max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=[], int[3] dilation=[], bool ceil_mode=False) -> (Tensor)
+- aten::mean(Tensor self, *, int? dtype=None) -> (Tensor)
+- aten::mean.dim(Tensor self, int[] dim, bool keepdim=False, *, int? dtype=None) -> (Tensor)
+- aten::min(Tensor self) -> (Tensor)
+- aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
+- aten::min.other(Tensor self, Tensor other) -> (Tensor)
+- aten::mul.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::mul.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::mul_.Tensor(Tensor(a!) self, Tensor other) -> (Tensor(a!))
+- aten::narrow(Tensor(a) self, int dim, int start, int length) -> (Tensor(a))
+- aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, int length) -> (Tensor(a))
+- aten::ne.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::ne.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::neg(Tensor self) -> (Tensor)
+- aten::norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -> (Tensor)
+- aten::permute(Tensor(a) self, int[] dims) -> (Tensor(a))
+- aten::pixel_shuffle(Tensor self, int upscale_factor) -> (Tensor)
+- aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -> (Tensor)
+- aten::pow.Tensor_Tensor(Tensor self, Tensor exponent) -> (Tensor)
+- aten::prelu(Tensor self, Tensor weight) -> (Tensor)
+- aten::prod(Tensor self, *, int? dtype=None) -> (Tensor)
+- aten::prod.dim_int(Tensor self, int dim, bool keepdim=False, *, int? dtype=None) -> (Tensor)
+- aten::reciprocal(Tensor self) -> (Tensor)
+- aten::reflection_pad1d(Tensor self, int[2] padding) -> (Tensor)
+- aten::reflection_pad2d(Tensor self, int[4] padding) -> (Tensor)
+- aten::relu(Tensor input) -> (Tensor)
+- aten::relu_(Tensor(a!) self) -> (Tensor(a!))
+- aten::repeat(Tensor self, int[] repeats) -> (Tensor)
+- aten::repeat_interleave.self_int(Tensor self, int repeats, int? dim=None, *, int? output_size=None) -> (Tensor)
+- aten::replication_pad1d(Tensor self, int[2] padding) -> (Tensor)
+- aten::replication_pad2d(Tensor self, int[4] padding) -> (Tensor)
+- aten::replication_pad3d(Tensor self, int[6] padding) -> (Tensor)
+- aten::reshape(Tensor self, int[] shape) -> (Tensor)
+- aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> (Tensor)
+- aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
+- aten::rsub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
+- aten::scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> (Tensor)
+- aten::scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> (Tensor)
+- aten::select.int(Tensor(a) self, int dim, int index) -> (Tensor(a))
+- aten::sigmoid(Tensor input) -> (Tensor)
+- aten::sigmoid_(Tensor(a!) self) -> (Tensor(a!))
+- aten::sin(Tensor self) -> (Tensor)
+- aten::sinh(Tensor self) -> (Tensor)
+- aten::slice.Tensor(Tensor(a) self, int dim=0, int? start=None, int? end=None, int step=1) -> (Tensor(a))
+- aten::softmax.int(Tensor self, int dim, int? dtype=None) -> (Tensor)
+- aten::split(Tensor self, int[] split_sizes, int dim=0) -> (Tensor[])
+- aten::split.Tensor(Tensor(a) self, int split_size, int dim=0) -> (Tensor[])
+- aten::split.sizes(Tensor(a -> *) self, int[] split_size, int dim=0) -> (Tensor[])
+- aten::split_with_sizes(Tensor(a) self, int[] split_sizes, int dim=0) -> (Tensor[])
+- aten::sqrt(Tensor self) -> (Tensor)
+- aten::square(Tensor self) -> (Tensor)
+- aten::squeeze.dim(Tensor(a) self, int dim) -> (Tensor(a))
+- aten::stack(Tensor[] tensors, int dim=0) -> (Tensor)
+- aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
+- aten::sub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
+- aten::sub_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> (Tensor(a!))
+- aten::sum(Tensor self, *, int? dtype=None) -> (Tensor)
+- aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, int? dtype=None) -> (Tensor)
+- aten::t(Tensor self) -> (Tensor)
+- aten::tan(Tensor self) -> (Tensor)
+- aten::tanh(Tensor input) -> (Tensor)
+- aten::tanh_(Tensor(a!) self) -> (Tensor(a!))
+- aten::to.device(Tensor(a) self, Device device, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -> (Tensor(a))
+- aten::to.dtype(Tensor self, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -> (Tensor)
+- aten::to.other(Tensor self, Tensor other, bool non_blocking=False, bool copy=False, int? memory_format=None) -> (Tensor)
+- aten::to.prim_Device(Tensor(a) self, Device? device, int? dtype=None, bool non_blocking=False, bool copy=False) -> (Tensor(a|b))
+- aten::topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)
+- aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> (Tensor(a))
+- aten::unbind.int(Tensor(a -> *) self, int dim=0) -> (Tensor[])
+- aten::unsqueeze(Tensor(a) self, int dim) -> (Tensor(a))
+- aten::upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> (Tensor)
+- aten::upsample_bilinear2d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> (Tensor)
+- aten::upsample_linear1d(Tensor self, int[1] output_size, bool align_corners, float? scales=None) -> (Tensor)
+- aten::upsample_linear1d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> (Tensor)
+- aten::upsample_nearest1d(Tensor self, int[1] output_size, float? scales=None) -> (Tensor)
+- aten::upsample_nearest1d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> (Tensor)
+- aten::upsample_nearest2d(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None) -> (Tensor)
+- aten::upsample_nearest2d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> (Tensor)
+- aten::upsample_nearest3d(Tensor self, int[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> (Tensor)
+- aten::upsample_nearest3d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> (Tensor)
+- aten::upsample_trilinear3d(Tensor self, int[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> (Tensor)
+- aten::upsample_trilinear3d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> (Tensor)
+- aten::view(Tensor(a) self, int[] size) -> (Tensor(a))
+- trt::const(Tensor self) -> (Tensor)
+
+Operators Currently Supported Through Evaluators
+-------------------------------------------------
+
+- aten::Bool.float(float b) -> (bool)
+- aten::Bool.int(int a) -> (bool)
+- aten::Float.Scalar(Scalar a) -> float
+- aten::Float.bool(bool a) -> float
+- aten::Float.int(int a) -> float
+- aten::Int.Scalar(Scalar a) -> int
+- aten::Int.bool(bool a) -> int
+- aten::Int.float(float a) -> int
+- aten::Int.int(int a) -> int
+- aten::__and__(int a, int b) -> (bool)
+- aten::__and__.bool(bool a, bool b) -> (bool)
+- aten::__derive_index(int idx, int start, int step) -> int
+- aten::__getitem__.t(t[](a) list, int idx) -> (t(*))
+- aten::__is__(t1 self, t2 obj) -> bool
+- aten::__isnot__(t1 self, t2 obj) -> bool
+- aten::__not__(bool self) -> bool
+- aten::__or__(int a, int b) -> (bool)
+- aten::__range_length(int lo, int hi, int step) -> int
+- aten::__round_to_zero_floordiv(int a, int b) -> (int)
+- aten::__xor__(int a, int b) -> (bool)
+- aten::add.float(float a, float b) -> (float)
+- aten::add.int(int a, int b) -> (int)
+- aten::add.str(str a, str b) -> (str)
+- aten::add_.t(t[](a!) self, t[] b) -> (t[])
+- aten::append.t(t[](a!) self, t(c -> *) el) -> (t[](a!))
+- aten::arange(Scalar end, *, int? dtype=None, int? layout=None,
+                            Device? device=None, bool? pin_memory=None) -> (Tensor)
+- aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None,
+                            Layout? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)
+- aten::arange.start_step(Scalar start, Scalar end, Scalar step, *, ScalarType? dtype=None,
+                        Layout? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)
+- aten::clone(Tensor self, *, int? memory_format=None) -> (Tensor)
+- aten::copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> (Tensor(a!))
+- aten::dim(Tensor self) -> int
+- aten::div.float(float a, float b) -> (float)
+- aten::div.int(int a, int b) -> (float)
+- aten::eq.bool(bool a, bool b) -> (bool)
+- aten::eq.float(float a, float b) -> (bool)
+- aten::eq.float_int(float a, int b) -> (bool)
+- aten::eq.int(int a, int b) -> (bool)
+- aten::eq.int_float(int a, float b) -> (bool)
+- aten::eq.str(str a, str b) -> (bool)
+- aten::extend.t(t[](a!) self, t[] other) -> ()
+- aten::floor.float(float a) -> (int)
+- aten::floor.int(int a) -> (int)
+- aten::floordiv.float(float a, float b) -> (int)
+- aten::floordiv.int(int a, int b) -> (int)
+- aten::format(str self, ...) -> (str)
+- aten::ge.bool(bool a, bool b) -> (bool)
+- aten::ge.float(float a, float b) -> (bool)
+- aten::ge.float_int(float a, int b) -> (bool)
+- aten::ge.int(int a, int b) -> (bool)
+- aten::ge.int_float(int a, float b) -> (bool)
+- aten::gt.bool(bool a, bool b) -> (bool)
+- aten::gt.float(float a, float b) -> (bool)
+- aten::gt.float_int(float a, int b) -> (bool)
+- aten::gt.int(int a, int b) -> (bool)
+- aten::gt.int_float(int a, float b) -> (bool)
+- aten::is_floating_point(Tensor self) -> (bool)
+- aten::le.bool(bool a, bool b) -> (bool)
+- aten::le.float(float a, float b) -> (bool)
+- aten::le.float_int(float a, int b) -> (bool)
+- aten::le.int(int a, int b) -> (bool)
+- aten::le.int_float(int a, float b) -> (bool)
+- aten::len.t(t[] a) -> (int)
+- aten::lt.bool(bool a, bool b) -> (bool)
+- aten::lt.float(float a, float b) -> (bool)
+- aten::lt.float_int(float a, int b) -> (bool)
+- aten::lt.int(int a, int b) -> (bool)
+- aten::lt.int_float(int a, float b) -> (bool)
+- aten::mul.float(float a, float b) -> (float)
+- aten::mul.int(int a, int b) -> (int)
+- aten::ne.bool(bool a, bool b) -> (bool)
+- aten::ne.float(float a, float b) -> (bool)
+- aten::ne.float_int(float a, int b) -> (bool)
+- aten::ne.int(int a, int b) -> (bool)
+- aten::ne.int_float(int a, float b) -> (bool)
+- aten::neg.int(int a) -> (int)
+- aten::numel(Tensor self) -> int
+- aten::pow.float(float a, float b) -> (float)
+- aten::pow.float_int(float a, int b) -> (float)
+- aten::pow.int(int a, int b) -> (float)
+- aten::pow.int_float(int a, float b) -> (float)
+- aten::size(Tensor self) -> (int[])
+- aten::size.int(Tensor self, int dim) -> (int)
+- aten::slice.t(t[] l, int start, int end=9223372036854775807, int step=1) -> (t[])
+- aten::sqrt.float(float a) -> (float)
+- aten::sqrt.int(int a) -> (float)
+- aten::sub.float(float a, float b) -> (float)
+- aten::sub.int(int a, int b) -> (int)
+- aten::tensor(t[] data, *, int? dtype=None, Device? device=None, bool requires_grad=False) -> (Tensor)
+- prim::TupleIndex(Any tup, int i) -> (Any)
+- prim::dtype(Tensor a) -> (int)
+- prim::max.bool(bool a, bool b) -> (bool)
+- prim::max.float(float a, float b) -> (bool)
+- prim::max.float_int(float a, int b) -> (bool)
+- prim::max.int(int a, int b) -> (bool)
+- prim::max.int_float(int a, float b) -> (bool)
+- prim::max.self_int(int[] self) -> (int)
+- prim::min.bool(bool a, bool b) -> (bool)
+- prim::min.float(float a, float b) -> (bool)
+- prim::min.float_int(float a, int b) -> (bool)
+- prim::min.int(int a, int b) -> (bool)
+- prim::min.int_float(int a, float b) -> (bool)
+- prim::min.self_int(int[] self) -> (int)
+- prim::shape(Tensor a) -> (int[])
diff --git a/docs/v1.2.0/_sources/py_api/fx.rst.txt b/docs/v1.2.0/_sources/py_api/fx.rst.txt
new file mode 100644
index 0000000000..8ce591f5ee
--- /dev/null
+++ b/docs/v1.2.0/_sources/py_api/fx.rst.txt
@@ -0,0 +1,31 @@
+.. _torch_tensorrt_fx_py:
+
+torch_tensorrt.fx
+===================
+
+.. currentmodule:: torch_tensorrt.fx
+
+.. automodule torch_tensorrt.ts
+   :undoc-members:
+
+.. automodule:: torch_tensorrt.fx
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Functions
+------------
+
+.. autofunction:: compile
+
+
+Classes
+--------
+
+.. autoclass:: TRTModule
+
+.. autoclass:: InputTensorSpec
+
+.. autoclass:: TRTInterpreter
+
+.. autoclass:: TRTInterpreterResult
\ No newline at end of file
diff --git a/docs/v1.2.0/_sources/py_api/logging.rst.txt b/docs/v1.2.0/_sources/py_api/logging.rst.txt
new file mode 100644
index 0000000000..7918fe7f86
--- /dev/null
+++ b/docs/v1.2.0/_sources/py_api/logging.rst.txt
@@ -0,0 +1,13 @@
+.. _torch_tensorrt_logging_py:
+
+torch_tensorrt.logging
+----------------------
+
+.. currentmodule:: torch_tensorrt.logging
+
+.. automodule:: torch_tensorrt.logging
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. autoclass:: py torch_tensorrt.logging.Level
diff --git a/docs/v1.2.0/_sources/py_api/ptq.rst.txt b/docs/v1.2.0/_sources/py_api/ptq.rst.txt
new file mode 100644
index 0000000000..ec83662efb
--- /dev/null
+++ b/docs/v1.2.0/_sources/py_api/ptq.rst.txt
@@ -0,0 +1,27 @@
+.. _torch_tensorrt_ptq_py:
+
+torch_tensorrt.ptq
+===================
+
+.. currentmodule:: torch_tensorrt.ptq
+
+.. automodule:: torch_tensorrt.ptq
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Classes
+---------
+
+.. autoclass:: DataLoaderCalibrator
+   :members:
+   :special-members: __init__
+
+.. autoclass:: CacheCalibrator
+   :members:
+   :special-members: __init__
+
+Enums
+-------
+
+.. autoclass:: CalibrationAlgo
diff --git a/docs/v1.2.0/_sources/py_api/torch_tensorrt.rst.txt b/docs/v1.2.0/_sources/py_api/torch_tensorrt.rst.txt
new file mode 100644
index 0000000000..2ccf7c60e2
--- /dev/null
+++ b/docs/v1.2.0/_sources/py_api/torch_tensorrt.rst.txt
@@ -0,0 +1,60 @@
+.. _torch_tensorrt_py:
+
+torch_tensorrt
+===============
+
+.. automodule torch_tensorrt
+   :undoc-members:
+
+
+
+.. automodule:: torch_tensorrt
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Functions
+------------
+
+.. autofunction:: set_device
+
+.. autofunction:: compile
+
+.. autofunction:: convert_method_to_trt_engine
+
+.. autofunction:: get_build_info
+
+.. autofunction:: dump_build_info
+
+Classes
+---------
+
+.. autoclass:: Input
+   :members:
+   :special-members: __init__
+
+.. autoclass:: Device
+   :members:
+   :special-members: __init__
+
+Enums
+-------
+
+.. autoclass:: dtype
+
+.. autoclass:: DeviceType
+
+.. autoclass:: EngineCapability
+
+.. autoclass:: TensorFormat
+
+Submodules
+----------
+
+.. toctree::
+   :maxdepth: 1
+
+   logging
+   ptq
+   ts
+   fx
diff --git a/docs/v1.2.0/_sources/py_api/ts.rst.txt b/docs/v1.2.0/_sources/py_api/ts.rst.txt
new file mode 100644
index 0000000000..45f6418eb7
--- /dev/null
+++ b/docs/v1.2.0/_sources/py_api/ts.rst.txt
@@ -0,0 +1,27 @@
+.. _torch_tensorrt_ts_py:
+
+torch_tensorrt.ts
+===================
+
+.. currentmodule:: torch_tensorrt.ts
+
+.. automodule torch_tensorrt.ts
+   :undoc-members:
+
+.. automodule:: torch_tensorrt.ts
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Functions
+------------
+
+.. autofunction:: compile
+
+.. autofunction:: convert_method_to_trt_engine
+
+.. autofunction:: check_method_op_support
+
+.. autofunction:: embed_engine_in_new_module
+
+.. autofunction:: TensorRTCompileSpec
diff --git a/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/changelog.rst.txt b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/changelog.rst.txt
new file mode 100644
index 0000000000..d1ad81a0df
--- /dev/null
+++ b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/changelog.rst.txt
@@ -0,0 +1,6 @@
+
+*********
+Changelog
+*********
+
+v0.0.1
diff --git a/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/configuring.rst.txt b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/configuring.rst.txt
new file mode 100644
index 0000000000..3afcb7bc62
--- /dev/null
+++ b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/configuring.rst.txt
@@ -0,0 +1,108 @@
+
+*************
+Configuration
+*************
+
+You can configure different parts of the theme.
+
+Project-wide Configuration
+==========================
+
+HTML Theme Options
+------------------
+
+The theme's project-wide options are defined in the ``pytorch_sphinx_theme/theme.conf``
+file of this repository, and can be defined in your project's ``conf.py`` via
+``html_theme_options``. For example:
+
+.. code:: python
+
+    html_theme_options = {
+        'canonical_url': '',
+        'analytics_id': '',
+        'logo_only': False,
+        'display_version': True,
+        'prev_next_buttons_location': 'bottom',
+        'style_external_links': False,
+        'vcs_pageview_mode': '',
+        # Toc options
+        'collapse_navigation': True,
+        'sticky_navigation': True,
+        'navigation_depth': 4,
+        'includehidden': True,
+        'titles_only': False
+    }
+
+The following options are available:
+
+Base options
+~~~~~~~~~~~~
+
+* ``canonical_url`` String. This will specify a `canonical url <https://en.wikipedia.org/wiki/Canonical_link_element>`__
+  to let search engines know they should give higher ranking to latest version of the docs.
+  The url points to the root of the documentation and requires a trailing slash.
+* ``analytics_id`` String. Change the Google Analytics ID that is included on pages.
+* ``display_version`` Bool. With this disabled, the version number isn't shown at the top of the sidebar.
+* ``prev_next_buttons_location`` String. can take the value ``bottom``, ``top``, ``both`` , or ``None``
+  and will display the "Next" and "Previous" buttons accordingly.
+* ``style_external_links`` Bool. Add an icon next to external links. Defaults to ``False``.
+* ``vcs_pageview_mode`` String. Changes how to view files when using `display_github`, `display_gitlab`, etc.
+  When using Github or Gitlab this can be: `blob` (default), `edit`, or `raw`,
+  on Bitbucket, this can be either: `view` (default) or `edit`.
+
+TOC Options
+~~~~~~~~~~~
+
+These effect how we display the Table of Contents in the side bar. You can read more about them here: http://www.sphinx-doc.org/en/stable/templating.html#toctree
+
+* ``collapse_navigation`` Bool. With this enabled, you will lose the ``[+]`` drop downs next to each section in the sidebar.
+* ``sticky_navigation`` Bool. This causes the sidebar to scroll with the main page content as you scroll the page.
+* ``navigation_depth`` Int. Indicate the max depth of the tree; by default, 4 levels are included;
+  set it to -1 to allow unlimited depth.
+* ``includehidden`` Bool. Specifies if the sidebar includes toctrees marked with the ``:hidden:`` option
+* ``titles_only`` Bool. If True, removes headers within a page from the sidebar.
+
+.. note::
+
+   Setting ``collapse_navigation`` to False and using a high ``navigation_depth``
+   can cause projects with many files and a deep file structure to generate HTML files
+   that are significantly larger in file size and much longer compilation times.
+
+
+HTML Context Options
+--------------------
+
+TODO.
+
+
+Page-level Configuration
+========================
+
+Pages support metadata that changes how the theme renders.
+You can currently add the following:
+
+* ``:github_url:`` This will force the "Edit on GitHub" to the configured URL
+* ``:bitbucket_url:`` This will force the "Edit on Bitbucket" to the configured URL
+* ``:gitlab_url:`` This will force the "Edit on GitLab" to the configured URL
+
+
+How the Table of Contents builds
+================================
+
+Currently the left menu will build based upon any ``toctree(s)`` defined in your ``index.rst`` file.
+It outputs 2 levels of depth, which should give your visitors a high level of access to your
+docs. If no toctrees are set the theme reverts to sphinx's usual local toctree.
+
+It's important to note that if you don't follow the same styling for your rST headers across
+your documents, the toctree will misbuild, and the resulting menu might not show the correct
+depth when it renders.
+
+Also note that by default the table of contents is set with ``includehidden=True``. This allows you
+to set a hidden toc in your index file with the `:hidden: <hidden_>`_ property that will allow you
+to build a toc without it rendering in your index.
+
+By default, the navigation will "stick" to the screen as you scroll. However if your toc
+is vertically too large, it will revert to static positioning. To disable the sticky nav
+altogether change the setting in ``conf.py``.
+
+.. _hidden: http://sphinx-doc.org/markup/toctree.html
diff --git a/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/api.rst.txt b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/api.rst.txt
new file mode 100644
index 0000000000..b49bc248ac
--- /dev/null
+++ b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/api.rst.txt
@@ -0,0 +1,53 @@
+
+*********************
+:mod:`test_py_module`
+*********************
+
+.. contents:: Table of Contents
+
+.. automodule:: test_py_module.test
+    :members:
+    :private-members:
+    :special-members:
+
+Generated Index
+===============
+
+Part of the sphinx build process in generate and index file: :ref:`genindex`.
+
+
+Optional parameter args
+=======================
+
+At this point optional parameters `cannot be generated from code`_.
+However, some projects will manually do it, like so:
+
+This example comes from `django-payments module docs`_.
+
+.. class:: payments.dotpay.DotpayProvider(seller_id, pin[, channel=0[, lock=False], lang='pl'])
+
+   This backend implements payments using a popular Polish gateway, `Dotpay.pl <http://www.dotpay.pl>`_.
+
+   Due to API limitations there is no support for transferring purchased items.
+
+
+   :param seller_id: Seller ID assigned by Dotpay
+   :param pin: PIN assigned by Dotpay
+   :param channel: Default payment channel (consult reference guide)
+   :param lang: UI language
+   :param lock: Whether to disable channels other than the default selected above
+
+.. _cannot be generated from code: https://groups.google.com/forum/#!topic/sphinx-users/_qfsVT5Vxpw
+.. _django-payments module docs: http://django-payments.readthedocs.org/en/latest/modules.html#payments.authorizenet.AuthorizeNetProvide
+
+
+Data
+====
+
+.. data:: Data_item_1
+          Data_item_2
+          Data_item_3
+
+    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Fusce congue elit eu hendrerit mattis.
+
+Some data link :data:`Data_item_1`.
diff --git a/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/demo.rst.txt b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/demo.rst.txt
new file mode 100644
index 0000000000..66f19cc899
--- /dev/null
+++ b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/demo.rst.txt
@@ -0,0 +1,475 @@
+.. This is a comment. Note how any initial comments are moved by
+   transforms to after the document title, subtitle, and docinfo.
+
+.. demo.rst from: http://docutils.sourceforge.net/docs/user/rst/demo.txt
+
+.. |EXAMPLE| image:: static/yi_jing_01_chien.jpg
+   :width: 1em
+
+**********************
+Paragraph Level Markup
+**********************
+
+.. contents:: Table of Contents
+
+Inline Markup
+=============
+
+Paragraphs contain text and may contain inline markup: *emphasis*, **strong emphasis**, ``inline literals``,
+standalone hyperlinks (http://www.python.org), external hyperlinks (Python_), internal cross-references (example_),
+external hyperlinks with embedded URIs (`Python web site <http://www.python.org>`__), footnote references
+(manually numbered [1]_, anonymous auto-numbered [#]_, labeled auto-numbered [#label]_, or symbolic [*]_),
+citation references ([12]_), substitution references (|example|), and _`inline hyperlink targets`
+(see Targets_ below for a reference back to here). Character-level inline markup is also possible
+(although exceedingly ugly!) in *re*\ ``Structured``\ *Text*. Problems are indicated by |problematic|
+text (generated by processing errors; this one is intentional).
+
+Also with ``sphinx.ext.autodoc``, which I use in the demo, I can link to :class:`test_py_module.test.Foo`.
+It will link you right my code documentation for it.
+
+The default role for interpreted text is `Title Reference`.  Here are some explicit interpreted text roles:
+a PEP reference (:PEP:`287`); an RFC reference (:RFC:`2822`); a :sub:`subscript`; a :sup:`superscript`;
+and explicit roles for :emphasis:`standard` :strong:`inline` :literal:`markup`.
+
+GUI labels are a useful way to indicate that :guilabel:`Some action` is to be taken by the user.
+The GUI label should not run over ``line-height`` so as not to :guilabel:`interfere` with text from adjacent lines.
+
+Key-bindings indicate that the read is to press a button on the keyboard or mouse,
+for example :kbd:`MMB` and :kbd:`Shift-MMB`. Another useful markup to indicate a user action
+is to use ``menuselection`` this can be used to show short and long menus in software.
+For example, and ``menuselection`` can be seen here that breaks is too long to fit on this line.
+:menuselection:`My --> Software --> Some menu --> Some sub menu 1 --> sub menu 2`.
+
+.. DO NOT RE-WRAP THE FOLLOWING PARAGRAPH!
+
+Let's test wrapping and whitespace significance in inline literals:
+``This is an example of --inline-literal --text, --including some--
+strangely--hyphenated-words.  Adjust-the-width-of-your-browser-window
+to see how the text is wrapped.  -- ---- --------  Now note    the
+spacing    between the    words of    this sentence    (words
+should    be grouped    in pairs).``
+
+If the ``--pep-references`` option was supplied, there should be a live link to PEP 258 here.
+
+Math
+====
+
+This is a test. Here is an equation:
+:math:`X_{0:5} = (X_0, X_1, X_2, X_3, X_4)`.
+Here is another:
+
+.. math::
+    :label: This is a label
+
+    \nabla^2 f =
+    \frac{1}{r^2} \frac{\partial}{\partial r}
+    \left( r^2 \frac{\partial f}{\partial r} \right) +
+    \frac{1}{r^2 \sin \theta} \frac{\partial f}{\partial \theta}
+    \left( \sin \theta \, \frac{\partial f}{\partial \theta} \right) +
+    \frac{1}{r^2 \sin^2\theta} \frac{\partial^2 f}{\partial \phi^2}
+
+You can add a link to equations like the one above :eq:`This is a label` by using ``:eq:``.
+
+Meta
+====
+
+.. meta::
+   :keywords: reStructuredText, demonstration, demo, parser
+   :description lang=en: A demonstration of the reStructuredText
+       markup language, containing examples of all basic
+       constructs and many advanced constructs.
+
+Blocks
+======
+
+Literal Blocks
+--------------
+
+Literal blocks are indicated with a double-colon ("::") at the end of
+the preceding paragraph (over there ``-->``).  They can be indented::
+
+    if literal_block:
+        text = 'is left as-is'
+        spaces_and_linebreaks = 'are preserved'
+        markup_processing = None
+
+Or they can be quoted without indentation::
+
+>> Great idea!
+>
+> Why didn't I think of that?
+
+Line Blocks
+-----------
+
+| This is a line block.  It ends with a blank line.
+|     Each new line begins with a vertical bar ("|").
+|     Line breaks and initial indents are preserved.
+| Continuation lines are wrapped portions of long lines;
+  they begin with a space in place of the vertical bar.
+|     The left edge of a continuation line need not be aligned with
+  the left edge of the text above it.
+
+| This is a second line block.
+|
+| Blank lines are permitted internally, but they must begin with a "|".
+
+Take it away, Eric the Orchestra Leader!
+
+    | A one, two, a one two three four
+    |
+    | Half a bee, philosophically,
+    |     must, *ipso facto*, half not be.
+    | But half the bee has got to be,
+    |     *vis a vis* its entity.  D'you see?
+    |
+    | But can a bee be said to be
+    |     or not to be an entire bee,
+    |         when half the bee is not a bee,
+    |             due to some ancient injury?
+    |
+    | Singing...
+
+Block Quotes
+------------
+
+Block quotes consist of indented body elements:
+
+    My theory by A. Elk.  Brackets Miss, brackets.  This theory goes
+    as follows and begins now.  All brontosauruses are thin at one
+    end, much much thicker in the middle and then thin again at the
+    far end.  That is my theory, it is mine, and belongs to me and I
+    own it, and what it is too.
+
+    -- Anne Elk (Miss)
+
+Doctest Blocks
+--------------
+
+>>> print 'Python-specific usage examples; begun with ">>>"'
+Python-specific usage examples; begun with ">>>"
+>>> print '(cut and pasted from interactive Python sessions)'
+(cut and pasted from interactive Python sessions)
+
+Code Blocks
+-----------
+
+.. parsed-literal::
+
+    # parsed-literal test
+    curl -O http://someurl/release-|version|.tar-gz
+
+
+.. code-block:: json
+    :caption: Code Blocks can have captions.
+
+    {
+    "windows": [
+        {
+        "panes": [
+            {
+            "shell_command": [
+                "echo 'did you know'",
+                "echo 'you can inline'"
+            ]
+            },
+            {
+            "shell_command": "echo 'single commands'"
+            },
+            "echo 'for panes'"
+        ],
+        "window_name": "long form"
+        }
+    ],
+    "session_name": "shorthands"
+    }
+
+Emphasized lines with line numbers
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: python
+   :linenos:
+   :emphasize-lines: 3,5
+
+   def some_function():
+       interesting = False
+       print 'This line is highlighted.'
+       print 'This one is not...'
+       print '...but this one is.'
+
+Sidebar
+=======
+
+.. sidebar:: Ch'ien / The Creative
+
+    .. image:: static/yi_jing_01_chien.jpg
+
+    *Above* CH'IEN THE CREATIVE, HEAVEN
+
+    *Below* CH'IEN THE CREATIVE, HEAVEN
+
+The first hexagram is made up of six unbroken lines. These unbroken lines stand for the primal power,
+which is light-giving, active, strong, and of the spirit. The hexagram is consistently strong in character,
+and since it is without weakness, its essence is power or energy. Its image is heaven.
+Its energy is represented as unrestricted by any fixed conditions in space and is therefore conceived of as motion.
+Time is regarded as the basis of this motion.
+Thus the hexagram includes also the power of time and the power of persisting in time, that is, duration.
+
+The power represented by the hexagram is to be interpreted in a dual sense in terms of its action
+on the universe and of its action on the world of men. In relation to the universe, the hexagram expresses the strong,
+creative action of the Deity. In relation to the human world, it denotes the creative action of the holy man or sage,
+of the ruler or leader of men, who through his power awakens and develops their higher nature.
+
+Code with Sidebar
+-----------------
+
+.. sidebar:: A code example
+
+    With a sidebar on the right.
+
+.. literalinclude:: test_py_module/test.py
+    :language: python
+    :caption: Literal includes can also have captions.
+    :linenos:
+    :lines: 1-40
+
+References
+==========
+
+Footnotes
+---------
+
+.. [1] A footnote contains body elements, consistently indented by at
+   least 3 spaces.
+
+   This is the footnote's second paragraph.
+
+.. [#label] Footnotes may be numbered, either manually (as in [1]_) or
+   automatically using a "#"-prefixed label.  This footnote has a
+   label so it can be referred to from multiple places, both as a
+   footnote reference ([#label]_) and as a hyperlink reference
+   (label_).
+
+.. [#] This footnote is numbered automatically and anonymously using a
+   label of "#" only.
+
+.. [*] Footnotes may also use symbols, specified with a "*" label.
+   Here's a reference to the next footnote: [*]_.
+
+.. [*] This footnote shows the next symbol in the sequence.
+
+.. [4] Here's an unreferenced footnote, with a reference to a
+   nonexistent footnote: [5]_.
+
+Citations
+---------
+
+.. [11] This is the citation I made, let's make this extremely long so that we can tell that it doesn't follow the normal responsive table stuff.
+
+.. [12] This citation has some ``code blocks`` in it, maybe some **bold** and
+       *italics* too. Heck, lets put a link to a meta citation [13]_ too.
+
+.. [13] This citation will have two backlinks.
+
+
+Here's a reference to the above, [12]_, and a [nonexistent]_ citation.
+
+Here is another type of citation: `citation`
+
+Glossary
+--------
+
+This is a glossary with definition terms for thing like :term:`Writing`:
+
+.. glossary::
+
+  Documentation
+     Provides users with the knowledge they need to use something.
+
+  Reading
+     The process of taking information into ones mind through the use of eyes.
+
+  Writing
+     The process of putting thoughts into a medium for other people to :term:`read <Reading>`.
+
+Targets
+-------
+
+.. _example:
+
+This paragraph is pointed to by the explicit "example" target.
+A reference can be found under `Inline Markup`_, above. `Inline
+hyperlink targets`_ are also possible.
+
+Section headers are implicit targets, referred to by name. See
+Targets_, which is a subsection of `Body Elements`_.
+
+Explicit external targets are interpolated into references such as "Python_".
+
+.. _Python: http://www.python.org/
+
+Targets may be indirect and anonymous.  Thus `this phrase`__ may also
+refer to the Targets_ section.
+
+__ Targets_
+
+Here's a `hyperlink reference without a target`_, which generates an error.
+
+
+Directives
+==========
+
+Contents
+--------
+
+.. contents:: :local:
+
+These are just a sample of the many reStructuredText Directives. For others, please see:
+http://docutils.sourceforge.net/docs/ref/rst/directives.html.
+
+
+Centered text
+-------------
+
+You can create a statement with centered text with ``.. centered::``
+
+.. centered:: This is centered text!
+
+Images & Figures
+----------------
+
+Images
+^^^^^^
+
+An image directive (also clickable -- a hyperlink reference):
+
+.. image:: static/yi_jing_01_chien.jpg
+   :target: directives_
+
+Figures
+^^^^^^^
+
+.. figure:: static/yi_jing_01_chien.jpg
+   :alt: reStructuredText, the markup syntax
+
+   A figure is an image with a caption and/or a legend:
+
+   +------------+-----------------------------------------------+
+   | re         | Revised, revisited, based on 're' module.     |
+   +------------+-----------------------------------------------+
+   | Structured | Structure-enhanced text, structuredtext.      |
+   +------------+-----------------------------------------------+
+   | Text       | Well it is, isn't it?                         |
+   +------------+-----------------------------------------------+
+
+   This paragraph is also part of the legend.
+
+A figure directive with center alignment
+
+.. figure:: static/yi_jing_01_chien.jpg
+   :align: center
+
+   This caption should be centered.
+
+Admonitions
+-----------
+
+.. Attention:: Directives at large.
+
+.. Caution:: Don't take any wooden nickels.
+
+.. DANGER:: Mad scientist at work!
+
+.. Error:: Does not compute.
+
+.. Hint:: It's bigger than a bread box.
+
+.. Important::
+   - Wash behind your ears.
+   - Clean up your room.
+
+     - Including the closet.
+     - The bathroom too.
+
+       - Take the trash out of the bathroom.
+       - Clean the sink.
+   - Call your mother.
+   - Back up your data.
+
+.. Note:: This is a note.
+   Equations within a note:
+   :math:`G_{\mu\nu} = 8 \pi G (T_{\mu\nu}  + \rho_\Lambda g_{\mu\nu})`.
+
+.. Tip:: 15% if the service is good.
+
+    +---------+
+    | Example |
+    +=========+
+    | Thing1  |
+    +---------+
+    | Thing2  |
+    +---------+
+    | Thing3  |
+    +---------+
+
+.. WARNING:: Strong prose may provoke extreme mental exertion.
+   Reader discretion is strongly advised.
+
+.. admonition:: And, by the way...
+
+   You can make up your own admonition too.
+
+Topics, Sidebars, and Rubrics
+-----------------------------
+
+.. sidebar:: Sidebar Title
+   :subtitle: Optional Subtitle
+
+   This is a sidebar.  It is for text outside the flow of the main
+   text.
+
+   .. rubric:: This is a rubric inside a sidebar
+
+   Sidebars often appears beside the main text with a border and
+   background color.
+
+.. topic:: Topic Title
+
+   This is a topic.
+
+.. rubric:: This is a rubric
+
+Target Footnotes
+----------------
+
+.. target-notes::
+
+Replacement Text
+----------------
+
+I recommend you try |Python|_.
+
+.. |Python| replace:: Python, *the* best language around
+
+Compound Paragraph
+------------------
+
+.. compound::
+
+   This paragraph contains a literal block::
+
+       Connecting... OK
+       Transmitting data... OK
+       Disconnecting... OK
+
+   and thus consists of a simple paragraph, a literal block, and
+   another simple paragraph.  Nonetheless it is semantically *one*
+   paragraph.
+
+This construct is called a *compound paragraph* and can be produced
+with the "compound" directive.
+
+Download Links
+==============
+
+:download:`This long long long long long long long long long long long long long long long download link should be blue, normal weight text with a leading icon, and should wrap white-spaces <static/yi_jing_01_chien.jpg>`
diff --git a/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/lists_tables.rst.txt b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/lists_tables.rst.txt
new file mode 100644
index 0000000000..a31d213924
--- /dev/null
+++ b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/lists_tables.rst.txt
@@ -0,0 +1,302 @@
+
+**************
+Lists & Tables
+**************
+
+.. contents:: Table of Contents
+
+Lists
+=====
+
+Enumerated Lists
+----------------
+
+1. Arabic numerals.
+
+   a) lower alpha)
+
+      (i) (lower roman)
+
+          A. upper alpha.
+
+             I) upper roman)
+
+2. Lists that don't start at 1:
+
+   3. Three
+
+   4. Four
+
+   C. C
+
+   D. D
+
+   iii. iii
+
+   iv. iv
+
+#. List items may also be auto-enumerated.
+
+Definition Lists
+----------------
+
+Term
+    Definition
+Term : classifier
+    Definition paragraph 1.
+
+    Definition paragraph 2.
+Term
+    Definition
+
+
+Option Lists
+------------
+
+For listing command-line options:
+
+-a            command-line option "a"
+-b file       options can have arguments
+              and long descriptions
+--long        options can be long also
+--input=file  long options can also have
+              arguments
+
+--very-long-option
+              The description can also start on the next line.
+
+              The description may contain multiple body elements,
+              regardless of where it starts.
+
+-x, -y, -z    Multiple options are an "option group".
+-v, --verbose  Commonly-seen: short & long options.
+-1 file, --one=file, --two file
+              Multiple options with arguments.
+/V            DOS/VMS-style options too
+
+There must be at least two spaces between the option and the description.
+
+Field list
+----------
+
+.. bibliographic fields (which also require a transform):
+
+:Author: David Goodger
+:Address: 123 Example Street
+          Example, EX  Canada
+          A1B 2C3
+:Contact: docutils-develop@lists.sourceforge.net
+:Authors: Me; Myself; I
+:organization: humankind
+:date: $Date: 2012-01-03 19:23:53 +0000 (Tue, 03 Jan 2012) $
+:status: This is a "work in progress"
+:revision: $Revision: 7302 $
+:version: 1
+:copyright: This document has been placed in the public domain. You
+            may do with it as you wish. You may copy, modify,
+            redistribute, reattribute, sell, buy, rent, lease,
+            destroy, or improve it, quote it at length, excerpt,
+            incorporate, collate, fold, staple, or mutilate it, or do
+            anything else to it that your or anyone else's heart
+            desires.
+:field name: This is a generic bibliographic field.
+:field name 2:
+    Generic bibliographic fields may contain multiple body elements.
+
+    Like this.
+
+:Dedication:
+
+    For Docutils users & co-developers.
+
+:abstract:
+
+    This document is a demonstration of the reStructuredText markup
+    language, containing examples of all basic reStructuredText
+    constructs and many advanced constructs.
+
+Bullet Lists
+------------
+
+- A bullet list
+
+  + Nested bullet list.
+  + Nested item 2.
+
+- Item 2.
+
+  Paragraph 2 of item 2.
+
+  * Nested bullet list.
+  * Nested item 2.
+
+    - Third level.
+    - Item 2.
+
+  * Nested item 3.
+
+- ``inline literall``
+- ``inline literall``
+- ``inline literall``
+
+Second list level
+^^^^^^^^^^^^^^^^^
+
+- here is a list in a second-level section.
+- `yahoo <http://www.yahoo.com>`_
+- `yahoo <http://www.yahoo.com>`_
+
+  - `yahoo <http://www.yahoo.com>`_
+  - here is an inner bullet ``oh``
+
+    - one more ``with an inline literally``. `yahoo <http://www.yahoo.com>`_
+
+      heh heh. child. try to beat this embed:
+
+      .. literalinclude:: test_py_module/test.py
+          :language: python
+          :linenos:
+          :lines: 1-10
+  - and another. `yahoo <http://www.yahoo.com>`_
+  - `yahoo <http://www.yahoo.com>`_
+  - ``hi``
+- and hehe
+
+But deeper down the rabbit hole
+"""""""""""""""""""""""""""""""
+
+- I kept saying that, "deeper down the rabbit hole". `yahoo <http://www.yahoo.com>`_
+
+  - I cackle at night `yahoo <http://www.yahoo.com>`_.
+- I'm so lonely here in GZ ``guangzhou``
+- A man of python destiny, hopes and dreams. `yahoo <http://www.yahoo.com>`_
+
+  - `yahoo <http://www.yahoo.com>`_
+
+    - `yahoo <http://www.yahoo.com>`_ ``hi``
+    - ``destiny``
+
+Hlists
+------
+
+.. hlist::
+    :columns: 2
+
+    - First item
+    - Second item
+    - Third item
+    - Forth item
+    - Fifth item
+    - Sixths item
+
+.. rubric:: Hlist with images
+
+.. hlist::
+    :columns: 2
+
+    - .. figure:: static/yi_jing_01_chien.jpg
+
+         This is a short caption for a figure.
+
+    - .. figure:: static/yi_jing_01_chien.jpg
+
+         This is a long caption for a figure. Lorem ipsum dolor sit amet, consectetur adipiscing elit.
+         Donec porttitor dolor in odio posuere, vitae ornare libero mattis. In lobortis justo vestibulum nibh aliquet, non.
+
+Numbered List
+-------------
+
+#. One,
+#. Two.
+#. Three with long text. Lorem ipsum dolor sit amet, consectetur adipiscing elit.
+   Sed feugiat sagittis neque quis eleifend. Duis rutrum lectus sit amet mattis suscipit.
+
+- A) Using bullets and letters. (A)
+- B) Using bullets and letters. (B)
+- C) Using bullets and letters. (C)
+
+Tables
+======
+
+Grid Tables
+-----------
+
+Here's a grid table followed by a simple table:
+
++------------------------+------------+----------+----------+
+| Header row, column 1   | Header 2   | Header 3 | Header 4 |
+| (header rows optional) |            |          |          |
++========================+============+==========+==========+
+| body row 1, column 1   | column 2   | column 3 | column 4 |
++------------------------+------------+----------+----------+
+| body row 2             | Cells may span columns.          |
++------------------------+------------+---------------------+
+| body row 3             | Cells may  | - Table cells       |
++------------------------+ span rows. | - contain           |
+| body row 4             |            | - body elements.    |
++------------------------+------------+----------+----------+
+| body row 5             | Cells may also be     |          |
+|                        | empty: ``-->``        |          |
++------------------------+-----------------------+----------+
+
+=====  =====  ======
+   Inputs     Output
+------------  ------
+  A      B    A or B
+=====  =====  ======
+False  False  False
+True   False  True
+False  True   True
+True   True   True
+=====  =====  ======
+
+Giant Tables
+^^^^^^^^^^^^
+
++------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+
+| Header 1   | Header 2   | Header 3  | Header 1   | Header 2   | Header 3  | Header 1   | Header 2   | Header 3  | Header 1   | Header 2   | Header 3  |
++============+============+===========+============+============+===========+============+============+===========+============+============+===========+
+| body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  |
++------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+
+| body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  |
++------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+
+| body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  |
++------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+
+| body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  |
++------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+
+
+List Tables
+-----------
+
+.. list-table:: List tables can have captions like this one.
+    :widths: 10 5 10 50
+    :header-rows: 1
+    :stub-columns: 1
+
+    * - List table
+      - Header 1
+      - Header 2
+      - Header 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.
+    * - Stub Row 1
+      - Row 1
+      - Column 2
+      - Column 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.
+    * - Stub Row 2
+      - Row 2
+      - Column 2
+      - Column 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.
+    * - Stub Row 3
+      - Row 3
+      - Column 2
+      - Column 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.
+
+.. list-table:: This is a list table with images in it.
+
+    * - .. figure:: static/yi_jing_01_chien.jpg
+
+           This is a short caption for a figure.
+
+      - .. figure:: static/yi_jing_01_chien.jpg
+
+           This is a long caption for a figure. Lorem ipsum dolor sit amet, consectetur adipiscing elit.
+           Donec porttitor dolor in odio posuere, vitae ornare libero mattis. In lobortis justo vestibulum nibh aliquet, non.
diff --git a/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/long.rst.txt b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/long.rst.txt
new file mode 100644
index 0000000000..26b1add9d1
--- /dev/null
+++ b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/long.rst.txt
@@ -0,0 +1,214 @@
+
+***************
+Long Sticky Nav
+***************
+
+.. contents:: Table of Contents
+
+This section demonstrates how the 'sticky_navigation' setting behaves when the menu is very long.
+When this section is selected, it will make the menu and the main area scroll when you are at the top of the page.
+
+
+Example Menu 1
+==============
+
+Just a place holder...
+
+
+Example Menu 2
+==============
+
+Just a place holder...
+
+
+Example Menu 3
+==============
+
+Just a place holder...
+
+
+Example Menu 4
+==============
+
+Just a place holder...
+
+
+Example Menu 5
+==============
+
+Just a place holder...
+
+
+Example Menu 6
+==============
+
+Just a place holder...
+
+
+Example Menu 7
+==============
+
+Just a place holder...
+
+
+Example Menu 8
+==============
+
+Just a place holder...
+
+
+Example Menu 9
+==============
+
+Just a place holder...
+
+
+Example Menu 10
+===============
+
+Just a place holder...
+
+
+Example Menu 11
+===============
+
+Just a place holder...
+
+
+Example Menu 12
+===============
+
+Just a place holder...
+
+
+Example Menu 13
+===============
+
+Just a place holder...
+
+
+Example Menu 14
+===============
+
+Just a place holder...
+
+
+Example Menu 15
+===============
+
+Just a place holder...
+
+
+Example Menu 16
+===============
+
+Just a place holder...
+
+
+Example Menu 17
+===============
+
+Just a place holder...
+
+
+Example Menu 18
+===============
+
+Just a place holder...
+
+
+Example Menu 19
+===============
+
+Just a place holder...
+
+
+Example Menu 20
+===============
+
+Just a place holder...
+
+Example Submenu 1
+=================
+
+Just a place holder...
+
+Submenu 1
+---------
+
+Just a place holder...
+
+Subsubmenu 1
+^^^^^^^^^^^^
+
+Just a place holder...
+
+Subsubmenu 2
+^^^^^^^^^^^^
+
+Just a place holder...
+
+Submenu 2
+---------
+
+Just a place holder...
+
+Subsubmenu 1
+^^^^^^^^^^^^
+
+Just a place holder...
+
+Submenu 3
+---------
+
+Just a place holder...
+
+Submenu 4
+---------
+
+Just a place holder...
+
+Submenu 5
+---------
+
+Just a place holder...
+
+Example Submenu 2
+=================
+
+Just a place holder...
+
+Submenu 1
+---------
+
+Just a place holder...
+
+Subsubmenu 1
+^^^^^^^^^^^^
+
+Just a place holder...
+
+Submenu 2
+---------
+
+Just a place holder...
+
+Subsubmenu 1
+^^^^^^^^^^^^
+
+Just a place holder...
+
+Submenu 3
+---------
+
+Just a place holder...
+
+Submenu 4
+---------
+
+Just a place holder...
+
+Submenu 5
+---------
+
+Just a place holder...
diff --git a/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/structure.rst.txt b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/structure.rst.txt
new file mode 100644
index 0000000000..d21fc96321
--- /dev/null
+++ b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/structure.rst.txt
@@ -0,0 +1,101 @@
+
+*******************
+Structural Elements
+*******************
+
+.. contents:: Table of Contents
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Donec lorem neque, interdum in ipsum nec,
+finibus dictum velit. Ut eu efficitur arcu, id aliquam erat. In sit amet diam gravida, imperdiet tellus eu,
+gravida nisl. Praesent aliquet odio eget libero elementum, quis rhoncus tellus tincidunt.
+Suspendisse quis volutpat ipsum. Sed lobortis scelerisque tristique. Aenean condimentum risus tellus,
+quis accumsan ipsum laoreet ut. Integer porttitor maximus suscipit. Mauris in posuere sapien.
+Aliquam accumsan feugiat ligula, nec fringilla libero commodo sed. Proin et erat pharetra.
+
+---------
+
+Etiam turpis ante, luctus sed velit tristique, finibus volutpat dui. Nam sagittis vel ante nec malesuada.
+Praesent dignissim mi nec ornare elementum. Nunc eu augue vel sem dignissim cursus sed et nulla.
+Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas.
+Pellentesque dictum dui sem, non placerat tortor rhoncus in. Sed placerat nulla at rhoncus iaculis.
+
+Document Section
+================
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed condimentum nulla vel neque venenatis,
+nec placerat lorem placerat. Cras purus eros, gravida vitae tincidunt id, vehicula nec nulla.
+Fusce aliquet auctor cursus. Phasellus ex neque, vestibulum non est vitae, viverra fringilla tortor.
+Donec vestibulum convallis justo, a faucibus lorem vulputate vel. Aliquam cursus odio eu felis sodales aliquet.
+Aliquam erat volutpat. Maecenas eget dictum mauris. Suspendisse arcu eros, condimentum eget risus sed,
+luctus efficitur arcu. Cras ut dictum mi. Nulla congue interdum lorem, semper semper enim commodo nec.
+
+Document Subsection
+-------------------
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam efficitur in eros et blandit. Nunc maximus,
+nisl at auctor vestibulum, justo ex sollicitudin ligula, id faucibus urna orci tristique nisl.
+Duis auctor rutrum orci, in ornare lacus condimentum quis. Quisque arcu velit, facilisis quis interdum ac,
+hendrerit auctor mauris. Curabitur urna nibh, porttitor at ante sit amet, vestibulum interdum dolor.
+Duis dictum elit orci, tincidunt imperdiet sem pellentesque et. In vehicula pellentesque varius.
+Phasellus a turpis sollicitudin, bibendum massa et, imperdiet neque. Integer quis sapien in magna rutrum bibendum.
+Integer cursus ex sed magna vehicula finibus. Proin tempus orci quis dolor tempus, nec condimentum odio vestibulum.
+Etiam efficitur sollicitudin libero, tincidunt volutpat ligula interdum sed.
+
+Document Subsubsection
+^^^^^^^^^^^^^^^^^^^^^^
+
+Donec non rutrum lorem. Aenean sagittis metus at pharetra fringilla. Nunc sapien dolor, cursus sed nisi at,
+pretium tristique lectus. Sed pellentesque leo lectus, et convallis ipsum euismod a.
+Integer at leo vitae felis pretium aliquam fringilla quis odio. Sed pharetra enim accumsan feugiat pretium.
+Maecenas at pharetra tortor. Morbi semper eget mi vel finibus. Cras rutrum nulla eros, id feugiat arcu pellentesque ut.
+Sed finibus tortor ac nisi ultrices viverra. Duis feugiat malesuada sapien, at commodo ante porttitor ac.
+Curabitur posuere mauris mi, vel ornare orci scelerisque sit amet. Suspendisse nec fringilla dui.
+
+Document Paragraph
+""""""""""""""""""
+
+Pellentesque nec est in odio ultrices elementum. Vestibulum et hendrerit sapien, quis vulputate turpis.
+Suspendisse potenti. Curabitur tristique sit amet lectus non viverra. Phasellus rutrum dapibus turpis sed imperdiet.
+Mauris maximus viverra ante. Donec eu egestas mauris. Morbi vulputate tincidunt euismod. Integer vel porttitor neque.
+Donec at lacus suscipit, lacinia lectus vel, sagittis lectus.
+
+*********************
+Structural Elements 2
+*********************
+
+Etiam turpis ante, luctus sed velit tristique, finibus volutpat dui. Nam sagittis vel ante nec malesuada.
+Praesent dignissim mi nec ornare elementum. Nunc eu augue vel sem dignissim cursus sed et nulla.
+Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas.
+Pellentesque dictum dui sem, non placerat tortor rhoncus in. Sed placerat nulla at rhoncus iaculis.
+
+Document Section
+================
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed condimentum nulla vel neque venenatis,
+nec placerat lorem placerat. Cras purus eros, gravida vitae tincidunt id, vehicula nec nulla.
+Fusce aliquet auctor cursus. Phasellus ex neque, vestibulum non est vitae, viverra fringilla tortor.
+Donec vestibulum convallis justo, a faucibus lorem vulputate vel. Aliquam cursus odio eu felis sodales aliquet.
+Aliquam erat volutpat. Maecenas eget dictum mauris. Suspendisse arcu eros, condimentum eget risus sed,
+luctus efficitur arcu. Cras ut dictum mi. Nulla congue interdum lorem, semper semper enim commodo nec.
+
+Document Subsection
+-------------------
+
+.. figure:: static/yi_jing_01_chien.jpg
+    :align: right
+    :figwidth: 200px
+
+    This is a caption for a figure. Text should wrap around the caption.
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam efficitur in eros et blandit. Nunc maximus,
+nisl at auctor vestibulum, justo ex sollicitudin ligula, id faucibus urna orci tristique nisl.
+Duis auctor rutrum orci, in ornare lacus condimentum quis. Quisque arcu velit, facilisis quis interdum ac,
+hendrerit auctor mauris. Curabitur urna nibh, porttitor at ante sit amet, vestibulum interdum dolor.
+Duis dictum elit orci, tincidunt imperdiet sem pellentesque et. In vehicula pellentesque varius.
+Phasellus a turpis sollicitudin, bibendum massa et, imperdiet neque. Integer quis sapien in magna rutrum bibendum.
+Integer cursus ex sed magna vehicula finibus. Proin tempus orci quis dolor tempus, nec condimentum odio vestibulum.
+Etiam efficitur sollicitudin libero, tincidunt volutpat ligula interdum sed. Praesent congue sagittis nisl et suscipit.
+Vivamus sagittis risus et egestas commodo.Cras venenatis arcu in pharetra interdum.
+Donec quis metus porttitor tellus cursus lobortis. Quisque et orci magna. Fusce rhoncus mi mi,
+at vehicula massa rhoncus quis. Mauris augue leo, pretium eget molestie vitae, efficitur nec nulla.
+In hac habitasse platea dictumst. Sed sit amet imperdiet purus.
diff --git a/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/index.rst.txt b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/index.rst.txt
new file mode 100644
index 0000000000..ba42326fd7
--- /dev/null
+++ b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/index.rst.txt
@@ -0,0 +1,27 @@
+.. include:: ../README.rst
+
+.. toctree::
+   :caption: Theme Documentation
+   :maxdepth: 2
+
+   installing
+   configuring
+   changelog
+
+
+.. toctree::
+    :maxdepth: 2
+    :numbered:
+    :caption: Demo Documents
+
+    demo/structure
+    demo/demo
+    demo/lists_tables
+    demo/api
+
+.. toctree::
+    :maxdepth: 3
+    :numbered:
+    :caption: This is an incredibly long caption for a long menu
+
+    demo/long
diff --git a/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/installing.rst.txt b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/installing.rst.txt
new file mode 100644
index 0000000000..df406b999d
--- /dev/null
+++ b/docs/v1.2.0/_sources/src/pytorch-sphinx-theme/docs/installing.rst.txt
@@ -0,0 +1,17 @@
+
+************
+Installation
+************
+
+Via Git or Download
+===================
+
+Symlink or subtree the ``pytorch_sphinx_theme`` repository into your documentation at
+``docs/_themes/pytorch_sphinx_theme`` then add the following two settings to your Sphinx
+``conf.py`` file:
+
+.. code:: python
+
+    html_theme = "pytorch_sphinx_theme"
+    html_theme_path = ["_themes", ]
+
diff --git a/docs/v1.2.0/_sources/tutorials/creating_torchscript_module_in_python.rst.txt b/docs/v1.2.0/_sources/tutorials/creating_torchscript_module_in_python.rst.txt
new file mode 100644
index 0000000000..1d1cdba574
--- /dev/null
+++ b/docs/v1.2.0/_sources/tutorials/creating_torchscript_module_in_python.rst.txt
@@ -0,0 +1,140 @@
+.. _creating_a_ts_mod:
+
+Creating a TorchScript Module
+------------------------------
+TorchScript is a way to create serializable and optimizable models from PyTorch code.
+PyTorch has detailed documentation on how to do this https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html but briefly here is the
+here is key background information and the process:
+
+PyTorch programs are based around ``Module`` s which can be used to compose higher level modules. ``Modules`` contain a constructor to set up the modules, parameters and sub-modules
+and a forward function which describes how to use the parameters and submodules when the module is invoked.
+
+For example, we can define a LeNet module like this:
+
+.. code-block:: python
+    :linenos:
+
+    import torch.nn as nn
+    import torch.nn.functional as F
+
+
+    class LeNetFeatExtractor(nn.Module):
+        def __init__(self):
+            super(LeNetFeatExtractor, self).__init__()
+            self.conv1 = nn.Conv2d(1, 6, 3)
+            self.conv2 = nn.Conv2d(6, 16, 3)
+
+        def forward(self, x):
+            x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
+            x = F.max_pool2d(F.relu(self.conv2(x)), 2)
+            return x
+
+
+    class LeNetClassifier(nn.Module):
+        def __init__(self):
+            super(LeNetClassifier, self).__init__()
+            self.fc1 = nn.Linear(16 * 6 * 6, 120)
+            self.fc2 = nn.Linear(120, 84)
+            self.fc3 = nn.Linear(84, 10)
+
+        def forward(self, x):
+            x = torch.flatten(x, 1)
+            x = F.relu(self.fc1(x))
+            x = F.relu(self.fc2(x))
+            x = self.fc3(x)
+            return x
+
+
+    class LeNet(nn.Module):
+        def __init__(self):
+            super(LeNet, self).__init__()
+            self.feat = LeNetFeatExtractor()
+            self.classifer = LeNetClassifier()
+
+        def forward(self, x):
+            x = self.feat(x)
+            x = self.classifer(x)
+            return x
+
+.
+
+    Obviously you may want to consolidate such a simple model into a single module but we can see the composability of PyTorch here
+
+From here are two pathways for going from PyTorch Python code to TorchScript code: Tracing and Scripting.
+
+Tracing follows the path of execution when the module is called and records what happens.
+To trace an instance of our LeNet module, we can call ``torch.jit.trace`` with an example input.
+
+.. code-block:: python
+
+    import torch
+
+    model = LeNet()
+    input_data = torch.empty([1, 1, 32, 32])
+    traced_model = torch.jit.trace(model, input_data)
+
+Scripting actually inspects your code with a compiler and generates an equivalent TorchScript program. The difference is that since tracing
+is following the execution of your module, it cannot pick up control flow for instance. By working from the Python code, the compiler can
+include these components. We can run the script compiler on our LeNet module by calling ``torch.jit.script``
+
+.. code-block:: python
+
+    import torch
+
+    model = LeNet()
+    script_model = torch.jit.script(model)
+
+There are reasons to use one path or another, the PyTorch documentation has information on how to choose. From a Torch-TensorRT prespective, there is
+better support (i.e your module is more likely to compile) for traced modules because it doesn't include all the complexities of a complete
+programming language, though both paths supported.
+
+After scripting or tracing your module, you are given back a TorchScript Module. This contains the code and parameters used to run the module stored
+in a intermediate representation that Torch-TensorRT can consume.
+
+Here is what the LeNet traced module IR looks like:
+
+.. code-block:: none
+
+    graph(%self.1 : __torch__.___torch_mangle_10.LeNet,
+        %input.1 : Float(1, 1, 32, 32)):
+        %129 : __torch__.___torch_mangle_9.LeNetClassifier = prim::GetAttr[name="classifer"](%self.1)
+        %119 : __torch__.___torch_mangle_5.LeNetFeatExtractor = prim::GetAttr[name="feat"](%self.1)
+        %137 : Tensor = prim::CallMethod[name="forward"](%119, %input.1)
+        %138 : Tensor = prim::CallMethod[name="forward"](%129, %137)
+        return (%138)
+
+and the LeNet scripted module IR:
+
+.. code-block:: none
+
+    graph(%self : __torch__.LeNet,
+        %x.1 : Tensor):
+        %2 : __torch__.LeNetFeatExtractor = prim::GetAttr[name="feat"](%self)
+        %x.3 : Tensor = prim::CallMethod[name="forward"](%2, %x.1) # x.py:38:12
+        %5 : __torch__.LeNetClassifier = prim::GetAttr[name="classifer"](%self)
+        %x.5 : Tensor = prim::CallMethod[name="forward"](%5, %x.3) # x.py:39:12
+        return (%x.5)
+
+You can see that the IR preserves the module structure we have in our python code.
+
+.. _ts_in_py:
+
+Working with TorchScript in Python
+-----------------------------------
+
+TorchScript Modules are run the same way you run normal PyTorch modules. You can run the forward pass using the
+``forward`` method or just calling the module ``torch_scirpt_module(in_tensor)`` The JIT compiler will compile
+and optimize the module on the fly and then returns the results.
+
+Saving TorchScript Module to Disk
+-----------------------------------
+
+For either traced or scripted modules, you can save the module to disk with the following command
+
+.. code-block:: python
+
+    import torch
+
+    model = LeNet()
+    script_model = torch.jit.script(model)
+    script_model.save("lenet_scripted.ts")
diff --git a/docs/v1.2.0/_sources/tutorials/getting_started_with_cpp_api.rst.txt b/docs/v1.2.0/_sources/tutorials/getting_started_with_cpp_api.rst.txt
new file mode 100644
index 0000000000..41b8a448c0
--- /dev/null
+++ b/docs/v1.2.0/_sources/tutorials/getting_started_with_cpp_api.rst.txt
@@ -0,0 +1,338 @@
+.. _getting_started:
+
+Getting Started with C++
+========================
+
+If you haven't already, acquire a tarball of the library by following the instructions in :ref:`Installation`
+
+Using Torch-TensorRT in C++
+***************************
+Torch-TensorRT C++ API accepts TorchScript modules (generated either from ``torch.jit.script`` or ``torch.jit.trace``) as an input and returns
+a Torchscript module (optimized using TensorRT). This requires users to use Pytorch (in python) to generate torchscript modules beforehand.
+Please refer to `Creating TorchScript modules in Python <https://nvidia.github.io/Torch-TensorRT/tutorials/creating_torchscript_module_in_python.html>`_ section to generate torchscript graphs.
+
+
+.. _torch_tensorrt_quickstart:
+
+[Torch-TensorRT Quickstart] Compiling TorchScript Modules with ``torchtrtc``
+---------------------------------------------------------------------------------
+
+An easy way to get started with Torch-TensorRT and to check if your model can be supported without extra work is to run it through
+``torchtrtc``, which supports almost all features of the compiler from the command line including post training quantization
+(given a previously created calibration cache). For example we can compile our lenet model by setting our preferred operating
+precision and input size. This new TorchScript file can be loaded into Python (note: you need to ``import torch_tensorrt`` before loading
+these compiled modules because the compiler extends the PyTorch the deserializer and runtime to execute compiled modules).
+
+.. code-block:: shell
+
+    ❯ torchtrtc -p f16 lenet_scripted.ts trt_lenet_scripted.ts "(1,1,32,32)"
+
+    ❯ python3
+    Python 3.6.9 (default, Apr 18 2020, 01:56:04)
+    [GCC 8.4.0] on linux
+    Type "help", "copyright", "credits" or "license" for more information.
+    >>> import torch
+    >>> import torch_tensorrt
+    >>> ts_model = torch.jit.load(“trt_lenet_scripted.ts”)
+    >>> ts_model(torch.randn((1,1,32,32)).to(“cuda”).half())
+
+You can learn more about ``torchtrtc`` usage here: :ref:`torchtrtc`
+
+.. _ts_in_cc:
+
+Working with TorchScript in C++
+--------------------------------
+
+If we are developing an application to deploy with C++, we can save either our traced or scripted module using ``torch.jit.save``
+which will serialize the TorchScript code, weights and other information into a package. This is also where our dependency on Python ends.
+
+.. code-block:: python
+
+    torch_script_module.save("lenet.jit.pt")
+
+From here we can now load our TorchScript module in C++
+
+.. code-block:: c++
+
+    #include <torch/script.h> // One-stop header.
+
+    #include <iostream>
+    #include <memory>
+
+    int main(int argc, const char* argv[]) {
+        torch::jit::Module module;
+        try {
+            // Deserialize the ScriptModule from a file using torch::jit::load().
+            module = torch::jit::load("<PATH TO SAVED TS MOD>");
+        }
+        catch (const c10::Error& e) {
+            std::cerr << "error loading the model\n";
+            return -1;
+        }
+
+        std::cout << "ok\n";
+
+
+You can do full training and inference in C++ with PyTorch / LibTorch if you would like, you can even define your modules in C++ and
+have access to the same powerful tensor library that backs PyTorch. (For more information: https://pytorch.org/cppdocs/).
+For instance we can do inference with our LeNet module like this:
+
+.. code-block:: c++
+
+        mod.eval();
+        torch::Tensor in = torch::randn({1, 1, 32, 32});
+        auto out = mod.forward(in);
+
+and to run on the GPU:
+
+.. code-block:: c++
+
+        mod.eval();
+        mod.to(torch::kCUDA);
+        torch::Tensor in = torch::randn({1, 1, 32, 32}, torch::kCUDA);
+        auto out = mod.forward(in);
+
+As you can see it is pretty similar to the Python API. When you call the ``forward`` method, you invoke the PyTorch JIT compiler, which will optimize and run your TorchScript code.
+
+.. _compile_cpp:
+
+Compiling with Torch-TensorRT in C++
+-------------------------------------
+We are also at the point were we can compile and optimize our module with Torch-TensorRT, but instead of in a JIT fashion we must do it ahead-of-time (AOT) i.e. before we start doing actual inference work
+since it takes a bit of time to optimize the module, it would not make sense to do this every time you run the module or even the first time you run it.
+
+With our module loaded, we can feed it into the Torch-TensorRT compiler. When we do so we must provide some information on the expected input size and also configure any additional settings.
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+    ...
+
+        mod.to(at::kCUDA);
+        mod.eval();
+
+        auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA});
+        auto trt_mod = torch_tensorrt::CompileGraph(mod, std::vector<torch_tensorrt::CompileSpec::InputRange>{{in.sizes()}});
+        auto out = trt_mod.forward({in});
+
+Thats it! Now the graph runs primarily not with the JIT compiler but using TensorRT (though we execute the graph using the JIT runtime).
+
+We can also set settings like operating precision to run in FP16.
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+    ...
+
+        mod.to(at::kCUDA);
+        mod.eval();
+
+        auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA}).to(torch::kHALF);
+        auto input_sizes = std::vector<torch_tensorrt::CompileSpec::InputRange>({in.sizes()});
+        torch_tensorrt::CompileSpec info(input_sizes);
+        info.enable_precisions.insert(torch::kHALF);
+        auto trt_mod = torch_tensorrt::CompileGraph(mod, info);
+        auto out = trt_mod.forward({in});
+
+And now we are running the module in FP16 precision. You can then save the module to load later.
+
+.. code-block:: c++
+
+    trt_mod.save("<PATH TO SAVED TRT/TS MOD>")
+
+Torch-TensorRT compiled TorchScript modules are loaded in the same way as normal TorchScript module. Make sure your deployment application is linked against ``libtorchtrt.so``
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+
+    int main(int argc, const char* argv[]) {
+        torch::jit::Module module;
+        try {
+            // Deserialize the ScriptModule from a file using torch::jit::load().
+            module = torch::jit::load("<PATH TO SAVED TRT/TS MOD>");
+        }
+        catch (const c10::Error& e) {
+            std::cerr << "error loading the model\n";
+            return -1;
+        }
+
+        torch::Tensor in = torch::randn({1, 1, 32, 32}, torch::kCUDA);
+        auto out = mod.forward(in);
+
+        std::cout << "ok\n";
+    }
+
+If you want to save the engine produced by Torch-TensorRT to use in a TensorRT application you can use the ``ConvertGraphToTRTEngine`` API.
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+    ...
+
+        mod.to(at::kCUDA);
+        mod.eval();
+
+        auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA}).to(torch::kHALF);
+        auto input_sizes = std::vector<torch_tensorrt::CompileSpec::InputRange>({in.sizes()});
+        torch_tensorrt::CompileSpec info(input_sizes);
+        info.enabled_precisions.insert(torch::kHALF);
+        auto trt_mod = torch_tensorrt::ConvertGraphToTRTEngine(mod, "forward", info);
+        std::ofstream out("/tmp/engine_converted_from_jit.trt");
+        out << engine;
+        out.close();
+
+.. _under_the_hood:
+
+Under The Hood
+---------------
+
+When a module is provided to Torch-TensorRT, the compiler starts by mapping a graph like you saw above to a graph like this:
+
+.. code-block:: none
+
+    graph(%input.2 : Tensor):
+        %2 : Float(84, 10) = prim::Constant[value=<Tensor>]()
+        %3 : Float(120, 84) = prim::Constant[value=<Tensor>]()
+        %4 : Float(576, 120) = prim::Constant[value=<Tensor>]()
+        %5 : int = prim::Constant[value=-1]() # x.py:25:0
+        %6 : int[] = prim::Constant[value=annotate(List[int], [])]()
+        %7 : int[] = prim::Constant[value=[2, 2]]()
+        %8 : int[] = prim::Constant[value=[0, 0]]()
+        %9 : int[] = prim::Constant[value=[1, 1]]()
+        %10 : bool = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+        %11 : int = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+        %12 : bool = prim::Constant[value=0]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+        %self.classifer.fc3.bias : Float(10) = prim::Constant[value= 0.0464  0.0383  0.0678  0.0932  0.1045 -0.0805 -0.0435 -0.0818  0.0208 -0.0358 [ CUDAFloatType{10} ]]()
+        %self.classifer.fc2.bias : Float(84) = prim::Constant[value=<Tensor>]()
+        %self.classifer.fc1.bias : Float(120) = prim::Constant[value=<Tensor>]()
+        %self.feat.conv2.weight : Float(16, 6, 3, 3) = prim::Constant[value=<Tensor>]()
+        %self.feat.conv2.bias : Float(16) = prim::Constant[value=<Tensor>]()
+        %self.feat.conv1.weight : Float(6, 1, 3, 3) = prim::Constant[value=<Tensor>]()
+        %self.feat.conv1.bias : Float(6) = prim::Constant[value= 0.0530 -0.1691  0.2802  0.1502  0.1056 -0.1549 [ CUDAFloatType{6} ]]()
+        %input0.4 : Tensor = aten::_convolution(%input.2, %self.feat.conv1.weight, %self.feat.conv1.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+        %input0.5 : Tensor = aten::relu(%input0.4) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+        %input1.2 : Tensor = aten::max_pool2d(%input0.5, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+        %input0.6 : Tensor = aten::_convolution(%input1.2, %self.feat.conv2.weight, %self.feat.conv2.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+        %input2.1 : Tensor = aten::relu(%input0.6) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+        %x.1 : Tensor = aten::max_pool2d(%input2.1, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+        %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0
+        %27 : Tensor = aten::matmul(%input.1, %4)
+        %28 : Tensor = trt::const(%self.classifer.fc1.bias)
+        %29 : Tensor = aten::add_(%28, %27, %11)
+        %input0.2 : Tensor = aten::relu(%29) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+        %31 : Tensor = aten::matmul(%input0.2, %3)
+        %32 : Tensor = trt::const(%self.classifer.fc2.bias)
+        %33 : Tensor = aten::add_(%32, %31, %11)
+        %input1.1 : Tensor = aten::relu(%33) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+        %35 : Tensor = aten::matmul(%input1.1, %2)
+        %36 : Tensor = trt::const(%self.classifer.fc3.bias)
+        %37 : Tensor = aten::add_(%36, %35, %11)
+        return (%37)
+    (CompileGraph)
+
+The graph has now been transformed from a collection of modules, each managing their own parameters into a single graph with the parameters inlined
+into the graph and all of the operations laid out. Torch-TensorRT has also executed a number of optimizations and mappings to make the graph easier to translate to TensorRT.
+From here the compiler can assemble the TensorRT engine by following the dataflow through the graph.
+
+When the graph construction phase is complete, Torch-TensorRT produces a serialized TensorRT engine. From here depending on the API, this engine is returned
+to the user or moves into the graph construction phase. Here Torch-TensorRT creates a JIT Module to execute the TensorRT engine which will be instantiated and managed
+by the Torch-TensorRT runtime.
+
+Here is the graph that you get back after compilation is complete:
+
+.. code-block:: none
+
+    graph(%self_1 : __torch__.lenet, %input_0 : Tensor):
+        %1 : ...trt.Engine = prim::GetAttr[name="lenet"](%self_1)
+        %3 : Tensor[] = prim::ListConstruct(%input_0)
+        %4 : Tensor[] = trt::execute_engine(%3, %1)
+        %5 : Tensor = prim::ListUnpack(%4)
+        return (%5)
+
+
+You can see the call where the engine is executed, after extracting the attribute containing the engine and constructing a list of inputs, then returns the tensors back to the user.
+
+.. _unsupported_ops:
+
+Working with Unsupported Operators
+-----------------------------------
+
+Torch-TensorRT is a new library and the PyTorch operator library is quite large, so there will be ops that aren't supported natively by the compiler. You can either use the composition techinques
+shown above to make modules are fully Torch-TensorRT supported and ones that are not and stitch the modules together in the deployment application or you can register converters for missing ops.
+
+    You can check support without going through the full compilation pipleine using the ``torch_tensorrt::CheckMethodOperatorSupport(const torch::jit::Module& module, std::string method_name)`` api
+    to see what operators are not supported. ``torchtrtc`` automatically checks modules with this method before starting compilation and will print out a list of operators that are not supported.
+
+.. _custom_converters:
+
+Registering Custom Converters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Operations are mapped to TensorRT through the use of modular converters, a function that takes a node from a the JIT graph and produces an equivalent layer or subgraph in TensorRT.
+Torch-TensorRT ships with a library of these converters stored in a registry, that will be executed depending on the node being parsed. For instance a ``aten::relu(%input0.4)`` instruction will trigger
+the relu converter to be run on it, producing an activation layer in the TensorRT graph. But since this library is not exhaustive you may need to write your own to get Torch-TensorRT
+to support your module.
+
+Shipped with the Torch-TensorRT distribution are the internal core API headers. You can therefore access the converter registry and add a converter for the op you need.
+
+For example, if we try to compile a graph with a build of Torch-TensorRT that doesn't support the flatten operation (``aten::flatten``) you may see this error:
+
+.. code-block:: none
+
+    terminate called after throwing an instance of 'torch_tensorrt::Error'
+    what():  [enforce fail at core/conversion/conversion.cpp:109] Expected converter to be true but got false
+    Unable to convert node: %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0 (conversion.AddLayer)
+    Schema: aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)
+    Converter for aten::flatten requested, but no such converter was found.
+    If you need a converter for this operator, you can try implementing one yourself
+    or request a converter: https://www.github.com/NVIDIA/Torch-TensorRT/issues
+
+We can register a converter for this operator in our application. All of the tools required to build a converter can be imported by including ``torch_tensorrt/core/conversion/converters/converters.h``.
+We start by creating an instance of the self-registering class ``torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns()`` which will register converters
+in the global converter registry, associating a function schema like ``aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)`` with a lambda that
+will take the state of the conversion, the node/operation in question to convert and all of the inputs to the node and produces as a side effect a new layer in the TensorRT network.
+Arguments are passed as a vector of inspectable unions of TensorRT ``ITensors`` and Torch ``IValues`` in the order arguments are listed in the schema.
+
+Below is a implementation of a ``aten::flatten`` converter that we can use in our application. You have full access to the Torch and TensorRT libraries in the converter implementation. So
+for example we can quickly get the output size by just running the operation in PyTorch instead of implementing the full calculation outself like we do below for this flatten converter.
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+    #include "torch_tensorrt/core/conversion/converters/converters.h"
+
+    static auto flatten_converter = torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns()
+        .pattern({
+            "aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)",
+            [](torch_tensorrt::core::conversion::ConversionCtx* ctx,
+               const torch::jit::Node* n,
+               torch_tensorrt::core::conversion::converters::args& args) -> bool {
+                auto in = args[0].ITensor();
+                auto start_dim = args[1].unwrapToInt();
+                auto end_dim = args[2].unwrapToInt();
+                auto in_shape = torch_tensorrt::core::util::toVec(in->getDimensions());
+                auto out_shape = torch::flatten(torch::rand(in_shape), start_dim, end_dim).sizes();
+
+                auto shuffle = ctx->net->addShuffle(*in);
+                shuffle->setReshapeDimensions(torch_tensorrt::core::util::toDims(out_shape));
+                shuffle->setName(torch_tensorrt::core::util::node_info(n).c_str());
+
+                auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle->getOutput(0));
+                return true;
+            }
+        });
+
+    int main() {
+        ...
+
+To use this converter in Python, it is recommended to use PyTorch's `C++ / CUDA Extention <https://pytorch.org/tutorials/advanced/cpp_extension.html#custom-c-and-cuda-extensions>`_
+template to wrap your library of converters into a ``.so`` that you can load with ``ctypes.CDLL()`` in your Python application.
+
+You can find more information on all the details of writing converters in the contributors documentation (:ref:`writing_converters`).
+If you find yourself with a large library of converter implementations, do consider upstreaming them, PRs are welcome and it would be great for the community to benefit as well.
diff --git a/docs/v1.2.0/_sources/tutorials/getting_started_with_fx_path.rst.txt b/docs/v1.2.0/_sources/tutorials/getting_started_with_fx_path.rst.txt
new file mode 100644
index 0000000000..3e8b8733ad
--- /dev/null
+++ b/docs/v1.2.0/_sources/tutorials/getting_started_with_fx_path.rst.txt
@@ -0,0 +1,333 @@
+.. _getting_started_with_fx:
+
+Torch-TensorRT (FX Frontend) User Guide
+========================
+Torch-TensorRT (FX Frontend) is a tool that can convert a PyTorch model through ``torch.fx`` to an
+TensorRT engine optimized targeting running on Nvidia GPUs. TensorRT is the inference engine
+developed by NVIDIA which composed of various kinds of optimization including kernel fusion,
+graph optimization, low precision, etc.. This tool is developed in Python environment which allows this
+workflow to be very accessible to researchers and engineers. There are a few stages that a
+user want to use this tool and we will introduce them here.
+
+> Torch-TensorRT (FX Frontend) is in ``Beta`` and currently it is recommended to work with PyTorch nightly.
+
+.. code-block:: shell
+
+    # Test an example by
+    $ python py/torch_tensorrt/fx/example/lower_example.py
+
+
+Converting a PyTorch Model to TensorRT Engine
+---------------------------------------------
+In general, users are welcome to use the ``compile()`` to finish the conversion from a model to tensorRT engine. It is a
+wrapper API that consists of the major steps needed to finish this converison. Please refer to an example usage in ``lower_example.py`` file under ``examples/fx``.
+
+.. code-block:: python
+
+    def compile(
+        module: nn.Module,
+        input,
+        max_batch_size=2048,
+        max_workspace_size=33554432,
+        explicit_batch_dimension=False,
+        lower_precision=LowerPrecision.FP16,
+        verbose_log=False,
+        timing_cache_prefix="",
+        save_timing_cache=False,
+        cuda_graph_batch_size=-1,
+        dynamic_batch=True,
+    ) -> nn.Module:
+
+        """
+        Takes in original module, input and lowering setting, run lowering workflow to turn module
+        into lowered module, or so called TRTModule.
+
+        Args:
+            module: Original module for lowering.
+            input: Input for module.
+            max_batch_size: Maximum batch size (must be >= 1 to be set, 0 means not set)
+            max_workspace_size: Maximum size of workspace given to TensorRT.
+            explicit_batch_dimension: Use explicit batch dimension in TensorRT if set True, otherwise use implicit batch dimension.
+            lower_precision: lower_precision config given to TRTModule.
+            verbose_log: Enable verbose log for TensorRT if set True.
+            timing_cache_prefix: Timing cache file name for timing cache used by fx2trt.
+            save_timing_cache: Update timing cache with current timing cache data if set to True.
+            cuda_graph_batch_size: Cuda graph batch size, default to be -1.
+            dynamic_batch: batch dimension (dim=0) is dynamic.
+        Returns:
+            A torch.nn.Module lowered by TensorRT.
+        """
+
+In this section, we will go through an example to illustrate the major steps that fx path uses.
+Users can refer to ``fx2trt_example.py`` file in ``examples/fx``.
+
+* **Step 1: Trace the model with acc_tracer**
+Acc_tracer is a tracer inheritated from FX tracer. It comes with args normalizer to convert all args to kwargs and pass to TRT converters.
+
+.. code-block:: python
+
+   import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
+
+   # Build the model which needs to be a PyTorch nn.Module.
+   my_pytorch_model = build_model()
+
+   # Prepare inputs to the model. Inputs have to be a List of Tensors
+   inputs = [Tensor, Tensor, ...]
+
+   # Trace the model with acc_tracer.
+   acc_mod = acc_tracer.trace(my_pytorch_model, inputs)
+
+*Common Errors:*
+
+symbolically traced variables cannot be used as inputs to control flow
+This means the model contains dynamic control flow. Please refer to section “Dynamic Control Flow” in `FX guide <https://pytorch.org/docs/stable/fx.html#dynamic-control-flow>`_.
+
+* **Step 2: Build TensorRT engine**
+There are `two different modes <https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#explicit-implicit-batch>`_ for how TensorRT handles batch dimension, explicit batch dimension and implicit batch dimension. This mode was used by early versions of TensorRT, and is now deprecated but continues to be supported for backwards compatibility. In explicit batch mode, all dimensions are explicit and can be dynamic, that is their length can change at execution time. Many new features, such as dynamic shapes and loops, are available only in this mode. User can still choose to use implicit batch mode when they set ``explicit_batch_dimension=False`` in ``compile()``. We do not recommend to use it since it will lack of support in future TensorRT versions.
+
+Explicit batch is the default mode and it must be set for dynamic shape. For most of vision task, user can choose to enable ``dynamic_batch`` in ``compile()`` if they want to get the similar effects as implicit mode where only batch dimension changes. It has some requirements:
+1. Shapes of inputs, outputs and activations are fixed except batch dimension.
+2. Inputs, outputs and activations have batch dimension as the major dimension.
+3. All the operators in the model do not modify batch dimension (permute, transpose, split, etc.) or compute over batch dimension (sum, softmax, etc.).
+
+For examples of the last path, if we have a 3D tensor t shaped as (batch, sequence, dimension), operations such as torch.transpose(0, 2). If any of these three are not satisfied, we’ll need to specify InputTensorSpec as inputs with dynamic range.
+
+.. code-block:: python
+
+    import torch_tensorrt.fx.converters
+    from torch_tensorrt.fx import InputTensorSpec, TRTInterpreter
+
+    # InputTensorSpec is a dataclass we use to store input information.
+    # There're two ways we can build input_specs.
+    # Option 1, build it manually.
+    input_specs = [
+      InputTensorSpec(shape=(1, 2, 3), dtype=torch.float32),
+      InputTensorSpec(shape=(1, 4, 5), dtype=torch.float32),
+    ]
+    # Option 2, build it using sample_inputs where user provide a sample
+    inputs = [
+    torch.rand((1,2,3), dtype=torch.float32),
+    torch.rand((1,4,5), dtype=torch.float32),
+    ]
+    input_specs = InputTensorSpec.from_tensors(inputs)
+
+    # IMPORTANT: If dynamic shape is needed, we need to build it slightly differently.
+    input_specs = [
+        InputTensorSpec(
+            shape=(-1, 2, 3),
+            dtype=torch.float32,
+            # Currently we only support one set of dynamic range. User may set other dimensions but it is not promised to work for any models
+            # (min_shape, optimize_target_shape, max_shape)
+            # For more information refer to fx/input_tensor_spec.py
+            shape_ranges = [
+                ((1, 2, 3), (4, 2, 3), (100, 2, 3)),
+            ],
+        ),
+        InputTensorSpec(shape=(1, 4, 5), dtype=torch.float32),
+    ]
+
+    # Build a TRT interpreter. Set explicit_batch_dimension accordingly.
+    interpreter = TRTInterpreter(
+        acc_mod, input_specs, explicit_batch_dimension=True/False
+    )
+
+    # The output of TRTInterpreter run() is wrapped as TRTInterpreterResult.
+    # The TRTInterpreterResult contains required parameter to build TRTModule,
+    # and other informational output from TRTInterpreter run.
+    class TRTInterpreterResult(NamedTuple):
+        engine: Any
+        input_names: Sequence[str]
+        output_names: Sequence[str]
+        serialized_cache: bytearray
+
+    #max_batch_size: set accordingly for maximum batch size you will use.
+    #max_workspace_size: set to the maximum size we can afford for temporary buffer
+    #lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).
+    #sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity
+    #force_fp32_output: force output to be fp32
+    #strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric #reasons.
+    #algorithm_selector: set up algorithm selection for certain layer
+    #timing_cache: enable timing cache for TensorRT
+    #profiling_verbosity: TensorRT logging level
+    trt_interpreter_result = interpreter.run(
+        max_batch_size=64,
+        max_workspace_size=1 << 25,
+        sparse_weights=False,
+        force_fp32_output=False,
+        strict_type_constraints=False,
+        algorithm_selector=None,
+        timing_cache=None,
+        profiling_verbosity=None,
+    )
+
+
+*Common Errors:*
+
+RuntimeError: Conversion of function xxx not currently supported!
+- This means we don’t have the support for this xxx operator. Please refer to section “How to add a missing op” below for further instructions.
+
+* **Step 3: Run the model**
+One way is using TRTModule, which is basically a PyTorch nn.Module.
+
+.. code-block:: python
+
+    from torch_tensorrt.fx import TRTModule
+    mod = TRTModule(
+        trt_interpreter_result.engine,
+        trt_interpreter_result.input_names,
+        trt_interpreter_result.output_names)
+    # Just like all other PyTorch modules
+    outputs = mod(*inputs)
+    torch.save(mod, "trt.pt")
+    reload_trt_mod = torch.load("trt.pt")
+    reload_model_output = reload_trt_mod(*inputs)
+
+So far, we give a detailed explanation of major steps in convterting a PyTorch model into TensorRT engine. Users are welcome to refer to the source code for some parameters explanations. In the converting scheme, there are two important actions in it. One is acc tracer which helps us to convert a PyTorch model to acc graph. The other is FX path converter which helps to convert the acc graph's operation to corresponding TensorRT operation and build up the TensoRT engine for it.
+
+Acc Tracer
+---------
+
+Acc tracer is a custom FX symbolic tracer. It does a couple more things compare to the vanilla FX symbolic tracer. We mainly depend on it to convert PyTorch ops or builtin ops to acc ops. There are two main purposes for fx2trt to use acc ops:
+
+1. there’re many ops that do similar things in PyTorch ops and builtin ops such like torch.add, builtin.add and torch.Tensor.add. Using acc tracer, we normalize these three ops to a single acc_ops.add. This helps reduce the number of converters we need to write.
+2. acc ops only have kwargs which makes writing converter easier as we don’t need to add additional logic to find arguments in args and kwargs.
+
+Torch-TensorRT FX Frontend
+--------
+After symbolic tracing, we have the graph representation of a PyTorch model. The Torch-TensorRT FX Frontend leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).
+
+Below is an example of a converter function. The decorator is used to register this converter function with the corresponding node. In this example, we register this converter to a fx node whose target is acc_ops.sigmoid.
+
+.. code-block:: python
+
+    @tensorrt_converter(acc_ops.sigmoid)
+    def acc_ops_sigmoid(network, target, args, kwargs, name):
+        """
+        network: TensorRT network. We'll be adding layers to it.
+
+        The rest arguments are attributes of fx node.
+        """
+        input_val = kwargs['input']
+
+        if not isinstance(input_val, trt.tensorrt.ITensor):
+            raise RuntimeError(f'Sigmoid received input {input_val} that is not part '
+                            'of the TensorRT region!')
+
+        layer = network.add_activation(input=input_val, type=trt.ActivationType.SIGMOID)
+        layer.name = name
+        return layer.get_output(0)
+
+How to Add a Missing Op
+****************
+
+You can actually add it wherever you want just need to remember import the file so that all acc ops and mapper will be registered before tracing with acc_tracer.
+
+* **Step 1. Add a new acc op**
+
+TODO: Need to explain more on the logistic of acc op like when we want to break down an op and when we want to reuse other ops.
+
+In `acc tracer <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_tracer.py>`_, we convert nodes in the graph to acc ops if there’s a mapping registered for the node to an acc op.
+
+In order to make the conversion to acc ops to happen, there’re two things required. One is that there should be an acc op function defined and the other is there should be a mapping registered.
+
+Defining an acc op is simple, we first just need a function and register the function as an acc op via this decorator `acc_normalizer.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py>`_. e.g. the following code adds an acc op named foo() which adds two given inputs.
+
+.. code-block:: shell
+
+    # NOTE: all acc ops should only take kwargs as inputs, therefore we need the "*"
+    # at the beginning.
+    @register_acc_op
+    def foo(*, input, other, alpha):
+        return input + alpha * other
+
+There’re two ways to register a mapping. One is `register_acc_op_mapping() <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L164>`_. Let’s register a mapping from torch.add to foo() we just created above. We need to add decorator register_acc_op_mapping to it.
+
+.. code-block:: shell
+
+    this_arg_is_optional = True
+
+    @register_acc_op_mapping(
+        op_and_target=("call_function", torch.add),
+        arg_replacement_tuples=[
+            ("input", "input"),
+            ("other", "other"),
+            ("alpha", "alpha", this_arg_is_optional),
+        ],
+    )
+    @register_acc_op
+    def foo(*, input, other, alpha=1.0):
+        return input + alpha * other
+
+``op_and_target`` determines which node will trigger this mapping. op and target are the attributes of FX node. In acc_normalization when we see a node with the same op and target as set in the ``op_and_target``, we will trigger the mapping. Since we want to map from ``torch.add``, then op would be call_function and target would be ``torch.add``. ``arg_replacement_tuples`` determines how we construct kwargs for new acc op node using args and kwargs from original node. Each tuple in ``arg_replacement_tuples`` represents one argument mapping rule. It contains two or three elements. The third element is a boolean variable that determines whether this kwarg is optional in *original node*. We only need to specify the third element if it’s True. The first element is the argument name in original node which will be used as the acc op node’s argument whose name is the second element in the tuple. The sequence of the tuples does matter because the position of the tuple determines where the argument is in original node’s args. We use this information to map args from original node to kwargs in acc op node.
+We don’t have to specify arg_replacement_tuples if none of the followings are true.
+
+1. kwargs of original nodes and acc op nodes have different name.
+2. there’re optional arguments.
+
+The other way to register a mapping is through `register_custom_acc_mapper_fn() <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L206>`_. This one is designed to reduce the redundant op registration as it allows you to use a function to map to one or more existing acc ops throught some combinations. In the function, you can do basically whatever you want. Let’s use an example to explain how it works.
+
+.. code-block:: shell
+
+    @register_acc_op
+    def foo(*, input, other, alpha=1.0):
+        return input + alpha * other
+
+    @register_custom_acc_mapper_fn(
+        op_and_target=("call_function", torch.add),
+        arg_replacement_tuples=[
+            ("input", "input"),
+            ("other", "other"),
+            ("alpha", "alpha", this_arg_is_optional),
+        ],
+    )
+    def custom_mapper(node: torch.fx.Node, _: nn.Module) -> torch.fx.Node:
+        """
+        `node` is original node, which is a call_function node with target
+        being torch.add.
+        """
+        alpha = 1
+        if "alpha" in node.kwargs:
+            alpha = node.kwargs["alpha"]
+        foo_kwargs = {"input": node["input"], "other": node["other"], "alpha": alpha}
+        with node.graph.inserting_before(node):
+            foo_node = node.graph.call_function(foo, kwargs=foo_kwargs)
+            foo_node.meta = node.meta.copy()
+            return foo_node
+
+
+In the custom mapper function, we construct an acc op node and return it. The node we returns here would take over all the children nodes of original nodes `acc_normalizer.py <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L379>`_.
+
+The last step would be *adding unit test* for the new acc op or mapper function we added. The place to add the unit test is here `test_acc_tracer.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/test/tracer/test_acc_tracer.py>`_.
+
+* **Step 2. Add a new converter**
+
+All the developed converters for acc ops are all in `acc_op_converter.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/converters/acc_ops_converters.py>`_. It could give you a good example of how the converter is added.
+
+Essentially, the converter is the mapping mechanism that maps the acc ops to a TensorRT layer. If we are able to find all the TensorRT layers we need we can get start to add a converter for the node using `TensorRT APIs <https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Graph/Network.html>`_.
+
+.. code-block:: shell
+
+    @tensorrt_converter(acc_ops.sigmoid)
+    def acc_ops_sigmoid(network, target, args, kwargs, name):
+        """
+        network: TensorRT network. We'll be adding layers to it.
+
+        The rest arguments are attributes of fx node.
+        """
+        input_val = kwargs['input']
+
+        if not isinstance(input_val, trt.tensorrt.ITensor):
+            raise RuntimeError(f'Sigmoid received input {input_val} that is not part '
+                            'of the TensorRT region!')
+
+        layer = network.add_activation(input=input_val, type=trt.ActivationType.SIGMOID)
+        layer.name = name
+        return layer.get_output(0)
+
+We need to use ``tensorrt_converter`` decorator to register the converter. The argument for the decorator is the target of the fx node that we need to convert. In the converter, we can find the inputs to the fx node in kwargs. As in the example, the original node is `acc_ops.sigmoid` which only has one argument “input” in acc_ops.py. We get the input and check if it’s a TensorRT tensor. After that, we add a sigmoid layer to TensorRT network and return the output of the layer. The output we returned will be passed to the children nodes of acc_ops.sigmoid by fx.Interpreter.
+
+**What if we can not find corresponding layers in TensorRT that do the same thing as the node.**
+
+In this case, we would need to do a bit more work. TensorRT provides plugins which serves as custom layers. *We have not implement this feature yet. We will update once it is enabled*.
+
+Last step would be adding the unit test for the new converter we added. User could add corresponding unit test in this `folder <https://github.com/pytorch/TensorRT/tree/master/py/torch_tensorrt/fx/test/converters/acc_op>`_.
diff --git a/docs/v1.2.0/_sources/tutorials/getting_started_with_python_api.rst.txt b/docs/v1.2.0/_sources/tutorials/getting_started_with_python_api.rst.txt
new file mode 100644
index 0000000000..b63665c980
--- /dev/null
+++ b/docs/v1.2.0/_sources/tutorials/getting_started_with_python_api.rst.txt
@@ -0,0 +1,47 @@
+.. _getting_started_with_python_api:
+
+Using Torch-TensorRT in Python
+*******************************
+
+Torch-TensorRT Python API accepts a ```torch.nn.Module`` as an input. Under the hood, it uses ``torch.jit.script`` to convert the input module into a
+TorchScript module. To compile your input ```torch.nn.Module`` with Torch-TensorRT, all you need to do is provide the module and inputs
+to Torch-TensorRT and you will be returned an optimized TorchScript module to run or add into another PyTorch module. Inputs
+is a list of ``torch_tensorrt.Input`` classes which define input's shape, datatype and memory format. You can also specify settings such as
+operating precision for the engine or target device. After compilation you can save the module just like any other module
+to load in a deployment application. In order to load a TensorRT/TorchScript module, make sure you first import ``torch_tensorrt``.
+
+.. code-block:: python
+
+    import torch_tensorrt
+
+    ...
+
+    model = MyModel().eval() # torch module needs to be in eval (not training) mode
+
+    inputs = [torch_tensorrt.Input(
+                min_shape=[1, 1, 16, 16],
+                opt_shape=[1, 1, 32, 32],
+                max_shape=[1, 1, 64, 64],
+                dtype=torch.half,
+            )]
+    enabled_precisions = {torch.float, torch.half} # Run with fp16
+
+    trt_ts_module = torch_tensorrt.compile(model, inputs=inputs, enabled_precisions=enabled_precisions)
+
+    input_data = input_data.to('cuda').half()
+    result = trt_ts_module(input_data)
+    torch.jit.save(trt_ts_module, "trt_ts_module.ts")
+
+.. code-block:: python
+
+    # Deployment application
+    import torch
+    import torch_tensorrt
+
+    trt_ts_module = torch.jit.load("trt_ts_module.ts")
+    input_data = input_data.to('cuda').half()
+    result = trt_ts_module(input_data)
+
+Torch-TensorRT python API also provides ``torch_tensorrt.ts.compile`` which accepts a TorchScript module as input.
+The torchscript module can be obtained via scripting or tracing (refer to :ref:`creating_torchscript_module_in_python`). ``torch_tensorrt.ts.compile`` accepts a Torchscript module
+and a list of ``torch_tensorrt.Input`` classes.
diff --git a/docs/v1.2.0/_sources/tutorials/installation.rst.txt b/docs/v1.2.0/_sources/tutorials/installation.rst.txt
new file mode 100644
index 0000000000..949fa2ddc9
--- /dev/null
+++ b/docs/v1.2.0/_sources/tutorials/installation.rst.txt
@@ -0,0 +1,387 @@
+.. _installation:
+
+Installation
+=============
+
+Precompiled Binaries
+*********************
+
+Dependencies
+---------------
+
+You need to have either PyTorch or LibTorch installed based on if you are using Python or C++
+and you must have CUDA, cuDNN and TensorRT installed.
+
+    * https://www.pytorch.org
+    * https://developer.nvidia.com/cuda
+    * https://developer.nvidia.com/cudnn
+    * https://developer.nvidia.com/tensorrt
+
+
+Python Package
+---------------
+
+You can install the python package using
+
+.. code-block:: sh
+
+    pip3 install torch-tensorrt -f https://github.com/NVIDIA/Torch-TensorRT/releases
+
+.. _bin-dist:
+
+C++ Binary Distribution
+------------------------
+
+Precompiled tarballs for releases are provided here: https://github.com/NVIDIA/Torch-TensorRT/releases
+
+.. _compile-from-source:
+
+Compiling From Source
+******************************************
+
+.. _installing-deps:
+
+Dependencies for Compilation
+-------------------------------
+
+Torch-TensorRT is built with Bazel, so begin by installing it.
+
+    * The easiest way is to install bazelisk using the method of your choosing https://github.com/bazelbuild/bazelisk
+    * Otherwise you can use the following instructions to install binaries https://docs.bazel.build/versions/master/install.html
+    * Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions
+
+    .. code-block:: shell
+
+        export BAZEL_VERSION=$(cat <PATH_TO_TORCHTRT_ROOT>/.bazelversion)
+        mkdir bazel
+        cd bazel
+        curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-dist.zip
+        unzip bazel-$BAZEL_VERSION-dist.zip
+        bash ./compile.sh
+        cp output/bazel /usr/local/bin/
+
+
+You will also need to have CUDA installed on the system (or if running in a container, the system must have
+the CUDA driver installed and the container must have CUDA)
+
+The correct LibTorch version will be pulled down for you by bazel.
+
+    NOTE: For best compatability with official PyTorch, use torch==1.10.0+cuda113, TensorRT 8.0 and cuDNN 8.2 for CUDA 11.3 however Torch-TensorRT itself supports
+    TensorRT and cuDNN for other CUDA versions for usecases such as using NVIDIA compiled distributions of PyTorch that use other versions of CUDA
+    e.g. aarch64 or custom compiled version of PyTorch.
+
+.. _abis:
+
+Choosing the Right ABI
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+Likely the most complicated thing about compiling Torch-TensorRT is selecting the correct ABI. There are two options
+which are incompatible with each other, pre-cxx11-abi and the cxx11-abi. The complexity comes from the fact that while
+the most popular distribution of PyTorch (wheels downloaded from pytorch.org/pypi directly) use the pre-cxx11-abi, most
+other distributions you might encounter (e.g. ones from NVIDIA - NGC containers, and builds for Jetson as well as certain
+libtorch builds and likely if you build PyTorch from source) use the cxx11-abi. It is important you compile Torch-TensorRT
+using the correct ABI to function properly. Below is a table with general pairings of PyTorch distribution sources and the
+recommended commands:
+
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| PyTorch Source                                              | Recommended C++ Compilation Command                      | Recommended Python Compilation Command                             |
++=============================================================+==========================================================+====================================================================+
+| PyTorch whl file from PyTorch.org                           | bazel build //:libtorchtrt -c opt --config pre_cxx11_abi | python3 setup.py bdist_wheel                                       |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| libtorch-shared-with-deps-*.zip from PyTorch.org            | bazel build //:libtorchtrt -c opt --config pre_cxx11_abi | python3 setup.py bdist_wheel                                       |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| libtorch-cxx11-abi-shared-with-deps-*.zip from PyTorch.org  | bazel build //:libtorchtrt -c opt                        | python3 setup.py bdist_wheel --use-cxx11-abi                       |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| PyTorch preinstalled in an NGC container                    | bazel build //:libtorchtrt -c opt                        | python3 setup.py bdist_wheel --use-cxx11-abi                       |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| PyTorch from the NVIDIA Forums for Jetson                   | bazel build //:libtorchtrt -c opt                        | python3 setup.py bdist_wheel --jetpack-version 4.6 --use-cxx11-abi |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| PyTorch built from Source                                   | bazel build //:libtorchtrt -c opt                        | python3 setup.py bdist_wheel --use-cxx11-abi                       |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+
+    NOTE: For all of the above cases you must correctly declare the source of PyTorch you intend to use in your WORKSPACE file for both Python and C++ builds. See below for more information
+
+You then have two compilation options:
+
+.. _build-from-archive:
+
+**Building using cuDNN & TensorRT tarball distributions**
+--------------------------------------------------------------
+
+    This is recommended so as to build Torch-TensorRT hermetically and insures any compilation errors are not caused by version issues
+
+    Make sure when running Torch-TensorRT that these versions of the libraries are prioritized in your ``$LD_LIBRARY_PATH``
+
+You need to download the tarball distributions of TensorRT and cuDNN from the NVIDIA website.
+    * https://developer.nvidia.com/cudnn
+    * https://developer.nvidia.com/tensorrt
+
+Place these files in a directory (the directories ``third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]`` exist for this purpose)
+
+Then compile referencing the directory with the tarballs
+
+    If you get errors regarding the packages, check their sha256 hashes and make sure they match the ones listed in ``WORKSPACE``
+
+Release Build
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt -c opt --distdir third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]
+
+A tarball with the include files and library can then be found in ``bazel-bin``
+
+.. _build-from-archive-debug:
+
+Debug Build
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+To build with debug symbols use the following command
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt -c dbg --distdir third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]
+
+A tarball with the include files and library can then be found in ``bazel-bin``
+
+Pre CXX11 ABI Build
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+To build using the pre-CXX11 ABI use the ``pre_cxx11_abi`` config
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt --config pre_cxx11_abi -c [dbg/opt] --distdir third_party/distdir/[x86_64-linux-gnu | aarch64-linux-gnu]
+
+A tarball with the include files and library can then be found in ``bazel-bin``
+
+.. _build-from-local:
+
+**Building using locally installed cuDNN & TensorRT**
+--------------------------------------------------------------
+
+    If you encounter bugs and you compiled using this method please disclose that you used local sources in the issue (an ldd dump would be nice too)
+
+Install TensorRT, CUDA and cuDNN on the system before starting to compile.
+
+In WORKSPACE comment out:
+
+.. code-block:: python
+
+    # Downloaded distributions to use with --distdir
+    http_archive(
+        name = "cudnn",
+        urls = ["<URL>",],
+
+        build_file = "@//third_party/cudnn/archive:BUILD",
+        sha256 = "<TAR SHA256>",
+        strip_prefix = "cuda"
+    )
+
+    http_archive(
+        name = "tensorrt",
+        urls = ["<URL>",],
+
+        build_file = "@//third_party/tensorrt/archive:BUILD",
+        sha256 = "<TAR SHA256>",
+        strip_prefix = "TensorRT-<VERSION>"
+    )
+
+and uncomment
+
+.. code-block:: python
+
+    # Locally installed dependencies
+    new_local_repository(
+        name = "cudnn",
+        path = "/usr/",
+        build_file = "@//third_party/cudnn/local:BUILD"
+    )
+
+    new_local_repository(
+    name = "tensorrt",
+    path = "/usr/",
+    build_file = "@//third_party/tensorrt/local:BUILD"
+    )
+
+Release Build
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+Compile using:
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt -c opt
+
+A tarball with the include files and library can then be found in ``bazel-bin``
+
+.. _build-from-local-debug:
+
+Debug Build
+^^^^^^^^^^^^
+
+To build with debug symbols use the following command
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt -c dbg
+
+
+A tarball with the include files and library can then be found in ``bazel-bin``
+
+Pre CXX11 ABI Build
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+To build using the pre-CXX11 ABI use the ``pre_cxx11_abi`` config
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt --config pre_cxx11_abi -c [dbg/opt]
+
+**Building the Python package**
+--------------------------------
+
+Begin by installing ``ninja``
+
+You can build the Python package using ``setup.py`` (this will also build the correct version of ``libtorchtrt.so``)
+
+.. code-block:: shell
+
+    python3 setup.py [install/bdist_wheel]
+
+Debug Build
+^^^^^^^^^^^^
+
+.. code-block:: shell
+
+    python3 setup.py develop [--user]
+
+This also compiles a debug build of ``libtorchtrt.so``
+
+**Building Natively on aarch64 (Jetson)**
+-------------------------------------------
+
+Prerequisites
+^^^^^^^^^^^^^^
+
+Install or compile a build of PyTorch/LibTorch for aarch64
+
+NVIDIA hosts builds the latest release branch for Jetson here:
+
+    https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048
+
+
+Enviorment Setup
+^^^^^^^^^^^^^^^^^
+
+To build natively on aarch64-linux-gnu platform, configure the ``WORKSPACE`` with local available dependencies.
+
+1. Disable the rules with ``http_archive`` for x86_64 by commenting the following rules:
+
+.. code-block:: shell
+
+    #http_archive(
+    #    name = "libtorch",
+    #    build_file = "@//third_party/libtorch:BUILD",
+    #    strip_prefix = "libtorch",
+    #    urls = ["https://download.pytorch.org/libtorch/cu102/libtorch-cxx11-abi-shared-with-deps-1.5.1.zip"],
+    #    sha256 = "cf0691493d05062fe3239cf76773bae4c5124f4b039050dbdd291c652af3ab2a"
+    #)
+
+    #http_archive(
+    #    name = "libtorch_pre_cxx11_abi",
+    #    build_file = "@//third_party/libtorch:BUILD",
+    #    strip_prefix = "libtorch",
+    #    sha256 = "818977576572eadaf62c80434a25afe44dbaa32ebda3a0919e389dcbe74f8656",
+    #    urls = ["https://download.pytorch.org/libtorch/cu102/libtorch-shared-with-deps-1.5.1.zip"],
+    #)
+
+    # Download these tarballs manually from the NVIDIA website
+    # Either place them in the distdir directory in third_party and use the --distdir flag
+    # or modify the urls to "file:///<PATH TO TARBALL>/<TARBALL NAME>.tar.gz
+
+    #http_archive(
+    #    name = "cudnn",
+    #    urls = ["https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.0.1.13/10.2_20200626/cudnn-10.2-linux-x64-v8.0.1.13.tgz"],
+    #    build_file = "@//third_party/cudnn/archive:BUILD",
+    #    sha256 = "0c106ec84f199a0fbcf1199010166986da732f9b0907768c9ac5ea5b120772db",
+    #    strip_prefix = "cuda"
+    #)
+
+    #http_archive(
+    #    name = "tensorrt",
+    #    urls = ["https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/7.1/tars/TensorRT-7.1.3.4.Ubuntu-18.04.x86_64-gnu.cuda-10.2.cudnn8.0.tar.gz"],
+    #    build_file = "@//third_party/tensorrt/archive:BUILD",
+    #    sha256 = "9205bed204e2ae7aafd2e01cce0f21309e281e18d5bfd7172ef8541771539d41",
+    #    strip_prefix = "TensorRT-7.1.3.4"
+    #)
+
+    NOTE: You may also need to configure the CUDA version to 10.2 by setting the path for the cuda new_local_repository
+
+
+2. Configure the correct paths to directory roots containing local dependencies in the ``new_local_repository`` rules:
+
+    NOTE: If you installed PyTorch using a pip package, the correct path is the path to the root of the python torch package.
+    In the case that you installed with ``sudo pip install`` this will be ``/usr/local/lib/python3.6/dist-packages/torch``.
+    In the case you installed with ``pip install --user`` this will be ``$HOME/.local/lib/python3.6/site-packages/torch``.
+
+In the case you are using NVIDIA compiled pip packages, set the path for both libtorch sources to the same path. This is because unlike
+PyTorch on x86_64, NVIDIA aarch64 PyTorch uses the CXX11-ABI. If you compiled for source using the pre_cxx11_abi and only would like to
+use that library, set the paths to the same path but when you compile make sure to add the flag ``--config=pre_cxx11_abi``
+
+.. code-block:: shell
+
+    new_local_repository(
+        name = "libtorch",
+        path = "/usr/local/lib/python3.6/dist-packages/torch",
+        build_file = "third_party/libtorch/BUILD"
+    )
+
+    new_local_repository(
+        name = "libtorch_pre_cxx11_abi",
+        path = "/usr/local/lib/python3.6/dist-packages/torch",
+        build_file = "third_party/libtorch/BUILD"
+    )
+
+    new_local_repository(
+        name = "cudnn",
+        path = "/usr/",
+        build_file = "@//third_party/cudnn/local:BUILD"
+    )
+
+    new_local_repository(
+        name = "tensorrt",
+        path = "/usr/",
+        build_file = "@//third_party/tensorrt/local:BUILD"
+    )
+
+Compile C++ Library and Compiler CLI
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+    NOTE: Due to shifting dependency locations between Jetpack 4.5 and 4.6 there is a now a flag to inform bazel of the Jetpack version
+
+    .. code-block:: shell
+
+        --platforms //toolchains:jetpack_4.x
+
+
+Compile Torch-TensorRT library using bazel command:
+
+.. code-block:: shell
+
+   bazel build //:libtorchtrt --platforms //toolchains:jetpack_4.6
+
+Compile Python API
+^^^^^^^^^^^^^^^^^^^^
+
+    NOTE: Due to shifting dependencies locations between Jetpack 4.5 and Jetpack 4.6 there is now a flag for ``setup.py`` which sets the jetpack version (default: 4.6)
+
+Compile the Python API using the following command from the ``//py`` directory:
+
+.. code-block:: shell
+
+    python3 setup.py install --use-cxx11-abi
+
+If you have a build of PyTorch that uses Pre-CXX11 ABI drop the ``--use-cxx11-abi`` flag
+
+If you are building for Jetpack 4.5 add the ``--jetpack-version 4.5`` flag
diff --git a/docs/v1.2.0/_sources/tutorials/notebooks.rst.txt b/docs/v1.2.0/_sources/tutorials/notebooks.rst.txt
new file mode 100644
index 0000000000..24279113ab
--- /dev/null
+++ b/docs/v1.2.0/_sources/tutorials/notebooks.rst.txt
@@ -0,0 +1,154 @@
+.. _notebooks:
+
+Example notebooks
+===================
+
+There exists a number of notebooks which cover specific using specific features and models
+with Torch-TensorRT
+
+Notebooks
+------------
+
+Compiling CitriNet with Torch-TensorRT
+********************************************
+
+Citrinet is an acoustic model used for the speech to text recognition task. It is a version
+of QuartzNet that extends ContextNet, utilizing subword encoding (via Word Piece tokenization)
+and Squeeze-and-Excitation(SE) mechanism and are therefore smaller than QuartzNet models. CitriNet
+models take in audio segments and transcribe them to letter, byte pair, or word piece sequences.
+
+This notebook demonstrates the steps for optimizing a pretrained CitriNet model with Torch-TensorRT,
+and running it to test the speedup obtained.
+
+* `Torch-TensorRT Getting Started - CitriNet <https://github.com/pytorch/TensorRT/blob/master/notebooks/CitriNet-example.ipynb>`_
+
+
+Compiling EfficentNet with Torch-TensorRT
+********************************************
+
+EfficentNet is a feedforward CNN designed to achieve better performance and accuracy than alternative architectures
+by using a "scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient".
+
+This notebook demonstrates the steps for optimizing a pretrained EfficentNet model with Torch-TensorRT,
+and running it to test the speedup obtained.
+
+* `Torch-TensorRT Getting Started - EfficientNet-B0 <https://github.com/pytorch/TensorRT/blob/master/notebooks/EfficientNet-example.ipynb>`_
+
+
+Masked Language Modeling (MLM) with Hugging Face BERT Transformer accelerated by Torch-TensorRT
+*************************************************************************************************
+
+"BERT is a transformer model pretrained on a large corpus of English data in a self-supervised fashion.
+This way, the model learns an inner representation of the English language that can then be used to extract
+features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train
+a standard classifier using the features produced by the BERT model as inputs." (https://huggingface.co/bert-base-uncased)
+
+This notebook demonstrates the steps for optimizing a pretrained EfficentNet model with Torch-TensorRT,
+and running it to test the speedup obtained.
+
+* `Masked Language Modeling (MLM) with Hugging Face BERT Transformer <https://github.com/pytorch/TensorRT/blob/master/notebooks/Hugging-Face-BERT.ipynb>`_
+
+
+Serving a model in C++ using Torch-TensorRT
+**********************************************
+
+This example shows how you can load a pretrained ResNet-50 model, convert it to a Torch-TensorRT
+optimized model (via the Torch-TensorRT Python API), save the model as a torchscript module, and
+then finally load and serve the model with the PyTorch C++ API.
+
+* `ResNet C++ Serving Example <https://github.com/pytorch/TensorRT/blob/master/notebooks/Resnet50-CPP.ipynb>`_
+
+
+Compiling ResNet50 with Torch-TensorRT
+********************************************
+
+This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a
+pretrained ResNet-50 network, and running it to test the speedup obtained.
+
+* `Torch-TensorRT Getting Started - ResNet 50 <https://github.com/pytorch/TensorRT/blob/master/notebooks/Resnet50-example.ipynb>`_
+
+
+Using Dynamic Shapes with Torch-TensorRT
+********************************************
+
+Making use of Dynamic Shaped Tensors in Torch TensorRT is quite simple. Let's say you are
+using the ``torch_tensorrt.compile(...)`` function  to compile a torchscript module. One
+of the args in this function in this function is ``input``: which defines an input to a
+module in terms of expected shape, data type and tensor format: ``torch_tensorrt.Input.``
+
+For the purposes of this walkthrough we just need three kwargs: `min_shape`, `opt_shape`` and `max_shape`.
+
+.. code-block:: py
+
+    torch_tensorrt.Input(
+            min_shape=(1, 224, 224, 3),
+            opt_shape=(1, 512, 512, 3),
+            max_shape=(1, 1024, 1024, 3),
+            dtype=torch.int32
+            format=torch.channel_last
+        )
+    ...
+
+In this example, we are going to use a simple ResNet model to demonstrate the use of the API.
+
+* `Torch-TensorRT - Using Dynamic Shapes <https://github.com/pytorch/TensorRT/blob/master/notebooks/dynamic-shapes.ipynb>`_
+
+Using the FX Frontend with Torch-TensorRT
+********************************************
+
+The purpose of this example is to demostrate the overall flow of lowering a PyTorch model to TensorRT
+conveniently with using FX.
+
+* `Using the FX Frontend with Torch-TensorRT <https://github.com/pytorch/TensorRT/blob/master/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb>`_
+
+
+Compiling a PyTorch model using FX Frontend with Torch-TensorRT
+*******************************************************************
+
+The purpose of this example is to demonstrate the overall flow of lowering a PyTorch
+model to TensorRT via FX with existing FX based tooling
+
+* `Compiling a PyTorch model using FX Frontend with Torch-TensorRT  <https://github.com/pytorch/TensorRT/blob/master/notebooks/getting_started_with_fx_path_module.ipynb>`_
+
+
+Compiling LeNet with Torch-TensorRT
+*******************************************************************
+
+This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a simple LeNet network.
+
+* `Torch-TensorRT Getting Started - LeNet  <https://github.com/pytorch/TensorRT/blob/master/notebooks/lenet-getting-started.ipynb>`_
+
+
+Accelerate Deep Learning Models using Quantization in Torch-TensorRT
+*******************************************************************
+
+Model Quantization is a popular way of optimization which reduces the size of models thereby
+accelerating inference, also opening up the possibilities of deployments on devices with lower
+computation power such as Jetson. Simply put, quantization is a process of mapping input values
+from a larger set to output values in a smaller set. In this notebook, we illustrate the workflow
+that you can adopt while quantizing a deep learning model in Torch-TensorRT. The notebook takes
+you through an example of Mobilenetv2 for a classification task on a subset of Imagenet Dataset
+called Imagenette which has 10 classes.
+
+* `Accelerate Deep Learning Models using Quantization in Torch-TensorRT <https://github.com/pytorch/TensorRT/blob/master/notebooks/qat-ptq-workflow.ipynb>`_
+
+
+Object Detection with Torch-TensorRT (SSD)
+*******************************************************************
+
+This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained SSD network, and running it to test the speedup obtained.
+
+* `Object Detection with Torch-TensorRT (SSD)  <https://github.com/pytorch/TensorRT/blob/master/notebooks/ssd-object-detection-demo.ipynb>`_
+
+
+Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT
+*****************************************************************************
+
+Quantization Aware training (QAT) simulates quantization during training by
+quantizing weights and activation layers. This will help to reduce the loss in
+accuracy when we convert the network trained in FP32 to INT8 for faster inference.
+QAT introduces additional nodes in the graph which will be used to learn the dynamic
+ranges of weights and activation layers. In this notebook, we illustrate the following
+steps from training to inference of a QAT model in Torch-TensorRT.
+
+* `Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT  <https://github.com/pytorch/TensorRT/blob/master/notebooks/vgg-qat.ipynb>`_
diff --git a/docs/v1.2.0/_sources/tutorials/ptq.rst.txt b/docs/v1.2.0/_sources/tutorials/ptq.rst.txt
new file mode 100644
index 0000000000..b62457109f
--- /dev/null
+++ b/docs/v1.2.0/_sources/tutorials/ptq.rst.txt
@@ -0,0 +1,205 @@
+.. _ptq:
+
+Post Training Quantization (PTQ)
+=================================
+
+Post Training Quantization (PTQ) is a technique to reduce the required computational resources for inference
+while still preserving the accuracy of your model by mapping the traditional FP32 activation space to a reduced
+INT8 space. TensorRT uses a calibration step which executes your model with sample data from the target domain
+and track the activations in FP32 to calibrate a mapping to INT8 that minimizes the information loss between
+FP32 inference and INT8 inference.
+
+Users writing TensorRT applications are required to setup a calibrator class which will provide sample data to
+the TensorRT calibrator. With Torch-TensorRT we look to leverage existing infrastructure in PyTorch to make implementing
+calibrators easier.
+
+LibTorch provides a ``DataLoader`` and ``Dataset`` API which steamlines preprocessing and batching input data.
+These APIs are exposed via both C++ and Python interface which makes it easier for the end user.
+For C++ interface, we use ``torch::Dataset`` and ``torch::data::make_data_loader`` objects to construct and perform pre-processing on datasets.
+The equivalent functionality in python interface uses ``torch.utils.data.Dataset`` and ``torch.utils.data.DataLoader``.
+This section of the PyTorch documentation has more information https://pytorch.org/tutorials/advanced/cpp_frontend.html#loading-data and https://pytorch.org/tutorials/recipes/recipes/loading_data_recipe.html.
+Torch-TensorRT uses Dataloaders as the base of a generic calibrator implementation. So you will be able to reuse or quickly
+implement a ``torch::Dataset`` for your target domain, place it in a DataLoader and create a INT8 Calibrator
+which you can provide to Torch-TensorRT to run INT8 Calibration during compliation of your module.
+
+.. _writing_ptq_cpp:
+
+How to create your own PTQ application in C++
+-----------------------------------------------
+
+Here is an example interface of a ``torch::Dataset`` class for CIFAR10:
+
+.. code-block:: c++
+    :linenos:
+
+    //cpp/ptq/datasets/cifar10.h
+    #pragma once
+
+    #include "torch/data/datasets/base.h"
+    #include "torch/data/example.h"
+    #include "torch/types.h"
+
+    #include <cstddef>
+    #include <string>
+
+    namespace datasets {
+    // The CIFAR10 Dataset
+    class CIFAR10 : public torch::data::datasets::Dataset<CIFAR10> {
+    public:
+        // The mode in which the dataset is loaded
+        enum class Mode { kTrain, kTest };
+
+        // Loads CIFAR10 from un-tarred file
+        // Dataset can be found https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz
+        // Root path should be the directory that contains the content of tarball
+        explicit CIFAR10(const std::string& root, Mode mode = Mode::kTrain);
+
+        // Returns the pair at index in the dataset
+        torch::data::Example<> get(size_t index) override;
+
+        // The size of the dataset
+        c10::optional<size_t> size() const override;
+
+        // The mode the dataset is in
+        bool is_train() const noexcept;
+
+        // Returns all images stacked into a single tensor
+        const torch::Tensor& images() const;
+
+        // Returns all targets stacked into a single tensor
+        const torch::Tensor& targets() const;
+
+        // Trims the dataset to the first n pairs
+        CIFAR10&& use_subset(int64_t new_size);
+
+
+    private:
+        Mode mode_;
+        torch::Tensor images_, targets_;
+    };
+    } // namespace datasets
+
+
+This class's implementation reads from the binary distribution of the CIFAR10 dataset and builds two tensors which hold the images and labels.
+
+We use a subset of the dataset to use for calibration, since we don't need the the full dataset for effective calibration and calibration does
+some take time, then define the preprocessing to apply to the images in the dataset and create a DataLoader from the dataset which will batch the data:
+
+.. code-block:: c++
+
+    auto calibration_dataset = datasets::CIFAR10(data_dir, datasets::CIFAR10::Mode::kTest)
+                                        .use_subset(320)
+                                        .map(torch::data::transforms::Normalize<>({0.4914, 0.4822, 0.4465},
+                                                                                {0.2023, 0.1994, 0.2010}))
+                                        .map(torch::data::transforms::Stack<>());
+    auto calibration_dataloader = torch::data::make_data_loader(std::move(calibration_dataset),
+                                                                torch::data::DataLoaderOptions().batch_size(32)
+                                                                                                .workers(2));
+
+
+Next we create a calibrator from the ``calibration_dataloader`` using the calibrator factory (found in ``torch_tensorrt/ptq.h``):
+
+.. code-block:: c++
+
+    #include "torch_tensorrt/ptq.h"
+    ...
+
+    auto calibrator = torch_tensorrt::ptq::make_int8_calibrator(std::move(calibration_dataloader), calibration_cache_file, true);
+
+Here we also define a location to write a calibration cache file to which we can use to reuse the calibration data without needing the dataset and whether or not
+we should use the cache file if it exists. There also exists a ``torch_tensorrt::ptq::make_int8_cache_calibrator`` factory which creates a calibrator that uses the cache
+only for cases where you may do engine building on a machine that has limited storage (i.e. no space for a full dataset) or to have a simpiler deployment application.
+
+The calibrator factories create a calibrator that inherits from a ``nvinfer1::IInt8Calibrator`` virtual class (``nvinfer1::IInt8EntropyCalibrator2`` by default) which
+defines the calibration algorithm used when calibrating. You can explicitly make the selection of calibration algorithm like this:
+
+.. code-block:: c++
+
+    // MinMax Calibrator is geared more towards NLP tasks
+    auto calibrator = torch_tensorrt::ptq::make_int8_calibrator<nvinfer1::IInt8MinMaxCalibrator>(std::move(calibration_dataloader), calibration_cache_file, true);
+
+Then all thats required to setup the module for INT8 calibration is to set the following compile settings in the `torch_tensorrt::CompileSpec` struct and compiling the module:
+
+.. code-block:: c++
+
+    std::vector<std::vector<int64_t>> input_shape = {{32, 3, 32, 32}};
+    /// Configure settings for compilation
+    auto compile_spec = torch_tensorrt::CompileSpec({input_shape});
+    /// Set operating precision to INT8
+    compile_spec.enabled_precisions.insert(torch::kF16);
+    compile_spec.enabled_precisions.insert(torch::kI8);
+    /// Use the TensorRT Entropy Calibrator
+    compile_spec.ptq_calibrator = calibrator;
+
+    auto trt_mod = torch_tensorrt::CompileGraph(mod, compile_spec);
+
+If you have an existing Calibrator implementation for TensorRT you may directly set the ``ptq_calibrator`` field with a pointer to your calibrator and it will work as well.
+From here not much changes in terms of how to execution works. You are still able to fully use LibTorch as the sole interface for inference. Data should remain
+in FP32 precision when it's passed into `trt_mod.forward`. There exists an example application in the Torch-TensorRT demo that takes you from training a VGG16 network on
+CIFAR10 to deploying in INT8 with Torch-TensorRT here: https://github.com/pytorch/TensorRT/tree/master/cpp/ptq
+
+.. _writing_ptq_python:
+
+How to create your own PTQ application in Python
+--------------------------------------------------
+
+Torch-TensorRT Python API provides an easy and convenient way to use pytorch dataloaders with TensorRT calibrators. ``DataLoaderCalibrator`` class can be used to create
+a TensorRT calibrator by providing desired configuration. The following code demonstrates an example on how to use it
+
+.. code-block:: python
+
+    testing_dataset = torchvision.datasets.CIFAR10(
+        root="./data",
+        train=False,
+        download=True,
+        transform=transforms.Compose(
+            [
+                transforms.ToTensor(),
+                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+            ]
+        ),
+    )
+
+    testing_dataloader = torch.utils.data.DataLoader(
+        testing_dataset, batch_size=1, shuffle=False, num_workers=1
+    )
+    calibrator = torch_tensorrt.ptq.DataLoaderCalibrator(
+        testing_dataloader,
+        cache_file="./calibration.cache",
+        use_cache=False,
+        algo_type=torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2,
+        device=torch.device("cuda:0"),
+    )
+
+    trt_mod = torch_tensorrt.compile(model, inputs=[torch_tensorrt.Input((1, 3, 32, 32))],
+                                        enabled_precisions={torch.float, torch.half, torch.int8},
+                                        calibrator=calibrator,
+                                        device={
+                                             "device_type": torch_tensorrt.DeviceType.GPU,
+                                             "gpu_id": 0,
+                                             "dla_core": 0,
+                                             "allow_gpu_fallback": False,
+                                             "disable_tf32": False
+                                         })
+
+In the cases where there is a pre-existing calibration cache file that users want to use, ``CacheCalibrator`` can be used without any dataloaders. The following example demonstrates how
+to use ``CacheCalibrator`` to use in INT8 mode.
+
+.. code-block:: python
+
+  calibrator = torch_tensorrt.ptq.CacheCalibrator("./calibration.cache")
+
+  trt_mod = torch_tensorrt.compile(model, inputs=[torch_tensorrt.Input([1, 3, 32, 32])],
+                                        enabled_precisions={torch.float, torch.half, torch.int8},
+                                        calibrator=calibrator)
+
+If you already have an existing calibrator class (implemented directly using TensorRT API), you can directly set the calibrator field to your class which can be very convenient.
+For a demo on how PTQ can be performed on a VGG network using Torch-TensorRT API, you can refer to https://github.com/pytorch/TensorRT/blob/master/tests/py/test_ptq_dataloader_calibrator.py
+and https://github.com/pytorch/TensorRT/blob/master/tests/py/test_ptq_trt_calibrator.py
+
+Citations
+^^^^^^^^^^^
+
+Krizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images.
+
+Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
diff --git a/docs/v1.2.0/_sources/tutorials/runtime.rst.txt b/docs/v1.2.0/_sources/tutorials/runtime.rst.txt
new file mode 100644
index 0000000000..0cfc93200f
--- /dev/null
+++ b/docs/v1.2.0/_sources/tutorials/runtime.rst.txt
@@ -0,0 +1,36 @@
+.. _runtime:
+
+Deploying Torch-TensorRT Programs
+====================================
+
+After compiling and saving Torch-TensorRT programs there is no longer a strict dependency on the full
+Torch-TensorRT library. All that is required to run a compiled program is the runtime. There are therfore a couple
+options to deploy your programs other than shipping the full Torch-TensorRT compiler with your applications.
+
+Torch-TensorRT package / libtorchtrt.so
+--------------------------------------------
+
+Once a program is compiled, you run it using the standard PyTorch APIs. All that is required is that the package
+must be imported in python or linked in C++.
+
+Runtime Library
+-----------------
+
+Distributed with the C++ distribution is ``libtorchtrt_runtime.so``. This library only contains the components
+necessary to run Torch-TensorRT programs. Instead of linking ``libtorchtrt.so`` or importing ``torch_tensorrt`` you can
+link ``libtorchtrt_runtime.so`` in your deployment programs or use ``DL_OPEN`` or ``LD_PRELOAD``. For python
+you can load the runtime with ``torch.ops.load_library("libtorchtrt_runtime.so")``. You can then continue to use
+programs just as you would otherwise via PyTorch API.
+
+.. note:: If you are using the standard distribution of PyTorch in Python on x86, likely you will need the pre-cxx11-abi variant of ``libtorchtrt_runtime.so``, check :ref:`Installation` documentation for more details.
+
+.. note:: If you are linking ``libtorchtrt_runtime.so``, likely using the following flags will help ``-Wl,--no-as-needed -ltorchtrt -Wl,--as-needed`` as theres no direct symbol dependency to anything in the Torch-TensorRT runtime for most Torch-TensorRT runtime applications
+
+An example of how to use ``libtorchtrt_runtime.so`` can be found here: https://github.com/pytorch/TensorRT/tree/master/examples/torchtrt_runtime_example
+
+Plugin Library
+---------------
+
+In the case you use Torch-TensorRT as a converter to a TensorRT engine and your engine uses plugins provided by Torch-TensorRT, Torch-TensorRT
+ships the library ``libtorchtrt_plugins.so`` which contains the implementation of the TensorRT plugins used by Torch-TensorRT during
+compilation. This library can be ``DL_OPEN`` or ``LD_PRELOAD`` similar to other TensorRT plugin libraries.
diff --git a/docs/v1.2.0/_sources/tutorials/serving_torch_tensorrt_with_triton.rst.txt b/docs/v1.2.0/_sources/tutorials/serving_torch_tensorrt_with_triton.rst.txt
new file mode 100644
index 0000000000..a713d563c2
--- /dev/null
+++ b/docs/v1.2.0/_sources/tutorials/serving_torch_tensorrt_with_triton.rst.txt
@@ -0,0 +1,216 @@
+.. _serving_torch_tensorrt_with_triton:
+
+Serving a Torch-TensorRT model with Triton
+==========================================
+
+Optimization and deployment go hand in hand in a discussion about Machine
+Learning infrastructure. Once network level optimzation are done
+to get the maximum performance, the next step would be to deploy it.
+
+However, serving this optimized model comes with it's own set of considerations
+and challenges like: building an infrastructure to support concorrent model
+executions, supporting clients over HTTP or gRPC and more.
+
+The `Triton Inference Server <https://github.com/triton-inference-server/server>`__
+solves the aforementioned and more. Let's discuss step-by-step, the process of
+optimizing a model with Torch-TensorRT, deploying it on Triton Inference
+Server, and building a client to query the model.
+
+Step 1: Optimize your model with Torch-TensorRT
+-----------------------------------------------
+
+Most Torch-TensorRT users will be familiar with this step. For the purpose of
+this demonstration, we will be using a ResNet50 model from Torchhub.
+
+Let’s first pull the `NGC PyTorch Docker container <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch>`__. You may need to create
+an account and get the API key from `here <https://ngc.nvidia.com/setup/>`__.
+Sign up and login with your key (follow the instructions
+`here <https://ngc.nvidia.com/setup/api-key>`__ after signing up).
+
+::
+
+   # <xx.xx> is the yy:mm for the publishing tag for NVIDIA's Pytorch
+   # container; eg. 22.04
+
+   docker run -it --gpus all -v ${PWD}:/scratch_space nvcr.io/nvidia/pytorch:<xx.xx>-py3
+   cd /scratch_space
+
+Once inside the container, we can proceed to download a ResNet model from
+Torchhub and optimize it with Torch-TensorRT.
+
+::
+
+   import torch
+   import torch_tensorrt
+   torch.hub._validate_not_a_forked_repo=lambda a,b,c: True
+
+   # load model
+   model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True).eval().to("cuda")
+
+   # Compile with Torch TensorRT;
+   trt_model = torch_tensorrt.compile(model,
+       inputs= [torch_tensorrt.Input((1, 3, 224, 224))],
+       enabled_precisions= { torch.half} # Run with FP32
+   )
+
+   # Save the model
+   torch.jit.save(trt_model, "model.pt")
+
+After copying the model, exit the container. The next step in the process
+is to set up a Triton Inference Server.
+
+Step 2: Set Up Triton Inference Server
+--------------------------------------
+
+If you are new to the Triton Inference Server and want to learn more, we
+highly recommend to checking our `Github
+Repository <https://github.com/triton-inference-server>`__.
+
+To use Triton, we need to make a model repository. A model repository, as the
+name suggested, is a repository of the models the Inference server hosts. While
+Triton can serve models from multiple repositories, in this example, we will
+discuss the simplest possible form of the model repository.
+
+The structure of this repository should look something like this:
+
+::
+
+   model_repository
+   |
+   +-- resnet50
+       |
+       +-- config.pbtxt
+       +-- 1
+           |
+           +-- model.pt
+
+There are two files that Triton requires to serve the model: the model itself
+and a model configuration file which is typically provided in ``config.pbtxt``.
+For the model we prepared in step 1, the following configuration can be used:
+
+::
+
+   name: "resnet50"
+   platform: "pytorch_libtorch"
+   max_batch_size : 0
+   input [
+     {
+       name: "input__0"
+       data_type: TYPE_FP32
+       dims: [ 3, 224, 224 ]
+       reshape { shape: [ 1, 3, 224, 224 ] }
+     }
+   ]
+   output [
+     {
+       name: "output__0"
+       data_type: TYPE_FP32
+       dims: [ 1, 1000 ,1, 1]
+       reshape { shape: [ 1, 1000 ] }
+     }
+   ]
+
+The ``config.pbtxt`` file is used to describe the exact model configuration
+with details like the names and shapes of the input and output layer(s),
+datatypes, scheduling and batching details and more. If you are new to Triton,
+we highly encourage you to check out this `section of our
+documentation <https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md>`__
+for more details.
+
+With the model repository setup, we can proceed to launch the Triton server
+with the docker command below. Refer `this page <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver>`__ for the pull tag for the container.
+
+::
+
+   # Make sure that the TensorRT version in the Triton container
+   # and TensorRT version in the environment used to optimize the model
+   # are the same.
+
+   docker run --gpus all --rm -p 8000:8000 -p 8001:8001 -p 8002:8002 -v /full/path/to/the_model_repository/model_repository:/models nvcr.io/nvidia/tritonserver:<xx.yy>-py3 tritonserver --model-repository=/models
+
+This should spin up a Triton Inference server. Next step, building a simple
+http client to query the server.
+
+Step 3: Building a Triton Client to Query the Server
+----------------------------------------------------
+
+Before proceeding, make sure to have a sample image on hand. If you don't
+have one, download an example image to test inference. In this section, we
+will be going over a very basic client. For a variety of more fleshed out
+examples, refer to the `Triton Client Repository <https://github.com/triton-inference-server/client/tree/main/src/python/examples>`__
+
+::
+
+   wget  -O img1.jpg "https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg"
+
+We then need to install dependencies for building a python client. These will
+change from client to client. For a full list of all languages supported by Triton,
+please refer to `Triton's client repository <https://github.com/triton-inference-server/client>`__.
+
+::
+
+   pip install torchvision
+   pip install attrdict
+   pip install nvidia-pyindex
+   pip install tritonclient[all]
+
+Let's jump into the client. Firstly, we write a small preprocessing function to
+resize and normalize the query image.
+
+::
+
+   import numpy as np
+   from torchvision import transforms
+   from PIL import Image
+   import tritonclient.http as httpclient
+   from tritonclient.utils import triton_to_np_dtype
+
+   # preprocessing function
+   def rn50_preprocess(img_path="img1.jpg"):
+       img = Image.open(img_path)
+       preprocess = transforms.Compose([
+           transforms.Resize(256),
+           transforms.CenterCrop(224),
+           transforms.ToTensor(),
+           transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+       ])
+       return preprocess(img).numpy()
+
+   transformed_img = rn50_preprocess()
+
+Building a client requires three basic points. Firstly, we setup a connection
+with the Triton Inference Server.
+
+::
+
+   # Setting up client
+   client = httpclient.InferenceServerClient(url="localhost:8000")
+
+Secondly, we specify the names of the input and output layer(s) of our model.
+
+::
+
+   inputs = httpclient.InferInput("input__0", transformed_img.shape, datatype="FP32")
+   inputs.set_data_from_numpy(transformed_img, binary_data=True)
+
+   outputs = httpclient.InferRequestedOutput("output__0", binary_data=True, class_count=1000)
+
+Lastly, we send an inference request to the Triton Inference Server.
+
+::
+
+   # Querying the server
+   results = client.infer(model_name="resnet50", inputs=[inputs], outputs=[outputs])
+   inference_output = results.as_numpy('output__0')
+   print(inference_output[:5])
+
+The output of the same should look like below:
+
+::
+
+   [b'12.468750:90' b'11.523438:92' b'9.664062:14' b'8.429688:136'
+    b'8.234375:11']
+
+The output format here is ``<confidence_score>:<classification_index>``.
+To learn how to map these to the label names and more, refer to Triton Inference Server's
+`documentation <https://github.com/triton-inference-server/server/blob/main/docs/protocol/extension_classification.md>`__.
diff --git a/docs/v1.2.0/_sources/tutorials/torchtrtc.rst.txt b/docs/v1.2.0/_sources/tutorials/torchtrtc.rst.txt
new file mode 100644
index 0000000000..b841c891e5
--- /dev/null
+++ b/docs/v1.2.0/_sources/tutorials/torchtrtc.rst.txt
@@ -0,0 +1,128 @@
+.. _torchtrtc:
+
+torchtrtc
+=================================
+
+``torchtrtc`` is a CLI application for using the Torch-TensorRT compiler. It serves as an easy way to compile a
+TorchScript Module with Torch-TensorRT from the command-line to quickly check support or as part of
+a deployment pipeline. All basic features of the compiler are supported including post training
+quantization (though you must already have a calibration cache file to use the PTQ feature). The compiler can
+output two formats, either a TorchScript program with the TensorRT engine embedded or
+the TensorRT engine itself as a PLAN file.
+
+All that is required to run the program after compilation is for C++ linking against ``libtorchtrt.so``
+or in Python importing the torch_tensorrt package. All other aspects of using compiled modules are identical
+to standard TorchScript. Load with ``torch.jit.load()`` and run like you would run any other module.
+
+.. code-block:: txt
+
+    torchtrtc [input_file_path] [output_file_path]
+      [input_specs...] {OPTIONS}
+
+      torchtrtc is a compiler for TorchScript, it will compile and optimize
+      TorchScript programs to run on NVIDIA GPUs using TensorRT
+
+    OPTIONS:
+
+        -h, --help                        Display this help menu
+        Verbiosity of the compiler
+          -v, --verbose                     Dumps debugging information about the
+                                            compilation process onto the console
+          -w, --warnings                    Disables warnings generated during
+                                            compilation onto the console (warnings
+                                            are on by default)
+          --i, --info                       Dumps info messages generated during
+                                            compilation onto the console
+        --build-debuggable-engine         Creates a debuggable engine
+        --allow-gpu-fallback              (Only used when targeting DLA
+                                          (device-type)) Lets engine run layers on
+                                          GPU if they are not supported on DLA
+        --require-full-compilation        Require that the model should be fully
+                                          compiled to TensorRT or throw an error
+        --check-method-support=[method_name]
+                                          Check the support for end to end
+                                          compilation of a specified method in the
+                                          TorchScript module
+        --disable-tf32                    Prevent Float32 layers from using the
+                                          TF32 data format
+        --sparse-weights                  Enable sparsity for weights of conv and
+                                          FC layers
+        -p[precision...],
+        --enable-precision=[precision...] (Repeatable) Enabling an operating
+                                          precision for kernels to use when
+                                          building the engine (Int8 requires a
+                                          calibration-cache argument) [ float |
+                                          float32 | f32 | fp32 | half | float16 |
+                                          f16 | fp16 | int8 | i8 | char ]
+                                          (default: float)
+        -d[type], --device-type=[type]    The type of device the engine should be
+                                          built for [ gpu | dla ] (default: gpu)
+        --gpu-id=[gpu_id]                 GPU id if running on multi-GPU platform
+                                          (defaults to 0)
+        --dla-core=[dla_core]             DLACore id if running on available DLA
+                                          (defaults to 0)
+        --engine-capability=[capability]  The type of device the engine should be
+                                          built for [ standard | safety |
+                                          dla_standalone ]
+        --calibration-cache-file=[file_path]
+                                          Path to calibration cache file to use
+                                          for post training quantization
+        --teo=[op_name...],
+        --torch-executed-op=[op_name...]  (Repeatable) Operator in the graph that
+                                          should always be run in PyTorch for
+                                          execution (partial compilation must be
+                                          enabled)
+        --tem=[module_name...],
+        --torch-executed-mod=[module_name...]
+                                          (Repeatable) Module that should always
+                                          be run in Pytorch for execution (partial
+                                          compilation must be enabled)
+        --mbs=[num_ops],
+        --min-block-size=[num_ops]        Minimum number of contiguous TensorRT
+                                          supported ops to compile a subgraph to
+                                          TensorRT
+        --embed-engine                    Whether to treat input file as a
+                                          serialized TensorRT engine and embed it
+                                          into a TorchScript module (device spec
+                                          must be provided)
+        --num-min-timing-iter=[num_iters] Number of minimization timing iterations
+                                          used to select kernels
+        --num-avg-timing-iters=[num_iters]
+                                          Number of averaging timing iterations
+                                          used to select kernels
+        --workspace-size=[workspace_size] Maximum size of workspace given to
+                                          TensorRT
+        -t[threshold],
+        --threshold=[threshold]           Maximum acceptable numerical deviation
+                                          from standard torchscript output
+                                          (default 2e-5)
+        --no-threshold-check              Skip checking threshold compliance
+        --truncate-long-double,
+        --truncate, --truncate-64bit      Truncate weights that are provided in
+                                          64bit to 32bit (Long, Double to Int,
+                                          Float)
+        --save-engine                     Instead of compiling a full a
+                                          TorchScript program, save the created
+                                          engine to the path specified as the
+                                          output path
+        input_file_path                   Path to input TorchScript file
+        output_file_path                  Path for compiled TorchScript (or
+                                          TensorRT engine) file
+        input_specs...                    Specs for inputs to engine, can either
+                                          be a single size or a range defined by
+                                          Min, Optimal, Max sizes, e.g.
+                                          "(N,..,C,H,W)"
+                                          "[(MIN_N,..,MIN_C,MIN_H,MIN_W);(OPT_N,..,OPT_C,OPT_H,OPT_W);(MAX_N,..,MAX_C,MAX_H,MAX_W)]".
+                                          Data Type and format can be specified by
+                                          adding an "@" followed by dtype and "%"
+                                          followed by format to the end of the
+                                          shape spec. e.g. "(3, 3, 32,
+                                          32)@f16%NHWC"
+        "--" can be used to terminate flag options and force all following
+        arguments to be treated as positional options
+
+e.g.
+
+.. code-block:: shell
+
+    torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous" -p f16
diff --git a/docs/v1.2.0/_sources/tutorials/use_from_pytorch.rst.txt b/docs/v1.2.0/_sources/tutorials/use_from_pytorch.rst.txt
new file mode 100644
index 0000000000..296e4c3c32
--- /dev/null
+++ b/docs/v1.2.0/_sources/tutorials/use_from_pytorch.rst.txt
@@ -0,0 +1,64 @@
+.. _use_from_pytorch:
+
+Using Torch-TensorRT Directly From PyTorch
+============================================
+
+You will now be able to directly access TensorRT from PyTorch APIs. The process to use this feature
+is very similar to the compilation workflow described in :ref:`getting_started_with_python_api`
+
+Start by loading ``torch_tensorrt`` into your application.
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+
+Then given a TorchScript module, you can compile it with TensorRT using the ``torch._C._jit_to_backend("tensorrt", ...)`` API.
+
+.. code-block:: python
+
+    import torchvision.models as models
+
+    model = models.mobilenet_v2(pretrained=True)
+    script_model = torch.jit.script(model)
+
+Unlike the ``compile`` API in Torch-TensorRT which assumes you are trying to compile the ``forward`` function of a module
+or the ``convert_method_to_trt_engine`` which converts a specified function to a TensorRT engine, the backend API
+will take a dictionary which maps names of functions to compile to Compilation Spec objects which wrap the same
+sort of dictionary you would provide to ``compile``. For more information on the compile spec dictionary take a look
+at the documentation for the Torch-TensorRT ``TensorRTCompileSpec`` API.
+
+.. code-block:: python
+
+    spec = {
+        "forward": torch_tensorrt.ts.TensorRTCompileSpec(
+            {
+                "inputs": [torch_tensorrt.Input([1, 3, 300, 300])],
+                "enabled_precisions": {torch.float, torch.half},
+                "refit": False,
+                "debug": False,
+                "device": {
+                    "device_type": torch_tensorrt.DeviceType.GPU,
+                    "gpu_id": 0,
+                    "dla_core": 0,
+                    "allow_gpu_fallback": True,
+                },
+                "capability": torch_tensorrt.EngineCapability.default,
+                "num_avg_timing_iters": 1,
+            }
+        )
+    }
+
+Now to compile with Torch-TensorRT, provide the target module objects and the spec dictionary to ``torch._C._jit_to_backend("tensorrt", ...)``
+
+.. code-block:: python
+
+    trt_model = torch._C._jit_to_backend("tensorrt", script_model, spec)
+
+To run explicitly call the function of the method you want to run (vs. how you can just call on the module itself in standard PyTorch)
+
+.. code-block:: python
+
+    input = torch.randn((1, 3, 300, 300)).to("cuda").to(torch.half)
+    print(trt_model.forward(input))
diff --git a/docs/v1.2.0/_sources/tutorials/using_dla.rst.txt b/docs/v1.2.0/_sources/tutorials/using_dla.rst.txt
new file mode 100644
index 0000000000..9b062f7053
--- /dev/null
+++ b/docs/v1.2.0/_sources/tutorials/using_dla.rst.txt
@@ -0,0 +1,47 @@
+.. _using_dla:
+
+DLA
+=================================
+
+``DLA`` NVIDIA Deep Learning Accelerator is a fixed-function accelerator engine targeted for deep learning operations. DLA is designed to do full hardware acceleration of convolutional neural networks. DLA supports various layers such as convolution, deconvolution, fully-connected, activation, pooling, batch normalization, etc. ``torch_tensorrt`` supports compilation of TorchScript Module and deployment pipeline on the DLA hardware available on NVIDIA embedded platforms.
+
+NOTE: DLA supports fp16 and int8 precision only.
+
+Using DLA with torchtrtc
+
+.. code-block:: shell
+
+    torchtrtc [input_file_path] [output_file_path] [input_shapes...] -p f16 -d dla {OPTIONS}
+
+Using DLA in a C++ application
+
+.. code-block:: c++
+
+    std::vector<std::vector<int64_t>> input_shape = {{32, 3, 32, 32}};
+    auto compile_spec = torch_tensorrt::CompileSpec({input_shape});
+
+    # Set a precision. DLA supports fp16 or int8 only
+    compile_spec.enabled_precisions = {torch::kF16};
+    compile_spec.device.device_type = torch_tensorrt::CompileSpec::DeviceType::kDLA;
+
+    # Make sure the gpu id is set to Xavier id for DLA
+    compile_spec.device.gpu_id = 0;
+
+    # Set the DLA core id
+    compile_spec.device.dla_core = 1;
+
+    # If a layer fails to run on DLA it will fallback to GPU
+    compile_spec.device.allow_gpu_fallback = true;
+
+
+Using DLA in a python application
+
+.. code-block:: python
+
+    compile_spec = {
+        "inputs": [torch_tensorrt.Input(self.input.shape)],
+        "device": torch_tensorrt.Device("dla:0", allow_gpu_fallback=True),
+        "enalbed_precisions": {torch.half},
+    }
+
+    trt_mod = torch_tensorrt.compile(self.scripted_model, compile_spec)
diff --git a/docs/v1.2.0/_static/basic.css b/docs/v1.2.0/_static/basic.css
new file mode 100644
index 0000000000..bf18350b65
--- /dev/null
+++ b/docs/v1.2.0/_static/basic.css
@@ -0,0 +1,906 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 230px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin: 10px 0 0 20px;
+    padding: 0;
+}
+
+ul.search li {
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 450px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+a.brackets:before,
+span.brackets > a:before{
+    content: "[";
+}
+
+a.brackets:after,
+span.brackets > a:after {
+    content: "]";
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+table.footnote td, table.footnote th {
+    border: 0 !important;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
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+}
+
+ol.upperalpha {
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+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
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+ul.simple p {
+    margin-bottom: 0;
+}
+
+dl.footnote > dt,
+dl.citation > dt {
+    float: left;
+    margin-right: 0.5em;
+}
+
+dl.footnote > dd,
+dl.citation > dd {
+    margin-bottom: 0em;
+}
+
+dl.footnote > dd:after,
+dl.citation > dd:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dt:after {
+    content: ":";
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
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+    text-decoration: underline;
+}
+
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+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/collapsible-lists/LICENSE.md b/docs/v1.2.0/_static/collapsible-lists/LICENSE.md
new file mode 100644
index 0000000000..ef81a64535
--- /dev/null
+++ b/docs/v1.2.0/_static/collapsible-lists/LICENSE.md
@@ -0,0 +1,7 @@
+This code is the fruit of Kate Morley's labor, taken from here:
+
+- http://code.iamkate.com/javascript/collapsible-lists/
+
+She includes a generous CC0 1.0 license for all materials on her site:
+
+- http://code.iamkate.com/
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diff --git a/docs/v1.2.0/_static/collapsible-lists/css/tree_view.css b/docs/v1.2.0/_static/collapsible-lists/css/tree_view.css
new file mode 100644
index 0000000000..fa21ac4e84
--- /dev/null
+++ b/docs/v1.2.0/_static/collapsible-lists/css/tree_view.css
@@ -0,0 +1,61 @@
+/* Source taken directly from:
+ *     view-source:http://code.iamkate.com/javascript/collapsible-lists/
+ *
+ * Kate Morley's license for this code is CC0:
+ *     Created by [Kate Morley](http://iamkate.com/). Except where explicitly
+ *     stated otherwise, all content is released under the terms of the
+ *     [CC0 1.0 Universal legal code](http://creativecommons.org/publicdomain/zero/1.0/legalcode).
+ */
+.treeView{
+  -moz-user-select:none;
+  position:relative;
+}
+
+.treeView ul{
+  margin:0 0 0 -1.5em ! important;
+  padding:0 0 0 1.5em ! important;
+}
+
+.treeView ul ul{
+  background:url('list-item-contents.png') repeat-y left ! important;
+}
+
+.treeView li.lastChild > ul{
+  background-image:none ! important;
+}
+
+.treeView li{
+  margin:0 ! important;
+  padding:0 ! important;
+  background:url('list-item-root.png') no-repeat top left ! important;
+  list-style-position:inside ! important;
+  list-style-image:url('button.png') ! important;
+  cursor:auto;
+}
+
+.treeView li.collapsibleListOpen{
+  list-style-image:url('button-open.png') ! important;
+  cursor:pointer;
+}
+
+.treeView li.collapsibleListClosed{
+  list-style-image:url('button-closed.png') ! important;
+  cursor:pointer;
+}
+
+.treeView li li{
+  background-image:url('list-item.png') ! important;
+  padding-left:1.5em ! important;
+}
+
+.treeView li.lastChild{
+  background-image:url('list-item-last.png') ! important;
+}
+
+.treeView li.collapsibleListOpen{
+  background-image:url('list-item-open.png') ! important;
+}
+
+.treeView li.collapsibleListOpen.lastChild{
+  background-image:url('list-item-last-open.png') ! important;
+}
diff --git a/docs/v1.2.0/_static/collapsible-lists/js/CollapsibleLists.compressed.js b/docs/v1.2.0/_static/collapsible-lists/js/CollapsibleLists.compressed.js
new file mode 100644
index 0000000000..429406cf39
--- /dev/null
+++ b/docs/v1.2.0/_static/collapsible-lists/js/CollapsibleLists.compressed.js
@@ -0,0 +1,83 @@
+/*
+
+CollapsibleLists.js
+
+An object allowing lists to dynamically expand and collapse
+
+Created by Kate Morley - http://code.iamkate.com/ - and released under
+the terms of the CC0 1.0 Universal legal code:
+
+http://creativecommons.org/publicdomain/zero/1.0/legalcode
+
+*/
+
+var CollapsibleLists=new function(){
+this.apply=function(_1){
+var _2=document.getElementsByTagName("ul");
+for(var _3=0;_3<_2.length;_3++){
+if(_2[_3].className.match(/(^| )collapsibleList( |$)/)){
+this.applyTo(_2[_3],true);
+if(!_1){
+var _4=_2[_3].getElementsByTagName("ul");
+for(var _5=0;_5<_4.length;_5++){
+_4[_5].className+=" collapsibleList";
+}
+}
+}
+}
+};
+this.applyTo=function(_6,_7){
+var _8=_6.getElementsByTagName("li");
+for(var _9=0;_9<_8.length;_9++){
+if(!_7||_6==_8[_9].parentNode){
+if(_8[_9].addEventListener){
+_8[_9].addEventListener("mousedown",function(e){
+e.preventDefault();
+},false);
+}else{
+_8[_9].attachEvent("onselectstart",function(){
+event.returnValue=false;
+});
+}
+if(_8[_9].addEventListener){
+_8[_9].addEventListener("click",_a(_8[_9]),false);
+}else{
+_8[_9].attachEvent("onclick",_a(_8[_9]));
+}
+_b(_8[_9]);
+}
+}
+};
+function _a(_c){
+return function(e){
+if(!e){
+e=window.event;
+}
+var _d=(e.target?e.target:e.srcElement);
+while(_d.nodeName!="LI"){
+_d=_d.parentNode;
+}
+if(_d==_c){
+_b(_c);
+}
+};
+};
+function _b(_e){
+var _f=_e.className.match(/(^| )collapsibleListClosed( |$)/);
+var uls=_e.getElementsByTagName("ul");
+for(var _10=0;_10<uls.length;_10++){
+var li=uls[_10];
+while(li.nodeName!="LI"){
+li=li.parentNode;
+}
+if(li==_e){
+uls[_10].style.display=(_f?"block":"none");
+}
+}
+_e.className=_e.className.replace(/(^| )collapsibleList(Open|Closed)( |$)/,"");
+if(uls.length>0){
+_e.className+=" collapsibleList"+(_f?"Open":"Closed");
+}
+};
+}();
+
diff --git a/docs/v1.2.0/_static/collapsible-lists/js/apply-collapsible-lists.js b/docs/v1.2.0/_static/collapsible-lists/js/apply-collapsible-lists.js
new file mode 100644
index 0000000000..e848bb9811
--- /dev/null
+++ b/docs/v1.2.0/_static/collapsible-lists/js/apply-collapsible-lists.js
@@ -0,0 +1,3 @@
+$(document).ready(function() {
+    CollapsibleLists.apply();
+});
diff --git a/docs/v1.2.0/_static/css/theme.css b/docs/v1.2.0/_static/css/theme.css
new file mode 100644
index 0000000000..335a5b8346
--- /dev/null
+++ b/docs/v1.2.0/_static/css/theme.css
@@ -0,0 +1,12441 @@
+@charset "UTF-8";
+/*!
+ * Bootstrap v4.0.0 (https://getbootstrap.com)
+ * Copyright 2011-2018 The Bootstrap Authors
+ * Copyright 2011-2018 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+:root {
+  --blue: #007bff;
+  --indigo: #6610f2;
+  --purple: #6f42c1;
+  --pink: #e83e8c;
+  --red: #dc3545;
+  --orange: #fd7e14;
+  --yellow: #ffc107;
+  --green: #28a745;
+  --teal: #20c997;
+  --cyan: #17a2b8;
+  --white: #fff;
+  --gray: #6c757d;
+  --gray-dark: #343a40;
+  --primary: #007bff;
+  --secondary: #6c757d;
+  --success: #28a745;
+  --info: #17a2b8;
+  --warning: #ffc107;
+  --danger: #dc3545;
+  --light: #f8f9fa;
+  --dark: #343a40;
+  --breakpoint-xs: 0;
+  --breakpoint-sm: 576px;
+  --breakpoint-md: 768px;
+  --breakpoint-lg: 992px;
+  --breakpoint-xl: 1200px;
+  --font-family-sans-serif: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
+  --font-family-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+}
+
+*,
+*::before,
+*::after {
+  -webkit-box-sizing: border-box;
+          box-sizing: border-box;
+}
+
+html {
+  font-family: sans-serif;
+  line-height: 1.15;
+  -webkit-text-size-adjust: 100%;
+  -ms-text-size-adjust: 100%;
+  -ms-overflow-style: scrollbar;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+
+@-ms-viewport {
+  width: device-width;
+}
+article, aside, dialog, figcaption, figure, footer, header, hgroup, main, nav, section {
+  display: block;
+}
+
+body {
+  margin: 0;
+  font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
+  font-size: 1rem;
+  font-weight: 400;
+  line-height: 1.5;
+  color: #212529;
+  text-align: left;
+  background-color: #fff;
+}
+
+[tabindex="-1"]:focus {
+  outline: 0 !important;
+}
+
+hr {
+  -webkit-box-sizing: content-box;
+          box-sizing: content-box;
+  height: 0;
+  overflow: visible;
+}
+
+h1, h2, h3, h4, h5, h6 {
+  margin-top: 0;
+  margin-bottom: 0.5rem;
+}
+
+p {
+  margin-top: 0;
+  margin-bottom: 1rem;
+}
+
+abbr[title],
+abbr[data-original-title] {
+  text-decoration: underline;
+  -webkit-text-decoration: underline dotted;
+          text-decoration: underline dotted;
+  cursor: help;
+  border-bottom: 0;
+}
+
+address {
+  margin-bottom: 1rem;
+  font-style: normal;
+  line-height: inherit;
+}
+
+ol,
+ul,
+dl {
+  margin-top: 0;
+  margin-bottom: 1rem;
+}
+
+ol ol,
+ul ul,
+ol ul,
+ul ol {
+  margin-bottom: 0;
+}
+
+dt {
+  font-weight: 700;
+}
+
+dd {
+  margin-bottom: .5rem;
+  margin-left: 0;
+}
+
+blockquote {
+  margin: 0 0 1rem;
+}
+
+dfn {
+  font-style: italic;
+}
+
+b,
+strong {
+  font-weight: bolder;
+}
+
+small {
+  font-size: 80%;
+}
+
+sub,
+sup {
+  position: relative;
+  font-size: 75%;
+  line-height: 0;
+  vertical-align: baseline;
+}
+
+sub {
+  bottom: -.25em;
+}
+
+sup {
+  top: -.5em;
+}
+
+a {
+  color: #007bff;
+  text-decoration: none;
+  background-color: transparent;
+  -webkit-text-decoration-skip: objects;
+}
+a:hover {
+  color: #0056b3;
+  text-decoration: underline;
+}
+
+a:not([href]):not([tabindex]) {
+  color: inherit;
+  text-decoration: none;
+}
+a:not([href]):not([tabindex]):hover, a:not([href]):not([tabindex]):focus {
+  color: inherit;
+  text-decoration: none;
+}
+a:not([href]):not([tabindex]):focus {
+  outline: 0;
+}
+
+pre,
+code,
+kbd,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+
+pre {
+  margin-top: 0;
+  margin-bottom: 1rem;
+  overflow: auto;
+  -ms-overflow-style: scrollbar;
+}
+
+figure {
+  margin: 0 0 1rem;
+}
+
+img {
+  vertical-align: middle;
+  border-style: none;
+}
+
+svg:not(:root) {
+  overflow: hidden;
+}
+
+table {
+  border-collapse: collapse;
+}
+
+caption {
+  padding-top: 0.75rem;
+  padding-bottom: 0.75rem;
+  color: #6c757d;
+  text-align: left;
+  caption-side: bottom;
+}
+
+th {
+  text-align: inherit;
+}
+
+label {
+  display: inline-block;
+  margin-bottom: .5rem;
+}
+
+button {
+  border-radius: 0;
+}
+
+button:focus {
+  outline: 1px dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+}
+
+input,
+button,
+select,
+optgroup,
+textarea {
+  margin: 0;
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+
+button,
+input {
+  overflow: visible;
+}
+
+button,
+select {
+  text-transform: none;
+}
+
+button,
+html [type="button"],
+[type="reset"],
+[type="submit"] {
+  -webkit-appearance: button;
+}
+
+button::-moz-focus-inner,
+[type="button"]::-moz-focus-inner,
+[type="reset"]::-moz-focus-inner,
+[type="submit"]::-moz-focus-inner {
+  padding: 0;
+  border-style: none;
+}
+
+input[type="radio"],
+input[type="checkbox"] {
+  -webkit-box-sizing: border-box;
+          box-sizing: border-box;
+  padding: 0;
+}
+
+input[type="date"],
+input[type="time"],
+input[type="datetime-local"],
+input[type="month"] {
+  -webkit-appearance: listbox;
+}
+
+textarea {
+  overflow: auto;
+  resize: vertical;
+}
+
+fieldset {
+  min-width: 0;
+  padding: 0;
+  margin: 0;
+  border: 0;
+}
+
+legend {
+  display: block;
+  width: 100%;
+  max-width: 100%;
+  padding: 0;
+  margin-bottom: .5rem;
+  font-size: 1.5rem;
+  line-height: inherit;
+  color: inherit;
+  white-space: normal;
+}
+
+progress {
+  vertical-align: baseline;
+}
+
+[type="number"]::-webkit-inner-spin-button,
+[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+
+[type="search"] {
+  outline-offset: -2px;
+  -webkit-appearance: none;
+}
+
+[type="search"]::-webkit-search-cancel-button,
+[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+
+::-webkit-file-upload-button {
+  font: inherit;
+  -webkit-appearance: button;
+}
+
+output {
+  display: inline-block;
+}
+
+summary {
+  display: list-item;
+  cursor: pointer;
+}
+
+template {
+  display: none;
+}
+
+[hidden] {
+  display: none !important;
+}
+
+h1, h2, h3, h4, h5, h6,
+.h1, .h2, .h3, .h4, .h5, .h6 {
+  margin-bottom: 0.5rem;
+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.2;
+  color: inherit;
+}
+
+h1, .h1 {
+  font-size: 2.5rem;
+}
+
+h2, .h2 {
+  font-size: 2rem;
+}
+
+h3, .h3 {
+  font-size: 1.75rem;
+}
+
+h4, .h4 {
+  font-size: 1.5rem;
+}
+
+h5, .h5 {
+  font-size: 1.25rem;
+}
+
+h6, .h6 {
+  font-size: 1rem;
+}
+
+.lead {
+  font-size: 1.25rem;
+  font-weight: 300;
+}
+
+.display-1 {
+  font-size: 6rem;
+  font-weight: 300;
+  line-height: 1.2;
+}
+
+.display-2 {
+  font-size: 5.5rem;
+  font-weight: 300;
+  line-height: 1.2;
+}
+
+.display-3 {
+  font-size: 4.5rem;
+  font-weight: 300;
+  line-height: 1.2;
+}
+
+.display-4 {
+  font-size: 3.5rem;
+  font-weight: 300;
+  line-height: 1.2;
+}
+
+hr {
+  margin-top: 1rem;
+  margin-bottom: 1rem;
+  border: 0;
+  border-top: 1px solid rgba(0, 0, 0, 0.1);
+}
+
+small,
+.small {
+  font-size: 80%;
+  font-weight: 400;
+}
+
+mark,
+.mark {
+  padding: 0.2em;
+  background-color: #fcf8e3;
+}
+
+.list-unstyled {
+  padding-left: 0;
+  list-style: none;
+}
+
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+}
+
+.list-inline-item {
+  display: inline-block;
+}
+.list-inline-item:not(:last-child) {
+  margin-right: 0.5rem;
+}
+
+.initialism {
+  font-size: 90%;
+  text-transform: uppercase;
+}
+
+.blockquote {
+  margin-bottom: 1rem;
+  font-size: 1.25rem;
+}
+
+.blockquote-footer {
+  display: block;
+  font-size: 80%;
+  color: #6c757d;
+}
+.blockquote-footer::before {
+  content: "\2014 \00A0";
+}
+
+.img-fluid {
+  max-width: 100%;
+  height: auto;
+}
+
+.img-thumbnail {
+  padding: 0.25rem;
+  background-color: #fff;
+  border: 1px solid #dee2e6;
+  border-radius: 0.25rem;
+  max-width: 100%;
+  height: auto;
+}
+
+.figure {
+  display: inline-block;
+}
+
+.figure-img {
+  margin-bottom: 0.5rem;
+  line-height: 1;
+}
+
+.figure-caption {
+  font-size: 90%;
+  color: #6c757d;
+}
+
+code,
+kbd,
+pre,
+samp {
+  font-family: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+}
+
+code {
+  font-size: 87.5%;
+  color: #e83e8c;
+  word-break: break-word;
+}
+a > code {
+  color: inherit;
+}
+
+kbd {
+  padding: 0.2rem 0.4rem;
+  font-size: 87.5%;
+  color: #fff;
+  background-color: #212529;
+  border-radius: 0.2rem;
+}
+kbd kbd {
+  padding: 0;
+  font-size: 100%;
+  font-weight: 700;
+}
+
+pre {
+  display: block;
+  font-size: 87.5%;
+  color: #212529;
+}
+pre code {
+  font-size: inherit;
+  color: inherit;
+  word-break: normal;
+}
+
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+
+.container {
+  width: 100%;
+  padding-right: 15px;
+  padding-left: 15px;
+  margin-right: auto;
+  margin-left: auto;
+}
+@media (min-width: 576px) {
+  .container {
+    max-width: 540px;
+  }
+}
+@media (min-width: 768px) {
+  .container {
+    max-width: 720px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    max-width: 960px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    max-width: 1140px;
+  }
+}
+
+.container-fluid {
+  width: 100%;
+  padding-right: 15px;
+  padding-left: 15px;
+  margin-right: auto;
+  margin-left: auto;
+}
+
+.row {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  margin-right: -15px;
+  margin-left: -15px;
+}
+
+.no-gutters {
+  margin-right: 0;
+  margin-left: 0;
+}
+.no-gutters > .col,
+.no-gutters > [class*="col-"] {
+  padding-right: 0;
+  padding-left: 0;
+}
+
+.col-1, .col-2, .col-3, .col-4, .col-5, .col-6, .col-7, .col-8, .col-9, .col-10, .col-11, .col-12, .col,
+.col-auto, .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12, .col-sm,
+.col-sm-auto, .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12, .col-md,
+.col-md-auto, .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12, .col-lg,
+.col-lg-auto, .col-xl-1, .col-xl-2, .col-xl-3, .col-xl-4, .col-xl-5, .col-xl-6, .col-xl-7, .col-xl-8, .col-xl-9, .col-xl-10, .col-xl-11, .col-xl-12, .col-xl,
+.col-xl-auto {
+  position: relative;
+  width: 100%;
+  min-height: 1px;
+  padding-right: 15px;
+  padding-left: 15px;
+}
+
+.col {
+  -ms-flex-preferred-size: 0;
+      flex-basis: 0;
+  -webkit-box-flex: 1;
+      -ms-flex-positive: 1;
+          flex-grow: 1;
+  max-width: 100%;
+}
+
+.col-auto {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 auto;
+          flex: 0 0 auto;
+  width: auto;
+  max-width: none;
+}
+
+.col-1 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 8.3333333333%;
+          flex: 0 0 8.3333333333%;
+  max-width: 8.3333333333%;
+}
+
+.col-2 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 16.6666666667%;
+          flex: 0 0 16.6666666667%;
+  max-width: 16.6666666667%;
+}
+
+.col-3 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 25%;
+          flex: 0 0 25%;
+  max-width: 25%;
+}
+
+.col-4 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 33.3333333333%;
+          flex: 0 0 33.3333333333%;
+  max-width: 33.3333333333%;
+}
+
+.col-5 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 41.6666666667%;
+          flex: 0 0 41.6666666667%;
+  max-width: 41.6666666667%;
+}
+
+.col-6 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 50%;
+          flex: 0 0 50%;
+  max-width: 50%;
+}
+
+.col-7 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 58.3333333333%;
+          flex: 0 0 58.3333333333%;
+  max-width: 58.3333333333%;
+}
+
+.col-8 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 66.6666666667%;
+          flex: 0 0 66.6666666667%;
+  max-width: 66.6666666667%;
+}
+
+.col-9 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 75%;
+          flex: 0 0 75%;
+  max-width: 75%;
+}
+
+.col-10 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 83.3333333333%;
+          flex: 0 0 83.3333333333%;
+  max-width: 83.3333333333%;
+}
+
+.col-11 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 91.6666666667%;
+          flex: 0 0 91.6666666667%;
+  max-width: 91.6666666667%;
+}
+
+.col-12 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 100%;
+          flex: 0 0 100%;
+  max-width: 100%;
+}
+
+.order-first {
+  -webkit-box-ordinal-group: 0;
+      -ms-flex-order: -1;
+          order: -1;
+}
+
+.order-last {
+  -webkit-box-ordinal-group: 14;
+      -ms-flex-order: 13;
+          order: 13;
+}
+
+.order-0 {
+  -webkit-box-ordinal-group: 1;
+      -ms-flex-order: 0;
+          order: 0;
+}
+
+.order-1 {
+  -webkit-box-ordinal-group: 2;
+      -ms-flex-order: 1;
+          order: 1;
+}
+
+.order-2 {
+  -webkit-box-ordinal-group: 3;
+      -ms-flex-order: 2;
+          order: 2;
+}
+
+.order-3 {
+  -webkit-box-ordinal-group: 4;
+      -ms-flex-order: 3;
+          order: 3;
+}
+
+.order-4 {
+  -webkit-box-ordinal-group: 5;
+      -ms-flex-order: 4;
+          order: 4;
+}
+
+.order-5 {
+  -webkit-box-ordinal-group: 6;
+      -ms-flex-order: 5;
+          order: 5;
+}
+
+.order-6 {
+  -webkit-box-ordinal-group: 7;
+      -ms-flex-order: 6;
+          order: 6;
+}
+
+.order-7 {
+  -webkit-box-ordinal-group: 8;
+      -ms-flex-order: 7;
+          order: 7;
+}
+
+.order-8 {
+  -webkit-box-ordinal-group: 9;
+      -ms-flex-order: 8;
+          order: 8;
+}
+
+.order-9 {
+  -webkit-box-ordinal-group: 10;
+      -ms-flex-order: 9;
+          order: 9;
+}
+
+.order-10 {
+  -webkit-box-ordinal-group: 11;
+      -ms-flex-order: 10;
+          order: 10;
+}
+
+.order-11 {
+  -webkit-box-ordinal-group: 12;
+      -ms-flex-order: 11;
+          order: 11;
+}
+
+.order-12 {
+  -webkit-box-ordinal-group: 13;
+      -ms-flex-order: 12;
+          order: 12;
+}
+
+.offset-1 {
+  margin-left: 8.3333333333%;
+}
+
+.offset-2 {
+  margin-left: 16.6666666667%;
+}
+
+.offset-3 {
+  margin-left: 25%;
+}
+
+.offset-4 {
+  margin-left: 33.3333333333%;
+}
+
+.offset-5 {
+  margin-left: 41.6666666667%;
+}
+
+.offset-6 {
+  margin-left: 50%;
+}
+
+.offset-7 {
+  margin-left: 58.3333333333%;
+}
+
+.offset-8 {
+  margin-left: 66.6666666667%;
+}
+
+.offset-9 {
+  margin-left: 75%;
+}
+
+.offset-10 {
+  margin-left: 83.3333333333%;
+}
+
+.offset-11 {
+  margin-left: 91.6666666667%;
+}
+
+@media (min-width: 576px) {
+  .col-sm {
+    -ms-flex-preferred-size: 0;
+        flex-basis: 0;
+    -webkit-box-flex: 1;
+        -ms-flex-positive: 1;
+            flex-grow: 1;
+    max-width: 100%;
+  }
+
+  .col-sm-auto {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 auto;
+            flex: 0 0 auto;
+    width: auto;
+    max-width: none;
+  }
+
+  .col-sm-1 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 8.3333333333%;
+            flex: 0 0 8.3333333333%;
+    max-width: 8.3333333333%;
+  }
+
+  .col-sm-2 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 16.6666666667%;
+            flex: 0 0 16.6666666667%;
+    max-width: 16.6666666667%;
+  }
+
+  .col-sm-3 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 25%;
+            flex: 0 0 25%;
+    max-width: 25%;
+  }
+
+  .col-sm-4 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 33.3333333333%;
+            flex: 0 0 33.3333333333%;
+    max-width: 33.3333333333%;
+  }
+
+  .col-sm-5 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 41.6666666667%;
+            flex: 0 0 41.6666666667%;
+    max-width: 41.6666666667%;
+  }
+
+  .col-sm-6 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 50%;
+            flex: 0 0 50%;
+    max-width: 50%;
+  }
+
+  .col-sm-7 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 58.3333333333%;
+            flex: 0 0 58.3333333333%;
+    max-width: 58.3333333333%;
+  }
+
+  .col-sm-8 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 66.6666666667%;
+            flex: 0 0 66.6666666667%;
+    max-width: 66.6666666667%;
+  }
+
+  .col-sm-9 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 75%;
+            flex: 0 0 75%;
+    max-width: 75%;
+  }
+
+  .col-sm-10 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 83.3333333333%;
+            flex: 0 0 83.3333333333%;
+    max-width: 83.3333333333%;
+  }
+
+  .col-sm-11 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 91.6666666667%;
+            flex: 0 0 91.6666666667%;
+    max-width: 91.6666666667%;
+  }
+
+  .col-sm-12 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 100%;
+            flex: 0 0 100%;
+    max-width: 100%;
+  }
+
+  .order-sm-first {
+    -webkit-box-ordinal-group: 0;
+        -ms-flex-order: -1;
+            order: -1;
+  }
+
+  .order-sm-last {
+    -webkit-box-ordinal-group: 14;
+        -ms-flex-order: 13;
+            order: 13;
+  }
+
+  .order-sm-0 {
+    -webkit-box-ordinal-group: 1;
+        -ms-flex-order: 0;
+            order: 0;
+  }
+
+  .order-sm-1 {
+    -webkit-box-ordinal-group: 2;
+        -ms-flex-order: 1;
+            order: 1;
+  }
+
+  .order-sm-2 {
+    -webkit-box-ordinal-group: 3;
+        -ms-flex-order: 2;
+            order: 2;
+  }
+
+  .order-sm-3 {
+    -webkit-box-ordinal-group: 4;
+        -ms-flex-order: 3;
+            order: 3;
+  }
+
+  .order-sm-4 {
+    -webkit-box-ordinal-group: 5;
+        -ms-flex-order: 4;
+            order: 4;
+  }
+
+  .order-sm-5 {
+    -webkit-box-ordinal-group: 6;
+        -ms-flex-order: 5;
+            order: 5;
+  }
+
+  .order-sm-6 {
+    -webkit-box-ordinal-group: 7;
+        -ms-flex-order: 6;
+            order: 6;
+  }
+
+  .order-sm-7 {
+    -webkit-box-ordinal-group: 8;
+        -ms-flex-order: 7;
+            order: 7;
+  }
+
+  .order-sm-8 {
+    -webkit-box-ordinal-group: 9;
+        -ms-flex-order: 8;
+            order: 8;
+  }
+
+  .order-sm-9 {
+    -webkit-box-ordinal-group: 10;
+        -ms-flex-order: 9;
+            order: 9;
+  }
+
+  .order-sm-10 {
+    -webkit-box-ordinal-group: 11;
+        -ms-flex-order: 10;
+            order: 10;
+  }
+
+  .order-sm-11 {
+    -webkit-box-ordinal-group: 12;
+        -ms-flex-order: 11;
+            order: 11;
+  }
+
+  .order-sm-12 {
+    -webkit-box-ordinal-group: 13;
+        -ms-flex-order: 12;
+            order: 12;
+  }
+
+  .offset-sm-0 {
+    margin-left: 0;
+  }
+
+  .offset-sm-1 {
+    margin-left: 8.3333333333%;
+  }
+
+  .offset-sm-2 {
+    margin-left: 16.6666666667%;
+  }
+
+  .offset-sm-3 {
+    margin-left: 25%;
+  }
+
+  .offset-sm-4 {
+    margin-left: 33.3333333333%;
+  }
+
+  .offset-sm-5 {
+    margin-left: 41.6666666667%;
+  }
+
+  .offset-sm-6 {
+    margin-left: 50%;
+  }
+
+  .offset-sm-7 {
+    margin-left: 58.3333333333%;
+  }
+
+  .offset-sm-8 {
+    margin-left: 66.6666666667%;
+  }
+
+  .offset-sm-9 {
+    margin-left: 75%;
+  }
+
+  .offset-sm-10 {
+    margin-left: 83.3333333333%;
+  }
+
+  .offset-sm-11 {
+    margin-left: 91.6666666667%;
+  }
+}
+@media (min-width: 768px) {
+  .col-md {
+    -ms-flex-preferred-size: 0;
+        flex-basis: 0;
+    -webkit-box-flex: 1;
+        -ms-flex-positive: 1;
+            flex-grow: 1;
+    max-width: 100%;
+  }
+
+  .col-md-auto {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 auto;
+            flex: 0 0 auto;
+    width: auto;
+    max-width: none;
+  }
+
+  .col-md-1 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 8.3333333333%;
+            flex: 0 0 8.3333333333%;
+    max-width: 8.3333333333%;
+  }
+
+  .col-md-2 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 16.6666666667%;
+            flex: 0 0 16.6666666667%;
+    max-width: 16.6666666667%;
+  }
+
+  .col-md-3 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 25%;
+            flex: 0 0 25%;
+    max-width: 25%;
+  }
+
+  .col-md-4 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 33.3333333333%;
+            flex: 0 0 33.3333333333%;
+    max-width: 33.3333333333%;
+  }
+
+  .col-md-5 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 41.6666666667%;
+            flex: 0 0 41.6666666667%;
+    max-width: 41.6666666667%;
+  }
+
+  .col-md-6 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 50%;
+            flex: 0 0 50%;
+    max-width: 50%;
+  }
+
+  .col-md-7 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 58.3333333333%;
+            flex: 0 0 58.3333333333%;
+    max-width: 58.3333333333%;
+  }
+
+  .col-md-8 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 66.6666666667%;
+            flex: 0 0 66.6666666667%;
+    max-width: 66.6666666667%;
+  }
+
+  .col-md-9 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 75%;
+            flex: 0 0 75%;
+    max-width: 75%;
+  }
+
+  .col-md-10 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 83.3333333333%;
+            flex: 0 0 83.3333333333%;
+    max-width: 83.3333333333%;
+  }
+
+  .col-md-11 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 91.6666666667%;
+            flex: 0 0 91.6666666667%;
+    max-width: 91.6666666667%;
+  }
+
+  .col-md-12 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 100%;
+            flex: 0 0 100%;
+    max-width: 100%;
+  }
+
+  .order-md-first {
+    -webkit-box-ordinal-group: 0;
+        -ms-flex-order: -1;
+            order: -1;
+  }
+
+  .order-md-last {
+    -webkit-box-ordinal-group: 14;
+        -ms-flex-order: 13;
+            order: 13;
+  }
+
+  .order-md-0 {
+    -webkit-box-ordinal-group: 1;
+        -ms-flex-order: 0;
+            order: 0;
+  }
+
+  .order-md-1 {
+    -webkit-box-ordinal-group: 2;
+        -ms-flex-order: 1;
+            order: 1;
+  }
+
+  .order-md-2 {
+    -webkit-box-ordinal-group: 3;
+        -ms-flex-order: 2;
+            order: 2;
+  }
+
+  .order-md-3 {
+    -webkit-box-ordinal-group: 4;
+        -ms-flex-order: 3;
+            order: 3;
+  }
+
+  .order-md-4 {
+    -webkit-box-ordinal-group: 5;
+        -ms-flex-order: 4;
+            order: 4;
+  }
+
+  .order-md-5 {
+    -webkit-box-ordinal-group: 6;
+        -ms-flex-order: 5;
+            order: 5;
+  }
+
+  .order-md-6 {
+    -webkit-box-ordinal-group: 7;
+        -ms-flex-order: 6;
+            order: 6;
+  }
+
+  .order-md-7 {
+    -webkit-box-ordinal-group: 8;
+        -ms-flex-order: 7;
+            order: 7;
+  }
+
+  .order-md-8 {
+    -webkit-box-ordinal-group: 9;
+        -ms-flex-order: 8;
+            order: 8;
+  }
+
+  .order-md-9 {
+    -webkit-box-ordinal-group: 10;
+        -ms-flex-order: 9;
+            order: 9;
+  }
+
+  .order-md-10 {
+    -webkit-box-ordinal-group: 11;
+        -ms-flex-order: 10;
+            order: 10;
+  }
+
+  .order-md-11 {
+    -webkit-box-ordinal-group: 12;
+        -ms-flex-order: 11;
+            order: 11;
+  }
+
+  .order-md-12 {
+    -webkit-box-ordinal-group: 13;
+        -ms-flex-order: 12;
+            order: 12;
+  }
+
+  .offset-md-0 {
+    margin-left: 0;
+  }
+
+  .offset-md-1 {
+    margin-left: 8.3333333333%;
+  }
+
+  .offset-md-2 {
+    margin-left: 16.6666666667%;
+  }
+
+  .offset-md-3 {
+    margin-left: 25%;
+  }
+
+  .offset-md-4 {
+    margin-left: 33.3333333333%;
+  }
+
+  .offset-md-5 {
+    margin-left: 41.6666666667%;
+  }
+
+  .offset-md-6 {
+    margin-left: 50%;
+  }
+
+  .offset-md-7 {
+    margin-left: 58.3333333333%;
+  }
+
+  .offset-md-8 {
+    margin-left: 66.6666666667%;
+  }
+
+  .offset-md-9 {
+    margin-left: 75%;
+  }
+
+  .offset-md-10 {
+    margin-left: 83.3333333333%;
+  }
+
+  .offset-md-11 {
+    margin-left: 91.6666666667%;
+  }
+}
+@media (min-width: 992px) {
+  .col-lg {
+    -ms-flex-preferred-size: 0;
+        flex-basis: 0;
+    -webkit-box-flex: 1;
+        -ms-flex-positive: 1;
+            flex-grow: 1;
+    max-width: 100%;
+  }
+
+  .col-lg-auto {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 auto;
+            flex: 0 0 auto;
+    width: auto;
+    max-width: none;
+  }
+
+  .col-lg-1 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 8.3333333333%;
+            flex: 0 0 8.3333333333%;
+    max-width: 8.3333333333%;
+  }
+
+  .col-lg-2 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 16.6666666667%;
+            flex: 0 0 16.6666666667%;
+    max-width: 16.6666666667%;
+  }
+
+  .col-lg-3 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 25%;
+            flex: 0 0 25%;
+    max-width: 25%;
+  }
+
+  .col-lg-4 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 33.3333333333%;
+            flex: 0 0 33.3333333333%;
+    max-width: 33.3333333333%;
+  }
+
+  .col-lg-5 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 41.6666666667%;
+            flex: 0 0 41.6666666667%;
+    max-width: 41.6666666667%;
+  }
+
+  .col-lg-6 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 50%;
+            flex: 0 0 50%;
+    max-width: 50%;
+  }
+
+  .col-lg-7 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 58.3333333333%;
+            flex: 0 0 58.3333333333%;
+    max-width: 58.3333333333%;
+  }
+
+  .col-lg-8 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 66.6666666667%;
+            flex: 0 0 66.6666666667%;
+    max-width: 66.6666666667%;
+  }
+
+  .col-lg-9 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 75%;
+            flex: 0 0 75%;
+    max-width: 75%;
+  }
+
+  .col-lg-10 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 83.3333333333%;
+            flex: 0 0 83.3333333333%;
+    max-width: 83.3333333333%;
+  }
+
+  .col-lg-11 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 91.6666666667%;
+            flex: 0 0 91.6666666667%;
+    max-width: 91.6666666667%;
+  }
+
+  .col-lg-12 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 100%;
+            flex: 0 0 100%;
+    max-width: 100%;
+  }
+
+  .order-lg-first {
+    -webkit-box-ordinal-group: 0;
+        -ms-flex-order: -1;
+            order: -1;
+  }
+
+  .order-lg-last {
+    -webkit-box-ordinal-group: 14;
+        -ms-flex-order: 13;
+            order: 13;
+  }
+
+  .order-lg-0 {
+    -webkit-box-ordinal-group: 1;
+        -ms-flex-order: 0;
+            order: 0;
+  }
+
+  .order-lg-1 {
+    -webkit-box-ordinal-group: 2;
+        -ms-flex-order: 1;
+            order: 1;
+  }
+
+  .order-lg-2 {
+    -webkit-box-ordinal-group: 3;
+        -ms-flex-order: 2;
+            order: 2;
+  }
+
+  .order-lg-3 {
+    -webkit-box-ordinal-group: 4;
+        -ms-flex-order: 3;
+            order: 3;
+  }
+
+  .order-lg-4 {
+    -webkit-box-ordinal-group: 5;
+        -ms-flex-order: 4;
+            order: 4;
+  }
+
+  .order-lg-5 {
+    -webkit-box-ordinal-group: 6;
+        -ms-flex-order: 5;
+            order: 5;
+  }
+
+  .order-lg-6 {
+    -webkit-box-ordinal-group: 7;
+        -ms-flex-order: 6;
+            order: 6;
+  }
+
+  .order-lg-7 {
+    -webkit-box-ordinal-group: 8;
+        -ms-flex-order: 7;
+            order: 7;
+  }
+
+  .order-lg-8 {
+    -webkit-box-ordinal-group: 9;
+        -ms-flex-order: 8;
+            order: 8;
+  }
+
+  .order-lg-9 {
+    -webkit-box-ordinal-group: 10;
+        -ms-flex-order: 9;
+            order: 9;
+  }
+
+  .order-lg-10 {
+    -webkit-box-ordinal-group: 11;
+        -ms-flex-order: 10;
+            order: 10;
+  }
+
+  .order-lg-11 {
+    -webkit-box-ordinal-group: 12;
+        -ms-flex-order: 11;
+            order: 11;
+  }
+
+  .order-lg-12 {
+    -webkit-box-ordinal-group: 13;
+        -ms-flex-order: 12;
+            order: 12;
+  }
+
+  .offset-lg-0 {
+    margin-left: 0;
+  }
+
+  .offset-lg-1 {
+    margin-left: 8.3333333333%;
+  }
+
+  .offset-lg-2 {
+    margin-left: 16.6666666667%;
+  }
+
+  .offset-lg-3 {
+    margin-left: 25%;
+  }
+
+  .offset-lg-4 {
+    margin-left: 33.3333333333%;
+  }
+
+  .offset-lg-5 {
+    margin-left: 41.6666666667%;
+  }
+
+  .offset-lg-6 {
+    margin-left: 50%;
+  }
+
+  .offset-lg-7 {
+    margin-left: 58.3333333333%;
+  }
+
+  .offset-lg-8 {
+    margin-left: 66.6666666667%;
+  }
+
+  .offset-lg-9 {
+    margin-left: 75%;
+  }
+
+  .offset-lg-10 {
+    margin-left: 83.3333333333%;
+  }
+
+  .offset-lg-11 {
+    margin-left: 91.6666666667%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-xl {
+    -ms-flex-preferred-size: 0;
+        flex-basis: 0;
+    -webkit-box-flex: 1;
+        -ms-flex-positive: 1;
+            flex-grow: 1;
+    max-width: 100%;
+  }
+
+  .col-xl-auto {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 auto;
+            flex: 0 0 auto;
+    width: auto;
+    max-width: none;
+  }
+
+  .col-xl-1 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 8.3333333333%;
+            flex: 0 0 8.3333333333%;
+    max-width: 8.3333333333%;
+  }
+
+  .col-xl-2 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 16.6666666667%;
+            flex: 0 0 16.6666666667%;
+    max-width: 16.6666666667%;
+  }
+
+  .col-xl-3 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 25%;
+            flex: 0 0 25%;
+    max-width: 25%;
+  }
+
+  .col-xl-4 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 33.3333333333%;
+            flex: 0 0 33.3333333333%;
+    max-width: 33.3333333333%;
+  }
+
+  .col-xl-5 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 41.6666666667%;
+            flex: 0 0 41.6666666667%;
+    max-width: 41.6666666667%;
+  }
+
+  .col-xl-6 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 50%;
+            flex: 0 0 50%;
+    max-width: 50%;
+  }
+
+  .col-xl-7 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 58.3333333333%;
+            flex: 0 0 58.3333333333%;
+    max-width: 58.3333333333%;
+  }
+
+  .col-xl-8 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 66.6666666667%;
+            flex: 0 0 66.6666666667%;
+    max-width: 66.6666666667%;
+  }
+
+  .col-xl-9 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 75%;
+            flex: 0 0 75%;
+    max-width: 75%;
+  }
+
+  .col-xl-10 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 83.3333333333%;
+            flex: 0 0 83.3333333333%;
+    max-width: 83.3333333333%;
+  }
+
+  .col-xl-11 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 91.6666666667%;
+            flex: 0 0 91.6666666667%;
+    max-width: 91.6666666667%;
+  }
+
+  .col-xl-12 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 100%;
+            flex: 0 0 100%;
+    max-width: 100%;
+  }
+
+  .order-xl-first {
+    -webkit-box-ordinal-group: 0;
+        -ms-flex-order: -1;
+            order: -1;
+  }
+
+  .order-xl-last {
+    -webkit-box-ordinal-group: 14;
+        -ms-flex-order: 13;
+            order: 13;
+  }
+
+  .order-xl-0 {
+    -webkit-box-ordinal-group: 1;
+        -ms-flex-order: 0;
+            order: 0;
+  }
+
+  .order-xl-1 {
+    -webkit-box-ordinal-group: 2;
+        -ms-flex-order: 1;
+            order: 1;
+  }
+
+  .order-xl-2 {
+    -webkit-box-ordinal-group: 3;
+        -ms-flex-order: 2;
+            order: 2;
+  }
+
+  .order-xl-3 {
+    -webkit-box-ordinal-group: 4;
+        -ms-flex-order: 3;
+            order: 3;
+  }
+
+  .order-xl-4 {
+    -webkit-box-ordinal-group: 5;
+        -ms-flex-order: 4;
+            order: 4;
+  }
+
+  .order-xl-5 {
+    -webkit-box-ordinal-group: 6;
+        -ms-flex-order: 5;
+            order: 5;
+  }
+
+  .order-xl-6 {
+    -webkit-box-ordinal-group: 7;
+        -ms-flex-order: 6;
+            order: 6;
+  }
+
+  .order-xl-7 {
+    -webkit-box-ordinal-group: 8;
+        -ms-flex-order: 7;
+            order: 7;
+  }
+
+  .order-xl-8 {
+    -webkit-box-ordinal-group: 9;
+        -ms-flex-order: 8;
+            order: 8;
+  }
+
+  .order-xl-9 {
+    -webkit-box-ordinal-group: 10;
+        -ms-flex-order: 9;
+            order: 9;
+  }
+
+  .order-xl-10 {
+    -webkit-box-ordinal-group: 11;
+        -ms-flex-order: 10;
+            order: 10;
+  }
+
+  .order-xl-11 {
+    -webkit-box-ordinal-group: 12;
+        -ms-flex-order: 11;
+            order: 11;
+  }
+
+  .order-xl-12 {
+    -webkit-box-ordinal-group: 13;
+        -ms-flex-order: 12;
+            order: 12;
+  }
+
+  .offset-xl-0 {
+    margin-left: 0;
+  }
+
+  .offset-xl-1 {
+    margin-left: 8.3333333333%;
+  }
+
+  .offset-xl-2 {
+    margin-left: 16.6666666667%;
+  }
+
+  .offset-xl-3 {
+    margin-left: 25%;
+  }
+
+  .offset-xl-4 {
+    margin-left: 33.3333333333%;
+  }
+
+  .offset-xl-5 {
+    margin-left: 41.6666666667%;
+  }
+
+  .offset-xl-6 {
+    margin-left: 50%;
+  }
+
+  .offset-xl-7 {
+    margin-left: 58.3333333333%;
+  }
+
+  .offset-xl-8 {
+    margin-left: 66.6666666667%;
+  }
+
+  .offset-xl-9 {
+    margin-left: 75%;
+  }
+
+  .offset-xl-10 {
+    margin-left: 83.3333333333%;
+  }
+
+  .offset-xl-11 {
+    margin-left: 91.6666666667%;
+  }
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 1rem;
+  background-color: transparent;
+}
+.table th,
+.table td {
+  padding: 0.75rem;
+  vertical-align: top;
+  border-top: 1px solid #dee2e6;
+}
+.table thead th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #dee2e6;
+}
+.table tbody + tbody {
+  border-top: 2px solid #dee2e6;
+}
+.table .table {
+  background-color: #fff;
+}
+
+.table-sm th,
+.table-sm td {
+  padding: 0.3rem;
+}
+
+.table-bordered {
+  border: 1px solid #dee2e6;
+}
+.table-bordered th,
+.table-bordered td {
+  border: 1px solid #dee2e6;
+}
+.table-bordered thead th,
+.table-bordered thead td {
+  border-bottom-width: 2px;
+}
+
+.table-striped tbody tr:nth-of-type(odd) {
+  background-color: rgba(0, 0, 0, 0.05);
+}
+
+.table-hover tbody tr:hover {
+  background-color: rgba(0, 0, 0, 0.075);
+}
+
+.table-primary,
+.table-primary > th,
+.table-primary > td {
+  background-color: #b8daff;
+}
+
+.table-hover .table-primary:hover {
+  background-color: #9fcdff;
+}
+.table-hover .table-primary:hover > td,
+.table-hover .table-primary:hover > th {
+  background-color: #9fcdff;
+}
+
+.table-secondary,
+.table-secondary > th,
+.table-secondary > td {
+  background-color: #d6d8db;
+}
+
+.table-hover .table-secondary:hover {
+  background-color: #c8cbcf;
+}
+.table-hover .table-secondary:hover > td,
+.table-hover .table-secondary:hover > th {
+  background-color: #c8cbcf;
+}
+
+.table-success,
+.table-success > th,
+.table-success > td {
+  background-color: #c3e6cb;
+}
+
+.table-hover .table-success:hover {
+  background-color: #b1dfbb;
+}
+.table-hover .table-success:hover > td,
+.table-hover .table-success:hover > th {
+  background-color: #b1dfbb;
+}
+
+.table-info,
+.table-info > th,
+.table-info > td {
+  background-color: #bee5eb;
+}
+
+.table-hover .table-info:hover {
+  background-color: #abdde5;
+}
+.table-hover .table-info:hover > td,
+.table-hover .table-info:hover > th {
+  background-color: #abdde5;
+}
+
+.table-warning,
+.table-warning > th,
+.table-warning > td {
+  background-color: #ffeeba;
+}
+
+.table-hover .table-warning:hover {
+  background-color: #ffe8a1;
+}
+.table-hover .table-warning:hover > td,
+.table-hover .table-warning:hover > th {
+  background-color: #ffe8a1;
+}
+
+.table-danger,
+.table-danger > th,
+.table-danger > td {
+  background-color: #f5c6cb;
+}
+
+.table-hover .table-danger:hover {
+  background-color: #f1b0b7;
+}
+.table-hover .table-danger:hover > td,
+.table-hover .table-danger:hover > th {
+  background-color: #f1b0b7;
+}
+
+.table-light,
+.table-light > th,
+.table-light > td {
+  background-color: #fdfdfe;
+}
+
+.table-hover .table-light:hover {
+  background-color: #ececf6;
+}
+.table-hover .table-light:hover > td,
+.table-hover .table-light:hover > th {
+  background-color: #ececf6;
+}
+
+.table-dark,
+.table-dark > th,
+.table-dark > td {
+  background-color: #c6c8ca;
+}
+
+.table-hover .table-dark:hover {
+  background-color: #b9bbbe;
+}
+.table-hover .table-dark:hover > td,
+.table-hover .table-dark:hover > th {
+  background-color: #b9bbbe;
+}
+
+.table-active,
+.table-active > th,
+.table-active > td {
+  background-color: rgba(0, 0, 0, 0.075);
+}
+
+.table-hover .table-active:hover {
+  background-color: rgba(0, 0, 0, 0.075);
+}
+.table-hover .table-active:hover > td,
+.table-hover .table-active:hover > th {
+  background-color: rgba(0, 0, 0, 0.075);
+}
+
+.table .thead-dark th {
+  color: #fff;
+  background-color: #212529;
+  border-color: #32383e;
+}
+.table .thead-light th {
+  color: #495057;
+  background-color: #e9ecef;
+  border-color: #dee2e6;
+}
+
+.table-dark {
+  color: #fff;
+  background-color: #212529;
+}
+.table-dark th,
+.table-dark td,
+.table-dark thead th {
+  border-color: #32383e;
+}
+.table-dark.table-bordered {
+  border: 0;
+}
+.table-dark.table-striped tbody tr:nth-of-type(odd) {
+  background-color: rgba(255, 255, 255, 0.05);
+}
+.table-dark.table-hover tbody tr:hover {
+  background-color: rgba(255, 255, 255, 0.075);
+}
+
+@media (max-width: 575.98px) {
+  .table-responsive-sm {
+    display: block;
+    width: 100%;
+    overflow-x: auto;
+    -webkit-overflow-scrolling: touch;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+  }
+  .table-responsive-sm > .table-bordered {
+    border: 0;
+  }
+}
+@media (max-width: 767.98px) {
+  .table-responsive-md {
+    display: block;
+    width: 100%;
+    overflow-x: auto;
+    -webkit-overflow-scrolling: touch;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+  }
+  .table-responsive-md > .table-bordered {
+    border: 0;
+  }
+}
+@media (max-width: 991.98px) {
+  .table-responsive-lg {
+    display: block;
+    width: 100%;
+    overflow-x: auto;
+    -webkit-overflow-scrolling: touch;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+  }
+  .table-responsive-lg > .table-bordered {
+    border: 0;
+  }
+}
+@media (max-width: 1199.98px) {
+  .table-responsive-xl {
+    display: block;
+    width: 100%;
+    overflow-x: auto;
+    -webkit-overflow-scrolling: touch;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+  }
+  .table-responsive-xl > .table-bordered {
+    border: 0;
+  }
+}
+.table-responsive {
+  display: block;
+  width: 100%;
+  overflow-x: auto;
+  -webkit-overflow-scrolling: touch;
+  -ms-overflow-style: -ms-autohiding-scrollbar;
+}
+.table-responsive > .table-bordered {
+  border: 0;
+}
+
+.form-control {
+  display: block;
+  width: 100%;
+  padding: 0.375rem 0.75rem;
+  font-size: 1rem;
+  line-height: 1.5;
+  color: #495057;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ced4da;
+  border-radius: 0.25rem;
+  -webkit-transition: border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out;
+  transition: border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out;
+  transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;
+  transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out;
+}
+.form-control::-ms-expand {
+  background-color: transparent;
+  border: 0;
+}
+.form-control:focus {
+  color: #495057;
+  background-color: #fff;
+  border-color: #80bdff;
+  outline: 0;
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+}
+.form-control::-webkit-input-placeholder {
+  color: #6c757d;
+  opacity: 1;
+}
+.form-control::-moz-placeholder {
+  color: #6c757d;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #6c757d;
+  opacity: 1;
+}
+.form-control::-ms-input-placeholder {
+  color: #6c757d;
+  opacity: 1;
+}
+.form-control::placeholder {
+  color: #6c757d;
+  opacity: 1;
+}
+.form-control:disabled, .form-control[readonly] {
+  background-color: #e9ecef;
+  opacity: 1;
+}
+
+select.form-control:not([size]):not([multiple]) {
+  height: calc(2.25rem + 2px);
+}
+select.form-control:focus::-ms-value {
+  color: #495057;
+  background-color: #fff;
+}
+
+.form-control-file,
+.form-control-range {
+  display: block;
+  width: 100%;
+}
+
+.col-form-label {
+  padding-top: calc(0.375rem + 1px);
+  padding-bottom: calc(0.375rem + 1px);
+  margin-bottom: 0;
+  font-size: inherit;
+  line-height: 1.5;
+}
+
+.col-form-label-lg {
+  padding-top: calc(0.5rem + 1px);
+  padding-bottom: calc(0.5rem + 1px);
+  font-size: 1.25rem;
+  line-height: 1.5;
+}
+
+.col-form-label-sm {
+  padding-top: calc(0.25rem + 1px);
+  padding-bottom: calc(0.25rem + 1px);
+  font-size: 0.875rem;
+  line-height: 1.5;
+}
+
+.form-control-plaintext {
+  display: block;
+  width: 100%;
+  padding-top: 0.375rem;
+  padding-bottom: 0.375rem;
+  margin-bottom: 0;
+  line-height: 1.5;
+  background-color: transparent;
+  border: solid transparent;
+  border-width: 1px 0;
+}
+.form-control-plaintext.form-control-sm, .input-group-sm > .form-control-plaintext.form-control,
+.input-group-sm > .input-group-prepend > .form-control-plaintext.input-group-text,
+.input-group-sm > .input-group-append > .form-control-plaintext.input-group-text,
+.input-group-sm > .input-group-prepend > .form-control-plaintext.btn,
+.input-group-sm > .input-group-append > .form-control-plaintext.btn, .form-control-plaintext.form-control-lg, .input-group-lg > .form-control-plaintext.form-control,
+.input-group-lg > .input-group-prepend > .form-control-plaintext.input-group-text,
+.input-group-lg > .input-group-append > .form-control-plaintext.input-group-text,
+.input-group-lg > .input-group-prepend > .form-control-plaintext.btn,
+.input-group-lg > .input-group-append > .form-control-plaintext.btn {
+  padding-right: 0;
+  padding-left: 0;
+}
+
+.form-control-sm, .input-group-sm > .form-control,
+.input-group-sm > .input-group-prepend > .input-group-text,
+.input-group-sm > .input-group-append > .input-group-text,
+.input-group-sm > .input-group-prepend > .btn,
+.input-group-sm > .input-group-append > .btn {
+  padding: 0.25rem 0.5rem;
+  font-size: 0.875rem;
+  line-height: 1.5;
+  border-radius: 0.2rem;
+}
+
+select.form-control-sm:not([size]):not([multiple]), .input-group-sm > select.form-control:not([size]):not([multiple]),
+.input-group-sm > .input-group-prepend > select.input-group-text:not([size]):not([multiple]),
+.input-group-sm > .input-group-append > select.input-group-text:not([size]):not([multiple]),
+.input-group-sm > .input-group-prepend > select.btn:not([size]):not([multiple]),
+.input-group-sm > .input-group-append > select.btn:not([size]):not([multiple]) {
+  height: calc(1.8125rem + 2px);
+}
+
+.form-control-lg, .input-group-lg > .form-control,
+.input-group-lg > .input-group-prepend > .input-group-text,
+.input-group-lg > .input-group-append > .input-group-text,
+.input-group-lg > .input-group-prepend > .btn,
+.input-group-lg > .input-group-append > .btn {
+  padding: 0.5rem 1rem;
+  font-size: 1.25rem;
+  line-height: 1.5;
+  border-radius: 0.3rem;
+}
+
+select.form-control-lg:not([size]):not([multiple]), .input-group-lg > select.form-control:not([size]):not([multiple]),
+.input-group-lg > .input-group-prepend > select.input-group-text:not([size]):not([multiple]),
+.input-group-lg > .input-group-append > select.input-group-text:not([size]):not([multiple]),
+.input-group-lg > .input-group-prepend > select.btn:not([size]):not([multiple]),
+.input-group-lg > .input-group-append > select.btn:not([size]):not([multiple]) {
+  height: calc(2.875rem + 2px);
+}
+
+.form-group {
+  margin-bottom: 1rem;
+}
+
+.form-text {
+  display: block;
+  margin-top: 0.25rem;
+}
+
+.form-row {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  margin-right: -5px;
+  margin-left: -5px;
+}
+.form-row > .col,
+.form-row > [class*="col-"] {
+  padding-right: 5px;
+  padding-left: 5px;
+}
+
+.form-check {
+  position: relative;
+  display: block;
+  padding-left: 1.25rem;
+}
+
+.form-check-input {
+  position: absolute;
+  margin-top: 0.3rem;
+  margin-left: -1.25rem;
+}
+.form-check-input:disabled ~ .form-check-label {
+  color: #6c757d;
+}
+
+.form-check-label {
+  margin-bottom: 0;
+}
+
+.form-check-inline {
+  display: -webkit-inline-box;
+  display: -ms-inline-flexbox;
+  display: inline-flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  padding-left: 0;
+  margin-right: 0.75rem;
+}
+.form-check-inline .form-check-input {
+  position: static;
+  margin-top: 0;
+  margin-right: 0.3125rem;
+  margin-left: 0;
+}
+
+.valid-feedback {
+  display: none;
+  width: 100%;
+  margin-top: 0.25rem;
+  font-size: 80%;
+  color: #28a745;
+}
+
+.valid-tooltip {
+  position: absolute;
+  top: 100%;
+  z-index: 5;
+  display: none;
+  max-width: 100%;
+  padding: .5rem;
+  margin-top: .1rem;
+  font-size: .875rem;
+  line-height: 1;
+  color: #fff;
+  background-color: rgba(40, 167, 69, 0.8);
+  border-radius: .2rem;
+}
+
+.was-validated .form-control:valid, .form-control.is-valid,
+.was-validated .custom-select:valid,
+.custom-select.is-valid {
+  border-color: #28a745;
+}
+.was-validated .form-control:valid:focus, .form-control.is-valid:focus,
+.was-validated .custom-select:valid:focus,
+.custom-select.is-valid:focus {
+  border-color: #28a745;
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25);
+}
+.was-validated .form-control:valid ~ .valid-feedback,
+.was-validated .form-control:valid ~ .valid-tooltip, .form-control.is-valid ~ .valid-feedback,
+.form-control.is-valid ~ .valid-tooltip,
+.was-validated .custom-select:valid ~ .valid-feedback,
+.was-validated .custom-select:valid ~ .valid-tooltip,
+.custom-select.is-valid ~ .valid-feedback,
+.custom-select.is-valid ~ .valid-tooltip {
+  display: block;
+}
+
+.was-validated .form-check-input:valid ~ .form-check-label, .form-check-input.is-valid ~ .form-check-label {
+  color: #28a745;
+}
+.was-validated .form-check-input:valid ~ .valid-feedback,
+.was-validated .form-check-input:valid ~ .valid-tooltip, .form-check-input.is-valid ~ .valid-feedback,
+.form-check-input.is-valid ~ .valid-tooltip {
+  display: block;
+}
+
+.was-validated .custom-control-input:valid ~ .custom-control-label, .custom-control-input.is-valid ~ .custom-control-label {
+  color: #28a745;
+}
+.was-validated .custom-control-input:valid ~ .custom-control-label::before, .custom-control-input.is-valid ~ .custom-control-label::before {
+  background-color: #71dd8a;
+}
+.was-validated .custom-control-input:valid ~ .valid-feedback,
+.was-validated .custom-control-input:valid ~ .valid-tooltip, .custom-control-input.is-valid ~ .valid-feedback,
+.custom-control-input.is-valid ~ .valid-tooltip {
+  display: block;
+}
+.was-validated .custom-control-input:valid:checked ~ .custom-control-label::before, .custom-control-input.is-valid:checked ~ .custom-control-label::before {
+  background-color: #34ce57;
+}
+.was-validated .custom-control-input:valid:focus ~ .custom-control-label::before, .custom-control-input.is-valid:focus ~ .custom-control-label::before {
+  -webkit-box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(40, 167, 69, 0.25);
+          box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(40, 167, 69, 0.25);
+}
+
+.was-validated .custom-file-input:valid ~ .custom-file-label, .custom-file-input.is-valid ~ .custom-file-label {
+  border-color: #28a745;
+}
+.was-validated .custom-file-input:valid ~ .custom-file-label::before, .custom-file-input.is-valid ~ .custom-file-label::before {
+  border-color: inherit;
+}
+.was-validated .custom-file-input:valid ~ .valid-feedback,
+.was-validated .custom-file-input:valid ~ .valid-tooltip, .custom-file-input.is-valid ~ .valid-feedback,
+.custom-file-input.is-valid ~ .valid-tooltip {
+  display: block;
+}
+.was-validated .custom-file-input:valid:focus ~ .custom-file-label, .custom-file-input.is-valid:focus ~ .custom-file-label {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25);
+}
+
+.invalid-feedback {
+  display: none;
+  width: 100%;
+  margin-top: 0.25rem;
+  font-size: 80%;
+  color: #dc3545;
+}
+
+.invalid-tooltip {
+  position: absolute;
+  top: 100%;
+  z-index: 5;
+  display: none;
+  max-width: 100%;
+  padding: .5rem;
+  margin-top: .1rem;
+  font-size: .875rem;
+  line-height: 1;
+  color: #fff;
+  background-color: rgba(220, 53, 69, 0.8);
+  border-radius: .2rem;
+}
+
+.was-validated .form-control:invalid, .form-control.is-invalid,
+.was-validated .custom-select:invalid,
+.custom-select.is-invalid {
+  border-color: #dc3545;
+}
+.was-validated .form-control:invalid:focus, .form-control.is-invalid:focus,
+.was-validated .custom-select:invalid:focus,
+.custom-select.is-invalid:focus {
+  border-color: #dc3545;
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25);
+}
+.was-validated .form-control:invalid ~ .invalid-feedback,
+.was-validated .form-control:invalid ~ .invalid-tooltip, .form-control.is-invalid ~ .invalid-feedback,
+.form-control.is-invalid ~ .invalid-tooltip,
+.was-validated .custom-select:invalid ~ .invalid-feedback,
+.was-validated .custom-select:invalid ~ .invalid-tooltip,
+.custom-select.is-invalid ~ .invalid-feedback,
+.custom-select.is-invalid ~ .invalid-tooltip {
+  display: block;
+}
+
+.was-validated .form-check-input:invalid ~ .form-check-label, .form-check-input.is-invalid ~ .form-check-label {
+  color: #dc3545;
+}
+.was-validated .form-check-input:invalid ~ .invalid-feedback,
+.was-validated .form-check-input:invalid ~ .invalid-tooltip, .form-check-input.is-invalid ~ .invalid-feedback,
+.form-check-input.is-invalid ~ .invalid-tooltip {
+  display: block;
+}
+
+.was-validated .custom-control-input:invalid ~ .custom-control-label, .custom-control-input.is-invalid ~ .custom-control-label {
+  color: #dc3545;
+}
+.was-validated .custom-control-input:invalid ~ .custom-control-label::before, .custom-control-input.is-invalid ~ .custom-control-label::before {
+  background-color: #efa2a9;
+}
+.was-validated .custom-control-input:invalid ~ .invalid-feedback,
+.was-validated .custom-control-input:invalid ~ .invalid-tooltip, .custom-control-input.is-invalid ~ .invalid-feedback,
+.custom-control-input.is-invalid ~ .invalid-tooltip {
+  display: block;
+}
+.was-validated .custom-control-input:invalid:checked ~ .custom-control-label::before, .custom-control-input.is-invalid:checked ~ .custom-control-label::before {
+  background-color: #e4606d;
+}
+.was-validated .custom-control-input:invalid:focus ~ .custom-control-label::before, .custom-control-input.is-invalid:focus ~ .custom-control-label::before {
+  -webkit-box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(220, 53, 69, 0.25);
+          box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(220, 53, 69, 0.25);
+}
+
+.was-validated .custom-file-input:invalid ~ .custom-file-label, .custom-file-input.is-invalid ~ .custom-file-label {
+  border-color: #dc3545;
+}
+.was-validated .custom-file-input:invalid ~ .custom-file-label::before, .custom-file-input.is-invalid ~ .custom-file-label::before {
+  border-color: inherit;
+}
+.was-validated .custom-file-input:invalid ~ .invalid-feedback,
+.was-validated .custom-file-input:invalid ~ .invalid-tooltip, .custom-file-input.is-invalid ~ .invalid-feedback,
+.custom-file-input.is-invalid ~ .invalid-tooltip {
+  display: block;
+}
+.was-validated .custom-file-input:invalid:focus ~ .custom-file-label, .custom-file-input.is-invalid:focus ~ .custom-file-label {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25);
+}
+
+.form-inline {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: horizontal;
+  -webkit-box-direction: normal;
+      -ms-flex-flow: row wrap;
+          flex-flow: row wrap;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+}
+.form-inline .form-check {
+  width: 100%;
+}
+@media (min-width: 576px) {
+  .form-inline label {
+    display: -webkit-box;
+    display: -ms-flexbox;
+    display: flex;
+    -webkit-box-align: center;
+        -ms-flex-align: center;
+            align-items: center;
+    -webkit-box-pack: center;
+        -ms-flex-pack: center;
+            justify-content: center;
+    margin-bottom: 0;
+  }
+  .form-inline .form-group {
+    display: -webkit-box;
+    display: -ms-flexbox;
+    display: flex;
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 auto;
+            flex: 0 0 auto;
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row wrap;
+            flex-flow: row wrap;
+    -webkit-box-align: center;
+        -ms-flex-align: center;
+            align-items: center;
+    margin-bottom: 0;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-plaintext {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    width: auto;
+  }
+  .form-inline .form-check {
+    display: -webkit-box;
+    display: -ms-flexbox;
+    display: flex;
+    -webkit-box-align: center;
+        -ms-flex-align: center;
+            align-items: center;
+    -webkit-box-pack: center;
+        -ms-flex-pack: center;
+            justify-content: center;
+    width: auto;
+    padding-left: 0;
+  }
+  .form-inline .form-check-input {
+    position: relative;
+    margin-top: 0;
+    margin-right: 0.25rem;
+    margin-left: 0;
+  }
+  .form-inline .custom-control {
+    -webkit-box-align: center;
+        -ms-flex-align: center;
+            align-items: center;
+    -webkit-box-pack: center;
+        -ms-flex-pack: center;
+            justify-content: center;
+  }
+  .form-inline .custom-control-label {
+    margin-bottom: 0;
+  }
+}
+
+.btn {
+  display: inline-block;
+  font-weight: 400;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: middle;
+  -webkit-user-select: none;
+     -moz-user-select: none;
+      -ms-user-select: none;
+          user-select: none;
+  border: 1px solid transparent;
+  padding: 0.375rem 0.75rem;
+  font-size: 1rem;
+  line-height: 1.5;
+  border-radius: 0.25rem;
+  -webkit-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out;
+  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out;
+  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;
+  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out;
+}
+.btn:hover, .btn:focus {
+  text-decoration: none;
+}
+.btn:focus, .btn.focus {
+  outline: 0;
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+}
+.btn.disabled, .btn:disabled {
+  opacity: 0.65;
+}
+.btn:not(:disabled):not(.disabled) {
+  cursor: pointer;
+}
+.btn:not(:disabled):not(.disabled):active, .btn:not(:disabled):not(.disabled).active {
+  background-image: none;
+}
+
+a.btn.disabled,
+fieldset:disabled a.btn {
+  pointer-events: none;
+}
+
+.btn-primary {
+  color: #fff;
+  background-color: #007bff;
+  border-color: #007bff;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #0069d9;
+  border-color: #0062cc;
+}
+.btn-primary:focus, .btn-primary.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+}
+.btn-primary.disabled, .btn-primary:disabled {
+  color: #fff;
+  background-color: #007bff;
+  border-color: #007bff;
+}
+.btn-primary:not(:disabled):not(.disabled):active, .btn-primary:not(:disabled):not(.disabled).active, .show > .btn-primary.dropdown-toggle {
+  color: #fff;
+  background-color: #0062cc;
+  border-color: #005cbf;
+}
+.btn-primary:not(:disabled):not(.disabled):active:focus, .btn-primary:not(:disabled):not(.disabled).active:focus, .show > .btn-primary.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+}
+
+.btn-secondary {
+  color: #fff;
+  background-color: #6c757d;
+  border-color: #6c757d;
+}
+.btn-secondary:hover {
+  color: #fff;
+  background-color: #5a6268;
+  border-color: #545b62;
+}
+.btn-secondary:focus, .btn-secondary.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+}
+.btn-secondary.disabled, .btn-secondary:disabled {
+  color: #fff;
+  background-color: #6c757d;
+  border-color: #6c757d;
+}
+.btn-secondary:not(:disabled):not(.disabled):active, .btn-secondary:not(:disabled):not(.disabled).active, .show > .btn-secondary.dropdown-toggle {
+  color: #fff;
+  background-color: #545b62;
+  border-color: #4e555b;
+}
+.btn-secondary:not(:disabled):not(.disabled):active:focus, .btn-secondary:not(:disabled):not(.disabled).active:focus, .show > .btn-secondary.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+}
+
+.btn-success {
+  color: #fff;
+  background-color: #28a745;
+  border-color: #28a745;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #218838;
+  border-color: #1e7e34;
+}
+.btn-success:focus, .btn-success.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+}
+.btn-success.disabled, .btn-success:disabled {
+  color: #fff;
+  background-color: #28a745;
+  border-color: #28a745;
+}
+.btn-success:not(:disabled):not(.disabled):active, .btn-success:not(:disabled):not(.disabled).active, .show > .btn-success.dropdown-toggle {
+  color: #fff;
+  background-color: #1e7e34;
+  border-color: #1c7430;
+}
+.btn-success:not(:disabled):not(.disabled):active:focus, .btn-success:not(:disabled):not(.disabled).active:focus, .show > .btn-success.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+}
+
+.btn-info {
+  color: #fff;
+  background-color: #17a2b8;
+  border-color: #17a2b8;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #138496;
+  border-color: #117a8b;
+}
+.btn-info:focus, .btn-info.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+}
+.btn-info.disabled, .btn-info:disabled {
+  color: #fff;
+  background-color: #17a2b8;
+  border-color: #17a2b8;
+}
+.btn-info:not(:disabled):not(.disabled):active, .btn-info:not(:disabled):not(.disabled).active, .show > .btn-info.dropdown-toggle {
+  color: #fff;
+  background-color: #117a8b;
+  border-color: #10707f;
+}
+.btn-info:not(:disabled):not(.disabled):active:focus, .btn-info:not(:disabled):not(.disabled).active:focus, .show > .btn-info.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+}
+
+.btn-warning {
+  color: #212529;
+  background-color: #ffc107;
+  border-color: #ffc107;
+}
+.btn-warning:hover {
+  color: #212529;
+  background-color: #e0a800;
+  border-color: #d39e00;
+}
+.btn-warning:focus, .btn-warning.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+}
+.btn-warning.disabled, .btn-warning:disabled {
+  color: #212529;
+  background-color: #ffc107;
+  border-color: #ffc107;
+}
+.btn-warning:not(:disabled):not(.disabled):active, .btn-warning:not(:disabled):not(.disabled).active, .show > .btn-warning.dropdown-toggle {
+  color: #212529;
+  background-color: #d39e00;
+  border-color: #c69500;
+}
+.btn-warning:not(:disabled):not(.disabled):active:focus, .btn-warning:not(:disabled):not(.disabled).active:focus, .show > .btn-warning.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+}
+
+.btn-danger {
+  color: #fff;
+  background-color: #dc3545;
+  border-color: #dc3545;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c82333;
+  border-color: #bd2130;
+}
+.btn-danger:focus, .btn-danger.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+}
+.btn-danger.disabled, .btn-danger:disabled {
+  color: #fff;
+  background-color: #dc3545;
+  border-color: #dc3545;
+}
+.btn-danger:not(:disabled):not(.disabled):active, .btn-danger:not(:disabled):not(.disabled).active, .show > .btn-danger.dropdown-toggle {
+  color: #fff;
+  background-color: #bd2130;
+  border-color: #b21f2d;
+}
+.btn-danger:not(:disabled):not(.disabled):active:focus, .btn-danger:not(:disabled):not(.disabled).active:focus, .show > .btn-danger.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+}
+
+.btn-light {
+  color: #212529;
+  background-color: #f8f9fa;
+  border-color: #f8f9fa;
+}
+.btn-light:hover {
+  color: #212529;
+  background-color: #e2e6ea;
+  border-color: #dae0e5;
+}
+.btn-light:focus, .btn-light.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+}
+.btn-light.disabled, .btn-light:disabled {
+  color: #212529;
+  background-color: #f8f9fa;
+  border-color: #f8f9fa;
+}
+.btn-light:not(:disabled):not(.disabled):active, .btn-light:not(:disabled):not(.disabled).active, .show > .btn-light.dropdown-toggle {
+  color: #212529;
+  background-color: #dae0e5;
+  border-color: #d3d9df;
+}
+.btn-light:not(:disabled):not(.disabled):active:focus, .btn-light:not(:disabled):not(.disabled).active:focus, .show > .btn-light.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+}
+
+.btn-dark {
+  color: #fff;
+  background-color: #343a40;
+  border-color: #343a40;
+}
+.btn-dark:hover {
+  color: #fff;
+  background-color: #23272b;
+  border-color: #1d2124;
+}
+.btn-dark:focus, .btn-dark.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+}
+.btn-dark.disabled, .btn-dark:disabled {
+  color: #fff;
+  background-color: #343a40;
+  border-color: #343a40;
+}
+.btn-dark:not(:disabled):not(.disabled):active, .btn-dark:not(:disabled):not(.disabled).active, .show > .btn-dark.dropdown-toggle {
+  color: #fff;
+  background-color: #1d2124;
+  border-color: #171a1d;
+}
+.btn-dark:not(:disabled):not(.disabled):active:focus, .btn-dark:not(:disabled):not(.disabled).active:focus, .show > .btn-dark.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+}
+
+.btn-outline-primary {
+  color: #007bff;
+  background-color: transparent;
+  background-image: none;
+  border-color: #007bff;
+}
+.btn-outline-primary:hover {
+  color: #fff;
+  background-color: #007bff;
+  border-color: #007bff;
+}
+.btn-outline-primary:focus, .btn-outline-primary.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+}
+.btn-outline-primary.disabled, .btn-outline-primary:disabled {
+  color: #007bff;
+  background-color: transparent;
+}
+.btn-outline-primary:not(:disabled):not(.disabled):active, .btn-outline-primary:not(:disabled):not(.disabled).active, .show > .btn-outline-primary.dropdown-toggle {
+  color: #fff;
+  background-color: #007bff;
+  border-color: #007bff;
+}
+.btn-outline-primary:not(:disabled):not(.disabled):active:focus, .btn-outline-primary:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-primary.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+}
+
+.btn-outline-secondary {
+  color: #6c757d;
+  background-color: transparent;
+  background-image: none;
+  border-color: #6c757d;
+}
+.btn-outline-secondary:hover {
+  color: #fff;
+  background-color: #6c757d;
+  border-color: #6c757d;
+}
+.btn-outline-secondary:focus, .btn-outline-secondary.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+}
+.btn-outline-secondary.disabled, .btn-outline-secondary:disabled {
+  color: #6c757d;
+  background-color: transparent;
+}
+.btn-outline-secondary:not(:disabled):not(.disabled):active, .btn-outline-secondary:not(:disabled):not(.disabled).active, .show > .btn-outline-secondary.dropdown-toggle {
+  color: #fff;
+  background-color: #6c757d;
+  border-color: #6c757d;
+}
+.btn-outline-secondary:not(:disabled):not(.disabled):active:focus, .btn-outline-secondary:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-secondary.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+}
+
+.btn-outline-success {
+  color: #28a745;
+  background-color: transparent;
+  background-image: none;
+  border-color: #28a745;
+}
+.btn-outline-success:hover {
+  color: #fff;
+  background-color: #28a745;
+  border-color: #28a745;
+}
+.btn-outline-success:focus, .btn-outline-success.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+}
+.btn-outline-success.disabled, .btn-outline-success:disabled {
+  color: #28a745;
+  background-color: transparent;
+}
+.btn-outline-success:not(:disabled):not(.disabled):active, .btn-outline-success:not(:disabled):not(.disabled).active, .show > .btn-outline-success.dropdown-toggle {
+  color: #fff;
+  background-color: #28a745;
+  border-color: #28a745;
+}
+.btn-outline-success:not(:disabled):not(.disabled):active:focus, .btn-outline-success:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-success.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+}
+
+.btn-outline-info {
+  color: #17a2b8;
+  background-color: transparent;
+  background-image: none;
+  border-color: #17a2b8;
+}
+.btn-outline-info:hover {
+  color: #fff;
+  background-color: #17a2b8;
+  border-color: #17a2b8;
+}
+.btn-outline-info:focus, .btn-outline-info.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+}
+.btn-outline-info.disabled, .btn-outline-info:disabled {
+  color: #17a2b8;
+  background-color: transparent;
+}
+.btn-outline-info:not(:disabled):not(.disabled):active, .btn-outline-info:not(:disabled):not(.disabled).active, .show > .btn-outline-info.dropdown-toggle {
+  color: #fff;
+  background-color: #17a2b8;
+  border-color: #17a2b8;
+}
+.btn-outline-info:not(:disabled):not(.disabled):active:focus, .btn-outline-info:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-info.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+}
+
+.btn-outline-warning {
+  color: #ffc107;
+  background-color: transparent;
+  background-image: none;
+  border-color: #ffc107;
+}
+.btn-outline-warning:hover {
+  color: #212529;
+  background-color: #ffc107;
+  border-color: #ffc107;
+}
+.btn-outline-warning:focus, .btn-outline-warning.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+}
+.btn-outline-warning.disabled, .btn-outline-warning:disabled {
+  color: #ffc107;
+  background-color: transparent;
+}
+.btn-outline-warning:not(:disabled):not(.disabled):active, .btn-outline-warning:not(:disabled):not(.disabled).active, .show > .btn-outline-warning.dropdown-toggle {
+  color: #212529;
+  background-color: #ffc107;
+  border-color: #ffc107;
+}
+.btn-outline-warning:not(:disabled):not(.disabled):active:focus, .btn-outline-warning:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-warning.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+}
+
+.btn-outline-danger {
+  color: #dc3545;
+  background-color: transparent;
+  background-image: none;
+  border-color: #dc3545;
+}
+.btn-outline-danger:hover {
+  color: #fff;
+  background-color: #dc3545;
+  border-color: #dc3545;
+}
+.btn-outline-danger:focus, .btn-outline-danger.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+}
+.btn-outline-danger.disabled, .btn-outline-danger:disabled {
+  color: #dc3545;
+  background-color: transparent;
+}
+.btn-outline-danger:not(:disabled):not(.disabled):active, .btn-outline-danger:not(:disabled):not(.disabled).active, .show > .btn-outline-danger.dropdown-toggle {
+  color: #fff;
+  background-color: #dc3545;
+  border-color: #dc3545;
+}
+.btn-outline-danger:not(:disabled):not(.disabled):active:focus, .btn-outline-danger:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-danger.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+}
+
+.btn-outline-light {
+  color: #f8f9fa;
+  background-color: transparent;
+  background-image: none;
+  border-color: #f8f9fa;
+}
+.btn-outline-light:hover {
+  color: #212529;
+  background-color: #f8f9fa;
+  border-color: #f8f9fa;
+}
+.btn-outline-light:focus, .btn-outline-light.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+}
+.btn-outline-light.disabled, .btn-outline-light:disabled {
+  color: #f8f9fa;
+  background-color: transparent;
+}
+.btn-outline-light:not(:disabled):not(.disabled):active, .btn-outline-light:not(:disabled):not(.disabled).active, .show > .btn-outline-light.dropdown-toggle {
+  color: #212529;
+  background-color: #f8f9fa;
+  border-color: #f8f9fa;
+}
+.btn-outline-light:not(:disabled):not(.disabled):active:focus, .btn-outline-light:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-light.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+}
+
+.btn-outline-dark {
+  color: #343a40;
+  background-color: transparent;
+  background-image: none;
+  border-color: #343a40;
+}
+.btn-outline-dark:hover {
+  color: #fff;
+  background-color: #343a40;
+  border-color: #343a40;
+}
+.btn-outline-dark:focus, .btn-outline-dark.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+}
+.btn-outline-dark.disabled, .btn-outline-dark:disabled {
+  color: #343a40;
+  background-color: transparent;
+}
+.btn-outline-dark:not(:disabled):not(.disabled):active, .btn-outline-dark:not(:disabled):not(.disabled).active, .show > .btn-outline-dark.dropdown-toggle {
+  color: #fff;
+  background-color: #343a40;
+  border-color: #343a40;
+}
+.btn-outline-dark:not(:disabled):not(.disabled):active:focus, .btn-outline-dark:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-dark.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+}
+
+.btn-link {
+  font-weight: 400;
+  color: #007bff;
+  background-color: transparent;
+}
+.btn-link:hover {
+  color: #0056b3;
+  text-decoration: underline;
+  background-color: transparent;
+  border-color: transparent;
+}
+.btn-link:focus, .btn-link.focus {
+  text-decoration: underline;
+  border-color: transparent;
+  -webkit-box-shadow: none;
+          box-shadow: none;
+}
+.btn-link:disabled, .btn-link.disabled {
+  color: #6c757d;
+}
+
+.btn-lg, .btn-group-lg > .btn {
+  padding: 0.5rem 1rem;
+  font-size: 1.25rem;
+  line-height: 1.5;
+  border-radius: 0.3rem;
+}
+
+.btn-sm, .btn-group-sm > .btn {
+  padding: 0.25rem 0.5rem;
+  font-size: 0.875rem;
+  line-height: 1.5;
+  border-radius: 0.2rem;
+}
+
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 0.5rem;
+}
+
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.show {
+  opacity: 1;
+}
+
+.collapse {
+  display: none;
+}
+.collapse.show {
+  display: block;
+}
+
+tr.collapse.show {
+  display: table-row;
+}
+
+tbody.collapse.show {
+  display: table-row-group;
+}
+
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition: height 0.35s ease;
+  transition: height 0.35s ease;
+}
+
+.dropup,
+.dropdown {
+  position: relative;
+}
+
+.dropdown-toggle::after {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 0.255em;
+  vertical-align: 0.255em;
+  content: "";
+  border-top: 0.3em solid;
+  border-right: 0.3em solid transparent;
+  border-bottom: 0;
+  border-left: 0.3em solid transparent;
+}
+.dropdown-toggle:empty::after {
+  margin-left: 0;
+}
+
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 10rem;
+  padding: 0.5rem 0;
+  margin: 0.125rem 0 0;
+  font-size: 1rem;
+  color: #212529;
+  text-align: left;
+  list-style: none;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 0.25rem;
+}
+
+.dropup .dropdown-menu {
+  margin-top: 0;
+  margin-bottom: 0.125rem;
+}
+.dropup .dropdown-toggle::after {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 0.255em;
+  vertical-align: 0.255em;
+  content: "";
+  border-top: 0;
+  border-right: 0.3em solid transparent;
+  border-bottom: 0.3em solid;
+  border-left: 0.3em solid transparent;
+}
+.dropup .dropdown-toggle:empty::after {
+  margin-left: 0;
+}
+
+.dropright .dropdown-menu {
+  margin-top: 0;
+  margin-left: 0.125rem;
+}
+.dropright .dropdown-toggle::after {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 0.255em;
+  vertical-align: 0.255em;
+  content: "";
+  border-top: 0.3em solid transparent;
+  border-bottom: 0.3em solid transparent;
+  border-left: 0.3em solid;
+}
+.dropright .dropdown-toggle:empty::after {
+  margin-left: 0;
+}
+.dropright .dropdown-toggle::after {
+  vertical-align: 0;
+}
+
+.dropleft .dropdown-menu {
+  margin-top: 0;
+  margin-right: 0.125rem;
+}
+.dropleft .dropdown-toggle::after {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 0.255em;
+  vertical-align: 0.255em;
+  content: "";
+}
+.dropleft .dropdown-toggle::after {
+  display: none;
+}
+.dropleft .dropdown-toggle::before {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-right: 0.255em;
+  vertical-align: 0.255em;
+  content: "";
+  border-top: 0.3em solid transparent;
+  border-right: 0.3em solid;
+  border-bottom: 0.3em solid transparent;
+}
+.dropleft .dropdown-toggle:empty::after {
+  margin-left: 0;
+}
+.dropleft .dropdown-toggle::before {
+  vertical-align: 0;
+}
+
+.dropdown-divider {
+  height: 0;
+  margin: 0.5rem 0;
+  overflow: hidden;
+  border-top: 1px solid #e9ecef;
+}
+
+.dropdown-item {
+  display: block;
+  width: 100%;
+  padding: 0.25rem 1.5rem;
+  clear: both;
+  font-weight: 400;
+  color: #212529;
+  text-align: inherit;
+  white-space: nowrap;
+  background-color: transparent;
+  border: 0;
+}
+.dropdown-item:hover, .dropdown-item:focus {
+  color: #16181b;
+  text-decoration: none;
+  background-color: #f8f9fa;
+}
+.dropdown-item.active, .dropdown-item:active {
+  color: #fff;
+  text-decoration: none;
+  background-color: #007bff;
+}
+.dropdown-item.disabled, .dropdown-item:disabled {
+  color: #6c757d;
+  background-color: transparent;
+}
+
+.dropdown-menu.show {
+  display: block;
+}
+
+.dropdown-header {
+  display: block;
+  padding: 0.5rem 1.5rem;
+  margin-bottom: 0;
+  font-size: 0.875rem;
+  color: #6c757d;
+  white-space: nowrap;
+}
+
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: -webkit-inline-box;
+  display: -ms-inline-flexbox;
+  display: inline-flex;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  -webkit-box-flex: 0;
+      -ms-flex: 0 1 auto;
+          flex: 0 1 auto;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover {
+  z-index: 1;
+}
+.btn-group > .btn:focus, .btn-group > .btn:active, .btn-group > .btn.active,
+.btn-group-vertical > .btn:focus,
+.btn-group-vertical > .btn:active,
+.btn-group-vertical > .btn.active {
+  z-index: 1;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group,
+.btn-group-vertical .btn + .btn,
+.btn-group-vertical .btn + .btn-group,
+.btn-group-vertical .btn-group + .btn,
+.btn-group-vertical .btn-group + .btn-group {
+  margin-left: -1px;
+}
+
+.btn-toolbar {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  -webkit-box-pack: start;
+      -ms-flex-pack: start;
+          justify-content: flex-start;
+}
+.btn-toolbar .input-group {
+  width: auto;
+}
+
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:not(:last-child):not(.dropdown-toggle),
+.btn-group > .btn-group:not(:last-child) > .btn {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+.btn-group > .btn:not(:first-child),
+.btn-group > .btn-group:not(:first-child) > .btn {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+
+.dropdown-toggle-split {
+  padding-right: 0.5625rem;
+  padding-left: 0.5625rem;
+}
+.dropdown-toggle-split::after {
+  margin-left: 0;
+}
+
+.btn-sm + .dropdown-toggle-split, .btn-group-sm > .btn + .dropdown-toggle-split {
+  padding-right: 0.375rem;
+  padding-left: 0.375rem;
+}
+
+.btn-lg + .dropdown-toggle-split, .btn-group-lg > .btn + .dropdown-toggle-split {
+  padding-right: 0.75rem;
+  padding-left: 0.75rem;
+}
+
+.btn-group-vertical {
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+  -webkit-box-align: start;
+      -ms-flex-align: start;
+          align-items: flex-start;
+  -webkit-box-pack: center;
+      -ms-flex-pack: center;
+          justify-content: center;
+}
+.btn-group-vertical .btn,
+.btn-group-vertical .btn-group {
+  width: 100%;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:last-child):not(.dropdown-toggle),
+.btn-group-vertical > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:not(:first-child),
+.btn-group-vertical > .btn-group:not(:first-child) > .btn {
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+}
+
+.btn-group-toggle > .btn,
+.btn-group-toggle > .btn-group > .btn {
+  margin-bottom: 0;
+}
+.btn-group-toggle > .btn input[type="radio"],
+.btn-group-toggle > .btn input[type="checkbox"],
+.btn-group-toggle > .btn-group > .btn input[type="radio"],
+.btn-group-toggle > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+
+.input-group {
+  position: relative;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  -webkit-box-align: stretch;
+      -ms-flex-align: stretch;
+          align-items: stretch;
+  width: 100%;
+}
+.input-group > .form-control,
+.input-group > .custom-select,
+.input-group > .custom-file {
+  position: relative;
+  -webkit-box-flex: 1;
+      -ms-flex: 1 1 auto;
+          flex: 1 1 auto;
+  width: 1%;
+  margin-bottom: 0;
+}
+.input-group > .form-control:focus,
+.input-group > .custom-select:focus,
+.input-group > .custom-file:focus {
+  z-index: 3;
+}
+.input-group > .form-control + .form-control,
+.input-group > .form-control + .custom-select,
+.input-group > .form-control + .custom-file,
+.input-group > .custom-select + .form-control,
+.input-group > .custom-select + .custom-select,
+.input-group > .custom-select + .custom-file,
+.input-group > .custom-file + .form-control,
+.input-group > .custom-file + .custom-select,
+.input-group > .custom-file + .custom-file {
+  margin-left: -1px;
+}
+.input-group > .form-control:not(:last-child),
+.input-group > .custom-select:not(:last-child) {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+.input-group > .form-control:not(:first-child),
+.input-group > .custom-select:not(:first-child) {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.input-group > .custom-file {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+}
+.input-group > .custom-file:not(:last-child) .custom-file-label, .input-group > .custom-file:not(:last-child) .custom-file-label::before {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+.input-group > .custom-file:not(:first-child) .custom-file-label, .input-group > .custom-file:not(:first-child) .custom-file-label::before {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+
+.input-group-prepend,
+.input-group-append {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+}
+.input-group-prepend .btn,
+.input-group-append .btn {
+  position: relative;
+  z-index: 2;
+}
+.input-group-prepend .btn + .btn,
+.input-group-prepend .btn + .input-group-text,
+.input-group-prepend .input-group-text + .input-group-text,
+.input-group-prepend .input-group-text + .btn,
+.input-group-append .btn + .btn,
+.input-group-append .btn + .input-group-text,
+.input-group-append .input-group-text + .input-group-text,
+.input-group-append .input-group-text + .btn {
+  margin-left: -1px;
+}
+
+.input-group-prepend {
+  margin-right: -1px;
+}
+
+.input-group-append {
+  margin-left: -1px;
+}
+
+.input-group-text {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  padding: 0.375rem 0.75rem;
+  margin-bottom: 0;
+  font-size: 1rem;
+  font-weight: 400;
+  line-height: 1.5;
+  color: #495057;
+  text-align: center;
+  white-space: nowrap;
+  background-color: #e9ecef;
+  border: 1px solid #ced4da;
+  border-radius: 0.25rem;
+}
+.input-group-text input[type="radio"],
+.input-group-text input[type="checkbox"] {
+  margin-top: 0;
+}
+
+.input-group > .input-group-prepend > .btn,
+.input-group > .input-group-prepend > .input-group-text,
+.input-group > .input-group-append:not(:last-child) > .btn,
+.input-group > .input-group-append:not(:last-child) > .input-group-text,
+.input-group > .input-group-append:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group > .input-group-append:last-child > .input-group-text:not(:last-child) {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+
+.input-group > .input-group-append > .btn,
+.input-group > .input-group-append > .input-group-text,
+.input-group > .input-group-prepend:not(:first-child) > .btn,
+.input-group > .input-group-prepend:not(:first-child) > .input-group-text,
+.input-group > .input-group-prepend:first-child > .btn:not(:first-child),
+.input-group > .input-group-prepend:first-child > .input-group-text:not(:first-child) {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+
+.custom-control {
+  position: relative;
+  display: block;
+  min-height: 1.5rem;
+  padding-left: 1.5rem;
+}
+
+.custom-control-inline {
+  display: -webkit-inline-box;
+  display: -ms-inline-flexbox;
+  display: inline-flex;
+  margin-right: 1rem;
+}
+
+.custom-control-input {
+  position: absolute;
+  z-index: -1;
+  opacity: 0;
+}
+.custom-control-input:checked ~ .custom-control-label::before {
+  color: #fff;
+  background-color: #007bff;
+}
+.custom-control-input:focus ~ .custom-control-label::before {
+  -webkit-box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+          box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+}
+.custom-control-input:active ~ .custom-control-label::before {
+  color: #fff;
+  background-color: #b3d7ff;
+}
+.custom-control-input:disabled ~ .custom-control-label {
+  color: #6c757d;
+}
+.custom-control-input:disabled ~ .custom-control-label::before {
+  background-color: #e9ecef;
+}
+
+.custom-control-label {
+  margin-bottom: 0;
+}
+.custom-control-label::before {
+  position: absolute;
+  top: 0.25rem;
+  left: 0;
+  display: block;
+  width: 1rem;
+  height: 1rem;
+  pointer-events: none;
+  content: "";
+  -webkit-user-select: none;
+     -moz-user-select: none;
+      -ms-user-select: none;
+          user-select: none;
+  background-color: #dee2e6;
+}
+.custom-control-label::after {
+  position: absolute;
+  top: 0.25rem;
+  left: 0;
+  display: block;
+  width: 1rem;
+  height: 1rem;
+  content: "";
+  background-repeat: no-repeat;
+  background-position: center center;
+  background-size: 50% 50%;
+}
+
+.custom-checkbox .custom-control-label::before {
+  border-radius: 0.25rem;
+}
+.custom-checkbox .custom-control-input:checked ~ .custom-control-label::before {
+  background-color: #007bff;
+}
+.custom-checkbox .custom-control-input:checked ~ .custom-control-label::after {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3E%3Cpath fill='%23fff' d='M6.564.75l-3.59 3.612-1.538-1.55L0 4.26 2.974 7.25 8 2.193z'/%3E%3C/svg%3E");
+}
+.custom-checkbox .custom-control-input:indeterminate ~ .custom-control-label::before {
+  background-color: #007bff;
+}
+.custom-checkbox .custom-control-input:indeterminate ~ .custom-control-label::after {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 4 4'%3E%3Cpath stroke='%23fff' d='M0 2h4'/%3E%3C/svg%3E");
+}
+.custom-checkbox .custom-control-input:disabled:checked ~ .custom-control-label::before {
+  background-color: rgba(0, 123, 255, 0.5);
+}
+.custom-checkbox .custom-control-input:disabled:indeterminate ~ .custom-control-label::before {
+  background-color: rgba(0, 123, 255, 0.5);
+}
+
+.custom-radio .custom-control-label::before {
+  border-radius: 50%;
+}
+.custom-radio .custom-control-input:checked ~ .custom-control-label::before {
+  background-color: #007bff;
+}
+.custom-radio .custom-control-input:checked ~ .custom-control-label::after {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3E%3Ccircle r='3' fill='%23fff'/%3E%3C/svg%3E");
+}
+.custom-radio .custom-control-input:disabled:checked ~ .custom-control-label::before {
+  background-color: rgba(0, 123, 255, 0.5);
+}
+
+.custom-select {
+  display: inline-block;
+  width: 100%;
+  height: calc(2.25rem + 2px);
+  padding: 0.375rem 1.75rem 0.375rem 0.75rem;
+  line-height: 1.5;
+  color: #495057;
+  vertical-align: middle;
+  background: #fff url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 4 5'%3E%3Cpath fill='%23343a40' d='M2 0L0 2h4zm0 5L0 3h4z'/%3E%3C/svg%3E") no-repeat right 0.75rem center;
+  background-size: 8px 10px;
+  border: 1px solid #ced4da;
+  border-radius: 0.25rem;
+  -webkit-appearance: none;
+     -moz-appearance: none;
+          appearance: none;
+}
+.custom-select:focus {
+  border-color: #80bdff;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075), 0 0 5px rgba(128, 189, 255, 0.5);
+          box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075), 0 0 5px rgba(128, 189, 255, 0.5);
+}
+.custom-select:focus::-ms-value {
+  color: #495057;
+  background-color: #fff;
+}
+.custom-select[multiple], .custom-select[size]:not([size="1"]) {
+  height: auto;
+  padding-right: 0.75rem;
+  background-image: none;
+}
+.custom-select:disabled {
+  color: #6c757d;
+  background-color: #e9ecef;
+}
+.custom-select::-ms-expand {
+  opacity: 0;
+}
+
+.custom-select-sm {
+  height: calc(1.8125rem + 2px);
+  padding-top: 0.375rem;
+  padding-bottom: 0.375rem;
+  font-size: 75%;
+}
+
+.custom-select-lg {
+  height: calc(2.875rem + 2px);
+  padding-top: 0.375rem;
+  padding-bottom: 0.375rem;
+  font-size: 125%;
+}
+
+.custom-file {
+  position: relative;
+  display: inline-block;
+  width: 100%;
+  height: calc(2.25rem + 2px);
+  margin-bottom: 0;
+}
+
+.custom-file-input {
+  position: relative;
+  z-index: 2;
+  width: 100%;
+  height: calc(2.25rem + 2px);
+  margin: 0;
+  opacity: 0;
+}
+.custom-file-input:focus ~ .custom-file-control {
+  border-color: #80bdff;
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+}
+.custom-file-input:focus ~ .custom-file-control::before {
+  border-color: #80bdff;
+}
+.custom-file-input:lang(en) ~ .custom-file-label::after {
+  content: "Browse";
+}
+
+.custom-file-label {
+  position: absolute;
+  top: 0;
+  right: 0;
+  left: 0;
+  z-index: 1;
+  height: calc(2.25rem + 2px);
+  padding: 0.375rem 0.75rem;
+  line-height: 1.5;
+  color: #495057;
+  background-color: #fff;
+  border: 1px solid #ced4da;
+  border-radius: 0.25rem;
+}
+.custom-file-label::after {
+  position: absolute;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  z-index: 3;
+  display: block;
+  height: calc(calc(2.25rem + 2px) - 1px * 2);
+  padding: 0.375rem 0.75rem;
+  line-height: 1.5;
+  color: #495057;
+  content: "Browse";
+  background-color: #e9ecef;
+  border-left: 1px solid #ced4da;
+  border-radius: 0 0.25rem 0.25rem 0;
+}
+
+.nav {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  padding-left: 0;
+  margin-bottom: 0;
+  list-style: none;
+}
+
+.nav-link {
+  display: block;
+  padding: 0.5rem 1rem;
+}
+.nav-link:hover, .nav-link:focus {
+  text-decoration: none;
+}
+.nav-link.disabled {
+  color: #6c757d;
+}
+
+.nav-tabs {
+  border-bottom: 1px solid #dee2e6;
+}
+.nav-tabs .nav-item {
+  margin-bottom: -1px;
+}
+.nav-tabs .nav-link {
+  border: 1px solid transparent;
+  border-top-left-radius: 0.25rem;
+  border-top-right-radius: 0.25rem;
+}
+.nav-tabs .nav-link:hover, .nav-tabs .nav-link:focus {
+  border-color: #e9ecef #e9ecef #dee2e6;
+}
+.nav-tabs .nav-link.disabled {
+  color: #6c757d;
+  background-color: transparent;
+  border-color: transparent;
+}
+.nav-tabs .nav-link.active,
+.nav-tabs .nav-item.show .nav-link {
+  color: #495057;
+  background-color: #fff;
+  border-color: #dee2e6 #dee2e6 #fff;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+}
+
+.nav-pills .nav-link {
+  border-radius: 0.25rem;
+}
+.nav-pills .nav-link.active,
+.nav-pills .show > .nav-link {
+  color: #fff;
+  background-color: #007bff;
+}
+
+.nav-fill .nav-item {
+  -webkit-box-flex: 1;
+      -ms-flex: 1 1 auto;
+          flex: 1 1 auto;
+  text-align: center;
+}
+
+.nav-justified .nav-item {
+  -ms-flex-preferred-size: 0;
+      flex-basis: 0;
+  -webkit-box-flex: 1;
+      -ms-flex-positive: 1;
+          flex-grow: 1;
+  text-align: center;
+}
+
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+
+.navbar {
+  position: relative;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  -webkit-box-pack: justify;
+      -ms-flex-pack: justify;
+          justify-content: space-between;
+  padding: 0.5rem 1rem;
+}
+.navbar > .container,
+.navbar > .container-fluid {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  -webkit-box-pack: justify;
+      -ms-flex-pack: justify;
+          justify-content: space-between;
+}
+
+.navbar-brand {
+  display: inline-block;
+  padding-top: 0.3125rem;
+  padding-bottom: 0.3125rem;
+  margin-right: 1rem;
+  font-size: 1.25rem;
+  line-height: inherit;
+  white-space: nowrap;
+}
+.navbar-brand:hover, .navbar-brand:focus {
+  text-decoration: none;
+}
+
+.navbar-nav {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+  padding-left: 0;
+  margin-bottom: 0;
+  list-style: none;
+}
+.navbar-nav .nav-link {
+  padding-right: 0;
+  padding-left: 0;
+}
+.navbar-nav .dropdown-menu {
+  position: static;
+  float: none;
+}
+
+.navbar-text {
+  display: inline-block;
+  padding-top: 0.5rem;
+  padding-bottom: 0.5rem;
+}
+
+.navbar-collapse {
+  -ms-flex-preferred-size: 100%;
+      flex-basis: 100%;
+  -webkit-box-flex: 1;
+      -ms-flex-positive: 1;
+          flex-grow: 1;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+}
+
+.navbar-toggler {
+  padding: 0.25rem 0.75rem;
+  font-size: 1.25rem;
+  line-height: 1;
+  background-color: transparent;
+  border: 1px solid transparent;
+  border-radius: 0.25rem;
+}
+.navbar-toggler:hover, .navbar-toggler:focus {
+  text-decoration: none;
+}
+.navbar-toggler:not(:disabled):not(.disabled) {
+  cursor: pointer;
+}
+
+.navbar-toggler-icon {
+  display: inline-block;
+  width: 1.5em;
+  height: 1.5em;
+  vertical-align: middle;
+  content: "";
+  background: no-repeat center center;
+  background-size: 100% 100%;
+}
+
+@media (max-width: 575.98px) {
+  .navbar-expand-sm > .container,
+  .navbar-expand-sm > .container-fluid {
+    padding-right: 0;
+    padding-left: 0;
+  }
+}
+@media (min-width: 576px) {
+  .navbar-expand-sm {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row nowrap;
+            flex-flow: row nowrap;
+    -webkit-box-pack: start;
+        -ms-flex-pack: start;
+            justify-content: flex-start;
+  }
+  .navbar-expand-sm .navbar-nav {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-direction: row;
+            flex-direction: row;
+  }
+  .navbar-expand-sm .navbar-nav .dropdown-menu {
+    position: absolute;
+  }
+  .navbar-expand-sm .navbar-nav .dropdown-menu-right {
+    right: 0;
+    left: auto;
+  }
+  .navbar-expand-sm .navbar-nav .nav-link {
+    padding-right: 0.5rem;
+    padding-left: 0.5rem;
+  }
+  .navbar-expand-sm > .container,
+  .navbar-expand-sm > .container-fluid {
+    -ms-flex-wrap: nowrap;
+        flex-wrap: nowrap;
+  }
+  .navbar-expand-sm .navbar-collapse {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+    -ms-flex-preferred-size: auto;
+        flex-basis: auto;
+  }
+  .navbar-expand-sm .navbar-toggler {
+    display: none;
+  }
+  .navbar-expand-sm .dropup .dropdown-menu {
+    top: auto;
+    bottom: 100%;
+  }
+}
+@media (max-width: 767.98px) {
+  .navbar-expand-md > .container,
+  .navbar-expand-md > .container-fluid {
+    padding-right: 0;
+    padding-left: 0;
+  }
+}
+@media (min-width: 768px) {
+  .navbar-expand-md {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row nowrap;
+            flex-flow: row nowrap;
+    -webkit-box-pack: start;
+        -ms-flex-pack: start;
+            justify-content: flex-start;
+  }
+  .navbar-expand-md .navbar-nav {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-direction: row;
+            flex-direction: row;
+  }
+  .navbar-expand-md .navbar-nav .dropdown-menu {
+    position: absolute;
+  }
+  .navbar-expand-md .navbar-nav .dropdown-menu-right {
+    right: 0;
+    left: auto;
+  }
+  .navbar-expand-md .navbar-nav .nav-link {
+    padding-right: 0.5rem;
+    padding-left: 0.5rem;
+  }
+  .navbar-expand-md > .container,
+  .navbar-expand-md > .container-fluid {
+    -ms-flex-wrap: nowrap;
+        flex-wrap: nowrap;
+  }
+  .navbar-expand-md .navbar-collapse {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+    -ms-flex-preferred-size: auto;
+        flex-basis: auto;
+  }
+  .navbar-expand-md .navbar-toggler {
+    display: none;
+  }
+  .navbar-expand-md .dropup .dropdown-menu {
+    top: auto;
+    bottom: 100%;
+  }
+}
+@media (max-width: 991.98px) {
+  .navbar-expand-lg > .container,
+  .navbar-expand-lg > .container-fluid {
+    padding-right: 0;
+    padding-left: 0;
+  }
+}
+@media (min-width: 992px) {
+  .navbar-expand-lg {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row nowrap;
+            flex-flow: row nowrap;
+    -webkit-box-pack: start;
+        -ms-flex-pack: start;
+            justify-content: flex-start;
+  }
+  .navbar-expand-lg .navbar-nav {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-direction: row;
+            flex-direction: row;
+  }
+  .navbar-expand-lg .navbar-nav .dropdown-menu {
+    position: absolute;
+  }
+  .navbar-expand-lg .navbar-nav .dropdown-menu-right {
+    right: 0;
+    left: auto;
+  }
+  .navbar-expand-lg .navbar-nav .nav-link {
+    padding-right: 0.5rem;
+    padding-left: 0.5rem;
+  }
+  .navbar-expand-lg > .container,
+  .navbar-expand-lg > .container-fluid {
+    -ms-flex-wrap: nowrap;
+        flex-wrap: nowrap;
+  }
+  .navbar-expand-lg .navbar-collapse {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+    -ms-flex-preferred-size: auto;
+        flex-basis: auto;
+  }
+  .navbar-expand-lg .navbar-toggler {
+    display: none;
+  }
+  .navbar-expand-lg .dropup .dropdown-menu {
+    top: auto;
+    bottom: 100%;
+  }
+}
+@media (max-width: 1199.98px) {
+  .navbar-expand-xl > .container,
+  .navbar-expand-xl > .container-fluid {
+    padding-right: 0;
+    padding-left: 0;
+  }
+}
+@media (min-width: 1200px) {
+  .navbar-expand-xl {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row nowrap;
+            flex-flow: row nowrap;
+    -webkit-box-pack: start;
+        -ms-flex-pack: start;
+            justify-content: flex-start;
+  }
+  .navbar-expand-xl .navbar-nav {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-direction: row;
+            flex-direction: row;
+  }
+  .navbar-expand-xl .navbar-nav .dropdown-menu {
+    position: absolute;
+  }
+  .navbar-expand-xl .navbar-nav .dropdown-menu-right {
+    right: 0;
+    left: auto;
+  }
+  .navbar-expand-xl .navbar-nav .nav-link {
+    padding-right: 0.5rem;
+    padding-left: 0.5rem;
+  }
+  .navbar-expand-xl > .container,
+  .navbar-expand-xl > .container-fluid {
+    -ms-flex-wrap: nowrap;
+        flex-wrap: nowrap;
+  }
+  .navbar-expand-xl .navbar-collapse {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+    -ms-flex-preferred-size: auto;
+        flex-basis: auto;
+  }
+  .navbar-expand-xl .navbar-toggler {
+    display: none;
+  }
+  .navbar-expand-xl .dropup .dropdown-menu {
+    top: auto;
+    bottom: 100%;
+  }
+}
+.navbar-expand {
+  -webkit-box-orient: horizontal;
+  -webkit-box-direction: normal;
+      -ms-flex-flow: row nowrap;
+          flex-flow: row nowrap;
+  -webkit-box-pack: start;
+      -ms-flex-pack: start;
+          justify-content: flex-start;
+}
+.navbar-expand > .container,
+.navbar-expand > .container-fluid {
+  padding-right: 0;
+  padding-left: 0;
+}
+.navbar-expand .navbar-nav {
+  -webkit-box-orient: horizontal;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: row;
+          flex-direction: row;
+}
+.navbar-expand .navbar-nav .dropdown-menu {
+  position: absolute;
+}
+.navbar-expand .navbar-nav .dropdown-menu-right {
+  right: 0;
+  left: auto;
+}
+.navbar-expand .navbar-nav .nav-link {
+  padding-right: 0.5rem;
+  padding-left: 0.5rem;
+}
+.navbar-expand > .container,
+.navbar-expand > .container-fluid {
+  -ms-flex-wrap: nowrap;
+      flex-wrap: nowrap;
+}
+.navbar-expand .navbar-collapse {
+  display: -webkit-box !important;
+  display: -ms-flexbox !important;
+  display: flex !important;
+  -ms-flex-preferred-size: auto;
+      flex-basis: auto;
+}
+.navbar-expand .navbar-toggler {
+  display: none;
+}
+.navbar-expand .dropup .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+}
+
+.navbar-light .navbar-brand {
+  color: rgba(0, 0, 0, 0.9);
+}
+.navbar-light .navbar-brand:hover, .navbar-light .navbar-brand:focus {
+  color: rgba(0, 0, 0, 0.9);
+}
+.navbar-light .navbar-nav .nav-link {
+  color: rgba(0, 0, 0, 0.5);
+}
+.navbar-light .navbar-nav .nav-link:hover, .navbar-light .navbar-nav .nav-link:focus {
+  color: rgba(0, 0, 0, 0.7);
+}
+.navbar-light .navbar-nav .nav-link.disabled {
+  color: rgba(0, 0, 0, 0.3);
+}
+.navbar-light .navbar-nav .show > .nav-link,
+.navbar-light .navbar-nav .active > .nav-link,
+.navbar-light .navbar-nav .nav-link.show,
+.navbar-light .navbar-nav .nav-link.active {
+  color: rgba(0, 0, 0, 0.9);
+}
+.navbar-light .navbar-toggler {
+  color: rgba(0, 0, 0, 0.5);
+  border-color: rgba(0, 0, 0, 0.1);
+}
+.navbar-light .navbar-toggler-icon {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg viewBox='0 0 30 30' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath stroke='rgba(0, 0, 0, 0.5)' stroke-width='2' stroke-linecap='round' stroke-miterlimit='10' d='M4 7h22M4 15h22M4 23h22'/%3E%3C/svg%3E");
+}
+.navbar-light .navbar-text {
+  color: rgba(0, 0, 0, 0.5);
+}
+.navbar-light .navbar-text a {
+  color: rgba(0, 0, 0, 0.9);
+}
+.navbar-light .navbar-text a:hover, .navbar-light .navbar-text a:focus {
+  color: rgba(0, 0, 0, 0.9);
+}
+
+.navbar-dark .navbar-brand {
+  color: #fff;
+}
+.navbar-dark .navbar-brand:hover, .navbar-dark .navbar-brand:focus {
+  color: #fff;
+}
+.navbar-dark .navbar-nav .nav-link {
+  color: rgba(255, 255, 255, 0.5);
+}
+.navbar-dark .navbar-nav .nav-link:hover, .navbar-dark .navbar-nav .nav-link:focus {
+  color: rgba(255, 255, 255, 0.75);
+}
+.navbar-dark .navbar-nav .nav-link.disabled {
+  color: rgba(255, 255, 255, 0.25);
+}
+.navbar-dark .navbar-nav .show > .nav-link,
+.navbar-dark .navbar-nav .active > .nav-link,
+.navbar-dark .navbar-nav .nav-link.show,
+.navbar-dark .navbar-nav .nav-link.active {
+  color: #fff;
+}
+.navbar-dark .navbar-toggler {
+  color: rgba(255, 255, 255, 0.5);
+  border-color: rgba(255, 255, 255, 0.1);
+}
+.navbar-dark .navbar-toggler-icon {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg viewBox='0 0 30 30' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath stroke='rgba(255, 255, 255, 0.5)' stroke-width='2' stroke-linecap='round' stroke-miterlimit='10' d='M4 7h22M4 15h22M4 23h22'/%3E%3C/svg%3E");
+}
+.navbar-dark .navbar-text {
+  color: rgba(255, 255, 255, 0.5);
+}
+.navbar-dark .navbar-text a {
+  color: #fff;
+}
+.navbar-dark .navbar-text a:hover, .navbar-dark .navbar-text a:focus {
+  color: #fff;
+}
+
+.card {
+  position: relative;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+  min-width: 0;
+  word-wrap: break-word;
+  background-color: #fff;
+  background-clip: border-box;
+  border: 1px solid rgba(0, 0, 0, 0.125);
+  border-radius: 0.25rem;
+}
+.card > hr {
+  margin-right: 0;
+  margin-left: 0;
+}
+.card > .list-group:first-child .list-group-item:first-child {
+  border-top-left-radius: 0.25rem;
+  border-top-right-radius: 0.25rem;
+}
+.card > .list-group:last-child .list-group-item:last-child {
+  border-bottom-right-radius: 0.25rem;
+  border-bottom-left-radius: 0.25rem;
+}
+
+.card-body {
+  -webkit-box-flex: 1;
+      -ms-flex: 1 1 auto;
+          flex: 1 1 auto;
+  padding: 1.25rem;
+}
+
+.card-title {
+  margin-bottom: 0.75rem;
+}
+
+.card-subtitle {
+  margin-top: -0.375rem;
+  margin-bottom: 0;
+}
+
+.card-text:last-child {
+  margin-bottom: 0;
+}
+
+.card-link:hover {
+  text-decoration: none;
+}
+.card-link + .card-link {
+  margin-left: 1.25rem;
+}
+
+.card-header {
+  padding: 0.75rem 1.25rem;
+  margin-bottom: 0;
+  background-color: rgba(0, 0, 0, 0.03);
+  border-bottom: 1px solid rgba(0, 0, 0, 0.125);
+}
+.card-header:first-child {
+  border-radius: calc(0.25rem - 1px) calc(0.25rem - 1px) 0 0;
+}
+.card-header + .list-group .list-group-item:first-child {
+  border-top: 0;
+}
+
+.card-footer {
+  padding: 0.75rem 1.25rem;
+  background-color: rgba(0, 0, 0, 0.03);
+  border-top: 1px solid rgba(0, 0, 0, 0.125);
+}
+.card-footer:last-child {
+  border-radius: 0 0 calc(0.25rem - 1px) calc(0.25rem - 1px);
+}
+
+.card-header-tabs {
+  margin-right: -0.625rem;
+  margin-bottom: -0.75rem;
+  margin-left: -0.625rem;
+  border-bottom: 0;
+}
+
+.card-header-pills {
+  margin-right: -0.625rem;
+  margin-left: -0.625rem;
+}
+
+.card-img-overlay {
+  position: absolute;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  padding: 1.25rem;
+}
+
+.card-img {
+  width: 100%;
+  border-radius: calc(0.25rem - 1px);
+}
+
+.card-img-top {
+  width: 100%;
+  border-top-left-radius: calc(0.25rem - 1px);
+  border-top-right-radius: calc(0.25rem - 1px);
+}
+
+.card-img-bottom {
+  width: 100%;
+  border-bottom-right-radius: calc(0.25rem - 1px);
+  border-bottom-left-radius: calc(0.25rem - 1px);
+}
+
+.card-deck {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+}
+.card-deck .card {
+  margin-bottom: 15px;
+}
+@media (min-width: 576px) {
+  .card-deck {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row wrap;
+            flex-flow: row wrap;
+    margin-right: -15px;
+    margin-left: -15px;
+  }
+  .card-deck .card {
+    display: -webkit-box;
+    display: -ms-flexbox;
+    display: flex;
+    -webkit-box-flex: 1;
+        -ms-flex: 1 0 0%;
+            flex: 1 0 0%;
+    -webkit-box-orient: vertical;
+    -webkit-box-direction: normal;
+        -ms-flex-direction: column;
+            flex-direction: column;
+    margin-right: 15px;
+    margin-bottom: 0;
+    margin-left: 15px;
+  }
+}
+
+.card-group {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+}
+.card-group > .card {
+  margin-bottom: 15px;
+}
+@media (min-width: 576px) {
+  .card-group {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row wrap;
+            flex-flow: row wrap;
+  }
+  .card-group > .card {
+    -webkit-box-flex: 1;
+        -ms-flex: 1 0 0%;
+            flex: 1 0 0%;
+    margin-bottom: 0;
+  }
+  .card-group > .card + .card {
+    margin-left: 0;
+    border-left: 0;
+  }
+  .card-group > .card:first-child {
+    border-top-right-radius: 0;
+    border-bottom-right-radius: 0;
+  }
+  .card-group > .card:first-child .card-img-top,
+  .card-group > .card:first-child .card-header {
+    border-top-right-radius: 0;
+  }
+  .card-group > .card:first-child .card-img-bottom,
+  .card-group > .card:first-child .card-footer {
+    border-bottom-right-radius: 0;
+  }
+  .card-group > .card:last-child {
+    border-top-left-radius: 0;
+    border-bottom-left-radius: 0;
+  }
+  .card-group > .card:last-child .card-img-top,
+  .card-group > .card:last-child .card-header {
+    border-top-left-radius: 0;
+  }
+  .card-group > .card:last-child .card-img-bottom,
+  .card-group > .card:last-child .card-footer {
+    border-bottom-left-radius: 0;
+  }
+  .card-group > .card:only-child {
+    border-radius: 0.25rem;
+  }
+  .card-group > .card:only-child .card-img-top,
+  .card-group > .card:only-child .card-header {
+    border-top-left-radius: 0.25rem;
+    border-top-right-radius: 0.25rem;
+  }
+  .card-group > .card:only-child .card-img-bottom,
+  .card-group > .card:only-child .card-footer {
+    border-bottom-right-radius: 0.25rem;
+    border-bottom-left-radius: 0.25rem;
+  }
+  .card-group > .card:not(:first-child):not(:last-child):not(:only-child) {
+    border-radius: 0;
+  }
+  .card-group > .card:not(:first-child):not(:last-child):not(:only-child) .card-img-top,
+  .card-group > .card:not(:first-child):not(:last-child):not(:only-child) .card-img-bottom,
+  .card-group > .card:not(:first-child):not(:last-child):not(:only-child) .card-header,
+  .card-group > .card:not(:first-child):not(:last-child):not(:only-child) .card-footer {
+    border-radius: 0;
+  }
+}
+
+.card-columns .card {
+  margin-bottom: 0.75rem;
+}
+@media (min-width: 576px) {
+  .card-columns {
+    -webkit-column-count: 3;
+       -moz-column-count: 3;
+            column-count: 3;
+    -webkit-column-gap: 1.25rem;
+       -moz-column-gap: 1.25rem;
+            column-gap: 1.25rem;
+  }
+  .card-columns .card {
+    display: inline-block;
+    width: 100%;
+  }
+}
+
+.breadcrumb {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  padding: 0.75rem 1rem;
+  margin-bottom: 1rem;
+  list-style: none;
+  background-color: #e9ecef;
+  border-radius: 0.25rem;
+}
+
+.breadcrumb-item + .breadcrumb-item::before {
+  display: inline-block;
+  padding-right: 0.5rem;
+  padding-left: 0.5rem;
+  color: #6c757d;
+  content: "/";
+}
+.breadcrumb-item + .breadcrumb-item:hover::before {
+  text-decoration: underline;
+}
+.breadcrumb-item + .breadcrumb-item:hover::before {
+  text-decoration: none;
+}
+.breadcrumb-item.active {
+  color: #6c757d;
+}
+
+.pagination {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  padding-left: 0;
+  list-style: none;
+  border-radius: 0.25rem;
+}
+
+.page-link {
+  position: relative;
+  display: block;
+  padding: 0.5rem 0.75rem;
+  margin-left: -1px;
+  line-height: 1.25;
+  color: #007bff;
+  background-color: #fff;
+  border: 1px solid #dee2e6;
+}
+.page-link:hover {
+  color: #0056b3;
+  text-decoration: none;
+  background-color: #e9ecef;
+  border-color: #dee2e6;
+}
+.page-link:focus {
+  z-index: 2;
+  outline: 0;
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+}
+.page-link:not(:disabled):not(.disabled) {
+  cursor: pointer;
+}
+
+.page-item:first-child .page-link {
+  margin-left: 0;
+  border-top-left-radius: 0.25rem;
+  border-bottom-left-radius: 0.25rem;
+}
+.page-item:last-child .page-link {
+  border-top-right-radius: 0.25rem;
+  border-bottom-right-radius: 0.25rem;
+}
+.page-item.active .page-link {
+  z-index: 1;
+  color: #fff;
+  background-color: #007bff;
+  border-color: #007bff;
+}
+.page-item.disabled .page-link {
+  color: #6c757d;
+  pointer-events: none;
+  cursor: auto;
+  background-color: #fff;
+  border-color: #dee2e6;
+}
+
+.pagination-lg .page-link {
+  padding: 0.75rem 1.5rem;
+  font-size: 1.25rem;
+  line-height: 1.5;
+}
+.pagination-lg .page-item:first-child .page-link {
+  border-top-left-radius: 0.3rem;
+  border-bottom-left-radius: 0.3rem;
+}
+.pagination-lg .page-item:last-child .page-link {
+  border-top-right-radius: 0.3rem;
+  border-bottom-right-radius: 0.3rem;
+}
+
+.pagination-sm .page-link {
+  padding: 0.25rem 0.5rem;
+  font-size: 0.875rem;
+  line-height: 1.5;
+}
+.pagination-sm .page-item:first-child .page-link {
+  border-top-left-radius: 0.2rem;
+  border-bottom-left-radius: 0.2rem;
+}
+.pagination-sm .page-item:last-child .page-link {
+  border-top-right-radius: 0.2rem;
+  border-bottom-right-radius: 0.2rem;
+}
+
+.badge {
+  display: inline-block;
+  padding: 0.25em 0.4em;
+  font-size: 75%;
+  font-weight: 700;
+  line-height: 1;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: 0.25rem;
+}
+.badge:empty {
+  display: none;
+}
+
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+
+.badge-pill {
+  padding-right: 0.6em;
+  padding-left: 0.6em;
+  border-radius: 10rem;
+}
+
+.badge-primary {
+  color: #fff;
+  background-color: #007bff;
+}
+.badge-primary[href]:hover, .badge-primary[href]:focus {
+  color: #fff;
+  text-decoration: none;
+  background-color: #0062cc;
+}
+
+.badge-secondary {
+  color: #fff;
+  background-color: #6c757d;
+}
+.badge-secondary[href]:hover, .badge-secondary[href]:focus {
+  color: #fff;
+  text-decoration: none;
+  background-color: #545b62;
+}
+
+.badge-success {
+  color: #fff;
+  background-color: #28a745;
+}
+.badge-success[href]:hover, .badge-success[href]:focus {
+  color: #fff;
+  text-decoration: none;
+  background-color: #1e7e34;
+}
+
+.badge-info {
+  color: #fff;
+  background-color: #17a2b8;
+}
+.badge-info[href]:hover, .badge-info[href]:focus {
+  color: #fff;
+  text-decoration: none;
+  background-color: #117a8b;
+}
+
+.badge-warning {
+  color: #212529;
+  background-color: #ffc107;
+}
+.badge-warning[href]:hover, .badge-warning[href]:focus {
+  color: #212529;
+  text-decoration: none;
+  background-color: #d39e00;
+}
+
+.badge-danger {
+  color: #fff;
+  background-color: #dc3545;
+}
+.badge-danger[href]:hover, .badge-danger[href]:focus {
+  color: #fff;
+  text-decoration: none;
+  background-color: #bd2130;
+}
+
+.badge-light {
+  color: #212529;
+  background-color: #f8f9fa;
+}
+.badge-light[href]:hover, .badge-light[href]:focus {
+  color: #212529;
+  text-decoration: none;
+  background-color: #dae0e5;
+}
+
+.badge-dark {
+  color: #fff;
+  background-color: #343a40;
+}
+.badge-dark[href]:hover, .badge-dark[href]:focus {
+  color: #fff;
+  text-decoration: none;
+  background-color: #1d2124;
+}
+
+.jumbotron {
+  padding: 2rem 1rem;
+  margin-bottom: 2rem;
+  background-color: #e9ecef;
+  border-radius: 0.3rem;
+}
+@media (min-width: 576px) {
+  .jumbotron {
+    padding: 4rem 2rem;
+  }
+}
+
+.jumbotron-fluid {
+  padding-right: 0;
+  padding-left: 0;
+  border-radius: 0;
+}
+
+.alert {
+  position: relative;
+  padding: 0.75rem 1.25rem;
+  margin-bottom: 1rem;
+  border: 1px solid transparent;
+  border-radius: 0.25rem;
+}
+
+.alert-heading {
+  color: inherit;
+}
+
+.alert-link {
+  font-weight: 700;
+}
+
+.alert-dismissible {
+  padding-right: 4rem;
+}
+.alert-dismissible .close {
+  position: absolute;
+  top: 0;
+  right: 0;
+  padding: 0.75rem 1.25rem;
+  color: inherit;
+}
+
+.alert-primary {
+  color: #004085;
+  background-color: #cce5ff;
+  border-color: #b8daff;
+}
+.alert-primary hr {
+  border-top-color: #9fcdff;
+}
+.alert-primary .alert-link {
+  color: #002752;
+}
+
+.alert-secondary {
+  color: #383d41;
+  background-color: #e2e3e5;
+  border-color: #d6d8db;
+}
+.alert-secondary hr {
+  border-top-color: #c8cbcf;
+}
+.alert-secondary .alert-link {
+  color: #202326;
+}
+
+.alert-success {
+  color: #155724;
+  background-color: #d4edda;
+  border-color: #c3e6cb;
+}
+.alert-success hr {
+  border-top-color: #b1dfbb;
+}
+.alert-success .alert-link {
+  color: #0b2e13;
+}
+
+.alert-info {
+  color: #0c5460;
+  background-color: #d1ecf1;
+  border-color: #bee5eb;
+}
+.alert-info hr {
+  border-top-color: #abdde5;
+}
+.alert-info .alert-link {
+  color: #062c33;
+}
+
+.alert-warning {
+  color: #856404;
+  background-color: #fff3cd;
+  border-color: #ffeeba;
+}
+.alert-warning hr {
+  border-top-color: #ffe8a1;
+}
+.alert-warning .alert-link {
+  color: #533f03;
+}
+
+.alert-danger {
+  color: #721c24;
+  background-color: #f8d7da;
+  border-color: #f5c6cb;
+}
+.alert-danger hr {
+  border-top-color: #f1b0b7;
+}
+.alert-danger .alert-link {
+  color: #491217;
+}
+
+.alert-light {
+  color: #818182;
+  background-color: #fefefe;
+  border-color: #fdfdfe;
+}
+.alert-light hr {
+  border-top-color: #ececf6;
+}
+.alert-light .alert-link {
+  color: #686868;
+}
+
+.alert-dark {
+  color: #1b1e21;
+  background-color: #d6d8d9;
+  border-color: #c6c8ca;
+}
+.alert-dark hr {
+  border-top-color: #b9bbbe;
+}
+.alert-dark .alert-link {
+  color: #040505;
+}
+
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 1rem 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 1rem 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  height: 1rem;
+  overflow: hidden;
+  font-size: 0.75rem;
+  background-color: #e9ecef;
+  border-radius: 0.25rem;
+}
+
+.progress-bar {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+  -webkit-box-pack: center;
+      -ms-flex-pack: center;
+          justify-content: center;
+  color: #fff;
+  text-align: center;
+  background-color: #007bff;
+  -webkit-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+
+.progress-bar-striped {
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 1rem 1rem;
+}
+
+.progress-bar-animated {
+  -webkit-animation: progress-bar-stripes 1s linear infinite;
+          animation: progress-bar-stripes 1s linear infinite;
+}
+
+.media {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: start;
+      -ms-flex-align: start;
+          align-items: flex-start;
+}
+
+.media-body {
+  -webkit-box-flex: 1;
+      -ms-flex: 1;
+          flex: 1;
+}
+
+.list-group {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+  padding-left: 0;
+  margin-bottom: 0;
+}
+
+.list-group-item-action {
+  width: 100%;
+  color: #495057;
+  text-align: inherit;
+}
+.list-group-item-action:hover, .list-group-item-action:focus {
+  color: #495057;
+  text-decoration: none;
+  background-color: #f8f9fa;
+}
+.list-group-item-action:active {
+  color: #212529;
+  background-color: #e9ecef;
+}
+
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 0.75rem 1.25rem;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid rgba(0, 0, 0, 0.125);
+}
+.list-group-item:first-child {
+  border-top-left-radius: 0.25rem;
+  border-top-right-radius: 0.25rem;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 0.25rem;
+  border-bottom-left-radius: 0.25rem;
+}
+.list-group-item:hover, .list-group-item:focus {
+  z-index: 1;
+  text-decoration: none;
+}
+.list-group-item.disabled, .list-group-item:disabled {
+  color: #6c757d;
+  background-color: #fff;
+}
+.list-group-item.active {
+  z-index: 2;
+  color: #fff;
+  background-color: #007bff;
+  border-color: #007bff;
+}
+
+.list-group-flush .list-group-item {
+  border-right: 0;
+  border-left: 0;
+  border-radius: 0;
+}
+.list-group-flush:first-child .list-group-item:first-child {
+  border-top: 0;
+}
+.list-group-flush:last-child .list-group-item:last-child {
+  border-bottom: 0;
+}
+
+.list-group-item-primary {
+  color: #004085;
+  background-color: #b8daff;
+}
+.list-group-item-primary.list-group-item-action:hover, .list-group-item-primary.list-group-item-action:focus {
+  color: #004085;
+  background-color: #9fcdff;
+}
+.list-group-item-primary.list-group-item-action.active {
+  color: #fff;
+  background-color: #004085;
+  border-color: #004085;
+}
+
+.list-group-item-secondary {
+  color: #383d41;
+  background-color: #d6d8db;
+}
+.list-group-item-secondary.list-group-item-action:hover, .list-group-item-secondary.list-group-item-action:focus {
+  color: #383d41;
+  background-color: #c8cbcf;
+}
+.list-group-item-secondary.list-group-item-action.active {
+  color: #fff;
+  background-color: #383d41;
+  border-color: #383d41;
+}
+
+.list-group-item-success {
+  color: #155724;
+  background-color: #c3e6cb;
+}
+.list-group-item-success.list-group-item-action:hover, .list-group-item-success.list-group-item-action:focus {
+  color: #155724;
+  background-color: #b1dfbb;
+}
+.list-group-item-success.list-group-item-action.active {
+  color: #fff;
+  background-color: #155724;
+  border-color: #155724;
+}
+
+.list-group-item-info {
+  color: #0c5460;
+  background-color: #bee5eb;
+}
+.list-group-item-info.list-group-item-action:hover, .list-group-item-info.list-group-item-action:focus {
+  color: #0c5460;
+  background-color: #abdde5;
+}
+.list-group-item-info.list-group-item-action.active {
+  color: #fff;
+  background-color: #0c5460;
+  border-color: #0c5460;
+}
+
+.list-group-item-warning {
+  color: #856404;
+  background-color: #ffeeba;
+}
+.list-group-item-warning.list-group-item-action:hover, .list-group-item-warning.list-group-item-action:focus {
+  color: #856404;
+  background-color: #ffe8a1;
+}
+.list-group-item-warning.list-group-item-action.active {
+  color: #fff;
+  background-color: #856404;
+  border-color: #856404;
+}
+
+.list-group-item-danger {
+  color: #721c24;
+  background-color: #f5c6cb;
+}
+.list-group-item-danger.list-group-item-action:hover, .list-group-item-danger.list-group-item-action:focus {
+  color: #721c24;
+  background-color: #f1b0b7;
+}
+.list-group-item-danger.list-group-item-action.active {
+  color: #fff;
+  background-color: #721c24;
+  border-color: #721c24;
+}
+
+.list-group-item-light {
+  color: #818182;
+  background-color: #fdfdfe;
+}
+.list-group-item-light.list-group-item-action:hover, .list-group-item-light.list-group-item-action:focus {
+  color: #818182;
+  background-color: #ececf6;
+}
+.list-group-item-light.list-group-item-action.active {
+  color: #fff;
+  background-color: #818182;
+  border-color: #818182;
+}
+
+.list-group-item-dark {
+  color: #1b1e21;
+  background-color: #c6c8ca;
+}
+.list-group-item-dark.list-group-item-action:hover, .list-group-item-dark.list-group-item-action:focus {
+  color: #1b1e21;
+  background-color: #b9bbbe;
+}
+.list-group-item-dark.list-group-item-action.active {
+  color: #fff;
+  background-color: #1b1e21;
+  border-color: #1b1e21;
+}
+
+.close {
+  float: right;
+  font-size: 1.5rem;
+  font-weight: 700;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: .5;
+}
+.close:hover, .close:focus {
+  color: #000;
+  text-decoration: none;
+  opacity: .75;
+}
+.close:not(:disabled):not(.disabled) {
+  cursor: pointer;
+}
+
+button.close {
+  padding: 0;
+  background-color: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+
+.modal-open {
+  overflow: hidden;
+}
+
+.modal {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  display: none;
+  overflow: hidden;
+  outline: 0;
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 0.5rem;
+  pointer-events: none;
+}
+.modal.fade .modal-dialog {
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  transition: -webkit-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+  transition: transform 0.3s ease-out, -webkit-transform 0.3s ease-out;
+  -webkit-transform: translate(0, -25%);
+          transform: translate(0, -25%);
+}
+.modal.show .modal-dialog {
+  -webkit-transform: translate(0, 0);
+          transform: translate(0, 0);
+}
+
+.modal-dialog-centered {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  min-height: calc(100% - (0.5rem * 2));
+}
+
+.modal-content {
+  position: relative;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+  width: 100%;
+  pointer-events: auto;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 0.3rem;
+  outline: 0;
+}
+
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+}
+.modal-backdrop.show {
+  opacity: 0.5;
+}
+
+.modal-header {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: start;
+      -ms-flex-align: start;
+          align-items: flex-start;
+  -webkit-box-pack: justify;
+      -ms-flex-pack: justify;
+          justify-content: space-between;
+  padding: 1rem;
+  border-bottom: 1px solid #e9ecef;
+  border-top-left-radius: 0.3rem;
+  border-top-right-radius: 0.3rem;
+}
+.modal-header .close {
+  padding: 1rem;
+  margin: -1rem -1rem -1rem auto;
+}
+
+.modal-title {
+  margin-bottom: 0;
+  line-height: 1.5;
+}
+
+.modal-body {
+  position: relative;
+  -webkit-box-flex: 1;
+      -ms-flex: 1 1 auto;
+          flex: 1 1 auto;
+  padding: 1rem;
+}
+
+.modal-footer {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  -webkit-box-pack: end;
+      -ms-flex-pack: end;
+          justify-content: flex-end;
+  padding: 1rem;
+  border-top: 1px solid #e9ecef;
+}
+.modal-footer > :not(:first-child) {
+  margin-left: .25rem;
+}
+.modal-footer > :not(:last-child) {
+  margin-right: .25rem;
+}
+
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+
+@media (min-width: 576px) {
+  .modal-dialog {
+    max-width: 500px;
+    margin: 1.75rem auto;
+  }
+
+  .modal-dialog-centered {
+    min-height: calc(100% - (1.75rem * 2));
+  }
+
+  .modal-sm {
+    max-width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    max-width: 800px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  margin: 0;
+  font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
+  font-style: normal;
+  font-weight: 400;
+  line-height: 1.5;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  letter-spacing: normal;
+  word-break: normal;
+  word-spacing: normal;
+  white-space: normal;
+  line-break: auto;
+  font-size: 0.875rem;
+  word-wrap: break-word;
+  opacity: 0;
+}
+.tooltip.show {
+  opacity: 0.9;
+}
+.tooltip .arrow {
+  position: absolute;
+  display: block;
+  width: 0.8rem;
+  height: 0.4rem;
+}
+.tooltip .arrow::before {
+  position: absolute;
+  content: "";
+  border-color: transparent;
+  border-style: solid;
+}
+
+.bs-tooltip-top, .bs-tooltip-auto[x-placement^="top"] {
+  padding: 0.4rem 0;
+}
+.bs-tooltip-top .arrow, .bs-tooltip-auto[x-placement^="top"] .arrow {
+  bottom: 0;
+}
+.bs-tooltip-top .arrow::before, .bs-tooltip-auto[x-placement^="top"] .arrow::before {
+  top: 0;
+  border-width: 0.4rem 0.4rem 0;
+  border-top-color: #000;
+}
+
+.bs-tooltip-right, .bs-tooltip-auto[x-placement^="right"] {
+  padding: 0 0.4rem;
+}
+.bs-tooltip-right .arrow, .bs-tooltip-auto[x-placement^="right"] .arrow {
+  left: 0;
+  width: 0.4rem;
+  height: 0.8rem;
+}
+.bs-tooltip-right .arrow::before, .bs-tooltip-auto[x-placement^="right"] .arrow::before {
+  right: 0;
+  border-width: 0.4rem 0.4rem 0.4rem 0;
+  border-right-color: #000;
+}
+
+.bs-tooltip-bottom, .bs-tooltip-auto[x-placement^="bottom"] {
+  padding: 0.4rem 0;
+}
+.bs-tooltip-bottom .arrow, .bs-tooltip-auto[x-placement^="bottom"] .arrow {
+  top: 0;
+}
+.bs-tooltip-bottom .arrow::before, .bs-tooltip-auto[x-placement^="bottom"] .arrow::before {
+  bottom: 0;
+  border-width: 0 0.4rem 0.4rem;
+  border-bottom-color: #000;
+}
+
+.bs-tooltip-left, .bs-tooltip-auto[x-placement^="left"] {
+  padding: 0 0.4rem;
+}
+.bs-tooltip-left .arrow, .bs-tooltip-auto[x-placement^="left"] .arrow {
+  right: 0;
+  width: 0.4rem;
+  height: 0.8rem;
+}
+.bs-tooltip-left .arrow::before, .bs-tooltip-auto[x-placement^="left"] .arrow::before {
+  left: 0;
+  border-width: 0.4rem 0 0.4rem 0.4rem;
+  border-left-color: #000;
+}
+
+.tooltip-inner {
+  max-width: 200px;
+  padding: 0.25rem 0.5rem;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 0.25rem;
+}
+
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: block;
+  max-width: 276px;
+  font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
+  font-style: normal;
+  font-weight: 400;
+  line-height: 1.5;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  letter-spacing: normal;
+  word-break: normal;
+  word-spacing: normal;
+  white-space: normal;
+  line-break: auto;
+  font-size: 0.875rem;
+  word-wrap: break-word;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 0.3rem;
+}
+.popover .arrow {
+  position: absolute;
+  display: block;
+  width: 1rem;
+  height: 0.5rem;
+  margin: 0 0.3rem;
+}
+.popover .arrow::before, .popover .arrow::after {
+  position: absolute;
+  display: block;
+  content: "";
+  border-color: transparent;
+  border-style: solid;
+}
+
+.bs-popover-top, .bs-popover-auto[x-placement^="top"] {
+  margin-bottom: 0.5rem;
+}
+.bs-popover-top .arrow, .bs-popover-auto[x-placement^="top"] .arrow {
+  bottom: calc((0.5rem + 1px) * -1);
+}
+.bs-popover-top .arrow::before, .bs-popover-auto[x-placement^="top"] .arrow::before,
+.bs-popover-top .arrow::after,
+.bs-popover-auto[x-placement^="top"] .arrow::after {
+  border-width: 0.5rem 0.5rem 0;
+}
+.bs-popover-top .arrow::before, .bs-popover-auto[x-placement^="top"] .arrow::before {
+  bottom: 0;
+  border-top-color: rgba(0, 0, 0, 0.25);
+}
+.bs-popover-top .arrow::after, .bs-popover-auto[x-placement^="top"] .arrow::after {
+  bottom: 1px;
+  border-top-color: #fff;
+}
+
+.bs-popover-right, .bs-popover-auto[x-placement^="right"] {
+  margin-left: 0.5rem;
+}
+.bs-popover-right .arrow, .bs-popover-auto[x-placement^="right"] .arrow {
+  left: calc((0.5rem + 1px) * -1);
+  width: 0.5rem;
+  height: 1rem;
+  margin: 0.3rem 0;
+}
+.bs-popover-right .arrow::before, .bs-popover-auto[x-placement^="right"] .arrow::before,
+.bs-popover-right .arrow::after,
+.bs-popover-auto[x-placement^="right"] .arrow::after {
+  border-width: 0.5rem 0.5rem 0.5rem 0;
+}
+.bs-popover-right .arrow::before, .bs-popover-auto[x-placement^="right"] .arrow::before {
+  left: 0;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.bs-popover-right .arrow::after, .bs-popover-auto[x-placement^="right"] .arrow::after {
+  left: 1px;
+  border-right-color: #fff;
+}
+
+.bs-popover-bottom, .bs-popover-auto[x-placement^="bottom"] {
+  margin-top: 0.5rem;
+}
+.bs-popover-bottom .arrow, .bs-popover-auto[x-placement^="bottom"] .arrow {
+  top: calc((0.5rem + 1px) * -1);
+}
+.bs-popover-bottom .arrow::before, .bs-popover-auto[x-placement^="bottom"] .arrow::before,
+.bs-popover-bottom .arrow::after,
+.bs-popover-auto[x-placement^="bottom"] .arrow::after {
+  border-width: 0 0.5rem 0.5rem 0.5rem;
+}
+.bs-popover-bottom .arrow::before, .bs-popover-auto[x-placement^="bottom"] .arrow::before {
+  top: 0;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+}
+.bs-popover-bottom .arrow::after, .bs-popover-auto[x-placement^="bottom"] .arrow::after {
+  top: 1px;
+  border-bottom-color: #fff;
+}
+.bs-popover-bottom .popover-header::before, .bs-popover-auto[x-placement^="bottom"] .popover-header::before {
+  position: absolute;
+  top: 0;
+  left: 50%;
+  display: block;
+  width: 1rem;
+  margin-left: -0.5rem;
+  content: "";
+  border-bottom: 1px solid #f7f7f7;
+}
+
+.bs-popover-left, .bs-popover-auto[x-placement^="left"] {
+  margin-right: 0.5rem;
+}
+.bs-popover-left .arrow, .bs-popover-auto[x-placement^="left"] .arrow {
+  right: calc((0.5rem + 1px) * -1);
+  width: 0.5rem;
+  height: 1rem;
+  margin: 0.3rem 0;
+}
+.bs-popover-left .arrow::before, .bs-popover-auto[x-placement^="left"] .arrow::before,
+.bs-popover-left .arrow::after,
+.bs-popover-auto[x-placement^="left"] .arrow::after {
+  border-width: 0.5rem 0 0.5rem 0.5rem;
+}
+.bs-popover-left .arrow::before, .bs-popover-auto[x-placement^="left"] .arrow::before {
+  right: 0;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.bs-popover-left .arrow::after, .bs-popover-auto[x-placement^="left"] .arrow::after {
+  right: 1px;
+  border-left-color: #fff;
+}
+
+.popover-header {
+  padding: 0.5rem 0.75rem;
+  margin-bottom: 0;
+  font-size: 1rem;
+  color: inherit;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-top-left-radius: calc(0.3rem - 1px);
+  border-top-right-radius: calc(0.3rem - 1px);
+}
+.popover-header:empty {
+  display: none;
+}
+
+.popover-body {
+  padding: 0.5rem 0.75rem;
+  color: #212529;
+}
+
+.carousel {
+  position: relative;
+}
+
+.carousel-inner {
+  position: relative;
+  width: 100%;
+  overflow: hidden;
+}
+
+.carousel-item {
+  position: relative;
+  display: none;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  width: 100%;
+  -webkit-transition: -webkit-transform 0.6s ease;
+  transition: -webkit-transform 0.6s ease;
+  transition: transform 0.6s ease;
+  transition: transform 0.6s ease, -webkit-transform 0.6s ease;
+  -webkit-backface-visibility: hidden;
+          backface-visibility: hidden;
+  -webkit-perspective: 1000px;
+          perspective: 1000px;
+}
+
+.carousel-item.active,
+.carousel-item-next,
+.carousel-item-prev {
+  display: block;
+}
+
+.carousel-item-next,
+.carousel-item-prev {
+  position: absolute;
+  top: 0;
+}
+
+.carousel-item-next.carousel-item-left,
+.carousel-item-prev.carousel-item-right {
+  -webkit-transform: translateX(0);
+          transform: translateX(0);
+}
+@supports (transform-style: preserve-3d) {
+  .carousel-item-next.carousel-item-left,
+  .carousel-item-prev.carousel-item-right {
+    -webkit-transform: translate3d(0, 0, 0);
+            transform: translate3d(0, 0, 0);
+  }
+}
+
+.carousel-item-next,
+.active.carousel-item-right {
+  -webkit-transform: translateX(100%);
+          transform: translateX(100%);
+}
+@supports (transform-style: preserve-3d) {
+  .carousel-item-next,
+  .active.carousel-item-right {
+    -webkit-transform: translate3d(100%, 0, 0);
+            transform: translate3d(100%, 0, 0);
+  }
+}
+
+.carousel-item-prev,
+.active.carousel-item-left {
+  -webkit-transform: translateX(-100%);
+          transform: translateX(-100%);
+}
+@supports (transform-style: preserve-3d) {
+  .carousel-item-prev,
+  .active.carousel-item-left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+            transform: translate3d(-100%, 0, 0);
+  }
+}
+
+.carousel-control-prev,
+.carousel-control-next {
+  position: absolute;
+  top: 0;
+  bottom: 0;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  -webkit-box-pack: center;
+      -ms-flex-pack: center;
+          justify-content: center;
+  width: 15%;
+  color: #fff;
+  text-align: center;
+  opacity: 0.5;
+}
+.carousel-control-prev:hover, .carousel-control-prev:focus,
+.carousel-control-next:hover,
+.carousel-control-next:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  opacity: .9;
+}
+
+.carousel-control-prev {
+  left: 0;
+}
+
+.carousel-control-next {
+  right: 0;
+}
+
+.carousel-control-prev-icon,
+.carousel-control-next-icon {
+  display: inline-block;
+  width: 20px;
+  height: 20px;
+  background: transparent no-repeat center center;
+  background-size: 100% 100%;
+}
+
+.carousel-control-prev-icon {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='%23fff' viewBox='0 0 8 8'%3E%3Cpath d='M5.25 0l-4 4 4 4 1.5-1.5-2.5-2.5 2.5-2.5-1.5-1.5z'/%3E%3C/svg%3E");
+}
+
+.carousel-control-next-icon {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='%23fff' viewBox='0 0 8 8'%3E%3Cpath d='M2.75 0l-1.5 1.5 2.5 2.5-2.5 2.5 1.5 1.5 4-4-4-4z'/%3E%3C/svg%3E");
+}
+
+.carousel-indicators {
+  position: absolute;
+  right: 0;
+  bottom: 10px;
+  left: 0;
+  z-index: 15;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-pack: center;
+      -ms-flex-pack: center;
+          justify-content: center;
+  padding-left: 0;
+  margin-right: 15%;
+  margin-left: 15%;
+  list-style: none;
+}
+.carousel-indicators li {
+  position: relative;
+  -webkit-box-flex: 0;
+      -ms-flex: 0 1 auto;
+          flex: 0 1 auto;
+  width: 30px;
+  height: 3px;
+  margin-right: 3px;
+  margin-left: 3px;
+  text-indent: -999px;
+  background-color: rgba(255, 255, 255, 0.5);
+}
+.carousel-indicators li::before {
+  position: absolute;
+  top: -10px;
+  left: 0;
+  display: inline-block;
+  width: 100%;
+  height: 10px;
+  content: "";
+}
+.carousel-indicators li::after {
+  position: absolute;
+  bottom: -10px;
+  left: 0;
+  display: inline-block;
+  width: 100%;
+  height: 10px;
+  content: "";
+}
+.carousel-indicators .active {
+  background-color: #fff;
+}
+
+.carousel-caption {
+  position: absolute;
+  right: 15%;
+  bottom: 20px;
+  left: 15%;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+}
+
+.align-baseline {
+  vertical-align: baseline !important;
+}
+
+.align-top {
+  vertical-align: top !important;
+}
+
+.align-middle {
+  vertical-align: middle !important;
+}
+
+.align-bottom {
+  vertical-align: bottom !important;
+}
+
+.align-text-bottom {
+  vertical-align: text-bottom !important;
+}
+
+.align-text-top {
+  vertical-align: text-top !important;
+}
+
+.bg-primary {
+  background-color: #007bff !important;
+}
+
+a.bg-primary:hover, a.bg-primary:focus,
+button.bg-primary:hover,
+button.bg-primary:focus {
+  background-color: #0062cc !important;
+}
+
+.bg-secondary {
+  background-color: #6c757d !important;
+}
+
+a.bg-secondary:hover, a.bg-secondary:focus,
+button.bg-secondary:hover,
+button.bg-secondary:focus {
+  background-color: #545b62 !important;
+}
+
+.bg-success {
+  background-color: #28a745 !important;
+}
+
+a.bg-success:hover, a.bg-success:focus,
+button.bg-success:hover,
+button.bg-success:focus {
+  background-color: #1e7e34 !important;
+}
+
+.bg-info {
+  background-color: #17a2b8 !important;
+}
+
+a.bg-info:hover, a.bg-info:focus,
+button.bg-info:hover,
+button.bg-info:focus {
+  background-color: #117a8b !important;
+}
+
+.bg-warning {
+  background-color: #ffc107 !important;
+}
+
+a.bg-warning:hover, a.bg-warning:focus,
+button.bg-warning:hover,
+button.bg-warning:focus {
+  background-color: #d39e00 !important;
+}
+
+.bg-danger {
+  background-color: #dc3545 !important;
+}
+
+a.bg-danger:hover, a.bg-danger:focus,
+button.bg-danger:hover,
+button.bg-danger:focus {
+  background-color: #bd2130 !important;
+}
+
+.bg-light {
+  background-color: #f8f9fa !important;
+}
+
+a.bg-light:hover, a.bg-light:focus,
+button.bg-light:hover,
+button.bg-light:focus {
+  background-color: #dae0e5 !important;
+}
+
+.bg-dark {
+  background-color: #343a40 !important;
+}
+
+a.bg-dark:hover, a.bg-dark:focus,
+button.bg-dark:hover,
+button.bg-dark:focus {
+  background-color: #1d2124 !important;
+}
+
+.bg-white {
+  background-color: #fff !important;
+}
+
+.bg-transparent {
+  background-color: transparent !important;
+}
+
+.border {
+  border: 1px solid #dee2e6 !important;
+}
+
+.border-top {
+  border-top: 1px solid #dee2e6 !important;
+}
+
+.border-right {
+  border-right: 1px solid #dee2e6 !important;
+}
+
+.border-bottom {
+  border-bottom: 1px solid #dee2e6 !important;
+}
+
+.border-left {
+  border-left: 1px solid #dee2e6 !important;
+}
+
+.border-0 {
+  border: 0 !important;
+}
+
+.border-top-0 {
+  border-top: 0 !important;
+}
+
+.border-right-0 {
+  border-right: 0 !important;
+}
+
+.border-bottom-0 {
+  border-bottom: 0 !important;
+}
+
+.border-left-0 {
+  border-left: 0 !important;
+}
+
+.border-primary {
+  border-color: #007bff !important;
+}
+
+.border-secondary {
+  border-color: #6c757d !important;
+}
+
+.border-success {
+  border-color: #28a745 !important;
+}
+
+.border-info {
+  border-color: #17a2b8 !important;
+}
+
+.border-warning {
+  border-color: #ffc107 !important;
+}
+
+.border-danger {
+  border-color: #dc3545 !important;
+}
+
+.border-light {
+  border-color: #f8f9fa !important;
+}
+
+.border-dark {
+  border-color: #343a40 !important;
+}
+
+.border-white {
+  border-color: #fff !important;
+}
+
+.rounded {
+  border-radius: 0.25rem !important;
+}
+
+.rounded-top {
+  border-top-left-radius: 0.25rem !important;
+  border-top-right-radius: 0.25rem !important;
+}
+
+.rounded-right {
+  border-top-right-radius: 0.25rem !important;
+  border-bottom-right-radius: 0.25rem !important;
+}
+
+.rounded-bottom {
+  border-bottom-right-radius: 0.25rem !important;
+  border-bottom-left-radius: 0.25rem !important;
+}
+
+.rounded-left {
+  border-top-left-radius: 0.25rem !important;
+  border-bottom-left-radius: 0.25rem !important;
+}
+
+.rounded-circle {
+  border-radius: 50% !important;
+}
+
+.rounded-0 {
+  border-radius: 0 !important;
+}
+
+.clearfix::after {
+  display: block;
+  clear: both;
+  content: "";
+}
+
+.d-none {
+  display: none !important;
+}
+
+.d-inline {
+  display: inline !important;
+}
+
+.d-inline-block {
+  display: inline-block !important;
+}
+
+.d-block {
+  display: block !important;
+}
+
+.d-table {
+  display: table !important;
+}
+
+.d-table-row {
+  display: table-row !important;
+}
+
+.d-table-cell {
+  display: table-cell !important;
+}
+
+.d-flex {
+  display: -webkit-box !important;
+  display: -ms-flexbox !important;
+  display: flex !important;
+}
+
+.d-inline-flex {
+  display: -webkit-inline-box !important;
+  display: -ms-inline-flexbox !important;
+  display: inline-flex !important;
+}
+
+@media (min-width: 576px) {
+  .d-sm-none {
+    display: none !important;
+  }
+
+  .d-sm-inline {
+    display: inline !important;
+  }
+
+  .d-sm-inline-block {
+    display: inline-block !important;
+  }
+
+  .d-sm-block {
+    display: block !important;
+  }
+
+  .d-sm-table {
+    display: table !important;
+  }
+
+  .d-sm-table-row {
+    display: table-row !important;
+  }
+
+  .d-sm-table-cell {
+    display: table-cell !important;
+  }
+
+  .d-sm-flex {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+  }
+
+  .d-sm-inline-flex {
+    display: -webkit-inline-box !important;
+    display: -ms-inline-flexbox !important;
+    display: inline-flex !important;
+  }
+}
+@media (min-width: 768px) {
+  .d-md-none {
+    display: none !important;
+  }
+
+  .d-md-inline {
+    display: inline !important;
+  }
+
+  .d-md-inline-block {
+    display: inline-block !important;
+  }
+
+  .d-md-block {
+    display: block !important;
+  }
+
+  .d-md-table {
+    display: table !important;
+  }
+
+  .d-md-table-row {
+    display: table-row !important;
+  }
+
+  .d-md-table-cell {
+    display: table-cell !important;
+  }
+
+  .d-md-flex {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+  }
+
+  .d-md-inline-flex {
+    display: -webkit-inline-box !important;
+    display: -ms-inline-flexbox !important;
+    display: inline-flex !important;
+  }
+}
+@media (min-width: 992px) {
+  .d-lg-none {
+    display: none !important;
+  }
+
+  .d-lg-inline {
+    display: inline !important;
+  }
+
+  .d-lg-inline-block {
+    display: inline-block !important;
+  }
+
+  .d-lg-block {
+    display: block !important;
+  }
+
+  .d-lg-table {
+    display: table !important;
+  }
+
+  .d-lg-table-row {
+    display: table-row !important;
+  }
+
+  .d-lg-table-cell {
+    display: table-cell !important;
+  }
+
+  .d-lg-flex {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+  }
+
+  .d-lg-inline-flex {
+    display: -webkit-inline-box !important;
+    display: -ms-inline-flexbox !important;
+    display: inline-flex !important;
+  }
+}
+@media (min-width: 1200px) {
+  .d-xl-none {
+    display: none !important;
+  }
+
+  .d-xl-inline {
+    display: inline !important;
+  }
+
+  .d-xl-inline-block {
+    display: inline-block !important;
+  }
+
+  .d-xl-block {
+    display: block !important;
+  }
+
+  .d-xl-table {
+    display: table !important;
+  }
+
+  .d-xl-table-row {
+    display: table-row !important;
+  }
+
+  .d-xl-table-cell {
+    display: table-cell !important;
+  }
+
+  .d-xl-flex {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+  }
+
+  .d-xl-inline-flex {
+    display: -webkit-inline-box !important;
+    display: -ms-inline-flexbox !important;
+    display: inline-flex !important;
+  }
+}
+@media print {
+  .d-print-none {
+    display: none !important;
+  }
+
+  .d-print-inline {
+    display: inline !important;
+  }
+
+  .d-print-inline-block {
+    display: inline-block !important;
+  }
+
+  .d-print-block {
+    display: block !important;
+  }
+
+  .d-print-table {
+    display: table !important;
+  }
+
+  .d-print-table-row {
+    display: table-row !important;
+  }
+
+  .d-print-table-cell {
+    display: table-cell !important;
+  }
+
+  .d-print-flex {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+  }
+
+  .d-print-inline-flex {
+    display: -webkit-inline-box !important;
+    display: -ms-inline-flexbox !important;
+    display: inline-flex !important;
+  }
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  width: 100%;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive::before {
+  display: block;
+  content: "";
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  bottom: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  border: 0;
+}
+
+.embed-responsive-21by9::before {
+  padding-top: 42.8571428571%;
+}
+
+.embed-responsive-16by9::before {
+  padding-top: 56.25%;
+}
+
+.embed-responsive-4by3::before {
+  padding-top: 75%;
+}
+
+.embed-responsive-1by1::before {
+  padding-top: 100%;
+}
+
+.flex-row {
+  -webkit-box-orient: horizontal !important;
+  -webkit-box-direction: normal !important;
+      -ms-flex-direction: row !important;
+          flex-direction: row !important;
+}
+
+.flex-column {
+  -webkit-box-orient: vertical !important;
+  -webkit-box-direction: normal !important;
+      -ms-flex-direction: column !important;
+          flex-direction: column !important;
+}
+
+.flex-row-reverse {
+  -webkit-box-orient: horizontal !important;
+  -webkit-box-direction: reverse !important;
+      -ms-flex-direction: row-reverse !important;
+          flex-direction: row-reverse !important;
+}
+
+.flex-column-reverse {
+  -webkit-box-orient: vertical !important;
+  -webkit-box-direction: reverse !important;
+      -ms-flex-direction: column-reverse !important;
+          flex-direction: column-reverse !important;
+}
+
+.flex-wrap {
+  -ms-flex-wrap: wrap !important;
+      flex-wrap: wrap !important;
+}
+
+.flex-nowrap {
+  -ms-flex-wrap: nowrap !important;
+      flex-wrap: nowrap !important;
+}
+
+.flex-wrap-reverse {
+  -ms-flex-wrap: wrap-reverse !important;
+      flex-wrap: wrap-reverse !important;
+}
+
+.justify-content-start {
+  -webkit-box-pack: start !important;
+      -ms-flex-pack: start !important;
+          justify-content: flex-start !important;
+}
+
+.justify-content-end {
+  -webkit-box-pack: end !important;
+      -ms-flex-pack: end !important;
+          justify-content: flex-end !important;
+}
+
+.justify-content-center {
+  -webkit-box-pack: center !important;
+      -ms-flex-pack: center !important;
+          justify-content: center !important;
+}
+
+.justify-content-between {
+  -webkit-box-pack: justify !important;
+      -ms-flex-pack: justify !important;
+          justify-content: space-between !important;
+}
+
+.justify-content-around {
+  -ms-flex-pack: distribute !important;
+      justify-content: space-around !important;
+}
+
+.align-items-start {
+  -webkit-box-align: start !important;
+      -ms-flex-align: start !important;
+          align-items: flex-start !important;
+}
+
+.align-items-end {
+  -webkit-box-align: end !important;
+      -ms-flex-align: end !important;
+          align-items: flex-end !important;
+}
+
+.align-items-center {
+  -webkit-box-align: center !important;
+      -ms-flex-align: center !important;
+          align-items: center !important;
+}
+
+.align-items-baseline {
+  -webkit-box-align: baseline !important;
+      -ms-flex-align: baseline !important;
+          align-items: baseline !important;
+}
+
+.align-items-stretch {
+  -webkit-box-align: stretch !important;
+      -ms-flex-align: stretch !important;
+          align-items: stretch !important;
+}
+
+.align-content-start {
+  -ms-flex-line-pack: start !important;
+      align-content: flex-start !important;
+}
+
+.align-content-end {
+  -ms-flex-line-pack: end !important;
+      align-content: flex-end !important;
+}
+
+.align-content-center {
+  -ms-flex-line-pack: center !important;
+      align-content: center !important;
+}
+
+.align-content-between {
+  -ms-flex-line-pack: justify !important;
+      align-content: space-between !important;
+}
+
+.align-content-around {
+  -ms-flex-line-pack: distribute !important;
+      align-content: space-around !important;
+}
+
+.align-content-stretch {
+  -ms-flex-line-pack: stretch !important;
+      align-content: stretch !important;
+}
+
+.align-self-auto {
+  -ms-flex-item-align: auto !important;
+      align-self: auto !important;
+}
+
+.align-self-start {
+  -ms-flex-item-align: start !important;
+      align-self: flex-start !important;
+}
+
+.align-self-end {
+  -ms-flex-item-align: end !important;
+      align-self: flex-end !important;
+}
+
+.align-self-center {
+  -ms-flex-item-align: center !important;
+      align-self: center !important;
+}
+
+.align-self-baseline {
+  -ms-flex-item-align: baseline !important;
+      align-self: baseline !important;
+}
+
+.align-self-stretch {
+  -ms-flex-item-align: stretch !important;
+      align-self: stretch !important;
+}
+
+@media (min-width: 576px) {
+  .flex-sm-row {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: row !important;
+            flex-direction: row !important;
+  }
+
+  .flex-sm-column {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: column !important;
+            flex-direction: column !important;
+  }
+
+  .flex-sm-row-reverse {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: row-reverse !important;
+            flex-direction: row-reverse !important;
+  }
+
+  .flex-sm-column-reverse {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: column-reverse !important;
+            flex-direction: column-reverse !important;
+  }
+
+  .flex-sm-wrap {
+    -ms-flex-wrap: wrap !important;
+        flex-wrap: wrap !important;
+  }
+
+  .flex-sm-nowrap {
+    -ms-flex-wrap: nowrap !important;
+        flex-wrap: nowrap !important;
+  }
+
+  .flex-sm-wrap-reverse {
+    -ms-flex-wrap: wrap-reverse !important;
+        flex-wrap: wrap-reverse !important;
+  }
+
+  .justify-content-sm-start {
+    -webkit-box-pack: start !important;
+        -ms-flex-pack: start !important;
+            justify-content: flex-start !important;
+  }
+
+  .justify-content-sm-end {
+    -webkit-box-pack: end !important;
+        -ms-flex-pack: end !important;
+            justify-content: flex-end !important;
+  }
+
+  .justify-content-sm-center {
+    -webkit-box-pack: center !important;
+        -ms-flex-pack: center !important;
+            justify-content: center !important;
+  }
+
+  .justify-content-sm-between {
+    -webkit-box-pack: justify !important;
+        -ms-flex-pack: justify !important;
+            justify-content: space-between !important;
+  }
+
+  .justify-content-sm-around {
+    -ms-flex-pack: distribute !important;
+        justify-content: space-around !important;
+  }
+
+  .align-items-sm-start {
+    -webkit-box-align: start !important;
+        -ms-flex-align: start !important;
+            align-items: flex-start !important;
+  }
+
+  .align-items-sm-end {
+    -webkit-box-align: end !important;
+        -ms-flex-align: end !important;
+            align-items: flex-end !important;
+  }
+
+  .align-items-sm-center {
+    -webkit-box-align: center !important;
+        -ms-flex-align: center !important;
+            align-items: center !important;
+  }
+
+  .align-items-sm-baseline {
+    -webkit-box-align: baseline !important;
+        -ms-flex-align: baseline !important;
+            align-items: baseline !important;
+  }
+
+  .align-items-sm-stretch {
+    -webkit-box-align: stretch !important;
+        -ms-flex-align: stretch !important;
+            align-items: stretch !important;
+  }
+
+  .align-content-sm-start {
+    -ms-flex-line-pack: start !important;
+        align-content: flex-start !important;
+  }
+
+  .align-content-sm-end {
+    -ms-flex-line-pack: end !important;
+        align-content: flex-end !important;
+  }
+
+  .align-content-sm-center {
+    -ms-flex-line-pack: center !important;
+        align-content: center !important;
+  }
+
+  .align-content-sm-between {
+    -ms-flex-line-pack: justify !important;
+        align-content: space-between !important;
+  }
+
+  .align-content-sm-around {
+    -ms-flex-line-pack: distribute !important;
+        align-content: space-around !important;
+  }
+
+  .align-content-sm-stretch {
+    -ms-flex-line-pack: stretch !important;
+        align-content: stretch !important;
+  }
+
+  .align-self-sm-auto {
+    -ms-flex-item-align: auto !important;
+        align-self: auto !important;
+  }
+
+  .align-self-sm-start {
+    -ms-flex-item-align: start !important;
+        align-self: flex-start !important;
+  }
+
+  .align-self-sm-end {
+    -ms-flex-item-align: end !important;
+        align-self: flex-end !important;
+  }
+
+  .align-self-sm-center {
+    -ms-flex-item-align: center !important;
+        align-self: center !important;
+  }
+
+  .align-self-sm-baseline {
+    -ms-flex-item-align: baseline !important;
+        align-self: baseline !important;
+  }
+
+  .align-self-sm-stretch {
+    -ms-flex-item-align: stretch !important;
+        align-self: stretch !important;
+  }
+}
+@media (min-width: 768px) {
+  .flex-md-row {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: row !important;
+            flex-direction: row !important;
+  }
+
+  .flex-md-column {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: column !important;
+            flex-direction: column !important;
+  }
+
+  .flex-md-row-reverse {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: row-reverse !important;
+            flex-direction: row-reverse !important;
+  }
+
+  .flex-md-column-reverse {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: column-reverse !important;
+            flex-direction: column-reverse !important;
+  }
+
+  .flex-md-wrap {
+    -ms-flex-wrap: wrap !important;
+        flex-wrap: wrap !important;
+  }
+
+  .flex-md-nowrap {
+    -ms-flex-wrap: nowrap !important;
+        flex-wrap: nowrap !important;
+  }
+
+  .flex-md-wrap-reverse {
+    -ms-flex-wrap: wrap-reverse !important;
+        flex-wrap: wrap-reverse !important;
+  }
+
+  .justify-content-md-start {
+    -webkit-box-pack: start !important;
+        -ms-flex-pack: start !important;
+            justify-content: flex-start !important;
+  }
+
+  .justify-content-md-end {
+    -webkit-box-pack: end !important;
+        -ms-flex-pack: end !important;
+            justify-content: flex-end !important;
+  }
+
+  .justify-content-md-center {
+    -webkit-box-pack: center !important;
+        -ms-flex-pack: center !important;
+            justify-content: center !important;
+  }
+
+  .justify-content-md-between {
+    -webkit-box-pack: justify !important;
+        -ms-flex-pack: justify !important;
+            justify-content: space-between !important;
+  }
+
+  .justify-content-md-around {
+    -ms-flex-pack: distribute !important;
+        justify-content: space-around !important;
+  }
+
+  .align-items-md-start {
+    -webkit-box-align: start !important;
+        -ms-flex-align: start !important;
+            align-items: flex-start !important;
+  }
+
+  .align-items-md-end {
+    -webkit-box-align: end !important;
+        -ms-flex-align: end !important;
+            align-items: flex-end !important;
+  }
+
+  .align-items-md-center {
+    -webkit-box-align: center !important;
+        -ms-flex-align: center !important;
+            align-items: center !important;
+  }
+
+  .align-items-md-baseline {
+    -webkit-box-align: baseline !important;
+        -ms-flex-align: baseline !important;
+            align-items: baseline !important;
+  }
+
+  .align-items-md-stretch {
+    -webkit-box-align: stretch !important;
+        -ms-flex-align: stretch !important;
+            align-items: stretch !important;
+  }
+
+  .align-content-md-start {
+    -ms-flex-line-pack: start !important;
+        align-content: flex-start !important;
+  }
+
+  .align-content-md-end {
+    -ms-flex-line-pack: end !important;
+        align-content: flex-end !important;
+  }
+
+  .align-content-md-center {
+    -ms-flex-line-pack: center !important;
+        align-content: center !important;
+  }
+
+  .align-content-md-between {
+    -ms-flex-line-pack: justify !important;
+        align-content: space-between !important;
+  }
+
+  .align-content-md-around {
+    -ms-flex-line-pack: distribute !important;
+        align-content: space-around !important;
+  }
+
+  .align-content-md-stretch {
+    -ms-flex-line-pack: stretch !important;
+        align-content: stretch !important;
+  }
+
+  .align-self-md-auto {
+    -ms-flex-item-align: auto !important;
+        align-self: auto !important;
+  }
+
+  .align-self-md-start {
+    -ms-flex-item-align: start !important;
+        align-self: flex-start !important;
+  }
+
+  .align-self-md-end {
+    -ms-flex-item-align: end !important;
+        align-self: flex-end !important;
+  }
+
+  .align-self-md-center {
+    -ms-flex-item-align: center !important;
+        align-self: center !important;
+  }
+
+  .align-self-md-baseline {
+    -ms-flex-item-align: baseline !important;
+        align-self: baseline !important;
+  }
+
+  .align-self-md-stretch {
+    -ms-flex-item-align: stretch !important;
+        align-self: stretch !important;
+  }
+}
+@media (min-width: 992px) {
+  .flex-lg-row {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: row !important;
+            flex-direction: row !important;
+  }
+
+  .flex-lg-column {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: column !important;
+            flex-direction: column !important;
+  }
+
+  .flex-lg-row-reverse {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: row-reverse !important;
+            flex-direction: row-reverse !important;
+  }
+
+  .flex-lg-column-reverse {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: column-reverse !important;
+            flex-direction: column-reverse !important;
+  }
+
+  .flex-lg-wrap {
+    -ms-flex-wrap: wrap !important;
+        flex-wrap: wrap !important;
+  }
+
+  .flex-lg-nowrap {
+    -ms-flex-wrap: nowrap !important;
+        flex-wrap: nowrap !important;
+  }
+
+  .flex-lg-wrap-reverse {
+    -ms-flex-wrap: wrap-reverse !important;
+        flex-wrap: wrap-reverse !important;
+  }
+
+  .justify-content-lg-start {
+    -webkit-box-pack: start !important;
+        -ms-flex-pack: start !important;
+            justify-content: flex-start !important;
+  }
+
+  .justify-content-lg-end {
+    -webkit-box-pack: end !important;
+        -ms-flex-pack: end !important;
+            justify-content: flex-end !important;
+  }
+
+  .justify-content-lg-center {
+    -webkit-box-pack: center !important;
+        -ms-flex-pack: center !important;
+            justify-content: center !important;
+  }
+
+  .justify-content-lg-between {
+    -webkit-box-pack: justify !important;
+        -ms-flex-pack: justify !important;
+            justify-content: space-between !important;
+  }
+
+  .justify-content-lg-around {
+    -ms-flex-pack: distribute !important;
+        justify-content: space-around !important;
+  }
+
+  .align-items-lg-start {
+    -webkit-box-align: start !important;
+        -ms-flex-align: start !important;
+            align-items: flex-start !important;
+  }
+
+  .align-items-lg-end {
+    -webkit-box-align: end !important;
+        -ms-flex-align: end !important;
+            align-items: flex-end !important;
+  }
+
+  .align-items-lg-center {
+    -webkit-box-align: center !important;
+        -ms-flex-align: center !important;
+            align-items: center !important;
+  }
+
+  .align-items-lg-baseline {
+    -webkit-box-align: baseline !important;
+        -ms-flex-align: baseline !important;
+            align-items: baseline !important;
+  }
+
+  .align-items-lg-stretch {
+    -webkit-box-align: stretch !important;
+        -ms-flex-align: stretch !important;
+            align-items: stretch !important;
+  }
+
+  .align-content-lg-start {
+    -ms-flex-line-pack: start !important;
+        align-content: flex-start !important;
+  }
+
+  .align-content-lg-end {
+    -ms-flex-line-pack: end !important;
+        align-content: flex-end !important;
+  }
+
+  .align-content-lg-center {
+    -ms-flex-line-pack: center !important;
+        align-content: center !important;
+  }
+
+  .align-content-lg-between {
+    -ms-flex-line-pack: justify !important;
+        align-content: space-between !important;
+  }
+
+  .align-content-lg-around {
+    -ms-flex-line-pack: distribute !important;
+        align-content: space-around !important;
+  }
+
+  .align-content-lg-stretch {
+    -ms-flex-line-pack: stretch !important;
+        align-content: stretch !important;
+  }
+
+  .align-self-lg-auto {
+    -ms-flex-item-align: auto !important;
+        align-self: auto !important;
+  }
+
+  .align-self-lg-start {
+    -ms-flex-item-align: start !important;
+        align-self: flex-start !important;
+  }
+
+  .align-self-lg-end {
+    -ms-flex-item-align: end !important;
+        align-self: flex-end !important;
+  }
+
+  .align-self-lg-center {
+    -ms-flex-item-align: center !important;
+        align-self: center !important;
+  }
+
+  .align-self-lg-baseline {
+    -ms-flex-item-align: baseline !important;
+        align-self: baseline !important;
+  }
+
+  .align-self-lg-stretch {
+    -ms-flex-item-align: stretch !important;
+        align-self: stretch !important;
+  }
+}
+@media (min-width: 1200px) {
+  .flex-xl-row {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: row !important;
+            flex-direction: row !important;
+  }
+
+  .flex-xl-column {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: column !important;
+            flex-direction: column !important;
+  }
+
+  .flex-xl-row-reverse {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: row-reverse !important;
+            flex-direction: row-reverse !important;
+  }
+
+  .flex-xl-column-reverse {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: column-reverse !important;
+            flex-direction: column-reverse !important;
+  }
+
+  .flex-xl-wrap {
+    -ms-flex-wrap: wrap !important;
+        flex-wrap: wrap !important;
+  }
+
+  .flex-xl-nowrap {
+    -ms-flex-wrap: nowrap !important;
+        flex-wrap: nowrap !important;
+  }
+
+  .flex-xl-wrap-reverse {
+    -ms-flex-wrap: wrap-reverse !important;
+        flex-wrap: wrap-reverse !important;
+  }
+
+  .justify-content-xl-start {
+    -webkit-box-pack: start !important;
+        -ms-flex-pack: start !important;
+            justify-content: flex-start !important;
+  }
+
+  .justify-content-xl-end {
+    -webkit-box-pack: end !important;
+        -ms-flex-pack: end !important;
+            justify-content: flex-end !important;
+  }
+
+  .justify-content-xl-center {
+    -webkit-box-pack: center !important;
+        -ms-flex-pack: center !important;
+            justify-content: center !important;
+  }
+
+  .justify-content-xl-between {
+    -webkit-box-pack: justify !important;
+        -ms-flex-pack: justify !important;
+            justify-content: space-between !important;
+  }
+
+  .justify-content-xl-around {
+    -ms-flex-pack: distribute !important;
+        justify-content: space-around !important;
+  }
+
+  .align-items-xl-start {
+    -webkit-box-align: start !important;
+        -ms-flex-align: start !important;
+            align-items: flex-start !important;
+  }
+
+  .align-items-xl-end {
+    -webkit-box-align: end !important;
+        -ms-flex-align: end !important;
+            align-items: flex-end !important;
+  }
+
+  .align-items-xl-center {
+    -webkit-box-align: center !important;
+        -ms-flex-align: center !important;
+            align-items: center !important;
+  }
+
+  .align-items-xl-baseline {
+    -webkit-box-align: baseline !important;
+        -ms-flex-align: baseline !important;
+            align-items: baseline !important;
+  }
+
+  .align-items-xl-stretch {
+    -webkit-box-align: stretch !important;
+        -ms-flex-align: stretch !important;
+            align-items: stretch !important;
+  }
+
+  .align-content-xl-start {
+    -ms-flex-line-pack: start !important;
+        align-content: flex-start !important;
+  }
+
+  .align-content-xl-end {
+    -ms-flex-line-pack: end !important;
+        align-content: flex-end !important;
+  }
+
+  .align-content-xl-center {
+    -ms-flex-line-pack: center !important;
+        align-content: center !important;
+  }
+
+  .align-content-xl-between {
+    -ms-flex-line-pack: justify !important;
+        align-content: space-between !important;
+  }
+
+  .align-content-xl-around {
+    -ms-flex-line-pack: distribute !important;
+        align-content: space-around !important;
+  }
+
+  .align-content-xl-stretch {
+    -ms-flex-line-pack: stretch !important;
+        align-content: stretch !important;
+  }
+
+  .align-self-xl-auto {
+    -ms-flex-item-align: auto !important;
+        align-self: auto !important;
+  }
+
+  .align-self-xl-start {
+    -ms-flex-item-align: start !important;
+        align-self: flex-start !important;
+  }
+
+  .align-self-xl-end {
+    -ms-flex-item-align: end !important;
+        align-self: flex-end !important;
+  }
+
+  .align-self-xl-center {
+    -ms-flex-item-align: center !important;
+        align-self: center !important;
+  }
+
+  .align-self-xl-baseline {
+    -ms-flex-item-align: baseline !important;
+        align-self: baseline !important;
+  }
+
+  .align-self-xl-stretch {
+    -ms-flex-item-align: stretch !important;
+        align-self: stretch !important;
+  }
+}
+.float-left {
+  float: left !important;
+}
+
+.float-right {
+  float: right !important;
+}
+
+.float-none {
+  float: none !important;
+}
+
+@media (min-width: 576px) {
+  .float-sm-left {
+    float: left !important;
+  }
+
+  .float-sm-right {
+    float: right !important;
+  }
+
+  .float-sm-none {
+    float: none !important;
+  }
+}
+@media (min-width: 768px) {
+  .float-md-left {
+    float: left !important;
+  }
+
+  .float-md-right {
+    float: right !important;
+  }
+
+  .float-md-none {
+    float: none !important;
+  }
+}
+@media (min-width: 992px) {
+  .float-lg-left {
+    float: left !important;
+  }
+
+  .float-lg-right {
+    float: right !important;
+  }
+
+  .float-lg-none {
+    float: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .float-xl-left {
+    float: left !important;
+  }
+
+  .float-xl-right {
+    float: right !important;
+  }
+
+  .float-xl-none {
+    float: none !important;
+  }
+}
+.position-static {
+  position: static !important;
+}
+
+.position-relative {
+  position: relative !important;
+}
+
+.position-absolute {
+  position: absolute !important;
+}
+
+.position-fixed {
+  position: fixed !important;
+}
+
+.position-sticky {
+  position: sticky !important;
+}
+
+.fixed-top {
+  position: fixed;
+  top: 0;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+
+.fixed-bottom {
+  position: fixed;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1030;
+}
+
+@supports (position: sticky) {
+  .sticky-top {
+    position: sticky;
+    top: 0;
+    z-index: 1020;
+  }
+}
+
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  -webkit-clip-path: inset(50%);
+          clip-path: inset(50%);
+  border: 0;
+}
+
+.sr-only-focusable:active, .sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  overflow: visible;
+  clip: auto;
+  white-space: normal;
+  -webkit-clip-path: none;
+          clip-path: none;
+}
+
+.w-25 {
+  width: 25% !important;
+}
+
+.w-50 {
+  width: 50% !important;
+}
+
+.w-75 {
+  width: 75% !important;
+}
+
+.w-100 {
+  width: 100% !important;
+}
+
+.h-25 {
+  height: 25% !important;
+}
+
+.h-50 {
+  height: 50% !important;
+}
+
+.h-75 {
+  height: 75% !important;
+}
+
+.h-100 {
+  height: 100% !important;
+}
+
+.mw-100 {
+  max-width: 100% !important;
+}
+
+.mh-100 {
+  max-height: 100% !important;
+}
+
+.m-0 {
+  margin: 0 !important;
+}
+
+.mt-0,
+.my-0 {
+  margin-top: 0 !important;
+}
+
+.mr-0,
+.mx-0 {
+  margin-right: 0 !important;
+}
+
+.mb-0,
+.my-0 {
+  margin-bottom: 0 !important;
+}
+
+.ml-0,
+.mx-0 {
+  margin-left: 0 !important;
+}
+
+.m-1 {
+  margin: 0.25rem !important;
+}
+
+.mt-1,
+.my-1 {
+  margin-top: 0.25rem !important;
+}
+
+.mr-1,
+.mx-1 {
+  margin-right: 0.25rem !important;
+}
+
+.mb-1,
+.my-1 {
+  margin-bottom: 0.25rem !important;
+}
+
+.ml-1,
+.mx-1 {
+  margin-left: 0.25rem !important;
+}
+
+.m-2 {
+  margin: 0.5rem !important;
+}
+
+.mt-2,
+.my-2 {
+  margin-top: 0.5rem !important;
+}
+
+.mr-2,
+.mx-2 {
+  margin-right: 0.5rem !important;
+}
+
+.mb-2,
+.my-2 {
+  margin-bottom: 0.5rem !important;
+}
+
+.ml-2,
+.mx-2 {
+  margin-left: 0.5rem !important;
+}
+
+.m-3 {
+  margin: 1rem !important;
+}
+
+.mt-3,
+.my-3 {
+  margin-top: 1rem !important;
+}
+
+.mr-3,
+.mx-3 {
+  margin-right: 1rem !important;
+}
+
+.mb-3,
+.my-3 {
+  margin-bottom: 1rem !important;
+}
+
+.ml-3,
+.mx-3 {
+  margin-left: 1rem !important;
+}
+
+.m-4 {
+  margin: 1.5rem !important;
+}
+
+.mt-4,
+.my-4 {
+  margin-top: 1.5rem !important;
+}
+
+.mr-4,
+.mx-4 {
+  margin-right: 1.5rem !important;
+}
+
+.mb-4,
+.my-4 {
+  margin-bottom: 1.5rem !important;
+}
+
+.ml-4,
+.mx-4 {
+  margin-left: 1.5rem !important;
+}
+
+.m-5 {
+  margin: 3rem !important;
+}
+
+.mt-5,
+.my-5 {
+  margin-top: 3rem !important;
+}
+
+.mr-5,
+.mx-5 {
+  margin-right: 3rem !important;
+}
+
+.mb-5,
+.my-5 {
+  margin-bottom: 3rem !important;
+}
+
+.ml-5,
+.mx-5 {
+  margin-left: 3rem !important;
+}
+
+.p-0 {
+  padding: 0 !important;
+}
+
+.pt-0,
+.py-0 {
+  padding-top: 0 !important;
+}
+
+.pr-0,
+.px-0 {
+  padding-right: 0 !important;
+}
+
+.pb-0,
+.py-0 {
+  padding-bottom: 0 !important;
+}
+
+.pl-0,
+.px-0 {
+  padding-left: 0 !important;
+}
+
+.p-1 {
+  padding: 0.25rem !important;
+}
+
+.pt-1,
+.py-1 {
+  padding-top: 0.25rem !important;
+}
+
+.pr-1,
+.px-1 {
+  padding-right: 0.25rem !important;
+}
+
+.pb-1,
+.py-1 {
+  padding-bottom: 0.25rem !important;
+}
+
+.pl-1,
+.px-1 {
+  padding-left: 0.25rem !important;
+}
+
+.p-2 {
+  padding: 0.5rem !important;
+}
+
+.pt-2,
+.py-2 {
+  padding-top: 0.5rem !important;
+}
+
+.pr-2,
+.px-2 {
+  padding-right: 0.5rem !important;
+}
+
+.pb-2,
+.py-2 {
+  padding-bottom: 0.5rem !important;
+}
+
+.pl-2,
+.px-2 {
+  padding-left: 0.5rem !important;
+}
+
+.p-3 {
+  padding: 1rem !important;
+}
+
+.pt-3,
+.py-3 {
+  padding-top: 1rem !important;
+}
+
+.pr-3,
+.px-3 {
+  padding-right: 1rem !important;
+}
+
+.pb-3,
+.py-3 {
+  padding-bottom: 1rem !important;
+}
+
+.pl-3,
+.px-3 {
+  padding-left: 1rem !important;
+}
+
+.p-4 {
+  padding: 1.5rem !important;
+}
+
+.pt-4,
+.py-4 {
+  padding-top: 1.5rem !important;
+}
+
+.pr-4,
+.px-4 {
+  padding-right: 1.5rem !important;
+}
+
+.pb-4,
+.py-4 {
+  padding-bottom: 1.5rem !important;
+}
+
+.pl-4,
+.px-4 {
+  padding-left: 1.5rem !important;
+}
+
+.p-5 {
+  padding: 3rem !important;
+}
+
+.pt-5,
+.py-5 {
+  padding-top: 3rem !important;
+}
+
+.pr-5,
+.px-5 {
+  padding-right: 3rem !important;
+}
+
+.pb-5,
+.py-5 {
+  padding-bottom: 3rem !important;
+}
+
+.pl-5,
+.px-5 {
+  padding-left: 3rem !important;
+}
+
+.m-auto {
+  margin: auto !important;
+}
+
+.mt-auto,
+.my-auto {
+  margin-top: auto !important;
+}
+
+.mr-auto,
+.mx-auto {
+  margin-right: auto !important;
+}
+
+.mb-auto,
+.my-auto {
+  margin-bottom: auto !important;
+}
+
+.ml-auto,
+.mx-auto {
+  margin-left: auto !important;
+}
+
+@media (min-width: 576px) {
+  .m-sm-0 {
+    margin: 0 !important;
+  }
+
+  .mt-sm-0,
+  .my-sm-0 {
+    margin-top: 0 !important;
+  }
+
+  .mr-sm-0,
+  .mx-sm-0 {
+    margin-right: 0 !important;
+  }
+
+  .mb-sm-0,
+  .my-sm-0 {
+    margin-bottom: 0 !important;
+  }
+
+  .ml-sm-0,
+  .mx-sm-0 {
+    margin-left: 0 !important;
+  }
+
+  .m-sm-1 {
+    margin: 0.25rem !important;
+  }
+
+  .mt-sm-1,
+  .my-sm-1 {
+    margin-top: 0.25rem !important;
+  }
+
+  .mr-sm-1,
+  .mx-sm-1 {
+    margin-right: 0.25rem !important;
+  }
+
+  .mb-sm-1,
+  .my-sm-1 {
+    margin-bottom: 0.25rem !important;
+  }
+
+  .ml-sm-1,
+  .mx-sm-1 {
+    margin-left: 0.25rem !important;
+  }
+
+  .m-sm-2 {
+    margin: 0.5rem !important;
+  }
+
+  .mt-sm-2,
+  .my-sm-2 {
+    margin-top: 0.5rem !important;
+  }
+
+  .mr-sm-2,
+  .mx-sm-2 {
+    margin-right: 0.5rem !important;
+  }
+
+  .mb-sm-2,
+  .my-sm-2 {
+    margin-bottom: 0.5rem !important;
+  }
+
+  .ml-sm-2,
+  .mx-sm-2 {
+    margin-left: 0.5rem !important;
+  }
+
+  .m-sm-3 {
+    margin: 1rem !important;
+  }
+
+  .mt-sm-3,
+  .my-sm-3 {
+    margin-top: 1rem !important;
+  }
+
+  .mr-sm-3,
+  .mx-sm-3 {
+    margin-right: 1rem !important;
+  }
+
+  .mb-sm-3,
+  .my-sm-3 {
+    margin-bottom: 1rem !important;
+  }
+
+  .ml-sm-3,
+  .mx-sm-3 {
+    margin-left: 1rem !important;
+  }
+
+  .m-sm-4 {
+    margin: 1.5rem !important;
+  }
+
+  .mt-sm-4,
+  .my-sm-4 {
+    margin-top: 1.5rem !important;
+  }
+
+  .mr-sm-4,
+  .mx-sm-4 {
+    margin-right: 1.5rem !important;
+  }
+
+  .mb-sm-4,
+  .my-sm-4 {
+    margin-bottom: 1.5rem !important;
+  }
+
+  .ml-sm-4,
+  .mx-sm-4 {
+    margin-left: 1.5rem !important;
+  }
+
+  .m-sm-5 {
+    margin: 3rem !important;
+  }
+
+  .mt-sm-5,
+  .my-sm-5 {
+    margin-top: 3rem !important;
+  }
+
+  .mr-sm-5,
+  .mx-sm-5 {
+    margin-right: 3rem !important;
+  }
+
+  .mb-sm-5,
+  .my-sm-5 {
+    margin-bottom: 3rem !important;
+  }
+
+  .ml-sm-5,
+  .mx-sm-5 {
+    margin-left: 3rem !important;
+  }
+
+  .p-sm-0 {
+    padding: 0 !important;
+  }
+
+  .pt-sm-0,
+  .py-sm-0 {
+    padding-top: 0 !important;
+  }
+
+  .pr-sm-0,
+  .px-sm-0 {
+    padding-right: 0 !important;
+  }
+
+  .pb-sm-0,
+  .py-sm-0 {
+    padding-bottom: 0 !important;
+  }
+
+  .pl-sm-0,
+  .px-sm-0 {
+    padding-left: 0 !important;
+  }
+
+  .p-sm-1 {
+    padding: 0.25rem !important;
+  }
+
+  .pt-sm-1,
+  .py-sm-1 {
+    padding-top: 0.25rem !important;
+  }
+
+  .pr-sm-1,
+  .px-sm-1 {
+    padding-right: 0.25rem !important;
+  }
+
+  .pb-sm-1,
+  .py-sm-1 {
+    padding-bottom: 0.25rem !important;
+  }
+
+  .pl-sm-1,
+  .px-sm-1 {
+    padding-left: 0.25rem !important;
+  }
+
+  .p-sm-2 {
+    padding: 0.5rem !important;
+  }
+
+  .pt-sm-2,
+  .py-sm-2 {
+    padding-top: 0.5rem !important;
+  }
+
+  .pr-sm-2,
+  .px-sm-2 {
+    padding-right: 0.5rem !important;
+  }
+
+  .pb-sm-2,
+  .py-sm-2 {
+    padding-bottom: 0.5rem !important;
+  }
+
+  .pl-sm-2,
+  .px-sm-2 {
+    padding-left: 0.5rem !important;
+  }
+
+  .p-sm-3 {
+    padding: 1rem !important;
+  }
+
+  .pt-sm-3,
+  .py-sm-3 {
+    padding-top: 1rem !important;
+  }
+
+  .pr-sm-3,
+  .px-sm-3 {
+    padding-right: 1rem !important;
+  }
+
+  .pb-sm-3,
+  .py-sm-3 {
+    padding-bottom: 1rem !important;
+  }
+
+  .pl-sm-3,
+  .px-sm-3 {
+    padding-left: 1rem !important;
+  }
+
+  .p-sm-4 {
+    padding: 1.5rem !important;
+  }
+
+  .pt-sm-4,
+  .py-sm-4 {
+    padding-top: 1.5rem !important;
+  }
+
+  .pr-sm-4,
+  .px-sm-4 {
+    padding-right: 1.5rem !important;
+  }
+
+  .pb-sm-4,
+  .py-sm-4 {
+    padding-bottom: 1.5rem !important;
+  }
+
+  .pl-sm-4,
+  .px-sm-4 {
+    padding-left: 1.5rem !important;
+  }
+
+  .p-sm-5 {
+    padding: 3rem !important;
+  }
+
+  .pt-sm-5,
+  .py-sm-5 {
+    padding-top: 3rem !important;
+  }
+
+  .pr-sm-5,
+  .px-sm-5 {
+    padding-right: 3rem !important;
+  }
+
+  .pb-sm-5,
+  .py-sm-5 {
+    padding-bottom: 3rem !important;
+  }
+
+  .pl-sm-5,
+  .px-sm-5 {
+    padding-left: 3rem !important;
+  }
+
+  .m-sm-auto {
+    margin: auto !important;
+  }
+
+  .mt-sm-auto,
+  .my-sm-auto {
+    margin-top: auto !important;
+  }
+
+  .mr-sm-auto,
+  .mx-sm-auto {
+    margin-right: auto !important;
+  }
+
+  .mb-sm-auto,
+  .my-sm-auto {
+    margin-bottom: auto !important;
+  }
+
+  .ml-sm-auto,
+  .mx-sm-auto {
+    margin-left: auto !important;
+  }
+}
+@media (min-width: 768px) {
+  .m-md-0 {
+    margin: 0 !important;
+  }
+
+  .mt-md-0,
+  .my-md-0 {
+    margin-top: 0 !important;
+  }
+
+  .mr-md-0,
+  .mx-md-0 {
+    margin-right: 0 !important;
+  }
+
+  .mb-md-0,
+  .my-md-0 {
+    margin-bottom: 0 !important;
+  }
+
+  .ml-md-0,
+  .mx-md-0 {
+    margin-left: 0 !important;
+  }
+
+  .m-md-1 {
+    margin: 0.25rem !important;
+  }
+
+  .mt-md-1,
+  .my-md-1 {
+    margin-top: 0.25rem !important;
+  }
+
+  .mr-md-1,
+  .mx-md-1 {
+    margin-right: 0.25rem !important;
+  }
+
+  .mb-md-1,
+  .my-md-1 {
+    margin-bottom: 0.25rem !important;
+  }
+
+  .ml-md-1,
+  .mx-md-1 {
+    margin-left: 0.25rem !important;
+  }
+
+  .m-md-2 {
+    margin: 0.5rem !important;
+  }
+
+  .mt-md-2,
+  .my-md-2 {
+    margin-top: 0.5rem !important;
+  }
+
+  .mr-md-2,
+  .mx-md-2 {
+    margin-right: 0.5rem !important;
+  }
+
+  .mb-md-2,
+  .my-md-2 {
+    margin-bottom: 0.5rem !important;
+  }
+
+  .ml-md-2,
+  .mx-md-2 {
+    margin-left: 0.5rem !important;
+  }
+
+  .m-md-3 {
+    margin: 1rem !important;
+  }
+
+  .mt-md-3,
+  .my-md-3 {
+    margin-top: 1rem !important;
+  }
+
+  .mr-md-3,
+  .mx-md-3 {
+    margin-right: 1rem !important;
+  }
+
+  .mb-md-3,
+  .my-md-3 {
+    margin-bottom: 1rem !important;
+  }
+
+  .ml-md-3,
+  .mx-md-3 {
+    margin-left: 1rem !important;
+  }
+
+  .m-md-4 {
+    margin: 1.5rem !important;
+  }
+
+  .mt-md-4,
+  .my-md-4 {
+    margin-top: 1.5rem !important;
+  }
+
+  .mr-md-4,
+  .mx-md-4 {
+    margin-right: 1.5rem !important;
+  }
+
+  .mb-md-4,
+  .my-md-4 {
+    margin-bottom: 1.5rem !important;
+  }
+
+  .ml-md-4,
+  .mx-md-4 {
+    margin-left: 1.5rem !important;
+  }
+
+  .m-md-5 {
+    margin: 3rem !important;
+  }
+
+  .mt-md-5,
+  .my-md-5 {
+    margin-top: 3rem !important;
+  }
+
+  .mr-md-5,
+  .mx-md-5 {
+    margin-right: 3rem !important;
+  }
+
+  .mb-md-5,
+  .my-md-5 {
+    margin-bottom: 3rem !important;
+  }
+
+  .ml-md-5,
+  .mx-md-5 {
+    margin-left: 3rem !important;
+  }
+
+  .p-md-0 {
+    padding: 0 !important;
+  }
+
+  .pt-md-0,
+  .py-md-0 {
+    padding-top: 0 !important;
+  }
+
+  .pr-md-0,
+  .px-md-0 {
+    padding-right: 0 !important;
+  }
+
+  .pb-md-0,
+  .py-md-0 {
+    padding-bottom: 0 !important;
+  }
+
+  .pl-md-0,
+  .px-md-0 {
+    padding-left: 0 !important;
+  }
+
+  .p-md-1 {
+    padding: 0.25rem !important;
+  }
+
+  .pt-md-1,
+  .py-md-1 {
+    padding-top: 0.25rem !important;
+  }
+
+  .pr-md-1,
+  .px-md-1 {
+    padding-right: 0.25rem !important;
+  }
+
+  .pb-md-1,
+  .py-md-1 {
+    padding-bottom: 0.25rem !important;
+  }
+
+  .pl-md-1,
+  .px-md-1 {
+    padding-left: 0.25rem !important;
+  }
+
+  .p-md-2 {
+    padding: 0.5rem !important;
+  }
+
+  .pt-md-2,
+  .py-md-2 {
+    padding-top: 0.5rem !important;
+  }
+
+  .pr-md-2,
+  .px-md-2 {
+    padding-right: 0.5rem !important;
+  }
+
+  .pb-md-2,
+  .py-md-2 {
+    padding-bottom: 0.5rem !important;
+  }
+
+  .pl-md-2,
+  .px-md-2 {
+    padding-left: 0.5rem !important;
+  }
+
+  .p-md-3 {
+    padding: 1rem !important;
+  }
+
+  .pt-md-3,
+  .py-md-3 {
+    padding-top: 1rem !important;
+  }
+
+  .pr-md-3,
+  .px-md-3 {
+    padding-right: 1rem !important;
+  }
+
+  .pb-md-3,
+  .py-md-3 {
+    padding-bottom: 1rem !important;
+  }
+
+  .pl-md-3,
+  .px-md-3 {
+    padding-left: 1rem !important;
+  }
+
+  .p-md-4 {
+    padding: 1.5rem !important;
+  }
+
+  .pt-md-4,
+  .py-md-4 {
+    padding-top: 1.5rem !important;
+  }
+
+  .pr-md-4,
+  .px-md-4 {
+    padding-right: 1.5rem !important;
+  }
+
+  .pb-md-4,
+  .py-md-4 {
+    padding-bottom: 1.5rem !important;
+  }
+
+  .pl-md-4,
+  .px-md-4 {
+    padding-left: 1.5rem !important;
+  }
+
+  .p-md-5 {
+    padding: 3rem !important;
+  }
+
+  .pt-md-5,
+  .py-md-5 {
+    padding-top: 3rem !important;
+  }
+
+  .pr-md-5,
+  .px-md-5 {
+    padding-right: 3rem !important;
+  }
+
+  .pb-md-5,
+  .py-md-5 {
+    padding-bottom: 3rem !important;
+  }
+
+  .pl-md-5,
+  .px-md-5 {
+    padding-left: 3rem !important;
+  }
+
+  .m-md-auto {
+    margin: auto !important;
+  }
+
+  .mt-md-auto,
+  .my-md-auto {
+    margin-top: auto !important;
+  }
+
+  .mr-md-auto,
+  .mx-md-auto {
+    margin-right: auto !important;
+  }
+
+  .mb-md-auto,
+  .my-md-auto {
+    margin-bottom: auto !important;
+  }
+
+  .ml-md-auto,
+  .mx-md-auto {
+    margin-left: auto !important;
+  }
+}
+@media (min-width: 992px) {
+  .m-lg-0 {
+    margin: 0 !important;
+  }
+
+  .mt-lg-0,
+  .my-lg-0 {
+    margin-top: 0 !important;
+  }
+
+  .mr-lg-0,
+  .mx-lg-0 {
+    margin-right: 0 !important;
+  }
+
+  .mb-lg-0,
+  .my-lg-0 {
+    margin-bottom: 0 !important;
+  }
+
+  .ml-lg-0,
+  .mx-lg-0 {
+    margin-left: 0 !important;
+  }
+
+  .m-lg-1 {
+    margin: 0.25rem !important;
+  }
+
+  .mt-lg-1,
+  .my-lg-1 {
+    margin-top: 0.25rem !important;
+  }
+
+  .mr-lg-1,
+  .mx-lg-1 {
+    margin-right: 0.25rem !important;
+  }
+
+  .mb-lg-1,
+  .my-lg-1 {
+    margin-bottom: 0.25rem !important;
+  }
+
+  .ml-lg-1,
+  .mx-lg-1 {
+    margin-left: 0.25rem !important;
+  }
+
+  .m-lg-2 {
+    margin: 0.5rem !important;
+  }
+
+  .mt-lg-2,
+  .my-lg-2 {
+    margin-top: 0.5rem !important;
+  }
+
+  .mr-lg-2,
+  .mx-lg-2 {
+    margin-right: 0.5rem !important;
+  }
+
+  .mb-lg-2,
+  .my-lg-2 {
+    margin-bottom: 0.5rem !important;
+  }
+
+  .ml-lg-2,
+  .mx-lg-2 {
+    margin-left: 0.5rem !important;
+  }
+
+  .m-lg-3 {
+    margin: 1rem !important;
+  }
+
+  .mt-lg-3,
+  .my-lg-3 {
+    margin-top: 1rem !important;
+  }
+
+  .mr-lg-3,
+  .mx-lg-3 {
+    margin-right: 1rem !important;
+  }
+
+  .mb-lg-3,
+  .my-lg-3 {
+    margin-bottom: 1rem !important;
+  }
+
+  .ml-lg-3,
+  .mx-lg-3 {
+    margin-left: 1rem !important;
+  }
+
+  .m-lg-4 {
+    margin: 1.5rem !important;
+  }
+
+  .mt-lg-4,
+  .my-lg-4 {
+    margin-top: 1.5rem !important;
+  }
+
+  .mr-lg-4,
+  .mx-lg-4 {
+    margin-right: 1.5rem !important;
+  }
+
+  .mb-lg-4,
+  .my-lg-4 {
+    margin-bottom: 1.5rem !important;
+  }
+
+  .ml-lg-4,
+  .mx-lg-4 {
+    margin-left: 1.5rem !important;
+  }
+
+  .m-lg-5 {
+    margin: 3rem !important;
+  }
+
+  .mt-lg-5,
+  .my-lg-5 {
+    margin-top: 3rem !important;
+  }
+
+  .mr-lg-5,
+  .mx-lg-5 {
+    margin-right: 3rem !important;
+  }
+
+  .mb-lg-5,
+  .my-lg-5 {
+    margin-bottom: 3rem !important;
+  }
+
+  .ml-lg-5,
+  .mx-lg-5 {
+    margin-left: 3rem !important;
+  }
+
+  .p-lg-0 {
+    padding: 0 !important;
+  }
+
+  .pt-lg-0,
+  .py-lg-0 {
+    padding-top: 0 !important;
+  }
+
+  .pr-lg-0,
+  .px-lg-0 {
+    padding-right: 0 !important;
+  }
+
+  .pb-lg-0,
+  .py-lg-0 {
+    padding-bottom: 0 !important;
+  }
+
+  .pl-lg-0,
+  .px-lg-0 {
+    padding-left: 0 !important;
+  }
+
+  .p-lg-1 {
+    padding: 0.25rem !important;
+  }
+
+  .pt-lg-1,
+  .py-lg-1 {
+    padding-top: 0.25rem !important;
+  }
+
+  .pr-lg-1,
+  .px-lg-1 {
+    padding-right: 0.25rem !important;
+  }
+
+  .pb-lg-1,
+  .py-lg-1 {
+    padding-bottom: 0.25rem !important;
+  }
+
+  .pl-lg-1,
+  .px-lg-1 {
+    padding-left: 0.25rem !important;
+  }
+
+  .p-lg-2 {
+    padding: 0.5rem !important;
+  }
+
+  .pt-lg-2,
+  .py-lg-2 {
+    padding-top: 0.5rem !important;
+  }
+
+  .pr-lg-2,
+  .px-lg-2 {
+    padding-right: 0.5rem !important;
+  }
+
+  .pb-lg-2,
+  .py-lg-2 {
+    padding-bottom: 0.5rem !important;
+  }
+
+  .pl-lg-2,
+  .px-lg-2 {
+    padding-left: 0.5rem !important;
+  }
+
+  .p-lg-3 {
+    padding: 1rem !important;
+  }
+
+  .pt-lg-3,
+  .py-lg-3 {
+    padding-top: 1rem !important;
+  }
+
+  .pr-lg-3,
+  .px-lg-3 {
+    padding-right: 1rem !important;
+  }
+
+  .pb-lg-3,
+  .py-lg-3 {
+    padding-bottom: 1rem !important;
+  }
+
+  .pl-lg-3,
+  .px-lg-3 {
+    padding-left: 1rem !important;
+  }
+
+  .p-lg-4 {
+    padding: 1.5rem !important;
+  }
+
+  .pt-lg-4,
+  .py-lg-4 {
+    padding-top: 1.5rem !important;
+  }
+
+  .pr-lg-4,
+  .px-lg-4 {
+    padding-right: 1.5rem !important;
+  }
+
+  .pb-lg-4,
+  .py-lg-4 {
+    padding-bottom: 1.5rem !important;
+  }
+
+  .pl-lg-4,
+  .px-lg-4 {
+    padding-left: 1.5rem !important;
+  }
+
+  .p-lg-5 {
+    padding: 3rem !important;
+  }
+
+  .pt-lg-5,
+  .py-lg-5 {
+    padding-top: 3rem !important;
+  }
+
+  .pr-lg-5,
+  .px-lg-5 {
+    padding-right: 3rem !important;
+  }
+
+  .pb-lg-5,
+  .py-lg-5 {
+    padding-bottom: 3rem !important;
+  }
+
+  .pl-lg-5,
+  .px-lg-5 {
+    padding-left: 3rem !important;
+  }
+
+  .m-lg-auto {
+    margin: auto !important;
+  }
+
+  .mt-lg-auto,
+  .my-lg-auto {
+    margin-top: auto !important;
+  }
+
+  .mr-lg-auto,
+  .mx-lg-auto {
+    margin-right: auto !important;
+  }
+
+  .mb-lg-auto,
+  .my-lg-auto {
+    margin-bottom: auto !important;
+  }
+
+  .ml-lg-auto,
+  .mx-lg-auto {
+    margin-left: auto !important;
+  }
+}
+@media (min-width: 1200px) {
+  .m-xl-0 {
+    margin: 0 !important;
+  }
+
+  .mt-xl-0,
+  .my-xl-0 {
+    margin-top: 0 !important;
+  }
+
+  .mr-xl-0,
+  .mx-xl-0 {
+    margin-right: 0 !important;
+  }
+
+  .mb-xl-0,
+  .my-xl-0 {
+    margin-bottom: 0 !important;
+  }
+
+  .ml-xl-0,
+  .mx-xl-0 {
+    margin-left: 0 !important;
+  }
+
+  .m-xl-1 {
+    margin: 0.25rem !important;
+  }
+
+  .mt-xl-1,
+  .my-xl-1 {
+    margin-top: 0.25rem !important;
+  }
+
+  .mr-xl-1,
+  .mx-xl-1 {
+    margin-right: 0.25rem !important;
+  }
+
+  .mb-xl-1,
+  .my-xl-1 {
+    margin-bottom: 0.25rem !important;
+  }
+
+  .ml-xl-1,
+  .mx-xl-1 {
+    margin-left: 0.25rem !important;
+  }
+
+  .m-xl-2 {
+    margin: 0.5rem !important;
+  }
+
+  .mt-xl-2,
+  .my-xl-2 {
+    margin-top: 0.5rem !important;
+  }
+
+  .mr-xl-2,
+  .mx-xl-2 {
+    margin-right: 0.5rem !important;
+  }
+
+  .mb-xl-2,
+  .my-xl-2 {
+    margin-bottom: 0.5rem !important;
+  }
+
+  .ml-xl-2,
+  .mx-xl-2 {
+    margin-left: 0.5rem !important;
+  }
+
+  .m-xl-3 {
+    margin: 1rem !important;
+  }
+
+  .mt-xl-3,
+  .my-xl-3 {
+    margin-top: 1rem !important;
+  }
+
+  .mr-xl-3,
+  .mx-xl-3 {
+    margin-right: 1rem !important;
+  }
+
+  .mb-xl-3,
+  .my-xl-3 {
+    margin-bottom: 1rem !important;
+  }
+
+  .ml-xl-3,
+  .mx-xl-3 {
+    margin-left: 1rem !important;
+  }
+
+  .m-xl-4 {
+    margin: 1.5rem !important;
+  }
+
+  .mt-xl-4,
+  .my-xl-4 {
+    margin-top: 1.5rem !important;
+  }
+
+  .mr-xl-4,
+  .mx-xl-4 {
+    margin-right: 1.5rem !important;
+  }
+
+  .mb-xl-4,
+  .my-xl-4 {
+    margin-bottom: 1.5rem !important;
+  }
+
+  .ml-xl-4,
+  .mx-xl-4 {
+    margin-left: 1.5rem !important;
+  }
+
+  .m-xl-5 {
+    margin: 3rem !important;
+  }
+
+  .mt-xl-5,
+  .my-xl-5 {
+    margin-top: 3rem !important;
+  }
+
+  .mr-xl-5,
+  .mx-xl-5 {
+    margin-right: 3rem !important;
+  }
+
+  .mb-xl-5,
+  .my-xl-5 {
+    margin-bottom: 3rem !important;
+  }
+
+  .ml-xl-5,
+  .mx-xl-5 {
+    margin-left: 3rem !important;
+  }
+
+  .p-xl-0 {
+    padding: 0 !important;
+  }
+
+  .pt-xl-0,
+  .py-xl-0 {
+    padding-top: 0 !important;
+  }
+
+  .pr-xl-0,
+  .px-xl-0 {
+    padding-right: 0 !important;
+  }
+
+  .pb-xl-0,
+  .py-xl-0 {
+    padding-bottom: 0 !important;
+  }
+
+  .pl-xl-0,
+  .px-xl-0 {
+    padding-left: 0 !important;
+  }
+
+  .p-xl-1 {
+    padding: 0.25rem !important;
+  }
+
+  .pt-xl-1,
+  .py-xl-1 {
+    padding-top: 0.25rem !important;
+  }
+
+  .pr-xl-1,
+  .px-xl-1 {
+    padding-right: 0.25rem !important;
+  }
+
+  .pb-xl-1,
+  .py-xl-1 {
+    padding-bottom: 0.25rem !important;
+  }
+
+  .pl-xl-1,
+  .px-xl-1 {
+    padding-left: 0.25rem !important;
+  }
+
+  .p-xl-2 {
+    padding: 0.5rem !important;
+  }
+
+  .pt-xl-2,
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+  .py-xl-3 {
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+  .pr-xl-3,
+  .px-xl-3 {
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+  }
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+  .pb-xl-3,
+  .py-xl-3 {
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+  .pl-xl-3,
+  .px-xl-3 {
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+  .pt-xl-4,
+  .py-xl-4 {
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+  .pr-xl-4,
+  .px-xl-4 {
+    padding-right: 1.5rem !important;
+  }
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+  .pb-xl-4,
+  .py-xl-4 {
+    padding-bottom: 1.5rem !important;
+  }
+
+  .pl-xl-4,
+  .px-xl-4 {
+    padding-left: 1.5rem !important;
+  }
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+  .p-xl-5 {
+    padding: 3rem !important;
+  }
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+  .pt-xl-5,
+  .py-xl-5 {
+    padding-top: 3rem !important;
+  }
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+  .pr-xl-5,
+  .px-xl-5 {
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+  }
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+  .pb-xl-5,
+  .py-xl-5 {
+    padding-bottom: 3rem !important;
+  }
+
+  .pl-xl-5,
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+    padding-left: 3rem !important;
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+  .mt-xl-auto,
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+  .mr-xl-auto,
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+  .mb-xl-auto,
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+    margin-bottom: auto !important;
+  }
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+  .ml-xl-auto,
+  .mx-xl-auto {
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+}
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+}
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+}
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+}
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+}
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+}
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+  .text-sm-center {
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+}
+@media (min-width: 768px) {
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+  .text-md-right {
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+  .text-md-center {
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+}
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+  .text-lg-right {
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+  .text-lg-center {
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+}
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+}
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+}
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+}
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+  a:not(.btn) {
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+  thead {
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+  tr,
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+  p,
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+  body {
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+  .container {
+    min-width: 992px !important;
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+
+  .navbar {
+    display: none;
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+
+  .badge {
+    border: 1px solid #000;
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+  .table {
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+  .table td,
+  .table th {
+    background-color: #fff !important;
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+}
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+}
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+}
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+  font-weight: 700;
+  font-style: normal;
+  src: url("../fonts/FreightSans/freight-sans-bold.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-bold.woff") format("woff");
+}
+@font-face {
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+  font-weight: 700;
+  font-style: italic;
+  src: url("../fonts/FreightSans/freight-sans-bold-italic.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-bold-italic.woff") format("woff");
+}
+@font-face {
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+  src: url("../fonts/FreightSans/freight-sans-medium.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-medium.woff") format("woff");
+}
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+  font-style: italic;
+  src: url("../fonts/FreightSans/freight-sans-medium-italic.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-medium-italic.woff") format("woff");
+}
+@font-face {
+  font-family: FreightSans;
+  font-weight: 100;
+  font-style: normal;
+  src: url("../fonts/FreightSans/freight-sans-light.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-light.woff") format("woff");
+}
+@font-face {
+  font-family: FreightSans;
+  font-weight: 100;
+  font-style: italic;
+  src: url("../fonts/FreightSans/freight-sans-light-italic.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-light-italic.woff") format("woff");
+}
+@font-face {
+  font-family: FreightSans;
+  font-weight: 400;
+  font-style: italic;
+  src: url("../fonts/FreightSans/freight-sans-book-italic.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-book-italic.woff") format("woff");
+}
+@font-face {
+  font-family: FreightSans;
+  font-weight: 400;
+  font-style: normal;
+  src: url("../fonts/FreightSans/freight-sans-book.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-book.woff") format("woff");
+}
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+  font-weight: 600;
+  font-style: normal;
+  unicode-range: u+0020-007f;
+  src: local("IBMPlexMono-SemiBold"), url("../fonts/IBMPlexMono/IBMPlexMono-SemiBold.woff2") format("woff2"), url("../fonts/IBMPlexMono/IBMPlexMono-SemiBold.woff") format("woff");
+}
+@font-face {
+  font-family: IBMPlexMono;
+  font-weight: 500;
+  font-style: normal;
+  unicode-range: u+0020-007f;
+  src: local("IBMPlexMono-Medium"), url("../fonts/IBMPlexMono/IBMPlexMono-Medium.woff2") format("woff2"), url("../fonts/IBMPlexMono/IBMPlexMono-Medium.woff") format("woff");
+}
+@font-face {
+  font-family: IBMPlexMono;
+  font-weight: 400;
+  font-style: normal;
+  unicode-range: u+0020-007f;
+  src: local("IBMPlexMono-Regular"), url("../fonts/IBMPlexMono/IBMPlexMono-Regular.woff2") format("woff2"), url("../fonts/IBMPlexMono/IBMPlexMono-Regular.woff") format("woff");
+}
+@font-face {
+  font-family: IBMPlexMono;
+  font-weight: 300;
+  font-style: normal;
+  unicode-range: u+0020-007f;
+  src: local("IBMPlexMono-Light"), url("../fonts/IBMPlexMono/IBMPlexMono-Light.woff2") format("woff2"), url("../fonts/IBMPlexMono/IBMPlexMono-Light.woff") format("woff");
+}
+html {
+  position: relative;
+  min-height: 100%;
+  font-size: 12px;
+}
+@media screen and (min-width: 768px) {
+  html {
+    font-size: 16px;
+  }
+}
+
+* {
+  -webkit-box-sizing: border-box;
+          box-sizing: border-box;
+}
+
+body {
+  font-family: FreightSans, Helvetica Neue, Helvetica, Arial, sans-serif;
+}
+
+a:link,
+a:visited,
+a:hover {
+  text-decoration: none;
+  color: #e44c2c;
+}
+
+a.with-right-arrow, .btn.with-right-arrow {
+  padding-right: 1.375rem;
+  position: relative;
+  background-image: url("../images/chevron-right-orange.svg");
+  background-size: 6px 13px;
+  background-position: center right 5px;
+  background-repeat: no-repeat;
+}
+@media screen and (min-width: 768px) {
+  a.with-right-arrow, .btn.with-right-arrow {
+    background-size: 8px 14px;
+    background-position: center right 12px;
+    padding-right: 2rem;
+  }
+}
+
+::-webkit-input-placeholder {
+  color: #e44c2c;
+}
+
+::-moz-placeholder {
+  color: #e44c2c;
+}
+
+:-ms-input-placeholder {
+  color: #e44c2c;
+}
+
+:-moz-placeholder {
+  color: #e44c2c;
+}
+
+.email-subscribe-form input.email {
+  color: #e44c2c;
+  border: none;
+  border-bottom: 1px solid #939393;
+  width: 100%;
+  background-color: transparent;
+  outline: none;
+  font-size: 1.125rem;
+  letter-spacing: 0.25px;
+  line-height: 2.25rem;
+}
+.email-subscribe-form input[type="submit"] {
+  position: absolute;
+  right: 0;
+  top: 10px;
+  height: 15px;
+  width: 15px;
+  background-image: url("../images/arrow-right-with-tail.svg");
+  background-color: transparent;
+  background-repeat: no-repeat;
+  background-size: 15px 15px;
+  background-position: center center;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  appearance: none;
+  border: 0;
+}
+
+.email-subscribe-form-fields-wrapper {
+  position: relative;
+}
+
+.anchorjs-link {
+  color: #6c6c6d !important;
+}
+@media screen and (min-width: 768px) {
+  .anchorjs-link:hover {
+    color: inherit;
+    text-decoration: none !important;
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+}
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+}
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+code, kbd, pre, samp {
+  font-family: IBMPlexMono,SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;
+}
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+  font-family: IBMPlexMono,SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;
+}
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+}
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+}
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+  line-height: 1.3125rem;
+}
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+code.highlighter-rouge {
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+  background-color: #f3f4f7;
+  padding: 2px 6px;
+}
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+a:link code.highlighter-rouge,
+a:visited code.highlighter-rouge,
+a:hover code.highlighter-rouge {
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+}
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+a:visited.has-code,
+a:hover.has-code {
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+}
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+p code,
+h1 code,
+h2 code,
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+}
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+pre {
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+  white-space: -moz-pre-wrap;
+  white-space: -pre-wrap;
+  white-space: -o-pre-wrap;
+  word-wrap: break-word;
+}
+
+.header-holder {
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+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  left: 0;
+  margin-left: auto;
+  margin-right: auto;
+  position: fixed;
+  right: 0;
+  top: 0;
+  width: 100%;
+  z-index: 9999;
+  background-color: #ffffff;
+  border-bottom: 1px solid #e2e2e2;
+}
+@media screen and (min-width: 1100px) {
+  .header-holder {
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+}
+
+.header-container {
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+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+}
+.header-container:before, .header-container:after {
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+  display: table;
+}
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+  clear: both;
+}
+.header-container {
+  *zoom: 1;
+}
+@media screen and (min-width: 1100px) {
+  .header-container {
+    display: block;
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+}
+
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+  height: 23px;
+  width: 93px;
+  background-image: url("../images/logo.svg");
+  background-repeat: no-repeat;
+  background-size: 93px 23px;
+  display: block;
+  float: left;
+  z-index: 10;
+}
+@media screen and (min-width: 1100px) {
+  .header-logo {
+    background-size: 108px 27px;
+    position: absolute;
+    height: 27px;
+    width: 108px;
+    top: 4px;
+    float: none;
+  }
+}
+
+.main-menu-open-button {
+  background-image: url("../images/icon-menu-dots.svg");
+  background-position: center center;
+  background-size: 25px 7px;
+  background-repeat: no-repeat;
+  width: 25px;
+  height: 17px;
+  position: absolute;
+  right: 0;
+  top: 4px;
+}
+@media screen and (min-width: 1100px) {
+  .main-menu-open-button {
+    display: none;
+  }
+}
+
+.header-holder .main-menu {
+  display: none;
+}
+@media screen and (min-width: 1100px) {
+  .header-holder .main-menu {
+    display: -webkit-box;
+    display: -ms-flexbox;
+    display: flex;
+    -webkit-box-align: center;
+        -ms-flex-align: center;
+            align-items: center;
+    -webkit-box-pack: end;
+        -ms-flex-pack: end;
+            justify-content: flex-end;
+  }
+}
+.header-holder .main-menu ul {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  margin: 0;
+}
+.header-holder .main-menu ul li {
+  display: inline-block;
+  margin-right: 40px;
+  position: relative;
+}
+.header-holder .main-menu ul li.active:after {
+  content: "•";
+  bottom: -24px;
+  color: #e44c2c;
+  font-size: 1.375rem;
+  left: 0;
+  position: absolute;
+  right: 0;
+  text-align: center;
+}
+.header-holder .main-menu ul li.active a {
+  color: #e44c2c;
+}
+.header-holder .main-menu ul li.docs-active:after {
+  content: "•";
+  bottom: -24px;
+  color: #e44c2c;
+  font-size: 1.375rem;
+  left: -24px;
+  position: absolute;
+  right: 0;
+  text-align: center;
+}
+.header-holder .main-menu ul li:last-of-type {
+  margin-right: 0;
+}
+.header-holder .main-menu ul li a {
+  color: #ffffff;
+  font-size: 1.3rem;
+  letter-spacing: 0;
+  line-height: 2.125rem;
+  text-align: center;
+  text-decoration: none;
+}
+@media screen and (min-width: 1100px) {
+  .header-holder .main-menu ul li a:hover {
+    color: #e44c2c;
+  }
+}
+
+.mobile-main-menu {
+  display: none;
+}
+.mobile-main-menu.open {
+  background-color: #262626;
+  display: block;
+  height: 100%;
+  left: 0;
+  margin-left: auto;
+  margin-right: auto;
+  min-height: 100%;
+  position: fixed;
+  right: 0;
+  top: 0;
+  width: 100%;
+  z-index: 99999;
+}
+
+.mobile-main-menu .container-fluid {
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  height: 68px;
+  position: relative;
+}
+.mobile-main-menu .container-fluid:before, .mobile-main-menu .container-fluid:after {
+  content: "";
+  display: table;
+}
+.mobile-main-menu .container-fluid:after {
+  clear: both;
+}
+.mobile-main-menu .container-fluid {
+  *zoom: 1;
+}
+
+.mobile-main-menu.open ul {
+  list-style-type: none;
+  padding: 0;
+}
+.mobile-main-menu.open ul li a, .mobile-main-menu.open .resources-mobile-menu-title {
+  font-size: 2rem;
+  color: #ffffff;
+  letter-spacing: 0;
+  line-height: 4rem;
+  text-decoration: none;
+}
+.mobile-main-menu.open ul li.active a {
+  color: #e44c2c;
+}
+
+.main-menu-close-button {
+  background-image: url("../images/icon-close.svg");
+  background-position: center center;
+  background-repeat: no-repeat;
+  background-size: 24px 24px;
+  height: 24px;
+  position: absolute;
+  right: 0;
+  width: 24px;
+  top: -4px;
+}
+
+.mobile-main-menu-header-container {
+  position: relative;
+}
+
+.mobile-main-menu-links-container {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  padding-left: 2.8125rem;
+  height: 90vh;
+  margin-top: -25px;
+  padding-top: 50%;
+  overflow-y: scroll;
+}
+.mobile-main-menu-links-container .main-menu {
+  height: 100vh;
+}
+
+.mobile-main-menu-links-container ul.resources-mobile-menu-items li {
+  padding-left: 15px;
+}
+
+.site-footer {
+  padding: 2.5rem 0;
+  width: 100%;
+  background: #000000;
+  background-size: 100%;
+  margin-left: 0;
+  margin-right: 0;
+  position: relative;
+  z-index: 201;
+}
+@media screen and (min-width: 768px) {
+  .site-footer {
+    padding: 5rem 0;
+  }
+}
+
+.site-footer p {
+  color: #ffffff;
+}
+.site-footer ul {
+  list-style-type: none;
+  padding-left: 0;
+  margin-bottom: 0;
+}
+
+.site-footer ul li {
+  font-size: 1.125rem;
+  line-height: 2rem;
+  color: #A0A0A1;
+  padding-bottom: 0.375rem;
+}
+.site-footer ul li.list-title {
+  padding-bottom: 0.75rem;
+  color: #ffffff;
+}
+.site-footer a:link,
+.site-footer a:visited {
+  color: inherit;
+}
+@media screen and (min-width: 768px) {
+  .site-footer a:hover {
+    color: #e44c2c;
+  }
+}
+
+.site-footer .privacy-policy {
+    background: #000000;
+    display: flex;
+    border-bottom: 1px solid white;
+    padding-bottom: 10px;
+}
+
+.site-footer .privacy-policy-links {
+    background: #000000;
+    display: flex;
+    padding-top: 1rem;
+    padding-right: 1rem;
+    display: inline-flex;
+    color: white;
+}
+
+.site-footer .footer-links-wrapper {
+    display: flex;
+    flex-wrap: wrap;
+    border-bottom: 1px solid white;
+    padding-bottom: 1rem;
+}
+
+.site-footer .copyright {
+    padding-top: 1rem;
+    padding-right: 1rem;
+    display: inline-flex;
+    color: white;
+}
+
+.site-footer .copyright p {
+    color: white;
+}
+
+.site-footer .copyright a {
+    color: red;
+}
+
+.docs-tutorials-resources {
+  background-color: #262626;
+  color: #ffffff;
+  padding-top: 2.5rem;
+  padding-bottom: 2.5rem;
+  position: relative;
+  z-index: 201;
+}
+@media screen and (min-width: 768px) {
+  .docs-tutorials-resources {
+    padding-top: 5rem;
+    padding-bottom: 5rem;
+  }
+}
+.docs-tutorials-resources p {
+  color: #929292;
+  font-size: 1.125rem;
+}
+.docs-tutorials-resources h2 {
+  font-size: 1.5rem;
+  letter-spacing: -0.25px;
+  text-transform: none;
+  margin-bottom: 0.25rem;
+}
+@media screen and (min-width: 768px) {
+  .docs-tutorials-resources h2 {
+    margin-bottom: 1.25rem;
+  }
+}
+.docs-tutorials-resources .col-md-4 {
+  margin-bottom: 2rem;
+  text-align: center;
+}
+@media screen and (min-width: 768px) {
+  .docs-tutorials-resources .col-md-4 {
+    margin-bottom: 0;
+  }
+}
+.docs-tutorials-resources .with-right-arrow {
+  margin-left: 12px;
+}
+.docs-tutorials-resources .with-right-arrow:hover {
+  background-image: url("../images/chevron-right-white.svg");
+}
+.docs-tutorials-resources p {
+  font-size: 1rem;
+  line-height: 1.5rem;
+  letter-spacing: 0.22px;
+  color: #939393;
+  margin-bottom: 0;
+}
+@media screen and (min-width: 768px) {
+  .docs-tutorials-resources p {
+    margin-bottom: 1.25rem;
+  }
+}
+.docs-tutorials-resources a {
+  font-size: 1.125rem;
+  color: #e44c2c;
+}
+.docs-tutorials-resources a:hover {
+  color: #ffffff;
+}
+
+.footer-container {
+  position: relative;
+}
+
+@media screen and (min-width: 768px) {
+  .footer-logo-wrapper {
+    position: absolute;
+    top: 0;
+    left: 30px;
+  }
+}
+
+.footer-logo {
+  background-image: url("../images/logo-icon.svg");
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: 20px 24px;
+  display: block;
+  height: 24px;
+  margin-bottom: 2.8125rem;
+  width: 20px;
+}
+@media screen and (min-width: 768px) {
+  .footer-logo {
+    background-size: 29px 36px;
+    height: 36px;
+    margin-bottom: 0;
+    margin-bottom: 0;
+    width: 29px;
+  }
+}
+
+.footer-links-wrapper {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+}
+@media screen and (min-width: 768px) {
+  .footer-links-wrapper {
+    -ms-flex-wrap: initial;
+        flex-wrap: initial;
+    -webkit-box-pack: end;
+        -ms-flex-pack: end;
+            justify-content: flex-end;
+  }
+}
+
+.footer-links-col {
+  margin-bottom: 3.75rem;
+  width: 50%;
+}
+@media screen and (min-width: 768px) {
+  .footer-links-col {
+    margin-bottom: 0;
+    width: 14%;
+    margin-right: 23px;
+  }
+  .footer-links-col.follow-us-col {
+    width: 18%;
+    margin-right: 0;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  .footer-links-col {
+    width: 18%;
+    margin-right: 30px;
+  }
+}
+
+.footer-social-icons {
+  margin: 8.5625rem 0 2.5rem 0;
+}
+.footer-social-icons a {
+  height: 32px;
+  width: 32px;
+  display: inline-block;
+  background-color: #CCCDD1;
+  border-radius: 50%;
+  margin-right: 5px;
+}
+.footer-social-icons a.facebook {
+  background-image: url("../images/logo-facebook-dark.svg");
+  background-position: center center;
+  background-size: 9px 18px;
+  background-repeat: no-repeat;
+}
+.footer-social-icons a.twitter {
+  background-image: url("../images/logo-twitter-dark.svg");
+  background-position: center center;
+  background-size: 17px 17px;
+  background-repeat: no-repeat;
+}
+.footer-social-icons a.youtube {
+  background-image: url("../images/logo-youtube-dark.svg");
+  background-position: center center;
+  background-repeat: no-repeat;
+}
+
+.site-footer .mc-field-group {
+  margin-top: -2px;
+}
+
+article.pytorch-article {
+  max-width: 920px;
+  margin: 0 auto;
+}
+article.pytorch-article h2,
+article.pytorch-article h3,
+article.pytorch-article h4,
+article.pytorch-article h5,
+article.pytorch-article h6 {
+  margin: 1.375rem 0;
+  color: #262626;
+}
+article.pytorch-article h2 {
+  font-size: 1.625rem;
+  letter-spacing: 1.33px;
+  line-height: 2rem;
+  text-transform: none;
+}
+article.pytorch-article h3 {
+  font-size: 1.5rem;
+  letter-spacing: -0.25px;
+  line-height: 1.875rem;
+  text-transform: none;
+}
+article.pytorch-article h4,
+article.pytorch-article h5,
+article.pytorch-article h6 {
+  font-size: 1.125rem;
+  letter-spacing: -0.19px;
+  line-height: 1.875rem;
+}
+article.pytorch-article p {
+  margin-bottom: 1.125rem;
+}
+article.pytorch-article p,
+article.pytorch-article ul li,
+article.pytorch-article ol li,
+article.pytorch-article dl dt,
+article.pytorch-article dl dd,
+article.pytorch-article blockquote {
+  font-size: 1rem;
+  line-height: 1.375rem;
+  color: #262626;
+  letter-spacing: 0.01px;
+  font-weight: 500;
+}
+article.pytorch-article table {
+  margin-bottom: 2.5rem;
+  width: 100%;
+}
+article.pytorch-article table thead {
+  border-bottom: 1px solid #cacaca;
+}
+article.pytorch-article table th {
+  padding: 0.625rem;
+  color: #262626;
+}
+article.pytorch-article table td {
+  padding: 0.3125rem;
+}
+article.pytorch-article table tr th:first-of-type,
+article.pytorch-article table tr td:first-of-type {
+  padding-left: 0;
+}
+article.pytorch-article table.docutils.field-list th.field-name {
+  padding: 0.3125rem;
+  padding-left: 0;
+}
+article.pytorch-article table.docutils.field-list td.field-body {
+  padding: 0.3125rem;
+}
+article.pytorch-article table.docutils.field-list td.field-body p:last-of-type {
+  margin-bottom: 0;
+}
+article.pytorch-article ul,
+article.pytorch-article ol {
+  margin: 1.5rem 0 3.125rem 0;
+}
+@media screen and (min-width: 768px) {
+  article.pytorch-article ul,
+  article.pytorch-article ol {
+    padding-left: 6.25rem;
+  }
+}
+article.pytorch-article ul li,
+article.pytorch-article ol li {
+  margin-bottom: 0.625rem;
+}
+article.pytorch-article dl {
+  margin-bottom: 1.5rem;
+}
+article.pytorch-article dl dt {
+  margin-bottom: 0.75rem;
+}
+article.pytorch-article pre {
+  margin-bottom: 2.5rem;
+}
+article.pytorch-article hr {
+  margin-top: 4.6875rem;
+  margin-bottom: 4.6875rem;
+}
+article.pytorch-article blockquote {
+  margin: 0 auto;
+  margin-bottom: 2.5rem;
+  width: 65%;
+}
+article.pytorch-article img {
+  width: 100%;
+}
+
+html {
+  height: 100%;
+}
+@media screen and (min-width: 768px) {
+  html {
+    font-size: 16px;
+  }
+}
+
+body {
+  background: #ffffff;
+  height: 100%;
+  margin: 0;
+}
+body.no-scroll {
+  height: 100%;
+  overflow: hidden;
+}
+
+p {
+  margin-top: 0;
+  margin-bottom: 1.125rem;
+}
+p a:link,
+p a:visited,
+p a:hover {
+  color: #e44c2c;
+  text-decoration: none;
+}
+@media screen and (min-width: 768px) {
+  p a:hover {
+    text-decoration: underline;
+  }
+}
+p a:link,
+p a:visited,
+p a:hover {
+  color: #ee4c2c;
+}
+
+.wy-breadcrumbs li a {
+  color: #ee4c2c;
+}
+
+ul.pytorch-breadcrumbs {
+  padding-left: 0;
+  list-style-type: none;
+}
+ul.pytorch-breadcrumbs li {
+  display: inline-block;
+  font-size: 0.875rem;
+}
+ul.pytorch-breadcrumbs a {
+  color: #ee4c2c;
+  text-decoration: none;
+}
+
+.table-of-contents-link-wrapper {
+  display: block;
+  margin-top: 0;
+  padding: 1.25rem 1.875rem;
+  background-color: #f3f4f7;
+  position: relative;
+  color: #262626;
+  font-size: 1.25rem;
+}
+.table-of-contents-link-wrapper.is-open .toggle-table-of-contents {
+  -webkit-transform: rotate(180deg);
+          transform: rotate(180deg);
+}
+@media screen and (min-width: 1100px) {
+  .table-of-contents-link-wrapper {
+    display: none;
+  }
+}
+
+.toggle-table-of-contents {
+  background-image: url("../images/chevron-down-grey.svg");
+  background-position: center center;
+  background-repeat: no-repeat;
+  background-size: 18px 18px;
+  height: 100%;
+  position: absolute;
+  right: 21px;
+  width: 30px;
+  top: 0;
+}
+
+.tutorials-header .header-logo {
+  background-image: url("../images/logo-dark.svg");
+}
+.tutorials-header .main-menu ul li a {
+  color: #262626;
+}
+.tutorials-header .main-menu-open-button {
+  background-image: url("../images/icon-menu-dots-dark.svg");
+}
+
+.rst-content footer .rating-hr.hr-top {
+  margin-bottom: -0.0625rem;
+}
+.rst-content footer .rating-hr.hr-bottom {
+  margin-top: -0.0625rem;
+}
+.rst-content footer .rating-container {
+  display: -webkit-inline-box;
+  display: -ms-inline-flexbox;
+  display: inline-flex;
+  font-size: 1.125rem;
+}
+.rst-content footer .rating-container .rating-prompt, .rst-content footer .rating-container .was-helpful-thank-you {
+  padding: 0.625rem 1.25rem 0.625rem 1.25rem;
+}
+.rst-content footer .rating-container .was-helpful-thank-you {
+  display: none;
+}
+.rst-content footer .rating-container .rating-prompt.yes-link, .rst-content footer .rating-container .rating-prompt.no-link {
+  color: #e44c2c;
+  cursor: pointer;
+}
+.rst-content footer .rating-container .rating-prompt.yes-link:hover, .rst-content footer .rating-container .rating-prompt.no-link:hover {
+  background-color: #e44c2c;
+  color: #ffffff;
+}
+.rst-content footer .rating-container .stars-outer {
+  display: inline-block;
+  position: relative;
+  font-family: FontAwesome;
+  padding: 0.625rem 1.25rem 0.625rem 1.25rem;
+}
+.rst-content footer .rating-container .stars-outer i {
+  cursor: pointer;
+}
+.rst-content footer .rating-container .stars-outer .star-fill {
+  color: #ee4c2c;
+}
+.rst-content footer div[role="contentinfo"] {
+  padding-top: 2.5rem;
+}
+.rst-content footer div[role="contentinfo"] p {
+  margin-bottom: 0;
+}
+
+h1 {
+  font-size: 2rem;
+  letter-spacing: 1.78px;
+  line-height: 2.5rem;
+  text-transform: uppercase;
+  margin: 1.375rem 0;
+}
+
+span.pre {
+  color: #6c6c6d;
+  background-color: #f3f4f7;
+  padding: 2px 0px;
+}
+
+pre {
+  padding: 1.375rem;
+}
+
+.highlight .c1 {
+  color: #6c6c6d;
+}
+
+.headerlink {
+  display: none !important;
+}
+
+a:link.has-code,
+a:hover.has-code,
+a:visited.has-code {
+  color: #4974D1;
+}
+a:link.has-code span,
+a:hover.has-code span,
+a:visited.has-code span {
+  color: #4974D1;
+}
+
+article.pytorch-article ul,
+article.pytorch-article ol {
+  padding-left: 1.875rem;
+  margin: 0;
+}
+article.pytorch-article ul li,
+article.pytorch-article ol li {
+  margin: 0;
+  line-height: 1.75rem;
+}
+article.pytorch-article ul p,
+article.pytorch-article ol p {
+  line-height: 1.75rem;
+  margin-bottom: 0;
+}
+article.pytorch-article ul ul,
+article.pytorch-article ul ol,
+article.pytorch-article ol ul,
+article.pytorch-article ol ol {
+  margin: 0;
+}
+article.pytorch-article h1,
+article.pytorch-article h2,
+article.pytorch-article h3,
+article.pytorch-article h4,
+article.pytorch-article h5,
+article.pytorch-article h6 {
+  font-weight: normal;
+}
+article.pytorch-article h1 a,
+article.pytorch-article h2 a,
+article.pytorch-article h3 a,
+article.pytorch-article h4 a,
+article.pytorch-article h5 a,
+article.pytorch-article h6 a {
+  color: #262626;
+}
+article.pytorch-article p.caption {
+  margin-top: 1.25rem;
+}
+
+article.pytorch-article .section:first-of-type h1:first-of-type {
+  margin-top: 0;
+}
+
+article.pytorch-article .sphx-glr-thumbcontainer {
+  margin: 0;
+  border: 1px solid #d6d7d8;
+  border-radius: 0;
+  width: 45%;
+  text-align: center;
+  margin-bottom: 5%;
+}
+@media screen and (max-width: 1100px) {
+  article.pytorch-article .sphx-glr-thumbcontainer:nth-child(odd) {
+    margin-left: 0;
+    margin-right: 2.5%;
+  }
+  article.pytorch-article .sphx-glr-thumbcontainer:nth-child(even) {
+    margin-right: 0;
+    margin-left: 2.5%;
+  }
+  article.pytorch-article .sphx-glr-thumbcontainer .figure {
+    width: 40%;
+  }
+}
+@media screen and (min-width: 1101px) {
+  article.pytorch-article .sphx-glr-thumbcontainer {
+    margin-right: 3%;
+    margin-bottom: 3%;
+    width: 30%;
+  }
+}
+article.pytorch-article .sphx-glr-thumbcontainer .caption-text a {
+  font-size: 1rem;
+  color: #262626;
+  letter-spacing: 0;
+  line-height: 1.5rem;
+  text-decoration: none;
+}
+article.pytorch-article .sphx-glr-thumbcontainer:hover {
+  -webkit-box-shadow: none;
+          box-shadow: none;
+  border-bottom-color: #ffffff;
+}
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+}
+
+a[rel~="prev"], a[rel~="next"] {
+  padding: 0.375rem 0 0 0;
+}
+
+img.next-page,
+img.previous-page {
+  width: 8px;
+  height: 10px;
+  position: relative;
+  top: -1px;
+}
+
+img.previous-page {
+  -webkit-transform: scaleX(-1);
+          transform: scaleX(-1);
+}
+
+.rst-footer-buttons {
+  margin-top: 1.875rem;
+  margin-bottom: 1.875rem;
+}
+.rst-footer-buttons .btn:focus,
+.rst-footer-buttons .btn.focus {
+  -webkit-box-shadow: none;
+          box-shadow: none;
+}
+
+article.pytorch-article blockquote {
+  margin-left: 3.75rem;
+  color: #6c6c6d;
+}
+
+article.pytorch-article .caption {
+  color: #6c6c6d;
+  letter-spacing: 0.25px;
+  line-height: 2.125rem;
+}
+
+article.pytorch-article .math {
+  color: #262626;
+  width: auto;
+  text-align: center;
+}
+article.pytorch-article .math img {
+  width: auto;
+}
+
+.pytorch-breadcrumbs-wrapper {
+  width: 100%;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-breadcrumbs-wrapper {
+    float: left;
+    margin-left: 3%;
+    width: 75%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-breadcrumbs-wrapper {
+    width: 850px;
+    margin-left: 1.875rem;
+  }
+}
+.pytorch-breadcrumbs-wrapper .pytorch-breadcrumbs-aside {
+  float: right;
+}
+.pytorch-breadcrumbs-wrapper .pytorch-breadcrumbs-aside .fa.fa-github {
+  margin-top: 5px;
+  display: block;
+}
+
+.pytorch-article .container {
+  padding-left: 0;
+  padding-right: 0;
+  max-width: none;
+}
+
+a:link,
+a:visited,
+a:hover {
+  color: #ee4c2c;
+}
+
+::-webkit-input-placeholder {
+  color: #ee4c2c;
+}
+
+::-moz-placeholder {
+  color: #ee4c2c;
+}
+
+:-ms-input-placeholder {
+  color: #ee4c2c;
+}
+
+:-moz-placeholder {
+  color: #ee4c2c;
+}
+
+@media screen and (min-width: 768px) {
+  .site-footer a:hover {
+    color: #ee4c2c;
+  }
+}
+
+.docs-tutorials-resources a {
+  color: #ee4c2c;
+}
+
+.header-holder {
+  position: relative;
+  z-index: 201;
+}
+
+.header-holder .main-menu ul li.active:after {
+  color: #ee4c2c;
+}
+.header-holder .main-menu ul li.active a {
+  color: #ee4c2c;
+}
+@media screen and (min-width: 1100px) {
+  .header-holder .main-menu ul li a:hover {
+    color: #ee4c2c;
+  }
+}
+
+.mobile-main-menu.open ul li.active a {
+  color: #ee4c2c;
+}
+
+.version {
+  padding-bottom: 1rem;
+}
+
+.pytorch-call-to-action-links {
+  padding-top: 0;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+}
+@media screen and (min-width: 768px) {
+  .pytorch-call-to-action-links {
+    padding-top: 2.5rem;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  .pytorch-call-to-action-links {
+    padding-top: 0;
+  }
+}
+@media (min-width: 1100px) and (max-width: 1239px) {
+  .pytorch-call-to-action-links {
+    padding-top: 2.5rem;
+  }
+}
+.pytorch-call-to-action-links #tutorial-type {
+  display: none;
+}
+.pytorch-call-to-action-links .call-to-action-img, .pytorch-call-to-action-links .call-to-action-notebook-img {
+  height: 1.375rem;
+  width: 1.375rem;
+  margin-right: 10px;
+}
+.pytorch-call-to-action-links .call-to-action-notebook-img {
+  height: 1rem;
+}
+.pytorch-call-to-action-links a {
+  padding-right: 1.25rem;
+  color: #000000;
+  cursor: pointer;
+}
+.pytorch-call-to-action-links a:hover {
+  color: #e44c2c;
+}
+.pytorch-call-to-action-links a .call-to-action-desktop-view {
+  display: none;
+}
+@media screen and (min-width: 768px) {
+  .pytorch-call-to-action-links a .call-to-action-desktop-view {
+    display: block;
+  }
+}
+.pytorch-call-to-action-links a .call-to-action-mobile-view {
+  display: block;
+}
+@media screen and (min-width: 768px) {
+  .pytorch-call-to-action-links a .call-to-action-mobile-view {
+    display: none;
+  }
+}
+.pytorch-call-to-action-links a #google-colab-link, .pytorch-call-to-action-links a #download-notebook-link,
+.pytorch-call-to-action-links a #github-view-link {
+  padding-bottom: 0.625rem;
+  border-bottom: 1px solid #f3f4f7;
+  padding-right: 2.5rem;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+}
+.pytorch-call-to-action-links a #google-colab-link:hover, .pytorch-call-to-action-links a #download-notebook-link:hover,
+.pytorch-call-to-action-links a #github-view-link:hover {
+  border-bottom-color: #e44c2c;
+  color: #e44c2c;
+}
+
+#tutorial-cards-container #tutorial-cards {
+  width: 100%;
+}
+#tutorial-cards-container .tutorials-nav {
+  padding-left: 0;
+  padding-right: 0;
+  padding-bottom: 0;
+}
+#tutorial-cards-container .tutorials-hr {
+  margin-top: 1rem;
+  margin-bottom: 1rem;
+}
+#tutorial-cards-container .card.tutorials-card {
+  border-radius: 0;
+  border-color: #f3f4f7;
+  height: 98px;
+  margin-bottom: 1.25rem;
+  margin-bottom: 1.875rem;
+  overflow: scroll;
+  background-color: #f3f4f7;
+  cursor: pointer;
+}
+@media screen and (min-width: 1240px) {
+  #tutorial-cards-container .card.tutorials-card {
+    height: 200px;
+    overflow: inherit;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  #tutorial-cards-container .card.tutorials-card {
+    height: 200px;
+    overflow: scroll;
+  }
+}
+#tutorial-cards-container .card.tutorials-card .tutorials-image {
+  position: absolute;
+  top: 0px;
+  right: 0px;
+  height: 96px;
+  width: 96px;
+  opacity: 0.5;
+}
+#tutorial-cards-container .card.tutorials-card .tutorials-image img {
+  height: 100%;
+  width: 100%;
+}
+@media screen and (min-width: 768px) {
+  #tutorial-cards-container .card.tutorials-card .tutorials-image {
+    height: 100%;
+    width: 25%;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  #tutorial-cards-container .card.tutorials-card .tutorials-image {
+    height: 100%;
+    width: 198px;
+  }
+}
+#tutorial-cards-container .card.tutorials-card .tutorials-image:before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  right: 0;
+  z-index: 1;
+  background: #000000;
+  opacity: .075;
+}
+#tutorial-cards-container .card.tutorials-card .card-title-container {
+  width: 70%;
+  display: -webkit-inline-box;
+  display: -ms-inline-flexbox;
+  display: inline-flex;
+}
+@media screen and (min-width: 768px) {
+  #tutorial-cards-container .card.tutorials-card .card-title-container {
+    width: 75%;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  #tutorial-cards-container .card.tutorials-card .card-title-container {
+    width: 70%;
+  }
+}
+#tutorial-cards-container .card.tutorials-card .card-title-container h4 {
+  margin-bottom: 1.125rem;
+  margin-top: 0;
+  font-size: 1.5rem;
+}
+#tutorial-cards-container .card.tutorials-card p.card-summary, #tutorial-cards-container .card.tutorials-card p.tags {
+  font-size: 0.9375rem;
+  line-height: 1.5rem;
+  margin-bottom: 0;
+  color: #6c6c6d;
+  font-weight: 400;
+  width: 70%;
+}
+@media screen and (min-width: 768px) {
+  #tutorial-cards-container .card.tutorials-card p.card-summary, #tutorial-cards-container .card.tutorials-card p.tags {
+    width: 75%;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  #tutorial-cards-container .card.tutorials-card p.card-summary, #tutorial-cards-container .card.tutorials-card p.tags {
+    width: 70%;
+  }
+}
+#tutorial-cards-container .card.tutorials-card p.tags {
+  margin-top: 30px;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+  overflow: hidden;
+}
+#tutorial-cards-container .card.tutorials-card h4 {
+  color: #262626;
+  margin-bottom: 1.125rem;
+}
+#tutorial-cards-container .card.tutorials-card a {
+  height: 100%;
+}
+@media screen and (min-width: 768px) {
+  #tutorial-cards-container .card.tutorials-card a {
+    min-height: 190px;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  #tutorial-cards-container .card.tutorials-card a {
+    min-height: 234px;
+  }
+}
+@media screen and (min-width: 768px) {
+  #tutorial-cards-container .card.tutorials-card:after {
+    content: "";
+    display: block;
+    width: 0;
+    height: 1px;
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    background-color: #e44c2c;
+    -webkit-transition: width .250s ease-in-out;
+    transition: width .250s ease-in-out;
+  }
+  #tutorial-cards-container .card.tutorials-card:hover:after {
+    width: 100%;
+  }
+}
+#tutorial-cards-container .card.tutorials-card:hover {
+  background-color: #ffffff;
+  border: 1px solid #e2e2e2;
+  border-bottom: none;
+}
+#tutorial-cards-container .card.tutorials-card:hover p.card-summary {
+  color: #262626;
+}
+#tutorial-cards-container .card.tutorials-card:hover .tutorials-image {
+  opacity: unset;
+}
+#tutorial-cards-container .tutorial-tags-container {
+  width: 75%;
+}
+#tutorial-cards-container .tutorial-tags-container.active {
+  width: 0;
+}
+#tutorial-cards-container .tutorial-filter-menu ul {
+  list-style-type: none;
+  padding-left: 1.25rem;
+}
+#tutorial-cards-container .tutorial-filter-menu ul li {
+  padding-right: 1.25rem;
+  word-break: break-all;
+}
+#tutorial-cards-container .tutorial-filter-menu ul li a {
+  color: #979797;
+}
+#tutorial-cards-container .tutorial-filter-menu ul li a:hover {
+  color: #e44c2c;
+}
+#tutorial-cards-container .tutorial-filter {
+  cursor: pointer;
+}
+#tutorial-cards-container .filter-btn {
+  color: #979797;
+  border: 1px solid #979797;
+  display: inline-block;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: middle;
+  padding: 0.375rem 0.75rem;
+  font-size: 1rem;
+  line-height: 1.5;
+  margin-bottom: 5px;
+}
+#tutorial-cards-container .filter-btn:hover {
+  border: 1px solid #e44c2c;
+  color: #e44c2c;
+}
+#tutorial-cards-container .filter-btn.selected {
+  background-color: #e44c2c;
+  border: 1px solid #e44c2c;
+  color: #ffffff;
+}
+#tutorial-cards-container .all-tag-selected {
+  background-color: #979797;
+  color: #ffffff;
+}
+#tutorial-cards-container .all-tag-selected:hover {
+  border-color: #979797;
+  color: #ffffff;
+}
+#tutorial-cards-container .pagination .page {
+  border: 1px solid #dee2e6;
+  padding: 0.5rem 0.75rem;
+}
+#tutorial-cards-container .pagination .active .page {
+  background-color: #dee2e6;
+}
+
+article.pytorch-article .tutorials-callout-container {
+  padding-bottom: 50px;
+}
+article.pytorch-article .tutorials-callout-container .col-md-6 {
+  padding-bottom: 10px;
+}
+article.pytorch-article .tutorials-callout-container .text-container {
+  padding: 10px 0px 30px 0px;
+  padding-bottom: 10px;
+}
+article.pytorch-article .tutorials-callout-container .text-container .body-paragraph {
+  color: #666666;
+  font-weight: 300;
+  font-size: 1.125rem;
+  line-height: 1.875rem;
+}
+article.pytorch-article .tutorials-callout-container .btn.callout-button {
+  font-size: 1.125rem;
+  border-radius: 0;
+  border: none;
+  background-color: #f3f4f7;
+  color: #6c6c6d;
+  font-weight: 400;
+  position: relative;
+  letter-spacing: 0.25px;
+}
+@media screen and (min-width: 768px) {
+  article.pytorch-article .tutorials-callout-container .btn.callout-button:after {
+    content: "";
+    display: block;
+    width: 0;
+    height: 1px;
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    background-color: #e44c2c;
+    -webkit-transition: width .250s ease-in-out;
+    transition: width .250s ease-in-out;
+  }
+  article.pytorch-article .tutorials-callout-container .btn.callout-button:hover:after {
+    width: 100%;
+  }
+}
+article.pytorch-article .tutorials-callout-container .btn.callout-button a {
+  color: inherit;
+}
+
+.pytorch-container {
+  margin: 0 auto;
+  padding: 0 1.875rem;
+  width: auto;
+  position: relative;
+}
+@media screen and (min-width: 1100px) {
+  .pytorch-container {
+    padding: 0;
+  }
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-container {
+    margin-left: 25%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-container {
+    margin-left: 350px;
+  }
+}
+.pytorch-container:before, .pytorch-container:after {
+  content: "";
+  display: table;
+}
+.pytorch-container:after {
+  clear: both;
+}
+.pytorch-container {
+  *zoom: 1;
+}
+
+.pytorch-content-wrap {
+  background-color: #ffffff;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  position: relative;
+  padding-top: 0;
+}
+.pytorch-content-wrap:before, .pytorch-content-wrap:after {
+  content: "";
+  display: table;
+}
+.pytorch-content-wrap:after {
+  clear: both;
+}
+.pytorch-content-wrap {
+  *zoom: 1;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-content-wrap {
+    padding-top: 45px;
+    float: left;
+    width: 100%;
+    display: block;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-content-wrap {
+    width: 100%;
+  }
+}
+
+.pytorch-content {
+  background: #ffffff;
+  width: 100%;
+  max-width: 700px;
+  position: relative;
+}
+
+.pytorch-content-left {
+  min-height: 100vh;
+  margin-top: 2.5rem;
+  width: 100%;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-content-left {
+    margin-top: 0;
+    margin-left: 3%;
+    width: 75%;
+    float: left;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-content-left {
+    width: 850px;
+    margin-left: 30px;
+  }
+}
+.pytorch-content-left .main-content {
+  padding-top: 0.9375rem;
+}
+.pytorch-content-left .main-content ul.simple {
+  padding-bottom: 1.25rem;
+}
+.pytorch-content-left .main-content .note:nth-child(1), .pytorch-content-left .main-content .warning:nth-child(1) {
+  margin-top: 0;
+}
+
+.pytorch-content-right {
+  display: none;
+  position: relative;
+  overflow-x: hidden;
+  overflow-y: hidden;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-content-right {
+    display: block;
+    margin-left: 0;
+    width: 19%;
+    float: left;
+    height: 100%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-content-right {
+    width: 280px;
+  }
+}
+
+@media screen and (min-width: 1101px) {
+  .pytorch-side-scroll {
+    position: relative;
+    overflow-x: hidden;
+    overflow-y: scroll;
+    height: 100%;
+  }
+}
+
+.pytorch-menu-vertical {
+  padding: 1.25rem 1.875rem 2.5rem 1.875rem;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-menu-vertical {
+    display: block;
+    padding-top: 0;
+    padding-right: 13.5%;
+    padding-bottom: 5.625rem;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-menu-vertical {
+    padding-left: 0;
+    padding-right: 1.5625rem;
+  }
+}
+
+.pytorch-left-menu {
+  display: none;
+  background-color: #f3f4f7;
+  color: #262626;
+  overflow: scroll;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-left-menu {
+    display: block;
+    overflow-x: hidden;
+    overflow-y: hidden;
+    padding-bottom: 110px;
+    padding: 0 1.875rem 0 0;
+    width: 25%;
+    z-index: 200;
+    float: left;
+  }
+  .pytorch-left-menu.make-fixed {
+    position: fixed;
+    top: 0;
+    bottom: 0;
+    left: 0;
+    float: none;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-left-menu {
+    padding: 0 0 0 1.875rem;
+    width: 350px;
+  }
+}
+
+.expand-menu, .hide-menu {
+  color: #6c6c6d;
+  padding-left: 10px;
+  cursor: pointer;
+}
+
+.collapse {
+  display: none;
+}
+
+.left-nav-top-caption {
+  padding-top: 1rem;
+}
+
+.pytorch-left-menu p.caption {
+  color: #262626;
+  display: block;
+  font-size: 1rem;
+  line-height: 1.375rem;
+  margin-bottom: 1rem;
+  text-transform: none;
+  white-space: normal;
+}
+
+.pytorch-left-menu-search {
+  margin-bottom: 2.5rem;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-left-menu-search {
+    margin: 1.25rem 0.625rem 1.875rem 0;
+  }
+}
+
+.pytorch-left-menu-search ::-webkit-input-placeholder {
+  color: #262626;
+}
+.pytorch-left-menu-search ::-moz-placeholder {
+  color: #262626;
+}
+.pytorch-left-menu-search :-ms-input-placeholder {
+  color: #262626;
+}
+.pytorch-left-menu-search ::-ms-input-placeholder {
+  color: #262626;
+}
+.pytorch-left-menu-search ::placeholder {
+  color: #262626;
+}
+
+.pytorch-left-menu-search :focus::-webkit-input-placeholder {
+  color: transparent;
+}
+.pytorch-left-menu-search :focus::-moz-placeholder {
+  color: transparent;
+}
+.pytorch-left-menu-search :focus:-ms-input-placeholder {
+  color: transparent;
+}
+.pytorch-left-menu-search :focus::-ms-input-placeholder {
+  color: transparent;
+}
+.pytorch-left-menu-search :focus::placeholder {
+  color: transparent;
+}
+
+.pytorch-left-menu-search input[type=text] {
+  border-radius: 0;
+  padding: 0.5rem 0.75rem;
+  border-color: #ffffff;
+  color: #262626;
+  border-style: solid;
+  font-size: 1rem;
+  width: 100%;
+  background-color: #f3f4f7;
+  background-image: url("../images/search-icon.svg");
+  background-repeat: no-repeat;
+  background-size: 18px 18px;
+  background-position: 12px 10px;
+  padding-left: 40px;
+  background-color: #ffffff;
+}
+.pytorch-left-menu-search input[type=text]:focus {
+  outline: 0;
+}
+
+@media screen and (min-width: 1101px) {
+  .pytorch-left-menu .pytorch-side-scroll {
+    width: 120%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-left-menu .pytorch-side-scroll {
+    width: 340px;
+  }
+}
+
+.pytorch-right-menu {
+  min-height: 100px;
+  overflow-x: hidden;
+  overflow-y: hidden;
+  left: 0;
+  z-index: 200;
+  padding-top: 0;
+  position: relative;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-right-menu {
+    width: 100%;
+  }
+  .pytorch-right-menu.scrolling-fixed {
+    position: fixed;
+    top: 45px;
+    left: 83.5%;
+    width: 14%;
+  }
+  .pytorch-right-menu.scrolling-absolute {
+    position: absolute;
+    left: 0;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-right-menu {
+    left: 0;
+    width: 380px;
+  }
+  .pytorch-right-menu.scrolling-fixed {
+    position: fixed;
+    top: 45px;
+    left: 1230px;
+  }
+  .pytorch-right-menu.scrolling-absolute {
+    position: absolute;
+    left: 0;
+  }
+}
+
+.pytorch-left-menu ul,
+.pytorch-right-menu ul {
+  list-style-type: none;
+  padding-left: 0;
+  margin-bottom: 2.5rem;
+}
+.pytorch-left-menu > ul,
+.pytorch-right-menu > ul {
+  margin-bottom: 2.5rem;
+}
+.pytorch-left-menu a:link,
+.pytorch-left-menu a:visited,
+.pytorch-left-menu a:hover,
+.pytorch-right-menu a:link,
+.pytorch-right-menu a:visited,
+.pytorch-right-menu a:hover {
+  color: #6c6c6d;
+  font-size: 0.875rem;
+  line-height: 1rem;
+  padding: 0;
+  text-decoration: none;
+}
+.pytorch-left-menu a:link.reference.internal,
+.pytorch-left-menu a:visited.reference.internal,
+.pytorch-left-menu a:hover.reference.internal,
+.pytorch-right-menu a:link.reference.internal,
+.pytorch-right-menu a:visited.reference.internal,
+.pytorch-right-menu a:hover.reference.internal {
+  margin-bottom: 0.3125rem;
+  position: relative;
+}
+.pytorch-left-menu li code,
+.pytorch-right-menu li code {
+  border: none;
+  background: inherit;
+  color: inherit;
+  padding-left: 0;
+  padding-right: 0;
+}
+.pytorch-left-menu li span.toctree-expand,
+.pytorch-right-menu li span.toctree-expand {
+  display: block;
+  float: left;
+  margin-left: -1.2em;
+  font-size: 0.8em;
+  line-height: 1.6em;
+}
+.pytorch-left-menu li.on a, .pytorch-left-menu li.current > a,
+.pytorch-right-menu li.on a,
+.pytorch-right-menu li.current > a {
+  position: relative;
+  border: none;
+}
+.pytorch-left-menu li.on a span.toctree-expand, .pytorch-left-menu li.current > a span.toctree-expand,
+.pytorch-right-menu li.on a span.toctree-expand,
+.pytorch-right-menu li.current > a span.toctree-expand {
+  display: block;
+  font-size: 0.8em;
+  line-height: 1.6em;
+}
+.pytorch-left-menu li.toctree-l1.current > a,
+.pytorch-right-menu li.toctree-l1.current > a {
+  color: #ee4c2c;
+}
+.pytorch-left-menu li.toctree-l1.current > a:before,
+.pytorch-right-menu li.toctree-l1.current > a:before {
+  content: "\2022";
+  display: inline-block;
+  position: absolute;
+  left: -15px;
+  top: -10%;
+  font-size: 1.375rem;
+  color: #ee4c2c;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-left-menu li.toctree-l1.current > a:before,
+  .pytorch-right-menu li.toctree-l1.current > a:before {
+    left: -20px;
+  }
+}
+.pytorch-left-menu li.toctree-l1.current li.toctree-l2 > ul, .pytorch-left-menu li.toctree-l2.current li.toctree-l3 > ul,
+.pytorch-right-menu li.toctree-l1.current li.toctree-l2 > ul,
+.pytorch-right-menu li.toctree-l2.current li.toctree-l3 > ul {
+  display: none;
+}
+.pytorch-left-menu li.toctree-l1.current li.toctree-l2.current > ul, .pytorch-left-menu li.toctree-l2.current li.toctree-l3.current > ul,
+.pytorch-right-menu li.toctree-l1.current li.toctree-l2.current > ul,
+.pytorch-right-menu li.toctree-l2.current li.toctree-l3.current > ul {
+  display: block;
+}
+.pytorch-left-menu li.toctree-l2.current li.toctree-l3 > a,
+.pytorch-right-menu li.toctree-l2.current li.toctree-l3 > a {
+  display: block;
+}
+.pytorch-left-menu li.toctree-l3,
+.pytorch-right-menu li.toctree-l3 {
+  font-size: 0.9em;
+}
+.pytorch-left-menu li.toctree-l3.current li.toctree-l4 > a,
+.pytorch-right-menu li.toctree-l3.current li.toctree-l4 > a {
+  display: block;
+}
+.pytorch-left-menu li.toctree-l4,
+.pytorch-right-menu li.toctree-l4 {
+  font-size: 0.9em;
+}
+.pytorch-left-menu li.current ul,
+.pytorch-right-menu li.current ul {
+  display: block;
+}
+.pytorch-left-menu li ul,
+.pytorch-right-menu li ul {
+  margin-bottom: 0;
+  display: none;
+}
+.pytorch-left-menu li ul li a,
+.pytorch-right-menu li ul li a {
+  margin-bottom: 0;
+}
+.pytorch-left-menu a,
+.pytorch-right-menu a {
+  display: inline-block;
+  position: relative;
+}
+.pytorch-left-menu a:hover,
+.pytorch-right-menu a:hover {
+  cursor: pointer;
+}
+.pytorch-left-menu a:active,
+.pytorch-right-menu a:active {
+  cursor: pointer;
+}
+
+.pytorch-left-menu ul {
+  padding-left: 0;
+}
+
+.pytorch-right-menu a:link,
+.pytorch-right-menu a:visited,
+.pytorch-right-menu a:hover {
+  color: #6c6c6d;
+}
+.pytorch-right-menu a:link span.pre,
+.pytorch-right-menu a:visited span.pre,
+.pytorch-right-menu a:hover span.pre {
+  color: #6c6c6d;
+}
+.pytorch-right-menu a.reference.internal.expanded:before {
+  content: "-";
+  font-family: monospace;
+  position: absolute;
+  left: -12px;
+}
+.pytorch-right-menu a.reference.internal.not-expanded:before {
+  content: "+";
+  font-family: monospace;
+  position: absolute;
+  left: -12px;
+}
+.pytorch-right-menu li.active > a {
+  color: #ee4c2c;
+}
+.pytorch-right-menu li.active > a span.pre, .pytorch-right-menu li.active > a:before {
+  color: #ee4c2c;
+}
+.pytorch-right-menu li.active > a:after {
+  content: "\2022";
+  color: #e44c2c;
+  display: inline-block;
+  font-size: 1.375rem;
+  left: -17px;
+  position: absolute;
+  top: 1px;
+}
+.pytorch-right-menu .pytorch-side-scroll > ul > li > ul > li {
+  margin-bottom: 0;
+}
+.pytorch-right-menu ul ul {
+  padding-left: 0;
+}
+.pytorch-right-menu ul ul li {
+  padding-left: 0px;
+}
+.pytorch-right-menu ul ul li a.reference.internal {
+  padding-left: 0;
+}
+.pytorch-right-menu ul ul li ul {
+  display: none;
+  padding-left: 10px;
+}
+.pytorch-right-menu ul ul li li a.reference.internal {
+  padding-left: 0;
+}
+.pytorch-right-menu li ul {
+  display: block;
+}
+
+.pytorch-right-menu .pytorch-side-scroll {
+  padding-top: 20px;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-right-menu .pytorch-side-scroll {
+    width: 120%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-right-menu .pytorch-side-scroll {
+    width: 400px;
+  }
+}
+.pytorch-right-menu .pytorch-side-scroll > ul {
+  padding-left: 10%;
+  padding-right: 10%;
+  margin-bottom: 0;
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-right-menu .pytorch-side-scroll > ul {
+    padding-left: 25px;
+  }
+}
+.pytorch-right-menu .pytorch-side-scroll > ul > li > a.reference.internal {
+  color: #262626;
+  font-weight: 500;
+}
+.pytorch-right-menu .pytorch-side-scroll ul li {
+  position: relative;
+}
+
+#pytorch-right-menu .side-scroll-highlight {
+  color: #ee4c2c;
+}
+
+.header-container {
+  max-width: none;
+  margin-top: 4px;
+}
+@media screen and (min-width: 1101px) {
+  .header-container {
+    margin-top: 0;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .header-container {
+    margin-top: 0;
+  }
+}
+
+.container-fluid.header-holder {
+  padding-right: 0;
+  padding-left: 0;
+}
+
+.header-holder .container {
+  max-width: none;
+  padding-right: 1.875rem;
+  padding-left: 1.875rem;
+}
+@media screen and (min-width: 1101px) {
+  .header-holder .container {
+    padding-right: 1.875rem;
+    padding-left: 1.875rem;
+  }
+}
+
+.header-holder .main-menu {
+  -webkit-box-pack: unset;
+      -ms-flex-pack: unset;
+          justify-content: unset;
+  position: relative;
+}
+@media screen and (min-width: 1101px) {
+  .header-holder .main-menu ul {
+    padding-left: 0;
+    margin-left: 26%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .header-holder .main-menu ul {
+    padding-left: 38px;
+    margin-left: 310px;
+  }
+}
+
+.pytorch-page-level-bar {
+  display: none;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  background-color: #ffffff;
+  border-bottom: 1px solid #e2e2e2;
+  width: 100%;
+  z-index: 201;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-page-level-bar {
+    left: 0;
+    display: -webkit-box;
+    display: -ms-flexbox;
+    display: flex;
+    height: 45px;
+    padding-left: 0;
+    width: 100%;
+    position: absolute;
+    z-index: 1;
+  }
+  .pytorch-page-level-bar.left-menu-is-fixed {
+    position: fixed;
+    top: 0;
+    left: 25%;
+    padding-left: 0;
+    right: 0;
+    width: 75%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-page-level-bar {
+    left: 0;
+    right: 0;
+    width: auto;
+    z-index: 1;
+  }
+  .pytorch-page-level-bar.left-menu-is-fixed {
+    left: 350px;
+    right: 0;
+    width: auto;
+  }
+}
+.pytorch-page-level-bar ul, .pytorch-page-level-bar li {
+  margin: 0;
+}
+
+.pytorch-shortcuts-wrapper {
+  display: none;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-shortcuts-wrapper {
+    font-size: 0.875rem;
+    float: left;
+    margin-left: 2%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-shortcuts-wrapper {
+    margin-left: 1.875rem;
+  }
+}
+
+.cookie-banner-wrapper {
+  display: none;
+}
+.cookie-banner-wrapper .container {
+  padding-left: 1.875rem;
+  padding-right: 1.875rem;
+  max-width: 1240px;
+}
+.cookie-banner-wrapper.is-visible {
+  display: block;
+  position: fixed;
+  bottom: 0;
+  background-color: #f3f4f7;
+  min-height: 100px;
+  width: 100%;
+  z-index: 401;
+  border-top: 3px solid #ededee;
+}
+.cookie-banner-wrapper .gdpr-notice {
+  color: #6c6c6d;
+  margin-top: 1.5625rem;
+  text-align: left;
+  max-width: 1440px;
+}
+@media screen and (min-width: 768px) {
+  .cookie-banner-wrapper .gdpr-notice {
+    width: 77%;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  .cookie-banner-wrapper .gdpr-notice {
+    width: inherit;
+  }
+}
+.cookie-banner-wrapper .gdpr-notice .cookie-policy-link {
+  color: #343434;
+}
+.cookie-banner-wrapper .close-button {
+  -webkit-appearance: none;
+     -moz-appearance: none;
+          appearance: none;
+  background: transparent;
+  border: 1px solid #f3f4f7;
+  height: 1.3125rem;
+  position: absolute;
+  bottom: 42px;
+  right: 0;
+  top: 0;
+  cursor: pointer;
+  outline: none;
+}
+@media screen and (min-width: 768px) {
+  .cookie-banner-wrapper .close-button {
+    right: 20%;
+    top: inherit;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  .cookie-banner-wrapper .close-button {
+    right: 0;
+    top: 0;
+  }
+}
+
+.main-menu ul li .resources-dropdown a {
+  cursor: pointer;
+}
+.main-menu ul li .dropdown-menu {
+  border-radius: 0;
+  padding: 0;
+}
+.main-menu ul li .dropdown-menu .dropdown-item {
+  color: #6c6c6d;
+  border-bottom: 1px solid #e2e2e2;
+}
+.main-menu ul li .dropdown-menu .dropdown-item:last-of-type {
+  border-bottom-color: transparent;
+}
+.main-menu ul li .dropdown-menu .dropdown-item:hover {
+  background-color: #e44c2c;
+}
+.main-menu ul li .dropdown-menu .dropdown-item p {
+  font-size: 1rem;
+  color: #979797;
+}
+.main-menu ul li .dropdown-menu a.dropdown-item:hover {
+  color: #ffffff;
+}
+.main-menu ul li .dropdown-menu a.dropdown-item:hover p {
+  color: #ffffff;
+}
+
+.resources-dropdown-menu {
+  left: -75px;
+  width: 226px;
+  display: none;
+  position: absolute;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 10rem;
+  padding: 0.5rem 0;
+  font-size: 1rem;
+  color: #212529;
+  text-align: left;
+  list-style: none;
+  background-color: #ffffff;
+  background-clip: padding-box;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 0.25rem;
+}
+
+.resources-dropdown:hover .resources-dropdown-menu {
+  display: block;
+}
+
+.main-menu ul li .resources-dropdown-menu {
+  border-radius: 0;
+  padding: 0;
+}
+.main-menu ul li.active:hover .resources-dropdown-menu {
+  display: block;
+}
+
+.main-menu ul li .resources-dropdown-menu .dropdown-item {
+  color: #6c6c6d;
+  border-bottom: 1px solid #e2e2e2;
+}
+
+.resources-dropdown .with-down-orange-arrow {
+  padding-right: 2rem;
+  position: relative;
+  background: url("../images/chevron-down-orange.svg");
+  background-size: 14px 18px;
+  background-position: top 7px right 10px;
+  background-repeat: no-repeat;
+}
+
+.with-down-arrow {
+  padding-right: 2rem;
+  position: relative;
+  background-image: url("../images/chevron-down-black.svg");
+  background-size: 14px 18px;
+  background-position: top 7px right 10px;
+  background-repeat: no-repeat;
+}
+.with-down-arrow:hover {
+  background-image: url("../images/chevron-down-orange.svg");
+  background-repeat: no-repeat;
+}
+
+.header-holder .main-menu ul li .resources-dropdown .doc-dropdown-option {
+  padding-top: 1rem;
+}
+
+.header-holder .main-menu ul li a.nav-dropdown-item {
+  display: block;
+  font-size: 1rem;
+  line-height: 1.3125rem;
+  width: 100%;
+  padding: 0.25rem 1.5rem;
+  clear: both;
+  font-weight: 400;
+  color: #979797;
+  text-align: center;
+  background-color: transparent;
+  border-bottom: 1px solid #e2e2e2;
+}
+.header-holder .main-menu ul li a.nav-dropdown-item:last-of-type {
+  border-bottom-color: transparent;
+}
+.header-holder .main-menu ul li a.nav-dropdown-item:hover {
+  background-color: #e44c2c;
+  color: white;
+}
+.header-holder .main-menu ul li a.nav-dropdown-item .dropdown-title {
+  font-size: 1.125rem;
+  color: #6c6c6d;
+  letter-spacing: 0;
+  line-height: 34px;
+}
+
+.header-holder .main-menu ul li a.nav-dropdown-item:hover .dropdown-title {
+  background-color: #e44c2c;
+  color: white;
+}
+
+/*# sourceMappingURL=theme.css.map */
diff --git a/docs/v1.2.0/_static/doctools.js b/docs/v1.2.0/_static/doctools.js
new file mode 100644
index 0000000000..e1bfd708b7
--- /dev/null
+++ b/docs/v1.2.0/_static/doctools.js
@@ -0,0 +1,358 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for all documentation.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/**
+ * select a different prefix for underscore
+ */
+$u = _.noConflict();
+
+/**
+ * make the code below compatible with browsers without
+ * an installed firebug like debugger
+if (!window.console || !console.firebug) {
+  var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
+    "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
+    "profile", "profileEnd"];
+  window.console = {};
+  for (var i = 0; i < names.length; ++i)
+    window.console[names[i]] = function() {};
+}
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+  if (!x) {
+    return x
+  }
+  return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+  if (typeof s === 'undefined')
+    s = document.location.search;
+  var parts = s.substr(s.indexOf('?') + 1).split('&');
+  var result = {};
+  for (var i = 0; i < parts.length; i++) {
+    var tmp = parts[i].split('=', 2);
+    var key = jQuery.urldecode(tmp[0]);
+    var value = jQuery.urldecode(tmp[1]);
+    if (key in result)
+      result[key].push(value);
+    else
+      result[key] = [value];
+  }
+  return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+  function highlight(node, addItems) {
+    if (node.nodeType === 3) {
+      var val = node.nodeValue;
+      var pos = val.toLowerCase().indexOf(text);
+      if (pos >= 0 &&
+          !jQuery(node.parentNode).hasClass(className) &&
+          !jQuery(node.parentNode).hasClass("nohighlight")) {
+        var span;
+        var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+        if (isInSVG) {
+          span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+        } else {
+          span = document.createElement("span");
+          span.className = className;
+        }
+        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+          document.createTextNode(val.substr(pos + text.length)),
+          node.nextSibling));
+        node.nodeValue = val.substr(0, pos);
+        if (isInSVG) {
+          var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+          var bbox = node.parentElement.getBBox();
+          rect.x.baseVal.value = bbox.x;
+          rect.y.baseVal.value = bbox.y;
+          rect.width.baseVal.value = bbox.width;
+          rect.height.baseVal.value = bbox.height;
+          rect.setAttribute('class', className);
+          addItems.push({
+              "parent": node.parentNode,
+              "target": rect});
+        }
+      }
+    }
+    else if (!jQuery(node).is("button, select, textarea")) {
+      jQuery.each(node.childNodes, function() {
+        highlight(this, addItems);
+      });
+    }
+  }
+  var addItems = [];
+  var result = this.each(function() {
+    highlight(this, addItems);
+  });
+  for (var i = 0; i < addItems.length; ++i) {
+    jQuery(addItems[i].parent).before(addItems[i].target);
+  }
+  return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+  jQuery.uaMatch = function(ua) {
+    ua = ua.toLowerCase();
+
+    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+      /(msie) ([\w.]+)/.exec(ua) ||
+      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+      [];
+
+    return {
+      browser: match[ 1 ] || "",
+      version: match[ 2 ] || "0"
+    };
+  };
+  jQuery.browser = {};
+  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+var Documentation = {
+
+  init : function() {
+    this.fixFirefoxAnchorBug();
+    this.highlightSearchWords();
+    this.initIndexTable();
+    this.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS : {},
+  PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
+  LOCALE : 'unknown',
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext : function(string) {
+    var translated = Documentation.TRANSLATIONS[string];
+    if (typeof translated === 'undefined')
+      return string;
+    return (typeof translated === 'string') ? translated : translated[0];
+  },
+
+  ngettext : function(singular, plural, n) {
+    var translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated === 'undefined')
+      return (n == 1) ? singular : plural;
+    return translated[Documentation.PLURALEXPR(n)];
+  },
+
+  addTranslations : function(catalog) {
+    for (var key in catalog.messages)
+      this.TRANSLATIONS[key] = catalog.messages[key];
+    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
+    this.LOCALE = catalog.locale;
+  },
+
+  /**
+   * add context elements like header anchor links
+   */
+  addContextElements : function() {
+    $('div[id] > :header:first').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this headline')).
+      appendTo(this);
+    });
+    $('dt[id]').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this definition')).
+      appendTo(this);
+    });
+  },
+
+  /**
+   * workaround a firefox stupidity
+   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
+   */
+  fixFirefoxAnchorBug : function() {
+    if (document.location.hash && $.browser.mozilla)
+      window.setTimeout(function() {
+        document.location.href += '';
+      }, 10);
+  },
+
+  /**
+   * highlight the search words provided in the url in the text
+   */
+  highlightSearchWords : function() {
+    var params = $.getQueryParameters();
+    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
+    if (terms.length) {
+      var body = $('div.body');
+      if (!body.length) {
+        body = $('body');
+      }
+      window.setTimeout(function() {
+        $.each(terms, function() {
+          body.highlightText(this.toLowerCase(), 'highlighted');
+        });
+      }, 10);
+      $('<p class="highlight-link"><a href="javascript:Documentation.' +
+        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
+          .appendTo($('#searchbox'));
+    }
+  },
+
+  /**
+   * init the domain index toggle buttons
+   */
+  initIndexTable : function() {
+    var togglers = $('img.toggler').click(function() {
+      var src = $(this).attr('src');
+      var idnum = $(this).attr('id').substr(7);
+      $('tr.cg-' + idnum).toggle();
+      if (src.substr(-9) === 'minus.png')
+        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
+      else
+        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
+    }).css('display', '');
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
+        togglers.click();
+    }
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords : function() {
+    $('#searchbox .highlight-link').fadeOut(300);
+    $('span.highlighted').removeClass('highlighted');
+    var url = new URL(window.location);
+    url.searchParams.delete('highlight');
+    window.history.replaceState({}, '', url);
+  },
+
+   /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar : function() {
+    $('input[name=q]').first().focus();
+  },
+
+  /**
+   * make the url absolute
+   */
+  makeURL : function(relativeURL) {
+    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
+  },
+
+  /**
+   * get the current relative url
+   */
+  getCurrentURL : function() {
+    var path = document.location.pathname;
+    var parts = path.split(/\//);
+    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
+      if (this === '..')
+        parts.pop();
+    });
+    var url = parts.join('/');
+    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
+  },
+
+  initOnKeyListeners: function() {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+        !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS)
+        return;
+
+    $(document).keydown(function(event) {
+      var activeElementType = document.activeElement.tagName;
+      // don't navigate when in search box, textarea, dropdown or button
+      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
+          && activeElementType !== 'BUTTON') {
+        if (event.altKey || event.ctrlKey || event.metaKey)
+          return;
+
+          if (!event.shiftKey) {
+            switch (event.key) {
+              case 'ArrowLeft':
+                if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS)
+                  break;
+                var prevHref = $('link[rel="prev"]').prop('href');
+                if (prevHref) {
+                  window.location.href = prevHref;
+                  return false;
+                }
+                break;
+              case 'ArrowRight':
+                if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS)
+                  break;
+                var nextHref = $('link[rel="next"]').prop('href');
+                if (nextHref) {
+                  window.location.href = nextHref;
+                  return false;
+                }
+                break;
+              case 'Escape':
+                if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS)
+                  break;
+                Documentation.hideSearchWords();
+                return false;
+          }
+        }
+
+        // some keyboard layouts may need Shift to get /
+        switch (event.key) {
+          case '/':
+            if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS)
+              break;
+            Documentation.focusSearchBar();
+            return false;
+        }
+      }
+    });
+  }
+};
+
+// quick alias for translations
+_ = Documentation.gettext;
+
+$(document).ready(function() {
+  Documentation.init();
+});
diff --git a/docs/v1.2.0/_static/documentation_options.js b/docs/v1.2.0/_static/documentation_options.js
new file mode 100644
index 0000000000..7e8ecffa6a
--- /dev/null
+++ b/docs/v1.2.0/_static/documentation_options.js
@@ -0,0 +1,14 @@
+var DOCUMENTATION_OPTIONS = {
+    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
+    VERSION: 'master',
+    LANGUAGE: 'None',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
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new file mode 100644
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diff --git a/docs/v1.2.0/_static/fonts/font-awesome.css b/docs/v1.2.0/_static/fonts/font-awesome.css
new file mode 100644
index 0000000000..e7dcefb822
--- /dev/null
+++ b/docs/v1.2.0/_static/fonts/font-awesome.css
@@ -0,0 +1 @@
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new file mode 100644
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+
+    function i(e) {
+        if (r[e]) return r[e].exports;
+        var t = r[e] = {
+            i: e,
+            l: !1,
+            exports: {}
+        };
+        return n[e].call(t.exports, t, t.exports, i), t.l = !0, t.exports
+    }
+    return i.m = n, i.c = r, i.d = function(e, t, n) {
+        i.o(e, t) || Object.defineProperty(e, t, {
+            enumerable: !0,
+            get: n
+        })
+    }, i.r = function(e) {
+        "undefined" != typeof Symbol && Symbol.toStringTag && Object.defineProperty(e, Symbol.toStringTag, {
+            value: "Module"
+        }), Object.defineProperty(e, "__esModule", {
+            value: !0
+        })
+    }, i.t = function(t, e) {
+        if (1 & e && (t = i(t)), 8 & e) return t;
+        if (4 & e && "object" == typeof t && t && t.__esModule) return t;
+        var n = Object.create(null);
+        if (i.r(n), Object.defineProperty(n, "default", {
+                enumerable: !0,
+                value: t
+            }), 2 & e && "string" != typeof t)
+            for (var r in t) i.d(n, r, function(e) {
+                return t[e]
+            }.bind(null, r));
+        return n
+    }, i.n = function(e) {
+        var t = e && e.__esModule ? function() {
+            return e.default
+        } : function() {
+            return e
+        };
+        return i.d(t, "a", t), t
+    }, i.o = function(e, t) {
+        return Object.prototype.hasOwnProperty.call(e, t)
+    }, i.p = "", i(i.s = 13)
+}([function(e, t, n) {
+    "use strict";
+    var r = {
+            Listener: function() {
+                function e(e, t, n) {
+                    var r = this;
+                    this.els_ = Array.prototype.slice.call("string" == typeof e ? document.querySelectorAll(e) : [].concat(e)), this.handler_ = "function" == typeof n ? {
+                        update: n
+                    } : n, this.events_ = [].concat(t), this.update_ = function(e) {
+                        return r.handler_.update(e)
+                    }
+                }
+                var t = e.prototype;
+                return t.listen = function() {
+                    var n = this;
+                    this.els_.forEach(function(t) {
+                        n.events_.forEach(function(e) {
+                            t.addEventListener(e, n.update_, !1)
+                        })
+                    }), "function" == typeof this.handler_.setup && this.handler_.setup()
+                }, t.unlisten = function() {
+                    var n = this;
+                    this.els_.forEach(function(t) {
+                        n.events_.forEach(function(e) {
+                            t.removeEventListener(e, n.update_)
+                        })
+                    }), "function" == typeof this.handler_.reset && this.handler_.reset()
+                }, e
+            }(),
+            MatchMedia: function(e, t) {
+                this.handler_ = function(e) {
+                    e.matches ? t.listen() : t.unlisten()
+                };
+                var n = window.matchMedia(e);
+                n.addListener(this.handler_), this.handler_(n)
+            }
+        },
+        i = {
+            Shadow: function() {
+                function e(e, t) {
+                    var n = "string" == typeof e ? document.querySelector(e) : e;
+                    if (!(n instanceof HTMLElement && n.parentNode instanceof HTMLElement)) throw new ReferenceError;
+                    if (this.el_ = n.parentNode, !((n = "string" == typeof t ? document.querySelector(t) : t) instanceof HTMLElement)) throw new ReferenceError;
+                    this.header_ = n, this.height_ = 0, this.active_ = !1
+                }
+                var t = e.prototype;
+                return t.setup = function() {
+                    for (var e = this.el_; e = e.previousElementSibling;) {
+                        if (!(e instanceof HTMLElement)) throw new ReferenceError;
+                        this.height_ += e.offsetHeight
+                    }
+                    this.update()
+                }, t.update = function(e) {
+                    if (!e || "resize" !== e.type && "orientationchange" !== e.type) {
+                        var t = window.pageYOffset >= this.height_;
+                        t !== this.active_ && (this.header_.dataset.mdState = (this.active_ = t) ? "shadow" : "")
+                    } else this.height_ = 0, this.setup()
+                }, t.reset = function() {
+                    this.header_.dataset.mdState = "", this.height_ = 0, this.active_ = !1
+                }, e
+            }(),
+            Title: function() {
+                function e(e, t) {
+                    var n = "string" == typeof e ? document.querySelector(e) : e;
+                    if (!(n instanceof HTMLElement)) throw new ReferenceError;
+                    if (this.el_ = n, !((n = "string" == typeof t ? document.querySelector(t) : t) instanceof HTMLHeadingElement)) throw new ReferenceError;
+                    this.header_ = n, this.active_ = !1
+                }
+                var t = e.prototype;
+                return t.setup = function() {
+                    var t = this;
+                    Array.prototype.forEach.call(this.el_.children, function(e) {
+                        e.style.width = t.el_.offsetWidth - 20 + "px"
+                    })
+                }, t.update = function(e) {
+                    var t = this,
+                        n = window.pageYOffset >= this.header_.offsetTop;
+                    n !== this.active_ && (this.el_.dataset.mdState = (this.active_ = n) ? "active" : ""), "resize" !== e.type && "orientationchange" !== e.type || Array.prototype.forEach.call(this.el_.children, function(e) {
+                        e.style.width = t.el_.offsetWidth - 20 + "px"
+                    })
+                }, t.reset = function() {
+                    this.el_.dataset.mdState = "", this.el_.style.width = "", this.active_ = !1
+                }, e
+            }()
+        },
+        o = {
+            Blur: function() {
+                function e(e) {
+                    this.els_ = "string" == typeof e ? document.querySelectorAll(e) : e, this.index_ = 0, this.offset_ = window.pageYOffset, this.dir_ = !1, this.anchors_ = [].reduce.call(this.els_, function(e, t) {
+                        var n = decodeURIComponent(t.hash);
+                        return e.concat(document.getElementById(n.substring(1)) || [])
+                    }, [])
+                }
+                var t = e.prototype;
+                return t.setup = function() {
+                    this.update()
+                }, t.update = function() {
+                    var e = window.pageYOffset,
+                        t = this.offset_ - e < 0;
+                    if (this.dir_ !== t && (this.index_ = this.index_ = t ? 0 : this.els_.length - 1), 0 !== this.anchors_.length) {
+                        if (this.offset_ <= e)
+                            for (var n = this.index_ + 1; n < this.els_.length && this.anchors_[n].offsetTop - 80 <= e; n++) 0 < n && (this.els_[n - 1].dataset.mdState = "blur"), this.index_ = n;
+                        else
+                            for (var r = this.index_; 0 <= r; r--) {
+                                if (!(this.anchors_[r].offsetTop - 80 > e)) {
+                                    this.index_ = r;
+                                    break
+                                }
+                                0 < r && (this.els_[r - 1].dataset.mdState = "")
+                            }
+                        this.offset_ = e, this.dir_ = t
+                    }
+                }, t.reset = function() {
+                    Array.prototype.forEach.call(this.els_, function(e) {
+                        e.dataset.mdState = ""
+                    }), this.index_ = 0, this.offset_ = window.pageYOffset
+                }, e
+            }(),
+            Collapse: function() {
+                function e(e) {
+                    var t = "string" == typeof e ? document.querySelector(e) : e;
+                    if (!(t instanceof HTMLElement)) throw new ReferenceError;
+                    this.el_ = t
+                }
+                var t = e.prototype;
+                return t.setup = function() {
+                    var e = this.el_.getBoundingClientRect().height;
+                    this.el_.style.display = e ? "block" : "none", this.el_.style.overflow = e ? "visible" : "hidden"
+                }, t.update = function() {
+                    var e = this,
+                        t = this.el_.getBoundingClientRect().height;
+                    this.el_.style.display = "block", this.el_.style.overflow = "";
+                    var r = this.el_.previousElementSibling.previousElementSibling.checked;
+                    if (r) this.el_.style.maxHeight = t + "px", requestAnimationFrame(function() {
+                        e.el_.setAttribute("data-md-state", "animate"), e.el_.style.maxHeight = "0px"
+                    });
+                    else {
+                        this.el_.setAttribute("data-md-state", "expand"), this.el_.style.maxHeight = "";
+                        var n = this.el_.getBoundingClientRect().height;
+                        this.el_.removeAttribute("data-md-state"), this.el_.style.maxHeight = "0px", requestAnimationFrame(function() {
+                            e.el_.setAttribute("data-md-state", "animate"), e.el_.style.maxHeight = n + "px"
+                        })
+                    }
+                    this.el_.addEventListener("transitionend", function e(t) {
+                        var n = t.target;
+                        if (!(n instanceof HTMLElement)) throw new ReferenceError;
+                        n.removeAttribute("data-md-state"), n.style.maxHeight = "", n.style.display = r ? "none" : "block", n.style.overflow = r ? "hidden" : "visible", n.removeEventListener("transitionend", e)
+                    }, !1)
+                }, t.reset = function() {
+                    this.el_.dataset.mdState = "", this.el_.style.maxHeight = "", this.el_.style.display = "", this.el_.style.overflow = ""
+                }, e
+            }(),
+            Scrolling: function() {
+                function e(e) {
+                    var t = "string" == typeof e ? document.querySelector(e) : e;
+                    if (!(t instanceof HTMLElement)) throw new ReferenceError;
+                    this.el_ = t
+                }
+                var t = e.prototype;
+                return t.setup = function() {
+                    this.el_.children[this.el_.children.length - 1].style.webkitOverflowScrolling = "touch";
+                    var e = this.el_.querySelectorAll("[data-md-toggle]");
+                    Array.prototype.forEach.call(e, function(e) {
+                        if (!(e instanceof HTMLInputElement)) throw new ReferenceError;
+                        if (e.checked) {
+                            var t = e.nextElementSibling;
+                            if (!(t instanceof HTMLElement)) throw new ReferenceError;
+                            for (;
+                                "NAV" !== t.tagName && t.nextElementSibling;) t = t.nextElementSibling;
+                            if (!(e.parentNode instanceof HTMLElement && e.parentNode.parentNode instanceof HTMLElement)) throw new ReferenceError;
+                            var n = e.parentNode.parentNode,
+                                r = t.children[t.children.length - 1];
+                            n.style.webkitOverflowScrolling = "", r.style.webkitOverflowScrolling = "touch"
+                        }
+                    })
+                }, t.update = function(e) {
+                    var t = e.target;
+                    if (!(t instanceof HTMLElement)) throw new ReferenceError;
+                    var n = t.nextElementSibling;
+                    if (!(n instanceof HTMLElement)) throw new ReferenceError;
+                    for (;
+                        "NAV" !== n.tagName && n.nextElementSibling;) n = n.nextElementSibling;
+                    if (!(t.parentNode instanceof HTMLElement && t.parentNode.parentNode instanceof HTMLElement)) throw new ReferenceError;
+                    var r = t.parentNode.parentNode,
+                        i = n.children[n.children.length - 1];
+                    if (r.style.webkitOverflowScrolling = "", i.style.webkitOverflowScrolling = "", !t.checked) {
+                        n.addEventListener("transitionend", function e() {
+                            n instanceof HTMLElement && (r.style.webkitOverflowScrolling = "touch", n.removeEventListener("transitionend", e))
+                        }, !1)
+                    }
+                    if (t.checked) {
+                        n.addEventListener("transitionend", function e() {
+                            n instanceof HTMLElement && (i.style.webkitOverflowScrolling = "touch", n.removeEventListener("transitionend", e))
+                        }, !1)
+                    }
+                }, t.reset = function() {
+                    this.el_.children[1].style.webkitOverflowScrolling = "";
+                    var e = this.el_.querySelectorAll("[data-md-toggle]");
+                    Array.prototype.forEach.call(e, function(e) {
+                        if (!(e instanceof HTMLInputElement)) throw new ReferenceError;
+                        if (e.checked) {
+                            var t = e.nextElementSibling;
+                            if (!(t instanceof HTMLElement)) throw new ReferenceError;
+                            for (;
+                                "NAV" !== t.tagName && t.nextElementSibling;) t = t.nextElementSibling;
+                            if (!(e.parentNode instanceof HTMLElement && e.parentNode.parentNode instanceof HTMLElement)) throw new ReferenceError;
+                            var n = e.parentNode.parentNode,
+                                r = t.children[t.children.length - 1];
+                            n.style.webkitOverflowScrolling = "", r.style.webkitOverflowScrolling = ""
+                        }
+                    })
+                }, e
+            }()
+        },
+        a = {
+            Lock: function() {
+                function e(e) {
+                    var t = "string" == typeof e ? document.querySelector(e) : e;
+                    if (!(t instanceof HTMLInputElement)) throw new ReferenceError;
+                    if (this.el_ = t, !document.body) throw new ReferenceError;
+                    this.lock_ = document.body
+                }
+                var t = e.prototype;
+                return t.setup = function() {
+                    this.update()
+                }, t.update = function() {
+                    var e = this;
+                    this.el_.checked ? (this.offset_ = window.pageYOffset, setTimeout(function() {
+                        window.scrollTo(0, 0), e.el_.checked && (e.lock_.dataset.mdState = "lock")
+                    }, 400)) : (this.lock_.dataset.mdState = "", setTimeout(function() {
+                        void 0 !== e.offset_ && window.scrollTo(0, e.offset_)
+                    }, 100))
+                }, t.reset = function() {
+                    "lock" === this.lock_.dataset.mdState && window.scrollTo(0, this.offset_), this.lock_.dataset.mdState = ""
+                }, e
+            }(),
+            Result: n(9).a
+        },
+        s = {
+            Position: function() {
+                function e(e, t) {
+                    var n = "string" == typeof e ? document.querySelector(e) : e;
+                    if (!(n instanceof HTMLElement && n.parentNode instanceof HTMLElement)) throw new ReferenceError;
+                    if (this.el_ = n, this.parent_ = n.parentNode, !((n = "string" == typeof t ? document.querySelector(t) : t) instanceof HTMLElement)) throw new ReferenceError;
+                    this.header_ = n, this.height_ = 0, this.pad_ = "fixed" === window.getComputedStyle(this.header_).position
+                }
+                var t = e.prototype;
+                return t.setup = function() {
+                    var e = Array.prototype.reduce.call(this.parent_.children, function(e, t) {
+                        return Math.max(e, t.offsetTop)
+                    }, 0);
+                    this.offset_ = e - (this.pad_ ? this.header_.offsetHeight : 0), this.update()
+                }, t.update = function(e) {
+                    var t = window.pageYOffset,
+                        n = window.innerHeight;
+                    e && "resize" === e.type && this.setup();
+                    var r = this.pad_ ? this.header_.offsetHeight : 0,
+                        i = this.parent_.offsetTop + this.parent_.offsetHeight,
+                        o = n - r - Math.max(0, this.offset_ - t) - Math.max(0, t + n - i);
+                    o !== this.height_ && (this.el_.style.height = (this.height_ = o) + "px"), t >= this.offset_ ? "lock" !== this.el_.dataset.mdState && (this.el_.dataset.mdState = "lock") : "lock" === this.el_.dataset.mdState && (this.el_.dataset.mdState = "")
+                }, t.reset = function() {
+                    this.el_.dataset.mdState = "", this.el_.style.height = "", this.height_ = 0
+                }, e
+            }()
+        },
+        c = n(6),
+        l = n.n(c);
+    var u = {
+            Adapter: {
+                GitHub: function(o) {
+                    var e, t;
+
+                    function n(e) {
+                        var t;
+                        t = o.call(this, e) || this;
+                        var n = /^.+github\.com\/([^/]+)\/?([^/]+)?.*$/.exec(t.base_);
+                        if (n && 3 === n.length) {
+                            var r = n[1],
+                                i = n[2];
+                            t.base_ = "https://api.github.com/users/" + r + "/repos", t.name_ = i
+                        }
+                        return t
+                    }
+                    return t = o, (e = n).prototype = Object.create(t.prototype), (e.prototype.constructor = e).__proto__ = t, n.prototype.fetch_ = function() {
+                        var i = this;
+                        return function n(r) {
+                            return void 0 === r && (r = 0), fetch(i.base_ + "?per_page=30&page=" + r).then(function(e) {
+                                return e.json()
+                            }).then(function(e) {
+                                if (!(e instanceof Array)) throw new TypeError;
+                                if (i.name_) {
+                                    var t = e.find(function(e) {
+                                        return e.name === i.name_
+                                    });
+                                    return t || 30 !== e.length ? t ? [i.format_(t.stargazers_count) + " Stars", i.format_(t.forks_count) + " Forks"] : [] : n(r + 1)
+                                }
+                                return [e.length + " Repositories"]
+                            })
+                        }()
+                    }, n
+                }(function() {
+                    function e(e) {
+                        var t = "string" == typeof e ? document.querySelector(e) : e;
+                        if (!(t instanceof HTMLAnchorElement)) throw new ReferenceError;
+                        this.el_ = t, this.base_ = this.el_.href, this.salt_ = this.hash_(this.base_)
+                    }
+                    var t = e.prototype;
+                    return t.fetch = function() {
+                        var n = this;
+                        return new Promise(function(t) {
+                            var e = l.a.getJSON(n.salt_ + ".cache-source");
+                            void 0 !== e ? t(e) : n.fetch_().then(function(e) {
+                                l.a.set(n.salt_ + ".cache-source", e, {
+                                    expires: 1 / 96
+                                }), t(e)
+                            })
+                        })
+                    }, t.fetch_ = function() {
+                        throw new Error("fetch_(): Not implemented")
+                    }, t.format_ = function(e) {
+                        return 1e4 < e ? (e / 1e3).toFixed(0) + "k" : 1e3 < e ? (e / 1e3).toFixed(1) + "k" : "" + e
+                    }, t.hash_ = function(e) {
+                        var t = 0;
+                        if (0 === e.length) return t;
+                        for (var n = 0, r = e.length; n < r; n++) t = (t << 5) - t + e.charCodeAt(n), t |= 0;
+                        return t
+                    }, e
+                }())
+            },
+            Repository: n(10).a
+        },
+        f = {
+            Toggle: function() {
+                function e(e) {
+                    var t = "string" == typeof e ? document.querySelector(e) : e;
+                    if (!(t instanceof Node)) throw new ReferenceError;
+                    this.el_ = t;
+                    var n = document.querySelector("[data-md-component=header]");
+                    this.height_ = n.offsetHeight, this.active_ = !1
+                }
+                var t = e.prototype;
+                return t.update = function() {
+                    var e = window.pageYOffset >= this.el_.children[0].offsetTop + (5 - this.height_);
+                    e !== this.active_ && (this.el_.dataset.mdState = (this.active_ = e) ? "hidden" : "")
+                }, t.reset = function() {
+                    this.el_.dataset.mdState = "", this.active_ = !1
+                }, e
+            }()
+        };
+    t.a = {
+        Event: r,
+        Header: i,
+        Nav: o,
+        Search: a,
+        Sidebar: s,
+        Source: u,
+        Tabs: f
+    }
+}, function(t, e, n) {
+    (function(e) {
+        t.exports = e.lunr = n(24)
+    }).call(this, n(4))
+}, function(e, f, d) {
+    "use strict";
+    (function(t) {
+        var e = d(8),
+            n = setTimeout;
+
+        function r() {}
+
+        function o(e) {
+            if (!(this instanceof o)) throw new TypeError("Promises must be constructed via new");
+            if ("function" != typeof e) throw new TypeError("not a function");
+            this._state = 0, this._handled = !1, this._value = void 0, this._deferreds = [], u(e, this)
+        }
+
+        function i(n, r) {
+            for (; 3 === n._state;) n = n._value;
+            0 !== n._state ? (n._handled = !0, o._immediateFn(function() {
+                var e = 1 === n._state ? r.onFulfilled : r.onRejected;
+                if (null !== e) {
+                    var t;
+                    try {
+                        t = e(n._value)
+                    } catch (e) {
+                        return void s(r.promise, e)
+                    }
+                    a(r.promise, t)
+                } else(1 === n._state ? a : s)(r.promise, n._value)
+            })) : n._deferreds.push(r)
+        }
+
+        function a(t, e) {
+            try {
+                if (e === t) throw new TypeError("A promise cannot be resolved with itself.");
+                if (e && ("object" == typeof e || "function" == typeof e)) {
+                    var n = e.then;
+                    if (e instanceof o) return t._state = 3, t._value = e, void c(t);
+                    if ("function" == typeof n) return void u((r = n, i = e, function() {
+                        r.apply(i, arguments)
+                    }), t)
+                }
+                t._state = 1, t._value = e, c(t)
+            } catch (e) {
+                s(t, e)
+            }
+            var r, i
+        }
+
+        function s(e, t) {
+            e._state = 2, e._value = t, c(e)
+        }
+
+        function c(e) {
+            2 === e._state && 0 === e._deferreds.length && o._immediateFn(function() {
+                e._handled || o._unhandledRejectionFn(e._value)
+            });
+            for (var t = 0, n = e._deferreds.length; t < n; t++) i(e, e._deferreds[t]);
+            e._deferreds = null
+        }
+
+        function l(e, t, n) {
+            this.onFulfilled = "function" == typeof e ? e : null, this.onRejected = "function" == typeof t ? t : null, this.promise = n
+        }
+
+        function u(e, t) {
+            var n = !1;
+            try {
+                e(function(e) {
+                    n || (n = !0, a(t, e))
+                }, function(e) {
+                    n || (n = !0, s(t, e))
+                })
+            } catch (e) {
+                if (n) return;
+                n = !0, s(t, e)
+            }
+        }
+        o.prototype.catch = function(e) {
+            return this.then(null, e)
+        }, o.prototype.then = function(e, t) {
+            var n = new this.constructor(r);
+            return i(this, new l(e, t, n)), n
+        }, o.prototype.finally = e.a, o.all = function(t) {
+            return new o(function(r, i) {
+                if (!t || void 0 === t.length) throw new TypeError("Promise.all accepts an array");
+                var o = Array.prototype.slice.call(t);
+                if (0 === o.length) return r([]);
+                var a = o.length;
+
+                function s(t, e) {
+                    try {
+                        if (e && ("object" == typeof e || "function" == typeof e)) {
+                            var n = e.then;
+                            if ("function" == typeof n) return void n.call(e, function(e) {
+                                s(t, e)
+                            }, i)
+                        }
+                        o[t] = e, 0 == --a && r(o)
+                    } catch (e) {
+                        i(e)
+                    }
+                }
+                for (var e = 0; e < o.length; e++) s(e, o[e])
+            })
+        }, o.resolve = function(t) {
+            return t && "object" == typeof t && t.constructor === o ? t : new o(function(e) {
+                e(t)
+            })
+        }, o.reject = function(n) {
+            return new o(function(e, t) {
+                t(n)
+            })
+        }, o.race = function(i) {
+            return new o(function(e, t) {
+                for (var n = 0, r = i.length; n < r; n++) i[n].then(e, t)
+            })
+        }, o._immediateFn = "function" == typeof t && function(e) {
+            t(e)
+        } || function(e) {
+            n(e, 0)
+        }, o._unhandledRejectionFn = function(e) {
+            "undefined" != typeof console && console && console.warn("Possible Unhandled Promise Rejection:", e)
+        }, f.a = o
+    }).call(this, d(21).setImmediate)
+}, function(e, t, n) {
+    "use strict";
+
+    function r(e, t) {
+        var n = document.createElement(e);
+        t && Array.prototype.forEach.call(Object.keys(t), function(e) {
+            n.setAttribute(e, t[e])
+        });
+        for (var r = arguments.length, i = new Array(2 < r ? r - 2 : 0), o = 2; o < r; o++) i[o - 2] = arguments[o];
+        return function t(e) {
+            Array.prototype.forEach.call(e, function(e) {
+                "string" == typeof e || "number" == typeof e ? n.textContent += e : Array.isArray(e) ? t(e) : void 0 !== e.__html ? n.innerHTML += e.__html : e instanceof Node && n.appendChild(e)
+            })
+        }(i), n
+    }
+    n.r(t), n.d(t, "createElement", function() {
+        return r
+    })
+}, function(e, t) {
+    var n;
+    n = function() {
+        return this
+    }();
+    try {
+        n = n || new Function("return this")()
+    } catch (e) {
+        "object" == typeof window && (n = window)
+    }
+    e.exports = n
+}, function(e, t, n) {
+    var r;
+    r = function() {
+        return function(n) {
+            var r = {};
+
+            function i(e) {
+                if (r[e]) return r[e].exports;
+                var t = r[e] = {
+                    i: e,
+                    l: !1,
+                    exports: {}
+                };
+                return n[e].call(t.exports, t, t.exports, i), t.l = !0, t.exports
+            }
+            return i.m = n, i.c = r, i.d = function(e, t, n) {
+                i.o(e, t) || Object.defineProperty(e, t, {
+                    enumerable: !0,
+                    get: n
+                })
+            }, i.r = function(e) {
+                "undefined" != typeof Symbol && Symbol.toStringTag && Object.defineProperty(e, Symbol.toStringTag, {
+                    value: "Module"
+                }), Object.defineProperty(e, "__esModule", {
+                    value: !0
+                })
+            }, i.t = function(t, e) {
+                if (1 & e && (t = i(t)), 8 & e) return t;
+                if (4 & e && "object" == typeof t && t && t.__esModule) return t;
+                var n = Object.create(null);
+                if (i.r(n), Object.defineProperty(n, "default", {
+                        enumerable: !0,
+                        value: t
+                    }), 2 & e && "string" != typeof t)
+                    for (var r in t) i.d(n, r, function(e) {
+                        return t[e]
+                    }.bind(null, r));
+                return n
+            }, i.n = function(e) {
+                var t = e && e.__esModule ? function() {
+                    return e.default
+                } : function() {
+                    return e
+                };
+                return i.d(t, "a", t), t
+            }, i.o = function(e, t) {
+                return Object.prototype.hasOwnProperty.call(e, t)
+            }, i.p = "", i(i.s = 0)
+        }([function(e, t, n) {
+            "use strict";
+            var i = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function(e) {
+                    return typeof e
+                } : function(e) {
+                    return e && "function" == typeof Symbol && e.constructor === Symbol && e !== Symbol.prototype ? "symbol" : typeof e
+                },
+                o = function() {
+                    function r(e, t) {
+                        for (var n = 0; n < t.length; n++) {
+                            var r = t[n];
+                            r.enumerable = r.enumerable || !1, r.configurable = !0, "value" in r && (r.writable = !0), Object.defineProperty(e, r.key, r)
+                        }
+                    }
+                    return function(e, t, n) {
+                        return t && r(e.prototype, t), n && r(e, n), e
+                    }
+                }(),
+                a = r(n(1)),
+                s = r(n(3)),
+                c = r(n(4));
+
+            function r(e) {
+                return e && e.__esModule ? e : {
+                    default: e
+                }
+            }
+            var l = function(e) {
+                function r(e, t) {
+                    ! function(e, t) {
+                        if (!(e instanceof t)) throw new TypeError("Cannot call a class as a function")
+                    }(this, r);
+                    var n = function(e, t) {
+                        if (!e) throw new ReferenceError("this hasn't been initialised - super() hasn't been called");
+                        return !t || "object" != typeof t && "function" != typeof t ? e : t
+                    }(this, (r.__proto__ || Object.getPrototypeOf(r)).call(this));
+                    return n.resolveOptions(t), n.listenClick(e), n
+                }
+                return function(e, t) {
+                    if ("function" != typeof t && null !== t) throw new TypeError("Super expression must either be null or a function, not " + typeof t);
+                    e.prototype = Object.create(t && t.prototype, {
+                        constructor: {
+                            value: e,
+                            enumerable: !1,
+                            writable: !0,
+                            configurable: !0
+                        }
+                    }), t && (Object.setPrototypeOf ? Object.setPrototypeOf(e, t) : e.__proto__ = t)
+                }(r, s.default), o(r, [{
+                    key: "resolveOptions",
+                    value: function() {
+                        var e = 0 < arguments.length && void 0 !== arguments[0] ? arguments[0] : {};
+                        this.action = "function" == typeof e.action ? e.action : this.defaultAction, this.target = "function" == typeof e.target ? e.target : this.defaultTarget, this.text = "function" == typeof e.text ? e.text : this.defaultText, this.container = "object" === i(e.container) ? e.container : document.body
+                    }
+                }, {
+                    key: "listenClick",
+                    value: function(e) {
+                        var t = this;
+                        this.listener = (0, c.default)(e, "click", function(e) {
+                            return t.onClick(e)
+                        })
+                    }
+                }, {
+                    key: "onClick",
+                    value: function(e) {
+                        var t = e.delegateTarget || e.currentTarget;
+                        this.clipboardAction && (this.clipboardAction = null), this.clipboardAction = new a.default({
+                            action: this.action(t),
+                            target: this.target(t),
+                            text: this.text(t),
+                            container: this.container,
+                            trigger: t,
+                            emitter: this
+                        })
+                    }
+                }, {
+                    key: "defaultAction",
+                    value: function(e) {
+                        return u("action", e)
+                    }
+                }, {
+                    key: "defaultTarget",
+                    value: function(e) {
+                        var t = u("target", e);
+                        if (t) return document.querySelector(t)
+                    }
+                }, {
+                    key: "defaultText",
+                    value: function(e) {
+                        return u("text", e)
+                    }
+                }, {
+                    key: "destroy",
+                    value: function() {
+                        this.listener.destroy(), this.clipboardAction && (this.clipboardAction.destroy(), this.clipboardAction = null)
+                    }
+                }], [{
+                    key: "isSupported",
+                    value: function() {
+                        var e = 0 < arguments.length && void 0 !== arguments[0] ? arguments[0] : ["copy", "cut"],
+                            t = "string" == typeof e ? [e] : e,
+                            n = !!document.queryCommandSupported;
+                        return t.forEach(function(e) {
+                            n = n && !!document.queryCommandSupported(e)
+                        }), n
+                    }
+                }]), r
+            }();
+
+            function u(e, t) {
+                var n = "data-clipboard-" + e;
+                if (t.hasAttribute(n)) return t.getAttribute(n)
+            }
+            e.exports = l
+        }, function(e, t, n) {
+            "use strict";
+            var r, i = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function(e) {
+                    return typeof e
+                } : function(e) {
+                    return e && "function" == typeof Symbol && e.constructor === Symbol && e !== Symbol.prototype ? "symbol" : typeof e
+                },
+                o = function() {
+                    function r(e, t) {
+                        for (var n = 0; n < t.length; n++) {
+                            var r = t[n];
+                            r.enumerable = r.enumerable || !1, r.configurable = !0, "value" in r && (r.writable = !0), Object.defineProperty(e, r.key, r)
+                        }
+                    }
+                    return function(e, t, n) {
+                        return t && r(e.prototype, t), n && r(e, n), e
+                    }
+                }(),
+                a = n(2),
+                s = (r = a) && r.__esModule ? r : {
+                    default: r
+                };
+            var c = function() {
+                function t(e) {
+                    ! function(e, t) {
+                        if (!(e instanceof t)) throw new TypeError("Cannot call a class as a function")
+                    }(this, t), this.resolveOptions(e), this.initSelection()
+                }
+                return o(t, [{
+                    key: "resolveOptions",
+                    value: function() {
+                        var e = 0 < arguments.length && void 0 !== arguments[0] ? arguments[0] : {};
+                        this.action = e.action, this.container = e.container, this.emitter = e.emitter, this.target = e.target, this.text = e.text, this.trigger = e.trigger, this.selectedText = ""
+                    }
+                }, {
+                    key: "initSelection",
+                    value: function() {
+                        this.text ? this.selectFake() : this.target && this.selectTarget()
+                    }
+                }, {
+                    key: "selectFake",
+                    value: function() {
+                        var e = this,
+                            t = "rtl" == document.documentElement.getAttribute("dir");
+                        this.removeFake(), this.fakeHandlerCallback = function() {
+                            return e.removeFake()
+                        }, this.fakeHandler = this.container.addEventListener("click", this.fakeHandlerCallback) || !0, this.fakeElem = document.createElement("textarea"), this.fakeElem.style.fontSize = "12pt", this.fakeElem.style.border = "0", this.fakeElem.style.padding = "0", this.fakeElem.style.margin = "0", this.fakeElem.style.position = "absolute", this.fakeElem.style[t ? "right" : "left"] = "-9999px";
+                        var n = window.pageYOffset || document.documentElement.scrollTop;
+                        this.fakeElem.style.top = n + "px", this.fakeElem.setAttribute("readonly", ""), this.fakeElem.value = this.text, this.container.appendChild(this.fakeElem), this.selectedText = (0, s.default)(this.fakeElem), this.copyText()
+                    }
+                }, {
+                    key: "removeFake",
+                    value: function() {
+                        this.fakeHandler && (this.container.removeEventListener("click", this.fakeHandlerCallback), this.fakeHandler = null, this.fakeHandlerCallback = null), this.fakeElem && (this.container.removeChild(this.fakeElem), this.fakeElem = null)
+                    }
+                }, {
+                    key: "selectTarget",
+                    value: function() {
+                        this.selectedText = (0, s.default)(this.target), this.copyText()
+                    }
+                }, {
+                    key: "copyText",
+                    value: function() {
+                        var t = void 0;
+                        try {
+                            t = document.execCommand(this.action)
+                        } catch (e) {
+                            t = !1
+                        }
+                        this.handleResult(t)
+                    }
+                }, {
+                    key: "handleResult",
+                    value: function(e) {
+                        this.emitter.emit(e ? "success" : "error", {
+                            action: this.action,
+                            text: this.selectedText,
+                            trigger: this.trigger,
+                            clearSelection: this.clearSelection.bind(this)
+                        })
+                    }
+                }, {
+                    key: "clearSelection",
+                    value: function() {
+                        this.trigger && this.trigger.focus(), window.getSelection().removeAllRanges()
+                    }
+                }, {
+                    key: "destroy",
+                    value: function() {
+                        this.removeFake()
+                    }
+                }, {
+                    key: "action",
+                    set: function() {
+                        var e = 0 < arguments.length && void 0 !== arguments[0] ? arguments[0] : "copy";
+                        if (this._action = e, "copy" !== this._action && "cut" !== this._action) throw new Error('Invalid "action" value, use either "copy" or "cut"')
+                    },
+                    get: function() {
+                        return this._action
+                    }
+                }, {
+                    key: "target",
+                    set: function(e) {
+                        if (void 0 !== e) {
+                            if (!e || "object" !== (void 0 === e ? "undefined" : i(e)) || 1 !== e.nodeType) throw new Error('Invalid "target" value, use a valid Element');
+                            if ("copy" === this.action && e.hasAttribute("disabled")) throw new Error('Invalid "target" attribute. Please use "readonly" instead of "disabled" attribute');
+                            if ("cut" === this.action && (e.hasAttribute("readonly") || e.hasAttribute("disabled"))) throw new Error('Invalid "target" attribute. You can\'t cut text from elements with "readonly" or "disabled" attributes');
+                            this._target = e
+                        }
+                    },
+                    get: function() {
+                        return this._target
+                    }
+                }]), t
+            }();
+            e.exports = c
+        }, function(e, t) {
+            e.exports = function(e) {
+                var t;
+                if ("SELECT" === e.nodeName) e.focus(), t = e.value;
+                else if ("INPUT" === e.nodeName || "TEXTAREA" === e.nodeName) {
+                    var n = e.hasAttribute("readonly");
+                    n || e.setAttribute("readonly", ""), e.select(), e.setSelectionRange(0, e.value.length), n || e.removeAttribute("readonly"), t = e.value
+                } else {
+                    e.hasAttribute("contenteditable") && e.focus();
+                    var r = window.getSelection(),
+                        i = document.createRange();
+                    i.selectNodeContents(e), r.removeAllRanges(), r.addRange(i), t = r.toString()
+                }
+                return t
+            }
+        }, function(e, t) {
+            function n() {}
+            n.prototype = {
+                on: function(e, t, n) {
+                    var r = this.e || (this.e = {});
+                    return (r[e] || (r[e] = [])).push({
+                        fn: t,
+                        ctx: n
+                    }), this
+                },
+                once: function(e, t, n) {
+                    var r = this;
+
+                    function i() {
+                        r.off(e, i), t.apply(n, arguments)
+                    }
+                    return i._ = t, this.on(e, i, n)
+                },
+                emit: function(e) {
+                    for (var t = [].slice.call(arguments, 1), n = ((this.e || (this.e = {}))[e] || []).slice(), r = 0, i = n.length; r < i; r++) n[r].fn.apply(n[r].ctx, t);
+                    return this
+                },
+                off: function(e, t) {
+                    var n = this.e || (this.e = {}),
+                        r = n[e],
+                        i = [];
+                    if (r && t)
+                        for (var o = 0, a = r.length; o < a; o++) r[o].fn !== t && r[o].fn._ !== t && i.push(r[o]);
+                    return i.length ? n[e] = i : delete n[e], this
+                }
+            }, e.exports = n
+        }, function(e, t, n) {
+            var d = n(5),
+                h = n(6);
+            e.exports = function(e, t, n) {
+                if (!e && !t && !n) throw new Error("Missing required arguments");
+                if (!d.string(t)) throw new TypeError("Second argument must be a String");
+                if (!d.fn(n)) throw new TypeError("Third argument must be a Function");
+                if (d.node(e)) return u = t, f = n, (l = e).addEventListener(u, f), {
+                    destroy: function() {
+                        l.removeEventListener(u, f)
+                    }
+                };
+                if (d.nodeList(e)) return a = e, s = t, c = n, Array.prototype.forEach.call(a, function(e) {
+                    e.addEventListener(s, c)
+                }), {
+                    destroy: function() {
+                        Array.prototype.forEach.call(a, function(e) {
+                            e.removeEventListener(s, c)
+                        })
+                    }
+                };
+                if (d.string(e)) return r = e, i = t, o = n, h(document.body, r, i, o);
+                throw new TypeError("First argument must be a String, HTMLElement, HTMLCollection, or NodeList");
+                var r, i, o, a, s, c, l, u, f
+            }
+        }, function(e, n) {
+            n.node = function(e) {
+                return void 0 !== e && e instanceof HTMLElement && 1 === e.nodeType
+            }, n.nodeList = function(e) {
+                var t = Object.prototype.toString.call(e);
+                return void 0 !== e && ("[object NodeList]" === t || "[object HTMLCollection]" === t) && "length" in e && (0 === e.length || n.node(e[0]))
+            }, n.string = function(e) {
+                return "string" == typeof e || e instanceof String
+            }, n.fn = function(e) {
+                return "[object Function]" === Object.prototype.toString.call(e)
+            }
+        }, function(e, t, n) {
+            var a = n(7);
+
+            function o(e, t, n, r, i) {
+                var o = function(t, n, e, r) {
+                    return function(e) {
+                        e.delegateTarget = a(e.target, n), e.delegateTarget && r.call(t, e)
+                    }
+                }.apply(this, arguments);
+                return e.addEventListener(n, o, i), {
+                    destroy: function() {
+                        e.removeEventListener(n, o, i)
+                    }
+                }
+            }
+            e.exports = function(e, t, n, r, i) {
+                return "function" == typeof e.addEventListener ? o.apply(null, arguments) : "function" == typeof n ? o.bind(null, document).apply(null, arguments) : ("string" == typeof e && (e = document.querySelectorAll(e)), Array.prototype.map.call(e, function(e) {
+                    return o(e, t, n, r, i)
+                }))
+            }
+        }, function(e, t) {
+            if ("undefined" != typeof Element && !Element.prototype.matches) {
+                var n = Element.prototype;
+                n.matches = n.matchesSelector || n.mozMatchesSelector || n.msMatchesSelector || n.oMatchesSelector || n.webkitMatchesSelector
+            }
+            e.exports = function(e, t) {
+                for (; e && 9 !== e.nodeType;) {
+                    if ("function" == typeof e.matches && e.matches(t)) return e;
+                    e = e.parentNode
+                }
+            }
+        }])
+    }, e.exports = r()
+}, function(r, i, o) {
+    var a, s;
+    ! function(e) {
+        if (void 0 === (s = "function" == typeof(a = e) ? a.call(i, o, i, r) : a) || (r.exports = s), !0, r.exports = e(), !!0) {
+            var t = window.Cookies,
+                n = window.Cookies = e();
+            n.noConflict = function() {
+                return window.Cookies = t, n
+            }
+        }
+    }(function() {
+        function m() {
+            for (var e = 0, t = {}; e < arguments.length; e++) {
+                var n = arguments[e];
+                for (var r in n) t[r] = n[r]
+            }
+            return t
+        }
+        return function e(h) {
+            function p(e, t, n) {
+                var r;
+                if ("undefined" != typeof document) {
+                    if (1 < arguments.length) {
+                        if ("number" == typeof(n = m({
+                                path: "/"
+                            }, p.defaults, n)).expires) {
+                            var i = new Date;
+                            i.setMilliseconds(i.getMilliseconds() + 864e5 * n.expires), n.expires = i
+                        }
+                        n.expires = n.expires ? n.expires.toUTCString() : "";
+                        try {
+                            r = JSON.stringify(t), /^[\{\[]/.test(r) && (t = r)
+                        } catch (e) {}
+                        t = h.write ? h.write(t, e) : encodeURIComponent(String(t)).replace(/%(23|24|26|2B|3A|3C|3E|3D|2F|3F|40|5B|5D|5E|60|7B|7D|7C)/g, decodeURIComponent), e = (e = (e = encodeURIComponent(String(e))).replace(/%(23|24|26|2B|5E|60|7C)/g, decodeURIComponent)).replace(/[\(\)]/g, escape);
+                        var o = "";
+                        for (var a in n) n[a] && (o += "; " + a, !0 !== n[a] && (o += "=" + n[a]));
+                        return document.cookie = e + "=" + t + o
+                    }
+                    e || (r = {});
+                    for (var s = document.cookie ? document.cookie.split("; ") : [], c = /(%[0-9A-Z]{2})+/g, l = 0; l < s.length; l++) {
+                        var u = s[l].split("="),
+                            f = u.slice(1).join("=");
+                        this.json || '"' !== f.charAt(0) || (f = f.slice(1, -1));
+                        try {
+                            var d = u[0].replace(c, decodeURIComponent);
+                            if (f = h.read ? h.read(f, d) : h(f, d) || f.replace(c, decodeURIComponent), this.json) try {
+                                f = JSON.parse(f)
+                            } catch (e) {}
+                            if (e === d) {
+                                r = f;
+                                break
+                            }
+                            e || (r[d] = f)
+                        } catch (e) {}
+                    }
+                    return r
+                }
+            }
+            return (p.set = p).get = function(e) {
+                return p.call(p, e)
+            }, p.getJSON = function() {
+                return p.apply({
+                    json: !0
+                }, [].slice.call(arguments))
+            }, p.defaults = {}, p.remove = function(e, t) {
+                p(e, "", m(t, {
+                    expires: -1
+                }))
+            }, p.withConverter = e, p
+        }(function() {})
+    })
+}, function(e, t, n) {
+    "use strict";
+    n.r(t);
+    var r = "function" == typeof fetch ? fetch.bind() : function(i, o) {
+        return o = o || {}, new Promise(function(e, t) {
+            var n = new XMLHttpRequest;
+            for (var r in n.open(o.method || "get", i, !0), o.headers) n.setRequestHeader(r, o.headers[r]);
+
+            function s() {
+                var r, i = [],
+                    o = [],
+                    a = {};
+                return n.getAllResponseHeaders().replace(/^(.*?):[^\S\n]*([\s\S]*?)$/gm, function(e, t, n) {
+                    i.push(t = t.toLowerCase()), o.push([t, n]), r = a[t], a[t] = r ? r + "," + n : n
+                }), {
+                    ok: 2 == (n.status / 100 | 0),
+                    status: n.status,
+                    statusText: n.statusText,
+                    url: n.responseURL,
+                    clone: s,
+                    text: function() {
+                        return Promise.resolve(n.responseText)
+                    },
+                    json: function() {
+                        return Promise.resolve(n.responseText).then(JSON.parse)
+                    },
+                    blob: function() {
+                        return Promise.resolve(new Blob([n.response]))
+                    },
+                    headers: {
+                        keys: function() {
+                            return i
+                        },
+                        entries: function() {
+                            return o
+                        },
+                        get: function(e) {
+                            return a[e.toLowerCase()]
+                        },
+                        has: function(e) {
+                            return e.toLowerCase() in a
+                        }
+                    }
+                }
+            }
+            n.withCredentials = "include" == o.credentials, n.onload = function() {
+                e(s())
+            }, n.onerror = t, n.send(o.body || null)
+        })
+    };
+    t.default = r
+}, function(e, t, n) {
+    "use strict";
+    t.a = function(t) {
+        var n = this.constructor;
+        return this.then(function(e) {
+            return n.resolve(t()).then(function() {
+                return e
+            })
+        }, function(e) {
+            return n.resolve(t()).then(function() {
+                return n.reject(e)
+            })
+        })
+    }
+}, function(e, n, r) {
+    "use strict";
+    (function(f) {
+        r.d(n, "a", function() {
+            return t
+        });
+        var e = r(1),
+            d = r.n(e),
+            h = function(e) {
+                var t = document.getElementsByName("lang:" + e)[0];
+                if (!(t instanceof HTMLMetaElement)) throw new ReferenceError;
+                return t.content
+            },
+            t = function() {
+                function e(e, t) {
+                    var n = "string" == typeof e ? document.querySelector(e) : e;
+                    if (!(n instanceof HTMLElement)) throw new ReferenceError;
+                    this.el_ = n;
+                    var r = Array.prototype.slice.call(this.el_.children),
+                        i = r[0],
+                        o = r[1];
+                    this.data_ = t, this.meta_ = i, this.list_ = o, this.message_ = {
+                        placeholder: this.meta_.textContent,
+                        none: h("search.result.none"),
+                        one: h("search.result.one"),
+                        other: h("search.result.other")
+                    };
+                    var a = h("search.tokenizer");
+                    a.length && (d.a.tokenizer.separator = a), this.lang_ = h("search.language").split(",").filter(Boolean).map(function(e) {
+                        return e.trim()
+                    })
+                }
+                return e.prototype.update = function(e) {
+                    var t, a = this;
+                    if ("focus" !== e.type || this.index_) {
+                        if ("focus" === e.type || "keyup" === e.type) {
+                            var n = e.target;
+                            if (!(n instanceof HTMLInputElement)) throw new ReferenceError;
+                            if (!this.index_ || n.value === this.value_) return;
+                            for (; this.list_.firstChild;) this.list_.removeChild(this.list_.firstChild);
+                            if (this.value_ = n.value, 0 === this.value_.length) return void(this.meta_.textContent = this.message_.placeholder);
+                            var r = this.index_.query(function(t) {
+                                    a.value_.toLowerCase().split(" ").filter(Boolean).forEach(function(e) {
+                                        t.term(e, {
+                                            wildcard: d.a.Query.wildcard.TRAILING
+                                        })
+                                    })
+                                }).reduce(function(e, t) {
+                                    var n = a.docs_.get(t.ref);
+                                    if (n.parent) {
+                                        var r = n.parent.location;
+                                        e.set(r, (e.get(r) || []).concat(t))
+                                    } else {
+                                        var i = n.location;
+                                        e.set(i, e.get(i) || [])
+                                    }
+                                    return e
+                                }, new Map),
+                                i = (t = this.value_.trim(), t.replace(/[|\\{}()[\]^$+*?.-]/g, "\\$&")).replace(new RegExp(d.a.tokenizer.separator, "img"), "|"),
+                                s = new RegExp("(^|" + d.a.tokenizer.separator + ")(" + i + ")", "img"),
+                                c = function(e, t, n) {
+                                    return t + "<em>" + n + "</em>"
+                                };
+                            this.stack_ = [], r.forEach(function(e, t) {
+                                var n, r = a.docs_.get(t),
+                                    i = f.createElement("li", {
+                                        class: "md-search-result__item"
+                                    }, f.createElement("a", {
+                                        href: r.location,
+                                        title: r.title,
+                                        class: "md-search-result__link",
+                                        tabindex: "-1"
+                                    }, f.createElement("article", {
+                                        class: "md-search-result__article md-search-result__article--document"
+                                    }, f.createElement("h1", {
+                                        class: "md-search-result__title"
+                                    }, {
+                                        __html: r.title.replace(s, c)
+                                    }), r.text.length ? f.createElement("p", {
+                                        class: "md-search-result__teaser"
+                                    }, {
+                                        __html: r.text.replace(s, c)
+                                    }) : {}))),
+                                    o = e.map(function(t) {
+                                        return function() {
+                                            var e = a.docs_.get(t.ref);
+                                            i.appendChild(f.createElement("a", {
+                                                href: e.location,
+                                                title: e.title,
+                                                class: "md-search-result__link",
+                                                "data-md-rel": "anchor",
+                                                tabindex: "-1"
+                                            }, f.createElement("article", {
+                                                class: "md-search-result__article"
+                                            }, f.createElement("h1", {
+                                                class: "md-search-result__title"
+                                            }, {
+                                                __html: e.title.replace(s, c)
+                                            }), e.text.length ? f.createElement("p", {
+                                                class: "md-search-result__teaser"
+                                            }, {
+                                                __html: function(e, t) {
+                                                    var n = t;
+                                                    if (e.length > n) {
+                                                        for (;
+                                                            " " !== e[n] && 0 < --n;);
+                                                        return e.substring(0, n) + "..."
+                                                    }
+                                                    return e
+                                                }(e.text.replace(s, c), 400)
+                                            }) : {})))
+                                        }
+                                    });
+                                (n = a.stack_).push.apply(n, [function() {
+                                    return a.list_.appendChild(i)
+                                }].concat(o))
+                            });
+                            var o = this.el_.parentNode;
+                            if (!(o instanceof HTMLElement)) throw new ReferenceError;
+                            for (; this.stack_.length && o.offsetHeight >= o.scrollHeight - 16;) this.stack_.shift()();
+                            var l = this.list_.querySelectorAll("[data-md-rel=anchor]");
+                            switch (Array.prototype.forEach.call(l, function(r) {
+                                ["click", "keydown"].forEach(function(n) {
+                                    r.addEventListener(n, function(e) {
+                                        if ("keydown" !== n || 13 === e.keyCode) {
+                                            var t = document.querySelector("[data-md-toggle=search]");
+                                            if (!(t instanceof HTMLInputElement)) throw new ReferenceError;
+                                            t.checked && (t.checked = !1, t.dispatchEvent(new CustomEvent("change"))), e.preventDefault(), setTimeout(function() {
+                                                document.location.href = r.href
+                                            }, 100)
+                                        }
+                                    })
+                                })
+                            }), r.size) {
+                                case 0:
+                                    this.meta_.textContent = this.message_.none;
+                                    break;
+                                case 1:
+                                    this.meta_.textContent = this.message_.one;
+                                    break;
+                                default:
+                                    this.meta_.textContent = this.message_.other.replace("#", r.size)
+                            }
+                        }
+                    } else {
+                        var u = function(e) {
+                            a.docs_ = e.reduce(function(e, t) {
+                                var n, r, i, o = t.location.split("#"),
+                                    a = o[0],
+                                    s = o[1];
+                                return t.text = (n = t.text, r = document.createTextNode(n), (i = document.createElement("p")).appendChild(r), i.innerHTML), s && (t.parent = e.get(a), t.parent && !t.parent.done && (t.parent.title = t.title, t.parent.text = t.text, t.parent.done = !0)), t.text = t.text.replace(/\n/g, " ").replace(/\s+/g, " ").replace(/\s+([,.:;!?])/g, function(e, t) {
+                                    return t
+                                }), t.parent && t.parent.title === t.title || e.set(t.location, t), e
+                            }, new Map);
+                            var i = a.docs_,
+                                o = a.lang_;
+                            a.stack_ = [], a.index_ = d()(function() {
+                                var e, t = this,
+                                    n = {
+                                        "search.pipeline.trimmer": d.a.trimmer,
+                                        "search.pipeline.stopwords": d.a.stopWordFilter
+                                    },
+                                    r = Object.keys(n).reduce(function(e, t) {
+                                        return h(t).match(/^false$/i) || e.push(n[t]), e
+                                    }, []);
+                                this.pipeline.reset(), r && (e = this.pipeline).add.apply(e, r), 1 === o.length && "en" !== o[0] && d.a[o[0]] ? this.use(d.a[o[0]]) : 1 < o.length && this.use(d.a.multiLanguage.apply(d.a, o)), this.field("title", {
+                                    boost: 10
+                                }), this.field("text"), this.ref("location"), i.forEach(function(e) {
+                                    return t.add(e)
+                                })
+                            });
+                            var t = a.el_.parentNode;
+                            if (!(t instanceof HTMLElement)) throw new ReferenceError;
+                            t.addEventListener("scroll", function() {
+                                for (; a.stack_.length && t.scrollTop + t.offsetHeight >= t.scrollHeight - 16;) a.stack_.splice(0, 10).forEach(function(e) {
+                                    return e()
+                                })
+                            })
+                        };
+                        setTimeout(function() {
+                            return "function" == typeof a.data_ ? a.data_().then(u) : u(a.data_)
+                        }, 250)
+                    }
+                }, e
+            }()
+    }).call(this, r(3))
+}, function(e, n, r) {
+    "use strict";
+    (function(t) {
+        r.d(n, "a", function() {
+            return e
+        });
+        var e = function() {
+            function e(e) {
+                var t = "string" == typeof e ? document.querySelector(e) : e;
+                if (!(t instanceof HTMLElement)) throw new ReferenceError;
+                this.el_ = t
+            }
+            return e.prototype.initialize = function(e) {
+                e.length && this.el_.children.length && this.el_.children[this.el_.children.length - 1].appendChild(t.createElement("ul", {
+                    class: "md-source__facts"
+                }, e.map(function(e) {
+                    return t.createElement("li", {
+                        class: "md-source__fact"
+                    }, e)
+                }))), this.el_.dataset.mdState = "done"
+            }, e
+        }()
+    }).call(this, r(3))
+}, , , function(e, n, c) {
+    "use strict";
+    c.r(n),
+        function(o) {
+            c.d(n, "app", function() {
+                return t
+            });
+            c(14), c(15), c(16), c(17), c(18), c(19), c(20);
+            var r = c(2),
+                e = c(5),
+                a = c.n(e),
+                i = c(0);
+            window.Promise = window.Promise || r.a;
+            var s = function(e) {
+                var t = document.getElementsByName("lang:" + e)[0];
+                if (!(t instanceof HTMLMetaElement)) throw new ReferenceError;
+                return t.content
+            };
+            var t = {
+                initialize: function(t) {
+                    new i.a.Event.Listener(document, "DOMContentLoaded", function() {
+                        if (!(document.body instanceof HTMLElement)) throw new ReferenceError;
+                        Modernizr.addTest("ios", function() {
+                            return !!navigator.userAgent.match(/(iPad|iPhone|iPod)/g)
+                        });
+                        var e = document.querySelectorAll("table:not([class])");
+                        if (Array.prototype.forEach.call(e, function(e) {
+                                var t = o.createElement("div", {
+                                    class: "md-typeset__scrollwrap"
+                                }, o.createElement("div", {
+                                    class: "md-typeset__table"
+                                }));
+                                e.nextSibling ? e.parentNode.insertBefore(t, e.nextSibling) : e.parentNode.appendChild(t), t.children[0].appendChild(e)
+                            }), a.a.isSupported()) {
+                            var t = document.querySelectorAll(".codehilite > pre, pre > code");
+                            Array.prototype.forEach.call(t, function(e, t) {
+                                var n = "__code_" + t,
+                                    r = o.createElement("button", {
+                                        class: "md-clipboard",
+                                        title: s("clipboard.copy"),
+                                        "data-clipboard-target": "#" + n + " pre, #" + n + " code"
+                                    }, o.createElement("span", {
+                                        class: "md-clipboard__message"
+                                    })),
+                                    i = e.parentNode;
+                                i.id = n, i.insertBefore(r, e)
+                            }), new a.a(".md-clipboard").on("success", function(e) {
+                                var t = e.trigger.querySelector(".md-clipboard__message");
+                                if (!(t instanceof HTMLElement)) throw new ReferenceError;
+                                e.clearSelection(), t.dataset.mdTimer && clearTimeout(parseInt(t.dataset.mdTimer, 10)), t.classList.add("md-clipboard__message--active"), t.innerHTML = s("clipboard.copied"), t.dataset.mdTimer = setTimeout(function() {
+                                    t.classList.remove("md-clipboard__message--active"), t.dataset.mdTimer = ""
+                                }, 2e3).toString()
+                            })
+                        }
+                        if (!Modernizr.details) {
+                            var n = document.querySelectorAll("details > summary");
+                            Array.prototype.forEach.call(n, function(e) {
+                                e.addEventListener("click", function(e) {
+                                    var t = e.target.parentNode;
+                                    t.hasAttribute("open") ? t.removeAttribute("open") : t.setAttribute("open", "")
+                                })
+                            })
+                        }
+                        var r = function() {
+                            if (document.location.hash) {
+                                var e = document.getElementById(document.location.hash.substring(1));
+                                if (!e) return;
+                                for (var t = e.parentNode; t && !(t instanceof HTMLDetailsElement);) t = t.parentNode;
+                                if (t && !t.open) {
+                                    t.open = !0;
+                                    var n = location.hash;
+                                    location.hash = " ", location.hash = n
+                                }
+                            }
+                        };
+                        if (window.addEventListener("hashchange", r), r(), Modernizr.ios) {
+                            var i = document.querySelectorAll("[data-md-scrollfix]");
+                            Array.prototype.forEach.call(i, function(t) {
+                                t.addEventListener("touchstart", function() {
+                                    var e = t.scrollTop;
+                                    0 === e ? t.scrollTop = 1 : e + t.offsetHeight === t.scrollHeight && (t.scrollTop = e - 1)
+                                })
+                            })
+                        }
+                    }).listen(), new i.a.Event.Listener(window, ["scroll", "resize", "orientationchange"], new i.a.Header.Shadow("[data-md-component=container]", "[data-md-component=header]")).listen(), new i.a.Event.Listener(window, ["scroll", "resize", "orientationchange"], new i.a.Header.Title("[data-md-component=title]", ".md-typeset h1")).listen(), document.querySelector("[data-md-component=hero]") && new i.a.Event.Listener(window, ["scroll", "resize", "orientationchange"], new i.a.Tabs.Toggle("[data-md-component=hero]")).listen(), document.querySelector("[data-md-component=tabs]") && new i.a.Event.Listener(window, ["scroll", "resize", "orientationchange"], new i.a.Tabs.Toggle("[data-md-component=tabs]")).listen(), new i.a.Event.MatchMedia("(min-width: 1220px)", new i.a.Event.Listener(window, ["scroll", "resize", "orientationchange"], new i.a.Sidebar.Position("[data-md-component=navigation]", "[data-md-component=header]"))), document.querySelector("[data-md-component=toc]") && new i.a.Event.MatchMedia("(min-width: 960px)", new i.a.Event.Listener(window, ["scroll", "resize", "orientationchange"], new i.a.Sidebar.Position("[data-md-component=toc]", "[data-md-component=header]"))), new i.a.Event.MatchMedia("(min-width: 960px)", new i.a.Event.Listener(window, "scroll", new i.a.Nav.Blur("[data-md-component=toc] .md-nav__link")));
+                    var e = document.querySelectorAll("[data-md-component=collapsible]");
+                    Array.prototype.forEach.call(e, function(e) {
+                            new i.a.Event.MatchMedia("(min-width: 1220px)", new i.a.Event.Listener(e.previousElementSibling, "click", new i.a.Nav.Collapse(e)))
+                        }), new i.a.Event.MatchMedia("(max-width: 1219px)", new i.a.Event.Listener("[data-md-component=navigation] [data-md-toggle]", "change", new i.a.Nav.Scrolling("[data-md-component=navigation] nav"))), document.querySelector("[data-md-component=search]") && (new i.a.Event.MatchMedia("(max-width: 959px)", new i.a.Event.Listener("[data-md-toggle=search]", "change", new i.a.Search.Lock("[data-md-toggle=search]")))),
+                        new i.a.Event.Listener(document.body, "keydown", function(e) {
+                            if (9 === e.keyCode) {
+                                var t = document.querySelectorAll("[data-md-component=navigation] .md-nav__link[for]:not([tabindex])");
+                                Array.prototype.forEach.call(t, function(e) {
+                                    e.offsetHeight && (e.tabIndex = 0)
+                                })
+                            }
+                        }).listen(), new i.a.Event.Listener(document.body, "mousedown", function() {
+                            var e = document.querySelectorAll("[data-md-component=navigation] .md-nav__link[tabindex]");
+                            Array.prototype.forEach.call(e, function(e) {
+                                e.removeAttribute("tabIndex")
+                            })
+                        }).listen(), document.body.addEventListener("click", function() {
+                            "tabbing" === document.body.dataset.mdState && (document.body.dataset.mdState = "")
+                        }), new i.a.Event.MatchMedia("(max-width: 959px)", new i.a.Event.Listener("[data-md-component=navigation] [href^='#']", "click", function() {
+                            var e = document.querySelector("[data-md-toggle=drawer]");
+                            if (!(e instanceof HTMLInputElement)) throw new ReferenceError;
+                            e.checked && (e.checked = !1, e.dispatchEvent(new CustomEvent("change")))
+                        })),
+                        function() {
+                            var e = document.querySelector("[data-md-source]");
+                            if (!e) return r.a.resolve([]);
+                            if (!(e instanceof HTMLAnchorElement)) throw new ReferenceError;
+                            switch (e.dataset.mdSource) {
+                                case "github":
+                                    return new i.a.Source.Adapter.GitHub(e).fetch();
+                                default:
+                                    return r.a.resolve([])
+                            }
+                        }().then(function(t) {
+                            var e = document.querySelectorAll("[data-md-source]");
+                            Array.prototype.forEach.call(e, function(e) {
+                                new i.a.Source.Repository(e).initialize(t)
+                            })
+                        });
+                    var n = function() {
+                        var e = document.querySelectorAll("details");
+                        Array.prototype.forEach.call(e, function(e) {
+                            e.setAttribute("open", "")
+                        })
+                    };
+                    new i.a.Event.MatchMedia("print", {
+                        listen: n,
+                        unlisten: function() {}
+                    }), window.onbeforeprint = n
+                }
+            }
+        }.call(this, c(3))
+}, function(e, t, n) {
+    e.exports = n.p + "assets/images/icons/bitbucket.1b09e088.svg"
+}, function(e, t, n) {
+    e.exports = n.p + "assets/images/icons/github.f0b8504a.svg"
+}, function(e, t, n) {
+    e.exports = n.p + "assets/images/icons/gitlab.6dd19c00.svg"
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+    e.exports = "/Users/squidfunk/Desktop/General/Sources/mkdocs-material/material/application.4031d38b.css"
+}, function(e, t) {
+    e.exports = "/Users/squidfunk/Desktop/General/Sources/mkdocs-material/material/application-palette.224b79ff.css"
+}, function(e, t) {
+    ! function() {
+        if ("undefined" != typeof window) try {
+            var e = new window.CustomEvent("test", {
+                cancelable: !0
+            });
+            if (e.preventDefault(), !0 !== e.defaultPrevented) throw new Error("Could not prevent default")
+        } catch (e) {
+            var t = function(e, t) {
+                var n, r;
+                return (t = t || {}).bubbles = !!t.bubbles, t.cancelable = !!t.cancelable, (n = document.createEvent("CustomEvent")).initCustomEvent(e, t.bubbles, t.cancelable, t.detail), r = n.preventDefault, n.preventDefault = function() {
+                    r.call(this);
+                    try {
+                        Object.defineProperty(this, "defaultPrevented", {
+                            get: function() {
+                                return !0
+                            }
+                        })
+                    } catch (e) {
+                        this.defaultPrevented = !0
+                    }
+                }, n
+            };
+            t.prototype = window.Event.prototype, window.CustomEvent = t
+        }
+    }()
+}, function(e, t, n) {
+    window.fetch || (window.fetch = n(7).default || n(7))
+}, function(e, i, o) {
+    (function(e) {
+        var t = void 0 !== e && e || "undefined" != typeof self && self || window,
+            n = Function.prototype.apply;
+
+        function r(e, t) {
+            this._id = e, this._clearFn = t
+        }
+        i.setTimeout = function() {
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+            return new r(n.call(setInterval, t, arguments), clearInterval)
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+            e && e.close()
+        }, r.prototype.unref = r.prototype.ref = function() {}, r.prototype.close = function() {
+            this._clearFn.call(t, this._id)
+        }, i.enroll = function(e, t) {
+            clearTimeout(e._idleTimeoutId), e._idleTimeout = t
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+            clearTimeout(e._idleTimeoutId), e._idleTimeout = -1
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+            clearTimeout(e._idleTimeoutId);
+            var t = e._idleTimeout;
+            0 <= t && (e._idleTimeoutId = setTimeout(function() {
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+        }, o(22), i.setImmediate = "undefined" != typeof self && self.setImmediate || void 0 !== e && e.setImmediate || this && this.setImmediate, i.clearImmediate = "undefined" != typeof self && self.clearImmediate || void 0 !== e && e.clearImmediate || this && this.clearImmediate
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+                        var e = !0,
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+                        l = !0;
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+                                var t = e.callback,
+                                    n = e.args;
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+                                    case 0:
+                                        t();
+                                        break;
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+                                        break;
+                                    case 2:
+                                        t(n[0], n[1]);
+                                        break;
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+                                        t(n[0], n[1], n[2]);
+                                        break;
+                                    default:
+                                        t.apply(r, n)
+                                }
+                            }(t)
+                        } finally {
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+                    }
+                }
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+}, function(e, t) {
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+    function o() {
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+    }! function() {
+        try {
+            n = "function" == typeof setTimeout ? setTimeout : o
+        } catch (e) {
+            n = o
+        }
+        try {
+            r = "function" == typeof clearTimeout ? clearTimeout : a
+        } catch (e) {
+            r = a
+        }
+    }();
+    var c, l = [],
+        u = !1,
+        f = -1;
+
+    function d() {
+        u && c && (u = !1, c.length ? l = c.concat(l) : f = -1, l.length && h())
+    }
+
+    function h() {
+        if (!u) {
+            var e = s(d);
+            u = !0;
+            for (var t = l.length; t;) {
+                for (c = l, l = []; ++f < t;) c && c[f].run();
+                f = -1, t = l.length
+            }
+            c = null, u = !1,
+                function(t) {
+                    if (r === clearTimeout) return clearTimeout(t);
+                    if ((r === a || !r) && clearTimeout) return r = clearTimeout, clearTimeout(t);
+                    try {
+                        r(t)
+                    } catch (e) {
+                        try {
+                            return r.call(null, t)
+                        } catch (e) {
+                            return r.call(this, t)
+                        }
+                    }
+                }(e)
+        }
+    }
+
+    function p(e, t) {
+        this.fun = e, this.array = t
+    }
+
+    function m() {}
+    i.nextTick = function(e) {
+        var t = new Array(arguments.length - 1);
+        if (1 < arguments.length)
+            for (var n = 1; n < arguments.length; n++) t[n - 1] = arguments[n];
+        l.push(new p(e, t)), 1 !== l.length || u || s(h)
+    }, p.prototype.run = function() {
+        this.fun.apply(null, this.array)
+    }, i.title = "browser", i.browser = !0, i.env = {}, i.argv = [], i.version = "", i.versions = {}, i.on = m, i.addListener = m, i.once = m, i.off = m, i.removeListener = m, i.removeAllListeners = m, i.emit = m, i.prependListener = m, i.prependOnceListener = m, i.listeners = function(e) {
+        return []
+    }, i.binding = function(e) {
+        throw new Error("process.binding is not supported")
+    }, i.cwd = function() {
+        return "/"
+    }, i.chdir = function(e) {
+        throw new Error("process.chdir is not supported")
+    }, i.umask = function() {
+        return 0
+    }
+}, function(i, o, a) {
+    var s, c;
+    /**
+     * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.6
+     * Copyright (C) 2019 Oliver Nightingale
+     * @license MIT
+     */
+    ! function() {
+        var t, l, u, e, n, f, d, h, p, m, y, v, g, w, _, E, x, b, k, S, T, L, R, O, C, r, D = function(e) {
+            var t = new D.Builder;
+            return t.pipeline.add(D.trimmer, D.stopWordFilter, D.stemmer), t.searchPipeline.add(D.stemmer), e.call(t, t), t.build()
+        };
+        D.version = "2.3.6", D.utils = {}, D.utils.warn = (t = this, function(e) {
+            t.console && console.warn && console.warn(e)
+        }), D.utils.asString = function(e) {
+            return null == e ? "" : e.toString()
+        }, D.utils.clone = function(e) {
+            if (null == e) return e;
+            for (var t = Object.create(null), n = Object.keys(e), r = 0; r < n.length; r++) {
+                var i = n[r],
+                    o = e[i];
+                if (Array.isArray(o)) t[i] = o.slice();
+                else {
+                    if ("string" != typeof o && "number" != typeof o && "boolean" != typeof o) throw new TypeError("clone is not deep and does not support nested objects");
+                    t[i] = o
+                }
+            }
+            return t
+        }, D.FieldRef = function(e, t, n) {
+            this.docRef = e, this.fieldName = t, this._stringValue = n
+        }, D.FieldRef.joiner = "/", D.FieldRef.fromString = function(e) {
+            var t = e.indexOf(D.FieldRef.joiner);
+            if (-1 === t) throw "malformed field ref string";
+            var n = e.slice(0, t),
+                r = e.slice(t + 1);
+            return new D.FieldRef(r, n, e)
+        }, D.FieldRef.prototype.toString = function() {
+            return null == this._stringValue && (this._stringValue = this.fieldName + D.FieldRef.joiner + this.docRef), this._stringValue
+        }, D.Set = function(e) {
+            if (this.elements = Object.create(null), e) {
+                this.length = e.length;
+                for (var t = 0; t < this.length; t++) this.elements[e[t]] = !0
+            } else this.length = 0
+        }, D.Set.complete = {
+            intersect: function(e) {
+                return e
+            },
+            union: function(e) {
+                return e
+            },
+            contains: function() {
+                return !0
+            }
+        }, D.Set.empty = {
+            intersect: function() {
+                return this
+            },
+            union: function(e) {
+                return e
+            },
+            contains: function() {
+                return !1
+            }
+        }, D.Set.prototype.contains = function(e) {
+            return !!this.elements[e]
+        }, D.Set.prototype.intersect = function(e) {
+            var t, n, r, i = [];
+            if (e === D.Set.complete) return this;
+            if (e === D.Set.empty) return e;
+            n = this.length < e.length ? (t = this, e) : (t = e, this), r = Object.keys(t.elements);
+            for (var o = 0; o < r.length; o++) {
+                var a = r[o];
+                a in n.elements && i.push(a)
+            }
+            return new D.Set(i)
+        }, D.Set.prototype.union = function(e) {
+            return e === D.Set.complete ? D.Set.complete : e === D.Set.empty ? this : new D.Set(Object.keys(this.elements).concat(Object.keys(e.elements)))
+        }, D.idf = function(e, t) {
+            var n = 0;
+            for (var r in e) "_index" != r && (n += Object.keys(e[r]).length);
+            var i = (t - n + .5) / (n + .5);
+            return Math.log(1 + Math.abs(i))
+        }, D.Token = function(e, t) {
+            this.str = e || "", this.metadata = t || {}
+        }, D.Token.prototype.toString = function() {
+            return this.str
+        }, D.Token.prototype.update = function(e) {
+            return this.str = e(this.str, this.metadata), this
+        }, D.Token.prototype.clone = function(e) {
+            return e = e || function(e) {
+                return e
+            }, new D.Token(e(this.str, this.metadata), this.metadata)
+        }, D.tokenizer = function(e, t) {
+            if (null == e || null == e) return [];
+            if (Array.isArray(e)) return e.map(function(e) {
+                return new D.Token(D.utils.asString(e).toLowerCase(), D.utils.clone(t))
+            });
+            for (var n = e.toString().trim().toLowerCase(), r = n.length, i = [], o = 0, a = 0; o <= r; o++) {
+                var s = o - a;
+                if (n.charAt(o).match(D.tokenizer.separator) || o == r) {
+                    if (0 < s) {
+                        var c = D.utils.clone(t) || {};
+                        c.position = [a, s], c.index = i.length, i.push(new D.Token(n.slice(a, o), c))
+                    }
+                    a = o + 1
+                }
+            }
+            return i
+        }, D.tokenizer.separator = /[\s\-]+/, D.Pipeline = function() {
+            this._stack = []
+        }, D.Pipeline.registeredFunctions = Object.create(null), D.Pipeline.registerFunction = function(e, t) {
+            t in this.registeredFunctions && D.utils.warn("Overwriting existing registered function: " + t), e.label = t, D.Pipeline.registeredFunctions[e.label] = e
+        }, D.Pipeline.warnIfFunctionNotRegistered = function(e) {
+            e.label && e.label in this.registeredFunctions || D.utils.warn("Function is not registered with pipeline. This may cause problems when serialising the index.\n", e)
+        }, D.Pipeline.load = function(e) {
+            var n = new D.Pipeline;
+            return e.forEach(function(e) {
+                var t = D.Pipeline.registeredFunctions[e];
+                if (!t) throw new Error("Cannot load unregistered function: " + e);
+                n.add(t)
+            }), n
+        }, D.Pipeline.prototype.add = function() {
+            Array.prototype.slice.call(arguments).forEach(function(e) {
+                D.Pipeline.warnIfFunctionNotRegistered(e), this._stack.push(e)
+            }, this)
+        }, D.Pipeline.prototype.after = function(e, t) {
+            D.Pipeline.warnIfFunctionNotRegistered(t);
+            var n = this._stack.indexOf(e);
+            if (-1 == n) throw new Error("Cannot find existingFn");
+            n += 1, this._stack.splice(n, 0, t)
+        }, D.Pipeline.prototype.before = function(e, t) {
+            D.Pipeline.warnIfFunctionNotRegistered(t);
+            var n = this._stack.indexOf(e);
+            if (-1 == n) throw new Error("Cannot find existingFn");
+            this._stack.splice(n, 0, t)
+        }, D.Pipeline.prototype.remove = function(e) {
+            var t = this._stack.indexOf(e); - 1 != t && this._stack.splice(t, 1)
+        }, D.Pipeline.prototype.run = function(e) {
+            for (var t = this._stack.length, n = 0; n < t; n++) {
+                for (var r = this._stack[n], i = [], o = 0; o < e.length; o++) {
+                    var a = r(e[o], o, e);
+                    if (void 0 !== a && "" !== a)
+                        if (Array.isArray(a))
+                            for (var s = 0; s < a.length; s++) i.push(a[s]);
+                        else i.push(a)
+                }
+                e = i
+            }
+            return e
+        }, D.Pipeline.prototype.runString = function(e, t) {
+            var n = new D.Token(e, t);
+            return this.run([n]).map(function(e) {
+                return e.toString()
+            })
+        }, D.Pipeline.prototype.reset = function() {
+            this._stack = []
+        }, D.Pipeline.prototype.toJSON = function() {
+            return this._stack.map(function(e) {
+                return D.Pipeline.warnIfFunctionNotRegistered(e), e.label
+            })
+        }, D.Vector = function(e) {
+            this._magnitude = 0, this.elements = e || []
+        }, D.Vector.prototype.positionForIndex = function(e) {
+            if (0 == this.elements.length) return 0;
+            for (var t = 0, n = this.elements.length / 2, r = n - t, i = Math.floor(r / 2), o = this.elements[2 * i]; 1 < r && (o < e && (t = i), e < o && (n = i), o != e);) r = n - t, i = t + Math.floor(r / 2), o = this.elements[2 * i];
+            return o == e ? 2 * i : e < o ? 2 * i : o < e ? 2 * (i + 1) : void 0
+        }, D.Vector.prototype.insert = function(e, t) {
+            this.upsert(e, t, function() {
+                throw "duplicate index"
+            })
+        }, D.Vector.prototype.upsert = function(e, t, n) {
+            this._magnitude = 0;
+            var r = this.positionForIndex(e);
+            this.elements[r] == e ? this.elements[r + 1] = n(this.elements[r + 1], t) : this.elements.splice(r, 0, e, t)
+        }, D.Vector.prototype.magnitude = function() {
+            if (this._magnitude) return this._magnitude;
+            for (var e = 0, t = this.elements.length, n = 1; n < t; n += 2) {
+                var r = this.elements[n];
+                e += r * r
+            }
+            return this._magnitude = Math.sqrt(e)
+        }, D.Vector.prototype.dot = function(e) {
+            for (var t = 0, n = this.elements, r = e.elements, i = n.length, o = r.length, a = 0, s = 0, c = 0, l = 0; c < i && l < o;)(a = n[c]) < (s = r[l]) ? c += 2 : s < a ? l += 2 : a == s && (t += n[c + 1] * r[l + 1], c += 2, l += 2);
+            return t
+        }, D.Vector.prototype.similarity = function(e) {
+            return this.dot(e) / this.magnitude() || 0
+        }, D.Vector.prototype.toArray = function() {
+            for (var e = new Array(this.elements.length / 2), t = 1, n = 0; t < this.elements.length; t += 2, n++) e[n] = this.elements[t];
+            return e
+        }, D.Vector.prototype.toJSON = function() {
+            return this.elements
+        }, D.stemmer = (l = {
+            ational: "ate",
+            tional: "tion",
+            enci: "ence",
+            anci: "ance",
+            izer: "ize",
+            bli: "ble",
+            alli: "al",
+            entli: "ent",
+            eli: "e",
+            ousli: "ous",
+            ization: "ize",
+            ation: "ate",
+            ator: "ate",
+            alism: "al",
+            iveness: "ive",
+            fulness: "ful",
+            ousness: "ous",
+            aliti: "al",
+            iviti: "ive",
+            biliti: "ble",
+            logi: "log"
+        }, u = {
+            icate: "ic",
+            ative: "",
+            alize: "al",
+            iciti: "ic",
+            ical: "ic",
+            ful: "",
+            ness: ""
+        }, e = "[aeiouy]", n = "[^aeiou][^aeiouy]*", f = new RegExp("^([^aeiou][^aeiouy]*)?[aeiouy][aeiou]*[^aeiou][^aeiouy]*"), d = new RegExp("^([^aeiou][^aeiouy]*)?[aeiouy][aeiou]*[^aeiou][^aeiouy]*[aeiouy][aeiou]*[^aeiou][^aeiouy]*"), h = new RegExp("^([^aeiou][^aeiouy]*)?[aeiouy][aeiou]*[^aeiou][^aeiouy]*([aeiouy][aeiou]*)?$"), p = new RegExp("^([^aeiou][^aeiouy]*)?[aeiouy]"), m = /^(.+?)(ss|i)es$/, y = /^(.+?)([^s])s$/, v = /^(.+?)eed$/, g = /^(.+?)(ed|ing)$/, w = /.$/, _ = /(at|bl|iz)$/, E = new RegExp("([^aeiouylsz])\\1$"), x = new RegExp("^" + n + e + "[^aeiouwxy]$"), b = /^(.+?[^aeiou])y$/, k = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/, S = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/, T = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/, L = /^(.+?)(s|t)(ion)$/, R = /^(.+?)e$/, O = /ll$/, C = new RegExp("^" + n + e + "[^aeiouwxy]$"), r = function(e) {
+            var t, n, r, i, o, a, s;
+            if (e.length < 3) return e;
+            if ("y" == (r = e.substr(0, 1)) && (e = r.toUpperCase() + e.substr(1)), o = y, (i = m).test(e) ? e = e.replace(i, "$1$2") : o.test(e) && (e = e.replace(o, "$1$2")), o = g, (i = v).test(e)) {
+                var c = i.exec(e);
+                (i = f).test(c[1]) && (i = w, e = e.replace(i, ""))
+            } else if (o.test(e)) {
+                t = (c = o.exec(e))[1], (o = p).test(t) && (a = E, s = x, (o = _).test(e = t) ? e += "e" : a.test(e) ? (i = w, e = e.replace(i, "")) : s.test(e) && (e += "e"))
+            }(i = b).test(e) && (e = (t = (c = i.exec(e))[1]) + "i");
+            (i = k).test(e) && (t = (c = i.exec(e))[1], n = c[2], (i = f).test(t) && (e = t + l[n]));
+            (i = S).test(e) && (t = (c = i.exec(e))[1], n = c[2], (i = f).test(t) && (e = t + u[n]));
+            if (o = L, (i = T).test(e)) t = (c = i.exec(e))[1], (i = d).test(t) && (e = t);
+            else if (o.test(e)) {
+                t = (c = o.exec(e))[1] + c[2], (o = d).test(t) && (e = t)
+            }(i = R).test(e) && (t = (c = i.exec(e))[1], o = h, a = C, ((i = d).test(t) || o.test(t) && !a.test(t)) && (e = t));
+            return o = d, (i = O).test(e) && o.test(e) && (i = w, e = e.replace(i, "")), "y" == r && (e = r.toLowerCase() + e.substr(1)), e
+        }, function(e) {
+            return e.update(r)
+        }), D.Pipeline.registerFunction(D.stemmer, "stemmer"), D.generateStopWordFilter = function(e) {
+            var t = e.reduce(function(e, t) {
+                return e[t] = t, e
+            }, {});
+            return function(e) {
+                if (e && t[e.toString()] !== e.toString()) return e
+            }
+        }, D.stopWordFilter = D.generateStopWordFilter(["a", "able", "about", "across", "after", "all", "almost", "also", "am", "among", "an", "and", "any", "are", "as", "at", "be", "because", "been", "but", "by", "can", "cannot", "could", "dear", "did", "do", "does", "either", "else", "ever", "every", "for", "from", "get", "got", "had", "has", "have", "he", "her", "hers", "him", "his", "how", "however", "i", "if", "in", "into", "is", "it", "its", "just", "least", "let", "like", "likely", "may", "me", "might", "most", "must", "my", "neither", "no", "nor", "not", "of", "off", "often", "on", "only", "or", "other", "our", "own", "rather", "said", "say", "says", "she", "should", "since", "so", "some", "than", "that", "the", "their", "them", "then", "there", "these", "they", "this", "tis", "to", "too", "twas", "us", "wants", "was", "we", "were", "what", "when", "where", "which", "while", "who", "whom", "why", "will", "with", "would", "yet", "you", "your"]), D.Pipeline.registerFunction(D.stopWordFilter, "stopWordFilter"), D.trimmer = function(e) {
+            return e.update(function(e) {
+                return e.replace(/^\W+/, "").replace(/\W+$/, "")
+            })
+        }, D.Pipeline.registerFunction(D.trimmer, "trimmer"), D.TokenSet = function() {
+            this.final = !1, this.edges = {}, this.id = D.TokenSet._nextId, D.TokenSet._nextId += 1
+        }, D.TokenSet._nextId = 1, D.TokenSet.fromArray = function(e) {
+            for (var t = new D.TokenSet.Builder, n = 0, r = e.length; n < r; n++) t.insert(e[n]);
+            return t.finish(), t.root
+        }, D.TokenSet.fromClause = function(e) {
+            return "editDistance" in e ? D.TokenSet.fromFuzzyString(e.term, e.editDistance) : D.TokenSet.fromString(e.term)
+        }, D.TokenSet.fromFuzzyString = function(e, t) {
+            for (var n = new D.TokenSet, r = [{
+                    node: n,
+                    editsRemaining: t,
+                    str: e
+                }]; r.length;) {
+                var i = r.pop();
+                if (0 < i.str.length) {
+                    var o, a = i.str.charAt(0);
+                    a in i.node.edges ? o = i.node.edges[a] : (o = new D.TokenSet, i.node.edges[a] = o), 1 == i.str.length && (o.final = !0), r.push({
+                        node: o,
+                        editsRemaining: i.editsRemaining,
+                        str: i.str.slice(1)
+                    })
+                }
+                if (0 != i.editsRemaining) {
+                    if ("*" in i.node.edges) var s = i.node.edges["*"];
+                    else {
+                        s = new D.TokenSet;
+                        i.node.edges["*"] = s
+                    }
+                    if (0 == i.str.length && (s.final = !0), r.push({
+                            node: s,
+                            editsRemaining: i.editsRemaining - 1,
+                            str: i.str
+                        }), 1 < i.str.length && r.push({
+                            node: i.node,
+                            editsRemaining: i.editsRemaining - 1,
+                            str: i.str.slice(1)
+                        }), 1 == i.str.length && (i.node.final = !0), 1 <= i.str.length) {
+                        if ("*" in i.node.edges) var c = i.node.edges["*"];
+                        else {
+                            c = new D.TokenSet;
+                            i.node.edges["*"] = c
+                        }
+                        1 == i.str.length && (c.final = !0), r.push({
+                            node: c,
+                            editsRemaining: i.editsRemaining - 1,
+                            str: i.str.slice(1)
+                        })
+                    }
+                    if (1 < i.str.length) {
+                        var l, u = i.str.charAt(0),
+                            f = i.str.charAt(1);
+                        f in i.node.edges ? l = i.node.edges[f] : (l = new D.TokenSet, i.node.edges[f] = l), 1 == i.str.length && (l.final = !0), r.push({
+                            node: l,
+                            editsRemaining: i.editsRemaining - 1,
+                            str: u + i.str.slice(2)
+                        })
+                    }
+                }
+            }
+            return n
+        }, D.TokenSet.fromString = function(e) {
+            for (var t = new D.TokenSet, n = t, r = 0, i = e.length; r < i; r++) {
+                var o = e[r],
+                    a = r == i - 1;
+                if ("*" == o)(t.edges[o] = t).final = a;
+                else {
+                    var s = new D.TokenSet;
+                    s.final = a, t.edges[o] = s, t = s
+                }
+            }
+            return n
+        }, D.TokenSet.prototype.toArray = function() {
+            for (var e = [], t = [{
+                    prefix: "",
+                    node: this
+                }]; t.length;) {
+                var n = t.pop(),
+                    r = Object.keys(n.node.edges),
+                    i = r.length;
+                n.node.final && (n.prefix.charAt(0), e.push(n.prefix));
+                for (var o = 0; o < i; o++) {
+                    var a = r[o];
+                    t.push({
+                        prefix: n.prefix.concat(a),
+                        node: n.node.edges[a]
+                    })
+                }
+            }
+            return e
+        }, D.TokenSet.prototype.toString = function() {
+            if (this._str) return this._str;
+            for (var e = this.final ? "1" : "0", t = Object.keys(this.edges).sort(), n = t.length, r = 0; r < n; r++) {
+                var i = t[r];
+                e = e + i + this.edges[i].id
+            }
+            return e
+        }, D.TokenSet.prototype.intersect = function(e) {
+            for (var t = new D.TokenSet, n = void 0, r = [{
+                    qNode: e,
+                    output: t,
+                    node: this
+                }]; r.length;) {
+                n = r.pop();
+                for (var i = Object.keys(n.qNode.edges), o = i.length, a = Object.keys(n.node.edges), s = a.length, c = 0; c < o; c++)
+                    for (var l = i[c], u = 0; u < s; u++) {
+                        var f = a[u];
+                        if (f == l || "*" == l) {
+                            var d = n.node.edges[f],
+                                h = n.qNode.edges[l],
+                                p = d.final && h.final,
+                                m = void 0;
+                            f in n.output.edges ? (m = n.output.edges[f]).final = m.final || p : ((m = new D.TokenSet).final = p, n.output.edges[f] = m), r.push({
+                                qNode: h,
+                                output: m,
+                                node: d
+                            })
+                        }
+                    }
+            }
+            return t
+        }, D.TokenSet.Builder = function() {
+            this.previousWord = "", this.root = new D.TokenSet, this.uncheckedNodes = [], this.minimizedNodes = {}
+        }, D.TokenSet.Builder.prototype.insert = function(e) {
+            var t, n = 0;
+            if (e < this.previousWord) throw new Error("Out of order word insertion");
+            for (var r = 0; r < e.length && r < this.previousWord.length && e[r] == this.previousWord[r]; r++) n++;
+            this.minimize(n), t = 0 == this.uncheckedNodes.length ? this.root : this.uncheckedNodes[this.uncheckedNodes.length - 1].child;
+            for (r = n; r < e.length; r++) {
+                var i = new D.TokenSet,
+                    o = e[r];
+                t.edges[o] = i, this.uncheckedNodes.push({
+                    parent: t,
+                    char: o,
+                    child: i
+                }), t = i
+            }
+            t.final = !0, this.previousWord = e
+        }, D.TokenSet.Builder.prototype.finish = function() {
+            this.minimize(0)
+        }, D.TokenSet.Builder.prototype.minimize = function(e) {
+            for (var t = this.uncheckedNodes.length - 1; e <= t; t--) {
+                var n = this.uncheckedNodes[t],
+                    r = n.child.toString();
+                r in this.minimizedNodes ? n.parent.edges[n.char] = this.minimizedNodes[r] : (n.child._str = r, this.minimizedNodes[r] = n.child), this.uncheckedNodes.pop()
+            }
+        }, D.Index = function(e) {
+            this.invertedIndex = e.invertedIndex, this.fieldVectors = e.fieldVectors, this.tokenSet = e.tokenSet, this.fields = e.fields, this.pipeline = e.pipeline
+        }, D.Index.prototype.search = function(t) {
+            return this.query(function(e) {
+                new D.QueryParser(t, e).parse()
+            })
+        }, D.Index.prototype.query = function(e) {
+            for (var t = new D.Query(this.fields), n = Object.create(null), r = Object.create(null), i = Object.create(null), o = Object.create(null), a = Object.create(null), s = 0; s < this.fields.length; s++) r[this.fields[s]] = new D.Vector;
+            e.call(t, t);
+            for (s = 0; s < t.clauses.length; s++) {
+                var c = t.clauses[s],
+                    l = null,
+                    u = D.Set.complete;
+                l = c.usePipeline ? this.pipeline.runString(c.term, {
+                    fields: c.fields
+                }) : [c.term];
+                for (var f = 0; f < l.length; f++) {
+                    var d = l[f];
+                    c.term = d;
+                    var h = D.TokenSet.fromClause(c),
+                        p = this.tokenSet.intersect(h).toArray();
+                    if (0 === p.length && c.presence === D.Query.presence.REQUIRED) {
+                        for (var m = 0; m < c.fields.length; m++) {
+                            o[Q = c.fields[m]] = D.Set.empty
+                        }
+                        break
+                    }
+                    for (var y = 0; y < p.length; y++) {
+                        var v = p[y],
+                            g = this.invertedIndex[v],
+                            w = g._index;
+                        for (m = 0; m < c.fields.length; m++) {
+                            var _ = g[Q = c.fields[m]],
+                                E = Object.keys(_),
+                                x = v + "/" + Q,
+                                b = new D.Set(E);
+                            if (c.presence == D.Query.presence.REQUIRED && (u = u.union(b), void 0 === o[Q] && (o[Q] = D.Set.complete)), c.presence != D.Query.presence.PROHIBITED) {
+                                if (r[Q].upsert(w, c.boost, function(e, t) {
+                                        return e + t
+                                    }), !i[x]) {
+                                    for (var k = 0; k < E.length; k++) {
+                                        var S, T = E[k],
+                                            L = new D.FieldRef(T, Q),
+                                            R = _[T];
+                                        void 0 === (S = n[L]) ? n[L] = new D.MatchData(v, Q, R) : S.add(v, Q, R)
+                                    }
+                                    i[x] = !0
+                                }
+                            } else void 0 === a[Q] && (a[Q] = D.Set.empty), a[Q] = a[Q].union(b)
+                        }
+                    }
+                }
+                if (c.presence === D.Query.presence.REQUIRED)
+                    for (m = 0; m < c.fields.length; m++) {
+                        o[Q = c.fields[m]] = o[Q].intersect(u)
+                    }
+            }
+            var O = D.Set.complete,
+                C = D.Set.empty;
+            for (s = 0; s < this.fields.length; s++) {
+                var Q;
+                o[Q = this.fields[s]] && (O = O.intersect(o[Q])), a[Q] && (C = C.union(a[Q]))
+            }
+            var P = Object.keys(n),
+                A = [],
+                I = Object.create(null);
+            if (t.isNegated()) {
+                P = Object.keys(this.fieldVectors);
+                for (s = 0; s < P.length; s++) {
+                    L = P[s];
+                    var M = D.FieldRef.fromString(L);
+                    n[L] = new D.MatchData
+                }
+            }
+            for (s = 0; s < P.length; s++) {
+                var N = (M = D.FieldRef.fromString(P[s])).docRef;
+                if (O.contains(N) && !C.contains(N)) {
+                    var j, F = this.fieldVectors[M],
+                        H = r[M.fieldName].similarity(F);
+                    if (void 0 !== (j = I[N])) j.score += H, j.matchData.combine(n[M]);
+                    else {
+                        var q = {
+                            ref: N,
+                            score: H,
+                            matchData: n[M]
+                        };
+                        I[N] = q, A.push(q)
+                    }
+                }
+            }
+            return A.sort(function(e, t) {
+                return t.score - e.score
+            })
+        }, D.Index.prototype.toJSON = function() {
+            var e = Object.keys(this.invertedIndex).sort().map(function(e) {
+                    return [e, this.invertedIndex[e]]
+                }, this),
+                t = Object.keys(this.fieldVectors).map(function(e) {
+                    return [e, this.fieldVectors[e].toJSON()]
+                }, this);
+            return {
+                version: D.version,
+                fields: this.fields,
+                fieldVectors: t,
+                invertedIndex: e,
+                pipeline: this.pipeline.toJSON()
+            }
+        }, D.Index.load = function(e) {
+            var t = {},
+                n = {},
+                r = e.fieldVectors,
+                i = Object.create(null),
+                o = e.invertedIndex,
+                a = new D.TokenSet.Builder,
+                s = D.Pipeline.load(e.pipeline);
+            e.version != D.version && D.utils.warn("Version mismatch when loading serialised index. Current version of lunr '" + D.version + "' does not match serialized index '" + e.version + "'");
+            for (var c = 0; c < r.length; c++) {
+                var l = (f = r[c])[0],
+                    u = f[1];
+                n[l] = new D.Vector(u)
+            }
+            for (c = 0; c < o.length; c++) {
+                var f, d = (f = o[c])[0],
+                    h = f[1];
+                a.insert(d), i[d] = h
+            }
+            return a.finish(), t.fields = e.fields, t.fieldVectors = n, t.invertedIndex = i, t.tokenSet = a.root, t.pipeline = s, new D.Index(t)
+        }, D.Builder = function() {
+            this._ref = "id", this._fields = Object.create(null), this._documents = Object.create(null), this.invertedIndex = Object.create(null), this.fieldTermFrequencies = {}, this.fieldLengths = {}, this.tokenizer = D.tokenizer, this.pipeline = new D.Pipeline, this.searchPipeline = new D.Pipeline, this.documentCount = 0, this._b = .75, this._k1 = 1.2, this.termIndex = 0, this.metadataWhitelist = []
+        }, D.Builder.prototype.ref = function(e) {
+            this._ref = e
+        }, D.Builder.prototype.field = function(e, t) {
+            if (/\//.test(e)) throw new RangeError("Field '" + e + "' contains illegal character '/'");
+            this._fields[e] = t || {}
+        }, D.Builder.prototype.b = function(e) {
+            this._b = e < 0 ? 0 : 1 < e ? 1 : e
+        }, D.Builder.prototype.k1 = function(e) {
+            this._k1 = e
+        }, D.Builder.prototype.add = function(e, t) {
+            var n = e[this._ref],
+                r = Object.keys(this._fields);
+            this._documents[n] = t || {}, this.documentCount += 1;
+            for (var i = 0; i < r.length; i++) {
+                var o = r[i],
+                    a = this._fields[o].extractor,
+                    s = a ? a(e) : e[o],
+                    c = this.tokenizer(s, {
+                        fields: [o]
+                    }),
+                    l = this.pipeline.run(c),
+                    u = new D.FieldRef(n, o),
+                    f = Object.create(null);
+                this.fieldTermFrequencies[u] = f, this.fieldLengths[u] = 0, this.fieldLengths[u] += l.length;
+                for (var d = 0; d < l.length; d++) {
+                    var h = l[d];
+                    if (null == f[h] && (f[h] = 0), f[h] += 1, null == this.invertedIndex[h]) {
+                        var p = Object.create(null);
+                        p._index = this.termIndex, this.termIndex += 1;
+                        for (var m = 0; m < r.length; m++) p[r[m]] = Object.create(null);
+                        this.invertedIndex[h] = p
+                    }
+                    null == this.invertedIndex[h][o][n] && (this.invertedIndex[h][o][n] = Object.create(null));
+                    for (var y = 0; y < this.metadataWhitelist.length; y++) {
+                        var v = this.metadataWhitelist[y],
+                            g = h.metadata[v];
+                        null == this.invertedIndex[h][o][n][v] && (this.invertedIndex[h][o][n][v] = []), this.invertedIndex[h][o][n][v].push(g)
+                    }
+                }
+            }
+        }, D.Builder.prototype.calculateAverageFieldLengths = function() {
+            for (var e = Object.keys(this.fieldLengths), t = e.length, n = {}, r = {}, i = 0; i < t; i++) {
+                var o = D.FieldRef.fromString(e[i]),
+                    a = o.fieldName;
+                r[a] || (r[a] = 0), r[a] += 1, n[a] || (n[a] = 0), n[a] += this.fieldLengths[o]
+            }
+            var s = Object.keys(this._fields);
+            for (i = 0; i < s.length; i++) {
+                var c = s[i];
+                n[c] = n[c] / r[c]
+            }
+            this.averageFieldLength = n
+        }, D.Builder.prototype.createFieldVectors = function() {
+            for (var e = {}, t = Object.keys(this.fieldTermFrequencies), n = t.length, r = Object.create(null), i = 0; i < n; i++) {
+                for (var o = D.FieldRef.fromString(t[i]), a = o.fieldName, s = this.fieldLengths[o], c = new D.Vector, l = this.fieldTermFrequencies[o], u = Object.keys(l), f = u.length, d = this._fields[a].boost || 1, h = this._documents[o.docRef].boost || 1, p = 0; p < f; p++) {
+                    var m, y, v, g = u[p],
+                        w = l[g],
+                        _ = this.invertedIndex[g]._index;
+                    void 0 === r[g] ? (m = D.idf(this.invertedIndex[g], this.documentCount), r[g] = m) : m = r[g], y = m * ((this._k1 + 1) * w) / (this._k1 * (1 - this._b + this._b * (s / this.averageFieldLength[a])) + w), y *= d, y *= h, v = Math.round(1e3 * y) / 1e3, c.insert(_, v)
+                }
+                e[o] = c
+            }
+            this.fieldVectors = e
+        }, D.Builder.prototype.createTokenSet = function() {
+            this.tokenSet = D.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())
+        }, D.Builder.prototype.build = function() {
+            return this.calculateAverageFieldLengths(), this.createFieldVectors(), this.createTokenSet(), new D.Index({
+                invertedIndex: this.invertedIndex,
+                fieldVectors: this.fieldVectors,
+                tokenSet: this.tokenSet,
+                fields: Object.keys(this._fields),
+                pipeline: this.searchPipeline
+            })
+        }, D.Builder.prototype.use = function(e) {
+            var t = Array.prototype.slice.call(arguments, 1);
+            t.unshift(this), e.apply(this, t)
+        }, D.MatchData = function(e, t, n) {
+            for (var r = Object.create(null), i = Object.keys(n || {}), o = 0; o < i.length; o++) {
+                var a = i[o];
+                r[a] = n[a].slice()
+            }
+            this.metadata = Object.create(null), void 0 !== e && (this.metadata[e] = Object.create(null), this.metadata[e][t] = r)
+        }, D.MatchData.prototype.combine = function(e) {
+            for (var t = Object.keys(e.metadata), n = 0; n < t.length; n++) {
+                var r = t[n],
+                    i = Object.keys(e.metadata[r]);
+                null == this.metadata[r] && (this.metadata[r] = Object.create(null));
+                for (var o = 0; o < i.length; o++) {
+                    var a = i[o],
+                        s = Object.keys(e.metadata[r][a]);
+                    null == this.metadata[r][a] && (this.metadata[r][a] = Object.create(null));
+                    for (var c = 0; c < s.length; c++) {
+                        var l = s[c];
+                        null == this.metadata[r][a][l] ? this.metadata[r][a][l] = e.metadata[r][a][l] : this.metadata[r][a][l] = this.metadata[r][a][l].concat(e.metadata[r][a][l])
+                    }
+                }
+            }
+        }, D.MatchData.prototype.add = function(e, t, n) {
+            if (!(e in this.metadata)) return this.metadata[e] = Object.create(null), void(this.metadata[e][t] = n);
+            if (t in this.metadata[e])
+                for (var r = Object.keys(n), i = 0; i < r.length; i++) {
+                    var o = r[i];
+                    o in this.metadata[e][t] ? this.metadata[e][t][o] = this.metadata[e][t][o].concat(n[o]) : this.metadata[e][t][o] = n[o]
+                } else this.metadata[e][t] = n
+        }, D.Query = function(e) {
+            this.clauses = [], this.allFields = e
+        }, D.Query.wildcard = new String("*"), D.Query.wildcard.NONE = 0, D.Query.wildcard.LEADING = 1, D.Query.wildcard.TRAILING = 2, D.Query.presence = {
+            OPTIONAL: 1,
+            REQUIRED: 2,
+            PROHIBITED: 3
+        }, D.Query.prototype.clause = function(e) {
+            return "fields" in e || (e.fields = this.allFields), "boost" in e || (e.boost = 1), "usePipeline" in e || (e.usePipeline = !0), "wildcard" in e || (e.wildcard = D.Query.wildcard.NONE), e.wildcard & D.Query.wildcard.LEADING && e.term.charAt(0) != D.Query.wildcard && (e.term = "*" + e.term), e.wildcard & D.Query.wildcard.TRAILING && e.term.slice(-1) != D.Query.wildcard && (e.term = e.term + "*"), "presence" in e || (e.presence = D.Query.presence.OPTIONAL), this.clauses.push(e), this
+        }, D.Query.prototype.isNegated = function() {
+            for (var e = 0; e < this.clauses.length; e++)
+                if (this.clauses[e].presence != D.Query.presence.PROHIBITED) return !1;
+            return !0
+        }, D.Query.prototype.term = function(e, t) {
+            if (Array.isArray(e)) return e.forEach(function(e) {
+                this.term(e, D.utils.clone(t))
+            }, this), this;
+            var n = t || {};
+            return n.term = e.toString(), this.clause(n), this
+        }, D.QueryParseError = function(e, t, n) {
+            this.name = "QueryParseError", this.message = e, this.start = t, this.end = n
+        }, D.QueryParseError.prototype = new Error, D.QueryLexer = function(e) {
+            this.lexemes = [], this.str = e, this.length = e.length, this.pos = 0, this.start = 0, this.escapeCharPositions = []
+        }, D.QueryLexer.prototype.run = function() {
+            for (var e = D.QueryLexer.lexText; e;) e = e(this)
+        }, D.QueryLexer.prototype.sliceString = function() {
+            for (var e = [], t = this.start, n = this.pos, r = 0; r < this.escapeCharPositions.length; r++) n = this.escapeCharPositions[r], e.push(this.str.slice(t, n)), t = n + 1;
+            return e.push(this.str.slice(t, this.pos)), this.escapeCharPositions.length = 0, e.join("")
+        }, D.QueryLexer.prototype.emit = function(e) {
+            this.lexemes.push({
+                type: e,
+                str: this.sliceString(),
+                start: this.start,
+                end: this.pos
+            }), this.start = this.pos
+        }, D.QueryLexer.prototype.escapeCharacter = function() {
+            this.escapeCharPositions.push(this.pos - 1), this.pos += 1
+        }, D.QueryLexer.prototype.next = function() {
+            if (this.pos >= this.length) return D.QueryLexer.EOS;
+            var e = this.str.charAt(this.pos);
+            return this.pos += 1, e
+        }, D.QueryLexer.prototype.width = function() {
+            return this.pos - this.start
+        }, D.QueryLexer.prototype.ignore = function() {
+            this.start == this.pos && (this.pos += 1), this.start = this.pos
+        }, D.QueryLexer.prototype.backup = function() {
+            this.pos -= 1
+        }, D.QueryLexer.prototype.acceptDigitRun = function() {
+            for (var e, t; 47 < (t = (e = this.next()).charCodeAt(0)) && t < 58;);
+            e != D.QueryLexer.EOS && this.backup()
+        }, D.QueryLexer.prototype.more = function() {
+            return this.pos < this.length
+        }, D.QueryLexer.EOS = "EOS", D.QueryLexer.FIELD = "FIELD", D.QueryLexer.TERM = "TERM", D.QueryLexer.EDIT_DISTANCE = "EDIT_DISTANCE", D.QueryLexer.BOOST = "BOOST", D.QueryLexer.PRESENCE = "PRESENCE", D.QueryLexer.lexField = function(e) {
+            return e.backup(), e.emit(D.QueryLexer.FIELD), e.ignore(), D.QueryLexer.lexText
+        }, D.QueryLexer.lexTerm = function(e) {
+            if (1 < e.width() && (e.backup(), e.emit(D.QueryLexer.TERM)), e.ignore(), e.more()) return D.QueryLexer.lexText
+        }, D.QueryLexer.lexEditDistance = function(e) {
+            return e.ignore(), e.acceptDigitRun(), e.emit(D.QueryLexer.EDIT_DISTANCE), D.QueryLexer.lexText
+        }, D.QueryLexer.lexBoost = function(e) {
+            return e.ignore(), e.acceptDigitRun(), e.emit(D.QueryLexer.BOOST), D.QueryLexer.lexText
+        }, D.QueryLexer.lexEOS = function(e) {
+            0 < e.width() && e.emit(D.QueryLexer.TERM)
+        }, D.QueryLexer.termSeparator = D.tokenizer.separator, D.QueryLexer.lexText = function(e) {
+            for (;;) {
+                var t = e.next();
+                if (t == D.QueryLexer.EOS) return D.QueryLexer.lexEOS;
+                if (92 != t.charCodeAt(0)) {
+                    if (":" == t) return D.QueryLexer.lexField;
+                    if ("~" == t) return e.backup(), 0 < e.width() && e.emit(D.QueryLexer.TERM), D.QueryLexer.lexEditDistance;
+                    if ("^" == t) return e.backup(), 0 < e.width() && e.emit(D.QueryLexer.TERM), D.QueryLexer.lexBoost;
+                    if ("+" == t && 1 === e.width()) return e.emit(D.QueryLexer.PRESENCE), D.QueryLexer.lexText;
+                    if ("-" == t && 1 === e.width()) return e.emit(D.QueryLexer.PRESENCE), D.QueryLexer.lexText;
+                    if (t.match(D.QueryLexer.termSeparator)) return D.QueryLexer.lexTerm
+                } else e.escapeCharacter()
+            }
+        }, D.QueryParser = function(e, t) {
+            this.lexer = new D.QueryLexer(e), this.query = t, this.currentClause = {}, this.lexemeIdx = 0
+        }, D.QueryParser.prototype.parse = function() {
+            this.lexer.run(), this.lexemes = this.lexer.lexemes;
+            for (var e = D.QueryParser.parseClause; e;) e = e(this);
+            return this.query
+        }, D.QueryParser.prototype.peekLexeme = function() {
+            return this.lexemes[this.lexemeIdx]
+        }, D.QueryParser.prototype.consumeLexeme = function() {
+            var e = this.peekLexeme();
+            return this.lexemeIdx += 1, e
+        }, D.QueryParser.prototype.nextClause = function() {
+            var e = this.currentClause;
+            this.query.clause(e), this.currentClause = {}
+        }, D.QueryParser.parseClause = function(e) {
+            var t = e.peekLexeme();
+            if (null != t) switch (t.type) {
+                case D.QueryLexer.PRESENCE:
+                    return D.QueryParser.parsePresence;
+                case D.QueryLexer.FIELD:
+                    return D.QueryParser.parseField;
+                case D.QueryLexer.TERM:
+                    return D.QueryParser.parseTerm;
+                default:
+                    var n = "expected either a field or a term, found " + t.type;
+                    throw 1 <= t.str.length && (n += " with value '" + t.str + "'"), new D.QueryParseError(n, t.start, t.end)
+            }
+        }, D.QueryParser.parsePresence = function(e) {
+            var t = e.consumeLexeme();
+            if (null != t) {
+                switch (t.str) {
+                    case "-":
+                        e.currentClause.presence = D.Query.presence.PROHIBITED;
+                        break;
+                    case "+":
+                        e.currentClause.presence = D.Query.presence.REQUIRED;
+                        break;
+                    default:
+                        var n = "unrecognised presence operator'" + t.str + "'";
+                        throw new D.QueryParseError(n, t.start, t.end)
+                }
+                var r = e.peekLexeme();
+                if (null == r) {
+                    n = "expecting term or field, found nothing";
+                    throw new D.QueryParseError(n, t.start, t.end)
+                }
+                switch (r.type) {
+                    case D.QueryLexer.FIELD:
+                        return D.QueryParser.parseField;
+                    case D.QueryLexer.TERM:
+                        return D.QueryParser.parseTerm;
+                    default:
+                        n = "expecting term or field, found '" + r.type + "'";
+                        throw new D.QueryParseError(n, r.start, r.end)
+                }
+            }
+        }, D.QueryParser.parseField = function(e) {
+            var t = e.consumeLexeme();
+            if (null != t) {
+                if (-1 == e.query.allFields.indexOf(t.str)) {
+                    var n = e.query.allFields.map(function(e) {
+                            return "'" + e + "'"
+                        }).join(", "),
+                        r = "unrecognised field '" + t.str + "', possible fields: " + n;
+                    throw new D.QueryParseError(r, t.start, t.end)
+                }
+                e.currentClause.fields = [t.str];
+                var i = e.peekLexeme();
+                if (null == i) {
+                    r = "expecting term, found nothing";
+                    throw new D.QueryParseError(r, t.start, t.end)
+                }
+                switch (i.type) {
+                    case D.QueryLexer.TERM:
+                        return D.QueryParser.parseTerm;
+                    default:
+                        r = "expecting term, found '" + i.type + "'";
+                        throw new D.QueryParseError(r, i.start, i.end)
+                }
+            }
+        }, D.QueryParser.parseTerm = function(e) {
+            var t = e.consumeLexeme();
+            if (null != t) {
+                e.currentClause.term = t.str.toLowerCase(), -1 != t.str.indexOf("*") && (e.currentClause.usePipeline = !1);
+                var n = e.peekLexeme();
+                if (null != n) switch (n.type) {
+                    case D.QueryLexer.TERM:
+                        return e.nextClause(), D.QueryParser.parseTerm;
+                    case D.QueryLexer.FIELD:
+                        return e.nextClause(), D.QueryParser.parseField;
+                    case D.QueryLexer.EDIT_DISTANCE:
+                        return D.QueryParser.parseEditDistance;
+                    case D.QueryLexer.BOOST:
+                        return D.QueryParser.parseBoost;
+                    case D.QueryLexer.PRESENCE:
+                        return e.nextClause(), D.QueryParser.parsePresence;
+                    default:
+                        var r = "Unexpected lexeme type '" + n.type + "'";
+                        throw new D.QueryParseError(r, n.start, n.end)
+                } else e.nextClause()
+            }
+        }, D.QueryParser.parseEditDistance = function(e) {
+            var t = e.consumeLexeme();
+            if (null != t) {
+                var n = parseInt(t.str, 10);
+                if (isNaN(n)) {
+                    var r = "edit distance must be numeric";
+                    throw new D.QueryParseError(r, t.start, t.end)
+                }
+                e.currentClause.editDistance = n;
+                var i = e.peekLexeme();
+                if (null != i) switch (i.type) {
+                    case D.QueryLexer.TERM:
+                        return e.nextClause(), D.QueryParser.parseTerm;
+                    case D.QueryLexer.FIELD:
+                        return e.nextClause(), D.QueryParser.parseField;
+                    case D.QueryLexer.EDIT_DISTANCE:
+                        return D.QueryParser.parseEditDistance;
+                    case D.QueryLexer.BOOST:
+                        return D.QueryParser.parseBoost;
+                    case D.QueryLexer.PRESENCE:
+                        return e.nextClause(), D.QueryParser.parsePresence;
+                    default:
+                        r = "Unexpected lexeme type '" + i.type + "'";
+                        throw new D.QueryParseError(r, i.start, i.end)
+                } else e.nextClause()
+            }
+        }, D.QueryParser.parseBoost = function(e) {
+            var t = e.consumeLexeme();
+            if (null != t) {
+                var n = parseInt(t.str, 10);
+                if (isNaN(n)) {
+                    var r = "boost must be numeric";
+                    throw new D.QueryParseError(r, t.start, t.end)
+                }
+                e.currentClause.boost = n;
+                var i = e.peekLexeme();
+                if (null != i) switch (i.type) {
+                    case D.QueryLexer.TERM:
+                        return e.nextClause(), D.QueryParser.parseTerm;
+                    case D.QueryLexer.FIELD:
+                        return e.nextClause(), D.QueryParser.parseField;
+                    case D.QueryLexer.EDIT_DISTANCE:
+                        return D.QueryParser.parseEditDistance;
+                    case D.QueryLexer.BOOST:
+                        return D.QueryParser.parseBoost;
+                    case D.QueryLexer.PRESENCE:
+                        return e.nextClause(), D.QueryParser.parsePresence;
+                    default:
+                        r = "Unexpected lexeme type '" + i.type + "'";
+                        throw new D.QueryParseError(r, i.start, i.end)
+                } else e.nextClause()
+            }
+        }, void 0 === (c = "function" == typeof(s = function() {
+            return D
+        }) ? s.call(o, a, o, i) : s) || (i.exports = c)
+    }()
+}]));
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.da.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.da.js
new file mode 100644
index 0000000000..34910dfe5f
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.da.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r,m,i;e.da=function(){this.pipeline.reset(),this.pipeline.add(e.da.trimmer,e.da.stopWordFilter,e.da.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.da.stemmer))},e.da.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.da.trimmer=e.trimmerSupport.generateTrimmer(e.da.wordCharacters),e.Pipeline.registerFunction(e.da.trimmer,"trimmer-da"),e.da.stemmer=(r=e.stemmerSupport.Among,m=e.stemmerSupport.SnowballProgram,i=new function(){var i,t,n,s=[new r("hed",-1,1),new r("ethed",0,1),new r("ered",-1,1),new r("e",-1,1),new r("erede",3,1),new r("ende",3,1),new r("erende",5,1),new r("ene",3,1),new r("erne",3,1),new r("ere",3,1),new r("en",-1,1),new r("heden",10,1),new r("eren",10,1),new r("er",-1,1),new r("heder",13,1),new r("erer",13,1),new r("s",-1,2),new r("heds",16,1),new r("es",16,1),new r("endes",18,1),new r("erendes",19,1),new r("enes",18,1),new r("ernes",18,1),new r("eres",18,1),new r("ens",16,1),new r("hedens",24,1),new r("erens",24,1),new r("ers",16,1),new r("ets",16,1),new r("erets",28,1),new r("et",-1,1),new r("eret",30,1)],o=[new r("gd",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1)],a=[new r("ig",-1,1),new r("lig",0,1),new r("elig",1,1),new r("els",-1,1),new r("løst",-1,2)],d=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],u=[239,254,42,3,0,0,0,0,0,0,0,0,0,0,0,0,16],c=new m;function l(){var e,r=c.limit-c.cursor;c.cursor>=t&&(e=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,c.find_among_b(o,4)?(c.bra=c.cursor,c.limit_backward=e,c.cursor=c.limit-r,c.cursor>c.limit_backward&&(c.cursor--,c.bra=c.cursor,c.slice_del())):c.limit_backward=e)}this.setCurrent=function(e){c.setCurrent(e)},this.getCurrent=function(){return c.getCurrent()},this.stem=function(){var e,r=c.cursor;return function(){var e,r=c.cursor+3;if(t=c.limit,0<=r&&r<=c.limit){for(i=r;;){if(e=c.cursor,c.in_grouping(d,97,248)){c.cursor=e;break}if((c.cursor=e)>=c.limit)return;c.cursor++}for(;!c.out_grouping(d,97,248);){if(c.cursor>=c.limit)return;c.cursor++}(t=c.cursor)<i&&(t=i)}}(),c.limit_backward=r,c.cursor=c.limit,function(){var e,r;if(c.cursor>=t&&(r=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,e=c.find_among_b(s,32),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del();break;case 2:c.in_grouping_b(u,97,229)&&c.slice_del()}}(),c.cursor=c.limit,l(),c.cursor=c.limit,function(){var e,r,i,n=c.limit-c.cursor;if(c.ket=c.cursor,c.eq_s_b(2,"st")&&(c.bra=c.cursor,c.eq_s_b(2,"ig")&&c.slice_del()),c.cursor=c.limit-n,c.cursor>=t&&(r=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,e=c.find_among_b(a,5),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del(),i=c.limit-c.cursor,l(),c.cursor=c.limit-i;break;case 2:c.slice_from("løs")}}(),c.cursor=c.limit,c.cursor>=t&&(e=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,c.out_grouping_b(d,97,248)?(c.bra=c.cursor,n=c.slice_to(n),c.limit_backward=e,c.eq_v_b(n)&&c.slice_del()):c.limit_backward=e),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}),e.Pipeline.registerFunction(e.da.stemmer,"stemmer-da"),e.da.stopWordFilter=e.generateStopWordFilter("ad af alle alt anden at blev blive bliver da de dem den denne der deres det dette dig din disse dog du efter eller en end er et for fra ham han hans har havde have hende hendes her hos hun hvad hvis hvor i ikke ind jeg jer jo kunne man mange med meget men mig min mine mit mod ned noget nogle nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.de.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.de.js
new file mode 100644
index 0000000000..1529892c82
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.de.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var _,p,r;e.de=function(){this.pipeline.reset(),this.pipeline.add(e.de.trimmer,e.de.stopWordFilter,e.de.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.de.stemmer))},e.de.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.de.trimmer=e.trimmerSupport.generateTrimmer(e.de.wordCharacters),e.Pipeline.registerFunction(e.de.trimmer,"trimmer-de"),e.de.stemmer=(_=e.stemmerSupport.Among,p=e.stemmerSupport.SnowballProgram,r=new function(){var r,n,i,s=[new _("",-1,6),new _("U",0,2),new _("Y",0,1),new _("ä",0,3),new _("ö",0,4),new _("ü",0,5)],o=[new _("e",-1,2),new _("em",-1,1),new _("en",-1,2),new _("ern",-1,1),new _("er",-1,1),new _("s",-1,3),new _("es",5,2)],c=[new _("en",-1,1),new _("er",-1,1),new _("st",-1,2),new _("est",2,1)],u=[new _("ig",-1,1),new _("lich",-1,1)],a=[new _("end",-1,1),new _("ig",-1,2),new _("ung",-1,1),new _("lich",-1,3),new _("isch",-1,2),new _("ik",-1,2),new _("heit",-1,3),new _("keit",-1,4)],t=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,32,8],d=[117,30,5],l=[117,30,4],m=new p;function h(e,r,n){return!(!m.eq_s(1,e)||(m.ket=m.cursor,!m.in_grouping(t,97,252)))&&(m.slice_from(r),m.cursor=n,!0)}function w(){for(;!m.in_grouping(t,97,252);){if(m.cursor>=m.limit)return!0;m.cursor++}for(;!m.out_grouping(t,97,252);){if(m.cursor>=m.limit)return!0;m.cursor++}return!1}function f(){return i<=m.cursor}function b(){return n<=m.cursor}this.setCurrent=function(e){m.setCurrent(e)},this.getCurrent=function(){return m.getCurrent()},this.stem=function(){var e=m.cursor;return function(){for(var e,r,n,i,s=m.cursor;;)if(e=m.cursor,m.bra=e,m.eq_s(1,"ß"))m.ket=m.cursor,m.slice_from("ss");else{if(e>=m.limit)break;m.cursor=e+1}for(m.cursor=s;;)for(r=m.cursor;;){if(n=m.cursor,m.in_grouping(t,97,252)){if(i=m.cursor,m.bra=i,h("u","U",n))break;if(m.cursor=i,h("y","Y",n))break}if(n>=m.limit)return m.cursor=r;m.cursor=n+1}}(),m.cursor=e,function(){i=m.limit,n=i;var e=m.cursor+3;0<=e&&e<=m.limit&&(r=e,w()||((i=m.cursor)<r&&(i=r),w()||(n=m.cursor)))}(),m.limit_backward=e,m.cursor=m.limit,function(){var e,r,n,i,s=m.limit-m.cursor;if(m.ket=m.cursor,(e=m.find_among_b(o,7))&&(m.bra=m.cursor,f()))switch(e){case 1:m.slice_del();break;case 2:m.slice_del(),m.ket=m.cursor,m.eq_s_b(1,"s")&&(m.bra=m.cursor,m.eq_s_b(3,"nis")&&m.slice_del());break;case 3:m.in_grouping_b(d,98,116)&&m.slice_del()}if(m.cursor=m.limit-s,m.ket=m.cursor,(e=m.find_among_b(c,4))&&(m.bra=m.cursor,f()))switch(e){case 1:m.slice_del();break;case 2:if(m.in_grouping_b(l,98,116)){var t=m.cursor-3;m.limit_backward<=t&&t<=m.limit&&(m.cursor=t,m.slice_del())}}if(m.cursor=m.limit-s,m.ket=m.cursor,(e=m.find_among_b(a,8))&&(m.bra=m.cursor,b()))switch(e){case 1:m.slice_del(),m.ket=m.cursor,m.eq_s_b(2,"ig")&&(m.bra=m.cursor,r=m.limit-m.cursor,m.eq_s_b(1,"e")||(m.cursor=m.limit-r,b()&&m.slice_del()));break;case 2:n=m.limit-m.cursor,m.eq_s_b(1,"e")||(m.cursor=m.limit-n,m.slice_del());break;case 3:if(m.slice_del(),m.ket=m.cursor,i=m.limit-m.cursor,!m.eq_s_b(2,"er")&&(m.cursor=m.limit-i,!m.eq_s_b(2,"en")))break;m.bra=m.cursor,f()&&m.slice_del();break;case 4:m.slice_del(),m.ket=m.cursor,(e=m.find_among_b(u,2))&&(m.bra=m.cursor,b()&&1==e&&m.slice_del())}}(),m.cursor=m.limit_backward,function(){for(var e,r;;){if(r=m.cursor,m.bra=r,!(e=m.find_among(s,6)))return;switch(m.ket=m.cursor,e){case 1:m.slice_from("y");break;case 2:case 5:m.slice_from("u");break;case 3:m.slice_from("a");break;case 4:m.slice_from("o");break;case 6:if(m.cursor>=m.limit)return;m.cursor++}}}(),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return r.setCurrent(e),r.stem(),r.getCurrent()}):(r.setCurrent(e),r.stem(),r.getCurrent())}),e.Pipeline.registerFunction(e.de.stemmer,"stemmer-de"),e.de.stopWordFilter=e.generateStopWordFilter("aber alle allem allen aller alles als also am an ander andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte während würde würden zu zum zur zwar zwischen über".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.du.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.du.js
new file mode 100644
index 0000000000..52632004a2
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.du.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var v,q,r;console.warn('[Lunr Languages] Please use the "nl" instead of the "du". The "nl" code is the standard code for Dutch language, and "du" will be removed in the next major versions.'),e.du=function(){this.pipeline.reset(),this.pipeline.add(e.du.trimmer,e.du.stopWordFilter,e.du.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.du.stemmer))},e.du.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.du.trimmer=e.trimmerSupport.generateTrimmer(e.du.wordCharacters),e.Pipeline.registerFunction(e.du.trimmer,"trimmer-du"),e.du.stemmer=(v=e.stemmerSupport.Among,q=e.stemmerSupport.SnowballProgram,r=new function(){var r,i,u,o=[new v("",-1,6),new v("á",0,1),new v("ä",0,1),new v("é",0,2),new v("ë",0,2),new v("í",0,3),new v("ï",0,3),new v("ó",0,4),new v("ö",0,4),new v("ú",0,5),new v("ü",0,5)],n=[new v("",-1,3),new v("I",0,2),new v("Y",0,1)],t=[new v("dd",-1,-1),new v("kk",-1,-1),new v("tt",-1,-1)],c=[new v("ene",-1,2),new v("se",-1,3),new v("en",-1,2),new v("heden",2,1),new v("s",-1,3)],a=[new v("end",-1,1),new v("ig",-1,2),new v("ing",-1,1),new v("lijk",-1,3),new v("baar",-1,4),new v("bar",-1,5)],l=[new v("aa",-1,-1),new v("ee",-1,-1),new v("oo",-1,-1),new v("uu",-1,-1)],m=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],d=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],f=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],_=new q;function s(e){return(_.cursor=e)>=_.limit||(_.cursor++,!1)}function w(){for(;!_.in_grouping(m,97,232);){if(_.cursor>=_.limit)return!0;_.cursor++}for(;!_.out_grouping(m,97,232);){if(_.cursor>=_.limit)return!0;_.cursor++}return!1}function b(){return i<=_.cursor}function p(){return r<=_.cursor}function g(){var e=_.limit-_.cursor;_.find_among_b(t,3)&&(_.cursor=_.limit-e,_.ket=_.cursor,_.cursor>_.limit_backward&&(_.cursor--,_.bra=_.cursor,_.slice_del()))}function h(){var e;u=!1,_.ket=_.cursor,_.eq_s_b(1,"e")&&(_.bra=_.cursor,b()&&(e=_.limit-_.cursor,_.out_grouping_b(m,97,232)&&(_.cursor=_.limit-e,_.slice_del(),u=!0,g())))}function k(){var 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diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.es.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.es.js
new file mode 100644
index 0000000000..9de6c09cb4
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.es.js
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diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.fi.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.fi.js
new file mode 100644
index 0000000000..2f9bf5aebd
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.fi.js
@@ -0,0 +1 @@
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v("impa",3,-1),new v("mmi",-1,1),new v("immi",5,-1),new v("mpi",-1,1),new v("impi",7,-1),new v("ejä",-1,-1),new v("mmä",-1,1),new v("immä",10,-1),new v("mpä",-1,1),new v("impä",12,-1)],_=[new v("i",-1,-1),new v("j",-1,-1)],k=[new v("mma",-1,1),new v("imma",0,-1)],b=[17,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8],d=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,32],e=[17,65,16,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,32],f=[17,97,24,1,0,0,0,0,0,0,0,0,0,0,0,0,8,0,32],h=new C;function p(){for(var i;i=h.cursor,!h.in_grouping(d,97,246);){if((h.cursor=i)>=h.limit)return!0;h.cursor++}for(h.cursor=i;!h.out_grouping(d,97,246);){if(h.cursor>=h.limit)return!0;h.cursor++}return!1}function g(){var i,e;if(h.cursor>=o)if(e=h.limit_backward,h.limit_backward=o,h.ket=h.cursor,i=h.find_among_b(r,10)){switch(h.bra=h.cursor,h.limit_backward=e,i){case 1:if(!h.in_grouping_b(f,97,246))return;break;case 2:if(!(l<=h.cursor))return}h.slice_del()}else h.limit_backward=e}function j(){return h.find_among_b(i,7)}function q(){return h.eq_s_b(1,"i")&&h.in_grouping_b(e,97,246)}this.setCurrent=function(i){h.setCurrent(i)},this.getCurrent=function(){return h.getCurrent()},this.stem=function(){var i,e=h.cursor;return o=h.limit,l=o,p()||(o=h.cursor,p()||(l=h.cursor)),n=!1,h.limit_backward=e,h.cursor=h.limit,g(),h.cursor=h.limit,function(){var i,e,r;if(h.cursor>=o)if(e=h.limit_backward,h.limit_backward=o,h.ket=h.cursor,i=h.find_among_b(c,9))switch(h.bra=h.cursor,h.limit_backward=e,i){case 1:r=h.limit-h.cursor,h.eq_s_b(1,"k")||(h.cursor=h.limit-r,h.slice_del());break;case 2:h.slice_del(),h.ket=h.cursor,h.eq_s_b(3,"kse")&&(h.bra=h.cursor,h.slice_from("ksi"));break;case 3:h.slice_del();break;case 4:h.find_among_b(s,6)&&h.slice_del();break;case 5:h.find_among_b(a,6)&&h.slice_del();break;case 6:h.find_among_b(u,2)&&h.slice_del()}else h.limit_backward=e}(),h.cursor=h.limit,function(){var i,e,r;if(h.cursor>=o)if(e=h.limit_backward,h.limit_backward=o,h.ket=h.cursor,i=h.find_among_b(m,30)){switch(h.bra=h.cursor,h.limit_backward=e,i){case 1:if(!h.eq_s_b(1,"a"))return;break;case 2:case 9:if(!h.eq_s_b(1,"e"))return;break;case 3:if(!h.eq_s_b(1,"i"))return;break;case 4:if(!h.eq_s_b(1,"o"))return;break;case 5:if(!h.eq_s_b(1,"ä"))return;break;case 6:if(!h.eq_s_b(1,"ö"))return;break;case 7:if(r=h.limit-h.cursor,!j()&&(h.cursor=h.limit-r,!h.eq_s_b(2,"ie"))){h.cursor=h.limit-r;break}if(h.cursor=h.limit-r,h.cursor<=h.limit_backward){h.cursor=h.limit-r;break}h.cursor--,h.bra=h.cursor;break;case 8:if(!h.in_grouping_b(d,97,246)||!h.out_grouping_b(d,97,246))return}h.slice_del(),n=!0}else h.limit_backward=e}(),h.cursor=h.limit,function(){var i,e,r;if(h.cursor>=l)if(e=h.limit_backward,h.limit_backward=l,h.ket=h.cursor,i=h.find_among_b(w,14)){if(h.bra=h.cursor,h.limit_backward=e,1==i){if(r=h.limit-h.cursor,h.eq_s_b(2,"po"))return;h.cursor=h.limit-r}h.slice_del()}else h.limit_backward=e}(),h.cursor=h.limit,h.cursor=(n?h.cursor>=o&&(i=h.limit_backward,h.limit_backward=o,h.ket=h.cursor,h.find_among_b(_,2)?(h.bra=h.cursor,h.limit_backward=i,h.slice_del()):h.limit_backward=i):(h.cursor=h.limit,function(){var i,e,r,n,t,s;if(h.cursor>=o){if(e=h.limit_backward,h.limit_backward=o,h.ket=h.cursor,h.eq_s_b(1,"t")&&(h.bra=h.cursor,r=h.limit-h.cursor,h.in_grouping_b(d,97,246)&&(h.cursor=h.limit-r,h.slice_del(),h.limit_backward=e,n=h.limit-h.cursor,h.cursor>=l&&(h.cursor=l,t=h.limit_backward,h.limit_backward=h.cursor,h.cursor=h.limit-n,h.ket=h.cursor,i=h.find_among_b(k,2))))){if(h.bra=h.cursor,h.limit_backward=t,1==i){if(s=h.limit-h.cursor,h.eq_s_b(2,"po"))return;h.cursor=h.limit-s}return h.slice_del()}h.limit_backward=e}}()),h.limit),function(){var i,e,r,n;if(h.cursor>=o){for(i=h.limit_backward,h.limit_backward=o,e=h.limit-h.cursor,j()&&(h.cursor=h.limit-e,h.ket=h.cursor,h.cursor>h.limit_backward&&(h.cursor--,h.bra=h.cursor,h.slice_del())),h.cursor=h.limit-e,h.ket=h.cursor,h.in_grouping_b(b,97,228)&&(h.bra=h.cursor,h.out_grouping_b(d,97,246)&&h.slice_del()),h.cursor=h.limit-e,h.ket=h.cursor,h.eq_s_b(1,"j")&&(h.bra=h.cursor,r=h.limit-h.cursor,h.eq_s_b(1,"o")?h.slice_del():(h.cursor=h.limit-r,h.eq_s_b(1,"u")&&h.slice_del())),h.cursor=h.limit-e,h.ket=h.cursor,h.eq_s_b(1,"o")&&(h.bra=h.cursor,h.eq_s_b(1,"j")&&h.slice_del()),h.cursor=h.limit-e,h.limit_backward=i;;){if(n=h.limit-h.cursor,h.out_grouping_b(d,97,246)){h.cursor=h.limit-n;break}if(h.cursor=h.limit-n,h.cursor<=h.limit_backward)return;h.cursor--}h.ket=h.cursor,h.cursor>h.limit_backward&&(h.cursor--,h.bra=h.cursor,t=h.slice_to(),h.eq_v_b(t)&&h.slice_del())}}(),!0}},function(i){return"function"==typeof i.update?i.update(function(i){return e.setCurrent(i),e.stem(),e.getCurrent()}):(e.setCurrent(i),e.stem(),e.getCurrent())}),i.Pipeline.registerFunction(i.fi.stemmer,"stemmer-fi"),i.fi.stopWordFilter=i.generateStopWordFilter("ei eivät emme en et ette että he heidän heidät heihin heille heillä heiltä heissä heistä heitä hän häneen hänelle hänellä häneltä hänen hänessä hänestä hänet häntä itse ja johon joiden joihin joiksi joilla joille joilta joina joissa joista joita joka joksi jolla jolle jolta jona jonka jos jossa josta jota jotka kanssa keiden keihin keiksi keille keillä keiltä keinä keissä keistä keitä keneen keneksi kenelle kenellä keneltä kenen kenenä kenessä kenestä kenet ketkä ketkä ketä koska kuin kuka kun me meidän meidät meihin meille meillä meiltä meissä meistä meitä mihin miksi mikä mille millä miltä minkä minkä minua minulla minulle minulta minun minussa minusta minut minuun minä minä missä mistä mitkä mitä mukaan mutta ne niiden niihin niiksi niille niillä niiltä niin niin niinä niissä niistä niitä noiden noihin noiksi noilla noille noilta noin noina noissa noista noita nuo nyt näiden näihin näiksi näille näillä näiltä näinä näissä näistä näitä nämä ole olemme olen olet olette oli olimme olin olisi olisimme olisin olisit olisitte olisivat olit olitte olivat olla olleet ollut on ovat poikki se sekä sen siihen siinä siitä siksi sille sillä sillä siltä sinua sinulla sinulle sinulta sinun sinussa sinusta sinut sinuun sinä sinä sitä tai te teidän teidät teihin teille teillä teiltä teissä teistä teitä tuo tuohon tuoksi tuolla tuolle tuolta tuon tuona tuossa tuosta tuota tähän täksi tälle tällä tältä tämä tämän tänä tässä tästä tätä vaan vai vaikka yli".split(" ")),i.Pipeline.registerFunction(i.fi.stopWordFilter,"stopWordFilter-fi")}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.fr.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.fr.js
new file mode 100644
index 0000000000..078d0cab70
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.fr.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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4:d.eq_s_b(2,"gu")&&d.slice_del()}d.limit_backward=s}}();d.cursor=d.limit,d.ket=d.cursor,d.eq_s_b(1,"Y")?(d.bra=d.cursor,d.slice_from("i")):(d.cursor=d.limit,d.eq_s_b(1,"ç")&&(d.bra=d.cursor,d.slice_from("c")))}this.setCurrent=function(e){d.setCurrent(e)},this.getCurrent=function(){return d.getCurrent()},this.stem=function(){var e,r=d.cursor;return function(){for(var e,r;;){if(e=d.cursor,d.in_grouping(_,97,251)){if(d.bra=d.cursor,r=d.cursor,k("u","U",e))continue;if(d.cursor=r,k("i","I",e))continue;if(d.cursor=r,p("y","Y",e))continue}if(d.cursor=e,!k("y","Y",d.bra=e)){if(d.cursor=e,d.eq_s(1,"q")&&(d.bra=d.cursor,p("u","U",e)))continue;if((d.cursor=e)>=d.limit)return;d.cursor++}}}(),d.cursor=r,function(){var e=d.cursor;if(t=d.limit,s=i=t,d.in_grouping(_,97,251)&&d.in_grouping(_,97,251)&&d.cursor<d.limit)d.cursor++;else if(d.cursor=e,!d.find_among(n,3)){d.cursor=e;do{if(d.cursor>=d.limit){d.cursor=t;break}d.cursor++}while(!d.in_grouping(_,97,251))}t=d.cursor,d.cursor=e,g()||(i=d.cursor,g()||(s=d.cursor))}(),d.limit_backward=r,d.cursor=d.limit,z(),d.cursor=d.limit,e=d.limit-d.cursor,d.find_among_b(m,5)&&(d.cursor=d.limit-e,d.ket=d.cursor,d.cursor>d.limit_backward&&(d.cursor--,d.bra=d.cursor,d.slice_del())),d.cursor=d.limit,function(){for(var e,r=1;d.out_grouping_b(_,97,251);)r--;if(r<=0){if(d.ket=d.cursor,e=d.limit-d.cursor,!d.eq_s_b(1,"é")&&(d.cursor=d.limit-e,!d.eq_s_b(1,"è")))return;d.bra=d.cursor,d.slice_from("e")}}(),d.cursor=d.limit_backward,function(){for(var e,r;r=d.cursor,d.bra=r,e=d.find_among(u,4);)switch(d.ket=d.cursor,e){case 1:d.slice_from("i");break;case 2:d.slice_from("u");break;case 3:d.slice_from("y");break;case 4:if(d.cursor>=d.limit)return;d.cursor++}}(),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return s.setCurrent(e),s.stem(),s.getCurrent()}):(s.setCurrent(e),s.stem(),s.getCurrent())}),e.Pipeline.registerFunction(e.fr.stemmer,"stemmer-fr"),e.fr.stopWordFilter=e.generateStopWordFilter("ai aie aient aies ait as au aura aurai auraient aurais aurait auras aurez auriez aurions aurons auront aux avaient avais avait avec avez aviez avions avons ayant ayez ayons c ce ceci celà ces cet cette d dans de des du elle en es est et eu eue eues eurent eus eusse eussent eusses eussiez eussions eut eux eûmes eût eûtes furent fus fusse fussent fusses fussiez fussions fut fûmes fût fûtes ici il ils j je l la le les leur leurs lui m ma mais me mes moi mon même n ne nos notre nous on ont ou par pas pour qu que quel quelle quelles quels qui s sa sans se sera serai seraient serais serait seras serez seriez serions serons seront ses soi soient sois soit sommes son sont soyez soyons suis sur t ta te tes toi ton tu un une vos votre vous y à étaient étais était étant étiez étions été étée étées étés êtes".split(" ")),e.Pipeline.registerFunction(e.fr.stopWordFilter,"stopWordFilter-fr")}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.hu.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.hu.js
new file mode 100644
index 0000000000..56a4b0dc19
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.hu.js
@@ -0,0 +1 @@
+!function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var p,_,n;e.hu=function(){this.pipeline.reset(),this.pipeline.add(e.hu.trimmer,e.hu.stopWordFilter,e.hu.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hu.stemmer))},e.hu.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.hu.trimmer=e.trimmerSupport.generateTrimmer(e.hu.wordCharacters),e.Pipeline.registerFunction(e.hu.trimmer,"trimmer-hu"),e.hu.stemmer=(p=e.stemmerSupport.Among,_=e.stemmerSupport.SnowballProgram,n=new function(){var r,i=[new p("cs",-1,-1),new p("dzs",-1,-1),new p("gy",-1,-1),new p("ly",-1,-1),new p("ny",-1,-1),new p("sz",-1,-1),new p("ty",-1,-1),new p("zs",-1,-1)],n=[new p("á",-1,1),new p("é",-1,2)],a=[new p("bb",-1,-1),new p("cc",-1,-1),new p("dd",-1,-1),new p("ff",-1,-1),new p("gg",-1,-1),new p("jj",-1,-1),new p("kk",-1,-1),new p("ll",-1,-1),new p("mm",-1,-1),new p("nn",-1,-1),new p("pp",-1,-1),new p("rr",-1,-1),new p("ccs",-1,-1),new p("ss",-1,-1),new p("zzs",-1,-1),new p("tt",-1,-1),new p("vv",-1,-1),new p("ggy",-1,-1),new p("lly",-1,-1),new p("nny",-1,-1),new p("tty",-1,-1),new p("ssz",-1,-1),new p("zz",-1,-1)],t=[new p("al",-1,1),new p("el",-1,2)],e=[new p("ba",-1,-1),new p("ra",-1,-1),new p("be",-1,-1),new p("re",-1,-1),new p("ig",-1,-1),new p("nak",-1,-1),new p("nek",-1,-1),new p("val",-1,-1),new p("vel",-1,-1),new p("ul",-1,-1),new p("nál",-1,-1),new p("nél",-1,-1),new p("ból",-1,-1),new p("ról",-1,-1),new p("tól",-1,-1),new p("bõl",-1,-1),new p("rõl",-1,-1),new p("tõl",-1,-1),new p("ül",-1,-1),new p("n",-1,-1),new p("an",19,-1),new p("ban",20,-1),new p("en",19,-1),new p("ben",22,-1),new p("képpen",22,-1),new p("on",19,-1),new p("ön",19,-1),new p("képp",-1,-1),new p("kor",-1,-1),new p("t",-1,-1),new p("at",29,-1),new p("et",29,-1),new p("ként",29,-1),new p("anként",32,-1),new p("enként",32,-1),new p("onként",32,-1),new p("ot",29,-1),new p("ért",29,-1),new p("öt",29,-1),new p("hez",-1,-1),new p("hoz",-1,-1),new p("höz",-1,-1),new p("vá",-1,-1),new p("vé",-1,-1)],s=[new p("án",-1,2),new p("én",-1,1),new p("ánként",-1,3)],c=[new p("stul",-1,2),new p("astul",0,1),new p("ástul",0,3),new p("stül",-1,2),new p("estül",3,1),new p("éstül",3,4)],w=[new p("á",-1,1),new p("é",-1,2)],o=[new p("k",-1,7),new p("ak",0,4),new p("ek",0,6),new p("ok",0,5),new p("ák",0,1),new p("ék",0,2),new p("ök",0,3)],l=[new p("éi",-1,7),new p("áéi",0,6),new p("ééi",0,5),new p("é",-1,9),new p("ké",3,4),new p("aké",4,1),new p("eké",4,1),new p("oké",4,1),new p("áké",4,3),new p("éké",4,2),new p("öké",4,1),new p("éé",3,8)],u=[new p("a",-1,18),new p("ja",0,17),new p("d",-1,16),new p("ad",2,13),new p("ed",2,13),new p("od",2,13),new p("ád",2,14),new p("éd",2,15),new p("öd",2,13),new p("e",-1,18),new p("je",9,17),new p("nk",-1,4),new p("unk",11,1),new p("ánk",11,2),new p("énk",11,3),new p("ünk",11,1),new p("uk",-1,8),new p("juk",16,7),new p("ájuk",17,5),new p("ük",-1,8),new p("jük",19,7),new p("éjük",20,6),new p("m",-1,12),new p("am",22,9),new p("em",22,9),new p("om",22,9),new p("ám",22,10),new p("ém",22,11),new p("o",-1,18),new p("á",-1,19),new p("é",-1,20)],m=[new p("id",-1,10),new p("aid",0,9),new p("jaid",1,6),new p("eid",0,9),new p("jeid",3,6),new p("áid",0,7),new p("éid",0,8),new p("i",-1,15),new p("ai",7,14),new p("jai",8,11),new p("ei",7,14),new p("jei",10,11),new p("ái",7,12),new p("éi",7,13),new p("itek",-1,24),new p("eitek",14,21),new p("jeitek",15,20),new p("éitek",14,23),new p("ik",-1,29),new p("aik",18,26),new p("jaik",19,25),new p("eik",18,26),new p("jeik",21,25),new p("áik",18,27),new p("éik",18,28),new p("ink",-1,20),new p("aink",25,17),new p("jaink",26,16),new p("eink",25,17),new p("jeink",28,16),new p("áink",25,18),new p("éink",25,19),new p("aitok",-1,21),new p("jaitok",32,20),new p("áitok",-1,22),new p("im",-1,5),new p("aim",35,4),new p("jaim",36,1),new p("eim",35,4),new p("jeim",38,1),new p("áim",35,2),new p("éim",35,3)],k=[17,65,16,0,0,0,0,0,0,0,0,0,0,0,0,0,1,17,52,14],f=new _;function b(){return r<=f.cursor}function d(){var e=f.limit-f.cursor;return!!f.find_among_b(a,23)&&(f.cursor=f.limit-e,!0)}function g(){if(f.cursor>f.limit_backward){f.cursor--,f.ket=f.cursor;var e=f.cursor-1;f.limit_backward<=e&&e<=f.limit&&(f.cursor=e,f.bra=e,f.slice_del())}}function h(){f.ket=f.cursor,f.find_among_b(e,44)&&(f.bra=f.cursor,b()&&(f.slice_del(),function(){var e;if(f.ket=f.cursor,(e=f.find_among_b(n,2))&&(f.bra=f.cursor,b()))switch(e){case 1:f.slice_from("a");break;case 2:f.slice_from("e")}}()))}this.setCurrent=function(e){f.setCurrent(e)},this.getCurrent=function(){return f.getCurrent()},this.stem=function(){var e=f.cursor;return function(){var e,n=f.cursor;if(r=f.limit,f.in_grouping(k,97,252))for(;;){if(e=f.cursor,f.out_grouping(k,97,252))return f.cursor=e,f.find_among(i,8)||(f.cursor=e)<f.limit&&f.cursor++,r=f.cursor;if((f.cursor=e)>=f.limit)return r=e;f.cursor++}if(f.cursor=n,f.out_grouping(k,97,252)){for(;!f.in_grouping(k,97,252);){if(f.cursor>=f.limit)return;f.cursor++}r=f.cursor}}(),f.limit_backward=e,f.cursor=f.limit,function(){var e;if(f.ket=f.cursor,(e=f.find_among_b(t,2))&&(f.bra=f.cursor,b())){if((1==e||2==e)&&!d())return;f.slice_del(),g()}}(),f.cursor=f.limit,h(),f.cursor=f.limit,function(){var e;if(f.ket=f.cursor,(e=f.find_among_b(s,3))&&(f.bra=f.cursor,b()))switch(e){case 1:f.slice_from("e");break;case 2:case 3:f.slice_from("a")}}(),f.cursor=f.limit,function(){var e;if(f.ket=f.cursor,(e=f.find_among_b(c,6))&&(f.bra=f.cursor,b()))switch(e){case 1:case 2:f.slice_del();break;case 3:f.slice_from("a");break;case 4:f.slice_from("e")}}(),f.cursor=f.limit,function(){var e;if(f.ket=f.cursor,(e=f.find_among_b(w,2))&&(f.bra=f.cursor,b())){if((1==e||2==e)&&!d())return;f.slice_del(),g()}}(),f.cursor=f.limit,function(){var e;if(f.ket=f.cursor,(e=f.find_among_b(l,12))&&(f.bra=f.cursor,b()))switch(e){case 1:case 4:case 7:case 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2:f.slice_from("e");break;case 3:case 4:case 5:case 6:case 7:f.slice_del()}}(),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}),e.Pipeline.registerFunction(e.hu.stemmer,"stemmer-hu"),e.hu.stopWordFilter=e.generateStopWordFilter("a abban ahhoz ahogy ahol aki akik akkor alatt amely amelyek amelyekben amelyeket amelyet amelynek ami amikor amit amolyan amíg annak arra arról az azok azon azonban azt aztán azután azzal azért be belül benne bár cikk cikkek cikkeket csak de e ebben eddig egy egyes egyetlen egyik egyre egyéb egész ehhez ekkor el ellen elsõ elég elõ elõször elõtt emilyen ennek erre ez ezek ezen ezt ezzel ezért fel felé hanem hiszen hogy hogyan igen ill ill. illetve ilyen ilyenkor ismét ison itt jobban jó jól kell kellett keressünk keresztül ki kívül között közül legalább legyen lehet lehetett lenne lenni lesz lett maga magát majd majd meg mellett mely melyek mert mi mikor milyen minden mindenki mindent mindig mint mintha mit mivel miért most már más másik még míg nagy nagyobb nagyon ne nekem neki nem nincs néha néhány nélkül olyan ott pedig persze rá s saját sem semmi sok sokat sokkal szemben szerint szinte számára talán tehát teljes tovább továbbá több ugyanis utolsó után utána vagy vagyis vagyok valaki valami valamint való van vannak vele vissza viszont volna volt voltak voltam voltunk által általában át én éppen és így õ õk õket össze úgy új újabb újra".split(" ")),e.Pipeline.registerFunction(e.hu.stopWordFilter,"stopWordFilter-hu")}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.it.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.it.js
new file mode 100644
index 0000000000..50dddaa04b
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.it.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. 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\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.ja.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.ja.js
new file mode 100644
index 0000000000..69f620250d
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.ja.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(m){if(void 0===m)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===m.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var l="2"==m.version[0];m.ja=function(){this.pipeline.reset(),this.pipeline.add(m.ja.trimmer,m.ja.stopWordFilter,m.ja.stemmer),l?this.tokenizer=m.ja.tokenizer:(m.tokenizer&&(m.tokenizer=m.ja.tokenizer),this.tokenizerFn&&(this.tokenizerFn=m.ja.tokenizer))};var j=new m.TinySegmenter;m.ja.tokenizer=function(e){var r,t,i,n,o,s,p,a,u;if(!arguments.length||null==e||null==e)return[];if(Array.isArray(e))return e.map(function(e){return l?new m.Token(e.toLowerCase()):e.toLowerCase()});for(r=(t=e.toString().toLowerCase().replace(/^\s+/,"")).length-1;0<=r;r--)if(/\S/.test(t.charAt(r))){t=t.substring(0,r+1);break}for(o=[],i=t.length,p=a=0;a<=i;a++)if(s=a-p,t.charAt(a).match(/\s/)||a==i){if(0<s)for(n=j.segment(t.slice(p,a)).filter(function(e){return!!e}),u=p,r=0;r<n.length;r++)l?o.push(new m.Token(n[r],{position:[u,n[r].length],index:o.length})):o.push(n[r]),u+=n[r].length;p=a+1}return o},m.ja.stemmer=function(e){return e},m.Pipeline.registerFunction(m.ja.stemmer,"stemmer-ja"),m.ja.wordCharacters="一二三四五六七八九十百千万億兆一-龠々〆ヵヶぁ-んァ-ヴーｱ-ﾝﾞa-zA-Zａ-ｚＡ-Ｚ0-9０-９",m.ja.trimmer=m.trimmerSupport.generateTrimmer(m.ja.wordCharacters),m.Pipeline.registerFunction(m.ja.trimmer,"trimmer-ja"),m.ja.stopWordFilter=m.generateStopWordFilter("これ それ あれ この その あの ここ そこ あそこ こちら どこ だれ なに なん 何 私 貴方 貴方方 我々 私達 あの人 あのかた 彼女 彼 です あります おります います は が の に を で え から まで より も どの と し それで しかし".split(" ")),m.Pipeline.registerFunction(m.ja.stopWordFilter,"stopWordFilter-ja"),m.jp=m.ja,m.Pipeline.registerFunction(m.jp.stemmer,"stemmer-jp"),m.Pipeline.registerFunction(m.jp.trimmer,"trimmer-jp"),m.Pipeline.registerFunction(m.jp.stopWordFilter,"stopWordFilter-jp")}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.jp.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.jp.js
new file mode 100644
index 0000000000..c055ebaf37
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.jp.js
@@ -0,0 +1 @@
+module.exports=require("./lunr.ja");
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.multi.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.multi.js
new file mode 100644
index 0000000000..8a145c9117
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.multi.js
@@ -0,0 +1 @@
+!function(e,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(e.lunr)}(this,function(){return function(o){o.multiLanguage=function(){for(var e=Array.prototype.slice.call(arguments),i=e.join("-"),t="",r=[],n=[],s=0;s<e.length;++s)"en"==e[s]?(t+="\\w",r.unshift(o.stopWordFilter),r.push(o.stemmer),n.push(o.stemmer)):(t+=o[e[s]].wordCharacters,r.unshift(o[e[s]].stopWordFilter),r.push(o[e[s]].stemmer),n.push(o[e[s]].stemmer));var p=o.trimmerSupport.generateTrimmer(t);return o.Pipeline.registerFunction(p,"lunr-multi-trimmer-"+i),r.unshift(p),function(){this.pipeline.reset(),this.pipeline.add.apply(this.pipeline,r),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add.apply(this.searchPipeline,n))}}}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.nl.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.nl.js
new file mode 100644
index 0000000000..273f4ad0be
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.nl.js
@@ -0,0 +1 @@
+!function(r,e){"function"==typeof define&&define.amd?define(e):"object"==typeof exports?module.exports=e():e()(r.lunr)}(this,function(){return function(r){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var v,q,e;r.nl=function(){this.pipeline.reset(),this.pipeline.add(r.nl.trimmer,r.nl.stopWordFilter,r.nl.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(r.nl.stemmer))},r.nl.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",r.nl.trimmer=r.trimmerSupport.generateTrimmer(r.nl.wordCharacters),r.Pipeline.registerFunction(r.nl.trimmer,"trimmer-nl"),r.nl.stemmer=(v=r.stemmerSupport.Among,q=r.stemmerSupport.SnowballProgram,e=new function(){var e,i,u,o=[new v("",-1,6),new v("á",0,1),new v("ä",0,1),new v("é",0,2),new v("ë",0,2),new v("í",0,3),new v("ï",0,3),new v("ó",0,4),new v("ö",0,4),new v("ú",0,5),new v("ü",0,5)],n=[new v("",-1,3),new v("I",0,2),new v("Y",0,1)],t=[new v("dd",-1,-1),new v("kk",-1,-1),new v("tt",-1,-1)],c=[new v("ene",-1,2),new v("se",-1,3),new v("en",-1,2),new v("heden",2,1),new v("s",-1,3)],a=[new v("end",-1,1),new v("ig",-1,2),new v("ing",-1,1),new v("lijk",-1,3),new v("baar",-1,4),new v("bar",-1,5)],l=[new v("aa",-1,-1),new v("ee",-1,-1),new v("oo",-1,-1),new v("uu",-1,-1)],m=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],d=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],f=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],_=new q;function s(r){return(_.cursor=r)>=_.limit||(_.cursor++,!1)}function w(){for(;!_.in_grouping(m,97,232);){if(_.cursor>=_.limit)return!0;_.cursor++}for(;!_.out_grouping(m,97,232);){if(_.cursor>=_.limit)return!0;_.cursor++}return!1}function b(){return i<=_.cursor}function p(){return e<=_.cursor}function g(){var r=_.limit-_.cursor;_.find_among_b(t,3)&&(_.cursor=_.limit-r,_.ket=_.cursor,_.cursor>_.limit_backward&&(_.cursor--,_.bra=_.cursor,_.slice_del()))}function h(){var r;u=!1,_.ket=_.cursor,_.eq_s_b(1,"e")&&(_.bra=_.cursor,b()&&(r=_.limit-_.cursor,_.out_grouping_b(m,97,232)&&(_.cursor=_.limit-r,_.slice_del(),u=!0,g())))}function k(){var r;b()&&(r=_.limit-_.cursor,_.out_grouping_b(m,97,232)&&(_.cursor=_.limit-r,_.eq_s_b(3,"gem")||(_.cursor=_.limit-r,_.slice_del(),g())))}this.setCurrent=function(r){_.setCurrent(r)},this.getCurrent=function(){return _.getCurrent()},this.stem=function(){var r=_.cursor;return function(){for(var r,e,i,n=_.cursor;;){if(_.bra=_.cursor,r=_.find_among(o,11))switch(_.ket=_.cursor,r){case 1:_.slice_from("a");continue;case 2:_.slice_from("e");continue;case 3:_.slice_from("i");continue;case 4:_.slice_from("o");continue;case 5:_.slice_from("u");continue;case 6:if(_.cursor>=_.limit)break;_.cursor++;continue}break}for(_.cursor=n,_.bra=n,_.eq_s(1,"y")?(_.ket=_.cursor,_.slice_from("Y")):_.cursor=n;;)if(e=_.cursor,_.in_grouping(m,97,232)){if(i=_.cursor,_.bra=i,_.eq_s(1,"i"))_.ket=_.cursor,_.in_grouping(m,97,232)&&(_.slice_from("I"),_.cursor=e);else if(_.cursor=i,_.eq_s(1,"y"))_.ket=_.cursor,_.slice_from("Y"),_.cursor=e;else if(s(e))break}else if(s(e))break}(),_.cursor=r,i=_.limit,e=i,w()||((i=_.cursor)<3&&(i=3),w()||(e=_.cursor)),_.limit_backward=r,_.cursor=_.limit,function(){var r,e,i,n,o,t,s=_.limit-_.cursor;if(_.ket=_.cursor,r=_.find_among_b(c,5))switch(_.bra=_.cursor,r){case 1:b()&&_.slice_from("heid");break;case 2:k();break;case 3:b()&&_.out_grouping_b(f,97,232)&&_.slice_del()}if(_.cursor=_.limit-s,h(),_.cursor=_.limit-s,_.ket=_.cursor,_.eq_s_b(4,"heid")&&(_.bra=_.cursor,p()&&(e=_.limit-_.cursor,_.eq_s_b(1,"c")||(_.cursor=_.limit-e,_.slice_del(),_.ket=_.cursor,_.eq_s_b(2,"en")&&(_.bra=_.cursor,k())))),_.cursor=_.limit-s,_.ket=_.cursor,r=_.find_among_b(a,6))switch(_.bra=_.cursor,r){case 1:if(p()){if(_.slice_del(),i=_.limit-_.cursor,_.ket=_.cursor,_.eq_s_b(2,"ig")&&(_.bra=_.cursor,p()&&(n=_.limit-_.cursor,!_.eq_s_b(1,"e")))){_.cursor=_.limit-n,_.slice_del();break}_.cursor=_.limit-i,g()}break;case 2:p()&&(o=_.limit-_.cursor,_.eq_s_b(1,"e")||(_.cursor=_.limit-o,_.slice_del()));break;case 3:p()&&(_.slice_del(),h());break;case 4:p()&&_.slice_del();break;case 5:p()&&u&&_.slice_del()}_.cursor=_.limit-s,_.out_grouping_b(d,73,232)&&(t=_.limit-_.cursor,_.find_among_b(l,4)&&_.out_grouping_b(m,97,232)&&(_.cursor=_.limit-t,_.ket=_.cursor,_.cursor>_.limit_backward&&(_.cursor--,_.bra=_.cursor,_.slice_del())))}(),_.cursor=_.limit_backward,function(){for(var r;;)if(_.bra=_.cursor,r=_.find_among(n,3))switch(_.ket=_.cursor,r){case 1:_.slice_from("y");break;case 2:_.slice_from("i");break;case 3:if(_.cursor>=_.limit)return;_.cursor++}}(),!0}},function(r){return"function"==typeof r.update?r.update(function(r){return e.setCurrent(r),e.stem(),e.getCurrent()}):(e.setCurrent(r),e.stem(),e.getCurrent())}),r.Pipeline.registerFunction(r.nl.stemmer,"stemmer-nl"),r.nl.stopWordFilter=r.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" ")),r.Pipeline.registerFunction(r.nl.stopWordFilter,"stopWordFilter-nl")}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.no.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.no.js
new file mode 100644
index 0000000000..3d156b9c19
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.no.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r,n,i;e.no=function(){this.pipeline.reset(),this.pipeline.add(e.no.trimmer,e.no.stopWordFilter,e.no.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.no.stemmer))},e.no.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.no.trimmer=e.trimmerSupport.generateTrimmer(e.no.wordCharacters),e.Pipeline.registerFunction(e.no.trimmer,"trimmer-no"),e.no.stemmer=(r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){var o,s,a=[new r("a",-1,1),new r("e",-1,1),new r("ede",1,1),new r("ande",1,1),new r("ende",1,1),new r("ane",1,1),new r("ene",1,1),new r("hetene",6,1),new r("erte",1,3),new r("en",-1,1),new r("heten",9,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",12,1),new r("s",-1,2),new r("as",14,1),new r("es",14,1),new r("edes",16,1),new r("endes",16,1),new r("enes",16,1),new r("hetenes",19,1),new r("ens",14,1),new r("hetens",21,1),new r("ers",14,1),new r("ets",14,1),new r("et",-1,1),new r("het",25,1),new r("ert",-1,3),new r("ast",-1,1)],m=[new r("dt",-1,-1),new r("vt",-1,-1)],l=[new r("leg",-1,1),new r("eleg",0,1),new r("ig",-1,1),new r("eig",2,1),new r("lig",2,1),new r("elig",4,1),new r("els",-1,1),new r("lov",-1,1),new r("elov",7,1),new r("slov",7,1),new r("hetslov",9,1)],u=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],d=[119,125,149,1],c=new n;this.setCurrent=function(e){c.setCurrent(e)},this.getCurrent=function(){return c.getCurrent()},this.stem=function(){var e,r,n,i,t=c.cursor;return function(){var e,r=c.cursor+3;if(s=c.limit,0<=r||r<=c.limit){for(o=r;;){if(e=c.cursor,c.in_grouping(u,97,248)){c.cursor=e;break}if(e>=c.limit)return;c.cursor=e+1}for(;!c.out_grouping(u,97,248);){if(c.cursor>=c.limit)return;c.cursor++}(s=c.cursor)<o&&(s=o)}}(),c.limit_backward=t,c.cursor=c.limit,function(){var e,r,n;if(c.cursor>=s&&(r=c.limit_backward,c.limit_backward=s,c.ket=c.cursor,e=c.find_among_b(a,29),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del();break;case 2:n=c.limit-c.cursor,c.in_grouping_b(d,98,122)?c.slice_del():(c.cursor=c.limit-n,c.eq_s_b(1,"k")&&c.out_grouping_b(u,97,248)&&c.slice_del());break;case 3:c.slice_from("er")}}(),c.cursor=c.limit,r=c.limit-c.cursor,c.cursor>=s&&(e=c.limit_backward,c.limit_backward=s,c.ket=c.cursor,c.find_among_b(m,2)?(c.bra=c.cursor,c.limit_backward=e,c.cursor=c.limit-r,c.cursor>c.limit_backward&&(c.cursor--,c.bra=c.cursor,c.slice_del())):c.limit_backward=e),c.cursor=c.limit,c.cursor>=s&&(i=c.limit_backward,c.limit_backward=s,c.ket=c.cursor,(n=c.find_among_b(l,11))?(c.bra=c.cursor,c.limit_backward=i,1==n&&c.slice_del()):c.limit_backward=i),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}),e.Pipeline.registerFunction(e.no.stemmer,"stemmer-no"),e.no.stopWordFilter=e.generateStopWordFilter("alle at av bare begge ble blei bli blir blitt både båe da de deg dei deim deira deires dem den denne der dere deres det dette di din disse ditt du dykk dykkar då eg ein eit eitt eller elles en enn er et ett etter for fordi fra før ha hadde han hans har hennar henne hennes her hjå ho hoe honom hoss hossen hun hva hvem hver hvilke hvilken hvis hvor hvordan hvorfor i ikke ikkje ikkje ingen ingi inkje inn inni ja jeg kan kom korleis korso kun kunne kva kvar kvarhelst kven kvi kvifor man mange me med medan meg meget mellom men mi min mine mitt mot mykje ned no noe noen noka noko nokon nokor nokre nå når og også om opp oss over på samme seg selv si si sia sidan siden sin sine sitt sjøl skal skulle slik so som som somme somt så sånn til um upp ut uten var vart varte ved vere verte vi vil ville vore vors vort vår være være vært å".split(" ")),e.Pipeline.registerFunction(e.no.stopWordFilter,"stopWordFilter-no")}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.pt.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.pt.js
new file mode 100644
index 0000000000..f50fc9fa6d
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.pt.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var j,C,r;e.pt=function(){this.pipeline.reset(),this.pipeline.add(e.pt.trimmer,e.pt.stopWordFilter,e.pt.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.pt.stemmer))},e.pt.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.pt.trimmer=e.trimmerSupport.generateTrimmer(e.pt.wordCharacters),e.Pipeline.registerFunction(e.pt.trimmer,"trimmer-pt"),e.pt.stemmer=(j=e.stemmerSupport.Among,C=e.stemmerSupport.SnowballProgram,r=new function(){var s,n,i,o=[new j("",-1,3),new j("ã",0,1),new j("õ",0,2)],a=[new j("",-1,3),new j("a~",0,1),new j("o~",0,2)],r=[new j("ic",-1,-1),new j("ad",-1,-1),new j("os",-1,-1),new j("iv",-1,1)],t=[new j("ante",-1,1),new j("avel",-1,1),new j("ível",-1,1)],u=[new j("ic",-1,1),new j("abil",-1,1),new j("iv",-1,1)],w=[new j("ica",-1,1),new j("ância",-1,1),new j("ência",-1,4),new j("ira",-1,9),new j("adora",-1,1),new j("osa",-1,1),new j("ista",-1,1),new j("iva",-1,8),new j("eza",-1,1),new j("logía",-1,2),new j("idade",-1,7),new j("ante",-1,1),new j("mente",-1,6),new j("amente",12,5),new j("ável",-1,1),new j("ível",-1,1),new j("ución",-1,3),new j("ico",-1,1),new j("ismo",-1,1),new j("oso",-1,1),new j("amento",-1,1),new j("imento",-1,1),new j("ivo",-1,8),new j("aça~o",-1,1),new j("ador",-1,1),new j("icas",-1,1),new j("ências",-1,4),new j("iras",-1,9),new j("adoras",-1,1),new j("osas",-1,1),new j("istas",-1,1),new j("ivas",-1,8),new j("ezas",-1,1),new j("logías",-1,2),new j("idades",-1,7),new j("uciones",-1,3),new j("adores",-1,1),new j("antes",-1,1),new j("aço~es",-1,1),new j("icos",-1,1),new j("ismos",-1,1),new j("osos",-1,1),new j("amentos",-1,1),new j("imentos",-1,1),new j("ivos",-1,8)],m=[new j("ada",-1,1),new j("ida",-1,1),new j("ia",-1,1),new j("aria",2,1),new j("eria",2,1),new j("iria",2,1),new j("ara",-1,1),new j("era",-1,1),new j("ira",-1,1),new j("ava",-1,1),new j("asse",-1,1),new j("esse",-1,1),new j("isse",-1,1),new j("aste",-1,1),new j("este",-1,1),new j("iste",-1,1),new j("ei",-1,1),new j("arei",16,1),new j("erei",16,1),new j("irei",16,1),new j("am",-1,1),new j("iam",20,1),new j("ariam",21,1),new j("eriam",21,1),new j("iriam",21,1),new j("aram",20,1),new j("eram",20,1),new j("iram",20,1),new j("avam",20,1),new j("em",-1,1),new j("arem",29,1),new j("erem",29,1),new j("irem",29,1),new j("assem",29,1),new j("essem",29,1),new j("issem",29,1),new j("ado",-1,1),new j("ido",-1,1),new j("ando",-1,1),new j("endo",-1,1),new j("indo",-1,1),new j("ara~o",-1,1),new j("era~o",-1,1),new j("ira~o",-1,1),new j("ar",-1,1),new j("er",-1,1),new j("ir",-1,1),new j("as",-1,1),new j("adas",47,1),new j("idas",47,1),new j("ias",47,1),new j("arias",50,1),new j("erias",50,1),new j("irias",50,1),new j("aras",47,1),new j("eras",47,1),new j("iras",47,1),new j("avas",47,1),new j("es",-1,1),new j("ardes",58,1),new j("erdes",58,1),new j("irdes",58,1),new j("ares",58,1),new 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diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.ro.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.ro.js
new file mode 100644
index 0000000000..b19627e1b3
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.ro.js
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\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.ru.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.ru.js
new file mode 100644
index 0000000000..ac99248044
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.ru.js
@@ -0,0 +1 @@
+!function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var h,g,n;e.ru=function(){this.pipeline.reset(),this.pipeline.add(e.ru.trimmer,e.ru.stopWordFilter,e.ru.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.ru.stemmer))},e.ru.wordCharacters="Ѐ-҄҇-ԯᴫᵸⷠ-ⷿꙀ-ꚟ︮︯",e.ru.trimmer=e.trimmerSupport.generateTrimmer(e.ru.wordCharacters),e.Pipeline.registerFunction(e.ru.trimmer,"trimmer-ru"),e.ru.stemmer=(h=e.stemmerSupport.Among,g=e.stemmerSupport.SnowballProgram,n=new function(){var n,e,r=[new h("в",-1,1),new h("ив",0,2),new h("ыв",0,2),new h("вши",-1,1),new h("ивши",3,2),new h("ывши",3,2),new h("вшись",-1,1),new h("ившись",6,2),new h("ывшись",6,2)],t=[new h("ее",-1,1),new h("ие",-1,1),new h("ое",-1,1),new h("ые",-1,1),new h("ими",-1,1),new h("ыми",-1,1),new h("ей",-1,1),new h("ий",-1,1),new h("ой",-1,1),new h("ый",-1,1),new h("ем",-1,1),new h("им",-1,1),new h("ом",-1,1),new h("ым",-1,1),new h("его",-1,1),new h("ого",-1,1),new h("ему",-1,1),new h("ому",-1,1),new h("их",-1,1),new h("ых",-1,1),new h("ею",-1,1),new h("ою",-1,1),new h("ую",-1,1),new h("юю",-1,1),new h("ая",-1,1),new h("яя",-1,1)],w=[new h("ем",-1,1),new h("нн",-1,1),new h("вш",-1,1),new h("ивш",2,2),new h("ывш",2,2),new h("щ",-1,1),new h("ющ",5,1),new h("ующ",6,2)],i=[new h("сь",-1,1),new h("ся",-1,1)],u=[new h("ла",-1,1),new h("ила",0,2),new h("ыла",0,2),new h("на",-1,1),new h("ена",3,2),new h("ете",-1,1),new h("ите",-1,2),new h("йте",-1,1),new h("ейте",7,2),new h("уйте",7,2),new h("ли",-1,1),new h("или",10,2),new h("ыли",10,2),new h("й",-1,1),new h("ей",13,2),new h("уй",13,2),new h("л",-1,1),new h("ил",16,2),new h("ыл",16,2),new h("ем",-1,1),new h("им",-1,2),new h("ым",-1,2),new h("н",-1,1),new h("ен",22,2),new h("ло",-1,1),new h("ило",24,2),new h("ыло",24,2),new h("но",-1,1),new h("ено",27,2),new h("нно",27,1),new h("ет",-1,1),new h("ует",30,2),new h("ит",-1,2),new h("ыт",-1,2),new h("ют",-1,1),new h("уют",34,2),new h("ят",-1,2),new h("ны",-1,1),new h("ены",37,2),new h("ть",-1,1),new h("ить",39,2),new h("ыть",39,2),new h("ешь",-1,1),new h("ишь",-1,2),new h("ю",-1,2),new h("ую",44,2)],s=[new h("а",-1,1),new h("ев",-1,1),new h("ов",-1,1),new h("е",-1,1),new h("ие",3,1),new h("ье",3,1),new h("и",-1,1),new h("еи",6,1),new h("ии",6,1),new h("ами",6,1),new h("ями",6,1),new h("иями",10,1),new h("й",-1,1),new h("ей",12,1),new h("ией",13,1),new h("ий",12,1),new h("ой",12,1),new h("ам",-1,1),new h("ем",-1,1),new h("ием",18,1),new h("ом",-1,1),new h("ям",-1,1),new h("иям",21,1),new h("о",-1,1),new h("у",-1,1),new h("ах",-1,1),new h("ях",-1,1),new h("иях",26,1),new h("ы",-1,1),new h("ь",-1,1),new h("ю",-1,1),new h("ию",30,1),new h("ью",30,1),new h("я",-1,1),new h("ия",33,1),new h("ья",33,1)],o=[new h("ост",-1,1),new h("ость",-1,1)],c=[new h("ейше",-1,1),new h("н",-1,2),new h("ейш",-1,1),new h("ь",-1,3)],m=[33,65,8,232],l=new g;function f(){for(;!l.in_grouping(m,1072,1103);){if(l.cursor>=l.limit)return!1;l.cursor++}return!0}function a(){for(;!l.out_grouping(m,1072,1103);){if(l.cursor>=l.limit)return!1;l.cursor++}return!0}function p(e,n){var r,t;if(l.ket=l.cursor,r=l.find_among_b(e,n)){switch(l.bra=l.cursor,r){case 1:if(t=l.limit-l.cursor,!l.eq_s_b(1,"а")&&(l.cursor=l.limit-t,!l.eq_s_b(1,"я")))return!1;case 2:l.slice_del()}return!0}return!1}function d(e,n){var r;return l.ket=l.cursor,!!(r=l.find_among_b(e,n))&&(l.bra=l.cursor,1==r&&l.slice_del(),!0)}function _(){return!!d(t,26)&&(p(w,8),!0)}function b(){var e;l.ket=l.cursor,(e=l.find_among_b(o,2))&&(l.bra=l.cursor,n<=l.cursor&&1==e&&l.slice_del())}this.setCurrent=function(e){l.setCurrent(e)},this.getCurrent=function(){return l.getCurrent()},this.stem=function(){return e=l.limit,n=e,f()&&(e=l.cursor,a()&&f()&&a()&&(n=l.cursor)),l.cursor=l.limit,!(l.cursor<e)&&(l.limit_backward=e,p(r,9)||(l.cursor=l.limit,d(i,2)||(l.cursor=l.limit),_()||(l.cursor=l.limit,p(u,46)||(l.cursor=l.limit,d(s,36)))),l.cursor=l.limit,l.ket=l.cursor,l.eq_s_b(1,"и")?(l.bra=l.cursor,l.slice_del()):l.cursor=l.limit,b(),l.cursor=l.limit,function(){var e;if(l.ket=l.cursor,e=l.find_among_b(c,4))switch(l.bra=l.cursor,e){case 1:if(l.slice_del(),l.ket=l.cursor,!l.eq_s_b(1,"н"))break;l.bra=l.cursor;case 2:if(!l.eq_s_b(1,"н"))break;case 3:l.slice_del()}}(),!0)}},function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}),e.Pipeline.registerFunction(e.ru.stemmer,"stemmer-ru"),e.ru.stopWordFilter=e.generateStopWordFilter("алло без близко более больше будем будет будете будешь будто буду будут будь бы бывает бывь был была были было быть в важная важное важные важный вам вами вас ваш ваша ваше ваши вверх вдали вдруг ведь везде весь вниз внизу во вокруг вон восемнадцатый восемнадцать восемь восьмой вот впрочем времени время все всегда всего всем всеми всему всех всею всю всюду вся всё второй вы г где говорил говорит год года году да давно даже далеко дальше даром два двадцатый двадцать две двенадцатый двенадцать двух девятнадцатый девятнадцать девятый девять действительно дел день десятый десять для до довольно долго должно другая другие других друго другое другой е его ее ей ему если есть еще ещё ею её ж же жизнь за занят занята занято заняты затем зато зачем здесь значит и из или им именно иметь ими имя иногда их к каждая каждое каждые каждый кажется как какая какой кем когда кого ком кому конечно которая которого которой которые который которых кроме кругом кто куда лет ли лишь лучше люди м мало между меля менее меньше меня миллионов мимо мира мне много многочисленная многочисленное многочисленные многочисленный мной мною мог могут мож может можно можхо мои мой мор мочь моя моё мы на наверху над надо назад наиболее наконец нам нами нас начала наш наша наше наши не него недавно недалеко нее ней нельзя нем немного нему непрерывно нередко несколько нет нею неё ни нибудь ниже низко никогда никуда ними них ничего но ну нужно нх о об оба обычно один одиннадцатый одиннадцать однажды однако одного одной около он она они оно опять особенно от отовсюду отсюда очень первый перед по под пожалуйста позже пока пор пора после посреди потом потому почему почти прекрасно при про просто против процентов пятнадцатый пятнадцать пятый пять раз разве рано раньше рядом с сам сама сами самим самими самих само самого самой самом самому саму свое своего своей свои своих свою сеаой себе себя сегодня седьмой сейчас семнадцатый семнадцать семь сих сказал сказала сказать сколько слишком сначала снова со собой собою совсем спасибо стал суть т та так такая также такие такое такой там твой твоя твоё те тебе тебя тем теми теперь тех то тобой тобою тогда того тоже только том тому тот тою третий три тринадцатый тринадцать ту туда тут ты тысяч у уж уже уметь хорошо хотеть хоть хотя хочешь часто чаще чего человек чем чему через четвертый четыре четырнадцатый четырнадцать что чтоб чтобы чуть шестнадцатый шестнадцать шестой шесть эта эти этим этими этих это этого этой этом этому этот эту я \ufeffа".split(" ")),e.Pipeline.registerFunction(e.ru.stopWordFilter,"stopWordFilter-ru")}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.stemmer.support.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.stemmer.support.js
new file mode 100644
index 0000000000..55fd4172c4
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.stemmer.support.js
@@ -0,0 +1 @@
+!function(r,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(r.lunr)}(this,function(){return function(r){r.stemmerSupport={Among:function(r,t,i,s){if(this.toCharArray=function(r){for(var t=r.length,i=new Array(t),s=0;s<t;s++)i[s]=r.charCodeAt(s);return i},!r&&""!=r||!t&&0!=t||!i)throw"Bad Among initialisation: s:"+r+", substring_i: "+t+", result: "+i;this.s_size=r.length,this.s=this.toCharArray(r),this.substring_i=t,this.result=i,this.method=s},SnowballProgram:function(){var b;return{bra:0,ket:0,limit:0,cursor:0,limit_backward:0,setCurrent:function(r){b=r,this.cursor=0,this.limit=r.length,this.limit_backward=0,this.bra=this.cursor,this.ket=this.limit},getCurrent:function(){var r=b;return b=null,r},in_grouping:function(r,t,i){if(this.cursor<this.limit){var s=b.charCodeAt(this.cursor);if(s<=i&&t<=s&&r[(s-=t)>>3]&1<<(7&s))return this.cursor++,!0}return!1},in_grouping_b:function(r,t,i){if(this.cursor>this.limit_backward){var s=b.charCodeAt(this.cursor-1);if(s<=i&&t<=s&&r[(s-=t)>>3]&1<<(7&s))return this.cursor--,!0}return!1},out_grouping:function(r,t,i){if(this.cursor<this.limit){var s=b.charCodeAt(this.cursor);if(i<s||s<t)return this.cursor++,!0;if(!(r[(s-=t)>>3]&1<<(7&s)))return this.cursor++,!0}return!1},out_grouping_b:function(r,t,i){if(this.cursor>this.limit_backward){var s=b.charCodeAt(this.cursor-1);if(i<s||s<t)return this.cursor--,!0;if(!(r[(s-=t)>>3]&1<<(7&s)))return this.cursor--,!0}return!1},eq_s:function(r,t){if(this.limit-this.cursor<r)return!1;for(var i=0;i<r;i++)if(b.charCodeAt(this.cursor+i)!=t.charCodeAt(i))return!1;return this.cursor+=r,!0},eq_s_b:function(r,t){if(this.cursor-this.limit_backward<r)return!1;for(var i=0;i<r;i++)if(b.charCodeAt(this.cursor-r+i)!=t.charCodeAt(i))return!1;return this.cursor-=r,!0},find_among:function(r,t){for(var i=0,s=t,e=this.cursor,n=this.limit,u=0,o=0,h=!1;;){for(var c=i+(s-i>>1),a=0,f=u<o?u:o,l=r[c],_=f;_<l.s_size;_++){if(e+f==n){a=-1;break}if(a=b.charCodeAt(e+f)-l.s[_])break;f++}if(a<0?(s=c,o=f):(i=c,u=f),s-i<=1){if(0<i||s==i||h)break;h=!0}}for(;;){if(u>=(l=r[i]).s_size){if(this.cursor=e+l.s_size,!l.method)return l.result;var m=l.method();if(this.cursor=e+l.s_size,m)return l.result}if((i=l.substring_i)<0)return 0}},find_among_b:function(r,t){for(var i=0,s=t,e=this.cursor,n=this.limit_backward,u=0,o=0,h=!1;;){for(var c=i+(s-i>>1),a=0,f=u<o?u:o,l=(_=r[c]).s_size-1-f;0<=l;l--){if(e-f==n){a=-1;break}if(a=b.charCodeAt(e-1-f)-_.s[l])break;f++}if(a<0?(s=c,o=f):(i=c,u=f),s-i<=1){if(0<i||s==i||h)break;h=!0}}for(;;){var _;if(u>=(_=r[i]).s_size){if(this.cursor=e-_.s_size,!_.method)return _.result;var m=_.method();if(this.cursor=e-_.s_size,m)return _.result}if((i=_.substring_i)<0)return 0}},replace_s:function(r,t,i){var s=i.length-(t-r);return b=b.substring(0,r)+i+b.substring(t),this.limit+=s,this.cursor>=t?this.cursor+=s:this.cursor>r&&(this.cursor=r),s},slice_check:function(){if(this.bra<0||this.bra>this.ket||this.ket>this.limit||this.limit>b.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),b.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.sv.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.sv.js
new file mode 100644
index 0000000000..6daf5f9d80
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.sv.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r,l,n;e.sv=function(){this.pipeline.reset(),this.pipeline.add(e.sv.trimmer,e.sv.stopWordFilter,e.sv.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sv.stemmer))},e.sv.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤff-stＡ-Ｚａ-ｚ",e.sv.trimmer=e.trimmerSupport.generateTrimmer(e.sv.wordCharacters),e.Pipeline.registerFunction(e.sv.trimmer,"trimmer-sv"),e.sv.stemmer=(r=e.stemmerSupport.Among,l=e.stemmerSupport.SnowballProgram,n=new function(){var n,t,i=[new r("a",-1,1),new r("arna",0,1),new r("erna",0,1),new r("heterna",2,1),new r("orna",0,1),new r("ad",-1,1),new r("e",-1,1),new r("ade",6,1),new r("ande",6,1),new r("arne",6,1),new r("are",6,1),new r("aste",6,1),new r("en",-1,1),new r("anden",12,1),new r("aren",12,1),new r("heten",12,1),new r("ern",-1,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",18,1),new r("or",-1,1),new r("s",-1,2),new r("as",21,1),new r("arnas",22,1),new r("ernas",22,1),new r("ornas",22,1),new r("es",21,1),new r("ades",26,1),new r("andes",26,1),new r("ens",21,1),new r("arens",29,1),new r("hetens",29,1),new r("erns",21,1),new r("at",-1,1),new r("andet",-1,1),new r("het",-1,1),new r("ast",-1,1)],s=[new r("dd",-1,-1),new r("gd",-1,-1),new r("nn",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1),new r("tt",-1,-1)],a=[new r("ig",-1,1),new r("lig",0,1),new r("els",-1,1),new r("fullt",-1,3),new r("löst",-1,2)],o=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,24,0,32],u=[119,127,149],m=new l;this.setCurrent=function(e){m.setCurrent(e)},this.getCurrent=function(){return m.getCurrent()},this.stem=function(){var e,r=m.cursor;return function(){var e,r=m.cursor+3;if(t=m.limit,0<=r||r<=m.limit){for(n=r;;){if(e=m.cursor,m.in_grouping(o,97,246)){m.cursor=e;break}if(m.cursor=e,m.cursor>=m.limit)return;m.cursor++}for(;!m.out_grouping(o,97,246);){if(m.cursor>=m.limit)return;m.cursor++}(t=m.cursor)<n&&(t=n)}}(),m.limit_backward=r,m.cursor=m.limit,function(){var e,r=m.limit_backward;if(m.cursor>=t&&(m.limit_backward=t,m.cursor=m.limit,m.ket=m.cursor,e=m.find_among_b(i,37),m.limit_backward=r,e))switch(m.bra=m.cursor,e){case 1:m.slice_del();break;case 2:m.in_grouping_b(u,98,121)&&m.slice_del()}}(),m.cursor=m.limit,e=m.limit_backward,m.cursor>=t&&(m.limit_backward=t,m.cursor=m.limit,m.find_among_b(s,7)&&(m.cursor=m.limit,m.ket=m.cursor,m.cursor>m.limit_backward&&(m.bra=--m.cursor,m.slice_del())),m.limit_backward=e),m.cursor=m.limit,function(){var e,r;if(m.cursor>=t){if(r=m.limit_backward,m.limit_backward=t,m.cursor=m.limit,m.ket=m.cursor,e=m.find_among_b(a,5))switch(m.bra=m.cursor,e){case 1:m.slice_del();break;case 2:m.slice_from("lös");break;case 3:m.slice_from("full")}m.limit_backward=r}}(),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}),e.Pipeline.registerFunction(e.sv.stemmer,"stemmer-sv"),e.sv.stopWordFilter=e.generateStopWordFilter("alla allt att av blev bli blir blivit de dem den denna deras dess dessa det detta dig din dina ditt du där då efter ej eller en er era ert ett från för ha hade han hans har henne hennes hon honom hur här i icke ingen inom inte jag ju kan kunde man med mellan men mig min mina mitt mot mycket ni nu när någon något några och om oss på samma sedan sig sin sina sitta själv skulle som så sådan sådana sådant till under upp ut utan vad var vara varför varit varje vars vart vem vi vid vilka vilkas vilken vilket vår våra vårt än är åt över".split(" ")),e.Pipeline.registerFunction(e.sv.stopWordFilter,"stopWordFilter-sv")}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.th.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.th.js
new file mode 100644
index 0000000000..ee8ef373a0
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/lunr.th.js
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(t){if(void 0===t)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===t.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==t.version[0];t.th=function(){this.pipeline.reset(),this.pipeline.add(t.th.trimmer),i?this.tokenizer=t.th.tokenizer:(t.tokenizer&&(t.tokenizer=t.th.tokenizer),this.tokenizerFn&&(this.tokenizerFn=t.th.tokenizer))},t.th.wordCharacters="[฀-๿]",t.th.trimmer=t.trimmerSupport.generateTrimmer(t.th.wordCharacters),t.Pipeline.registerFunction(t.th.trimmer,"trimmer-th");var n=t.wordcut;n.init(),t.th.tokenizer=function(e){if(!arguments.length||null==e||null==e)return[];if(Array.isArray(e))return e.map(function(e){return i?new t.Token(e):e});var r=e.toString().replace(/^\s+/,"");return n.cut(r).split("|")}}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/lunr.tr.js b/docs/v1.2.0/_static/javascripts/lunr/lunr.tr.js
new file mode 100644
index 0000000000..e8fb5a7df6
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herkesin hiç hiçbir iki ile ilgili ise itibaren itibariyle için işte kadar karşın katrilyon kendi kendilerine kendini kendisi kendisine kendisini kez ki kim kimden kime kimi kimse kırk milyar milyon mu mü mı nasıl ne neden nedenle nerde nerede nereye niye niçin o olan olarak oldu olduklarını olduğu olduğunu olmadı olmadığı olmak olması olmayan olmaz olsa olsun olup olur olursa oluyor on ona ondan onlar onlardan onları onların onu onun otuz oysa pek rağmen sadece sanki sekiz seksen sen senden seni senin siz sizden sizi sizin tarafından trilyon tüm var vardı ve veya ya yani yapacak yapmak yaptı yaptıkları yaptığı yaptığını yapılan yapılması yapıyor yedi yerine yetmiş yine yirmi yoksa yüz zaten çok çünkü öyle üzere üç şey şeyden şeyi şeyler şu şuna şunda şundan şunları şunu şöyle".split(" ")),r.Pipeline.registerFunction(r.tr.stopWordFilter,"stopWordFilter-tr")}});
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/lunr/tinyseg.js b/docs/v1.2.0/_static/javascripts/lunr/tinyseg.js
new file mode 100644
index 0000000000..45bffe0923
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/lunr/tinyseg.js
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e=0;e<1;e++)words="ก.ก.\nก.ก.น.\nก.ข.ค.\nก.ค.\nก.จ.\nก.ช.น.\nก.ฌ.\nก.ต.\nก.ต.ง.\nก.ต.ช.\nก.ตร.\nก.ท.\nก.น.ช.\nก.บช.\nก.บถ.\nก.ป.ส.\nก.พ.\nก.ม.\nก.ย.\nก.ร.\nก.ล.ต.\nก.ว.\nก.ศ.ว.\nก.ส.ท.\nก.ส.ธ.\nก.ส.อ.\nก.อ.\nกก.ตชด.\nกก.ตร.น.\nกก.ภ.จว.\nกก.รสช.\nกกบ.ขส.ทบ.\nกกล.รพน.\nกง.กห.\nกง.ทบ.\nกง.ทร.\nกซข.ป.\nกซม.ป.\nกทม.กรุงเทพมหานคร\nกบ.ทบ.\nกป.สป.\nกพ.ทบ.\nกพ.ทร.\nกพ.ทหาร\nกร.ทบ.\nกรป.กลาง\nกรอ.พอ.\nกศ.ด.\nกศ.บ.\nกศ.บป.\nกศ.ม.\nกษ.ด.\nกษ.บ.\nกษ.ม.\nกส.ด.\nกส.ทบ.\nกส.บ.\nกส.ม.\nกอ.ปค.\nกอ.รพน.\nกอ.รมน.\nกอ.รสต.\nข.ต.ว.\nขว.ทบ.\nขว.ทร.\nขว.ทหาร\nขส.ทบ.\nขส.ทร.\nขส.ทอ.\nค.ด.\nค.บ.\nค.พ.ศ.\nค.ม.\nค.ร.น.\nค.ร.ฟ.\nค.ร.ม.\nค.ศ.\nค.อ.ด.\nค.อ.บ.\nค.อ.ม.\nคศ.ด.\nคศ.บ.\nคศ.ม.\nง.ด.\nจ.จ.\nจ.จ.จ.\nจ.ช.\nจ.ต.\nจ.ท.\nจ.ป.ร.\nจ.ม.\nจ.ศ.\nจ.ส.ต.\nจ.ส.ท.\nจ.ส.อ.\nจ.อ.\nจ.อ.ร.\nจ.๑๘\nจก.ธน.\nจก.สน.\nช.ค.\nช.ค.บ.\nช.พ.ค.\nช.ส.\nช.ส.ค.\nฌ.ป.ค.\nฌ.ศ.ร.\nฌ.ส.อ.\nฐท.สห.\nด.ช.\nด.ญ.\nด.ต.\nด.ศ.ค.\nด.ศ.ร.\nดย.ทร.\nต.ก.\nต.ค.\nต.จ.\nต.จ.ว.\nต.ช.\nต.ต.\nต.บ.\nต.ม.\nต.ร.\nต.ศ.ร.\nต.ห.\nต.อ.\nต.อ.จ.\nตร.กม.\nตร.ซม.\nตร.ต.\nตร.ทล.\nตร.น.\nตร.ปม.\nตร.ภ.\nตร.ม.\nตร.รฟ.\nตร.ว.\nตร.ส.\nตร.สข.\nท.จ.\nท.จ.ว.\nท.ช.\nท.ญ.\nท.ด.\nท.ท.ท.\nท.ทบ.\nท.บ.\nท.พ.\nท.ม.\nท.ศ.\nทก.ด.\nทก.บ.\nทก.ม.\nทส.ปช.\nทส.รมว.กห.\nทุ.ส.นิ.ม.\nธ.ก.ส.\nธ.ค.\nธ.ญ\nธ.บ.\nน.ช.\nน.ญ.\nน.ด.\nน.ต.\nน.ท.\nน.น.\nน.บ.\nน.บ.ท.\nน.ป.ท.\nน.พ.\nน.ม.\nน.ร.\nน.ว.\nน.ศ.\nน.ส.\nน.ส.พ.\nน.ส.๓\nน.สพ.\nน.อ.\nนปพ.ภ.\nนศ.ด.\nนศ.บ.\nนศ.ม.\nบ.ก.\nบ.ข.ส.\nบ.ช.\nบ.ด.ท.\nบ.ตร.\nบ.ภ.\nบ.ม.\nบก.จร.\nบก.ตชด.\nบก.ตม.\nบก.ทล.\nบก.น.\nบก.ป.\nบก.ปค.\nบก.ปม.\nบก.ภ.เขต\nบก.รน.\nบก.รฟ.\nบก.ร้อย.ตชด.\nบก.ส.\nบกข.ป.\nบจพ.ป.\nบช.ก.\nบช.ด.\nบช.ตชด.\nบช.น.\nบช.บ.\nบช.ปส.\nบช.ภ.\nบช.ม.\nบชท.ป.\nบชน.ป.\nบชส.ป.\nบธ.ด.\nบธ.บ.\nบธ.ม.\nบนท.ป.\nบนอ.ป.\nบปช.ป.\nป.กท.\nป.กศ.\nป.กศ.สูง\nป.จ.\nป.จ.ว.\nป.ช.\nป.ธ.\nป.ป.\nป.ป.ก.\nป.ป.ช.\nป.ป.ป.\nป.ป.ร.\nป.ป.ส.\nป.พ.\nป.พ.พ.\nป.พย.\nป.ม.\nป.ม.ก.\nป.ม.ช.\nป.ม.ธ.\nป.ม.ศ.\nป.ม.อ.\nป.ร.ร.๔\nป.ร.ร.๕\nป.ร.ร.๖\nป.ล.\nป.ว.พ.\nป.วิ.อ.\nป.ส.ส.\nป.อ.\nป.อ.ร.ส.\nป.๑\nปม.วส.\nปอ.พ.\nผกก.ภ.\nผช.ผอ.\nผต.มท.\nผบ.ตร.\nผบ.ทบ.\nผบ.ทร.\nผบ.ทสส.\nผบ.ทอ.\nผบก.น.\nผบก.ป.\nผบก.ปค.\nผบก.ปม.\nผบก.ภ.\nผบช.ก.\nผบช.ตชด.\nผบช.น.\nผบช.ภ.\nผว.กทม.\nผอ.ปจ.\nพ.ก.ง.\nพ.กศ.\nพ.ข.ต.\nพ.ค.\nพ.ค.ช.\nพ.ค.ว.\nพ.ค.ศ.\nพ.จ.ต.\nพ.จ.ท.\nพ.จ.อ.\nพ.ช.\nพ.ช.ค.\nพ.ด.\nพ.ต.\nพ.ต.ต.\nพ.ต.ท.\nพ.ต.อ.\nพ.ต.อ.พิเศษ\nพ.ท.\nพ.บ.\nพ.ป.\nพ.ภ.ม.\nพ.ม.\nพ.ม.ช.\nพ.ย.\nพ.ร.ก.\nพ.ร.ฎ.\nพ.ร.ต.\nพ.ร.ธ.\nพ.ร.บ.\nพ.ศ.\nพ.ศ.บ.\nพ.ส.ร.\nพ.ส.ล.\nพ.อ.\nพ.อ.ต.\nพ.อ.ท.\nพ.อ.พิเศษ\nพ.อ.อ.\nพณ.ด.\nพณ.บ.\nพณ.ม.\nพธ.ด.\nพธ.บ.\nพธ.ม.\nพบ.ด.\nพบ.บ.\nพบ.ม.\nพย.ด.\nพย.บ.\nพย.ม.\nพล.จ.\nพล.ต.\nพล.ต.จ.\nพล.ต.ต.\nพล.ต.ท.\nพล.ต.อ.\nพล.ท.\nพล.ปตอ.\nพล.ม.\nพล.ม.๒\nพล.ร.จ.\nพล.ร.ต.\nพล.ร.ท.\nพล.ร.อ.\nพล.อ.\nพล.อ.จ.\nพล.อ.ต.\nพล.อ.ท.\nพล.อ.อ.\nพลา.ทร.\nพศ.ด.\nพศ.บ.\nพศ.ม.\nพอ.สว.\nภ.ง.ด.\nภ.ง.ด.๙\nภ.ด.\nภ.บ.\nภ.บ.ท.๕\nภ.ป.ร.\nภ.พ.\nภ.ม.\nภ.สถ.บ.\nม.ค.\nม.จ.\nม.ป.ท.\nม.ป.ป.\nม.ป.พ.\nม.ร.ว.\nม.ศ.\nม.อ.\nม.อ.ปัตตานี\nมิ.ย.\nมี.ค.\nยศ.ทบ.\nยศ.ทร.\nยศ.ทอ.\nร.ง.\nร.ด.\nร.ต.\nร.ต.ต.\nร.ต.ท.\nร.ต.อ.\nร.ท.\nร.น.\nร.บ.\nร.พ.\nร.ฟ.ล.\nร.ย.ล.\nร.ย.ส.ท.\nร.ล.\nร.ศ.\nร.ส.พ.\nร.อ.\nรป.ม.\nรร.จปร.\nรร.จอ.\nรร.ชท.\nรร.ตท.\nรร.นร.\nรร.นรต.\nรร.นอ.\nล.ญ.\nล.ว.\nลส.ชบ.\nว.ค.\nว.ฉ.\nว.ช.\nว.ด.ป.\nว.ป.ถ.\nวท.บ.\nศ.บ.\nศ.ป.ก.\nศ.ศ.ป.\nศฝร.ภ.\nศศ.บ.\nศษ.บ.\nศส.บ.\nส.ก.\nส.ก.ศ.ท.\nส.ค.\nส.ค.1\nส.ค.ร.\nส.ค.ส.\nส.ต.\nส.ต.ต.\nส.ต.ท.\nส.ต.อ.\nส.ท.\nส.ทร.\nส.ป.ช.\nส.ป.ส.ท.\nส.ป.อ.\nส.ร.\nส.ล.น.\nส.ว.\nส.ว.ท.\nส.ว.ส.ท.\nส.ส.\nส.ส.ท.\nส.ส.ร.\nส.ห.\nส.อ.\nสถ.บ.\nสนง.สสอ.\nสพ.ญ.\nสพ.บ.\nสว.จร.\nสว.ธร.\nสว.ส.\nสว.สป.\nสว.สส.\nสว.อก.\nสส.บ.\nสุ.จิ.ปุ.ลิ.\nห.ร.ม.\nอ.ก.ค.\nอ.ก.จ.\nอ.จ.\nอ.ช.พ.\nอ.ตร.\nอ.บ.\nอ.ส.ท.\nอ.ส.ม.ท.\nอ.ส.ย.\nอ.อ.ป.\nอส.รด.\nอุ.อา.ก.ส.\nฮ.จ.\nฮ.ท.\nฮ.ฝ.\nฮ.ล.\nฮ.ศ.\nเม.ย.\n\nกรีนิช\nกลันตัน\nกัลกัตตา\nกัวลาลัมเปอร์\nกัศมีร์\nกาฐมาณฑุ\nโกลกาตา\nควิเบก\nคอนเนตทิคัต\nคาบูล\nคุชราต\nคุนหมิง\nเคนตักกี\nเคนทักกี\nเคมบริดจ์\nแคชเมียร์\nแคนซัส\nแคนเบอร์รา\nแคโรไลนา\nแคลิฟอร์เนีย\nโคเปนเฮเกน\nโคลัมโบ\nโคโลราโด\nไครสต์เชิร์ช\nไคโร\nจาการ์ตา\nจำปาศักดิ์\nเจนไน\nเจนีวา\nเจ้อเจียง\nฉงชิ่ง\nเฉิงตู\nชานตง\nชิคาโก\nเชนไน\nเชอร์โนบิล\nซัปโปโร\nซานมารีโน\nซาบาห์\nซาราเยโว\nซาราวัก\nซิดนีย์\nซีอาน\nซีแอตเทิล\nซูริก\nซูริค\nเซเชลส์\nเซนได\nเซี่ยงไฮ้\nโซเฟีย\nโซล\nโซโลมอน\nไซ่ง่อน\nไซบีเรีย\nดัลลัส\nดาโคตา\nดานัง\nดีทรอยต์\nดูไบ\nเดนเวอร์\nเดลาแวร์\nเดียนเบียนฟู\nโดเวอร์\nโดฮา\nไดฟุกุ\nไดฟูกุ\nตรังกานู\nตริโปลี\nตูวาลู\nเตหะราน\nโตเกียว\nโตรอนโต\nทมิฬนาฑู\nทริโปลี\nทิเบต\nเทกซัส\nเท็กซัส\nเทนเนสซี\nเทลอาวีฟ\nแทสเมเนีย\nโทรอนโต\nไทเป\nธากา\nนางาซากิ\nนาริตะ\nนิวเจอร์ซีย์\nนิวเดลี\nนิวยอร์ก\nนิวยอร์ค\nนิวแฮมป์เชียร์\nเนบราสกา\nเนแบรสกา\nเนวาดา\nบรัสเซลส์\nบราซิเลีย\nบอมเบย์\nบอสตัน\nบังกาลอร์\nบังคาลอร์\nบูคาเรสต์\nบูดาเปสต์\nเบงกาซี\nเบนกาซี\nเบรุต\nเบลเกรด\nเบอร์ลิน\nแบกแดด\nปอยเปต\nปะลิส\nปะหัง\nปักกิ่ง\nปัญจาบ\nปัฏนา\nปารีส\nปีนัง\nเประ\nเปียงยาง\nพนมเปญ\nพระตะบอง\nพะโค\nพะสิม\nพาราณสี\nพิหารี\nเพนซิลวาเนีย\nเพนซิลเวเนีย\nฟรานซ์\nฟลอริดา\nฟิลาเดลเฟีย\nฟุกุชิมะ\nฟุกุชิมา\nฟุกุโอกะ\nฟูกูโอกะ\nแฟรงก์เฟิร์ต\nมอสโก\nมะนิลา\nมะละแหม่ง\nมัณฑะเลย์\nมัทราส\nมาดริด\nมิชิแกน\nมินนิโซตา\nมิยางิ\nมิลาน\nมิวนิก\nมิสซูรี\nมุมไบ\nเมน\nเมลเบิร์น\nเมาะตะมะ\nเมาะลำเลิง\nแมนจูเรีย\nแมนเชสเตอร์\nแมนฮัตตัน\nแมริแลนด์\nแมรีแลนด์\nแมสซาชูเซตส์\nยะไข่\nย่างกุ้ง\nยูทาห์\nยูนนาน\nเยรูซาเล็ม\nโยโกฮามา\nริยาด\nรีโอเดจาเนโร\nโรดไอแลนด์\nลอนดอน\nลอสแองเจลิส\nลาปาซ\nลาสเวกัส\nลิสบอน\nลุยเซียนา\nโลซาน\nโลซานน์\nวอชิงตัน\nวอร์ซอ\nวิสคอนซิน\nเวนิส\nเวลส์\nเวอร์จิเนีย\nเวอร์มอนต์\nเวียงจันทน์\nเวียนนา\nแวนคูเวอร์\nไวโอมิง\nสกอตแลนด์\nสก็อตแลนด์\nสตอกโฮล์ม\nสลังงอร์\nเสฉวน\nเสียมราฐ\nเสียมเรียบ\nหงสา\nหงสาวดี\nหนานไห่\nหลวงพระบาง\nหูเป่ย\nหูเป่ย์\nหูหนาน\nเหอเป่ย\nเหอเป่ย์\nเหอหนาน\nอชันตา\nอลาสกา\nอวันตี\nออริกอน\nออสโล\nอะแลสกา\nอัตตะปือ\nอัมมาน\nอัมสเตอร์ดัม\nอัสสัม\nอาบูดาบี\nอาร์คันซอ\nอินเดียนา\nอิบารากิ\nอิลลินอยส์\nอิสตันบูล\nอิสลามาบัด\nอุรุมชี\nอูลานบาตอร์\nเอดินบะระ\nเอเธนส์\nแอตแลนตา\nแอริโซนา\nแอลเจียร์\nโอคลาโฮมา\nโอค็อตสค์\nโอกินาวา\nโอซากา\nโอริสสา\nโอเรกอน\nโอไฮโอ\nไอดาโฮ\nไอโอวา\nฮอนโนลูลู\nฮานอย\nฮาเนดะ\nฮาราเร\nฮาวาย\nฮิโรชิมา\nฮุสตัน\nเฮลซิงกิ\n\nมกรา\nกุมภา\nมีนา\nเมษา\nพฤษภา\nมิถุนา\nกรกฎา\nสิงหา\nกันยา\nตุลา\nพฤศจิกา\nธันวา\nเอ\nบี\nซี\nดี\nอี\nเอฟ\nจี\nเอช\nไอ\nเจ\nเค\nแอล\nเอ็ม\nเอ็น\nโอ\nพี\nคิว\nอาร์\nเอส\nที\nยู\nวี\nดับเบิล\nดับบลิว\nเอ็กซ์\nเอ๊กซ์\nวาย\nแซด\nแอลฟา\nแอลฟ่า\nเบตา\nเบต้า\nแกมมา\nแกมม่า\nเดลตา\nเดลต้า\nโอเมกา\nโอเมก้า\nเมกะ\nกิกะ\nนาโน\nไมโคร\n\nกรรมาชน\nกรอบรูป\nกระดี๊กระด๊า\nกระบับ\nกราวนด์\nกรีน\nกรุ๊ป\nกฤษณ์\nกลาส\nก๊วน\nกษัตริยา\nกษัตริยาธิราช\nก่อนหน้า\nกะบับ\nกับดัก\nกัมมันตะ\nก๊าก\nก๋ากั่น\nกาญจน์\nกาญจนาภิเษก\nกามิกาเซ่\nการันตี\nกาหลิบ\nกิฟท์\nกิมจิ\nกีวี\nกึ๊ก\nกึ๋ย\nกุนซือ\nกุมภาพันธ์\nกู๋\nเกจิ\nเกมส์\nเกย์\nเกรด\nเกรย์\nเกสต์เฮาส์\nเก๊ะ\nเก๋ากี้\nเกิร์ล\nแกงค์\nแกรนด์\nแกสโซฮอล์\nแก๊สโซฮอล์\nโกเต็กซ์\nโกลด์\nโกะ\nโก๊ะ\nไกด์\nขั้นตอน\nเขวี้ยง\nคณาญาติ\nครัวซอง\nครัวซองต์\nคร่ำครวญ\nครีเอทีฟ\nครูเสด\nคลับ\nคลาสสิก\nคลิตอริส\nคลิป\nความหมาย\nควิก\nควีน\nคองเกรส\nคอนซูเมอร์\nคอนเซปต์\nคอนเซ็ปต์\nคอนโด\nคอนโดมิเนียม\nคอนเทนเนอร์\nคอนแทค\nคอนแท็ค\nคอนโทรล\nคอนเฟิร์ม\nคอปเตอร์\nคอมพ์\nคอมเพล็กซ์\nคอมมอนส์\nคอมเมนท์\nคอมเมนต์\nคอร์ป\nคอร์ปอเรชั่น\nคอร์รัปชัน\nคอร์รัปชั่น\nคอรัปชัน\nคอรัปชั่น\nคอร์ส\nคอลเล็กชั่น\nคอลัมน์\nคอลัมนิสต์\nคัตเอาต์\nคันคาก\nคันถธุระ\nคันธาระ\nคันยิ\nคัสตาร์ด\nคาราโอเกะ\nคีตกวี\nคีตปฏิภาณ\nคีตราชัน\nคาปูชิโน\nคามิคาเซ่\nคาเฟ่\nคาร์\nคาร์โก้\nคาราเมล\nคาแรกเตอร์\nคาแร็กเตอร์\nคาแรคเตอร์\nคาแร็คเตอร์\nคาวบอย\nคาสิโน\nคิกขุ\nคิวบิก\nคูลเลอร์\nเคบับ\nเครป\nเคลม\nเคลียร์\nเคลื่อนย้าย\nเคส\nเคอร์ฟิว\nแคชเชียร์\nแคทวอล์ค\nแคนดิเดต\nแคนตาลูป\nแคนยอน\nแคนู\nแคป\nแคมป์\nแคมปัส\nแคมเปญ\nแคร์\nแครกเกอร์\nแคร็กเกอร์\nแครอท\nแคสต์\nแคสติง\nแคสติ้ง\nโค้ก\nโค้ช\nโคโยตี\nโคโยตี้\nโครนา\nโคอะล่า\nโคอาลา\nโคอาล่า\nไคลแมกซ์\nไคลแม็กซ์\nงั้น\nง่าว\nงี้\nเง็ง\nโง่เขลา\nไง\nจตุคาม\nจ๊อกกี้\nจอหงวน\nจังโก้\nจัมโบ้\nจ๊าบ\nจารกรรม\nจารชน\nจิ๊ก\nจิ๊กโก๋\nจิ๊กซอว์\nจิตพิสัย\nจิตเภท\nจีดีพี\nจึ๊ก\nจุ๊ย\nจูน\nจูเนียร์\nเจ๊\nเจได\nเจ็ต\nเจล\nเจ๊าะแจ๊ะ\nเจี๊ยว\nแจ็กเก็ต\nแจ๊กเก็ต\nแจ็กพอต\nแจ็กพ็อต\nแจ๊กพอต\nแจ๊กพ็อต\nแจม\nแจ๊ส\nโจ๋\nฉลุย\nเฉิ่ม\nชนะเลิศ\nช็อค\nช็อต\nช็อป\nช็อปปิ้ง\nช็อปเปอร์\nชะโนด\nชัตเตอร์\nชัวร์\nชาร์จ\nชาร์ต\nชาร์ป\nชินบัญชร\nชิฟฟอน\nชีส\nชีอะห์\nเช็ก\nเช็งเม้ง\nเชฟ\nเชลียร์\nเชอร์รี่\nแชเชือน\nแช่แข็ง\nแชมป์\nแชมปิยอง\nแชมเปญ\nแชมเปี้ยน\nแชมพู\nโชว์รูม\nโชห่วย\nใช้งาน\nไชน่า\nซ้อ\nซอมบี้\nซะ\nซังเต\nซันตาคลอส\nซัพพลาย\nซัพพลายเออร์\nซัมเมอร์\nซากุระ\nซาดิสต์\nซาดิสม์\nซาตาน\nซานตาคลอส\nซาฟารี\nซาบะ\nซามูไร\nซาร์\nซาร์ดีน\nซาเล้ง\nซิง\nซิ่ง\nซิงเกิล\nซิตี\nซิตี้\nซินโดรม\nซิม\nซิ้ม\nซิมโฟนี\nซิมโฟนี่\nซิลเวอร์\nซี้\nซี้ซั้ว\nซีดาน\nซีน\nซีนีเพล็กซ์\nซีเนียร์\nซีร็อกซ์\nซีรีส์\nซีเรียส\nซีอีโอ\nซื่อบื้อ\nซุนหนี่\nซุปเปอร์\nซูชิ\nซูเปอร์\nซูม\nซูโม่\nซูเอี๋ย\nซูฮก\nเซ็กซ์\nเซ็กซี่\nเซ็กส์\nเซนเซอร์\nเซ็นเซอร์\nเซนเตอร์\nเซ็นเตอร์\nเซ็นทรัล\nเซนส์\nเซ่นไหว้\nเซฟตี้\nเซรามิก\nเซลส์\nเซลส์แมน\nเซอร์\nเซอร์ไพรส์\nเซอร์วิส\nเซาท์\nเซี้ยว\nแซ็ก\nแซกโซโฟน\nแซ็กโซโฟน\nแซนด์วิช\nแซมบ้า\nแซลมอน\nแซว\nโซเชียล\nโซน\nโซนี่\nโซลาร์\nโซโล\nโซโล่\nญาณทัสสนะ\nดยุก\nดยุค\nดร็อป\nดรัมเมเยอร์\nดรามา\nดราม่า\nดอกเตอร์\nด็อกเตอร์\nดัมพ์\nดั๊มพ์\nดาวน์\nดิกชันนารี\nดิสเครดิต\nดีกรี\nดีเจ\nดีไซน์\nดีไซน์เนอร์\nดีไซเนอร์\nดีเบต\nดีพาร์ตเมนต์\nดีพาร์ตเมนท์\nดีพาร์ทเมนต์\nดีพาร์ทเมนท์\nดีมานด์\nดีล\nดีลเลอร์\nดีเลย์\nเดชานุภาพ\nเดบิต\nเดโม\nเดย์\nเด้อ\nเดอะ\nเด๊ะ\nเดี้ยง\nเดี๊ยะ\nแดนซ์\nแดนเซอร์\nแดรี่\nโดนัท\nโดมิโน\nโดรายากิ\nไดเอ็ต\nตถตา\nตนเอง\nตรวจทาน\nตรวจสอบ\nตอกย้ำ\nต๊อง\nต่อยอด\nต่อรอง\nตะหงิด\nตังค์\nตันเถียน\nตัวตน\nตัวเอง\nตาปรือ\nต้าอ่วย\nติงต๊อง\nติ๋ม\nติ่มซำ\nติว\nติวเตอร์\nตี๋\nตื้บ\nตุ๊ก\nตุ๊กตุ๊ก\nตุ๊ด\nตุ๋ย\nตู้เซฟ\nเต๊ะ\nเตี๊ยม\nแตงกวา\nแตงโม\nแต๋ว\nโต๋เต๋\nโต๊ะจีน\nไตรมาส\nถ่ายทำ\nถูกต้อง\nทงคัตสึ\nทริป\nทรู\nทอม\nท็อป\nทอร์นาโด\nทอล์ค\nทักซิโด\nทันตกรรม\nทันตแพทยศาสตร์\nทับซ้อน\nทัวร์\nทัวร์นาเมนต์\nทัวร์นาเมนท์\nทัวริสต์\nทาเลนต์\nทาวน์\nทาวน์เฮาส์\nทำงาน\nทิป\nทิพยสมบัติ\nทิวลิป\nทีรามิสุ\nทีวี\nทูน่า\nเท็กซ์\nเทค\nเทคโน\nเทคโนแครต\nเทควันโด\nเทป\nเทรด\nเทรนด์\nเทรนเนอร์\nเทรลเลอร์\nเทรลเล่อร์\nเทเลกราฟ\nเทวบัญชา\nเทวบุตร\nเทวา\nเทวาธิราช\nเทโวโรหนะ\nเทอร์โบ\nเที่ยงคืน\nเที่ยงวัน\nเทียมทาน\nแทกติค\nแทคติค\nแทงกั๊ก\nแทงโก้\nโทมาฮอก\nโทมาฮอว์ก\nโทมาฮอว์ค\nโทร\nโทรโข่ง\nไทม์\nไทยแลนด์\nไทเฮา\nธรรมา\nธรรมาภิบาล\nธัมโม\nธีม\nธุรกรรม\nธุหร่ำ\nเธค\nนพมาศ\nนรีแพทย์\nน็อก\nน็อค\nน้องใหม่\nนอมินี\nนอร์ท\nน่ะ\nนางแบบ\nนาฏยศาลา\nนายแบบ\nนายพราน\nนินจา\nนิรันดร์\nนิว\nนิวส์\nนู้ด\nเนอะ\nเนิร์สเซอรี\nเนิร์สเซอรี่\nเนี้ยบ\nโนติส\nไนท์\nไนน์\nบรรพชน\nบร็อกโคลี\nบร็อคโคลี\nบรา\nบริกร\nบริวเวอรี่ส์\nบลอนด์\nบลูเบอร์รี\nบลูเบอร์รี่\nบ๊วย\nบอกซ์\nบ็อกซ์\nบ๊อกซ์\nบอดี้\nบอนด์\nบ๊อบ\nบอมบ์\nบ๋อย\nบอยคอต\nบอยคอตต์\nบอร์ด\nบังเกอร์\nบัตเตอร์\nบัลลาสต์\nบัส\nบาบูน\nบาร์บีคิว\nบาร์บี้\nบาลานซ์\nบิ๊ก\nบิล\nบึม\nบึ้ม\nบุญคุณ\nบุ๋น\nบุปผา\nบู๊\nบูชิโด\nบูติก\nบูติค\nบูม\nเบเกอรี่\nเบญจมบพิตร\nเบตาดีน\nเบนโตะ\nเบนโล\nเบบี้\nเบลอ\nเบอร์เกอร์\nเบอร์รี\nเบิร์ด\nเบิร์น\nแบ็กโฮ\nแบคโฮ\nแบด\nแบต\nแบนเนอร์\nแบรนด์\nแบล็ก\nแบล็ค\nไบโอ\nโบกี้\nโบตั๋น\nโบ้ย\nโบรกเกอร์\nโบรชัวร์\nโบว์\nโบว์ลิ่ง\nไบเบิล\nปฏิสัมพันธ์\nป๊อก\nปอดแหก\nป๊อป\nป๋อหลอ\nปักขคณนา\nปัจเจกชน\nปัจฉิมนิเทศ\nป๊า\nป๋า\nป่าไม้\nปาร์ตี้\nปาสกาล\nปาสคาล\nปาสเตอร์\nปิกอัพ\nปิ๊ง\nปิโตรเคมี\nปิยมิตร\nปึ้ก\nปูอัด\nเปโซ\nเป็นไง\nเปปเปอร์มินต์\nเปเปอร์\nเปราะบาง\nเป๊ะ\nเป่ายิงฉุบ\nเป่ายิ้งฉุบ\nเปียโน\nแป้ก\nแป๋ว\nแป๊ะเจี๊ยะ\nโปร\nโปรเจกต์\nโปรเจ็กต์\nโปรเจกเตอร์\nโปรเจ็กเตอร์\nโปรเจคท์\nโปรเจ็คท์\nโปรดักชั่น\nโปรดิวเซอร์\nโปรโมชั่น\nโปรโมต\nโปรโมเตอร์\nโปรโมท\nโปลิศ\nโปสเตอร์\nผลไม้\nผลักดัน\nผ้าห่ม\nผิดพลาด\nผู้นำ\nแผดเผา\nเฝอ\nพงษ์\nพริตตี้\nพรีเซนต์\nพรีเซ็นเตอร์\nพรีเมียม\nพรีเมียร์\nพฤหัส\nพล็อต\nพลาซ่า\nพลานุภาพ\nพ่อค้า\nพอเพียง\nพะเรอ\nพันธกิจ\nพันธุวิศวกรรม\nพาร์\nพาร์ตเนอร์\nพาร์ทเนอร์\nพาวเวอร์\nพาสเจอร์ไรส์\nพาสตา\nพาสต้า\nพาสปอร์ต\nพาเหรด\nพิซซ่า\nพีเรียด\nพุดดิ้ง\nพุทธภูมิ\nพุทธศตวรรษ\nพุทโธ\nพูล\nเพทนาการ\nเพนกวิน\nเพนตากอน\nเพรส\nเพรียวบาง\nเพลซ\nเพลท\nเพลย์บอย\nเพียบแปร้\nเพียว\nเพาเวอร์\nแพกเกจ\nแพ็ค\nแพตเทิร์น\nแพทเทิร์น\nแพทยสภา\nแพนงเชิญ\nแพนดา\nแพนด้า\nแพลน\nโพลล์\nโพลารอยด์\nโพสต์\nไพลิน\nฟยอร์ด\nฟรังก์\nฟรุต\nฟลอร์\nฟลุก\nฟลุค\nฟลุต\nฟลุท\nฟอยล์\nฟอร์ม\nฟันด์\nฟาวล์\nฟาสต์ฟู้ด\nฟินิกซ์\nฟิวเจอร์\nฟีด\nฟีเวอร์\nฟุตบาท\nเฟรช\nเฟรชชี่\nเฟรม\nเฟมินิสต์\nเฟส\nเฟอร์นิเจอร์\nเฟอร์รี่\nเฟิร์ม\nเฟี้ยวฟ้าว\nแฟกซ์\nแฟ็กซ์\nแฟนซี\nแฟนตาซี\nแฟ้บ\nแฟร์\nแฟรนไชส์\nแฟรี\nแฟรี่\nแฟลช\nแฟล็ต\nโฟน\nโฟม\nโฟล์ค\nไฟต์\nไฟแนนซ์\nไฟลต์\nไฟลท์\nภควัทคีตา\nภควัมบดี\nภควัมปติ\nภคันทลาพาธ\nภววิสัย\nภารตะ\nภูมิทัศน์\nม้ง\nมวลชน\nมยุราภิรมย์\nมลภาวะ\nมหภาค\nมหาอุปราชา\nมอคคา\nมอคค่า\nมอนสเตอร์\nม็อบ\nมอบตัว\nมอยส์เจอไรเซอร์\nมอลล์\nมะกัน\nมั้ง\nมัฟฟิน\nมั้ย\nม้านั่ง\nมาเฟีย\nมาม่า\nมายองเนส\nมายาคติ\nมาร์ก\nมาร์เก็ต\nมาร์เก็ตติ้ง\nมาร์ค\nมาร์จิน\nมาร์ช\nมาร์ต\nมาร์ท\nมาราธอน\nม้าหินอ่อน\nมินต์\nมินท์\nมินิ\nมิลค์\nมิวสิค\nมิสซัง\nมิสไซล์\nมิสเตอร์\nมือถือ\nมุมมอง\nเมคอัพ\nเมจิก\nเมจิค\nเมทัล\nเมเปิล\nเมาท์\nเมี่ยงคำ\nแมกกาซีน\nแม็กกาซีน\nแมคเคอเรล\nแม่ค้า\nแมชชีน\nแมชีน\nแมนชั่น\nแมมบ้า\nแมมโบ้\nโมจิ\nโมเดล\nโมเดิร์น\nโมเต็ล\nโมโนเรล\nโมหจริต\nไมค์\nไมเกรน\nยนตรกรรม\nยอมรับ\nยะเยือก\nยังไง\nยากูซ่า\nยาวี\nยิม\nยิว\nยุวทูต\nยูโทเปีย\nยูโร\nยูวี\nเยน\nเยลลี่\nเย้ว\nเยอบีรา\nเยอบีร่า\nเยอร์บีรา\nเยอร์บีร่า\nแยมโรล\nโยเกิร์ต\nโยโย่\nรวมมิตร\nร็อค\nร็อคเก็ต\nรองรับ\nรอมฎอน\nรอยัลตี้\nระโงก\nรันเวย์\nรัม\nรากหญ้า\nราชบัณฑิตยสถาน\nราชานุญาต\nราชานุสาวรีย์\nรามเทพ\nรามาธิบดี\nรามายณะ\nราเม็ง\nราเมน\nรายชื่อ\nราสเบอร์รี\nริกเตอร์\nริคเตอร์\nรีไซเคิล\nรีดไถ\nรีทัช\nรีเทิร์น\nรีไทร์\nรีแบรนด์\nรีพอร์ท\nรีโมต\nรีโมท\nรีวิว\nรีสอร์ต\nรีสอร์ท\nรีเสิร์ช\nรุมบ้า\nรุสโซ\nรูบิก\nรูบิค\nเรซิน\nเรซิ่น\nเรดิโอ\nเรต\nเรตติ้ง\nแรงใจ\nแรงดูด\nแรงผลัก\nแรลลี\nแรลลี่\nโรดแมป\nโรเนียว\nโรแมนติก\nโรแมนติค\nโรล\nโรลออน\nไรเฟิล\nล็อกเกอร์\nลอจิสติกส์\nล็อต\nล็อบบี้\nลอร์ด\nล้มเหลว\nละติน\nละอ่อน\nลาซานญ่า\nลาติน\nลาเต้\nลานีญา\nลามะ\nลิมิต\nลิมูซีน\nลิสต์\nลีก\nลีด\nลีดเดอร์\nลีเมอร์\nลีลาวดี\nลุค\nลูกชาย\nลูกสาว\nเลกเชอร์\nเลคเชอร์\nเลดี้\nเลสเบี้ยน\nเลิฟ\nแลนด์\nแล็บ\nโลโก้\nโลชั่น\nไลท์\nไลน์\nไลฟ์\nวนาราม\nวราราม\nวโรกาส\nว้อดก้า\nวอเตอร์\nวอฟเฟิล\nว้อย\nวอร์ม\nวอร์มอัพ\nวอร์รูม\nวอล์ก\nวอล์ค\nวอลซ์\nวอลนัต\nวอลนัท\nวอลล์\nว่ะ\nวันเวย์\nวัสสา\nวาซาบิ\nวาทกรรม\nวาทะ\nวานิลลา\nวานิลา\nวาฟเฟิล\nวาริชศาสตร์\nว้าว\nวัคค์\nวัจนะ\nวาไรตี้\nวิก\nวิดีโอ\nวิทย์\nวิน\nวิป\nวิปปิ้ง\nวิภัชภาค\nวิว\nวิลล์\nวิลเลจ\nวีเจ\nวีซ่า\nวีดิทัศน์\nวีน\nวีไอพี\nวืด\nเวณิกา\nเวเฟอร์\nเวสต์\nเวอร์\nเวิร์ก\nเวิร์กช็อป\nเวิร์ค\nเวิร์ลด์\nเวิลด์\nแวมไพร์\nไวกิ้ง\nไวเบรเตอร์\nไวอะกร้า\nไวอากร้า\nศากยบุตร\nศิรินทร์\nศิลปวัฒนธรรม\nศิลปากร\nศิวิไลซ์\nศึกษาศาสตร์\nสกรัม\nสกาย\nสกู๊ป\nสเกตช์\nสเก็ตช์\nสคริปต์\nสแควร์\nสงบสุข\nสจ๊วต\nสตรอเบอร์รี\nสตรอเบอรี\nสตรอว์เบอร์รี\nสตริง\nสต็อก\nสต๊อก\nสต็อค\nสต๊อค\nสตอรี\nสตาร์\nสตาร์ท\nสติกเกอร์\nสติ๊กเกอร์\nสตีล\nสตูดิโอ\nสเตชัน\nสเตชั่น\nสเตเดียม\nสเตนเลส\nสเต็ป\nสเตย์\nสเตริโอ\nสเตอริโอ\nสแตนดาร์ด\nสแตนเลส\nสโตน\nสโตร์\nสไตรค์\nสไตล์\nสถาปัตย์\nสไนเปอร์\nสปอต\nสป็อต\nสปอนเซอร์\nสปอร์ต\nสปา\nสปาย\nสปิริต\nสเปก\nสเปค\nสไปเดอร์\nสมณพราหมณ์\nสมาพันธ์\nสมิติเวช\nสโรชา\nสลัม\nสแล็ก\nสโลแกน\nสโลว์\nสไลด์\nสวีท\nสหรัฐ\nสหัชญาณ\nสหัสวรรษ\nสะกอม\nสะเด่า\nสะบึม\nสะบึมส์\nสะออน\nสังโฆ\nสะโหลสะเหล\nสันทนาการ\nสัมนา\nสามช่า\nสามแยก\nสารขัณฑ์\nสี่แยก\nสึนามิ\nสุนทรีย์\nสุริยยาตร\nสุริยยาตร์\nสุหนี่\nเสกสรรค์\nเสพติด\nเสือโคร่ง\nหงวน\nหน่อมแน้ม\nหมวย\nหมั่นโถว\nหม่านโถว\nหมายปอง\nหมิง\nหยวน\nหลวงตา\nหลวงปู่\nหลวงพี่\nหล่อฮังก้วย\nหลินจือ\nห่วย\nเห็นด้วย\nเหมย\nเห่ย\nเหี่ยวย่น\nแหม็บ\nแหวว\nโหงว\nโหงวเฮ้ง\nโหลน\nโหลยโท่ย\nไหง\nไหร่\nอพาร์ตเมนต์\nอพาร์ตเมนท์\nอพาร์ทเมนต์\nอพาร์ทเมนท์\nอมาตยาธิปไตย\nอยุติธรรม\nอริยสงฆ์\nอ่วม\nอวอร์ด\nออกแบบ\nออดิชั่น\nออดิทอเรียม\nออเดอร์\nออโต้\nออทิสติก\nอ่อนด้อย\nออฟ\nออยล์\nออร์แกน\nออร์แกนิก\nออร์แกนิค\nออร์เดอร์\nออรัล\nออสซี่\nอะ\nอัตลักษณ์\nอัตวิสัย\nอันเดอร์\nอันตรกิริยา\nอัลตรา\nอัลไซเมอร์\nอัลบัม\nอัลบั้ม\nอัลมอนด์\nอาข่า\nอาโนเนะ\nอาฟเตอร์\nอาร์ติสต์\nอาร์พีจี\nอาว์\nอาสวะ\nอิกัวนา\nอินดอร์\nอินดัสตรีส์\nอินเตอร์\nอิ่มแปร้\nอิมพีเรียล\nอิเล็กทริก\nอิเล็กทริค\nอิเลียด\nอิสรชน\nอิเหนา\nอิออน\nอีแต๋น\nอีโรติก\nอีเวนท์\nอีสต์\nอีสเตอร์\nอึ๊บ\nอึ้ม\nอึ๋ม\nอึมครึม\nอุด้ง\nอุตสาหการ\nอุเทน\nอุปการคุณ\nอุปทาน\nอุปนายก\nอุปนายิกา\nอุปสงค์\nอุปัทวเหตุ\nอุรังคธาตุ\nอูคูเลเล่\nอู้ฟู่\nเอ๋\nเอ็กซ์โป\nเอ็กซ์เพรส\nเอ็กโซเซต์\nเอ็กโซเซ่ต์\nเอเซีย\nเอ็นจีโอ\nเอ็นเตอร์เทน\nเอนทรานซ์\nเอ็นทรานซ์\nเอฟเฟ็กต์\nเอเยนต์\nเอลนีโญ\nเอสเปรสโซ\nเอสเพรสโซ\nเอ๋อ\nเอาต์\nเอาท์\nเอาท์ดอร์\nเอ๊าะ\nแอ็กชั่น\nแอ็คชั่น\nแอคทีฟ\nแอดมิชชั่น\nแอดมิสชัน\nแอนด์\nแอ๊บแบ๊ว\nแอปเปิล\nแอปเปิ้ล\nแอปพริคอท\nแอพพริคอท\nแอพริคอต\nแอร์\nแอโรบิก\nแอโรบิค\nแอลมอนด์\nแอสเตอร์\nโอเค\nโอเปอเรเตอร์\nโอเปร่า\nโอเพ่น\nโอ้ย\nโอยัวะ\nโอรสาธิราช\nโอเลี้ยง\nโอวัลติน\nโอเวอร์\nไอซ์\nไอซียู\nไอดอล\nไอเดีย\nไอติม\nฮวงจุ้ย\nฮ่องเต้\nฮองเฮา\nฮอต\nฮ็อต\nฮอตดอก\nฮ็อตด็อก\nฮันนีมูน\nฮัม\nฮัลโลวีน\nฮัลโหล\nฮากกา\nฮาร์ด\nฮาราคีรี\nฮาลาล\nฮาโลวีน\nฮิ\nฮิต\nฮิบรู\nฮิปโป\nฮิปฮอป\nฮีโร่\nฮูลาฮูป\nฮูล่าฮูป\nเฮฟวี\nเฮฟวี่\nเฮอร์ริเคน\nเฮีย\nแฮนด์\nแฮปปี้\nแฮมเบอร์เกอร์\nโฮป\nโฮม\nโฮลดิงส์\nโฮลวีต\nโฮสเตส\nไฮกุ\nไฮแจ็ค\nไฮโซ\nไฮเทค\nไฮบริด\nไฮเปอร์\nไฮไลต์\nไฮไลท์\nไฮเวย์\nไฮสคูล\nไฮเอนด์\n\nกรีซ\nกัมพูชา\nกัวเตมาลา\nกาตาร์\nกานา\nกาบอง\nกายอานา\nกินี\nเกรนาดีนส์\nเกรเนดา\nเกาหลี\nแกมเบีย\nโกตดิวัวร์\nคองโก\nคอโมโรส\nคอสตาริกา\nคาซัคสถาน\nคิตส์\nคิริบาตี\nคิริบาส\nคิวบา\nคีร์กีซสถาน\nคูเวต\nเคนยา\nเคปเวิร์ด\nเคย์แมน\nแคนาดา\nแคเมอรูน\nโครเอเชีย\nโคลอมเบีย\nจอร์เจีย\nจอร์แดน\nจาเมกา\nจิบูตี\nจีน\nชาด\nชิลี\nเช็ก\nซามัว\nซาอุ\nซิมบับเว\nซีเรีย\nซูดาน\nซูรินาเม\nเซนต์\nเซเนกัล\nเซอร์เบีย\nเซาตูเม\nเซียร์รา\nแซมเบีย\nโซมาเลีย\nโซเวียต\nไซปรัส\nญี่ปุ่น\nดารุสซาลาม\nเดนมาร์ก\nโดมินิกัน\nโดมินิกา\nตรินิแดด\nตองกา\nติมอร์\nตุรกี\nตูนิเซีย\nเติร์กเมนิสถาน\nโตโก\nโตเบโก\nไต้หวัน\nทาจิกิสถาน\nแทนซาเนีย\nนอร์เวย์\nนามิเบีย\nนาอูรู\nนิการากัว\nนิวซีแลนด์\nเนเธอร์แลนด์\nเนปาล\nเนวิส\nไนจีเรีย\nไนเจอร์\nบราซิล\nบริติช\nบริเตน\nบรูไน\nบอตสวานา\nบอสเนีย\nบังกลาเทศ\nบังคลาเทศ\nบัลแกเรีย\nบาร์บูดา\nบาร์เบโดส\nบาห์เรน\nบาฮามาส\nบิสเซา\nบุรุนดี\nบูร์กินาฟาโซ\nเบนิน\nเบลเยียม\nเบลารุส\nเบลีซ\nเบอร์มิวดา\nโบลิเวีย\nปรินซิปี\nปากีสถาน\nปานามา\nปาปัวนิวกินี\nปารากวัย\nปาเลสไตน์\nปาเลา\nเปรู\nเปอร์เซีย\nเปอร์โตริโก\nโปรตุเกส\nโปแลนด์\nฝรั่งเศส\nพม่า\nฟิจิ\nฟินแลนด์\nฟิลิปปินส์\nเฟรนช์\nภูฏาน\nภูฐาน\nมองโกเลีย\nมอนเตเนโกร\nมอนแทนา\nมอริเชียส\nมอริเตเนีย\nมอลโดวา\nมอลตา\nมัลดีฟส์\nมาเก๊า\nมาซิโดเนีย\nมาดากัสการ์\nมาร์แชลล์\nมาลาวี\nมาลี\nมาเลเซีย\nเม็กซิโก\nเมียนมาร์\nโมซัมบิก\nโมนาโก\nโมนาโค\nโมร็อกโก\nไมโครนีเซีย\nยูกันดา\nยูโกสลาเวีย\nยูเครน\nเยเมน\nเยอรมนี\nรวันดา\nรัสเซีย\nโรมาเนีย\nลักเซมเบิร์ก\nลัตเวีย\nลาว\nลิกเตนสไตน์\nลิทัวเนีย\nลิเบีย\nลีโอน\nลูเซีย\nเลโซโท\nเลบานอน\nเลสเต\nไลบีเรีย\nวาติกัน\nวานูอาตู\nวินเซนต์\nเวเนซุเอลา\nเวียดนาม\nศรีลังกา\nสเปน\nสโลวะเกีย\nสโลวัก\nสโลวีเนีย\nสวาซิแลนด์\nสวิตเซอร์แลนด์\nสวีเดน\nสหรัฐ\nสหราชอาณาจักร\nสิกขิม\nสิงคโปร์\nอเมริกา\nออสเตรเลีย\nออสเตรีย\nอันดอร์รา\nอัฟกานิสถาน\nอาเซอร์ไบจาน\nอาร์เจนตินา\nอาร์เมเนีย\nอาระเบีย\nอิเควทอเรียล\nอิตาลี\nอินเดีย\nอินโดนีเซีย\nอิรัก\nอิสราเอล\nอิหร่าน\nอียิปต์\nอุซเบกิสถาน\nอุรุกวัย\nเอกวาดอร์\nเอธิโอเปีย\nเอมิเรตส์\nเอริเทรีย\nเอลซัลวาดอร์\nเอสโตเนีย\nแองโกลา\nแอนติกา\nแอลจีเรีย\nแอลเบเนีย\nโอมาน\nไอซ์แลนด์\nไอร์แลนด์\nฮ่องกง\nฮอนดูรัส\nฮังการี\nเฮติ\nเฮอร์เซโกวีนา\n\nกระบี่\nกรุงเทพ\nกาญจนบุรี\nกาฬสินธุ์\nกำแพงเพชร\nขอนแก่น\nจันทบุรี\nฉะเชิงเทรา\nชลบุรี\nชัยนาท\nชัยภูมิ\nชุมพร\nเชียงราย\nเชียงใหม่\nตรัง\nตราด\nตาก\nนครนายก\nนครปฐม\nนครพนม\nนครราชสีมา\nนครศรีธรรมราช\nนครสวรรค์\nนนทบุรี\nนราธิวาส\nน่าน\nบึงกาฬ\nบุรีรัมย์\nปทุมธานี\nประจวบคีรีขันธ์\nปราจีนบุรี\nปัตตานี\nพะเยา\nพังงา\nพัทลุง\nพิจิตร\nพิษณุโลก\nเพชรบุรี\nเพชรบูรณ์\nแพร่\nภูเก็ต\nมหาสารคาม\nมุกดาหาร\nแม่ฮ่องสอน\nยโสธร\nยะลา\nร้อยเอ็ด\nระนอง\nระยอง\nราชบุรี\nลพบุรี\nลำปาง\nลำพูน\nเลย\nศรีสะเกษ\nสกลนคร\nสงขลา\nสตูล\nสมุทรปราการ\nสมุทรสงคราม\nสมุทรสาคร\nสระแก้ว\nสระบุรี\nสิงห์บุรี\nสุโขทัย\nสุพรรณบุรี\nสุราษฎร์\nสุราษฎร์ธานี\nสุรินทร์\nหนองคาย\nหนองบัวลำภู\nอยุธยา\nอ่างทอง\nอำนาจเจริญ\nอุดรธานี\nอุตรดิตถ์\nอุทัยธานี\nอุบลราชธานี\nกันทรลักษ์\nจตุจักร\nไชยา\nซีคอน\nดอนเมือง\nถลาง\nไทรโยค\nธนบุรี\nธัญบุรี\nบางกอก\nบางปะกง\nบางระจัน\nปะทิว\nปาย\nพญาไท\nพัฒน์พงษ์\nพัทยา\nพารากอน\nภูมิซรอล\nรัตนาธิเบศร์\nรังสิต\nลันตา\nลาดพร้าว\nวโรรส\nวิภาวดี\nสตึก\nสมุย\nสัตหีบ\nสิมิลัน\nสุขุมวิท\nสุไหง\nเสลภูมิ\nอังรีดูนังต์\nอ่างขาง\nอินทนนท์\nเอ็มโพเรียม\n\nคิวชู\nแคริบเบียน\nแคสเปียน\nดานูบ\nตะนาวศรี\nนอร์วีเจียน\nนิโคบาร์\nเนรัญชรา\nไนล์\nบอร์เนียว\nบอลติก\nเบงกอล\nปิง\nแปซิฟิก\nมะละกา\nมินดาเนา\nมิสซิสซิปปี\nเมดิเตอร์เรเนียน\nเมโสโปเตเมีย\nยมุนา\nยุโรป\nยูเรเชีย\nยูเรเซีย\nแยงซี\nแยงซีเกียง\nสแกนดิเนเวีย\nสะโตง\nสาละวิน\nสุมาตรา\nสุเอซ\nอะเมซอน\nอันดามัน\nอัลไต\nอาร์กติก\nอาหรับ\nอินโดจีน\nอิรวดี\nอิระวดี\nอีเจียน\nอุษาคเณย์\nอูราล\nเอเชีย\nเอเดรียติก\nเอเวอเรสต์\nแอตแลนติก\nแอนตาร์กติก\nแอนตาร์กติกา\nแอฟริกา\nโอลิมปัส\nไอโอเนียน\nฮวงโห\nฮอกไกโด\nฮอนชู\n\nกบิลพัสดุ์\nกุสินารา\nโกลิยะ\nโกสัมพี\nโคตรบอง\nโคตรบูรณ์\nตองอู\nทรอย\nทวารวดี\nทวาราวดี\nเทวทหะ\nไทรบุรี\nนาลันทา\nไบแซนไทน์\nปรัสเซีย\nปัลลวะ\nปาฏลีบุตร\nพุทธคยา\nมถุรา\nมายัน\nมิถิลา\nราชคฤห์\nล้านช้าง\nล้านนา\nลุมพินี\nวัชชี\nเวสาลี\nสารนาถ\nสาวัตถี\nหริภุญชัย\nอโยธยา\nออตโตมัน\nอังวะ\nอินทปัตถ์\nอุชเชนี\n\nกราฟิก\nกราฟิกส์\nกราฟิค\nกริด\nกิกะไบต์\nกูเกิล\nกูเกิ้ล\nเกตเวย์\nโกลบอล\nคลัสเตอร์\nคลาส\nคลิก\nคลิปอาร์ต\nคอนโซล\nคอนเทนต์\nคอมพิวติ้ง\nคอมไพล์\nคอมไพเลอร์\nคอมมูนิเคชั่น\nคอร์\nคีย์\nคีย์บอร์ด\nเครือข่าย\nเคอร์เซอร์\nเคอร์เนล\nแคช\nแคมฟรอก\nแคมฟร็อก\nแคร็ก\nโค้ด\nจาวา\nจีพีเอส\nชิป\nชิพ\nเชลล์\nแช็ต\nแชนเนล\nแชนแนล\nซ็อกเก็ต\nซอฟต์แวร์\nซอฟท์แวร์\nซอร์ส\nซัพพอร์ต\nซัพพอร์ท\nซีดี\nซีดีรอม\nซีเนอร์\nเซิร์ฟเวอร์\nโซลูชัน\nโซลูชั่น\nไซต์\nไซเบอร์\nทรานแซกชัน\nทรานแซกชั่น\nทรานแซ็กชัน\nทรานแซ็กชั่น\nทรานแซคชัน\nทรานแซคชั่น\nทรานแซ็คชัน\nทรานแซ็คชั่น\nทวิตเตอร์\nทวีต\nทัชแพด\nเทมเพลต\nเทอร์มินัล\nแท็ก\nแท็บ\nแทบเล็ต\nโทรจัน\nเน็ต\nเน็ตบุ๊ก\nเน็ตบุค\nเน็ตบุ๊ค\nเน็ตเวิร์ก\nเน็ตเวิร์ค\nโน้ตบุ๊ก\nโน้ตบุค\nโน้ตบุ๊ค\nดอส\nดาวน์เกรด\nดาวน์โหลด\nดิจิตอล\nดิจิทัล\nดีบั๊ก\nดีวีดี\nดีไวซ์\nเดเบียน\nเดลไฟ\nเดสก์ท็อป\nโดเมน\nไดรว์\nไดรเวอร์\nไดเรกทอรี\nไดโอด\nเทเลคอม\nบล็อกเกอร์\nบรอดแบนด์\nบราวเซอร์\nบลูทูท\nบลูทูธ\nบลูเรย์\nบั๊ก\nบัฟเฟอร์\nบิต\nบิท\nบูต\nเบราว์เซอร์\nแบนด์วิดท์\nไบต์\nไบนารี\nโปรแกรมเมอร์\nโปรเซส\nโปรเซสเซอร์\nโปรโตคอล\nพร็อกซี\nพอร์ต\nพอร์ท\nพาเนล\nพาร์ทิชัน\nพารามิเตอร์\nพาสเวิร์ด\nพิกเซล\nเพจ\nเพจเจอร์\nแพกเก็ต\nแพตช์\nแพลตฟอร์ม\nโพรเซส\nโพรเซสเซอร์\nโพรโทคอล\nไพธอน\nฟล็อปส์\nฟอนต์\nฟอร์แมต\nฟอร์เวิร์ด\nฟอรัม\nฟีเจอร์\nเฟซบุ๊ก\nเฟิร์มแวร์\nแฟล็ก\nโฟลเดอร์\nไฟร์ฟอกซ์\nไฟร์วอลล์\nไฟล์\nมอดูล\nมอนิเตอร์\nมัลติ\nมัลติทัช\nมัลติเพล็กซ์\nมัลแวร์\nมาสเตอร์\nมีเดีย\nเมนู\nเมมโมรี\nเมล\nเมาส์\nแมค\nโมดูล\nโมเด็ม\nโมบาย\nโมบายล์\nโมไบล์\nไมโครซอฟท์\nยูนิกซ์\nยูนิโคด\nยูนิโค้ด\nริงโทน\nรีเฟรช\nรีเลย์\nเราเตอร์\nเรียลไทม์\nลิงก์\nลินุกซ์\nลีนุกซ์\nลูป\nเลเยอร์\nแล็ปท็อป\nไลเซนส์\nไลบรารี\nวิกิ\nวิกิพีเดีย\nวินโดวส์\nวินโดว์ส\nเว็บ\nเวอร์ชวล\nเวอร์ชัน\nเวอร์ชั่น\nเวิร์กสเตชัน\nเวิร์กสเตชั่น\nเวิร์คสเตชัน\nเวิร์คสเตชั่น\nเวิร์ด\nเวิร์ม\nไวแมกซ์\nสกรีน\nสแกน\nสแกนเนอร์\nสแต็ก\nสนิฟเฟอร์\nสปายแวร์\nสเปซ\nสแปม\nสมาร์ท\nสล็อต\nเสิร์ช\nโหลด\nออนไลน์\nออปติก\nออปติคอล\nออปติคัล\nออฟไลน์\nออราเคิล\nอัพเกรด\nอัพเดต\nอัพโหลด\nอัปเกรด\nอัปเดต\nอัปโหลด\nอัลกอริทึม\nอาร์กิวเมนต์\nอินเตอร์เน็ต\nอินทิเกรเตอร์\nอินเทอร์เน็ต\nอินเทอร์เฟซ\nอินเทล\nอินพุต\nอินพุท\nอีเมล\nอีเมล์\nอูบุนตู\nอูบันตู\nเอนจิน\nเอ็นจิน\nเอาต์พุต\nเอาต์พุท\nเอาท์พุต\nเอาท์พุท\nแอนะล็อก\nแอนิเมชัน\nแอนิเมชั่น\nแอปพลิเคชัน\nแอปพลิเคชั่น\nแอพพลิเคชัน\nแอพพลิเคชั่น\nแอสเซมบลี\nแอสเซมเบลอร์\nโอเพน\nไอคอน\nไอซี\nไอพอด\nไอพ็อด\nไอแพด\nไอโฟน\nฮับ\nฮาร์ดดิสก์\nฮาร์ดแวร์\nแฮกเกอร์\nแฮ็กเกอร์\nแฮนด์เฮลด์\nโฮสต์\n\nกรีก\nกัณณาฑ\nกัศมีรี\nคันจิ\nคาตาคานะ\nคุชราตี\nคุรุมุขี\nซีริลลิก\nซูลู\nเซลติก\nเซิร์บ\nตากาล็อก\nเตลุคู\nเติร์ก\nทราวิฑ\nเบงกาลี\nปัญจาบี\nพินอิน\nมลยาฬัม\nมองโกล\nมาราฐี\nมาเลย์\nเม็กซิกัน\nแมนจู\nแมนดาริน\nไมถิลี\nเยอรมัน\nรัสเซียน\nสวาฮิลี\nสวิส\nสินธี\nอูรดู\nอัสสมี\nอารบิก\nอิตาเลียน\nอุยกูร์\nแอฟริกัน\nโอริยา\nไอริช\nฮันกึล\nฮินดี\nฮิรางานะ\nฮีบรู\n\nกรีนพีซ\nกรือเซะ\nกวนอิม\nกวนอู\nกัดดาฟี\nกัลยาณวัตร\nกัสสปะ\nกาลิเลโอ\nกินเนส\nกุมภกรรณ\nกูเตนเบิร์ก\nเกตส์\nเกษมณี\nโกณฑัญญะ\nขงเบ้ง\nคริสโตเฟอร์\nคองคอร์ด\nคอลเกต\nคานธี\nคาเบรียล\nคาร์ฟูร์\nคาร์สัน\nคาราบาว\nคาสิโอ\nคิริน\nคุนลุ้น\nคูโบต้า\nเครมลิน\nแคทรีนา\nโคตมะ\nโคตมี\nโคลัมบัส\nไคฟง\nไครสเลอร์\nง้อไบ๊\nจตุพร\nจ็อบส์\nจอห์น\nจิ้น\nจิม\nจิ๋ม\nจิว\nจุฬาภรณ์\nจุฬาลงกรณ์\nเจมส์\nแจ็กสัน\nโจเซฟ\nโจว\nชมัยมรุเชฐ\nชมัยมรุเชษฐ์\nชเวดากอง\nชาร์ลส์\nชินราช\nชินวัตร\nชุนชิว\nเช็ง\nเชตวัน\nเชฟรอน\nเชฟโรเลต\nเชลซี\nไชยานุชิต\nซ่ง\nซังฮี้\nซัดดัม\nซันซิล\nซัมซุง\nซัวเจ๋ง\nซินหัว\nซีซาร์\nซีแพค\nซูซาน\nซูซูกิ\nซูบารุ\nเซ็นทารา\nเซเวน\nเซเว่น\nโซฟิเทล\nโซยุซ\nโซยูซ\nณัฐวุฒิ\nดาร์ลี่\nดาวโจนส์\nดิสนีย์\nดีแทค\nดูปองท์\nเดโมแครต\nเดลล์\nเดลินิวส์\nเดวิด\nแดวู\nโดนัลด์\nโดราเอมอน\nโดเรมอน\nต๋อง\nตั๊กม้อ\nตากสิน\nตาเมือน\nตาลีบัน\nตูน\nเตมีย์\nโต๋\nโตชิบา\nโตโยต้า\nถังซัมจั๋ง\nถังซำจั๋ง\nทรพา\nทราเวล\nทรูมูฟ\nทีปังกร\nเทปโก\nเทพรัตน\nเทวทัต\nเทสโก้\nโทมัส\nไททานิก\nไททานิค\nไทยรัฐ\nธีออส\nนครินทรา\nนโปเลียน\nนพดล\nนราดูร\nนเรนทร\nนอสตราดามุส\nนาซา\nนาซ่า\nนาซี\nนาโต\nนาโต้\nนาลแก\nนิคอน\nนิโคลัส\nนิด้า\nนินเทนโด\nนิปปอน\nนิวตัน\nนิสสัน\nเนคเทค\nเนชั่น\nเนชันแนล\nเนชั่นแนล\nเนวิน\nเนสเล่\nเนสาด\nแนท\nแนสแดค\nโนเกีย\nโนเบล\nโนเวลล์\nโนโวเทล\nไนเม็กซ์\nบรอดเวย์\nบรัดเลย์\nบรู๊ซ\nบัลเมอร์\nบารัก\nบารัค\nบู๊ตึ๊ง\nเบญกาย\nเบนซ์\nเบ็นซ์\nเบนจามิน\nโบตัน\nไบโอเทค\nประชาธิปัตย์\nปวีณา\nปอเต็กตึ๊ง\nปอเต๊กตึ๊ง\nป่อเต็กตึ๊ง\nปัตตะโชติ\nปารุสก์\nปีเตอร์\nปูติน\nเป๊ปซี่\nเป้ย\nเปอร์โยต์\nเปาบุ้นจิ้น\nโปเกมอน\nโป๊ยก่าย\nพรหมทัต\nพลาโต\nพอลล่า\nพานาโซนิค\nพานาโซนิก\nพิทยานุกูล\nพิมพิสาร\nเพนแทกซ์\nเพลโต\nไพโอเนียร์\nฟรอยด์\nฟรังซิส\nฟรานซิส\nฟลอเรนซ์\nฟอร์ด\nฟิลิปส์\nฟูจิ\nแฟซ่า\nโฟร์โมสต์\nภูมิพล\nภูริทัต\nมงฟอร์ต\nมณโฑ\nมติชน\nมหิตลาธิเบศร\nมโหสถ\nมัจฉานุ\nมาร์กาเร็ต\nมาร์ติน\nมาสด้า\nมิตซูบิชิ\nมิราเคิล\nมุสโสลินี\nเม้ง\nเมจิ\nเมอร์ซีเดส\nเมอร์เซเดส\nแมกซ์เวลล์\nแมกไซไซ\nแมคอินทอช\nแมชีนเนอรี่\nโมคคัลลานะ\nโมโตโรลา\nโมโตโรล่า\nโมเนีย\nไมเคิล\nไมยราพณ์\nยโสธรา\nยะโฮวา\nยะโฮวาห์\nยามาฮ่า\nยาเวห์\nยาฮู\nยูนิเซฟ\nยูเนสโก\nยูไล\nเยโฮวาห์\nรอยเตอร์\nรอยัล\nรัชดา\nรัสปูติน\nราฟาเอล\nรามาวตาร\nราเมศวร\nราหุล\nริชาร์ด\nรีพับลิกัน\nรูนีย์\nเรนโบว์\nแรมโบ้\nโรตารี\nโรนัลโด\nโรนัลโด้\nโรบินสัน\nโรเบิร์ต\nล็อกซเล่ย์\nลิงคอล์น\nลิจฉวี\nลิไท\nลิไทย\nลินคอล์น\nลิเวอร์พูล\nเลโนโว\nเลียดก๊ก\nโลตัส\nวชิราลงกรณ์\nวลาดิเมียร์\nวอลล์สตรีท\nวาเลนไทน์\nวิกตอเรีย\nวิทยานุสรณ์\nวิทยายน\nวิมเบิลดัน\nวิลเลียม\nวีระ\nวุฒิชัย\nเวียดกง\nไวตามิลค์\nศกุนตลา\nศรีวิชัย\nศิริพงษ์\nศิริราช\nศุภชลาศัย\nสดกก๊อกธม\nสดายุ\nสตาลิน\nสตีฟ\nสแตนฟอร์ด\nสวรินทิรา\nสังกัจจายน์\nสาทิตย์\nสารีบุตร\nสิริกิติ์\nสิรินธร\nสิหิงค์\nสีวลี\nสีหนุ\nสีหมุนี\nสีหโมนี\nสุครีพ\nสุทโธทนะ\nสุเทพ\nสุนทราภรณ์\nสุนีย์\nสุรนารี\nสุรยุทธ์\nสุริยาสน์\nเส้าหลิน\nโสกราตีส\nโสภิต\nหนุมาน\nหลินฮุ่ย\nหลุยส์\nเห้งเจีย\nไหหม่า\nองคต\nองคุลิมาล\nอชาตศัตรู\nอดุลยเดช\nอพอลโล\nอริสโตเติล\nอริสมันต์\nอลิซาเบธ\nอ๋อม\nออร์คิด\nออสการ์\nอะพอลโล\nอับราฮัม\nอั้ม\nอัลกออิดะห์\nอัลคาเทล\nอัลจาซีราห์\nอัลเฟรด\nอัลเลาะห์\nอัสซุส\nอัสสชิ\nอัสสัมชัญ\nอาเซม\nอาเซ็ม\nอาเซียน\nอาฟต้า\nอาร์เซนอล\nอินทรชิต\nอินทราทิตย์\nอีซูซุ\nอีเลฟเวน\nอีเลฟเว่น\nอุณรุท\nอุบลรัตน์\nอุบาลี\nอุ๋ย\nเอกทัศน์\nเอเซอร์\nเอ็ดเวิร์ด\nเอดิสัน\nเอแบค\nเอลิซาเบธ\nเอสพลานาด\nเอสพลานาร์ด\nแอคคอร์\nแอคคอร์ด\nแองเจลิน่า\nแอตแลนติส\nแอน\nแอ๋ม\nแอมบาสซาเดอร์\nแอมบาสเดอร์\nแอมเวย์\nแอ๋ว\nโอดีสซีย์\nโอเดียน\nโอบามา\nโอรสาราม\nโอลิมเปีย\nโออิชิ\nไอน์สไตน์\nฮอนด้า\nฮอปกินส์\nฮอลลีวูด\nฮอลลีวู้ด\nฮานามิ\nฮามาส\nฮิตเลอร์\nฮิตาชิ\nฮุนเซน\nฮุนเซ็น\nฮุนได\nฮุสเซ็น\nเฮนรี\nเฮนรี่\nเฮเลน\nโฮจิมินห์\nโฮปเวลล์\nโฮเมอร์\n\nกลีเซอรีน\nกำทอน\nแกนีมีด\nครอส\nคริสตัล\nคลอโรพลาสต์\nคลอไรด์\nควอนตัม\nคอนดักเตอร์\nคอปเปอร์\nคอลลาเจน\nคอเลสเตอรอล\nคอสมิก\nคูลอมบ์\nเคอราติน\nแคโรทีน\nแคสสินี\nโครมาโทกราฟี\nไคโตซาน\nจีโนม\nจุลชีววิทยา\nชิคุนกุนยา\nซัลฟิวริก\nซัลเฟต\nซัลไฟด์\nซิงค์\nซิริอุส\nซิลิกา\nซิลิเกต\nซิลิโคน\nซีเทน\nซีเวิร์ต\nเซ็กเตอร์\nเซ็กเมนต์\nเซมิ\nโซนาร์\nไซบอร์ก\nดอปเปลอร์\nดอปเพลอร์\nไดนามิก\nไดนามิกส์\nไดนามิค\nไดนามิคส์\nไดออกไซด์\nทรานส์\nทามิฟลู\nเทฟลอน\nเทสโทสเตอโรน\nเทอร์โม\nแทนนิน\nไททัน\nไทฟอยด์\nไทรอยด์\nธาลัสซีเมีย\nเนกาตีฟ\nโนวา\nบอแรกซ์\nโบทอกซ์\nโบท็อกซ์\nไบโอติน\nปฏิยานุพันธ์\nโปรเจสเตอโรน\nพอลิเมอร์\nพันธุศาสตร์\nพัลซาร์\nพาร์กินสัน\nพาราเซตามอล\nพาราโบลา\nเพอร์ออกไซด์\nโพรเจสเทอโรน\nโพลาไรซ์\nโพลิเมอร์\nโพลีเอทิลีน\nไพรเมต\nฟลาโวนอยด์\nฟลูออเรสเซนซ์\nฟลูออไรด์\nฟอสซิล\nฟิชชัน\nฟิวชัน\nฟีโรโมน\nไฟเบอร์\nมอนอกไซด์\nมิราจ\nเมตริกซ์\nเมทริกซ์\nเมลานิน\nเมลามีน\nโมเมนตัม\nไมโตคอนเดรีย\nไมโทคอนเดรีย\nยีสต์\nยูริก\nยูเรีย\nรูมาตอยด์\nวีก้า\nเวกเตอร์\nเวก้า\nเวสิเคิล\nโวลต์\nสเกล\nสเกลาร์\nสเต็ม\nสเตียรอยด์\nสปีชีส์\nสเปิร์ม\nสัมพัทธภาพ\nสุริยจักรวาล\nออกเทน\nออโรรา\nออโรร่า\nอะคริลิก\nอะครีลิก\nอะซีติก\nอะซีโตน\nอะมิโน\nอะลูมินา\nอันโดรเมดา\nอัลคาไลน์\nอัลตราซาวด์\nอัลตราซาวนด์\nอัลลอยด์\nอินทิกรัล\nอินทิเกรต\nอีโบลา\nอีโบล่า\nเอ็กซ์โพเนนเชียล\nเอทานอล\nเอทิลีน\nเอนโทรปี\nเอสเตอร์\nเอสโตรเจน\nเอสโทรเจน\nแอนดรอยด์\nแอนแทร็กซ์\nแอมพลิจูด\nแอมโมเนียม\nแอลกอฮอลิซึม\nแอสพาร์แตม\nโอเซลทามิเวียร์\nฮับเบิล\nฮิวมัส\nไฮดรอกไซด์\nไฮดรอลิก\nไฮโดรลิก\nไฮเพอร์โบลา\n\nกงเต็ก\nกฎุมพี\nกฏ\nกฏหมาย\nกบฎ\nกราไฟต์\nก๊อปปี้\nกะทะ\nกังวาล\nกุฎฐัง\nกุฏุมพี\nฃวด\nคฑา\nคลินิค\nคลีนิก\nคลีนิค\nคาทอลิค\nคึ่นฉ่าย\nแคตตาล็อก\nโควต้า\nฅน\nจุมพฎ\nช็อคโกแลต\nแซ่ด\nดัทช์\nทนง\nทรมาณ\nทราก\nทะแยง\nทิฏฐิ\nบล็อค\nบ๊องแบ๊ว\nบัลเล่ต์\nเบรค\nแบงค์\nปรากฎ\nปัคคหะ\nปาฏิโมกข์\nปิติ\nปิรามิด\nโปรเตสแตนท์\nพนิช\nพยักเพยิด\nพริ้ว\nพลูโตเนียม\nพากษ์\nเฟิร์น\nยากี้\nเยภุยยสิกา\nรุสเซีย\nฤาษี\nล็อค\nล็อคเกอร์\nวันทยาหัตถ์\nวานิช\nวิญญาน\nวิศิษฐ์\nศรีษะ\nสเปกโทรสโคป\nสฤษฎ์\nสลิ่ม\nสัตตสดก\nสาราณียากร\nสุกี้\nสุกี้ยากี้\nสูญญากาศ\nหยอมแหยม\nหยอย\nหล่ะ\nอะดรีนาลีน\nอะหลั่ย\nอัตคัต\nอัฟริกา\nอานิสงค์\nอาฟริกา\nอิริยาบท\nอิเลคโทรนิคส์\nอีรุงตุงนัง\nอุตรายัน\nอุลตรา\nอุลตร้า\nโอกาศ\n\nกกหู\nกงการ\nกงจักร\nกฎเกณฑ์\nกฎบัตร\nกฎหมาย\nกฎหมู่\nกดขี่\nกดดัน\nก้นกบ\nก้นบึ้ง\nก้นปล่อง\nกนิษฐภคินี\nกนิษฐภาดา\nกรงเล็บ\nกรมการ\nกรมท่า\nกรมธรรม์\nกรมนา\nกรมวัง\nกรรมกร\nกรรมการ\nกรรมฐาน\nกรรมบถ\nกรรมพันธุ์\nกรรมวิธี\nกรรมสิทธิ์\nกระจกเงา\nกระจกตา\nกระจกนูน\nกระจกเว้า\nกระจอกชวา\nกระจอกเทศ\nกระจ้อยร่อย\nกระจับบก\nกระจับปิ้ง\nกระจับปี่\nกระโชกโฮกฮาก\nกระดานดำ\nกระดานหก\nกระดาษแก้ว\nกระดาษไข\nกระดาษทราย\nกระดาษสา\nกระดูกงู\nกระทาชาย\nกระเท่เร่\nกระบวนการ\nกระบองเพชร\nกระผีกริ้น\nกระยาทิพย์\nกระยาสารท\nกระยาหาร\nกระสอบทราย\nกระสุนปืน\nกระแสจิต\nกระแสน้ำ\nกระแสลม\nกรับคู่\nกรับพวง\nกรับเสภา\nกราดเกรี้ยว\nกราวรูด\nกริ่งเกรง\nกรีฑาสถาน\nกรีดกราย\nกรี๊ดกร๊าด\nกลไก\nกลบท\nกลเม็ด\nกลยุทธ์\nกลวิธี\nกลศาสตร์\nกลอักษร\nกลบเกลื่อน\nกลมกล่อม\nกลมกลืน\nกลมเกลียว\nกล้วยแขก\nกล้วยไม้\nกล่องเสียง\nกล่อมเกลา\nกล่อมเกลี้ยง\nกลัดกลุ้ม\nกลัดมัน\nกลั่นกรอง\nกลั่นแกล้ง\nกลับกลอก\nกลางคน\nกลางคัน\nกลางค่ำ\nกลางคืน\nกลางแจ้ง\nกลางแปลง\nกลางวัน\nกลาดเกลื่อน\nกล่าวขวัญ\nกล่าวโทษ\nกล่าวหา\nกล้ำกราย\nกล้ำกลืน\nกลิ้งกลอก\nกลิ้งเกลือก\nกลิ่นอาย\nกลียุค\nกวดขัน\nกวัดแกว่ง\nกวัดไกว\nกวีนิพนธ์\nก่อกวน\nก่อการ\nก่อตั้ง\nก่อสร้าง\nก่อหวอด\nก้อร่อก้อติก\nกองกลาง\nกองเกิน\nกองโจร\nกองทัพ\nกองทุน\nกองพล\nกองพัน\nกองฟอน\nกองร้อย\nกองหนุน\nกอบโกย\nกะเกณฑ์\nกะบังลม\nกักกัน\nกักขัง\nกักตัว\nกักตุน\nกัณฑ์เทศน์\nกัดฟัน\nกันชน\nกันท่า\nกันสาด\nกันเอง\nกับแกล้ม\nกับข้าว\nกับระเบิด\nกากเพชร\nกากหมู\nกาชาด\nกาญจนาภิเษก\nก้านคอ\nกาฝาก\nก้ามกราม\nกามกิจ\nกามคุณ\nกามเทพ\nกามโรค\nก้ามปู\nกายกรรม\nกายบริหาร\nกายภาพ\nกายวิภาค\nกายสิทธิ์\nก่ายกอง\nการคลัง\nการเงิน\nการบ้าน\nการเปรียญ\nการเมือง\nการเรือน\nการละเล่น\nกาลกิริยา\nกาลเทศะ\nก้าวก่าย\nก้าวร้าว\nก้าวหน้า\nกาสาวพัสตร์\nกาฬพฤกษ์\nกาฬโรค\nกำปั้น\nกำมือ\nกำแพงขาว\nกำลังใจ\nกำลังม้า\nกี่งอำเภอ\nกิจกรรม\nกิจการ\nกิจวัตร\nกิจจะลักษณะ\nกิตติคุณ\nกิตติศัพท์\nกินขาด\nกินใจ\nกินดอง\nกินโต๊ะ\nกินแบ่ง\nกินเปล่า\nกินเลี้ยง\nกินเส้น\nกินแหนง\nกิโลกรัม\nกิโลเมตร\nกิโลลิตร\nกิโลเฮิรตซ์\nกีดกัน\nกีดกั้น\nกีดขวาง\nกึกก้อง\nกึกกัก\nกึกกือ\nกึ่งกลาง\nกุกกัก\nกุ๊กกิ๊ก\nกุ้งฝอย\nกุ้งมังกร\nกุ้งแห้ง\nกุ้งเต้น\nกุ้งยิง\nกุญแจผี\nกุญแจมือ\nกุญแจเลื่อน\nกุญแจเสียง\nกุลธิดา\nกุลบุตร\nกุลสตรี\nกู้ยืม\nเก้งก้าง\nเก็บกวาด\nเก็บเกี่ยว\nเก็บงำ\nเก็บตก\nเกรงกลัว\nเกรงใจ\nเกรงขาม\nเกรียงไกร\nเกรียมกรม\nเกรี้ยวกราด\nเกล็ดเลือด\nเกลี้ยกล่อม\nเกลี้ยงเกลา\nเกลือกกลั้ว\nเกลือกกลิ้ง\nเกลื่อนกล่น\nเกลื่อนกลาด\nเกศธาตุ\nเกษตรกร\nเกษตรกรรม\nเกษตรศาสตร์\nเกษมสันต์\nเกษียรสมุทร\nเก้อเขิน\nเกาะแกะ\nเกี่ยงงอน\nเกียจคร้าน\nเกียรติคุณ\nเกียรตินิยม\nเกียรติประวัติ\nเกียรติภูมิ\nเกียรติยศ\nเกียรติศักดิ์\nเกียรติมุข\nเกี่ยวข้อง\nเกี่ยวดอง\nเกี่ยวพัน\nเกี่ยวโยง\nเกี้ยวพาน\nเกี้ยวพาราสี\nแก่แดด\nแก้ขัด\nแก้ไข\nแก้ตัว\nแก้เผ็ด\nแก้ลำ\nแกงคั่ว\nแกงจืด\nแกงบวด\nแกงป่า\nแกงเผ็ด\nแกงส้ม\nแก่งแย่ง\nแก่นแก้ว\nแก่นสาร\nแก้วตา\nแก้วหู\nแกว่งกวัด\nแกว่งไกว\nแกะรอย\nโก้เก๋\nโกรธเกรี้ยว\nโกรธขึ้ง\nไก่เขี่ย\nไก่ชน\nไก่บ้าน\nไก่ป่า\nไก่ฟ้า\nไกล่เกลี่ย\nขจัดขจาย\nขนเพชร\nขนสัตว์\nขนหนู\nขนส่ง\nขนมจีน\nขบขัน\nขบวนการ\nข่มขี่\nข่มขู่\nข่มขืน\nข่มเหง\nขมหิน\nขมิ้นชัน\nขมุบขมิบ\nขยะแขยง\nขยักขย่อน\nขยักขย้อน\nขยับขยาย\nขยับเขยื้อน\nขวดโหล\nขวยเขิน\nขวัญใจ\nขวัญตา\nขวัญอ่อน\nขวากหนาม\nขวางโลก\nของ้าว\nขอสับ\nขอขมา\nขอทาน\nขอโทษ\nขอร้อง\nขออภัย\nข้อเขียน\nข้อความ\nข้อเท็จจริง\nของกลาง\nของขวัญ\nของแข็ง\nของชำ\nของลับ\nของเล่น\nของว่าง\nของเหลว\nของไหล\nของไหว้\nข้องใจ\nข้องแวะ\nขอบข่าย\nขอบเขต\nขอบคุณ\nขอบใจ\nขอบพระคุณ\nข้อมูล\nข้อแม้\nข้อหา\nข้อสังเกต\nขัดข้อง\nขัดขืน\nขัดเขิน\nขัดจังหวะ\nขัดดอก\nขัดแตะ\nขัดยอก\nขัดแย้ง\nขัดสมาธิ\nขันหมาก\nขันอาสา\nขับขี่\nขับเคี่ยว\nขั้วโลก\nขาจร\nขาประจำ\nข้าราชการ\nข้าศึก\nข้าหลวง\nขาดแคลน\nขาดใจ\nขาดตอน\nขาดตัว\nขาดทุน\nขาดเหลือ\nขายหน้า\nข่าวกรอง\nข่าวคราว\nข่าวล่า\nข่าวลือ\nข่าวสาร\nข้าวเกรียบ\nข้าวแกง\nข้าวของ\nข้าวจี่\nข้าวเจ้า\nข้าวซอย\nข้าวต้ม\nข้าวตอก\nข้าวตัง\nข้าวแตน\nข้าวทิพย์\nข้าวบิณฑ์\nข้าวเปลือก\nข้าวโพด\nข้าวฟ่าง\nข้าวเม่า\nข้าวสวย\nข้าวสาร\nข้าวเหนียว\nข้าวหมาก\nข้าวหลาม\nขี้เกียจ\nขี้ข้า\nขี้ครอก\nขี้คร้าน\nขี้คุก\nขี้ไคล\nขี้เซา\nขีดขั้น\nขีดคร่อม\nขีดคั่น\nขีดฆ่า\nขี้ตา\nขี้ตืด\nขี้เถ้า\nขี้ทูด\nขี้ปะติ๋ว\nขี้ผึ้ง\nขี้มูก\nขี้ยา\nขี้แย\nขี้ริ้ว\nขี้เรื้อน\nขี้เล็บ\nขี้หู\nขี้หน้า\nขี้เหนียว\nขี้เหล็ก\nขี้เหร่\nขึงขัง\nขึงพืด\nขึ้งโกรธ\nขึ้นใจ\nขึ้นชื่อ\nขึ้นมือ\nขืนใจ\nขื่นขม\nขุดคุ้ย\nขุนทอง\nขุนนาง\nขุนพล\nขุนศึก\nขุนหลวง\nขูดรีด\nเข็มกลัด\nเข็มขัด\nเข้มข้น\nเข้มแข็ง\nเข้มงวด\nเข็มทิศ\nเข็มหมุด\nเข้าขา\nเข้าเค้า\nเข้าใจ\nเข้าชื่อ\nเข้าตัว\nเข้าถึง\nเข้าทรง\nเข้าท่า\nเข้าที\nเข้าเนื้อ\nเข้าเล่ม\nเขียวเสวย\nเขียวหวาน\nแขกเต้า\nแข็งกร้าว\nแข็งกล้า\nแข็งแกร่ง\nแข็งข้อ\nแข็งขัน\nแข่งขัน\nแข็งใจ\nแข็งตัว\nแข็งเมือง\nแข็งแรง\nแขวนลอย\nโขกสับ\nโขยกเขยก\nไขข้อ\nไขควง\nไขมัน\nไข่มุก\nไขว่ห้าง\nไขสันหลัง\nไขสือ\nไข่เค็ม\nไข่เยี่ยวม้า\nไข่หงส์\nไข่เหี้ย\nคงกระพัน\nคงตัว\nคงทน\nคงที่\nคชลักษณ์\nคชสาร\nคชสีห์\nคชราช\nคณิตศาสตร์\nคดเคี้ยว\nคติธรรม\nคติพจน์\nคนกลาง\nคนไข้\nคนใช้\nคนทรง\nคบไฟ\nคบเพลิง\nคบค้า\nคบคิด\nคบหา\nคมคาย\nครบครัน\nครบถ้วน\nครอบครอง\nครอบคลุม\nครอบครัว\nครอบงำ\nครอบจักรวาล\nคริสตกาล\nคริสตจักร\nคริสต์มาส\nคริสต์ศตวรรษ\nคริสต์ศักราช\nคริสตัง\nคริสเตียน\nครุกรรม\nครุภัณฑ์\nครุศาสตร์\nครุฑพ่าห์\nครุ่นคิด\nคลอเคลีย\nคล่องแคล่ว\nคล่องตัว\nคล่องมือ\nคลั่งไคล้\nคลาคล่ำ\nคลาไคล\nคลาดเคลื่อน\nคลาดแคล้ว\nคลี่คลาย\nคลึงเคล้น\nคลึงเคล้า\nคลื่นไส้\nคลื่นเหียน\nคลุกคลี\nคลุกคลาน\nคลุมเครือ\nคลุมโปง\nคลุ้มคลั่ง\nควงสว่าน\nควบคุม\nควบคู่\nควบแน่น\nควันหลง\nความคิด\nความหลัง\nความเห็น\nคอหอย\nคอห่าน\nคอแห้ง\nค่อนขอด\nค่อนแคะ\nค้อนควัก\nคั่งค้าง\nคั่งแค้น\nคัดค้าน\nคัดง้าง\nคัดท้าย\nคัดเลือก\nคันจาม\nคันฉ่อง\nคันฉาย\nคันชัก\nคันชั่ง\nคันไถ\nคันนา\nคันเร่ง\nคับขัน\nคับคั่ง\nคับแค้น\nคับแคบ\nคางทูม\nคางหมู\nค้างคืน\nค้างปี\nคาดคั้น\nคาดเชือก\nคาดโทษ\nคาดหมาย\nคานหาม\nคาบเกี่ยว\nคาบศิลา\nคาบสมุทร\nคำขาด\nคำนำ\nคิดค้น\nคืนดี\nคืนตัว\nคุกเข่า\nคุณค่า\nคุณชาย\nคุณธรรม\nคุณนาย\nคุณภาพ\nคุณลักษณะ\nคุณวุฒิ\nคุณศัพท์\nคุณสมบัติ\nคุณหญิง\nคุณากร\nคุณูปการ\nคุโณปการ\nคุมเชิง\nคุ้มกัน\nคุยเขื่อง\nคุยโต\nคุ้ยเขี่ย\nคุ้มครอง\nคู่กรณี\nคู่กัด\nคู่ขา\nคู่แข่ง\nคู่ครอง\nคู่ควร\nคู่คิด\nคู่คี่\nคู่ใจ\nคู่ชีพ\nคู่ชีวิต\nคู่บารมี\nคู่บุญ\nคู่ปรปักษ์\nคู่ปรับ\nคู่ผสม\nคู่มือ\nคู่รัก\nคู่ลำดับ\nคู่สาย\nคู่หมั้น\nคู่หู\nคู่อริ\nคู่อาฆาต\nเคมีภัณฑ์\nเคยตัว\nเคร่งขรึม\nเคร่งครัด\nเคร่งเครียด\nเครดิตฟองซิเอร์\nเครื่องกล\nเครื่องกัณฑ์\nเครื่องแกง\nเครื่องเขิน\nเครื่องครัว\nเครื่องเคียง\nเครื่องเงิน\nเครื่องจักร\nเครื่องเซ่น\nเครื่องดนตรี\nเครื่องต้น\nเครื่องทุ่นแรง\nเครื่องเทศ\nเครื่องใน\nเครื่องบิน\nเครื่องบูชา\nเครื่องแบบ\nเครื่องประดับ\nเครื่องปรุง\nเครื่องปรุงรส\nเครื่องมือ\nเครื่องยนต์\nเครื่องร่อน\nเครื่องราง\nเครื่องเรือน\nเครื่องล่าง\nเครื่องเล่น\nเครื่องสาย\nเครื่องสำอาง\nเครื่องสุกำศพ\nเครื่องหมาย\nเครือรัฐ\nเคลียคลอ\nเคลื่อนที่\nเคลื่อนไหว\nเคลือบแคลง\nเคลือบแฝง\nเคลือบฟัน\nเคว้งคว้าง\nเคหสถาน\nเค้าโครง\nเคียดแค้น\nเคี่ยวเข็ญ\nเคี้ยวเอื้อง\nเคืองขุ่น\nโคนม\nโคบาล\nโคมูตร\nโคมลอย\nโครงการ\nโครงเรื่อง\nโครงงาน\nโครงสร้าง\nโครมคราม\nโคลงเคลง\nฆ้องกระแต\nฆ้องชัย\nฆ้องวง\nฆ้องหุ่ย\nฆ้องเหม่ง\nฆ้องโหม่ง\nฆาตกร\nฆาตกรรม\nฆานประสาท\nงงงวย\nงงงัน\nงดเว้น\nงบดุล\nงบประมาณ\nงมโข่ง\nงมงาย\nง่วงงุน\nง่วงเหงา\nงอหาย\nง้องอน\nงอนง้อ\nงอมแงม\nงาช้าง\nง่าเงย\nงานการ\nง่ายดาย\nงึมงำ\nเงินเดือน\nเงินตรา\nเงินยวง\nเงียบกริบ\nเงียบเชียบ\nเงียบเหงา\nเงื่องหงอย\nเงื่อนไข\nเงื่อนงำ\nเงื่อนเวลา\nเงื้อมมือ\nแง่งอน\nจงใจ\nจงรัก\nจดจ่อ\nจดจำ\nจดหมาย\nจดหมายเหตุ\nจรจัด\nจรรยาบรรณ\nจริงจัง\nจริงใจ\nจอมขวัญ\nจอมใจ\nจอมทัพ\nจอมปลวก\nจอมพล\nจ๊ะเอ๋\nจักสาน\nจักรพรรดิ\nจักรภพ\nจักรยาน\nจักรยานยนต์\nจักรราศี\nจักรวรรดิ\nจักรวรรดินิยม\nจักรวาล\nจังหนับ\nจัดการ\nจัดจ้าน\nจัดเจน\nจัดแจง\nจัดตั้ง\nจัดสรร\nจับกุม\nจับจด\nจับเจ่า\nจ่าหน้า\nจาตุทสี\nจาตุมหาราช\nจาตุมหาราชิก\nจาตุมหาราชิกา\nจาตุรงคสันนิบาต\nจาตุรราชการ\nจานเชิง\nจานบิน\nจานผี\nจานเสียง\nจาบจ้วง\nจำเป็น\nจำพรรษา\nจำวัด\nจ้ำจี้จ้ำไช\nจำเลาะตา\nจิงโจ้น้ำ\nจิตใจ\nจิตตภาวนา\nจิตตัง\nจิตตานุปัสสนา\nจิตนิยม\nจิตบำบัด\nจิตแพทย์\nจิตวิสัย\nจิตรกร\nจิตรกรรม\nจิตรลดา\nจิตวิทยา\nจิตเวช\nจิตเวชศาสตร์\nจินตกวี\nจินตนา\nจินตนาการ\nจินตภาพ\nจุฑามณี\nจุฑามาศ\nจุฑารัตน์\nจุนเจือ\nจุ้นจ้าน\nจุลชีพ\nจุลชีวัน\nจุลชีวิน\nจุลทรรศน์\nจุลภาค\nจุลวรรค\nจุลศักราช\nจุลสาร\nจุลินทรีย์\nจุฬามณี\nจุฬาลักษณ์\nเจตคติ\nเจตจำนง\nเจตนารมณ์\nเจตภูต\nเจริญพร\nเจ้ากรม\nเจ้ากรรม\nเจ้าของ\nเจ้าขา\nเจ้าข้า\nเจ้าคณะ\nเจ้าค่ะ\nเจ้าจอม\nเจ้าชู้\nเจ้าตัว\nเจ้าถิ่น\nเจ้าท่า\nเจ้าที่\nเจ้าทุกข์\nเจ้านาย\nเจ้าเนื้อ\nเจ้าบ้าน\nเจ้าบ่าว\nเจ้าประคุณ\nเจ้าประคู้น\nเจ้าพนักงาน\nเจ้าพระคุณ\nเจ้าพระยา\nเจ้าพ่อ\nเจ้าพายุ\nเจ้าฟ้า\nเจ้าภาพ\nเจ้ามือ\nเจ้าแม่\nเจ้าเรือน\nเจ้าสังกัด\nเจ้าสัว\nเจ้าสาว\nเจ้าหน้าที่\nเจ้าหนี้\nเจ้าอาวาส\nเจาะจง\nเจือจาง\nเจือจาน\nเจือปน\nเจื้อยแจ้ว\nแจกจ่าย\nแจ่มแจ้ง\nแจ่มใส\nโจงกระเบน\nโจมตี\nโจรกรรม\nโจรสลัด\nใจความ\nใจคอ\nฉกฉวย\nฉกชิง\nฉลองได\nฉ้อฉล\nฉัตรมงคล\nฉันทลักษณ์\nฉายาลักษณ์\nฉิบหาย\nฉุกเฉิน\nฉุกละหุก\nฉุนเฉียว\nฉุปศาสตร์\nเฉไฉ\nเฉยเมย\nเฉาโฉด\nเฉิดฉัน\nเฉิดฉาย\nเฉิดฉิน\nเฉียบขาด\nเฉียบพลัน\nเฉียบแหลม\nเฉื่อยชา\nแฉะแบะ\nโฉดเฉา\nโฉมงาม\nโฉมฉาย\nโฉมเฉลา\nโฉมตรู\nโฉมยง\nโฉมศรี\nโฉมหน้า\nชดช้อย\nชดเชย\nชดใช้\nชนบท\nชนินทร์\nชนกกรรม\nชนมพรรษา\nชนมายุ\nชมเชย\nชมพูทวีป\nชมพูนท\nชมพูนุท\nชราธรรม\nชราภาพ\nชลจร\nชลธาร\nชลธี\nชลนัยน์\nชลนา\nชลเนตร\nชลประทาน\nชลมารค\nชลาธาร\nชลาลัย\nชลาศัย\nชลาสินธุ์\nชโลทร\nช่วงชิง\nช่วงใช้\nชวนชม\nชวนหัว\nช่วยเหลือ\nช่อฟ้า\nช่อม่วง\nชอกช้ำ\nช่องเขา\nช่องแคบ\nช่องไฟ\nช่องว่าง\nช้องนาง\nชอบกล\nชอบใจ\nชอบธรรม\nชอบพอ\nชักโครก\nชักเงา\nชักจูง\nชักชวน\nชักนำ\nชักเนื้อ\nชักพระ\nชักเย่อ\nชักใย\nชั่งใจ\nชังฆวิหาร\nชัดเจน\nชั้นเชิง\nชั่วคน\nชั่วคราว\nชั่วช้า\nชั่วโมง\nชั่วแล่น\nชาเย็น\nช้านาน\nช่างเครื่อง\nช่างฝีมือ\nช่างฟิต\nช่างไฟ\nช้างน้ำ\nช้างเผือก\nช้างพลาย\nช้างพัง\nช้างสาร\nช้างสีดอ\nชาติธรรม\nชาตินิยม\nชาติพันธุ์\nชาติพันธุ์วิทยา\nชาติภูมิ\nชานชาลา\nชายชาตรี\nชายคา\nชายฝั่ง\nชายทะเล\nชาวเล\nชาววัง\nช้ำใจ\nช้ำชอก\nชิงชัง\nชิงพลบ\nชินชา\nชินบุตร\nชิ้นเอก\nชิมลาง\nชีเปลือย\nชี้ขาด\nชี้แจง\nชี้นำ\nชี้แนะ\nชี้ฟ้า\nชีพจร\nชีพิตักษัย\nชื่นชม\nชื่นบาน\nชื่นมื่น\nชื่อย่อ\nชื่อรอง\nชื่อเล่น\nชื่อเสียง\nชุกชุม\nชุติมา\nชุบตัว\nชุบเลี้ยง\nชุมชน\nชุมทาง\nชุมสาย\nชุ่มใจ\nชุ่มชื่น\nชุ่มชื้น\nชุมนุมชน\nชูชีพ\nชูโรง\nชู้สาว\nเชยชม\nเชลยศักดิ์\nเชลยศึก\nเช่าซื้อ\nเช้าตรู่\nเช้ามืด\nเชิงกราน\nเชิงกล\nเชิงชั้น\nเชิงชาย\nเชิงซ้อน\nเชิงเดียว\nเชิงเดี่ยว\nเชิงตะกอน\nเชิงเทิน\nเชิงมุม\nเชิดชู\nเชิงอรรถ\nเชี่ยนหมาก\nเชี่ยวชาญ\nเชื่องช้า\nเชื่อใจ\nเชื่อถือ\nเชื่อฟัง\nเชื่อมือ\nเชื้อชาติ\nเชื้อเพลิง\nเชื้อไฟ\nเชื้อโรค\nเชื้อสาย\nเชื้อเชิญ\nเชื่องช้า\nเชือนแช\nเชื่อวัน\nแช่เย็น\nแช่อิ่ม\nแช่มช้อย\nแช่มชื่น\nโชกโชน\nโชติช่วง\nโชติรส\nใช้สอย\nซบเซา\nซมซาน\nซวนเซ\nซอกซอน\nซอกแซก\nซ่องสุม\nซ่องเสพ\nซ่องแซ่ง\nซ่อนรูป\nซ่อนเร้น\nซ่อนหา\nซ่อนกลิ่น\nซ่อนทราย\nซ่อมแซม\nซักค้าน\nซักซ้อม\nซักไซ้\nซักฟอก\nซักแห้ง\nซังกะตาย\nซังตาย\nซัดเซ\nซัดทอด\nซับซ้อน\nซับใน\nซับพระพักตร์\nซากศพ\nซ่านเซ็น\nซ้ำซ้อน\nซ้ำซาก\nซ้ำเติม\nซ้ำร้าย\nซี่โครง\nซีดเซียว\nซึมกะทือ\nซึมซาบ\nซึมเซา\nซึมทราบ\nซึมเศร้า\nซื่อตรง\nซื่อสัตย์\nซื้อขาย\nซุกซน\nซุกซ่อน\nซุบซิบ\nซู่ซ่า\nเซซัง\nเซ่อซ่า\nแซ่ซ้อง\nโซดาไฟ\nญาณทัสนะ\nญาณวิทยา\nญาณศาสตร์\nญาติกา\nฐานราก\nดกดื่น\nดงดิบ\nดลใจ\nดลบันดาล\nดวงแก้ว\nดวงใจ\nดวงเดือน\nดวงตรา\nดวงตา\nดวงสมร\nดอกจัน\nดอกจิก\nดอกบัว\nดอกเบี้ย\nดอกฟ้า\nดอกไม้\nดอกยาง\nดอกเล็บ\nดอกทอง\nดอกสร้อย\nดองยา\nดักคอ\nดักฟัง\nดังนั้น\nดังนี้\nดังหนึ่ง\nดั้งเดิม\nดัดจริต\nดัดแปลง\nดันทุรัง\nดับขันธ์\nดับจิต\nดับชีพ\nด่าทอ\nด่างทับทิม\nด่างพร้อย\nดาดฟ้า\nดาราศาสตร์\nดาลเดือด\nดาวกระจาย\nดาวเคราะห์\nดาวตก\nดาวเทียม\nดาวรุ่ง\nดาวเรือง\nดาวฤกษ์\nดาวหาง\nดาวเหนือ\nดาษดื่น\nดินขาว\nดินดาน\nดินดำ\nดินประสิว\nดินปืน\nดินระเบิด\nดินสอ\nดินสอพอง\nดิ้นรน\nดิบดี\nดีเกลือ\nดีใจ\nดีซ่าน\nดีดัก\nดีเดือด\nดีฝ่อ\nดีดดิ้น\nดึกดำบรรพ์\nดึกดื่น\nดึงดัน\nดึงดูด\nดื่มด่ำ\nดื้อด้าน\nดื้อดึง\nดื้อแพ่ง\nดื้อยา\nดื้อรั้น\nดุดัน\nดุเดือด\nดุร้าย\nดุลการค้า\nดุลพินิจ\nดุลภาค\nดุลยพินิจ\nดุลยภาพ\nดุษฎีนิพนธ์\nดุษฎีบัณฑิต\nดุษณีภาพ\nดูแคลน\nดูถูก\nดูดาย\nดูเบา\nดูแล\nดูหมิ่น\nดูเหมือน\nดูดดื่ม\nเด็ดขาด\nเด็ดดวง\nเด็ดเดี่ยว\nเดนตาย\nเดาสวด\nเดาสุ่ม\nเดินทาง\nเดินสะพัด\nเดินสาย\nเดินเหิน\nเดิมพัน\nเดียงสา\nเดียดฉันท์\nเดียวกัน\nเดียวดาย\nเดี๋ยวเดียว\nเดี๋ยวนี้\nเดือดดาล\nเดือดร้อน\nเดือนมืด\nเดือนหงาย\nแดดาล\nแดดิ้น\nแดกดัน\nโด่เด่\nโด่งดัง\nโดดเดี่ยว\nโดยสาร\nได้การ\nได้แก่\nได้ใจ\nได้ที\nได้ยิน\nได้เสีย\nตกเขียว\nตกค้าง\nตกใจ\nตกต่ำ\nตกแต่ง\nตกทอด\nตกฟาก\nตกมัน\nตกยาก\nตกลง\nตกหล่น\nต้นขั้ว\nต้นคิด\nต้นฉบับ\nต้นตอ\nต้นตำรับ\nต้นทุน\nต้นแบบ\nต้นเพลิง\nต้นมือ\nต้นไม้\nต้นร่าง\nต้นเรื่อง\nต้นสังกัด\nต้นหน\nต้นเหตุ\nตบตา\nตบแต่ง\nตบแผละ\nตบมือ\nต้มข่า\nต้มโคล้ง\nต้มยำ\nต้มส้ม\nตรมตรอม\nตรรกวิทยา\nตรรกศาสตร์\nตรวจการ\nตรวจการณ์\nตรวจตรา\nตระบัดสัตย์\nตรัสรู้\nตราตั้ง\nตราบาป\nตรายาง\nตราสาร\nตริตรอง\nตรีกฏุก\nตรีกาย\nตรีโกณ\nตรีโกณมิติ\nตรีคูณ\nตรีทูต\nตรีปิฎก\nตรีภพ\nตรีมูรติ\nตรึกตรอง\nตรึงตรา\nตรุษจีน\nตฤณชาติ\nตฤณมัย\nตลกบาตร\nตลบตะแลง\nตลบหลัง\nตลาดนัด\nตลาดน้ำ\nตลาดมืด\nตลาดสด\nต่อตี\nต่อเติม\nต่อว่า\nต่อสู้\nต่อกร\nต่อต้าน\nต่อแย้ง\nต้องการ\nต้องโทษ\nต้องหา\nต้อนรับ\nตอบโต้\nตอบแทน\nต่อยหอย\nตะพาบน้ำ\nตักตวง\nตักบาตร\nตั้งเข็ม\nตั้งไข่\nตั้งเค้า\nตั้งแง่\nตั้งใจ\nตั้งต้น\nตั้งแต่\nตั้งท้อง\nตัดขาด\nตัดใจ\nตัดเชือก\nตัดตอน\nตัดทอน\nตัดบท\nตัดพ้อ\nตัดรอน\nตัดสิน\nตับเต่า\nตับแลบ\nตับอ่อน\nตัวกลาง\nตัวการ\nตัวเก็ง\nตัวดี\nตัวตั้ง\nตัวเต็ง\nตัวถัง\nตัวแทน\nตัวประกอบ\nตัวประกัน\nตัวแปร\nตัวผู้\nตัวพิมพ์\nตัวเมีย\nตัวยืน\nตัวเลข\nตัวอย่าง\nตั๋วเงิน\nตั๋วแลกเงิน\nตากล้อง\nตาไก่\nตาข่าย\nตาชั่ง\nตาตุ่ม\nตาทวด\nตาปลา\nตาราง\nต่างหาก\nต้านทาน\nตามใจ\nตายใจ\nตายซาก\nตายด้าน\nตายตัว\nตายทั้งกลม\nตายห่า\nตายโหง\nตาลปัตร\nต่ำช้า\nต่ำต้อย\nตำส้ม\nติเตียน\nติณชาติ\nติดขัด\nติดใจ\nติดต่อ\nติดตั้ง\nติดตาม\nติดตื้น\nติดพัน\nติดลม\nติดอ่าง\nตีเกลียว\nตีขลุม\nตีความ\nตีคู่\nตีจาก\nตีตื้น\nตีแผ่\nตีรวน\nตีลังกา\nตีวง\nตีเสมอ\nตีนกา\nตีนคู้\nตีนจก\nตีนตะขาบ\nตีนผี\nตีนเหยียด\nตึกแถว\nตึกระฟ้า\nตึงเครียด\nตึงตัง\nตื้นตัน\nตื่นตัว\nตื่นตูม\nตื่นเต้น\nตุ๊ต๊ะ\nตุ้บตั้บ\nตุ้มหู\nตุลาการ\nตุลาคม\nตู้นิรภัย\nตูมตาม\nตู้เสบียง\nเตโชธาตุ\nเตร็ดเตร่\nเต้นรำ\nเตาแก๊ส\nเตาผิง\nเตาฟู่\nเตาไฟ\nเตารีด\nเตาสูบ\nเต่าทอง\nเต้ารับ\nเต้าส่วน\nเต้าเสียบ\nเต้าหู้ยี้\nแต่ละ\nแตกคอ\nแตกคอก\nแตกฉาน\nแตกดับ\nแตกตื่น\nแตกพาน\nแตกแยก\nแตกร้าว\nแตกหัก\nแต่งงาน\nแต่งตั้ง\nแต้มคู\nแต้มต่อ\nแตรงอน\nแตรเดี่ยว\nแตรฝรั่ง\nแตรฟันฟาร์\nแตรวง\nโต้ตอบ\nโต้เถียง\nโต้แย้ง\nโต๊ะหมู่\nโต๊ะอิหม่าม\nใต้ถุน\nไต้ก๋ง\nไต่คู้\nไต่เต้า\nไต่ถาม\nไต้ฝุ่น\nไตรจักร\nไตรจีวร\nไตรตรึงษ์\nไตรทวาร\nไตรปิฎก\nไตรเพท\nไตรภพ\nไตรภูมิ\nไตรภาคี\nไตรยางศ์\nไตรรงค์\nไตรรัตน์\nไตรลักษณ์\nไตรโลก\nไตรสรณคมน์\nไตรสิกขา\nไต่สวน\nถกเถียง\nถดถอย\nถนัดถนี่\nถนิมสร้อย\nถมถืด\nถมเถ\nถมไป\nถลากไถล\nถ้วนถี่\nถ้วยฟู\nถ่องแท้\nถอดถอน\nถ้อยคำ\nถ้อยแถลง\nถากถาง\nถ่านไฟฉาย\nถ่านหิน\nถามไถ่\nถ่ายทอด\nถ่ายทุกข์\nถ่ายเท\nถาวรวัตถุ\nถ้ำมอง\nถี่ถ้วน\nถึงใจ\nถูกใจ\nถูกชะตา\nเถรวาท\nเถ้าแก่\nเถ้าแก่เนี้ย\nแถมพก\nแถลงการณ์\nไถ่ถอน\nไถ่ถาม\nทดแทน\nทดรอง\nทดลอง\nทดสอบ\nทนทาน\nทนายความ\nทบทวน\nทแยงมุม\nทรงกลด\nทรงเครื่อง\nทรงเจ้า\nทรัพย์สิน\nทรามชม\nทรามเชย\nทรามวัย\nทรามสงวน\nทรามสวาท\nทรุดโทรม\nทฤษฎีบท\nท้วงติง\nท่วมท้น\nทวาทศ\nทวาทศมาส\nทวาบรยุค\nทวารบาล\nทวิบถ\nทวิบท\nทวิบาท\nทวิภาค\nทวิภาคี\nทวีคูณ\nทศกัณฐ์\nทศชาติ\nทศทิศ\nทศนิยม\nทศพร\nทศพล\nทศพิธราชธรรม\nทศมาส\nทศวรรษ\nท่อไอเสีย\nท้อถอย\nท้อแท้\nทองขาว\nทองคำ\nทองคำขาว\nทองคำเปลว\nทองเค\nทองแดง\nทองบรอนซ์\nทองม้วน\nทองย้อย\nทองสัมฤทธิ์\nทองหยอด\nทองหยิบ\nทองเหลือง\nทองเอก\nท่องเที่ยว\nท้องตรา\nท้องถิ่น\nท้องที่\nท้องน้อย\nท้องร่อง\nท้องเรื่อง\nทอดมัน\nทอดทิ้ง\nทอดน่อง\nทอดยอด\nทอดหุ่ย\nทอยกอง\nทะเบียนบ้าน\nทะเลทราย\nทะเลสาบ\nทะเลหลวง\nทักขิณาวัฏ\nทักท้วง\nทักทาย\nทักษิณาวรรต\nทักษิณาทาน\nทักษิณานุประทาน\nทั้งกลม\nทั้งคน\nทั้งดุ้น\nทั้งที\nทั้งนั้น\nทั้งนี้\nทั้งปวง\nทั้งผอง\nทั้งเพ\nทั้งมวล\nทั้งสิ้น\nทั้งหมด\nทั้งหลาย\nทัณฑ์บน\nทัดทาน\nทัดเทียม\nทันควัน\nทันใจ\nทันใด\nทันตา\nทันสมัย\nทันที\nทับถม\nทับทรวง\nทับศัพท์\nทั่วถึง\nทั่วไป\nท่าทาง\nท่าที\nท้าทาย\nทางการ\nทางข้าม\nทางด่วน\nทางเท้า\nทางโท\nทางใน\nทางผ่าน\nทางม้าลาย\nทางหลวง\nทางออก\nทางเอก\nทานกัณฑ์\nทานตะวัน\nท่านชาย\nทานบารมี\nท่านผู้หญิง\nท่านหญิง\nทาบทาม\nท้ายทอย\nทารุณกรรม\nทำคลอด\nทำใจ\nทำซ้ำ\nทำท่า\nทำที\nทำแท้ง\nทำโทษ\nทำบาป\nทำบุญ\nทำพิษ\nทำฟัน\nทำร้าย\nทำวัตร\nทำสาว\nทำเสน่ห์\nทำหมัน\nทำให้\nทิ้งขว้าง\nทิ้งทวน\nทิ้งท้าย\nทินกร\nทิพจักขุ\nทิพโสต\nทิพยจักษุ\nทิพยญาณ\nทิพยเนตร\nทิพยรส\nทิพากร\nทิ่มตำ\nทิ่มแทง\nทิวากร\nทิวากาล\nทิศทาง\nทีเด็ด\nทีท่า\nทีนี้\nทีหลัง\nทีฆนิกาย\nทีฆสระ\nที่ดิน\nที่นอน\nที่นั่ง\nที่ปรึกษา\nที่พึ่ง\nที่มั่น\nที่ราบ\nที่ว่าการ\nที่สุด\nที่หมาย\nที่ไหน\nทุกที\nทุกเมื่อ\nทุกข์สุข\nทุนทรัพย์\nทุนนิยม\nทุนรอน\nทุนสำรอง\nทุ่มเถียง\nทุ่มเท\nทูนหัว\nทูลกระหม่อม\nเทกระจาด\nเทครัว\nเทพเจ้า\nเทพดา\nเทพธิดา\nเทพนม\nเทพนิยม\nเทพนิยาย\nเทพบุตร\nเทพสังหรณ์\nเทศกาล\nเทศนาโวหาร\nเทศบัญญัติ\nเทศบาล\nเทศมนตรี\nเทห์ฟากฟ้า\nเท่ากับ\nเท่าใด\nเท่าตัว\nเท่าทัน\nเท่าทุน\nเท่าเทียม\nเท่านั้น\nเท่าไร\nเท้าช้าง\nเทิดทูน\nเที่ยงตรง\nเที่ยงแท้\nเที่ยงธรรม\nเทียนชนวน\nเทียนพรรษา\nเทียบเคียง\nเทียบเท่า\nเทือกเขา\nเทือกเถา\nแท็งก์น้ำ\nแท่นพิมพ์\nแท่นมณฑล\nแท่นหมึก\nแทรกซอน\nแทรกซ้อน\nแทรกซึม\nแทรกแซง\nแทะโลม\nไทยดำ\nไทยทาน\nไทยธรรม\nไทยน้อย\nไทยใหญ่\nธงชัย\nธงชาติ\nธงทิว\nธรณีวิทยา\nธรณีสงฆ์\nธรรมกาย\nธรรมการ\nธรรมเกษตร\nธรรมขันธ์\nธรรมคุณ\nธรรมจรรยา\nธรรมจริยา\nธรรมจักร\nธรรมจักษุ\nธรรมจาคะ\nธรรมจารี\nธรรมชาติ\nธรรมดา\nธรรมเนียม\nธรรมราชา\nธรรมศาสตร์\nธรรมสภา\nธรรมสังเวช\nธัญพืช\nธารพระกร\nธีรภาพ\nธีรราช\nนกเขา\nนกต่อ\nนกยูง\nนกรู้\nนกหวีด\nนครบาล\nนครรัฐ\nนงคราญ\nนงนุช\nนงพะงา\nนงเยาว์\nนงราม\nนงลักษณ์\nนบนอบ\nนพเก้า\nนพคุณ\nนพเคราะห์\nนพปฎล\nนพพล\nนพรัตน์\nนพศก\nนพศูล\nนมข้น\nนมผง\nนมไม้\nนมนาน\nนมหนู\nนมแมว\nนรีเวช\nนรีเวชวิทยา\nนวดฟั้น\nนวยนาด\nนวลระหง\nนวลลออ\nนวลละออง\nนวลจันทร์\nนอกครู\nนอกคอก\nนอกจาก\nนอกใจ\nนอกชาน\nนอกรีต\nนอกเหนือ\nนองเนือง\nนองเลือด\nนอนก้น\nนอนใจ\nนอบนบ\nนอนเล่น\nนอบน้อม\nน้อมนำ\nน้อยใจ\nน้อยหน้า\nนักการ\nนักการเมือง\nนักกีฬา\nนักข่าว\nนักท่องเที่ยว\nนักเทศน์\nนักโทษ\nนักธรรม\nนักบวช\nนักบิน\nนักบุญ\nนักปราชญ์\nนักพรต\nนักรบ\nนักเรียน\nนักเลง\nนักวิชาการ\nนักศึกษา\nนักสิทธิ์\nนักสืบ\nนักหนา\nนั่งเทียน\nนั่งร้าน\nนัดแนะ\nนัดหมาย\nนั่นแหละ\nนั่นเอง\nนับถือ\nนับประสา\nนัยน์ตา\nนาดำ\nนาปรัง\nนาปี\nนาสวน\nนาหว่าน\nนาคบาศ\nนาคปรก\nนาคราช\nนางกวัก\nนางกำนัล\nนางงาม\nนางใน\nนางบำเรอ\nนางแบบ\nนางพญา\nนางฟ้า\nนางไม้\nนางโลม\nนางสาว\nนางห้าม\nนางเอก\nนาฏกรรม\nนาฏดนตรี\nนาฏศิลป์\nนานนม\nน่านน้ำ\nน่านฟ้า\nนามกร\nนามธรรม\nนามไธย\nนามบัตร\nนามปากกา\nนามแฝง\nนามสกุล\nนามสงเคราะห์\nนามสมญา\nนายทะเบียน\nนายท่า\nนายท้าย\nนายทุน\nนายประกัน\nนายหน้า\nนายอำเภอ\nนารายณ์หัตถ์\nนารีผล\nนาวิกโยธิน\nนำจับ\nนำพา\nนำทาง\nนำร่อง\nนำสืบ\nนำแสดง\nน้ำกรด\nน้ำกาม\nน้ำเกลือ\nน้ำข้าว\nน้ำแข็ง\nน้ำแข็งไส\nน้ำแข็งแห้ง\nน้ำครำ\nน้ำคร่ำ\nน้ำค้าง\nน้ำค้างแข็ง\nน้ำคาวปลา\nน้ำคำ\nน้ำเค็ม\nน้ำเคย\nน้ำเงิน\nน้ำเงี้ยว\nน้ำจัณฑ์\nน้ำจิ้ม\nน้ำใจ\nน้ำเชื้อ\nน้ำเชื่อม\nน้ำซาวข้าว\nน้ำดอกไม้\nน้ำดี\nน้ำตก\nน้ำตา\nน้ำตาล\nน้ำท่า\nน้ำนม\nน้ำนวล\nน้ำบาดาล\nน้ำประสานทอง\nน้ำประปา\nน้ำปลา\nน้ำป่า\nน้ำผึ้ง\nน้ำพริก\nน้ำพริกเผา\nน้ำพี้\nน้ำพุ\nน้ำมนต์\nน้ำมนตร์\nน้ำมัน\nน้ำมือ\nน้ำมูก\nน้ำเมา\nน้ำย่อย\nน้ำยา\nน้ำรัก\nน้ำแร่\nน้ำลาย\nน้ำเลี้ยง\nน้ำสต๊อก\nน้ำส้ม\nน้ำส้มสายชู\nน้ำสังข์\nน้ำสาบาน\nน้ำเสียง\nน้ำหนวก\nน้ำหนอง\nน้ำหนัก\nน้ำหน้า\nน้ำหนึ่ง\nน้ำหมึก\nน้ำหอม\nน้ำเหลือง\nน้ำอบ\nน้ำอ้อย\nน้ำอัดลม\nนิ่งเฉย\nนิจศีล\nนิดเดียว\nนิดหน่อย\nนิติกร\nนิติกรรม\nนิติธรรม\nนิตินัย\nนิติบัญญัติ\nนิติบุคคล\nนิติภาวะ\nนิติวิทยาศาสตร์\nนิติเวช\nนิติเวชศาสตร์\nนิติศาสตร์\nนิเทศศาสตร์\nนิ่มนวล\nนิรุกติศาสตร์\nนิเวศวิทยา\nนิศากร\nนิศากาล\nนิศาชล\nนิศารัตน์\nนี่แน่ะ\nนี่แหละ\nนี่เอง\nนึกคิด\nนุงถุง\nนุ่งห่ม\nนุ่มนวล\nนุ่มนิ่ม\nเนตรนารี\nเนติบัณฑิต\nเนยเทียม\nเนยใส\nเนิ่นนาน\nเนิบนาบ\nเนื้อความ\nเนื้อคู่\nเนื้อเค็ม\nเนื้องอก\nเนื้อตัว\nเนื้อตาย\nเนื้อที่\nเนื้อแท้\nเนื้อเปื่อย\nเนื้อผ้า\nเนื้อเพลง\nเนื้อไม้\nเนื้อเยื่อ\nเนื้อร้อง\nเนื้อร้าย\nเนื้อเรื่อง\nเนื้อหา\nเนืองนอง\nเนืองนิตย์\nเนืองแน่น\nแน่ใจ\nแน่ชัด\nแน่แท้\nแน่นอน\nแน่นิ่ง\nแน่แน่ว\nแน่นแฟ้น\nแน่นหนา\nแนบเนียน\nแนบแน่น\nแนวคิด\nแนวทาง\nแนวโน้ม\nแนวป่า\nแนวรบ\nแนวร่วม\nแนวหน้า\nแนวหลัง\nแน่วแน่\nแนะนำ\nแนะแนว\nโน้มน้าว\nในหลวง\nบกพร่อง\nบงกช\nบงการ\nบดบัง\nบทกลอน\nบทกวี\nบทความ\nบทคัดย่อ\nบทเฉพาะกาล\nบทนำ\nบทบัญญัติ\nบทบาท\nบทประพันธ์\nบทเพลง\nบทร้อง\nบทเรียน\nบทลงโทษ\nบทสนทนา\nบทอัศจรรย์\nบทจร\nบทบงสุ์\nบทมาลย์\nบทรัช\nบทเรศ\nบทวลัญช์\nบนบาน\nบรมครู\nบรมธาตุ\nบรมบพิตร\nบรมวงศานุวงศ์\nบรมอัฐิ\nบรรณพิภพ\nบรรณศาลา\nบรรณาการ\nบรรณาธิการ\nบรรณานุกรม\nบรรณารักษ์\nบรรณารักษศาสตร์\nบรรดามี\nบรรดาศักดิ์\nบรรทัดฐาน\nบรรพบุรุษ\nบรรลัยกัลป์\nบรรลัยจักร\nบริคณห์สนธิ\nบวงสรวง\nบ่วงบาศ\nบ้วนพระโอษฐ์\nบ่อเกิด\nบอกกล่าว\nบอกบท\nบอกบุญ\nบอกใบ้\nบอกปัด\nบ้องกัญชา\nบ้องตื้น\nบ้องไฟ\nบ้องหู\nบอดสี\nบ่อนทำลาย\nบอบช้ำ\nบอบบาง\nบอบแบบ\nบังโกลน\nบังโคลน\nบังใบ\nบั้งไฟ\nบังคับการ\nบังคับบัญชา\nบัญชาการ\nบัณฑุกัมพล\nบัดดล\nบัดเดี๋ยว\nบัดนั้น\nบัดนี้\nบัดสีบัดเถลิง\nบัตรเครดิต\nบัตรพลี\nบัตรสนเท่ห์\nบัตรสินเชื่อ\nบั่นทอน\nบั้นท้าย\nบั้นปลาย\nบั้นพระองค์\nบั้นเอว\nบันไดลิง\nบันไดเลื่อน\nบันเทิงคดี\nบัวลอย\nบัวบก\nบ้าจี้\nบ้าดีเดือด\nบ้าน้ำลาย\nบ้าบิ่น\nบ้าระห่ำ\nบ้าเลือด\nบ้าหอบฟาง\nบากบั่น\nบากหน้า\nบางตา\nบางเบา\nบางที\nบาดเจ็บ\nบาดแผล\nบาดหมาง\nบาตรใหญ่\nบาทบงกช\nบาทบงสุ์\nบาทบริจาริกา\nบาทวิถี\nบานเกล็ด\nบานตะเกียง\nบานตะไท\nบานเบอะ\nบานปลาย\nบานแผละ\nบานพับ\nบ้านจัดสรร\nบ้านช่อง\nบ้านนอก\nบ้านพัก\nบ้านเมือง\nบ้านรับรอง\nบ้านเรือน\nบาปกรรม\nบายศรี\nบ่ายเบี่ยง\nบ่ายหน้า\nบ่าวไพร่\nบิดเบี้ยว\nบิดเบือน\nบิดพลิ้ว\nบี้แบน\nบีบคั้น\nบีบรัด\nบึ้งตึง\nบึ้งบูด\nบุกบั่น\nบุกเบิก\nบุกรุก\nบุคลิกภาพ\nบุคลิกลักษณะ\nบุญธรรม\nบุญนิธิ\nบุญฤทธิ์\nบุบสลาย\nบุ้ยใบ้\nบุรุษเพศ\nบุหงารำไป\nบู้บี้\nบูชายัญ\nบูดบึ้ง\nบูดเบี้ยว\nเบาความ\nเบาใจ\nเบาบาง\nเบาปัญญา\nเบามือ\nเบาแรง\nเบาสมอง\nเบาหวาน\nเบาโหวง\nเบ้าตา\nเบาะแส\nเบิกความ\nเบิกบาน\nเบี้ยล่าง\nเบี้ยเลี้ยง\nเบี้ยหวัด\nเบี่ยงบ่าย\nเบียดบัง\nเบียดเบียน\nเบียดเสียด\nเบื้องต้น\nเบื้องบน\nเบื้องหน้า\nเบื้องหลัง\nแบกะดิน\nแบเบาะ\nแบ่งเบา\nแบ่งปัน\nแบ่งแยก\nแบบฉบับ\nแบบแปลน\nแบบแผน\nแบบฝึกหัด\nแบบพิมพ์\nแบบสอบถาม\nแบบอย่าง\nแบะแฉะ\nแบะท่า\nโบแดง\nโบราณคดี\nโบราณวัตถุ\nโบราณสถาน\nใบขับขี่\nใบจอง\nใบตอง\nใบแทรก\nใบบอก\nใบบุญ\nใบเบิกทาง\nใบปลิว\nใบพัด\nใบโพ\nใบไม้\nใบระกา\nใบรับรอง\nใบลา\nใบเลี้ยง\nใบสั่ง\nใบสำคัญ\nใบสุทธิ\nใบเสร็จ\nใบหน้า\nใบอนุญาต\nใบระกา\nปกครอง\nปกคลุม\nปกป้อง\nปกปิด\nปฏิบัติการ\nปฏิบัติบูชา\nปฐพีวิทยา\nปฐมฌาน\nปฐมทัศน์\nปฐมเทศนา\nปฐมนิเทศ\nปฐมพยาบาล\nปฐมยาม\nปฐมฤกษ์\nปฐมวัย\nปฐมสมโพธิ\nปนเป\nป่นปี้\nปมเขื่อง\nปมเด่น\nปมด้อย\nปรนเปรอ\nปรบไก่\nปรบมือ\nปรสิตวิทยา\nประโปรย\nประพรม\nประกันชีวิต\nประกันภัย\nประจักษ์พยาน\nประจัญบาน\nประจันหน้า\nประจำการ\nประจำเดือน\nประจำเมือง\nประจำยาม\nประชดประชัน\nประชากร\nประชากรศาสตร์\nประชาคม\nประชาชน\nประชาราษฎร์\nประชาชาติ\nประชาชี\nประชาทัณฑ์\nประชาบาล\nประชาพิจารณ์\nประชาภิบาล\nประชามติ\nประชาสงเคราะห์\nประชาสัมพันธ์\nประดับประดา\nประดามี\nประดาน้ำ\nประเดี๋ยวเดียว\nประเดี๋ยวนี้\nประทับใจ\nประทุษร้าย\nประเทศราช\nประพาสต้น\nประเพณีนิยม\nประลัยกัลป์\nประวัติการณ์\nประวัติศาสตร์\nประสบการณ์\nประสบการณ์นิยม\nประสาทการ\nประสูติการ\nประสูติกาล\nประเส\nปรับทุกข์\nปรับโทษ\nปรับปรุง\nปรากฏการณ์\nปราดเปรียว\nปราดเปรื่อง\nปราบปราม\nปริญญาบัตร\nปรัยัติธรรม\nปรุโปร่ง\nปลงใจ\nปลงตก\nปลดทุกข์\nปลดปลง\nปลดปล่อย\nปลดเปลื้อง\nปลดระวาง\nปลดแอก\nปล้นสะดม\nปลอกกระสุน\nปลอกคอ\nปลอดโปร่ง\nปลอดภัย\nปลอมปน\nปลอมแปลง\nปลอบโยน\nปล่อยใจ\nปล่อยตัว\nปล่อยปละ\nปลั๊กไฟ\nปลากริม\nปลาเค็ม\nปลาจ่อม\nปลาเจ่า\nปลาแดก\nปลาตู้\nปลาทอง\nปลาร้า\nปลาส้ม\nปลาดาว\nปลาบิน\nปลาฝา\nปลาวาฬ\nปลาหมึก\nปลาบปลื้ม\nปลายข้าว\nปลายแถว\nปลายทาง\nปลิ้นปลอก\nปลิ้นปล้อน\nปลีกตัว\nปลีกย่อย\nปลุกใจ\nปลุกปล้ำ\nปลุกปั่น\nปลุกระดม\nปลุกเสก\nปลูกฝัง\nปลูกสร้าง\nปวดถ่วง\nปวดมวน\nปวดร้าว\nป่วนปั่น\nป่วยการ\nปอกลอก\nป้องกัน\nปักใจ\nปักดำ\nปักหลัก\nปัจเจกบุคคล\nปัจเจกพุทธะ\nปัจเจกโพธิ\nปัจฉิมชน\nปัจฉิมทิศ\nปัจฉิมภาค\nปัจฉิมยาม\nปัจฉิมลิขิต\nปัจฉิมวัย\nปัจฉิมวาจา\nปัญญาชน\nปัญญาวิมุติ\nปัญญาอ่อน\nปัดเป่า\nปันส่วน\nปั่นป่วน\nปั่นแปะ\nปั่นหัว\nปั้นจิ้ม\nปั้นเจ๋อ\nปั้นปึ่ง\nปั้นสิบ\nปั๊มน้ำมัน\nป่าช้า\nป่าชายเลน\nป่าดง\nป่าดงดิบ\nป่าดิบ\nป่าเถื่อน\nป่าเบญจพรรณ\nป่าละเมาะ\nปากกา\nปากขอ\nปากแข็ง\nปากคอ\nปากคำ\nปากคีบ\nปากจัด\nปากน้ำ\nปากเปล่า\nปากเสียง\nปานกลาง\nป่านนี้\nป้านลม\nป้ายสี\nป่าวร้อง\nปิดฉาก\nปิดบัง\nปิตุฆาต\nปิตุภูมิ\nปีมะโว้\nปีแสง\nปี่กลาง\nปี่ไฉน\nปี่ชวา\nปี่นอก\nปี่ใน\nปี่พาทย์\nปี่อ้อ\nปีกกา\nปีนเกลียว\nปีนป่าย\nปึกแผ่น\nปึงปัง\nปืนกล\nปืนครก\nปืนพก\nปืนยา\nปืนยาว\nปืนลม\nปืนเล็ก\nปืนเล็กยาว\nปืนสั้น\nปืนใหญ่\nปุบปับ\nปุ๊บปั๊บ\nปุ่มเปือก\nปุยฝ้าย\nปุ๋ยคอก\nปุ๋ยเคมี\nปุ๋ยวิทยาศาสตร์\nปุ๋ยหมัก\nปุ๋ยอินทรีย์\nปูจ๋า\nปูเสฉวน\nปู่เจ้า\nปู่ทวด\nปูนขาว\nปูนซีเมนต์\nปูนดิบ\nปูนแดง\nปูนปลาสเตอร์\nปูนปั้น\nเป็ดเทศ\nเป็ดน้ำ\nเป็นกลาง\nเป็นใจ\nเป็นต้น\nเป็นต่อ\nเป็นรอง\nเป็นไร\nเป็นลม\nเป็นห่วง\nเป็นอยู่\nเปรมปรีดิ์\nเปรอะเปื้อน\nเปรียบเทียบ\nเปรียบเปรย\nเปรี้ยวปาก\nเปรี้ยวหวาน\nเปรื่องปราด\nเปลญวน\nเปล่งปลั่ง\nเปล่าดาย\nเปล่าเปลี่ยว\nเปลี่ยนใจ\nเปลี่ยนตัว\nเปลี่ยนแปลง\nเปลี่ยนมือ\nเปลี่ยนหน้า\nเป๋อเหลอ\nเปะปะ\nเป่ากบ\nเป้านิ่ง\nเป้าหมาย\nเปิดฉาก\nเปิดเปิง\nเปิดโปง\nเปิดผนึก\nเปิดเผย\nเปียกปูน\nแป้งสาลี\nแป้งนวล\nแป้งเปียก\nแป้งมัน\nแป้งฝุ่น\nแป้งร่ำ\nแป้งสิงคโปร์\nแป้งหมี่\nแปดปน\nแปดเปื้อน\nแปรปรวน\nแปรผัน\nแปรพักตร์\nแปรรูป\nแปรอักษร\nแปลกปลอม\nแปะโป้ง\nโป้ปด\nโปร่งแสง\nโปร่งใส\nโปรดปราน\nโปรยทาน\nโปรยปราย\nโปโลน้ำ\nผกผัน\nผกากรอง\nผงขาว\nผงชูรส\nผงซักฟอก\nผงฟู\nผดุงครรภ์\nผมไฟ\nผลพลอยได้\nผลลัพธ์\nผลัดเปลี่ยน\nผลิตผล\nผลิตภัณฑ์\nผลุบโผล่\nผสมเทียม\nผสมผสาน\nผสมผเส\nผสมพันธุ์\nผสมโรง\nผสมเสร็จ\nผ่องแผ้ว\nผ่องใส\nผ่อนคลาย\nผ่อนชำระ\nผ่อนปรน\nผ่อนผัน\nผ่อนส่ง\nผอมโซ\nผอมแห้ง\nผักชี\nผักตบชวา\nผักบุ้ง\nผังเมือง\nผัดผ่อน\nผันแปร\nผันผวน\nผ่าตัด\nผ่าเผย\nผ่าหมาก\nผ่าเหล่า\nผ้าขนหนู\nผ้าขาวม้า\nผ้าขี้ริ้ว\nผ้าเช็ดตัว\nผ้าเช็ดปาก\nผ้าเช็ดมือ\nผ้าเช็ดหน้า\nผ้าดิบ\nผ้าต่วน\nผ้าไตร\nผ้าถุง\nผ้าแถบ\nผ้านวม\nผ้านุ่ง\nผ้าใบ\nผ้าป่า\nผ้าป่าน\nผ้าผ่อน\nผ้าพันคอ\nผ้าพันแผล\nผ้าแพร\nผ้าโพกหัว\nผ้ามัดหมี่\nผ้ายาง\nผ้าลูกไม้\nผ้าเหลือง\nผ้าอนามัย\nผ้าอ้อม\nผาดโผน\nผาติกรรม\nผิดหวัง\nผิวเผิน\nผิวพรรณ\nผิวหนัง\nผีกระสือ\nผีกระหัง\nผีกองกอย\nผีโขมด\nผีดิบ\nผีตองเหลือง\nผีถ้วยแก้ว\nผีแถน\nผีทะเล\nผีบุญ\nผีปอบ\nผีพุ่งไต้\nผีฟ้า\nผีเรือน\nผีสาง\nผีเสื้อ\nผีห่า\nผึ่งผาย\nผุดผ่อง\nผุดผาด\nผู้คน\nผู้คุม\nผู้จัดการ\nผู้ชาย\nผู้เชี่ยวชาญ\nผู้ดี\nผู้โดยสาร\nผู้ต้องขัง\nผู้ต้องหา\nผู้แทน\nผู้น้อย\nผู้บริโภค\nผู้บังคับบัญชา\nผู้ปกครอง\nผู้ประกอบการ\nผู้ป่วย\nผู้พิพากษา\nผู้เยาว์\nผู้ร้าย\nผู้วิเศษ\nผู้สื่อข่าว\nผู้เสียหาย\nผู้หญิง\nผู้ใหญ่\nผู้ใหญ่บ้าน\nผูกขวัญ\nผูกขาด\nผูกพัน\nผูกมัด\nเผชิญหน้า\nเผด็จการ\nเผด็จศึก\nเผยแผ่\nเผยแพร่\nเผละผละ\nเผ่าพันธุ์\nเผื่อแผ่\nแผงลอย\nแผนการ\nแผนงาน\nแผนที่\nแผนผัง\nแผนภาพ\nแผนภูมิ\nแผ่นดิน\nแผ่นเสียง\nแผ้วพาน\nโผงผาง\nฝนทอง\nฝอยทอง\nฝักแค\nฝักบัว\nฝักฝ่าย\nฝักใฝ่\nฝังใจ\nฝังหัว\nฝาชี\nฝาแฝด\nฝาละมี\nฝ่าพระบาท\nฝ่าฝืน\nฝ่าฟัน\nฝ้าฟาง\nฝากตัว\nฝากฝัง\nฝีดาษ\nฝีมะม่วง\nฝีจักร\nฝีเท้า\nฝีปาก\nฝีพาย\nฝีมือ\nฝีเย็บ\nฝึกงาน\nฝึกปรือ\nฝึกฝน\nฝึกสอน\nฝึกหัด\nฝืดเคือง\nใฝ่ฝัน\nพงพี\nพงศ์พันธุ์\nพญาโศก\nพญาไฟ\nพบปะ\nพบพาน\nพรสวรรค์\nพรมคด\nพรมแดน\nพรมมิ\nพรรคพวก\nพรรณราย\nพรวดพราด\nพรหมชาติ\nพรหมลิขิต\nพรหมโลก\nพรหมวิหาร\nพร้อมใจ\nพร้อมพรั่ง\nพร้อมเพรียง\nพร้อมมูล\nพร้อมสรรพ\nพร้อมหน้า\nพระครู\nพระคุณ\nพระเคราะห์\nพระเครื่อง\nพระเจ้า\nพระเจ้าอยู่หัว\nพระชายา\nพระทัย\nพระนาง\nพระนางเจ้า\nพระเป็นเจ้า\nพระผู้เป็นเจ้า\nพระพิมพ์\nพระพุทธเจ้า\nพระพุทธองค์\nพระภูมิ\nพระยา\nพระรอง\nพระสนม\nพระสนมเอก\nพระองค์\nพระองค์เจ้า\nพระเอก\nพรั่งพร้อม\nพรั่งพรู\nพรั่นพรึง\nพร่างพราว\nพรายน้ำ\nพรายแพรว\nพราวแพรว\nพร่ำพลอด\nพร่ำเพรื่อ\nพร่ำเพ้อ\nพริกไทย\nพริ้งพราย\nพริ้งเพรา\nพริ้งเพริศ\nพริบตา\nพริ้มพราย\nพริ้มเพรา\nพรุ่งนี้\nพฤติกรรม\nพฤติการณ์\nพฤตินัย\nพลการ\nพลขับ\nพลความ\nพลเมือง\nพลรบ\nพลร่ม\nพลเรือน\nพลโลก\nพลศึกษา\nพลบค่ำ\nพลอดรัก\nพลังงาน\nพลังเงียบ\nพลังจิต\nพลั้งปาก\nพลั้งเผลอ\nพลั้งพลาด\nพลัดถิ่น\nพลัดพราก\nพลาดท่า\nพลาดพลั้ง\nพลิกแพลง\nพลีกรรม\nพลุ่งพล่าน\nพวกพ้อง\nพวงมาลัย\nพวงมาลา\nพวงหรีด\nพวงคราม\nพวงชมพู\nพวงแสด\nพ่วงพี\nพวยน้ำ\nพวยพุ่ง\nพสกนิกร\nพหุคูณ\nพหุภาคี\nพหูพจน์\nพหูสูต\nพอควร\nพอใจ\nพอใช้\nพอใช้ได้\nพอดี\nพอตัว\nพอทำเนา\nพอประมาณ\nพอเพียง\nพอแรง\nพอสมควร\nพอเหมาะ\nพ่อขุน\nพ่อครัว\nพ่อตา\nพ่อบ้าน\nพ่อพันธุ์\nพ่อม่าย\nพ่อเมือง\nพ่อเลี้ยง\nพ่อสื่อ\nพอกพูน\nพ้องพาน\nพักผ่อน\nพักพิง\nพักฟื้น\nพักร้อน\nพักแรม\nพัดยศ\nพัดลม\nพันพัว\nพับฐาน\nพับเพียบ\nพัวพัน\nพาซื่อ\nพาดพิง\nพิณพาทย์\nพิธีกร\nพิธีกรรม\nพิธีการ\nพิธีรีตอง\nพิธีสาร\nพินัยกรรม\nพิมพ์เขียว\nพิมพ์ใจ\nพิมพ์ดีด\nพิษสง\nพี่น้อง\nพี่เบิ้ม\nพี่เลี้ยง\nพึงใจ\nพึงพอใจ\nพึ่งพา\nพึ่งพิง\nพืชพันธุ์\nพืชมงคล\nพื้นฐาน\nพื้นที่\nพื้นบ้าน\nพื้นเพ\nพื้นเมือง\nพื้นเสีย\nพุพอง\nพุทธกาล\nพุทธคุณ\nพุทธจักร\nพุทธเจดีย์\nพุทธฎีกา\nพุทธปฏิมา\nพุทธปฏิมากร\nพุทธมามกะ\nพุทธศักราช\nพุทธศาสนิกชน\nพุทธองค์\nพุทธชาด\nพุทธรักษา\nพุ่มพวง\nพุ่มไม้\nพู่กัน\nพูดจา\nเพ่งเล็ง\nเพดานบิน\nเพดานปาก\nเพริศพราย\nเพริศพริ้ง\nเพริศแพร้ว\nเพรียกพร้อง\nเพรียวลม\nเพลงเชิด\nเพลงยาว\nเพลิงกัลป์\nเพลินใจ\nเพลินตา\nเพลี่ยงพล้ำ\nเพ้อฝัน\nเพาะกาย\nเพาะชำ\nเพาะปลูก\nเพิกถอน\nเพิกเฉย\nเพิ่มเติม\nเพิ่มพูน\nเพียงตา\nเพียงพอ\nเพียบแประ\nเพียบพร้อม\nเพื่อนเกลอ\nเพื่อนตาย\nเพื่อนบ้าน\nเพื่อนฝูง\nเพื่อนยาก\nแพ้ท้อง\nแพร่หลาย\nแพร่งพราย\nแพรวพราว\nโพธิญาณ\nโพธิบัลลังก์\nโพธิสมภาร\nโพธิสัตว์\nโพ้นทะเล\nโพยภัย\nไพ่ตาย\nไพ่ป๊อก\nไพรวัน\nไพรสณฑ์\nไพรสัณฑ์\nไพร่พล\nไพร่ฟ้า\nไพร่สม\nไพร่ส่วย\nไพร่หลวง\nฟกช้ำ\nฟองเต้าหู้\nฟองน้ำ\nฟองมัน\nฟ้องกลับ\nฟ้องร้อง\nฟอนเฟะ\nฟักทอง\nฟัดเฟียด\nฟันดาบ\nฟันฝ่า\nฟันแท้\nฟันน้ำนม\nฟันปลา\nฟันฟาง\nฟันเฟือง\nฟันม้า\nฟันเลื่อย\nฟันหนู\nฟั่นเฝือ\nฟั่นเฟือน\nฟื้นตัว\nฟื้นฝอย\nฟื้นฟู\nฟุ้งซ่าน\nฟุ้งเฟ้อ\nฟุ้งเฟื่อง\nฟุตบอล\nฟูฟ่อง\nฟูเฟื่อง\nฟูมฟัก\nฟูมฟาย\nเฟะฟะ\nเฟื่องฟ้า\nเฟื่องฟุ้ง\nเฟื่องฟู\nไฟฉาย\nไฟแช็ก\nไฟธาตุ\nไฟฟ้า\nภัตกิจ\nภาคทัณฑ์\nภาคพื้น\nภาคเรียน\nภาคภูมิ\nภาพถ่าย\nภาพนิ่ง\nภาพประกอบ\nภาพพจน์\nภาพยนตร์\nภาพลวงตา\nภาพลักษณ์\nภายนอก\nภายใน\nภายหน้า\nภายหลัง\nภารกิจ\nภารธุระ\nภารโรง\nภารตวิทยา\nภาษาศาสตร์\nภาสกร\nภิญโญภาพ\nภินชาติ\nภูธร\nภูธเรศ\nภูบาล\nภูเบศ\nภูเบศวร์\nภูเขา\nภูเขาไฟ\nภูผา\nภูตคาม\nภูตบดี\nภูตรูป\nภูเตศวร\nภูมินทร์\nภูมิบาล\nภูมิประเทศ\nภูมิภาค\nภูมิรัฐศาสตร์\nภูมิลำเนา\nภูมิศาสตร์\nภูมิอากาศ\nภูมิธรรม\nภูมิปัญญา\nภูมิรู้\nภูมิใจ\nภูมิฐาน\nภูมิคุ้มกัน\nภูมิแพ้\nภูษาโยง\nเภทภัย\nเภสัชกร\nเภสัชกรรม\nเภสัชวิทยา\nเภสัชศาสตร์\nโภคทรัพย์\nโภคภัณฑ์\nโภชนากร\nโภชนาการ\nมกุฎราชกุมาร\nมงคลแฝด\nมงคลสูตร\nมงคลหัตถี\nมณเฑียรบาล\nมดดำ\nมดแดง\nมดเท็จ\nมดยอบ\nมดลูก\nมธุปายาส\nมธุรส\nมนเทียรบาล\nมนุษย์กบ\nมโนกรรม\nมโนคติ\nมโนทุจริต\nมโนธรรม\nมโนภาพ\nมโนมัย\nมโนรถ\nมโนรมย์\nมโนสุจริต\nมรรคนายก\nมรรคผล\nมฤคชาติ\nมฤคทายวัน\nมฤคราช\nมลทิน\nมลพิษ\nมลสาร\nมวกเหล็ก\nม้วนหน้า\nมวยไทย\nมวยปล้ำ\nมวยล้ม\nมวยวัด\nมวยสากล\nมวยหมู่\nมวลสาร\nมอคราม\nมอซอ\nมอหมึก\nมองเมียง\nมอบตัว\nมอบหมาย\nมอมเมา\nมะขามเทศ\nมะขามป้อม\nมะขามเปียก\nมะเขือเทศ\nมะเขือพวง\nมะพร้าวแก้ว\nมักคุ้น\nมักจี่\nมักง่าย\nมักน้อย\nมักมาก\nมักใหญ่\nมั่งคั่ง\nมั่งมี\nมัจจุราช\nมัชฌิมนิกาย\nมัชฌิมประเทศ\nมัชฌิมยาม\nมัชฌิมวัย\nมัดจำ\nมัดหมี่\nมัธยมกาล\nมัธยมศึกษา\nมันแกว\nมันเทศ\nมันฝรั่ง\nมันเปลว\nมันสมอง\nมั่นคง\nมั่นใจ\nมั่นหมาย\nมั่นเหมาะ\nมัวเมา\nมัวหมอง\nมั่วสุม\nม้าเทศ\nม้าน้ำ\nม้ามืด\nม้าเร็ว\nม้าล่อ\nม้าลาย\nมากมาย\nมาตรการ\nมาตรฐาน\nมาตราส่วน\nมาตุคาม\nมาตุฆาต\nมาตุภูมิ\nม่านตา\nม่านบังตา\nมายากร\nมายากล\nมายาการ\nมายาวี\nมารผจญ\nมารวิชัย\nมารสังคม\nมารหัวขน\nมาลาการ\nมิ่งขวัญ\nมิ่งมิตร\nมิจฉาจาร\nมิจฉาชีพ\nมิดชิด\nมิดเมี้ยน\nมิดหมี\nมิตรจิต\nมิตรภาพ\nมิตรสหาย\nมิน่า\nมีหน้า\nมีดโกน\nมีดดาบ\nมีดโต้\nมีดพก\nมีดพับ\nมีดสั้น\nมึนงง\nมึนชา\nมึนตึง\nมึนเมา\nมืดครึ้ม\nมืดมน\nมืดมัว\nมือจับ\nมือดี\nมือเติบ\nมือปืน\nมือเปล่า\nมือมืด\nมือสอง\nมือเสือ\nมือหนึ่ง\nมือใหม่\nมุกตลก\nมุขปาฐะ\nมุขมนตรี\nมุ่งมั่น\nมุ่งมาด\nมุ่งหน้า\nมุ่งหมาย\nมุ่งหวัง\nมุ้งลวด\nมุ้งสายบัว\nมุมก้ม\nมุมกลับ\nมุมเงย\nมุมฉาก\nมุมตรง\nมุมป้าน\nมุมมืด\nมุมแย้ง\nมุมสะท้อน\nมุมหักเห\nมุมแหลม\nมุสาวาท\nมูกเลือด\nมูกมัน\nมูกหลวง\nมูนดิน\nมูลฐาน\nมูลนาย\nมูลนิธิ\nมูลเหตุ\nมูลค่า\nมูลฝอย\nเม็ดเงิน\nเม็ดเลือด\nเม็ดโลหิต\nเม่นทะเล\nเมรุมาศ\nเมรุราช\nเมล์อากาศ\nเมาดิบ\nเมามัน\nเมามัว\nเมามาย\nเมินเฉย\nเมียน้อย\nเมียหลวง\nเมียงมอง\nเมี่ยงลาว\nเมี่ยงส้ม\nเมื่อกี้\nเมื่อตะกี้\nเมื่อใด\nเมื่อไร\nเมื่อไหร่\nเมื่อนั้น\nเมืองขึ้น\nเมืองท่า\nเมืองนอก\nเมืองหลวง\nเมื่อยขบ\nเมื่อยล้า\nแม่กอง\nแม่กุญแจ\nแม่คุณ\nแม่งาน\nแม่เจ้า\nแม่ชี\nแม่ทัพ\nแม่นม\nแม่น้ำ\nแม่บท\nแม่บ้าน\nแม่เบี้ย\nแม่พระ\nแม่พิมพ์\nแม่เพลง\nแม่มด\nแม่ม่าย\nแม่ไม้\nแม่ยก\nแม่ยาย\nแม่ร้าง\nแม่เรือน\nแม่แรง\nแม่เล้า\nแม่เลี้ยง\nแม่สี\nแม่สื่อ\nแม่เหล็ก\nแมงมุม\nแม่นยำ\nแมลงช้าง\nแมลงวัน\nแมลงปอ\nแมลงภู่\nแมลงเม่า\nแมวเซา\nแมวน้ำ\nแมวป่า\nแมวมอง\nไม้กลัด\nไม้กวาด\nไม้กางเขน\nไม้เกาหลัง\nไม้ขีดไฟ\nไม้จิ้มฟัน\nไม้เด็ด\nไม้ตาย\nไม้ตีพริก\nไม้ที\nไม้เท้า\nไม้บรรทัด\nไม้เมตร\nไม้ระแนง\nไม้เรียว\nไม้หมอน\nไม้อัด\nไม้จัตวา\nไม้ตรี\nไม้ไต่คู้\nไม้โท\nไม้ผัด\nไม้มลาย\nไม้ม้วน\nไม้ยมก\nไม้หน้า\nไม้หันอากาศ\nไม้เอก\nยกกลีบ\nยกครู\nยกเครื่อง\nยกเค้า\nยกทรง\nยกฟ้อง\nยกเมฆ\nยกยอ\nยกย่อง\nยกเลิก\nยกเว้น\nย่นย่อ\nยมทูต\nยมบาล\nยมราช\nยมโลก\nยวดยิ่ง\nยวดยาน\nยวนยี\nยวบยาบ\nย่อท้อ\nย่อส่วน\nย่อหน้า\nย่อหย่อน\nยอกย้อน\nยองใย\nย่องเบา\nย่องแย่ง\nยอดเยี่ยม\nยอดอก\nย้อนยอก\nย้อนรอย\nย้อนศร\nย้อนแสง\nย้อนหลัง\nยอบแยบ\nยอมความ\nย่อมเยา\nย่อยยับ\nยักยอก\nยักย้าย\nยักเยื้อง\nยัญกรรม\nยัญพิธี\nยัดเยียด\nยับเยิน\nยับยั้ง\nยั่วยวน\nยั่วยุ\nยั่วเย้า\nยากวาด\nยากันยุง\nยาเขียว\nยาใจ\nยาฉุน\nยาชา\nยาซัด\nยาดอง\nยาแดง\nยาถ่าย\nยาธาตุ\nยานัตถุ์\nยาเบื่อ\nยาโป๊\nยาแฝด\nยาพิษ\nยาระบาย\nยาสลบ\nยาสั่ง\nยาสีฟัน\nยาสูบ\nยาเส้น\nยาเสพติด\nยาหม่อง\nยาเหลือง\nย่าทวด\nย่านาง\nยากแค้น\nยากจน\nยากเย็น\nยากไร้\nยางนอก\nยางใน\nยางมะตอย\nยางมะตูม\nยางลบ\nยางสน\nยางอาย\nย่างกราย\nย่างเยื้อง\nย่างสด\nย่างสามขุม\nย่างเหยียบ\nยานเกราะ\nยานพาหนะ\nยานอวกาศ\nยานคาง\nยายทวด\nยาวเฟื้อย\nยาวยืด\nยาวเหยียด\nยำทวาย\nยำใหญ่\nยำเกรง\nยำเยง\nย่ำต๊อก\nย่ำยี\nย่ำแย่\nยิงเป้า\nยิ่งนัก\nยิ่งยวด\nยิ่งใหญ่\nยินดี\nยินยอม\nยินร้าย\nยิ้มกริ่ม\nยิ้มแฉ่ง\nยิ้มแต้\nยิ้มแป้น\nยิ้มเผล่\nยิ้มเยาะ\nยิ้มแย้ม\nยียวน\nยึดครอง\nยึดถือ\nยึดมั่น\nยึดเหนี่ยว\nยืดยาด\nยืดยาว\nยืดเยื้อ\nยืดหยุ่น\nยืดอก\nยืนกราน\nยืนต้น\nยืนพื้น\nยืนยง\nยืนยัน\nยืนหยัด\nยื้อยุด\nยุยง\nยุแยง\nยุแหย่\nยุคลบาท\nยุคเข็ญ\nยุคทอง\nยุคมืด\nยุ่งขิง\nยุ่งยาก\nยุ่งเหยิง\nยุติธรรม\nยุทธการ\nยุทธนาวี\nยุทธปัจจัย\nยุทธภัณฑ์\nยุทธภูมิ\nยุทธวิธี\nยุทธศาสตร์\nยุทธหัตถี\nยุทธนาการ\nยุทธนาธิการ\nยุบยับ\nยุบยิบ\nยุพราช\nยู่ยี่\nเย็นเจี๊ยบ\nเย็นใจ\nเย็นฉ่ำ\nเย็นเฉียบ\nเย็นชา\nเย็นชืด\nเย็นตา\nเย็นเยียบ\nเย็นเยือก\nเย็นวาบ\nเย็นวูบ\nเย็บกี่\nเย็บจักร\nเย็บด้าย\nเย้ยหยัน\nเย้าหยอก\nเยาะเย้ย\nเยี่ยมกราย\nเยี่ยมเยียน\nเยี่ยมเยือน\nเยี่ยมยอด\nเยื่อเคย\nเยื่อใย\nเยือกเย็น\nเยื้องกราย\nเยื้องยัก\nเยื้องย่าง\nแยกย้าย\nแยกแยะ\nแย่งชิง\nแยบคาย\nแยบยล\nแย้มพราย\nแย้มยิ้ม\nแย้มสรวล\nโยเย\nโย้เย้\nโยกโคลง\nโยกย้าย\nโยกโย้\nโยนกลอง\nใยหิน\nรกชัฏ\nรกร้าง\nรกเรี้ยว\nรกเรื้อ\nรกราก\nรงควัตถุ\nรชนีกร\nรถกระบะ\nรถเก๋ง\nรถเข็น\nรถแข่ง\nรถจักร\nรถจี๊ป\nรถตู้\nรถทัวร์\nรถบรรทุก\nรถพ่วง\nรถพยาบาล\nรถไฟ\nรถไฟฟ้า\nรถม้า\nรถเมล์\nรถยนต์\nรถราง\nรถลาก\nรถสปอร์ต\nรถสิบล้อ\nรบกวน\nรบรา\nรบเร้า\nรมดำ\nร่มเกล้า\nร่มชูชีพ\nร่มเย็น\nร่มรื่น\nร่วงโรย\nรวงผึ้ง\nรวงรัง\nรวดเร็ว\nรวนเร\nรวบยอด\nรวบรวม\nรวบรัด\nรวมพล\nรวมหัว\nร่วมใจ\nร่วมเพศ\nร่วมมือ\nร่วมรัก\nร่วมสมัย\nรวยริน\nรวยรื่น\nรสชาติ\nรสนิยม\nรองท้อง\nรองเท้า\nรองพื้น\nร่องน้ำ\nร่องรอย\nร้องขอ\nร้องทุกข์\nร้องเรียน\nร้องห่ม\nร้องไห้\nรองช้ำ\nรองทรง\nรอดชีวิต\nรอดตัว\nรอดตาย\nรอนแรม\nร่อนเร่\nร้อนใจ\nร้อนตัว\nร้อนรน\nร้อนรุ่ม\nร้อนวิชา\nร้อนอาสน์\nรอบจัด\nรอบเดือน\nรอบรู้\nรอยร้าว\nร่อยหรอ\nร้อยละ\nร้อยกรอง\nร้อยแก้ว\nร้อยหวาย\nระนาดทุ้ม\nระนาดเอก\nระเบิดขวด\nระเบิดมือ\nระเบียบการ\nรักใคร่\nรักษาการ\nรักษาการณ์\nรังไข่\nรังแตน\nรังนก\nรังผึ้ง\nรังเพลิง\nรังมด\nรังสรรค์\nรังสฤษฏ์\nรั้งรอ\nรังสีแพทย์\nรังสีวิทยา\nรัชกาล\nรัชทายาท\nรัชนีกร\nรัฐธรรมนูญ\nรัฐบาล\nรัฐบุรุษ\nรัฐประศาสน์\nรัฐประหาร\nรัฐพิธี\nรัฐมนตรี\nรัฐวิสาหกิจ\nรัฐศาสตร์\nรัฐสภา\nรัดกุม\nรัดเกล้า\nรัดตัว\nรัดประคด\nรัดรึง\nรัดรูป\nรัตติกาล\nรับขวัญ\nรับจ้าง\nรับช่วง\nรับใช้\nรับซื้อ\nรับทราบ\nรับประกัน\nรับประทาน\nรับปาก\nรับผิด\nรับผิดชอบ\nรับฟ้อง\nรับฟัง\nรับมือ\nรับรอง\nรับรู้\nรับสมัคร\nรับสั่ง\nรับหน้า\nรับเหมา\nรั่วไหล\nรามือ\nร่าเริง\nรากแก้ว\nรากขวัญ\nรากฐาน\nรากฟัน\nรากศัพท์\nรากเหง้า\nร่างกาย\nร่างแห\nร้างรา\nราชกรณียกิจ\nราชการ\nราชกิจ\nราชครู\nราชฐาน\nราชทัณฑ์\nราชทินนาม\nราชทูต\nราชธานี\nราชนาวี\nราชบัณฑิต\nราชบัลลังก์\nราชบาตร\nราชบุตร\nราชปะแตน\nราชภัฏ\nราชมัล\nราชยาน\nราชรถ\nราชลัญจกร\nราชเลขาธิการ\nราชเลขานุการ\nราชวงศ์\nราชวัติ\nราชสกุล\nราชสมบัติ\nราชสาส์น\nราชหัตถเลขา\nราชองครักษ์\nราชโองการ\nราชาคณะ\nราชาศัพท์\nราชินีนาถ\nร้านชำ\nร้านรวง\nราบคาบ\nราบรื่น\nราบเรียบ\nรายการ\nรายงาน\nรายจ่าย\nรายได้\nรายทาง\nรายรับ\nรายล้อม\nรายละเอียด\nรายวิชา\nร่ายยาว\nร่ายรำ\nร้ายกาจ\nร้ายแรง\nราวนม\nราวป่า\nร้าวฉาน\nร้าวราน\nรำพัด\nรำแพน\nรำวง\nร่ำไป\nร่ำร้อง\nร่ำเรียน\nร่ำไร\nร่ำลา\nร่ำไห้\nริเริ่ม\nริอ่าน\nริมฝีปาก\nริ้วรอย\nรีบร้อน\nรีบรุด\nรีบเร่ง\nรื่นรมย์\nรื่นเริง\nรื้อถอน\nรื้อฟื้น\nรุกฆาต\nรุกราน\nรุกล้ำ\nรุกไล่\nรุ่งขึ้น\nรุ่งแจ้ง\nรุ่งเช้า\nรุ่งเรือง\nรุ่งโรจน์\nรุ่งสว่าง\nรุ่งสาง\nรุ่งอรุณ\nรุจิเรข\nรุดหน้า\nรุนแรง\nรุมเร้า\nรุมล้อม\nรุ่มรวย\nรุ่มร้อน\nรุ่ยร่าย\nรู้แกว\nรู้ความ\nรู้คุณ\nรู้งาน\nรู้จัก\nรู้แจ้ง\nรู้ใจ\nรู้เชิง\nรู้ตัว\nรู้ทัน\nรู้เท่า\nรู้เรื่อง\nรู้สำนึก\nรู้สึก\nรู้เห็น\nรูปการณ์\nรูปโฉม\nรูปฌาน\nรูปถ่าย\nรูปทรง\nรูปธรรม\nรูปแบบ\nรูปพรรณ\nรูปพรหม\nรูปภพ\nรูปภาพ\nรูปร่าง\nรูปสมบัติ\nเร่ร่อน\nเร่งด่วน\nเร่งมือ\nเร่งรัด\nเร่งรีบ\nเร่งเร้า\nเร้นลับ\nเร่อร่า\nเร่าร้อน\nเราะราย\nเราะร้าย\nเริงใจ\nเริงรมย์\nเริดร้าง\nเริ่มต้น\nเริ่มแรก\nเรี่ยราด\nเรี่ยไร\nเรียกคืน\nเรียกตัว\nเรียกร้อง\nเรียกหา\nเรียบร้อย\nเรียงความ\nเรียงตัว\nเรียงเบอร์\nเรียงพิมพ์\nเรียงเม็ด\nเรียงราย\nเรียนรู้\nเรียบร้อย\nเรียบเรียง\nเรียบวุธ\nเรี่ยมเร้\nเรี่ยวแรง\nเรือกลไฟ\nเรือกอและ\nเรือกำปั่น\nเรือจ้าง\nเรือดำน้ำ\nเรือโดยสาร\nเรือตรวจการณ์\nเรือตังเก\nเรือธง\nเรือนำร่อง\nเรือบด\nเรือบิน\nเรือใบ\nเรือประมง\nเรือพ่วง\nเรือพิฆาต\nเรือยนต์\nเรือยาว\nเรือโยง\nเรือรบ\nเรือลากจูง\nเรือสำปั้น\nเรือสำเภา\nเรือหลวง\nเรือหางยาว\nเรืออีโปง\nเรือเอี้ยมจุ๊น\nเรื้อรัง\nเรือกสวน\nเรืองนาม\nเรืองรอง\nเรืองแสง\nเรื่องราว\nเรื่องสั้น\nเรือนแก้ว\nเรือนจำ\nเรือนเบี้ย\nเรือนแพ\nเรือนหอ\nเรื่อยเจื้อย\nเรื่อยเฉื่อย\nเรื่อยเปื่อย\nแรเงา\nแรกนา\nแรกนาขวัญ\nแรงงาน\nแรงดึงดูด\nแรงเทียน\nแรงม้า\nแรงเหวี่ยง\nแรมรอน\nแรมรา\nแรมโรย\nโรคจิต\nโรงครัว\nโรงงาน\nโรงเจ\nโรงเตี๊ยม\nโรงทาน\nโรงนา\nโรงพยาบาล\nโรงพัก\nโรงพิมพ์\nโรงเรียน\nโรงเรือน\nโรงแรม\nโรงเลี้ยง\nโรงเลื่อย\nโรงสี\nโรงสีข้าว\nโรงอาหาร\nโรมรัน\nโรยรา\nฤชากร\nฤดูกาล\nลงขัน\nลงแขก\nลงคอ\nลงตัว\nลงท้าย\nลงทุน\nลงโทษ\nลงพุง\nลงมือ\nลงรัก\nลงรอย\nลงแรง\nลงโรง\nลงเอย\nลดตัว\nลดละ\nลดเลี้ยว\nลดหย่อน\nลดหลั่น\nลนลาน\nล้นพ้น\nล้นหลาม\nล้นเหลือ\nลบล้าง\nลบเลือน\nลบหลู่\nลมกรด\nลมค้า\nลมงวง\nลมแดด\nลมทะเล\nลมบก\nลมบน\nลมบ้าหมู\nลมปราณ\nลมปาก\nลมพิษ\nลมว่าว\nลมเสีย\nลมหนาว\nลมหายใจ\nล่มจม\nล่มสลาย\nล้มละลาย\nล้มลุก\nล้มเลิก\nล่วงเกิน\nล่วงรู้\nล่วงละเมิด\nล่วงลับ\nล่วงล้ำ\nล่วงเลย\nล่วงหน้า\nลวดลาย\nลวดสปริง\nลวดหนาม\nล้วนแล้ว\nลหุโทษ\nล่อลวง\nล่อหลอก\nล่อแหลม\nล้อต๊อก\nล้อเลื่อน\nล้อเล่น\nล้อเลียน\nล้อหลอก\nลองเชิง\nลองดี\nลองภูมิ\nล่องหน\nลอดช่อง\nล่อนจ้อน\nลอบกัด\nล้อมวง\nลอยแก้ว\nลอยชาย\nลอยตัว\nลอยนวล\nลอยแพ\nลอยลำ\nละทิ้ง\nละเลย\nละเว้น\nละครนอก\nละครใน\nละครเพลง\nละครร้อง\nละครรำ\nละครลิง\nละครสัตว์\nละเอียดอ่อน\nลักไก่\nลักพา\nลักเพศ\nลักยิ้ม\nลักลอบ\nลักลั่น\nลักหลับ\nลัดเลาะ\nลับตา\nลับแล\nลับหลัง\nลาออก\nล่าช้า\nล่าทัพ\nล้าสมัย\nล้าหลัง\nลากข้าง\nล้างบาง\nล้างผลาญ\nลาดเขา\nลาดตระเวน\nลาดเท\nลาดยาง\nลานบิน\nลาภปาก\nลาภลอย\nลามปาม\nลามเลีย\nลายคราม\nลายเซ็น\nลายแทง\nลายน้ำ\nลายพร้อย\nลายมือ\nลายลักษณ์\nลายเส้น\nลำกล้อง\nลำแข้ง\nลำธาร\nลำแสง\nลำไส้\nลำตัด\nลำนำ\nล่ำสัน\nล้ำยุค\nล้ำสมัย\nล้ำลึก\nล้ำเลิศ\nล้ำเส้น\nล้ำหน้า\nลิงจุ่น\nลิงลม\nลิงโลด\nลิดรอน\nลิ้นไก่\nลิ้นชัก\nลิ้นปี่\nลิ้นควาย\nลิ้นงูเห่า\nลิ้นหมา\nลิบลับ\nลิบลิ่ว\nลิ่มเลือด\nลี้ภัย\nลี้ลับ\nลึกซึ้ง\nลึกลับ\nลึกล้ำ\nลืมตน\nลืมต้น\nลืมตัว\nลืมตา\nลืมเลือน\nลือชา\nลือชื่อ\nลือลั่น\nลุล่วง\nลุกลน\nลุกลาม\nลุกลี้ลุกลน\nลุกฮือ\nลุ่มน้ำ\nลุ่มลึก\nลุ่มหลง\nลุ่ทาง\nลูกกรง\nลูกกรอก\nลูกกรุง\nลูกกลอน\nลูกกลิ้ง\nลูกกวาด\nลูกกะจ๊อก\nลูกกุญแจ\nลูกเกด\nลูกแก้ว\nลูกขนไก่\nลูกข่าง\nลูกขุน\nลูกเขย\nลูกครึ่ง\nลูกคลื่น\nลูกความ\nลูกคอ\nลูกค้า\nลูกคิด\nลูกคู่\nลูกจ้าง\nลูกช้าง\nลูกชิด\nลูกชิ้น\nลูกชุบ\nลูกซอง\nลูกโซ่\nลูกดอก\nลูกดิ่ง\nลูกตะกั่ว\nลูกตุ้ม\nลูกเต้า\nลูกเต๋า\nลูกถ้วย\nลูกทุ่ง\nลูกเธอ\nลูกน้อง\nลูกน้ำ\nลูกนิมิต\nลูกบอล\nลูกบ้าน\nลูกบาศก์\nลูกบิด\nลูกเบี้ยว\nลูกประคบ\nลูกประคำ\nลูกปัด\nลูกปืน\nลูกโป่ง\nลูกผสม\nลูกผู้ชาย\nลูกผู้หญิง\nลูกพรรค\nลูกพี่\nลูกฟูก\nลูกไฟ\nลูกมือ\nลูกโม่\nลูกไม้\nลูกยาเธอ\nลูกรอก\nลูกรัง\nลูกเรือ\nลูกล้อ\nลูกลอย\nลูกเล่น\nลูกเลี้ยง\nลูกโลก\nลูกวัด\nลูกศร\nลูกศิษย์\nลูกสมุน\nลูกสะใภ้\nลูกสูบ\nลูกเสือ\nลูกหนัง\nลูกหนี้\nลูกหนู\nลูกหมาก\nลูกหลง\nลูกหลาน\nลูกหาบ\nลูกหิน\nลูกเห็บ\nลูกเหม็น\nลูกแหง่\nลูกอม\nลูกหม้อ\nลูบคม\nลูบคลำ\nลูบไล้\nเล็กน้อย\nเลขคณิต\nเลขผา\nเลขหมาย\nเล็ดลอด\nเล่นงาน\nเล่นแง่\nเล่นชู้\nเล่นตัว\nเล่นลิ้น\nเล่นหัว\nเลนส์นูน\nเลนส์เว้า\nเล็บครุฑ\nเลยเถิด\nเลศนัย\nเล่ห์กล\nเล่ห์เหลี่ยม\nเลอโฉม\nเลอมาน\nเลอเลิศ\nเลอสรวง\nเล่อล่า\nเลอะเลือน\nเล่าเรียน\nเล่าลือ\nเลาะลัด\nเลิกรา\nเลิกร้าง\nเลิกล้ม\nเลิศเลอ\nเลี้ยงชีพ\nเลี้ยงดู\nเลี้ยงต้อย\nเลียบเคียง\nเลี้ยวลด\nเลือกตั้ง\nเลือกเฟ้น\nเลือกสรร\nเลื่องลือ\nเลือดกำเดา\nเลือดเนื้อ\nเลือดฝาด\nเลือดเย็น\nเลือดร้อน\nเลือดหมู\nเลือดอุ่น\nเลือนราง\nเลื่อนเปื้อน\nเลื่อนลอย\nเลื่อมพราย\nเลื่อมใส\nเลื่อยฉลุ\nเลื่อยลันดา\nเลื่อยวงเดือน\nเลื้อยคลาน\nแลเหลียว\nแลกเปลี่ยน\nแล้วกัน\nและเล็ม\nโล่งใจ\nโล่งโถง\nโล่งอก\nโลดเต้น\nโลดโผน\nโลดลิ่ว\nโลดแล่น\nไล่ที่\nไล่เบี้ย\nไล่เลี่ย\nไล่เลียง\nไล่หลัง\nไล่ออก\nวกวน\nวงกบ\nวงกลม\nวงการ\nวงแขน\nวงเงิน\nวงจร\nวงนอก\nวงใน\nวงรี\nวงเล็บ\nวงเวียน\nวงแหวน\nวงศ์วาน\nวจีกรรม\nวจีเภท\nวจีภาค\nวนเวียน\nวอดวาย\nว็อบแว็บ\nวังวน\nวังหน้า\nวังหลวง\nวังหลัง\nวัดราษฎร์\nวัดวา\nวัดหลวง\nวัดผล\nวัดพื้น\nวัตถุนิยม\nวัตถุประสงค์\nวัตรปฏิบัติ\nวันโกน\nวันพระ\nวันเพ็ญ\nวัยรุ่น\nวัยวุฒิ\nว่ากล่าว\nว่าจ้าง\nว่าด้วย\nว่าที่\nวางก้าม\nวางใจ\nวางตัว\nวางตา\nวางโต\nวางท่า\nวางมวย\nวางมาด\nวางมือ\nวางวาย\nว่างเปล่า\nว่างเว้น\nวาดเขียน\nว่านเครือ\nวาบหวาม\nวายชนม์\nวายปราณ\nวายวาง\nวายวอด\nวายร้าย\nวายุภักษ์\nวาววับ\nวาววาม\nวาวแวว\nวาวแสง\nวิกฤตการณ์\nวิกฤติการณ์\nวิกฤตกาล\nวิกฤติกาล\nวิกลจริต\nวิงเวียน\nวิ่งเต้น\nวิ่งผลัด\nวิ่งรอก\nวิ่งราว\nวิจิตรศิลป์\nวิชาการ\nวิชาชีพ\nวิชาธร\nวิญญูชน\nวิดพื้น\nวิตกจริต\nวิถีทาง\nวิทยากร\nวิทยากล\nวิทยาการ\nวิทยาเขต\nวิทยาทาน\nวิทยาธร\nวิทยานิพนธ์\nวิทยาศาสตร์\nวิเทศสัมพันธ์\nวิธีการ\nวินัยธร\nวินัยปิฎก\nวินาศกรรม\nวินาศภัย\nวินาศสันตะโร\nวิภัชพยากรณ์\nวิภัชวาที\nวิไลวรรณ\nวิสัญญีแพทย์\nวิสัญญีภาพ\nวิสัญญีวิทยา\nวุฒิบัตร\nวุฒิสภา\nวุฒิสมาชิก\nวุ่นวาย\nวุ้นเส้น\nวูบวาบ\nเวจกุฎี\nเวจมรรค\nเวชกรรม\nเวชภัณฑ์\nเวชศาสตร์\nเวทมนตร์\nเวนคืน\nเวรกรรม\nเวฬุการ\nเวฬุวัน\nเว้าวอน\nเวิ้งว้าง\nเวียงวัง\nเวียนเทียน\nแว้งกัด\nแวดล้อม\nแวดวง\nแว่นขยาย\nแว่นแคว้น\nแว่นตา\nแวบวับ\nแววตา\nแวววาม\nแวววาว\nแวะเวียน\nโวยวาย\nไวไฟ\nไว้ใจ\nไว้ชื่อ\nไว้ตัว\nไว้ทุกข์\nไว้ลาย\nไว้หน้า\nไว้อาลัย\nศนิวาร\nศอกกลับ\nศอกกำ\nศอกกำมา\nศักดิ์ศรี\nศักดิ์สิทธิ์\nศารทวิษุวัติ\nศาลแขวง\nศาลจังหวัด\nศาลชั้นต้น\nศาลฎีกา\nศาลเตี้ย\nศาลทหาร\nศาลปกครอง\nศาลพระภูมิ\nศาลเพียงตา\nศาลแพ่ง\nศาลรัฐธรรมนูญ\nศาลแรงงาน\nศาลล้มละลาย\nศาลโลก\nศาลสูง\nศาลสูงสุด\nศาลอาญา\nศาลอุทธรณ์\nศาลากลาง\nศาลาดิน\nศาลาราย\nศาลาวัด\nศิลาฤกษ์\nศิลาแลง\nศิษย์เก่า\nศิษย์เอก\nศีลจุ่ม\nศีลธรรม\nศีลวัต\nศีลอด\nศูนย์กลาง\nศูนย์การค้า\nศูนย์ถ่วง\nศูนย์สูตร\nศูนย์หน้า\nเศร้าใจ\nเศร้าโศก\nเศร้าสร้อย\nเศร้าสลด\nเศร้าหมอง\nเศวตฉัตร\nเศษเกิน\nเศษซ้อน\nเศษวรรค\nเศษส่วน\nเศษเหล็ก\nโศกนาฏกรรม\nโศกศัลย์\nโศกเศร้า\nโศกสลด\nสกลโลก\nส่งเดช\nส่งท้าย\nส่งเสริม\nส่งเสีย\nส่งเสียง\nสงบเงียบ\nสงบเสงี่ยม\nสง่างาม\nสง่าราศี\nสดชื่น\nสดใส\nสตรีเพศ\nสติปัญญา\nสถลมารค\nสถานกงสุล\nสถานที่\nสถานทูต\nสถานการณ์\nสถานภาพ\nสถิติศาสตร์\nสนตะพาย\nสนใจ\nส้นตีน\nสนธิสัญญา\nสนนราคา\nสนับแข้ง\nสนับเพลา\nสนับมือ\nสนามบิน\nสนามเพลาะ\nสนิทสนม\nสนิมขุม\nสนิมสร้อย\nสนุกสนาน\nสบประมาท\nสบายใจ\nสภาพธรรม\nสมควร\nสมจริง\nสมใจ\nสมนัย\nสมน้ำหน้า\nสมประกอบ\nสมส่วน\nสมหวัง\nสมคบ\nสมทบ\nสมยอม\nสมรัก\nสมรู้\nสมสู่\nส้มฉุน\nส้มตำ\nส้มลิ้ม\nส้มกุ้ง\nส้มเช้า\nสมญานาม\nสมมติฐาน\nสมมุติฐาน\nสมมติเทพ\nสมรภูมิ\nสมัครใจ\nสมัยนิยม\nสมุทรศาสตร์\nสมุทรเสนา\nสยดสยอง\nสยองขวัญ\nสยามรัฐ\nสรรหา\nสรวมชีพ\nสรวลเส\nสร้อยเศร้า\nสร้างสรรค์\nสร้างเสริม\nสลดใจ\nสลบไสล\nสละสลวย\nสลาเหิน\nสลากภัต\nสวนครัว\nสวนป่า\nสวนสนุก\nสวนหย่อม\nส่วนกลาง\nส่วนเกิน\nส่วนตัว\nส่วนบุญ\nส่วนแบ่ง\nส่วนประกอบ\nส่วนพระองค์\nส่วนผสม\nส่วนรวม\nส่วนร่วม\nส่วนลด\nส่วนสัด\nสวมกอด\nสวมเขา\nสวมรอย\nสวยมภู\nสว่างไสว\nสวามิภักดิ์\nสวิงสวาย\nสสารนิยม\nส่อเสียด\nสอดคล้อง\nสอดแทรก\nสอดแนม\nสอบถาม\nสอบทาน\nสอบไล่\nสอบสวน\nส้อมเสียง\nสะสวย\nสะแกวัลย์\nสะแกแสง\nสะใจ\nสะเด็ดยาด\nสะเทือนใจ\nสะบัดช่อ\nสั่งสม\nสั่งสอน\nสั่งเสีย\nสังเกตการณ์\nสังคมนิยม\nสังคมวิทยา\nสังคมศาสตร์\nสังคมศึกษา\nสังคมสงเคราะห์\nสัญญาบัตร\nสัดส่วน\nสัตการ\nสัตบุรุษ\nสัตบริภัณฑ์\nสัตภัณฑ์\nสัตมหาสถาน\nสัตโลหะ\nสันเขา\nสันดอน\nสันหลัง\nสั่นเทา\nสั่นเทิ้ม\nสันติบาล\nสันติภาพ\nสันติวิธี\nสันติสุข\nสับเปลี่ยน\nสับสน\nสับหลีก\nสับหว่าง\nสัมมาคารวะ\nสัมมาชีพ\nส่าเหล้า\nสากกะเบือ\nสาทิสลักษณ์\nสาธุการ\nสาธุชน\nสาบเสือ\nสาปสรร\nสาปแช่ง\nสาปส่ง\nสามง่าม\nสามล้อ\nสามเหลี่ยม\nสามเวท\nสามัญชน\nสามัญสำนึก\nสายดิ่ง\nสายดิน\nสายตรวจ\nสายน้ำ\nสายบัว\nสายพาน\nสายฟ้า\nสายยาง\nสายยู\nสายใย\nสายรก\nสายรุ้ง\nสายล่อฟ้า\nสายลับ\nสายเลือด\nสายโลหิต\nสายวัด\nสายส่ง\nสายสวาท\nสายสะดือ\nสายสะพาย\nสายสัมพันธ์\nสายสิญจน์\nสายสืบ\nสายไหม\nสายอากาศ\nสายตา\nสายหยุด\nสารตรา\nสารประกอบ\nสารละลาย\nสารส้ม\nสารหนู\nสารทฤดู\nสาวใช้\nสาวน้อย\nสาวใหญ่\nสำนักงาน\nสำนักพิมพ์\nสำนักสงฆ์\nสำมะโนครัว\nสำเร็จรูป\nสิกขาบท\nสิงสถิต\nสิงสู่\nสิ่งก่อสร้าง\nสิ่งของ\nสิ่งปฏิกูล\nสิ่งพิมพ์\nสิ่งแวดล้อม\nสิ่งศักดิ์สิทธิ์\nสิทธิกร\nสิทธิ์ขาด\nสิทธิชัย\nสิทธิโชค\nสิทธิบัตร\nสินค้า\nสินจ้าง\nสินเชื่อ\nสินไถ่\nสินทรัพย์\nสินน้ำใจ\nสินบน\nสินแร่\nสินสมรส\nสินสอด\nสินไหม\nสิ้นเชิง\nสิ้นสุด\nสีผึ้ง\nสีลม\nสีชอล์ก\nสีถ่าน\nสีเทียน\nสีน้ำ\nสีน้ำมัน\nสีโปสเตอร์\nสีฝุ่น\nสี่เหลี่ยม\nสีหน้า\nสึกหรอ\nสืบทอด\nสืบค้น\nสืบสวน\nสืบสาว\nสืบเสาะ\nสื่อผสม\nสื่อมวลชน\nสื่อสาร\nสุกงอม\nสุกดิบ\nสุกปลั่ง\nสุกใส\nสุขนาฏกรรม\nสุขภัณฑ์\nสุขภาพ\nสุขลักษณะ\nสุขวิทยา\nสุขศาลา\nสุขศึกษา\nสุดท้าย\nสุตกวี\nสุนทรพจน์\nสุภาพชน\nสู่ขอ\nสู่รู้\nสู่สม\nสูงส่ง\nสูญเปล่า\nสูญสิ้น\nสูญเสีย\nสูญหาย\nเสสรวล\nเสแสร้ง\nเสกสรร\nเสถียรภาพ\nเส้นชัย\nเส้นตรง\nเส้นตาย\nเส้นทาง\nเส้นใย\nเส้นรุ้ง\nเส้นเลือด\nเส้นแวง\nเส้นสาย\nเส้นเสียง\nเส้นหมี่\nเส้นเอ็น\nเสบียงกรัง\nเสมอภาค\nเสมอหน้า\nเสมอเหมือน\nเสมียนตรา\nเสร็จสรรพ\nเสร็จสิ้น\nเสริมส่ง\nเสริมสร้าง\nเสริมสวย\nเสรีไทย\nเสรีธรรม\nเสรีนิยม\nเสรีภาพ\nเสาเข็ม\nเสาธง\nเสียใจ\nเสียเชิง\nเสียดาย\nเสียที\nเสียเที่ยว\nเสียเปรียบ\nเสียเปล่า\nเสียรู้\nเสียแรง\nเสียสละ\nเสียหลัก\nเสียหาย\nเสี่ยงทาย\nเสียดแทง\nเสียดแทรก\nเสียดสี\nเสี้ยนศึก\nเสี้ยนหนาม\nเสี้ยมสอน\nเสียวซ่าน\nเสียวไส้\nเสือดาว\nเสือดำ\nเสือปลา\nเสือป่า\nเสือไฟ\nเสื่อกก\nเสื่อกระจูด\nเสื่อน้ำมัน\nเสื่อลำแพน\nเสื้อกล้าม\nเสื้อกั๊ก\nเสื้อเกราะ\nเสื้อครุย\nเสื้อแสง\nเสื้อเมือง\nเสือกคลาน\nเสือกสน\nเสือกไส\nเสื่อมคลาย\nเสื่อมถอย\nเสื่อมทราม\nเสื่อมโทรม\nเสื่อมสลาย\nเสื่อมสูญ\nเสื่อมเสีย\nเสือหมอบ\nแสกหน้า\nแสดงออก\nแสเถา\nแสนกล\nแสนรู้\nแสร้งว่า\nใส่ความ\nใส่ไคล้\nใส่ใจ\nใส่ไฟ\nไส้กรอก\nไส้ไก่\nไส้ติ่ง\nไส้ศึก\nไส้อั่ว\nไส้เดือน\nไส้ตัน\nไสยเวท\nไสยศาสตร์\nหกล้ม\nหงส์หยก\nหงอนไก่\nหงอยก๋อย\nหงอยเหงา\nหงายท้อง\nหงายหลัง\nหงำเหงอะ\nหงำเหงือก\nหดหาย\nหดหู่\nหนทาง\nหนวกหู\nหน่วงเหนี่ยว\nหน่วยก้าน\nหน่อไม้\nหนองใน\nหนองแซง\nหนักข้อ\nหนักใจ\nหนักแน่น\nหนักหน่วง\nหนักหนา\nหนังกลับ\nหนังตะลุง\nหนังเรียด\nหนังสด\nหนังใหญ่\nหนังสือพิมพ์\nหนาแน่น\nหน้ากระดาน\nหน้ากาก\nหน้ากาฬ\nหน้าแข้ง\nหน้าจั่ว\nหน้าฉาน\nหน้าตัก\nหน้าตา\nหน้าต่าง\nหน้าท้อง\nหน้าทับ\nหน้าที่\nหน้าที่นั่ง\nหน้าบัน\nหน้าปัด\nหน้าผา\nหน้าผาก\nหน้าม้า\nหน้ามุข\nหน้าไม้\nหน้าเลือด\nหน้าอก\nหนามเตย\nหน่ายหนี\nหน่ายแหนง\nหนาวเหน็บ\nหนำ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nกช\nกฎ\nกฏุก\nกฐิน\nกณิกนันต์\nกณิการ์\nกด\nกตเวทิตา\nกตเวที\nกตัญชลี\nกตัญญุตา\nกตัญญู\nกตาธิการ\nกตาภินิหาร\nกติกา\nกถา\nกถิกาจารย์\nกทลี\nกน\nก่น\nก้น\nกนก\nกนิษฐ์\nกนิษฐา\nกบ\nกบฏ\nกบดาน\nกบทู\nกบาล\nกบินทร์\nกบิล\nกบี่\nกบูร\nกเบนทร์\nกม\nก้ม\nกมณฑลาภิเษก\nกมณฑโลทก\nกมล\nกมลา\nกมลาศ\nกมลาสน์\nกมเลศ\nกมัณฑลุ\nกมุท\nกร\nกรกฎ\nกรกฎาคม\nกรกฏ\nกรง\nกรชกาย\nกรณฑ์\nกรณิการ์\nกรณี\nกรณีย์\nกรณียกิจ\nกรณียะ\nกรด\nกรน\nกรบ\nกรบูร\nกรพินธุ์\nกรม\nกรรกฎ\nกรรกศ\nกรรเกด\nกรรไกร\nกรรเจียก\nกรรชิง\nกรรเชียง\nกรรโชก\nกรรฐ์\nกรรฐา\nกรรณ\nกรรณา\nกรรณิกา\nกรรณิการ์\nกรรดิ\nกรรดิก\nกรรดึก\nกรรตุ\nกรรไตร\nกรรทบ\nกรรแทก\nกรรบิด\nกรรบูร\nกรรภิรมย์\nกรรม\nกรรม์\nกรรม์ภิรมย์\nกรรมชวาต\nกรรมัชวาต\nกรรมาชีพ\nกรรมาธิการ\nกรรมาร\nกรรษก\nกรรสะ\nกรรแสง\nกรวด\nกรวบ\nกรวม\nกร้วม\nกรวย\nกรวิก\nกรสาปน์\nกรสุทธิ์\nกรอ\nกร้อ\nกรอก\nกร็อกกร๋อย\nกรอกแกรก\nกรอง\nกรองกรอย\nกรอด\nกร่อน\nกรอบ\nกรอม\nกร่อย\nกระ\nกระกร\nกระกรุ่น\nกระกลับกลอก\nกระกี้\nกระเกรอก\nกระเกริก\nกระเกริ่น\nกระคน\nกระคาย\nกระงกกระเงิ่น\nกระง่องกระแง่ง\nกระง่อนกระแง่น\nกระเง้ากระงอด\nกระโงก\nกระจก\nกระจง\nกระจร\nกระจอก\nกระจองหง่อง\nกระจ๋องหง่อง\nกระจองอแง\nกระจ้อน\nกระจอนหู\nกระจ้อย\nกระจ๋อหวอ\nกระจะ\nกระจัก\nกระจัง\nกระจัด\nกระจับ\nกระจ่า\nกระจ่าง\nกระจาด\nกระจาน\nกระจาบ\nกระจาม\nกระจาย\nกระจาว\nกระจิก\nกระจิ๋ง\nกระจิด\nกระจิบ\nกระจิ๋ม\nกระจิริด\nกระจิ๋ว\nกระจี้\nกระจี๋\nกระจุก\nกระจุ๋งกระจิ๋ง\nกระจุบ\nกระจุ๊บ\nกระจุ๋มกระจิ๋ม\nกระจุย\nกระจู้\nกระจู๋กระจี๋\nกระจูด\nกระเจอะกระเจิง\nกระเจา\nกระเจ่า\nกระเจ้า\nกระเจาะ\nกระเจิง\nกระเจิดกระเจิง\nกระเจี้ยง\nกระเจี๊ยบ\nกระเจียว\nกระเจี๊ยว\nกระแจะ\nกระโจน\nกระโจม\nกระฉอก\nกระฉ่อน\nกระฉับกระเฉง\nกระฉิ่ง\nกระฉีก\nกระฉูด\nกระเฉก\nกระเฉด\nกระแฉก\nกระโฉกกระเฉก\nกระโฉม\nกระชดกระช้อย\nกระชอน\nกระชอมดอก\nกระช้อย\nกระชัง\nกระชั้น\nกระชับ\nกระชาก\nกระชาย\nกระชิง\nกระชิด\nกระชุ\nกระชุก\nกระชุ่มกระชวย\nกระเชอ\nกระเชา\nกระเช้า\nกระเชียง\nกระแชง\nกระแชะ\nกระโชก\nกระซ่องกระแซ่ง\nกระซับ\nกระซาบ\nกระซิก\nกระซิบ\nกระซี้\nกระซุง\nกระซุบกระซิบ\nกระซุ้ม\nกระซู่\nกระเซ็น\nกระเซอ\nกระเซอะกระเซอ\nกระเซอะกระเซิง\nกระเซ้า\nกระเซิง\nกระแซ\nกระแซะ\nกระโซกระเซ\nกระฎี\nกระฎุมพี\nกระดก\nกระด้ง\nกระดนโด่\nกระดวง\nกระดวน\nกระด้วมกระเดี้ยม\nกระดอ\nกระดอง\nกระดองหาย\nกระดอน\nกระดอม\nกระดักกระเดี้ย\nกระดังงัว\nกระดังงา\nกระดาก\nกระด้าง\nกระดางลาง\nกระดาด\nกระดาดขาว\nกระดาน\nกระดานพน\nกระดาษ\nกระดำกระด่าง\nกระดิก\nกระดิ่ง\nกระดิ้ง\nกระดิบ\nกระดี่\nกระดี้กระเดียม\nกระดึง\nกระดืบ\nกระดุ\nกระดุกกระดิก\nกระดุ้งกระดิ้ง\nกระดุบ\nกระดุบกระดิบ\nกระดุม\nกระดูก\nกระเดก\nกระเด้ง\nกระเด็น\nกระเด้า\nกระเดาะ\nกระเดิด\nกระเดี้ย\nกระเดียด\nกระเดือก\nกระเดื่อง\nกระแด็ก\nกระแด้ง\nกระแด้แร่\nกระแด่ว\nกระแดะ\nกระโดก\nกระโดง\nกระโดด\nกระโดน\nกระได\nกระตรับ\nกระตราก\nกระตรุด\nกระตรุม\nกระต้วมกระเตี้ยม\nกระต้อ\nกระต่องกระแต่ง\nกระต๊อบ\nกระต้อยตีวิด\nกระตัก\nกระตั้ว\nกระต่าย\nกระติก\nกระติ๊ด\nกระติบ\nกระตือรือร้น\nกระตุก\nกระตุ้งกระติ้ง\nกระตุ่น\nกระตุ้น\nกระตูบ\nกระเตง\nกระเต็น\nกระเตอะ\nกระเตาะ\nกระเตาะกระแตะ\nกระเตื้อง\nกระแต\nกระแตแต้แว้ด\nกระโตกกระตาก\nกระโตน\nกระถด\nกระถอบ\nกระถั่ว\nกระถาง\nกระถิก\nกระถิน\nกระเถิบ\nกระโถน\nกระทก\nกระทง\nกระทบ\nกระทรวง\nกระทอก\nกระท้อน\nกระท่อนกระแท่น\nกระท่อม\nกระท้อมกระแท้ม\nกระทะ\nกระทั่ง\nกระทั้น\nกระทา\nกระทาย\nกระทาสี\nกระทาหอง\nกระทำ\nกระทิกกระทวย\nกระทิง\nกระทึง\nกระทืบ\nกระทุ\nกระทุง\nกระทุ้ง\nกระทุ่ม\nกระทู้\nกระเท่\nกระเทียบ\nกระเทียม\nกระเทือน\nกระเทื้อม\nกระแทก\nกระแท่น\nกระแทะ\nกระไทชาย\nกระน่อง\nกระนั้น\nกระนี้\nกระแนะกระแหน\nกระโน้น\nกระไน\nกระบก\nกระบม\nกระบวน\nกระบวย\nกระบวร\nกระบอก\nกระบอง\nกระบะ\nกระบัด\nกระบั้วกระเบี้ย\nกระบ่า\nกระบ้า\nกระบาก\nกระบาย\nกระบาล\nกระบิ\nกระบิ้ง\nกระบิด\nกระบิล\nกระบี่\nกระบือ\nกระบุง\nกระบุ่มกระบ่าม\nกระบู้กระบี้\nกระบูน\nกระบูร\nกระเบง\nกระเบญ\nกระเบ็ดกระบวน\nกระเบน\nกระเบา\nกระเบิก\nกระเบียด\nกระเบียน\nกระเบื้อง\nกระแบก\nกระแบะ\nกระโบม\nกระปมกระปำ\nกระปมกระเปา\nกระปรอก\nกระปรอกว่าว\nกระปรี้กระเปร่า\nกระปอก\nกระป้อกระแป้\nกระป่อง\nกระป๋อง\nกระปอดกระแปด\nกระป๋อหลอ\nกระปั้วกระเปี้ย\nกระป่ำ\nกระปุก\nกระปุ๊กลุก\nกระปุ่ม\nกระปุ่มกระป่ำ\nกระปุ่มกระปิ่ม\nกระเป๋า\nกระเปาะ\nกระโปก\nกระโปรง\nกระผม\nกระผลีกระผลาม\nกระผาน\nกระผีก\nกระพรวน\nกระพริ้ม\nกระพอก\nกระพอง\nกระพ้อม\nกระพัก\nกระพัง\nกระพังเหิร\nกระพังโหม\nกระพัด\nกระพัตร\nกระพัน\nกระพั่น\nกระพา\nกระพาก\nกระพี้\nกระพือ\nกระพุ้ง\nกระพุ่ม\nกระเพาะ\nกระเพิง\nกระเพื่อม\nกระแพ้ง\nกระฟัดกระเฟียด\nกระฟูมกระฟาย\nกระมล\nกระมอบ\nกระมอมกระแมม\nกระมัง\nกระมัน\nกระมิดกระเมี้ยน\nกระมุท\nกระเมาะ\nกระย่อง\nกระย่องกระแย่ง\nกระย่อน\nกระย่อม\nกระยา\nกระยาง\nกระยาจก\nกระยาหงัน\nกระยิก\nกระยิ้มกระย่อง\nกระยึกกระยือ\nกระยืดกระยาด\nกระเย้อกระแหย่ง\nกระรอก\nกระเรียน\nกระโรกน้ำข้าว\nกระโรกใหญ่\nกระไร\nกระลด\nกระลบ\nกระลอก\nกระลอม\nกระละหล่ำ\nกระลัด\nกระลับ\nกระลัมพร\nกระลา\nกระลำ\nกระลำพัก\nกระลำพุก\nกระลิง\nกระลี\nกระลุมพาง\nกระลุมพุก\nกระลุมพู\nกระลูน\nกระลู่น์\nกระเล็น\nกระเลียด\nกระเลือก\nกระโลง\nกระวน\nกระวัด\nกระวาด\nกระวาน\nกระวาย\nกระวิน\nกระวี\nกระวีกระวาด\nกระวูดกระวาด\nกระเวน\nกระเวยกระวาย\nกระแวน\nกระโวยกระวาย\nกระษัย\nกระษาปณ์\nกระสง\nกระสบ\nกระสม\nกระสรวล\nกระสร้อย\nกระสวน\nกระสวย\nกระสอบ\nกระสะ\nกระสัง\nกระสัน\nกระสับกระส่าย\nกระสา\nกระสานติ์\nกระสาบ\nกระสาย\nกระสือ\n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nกาก\nกากบาท\nกากะทิง\nกากะเยีย\nกากี\nกาง\nก้าง\nกางเกง\nกางเขน\nก๊าซ\nกาซะลอง\nกาญจนา\nกาฐ\nกาด\nก๊าด\nกาน\nก่าน\nก้าน\nก๊าน\nกานดา\nกานต์\nกานน\nก้านพร้าว\nกานพลู\nกาน้า\nกาบ\nก้าบ\nกาบู\nกาพย์\nกาเฟอีน\nกาแฟ\nกาม\nก้าม\nกามารมณ์\nกามินี\nกาเมสุมิจฉาจาร\nกาย\nก่าย\nกาเยน\nการ\nการณ์\nการ์ด\nการ์ตูน\nการบูร\nการย์\nการวิก\nการเวก\nการะเกด\nการะบุหนิง\nการัณย์\nการันต์\nการางหัวขวาน\nการิตการก\nการิตวาจก\nการุญ\nการุณย์\nกาเรการ่อน\nกาล\nกาลกรรณี\nกาลกิณี\nกาลจักร\nกาลัญญุตา\nกาลัญญู\nกาลัด\nกาลานุกาล\nกาลิก\nกาลี\nกาแล\nกาแล็กซี\nกาแล็กโทส\nกาว\nก้าว\nกาววาว\nกาวาง\nกาแวน\nกาศิก\nกาษฐะ\nกาษา\nกาสร\nกาสะ\nกาสา\nกาสาร\nกาสาวะ\nกาสิโน\nกาหล\nกาหลง\nกาหลา\nกาเหว่า\nกาไหล่\nกาฬ\nกาฬาวก\nกาฮัง\nกำ\nก่ำ\nกำกวม\nกำกัด\nกำกับ\nก้ำกึ่ง\nกำกูน\nก้ำเกิน\nกำเกียง\nกำคูน\nกำจร\nกำจัด\nกำจาย\nกำชับ\nกำชำ\nกำซาบ\nกำซำ\nกำด้น\nกำดัด\nกำดาล\nกำเดา\nกำธร\nกำนล\nกำนัน\nกำนัล\nกำเนิด\nกำบัง\nก่ำบึ้ง\nกำเบ้อ\nกำปอ\nกำปั่น\nกำผลา\nกำพง\nกำพด\nกำพต\nกำพร้า\nกำพราก\nกำพวด\nกำพอง\nกำพืด\nกำพุด\nกำพู\nกำเพลิง\nกำแพง\nกำภู\nกำมลาศน์\nกำมเลศ\nกำมะถัน\nกำมะลอ\nกำมะหยี่\nกำมะหริด\nกำมังละการ\nกำมังวิลิต\nกำมัชพล\nกำยาน\nกำยำ\nกำรอ\nกำราบ\nกำราล\nกำเริบ\nกำไร\nกำลัง\nกำลุง\nกำเลา\nกำไล\nกำสรด\nกำสรวล\nกำหนด\nกำหนัด\nกำเหน็จ\nกำแหง\nกิก\nกิ๊ก\nกิ่ง\nกิ้งก่า\nกิ้งกือ\nกิ้งโครง\nกิจ\nกิจจะ\nกิจจา\nกิดาการ\nกิดาหยัน\nกิตติ\nกิตติมศักดิ์\nกิน\nกินนร\nกินปลี\nกินเปี้ยว\nกินริน\nกินรี\nกิ๊บ\nกิมตึ๋ง\nกิมิชาติ\nกิมิวิทยา\nกิโมโน\nกิโยตีน\nกิระ\nกิริณี\nกิรินท\nกิริเนศวร\nกิริยา\nกิเลน\nกิเลส\nกิโล\nกิโลมกะ\nกิ่ว\nกิ๋ว\nกี\nกี่\nกี้\nกี๊\nกี๋\nกีฏวิทยา\nกีด\nกีตาร์\nกีบ\nกีรติ\nกีฬา\nกึก\nกึง\nกึ่ง\nกึ๋น\nกุ\nกุก\nกุ๊ก\nกุกกุฏ\nกุกกุร\nกุกรรม\nกุ้ง\nกุงอน\nกุงาน\nกุโงก\nกุจี\nกุญแจ\nกุญชร\nกุฎ\nกุฎา\nกุฎี\nกุฎุมพี\nกุฏฐัง\nกุฏิ\nกุณฑ์\nกุณฑล\nกุณฑี\nกุณโฑ\nกุณาล\nกุณี\nกุด\nกุดัง\nกุดั่น\nกุดา\nกุทัณฑ์\nกุน\nกุ๊น\nกุนเชียง\nกุนที\nกุโนกามอ\nกุบ\nกุบกับ\nกุม\nกุ่ม\nกุมฝอย\nกุมภ์\nกุมภนิยา\nกุมภัณฑ์\nกุมภา\nกุมภิล\nกุมภีล์\nกุมเหง\nกุมาร\nกุมารา\nกุมารี\nกุมุท\nกุย\nกุ๊ย\nกุ๋ย\nกุยช่าย\nกุยเฮง\nกุรระ\nกุรุพินท์\nกุเรา\nกุล\nกุลา\nกุลาหล\nกุลี\nกุลีกุจอ\nกุเลา\nกุแล\nกุเวร\nกุศราช\nกุศล\nกุศโลบาย\nกุสุม\nกุสุมภ์\nกุสุมา\nกุสุมาลย์\nกุสุมิตลดาเวลลิตา\nกุหนี\nกุหนุง\nกุหร่า\nกุหล่า\nกุหลาบ\nกุแหละ\nกู\nกู่\nกู้\nกู๊ก\nกูฏ\nกูฏา\nกูณฑ์\nกูด\nกูบ\nกูปรี\nกูรมะ\nกูรมาวตาร\nเก\nเก้\nเก๊\nเก๋\nเกก\nเก๊ก\nเกกมะเหรก\nเก๊กฮวย\nเก้กัง\nเก็ง\nเก่ง\nเก้ง\nเก๋ง\nเกงกอย\nเก่งกาจ\nเกงเขง\nเก๋งเคง\nเก็จ\nเกจิอาจารย์\nเกชา\nเกณฑ์\nเกด\nเก็ด\nเกตุ\nเกน\nเก็บ\nเกม\nเกย\nเกยูร\nเกรง\nเกร็ง\nเกร็ด\nเกรน\nเกร่อ\nเกรอะ\nเกราะ\nเกริก\nเกริน\nเกริ่น\nเกรียก\nเกรียง\nเกรียด\nเกรียน\nเกรียบ\nเกรียม\nเกรียว\nเกรี้ยว\nเกเร\nเกล็ด\nเกลศ\nเกลอ\nเกลา\nเกล้า\nเกลาะ\nเกลี่ย\nเกลี้ย\nเกลียง\nเกลี้ยง\nเกลียด\nเกลียว\nเกลือ\nเกลื้อ\nเกลือก\nเกลื่อน\nเกลื้อน\nเกวัฏ\nเกวียน\nเกศ\nเกศว\nเกศวะ\nเกศา\nเกศินี\nเกศี\nเกษตร\nเกษม\nเกษียณ\nเกษียน\nเกษียร\nเกส\nเกสร\nเกสรี\nเกสา\nเกสี\nเก้อ\nเกอิชา\nเกะ\nเกะกะ\nเกา\nเก่า\nเก้า\nเก๋า\nเกาต์\nเกาทัณฑ์\nเกาบิล\nเกาลัด\nเกาลิน\nเกาไศย\nเกาหลี\nเกาเหลา\nเกาเหลียง\nเก้าอี้\nเกาะ\nเกิ้ง\nเกิด\nเกิน\nเกิบ\nเกีย\nเกียกกาย\nเกียง\nเกี่ยง\nเกี๋ยง\nเกียจ\nเกียด\nเกียน\nเกี้ยมไฉ่\nเกี้ยมอี๋\nเกียร์\nเกียรติ\nเกียรติ์\nเกี่ยว\nเกี้ยว\nเกี๊ยว\nเกี๊ยะ\nเกื้อ\nเกือก\nเกื้อกูล\nเกือบ\nแก\nแก่\nแก้\nแกง\nแก่ง\nแก้ง\nแก๊ง\nแกงได\nแกงแนง\nแกโดลิเนียม\nแกน\nแก่น\nแก๊ป\nแกม\nแก้ม\nแกมมา\nแกรก\nแกร่ง\nแกร็น\nแกรนิต\nแกรไฟต์\nแกร่ว\nแกระ\nแกล\nแกล้ง\nแกลน\nแกลบ\nแกล้ม\nแกลลอน\nแกลเลียม\nแกล้ว\nแกละ\nแกแล\nแกว\nแก้ว\nแกว่ง\nแก๊ส\nแกะ\nโก\nโก่\nโก้\nโก๋\nโกก\nโกกนุท\nโกกิลา\nโกโก้\nโกง\nโก่ง\nโกงกาง\nโก้งเก้ง\nโกงโก้\nโก้งโค้ง\nโกเชาว์\nโกญจนาท\nโกญจา\nโกฏิ\nโกฐ\nโกฐาส\nโกณะ\nโกดัง\nโกทัณฑ์\nโกน\nโก่น\nโก๋น\nโกมล\nโกมุท\nโกเมน\nโกเมศ\nโกย\nโกรก\nโกรกกราก\nโกรง\nโกร่ง\nโกร่งกร่าง\nโกรงเกรง\nโกร๋งเกร๋ง\nโกรญจ\nโกรต๋น\nโกรธ\nโกรธา\nโกร๋น\nโกรม\nโกรย\nโกรศ\nโกโรโกเต\nโกโรโกโรก\nโกโรโกโส\nโกลน\nโกลาหล\nโกไล\nโกวิท\nโกศ\nโกศล\nโกษม\nโกสน\nโกสัช\nโกสินทร์\nโกสีย์\nโกสุม\nโกไสย\nโกหก\nใกล้\nไก\nไก่\nไก๊\nไก๋\nไก่กอม\nไกพัล\nไกร\nไกรพ\nไกรลาส\nไกรศร\nไกรศรี\nไกรสร\nไกรสรี\nไกรสิทธิ\nไกล\nไกล่\nไกลาส\nไกว\nไกวัล\nขงจื๊อ\nขจร\nขจรจบ\nขจัด\nขจ่าง\nขจาย\nขจาว\nขจิต\nขจี\nขจุย\nขเจา\nขณะ\nขด\nขน\nข้น\nขนง\nขนด\nขนบ\nขนม\nขนอง\nขนอน\nขนอบ\nขนัด\nขนัน\nขนาก\nขนาง\nขนาด\nขนาน\nขนาบ\nขนาย\nขนำ\nขนิษฐ\nขนิษฐา\nขนุน\nขนุนนก\nขบ\nขบถ\nขบวน\nขบวร\nขม\nข่ม\nขมงโกรย\nขมวด\nขมวน\nขมอง\nขม่อม\nขมัง\nขมับ\nขมา\nขม้ำ\nขมิ้น\nขมิบ\nขมีขมัน\nขมึง\nขมึงทึง\nขมุ\nขมุกขมัว\nขมุบ\nขโมย\nขยด\nขยม\nขย่ม\nขยอก\nขยอง\nขย่อน\nขย้อน\nขยะ\nขยัก\nขยัน\nขยั้น\nขยับ\nขยาด\nขยาย\nขยำ\nขย้ำ\nขยิก\nขยิบ\nขยิ่ม\nขยี้\nขยุกขยิก\nขยุกขยุย\nขยุบ\nขยุบขยิบ\nขยุม\nขยุ้ม\nขยุย\nขรม\nขรรค์\nขรัว\nขริบ\nขรี\nขรึม\nขรุขระ\nขลบ\nขล้อ\nขลัง\nขลับ\nขลาด\nขลาย\nขลิบ\nขลุก\nขลุกขลัก\nขลุกขลิก\nขลุบ\nขลุม\nขลุ่ย\nขลู\nขลู่\nขวง\nข่วง\nขวด\nข่วน\nขวนขวาย\nขวบ\nขวย\nขวักไขว่\nขวัญ\nขวั้น\nขวับ\nขวับเขวียว\nขวา\nขวาก\nขวาง\nขว้าง\nขวาด\nขวาน\nขวายขวน\nขวาว\nขว้าว\nขวิด\nขอ\nข่อ\nข้อ\nของ\nข้อง\nขอด\nขอน\nข้อน\nขอบ\nขอม\nข่อย\nข้อย\nข่อยหยอง\nขะแจะ\nขะเน็ด\nขะมอมขะแมม\nขะมักเขม้น\nขะมุกขะมอม\nขะยิก\nขะยุก\nขะเย้อแขย่ง\nขัค\nขัง\nขังขอก\nขัช\nขัณฑสกร\nขัณฑสีมา\nขัด\nขัดมอน\nขัตติยมานะ\nขัน\nขั้น\nขันติ\nขันตี\nขันโตก\nขันที\nขันธ์\nขันธาวาร\nขับ\nขัว\nขั้ว\nขา\nข่า\nข้า\nขาก\nขาก๊วย\nขาง\nข่าง\nข้าง\nขาณุ\nขาด\nขาทนียะ\nขาน\nขาบ\nข้าพเจ้า\nขาม\nข่าม\nข้าม\nขาย\nข่าย\nขาล\nขาว\nข่าว\nข้าว\nข้าวอังกุลี\nขำ\nขิก\nขิง\nขิงแกลง\nขิงแครง\nขิด\nขิปสัทโท\nขิม\nขี่\nขี้\nขี้เข็บ\nขีณาสพ\nขีด\nขี้ตังนี\nขีปนาวุธ\nขี้ยอก\nขีระ\nขึง\nขึ้ง\nขึ้น\nขึ้นฉ่าย\nขืน\nขื่น\nขื่อ\nขุก\nขุด\nขุน\nขุ่น\nขุนเพ็ด\nขุม\nขุย\nขู่\nขูด\nเข\nเข้\nเขก\nเข็ง\nเข่ง\nเขจร\nเข็ญ\nเข็ด\nเขดา\nเขต\nเขน\nเข็น\nเข่น\nเขนง\nเขน็ด\nเขนย\nเขบ็จขบวน\nเขบ็ต\nเขม\nเข็ม\nเข้ม\nเข้มขาบ\nเขม็ง\nเขม็ดแขม่\nเขม่น\nเขม้น\nเขม้นขะมัก\nเขมร\nเขมา\nเขม่า\nเขมือบ\nเขย\nเขยก\nเขย่ง\nเขย้อแขย่ง\nเขย่า\nเขยิน\nเขยิบ\nเขยื้อน\nเขรอะ\nเขลง\nเขลอะ\nเขละ\nเขลา\nเขลาะ\nเขว\nเขษม\nเขฬะ\nเขะขะ\nเขา\nเข่า\nเข้า\nเขิง\nเขิน\nเขิบ\nเขี่ย\nเขียง\nเขียด\nเขียดตะปาด\nเขียน\nเขี่ยน\nเขียม\nเขียว\nเขี้ยว\nเขียะ\nเขือ\nเขือง\nเขื่อง\nเขื่อน\nเขือม\nแข\nแข้\nแขก\nแข็ง\nแข่ง\nแข้ง\nแขน\nแข่น\nแข้น\nแขนง\nแขม\nแขม็บ\nแขม่ว\nแขยง\nแขย่ง\nแขวก\nแขวง\nแขวน\nแขวะ\nโข\nโขก\nโขง\nโข่ง\nโขด\nโขดง\nโขน\nโขนง\nโขม\nโขมง\nโขมด\nโขยก\nโขยง\nโขย่ง\nโขยด\nโขลก\nโขลง\nโขลน\nโขษม\nไข\nไข่\nไข้\nไขว่\nไขว้\nคคนะ\nคคนัมพร\nคคนางค์\nคคนานต์\nคง\nคงคา\nคงไคย\nคช\nคชาชาติ\nคชาชีพ\nคชาธาร\nคชาภรณ์\nคณนา\nคณบดี\nคณะ\nคณาจารย์\nคณาธิการ\nคณาธิปไตย\nคณานับ\nคณิกา\nคณิต\nคเณศ\nคด\nคดี\nคติ\nคทา\nคน\nค้น\nคนทา\nคนทิสอ\nคนที\nคนโท\nคนธ์\nคันธ์\nคนธรรพ์\nคเนจร\nคบ\nคม\nคมน์\nคมนาการ\nคมนาคม\nคมิกภัต\nครก\nครบ\nครรชิต\nครรภ\nครรภ์\nครรลอง\nครรโลง\nครรไล\nครวญ\nครวี\nครหา\nครอก\nครอง\nครองแครง\nคร่อเงาะ\nคร่อเทียน\nครอบ\nคร่อม\nคระเมิม\nคระแลง\nคระไล\nคระแวง\nคระหน\nคระหวน\nคระหาย\nคระโหย\nครั่ง\nครั้ง\nครัดเคร่ง\nครัน\nครั่น\nครั้น\nครับ\nครัว\nครา\nคร่า\nคราก\nคราง\nคราญ\nคราด\nคร้าน\nคราบ\nคราม\nคร้าม\nครามครัน\nคราว\nคร่าว\nคราส\nครำ\nคร่ำ\nคร่ำเคร่ง\nคริปทอน\nคริสต์\nครีบ\nครีม\nครีษมายัน\nครึ\nครึกครื้น\nครึกโครม\nครึ่ง\nครึ่ด\nครึน\nครึ้ม\nครืด\nครืน\nครื้น\nครืนครั่น\nครื้นครั่น\nครื้นครึก\nครื้นเครง\nครือ\nครุ\nครุคระ\nครุฑ\nครุ่น\nครุมเครือ\nครุย\nครุวนา\nครู\nครู่\nครูด\nคฤโฆษ\nคฤนถ์\nคฤหบดี\nคฤหัสถ์\nคฤหาสน์\nคลวง\nคลอ\nคล้อ\nคลอก\nคลอง\nคล่อง\nคล้อง\nคลอด\nคลอน\nคล้อย\nคลอรีน\nคลอโรฟอร์ม\nคลอโรฟีลล์\nคละ\nคละคลุ้ง\nคลัก\nคลั่ก\nคลัง\nคลั่ง\nคลัตช์\nคลับคล้าย\nคลับคลา\nคลา\nคล้า\nคลางแคลง\nคลาด\nคลาน\nคลาย\nคล้าย\nคล้ายคลึง\nคล่าว\nคลำ\nคล่ำ\nคล้ำ\nคลิ้งโคลง\nคลิด\nคลินิก\nคลี\nคลี่\nคลึง\nคลื่น\nคลุก\nคลุ้ง\nคลุบ\nคลุม\nคลุ่ม\nคลุ้ม\nควง\nควณ\nควน\nควบ\nควย\nควร\nควัก\nควั่ก\nควั่งคว้าง\nควัน\nควั่น\nคว้า\nควาก\nคว้าง\nควาญ\nควาน\nคว้าน\nความ\nควาย\nคว่าว\nคว่ำ\nควินิน\nควิวคว่าง\nคหกรรม\nคหกรรมศาสตร์\nคหบดี\nคหัฐ\nคอ\nค้อ\nคอก\nค็อกคัส\nค็อกเทล\nคอเคซอยด์\nค่องอ้อย\nคอด\nคอแดง\nคอน\nค่อน\nค้อน\nคอนกรีต\nคอนเดนเซอร์\nคอนแวนต์\nคอนเสิร์ต\nคอม\nค่อม\nค้อม\nคอมพิวเตอร์\nคอมมานโด\nคอมมิวนิสต์\nคอย\nค่อย\nค้อย\nคอยล์\nคอร์ด\nคอแลน\nคอสติกโซดา\nคะ\nค่ะ\nคะไขว่\nคะค้อย\nคะคาน\nคะนน\nคะนอง\nคะน้า\nคะนึง\nคะเน\nคะเนงร้าย\nคะเน็ด\nคะแนน\nคะมำ\nคะยั้นคะยอ\nคะเยอ\nคัก\nคั่ก\nคัคนะ\nคัคนัมพร\nคัคนางค์\nคัคนานต์\nคั่ง\nคังไคย\nคัจฉ\nคัณฑมาลา\nคัณฑสูตร\nคัด\nคัดเค้า\nคัดมอน\nคัดเม็ง\nคัทลียา\nคัน\nคั่น\nคั้น\nคันถรจนาจารย์\nคันธกุฎี\nคันธมาทน์\nคันธารราษฎร์\nคับ\nคับค้อน\nคับคา\nคับแค\nคัพภ์\nคัมภีร์\nคัมภีรภาพ\nคัล\nคั่ว\nคา\nค่า\nค้า\nค่าคบ\nคาง\nค่าง\nค้าง\nคางคก\nค้างคาว\nคาด\nคาถา\nคาทอลิก\nคาน\nค้าน\nคาบ\nคาพยุต\nคาม\nคามวาสี\nคามณีย์\nคามภีร์\nคาย\nค่าย\nคาร์บอน\nคาร์บอเนต\nคาร์บอลิก\nคาร์บูเรเตอร์\nคาร์โบรันดัม\nคาร์โบไฮเดรต\nคารม\nคารวะ\nคาราเต้\nคาราวาน\nคาว\nค่าว\nค้าว\nคาวตอง\nคาวี\nคาวุต\nคาส\nคำ\nค่ำ\nค้ำ\nคำนวณ\nคำนวร\nคำนับ\nคำนัล\nคำนึง\nคำนูณ\nคำฝอย\nคำเพลิง\nคำรน\nคำรบ\nคำราม\nคำแสด\nคำแหง\nคำโอง\nคิก\nคิง\nคิด\nคิมหันต์\nคิรี\nคิลาน\nคิลานะ\nคิว\nคิ้ว\nคี่\nคีต\nคีบ\nคีม\nคีรี\nคีรีบูน\nคึก\nคึ่ก\nคึกคัก\nคืน\nคืบ\nคือ\nคุ\nคุก\nคุกกี้\nคุกคาม\nคุกพาทย์\nคุ้ง\nคุณ\nคุด\nคุดทะราด\nคุต\nคุตติ\nคุ่น\nคุ้น\nคุป\nคุปต์\nคุปติ\nคุม\nคุ่ม\nคุ้ม\nคุย\nคุ้ย\nคุยหฐาน\nคุยหประเทศ\nคุรุ\nคุลา\nคุลิก่า\nคุลีการ\nคุหา\nคู\nคู่\nคู้\nคูณ\nคูถ\nคูน\nคูปอง\nคูเรียม\nคูหา\nเค้ก\nเค้เก้\nเค้ง\nเคณฑะ\nเคด\nเค็ด\nเคน\nเค้น\nเคเบิล\nเค็ม\nเคมี\nเคย\nเครง\nเคร่ง\nเครงครา\nเครงครำ\nเครดิต\nเครน\nเครา\nเคร่า\nเคราหณี\nเคราะห์\nเครียด\nเครียว\nเครือ\nเครื่อง\nเคล้ง\nเคล็ด\nเคล้น\nเคล้า\nเคล่าคล่อง\nเคลิบเคลิ้ม\nเคลิ้ม\nเคลีย\nเคลื่อน\nเคลือบ\nเคว้ง\nเคหะ\nเคหา\nเคอะ\nเค้า\nเคาน์เตอร์\nเคารพ\nเคาะ\nเคาะแคะ\nเคียง\nเคียด\nเคียน\nเคียม\nเคี่ยม\nเคียร\nเคียว\nเคี่ยว\nเคี้ยว\nเคือง\nแค\nแค่\nแค้\nแคแล\nแคดเมียม\nแค็ตตาล็อก\nแคแตร\nแคโทด\nแคน\nแค่น\nแค้น\nแคบ\nแคบหมู\nแคปซูล\nแคม\nแคร่\nแครก\nแครง\nแคระ\nแคลคูลัส\nแคลง\nแคลเซียม\nแคลน\nแคล้ว\nแคล่วคล่อง\nแคลอรี\nแคลิฟอร์เนียม\nแคว\nแควก\nแคว้น\nแคแสด\nแคะ\nโค\nโคก\nโคเคน\nโค่ง\nโค้ง\nโคจร\nโคเซแคนต์\nโคไซน์\nโคตร\nโคแทนเจนต์\nโคธา\nโคน\nโค่น\nโคบอลต์\nโคปผกะ\nโคม\nโคม่า\nโครก\nโครกคราก\nโครง\nโคร่ง\nโคร่งคร่าง\nโครม\nโครเมียม\nโครโมโซม\nโคราช\nโครำ\nโคล\nโคลง\nโคลน\nโควตา\nโคออร์ดิเนต\nใคร\nใคร่\nใคร่ครวญ\nไค\nไค้\nไคร้\nไคร้เครือ\nไคล\nไคล้\nฆนะ\nฆราวาส\nฆ้อง\nฆ่า\nฆาต\nฆาน\nฆานินทรีย์\nเฆี่ยน\nโฆรวิส\nโฆษก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าด\nเฉ\nเฉก\nเฉโก\nเฉ่ง\nเฉด\nเฉท\nเฉนียน\nเฉพาะ\nเฉย\nเฉลย\nเฉลว\nเฉลา\nเฉลิม\nเฉลี่ย\nเฉลียง\nเฉลี่ยง\nเฉลียบ\nเฉลียว\nเฉวียง\nเฉวียน\nเฉอะแฉะ\nเฉา\nเฉาก๊วย\nเฉาฮื้อ\nเฉาะ\nเฉิด\nเฉิบ\nเฉียง\nเฉียงพร้านางแอ\nเฉียด\nเฉียบ\nเฉียว\nเฉี่ยว\nเฉือน\nเฉื่อย\nแฉ\nแฉ่\nแฉก\nแฉง\nแฉ่ง\nแฉลบ\nแฉล้ม\nแฉะ\nโฉ\nโฉ่\nโฉเก\nโฉ่งฉ่าง\nโฉงเฉง\nโฉด\nโฉนด\nโฉบ\nโฉเบ๊\nโฉม\nโฉลก\nไฉน\nไฉไล\nชก\nชคัตตรัย\nชง\nชงโค\nชงฆ์\nชงฆา\nชงโลง\nชฎา\nชฎามังษี\nชฎามังสี\nชฎิล\nชด\nชน\nชนก\nชนนี\nชนม์\nชนวน\nชนะ\nชนัก\nชนา\nชนาง\nชนิด\nชเนตตี\nชบา\nชม\nชมดชม้อย\nชมนาด\nชมพู\nชมพู่\nชมรม\nชม้อย\nชม้าย\nชไม\nชยา\nชโย\nชรทึง\nชรริน\nชรอุ่ม\nชระล้ำ\nชระลุ\nชระอาบ\nชระเอม\nชรัด\nชรา\nชล\nชโลง\nชโลม\nช่วง\nชวด\nชวน\nชวย\nช่วย\nชวร\nชวลิต\nชวา\nชวาล\nชวาลา\nช่อ\nชอก\nช็อก\nช็อกโกเลต\nช็อกโกแลต\nชอง\nช่อง\nช้อง\nชองระอา\nชอน\nช่อน\nช้อน\nชอบ\nชอม\nช้อย\nชอล์ก\nชอ่ำ\nชอุ่ม\nชะ\nชะคราม\nชะงอก\nชะง่อน\nชะงัก\nชะงัด\nชะง้ำ\nชะงุ้ม\nชะเง้อ\nชะเงื้อม\nชะแง้\nชะโงก\nชะฉ่า\nชะช่อง\nชะชะ\nชะช้า\nชะโด\nชะตา\nชะต้า\nชะนี\nชะเนาะ\nชะเนียง\nชะพลู\nชะเพลิง\nชะมด\nชะมบ\nชะมวง\nชะมัง\nชะมัด\nชะแม่\nชะรอย\nชะลอ\nชะลอม\nชะล่า\nชะลาน\nชะลิน\nชะลูด\nชะเลง\nชะเลย\nชะแล็ก\nชะแลง\nชะวาก\nชะวาด\nชะเวิกชะวาก\nชะแวง\nชะอม\nชะอ้อน\nชะเอม\nชะโอน\nชัก\nชักคราม\nชักช้า\nชัง\nชั่ง\nชังคา\nชังฆ\nชัชวาล\nชัฏ\nชัด\nชัดช้า\nชัน\nชั้น\nชันกาด\nชันชี\nชันตุ\nชันนะตุ\nชันนุ\nชันโรง\nชันษา\nชันสูตร\nชัปนะ\nชัพ\nชัมพูนท\nชัย\nชัยพฤกษ์\nชัยภูมิ\nชัลลุกา\nชั่ว\nชั้ว\nชัวชม\nชา\nช้า\nชาคระ\nชาคริต\nชาคริยานุโยค\nช่าง\nช้าง\nช้าช่อน\nชาญ\nชาด\nชาดก\nชาต\nชาตบุษย์\nชาตรี\nชาตะ\nชาตา\nชาติ\nชาน\nชานุ\nช้าปี่\nชาปีไหน\nช้าแป้น\nช้าพลู\nชาม\nชามพูนท\nชามาดร\nชามาดา\nชามาตุ\nชาย\nชายา\nชาล\nชาลา\nชาลินี\nช้าเลือด\nชาว\nชาวี\nชำ\nช่ำ\nช้ำ\nชำงัด\nชำงาย\nช่ำชอง\nชำนะ\nชำนัญ\nชำนัน\nชำนาญ\nชำนิ\nชำเนียร\nชำมะนาด\nชำมะเลียง\nชำร่วย\nชำระ\nช้ำรั่ว\nชำรุด\nชำเรา\nชำเราะ\nชำแรก\nชำแระ\nชำเลือง\nชำแหละ\nชิ\nชิง\nชิ่ง\nชิงชัน\nชิงช้า\nชิงช้าชาลี\nชิงชี่\nชิงฮื้อ\nชิชะ\nชิชิ\nชิณณะ\nชิด\nชิเดนทรีย์\nชิต\nชิตินทรีย์\nชิน\nชิ้น\nชินโต\nชิโนรส\nชิม\nชิมแปนซี\nชิยา\nชิรณะ\nชิระ\nชิวหา\nชิสา\nชี\nชี่\nชี้\nชีปะขาว\nชีผะขาว\nชีผ้าขาว\nชีพ\nชีฟอง\nชีรณ\nชีระ\nชีวเคมี\nชีวประวัติ\nชีวภาพ\nชีววิทยา\nชีวะ\nชีวัน\nชีวา\nชีวาตม์\nชีวาลัย\nชีวิต\nชีวิตักษัย\nชีวิน\nชีวี\nชืด\nชื่น\nชื้น\nชื่อ\nชุก\nชุกชี\nชุ้ง\nชุณห\nชุด\nชุติ\nชุน\nชุบ\nชุม\nชุ่ม\nชุมนุม\nชุมพร\nชุมพา\nชุมแพรก\nชุมรุม\nชุมแสง\nชุมเห็ด\nชุ่ย\nชุลมุน\nชุลี\nชุษณะ\nชู\nชู้\nเช็ค\nเช้ง\nเช้งวับ\nเชงเลง\nเช็ด\nเชน\nเช่น\nเชย\nเชลง\nเชลย\nเชลแล็ก\nเชลียง\nเชวง\nเชษฐะ\nเชษฐา\nเชอ\nเช่า\nเช้า\nเชาว์\nเชาวน์\nเชิง\nเชิญ\nเชิด\nเชิ้ต\nเชียง\nเชี่ยน\nเชียบ\nเชี่ยม\nเชียร\nเชียร์\nเชียว\nเชี่ยว\nเชื่อ\nเชื้อ\nเชือก\nเชื่อง\nเชือด\nเชือน\nเชื่อม\nแช\nแช่\nแช่ง\nแชงมา\nแชบ๊วย\nแช่ม\nแชร์\nแชล่ม\nแชสซี\nแชะ\nโชก\nโชค\nโชงโลง\nโชดก\nโชดึก\nโชต\nโชตก\nโชติ\nโชติก\nโชน\nโชมโรม\nโชย\nโชยงการ\nโชยชาย\nโชยติส\nโชว์\nใช่\nใช้\nไช\nไชนะ\nไชย\nไชโย\nซก\nซ่ก\nซงดำ\nซ่งฮื้อ\nซด\nซน\nซ้น\nซบ\nซม\nซวดเซ\nซวน\nซวย\nซอ\nซอก\nซอง\nซ่อง\nซ้อง\nซองแมว\nซ้องแมว\nซอน\nซ่อน\nซ้อน\nซอม\nซ่อม\nซ้อม\nซอมซ่อ\nซอย\nซอส\nซัก\nซักส้าว\nซัง\nซั้ง\nซัด\nซับ\nซัลฟา\nซั้ว\nซา\nซ่า\nซาก\nซาง\nซ่าง\nซาด\nซาน\nซ่าน\nซาบซ่าน\nซาบซึ้ง\nซ่าโบะ\nซาแมเรียม\nซ้าย\nซาลาเปา\nซาว\nซ่าหริ่ม\nซำ\nซ้ำ\nซิ\nซี\nซิก\nซิกข์\nซิกซี้\nซิกแซ็ก\nซิการ์\nซิงโคนา\nซิ่น\nซินนามิก\nซินแส\nซิบ\nซิป\nซิฟิลิส\nซิลิคอน\nซิว\nซี่\nซีก\nซีเซียม\nซีด\nซี้ด\nซีนอน\nซีป่าย\nซีเมนต์\nซีเรียม\nซีลีเนียม\nซีอิ๊ว\nซึก\nซึง\nซึ่ง\nซึ้ง\nซึม\nซื่อ\nซื้อ\nซุก\nซุง\nซุน\nซุบ\nซุป\nซุ่ม\nซุ้ม\nซุ่มซ่าม\nซุย\nซู่\nซูโครส\nซูด\nซู้ด\nซูดซาด\nซูบ\nเซ\nเซ็ก\nเซแคนต์\nเซ็ง\nเซ่ง\nเซ้ง\nเซ็งแซ่\nเซต\nเซน\nเซ็น\nเซ่น\nเซนติกรัม\nเซนติเกรด\nเซนติเมตร\nเซนติลิตร\nเซปักตะกร้อ\nเซราะ\nเซรุ่ม\nเซลเซียส\nเซลล์\nเซลลูลอยด์\nเซลลูโลส\nเซ่อ\nเซอร์โคเนียม\nเซอะ\nเซา\nเซ้าซี้\nเซาะ\nเซิง\nเซิ้ง\nเซียน\nเซียบ\nเซียมซี\nเซียว\nเซี่ยว\nเซี่ยวกาง\nเซื่อง\nแซ\nแซ่\nแซ็กคาริน\nแซง\nแซงแซว\nแซด\nแซบ\nแซม\nแซยิด\nแซ่ว\nแซะ\nโซ\nโซ่\nโซก\nโซ่ง\nโซงโขดง\nโซเซ\nโซดา\nโซเดียม\nโซม\nโซรม\nโซลา\nไซ\nไซ้\nไซเกิล\nไซโคลน\nไซน์\nไซยาไนด์\nไซร้\nไซเรน\nไซโล\nฌาน\nฌาปน\nฌาปนกิจ\nฌาปนสถาน\nเฌอ\nเฌอเอม\nญวน\nญัตติ\nญาณ\nญาติ\nญานาซะฮ์\nญิบ\nญี่ปุ่น\nเญยธรรม\nไญยธรรม\nฎีกา\nฐกัด\nฐากูร\nฐาน\nฐานะ\nฐานันดร\nฐานานุกรม\nฐานานุรูป\nฐานานุศักดิ์\nฐานียะ\nฐาปน\nฐาปนา\nฐายี\nฐิต\nฐิติ\nฑาหก\nฑาหะ\nเฒ่า\nณรงค์\nเณร\nดก\nดง\nด้ง\nด้น\nดนโด่\nดนตรี\nดนัย\nดนุ\nดนู\nดบัสวิน\nดบัสวี\nดม\nดรงค์\nดรณี\nดรรชนี\nดราฟต์\nดรุณ\nดรุณี\nดล\nดวง\nด้วง\nดวด\nด่วน\nด้วน\nด้วย\nดอก\nดอง\nด่อง\nด้อง\nดองฉาย\nดองดึง\nดอด\nดอน\nด่อน\nดอม\nด้อม\nดอย\nด้อย\nดอลลาร์\nดะ\nดะโต๊ะ\nดะหมัง\nดัก\nดักดาน\nดักแด้\nดัง\nดั่ง\nดั้ง\nดัชนี\nดัด\nดัตช์\nดัน\nดั้น\nดับ\nดัมพ์\nดั้วเดี้ย\nดัสกร\nดา\nด่า\nดาก\nด่าง\nด้าง\nดาด\nดาน\nด่าน\nด้าน\nดาบ\nดาบส\nดาม\nด้าม\nด้ามจิ้ว\nดามพ์\nดาย\nด้าย\nดารกะ\nดารณี\nดารดาษ\nดาระ\nดารา\nดาล\nดาลัด\nดาว\nด่าว\nด้าว\nดาวดึงส์\nดาวบส\nดาษ\nดาษดา\nดำ\nด่ำ\nด้ำ\nดำกล\nดำเกิง\nดำแคง\nดำดง\nดำนาณ\nดำเนิน\nดำบล\nดำรง\nดำรวจ\nดำรัส\nดำริ\nดำรี\nดำรู\nดำฤษณา\nดำเลิง\nดิก\nดิ่ง\nดิฉัน\nดิฐ\nดิตถ์\nดิถี\nดิน\nดิ้น\nดิบ\nดิรัจฉาน\nดิลก\nดิ่ว\nดิ้ว\nดิ้วเดี้ยว\nดิษฐ์\nดิสโพรเซียม\nดี\nดีเซล\nดีด\nดีดีที\nดีบุก\nดีปลี\nดีเปรสชัน\nดีหมี\nดีหลี\nดึก\nดึง\nดึ่ง\nดึ่ม\nดื่น\nดื่ม\nดือ\nดื้อ\nดุ\nดุก\nดุกดิก\nดุกทะเล\nดุ้ง\nดุ้งดิ้ง\nดุจ\nดุด\nดุน\nดุ้น\nดุบ\nดุม\nดุ่ม\nดุ่ย\nดุรงค์\nดุริยะ\nดุริยางค์\nดุริยางคศาสตร์\nดุริยางคศิลป์\nดุล\nดุษฎี\nดุษณี\nดุษณีภาพ\nดุษิต\nดุสิต\nดุเหว่า\nดู\nดูกค่าง\nดูกร\nดูด\nดูรา\nดูแล\nเด\nเด่\nเดก\nเด็ก\nเดกซ์โทรส\nเดคากรัม\nเดคาเมตร\nเดคาลิตร\nเด้ง\nเด็จ\nเดช\nเดชน์\nเดชนะ\nเดชะ\nเดโช\nเดซิกรัม\nเดซิเมตร\nเดซิลิตร\nเด็ด\nเดน\nเด่น\nเดนมาร์ก\nเดรัจฉาน\nเด๋อ\nเด๋อด๋า\nเดา\nเด้า\nเดาะ\nเดิน\nเดิ่น\nเดิม\nเดียง\nเดียด\nเดียรดาษ\nเดียรถีย์\nเดียรัจฉาน\nเดียว\nเดี่ยว\nเดี๋ยว\nเดียะ\nเดื่อ\nเดือก\nเดื่อง\nเดือด\nเดือน\nเดือย\nแด\nแด่\nแดก\nแด็ก\nแดกงา\nแดกแด้\nแดง\nแดด\nแดน\nแด่น\nแด่ว\nแดะ\nแดะแด๋\nโด\nโด่\nโดกเดก\nโด่ง\nโดด\nโดน\nโดม\nโดมร\nโดย\nโดรณ\nใด\nได\nได้\nไดแซ็กคาไรด์\nไดนาโม\nไดนาไมต์\nไดโนเสาร์\nไดเรกตริกซ์\nตก\nต๊กโต\nตง\nต๋ง\nตงฉิน\nตงิด\nตงุ่น\nตด\nตติย\nตถาคต\nตน\nต้น\nตนัย\nตนุ\nตบ\nตบะ\nตปนียะ\nตม\nต้ม\nตมูก\nตยาคี\nตรง\nตรณี\nตรม\nตรรก\nตรรกะ\nตรลบ\nตรลอด\nตรลาด\nตรวจ\nตรวน\nตรอก\nตรอง\nตรอมใจ\nตรอมตรม\nตระ\nตระกล\nตระกวน\nตระกอง\nตระการ\nตระกูล\nตระคัร\nตระเตรียม\nตระนาว\nตระบก\nตระบอก\nตระบอง\nตระบัด\nตระบัน\nตระเบ็ง\nตระแบก\nตระแบง\nตระโบม\nตระพอง\nตระพัง\nตระลาการ\nตระวัน\nตระเว็ด\nตระเวน\nตระสัก\nตระหง่าน\nตระหนก\nตระหนัก\nตระหน่ำ\nตระหนี่\nตรัง\nตรังค์\nตรับ\nตรับฟัง\nตรัย\nตรัยตรึงศ์\nตรัส\nตรัสสา\nตรา\nตรากตรำ\nตราชู\nตราบ\nตราสัง\nตรำ\nตริ\nตริว\nตรี\nตรีปวาย\nตรีพิธพรรณ\nตรียัมปวาย\nตรึก\nตรึง\nตรุ\nตรุณ\nตรุณะ\nตรุษ\nตรู\nตรู่\nตฤณ\nตฤตีย\nตฤษณา\nตลก\nตลบ\nตลอด\nตลับ\nตลาด\nตลิ่ง\nตลึง\nตวง\nต่วน\nต้วมเตี้ยม\nตวัก\nตวัด\nตวาด\nตอ\nตอม่อ\nต่อ\nต้อ\nตอก\nต๊อก\nต๊อกต๋อย\nตอง\nต้อง\nตองกราย\nต้องเต\nตองแตก\nต่องแต่ง\nตองเหลือง\nตอด\nตอน\nต้อน\nตอบ\nตอเบา\nตอม\nต่อม\nต๋อม\nต่อย\nต้อย\nต้อยตริ่ง\nต้อยติ่ง\nต้อยตีวิด\nตอแย\nตอร์ปิโด\nต่อไส้\nตอแหล\nตะ\nตะกรน\nตะกร้อ\nตะกรัน\nตะกรับ\nตะกร้า\nตะกราม\nตะกรุด\nตะกรุม\nตะกรุมตะกราม\nตะกละ\nตะกลาม\nตะกวด\nตะกอ\nตะกอน\nตะกัง\nตะกั่ว\nตะกาง\nตะกาด\nตะกาย\nตะกาว\nตะกุกตะกัก\nตะกุย\nตะกู\nตะกูด\nตะเกียกตะกาย\nตะเกียง\nตะเกียบ\nตะแก\nตะแก่\nตะแกรง\nตะโก\nตะโก้\nตะโกก\nตะโกน\nตะโกรง\nตะโกรม\nตะไกร\nตะขบ\nตะขอ\nตะขาบ\nตะขิดตะขวง\nตะเข้\nตะเข็บ\nตะโขง\nตะคร้อ\nตะครอง\nตะครั่นตะครอ\nตะคร้ำ\nตะคริว\nตะคิว\nตะครุบ\nตะคอก\nตะคัน\nตะค้า\nตะคาก\nตะค้าน\nตะคุ่ม\nตะเครียว\nตะเคียว\nตะเคียน\nตะแคง\nตะไคร่\nตะไคร้\nตะเฆ่\nตะนอย\nตะนาว\nตะบม\nตะบอง\nตะบอย\nตะบัน\nตะบิ้ง\nตะบิด\nตะบิดตะบอย\nตะบี้ตะบัน\nตะบึง\nตะบูน\nตะเบ็ง\nตะเบ็งมาน\nตะเบ๊ะ\nตะแบก\nตะแบง\nตะโบม\nตะไบ\nตะปบ\nตะปลิง\nตะปิ้ง\nตะปุ่มตะป่ำ\nตะปู\nตะพง\nตะพด\nตะพอง\nตะพัก\nตะพัง\nตะพัด\nตะพั้น\nตะพาก\nตะพาน\nตะพาบ\nตะพาย\nตะพึด\nตะพึดตะพือ\nตะพุ่น\nตะเพรา\nตะเพิง\nตะเพิด\nตะเพียน\nตะโพก\nตะโพง\nตะโพน\nตะเภา\nตะใภ้\nตะม่อ\nตะมอย\nตะรังกะนู\nตะรังตังกวาง\nตะรังตังช้าง\nตะราง\nตะลอง\nตะลอน\nตะล่อม\nตะละ\nตะลาน\nตะลิงปลิง\nตะลิบ\nตะลีตะลาน\nตะลึง\nตะลึงพรึงเพริด\nตะลุง\nตะลุ่ม\nตะลุ่มนก\nตะลุมบอน\nตะลุ่มโปง\nตะลุมพอ\nตะลุมพุก\nตะลุย\nตะเลง\nตะแลงแกง\nตะไล\nตะวัน\nตะเวน\nตะหลิว\nตะหลุก\nตะหลุง\nตะแหง่ว\nตะแหมะแขะ\nตะโหงก\nตัก\nตักกะ\nตักเตือน\nตั๊กแตน\nตักษัย\nตัง\nตั่ง\nตั้ง\nตังเก\nตังฉ่าย\nตังเม\nตังวาย\nตังโอ๋\nตัจฉก\nตัจฉนี\nตัณฑุล\nตัณหา\nตัด\nตัน\nตันตระ\nตันติ\nตันหยง\nตับ\nตับปิ้ง\nตัว\nตั๋ว\nตัวจี๊ด\nตัวตืด\nตั้วโผ\nตั้วเหี่ย\nตา\nตาก\nตากวาง\nต่าง\nตาด\nตาน\nต่าน\nต้าน\nตานนกกด\nตานี\nตาบ\nตาม\nตามะแน\nตามิน\nตาย\nตาราไต\nตาล\nตาลุ\nต๋าว\nตาเสือ\nตาหนู\nตาฬ\nตำ\nต่ำ\nตำนาน\nตำบล\nตำแบ\nตำแย\nตำรวจ\nตำรับ\nตำรา\nตำรุ\nตำลึง\nตำเสา\nตำหนัก\nตำหนิ\nตำแหน่ง\nติ\nติก\nติ๊ก\nติกะ\nติกาหรัง\nติง\nติ่ง\nติ๋ง\nติ่งตั่ง\nติ๋งต่าง\nติงส\nติงสติ\nติณ\nติด\nติตติกะ\nติตติร\nติตถ\nติตถะ\nติถี\nติมิงคละ\nติรัจฉาน\nติลก\nติละ\nติ้ว\nตี\nตี่\nตีน\nตีบ\nตีรถะ\nตีระ\nตึ\nตึก\nตึ้ก\nตึ้กตั้ก\nตึง\nตึดตื๋อ\nตึ๊ดตื๋อ\nตืด\nตื่น\nตื้น\nตื้อ\nตื๊อ\nตื๋อ\nตุ\nตุ๊\nตุ๊กแก\nตุ๊กตา\nตุ๊กต่ำ\nตุกติก\nตุ๊กติ๊ก\nตุ๊กตุ่น\nตุ๊กตุ๋ย\nตุง\nตุ้งก่า\nตุ้งติ้ง\nตุ๊ดตู่\nตุน\nตุ่น\nตุ๋น\nตุนาหงัน\nตุบ\nตุ้บ\nตุ๊บป่อง\nตุปัดตุป่อง\nตุปัดตุเป๋\nตุ่ม\nตุ้ม\nตุ๋ม\nตุ้มกว้าว\nตุมกา\nตุ้มแซะ\nตุมตัง\nตุ้มเต๋น\nตุ้มปี่\nตุมพะ\nตุ่ย\nตุ้ย\nตุ๊ย\nตุ๋ยตุ่ย\nตุรคะ\nตุรงค์\nตุล\nตุลา\nตุหรัดตุเหร่\nตู\nตู่\nตู้\nตูก\nตูด\nตูบ\nตูม\nเต๊ก\nเต็ง\nเต่ง\nเตช\nเตโช\nเต้น\nเต็นท์\nเต็ม\nเตย\nเตร่\nเตร็ด\nเตรตา\nเตรียม\nเตรียมตรม\nเตละ\nเตลิด\nเตว็ด\nเต่อ\nเตอะ\nเตะ\nเตา\nเต่า\nเต้า\nเต๋า\nเต่าเกียด\nเต้าเจี้ยว\nเต้าทึง\nเต้าหู้\nเต้าฮวย\nเต๊าะ\nเตาะแตะ\nเติ่ง\nเติบ\nเติม\nเตี้ย\nเตียง\nเตียน\nเตียบ\nเตี๋ยม\nเตียรถ์\nเตียว\nเตี่ยว\nเตือน\nแต่\nแต้\nแตก\nแตง\nแต่ง\nแตงเม\nแต้จิ๋ว\nแตด\nแต๊ดแต๋\nแตน\nแต้ม\nแตร\nแตระ\nแต้ว\nแต้วแร้ว\nแต้วแล้ว\nแต่ว่า\nแต้แว้ด\nแตะ\nโต\nโต้\nโตก\nโต่ง\nโต้ง\nโตงเตง\nโตฎก\nโต๊ด\nโตน\nโตนด\nโต้โผ\nโตมร\nโตย\nโตรก\nโต๊ะ\nใต้\nไต\nไต่\nไต้\nไต๋\nไตร\nไตรกิศยา\nไตรดายุค\nไตร่ตรอง\nไตรย\nไต้หวัน\nถก\nถกล\nถงาด\nถด\nถนน\nถนอม\nถนัด\nถนัน\nถนำ\nถนิม\nถม\nถ่ม\nถมอ\nถมึงทึง\nถลก\nถลกบาตร\nถลน\nถล่ม\nถลอก\nถลัน\nถลา\nถลาก\nถลาย\nถลำ\nถลึงตา\nถลุง\nถ่วง\nถ้วน\nถ้วย\nถวัล\nถวัลย์\nถวาย\nถวิน\nถวิล\nถ่อ\nถ้อ\nถอก\nถอง\nถ่อง\nถ้อง\nถอด\nถอน\nถอบ\nถอบแถบ\nถ่อม\nถอย\nถ่อย\nถ้อย\nถะ\nถะถั่น\nถะมัดถะแมง\nถัก\nถัง\nถั่ง\nถัด\nถัทธ\nถัน\nถั่น\nถับ\nถัมภ์\nถัว\nถั่ว\nถา\nถ้า\nถาก\nถาง\nถ่าง\nถาด\nถาน\nถ่าน\nถาบ\nถาม\nถามะ\nถ่าย\nถ่าว\nถาวร\nถาวรธิรา\nถ้ำ\nถิ่น\nถี่\nถีบ\nถึก\nถึง\nถือ\nถุง\nถุน\nถุย\nถู\nถูก\nเถกิง\nเถน\nเถร\nเถระ\nเถรานุเถระ\nเถรี\nเถลไถล\nเถลิง\nเถลือกถลน\nเถ่อ\nเถอะ\nเถา\nเถ้า\nเถาวัลย์\nเถาะ\nเถิก\nเถิง\nเถิด\nเถิดเทิง\nเถิน\nเถียง\nเถียร\nเถือ\nเถือก\nเถื่อน\nแถ\nแถก\nแถง\nแถน\nแถบ\nแถม\nแถลง\nแถลบ\nแถว\nโถ\nโถง\nโถงเถง\nโถบ\nโถม\nโถมนาการ\nไถ\nไถ่\nไถ้\nไถง\nไถล\nทกล้า\nทแกล้ว\nท่ง\nทด\nทน\nท้น\nทนต์\nทนโท่\nทนาย\nทบ\nทบวง\nทมอ\nทมะ\nทมิฬ\nทโมน\nทยอย\nทแยง\nทรกรรม\nทรชน\nทรชาติ\nทรพิษ\nทรยศ\nทรราช\nทรลักษณ์\nทรง\nทรพี\nทรมาทรกรรม\nทรมาน\nทรรทึง\nทรรป\nทรรปณ์\nทรรปณะ\nทรรศนะ\nทรรศนาการ\nทรรศนีย์\nทรวง\nทรวดทรง\nทรวาร\nทรหด\nทรหวล\nทรหึง\nทรอมโบน\nทระนง\nทรัพย์\nทรัพยากร\nทรัมเป็ต\nทรานซิสเตอร์\nทราบ\nทราม\nทราย\nทรุด\nทฤษฎี\nทลาย\nทลิท\nทลิททก\nทวง\nท้วง\nท่วงท่า\nท่วงทำนอง\nท่วงที\nทวด\nทวน\nท้วน\nท่วม\nท้วม\nทวย\nท่วย\nท้วย\nทวอย\nทวัตดึงส์\nทวัย\nทวา\nทวาบร\nทว่า\nทวาย\nทวาร\nทวิ\nทวิช\nทวิตีย์\nทวิตียา\nทวี\nทวีธาภิเษก\nทวีป\nทศ\nทศมี\nทศางค์\nทหระ\nทหาร\nทอ\nท่อ\nท้อ\nทอก\nทอง\nท่อง\nท้อง\nทองกวาว\nทองภู\nทองลิน\nทองหลาง\nทองโหลง\nทองอุไร\nทอด\nทอน\nท่อน\nทอนซิล\nทอฟฟี่\nท่อม\nทอย\nทอเรียม\nทะ\nทะงัน\nทะนง\nทะนน\nทะนาน\nทะนุ\nทะเบียน\nทะมัดทะแมง\nทะมึน\nทะมื่น\nทะแม่ง\nทะยาน\nทะเยอทะยาน\nทะแย\nทะร่อทะแร่\nทะลวง\nทะลอก\nทะลัก\nทะลาย\nทะลึ่ง\nทะลุ\nทะลุดทะลาด\nทะเล\nทะเล้น\nทะเล่อทะล่า\nทะเลาะ\nทะเลิ่กทะลั่ก\nทะวาย\nทัก\nทักข์\nทักขิญ\nทักขิณ\nทักขิณา\nทักขิณาวัฏ\nทักขิโณทก\nทักขิไณยบุคคล\nทักทิน\nทักษะ\nทักษา\nทักษิณ\nทักษิณา\nทักษิโณทก\nทัง\nทั่ง\nทั้ง\nทังวล\nทังวี้ทังวล\nทังสเตน\nทัณฑ์\nทัณฑกรรม\nทัณฑฆาต\nทัณฑสถาน\nทัณฑะ\nทัณฑิกา\nทัณฑิมา\nทัณฑีบท\nทัด\nทัดทา\nทัต\nทัน\nทันต์\nทันตชะ\nทันตแพทย์\nทันติน\nทันตี\nทันธ์\nทับ\nทับทิม\nทับสมิงคลา\nทัพ\nทัพพะ\nทัพพี\nทั่ว\nทัศ\nทัศน์\nทัศนะ\nทัศนา\nทัศนคติ\nทัศนวิสัย\nทัศนศาสตร์\nทัศนศิลป์\nทัศนศึกษา\nทัศนาการ\nทัศนาจร\nทัศนีย์\nทัศนียภาพ\nทัศนูปกรณ์\nทัศไนย\nทัสนานุตริยะ\nทัฬหะ\nทัฬหิ\nทัฬหี\nทา\nท่า\nท้า\nทาก\nทาง\nท้าง\nทาฐะ\nทาฐิกะ\nทาฒะ\nทาฒิกะ\nทาน\nท่าน\nทานต์\nทานพ\nทาบ\nทาม\nท่ามกลาง\nทาย\nท้าย\nทายก\nทายัช\nทายาด\nทายาท\nทายิกา\nทารก\nทารพี\nทาริกา\nทารุณ\nทาว\nท่าว\nท้าว\nทาส\nทาสี\nทำ\nทำนบ\nทำนอง\nทำนาย\nทำนุ\nทำนูล\nทำเนา\nทำเนียบ\nทำไม\nทำลาย\nทำเล\nทิคัมพร\nทิฆัมพร\nทิ้ง\nทิงเจอร์\nทิ้งถ่อน\nทิ้งทูด\nทิชะ\nทิชากร\nทิชาชาติ\nทิฏฐะ\nทิฏฐานุคติ\nทิฏฐุชุกรรม\nทิฐธรรม\nทิฐิ\nทิด\nทิต\nทิน\nทิพ\nทิพย์\nทิพา\nทิม\nทิ่ม\nทิมทอง\nทิว\nทิวงคต\nทิวทัศน์\nทิวา\nทิศ\nทิศา\nทิศานุทิศ\nที\nที่\nทีฆชาติ\nทีฆนิกาย\nทีฆสระ\nทีฆายุ\nทีป\nทีม\nทีเอ็นที\nทึก\nทึกทัก\nทึ่ง\nทึ้ง\nทึดทือ\nทึนทึก\nทึบ\nทึม\nทึ่ม\nทื่อ\nทุ\nทุก\nทุกข์\nทุกขลาภ\nทุกขเวทนา\nทุกขารมณ์\nทุกฏ\nทุกรกิริยา\nทุกะ\nทุกัง\nทุกูล\nทุคตะ\nทุคติ\nทุ่ง\nทุ้ง\nทุงงะ\nทุจริต\nทุด\nทุทรรศนนิยม\nทุนิยม\nทุน\nทุ่น\nทุนนิมิต\nทุบ\nทุบทู\nทุปปัญญา\nทุพพรรณ\nทุพพล\nทุพพลภาพ\nทุพภิกขภัย\nทุม\nทุ่ม\nทุ้ม\nทุย\nทุ้ย\nทุรกันดาร\nทุรชน\nทุรชาติ\nทุรพล\nทุรลักษณ์\nทุรน\nทุรนทุราย\nทุรัศ\nทุราคม\nทุราจาร\nทุเรศ\nทุเรียน\nทุลักทุเล\nทุเลา\nทุศีล\nทุสสะ\nทุสสีล\nทู\nทูโม่ง\nทู่\nทู้\nทูกัง\nทู่ซี้\nทูต\nทูตานุทูต\nทูน\nทูบ\nทูม\nทูล\nทูเลียม\nเท\nเท่\nเทคนิค\nเทคนีเชียม\nเทคโนโลยี\nเท้ง\nเท้งเต้ง\nเท็จ\nเทนนิส\nเทพ\nเทพา\nเทพารักษ์\nเทพยเจ้า\nเทพยดา\nเทพยุดา\nเทพิน\nเทพินทร์\nเทพี\nเทเพนทร์\nเทโพ\nเทริด\nเทลลูเรียม\nเทวทัณฑ์\nเทวดา\nเทวทูต\nเทวธรรม\nเทวนาครี\nเทวนิยม\nเทวรูป\nเทวโลก\nเทววิทยา\nเทวสถาน\nเทวศ\nเทวษ\nเทวัญ\nเทวัน\nเทวาลัย\nเทวินทร์\nเทวี\nเทเวศ\nเทเวศร์\nเทเวศวร์\nเทศ\nเทศะ\nเทศาภิบาล\nเทศน์\nเทศนา\nเทห์\nเท่ห์\nเทห์ฟากฟ้า\nเทหวัตถุ\nเท่อ\nเท้อ\nเทอญ\nเทอม\nเทอร์เบียม\nเทอร์โมมิเตอร์\nเทอะทะ\nเทา\nเท่า\nเท้า\nเท้ายายม่อม\nเท่ารึง\nเทิ่ง\nเทิด\nเทิน\nเทิบ\nเทิบทาบ\nเทิ้ม\nเที่ยง\nเทียด\nเทียน\nเที้ยน\nเทียบ\nเทียม\nเทียร\nเที้ยร\nเทียว\nเที่ยว\nเทือ\nเทื่อ\nเทื้อ\nเทือก\nแท้\nแท็กซี่\nแทง\nแท่ง\nแท้ง\nแท็งก์\nแทงทวย\nแทงวิสัย\nแทตย์\nแทน\nแท่น\nแทนเจนต์\nแทนทาลัม\nแทบ\nแทรก\nแทรกเตอร์\nแทลเลียม\nแทะ\nโท\nโท่\nโทกเทก\nโทง\nโทงเทง\nโทณะ\nโทน\nโทนโท่\nโทมนัส\nโทรคมนาคม\nโทรทรรศน์\nโทรทัศน์\nโทรพิมพ์\nโทรภาพ\nโทรเลข\nโทรศัพท์\nโทรสาร\nโทรม\nโทษ\nโทษา\nโทษานุโทษ\nโทสะ\nโทสาคติ\nโทโส\nโทหฬินี\nไท\nไท้\nไทเทเนียม\nไทเทรต\nไทย\nไทร\nไทวะ\nธง\nธงก์\nธชะ\nธชี\nธตรฐ\nธนบัตร\nธนสมบัติ\nธนสาร\nธนะ\nธนา\nธนาคม\nธนาคาร\nธนาณัติ\nธเนศ\nธโนปจัย\nธไนศวรรย์\nธนิต\nธนิษฐะ\nธนิษฐา\nธนุ\nธนุรวิทยา\nธนุรเวท\nธนู\nธม\nธมกรก\nธรณะ\nธรณิน\nธรณินทร์\nธรณิศ\nธรณิศร\nธรณิศวร์\nธรณี\nธรมาน\nธรรม\nธรรมนูญ\nธรรมยุต\nธรรมยุติกนิกาย\nธรรมะ\nธรรมาทิตย์\nธรรมาธรรม\nธรรมาธิปไตย\nธรรมาธิษฐาน\nธรรมานุสาร\nธรรมาภิมุข\nธรรมาภิสมัย\nธรรมายตนะ\nธรรมารมณ์\nธรรมาสน์\nธรรมิก\nธรา\nธราดล\nธราธร\nธราธาร\nธราธิบดี\nธราธิป\nธริษตรี\nธเรษตรี\nธเรศ\nธวัช\nธัช\nธัญ\nธัญญาหาร\nธันยา\nธันยาวาท\nธันวาคม\nธัมมะ\nธาดา\nธาตรี\nธาตวากร\nธาตุ\nธาตุโขภ\nธาตุมมิสสา\nธานิน\nธานินทร์\nธานี\nธาร\nธารกำนัล\nธารคำนัล\nธารณะ\nธารณา\nธารา\nธาษตรี\nธำมรงค์\nธำรง\nธิดา\nธิติ\nธีระ\nธุช\nธุดงค์\nธุดงควัตร\nธุต\nธุตตะ\nธุมเกตุ\nธุมา\nธุรการ\nธุรกิจ\nธุระ\nธุรำ\nธุลี\nธุวดารา\nธุวภาค\nธุวมณฑล\nธูป\nเธนุ\nเธอ\nเธียร\nโธ่\nโธวนะ\nนก\nนกุล\nนขลิขิต\nนขะ\nนขา\n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ุลา\nบิน\nบิ่น\nบินยา\nบิลเลียด\nบิวเรตต์\nบิสมัท\nบี้\nบีฑา\nบีตา\nบีบ\nบีเยศ\nบึก\nบึกบึน\nบึง\nบึ่ง\nบึ้ง\nบุ\nบุก\nบุคคล\nบุคลากร\nบุคลาธิษฐาน\nบุคลิก\nบุง\nบุ่ง\nบุ้ง\nบุ้งกี๋\nบุญ\nบุญญาธิการ\nบุญญานุภาพ\nบุญญาภินิหาร\nบุญญาภิสังขาร\nบุณฑริก\nบุณมี\nบุณย์\nบุตร\nบุตรี\nบุถุชน\nบุทคล\nบุนนาค\nบุบ\nบุบบิบ\nบุปผชาติ\nบุพกรรม\nบุพการี\nบุพกิจ\nบุพชาติ\nบุพทักษิณ\nบุพนิมิต\nบุพบท\nบุพพาจารย์\nบุพเพสันนิวาส\nบุพโพ\nบุ๋ม\nบุ่มบ่าม\nบุ้ย\nบุรณะ\nบุรพทิศ\nบุรพบท\nบุรพาจารย์\nบูรพาจารย์\nบุระ\nบุราณ\nบุรินทร์\nบุริมทิศ\nบุริมพรรษา\nบุริมสิทธิ\nบุรี\nบุรุษ\nบุโรทั่ง\nบุษกร\nบุษบก\nบุษบง\nบุษบัน\nบุษบา\nบุษบามินตรา\nบุษปราค\nบุษปะ\nบุษย์\nบุษย์น้ำทอง\nบุษยมาส\nบุษยะ\nบุษราคัม\nบุหงัน\nบุหงา\nบุหรง\nบุหรี่\nบุหลัน\nบู่\nบู้\nบูชนียสถาน\nบูชา\nบูชิต\nบูด\nบูดู\nบูร\nบูรณ์\nบูรณภาพ\nบูรณมี\nบูรณะ\nบูรณาการ\nบูรพ์\nบูรพะ\nบูรพา\nเบ้\nเบ่ง\nเบ๊จี๋\nเบญกานี\nเบญจกัลยาณี\nเบญจกามคุณ\nเบญจขันธ์\nเบญจดุริยางค์\nเบญจธรรม\nเบญจบรรพต\nเบญจพรรณ\nเบญจเพส\nเบญจมาศ\nเบญจรงค์\nเบญจศก\nเบญจศีล\nเบญจะ\nเบญจา\nเบญจางค์\nเบญจางคประดิษฐ์\nเบญญา\nเบญพาด\nเบ็ด\nเบ็ดเตล็ด\nเบ็ดเสร็จ\nเบน\nเบนซิน\nเบรก\nเบริลเลียม\nเบส\nเบ้อ\nเบอร์\nเบอร์คีเลียม\nเบ้อเร่อ\nเบ้อเร่อเท่อ\nเบ้อเริ่ม\nเบ้อเริ่มเทิ่ม\nเบอะ\nเบอะบะ\nเบะ\nเบา\nเบ้า\nเบาราณ\nเบาะ\nเบิก\nเบิ่ง\nเบี้ย\nเบี่ยง\nเบียด\nเบียน\nเบียร์\nเบี้ยว\nเบือ\nเบื่อ\nเบื้อ\nเบื้อง\nเบือน\nแบ\nแบ้\nแบก\nแบคทีเรีย\nแบ่ง\nแบงก์\nแบดมินตัน\nแบตเตอรี่\nแบน\nแบนโจ\nแบบ\nแบ็บ\nแบเรียม\nแบหลา\nแบะ\nโบ\nโบ้\nโบ๋\nโบก\nโบกขรณี\nโบกขรพรรษ\nโบชุก\nโบต\nโบนัส\nโบ๊เบ๊\nโบย\nโบรมีน\nโบรอน\nโบราณ\nโบสถ์\nใบ\nใบ้\nไบ่\nปก\nปกติ\nปกรณ์\nปกรณัม\nปกิณกะ\nปกีรณัม\nปโกฏิ\nปง\nป่ง\nปงช้าง\nปฎล\nปฏัก\nปฏิกรณ์\nปฏิกรรม\nปฏิการะ\nปฏิกิริยา\nปฏิกูล\nปฏิคม\nปฏิคหิต\nปฏิคาหก\nปฏิฆะ\nปฏิชีวนะ\nปฏิญญา\nปฏิญาณ\nปฏิทิน\nปฏิบถ\nปฏิบัติ\nปฏิปทา\nปฏิปักษ์\nปฏิปัน\nปฏิปุจฉาพยากรณ์\nปฏิปุจฉาวาที\nปฏิพัทธ์\nปฏิพากย์\nปฏิภาค\nปฏิภาณ\nปฏิมา\nปฏิมากร\nปฏิยุทธ์\nปฏิรพ\nปฏิรูป\nปฏิโลม\nปฏิวัติ\nปฏิวาต\nปฏิวาท\nปฏิเวธ\nปฏิสนธิ\nปฏิสวะ\nปฏิสังขรณ์\nปฏิสันถาร\nปฏิสัมภิทา\nปฏิเสธ\nปฐพี\nปฐม\nปฐวี\nปณต\nปณาม\nปณิธาน\nปณิธิ\nปณีต\nปด\nปดิวรัดา\nปติ\nปถพี\nปถมัง\nปถวี\nปทัฏฐาน\nปทัสถาน\nปทานุกรม\nปทีป\nปทุม\nปน\nป่น\nปนัดดา\nปปัญจะ\nปม\nปรนัย\nปรปักษ์\nปรโลก\nปรวาที\nปรก\nปรกติ\nปรง\nปรตยักษ์\nปรน\nปรนนิบัติ\nปรนิมมิตวสวัตดี\nปรบ\nปรปักษ์\nปรมัตถ์\nปรมาจารย์\nปรมาณู\nปรมาภิไธย\nปรมาภิเษก\nปรมินทร์\nบรเมนทร์\nปรเมศวร์\nปรเมษฐ์\nปรวด\nปรวนแปร\nปรศุ\nปรสิต\nปร๋อ\nปรองดอง\nปรอด\nปรอท\nปรอย\nประ\nประกบ\nประกฤต\nประกฤติ\nประกล\nประกวด\nประกวดประขัน\nประกอบ\nประกัน\nประกับ\nประกาย\nประกายพรึก\nประการ\nประกาศ\nประกาศนียบัตร\nประกาศิต\nประกำ\nประกิด\nประกิต\nประคด\nประคนธรรพ\nประคนธรรพ์\nประคบ\nประคบประหงม\nประคอง\nประคับประคอง\nประคัลภ์\nประคำ\nประคิ่น\nประคุณ\nประเคน\nประเคราะห์\nประโคน\nประโคนธรรพ\nประโคนธรรพ์\nประโคม\nประจง\nประจญ\nประจบ\nประจบประแจง\nประจวบ\nประจ๋อประแจ๋\nประจักษ์\nประจักษนิยม\nประจัญ\nประจัน\nประจาก\nประจาค\nประจาน\nประจำ\nประจิม\nประจิ้มประเจ๋อ\nประจุ\nประจุคมน์\nประจุบัน\nประเจก\nประเจิด\nประเจิดประเจ้อ\nประเจียด\nประแจ\nประชด\nประชน\nประชวม\nประชวร\nประชัน\nประชา\nประชาธิปไตย\nประชิด\nประชี\nประชุม\nประเชิญ\nประณต\nประณม\nประณาม\nประณิธาน\nประณิธิ\nประณีต\nประณุท\nประดง\nประดน\nประดวน\nประดอน\nประดอย\nประดัก\nประดักประเดิด\nประดัง\nประดับ\nประดา\nประดาก\nประดาป\nประดาษ\nประดิชญา\nประดิดประดอย\nประดิทิน\nประดิษฐ์\nประดิษฐกรรม\nประดิษฐาน\nประดุง\nประดุจ\nประดู่\nประเด\nประเด็น\nประเดิม\nประเดียง\nประเดี๋ยว\nประเดี๋ยวประด๋าว\nประแดง\nประแดะ\nประโดง\nประโดย\nประตง\nประตัก\nประตาป\nประติชญา\nประติญาณ\nประติทิน\nประติมากร\nประติมากรรม\nประติรพ\nประตู\nประถม\nประถมจินดา\nประทม\nประท้วง\nประทวน\nประทักษ์\nประทักษิณ\nประทัง\nประทัด\nประทับ\nประทาน\nประทาย\nประทาศี\nประทิน\nประทิ่น\nประทีป\nประทุฐ\nประทุน\nประทุษ\nประทุษฐ์\nประเทศ\nประเทา\nประเทียด\nประเทียบ\nประเทือง\nประธาน\nประธานาธิบดี\nประนม\nประนอ\nประนอม\nประนัง\nประนัปดา\nประนีประนอม\nประปราน\nประปราย\nประปา\nประเปรี้ยง\nประเปรียว\nประพจน์\nประพนธ์\nประพรม\nประพฤติ\nประพฤทธิ์\nประพัด\nประพัทธ์\nประพันธ์\nประพาต\nประพาส\nประพาสมหรณพ\nประพาฬ\nประพิณ\nประพิมพ์ประพาย\nประพุทธ์\nประเพณี\nประโพธ\nประไพ\nประไพร\nประภพ\nประภัสสร\nประภา\nประภาคาร\nประภาพ\nประภาษ\nประภาส\nประเภท\nประมง\nประมวล\nประมาณ\nประมาท\nประมุข\nประมุท\nประมูล\nประเมิน\nประโมง\nประโมทย์\nประยงค์\nประยุกต์\nประยุทธ์\nประยุร\nประยูร\nประโยค\nประโยชน์\nประโรหิต\nประลมพ์\nประลอง\nประลัย\nประลาต\nประลาย\nประลุ\nประเล่ห์\nประเล้าประโลม\nประโลม\nประวรรต\nประวรรตน์\nประวัติ\nประวาล\nประวาลปัทม์\nประวาส\nประวิง\nประวิช\nประวิตร\nประวิน\nประวีณ\nประเวณี\nประเวศ\nประเวศน์\nประศม\nประศาสน์\nประศุ\nประสก\nประสงค์\nประสบ\nประสพ\nประสม\nประสะ\nประสัก\nประสันนาการ\nประสัยห์\nประสา\nประสาท\nประสาธน์\nประสาน\nประสาร\nประสิทธิ์\nประสิทธิผล\nประสิทธิภาพ\nประสีประสา\nประสูต\nประสูติ\nประเสบัน\nประเสบันอากง\nประเสริฐ\nประหนึ่ง\nประหม่า\nประหยัด\nประหลาด\nประหล่ำ\nประหวัด\nประหวั่น\nประหัตประหาร\nประหาณ\nประหาร\nประเหล\nประเหส\nประไหมสุหรี\nประอบ\nประอร\nปรัก\nปรักปรำ\nปรักมะ\nปรัง\nปรัชญา\nปรัตถจริยา\nปรัตยุบัน\nปรัน\nปรับ\nปรัมปรา\nปรัศจิม\nปรัศนา\nปรัศนี\nปรัศว์\nปรัสสบท\nปร่า\nปรากฏ\nปรากรม\nปรากฤต\nปราการ\nปราง\nปรางค์\nปราจีน\nปราชญ์\n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าย\nผคม\nผง\nผงก\nผงม\nผงะ\nผงาด\nผง่าน\nผจง\nผจญ\nผจัญ\nผจาน\nผชุม\nผณิน\nผณินทร\nผณิศวร\nผด\nผดุง\nผเดิน\nผทม\nผนวก\nผนวช\nผนัง\nผนิด\nผนึก\nผม\nผยอง\nผรณาปีติ\nผรสุ\nผริต\nผรุสวาท\nผล\nผลคุน\nผลคุนี\nผล็อย\nผละ\nผลัก\nผลัด\nผลับ\nผลัวะ\nผลา\nผลาญ\nผลานิสงส์\nผลาผล\nผลาหาร\nผลิ\nผลิกะ\nผลิต\nผลิน\nผลี\nผลีผลาม\nผลึก\nผลึ่ง\nผลือ\nผลุ\nผลุง\nผลุด\nผลุน\nผลุนผลัน\nผลุบ\nผลุบผลับ\nผลุย\nผลู\nผวน\nผวย\nผวา\nผสม\nผสาน\nผอง\nผ่อง\nผ่อน\nผอบ\nผอม\nผ็อย\nผอูน\nผะ\nผะดา\nผะสา\nผัก\nผัคคุณ\nผัคคุณี\nผัง\nผัด\nผัน\nผับ\nผัว\nผัวะ\nผัสสะ\nผัสสาหาร\nผา\nผ่า\nผ้า\nผาก\nผาง\nผ่าง\nผาณิต\nผาด\nผาติ\nผ่าน\nผาม\nผาย\nผ่ายผอม\nผาล\nผาลคุน\nผาลา\nผ่าว\nผาสุก\nผ้าฮาด\nผำ\nผ้ำ\nผิ\nผิง\nผิด\nผิตะ\nผิน\nผิว\nผี\nผี้ว์\nผึง\nผึ่ง\nผึ้ง\nผึ้งรวง\nผืน\nผื่น\nผุ\nผุด\nผุยผง\nผุสราคา\nผู้\nผูก\nเผ\nเผง\nเผชิญ\nเผ็ด\nเผด็จ\nเผดิม\nเผดียง\nเผ่น\nเผนิก\nเผย\nเผยอ\nเผยิบ\nเผยิบผยาบ\nเผล\nเผล่\nเผล็ด\nเผลอ\nเผลอไผล\nเผละ\nเผลาะ\nเผลาะแผละ\nเผลียง\nเผอเรอ\nเผอิญ\nเผอิล\nเผะ\nเผา\nเผ่า\nเผ้า\nเผาะ\nเผิ้ง\nเผิน\nเผียน\nเผือ\nเผื่อ\nเผือก\nเผือด\nเผือน\nเผื่อน\nแผ่\nแผก\nแผง\nแผด\nแผน\nแผ่น\nแผนก\nแผล\nแผลง\nแผล็บ\nแผล็ว\nแผละ\nแผ่ว\nแผ้ว\nโผ\nโผง\nโผฏฐัพพะ\nโผน\nโผเผ\nโผย\nโผล่\nโผลกเผลก\nโผละ\nโผอน\nโผะ\nไผ\nไผ่\nไผท\nฝน\nฝรั่ง\nฝรั่งเศส\nฝ่อ\nฝอย\nฝัก\nฝัง\nฝั่ง\nฝัด\nฝัน\nฝา\nฝ่า\nฝ้า\nฝาก\nฝาง\nฝาด\nฝาน\nฝาย\nฝ่าย\nฝ้าย\nฝิ่น\nฝี\nฝี่\nฝีก\nฝึก\nฝืด\nฝืน\nฝุ่น\nฝูง\nเฝ้า\nเฝือ\nเฝือก\nเฝือง\nเฝื่อน\nแฝก\nแฝง\nแฝด\nใฝ่\nไฝ\nพก\nพกุล\nพง\nพงศ์\nพงศกร\nพงศธร\nพงศา\nพงศาวดาร\nพจน์\nพจนา\nพจนานุกรม\nพจนารถ\nพจนีย์\nพจมาน\nพจี\nพชระ\nพญา\nพญาลอ\nพณิช\nพณิชย์\nพดด้วง\nพธู\nพนันดร\nพนาดร\nพนาดอน\nพนาราม\nพนาลัย\nพนาลี\nพนาวาส\nพนาเวศ\nพนาศรม\nพนาสณฑ์\nพนาสัณฑ์\nพเนจร\nพ่น\nพ้น\nพนม\nพนอง\nพนอม\nพนัก\nพนักงาน\nพนัง\nพนัน\nพนัส\nพนา\nพนาย\nพนิดา\nพนิต\nพบ\nพม่า\nพยนต์\nพยศ\nพยัก\nพยักพเยิด\nพยัคฆ์\nพยัคฆา\nพยัคฆิน\nพยัคฆี\nพยัชน์\nพยัญชนะ\nพยัต\nพยับ\nพยากรณ์\nพยาฆร์\nพยางค์\nพยาธิ\nพยาน\nพยาบาท\nพยาบาล\nพยาม\nพยามะ\nพยายาม\nพยุ\nพยุง\nพยุหยาตรา\nพยุหโยธา\nพยุหเสนา\nพยุหะ\nพยู่ห์\nพเยีย\nพร\nพรต\nพรม\nพรรค\nพรรค์\nพรรคานต์\nพรรณ\nพรรณนา\nพรรดึก\nพรรลาย\nพรรษ\nพรรษา\nพรรเอิญ\nพรวด\nพรวน\nพรหม\nพรหมจรรย์\nพรหมจาริณี\nพรหมจารี\nพรหมา\nพรหมาสตร์\nพรหมินทร์\nพรอก\nพร่อง\nพร้อง\nพรอด\nพร้อม\nพร้อมพรัก\nพร่อย\nพร้อย\nพระ\nพระนอม\nพระนาด\nพระฮาม\nพรักพร้อม\nพรั่ง\nพรัด\nพรั่น\nพรับ\nพร่า\nพร้า\nพราก\nพราง\nพร่าง\nพราด\nพราน\nพราย\nพราว\nพราหมณ์\nพราหมณะ\nพราหมณี\nพราหมี\nพรำ\nพร่ำ\nพริก\nพริ้ง\nพริบ\nพริ้ม\nพรึง\nพรึน\nพรึบ\nพรึ่บ\nพรืด\nพรุ\nพรุ่ง\nพรุน\nพรู\nพรูด\nพฤกษ์\nพฤกษชาติ\nพฤกษเทวดา\nพฤกษราช\nพฤกษศาสตร์\nพฤกษา\nพฤฒ\nพฤฒา\nพฤฒาจารย์\nพฤฒิ\nพฤต\nพฤติ\nพฤทธ์\nพฤทธิ์\nพฤนต์\nพฤนท์\nพฤศจิก\nพฤศจิกายน\nพฤษภ\nพฤษภาคม\nพฤหัสบดี\nพล\nพละ\nพลากร\nพลาดิศัย\nพลาธิการ\nพลานามัย\nพลบ\nพลวก\nพลวง\nพลวัต\nพลศาสตร์\nพลอ\nพล้อ\nพลอง\nพลอด\nพลอน\nพลอมแพลม\nพลอย\nพล่อย\nพลั่ก\nพลัง\nพลั่ง\nพลั้ง\nพลัด\nพลัน\nพลับ\nพลับพลา\nพลับพลึง\nพลั่ว\nพล่า\nพลาง\nพลาญ\nพลาด\nพล่าน\nพลาม\nพล่าม\nพลาย\nพลาสติก\nพลาสมา\nพลาหก\nพลำ\nพล้ำ\nพลำภัง\nพลิก\nพลิพัท\nพลิ้ว\nพลี\nพลุ\nพลุก\nพลุ่ก\nพลุกพล่าน\nพลุ่ง\nพลุ้ย\nพลู\nพลูโต\nพลูโทเนียม\nพวก\nพวง\nพ่วง\nพวน\nพวย\nพสก\nพสุ\nพสุธา\nพสุสงกรานต์\nพหล\nพหุ\nพหุล\nพหู\nพอ\nพ่อ\nพ้อ\nพอก\nพอง\nพ้อง\nพอน\nพ้อม\nพอโลเนียม\nพะ\nพะงา\nพะงาบ\nพะจง\nพะทำมะรง\nพะนอ\nพะเน้าพะนอ\nพะเนิน\nพะเนียง\nพะแนง\nพะพาน\nพะพิง\nพะเพิง\nพะยอม\nพ่ะย่ะค่ะ\nพะยูง\nพะยูน\nพะเยิบ\nพะเยิบพะยาบ\nพะรุงพะรัง\nพะโล้\nพะไล\nพะวง\nพะวักพะวน\nพะวา\nพะว้าพะวัง\nพะอง\nพะอากพะอำ\nพะอืดพะอม\nพัก\nพักตร์\nพักตรา\nพักร\nพัง\nพังกา\nพังงา\nพังผืด\nพังพวย\nพังพอน\nพังพาน\nพังพาบ\nพังเพย\nพัช\nพัชนี\nพัชระ\nพัญจน์\nพัฒนะ\nพัฒนา\nพัฒนากร\nพัฒนาการ\nพัด\nพัดชา\nพัดดึงส์\nพัตร\nพัทธ์\nพัทธยา\nพัทธสีมา\nพัทร\nพัน\nพันตู\nพันทาง\nพันธ์\nพันธะ\nพันธกรณี\nพันธบัตร\nพันธมิตร\nพันธนะ\nพันธนาคาร\nพันธนาการ\nพันธุ์\nพันธุกรรม\nพันลึก\nพันลือ\nพันเลิศ\nพันเอิญ\nพับ\nพัลลภ\nพัลวัน\nพัว\nพัวะ\nพัศดี\nพัสดุ\nพัสตร์\nพัสถาน\nพา\nพาก\nพากเพียร\nพากย์\nพาง\nพ่าง\nพาชี\nพาณิช\nพาณิชย์\nพาณิชยกรรม\nพาณิชยการ\nพาณิชยศาสตร์\nพาณิชยศิลป์\nพาณินี\nพาณี\nพาณีนี\nพาด\nพาท\nพาทย์\nพาธ\nพาธา\nพาน\nพ่าน\nพานร\nพานรินทร์\nพาม\nพาย\nพ่าย\nพายม้า\nพายัพ\nพายุ\nพาร์เซก\nพารณ\nพารา\nพาราฟิน\nพาล\nพาลา\nพาลี\nพาลุก\nพาโล\nพาไล\nพาส\nพาสน์\nพาสนา\nพาสุกรี\nพ่าห์\nพาหนะ\nพาหะ\nพาหา\nพาหิรกะ\nพาหิระ\nพาหุ\nพาหุรัด\nพาหุสัจจะ\nพาเหียร\nพาฬ\nพำ\nพำนัก\nพำพึม\nพำลา\nพิกล\nพิกสิต\nพิกัด\nพิกัติ\nพิกัน\nพิการ\nพิกุล\nพิเคราะห์\nพิฆน์\nพิฆเนศ\nพิฆเนศวร\nพิฆาต\nพิง\nพิจัย\nพิจาร\nพิจารณ์\nพิจารณา\nพิจิก\nพิจิต\nพิจิตร\nพิชญ์\nพิชัย\nพิชาน\nพิชิต\nพิเชฐ\nพิเชียร\nพิฑูรย์\nพิณ\nพิดทูล\nพิดาน\nพิโดร\nพิตร\nพิถี\nพิถีพิถัน\nพิทย\nพิทย์\nพิทยา\nพิทยาคม\nพิทยาคาร\nพิทยาธร\nพิทยาลัย\nพิทักษ์\nพิทูร\nพิเทศ\nพิธาน\nพิธี\nพิธุ\nพินทุ\nพินอบพิเทา\nพินัย\nพินาศ\nพินิจ\nพินิต\nพินิศ\nพินิศจัย\nพิเนต\nพิบัติ\nพิบุล\nพิบูล\nพิปริต\nพิปลาส\nพิพรรธ\nพิพรรธน์\nพิพักพิพ่วน\nพิพัฒ\nพิพัฒน์\nพิพากษ์\nพิพากษา\nพิพาท\nพิพิธ\nพิพิธภัณฑ์\nพิพิธภัณฑสถาน\nพิภพ\nพิภัช\nพิภาค\nพิภูษณะ\nพิเภก\nพิมปะการัง\nพิมพ์\nพิมพการัง\nพิมพา\nพิมพาภรณ์\nพิมล\nพิมเสน\nพิมาน\nพิมุข\nพิโมกข์\nพิโมกษ์\nพิโยกพิเกน\nพิโยค\nพิรอด\nพิระ\nพิรากล\nพิราบ\nพิราม\nพิราลัย\nพิริยะ\nพิรี้พิไร\nพิรุณ\nพิรุธ\nพิรุฬห์\nพิเรนทร์\nพิเราะ\nพิโรธ\nพิไร\nพิลังกาสา\nพิลาป\nพิลาส\nพิลิปดา\nพิลึก\nพิลึกกึกกือ\nพิลึกพิลั่น\nพิโลน\nพิไล\nพิศ\nพิศวง\nพิศวาส\nพิศาล\nพิศุทธ์\nพิศุทธิ์\nพิเศษ\nพิษ\nพิษฐาน\nพิษนาศน์\nพิสดาร\nพิสมร\nพิสมัย\nพิสัง\nพิสัช\nพิสัย\nพิสิฐ\nพิสุทธิ์\nพิสูจน์\nพิหค\nพิหาร\nพิฬาร\nพี\nพี่\nพี้\nพีชคณิต\nพีระมิด\nพึง\nพึ่ง\nพึ่บ\nพึ่บพั่บ\nพึม\nพึมพำ\nพืช\nพืด\nพื้น\nพุ\nพุก\nพุกาม\nพุง\nพุ่ง\nพุงดอ\nพุฒ\nพุฒิ\nพุด\nพุดตาน\nพุทธ\nพุทธะ\nพุทธังกูร\nพุทธางกูร\nพุทธันดร\nพุทธาภิเษก\nพุทธาวาส\nพุทธิ\nพุทโธ่\nพุทรา\nพุธ\nพุ่ม\nพุมเรียง\nพุ้ย\nพู\nพูพอน\nพู่\nพูด\nพูน\nพู้น\nพู่ระหง\nเพ\nเพ็ก\nเพกา\nเพคะ\nเพ็ง\nเพ่ง\nเพ็จ\nเพชฉลูกรรม\nเพชฌฆาต\nเพชร\nเพชรดา\nเพชรปาณี\nเพชรฤกษ์\nเพชรายุธ\nเพชรกลับ\nเพชรสังฆาต\nเพชรหลีก\nเพชรหึง\nเพ็ญ\nเพฑูริย์\nเพณี\nเพ็ดทูล\nเพดาน\nเพท\nเพทนา\nเพทาย\nเพทุบาย\nเพโทบาย\nเพ่นพ่าน\nเพนียด\nเพไนย\nเพ้ย\nเพรง\nเพรซีโอดิเมียม\nเพรา\nเพราะ\nเพริด\nเพริศ\nเพรียก\nเพรียง\nเพรียบ\nเพรี้ยม\nเพรียว\nเพรื่อ\nเพรือง\nเพล\nเพลง\nเพล็ด\nเพล้โพล้\nเพลา\nเพลาะ\nเพลิง\nเพลิดเพลิน\nเพลิน\nเพลีย\nเพลี้ย\nเพลี่ยง\nเพศ\nเพส\nเพสลาด\nเพ่อ\nเพ้อ\nเพ้อเจ้อ\nเพอิญ\nเพะ\nเพา\nเพาะ\nเพิก\nเพิง\nเพิ่ง\nเพิดเพ้ย\nเพิ่ม\nเพี้ย\nเพียง\nเพี้ยง\nเพียงออ\nเพี้ยน\nเพียบ\nเพียร\nเพื่อ\nเพื่อน\nแพ\nแพ้\nแพง\nแพ่ง\nแพงพวย\nแพทย์\nแพทยศาสตร์\nแพน\nแพ่น\nแพนก\nแพนงเชิง\nแพร\nแพร่\nแพรก\nแพร่ง\nแพรว\nแพร้ว\nแพลง\nแพลทินัม\nแพล็บ\nแพลม\nแพลเลเดียม\nแพละ\nแพละโลม\nแพว\nแพ้ว\nแพศย์\nแพศยา\nแพะ\nโพ\nโพก\nโพกพาย\nโพง\nโพงพาง\nโพชฌงค์\nโพซิตรอน\nโพด\nโพทะเล\nโพแทสเซียม\nโพธ\nโพธิ\nโพธิ์\nโพน\nโพ้น\nโพนทะนา\nโพบาย\nโพย\nโพยก๊วน\nโพยม\nโพรก\nโพรง\nโพรโทแอกทิเนียม\nโพรมีเทียม\nโพระดก\nโพล่\nโพลง\nโพล่ง\nโพล้ง\nโพลน\nโพล้เพล้\nโพละ\nโพสพ\nไพ\nไพ่\nไพจิตร\nไพชน\nไพชยนต์\nไพฑูรย์\nไพที\nไพบูลย์\nไพพรรณ\nไพร\nไพร่\nไพรจิตร\nไพรชน\nไพรชยนต์\nไพรฑูรย์\nไพรที\nไพรบูลย์\nไพรเราะ\nไพรัช\nไพรำ\nไพริน\nไพรินทร์\nไพรี\nไพเราะ\nไพโรจน์\nไพล\nไพล่\nไพศาขะ\nไพศาล\nไพเศษ\nไพสพ\nไพสิฐ\nไพหาร\nฟก\nฟ้ง\nฟรักโทส\nฟรี\nฟลูออรีน\nฟ่อ\nฟ้อ\nฟอก\nฟอง\nฟ่อง\nฟ้อง\nฟอด\nฟอน\nฟ่อน\nฟ้อน\nฟ้อแฟ้\nฟอร์มาลดีไฮด์\nฟอร์มาลิน\nฟอสฟอรัส\nฟอสเฟต\nฟัก\nฟักฟุ้น\nฟัง\nฟังก์ชัน\nฟัด\nฟัน\nฟั่น\nฟั้น\nฟ้า\nฟาก\nฟาง\nฟ่าง\nฟาด\nฟาทอม\nฟาน\nฟ่าม\nฟาย\nฟาร์ม\nฟาสซิสต์\nฟิด\nฟิต\nฟิบ\nฟิล์ม\nฟิวส์\nฟิสิกส์\nฟี่\nฟี้\nฟืดฟาด\nฟืน\nฟื้น\nฟืม\nฟุ\nฟุ้ง\nฟุต\nฟุน\nฟุบ\nฟุ่บ\nฟุ่มเฟือย\nฟุลสแก๊ป\nฟู\nฟู่\nฟูก\nฟูด\nฟูม\nเฟ็ด\nเฟ้น\nเฟลด์สปาร์\nเฟ้อ\nเฟอร์เมียม\nเฟอะ\nเฟอะฟะ\nเฟะ\nเฟะฟะ\nเฟิน\nเฟี้ยม\nเฟี้ยว\nเฟือ\nเฟื้อ\nเฟือง\nเฟื่อง\nเฟื้อง\nเฟือน\nเฟือย\nเฟื้อย\nแฟ่\nแฟง\nแฟชั่น\nแฟน\nแฟบ\nแฟ้ม\nแฟรนเซียม\nแฟลกซ์\nแฟลต\nแฟะ\nโฟกัส\nไฟ\nภควดี\nภควัต\nภควันต์\nภควัม\nภควา\nภควาน\nภคะ\nภคันทลา\nภคินี\nภณะ\nภณิดา\nภพ\nภมร\nภมริน\nภมรี\nภมุกา\nภยันตราย\nภยาคติ\nภระ\nภรณี\nภรต\nภรรดร\nภรรดา\nภรรยา\nภระมร\nภระมรี\nภราดร\nภราดรภาพ\nภราดา\nภริยา\nภฤศ\nภวะ\nภวตัณหา\nภวนะ\nภวังค์\nภวังคจิต\nภักดี\nภักตะ\nภักติ\nภักษ์\nภักษา\nภักษาหาร\nภัค\nภัคน์\nภังคะ\nภังคี\nภัจ\nภัณฑ์\nภัณฑาคาร\nภัณฑาคาริก\nภัณฑารักษ์\nภัณฑนะ\nภัณฑู\nภัต\nภัตตาคาร\nภัตตาหาร\nภัตร\nภัทระ\nภัทรกัป\nภัพ\nภัย\nภัสดา\nภัสตรา\nภัสมะ\nภัสสร\nภา\nภาค\nภาคย์\nภาคยานุวัติ\nภาคินี\nภาคิไนย\nภาคี\nภาคียะ\nภาชนะ\nภาชี\nภาณ\nภาณวาร\nภาณกะ\nภาณี\nภาณุ\nภาดร\nภาดา\nภาตระ\nภาตา\nภาตุ\nภาติกะ\nภาติยะ\nภาพ\nภาพย์\nภาม\nภาย\nภาร\nภาระ\nภารดี\nภารต\nภารตี\nภารยทรัพย์\nภารยา\nภารา\nภาวนา\nภาวะ\nภาษ\nภาษณ์\nภาษา\nภาษิต\nภาษี\nภาส\nภาสน์\nภาสวร\nภาสา\nภาสุระ\nภิกขา\nภิกขาจาร\nภิกขุ\nภิกขุนี\nภิกษา\nภิกษาจาร\nภิกษาหาร\nภิกษุ\nภิกษุณี\nภิงคาร\nภิญโญ\nภิตติ\nภินท์\nภินทนาการ\nภิยโย\nภิรมย์\nภิรมย์สุรางค์\nภิษัช\nภิสัก\nภีตะ\nภีมะ\nภีรุ\nภุกต์\nภุขัน\nภุช\nภุชงค์\nภุต\nภุมมะ\nภุมรัตน์\nภุมวาร\nภุมรา\nภุมริน\nภุมรี\nภุมเรศ\nภู\nภู่\nภูต\nภูติ\nภูม\nภูมิ\nภูมี\nภูริ\nภูรี\nภูวดล\nภูวนาถ\nภูวเนตร\nภูวไนย\nภูษา\nภูษิต\nเภกะ\nเภตรา\nเภท\nเภทุบาย\nเภรี\nเภสัช\nโภค\nโภคะ\nโภคิน\nโภคี\nโภไคย\nโภไคศวรรย์\nโภช\nโภชย์\nโภชก\nโภชนะ\nโภชนา\nโภชนาหาร\nโภชนียะ\nไภริน\nไภรี\nไภษัชคุรุ\nไภษัชย์\nมกร\nมกราคม\nมกุฎ\nมคธ\nมฆวัน\nมฆะ\nมฆา\nม่ง\nมงกุฎ\nมงโกรย\nมงคล\nมงคลวาร\nมณฑ์\nมณฑก\nมณฑนะ\nมณฑป\nมณฑล\nมณฑา\nมณฑารพ\nมณฑิระ\nมณเฑียร\nมณี\nมด\nมตะ\nมตกภัต\nมติ\nมทนะ\nมทะ\nมธุ\nมธุกร\nมธุการี\nมธุลีห์\nมธุระ\nมธุรพจน์\nมน\nมนินทรีย์\nม่น\nมนต์\nมนตร์\nมนตรี\nมนท์\nมนทิราลัย\nมนเทียร\nมนสิการ\nมนัส\nมนัสวี\nมนินทรีย์\nมนิมนา\nมนิลา\nมนุญ\nมนุษย์\nมนุษยชาติ\nมนุษยธรรม\nมนุษย์มนา\nมนุษยโลก\nมนุษยศาสตร์\nมนุษยสัมพันธ์\nมนุสาร\nมนู\nมนูสาร\nมโน\nมโนช\nมโนชญ์\nมโนราห์\nมโนสาเร่\nมโนห์รา\nมมังการ\nมยุรฉัตร\nมยุระ\nมยุรา\nมยุรี\nมยุเรศ\nมยูร\nมรกต\nมรคา\nมรฑป\nมรณ์\nมรณะ\nมรณกรรม\nมรณบัตร\nมรณภัย\nมรณภาพ\nมรดก\nมรรค\nมรรคา\nมรรตัย\nมรรยาท\nมรรษ\nมรสุม\nมริจ\nมริยาท\nมรีจิ\nมรุต\nมฤค\nมฤคย์\nมฤคศิระ\nมฤคศิรมาส\nมฤคเศียร\nมฤคินทร์\nมฤเคนทร์\nมฤดก\nมฤต\nมฤตยู\nมฤทุ\nมล\nมละ\nมลัก\nมลังเมลือง\nมล้าง\nมลาย\nมลายู\nมวก\nม่วง\nมวน\nม่วน\nม้วน\nม้วนต้วน\nมวย\nม้วย\nมวล\nมหกรรม\nมหรณพ\nมหรรณพ\nมหรสพ\nมหัจฉริยะ\nมหัต\nมหัทธนะ\nมหันต์\nมหันตโทษ\nมหัพภาค\nมหัศจรรย์\nมหา\nมหากฐิน\nมหากาฬ\nมหาขันธกะ\nมหาจักร\nมหาชน\nมหาชัย\nมหาชาติ\nมหาโชตรัต\nมหาดไทย\nมหาดเล็ก\nมหาตมะ\nมหาไถ่\nมหาเทพ\nมหาเทพี\nมหาเทวี\nมหาธาตุ\nมหานิกาย\nมหานิล\nมหาบพิตร\nมหาบัณฑิต\nมหาพน\n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ียม\nรูป\nรูปิยะ\nรูปี\nรูเล็ตต์\nเร่\nเรข\nเรขา\nเรขาคณิต\nเร็ง\nเร่ง\nเร้ง\nเรณุ\nเรณู\nเรดอน\nเรดาร์\nเรเดียม\nเร้น\nเรรวน\nเรไร\nเร็ว\nเร่ว\nเรวดี\nเรอ\nเร่อ\nเรา\nเร่า\nเร้า\nเราะ\nเริง\nเริด\nเริม\nเริ่ม\nเริ้ม\nเริศร้าง\nเรี่ย\nเรี้ย\nเรียก\nเรียง\nเรียด\nเรียน\nเรียบ\nเรียม\nเรี่ยม\nเรียว\nเรี่ยว\nเรี้ยวรก\nเรือ\nเรื่อ\nเรื้อ\nเรือก\nเรือง\nเรื่อง\nเรื้อง\nเรืองรอง\nเรือด\nเรือน\nเรื้อน\nเรื่อย\nแร\nแร่\nแรก\nแร็กเกต\nแรง\nแร่ง\nแร้ง\nแรด\nแร้นแค้น\nแรม\nแร้ว\nแระ\nโร\nโร่\nโรค\nโรคา\nโรคาพาธ\nโรง\nโรจ\nโรจน์\nโรเดียม\nโรตี\nโรท\nโรธ\nโรม\nโรมัน\nโรเมอร์\nโรย\nโรเร\nโรหิณี\nโรหิต\nไร\nไร่\nไร้\nไรย์\nฤกษ์\nฤกษณะ\nฤคเวท\nฤชา\nฤชุ\nฤณ\nฤดี\nฤดียา\nฤดู\nฤต\nฤติยา\nฤตุ\nฤทธา\nฤทธิ์\nฤทัย\nฤษภ\nฤษยา\nฤษี\nฤๅ\nฤๅดี\nฤๅทัย\nฤๅษี\nฤๅสาย\nลก\nล่ก\nลฆุ\nลง\nล่ง\nลงกา\nล้งเล้ง\nลด\nลดา\nลดาวัลย์\nลน\nล้น\nลบ\nลบอง\nลพ\nลพุช\nลม\nล่ม\nล้ม\nลมาด\nลรรลุง\nลลนา\nลลิต\nลวก\nลวง\nล่วง\nล้วง\nลวณะ\nลวด\nล้วน\nลวนลาม\nลวนะ\nล่วม\nลวะ\nลวิตร\nลหุ\nลหุกาบัติ\nล่อ\nล้อ\nลอก\nล็อก\nล็อกเกต\nลอกแลก\nลอการิทึม\nลอง\nล่อง\nลองกอง\nลองจิจูด\nลองไน\nลอด\nลอตเตอรี่\nลอน\nล่อน\nลอบ\nลอม\nล้อม\nลอมชอม\nลอมพอก\nลอย\nล่อย\nล่อแล่\nลอว์เรนเซียม\nลออ\nละ\nล่ะ\nละคร\nละติจูด\nละบม\nละบอง\nละบือ\nละเบ็ง\nละโบม\nละม่อม\nละมั่ง\nละมาน\nละม้าย\nละมุ\nละมุด\nละมุน\nละเมอ\nละเมาะ\nละเมิด\nละเมียด\nละแมะ\nละโมก\nละโมบ\nละไม\nละลวย\nละลอก\nละล้า\nละล้าละลัง\nละลาน\nละลาบละล้วง\nละลาย\nละล้าว\nละล่ำละลัก\nละลิบ\nละลุม\nละเลง\nละเล้า\nละเลาะ\nละเลิง\nละเลียด\nละเลียบ\nละไล้\nละว้า\nละวาด\nละเวง\nละแวก\nละโว้\nละหมาด\nละห้อย\nละหาน\nละหาร\nละหุ่ง\nละเหย\nละเหี่ย\nละอง\nละออง\nละอาย\nละเอียด\nละแอน\nลัก\nลักขณะ\nลักขณา\nลักขะ\nลักขี\nลักจั่น\nลักปิดลักเปิด\nลักษณ์\nลักษณนาม\nลักษณะ\nลักษณาการ\nลักษมณ์\nลักษมาณา\nลักษมี\nลักษะ\nลัคคะ\nลัคน์\nลัคนา\nลัง\nลั่ง\nลังกา\nลังคี\nลังถึง\nลังลอง\nลังเล\nลังสาด\nลัชชา\nลัชชี\nลัญจ์\nลัญจกร\nลัญฉกร\nลัญฉน์\nลัฐิ\nลัฐิกา\nลัด\nลัดา\nลัทธ์\nลัทธิ\nลัน\nลั่น\nลันเต\nลันเตา\nลันไต\nลั่นทม\nลันโทม\nลับ\nลัพธ์\nลัพธิ\nลัภ\nลัภนะ\nลัภย์\nลัมพ์\nลัย\nลา\nล่า\nล้า\nลาก\nลาง\nล่าง\nล้าง\nลางลิง\nลางสาด\nลาช\nลาชะ\nลาชา\nลาญ\nลาด\nลาดเลา\nล้าต้า\nล่าเตียง\nลาน\nล่าน\nล้าน\nลาบ\nลาพอน\nลาภ\nลาม\nล่าม\nลามก\nลาย\nล้าย\nลายสือ\nลาลา\nลาว\nลาวัณย์\nลาวา\nลำ\nล่ำ\nล้ำ\nลำเข็ญ\nลำแข\nลำเค็ญ\nลำเจียก\nลำดวน\nลำดับ\nลำเนา\nลำบอง\nลำบาก\nลำปำ\nลำพวน\nลำพอง\nลำพัง\nลำพู\nลำเพ็ญ\nลำเพา\nลำแพน\nลำโพง\nลำไพ่\nลำภุขัน\nลำมะลอก\nลำยอง\nลำไย\nลำลอง\nล่ำลา\nลำลาบ\nลำลึก\nลำเลาะ\nลำเลิก\nลำเลียง\nลำเวียง\nลำเอียก\nลำเอียง\nลิ\nลิกขา\nลิกไนต์\nลิกู\nลิเก\nลิขนะ\nลิขสิทธิ์\nลิขิต\nลิง\nลิงค์\nลิด\nลิต\nลิตมัส\nลิตร\nลิเทียม\nลิ่น\nลิ้น\nลินจง\nลิ้นจี่\nลินลา\nลินสีด\nลิ่นฮื้อ\nลินิน\nลิบ\nลิปดา\nลิปสติก\nลิปิ\nลิฟต์\nลิเภา\nลิ่ม\nลิ้ม\nลิมป์\nลิมปนะ\nลิลิต\nลิว\nลิ่ว\nลิสง\nลี\nลี่\nลี้\nลีซอ\nลีบ\nลีลา\nลีลาศ\nลีฬหา\nลึก\nลึงค์\nลืด\nลื่น\nลื้น\nลืบ\nลืม\nลือ\nลื่อ\nลื้อ\nลุ\nลุก\nลุง\nลุ้ง\nลุ่น\nลุ้น\nลุพธ์\nลุ่ม\nลุมพี\nลุมพู\nลุย\nลุ่ย\nลุ้ย\nลู่\nลูก\nลูกระมาศ\nลูกเอ็น\nลูขะ\nลูทีเชียม\nลูบ\nเลก\nเล็ก\nเลข\nเลขา\nเลขาธิการ\nเลขานุการ\nเล็ง\nเล้ง\nเล่งฮื้อ\nเลเซอร์\nเลฑฑุ\nเลณฑุ\nเลณะ\nเล็ด\nเลน\nเล็น\nเล่น\nเลนส์\nเล็บ\nเลบง\nเลปกร\nเลปน์\nเลเป\nเลเพ\nเล็ม\nเล่ม\nเลย\nเลว\nเลวง\nเลวูโลส\nเลศ\nเลษฏุ\nเล่ห์\nเล่ห์กระเท่ห์\nเลหลัง\nเลหะ\nเลอ\nเล่อ\nเลอะ\nเลอะเทอะ\nเละ\nเละเทะ\nเลา\nเล่า\nเล้า\nเลากัย\nเล้าโลม\nเลาะ\nเลิก\nเลิ่กลั่ก\nเลิง\nเลิ้ง\nเลินเล่อ\nเลิศ\nเลีย\nเลียง\nเลี่ยง\nเลี้ยง\nเลียงผา\nเลียงฝ้าย\nเลียงมัน\nเลียน\nเลี่ยน\nเลียนไฟ\nเลียบ\nเลี่ยม\nเลียว\nเลี้ยว\nเลือก\nเลือง\nเลื่อง\nเลือด\nเลือน\nเลื่อน\nเลื่อม\nเลื่อย\nเลื้อย\nเลื่อยล้า\nแล\nแล่\nแล้\nแลก\nแล็กเกอร์\nแล็กโทส\nแลง\nแล่ง\nแล้ง\nแลน\nแล่น\nแลนทานัม\nแลบ\nแล้ว\nและ\nโล่\nโล้\nโลก\nโลกเชษฐ์\nโลกธรรม\nโลกธาตุ\nโลกนาถ\nโลกบาล\nโลกย์\nโลกัย\nโลกวัชชะ\nโลกวิทู\nโลกัตถจริยา\nโลกันตร์\nโลกา\nโลกาธิบดี\nโลกาธิปไตย\nโลกานุวัตร\nโลกาภิวัตน์\nโลกามิส\nโลกายัต\nโลกาวินาศ\nโลกิยะ\nโลกีย์\nโลกียวัตร\nโลกียวิสัย\nโลกียสุข\nโลกุตระ\nโลกุตรธรรม\nโลกุตรภูมิ\nโลง\nโล่ง\nโล้ง\nโล่งโจ้ง\nโล่งโต้ง\nโล้งโต้ง\nโลจนะ\nโลณะ\nโลด\nโล่ติ๊น\nโลโต\nโลท\nโลน\nโล้น\nโลภ\nโลม\nโลมเล้า\nโลมะ\nโลมา\nโลลุป\nโลเล\nโลโล\nโลโล้\nโลหะ\nโลหกุมภี\nโลหัช\nโลหิต\nไล่\nไล้\nไลย\nไลลา\nไล่เลี่ย\nฦๅ\nฦๅชา\nฦๅสาย\nวก\nวง\nวงก์\nวงกต\nวงศ์\nวงศกร\nวงศา\nวงษ์\nวจนะ\nวจี\nวชิระ\nวชิรปาณี\nวชิรหัตถ์\nวชิราวุธ\nวฏะ\nวฏาการ\nวณิช\nวณิชชา\nวณิชย์\nวณิชยา\nวณิพก\nวดี\nวทนะ\nวทัญญุตา\nวทัญญู\nวธุกา\nวธู\nวน\nวนศาสตร์\nวนสณฑ์\nวนสัณฑ์\nวนอุทยาน\nวนัส\nวนัสบดี\nวนา\nวนาดร\nวนาดอน\nวนานต์\nวนาลัย\nวนาลี\nวนาวาส\nวนาศรม\nวนาสณฑ์\nวนาสัณฑ์\nวนิดา\nวนิพก\nวเนจร\nวโนทยาน\nวยัคฆ์\nวยากรณ์\nวรดนู\nวรทาน\nวรมหาวิหาร\nวรงค์\nวรณะ\nวรรค\nวรรคย์\nวรรช\nวรรชย์\nวรรณะ\nวรรณกรรม\nวรรณคดี\nวรรณยุกต์\nวรรณยุต\nวรรณศิลป์\nวรรณนา\nวรรณพฤติ\nวรรณึก\nวรรธกะ\nวรรธนะ\nวรรษ\nวรรษา\nวรวิหาร\nวรัญญู\nวรางคณา\nวรางคนา\nวราห์\nวราหะ\nวรุณ\nวโรดม\nวฤก\nวลัช\nวลัญช์\nวลัญชน์\nวลัย\nวลาหก\nวลี\nวศค\nวศะ\nวศิน\nวสนะ\nวสภะ\nวสละ\nวสวัดดี\nวสวัตตี\nวสะ\nวสันต์\nวสันตดิลก\nวสันตฤดู\nวสันตวิษุวัต\nวสา\nวสี\nวสุ\nวสุธา\nวสุนธรา\nวสุมดี\nวหะ\nวอ\nวอก\nวอกแวก\nว่องไว\nวอด\nวอน\nว่อน\nว็อบ\nวอมแวม\nวอลเลย์บอล\nวอแว\nวะ\nวัก\nวักกะ\nวัคคีย์\nวัคคุ\nวัคซีน\nวัง\nวังก์\nวังชา\nวังเวง\nวังศะ\nวังสะ\nวัจจะ\nวัจกุฎี\nวัจฉละ\nวัจน์\nวัช\nวัชชะ\nวัชพืช\nวัชฌ์\nวัชระ\nวัชรปาณี\nวัชรยาน\nวัชรอาสน์\nวัชราสน์\nวัชรินทร์\nวัชรี\nวัชเรนทร์\nวัฏ\nวัฏฏะ\nวัฏจักร\nวัฏทุกข์\nวัฏสงสาร\nวัฏกะ\nวัฏฏิ\nวัฒกะ\nวัฒกี\nวัฒนธรรม\nวัฒนะ\nวัฒนา\nวัณ\nวัณโรค\nวัณฏ์\nวัณณะ\nวัณนา\nวัด\nวัต\nวัตต์\nวัตตา\nวัตถ์\nวัตถาภรณ์\nวัตถาลังการ\nวัตถุ\nวัตนะ\nวัตร\nวัตสดร\nวัตสะ\nวัติ\nวัทน์\nวัน\nวันต์\nวันทนา\nวันทนาการ\nวันทนีย์\nวันทยหัตถ์\nวันทยาวุธ\nวันทา\nวันทิ\nวับ\nวับวาบ\nวับวาม\nวับแวบ\nวับแวม\nวัปปะ\nวัมมิกะ\nวัย\nวัลก์\nวัลคุ\nวัลย์\nวัลลภ\nวัลลี\nวัว\nวัสสะ\nวัสโสทก\nวัสดุ\nวัสตร์\nวัสน์\nวัสนะ\nวัสสานะ\nวัสสานฤดู\nวา\nว่า\nว้า\nว้าเหว่\nวาก\nว้าก\nวากยสัมพันธ์\nวากยะ\nวาง\nว่าง\nว้าง\nวาจก\nวาจา\nวาจาไปยะ\nวาจาล\nวาชเปยะ\nวาณิช\nวาณิชกะ\nวาณิชย์\nวาณี\nวาด\nวาต\nวาตะ\nวาตภัย\nวาท\nวาทศาสตร์\nวาทศิลป์\nวาทกะ\nวาทนะ\nวาทย์\nวาทยกร\nวาทิต\nวาทิน\nวาที\nวาน\nวานซืน\nว่าน\nวานร\nวานรินทร์\nวาเนเดียม\nวาบ\nวาปี\nวาม\nวามน\nวามนาวตาร\nวามะ\nวาย\nว่าย\nว้าย\nวายะ\nวาโย\nวายามะ\nวายุ\nวายุกูล\nวาร\nวาระ\nวารสาร\nวารสารศาสตร์\nวาริ\nวารี\nวาริช\nวารีช\nวาริท\nวาริธร\nวารุณ\nวารุณี\nวาล\nวาลวีชนี\nวาล์ว\nวาลิกา\nวาลุกา\nวาว\nว่าว\nว้าว่อน\nว้าวุ่น\nวาสนะ\nวาสนา\nวาสพ\nวาสะ\nวาสิน\nวาสี\nวาสุกรี\nวาสุกี\nวาสุเทพ\nวาหนะ\nวาหะ\nวาหินี\nวาฬ\nวิกขัมภ์\nวิกขัมภนะ\nวิกเขป\nวิกรม\nวิกรัย\nวิกรานต์\nวิกฤต\nวิกฤติ\nวิกล\nวิกสิต\nวิกัต\nวิกัติ\nวิกัติการก\nวิกัป\nวิกัย\nวิการ\nวิกาล\nวิกาลโภชน์\nวิคหะ\nวิเคราะห์\nวิฆเนศ\nวิฆเนศวร\nวิฆาต\nวิง\nวิ่ง\nวิ่งเปี้ยว\nวิงวอน\nวิจฉิกะ\nวิจล\nวิจักขณ์\nวิจักษ์\nวิจักษณ์\nวิจัย\nวิจาร\nวิจารณ์\nวิจารณญาณ\nวิจิ\nวิจิกิจฉา\nวิจิต\nวิจิตร\nวิจิน\nวิจุณ\nวิจุรณ\nวิชชา\nวิชชุ\nวิชชุดา\nวิชชุตา\nวิชชุลดา\nวิชญะ\nวิชน\nวิชนี\nวิชย\nวิชัย\nวิชา\nวิชานนะ\nวิชิต\nวิเชียร\nวิญญัตติ\nวิญญาณ\nวิญญาณกทรัพย์\nวิญญู\nวิฑูรย์\nวิด\nวิตก\nวิตถาร\nวิตามิน\nวิถี\nวิทธะ\nวิทยฐานะ\nวิทยา\nวิทยาคม\nวิทยาคาร\nวิทยาลัย\nวิทยุ\nวิทยุต\nวิทวัส\nวิทัตถิ\nวิทัศน์\nวิทารณ์\nวิทิต\nวิทู\nวิทูร\nวิเทศ\nวิเทโศบาย\nวิธ\nวิธวา\nวิธาน\nวิธี\nวิธุระ\nวิธู\nวิธูปนะ\nวิ่น\nวินตกะ\nวินัย\nวินาที\nวินายก\nวินาศ\nวินิจ\nวินิจฉัย\nวินิต\nวินิบาต\nวินิปาติก\nวิเนต\nวิบัติ\nวิบาก\nวิบุล\nวิบุลย์\nวิบูล\nวิบูลย์\nวิปการ\nวิปฏิสาร\nวิปโยค\nวิประโยค\nวิปริต\nวิปลาส\nวิปวาส\nวิปักษ์\nวิปัสสก\nวิปัสสนา\nวิปัสสนายานิก\nวิพากษ์\nวิพิธทัศนา\nวิพุธ\nวิภว\nวิภวตัณหา\nวิภังค์\nวิภัช\nวิภัตติ\nวิภา\nวิภาค\nวิภาช\nวิภาดา\nวิภาวี\nวิภาษ\nวิภาส\nวิภู\nวิภูษณะ\nวิภูษา\nวิภูษิต\nวิมน\nวิมล\nวิมลัก\nวิมังสา\nวิมัติ\nวิมาน\nวิมุข\nวิมุต\nวิมุตติ\nวิเมลือง\nวิโมกข์\nวิโยค\nวิระ\nวิรงรอง\nวิรังรอง\nวิรัช\nวิรัต\nวิรัติ\nวิราคะ\nวิราม\nวิริยภาพ\nวิริยะ\nวิรุธ\nวิรุฬห์\nวิรุฬหก\nวิรูป\nวิรูปักษ์\nวิเรนทร์\nวิโรจ\nวิโรจน์\nวิโรฒ\nวิโรธ\nวิลันดา\nวิลัย\nวิลาด\nวิลาศ\nวิลาป\nวิลาวัณย์\nวิลาส\nวิลาสินี\nวิลิปดา\nวิลิศมาหรา\nวิเลป\nวิเลปนะ\nวิโลกนะ\nวิโลม\nวิไล\nวิไลวรรณ\nวิวรณ์\nวิวรรธน์\nวิวัฏ\nวิวัฒน์\nวิวัฒนาการ\nวิวัน\nวิวาท\nวิวาห์\nวิวาหมงคล\nวิวาหะ\nวิวิต\nวิวิธ\nวิเวก\nวิศรุต\nวิศว\nวิศวกร\nวิศวกรรม\nวิศวกรรมศาสตร์\nวิศัลย์\nวิศาขบูชา\nวิศาขา\nวิศาล\nวิศิษฏ์\nวิศุทธ์\nวิศุทธิ์\nวิเศษ\nวิเศษณ์\nวิษณุ\nวิษณุกรรม\nวิษธร\nวิษัย\nวิษาณ\nวิษุวัต\nวิสกี้\nวิสรรชนีย์\nวิสฤต\nวิสสุกรรม\nวิสัชนา\nวิสัญญี\nวิสัย\nวิสัยทัศน์\nวิสาขบูชา\nวิสาขะ\nวิสาขา\nวิสามัญ\nวิสามานยนาม\nวิสาร\nวิสารทะ\nวิสาล\nวิสาสะ\nวิสาหกิจ\nวิสิฐ\nวิสุงคามสีมา\nวิสุทธ์\nวิสุทธิ์\nวิสูตร\nวิเสท\nวิหค\nวิหลั่น\nวิหาร\nวิหิงสา\nวิเหสา\nวิฬาร\nวิฬาร์\nวี\nวีจิ\nวีชนี\nวีณา\nวี้ด\nวีรกรรม\nวีรชน\nวีรบุรุษ\nวีรสตรี\nวี่วัน\nวี่แวว\nวีสะ\nวุ้ง\nวุฐิ\nวุฒ\nวุฒิ\nวุด\nวุ่น\nวุ้น\nวุบ\nวุ้ย\nวุลแฟรม\nวู้\nวูดวาด\nวูบ\nวู่วาม\nเว้\nเวค\nเวคิน\nเวคี\nเวจ\nเวช\nเวชยันต์\nเวฐน์\nเวณิ\nเวณิก\nเวณุ\nเวตน์\nเวตร\nเวตาล\nเวท\nเวทคู\nเวทนา\nเวทย์\nเวทัลละ\nเวทางค์\nเวทางคศาสตร์\nเวทานต์\nเวทานตะ\nเวทิ\nเวที\nเวธะ\nเวน\nเว้น\nเวนไตย\nเวไนย\nเวมัต\nเว้ย\nเวยยากรณะ\nเวร\nเวรมณี\nเวรี\nเวโรจน์\nเวลา\nเวเลนซี\nเวศม์\nเวศย์\nเวศยา\nเวสน์\nเวสภู\nเวสม์\nเวสวัณ\nเวสสะ\nเวสสันดร\nเวสสุกรรม\nเวสสุวัณ\nเวสารัช\nเวสิยา\nเวหน\nเวหะ\nเวหา\nเวหาส\nเวฬุ\nเวฬุริยะ\nเว่อ\nเว้า\nเวิก\nเวิ้ง\nเวี่ย\nเวียง\nเวียด\nเวียดนาม\nเวียน\nเวียร\nเวี่ยว\nแว้\nแวง\nแว้ง\nแวด\nแว้ด\nแวน\nแว่น\nแวนดา\nแวบ\nแว็บ\nแวม\nแว็ม\nแวว\nแว่ว\nแวะ\nโว\nโว่\nโวการ\nโว่ง\nโวทาน\nโวย\nโว้ย\nโว้เว้\nโวหาร\nไว\nไว้\nไวกูณฐ์\nไวฑูรย์\nไวทย์\nไวน์\nไวพจน์\nไวยากรณ์\nไวยาวัจกร\nไวยาวัจมัย\nไวรัส\nไววรรณ\nไวษณพ\nไวโอลิน\nศก\nศกุน\nศกุนต์\nศกุนิ\nศกุนี\nศจี\nศตะ\nศตภิษัช\nศตวรรษ\nศตพรรษ\nศตกะ\nศนิ\nศพ\nศมนะ\nศมะ\nศยาม\nศยามล\nศร\nศรายุธ\nศราวรณ์\nศรรกรา\nศรวณะ\nศรวณีย์\nศรวิษฐา\nศรัณย์\nศรัณยู\nศรัท\nศรัทธา\nศรัย\nศราทธ์\nศราทธพรต\nศราพก\nศราวก\nศราวณะ\nศรี\nศรีตรัง\nศรุติ\nศฤคาล\nศฤงค์\nศฤงคาร\nศฤงคาริน\nศฤงคารี\nศลิษฏ์\nศลิษา\nศวะ\nศวัส\nศวา\nศวาน\nศศะ\nศศธร\nศศพินทุ์\nศศลักษณ์\nศศิ\nศศิน\nศศี\nศศิขัณฑ์\nศศิธร\nศศิมณฑล\nศศิวิมล\nศอ\nศอก\nศักดา\nศักดิ\nศักดิ์\nศักดินา\nศักติ\nศักย\nศักยภาพ\nศักย์\nศักยะ\nศักร\nศักรินทร์\nศักเรนทร์\nศักราช\nศังกร\nศัตรู\nศันสนะ\nศันสนีย์\nศัพท์\nศัยยา\nศัล\nศัลย์\nศัลยกรรม\nศัลยแพทย์\nศัลยศาสตร์\nศัสดร\nศัสตร\nศัสตรศาสตร์\nศัสตรา\nศัสตราวุธ\nศากตะ\nศากย\nศากยะ\nศากยพุทธ\nศากยมุนี\nศาฎก\nศาณ\nศานต์\nศานติ\nศาป\nศารท\nศารทูล\nศาริกา\nศาล\nศาลา\nศาศวัต\nศาสดา\nศาสตร์\nศาสตรา\nศาสตราจารย์\nศาสนา\nศาสนกิจ\nศาสนจักร\nศาสนธรรม\nศาสนบุคคล\nศาสนพิธี\nศาสนวัตถุ\nศาสนศาสตร์\nศาสนสถาน\nศาสนสมบัติ\nศาสนิกชน\nศาสนีย์\nศาสนูปถัมภก\nศาสน์\nศิกษก\nศิการ\nศิขร\nศิขริน\nศิขรี\nศิขัณฑ์\nศิคาล\nศิงขร\nศิงขริน\nศิตะ\nศิถี\nศิพิระ\nศิระ\nศิรประภา\nศิราภรณ์\nศิโรรัตน์\nศิโรเวฐน์\nศิรา\nศิรามพุช\nศิโรราบ\nศิลป\nศิลป์\nศิลปะ\nศิลปกร\nศิลปกรรม\nศิลปกิจ\nศิลปวัตถุ\nศิลปวิทยา\nศิลปศาสตร์\nศิลปศึกษา\nศิลปหัตถกรรม\nศิลปิน\nศิลปี\nศิลา\nศิวะ\nศิวโมกข์\nศิวลึงค์\nศิวเวท\nศิวาลัย\nศิศีระ\nศิษฎิ\nศิษฏ์\nศิษย์\nศิษยานุศิษย์\nศีขร\nศีต\nศีตกาล\nศีรษะ\nศีล\nศึก\nศึกษา\nศึกษาธิการ\nศึกษานิเทศก์\nศุกร์\nศุกรวรรณ\nศุกรวาร\nศุกระ\nศุกล\nศุกลปักษ์\nศุจิ\nศุทธะ\nศุทธิ\nศุนะ\nศุนิ\nศุภกร\nศุภเคราะห์\nศุภนิมิต\nศุภมัสดุ\nศุภมาตรา\nศุภมาส\nศุภอักษร\nศุภางค์\nศูกร\nศุลกากร\nศุลการักษ์\nศุลี\nศุษิร\nศูทร\nศูนย์\nศูนยวาท\nศูละ\nศูลิน\nเศรณี\nเศรษฐ\nเศรษฐ์\nเศรษฐกิจ\nเศรษฐศาสตร์\nเศรษฐี\nเศร้า\nเศลษ\nเศวต\nเศวตร\nเศวตัมพร\nเศษ\nเศาจ\nเศาร์\nเศารยะ\nเศิก\nเศียร\nโศก\nโศกา\nโศกาดูร\nโศกาลัย\nโศกี\nโศจิ\nโศธนะ\nโศภน\nโศภะ\nโศภา\nโศภิต\nโศภิน\nโศภิษฐ์\nโศภี\nโศรดา\nโศรตร\nโศลก\nไศล\nไศวะ\nษมา\nษัฏ\nษัฑ\nษัณ\nษัษ\nษัษฐะ\nษัษฐี\nโษฑศัน\nสก\nสกวาที\nสกฏะ\nสกทาคามิผล\nสกิทาคามิผล\nสกทาคามิมรรค\nสกิทาคามิมรรค\nสกทาคามี\nสกิทาคามี\nสกนธ์\nสกปรก\nสกรณีย์\nสกรรจ์\nสกรรมกริยา\nสกล\nสกลมหาสังฆปริณายก\nสกัด\nสกา\nสกาว\nสกี\nสกุณ\nสกุณา\nสกุณี\nสกุน\nสกุนต์\nสกุล\nสเกต\nสแกนเดียม\nสขะ\nสง\nส่ง\nสงกร\nสงกรานต์\nสงกา\nสงค์\nสงคร\nสงคราม\nสงเคราะห์\nสงฆ์\nสงบ\nสงวน\nส่งสการ\nสงสัย\nสงสาร\nสงสารวัฏ\nสงัด\nสง่า\nสฐะ\nสณฑ์\nสด\nสดมภ์\nสดับ\nสดับปกรณ์\nสดำ\nสดุดี\nสตะ\nสตน\nสตภิสชะ\nสตรอนเชียม\nสตริกนิน\nสตรี\nสตัฟฟ์\nสตัมภ์\nสตางค์\nสติ\nสติปัฏฐาน\nสตี\nสตู\nสตูป\nสเต๊ก\nสถบดี\nสถล\nสถวีระ\nสถาน\nสถานะ\nสถานี\nสถาบัน\nสถาปนา\nสถาปนิก\nสถาปัตยกรรม\nสถาปัตยกรรมศาสตร์\nสถาปัตยเรขา\nสถาปัตยเวท\nสถาพร\nสถาวร\nสถิต\nสถิตยศาสตร์\nสถิติ\nสถิร\nสถีรวาท\nสถุล\nสถูป\nสทิง\nสทึง\nสทุม\nสธนะ\nสาธุสะ\nสน\nส้น\nสนทนา\nสนทรรศ\nสนทรรศน์\nสนเทศ\nสนเท่ห์\nสนธยา\nสนธิ\nสนน\nสนม\nสนวน\nสนอง\nสนอบ\nสนอม\nสนะ\nสนัด\nสนั่น\nสนับ\nสนับทึบ\nสนับสนุน\nสนาน\nสนาม\nสนายุ\nสนิกะ\nสนิท\nสนิธ\nสนิม\nสนุก\nสนุกเกอร์\nสนุข\nสนุต\nสนุ่น\nสบ\nสบง\nสบถ\nสบัน\nสบาย\nสบู่\nสไบ\nสปริง\nสปอร์\nสปาเกตตี\nสเปกตรัม\nสเปกโทรสโกป\nสไปริลลัม\nสพาบ\nสภา\nสภาพ\nสภาวการณ์\nสภาวะ\nสม\nสมการ\nสมจารี\nสมดุล\nสมมูล\nส้ม\nสมญา\nสมณะ\nสมณบริขาร\nสมณศักดิ์\nสมณสารูป\nสมเด็จ\nสมถะ\nสมถยานิก\nสมถวิปัสสนา\nสมนาคุณ\nสมบัติ\nสมบุกสมบัน\nสมบูรณ์\nสมบูรณาญาสิทธิราชย์\nสมประดี\nสมปฤดี\nสมปฤๅดี\nส้มป่อย\nสมปัก\nสมผุส\nสมพง\nสมพงศ์\nสมพล\nสมพัตสร\nสมพาส\nสมเพช\nสมโพธน์\nสมโพธิ\nสมภพ\nสมภาร\nสมโภค\nสมโภช\nสมมต\nสมมติ\nสมมุติ\nสมมาตร\nส้มมือ\nสมโมท\nสมโยค\nสมร\nสมรด\nสมรรถ\nสมรรถนะ\nสมรรถภาพ\nสมรส\nสมฤดี\nสมฤติ\nสมวายะ\nสมเสร็จ\nสมอ\nสมอง\nสมะ\nสมัคร\nสมังคี\nสมัช\nสมัชชา\nสมัญญา\nสมัต\nสมัน\nสมันต์\nสมัย\nสมา\nสมาคม\nสมาจาร\nสมาชิก\nสมาทาน\nสมาธิ\nสมาน\nสมานฉันท์\nสมาบัติ\nสมาพันธรัฐ\nสมาส\nสม่ำเสมอ\nสมิง\nสมิต\nสมิติ\nสมิทธ์\nสมิทธิ\nสมี\nสมุก\nสมุจจัย\nสมุจเฉท\nสมุฏฐาน\nสมุด\nสมุทร\nสมุทรโคดม\nสมุทัย\nสมุน\nสมุนไพร\nสมุลแว้ง\nสมุห\nสมุห์\nสมุหกลาโหม\nสมุหเทศาภิบาล\nสมุหนาม\nสมุหนายก\nสโมธาน\nสโมสร\nสยด\nสยนะ\nสยบ\nสยมพร\nสยมภู\nสยอง\nสยอน\nสยัมวรา\nสยาม\nสยามานุสติ\nสยามินทร์\nสยาย\nสยิว\nสยิ้ว\nสยุมพร\nสยุมภู\nสร\nสรง\nสร่ง\nสรณะ\nสรณคมน์\nสรณาคมน์\nสรณตรัย\nสรตะ\nสรทะ\nสรนุก\nสรเนาะ\nสรไน\nสรเพชญ\nสรภะ\nสรภัญญะ\nสรภู\nสรม\nสรร\nสรรค์\nสรรพ\nสรรพคุณ\nสรรพนาม\nสรรพสามิต\nสรรพัชญ\nสรรพากร\nสรรพางค์\nสรรเพชญ\nสรรเพชุดา\nสรรเสริญ\nสรลอน\nสรเลข\nสรวง\nสรวม\nสรวล\nสรเสริญ\nสร้อย\nสระ\nสระกอ\nสระท้อน\nสระพรั่ง\nสระอาด\nสรั่ง\nสรัสวดี\nสร่าง\nสร้าง\nสราญ\nสรี้\nสรีระ\nสรีรกิจ\nสรีรธาตุ\nสรีรวิทยา\nสรีรศาสตร์\nสรีรังคาร\nสรีรางคาร\nสรุป\nสโรช\nสโรชะ\nสฤก\nสฤต\nสฤษฎิ\nสฤษฎี\nสฤษฏ์\nสฤษดิ์\nสลด\nสลบ\nสลวน\nสลวย\nสลอด\nสลอน\nสลอย\nสละ\nสลัก\nสลัด\nสลัดได\nสลับ\nสลัว\nสลา\nสลาก\nสลาง\nสล้าง\nสลาด\nสลาตัน\nสลาบ\nสลาย\nสลิด\nสลิล\nสลึก\nสลึง\nสลุต\nสลุบ\nสลุมพร\nสแลง\nสวการย์\nสวภาพ\nสวราชย์\nสวก\nส้วง\nสวด\nสวน\nสวนะ\nสวนาการ\nส่วน\nสวนิต\nสวบ\nสวม\nส้วม\nสวย\nส่วย\nส้วย\nสวยม\nสวรรค\nสวรรค์\nสวรรคต\nสวรรคาลัย\nสวรรยา\nสวระ\nสวะ\nสวัสดิ\nสวัสดิ์\nสวัสดิการ\nสวัสดิภาพ\nสวัสดิมงคล\nสวัสดี\nสวัสติ\nสวาตี\nสวัสติกะ\nสวา\nสวาปาม\nสวาคตะ\nสวาง\nสว่าง\nสวาด\nสวาดิ\nสวาท\nสว่าน\nสว้าน\nสวาบ\nสวามิ\nสวามี\nสวามินี\nสวาย\nสวาสดิ์\nสวาหะ\nสวิง\nสวิญญาณกทรัพย์\nสวิตช์\nสสาร\nสสุระ\nสสุรี\nสหกรณ์\nสหการ\nสหจร\nสหชาต\nสหชาติ\nสหธรรม\nสหธรรมิก\nสหประชาชาติ\nสหพันธ์\nสหพันธรัฐ\nสหภาพ\nสหศึกษา\nสหัช\nสหัมบดี\nสหัส\nสหัสสะ\nสหัสธารา\nสหัสนัยน์\nสหัสเนตร\nสหัสรังสี\nสหัสา\nสหาย\nสอ\nส่อ\nสอง\nส่อง\nส้อง\nสอด\nสอน\nส่อน\nสอบ\nสอพลอ\nส้อม\nสอย\nสะ\nสะกด\nสะกอ\nสะกาง\nสะการะ\nสะกิด\nสะกิดสะเกา\nสะเก็ด\nสะแก\nสะคร้อ\nสะคราญ\nสะค้าน\nสะเงาะสะแงะ\nสะดม\nสะดวก\nสะดิ้ง\nสะดึง\nสะดือ\nสะดุ้ง\nสะดุด\nสะเด็ด\nสะเดา\nสะเดาะ\nสะตอ\nสะตาหมัน\nสะตึ\nสะตือ\nสะตุ\nสะเต๊ะ\nสะโตก\nสะทก\nสะท้อน\nสะท้าน\nสะทึก\nสะเทิน\nสะเทิ้น\nสะเทือน\nสะเทื้อน\nสะบะ\nสะบัก\nสะบักสะบอม\nสะบัด\nสะบัดสะบิ้ง\nสะบั้น\nสะบันงา\nสะบ้า\nสะบู\nสะแบง\nสะเปะสะปะ\nสะพรั่ง\nสะพรึงกลัว\nสะพรึบ\nสะพรึ่บ\nสะพัก\nสะพัง\nสะพัด\nสะพั้น\nสะพาน\nสะพาย\nสะเพร่า\nสะโพก\nสะเภา\nสะใภ้\nสะโมง\nสะระตะ\nสะระแหน่\nสะลาง\nสะลาบ\nสะลึมสะลือ\nสะวี้ดสะว้าด\nสะสม\nสะสวย\nสะสาง\nสะเหล่อ\nสะอาง\nสะอาด\nสะอ้าน\nสะอิ้ง\nสะอิดสะเอียน\nสะอึก\nสะอื้น\nสะเอ้ง\nสะเอว\nสะเออะ\nสะโอดสะอง\nสะไอ\nสัก\nสักกะ\nสักยะ\nสักกัจจะ\nสักกายทิฐิ\nสักการ\nสักการะ\nสักขี\nสักวา\nสักหลาด\nสัค\nสัคคะ\nสั่ง\nสังกร\nสังกรณี\nสังกรประโยค\nสังกะตัง\nสังกะวัง\nสังกะวาด\nสังกะสี\nสังกัด\nสังกัปปะ\nสังกา\nสังการ\nสังกาศ\nสังกิเลส\nสังเกต\nสังข์\nสังขกร\nสังขตธรรม\nสังขตะ\nสังขยา\nสังขลิก\nสังขลิกา\nสังขาร\nสังขารา\nสังเขป\nสังค์\nสังคญาติ\nสังคม\nสังคหะ\nสังคัง\nสังคายนา\nสังคายนาย\nสังคีต\nสังคีติ\nสังเค็ด\nสังเคราะห์\nสังฆกรรม\nสังฆการี\nสังฆเถระ\nสังฆทาน\nสังฆนายก\nสังฆปาโมกข์\nสังฆภัต\nสังฆเภท\nสังฆมณฑล\nสังฆมนตรี\nสังฆราช\nสังฆสภา\nสังฆาณัติ\nสังฆาฏิ\nสังฆาทิเสส\nสังฆาธิการ\nสังฆานุสติ\nสังฆาวาส\nสังยุตนิกาย\nสังโยค\nสังโยชน์\nสังวร\nสังวัจฉระ\nสังวัธยาย\nสังวาล\nสังวาส\nสังเวคะ\nสังเวช\nสังเวชนียสถาน\nสังเวย\nสังเวียน\nสังสกฤต\nสังสการ\nสังสนทนา\nสั่งสนทนา\nสังสรรค์\nสังสารวัฏ\nสังสิทธิ\nสังสุทธ์\nสังสุทธิ\nสังหร\nสังหรณ์\nสังหาร\nสังหาริมทรัพย์\nสังหาริมะ\nสังหิต\nสัจ\nสัจกิริยา\nสัจจะ\nสัจญาณ\nสัจธรรม\nสัจนิยม\nสัจพจน์\nสัชฌะ\nสัชฌุ\nสัญจร\nสัญเจตนา\nสัญชาตญาณ\nสัญชาติ\nสัญฌา\nสัญญา\nสัญญาณ\nสัญญี\nสัญโญชน์\nสัญนิยม\nสัญประกาศ\nสัญลักษณ์\nสัฐิ\nสัณฐาน\nสัณฐิติ\nสัณฑ์\nสัณห์\nสัด\nสัดจอง\nสัต\nสัตตะ\nสัตตาหกรณียะ\nสัตตาหกาลิก\nสัตมวาร\nสัตสดก\nสัตตบงกช\nสัตตบรรณ\nสัตตบุษย์\nสัตตู\nสัตถันดร\nสัตถา\nสัตถิ\nสัตถุ\nสัตถุศาสนา\nสัตบรรณ\nสัตย์\nสัตยพรต\nสัตยวาที\nสัตยาเคราะห์\nสัตยาธิษฐาน\nสัตยาบัน\nสัตว์\nสัตวชาติ\nสัตวบาล\nสัตวแพทย์\nสัตววิทยา\nสัตวา\nสัทธรรม\nสัทธา\nสัทธาจริต\nสัทธาธิกะ\nสัทธินทรีย์\nสัทธิงวิหาริก\nสัทธิวิหาริก\nสัทวิทยา\nสัทศาสตร์\nสัทอักษร\nสัน\nสั่น\nสั้น\nสันดาน\nสันดาป\nสันโดษ\nสันต์\nสันตติ\nสันตะปาปา\nสันตะวา\nสันติ\nสันตุฏฐี\nสันถวไมตรี\nสันถวะ\nสันถัต\nสันถาร\nสันทนะ\nสันทะ\nสันทัด\nสันทัสนะ\nสันทาน\nสันทิฐิก\nสันทิส\nสันเทหะ\nสันธาน\nสันนิธิ\nสันนิบาต\nสันนิวาส\nสันนิษฐาน\nสันสกฤต\nสับ\nสับปลับ\nสับปลี้\nสับปะรด\nสัปคับ\nสัปดาห์\nสัปดาหะ\nสัปดน\nสัปตศก\nสัปทน\nสัปปะ\nสัปปิ\nสัปปุริส\nสัปปุรุษ\nสัประยุทธ์\nสัปหงก\nสัปเหร่อ\nสัพ\nสัพพะ\nสัพพัญญู\nสัพเพเหระ\nสัพยอก\nสัมบูรณ์\nสัมปชัญญะ\nสัมปทา\nสัมปทาน\nสัมปยุต\nสัมปโยค\nสัมประสิทธิ์\nสัมประหาร\nสัมปรายภพ\nสัมปรายิกภพ\nสัมปัตติ\nสัมผัปลาป\nสัมผัปลาปะ\nสัมผัส\nสัมพล\nสัมพหุลา\nสัมพัจฉรฉินท์\nสัมพัตสร\nสัมพัทธ์\nสัมพันธ์\nสัมพันธน์\nสัมพันธภาพ\nสัมพันธมิตร\nสัมพันธไมตรี\nสัมพาหะ\nสัมพุทธ\nสัมพุทธะ\nสัมโพธิ\nสัมภวะ\nสัมภเวสี\nสัมภัต\nสัมภัตตะ\nสัมภาระ\nสัมภาษณ์\nสัมโภคกาย\nสัมมนา\nสัมมัปธาน\nสัมมา\nสัมโมทนียกถา\nสัมฤทธิ\nสัมฤทธิ์\nสัมฤทธิศก\nสัยน์\nสัลเลข\nสัสดี\nสัสตทิฐิ\nสัสสะ\nสัสสุ\nสัสสู\nสา\nส่า\nสาก\nสากรรจ์\nสากล\nสากัจฉา\nสากัลย์\nสากิยะ\nสาเก\nสาขา\nสาคร\nสาคเรศ\nสาคู\nสาง\nส้าง\nสาชล\nสาฎก\nสาฏิก\nสาณี\nสาด\nสาไถย\nสาทร\nสาทิส\nสาทุ\nสาโท\nสาธก\nสาธยะ\nสาธยาย\nสาธารณะ\nสาธารณชน\nสาธารณประโยชน์\nสาธารณภัย\nสาธารณรัฐ\nสาธารณสถาน\nสาธารณสมบัติ\nสาธารณสุข\nสาธารณูปการ\nสาธารณูปโภค\nสาธารณ์\nสาธิต\nสาธุ\nสาน\nส่าน\nสานุ\nสานู\nสานุศิษย์\nสาบ\nสาบสูญ\nสาบาน\nสาป\nสาปไตย\nสาม\nสามชุก\nสามเณร\nสามเณรี\nสามนต์\nสามนตราช\nสามยทรัพย์\nสามล\nสามหาว\nสามะ\nสามัคคี\nสามัญ\nสามัตถิยะ\nสามานย์\nสามานยนาม\nสามารถ\nสามิต\nสามินี\nสามิภักดิ์\nสามี\nสามีจิกรรม\nสาย\nส่าย\nส้าย\nสายชู\nสายัณห์\nสายาห์\nสาร\nสารคดี\nสารธรรม\nสารนิเทศ\nสารบบ\nสารบรรณ\nสารบัญ\nสารบาญ\nสารบาญชี\nสารประโยชน์\nสารสนเทศ\nสารทุกข์\nสารถี\nสารท\nสารพัด\nสารพัน\nสารพางค์\nสารภาพ\nสารภี\nสารวัตร\nสาระ\nสาระแน\nสาระพา\nสาระยำ\nสาระวารี\nสาระสะมา\nสารัตถประโยชน์\nสารัตถศึกษา\nสารัตถะ\nสารัทธ์\nสารัมภ์\nสาราณียกร\nสาราณียธรรม\nสาราณียะ\nสารานุกรม\nสารีริกธาตุ\nสารูป\nสาโรช\nสาละ\nสาละวน\nสาลิ\nสาลิกา\nสาลินี\nสาลี\nสาลี่\nสาลู\nสาโลหิต\nสาว\nสาวก\nสาวิกา\nสาวิตร\nสาวิตรี\nสาสน\nสาสน์\nสาส์น\nสาสนา\nสาสม\nสาหร่าย\nสาหรี\nส่าหรี\nสาหัส\nสาเหตุ\nสาแหรก\nสำ\nสำส่อน\nส่ำ\nสำคัญ\nสำซ่าง\nสำแดง\nสำทับ\nสำนวน\nสำนอง\nสำนัก\nสำนาน\nสำนึก\nสำนึง\nสำเนา\nสำเนียง\nสำบัด\nสำปะลอ\nสำปะหลัง\nสำปั้น\nสำปันนี\nสำเภา\nสำมะงา\nสำมะโน\nสำมะลอ\nสำมะเลเทเมา\nสำมะหา\nสำรด\nสำรวจ\nสำรวม\nสำรวย\nสำรวล\nสำรอก\nสำรอง\nสำรับ\nสำราก\nสำราญ\nสำริด\nสำเร็จ\nสำเรา\nสำเริง\nสำโรง\nสำลัก\nสำลาน\nสำลี\nสำแลง\nสำหรวด\nสำหรับ\nสำหา\nสำเหนียก\nสำเหร่\nสำออย\nสำอาง\nสิ\nสิกข์\nสิข\nสิกขมานา\nสิกขา\nสิขร\nสิขรี\nสิขเรศ\nสิขา\nสิขานล\nสิขี\nสิคาล\nสิง\nสิ่ง\nสิงขร\nสิงค์\nสิงคลิ้ง\nสิงคลี\nสิงคาร\nสิงคาล\nสิงคี\nสิงโต\nสิงห์\nสิงหนาท\nสิงหบัญชร\nสิงหรา\nสิงหราช\nสิงหาคม\nสิงหาสน์\nสิงหล\nสิญจน์\nสิตะ\nสิตางศุ์\nสิถิล\nสิทธ์\nสิทธัตถะ\nสิทธา\nสิทธาจารย์\nสิทธารถ\nสิทธิ\nสิทธิ์\nสิทธิการิยะ\nสิธยะ\nสิน\nสิ้น\nสินเทา\nสินธพ\nสินธุ\nสินธุ์\nสินธุระ\nสินธู\nสินเธาว์\nสินาด\nสินิทธ์\nสินี\nสิเนรุ\nสิเนหก\nสิเนหะ\nสิเนหา\nสิเน่หา\nสิบ\nสิปปะ\nสิมพลี\nสิระ\nสิโรดม\nสิโรตม์\nสิริ\nสิรี\nสิลา\nสิว\nสิ่ว\nสิวะ\nสิวาลัย\nสิวิกา\nสี\nสี่\nสี้\nสีกา\nสีกุน\nสีข้าง\nสีด\nสีดอ\nสีดา\nสีตลรัศมี\nสีตโลทก\nสีโตทก\nสีทันดร\nสีมันต์\nสีมา\nสีละมัน\nสีวิกา\nสีสอ\nสีสะ\nสีสา\nสีสุก\nสีเสียด\nสีห์\nสีหนาท\nสีหบัญชร\nสีหราช\nสีหไสยา\nสีหไสยาสน์\nสีหะ\nสึก\nสึง\nสืบ\nสื่อ\nสุ\nสุก\nสุกข์\nสุกร\nสุกรม\nสุกำศพ\nสุกียากี้\nสุข\nสุขา\nสุขาภิบาล\nสุขารมณ์\nสุขาวดี\nสุขิน\nสุขี\nสุขุม\nสุขุมาล\nสุโข\nสุคต\nสุคติ\nสุคนธ\nสุคนธ์\nสุคนธชาติ\nสุคนธรส\nสุคันธ์\nสุคันธรส\nสุงกะ\nสุงกากร\nสุงสิง\nสุงสุมาร\nสุจริต\nสุจหนี่\nสุจิ\nสุจิต\nสุจิตร\nสุชน\nสุชัมบดี\nสุชา\nสุชาดา\nสุญ\nสุญญากาศ\nสุญตา\nสุญนิยม\nสุณ\nสุณิสา\nสุด\nสุดา\nสุต\nสุตตนิบาต\nสุตตะ\nสุตตันตปิฎก\nสุตตันตะ\nสุติ\nสุทธ\nสุทธ์\nสุทธาวาส\nสุทธิ\nสุทรรศน์\nสุทัศน์\nสุธา\nสุธาโภชน์\nสุธารส\nสุธาสินี\nสุธาสี\nสุธี\nสุนทร\nสุนทรี\nสุนทรียภาพ\nสุนทรียศาสตร์\nสุนทรียะ\nสุนัข\nสุนันท์\nสุโนก\nสุบดี\nสุบรรณ\nสุบิน\nสุปรีดิ์\nสุปรีย์\nสุปาณี\nสุพพัต\nสุพรรณ\nสุพรรณบัฏ\nสุพรรณภาชน์\nสุพรรณราช\nสุพรรณศรี\nสุพรรณถัน\nสุพรรณิการ์\nสุภร\nสุภัค\nสุภา\nสุภาพ\nสุภาษิต\nสุม\nสุ่ม\nสุมทุม\nสุมน\nสุมนะ\nสุมนัส\nสุมนา\nสุ้มเสียง\nสุมะ\nสุมาลี\nสุเมธ\nสุเมรุ\nสุรคต\nสุรเชษฐ์\nสุรบดี\nสุรภาพ\nสุรโลก\nสุรสีหนาท\nสุรเสียง\nสุรงค์\nสุรังค์\nสุรภี\nสุรัติ\nสุรัสวดี\nสุรา\nสุรางค์จำเรียง\nสุรางคนา\nสุรางคนางค์\nสุรารักษ์\nสุราลัย\nสุรินทร์\nสุรินทราหู\nสุริยะ\nสุริยกันต์\nสุริยกานต์\nสุริยการ\nสุริยกาล\nสุริยคติ\nสุริยคราส\nสุริยมณฑล\nสุริยวงศ์\nสุริยง\nสุริยา\nสุริเยนทร์\nสุริเยศ\nสุริโย\nสุริยน\nสุริยัน\nสุริยุปราคา\nสุรีย์\nสุรุ่ยสุร่าย\nสุลต่าน\nสุวคนธ์\nสุวภาพ\nสุวรรณ\nสุวรรณภูมิ\nสุวะ\nสุวาน\nสุวินัย\nสุวิมล\nสุษิระ\nสุสาน\nสุหนัต\nสุหร่ง\nสุหร่าย\nสุหฤท\nสุหัท\nสุเหร่า\nสู\nสู่\nสู้\nสูง\nสูจิ\nสูจิบัตร\nสูญ\nสูด\nสูต\nสูตร\nสูติ\nสูติกรรม\nสูตินรีเวช\nสูติบัตร\nสูติแพทย์\nสูติศาสตร์\nสูท\nสูทกรรม\nสูทศาสตร์\nสูบ\nสูปะ\nสูร\nสูรย์\nสูรยกานต์\nสูริ\nสูสี\nเส\nเสก\nเสกขบุคคล\nเสกขะ\nเสขบุคคล\nเสขะ\nเส็ง\nเส้ง\nเส็งเคร็ง\nเสงี่ยม\nเสฏฐี\nเสณี\nเสด\nเสด็จ\nเสตุ\nเสถียร\nเสทะ\nเสโท\nเสน\nเส้น\nเสนง\nเสน่ง\nเสน่ห์\nเสนหา\nเสน่หา\nเสนอ\nเสนะ\nเสนา\nเสนาธิการ\nเสนาบดี\nเสน่า\nเสนากุฎ\nเสนางค์\nเสนางคนิกร\nเสนานี\nเสนาสนะ\nเสนาะ\nเสนี\nเสนีย์\nเสนียะ\nเสนียด\nเสบย\nเสบียง\nเสพ\nเสพย์\nเสเพล\nเสภา\nเสม็ด\nเสมหะ\nเสมอ\nเสมา\nเสมียน\nเสมือน\nเสย\nเสร็จ\nเสริด\nเสริม\nเสรี\nเสลด\nเสลบรรพต\nเสลา\nเสลี่ยง\nเสลือกสลน\nเสโล\nเสวก\nเสวกามาตย์\nเสวนะ\nเสวนา\nเสวย\nเสวียน\nเสสรวง\nเสสรวล\nเสา\nเส้า\nเสาร์\nเสารภย์\nเสารี\nเสาวคนธ์\nเสาวธาร\nเสาวภา\nเสาวภาคย์\nเสาวภาพ\nเสาวรภย์\nเสาวรส\nเสาวลักษณ์\nเสาวณิต\nเสาวนะ\nเสาวนา\nเสาวนีย์\nเสาหฤท\nเสาะ\nเสาะแสะ\nเสิร์จ\nเสิร์ฟ\nเสีย\nเสียง\nเสี่ยง\nเสียด\nเสี้ยน\nเสียบ\nเสียม\nเสี่ยม\nเสี้ยม\nเสียว\nเสี่ยว\nเสี้ยว\nเสือ\nเสื่อ\nเสื้อ\nเสือก\nเสื่อม\nแส\nแส่\nแส้\nแสก\nแสง\nแสด\nแสดง\nแสตมป์\nแสน\nแสนย์\nแสนยากร\nแสนยานุภาพ\nแสบ\nแสม\nแสยก\nแสยง\nแสยะ\nแสรก\nแสร้ง\nแสลง\nแสล้ม\nแสวง\nแสะ\nโสก\nโสกโดก\nโสกันต์\nโสโครก\nโสณฑ์\nโสณิ\nโสณี\nโสด\nโสดก\nโสดม\nโสดา\nโสดาบัน\nโสดาปัตติผล\nโสดาปัตติมรรค\nโสต\nโสตทัศนวัสดุ\nโสตทัศนอุปกรณ์\nโสตทัศนูปกรณ์\nโสตินทรีย์\nโสตถิ\nโสทก\nโสทร\nโสธก\nโสธนะ\nโสน\nโสภณ\nโสภา\nโสภี\nโสภิณี\nโสเภณี\nโสม\nโสมนัส\nโสมม\nโสมย์\nโสร่ง\nโสรจ\nโสรวาร\nโสโร\nโสวรรณ\nโสหุ้ย\nโสฬส\nใส\nใส่\nไส\nไส้\nไสย\nไสยา\nไสยาสน์\nไสร้\nไสว\nหก\nหกคะเมน\nหง\nหงก\nหงส์\nหงสบาท\nหงสรถ\nหงอ\nหงอก\nหง่อง\nหงองแหงง\nหงอด\nหงอน\nหง่อม\nหงอย\nหง่อย\nหงัก\nหงับ\nหง่าง\nหงาย\nหง่าว\nหงำ\nหงิก\nหงิง\nหงิม\nหงึก\nหงุงหงิง\nหงุดหงิด\nหงุบ\nหงุ่ย\nหญ้า\nหญ้าฝรั่น\nหญ้ายายเภา\nหญิง\nหญิบ\nหด\nหตะ\nหทัย\nหน\nหนวก\nหน่วง\nหนวด\nหน่วย\nหน่วยกิต\nหนอ\nหน่อ\nหนอก\nหนอง\nหนอน\nหนอนตายหยาก\nหน่อย\nหน็อยแน่\nหนัก\nหนัง\nหนังสติ๊ก\nหนังสือ\nหนั่น\nหนับ\nหนา\nหน้า\nหน่าง\nหนาด\nหนาน\nหนาม\nหน่าย\nหนาว\nหนำ\nหนำเลี้ยบ\nหนี\nหนี้\nหนีบ\nหนึก\nหนึ่ง\nหนึบ\nหนืด\nหนุ\nหนุน\nหนุบ\nหนุ่ม\nหนุ่ย\nหนู\nห่ม\nหมก\nหมด\nหม่น\nหมวก\nหมวด\nหมวน\nหมอ\nหม่อ\nหม้อ\nหมอก\nหมอง\nหม่อง\nหมอน\nหม่อน\nหมอบ\nหม่อม\nหมอย\nหม้อห้อม\nหมัก\nหมักหมม\nหมัด\nหมัน\nหมั่น\nหมั้น\nหมับ\nหมา\nหม่า\nหมาก\nหมากฮอส\nหมาง\nหมาด\nหมามุ่ย\nหมามุ้ย\nหมาย\nหม้าย\nหมาร่า\nหม่ำ\nหม้ำ\nหมิ่น\nหมี\nหมี่\nหมึก\nหมืน\nหมื่น\nหมุด\nหมุน\nหมุบ\nหมุบหมับ\nหมุบหมิบ\nหมุ่ย\nหมุยขาว\nหมู\nหมู่\nหมูหริ่ง\nหยก\nหย่ง\nหยด\nหยวก\nหยวบ\nหยอก\nหยอกเอิน\nหย็อกหย็อย\nหยอง\nหย็อง\nหย่อง\nหย็องกรอด\nหย็องแหย็ง\nหยอด\nหยอน\nหย่อน\nหย่อม\nหย็อมแหย็ม\nหย็อย\nหย่อย\nหยัก\nหยักไย่\nหยักเหยา\nหยัง\nหยั่ง\nหยังหยัง\nหยัด\nหยัน\nหยับ\nหยั่วเมือง\nหย่า\nหยากเยื่อ\nหยากไย่\nหยาด\nหยาบ\nหยาม\nหยาว\nหย้าว\nหยำเป\nหยำเหยอะ\nหยำแหยะ\nหยิก\nหยิ่ง\nหยิบ\nหยิม\nหยี\nหยี่\nหยุกหยิก\nหยุด\nหยุ่น\nหยุบ\nหยุมหยิม\nหยูกยา\nหโยดม\nหรคุณ\nหรณะ\nหรดาล\nหรดี\nหรรษ์\nหรรษา\nหรอ\nหรอก\nหร็อมแหร็ม\nหรอย\nหระ\nหรับ\nหรา\nหริ\nหริ่ง\nหริณะ\nหริต\nหริตกี\nหรีตกี\nหรี่\nหรีด\nหรือ\nหรุบ\nหรุบรู่\nหรุบหรู่\nหรุ่ม\nหรู\nหรูหรา\nหฤทัย\nหฤทย์\nหฤษฎ์\nหฤษฎี\nหฤหรรษ์\nหฤโหด\nหลง\nหลงใหล\nหลงจู๊\nหลด\nหลน\nหล่น\nหลบ\nหล่ม\nหลวง\nหลวม\nหลอ\nหล่อ\nหลอก\nหลอด\nหลอน\nหล็อน\nหล่อน\nหลอม\nหละ\nหละหลวม\nหลัก\nหลัง\nหลั่ง\nหลัด\nหลั่น\nหลับ\nหลัว\nหลา\nหล้า\nหลาก\nหลาน\nหลาบ\nหลาม\nหลาย\nหลาว\nหลิ่ง\nหลิท\nหลิน\nหลิม\nหลิว\nหลิ่ว\nหลี\nหลีก\nหลีโก\nหลีบ\nหลีฮื้อ\nหลืบ\nหลุกหลิก\nหลุด\nหลุน\nหลุบ\nหลุม\nหลุมพอ\nหลุมพี\nหลู่\nหวง\nห่วง\nห้วง\nหวด\nหวน\nห้วน\nหวย\nห้วย\nหวอ\nหวอด\nหวะ\nหวัง\nหวัด\nหวั่น\nหวันยิหวา\nหวัว\nหวัวร่อ\nหวัวเราะ\nหวา\nหว่า\nหว้า\nหวาก\nหว่าง\nหวาด\nหวาน\nหว่าน\nหวาม\nหวาย\nหวำ\nหวิด\nหวิว\nหวี\nหวี่\nหวีด\nหวือ\nหวุดหวิด\nหวูด\nหอ\nห่อ\nห้อ\nหอก\nหอง\nห้อง\nหอน\nห่อน\nหอบ\nหอม\nห้อม\nหอย\nห้อย\nหะ\nหะยี\nหะหาย\nหัก\nหัจญ์\nหัจญี\nหัช\nหัฏฐะ\nหัด\nหัต\nหัตถ์\nหัตถกรรม\nหัตถการ\nหัตถกิจ\nหัตถบาส\nหัตถพันธ์\nหัตถาภรณ์\nหัตถศาสตร์\nหัตถศิลป์\nหัตถศึกษา\nหัตถาจารย์\nหัตถินี\nหัตถี\nหัน\nหั่น\nหั้น\nหันตรา\nหับ\nหัย\nหัว\nหัวร่อ\nหัวเราะ\nหัส\nหัสดิน\nหัสดี\nหัสต์\nหัสตะ\nหา\nห่า\nห้า\nหาก\nหาง\nห่าง\nห้าง\nหาญ\nหาด\nห่าน\nหาบ\nหาม\nห่าม\nห้าม\nหาย\nหายใจ\nหายนะ\nหาร\nหารือ\nหาว\nห้าว\nหาสะ\nหำ\nห้ำ\nหิ้ง\nหิงคุ\nหิงสา\nหิงห้อย\nหิ่งห้อย\nหิ่งหาย\nหิด\nหิต\nหิตานุหิตประโยชน์\nหิน\nหิมพาน\nหิมพานต์\nหิมวัต\nหิมวันต์\nหิมวา\nหิมะ\nหิมาลัย\nหิรัญ\nหิรัญญิการ์\nหิรัญบัฏ\nหิรัณย์\nหิรัณยรัศมี\nหิริ\nหิว\nหิ้ว\nหี\nหีด\nหีนยาน\nหีบ\nหึ\nหึง\nหึ่ง\nหึงสา\nหืด\nหืน\nหื่น\nหือ\nหื้อ\nหุง\nหุน\nหุ่น\nหุ้น\nหุนหัน\nหุบ\nหุ้ม\nหุยฮา\nหู\nหู่\nหูก\nหูด\nเห\nเห่\nเหง\nเหง่ง\nเหงา\nเหง้า\nเหงื่อ\nเหงือก\nเห็จ\nเห็ด\nเหติ\nเหตุ\nเห็น\nเหน่ง\nเหนงนายพราน\nเหน็ดเหนื่อย\nเหน็บ\nเหน่อ\nเห็นอ้ม\nเหนอะ\nเหนอะหนะ\nเหน้า\nเหนาะ\nเหนียง\nเหนี่ยง\nเหนี่ยน\nเหนียม\nเหนียว\nเหนี่ยว\nเหนือ\nเหนื่อย\nเห็บ\nเหม\nเหม่\nเหม็ง\nเหม่ง\nเหม็น\nเหมวดี\nเหม่อ\nเหมันต์\nเหมันตฤดู\nเหมา\nเหมายัน\nเหมาะ\nเหมียว\nเหมี่ยว\nเหมือง\nเหมือด\nเหมือน\nเหมื่อย\nเหย\nเหยง\nเหย่อย\nเหยา\nเหย่า\nเหย้า\nเหยาะ\nเหยาะแหยะ\nเหยิง\nเหยิบ\nเหยียด\nเหยียบ\nเหยี่ยว\nเหยื่อ\nเหยือก\nเหรอ\nเหรอะ\nเหรัญญิก\nเหรา\nเหราะ\nเหรียญ\nเหล่\nเหล็ก\nเหลน\nเหลว\nเหลอ\nเหลา\nเหล่า\nเหล้า\nเหลาะแหละ\nเหลิง\nเหลิงเจิ้ง\nเหลียน\nเหลี่ยม\nเหลียว\nเหลือ\nเหลือก\nเหลือง\nเหลือบ\nเหลือม\nเหลื่อม\nเหว\nเหว่\nเหวง\nเหวย\nเหวอะ\nเหวอะหวะ\nเหวี่ยง\nเห่อ\nเหอะ\nเหะ\nเหะหะ\nเหา\nเห่า\nเหาะ\nเหิน\nเหิม\nเหี้ย\nเหียง\nเหียน\nเหี้ยน\nเหี้ยม\nเหี่ยว\nเหื่อ\nเหือด\nแห\nแห่\nแห้\nแหก\nแหง\nแหง่\nแห่ง\nแห้ง\nแหงแก๋\nแหง่ง\nแหงน\nแหน\nแห้น\nแหนง\nแหนบ\nแหนม\nแหบ\nแหม\nแหม่\nแหม่ม\nแหมะ\nแหย\nแหย่\nแหยง\nแหย่ง\nแหยม\nแหย็ม\nแหยะ\nแหล่\nแหลก\nแหล่ง\nแหลน\nแหลม\nแหละ\nแหว\nแห้ว\nแหวก\nแหว่ง\nแหวด\nแหวน\nแหวะ\nแหะ\nโห่\nโหง\nโหด\nโหน\nโหนก\nโหน่ง\nโหม\nโหม่ง\nโหมด\nโหย\nโหยกเหยก\nโหยง\nโหย่ง\nโหร\nโหรง\nโหรงเหรง\nโหรดาจารย์\nโหระพา\nโหรา\nโหราจารย์\nโหราศาสตร์\nโหล\nโหล่\nโหลงโจ้ง\nโหว\nโหว่\nโหว้\nโหวกเหวก\nโหวง\nโหวด\nโหวต\nให้\nใหญ่\nใหม่\nไห\nไห่\nไห้\nไหน\nไหม\nไหม้\nไหรณย์\nไหล\nไหล่\nไหว\nไหว้\nไหหลำ\nอก\nอกตเวทิตา\nอกตเวที\nอกตัญญุตา\nอกตัญญู\nอกนิษฐ์\nอกรณีย์\nอกรรมกริยา\nอกัปปิยวัตถุ\nอกัปปิยะ\nอกุศล\nอคติ\nอคาธ\nอโฆษะ\nองก์\nองค์\nองคชาต\nองคมนตรี\nองครักษ์\nองคาพยพ\nองคุลี\nองศ์\nองศา\nองอาจ\nองุ่น\nอจลา\nอจินตา\nอจินไตย\nอจิระ\nอเจลก\nอเจละ\nอชะ\nอชิน\nอชินี\nอชิระ\nอฏวี\nอณิ\nอณู\nอโณทัย\nอด\nอดิถี\nอดิเทพ\nอดิเรก\nอดิศร\nอดิศวร\nอดิศัย\nอดีต\nอดุล\nอดุลย์\nอติ\nอติชาต\nอติมานะ\nอติราช\nอติเรก\nอติสาร\nอถรรพเวท\nอาถรรพเวท\nอทระ\nอทินนาทาน\nอธรรม\nอธิ\nอธิกมาส\nอธิกรณ์\nอธิกวาร\nอธิกสุรทิน\nอธิการ\nอธิคม\nอธิฏฐาน\nอธิบดี\nอธิบาย\nอธิป\nอธิปไตย\nอธิมาตร\nอธิมุตติ\nอธิโมกข์\nอธิราช\nอธิวาส\nอธิวาสนะ\nอธิศีล\nอธิษฐาน\nอธึก\nอ้น\nอนงค์\nอนงคณะ\nอนงคเลขา\nอนธการ\nอนนต์\nอนยะ\nอนรรฆ\nอนรรถ\nอนล\nอนวัช\nอนัญ\nอนัตตา\nอนันต์\nอนันตริยกรรม\nอนัม\nอนาคต\nอนาคามิผล\nอนาคามิมรรค\nอนาคามี\nอนาจาร\nอนาถ\nอนาถา\nอนาทร\nอนาธิปไตย\nอนามัย\nอนามิกา\nอนารยชน\nอนารยธรรม\nอนารยะ\nอนาลัย\nอนำ\nอนิจ\nอนิจจัง\nอนิจจา\nอนิฏฐารมณ์\nอนิยต\nอนิยม\nอนิล\nอนิวรรต\nอนิวรรตน์\nอนีกะ\nอนีจะ\nอนึก\nอนึ่ง\nอนุ\nอนุกร\nอนุกรม\nอนุกรรมการ\nอนุกระเบียด\nอนุกาชาด\nอนุการ\nอนุกูล\nอนุคามิก\nอนุเคราะห์\nอนุจร\nอนุช\nอนุชน\nอนุชา\nอนุชาต\nอนุชิต\nอนุญาต\nอนุญาโตตุลาการ\nอนุตร\nอนุเถระ\nอนุทิน\nอนุบท\nอนุบาล\nอนุประโยค\nอนุปริญญา\nอนุปสัมบัน\nอนุปัสนา\nอนุพงศ์\nอนุพัทธ์\nอนุพันธ์\nอนุโพธ\nอนุภรรยา\nอนุภริยา\nอนุภาค\nอนุภาษ\nอนุมัติ\nอนุมาตรา\nอนุมาน\nอนุมูล\nอนุโมทนา\nอนุโยค\nอนุรักษ์\nอนุรักษนิยม\nอนุราช\nอนุราธ\nอนุราธะ\nอนุราธา\nอนุรูป\nอนุโลม\nอนุวงศ์\nอนุวรรตน์\nอนุวัต\nอนุวัตน์\nอนุวัตร\nอนุวัติ\nอนุวาต\nอนุศาสก\nอนุศาสน์\nอนุศาสนาจารย์\nอนุศิษฏ์\nอนุสติ\nอนุสนธิ\nอนุสร\nอนุสรณ์\nอนุสัญญา\nอนุสัย\nอนุสาวรีย์\nอนุสาสนี\nอเนก\nอเนกคุณ\nอเนกรรถประโยค\nอเนจอนาถ\nอโนชา\nอโนดาต\nอบ\nอบเชย\nอบาย\nอปจายนธรรม\nอปจายนมัย\nอปมงคล\nอปยศ\nอประไมย\nอปรัณณชาติ\nอปรา\nอปราชัย\nอปราชิต\nอปริมาณ\nอปลักษณ์\nอปโลกน์\nอปวาท\nอเปหิ\nอพพะ\nอพยพ\nอภัพ\nอภัย\nอภิ\nอภิฆาต\nอภิชฌา\nอภิชน\nอภิชนาธิปไตย\nอภิชัย\nอภิชาต\nอภิชิต\nอภิญญา\nอภิญญาณ\nอภิธรรม\nอภิธาน\nอภิไธย\nอภินันท์\nอภินันทนาการ\nอภินัย\nอภินิหาร\nอภิเนษกรมณ์\nอภิบาล\nอภิปรัชญา\nอภิปราย\nอภิมหาอำนาจ\nอภิมานะ\nอภิมุข\nอภิรดี\nอภิรติ\nอภิรมย์\nอภิรักษ์\nอภิราม\nอภิรุต\nอภิรุม\nอภิรูป\nอภิลักขิต\nอภิลักขิตสมัย\nอภิเลปน์\nอภิวันท์\nอภิวาท\nอภิวาทน์\nอภิเษก\nอภิสมโพธิ\nอภิสมัย\nอภิสมาจาร\nอภิสัมโพธิ\nอภิสัมโพธิญาณ\nอภิสิต\nอภิสิทธิ์\nอภูตะ\nอม\nอมพะนำ\nอ้ม\nอมตธรรม\nอมตบท\nอมตะ\nอมนุษย์\nอมร\nอมรา\nอมราวดี\nอมรินทร์\nอมเรนทร์\nอมเรศ\nอมฤต\nอมัตร\nอมาตย์\nอมาวสี\nอมาวสุ\nอมาวาสี\nอมิตร\nอเมริกัน\nอย่า\nอยาก\nอย่าง\nอยุทธ์\nอยู่\nอร\nอรชร\nอรชุน\nอรดี\nอรติ\nอรทัย\nอรไท\nอรนุช\nอรพินท์\nอรพิม\nอรรค\nอรรฆ\nอรรฆย์\nอรรจน์\nอรรณพ\nอรรถ\nอรรถกถา\nอรรถกถาจารย์\nอรรถาธิบาย\nอรรธ\nอรสุม\nอรหะ\nอรหัง\nอรหัต\nอรหัตผล\nอรหัตมรรค\nอรหัน\nอรหันต์\nอรหันตฆาต\nอร่อย\nอรัญ\nอรัญญิก\nอรัญวาส\nอรัญวาสี\nอรัณย์\nอราดี\nอร่าม\nอริ\nอรินทร์\nอริน\nอริยกะ\nอริยทรัพย์\nอริยบุคคล\nอริยผล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ียม\nแฮม\nแฮะ\nโฮ\nโฮก\nโฮ่ง\nโฮ้ง\nโฮเต็ล\nโฮลเมียม\nโฮะ\nไฮ้\nไฮโกรมิเตอร์\nไฮดรา\nไฮโดร\nไฮโดรคาร์บอน\nไฮโดรเจน\nไฮโดรมิเตอร์\nไฮไฟ\nไฮโล\nไฮฮี\n".split(/[\r\n]+/).filter(function(n){return 1<n.length}),this.addWords(words,!1);t&&this.finalizeDict()},dictSeek:function(n,t,e,r,i){for(var o=null;n<=t;){var s=Math.floor((n+t)/2),a=this.dict[s];if(a.length<=r)n=s+1;else{var c=a[r];c<e?n=s+1:e<c?t=s-1:(o=s,0==i?t=s-1:n=s+1)}}return o},isFinal:function(n){return this.dict[n.l].length==n.strOffset},createAcceptor:function(){return{l:0,r:this.dict.length-1,strOffset:0,isFinal:!1,dict:this,transit:function(n){return this.dict.transit(this,n)},isError:!1,tag:"DICT",w:1,type:"DICT"}},transit:function(n,t){var e=this.dictSeek(n.l,n.r,t,n.strOffset,0);if(null!==e){var r=this.dictSeek(e,n.r,t,n.strOffset,1);n.l=e,n.r=r,n.strOffset++,n.isFinal=this.isFinal(n)}else n.isError=!0;return n},sortuniq:function(n){return n.sort().filter(function(n,t,e){return!t||n!=e[t-1]})},flatten:function(n){return[].concat.apply([],n)}});e.exports=n}).call(this,"/dist/tmp")},{glob:16,path:22}],3:[function(n,t,e){var r={tag:"SPACE_RULE",createAcceptor:function(n){return n.SPACE_RULE?null:{strOffset:0,isFinal:!1,transit:function(n){return" "==n||"\t"==n||"\r"==n||"\n"==n||" "==n||" "==n?(this.isFinal=!0,this.strOffset++):this.isError=!0,this},isError:!1,tag:r.tag,w:1,type:"SPACE_RULE"}}},i=[{createAcceptor:function(n){return n.WORD_RULE?null:{strOffset:0,isFinal:!1,transit:function(n){var t=n.toLowerCase();return"a"<=t&&t<="z"?(this.isFinal=!0,this.strOffset++):this.isError=!0,this},isError:!1,tag:"WORD_RULE",type:"WORD_RULE",w:1}}},r,{tag:"SINSYM",createAcceptor:function(n){return{strOffset:0,isFinal:!1,transit:function(n){return 0==this.strOffset&&n.match(/^[\@\(\)\/\,\-\."`]$/)?(this.isFinal=!0,this.strOffset++):this.isError=!0,this},isError:!1,tag:"SINSYM",w:1,type:"SINSYM"}}},{createAcceptor:function(n){return n.NUMBER_RULE?null:{strOffset:0,isFinal:!1,transit:function(n){return"0"<=n&&n<="9"?(this.isFinal=!0,this.strOffset++):this.isError=!0,this},isError:!1,tag:"NUMBER_RULE",type:"NUMBER_RULE",w:1}}}];t.exports=i},{}],4:[function(n,t,e){var 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r=0;r<e.length;r++)e[r]=t._mark(e[r]);t.nodir&&(e=e.filter(function(n){return!/\/$/.test(n)}))}t.ignore.length&&(e=e.filter(function(n){return!h(t,n)}));t.found=e},p.mark=function(n,t){var e=f(n,t),r=n.cache[e],i=t;if(r){var o="DIR"===r||Array.isArray(r),s="/"===t.slice(-1);if(o&&!s?i+="/":!o&&s&&(i=i.slice(0,-1)),i!==t){var a=f(n,i);n.statCache[a]=n.statCache[e],n.cache[a]=n.cache[e]}}return i},p.isIgnored=h,p.childrenIgnored=function(n,t){return!!n.ignore.length&&n.ignore.some(function(n){return!(!n.gmatcher||!n.gmatcher.match(t))})};var s=t("path"),n=t("minimatch"),e=t("path-is-absolute"),a=n.Minimatch;function c(n,t){return n.toLowerCase().localeCompare(t.toLowerCase())}function u(n,t){return n.localeCompare(t)}function l(n){var t=null;if("/**"===n.slice(-3)){var e=n.replace(/(\/\*\*)+$/,"");t=new a(e)}return{matcher:new a(n),gmatcher:t}}function f(n,t){return"/"===t.charAt(0)?s.join(n.root,t):e(t)||""===t?t:n.changedCwd?s.resolve(n.cwd,t):s.resolve(t)}function 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r=this.minimatch.set.length;this.matches=new Array(r),"function"==typeof e&&(e=m(e),this.on("error",e),this.on("end",function(n){e(null,n)}));var i=this;r=this.minimatch.set.length;if(this._processing=0,this.matches=new Array(r),this._emitQueue=[],this._processQueue=[],this.paused=!1,this.noprocess)return this;if(0===r)return s();for(var o=0;o<r;o++)this._process(this.minimatch.set[o],o,!1,s);function s(){--i._processing,i._processing<=0&&i._finish()}}(i.glob=i).hasMagic=function(n,t){var e=o._extend({},t);e.noprocess=!0;var r=new b(n,e).minimatch.set;if(1<r.length)return!0;for(var i=0;i<r[0].length;i++)if("string"!=typeof r[0][i])return!0;return!1},n(i.Glob=b,t),b.prototype._finish=function(){if(f(this instanceof b),!this.aborted){if(this.realpath&&!this._didRealpath)return this._realpath();e.finish(this),this.emit("end",this.found)}},b.prototype._realpath=function(){if(!this._didRealpath){this._didRealpath=!0;var n=this.matches.length;if(0===n)return this._finish();for(var t=this,e=0;e<this.matches.length;e++)this._realpathSet(e,r)}function r(){0==--n&&t._finish()}},b.prototype._realpathSet=function(r,i){var n=this.matches[r];if(!n)return i();var t=Object.keys(n),o=this,s=t.length;if(0===s)return i();var a=this.matches[r]=Object.create(null);t.forEach(function(e,n){e=o._makeAbs(e),u.realpath(e,o.realpathCache,function(n,t){n?"stat"===n.syscall?a[e]=!0:o.emit("error",n):a[t]=!0,0==--s&&(o.matches[r]=a,i())})})},b.prototype._mark=function(n){return e.mark(this,n)},b.prototype._makeAbs=function(n){return e.makeAbs(this,n)},b.prototype.abort=function(){this.aborted=!0,this.emit("abort")},b.prototype.pause=function(){this.paused||(this.paused=!0,this.emit("pause"))},b.prototype.resume=function(){if(this.paused){if(this.emit("resume"),this.paused=!1,this._emitQueue.length)for(var n=this._emitQueue.slice(0),t=this._emitQueue.length=0;t<n.length;t++){var e=n[t];this._emitMatch(e[0],e[1])}if(this._processQueue.length){var r=this._processQueue.slice(0);for(t=this._processQueue.length=0;t<r.length;t++){var i=r[t];this._processing--,this._process(i[0],i[1],i[2],i[3])}}}},b.prototype._process=function(n,t,e,r){if(f(this instanceof b),f("function"==typeof r),!this.aborted)if(this._processing++,this.paused)this._processQueue.push([n,t,e,r]);else{for(var i,o=0;"string"==typeof n[o];)o++;switch(o){case n.length:return void this._processSimple(n.join("/"),t,r);case 0:i=null;break;default:i=n.slice(0,o).join("/")}var s,a=n.slice(o);s=null===i?".":((h(i)||h(n.join("/")))&&(i&&h(i)||(i="/"+i)),i);var c=this._makeAbs(s);if(v(this,s))return r();a[0]===l.GLOBSTAR?this._processGlobStar(i,s,c,a,t,e,r):this._processReaddir(i,s,c,a,t,e,r)}},b.prototype._processReaddir=function(e,r,i,o,s,a,c){var u=this;this._readdir(i,a,function(n,t){return u._processReaddir2(e,r,i,o,s,a,t,c)})},b.prototype._processReaddir2=function(n,t,e,r,i,o,s,a){if(!s)return a();for(var c=r[0],u=!!this.minimatch.negate,l=c._glob,f=this.dot||"."===l.charAt(0),h=[],p=0;p<s.length;p++){if("."!==(v=s[p]).charAt(0)||f)(u&&!n?!v.match(c):v.match(c))&&h.push(v)}var d=h.length;if(0===d)return a();if(1===r.length&&!this.mark&&!this.stat){this.matches[i]||(this.matches[i]=Object.create(null));for(p=0;p<d;p++){var v=h[p];n&&(v="/"!==n?n+"/"+v:n+v),"/"!==v.charAt(0)||this.nomount||(v=g.join(this.root,v)),this._emitMatch(i,v)}return a()}r.shift();for(p=0;p<d;p++){v=h[p];n&&(v="/"!==n?n+"/"+v:n+v),this._process([v].concat(r),i,o,a)}a()},b.prototype._emitMatch=function(n,t){if(!this.aborted&&!this.matches[n][t]&&!c(this,t))if(this.paused)this._emitQueue.push([n,t]);else{var e=this._makeAbs(t);if(this.nodir){var r=this.cache[e];if("DIR"===r||Array.isArray(r))return}this.mark&&(t=this._mark(t)),this.matches[n][t]=!0;var i=this.statCache[e];i&&this.emit("stat",t,i),this.emit("match",t)}},b.prototype._readdirInGlobStar=function(r,i){if(!this.aborted){if(this.follow)return this._readdir(r,!1,i);var o=this,n=d("lstat\0"+r,function(n,t){if(n)return i();var e=t.isSymbolicLink();(o.symlinks[r]=e)||t.isDirectory()?o._readdir(r,!1,i):(o.cache[r]="FILE",i())});n&&u.lstat(r,n)}},b.prototype._readdir=function(n,t,e){if(!this.aborted&&(e=d("readdir\0"+n+"\0"+t,e))){if(t&&!p(this.symlinks,n))return this._readdirInGlobStar(n,e);if(p(this.cache,n)){var r=this.cache[n];if(!r||"FILE"===r)return e();if(Array.isArray(r))return e(null,r)}var i,o,s;u.readdir(n,(i=this,o=n,s=e,function(n,t){n?i._readdirError(o,n,s):i._readdirEntries(o,t,s)}))}},b.prototype._readdirEntries=function(n,t,e){if(!this.aborted){if(!this.mark&&!this.stat)for(var r=0;r<t.length;r++){var i=t[r];i="/"===n?n+i:n+"/"+i,this.cache[i]=!0}return e(null,this.cache[n]=t)}},b.prototype._readdirError=function(n,t,e){if(!this.aborted){switch(t.code){case"ENOTSUP":case"ENOTDIR":this.cache[this._makeAbs(n)]="FILE";break;case"ENOENT":case"ELOOP":case"ENAMETOOLONG":case"UNKNOWN":this.cache[this._makeAbs(n)]=!1;break;default:this.cache[this._makeAbs(n)]=!1,this.strict&&(this.emit("error",t),this.abort()),this.silent||console.error("glob error",t)}return e()}},b.prototype._processGlobStar=function(e,r,i,o,s,a,c){var u=this;this._readdir(i,a,function(n,t){u._processGlobStar2(e,r,i,o,s,a,t,c)})},b.prototype._processGlobStar2=function(n,t,e,r,i,o,s,a){if(!s)return a();var c=r.slice(1),u=n?[n]:[],l=u.concat(c);this._process(l,i,!1,a);var f=this.symlinks[e],h=s.length;if(f&&o)return a();for(var p=0;p<h;p++){if("."!==s[p].charAt(0)||this.dot){var d=u.concat(s[p],c);this._process(d,i,!0,a);var v=u.concat(s[p],r);this._process(v,i,!0,a)}}a()},b.prototype._processSimple=function(e,r,i){var o=this;this._stat(e,function(n,t){o._processSimple2(e,r,n,t,i)})},b.prototype._processSimple2=function(n,t,e,r,i){if(this.matches[t]||(this.matches[t]=Object.create(null)),!r)return i();if(n&&h(n)&&!this.nomount){var o=/[\/\\]$/.test(n);"/"===n.charAt(0)?n=g.join(this.root,n):(n=g.resolve(this.root,n),o&&(n+="/"))}"win32"===s.platform&&(n=n.replace(/\\/g,"/")),this._emitMatch(t,n),i()},b.prototype._stat=function(r,i){var o=this._makeAbs(r),n="/"===r.slice(-1);if(r.length>this.maxLength)return i();if(!this.stat&&p(this.cache,o)){var t=this.cache[o];if(Array.isArray(t)&&(t="DIR"),!n||"DIR"===t)return i(null,t);if(n&&"FILE"===t)return i()}var e=this.statCache[o];if(void 0!==e){if(!1===e)return i(null,e);var s=e.isDirectory()?"DIR":"FILE";return n&&"FILE"===s?i():i(null,s,e)}var a=this,c=d("stat\0"+o,function(n,e){{if(e&&e.isSymbolicLink())return u.stat(o,function(n,t){n?a._stat2(r,o,null,e,i):a._stat2(r,o,n,t,i)});a._stat2(r,o,n,e,i)}});c&&u.lstat(o,c)},b.prototype._stat2=function(n,t,e,r,i){if(e)return this.statCache[t]=!1,i();var o="/"===n.slice(-1);if(this.statCache[t]=r,"/"===t.slice(-1)&&!r.isDirectory())return i(null,!1,r);var s=r.isDirectory()?"DIR":"FILE";return this.cache[t]=this.cache[t]||s,o&&"DIR"!==s?i():i(null,s,r)}}).call(this,_("_process"))},{"./common.js":15,"./sync.js":17,_process:24,assert:9,events:14,fs:12,inflight:18,inherits:19,minimatch:20,once:21,path:22,"path-is-absolute":23,util:28}],17:[function(e,r,n){(function(i){(r.exports=n).GlobSync=h;var s=e("fs"),c=e("minimatch"),g=(c.Minimatch,e("./glob.js").Glob,e("util"),e("path")),u=e("assert"),l=e("path-is-absolute"),t=e("./common.js"),o=(t.alphasort,t.alphasorti,t.setopts),a=t.ownProp,f=t.childrenIgnored;function n(n,t){if("function"==typeof t||3===arguments.length)throw new TypeError("callback provided to sync glob\nSee: https://github.com/isaacs/node-glob/issues/167");return new h(n,t).found}function h(n,t){if(!n)throw new Error("must provide pattern");if("function"==typeof t||3===arguments.length)throw new TypeError("callback provided to sync glob\nSee: https://github.com/isaacs/node-glob/issues/167");if(!(this instanceof h))return new h(n,t);if(o(this,n,t),this.noprocess)return this;var e=this.minimatch.set.length;this.matches=new Array(e);for(var r=0;r<e;r++)this._process(this.minimatch.set[r],r,!1);this._finish()}h.prototype._finish=function(){if(u(this instanceof h),this.realpath){var i=this;this.matches.forEach(function(n,t){var e=i.matches[t]=Object.create(null);for(var r in n)try{r=i._makeAbs(r),e[s.realpathSync(r,i.realpathCache)]=!0}catch(n){if("stat"!==n.syscall)throw n;e[i._makeAbs(r)]=!0}})}t.finish(this)},h.prototype._process=function(n,t,e){u(this instanceof h);for(var r,i=0;"string"==typeof n[i];)i++;switch(i){case n.length:return void this._processSimple(n.join("/"),t);case 0:r=null;break;default:r=n.slice(0,i).join("/")}var o,s=n.slice(i);o=null===r?".":((l(r)||l(n.join("/")))&&(r&&l(r)||(r="/"+r)),r);var a=this._makeAbs(o);f(this,o)||(s[0]===c.GLOBSTAR?this._processGlobStar(r,o,a,s,t,e):this._processReaddir(r,o,a,s,t,e))},h.prototype._processReaddir=function(n,t,e,r,i,o){var s=this._readdir(e,o);if(s){for(var a=r[0],c=!!this.minimatch.negate,u=a._glob,l=this.dot||"."===u.charAt(0),f=[],h=0;h<s.length;h++){if("."!==(v=s[h]).charAt(0)||l)(c&&!n?!v.match(a):v.match(a))&&f.push(v)}var p=f.length;if(0!==p)if(1!==r.length||this.mark||this.stat){r.shift();for(h=0;h<p;h++){var d;v=f[h];d=n?[n,v]:[v],this._process(d.concat(r),i,o)}}else{this.matches[i]||(this.matches[i]=Object.create(null));for(var h=0;h<p;h++){var v=f[h];n&&(v="/"!==n.slice(-1)?n+"/"+v:n+v),"/"!==v.charAt(0)||this.nomount||(v=g.join(this.root,v)),this.matches[i][v]=!0}}}},h.prototype._emitMatch=function(n,t){this._makeAbs(t);if(this.mark&&(t=this._mark(t)),!this.matches[n][t]){if(this.nodir){var e=this.cache[this._makeAbs(t)];if("DIR"===e||Array.isArray(e))return}this.matches[n][t]=!0,this.stat&&this._stat(t)}},h.prototype._readdirInGlobStar=function(n){if(this.follow)return this._readdir(n,!1);var t,e;try{e=s.lstatSync(n)}catch(n){return null}var r=e.isSymbolicLink();return(this.symlinks[n]=r)||e.isDirectory()?t=this._readdir(n,!1):this.cache[n]="FILE",t},h.prototype._readdir=function(t,n){if(n&&!a(this.symlinks,t))return this._readdirInGlobStar(t);if(a(this.cache,t)){var e=this.cache[t];if(!e||"FILE"===e)return null;if(Array.isArray(e))return e}try{return this._readdirEntries(t,s.readdirSync(t))}catch(n){return this._readdirError(t,n),null}},h.prototype._readdirEntries=function(n,t){if(!this.mark&&!this.stat)for(var e=0;e<t.length;e++){var r=t[e];r="/"===n?n+r:n+"/"+r,this.cache[r]=!0}return this.cache[n]=t},h.prototype._readdirError=function(n,t){switch(t.code){case"ENOTSUP":case"ENOTDIR":this.cache[this._makeAbs(n)]="FILE";break;case"ENOENT":case"ELOOP":case"ENAMETOOLONG":case"UNKNOWN":this.cache[this._makeAbs(n)]=!1;break;default:if(this.cache[this._makeAbs(n)]=!1,this.strict)throw t;this.silent||console.error("glob error",t)}},h.prototype._processGlobStar=function(n,t,e,r,i,o){var s=this._readdir(e,o);if(s){var a=r.slice(1),c=n?[n]:[],u=c.concat(a);this._process(u,i,!1);var l=s.length;if(!this.symlinks[e]||!o)for(var f=0;f<l;f++){if("."!==s[f].charAt(0)||this.dot){var h=c.concat(s[f],a);this._process(h,i,!0);var p=c.concat(s[f],r);this._process(p,i,!0)}}}},h.prototype._processSimple=function(n,t){var e=this._stat(n);if(this.matches[t]||(this.matches[t]=Object.create(null)),e){if(n&&l(n)&&!this.nomount){var r=/[\/\\]$/.test(n);"/"===n.charAt(0)?n=g.join(this.root,n):(n=g.resolve(this.root,n),r&&(n+="/"))}"win32"===i.platform&&(n=n.replace(/\\/g,"/")),this.matches[t][n]=!0}},h.prototype._stat=function(n){var t=this._makeAbs(n),e="/"===n.slice(-1);if(n.length>this.maxLength)return!1;if(!this.stat&&a(this.cache,t)){var r=this.cache[t];if(Array.isArray(r)&&(r="DIR"),!e||"DIR"===r)return r;if(e&&"FILE"===r)return!1}var i=this.statCache[t];if(!i){var o;try{o=s.lstatSync(t)}catch(n){return!1}if(o.isSymbolicLink())try{i=s.statSync(t)}catch(n){i=o}else i=o}r=(this.statCache[t]=i).isDirectory()?"DIR":"FILE";return this.cache[t]=this.cache[t]||r,(!e||"DIR"===r)&&r},h.prototype._mark=function(n){return t.mark(this,n)},h.prototype._makeAbs=function(n){return t.makeAbs(this,n)}}).call(this,e("_process"))},{"./common.js":15,"./glob.js":16,_process:24,assert:9,fs:12,minimatch:20,path:22,"path-is-absolute":23,util:28}],18:[function(t,r,n){(function(s){var n=t("wrappy"),a=Object.create(null),e=t("once");r.exports=n(function(n,t){return a[n]?(a[n].push(t),null):(a[n]=[t],o=n,e(function n(){var t=a[o],e=t.length,r=function(n){for(var t=n.length,e=[],r=0;r<t;r++)e[r]=n[r];return e}(arguments);try{for(var i=0;i<e;i++)t[i].apply(null,r)}finally{t.length>e?(t.splice(0,e),s.nextTick(function(){n.apply(null,r)})):delete a[o]}}));var o})}).call(this,t("_process"))},{_process:24,once:21,wrappy:29}],19:[function(n,t,e){"function"==typeof Object.create?t.exports=function(n,t){n.super_=t,n.prototype=Object.create(t.prototype,{constructor:{value:n,enumerable:!1,writable:!0,configurable:!0}})}:t.exports=function(n,t){n.super_=t;var e=function(){};e.prototype=t.prototype,n.prototype=new e,n.prototype.constructor=n}},{}],20:[function(n,t,e){(t.exports=s).Minimatch=i;var u={sep:"/"};try{u=n("path")}catch(n){}var M=s.GLOBSTAR=i.GLOBSTAR={},r=n("brace-expansion"),C={"!":{open:"(?:(?!(?:",close:"))[^/]*?)"},"?":{open:"(?:",close:")?"},"+":{open:"(?:",close:")+"},"*":{open:"(?:",close:")*"},"@":{open:"(?:",close:")"}},P="[^/]",z=P+"*?",B="().*{}+?[]^$\\!".split("").reduce(function(n,t){return n[t]=!0,n},{});var l=/\/+/;function o(t,e){t=t||{},e=e||{};var r={};return Object.keys(e).forEach(function(n){r[n]=e[n]}),Object.keys(t).forEach(function(n){r[n]=t[n]}),r}function s(n,t,e){if("string"!=typeof t)throw new TypeError("glob pattern string required");return e||(e={}),!(!e.nocomment&&"#"===t.charAt(0))&&(""===t.trim()?""===n:new i(t,e).match(n))}function i(n,t){if(!(this instanceof i))return new i(n,t);if("string"!=typeof n)throw new TypeError("glob pattern string required");t||(t={}),n=n.trim(),"/"!==u.sep&&(n=n.split(u.sep).join("/")),this.options=t,this.set=[],this.pattern=n,this.regexp=null,this.negate=!1,this.comment=!1,this.empty=!1,this.make()}function a(n,t){if(t||(t=this instanceof 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diff --git a/docs/v1.2.0/_static/javascripts/modernizr.js b/docs/v1.2.0/_static/javascripts/modernizr.js
new file mode 100644
index 0000000000..36eaf6a045
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/modernizr.js
@@ -0,0 +1 @@
+!function(e,t){for(var n in t)e[n]=t[n]}(window,function(n){var r={};function o(e){if(r[e])return r[e].exports;var t=r[e]={i:e,l:!1,exports:{}};return n[e].call(t.exports,t,t.exports,o),t.l=!0,t.exports}return o.m=n,o.c=r,o.d=function(e,t,n){o.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:n})},o.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},o.t=function(t,e){if(1&e&&(t=o(t)),8&e)return t;if(4&e&&"object"==typeof t&&t&&t.__esModule)return t;var n=Object.create(null);if(o.r(n),Object.defineProperty(n,"default",{enumerable:!0,value:t}),2&e&&"string"!=typeof t)for(var r in t)o.d(n,r,function(e){return t[e]}.bind(null,r));return n},o.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return o.d(t,"a",t),t},o.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},o.p="",o(o.s=11)}({11:function(e,t,n){"use strict";n.r(t);n(12)},12:function(e,t){var n;(function(i,d,p){function y(e,t){return typeof e===t}function s(e){var t=S.className,n=c._config.classPrefix||"";if(b&&(t=t.baseVal),c._config.enableJSClass){var r=new RegExp("(^|\\s)"+n+"no-js(\\s|$)");t=t.replace(r,"$1"+n+"js$2")}c._config.enableClasses&&(0<e.length&&(t+=" "+n+e.join(" "+n)),b?S.className.baseVal=t:S.className=t)}function a(e,t){if("object"==typeof e)for(var n in e)v(e,n)&&a(n,e[n]);else{var r=(e=e.toLowerCase()).split("."),o=c[r[0]];if(2===r.length&&(o=o[r[1]]),void 0!==o)return c;t="function"==typeof t?t():t,1===r.length?c[r[0]]=t:(!c[r[0]]||c[r[0]]instanceof Boolean||(c[r[0]]=new Boolean(c[r[0]])),c[r[0]][r[1]]=t),s([(t&&!1!==t?"":"no-")+r.join("-")]),c._trigger(e,t)}return c}function m(){return"function"!=typeof d.createElement?d.createElement(arguments[0]):b?d.createElementNS.call(d,"http://www.w3.org/2000/svg",arguments[0]):d.createElement.apply(d,arguments)}function o(e,t,n,r){var o,i,s,a,l,u="modernizr",f=m("div"),c=((l=d.body)||((l=m(b?"svg":"body")).fake=!0),l);if(parseInt(n,10))for(;n--;)(s=m("div")).id=r?r[n]:u+(n+1),f.appendChild(s);return(o=m("style")).type="text/css",o.id="s"+u,(c.fake?c:f).appendChild(o),c.appendChild(f),o.styleSheet?o.styleSheet.cssText=e:o.appendChild(d.createTextNode(e)),f.id=u,c.fake&&(c.style.background="",c.style.overflow="hidden",a=S.style.overflow,S.style.overflow="hidden",S.appendChild(c)),i=t(f,e),c.fake?(c.parentNode.removeChild(c),S.style.overflow=a,S.offsetHeight):f.parentNode.removeChild(f),!!i}function l(e){return e.replace(/([A-Z])/g,function(e,t){return"-"+t.toLowerCase()}).replace(/^ms-/,"-ms-")}function h(e,t){var n=e.length;if("CSS"in i&&"supports"in i.CSS){for(;n--;)if(i.CSS.supports(l(e[n]),t))return!0;return!1}if("CSSSupportsRule"in i){for(var r=[];n--;)r.push("("+l(e[n])+":"+t+")");return o("@supports ("+(r=r.join(" or "))+") { #modernizr { position: absolute; } }",function(e){return"absolute"===function(e,t,n){var r;if("getComputedStyle"in i){r=getComputedStyle.call(i,e,t);var o=i.console;null!==r?n&&(r=r.getPropertyValue(n)):o&&o[o.error?"error":"log"].call(o,"getComputedStyle returning null, its possible modernizr test results are inaccurate")}else r=!t&&e.currentStyle&&e.currentStyle[n];return r}(e,null,"position")})}return p}function u(e,t){return function(){return e.apply(t,arguments)}}function r(e,t,n,r,o){var i=e.charAt(0).toUpperCase()+e.slice(1),s=(e+" "+w.join(i+" ")+i).split(" ");return y(t,"string")||y(t,"undefined")?function(e,t,n,r){function o(){s&&(delete T.style,delete T.modElem)}if(r=!y(r,"undefined")&&r,!y(n,"undefined")){var i=h(e,n);if(!y(i,"undefined"))return i}for(var s,a,l,u,f,c=["modernizr","tspan","samp"];!T.style&&c.length;)s=!0,T.modElem=m(c.shift()),T.style=T.modElem.style;for(l=e.length,a=0;a<l;a++)if(u=e[a],f=T.style[u],!!~(""+u).indexOf("-")&&(u=u.replace(/([a-z])-([a-z])/g,function(e,t,n){return t+n.toUpperCase()}).replace(/^-/,"")),T.style[u]!==p){if(r||y(n,"undefined"))return o(),"pfx"!==t||u;try{T.style[u]=n}catch(e){}if(T.style[u]!==f)return o(),"pfx"!==t||u}return o(),!1}(s,t,r,o):function(e,t,n){var r;for(var o in e)if(e[o]in t)return!1===n?e[o]:y(r=t[e[o]],"function")?u(r,n||t):r;return!1}(s=(e+" "+P.join(i+" ")+i).split(" "),t,n)}function e(e,t,n){return r(e,p,p,t,n)}var f=[],t={_version:"3.7.1",_config:{classPrefix:"",enableClasses:!0,enableJSClass:!0,usePrefixes:!0},_q:[],on:function(e,t){var n=this;setTimeout(function(){t(n[e])},0)},addTest:function(e,t,n){f.push({name:e,fn:t,options:n})},addAsyncTest:function(e){f.push({name:null,fn:e})}},c=function(){};c.prototype=t,c=new c;var v,n,g=[],S=d.documentElement,b="svg"===S.nodeName.toLowerCase();v=y(n={}.hasOwnProperty,"undefined")||y(n.call,"undefined")?function(e,t){return t in e&&y(e.constructor.prototype[t],"undefined")}:function(e,t){return n.call(e,t)},t._l={},t.on=function(e,t){this._l[e]||(this._l[e]=[]),this._l[e].push(t),c.hasOwnProperty(e)&&setTimeout(function(){c._trigger(e,c[e])},0)},t._trigger=function(e,t){if(this._l[e]){var n=this._l[e];setTimeout(function(){var e;for(e=0;e<n.length;e++)(0,n[e])(t)},0),delete this._l[e]}},c._q.push(function(){t.addTest=a}),c.addTest("json","JSON"in i&&"parse"in JSON&&"stringify"in JSON),c.addTest("svg",!!d.createElementNS&&!!d.createElementNS("http://www.w3.org/2000/svg","svg").createSVGRect);var C=t.testStyles=o;c.addTest("checked",function(){return C("#modernizr {position:absolute} #modernizr input {margin-left:10px} #modernizr :checked {margin-left:20px;display:block}",function(e){var t=m("input");return t.setAttribute("type","checkbox"),t.setAttribute("checked","checked"),e.appendChild(t),20===t.offsetLeft})}),c.addTest("target",function(){var e=i.document;if(!("querySelectorAll"in e))return!1;try{return e.querySelectorAll(":target"),!0}catch(e){return!1}}),c.addTest("dataset",function(){var e=m("div");return e.setAttribute("data-a-b","c"),!(!e.dataset||"c"!==e.dataset.aB)}),c.addTest("details",function(){var t,n=m("details");return"open"in n&&(C("#modernizr details{display:block}",function(e){e.appendChild(n),n.innerHTML="<summary>a</summary>b",t=n.offsetHeight,n.open=!0,t=t!==n.offsetHeight}),t)}),c.addTest("fetch","fetch"in i);var _="Moz O ms Webkit",w=t._config.usePrefixes?_.split(" "):[];t._cssomPrefixes=w;var x={elem:m("modernizr")};c._q.push(function(){delete x.elem});var T={style:x.elem.style};c._q.unshift(function(){delete T.style});var P=t._config.usePrefixes?_.toLowerCase().split(" "):[];t._domPrefixes=P,t.testAllProps=r,t.testAllProps=e;var j="CSS"in i&&"supports"in i.CSS,O="supportsCSS"in i;c.addTest("supports",j||O),c.addTest("csstransforms3d",function(){return!!e("perspective","1px",!0)}),function(){var e,t,n,r,o,i;for(var s in f)if(f.hasOwnProperty(s)){if(e=[],(t=f[s]).name&&(e.push(t.name.toLowerCase()),t.options&&t.options.aliases&&t.options.aliases.length))for(n=0;n<t.options.aliases.length;n++)e.push(t.options.aliases[n].toLowerCase());for(r=y(t.fn,"function")?t.fn():t.fn,o=0;o<e.length;o++)1===(i=e[o].split(".")).length?c[i[0]]=r:(!c[i[0]]||c[i[0]]instanceof Boolean||(c[i[0]]=new Boolean(c[i[0]])),c[i[0]][i[1]]=r),g.push((r?"":"no-")+i.join("-"))}}(),s(g),delete t.addTest,delete t.addAsyncTest;for(var z=0;z<c._q.length;z++)c._q[z]();i.Modernizr=c})(n=window,document),e.exports=n.Modernizr}}));
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/javascripts/version_dropdown.js b/docs/v1.2.0/_static/javascripts/version_dropdown.js
new file mode 100644
index 0000000000..e09b6c0b00
--- /dev/null
+++ b/docs/v1.2.0/_static/javascripts/version_dropdown.js
@@ -0,0 +1,29 @@
+function add_version_dropdown(json_loc, target_loc, text) {
+
+    var dropdown = document.createElement("div");
+    dropdown.className = "md-flex__cell md-flex__cell--shrink dropdown";
+    var button = document.createElement("button");
+    button.className = "dropdownbutton";
+    var content = document.createElement("div");
+    content.className = "dropdown-content md-hero";
+    dropdown.appendChild(button);
+    dropdown.appendChild(content);
+    $.getJSON(json_loc, function(versions) {
+        for (var key in versions) {
+            if (versions.hasOwnProperty(key)) {
+                console.log(key, versions[key]);
+                var a = document.createElement("a");
+                a.innerHTML = key;
+                a.title = key;
+                a.href = target_loc + versions[key];
+                content.appendChild(a);
+            }
+        }
+    }).done(function() {
+        button.innerHTML = text;
+    }).fail(function() {
+        button.innerHTML = "Other Versions Not Found";
+    }).always(function() {
+        $(".navheader").append(dropdown);
+    });
+};
diff --git a/docs/v1.2.0/_static/jquery-3.5.1.js b/docs/v1.2.0/_static/jquery-3.5.1.js
new file mode 100644
index 0000000000..50937333b9
--- /dev/null
+++ b/docs/v1.2.0/_static/jquery-3.5.1.js
@@ -0,0 +1,10872 @@
+/*!
+ * jQuery JavaScript Library v3.5.1
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2020-05-04T22:49Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+      // Support: Chrome <=57, Firefox <=52
+      // In some browsers, typeof returns "function" for HTML <object> elements
+      // (i.e., `typeof document.createElement( "object" ) === "function"`).
+      // We don't want to classify *any* DOM node as a function.
+      return typeof obj === "function" && typeof obj.nodeType !== "number";
+  };
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.5.1",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+					[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+function( _i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.5
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2020-03-14
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem.namespaceURI,
+		docElem = ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+};
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the master Deferred
+			master = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						master.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( master.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return master.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+		}
+
+		return master.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+					dataPriv.get( this, "events" ) || Object.create( null )
+				)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+							return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+							return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+						return result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			if ( button & 1 ) {
+				return 1;
+			}
+
+			if ( button & 2 ) {
+				return 3;
+			}
+
+			if ( button & 4 ) {
+				return 2;
+			}
+
+			return 0;
+		}
+
+		return event.which;
+	}
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px";
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, dimension, extra );
+						} ) :
+						getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+				) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+					jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+			/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+			doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+					return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = (
+					dataPriv.get( cur, "events" ) || Object.create( null )
+				)[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {
+		xml = undefined;
+	}
+
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} )
+		.filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} )
+		.map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+	originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+					jQuery( callbackContext ) :
+					jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script
+			if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			if ( typeof props.top === "number" ) {
+				props.top += "px";
+			}
+			if ( typeof props.left === "number" ) {
+				props.left += "px";
+			}
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	} );
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/v1.2.0/_static/jquery.js b/docs/v1.2.0/_static/jquery.js
new file mode 100644
index 0000000000..b0614034ad
--- /dev/null
+++ b/docs/v1.2.0/_static/jquery.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.5.1 | (c) JS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var 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diff --git a/docs/v1.2.0/_static/js/modernizr.min.js b/docs/v1.2.0/_static/js/modernizr.min.js
new file mode 100644
index 0000000000..f65d479747
--- /dev/null
+++ b/docs/v1.2.0/_static/js/modernizr.min.js
@@ -0,0 +1,4 @@
+/* Modernizr 2.6.2 (Custom Build) | MIT & BSD
+ * Build: http://modernizr.com/download/#-fontface-backgroundsize-borderimage-borderradius-boxshadow-flexbox-hsla-multiplebgs-opacity-rgba-textshadow-cssanimations-csscolumns-generatedcontent-cssgradients-cssreflections-csstransforms-csstransforms3d-csstransitions-applicationcache-canvas-canvastext-draganddrop-hashchange-history-audio-video-indexeddb-input-inputtypes-localstorage-postmessage-sessionstorage-websockets-websqldatabase-webworkers-geolocation-inlinesvg-smil-svg-svgclippaths-touch-webgl-shiv-mq-cssclasses-addtest-prefixed-teststyles-testprop-testallprops-hasevent-prefixes-domprefixes-load
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+
+    function filterSelectedTags(cardTags, selectedTags) {
+      return cardTags.some(function(tag) {
+        return selectedTags.some(function(selectedTag) {
+          return selectedTag == tag;
+        });
+      });
+    }
+
+    function updateList() {
+      var selectedTags = [];
+
+      $(".selected").each(function() {
+        selectedTags.push($(this).data("tag"));
+      });
+
+      tutorialList.filter(function(item) {
+        var cardTags;
+
+        if (item.values().tags == null) {
+          cardTags = [""];
+        } else {
+          cardTags = item.values().tags.split(",");
+        }
+
+        if (selectedTags.length == 0) {
+          return true;
+        } else {
+          return filterSelectedTags(cardTags, selectedTags);
+        }
+      });
+    }
+
+    $(".filter-btn").on("click", function() {
+      if ($(this).data("tag") == "all") {
+        $(this).addClass("all-tag-selected");
+        $(".filter").removeClass("selected");
+      } else {
+        $(this).toggleClass("selected");
+        $("[data-tag='all']").removeClass("all-tag-selected");
+      }
+
+      // If no tags are selected then highlight the 'All' tag
+
+      if (!$(".selected")[0]) {
+        $("[data-tag='all']").addClass("all-tag-selected");
+      }
+
+      updateList();
+    });
+  }
+};
+
+},{}],4:[function(require,module,exports){
+// Modified from https://stackoverflow.com/a/32396543
+window.highlightNavigation = {
+  navigationListItems: document.querySelectorAll("#pytorch-right-menu li"),
+  sections: document.querySelectorAll(".pytorch-article .section"),
+  sectionIdTonavigationLink: {},
+
+  bind: function() {
+    if (!sideMenus.displayRightMenu) {
+      return;
+    };
+
+    for (var i = 0; i < highlightNavigation.sections.length; i++) {
+      var id = highlightNavigation.sections[i].id;
+      highlightNavigation.sectionIdTonavigationLink[id] =
+        document.querySelectorAll('#pytorch-right-menu li a[href="#' + id + '"]')[0];
+    }
+
+    $(window).scroll(utilities.throttle(highlightNavigation.highlight, 100));
+  },
+
+  highlight: function() {
+    var rightMenu = document.getElementById("pytorch-right-menu");
+
+    // If right menu is not on the screen don't bother
+    if (rightMenu.offsetWidth === 0 && rightMenu.offsetHeight === 0) {
+      return;
+    }
+
+    var scrollPosition = utilities.scrollTop();
+    var OFFSET_TOP_PADDING = 25;
+    var offset = document.getElementById("header-holder").offsetHeight +
+                 document.getElementById("pytorch-page-level-bar").offsetHeight +
+                 OFFSET_TOP_PADDING;
+
+    var sections = highlightNavigation.sections;
+
+    for (var i = (sections.length - 1); i >= 0; i--) {
+      var currentSection = sections[i];
+      var sectionTop = utilities.offset(currentSection).top;
+
+      if (scrollPosition >= sectionTop - offset) {
+        var navigationLink = highlightNavigation.sectionIdTonavigationLink[currentSection.id];
+        var navigationListItem = utilities.closest(navigationLink, "li");
+
+        if (navigationListItem && !navigationListItem.classList.contains("active")) {
+          for (var i = 0; i < highlightNavigation.navigationListItems.length; i++) {
+            var el = highlightNavigation.navigationListItems[i];
+            if (el.classList.contains("active")) {
+              el.classList.remove("active");
+            }
+          }
+
+          navigationListItem.classList.add("active");
+
+          // Scroll to active item. Not a requested feature but we could revive it. Needs work.
+
+          // var menuTop = $("#pytorch-right-menu").position().top;
+          // var itemTop = navigationListItem.getBoundingClientRect().top;
+          // var TOP_PADDING = 20
+          // var newActiveTop = $("#pytorch-side-scroll-right").scrollTop() + itemTop - menuTop - TOP_PADDING;
+
+          // $("#pytorch-side-scroll-right").animate({
+          //   scrollTop: newActiveTop
+          // }, 100);
+        }
+
+        break;
+      }
+    }
+  }
+};
+
+},{}],5:[function(require,module,exports){
+window.mainMenuDropdown = {
+  bind: function() {
+    $("[data-toggle='ecosystem-dropdown']").on("click", function() {
+      toggleDropdown($(this).attr("data-toggle"));
+    });
+
+    $("[data-toggle='resources-dropdown']").on("click", function() {
+      toggleDropdown($(this).attr("data-toggle"));
+    });
+
+    function toggleDropdown(menuToggle) {
+      var showMenuClass = "show-menu";
+      var menuClass = "." + menuToggle + "-menu";
+
+      if ($(menuClass).hasClass(showMenuClass)) {
+        $(menuClass).removeClass(showMenuClass);
+      } else {
+        $("[data-toggle=" + menuToggle + "].show-menu").removeClass(
+          showMenuClass
+        );
+        $(menuClass).addClass(showMenuClass);
+      }
+    }
+  }
+};
+
+},{}],6:[function(require,module,exports){
+window.mobileMenu = {
+  bind: function() {
+    $("[data-behavior='open-mobile-menu']").on('click', function(e) {
+      e.preventDefault();
+      $(".mobile-main-menu").addClass("open");
+      $("body").addClass('no-scroll');
+
+      mobileMenu.listenForResize();
+    });
+
+    $("[data-behavior='close-mobile-menu']").on('click', function(e) {
+      e.preventDefault();
+      mobileMenu.close();
+    });
+  },
+
+  listenForResize: function() {
+    $(window).on('resize.ForMobileMenu', function() {
+      if ($(this).width() > 768) {
+        mobileMenu.close();
+      }
+    });
+  },
+
+  close: function() {
+    $(".mobile-main-menu").removeClass("open");
+    $("body").removeClass('no-scroll');
+    $(window).off('resize.ForMobileMenu');
+  }
+};
+
+},{}],7:[function(require,module,exports){
+window.mobileTOC = {
+  bind: function() {
+    $("[data-behavior='toggle-table-of-contents']").on("click", function(e) {
+      e.preventDefault();
+
+      var $parent = $(this).parent();
+
+      if ($parent.hasClass("is-open")) {
+        $parent.removeClass("is-open");
+        $(".pytorch-left-menu").slideUp(200, function() {
+          $(this).css({display: ""});
+        });
+      } else {
+        $parent.addClass("is-open");
+        $(".pytorch-left-menu").slideDown(200);
+      }
+    });
+  }
+}
+
+},{}],8:[function(require,module,exports){
+window.pytorchAnchors = {
+  bind: function() {
+    // Replace Sphinx-generated anchors with anchorjs ones
+    $(".headerlink").text("");
+
+    window.anchors.add(".pytorch-article .headerlink");
+
+    $(".anchorjs-link").each(function() {
+      var $headerLink = $(this).closest(".headerlink");
+      var href = $headerLink.attr("href");
+      var clone = this.outerHTML;
+
+      $clone = $(clone).attr("href", href);
+      $headerLink.before($clone);
+      $headerLink.remove();
+    });
+  }
+};
+
+},{}],9:[function(require,module,exports){
+// Modified from https://stackoverflow.com/a/13067009
+// Going for a JS solution to scrolling to an anchor so we can benefit from
+// less hacky css and smooth scrolling.
+
+window.scrollToAnchor = {
+  bind: function() {
+    var document = window.document;
+    var history = window.history;
+    var location = window.location
+    var HISTORY_SUPPORT = !!(history && history.pushState);
+
+    var anchorScrolls = {
+      ANCHOR_REGEX: /^#[^ ]+$/,
+      offsetHeightPx: function() {
+        var OFFSET_HEIGHT_PADDING = 20;
+        // TODO: this is a little janky. We should try to not rely on JS for this
+        return utilities.headersHeight() + OFFSET_HEIGHT_PADDING;
+      },
+
+      /**
+       * Establish events, and fix initial scroll position if a hash is provided.
+       */
+      init: function() {
+        this.scrollToCurrent();
+        // This interferes with clicks below it, causing a double fire
+        // $(window).on('hashchange', $.proxy(this, 'scrollToCurrent'));
+        $('body').on('click', 'a', $.proxy(this, 'delegateAnchors'));
+        $('body').on('click', '#pytorch-right-menu li span', $.proxy(this, 'delegateSpans'));
+      },
+
+      /**
+       * Return the offset amount to deduct from the normal scroll position.
+       * Modify as appropriate to allow for dynamic calculations
+       */
+      getFixedOffset: function() {
+        return this.offsetHeightPx();
+      },
+
+      /**
+       * If the provided href is an anchor which resolves to an element on the
+       * page, scroll to it.
+       * @param  {String} href
+       * @return {Boolean} - Was the href an anchor.
+       */
+      scrollIfAnchor: function(href, pushToHistory) {
+        var match, anchorOffset;
+
+        if(!this.ANCHOR_REGEX.test(href)) {
+          return false;
+        }
+
+        match = document.getElementById(href.slice(1));
+
+        if(match) {
+          var anchorOffset = $(match).offset().top - this.getFixedOffset();
+
+          $('html, body').scrollTop(anchorOffset);
+
+          // Add the state to history as-per normal anchor links
+          if(HISTORY_SUPPORT && pushToHistory) {
+            history.pushState({}, document.title, location.pathname + href);
+          }
+        }
+
+        return !!match;
+      },
+
+      /**
+       * Attempt to scroll to the current location's hash.
+       */
+      scrollToCurrent: function(e) {
+        if(this.scrollIfAnchor(window.location.hash) && e) {
+          e.preventDefault();
+        }
+      },
+
+      delegateSpans: function(e) {
+        var elem = utilities.closest(e.target, "a");
+
+        if(this.scrollIfAnchor(elem.getAttribute('href'), true)) {
+          e.preventDefault();
+        }
+      },
+
+      /**
+       * If the click event's target was an anchor, fix the scroll position.
+       */
+      delegateAnchors: function(e) {
+        var elem = e.target;
+
+        if(this.scrollIfAnchor(elem.getAttribute('href'), true)) {
+          e.preventDefault();
+        }
+      }
+    };
+
+    $(document).ready($.proxy(anchorScrolls, 'init'));
+  }
+};
+
+},{}],10:[function(require,module,exports){
+window.sideMenus = {
+  rightMenuIsOnScreen: function() {
+    return document.getElementById("pytorch-content-right").offsetParent !== null;
+  },
+
+  isFixedToBottom: false,
+
+  bind: function() {
+    sideMenus.handleLeftMenu();
+
+    var rightMenuLinks = document.querySelectorAll("#pytorch-right-menu li");
+    var rightMenuHasLinks = rightMenuLinks.length > 1;
+
+    if (!rightMenuHasLinks) {
+      for (var i = 0; i < rightMenuLinks.length; i++) {
+        rightMenuLinks[i].style.display = "none";
+      }
+    }
+
+    if (rightMenuHasLinks) {
+      // Don't show the Shortcuts menu title text unless there are menu items
+      document.getElementById("pytorch-shortcuts-wrapper").style.display = "block";
+
+      // We are hiding the titles of the pages in the right side menu but there are a few
+      // pages that include other pages in the right side menu (see 'torch.nn' in the docs)
+      // so if we exclude those it looks confusing. Here we add a 'title-link' class to these
+      // links so we can exclude them from normal right side menu link operations
+      var titleLinks = document.querySelectorAll(
+        "#pytorch-right-menu #pytorch-side-scroll-right \
+         > ul > li > a.reference.internal"
+      );
+
+      for (var i = 0; i < titleLinks.length; i++) {
+        var link = titleLinks[i];
+
+        link.classList.add("title-link");
+
+        if (
+          link.nextElementSibling &&
+          link.nextElementSibling.tagName === "UL" &&
+          link.nextElementSibling.children.length > 0
+        ) {
+          link.classList.add("has-children");
+        }
+      }
+
+      // Add + expansion signifiers to normal right menu links that have sub menus
+      var menuLinks = document.querySelectorAll(
+        "#pytorch-right-menu ul li ul li a.reference.internal"
+      );
+
+      for (var i = 0; i < menuLinks.length; i++) {
+        if (
+          menuLinks[i].nextElementSibling &&
+          menuLinks[i].nextElementSibling.tagName === "UL"
+        ) {
+          menuLinks[i].classList.add("not-expanded");
+        }
+      }
+
+      // If a hash is present on page load recursively expand menu items leading to selected item
+      var linkWithHash =
+        document.querySelector(
+          "#pytorch-right-menu a[href=\"" + window.location.hash + "\"]"
+        );
+
+      if (linkWithHash) {
+        // Expand immediate sibling list if present
+        if (
+          linkWithHash.nextElementSibling &&
+          linkWithHash.nextElementSibling.tagName === "UL" &&
+          linkWithHash.nextElementSibling.children.length > 0
+        ) {
+          linkWithHash.nextElementSibling.style.display = "block";
+          linkWithHash.classList.add("expanded");
+        }
+
+        // Expand ancestor lists if any
+        sideMenus.expandClosestUnexpandedParentList(linkWithHash);
+      }
+
+      // Bind click events on right menu links
+      $("#pytorch-right-menu a.reference.internal").on("click", function() {
+        if (this.classList.contains("expanded")) {
+          this.nextElementSibling.style.display = "none";
+          this.classList.remove("expanded");
+          this.classList.add("not-expanded");
+        } else if (this.classList.contains("not-expanded")) {
+          this.nextElementSibling.style.display = "block";
+          this.classList.remove("not-expanded");
+          this.classList.add("expanded");
+        }
+      });
+
+      sideMenus.handleRightMenu();
+    }
+
+    $(window).on('resize scroll', function(e) {
+      sideMenus.handleNavBar();
+
+      sideMenus.handleLeftMenu();
+
+      if (sideMenus.rightMenuIsOnScreen()) {
+        sideMenus.handleRightMenu();
+      }
+    });
+  },
+
+  leftMenuIsFixed: function() {
+    return document.getElementById("pytorch-left-menu").classList.contains("make-fixed");
+  },
+
+  handleNavBar: function() {
+    var mainHeaderHeight = document.getElementById('header-holder').offsetHeight;
+
+    // If we are scrolled past the main navigation header fix the sub menu bar to top of page
+    if (utilities.scrollTop() >= mainHeaderHeight) {
+      document.getElementById("pytorch-left-menu").classList.add("make-fixed");
+      document.getElementById("pytorch-page-level-bar").classList.add("left-menu-is-fixed");
+    } else {
+      document.getElementById("pytorch-left-menu").classList.remove("make-fixed");
+      document.getElementById("pytorch-page-level-bar").classList.remove("left-menu-is-fixed");
+    }
+  },
+
+  expandClosestUnexpandedParentList: function (el) {
+    var closestParentList = utilities.closest(el, "ul");
+
+    if (closestParentList) {
+      var closestParentLink = closestParentList.previousElementSibling;
+      var closestParentLinkExists = closestParentLink &&
+                                    closestParentLink.tagName === "A" &&
+                                    closestParentLink.classList.contains("reference");
+
+      if (closestParentLinkExists) {
+        // Don't add expansion class to any title links
+         if (closestParentLink.classList.contains("title-link")) {
+           return;
+         }
+
+        closestParentList.style.display = "block";
+        closestParentLink.classList.remove("not-expanded");
+        closestParentLink.classList.add("expanded");
+        sideMenus.expandClosestUnexpandedParentList(closestParentLink);
+      }
+    }
+  },
+
+  handleLeftMenu: function () {
+    var windowHeight = utilities.windowHeight();
+    var topOfFooterRelativeToWindow = document.getElementById("docs-tutorials-resources").getBoundingClientRect().top;
+
+    if (topOfFooterRelativeToWindow >= windowHeight) {
+      document.getElementById("pytorch-left-menu").style.height = "100%";
+    } else {
+      var howManyPixelsOfTheFooterAreInTheWindow = windowHeight - topOfFooterRelativeToWindow;
+      var leftMenuDifference = howManyPixelsOfTheFooterAreInTheWindow;
+      document.getElementById("pytorch-left-menu").style.height = (windowHeight - leftMenuDifference) + "px";
+    }
+  },
+
+  handleRightMenu: function() {
+    var rightMenuWrapper = document.getElementById("pytorch-content-right");
+    var rightMenu = document.getElementById("pytorch-right-menu");
+    var rightMenuList = rightMenu.getElementsByTagName("ul")[0];
+    var article = document.getElementById("pytorch-article");
+    var articleHeight = article.offsetHeight;
+    var articleBottom = utilities.offset(article).top + articleHeight;
+    var mainHeaderHeight = document.getElementById('header-holder').offsetHeight;
+
+    if (utilities.scrollTop() < mainHeaderHeight) {
+      rightMenuWrapper.style.height = "100%";
+      rightMenu.style.top = 0;
+      rightMenu.classList.remove("scrolling-fixed");
+      rightMenu.classList.remove("scrolling-absolute");
+    } else {
+      if (rightMenu.classList.contains("scrolling-fixed")) {
+        var rightMenuBottom =
+          utilities.offset(rightMenuList).top + rightMenuList.offsetHeight;
+
+        if (rightMenuBottom >= articleBottom) {
+          rightMenuWrapper.style.height = articleHeight + mainHeaderHeight + "px";
+          rightMenu.style.top = utilities.scrollTop() - mainHeaderHeight + "px";
+          rightMenu.classList.add("scrolling-absolute");
+          rightMenu.classList.remove("scrolling-fixed");
+        }
+      } else {
+        rightMenuWrapper.style.height = articleHeight + mainHeaderHeight + "px";
+        rightMenu.style.top =
+          articleBottom - mainHeaderHeight - rightMenuList.offsetHeight + "px";
+        rightMenu.classList.add("scrolling-absolute");
+      }
+
+      if (utilities.scrollTop() < articleBottom - rightMenuList.offsetHeight) {
+        rightMenuWrapper.style.height = "100%";
+        rightMenu.style.top = "";
+        rightMenu.classList.remove("scrolling-absolute");
+        rightMenu.classList.add("scrolling-fixed");
+      }
+    }
+
+    var rightMenuSideScroll = document.getElementById("pytorch-side-scroll-right");
+    var sideScrollFromWindowTop = rightMenuSideScroll.getBoundingClientRect().top;
+
+    rightMenuSideScroll.style.height = utilities.windowHeight() - sideScrollFromWindowTop + "px";
+  }
+};
+
+},{}],"pytorch-sphinx-theme":[function(require,module,exports){
+var jQuery = (typeof(window) != 'undefined') ? window.jQuery : require('jquery');
+
+// Sphinx theme nav state
+function ThemeNav () {
+
+    var nav = {
+        navBar: null,
+        win: null,
+        winScroll: false,
+        winResize: false,
+        linkScroll: false,
+        winPosition: 0,
+        winHeight: null,
+        docHeight: null,
+        isRunning: false
+    };
+
+    nav.enable = function (withStickyNav) {
+        var self = this;
+
+        // TODO this can likely be removed once the theme javascript is broken
+        // out from the RTD assets. This just ensures old projects that are
+        // calling `enable()` get the sticky menu on by default. All other cals
+        // to `enable` should include an argument for enabling the sticky menu.
+        if (typeof(withStickyNav) == 'undefined') {
+            withStickyNav = true;
+        }
+
+        if (self.isRunning) {
+            // Only allow enabling nav logic once
+            return;
+        }
+
+        self.isRunning = true;
+        jQuery(function ($) {
+            self.init($);
+
+            self.reset();
+            self.win.on('hashchange', self.reset);
+
+            if (withStickyNav) {
+                // Set scroll monitor
+                self.win.on('scroll', function () {
+                    if (!self.linkScroll) {
+                        if (!self.winScroll) {
+                            self.winScroll = true;
+                            requestAnimationFrame(function() { self.onScroll(); });
+                        }
+                    }
+                });
+            }
+
+            // Set resize monitor
+            self.win.on('resize', function () {
+                if (!self.winResize) {
+                    self.winResize = true;
+                    requestAnimationFrame(function() { self.onResize(); });
+                }
+            });
+
+            self.onResize();
+        });
+
+    };
+
+    // TODO remove this with a split in theme and Read the Docs JS logic as
+    // well, it's only here to support 0.3.0 installs of our theme.
+    nav.enableSticky = function() {
+        this.enable(true);
+    };
+
+    nav.init = function ($) {
+        var doc = $(document),
+            self = this;
+
+        this.navBar = $('div.pytorch-side-scroll:first');
+        this.win = $(window);
+
+        // Set up javascript UX bits
+        $(document)
+            // Shift nav in mobile when clicking the menu.
+            .on('click', "[data-toggle='pytorch-left-menu-nav-top']", function() {
+                $("[data-toggle='wy-nav-shift']").toggleClass("shift");
+                $("[data-toggle='rst-versions']").toggleClass("shift");
+            })
+
+            // Nav menu link click operations
+            .on('click', ".pytorch-menu-vertical .current ul li a", function() {
+                var target = $(this);
+                // Close menu when you click a link.
+                $("[data-toggle='wy-nav-shift']").removeClass("shift");
+                $("[data-toggle='rst-versions']").toggleClass("shift");
+                // Handle dynamic display of l3 and l4 nav lists
+                self.toggleCurrent(target);
+                self.hashChange();
+            })
+            .on('click', "[data-toggle='rst-current-version']", function() {
+                $("[data-toggle='rst-versions']").toggleClass("shift-up");
+            })
+
+        // Make tables responsive
+        $("table.docutils:not(.field-list,.footnote,.citation)")
+            .wrap("<div class='wy-table-responsive'></div>");
+
+        // Add extra class to responsive tables that contain
+        // footnotes or citations so that we can target them for styling
+        $("table.docutils.footnote")
+            .wrap("<div class='wy-table-responsive footnote'></div>");
+        $("table.docutils.citation")
+            .wrap("<div class='wy-table-responsive citation'></div>");
+
+        // Add expand links to all parents of nested ul
+        $('.pytorch-menu-vertical ul').not('.simple').siblings('a').each(function () {
+            var link = $(this);
+                expand = $('<span class="toctree-expand"></span>');
+            expand.on('click', function (ev) {
+                self.toggleCurrent(link);
+                ev.stopPropagation();
+                return false;
+            });
+            link.prepend(expand);
+        });
+    };
+
+    nav.reset = function () {
+        // Get anchor from URL and open up nested nav
+        var anchor = encodeURI(window.location.hash) || '#';
+
+        try {
+            var vmenu = $('.pytorch-menu-vertical');
+            var link = vmenu.find('[href="' + anchor + '"]');
+            if (link.length === 0) {
+                // this link was not found in the sidebar.
+                // Find associated id element, then its closest section
+                // in the document and try with that one.
+                var id_elt = $('.document [id="' + anchor.substring(1) + '"]');
+                var closest_section = id_elt.closest('div.section');
+                link = vmenu.find('[href="#' + closest_section.attr("id") + '"]');
+                if (link.length === 0) {
+                    // still not found in the sidebar. fall back to main section
+                    link = vmenu.find('[href="#"]');
+                }
+            }
+            // If we found a matching link then reset current and re-apply
+            // otherwise retain the existing match
+            if (link.length > 0) {
+                $('.pytorch-menu-vertical .current').removeClass('current');
+                link.addClass('current');
+                link.closest('li.toctree-l1').addClass('current');
+                link.closest('li.toctree-l1').parent().addClass('current');
+                link.closest('li.toctree-l1').addClass('current');
+                link.closest('li.toctree-l2').addClass('current');
+                link.closest('li.toctree-l3').addClass('current');
+                link.closest('li.toctree-l4').addClass('current');
+            }
+        }
+        catch (err) {
+            console.log("Error expanding nav for anchor", err);
+        }
+
+    };
+
+    nav.onScroll = function () {
+        this.winScroll = false;
+        var newWinPosition = this.win.scrollTop(),
+            winBottom = newWinPosition + this.winHeight,
+            navPosition = this.navBar.scrollTop(),
+            newNavPosition = navPosition + (newWinPosition - this.winPosition);
+        if (newWinPosition < 0 || winBottom > this.docHeight) {
+            return;
+        }
+        this.navBar.scrollTop(newNavPosition);
+        this.winPosition = newWinPosition;
+    };
+
+    nav.onResize = function () {
+        this.winResize = false;
+        this.winHeight = this.win.height();
+        this.docHeight = $(document).height();
+    };
+
+    nav.hashChange = function () {
+        this.linkScroll = true;
+        this.win.one('hashchange', function () {
+            this.linkScroll = false;
+        });
+    };
+
+    nav.toggleCurrent = function (elem) {
+        var parent_li = elem.closest('li');
+        parent_li.siblings('li.current').removeClass('current');
+        parent_li.siblings().find('li.current').removeClass('current');
+        parent_li.find('> ul li.current').removeClass('current');
+        parent_li.toggleClass('current');
+    }
+
+    return nav;
+};
+
+module.exports.ThemeNav = ThemeNav();
+
+if (typeof(window) != 'undefined') {
+    window.SphinxRtdTheme = {
+        Navigation: module.exports.ThemeNav,
+        // TODO remove this once static assets are split up between the theme
+        // and Read the Docs. For now, this patches 0.3.0 to be backwards
+        // compatible with a pre-0.3.0 layout.html
+        StickyNav: module.exports.ThemeNav,
+    };
+}
+
+
+// requestAnimationFrame polyfill by Erik Möller. fixes from Paul Irish and Tino Zijdel
+// https://gist.github.com/paulirish/1579671
+// MIT license
+
+(function() {
+    var lastTime = 0;
+    var vendors = ['ms', 'moz', 'webkit', 'o'];
+    for(var x = 0; x < vendors.length && !window.requestAnimationFrame; ++x) {
+        window.requestAnimationFrame = window[vendors[x]+'RequestAnimationFrame'];
+        window.cancelAnimationFrame = window[vendors[x]+'CancelAnimationFrame']
+                                   || window[vendors[x]+'CancelRequestAnimationFrame'];
+    }
+
+    if (!window.requestAnimationFrame)
+        window.requestAnimationFrame = function(callback, element) {
+            var currTime = new Date().getTime();
+            var timeToCall = Math.max(0, 16 - (currTime - lastTime));
+            var id = window.setTimeout(function() { callback(currTime + timeToCall); },
+              timeToCall);
+            lastTime = currTime + timeToCall;
+            return id;
+        };
+
+    if (!window.cancelAnimationFrame)
+        window.cancelAnimationFrame = function(id) {
+            clearTimeout(id);
+        };
+}());
+
+$(".sphx-glr-thumbcontainer").removeAttr("tooltip");
+$("table").removeAttr("border");
+
+// This code replaces the default sphinx gallery download buttons
+// with the 3 download buttons at the top of the page
+
+var downloadNote = $(".sphx-glr-download-link-note.admonition.note");
+if (downloadNote.length >= 1) {
+    var tutorialUrlArray = $("#tutorial-type").text().split('/');
+        tutorialUrlArray[0] = tutorialUrlArray[0] + "_source"
+
+    var githubLink = "https://github.com/pytorch/tutorials/blob/master/" + tutorialUrlArray.join("/") + ".py",
+        notebookLink = $(".reference.download")[1].href,
+        notebookDownloadPath = notebookLink.split('_downloads')[1],
+        colabLink = "https://colab.research.google.com/github/pytorch/tutorials/blob/gh-pages/_downloads" + notebookDownloadPath;
+
+    $("#google-colab-link").wrap("<a href=" + colabLink + " data-behavior='call-to-action-event' data-response='Run in Google Colab' target='_blank'/>");
+    $("#download-notebook-link").wrap("<a href=" + notebookLink + " data-behavior='call-to-action-event' data-response='Download Notebook'/>");
+    $("#github-view-link").wrap("<a href=" + githubLink + " data-behavior='call-to-action-event' data-response='View on Github' target='_blank'/>");
+} else {
+    $(".pytorch-call-to-action-links").hide();
+}
+
+//This code handles the Expand/Hide toggle for the Docs/Tutorials left nav items
+
+$(document).ready(function() {
+  var caption = "#pytorch-left-menu p.caption";
+  var collapseAdded = $(this).not("checked");
+  $(caption).each(function () {
+    var menuName = this.innerText.replace(/[^\w\s]/gi, "").trim();
+    $(this).find("span").addClass("checked");
+    if (collapsedSections.includes(menuName) == true && collapseAdded && sessionStorage.getItem(menuName) !== "expand" || sessionStorage.getItem(menuName) == "collapse") {
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+      $(this.firstChild).after("<span class='hide-menu collapse'>[ - ]</span>");
+      $(this).next("ul").hide();
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+      $(this.firstChild).after("<span class='expand-menu collapse'>[ + ]</span>");
+      $(this.firstChild).after("<span class='hide-menu'>[ - ]</span>");
+    }
+  });
+
+  $(".expand-menu").on("click", function () {
+    $(this).prev(".hide-menu").toggle();
+    $(this).parent().next("ul").toggle();
+    var menuName = $(this).parent().text().replace(/[^\w\s]/gi, "").trim();
+    if (sessionStorage.getItem(menuName) == "collapse") {
+      sessionStorage.removeItem(menuName);
+    }
+    sessionStorage.setItem(menuName, "expand");
+    toggleList(this);
+  });
+
+  $(".hide-menu").on("click", function () {
+    $(this).next(".expand-menu").toggle();
+    $(this).parent().next("ul").toggle();
+    var menuName = $(this).parent().text().replace(/[^\w\s]/gi, "").trim();
+    if (sessionStorage.getItem(menuName) == "expand") {
+      sessionStorage.removeItem(menuName);
+    }
+    sessionStorage.setItem(menuName, "collapse");
+    toggleList(this);
+  });
+
+  function toggleList(menuCommand) {
+    $(menuCommand).toggle();
+  }
+});
+
+// Build an array from each tag that's present
+
+var tagList = $(".tutorials-card-container").map(function() {
+    return $(this).data("tags").split(",").map(function(item) {
+        return item.trim();
+      });
+}).get();
+
+function unique(value, index, self) {
+      return self.indexOf(value) == index && value != ""
+    }
+
+// Only return unique tags
+
+var tags = tagList.sort().filter(unique);
+
+// Add filter buttons to the top of the page for each tag
+
+function createTagMenu() {
+    tags.forEach(function(item){
+    $(".tutorial-filter-menu").append(" <div class='tutorial-filter filter-btn filter' data-tag='" + item + "'>" + item + "</div>")
+  })
+};
+
+createTagMenu();
+
+// Remove hyphens if they are present in the filter buttons
+
+$(".tags").each(function(){
+    var tags = $(this).text().split(",");
+    tags.forEach(function(tag, i ) {
+       tags[i] = tags[i].replace(/-/, ' ')
+    })
+    $(this).html(tags.join(", "));
+});
+
+// Remove hyphens if they are present in the card body
+
+$(".tutorial-filter").each(function(){
+    var tag = $(this).text();
+    $(this).html(tag.replace(/-/, ' '))
+})
+
+// Remove any empty p tags that Sphinx adds
+
+$("#tutorial-cards p").each(function(index, item) {
+    if(!$(item).text().trim()) {
+        $(item).remove();
+    }
+});
+
+// Jump back to top on pagination click
+
+$(document).on("click", ".page", function() {
+    $('html, body').animate(
+      {scrollTop: $("#dropdown-filter-tags").position().top},
+      'slow'
+    );
+});
+
+var link = $("a[href='intermediate/speech_command_recognition_with_torchaudio.html']");
+
+if (link.text() == "SyntaxError") {
+    console.log("There is an issue with the intermediate/speech_command_recognition_with_torchaudio.html menu item.");
+    link.text("Speech Command Recognition with torchaudio");
+}
+
+$(".stars-outer > i").hover(function() {
+    $(this).prevAll().addBack().toggleClass("fas star-fill");
+});
+
+$(".stars-outer > i").on("click", function() {
+    $(this).prevAll().each(function() {
+        $(this).addBack().addClass("fas star-fill");
+    });
+
+    $(".stars-outer > i").each(function() {
+        $(this).unbind("mouseenter mouseleave").css({
+            "pointer-events": "none"
+        });
+    });
+})
+
+$("#pytorch-side-scroll-right li a").on("click", function (e) {
+  var href = $(this).attr("href");
+  $('html, body').stop().animate({
+    scrollTop: $(href).offset().top - 100
+  }, 850);
+  e.preventDefault;
+});
+
+var lastId,
+  topMenu = $("#pytorch-side-scroll-right"),
+  topMenuHeight = topMenu.outerHeight() + 1,
+  // All sidenav items
+  menuItems = topMenu.find("a"),
+  // Anchors for menu items
+  scrollItems = menuItems.map(function () {
+    var item = $(this).attr("href");
+    if (item.length) {
+      return item;
+    }
+  });
+
+$(window).scroll(function () {
+  var fromTop = $(this).scrollTop() + topMenuHeight;
+  var article = ".section";
+
+  $(article).each(function (i) {
+    var offsetScroll = $(this).offset().top - $(window).scrollTop();
+    if (
+      offsetScroll <= topMenuHeight + 200 &&
+      offsetScroll >= topMenuHeight - 200 &&
+      scrollItems[i] == "#" + $(this).attr("id") &&
+      $(".hidden:visible")
+    ) {
+      $(menuItems).removeClass("side-scroll-highlight");
+      $(menuItems[i]).addClass("side-scroll-highlight");
+    }
+  });
+});
+
+
+},{"jquery":"jquery"}]},{},[1,2,3,4,5,6,7,8,9,10,"pytorch-sphinx-theme"]);
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new file mode 100644
index 0000000000..1216eeac26
--- /dev/null
+++ b/docs/v1.2.0/_static/js/vendor/anchor.min.js
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+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+//
+// AnchorJS - v4.3.0 - 2020-10-21
+// https://www.bryanbraun.com/anchorjs/
+// Copyright (c) 2020 Bryan Braun; Licensed MIT
+//
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
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\ No newline at end of file
diff --git a/docs/v1.2.0/_static/js/vendor/bootstrap.min.js b/docs/v1.2.0/_static/js/vendor/bootstrap.min.js
new file mode 100644
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+/*!
+  * Bootstrap v4.0.0 (https://getbootstrap.com)
+  * Copyright 2011-2018 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
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37:t.preventDefault(),this.prev();break;case 39:t.preventDefault(),this.next()}},C._getItemIndex=function(e){return this._items=t.makeArray(t(e).parent().find(y.ITEM)),this._items.indexOf(e)},C._getItemByDirection=function(t,e){var n=t===h,i=t===c,s=this._getItemIndex(e),r=this._items.length-1;if((i&&0===s||n&&s===r)&&!this._config.wrap)return e;var o=(s+(t===c?-1:1))%this._items.length;return-1===o?this._items[this._items.length-1]:this._items[o]},C._triggerSlideEvent=function(e,n){var i=this._getItemIndex(e),s=this._getItemIndex(t(this._element).find(y.ACTIVE_ITEM)[0]),r=t.Event(d.SLIDE,{relatedTarget:e,direction:n,from:s,to:i});return t(this._element).trigger(r),r},C._setActiveIndicatorElement=function(e){if(this._indicatorsElement){t(this._indicatorsElement).find(y.ACTIVE).removeClass(g);var n=this._indicatorsElement.children[this._getItemIndex(e)];n&&t(n).addClass(g)}},C._slide=function(e,n){var i,s,r,o=this,a=t(this._element).find(y.ACTIVE_ITEM)[0],l=this._getItemIndex(a),c=n||a&&this._getItemByDirection(e,a),_=this._getItemIndex(c),C=Boolean(this._interval);if(e===h?(i=v,s=E,r=u):(i=m,s=T,r=f),c&&t(c).hasClass(g))this._isSliding=!1;else if(!this._triggerSlideEvent(c,r).isDefaultPrevented()&&a&&c){this._isSliding=!0,C&&this.pause(),this._setActiveIndicatorElement(c);var I=t.Event(d.SLID,{relatedTarget:c,direction:r,from:l,to:_});P.supportsTransitionEnd()&&t(this._element).hasClass(p)?(t(c).addClass(s),P.reflow(c),t(a).addClass(i),t(c).addClass(i),t(a).one(P.TRANSITION_END,function(){t(c).removeClass(i+" "+s).addClass(g),t(a).removeClass(g+" "+s+" "+i),o._isSliding=!1,setTimeout(function(){return t(o._element).trigger(I)},0)}).emulateTransitionEnd(600)):(t(a).removeClass(g),t(c).addClass(g),this._isSliding=!1,t(this._element).trigger(I)),C&&this.cycle()}},o._jQueryInterface=function(e){return this.each(function(){var i=t(this).data(n),s=r({},a,t(this).data());"object"==typeof 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e=t(this),s=e.data(n)?"toggle":i.data();m._jQueryInterface.call(e,s)})}),t.fn[e]=m._jQueryInterface,t.fn[e].Constructor=m,t.fn[e].noConflict=function(){return t.fn[e]=o,m._jQueryInterface},m}(e),W=function(t){var e="dropdown",i="bs.dropdown",o="."+i,a=".data-api",l=t.fn[e],h=new RegExp("38|40|27"),c={HIDE:"hide"+o,HIDDEN:"hidden"+o,SHOW:"show"+o,SHOWN:"shown"+o,CLICK:"click"+o,CLICK_DATA_API:"click"+o+a,KEYDOWN_DATA_API:"keydown"+o+a,KEYUP_DATA_API:"keyup"+o+a},u="disabled",f="show",d="dropup",_="dropright",g="dropleft",p="dropdown-menu-right",m="dropdown-menu-left",v="position-static",E='[data-toggle="dropdown"]',T=".dropdown form",y=".dropdown-menu",C=".navbar-nav",I=".dropdown-menu .dropdown-item:not(.disabled)",A="top-start",b="top-end",D="bottom-start",S="bottom-end",w="right-start",N="left-start",O={offset:0,flip:!0,boundary:"scrollParent"},k={offset:"(number|string|function)",flip:"boolean",boundary:"(string|element)"},L=function(){function a(t,e){this._element=t,this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar(),this._addEventListeners()}var l=a.prototype;return l.toggle=function(){if(!this._element.disabled&&!t(this._element).hasClass(u)){var e=a._getParentFromElement(this._element),i=t(this._menu).hasClass(f);if(a._clearMenus(),!i){var s={relatedTarget:this._element},r=t.Event(c.SHOW,s);if(t(e).trigger(r),!r.isDefaultPrevented()){if(!this._inNavbar){if("undefined"==typeof n)throw new TypeError("Bootstrap dropdown require Popper.js (https://popper.js.org)");var o=this._element;t(e).hasClass(d)&&(t(this._menu).hasClass(m)||t(this._menu).hasClass(p))&&(o=e),"scrollParent"!==this._config.boundary&&t(e).addClass(v),this._popper=new n(o,this._menu,this._getPopperConfig())}"ontouchstart"in document.documentElement&&0===t(e).closest(C).length&&t("body").children().on("mouseover",null,t.noop),this._element.focus(),this._element.setAttribute("aria-expanded",!0),t(this._menu).toggleClass(f),t(e).toggleClass(f).trigger(t.Event(c.SHOWN,s))}}}},l.dispose=function(){t.removeData(this._element,i),t(this._element).off(o),this._element=null,this._menu=null,null!==this._popper&&(this._popper.destroy(),this._popper=null)},l.update=function(){this._inNavbar=this._detectNavbar(),null!==this._popper&&this._popper.scheduleUpdate()},l._addEventListeners=function(){var e=this;t(this._element).on(c.CLICK,function(t){t.preventDefault(),t.stopPropagation(),e.toggle()})},l._getConfig=function(n){return n=r({},this.constructor.Default,t(this._element).data(),n),P.typeCheckConfig(e,n,this.constructor.DefaultType),n},l._getMenuElement=function(){if(!this._menu){var e=a._getParentFromElement(this._element);this._menu=t(e).find(y)[0]}return this._menu},l._getPlacement=function(){var e=t(this._element).parent(),n=D;return e.hasClass(d)?(n=A,t(this._menu).hasClass(p)&&(n=b)):e.hasClass(_)?n=w:e.hasClass(g)?n=N:t(this._menu).hasClass(p)&&(n=S),n},l._detectNavbar=function(){return t(this._element).closest(".navbar").length>0},l._getPopperConfig=function(){var t=this,e={};return"function"==typeof this._config.offset?e.fn=function(e){return e.offsets=r({},e.offsets,t._config.offset(e.offsets)||{}),e}:e.offset=this._config.offset,{placement:this._getPlacement(),modifiers:{offset:e,flip:{enabled:this._config.flip},preventOverflow:{boundariesElement:this._config.boundary}}}},a._jQueryInterface=function(e){return this.each(function(){var n=t(this).data(i);if(n||(n=new a(this,"object"==typeof e?e:null),t(this).data(i,n)),"string"==typeof e){if("undefined"==typeof n[e])throw new TypeError('No method named "'+e+'"');n[e]()}})},a._clearMenus=function(e){if(!e||3!==e.which&&("keyup"!==e.type||9===e.which))for(var n=t.makeArray(t(E)),s=0;s<n.length;s++){var r=a._getParentFromElement(n[s]),o=t(n[s]).data(i),l={relatedTarget:n[s]};if(o){var h=o._menu;if(t(r).hasClass(f)&&!(e&&("click"===e.type&&/input|textarea/i.test(e.target.tagName)||"keyup"===e.type&&9===e.which)&&t.contains(r,e.target))){var u=t.Event(c.HIDE,l);t(r).trigger(u),u.isDefaultPrevented()||("ontouchstart"in document.documentElement&&t("body").children().off("mouseover",null,t.noop),n[s].setAttribute("aria-expanded","false"),t(h).removeClass(f),t(r).removeClass(f).trigger(t.Event(c.HIDDEN,l)))}}}},a._getParentFromElement=function(e){var n,i=P.getSelectorFromElement(e);return i&&(n=t(i)[0]),n||e.parentNode},a._dataApiKeydownHandler=function(e){if((/input|textarea/i.test(e.target.tagName)?!(32===e.which||27!==e.which&&(40!==e.which&&38!==e.which||t(e.target).closest(y).length)):h.test(e.which))&&(e.preventDefault(),e.stopPropagation(),!this.disabled&&!t(this).hasClass(u))){var n=a._getParentFromElement(this),i=t(n).hasClass(f);if((i||27===e.which&&32===e.which)&&(!i||27!==e.which&&32!==e.which)){var s=t(n).find(I).get();if(0!==s.length){var r=s.indexOf(e.target);38===e.which&&r>0&&r--,40===e.which&&r<s.length-1&&r++,r<0&&(r=0),s[r].focus()}}else{if(27===e.which){var o=t(n).find(E)[0];t(o).trigger("focus")}t(this).trigger("click")}}},s(a,null,[{key:"VERSION",get:function(){return"4.0.0"}},{key:"Default",get:function(){return O}},{key:"DefaultType",get:function(){return k}}]),a}();return t(document).on(c.KEYDOWN_DATA_API,E,L._dataApiKeydownHandler).on(c.KEYDOWN_DATA_API,y,L._dataApiKeydownHandler).on(c.CLICK_DATA_API+" "+c.KEYUP_DATA_API,L._clearMenus).on(c.CLICK_DATA_API,E,function(e){e.preventDefault(),e.stopPropagation(),L._jQueryInterface.call(t(this),"toggle")}).on(c.CLICK_DATA_API,T,function(t){t.stopPropagation()}),t.fn[e]=L._jQueryInterface,t.fn[e].Constructor=L,t.fn[e].noConflict=function(){return t.fn[e]=l,L._jQueryInterface},L}(e),M=function(t){var e="modal",n="bs.modal",i="."+n,o=t.fn.modal,a={backdrop:!0,keyboard:!0,focus:!0,show:!0},l={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean",show:"boolean"},h={HIDE:"hide"+i,HIDDEN:"hidden"+i,SHOW:"show"+i,SHOWN:"shown"+i,FOCUSIN:"focusin"+i,RESIZE:"resize"+i,CLICK_DISMISS:"click.dismiss"+i,KEYDOWN_DISMISS:"keydown.dismiss"+i,MOUSEUP_DISMISS:"mouseup.dismiss"+i,MOUSEDOWN_DISMISS:"mousedown.dismiss"+i,CLICK_DATA_API:"click"+i+".data-api"},c="modal-scrollbar-measure",u="modal-backdrop",f="modal-open",d="fade",_="show",g={DIALOG:".modal-dialog",DATA_TOGGLE:'[data-toggle="modal"]',DATA_DISMISS:'[data-dismiss="modal"]',FIXED_CONTENT:".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",STICKY_CONTENT:".sticky-top",NAVBAR_TOGGLER:".navbar-toggler"},p=function(){function o(e,n){this._config=this._getConfig(n),this._element=e,this._dialog=t(e).find(g.DIALOG)[0],this._backdrop=null,this._isShown=!1,this._isBodyOverflowing=!1,this._ignoreBackdropClick=!1,this._originalBodyPadding=0,this._scrollbarWidth=0}var p=o.prototype;return p.toggle=function(t){return this._isShown?this.hide():this.show(t)},p.show=function(e){var n=this;if(!this._isTransitioning&&!this._isShown){P.supportsTransitionEnd()&&t(this._element).hasClass(d)&&(this._isTransitioning=!0);var i=t.Event(h.SHOW,{relatedTarget:e});t(this._element).trigger(i),this._isShown||i.isDefaultPrevented()||(this._isShown=!0,this._checkScrollbar(),this._setScrollbar(),this._adjustDialog(),t(document.body).addClass(f),this._setEscapeEvent(),this._setResizeEvent(),t(this._element).on(h.CLICK_DISMISS,g.DATA_DISMISS,function(t){return n.hide(t)}),t(this._dialog).on(h.MOUSEDOWN_DISMISS,function(){t(n._element).one(h.MOUSEUP_DISMISS,function(e){t(e.target).is(n._element)&&(n._ignoreBackdropClick=!0)})}),this._showBackdrop(function(){return n._showElement(e)}))}},p.hide=function(e){var n=this;if(e&&e.preventDefault(),!this._isTransitioning&&this._isShown){var i=t.Event(h.HIDE);if(t(this._element).trigger(i),this._isShown&&!i.isDefaultPrevented()){this._isShown=!1;var s=P.supportsTransitionEnd()&&t(this._element).hasClass(d);s&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),t(document).off(h.FOCUSIN),t(this._element).removeClass(_),t(this._element).off(h.CLICK_DISMISS),t(this._dialog).off(h.MOUSEDOWN_DISMISS),s?t(this._element).one(P.TRANSITION_END,function(t){return n._hideModal(t)}).emulateTransitionEnd(300):this._hideModal()}}},p.dispose=function(){t.removeData(this._element,n),t(window,document,this._element,this._backdrop).off(i),this._config=null,this._element=null,this._dialog=null,this._backdrop=null,this._isShown=null,this._isBodyOverflowing=null,this._ignoreBackdropClick=null,this._scrollbarWidth=null},p.handleUpdate=function(){this._adjustDialog()},p._getConfig=function(t){return t=r({},a,t),P.typeCheckConfig(e,t,l),t},p._showElement=function(e){var n=this,i=P.supportsTransitionEnd()&&t(this._element).hasClass(d);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.appendChild(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.scrollTop=0,i&&P.reflow(this._element),t(this._element).addClass(_),this._config.focus&&this._enforceFocus();var s=t.Event(h.SHOWN,{relatedTarget:e}),r=function(){n._config.focus&&n._element.focus(),n._isTransitioning=!1,t(n._element).trigger(s)};i?t(this._dialog).one(P.TRANSITION_END,r).emulateTransitionEnd(300):r()},p._enforceFocus=function(){var e=this;t(document).off(h.FOCUSIN).on(h.FOCUSIN,function(n){document!==n.target&&e._element!==n.target&&0===t(e._element).has(n.target).length&&e._element.focus()})},p._setEscapeEvent=function(){var e=this;this._isShown&&this._config.keyboard?t(this._element).on(h.KEYDOWN_DISMISS,function(t){27===t.which&&(t.preventDefault(),e.hide())}):this._isShown||t(this._element).off(h.KEYDOWN_DISMISS)},p._setResizeEvent=function(){var e=this;this._isShown?t(window).on(h.RESIZE,function(t){return e.handleUpdate(t)}):t(window).off(h.RESIZE)},p._hideModal=function(){var e=this;this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._isTransitioning=!1,this._showBackdrop(function(){t(document.body).removeClass(f),e._resetAdjustments(),e._resetScrollbar(),t(e._element).trigger(h.HIDDEN)})},p._removeBackdrop=function(){this._backdrop&&(t(this._backdrop).remove(),this._backdrop=null)},p._showBackdrop=function(e){var n=this,i=t(this._element).hasClass(d)?d:"";if(this._isShown&&this._config.backdrop){var s=P.supportsTransitionEnd()&&i;if(this._backdrop=document.createElement("div"),this._backdrop.className=u,i&&t(this._backdrop).addClass(i),t(this._backdrop).appendTo(document.body),t(this._element).on(h.CLICK_DISMISS,function(t){n._ignoreBackdropClick?n._ignoreBackdropClick=!1:t.target===t.currentTarget&&("static"===n._config.backdrop?n._element.focus():n.hide())}),s&&P.reflow(this._backdrop),t(this._backdrop).addClass(_),!e)return;if(!s)return void e();t(this._backdrop).one(P.TRANSITION_END,e).emulateTransitionEnd(150)}else 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s=t(i)[0].style.paddingRight,r=t(i).css("padding-right");t(i).data("padding-right",s).css("padding-right",parseFloat(r)+e._scrollbarWidth+"px")}),t(g.STICKY_CONTENT).each(function(n,i){var s=t(i)[0].style.marginRight,r=t(i).css("margin-right");t(i).data("margin-right",s).css("margin-right",parseFloat(r)-e._scrollbarWidth+"px")}),t(g.NAVBAR_TOGGLER).each(function(n,i){var s=t(i)[0].style.marginRight,r=t(i).css("margin-right");t(i).data("margin-right",s).css("margin-right",parseFloat(r)+e._scrollbarWidth+"px")});var n=document.body.style.paddingRight,i=t("body").css("padding-right");t("body").data("padding-right",n).css("padding-right",parseFloat(i)+this._scrollbarWidth+"px")}},p._resetScrollbar=function(){t(g.FIXED_CONTENT).each(function(e,n){var i=t(n).data("padding-right");"undefined"!=typeof i&&t(n).css("padding-right",i).removeData("padding-right")}),t(g.STICKY_CONTENT+", "+g.NAVBAR_TOGGLER).each(function(e,n){var i=t(n).data("margin-right");"undefined"!=typeof i&&t(n).css("margin-right",i).removeData("margin-right")});var e=t("body").data("padding-right");"undefined"!=typeof e&&t("body").css("padding-right",e).removeData("padding-right")},p._getScrollbarWidth=function(){var t=document.createElement("div");t.className=c,document.body.appendChild(t);var e=t.getBoundingClientRect().width-t.clientWidth;return document.body.removeChild(t),e},o._jQueryInterface=function(e,i){return this.each(function(){var s=t(this).data(n),a=r({},o.Default,t(this).data(),"object"==typeof e&&e);if(s||(s=new o(this,a),t(this).data(n,s)),"string"==typeof e){if("undefined"==typeof s[e])throw new TypeError('No method named "'+e+'"');s[e](i)}else a.show&&s.show(i)})},s(o,null,[{key:"VERSION",get:function(){return"4.0.0"}},{key:"Default",get:function(){return a}}]),o}();return t(document).on(h.CLICK_DATA_API,g.DATA_TOGGLE,function(e){var i,s=this,o=P.getSelectorFromElement(this);o&&(i=t(o)[0]);var 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r=this.getTipElement(),o=P.getUID(this.constructor.NAME);r.setAttribute("id",o),this.element.setAttribute("aria-describedby",o),this.setContent(),this.config.animation&&t(r).addClass(g);var l="function"==typeof this.config.placement?this.config.placement.call(this,r,this.element):this.config.placement,h=this._getAttachment(l);this.addAttachmentClass(h);var c=!1===this.config.container?document.body:t(this.config.container);t(r).data(this.constructor.DATA_KEY,this),t.contains(this.element.ownerDocument.documentElement,this.tip)||t(r).appendTo(c),t(this.element).trigger(this.constructor.Event.INSERTED),this._popper=new n(this.element,r,{placement:h,modifiers:{offset:{offset:this.config.offset},flip:{behavior:this.config.fallbackPlacement},arrow:{element:v},preventOverflow:{boundariesElement:this.config.boundary}},onCreate:function(t){t.originalPlacement!==t.placement&&e._handlePopperPlacementChange(t)},onUpdate:function(t){e._handlePopperPlacementChange(t)}}),t(r).addClass(p),"ontouchstart"in document.documentElement&&t("body").children().on("mouseover",null,t.noop);var u=function(){e.config.animation&&e._fixTransition();var n=e._hoverState;e._hoverState=null,t(e.element).trigger(e.constructor.Event.SHOWN),n===d&&e._leave(null,e)};P.supportsTransitionEnd()&&t(this.tip).hasClass(g)?t(this.tip).one(P.TRANSITION_END,u).emulateTransitionEnd(a._TRANSITION_DURATION):u()}},I.hide=function(e){var n=this,i=this.getTipElement(),s=t.Event(this.constructor.Event.HIDE),r=function(){n._hoverState!==f&&i.parentNode&&i.parentNode.removeChild(i),n._cleanTipClass(),n.element.removeAttribute("aria-describedby"),t(n.element).trigger(n.constructor.Event.HIDDEN),null!==n._popper&&n._popper.destroy(),e&&e()};t(this.element).trigger(s),s.isDefaultPrevented()||(t(i).removeClass(p),"ontouchstart"in document.documentElement&&t("body").children().off("mouseover",null,t.noop),this._activeTrigger[y]=!1,this._activeTrigger[T]=!1,this._activeTrigger[E]=!1,P.supportsTransitionEnd()&&t(this.tip).hasClass(g)?t(i).one(P.TRANSITION_END,r).emulateTransitionEnd(150):r(),this._hoverState="")},I.update=function(){null!==this._popper&&this._popper.scheduleUpdate()},I.isWithContent=function(){return Boolean(this.getTitle())},I.addAttachmentClass=function(e){t(this.getTipElement()).addClass("bs-tooltip-"+e)},I.getTipElement=function(){return this.tip=this.tip||t(this.config.template)[0],this.tip},I.setContent=function(){var e=t(this.getTipElement());this.setElementContent(e.find(m),this.getTitle()),e.removeClass(g+" "+p)},I.setElementContent=function(e,n){var i=this.config.html;"object"==typeof n&&(n.nodeType||n.jquery)?i?t(n).parent().is(e)||e.empty().append(n):e.text(t(n).text()):e[i?"html":"text"](n)},I.getTitle=function(){var t=this.element.getAttribute("data-original-title");return t||(t="function"==typeof this.config.title?this.config.title.call(this.element):this.config.title),t},I._getAttachment=function(t){return c[t.toUpperCase()]},I._setListeners=function(){var e=this;this.config.trigger.split(" ").forEach(function(n){if("click"===n)t(e.element).on(e.constructor.Event.CLICK,e.config.selector,function(t){return e.toggle(t)});else if(n!==C){var i=n===E?e.constructor.Event.MOUSEENTER:e.constructor.Event.FOCUSIN,s=n===E?e.constructor.Event.MOUSELEAVE:e.constructor.Event.FOCUSOUT;t(e.element).on(i,e.config.selector,function(t){return e._enter(t)}).on(s,e.config.selector,function(t){return e._leave(t)})}t(e.element).closest(".modal").on("hide.bs.modal",function(){return e.hide()})}),this.config.selector?this.config=r({},this.config,{trigger:"manual",selector:""}):this._fixTitle()},I._fixTitle=function(){var t=typeof this.element.getAttribute("data-original-title");(this.element.getAttribute("title")||"string"!==t)&&(this.element.setAttribute("data-original-title",this.element.getAttribute("title")||""),this.element.setAttribute("title",""))},I._enter=function(e,n){var i=this.constructor.DATA_KEY;(n=n||t(e.currentTarget).data(i))||(n=new this.constructor(e.currentTarget,this._getDelegateConfig()),t(e.currentTarget).data(i,n)),e&&(n._activeTrigger["focusin"===e.type?T:E]=!0),t(n.getTipElement()).hasClass(p)||n._hoverState===f?n._hoverState=f:(clearTimeout(n._timeout),n._hoverState=f,n.config.delay&&n.config.delay.show?n._timeout=setTimeout(function(){n._hoverState===f&&n.show()},n.config.delay.show):n.show())},I._leave=function(e,n){var i=this.constructor.DATA_KEY;(n=n||t(e.currentTarget).data(i))||(n=new this.constructor(e.currentTarget,this._getDelegateConfig()),t(e.currentTarget).data(i,n)),e&&(n._activeTrigger["focusout"===e.type?T:E]=!1),n._isWithActiveTrigger()||(clearTimeout(n._timeout),n._hoverState=d,n.config.delay&&n.config.delay.hide?n._timeout=setTimeout(function(){n._hoverState===d&&n.hide()},n.config.delay.hide):n.hide())},I._isWithActiveTrigger=function(){for(var t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1},I._getConfig=function(n){return"number"==typeof(n=r({},this.constructor.Default,t(this.element).data(),n)).delay&&(n.delay={show:n.delay,hide:n.delay}),"number"==typeof n.title&&(n.title=n.title.toString()),"number"==typeof n.content&&(n.content=n.content.toString()),P.typeCheckConfig(e,n,this.constructor.DefaultType),n},I._getDelegateConfig=function(){var t={};if(this.config)for(var e in this.config)this.constructor.Default[e]!==this.config[e]&&(t[e]=this.config[e]);return t},I._cleanTipClass=function(){var e=t(this.getTipElement()),n=e.attr("class").match(l);null!==n&&n.length>0&&e.removeClass(n.join(""))},I._handlePopperPlacementChange=function(t){this._cleanTipClass(),this.addAttachmentClass(this._getAttachment(t.placement))},I._fixTransition=function(){var e=this.getTipElement(),n=this.config.animation;null===e.getAttribute("x-placement")&&(t(e).removeClass(g),this.config.animation=!1,this.hide(),this.show(),this.config.animation=n)},a._jQueryInterface=function(e){return this.each(function(){var n=t(this).data(i),s="object"==typeof e&&e;if((n||!/dispose|hide/.test(e))&&(n||(n=new a(this,s),t(this).data(i,n)),"string"==typeof e)){if("undefined"==typeof n[e])throw new TypeError('No method named "'+e+'"');n[e]()}})},s(a,null,[{key:"VERSION",get:function(){return"4.0.0"}},{key:"Default",get:function(){return u}},{key:"NAME",get:function(){return e}},{key:"DATA_KEY",get:function(){return i}},{key:"Event",get:function(){return _}},{key:"EVENT_KEY",get:function(){return o}},{key:"DefaultType",get:function(){return h}}]),a}();return t.fn[e]=I._jQueryInterface,t.fn[e].Constructor=I,t.fn[e].noConflict=function(){return t.fn[e]=a,I._jQueryInterface},I}(e),x=function(t){var e="popover",n="bs.popover",i="."+n,o=t.fn[e],a=new 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this.tip=this.tip||t(this.config.template)[0],this.tip},m.setContent=function(){var e=t(this.getTipElement());this.setElementContent(e.find(f),this.getTitle());var n=this._getContent();"function"==typeof n&&(n=n.call(this.element)),this.setElementContent(e.find(d),n),e.removeClass(c+" "+u)},m._getContent=function(){return this.element.getAttribute("data-content")||this.config.content},m._cleanTipClass=function(){var e=t(this.getTipElement()),n=e.attr("class").match(a);null!==n&&n.length>0&&e.removeClass(n.join(""))},p._jQueryInterface=function(e){return this.each(function(){var i=t(this).data(n),s="object"==typeof e?e:null;if((i||!/destroy|hide/.test(e))&&(i||(i=new p(this,s),t(this).data(n,i)),"string"==typeof e)){if("undefined"==typeof i[e])throw new TypeError('No method named "'+e+'"');i[e]()}})},s(p,null,[{key:"VERSION",get:function(){return"4.0.0"}},{key:"Default",get:function(){return l}},{key:"NAME",get:function(){return e}},{key:"DATA_KEY",get:function(){return 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diff --git a/docs/v1.2.0/_static/js/vendor/popper.min.js b/docs/v1.2.0/_static/js/vendor/popper.min.js
new file mode 100644
index 0000000000..bb1aaae3e8
--- /dev/null
+++ b/docs/v1.2.0/_static/js/vendor/popper.min.js
@@ -0,0 +1,5 @@
+/*
+ Copyright (C) Federico Zivolo 2020
+ Distributed under the MIT License (license terms are at http://opensource.org/licenses/MIT).
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Popper.js!');var s,d,a=void 0===r?t.gpuAcceleration:r,l=p(e.instance.popper),f=u(l),m={position:i.position},h=q(e,2>window.devicePixelRatio||!fe),c='bottom'===o?'top':'bottom',g='right'===n?'left':'right',b=B('transform');if(d='bottom'==c?'HTML'===l.nodeName?-l.clientHeight+h.bottom:-f.height+h.bottom:h.top,s='right'==g?'HTML'===l.nodeName?-l.clientWidth+h.right:-f.width+h.right:h.left,a&&b)m[b]='translate3d('+s+'px, '+d+'px, 0)',m[c]=0,m[g]=0,m.willChange='transform';else{var w='bottom'==c?-1:1,y='right'==g?-1:1;m[c]=d*w,m[g]=s*y,m.willChange=c+', '+g}var E={"x-placement":e.placement};return e.attributes=le({},E,e.attributes),e.styles=le({},m,e.styles),e.arrowStyles=le({},e.offsets.arrow,e.arrowStyles),e},gpuAcceleration:!0,x:'bottom',y:'right'},applyStyle:{order:900,enabled:!0,fn:function(e){return V(e.instance.popper,e.styles),j(e.instance.popper,e.attributes),e.arrowElement&&Object.keys(e.arrowStyles).length&&V(e.arrowElement,e.arrowStyles),e},onLoad:function(e,t,o,n,i){var r=L(i,t,e,o.positionFixed),p=O(o.placement,r,t,e,o.modifiers.flip.boundariesElement,o.modifiers.flip.padding);return t.setAttribute('x-placement',p),V(t,{position:o.positionFixed?'fixed':'absolute'}),o},gpuAcceleration:void 0}}},ge});
+//# sourceMappingURL=popper.min.js.map
diff --git a/docs/v1.2.0/_static/language_data.js b/docs/v1.2.0/_static/language_data.js
new file mode 100644
index 0000000000..ebe2f03bf0
--- /dev/null
+++ b/docs/v1.2.0/_static/language_data.js
@@ -0,0 +1,297 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
+
+
+/* Non-minified version is copied as a separate JS file, is available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
+
+
+
+var splitChars = (function() {
+    var result = {};
+    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
+         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
+         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
+         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
+         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
+         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
+         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
+         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
+         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
+         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
+    var i, j, start, end;
+    for (i = 0; i < singles.length; i++) {
+        result[singles[i]] = true;
+    }
+    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
+         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
+         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
+         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
+         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
+         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
+         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
+         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
+         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
+         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
+         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
+         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
+         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
+         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
+         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
+         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
+         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
+         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
+         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
+         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
+         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
+         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
+         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
+         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
+         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
+         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
+         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
+         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
+         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
+         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
+         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
+         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
+         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
+         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
+         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
+         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
+         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
+         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
+         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
+         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
+         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
+         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
+         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
+         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
+         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
+         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
+         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
+         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
+         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
+    for (i = 0; i < ranges.length; i++) {
+        start = ranges[i][0];
+        end = ranges[i][1];
+        for (j = start; j <= end; j++) {
+            result[j] = true;
+        }
+    }
+    return result;
+})();
+
+function splitQuery(query) {
+    var result = [];
+    var start = -1;
+    for (var i = 0; i < query.length; i++) {
+        if (splitChars[query.charCodeAt(i)]) {
+            if (start !== -1) {
+                result.push(query.slice(start, i));
+                start = -1;
+            }
+        } else if (start === -1) {
+            start = i;
+        }
+    }
+    if (start !== -1) {
+        result.push(query.slice(start));
+    }
+    return result;
+}
+
+
diff --git a/docs/v1.2.0/_static/material.css b/docs/v1.2.0/_static/material.css
new file mode 100644
index 0000000000..a3d9b27d2d
--- /dev/null
+++ b/docs/v1.2.0/_static/material.css
@@ -0,0 +1 @@
+@charset "utf-8";.dropdown{width:125px;vertical-align:middle}.dropdownbutton{color:inherit;font-weight:700;font-size:.65rem}.dropdown-content{display:none;position:absolute;z-index:1;background-color:inherit}.dropdown-content a{display:block;width:125px;margin:8px;font-size:.65rem;font-weight:200}.dropdown-content a:hover{}.dropdown:hover .dropdown-content{display:block}.dropdown:hover .dropbtn{}
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/minus.png b/docs/v1.2.0/_static/minus.png
new file mode 100644
index 0000000000000000000000000000000000000000..d96755fdaf8bb2214971e0db9c1fd3077d7c419d
GIT binary patch
literal 90
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kDsEF_5m^0CR;1wuP-*O&G^0G}KYk!hp00i_>zopr08q^qX#fBK

literal 0
HcmV?d00001

diff --git a/docs/v1.2.0/_static/plus.png b/docs/v1.2.0/_static/plus.png
new file mode 100644
index 0000000000000000000000000000000000000000..7107cec93a979b9a5f64843235a16651d563ce2d
GIT binary patch
literal 90
zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu>-2
m3q%Vub%g%s<8sJhVPMczOq}xhg9DJoz~JfX=d#Wzp$Pyb1r*Kz

literal 0
HcmV?d00001

diff --git a/docs/v1.2.0/_static/pygments.css b/docs/v1.2.0/_static/pygments.css
new file mode 100644
index 0000000000..691aeb82d0
--- /dev/null
+++ b/docs/v1.2.0/_static/pygments.css
@@ -0,0 +1,74 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #eeffcc; }
+.highlight .c { color: #408090; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #007020; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408090; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408090; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #007020 } /* Comment.Preproc */
+.highlight .cpf { color: #408090; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408090; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #333333 } /* Generic.Output */
+.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #007020 } /* Keyword.Pseudo */
+.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #902000 } /* Keyword.Type */
+.highlight .m { color: #208050 } /* Literal.Number */
+.highlight .s { color: #4070a0 } /* Literal.String */
+.highlight .na { color: #4070a0 } /* Name.Attribute */
+.highlight .nb { color: #007020 } /* Name.Builtin */
+.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
+.highlight .no { color: #60add5 } /* Name.Constant */
+.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #007020 } /* Name.Exception */
+.highlight .nf { color: #06287e } /* Name.Function */
+.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
+.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #208050 } /* Literal.Number.Bin */
+.highlight .mf { color: #208050 } /* Literal.Number.Float */
+.highlight .mh { color: #208050 } /* Literal.Number.Hex */
+.highlight .mi { color: #208050 } /* Literal.Number.Integer */
+.highlight .mo { color: #208050 } /* Literal.Number.Oct */
+.highlight .sa { color: #4070a0 } /* Literal.String.Affix */
+.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070a0 } /* Literal.String.Char */
+.highlight .dl { color: #4070a0 } /* Literal.String.Delimiter */
+.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
+.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
+.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
+.highlight .sx { color: #c65d09 } /* Literal.String.Other */
+.highlight .sr { color: #235388 } /* Literal.String.Regex */
+.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
+.highlight .ss { color: #517918 } /* Literal.String.Symbol */
+.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #06287e } /* Name.Function.Magic */
+.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
+.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
+.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
+.highlight .vm { color: #bb60d5 } /* Name.Variable.Magic */
+.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/searchtools.js b/docs/v1.2.0/_static/searchtools.js
new file mode 100644
index 0000000000..0a44e8582f
--- /dev/null
+++ b/docs/v1.2.0/_static/searchtools.js
@@ -0,0 +1,525 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+if (!Scorer) {
+  /**
+   * Simple result scoring code.
+   */
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [filename, title, anchor, descr, score]
+    // and returns the new score.
+    /*
+    score: function(result) {
+      return result[4];
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {0:  15,   // used to be importantResults
+              1:  5,   // used to be objectResults
+              2: -5},  // used to be unimportantResults
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2
+  };
+}
+
+if (!splitQuery) {
+  function splitQuery(query) {
+    return query.split(/\s+/);
+  }
+}
+
+/**
+ * Search Module
+ */
+var Search = {
+
+  _index : null,
+  _queued_query : null,
+  _pulse_status : -1,
+
+  htmlToText : function(htmlString) {
+      var virtualDocument = document.implementation.createHTMLDocument('virtual');
+      var htmlElement = $(htmlString, virtualDocument);
+      htmlElement.find('.headerlink').remove();
+      docContent = htmlElement.find('[role=main]')[0];
+      if(docContent === undefined) {
+          console.warn("Content block not found. Sphinx search tries to obtain it " +
+                       "via '[role=main]'. Could you check your theme or template.");
+          return "";
+      }
+      return docContent.textContent || docContent.innerText;
+  },
+
+  init : function() {
+      var params = $.getQueryParameters();
+      if (params.q) {
+          var query = params.q[0];
+          $('input[name="q"]')[0].value = query;
+          this.performSearch(query);
+      }
+  },
+
+  loadIndex : function(url) {
+    $.ajax({type: "GET", url: url, data: null,
+            dataType: "script", cache: true,
+            complete: function(jqxhr, textstatus) {
+              if (textstatus != "success") {
+                document.getElementById("searchindexloader").src = url;
+              }
+            }});
+  },
+
+  setIndex : function(index) {
+    var q;
+    this._index = index;
+    if ((q = this._queued_query) !== null) {
+      this._queued_query = null;
+      Search.query(q);
+    }
+  },
+
+  hasIndex : function() {
+      return this._index !== null;
+  },
+
+  deferQuery : function(query) {
+      this._queued_query = query;
+  },
+
+  stopPulse : function() {
+      this._pulse_status = 0;
+  },
+
+  startPulse : function() {
+    if (this._pulse_status >= 0)
+        return;
+    function pulse() {
+      var i;
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      var dotString = '';
+      for (i = 0; i < Search._pulse_status; i++)
+        dotString += '.';
+      Search.dots.text(dotString);
+      if (Search._pulse_status > -1)
+        window.setTimeout(pulse, 500);
+    }
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch : function(query) {
+    // create the required interface elements
+    this.out = $('#search-results');
+    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
+    this.dots = $('<span></span>').appendTo(this.title);
+    this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
+    this.output = $('<ul class="search"/>').appendTo(this.out);
+
+    $('#search-progress').text(_('Preparing search...'));
+    this.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (this.hasIndex())
+      this.query(query);
+    else
+      this.deferQuery(query);
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query : function(query) {
+    var i;
+
+    // stem the searchterms and add them to the correct list
+    var stemmer = new Stemmer();
+    var searchterms = [];
+    var excluded = [];
+    var hlterms = [];
+    var tmp = splitQuery(query);
+    var objectterms = [];
+    for (i = 0; i < tmp.length; i++) {
+      if (tmp[i] !== "") {
+          objectterms.push(tmp[i].toLowerCase());
+      }
+
+      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
+        // skip this "word"
+        continue;
+      }
+      // stem the word
+      var word = stemmer.stemWord(tmp[i].toLowerCase());
+      var toAppend;
+      // select the correct list
+      if (word[0] == '-') {
+        toAppend = excluded;
+        word = word.substr(1);
+      }
+      else {
+        toAppend = searchterms;
+        hlterms.push(tmp[i].toLowerCase());
+      }
+      // only add if not already in the list
+      if (!$u.contains(toAppend, word))
+        toAppend.push(word);
+    }
+    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
+
+    // console.debug('SEARCH: searching for:');
+    // console.info('required: ', searchterms);
+    // console.info('excluded: ', excluded);
+
+    // prepare search
+    var terms = this._index.terms;
+    var titleterms = this._index.titleterms;
+
+    // array of [filename, title, anchor, descr, score]
+    var results = [];
+    $('#search-progress').empty();
+
+    // lookup as object
+    for (i = 0; i < objectterms.length; i++) {
+      var others = [].concat(objectterms.slice(0, i),
+                             objectterms.slice(i+1, objectterms.length));
+      results = results.concat(this.performObjectSearch(objectterms[i], others));
+    }
+
+    // lookup as search terms in fulltext
+    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      for (i = 0; i < results.length; i++)
+        results[i][4] = Scorer.score(results[i]);
+    }
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort(function(a, b) {
+      var left = a[4];
+      var right = b[4];
+      if (left > right) {
+        return 1;
+      } else if (left < right) {
+        return -1;
+      } else {
+        // same score: sort alphabetically
+        left = a[1].toLowerCase();
+        right = b[1].toLowerCase();
+        return (left > right) ? -1 : ((left < right) ? 1 : 0);
+      }
+    });
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    //console.info('search results:', Search.lastresults);
+
+    // print the results
+    var resultCount = results.length;
+    function displayNextItem() {
+      // results left, load the summary and display it
+      if (results.length) {
+        var item = results.pop();
+        var listItem = $('<li></li>');
+        var requestUrl = "";
+        var linkUrl = "";
+        if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
+          // dirhtml builder
+          var dirname = item[0] + '/';
+          if (dirname.match(/\/index\/$/)) {
+            dirname = dirname.substring(0, dirname.length-6);
+          } else if (dirname == 'index/') {
+            dirname = '';
+          }
+          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
+          linkUrl = requestUrl;
+
+        } else {
+          // normal html builders
+          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+          linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+        }
+        listItem.append($('<a/>').attr('href',
+            linkUrl +
+            highlightstring + item[2]).html(item[1]));
+        if (item[3]) {
+          listItem.append($('<span> (' + item[3] + ')</span>'));
+          Search.output.append(listItem);
+          setTimeout(function() {
+            displayNextItem();
+          }, 5);
+        } else if (DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY) {
+          $.ajax({url: requestUrl,
+                  dataType: "text",
+                  complete: function(jqxhr, textstatus) {
+                    var data = jqxhr.responseText;
+                    if (data !== '' && data !== undefined) {
+                      var summary = Search.makeSearchSummary(data, searchterms, hlterms);
+                      if (summary) {
+                        listItem.append(summary);
+                      }
+                    }
+                    Search.output.append(listItem);
+                    setTimeout(function() {
+                      displayNextItem();
+                    }, 5);
+                  }});
+        } else {
+          // just display title
+          Search.output.append(listItem);
+          setTimeout(function() {
+            displayNextItem();
+          }, 5);
+        }
+      }
+      // search finished, update title and status message
+      else {
+        Search.stopPulse();
+        Search.title.text(_('Search Results'));
+        if (!resultCount)
+          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
+        else
+            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
+        Search.status.fadeIn(500);
+      }
+    }
+    displayNextItem();
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch : function(object, otherterms) {
+    var filenames = this._index.filenames;
+    var docnames = this._index.docnames;
+    var objects = this._index.objects;
+    var objnames = this._index.objnames;
+    var titles = this._index.titles;
+
+    var i;
+    var results = [];
+
+    for (var prefix in objects) {
+      for (var iMatch = 0; iMatch != objects[prefix].length; ++iMatch) {
+        var match = objects[prefix][iMatch];
+        var name = match[4];
+        var fullname = (prefix ? prefix + '.' : '') + name;
+        var fullnameLower = fullname.toLowerCase()
+        if (fullnameLower.indexOf(object) > -1) {
+          var score = 0;
+          var parts = fullnameLower.split('.');
+          // check for different match types: exact matches of full name or
+          // "last name" (i.e. last dotted part)
+          if (fullnameLower == object || parts[parts.length - 1] == object) {
+            score += Scorer.objNameMatch;
+          // matches in last name
+          } else if (parts[parts.length - 1].indexOf(object) > -1) {
+            score += Scorer.objPartialMatch;
+          }
+          var objname = objnames[match[1]][2];
+          var title = titles[match[0]];
+          // If more than one term searched for, we require other words to be
+          // found in the name/title/description
+          if (otherterms.length > 0) {
+            var haystack = (prefix + ' ' + name + ' ' +
+                            objname + ' ' + title).toLowerCase();
+            var allfound = true;
+            for (i = 0; i < otherterms.length; i++) {
+              if (haystack.indexOf(otherterms[i]) == -1) {
+                allfound = false;
+                break;
+              }
+            }
+            if (!allfound) {
+              continue;
+            }
+          }
+          var descr = objname + _(', in ') + title;
+
+          var anchor = match[3];
+          if (anchor === '')
+            anchor = fullname;
+          else if (anchor == '-')
+            anchor = objnames[match[1]][1] + '-' + fullname;
+          // add custom score for some objects according to scorer
+          if (Scorer.objPrio.hasOwnProperty(match[2])) {
+            score += Scorer.objPrio[match[2]];
+          } else {
+            score += Scorer.objPrioDefault;
+          }
+          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
+        }
+      }
+    }
+
+    return results;
+  },
+
+  /**
+   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
+   */
+  escapeRegExp : function(string) {
+    return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
+    var docnames = this._index.docnames;
+    var filenames = this._index.filenames;
+    var titles = this._index.titles;
+
+    var i, j, file;
+    var fileMap = {};
+    var scoreMap = {};
+    var results = [];
+
+    // perform the search on the required terms
+    for (i = 0; i < searchterms.length; i++) {
+      var word = searchterms[i];
+      var files = [];
+      var _o = [
+        {files: terms[word], score: Scorer.term},
+        {files: titleterms[word], score: Scorer.title}
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        var word_regex = this.escapeRegExp(word);
+        for (var w in terms) {
+          if (w.match(word_regex) && !terms[word]) {
+            _o.push({files: terms[w], score: Scorer.partialTerm})
+          }
+        }
+        for (var w in titleterms) {
+          if (w.match(word_regex) && !titleterms[word]) {
+              _o.push({files: titleterms[w], score: Scorer.partialTitle})
+          }
+        }
+      }
+
+      // no match but word was a required one
+      if ($u.every(_o, function(o){return o.files === undefined;})) {
+        break;
+      }
+      // found search word in contents
+      $u.each(_o, function(o) {
+        var _files = o.files;
+        if (_files === undefined)
+          return
+
+        if (_files.length === undefined)
+          _files = [_files];
+        files = files.concat(_files);
+
+        // set score for the word in each file to Scorer.term
+        for (j = 0; j < _files.length; j++) {
+          file = _files[j];
+          if (!(file in scoreMap))
+            scoreMap[file] = {};
+          scoreMap[file][word] = o.score;
+        }
+      });
+
+      // create the mapping
+      for (j = 0; j < files.length; j++) {
+        file = files[j];
+        if (file in fileMap && fileMap[file].indexOf(word) === -1)
+          fileMap[file].push(word);
+        else
+          fileMap[file] = [word];
+      }
+    }
+
+    // now check if the files don't contain excluded terms
+    for (file in fileMap) {
+      var valid = true;
+
+      // check if all requirements are matched
+      var filteredTermCount = // as search terms with length < 3 are discarded: ignore
+        searchterms.filter(function(term){return term.length > 2}).length
+      if (
+        fileMap[file].length != searchterms.length &&
+        fileMap[file].length != filteredTermCount
+      ) continue;
+
+      // ensure that none of the excluded terms is in the search result
+      for (i = 0; i < excluded.length; i++) {
+        if (terms[excluded[i]] == file ||
+            titleterms[excluded[i]] == file ||
+            $u.contains(terms[excluded[i]] || [], file) ||
+            $u.contains(titleterms[excluded[i]] || [], file)) {
+          valid = false;
+          break;
+        }
+      }
+
+      // if we have still a valid result we can add it to the result list
+      if (valid) {
+        // select one (max) score for the file.
+        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
+        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
+        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
+      }
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words, hlwords is the list of normal, unstemmed
+   * words. the first one is used to find the occurrence, the
+   * latter for highlighting it.
+   */
+  makeSearchSummary : function(htmlText, keywords, hlwords) {
+    var text = Search.htmlToText(htmlText);
+    if (text == "") {
+      return null;
+    }
+    var textLower = text.toLowerCase();
+    var start = 0;
+    $.each(keywords, function() {
+      var i = textLower.indexOf(this.toLowerCase());
+      if (i > -1)
+        start = i;
+    });
+    start = Math.max(start - 120, 0);
+    var excerpt = ((start > 0) ? '...' : '') +
+      $.trim(text.substr(start, 240)) +
+      ((start + 240 - text.length) ? '...' : '');
+    var rv = $('<p class="context"></p>').text(excerpt);
+    $.each(hlwords, function() {
+      rv = rv.highlightText(this, 'highlighted');
+    });
+    return rv;
+  }
+};
+
+$(document).ready(function() {
+  Search.init();
+});
diff --git a/docs/v1.2.0/_static/stylesheets/application-fixes.css b/docs/v1.2.0/_static/stylesheets/application-fixes.css
new file mode 100644
index 0000000000..bbe6ad0e7d
--- /dev/null
+++ b/docs/v1.2.0/_static/stylesheets/application-fixes.css
@@ -0,0 +1 @@
+@charset "utf-8";.md-nav--primary ul,.md-nav--primary ul li ul,.md-nav--secondary ul,.md-nav--secondary ul li ul{margin:0;padding:0;list-style:none}.md-nav--primary ul li,.md-nav--primary ul li ul li,.md-nav--secondary ul li,.md-nav--secondary ul li ul li{padding:0 .6rem}.md-nav--primary a.reference,.md-nav--secondary a.reference{display:block;margin-top:.625em;transition:color .125s;text-overflow:ellipsis;cursor:pointer;overflow:hidden}.md-typeset td p,.md-typeset th p{margin:0}.md-typeset .admonition,.md-typeset details{font-size:.8rem}.classifier:before{font-style:normal;margin:.5em;content:":"}dl.footnote>dt,dl.citation>dt{float:left}code.xref{background-color:transparent;font-weight:bold}table.docutils{width:100%}.longtable tr td:first-child{width:50%;white-space:nowrap}dt:target{margin-top:-3.55rem;padding-top:3.45rem}.md-typeset code{margin:0}ul.search li div.context{color:#888;margin:2px 0 0 30px;text-align:left}span.highlighted{background-color:#fbe54e}p.rubric{margin-top:1rem;font-weight:bold}dl.field-list>dt{font-weight:bold;word-break:break-word;padding-left:.5em;padding-right:5px}table.longtable{border-collapse:collapse}.longtable tr{border:solid;border-width:1px 0}.longtable tr:first-child{border-top:none}.md-tabs code,kbd,pre,.md-footer-nav code,kbd,pre{color:#fff}.toctree-wrapper.compound li{list-style:none;padding:0;margin:0 0 .1rem .2rem}table.indextable.genindextable li{margin:0 .5rem}table.indextable.genindextable li ul li{list-style:none}p.highlight-link{margin:.625rem 0 0 0}table.longtable.docutils.align-default{width:100%;overflow-x:auto;-webkit-overflow-scrolling:touch}@media only screen and (max-width:40em){table.longtable.docutils.align-default{display:block}.longtable.docutils.align-default td{padding-right:1rem}}.md-nav__extra_link:after{font-family:Material Icons;font-style:normal;font-variant:normal;font-weight:400;line-height:1;text-transform:none;white-space:nowrap;speak:none;word-wrap:normal;direction:ltr}.md-nav__extra_link{display:block;margin-top:.625em;transition:color .125s;text-overflow:ellipsis;cursor:pointer;overflow:hidden}.md-nav__extra_link:active{color:#3f51b5}.md-nav__extra_link:focus,.md-nav__extra_link:hover{color:#536dfe}@media only screen and (max-width:76.1875em){.md-nav--primary .md-nav--secondary .md-nav__extra_link{position:static}.md-nav--primary .md-nav--secondary .md-nav .md-nav__extra_link{padding-left:1.4rem}.md-nav--primary .md-nav--secondary .md-nav .md-nav .md-nav .md-nav__extra_link{padding-left:2.6rem}}[data-md-color-primary=red] .md-nav__extra_link:active{color:#ef5350}[data-md-color-primary=red] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=pink] .md-nav__extra_link:active{color:#e91e63}[data-md-color-primary=pink] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=purple] .md-nav__extra_link:active{color:#ab47bc}[data-md-color-primary=purple] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=deep-purple] .md-nav__extra_link:active{color:#7e57c2}[data-md-color-primary=deep-purple] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=indigo] .md-nav__extra_link:active{color:#3f51b5}[data-md-color-primary=indigo] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=blue] .md-nav__extra_link:active{color:#2196f3}[data-md-color-primary=blue] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=light-blue] .md-nav__extra_link:active{color:#03a9f4}[data-md-color-primary=light-blue] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=cyan] .md-nav__extra_link:active{color:#00bcd4}[data-md-color-primary=cyan] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=teal] .md-nav__extra_link:active{color:#009688}[data-md-color-primary=teal] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=green] .md-nav__extra_link:active{color:#4caf50}[data-md-color-primary=green] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=light-green] .md-nav__extra_link:active{color:#7cb342}[data-md-color-primary=light-green] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=lime] .md-nav__extra_link:active{color:#c0ca33}[data-md-color-primary=lime] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=yellow] .md-nav__extra_link:active{color:#f9a825}[data-md-color-primary=yellow] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=amber] .md-nav__extra_link:active{color:#ffa000}[data-md-color-primary=amber] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=orange] .md-nav__extra_link:active{color:#fb8c00}[data-md-color-primary=orange] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=deep-orange] .md-nav__extra_link:active{color:#ff7043}[data-md-color-primary=deep-orange] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=brown] .md-nav__extra_link:active{color:#795548}[data-md-color-primary=brown] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=grey] .md-nav__extra_link:active{color:#757575}[data-md-color-primary=grey] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-primary=blue-grey] .md-nav__extra_link:active{color:#546e7a}[data-md-color-primary=blue-grey] .md-nav__item--nested>.md-nav__extra_link{color:inherit}[data-md-color-accent=red] .md-nav__extra_link:focus,[data-md-color-accent=red] .md-nav__extra_link:hover{color:#ff1744}[data-md-color-accent=pink] .md-nav__extra_link:focus,[data-md-color-accent=pink] .md-nav__extra_link:hover{color:#f50057}[data-md-color-accent=purple] .md-nav__extra_link:focus,[data-md-color-accent=purple] .md-nav__extra_link:hover{color:#e040fb}[data-md-color-accent=deep-purple] .md-nav__extra_link:focus,[data-md-color-accent=deep-purple] .md-nav__extra_link:hover{color:#7c4dff}[data-md-color-accent=indigo] .md-nav__extra_link:focus,[data-md-color-accent=indigo] .md-nav__extra_link:hover{color:#536dfe}[data-md-color-accent=blue] .md-nav__extra_link:focus,[data-md-color-accent=blue] .md-nav__extra_link:hover{color:#448aff}[data-md-color-accent=light-blue] .md-nav__extra_link:focus,[data-md-color-accent=light-blue] .md-nav__extra_link:hover{color:#0091ea}[data-md-color-accent=cyan] .md-nav__extra_link:focus,[data-md-color-accent=cyan] .md-nav__extra_link:hover{color:#00b8d4}[data-md-color-accent=teal] .md-nav__extra_link:focus,[data-md-color-accent=teal] .md-nav__extra_link:hover{color:#00bfa5}[data-md-color-accent=green] .md-nav__extra_link:focus,[data-md-color-accent=green] .md-nav__extra_link:hover{color:#00c853}[data-md-color-accent=light-green] .md-nav__extra_link:focus,[data-md-color-accent=light-green] .md-nav__extra_link:hover{color:#64dd17}[data-md-color-accent=lime] .md-nav__extra_link:focus,[data-md-color-accent=lime] .md-nav__extra_link:hover{color:#aeea00}[data-md-color-accent=yellow] .md-nav__extra_link:focus,[data-md-color-accent=yellow] .md-nav__extra_link:hover{color:#ffd600}[data-md-color-accent=amber] .md-nav__extra_link:focus,[data-md-color-accent=amber] .md-nav__extra_link:hover{color:#ffab00}[data-md-color-accent=orange] .md-nav__extra_link:focus,[data-md-color-accent=orange] .md-nav__extra_link:hover{color:#ff9100}[data-md-color-accent=deep-orange] .md-nav__extra_link:focus,[data-md-color-accent=deep-orange] .md-nav__extra_link:hover{color:#ff6e40}div.rendered_html table{font-size:.8rem !important}.md-nav span.caption{font-weight:700;pointer-events:none}.md-nav span.caption:hover,.md-nav span.caption:active{color:#000}.md-typeset img.align-right{clear:right;float:right;margin-left:1em}.md-typeset img.align-center{display:block;margin-left:auto;margin-right:auto}dl.citation dt span.brackets{margin-right:.3rem}
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/stylesheets/application-palette.css b/docs/v1.2.0/_static/stylesheets/application-palette.css
new file mode 100644
index 0000000000..4623f093cb
--- /dev/null
+++ b/docs/v1.2.0/_static/stylesheets/application-palette.css
@@ -0,0 +1 @@
+@charset "utf-8";button[data-md-color-accent],button[data-md-color-primary]{width:6.5rem;margin-bottom:.2rem;padding:1.2rem .4rem .2rem;transition:background-color .25s,opacity .25s;border-radius:.1rem;color:#fff;font-size:.64rem;text-align:left;cursor:pointer}button[data-md-color-accent]:hover,button[data-md-color-primary]:hover{opacity:.75}button[data-md-color-primary=red]{background-color:#ef5350}[data-md-color-primary=red] .md-typeset a{color:#ef5350}[data-md-color-primary=red] .md-header,[data-md-color-primary=red] .md-hero{background-color:#ef5350}[data-md-color-primary=red] .md-nav__link--active,[data-md-color-primary=red] .md-nav__link:active{color:#ef5350}[data-md-color-primary=red] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=pink]{background-color:#e91e63}[data-md-color-primary=pink] .md-typeset a{color:#e91e63}[data-md-color-primary=pink] .md-header,[data-md-color-primary=pink] .md-hero{background-color:#e91e63}[data-md-color-primary=pink] .md-nav__link--active,[data-md-color-primary=pink] .md-nav__link:active{color:#e91e63}[data-md-color-primary=pink] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=purple]{background-color:#ab47bc}[data-md-color-primary=purple] .md-typeset a{color:#ab47bc}[data-md-color-primary=purple] .md-header,[data-md-color-primary=purple] .md-hero{background-color:#ab47bc}[data-md-color-primary=purple] .md-nav__link--active,[data-md-color-primary=purple] .md-nav__link:active{color:#ab47bc}[data-md-color-primary=purple] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=deep-purple]{background-color:#7e57c2}[data-md-color-primary=deep-purple] .md-typeset a{color:#7e57c2}[data-md-color-primary=deep-purple] .md-header,[data-md-color-primary=deep-purple] .md-hero{background-color:#7e57c2}[data-md-color-primary=deep-purple] .md-nav__link--active,[data-md-color-primary=deep-purple] .md-nav__link:active{color:#7e57c2}[data-md-color-primary=deep-purple] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=indigo]{background-color:#3f51b5}[data-md-color-primary=indigo] .md-typeset a{color:#3f51b5}[data-md-color-primary=indigo] .md-header,[data-md-color-primary=indigo] .md-hero{background-color:#3f51b5}[data-md-color-primary=indigo] .md-nav__link--active,[data-md-color-primary=indigo] .md-nav__link:active{color:#3f51b5}[data-md-color-primary=indigo] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=blue]{background-color:#2196f3}[data-md-color-primary=blue] .md-typeset a{color:#2196f3}[data-md-color-primary=blue] .md-header,[data-md-color-primary=blue] .md-hero{background-color:#2196f3}[data-md-color-primary=blue] .md-nav__link--active,[data-md-color-primary=blue] .md-nav__link:active{color:#2196f3}[data-md-color-primary=blue] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=light-blue]{background-color:#03a9f4}[data-md-color-primary=light-blue] .md-typeset a{color:#03a9f4}[data-md-color-primary=light-blue] .md-header,[data-md-color-primary=light-blue] .md-hero{background-color:#03a9f4}[data-md-color-primary=light-blue] .md-nav__link--active,[data-md-color-primary=light-blue] .md-nav__link:active{color:#03a9f4}[data-md-color-primary=light-blue] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=cyan]{background-color:#00bcd4}[data-md-color-primary=cyan] .md-typeset a{color:#00bcd4}[data-md-color-primary=cyan] .md-header,[data-md-color-primary=cyan] .md-hero{background-color:#00bcd4}[data-md-color-primary=cyan] .md-nav__link--active,[data-md-color-primary=cyan] .md-nav__link:active{color:#00bcd4}[data-md-color-primary=cyan] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=teal]{background-color:#009688}[data-md-color-primary=teal] .md-typeset a{color:#009688}[data-md-color-primary=teal] .md-header,[data-md-color-primary=teal] .md-hero{background-color:#009688}[data-md-color-primary=teal] .md-nav__link--active,[data-md-color-primary=teal] .md-nav__link:active{color:#009688}[data-md-color-primary=teal] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=green]{background-color:#4caf50}[data-md-color-primary=green] .md-typeset a{color:#4caf50}[data-md-color-primary=green] .md-header,[data-md-color-primary=green] .md-hero{background-color:#4caf50}[data-md-color-primary=green] .md-nav__link--active,[data-md-color-primary=green] .md-nav__link:active{color:#4caf50}[data-md-color-primary=green] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=light-green]{background-color:#7cb342}[data-md-color-primary=light-green] .md-typeset a{color:#7cb342}[data-md-color-primary=light-green] .md-header,[data-md-color-primary=light-green] .md-hero{background-color:#7cb342}[data-md-color-primary=light-green] .md-nav__link--active,[data-md-color-primary=light-green] .md-nav__link:active{color:#7cb342}[data-md-color-primary=light-green] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=lime]{background-color:#c0ca33}[data-md-color-primary=lime] .md-typeset a{color:#c0ca33}[data-md-color-primary=lime] .md-header,[data-md-color-primary=lime] .md-hero{background-color:#c0ca33}[data-md-color-primary=lime] .md-nav__link--active,[data-md-color-primary=lime] .md-nav__link:active{color:#c0ca33}[data-md-color-primary=lime] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=yellow]{background-color:#f9a825}[data-md-color-primary=yellow] .md-typeset a{color:#f9a825}[data-md-color-primary=yellow] .md-header,[data-md-color-primary=yellow] .md-hero{background-color:#f9a825}[data-md-color-primary=yellow] .md-nav__link--active,[data-md-color-primary=yellow] .md-nav__link:active{color:#f9a825}[data-md-color-primary=yellow] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=amber]{background-color:#ffa000}[data-md-color-primary=amber] .md-typeset a{color:#ffa000}[data-md-color-primary=amber] .md-header,[data-md-color-primary=amber] .md-hero{background-color:#ffa000}[data-md-color-primary=amber] .md-nav__link--active,[data-md-color-primary=amber] .md-nav__link:active{color:#ffa000}[data-md-color-primary=amber] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=orange]{background-color:#fb8c00}[data-md-color-primary=orange] .md-typeset a{color:#fb8c00}[data-md-color-primary=orange] .md-header,[data-md-color-primary=orange] .md-hero{background-color:#fb8c00}[data-md-color-primary=orange] .md-nav__link--active,[data-md-color-primary=orange] .md-nav__link:active{color:#fb8c00}[data-md-color-primary=orange] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=deep-orange]{background-color:#ff7043}[data-md-color-primary=deep-orange] .md-typeset a{color:#ff7043}[data-md-color-primary=deep-orange] .md-header,[data-md-color-primary=deep-orange] .md-hero{background-color:#ff7043}[data-md-color-primary=deep-orange] .md-nav__link--active,[data-md-color-primary=deep-orange] .md-nav__link:active{color:#ff7043}[data-md-color-primary=deep-orange] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=brown]{background-color:#795548}[data-md-color-primary=brown] .md-typeset a{color:#795548}[data-md-color-primary=brown] .md-header,[data-md-color-primary=brown] .md-hero{background-color:#795548}[data-md-color-primary=brown] .md-nav__link--active,[data-md-color-primary=brown] .md-nav__link:active{color:#795548}[data-md-color-primary=brown] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=grey]{background-color:#757575}[data-md-color-primary=grey] .md-typeset a{color:#757575}[data-md-color-primary=grey] .md-header,[data-md-color-primary=grey] .md-hero{background-color:#757575}[data-md-color-primary=grey] .md-nav__link--active,[data-md-color-primary=grey] .md-nav__link:active{color:#757575}[data-md-color-primary=grey] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=blue-grey]{background-color:#546e7a}[data-md-color-primary=blue-grey] .md-typeset a{color:#546e7a}[data-md-color-primary=blue-grey] .md-header,[data-md-color-primary=blue-grey] .md-hero{background-color:#546e7a}[data-md-color-primary=blue-grey] .md-nav__link--active,[data-md-color-primary=blue-grey] .md-nav__link:active{color:#546e7a}[data-md-color-primary=blue-grey] .md-nav__item--nested>.md-nav__link{color:inherit}button[data-md-color-primary=white]{box-shadow:inset 0 0 .05rem rgba(0,0,0,.54)}[data-md-color-primary=white] .md-header,[data-md-color-primary=white] .md-hero,button[data-md-color-primary=white]{background-color:#fff;color:rgba(0,0,0,.87)}[data-md-color-primary=white] .md-hero--expand{border-bottom:.05rem solid rgba(0,0,0,.07)}button[data-md-color-accent=red]{background-color:#ff1744}[data-md-color-accent=red] .md-typeset a:active,[data-md-color-accent=red] .md-typeset a:hover{color:#ff1744}[data-md-color-accent=red] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=red] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-nav__link:focus,[data-md-color-accent=red] .md-nav__link:hover,[data-md-color-accent=red] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=red] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=red] .md-typeset .md-clipboard:active:before,[data-md-color-accent=red] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=red] .md-typeset [id] .headerlink:focus,[data-md-color-accent=red] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=red] .md-typeset [id]:target .headerlink{color:#ff1744}[data-md-color-accent=red] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-search-result__link:hover,[data-md-color-accent=red] .md-search-result__link[data-md-state=active]{background-color:rgba(255,23,68,.1)}[data-md-color-accent=red] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#ff1744}[data-md-color-accent=red] .md-source-file:hover:before{background-color:#ff1744}button[data-md-color-accent=pink]{background-color:#f50057}[data-md-color-accent=pink] .md-typeset a:active,[data-md-color-accent=pink] .md-typeset a:hover{color:#f50057}[data-md-color-accent=pink] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=pink] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-nav__link:focus,[data-md-color-accent=pink] .md-nav__link:hover,[data-md-color-accent=pink] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=pink] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=pink] .md-typeset .md-clipboard:active:before,[data-md-color-accent=pink] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=pink] .md-typeset [id] .headerlink:focus,[data-md-color-accent=pink] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=pink] .md-typeset [id]:target .headerlink{color:#f50057}[data-md-color-accent=pink] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-search-result__link:hover,[data-md-color-accent=pink] .md-search-result__link[data-md-state=active]{background-color:rgba(245,0,87,.1)}[data-md-color-accent=pink] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#f50057}[data-md-color-accent=pink] .md-source-file:hover:before{background-color:#f50057}button[data-md-color-accent=purple]{background-color:#e040fb}[data-md-color-accent=purple] .md-typeset a:active,[data-md-color-accent=purple] .md-typeset a:hover{color:#e040fb}[data-md-color-accent=purple] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=purple] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-nav__link:focus,[data-md-color-accent=purple] .md-nav__link:hover,[data-md-color-accent=purple] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=purple] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=purple] .md-typeset .md-clipboard:active:before,[data-md-color-accent=purple] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=purple] .md-typeset [id] .headerlink:focus,[data-md-color-accent=purple] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=purple] .md-typeset [id]:target .headerlink{color:#e040fb}[data-md-color-accent=purple] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-search-result__link:hover,[data-md-color-accent=purple] .md-search-result__link[data-md-state=active]{background-color:rgba(224,64,251,.1)}[data-md-color-accent=purple] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#e040fb}[data-md-color-accent=purple] .md-source-file:hover:before{background-color:#e040fb}button[data-md-color-accent=deep-purple]{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-typeset a:active,[data-md-color-accent=deep-purple] .md-typeset a:hover{color:#7c4dff}[data-md-color-accent=deep-purple] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=deep-purple] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-nav__link:focus,[data-md-color-accent=deep-purple] .md-nav__link:hover,[data-md-color-accent=deep-purple] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=deep-purple] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=deep-purple] .md-typeset .md-clipboard:active:before,[data-md-color-accent=deep-purple] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=deep-purple] .md-typeset [id] .headerlink:focus,[data-md-color-accent=deep-purple] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=deep-purple] .md-typeset [id]:target .headerlink{color:#7c4dff}[data-md-color-accent=deep-purple] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-search-result__link:hover,[data-md-color-accent=deep-purple] .md-search-result__link[data-md-state=active]{background-color:rgba(124,77,255,.1)}[data-md-color-accent=deep-purple] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#7c4dff}[data-md-color-accent=deep-purple] .md-source-file:hover:before{background-color:#7c4dff}button[data-md-color-accent=indigo]{background-color:#536dfe}[data-md-color-accent=indigo] .md-typeset a:active,[data-md-color-accent=indigo] .md-typeset a:hover{color:#536dfe}[data-md-color-accent=indigo] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=indigo] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-nav__link:focus,[data-md-color-accent=indigo] .md-nav__link:hover,[data-md-color-accent=indigo] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=indigo] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=indigo] .md-typeset .md-clipboard:active:before,[data-md-color-accent=indigo] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=indigo] .md-typeset [id] .headerlink:focus,[data-md-color-accent=indigo] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=indigo] .md-typeset [id]:target .headerlink{color:#536dfe}[data-md-color-accent=indigo] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-search-result__link:hover,[data-md-color-accent=indigo] .md-search-result__link[data-md-state=active]{background-color:rgba(83,109,254,.1)}[data-md-color-accent=indigo] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#536dfe}[data-md-color-accent=indigo] .md-source-file:hover:before{background-color:#536dfe}button[data-md-color-accent=blue]{background-color:#448aff}[data-md-color-accent=blue] .md-typeset a:active,[data-md-color-accent=blue] .md-typeset a:hover{color:#448aff}[data-md-color-accent=blue] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=blue] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-nav__link:focus,[data-md-color-accent=blue] .md-nav__link:hover,[data-md-color-accent=blue] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=blue] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=blue] .md-typeset .md-clipboard:active:before,[data-md-color-accent=blue] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=blue] .md-typeset [id] .headerlink:focus,[data-md-color-accent=blue] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=blue] .md-typeset [id]:target .headerlink{color:#448aff}[data-md-color-accent=blue] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-search-result__link:hover,[data-md-color-accent=blue] .md-search-result__link[data-md-state=active]{background-color:rgba(68,138,255,.1)}[data-md-color-accent=blue] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#448aff}[data-md-color-accent=blue] .md-source-file:hover:before{background-color:#448aff}button[data-md-color-accent=light-blue]{background-color:#0091ea}[data-md-color-accent=light-blue] .md-typeset a:active,[data-md-color-accent=light-blue] .md-typeset a:hover{color:#0091ea}[data-md-color-accent=light-blue] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=light-blue] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-nav__link:focus,[data-md-color-accent=light-blue] .md-nav__link:hover,[data-md-color-accent=light-blue] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=light-blue] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=light-blue] .md-typeset .md-clipboard:active:before,[data-md-color-accent=light-blue] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=light-blue] .md-typeset [id] .headerlink:focus,[data-md-color-accent=light-blue] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=light-blue] .md-typeset [id]:target .headerlink{color:#0091ea}[data-md-color-accent=light-blue] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-search-result__link:hover,[data-md-color-accent=light-blue] .md-search-result__link[data-md-state=active]{background-color:rgba(0,145,234,.1)}[data-md-color-accent=light-blue] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#0091ea}[data-md-color-accent=light-blue] .md-source-file:hover:before{background-color:#0091ea}button[data-md-color-accent=cyan]{background-color:#00b8d4}[data-md-color-accent=cyan] .md-typeset a:active,[data-md-color-accent=cyan] .md-typeset a:hover{color:#00b8d4}[data-md-color-accent=cyan] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=cyan] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-nav__link:focus,[data-md-color-accent=cyan] .md-nav__link:hover,[data-md-color-accent=cyan] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=cyan] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=cyan] .md-typeset .md-clipboard:active:before,[data-md-color-accent=cyan] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=cyan] .md-typeset [id] .headerlink:focus,[data-md-color-accent=cyan] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=cyan] .md-typeset [id]:target .headerlink{color:#00b8d4}[data-md-color-accent=cyan] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-search-result__link:hover,[data-md-color-accent=cyan] .md-search-result__link[data-md-state=active]{background-color:rgba(0,184,212,.1)}[data-md-color-accent=cyan] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00b8d4}[data-md-color-accent=cyan] .md-source-file:hover:before{background-color:#00b8d4}button[data-md-color-accent=teal]{background-color:#00bfa5}[data-md-color-accent=teal] .md-typeset a:active,[data-md-color-accent=teal] .md-typeset a:hover{color:#00bfa5}[data-md-color-accent=teal] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=teal] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#00bfa5}[data-md-color-accent=teal] .md-nav__link:focus,[data-md-color-accent=teal] .md-nav__link:hover,[data-md-color-accent=teal] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=teal] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=teal] .md-typeset .md-clipboard:active:before,[data-md-color-accent=teal] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=teal] .md-typeset [id] .headerlink:focus,[data-md-color-accent=teal] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=teal] .md-typeset [id]:target .headerlink{color:#00bfa5}[data-md-color-accent=teal] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00bfa5}[data-md-color-accent=teal] .md-search-result__link:hover,[data-md-color-accent=teal] .md-search-result__link[data-md-state=active]{background-color:rgba(0,191,165,.1)}[data-md-color-accent=teal] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00bfa5}[data-md-color-accent=teal] .md-source-file:hover:before{background-color:#00bfa5}button[data-md-color-accent=green]{background-color:#00c853}[data-md-color-accent=green] .md-typeset a:active,[data-md-color-accent=green] .md-typeset a:hover{color:#00c853}[data-md-color-accent=green] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=green] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#00c853}[data-md-color-accent=green] .md-nav__link:focus,[data-md-color-accent=green] .md-nav__link:hover,[data-md-color-accent=green] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=green] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=green] .md-typeset .md-clipboard:active:before,[data-md-color-accent=green] .md-typeset .md-clipboard:hover:before,[data-md-color-accent=green] .md-typeset [id] .headerlink:focus,[data-md-color-accent=green] .md-typeset [id]:hover .headerlink:hover,[data-md-color-accent=green] .md-typeset [id]:target .headerlink{color:#00c853}[data-md-color-accent=green] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00c853}[data-md-color-accent=green] .md-search-result__link:hover,[data-md-color-accent=green] .md-search-result__link[data-md-state=active]{background-color:rgba(0,200,83,.1)}[data-md-color-accent=green] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#00c853}[data-md-color-accent=green] .md-source-file:hover:before{background-color:#00c853}button[data-md-color-accent=light-green]{background-color:#64dd17}[data-md-color-accent=light-green] .md-typeset a:active,[data-md-color-accent=light-green] .md-typeset a:hover{color:#64dd17}[data-md-color-accent=light-green] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,[data-md-color-accent=light-green] .md-typeset pre code::-webkit-scrollbar-thumb:hover{background-color:#64dd17}[data-md-color-accent=light-green] .md-nav__link:focus,[data-md-color-accent=light-green] .md-nav__link:hover,[data-md-color-accent=light-green] .md-typeset .footnote li:hover .footnote-backref:hover,[data-md-color-accent=light-green] .md-typeset .footnote li:target .footnote-backref,[data-md-color-accent=light-green] 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.md-nav__link{padding-right:3.2rem;padding-left:0}.md-nav__toggle~.md-nav{display:flex;-webkit-transform:translateX(100%);transform:translateX(100%);transition:opacity .125s .05s,-webkit-transform .25s cubic-bezier(.8,0,.6,1);transition:transform .25s cubic-bezier(.8,0,.6,1),opacity .125s .05s;transition:transform .25s cubic-bezier(.8,0,.6,1),opacity .125s .05s,-webkit-transform .25s cubic-bezier(.8,0,.6,1);opacity:0}[dir=rtl] .md-nav__toggle~.md-nav{-webkit-transform:translateX(-100%);transform:translateX(-100%)}.no-csstransforms3d .md-nav__toggle~.md-nav{display:none}.md-nav__toggle:checked~.md-nav{-webkit-transform:translateX(0);transform:translateX(0);transition:opacity .125s .125s,-webkit-transform .25s cubic-bezier(.4,0,.2,1);transition:transform .25s cubic-bezier(.4,0,.2,1),opacity .125s .125s;transition:transform .25s cubic-bezier(.4,0,.2,1),opacity .125s .125s,-webkit-transform .25s cubic-bezier(.4,0,.2,1);opacity:1}.no-csstransforms3d .md-nav__toggle:checked~.md-nav{display:flex}.md-sidebar--primary{position:fixed;top:0;left:-12.1rem;width:12.1rem;height:100%;-webkit-transform:translateX(0);transform:translateX(0);transition:box-shadow .25s,-webkit-transform .25s cubic-bezier(.4,0,.2,1);transition:transform .25s cubic-bezier(.4,0,.2,1),box-shadow .25s;transition:transform .25s cubic-bezier(.4,0,.2,1),box-shadow .25s,-webkit-transform .25s cubic-bezier(.4,0,.2,1);background-color:#fff;z-index:3}[dir=rtl] .md-sidebar--primary{right:-12.1rem;left:auto}.no-csstransforms3d .md-sidebar--primary{display:none}[data-md-toggle=drawer]:checked~.md-container .md-sidebar--primary{box-shadow:0 8px 10px 1px rgba(0,0,0,.14),0 3px 14px 2px rgba(0,0,0,.12),0 5px 5px -3px rgba(0,0,0,.4);-webkit-transform:translateX(12.1rem);transform:translateX(12.1rem)}[dir=rtl] [data-md-toggle=drawer]:checked~.md-container .md-sidebar--primary{-webkit-transform:translateX(-12.1rem);transform:translateX(-12.1rem)}.no-csstransforms3d [data-md-toggle=drawer]:checked~.md-container .md-sidebar--primary{display:block}.md-sidebar--primary .md-sidebar__scrollwrap{overflow:hidden;position:absolute;top:0;right:0;bottom:0;left:0;margin:0}.md-tabs{display:none}}@media only screen and (min-width:60em){.md-content{margin-right:12.1rem}[dir=rtl] .md-content{margin-right:0;margin-left:12.1rem}.md-header-nav__button.md-icon--search{display:none}.md-header-nav__source{display:block;width:11.7rem;max-width:11.7rem;padding-right:.6rem}[dir=rtl] .md-header-nav__source{padding-right:0;padding-left:.6rem}.md-search{padding:.2rem}.md-search__overlay{position:fixed;top:0;left:0;width:0;height:0;transition:width 0 .25s,height 0 .25s,opacity .25s;background-color:rgba(0,0,0,.54);cursor:pointer}[dir=rtl] .md-search__overlay{right:0;left:auto}[data-md-toggle=search]:checked~.md-header .md-search__overlay{width:100%;height:100%;transition:width 0,height 0,opacity .25s;opacity:1}.md-search__inner{position:relative;width:11.5rem;margin-right:.8rem;padding:.1rem 0;float:right;transition:width .25s cubic-bezier(.1,.7,.1,1)}[dir=rtl] .md-search__inner{margin-right:0;margin-left:.8rem;float:left}.md-search__form,.md-search__input{border-radius:.1rem}.md-search__input{width:100%;height:1.8rem;padding-left:2.2rem;transition:background-color .25s cubic-bezier(.1,.7,.1,1),color .25s cubic-bezier(.1,.7,.1,1);background-color:rgba(0,0,0,.26);color:inherit;font-size:.8rem}[dir=rtl] .md-search__input{padding-right:2.2rem}.md-search__input+.md-search__icon{color:inherit}.md-search__input::-webkit-input-placeholder{color:hsla(0,0%,100%,.7)}.md-search__input:-ms-input-placeholder{color:hsla(0,0%,100%,.7)}.md-search__input::-ms-input-placeholder{color:hsla(0,0%,100%,.7)}.md-search__input::placeholder{color:hsla(0,0%,100%,.7)}.md-search__input:hover{background-color:hsla(0,0%,100%,.12)}[data-md-toggle=search]:checked~.md-header .md-search__input{border-radius:.1rem .1rem 0 0;background-color:#fff;color:rgba(0,0,0,.87);text-overflow:clip}[data-md-toggle=search]:checked~.md-header .md-search__input+.md-search__icon,[data-md-toggle=search]:checked~.md-header .md-search__input::-webkit-input-placeholder{color:rgba(0,0,0,.54)}[data-md-toggle=search]:checked~.md-header .md-search__input+.md-search__icon,[data-md-toggle=search]:checked~.md-header .md-search__input:-ms-input-placeholder{color:rgba(0,0,0,.54)}[data-md-toggle=search]:checked~.md-header .md-search__input+.md-search__icon,[data-md-toggle=search]:checked~.md-header .md-search__input::-ms-input-placeholder{color:rgba(0,0,0,.54)}[data-md-toggle=search]:checked~.md-header .md-search__input+.md-search__icon,[data-md-toggle=search]:checked~.md-header .md-search__input::placeholder{color:rgba(0,0,0,.54)}.md-search__output{top:1.9rem;transition:opacity .4s;opacity:0}[data-md-toggle=search]:checked~.md-header .md-search__output{box-shadow:0 6px 10px 0 rgba(0,0,0,.14),0 1px 18px 0 rgba(0,0,0,.12),0 3px 5px -1px rgba(0,0,0,.4);opacity:1}.md-search__scrollwrap{max-height:0}[data-md-toggle=search]:checked~.md-header .md-search__scrollwrap{max-height:75vh}.md-search__scrollwrap::-webkit-scrollbar{width:.2rem;height:.2rem}.md-search__scrollwrap::-webkit-scrollbar-thumb{background-color:rgba(0,0,0,.26)}.md-search__scrollwrap::-webkit-scrollbar-thumb:hover{background-color:#536dfe}.md-search-result__meta{padding-left:2.2rem}[dir=rtl] .md-search-result__meta{padding-right:2.2rem;padding-left:0}.md-search-result__article{padding-left:2.2rem}[dir=rtl] .md-search-result__article{padding-right:2.2rem;padding-left:.8rem}.md-sidebar--secondary{display:block;margin-left:100%;-webkit-transform:translate(-100%);transform:translate(-100%)}[dir=rtl] .md-sidebar--secondary{margin-right:100%;margin-left:0;-webkit-transform:translate(100%);transform:translate(100%)}}@media only screen and (min-width:88.25em){.md-sidebar--secondary{margin-left:73rem}[dir=rtl] .md-sidebar--secondary{margin-right:73rem;margin-left:0}}@media only screen and (min-width:76.25em){.md-content{margin-left:12.1rem}[dir=rtl] .md-content{margin-right:12.1rem}.md-content__inner{margin-right:1.2rem;margin-left:1.2rem}.md-header-nav__button.md-icon--menu{display:none}.md-nav[data-md-state=animate]{transition:max-height .25s cubic-bezier(.86,0,.07,1)}.md-nav__toggle~.md-nav{max-height:0;overflow:hidden}.no-js .md-nav__toggle~.md-nav{display:none}.md-nav[data-md-state=expand],.md-nav__toggle:checked~.md-nav{max-height:100%}.no-js .md-nav[data-md-state=expand],.no-js .md-nav__toggle:checked~.md-nav{display:block}.md-nav__item--nested>.md-nav>.md-nav__title{display:none}.md-nav__item--nested>.md-nav__link:after{display:inline-block;-webkit-transform-origin:.45em .45em;transform-origin:.45em .45em;-webkit-transform-style:preserve-3d;transform-style:preserve-3d;vertical-align:-.125em}.js .md-nav__item--nested>.md-nav__link:after{transition:-webkit-transform .4s;transition:transform .4s;transition:transform .4s,-webkit-transform .4s}.md-nav__item--nested .md-nav__toggle:checked~.md-nav__link:after{-webkit-transform:rotateX(180deg);transform:rotateX(180deg)}.md-search__inner{margin-right:1.2rem}[dir=rtl] .md-search__inner{margin-left:1.2rem}.md-search__scrollwrap,[data-md-toggle=search]:checked~.md-header .md-search__inner{width:34.4rem}.md-tabs~.md-main .md-nav--primary>.md-nav__list>.md-nav__item--nested{font-size:0;visibility:hidden}.md-tabs--active~.md-main .md-nav--primary .md-nav__title{display:block;padding:0}.md-tabs--active~.md-main .md-nav--primary .md-nav__title--site{display:none}.no-js .md-tabs--active~.md-main .md-nav--primary .md-nav{display:block}.md-tabs--active~.md-main .md-nav--primary>.md-nav__list>.md-nav__item{font-size:0;visibility:hidden}.md-tabs--active~.md-main .md-nav--primary>.md-nav__list>.md-nav__item--nested{display:none;font-size:.7rem;overflow:auto;visibility:visible}.md-tabs--active~.md-main .md-nav--primary>.md-nav__list>.md-nav__item--nested>.md-nav__link{display:none}.md-tabs--active~.md-main .md-nav--primary>.md-nav__list>.md-nav__item--active{display:block}.md-tabs--active~.md-main .md-nav[data-md-level=1]{max-height:none;overflow:visible}.md-tabs--active~.md-main .md-nav[data-md-level=1]>.md-nav__list>.md-nav__item{padding-left:0}.md-tabs--active~.md-main .md-nav[data-md-level=1] .md-nav .md-nav__title{display:none}}@media only screen and (min-width:45em){.md-footer-nav__link{width:50%}.md-footer-copyright{max-width:75%;float:left}[dir=rtl] .md-footer-copyright{float:right}.md-footer-social{padding:.6rem 0;float:right}[dir=rtl] .md-footer-social{float:left}}@media only screen and (max-width:29.9375em){[data-md-toggle=search]:checked~.md-header .md-search__overlay{-webkit-transform:scale(45);transform:scale(45)}}@media only screen and (min-width:30em) and (max-width:44.9375em){[data-md-toggle=search]:checked~.md-header .md-search__overlay{-webkit-transform:scale(60);transform:scale(60)}}@media only screen and (min-width:45em) and (max-width:59.9375em){[data-md-toggle=search]:checked~.md-header .md-search__overlay{-webkit-transform:scale(75);transform:scale(75)}}@media only screen and (min-width:60em) and (max-width:76.1875em){.md-search__scrollwrap,[data-md-toggle=search]:checked~.md-header .md-search__inner{width:23.4rem}.md-search-result__teaser{max-height:2.5rem;-webkit-line-clamp:3}}
\ No newline at end of file
diff --git a/docs/v1.2.0/_static/underscore-1.13.1.js b/docs/v1.2.0/_static/underscore-1.13.1.js
new file mode 100644
index 0000000000..ffd77af964
--- /dev/null
+++ b/docs/v1.2.0/_static/underscore-1.13.1.js
@@ -0,0 +1,2042 @@
+(function (global, factory) {
+  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+  typeof define === 'function' && define.amd ? define('underscore', factory) :
+  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
+    var current = global._;
+    var exports = global._ = factory();
+    exports.noConflict = function () { global._ = current; return exports; };
+  }()));
+}(this, (function () {
+  //     Underscore.js 1.13.1
+  //     https://underscorejs.org
+  //     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
+  //     Underscore may be freely distributed under the MIT license.
+
+  // Current version.
+  var VERSION = '1.13.1';
+
+  // Establish the root object, `window` (`self`) in the browser, `global`
+  // on the server, or `this` in some virtual machines. We use `self`
+  // instead of `window` for `WebWorker` support.
+  var root = typeof self == 'object' && self.self === self && self ||
+            typeof global == 'object' && global.global === global && global ||
+            Function('return this')() ||
+            {};
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
+  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var push = ArrayProto.push,
+      slice = ArrayProto.slice,
+      toString = ObjProto.toString,
+      hasOwnProperty = ObjProto.hasOwnProperty;
+
+  // Modern feature detection.
+  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
+      supportsDataView = typeof DataView !== 'undefined';
+
+  // All **ECMAScript 5+** native function implementations that we hope to use
+  // are declared here.
+  var nativeIsArray = Array.isArray,
+      nativeKeys = Object.keys,
+      nativeCreate = Object.create,
+      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
+
+  // Create references to these builtin functions because we override them.
+  var _isNaN = isNaN,
+      _isFinite = isFinite;
+
+  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
+  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
+  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
+    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
+
+  // The largest integer that can be represented exactly.
+  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
+
+  // Some functions take a variable number of arguments, or a few expected
+  // arguments at the beginning and then a variable number of values to operate
+  // on. This helper accumulates all remaining arguments past the function’s
+  // argument length (or an explicit `startIndex`), into an array that becomes
+  // the last argument. Similar to ES6’s "rest parameter".
+  function restArguments(func, startIndex) {
+    startIndex = startIndex == null ? func.length - 1 : +startIndex;
+    return function() {
+      var length = Math.max(arguments.length - startIndex, 0),
+          rest = Array(length),
+          index = 0;
+      for (; index < length; index++) {
+        rest[index] = arguments[index + startIndex];
+      }
+      switch (startIndex) {
+        case 0: return func.call(this, rest);
+        case 1: return func.call(this, arguments[0], rest);
+        case 2: return func.call(this, arguments[0], arguments[1], rest);
+      }
+      var args = Array(startIndex + 1);
+      for (index = 0; index < startIndex; index++) {
+        args[index] = arguments[index];
+      }
+      args[startIndex] = rest;
+      return func.apply(this, args);
+    };
+  }
+
+  // Is a given variable an object?
+  function isObject(obj) {
+    var type = typeof obj;
+    return type === 'function' || type === 'object' && !!obj;
+  }
+
+  // Is a given value equal to null?
+  function isNull(obj) {
+    return obj === null;
+  }
+
+  // Is a given variable undefined?
+  function isUndefined(obj) {
+    return obj === void 0;
+  }
+
+  // Is a given value a boolean?
+  function isBoolean(obj) {
+    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
+  }
+
+  // Is a given value a DOM element?
+  function isElement(obj) {
+    return !!(obj && obj.nodeType === 1);
+  }
+
+  // Internal function for creating a `toString`-based type tester.
+  function tagTester(name) {
+    var tag = '[object ' + name + ']';
+    return function(obj) {
+      return toString.call(obj) === tag;
+    };
+  }
+
+  var isString = tagTester('String');
+
+  var isNumber = tagTester('Number');
+
+  var isDate = tagTester('Date');
+
+  var isRegExp = tagTester('RegExp');
+
+  var isError = tagTester('Error');
+
+  var isSymbol = tagTester('Symbol');
+
+  var isArrayBuffer = tagTester('ArrayBuffer');
+
+  var isFunction = tagTester('Function');
+
+  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
+  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
+  var nodelist = root.document && root.document.childNodes;
+  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
+    isFunction = function(obj) {
+      return typeof obj == 'function' || false;
+    };
+  }
+
+  var isFunction$1 = isFunction;
+
+  var hasObjectTag = tagTester('Object');
+
+  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
+  // In IE 11, the most common among them, this problem also applies to
+  // `Map`, `WeakMap` and `Set`.
+  var hasStringTagBug = (
+        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
+      ),
+      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
+
+  var isDataView = tagTester('DataView');
+
+  // In IE 10 - Edge 13, we need a different heuristic
+  // to determine whether an object is a `DataView`.
+  function ie10IsDataView(obj) {
+    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
+  }
+
+  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native `Array.isArray`.
+  var isArray = nativeIsArray || tagTester('Array');
+
+  // Internal function to check whether `key` is an own property name of `obj`.
+  function has$1(obj, key) {
+    return obj != null && hasOwnProperty.call(obj, key);
+  }
+
+  var isArguments = tagTester('Arguments');
+
+  // Define a fallback version of the method in browsers (ahem, IE < 9), where
+  // there isn't any inspectable "Arguments" type.
+  (function() {
+    if (!isArguments(arguments)) {
+      isArguments = function(obj) {
+        return has$1(obj, 'callee');
+      };
+    }
+  }());
+
+  var isArguments$1 = isArguments;
+
+  // Is a given object a finite number?
+  function isFinite$1(obj) {
+    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
+  }
+
+  // Is the given value `NaN`?
+  function isNaN$1(obj) {
+    return isNumber(obj) && _isNaN(obj);
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function constant(value) {
+    return function() {
+      return value;
+    };
+  }
+
+  // Common internal logic for `isArrayLike` and `isBufferLike`.
+  function createSizePropertyCheck(getSizeProperty) {
+    return function(collection) {
+      var sizeProperty = getSizeProperty(collection);
+      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
+    }
+  }
+
+  // Internal helper to generate a function to obtain property `key` from `obj`.
+  function shallowProperty(key) {
+    return function(obj) {
+      return obj == null ? void 0 : obj[key];
+    };
+  }
+
+  // Internal helper to obtain the `byteLength` property of an object.
+  var getByteLength = shallowProperty('byteLength');
+
+  // Internal helper to determine whether we should spend extensive checks against
+  // `ArrayBuffer` et al.
+  var isBufferLike = createSizePropertyCheck(getByteLength);
+
+  // Is a given value a typed array?
+  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
+  function isTypedArray(obj) {
+    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
+    // Otherwise, fall back on the above regular expression.
+    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
+                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
+  }
+
+  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
+
+  // Internal helper to obtain the `length` property of an object.
+  var getLength = shallowProperty('length');
+
+  // Internal helper to create a simple lookup structure.
+  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
+  // circular imports. `emulatedSet` is a one-off solution that only works for
+  // arrays of strings.
+  function emulatedSet(keys) {
+    var hash = {};
+    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
+    return {
+      contains: function(key) { return hash[key]; },
+      push: function(key) {
+        hash[key] = true;
+        return keys.push(key);
+      }
+    };
+  }
+
+  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
+  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
+  // needed.
+  function collectNonEnumProps(obj, keys) {
+    keys = emulatedSet(keys);
+    var nonEnumIdx = nonEnumerableProps.length;
+    var constructor = obj.constructor;
+    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
+
+    // Constructor is a special case.
+    var prop = 'constructor';
+    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
+
+    while (nonEnumIdx--) {
+      prop = nonEnumerableProps[nonEnumIdx];
+      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
+        keys.push(prop);
+      }
+    }
+  }
+
+  // Retrieve the names of an object's own properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`.
+  function keys(obj) {
+    if (!isObject(obj)) return [];
+    if (nativeKeys) return nativeKeys(obj);
+    var keys = [];
+    for (var key in obj) if (has$1(obj, key)) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  function isEmpty(obj) {
+    if (obj == null) return true;
+    // Skip the more expensive `toString`-based type checks if `obj` has no
+    // `.length`.
+    var length = getLength(obj);
+    if (typeof length == 'number' && (
+      isArray(obj) || isString(obj) || isArguments$1(obj)
+    )) return length === 0;
+    return getLength(keys(obj)) === 0;
+  }
+
+  // Returns whether an object has a given set of `key:value` pairs.
+  function isMatch(object, attrs) {
+    var _keys = keys(attrs), length = _keys.length;
+    if (object == null) return !length;
+    var obj = Object(object);
+    for (var i = 0; i < length; i++) {
+      var key = _keys[i];
+      if (attrs[key] !== obj[key] || !(key in obj)) return false;
+    }
+    return true;
+  }
+
+  // If Underscore is called as a function, it returns a wrapped object that can
+  // be used OO-style. This wrapper holds altered versions of all functions added
+  // through `_.mixin`. Wrapped objects may be chained.
+  function _$1(obj) {
+    if (obj instanceof _$1) return obj;
+    if (!(this instanceof _$1)) return new _$1(obj);
+    this._wrapped = obj;
+  }
+
+  _$1.VERSION = VERSION;
+
+  // Extracts the result from a wrapped and chained object.
+  _$1.prototype.value = function() {
+    return this._wrapped;
+  };
+
+  // Provide unwrapping proxies for some methods used in engine operations
+  // such as arithmetic and JSON stringification.
+  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
+
+  _$1.prototype.toString = function() {
+    return String(this._wrapped);
+  };
+
+  // Internal function to wrap or shallow-copy an ArrayBuffer,
+  // typed array or DataView to a new view, reusing the buffer.
+  function toBufferView(bufferSource) {
+    return new Uint8Array(
+      bufferSource.buffer || bufferSource,
+      bufferSource.byteOffset || 0,
+      getByteLength(bufferSource)
+    );
+  }
+
+  // We use this string twice, so give it a name for minification.
+  var tagDataView = '[object DataView]';
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function eq(a, b, aStack, bStack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
+    if (a === b) return a !== 0 || 1 / a === 1 / b;
+    // `null` or `undefined` only equal to itself (strict comparison).
+    if (a == null || b == null) return false;
+    // `NaN`s are equivalent, but non-reflexive.
+    if (a !== a) return b !== b;
+    // Exhaust primitive checks
+    var type = typeof a;
+    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
+    return deepEq(a, b, aStack, bStack);
+  }
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function deepEq(a, b, aStack, bStack) {
+    // Unwrap any wrapped objects.
+    if (a instanceof _$1) a = a._wrapped;
+    if (b instanceof _$1) b = b._wrapped;
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className !== toString.call(b)) return false;
+    // Work around a bug in IE 10 - Edge 13.
+    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
+      if (!isDataView$1(b)) return false;
+      className = tagDataView;
+    }
+    switch (className) {
+      // These types are compared by value.
+      case '[object RegExp]':
+        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return '' + a === '' + b;
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive.
+        // Object(NaN) is equivalent to NaN.
+        if (+a !== +a) return +b !== +b;
+        // An `egal` comparison is performed for other numeric values.
+        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a === +b;
+      case '[object Symbol]':
+        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
+      case '[object ArrayBuffer]':
+      case tagDataView:
+        // Coerce to typed array so we can fall through.
+        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
+    }
+
+    var areArrays = className === '[object Array]';
+    if (!areArrays && isTypedArray$1(a)) {
+        var byteLength = getByteLength(a);
+        if (byteLength !== getByteLength(b)) return false;
+        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
+        areArrays = true;
+    }
+    if (!areArrays) {
+      if (typeof a != 'object' || typeof b != 'object') return false;
+
+      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
+      // from different frames are.
+      var aCtor = a.constructor, bCtor = b.constructor;
+      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
+                               isFunction$1(bCtor) && bCtor instanceof bCtor)
+                          && ('constructor' in a && 'constructor' in b)) {
+        return false;
+      }
+    }
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+
+    // Initializing stack of traversed objects.
+    // It's done here since we only need them for objects and arrays comparison.
+    aStack = aStack || [];
+    bStack = bStack || [];
+    var length = aStack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (aStack[length] === a) return bStack[length] === b;
+    }
+
+    // Add the first object to the stack of traversed objects.
+    aStack.push(a);
+    bStack.push(b);
+
+    // Recursively compare objects and arrays.
+    if (areArrays) {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      length = a.length;
+      if (length !== b.length) return false;
+      // Deep compare the contents, ignoring non-numeric properties.
+      while (length--) {
+        if (!eq(a[length], b[length], aStack, bStack)) return false;
+      }
+    } else {
+      // Deep compare objects.
+      var _keys = keys(a), key;
+      length = _keys.length;
+      // Ensure that both objects contain the same number of properties before comparing deep equality.
+      if (keys(b).length !== length) return false;
+      while (length--) {
+        // Deep compare each member
+        key = _keys[length];
+        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    aStack.pop();
+    bStack.pop();
+    return true;
+  }
+
+  // Perform a deep comparison to check if two objects are equal.
+  function isEqual(a, b) {
+    return eq(a, b);
+  }
+
+  // Retrieve all the enumerable property names of an object.
+  function allKeys(obj) {
+    if (!isObject(obj)) return [];
+    var keys = [];
+    for (var key in obj) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Since the regular `Object.prototype.toString` type tests don't work for
+  // some types in IE 11, we use a fingerprinting heuristic instead, based
+  // on the methods. It's not great, but it's the best we got.
+  // The fingerprint method lists are defined below.
+  function ie11fingerprint(methods) {
+    var length = getLength(methods);
+    return function(obj) {
+      if (obj == null) return false;
+      // `Map`, `WeakMap` and `Set` have no enumerable keys.
+      var keys = allKeys(obj);
+      if (getLength(keys)) return false;
+      for (var i = 0; i < length; i++) {
+        if (!isFunction$1(obj[methods[i]])) return false;
+      }
+      // If we are testing against `WeakMap`, we need to ensure that
+      // `obj` doesn't have a `forEach` method in order to distinguish
+      // it from a regular `Map`.
+      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
+    };
+  }
+
+  // In the interest of compact minification, we write
+  // each string in the fingerprints only once.
+  var forEachName = 'forEach',
+      hasName = 'has',
+      commonInit = ['clear', 'delete'],
+      mapTail = ['get', hasName, 'set'];
+
+  // `Map`, `WeakMap` and `Set` each have slightly different
+  // combinations of the above sublists.
+  var mapMethods = commonInit.concat(forEachName, mapTail),
+      weakMapMethods = commonInit.concat(mapTail),
+      setMethods = ['add'].concat(commonInit, forEachName, hasName);
+
+  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
+
+  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
+
+  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
+
+  var isWeakSet = tagTester('WeakSet');
+
+  // Retrieve the values of an object's properties.
+  function values(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var values = Array(length);
+    for (var i = 0; i < length; i++) {
+      values[i] = obj[_keys[i]];
+    }
+    return values;
+  }
+
+  // Convert an object into a list of `[key, value]` pairs.
+  // The opposite of `_.object` with one argument.
+  function pairs(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var pairs = Array(length);
+    for (var i = 0; i < length; i++) {
+      pairs[i] = [_keys[i], obj[_keys[i]]];
+    }
+    return pairs;
+  }
+
+  // Invert the keys and values of an object. The values must be serializable.
+  function invert(obj) {
+    var result = {};
+    var _keys = keys(obj);
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      result[obj[_keys[i]]] = _keys[i];
+    }
+    return result;
+  }
+
+  // Return a sorted list of the function names available on the object.
+  function functions(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (isFunction$1(obj[key])) names.push(key);
+    }
+    return names.sort();
+  }
+
+  // An internal function for creating assigner functions.
+  function createAssigner(keysFunc, defaults) {
+    return function(obj) {
+      var length = arguments.length;
+      if (defaults) obj = Object(obj);
+      if (length < 2 || obj == null) return obj;
+      for (var index = 1; index < length; index++) {
+        var source = arguments[index],
+            keys = keysFunc(source),
+            l = keys.length;
+        for (var i = 0; i < l; i++) {
+          var key = keys[i];
+          if (!defaults || obj[key] === void 0) obj[key] = source[key];
+        }
+      }
+      return obj;
+    };
+  }
+
+  // Extend a given object with all the properties in passed-in object(s).
+  var extend = createAssigner(allKeys);
+
+  // Assigns a given object with all the own properties in the passed-in
+  // object(s).
+  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
+  var extendOwn = createAssigner(keys);
+
+  // Fill in a given object with default properties.
+  var defaults = createAssigner(allKeys, true);
+
+  // Create a naked function reference for surrogate-prototype-swapping.
+  function ctor() {
+    return function(){};
+  }
+
+  // An internal function for creating a new object that inherits from another.
+  function baseCreate(prototype) {
+    if (!isObject(prototype)) return {};
+    if (nativeCreate) return nativeCreate(prototype);
+    var Ctor = ctor();
+    Ctor.prototype = prototype;
+    var result = new Ctor;
+    Ctor.prototype = null;
+    return result;
+  }
+
+  // Creates an object that inherits from the given prototype object.
+  // If additional properties are provided then they will be added to the
+  // created object.
+  function create(prototype, props) {
+    var result = baseCreate(prototype);
+    if (props) extendOwn(result, props);
+    return result;
+  }
+
+  // Create a (shallow-cloned) duplicate of an object.
+  function clone(obj) {
+    if (!isObject(obj)) return obj;
+    return isArray(obj) ? obj.slice() : extend({}, obj);
+  }
+
+  // Invokes `interceptor` with the `obj` and then returns `obj`.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  function tap(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  }
+
+  // Normalize a (deep) property `path` to array.
+  // Like `_.iteratee`, this function can be customized.
+  function toPath$1(path) {
+    return isArray(path) ? path : [path];
+  }
+  _$1.toPath = toPath$1;
+
+  // Internal wrapper for `_.toPath` to enable minification.
+  // Similar to `cb` for `_.iteratee`.
+  function toPath(path) {
+    return _$1.toPath(path);
+  }
+
+  // Internal function to obtain a nested property in `obj` along `path`.
+  function deepGet(obj, path) {
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      if (obj == null) return void 0;
+      obj = obj[path[i]];
+    }
+    return length ? obj : void 0;
+  }
+
+  // Get the value of the (deep) property on `path` from `object`.
+  // If any property in `path` does not exist or if the value is
+  // `undefined`, return `defaultValue` instead.
+  // The `path` is normalized through `_.toPath`.
+  function get(object, path, defaultValue) {
+    var value = deepGet(object, toPath(path));
+    return isUndefined(value) ? defaultValue : value;
+  }
+
+  // Shortcut function for checking if an object has a given property directly on
+  // itself (in other words, not on a prototype). Unlike the internal `has`
+  // function, this public version can also traverse nested properties.
+  function has(obj, path) {
+    path = toPath(path);
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      var key = path[i];
+      if (!has$1(obj, key)) return false;
+      obj = obj[key];
+    }
+    return !!length;
+  }
+
+  // Keep the identity function around for default iteratees.
+  function identity(value) {
+    return value;
+  }
+
+  // Returns a predicate for checking whether an object has a given set of
+  // `key:value` pairs.
+  function matcher(attrs) {
+    attrs = extendOwn({}, attrs);
+    return function(obj) {
+      return isMatch(obj, attrs);
+    };
+  }
+
+  // Creates a function that, when passed an object, will traverse that object’s
+  // properties down the given `path`, specified as an array of keys or indices.
+  function property(path) {
+    path = toPath(path);
+    return function(obj) {
+      return deepGet(obj, path);
+    };
+  }
+
+  // Internal function that returns an efficient (for current engines) version
+  // of the passed-in callback, to be repeatedly applied in other Underscore
+  // functions.
+  function optimizeCb(func, context, argCount) {
+    if (context === void 0) return func;
+    switch (argCount == null ? 3 : argCount) {
+      case 1: return function(value) {
+        return func.call(context, value);
+      };
+      // The 2-argument case is omitted because we’re not using it.
+      case 3: return function(value, index, collection) {
+        return func.call(context, value, index, collection);
+      };
+      case 4: return function(accumulator, value, index, collection) {
+        return func.call(context, accumulator, value, index, collection);
+      };
+    }
+    return function() {
+      return func.apply(context, arguments);
+    };
+  }
+
+  // An internal function to generate callbacks that can be applied to each
+  // element in a collection, returning the desired result — either `_.identity`,
+  // an arbitrary callback, a property matcher, or a property accessor.
+  function baseIteratee(value, context, argCount) {
+    if (value == null) return identity;
+    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
+    if (isObject(value) && !isArray(value)) return matcher(value);
+    return property(value);
+  }
+
+  // External wrapper for our callback generator. Users may customize
+  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
+  // This abstraction hides the internal-only `argCount` argument.
+  function iteratee(value, context) {
+    return baseIteratee(value, context, Infinity);
+  }
+  _$1.iteratee = iteratee;
+
+  // The function we call internally to generate a callback. It invokes
+  // `_.iteratee` if overridden, otherwise `baseIteratee`.
+  function cb(value, context, argCount) {
+    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
+    return baseIteratee(value, context, argCount);
+  }
+
+  // Returns the results of applying the `iteratee` to each element of `obj`.
+  // In contrast to `_.map` it returns an object.
+  function mapObject(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = keys(obj),
+        length = _keys.length,
+        results = {};
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys[index];
+      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function noop(){}
+
+  // Generates a function for a given object that returns a given property.
+  function propertyOf(obj) {
+    if (obj == null) return noop;
+    return function(path) {
+      return get(obj, path);
+    };
+  }
+
+  // Run a function **n** times.
+  function times(n, iteratee, context) {
+    var accum = Array(Math.max(0, n));
+    iteratee = optimizeCb(iteratee, context, 1);
+    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
+    return accum;
+  }
+
+  // Return a random integer between `min` and `max` (inclusive).
+  function random(min, max) {
+    if (max == null) {
+      max = min;
+      min = 0;
+    }
+    return min + Math.floor(Math.random() * (max - min + 1));
+  }
+
+  // A (possibly faster) way to get the current timestamp as an integer.
+  var now = Date.now || function() {
+    return new Date().getTime();
+  };
+
+  // Internal helper to generate functions for escaping and unescaping strings
+  // to/from HTML interpolation.
+  function createEscaper(map) {
+    var escaper = function(match) {
+      return map[match];
+    };
+    // Regexes for identifying a key that needs to be escaped.
+    var source = '(?:' + keys(map).join('|') + ')';
+    var testRegexp = RegExp(source);
+    var replaceRegexp = RegExp(source, 'g');
+    return function(string) {
+      string = string == null ? '' : '' + string;
+      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
+    };
+  }
+
+  // Internal list of HTML entities for escaping.
+  var escapeMap = {
+    '&': '&amp;',
+    '<': '&lt;',
+    '>': '&gt;',
+    '"': '&quot;',
+    "'": '&#x27;',
+    '`': '&#x60;'
+  };
+
+  // Function for escaping strings to HTML interpolation.
+  var _escape = createEscaper(escapeMap);
+
+  // Internal list of HTML entities for unescaping.
+  var unescapeMap = invert(escapeMap);
+
+  // Function for unescaping strings from HTML interpolation.
+  var _unescape = createEscaper(unescapeMap);
+
+  // By default, Underscore uses ERB-style template delimiters. Change the
+  // following template settings to use alternative delimiters.
+  var templateSettings = _$1.templateSettings = {
+    evaluate: /<%([\s\S]+?)%>/g,
+    interpolate: /<%=([\s\S]+?)%>/g,
+    escape: /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `_.templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /(.)^/;
+
+  // Certain characters need to be escaped so that they can be put into a
+  // string literal.
+  var escapes = {
+    "'": "'",
+    '\\': '\\',
+    '\r': 'r',
+    '\n': 'n',
+    '\u2028': 'u2028',
+    '\u2029': 'u2029'
+  };
+
+  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
+
+  function escapeChar(match) {
+    return '\\' + escapes[match];
+  }
+
+  // In order to prevent third-party code injection through
+  // `_.templateSettings.variable`, we test it against the following regular
+  // expression. It is intentionally a bit more liberal than just matching valid
+  // identifiers, but still prevents possible loopholes through defaults or
+  // destructuring assignment.
+  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  // NB: `oldSettings` only exists for backwards compatibility.
+  function template(text, settings, oldSettings) {
+    if (!settings && oldSettings) settings = oldSettings;
+    settings = defaults({}, settings, _$1.templateSettings);
+
+    // Combine delimiters into one regular expression via alternation.
+    var matcher = RegExp([
+      (settings.escape || noMatch).source,
+      (settings.interpolate || noMatch).source,
+      (settings.evaluate || noMatch).source
+    ].join('|') + '|$', 'g');
+
+    // Compile the template source, escaping string literals appropriately.
+    var index = 0;
+    var source = "__p+='";
+    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
+      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
+      index = offset + match.length;
+
+      if (escape) {
+        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
+      } else if (interpolate) {
+        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
+      } else if (evaluate) {
+        source += "';\n" + evaluate + "\n__p+='";
+      }
+
+      // Adobe VMs need the match returned to produce the correct offset.
+      return match;
+    });
+    source += "';\n";
+
+    var argument = settings.variable;
+    if (argument) {
+      // Insure against third-party code injection. (CVE-2021-23358)
+      if (!bareIdentifier.test(argument)) throw new Error(
+        'variable is not a bare identifier: ' + argument
+      );
+    } else {
+      // If a variable is not specified, place data values in local scope.
+      source = 'with(obj||{}){\n' + source + '}\n';
+      argument = 'obj';
+    }
+
+    source = "var __t,__p='',__j=Array.prototype.join," +
+      "print=function(){__p+=__j.call(arguments,'');};\n" +
+      source + 'return __p;\n';
+
+    var render;
+    try {
+      render = new Function(argument, '_', source);
+    } catch (e) {
+      e.source = source;
+      throw e;
+    }
+
+    var template = function(data) {
+      return render.call(this, data, _$1);
+    };
+
+    // Provide the compiled source as a convenience for precompilation.
+    template.source = 'function(' + argument + '){\n' + source + '}';
+
+    return template;
+  }
+
+  // Traverses the children of `obj` along `path`. If a child is a function, it
+  // is invoked with its parent as context. Returns the value of the final
+  // child, or `fallback` if any child is undefined.
+  function result(obj, path, fallback) {
+    path = toPath(path);
+    var length = path.length;
+    if (!length) {
+      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
+    }
+    for (var i = 0; i < length; i++) {
+      var prop = obj == null ? void 0 : obj[path[i]];
+      if (prop === void 0) {
+        prop = fallback;
+        i = length; // Ensure we don't continue iterating.
+      }
+      obj = isFunction$1(prop) ? prop.call(obj) : prop;
+    }
+    return obj;
+  }
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  function uniqueId(prefix) {
+    var id = ++idCounter + '';
+    return prefix ? prefix + id : id;
+  }
+
+  // Start chaining a wrapped Underscore object.
+  function chain(obj) {
+    var instance = _$1(obj);
+    instance._chain = true;
+    return instance;
+  }
+
+  // Internal function to execute `sourceFunc` bound to `context` with optional
+  // `args`. Determines whether to execute a function as a constructor or as a
+  // normal function.
+  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
+    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
+    var self = baseCreate(sourceFunc.prototype);
+    var result = sourceFunc.apply(self, args);
+    if (isObject(result)) return result;
+    return self;
+  }
+
+  // Partially apply a function by creating a version that has had some of its
+  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
+  // as a placeholder by default, allowing any combination of arguments to be
+  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
+  var partial = restArguments(function(func, boundArgs) {
+    var placeholder = partial.placeholder;
+    var bound = function() {
+      var position = 0, length = boundArgs.length;
+      var args = Array(length);
+      for (var i = 0; i < length; i++) {
+        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
+      }
+      while (position < arguments.length) args.push(arguments[position++]);
+      return executeBound(func, bound, this, this, args);
+    };
+    return bound;
+  });
+
+  partial.placeholder = _$1;
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally).
+  var bind = restArguments(function(func, context, args) {
+    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
+    var bound = restArguments(function(callArgs) {
+      return executeBound(func, bound, context, this, args.concat(callArgs));
+    });
+    return bound;
+  });
+
+  // Internal helper for collection methods to determine whether a collection
+  // should be iterated as an array or as an object.
+  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
+  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
+  var isArrayLike = createSizePropertyCheck(getLength);
+
+  // Internal implementation of a recursive `flatten` function.
+  function flatten$1(input, depth, strict, output) {
+    output = output || [];
+    if (!depth && depth !== 0) {
+      depth = Infinity;
+    } else if (depth <= 0) {
+      return output.concat(input);
+    }
+    var idx = output.length;
+    for (var i = 0, length = getLength(input); i < length; i++) {
+      var value = input[i];
+      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
+        // Flatten current level of array or arguments object.
+        if (depth > 1) {
+          flatten$1(value, depth - 1, strict, output);
+          idx = output.length;
+        } else {
+          var j = 0, len = value.length;
+          while (j < len) output[idx++] = value[j++];
+        }
+      } else if (!strict) {
+        output[idx++] = value;
+      }
+    }
+    return output;
+  }
+
+  // Bind a number of an object's methods to that object. Remaining arguments
+  // are the method names to be bound. Useful for ensuring that all callbacks
+  // defined on an object belong to it.
+  var bindAll = restArguments(function(obj, keys) {
+    keys = flatten$1(keys, false, false);
+    var index = keys.length;
+    if (index < 1) throw new Error('bindAll must be passed function names');
+    while (index--) {
+      var key = keys[index];
+      obj[key] = bind(obj[key], obj);
+    }
+    return obj;
+  });
+
+  // Memoize an expensive function by storing its results.
+  function memoize(func, hasher) {
+    var memoize = function(key) {
+      var cache = memoize.cache;
+      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
+      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
+      return cache[address];
+    };
+    memoize.cache = {};
+    return memoize;
+  }
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  var delay = restArguments(function(func, wait, args) {
+    return setTimeout(function() {
+      return func.apply(null, args);
+    }, wait);
+  });
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  var defer = partial(delay, _$1, 1);
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time. Normally, the throttled function will run
+  // as much as it can, without ever going more than once per `wait` duration;
+  // but if you'd like to disable the execution on the leading edge, pass
+  // `{leading: false}`. To disable execution on the trailing edge, ditto.
+  function throttle(func, wait, options) {
+    var timeout, context, args, result;
+    var previous = 0;
+    if (!options) options = {};
+
+    var later = function() {
+      previous = options.leading === false ? 0 : now();
+      timeout = null;
+      result = func.apply(context, args);
+      if (!timeout) context = args = null;
+    };
+
+    var throttled = function() {
+      var _now = now();
+      if (!previous && options.leading === false) previous = _now;
+      var remaining = wait - (_now - previous);
+      context = this;
+      args = arguments;
+      if (remaining <= 0 || remaining > wait) {
+        if (timeout) {
+          clearTimeout(timeout);
+          timeout = null;
+        }
+        previous = _now;
+        result = func.apply(context, args);
+        if (!timeout) context = args = null;
+      } else if (!timeout && options.trailing !== false) {
+        timeout = setTimeout(later, remaining);
+      }
+      return result;
+    };
+
+    throttled.cancel = function() {
+      clearTimeout(timeout);
+      previous = 0;
+      timeout = context = args = null;
+    };
+
+    return throttled;
+  }
+
+  // When a sequence of calls of the returned function ends, the argument
+  // function is triggered. The end of a sequence is defined by the `wait`
+  // parameter. If `immediate` is passed, the argument function will be
+  // triggered at the beginning of the sequence instead of at the end.
+  function debounce(func, wait, immediate) {
+    var timeout, previous, args, result, context;
+
+    var later = function() {
+      var passed = now() - previous;
+      if (wait > passed) {
+        timeout = setTimeout(later, wait - passed);
+      } else {
+        timeout = null;
+        if (!immediate) result = func.apply(context, args);
+        // This check is needed because `func` can recursively invoke `debounced`.
+        if (!timeout) args = context = null;
+      }
+    };
+
+    var debounced = restArguments(function(_args) {
+      context = this;
+      args = _args;
+      previous = now();
+      if (!timeout) {
+        timeout = setTimeout(later, wait);
+        if (immediate) result = func.apply(context, args);
+      }
+      return result;
+    });
+
+    debounced.cancel = function() {
+      clearTimeout(timeout);
+      timeout = args = context = null;
+    };
+
+    return debounced;
+  }
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  function wrap(func, wrapper) {
+    return partial(wrapper, func);
+  }
+
+  // Returns a negated version of the passed-in predicate.
+  function negate(predicate) {
+    return function() {
+      return !predicate.apply(this, arguments);
+    };
+  }
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  function compose() {
+    var args = arguments;
+    var start = args.length - 1;
+    return function() {
+      var i = start;
+      var result = args[start].apply(this, arguments);
+      while (i--) result = args[i].call(this, result);
+      return result;
+    };
+  }
+
+  // Returns a function that will only be executed on and after the Nth call.
+  function after(times, func) {
+    return function() {
+      if (--times < 1) {
+        return func.apply(this, arguments);
+      }
+    };
+  }
+
+  // Returns a function that will only be executed up to (but not including) the
+  // Nth call.
+  function before(times, func) {
+    var memo;
+    return function() {
+      if (--times > 0) {
+        memo = func.apply(this, arguments);
+      }
+      if (times <= 1) func = null;
+      return memo;
+    };
+  }
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  var once = partial(before, 2);
+
+  // Returns the first key on an object that passes a truth test.
+  function findKey(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = keys(obj), key;
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      key = _keys[i];
+      if (predicate(obj[key], key, obj)) return key;
+    }
+  }
+
+  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
+  function createPredicateIndexFinder(dir) {
+    return function(array, predicate, context) {
+      predicate = cb(predicate, context);
+      var length = getLength(array);
+      var index = dir > 0 ? 0 : length - 1;
+      for (; index >= 0 && index < length; index += dir) {
+        if (predicate(array[index], index, array)) return index;
+      }
+      return -1;
+    };
+  }
+
+  // Returns the first index on an array-like that passes a truth test.
+  var findIndex = createPredicateIndexFinder(1);
+
+  // Returns the last index on an array-like that passes a truth test.
+  var findLastIndex = createPredicateIndexFinder(-1);
+
+  // Use a comparator function to figure out the smallest index at which
+  // an object should be inserted so as to maintain order. Uses binary search.
+  function sortedIndex(array, obj, iteratee, context) {
+    iteratee = cb(iteratee, context, 1);
+    var value = iteratee(obj);
+    var low = 0, high = getLength(array);
+    while (low < high) {
+      var mid = Math.floor((low + high) / 2);
+      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
+    }
+    return low;
+  }
+
+  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
+  function createIndexFinder(dir, predicateFind, sortedIndex) {
+    return function(array, item, idx) {
+      var i = 0, length = getLength(array);
+      if (typeof idx == 'number') {
+        if (dir > 0) {
+          i = idx >= 0 ? idx : Math.max(idx + length, i);
+        } else {
+          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
+        }
+      } else if (sortedIndex && idx && length) {
+        idx = sortedIndex(array, item);
+        return array[idx] === item ? idx : -1;
+      }
+      if (item !== item) {
+        idx = predicateFind(slice.call(array, i, length), isNaN$1);
+        return idx >= 0 ? idx + i : -1;
+      }
+      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
+        if (array[idx] === item) return idx;
+      }
+      return -1;
+    };
+  }
+
+  // Return the position of the first occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
+
+  // Return the position of the last occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  var lastIndexOf = createIndexFinder(-1, findLastIndex);
+
+  // Return the first value which passes a truth test.
+  function find(obj, predicate, context) {
+    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
+    var key = keyFinder(obj, predicate, context);
+    if (key !== void 0 && key !== -1) return obj[key];
+  }
+
+  // Convenience version of a common use case of `_.find`: getting the first
+  // object containing specific `key:value` pairs.
+  function findWhere(obj, attrs) {
+    return find(obj, matcher(attrs));
+  }
+
+  // The cornerstone for collection functions, an `each`
+  // implementation, aka `forEach`.
+  // Handles raw objects in addition to array-likes. Treats all
+  // sparse array-likes as if they were dense.
+  function each(obj, iteratee, context) {
+    iteratee = optimizeCb(iteratee, context);
+    var i, length;
+    if (isArrayLike(obj)) {
+      for (i = 0, length = obj.length; i < length; i++) {
+        iteratee(obj[i], i, obj);
+      }
+    } else {
+      var _keys = keys(obj);
+      for (i = 0, length = _keys.length; i < length; i++) {
+        iteratee(obj[_keys[i]], _keys[i], obj);
+      }
+    }
+    return obj;
+  }
+
+  // Return the results of applying the iteratee to each element.
+  function map(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length,
+        results = Array(length);
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      results[index] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Internal helper to create a reducing function, iterating left or right.
+  function createReduce(dir) {
+    // Wrap code that reassigns argument variables in a separate function than
+    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
+    var reducer = function(obj, iteratee, memo, initial) {
+      var _keys = !isArrayLike(obj) && keys(obj),
+          length = (_keys || obj).length,
+          index = dir > 0 ? 0 : length - 1;
+      if (!initial) {
+        memo = obj[_keys ? _keys[index] : index];
+        index += dir;
+      }
+      for (; index >= 0 && index < length; index += dir) {
+        var currentKey = _keys ? _keys[index] : index;
+        memo = iteratee(memo, obj[currentKey], currentKey, obj);
+      }
+      return memo;
+    };
+
+    return function(obj, iteratee, memo, context) {
+      var initial = arguments.length >= 3;
+      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
+    };
+  }
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`.
+  var reduce = createReduce(1);
+
+  // The right-associative version of reduce, also known as `foldr`.
+  var reduceRight = createReduce(-1);
+
+  // Return all the elements that pass a truth test.
+  function filter(obj, predicate, context) {
+    var results = [];
+    predicate = cb(predicate, context);
+    each(obj, function(value, index, list) {
+      if (predicate(value, index, list)) results.push(value);
+    });
+    return results;
+  }
+
+  // Return all the elements for which a truth test fails.
+  function reject(obj, predicate, context) {
+    return filter(obj, negate(cb(predicate)), context);
+  }
+
+  // Determine whether all of the elements pass a truth test.
+  function every(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (!predicate(obj[currentKey], currentKey, obj)) return false;
+    }
+    return true;
+  }
+
+  // Determine if at least one element in the object passes a truth test.
+  function some(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (predicate(obj[currentKey], currentKey, obj)) return true;
+    }
+    return false;
+  }
+
+  // Determine if the array or object contains a given item (using `===`).
+  function contains(obj, item, fromIndex, guard) {
+    if (!isArrayLike(obj)) obj = values(obj);
+    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
+    return indexOf(obj, item, fromIndex) >= 0;
+  }
+
+  // Invoke a method (with arguments) on every item in a collection.
+  var invoke = restArguments(function(obj, path, args) {
+    var contextPath, func;
+    if (isFunction$1(path)) {
+      func = path;
+    } else {
+      path = toPath(path);
+      contextPath = path.slice(0, -1);
+      path = path[path.length - 1];
+    }
+    return map(obj, function(context) {
+      var method = func;
+      if (!method) {
+        if (contextPath && contextPath.length) {
+          context = deepGet(context, contextPath);
+        }
+        if (context == null) return void 0;
+        method = context[path];
+      }
+      return method == null ? method : method.apply(context, args);
+    });
+  });
+
+  // Convenience version of a common use case of `_.map`: fetching a property.
+  function pluck(obj, key) {
+    return map(obj, property(key));
+  }
+
+  // Convenience version of a common use case of `_.filter`: selecting only
+  // objects containing specific `key:value` pairs.
+  function where(obj, attrs) {
+    return filter(obj, matcher(attrs));
+  }
+
+  // Return the maximum element (or element-based computation).
+  function max(obj, iteratee, context) {
+    var result = -Infinity, lastComputed = -Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value > result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Return the minimum element (or element-based computation).
+  function min(obj, iteratee, context) {
+    var result = Infinity, lastComputed = Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value < result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed < lastComputed || computed === Infinity && result === Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Sample **n** random values from a collection using the modern version of the
+  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
+  // If **n** is not specified, returns a single random element.
+  // The internal `guard` argument allows it to work with `_.map`.
+  function sample(obj, n, guard) {
+    if (n == null || guard) {
+      if (!isArrayLike(obj)) obj = values(obj);
+      return obj[random(obj.length - 1)];
+    }
+    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
+    var length = getLength(sample);
+    n = Math.max(Math.min(n, length), 0);
+    var last = length - 1;
+    for (var index = 0; index < n; index++) {
+      var rand = random(index, last);
+      var temp = sample[index];
+      sample[index] = sample[rand];
+      sample[rand] = temp;
+    }
+    return sample.slice(0, n);
+  }
+
+  // Shuffle a collection.
+  function shuffle(obj) {
+    return sample(obj, Infinity);
+  }
+
+  // Sort the object's values by a criterion produced by an iteratee.
+  function sortBy(obj, iteratee, context) {
+    var index = 0;
+    iteratee = cb(iteratee, context);
+    return pluck(map(obj, function(value, key, list) {
+      return {
+        value: value,
+        index: index++,
+        criteria: iteratee(value, key, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria;
+      var b = right.criteria;
+      if (a !== b) {
+        if (a > b || a === void 0) return 1;
+        if (a < b || b === void 0) return -1;
+      }
+      return left.index - right.index;
+    }), 'value');
+  }
+
+  // An internal function used for aggregate "group by" operations.
+  function group(behavior, partition) {
+    return function(obj, iteratee, context) {
+      var result = partition ? [[], []] : {};
+      iteratee = cb(iteratee, context);
+      each(obj, function(value, index) {
+        var key = iteratee(value, index, obj);
+        behavior(result, value, key);
+      });
+      return result;
+    };
+  }
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  var groupBy = group(function(result, value, key) {
+    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
+  });
+
+  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
+  // when you know that your index values will be unique.
+  var indexBy = group(function(result, value, key) {
+    result[key] = value;
+  });
+
+  // Counts instances of an object that group by a certain criterion. Pass
+  // either a string attribute to count by, or a function that returns the
+  // criterion.
+  var countBy = group(function(result, value, key) {
+    if (has$1(result, key)) result[key]++; else result[key] = 1;
+  });
+
+  // Split a collection into two arrays: one whose elements all pass the given
+  // truth test, and one whose elements all do not pass the truth test.
+  var partition = group(function(result, value, pass) {
+    result[pass ? 0 : 1].push(value);
+  }, true);
+
+  // Safely create a real, live array from anything iterable.
+  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
+  function toArray(obj) {
+    if (!obj) return [];
+    if (isArray(obj)) return slice.call(obj);
+    if (isString(obj)) {
+      // Keep surrogate pair characters together.
+      return obj.match(reStrSymbol);
+    }
+    if (isArrayLike(obj)) return map(obj, identity);
+    return values(obj);
+  }
+
+  // Return the number of elements in a collection.
+  function size(obj) {
+    if (obj == null) return 0;
+    return isArrayLike(obj) ? obj.length : keys(obj).length;
+  }
+
+  // Internal `_.pick` helper function to determine whether `key` is an enumerable
+  // property name of `obj`.
+  function keyInObj(value, key, obj) {
+    return key in obj;
+  }
+
+  // Return a copy of the object only containing the allowed properties.
+  var pick = restArguments(function(obj, keys) {
+    var result = {}, iteratee = keys[0];
+    if (obj == null) return result;
+    if (isFunction$1(iteratee)) {
+      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
+      keys = allKeys(obj);
+    } else {
+      iteratee = keyInObj;
+      keys = flatten$1(keys, false, false);
+      obj = Object(obj);
+    }
+    for (var i = 0, length = keys.length; i < length; i++) {
+      var key = keys[i];
+      var value = obj[key];
+      if (iteratee(value, key, obj)) result[key] = value;
+    }
+    return result;
+  });
+
+  // Return a copy of the object without the disallowed properties.
+  var omit = restArguments(function(obj, keys) {
+    var iteratee = keys[0], context;
+    if (isFunction$1(iteratee)) {
+      iteratee = negate(iteratee);
+      if (keys.length > 1) context = keys[1];
+    } else {
+      keys = map(flatten$1(keys, false, false), String);
+      iteratee = function(value, key) {
+        return !contains(keys, key);
+      };
+    }
+    return pick(obj, iteratee, context);
+  });
+
+  // Returns everything but the last entry of the array. Especially useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N.
+  function initial(array, n, guard) {
+    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
+  }
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. The **guard** check allows it to work with `_.map`.
+  function first(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[0];
+    return initial(array, array.length - n);
+  }
+
+  // Returns everything but the first entry of the `array`. Especially useful on
+  // the `arguments` object. Passing an **n** will return the rest N values in the
+  // `array`.
+  function rest(array, n, guard) {
+    return slice.call(array, n == null || guard ? 1 : n);
+  }
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array.
+  function last(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[array.length - 1];
+    return rest(array, Math.max(0, array.length - n));
+  }
+
+  // Trim out all falsy values from an array.
+  function compact(array) {
+    return filter(array, Boolean);
+  }
+
+  // Flatten out an array, either recursively (by default), or up to `depth`.
+  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
+  function flatten(array, depth) {
+    return flatten$1(array, depth, false);
+  }
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  var difference = restArguments(function(array, rest) {
+    rest = flatten$1(rest, true, true);
+    return filter(array, function(value){
+      return !contains(rest, value);
+    });
+  });
+
+  // Return a version of the array that does not contain the specified value(s).
+  var without = restArguments(function(array, otherArrays) {
+    return difference(array, otherArrays);
+  });
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // The faster algorithm will not work with an iteratee if the iteratee
+  // is not a one-to-one function, so providing an iteratee will disable
+  // the faster algorithm.
+  function uniq(array, isSorted, iteratee, context) {
+    if (!isBoolean(isSorted)) {
+      context = iteratee;
+      iteratee = isSorted;
+      isSorted = false;
+    }
+    if (iteratee != null) iteratee = cb(iteratee, context);
+    var result = [];
+    var seen = [];
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var value = array[i],
+          computed = iteratee ? iteratee(value, i, array) : value;
+      if (isSorted && !iteratee) {
+        if (!i || seen !== computed) result.push(value);
+        seen = computed;
+      } else if (iteratee) {
+        if (!contains(seen, computed)) {
+          seen.push(computed);
+          result.push(value);
+        }
+      } else if (!contains(result, value)) {
+        result.push(value);
+      }
+    }
+    return result;
+  }
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  var union = restArguments(function(arrays) {
+    return uniq(flatten$1(arrays, true, true));
+  });
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays.
+  function intersection(array) {
+    var result = [];
+    var argsLength = arguments.length;
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var item = array[i];
+      if (contains(result, item)) continue;
+      var j;
+      for (j = 1; j < argsLength; j++) {
+        if (!contains(arguments[j], item)) break;
+      }
+      if (j === argsLength) result.push(item);
+    }
+    return result;
+  }
+
+  // Complement of zip. Unzip accepts an array of arrays and groups
+  // each array's elements on shared indices.
+  function unzip(array) {
+    var length = array && max(array, getLength).length || 0;
+    var result = Array(length);
+
+    for (var index = 0; index < length; index++) {
+      result[index] = pluck(array, index);
+    }
+    return result;
+  }
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  var zip = restArguments(unzip);
+
+  // Converts lists into objects. Pass either a single array of `[key, value]`
+  // pairs, or two parallel arrays of the same length -- one of keys, and one of
+  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
+  function object(list, values) {
+    var result = {};
+    for (var i = 0, length = getLength(list); i < length; i++) {
+      if (values) {
+        result[list[i]] = values[i];
+      } else {
+        result[list[i][0]] = list[i][1];
+      }
+    }
+    return result;
+  }
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](https://docs.python.org/library/functions.html#range).
+  function range(start, stop, step) {
+    if (stop == null) {
+      stop = start || 0;
+      start = 0;
+    }
+    if (!step) {
+      step = stop < start ? -1 : 1;
+    }
+
+    var length = Math.max(Math.ceil((stop - start) / step), 0);
+    var range = Array(length);
+
+    for (var idx = 0; idx < length; idx++, start += step) {
+      range[idx] = start;
+    }
+
+    return range;
+  }
+
+  // Chunk a single array into multiple arrays, each containing `count` or fewer
+  // items.
+  function chunk(array, count) {
+    if (count == null || count < 1) return [];
+    var result = [];
+    var i = 0, length = array.length;
+    while (i < length) {
+      result.push(slice.call(array, i, i += count));
+    }
+    return result;
+  }
+
+  // Helper function to continue chaining intermediate results.
+  function chainResult(instance, obj) {
+    return instance._chain ? _$1(obj).chain() : obj;
+  }
+
+  // Add your own custom functions to the Underscore object.
+  function mixin(obj) {
+    each(functions(obj), function(name) {
+      var func = _$1[name] = obj[name];
+      _$1.prototype[name] = function() {
+        var args = [this._wrapped];
+        push.apply(args, arguments);
+        return chainResult(this, func.apply(_$1, args));
+      };
+    });
+    return _$1;
+  }
+
+  // Add all mutator `Array` functions to the wrapper.
+  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    _$1.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) {
+        method.apply(obj, arguments);
+        if ((name === 'shift' || name === 'splice') && obj.length === 0) {
+          delete obj[0];
+        }
+      }
+      return chainResult(this, obj);
+    };
+  });
+
+  // Add all accessor `Array` functions to the wrapper.
+  each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    _$1.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) obj = method.apply(obj, arguments);
+      return chainResult(this, obj);
+    };
+  });
+
+  // Named Exports
+
+  var allExports = {
+    __proto__: null,
+    VERSION: VERSION,
+    restArguments: restArguments,
+    isObject: isObject,
+    isNull: isNull,
+    isUndefined: isUndefined,
+    isBoolean: isBoolean,
+    isElement: isElement,
+    isString: isString,
+    isNumber: isNumber,
+    isDate: isDate,
+    isRegExp: isRegExp,
+    isError: isError,
+    isSymbol: isSymbol,
+    isArrayBuffer: isArrayBuffer,
+    isDataView: isDataView$1,
+    isArray: isArray,
+    isFunction: isFunction$1,
+    isArguments: isArguments$1,
+    isFinite: isFinite$1,
+    isNaN: isNaN$1,
+    isTypedArray: isTypedArray$1,
+    isEmpty: isEmpty,
+    isMatch: isMatch,
+    isEqual: isEqual,
+    isMap: isMap,
+    isWeakMap: isWeakMap,
+    isSet: isSet,
+    isWeakSet: isWeakSet,
+    keys: keys,
+    allKeys: allKeys,
+    values: values,
+    pairs: pairs,
+    invert: invert,
+    functions: functions,
+    methods: functions,
+    extend: extend,
+    extendOwn: extendOwn,
+    assign: extendOwn,
+    defaults: defaults,
+    create: create,
+    clone: clone,
+    tap: tap,
+    get: get,
+    has: has,
+    mapObject: mapObject,
+    identity: identity,
+    constant: constant,
+    noop: noop,
+    toPath: toPath$1,
+    property: property,
+    propertyOf: propertyOf,
+    matcher: matcher,
+    matches: matcher,
+    times: times,
+    random: random,
+    now: now,
+    escape: _escape,
+    unescape: _unescape,
+    templateSettings: templateSettings,
+    template: template,
+    result: result,
+    uniqueId: uniqueId,
+    chain: chain,
+    iteratee: iteratee,
+    partial: partial,
+    bind: bind,
+    bindAll: bindAll,
+    memoize: memoize,
+    delay: delay,
+    defer: defer,
+    throttle: throttle,
+    debounce: debounce,
+    wrap: wrap,
+    negate: negate,
+    compose: compose,
+    after: after,
+    before: before,
+    once: once,
+    findKey: findKey,
+    findIndex: findIndex,
+    findLastIndex: findLastIndex,
+    sortedIndex: sortedIndex,
+    indexOf: indexOf,
+    lastIndexOf: lastIndexOf,
+    find: find,
+    detect: find,
+    findWhere: findWhere,
+    each: each,
+    forEach: each,
+    map: map,
+    collect: map,
+    reduce: reduce,
+    foldl: reduce,
+    inject: reduce,
+    reduceRight: reduceRight,
+    foldr: reduceRight,
+    filter: filter,
+    select: filter,
+    reject: reject,
+    every: every,
+    all: every,
+    some: some,
+    any: some,
+    contains: contains,
+    includes: contains,
+    include: contains,
+    invoke: invoke,
+    pluck: pluck,
+    where: where,
+    max: max,
+    min: min,
+    shuffle: shuffle,
+    sample: sample,
+    sortBy: sortBy,
+    groupBy: groupBy,
+    indexBy: indexBy,
+    countBy: countBy,
+    partition: partition,
+    toArray: toArray,
+    size: size,
+    pick: pick,
+    omit: omit,
+    first: first,
+    head: first,
+    take: first,
+    initial: initial,
+    last: last,
+    rest: rest,
+    tail: rest,
+    drop: rest,
+    compact: compact,
+    flatten: flatten,
+    without: without,
+    uniq: uniq,
+    unique: uniq,
+    union: union,
+    intersection: intersection,
+    difference: difference,
+    unzip: unzip,
+    transpose: unzip,
+    zip: zip,
+    object: object,
+    range: range,
+    chunk: chunk,
+    mixin: mixin,
+    'default': _$1
+  };
+
+  // Default Export
+
+  // Add all of the Underscore functions to the wrapper object.
+  var _ = mixin(allExports);
+  // Legacy Node.js API.
+  _._ = _;
+
+  return _;
+
+})));
+//# sourceMappingURL=underscore-umd.js.map
diff --git a/docs/v1.2.0/_static/underscore-1.3.1.js b/docs/v1.2.0/_static/underscore-1.3.1.js
new file mode 100644
index 0000000000..208d4cd890
--- /dev/null
+++ b/docs/v1.2.0/_static/underscore-1.3.1.js
@@ -0,0 +1,999 @@
+//     Underscore.js 1.3.1
+//     (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
+//     Underscore is freely distributable under the MIT license.
+//     Portions of Underscore are inspired or borrowed from Prototype,
+//     Oliver Steele's Functional, and John Resig's Micro-Templating.
+//     For all details and documentation:
+//     http://documentcloud.github.com/underscore
+
+(function() {
+
+  // Baseline setup
+  // --------------
+
+  // Establish the root object, `window` in the browser, or `global` on the server.
+  var root = this;
+
+  // Save the previous value of the `_` variable.
+  var previousUnderscore = root._;
+
+  // Establish the object that gets returned to break out of a loop iteration.
+  var breaker = {};
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var slice            = ArrayProto.slice,
+      unshift          = ArrayProto.unshift,
+      toString         = ObjProto.toString,
+      hasOwnProperty   = ObjProto.hasOwnProperty;
+
+  // All **ECMAScript 5** native function implementations that we hope to use
+  // are declared here.
+  var
+    nativeForEach      = ArrayProto.forEach,
+    nativeMap          = ArrayProto.map,
+    nativeReduce       = ArrayProto.reduce,
+    nativeReduceRight  = ArrayProto.reduceRight,
+    nativeFilter       = ArrayProto.filter,
+    nativeEvery        = ArrayProto.every,
+    nativeSome         = ArrayProto.some,
+    nativeIndexOf      = ArrayProto.indexOf,
+    nativeLastIndexOf  = ArrayProto.lastIndexOf,
+    nativeIsArray      = Array.isArray,
+    nativeKeys         = Object.keys,
+    nativeBind         = FuncProto.bind;
+
+  // Create a safe reference to the Underscore object for use below.
+  var _ = function(obj) { return new wrapper(obj); };
+
+  // Export the Underscore object for **Node.js**, with
+  // backwards-compatibility for the old `require()` API. If we're in
+  // the browser, add `_` as a global object via a string identifier,
+  // for Closure Compiler "advanced" mode.
+  if (typeof exports !== 'undefined') {
+    if (typeof module !== 'undefined' && module.exports) {
+      exports = module.exports = _;
+    }
+    exports._ = _;
+  } else {
+    root['_'] = _;
+  }
+
+  // Current version.
+  _.VERSION = '1.3.1';
+
+  // Collection Functions
+  // --------------------
+
+  // The cornerstone, an `each` implementation, aka `forEach`.
+  // Handles objects with the built-in `forEach`, arrays, and raw objects.
+  // Delegates to **ECMAScript 5**'s native `forEach` if available.
+  var each = _.each = _.forEach = function(obj, iterator, context) {
+    if (obj == null) return;
+    if (nativeForEach && obj.forEach === nativeForEach) {
+      obj.forEach(iterator, context);
+    } else if (obj.length === +obj.length) {
+      for (var i = 0, l = obj.length; i < l; i++) {
+        if (i in obj && iterator.call(context, obj[i], i, obj) === breaker) return;
+      }
+    } else {
+      for (var key in obj) {
+        if (_.has(obj, key)) {
+          if (iterator.call(context, obj[key], key, obj) === breaker) return;
+        }
+      }
+    }
+  };
+
+  // Return the results of applying the iterator to each element.
+  // Delegates to **ECMAScript 5**'s native `map` if available.
+  _.map = _.collect = function(obj, iterator, context) {
+    var results = [];
+    if (obj == null) return results;
+    if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context);
+    each(obj, function(value, index, list) {
+      results[results.length] = iterator.call(context, value, index, list);
+    });
+    if (obj.length === +obj.length) results.length = obj.length;
+    return results;
+  };
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`. Delegates to **ECMAScript 5**'s native `reduce` if available.
+  _.reduce = _.foldl = _.inject = function(obj, iterator, memo, context) {
+    var initial = arguments.length > 2;
+    if (obj == null) obj = [];
+    if (nativeReduce && obj.reduce === nativeReduce) {
+      if (context) iterator = _.bind(iterator, context);
+      return initial ? obj.reduce(iterator, memo) : obj.reduce(iterator);
+    }
+    each(obj, function(value, index, list) {
+      if (!initial) {
+        memo = value;
+        initial = true;
+      } else {
+        memo = iterator.call(context, memo, value, index, list);
+      }
+    });
+    if (!initial) throw new TypeError('Reduce of empty array with no initial value');
+    return memo;
+  };
+
+  // The right-associative version of reduce, also known as `foldr`.
+  // Delegates to **ECMAScript 5**'s native `reduceRight` if available.
+  _.reduceRight = _.foldr = function(obj, iterator, memo, context) {
+    var initial = arguments.length > 2;
+    if (obj == null) obj = [];
+    if (nativeReduceRight && obj.reduceRight === nativeReduceRight) {
+      if (context) iterator = _.bind(iterator, context);
+      return initial ? obj.reduceRight(iterator, memo) : obj.reduceRight(iterator);
+    }
+    var reversed = _.toArray(obj).reverse();
+    if (context && !initial) iterator = _.bind(iterator, context);
+    return initial ? _.reduce(reversed, iterator, memo, context) : _.reduce(reversed, iterator);
+  };
+
+  // Return the first value which passes a truth test. Aliased as `detect`.
+  _.find = _.detect = function(obj, iterator, context) {
+    var result;
+    any(obj, function(value, index, list) {
+      if (iterator.call(context, value, index, list)) {
+        result = value;
+        return true;
+      }
+    });
+    return result;
+  };
+
+  // Return all the elements that pass a truth test.
+  // Delegates to **ECMAScript 5**'s native `filter` if available.
+  // Aliased as `select`.
+  _.filter = _.select = function(obj, iterator, context) {
+    var results = [];
+    if (obj == null) return results;
+    if (nativeFilter && obj.filter === nativeFilter) return obj.filter(iterator, context);
+    each(obj, function(value, index, list) {
+      if (iterator.call(context, value, index, list)) results[results.length] = value;
+    });
+    return results;
+  };
+
+  // Return all the elements for which a truth test fails.
+  _.reject = function(obj, iterator, context) {
+    var results = [];
+    if (obj == null) return results;
+    each(obj, function(value, index, list) {
+      if (!iterator.call(context, value, index, list)) results[results.length] = value;
+    });
+    return results;
+  };
+
+  // Determine whether all of the elements match a truth test.
+  // Delegates to **ECMAScript 5**'s native `every` if available.
+  // Aliased as `all`.
+  _.every = _.all = function(obj, iterator, context) {
+    var result = true;
+    if (obj == null) return result;
+    if (nativeEvery && obj.every === nativeEvery) return obj.every(iterator, context);
+    each(obj, function(value, index, list) {
+      if (!(result = result && iterator.call(context, value, index, list))) return breaker;
+    });
+    return result;
+  };
+
+  // Determine if at least one element in the object matches a truth test.
+  // Delegates to **ECMAScript 5**'s native `some` if available.
+  // Aliased as `any`.
+  var any = _.some = _.any = function(obj, iterator, context) {
+    iterator || (iterator = _.identity);
+    var result = false;
+    if (obj == null) return result;
+    if (nativeSome && obj.some === nativeSome) return obj.some(iterator, context);
+    each(obj, function(value, index, list) {
+      if (result || (result = iterator.call(context, value, index, list))) return breaker;
+    });
+    return !!result;
+  };
+
+  // Determine if a given value is included in the array or object using `===`.
+  // Aliased as `contains`.
+  _.include = _.contains = function(obj, target) {
+    var found = false;
+    if (obj == null) return found;
+    if (nativeIndexOf && obj.indexOf === nativeIndexOf) return obj.indexOf(target) != -1;
+    found = any(obj, function(value) {
+      return value === target;
+    });
+    return found;
+  };
+
+  // Invoke a method (with arguments) on every item in a collection.
+  _.invoke = function(obj, method) {
+    var args = slice.call(arguments, 2);
+    return _.map(obj, function(value) {
+      return (_.isFunction(method) ? method || value : value[method]).apply(value, args);
+    });
+  };
+
+  // Convenience version of a common use case of `map`: fetching a property.
+  _.pluck = function(obj, key) {
+    return _.map(obj, function(value){ return value[key]; });
+  };
+
+  // Return the maximum element or (element-based computation).
+  _.max = function(obj, iterator, context) {
+    if (!iterator && _.isArray(obj)) return Math.max.apply(Math, obj);
+    if (!iterator && _.isEmpty(obj)) return -Infinity;
+    var result = {computed : -Infinity};
+    each(obj, function(value, index, list) {
+      var computed = iterator ? iterator.call(context, value, index, list) : value;
+      computed >= result.computed && (result = {value : value, computed : computed});
+    });
+    return result.value;
+  };
+
+  // Return the minimum element (or element-based computation).
+  _.min = function(obj, iterator, context) {
+    if (!iterator && _.isArray(obj)) return Math.min.apply(Math, obj);
+    if (!iterator && _.isEmpty(obj)) return Infinity;
+    var result = {computed : Infinity};
+    each(obj, function(value, index, list) {
+      var computed = iterator ? iterator.call(context, value, index, list) : value;
+      computed < result.computed && (result = {value : value, computed : computed});
+    });
+    return result.value;
+  };
+
+  // Shuffle an array.
+  _.shuffle = function(obj) {
+    var shuffled = [], rand;
+    each(obj, function(value, index, list) {
+      if (index == 0) {
+        shuffled[0] = value;
+      } else {
+        rand = Math.floor(Math.random() * (index + 1));
+        shuffled[index] = shuffled[rand];
+        shuffled[rand] = value;
+      }
+    });
+    return shuffled;
+  };
+
+  // Sort the object's values by a criterion produced by an iterator.
+  _.sortBy = function(obj, iterator, context) {
+    return _.pluck(_.map(obj, function(value, index, list) {
+      return {
+        value : value,
+        criteria : iterator.call(context, value, index, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria, b = right.criteria;
+      return a < b ? -1 : a > b ? 1 : 0;
+    }), 'value');
+  };
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  _.groupBy = function(obj, val) {
+    var result = {};
+    var iterator = _.isFunction(val) ? val : function(obj) { return obj[val]; };
+    each(obj, function(value, index) {
+      var key = iterator(value, index);
+      (result[key] || (result[key] = [])).push(value);
+    });
+    return result;
+  };
+
+  // Use a comparator function to figure out at what index an object should
+  // be inserted so as to maintain order. Uses binary search.
+  _.sortedIndex = function(array, obj, iterator) {
+    iterator || (iterator = _.identity);
+    var low = 0, high = array.length;
+    while (low < high) {
+      var mid = (low + high) >> 1;
+      iterator(array[mid]) < iterator(obj) ? low = mid + 1 : high = mid;
+    }
+    return low;
+  };
+
+  // Safely convert anything iterable into a real, live array.
+  _.toArray = function(iterable) {
+    if (!iterable)                return [];
+    if (iterable.toArray)         return iterable.toArray();
+    if (_.isArray(iterable))      return slice.call(iterable);
+    if (_.isArguments(iterable))  return slice.call(iterable);
+    return _.values(iterable);
+  };
+
+  // Return the number of elements in an object.
+  _.size = function(obj) {
+    return _.toArray(obj).length;
+  };
+
+  // Array Functions
+  // ---------------
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. Aliased as `head`. The **guard** check allows it to work
+  // with `_.map`.
+  _.first = _.head = function(array, n, guard) {
+    return (n != null) && !guard ? slice.call(array, 0, n) : array[0];
+  };
+
+  // Returns everything but the last entry of the array. Especcialy useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N. The **guard** check allows it to work with
+  // `_.map`.
+  _.initial = function(array, n, guard) {
+    return slice.call(array, 0, array.length - ((n == null) || guard ? 1 : n));
+  };
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array. The **guard** check allows it to work with `_.map`.
+  _.last = function(array, n, guard) {
+    if ((n != null) && !guard) {
+      return slice.call(array, Math.max(array.length - n, 0));
+    } else {
+      return array[array.length - 1];
+    }
+  };
+
+  // Returns everything but the first entry of the array. Aliased as `tail`.
+  // Especially useful on the arguments object. Passing an **index** will return
+  // the rest of the values in the array from that index onward. The **guard**
+  // check allows it to work with `_.map`.
+  _.rest = _.tail = function(array, index, guard) {
+    return slice.call(array, (index == null) || guard ? 1 : index);
+  };
+
+  // Trim out all falsy values from an array.
+  _.compact = function(array) {
+    return _.filter(array, function(value){ return !!value; });
+  };
+
+  // Return a completely flattened version of an array.
+  _.flatten = function(array, shallow) {
+    return _.reduce(array, function(memo, value) {
+      if (_.isArray(value)) return memo.concat(shallow ? value : _.flatten(value));
+      memo[memo.length] = value;
+      return memo;
+    }, []);
+  };
+
+  // Return a version of the array that does not contain the specified value(s).
+  _.without = function(array) {
+    return _.difference(array, slice.call(arguments, 1));
+  };
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // Aliased as `unique`.
+  _.uniq = _.unique = function(array, isSorted, iterator) {
+    var initial = iterator ? _.map(array, iterator) : array;
+    var result = [];
+    _.reduce(initial, function(memo, el, i) {
+      if (0 == i || (isSorted === true ? _.last(memo) != el : !_.include(memo, el))) {
+        memo[memo.length] = el;
+        result[result.length] = array[i];
+      }
+      return memo;
+    }, []);
+    return result;
+  };
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  _.union = function() {
+    return _.uniq(_.flatten(arguments, true));
+  };
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays. (Aliased as "intersect" for back-compat.)
+  _.intersection = _.intersect = function(array) {
+    var rest = slice.call(arguments, 1);
+    return _.filter(_.uniq(array), function(item) {
+      return _.every(rest, function(other) {
+        return _.indexOf(other, item) >= 0;
+      });
+    });
+  };
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  _.difference = function(array) {
+    var rest = _.flatten(slice.call(arguments, 1));
+    return _.filter(array, function(value){ return !_.include(rest, value); });
+  };
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  _.zip = function() {
+    var args = slice.call(arguments);
+    var length = _.max(_.pluck(args, 'length'));
+    var results = new Array(length);
+    for (var i = 0; i < length; i++) results[i] = _.pluck(args, "" + i);
+    return results;
+  };
+
+  // If the browser doesn't supply us with indexOf (I'm looking at you, **MSIE**),
+  // we need this function. Return the position of the first occurrence of an
+  // item in an array, or -1 if the item is not included in the array.
+  // Delegates to **ECMAScript 5**'s native `indexOf` if available.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  _.indexOf = function(array, item, isSorted) {
+    if (array == null) return -1;
+    var i, l;
+    if (isSorted) {
+      i = _.sortedIndex(array, item);
+      return array[i] === item ? i : -1;
+    }
+    if (nativeIndexOf && array.indexOf === nativeIndexOf) return array.indexOf(item);
+    for (i = 0, l = array.length; i < l; i++) if (i in array && array[i] === item) return i;
+    return -1;
+  };
+
+  // Delegates to **ECMAScript 5**'s native `lastIndexOf` if available.
+  _.lastIndexOf = function(array, item) {
+    if (array == null) return -1;
+    if (nativeLastIndexOf && array.lastIndexOf === nativeLastIndexOf) return array.lastIndexOf(item);
+    var i = array.length;
+    while (i--) if (i in array && array[i] === item) return i;
+    return -1;
+  };
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](http://docs.python.org/library/functions.html#range).
+  _.range = function(start, stop, step) {
+    if (arguments.length <= 1) {
+      stop = start || 0;
+      start = 0;
+    }
+    step = arguments[2] || 1;
+
+    var len = Math.max(Math.ceil((stop - start) / step), 0);
+    var idx = 0;
+    var range = new Array(len);
+
+    while(idx < len) {
+      range[idx++] = start;
+      start += step;
+    }
+
+    return range;
+  };
+
+  // Function (ahem) Functions
+  // ------------------
+
+  // Reusable constructor function for prototype setting.
+  var ctor = function(){};
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally). Binding with arguments is also known as `curry`.
+  // Delegates to **ECMAScript 5**'s native `Function.bind` if available.
+  // We check for `func.bind` first, to fail fast when `func` is undefined.
+  _.bind = function bind(func, context) {
+    var bound, args;
+    if (func.bind === nativeBind && nativeBind) return nativeBind.apply(func, slice.call(arguments, 1));
+    if (!_.isFunction(func)) throw new TypeError;
+    args = slice.call(arguments, 2);
+    return bound = function() {
+      if (!(this instanceof bound)) return func.apply(context, args.concat(slice.call(arguments)));
+      ctor.prototype = func.prototype;
+      var self = new ctor;
+      var result = func.apply(self, args.concat(slice.call(arguments)));
+      if (Object(result) === result) return result;
+      return self;
+    };
+  };
+
+  // Bind all of an object's methods to that object. Useful for ensuring that
+  // all callbacks defined on an object belong to it.
+  _.bindAll = function(obj) {
+    var funcs = slice.call(arguments, 1);
+    if (funcs.length == 0) funcs = _.functions(obj);
+    each(funcs, function(f) { obj[f] = _.bind(obj[f], obj); });
+    return obj;
+  };
+
+  // Memoize an expensive function by storing its results.
+  _.memoize = function(func, hasher) {
+    var memo = {};
+    hasher || (hasher = _.identity);
+    return function() {
+      var key = hasher.apply(this, arguments);
+      return _.has(memo, key) ? memo[key] : (memo[key] = func.apply(this, arguments));
+    };
+  };
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  _.delay = function(func, wait) {
+    var args = slice.call(arguments, 2);
+    return setTimeout(function(){ return func.apply(func, args); }, wait);
+  };
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  _.defer = function(func) {
+    return _.delay.apply(_, [func, 1].concat(slice.call(arguments, 1)));
+  };
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time.
+  _.throttle = function(func, wait) {
+    var context, args, timeout, throttling, more;
+    var whenDone = _.debounce(function(){ more = throttling = false; }, wait);
+    return function() {
+      context = this; args = arguments;
+      var later = function() {
+        timeout = null;
+        if (more) func.apply(context, args);
+        whenDone();
+      };
+      if (!timeout) timeout = setTimeout(later, wait);
+      if (throttling) {
+        more = true;
+      } else {
+        func.apply(context, args);
+      }
+      whenDone();
+      throttling = true;
+    };
+  };
+
+  // Returns a function, that, as long as it continues to be invoked, will not
+  // be triggered. The function will be called after it stops being called for
+  // N milliseconds.
+  _.debounce = function(func, wait) {
+    var timeout;
+    return function() {
+      var context = this, args = arguments;
+      var later = function() {
+        timeout = null;
+        func.apply(context, args);
+      };
+      clearTimeout(timeout);
+      timeout = setTimeout(later, wait);
+    };
+  };
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  _.once = function(func) {
+    var ran = false, memo;
+    return function() {
+      if (ran) return memo;
+      ran = true;
+      return memo = func.apply(this, arguments);
+    };
+  };
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  _.wrap = function(func, wrapper) {
+    return function() {
+      var args = [func].concat(slice.call(arguments, 0));
+      return wrapper.apply(this, args);
+    };
+  };
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  _.compose = function() {
+    var funcs = arguments;
+    return function() {
+      var args = arguments;
+      for (var i = funcs.length - 1; i >= 0; i--) {
+        args = [funcs[i].apply(this, args)];
+      }
+      return args[0];
+    };
+  };
+
+  // Returns a function that will only be executed after being called N times.
+  _.after = function(times, func) {
+    if (times <= 0) return func();
+    return function() {
+      if (--times < 1) { return func.apply(this, arguments); }
+    };
+  };
+
+  // Object Functions
+  // ----------------
+
+  // Retrieve the names of an object's properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`
+  _.keys = nativeKeys || function(obj) {
+    if (obj !== Object(obj)) throw new TypeError('Invalid object');
+    var keys = [];
+    for (var key in obj) if (_.has(obj, key)) keys[keys.length] = key;
+    return keys;
+  };
+
+  // Retrieve the values of an object's properties.
+  _.values = function(obj) {
+    return _.map(obj, _.identity);
+  };
+
+  // Return a sorted list of the function names available on the object.
+  // Aliased as `methods`
+  _.functions = _.methods = function(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (_.isFunction(obj[key])) names.push(key);
+    }
+    return names.sort();
+  };
+
+  // Extend a given object with all the properties in passed-in object(s).
+  _.extend = function(obj) {
+    each(slice.call(arguments, 1), function(source) {
+      for (var prop in source) {
+        obj[prop] = source[prop];
+      }
+    });
+    return obj;
+  };
+
+  // Fill in a given object with default properties.
+  _.defaults = function(obj) {
+    each(slice.call(arguments, 1), function(source) {
+      for (var prop in source) {
+        if (obj[prop] == null) obj[prop] = source[prop];
+      }
+    });
+    return obj;
+  };
+
+  // Create a (shallow-cloned) duplicate of an object.
+  _.clone = function(obj) {
+    if (!_.isObject(obj)) return obj;
+    return _.isArray(obj) ? obj.slice() : _.extend({}, obj);
+  };
+
+  // Invokes interceptor with the obj, and then returns obj.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  _.tap = function(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  };
+
+  // Internal recursive comparison function.
+  function eq(a, b, stack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the Harmony `egal` proposal: http://wiki.ecmascript.org/doku.php?id=harmony:egal.
+    if (a === b) return a !== 0 || 1 / a == 1 / b;
+    // A strict comparison is necessary because `null == undefined`.
+    if (a == null || b == null) return a === b;
+    // Unwrap any wrapped objects.
+    if (a._chain) a = a._wrapped;
+    if (b._chain) b = b._wrapped;
+    // Invoke a custom `isEqual` method if one is provided.
+    if (a.isEqual && _.isFunction(a.isEqual)) return a.isEqual(b);
+    if (b.isEqual && _.isFunction(b.isEqual)) return b.isEqual(a);
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className != toString.call(b)) return false;
+    switch (className) {
+      // Strings, numbers, dates, and booleans are compared by value.
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return a == String(b);
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive. An `egal` comparison is performed for
+        // other numeric values.
+        return a != +a ? b != +b : (a == 0 ? 1 / a == 1 / b : a == +b);
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a == +b;
+      // RegExps are compared by their source patterns and flags.
+      case '[object RegExp]':
+        return a.source == b.source &&
+               a.global == b.global &&
+               a.multiline == b.multiline &&
+               a.ignoreCase == b.ignoreCase;
+    }
+    if (typeof a != 'object' || typeof b != 'object') return false;
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+    var length = stack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (stack[length] == a) return true;
+    }
+    // Add the first object to the stack of traversed objects.
+    stack.push(a);
+    var size = 0, result = true;
+    // Recursively compare objects and arrays.
+    if (className == '[object Array]') {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      size = a.length;
+      result = size == b.length;
+      if (result) {
+        // Deep compare the contents, ignoring non-numeric properties.
+        while (size--) {
+          // Ensure commutative equality for sparse arrays.
+          if (!(result = size in a == size in b && eq(a[size], b[size], stack))) break;
+        }
+      }
+    } else {
+      // Objects with different constructors are not equivalent.
+      if ('constructor' in a != 'constructor' in b || a.constructor != b.constructor) return false;
+      // Deep compare objects.
+      for (var key in a) {
+        if (_.has(a, key)) {
+          // Count the expected number of properties.
+          size++;
+          // Deep compare each member.
+          if (!(result = _.has(b, key) && eq(a[key], b[key], stack))) break;
+        }
+      }
+      // Ensure that both objects contain the same number of properties.
+      if (result) {
+        for (key in b) {
+          if (_.has(b, key) && !(size--)) break;
+        }
+        result = !size;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    stack.pop();
+    return result;
+  }
+
+  // Perform a deep comparison to check if two objects are equal.
+  _.isEqual = function(a, b) {
+    return eq(a, b, []);
+  };
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  _.isEmpty = function(obj) {
+    if (_.isArray(obj) || _.isString(obj)) return obj.length === 0;
+    for (var key in obj) if (_.has(obj, key)) return false;
+    return true;
+  };
+
+  // Is a given value a DOM element?
+  _.isElement = function(obj) {
+    return !!(obj && obj.nodeType == 1);
+  };
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native Array.isArray
+  _.isArray = nativeIsArray || function(obj) {
+    return toString.call(obj) == '[object Array]';
+  };
+
+  // Is a given variable an object?
+  _.isObject = function(obj) {
+    return obj === Object(obj);
+  };
+
+  // Is a given variable an arguments object?
+  _.isArguments = function(obj) {
+    return toString.call(obj) == '[object Arguments]';
+  };
+  if (!_.isArguments(arguments)) {
+    _.isArguments = function(obj) {
+      return !!(obj && _.has(obj, 'callee'));
+    };
+  }
+
+  // Is a given value a function?
+  _.isFunction = function(obj) {
+    return toString.call(obj) == '[object Function]';
+  };
+
+  // Is a given value a string?
+  _.isString = function(obj) {
+    return toString.call(obj) == '[object String]';
+  };
+
+  // Is a given value a number?
+  _.isNumber = function(obj) {
+    return toString.call(obj) == '[object Number]';
+  };
+
+  // Is the given value `NaN`?
+  _.isNaN = function(obj) {
+    // `NaN` is the only value for which `===` is not reflexive.
+    return obj !== obj;
+  };
+
+  // Is a given value a boolean?
+  _.isBoolean = function(obj) {
+    return obj === true || obj === false || toString.call(obj) == '[object Boolean]';
+  };
+
+  // Is a given value a date?
+  _.isDate = function(obj) {
+    return toString.call(obj) == '[object Date]';
+  };
+
+  // Is the given value a regular expression?
+  _.isRegExp = function(obj) {
+    return toString.call(obj) == '[object RegExp]';
+  };
+
+  // Is a given value equal to null?
+  _.isNull = function(obj) {
+    return obj === null;
+  };
+
+  // Is a given variable undefined?
+  _.isUndefined = function(obj) {
+    return obj === void 0;
+  };
+
+  // Has own property?
+  _.has = function(obj, key) {
+    return hasOwnProperty.call(obj, key);
+  };
+
+  // Utility Functions
+  // -----------------
+
+  // Run Underscore.js in *noConflict* mode, returning the `_` variable to its
+  // previous owner. Returns a reference to the Underscore object.
+  _.noConflict = function() {
+    root._ = previousUnderscore;
+    return this;
+  };
+
+  // Keep the identity function around for default iterators.
+  _.identity = function(value) {
+    return value;
+  };
+
+  // Run a function **n** times.
+  _.times = function (n, iterator, context) {
+    for (var i = 0; i < n; i++) iterator.call(context, i);
+  };
+
+  // Escape a string for HTML interpolation.
+  _.escape = function(string) {
+    return (''+string).replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;').replace(/"/g, '&quot;').replace(/'/g, '&#x27;').replace(/\//g,'&#x2F;');
+  };
+
+  // Add your own custom functions to the Underscore object, ensuring that
+  // they're correctly added to the OOP wrapper as well.
+  _.mixin = function(obj) {
+    each(_.functions(obj), function(name){
+      addToWrapper(name, _[name] = obj[name]);
+    });
+  };
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  _.uniqueId = function(prefix) {
+    var id = idCounter++;
+    return prefix ? prefix + id : id;
+  };
+
+  // By default, Underscore uses ERB-style template delimiters, change the
+  // following template settings to use alternative delimiters.
+  _.templateSettings = {
+    evaluate    : /<%([\s\S]+?)%>/g,
+    interpolate : /<%=([\s\S]+?)%>/g,
+    escape      : /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /.^/;
+
+  // Within an interpolation, evaluation, or escaping, remove HTML escaping
+  // that had been previously added.
+  var unescape = function(code) {
+    return code.replace(/\\\\/g, '\\').replace(/\\'/g, "'");
+  };
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  _.template = function(str, data) {
+    var c  = _.templateSettings;
+    var tmpl = 'var __p=[],print=function(){__p.push.apply(__p,arguments);};' +
+      'with(obj||{}){__p.push(\'' +
+      str.replace(/\\/g, '\\\\')
+         .replace(/'/g, "\\'")
+         .replace(c.escape || noMatch, function(match, code) {
+           return "',_.escape(" + unescape(code) + "),'";
+         })
+         .replace(c.interpolate || noMatch, function(match, code) {
+           return "'," + unescape(code) + ",'";
+         })
+         .replace(c.evaluate || noMatch, function(match, code) {
+           return "');" + unescape(code).replace(/[\r\n\t]/g, ' ') + ";__p.push('";
+         })
+         .replace(/\r/g, '\\r')
+         .replace(/\n/g, '\\n')
+         .replace(/\t/g, '\\t')
+         + "');}return __p.join('');";
+    var func = new Function('obj', '_', tmpl);
+    if (data) return func(data, _);
+    return function(data) {
+      return func.call(this, data, _);
+    };
+  };
+
+  // Add a "chain" function, which will delegate to the wrapper.
+  _.chain = function(obj) {
+    return _(obj).chain();
+  };
+
+  // The OOP Wrapper
+  // ---------------
+
+  // If Underscore is called as a function, it returns a wrapped object that
+  // can be used OO-style. This wrapper holds altered versions of all the
+  // underscore functions. Wrapped objects may be chained.
+  var wrapper = function(obj) { this._wrapped = obj; };
+
+  // Expose `wrapper.prototype` as `_.prototype`
+  _.prototype = wrapper.prototype;
+
+  // Helper function to continue chaining intermediate results.
+  var result = function(obj, chain) {
+    return chain ? _(obj).chain() : obj;
+  };
+
+  // A method to easily add functions to the OOP wrapper.
+  var addToWrapper = function(name, func) {
+    wrapper.prototype[name] = function() {
+      var args = slice.call(arguments);
+      unshift.call(args, this._wrapped);
+      return result(func.apply(_, args), this._chain);
+    };
+  };
+
+  // Add all of the Underscore functions to the wrapper object.
+  _.mixin(_);
+
+  // Add all mutator Array functions to the wrapper.
+  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    wrapper.prototype[name] = function() {
+      var wrapped = this._wrapped;
+      method.apply(wrapped, arguments);
+      var length = wrapped.length;
+      if ((name == 'shift' || name == 'splice') && length === 0) delete wrapped[0];
+      return result(wrapped, this._chain);
+    };
+  });
+
+  // Add all accessor Array functions to the wrapper.
+  each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    wrapper.prototype[name] = function() {
+      return result(method.apply(this._wrapped, arguments), this._chain);
+    };
+  });
+
+  // Start chaining a wrapped Underscore object.
+  wrapper.prototype.chain = function() {
+    this._chain = true;
+    return this;
+  };
+
+  // Extracts the result from a wrapped and chained object.
+  wrapper.prototype.value = function() {
+    return this._wrapped;
+  };
+
+}).call(this);
diff --git a/docs/v1.2.0/_static/underscore.js b/docs/v1.2.0/_static/underscore.js
new file mode 100644
index 0000000000..cf177d4285
--- /dev/null
+++ b/docs/v1.2.0/_static/underscore.js
@@ -0,0 +1,6 @@
+!function(n,r){"object"==typeof exports&&"undefined"!=typeof module?module.exports=r():"function"==typeof define&&define.amd?define("underscore",r):(n="undefined"!=typeof globalThis?globalThis:n||self,function(){var t=n._,e=n._=r();e.noConflict=function(){return n._=t,e}}())}(this,(function(){
+//     Underscore.js 1.13.1
+//     https://underscorejs.org
+//     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
+//     Underscore may be freely distributed under the MIT license.
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+
+  <section id="torchtrtc">
+<span id="id1"></span><h1>torchtrtc<a class="headerlink" href="#torchtrtc" title="Permalink to this headline">¶</a></h1>
+<p><code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code> is a CLI application for using the Torch-TensorRT compiler. It serves as an easy way to compile a
+TorchScript Module with Torch-TensorRT from the command-line to quickly check support or as part of
+a deployment pipeline. All basic features of the compiler are supported including post training
+quantization (though you must already have a calibration cache file to use the PTQ feature). The compiler can
+output two formats, either a TorchScript program with the TensorRT engine embedded or
+the TensorRT engine itself as a PLAN file.</p>
+<p>All that is required to run the program after compilation is for C++ linking against <code class="docutils literal notranslate"><span class="pre">libtorchtrt.so</span></code>
+or in Python importing the torch_tensorrt package. All other aspects of using compiled modules are identical
+to standard TorchScript. Load with <code class="docutils literal notranslate"><span class="pre">torch.jit.load()</span></code> and run like you would run any other module.</p>
+<div class="highlight-txt notranslate"><div class="highlight"><pre><span></span>torchtrtc [input_file_path] [output_file_path]
+  [input_specs...] {OPTIONS}
+
+  torchtrtc is a compiler for TorchScript, it will compile and optimize
+  TorchScript programs to run on NVIDIA GPUs using TensorRT
+
+OPTIONS:
+
+    -h, --help                        Display this help menu
+    Verbiosity of the compiler
+      -v, --verbose                     Dumps debugging information about the
+                                        compilation process onto the console
+      -w, --warnings                    Disables warnings generated during
+                                        compilation onto the console (warnings
+                                        are on by default)
+      --i, --info                       Dumps info messages generated during
+                                        compilation onto the console
+    --build-debuggable-engine         Creates a debuggable engine
+    --allow-gpu-fallback              (Only used when targeting DLA
+                                      (device-type)) Lets engine run layers on
+                                      GPU if they are not supported on DLA
+    --require-full-compilation        Require that the model should be fully
+                                      compiled to TensorRT or throw an error
+    --check-method-support=[method_name]
+                                      Check the support for end to end
+                                      compilation of a specified method in the
+                                      TorchScript module
+    --disable-tf32                    Prevent Float32 layers from using the
+                                      TF32 data format
+    --sparse-weights                  Enable sparsity for weights of conv and
+                                      FC layers
+    -p[precision...],
+    --enable-precision=[precision...] (Repeatable) Enabling an operating
+                                      precision for kernels to use when
+                                      building the engine (Int8 requires a
+                                      calibration-cache argument) [ float |
+                                      float32 | f32 | fp32 | half | float16 |
+                                      f16 | fp16 | int8 | i8 | char ]
+                                      (default: float)
+    -d[type], --device-type=[type]    The type of device the engine should be
+                                      built for [ gpu | dla ] (default: gpu)
+    --gpu-id=[gpu_id]                 GPU id if running on multi-GPU platform
+                                      (defaults to 0)
+    --dla-core=[dla_core]             DLACore id if running on available DLA
+                                      (defaults to 0)
+    --engine-capability=[capability]  The type of device the engine should be
+                                      built for [ standard | safety |
+                                      dla_standalone ]
+    --calibration-cache-file=[file_path]
+                                      Path to calibration cache file to use
+                                      for post training quantization
+    --teo=[op_name...],
+    --torch-executed-op=[op_name...]  (Repeatable) Operator in the graph that
+                                      should always be run in PyTorch for
+                                      execution (partial compilation must be
+                                      enabled)
+    --tem=[module_name...],
+    --torch-executed-mod=[module_name...]
+                                      (Repeatable) Module that should always
+                                      be run in Pytorch for execution (partial
+                                      compilation must be enabled)
+    --mbs=[num_ops],
+    --min-block-size=[num_ops]        Minimum number of contiguous TensorRT
+                                      supported ops to compile a subgraph to
+                                      TensorRT
+    --embed-engine                    Whether to treat input file as a
+                                      serialized TensorRT engine and embed it
+                                      into a TorchScript module (device spec
+                                      must be provided)
+    --num-avg-timing-iters=[num_iters]
+                                      Number of averaging timing iterations
+                                      used to select kernels
+    --workspace-size=[workspace_size] Maximum size of workspace given to
+                                      TensorRT
+    --dla-sram-size=[dla_sram_size]   Fast software managed RAM used by DLA
+                                      to communicate within a layer.
+    --dla-local-dram-size=[dla_local_dram_size]  Host RAM used by DLA to share
+                                      intermediate tensor data across operations.
+    --dla-global-dram-size=[dla_global_dram_size] Host RAM used by DLA to store
+                                      weights and metadata for execution
+    --atol=[atol]                     Absolute tolerance threshold for acceptable
+                                      numerical deviation from standard torchscript
+                                      output (default 1e-8)
+    --rtol=[rtol]                     Relative tolerance threshold for acceptable
+                                      numerical deviation from standard torchscript
+                                      output  (default 1e-5)
+    --no-threshold-check              Skip checking threshold compliance
+    --truncate-long-double,
+    --truncate, --truncate-64bit      Truncate weights that are provided in
+                                      64bit to 32bit (Long, Double to Int,
+                                      Float)
+    --save-engine                     Instead of compiling a full a
+                                      TorchScript program, save the created
+                                      engine to the path specified as the
+                                      output path
+    --custom-torch-ops                (repeatable) Shared object/DLL containing custom torch operators
+    --custom-converters               (repeatable) Shared object/DLL containing custom converters
+    input_file_path                   Path to input TorchScript file
+    output_file_path                  Path for compiled TorchScript (or
+                                      TensorRT engine) file
+    input_specs...                    Specs for inputs to engine, can either
+                                      be a single size or a range defined by
+                                      Min, Optimal, Max sizes, e.g.
+                                      &quot;(N,..,C,H,W)&quot;
+                                      &quot;[(MIN_N,..,MIN_C,MIN_H,MIN_W);(OPT_N,..,OPT_C,OPT_H,OPT_W);(MAX_N,..,MAX_C,MAX_H,MAX_W)]&quot;.
+                                      Data Type and format can be specified by
+                                      adding an &quot;@&quot; followed by dtype and &quot;%&quot;
+                                      followed by format to the end of the
+                                      shape spec. e.g. &quot;(3, 3, 32,
+                                      32)@f16%NHWC&quot;
+    &quot;--&quot; can be used to terminate flag options and force all following
+    arguments to be treated as positional options
+</pre></div>
+</div>
+<p>e.g.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts <span class="s2">&quot;[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous&quot;</span> -p f16
+</pre></div>
+</div>
+<ul class="simple">
+<li><p>To include a set of custom operators</p></li>
+</ul>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-torch-ops<span class="o">=</span>&lt;path to custom library .so file&gt; <span class="s2">&quot;[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous&quot;</span> -p f16
+</pre></div>
+</div>
+<ul class="simple">
+<li><p>To include a set of custom converters</p></li>
+</ul>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-converters<span class="o">=</span>&lt;path to custom library .so file&gt; <span class="s2">&quot;[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous&quot;</span> -p f16
+</pre></div>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
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+</ul>
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+
+  <section id="conversion-phase">
+<span id="conversion"></span><h1>Conversion Phase<a class="headerlink" href="#conversion-phase" title="Permalink to this headline">¶</a></h1>
+<p>Once the graph has be simplified to a form thats easy to convert, we then set up a conversion context
+to manage the construction of a TensorRT <code class="docutils literal notranslate"><span class="pre">INetworkDefinition</span></code> from the blocks nodes. The conversion context
+records the set of converted nodes, block inputs and outputs and other information about the conversion
+of the graph. This data is then used to help converters link together layers and also hold build time
+information like weights required to construct the engine. After the context is created, the block
+converter starts iterating through the list of nodes, for each node, the converter will look at its
+inputs and assemble an array of resources to pass to the converter. Inputs can be in a couple of states:</p>
+<ul class="simple">
+<li><p>The input is a block parameter</p>
+<ul>
+<li><p>In this case the input should have already been stored in as an IValue in the
+conversion context <code class="docutils literal notranslate"><span class="pre">evaluated_value_map</span></code>. The conversion stage will add the IValue to the list of args for the
+converter</p></li>
+</ul>
+</li>
+<li><p>The input is an output of a node that has already been converted</p>
+<ul>
+<li><p>In this case the ITensor of the output has added to the <code class="docutils literal notranslate"><span class="pre">value_tensor_map</span></code>,
+The conversion stage will add the ITensor to the list of args for the converter</p></li>
+</ul>
+</li>
+<li><p>The input is from a node that produces a static value</p>
+<ul>
+<li><p>There are nodes that produce static values, typically used to store parameters for operators, we need to
+evaluate these nodes at conversion time to be able to convert a op. The conversion system will look for a node
+evaluator in the evaluator registry and run it on the node. The IValue produced will be entered in the
+conversion context <code class="docutils literal notranslate"><span class="pre">evaluated_value_map</span></code> and added to the list of args for the converter. If the node
+to be evaluated takes inputs, the conversion stage will recursively resolve dependencies until the final
+static value has been evaluated</p></li>
+</ul>
+</li>
+<li><p>The input is from a node that has not been converted</p>
+<ul>
+<li><p>Torch-TensorRT will error out here</p></li>
+</ul>
+</li>
+</ul>
+<section id="node-evaluation">
+<h2>Node Evaluation<a class="headerlink" href="#node-evaluation" title="Permalink to this headline">¶</a></h2>
+<p>There are some nodes that contain static data and are resources for operations. These can be evaluated at
+conversion time so that you can use those values when doing node conversion. In theory any node kind can have
+a conversion time evaluator as long as it produces a static IValue, This IValue will be stored in the conversion
+context so it can be consumed by any node that takes the evaluated node as an input. Common node types are
+<code class="docutils literal notranslate"><span class="pre">prim::Constant</span></code> which emits a constant and <code class="docutils literal notranslate"><span class="pre">prim::ListConstruct</span></code> which makes lists.</p>
+</section>
+<section id="node-converters">
+<h2>Node Converters<a class="headerlink" href="#node-converters" title="Permalink to this headline">¶</a></h2>
+<p>Node converters map JIT nodes to layers or subgraphs of layers. They then associate outputs from the JIT graph
+and the TRT graph together in the conversion context. This allows the conversion stage to assemble the inputs
+for the next node. There are some cases where a node produces an output that is not a Tensor but a static result
+from a calculation done on inputs which need to be converted first. In this case the converter may associate the outputs in
+the <code class="docutils literal notranslate"><span class="pre">evaluated_value_map</span></code> instead of the <code class="docutils literal notranslate"><span class="pre">value_tensor_map</span></code>. For more information take a look at: <a class="reference internal" href="writing_converters.html#writing-converters"><span class="std std-ref">Writing Converters</span></a></p>
+</section>
+</section>
+
+
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diff --git a/docs/v1.2.0/contributors/lowering.html b/docs/v1.2.0/contributors/lowering.html
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
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+</ul>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+
+  <section id="lowering-phase">
+<span id="lowering"></span><h1>Lowering Phase<a class="headerlink" href="#lowering-phase" title="Permalink to this headline">¶</a></h1>
+<p>The lowering phase is made up out of passes which are operations which map a graph from a high level representation
+to a lower level one. Each pass does something specific for instance inlining method calls. The idea is to
+significantly reduce what the conversion phase needs to be able to handle when actually mapping to TensorRT.
+We aim for closer to 1-&gt;1 op conversion vs looking for applicable subgraphs, limiting the number of converters and
+reduce the scope of each converter.</p>
+<p>You can see the effects of each pass by setting the log level to <code class="docutils literal notranslate"><span class="pre">Level::kGraph</span></code></p>
+<section id="passes-used">
+<h2>Passes Used<a class="headerlink" href="#passes-used" title="Permalink to this headline">¶</a></h2>
+<section id="eliminatecommonsubexpression">
+<h3>EliminateCommonSubexpression<a class="headerlink" href="#eliminatecommonsubexpression" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/common_subexpression_elimination.h">torch/csrc/jit/passes/common_subexpression_elimination.h</a></p>
+</div></blockquote>
+<p>Removes common subexpressions in the graph</p>
+</section>
+<section id="eliminate-dead-code">
+<h3>Eliminate Dead Code<a class="headerlink" href="#eliminate-dead-code" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/dead_code_elimination.h">torch/csrc/jit/passes/dead_code_elimination.h</a></p>
+</div></blockquote>
+<p>Dead code elimination will check if a node has side effects and not delete it if it does.</p>
+</section>
+<section id="eliminate-exeception-or-pass-pattern">
+<h3>Eliminate Exeception Or Pass Pattern<a class="headerlink" href="#eliminate-exeception-or-pass-pattern" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/exception_elimination.cpp">Torch-TensorRT/core/lowering/passes/exception_elimination.cpp</a></p>
+</div></blockquote>
+<p>A common pattern in scripted modules are dimension gaurds which will throw execptions if
+the input dimension is not what was expected.</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%1013 : bool = aten::ne(%1012, %24) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py:248:11
+    = prim::If(%1013) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py:248:8
+    block0():
+        = prim::RaiseException(%23) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py:249:12
+    -&gt; ()
+    block1():
+    -&gt; ()
+</pre></div>
+</div>
+<p>Since we are resolving all of this at compile time and there are no execptions in the TensorRT graph, we just remove it.</p>
+</section>
+<section id="eliminate-redundant-gaurds">
+<h3>Eliminate Redundant Gaurds<a class="headerlink" href="#eliminate-redundant-gaurds" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/guard_elimination.h">torch/csrc/jit/passes/guard_elimination.h</a></p>
+</div></blockquote>
+<p>Eliminate redundant guards for ops whose outputs are fully determined by their inputs i.e. if inputs to such ops are
+guarded we are allowed to remove a guard on ops’ outputs</p>
+</section>
+<section id="freeze-module">
+<h3>Freeze Module<a class="headerlink" href="#freeze-module" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/freeze_module.h">torch/csrc/jit/passes/freeze_module.h</a></p>
+</div></blockquote>
+<p>Freeze attributes and inline constants and modules. Propogates constants in the graph.</p>
+</section>
+<section id="fuse-addmm-branches">
+<h3>Fuse AddMM Branches<a class="headerlink" href="#fuse-addmm-branches" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/fuse_addmm_branches.cpp">Torch-TensorRT/core/lowering/passes/fuse_addmm_branches.cpp</a></p>
+</div></blockquote>
+<p>A common pattern in scripted modules is tensors of different dimensions use different constructions for implementing linear layers. We fuse these
+different varients into a single one that will get caught by the Unpack AddMM pass.</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%ret : Tensor = prim::If(%622)
+block0():
+  %ret.1 : Tensor = aten::addmm(%self.fc.bias, %x9.1, %3677, %3, %3)
+  -&gt; (%ret.1)
+block1():
+  %output.1 : Tensor = aten::matmul(%x9.1, %3677)
+  %output0.1 : Tensor = aten::add_(%output.1, %self.fc.bias, %3)
+  -&gt; (%output0.1)
+</pre></div>
+</div>
+<p>We fuse this set of blocks into a graph like this:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%ret : Tensor = aten::addmm(%self.fc.bias, %x9.1, %3677, %3, %3)
+</pre></div>
+</div>
+</section>
+<section id="fuse-linear">
+<h3>Fuse Linear<a class="headerlink" href="#fuse-linear" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/fuse_linear.h">torch/csrc/jit/passes/fuse_linear.h</a></p>
+</div></blockquote>
+<p>Match the <code class="docutils literal notranslate"><span class="pre">aten::linear</span></code> pattern and fuse it into a single <code class="docutils literal notranslate"><span class="pre">aten::linear</span></code>
+This pass fuse the addmm or matmul + add generated by JIT back to linear</p>
+</section>
+<section id="fuse-flatten-linear">
+<h3>Fuse Flatten Linear<a class="headerlink" href="#fuse-flatten-linear" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/fuse_flatten_linear.cpp">Torch-TensorRT/core/lowering/passes/fuse_flatten_linear.cpp</a></p>
+</div></blockquote>
+<p>TensorRT implicity flattens input layers into fully connected layers when they are higher than 1D. So when there is a
+<code class="docutils literal notranslate"><span class="pre">aten::flatten</span></code> -&gt; <code class="docutils literal notranslate"><span class="pre">aten::linear</span></code> pattern we remove the <code class="docutils literal notranslate"><span class="pre">aten::flatten</span></code>.</p>
+</section>
+<section id="lower-graph">
+<h3>Lower Graph<a class="headerlink" href="#lower-graph" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/lower_graph.h">torch/csrc/jit/passes/lower_graph.h</a></p>
+</div></blockquote>
+<p>Given a graph with of a method which first argument is %self, lower it to a graph where
+all attributes accesses are replaced with explicit inputs of the graph
+(rather than results of prim::GetAttr executed on %self). Returns a tuple
+(graph, parameters) where the last module.parameters.size() inputs to the
+graph are the trainable parameters used in this method. The remaining inputs
+are the true inputs to the function.</p>
+</section>
+<section id="lower-tuples">
+<h3>Lower Tuples<a class="headerlink" href="#lower-tuples" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/lower_tuples.h">torch/csrc/jit/passes/lower_tuples.h</a></p>
+</div></blockquote>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">LowerSimpleTuples</span></code>:</p></li>
+</ul>
+<p>Removes tuples where TupleConstruct and TupleUnpack are matched but leaves tuples in place across if statements, loops, and as inputs/outputs</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">LowerAllTuples</span></code>:</p></li>
+</ul>
+<p>Removes _all_ tuples and raises an error if some cannot be removed, this is used by ONNX to ensure there are not tuples before conversion, but will not work on graphs whose inputs contain tuples.</p>
+</section>
+<section id="module-fallback">
+<h3>Module Fallback<a class="headerlink" href="#module-fallback" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/module_fallback.cpp">Torch-TensorRT/core/lowering/passes/module_fallback.cpp</a></p>
+</div></blockquote>
+<p>Module fallback consists of two lowering passes that must be run as a pair. The first pass is run before freezing to place delimiters in the graph around modules
+that should run in PyTorch. The second pass marks nodes between these delimiters after freezing to signify they should run in PyTorch.</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">NotateModuleForFallback</span></code></p></li>
+</ul>
+<p>Places delimiting nodes around module calls pre freezing to signify where in the graph nodes should run in PyTorch</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">MarkNodesForFallback</span></code></p></li>
+</ul>
+<p>Looks for delimiters then marks all nodes between the delimiters to tell partitioning to run them in PyTorch</p>
+</section>
+<section id="peephole-optimze">
+<h3>Peephole Optimze<a class="headerlink" href="#peephole-optimze" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/ppeephole_optimze.h">torch/csrc/jit/passes/peephole_optimze.h</a></p>
+</div></blockquote>
+<p>The intent for this optimization pass is to catch all of the small, easy to catch peephole optimizations you might be interested in doing.</p>
+<dl class="simple">
+<dt>Right now, it does:</dt><dd><ul class="simple">
+<li><p>Eliminate no-op ‘expand’ nodes</p></li>
+<li><p>Simply x.t().t() to x</p></li>
+</ul>
+</dd>
+</dl>
+</section>
+<section id="remove-contiguous">
+<h3>Remove Contiguous<a class="headerlink" href="#remove-contiguous" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/remove_contiguous.cpp">Torch-TensorRT/core/lowering/passes/remove_contiguous.cpp</a></p>
+</div></blockquote>
+<p>Removes contiguous operators since we are doing TensorRT memory is already contiguous.</p>
+</section>
+<section id="remove-dropout">
+<h3>Remove Dropout<a class="headerlink" href="#remove-dropout" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/remove_dropout.cpp">Torch-TensorRT/core/lowering/passes/remove_dropout.cpp</a></p>
+</div></blockquote>
+<p>Removes dropout operators since we are doing inference.</p>
+</section>
+<section id="remove-to">
+<h3>Remove To<a class="headerlink" href="#remove-to" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/remove_to.cpp">Torch-TensorRT/core/lowering/passes/remove_to.cpp</a></p>
+</div></blockquote>
+<p>Removes <code class="docutils literal notranslate"><span class="pre">aten::to</span></code> operators that do casting, since TensorRT mangages it itself. It is important that this is one of the last passes run so that
+other passes have a change to move required cast operators out of the main namespace.</p>
+</section>
+<section id="unpack-addmm">
+<h3>Unpack AddMM<a class="headerlink" href="#unpack-addmm" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/unpack_addmm.cpp">Torch-TensorRT/core/lowering/passes/unpack_addmm.cpp</a></p>
+</div></blockquote>
+<p>Unpacks <code class="docutils literal notranslate"><span class="pre">aten::addmm</span></code> into <code class="docutils literal notranslate"><span class="pre">aten::matmul</span></code> and <code class="docutils literal notranslate"><span class="pre">aten::add_</span></code> (with an additional <code class="docutils literal notranslate"><span class="pre">trt::const</span></code>
+op to freeze the bias in the TensorRT graph). This lets us reuse the <code class="docutils literal notranslate"><span class="pre">aten::matmul</span></code> and <code class="docutils literal notranslate"><span class="pre">aten::add_</span></code>
+converters instead of needing a dedicated converter.</p>
+</section>
+<section id="unpack-logsoftmax">
+<h3>Unpack LogSoftmax<a class="headerlink" href="#unpack-logsoftmax" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/unpack_log_softmax.cpp">Torch-TensorRT/core/lowering/passes/unpack_log_softmax.cpp</a></p>
+</div></blockquote>
+<p>Unpacks <code class="docutils literal notranslate"><span class="pre">aten::logsoftmax</span></code> into <code class="docutils literal notranslate"><span class="pre">aten::softmax</span></code> and <code class="docutils literal notranslate"><span class="pre">aten::log</span></code>. This lets us reuse the
+<code class="docutils literal notranslate"><span class="pre">aten::softmax</span></code> and <code class="docutils literal notranslate"><span class="pre">aten::log</span></code> converters instead of needing a dedicated converter.</p>
+</section>
+<section id="unroll-loops">
+<h3>Unroll Loops<a class="headerlink" href="#unroll-loops" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/loop_unrolling.h">torch/csrc/jit/passes/loop_unrolling.h</a></p>
+</div></blockquote>
+<p>Unrolls the operations of compatable loops (e.g. sufficently short) so that you only have to go through the loop once.</p>
+</section>
+</section>
+</section>
+
+
+             </article>
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+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
+              <ul>
+<li><a class="reference internal" href="#">Lowering Phase</a><ul>
+<li><a class="reference internal" href="#passes-used">Passes Used</a><ul>
+<li><a class="reference internal" href="#eliminatecommonsubexpression">EliminateCommonSubexpression</a></li>
+<li><a class="reference internal" href="#eliminate-dead-code">Eliminate Dead Code</a></li>
+<li><a class="reference internal" href="#eliminate-exeception-or-pass-pattern">Eliminate Exeception Or Pass Pattern</a></li>
+<li><a class="reference internal" href="#eliminate-redundant-gaurds">Eliminate Redundant Gaurds</a></li>
+<li><a class="reference internal" href="#freeze-module">Freeze Module</a></li>
+<li><a class="reference internal" href="#fuse-addmm-branches">Fuse AddMM Branches</a></li>
+<li><a class="reference internal" href="#fuse-linear">Fuse Linear</a></li>
+<li><a class="reference internal" href="#fuse-flatten-linear">Fuse Flatten Linear</a></li>
+<li><a class="reference internal" href="#lower-graph">Lower Graph</a></li>
+<li><a class="reference internal" href="#lower-tuples">Lower Tuples</a></li>
+<li><a class="reference internal" href="#module-fallback">Module Fallback</a></li>
+<li><a class="reference internal" href="#peephole-optimze">Peephole Optimze</a></li>
+<li><a class="reference internal" href="#remove-contiguous">Remove Contiguous</a></li>
+<li><a class="reference internal" href="#remove-dropout">Remove Dropout</a></li>
+<li><a class="reference internal" href="#remove-to">Remove To</a></li>
+<li><a class="reference internal" href="#unpack-addmm">Unpack AddMM</a></li>
+<li><a class="reference internal" href="#unpack-logsoftmax">Unpack LogSoftmax</a></li>
+<li><a class="reference internal" href="#unroll-loops">Unroll Loops</a></li>
+</ul>
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diff --git a/docs/v1.2.0/contributors/partitioning.html b/docs/v1.2.0/contributors/partitioning.html
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+
+  <section id="partitioning-phase">
+<span id="partitioning"></span><h1>Partitioning Phase<a class="headerlink" href="#partitioning-phase" title="Permalink to this headline">¶</a></h1>
+<p>The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
+Criteria for separation include: Lack of a converter, operator is explicitly set to run in PyTorch by the user or the node has a flag which tells partitioning to
+run in PyTorch by the module fallback passes.</p>
+<p>On a high level, Torch-TensorRT partitioning phase does the following:</p>
+<ul class="simple">
+<li><p>Segmentation. Go through the set of operators in order and verify if there is converter for each operator. Then, roughly separate the graph into parts that Torch-TensorRT can support and parts Torch-TensorRT cannot.</p></li>
+<li><p>Dependency Analysis. For every to be compiled operator there is a “complete dependency graph”, which means that every input can to traced back to an input as Tensor or TensorList. Go through all segments after segmentation then do dependency analysis to ensure that there are only Tensor/TensorList inputs and outputs for TensorRT segments.</p></li>
+<li><p>Shape Analysis. For each segments, figure out the input and outputs shapes starting from the provided input shape from the user. Shapes can be calculated by running the graphs with JIT.</p></li>
+<li><p>Conversion. Every TensorRT segments will be converted to TensorRT engine. This part is done in compiler.cpp, but it’s still a phase in our partitioning process.</p></li>
+<li><p>Stitching. Stitch all TensorRT engines with PyTorch nodes altogether.</p></li>
+</ul>
+<p>Here are the brief description of these functions of each file:</p>
+<section id="partitoninfo-h-cpp">
+<h2>PartitonInfo.h/.cpp<a class="headerlink" href="#partitoninfo-h-cpp" title="Permalink to this headline">¶</a></h2>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/partitioning/PartitionInfo.h">core/partitioning/PartitionInfo.h</a></p>
+</div></blockquote>
+<p>The automatic fallback APIs that is used for partitioning.</p>
+</section>
+<section id="segmentedblock-h-cpp">
+<h2>SegmentedBlock.h/.cpp<a class="headerlink" href="#segmentedblock-h-cpp" title="Permalink to this headline">¶</a></h2>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/partitioning/SegmentedBlock.h">core/partitioning/SegmentedBlock.h</a></p>
+</div></blockquote>
+<p>The main data structures that is used to maintain information for each segments after segmentation.</p>
+</section>
+<section id="shape-analysis-h-cpp">
+<h2>shape_analysis.h/.cpp<a class="headerlink" href="#shape-analysis-h-cpp" title="Permalink to this headline">¶</a></h2>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/partitioning/shape_analysis.h">core/partitioning/shape_analysis.h</a></p>
+</div></blockquote>
+<p>Code implementation to get the shapes for each segments by running them in JIT.</p>
+</section>
+<section id="partitioning-h-cpp">
+<h2>partitioning.h/.cpp<a class="headerlink" href="#partitioning-h-cpp" title="Permalink to this headline">¶</a></h2>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/partitioning/partitioning.h">core/partitioning/partitioning.h</a></p>
+</div></blockquote>
+<p>APIs and main code implementation for partitioning phase.</p>
+</section>
+</section>
+<section id="automatic-fallback">
+<h1>Automatic Fallback<a class="headerlink" href="#automatic-fallback" title="Permalink to this headline">¶</a></h1>
+<p>To enable automatic fallback feature, you can set following attributes in Python:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import torch
+import torch_tensorrt as torchtrt
+
+...
+model = MyModel()
+ts_model = torch.jit.script(model)
+trt_model = torchtrt.ts.compile(model, **{
+  ...
+  &quot;min_block_size&quot; : 3,
+  &quot;torch_executed_ops&quot;: [&quot;aten::add&quot;],
+  &quot;torch_executed_modules&quot;: [],
+})
+</pre></div>
+</div>
+<ul class="simple">
+<li><p>enabled: By default automatic fallback will be off. It is enabled by setting it to True.</p></li>
+<li><p>min_block_size: The minimum number of consecutive operations that must satisfy to be converted to TensorRT. For example, if it’s set to 3, then there must be 3 consecutive supported operators then this segments will be converted.</p></li>
+<li><p>forced_fallback_ops: A list of strings that will be the names of operations that the user explicitly want to be in PyTorch nodes.</p></li>
+</ul>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>#include &quot;torch/script.h&quot;
+#include &quot;torch_tensorrt/torch_tensorrt.h&quot;
+
+...
+auto in = torch::randn({1, 3, 224, 224}, {torch::kCUDA});
+
+auto mod = torch::jit::load(&quot;trt_ts_module.ts&quot;);
+auto input_sizes =  std::vector&lt;torchtrt::InputRange&gt;{{in.sizes()}};
+torchtrt::ts::CompileSpec cfg(input_sizes);
+cfg.min_block_size = 2;
+cfg.torch_executed_ops.push_back(&quot;aten::relu&quot;);
+auto trt_mod = torchtrt::ts::compile(mod, cfg);
+auto out = trt_mod.forward({in});
+</pre></div>
+</div>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+
+  <section id="compiler-phases">
+<h1>Compiler Phases<a class="headerlink" href="#compiler-phases" title="Permalink to this headline">¶</a></h1>
+<div class="toctree-wrapper compound">
+</div>
+<section id="lowering">
+<h2>Lowering<a class="headerlink" href="#lowering" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference internal" href="lowering.html#lowering"><span class="std std-ref">Lowering Phase</span></a></p>
+<p>The lowering is made up of a set of passes (some from PyTorch and some specific to Torch-TensorRT)
+run over the graph IR to map the large PyTorch opset to a reduced opset that is easier to convert to
+TensorRT.</p>
+</section>
+<section id="partitioning">
+<h2>Partitioning<a class="headerlink" href="#partitioning" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference internal" href="partitioning.html#partitioning"><span class="std std-ref">Partitioning Phase</span></a></p>
+<p>The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
+Criteria for separation include: Lack of a converter, operator is explicitly set to run in PyTorch by the user or the node has a flag which tells partitioning to
+run in PyTorch by the module fallback passes.</p>
+</section>
+<section id="conversion">
+<h2>Conversion<a class="headerlink" href="#conversion" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference internal" href="conversion.html#conversion"><span class="std std-ref">Conversion Phase</span></a></p>
+<p>In the conversion phase we traverse the lowered graph and construct an equivalent TensorRT graph.
+The conversion phase is made up of three main components, a context to manage compile time data,
+a evaluator library which will execute operations that can be resolved at compile time and a converter
+library which maps an op from JIT to TensorRT.</p>
+</section>
+<section id="compilation-and-runtime">
+<h2>Compilation and Runtime<a class="headerlink" href="#compilation-and-runtime" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference internal" href="../tutorials/runtime.html#runtime"><span class="std std-ref">Deploying Torch-TensorRT Programs</span></a></p>
+<p>The final compilation phase constructs a TorchScript program to run the converted TensorRT engine. It
+takes a serialized engine and instantiates it within a engine manager, then the compiler will
+build out a JIT graph that references this engine and wraps it in a module to return to the user.
+When the user executes the module, the JIT program run in the JIT runtime extended by Torch-TensorRT with the data providied from the user.</p>
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+<li><a class="reference internal" href="#lowering">Lowering</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+
+  <section id="runtime-phase">
+<span id="execution"></span><h1>Runtime Phase<a class="headerlink" href="#runtime-phase" title="Permalink to this headline">¶</a></h1>
+<p>The Runtime phase is responsible for constructing self standing TorchScript graphs with embedded TensorRT engines and serving as the runtime
+when these engines are called. The main interface accepts a serialized TensorRT engine. The execution phase
+will deserialize and wrap this engine in a class which maintains a execution context for each engine
+and some metadata about its inputs and outputs and is compatable with the TorchScript interpreter so that
+it can be moved around and used like other TorchScript IValues. The engine is run by providing it and inputs
+to the <code class="docutils literal notranslate"><span class="pre">tensorrt::execute_engine</span></code> operator which will take the engine and its inputs and return the results of engine exeuction.</p>
+<section id="background">
+<h2>Background<a class="headerlink" href="#background" title="Permalink to this headline">¶</a></h2>
+<p>PyTorch JIT’s runtime is based around a stack machine, all operators pop off arguments from the stack, pass them to
+some implementation of the operator then push results back onto the stack. The actual elements of the stack
+are <code class="docutils literal notranslate"><span class="pre">torch::jit::IValues</span></code>, the same type we evaluate in the conversion phase (the realization of the abstract
+torch::jit::Value type).</p>
+</section>
+<section id="tensorrt-engine-executor-op">
+<h2>TensorRT Engine Executor Op<a class="headerlink" href="#tensorrt-engine-executor-op" title="Permalink to this headline">¶</a></h2>
+<p>When the Torch-TensorRT is loaded, it registers an operator in the PyTorch JIT operator library called
+<code class="docutils literal notranslate"><span class="pre">trt::execute_engine(Tensor[]</span> <span class="pre">inputs,</span> <span class="pre">__torch__.torch.classes.tensorrt.Engine</span> <span class="pre">engine)</span> <span class="pre">-&gt;</span> <span class="pre">Tensor[]</span></code> which takes an
+instantiated engine and list of inputs. Compiled graphs store this engine in an attribute so that it is portable and serializable.
+When the op is called, an instnantiated engine and input tensors are popped off the runtime stack. These inputs are passed into a generic engine execution function which
+will run the tensors through the TensorRT engine and return new tensors as results. These tensors are pushed on to the
+stack so that the next op whatever it is can use it.</p>
+</section>
+<section id="constructing-the-resulting-graph">
+<h2>Constructing the Resulting Graph<a class="headerlink" href="#constructing-the-resulting-graph" title="Permalink to this headline">¶</a></h2>
+<p>Once the engine is deserialized and instantiated, the compiler will construct a graph that will execute the engine when the module is called.
+Here is an example:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">graph</span><span class="p">(</span><span class="o">%</span><span class="n">self_1</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">__torch__</span><span class="p">.</span><span class="n">torchvision</span><span class="p">.</span><span class="n">models</span><span class="p">.</span><span class="n">resnet</span><span class="p">.</span><span class="n">___torch_mangle_4847</span><span class="p">.</span><span class="n">ResNet_trt</span><span class="p">,</span><span class="w"></span>
+<span class="w">  </span><span class="o">%</span><span class="n">input_0</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">)</span><span class="o">:</span><span class="w"></span>
+<span class="w">    </span><span class="o">%</span><span class="mi">1</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">__torch__</span><span class="p">.</span><span class="n">torch</span><span class="p">.</span><span class="n">classes</span><span class="p">.</span><span class="n">tensorrt</span><span class="p">.</span><span class="n">Engine</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">GetAttr</span><span class="p">[</span><span class="n">name</span><span class="o">=</span><span class="s">&quot;__torch___torchvision_models_resnet____torch_mangle_4847_ResNet_trt_engine&quot;</span><span class="p">](</span><span class="o">%</span><span class="n">self_1</span><span class="p">)</span><span class="w"></span>
+<span class="w">    </span><span class="o">%</span><span class="mi">3</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">ListConstruct</span><span class="p">(</span><span class="o">%</span><span class="n">input_0</span><span class="p">)</span><span class="w"></span>
+<span class="w">    </span><span class="o">%</span><span class="mi">4</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">trt</span><span class="o">::</span><span class="n">execute_engine</span><span class="p">(</span><span class="o">%</span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="o">%</span><span class="mi">1</span><span class="p">)</span><span class="w"></span>
+<span class="w">    </span><span class="o">%</span><span class="mi">5</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">ListUnpack</span><span class="p">(</span><span class="o">%</span><span class="mi">4</span><span class="p">)</span><span class="w"></span>
+<span class="k">return</span><span class="w"> </span><span class="p">(</span><span class="o">%</span><span class="mi">5</span><span class="p">)</span><span class="w"></span>
+</pre></div>
+</div>
+<p>You can see the engine attribute in the graph and the <code class="docutils literal notranslate"><span class="pre">trt::execute_engine</span></code> op taking a list of input tensors and an engine in
+and produces a list of output tensors which is returned. When <code class="docutils literal notranslate"><span class="pre">forward</span></code> is called on the module this graph is executed, thereby
+running the TensorRT engine.</p>
+<p>In the case of multiple outputs, the compiled graph may repack output tensors into a Tuple to return back to the user.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">graph</span><span class="p">(</span><span class="o">%</span><span class="n">self_1</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">__torch__</span><span class="p">.</span><span class="n">PyTorch</span><span class="p">.</span><span class="n">Detection</span><span class="p">.</span><span class="n">SSD</span><span class="p">.</span><span class="n">src</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">SSD300_trt</span><span class="p">,</span><span class="w"></span>
+<span class="w">  </span><span class="o">%</span><span class="n">input_0</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">)</span><span class="o">:</span><span class="w"></span>
+<span class="w">    </span><span class="o">%</span><span class="mi">1</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">__torch__</span><span class="p">.</span><span class="n">torch</span><span class="p">.</span><span class="n">classes</span><span class="p">.</span><span class="n">tensorrt</span><span class="p">.</span><span class="n">Engine</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">GetAttr</span><span class="p">[</span><span class="n">name</span><span class="o">=</span><span class="s">&quot;__torch___PyTorch_Detection_SSD_src_model_SSD300_trt_engine&quot;</span><span class="p">](</span><span class="o">%</span><span class="n">self_1</span><span class="p">)</span><span class="w"></span>
+<span class="w">    </span><span class="o">%</span><span class="mi">3</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">ListConstruct</span><span class="p">(</span><span class="o">%</span><span class="n">input_0</span><span class="p">)</span><span class="w"></span>
+<span class="w">    </span><span class="o">%</span><span class="mi">4</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">trt</span><span class="o">::</span><span class="n">execute_engine</span><span class="p">(</span><span class="o">%</span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="o">%</span><span class="mi">1</span><span class="p">)</span><span class="w"></span>
+<span class="w">    </span><span class="o">%</span><span class="mi">5</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">,</span><span class="w"> </span><span class="o">%</span><span class="mi">6</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">ListUnpack</span><span class="p">(</span><span class="o">%</span><span class="mi">4</span><span class="p">)</span><span class="w"></span>
+<span class="w">    </span><span class="o">%</span><span class="mi">7</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="p">(</span><span class="n">Tensor</span><span class="p">,</span><span class="w"> </span><span class="n">Tensor</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">TupleConstruct</span><span class="p">(</span><span class="o">%</span><span class="mi">5</span><span class="p">,</span><span class="w"> </span><span class="o">%</span><span class="mi">6</span><span class="p">)</span><span class="w"></span>
+<span class="k">return</span><span class="w"> </span><span class="p">(</span><span class="o">%</span><span class="mi">7</span><span class="p">)</span><span class="w"></span>
+</pre></div>
+</div>
+</section>
+<section id="serialization-and-deserialization">
+<h2>Serialization and Deserialization<a class="headerlink" href="#serialization-and-deserialization" title="Permalink to this headline">¶</a></h2>
+<p>Serialization and deserialization of TensorRT engines embedded in TorchScript graphs are handled by the holder class for the engine and TorchBind.
+When a TorchScript module is saved, the pickler will run serilization on the cuda engine and store the serialized engine in the zip file created.
+When deserializing, the depickler will call a constructor for the engine holder class with the serialized engine so that it can be set up again for
+execution.</p>
+</section>
+</section>
+<section id="abi-versioning-and-serialization-format">
+<h1>ABI Versioning and Serialization Format<a class="headerlink" href="#abi-versioning-and-serialization-format" title="Permalink to this headline">¶</a></h1>
+<p>Torch-TensorRT programs are standard TorchScript with TensorRT engines as objects embedded in the graph. Therefore there is a serialization format
+for the TensorRT engines. The format for Torch-TensorRT serialized programs are versioned with an “ABI” version which tells the runtime about runtime compatibility.</p>
+<p>&gt; Current ABI version is 3</p>
+<p>The format is a vector of serialized strings. They encode the following information</p>
+<ul class="simple">
+<li><p>ABI Version for the program</p></li>
+<li><p>Name of the TRT engine</p></li>
+<li><p>Device information: Includes the target device the engine was built on, SM capability and other device information. This information is used at deserialization time to select the correct device to run the engine</p></li>
+<li><p>Serialized TensorRT engine</p></li>
+</ul>
+</section>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/logging.html">torch_tensorrt.logging</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">System Overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="writing_converters.html">Writing Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+
+  <section id="system-overview">
+<span id="id1"></span><h1>System Overview<a class="headerlink" href="#system-overview" title="Permalink to this headline">¶</a></h1>
+<p>Torch-TensorRT is primarily a C++ Library with a Python API planned. We use Bazel as our build system and target Linux x86_64 and
+Linux aarch64 (only natively) right now. The compiler we use is GCC 7.5.0 and the library is untested with compilers before that
+version so there may be compilation errors if you try to use an older compiler.</p>
+<p>The repository is structured into:</p>
+<ul class="simple">
+<li><p>core: Main compiler source code</p></li>
+<li><p>cpp: C++ API</p></li>
+<li><p>tests: tests of the C++ API, the core and converters</p></li>
+<li><p>py: Python API</p></li>
+<li><p>notebooks: Example applications built with Torch-TensorRT</p></li>
+<li><p>docs: Documentation</p></li>
+<li><p>docsrc: Documentation Source</p></li>
+<li><p>third_party: BUILD files for dependency libraries</p></li>
+<li><p>toolchains: Toolchains for different platforms</p></li>
+</ul>
+<p>The C++ API is unstable and subject to change until the library matures, though most work is done under the hood in the core.</p>
+<p>The core has a couple major parts: The top level compiler interface which coordinates ingesting a module, lowering,
+converting and generating a new module and returning it back to the user. There are the three main phases of the
+compiler, the lowering phase, the conversion phase, and the execution phase.</p>
+<section id="compiler-phases">
+<h2>Compiler Phases<a class="headerlink" href="#compiler-phases" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+</div>
+<section id="lowering">
+<h3>Lowering<a class="headerlink" href="#lowering" title="Permalink to this headline">¶</a></h3>
+<p><a class="reference internal" href="lowering.html#lowering"><span class="std std-ref">Lowering Phase</span></a></p>
+<p>The lowering is made up of a set of passes (some from PyTorch and some specific to Torch-TensorRT)
+run over the graph IR to map the large PyTorch opset to a reduced opset that is easier to convert to
+TensorRT.</p>
+</section>
+<section id="partitioning">
+<h3>Partitioning<a class="headerlink" href="#partitioning" title="Permalink to this headline">¶</a></h3>
+<p><a class="reference internal" href="partitioning.html#partitioning"><span class="std std-ref">Partitioning Phase</span></a></p>
+<p>The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
+Criteria for separation include: Lack of a converter, operator is explicitly set to run in PyTorch by the user or the node has a flag which tells partitioning to
+run in PyTorch by the module fallback passes.</p>
+</section>
+<section id="conversion">
+<h3>Conversion<a class="headerlink" href="#conversion" title="Permalink to this headline">¶</a></h3>
+<p><a class="reference internal" href="conversion.html#conversion"><span class="std std-ref">Conversion Phase</span></a></p>
+<p>In the conversion phase we traverse the lowered graph and construct an equivalent TensorRT graph.
+The conversion phase is made up of three main components, a context to manage compile time data,
+a evaluator library which will execute operations that can be resolved at compile time and a converter
+library which maps an op from JIT to TensorRT.</p>
+</section>
+<section id="compilation-and-runtime">
+<h3>Compilation and Runtime<a class="headerlink" href="#compilation-and-runtime" title="Permalink to this headline">¶</a></h3>
+<p><a class="reference internal" href="../tutorials/runtime.html#runtime"><span class="std std-ref">Deploying Torch-TensorRT Programs</span></a></p>
+<p>The final compilation phase constructs a TorchScript program to run the converted TensorRT engine. It
+takes a serialized engine and instantiates it within a engine manager, then the compiler will
+build out a JIT graph that references this engine and wraps it in a module to return to the user.
+When the user executes the module, the JIT program run in the JIT runtime extended by Torch-TensorRT with the data providied from the user.</p>
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diff --git a/docs/v1.2.0/contributors/useful_links.html b/docs/v1.2.0/contributors/useful_links.html
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<section id="tensorrt-available-layers-and-expected-dimensions">
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+<li><p><a class="reference external" href="https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/python_api/index.html">https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/python_api/index.html</a></p></li>
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+
+  <section id="writing-converters">
+<span id="id1"></span><h1>Writing Converters<a class="headerlink" href="#writing-converters" title="Permalink to this headline">¶</a></h1>
+<section id="background">
+<h2>Background<a class="headerlink" href="#background" title="Permalink to this headline">¶</a></h2>
+<p>In the JIT IR, operations are represented as nodes in a graph. A node has inputs and outputs, represented by <code class="docutils literal notranslate"><span class="pre">torch::jit::Values</span></code>
+which are typed abstract representation of data flowing into and out of a node. TensorRT represents its graph though the
+use of <code class="docutils literal notranslate"><span class="pre">nvinfer1::ILayers</span></code> and <code class="docutils literal notranslate"><span class="pre">nvinfer1::ITensors</span></code> which are its analogues to nodes and values. The goal of
+converters create new ILayers and subgraphs that do operation specified by the node and associate produced ITensors
+and Values together.</p>
+</section>
+<section id="converters">
+<h2>Converters<a class="headerlink" href="#converters" title="Permalink to this headline">¶</a></h2>
+<p>Converters should be functions which will use a list of inputs (either <code class="docutils literal notranslate"><span class="pre">nvinfer1::ITensors</span></code> or <code class="docutils literal notranslate"><span class="pre">torch::jit::IValues</span></code>) to
+construct an equivalent layer to the LibTorch op.</p>
+<p>Converters can be registered using the <code class="docutils literal notranslate"><span class="pre">RegisterNodeConversionPatterns</span></code> helper class where you instantiate a
+RegisterNodeConversionPatterns object and call the pattern function on it (like below) which takes a string
+which describes the function schema of the op that will cause the converter to be run and a lambda or function
+which will do the actual conversion:</p>
+<blockquote>
+<div><p>Note the pattern function can be chained</p>
+</div></blockquote>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">acthardtanh</span><span class="w"> </span><span class="n">TORCHTRT_UNUSED</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">RegisterNodeConversionPatterns</span><span class="p">()</span><span class="w"></span>
+<span class="w">    </span><span class="p">.</span><span class="n">pattern</span><span class="p">({</span><span class="w"></span>
+<span class="w">        </span><span class="s">&quot;aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -&gt; (Tensor)&quot;</span><span class="p">,</span><span class="w"></span>
+<span class="w">        </span><span class="p">[](</span><span class="n">ConversionCtx</span><span class="o">*</span><span class="w"> </span><span class="n">ctx</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Node</span><span class="o">*</span><span class="w"> </span><span class="n">n</span><span class="p">,</span><span class="w"> </span><span class="n">args</span><span class="o">&amp;</span><span class="w"> </span><span class="n">args</span><span class="p">)</span><span class="w"> </span><span class="o">-&gt;</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">ITensor</span><span class="p">();</span><span class="w"></span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">min</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">unwrapToDouble</span><span class="p">();</span><span class="w"></span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">max</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">2</span><span class="p">].</span><span class="n">unwrapToDouble</span><span class="p">();</span><span class="w"></span>
+
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">new_layer</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ctx</span><span class="o">-&gt;</span><span class="n">net</span><span class="o">-&gt;</span><span class="n">addActivation</span><span class="p">(</span><span class="o">*</span><span class="n">in</span><span class="p">,</span><span class="w"> </span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">ActivationType</span><span class="o">::</span><span class="n">kCLIP</span><span class="p">);</span><span class="w"></span>
+<span class="w">            </span><span class="n">TORCHTRT_CHECK</span><span class="p">(</span><span class="n">new_layer</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;Unable to create layer for aten::hardtanh&quot;</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">            </span><span class="n">new_layer</span><span class="o">-&gt;</span><span class="n">setAlpha</span><span class="p">(</span><span class="n">min</span><span class="p">);</span><span class="w"></span>
+<span class="w">            </span><span class="n">new_layer</span><span class="o">-&gt;</span><span class="n">setBeta</span><span class="p">(</span><span class="n">max</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">            </span><span class="n">new_layer</span><span class="o">-&gt;</span><span class="n">setName</span><span class="p">(</span><span class="n">util</span><span class="o">::</span><span class="n">node_info</span><span class="p">(</span><span class="n">n</span><span class="p">).</span><span class="n">c_str</span><span class="p">());</span><span class="w"></span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">out_tensor</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ctx</span><span class="o">-&gt;</span><span class="n">AssociateValueAndTensor</span><span class="p">(</span><span class="n">n</span><span class="o">-&gt;</span><span class="n">outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">new_layer</span><span class="o">-&gt;</span><span class="n">getOutput</span><span class="p">(</span><span class="mi">0</span><span class="p">));</span><span class="w"></span>
+
+<span class="w">            </span><span class="n">LOG_DEBUG</span><span class="p">(</span><span class="s">&quot;Output shape: &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">out_tensor</span><span class="o">-&gt;</span><span class="n">getDimensions</span><span class="p">());</span><span class="w"></span>
+<span class="w">            </span><span class="k">return</span><span class="w"> </span><span class="nb">true</span><span class="p">;</span><span class="w"></span>
+<span class="w">        </span><span class="p">}</span><span class="w"></span>
+<span class="w">    </span><span class="p">});</span><span class="w"></span>
+</pre></div>
+</div>
+</section>
+<section id="converter-contract">
+<h2>Converter Contract<a class="headerlink" href="#converter-contract" title="Permalink to this headline">¶</a></h2>
+<section id="what-is-guaranteed-to-converters">
+<h3>What is guaranteed to converters<a class="headerlink" href="#what-is-guaranteed-to-converters" title="Permalink to this headline">¶</a></h3>
+<ol class="arabic simple">
+<li><p>In the args there will be an entry for each node input value, either a ITensor or IValue</p></li>
+<li><p>Inputs will be provided in order according to the function schema</p></li>
+</ol>
+</section>
+<section id="responsibilities-of-a-converter">
+<h3>Responsibilities of a converter<a class="headerlink" href="#responsibilities-of-a-converter" title="Permalink to this headline">¶</a></h3>
+<ol class="arabic simple">
+<li><p>Args must be guaranteed to be a type to unwrap the Arg union without checking, typically input tensor arguments can be expected to be ITensors</p></li>
+<li><p>Any weights or static values must guaranteed to be valid until the end of conversion time</p>
+<ol class="loweralpha simple">
+<li><p>A helpful tool is the Weights helper class described below</p></li>
+</ol>
+</li>
+<li><p>Converters are expected to produce an IValue or ITensor for each output of a node. The compiler will check this and produce warnings if there are Values that don’t have associated ITensors or IValues.</p></li>
+<li><p>Outputs must be annotated</p>
+<ol class="loweralpha simple">
+<li><p>There must be an association between a JIT nodes output values and the new TRT layers output tensors in the <code class="docutils literal notranslate"><span class="pre">value_tensor_map</span></code> in the conversion context</p></li>
+</ol>
+</li>
+<li><p>Name your layers</p>
+<ol class="loweralpha simple">
+<li><p>Its much easier to debug when we can track which layers and nodes correspond with each other. The system we are currently using is to use the “node info” of the node as the name of the layer</p></li>
+</ol>
+</li>
+<li><p>Name your tensors</p>
+<ol class="loweralpha simple">
+<li><p>Use the output value debug name as the name for the new ITensor (again for debugging)</p></li>
+</ol>
+</li>
+</ol>
+</section>
+</section>
+<section id="conversion-context">
+<h2>Conversion Context<a class="headerlink" href="#conversion-context" title="Permalink to this headline">¶</a></h2>
+<p>The conversion context maintains the state of conversion, it manages the Network Definition, two maps
+one that stores associations between Values and IValues (the evaluated_value_map) and one that stores
+associations between Values and ITensors, and any sort of memory that needs to live until the end of
+conversion. The main apis that you will interface with in converters is directly accessing the network
+definition to add layers <code class="docutils literal notranslate"><span class="pre">ctx-&gt;net</span></code> and data association functions <code class="docutils literal notranslate"><span class="pre">ctx-&gt;AssociateValueAndTensor()</span></code>
+and <code class="docutils literal notranslate"><span class="pre">ctx-&gt;AssociateValueAndIValue()</span></code>, which you will use to add layers to the TRT layers and log
+pairs of node outputs and static values or TensorRT layer outputs.</p>
+</section>
+<section id="args">
+<h2>Args<a class="headerlink" href="#args" title="Permalink to this headline">¶</a></h2>
+<p>Arguments provided to the converter are inspectable unions of <code class="docutils literal notranslate"><span class="pre">nvinfer1::ITensors</span></code> and <code class="docutils literal notranslate"><span class="pre">torch::jit::IValues</span></code> (i.e.
+abstract dataflow in the TensorRT graph and static values). You are guaranteed that you will have some
+argument for each input value for the node. They are provided in the order of the function schema.
+It can be expected that inputs (meaning the parameters that would be passed into the forward
+function of a module in PyTorch) will be ITensors but the Arg class also has mechanisms to inspect arguments safely
+before unwrapping if you are unsure. Args also have deep unwrap methods that let you get straight to the
+underlying data in an IValue if you know it’s safe. You can also pass in a fallback value if there is a
+chance the IValue is None. IValues have been extended to be able to hold a wrapper around ITensors only in the case of TensorLists.
+You can get an ITensor from an IValue by a pattern similar to this: <code class="docutils literal notranslate"><span class="pre">ivalue.toCustomClass&lt;TensorContainer&gt;()-&gt;tensor()</span></code>.
+You can tell if an IValue contains a Tensor or an ITensor by using <code class="docutils literal notranslate"><span class="pre">ivalue.isTensor()</span></code> or <code class="docutils literal notranslate"><span class="pre">ivalue.isCustomClass()</span></code>.</p>
+</section>
+<section id="weights">
+<h2>Weights<a class="headerlink" href="#weights" title="Permalink to this headline">¶</a></h2>
+<p>Weights are used during build time, so any weights need to be guaranteed to live until the end of the conversion phase.
+TensorRT also uses its own weights structure to hold the weights. There is a wrapper around this class available
+to converts which abstracts a lot of this.</p>
+<p>The weights wrapper class can accept either <code class="docutils literal notranslate"><span class="pre">at::Tensors</span></code> or singular values (right now). You also need to pass the
+conversion context when constructing these weights because internally the weights class will allocate memory managed
+by the conversion context to store a copy of the tensor data. This data gets freed when the conversion context
+destructor gets destroyed so converters don’t really need to think about it.</p>
+<p>There is metadata generated from the shape of the input data which becomes useful in interfacing with TensorRT, such
+as number of input maps, number of output maps and kernel shape.</p>
+</section>
+<section id="other-advice">
+<h2>Other advice<a class="headerlink" href="#other-advice" title="Permalink to this headline">¶</a></h2>
+<p>You have the benefit of the full aten library when dealing with weights and other static values. This means that you
+can do quite a bit of work during conversion time to produce efficient conversion. A good example is batch_norm
+converter where the converter does fusion of operations with PyTorch before creating the TensorRT layer.</p>
+</section>
+</section>
+
+
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+              <ul>
+<li><a class="reference internal" href="#">Writing Converters</a><ul>
+<li><a class="reference internal" href="#background">Background</a></li>
+<li><a class="reference internal" href="#converters">Converters</a></li>
+<li><a class="reference internal" href="#converter-contract">Converter Contract</a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/notebooks.html">Example notebooks</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="contributors/system_overview.html">System Overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="contributors/writing_converters.html">Writing Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="indices/supported_ops.html">Operators Supported</a></li>
+</ul>
+
+
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+<div class="genindex-jumpbox">
+ <a href="#_"><strong>_</strong></a>
+ | <a href="#A"><strong>A</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#W"><strong>W</strong></a>
+ | <a href="#X"><strong>X</strong></a>
+
+</div>
+<h2 id="_">_</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.__init__">__init__() (torch_tensorrt.Device method)</a>
+
+      <ul>
+        <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.__init__">(torch_tensorrt.Input method)</a>
+</li>
+        <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator.__init__">(torch_tensorrt.ptq.CacheCalibrator method)</a>
+</li>
+        <li><a href="py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator.__init__">(torch_tensorrt.ptq.DataLoaderCalibrator method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.allow_gpu_fallback">allow_gpu_fallback (torch_tensorrt.Device attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#id1">CacheCalibrator (class in torch_tensorrt.ptq)</a>, <a href="py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator">[1]</a>
+</li>
+      <li><a href="py_api/ptq.html#id2">CalibrationAlgo (class in torch_tensorrt.ptq)</a>, <a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo">[1]</a>
+</li>
+      <li><a href="py_api/ts.html#torch_tensorrt.ts.check_method_op_support">check_method_op_support() (in module torch_tensorrt.ts)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.compile">compile() (in module torch_tensorrt)</a>
+
+      <ul>
+        <li><a href="py_api/fx.html#torch_tensorrt.fx.compile">(in module torch_tensorrt.fx)</a>
+</li>
+        <li><a href="py_api/ts.html#torch_tensorrt.ts.compile">(in module torch_tensorrt.ts)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.convert_method_to_trt_engine">convert_method_to_trt_engine() (in module torch_tensorrt)</a>
+
+      <ul>
+        <li><a href="py_api/ts.html#torch_tensorrt.ts.convert_method_to_trt_engine">(in module torch_tensorrt.ts)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#id0">DataLoaderCalibrator (class in torch_tensorrt.ptq)</a>, <a href="py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator">[1]</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.debug">debug (class in torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Debug">Debug (torch_tensorrt.logging.Level attribute)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device">Device (class in torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.device_type">device_type (torch_tensorrt.Device attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.DeviceType">DeviceType (class in torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.dla_core">dla_core (torch_tensorrt.Device attribute)</a>
+</li>
+      <li><a href="src/pytorch-sphinx-theme/docs/demo/demo.html#term-Documentation"><strong>Documentation</strong></a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.dtype">dtype (class in torch_tensorrt)</a>
+
+      <ul>
+        <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.dtype">(torch_tensorrt.Input attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.dump_build_info">dump_build_info() (in module torch_tensorrt)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ts.html#torch_tensorrt.ts.embed_engine_in_new_module">embed_engine_in_new_module() (in module torch_tensorrt.ts)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.EngineCapability">EngineCapability (class in torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION">ENTROPY_CALIBRATION (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2">ENTROPY_CALIBRATION_2 (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Error">Error (torch_tensorrt.logging.Level attribute)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.errors">errors (class in torch_tensorrt.logging)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.format">format (torch_tensorrt.Input attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.get_batch">get_batch() (in module torch_tensorrt.ptq)</a>
+</li>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.get_batch_size">get_batch_size() (in module torch_tensorrt.ptq)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.get_build_info">get_build_info() (in module torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.get_cache_mode_batch">get_cache_mode_batch() (in module torch_tensorrt.ptq)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.get_is_colored_output_on">get_is_colored_output_on() (in module torch_tensorrt.logging)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.get_logging_prefix">get_logging_prefix() (in module torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.get_reportable_log_level">get_reportable_log_level() (in module torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.gpu_id">gpu_id (torch_tensorrt.Device attribute)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Graph">Graph (torch_tensorrt.logging.Level attribute)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.graphs">graphs (class in torch_tensorrt.logging)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.info">info (class in torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Info">Info (torch_tensorrt.logging.Level attribute)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input">Input (class in torch_tensorrt)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/fx.html#torch_tensorrt.fx.InputTensorSpec">InputTensorSpec (class in torch_tensorrt.fx)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.internal_errors">internal_errors (class in torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.InternalError">InternalError (torch_tensorrt.logging.Level attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.LEGACY_CALIBRATION">LEGACY_CALIBRATION (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level">Level (class in torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.log">log() (in module torch_tensorrt.logging)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.MINMAX_CALIBRATION">MINMAX_CALIBRATION (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
+</li>
+      <li>
+    module
+
+      <ul>
+        <li><a href="py_api/torch_tensorrt.html#module-torch_tensorrt">torch_tensorrt</a>
+</li>
+        <li><a href="py_api/fx.html#module-torch_tensorrt.fx">torch_tensorrt.fx</a>
+</li>
+        <li><a href="py_api/logging.html#module-torch_tensorrt.logging">torch_tensorrt.logging</a>
+</li>
+        <li><a href="py_api/ptq.html#module-torch_tensorrt.ptq">torch_tensorrt.ptq</a>
+</li>
+        <li><a href="py_api/ts.html#module-torch_tensorrt.ts">torch_tensorrt.ts</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    Python Enhancement Proposals
+
+      <ul>
+        <li><a href="src/pytorch-sphinx-theme/docs/demo/demo.html#index-0">PEP 287</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.read_calibration_cache">read_calibration_cache() (in module torch_tensorrt.ptq)</a>
+</li>
+      <li><a href="src/pytorch-sphinx-theme/docs/demo/demo.html#term-Reading"><strong>Reading</strong></a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    RFC
+
+      <ul>
+        <li><a href="src/pytorch-sphinx-theme/docs/demo/demo.html#index-1">RFC 2822</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.set_device">set_device() (in module torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.set_is_colored_output_on">set_is_colored_output_on() (in module torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.set_logging_prefix">set_logging_prefix() (in module torch_tensorrt.logging)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.set_reportable_log_level">set_reportable_log_level() (in module torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.shape">shape (torch_tensorrt.Input attribute)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.shape_mode">shape_mode (torch_tensorrt.Input attribute)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html#c.STR">STR (C macro)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.TensorFormat">TensorFormat (class in torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/ts.html#torch_tensorrt.ts.TensorRTCompileSpec">TensorRTCompileSpec() (in module torch_tensorrt.ts)</a>
+</li>
+      <li>
+    torch_tensorrt
+
+      <ul>
+        <li><a href="py_api/torch_tensorrt.html#module-torch_tensorrt">module</a>
+</li>
+      </ul></li>
+      <li>
+    torch_tensorrt.fx
+
+      <ul>
+        <li><a href="py_api/fx.html#module-torch_tensorrt.fx">module</a>
+</li>
+      </ul></li>
+      <li>
+    torch_tensorrt.logging
+
+      <ul>
+        <li><a href="py_api/logging.html#module-torch_tensorrt.logging">module</a>
+</li>
+      </ul></li>
+      <li>
+    torch_tensorrt.ptq
+
+      <ul>
+        <li><a href="py_api/ptq.html#module-torch_tensorrt.ptq">module</a>
+</li>
+      </ul></li>
+      <li>
+    torch_tensorrt.ts
+
+      <ul>
+        <li><a href="py_api/ts.html#module-torch_tensorrt.ts">module</a>
+</li>
+      </ul></li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE">torch_tensorrt::DataType (C++ class)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType8DataTypeE5Value">torch_tensorrt::DataType::DataType (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType8DataTypeEN3c1010ScalarTypeE">[1]</a>, <a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType8DataTypeEv">[2]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypecvbEv">torch_tensorrt::DataType::operator bool (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4NK14torch_tensorrt8DataTypecv5ValueEv">torch_tensorrt::DataType::operator Value (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4NK14torch_tensorrt8DataTypeneE8DataType">torch_tensorrt::DataType::operator!= (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4NK14torch_tensorrt8DataTypeneEN8DataType5ValueE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4NK14torch_tensorrt8DataTypeeqE8DataType">torch_tensorrt::DataType::operator== (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4NK14torch_tensorrt8DataTypeeqEN8DataType5ValueE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5ValueE">torch_tensorrt::DataType::Value (C++ enum)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value5kBoolE">torch_tensorrt::DataType::Value::kBool (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value5kCharE">torch_tensorrt::DataType::Value::kChar (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value6kFloatE">torch_tensorrt::DataType::Value::kFloat (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value5kHalfE">torch_tensorrt::DataType::Value::kHalf (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value4kIntE">torch_tensorrt::DataType::Value::kInt (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value8kUnknownE">torch_tensorrt::DataType::Value::kUnknown (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6DeviceE">torch_tensorrt::Device (C++ struct)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device18allow_gpu_fallbackE">torch_tensorrt::Device::allow_gpu_fallback (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device6DeviceEv">torch_tensorrt::Device::Device (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device11device_typeE">torch_tensorrt::Device::device_type (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceTypeE">torch_tensorrt::Device::DeviceType (C++ class)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceTypeE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeE5Value">torch_tensorrt::Device::DeviceType::DeviceType (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEN3c1010DeviceTypeE">[1]</a>, <a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEv">[2]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeE5Value">[3]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEN3c1010DeviceTypeE">[4]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEv">[5]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceTypecvbEv">torch_tensorrt::Device::DeviceType::operator bool (C++ function)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceTypecvbEv">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4NK14torch_tensorrt6Device10DeviceTypecv5ValueEv">torch_tensorrt::Device::DeviceType::operator Value (C++ function)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4NK14torch_tensorrt6Device10DeviceTypecv5ValueEv">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4NK14torch_tensorrt6Device10DeviceTypeneE10DeviceType">torch_tensorrt::Device::DeviceType::operator!= (C++ function)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4NK14torch_tensorrt6Device10DeviceTypeneE10DeviceType">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4NK14torch_tensorrt6Device10DeviceTypeeqE10DeviceType">torch_tensorrt::Device::DeviceType::operator== (C++ function)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4NK14torch_tensorrt6Device10DeviceTypeeqE10DeviceType">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE">torch_tensorrt::Device::DeviceType::Value (C++ enum)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kDLAE">torch_tensorrt::Device::DeviceType::Value::kDLA (C++ enumerator)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kDLAE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kGPUE">torch_tensorrt::Device::DeviceType::Value::kGPU (C++ enumerator)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kGPUE">[1]</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device8dla_coreE">torch_tensorrt::Device::dla_core (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device6gpu_idE">torch_tensorrt::Device::gpu_id (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html#_CPPv4N14torch_tensorrt15dump_build_infoEv">torch_tensorrt::dump_build_info (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapabilityE">torch_tensorrt::EngineCapability (C++ enum)</a>
+</li>
+      <li><a href="_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapability15kDLA_STANDALONEE">torch_tensorrt::EngineCapability::kDLA_STANDALONE (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapability7kSAFETYE">torch_tensorrt::EngineCapability::kSAFETY (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapability9kSTANDARDE">torch_tensorrt::EngineCapability::kSTANDARD (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html#_CPPv4N14torch_tensorrt14get_build_infoEv">torch_tensorrt::get_build_info (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1GraphInputs.html#_CPPv4N14torch_tensorrt11GraphInputsE">torch_tensorrt::GraphInputs (C++ struct)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1GraphInputs.html#_CPPv4N14torch_tensorrt11GraphInputs15input_signatureE">torch_tensorrt::GraphInputs::input_signature (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1GraphInputs.html#_CPPv4N14torch_tensorrt11GraphInputs6inputsE">torch_tensorrt::GraphInputs::inputs (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5InputE">torch_tensorrt::Input (C++ struct)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5dtypeE">torch_tensorrt::Input::dtype (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input6formatE">torch_tensorrt::Input::format (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN2at6TensorE">torch_tensorrt::Input::Input (C++ function)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE12TensorFormat">[1]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">[2]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE12TensorFormat">[3]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">[4]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE12TensorFormat">[5]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataType12TensorFormat">[6]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE12TensorFormat">[7]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataType12TensorFormat">[8]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEv">[9]</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input9max_shapeE">torch_tensorrt::Input::max_shape (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input9min_shapeE">torch_tensorrt::Input::min_shape (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input9opt_shapeE">torch_tensorrt::Input::opt_shape (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5shapeE">torch_tensorrt::Input::shape (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html#_CPPv4N14torch_tensorrt7logging24get_is_colored_output_onEv">torch_tensorrt::logging::get_is_colored_output_on (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html#_CPPv4N14torch_tensorrt7logging18get_logging_prefixEv">torch_tensorrt::logging::get_logging_prefix (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html#_CPPv4N14torch_tensorrt7logging24get_reportable_log_levelEv">torch_tensorrt::logging::get_reportable_log_level (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5LevelE">torch_tensorrt::logging::Level (C++ enum)</a>
+</li>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level6kDEBUGE">torch_tensorrt::logging::Level::kDEBUG (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level6kERRORE">torch_tensorrt::logging::Level::kERROR (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level6kGRAPHE">torch_tensorrt::logging::Level::kGRAPH (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level5kINFOE">torch_tensorrt::logging::Level::kINFO (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level15kINTERNAL_ERRORE">torch_tensorrt::logging::Level::kINTERNAL_ERROR (C++ enumerator)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level8kWARNINGE">torch_tensorrt::logging::Level::kWARNING (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html#_CPPv4N14torch_tensorrt7logging3logE5LevelNSt6stringE">torch_tensorrt::logging::log (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html#_CPPv4N14torch_tensorrt7logging24set_is_colored_output_onEb">torch_tensorrt::logging::set_is_colored_output_on (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1af8f3443813315af7901903d25dd495cc.html#_CPPv4N14torch_tensorrt7logging18set_logging_prefixENSt6stringE">torch_tensorrt::logging::set_logging_prefix (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html#_CPPv4N14torch_tensorrt7logging24set_reportable_log_levelE5Level">torch_tensorrt::logging::set_reportable_log_level (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE">torch_tensorrt::ptq::Int8CacheCalibrator (C++ class)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator8getBatchEA_PvA_PKci">torch_tensorrt::ptq::Int8CacheCalibrator::getBatch (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4NK14torch_tensorrt3ptq19Int8CacheCalibrator12getBatchSizeEv">torch_tensorrt::ptq::Int8CacheCalibrator::getBatchSize (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator19Int8CacheCalibratorERKNSt6stringE">torch_tensorrt::ptq::Int8CacheCalibrator::Int8CacheCalibrator (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibratorcvPN8nvinfer115IInt8CalibratorEEv">torch_tensorrt::ptq::Int8CacheCalibrator::operator nvinfer1::IInt8Calibrator* (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator20readCalibrationCacheER6size_t">torch_tensorrt::ptq::Int8CacheCalibrator::readCalibrationCache (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator21writeCalibrationCacheEPKv6size_t">torch_tensorrt::ptq::Int8CacheCalibrator::writeCalibrationCache (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE">torch_tensorrt::ptq::Int8Calibrator (C++ class)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator8getBatchEA_PvA_PKci">torch_tensorrt::ptq::Int8Calibrator::getBatch (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4NK14torch_tensorrt3ptq14Int8Calibrator12getBatchSizeEv">torch_tensorrt::ptq::Int8Calibrator::getBatchSize (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator14Int8CalibratorE19DataLoaderUniquePtrRKNSt6stringEb">torch_tensorrt::ptq::Int8Calibrator::Int8Calibrator (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4N14torch_tensorrt3ptq14Int8CalibratorcvPN8nvinfer115IInt8CalibratorEEv">torch_tensorrt::ptq::Int8Calibrator::operator nvinfer1::IInt8Calibrator* (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator20readCalibrationCacheER6size_t">torch_tensorrt::ptq::Int8Calibrator::readCalibrationCache (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator21writeCalibrationCacheEPKv6size_t">torch_tensorrt::ptq::Int8Calibrator::writeCalibrationCache (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.html#_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE">torch_tensorrt::ptq::make_int8_cache_calibrator (C++ function)</a>, <a href="_cpp_api/function_ptq_8h_1a9835f6e605dce1abf442a55b64d6dffa.html#_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE">[1]</a>
+</li>
+      <li><a href="_cpp_api/function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb">torch_tensorrt::ptq::make_int8_calibrator (C++ function)</a>, <a href="_cpp_api/function_ptq_8h_1a83ff2be7e0b80bc7434de415861dc039.html#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb">[1]</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html#_CPPv4N14torch_tensorrt10set_deviceEKi">torch_tensorrt::set_device (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE">torch_tensorrt::TensorFormat (C++ class)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatcvbEv">torch_tensorrt::TensorFormat::operator bool (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4NK14torch_tensorrt12TensorFormatcv5ValueEv">torch_tensorrt::TensorFormat::operator Value (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4NK14torch_tensorrt12TensorFormatneE12TensorFormat">torch_tensorrt::TensorFormat::operator!= (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4NK14torch_tensorrt12TensorFormatneEN12TensorFormat5ValueE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4NK14torch_tensorrt12TensorFormateqE12TensorFormat">torch_tensorrt::TensorFormat::operator== (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4NK14torch_tensorrt12TensorFormateqEN12TensorFormat5ValueE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatE5Value">torch_tensorrt::TensorFormat::TensorFormat (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatEN2at12MemoryFormatE">[1]</a>, <a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatEv">[2]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5ValueE">torch_tensorrt::TensorFormat::Value (C++ enum)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value13kChannelsLastE">torch_tensorrt::TensorFormat::Value::kChannelsLast (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE">torch_tensorrt::TensorFormat::Value::kContiguous (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value8kUnknownE">torch_tensorrt::TensorFormat::Value::kUnknown (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html#_CPPv4N14torch_tensorrt11torchscript29check_method_operator_supportERKN5torch3jit6ModuleENSt6stringE">torch_tensorrt::torchscript::check_method_operator_support (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html#_CPPv4N14torch_tensorrt11torchscript7compileERKN5torch3jit6ModuleE11CompileSpec">torch_tensorrt::torchscript::compile (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpecE">torch_tensorrt::torchscript::CompileSpec (C++ struct)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec10capabilityE">torch_tensorrt::torchscript::CompileSpec::capability (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecEN5torch3jit6IValueE">torch_tensorrt::torchscript::CompileSpec::CompileSpec (C++ function)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorI5InputEE">[1]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorIN3c108ArrayRefI7int64_tEEEE">[2]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorINSt6vectorI7int64_tEEEE">[3]</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec5debugE">torch_tensorrt::torchscript::CompileSpec::debug (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec6deviceE">torch_tensorrt::torchscript::CompileSpec::device (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec12disable_tf32E">torch_tensorrt::torchscript::CompileSpec::disable_tf32 (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec20dla_global_dram_sizeE">torch_tensorrt::torchscript::CompileSpec::dla_global_dram_size (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec19dla_local_dram_sizeE">torch_tensorrt::torchscript::CompileSpec::dla_local_dram_size (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec13dla_sram_sizeE">torch_tensorrt::torchscript::CompileSpec::dla_sram_size (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec18enabled_precisionsE">torch_tensorrt::torchscript::CompileSpec::enabled_precisions (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec12graph_inputsE">torch_tensorrt::torchscript::CompileSpec::graph_inputs (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14min_block_sizeE">torch_tensorrt::torchscript::CompileSpec::min_block_size (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec20num_avg_timing_itersE">torch_tensorrt::torchscript::CompileSpec::num_avg_timing_iters (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14ptq_calibratorE">torch_tensorrt::torchscript::CompileSpec::ptq_calibrator (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec5refitE">torch_tensorrt::torchscript::CompileSpec::refit (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec24require_full_compilationE">torch_tensorrt::torchscript::CompileSpec::require_full_compilation (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14sparse_weightsE">torch_tensorrt::torchscript::CompileSpec::sparse_weights (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec22torch_executed_modulesE">torch_tensorrt::torchscript::CompileSpec::torch_executed_modules (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec18torch_executed_opsE">torch_tensorrt::torchscript::CompileSpec::torch_executed_ops (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec24truncate_long_and_doubleE">torch_tensorrt::torchscript::CompileSpec::truncate_long_and_double (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14workspace_sizeE">torch_tensorrt::torchscript::CompileSpec::workspace_size (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html#_CPPv4N14torch_tensorrt11torchscript28convert_method_to_trt_engineERKN5torch3jit6ModuleENSt6stringE11CompileSpec">torch_tensorrt::torchscript::convert_method_to_trt_engine (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1a710df824a7718b440e4bc17bf9693cef.html#_CPPv4N14torch_tensorrt11torchscript26embed_engine_in_new_moduleERKNSt6stringE6Device">torch_tensorrt::torchscript::embed_engine_in_new_module (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html#c.TORCH_TENSORRT_MAJOR_VERSION">TORCH_TENSORRT_MAJOR_VERSION (C macro)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html#c.TORCH_TENSORRT_MINOR_VERSION">TORCH_TENSORRT_MINOR_VERSION (C macro)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html#c.TORCH_TENSORRT_PATCH_VERSION">TORCH_TENSORRT_PATCH_VERSION (C macro)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html#c.TORCH_TENSORRT_VERSION">TORCH_TENSORRT_VERSION (C macro)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html#c.TORCHTRT_API">TORCHTRT_API (C macro)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html#c.TORCHTRT_HIDDEN">TORCHTRT_HIDDEN (C macro)</a>
+</li>
+      <li><a href="py_api/fx.html#torch_tensorrt.fx.TRTInterpreter">TRTInterpreter (class in torch_tensorrt.fx)</a>
+</li>
+      <li><a href="py_api/fx.html#torch_tensorrt.fx.TRTInterpreterResult">TRTInterpreterResult (class in torch_tensorrt.fx)</a>
+</li>
+      <li><a href="py_api/fx.html#torch_tensorrt.fx.TRTModule">TRTModule (class in torch_tensorrt.fx)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="W">W</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Warning">Warning (torch_tensorrt.logging.Level attribute)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.warnings">warnings (class in torch_tensorrt.logging)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.write_calibration_cache">write_calibration_cache() (in module torch_tensorrt.ptq)</a>
+</li>
+      <li><a href="src/pytorch-sphinx-theme/docs/demo/demo.html#term-Writing"><strong>Writing</strong></a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="X">X</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
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diff --git a/docs/v1.2.0/getting_started/getting_started_with_cpp_api.html b/docs/v1.2.0/getting_started/getting_started_with_cpp_api.html
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+<ul>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</a></li>
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+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
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+</ul>
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+
+  <section id="using-torch-tensorrt-in-c">
+<span id="getting-started-cpp"></span><h1>Using Torch-TensorRT in  C++<a class="headerlink" href="#using-torch-tensorrt-in-c" title="Permalink to this headline">¶</a></h1>
+<p>If you haven’t already, acquire a tarball of the library by following the instructions in <a class="reference internal" href="installation.html#installation"><span class="std std-ref">Installation</span></a></p>
+<section id="id1">
+<h2>Using Torch-TensorRT in C++<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+<p>Torch-TensorRT C++ API accepts TorchScript modules (generated either from <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.jit.trace</span></code>) as an input and returns
+a Torchscript module (optimized using TensorRT). This requires users to use Pytorch (in python) to generate torchscript modules beforehand.
+Please refer to <a class="reference external" href="https://nvidia.github.io/Torch-TensorRT/tutorials/creating_torchscript_module_in_python.html">Creating TorchScript modules in Python</a> section to generate torchscript graphs.</p>
+<section id="torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc">
+<span id="torch-tensorrt-quickstart"></span><h3>[Torch-TensorRT Quickstart] Compiling TorchScript Modules with <code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code><a class="headerlink" href="#torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc" title="Permalink to this headline">¶</a></h3>
+<p>An easy way to get started with Torch-TensorRT and to check if your model can be supported without extra work is to run it through
+<code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code>, which supports almost all features of the compiler from the command line including post training quantization
+(given a previously created calibration cache). For example we can compile our lenet model by setting our preferred operating
+precision and input size. This new TorchScript file can be loaded into Python (note: you need to <code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">torch_tensorrt</span></code> before loading
+these compiled modules because the compiler extends the PyTorch the deserializer and runtime to execute compiled modules).</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>❯ torchtrtc -p f16 lenet_scripted.ts trt_lenet_scripted.ts <span class="s2">&quot;(1,1,32,32)&quot;</span>
+
+❯ python3
+Python <span class="m">3</span>.6.9 <span class="o">(</span>default, Apr <span class="m">18</span> <span class="m">2020</span>, <span class="m">01</span>:56:04<span class="o">)</span>
+<span class="o">[</span>GCC <span class="m">8</span>.4.0<span class="o">]</span> on linux
+Type <span class="s2">&quot;help&quot;</span>, <span class="s2">&quot;copyright&quot;</span>, <span class="s2">&quot;credits&quot;</span> or <span class="s2">&quot;license&quot;</span> <span class="k">for</span> more information.
+&gt;&gt;&gt; import torch
+&gt;&gt;&gt; import torch_tensorrt
+&gt;&gt;&gt; <span class="nv">ts_model</span> <span class="o">=</span> torch.jit.load<span class="o">(</span>“trt_lenet_scripted.ts”<span class="o">)</span>
+&gt;&gt;&gt; ts_model<span class="o">(</span>torch.randn<span class="o">((</span><span class="m">1</span>,1,32,32<span class="o">))</span>.to<span class="o">(</span>“cuda”<span class="o">)</span>.half<span class="o">())</span>
+</pre></div>
+</div>
+<p>You can learn more about <code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code> usage here: <a class="reference internal" href="../cli/torchtrtc.html#torchtrtc"><span class="std std-ref">torchtrtc</span></a></p>
+</section>
+<section id="working-with-torchscript-in-c">
+<span id="ts-in-cc"></span><h3>Working with TorchScript in C++<a class="headerlink" href="#working-with-torchscript-in-c" title="Permalink to this headline">¶</a></h3>
+<p>If we are developing an application to deploy with C++, we can save either our traced or scripted module using <code class="docutils literal notranslate"><span class="pre">torch.jit.save</span></code>
+which will serialize the TorchScript code, weights and other information into a package. This is also where our dependency on Python ends.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">torch_script_module</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;lenet.jit.pt&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>From here we can now load our TorchScript module in C++</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;torch/script.h&gt;</span><span class="c1"> // One-stop header.</span><span class="cp"></span>
+
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;memory&gt;</span><span class="cp"></span>
+
+<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="w"> </span><span class="k">module</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="k">try</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">        </span><span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
+<span class="w">        </span><span class="k">module</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;&lt;PATH TO SAVED TS MOD&gt;&quot;</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="p">}</span><span class="w"></span>
+<span class="w">    </span><span class="k">catch</span><span class="w"> </span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&amp;</span><span class="w"> </span><span class="n">e</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">cerr</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;error loading the model</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span><span class="w"></span>
+<span class="w">        </span><span class="k">return</span><span class="w"> </span><span class="mi">-1</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="p">}</span><span class="w"></span>
+
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;ok</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span><span class="w"></span>
+</pre></div>
+</div>
+<p>You can do full training and inference in C++ with PyTorch / LibTorch if you would like, you can even define your modules in C++ and
+have access to the same powerful tensor library that backs PyTorch. (For more information: <a class="reference external" href="https://pytorch.org/cppdocs/">https://pytorch.org/cppdocs/</a>).
+For instance we can do inference with our LeNet module like this:</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span><span class="w"></span>
+<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">});</span><span class="w"></span>
+<span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span><span class="w"></span>
+</pre></div>
+</div>
+<p>and to run on the GPU:</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span><span class="w"></span>
+<span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span><span class="w"></span>
+<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span><span class="w"></span>
+<span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span><span class="w"></span>
+</pre></div>
+</div>
+<p>As you can see it is pretty similar to the Python API. When you call the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method, you invoke the PyTorch JIT compiler, which will optimize and run your TorchScript code.</p>
+</section>
+<section id="compiling-with-torch-tensorrt-in-c">
+<span id="compile-cpp"></span><h3>Compiling with Torch-TensorRT in C++<a class="headerlink" href="#compiling-with-torch-tensorrt-in-c" title="Permalink to this headline">¶</a></h3>
+<p>We are also at the point were we can compile and optimize our module with Torch-TensorRT, but instead of in a JIT fashion we must do it ahead-of-time (AOT) i.e. before we start doing actual inference work
+since it takes a bit of time to optimize the module, it would not make sense to do this every time you run the module or even the first time you run it.</p>
+<p>With our module loaded, we can feed it into the Torch-TensorRT compiler. When we do so we must provide some information on the expected input size and also configure any additional settings.</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span><span class="cp"></span>
+<span class="p">...</span><span class="w"></span>
+
+<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span><span class="w"></span>
+
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">});</span><span class="w"></span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">trt_mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">{{</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()}});</span><span class="w"></span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">trt_mod</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">in</span><span class="p">});</span><span class="w"></span>
+</pre></div>
+</div>
+<p>Thats it! Now the graph runs primarily not with the JIT compiler but using TensorRT (though we execute the graph using the JIT runtime).</p>
+<p>We can also set settings like operating precision to run in FP16.</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span><span class="cp"></span>
+<span class="p">...</span><span class="w"></span>
+
+<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span><span class="w"></span>
+
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">}).</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">input_sizes</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">({</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()});</span><span class="w"></span>
+<span class="w">    </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="w"> </span><span class="nf">info</span><span class="p">(</span><span class="n">input_sizes</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="n">info</span><span class="p">.</span><span class="n">enable_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">trt_mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span><span class="w"> </span><span class="n">info</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">trt_mod</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">in</span><span class="p">});</span><span class="w"></span>
+</pre></div>
+</div>
+<p>And now we are running the module in FP16 precision. You can then save the module to load later.</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">trt_mod</span><span class="p">.</span><span class="n">save</span><span class="p">(</span><span class="s">&quot;&lt;PATH TO SAVED TRT/TS MOD&gt;&quot;</span><span class="p">)</span><span class="w"></span>
+</pre></div>
+</div>
+<p>Torch-TensorRT compiled TorchScript modules are loaded in the same way as normal TorchScript module. Make sure your deployment application is linked against <code class="docutils literal notranslate"><span class="pre">libtorchtrt.so</span></code></p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span><span class="cp"></span>
+
+<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="w"> </span><span class="k">module</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="k">try</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">        </span><span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
+<span class="w">        </span><span class="k">module</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;&lt;PATH TO SAVED TRT/TS MOD&gt;&quot;</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="p">}</span><span class="w"></span>
+<span class="w">    </span><span class="k">catch</span><span class="w"> </span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&amp;</span><span class="w"> </span><span class="n">e</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">cerr</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;error loading the model</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span><span class="w"></span>
+<span class="w">        </span><span class="k">return</span><span class="w"> </span><span class="mi">-1</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="p">}</span><span class="w"></span>
+
+<span class="w">    </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span><span class="w"></span>
+
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;ok</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span><span class="w"></span>
+<span class="p">}</span><span class="w"></span>
+</pre></div>
+</div>
+<p>If you want to save the engine produced by Torch-TensorRT to use in a TensorRT application you can use the <code class="docutils literal notranslate"><span class="pre">ConvertGraphToTRTEngine</span></code> API.</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span><span class="cp"></span>
+<span class="p">...</span><span class="w"></span>
+
+<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span><span class="w"></span>
+
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">}).</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">input_sizes</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">({</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()});</span><span class="w"></span>
+<span class="w">    </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="w"> </span><span class="nf">info</span><span class="p">(</span><span class="n">input_sizes</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="n">info</span><span class="p">.</span><span class="n">enabled_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">trt_mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">ConvertGraphToTRTEngine</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;forward&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">info</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ofstream</span><span class="w"> </span><span class="n">out</span><span class="p">(</span><span class="s">&quot;/tmp/engine_converted_from_jit.trt&quot;</span><span class="p">);</span><span class="w"></span>
+<span class="w">    </span><span class="n">out</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">engine</span><span class="p">;</span><span class="w"></span>
+<span class="w">    </span><span class="n">out</span><span class="p">.</span><span class="n">close</span><span class="p">();</span><span class="w"></span>
+</pre></div>
+</div>
+</section>
+<section id="under-the-hood">
+<span id="id2"></span><h3>Under The Hood<a class="headerlink" href="#under-the-hood" title="Permalink to this headline">¶</a></h3>
+<p>When a module is provided to Torch-TensorRT, the compiler starts by mapping a graph like you saw above to a graph like this:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%input.2 : Tensor):
+    %2 : Float(84, 10) = prim::Constant[value=&lt;Tensor&gt;]()
+    %3 : Float(120, 84) = prim::Constant[value=&lt;Tensor&gt;]()
+    %4 : Float(576, 120) = prim::Constant[value=&lt;Tensor&gt;]()
+    %5 : int = prim::Constant[value=-1]() # x.py:25:0
+    %6 : int[] = prim::Constant[value=annotate(List[int], [])]()
+    %7 : int[] = prim::Constant[value=[2, 2]]()
+    %8 : int[] = prim::Constant[value=[0, 0]]()
+    %9 : int[] = prim::Constant[value=[1, 1]]()
+    %10 : bool = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+    %11 : int = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+    %12 : bool = prim::Constant[value=0]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+    %self.classifer.fc3.bias : Float(10) = prim::Constant[value= 0.0464  0.0383  0.0678  0.0932  0.1045 -0.0805 -0.0435 -0.0818  0.0208 -0.0358 [ CUDAFloatType{10} ]]()
+    %self.classifer.fc2.bias : Float(84) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.classifer.fc1.bias : Float(120) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.feat.conv2.weight : Float(16, 6, 3, 3) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.feat.conv2.bias : Float(16) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.feat.conv1.weight : Float(6, 1, 3, 3) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.feat.conv1.bias : Float(6) = prim::Constant[value= 0.0530 -0.1691  0.2802  0.1502  0.1056 -0.1549 [ CUDAFloatType{6} ]]()
+    %input0.4 : Tensor = aten::_convolution(%input.2, %self.feat.conv1.weight, %self.feat.conv1.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+    %input0.5 : Tensor = aten::relu(%input0.4) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+    %input1.2 : Tensor = aten::max_pool2d(%input0.5, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+    %input0.6 : Tensor = aten::_convolution(%input1.2, %self.feat.conv2.weight, %self.feat.conv2.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+    %input2.1 : Tensor = aten::relu(%input0.6) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+    %x.1 : Tensor = aten::max_pool2d(%input2.1, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+    %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0
+    %27 : Tensor = aten::matmul(%input.1, %4)
+    %28 : Tensor = trt::const(%self.classifer.fc1.bias)
+    %29 : Tensor = aten::add_(%28, %27, %11)
+    %input0.2 : Tensor = aten::relu(%29) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+    %31 : Tensor = aten::matmul(%input0.2, %3)
+    %32 : Tensor = trt::const(%self.classifer.fc2.bias)
+    %33 : Tensor = aten::add_(%32, %31, %11)
+    %input1.1 : Tensor = aten::relu(%33) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+    %35 : Tensor = aten::matmul(%input1.1, %2)
+    %36 : Tensor = trt::const(%self.classifer.fc3.bias)
+    %37 : Tensor = aten::add_(%36, %35, %11)
+    return (%37)
+(CompileGraph)
+</pre></div>
+</div>
+<p>The graph has now been transformed from a collection of modules, each managing their own parameters into a single graph with the parameters inlined
+into the graph and all of the operations laid out. Torch-TensorRT has also executed a number of optimizations and mappings to make the graph easier to translate to TensorRT.
+From here the compiler can assemble the TensorRT engine by following the dataflow through the graph.</p>
+<p>When the graph construction phase is complete, Torch-TensorRT produces a serialized TensorRT engine. From here depending on the API, this engine is returned
+to the user or moves into the graph construction phase. Here Torch-TensorRT creates a JIT Module to execute the TensorRT engine which will be instantiated and managed
+by the Torch-TensorRT runtime.</p>
+<p>Here is the graph that you get back after compilation is complete:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%self_1 : __torch__.lenet, %input_0 : Tensor):
+    %1 : ...trt.Engine = prim::GetAttr[name=&quot;lenet&quot;](%self_1)
+    %3 : Tensor[] = prim::ListConstruct(%input_0)
+    %4 : Tensor[] = trt::execute_engine(%3, %1)
+    %5 : Tensor = prim::ListUnpack(%4)
+    return (%5)
+</pre></div>
+</div>
+<p>You can see the call where the engine is executed, after extracting the attribute containing the engine and constructing a list of inputs, then returns the tensors back to the user.</p>
+</section>
+<section id="working-with-unsupported-operators">
+<span id="unsupported-ops"></span><h3>Working with Unsupported Operators<a class="headerlink" href="#working-with-unsupported-operators" title="Permalink to this headline">¶</a></h3>
+<p>Torch-TensorRT is a new library and the PyTorch operator library is quite large, so there will be ops that aren’t supported natively by the compiler. You can either use the composition techinques
+shown above to make modules are fully Torch-TensorRT supported and ones that are not and stitch the modules together in the deployment application or you can register converters for missing ops.</p>
+<blockquote>
+<div><p>You can check support without going through the full compilation pipleine using the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt::CheckMethodOperatorSupport(const</span> <span class="pre">torch::jit::Module&amp;</span> <span class="pre">module,</span> <span class="pre">std::string</span> <span class="pre">method_name)</span></code> api
+to see what operators are not supported. <code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code> automatically checks modules with this method before starting compilation and will print out a list of operators that are not supported.</p>
+</div></blockquote>
+<section id="registering-custom-converters">
+<span id="custom-converters"></span><h4>Registering Custom Converters<a class="headerlink" href="#registering-custom-converters" title="Permalink to this headline">¶</a></h4>
+<p>Operations are mapped to TensorRT through the use of modular converters, a function that takes a node from a the JIT graph and produces an equivalent layer or subgraph in TensorRT.
+Torch-TensorRT ships with a library of these converters stored in a registry, that will be executed depending on the node being parsed. For instance a <code class="docutils literal notranslate"><span class="pre">aten::relu(%input0.4)</span></code> instruction will trigger
+the relu converter to be run on it, producing an activation layer in the TensorRT graph. But since this library is not exhaustive you may need to write your own to get Torch-TensorRT
+to support your module.</p>
+<p>Shipped with the Torch-TensorRT distribution are the internal core API headers. You can therefore access the converter registry and add a converter for the op you need.</p>
+<p>For example, if we try to compile a graph with a build of Torch-TensorRT that doesn’t support the flatten operation (<code class="docutils literal notranslate"><span class="pre">aten::flatten</span></code>) you may see this error:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>terminate called after throwing an instance of &#39;torch_tensorrt::Error&#39;
+what():  [enforce fail at core/conversion/conversion.cpp:109] Expected converter to be true but got false
+Unable to convert node: %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0 (conversion.AddLayer)
+Schema: aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -&gt; (Tensor)
+Converter for aten::flatten requested, but no such converter was found.
+If you need a converter for this operator, you can try implementing one yourself
+or request a converter: https://www.github.com/NVIDIA/Torch-TensorRT/issues
+</pre></div>
+</div>
+<p>We can register a converter for this operator in our application. All of the tools required to build a converter can be imported by including <code class="docutils literal notranslate"><span class="pre">torch_tensorrt/core/conversion/converters/converters.h</span></code>.
+We start by creating an instance of the self-registering class <code class="docutils literal notranslate"><span class="pre">torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns()</span></code> which will register converters
+in the global converter registry, associating a function schema like <code class="docutils literal notranslate"><span class="pre">aten::flatten.using_ints(Tensor</span> <span class="pre">self,</span> <span class="pre">int</span> <span class="pre">start_dim=0,</span> <span class="pre">int</span> <span class="pre">end_dim=-1)</span> <span class="pre">-&gt;</span> <span class="pre">(Tensor)</span></code> with a lambda that
+will take the state of the conversion, the node/operation in question to convert and all of the inputs to the node and produces as a side effect a new layer in the TensorRT network.
+Arguments are passed as a vector of inspectable unions of TensorRT <code class="docutils literal notranslate"><span class="pre">ITensors</span></code> and Torch <code class="docutils literal notranslate"><span class="pre">IValues</span></code> in the order arguments are listed in the schema.</p>
+<p>Below is a implementation of a <code class="docutils literal notranslate"><span class="pre">aten::flatten</span></code> converter that we can use in our application. You have full access to the Torch and TensorRT libraries in the converter implementation. So
+for example we can quickly get the output size by just running the operation in PyTorch instead of implementing the full calculation outself like we do below for this flatten converter.</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span><span class="cp"></span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/core/conversion/converters/converters.h&quot;</span><span class="cp"></span>
+
+<span class="k">static</span><span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">flatten_converter</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">converters</span><span class="o">::</span><span class="n">RegisterNodeConversionPatterns</span><span class="p">()</span><span class="w"></span>
+<span class="w">    </span><span class="p">.</span><span class="n">pattern</span><span class="p">({</span><span class="w"></span>
+<span class="w">        </span><span class="s">&quot;aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -&gt; (Tensor)&quot;</span><span class="p">,</span><span class="w"></span>
+<span class="w">        </span><span class="p">[](</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">ConversionCtx</span><span class="o">*</span><span class="w"> </span><span class="n">ctx</span><span class="p">,</span><span class="w"></span>
+<span class="w">           </span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Node</span><span class="o">*</span><span class="w"> </span><span class="n">n</span><span class="p">,</span><span class="w"></span>
+<span class="w">           </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">converters</span><span class="o">::</span><span class="n">args</span><span class="o">&amp;</span><span class="w"> </span><span class="n">args</span><span class="p">)</span><span class="w"> </span><span class="o">-&gt;</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">ITensor</span><span class="p">();</span><span class="w"></span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">start_dim</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">unwrapToInt</span><span class="p">();</span><span class="w"></span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">end_dim</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">2</span><span class="p">].</span><span class="n">unwrapToInt</span><span class="p">();</span><span class="w"></span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">in_shape</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">toVec</span><span class="p">(</span><span class="n">in</span><span class="o">-&gt;</span><span class="n">getDimensions</span><span class="p">());</span><span class="w"></span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">out_shape</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">flatten</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">rand</span><span class="p">(</span><span class="n">in_shape</span><span class="p">),</span><span class="w"> </span><span class="n">start_dim</span><span class="p">,</span><span class="w"> </span><span class="n">end_dim</span><span class="p">).</span><span class="n">sizes</span><span class="p">();</span><span class="w"></span>
+
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">shuffle</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ctx</span><span class="o">-&gt;</span><span class="n">net</span><span class="o">-&gt;</span><span class="n">addShuffle</span><span class="p">(</span><span class="o">*</span><span class="n">in</span><span class="p">);</span><span class="w"></span>
+<span class="w">            </span><span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">setReshapeDimensions</span><span class="p">(</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">toDims</span><span class="p">(</span><span class="n">out_shape</span><span class="p">));</span><span class="w"></span>
+<span class="w">            </span><span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">setName</span><span class="p">(</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">node_info</span><span class="p">(</span><span class="n">n</span><span class="p">).</span><span class="n">c_str</span><span class="p">());</span><span class="w"></span>
+
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">out_tensor</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ctx</span><span class="o">-&gt;</span><span class="n">AssociateValueAndTensor</span><span class="p">(</span><span class="n">n</span><span class="o">-&gt;</span><span class="n">outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">getOutput</span><span class="p">(</span><span class="mi">0</span><span class="p">));</span><span class="w"></span>
+<span class="w">            </span><span class="k">return</span><span class="w"> </span><span class="nb">true</span><span class="p">;</span><span class="w"></span>
+<span class="w">        </span><span class="p">}</span><span class="w"></span>
+<span class="w">    </span><span class="p">});</span><span class="w"></span>
+
+<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="p">...</span><span class="w"></span>
+</pre></div>
+</div>
+<p>To use this converter in Python, it is recommended to use PyTorch’s <a class="reference external" href="https://pytorch.org/tutorials/advanced/cpp_extension.html#custom-c-and-cuda-extensions">C++ / CUDA Extention</a>
+template to wrap your library of converters into a <code class="docutils literal notranslate"><span class="pre">.so</span></code> that you can load with <code class="docutils literal notranslate"><span class="pre">ctypes.CDLL()</span></code> in your Python application.</p>
+<p>You can find more information on all the details of writing converters in the contributors documentation (<a class="reference internal" href="../contributors/writing_converters.html#writing-converters"><span class="std std-ref">Writing Converters</span></a>).
+If you find yourself with a large library of converter implementations, do consider upstreaming them, PRs are welcome and it would be great for the community to benefit as well.</p>
+</section>
+</section>
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+              <ul>
+<li><a class="reference internal" href="#">Using Torch-TensorRT in  C++</a><ul>
+<li><a class="reference internal" href="#id1">Using Torch-TensorRT in C++</a><ul>
+<li><a class="reference internal" href="#torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc">[Torch-TensorRT Quickstart] Compiling TorchScript Modules with <code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code></a></li>
+<li><a class="reference internal" href="#working-with-torchscript-in-c">Working with TorchScript in C++</a></li>
+<li><a class="reference internal" href="#compiling-with-torch-tensorrt-in-c">Compiling with Torch-TensorRT in C++</a></li>
+<li><a class="reference internal" href="#under-the-hood">Under The Hood</a></li>
+<li><a class="reference internal" href="#working-with-unsupported-operators">Working with Unsupported Operators</a><ul>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
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+
+  <section id="using-torch-tensorrt-in-python">
+<span id="getting-started-with-python-api"></span><h1>Using Torch-TensorRT in Python<a class="headerlink" href="#using-torch-tensorrt-in-python" title="Permalink to this headline">¶</a></h1>
+<p>The Torch-TensorRT Python API supports a number of unique usecases compared to the CLI and C++ APIs which solely support TorchScript compilation.</p>
+<p>Torch-TensorRT Python API can accept a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>, <code class="docutils literal notranslate"><span class="pre">torch.jit.ScriptModule</span></code>, or <code class="docutils literal notranslate"><span class="pre">torch.fx.GraphModule</span></code> as an input.
+Depending on what is provided one of the two frontends (TorchScript or FX) will be selected to compile the module. Provided the
+module type is supported, users may explicitly set which frontend they would like to use using the <code class="docutils literal notranslate"><span class="pre">ir</span></code> flag for <code class="docutils literal notranslate"><span class="pre">compile</span></code>.
+If given a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> and the <code class="docutils literal notranslate"><span class="pre">ir</span></code> flag is set to either <code class="docutils literal notranslate"><span class="pre">default</span></code> or <code class="docutils literal notranslate"><span class="pre">torchscript</span></code> the module will be run through
+<code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code> to convert the input module into a  TorchScript module.</p>
+<p>To compile your input <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> with Torch-TensorRT, all you need to do is provide the module and inputs
+to Torch-TensorRT and you will be returned an optimized TorchScript module to run or add into another PyTorch module. Inputs
+is a list of <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.Input</span></code> classes which define input’s shape, datatype and memory format. You can also specify settings such as
+operating precision for the engine or target device. After compilation you can save the module just like any other module
+to load in a deployment application. In order to load a TensorRT/TorchScript module, make sure you first import <code class="docutils literal notranslate"><span class="pre">torch_tensorrt</span></code>.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="o">...</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>  <span class="c1"># torch module needs to be in eval (not training) mode</span>
+
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span>
+        <span class="n">min_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">],</span>
+        <span class="n">opt_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span>
+        <span class="n">max_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span>
+        <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">,</span>
+    <span class="p">)</span>
+<span class="p">]</span>
+<span class="n">enabled_precisions</span> <span class="o">=</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">}</span>  <span class="c1"># Run with fp16</span>
+
+<span class="n">trt_ts_module</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
+    <span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions</span>
+<span class="p">)</span>
+
+<span class="n">input_data</span> <span class="o">=</span> <span class="n">input_data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">trt_ts_module</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">trt_ts_module</span><span class="p">,</span> <span class="s2">&quot;trt_ts_module.ts&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Deployment application</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="n">trt_ts_module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;trt_ts_module.ts&quot;</span><span class="p">)</span>
+<span class="n">input_data</span> <span class="o">=</span> <span class="n">input_data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">trt_ts_module</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Torch-TensorRT Python API also provides <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.ts.compile</span></code> which accepts a TorchScript module as input and <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.fx.compile</span></code> which accepts a FX GraphModule as input.</p>
+</section>
+
+
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diff --git a/docs/v1.2.0/getting_started/getting_started_with_windows.html b/docs/v1.2.0/getting_started/getting_started_with_windows.html
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+<li class="toctree-l1"><a class="reference internal" href="getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/logging.html">torch_tensorrt.logging</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/writing_converters.html">Writing Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
+</ul>
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+
+  <section id="building-torch-tensorrt-on-windows">
+<span id="getting-started-windows"></span><h1>Building Torch-TensorRT on Windows<a class="headerlink" href="#building-torch-tensorrt-on-windows" title="Permalink to this headline">¶</a></h1>
+<p>Torch-TensorRT has community support for Windows platform using CMake</p>
+<p>Prerequisite:</p>
+<ul class="simple">
+<li><p>Microsoft Visual Studio</p></li>
+<li><p>LibTorch</p></li>
+<li><p>TensorRT</p></li>
+<li><p>CUDA</p></li>
+<li><p>cuDNN</p></li>
+</ul>
+<section id="build-configuration">
+<h2>Build configuration<a class="headerlink" href="#build-configuration" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>Open Microsoft Visual Studio</p></li>
+<li><p>Open Torch-TensorRT source code folder</p></li>
+<li><p>Open Manage configurations -&gt; Edit JSON to open CMakeSettings.json file.</p></li>
+<li><p>Configure the CMake build configurations. Following is an example configuration:</p></li>
+</ul>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>{
+  &quot;configurations&quot;: [
+    {
+      &quot;name&quot;: &quot;x64-Debug&quot;,
+      &quot;generator&quot;: &quot;Ninja&quot;,
+      &quot;configurationType&quot;: &quot;Debug&quot;,
+      &quot;inheritEnvironments&quot;: [ &quot;msvc_x64_x64&quot; ],
+      &quot;buildRoot&quot;: &quot;${projectDir}\\out\\build\\${name}&quot;,
+      &quot;installRoot&quot;: &quot;${projectDir}\\out\\install\\${name}&quot;,
+      &quot;cmakeCommandArgs&quot;: &quot;-S . -B out&quot;,
+      &quot;buildCommandArgs&quot;: &quot;cmake --build out&quot;,
+      &quot;ctestCommandArgs&quot;: &quot;&quot;,
+      &quot;variables&quot;: [
+        {
+          &quot;name&quot;: &quot;CMAKE_MODULE_PATH&quot;,
+          &quot;value&quot;: &quot;$PWD\cmake\Modules&quot;,
+          &quot;type&quot;: &quot;FILEPATH&quot;
+        },
+        {
+          &quot;name&quot;: &quot;Torch_DIR&quot;,
+          &quot;value&quot;: &quot;&lt;Path to libtorch&gt;\share\cmake\Torch&quot;,
+          &quot;type&quot;: &quot;FILEPATH&quot;
+        },
+        {
+          &quot;name&quot;: &quot;TensorRT_ROOT&quot;,
+          &quot;value&quot;: &quot;&lt;Path to TensorRT directory&gt;&quot;,
+          &quot;type&quot;: &quot;FILEPATH&quot;
+        },
+        {
+          &quot;name&quot;: &quot;CMAKE_BUILD_TYPE&quot;,
+          &quot;value&quot;: &quot;Release&quot;,
+          &quot;type&quot;: &quot; STRING&quot;
+        }
+      ]
+    }
+  ]
+}
+</pre></div>
+</div>
+</section>
+<section id="compilation">
+<h2>Compilation<a class="headerlink" href="#compilation" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>Click Build -&gt; Build All or directly press Ctrl + Shift + B</p></li>
+</ul>
+<p>Note: After successful compilation, the build artifacts will be present at buildRoot path configured.</p>
+</section>
+<section id="installation">
+<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>Build -&gt; Install Torch-TensorRT</p></li>
+</ul>
+<p>Note: After successful installation, the artifacts will be present at installRoot.</p>
+</section>
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+<li><a class="reference internal" href="#build-configuration">Build configuration</a></li>
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diff --git a/docs/v1.2.0/getting_started/installation.html b/docs/v1.2.0/getting_started/installation.html
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@@ -0,0 +1,1036 @@
+
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+<li class="toctree-l1"><a class="reference internal" href="getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Example notebooks</a></li>
+</ul>
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+<ul>
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+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+
+  <section id="installation">
+<span id="id1"></span><h1>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h1>
+<section id="precompiled-binaries">
+<h2>Precompiled Binaries<a class="headerlink" href="#precompiled-binaries" title="Permalink to this headline">¶</a></h2>
+<section id="dependencies">
+<h3>Dependencies<a class="headerlink" href="#dependencies" title="Permalink to this headline">¶</a></h3>
+<p>You need to have either PyTorch or LibTorch installed based on if you are using Python or C++
+and you must have CUDA, cuDNN and TensorRT installed.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><a class="reference external" href="https://www.pytorch.org">https://www.pytorch.org</a></p></li>
+<li><p><a class="reference external" href="https://developer.nvidia.com/cuda">https://developer.nvidia.com/cuda</a></p></li>
+<li><p><a class="reference external" href="https://developer.nvidia.com/cudnn">https://developer.nvidia.com/cudnn</a></p></li>
+<li><p><a class="reference external" href="https://developer.nvidia.com/tensorrt">https://developer.nvidia.com/tensorrt</a></p></li>
+</ul>
+</div></blockquote>
+</section>
+<section id="python-package">
+<h3>Python Package<a class="headerlink" href="#python-package" title="Permalink to this headline">¶</a></h3>
+<p>You can install the python package using</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>pip3 install torch-tensorrt -f https://github.com/pytorch/TensorRT/releases
+</pre></div>
+</div>
+</section>
+<section id="c-binary-distribution">
+<span id="bin-dist"></span><h3>C++ Binary Distribution<a class="headerlink" href="#c-binary-distribution" title="Permalink to this headline">¶</a></h3>
+<p>Precompiled tarballs for releases are provided here: <a class="reference external" href="https://github.com/pytorch/TensorRT/releases">https://github.com/pytorch/TensorRT/releases</a></p>
+</section>
+</section>
+<section id="compiling-from-source">
+<span id="compile-from-source"></span><h2>Compiling From Source<a class="headerlink" href="#compiling-from-source" title="Permalink to this headline">¶</a></h2>
+<section id="dependencies-for-compilation">
+<span id="installing-deps"></span><h3>Dependencies for Compilation<a class="headerlink" href="#dependencies-for-compilation" title="Permalink to this headline">¶</a></h3>
+<p>Torch-TensorRT is built with Bazel, so begin by installing it.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>The easiest way is to install bazelisk using the method of your choosing <a class="reference external" href="https://github.com/bazelbuild/bazelisk">https://github.com/bazelbuild/bazelisk</a></p></li>
+<li><p>Otherwise you can use the following instructions to install binaries <a class="reference external" href="https://docs.bazel.build/versions/master/install.html">https://docs.bazel.build/versions/master/install.html</a></p></li>
+<li><p>Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions</p></li>
+</ul>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span> <span class="nv">BAZEL_VERSION</span><span class="o">=</span><span class="k">$(</span>cat &lt;PATH_TO_TORCHTRT_ROOT&gt;/.bazelversion<span class="k">)</span>
+mkdir bazel
+<span class="nb">cd</span> bazel
+curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/<span class="nv">$BAZEL_VERSION</span>/bazel-<span class="nv">$BAZEL_VERSION</span>-dist.zip
+unzip bazel-<span class="nv">$BAZEL_VERSION</span>-dist.zip
+bash ./compile.sh
+cp output/bazel /usr/local/bin/
+</pre></div>
+</div>
+</div></blockquote>
+<p>You will also need to have CUDA installed on the system (or if running in a container, the system must have
+the CUDA driver installed and the container must have CUDA)</p>
+<p>The correct LibTorch version will be pulled down for you by bazel.</p>
+<blockquote>
+<div><p>NOTE: For best compatability with official PyTorch, use torch==1.10.0+cuda113, TensorRT 8.0 and cuDNN 8.2 for CUDA 11.3 however Torch-TensorRT itself supports
+TensorRT and cuDNN for other CUDA versions for usecases such as using NVIDIA compiled distributions of PyTorch that use other versions of CUDA
+e.g. aarch64 or custom compiled version of PyTorch.</p>
+</div></blockquote>
+<section id="choosing-the-right-abi">
+<span id="abis"></span><h4>Choosing the Right ABI<a class="headerlink" href="#choosing-the-right-abi" title="Permalink to this headline">¶</a></h4>
+<p>Likely the most complicated thing about compiling Torch-TensorRT is selecting the correct ABI. There are two options
+which are incompatible with each other, pre-cxx11-abi and the cxx11-abi. The complexity comes from the fact that while
+the most popular distribution of PyTorch (wheels downloaded from pytorch.org/pypi directly) use the pre-cxx11-abi, most
+other distributions you might encounter (e.g. ones from NVIDIA - NGC containers, and builds for Jetson as well as certain
+libtorch builds and likely if you build PyTorch from source) use the cxx11-abi. It is important you compile Torch-TensorRT
+using the correct ABI to function properly. Below is a table with general pairings of PyTorch distribution sources and the
+recommended commands:</p>
+<table class="docutils align-default">
+<colgroup>
+<col style="width: 33%" />
+<col style="width: 31%" />
+<col style="width: 36%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>PyTorch Source</p></th>
+<th class="head"><p>Recommended C++ Compilation Command</p></th>
+<th class="head"><p>Recommended Python Compilation Command</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>PyTorch whl file from PyTorch.org</p></td>
+<td><p>bazel build //:libtorchtrt -c opt –config pre_cxx11_abi</p></td>
+<td><p>python3 setup.py bdist_wheel</p></td>
+</tr>
+<tr class="row-odd"><td><p>libtorch-shared-with-deps-<a href="#id2"><span class="problematic" id="id3">*</span></a>.zip from PyTorch.org</p></td>
+<td><p>bazel build //:libtorchtrt -c opt –config pre_cxx11_abi</p></td>
+<td><p>python3 setup.py bdist_wheel</p></td>
+</tr>
+<tr class="row-even"><td><p>libtorch-cxx11-abi-shared-with-deps-<a href="#id4"><span class="problematic" id="id5">*</span></a>.zip from PyTorch.org</p></td>
+<td><p>bazel build //:libtorchtrt -c opt</p></td>
+<td><p>python3 setup.py bdist_wheel –use-cxx11-abi</p></td>
+</tr>
+<tr class="row-odd"><td><p>PyTorch preinstalled in an NGC container</p></td>
+<td><p>bazel build //:libtorchtrt -c opt</p></td>
+<td><p>python3 setup.py bdist_wheel –use-cxx11-abi</p></td>
+</tr>
+<tr class="row-even"><td><p>PyTorch from the NVIDIA Forums for Jetson</p></td>
+<td><p>bazel build //:libtorchtrt -c opt</p></td>
+<td><p>python3 setup.py bdist_wheel –jetpack-version 4.6 –use-cxx11-abi</p></td>
+</tr>
+<tr class="row-odd"><td><p>PyTorch built from Source</p></td>
+<td><p>bazel build //:libtorchtrt -c opt</p></td>
+<td><p>python3 setup.py bdist_wheel –use-cxx11-abi</p></td>
+</tr>
+</tbody>
+</table>
+<blockquote>
+<div><p>NOTE: For all of the above cases you must correctly declare the source of PyTorch you intend to use in your WORKSPACE file for both Python and C++ builds. See below for more information</p>
+</div></blockquote>
+<p>You then have two compilation options:</p>
+</section>
+</section>
+<section id="building-using-cudnn-tensorrt-tarball-distributions">
+<span id="build-from-archive"></span><h3><strong>Building using cuDNN &amp; TensorRT tarball distributions</strong><a class="headerlink" href="#building-using-cudnn-tensorrt-tarball-distributions" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p>This is recommended so as to build Torch-TensorRT hermetically and insures any compilation errors are not caused by version issues</p>
+<p>Make sure when running Torch-TensorRT that these versions of the libraries are prioritized in your <code class="docutils literal notranslate"><span class="pre">$LD_LIBRARY_PATH</span></code></p>
+</div></blockquote>
+<dl class="simple">
+<dt>You need to download the tarball distributions of TensorRT and cuDNN from the NVIDIA website.</dt><dd><ul class="simple">
+<li><p><a class="reference external" href="https://developer.nvidia.com/cudnn">https://developer.nvidia.com/cudnn</a></p></li>
+<li><p><a class="reference external" href="https://developer.nvidia.com/tensorrt">https://developer.nvidia.com/tensorrt</a></p></li>
+</ul>
+</dd>
+</dl>
+<p>Place these files in a directory (the directories <code class="docutils literal notranslate"><span class="pre">third_party/distdir/[x86_64-linux-gnu</span> <span class="pre">|</span> <span class="pre">aarch64-linux-gnu]</span></code> exist for this purpose)</p>
+<p>Then compile referencing the directory with the tarballs</p>
+<blockquote>
+<div><p>If you get errors regarding the packages, check their sha256 hashes and make sure they match the ones listed in <code class="docutils literal notranslate"><span class="pre">WORKSPACE</span></code></p>
+</div></blockquote>
+<section id="release-build">
+<h4>Release Build<a class="headerlink" href="#release-build" title="Permalink to this headline">¶</a></h4>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt -c opt --distdir third_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
+</pre></div>
+</div>
+<p>A tarball with the include files and library can then be found in <code class="docutils literal notranslate"><span class="pre">bazel-bin</span></code></p>
+</section>
+<section id="debug-build">
+<span id="build-from-archive-debug"></span><h4>Debug Build<a class="headerlink" href="#debug-build" title="Permalink to this headline">¶</a></h4>
+<p>To build with debug symbols use the following command</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt -c dbg --distdir third_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
+</pre></div>
+</div>
+<p>A tarball with the include files and library can then be found in <code class="docutils literal notranslate"><span class="pre">bazel-bin</span></code></p>
+</section>
+<section id="pre-cxx11-abi-build">
+<h4>Pre CXX11 ABI Build<a class="headerlink" href="#pre-cxx11-abi-build" title="Permalink to this headline">¶</a></h4>
+<p>To build using the pre-CXX11 ABI use the <code class="docutils literal notranslate"><span class="pre">pre_cxx11_abi</span></code> config</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt --config pre_cxx11_abi -c <span class="o">[</span>dbg/opt<span class="o">]</span> --distdir third_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
+</pre></div>
+</div>
+<p>A tarball with the include files and library can then be found in <code class="docutils literal notranslate"><span class="pre">bazel-bin</span></code></p>
+</section>
+</section>
+<section id="building-using-locally-installed-cudnn-tensorrt">
+<span id="build-from-local"></span><h3><strong>Building using locally installed cuDNN &amp; TensorRT</strong><a class="headerlink" href="#building-using-locally-installed-cudnn-tensorrt" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p>If you encounter bugs and you compiled using this method please disclose that you used local sources in the issue (an ldd dump would be nice too)</p>
+</div></blockquote>
+<p>Install TensorRT, CUDA and cuDNN on the system before starting to compile.</p>
+<p>In WORKSPACE comment out:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Downloaded distributions to use with --distdir</span>
+<span class="n">http_archive</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;cudnn&quot;</span><span class="p">,</span>
+    <span class="n">urls</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;&lt;URL&gt;&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+    <span class="n">build_file</span><span class="o">=</span><span class="s2">&quot;@//third_party/cudnn/archive:BUILD&quot;</span><span class="p">,</span>
+    <span class="n">sha256</span><span class="o">=</span><span class="s2">&quot;&lt;TAR SHA256&gt;&quot;</span><span class="p">,</span>
+    <span class="n">strip_prefix</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">http_archive</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;tensorrt&quot;</span><span class="p">,</span>
+    <span class="n">urls</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;&lt;URL&gt;&quot;</span><span class="p">,</span>
+    <span class="p">],</span>
+    <span class="n">build_file</span><span class="o">=</span><span class="s2">&quot;@//third_party/tensorrt/archive:BUILD&quot;</span><span class="p">,</span>
+    <span class="n">sha256</span><span class="o">=</span><span class="s2">&quot;&lt;TAR SHA256&gt;&quot;</span><span class="p">,</span>
+    <span class="n">strip_prefix</span><span class="o">=</span><span class="s2">&quot;TensorRT-&lt;VERSION&gt;&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>and uncomment</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Locally installed dependencies</span>
+<span class="n">new_local_repository</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;cudnn&quot;</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="s2">&quot;/usr/&quot;</span><span class="p">,</span> <span class="n">build_file</span><span class="o">=</span><span class="s2">&quot;@//third_party/cudnn/local:BUILD&quot;</span>
+<span class="p">)</span>
+
+<span class="n">new_local_repository</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;tensorrt&quot;</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="s2">&quot;/usr/&quot;</span><span class="p">,</span> <span class="n">build_file</span><span class="o">=</span><span class="s2">&quot;@//third_party/tensorrt/local:BUILD&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<section id="id6">
+<h4>Release Build<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h4>
+<p>Compile using:</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt -c opt
+</pre></div>
+</div>
+<p>A tarball with the include files and library can then be found in <code class="docutils literal notranslate"><span class="pre">bazel-bin</span></code></p>
+</section>
+<section id="build-from-local-debug">
+<span id="id7"></span><h4>Debug Build<a class="headerlink" href="#build-from-local-debug" title="Permalink to this headline">¶</a></h4>
+<p>To build with debug symbols use the following command</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt -c dbg
+</pre></div>
+</div>
+<p>A tarball with the include files and library can then be found in <code class="docutils literal notranslate"><span class="pre">bazel-bin</span></code></p>
+</section>
+<section id="id8">
+<h4>Pre CXX11 ABI Build<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h4>
+<p>To build using the pre-CXX11 ABI use the <code class="docutils literal notranslate"><span class="pre">pre_cxx11_abi</span></code> config</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt --config pre_cxx11_abi -c <span class="o">[</span>dbg/opt<span class="o">]</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="building-with-cmake">
+<h3><strong>Building with CMake</strong><a class="headerlink" href="#building-with-cmake" title="Permalink to this headline">¶</a></h3>
+<p>It is possible to build the API libraries (in cpp/) and the torchtrtc executable using CMake instead of Bazel.
+Currently, the python API and the tests cannot be built with CMake.
+Begin by installing CMake.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>Latest releases of CMake and instructions on how to install are available for different platforms
+[on their website](<a class="reference external" href="https://cmake.org/download/">https://cmake.org/download/</a>).</p></li>
+</ul>
+</div></blockquote>
+<p>A few useful CMake options include:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>CMake finders for TensorRT and cuDNN are provided in <cite>cmake/Modules</cite>. In order for CMake to use them, pass
+<cite>-DCMAKE_MODULE_PATH=cmake/Modules</cite> when configuring the project with CMake.</p></li>
+<li><p>Libtorch provides its own CMake finder. In case CMake doesn’t find it, pass the path to your install of
+libtorch with <cite>-DTorch_DIR=&lt;path to libtorch&gt;/share/cmake/Torch</cite></p></li>
+<li><p>If TensorRT is not found with the provided cmake finder, specify <cite>-DTensorRT_ROOT=&lt;path to TensorRT&gt;</cite></p></li>
+<li><p>Finally, configure and build the project in a build directory of your choice with the following command
+from the root of Torch-TensorRT project:</p></li>
+</ul>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>cmake -S. -B&lt;build directory&gt; <span class="se">\</span>
+    <span class="o">[</span>-DCMAKE_MODULE_PATH<span class="o">=</span>cmake/Module<span class="o">]</span> <span class="se">\</span>
+    <span class="o">[</span>-DTorch_DIR<span class="o">=</span>&lt;path to libtorch&gt;/share/cmake/Torch<span class="o">]</span> <span class="se">\</span>
+    <span class="o">[</span>-DTensorRT_ROOT<span class="o">=</span>&lt;path to TensorRT&gt;<span class="o">]</span> <span class="se">\</span>
+    <span class="o">[</span>-DCMAKE_BUILD_TYPE<span class="o">=</span>Debug<span class="p">|</span>Release<span class="o">]</span>
+cmake --build &lt;build directory&gt;
+</pre></div>
+</div>
+</div></blockquote>
+</section>
+<section id="building-the-python-package">
+<h3><strong>Building the Python package</strong><a class="headerlink" href="#building-the-python-package" title="Permalink to this headline">¶</a></h3>
+<p>Begin by installing <code class="docutils literal notranslate"><span class="pre">ninja</span></code></p>
+<p>You can build the Python package using <code class="docutils literal notranslate"><span class="pre">setup.py</span></code> (this will also build the correct version of <code class="docutils literal notranslate"><span class="pre">libtorchtrt.so</span></code>)</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>python3 setup.py <span class="o">[</span>install/bdist_wheel<span class="o">]</span>
+</pre></div>
+</div>
+<section id="id9">
+<h4>Debug Build<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h4>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>python3 setup.py develop <span class="o">[</span>--user<span class="o">]</span>
+</pre></div>
+</div>
+<p>This also compiles a debug build of <code class="docutils literal notranslate"><span class="pre">libtorchtrt.so</span></code></p>
+</section>
+</section>
+<section id="building-natively-on-aarch64-jetson">
+<h3><strong>Building Natively on aarch64 (Jetson)</strong><a class="headerlink" href="#building-natively-on-aarch64-jetson" title="Permalink to this headline">¶</a></h3>
+<section id="prerequisites">
+<h4>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h4>
+<p>Install or compile a build of PyTorch/LibTorch for aarch64</p>
+<p>NVIDIA hosts builds the latest release branch for Jetson here:</p>
+<blockquote>
+<div><p><a class="reference external" href="https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048">https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048</a></p>
+</div></blockquote>
+</section>
+<section id="enviorment-setup">
+<h4>Enviorment Setup<a class="headerlink" href="#enviorment-setup" title="Permalink to this headline">¶</a></h4>
+<p>To build natively on aarch64-linux-gnu platform, configure the <code class="docutils literal notranslate"><span class="pre">WORKSPACE</span></code> with local available dependencies.</p>
+<ol class="arabic">
+<li><p>Replace <code class="docutils literal notranslate"><span class="pre">WORKSPACE</span></code> with the corresponding WORKSPACE file in <code class="docutils literal notranslate"><span class="pre">//toolchains/jp_workspaces</span></code></p></li>
+<li><p>Configure the correct paths to directory roots containing local dependencies in the <code class="docutils literal notranslate"><span class="pre">new_local_repository</span></code> rules:</p>
+<blockquote>
+<div><p>NOTE: If you installed PyTorch using a pip package, the correct path is the path to the root of the python torch package.
+In the case that you installed with <code class="docutils literal notranslate"><span class="pre">sudo</span> <span class="pre">pip</span> <span class="pre">install</span></code> this will be <code class="docutils literal notranslate"><span class="pre">/usr/local/lib/python3.8/dist-packages/torch</span></code>.
+In the case you installed with <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">--user</span></code> this will be <code class="docutils literal notranslate"><span class="pre">$HOME/.local/lib/python3.8/site-packages/torch</span></code>.</p>
+</div></blockquote>
+</li>
+</ol>
+<p>In the case you are using NVIDIA compiled pip packages, set the path for both libtorch sources to the same path. This is because unlike
+PyTorch on x86_64, NVIDIA aarch64 PyTorch uses the CXX11-ABI. If you compiled for source using the pre_cxx11_abi and only would like to
+use that library, set the paths to the same path but when you compile make sure to add the flag <code class="docutils literal notranslate"><span class="pre">--config=pre_cxx11_abi</span></code></p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>new_local_repository<span class="o">(</span>
+    <span class="nv">name</span> <span class="o">=</span> <span class="s2">&quot;libtorch&quot;</span>,
+    <span class="nv">path</span> <span class="o">=</span> <span class="s2">&quot;/usr/local/lib/python3.8/dist-packages/torch&quot;</span>,
+    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">&quot;third_party/libtorch/BUILD&quot;</span>
+<span class="o">)</span>
+
+new_local_repository<span class="o">(</span>
+    <span class="nv">name</span> <span class="o">=</span> <span class="s2">&quot;libtorch_pre_cxx11_abi&quot;</span>,
+    <span class="nv">path</span> <span class="o">=</span> <span class="s2">&quot;/usr/local/lib/python3.8/dist-packages/torch&quot;</span>,
+    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">&quot;third_party/libtorch/BUILD&quot;</span>
+<span class="o">)</span>
+</pre></div>
+</div>
+</section>
+<section id="compile-c-library-and-compiler-cli">
+<h4>Compile C++ Library and Compiler CLI<a class="headerlink" href="#compile-c-library-and-compiler-cli" title="Permalink to this headline">¶</a></h4>
+<blockquote>
+<div><p>NOTE: Due to shifting dependency locations between Jetpack 4.5 and 4.6 there is a now a flag to inform bazel of the Jetpack version</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>--platforms //toolchains:jetpack_x.x
+</pre></div>
+</div>
+</div></blockquote>
+<p>Compile Torch-TensorRT library using bazel command:</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel build //:libtorchtrt --platforms //toolchains:jetpack_5.0
+</pre></div>
+</div>
+</section>
+<section id="compile-python-api">
+<h4>Compile Python API<a class="headerlink" href="#compile-python-api" title="Permalink to this headline">¶</a></h4>
+<blockquote>
+<div><p>NOTE: Due to shifting dependencies locations between Jetpack 4.5 and newer Jetpack verisons there is now a flag for <code class="docutils literal notranslate"><span class="pre">setup.py</span></code> which sets the jetpack version (default: 5.0)</p>
+</div></blockquote>
+<p>Compile the Python API using the following command from the <code class="docutils literal notranslate"><span class="pre">//py</span></code> directory:</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>python3 setup.py install --use-cxx11-abi
+</pre></div>
+</div>
+<p>If you have a build of PyTorch that uses Pre-CXX11 ABI drop the <code class="docutils literal notranslate"><span class="pre">--use-cxx11-abi</span></code> flag</p>
+<p>If you are building for Jetpack 4.5 add the <code class="docutils literal notranslate"><span class="pre">--jetpack-version</span> <span class="pre">5.0</span></code> flag</p>
+</section>
+</section>
+</section>
+</section>
+
+
+             </article>
+
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+
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
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+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
+              <ul>
+<li><a class="reference internal" href="#">Installation</a><ul>
+<li><a class="reference internal" href="#precompiled-binaries">Precompiled Binaries</a><ul>
+<li><a class="reference internal" href="#dependencies">Dependencies</a></li>
+<li><a class="reference internal" href="#python-package">Python Package</a></li>
+<li><a class="reference internal" href="#c-binary-distribution">C++ Binary Distribution</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#compiling-from-source">Compiling From Source</a><ul>
+<li><a class="reference internal" href="#dependencies-for-compilation">Dependencies for Compilation</a><ul>
+<li><a class="reference internal" href="#choosing-the-right-abi">Choosing the Right ABI</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#building-using-cudnn-tensorrt-tarball-distributions"><strong>Building using cuDNN &amp; TensorRT tarball distributions</strong></a><ul>
+<li><a class="reference internal" href="#release-build">Release Build</a></li>
+<li><a class="reference internal" href="#debug-build">Debug Build</a></li>
+<li><a class="reference internal" href="#pre-cxx11-abi-build">Pre CXX11 ABI Build</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#building-using-locally-installed-cudnn-tensorrt"><strong>Building using locally installed cuDNN &amp; TensorRT</strong></a><ul>
+<li><a class="reference internal" href="#id6">Release Build</a></li>
+<li><a class="reference internal" href="#build-from-local-debug">Debug Build</a></li>
+<li><a class="reference internal" href="#id8">Pre CXX11 ABI Build</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#building-with-cmake"><strong>Building with CMake</strong></a></li>
+<li><a class="reference internal" href="#building-the-python-package"><strong>Building the Python package</strong></a><ul>
+<li><a class="reference internal" href="#id9">Debug Build</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#building-natively-on-aarch64-jetson"><strong>Building Natively on aarch64 (Jetson)</strong></a><ul>
+<li><a class="reference internal" href="#prerequisites">Prerequisites</a></li>
+<li><a class="reference internal" href="#enviorment-setup">Enviorment Setup</a></li>
+<li><a class="reference internal" href="#compile-c-library-and-compiler-cli">Compile C++ Library and Compiler CLI</a></li>
+<li><a class="reference internal" href="#compile-python-api">Compile Python API</a></li>
+</ul>
+</li>
+</ul>
+</li>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="tutorials/notebooks.html">Example notebooks</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="py_api/ptq.html">torch_tensorrt.ptq</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="py_api/fx.html">torch_tensorrt.fx</a></li>
+</ul>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
+<li class="toctree-l1"><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
+<li class="toctree-l1"><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="indices/supported_ops.html">Operators Supported</a></li>
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+
+  <section id="torch-tensorrt">
+<h1>Torch-TensorRT<a class="headerlink" href="#torch-tensorrt" title="Permalink to this headline">¶</a></h1>
+<section id="ahead-of-time-compilation-of-torchscript-pytorch-jit-for-nvidia-gpus">
+<h2>Ahead-of-time compilation of TorchScript / PyTorch JIT for NVIDIA GPUs<a class="headerlink" href="#ahead-of-time-compilation-of-torchscript-pytorch-jit-for-nvidia-gpus" title="Permalink to this headline">¶</a></h2>
+<p>Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA’s TensorRT Deep Learning Optimizer and Runtime.
+Unlike PyTorch’s Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your
+TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into an module targeting
+a TensorRT engine. Torch-TensorRT operates as a PyTorch extention and compiles modules that integrate into the JIT runtime seamlessly.
+After compilation using the optimized graph should feel no different than running a TorchScript module.
+You also have access to TensorRT’s suite of configurations at compile time, so you are able to specify
+operating precision (FP32/FP16/INT8) and other settings for your module.</p>
+<p>More Information / System Architecture:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://developer.nvidia.com/gtc/2020/video/s21671">GTC 2020 Talk</a></p></li>
+</ul>
+</section>
+<section id="getting-started">
+<h2>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="getting_started/installation.html#installation"><span class="std std-ref">Installation</span></a></p></li>
+<li><p><a class="reference internal" href="getting_started/getting_started_with_python_api.html#getting-started-with-python-api"><span class="std std-ref">Using Torch-TensorRT in Python</span></a></p></li>
+<li><p><a class="reference internal" href="getting_started/getting_started_with_cpp_api.html#getting-started-cpp"><span class="std std-ref">Using Torch-TensorRT in  C++</span></a></p></li>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="tutorials">
+<h2>Tutorials<a class="headerlink" href="#tutorials" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="tutorials/creating_torchscript_module_in_python.html#creating-a-ts-mod"><span class="std std-ref">Creating a TorchScript Module</span></a></p></li>
+<li><p><a class="reference internal" href="tutorials/getting_started_with_fx_path.html#getting-started-with-fx"><span class="std std-ref">Torch-TensorRT (FX Frontend) User Guide</span></a></p></li>
+<li><p><a class="reference internal" href="tutorials/ptq.html#ptq"><span class="std std-ref">Post Training Quantization (PTQ)</span></a></p></li>
+<li><p><a class="reference internal" href="tutorials/runtime.html#runtime"><span class="std std-ref">Deploying Torch-TensorRT Programs</span></a></p></li>
+<li><p><a class="reference internal" href="tutorials/serving_torch_tensorrt_with_triton.html#serving-torch-tensorrt-with-triton"><span class="std std-ref">Serving a Torch-TensorRT model with Triton</span></a></p></li>
+<li><p><a class="reference internal" href="tutorials/use_from_pytorch.html#use-from-pytorch"><span class="std std-ref">Using Torch-TensorRT Directly From PyTorch</span></a></p></li>
+<li><p><a class="reference internal" href="tutorials/using_dla.html#using-dla"><span class="std std-ref">DLA</span></a></p></li>
+<li><p><a class="reference internal" href="tutorials/notebooks.html#notebooks"><span class="std std-ref">Example notebooks</span></a></p></li>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="python-api-documenation">
+<h2>Python API Documenation<a class="headerlink" href="#python-api-documenation" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="py_api/torch_tensorrt.html#torch-tensorrt-py"><span class="std std-ref">torch_tensorrt</span></a></p></li>
+<li><p><a class="reference internal" href="py_api/logging.html#torch-tensorrt-logging-py"><span class="std std-ref">torch_tensorrt.logging</span></a></p></li>
+<li><p><a class="reference internal" href="py_api/ptq.html#torch-tensorrt-ptq-py"><span class="std std-ref">torch_tensorrt.ptq</span></a></p></li>
+<li><p><a class="reference internal" href="py_api/ts.html#torch-tensorrt-ts-py"><span class="std std-ref">torch_tensorrt.ts</span></a></p></li>
+<li><p><a class="reference internal" href="py_api/fx.html#torch-tensorrt-fx-py"><span class="std std-ref">torch_tensorrt.fx</span></a></p></li>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="c-api-documenation">
+<h2>C++ API Documenation<a class="headerlink" href="#c-api-documenation" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt.html#namespace-torch-tensorrt"><span class="std std-ref">Namespace torch_tensorrt</span></a></p></li>
+<li><p><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt__logging.html#namespace-torch-tensorrt-logging"><span class="std std-ref">Namespace torch_tensorrt::logging</span></a></p></li>
+<li><p><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt__ptq.html#namespace-torch-tensorrt-ptq"><span class="std std-ref">Namespace torch_tensorrt::ptq</span></a></p></li>
+<li><p><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt__torchscript.html#namespace-torch-tensorrt-torchscript"><span class="std std-ref">Namespace torch_tensorrt::torchscript</span></a></p></li>
+</ul>
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+</div>
+</section>
+<section id="cli-documentation">
+<h2>CLI Documentation<a class="headerlink" href="#cli-documentation" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="cli/torchtrtc.html#torchtrtc"><span class="std std-ref">torchtrtc</span></a></p></li>
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+<li><p><a class="reference internal" href="contributors/useful_links.html#useful-links"><span class="std std-ref">Useful Links for Torch-TensorRT Development</span></a></p></li>
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+</section>
+<section id="indices">
+<h2>Indices<a class="headerlink" href="#indices" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="indices/supported_ops.html#supported-ops"><span class="std std-ref">Operators Supported</span></a></p></li>
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+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">Operators Supported</a></li>
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+
+  <section id="operators-supported">
+<span id="supported-ops"></span><h1>Operators Supported<a class="headerlink" href="#operators-supported" title="Permalink to this headline">¶</a></h1>
+<section id="operators-currently-supported-through-converters">
+<h2>Operators Currently Supported Through Converters<a class="headerlink" href="#operators-currently-supported-through-converters" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>aten::_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -&gt; (Tensor)</p></li>
+<li><p>aten::_convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled) -&gt; (Tensor)</p></li>
+<li><p>aten::abs(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::acos(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::acosh(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::adaptive_avg_pool1d(Tensor self, int[1] output_size) -&gt; (Tensor)</p></li>
+<li><p>aten::adaptive_avg_pool2d(Tensor self, int[2] output_size) -&gt; (Tensor)</p></li>
+<li><p>aten::adaptive_avg_pool3d(Tensor self, int[3] output_size) -&gt; (Tensor)</p></li>
+<li><p>aten::adaptive_max_pool1d(Tensor self, int[2] output_size) -&gt; (Tensor, Tensor)</p></li>
+<li><p>aten::adaptive_max_pool2d(Tensor self, int[2] output_size) -&gt; (Tensor, Tensor)</p></li>
+<li><p>aten::adaptive_max_pool3d(Tensor self, int[3] output_size) -&gt; (Tensor, Tensor)</p></li>
+<li><p>aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -&gt; (Tensor)</p></li>
+<li><p>aten::add.Tensor(Tensor self, Tensor other, Scalar alpha=1) -&gt; (Tensor)</p></li>
+<li><p>aten::<a href="#id41"><span class="problematic" id="id42">add_</span></a>.Tensor(Tensor(a!) self, Tensor other, <a href="#id1"><span class="problematic" id="id2">*</span></a>, Scalar alpha=1) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::argmax(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor)</p></li>
+<li><p>aten::argmin(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor)</p></li>
+<li><p>aten::asin(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::asinh(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::atan(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::atanh(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::avg_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=[0], bool ceil_mode=False, bool count_include_pad=True) -&gt; (Tensor)</p></li>
+<li><p>aten::avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=[0, 0], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -&gt; (Tensor)</p></li>
+<li><p>aten::avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=[], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -&gt; (Tensor)</p></li>
+<li><p>aten::batch_norm(Tensor input, Tensor? gamma, Tensor? beta, Tensor? mean, Tensor? var, bool training, float momentum, float eps, bool cudnn_enabled) -&gt; (Tensor)</p></li>
+<li><p>aten::bitwise_not(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::bmm(Tensor self, Tensor mat2) -&gt; (Tensor)</p></li>
+<li><p>aten::cat(Tensor[] tensors, int dim=0) -&gt; (Tensor)</p></li>
+<li><p>aten::ceil(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::clamp(Tensor self, Scalar? min=None, Scalar? max=None) -&gt; (Tensor)</p></li>
+<li><p>aten::clamp_max(Tensor self, Scalar max) -&gt; (Tensor)</p></li>
+<li><p>aten::clamp_min(Tensor self, Scalar min) -&gt; (Tensor)</p></li>
+<li><p>aten::constant_pad_nd(Tensor self, int[] pad, Scalar value=0) -&gt; (Tensor)</p></li>
+<li><p>aten::cos(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::cosh(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::cumsum(Tensor self, int dim, <a href="#id3"><span class="problematic" id="id4">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::div.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::div.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::div.Tensor_mode(Tensor self, Tensor other, <a href="#id5"><span class="problematic" id="id6">*</span></a>, str? rounding_mode) -&gt; (Tensor)</p></li>
+<li><p>aten::<a href="#id43"><span class="problematic" id="id44">div_</span></a>.Scalar(Tensor(a!) self, Scalar other) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::<a href="#id45"><span class="problematic" id="id46">div_</span></a>.Tensor(Tensor(a!) self, Tensor other) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -&gt; (Tensor)</p></li>
+<li><p>aten::embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -&gt; (Tensor)</p></li>
+<li><p>aten::eq.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::eq.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::erf(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::exp(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::expand(Tensor(a) self, int[] size, <a href="#id7"><span class="problematic" id="id8">*</span></a>, bool implicit=False) -&gt; (Tensor(a))</p></li>
+<li><p>aten::expand_as(Tensor(a) self, Tensor other) -&gt; (Tensor(a))</p></li>
+<li><p>aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -&gt; (Tensor)</p></li>
+<li><p>aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -&gt; (Tensor)</p></li>
+<li><p>aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -&gt; (Tensor)</p></li>
+<li><p>aten::floor(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::floor_divide(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::floor_divide.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::ge.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::ge.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -&gt; (Tensor)</p></li>
+<li><p>aten::gt.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::gt.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -&gt; (Tensor)</p></li>
+<li><p>aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::index.Tensor(Tensor self, Tensor?[] indices) -&gt; (Tensor)</p></li>
+<li><p>aten::instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -&gt; (Tensor)</p></li>
+<li><p>aten::layer_norm(Tensor input, int[] normalized_shape, Tensor? gamma, Tensor? beta, float eps, bool cudnn_enabled) -&gt; (Tensor)</p></li>
+<li><p>aten::le.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::le.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -&gt; (Tensor)</p></li>
+<li><p>aten::leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -&gt; (Tensor)</p></li>
+<li><p>aten::log(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -&gt; (Tensor, Tensor)</p></li>
+<li><p>aten::lt.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::lt.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -&gt; (Tensor)</p></li>
+<li><p>aten::matmul(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::max(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::max.dim(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor values, Tensor indices)</p></li>
+<li><p>aten::max.other(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=[], int[1] dilation=[], bool ceil_mode=False) -&gt; (Tensor)</p></li>
+<li><p>aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=[0, 0], int[2] dilation=[1, 1], bool ceil_mode=False) -&gt; (Tensor)</p></li>
+<li><p>aten::max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=[], int[3] dilation=[], bool ceil_mode=False) -&gt; (Tensor)</p></li>
+<li><p>aten::mean(Tensor self, <a href="#id9"><span class="problematic" id="id10">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::mean.dim(Tensor self, int[] dim, bool keepdim=False, <a href="#id11"><span class="problematic" id="id12">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::min(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::min.dim(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor values, Tensor indices)</p></li>
+<li><p>aten::min.other(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::mul.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::mul.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::<a href="#id47"><span class="problematic" id="id48">mul_</span></a>.Tensor(Tensor(a!) self, Tensor other) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::narrow(Tensor(a) self, int dim, int start, int length) -&gt; (Tensor(a))</p></li>
+<li><p>aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, int length) -&gt; (Tensor(a))</p></li>
+<li><p>aten::ne.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::ne.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::neg(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -&gt; (Tensor)</p></li>
+<li><p>aten::permute(Tensor(a) self, int[] dims) -&gt; (Tensor(a))</p></li>
+<li><p>aten::pixel_shuffle(Tensor self, int upscale_factor) -&gt; (Tensor)</p></li>
+<li><p>aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -&gt; (Tensor)</p></li>
+<li><p>aten::pow.Tensor_Tensor(Tensor self, Tensor exponent) -&gt; (Tensor)</p></li>
+<li><p>aten::prelu(Tensor self, Tensor weight) -&gt; (Tensor)</p></li>
+<li><p>aten::prod(Tensor self, <a href="#id13"><span class="problematic" id="id14">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::prod.dim_int(Tensor self, int dim, bool keepdim=False, <a href="#id15"><span class="problematic" id="id16">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::reciprocal(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::reflection_pad1d(Tensor self, int[2] padding) -&gt; (Tensor)</p></li>
+<li><p>aten::reflection_pad2d(Tensor self, int[4] padding) -&gt; (Tensor)</p></li>
+<li><p>aten::relu(Tensor input) -&gt; (Tensor)</p></li>
+<li><p>aten::relu_(Tensor(a!) self) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::repeat(Tensor self, int[] repeats) -&gt; (Tensor)</p></li>
+<li><p>aten::repeat_interleave.self_int(Tensor self, int repeats, int? dim=None, <a href="#id17"><span class="problematic" id="id18">*</span></a>, int? output_size=None) -&gt; (Tensor)</p></li>
+<li><p>aten::replication_pad1d(Tensor self, int[2] padding) -&gt; (Tensor)</p></li>
+<li><p>aten::replication_pad2d(Tensor self, int[4] padding) -&gt; (Tensor)</p></li>
+<li><p>aten::replication_pad3d(Tensor self, int[6] padding) -&gt; (Tensor)</p></li>
+<li><p>aten::reshape(Tensor self, int[] shape) -&gt; (Tensor)</p></li>
+<li><p>aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -&gt; (Tensor)</p></li>
+<li><p>aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -&gt; (Tensor)</p></li>
+<li><p>aten::rsub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -&gt; (Tensor)</p></li>
+<li><p>aten::scatter.src(Tensor self, int dim, Tensor index, Tensor src) -&gt; (Tensor)</p></li>
+<li><p>aten::scatter.value(Tensor self, int dim, Tensor index, Scalar value) -&gt; (Tensor)</p></li>
+<li><p>aten::select.int(Tensor(a) self, int dim, int index) -&gt; (Tensor(a))</p></li>
+<li><p>aten::sigmoid(Tensor input) -&gt; (Tensor)</p></li>
+<li><p>aten::sigmoid_(Tensor(a!) self) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::sin(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::sinh(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::slice.Tensor(Tensor(a) self, int dim=0, int? start=None, int? end=None, int step=1) -&gt; (Tensor(a))</p></li>
+<li><p>aten::softmax.int(Tensor self, int dim, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::split(Tensor self, int[] split_sizes, int dim=0) -&gt; (Tensor[])</p></li>
+<li><p>aten::split.Tensor(Tensor(a) self, int split_size, int dim=0) -&gt; (Tensor[])</p></li>
+<li><p>aten::split.sizes(Tensor(a -&gt; <a href="#id19"><span class="problematic" id="id20">*</span></a>) self, int[] split_size, int dim=0) -&gt; (Tensor[])</p></li>
+<li><p>aten::split_with_sizes(Tensor(a) self, int[] split_sizes, int dim=0) -&gt; (Tensor[])</p></li>
+<li><p>aten::sqrt(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::square(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::squeeze.dim(Tensor(a) self, int dim) -&gt; (Tensor(a))</p></li>
+<li><p>aten::stack(Tensor[] tensors, int dim=0) -&gt; (Tensor)</p></li>
+<li><p>aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -&gt; (Tensor)</p></li>
+<li><p>aten::sub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -&gt; (Tensor)</p></li>
+<li><p>aten::<a href="#id49"><span class="problematic" id="id50">sub_</span></a>.Tensor(Tensor(a!) self, Tensor other, <a href="#id21"><span class="problematic" id="id22">*</span></a>, Scalar alpha=1) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::sum(Tensor self, <a href="#id23"><span class="problematic" id="id24">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, <a href="#id25"><span class="problematic" id="id26">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::t(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::tan(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::tanh(Tensor input) -&gt; (Tensor)</p></li>
+<li><p>aten::tanh_(Tensor(a!) self) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::to.device(Tensor(a) self, Device device, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -&gt; (Tensor(a))</p></li>
+<li><p>aten::to.dtype(Tensor self, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -&gt; (Tensor)</p></li>
+<li><p>aten::to.other(Tensor self, Tensor other, bool non_blocking=False, bool copy=False, int? memory_format=None) -&gt; (Tensor)</p></li>
+<li><p>aten::to.prim_Device(Tensor(a) self, Device? device, int? dtype=None, bool non_blocking=False, bool copy=False) -&gt; (Tensor(a|b))</p></li>
+<li><p>aten::topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -&gt; (Tensor values, Tensor indices)</p></li>
+<li><p>aten::transpose.int(Tensor(a) self, int dim0, int dim1) -&gt; (Tensor(a))</p></li>
+<li><p>aten::unbind.int(Tensor(a -&gt; <a href="#id27"><span class="problematic" id="id28">*</span></a>) self, int dim=0) -&gt; (Tensor[])</p></li>
+<li><p>aten::unsqueeze(Tensor(a) self, int dim) -&gt; (Tensor(a))</p></li>
+<li><p>aten::upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_bilinear2d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_linear1d(Tensor self, int[1] output_size, bool align_corners, float? scales=None) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_linear1d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_nearest1d(Tensor self, int[1] output_size, float? scales=None) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_nearest1d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_nearest2d(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_nearest2d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_nearest3d(Tensor self, int[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_nearest3d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_trilinear3d(Tensor self, int[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_trilinear3d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -&gt; (Tensor)</p></li>
+<li><p>aten::view(Tensor(a) self, int[] size) -&gt; (Tensor(a))</p></li>
+<li><p>trt::const(Tensor self) -&gt; (Tensor)</p></li>
+</ul>
+</section>
+<section id="operators-currently-supported-through-evaluators">
+<h2>Operators Currently Supported Through Evaluators<a class="headerlink" href="#operators-currently-supported-through-evaluators" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>aten::Bool.float(float b) -&gt; (bool)</p></li>
+<li><p>aten::Bool.int(int a) -&gt; (bool)</p></li>
+<li><p>aten::Float.Scalar(Scalar a) -&gt; float</p></li>
+<li><p>aten::Float.bool(bool a) -&gt; float</p></li>
+<li><p>aten::Float.int(int a) -&gt; float</p></li>
+<li><p>aten::Int.Scalar(Scalar a) -&gt; int</p></li>
+<li><p>aten::Int.bool(bool a) -&gt; int</p></li>
+<li><p>aten::Int.float(float a) -&gt; int</p></li>
+<li><p>aten::Int.int(int a) -&gt; int</p></li>
+<li><p>aten::__and__(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::__and__.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::__derive_index(int idx, int start, int step) -&gt; int</p></li>
+<li><p>aten::__getitem__.t(t[](a) list, int idx) -&gt; (t(*))</p></li>
+<li><p>aten::__is__(t1 self, t2 obj) -&gt; bool</p></li>
+<li><p>aten::__isnot__(t1 self, t2 obj) -&gt; bool</p></li>
+<li><p>aten::__not__(bool self) -&gt; bool</p></li>
+<li><p>aten::__or__(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::__range_length(int lo, int hi, int step) -&gt; int</p></li>
+<li><p>aten::__round_to_zero_floordiv(int a, int b) -&gt; (int)</p></li>
+<li><p>aten::__xor__(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::add.float(float a, float b) -&gt; (float)</p></li>
+<li><p>aten::add.int(int a, int b) -&gt; (int)</p></li>
+<li><p>aten::add.str(str a, str b) -&gt; (str)</p></li>
+<li><p>aten::<a href="#id51"><span class="problematic" id="id52">add_</span></a>.t(t[](a!) self, t[] b) -&gt; (t[])</p></li>
+<li><p>aten::append.t(t[](a!) self, t(c -&gt; <a href="#id29"><span class="problematic" id="id30">*</span></a>) el) -&gt; (t[](a!))</p></li>
+<li><dl class="simple">
+<dt>aten::arange(Scalar end, <a href="#id31"><span class="problematic" id="id32">*</span></a>, int? dtype=None, int? layout=None,</dt><dd><p>Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>aten::arange.start(Scalar start, Scalar end, <a href="#id33"><span class="problematic" id="id34">*</span></a>, ScalarType? dtype=None,</dt><dd><p>Layout? layout=None, Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>aten::arange.start_step(Scalar start, Scalar end, Scalar step, <a href="#id35"><span class="problematic" id="id36">*</span></a>, ScalarType? dtype=None,</dt><dd><p>Layout? layout=None, Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
+</dd>
+</dl>
+</li>
+<li><p>aten::clone(Tensor self, <a href="#id37"><span class="problematic" id="id38">*</span></a>, int? memory_format=None) -&gt; (Tensor)</p></li>
+<li><p>aten::copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::dim(Tensor self) -&gt; int</p></li>
+<li><p>aten::div.float(float a, float b) -&gt; (float)</p></li>
+<li><p>aten::div.int(int a, int b) -&gt; (float)</p></li>
+<li><p>aten::eq.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::eq.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>aten::eq.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>aten::eq.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::eq.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>aten::eq.str(str a, str b) -&gt; (bool)</p></li>
+<li><p>aten::extend.t(t[](a!) self, t[] other) -&gt; ()</p></li>
+<li><p>aten::floor.float(float a) -&gt; (int)</p></li>
+<li><p>aten::floor.int(int a) -&gt; (int)</p></li>
+<li><p>aten::floordiv.float(float a, float b) -&gt; (int)</p></li>
+<li><p>aten::floordiv.int(int a, int b) -&gt; (int)</p></li>
+<li><p>aten::format(str self, …) -&gt; (str)</p></li>
+<li><p>aten::ge.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::ge.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>aten::ge.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>aten::ge.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::ge.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>aten::gt.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::gt.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>aten::gt.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>aten::gt.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::gt.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>aten::is_floating_point(Tensor self) -&gt; (bool)</p></li>
+<li><p>aten::le.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::le.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>aten::le.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>aten::le.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::le.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>aten::len.t(t[] a) -&gt; (int)</p></li>
+<li><p>aten::lt.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::lt.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>aten::lt.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>aten::lt.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::lt.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>aten::mul.float(float a, float b) -&gt; (float)</p></li>
+<li><p>aten::mul.int(int a, int b) -&gt; (int)</p></li>
+<li><p>aten::ne.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::ne.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>aten::ne.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>aten::ne.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::ne.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>aten::neg.int(int a) -&gt; (int)</p></li>
+<li><p>aten::numel(Tensor self) -&gt; int</p></li>
+<li><p>aten::pow.float(float a, float b) -&gt; (float)</p></li>
+<li><p>aten::pow.float_int(float a, int b) -&gt; (float)</p></li>
+<li><p>aten::pow.int(int a, int b) -&gt; (float)</p></li>
+<li><p>aten::pow.int_float(int a, float b) -&gt; (float)</p></li>
+<li><p>aten::size(Tensor self) -&gt; (int[])</p></li>
+<li><p>aten::size.int(Tensor self, int dim) -&gt; (int)</p></li>
+<li><p>aten::slice.t(t[] l, int start, int end=9223372036854775807, int step=1) -&gt; (t[])</p></li>
+<li><p>aten::sqrt.float(float a) -&gt; (float)</p></li>
+<li><p>aten::sqrt.int(int a) -&gt; (float)</p></li>
+<li><p>aten::sub.float(float a, float b) -&gt; (float)</p></li>
+<li><p>aten::sub.int(int a, int b) -&gt; (int)</p></li>
+<li><p>aten::tensor(t[] data, <a href="#id39"><span class="problematic" id="id40">*</span></a>, int? dtype=None, Device? device=None, bool requires_grad=False) -&gt; (Tensor)</p></li>
+<li><p>prim::TupleIndex(Any tup, int i) -&gt; (Any)</p></li>
+<li><p>prim::dtype(Tensor a) -&gt; (int)</p></li>
+<li><p>prim::max.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>prim::max.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>prim::max.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>prim::max.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>prim::max.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>prim::max.self_int(int[] self) -&gt; (int)</p></li>
+<li><p>prim::min.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>prim::min.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>prim::min.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>prim::min.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>prim::min.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>prim::min.self_int(int[] self) -&gt; (int)</p></li>
+<li><p>prim::shape(Tensor a) -&gt; (int[])</p></li>
+</ul>
+</section>
+</section>
+
+
+             </article>
+
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+<li class="toctree-l1"><a class="reference internal" href="getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="indices/supported_ops.html">Operators Supported</a></li>
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+
+  <section id="torch-tensorrt-fx">
+<span id="torch-tensorrt-fx-py"></span><h1>torch_tensorrt.fx<a class="headerlink" href="#torch-tensorrt-fx" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.fx"></span><section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.fx.compile">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.fx.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.nn.modules.module.Module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_batch_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_workspace_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">33554432</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">explicit_batch_dimension</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">LowerPrecision.FP16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timing_cache_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_timing_cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cuda_graph_batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_batch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.nn.modules.module.Module</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/fx/lower.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.fx.compile" title="Permalink to this definition">¶</a></dt>
+<dd><p>Takes in original module, input and lowering setting, run lowering workflow to turn module
+into lowered module, or so called TRTModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> – Original module for lowering.</p></li>
+<li><p><strong>input</strong> – Input for module.</p></li>
+<li><p><strong>max_batch_size</strong> – Maximum batch size (must be &gt;= 1 to be set, 0 means not set)</p></li>
+<li><p><strong>max_workspace_size</strong> – Maximum size of workspace given to TensorRT.</p></li>
+<li><p><strong>explicit_batch_dimension</strong> – Use explicit batch dimension in TensorRT if set True, otherwise use implicit batch dimension.</p></li>
+<li><p><strong>lower_precision</strong> – lower_precision config given to TRTModule.</p></li>
+<li><p><strong>verbose_log</strong> – Enable verbose log for TensorRT if set True.</p></li>
+<li><p><strong>timing_cache_prefix</strong> – Timing cache file name for timing cache used by fx2trt.</p></li>
+<li><p><strong>save_timing_cache</strong> – Update timing cache with current timing cache data if set to True.</p></li>
+<li><p><strong>cuda_graph_batch_size</strong> – Cuda graph batch size, default to be -1.</p></li>
+<li><p><strong>dynamic_batch</strong> – batch dimension (dim=0) is dynamic.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>A torch.nn.Module lowered by TensorRT.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="classes">
+<h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.fx.TRTModule">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.fx.</span></span><span class="sig-name descname"><span class="pre">TRTModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">engine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cuda_graph_batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/fx/trt_module.html#TRTModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.fx.TRTModule" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.fx.InputTensorSpec">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.fx.</span></span><span class="sig-name descname"><span class="pre">InputTensorSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.device</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">device(type='cpu')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape_ranges</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_batch_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/fx/input_tensor_spec.html#InputTensorSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.fx.InputTensorSpec" title="Permalink to this definition">¶</a></dt>
+<dd><p>This class contains the information of a input tensor.</p>
+<p>shape: shape of the tensor.</p>
+<p>dtype: dtyep of the tensor.</p>
+<dl class="simple">
+<dt>device: device of the tensor. This is only used to generate inputs to the given model</dt><dd><p>in order to run shape prop. For TensorRT engine, inputs have to be on cuda device.</p>
+</dd>
+<dt>shape_ranges: If dynamic shape is needed (shape has dimensions of -1), then this field</dt><dd><p>has to be provided (default is empty list). Every shape_range is a tuple of three
+tuples ((min_input_shape), (optimized_input_shape), (max_input_shape)). Each shape_range
+is used to populate a TensorRT optimization profile.
+e.g. If the input shape varies from (1, 224) to (100, 224) and we want to optimize
+for (25, 224) because it’s the most common input shape, then we set shape_ranges to
+((1, 224), (25, 225), (100, 224)).</p>
+</dd>
+<dt>has_batch_dim: Whether the shape includes batch dimension. Batch dimension has to be provided</dt><dd><p>if the engine want to run with dynamic shape.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.fx.TRTInterpreter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.fx.</span></span><span class="sig-name descname"><span class="pre">TRTInterpreter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.fx.graph_module.GraphModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_specs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch_tensorrt.fx.InputTensorSpec" title="torch_tensorrt.fx.input_tensor_spec.InputTensorSpec"><span class="pre">torch_tensorrt.fx.input_tensor_spec.InputTensorSpec</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">explicit_batch_dimension</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">explicit_precision</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/fx/fx2trt.html#TRTInterpreter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.fx.TRTInterpreter" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.fx.TRTInterpreterResult">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.fx.</span></span><span class="sig-name descname"><span class="pre">TRTInterpreterResult</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">engine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">serialized_cache</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/fx/fx2trt.html#TRTInterpreterResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.fx.TRTInterpreterResult" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+</section>
+
+
+             </article>
+
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diff --git a/docs/v1.2.0/py_api/logging.html b/docs/v1.2.0/py_api/logging.html
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+<li class="toctree-l1"><a class="reference internal" href="fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
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+</ul>
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+<ul>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
+</ul>
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+
+  <section id="torch-tensorrt-logging">
+<span id="torch-tensorrt-logging-py"></span><h1>torch_tensorrt.logging<a class="headerlink" href="#torch-tensorrt-logging" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.logging"></span><dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">Level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#Level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.Level" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">enum.Enum</span></code></p>
+<p>Enum to set the minimum required logging level to print a message to stdout</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Debug">
+<span class="sig-name descname"><span class="pre">Debug</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.DEBUG:</span> <span class="pre">4&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Debug" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Error">
+<span class="sig-name descname"><span class="pre">Error</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.ERROR:</span> <span class="pre">1&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Error" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Graph">
+<span class="sig-name descname"><span class="pre">Graph</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.GRAPH:</span> <span class="pre">5&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Graph" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Info">
+<span class="sig-name descname"><span class="pre">Info</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.INFO:</span> <span class="pre">3&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Info" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.InternalError">
+<span class="sig-name descname"><span class="pre">InternalError</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.INTERNAL_ERROR:</span> <span class="pre">0&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.InternalError" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Warning">
+<span class="sig-name descname"><span class="pre">Warning</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.WARNING:</span> <span class="pre">2&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Warning" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.debug">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">debug</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#debug"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.debug" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to display full debug information through the logger</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.debug():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id1"><span class="problematic" id="id2">**</span></a>spec)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.errors">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">errors</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#errors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.errors" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to limit displayed log messages to just errors and above</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.errors():</dt><dd><p>outputs = model_torchtrt(inputs)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.get_is_colored_output_on">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">get_is_colored_output_on</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#get_is_colored_output_on"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.get_is_colored_output_on" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get if colored output is enabled for logging</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>If colored output is one</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.get_logging_prefix">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">get_logging_prefix</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#get_logging_prefix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.get_logging_prefix" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get the prefix set for logging messages</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Prefix used for logger</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.get_reportable_log_level">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">get_reportable_log_level</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><span class="pre">torch_tensorrt.logging.Level</span></a></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#get_reportable_log_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.get_reportable_log_level" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get the level required for a message to be printed in the log</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>The enum representing the level required to print</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level">torch_tensorrt.logging.Level</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.graphs">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">graphs</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#graphs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.graphs" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to display the results of intermediate lowering passes
+as well as full debug information through the logger</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.graphs():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id3"><span class="problematic" id="id4">**</span></a>spec)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.info">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">info</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.info" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to display all info and greater severity messages</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.info():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id5"><span class="problematic" id="id6">**</span></a>spec)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.internal_errors">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">internal_errors</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#internal_errors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.internal_errors" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to limit displayed log messages to just internal errors</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.internal_errors():</dt><dd><p>outputs = model_torchtrt(inputs)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.log">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">log</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><span class="pre">torch_tensorrt.logging.Level</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#log"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.log" title="Permalink to this definition">¶</a></dt>
+<dd><p>Add a new message to the log</p>
+<p>Adds a new message to the log at a specified level. The message
+will only get printed out if Level &gt; reportable_log_level</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>level</strong> (<a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><em>torch_tensorrt.logging.Level</em></a>) – Severity of the message</p></li>
+<li><p><strong>msg</strong> (<em>str</em>) – Actual message text</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.set_is_colored_output_on">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">set_is_colored_output_on</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">colored_output_on</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#set_is_colored_output_on"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.set_is_colored_output_on" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enable or disable color in the log output</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>colored_output_on</strong> (<em>bool</em>) – If colored output should be enabled or not</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.set_logging_prefix">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">set_logging_prefix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#set_logging_prefix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.set_logging_prefix" title="Permalink to this definition">¶</a></dt>
+<dd><p>Set the prefix used when logging messages</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – Prefix to use for logging messages</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.set_reportable_log_level">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">set_reportable_log_level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><span class="pre">torch_tensorrt.logging.Level</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#set_reportable_log_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.set_reportable_log_level" title="Permalink to this definition">¶</a></dt>
+<dd><p>Set the level required for a message to be printed to the log</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>level</strong> (<a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><em>torch_tensorrt.logging.Level</em></a>) – The enum representing the level required to print</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.warnings">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">warnings</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#warnings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.warnings" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to limit displayed log messages to just warnings and above</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.warnings():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id7"><span class="problematic" id="id8">**</span></a>spec)</p>
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+
+  <section id="torch-tensorrt-ptq">
+<span id="torch-tensorrt-ptq-py"></span><h1>torch_tensorrt.ptq<a class="headerlink" href="#torch-tensorrt-ptq" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.ptq"></span><dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CacheCalibrator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CacheCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CacheCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.CacheCalibrator" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Constructs a calibrator class in TensorRT which directly uses pre-existing cache file for calibration.
+:param cache_file: path to cache file.
+:param algo_type: choice of calibration algorithm.</p>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CalibrationAlgo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CalibrationAlgo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">enum.Enum</span></code></p>
+<p>An enumeration.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION">
+<span class="sig-name descname"><span class="pre">ENTROPY_CALIBRATION</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.ENTROPY_CALIBRATION:</span> <span class="pre">1&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2">
+<span class="sig-name descname"><span class="pre">ENTROPY_CALIBRATION_2</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.ENTROPY_CALIBRATION_2:</span> <span class="pre">2&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.LEGACY_CALIBRATION">
+<span class="sig-name descname"><span class="pre">LEGACY_CALIBRATION</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.LEGACY_CALIBRATION:</span> <span class="pre">0&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.LEGACY_CALIBRATION" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.MINMAX_CALIBRATION">
+<span class="sig-name descname"><span class="pre">MINMAX_CALIBRATION</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.MINMAX_CALIBRATION:</span> <span class="pre">3&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.MINMAX_CALIBRATION" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.DataLoaderCalibrator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">DataLoaderCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#DataLoaderCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.DataLoaderCalibrator" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces
+data which is passed during calibration.
+:param dataloader: an instance of pytorch dataloader which iterates through a given dataset.
+:param algo_type: choice of calibration algorithm.
+:param cache_file: path to cache file.
+:param use_cache: flag which enables usage of pre-existing cache.
+:param device: device on which calibration data is copied to.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.get_batch">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">get_batch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#get_batch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.get_batch" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.get_batch_size">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">get_batch_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#get_batch_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.get_batch_size" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.get_cache_mode_batch">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">get_cache_mode_batch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#get_cache_mode_batch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.get_cache_mode_batch" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.read_calibration_cache">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">read_calibration_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#read_calibration_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.read_calibration_cache" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.write_calibration_cache">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">write_calibration_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#write_calibration_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.write_calibration_cache" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<section id="classes">
+<h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="id0">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">DataLoaderCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#DataLoaderCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id0" title="Permalink to this definition">¶</a></dt>
+<dd><p>Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces
+data which is passed during calibration.
+:param dataloader: an instance of pytorch dataloader which iterates through a given dataset.
+:param algo_type: choice of calibration algorithm.
+:param cache_file: path to cache file.
+:param use_cache: flag which enables usage of pre-existing cache.
+:param device: device on which calibration data is copied to.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.DataLoaderCalibrator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#DataLoaderCalibrator.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.DataLoaderCalibrator.__init__" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="id1">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CacheCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CacheCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id1" title="Permalink to this definition">¶</a></dt>
+<dd><p>Constructs a calibrator class in TensorRT which directly uses pre-existing cache file for calibration.
+:param cache_file: path to cache file.
+:param algo_type: choice of calibration algorithm.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CacheCalibrator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CacheCalibrator.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.CacheCalibrator.__init__" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="enums">
+<h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="id2">
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+
+  <section id="torch-tensorrt">
+<span id="torch-tensorrt-py"></span><h1>torch_tensorrt<a class="headerlink" href="#torch-tensorrt" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt"></span><section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.set_device">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gpu_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_util.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.set_device" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.compile">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">typing.Any</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ir='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={&lt;dtype.float:</span> <span class="pre">0&gt;}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_compile.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.compile" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compile a PyTorch module for NVIDIA GPUs using TensorRT</p>
+<p>Takes a existing PyTorch module and a set of settings to configure the compiler
+and using the path specified in <code class="docutils literal notranslate"><span class="pre">ir</span></code> lower and compile the module to TensorRT
+returning a PyTorch Module back</p>
+<p>Converts specifically the forward method of a Module</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>module</strong> (<em>Union</em><em>(</em><em>torch.nn.Module</em><em>,</em><em>torch.jit.ScriptModule</em>) – Source module</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>ir</strong> (<em>str</em>) – The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</p></li>
+<li><p><strong>**kwargs</strong> – Additional settings for the specific requested strategy (See submodules for more info)</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Compiled Module, when run it will execute via TensorRT</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>torch.nn.Module</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.convert_method_to_trt_engine">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">convert_method_to_trt_engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">typing.Any</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name:</span> <span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ir='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={&lt;dtype.float:</span> <span class="pre">0&gt;}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_compile.html#convert_method_to_trt_engine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.convert_method_to_trt_engine" title="Permalink to this definition">¶</a></dt>
+<dd><p>Convert a TorchScript module method to a serialized TensorRT engine</p>
+<p>Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>module</strong> (<em>Union</em><em>(</em><em>torch.nn.Module</em><em>,</em><em>torch.jit.ScriptModule</em>) – Source module</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>ir</strong> (<em>str</em>) – The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</p></li>
+<li><p><strong>**kwargs</strong> – Additional settings for the specific requested strategy (See submodules for more info)</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>bytes</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.get_build_info">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">get_build_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_util.html#get_build_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.get_build_info" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a string containing the build information of torch_tensorrt distribution</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>String containing the build information for torch_tensorrt distribution</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.dump_build_info">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">dump_build_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_util.html#dump_build_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dump_build_info" title="Permalink to this definition">¶</a></dt>
+<dd><p>Prints build information about the torch_tensorrt distribution to stdout</p>
+</dd></dl>
+
+</section>
+<section id="classes">
+<h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.Input">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">Input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input" title="Permalink to this definition">¶</a></dt>
+<dd><p>Defines an input to a module in terms of expected shape, data type and tensor format.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Variables</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape_mode</strong> (<em>torch_tensorrt.Input._ShapeMode</em>) – Is input statically or dynamically shaped</p></li>
+<li><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>Dict</em>) – <p>Either a single Tuple or a dict of tuples defining the input shape.
+Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form
+<a href="#id1"><span class="problematic" id="id2">``</span></a>{</p>
+<blockquote>
+<div><p>”min_shape”: Tuple,
+“opt_shape”: Tuple,
+“max_shape”: Tuple</p>
+</div></blockquote>
+<p>}``</p>
+</p></li>
+<li><p><strong>dtype</strong> (<em>torch_tensorrt.dpython:type</em>) – The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)</p></li>
+<li><p><strong>format</strong> (<a class="reference internal" href="#torch_tensorrt.TensorFormat" title="torch_tensorrt.TensorFormat"><em>torch_tensorrt.TensorFormat</em></a>) – The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input.__init__" title="Permalink to this definition">¶</a></dt>
+<dd><p>__init__ Method for torch_tensorrt.Input</p>
+<p>Input accepts one of a few construction patterns</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Static shape of input tensor</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Static shape of input tensor</p></li>
+<li><p><strong>min_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Min size of input tensor’s shape range
+Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
+<li><p><strong>opt_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Opt size of input tensor’s shape range
+Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
+<li><p><strong>max_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Max size of input tensor’s shape range
+Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
+<li><p><strong>dtype</strong> (<em>torch.dpython:type</em><em> or </em><em>torch_tensorrt.dpython:type</em>) – Expected data type for input tensor (default: torch_tensorrt.dtype.float32)</p></li>
+<li><p><strong>format</strong> (<em>torch.memory_format</em><em> or </em><a class="reference internal" href="#torch_tensorrt.TensorFormat" title="torch_tensorrt.TensorFormat"><em>torch_tensorrt.TensorFormat</em></a>) – The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<ul class="simple">
+<li><p>Input([1,3,32,32], dtype=torch.float32, format=torch.channel_last)</p></li>
+<li><p>Input(shape=(1,3,32,32), dtype=torch_tensorrt.dtype.int32, format=torch_tensorrt.TensorFormat.NCHW)</p></li>
+<li><p>Input(min_shape=(1,3,32,32), opt_shape=[2,3,32,32], max_shape=(3,3,32,32)) #Implicitly dtype=torch_tensorrt.dtype.float32, format=torch_tensorrt.TensorFormat.NCHW</p></li>
+</ul>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.dtype">
+<span class="sig-name descname"><span class="pre">dtype</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;dtype.unknown:</span> <span class="pre">5&gt;</span></em><a class="headerlink" href="#torch_tensorrt.Input.dtype" title="Permalink to this definition">¶</a></dt>
+<dd><p>torch_tensorrt.dtype.float32)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type</dt>
+<dd class="field-odd"><p>The expected data type of the input tensor (default</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.format">
+<span class="sig-name descname"><span class="pre">format</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;TensorFormat.contiguous:</span> <span class="pre">0&gt;</span></em><a class="headerlink" href="#torch_tensorrt.Input.format" title="Permalink to this definition">¶</a></dt>
+<dd><p>torch_tensorrt.TensorFormat.NCHW)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type</dt>
+<dd class="field-odd"><p>The expected format of the input tensor (default</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.shape">
+<span class="sig-name descname"><span class="pre">shape</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#torch_tensorrt.Input.shape" title="Permalink to this definition">¶</a></dt>
+<dd><p>Either a single Tuple or a dict of tuples defining the input shape. Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form <code class="docutils literal notranslate"><span class="pre">{</span> <span class="pre">&quot;min_shape&quot;:</span> <span class="pre">Tuple,</span> <span class="pre">&quot;opt_shape&quot;:</span> <span class="pre">Tuple,</span> <span class="pre">&quot;max_shape&quot;:</span> <span class="pre">Tuple</span> <span class="pre">}</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Type</dt>
+<dd class="field-odd"><p>(Tuple or Dict)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.shape_mode">
+<span class="sig-name descname"><span class="pre">shape_mode</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#torch_tensorrt.Input.shape_mode" title="Permalink to this definition">¶</a></dt>
+<dd><p>Is input statically or dynamically shaped</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type</dt>
+<dd class="field-odd"><p>(torch_tensorrt.Input._ShapeMode)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.Device">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">Device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Device.html#Device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Device" title="Permalink to this definition">¶</a></dt>
+<dd><p>Defines a device that can be used to specify target devices for engines</p>
+<dl class="field-list simple">
+<dt class="field-odd">Variables</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>device_type</strong> (<a class="reference internal" href="#torch_tensorrt.DeviceType" title="torch_tensorrt.DeviceType"><em>torch_tensorrt.DeviceType</em></a>) – Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.</p></li>
+<li><p><strong>gpu_id</strong> (<em>python:int</em>) – Device ID for target GPU</p></li>
+<li><p><strong>dla_core</strong> (<em>python:int</em>) – Core ID for target DLA core</p></li>
+<li><p><strong>allow_gpu_fallback</strong> (<em>bool</em>) – Whether falling back to GPU if DLA cannot support an op should be allowed</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.Device.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Device.html#Device.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Device.__init__" title="Permalink to this definition">¶</a></dt>
+<dd><p>__init__ Method for torch_tensorrt.Device</p>
+<p>Device accepts one of a few construction patterns</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>spec</strong> (<em>str</em>) – String with device spec e.g. “dla:0” for dla, core_id 0</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>gpu_id</strong> (<em>python:int</em>) – ID of target GPU (will get overrided if dla_core is specified to the GPU managing DLA). If specified, no positional arguments should be provided</p></li>
+<li><p><strong>dla_core</strong> (<em>python:int</em>) – ID of target DLA core. If specified, no positional arguments should be provided.</p></li>
+<li><p><strong>allow_gpu_fallback</strong> (<em>bool</em>) – Allow TensorRT to schedule operations on GPU if they are not supported on DLA (ignored if device type is not DLA)</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<ul class="simple">
+<li><p>Device(“gpu:1”)</p></li>
+<li><p>Device(“cuda:1”)</p></li>
+<li><p>Device(“dla:0”, allow_gpu_fallback=True)</p></li>
+<li><p>Device(gpu_id=0, dla_core=0, allow_gpu_fallback=True)</p></li>
+<li><p>Device(dla_core=0, allow_gpu_fallback=True)</p></li>
+<li><p>Device(gpu_id=1)</p></li>
+</ul>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Device.allow_gpu_fallback">
+<span class="sig-name descname"><span class="pre">allow_gpu_fallback</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#torch_tensorrt.Device.allow_gpu_fallback" title="Permalink to this definition">¶</a></dt>
+<dd><p>(bool) Whether falling back to GPU if DLA cannot support an op should be allowed</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Device.device_type">
+<span class="sig-name descname"><span class="pre">device_type</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#torch_tensorrt.Device.device_type" title="Permalink to this definition">¶</a></dt>
+<dd><p>Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type</dt>
+<dd class="field-odd"><p>(<a class="reference internal" href="#torch_tensorrt.DeviceType" title="torch_tensorrt.DeviceType">torch_tensorrt.DeviceType</a>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Device.dla_core">
+<span class="sig-name descname"><span class="pre">dla_core</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">-1</span></em><a class="headerlink" href="#torch_tensorrt.Device.dla_core" title="Permalink to this definition">¶</a></dt>
+<dd><p>(int) Core ID for target DLA core</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Device.gpu_id">
+<span class="sig-name descname"><span class="pre">gpu_id</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">-1</span></em><a class="headerlink" href="#torch_tensorrt.Device.gpu_id" title="Permalink to this definition">¶</a></dt>
+<dd><p>(int) Device ID for target GPU</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="enums">
+<h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.dtype">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">dtype</span></span><a class="headerlink" href="#torch_tensorrt.dtype" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enum to specifiy operating precision for engine execution</p>
+<p>Members:</p>
+<blockquote>
+<div><p>float : 32 bit floating point number</p>
+<p>float32 : 32 bit floating point number</p>
+<p>half : 16 bit floating point number</p>
+<p>float16 : 16 bit floating point number</p>
+<p>int8 : 8 bit integer number</p>
+<p>int32 : 32 bit integer number</p>
+<p>bool : Boolean value</p>
+<p>unknown : Unknown data type</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.DeviceType">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">DeviceType</span></span><a class="headerlink" href="#torch_tensorrt.DeviceType" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enum to specify device kinds to build TensorRT engines for</p>
+<p>Members:</p>
+<blockquote>
+<div><p>GPU : Specify using GPU to execute TensorRT Engine</p>
+<p>DLA : Specify using DLA to execute TensorRT Engine (Jetson Only)</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.EngineCapability">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">EngineCapability</span></span><a class="headerlink" href="#torch_tensorrt.EngineCapability" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enum to specify engine capability settings (selections of kernels to meet safety requirements)</p>
+<p>Members:</p>
+<blockquote>
+<div><p>safe_gpu : Use safety GPU kernels only</p>
+<p>safe_dla : Use safety DLA kernels only</p>
+<p>default : Use default behavior</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.TensorFormat">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">TensorFormat</span></span><a class="headerlink" href="#torch_tensorrt.TensorFormat" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enum to specifiy the memory layout of tensors</p>
+<p>Members:</p>
+<blockquote>
+<div><p>contiguous : Contiguous memory layout (NCHW / Linear)</p>
+<p>channels_last : Channels last memory layout (NHWC)</p>
+</div></blockquote>
+</dd></dl>
+
+</section>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="logging.html">torch_tensorrt.logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ptq.html">torch_tensorrt.ptq</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fx.html">torch_tensorrt.fx</a></li>
+</ul>
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+  <section id="torch-tensorrt-ts">
+<span id="torch-tensorrt-ts-py"></span><h1>torch_tensorrt.ts<a class="headerlink" href="#torch-tensorrt-ts" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.ts"></span><section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.compile">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_signature=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=&lt;torch_tensorrt._Device.Device</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_full_compilation=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_block_size=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_ops=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_modules=[]</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.jit._script.ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.compile" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compile a TorchScript module for NVIDIA GPUs using TensorRT</p>
+<p>Takes a existing TorchScript module and a set of settings to configure the compiler
+and will convert methods to JIT Graphs which call equivalent TensorRT engines</p>
+<p>Converts specifically the forward method of a TorchScript Module</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
+<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>Union</strong> (<em>input_signature</em>) – <p>A formatted collection of input specifications for the module. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum to select device type. <strong>This API should be considered beta-level stable and may change in the future</strong></p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input_signature=([
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+], torch.randn((1, 3, 224, 244))) # Use an example tensor and let torch_tensorrt infer settings for input #3
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
+<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
+<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
+<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>torch_tensorrt.EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
+<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
+<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
+<li><p><strong>dla_sram_size</strong> (<em>python:int</em>) – Fast software managed RAM used by DLA to communicate within a layer.</p></li>
+<li><p><strong>dla_local_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to share intermediate tensor data across operations</p></li>
+<li><p><strong>dla_global_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to store weights and metadata for execution</p></li>
+<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
+<li><p><strong>calibrator</strong> (<em>Union</em><em>(</em><em>torch_tensorrt._C.IInt8Calibrator</em><em>, </em><em>tensorrt.IInt8Calibrator</em><em>)</em>) – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
+<li><p><strong>require_full_compilation</strong> (<em>bool</em>) – Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</p></li>
+<li><p><strong>min_block_size</strong> (<em>python:int</em>) – The minimum number of contiguous TensorRT convertable operations in order to run a set of operations in TensorRT</p></li>
+<li><p><strong>torch_executed_ops</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of aten operators that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
+<li><p><strong>torch_executed_modules</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Compiled TorchScript Module, when run it will execute via TensorRT</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>torch.jit.ScriptModule</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.convert_method_to_trt_engine">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">convert_method_to_trt_engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name:</span> <span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=&lt;torch_tensorrt._Device.Device</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#convert_method_to_trt_engine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.convert_method_to_trt_engine" title="Permalink to this definition">¶</a></dt>
+<dd><p>Convert a TorchScript module method to a serialized TensorRT engine</p>
+<p>Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
+<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p></li>
+<li><p><strong>method_name</strong> (<em>str</em>) – Name of method to convert</p></li>
+</ul>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>Union</strong> (<em>input_signature</em>) – <p>A formatted collection of input specifications for the module. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum to select device type. <strong>This API should be considered beta-level stable and may change in the future</strong></p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input_signature=([
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+], torch.randn((1, 3, 224, 244))) # Use an example tensor and let torch_tensorrt infer settings for input #3
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
+<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
+<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
+<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>torch_tensorrt.EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
+<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
+<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
+<li><p><strong>dla_sram_size</strong> (<em>python:int</em>) – Fast software managed RAM used by DLA to communicate within a layer.</p></li>
+<li><p><strong>dla_local_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to share intermediate tensor data across operations</p></li>
+<li><p><strong>dla_global_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to store weights and metadata for execution</p></li>
+<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
+<li><p><strong>calibrator</strong> (<em>Union</em><em>(</em><em>torch_tensorrt._C.IInt8Calibrator</em><em>, </em><em>tensorrt.IInt8Calibrator</em><em>)</em>) – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>bytes</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.check_method_op_support">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">check_method_op_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#check_method_op_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.check_method_op_support" title="Permalink to this definition">¶</a></dt>
+<dd><p>Checks to see if a method is fully supported by torch_tensorrt</p>
+<p>Checks if a method of a TorchScript module can be compiled by torch_tensorrt, if not, a list of operators
+that are not supported are printed out and the function returns false, else true.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
+<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p></li>
+<li><p><strong>method_name</strong> (<em>str</em>) – Name of method to check</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>True if supported Method</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.embed_engine_in_new_module">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">embed_engine_in_new_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">serialized_engine:</span> <span class="pre">bytes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=&lt;torch_tensorrt._Device.Device</span> <span class="pre">object&gt;</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.jit._script.ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#embed_engine_in_new_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.embed_engine_in_new_module" title="Permalink to this definition">¶</a></dt>
+<dd><p>Takes a pre-built serialized TensorRT engine and embeds it within a TorchScript module</p>
+<p>Takes a pre-built serialied TensorRT engine (as bytes) and embeds it within a TorchScript module.
+Registers the forward method to execute the TensorRT engine with the function signature:</p>
+<blockquote>
+<div><p>forward(Tensor[]) -&gt; Tensor[]</p>
+</div></blockquote>
+<dl>
+<dt>TensorRT bindings must have names with the following format:</dt><dd><ul class="simple">
+<li><p>[symbol].[index in input / output array]</p></li>
+</ul>
+<p>ex.
+- [x.0, x.1, x.2] -&gt; [y.0]</p>
+</dd>
+</dl>
+<p>Module can be save with engine embedded with torch.jit.save and moved / loaded according to torch_tensorrt portability rules</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>serialized_engine</strong> (<em>bytes</em>) – Serialized TensorRT engine from either torch_tensorrt or TensorRT APIs</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – Target device to run engine on. Must be compatible with engine provided. Default: Current active device</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>New TorchScript module with engine embedded</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>torch.jit.ScriptModule</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.TensorRTCompileSpec">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">TensorRTCompileSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs=[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_signature=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=&lt;torch_tensorrt._Device.Device</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions={}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability=&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size=1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size=1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size=536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator=None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">&lt;torch.ScriptClass</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7f6a8d162db0&gt;</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compile_spec.html#TensorRTCompileSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.TensorRTCompileSpec" title="Permalink to this definition">¶</a></dt>
+<dd><p>Utility to create a formated spec dictionary for using the PyTorch TensorRT backend</p>
+<dl class="field-list simple">
+<dt class="field-odd">Keyword Arguments</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>torch_tensorrt.Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>torch_tensorrt.Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
+<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
+<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
+<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>torch_tensorrt.EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
+<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
+<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
+<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
+<li><p><strong>calibrator</strong> – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
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diff --git a/docs/v1.2.0/src/pytorch-sphinx-theme/docs/configuring.html b/docs/v1.2.0/src/pytorch-sphinx-theme/docs/configuring.html
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@@ -0,0 +1,760 @@
+
+
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+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<h1>Configuration<a class="headerlink" href="#configuration" title="Permalink to this headline">¶</a></h1>
+<p>You can configure different parts of the theme.</p>
+<section id="project-wide-configuration">
+<h2>Project-wide Configuration<a class="headerlink" href="#project-wide-configuration" title="Permalink to this headline">¶</a></h2>
+<section id="html-theme-options">
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+file of this repository, and can be defined in your project’s <code class="docutils literal notranslate"><span class="pre">conf.py</span></code> via
+<code class="docutils literal notranslate"><span class="pre">html_theme_options</span></code>. For example:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">html_theme_options</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s1">&#39;canonical_url&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;analytics_id&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;logo_only&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="s1">&#39;display_version&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="s1">&#39;prev_next_buttons_location&#39;</span><span class="p">:</span> <span class="s1">&#39;bottom&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;style_external_links&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="s1">&#39;vcs_pageview_mode&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
+    <span class="c1"># Toc options</span>
+    <span class="s1">&#39;collapse_navigation&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="s1">&#39;sticky_navigation&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="s1">&#39;navigation_depth&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
+    <span class="s1">&#39;includehidden&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="s1">&#39;titles_only&#39;</span><span class="p">:</span> <span class="kc">False</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>The following options are available:</p>
+<section id="base-options">
+<h4>Base options<a class="headerlink" href="#base-options" title="Permalink to this headline">¶</a></h4>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">canonical_url</span></code> String. This will specify a <a class="reference external" href="https://en.wikipedia.org/wiki/Canonical_link_element">canonical url</a>
+to let search engines know they should give higher ranking to latest version of the docs.
+The url points to the root of the documentation and requires a trailing slash.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">analytics_id</span></code> String. Change the Google Analytics ID that is included on pages.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">display_version</span></code> Bool. With this disabled, the version number isn’t shown at the top of the sidebar.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">prev_next_buttons_location</span></code> String. can take the value <code class="docutils literal notranslate"><span class="pre">bottom</span></code>, <code class="docutils literal notranslate"><span class="pre">top</span></code>, <code class="docutils literal notranslate"><span class="pre">both</span></code> , or <code class="docutils literal notranslate"><span class="pre">None</span></code>
+and will display the “Next” and “Previous” buttons accordingly.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">style_external_links</span></code> Bool. Add an icon next to external links. Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">vcs_pageview_mode</span></code> String. Changes how to view files when using <cite>display_github</cite>, <cite>display_gitlab</cite>, etc.
+When using Github or Gitlab this can be: <cite>blob</cite> (default), <cite>edit</cite>, or <cite>raw</cite>,
+on Bitbucket, this can be either: <cite>view</cite> (default) or <cite>edit</cite>.</p></li>
+</ul>
+</section>
+<section id="toc-options">
+<h4>TOC Options<a class="headerlink" href="#toc-options" title="Permalink to this headline">¶</a></h4>
+<p>These effect how we display the Table of Contents in the side bar. You can read more about them here: <a class="reference external" href="http://www.sphinx-doc.org/en/stable/templating.html#toctree">http://www.sphinx-doc.org/en/stable/templating.html#toctree</a></p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">collapse_navigation</span></code> Bool. With this enabled, you will lose the <code class="docutils literal notranslate"><span class="pre">[+]</span></code> drop downs next to each section in the sidebar.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sticky_navigation</span></code> Bool. This causes the sidebar to scroll with the main page content as you scroll the page.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">navigation_depth</span></code> Int. Indicate the max depth of the tree; by default, 4 levels are included;
+set it to -1 to allow unlimited depth.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">includehidden</span></code> Bool. Specifies if the sidebar includes toctrees marked with the <code class="docutils literal notranslate"><span class="pre">:hidden:</span></code> option</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">titles_only</span></code> Bool. If True, removes headers within a page from the sidebar.</p></li>
+</ul>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Setting <code class="docutils literal notranslate"><span class="pre">collapse_navigation</span></code> to False and using a high <code class="docutils literal notranslate"><span class="pre">navigation_depth</span></code>
+can cause projects with many files and a deep file structure to generate HTML files
+that are significantly larger in file size and much longer compilation times.</p>
+</div>
+</section>
+</section>
+<section id="html-context-options">
+<h3>HTML Context Options<a class="headerlink" href="#html-context-options" title="Permalink to this headline">¶</a></h3>
+<p>TODO.</p>
+</section>
+</section>
+<section id="page-level-configuration">
+<h2>Page-level Configuration<a class="headerlink" href="#page-level-configuration" title="Permalink to this headline">¶</a></h2>
+<p>Pages support metadata that changes how the theme renders.
+You can currently add the following:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">:github_url:</span></code> This will force the “Edit on GitHub” to the configured URL</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">:bitbucket_url:</span></code> This will force the “Edit on Bitbucket” to the configured URL</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">:gitlab_url:</span></code> This will force the “Edit on GitLab” to the configured URL</p></li>
+</ul>
+</section>
+<section id="how-the-table-of-contents-builds">
+<h2>How the Table of Contents builds<a class="headerlink" href="#how-the-table-of-contents-builds" title="Permalink to this headline">¶</a></h2>
+<p>Currently the left menu will build based upon any <code class="docutils literal notranslate"><span class="pre">toctree(s)</span></code> defined in your <code class="docutils literal notranslate"><span class="pre">index.rst</span></code> file.
+It outputs 2 levels of depth, which should give your visitors a high level of access to your
+docs. If no toctrees are set the theme reverts to sphinx’s usual local toctree.</p>
+<p>It’s important to note that if you don’t follow the same styling for your rST headers across
+your documents, the toctree will misbuild, and the resulting menu might not show the correct
+depth when it renders.</p>
+<p>Also note that by default the table of contents is set with <code class="docutils literal notranslate"><span class="pre">includehidden=True</span></code>. This allows you
+to set a hidden toc in your index file with the <a class="reference external" href="http://sphinx-doc.org/markup/toctree.html">:hidden:</a> property that will allow you
+to build a toc without it rendering in your index.</p>
+<p>By default, the navigation will “stick” to the screen as you scroll. However if your toc
+is vertically too large, it will revert to static positioning. To disable the sticky nav
+altogether change the setting in <code class="docutils literal notranslate"><span class="pre">conf.py</span></code>.</p>
+</section>
+</section>
+
+
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diff --git a/docs/v1.2.0/src/pytorch-sphinx-theme/docs/demo/api.html b/docs/v1.2.0/src/pytorch-sphinx-theme/docs/demo/api.html
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+
+  <section id="mod-test-py-module">
+<h1><a class="toc-backref" href="#id5"><span class="section-number">5. </span><a href="#id1"><span class="problematic" id="id2">:mod:`test_py_module`</span></a></a><a class="headerlink" href="#mod-test-py-module" title="Permalink to this headline">¶</a></h1>
+<div class="contents topic" id="table-of-contents">
+<p class="topic-title">Table of Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#mod-test-py-module" id="id5">:mod:`test_py_module`</a></p>
+<ul>
+<li><p><a class="reference internal" href="#generated-index" id="id6">Generated Index</a></p></li>
+<li><p><a class="reference internal" href="#optional-parameter-args" id="id7">Optional parameter args</a></p></li>
+<li><p><a class="reference internal" href="#data" id="id8">Data</a></p></li>
+</ul>
+</li>
+</ul>
+</div>
+<section id="generated-index">
+<h2><a class="toc-backref" href="#id6"><span class="section-number">5.1. </span>Generated Index</a><a class="headerlink" href="#generated-index" title="Permalink to this headline">¶</a></h2>
+<p>Part of the sphinx build process in generate and index file: <a class="reference internal" href="../../../../genindex.html"><span class="std std-ref">Index</span></a>.</p>
+</section>
+<section id="optional-parameter-args">
+<h2><a class="toc-backref" href="#id7"><span class="section-number">5.2. </span>Optional parameter args</a><a class="headerlink" href="#optional-parameter-args" title="Permalink to this headline">¶</a></h2>
+<p>At this point optional parameters <a class="reference external" href="https://groups.google.com/forum/#!topic/sphinx-users/_qfsVT5Vxpw">cannot be generated from code</a>.
+However, some projects will manually do it, like so:</p>
+<p>This example comes from <a class="reference external" href="http://django-payments.readthedocs.org/en/latest/modules.html#payments.authorizenet.AuthorizeNetProvide">django-payments module docs</a>.</p>
+<dl class="cpp class">
+<dt class="sig sig-object cpp">
+<span class="sig-name descname"><span class="pre">payments.dotpay.DotpayProvider(seller_id,</span> <span class="pre">pin[,</span> <span class="pre">channel=0[,</span> <span class="pre">lock=False],</span> <span class="pre">lang='pl'])</span></span></dt>
+<dd><p>This backend implements payments using a popular Polish gateway, <a class="reference external" href="http://www.dotpay.pl">Dotpay.pl</a>.</p>
+<p>Due to API limitations there is no support for transferring purchased items.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param seller_id</dt>
+<dd class="field-odd"><p>Seller ID assigned by Dotpay</p>
+</dd>
+<dt class="field-even">Param pin</dt>
+<dd class="field-even"><p>PIN assigned by Dotpay</p>
+</dd>
+<dt class="field-odd">Param channel</dt>
+<dd class="field-odd"><p>Default payment channel (consult reference guide)</p>
+</dd>
+<dt class="field-even">Param lang</dt>
+<dd class="field-even"><p>UI language</p>
+</dd>
+<dt class="field-odd">Param lock</dt>
+<dd class="field-odd"><p>Whether to disable channels other than the default selected above</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="data">
+<h2><a class="toc-backref" href="#id8"><span class="section-number">5.3. </span>Data</a><a class="headerlink" href="#data" title="Permalink to this headline">¶</a></h2>
+<p>Some data link <a href="#id3"><span class="problematic" id="id4">:data:`Data_item_1`</span></a>.</p>
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diff --git a/docs/v1.2.0/src/pytorch-sphinx-theme/docs/demo/demo.html b/docs/v1.2.0/src/pytorch-sphinx-theme/docs/demo/demo.html
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@@ -0,0 +1,1267 @@
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
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+        <div class="pytorch-breadcrumbs-wrapper">
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+  <ul class="pytorch-breadcrumbs">
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+      <li>
+        <a href="../../../../index.html">
+
+            Docs
+
+        </a> &gt;
+      </li>
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+      <li><span class="section-number">3. </span>Paragraph Level Markup</li>
+
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+      <li class="pytorch-breadcrumbs-aside">
+
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+            <a href="../../../../_sources/src/pytorch-sphinx-theme/docs/demo/demo.rst.txt" rel="nofollow"><img src="../../../../_static/images/view-page-source-icon.svg"></a>
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+        </div>
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+        <div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper">
+          Shortcuts
+        </div>
+      </div>
+
+      <section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
+        <div class="pytorch-content-left">
+
+
+
+          <div class="rst-content">
+
+            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+
+  <section id="paragraph-level-markup">
+<h1><a class="toc-backref" href="#id33"><span class="section-number">3. </span>Paragraph Level Markup</a><a class="headerlink" href="#paragraph-level-markup" title="Permalink to this headline">¶</a></h1>
+<div class="contents topic" id="table-of-contents">
+<p class="topic-title">Table of Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#paragraph-level-markup" id="id33">Paragraph Level Markup</a></p>
+<ul>
+<li><p><a class="reference internal" href="#inline-markup" id="id34">Inline Markup</a></p></li>
+<li><p><a class="reference internal" href="#math" id="id35">Math</a></p></li>
+<li><p><a class="reference internal" href="#meta" id="id36">Meta</a></p></li>
+<li><p><a class="reference internal" href="#blocks" id="id37">Blocks</a></p>
+<ul>
+<li><p><a class="reference internal" href="#literal-blocks" id="id38">Literal Blocks</a></p></li>
+<li><p><a class="reference internal" href="#line-blocks" id="id39">Line Blocks</a></p></li>
+<li><p><a class="reference internal" href="#block-quotes" id="id40">Block Quotes</a></p></li>
+<li><p><a class="reference internal" href="#doctest-blocks" id="id41">Doctest Blocks</a></p></li>
+<li><p><a class="reference internal" href="#code-blocks" id="id42">Code Blocks</a></p>
+<ul>
+<li><p><a class="reference internal" href="#emphasized-lines-with-line-numbers" id="id43">Emphasized lines with line numbers</a></p></li>
+</ul>
+</li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#sidebar" id="id44">Sidebar</a></p>
+<ul>
+<li><p><a class="reference internal" href="#code-with-sidebar" id="id45">Code with Sidebar</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#references" id="id46">References</a></p>
+<ul>
+<li><p><a class="reference internal" href="#footnotes" id="id47">Footnotes</a></p></li>
+<li><p><a class="reference internal" href="#citations" id="id48">Citations</a></p></li>
+<li><p><a class="reference internal" href="#glossary" id="id49">Glossary</a></p></li>
+<li><p><a class="reference internal" href="#targets" id="id50">Targets</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#directives" id="id51">Directives</a></p>
+<ul>
+<li><p><a class="reference internal" href="#contents" id="id52">Contents</a></p></li>
+<li><p><a class="reference internal" href="#centered-text" id="id53">Centered text</a></p></li>
+<li><p><a class="reference internal" href="#images-figures" id="id54">Images &amp; Figures</a></p>
+<ul>
+<li><p><a class="reference internal" href="#images" id="id55">Images</a></p></li>
+<li><p><a class="reference internal" href="#figures" id="id56">Figures</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#admonitions" id="id57">Admonitions</a></p></li>
+<li><p><a class="reference internal" href="#topics-sidebars-and-rubrics" id="id58">Topics, Sidebars, and Rubrics</a></p></li>
+<li><p><a class="reference internal" href="#target-footnotes" id="id59">Target Footnotes</a></p></li>
+<li><p><a class="reference internal" href="#replacement-text" id="id60">Replacement Text</a></p></li>
+<li><p><a class="reference internal" href="#compound-paragraph" id="id61">Compound Paragraph</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#download-links" id="id62">Download Links</a></p></li>
+</ul>
+</li>
+</ul>
+</div>
+<section id="inline-markup">
+<h2><a class="toc-backref" href="#id34"><span class="section-number">3.1. </span>Inline Markup</a><a class="headerlink" href="#inline-markup" title="Permalink to this headline">¶</a></h2>
+<p>Paragraphs contain text and may contain inline markup: <em>emphasis</em>, <strong>strong emphasis</strong>, <code class="docutils literal notranslate"><span class="pre">inline</span> <span class="pre">literals</span></code>,
+standalone hyperlinks (<a class="reference external" href="http://www.python.org">http://www.python.org</a>), external hyperlinks (<a class="reference external" href="http://www.python.org/">Python</a> <a class="footnote-reference brackets" href="#id29" id="id30">5</a>), internal cross-references (<a class="reference internal" href="#example">example</a>),
+external hyperlinks with embedded URIs (<a class="reference external" href="http://www.python.org">Python web site</a>), footnote references
+(manually numbered <a class="footnote-reference brackets" href="#id6" id="id1">1</a>, anonymous auto-numbered <a class="footnote-reference brackets" href="#id9" id="id2">3</a>, labeled auto-numbered <a class="footnote-reference brackets" href="#label" id="id3">2</a>, or symbolic <a class="footnote-reference brackets" href="#id10" id="id4">*</a>),
+citation references (<a class="footnote-reference brackets" href="#id16" id="id5">12</a>), substitution references (<a class="reference internal" href="../../../../_images/yi_jing_01_chien.jpg"><img alt="EXAMPLE" src="../../../../_images/yi_jing_01_chien.jpg" style="width: 1em;" /></a>), and <span class="target" id="inline-hyperlink-targets">inline hyperlink targets</span>
+(see <a class="reference internal" href="#targets">Targets</a> below for a reference back to here). Character-level inline markup is also possible
+(although exceedingly ugly!) in <em>re</em><code class="docutils literal notranslate"><span class="pre">Structured</span></code><em>Text</em>. Problems are indicated by <a href="#id27"><span class="problematic" id="id28">|problematic|</span></a>
+text (generated by processing errors; this one is intentional).</p>
+<p>Also with <code class="docutils literal notranslate"><span class="pre">sphinx.ext.autodoc</span></code>, which I use in the demo, I can link to <code class="xref cpp cpp-class docutils literal notranslate"><span class="pre">test_py_module.test.Foo</span></code>.
+It will link you right my code documentation for it.</p>
+<p>The default role for interpreted text is <cite>Title Reference</cite>.  Here are some explicit interpreted text roles:
+a PEP reference (<span class="target" id="index-0"></span><a class="pep reference external" href="https://peps.python.org/pep-0287/"><strong>PEP 287</strong></a>); an RFC reference (<span class="target" id="index-1"></span><a class="rfc reference external" href="https://datatracker.ietf.org/doc/html/rfc2822.html"><strong>RFC 2822</strong></a>); a <sub>subscript</sub>; a <sup>superscript</sup>;
+and explicit roles for <em>standard</em> <strong>inline</strong> <code class="docutils literal notranslate"><span class="pre">markup</span></code>.</p>
+<p>GUI labels are a useful way to indicate that <span class="guilabel">Some action</span> is to be taken by the user.
+The GUI label should not run over <code class="docutils literal notranslate"><span class="pre">line-height</span></code> so as not to <span class="guilabel">interfere</span> with text from adjacent lines.</p>
+<p>Key-bindings indicate that the read is to press a button on the keyboard or mouse,
+for example <kbd class="kbd docutils literal notranslate">MMB</kbd> and <kbd class="kbd compound docutils literal notranslate"><kbd class="kbd docutils literal notranslate">Shift</kbd>-<kbd class="kbd docutils literal notranslate">MMB</kbd></kbd>. Another useful markup to indicate a user action
+is to use <code class="docutils literal notranslate"><span class="pre">menuselection</span></code> this can be used to show short and long menus in software.
+For example, and <code class="docutils literal notranslate"><span class="pre">menuselection</span></code> can be seen here that breaks is too long to fit on this line.
+<span class="menuselection">My ‣ Software ‣ Some menu ‣ Some sub menu 1 ‣ sub menu 2</span>.</p>
+<p>Let’s test wrapping and whitespace significance in inline literals:
+<code class="docutils literal notranslate"><span class="pre">This</span> <span class="pre">is</span> <span class="pre">an</span> <span class="pre">example</span> <span class="pre">of</span> <span class="pre">--inline-literal</span> <span class="pre">--text,</span> <span class="pre">--including</span> <span class="pre">some--</span>
+<span class="pre">strangely--hyphenated-words.</span>&#160; <span class="pre">Adjust-the-width-of-your-browser-window</span>
+<span class="pre">to</span> <span class="pre">see</span> <span class="pre">how</span> <span class="pre">the</span> <span class="pre">text</span> <span class="pre">is</span> <span class="pre">wrapped.</span>&#160; <span class="pre">--</span> <span class="pre">----</span> <span class="pre">--------</span>&#160; <span class="pre">Now</span> <span class="pre">note</span>&#160;&#160;&#160; <span class="pre">the</span>
+<span class="pre">spacing</span>&#160;&#160;&#160; <span class="pre">between</span> <span class="pre">the</span>&#160;&#160;&#160; <span class="pre">words</span> <span class="pre">of</span>&#160;&#160;&#160; <span class="pre">this</span> <span class="pre">sentence</span>&#160;&#160;&#160; <span class="pre">(words</span>
+<span class="pre">should</span>&#160;&#160;&#160; <span class="pre">be</span> <span class="pre">grouped</span>&#160;&#160;&#160; <span class="pre">in</span> <span class="pre">pairs).</span></code></p>
+<p>If the <code class="docutils literal notranslate"><span class="pre">--pep-references</span></code> option was supplied, there should be a live link to PEP 258 here.</p>
+</section>
+<section id="math">
+<h2><a class="toc-backref" href="#id35"><span class="section-number">3.2. </span>Math</a><a class="headerlink" href="#math" title="Permalink to this headline">¶</a></h2>
+<p>This is a test. Here is an equation:
+<span class="math notranslate nohighlight">\(X_{0:5} = (X_0, X_1, X_2, X_3, X_4)\)</span>.
+Here is another:</p>
+<div class="math notranslate nohighlight" id="equation-this-is-a-label">
+<span class="eqno">(1)<a class="headerlink" href="#equation-this-is-a-label" title="Permalink to this equation">¶</a></span>\[\nabla^2 f =
+\frac{1}{r^2} \frac{\partial}{\partial r}
+\left( r^2 \frac{\partial f}{\partial r} \right) +
+\frac{1}{r^2 \sin \theta} \frac{\partial f}{\partial \theta}
+\left( \sin \theta \, \frac{\partial f}{\partial \theta} \right) +
+\frac{1}{r^2 \sin^2\theta} \frac{\partial^2 f}{\partial \phi^2}\]</div>
+<p>You can add a link to equations like the one above <a class="reference internal" href="#equation-this-is-a-label">(1)</a> by using <code class="docutils literal notranslate"><span class="pre">:eq:</span></code>.</p>
+</section>
+<section id="meta">
+<h2><a class="toc-backref" href="#id36"><span class="section-number">3.3. </span>Meta</a><a class="headerlink" href="#meta" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="blocks">
+<h2><a class="toc-backref" href="#id37"><span class="section-number">3.4. </span>Blocks</a><a class="headerlink" href="#blocks" title="Permalink to this headline">¶</a></h2>
+<section id="literal-blocks">
+<h3><a class="toc-backref" href="#id38"><span class="section-number">3.4.1. </span>Literal Blocks</a><a class="headerlink" href="#literal-blocks" title="Permalink to this headline">¶</a></h3>
+<p>Literal blocks are indicated with a double-colon (“::”) at the end of
+the preceding paragraph (over there <code class="docutils literal notranslate"><span class="pre">--&gt;</span></code>).  They can be indented:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>if literal_block:
+    text = &#39;is left as-is&#39;
+    spaces_and_linebreaks = &#39;are preserved&#39;
+    markup_processing = None
+</pre></div>
+</div>
+<p>Or they can be quoted without indentation:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>&gt;&gt; Great idea!
+&gt;
+&gt; Why didn&#39;t I think of that?
+</pre></div>
+</div>
+</section>
+<section id="line-blocks">
+<h3><a class="toc-backref" href="#id39"><span class="section-number">3.4.2. </span>Line Blocks</a><a class="headerlink" href="#line-blocks" title="Permalink to this headline">¶</a></h3>
+<div class="line-block">
+<div class="line">This is a line block.  It ends with a blank line.</div>
+<div class="line-block">
+<div class="line">Each new line begins with a vertical bar (“|”).</div>
+<div class="line">Line breaks and initial indents are preserved.</div>
+</div>
+<div class="line">Continuation lines are wrapped portions of long lines;
+they begin with a space in place of the vertical bar.</div>
+<div class="line-block">
+<div class="line">The left edge of a continuation line need not be aligned with
+the left edge of the text above it.</div>
+</div>
+</div>
+<div class="line-block">
+<div class="line">This is a second line block.</div>
+<div class="line"><br /></div>
+<div class="line">Blank lines are permitted internally, but they must begin with a “|”.</div>
+</div>
+<p>Take it away, Eric the Orchestra Leader!</p>
+<blockquote>
+<div><div class="line-block">
+<div class="line">A one, two, a one two three four</div>
+<div class="line"><br /></div>
+<div class="line">Half a bee, philosophically,</div>
+<div class="line-block">
+<div class="line">must, <em>ipso facto</em>, half not be.</div>
+</div>
+<div class="line">But half the bee has got to be,</div>
+<div class="line-block">
+<div class="line"><em>vis a vis</em> its entity.  D’you see?</div>
+<div class="line"><br /></div>
+</div>
+<div class="line">But can a bee be said to be</div>
+<div class="line-block">
+<div class="line">or not to be an entire bee,</div>
+<div class="line-block">
+<div class="line">when half the bee is not a bee,</div>
+<div class="line-block">
+<div class="line">due to some ancient injury?</div>
+<div class="line"><br /></div>
+</div>
+</div>
+</div>
+<div class="line">Singing…</div>
+</div>
+</div></blockquote>
+</section>
+<section id="block-quotes">
+<h3><a class="toc-backref" href="#id40"><span class="section-number">3.4.3. </span>Block Quotes</a><a class="headerlink" href="#block-quotes" title="Permalink to this headline">¶</a></h3>
+<p>Block quotes consist of indented body elements:</p>
+<blockquote>
+<div><p>My theory by A. Elk.  Brackets Miss, brackets.  This theory goes
+as follows and begins now.  All brontosauruses are thin at one
+end, much much thicker in the middle and then thin again at the
+far end.  That is my theory, it is mine, and belongs to me and I
+own it, and what it is too.</p>
+<p class="attribution">—Anne Elk (Miss)</p>
+</div></blockquote>
+</section>
+<section id="doctest-blocks">
+<h3><a class="toc-backref" href="#id41"><span class="section-number">3.4.4. </span>Doctest Blocks</a><a class="headerlink" href="#doctest-blocks" title="Permalink to this headline">¶</a></h3>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="s1">&#39;Python-specific usage examples; begun with &quot;&gt;&gt;&gt;&quot;&#39;</span>
+<span class="go">Python-specific usage examples; begun with &quot;&gt;&gt;&gt;&quot;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="s1">&#39;(cut and pasted from interactive Python sessions)&#39;</span>
+<span class="go">(cut and pasted from interactive Python sessions)</span>
+</pre></div>
+</div>
+</section>
+<section id="code-blocks">
+<h3><a class="toc-backref" href="#id42"><span class="section-number">3.4.5. </span>Code Blocks</a><a class="headerlink" href="#code-blocks" title="Permalink to this headline">¶</a></h3>
+<pre class="literal-block"># parsed-literal test
+curl -O <a class="reference external" href="http://someurl/release">http://someurl/release</a>-master (1.2.0).tar-gz</pre>
+<div class="literal-block-wrapper docutils container" id="id23">
+<div class="code-block-caption"><span class="caption-text">Code Blocks can have captions.</span><a class="headerlink" href="#id23" title="Permalink to this code">¶</a></div>
+<div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="w"></span>
+<span class="nt">&quot;windows&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="w"></span>
+<span class="w">    </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="nt">&quot;panes&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="w"></span>
+<span class="w">        </span><span class="p">{</span><span class="w"></span>
+<span class="w">        </span><span class="nt">&quot;shell_command&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="w"></span>
+<span class="w">            </span><span class="s2">&quot;echo &#39;did you know&#39;&quot;</span><span class="p">,</span><span class="w"></span>
+<span class="w">            </span><span class="s2">&quot;echo &#39;you can inline&#39;&quot;</span><span class="w"></span>
+<span class="w">        </span><span class="p">]</span><span class="w"></span>
+<span class="w">        </span><span class="p">},</span><span class="w"></span>
+<span class="w">        </span><span class="p">{</span><span class="w"></span>
+<span class="w">        </span><span class="nt">&quot;shell_command&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;echo &#39;single commands&#39;&quot;</span><span class="w"></span>
+<span class="w">        </span><span class="p">},</span><span class="w"></span>
+<span class="w">        </span><span class="s2">&quot;echo &#39;for panes&#39;&quot;</span><span class="w"></span>
+<span class="w">    </span><span class="p">],</span><span class="w"></span>
+<span class="w">    </span><span class="nt">&quot;window_name&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;long form&quot;</span><span class="w"></span>
+<span class="w">    </span><span class="p">}</span><span class="w"></span>
+<span class="p">],</span><span class="w"></span>
+<span class="nt">&quot;session_name&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;shorthands&quot;</span><span class="w"></span>
+<span class="p">}</span><span class="w"></span>
+</pre></div>
+</div>
+</div>
+<section id="emphasized-lines-with-line-numbers">
+<h4><a class="toc-backref" href="#id43"><span class="section-number">3.4.5.1. </span>Emphasized lines with line numbers</a><a class="headerlink" href="#emphasized-lines-with-line-numbers" title="Permalink to this headline">¶</a></h4>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos">1</span><span class="k">def</span> <span class="nf">some_function</span><span class="p">():</span>
+<span class="linenos">2</span>    <span class="n">interesting</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="hll"><span class="linenos">3</span>    <span class="nb">print</span> <span class="s1">&#39;This line is highlighted.&#39;</span>
+</span><span class="linenos">4</span>    <span class="nb">print</span> <span class="s1">&#39;This one is not...&#39;</span>
+<span class="hll"><span class="linenos">5</span>    <span class="nb">print</span> <span class="s1">&#39;...but this one is.&#39;</span>
+</span></pre></div>
+</div>
+</section>
+</section>
+</section>
+<section id="sidebar">
+<h2><a class="toc-backref" href="#id44"><span class="section-number">3.5. </span>Sidebar</a><a class="headerlink" href="#sidebar" title="Permalink to this headline">¶</a></h2>
+<aside class="sidebar">
+<p class="sidebar-title">Ch’ien / The Creative</p>
+<img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" />
+<p><em>Above</em> CH’IEN THE CREATIVE, HEAVEN</p>
+<p><em>Below</em> CH’IEN THE CREATIVE, HEAVEN</p>
+</aside>
+<p>The first hexagram is made up of six unbroken lines. These unbroken lines stand for the primal power,
+which is light-giving, active, strong, and of the spirit. The hexagram is consistently strong in character,
+and since it is without weakness, its essence is power or energy. Its image is heaven.
+Its energy is represented as unrestricted by any fixed conditions in space and is therefore conceived of as motion.
+Time is regarded as the basis of this motion.
+Thus the hexagram includes also the power of time and the power of persisting in time, that is, duration.</p>
+<p>The power represented by the hexagram is to be interpreted in a dual sense in terms of its action
+on the universe and of its action on the world of men. In relation to the universe, the hexagram expresses the strong,
+creative action of the Deity. In relation to the human world, it denotes the creative action of the holy man or sage,
+of the ruler or leader of men, who through his power awakens and develops their higher nature.</p>
+<section id="code-with-sidebar">
+<h3><a class="toc-backref" href="#id45"><span class="section-number">3.5.1. </span>Code with Sidebar</a><a class="headerlink" href="#code-with-sidebar" title="Permalink to this headline">¶</a></h3>
+<aside class="sidebar">
+<p class="sidebar-title">A code example</p>
+<p>With a sidebar on the right.</p>
+</aside>
+<div class="literal-block-wrapper docutils container" id="id24">
+<div class="code-block-caption"><span class="caption-text">Literal includes can also have captions.</span><a class="headerlink" href="#id24" title="Permalink to this code">¶</a></div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="linenos"> 2</span><span class="sd">&quot;&quot;&quot;Test Module for sphinx_rtd_theme.&quot;&quot;&quot;</span>
+<span class="linenos"> 3</span>
+<span class="linenos"> 4</span>
+<span class="linenos"> 5</span><span class="k">class</span> <span class="nc">Foo</span><span class="p">:</span>
+<span class="linenos"> 6</span>
+<span class="linenos"> 7</span>    <span class="sd">&quot;&quot;&quot;Docstring for class Foo.</span>
+<span class="linenos"> 8</span>
+<span class="linenos"> 9</span><span class="sd">    This text tests for the formatting of docstrings generated from output</span>
+<span class="linenos">10</span><span class="sd">    ``sphinx.ext.autodoc``. Which contain reST, but sphinx nests it in the</span>
+<span class="linenos">11</span><span class="sd">    ``&lt;dl&gt;``, and ``&lt;dt&gt;`` tags. Also, ``&lt;tt&gt;`` is used for class, method names</span>
+<span class="linenos">12</span><span class="sd">    and etc, but those will *always* have the ``.descname`` or</span>
+<span class="linenos">13</span><span class="sd">    ``.descclassname`` class.</span>
+<span class="linenos">14</span>
+<span class="linenos">15</span><span class="sd">    Normal ``&lt;tt&gt;`` (like the &lt;tt&gt; I just wrote here) needs to be shown with</span>
+<span class="linenos">16</span><span class="sd">    the same style as anything else with ````this type of markup````.</span>
+<span class="linenos">17</span>
+<span class="linenos">18</span><span class="sd">    It&#39;s common for programmers to give a code example inside of their</span>
+<span class="linenos">19</span><span class="sd">    docstring::</span>
+<span class="linenos">20</span>
+<span class="linenos">21</span><span class="sd">        from test_py_module import Foo</span>
+<span class="linenos">22</span>
+<span class="linenos">23</span><span class="sd">        myclass = Foo()</span>
+<span class="linenos">24</span><span class="sd">        myclass.dothismethod(&#39;with this argument&#39;)</span>
+<span class="linenos">25</span><span class="sd">        myclass.flush()</span>
+<span class="linenos">26</span>
+<span class="linenos">27</span><span class="sd">        print(myclass)</span>
+<span class="linenos">28</span>
+<span class="linenos">29</span>
+<span class="linenos">30</span><span class="sd">    Here is a link to :py:meth:`capitalize`.</span>
+<span class="linenos">31</span><span class="sd">    Here is a link to :py:meth:`__init__`.</span>
+<span class="linenos">32</span>
+<span class="linenos">33</span><span class="sd">    &quot;&quot;&quot;</span>
+<span class="linenos">34</span>
+<span class="linenos">35</span>    <span class="c1">#: Doc comment for class attribute Foo.bar.</span>
+<span class="linenos">36</span>    <span class="c1">#: It can have multiple lines.</span>
+<span class="linenos">37</span>    <span class="n">bar</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="linenos">38</span>
+<span class="linenos">39</span>    <span class="n">flox</span> <span class="o">=</span> <span class="mf">1.5</span>   <span class="c1">#: Doc comment for Foo.flox. One line only.</span>
+<span class="linenos">40</span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+<section id="references">
+<h2><a class="toc-backref" href="#id46"><span class="section-number">3.6. </span>References</a><a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>
+<section id="footnotes">
+<h3><a class="toc-backref" href="#id47"><span class="section-number">3.6.1. </span>Footnotes</a><a class="headerlink" href="#footnotes" title="Permalink to this headline">¶</a></h3>
+<dl class="footnote brackets">
+<dt class="label" id="id6"><span class="brackets">1</span><span class="fn-backref">(<a href="#id1">1</a>,<a href="#id7">2</a>)</span></dt>
+<dd><p>A footnote contains body elements, consistently indented by at
+least 3 spaces.</p>
+<p>This is the footnote’s second paragraph.</p>
+</dd>
+<dt class="label" id="label"><span class="brackets">2</span><span class="fn-backref">(<a href="#id3">1</a>,<a href="#id8">2</a>)</span></dt>
+<dd><p>Footnotes may be numbered, either manually (as in <a class="footnote-reference brackets" href="#id6" id="id7">1</a>) or
+automatically using a “#”-prefixed label.  This footnote has a
+label so it can be referred to from multiple places, both as a
+footnote reference (<a class="footnote-reference brackets" href="#label" id="id8">2</a>) and as a hyperlink reference
+(<a class="reference internal" href="#label">label</a>).</p>
+</dd>
+<dt class="label" id="id9"><span class="brackets"><a class="fn-backref" href="#id2">3</a></span></dt>
+<dd><p>This footnote is numbered automatically and anonymously using a
+label of “#” only.</p>
+</dd>
+<dt class="label" id="id10"><span class="brackets"><a class="fn-backref" href="#id4">*</a></span></dt>
+<dd><p>Footnotes may also use symbols, specified with a “*” label.
+Here’s a reference to the next footnote: <a class="footnote-reference brackets" href="#id12" id="id11">†</a>.</p>
+</dd>
+<dt class="label" id="id12"><span class="brackets"><a class="fn-backref" href="#id11">†</a></span></dt>
+<dd><p>This footnote shows the next symbol in the sequence.</p>
+</dd>
+<dt class="label" id="id13"><span class="brackets">4</span></dt>
+<dd><p>Here’s an unreferenced footnote, with a reference to a
+nonexistent footnote: <a href="#id63"><span class="problematic" id="id14">[5]_</span></a>.</p>
+</dd>
+</dl>
+</section>
+<section id="citations">
+<h3><a class="toc-backref" href="#id48"><span class="section-number">3.6.2. </span>Citations</a><a class="headerlink" href="#citations" title="Permalink to this headline">¶</a></h3>
+<dl class="footnote brackets">
+<dt class="label" id="id15"><span class="brackets">11</span></dt>
+<dd><p>This is the citation I made, let’s make this extremely long so that we can tell that it doesn’t follow the normal responsive table stuff.</p>
+</dd>
+<dt class="label" id="id16"><span class="brackets">12</span><span class="fn-backref">(<a href="#id5">1</a>,<a href="#id19">2</a>)</span></dt>
+<dd><p>This citation has some <code class="docutils literal notranslate"><span class="pre">code</span> <span class="pre">blocks</span></code> in it, maybe some <strong>bold</strong> and
+<em>italics</em> too. Heck, lets put a link to a meta citation <a class="footnote-reference brackets" href="#id18" id="id17">13</a> too.</p>
+</dd>
+<dt class="label" id="id18"><span class="brackets"><a class="fn-backref" href="#id17">13</a></span></dt>
+<dd><p>This citation will have two backlinks.</p>
+</dd>
+</dl>
+<p>Here’s a reference to the above, <a class="footnote-reference brackets" href="#id16" id="id19">12</a>, and a <span id="id20">[nonexistent]</span> citation.</p>
+<p>Here is another type of citation: <cite>citation</cite></p>
+</section>
+<section id="glossary">
+<h3><a class="toc-backref" href="#id49"><span class="section-number">3.6.3. </span>Glossary</a><a class="headerlink" href="#glossary" title="Permalink to this headline">¶</a></h3>
+<p>This is a glossary with definition terms for thing like <a class="reference internal" href="#term-Writing"><span class="xref std std-term">Writing</span></a>:</p>
+<dl class="glossary simple">
+<dt id="term-Documentation">Documentation<a class="headerlink" href="#term-Documentation" title="Permalink to this term">¶</a></dt><dd><p>Provides users with the knowledge they need to use something.</p>
+</dd>
+<dt id="term-Reading">Reading<a class="headerlink" href="#term-Reading" title="Permalink to this term">¶</a></dt><dd><p>The process of taking information into ones mind through the use of eyes.</p>
+</dd>
+<dt id="term-Writing">Writing<a class="headerlink" href="#term-Writing" title="Permalink to this term">¶</a></dt><dd><p>The process of putting thoughts into a medium for other people to <a class="reference internal" href="#term-Reading"><span class="xref std std-term">read</span></a>.</p>
+</dd>
+</dl>
+</section>
+<section id="targets">
+<h3><a class="toc-backref" href="#id50"><span class="section-number">3.6.4. </span>Targets</a><a class="headerlink" href="#targets" title="Permalink to this headline">¶</a></h3>
+<p id="example">This paragraph is pointed to by the explicit “example” target.
+A reference can be found under <a class="reference internal" href="#inline-markup">Inline Markup</a>, above. <a class="reference internal" href="#inline-hyperlink-targets">Inline
+hyperlink targets</a> are also possible.</p>
+<p>Section headers are implicit targets, referred to by name. See
+<a class="reference internal" href="#targets">Targets</a>, which is a subsection of <a href="#id64"><span class="problematic" id="id65">`Body Elements`_</span></a>.</p>
+<p>Explicit external targets are interpolated into references such as “<a class="reference external" href="http://www.python.org/">Python</a> <a class="footnote-reference brackets" href="#id29" id="id31">5</a>”.</p>
+<p>Targets may be indirect and anonymous.  Thus <a class="reference internal" href="#targets">this phrase</a> may also
+refer to the <a class="reference internal" href="#targets">Targets</a> section.</p>
+<p>Here’s a <a href="#id66"><span class="problematic" id="id67">`hyperlink reference without a target`_</span></a>, which generates an error.</p>
+</section>
+</section>
+<section id="directives">
+<h2><a class="toc-backref" href="#id51"><span class="section-number">3.7. </span>Directives</a><a class="headerlink" href="#directives" title="Permalink to this headline">¶</a></h2>
+<section id="contents">
+<h3><a class="toc-backref" href="#id52"><span class="section-number">3.7.1. </span>Contents</a><a class="headerlink" href="#contents" title="Permalink to this headline">¶</a></h3>
+<p>These are just a sample of the many reStructuredText Directives. For others, please see:
+<a class="reference external" href="http://docutils.sourceforge.net/docs/ref/rst/directives.html">http://docutils.sourceforge.net/docs/ref/rst/directives.html</a>.</p>
+</section>
+<section id="centered-text">
+<h3><a class="toc-backref" href="#id53"><span class="section-number">3.7.2. </span>Centered text</a><a class="headerlink" href="#centered-text" title="Permalink to this headline">¶</a></h3>
+<p>You can create a statement with centered text with <code class="docutils literal notranslate"><span class="pre">..</span> <span class="pre">centered::</span></code></p>
+<p class="centered">
+<strong>This is centered text!</strong></p></section>
+<section id="images-figures">
+<h3><a class="toc-backref" href="#id54"><span class="section-number">3.7.3. </span>Images &amp; Figures</a><a class="headerlink" href="#images-figures" title="Permalink to this headline">¶</a></h3>
+<section id="images">
+<h4><a class="toc-backref" href="#id55"><span class="section-number">3.7.3.1. </span>Images</a><a class="headerlink" href="#images" title="Permalink to this headline">¶</a></h4>
+<p>An image directive (also clickable – a hyperlink reference):</p>
+<a class="reference internal image-reference" href="#directives"><img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" /></a>
+</section>
+<section id="figures">
+<h4><a class="toc-backref" href="#id56"><span class="section-number">3.7.3.2. </span>Figures</a><a class="headerlink" href="#figures" title="Permalink to this headline">¶</a></h4>
+<figure class="align-default" id="id25">
+<img alt="reStructuredText, the markup syntax" src="../../../../_images/yi_jing_01_chien.jpg" />
+<figcaption>
+<p><span class="caption-text">A figure is an image with a caption and/or a legend:</span><a class="headerlink" href="#id25" title="Permalink to this image">¶</a></p>
+<div class="legend">
+<table class="docutils align-default">
+<colgroup>
+<col style="width: 20%" />
+<col style="width: 80%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p>re</p></td>
+<td><p>Revised, revisited, based on ‘re’ module.</p></td>
+</tr>
+<tr class="row-even"><td><p>Structured</p></td>
+<td><p>Structure-enhanced text, structuredtext.</p></td>
+</tr>
+<tr class="row-odd"><td><p>Text</p></td>
+<td><p>Well it is, isn’t it?</p></td>
+</tr>
+</tbody>
+</table>
+<p>This paragraph is also part of the legend.</p>
+</div>
+</figcaption>
+</figure>
+<p>A figure directive with center alignment</p>
+<figure class="align-center" id="id26">
+<img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" />
+<figcaption>
+<p><span class="caption-text">This caption should be centered.</span><a class="headerlink" href="#id26" title="Permalink to this image">¶</a></p>
+</figcaption>
+</figure>
+</section>
+</section>
+<section id="admonitions">
+<h3><a class="toc-backref" href="#id57"><span class="section-number">3.7.4. </span>Admonitions</a><a class="headerlink" href="#admonitions" title="Permalink to this headline">¶</a></h3>
+<div class="admonition attention">
+<p class="admonition-title">Attention</p>
+<p>Directives at large.</p>
+</div>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p>Don’t take any wooden nickels.</p>
+</div>
+<div class="admonition danger">
+<p class="admonition-title">Danger</p>
+<p>Mad scientist at work!</p>
+</div>
+<div class="admonition error">
+<p class="admonition-title">Error</p>
+<p>Does not compute.</p>
+</div>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>It’s bigger than a bread box.</p>
+</div>
+<div class="admonition important">
+<p class="admonition-title">Important</p>
+<ul class="simple">
+<li><p>Wash behind your ears.</p></li>
+<li><p>Clean up your room.</p>
+<ul>
+<li><p>Including the closet.</p></li>
+<li><p>The bathroom too.</p>
+<ul>
+<li><p>Take the trash out of the bathroom.</p></li>
+<li><p>Clean the sink.</p></li>
+</ul>
+</li>
+</ul>
+</li>
+<li><p>Call your mother.</p></li>
+<li><p>Back up your data.</p></li>
+</ul>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This is a note.
+Equations within a note:
+<span class="math notranslate nohighlight">\(G_{\mu\nu} = 8 \pi G (T_{\mu\nu}  + \rho_\Lambda g_{\mu\nu})\)</span>.</p>
+</div>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>15% if the service is good.</p>
+<table class="docutils align-default">
+<colgroup>
+<col style="width: 100%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Example</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Thing1</p></td>
+</tr>
+<tr class="row-odd"><td><p>Thing2</p></td>
+</tr>
+<tr class="row-even"><td><p>Thing3</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Strong prose may provoke extreme mental exertion.
+Reader discretion is strongly advised.</p>
+</div>
+<div class="admonition-and-by-the-way admonition">
+<p class="admonition-title">And, by the way…</p>
+<p>You can make up your own admonition too.</p>
+</div>
+</section>
+<section id="topics-sidebars-and-rubrics">
+<h3><a class="toc-backref" href="#id58"><span class="section-number">3.7.5. </span>Topics, Sidebars, and Rubrics</a><a class="headerlink" href="#topics-sidebars-and-rubrics" title="Permalink to this headline">¶</a></h3>
+<aside class="sidebar">
+<p class="sidebar-title">Sidebar Title</p>
+<p class="sidebar-subtitle">Optional Subtitle</p>
+<p>This is a sidebar.  It is for text outside the flow of the main
+text.</p>
+<p class="rubric">This is a rubric inside a sidebar</p>
+<p>Sidebars often appears beside the main text with a border and
+background color.</p>
+</aside>
+<div class="topic">
+<p class="topic-title">Topic Title</p>
+<p>This is a topic.</p>
+</div>
+<p class="rubric">This is a rubric</p>
+</section>
+<section id="target-footnotes">
+<h3><a class="toc-backref" href="#id59"><span class="section-number">3.7.6. </span>Target Footnotes</a><a class="headerlink" href="#target-footnotes" title="Permalink to this headline">¶</a></h3>
+<dl class="footnote brackets">
+<dt class="label" id="id29"><span class="brackets">5</span><span class="fn-backref">(<a href="#id30">1</a>,<a href="#id31">2</a>,<a href="#id32">3</a>)</span></dt>
+<dd><p><a class="reference external" href="http://www.python.org/">http://www.python.org/</a></p>
+</dd>
+</dl>
+</section>
+<section id="replacement-text">
+<h3><a class="toc-backref" href="#id60"><span class="section-number">3.7.7. </span>Replacement Text</a><a class="headerlink" href="#replacement-text" title="Permalink to this headline">¶</a></h3>
+<p>I recommend you try <a class="reference external" href="http://www.python.org/">Python, <em>the</em> best language around</a> <a class="footnote-reference brackets" href="#id29" id="id32">5</a>.</p>
+</section>
+<section id="compound-paragraph">
+<h3><a class="toc-backref" href="#id61"><span class="section-number">3.7.8. </span>Compound Paragraph</a><a class="headerlink" href="#compound-paragraph" title="Permalink to this headline">¶</a></h3>
+<div class="compound">
+<p class="compound-first">This paragraph contains a literal block:</p>
+<div class="compound-middle highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">Connecting</span><span class="p">...</span><span class="w"> </span><span class="n">OK</span><span class="w"></span>
+<span class="n">Transmitting</span><span class="w"> </span><span class="n">data</span><span class="p">...</span><span class="w"> </span><span class="n">OK</span><span class="w"></span>
+<span class="n">Disconnecting</span><span class="p">...</span><span class="w"> </span><span class="n">OK</span><span class="w"></span>
+</pre></div>
+</div>
+<p class="compound-last">and thus consists of a simple paragraph, a literal block, and
+another simple paragraph.  Nonetheless it is semantically <em>one</em>
+paragraph.</p>
+</div>
+<p>This construct is called a <em>compound paragraph</em> and can be produced
+with the “compound” directive.</p>
+</section>
+</section>
+<section id="download-links">
+<h2><a class="toc-backref" href="#id62"><span class="section-number">3.8. </span>Download Links</a><a class="headerlink" href="#download-links" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference download internal" download="" href="../../../../_downloads/26d49aeeb9c710e27197fda28b7c3516/yi_jing_01_chien.jpg"><code class="xref download docutils literal notranslate"><span class="pre">This</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">download</span> <span class="pre">link</span> <span class="pre">should</span> <span class="pre">be</span> <span class="pre">blue,</span> <span class="pre">normal</span> <span class="pre">weight</span> <span class="pre">text</span> <span class="pre">with</span> <span class="pre">a</span> <span class="pre">leading</span> <span class="pre">icon,</span> <span class="pre">and</span> <span class="pre">should</span> <span class="pre">wrap</span> <span class="pre">white-spaces</span></code></a></p>
+</section>
+</section>
+
+
+             </article>
+
+            </div>
+            <footer>
+
+
+
+
+    <hr>
+
+
+
+  <div role="contentinfo">
+    <p>
+        &copy; Copyright 2021, NVIDIA Corporation.
+
+    </p>
+  </div>
+
+      <div>
+        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+      </div>
+
+
+</footer>
+
+          </div>
+        </div>
+
+        <div class="pytorch-content-right" id="pytorch-content-right">
+          <div class="pytorch-right-menu" id="pytorch-right-menu">
+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
+              <ul>
+<li><a class="reference internal" href="#">3. Paragraph Level Markup</a><ul>
+<li><a class="reference internal" href="#inline-markup">3.1. Inline Markup</a></li>
+<li><a class="reference internal" href="#math">3.2. Math</a></li>
+<li><a class="reference internal" href="#meta">3.3. Meta</a></li>
+<li><a class="reference internal" href="#blocks">3.4. Blocks</a><ul>
+<li><a class="reference internal" href="#literal-blocks">3.4.1. Literal Blocks</a></li>
+<li><a class="reference internal" href="#line-blocks">3.4.2. Line Blocks</a></li>
+<li><a class="reference internal" href="#block-quotes">3.4.3. Block Quotes</a></li>
+<li><a class="reference internal" href="#doctest-blocks">3.4.4. Doctest Blocks</a></li>
+<li><a class="reference internal" href="#code-blocks">3.4.5. Code Blocks</a><ul>
+<li><a class="reference internal" href="#emphasized-lines-with-line-numbers">3.4.5.1. Emphasized lines with line numbers</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li><a class="reference internal" href="#sidebar">3.5. Sidebar</a><ul>
+<li><a class="reference internal" href="#code-with-sidebar">3.5.1. Code with Sidebar</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#references">3.6. References</a><ul>
+<li><a class="reference internal" href="#footnotes">3.6.1. Footnotes</a></li>
+<li><a class="reference internal" href="#citations">3.6.2. Citations</a></li>
+<li><a class="reference internal" href="#glossary">3.6.3. Glossary</a></li>
+<li><a class="reference internal" href="#targets">3.6.4. Targets</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#directives">3.7. Directives</a><ul>
+<li><a class="reference internal" href="#contents">3.7.1. Contents</a></li>
+<li><a class="reference internal" href="#centered-text">3.7.2. Centered text</a></li>
+<li><a class="reference internal" href="#images-figures">3.7.3. Images &amp; Figures</a><ul>
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diff --git a/docs/v1.2.0/src/pytorch-sphinx-theme/docs/demo/lists_tables.html b/docs/v1.2.0/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/notebooks.html">Example notebooks</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../py_api/logging.html">torch_tensorrt.logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../py_api/ptq.html">torch_tensorrt.ptq</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../py_api/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../py_api/fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../contributors/system_overview.html">System Overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../contributors/writing_converters.html">Writing Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../indices/supported_ops.html">Operators Supported</a></li>
+</ul>
+
+
+
+        </div>
+      </div>
+    </nav>
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+    <div class="pytorch-container">
+      <div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
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+
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+      <li class="pytorch-breadcrumbs-aside">
+
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+        <div class="pytorch-content-left">
+
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+          <div class="rst-content">
+
+            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+
+  <section id="lists-tables">
+<h1><a class="toc-backref" href="#id17"><span class="section-number">4. </span>Lists &amp; Tables</a><a class="headerlink" href="#lists-tables" title="Permalink to this headline">¶</a></h1>
+<div class="contents topic" id="table-of-contents">
+<p class="topic-title">Table of Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#lists-tables" id="id17">Lists &amp; Tables</a></p>
+<ul>
+<li><p><a class="reference internal" href="#lists" id="id18">Lists</a></p>
+<ul>
+<li><p><a class="reference internal" href="#enumerated-lists" id="id19">Enumerated Lists</a></p></li>
+<li><p><a class="reference internal" href="#definition-lists" id="id20">Definition Lists</a></p></li>
+<li><p><a class="reference internal" href="#option-lists" id="id21">Option Lists</a></p></li>
+<li><p><a class="reference internal" href="#field-list" id="id22">Field list</a></p></li>
+<li><p><a class="reference internal" href="#bullet-lists" id="id23">Bullet Lists</a></p>
+<ul>
+<li><p><a class="reference internal" href="#second-list-level" id="id24">Second list level</a></p>
+<ul>
+<li><p><a class="reference internal" href="#but-deeper-down-the-rabbit-hole" id="id25">But deeper down the rabbit hole</a></p></li>
+</ul>
+</li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#hlists" id="id26">Hlists</a></p></li>
+<li><p><a class="reference internal" href="#numbered-list" id="id27">Numbered List</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#tables" id="id28">Tables</a></p>
+<ul>
+<li><p><a class="reference internal" href="#grid-tables" id="id29">Grid Tables</a></p>
+<ul>
+<li><p><a class="reference internal" href="#giant-tables" id="id30">Giant Tables</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#list-tables" id="id31">List Tables</a></p></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+<section id="lists">
+<h2><a class="toc-backref" href="#id18"><span class="section-number">4.1. </span>Lists</a><a class="headerlink" href="#lists" title="Permalink to this headline">¶</a></h2>
+<section id="enumerated-lists">
+<h3><a class="toc-backref" href="#id19"><span class="section-number">4.1.1. </span>Enumerated Lists</a><a class="headerlink" href="#enumerated-lists" title="Permalink to this headline">¶</a></h3>
+<ol class="arabic">
+<li><p>Arabic numerals.</p>
+<ol class="loweralpha simple">
+<li><p>lower alpha)</p>
+<ol class="lowerroman simple">
+<li><p>(lower roman)</p>
+<ol class="upperalpha simple">
+<li><p>upper alpha.</p>
+<ol class="upperroman simple">
+<li><p>upper roman)</p></li>
+</ol>
+</li>
+</ol>
+</li>
+</ol>
+</li>
+</ol>
+</li>
+<li><p>Lists that don’t start at 1:</p>
+<ol class="arabic simple" start="3">
+<li><p>Three</p></li>
+<li><p>Four</p></li>
+</ol>
+<ol class="upperalpha simple" start="3">
+<li><p>C</p></li>
+<li><p>D</p></li>
+</ol>
+<ol class="lowerroman simple" start="3">
+<li><p>iii</p></li>
+<li><p>iv</p></li>
+</ol>
+</li>
+<li><p>List items may also be auto-enumerated.</p></li>
+</ol>
+</section>
+<section id="definition-lists">
+<h3><a class="toc-backref" href="#id20"><span class="section-number">4.1.2. </span>Definition Lists</a><a class="headerlink" href="#definition-lists" title="Permalink to this headline">¶</a></h3>
+<dl>
+<dt>Term</dt><dd><p>Definition</p>
+</dd>
+<dt>Term<span class="classifier">classifier</span></dt><dd><p>Definition paragraph 1.</p>
+<p>Definition paragraph 2.</p>
+</dd>
+<dt>Term</dt><dd><p>Definition</p>
+</dd>
+</dl>
+</section>
+<section id="option-lists">
+<h3><a class="toc-backref" href="#id21"><span class="section-number">4.1.3. </span>Option Lists</a><a class="headerlink" href="#option-lists" title="Permalink to this headline">¶</a></h3>
+<p>For listing command-line options:</p>
+<dl class="option-list">
+<dt><kbd><span class="option">-a</span></kbd></dt>
+<dd><p>command-line option “a”</p>
+</dd>
+<dt><kbd><span class="option">-b <var>file</var></span></kbd></dt>
+<dd><p>options can have arguments
+and long descriptions</p>
+</dd>
+<dt><kbd><span class="option">--long</span></kbd></dt>
+<dd><p>options can be long also</p>
+</dd>
+<dt><kbd><span class="option">--input=<var>file</var></span></kbd></dt>
+<dd><p>long options can also have
+arguments</p>
+</dd>
+<dt><kbd><span class="option">--very-long-option</span></kbd></dt>
+<dd><p>The description can also start on the next line.</p>
+<p>The description may contain multiple body elements,
+regardless of where it starts.</p>
+</dd>
+<dt><kbd><span class="option">-x</span>, <span class="option">-y</span>, <span class="option">-z</span></kbd></dt>
+<dd><p>Multiple options are an “option group”.</p>
+</dd>
+<dt><kbd><span class="option">-v</span>, <span class="option">--verbose</span></kbd></dt>
+<dd><p>Commonly-seen: short &amp; long options.</p>
+</dd>
+<dt><kbd><span class="option">-1 <var>file</var></span>, <span class="option">--one=<var>file</var></span>, <span class="option">--two <var>file</var></span></kbd></dt>
+<dd><p>Multiple options with arguments.</p>
+</dd>
+<dt><kbd><span class="option">/V</span></kbd></dt>
+<dd><p>DOS/VMS-style options too</p>
+</dd>
+</dl>
+<p>There must be at least two spaces between the option and the description.</p>
+</section>
+<section id="field-list">
+<h3><a class="toc-backref" href="#id22"><span class="section-number">4.1.4. </span>Field list</a><a class="headerlink" href="#field-list" title="Permalink to this headline">¶</a></h3>
+<dl class="field-list">
+<dt class="field-odd">Author</dt>
+<dd class="field-odd"><p>David Goodger</p>
+</dd>
+<dt class="field-even">Address</dt>
+<dd class="field-even"><p>123 Example Street
+Example, EX  Canada
+A1B 2C3</p>
+</dd>
+<dt class="field-odd">Contact</dt>
+<dd class="field-odd"><p><a class="reference external" href="mailto:docutils-develop&#37;&#52;&#48;lists&#46;sourceforge&#46;net">docutils-develop<span>&#64;</span>lists<span>&#46;</span>sourceforge<span>&#46;</span>net</a></p>
+</dd>
+<dt class="field-even">Authors</dt>
+<dd class="field-even"><p>Me; Myself; I</p>
+</dd>
+<dt class="field-odd">organization</dt>
+<dd class="field-odd"><p>humankind</p>
+</dd>
+<dt class="field-even">date</dt>
+<dd class="field-even"><p>$Date: 2012-01-03 19:23:53 +0000 (Tue, 03 Jan 2012) $</p>
+</dd>
+<dt class="field-odd">status</dt>
+<dd class="field-odd"><p>This is a “work in progress”</p>
+</dd>
+<dt class="field-even">revision</dt>
+<dd class="field-even"><p>$Revision: 7302 $</p>
+</dd>
+<dt class="field-odd">version</dt>
+<dd class="field-odd"><p>1</p>
+</dd>
+<dt class="field-even">copyright</dt>
+<dd class="field-even"><p>This document has been placed in the public domain. You
+may do with it as you wish. You may copy, modify,
+redistribute, reattribute, sell, buy, rent, lease,
+destroy, or improve it, quote it at length, excerpt,
+incorporate, collate, fold, staple, or mutilate it, or do
+anything else to it that your or anyone else’s heart
+desires.</p>
+</dd>
+<dt class="field-odd">field name</dt>
+<dd class="field-odd"><p>This is a generic bibliographic field.</p>
+</dd>
+<dt class="field-even">field name 2</dt>
+<dd class="field-even"><p>Generic bibliographic fields may contain multiple body elements.</p>
+<p>Like this.</p>
+</dd>
+<dt class="field-odd">Dedication</dt>
+<dd class="field-odd"><p>For Docutils users &amp; co-developers.</p>
+</dd>
+<dt class="field-even">abstract</dt>
+<dd class="field-even"><p>This document is a demonstration of the reStructuredText markup
+language, containing examples of all basic reStructuredText
+constructs and many advanced constructs.</p>
+</dd>
+</dl>
+</section>
+<section id="bullet-lists">
+<h3><a class="toc-backref" href="#id23"><span class="section-number">4.1.5. </span>Bullet Lists</a><a class="headerlink" href="#bullet-lists" title="Permalink to this headline">¶</a></h3>
+<ul>
+<li><p>A bullet list</p>
+<ul class="simple">
+<li><p>Nested bullet list.</p></li>
+<li><p>Nested item 2.</p></li>
+</ul>
+</li>
+<li><p>Item 2.</p>
+<p>Paragraph 2 of item 2.</p>
+<ul class="simple">
+<li><p>Nested bullet list.</p></li>
+<li><p>Nested item 2.</p>
+<ul>
+<li><p>Third level.</p></li>
+<li><p>Item 2.</p></li>
+</ul>
+</li>
+<li><p>Nested item 3.</p></li>
+</ul>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">inline</span> <span class="pre">literall</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">inline</span> <span class="pre">literall</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">inline</span> <span class="pre">literall</span></code></p></li>
+</ul>
+<section id="second-list-level">
+<h4><a class="toc-backref" href="#id24"><span class="section-number">4.1.5.1. </span>Second list level</a><a class="headerlink" href="#second-list-level" title="Permalink to this headline">¶</a></h4>
+<ul>
+<li><p>here is a list in a second-level section.</p></li>
+<li><p><a class="reference external" href="http://www.yahoo.com">yahoo</a></p></li>
+<li><p><a class="reference external" href="http://www.yahoo.com">yahoo</a></p>
+<ul>
+<li><p><a class="reference external" href="http://www.yahoo.com">yahoo</a></p></li>
+<li><p>here is an inner bullet <code class="docutils literal notranslate"><span class="pre">oh</span></code></p>
+<ul>
+<li><p>one more <code class="docutils literal notranslate"><span class="pre">with</span> <span class="pre">an</span> <span class="pre">inline</span> <span class="pre">literally</span></code>. <a class="reference external" href="http://www.yahoo.com">yahoo</a></p>
+<p>heh heh. child. try to beat this embed:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="linenos"> 2</span><span class="sd">&quot;&quot;&quot;Test Module for sphinx_rtd_theme.&quot;&quot;&quot;</span>
+<span class="linenos"> 3</span>
+<span class="linenos"> 4</span>
+<span class="linenos"> 5</span><span class="k">class</span> <span class="nc">Foo</span><span class="p">:</span>
+<span class="linenos"> 6</span>
+<span class="linenos"> 7</span>    <span class="s2">&quot;&quot;&quot;Docstring for class Foo.</span>
+<span class="linenos"> 8</span>
+<span class="linenos"> 9</span><span class="s2">    This text tests for the formatting of docstrings generated from output</span>
+<span class="linenos">10</span><span class="s2">    ``sphinx.ext.autodoc``. Which contain reST, but sphinx nests it in the</span>
+</pre></div>
+</div>
+</li>
+</ul>
+</li>
+<li><p>and another. <a class="reference external" href="http://www.yahoo.com">yahoo</a></p></li>
+<li><p><a class="reference external" href="http://www.yahoo.com">yahoo</a></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">hi</span></code></p></li>
+</ul>
+</li>
+<li><p>and hehe</p></li>
+</ul>
+<section id="but-deeper-down-the-rabbit-hole">
+<h5><a class="toc-backref" href="#id25"><span class="section-number">4.1.5.1.1. </span>But deeper down the rabbit hole</a><a class="headerlink" href="#but-deeper-down-the-rabbit-hole" title="Permalink to this headline">¶</a></h5>
+<ul class="simple">
+<li><p>I kept saying that, “deeper down the rabbit hole”. <a class="reference external" href="http://www.yahoo.com">yahoo</a></p>
+<ul>
+<li><p>I cackle at night <a class="reference external" href="http://www.yahoo.com">yahoo</a>.</p></li>
+</ul>
+</li>
+<li><p>I’m so lonely here in GZ <code class="docutils literal notranslate"><span class="pre">guangzhou</span></code></p></li>
+<li><p>A man of python destiny, hopes and dreams. <a class="reference external" href="http://www.yahoo.com">yahoo</a></p>
+<ul>
+<li><p><a class="reference external" href="http://www.yahoo.com">yahoo</a></p>
+<ul>
+<li><p><a class="reference external" href="http://www.yahoo.com">yahoo</a> <code class="docutils literal notranslate"><span class="pre">hi</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">destiny</span></code></p></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</section>
+</section>
+</section>
+<section id="hlists">
+<h3><a class="toc-backref" href="#id26"><span class="section-number">4.1.6. </span>Hlists</a><a class="headerlink" href="#hlists" title="Permalink to this headline">¶</a></h3>
+<table class="hlist"><tr><td><ul class="simple">
+<li><p>First item</p></li>
+<li><p>Second item</p></li>
+<li><p>Third item</p></li>
+</ul>
+</td><td><ul class="simple">
+<li><p>Forth item</p></li>
+<li><p>Fifth item</p></li>
+<li><p>Sixths item</p></li>
+</ul>
+</td></tr></table>
+<p class="rubric">Hlist with images</p>
+<table class="hlist"><tr><td><ul>
+<li><figure class="align-default" id="id11">
+<img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" />
+<figcaption>
+<p><span class="caption-text">This is a short caption for a figure.</span><a class="headerlink" href="#id11" title="Permalink to this image">¶</a></p>
+</figcaption>
+</figure>
+</li>
+</ul>
+</td><td><ul>
+<li><figure class="align-default" id="id12">
+<img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" />
+<figcaption>
+<p><span class="caption-text">This is a long caption for a figure. Lorem ipsum dolor sit amet, consectetur adipiscing elit.
+Donec porttitor dolor in odio posuere, vitae ornare libero mattis. In lobortis justo vestibulum nibh aliquet, non.</span><a class="headerlink" href="#id12" title="Permalink to this image">¶</a></p>
+</figcaption>
+</figure>
+</li>
+</ul>
+</td></tr></table>
+</section>
+<section id="numbered-list">
+<h3><a class="toc-backref" href="#id27"><span class="section-number">4.1.7. </span>Numbered List</a><a class="headerlink" href="#numbered-list" title="Permalink to this headline">¶</a></h3>
+<ol class="arabic simple">
+<li><p>One,</p></li>
+<li><p>Two.</p></li>
+<li><p>Three with long text. Lorem ipsum dolor sit amet, consectetur adipiscing elit.
+Sed feugiat sagittis neque quis eleifend. Duis rutrum lectus sit amet mattis suscipit.</p></li>
+</ol>
+<ul class="simple">
+<li><ol class="upperalpha simple">
+<li><p>Using bullets and letters. (A)</p></li>
+</ol>
+</li>
+<li><ol class="upperalpha simple" start="2">
+<li><p>Using bullets and letters. (B)</p></li>
+</ol>
+</li>
+<li><ol class="upperalpha simple" start="3">
+<li><p>Using bullets and letters. (C)</p></li>
+</ol>
+</li>
+</ul>
+</section>
+</section>
+<section id="tables">
+<h2><a class="toc-backref" href="#id28"><span class="section-number">4.2. </span>Tables</a><a class="headerlink" href="#tables" title="Permalink to this headline">¶</a></h2>
+<section id="grid-tables">
+<h3><a class="toc-backref" href="#id29"><span class="section-number">4.2.1. </span>Grid Tables</a><a class="headerlink" href="#grid-tables" title="Permalink to this headline">¶</a></h3>
+<p>Here’s a grid table followed by a simple table:</p>
+<table class="docutils align-default">
+<colgroup>
+<col style="width: 43%" />
+<col style="width: 21%" />
+<col style="width: 18%" />
+<col style="width: 18%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Header row, column 1
+(header rows optional)</p></th>
+<th class="head"><p>Header 2</p></th>
+<th class="head"><p>Header 3</p></th>
+<th class="head"><p>Header 4</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>body row 1, column 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>column 4</p></td>
+</tr>
+<tr class="row-odd"><td><p>body row 2</p></td>
+<td colspan="3"><p>Cells may span columns.</p></td>
+</tr>
+<tr class="row-even"><td><p>body row 3</p></td>
+<td rowspan="2"><p>Cells may
+span rows.</p></td>
+<td colspan="2" rowspan="2"><ul class="simple">
+<li><p>Table cells</p></li>
+<li><p>contain</p></li>
+<li><p>body elements.</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p>body row 4</p></td>
+</tr>
+<tr class="row-even"><td><p>body row 5</p></td>
+<td colspan="2"><p>Cells may also be
+empty: <code class="docutils literal notranslate"><span class="pre">--&gt;</span></code></p></td>
+<td></td>
+</tr>
+</tbody>
+</table>
+<table class="docutils align-default">
+<colgroup>
+<col style="width: 31%" />
+<col style="width: 31%" />
+<col style="width: 38%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head" colspan="2"><p>Inputs</p></th>
+<th class="head"><p>Output</p></th>
+</tr>
+<tr class="row-even"><th class="head"><p>A</p></th>
+<th class="head"><p>B</p></th>
+<th class="head"><p>A or B</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-odd"><td><p>False</p></td>
+<td><p>False</p></td>
+<td><p>False</p></td>
+</tr>
+<tr class="row-even"><td><p>True</p></td>
+<td><p>False</p></td>
+<td><p>True</p></td>
+</tr>
+<tr class="row-odd"><td><p>False</p></td>
+<td><p>True</p></td>
+<td><p>True</p></td>
+</tr>
+<tr class="row-even"><td><p>True</p></td>
+<td><p>True</p></td>
+<td><p>True</p></td>
+</tr>
+</tbody>
+</table>
+<section id="giant-tables">
+<h4><a class="toc-backref" href="#id30"><span class="section-number">4.2.1.1. </span>Giant Tables</a><a class="headerlink" href="#giant-tables" title="Permalink to this headline">¶</a></h4>
+<table class="docutils align-default">
+<colgroup>
+<col style="width: 9%" />
+<col style="width: 9%" />
+<col style="width: 8%" />
+<col style="width: 9%" />
+<col style="width: 9%" />
+<col style="width: 8%" />
+<col style="width: 9%" />
+<col style="width: 9%" />
+<col style="width: 8%" />
+<col style="width: 9%" />
+<col style="width: 9%" />
+<col style="width: 8%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Header 1</p></th>
+<th class="head"><p>Header 2</p></th>
+<th class="head"><p>Header 3</p></th>
+<th class="head"><p>Header 1</p></th>
+<th class="head"><p>Header 2</p></th>
+<th class="head"><p>Header 3</p></th>
+<th class="head"><p>Header 1</p></th>
+<th class="head"><p>Header 2</p></th>
+<th class="head"><p>Header 3</p></th>
+<th class="head"><p>Header 1</p></th>
+<th class="head"><p>Header 2</p></th>
+<th class="head"><p>Header 3</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+</tr>
+<tr class="row-odd"><td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+</tr>
+<tr class="row-even"><td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+</tr>
+<tr class="row-odd"><td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="list-tables">
+<h3><a class="toc-backref" href="#id31"><span class="section-number">4.2.2. </span>List Tables</a><a class="headerlink" href="#list-tables" title="Permalink to this headline">¶</a></h3>
+<table class="colwidths-given docutils align-default" id="id13">
+<caption><span class="caption-text">List tables can have captions like this one.</span><a class="headerlink" href="#id13" title="Permalink to this table">¶</a></caption>
+<colgroup>
+<col style="width: 13%" />
+<col style="width: 7%" />
+<col style="width: 13%" />
+<col style="width: 67%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head stub"><p>List table</p></th>
+<th class="head"><p>Header 1</p></th>
+<th class="head"><p>Header 2</p></th>
+<th class="head"><p>Header 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><th class="stub"><p>Stub Row 1</p></th>
+<td><p>Row 1</p></td>
+<td><p>Column 2</p></td>
+<td><p>Column 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.</p></td>
+</tr>
+<tr class="row-odd"><th class="stub"><p>Stub Row 2</p></th>
+<td><p>Row 2</p></td>
+<td><p>Column 2</p></td>
+<td><p>Column 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.</p></td>
+</tr>
+<tr class="row-even"><th class="stub"><p>Stub Row 3</p></th>
+<td><p>Row 3</p></td>
+<td><p>Column 2</p></td>
+<td><p>Column 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.</p></td>
+</tr>
+</tbody>
+</table>
+<table class="docutils align-default" id="id14">
+<caption><span class="caption-text">This is a list table with images in it.</span><a class="headerlink" href="#id14" title="Permalink to this table">¶</a></caption>
+<colgroup>
+<col style="width: 50%" />
+<col style="width: 50%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><figure class="align-default" id="id15">
+<img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" />
+<figcaption>
+<p><span class="caption-text">This is a short caption for a figure.</span><a class="headerlink" href="#id15" title="Permalink to this image">¶</a></p>
+</figcaption>
+</figure>
+</td>
+<td><figure class="align-default" id="id16">
+<img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" />
+<figcaption>
+<p><span class="caption-text">This is a long caption for a figure. Lorem ipsum dolor sit amet, consectetur adipiscing elit.
+Donec porttitor dolor in odio posuere, vitae ornare libero mattis. In lobortis justo vestibulum nibh aliquet, non.</span><a class="headerlink" href="#id16" title="Permalink to this image">¶</a></p>
+</figcaption>
+</figure>
+</td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+</section>
+
+
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+<li><a class="reference internal" href="#tables">4.2. Tables</a><ul>
+<li><a class="reference internal" href="#grid-tables">4.2.1. Grid Tables</a><ul>
+<li><a class="reference internal" href="#giant-tables">4.2.1.1. Giant Tables</a></li>
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diff --git a/docs/v1.2.0/src/pytorch-sphinx-theme/docs/demo/long.html b/docs/v1.2.0/src/pytorch-sphinx-theme/docs/demo/long.html
new file mode 100644
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--- /dev/null
+++ b/docs/v1.2.0/src/pytorch-sphinx-theme/docs/demo/long.html
@@ -0,0 +1,921 @@
+
+
+
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
+<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
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+</head>
+
+<div class="container-fluid header-holder tutorials-header" id="header-holder">
+  <div class="container">
+    <div class="header-container">
+      <a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>
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+            <a href="https://pytorch.org/get-started">Get Started</a>
+          </li>
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+              </div>
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+          <li>
+            <a href="https://github.com/pytorch/pytorch">GitHub</a>
+          </li>
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+      <a class="main-menu-open-button" href="#" data-behavior="open-mobile-menu"></a>
+    </div>
+  </div>
+</div>
+
+<body class="pytorch-body">
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+
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+    <div class="table-of-contents-link-wrapper">
+      <span>Table of Contents</span>
+      <a href="#" class="toggle-table-of-contents" data-behavior="toggle-table-of-contents"></a>
+    </div>
+
+    <nav data-toggle="wy-nav-shift" class="pytorch-left-menu" id="pytorch-left-menu">
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+          <div class="pytorch-left-menu-search">
+
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+
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+                <div class="version">
+                  master (1.2.0)
+                </div>
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+  </form>
+</div>
+
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+          </div>
+
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+
+
+
+
+
+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/using_dla.html">DLA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/notebooks.html">Example notebooks</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
+<ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../py_api/ptq.html">torch_tensorrt.ptq</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../py_api/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../py_api/fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../contributors/system_overview.html">System Overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../contributors/writing_converters.html">Writing Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../indices/supported_ops.html">Operators Supported</a></li>
+</ul>
+
+
+
+        </div>
+      </div>
+    </nav>
+
+    <div class="pytorch-container">
+      <div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
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+        <a href="../../../../index.html">
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+            Docs
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+
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+      <li><span class="section-number">1. </span>Long Sticky Nav</li>
+
+
+      <li class="pytorch-breadcrumbs-aside">
+
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+            <a href="../../../../_sources/src/pytorch-sphinx-theme/docs/demo/long.rst.txt" rel="nofollow"><img src="../../../../_static/images/view-page-source-icon.svg"></a>
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+          Shortcuts
+        </div>
+      </div>
+
+      <section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
+        <div class="pytorch-content-left">
+
+
+
+          <div class="rst-content">
+
+            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+
+  <section id="long-sticky-nav">
+<h1><a class="toc-backref" href="#id9"><span class="section-number">1. </span>Long Sticky Nav</a><a class="headerlink" href="#long-sticky-nav" title="Permalink to this headline">¶</a></h1>
+<div class="contents topic" id="table-of-contents">
+<p class="topic-title">Table of Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#long-sticky-nav" id="id9">Long Sticky Nav</a></p>
+<ul>
+<li><p><a class="reference internal" href="#example-menu-1" id="id10">Example Menu 1</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-2" id="id11">Example Menu 2</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-3" id="id12">Example Menu 3</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-4" id="id13">Example Menu 4</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-5" id="id14">Example Menu 5</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-6" id="id15">Example Menu 6</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-7" id="id16">Example Menu 7</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-8" id="id17">Example Menu 8</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-9" id="id18">Example Menu 9</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-10" id="id19">Example Menu 10</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-11" id="id20">Example Menu 11</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-12" id="id21">Example Menu 12</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-13" id="id22">Example Menu 13</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-14" id="id23">Example Menu 14</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-15" id="id24">Example Menu 15</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-16" id="id25">Example Menu 16</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-17" id="id26">Example Menu 17</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-18" id="id27">Example Menu 18</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-19" id="id28">Example Menu 19</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-20" id="id29">Example Menu 20</a></p></li>
+<li><p><a class="reference internal" href="#example-submenu-1" id="id30">Example Submenu 1</a></p>
+<ul>
+<li><p><a class="reference internal" href="#submenu-1" id="id31">Submenu 1</a></p>
+<ul>
+<li><p><a class="reference internal" href="#subsubmenu-1" id="id32">Subsubmenu 1</a></p></li>
+<li><p><a class="reference internal" href="#subsubmenu-2" id="id33">Subsubmenu 2</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#submenu-2" id="id34">Submenu 2</a></p>
+<ul>
+<li><p><a class="reference internal" href="#id1" id="id35">Subsubmenu 1</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#submenu-3" id="id36">Submenu 3</a></p></li>
+<li><p><a class="reference internal" href="#submenu-4" id="id37">Submenu 4</a></p></li>
+<li><p><a class="reference internal" href="#submenu-5" id="id38">Submenu 5</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#example-submenu-2" id="id39">Example Submenu 2</a></p>
+<ul>
+<li><p><a class="reference internal" href="#id2" id="id40">Submenu 1</a></p>
+<ul>
+<li><p><a class="reference internal" href="#id3" id="id41">Subsubmenu 1</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#id4" id="id42">Submenu 2</a></p>
+<ul>
+<li><p><a class="reference internal" href="#id5" id="id43">Subsubmenu 1</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#id6" id="id44">Submenu 3</a></p></li>
+<li><p><a class="reference internal" href="#id7" id="id45">Submenu 4</a></p></li>
+<li><p><a class="reference internal" href="#id8" id="id46">Submenu 5</a></p></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+<p>This section demonstrates how the ‘sticky_navigation’ setting behaves when the menu is very long.
+When this section is selected, it will make the menu and the main area scroll when you are at the top of the page.</p>
+<section id="example-menu-1">
+<h2><a class="toc-backref" href="#id10"><span class="section-number">1.1. </span>Example Menu 1</a><a class="headerlink" href="#example-menu-1" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-2">
+<h2><a class="toc-backref" href="#id11"><span class="section-number">1.2. </span>Example Menu 2</a><a class="headerlink" href="#example-menu-2" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-3">
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+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<section id="example-menu-10">
+<h2><a class="toc-backref" href="#id19"><span class="section-number">1.10. </span>Example Menu 10</a><a class="headerlink" href="#example-menu-10" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
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+<h2><a class="toc-backref" href="#id20"><span class="section-number">1.11. </span>Example Menu 11</a><a class="headerlink" href="#example-menu-11" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
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+<h2><a class="toc-backref" href="#id21"><span class="section-number">1.12. </span>Example Menu 12</a><a class="headerlink" href="#example-menu-12" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<h2><a class="toc-backref" href="#id23"><span class="section-number">1.14. </span>Example Menu 14</a><a class="headerlink" href="#example-menu-14" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<h2><a class="toc-backref" href="#id25"><span class="section-number">1.16. </span>Example Menu 16</a><a class="headerlink" href="#example-menu-16" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
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+<h2><a class="toc-backref" href="#id26"><span class="section-number">1.17. </span>Example Menu 17</a><a class="headerlink" href="#example-menu-17" title="Permalink to this headline">¶</a></h2>
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+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<section id="example-submenu-1">
+<h2><a class="toc-backref" href="#id30"><span class="section-number">1.21. </span>Example Submenu 1</a><a class="headerlink" href="#example-submenu-1" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+<section id="submenu-1">
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+<p>Just a place holder…</p>
+<section id="subsubmenu-1">
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+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<h4><a class="toc-backref" href="#id35"><span class="section-number">1.21.2.1. </span>Subsubmenu 1</a><a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h4>
+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<section id="submenu-4">
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+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<section id="example-submenu-2">
+<h2><a class="toc-backref" href="#id39"><span class="section-number">1.22. </span>Example Submenu 2</a><a class="headerlink" href="#example-submenu-2" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
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+<h3><a class="toc-backref" href="#id40"><span class="section-number">1.22.1. </span>Submenu 1</a><a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
+<p>Just a place holder…</p>
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+<h4><a class="toc-backref" href="#id41"><span class="section-number">1.22.1.1. </span>Subsubmenu 1</a><a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h4>
+<p>Just a place holder…</p>
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+<h3><a class="toc-backref" href="#id42"><span class="section-number">1.22.2. </span>Submenu 2</a><a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
+<p>Just a place holder…</p>
+<section id="id5">
+<h4><a class="toc-backref" href="#id43"><span class="section-number">1.22.2.1. </span>Subsubmenu 1</a><a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h4>
+<p>Just a place holder…</p>
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+<section id="id6">
+<h3><a class="toc-backref" href="#id44"><span class="section-number">1.22.3. </span>Submenu 3</a><a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h3>
+<p>Just a place holder…</p>
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+<section id="id7">
+<h3><a class="toc-backref" href="#id45"><span class="section-number">1.22.4. </span>Submenu 4</a><a class="headerlink" href="#id7" title="Permalink to this headline">¶</a></h3>
+<p>Just a place holder…</p>
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+<section id="id8">
+<h3><a class="toc-backref" href="#id46"><span class="section-number">1.22.5. </span>Submenu 5</a><a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h3>
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diff --git a/docs/v1.2.0/src/pytorch-sphinx-theme/docs/demo/structure.html b/docs/v1.2.0/src/pytorch-sphinx-theme/docs/demo/structure.html
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+  <section id="structural-elements">
+<h1><a class="toc-backref" href="#id4"><span class="section-number">1. </span>Structural Elements</a><a class="headerlink" href="#structural-elements" title="Permalink to this headline">¶</a></h1>
+<div class="contents topic" id="table-of-contents">
+<p class="topic-title">Table of Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#structural-elements" id="id4">Structural Elements</a></p>
+<ul>
+<li><p><a class="reference internal" href="#document-section" id="id5">Document Section</a></p>
+<ul>
+<li><p><a class="reference internal" href="#document-subsection" id="id6">Document Subsection</a></p>
+<ul>
+<li><p><a class="reference internal" href="#document-subsubsection" id="id7">Document Subsubsection</a></p>
+<ul>
+<li><p><a class="reference internal" href="#document-paragraph" id="id8">Document Paragraph</a></p></li>
+</ul>
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+</li>
+<li><p><a class="reference internal" href="#structural-elements-2" id="id9">Structural Elements 2</a></p>
+<ul>
+<li><p><a class="reference internal" href="#id1" id="id10">Document Section</a></p>
+<ul>
+<li><p><a class="reference internal" href="#id2" id="id11">Document Subsection</a></p></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+<p>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Donec lorem neque, interdum in ipsum nec,
+finibus dictum velit. Ut eu efficitur arcu, id aliquam erat. In sit amet diam gravida, imperdiet tellus eu,
+gravida nisl. Praesent aliquet odio eget libero elementum, quis rhoncus tellus tincidunt.
+Suspendisse quis volutpat ipsum. Sed lobortis scelerisque tristique. Aenean condimentum risus tellus,
+quis accumsan ipsum laoreet ut. Integer porttitor maximus suscipit. Mauris in posuere sapien.
+Aliquam accumsan feugiat ligula, nec fringilla libero commodo sed. Proin et erat pharetra.</p>
+<hr class="docutils" />
+<p>Etiam turpis ante, luctus sed velit tristique, finibus volutpat dui. Nam sagittis vel ante nec malesuada.
+Praesent dignissim mi nec ornare elementum. Nunc eu augue vel sem dignissim cursus sed et nulla.
+Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas.
+Pellentesque dictum dui sem, non placerat tortor rhoncus in. Sed placerat nulla at rhoncus iaculis.</p>
+<section id="document-section">
+<h2><a class="toc-backref" href="#id5"><span class="section-number">1.1. </span>Document Section</a><a class="headerlink" href="#document-section" title="Permalink to this headline">¶</a></h2>
+<p>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed condimentum nulla vel neque venenatis,
+nec placerat lorem placerat. Cras purus eros, gravida vitae tincidunt id, vehicula nec nulla.
+Fusce aliquet auctor cursus. Phasellus ex neque, vestibulum non est vitae, viverra fringilla tortor.
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+luctus efficitur arcu. Cras ut dictum mi. Nulla congue interdum lorem, semper semper enim commodo nec.</p>
+<section id="document-subsection">
+<h3><a class="toc-backref" href="#id6"><span class="section-number">1.1.1. </span>Document Subsection</a><a class="headerlink" href="#document-subsection" title="Permalink to this headline">¶</a></h3>
+<p>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam efficitur in eros et blandit. Nunc maximus,
+nisl at auctor vestibulum, justo ex sollicitudin ligula, id faucibus urna orci tristique nisl.
+Duis auctor rutrum orci, in ornare lacus condimentum quis. Quisque arcu velit, facilisis quis interdum ac,
+hendrerit auctor mauris. Curabitur urna nibh, porttitor at ante sit amet, vestibulum interdum dolor.
+Duis dictum elit orci, tincidunt imperdiet sem pellentesque et. In vehicula pellentesque varius.
+Phasellus a turpis sollicitudin, bibendum massa et, imperdiet neque. Integer quis sapien in magna rutrum bibendum.
+Integer cursus ex sed magna vehicula finibus. Proin tempus orci quis dolor tempus, nec condimentum odio vestibulum.
+Etiam efficitur sollicitudin libero, tincidunt volutpat ligula interdum sed.</p>
+<section id="document-subsubsection">
+<h4><a class="toc-backref" href="#id7"><span class="section-number">1.1.1.1. </span>Document Subsubsection</a><a class="headerlink" href="#document-subsubsection" title="Permalink to this headline">¶</a></h4>
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+Integer at leo vitae felis pretium aliquam fringilla quis odio. Sed pharetra enim accumsan feugiat pretium.
+Maecenas at pharetra tortor. Morbi semper eget mi vel finibus. Cras rutrum nulla eros, id feugiat arcu pellentesque ut.
+Sed finibus tortor ac nisi ultrices viverra. Duis feugiat malesuada sapien, at commodo ante porttitor ac.
+Curabitur posuere mauris mi, vel ornare orci scelerisque sit amet. Suspendisse nec fringilla dui.</p>
+<section id="document-paragraph">
+<h5><a class="toc-backref" href="#id8"><span class="section-number">1.1.1.1.1. </span>Document Paragraph</a><a class="headerlink" href="#document-paragraph" title="Permalink to this headline">¶</a></h5>
+<p>Pellentesque nec est in odio ultrices elementum. Vestibulum et hendrerit sapien, quis vulputate turpis.
+Suspendisse potenti. Curabitur tristique sit amet lectus non viverra. Phasellus rutrum dapibus turpis sed imperdiet.
+Mauris maximus viverra ante. Donec eu egestas mauris. Morbi vulputate tincidunt euismod. Integer vel porttitor neque.
+Donec at lacus suscipit, lacinia lectus vel, sagittis lectus.</p>
+</section>
+</section>
+</section>
+</section>
+</section>
+<section id="structural-elements-2">
+<h1><a class="toc-backref" href="#id9"><span class="section-number">2. </span>Structural Elements 2</a><a class="headerlink" href="#structural-elements-2" title="Permalink to this headline">¶</a></h1>
+<p>Etiam turpis ante, luctus sed velit tristique, finibus volutpat dui. Nam sagittis vel ante nec malesuada.
+Praesent dignissim mi nec ornare elementum. Nunc eu augue vel sem dignissim cursus sed et nulla.
+Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas.
+Pellentesque dictum dui sem, non placerat tortor rhoncus in. Sed placerat nulla at rhoncus iaculis.</p>
+<section id="id1">
+<h2><a class="toc-backref" href="#id10"><span class="section-number">2.1. </span>Document Section</a><a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+<p>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed condimentum nulla vel neque venenatis,
+nec placerat lorem placerat. Cras purus eros, gravida vitae tincidunt id, vehicula nec nulla.
+Fusce aliquet auctor cursus. Phasellus ex neque, vestibulum non est vitae, viverra fringilla tortor.
+Donec vestibulum convallis justo, a faucibus lorem vulputate vel. Aliquam cursus odio eu felis sodales aliquet.
+Aliquam erat volutpat. Maecenas eget dictum mauris. Suspendisse arcu eros, condimentum eget risus sed,
+luctus efficitur arcu. Cras ut dictum mi. Nulla congue interdum lorem, semper semper enim commodo nec.</p>
+<section id="id2">
+<h3><a class="toc-backref" href="#id11"><span class="section-number">2.1.1. </span>Document Subsection</a><a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
+<figure class="align-right" id="id3" style="width: 200px">
+<img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" />
+<figcaption>
+<p><span class="caption-text">This is a caption for a figure. Text should wrap around the caption.</span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
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+<p>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam efficitur in eros et blandit. Nunc maximus,
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diff --git a/docs/v1.2.0/src/pytorch-sphinx-theme/docs/installing.html b/docs/v1.2.0/src/pytorch-sphinx-theme/docs/installing.html
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@@ -0,0 +1,669 @@
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+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
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+  <section id="creating-a-torchscript-module">
+<span id="creating-a-ts-mod"></span><h1>Creating a TorchScript Module<a class="headerlink" href="#creating-a-torchscript-module" title="Permalink to this headline">¶</a></h1>
+<p>TorchScript is a way to create serializable and optimizable models from PyTorch code.
+PyTorch has detailed documentation on how to do this <a class="reference external" href="https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html">https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html</a> but briefly here is the
+here is key background information and the process:</p>
+<p>PyTorch programs are based around <code class="docutils literal notranslate"><span class="pre">Module</span></code> s which can be used to compose higher level modules. <code class="docutils literal notranslate"><span class="pre">Modules</span></code> contain a constructor to set up the modules, parameters and sub-modules
+and a forward function which describes how to use the parameters and submodules when the module is invoked.</p>
+<p>For example, we can define a LeNet module like this:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="linenos"> 2</span><span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
+<span class="linenos"> 3</span>
+<span class="linenos"> 4</span>
+<span class="linenos"> 5</span><span class="k">class</span> <span class="nc">LeNetFeatExtractor</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="linenos"> 6</span>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="linenos"> 7</span>        <span class="nb">super</span><span class="p">(</span><span class="n">LeNetFeatExtractor</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+<span class="linenos"> 8</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="linenos"> 9</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="linenos">10</span>
+<span class="linenos">11</span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="linenos">12</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+<span class="linenos">13</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="mi">2</span><span class="p">)</span>
+<span class="linenos">14</span>        <span class="k">return</span> <span class="n">x</span>
+<span class="linenos">15</span>
+<span class="linenos">16</span>
+<span class="linenos">17</span><span class="k">class</span> <span class="nc">LeNetClassifier</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="linenos">18</span>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="linenos">19</span>        <span class="nb">super</span><span class="p">(</span><span class="n">LeNetClassifier</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+<span class="linenos">20</span>        <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">16</span> <span class="o">*</span> <span class="mi">6</span> <span class="o">*</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">120</span><span class="p">)</span>
+<span class="linenos">21</span>        <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span> <span class="mi">84</span><span class="p">)</span>
+<span class="linenos">22</span>        <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">84</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+<span class="linenos">23</span>
+<span class="linenos">24</span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="linenos">25</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="linenos">26</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+<span class="linenos">27</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+<span class="linenos">28</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">29</span>        <span class="k">return</span> <span class="n">x</span>
+<span class="linenos">30</span>
+<span class="linenos">31</span>
+<span class="linenos">32</span><span class="k">class</span> <span class="nc">LeNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="linenos">33</span>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="linenos">34</span>        <span class="nb">super</span><span class="p">(</span><span class="n">LeNet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+<span class="linenos">35</span>        <span class="bp">self</span><span class="o">.</span><span class="n">feat</span> <span class="o">=</span> <span class="n">LeNetFeatExtractor</span><span class="p">()</span>
+<span class="linenos">36</span>        <span class="bp">self</span><span class="o">.</span><span class="n">classifer</span> <span class="o">=</span> <span class="n">LeNetClassifier</span><span class="p">()</span>
+<span class="linenos">37</span>
+<span class="linenos">38</span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="linenos">39</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feat</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">40</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">41</span>        <span class="k">return</span> <span class="n">x</span>
+</pre></div>
+</div>
+<p>.</p>
+<blockquote>
+<div><p>Obviously you may want to consolidate such a simple model into a single module but we can see the composability of PyTorch here</p>
+</div></blockquote>
+<p>From here are two pathways for going from PyTorch Python code to TorchScript code: Tracing and Scripting.</p>
+<p>Tracing follows the path of execution when the module is called and records what happens.
+To trace an instance of our LeNet module, we can call <code class="docutils literal notranslate"><span class="pre">torch.jit.trace</span></code> with an example input.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">()</span>
+<span class="n">input_data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">])</span>
+<span class="n">traced_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">input_data</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Scripting actually inspects your code with a compiler and generates an equivalent TorchScript program. The difference is that since tracing
+is following the execution of your module, it cannot pick up control flow for instance. By working from the Python code, the compiler can
+include these components. We can run the script compiler on our LeNet module by calling <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">()</span>
+<span class="n">script_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>There are reasons to use one path or another, the PyTorch documentation has information on how to choose. From a Torch-TensorRT prespective, there is
+better support (i.e your module is more likely to compile) for traced modules because it doesn’t include all the complexities of a complete
+programming language, though both paths supported.</p>
+<p>After scripting or tracing your module, you are given back a TorchScript Module. This contains the code and parameters used to run the module stored
+in a intermediate representation that Torch-TensorRT can consume.</p>
+<p>Here is what the LeNet traced module IR looks like:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%self.1 : __torch__.___torch_mangle_10.LeNet,
+    %input.1 : Float(1, 1, 32, 32)):
+    %129 : __torch__.___torch_mangle_9.LeNetClassifier = prim::GetAttr[name=&quot;classifer&quot;](%self.1)
+    %119 : __torch__.___torch_mangle_5.LeNetFeatExtractor = prim::GetAttr[name=&quot;feat&quot;](%self.1)
+    %137 : Tensor = prim::CallMethod[name=&quot;forward&quot;](%119, %input.1)
+    %138 : Tensor = prim::CallMethod[name=&quot;forward&quot;](%129, %137)
+    return (%138)
+</pre></div>
+</div>
+<p>and the LeNet scripted module IR:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%self : __torch__.LeNet,
+    %x.1 : Tensor):
+    %2 : __torch__.LeNetFeatExtractor = prim::GetAttr[name=&quot;feat&quot;](%self)
+    %x.3 : Tensor = prim::CallMethod[name=&quot;forward&quot;](%2, %x.1) # x.py:38:12
+    %5 : __torch__.LeNetClassifier = prim::GetAttr[name=&quot;classifer&quot;](%self)
+    %x.5 : Tensor = prim::CallMethod[name=&quot;forward&quot;](%5, %x.3) # x.py:39:12
+    return (%x.5)
+</pre></div>
+</div>
+<p>You can see that the IR preserves the module structure we have in our python code.</p>
+</section>
+<section id="working-with-torchscript-in-python">
+<span id="ts-in-py"></span><h1>Working with TorchScript in Python<a class="headerlink" href="#working-with-torchscript-in-python" title="Permalink to this headline">¶</a></h1>
+<p>TorchScript Modules are run the same way you run normal PyTorch modules. You can run the forward pass using the
+<code class="docutils literal notranslate"><span class="pre">forward</span></code> method or just calling the module <code class="docutils literal notranslate"><span class="pre">torch_scirpt_module(in_tensor)</span></code> The JIT compiler will compile
+and optimize the module on the fly and then returns the results.</p>
+</section>
+<section id="saving-torchscript-module-to-disk">
+<h1>Saving TorchScript Module to Disk<a class="headerlink" href="#saving-torchscript-module-to-disk" title="Permalink to this headline">¶</a></h1>
+<p>For either traced or scripted modules, you can save the module to disk with the following command</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">()</span>
+<span class="n">script_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+<span class="n">script_model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;lenet_scripted.ts&quot;</span><span class="p">)</span>
+</pre></div>
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diff --git a/docs/v1.2.0/tutorials/getting_started_with_cpp_api.html b/docs/v1.2.0/tutorials/getting_started_with_cpp_api.html
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+         <ul class="md-nav__list">
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+            Installation
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+            Getting Started with C++
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+             <li class="md-nav__item">
+              <a class="md-nav__link" href="#tutorials-getting-started-with-cpp-api--page-root">
+               Getting Started with C++
+              </a>
+              <nav class="md-nav">
+               <ul class="md-nav__list">
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#using-torch-tensorrt-in-c">
+                  Using Torch-TensorRT in C++
+                 </a>
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+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc">
+                     [Torch-TensorRT Quickstart] Compiling TorchScript Modules with
+                     <code class="docutils literal notranslate">
+                      <span class="pre">
+                       torchtrtc
+                      </span>
+                     </code>
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#working-with-torchscript-in-c">
+                     Working with TorchScript in C++
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#compiling-with-torch-tensorrt-in-c">
+                     Compiling with Torch-TensorRT in C++
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#under-the-hood">
+                     Under The Hood
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#working-with-unsupported-operators">
+                     Working with Unsupported Operators
+                    </a>
+                    <nav class="md-nav">
+                     <ul class="md-nav__list">
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#registering-custom-converters">
+                        Registering Custom Converters
+                       </a>
+                      </li>
+                     </ul>
+                    </nav>
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+            </ul>
+           </nav>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="getting_started_with_python_api.html">
+            Using Torch-TensorRT in Python
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="creating_torchscript_module_in_python.html">
+            Creating a TorchScript Module
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="creating_torchscript_module_in_python.html#working-with-torchscript-in-python">
+            Working with TorchScript in Python
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">
+            Saving TorchScript Module to Disk
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="ptq.html">
+            Post Training Quantization (PTQ)
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="torchtrtc.html">
+            torchtrtc
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="use_from_pytorch.html">
+            Using Torch-TensorRT Directly From PyTorch
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="runtime.html">
+            Deploying Torch-TensorRT Programs
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="using_dla.html">
+            DLA
+           </a>
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+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             Notebooks
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/CitriNet-example.html">
+            Torch-TensorRT Getting Started - CitriNet
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/dynamic-shapes.html">
+            Torch-TensorRT - Using Dynamic Shapes
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/EfficientNet-example.html">
+            Torch-TensorRT Getting Started - EfficientNet-B0
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/Hugging-Face-BERT.html">
+            Masked Language Modeling (MLM) with Hugging Face BERT Transformer
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/lenet-getting-started.html">
+            Torch-TensorRT Getting Started - LeNet
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/Resnet50-example.html">
+            Torch-TensorRT Getting Started - ResNet 50
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/ssd-object-detection-demo.html">
+            Object Detection with Torch-TensorRT (SSD)
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/vgg-qat.html">
+            Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             Python API Documenation
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../py_api/torch_tensorrt.html">
+            torch_tensorrt
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../py_api/logging.html">
+            torch_tensorrt.logging
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../py_api/ptq.html">
+            torch_tensorrt.ptq
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../py_api/ts.html">
+            torch_tensorrt.ts
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             C++ API Documenation
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/torch_tensort_cpp.html">
+            Torch-TensorRT C++ API
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt.html">
+            Namespace torch_tensorrt
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__logging.html">
+            Namespace torch_tensorrt::logging
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">
+            Namespace torch_tensorrt::torchscript
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">
+            Namespace torch_tensorrt::ptq
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             Contributor Documentation
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../contributors/system_overview.html">
+            System Overview
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../contributors/writing_converters.html">
+            Writing Converters
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../contributors/useful_links.html">
+            Useful Links for Torch-TensorRT Development
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             Indices
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../indices/supported_ops.html">
+            Operators Supported
+           </a>
+          </li>
+         </ul>
+        </nav>
+       </div>
+      </div>
+     </div>
+     <div class="md-sidebar md-sidebar--secondary" data-md-component="toc">
+      <div class="md-sidebar__scrollwrap">
+       <div class="md-sidebar__inner">
+        <nav class="md-nav md-nav--secondary">
+         <label class="md-nav__title" for="__toc">
+          Contents
+         </label>
+         <ul class="md-nav__list" data-md-scrollfix="">
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="#tutorials-getting-started-with-cpp-api--page-root">
+            Getting Started with C++
+           </a>
+           <nav class="md-nav">
+            <ul class="md-nav__list">
+             <li class="md-nav__item">
+              <a class="md-nav__link" href="#using-torch-tensorrt-in-c">
+               Using Torch-TensorRT in C++
+              </a>
+              <nav class="md-nav">
+               <ul class="md-nav__list">
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc">
+                  [Torch-TensorRT Quickstart] Compiling TorchScript Modules with
+                  <code class="docutils literal notranslate">
+                   <span class="pre">
+                    torchtrtc
+                   </span>
+                  </code>
+                 </a>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#working-with-torchscript-in-c">
+                  Working with TorchScript in C++
+                 </a>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#compiling-with-torch-tensorrt-in-c">
+                  Compiling with Torch-TensorRT in C++
+                 </a>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#under-the-hood">
+                  Under The Hood
+                 </a>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#working-with-unsupported-operators">
+                  Working with Unsupported Operators
+                 </a>
+                 <nav class="md-nav">
+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#registering-custom-converters">
+                     Registering Custom Converters
+                    </a>
+                   </li>
+                  </ul>
+                 </nav>
+                </li>
+               </ul>
+              </nav>
+             </li>
+            </ul>
+           </nav>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__extra_link" href="../_sources/tutorials/getting_started_with_cpp_api.rst.txt">
+            Show Source
+           </a>
+          </li>
+          <li class="md-nav__item" id="searchbox">
+          </li>
+         </ul>
+        </nav>
+       </div>
+      </div>
+     </div>
+     <div class="md-content">
+      <article class="md-content__inner md-typeset" role="main">
+       <span id="getting-started">
+       </span>
+       <h1 id="tutorials-getting-started-with-cpp-api--page-root">
+        Getting Started with C++
+        <a class="headerlink" href="#tutorials-getting-started-with-cpp-api--page-root" title="Permalink to this headline">
+         ¶
+        </a>
+       </h1>
+       <p>
+        If you haven’t already, acquire a tarball of the library by following the instructions in
+        <a class="reference internal" href="installation.html#installation">
+         <span class="std std-ref">
+          Installation
+         </span>
+        </a>
+       </p>
+       <h2 id="using-torch-tensorrt-in-c">
+        Using Torch-TensorRT in C++
+        <a class="headerlink" href="#using-torch-tensorrt-in-c" title="Permalink to this headline">
+         ¶
+        </a>
+       </h2>
+       <p>
+        Torch-TensorRT C++ API accepts TorchScript modules (generated either from
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch.jit.script
+         </span>
+        </code>
+        or
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch.jit.trace
+         </span>
+        </code>
+        ) as an input and returns
+a Torchscript module (optimized using TensorRT). This requires users to use Pytorch (in python) to generate torchscript modules beforehand.
+Please refer to
+        <a class="reference external" href="https://nvidia.github.io/Torch-TensorRT/tutorials/creating_torchscript_module_in_python.html">
+         Creating TorchScript modules in Python
+        </a>
+        section to generate torchscript graphs.
+       </p>
+       <span id="torch-tensorrt-quickstart">
+       </span>
+       <h3 id="torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc">
+        [Torch-TensorRT Quickstart] Compiling TorchScript Modules with
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torchtrtc
+         </span>
+        </code>
+        <a class="headerlink" href="#torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        An easy way to get started with Torch-TensorRT and to check if your model can be supported without extra work is to run it through
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torchtrtc
+         </span>
+        </code>
+        , which supports almost all features of the compiler from the command line including post training quantization
+(given a previously created calibration cache). For example we can compile our lenet model by setting our preferred operating
+precision and input size. This new TorchScript file can be loaded into Python (note: you need to
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          import
+         </span>
+         <span class="pre">
+          torch_tensorrt
+         </span>
+        </code>
+        before loading
+these compiled modules because the compiler extends the PyTorch the deserializer and runtime to execute compiled modules).
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>❯ torchtrtc -p f16 lenet_scripted.ts trt_lenet_scripted.ts <span class="s2">"(1,1,32,32)"</span>
+
+❯ python3
+Python <span class="m">3</span>.6.9 <span class="o">(</span>default, Apr <span class="m">18</span> <span class="m">2020</span>, <span class="m">01</span>:56:04<span class="o">)</span>
+<span class="o">[</span>GCC <span class="m">8</span>.4.0<span class="o">]</span> on linux
+Type <span class="s2">"help"</span>, <span class="s2">"copyright"</span>, <span class="s2">"credits"</span> or <span class="s2">"license"</span> <span class="k">for</span> more information.
+&gt;&gt;&gt; import torch
+&gt;&gt;&gt; import torch_tensorrt
+&gt;&gt;&gt; <span class="nv">ts_model</span> <span class="o">=</span> torch.jit.load<span class="o">(</span>“trt_lenet_scripted.ts”<span class="o">)</span>
+&gt;&gt;&gt; ts_model<span class="o">(</span>torch.randn<span class="o">((</span><span class="m">1</span>,1,32,32<span class="o">))</span>.to<span class="o">(</span>“cuda”<span class="o">)</span>.half<span class="o">())</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        You can learn more about
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torchtrtc
+         </span>
+        </code>
+        usage here:
+        <a class="reference internal" href="torchtrtc.html#torchtrtc">
+         <span class="std std-ref">
+          torchtrtc
+         </span>
+        </a>
+       </p>
+       <span id="ts-in-cc">
+       </span>
+       <h3 id="working-with-torchscript-in-c">
+        Working with TorchScript in C++
+        <a class="headerlink" href="#working-with-torchscript-in-c" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        If we are developing an application to deploy with C++, we can save either our traced or scripted module using
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch.jit.save
+         </span>
+        </code>
+        which will serialize the TorchScript code, weights and other information into a package. This is also where our dependency on Python ends.
+       </p>
+       <div class="highlight-python notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="n">torch_script_module</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"lenet.jit.pt"</span><span class="p">)</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        From here we can now load our TorchScript module in C++
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="cp">#include</span> <span class="cpf">&lt;torch/script.h&gt; // One-stop header.</span><span class="cp"></span>
+
+<span class="cp">#include</span> <span class="cpf">&lt;iostream&gt;</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">&lt;memory&gt;</span><span class="cp"></span>
+
+<span class="kt">int</span> <span class="nf">main</span><span class="p">(</span><span class="kt">int</span> <span class="n">argc</span><span class="p">,</span> <span class="k">const</span> <span class="kt">char</span><span class="o">*</span> <span class="n">argv</span><span class="p">[])</span> <span class="p">{</span>
+    <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span> <span class="k">module</span><span class="p">;</span>
+    <span class="k">try</span> <span class="p">{</span>
+        <span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
+        <span class="k">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="s">"&lt;PATH TO SAVED TS MOD&gt;"</span><span class="p">);</span>
+    <span class="p">}</span>
+    <span class="k">catch</span> <span class="p">(</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&amp;</span> <span class="n">e</span><span class="p">)</span> <span class="p">{</span>
+        <span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o">&lt;&lt;</span> <span class="s">"error loading the model</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span>
+        <span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="p">;</span>
+    <span class="p">}</span>
+
+    <span class="n">std</span><span class="o">::</span><span class="n">cout</span> <span class="o">&lt;&lt;</span> <span class="s">"ok</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        You can do full training and inference in C++ with PyTorch / LibTorch if you would like, you can even define your modules in C++ and
+have access to the same powerful tensor library that backs PyTorch. (For more information:
+        <a class="reference external" href="https://pytorch.org/cppdocs/">
+         https://pytorch.org/cppdocs/
+        </a>
+        ).
+For instance we can do inference with our LeNet module like this:
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">in</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">});</span>
+<span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        and to run on the GPU:
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+<span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">in</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+<span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        As you can see it is pretty similar to the Python API. When you call the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          forward
+         </span>
+        </code>
+        method, you invoke the PyTorch JIT compiler, which will optimize and run your TorchScript code.
+       </p>
+       <span id="compile-cpp">
+       </span>
+       <h3 id="compiling-with-torch-tensorrt-in-c">
+        Compiling with Torch-TensorRT in C++
+        <a class="headerlink" href="#compiling-with-torch-tensorrt-in-c" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        We are also at the point were we can compile and optimize our module with Torch-TensorRT, but instead of in a JIT fashion we must do it ahead-of-time (AOT) i.e. before we start doing actual inference work
+since it takes a bit of time to optimize the module, it would not make sense to do this every time you run the module or even the first time you run it.
+       </p>
+       <p>
+        With our module loaded, we can feed it into the Torch-TensorRT compiler. When we do so we must provide some information on the expected input size and also configure any additional settings.
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
+<span class="p">...</span>
+
+    <span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+    <span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+
+    <span class="k">auto</span> <span class="n">in</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">});</span>
+    <span class="k">auto</span> <span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">{{</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()}});</span>
+    <span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">trt_mod</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">in</span><span class="p">});</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        Thats it! Now the graph runs primarily not with the JIT compiler but using TensorRT (though we execute the graph using the JIT runtime).
+       </p>
+       <p>
+        We can also set settings like operating precision to run in FP16.
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
+<span class="p">...</span>
+
+    <span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+    <span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+
+    <span class="k">auto</span> <span class="n">in</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">}).</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
+    <span class="k">auto</span> <span class="n">input_sizes</span> <span class="o">=</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">({</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()});</span>
+    <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span> <span class="n">info</span><span class="p">(</span><span class="n">input_sizes</span><span class="p">);</span>
+    <span class="n">info</span><span class="p">.</span><span class="n">enable_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
+    <span class="k">auto</span> <span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">info</span><span class="p">);</span>
+    <span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">trt_mod</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">in</span><span class="p">});</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        And now we are running the module in FP16 precision. You can then save the module to load later.
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="n">trt_mod</span><span class="p">.</span><span class="n">save</span><span class="p">(</span><span class="s">"&lt;PATH TO SAVED TRT/TS MOD&gt;"</span><span class="p">)</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        Torch-TensorRT compiled TorchScript modules are loaded in the same way as normal TorchScript module. Make sure your deployment application is linked against
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          libtorchtrt.so
+         </span>
+        </code>
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
+
+<span class="kt">int</span> <span class="nf">main</span><span class="p">(</span><span class="kt">int</span> <span class="n">argc</span><span class="p">,</span> <span class="k">const</span> <span class="kt">char</span><span class="o">*</span> <span class="n">argv</span><span class="p">[])</span> <span class="p">{</span>
+    <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span> <span class="k">module</span><span class="p">;</span>
+    <span class="k">try</span> <span class="p">{</span>
+        <span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
+        <span class="k">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="s">"&lt;PATH TO SAVED TRT/TS MOD&gt;"</span><span class="p">);</span>
+    <span class="p">}</span>
+    <span class="k">catch</span> <span class="p">(</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&amp;</span> <span class="n">e</span><span class="p">)</span> <span class="p">{</span>
+        <span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o">&lt;&lt;</span> <span class="s">"error loading the model</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span>
+        <span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="p">;</span>
+    <span class="p">}</span>
+
+    <span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">in</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+    <span class="k">auto</span> <span class="n">out</span> <span class="o">=</span> <span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span>
+
+    <span class="n">std</span><span class="o">::</span><span class="n">cout</span> <span class="o">&lt;&lt;</span> <span class="s">"ok</span><span class="se">\n</span><span class="s">"</span><span class="p">;</span>
+<span class="p">}</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        If you want to save the engine produced by Torch-TensorRT to use in a TensorRT application you can use the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          ConvertGraphToTRTEngine
+         </span>
+        </code>
+        API.
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
+<span class="p">...</span>
+
+    <span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+    <span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+
+    <span class="k">auto</span> <span class="n">in</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">},</span> <span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">}).</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
+    <span class="k">auto</span> <span class="n">input_sizes</span> <span class="o">=</span> <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">({</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()});</span>
+    <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span> <span class="n">info</span><span class="p">(</span><span class="n">input_sizes</span><span class="p">);</span>
+    <span class="n">info</span><span class="p">.</span><span class="n">enabled_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
+    <span class="k">auto</span> <span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">ConvertGraphToTRTEngine</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="s">"forward"</span><span class="p">,</span> <span class="n">info</span><span class="p">);</span>
+    <span class="n">std</span><span class="o">::</span><span class="n">ofstream</span> <span class="n">out</span><span class="p">(</span><span class="s">"/tmp/engine_converted_from_jit.trt"</span><span class="p">);</span>
+    <span class="n">out</span> <span class="o">&lt;&lt;</span> <span class="n">engine</span><span class="p">;</span>
+    <span class="n">out</span><span class="p">.</span><span class="n">close</span><span class="p">();</span>
+</pre>
+        </div>
+       </div>
+       <span id="id1">
+       </span>
+       <h3 id="under-the-hood">
+        Under The Hood
+        <a class="headerlink" href="#under-the-hood" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        When a module is provided to Torch-TensorRT, the compiler starts by mapping a graph like you saw above to a graph like this:
+       </p>
+       <div class="highlight-none notranslate">
+        <div class="highlight">
+         <pre><span></span>graph(%input.2 : Tensor):
+    %2 : Float(84, 10) = prim::Constant[value=&lt;Tensor&gt;]()
+    %3 : Float(120, 84) = prim::Constant[value=&lt;Tensor&gt;]()
+    %4 : Float(576, 120) = prim::Constant[value=&lt;Tensor&gt;]()
+    %5 : int = prim::Constant[value=-1]() # x.py:25:0
+    %6 : int[] = prim::Constant[value=annotate(List[int], [])]()
+    %7 : int[] = prim::Constant[value=[2, 2]]()
+    %8 : int[] = prim::Constant[value=[0, 0]]()
+    %9 : int[] = prim::Constant[value=[1, 1]]()
+    %10 : bool = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+    %11 : int = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+    %12 : bool = prim::Constant[value=0]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+    %self.classifer.fc3.bias : Float(10) = prim::Constant[value= 0.0464  0.0383  0.0678  0.0932  0.1045 -0.0805 -0.0435 -0.0818  0.0208 -0.0358 [ CUDAFloatType{10} ]]()
+    %self.classifer.fc2.bias : Float(84) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.classifer.fc1.bias : Float(120) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.feat.conv2.weight : Float(16, 6, 3, 3) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.feat.conv2.bias : Float(16) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.feat.conv1.weight : Float(6, 1, 3, 3) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.feat.conv1.bias : Float(6) = prim::Constant[value= 0.0530 -0.1691  0.2802  0.1502  0.1056 -0.1549 [ CUDAFloatType{6} ]]()
+    %input0.4 : Tensor = aten::_convolution(%input.2, %self.feat.conv1.weight, %self.feat.conv1.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+    %input0.5 : Tensor = aten::relu(%input0.4) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+    %input1.2 : Tensor = aten::max_pool2d(%input0.5, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+    %input0.6 : Tensor = aten::_convolution(%input1.2, %self.feat.conv2.weight, %self.feat.conv2.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+    %input2.1 : Tensor = aten::relu(%input0.6) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+    %x.1 : Tensor = aten::max_pool2d(%input2.1, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+    %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0
+    %27 : Tensor = aten::matmul(%input.1, %4)
+    %28 : Tensor = trt::const(%self.classifer.fc1.bias)
+    %29 : Tensor = aten::add_(%28, %27, %11)
+    %input0.2 : Tensor = aten::relu(%29) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+    %31 : Tensor = aten::matmul(%input0.2, %3)
+    %32 : Tensor = trt::const(%self.classifer.fc2.bias)
+    %33 : Tensor = aten::add_(%32, %31, %11)
+    %input1.1 : Tensor = aten::relu(%33) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+    %35 : Tensor = aten::matmul(%input1.1, %2)
+    %36 : Tensor = trt::const(%self.classifer.fc3.bias)
+    %37 : Tensor = aten::add_(%36, %35, %11)
+    return (%37)
+(CompileGraph)
+</pre>
+        </div>
+       </div>
+       <p>
+        The graph has now been transformed from a collection of modules, each managing their own parameters into a single graph with the parameters inlined
+into the graph and all of the operations laid out. Torch-TensorRT has also executed a number of optimizations and mappings to make the graph easier to translate to TensorRT.
+From here the compiler can assemble the TensorRT engine by following the dataflow through the graph.
+       </p>
+       <p>
+        When the graph construction phase is complete, Torch-TensorRT produces a serialized TensorRT engine. From here depending on the API, this engine is returned
+to the user or moves into the graph construction phase. Here Torch-TensorRT creates a JIT Module to execute the TensorRT engine which will be instantiated and managed
+by the Torch-TensorRT runtime.
+       </p>
+       <p>
+        Here is the graph that you get back after compilation is complete:
+       </p>
+       <div class="highlight-none notranslate">
+        <div class="highlight">
+         <pre><span></span>graph(%self_1 : __torch__.lenet, %input_0 : Tensor):
+    %1 : ...trt.Engine = prim::GetAttr[name="lenet"](%self_1)
+    %3 : Tensor[] = prim::ListConstruct(%input_0)
+    %4 : Tensor[] = trt::execute_engine(%3, %1)
+    %5 : Tensor = prim::ListUnpack(%4)
+    return (%5)
+</pre>
+        </div>
+       </div>
+       <p>
+        You can see the call where the engine is executed, after extracting the attribute containing the engine and constructing a list of inputs, then returns the tensors back to the user.
+       </p>
+       <span id="unsupported-ops">
+       </span>
+       <h3 id="working-with-unsupported-operators">
+        Working with Unsupported Operators
+        <a class="headerlink" href="#working-with-unsupported-operators" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        Torch-TensorRT is a new library and the PyTorch operator library is quite large, so there will be ops that aren’t supported natively by the compiler. You can either use the composition techinques
+shown above to make modules are fully Torch-TensorRT supported and ones that are not and stitch the modules together in the deployment application or you can register converters for missing ops.
+       </p>
+       <blockquote>
+        <div>
+         <p>
+          You can check support without going through the full compilation pipleine using the
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            torch_tensorrt::CheckMethodOperatorSupport(const
+           </span>
+           <span class="pre">
+            torch::jit::Module&amp;
+           </span>
+           <span class="pre">
+            module,
+           </span>
+           <span class="pre">
+            std::string
+           </span>
+           <span class="pre">
+            method_name)
+           </span>
+          </code>
+          api
+to see what operators are not supported.
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            torchtrtc
+           </span>
+          </code>
+          automatically checks modules with this method before starting compilation and will print out a list of operators that are not supported.
+         </p>
+        </div>
+       </blockquote>
+       <span id="custom-converters">
+       </span>
+       <h4 id="registering-custom-converters">
+        Registering Custom Converters
+        <a class="headerlink" href="#registering-custom-converters" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        Operations are mapped to TensorRT through the use of modular converters, a function that takes a node from a the JIT graph and produces an equivalent layer or subgraph in TensorRT.
+Torch-TensorRT ships with a library of these converters stored in a registry, that will be executed depending on the node being parsed. For instance a
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          aten::relu(%input0.4)
+         </span>
+        </code>
+        instruction will trigger
+the relu converter to be run on it, producing an activation layer in the TensorRT graph. But since this library is not exhaustive you may need to write your own to get Torch-TensorRT
+to support your module.
+       </p>
+       <p>
+        Shipped with the Torch-TensorRT distribution are the internal core API headers. You can therefore access the converter registry and add a converter for the op you need.
+       </p>
+       <p>
+        For example, if we try to compile a graph with a build of Torch-TensorRT that doesn’t support the flatten operation (
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          aten::flatten
+         </span>
+        </code>
+        ) you may see this error:
+       </p>
+       <div class="highlight-none notranslate">
+        <div class="highlight">
+         <pre><span></span>terminate called after throwing an instance of 'torch_tensorrt::Error'
+what():  [enforce fail at core/conversion/conversion.cpp:109] Expected converter to be true but got false
+Unable to convert node: %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0 (conversion.AddLayer)
+Schema: aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -&gt; (Tensor)
+Converter for aten::flatten requested, but no such converter was found.
+If you need a converter for this operator, you can try implementing one yourself
+or request a converter: https://www.github.com/NVIDIA/Torch-TensorRT/issues
+</pre>
+        </div>
+       </div>
+       <p>
+        We can register a converter for this operator in our application. All of the tools required to build a converter can be imported by including
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt/core/conversion/converters/converters.h
+         </span>
+        </code>
+        .
+We start by creating an instance of the self-registering class
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns()
+         </span>
+        </code>
+        which will register converters
+in the global converter registry, associating a function schema like
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          aten::flatten.using_ints(Tensor
+         </span>
+         <span class="pre">
+          self,
+         </span>
+         <span class="pre">
+          int
+         </span>
+         <span class="pre">
+          start_dim=0,
+         </span>
+         <span class="pre">
+          int
+         </span>
+         <span class="pre">
+          end_dim=-1)
+         </span>
+         <span class="pre">
+          -&gt;
+         </span>
+         <span class="pre">
+          (Tensor)
+         </span>
+        </code>
+        with a lambda that
+will take the state of the conversion, the node/operation in question to convert and all of the inputs to the node and produces as a side effect a new layer in the TensorRT network.
+Arguments are passed as a vector of inspectable unions of TensorRT
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          ITensors
+         </span>
+        </code>
+        and Torch
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          IValues
+         </span>
+        </code>
+        in the order arguments are listed in the schema.
+       </p>
+       <p>
+        Below is a implementation of a
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          aten::flatten
+         </span>
+        </code>
+        converter that we can use in our application. You have full access to the Torch and TensorRT libraries in the converter implementation. So
+for example we can quickly get the output size by just running the operation in PyTorch instead of implementing the full calculation outself like we do below for this flatten converter.
+       </p>
+       <div class="highlight-c++ notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="cp">#include</span> <span class="cpf">"torch/script.h"</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/torch_tensorrt.h"</span><span class="cp"></span>
+<span class="cp">#include</span> <span class="cpf">"torch_tensorrt/core/conversion/converters/converters.h"</span><span class="cp"></span>
+
+<span class="k">static</span> <span class="k">auto</span> <span class="n">flatten_converter</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">converters</span><span class="o">::</span><span class="n">RegisterNodeConversionPatterns</span><span class="p">()</span>
+    <span class="p">.</span><span class="n">pattern</span><span class="p">({</span>
+        <span class="s">"aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -&gt; (Tensor)"</span><span class="p">,</span>
+        <span class="p">[](</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">ConversionCtx</span><span class="o">*</span> <span class="n">ctx</span><span class="p">,</span>
+           <span class="k">const</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Node</span><span class="o">*</span> <span class="n">n</span><span class="p">,</span>
+           <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">converters</span><span class="o">::</span><span class="n">args</span><span class="o">&amp;</span> <span class="n">args</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kt">bool</span> <span class="p">{</span>
+            <span class="k">auto</span> <span class="n">in</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">ITensor</span><span class="p">();</span>
+            <span class="k">auto</span> <span class="n">start_dim</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">unwrapToInt</span><span class="p">();</span>
+            <span class="k">auto</span> <span class="n">end_dim</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">2</span><span class="p">].</span><span class="n">unwrapToInt</span><span class="p">();</span>
+            <span class="k">auto</span> <span class="n">in_shape</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">toVec</span><span class="p">(</span><span class="n">in</span><span class="o">-&gt;</span><span class="n">getDimensions</span><span class="p">());</span>
+            <span class="k">auto</span> <span class="n">out_shape</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">flatten</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">rand</span><span class="p">(</span><span class="n">in_shape</span><span class="p">),</span> <span class="n">start_dim</span><span class="p">,</span> <span class="n">end_dim</span><span class="p">).</span><span class="n">sizes</span><span class="p">();</span>
+
+            <span class="k">auto</span> <span class="n">shuffle</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">-&gt;</span><span class="n">net</span><span class="o">-&gt;</span><span class="n">addShuffle</span><span class="p">(</span><span class="o">*</span><span class="n">in</span><span class="p">);</span>
+            <span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">setReshapeDimensions</span><span class="p">(</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">toDims</span><span class="p">(</span><span class="n">out_shape</span><span class="p">));</span>
+            <span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">setName</span><span class="p">(</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">node_info</span><span class="p">(</span><span class="n">n</span><span class="p">).</span><span class="n">c_str</span><span class="p">());</span>
+
+            <span class="k">auto</span> <span class="n">out_tensor</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">-&gt;</span><span class="n">AssociateValueAndTensor</span><span class="p">(</span><span class="n">n</span><span class="o">-&gt;</span><span class="n">outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">getOutput</span><span class="p">(</span><span class="mi">0</span><span class="p">));</span>
+            <span class="k">return</span> <span class="nb">true</span><span class="p">;</span>
+        <span class="p">}</span>
+    <span class="p">});</span>
+
+<span class="kt">int</span> <span class="nf">main</span><span class="p">()</span> <span class="p">{</span>
+    <span class="p">...</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        To use this converter in Python, it is recommended to use PyTorch’s
+        <a class="reference external" href="https://pytorch.org/tutorials/advanced/cpp_extension.html#custom-c-and-cuda-extensions">
+         C++ / CUDA Extention
+        </a>
+        template to wrap your library of converters into a
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          .so
+         </span>
+        </code>
+        that you can load with
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          ctypes.CDLL()
+         </span>
+        </code>
+        in your Python application.
+       </p>
+       <p>
+        You can find more information on all the details of writing converters in the contributors documentation (
+        <a class="reference internal" href="../contributors/writing_converters.html#writing-converters">
+         <span class="std std-ref">
+          Writing Converters
+         </span>
+        </a>
+        ).
+If you find yourself with a large library of converter implementations, do consider upstreaming them, PRs are welcome and it would be great for the community to benefit as well.
+       </p>
+      </article>
+     </div>
+    </div>
+   </main>
+  </div>
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+
+  <section id="torch-tensorrt-fx-frontend-user-guide">
+<span id="getting-started-with-fx"></span><h1>Torch-TensorRT (FX Frontend) User Guide<a class="headerlink" href="#torch-tensorrt-fx-frontend-user-guide" title="Permalink to this headline">¶</a></h1>
+<p>Torch-TensorRT (FX Frontend) is a tool that can convert a PyTorch model through <code class="docutils literal notranslate"><span class="pre">torch.fx</span></code> to an
+TensorRT engine optimized targeting running on Nvidia GPUs. TensorRT is the inference engine
+developed by NVIDIA which composed of various kinds of optimization including kernel fusion,
+graph optimization, low precision, etc.. This tool is developed in Python environment which allows this
+workflow to be very accessible to researchers and engineers. There are a few stages that a
+user want to use this tool and we will introduce them here.</p>
+<p>&gt; Torch-TensorRT (FX Frontend) is in <code class="docutils literal notranslate"><span class="pre">Beta</span></code> and currently it is recommended to work with PyTorch nightly.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="c1"># Test an example by</span>
+$ python py/torch_tensorrt/fx/example/lower_example.py
+</pre></div>
+</div>
+<section id="converting-a-pytorch-model-to-tensorrt-engine">
+<h2>Converting a PyTorch Model to TensorRT Engine<a class="headerlink" href="#converting-a-pytorch-model-to-tensorrt-engine" title="Permalink to this headline">¶</a></h2>
+<p>In general, users are welcome to use the <code class="docutils literal notranslate"><span class="pre">compile()</span></code> to finish the conversion from a model to tensorRT engine. It is a
+wrapper API that consists of the major steps needed to finish this converison. Please refer to an example usage in <code class="docutils literal notranslate"><span class="pre">lower_example.py</span></code> file under <code class="docutils literal notranslate"><span class="pre">examples/fx</span></code>.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span>
+    <span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
+    <span class="nb">input</span><span class="p">,</span>
+    <span class="n">max_batch_size</span><span class="o">=</span><span class="mi">2048</span><span class="p">,</span>
+    <span class="n">max_workspace_size</span><span class="o">=</span><span class="mi">33554432</span><span class="p">,</span>
+    <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">lower_precision</span><span class="o">=</span><span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span><span class="p">,</span>
+    <span class="n">verbose_log</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">timing_cache_prefix</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
+    <span class="n">save_timing_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">cuda_graph_batch_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">dynamic_batch</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
+
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Takes in original module, input and lowering setting, run lowering workflow to turn module</span>
+<span class="sd">    into lowered module, or so called TRTModule.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        module: Original module for lowering.</span>
+<span class="sd">        input: Input for module.</span>
+<span class="sd">        max_batch_size: Maximum batch size (must be &gt;= 1 to be set, 0 means not set)</span>
+<span class="sd">        max_workspace_size: Maximum size of workspace given to TensorRT.</span>
+<span class="sd">        explicit_batch_dimension: Use explicit batch dimension in TensorRT if set True, otherwise use implicit batch dimension.</span>
+<span class="sd">        lower_precision: lower_precision config given to TRTModule.</span>
+<span class="sd">        verbose_log: Enable verbose log for TensorRT if set True.</span>
+<span class="sd">        timing_cache_prefix: Timing cache file name for timing cache used by fx2trt.</span>
+<span class="sd">        save_timing_cache: Update timing cache with current timing cache data if set to True.</span>
+<span class="sd">        cuda_graph_batch_size: Cuda graph batch size, default to be -1.</span>
+<span class="sd">        dynamic_batch: batch dimension (dim=0) is dynamic.</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        A torch.nn.Module lowered by TensorRT.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+</pre></div>
+</div>
+<p>In this section, we will go through an example to illustrate the major steps that fx path uses.
+Users can refer to <code class="docutils literal notranslate"><span class="pre">fx2trt_example.py</span></code> file in <code class="docutils literal notranslate"><span class="pre">examples/fx</span></code>.</p>
+<ul class="simple">
+<li><p><strong>Step 1: Trace the model with acc_tracer</strong></p></li>
+</ul>
+<p>Acc_tracer is a tracer inheritated from FX tracer. It comes with args normalizer to convert all args to kwargs and pass to TRT converters.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt.fx.tracer.acc_tracer.acc_tracer</span> <span class="k">as</span> <span class="nn">acc_tracer</span>
+
+<span class="c1"># Build the model which needs to be a PyTorch nn.Module.</span>
+<span class="n">my_pytorch_model</span> <span class="o">=</span> <span class="n">build_model</span><span class="p">()</span>
+
+<span class="c1"># Prepare inputs to the model. Inputs have to be a List of Tensors</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span>
+
+<span class="c1"># Trace the model with acc_tracer.</span>
+<span class="n">acc_mod</span> <span class="o">=</span> <span class="n">acc_tracer</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">my_pytorch_model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+<p><em>Common Errors:</em></p>
+<p>symbolically traced variables cannot be used as inputs to control flow
+This means the model contains dynamic control flow. Please refer to section “Dynamic Control Flow” in <a class="reference external" href="https://pytorch.org/docs/stable/fx.html#dynamic-control-flow">FX guide</a>.</p>
+<ul class="simple">
+<li><p><strong>Step 2: Build TensorRT engine</strong></p></li>
+</ul>
+<p>There are <a class="reference external" href="https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#explicit-implicit-batch">two different modes</a> for how TensorRT handles batch dimension, explicit batch dimension and implicit batch dimension. This mode was used by early versions of TensorRT, and is now deprecated but continues to be supported for backwards compatibility. In explicit batch mode, all dimensions are explicit and can be dynamic, that is their length can change at execution time. Many new features, such as dynamic shapes and loops, are available only in this mode. User can still choose to use implicit batch mode when they set <code class="docutils literal notranslate"><span class="pre">explicit_batch_dimension=False</span></code> in <code class="docutils literal notranslate"><span class="pre">compile()</span></code>. We do not recommend to use it since it will lack of support in future TensorRT versions.</p>
+<p>Explicit batch is the default mode and it must be set for dynamic shape. For most of vision task, user can choose to enable <code class="docutils literal notranslate"><span class="pre">dynamic_batch</span></code> in <code class="docutils literal notranslate"><span class="pre">compile()</span></code> if they want to get the similar effects as implicit mode where only batch dimension changes. It has some requirements:
+1. Shapes of inputs, outputs and activations are fixed except batch dimension.
+2. Inputs, outputs and activations have batch dimension as the major dimension.
+3. All the operators in the model do not modify batch dimension (permute, transpose, split, etc.) or compute over batch dimension (sum, softmax, etc.).</p>
+<p>For examples of the last path, if we have a 3D tensor t shaped as (batch, sequence, dimension), operations such as torch.transpose(0, 2). If any of these three are not satisfied, we’ll need to specify InputTensorSpec as inputs with dynamic range.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt.fx.converters</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.fx</span> <span class="kn">import</span> <span class="n">InputTensorSpec</span><span class="p">,</span> <span class="n">TRTInterpreter</span>
+
+<span class="c1"># InputTensorSpec is a dataclass we use to store input information.</span>
+<span class="c1"># There&#39;re two ways we can build input_specs.</span>
+<span class="c1"># Option 1, build it manually.</span>
+<span class="n">input_specs</span> <span class="o">=</span> <span class="p">[</span>
+  <span class="n">InputTensorSpec</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
+  <span class="n">InputTensorSpec</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
+<span class="p">]</span>
+<span class="c1"># Option 2, build it using sample_inputs where user provide a sample</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
+<span class="p">]</span>
+<span class="n">input_specs</span> <span class="o">=</span> <span class="n">InputTensorSpec</span><span class="o">.</span><span class="n">from_tensors</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+
+<span class="c1"># IMPORTANT: If dynamic shape is needed, we need to build it slightly differently.</span>
+<span class="n">input_specs</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">InputTensorSpec</span><span class="p">(</span>
+        <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
+        <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
+        <span class="c1"># Currently we only support one set of dynamic range. User may set other dimensions but it is not promised to work for any models</span>
+        <span class="c1"># (min_shape, optimize_target_shape, max_shape)</span>
+        <span class="c1"># For more information refer to fx/input_tensor_spec.py</span>
+        <span class="n">shape_ranges</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)),</span>
+        <span class="p">],</span>
+    <span class="p">),</span>
+    <span class="n">InputTensorSpec</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
+<span class="p">]</span>
+
+<span class="c1"># Build a TRT interpreter. Set explicit_batch_dimension accordingly.</span>
+<span class="n">interpreter</span> <span class="o">=</span> <span class="n">TRTInterpreter</span><span class="p">(</span>
+    <span class="n">acc_mod</span><span class="p">,</span> <span class="n">input_specs</span><span class="p">,</span> <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="kc">True</span><span class="o">/</span><span class="kc">False</span>
+<span class="p">)</span>
+
+<span class="c1"># The output of TRTInterpreter run() is wrapped as TRTInterpreterResult.</span>
+<span class="c1"># The TRTInterpreterResult contains required parameter to build TRTModule,</span>
+<span class="c1"># and other informational output from TRTInterpreter run.</span>
+<span class="k">class</span> <span class="nc">TRTInterpreterResult</span><span class="p">(</span><span class="n">NamedTuple</span><span class="p">):</span>
+    <span class="n">engine</span><span class="p">:</span> <span class="n">Any</span>
+    <span class="n">input_names</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
+    <span class="n">output_names</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
+    <span class="n">serialized_cache</span><span class="p">:</span> <span class="nb">bytearray</span>
+
+<span class="c1">#max_batch_size: set accordingly for maximum batch size you will use.</span>
+<span class="c1">#max_workspace_size: set to the maximum size we can afford for temporary buffer</span>
+<span class="c1">#lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).</span>
+<span class="c1">#sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity</span>
+<span class="c1">#force_fp32_output: force output to be fp32</span>
+<span class="c1">#strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric #reasons.</span>
+<span class="c1">#algorithm_selector: set up algorithm selection for certain layer</span>
+<span class="c1">#timing_cache: enable timing cache for TensorRT</span>
+<span class="c1">#profiling_verbosity: TensorRT logging level</span>
+<span class="n">trt_interpreter_result</span> <span class="o">=</span> <span class="n">interpreter</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+    <span class="n">max_batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
+    <span class="n">max_workspace_size</span><span class="o">=</span><span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="mi">25</span><span class="p">,</span>
+    <span class="n">sparse_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">force_fp32_output</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">strict_type_constraints</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">algorithm_selector</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">timing_cache</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">profiling_verbosity</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p><em>Common Errors:</em></p>
+<p>RuntimeError: Conversion of function xxx not currently supported!
+- This means we don’t have the support for this xxx operator. Please refer to section “How to add a missing op” below for further instructions.</p>
+<ul class="simple">
+<li><p><strong>Step 3: Run the model</strong></p></li>
+</ul>
+<p>One way is using TRTModule, which is basically a PyTorch nn.Module.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch_tensorrt.fx</span> <span class="kn">import</span> <span class="n">TRTModule</span>
+<span class="n">mod</span> <span class="o">=</span> <span class="n">TRTModule</span><span class="p">(</span>
+    <span class="n">trt_interpreter_result</span><span class="o">.</span><span class="n">engine</span><span class="p">,</span>
+    <span class="n">trt_interpreter_result</span><span class="o">.</span><span class="n">input_names</span><span class="p">,</span>
+    <span class="n">trt_interpreter_result</span><span class="o">.</span><span class="n">output_names</span><span class="p">)</span>
+<span class="c1"># Just like all other PyTorch modules</span>
+<span class="n">outputs</span> <span class="o">=</span> <span class="n">mod</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="s2">&quot;trt.pt&quot;</span><span class="p">)</span>
+<span class="n">reload_trt_mod</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;trt.pt&quot;</span><span class="p">)</span>
+<span class="n">reload_model_output</span> <span class="o">=</span> <span class="n">reload_trt_mod</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>So far, we give a detailed explanation of major steps in convterting a PyTorch model into TensorRT engine. Users are welcome to refer to the source code for some parameters explanations. In the converting scheme, there are two important actions in it. One is acc tracer which helps us to convert a PyTorch model to acc graph. The other is FX path converter which helps to convert the acc graph’s operation to corresponding TensorRT operation and build up the TensoRT engine for it.</p>
+</section>
+<section id="acc-tracer">
+<h2>Acc Tracer<a class="headerlink" href="#acc-tracer" title="Permalink to this headline">¶</a></h2>
+<p>Acc tracer is a custom FX symbolic tracer. It does a couple more things compare to the vanilla FX symbolic tracer. We mainly depend on it to convert PyTorch ops or builtin ops to acc ops. There are two main purposes for fx2trt to use acc ops:</p>
+<ol class="arabic simple">
+<li><p>there’re many ops that do similar things in PyTorch ops and builtin ops such like torch.add, builtin.add and torch.Tensor.add. Using acc tracer, we normalize these three ops to a single acc_ops.add. This helps reduce the number of converters we need to write.</p></li>
+<li><p>acc ops only have kwargs which makes writing converter easier as we don’t need to add additional logic to find arguments in args and kwargs.</p></li>
+</ol>
+</section>
+<section id="torch-tensorrt-fx-frontend">
+<h2>Torch-TensorRT FX Frontend<a class="headerlink" href="#torch-tensorrt-fx-frontend" title="Permalink to this headline">¶</a></h2>
+<p>After symbolic tracing, we have the graph representation of a PyTorch model. The Torch-TensorRT FX Frontend leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).</p>
+<p>Below is an example of a converter function. The decorator is used to register this converter function with the corresponding node. In this example, we register this converter to a fx node whose target is acc_ops.sigmoid.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@tensorrt_converter</span><span class="p">(</span><span class="n">acc_ops</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">acc_ops_sigmoid</span><span class="p">(</span><span class="n">network</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    network: TensorRT network. We&#39;ll be adding layers to it.</span>
+
+<span class="sd">    The rest arguments are attributes of fx node.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">input_val</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_val</span><span class="p">,</span> <span class="n">trt</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Sigmoid received input </span><span class="si">{</span><span class="n">input_val</span><span class="si">}</span><span class="s1"> that is not part &#39;</span>
+                        <span class="s1">&#39;of the TensorRT region!&#39;</span><span class="p">)</span>
+
+    <span class="n">layer</span> <span class="o">=</span> <span class="n">network</span><span class="o">.</span><span class="n">add_activation</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">input_val</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="n">trt</span><span class="o">.</span><span class="n">ActivationType</span><span class="o">.</span><span class="n">SIGMOID</span><span class="p">)</span>
+    <span class="n">layer</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
+    <span class="k">return</span> <span class="n">layer</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="how-to-add-a-missing-op">
+<h3>How to Add a Missing Op<a class="headerlink" href="#how-to-add-a-missing-op" title="Permalink to this headline">¶</a></h3>
+<p>You can actually add it wherever you want just need to remember import the file so that all acc ops and mapper will be registered before tracing with acc_tracer.</p>
+<ul class="simple">
+<li><p><strong>Step 1. Add a new acc op</strong></p></li>
+</ul>
+<p>TODO: Need to explain more on the logistic of acc op like when we want to break down an op and when we want to reuse other ops.</p>
+<p>In <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_tracer.py">acc tracer</a>, we convert nodes in the graph to acc ops if there’s a mapping registered for the node to an acc op.</p>
+<p>In order to make the conversion to acc ops to happen, there’re two things required. One is that there should be an acc op function defined and the other is there should be a mapping registered.</p>
+<p>Defining an acc op is simple, we first just need a function and register the function as an acc op via this decorator <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py">acc_normalizer.py</a>. e.g. the following code adds an acc op named foo() which adds two given inputs.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="c1"># NOTE: all acc ops should only take kwargs as inputs, therefore we need the &quot;*&quot;</span>
+<span class="c1"># at the beginning.</span>
+@register_acc_op
+def foo<span class="o">(</span>*, input, other, alpha<span class="o">)</span>:
+    <span class="k">return</span> input + alpha * other
+</pre></div>
+</div>
+<p>There’re two ways to register a mapping. One is <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L164">register_acc_op_mapping()</a>. Let’s register a mapping from torch.add to foo() we just created above. We need to add decorator register_acc_op_mapping to it.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nv">this_arg_is_optional</span> <span class="o">=</span> True
+
+@register_acc_op_mapping<span class="o">(</span>
+    <span class="nv">op_and_target</span><span class="o">=(</span><span class="s2">&quot;call_function&quot;</span>, torch.add<span class="o">)</span>,
+    <span class="nv">arg_replacement_tuples</span><span class="o">=[</span>
+        <span class="o">(</span><span class="s2">&quot;input&quot;</span>, <span class="s2">&quot;input&quot;</span><span class="o">)</span>,
+        <span class="o">(</span><span class="s2">&quot;other&quot;</span>, <span class="s2">&quot;other&quot;</span><span class="o">)</span>,
+        <span class="o">(</span><span class="s2">&quot;alpha&quot;</span>, <span class="s2">&quot;alpha&quot;</span>, this_arg_is_optional<span class="o">)</span>,
+    <span class="o">]</span>,
+<span class="o">)</span>
+@register_acc_op
+def foo<span class="o">(</span>*, input, other, <span class="nv">alpha</span><span class="o">=</span><span class="m">1</span>.0<span class="o">)</span>:
+    <span class="k">return</span> input + alpha * other
+</pre></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">op_and_target</span></code> determines which node will trigger this mapping. op and target are the attributes of FX node. In acc_normalization when we see a node with the same op and target as set in the <code class="docutils literal notranslate"><span class="pre">op_and_target</span></code>, we will trigger the mapping. Since we want to map from <code class="docutils literal notranslate"><span class="pre">torch.add</span></code>, then op would be call_function and target would be <code class="docutils literal notranslate"><span class="pre">torch.add</span></code>. <code class="docutils literal notranslate"><span class="pre">arg_replacement_tuples</span></code> determines how we construct kwargs for new acc op node using args and kwargs from original node. Each tuple in <code class="docutils literal notranslate"><span class="pre">arg_replacement_tuples</span></code> represents one argument mapping rule. It contains two or three elements. The third element is a boolean variable that determines whether this kwarg is optional in <em>original node</em>. We only need to specify the third element if it’s True. The first element is the argument name in original node which will be used as the acc op node’s argument whose name is the second element in the tuple. The sequence of the tuples does matter because the position of the tuple determines where the argument is in original node’s args. We use this information to map args from original node to kwargs in acc op node.
+We don’t have to specify arg_replacement_tuples if none of the followings are true.</p>
+<ol class="arabic simple">
+<li><p>kwargs of original nodes and acc op nodes have different name.</p></li>
+<li><p>there’re optional arguments.</p></li>
+</ol>
+<p>The other way to register a mapping is through <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L206">register_custom_acc_mapper_fn()</a>. This one is designed to reduce the redundant op registration as it allows you to use a function to map to one or more existing acc ops throught some combinations. In the function, you can do basically whatever you want. Let’s use an example to explain how it works.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>@register_acc_op
+def foo<span class="o">(</span>*, input, other, <span class="nv">alpha</span><span class="o">=</span><span class="m">1</span>.0<span class="o">)</span>:
+    <span class="k">return</span> input + alpha * other
+
+@register_custom_acc_mapper_fn<span class="o">(</span>
+    <span class="nv">op_and_target</span><span class="o">=(</span><span class="s2">&quot;call_function&quot;</span>, torch.add<span class="o">)</span>,
+    <span class="nv">arg_replacement_tuples</span><span class="o">=[</span>
+        <span class="o">(</span><span class="s2">&quot;input&quot;</span>, <span class="s2">&quot;input&quot;</span><span class="o">)</span>,
+        <span class="o">(</span><span class="s2">&quot;other&quot;</span>, <span class="s2">&quot;other&quot;</span><span class="o">)</span>,
+        <span class="o">(</span><span class="s2">&quot;alpha&quot;</span>, <span class="s2">&quot;alpha&quot;</span>, this_arg_is_optional<span class="o">)</span>,
+    <span class="o">]</span>,
+<span class="o">)</span>
+def custom_mapper<span class="o">(</span>node: torch.fx.Node, _: nn.Module<span class="o">)</span> -&gt; torch.fx.Node:
+    <span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    `node` is original node, which is a call_function node with target</span>
+<span class="s2">    being torch.add.</span>
+<span class="s2">    &quot;&quot;&quot;</span>
+    <span class="nv">alpha</span> <span class="o">=</span> <span class="m">1</span>
+    <span class="k">if</span> <span class="s2">&quot;alpha&quot;</span> <span class="k">in</span> node.kwargs:
+        <span class="nv">alpha</span> <span class="o">=</span> node.kwargs<span class="o">[</span><span class="s2">&quot;alpha&quot;</span><span class="o">]</span>
+    <span class="nv">foo_kwargs</span> <span class="o">=</span> <span class="o">{</span><span class="s2">&quot;input&quot;</span>: node<span class="o">[</span><span class="s2">&quot;input&quot;</span><span class="o">]</span>, <span class="s2">&quot;other&quot;</span>: node<span class="o">[</span><span class="s2">&quot;other&quot;</span><span class="o">]</span>, <span class="s2">&quot;alpha&quot;</span>: alpha<span class="o">}</span>
+    with node.graph.inserting_before<span class="o">(</span>node<span class="o">)</span>:
+        <span class="nv">foo_node</span> <span class="o">=</span> node.graph.call_function<span class="o">(</span>foo, <span class="nv">kwargs</span><span class="o">=</span>foo_kwargs<span class="o">)</span>
+        foo_node.meta <span class="o">=</span> node.meta.copy<span class="o">()</span>
+        <span class="k">return</span> foo_node
+</pre></div>
+</div>
+<p>In the custom mapper function, we construct an acc op node and return it. The node we returns here would take over all the children nodes of original nodes <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L379">acc_normalizer.py</a>.</p>
+<p>The last step would be <em>adding unit test</em> for the new acc op or mapper function we added. The place to add the unit test is here <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/test/tracer/test_acc_tracer.py">test_acc_tracer.py</a>.</p>
+<ul class="simple">
+<li><p><strong>Step 2. Add a new converter</strong></p></li>
+</ul>
+<p>All the developed converters for acc ops are all in <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/converters/acc_ops_converters.py">acc_op_converter.py</a>. It could give you a good example of how the converter is added.</p>
+<p>Essentially, the converter is the mapping mechanism that maps the acc ops to a TensorRT layer. If we are able to find all the TensorRT layers we need we can get start to add a converter for the node using <a class="reference external" href="https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Graph/Network.html">TensorRT APIs</a>.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>@tensorrt_converter<span class="o">(</span>acc_ops.sigmoid<span class="o">)</span>
+def acc_ops_sigmoid<span class="o">(</span>network, target, args, kwargs, name<span class="o">)</span>:
+    <span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    network: TensorRT network. We&#39;ll be adding layers to it.</span>
+
+<span class="s2">    The rest arguments are attributes of fx node.</span>
+<span class="s2">    &quot;&quot;&quot;</span>
+    <span class="nv">input_val</span> <span class="o">=</span> kwargs<span class="o">[</span><span class="s1">&#39;input&#39;</span><span class="o">]</span>
+
+    <span class="k">if</span> not isinstance<span class="o">(</span>input_val, trt.tensorrt.ITensor<span class="o">)</span>:
+        raise RuntimeError<span class="o">(</span>f<span class="s1">&#39;Sigmoid received input {input_val} that is not part &#39;</span>
+                        <span class="s1">&#39;of the TensorRT region!&#39;</span><span class="o">)</span>
+
+    <span class="nv">layer</span> <span class="o">=</span> network.add_activation<span class="o">(</span><span class="nv">input</span><span class="o">=</span>input_val, <span class="nv">type</span><span class="o">=</span>trt.ActivationType.SIGMOID<span class="o">)</span>
+    layer.name <span class="o">=</span> name
+    <span class="k">return</span> layer.get_output<span class="o">(</span><span class="m">0</span><span class="o">)</span>
+</pre></div>
+</div>
+<p>We need to use <code class="docutils literal notranslate"><span class="pre">tensorrt_converter</span></code> decorator to register the converter. The argument for the decorator is the target of the fx node that we need to convert. In the converter, we can find the inputs to the fx node in kwargs. As in the example, the original node is <cite>acc_ops.sigmoid</cite> which only has one argument “input” in acc_ops.py. We get the input and check if it’s a TensorRT tensor. After that, we add a sigmoid layer to TensorRT network and return the output of the layer. The output we returned will be passed to the children nodes of acc_ops.sigmoid by fx.Interpreter.</p>
+<p><strong>What if we can not find corresponding layers in TensorRT that do the same thing as the node.</strong></p>
+<p>In this case, we would need to do a bit more work. TensorRT provides plugins which serves as custom layers. <em>We have not implement this feature yet. We will update once it is enabled</em>.</p>
+<p>Last step would be adding the unit test for the new converter we added. User could add corresponding unit test in this <a class="reference external" href="https://github.com/pytorch/TensorRT/tree/master/py/torch_tensorrt/fx/test/converters/acc_op">folder</a>.</p>
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+           <a class="md-nav__link" href="creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">
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+            Deploying Torch-TensorRT Programs
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+            Torch-TensorRT - Using Dynamic Shapes
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+            Torch-TensorRT Getting Started - EfficientNet-B0
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+            Masked Language Modeling (MLM) with Hugging Face BERT Transformer
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+           <a class="md-nav__link" href="../_notebooks/Resnet50-example.html">
+            Torch-TensorRT Getting Started - ResNet 50
+           </a>
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+           <a class="md-nav__link" href="../_notebooks/ssd-object-detection-demo.html">
+            Object Detection with Torch-TensorRT (SSD)
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/vgg-qat.html">
+            Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT
+           </a>
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+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
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+           <a class="md-nav__link" href="../py_api/torch_tensorrt.html">
+            torch_tensorrt
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+            torch_tensorrt.logging
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+            torch_tensorrt.ptq
+           </a>
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+          <li class="md-nav__item">
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+            torch_tensorrt.ts
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+            Namespace torch_tensorrt
+           </a>
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+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__logging.html">
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+           </a>
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+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">
+            Namespace torch_tensorrt::torchscript
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">
+            Namespace torch_tensorrt::ptq
+           </a>
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+           <span class="md-nav__link caption">
+            <span class="caption-text">
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+            </span>
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+            System Overview
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+           <a class="md-nav__link" href="../contributors/writing_converters.html">
+            Writing Converters
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+            Useful Links for Torch-TensorRT Development
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+             Indices
+            </span>
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+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../indices/supported_ops.html">
+            Operators Supported
+           </a>
+          </li>
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+      </div>
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+     <div class="md-content">
+      <article class="md-content__inner md-typeset" role="main">
+       <span id="getting-started-with-python-api">
+       </span>
+       <h1 id="tutorials-getting-started-with-python-api--page-root">
+        Using Torch-TensorRT in Python
+        <a class="headerlink" href="#tutorials-getting-started-with-python-api--page-root" title="Permalink to this headline">
+         ¶
+        </a>
+       </h1>
+       <p>
+        Torch-TensorRT Python API accepts a
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          `torch.nn.Module
+         </span>
+        </code>
+        as an input. Under the hood, it uses
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch.jit.script
+         </span>
+        </code>
+        to convert the input module into a
+TorchScript module. To compile your input
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          `torch.nn.Module
+         </span>
+        </code>
+        with Torch-TensorRT, all you need to do is provide the module and inputs
+to Torch-TensorRT and you will be returned an optimized TorchScript module to run or add into another PyTorch module. Inputs
+is a list of
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt.Input
+         </span>
+        </code>
+        classes which define input’s shape, datatype and memory format. You can also specify settings such as
+operating precision for the engine or target device. After compilation you can save the module just like any other module
+to load in a deployment application. In order to load a TensorRT/TorchScript module, make sure you first import
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt
+         </span>
+        </code>
+        .
+       </p>
+       <div class="highlight-python notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="o">...</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> <span class="c1"># torch module needs to be in eval (not training) mode</span>
+
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span>
+            <span class="n">min_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">],</span>
+            <span class="n">opt_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span>
+            <span class="n">max_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span>
+            <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">,</span>
+        <span class="p">)]</span>
+<span class="n">enabled_precisions</span> <span class="o">=</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">}</span> <span class="c1"># Run with fp16</span>
+
+<span class="n">trt_ts_module</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions</span><span class="p">)</span>
+
+<span class="n">input_data</span> <span class="o">=</span> <span class="n">input_data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">trt_ts_module</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">trt_ts_module</span><span class="p">,</span> <span class="s2">"trt_ts_module.ts"</span><span class="p">)</span>
+</pre>
+        </div>
+       </div>
+       <div class="highlight-python notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="c1"># Deployment application</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="n">trt_ts_module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"trt_ts_module.ts"</span><span class="p">)</span>
+<span class="n">input_data</span> <span class="o">=</span> <span class="n">input_data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">trt_ts_module</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        Torch-TensorRT python API also provides
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt.ts.compile
+         </span>
+        </code>
+        which accepts a TorchScript module as input.
+The torchscript module can be obtained via scripting or tracing (refer to
+        <span class="xref std std-ref">
+         creating_torchscript_module_in_python
+        </span>
+        ).
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt.ts.compile
+         </span>
+        </code>
+        accepts a Torchscript module
+and a list of
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch_tensorrt.Input
+         </span>
+        </code>
+        classes.
+       </p>
+      </article>
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diff --git a/docs/v1.2.0/tutorials/installation.html b/docs/v1.2.0/tutorials/installation.html
new file mode 100644
index 0000000000..2a810c0419
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+       </a>
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+       </a>
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+                     <ul class="md-nav__list">
+                      <li class="md-nav__item">
+                       <a class="md-nav__link" href="#choosing-the-right-abi">
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+                    </nav>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#building-using-cudnn-tensorrt-tarball-distributions">
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+               Show Source
+              </a>
+             </li>
+            </ul>
+           </nav>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="getting_started_with_cpp_api.html">
+            Getting Started with C++
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="getting_started_with_python_api.html">
+            Using Torch-TensorRT in Python
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="creating_torchscript_module_in_python.html">
+            Creating a TorchScript Module
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="creating_torchscript_module_in_python.html#working-with-torchscript-in-python">
+            Working with TorchScript in Python
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">
+            Saving TorchScript Module to Disk
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="ptq.html">
+            Post Training Quantization (PTQ)
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="torchtrtc.html">
+            torchtrtc
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="use_from_pytorch.html">
+            Using Torch-TensorRT Directly From PyTorch
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="runtime.html">
+            Deploying Torch-TensorRT Programs
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="using_dla.html">
+            DLA
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             Notebooks
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/CitriNet-example.html">
+            Torch-TensorRT Getting Started - CitriNet
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/dynamic-shapes.html">
+            Torch-TensorRT - Using Dynamic Shapes
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/EfficientNet-example.html">
+            Torch-TensorRT Getting Started - EfficientNet-B0
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/Hugging-Face-BERT.html">
+            Masked Language Modeling (MLM) with Hugging Face BERT Transformer
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/lenet-getting-started.html">
+            Torch-TensorRT Getting Started - LeNet
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/Resnet50-example.html">
+            Torch-TensorRT Getting Started - ResNet 50
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/ssd-object-detection-demo.html">
+            Object Detection with Torch-TensorRT (SSD)
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_notebooks/vgg-qat.html">
+            Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             Python API Documenation
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../py_api/torch_tensorrt.html">
+            torch_tensorrt
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../py_api/logging.html">
+            torch_tensorrt.logging
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../py_api/ptq.html">
+            torch_tensorrt.ptq
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../py_api/ts.html">
+            torch_tensorrt.ts
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             C++ API Documenation
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/torch_tensort_cpp.html">
+            Torch-TensorRT C++ API
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt.html">
+            Namespace torch_tensorrt
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__logging.html">
+            Namespace torch_tensorrt::logging
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">
+            Namespace torch_tensorrt::torchscript
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">
+            Namespace torch_tensorrt::ptq
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             Contributor Documentation
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../contributors/system_overview.html">
+            System Overview
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../contributors/writing_converters.html">
+            Writing Converters
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../contributors/useful_links.html">
+            Useful Links for Torch-TensorRT Development
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <span class="md-nav__link caption">
+            <span class="caption-text">
+             Indices
+            </span>
+           </span>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="../indices/supported_ops.html">
+            Operators Supported
+           </a>
+          </li>
+         </ul>
+        </nav>
+       </div>
+      </div>
+     </div>
+     <div class="md-sidebar md-sidebar--secondary" data-md-component="toc">
+      <div class="md-sidebar__scrollwrap">
+       <div class="md-sidebar__inner">
+        <nav class="md-nav md-nav--secondary">
+         <label class="md-nav__title" for="__toc">
+          Contents
+         </label>
+         <ul class="md-nav__list" data-md-scrollfix="">
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="#tutorials-installation--page-root">
+            Installation
+           </a>
+           <nav class="md-nav">
+            <ul class="md-nav__list">
+             <li class="md-nav__item">
+              <a class="md-nav__link" href="#precompiled-binaries">
+               Precompiled Binaries
+              </a>
+              <nav class="md-nav">
+               <ul class="md-nav__list">
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#dependencies">
+                  Dependencies
+                 </a>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#python-package">
+                  Python Package
+                 </a>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#c-binary-distribution">
+                  C++ Binary Distribution
+                 </a>
+                </li>
+               </ul>
+              </nav>
+             </li>
+             <li class="md-nav__item">
+              <a class="md-nav__link" href="#compiling-from-source">
+               Compiling From Source
+              </a>
+              <nav class="md-nav">
+               <ul class="md-nav__list">
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#dependencies-for-compilation">
+                  Dependencies for Compilation
+                 </a>
+                 <nav class="md-nav">
+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#choosing-the-right-abi">
+                     Choosing the Right ABI
+                    </a>
+                   </li>
+                  </ul>
+                 </nav>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#building-using-cudnn-tensorrt-tarball-distributions">
+                  <strong>
+                   Building using cuDNN &amp; TensorRT tarball distributions
+                  </strong>
+                 </a>
+                 <nav class="md-nav">
+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#release-build">
+                     Release Build
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#debug-build">
+                     Debug Build
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#pre-cxx11-abi-build">
+                     Pre CXX11 ABI Build
+                    </a>
+                   </li>
+                  </ul>
+                 </nav>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#building-using-locally-installed-cudnn-tensorrt">
+                  <strong>
+                   Building using locally installed cuDNN &amp; TensorRT
+                  </strong>
+                 </a>
+                 <nav class="md-nav">
+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#id6">
+                     Release Build
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#build-from-local-debug">
+                     Debug Build
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#id8">
+                     Pre CXX11 ABI Build
+                    </a>
+                   </li>
+                  </ul>
+                 </nav>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#building-the-python-package">
+                  <strong>
+                   Building the Python package
+                  </strong>
+                 </a>
+                 <nav class="md-nav">
+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#id9">
+                     Debug Build
+                    </a>
+                   </li>
+                  </ul>
+                 </nav>
+                </li>
+                <li class="md-nav__item">
+                 <a class="md-nav__link" href="#building-natively-on-aarch64-jetson">
+                  <strong>
+                   Building Natively on aarch64 (Jetson)
+                  </strong>
+                 </a>
+                 <nav class="md-nav">
+                  <ul class="md-nav__list">
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#prerequisites">
+                     Prerequisites
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#enviorment-setup">
+                     Enviorment Setup
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#compile-c-library-and-compiler-cli">
+                     Compile C++ Library and Compiler CLI
+                    </a>
+                   </li>
+                   <li class="md-nav__item">
+                    <a class="md-nav__link" href="#compile-python-api">
+                     Compile Python API
+                    </a>
+                   </li>
+                  </ul>
+                 </nav>
+                </li>
+               </ul>
+              </nav>
+             </li>
+            </ul>
+           </nav>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__extra_link" href="../_sources/tutorials/installation.rst.txt">
+            Show Source
+           </a>
+          </li>
+          <li class="md-nav__item" id="searchbox">
+          </li>
+         </ul>
+        </nav>
+       </div>
+      </div>
+     </div>
+     <div class="md-content">
+      <article class="md-content__inner md-typeset" role="main">
+       <span id="id1">
+       </span>
+       <h1 id="tutorials-installation--page-root">
+        Installation
+        <a class="headerlink" href="#tutorials-installation--page-root" title="Permalink to this headline">
+         ¶
+        </a>
+       </h1>
+       <h2 id="precompiled-binaries">
+        Precompiled Binaries
+        <a class="headerlink" href="#precompiled-binaries" title="Permalink to this headline">
+         ¶
+        </a>
+       </h2>
+       <h3 id="dependencies">
+        Dependencies
+        <a class="headerlink" href="#dependencies" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        You need to have either PyTorch or LibTorch installed based on if you are using Python or C++
+and you must have CUDA, cuDNN and TensorRT installed.
+       </p>
+       <blockquote>
+        <div>
+         <ul class="simple">
+          <li>
+           <p>
+            <a class="reference external" href="https://www.pytorch.org">
+             https://www.pytorch.org
+            </a>
+           </p>
+          </li>
+          <li>
+           <p>
+            <a class="reference external" href="https://developer.nvidia.com/cuda">
+             https://developer.nvidia.com/cuda
+            </a>
+           </p>
+          </li>
+          <li>
+           <p>
+            <a class="reference external" href="https://developer.nvidia.com/cudnn">
+             https://developer.nvidia.com/cudnn
+            </a>
+           </p>
+          </li>
+          <li>
+           <p>
+            <a class="reference external" href="https://developer.nvidia.com/tensorrt">
+             https://developer.nvidia.com/tensorrt
+            </a>
+           </p>
+          </li>
+         </ul>
+        </div>
+       </blockquote>
+       <h3 id="python-package">
+        Python Package
+        <a class="headerlink" href="#python-package" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        You can install the python package using
+       </p>
+       <div class="highlight-sh notranslate">
+        <div class="highlight">
+         <pre><span></span>pip3 install torch-tensorrt -f https://github.com/NVIDIA/Torch-TensorRT/releases
+</pre>
+        </div>
+       </div>
+       <span id="bin-dist">
+       </span>
+       <h3 id="c-binary-distribution">
+        C++ Binary Distribution
+        <a class="headerlink" href="#c-binary-distribution" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        Precompiled tarballs for releases are provided here:
+        <a class="reference external" href="https://github.com/NVIDIA/Torch-TensorRT/releases">
+         https://github.com/NVIDIA/Torch-TensorRT/releases
+        </a>
+       </p>
+       <span id="compile-from-source">
+       </span>
+       <h2 id="compiling-from-source">
+        Compiling From Source
+        <a class="headerlink" href="#compiling-from-source" title="Permalink to this headline">
+         ¶
+        </a>
+       </h2>
+       <span id="installing-deps">
+       </span>
+       <h3 id="dependencies-for-compilation">
+        Dependencies for Compilation
+        <a class="headerlink" href="#dependencies-for-compilation" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        Torch-TensorRT is built with Bazel, so begin by installing it.
+       </p>
+       <blockquote>
+        <div>
+         <ul class="simple">
+          <li>
+           <p>
+            The easiest way is to install bazelisk using the method of your choosing
+            <a class="reference external" href="https://github.com/bazelbuild/bazelisk">
+             https://github.com/bazelbuild/bazelisk
+            </a>
+           </p>
+          </li>
+          <li>
+           <p>
+            Otherwise you can use the following instructions to install binaries
+            <a class="reference external" href="https://docs.bazel.build/versions/master/install.html">
+             https://docs.bazel.build/versions/master/install.html
+            </a>
+           </p>
+          </li>
+          <li>
+           <p>
+            Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions
+           </p>
+          </li>
+         </ul>
+         <div class="highlight-shell notranslate">
+          <div class="highlight">
+           <pre><span></span><span class="nb">export</span> <span class="nv">BAZEL_VERSION</span><span class="o">=</span><span class="k">$(</span>cat &lt;PATH_TO_TORCHTRT_ROOT&gt;/.bazelversion<span class="k">)</span>
+mkdir bazel
+<span class="nb">cd</span> bazel
+curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/<span class="nv">$BAZEL_VERSION</span>/bazel-<span class="nv">$BAZEL_VERSION</span>-dist.zip
+unzip bazel-<span class="nv">$BAZEL_VERSION</span>-dist.zip
+bash ./compile.sh
+cp output/bazel /usr/local/bin/
+</pre>
+          </div>
+         </div>
+        </div>
+       </blockquote>
+       <p>
+        You will also need to have CUDA installed on the system (or if running in a container, the system must have
+the CUDA driver installed and the container must have CUDA)
+       </p>
+       <p>
+        The correct LibTorch version will be pulled down for you by bazel.
+       </p>
+       <blockquote>
+        <div>
+         <p>
+          NOTE: For best compatability with official PyTorch, use torch==1.10.0+cuda113, TensorRT 8.0 and cuDNN 8.2 for CUDA 11.3 however Torch-TensorRT itself supports
+TensorRT and cuDNN for other CUDA versions for usecases such as using NVIDIA compiled distributions of PyTorch that use other versions of CUDA
+e.g. aarch64 or custom compiled version of PyTorch.
+         </p>
+        </div>
+       </blockquote>
+       <span id="abis">
+       </span>
+       <h4 id="choosing-the-right-abi">
+        Choosing the Right ABI
+        <a class="headerlink" href="#choosing-the-right-abi" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        Likely the most complicated thing about compiling Torch-TensorRT is selecting the correct ABI. There are two options
+which are incompatible with each other, pre-cxx11-abi and the cxx11-abi. The complexity comes from the fact that while
+the most popular distribution of PyTorch (wheels downloaded from pytorch.org/pypi directly) use the pre-cxx11-abi, most
+other distributions you might encounter (e.g. ones from NVIDIA - NGC containers, and builds for Jetson as well as certain
+libtorch builds and likely if you build PyTorch from source) use the cxx11-abi. It is important you compile Torch-TensorRT
+using the correct ABI to function properly. Below is a table with general pairings of PyTorch distribution sources and the
+recommended commands:
+       </p>
+       <table>
+        <colgroup>
+         <col style="width: 33%"/>
+         <col style="width: 31%"/>
+         <col style="width: 36%"/>
+        </colgroup>
+        <thead>
+         <tr class="row-odd">
+          <th class="head">
+           <p>
+            PyTorch Source
+           </p>
+          </th>
+          <th class="head">
+           <p>
+            Recommended C++ Compilation Command
+           </p>
+          </th>
+          <th class="head">
+           <p>
+            Recommended Python Compilation Command
+           </p>
+          </th>
+         </tr>
+        </thead>
+        <tbody>
+         <tr class="row-even">
+          <td>
+           <p>
+            PyTorch whl file from PyTorch.org
+           </p>
+          </td>
+          <td>
+           <p>
+            bazel build //:libtorchtrt -c opt –config pre_cxx11_abi
+           </p>
+          </td>
+          <td>
+           <p>
+            python3 setup.py bdist_wheel
+           </p>
+          </td>
+         </tr>
+         <tr class="row-odd">
+          <td>
+           <p>
+            libtorch-shared-with-deps-
+            <a href="#id2">
+             <span class="problematic" id="id3">
+              *
+             </span>
+            </a>
+            .zip from PyTorch.org
+           </p>
+          </td>
+          <td>
+           <p>
+            bazel build //:libtorchtrt -c opt –config pre_cxx11_abi
+           </p>
+          </td>
+          <td>
+           <p>
+            python3 setup.py bdist_wheel
+           </p>
+          </td>
+         </tr>
+         <tr class="row-even">
+          <td>
+           <p>
+            libtorch-cxx11-abi-shared-with-deps-
+            <a href="#id4">
+             <span class="problematic" id="id5">
+              *
+             </span>
+            </a>
+            .zip from PyTorch.org
+           </p>
+          </td>
+          <td>
+           <p>
+            bazel build //:libtorchtrt -c opt
+           </p>
+          </td>
+          <td>
+           <p>
+            python3 setup.py bdist_wheel –use-cxx11-abi
+           </p>
+          </td>
+         </tr>
+         <tr class="row-odd">
+          <td>
+           <p>
+            PyTorch preinstalled in an NGC container
+           </p>
+          </td>
+          <td>
+           <p>
+            bazel build //:libtorchtrt -c opt
+           </p>
+          </td>
+          <td>
+           <p>
+            python3 setup.py bdist_wheel –use-cxx11-abi
+           </p>
+          </td>
+         </tr>
+         <tr class="row-even">
+          <td>
+           <p>
+            PyTorch from the NVIDIA Forums for Jetson
+           </p>
+          </td>
+          <td>
+           <p>
+            bazel build //:libtorchtrt -c opt
+           </p>
+          </td>
+          <td>
+           <p>
+            python3 setup.py bdist_wheel –jetpack-version 4.6 –use-cxx11-abi
+           </p>
+          </td>
+         </tr>
+         <tr class="row-odd">
+          <td>
+           <p>
+            PyTorch built from Source
+           </p>
+          </td>
+          <td>
+           <p>
+            bazel build //:libtorchtrt -c opt
+           </p>
+          </td>
+          <td>
+           <p>
+            python3 setup.py bdist_wheel –use-cxx11-abi
+           </p>
+          </td>
+         </tr>
+        </tbody>
+       </table>
+       <blockquote>
+        <div>
+         <p>
+          NOTE: For all of the above cases you must correctly declare the source of PyTorch you intend to use in your WORKSPACE file for both Python and C++ builds. See below for more information
+         </p>
+        </div>
+       </blockquote>
+       <p>
+        You then have two compilation options:
+       </p>
+       <span id="build-from-archive">
+       </span>
+       <h3 id="building-using-cudnn-tensorrt-tarball-distributions">
+        <strong>
+         Building using cuDNN &amp; TensorRT tarball distributions
+        </strong>
+        <a class="headerlink" href="#building-using-cudnn-tensorrt-tarball-distributions" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <blockquote>
+        <div>
+         <p>
+          This is recommended so as to build Torch-TensorRT hermetically and insures any compilation errors are not caused by version issues
+         </p>
+         <p>
+          Make sure when running Torch-TensorRT that these versions of the libraries are prioritized in your
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            $LD_LIBRARY_PATH
+           </span>
+          </code>
+         </p>
+        </div>
+       </blockquote>
+       <dl class="simple">
+        <dt>
+         You need to download the tarball distributions of TensorRT and cuDNN from the NVIDIA website.
+        </dt>
+        <dd>
+         <ul class="simple">
+          <li>
+           <p>
+            <a class="reference external" href="https://developer.nvidia.com/cudnn">
+             https://developer.nvidia.com/cudnn
+            </a>
+           </p>
+          </li>
+          <li>
+           <p>
+            <a class="reference external" href="https://developer.nvidia.com/tensorrt">
+             https://developer.nvidia.com/tensorrt
+            </a>
+           </p>
+          </li>
+         </ul>
+        </dd>
+       </dl>
+       <p>
+        Place these files in a directory (the directories
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          third_party/distdir/[x86_64-linux-gnu
+         </span>
+         <span class="pre">
+          |
+         </span>
+         <span class="pre">
+          aarch64-linux-gnu]
+         </span>
+        </code>
+        exist for this purpose)
+       </p>
+       <p>
+        Then compile referencing the directory with the tarballs
+       </p>
+       <blockquote>
+        <div>
+         <p>
+          If you get errors regarding the packages, check their sha256 hashes and make sure they match the ones listed in
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            WORKSPACE
+           </span>
+          </code>
+         </p>
+        </div>
+       </blockquote>
+       <h4 id="release-build">
+        Release Build
+        <a class="headerlink" href="#release-build" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt -c opt --distdir third_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        A tarball with the include files and library can then be found in
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          bazel-bin
+         </span>
+        </code>
+       </p>
+       <span id="build-from-archive-debug">
+       </span>
+       <h4 id="debug-build">
+        Debug Build
+        <a class="headerlink" href="#debug-build" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        To build with debug symbols use the following command
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt -c dbg --distdir third_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        A tarball with the include files and library can then be found in
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          bazel-bin
+         </span>
+        </code>
+       </p>
+       <h4 id="pre-cxx11-abi-build">
+        Pre CXX11 ABI Build
+        <a class="headerlink" href="#pre-cxx11-abi-build" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        To build using the pre-CXX11 ABI use the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          pre_cxx11_abi
+         </span>
+        </code>
+        config
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt --config pre_cxx11_abi -c <span class="o">[</span>dbg/opt<span class="o">]</span> --distdir third_party/distdir/<span class="o">[</span>x86_64-linux-gnu <span class="p">|</span> aarch64-linux-gnu<span class="o">]</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        A tarball with the include files and library can then be found in
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          bazel-bin
+         </span>
+        </code>
+       </p>
+       <span id="build-from-local">
+       </span>
+       <h3 id="building-using-locally-installed-cudnn-tensorrt">
+        <strong>
+         Building using locally installed cuDNN &amp; TensorRT
+        </strong>
+        <a class="headerlink" href="#building-using-locally-installed-cudnn-tensorrt" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <blockquote>
+        <div>
+         <p>
+          If you encounter bugs and you compiled using this method please disclose that you used local sources in the issue (an ldd dump would be nice too)
+         </p>
+        </div>
+       </blockquote>
+       <p>
+        Install TensorRT, CUDA and cuDNN on the system before starting to compile.
+       </p>
+       <p>
+        In WORKSPACE comment out:
+       </p>
+       <div class="highlight-python notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="c1"># Downloaded distributions to use with --distdir</span>
+<span class="n">http_archive</span><span class="p">(</span>
+    <span class="n">name</span> <span class="o">=</span> <span class="s2">"cudnn"</span><span class="p">,</span>
+    <span class="n">urls</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"&lt;URL&gt;"</span><span class="p">,],</span>
+
+    <span class="n">build_file</span> <span class="o">=</span> <span class="s2">"@//third_party/cudnn/archive:BUILD"</span><span class="p">,</span>
+    <span class="n">sha256</span> <span class="o">=</span> <span class="s2">"&lt;TAR SHA256&gt;"</span><span class="p">,</span>
+    <span class="n">strip_prefix</span> <span class="o">=</span> <span class="s2">"cuda"</span>
+<span class="p">)</span>
+
+<span class="n">http_archive</span><span class="p">(</span>
+    <span class="n">name</span> <span class="o">=</span> <span class="s2">"tensorrt"</span><span class="p">,</span>
+    <span class="n">urls</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"&lt;URL&gt;"</span><span class="p">,],</span>
+
+    <span class="n">build_file</span> <span class="o">=</span> <span class="s2">"@//third_party/tensorrt/archive:BUILD"</span><span class="p">,</span>
+    <span class="n">sha256</span> <span class="o">=</span> <span class="s2">"&lt;TAR SHA256&gt;"</span><span class="p">,</span>
+    <span class="n">strip_prefix</span> <span class="o">=</span> <span class="s2">"TensorRT-&lt;VERSION&gt;"</span>
+<span class="p">)</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        and uncomment
+       </p>
+       <div class="highlight-python notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="c1"># Locally installed dependencies</span>
+<span class="n">new_local_repository</span><span class="p">(</span>
+    <span class="n">name</span> <span class="o">=</span> <span class="s2">"cudnn"</span><span class="p">,</span>
+    <span class="n">path</span> <span class="o">=</span> <span class="s2">"/usr/"</span><span class="p">,</span>
+    <span class="n">build_file</span> <span class="o">=</span> <span class="s2">"@//third_party/cudnn/local:BUILD"</span>
+<span class="p">)</span>
+
+<span class="n">new_local_repository</span><span class="p">(</span>
+<span class="n">name</span> <span class="o">=</span> <span class="s2">"tensorrt"</span><span class="p">,</span>
+<span class="n">path</span> <span class="o">=</span> <span class="s2">"/usr/"</span><span class="p">,</span>
+<span class="n">build_file</span> <span class="o">=</span> <span class="s2">"@//third_party/tensorrt/local:BUILD"</span>
+<span class="p">)</span>
+</pre>
+        </div>
+       </div>
+       <h4 id="id6">
+        Release Build
+        <a class="headerlink" href="#id6" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        Compile using:
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt -c opt
+</pre>
+        </div>
+       </div>
+       <p>
+        A tarball with the include files and library can then be found in
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          bazel-bin
+         </span>
+        </code>
+       </p>
+       <span id="id7">
+       </span>
+       <h4 id="build-from-local-debug">
+        Debug Build
+        <a class="headerlink" href="#build-from-local-debug" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        To build with debug symbols use the following command
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt -c dbg
+</pre>
+        </div>
+       </div>
+       <p>
+        A tarball with the include files and library can then be found in
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          bazel-bin
+         </span>
+        </code>
+       </p>
+       <h4 id="id8">
+        Pre CXX11 ABI Build
+        <a class="headerlink" href="#id8" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        To build using the pre-CXX11 ABI use the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          pre_cxx11_abi
+         </span>
+        </code>
+        config
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt --config pre_cxx11_abi -c <span class="o">[</span>dbg/opt<span class="o">]</span>
+</pre>
+        </div>
+       </div>
+       <h3 id="building-the-python-package">
+        <strong>
+         Building the Python package
+        </strong>
+        <a class="headerlink" href="#building-the-python-package" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <p>
+        Begin by installing
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          ninja
+         </span>
+        </code>
+       </p>
+       <p>
+        You can build the Python package using
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          setup.py
+         </span>
+        </code>
+        (this will also build the correct version of
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          libtorchtrt.so
+         </span>
+        </code>
+        )
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>python3 setup.py <span class="o">[</span>install/bdist_wheel<span class="o">]</span>
+</pre>
+        </div>
+       </div>
+       <h4 id="id9">
+        Debug Build
+        <a class="headerlink" href="#id9" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>python3 setup.py develop <span class="o">[</span>--user<span class="o">]</span>
+</pre>
+        </div>
+       </div>
+       <p>
+        This also compiles a debug build of
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          libtorchtrt.so
+         </span>
+        </code>
+       </p>
+       <h3 id="building-natively-on-aarch64-jetson">
+        <strong>
+         Building Natively on aarch64 (Jetson)
+        </strong>
+        <a class="headerlink" href="#building-natively-on-aarch64-jetson" title="Permalink to this headline">
+         ¶
+        </a>
+       </h3>
+       <h4 id="prerequisites">
+        Prerequisites
+        <a class="headerlink" href="#prerequisites" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        Install or compile a build of PyTorch/LibTorch for aarch64
+       </p>
+       <p>
+        NVIDIA hosts builds the latest release branch for Jetson here:
+       </p>
+       <blockquote>
+        <div>
+         <p>
+          <a class="reference external" href="https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048">
+           https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048
+          </a>
+         </p>
+        </div>
+       </blockquote>
+       <h4 id="enviorment-setup">
+        Enviorment Setup
+        <a class="headerlink" href="#enviorment-setup" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <p>
+        To build natively on aarch64-linux-gnu platform, configure the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          WORKSPACE
+         </span>
+        </code>
+        with local available dependencies.
+       </p>
+       <ol class="arabic simple">
+        <li>
+         <p>
+          Disable the rules with
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            http_archive
+           </span>
+          </code>
+          for x86_64 by commenting the following rules:
+         </p>
+        </li>
+       </ol>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span><span class="c1">#http_archive(</span>
+<span class="c1">#    name = "libtorch",</span>
+<span class="c1">#    build_file = "@//third_party/libtorch:BUILD",</span>
+<span class="c1">#    strip_prefix = "libtorch",</span>
+<span class="c1">#    urls = ["https://download.pytorch.org/libtorch/cu102/libtorch-cxx11-abi-shared-with-deps-1.5.1.zip"],</span>
+<span class="c1">#    sha256 = "cf0691493d05062fe3239cf76773bae4c5124f4b039050dbdd291c652af3ab2a"</span>
+<span class="c1">#)</span>
+
+<span class="c1">#http_archive(</span>
+<span class="c1">#    name = "libtorch_pre_cxx11_abi",</span>
+<span class="c1">#    build_file = "@//third_party/libtorch:BUILD",</span>
+<span class="c1">#    strip_prefix = "libtorch",</span>
+<span class="c1">#    sha256 = "818977576572eadaf62c80434a25afe44dbaa32ebda3a0919e389dcbe74f8656",</span>
+<span class="c1">#    urls = ["https://download.pytorch.org/libtorch/cu102/libtorch-shared-with-deps-1.5.1.zip"],</span>
+<span class="c1">#)</span>
+
+<span class="c1"># Download these tarballs manually from the NVIDIA website</span>
+<span class="c1"># Either place them in the distdir directory in third_party and use the --distdir flag</span>
+<span class="c1"># or modify the urls to "file:///&lt;PATH TO TARBALL&gt;/&lt;TARBALL NAME&gt;.tar.gz</span>
+
+<span class="c1">#http_archive(</span>
+<span class="c1">#    name = "cudnn",</span>
+<span class="c1">#    urls = ["https://developer.nvidia.com/compute/machine-learning/cudnn/secure/8.0.1.13/10.2_20200626/cudnn-10.2-linux-x64-v8.0.1.13.tgz"],</span>
+<span class="c1">#    build_file = "@//third_party/cudnn/archive:BUILD",</span>
+<span class="c1">#    sha256 = "0c106ec84f199a0fbcf1199010166986da732f9b0907768c9ac5ea5b120772db",</span>
+<span class="c1">#    strip_prefix = "cuda"</span>
+<span class="c1">#)</span>
+
+<span class="c1">#http_archive(</span>
+<span class="c1">#    name = "tensorrt",</span>
+<span class="c1">#    urls = ["https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/7.1/tars/TensorRT-7.1.3.4.Ubuntu-18.04.x86_64-gnu.cuda-10.2.cudnn8.0.tar.gz"],</span>
+<span class="c1">#    build_file = "@//third_party/tensorrt/archive:BUILD",</span>
+<span class="c1">#    sha256 = "9205bed204e2ae7aafd2e01cce0f21309e281e18d5bfd7172ef8541771539d41",</span>
+<span class="c1">#    strip_prefix = "TensorRT-7.1.3.4"</span>
+<span class="c1">#)</span>
+
+NOTE: You may also need to configure the CUDA version to <span class="m">10</span>.2 by setting the path <span class="k">for</span> the cuda new_local_repository
+</pre>
+        </div>
+       </div>
+       <ol class="arabic" start="2">
+        <li>
+         <p>
+          Configure the correct paths to directory roots containing local dependencies in the
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            new_local_repository
+           </span>
+          </code>
+          rules:
+         </p>
+         <blockquote>
+          <div>
+           <p>
+            NOTE: If you installed PyTorch using a pip package, the correct path is the path to the root of the python torch package.
+In the case that you installed with
+            <code class="docutils literal notranslate">
+             <span class="pre">
+              sudo
+             </span>
+             <span class="pre">
+              pip
+             </span>
+             <span class="pre">
+              install
+             </span>
+            </code>
+            this will be
+            <code class="docutils literal notranslate">
+             <span class="pre">
+              /usr/local/lib/python3.6/dist-packages/torch
+             </span>
+            </code>
+            .
+In the case you installed with
+            <code class="docutils literal notranslate">
+             <span class="pre">
+              pip
+             </span>
+             <span class="pre">
+              install
+             </span>
+             <span class="pre">
+              --user
+             </span>
+            </code>
+            this will be
+            <code class="docutils literal notranslate">
+             <span class="pre">
+              $HOME/.local/lib/python3.6/site-packages/torch
+             </span>
+            </code>
+            .
+           </p>
+          </div>
+         </blockquote>
+        </li>
+       </ol>
+       <p>
+        In the case you are using NVIDIA compiled pip packages, set the path for both libtorch sources to the same path. This is because unlike
+PyTorch on x86_64, NVIDIA aarch64 PyTorch uses the CXX11-ABI. If you compiled for source using the pre_cxx11_abi and only would like to
+use that library, set the paths to the same path but when you compile make sure to add the flag
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          --config=pre_cxx11_abi
+         </span>
+        </code>
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>new_local_repository<span class="o">(</span>
+    <span class="nv">name</span> <span class="o">=</span> <span class="s2">"libtorch"</span>,
+    <span class="nv">path</span> <span class="o">=</span> <span class="s2">"/usr/local/lib/python3.6/dist-packages/torch"</span>,
+    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">"third_party/libtorch/BUILD"</span>
+<span class="o">)</span>
+
+new_local_repository<span class="o">(</span>
+    <span class="nv">name</span> <span class="o">=</span> <span class="s2">"libtorch_pre_cxx11_abi"</span>,
+    <span class="nv">path</span> <span class="o">=</span> <span class="s2">"/usr/local/lib/python3.6/dist-packages/torch"</span>,
+    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">"third_party/libtorch/BUILD"</span>
+<span class="o">)</span>
+
+new_local_repository<span class="o">(</span>
+    <span class="nv">name</span> <span class="o">=</span> <span class="s2">"cudnn"</span>,
+    <span class="nv">path</span> <span class="o">=</span> <span class="s2">"/usr/"</span>,
+    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">"@//third_party/cudnn/local:BUILD"</span>
+<span class="o">)</span>
+
+new_local_repository<span class="o">(</span>
+    <span class="nv">name</span> <span class="o">=</span> <span class="s2">"tensorrt"</span>,
+    <span class="nv">path</span> <span class="o">=</span> <span class="s2">"/usr/"</span>,
+    <span class="nv">build_file</span> <span class="o">=</span> <span class="s2">"@//third_party/tensorrt/local:BUILD"</span>
+<span class="o">)</span>
+</pre>
+        </div>
+       </div>
+       <h4 id="compile-c-library-and-compiler-cli">
+        Compile C++ Library and Compiler CLI
+        <a class="headerlink" href="#compile-c-library-and-compiler-cli" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <blockquote>
+        <div>
+         <p>
+          NOTE: Due to shifting dependency locations between Jetpack 4.5 and 4.6 there is a now a flag to inform bazel of the Jetpack version
+         </p>
+         <div class="highlight-shell notranslate">
+          <div class="highlight">
+           <pre><span></span>--platforms //toolchains:jetpack_4.x
+</pre>
+          </div>
+         </div>
+        </div>
+       </blockquote>
+       <p>
+        Compile Torch-TensorRT library using bazel command:
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>bazel build //:libtorchtrt --platforms //toolchains:jetpack_4.6
+</pre>
+        </div>
+       </div>
+       <h4 id="compile-python-api">
+        Compile Python API
+        <a class="headerlink" href="#compile-python-api" title="Permalink to this headline">
+         ¶
+        </a>
+       </h4>
+       <blockquote>
+        <div>
+         <p>
+          NOTE: Due to shifting dependencies locations between Jetpack 4.5 and Jetpack 4.6 there is now a flag for
+          <code class="docutils literal notranslate">
+           <span class="pre">
+            setup.py
+           </span>
+          </code>
+          which sets the jetpack version (default: 4.6)
+         </p>
+        </div>
+       </blockquote>
+       <p>
+        Compile the Python API using the following command from the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          //py
+         </span>
+        </code>
+        directory:
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>python3 setup.py install --use-cxx11-abi
+</pre>
+        </div>
+       </div>
+       <p>
+        If you have a build of PyTorch that uses Pre-CXX11 ABI drop the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          --use-cxx11-abi
+         </span>
+        </code>
+        flag
+       </p>
+       <p>
+        If you are building for Jetpack 4.5 add the
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          --jetpack-version
+         </span>
+         <span class="pre">
+          4.5
+         </span>
+        </code>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="using_dla.html">DLA</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Example notebooks</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+
+  <section id="example-notebooks">
+<span id="notebooks"></span><h1>Example notebooks<a class="headerlink" href="#example-notebooks" title="Permalink to this headline">¶</a></h1>
+<p>There exists a number of notebooks which cover specific using specific features and models
+with Torch-TensorRT</p>
+<section id="id1">
+<h2>Notebooks<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+<section id="compiling-citrinet-with-torch-tensorrt">
+<h3>Compiling CitriNet with Torch-TensorRT<a class="headerlink" href="#compiling-citrinet-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>Citrinet is an acoustic model used for the speech to text recognition task. It is a version
+of QuartzNet that extends ContextNet, utilizing subword encoding (via Word Piece tokenization)
+and Squeeze-and-Excitation(SE) mechanism and are therefore smaller than QuartzNet models. CitriNet
+models take in audio segments and transcribe them to letter, byte pair, or word piece sequences.</p>
+<p>This notebook demonstrates the steps for optimizing a pretrained CitriNet model with Torch-TensorRT,
+and running it to test the speedup obtained.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/CitriNet-example.ipynb">Torch-TensorRT Getting Started - CitriNet</a></p></li>
+</ul>
+</section>
+<section id="compiling-efficentnet-with-torch-tensorrt">
+<h3>Compiling EfficentNet with Torch-TensorRT<a class="headerlink" href="#compiling-efficentnet-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>EfficentNet is a feedforward CNN designed to achieve better performance and accuracy than alternative architectures
+by using a “scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient”.</p>
+<p>This notebook demonstrates the steps for optimizing a pretrained EfficentNet model with Torch-TensorRT,
+and running it to test the speedup obtained.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/EfficientNet-example.ipynb">Torch-TensorRT Getting Started - EfficientNet-B0</a></p></li>
+</ul>
+</section>
+<section id="masked-language-modeling-mlm-with-hugging-face-bert-transformer-accelerated-by-torch-tensorrt">
+<h3>Masked Language Modeling (MLM) with Hugging Face BERT Transformer accelerated by Torch-TensorRT<a class="headerlink" href="#masked-language-modeling-mlm-with-hugging-face-bert-transformer-accelerated-by-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>“BERT is a transformer model pretrained on a large corpus of English data in a self-supervised fashion.
+This way, the model learns an inner representation of the English language that can then be used to extract
+features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train
+a standard classifier using the features produced by the BERT model as inputs.” (<a class="reference external" href="https://huggingface.co/bert-base-uncased">https://huggingface.co/bert-base-uncased</a>)</p>
+<p>This notebook demonstrates the steps for optimizing a pretrained EfficentNet model with Torch-TensorRT,
+and running it to test the speedup obtained.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/Hugging-Face-BERT.ipynb">Masked Language Modeling (MLM) with Hugging Face BERT Transformer</a></p></li>
+</ul>
+</section>
+<section id="serving-a-model-in-c-using-torch-tensorrt">
+<h3>Serving a model in C++ using Torch-TensorRT<a class="headerlink" href="#serving-a-model-in-c-using-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>This example shows how you can load a pretrained ResNet-50 model, convert it to a Torch-TensorRT
+optimized model (via the Torch-TensorRT Python API), save the model as a torchscript module, and
+then finally load and serve the model with the PyTorch C++ API.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/Resnet50-CPP.ipynb">ResNet C++ Serving Example</a></p></li>
+</ul>
+</section>
+<section id="compiling-resnet50-with-torch-tensorrt">
+<h3>Compiling ResNet50 with Torch-TensorRT<a class="headerlink" href="#compiling-resnet50-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a
+pretrained ResNet-50 network, and running it to test the speedup obtained.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/Resnet50-example.ipynb">Torch-TensorRT Getting Started - ResNet 50</a></p></li>
+</ul>
+</section>
+<section id="using-dynamic-shapes-with-torch-tensorrt">
+<h3>Using Dynamic Shapes with Torch-TensorRT<a class="headerlink" href="#using-dynamic-shapes-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>Making use of Dynamic Shaped Tensors in Torch TensorRT is quite simple. Let’s say you are
+using the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.compile(...)</span></code> function  to compile a torchscript module. One
+of the args in this function in this function is <code class="docutils literal notranslate"><span class="pre">input</span></code>: which defines an input to a
+module in terms of expected shape, data type and tensor format: <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.Input.</span></code></p>
+<p>For the purposes of this walkthrough we just need three kwargs: <cite>min_shape</cite>, <cite>opt_shape`</cite> and <cite>max_shape</cite>.</p>
+<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span>
+        <span class="n">min_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
+        <span class="n">opt_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
+        <span class="n">max_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
+        <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span>
+        <span class="nb">format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channel_last</span>
+    <span class="p">)</span>
+<span class="o">...</span>
+</pre></div>
+</div>
+<p>In this example, we are going to use a simple ResNet model to demonstrate the use of the API.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/dynamic-shapes.ipynb">Torch-TensorRT - Using Dynamic Shapes</a></p></li>
+</ul>
+</section>
+<section id="using-the-fx-frontend-with-torch-tensorrt">
+<h3>Using the FX Frontend with Torch-TensorRT<a class="headerlink" href="#using-the-fx-frontend-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>The purpose of this example is to demostrate the overall flow of lowering a PyTorch model to TensorRT
+conveniently with using FX.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb">Using the FX Frontend with Torch-TensorRT</a></p></li>
+</ul>
+</section>
+<section id="compiling-a-pytorch-model-using-fx-frontend-with-torch-tensorrt">
+<h3>Compiling a PyTorch model using FX Frontend with Torch-TensorRT<a class="headerlink" href="#compiling-a-pytorch-model-using-fx-frontend-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>The purpose of this example is to demonstrate the overall flow of lowering a PyTorch
+model to TensorRT via FX with existing FX based tooling</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/getting_started_with_fx_path_module.ipynb">Compiling a PyTorch model using FX Frontend with Torch-TensorRT</a></p></li>
+</ul>
+</section>
+<section id="compiling-lenet-with-torch-tensorrt">
+<h3>Compiling LeNet with Torch-TensorRT<a class="headerlink" href="#compiling-lenet-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a simple LeNet network.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/lenet-getting-started.ipynb">Torch-TensorRT Getting Started - LeNet</a></p></li>
+</ul>
+</section>
+<section id="accelerate-deep-learning-models-using-quantization-in-torch-tensorrt">
+<h3>Accelerate Deep Learning Models using Quantization in Torch-TensorRT<a class="headerlink" href="#accelerate-deep-learning-models-using-quantization-in-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>Model Quantization is a popular way of optimization which reduces the size of models thereby
+accelerating inference, also opening up the possibilities of deployments on devices with lower
+computation power such as Jetson. Simply put, quantization is a process of mapping input values
+from a larger set to output values in a smaller set. In this notebook, we illustrate the workflow
+that you can adopt while quantizing a deep learning model in Torch-TensorRT. The notebook takes
+you through an example of Mobilenetv2 for a classification task on a subset of Imagenet Dataset
+called Imagenette which has 10 classes.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/qat-ptq-workflow.ipynb">Accelerate Deep Learning Models using Quantization in Torch-TensorRT</a></p></li>
+</ul>
+</section>
+<section id="object-detection-with-torch-tensorrt-ssd">
+<h3>Object Detection with Torch-TensorRT (SSD)<a class="headerlink" href="#object-detection-with-torch-tensorrt-ssd" title="Permalink to this headline">¶</a></h3>
+<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained SSD network, and running it to test the speedup obtained.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/ssd-object-detection-demo.ipynb">Object Detection with Torch-TensorRT (SSD)</a></p></li>
+</ul>
+</section>
+<section id="deploying-quantization-aware-trained-models-in-int8-using-torch-tensorrt">
+<h3>Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT<a class="headerlink" href="#deploying-quantization-aware-trained-models-in-int8-using-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>Quantization Aware training (QAT) simulates quantization during training by
+quantizing weights and activation layers. This will help to reduce the loss in
+accuracy when we convert the network trained in FP32 to INT8 for faster inference.
+QAT introduces additional nodes in the graph which will be used to learn the dynamic
+ranges of weights and activation layers. In this notebook, we illustrate the following
+steps from training to inference of a QAT model in Torch-TensorRT.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/vgg-qat.ipynb">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></p></li>
+</ul>
+</section>
+</section>
+</section>
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+<li><a class="reference internal" href="#id1">Notebooks</a><ul>
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+<li><a class="reference internal" href="#compiling-efficentnet-with-torch-tensorrt">Compiling EfficentNet with Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#masked-language-modeling-mlm-with-hugging-face-bert-transformer-accelerated-by-torch-tensorrt">Masked Language Modeling (MLM) with Hugging Face BERT Transformer accelerated by Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#serving-a-model-in-c-using-torch-tensorrt">Serving a model in C++ using Torch-TensorRT</a></li>
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+<li><a class="reference internal" href="#using-the-fx-frontend-with-torch-tensorrt">Using the FX Frontend with Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#compiling-a-pytorch-model-using-fx-frontend-with-torch-tensorrt">Compiling a PyTorch model using FX Frontend with Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#compiling-lenet-with-torch-tensorrt">Compiling LeNet with Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#accelerate-deep-learning-models-using-quantization-in-torch-tensorrt">Accelerate Deep Learning Models using Quantization in Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#object-detection-with-torch-tensorrt-ssd">Object Detection with Torch-TensorRT (SSD)</a></li>
+<li><a class="reference internal" href="#deploying-quantization-aware-trained-models-in-int8-using-torch-tensorrt">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></li>
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+  <section id="post-training-quantization-ptq">
+<span id="ptq"></span><h1>Post Training Quantization (PTQ)<a class="headerlink" href="#post-training-quantization-ptq" title="Permalink to this headline">¶</a></h1>
+<p>Post Training Quantization (PTQ) is a technique to reduce the required computational resources for inference
+while still preserving the accuracy of your model by mapping the traditional FP32 activation space to a reduced
+INT8 space. TensorRT uses a calibration step which executes your model with sample data from the target domain
+and track the activations in FP32 to calibrate a mapping to INT8 that minimizes the information loss between
+FP32 inference and INT8 inference.</p>
+<p>Users writing TensorRT applications are required to setup a calibrator class which will provide sample data to
+the TensorRT calibrator. With Torch-TensorRT we look to leverage existing infrastructure in PyTorch to make implementing
+calibrators easier.</p>
+<p>LibTorch provides a <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code> and <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> API which steamlines preprocessing and batching input data.
+These APIs are exposed via both C++ and Python interface which makes it easier for the end user.
+For C++ interface, we use <code class="docutils literal notranslate"><span class="pre">torch::Dataset</span></code> and <code class="docutils literal notranslate"><span class="pre">torch::data::make_data_loader</span></code> objects to construct and perform pre-processing on datasets.
+The equivalent functionality in python interface uses <code class="docutils literal notranslate"><span class="pre">torch.utils.data.Dataset</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code>.
+This section of the PyTorch documentation has more information <a class="reference external" href="https://pytorch.org/tutorials/advanced/cpp_frontend.html#loading-data">https://pytorch.org/tutorials/advanced/cpp_frontend.html#loading-data</a> and <a class="reference external" href="https://pytorch.org/tutorials/recipes/recipes/loading_data_recipe.html">https://pytorch.org/tutorials/recipes/recipes/loading_data_recipe.html</a>.
+Torch-TensorRT uses Dataloaders as the base of a generic calibrator implementation. So you will be able to reuse or quickly
+implement a <code class="docutils literal notranslate"><span class="pre">torch::Dataset</span></code> for your target domain, place it in a DataLoader and create a INT8 Calibrator
+which you can provide to Torch-TensorRT to run INT8 Calibration during compliation of your module.</p>
+<section id="how-to-create-your-own-ptq-application-in-c">
+<span id="writing-ptq-cpp"></span><h2>How to create your own PTQ application in C++<a class="headerlink" href="#how-to-create-your-own-ptq-application-in-c" title="Permalink to this headline">¶</a></h2>
+<p>Here is an example interface of a <code class="docutils literal notranslate"><span class="pre">torch::Dataset</span></code> class for CIFAR10:</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="c1">//cpp/ptq/datasets/cifar10.h</span>
+<span class="linenos"> 2</span><span class="cp">#pragma once</span>
+<span class="linenos"> 3</span>
+<span class="linenos"> 4</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/data/datasets/base.h&quot;</span><span class="cp"></span>
+<span class="linenos"> 5</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/data/example.h&quot;</span><span class="cp"></span>
+<span class="linenos"> 6</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/types.h&quot;</span><span class="cp"></span>
+<span class="linenos"> 7</span>
+<span class="linenos"> 8</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cstddef&gt;</span><span class="cp"></span>
+<span class="linenos"> 9</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;string&gt;</span><span class="cp"></span>
+<span class="linenos">10</span>
+<span class="linenos">11</span><span class="k">namespace</span><span class="w"> </span><span class="nn">datasets</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="linenos">12</span><span class="c1">// The CIFAR10 Dataset</span>
+<span class="linenos">13</span><span class="k">class</span><span class="w"> </span><span class="nc">CIFAR10</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="k">public</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">datasets</span><span class="o">::</span><span class="n">Dataset</span><span class="o">&lt;</span><span class="n">CIFAR10</span><span class="o">&gt;</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
+<span class="linenos">14</span><span class="k">public</span><span class="o">:</span><span class="w"></span>
+<span class="linenos">15</span><span class="w">    </span><span class="c1">// The mode in which the dataset is loaded</span>
+<span class="linenos">16</span><span class="w">    </span><span class="k">enum</span><span class="w"> </span><span class="k">class</span><span class="w"> </span><span class="nc">Mode</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">kTrain</span><span class="p">,</span><span class="w"> </span><span class="n">kTest</span><span class="w"> </span><span class="p">};</span><span class="w"></span>
+<span class="linenos">17</span>
+<span class="linenos">18</span><span class="w">    </span><span class="c1">// Loads CIFAR10 from un-tarred file</span>
+<span class="linenos">19</span><span class="w">    </span><span class="c1">// Dataset can be found https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz</span>
+<span class="linenos">20</span><span class="w">    </span><span class="c1">// Root path should be the directory that contains the content of tarball</span>
+<span class="linenos">21</span><span class="w">    </span><span class="k">explicit</span><span class="w"> </span><span class="n">CIFAR10</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">root</span><span class="p">,</span><span class="w"> </span><span class="n">Mode</span><span class="w"> </span><span class="n">mode</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Mode</span><span class="o">::</span><span class="n">kTrain</span><span class="p">);</span><span class="w"></span>
+<span class="linenos">22</span>
+<span class="linenos">23</span><span class="w">    </span><span class="c1">// Returns the pair at index in the dataset</span>
+<span class="linenos">24</span><span class="w">    </span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">Example</span><span class="o">&lt;&gt;</span><span class="w"> </span><span class="n">get</span><span class="p">(</span><span class="kt">size_t</span><span class="w"> </span><span class="n">index</span><span class="p">)</span><span class="w"> </span><span class="k">override</span><span class="p">;</span><span class="w"></span>
+<span class="linenos">25</span>
+<span class="linenos">26</span><span class="w">    </span><span class="c1">// The size of the dataset</span>
+<span class="linenos">27</span><span class="w">    </span><span class="n">c10</span><span class="o">::</span><span class="n">optional</span><span class="o">&lt;</span><span class="kt">size_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">size</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="k">override</span><span class="p">;</span><span class="w"></span>
+<span class="linenos">28</span>
+<span class="linenos">29</span><span class="w">    </span><span class="c1">// The mode the dataset is in</span>
+<span class="linenos">30</span><span class="w">    </span><span class="kt">bool</span><span class="w"> </span><span class="nf">is_train</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="k">noexcept</span><span class="p">;</span><span class="w"></span>
+<span class="linenos">31</span>
+<span class="linenos">32</span><span class="w">    </span><span class="c1">// Returns all images stacked into a single tensor</span>
+<span class="linenos">33</span><span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&amp;</span><span class="w"> </span><span class="nf">images</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span><span class="w"></span>
+<span class="linenos">34</span>
+<span class="linenos">35</span><span class="w">    </span><span class="c1">// Returns all targets stacked into a single tensor</span>
+<span class="linenos">36</span><span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&amp;</span><span class="w"> </span><span class="nf">targets</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span><span class="w"></span>
+<span class="linenos">37</span>
+<span class="linenos">38</span><span class="w">    </span><span class="c1">// Trims the dataset to the first n pairs</span>
+<span class="linenos">39</span><span class="w">    </span><span class="n">CIFAR10</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="nf">use_subset</span><span class="p">(</span><span class="kt">int64_t</span><span class="w"> </span><span class="n">new_size</span><span class="p">);</span><span class="w"></span>
+<span class="linenos">40</span>
+<span class="linenos">41</span>
+<span class="linenos">42</span><span class="k">private</span><span class="o">:</span><span class="w"></span>
+<span class="linenos">43</span><span class="w">    </span><span class="n">Mode</span><span class="w"> </span><span class="n">mode_</span><span class="p">;</span><span class="w"></span>
+<span class="linenos">44</span><span class="w">    </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">images_</span><span class="p">,</span><span class="w"> </span><span class="n">targets_</span><span class="p">;</span><span class="w"></span>
+<span class="linenos">45</span><span class="p">};</span><span class="w"></span>
+<span class="linenos">46</span><span class="p">}</span><span class="w"> </span><span class="c1">// namespace datasets</span>
+</pre></div>
+</div>
+<p>This class’s implementation reads from the binary distribution of the CIFAR10 dataset and builds two tensors which hold the images and labels.</p>
+<p>We use a subset of the dataset to use for calibration, since we don’t need the the full dataset for effective calibration and calibration does
+some take time, then define the preprocessing to apply to the images in the dataset and create a DataLoader from the dataset which will batch the data:</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">calibration_dataset</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">datasets</span><span class="o">::</span><span class="n">CIFAR10</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span><span class="w"> </span><span class="n">datasets</span><span class="o">::</span><span class="n">CIFAR10</span><span class="o">::</span><span class="n">Mode</span><span class="o">::</span><span class="n">kTest</span><span class="p">)</span><span class="w"></span>
+<span class="w">                                    </span><span class="p">.</span><span class="n">use_subset</span><span class="p">(</span><span class="mi">320</span><span class="p">)</span><span class="w"></span>
+<span class="w">                                    </span><span class="p">.</span><span class="n">map</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">transforms</span><span class="o">::</span><span class="n">Normalize</span><span class="o">&lt;&gt;</span><span class="p">({</span><span class="mf">0.4914</span><span class="p">,</span><span class="w"> </span><span class="mf">0.4822</span><span class="p">,</span><span class="w"> </span><span class="mf">0.4465</span><span class="p">},</span><span class="w"></span>
+<span class="w">                                                                            </span><span class="p">{</span><span class="mf">0.2023</span><span class="p">,</span><span class="w"> </span><span class="mf">0.1994</span><span class="p">,</span><span class="w"> </span><span class="mf">0.2010</span><span class="p">}))</span><span class="w"></span>
+<span class="w">                                    </span><span class="p">.</span><span class="n">map</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">transforms</span><span class="o">::</span><span class="n">Stack</span><span class="o">&lt;&gt;</span><span class="p">());</span><span class="w"></span>
+<span class="k">auto</span><span class="w"> </span><span class="n">calibration_dataloader</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">make_data_loader</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">move</span><span class="p">(</span><span class="n">calibration_dataset</span><span class="p">),</span><span class="w"></span>
+<span class="w">                                                            </span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">DataLoaderOptions</span><span class="p">().</span><span class="n">batch_size</span><span class="p">(</span><span class="mi">32</span><span class="p">)</span><span class="w"></span>
+<span class="w">                                                                                            </span><span class="p">.</span><span class="n">workers</span><span class="p">(</span><span class="mi">2</span><span class="p">));</span><span class="w"></span>
+</pre></div>
+</div>
+<p>Next we create a calibrator from the <code class="docutils literal notranslate"><span class="pre">calibration_dataloader</span></code> using the calibrator factory (found in <code class="docutils literal notranslate"><span class="pre">torch_tensorrt/ptq.h</span></code>):</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/ptq.h&quot;</span><span class="cp"></span>
+<span class="p">...</span><span class="w"></span>
+
+<span class="k">auto</span><span class="w"> </span><span class="n">calibrator</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">ptq</span><span class="o">::</span><span class="n">make_int8_calibrator</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">move</span><span class="p">(</span><span class="n">calibration_dataloader</span><span class="p">),</span><span class="w"> </span><span class="n">calibration_cache_file</span><span class="p">,</span><span class="w"> </span><span class="nb">true</span><span class="p">);</span><span class="w"></span>
+</pre></div>
+</div>
+<p>Here we also define a location to write a calibration cache file to which we can use to reuse the calibration data without needing the dataset and whether or not
+we should use the cache file if it exists. There also exists a <code class="docutils literal notranslate"><span class="pre">torch_tensorrt::ptq::make_int8_cache_calibrator</span></code> factory which creates a calibrator that uses the cache
+only for cases where you may do engine building on a machine that has limited storage (i.e. no space for a full dataset) or to have a simpiler deployment application.</p>
+<p>The calibrator factories create a calibrator that inherits from a <code class="docutils literal notranslate"><span class="pre">nvinfer1::IInt8Calibrator</span></code> virtual class (<code class="docutils literal notranslate"><span class="pre">nvinfer1::IInt8EntropyCalibrator2</span></code> by default) which
+defines the calibration algorithm used when calibrating. You can explicitly make the selection of calibration algorithm like this:</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="c1">// MinMax Calibrator is geared more towards NLP tasks</span>
+<span class="k">auto</span><span class="w"> </span><span class="n">calibrator</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">ptq</span><span class="o">::</span><span class="n">make_int8_calibrator</span><span class="o">&lt;</span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8MinMaxCalibrator</span><span class="o">&gt;</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">move</span><span class="p">(</span><span class="n">calibration_dataloader</span><span class="p">),</span><span class="w"> </span><span class="n">calibration_cache_file</span><span class="p">,</span><span class="w"> </span><span class="nb">true</span><span class="p">);</span><span class="w"></span>
+</pre></div>
+</div>
+<p>Then all thats required to setup the module for INT8 calibration is to set the following compile settings in the <cite>torch_tensorrt::CompileSpec</cite> struct and compiling the module:</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">input_shape</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{{</span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">}};</span><span class="w"></span>
+<span class="c1">/// Configure settings for compilation</span>
+<span class="k">auto</span><span class="w"> </span><span class="n">compile_spec</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="p">({</span><span class="n">input_shape</span><span class="p">});</span><span class="w"></span>
+<span class="c1">/// Set operating precision to INT8</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">enabled_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kF16</span><span class="p">);</span><span class="w"></span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">enabled_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kI8</span><span class="p">);</span><span class="w"></span>
+<span class="c1">/// Use the TensorRT Entropy Calibrator</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">ptq_calibrator</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">calibrator</span><span class="p">;</span><span class="w"></span>
+
+<span class="k">auto</span><span class="w"> </span><span class="n">trt_mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span><span class="w"> </span><span class="n">compile_spec</span><span class="p">);</span><span class="w"></span>
+</pre></div>
+</div>
+<p>If you have an existing Calibrator implementation for TensorRT you may directly set the <code class="docutils literal notranslate"><span class="pre">ptq_calibrator</span></code> field with a pointer to your calibrator and it will work as well.
+From here not much changes in terms of how to execution works. You are still able to fully use LibTorch as the sole interface for inference. Data should remain
+in FP32 precision when it’s passed into <cite>trt_mod.forward</cite>. There exists an example application in the Torch-TensorRT demo that takes you from training a VGG16 network on
+CIFAR10 to deploying in INT8 with Torch-TensorRT here: <a class="reference external" href="https://github.com/pytorch/TensorRT/tree/master/cpp/ptq">https://github.com/pytorch/TensorRT/tree/master/cpp/ptq</a></p>
+</section>
+<section id="how-to-create-your-own-ptq-application-in-python">
+<span id="writing-ptq-python"></span><h2>How to create your own PTQ application in Python<a class="headerlink" href="#how-to-create-your-own-ptq-application-in-python" title="Permalink to this headline">¶</a></h2>
+<p>Torch-TensorRT Python API provides an easy and convenient way to use pytorch dataloaders with TensorRT calibrators. <code class="docutils literal notranslate"><span class="pre">DataLoaderCalibrator</span></code> class can be used to create
+a TensorRT calibrator by providing desired configuration. The following code demonstrates an example on how to use it</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">testing_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">CIFAR10</span><span class="p">(</span>
+    <span class="n">root</span><span class="o">=</span><span class="s2">&quot;./data&quot;</span><span class="p">,</span>
+    <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">transform</span><span class="o">=</span><span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span>
+        <span class="p">[</span>
+            <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
+            <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.4914</span><span class="p">,</span> <span class="mf">0.4822</span><span class="p">,</span> <span class="mf">0.4465</span><span class="p">),</span> <span class="p">(</span><span class="mf">0.2023</span><span class="p">,</span> <span class="mf">0.1994</span><span class="p">,</span> <span class="mf">0.2010</span><span class="p">)),</span>
+        <span class="p">]</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">testing_dataloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
+    <span class="n">testing_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">1</span>
+<span class="p">)</span>
+<span class="n">calibrator</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ptq</span><span class="o">.</span><span class="n">DataLoaderCalibrator</span><span class="p">(</span>
+    <span class="n">testing_dataloader</span><span class="p">,</span>
+    <span class="n">cache_file</span><span class="o">=</span><span class="s2">&quot;./calibration.cache&quot;</span><span class="p">,</span>
+    <span class="n">use_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">algo_type</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ptq</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">,</span>
+    <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda:0&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">))],</span>
+                                    <span class="n">enabled_precisions</span><span class="o">=</span><span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">int8</span><span class="p">},</span>
+                                    <span class="n">calibrator</span><span class="o">=</span><span class="n">calibrator</span><span class="p">,</span>
+                                    <span class="n">device</span><span class="o">=</span><span class="p">{</span>
+                                         <span class="s2">&quot;device_type&quot;</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span><span class="p">,</span>
+                                         <span class="s2">&quot;gpu_id&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+                                         <span class="s2">&quot;dla_core&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+                                         <span class="s2">&quot;allow_gpu_fallback&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+                                         <span class="s2">&quot;disable_tf32&quot;</span><span class="p">:</span> <span class="kc">False</span>
+                                     <span class="p">})</span>
+</pre></div>
+</div>
+<p>In the cases where there is a pre-existing calibration cache file that users want to use, <code class="docutils literal notranslate"><span class="pre">CacheCalibrator</span></code> can be used without any dataloaders. The following example demonstrates how
+to use <code class="docutils literal notranslate"><span class="pre">CacheCalibrator</span></code> to use in INT8 mode.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ptq</span><span class="o">.</span><span class="n">CacheCalibrator</span><span class="p">(</span><span class="s2">&quot;./calibration.cache&quot;</span><span class="p">)</span>
+
+<span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">])],</span>
+                                      <span class="n">enabled_precisions</span><span class="o">=</span><span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">int8</span><span class="p">},</span>
+                                      <span class="n">calibrator</span><span class="o">=</span><span class="n">calibrator</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>If you already have an existing calibrator class (implemented directly using TensorRT API), you can directly set the calibrator field to your class which can be very convenient.
+For a demo on how PTQ can be performed on a VGG network using Torch-TensorRT API, you can refer to <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/tests/py/test_ptq_dataloader_calibrator.py">https://github.com/pytorch/TensorRT/blob/master/tests/py/test_ptq_dataloader_calibrator.py</a>
+and <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/tests/py/test_ptq_trt_calibrator.py">https://github.com/pytorch/TensorRT/blob/master/tests/py/test_ptq_trt_calibrator.py</a></p>
+<section id="citations">
+<h3>Citations<a class="headerlink" href="#citations" title="Permalink to this headline">¶</a></h3>
+<p>Krizhevsky, A., &amp; Hinton, G. (2009). Learning multiple layers of features from tiny images.</p>
+<p>Simonyan, K., &amp; Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.</p>
+</section>
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+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1"><a class="reference internal" href="serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="use_from_pytorch.html">Using Torch-TensorRT Directly From PyTorch</a></li>
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+  <section id="deploying-torch-tensorrt-programs">
+<span id="runtime"></span><h1>Deploying Torch-TensorRT Programs<a class="headerlink" href="#deploying-torch-tensorrt-programs" title="Permalink to this headline">¶</a></h1>
+<p>After compiling and saving Torch-TensorRT programs there is no longer a strict dependency on the full
+Torch-TensorRT library. All that is required to run a compiled program is the runtime. There are therfore a couple
+options to deploy your programs other than shipping the full Torch-TensorRT compiler with your applications.</p>
+<section id="torch-tensorrt-package-libtorchtrt-so">
+<h2>Torch-TensorRT package / libtorchtrt.so<a class="headerlink" href="#torch-tensorrt-package-libtorchtrt-so" title="Permalink to this headline">¶</a></h2>
+<p>Once a program is compiled, you run it using the standard PyTorch APIs. All that is required is that the package
+must be imported in python or linked in C++.</p>
+</section>
+<section id="runtime-library">
+<h2>Runtime Library<a class="headerlink" href="#runtime-library" title="Permalink to this headline">¶</a></h2>
+<p>Distributed with the C++ distribution is <code class="docutils literal notranslate"><span class="pre">libtorchtrt_runtime.so</span></code>. This library only contains the components
+necessary to run Torch-TensorRT programs. Instead of linking <code class="docutils literal notranslate"><span class="pre">libtorchtrt.so</span></code> or importing <code class="docutils literal notranslate"><span class="pre">torch_tensorrt</span></code> you can
+link <code class="docutils literal notranslate"><span class="pre">libtorchtrt_runtime.so</span></code> in your deployment programs or use <code class="docutils literal notranslate"><span class="pre">DL_OPEN</span></code> or <code class="docutils literal notranslate"><span class="pre">LD_PRELOAD</span></code>. For python
+you can load the runtime with <code class="docutils literal notranslate"><span class="pre">torch.ops.load_library(&quot;libtorchtrt_runtime.so&quot;)</span></code>. You can then continue to use
+programs just as you would otherwise via PyTorch API.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If you are using the standard distribution of PyTorch in Python on x86, likely you will need the pre-cxx11-abi variant of <code class="docutils literal notranslate"><span class="pre">libtorchtrt_runtime.so</span></code>, check <a class="reference internal" href="../getting_started/installation.html#installation"><span class="std std-ref">Installation</span></a> documentation for more details.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If you are linking <code class="docutils literal notranslate"><span class="pre">libtorchtrt_runtime.so</span></code>, likely using the following flags will help <code class="docutils literal notranslate"><span class="pre">-Wl,--no-as-needed</span> <span class="pre">-ltorchtrt</span> <span class="pre">-Wl,--as-needed</span></code> as theres no direct symbol dependency to anything in the Torch-TensorRT runtime for most Torch-TensorRT runtime applications</p>
+</div>
+<p>An example of how to use <code class="docutils literal notranslate"><span class="pre">libtorchtrt_runtime.so</span></code> can be found here: <a class="reference external" href="https://github.com/pytorch/TensorRT/tree/master/examples/torchtrt_runtime_example">https://github.com/pytorch/TensorRT/tree/master/examples/torchtrt_runtime_example</a></p>
+</section>
+<section id="plugin-library">
+<h2>Plugin Library<a class="headerlink" href="#plugin-library" title="Permalink to this headline">¶</a></h2>
+<p>In the case you use Torch-TensorRT as a converter to a TensorRT engine and your engine uses plugins provided by Torch-TensorRT, Torch-TensorRT
+ships the library <code class="docutils literal notranslate"><span class="pre">libtorchtrt_plugins.so</span></code> which contains the implementation of the TensorRT plugins used by Torch-TensorRT during
+compilation. This library can be <code class="docutils literal notranslate"><span class="pre">DL_OPEN</span></code> or <code class="docutils literal notranslate"><span class="pre">LD_PRELOAD</span></code> similar to other TensorRT plugin libraries.</p>
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+<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="runtime.html">Deploying Torch-TensorRT Programs</a></li>
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+
+  <section id="serving-a-torch-tensorrt-model-with-triton">
+<span id="serving-torch-tensorrt-with-triton"></span><h1>Serving a Torch-TensorRT model with Triton<a class="headerlink" href="#serving-a-torch-tensorrt-model-with-triton" title="Permalink to this headline">¶</a></h1>
+<p>Optimization and deployment go hand in hand in a discussion about Machine
+Learning infrastructure. Once network level optimzation are done
+to get the maximum performance, the next step would be to deploy it.</p>
+<p>However, serving this optimized model comes with it’s own set of considerations
+and challenges like: building an infrastructure to support concorrent model
+executions, supporting clients over HTTP or gRPC and more.</p>
+<p>The <a class="reference external" href="https://github.com/triton-inference-server/server">Triton Inference Server</a>
+solves the aforementioned and more. Let’s discuss step-by-step, the process of
+optimizing a model with Torch-TensorRT, deploying it on Triton Inference
+Server, and building a client to query the model.</p>
+<section id="step-1-optimize-your-model-with-torch-tensorrt">
+<h2>Step 1: Optimize your model with Torch-TensorRT<a class="headerlink" href="#step-1-optimize-your-model-with-torch-tensorrt" title="Permalink to this headline">¶</a></h2>
+<p>Most Torch-TensorRT users will be familiar with this step. For the purpose of
+this demonstration, we will be using a ResNet50 model from Torchhub.</p>
+<p>Let’s first pull the <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">NGC PyTorch Docker container</a>. You may need to create
+an account and get the API key from <a class="reference external" href="https://ngc.nvidia.com/setup/">here</a>.
+Sign up and login with your key (follow the instructions
+<a class="reference external" href="https://ngc.nvidia.com/setup/api-key">here</a> after signing up).</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp"># &lt;xx.xx&gt; is the yy:mm for the publishing tag for NVIDIA&#39;s Pytorch</span>
+<span class="cp"># container; eg. 22.04</span>
+
+<span class="n">docker</span><span class="w"> </span><span class="n">run</span><span class="w"> </span><span class="o">-</span><span class="n">it</span><span class="w"> </span><span class="o">--</span><span class="n">gpus</span><span class="w"> </span><span class="n">all</span><span class="w"> </span><span class="o">-</span><span class="n">v</span><span class="w"> </span><span class="n">$</span><span class="p">{</span><span class="n">PWD</span><span class="p">}</span><span class="o">:/</span><span class="n">scratch_space</span><span class="w"> </span><span class="n">nvcr</span><span class="p">.</span><span class="n">io</span><span class="o">/</span><span class="n">nvidia</span><span class="o">/</span><span class="n">pytorch</span><span class="o">:&lt;</span><span class="n">xx</span><span class="p">.</span><span class="n">xx</span><span class="o">&gt;-</span><span class="n">py3</span><span class="w"></span>
+<span class="n">cd</span><span class="w"> </span><span class="o">/</span><span class="n">scratch_space</span><span class="w"></span>
+</pre></div>
+</div>
+<p>Once inside the container, we can proceed to download a ResNet model from
+Torchhub and optimize it with Torch-TensorRT.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>import torch
+import torch_tensorrt
+torch.hub._validate_not_a_forked_repo=lambda a,b,c: True
+
+# load model
+model = torch.hub.load(&#39;pytorch/vision:v0.10.0&#39;, &#39;resnet50&#39;, pretrained=True).eval().to(&quot;cuda&quot;)
+
+# Compile with Torch TensorRT;
+trt_model = torch_tensorrt.compile(model,
+    inputs= [torch_tensorrt.Input((1, 3, 224, 224))],
+    enabled_precisions= { torch.half} # Run with FP32
+)
+
+# Save the model
+torch.jit.save(trt_model, &quot;model.pt&quot;)
+</pre></div>
+</div>
+<p>After copying the model, exit the container. The next step in the process
+is to set up a Triton Inference Server.</p>
+</section>
+<section id="step-2-set-up-triton-inference-server">
+<h2>Step 2: Set Up Triton Inference Server<a class="headerlink" href="#step-2-set-up-triton-inference-server" title="Permalink to this headline">¶</a></h2>
+<p>If you are new to the Triton Inference Server and want to learn more, we
+highly recommend to checking our <a class="reference external" href="https://github.com/triton-inference-server">Github
+Repository</a>.</p>
+<p>To use Triton, we need to make a model repository. A model repository, as the
+name suggested, is a repository of the models the Inference server hosts. While
+Triton can serve models from multiple repositories, in this example, we will
+discuss the simplest possible form of the model repository.</p>
+<p>The structure of this repository should look something like this:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">model_repository</span><span class="w"></span>
+<span class="o">|</span><span class="w"></span>
+<span class="o">+--</span><span class="w"> </span><span class="n">resnet50</span><span class="w"></span>
+<span class="w">    </span><span class="o">|</span><span class="w"></span>
+<span class="w">    </span><span class="o">+--</span><span class="w"> </span><span class="n">config</span><span class="p">.</span><span class="n">pbtxt</span><span class="w"></span>
+<span class="w">    </span><span class="o">+--</span><span class="w"> </span><span class="mi">1</span><span class="w"></span>
+<span class="w">        </span><span class="o">|</span><span class="w"></span>
+<span class="w">        </span><span class="o">+--</span><span class="w"> </span><span class="n">model</span><span class="p">.</span><span class="n">pt</span><span class="w"></span>
+</pre></div>
+</div>
+<p>There are two files that Triton requires to serve the model: the model itself
+and a model configuration file which is typically provided in <code class="docutils literal notranslate"><span class="pre">config.pbtxt</span></code>.
+For the model we prepared in step 1, the following configuration can be used:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="nl">name</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;resnet50&quot;</span><span class="w"></span>
+<span class="nl">platform</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;pytorch_libtorch&quot;</span><span class="w"></span>
+<span class="nl">max_batch_size</span><span class="w"> </span><span class="p">:</span><span class="w"> </span><span class="mi">0</span><span class="w"></span>
+<span class="n">input</span><span class="w"> </span><span class="p">[</span><span class="w"></span>
+<span class="w">  </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="nl">name</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;input__0&quot;</span><span class="w"></span>
+<span class="w">    </span><span class="nl">data_type</span><span class="p">:</span><span class="w"> </span><span class="n">TYPE_FP32</span><span class="w"></span>
+<span class="w">    </span><span class="nl">dims</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="w"> </span><span class="p">]</span><span class="w"></span>
+<span class="w">    </span><span class="n">reshape</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">shape</span><span class="o">:</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="w"> </span><span class="p">]</span><span class="w"> </span><span class="p">}</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+<span class="p">]</span><span class="w"></span>
+<span class="n">output</span><span class="w"> </span><span class="p">[</span><span class="w"></span>
+<span class="w">  </span><span class="p">{</span><span class="w"></span>
+<span class="w">    </span><span class="nl">name</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;output__0&quot;</span><span class="w"></span>
+<span class="w">    </span><span class="nl">data_type</span><span class="p">:</span><span class="w"> </span><span class="n">TYPE_FP32</span><span class="w"></span>
+<span class="w">    </span><span class="nl">dims</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1000</span><span class="w"> </span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">]</span><span class="w"></span>
+<span class="w">    </span><span class="n">reshape</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">shape</span><span class="o">:</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1000</span><span class="w"> </span><span class="p">]</span><span class="w"> </span><span class="p">}</span><span class="w"></span>
+<span class="w">  </span><span class="p">}</span><span class="w"></span>
+<span class="p">]</span><span class="w"></span>
+</pre></div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">config.pbtxt</span></code> file is used to describe the exact model configuration
+with details like the names and shapes of the input and output layer(s),
+datatypes, scheduling and batching details and more. If you are new to Triton,
+we highly encourage you to check out this <a class="reference external" href="https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md">section of our
+documentation</a>
+for more details.</p>
+<p>With the model repository setup, we can proceed to launch the Triton server
+with the docker command below. Refer <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver">this page</a> for the pull tag for the container.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp"># Make sure that the TensorRT version in the Triton container</span>
+<span class="cp"># and TensorRT version in the environment used to optimize the model</span>
+<span class="cp"># are the same.</span>
+
+<span class="n">docker</span><span class="w"> </span><span class="n">run</span><span class="w"> </span><span class="o">--</span><span class="n">gpus</span><span class="w"> </span><span class="n">all</span><span class="w"> </span><span class="o">--</span><span class="n">rm</span><span class="w"> </span><span class="o">-</span><span class="n">p</span><span class="w"> </span><span class="mi">8000</span><span class="o">:</span><span class="mi">8000</span><span class="w"> </span><span class="o">-</span><span class="n">p</span><span class="w"> </span><span class="mi">8001</span><span class="o">:</span><span class="mi">8001</span><span class="w"> </span><span class="o">-</span><span class="n">p</span><span class="w"> </span><span class="mi">8002</span><span class="o">:</span><span class="mi">8002</span><span class="w"> </span><span class="o">-</span><span class="n">v</span><span class="w"> </span><span class="o">/</span><span class="n">full</span><span class="o">/</span><span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">the_model_repository</span><span class="o">/</span><span class="n">model_repository</span><span class="o">:/</span><span class="n">models</span><span class="w"> </span><span class="n">nvcr</span><span class="p">.</span><span class="n">io</span><span class="o">/</span><span class="n">nvidia</span><span class="o">/</span><span class="n">tritonserver</span><span class="o">:&lt;</span><span class="n">xx</span><span class="p">.</span><span class="n">yy</span><span class="o">&gt;-</span><span class="n">py3</span><span class="w"> </span><span class="n">tritonserver</span><span class="w"> </span><span class="o">--</span><span class="n">model</span><span class="o">-</span><span class="n">repository</span><span class="o">=/</span><span class="n">models</span><span class="w"></span>
+</pre></div>
+</div>
+<p>This should spin up a Triton Inference server. Next step, building a simple
+http client to query the server.</p>
+</section>
+<section id="step-3-building-a-triton-client-to-query-the-server">
+<h2>Step 3: Building a Triton Client to Query the Server<a class="headerlink" href="#step-3-building-a-triton-client-to-query-the-server" title="Permalink to this headline">¶</a></h2>
+<p>Before proceeding, make sure to have a sample image on hand. If you don’t
+have one, download an example image to test inference. In this section, we
+will be going over a very basic client. For a variety of more fleshed out
+examples, refer to the <a class="reference external" href="https://github.com/triton-inference-server/client/tree/main/src/python/examples">Triton Client Repository</a></p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">wget</span><span class="w">  </span><span class="o">-</span><span class="n">O</span><span class="w"> </span><span class="n">img1</span><span class="p">.</span><span class="n">jpg</span><span class="w"> </span><span class="s">&quot;https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg&quot;</span><span class="w"></span>
+</pre></div>
+</div>
+<p>We then need to install dependencies for building a python client. These will
+change from client to client. For a full list of all languages supported by Triton,
+please refer to <a class="reference external" href="https://github.com/triton-inference-server/client">Triton’s client repository</a>.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">torchvision</span><span class="w"></span>
+<span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">attrdict</span><span class="w"></span>
+<span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">nvidia</span><span class="o">-</span><span class="n">pyindex</span><span class="w"></span>
+<span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">tritonclient</span><span class="p">[</span><span class="n">all</span><span class="p">]</span><span class="w"></span>
+</pre></div>
+</div>
+<p>Let’s jump into the client. Firstly, we write a small preprocessing function to
+resize and normalize the query image.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">import</span><span class="w"> </span><span class="n">numpy</span><span class="w"> </span><span class="n">as</span><span class="w"> </span><span class="n">np</span><span class="w"></span>
+<span class="n">from</span><span class="w"> </span><span class="n">torchvision</span><span class="w"> </span><span class="k">import</span><span class="w"> </span><span class="n">transforms</span><span class="w"></span>
+<span class="n">from</span><span class="w"> </span><span class="n">PIL</span><span class="w"> </span><span class="k">import</span><span class="w"> </span><span class="n">Image</span><span class="w"></span>
+<span class="k">import</span><span class="w"> </span><span class="n">tritonclient</span><span class="p">.</span><span class="n">http</span><span class="w"> </span><span class="n">as</span><span class="w"> </span><span class="n">httpclient</span><span class="w"></span>
+<span class="n">from</span><span class="w"> </span><span class="n">tritonclient</span><span class="p">.</span><span class="n">utils</span><span class="w"> </span><span class="k">import</span><span class="w"> </span><span class="n">triton_to_np_dtype</span><span class="w"></span>
+
+<span class="cp"># preprocessing function</span>
+<span class="n">def</span><span class="w"> </span><span class="n">rn50_preprocess</span><span class="p">(</span><span class="n">img_path</span><span class="o">=</span><span class="s">&quot;img1.jpg&quot;</span><span class="p">)</span><span class="o">:</span><span class="w"></span>
+<span class="w">    </span><span class="n">img</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Image</span><span class="p">.</span><span class="n">open</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span><span class="w"></span>
+<span class="w">    </span><span class="n">preprocess</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">transforms</span><span class="p">.</span><span class="n">Compose</span><span class="p">([</span><span class="w"></span>
+<span class="w">        </span><span class="n">transforms</span><span class="p">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span><span class="w"></span>
+<span class="w">        </span><span class="n">transforms</span><span class="p">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span><span class="w"></span>
+<span class="w">        </span><span class="n">transforms</span><span class="p">.</span><span class="n">ToTensor</span><span class="p">(),</span><span class="w"></span>
+<span class="w">        </span><span class="n">transforms</span><span class="p">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span><span class="w"> </span><span class="mf">0.456</span><span class="p">,</span><span class="w"> </span><span class="mf">0.406</span><span class="p">],</span><span class="w"> </span><span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span><span class="w"> </span><span class="mf">0.224</span><span class="p">,</span><span class="w"> </span><span class="mf">0.225</span><span class="p">]),</span><span class="w"></span>
+<span class="w">    </span><span class="p">])</span><span class="w"></span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">preprocess</span><span class="p">(</span><span class="n">img</span><span class="p">).</span><span class="n">numpy</span><span class="p">()</span><span class="w"></span>
+
+<span class="n">transformed_img</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">rn50_preprocess</span><span class="p">()</span><span class="w"></span>
+</pre></div>
+</div>
+<p>Building a client requires three basic points. Firstly, we setup a connection
+with the Triton Inference Server.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp"># Setting up client</span>
+<span class="n">client</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">httpclient</span><span class="p">.</span><span class="n">InferenceServerClient</span><span class="p">(</span><span class="n">url</span><span class="o">=</span><span class="s">&quot;localhost:8000&quot;</span><span class="p">)</span><span class="w"></span>
+</pre></div>
+</div>
+<p>Secondly, we specify the names of the input and output layer(s) of our model.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">inputs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">httpclient</span><span class="p">.</span><span class="n">InferInput</span><span class="p">(</span><span class="s">&quot;input__0&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">transformed_img</span><span class="p">.</span><span class="n">shape</span><span class="p">,</span><span class="w"> </span><span class="n">datatype</span><span class="o">=</span><span class="s">&quot;FP32&quot;</span><span class="p">)</span><span class="w"></span>
+<span class="n">inputs</span><span class="p">.</span><span class="n">set_data_from_numpy</span><span class="p">(</span><span class="n">transformed_img</span><span class="p">,</span><span class="w"> </span><span class="n">binary_data</span><span class="o">=</span><span class="n">True</span><span class="p">)</span><span class="w"></span>
+
+<span class="n">outputs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">httpclient</span><span class="p">.</span><span class="n">InferRequestedOutput</span><span class="p">(</span><span class="s">&quot;output__0&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">binary_data</span><span class="o">=</span><span class="n">True</span><span class="p">,</span><span class="w"> </span><span class="n">class_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span><span class="w"></span>
+</pre></div>
+</div>
+<p>Lastly, we send an inference request to the Triton Inference Server.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span># Querying the server
+results = client.infer(model_name=&quot;resnet50&quot;, inputs=[inputs], outputs=[outputs])
+inference_output = results.as_numpy(&#39;output__0&#39;)
+print(inference_output[:5])
+</pre></div>
+</div>
+<p>The output of the same should look like below:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>[b&#39;12.468750:90&#39; b&#39;11.523438:92&#39; b&#39;9.664062:14&#39; b&#39;8.429688:136&#39;
+ b&#39;8.234375:11&#39;]
+</pre></div>
+</div>
+<p>The output format here is <code class="docutils literal notranslate"><span class="pre">&lt;confidence_score&gt;:&lt;classification_index&gt;</span></code>.
+To learn how to map these to the label names and more, refer to Triton Inference Server’s
+<a class="reference external" href="https://github.com/triton-inference-server/server/blob/main/docs/protocol/extension_classification.md">documentation</a>.</p>
+</section>
+</section>
+
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+              <ul>
+<li><a class="reference internal" href="#">Serving a Torch-TensorRT model with Triton</a><ul>
+<li><a class="reference internal" href="#step-1-optimize-your-model-with-torch-tensorrt">Step 1: Optimize your model with Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#step-2-set-up-triton-inference-server">Step 2: Set Up Triton Inference Server</a></li>
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diff --git a/docs/v1.2.0/tutorials/torchtrtc.html b/docs/v1.2.0/tutorials/torchtrtc.html
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@@ -0,0 +1,700 @@
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+           <a class="md-nav__link" href="creating_torchscript_module_in_python.html#working-with-torchscript-in-python">
+            Working with TorchScript in Python
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">
+            Saving TorchScript Module to Disk
+           </a>
+          </li>
+          <li class="md-nav__item">
+           <a class="md-nav__link" href="ptq.html">
+            Post Training Quantization (PTQ)
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+            Torch-TensorRT Getting Started - EfficientNet-B0
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+            Torch-TensorRT Getting Started - ResNet 50
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+            Object Detection with Torch-TensorRT (SSD)
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+            Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT
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+            torch_tensorrt.ptq
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+            torch_tensorrt.ts
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+            Writing Converters
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+            Useful Links for Torch-TensorRT Development
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+            Operators Supported
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+     </div>
+     <div class="md-content">
+      <article class="md-content__inner md-typeset" role="main">
+       <span id="id1">
+       </span>
+       <h1 id="tutorials-torchtrtc--page-root">
+        torchtrtc
+        <a class="headerlink" href="#tutorials-torchtrtc--page-root" title="Permalink to this headline">
+         ¶
+        </a>
+       </h1>
+       <p>
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torchtrtc
+         </span>
+        </code>
+        is a CLI application for using the Torch-TensorRT compiler. It serves as an easy way to compile a
+TorchScript Module with Torch-TensorRT from the command-line to quickly check support or as part of
+a deployment pipeline. All basic features of the compiler are supported including post training
+quantization (though you must already have a calibration cache file to use the PTQ feature). The compiler can
+output two formats, either a TorchScript program with the TensorRT engine embedded or
+the TensorRT engine itself as a PLAN file.
+       </p>
+       <p>
+        All that is required to run the program after compilation is for C++ linking against
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          libtorchtrt.so
+         </span>
+        </code>
+        or in Python importing the torch_tensorrt package. All other aspects of using compiled modules are identical
+to standard TorchScript. Load with
+        <code class="docutils literal notranslate">
+         <span class="pre">
+          torch.jit.load()
+         </span>
+        </code>
+        and run like you would run any other module.
+       </p>
+       <div class="highlight-txt notranslate">
+        <div class="highlight">
+         <pre><span></span>torchtrtc [input_file_path] [output_file_path]
+  [input_specs...] {OPTIONS}
+
+  torchtrtc is a compiler for TorchScript, it will compile and optimize
+  TorchScript programs to run on NVIDIA GPUs using TensorRT
+
+OPTIONS:
+
+    -h, --help                        Display this help menu
+    Verbiosity of the compiler
+      -v, --verbose                     Dumps debugging information about the
+                                        compilation process onto the console
+      -w, --warnings                    Disables warnings generated during
+                                        compilation onto the console (warnings
+                                        are on by default)
+      --i, --info                       Dumps info messages generated during
+                                        compilation onto the console
+    --build-debuggable-engine         Creates a debuggable engine
+    --allow-gpu-fallback              (Only used when targeting DLA
+                                      (device-type)) Lets engine run layers on
+                                      GPU if they are not supported on DLA
+    --require-full-compilation        Require that the model should be fully
+                                      compiled to TensorRT or throw an error
+    --check-method-support=[method_name]
+                                      Check the support for end to end
+                                      compilation of a specified method in the
+                                      TorchScript module
+    --disable-tf32                    Prevent Float32 layers from using the
+                                      TF32 data format
+    --sparse-weights                  Enable sparsity for weights of conv and
+                                      FC layers
+    -p[precision...],
+    --enable-precision=[precision...] (Repeatable) Enabling an operating
+                                      precision for kernels to use when
+                                      building the engine (Int8 requires a
+                                      calibration-cache argument) [ float |
+                                      float32 | f32 | fp32 | half | float16 |
+                                      f16 | fp16 | int8 | i8 | char ]
+                                      (default: float)
+    -d[type], --device-type=[type]    The type of device the engine should be
+                                      built for [ gpu | dla ] (default: gpu)
+    --gpu-id=[gpu_id]                 GPU id if running on multi-GPU platform
+                                      (defaults to 0)
+    --dla-core=[dla_core]             DLACore id if running on available DLA
+                                      (defaults to 0)
+    --engine-capability=[capability]  The type of device the engine should be
+                                      built for [ standard | safety |
+                                      dla_standalone ]
+    --calibration-cache-file=[file_path]
+                                      Path to calibration cache file to use
+                                      for post training quantization
+    --teo=[op_name...],
+    --torch-executed-op=[op_name...]  (Repeatable) Operator in the graph that
+                                      should always be run in PyTorch for
+                                      execution (partial compilation must be
+                                      enabled)
+    --tem=[module_name...],
+    --torch-executed-mod=[module_name...]
+                                      (Repeatable) Module that should always
+                                      be run in Pytorch for execution (partial
+                                      compilation must be enabled)
+    --mbs=[num_ops],
+    --min-block-size=[num_ops]        Minimum number of contiguous TensorRT
+                                      supported ops to compile a subgraph to
+                                      TensorRT
+    --embed-engine                    Whether to treat input file as a
+                                      serialized TensorRT engine and embed it
+                                      into a TorchScript module (device spec
+                                      must be provided)
+    --num-min-timing-iter=[num_iters] Number of minimization timing iterations
+                                      used to select kernels
+    --num-avg-timing-iters=[num_iters]
+                                      Number of averaging timing iterations
+                                      used to select kernels
+    --workspace-size=[workspace_size] Maximum size of workspace given to
+                                      TensorRT
+    -t[threshold],
+    --threshold=[threshold]           Maximum acceptable numerical deviation
+                                      from standard torchscript output
+                                      (default 2e-5)
+    --no-threshold-check              Skip checking threshold compliance
+    --truncate-long-double,
+    --truncate, --truncate-64bit      Truncate weights that are provided in
+                                      64bit to 32bit (Long, Double to Int,
+                                      Float)
+    --save-engine                     Instead of compiling a full a
+                                      TorchScript program, save the created
+                                      engine to the path specified as the
+                                      output path
+    input_file_path                   Path to input TorchScript file
+    output_file_path                  Path for compiled TorchScript (or
+                                      TensorRT engine) file
+    input_specs...                    Specs for inputs to engine, can either
+                                      be a single size or a range defined by
+                                      Min, Optimal, Max sizes, e.g.
+                                      "(N,..,C,H,W)"
+                                      "[(MIN_N,..,MIN_C,MIN_H,MIN_W);(OPT_N,..,OPT_C,OPT_H,OPT_W);(MAX_N,..,MAX_C,MAX_H,MAX_W)]".
+                                      Data Type and format can be specified by
+                                      adding an "@" followed by dtype and "%"
+                                      followed by format to the end of the
+                                      shape spec. e.g. "(3, 3, 32,
+                                      32)@f16%NHWC"
+    "--" can be used to terminate flag options and force all following
+    arguments to be treated as positional options
+</pre>
+        </div>
+       </div>
+       <p>
+        e.g.
+       </p>
+       <div class="highlight-shell notranslate">
+        <div class="highlight">
+         <pre><span></span>torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts <span class="s2">"[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous"</span> -p f16
+</pre>
+        </div>
+       </div>
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+  <section id="using-torch-tensorrt-directly-from-pytorch">
+<span id="use-from-pytorch"></span><h1>Using Torch-TensorRT Directly From PyTorch<a class="headerlink" href="#using-torch-tensorrt-directly-from-pytorch" title="Permalink to this headline">¶</a></h1>
+<p>You will now be able to directly access TensorRT from PyTorch APIs. The process to use this feature
+is very similar to the compilation workflow described in <a class="reference internal" href="../getting_started/getting_started_with_python_api.html#getting-started-with-python-api"><span class="std std-ref">Using Torch-TensorRT in Python</span></a></p>
+<p>Start by loading <code class="docutils literal notranslate"><span class="pre">torch_tensorrt</span></code> into your application.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+</pre></div>
+</div>
+<p>Then given a TorchScript module, you can compile it with TensorRT using the <code class="docutils literal notranslate"><span class="pre">torch._C._jit_to_backend(&quot;tensorrt&quot;,</span> <span class="pre">...)</span></code> API.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">mobilenet_v2</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">script_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Unlike the <code class="docutils literal notranslate"><span class="pre">compile</span></code> API in Torch-TensorRT which assumes you are trying to compile the <code class="docutils literal notranslate"><span class="pre">forward</span></code> function of a module
+or the <code class="docutils literal notranslate"><span class="pre">convert_method_to_trt_engine</span></code> which converts a specified function to a TensorRT engine, the backend API
+will take a dictionary which maps names of functions to compile to Compilation Spec objects which wrap the same
+sort of dictionary you would provide to <code class="docutils literal notranslate"><span class="pre">compile</span></code>. For more information on the compile spec dictionary take a look
+at the documentation for the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">TensorRTCompileSpec</span></code> API.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">spec</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;forward&quot;</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ts</span><span class="o">.</span><span class="n">TensorRTCompileSpec</span><span class="p">(</span>
+        <span class="p">{</span>
+            <span class="s2">&quot;inputs&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">])],</span>
+            <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">},</span>
+            <span class="s2">&quot;refit&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+            <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+            <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="p">{</span>
+                <span class="s2">&quot;device_type&quot;</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span><span class="p">,</span>
+                <span class="s2">&quot;gpu_id&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+                <span class="s2">&quot;dla_core&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+                <span class="s2">&quot;allow_gpu_fallback&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="s2">&quot;capability&quot;</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">EngineCapability</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
+            <span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="p">}</span>
+    <span class="p">)</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>Now to compile with Torch-TensorRT, provide the target module objects and the spec dictionary to <code class="docutils literal notranslate"><span class="pre">torch._C._jit_to_backend(&quot;tensorrt&quot;,</span> <span class="pre">...)</span></code></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">trt_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_jit_to_backend</span><span class="p">(</span><span class="s2">&quot;tensorrt&quot;</span><span class="p">,</span> <span class="n">script_model</span><span class="p">,</span> <span class="n">spec</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>To run explicitly call the function of the method you want to run (vs. how you can just call on the module itself in standard PyTorch)</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">trt_model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="nb">input</span><span class="p">))</span>
+</pre></div>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+
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+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+
+  <section id="dla">
+<span id="using-dla"></span><h1>DLA<a class="headerlink" href="#dla" title="Permalink to this headline">¶</a></h1>
+<p><code class="docutils literal notranslate"><span class="pre">DLA</span></code> NVIDIA Deep Learning Accelerator is a fixed-function accelerator engine targeted for deep learning operations. DLA is designed to do full hardware acceleration of convolutional neural networks. DLA supports various layers such as convolution, deconvolution, fully-connected, activation, pooling, batch normalization, etc. <code class="docutils literal notranslate"><span class="pre">torch_tensorrt</span></code> supports compilation of TorchScript Module and deployment pipeline on the DLA hardware available on NVIDIA embedded platforms.</p>
+<p>NOTE: DLA supports fp16 and int8 precision only.</p>
+<p>Using DLA with torchtrtc</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torchtrtc <span class="o">[</span>input_file_path<span class="o">]</span> <span class="o">[</span>output_file_path<span class="o">]</span> <span class="o">[</span>input_shapes...<span class="o">]</span> -p f16 -d dla <span class="o">{</span>OPTIONS<span class="o">}</span>
+</pre></div>
+</div>
+<p>Using DLA in a C++ application</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">input_shape</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{{</span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">}};</span><span class="w"></span>
+<span class="k">auto</span><span class="w"> </span><span class="n">compile_spec</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="p">({</span><span class="n">input_shape</span><span class="p">});</span><span class="w"></span>
+
+<span class="cp"># Set a precision. DLA supports fp16 or int8 only</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">enabled_precisions</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kF16</span><span class="p">};</span><span class="w"></span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">device</span><span class="p">.</span><span class="n">device_type</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">DeviceType</span><span class="o">::</span><span class="n">kDLA</span><span class="p">;</span><span class="w"></span>
+
+<span class="cp"># Make sure the gpu id is set to Xavier id for DLA</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">device</span><span class="p">.</span><span class="n">gpu_id</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span><span class="w"></span>
+
+<span class="cp"># Set the DLA core id</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">device</span><span class="p">.</span><span class="n">dla_core</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span><span class="w"></span>
+
+<span class="cp"># If a layer fails to run on DLA it will fallback to GPU</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">device</span><span class="p">.</span><span class="n">allow_gpu_fallback</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">true</span><span class="p">;</span><span class="w"></span>
+</pre></div>
+</div>
+<p>Using DLA in a python application</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">compile_spec</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;inputs&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input</span><span class="o">.</span><span class="n">shape</span><span class="p">)],</span>
+    <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Device</span><span class="p">(</span><span class="s2">&quot;dla:0&quot;</span><span class="p">,</span> <span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <span class="s2">&quot;enalbed_precisions&quot;</span><span class="p">:</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">},</span>
+<span class="p">}</span>
+
+<span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scripted_model</span><span class="p">,</span> <span class="n">compile_spec</span><span class="p">)</span>
+</pre></div>
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diff --git a/docsrc/indices/supported_ops.rst b/docsrc/indices/supported_ops.rst
index 2ce7b34ae7..0367c02710 100644
--- a/docsrc/indices/supported_ops.rst
+++ b/docsrc/indices/supported_ops.rst
@@ -23,6 +23,8 @@ Operators Currently Supported Through Converters
 - aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
 - aten::add.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
 - aten::add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> (Tensor(a!))
+- aten::argmax(Tensor self, int dim, bool keepdim=False) -> (Tensor)
+- aten::argmin(Tensor self, int dim, bool keepdim=False) -> (Tensor)
 - aten::asin(Tensor self) -> (Tensor)
 - aten::asinh(Tensor self) -> (Tensor)
 - aten::atan(Tensor self) -> (Tensor)
@@ -31,6 +33,7 @@ Operators Currently Supported Through Converters
 - aten::avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=[0, 0], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> (Tensor)
 - aten::avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=[], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> (Tensor)
 - aten::batch_norm(Tensor input, Tensor? gamma, Tensor? beta, Tensor? mean, Tensor? var, bool training, float momentum, float eps, bool cudnn_enabled) -> (Tensor)
+- aten::bitwise_not(Tensor self) -> (Tensor)
 - aten::bmm(Tensor self, Tensor mat2) -> (Tensor)
 - aten::cat(Tensor[] tensors, int dim=0) -> (Tensor)
 - aten::ceil(Tensor self) -> (Tensor)
@@ -90,6 +93,7 @@ Operators Currently Supported Through Converters
 - aten::mean(Tensor self, *, int? dtype=None) -> (Tensor)
 - aten::mean.dim(Tensor self, int[] dim, bool keepdim=False, *, int? dtype=None) -> (Tensor)
 - aten::min(Tensor self) -> (Tensor)
+- aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
 - aten::min.other(Tensor self, Tensor other) -> (Tensor)
 - aten::mul.Scalar(Tensor self, Scalar other) -> (Tensor)
 - aten::mul.Tensor(Tensor self, Tensor other) -> (Tensor)
@@ -113,6 +117,7 @@ Operators Currently Supported Through Converters
 - aten::relu(Tensor input) -> (Tensor)
 - aten::relu_(Tensor(a!) self) -> (Tensor(a!))
 - aten::repeat(Tensor self, int[] repeats) -> (Tensor)
+- aten::repeat_interleave.self_int(Tensor self, int repeats, int? dim=None, *, int? output_size=None) -> (Tensor)
 - aten::replication_pad1d(Tensor self, int[2] padding) -> (Tensor)
 - aten::replication_pad2d(Tensor self, int[4] padding) -> (Tensor)
 - aten::replication_pad3d(Tensor self, int[6] padding) -> (Tensor)
@@ -120,6 +125,8 @@ Operators Currently Supported Through Converters
 - aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> (Tensor)
 - aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
 - aten::rsub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
+- aten::scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> (Tensor)
+- aten::scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> (Tensor)
 - aten::select.int(Tensor(a) self, int dim, int index) -> (Tensor(a))
 - aten::sigmoid(Tensor input) -> (Tensor)
 - aten::sigmoid_(Tensor(a!) self) -> (Tensor(a!))
@@ -129,8 +136,10 @@ Operators Currently Supported Through Converters
 - aten::softmax.int(Tensor self, int dim, int? dtype=None) -> (Tensor)
 - aten::split(Tensor self, int[] split_sizes, int dim=0) -> (Tensor[])
 - aten::split.Tensor(Tensor(a) self, int split_size, int dim=0) -> (Tensor[])
+- aten::split.sizes(Tensor(a -> *) self, int[] split_size, int dim=0) -> (Tensor[])
 - aten::split_with_sizes(Tensor(a) self, int[] split_sizes, int dim=0) -> (Tensor[])
 - aten::sqrt(Tensor self) -> (Tensor)
+- aten::square(Tensor self) -> (Tensor)
 - aten::squeeze.dim(Tensor(a) self, int dim) -> (Tensor(a))
 - aten::stack(Tensor[] tensors, int dim=0) -> (Tensor)
 - aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
@@ -179,6 +188,7 @@ Operators Currently Supported Through Evaluators
 - aten::Int.int(int a) -> int
 - aten::__and__(int a, int b) -> (bool)
 - aten::__and__.bool(bool a, bool b) -> (bool)
+- aten::__derive_index(int idx, int start, int step) -> int
 - aten::__getitem__.t(t[](a) list, int idx) -> (t(*))
 - aten::__is__(t1 self, t2 obj) -> bool
 - aten::__isnot__(t1 self, t2 obj) -> bool
@@ -258,6 +268,7 @@ Operators Currently Supported Through Evaluators
 - aten::sub.float(float a, float b) -> (float)
 - aten::sub.int(int a, int b) -> (int)
 - aten::tensor(t[] data, *, int? dtype=None, Device? device=None, bool requires_grad=False) -> (Tensor)
+- prim::TupleIndex(Any tup, int i) -> (Any)
 - prim::dtype(Tensor a) -> (int)
 - prim::max.bool(bool a, bool b) -> (bool)
 - prim::max.float(float a, float b) -> (bool)
diff --git a/docsrc/tutorials/getting_started_with_fx_path.rst b/docsrc/tutorials/getting_started_with_fx_path.rst
index 7d782445a2..3e8b8733ad 100644
--- a/docsrc/tutorials/getting_started_with_fx_path.rst
+++ b/docsrc/tutorials/getting_started_with_fx_path.rst
@@ -22,7 +22,7 @@ Converting a PyTorch Model to TensorRT Engine
 In general, users are welcome to use the ``compile()`` to finish the conversion from a model to tensorRT engine. It is a
 wrapper API that consists of the major steps needed to finish this converison. Please refer to an example usage in ``lower_example.py`` file under ``examples/fx``.
 
-.. code-block:: shell
+.. code-block:: python
 
     def compile(
         module: nn.Module,
@@ -64,7 +64,7 @@ Users can refer to ``fx2trt_example.py`` file in ``examples/fx``.
 * **Step 1: Trace the model with acc_tracer**
 Acc_tracer is a tracer inheritated from FX tracer. It comes with args normalizer to convert all args to kwargs and pass to TRT converters.
 
-.. code-block:: shell
+.. code-block:: python
 
    import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
 
@@ -92,10 +92,10 @@ Explicit batch is the default mode and it must be set for dynamic shape. For mos
 
 For examples of the last path, if we have a 3D tensor t shaped as (batch, sequence, dimension), operations such as torch.transpose(0, 2). If any of these three are not satisfied, we’ll need to specify InputTensorSpec as inputs with dynamic range.
 
-c
+.. code-block:: python
 
-    import deeplearning.trt.fx2trt.converter.converters
-    from torch.fx.experimental.fx2trt.fx2trt import InputTensorSpec, TRTInterpreter
+    import torch_tensorrt.fx.converters
+    from torch_tensorrt.fx import InputTensorSpec, TRTInterpreter
 
     # InputTensorSpec is a dataclass we use to store input information.
     # There're two ways we can build input_specs.
@@ -169,7 +169,7 @@ RuntimeError: Conversion of function xxx not currently supported!
 * **Step 3: Run the model**
 One way is using TRTModule, which is basically a PyTorch nn.Module.
 
-.. code-block:: shell
+.. code-block:: python
 
     from torch_tensorrt.fx import TRTModule
     mod = TRTModule(
@@ -192,13 +192,13 @@ Acc tracer is a custom FX symbolic tracer. It does a couple more things compare
 1. there’re many ops that do similar things in PyTorch ops and builtin ops such like torch.add, builtin.add and torch.Tensor.add. Using acc tracer, we normalize these three ops to a single acc_ops.add. This helps reduce the number of converters we need to write.
 2. acc ops only have kwargs which makes writing converter easier as we don’t need to add additional logic to find arguments in args and kwargs.
 
-FX2TRT
+Torch-TensorRT FX Frontend
 --------
-After symbolic tracing, we have the graph representation of a PyTorch model. fx2trt leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).
+After symbolic tracing, we have the graph representation of a PyTorch model. The Torch-TensorRT FX Frontend leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).
 
 Below is an example of a converter function. The decorator is used to register this converter function with the corresponding node. In this example, we register this converter to a fx node whose target is acc_ops.sigmoid.
 
-.. code-block:: shell
+.. code-block:: python
 
     @tensorrt_converter(acc_ops.sigmoid)
     def acc_ops_sigmoid(network, target, args, kwargs, name):
diff --git a/docsrc/tutorials/notebooks.rst b/docsrc/tutorials/notebooks.rst
index df903fc353..24279113ab 100644
--- a/docsrc/tutorials/notebooks.rst
+++ b/docsrc/tutorials/notebooks.rst
@@ -125,10 +125,10 @@ Accelerate Deep Learning Models using Quantization in Torch-TensorRT
 Model Quantization is a popular way of optimization which reduces the size of models thereby
 accelerating inference, also opening up the possibilities of deployments on devices with lower
 computation power such as Jetson. Simply put, quantization is a process of mapping input values
- from a larger set to output values in a smaller set. In this notebook, we illustrate the workflow
- that you can adopt while quantizing a deep learning model in Torch-TensorRT. The notebook takes
- you through an example of Mobilenetv2 for a classification task on a subset of Imagenet Dataset
- called Imagenette which has 10 classes.
+from a larger set to output values in a smaller set. In this notebook, we illustrate the workflow
+that you can adopt while quantizing a deep learning model in Torch-TensorRT. The notebook takes
+you through an example of Mobilenetv2 for a classification task on a subset of Imagenet Dataset
+called Imagenette which has 10 classes.
 
 * `Accelerate Deep Learning Models using Quantization in Torch-TensorRT <https://github.com/pytorch/TensorRT/blob/master/notebooks/qat-ptq-workflow.ipynb>`_
 
diff --git a/py/setup.py b/py/setup.py
index 88626a9e04..c0ebc37ac6 100644
--- a/py/setup.py
+++ b/py/setup.py
@@ -28,8 +28,8 @@
 
 CI_RELEASE = False
 
-__version__ = "1.2.0a0"
-__cuda_version__ = "11.3"
+__version__ = "1.2.0"
+__cuda_version__ = "11.6"
 __cudnn_version__ = "8.4"
 __tensorrt_version__ = "8.4"
 
@@ -376,7 +376,7 @@ def run(self):
     long_description=long_description,
     ext_modules=ext_modules,
     install_requires=[
-        "torch>=1.12.0+cu113,<1.13.0",
+        "torch>=1.12.0+cu116,<1.13.0",
     ],
     setup_requires=[],
     cmdclass={