AE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.python.keras import Input\n",
    "from tensorflow.python.keras.models import Model\n",
    "from tensorflow.python.keras.engine.keras_tensor import KerasTensor\n",
    "from tensorflow.python.keras.engine.functional import Functional\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.python.keras.regularizers import l2\n",
    "import os\n",
    "\n",
    "from tensorflow_model_optimization.python.core.sparsity.keras import prune, pruning_callbacks, pruning_schedule\n",
    "from tensorflow_model_optimization.sparsity.keras import strip_pruning, prune_low_magnitude\n",
    "\n",
    "from callbacks import all_callbacks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the MNIST data class and call it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original dataset and modified dataset with modifiable resolution is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 196)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import MNIST_dataset as mnist\n",
    "size_final = 14\n",
    "\n",
    "data_zoom = mnist.MNISTData(size_initial=20,size_final=size_final, color_depth=5)\n",
    "test = data_zoom.x_test\n",
    "\n",
    "plt.imshow(test[0].reshape(size_final,size_final), cmap='gray')\n",
    "print(data_zoom.x_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "markdown",
     "checksum": "cec58d97b4c05e29e9afdeb102569f20",
     "grade": false,
     "grade_id": "cell-6ee6a27aac2a7e5b",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "source": [
    "# Autoencoder using Keras\n",
    "The aim of this class is to implement a simple autoencoder for the MNIST data and then build an autoencoder that is\n",
    "able to classify MNIST data in its latent dimension.\n",
    "\n",
    "Code partially adapted from [Keras Documentation](https://blog.keras.io/building-autoencoders-in-keras.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "8f093c3123887a615df510d5cb9d3bee",
     "grade": false,
     "grade_id": "cell-b3452809a3ed4ee3",
     "locked": true,
     "schema_version": 3,
     "solution": false,
     "task": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class Autoencoder:\n",
    "    \"\"\"Autoencoder Class\"\"\"\n",
    "\n",
    "    def __init__(self, data: mnist.MNISTData, num_classes=10, pruned = False):\n",
    "        self.x_train = data.x_train\n",
    "        self.x_test = data.x_test\n",
    "        self.y_train = data.y_train\n",
    "        self.y_test = data.y_test\n",
    "\n",
    "        self.num_classes = num_classes\n",
    "        self.pruned = pruned\n",
    "        self.input_shape = (self.x_train.shape[-1],)\n",
    "        self.input = Input(shape=self.input_shape, name='encoder_input')\n",
    "        self.latent_dim = 2\n",
    "\n",
    "        self.encoder = None\n",
    "        self.encoder_model = None\n",
    "        self.decoder = None\n",
    "        self.latent_classifier = None\n",
    "        self.autoencoder = None\n",
    "\n",
    "        self.history = None\n",
    "\n",
    "        self.pruning_params = {\"pruning_schedule\" : pruning_schedule.ConstantSparsity(0.75, begin_step=2000, frequency=100)}\n",
    "    \n",
    "    def build_encoder(self):\n",
    "        \"\"\"Build your encoder architecture and store the output in self.encoder. \n",
    "        The final encoding dimension is 2.\"\"\"\n",
    "\n",
    "        self.encoder = Dense(32,  activation='relu')(self.input)\n",
    "        \n",
    "        if self.pruned:\n",
    "            self.encoder = prune_low_magnitude(Dense(16,  activation='relu'), **self.pruning_params)(self.encoder) \n",
    "        \n",
    "        if not self.pruned:\n",
    "            self.encoder = Dense(16,  activation='relu')(self.encoder)   \n",
    "\n",
    "        self.encoder = Dense(self.latent_dim,  activation='relu', name = 'encoder_output')(self.encoder)\n",
    "        #building a model for the encoder in order to be able to predict and plot the latent dimension\n",
    "        self.encoder_model = Model(self.input, self.encoder, name='encoder')\n",
    "\n",
    "    def build_decoder(self):\n",
    "        \"\"\"Build the decoder architecture.\"\"\"\n",
    "        if self.encoder is None:\n",
    "            raise RuntimeError(\"The encoder has to be built before you can build the decoder!\")\n",
    "\n",
    "        if self.pruned:\n",
    "            self.decoder = prune_low_magnitude(Dense(16,  activation='relu'), **self.pruning_params)(self.encoder)\n",
    "\n",
    "        if not self.pruned:\n",
    "            self.decoder = Dense(16, activation='relu')(self.encoder)\n",
    "        self.decoder = Dense(32,activation='relu')(self.decoder)     \n",
    "        \n",
    "        self.decoder = Dense(self.input_shape[0],activation='sigmoid', name='ecoder_output')(self.decoder)\n",
    "\n",
    "    def build_classifier(self):\n",
    "        \"\"\" Building the classifier architecture.\"\"\"\n",
    "        if self.encoder is None:\n",
    "            raise RuntimeError(\"The encoder has to be built before you can build the classifier!\")\n",
    "        \n",
    "        if self.pruned:\n",
    "            self.latent_classifier = prune_low_magnitude(Dense(16,  activation='relu'), **self.pruning_params)(self.encoder)\n",
    "        \n",
    "        if not self.pruned:\n",
    "            self.latent_classifier = Dense(16, activation='relu')(self.encoder)\n",
    "        \n",
    "        self.latent_classifier = Dense(32, activation='relu')(self.latent_classifier)\n",
    "        self.latent_classifier = Dense(self.num_classes, activation='softmax',name='classifier_output')(self.latent_classifier)\n",
    "\n",
    "    def build_autoencoder(self, use_latent_classifier=False):\n",
    "        \"\"\" Build the whole autoencoder Model from self.encoder and self.decoder.\"\"\"\n",
    "        self.dir()\n",
    "        \n",
    "        if self.encoder is None:\n",
    "            raise RuntimeError(\"The encoder has to be built before you can build the autoencoder!\")\n",
    "        if self.decoder is None:\n",
    "            raise RuntimeError(\"The decoder has to be built before you can build the autoencoder!\")\n",
    "        \n",
    "        if use_latent_classifier:\n",
    "            if self.latent_classifier is None:\n",
    "                raise RuntimeError(\"If you want to use the option with the latent classifier, you have to build it \"\n",
    "                                   \"beforehand!\")\n",
    "            self.autoencoder = Model(self.input, outputs=[self.decoder, self.latent_classifier])\n",
    "            self.autoencoder.compile(loss=['mse', 'categorical_crossentropy'], loss_weights=[1, 0.1], optimizer='adam',\n",
    "                                     metrics=\"accuracy\")\n",
    "            if not self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_model_w_classifier.h5')\n",
    "            if self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_pruned_model_w_classifier.h5')\n",
    "    \n",
    "        else:\n",
    "            self.autoencoder = Model(self.input, outputs=self.decoder, name = 'autoencoder')\n",
    "            self.autoencoder.compile(loss='mse', optimizer='adam') \n",
    "            if not self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_model_wo_classifier.h5')\n",
    "            if self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_pruned_model_wo_classifier.h5')\n",
    "        self.autoencoder.summary()\n",
    "\n",
    "    def fit_data(self, batch_size=256, epochs=20, use_latent_classifier=False):\n",
    "        \"\"\"Write the fit function for the autoencoder. \n",
    "        Storing the fit history in self.history to be able to plot the fitting scores.\"\"\"\n",
    "        \n",
    "        callbacks = all_callbacks(stop_patience = 1000,\n",
    "                              lr_factor = 0.5,\n",
    "                              lr_patience = 10,\n",
    "                              lr_epsilon = 0.000001,\n",
    "                              lr_cooldown = 2,\n",
    "                              lr_minimum = 0.0000001,\n",
    "                              outputDir = 'model/AE_model/model_2')\n",
    "        callbacks.callbacks.append(pruning_callbacks.UpdatePruningStep()) \n",
    "        \n",
    "        if use_latent_classifier:\n",
    "            self.history = self.autoencoder.fit(self.x_train, [self.x_train, self.y_train],\n",
    "                                                validation_data=(self.x_test, [self.x_test, self.y_test]),\n",
    "                                                batch_size=batch_size, epochs=epochs,\n",
    "                                                shuffle=True, callbacks=callbacks.callbacks)\n",
    "            self.autoencoder = strip_pruning(self.autoencoder)\n",
    "            if not self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_best_model_classifier.h5')\n",
    "            if self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_pruned_best_model_classifier.h5')\n",
    "        else:                         \n",
    "            self.history = self.autoencoder.fit(self.x_train, self.x_train,\n",
    "                                                validation_data=(self.x_test, self.x_test),\n",
    "                                                batch_size=batch_size, epochs=epochs,\n",
    "                                                shuffle=True, callbacks=callbacks.callbacks)  \n",
    "            self.autoencoder = strip_pruning(self.autoencoder)\n",
    "            if not self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_best_model.h5')\n",
    "            if self.pruned:\n",
    "                self.autoencoder.save('model/AE_model/KERAS_check_pruned_best_model.h5')\n",
    "\n",
    "        self.history = self.history.history\n",
    "\n",
    "    def dir(self):\n",
    "        \"\"\"Creation of the folders path to store the results.\"\"\"\n",
    "        dir = os.path.join(\"images\")\n",
    "        if not os.path.exists(dir):\n",
    "            os.mkdir(dir)\n",
    "        dir2 = os.path.join(\"./images/AE\")\n",
    "        if not os.path.exists(dir2):\n",
    "            os.mkdir(dir2)\n",
    "        dir3 = os.path.join(\"model\")\n",
    "        if not os.path.exists(dir3):\n",
    "            os.mkdir(dir3)\n",
    "        dir4 = os.path.join(\"./model/AE_model\")\n",
    "        if not os.path.exists(dir4):\n",
    "            os.mkdir(dir4)\n",
    "        \n",
    "    \n",
    "    def plot_score(self, use_latent_classifier = False, model_name=None):\n",
    "        \"\"\"Plots the scores achieved during the fitting.\"\"\"\n",
    "        if use_latent_classifier is False:\n",
    "            plt.plot(self.history['loss'])\n",
    "            plt.plot(self.history['val_loss'])\n",
    "            plt.ylabel('Model Accuracy')\n",
    "        else:\n",
    "            plt.plot(self.history['classifier_output_accuracy'])\n",
    "            plt.plot(self.history['val_classifier_output_accuracy'])\n",
    "            plt.ylabel('Classifier Accuracy')\n",
    "        \n",
    "        plt.xlabel('Epoch')        \n",
    "        plt.legend(['Train', 'Test'], loc='best')\n",
    "        plt.title('Accuracy of {model_name}'.format(model_name=model_name))\n",
    "        \n",
    "        plt.savefig('./images/AE/Accuracy of {model_name}.png'.format(model_name=model_name))\n",
    "        \n",
    "        plt.show()\n",
    "\n",
    "    def plot_latent_dimension(self, model_name = None):\n",
    "        \"\"\"Plots the latent dimension of the autoencoder.\"\"\"\n",
    "        prediction = self.encoder_model.predict(self.x_test)\n",
    "        fig = plt.figure(figsize=(10, 10))\n",
    "        fig.patch.set_facecolor(\"white\")\n",
    "        plt.scatter(prediction[:, 0], prediction[:, 1], c=np.argmax(self.y_test, axis=1), cmap=\"Set3\")\n",
    "        plt.colorbar()\n",
    "        plt.title('Latent space {model_name}'.format(model_name=model_name))\n",
    "\n",
    "\n",
    "        plt.savefig('./images/AE/Latent dimension of {model_name}.png'.format(model_name=model_name))\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def plot_reco(self, use_latent_classifier = False):\n",
    "        \"\"\"Plotting reconstructed images\"\"\"\n",
    "        \n",
    "        n = 6\n",
    "        plt.figure(figsize=(10, 3))\n",
    "        reco_imgs = self.autoencoder.predict(self.x_test)\n",
    "        img_size = int(np.sqrt(self.input_shape[0]))\n",
    "\n",
    "        \n",
    "        if use_latent_classifier :\n",
    "            raise RuntimeError(\"You are considering the autoencoder with classifier!\")\n",
    "        else:\n",
    "        \n",
    "            for i in range(n):\n",
    "                # Display original MNIST\n",
    "                ax = plt.subplot(2, n, i + 1)\n",
    "                plt.imshow(self.x_test[i].reshape(img_size,img_size), cmap='gray_r')\n",
    "                ax.get_xaxis().set_visible(False)\n",
    "                ax.get_yaxis().set_visible(False)\n",
    "\n",
    "                # Display reconstructed MNIST            \n",
    "                ax = plt.subplot(2, n, i + n + 1)\n",
    "                plt.imshow(reco_imgs[i].reshape(img_size,img_size), cmap='gray_r')\n",
    "                ax.get_xaxis().set_visible(False)\n",
    "                ax.get_yaxis().set_visible(False)\n",
    "            plt.savefig('./images/AE/reconstructed images.png')\n",
    "            plt.show()\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is built the standard autoencoder, *without classifier* here, and the MNIST data class called before is used. \n",
    "The model is fitted and adequate plots are reproduced, \n",
    "as well as the comparison between orginal MNIST dataset and reconstructed images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"autoencoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "encoder_input (InputLayer)   [(None, 196)]             0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 32)                6304      \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "encoder_output (Dense)       (None, 2)                 34        \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 16)                48        \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 32)                544       \n",
      "_________________________________________________________________\n",
      "ecoder_output (Dense)        (None, 196)               6468      \n",
      "=================================================================\n",
      "Total params: 13,926\n",
      "Trainable params: 13,926\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "WARNING:tensorflow:`epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n",
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
      "Epoch 1/20\n",
      "  2/235 [..............................] - ETA: 49s - loss: 0.1309WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.4227s). Check your callbacks.\n",
      "198/235 [========================>.....] - ETA: 0s - loss: 0.0828\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.05054, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.05054, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 4ms/step - loss: 0.0778 - val_loss: 0.0505\n",
      "Epoch 2/20\n",
      "214/235 [==========================>...] - ETA: 0s - loss: 0.0465\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.05054 to 0.04136, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.05054 to 0.04136, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0461 - val_loss: 0.0414\n",
      "Epoch 3/20\n",
      "208/235 [=========================>....] - ETA: 0s - loss: 0.0398\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.04136 to 0.03784, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.04136 to 0.03784, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0396 - val_loss: 0.0378\n",
      "Epoch 4/20\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.0376\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.03784 to 0.03675, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.03784 to 0.03675, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0376 - val_loss: 0.0368\n",
      "Epoch 5/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0365\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.03675 to 0.03570, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.03675 to 0.03570, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0365 - val_loss: 0.0357\n",
      "Epoch 6/20\n",
      "218/235 [==========================>...] - ETA: 0s - loss: 0.0358- ETA: 0s - loss: 0.\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.03570 to 0.03507, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.03570 to 0.03507, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0357 - val_loss: 0.0351\n",
      "Epoch 7/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0351\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.03507 to 0.03459, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.03507 to 0.03459, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0351 - val_loss: 0.0346\n",
      "Epoch 8/20\n",
      "210/235 [=========================>....] - ETA: 0s - loss: 0.0347\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.03459 to 0.03406, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.03459 to 0.03406, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0346 - val_loss: 0.0341\n",
      "Epoch 9/20\n",
      "222/235 [===========================>..] - ETA: 0s - loss: 0.0342\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.03406 to 0.03366, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.03406 to 0.03366, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0342 - val_loss: 0.0337\n",
      "Epoch 10/20\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.0339\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.03366 to 0.03344, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.03366 to 0.03344, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_epoch10.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0339 - val_loss: 0.0334\n",
      "Epoch 11/20\n",
      "230/235 [============================>.] - ETA: 0s - loss: 0.0337\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.03344 to 0.03328, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.03344 to 0.03328, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0337 - val_loss: 0.0333\n",
      "Epoch 12/20\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.0335\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.03328 to 0.03297, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.03328 to 0.03297, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0335 - val_loss: 0.0330\n",
      "Epoch 13/20\n",
      "230/235 [============================>.] - ETA: 0s - loss: 0.0332\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.03297\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 0.03297\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0332 - val_loss: 0.0330\n",
      "Epoch 14/20\n",
      "222/235 [===========================>..] - ETA: 0s - loss: 0.0331\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.03297 to 0.03267, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.03297 to 0.03267, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0330 - val_loss: 0.0327\n",
      "Epoch 15/20\n",
      "202/235 [========================>.....] - ETA: 0s - loss: 0.0329- ETA: 0s - loss: 0.\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.03267\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 0.03267\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0329 - val_loss: 0.0328\n",
      "Epoch 16/20\n",
      "225/235 [===========================>..] - ETA: 0s - loss: 0.0327\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.03267 to 0.03236, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.03267 to 0.03236, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0327 - val_loss: 0.0324\n",
      "Epoch 17/20\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.0326\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.03236 to 0.03230, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.03236 to 0.03230, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0326 - val_loss: 0.0323\n",
      "Epoch 18/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.0325\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.03230 to 0.03214, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.03230 to 0.03214, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0324 - val_loss: 0.0321\n",
      "Epoch 19/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.0323\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.03214 to 0.03203, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.03214 to 0.03203, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0323 - val_loss: 0.0320\n",
      "Epoch 20/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0322\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.03203\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 0.03203\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_epoch20.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0322 - val_loss: 0.0321\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x216 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "network = Autoencoder(data_zoom, pruned=False)\n",
    "network.build_encoder()\n",
    "network.build_decoder()\n",
    "network.build_autoencoder()\n",
    "network.fit_data(epochs=20)\n",
    "network.plot_latent_dimension(\"Autoencoder without classifier\")\n",
    "network.plot_reco()\n",
    "network.plot_score(use_latent_classifier=False,model_name=\"Autoencoder without classifier\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\loren\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow_model_optimization\\python\\core\\sparsity\\keras\\pruning_wrapper.py:212: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.add_weight` method instead.\n",
      "Model: \"autoencoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "encoder_input (InputLayer)   [(None, 196)]             0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 32)                6304      \n",
      "_________________________________________________________________\n",
      "prune_low_magnitude_dense_5  (None, 16)                1042      \n",
      "_________________________________________________________________\n",
      "encoder_output (Dense)       (None, 2)                 34        \n",
      "_________________________________________________________________\n",
      "prune_low_magnitude_dense_6  (None, 16)                82        \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 32)                544       \n",
      "_________________________________________________________________\n",
      "ecoder_output (Dense)        (None, 196)               6468      \n",
      "=================================================================\n",
      "Total params: 14,474\n",
      "Trainable params: 13,926\n",
      "Non-trainable params: 548\n",
      "_________________________________________________________________\n",
      "WARNING:tensorflow:`epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n",
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
      "Epoch 1/20\n",
      "  2/235 [..............................] - ETA: 1:05 - loss: 0.1298WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0000s vs `on_train_batch_end` time: 0.5658s). Check your callbacks.\n",
      "234/235 [============================>.] - ETA: 0s - loss: 0.0957\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.06874, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.06874, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.0957 - val_loss: 0.0687\n",
      "Epoch 2/20\n",
      "213/235 [==========================>...] - ETA: 0s - loss: 0.0525\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.06874 to 0.04456, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.06874 to 0.04456, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0519 - val_loss: 0.0446\n",
      "Epoch 3/20\n",
      "220/235 [===========================>..] - ETA: 0s - loss: 0.0434\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.04456 to 0.04167, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.04456 to 0.04167, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0433 - val_loss: 0.0417\n",
      "Epoch 4/20\n",
      "198/235 [========================>.....] - ETA: 0s - loss: 0.0413\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.04167 to 0.04021, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.04167 to 0.04021, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0412 - val_loss: 0.0402\n",
      "Epoch 5/20\n",
      "203/235 [========================>.....] - ETA: 0s - loss: 0.0401\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.04021 to 0.03911, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.04021 to 0.03911, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0400 - val_loss: 0.0391\n",
      "Epoch 6/20\n",
      "210/235 [=========================>....] - ETA: 0s - loss: 0.0392\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.03911 to 0.03849, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.03911 to 0.03849, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0392 - val_loss: 0.0385\n",
      "Epoch 7/20\n",
      "226/235 [===========================>..] - ETA: 0s - loss: 0.0386\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.03849 to 0.03786, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.03849 to 0.03786, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0386 - val_loss: 0.0379\n",
      "Epoch 8/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0381\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.03786 to 0.03765, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.03786 to 0.03765, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0381 - val_loss: 0.0376\n",
      "Epoch 9/20\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.0476\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.03765\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.03765\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0473 - val_loss: 0.0406\n",
      "Epoch 10/20\n",
      "214/235 [==========================>...] - ETA: 0s - loss: 0.0394\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.03765\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.03765\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_epoch10.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0393 - val_loss: 0.0381\n",
      "Epoch 11/20\n",
      "221/235 [===========================>..] - ETA: 0s - loss: 0.0379\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.03765 to 0.03729, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.03765 to 0.03729, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0379 - val_loss: 0.0373\n",
      "Epoch 12/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.0374\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.03729 to 0.03686, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.03729 to 0.03686, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0374 - val_loss: 0.0369\n",
      "Epoch 13/20\n",
      "214/235 [==========================>...] - ETA: 0s - loss: 0.0370\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.03686 to 0.03660, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.03686 to 0.03660, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0370 - val_loss: 0.0366\n",
      "Epoch 14/20\n",
      "215/235 [==========================>...] - ETA: 0s - loss: 0.0367\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.03660 to 0.03627, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.03660 to 0.03627, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0367 - val_loss: 0.0363\n",
      "Epoch 15/20\n",
      "208/235 [=========================>....] - ETA: 0s - loss: 0.0364\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.03627 to 0.03601, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.03627 to 0.03601, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0364 - val_loss: 0.0360\n",
      "Epoch 16/20\n",
      "217/235 [==========================>...] - ETA: 0s - loss: 0.0361\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.03601 to 0.03575, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.03601 to 0.03575, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0361 - val_loss: 0.0358\n",
      "Epoch 17/20\n",
      "206/235 [=========================>....] - ETA: 0s - loss: 0.0359\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.03575 to 0.03553, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.03575 to 0.03553, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0359 - val_loss: 0.0355\n",
      "Epoch 18/20\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.0356\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.03553 to 0.03537, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.03553 to 0.03537, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0357 - val_loss: 0.0354\n",
      "Epoch 19/20\n",
      "205/235 [=========================>....] - ETA: 0s - loss: 0.0355\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.03537\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.03537\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0355 - val_loss: 0.0354\n",
      "Epoch 20/20\n",
      "218/235 [==========================>...] - ETA: 0s - loss: 0.0354\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.03537 to 0.03503, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.03537 to 0.03503, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_epoch20.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0353 - val_loss: 0.0350\n"
     ]
    },
    {
     "data": {
      "image/png": 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01vT29vLss8/S09PDJZdcgsfj4dvf/jaWZfG73/2OzZs3D/s8Hn/8cb785S+zY8eO5Fd/C01bW9tJvy6XXnop+/fvZ/PmzclWtf5WvCuvvDLtOaWlpRw7dgzXdcd0jbvvvptnn32Wl156Ccuy+OY3v4nf70+GvJUrV/Lzn/8cx3F4/vnn+ctf/pJyrba2Nrq6utKWbRgGH/3oR/n85z9PfX09juPw1ltvEYvFUo6Lx+PEYjGKi4vxeDw899xzvPDCC8nH//CHP3Do0CG01uTm5mKaJoZhsH//fl5++WVisRiBQICMjIyUn6Ox+tu//Vu+9KUvJcNZS0sLTz311EmXI8REkVByFrvpppvIyMhIfj300EN89rOfJRKJUFRUxMUXX8yNN96Ycs5nPvMZfvOb35Cfn8/f//3fk52dzQsvvMAvfvELKioqKCsr48EHHxzyYTqchx56iAceeIC8vDx+9atfDXl8y5YtrF27lqysLG699VYeeeQRZs+enXz8tttuY/Xq1axcuZKbb76Zj33sYwB8+ctf5u233yY3N5ebb76ZO+64I3mOaZo888wzHDp0iOnTpzNt2rTk+he33347Dz74IPfeey85OTksXbqU5557btj6/+Y3v+Gmm27innvuITc3l6VLl7J161auu+46AJ544gmOHTtGRUUFt99+O1/5yleSj52KD3/4w3zkIx+hrKyMaDTKt7/97WGPHem5dHd38/GPf5z8/HxmzJhBYWEh//iP/wgkWjFmzpxJTk4Ojz76KD/72c8AWLNmDd/73vf49Kc/TX5+PnPnzuVHP/oRAD6fj9/97nf86Ec/oqCggF/+8pcpr/lAGzdupLq6mk996lOUlZUlv2699Vbmzp3LE088kTw2Ly8vZZ2Sb33rW2nLDAaDrFq1iiVLluDz+QC45JJLmDFjBiUlJWnPueuuu4BEN9yqVauGfR37LViwgJ/+9Kf8j//xPygqKuKZZ57hmWeeSV7vkUce4Zlnnkl2Yb7//e9Pnrtw4ULuu+8+Zs+eTV5eXtquk//4j/9g2bJlXHjhhRQUFPDggw8OCUzZ2dl8+9vf5u677yY/P5+f//zn3HrrrcnHDx48yHXXXUdWVhaXXHIJn/zkJ7n66quJxWJ88YtfpKioiLKyMpqbm/na17426nMe7DOf+Qy33norN9xwA9nZ2Vx88cVs2rTppMsRYqIoPbC9VohJpJTi4MGDzJ0790xXZVJcddVVfOhDH0pZfVMIIcQJ0lIihBBCiClBQokQQgghJsQjjzzC0qVLWbJkCQ8//PCox0v3jRBCCCHG3e7du7n33nvZvHkzPp+PG2+8kUcffXTELntpKRFCCCHEuHv33XdZu3YtmZmZeDwe1q1bx+9+97sRzxm6+tQEKioqYubMmZN5SSGEEOKMOXbsWMoCiRPtxmtn0toWmZRrRayi5CKRAOvXr2f9+vXJ20uXLuVLX/oSbW1tZGRk8Mc//pE1a9aMWOakhpKZM2eydevWybykEEIIccaM9kt4vLW2Rdj68n2Tcq01N7w+4u/0RYsW8eCDD3LDDTcQDAZZuXLlsOsy9ZPuGyGEEEJMiI997GNs27aNV199lfz8fObPnz/i8ZPaUiKEEEKI80dzczMlJSUcP36c3/3ud2zcuHHE4yWUCCGEEGJCfOADH6CtrQ2v18t3vvMd8vLyRjxeQokQQgghJsRrr712UsfLmBIhhBBCTAkSSoQQQggxJUgoEUIIIcSUIKFECCGEEFOChBIhhBBCTAkSSoQQQggxJUgoEUIIIcSUIKFECCGEEFOChBIhhBBCTAkSSoQQQggxJUgoEUIIIcSUIKFECCGEEFOChBIhhBBCTAkSSoQQQggxJUgoEUIIIcSU4DnTFRDizOoBagAbKAeKAHVGaySEEOcrCSXiPHYM2Au4fbfrgBJgFRJMhBBi8kn3jThPxUkNJAAO0Ay0nJEaCSHE+U5CiThPtZC+NcQB6ie5LkIIIUBCiTgvaSDc9990zEmsixBCiH4ypkScZ6LAW0CE1K6bfiZQNak1EkIIkSChRJxn3gZ609yv+r7mAXmTWSEhhBB9JJSI84ADhIBOoH2YY7zAFUDGJNVJCCHEYBJKxDlMA/uAw2M41kACiRBCnFky0FWcw2oYWyBRJBZOE0IIcSZJKBHnsANjPE4DuRNZESGEEGMgoUScw+Incewe0s/GEUIIMVkklIhzWM5JHKuB7omqiBBCiDGQUCLOYctO4liN/DgIIcSZJZ/C4hwVJjHzZqz8QPYE1UUIIcRYyJRgcQ5ygDcY+5gSBVyE7AwshBBnlrSUiHOMBo4CFsPvbTOYAWRNWI2EEEKMjbSUiHNIN7CN9MvIj0RaSIQQYiqQUCLOERbwJmCfwrmZ41wXIYQQp0K6b8Q5ooGxd9cMNnc8KyKEEOIUSSgR54gQiQGuJyuALDEvhBBTg4QScQ6IAdWneO4CZEyJEEJMDRJKxDngMKfWSmIAFeNcFyGEEKdKQok4BzSewjkGsBwwx7kuQgghTpXMvhHngJPtflHApUDe+FdFCCHOoDD5bHPvnqSrvT7uJUpLiTjLxUksKX8yFIll5YUQQkwlEkrEWa72FM5xkUZCIYSYeiSUiLNciFNbn2QbiXAihBBiqhg1lESjUS666CJWrFjBkiVL+PKXvwzARz7yEWbNmsXKlStZuXIlO3bsmOi6CjGIZuyb7g3WAdSNY12EEEKcrlHbsP1+Py+//DJZWVlYlsXll1/Oe9/7XgC+8Y1vcOedd054JYVIrwZoPsVzHRKhpGr8qiOEEOK0jNpSopQiKyuxg6plWViWhVKy2JSYCg5xel0w0nsphBBTyZg+lR3HYeXKlZSUlHD99dezdu1aAL70pS+xfPlyPve5zxGLxdKe+9hjj7FmzRrWrFlDS0vL+NVcCNK/58bGAKaPV0WEEEKMgzGFEtM02bFjB7W1tWzevJndu3fzta99jX379rFlyxba29v5t3/7t7Tnrl+/nq1bt7J161aKi4vHtfLifJd7GueWA6XjVREhhBDj4KTar/Py8rj66qt5/vnnKS8vRymF3+/nr//6r9m8efNE1VGIYSw6xfOKgAuQPW+EEGJqGTWUtLS00NnZCUAkEuHFF19k4cKFNDQ0AKC15sknn2Tp0qUTWlEhEqLAVuBZ4K1TON8HyHtVCCGmolFn3zQ0NPDAAw/gOA6u63L33Xdzyy23cM0119DS0oLWmpUrV/Loo49ORn3Fec0hsaxxtO/2qaxPEgc2AGuAsvGplhBCiHExaihZvnw527dvH3L/yy+/PCEVEmJ4DYA1TmVtA25CunCEEGLqkDmR4iwSItFaMh40iQXUhBBCTJT/+3//L0uWLGHp0qXcd999RKPREY+XUCLOIsFxLm+8Ao4QQojB6urq+Pa3v83WrVvZvXs3juPwi1/8YsRzJJSIs0hknMsrGOfyhBBCDGTbNpFIBNu2CYfDVFRUjHi8hBJxFqkex7IWAeY4lieEEOeXlpaW5OKoa9as4bHHHkt5vLKyki984QtMnz6d8vJycnNzueGGG0YsU/ZvF2cRe5zKyQbmjFNZQghxfiouLmbr1q3DPt7R0cFTTz3F0aNHycvL46677uKnP/0pH/rQh4Y9R1pKxFnkVKYAD6aAi8ehHCGEECP585//zKxZsyguLsbr9XLHHXfw5ptvjniOhBJxltCc3uZ7/RTythdCiIk3ffp0Nm7cSDgcRmvNSy+9xKJFI6/ELZ/O4izRNk7laGDvOJUlhBBiOGvXruXOO+9k1apVLFu2DNd1Wb9+/YjnyJgScRZwgE3jVJYmsQjbinEqTwghxHC+8pWv8JWvfGXMx0tLiTgLNDA+40n6jWdZQgghxouEEnEWiI1jWQrZ80YIIaYmCSXiLDCei5wFgMXjWJ4QQojxIqFETHEx4Og4laWAiwD/OJUnhBBiPMlAVzGFucDrwMgbOJ2cXhKLpwkhhJhqpKVETGGNJFpKxmtgqkbe8kIIMXXJJ7SYgqIkWjTqGJ8F0wZ6GwiNc5lCCCHGg3TfiCkkAmwDuvtuj3cggcT+Oe8CF05A2UIIIU6HhBIxRWjgTRKtJBO9jkj7BJcvhBDiVEj3jZgiWgGLyVnYzDcJ1xBCCHGyJJSIKWIyWkgATGD2JFxHCCHEyZLuGzFF5DGxoaQ/f88Cpk/gdYQQQpwqCSViisgGSknsczPeFIkwMhfwTkD5QgghxoN034gpZBWQNQHlahLdQxJIhBBiKpNQIqYIh8R04IlaQ6QB6JmgsoUQQowHCSViitgNNE/wNVonuHwhhBCnQ0KJmAIcJmb11oEUMhVYCCGmNgklYgpwmPjpwIrEQFohhBBTlYQSMQV4gcAEl78WmWwmhBBTm4QSMQUoYBkT93a8DMifoLKFEEKMFwklYoooADImqGxzgsoVQggxniSUiCniGIldgsebQtYnEUKIs4OEEjFFTNTsmwKkpUQIIc4OEkrEFDFRwaEDeAOwJ6h8IYQQ40WmI4gpYiawk/GfGuwC3cA+YOk4ly2EEFOL06zp/E70TFfjlElLiZgiKoGcCSrbBWonqGwhhBDjRUKJmCIUMGcCy5/I1WKFEEKMBwklYoqIAjvGoZzMYe6X1VyFEGKqk1AipgALeJXTb80wSYxN8XLirW0CfmDxaZYthBBioslAV3GGRYBtQHwcyjKBGcA0EmNIeoDcvtvyVhdCiKlOPqnFJNNADXCARJfNePEBl5IIJiYwexzLFkIIMRmk+0ZMsqPAbsY3kEBiHZKGcS5TCCHEZJJQIiZRHbCXiZkJ4wIHGZ9uICGEEGeChBIxSXoZn9k1I3GBLUDnBF9HCCHERJBQIiZJDeO/Wms6HcBbQMskXEsIIcR4klAiJslkdqs4wDuTeD0hhBDjQUKJmCQlJFZtnSwREuFECCHE2UJCiZgkpUD2JF7PQN7eQghxdpFPbTFJFHAZUDgJ1zKAKia3ZUYIIcTpklAiJpEJXEJihdWJKt8AypFl5YUQ4uwjK7qKSWQB25m4mTHlJHYansxuIiGEEONFWkrEJNoKtDJxU4PrgNeB9gkqXwghxESSUCImSS+JNUQmYjXXfprEjJvtTM6aKEIIIcaThBIxSWJM3sDTOBCepGsJIYQYLxJKxCQxmLx1QzTy1hZCiLOPfHKLSVIzidfKBjIm8XpCCCHGg4QSMUlCE1CmIhFADBLTgU0gAKyegGsJIYSYaDIlWEygeuAQifEkXhIhYjwHoC4CZgE9JAbRBoBiJGsLIcTZSUKJmCCHgIOcGEcSG+fyZ5AIJArI6fsSQghxNhv1T8poNMpFF13EihUrWLJkCV/+8pcBOHr0KGvXrmXu3Lncc889xOOTuQusmNocUgPJRDgOvABUT+A1hBBCTKZRQ4nf7+fll19m586d7Nixg+eff56NGzfy4IMP8rnPfY5Dhw6Rn5/PD37wg8morzgr9E7CNTSJFWL3kugmEkIIcbYbNZQopcjKygLAsiwsy0Ipxcsvv8ydd94JwAMPPMCTTz45oRUVZxM/kzf91wH2T9K1hBBCjNX+/ftZuXJl8isnJ4eHH354xHPGNKbEcRxWr17NoUOH+NSnPsWcOXPIy8vD40mcPm3aNOrq6tKe+9hjj/HYY48B0NIyUXueiKnFT2ImzGQFk+gkXUcIIcRYLViwgB07dgCJHFFZWcntt98+4jljmqZgmiY7duygtraWzZs3s2/fvjFXav369WzdupWtW7dSXFw85vPE2SgC7AY2kAglk0U24BNCiKnspZdeYs6cOcyYMWPE405q9k1eXh5XX301b731Fp2dndi2jcfjoba2lsrKytOqsDjbRYBXAZvJ3XfGJDE1WAghxGRqaWlhzZo1ydvr169n/fr1aY/9xS9+wX333TdqmaO2lLS0tNDZ2QlAJBLhxRdfZNGiRVx99dX85je/AeDxxx/ntttuG8tzEOesg0x+IAFYCBRO8jWFEEIUFxcne0K2bt06bCCJx+M8/fTT3HXXXaOWOWpLSUNDAw888ACO4+C6LnfffTe33HILixcv5t577+Wf//mfueCCC/jYxz528s9InENaOTM781pn4JpCCCHG6rnnnmPVqlWUlpaOeuyooWT58uVs3759yP2zZ89m8+bNp1ZDcQ4KcGZ25g2cgWsKIYQYqyeeeGJMXTcg63GLcTOHyR3cCom3b/kkX1MIIcRY9fb28uKLL3LHHXeM6XhZZl6Mk1JgAYk1QzTgTsI1/STGsXgn4VpCCCFOVjAYpK2tbczHS0uJGEezgRuAC0jsSTPRooB0IQohxLlCQokYZxoY+zo2p3+tMIldgoUQQpztJJSIcXaYRFCYrJk4CpmBI4QQ5wYZUyLGiQO8A9RO8nU1kDvJ1xRCCDERJJSIcbILaJjka5rAYiZ/1o8QQoiJIKFEjAOLRCCZjBk35UCMxMybWUDBJFxTCCHEZJBQIsZBnMmZbZMFrJ6E6wghhDgTZKCrGAcZTE4oyZuEawghhDhTJJSIcdAFZE/CdeqQmTZCCHHuku4bcZoOkJgG7EzCtTSJ2T2zJuFaQghx9vEWeKm6b9rkXOyZ8S9SWkrEaQgDh5icQNKvfhKvJYQQYjJJKBGnofkMXDNyBq4phBBiMkgoEafIBY4xOdOABwpO8vWEEEJMFgkl4hQdAXon+ZoGMG+SrymEEGKyyEBXcYpqmLz9bQB8wDKgaBKvKYQQYjJJKBGnaDIDCcDVgHeSrymEEGIySfeNOEUVk3y9yd5XRwghxGSTUCJO0Vy0NtGDGkwG3x4/XRNVsBBCiClCQok4JbZj8JM3pxG1FLabCCNag5qw1eYne1CtEEKIySahRJySvQ3d1HWa/OiNUurbvdR2+NhTl4HlTFQq6Z6gcoUQQkwVMtBVnJK6jjB3rKpnflkUy1EYSlOeB8aEtZRM9sBaIYQQk01CiRiV1ppNR9rYeKSNiOUwvSCTNTO6mF8WRSnweXTfcRPZfTMZG/4JIYQ4k6T7Rozq+d0NvLyvia6IRdx2OdQcojyvPiWAuC64E9aYoZBF04QQ4twnoUSMKBy3ebu6A8tJTRw+M3V5+d31mTju0GYSyx6PppPpQPE4lCOEEGIqk1AiRtQWimOmGShyrC2QMv33L/vzOdoSID4ghFg2NPd4CcdOJ5gYJEKJEEKIc52EEjGi3AwvTpp+mRf25OPqE+uS2I7il1tLeHFvPg1dXpq7vWzYn8dP3yrBdk/nbeYia5QIIcT5QQa6ihHlZHiZW5LFwaYe+ntwllX28N5lHSkzbRZV9LL1aDZbj+Ww9VhO8v78TIvsgHOatdgPVJEYWyKEEOJcJS0lYlQ3LMhnZm7irTK9IMotK9rJ8LkodWK2zdULOllQHsZn9gcQjc90uX1V6zjMyIkD9ukWIoQQYoqTlhIxqng0wqICk6NdLpfN7cJrDu3O8Xs1d65uxXIUv9laRFO3l49e3kRu5um2kkCihcQch3KEEEJMZdJSIkbl8Xho7HVRQF6mPWzLh1LgNTW3r2rDcgyyM8YjkEBi4TRZ0VUIIc51EkrEqHJycvCaCgVUt/lx3OGPVQoMpSnNsXhmRz6OOx6b9Glg7+kWIoQQYoqTUCJG5fV6WT23gsr8KMurejHUyEFDA1l+G68HfrutiHBcjUMwkRk4QghxrpNQIkYRApoozDP58KVt+D06ZYBrurBhGnCkJYOr5nWytDJMQ6d/HOrhHYcyhBBCTGUy0FUMw6G1ZxvP71ZUtwWYWVTNPRcOnQGjVGKJecNILDNvO4oN+/MIWyb/989ViVYV4Mr5nVw2t/sUZ+KYwJzTfD5CCCGmOgklIq2e6B5+ttHLsmkhVs/sJhwzcVzwpJkE0xry0BXxErEMth3L5ni7n7klEWYURglFTd6py+Kldws40JTBRy5twhhT+1z/jBsXmNn3JYQQ4lwmoUSkodlytItPXNWNaWi8ZmLJ+HSBxHHhrcO57KjJAhSm4fKRyxopy43j92gsR3H1ok6ee6eAeSURInFFMDCWASYGcDGQhbxNhRDi/CCf9iJFJG7zyr4mFldE8Xl0ctVWryfRTdMbM2jq9pGbYVOYZWMouGp+N/mZNluOZXPNwnam5ccw+1pD+tc0uW1lG1ozxlYSSLSUxIC88X2CQgghpiwJJSLJdly+9+oRuiIxblwWS1lGXmv4054CtlVn4TE0jquoyItx95oW2poqKVMerpkZY07JiUAy0MDBsWN32lN2hBBCnEVk9o1I2lPfRShm4Wpw3NQE8XZ1FtuPZ+G4BjHbxHYN6jr8PLOrkNzcLgKBGCvm1Y7DPjf9XKBonMoSQghxNpBQIpJq28NYjgYUO2uC2APyxcYjOVhO6tvF0QaHmjPxZ4aoqKjDMPQ47HMDibflCqQhTwghzi8SSkRSYZYfT1+fzQt7Cqjt8BO3FTFLEYmnf6soNC4uSo2wzOtJuxyoHMfyhBBCnA3kT1GRtLwqjw37m7FdjeUYPP5mOaU5MYqzbfKDXsJxF01qU0imzx3HLptkqeNcnhBCiLOBhBKRlOnzcNUC2H48TmsosYKq3+Ny0awuWrojtIXyiTsKxzVQSmMqzfJpIX65pZi2kIecDIeLZ/cwrzRyGrXwIW9LIYQ4P8mnv0iybJuV049y8RxNOG5gKE3Aq7EdKAhazC0Ns+VoDtXtATJ9DtVtAV4/lNt3tqI1BDXtft63oo1FFWHMvkaVkxtnEhjnZyWEEOJsIaFEJG0/fpzlVWA7cKQlQHuvl5LsOPNLI2T4NKC5dnEnWsO3/1xJ1DJgQHdOUVacBy5txGNqtKvAPJWBrz2Ahex1I4QQ5x8JJSJpe02IOSUmj79ZRswyiDsKn6m5dG4nV8zvTq5b0tTtJRw3IWV8iea+tc1k+t2U9U1OjaxPIoQQ5yMJJQKAcNymqcvg99uLCEXN5IDWuKOozI+nBA3bMRg03pWSbIssvzMOgSSLxLgSIYQQ5xuZEiwA+M3WGjRQ1+EfMsOmJ2riDpjxW54XwxjUmuE1Ne64NHBcMB6FCCGEOAtJS4kgFLWpbuvtuzW0qWPzkRxMBTtrgziuYvm0Xt63opXfby/GdhWgaOjyoscllGSPRyFCCCHOQhJKzlNaa96u7mDjkVY6w9aIrRyN3X6e3lmI4yYa1uo7/VTkxbhiXgc7a7PojnhxXAjHDTJ8p7tmSR0w7TTLEEIIcTaS7pvz1J/2NPKnPQ20huLYQxKJxlCJ2Tb9X/2BBMByDOo7/RRl23j67jYMyA+OxyJqR8ahDCGEEGcjCSXnod6YzbZj7X373KSjULhcu6iDirxomg6dRDCpbgvguArbNfAY0Nk7Hg1v8XEoQwghxFTQ2dnJnXfeycKFC1m0aBFvvfXWiMdL9815qLk7immoNC0kCUsrQ9y0rI2AV5OXYfP0Tj+WkxpNTMMl0+dSEIyzZmYPq2f2oLVKjis59Y35Sk71RCGEEFPMZz7zGW688UZ+85vfEI/HCYfDIx4voeQ8lJvhxRkmkCwsC3H7Ba0YfW1oC8oiGLuGHmsoWF4VYl6JoijbwWtC//oijpv4p6FOJZwUnewJQgghpqCuri5effVVfvSjHwHg8/nw+UZe8kFCyXmoK2KlCSUaUNx2QVsykAB4PZq/urSJJzaVELeNvpChuWNVK1pDWa6dcjyAaYDWp9paEjqVk4QQQgAB12ROKGtSrtXS0sKaNWuSt9evX8/69euTt48ePUpxcTF//dd/zc6dO1m9ejWPPPIIwWBw2DIllJxnbMfll1uOD1kzNcPrUpRl4fcMbRWpyIvz+Rtqqe3w0dLjZV5JmOwMza+3FHPnmpZxrqEsLy+EEGeD4uJitm7dOuzjtm3z9ttv85//+Z+sXbuWz3zmM3z961/nX/7lX4Y9Z9SBrjU1NVx99dUsXryYJUuW8MgjjwDw0EMPUVlZycqVK1m5ciV//OMfT+EpiclW3RZGD1pQpCQnzoqqEOsW9A5zVqLVo6ogzrzSCC/vLwAgaitCseHfQsda/adQw4pTOEcIIcRUM23aNKZNm8batWsBuPPOO3n77bdHPGfUlhKPx8M3v/lNVq1aRU9PD6tXr+b6668H4HOf+xxf+MIXxqHqYrJYrkM8OetGc+vKVpZUhDHU2FZk7Y54kmHj/rXNuDp9V41SUJRl0RszCPrdNCWlYwKnEmSEEEJMNWVlZVRVVbF//34WLFjASy+9xOLFi0c8Z9RQUl5eTnl5OQDZ2dksWrSIurq68amxmHTh2Im1RBaVh1lSEcY3qMtmuBk0rgubj2bz3mXtQGJtEqPv/nTjRzym5mhrBovKRx5tfcKCMR4nhBDibPCf//mf3H///cTjcWbPns1///d/j3j8Sa1TcuzYMbZv355sivmv//ovli9fzkc/+lE6OjrSnvPYY4+xZs0a1qxZQ0vLeI8/EGPnAgdo7j6QvOeC6aEhgQTAcqCu05dsBUlScPXCTuYUR1OO74maQ8pwXDjUnEHmmFd4LQJmjfFYIYQQZ4OVK1eydetWdu3axZNPPkl+fv6Ix485lIRCIT7wgQ/w8MMPk5OTw9/93d9x+PBhduzYQXl5Of/wD/+Q9rz169ezdetWtm7dSnFx8ck9GzGOdgKHKMyK4TUT3SmJVVuHcrWipt2P7aiUFhBDQabPHdLNE3cUMUth9eWPuK2IxA3eOpTD9ILYKPVSwEXAxaTbd0cIIcT5Y0yzbyzL4gMf+AD3338/d9xxBwClpaXJxz/+8Y9zyy23TEwNxTiIAA2Ay/JpIV7Zl4flaHbWZFFVEEvbWmIohrlfowHLVvRETbIDDsXZNrtrM2jr9VGSHaeuw8+h5gzuvLBlDNOCTWTGjRBCCBhDKNFa87GPfYxFixbx+c9/Pnl/Q0NDcqzJ73//e5YuXTpxtRSnKUTf6A/83sTg1l9tKWF3XZAllb3MLIriMzW2C1orfrO1mEy/S8wK4femBhNDO7y02WRzexVKJVY3WTurm2sXdWI7Ueo6/SypDHPt4s4xrlPiAsPPWRdCCHH+GDWUvPHGG/zkJz9h2bJlrFy5EoCvfvWrPPHEE+zYsQOlFDNnzuS73/3uRNdVnLIgiV/+CR4jseGeow1+sbmEnIDN8mk9FGQ5FGTaXLmgi711mYQtA4/hYPYNGdGuy1vvZrCltRh7QM/f5qM5ZPhcLpvbzcyiKGNnANOBkVf4E0IIcX4YNZRcfvnlQ9a1ALjpppsmpEJiImSSGEjaStzWPLWjCEf3N2MouqNeNh3N4xPr6inMsgEoy4nTGvJwtCXAovIwfo9GAW/WlmKROrDVcgzePJTLZXO7T7JeLhAm0b2UcTpPUAghxDlAdgk+b6wGprGtOptQzGTwoFLHVWw6kpO87fNoirIsGjp9bD8eBDTKMIhY6d8ykfipvpWagdeBsc7SEUIIca6SZebPGyawnN11h0m0TKRyteJQc2prhc8D713WgVLQG/Pw53fzhi29ONs6jbrZJAbiTjuNMoQQQpztJJScZ7L9Q9cU6Rf0D22tMIzEFN/HXi0nFDXRQ6btagwF71nafhq1coCe0zhfCCHEuUC6b84zl85Nv1aMx3S5ZE76MSG7aoJELSNNIAFQZPocZhaezADXwUwg+zTOF0IIcS6QUHKe6ArHqe0IU5YboCLPR2Iyb/8X+EyX8tw4ljNoFVegttOP5Qz/VumNmRxt9Q85b+y8QPmpniyEEOIcId0357io5fDbbceI2130xgzaez1oXMxMByfsoX/Aazju4dsvVbK4opf3X9CKd0AvT1GWhcdwsd30wUQDT2wu5fK5XVwxvwtDpd8LJz0PcBkwfLeSEEKI84OEknPcrtod3LWmEVcn1ieJd4Y4vrWa34ZvIHUGjqYyP0ZRlk17r5f8TAtf37tjZVWI1w/mYbua9EvBKxxX8ZcDefTETN634mTGl9hAJzIlWAghhHTfnMMi8UZWVjXi82gCXo3HhIy8DIpXL8BOyaOaO1a18leXNLFuQScFmRYeg+QeN1kBl7++rIHSnDiDu31SKfbUBYfsjdP/2PAOntoTFEIIcU6RlpJzThioBSw8ZiteMzUhKNMkL1+Tl2HRGUmspLqwPMz8snByrxtjwLvCcRNjTEpzLf72qga6owY/fr2EzogfJ034sB2DuG0Q8LqDHhlpwEkPiRYTeTsKIcT5TH4LnFOagG30t2R4hmkHU65Dtv9EKFkxLYR/0OZ7PVGTp3cUcqQlAw3MKIhy68o28jJtumPeASvCpvJ5XPyewYFkNAoJJUIIIaT75pzhANtJLN2eCBhKDZ1J03//0p7NmNj4iA9pw3Bd+OFrZRxuycDVCq0V1W0Bvv9aOXFb8cG1zXgMFzXoTK/pctXCjpMY5Jo8E/Cf7ElCCCHOMRJKzhkdae9VipQxHtq20e++Q4VuYDp1KFx21mQTs08kiYPNGYTjJnpAa4hGYTmKvQ1BZhTG+P+99zj3rm2iJCeOqRLdQTcva+OiWaGTrLcJLGPkMSdCCCHOB9Jefs4YPl929ZrEHIOeqMnBwxb5rbm8zC19g10V+xs1e+szWVIRxjQ0zd1eHHdoSLAcg731mRhKM68kwrySKPNL60+z3iuAstMsQwghxLlAQsk5I590wSRuK/60t5D9jZl992hMoxInZc0RxdM7itl8NMbs4igt3d60g1hBc7QlQHVbAK3h5uVtrJzeexp1VkDhaZwvhBDiXCLdN+cMhdYXErUUMUsRtxPdLTtrguxvTKwBUpgV51PX1OKmaQUBaOzy8+ahXA42Z5IIDAOTSWKNEkcbWI6B7Ro8u6uQrvDpLHqWi4wlEUII0U9CyTlE6zx+samU2g4fzT1ent5RyB/fKaQ/YHz44ibyMx085nDTc0/c7zFcKvNi+EyX4abzamBPffAUa2sAa07xXCGEEOci6b45hxjGAf7q0kZU3zLv2Ys62F2XCA1luXHeqQ3i82pWTAuxoyZr0LLxGq/holHkBBwundvJzprsEVcXcVxFKGrgajBOapxqgMTS8oGTfo5CCCHOXRJKzhkR4CDGgJwRtYxkJ0xTl4+WHm9yjsu0/Bi1HYlN9PrXHLFcE6/pkul3CEVNGrp8w+53k6DYfjybqxZ2JRdeG10QuAqZbSOEEGIw6b45Zxwbck9PzKQsJwZoNArHTYwFsV2D2g4/D1zaSCIc9H8lZtg0dfvYUp0zSiBJiNomcftkAkYvcLLThoUQQpwPJJScM2JD7plZGGV64dD7AVDw1uEcDGNoC4flGFj2cG+NocfvqQ9iOydT19eAjSQWfBNCCCESJJScM0qT/+pfxdVjktKdM5DdtxCa5Qxt5VBKU5wdx2OkWy5+6PEvvZtHZ9iTdvXY9FygHdg71hOEEEKcBySUnDPK0NrPO7VBfvJWKT96o5TNR7KJWmqY0RsDu21S04RpaG5c1k5FXn8wGSltaBxXsbsu8ySXl3dJbBwohBBCJMhA13OG4ndvz2N/YzeWk8ia1W2B5GMngkX65OAxXExD42jFe5e0My0/zgfXNvGrLSUcaR1+lowC1i3o5Ip53adQZ4f+9U+EEEIICSXniIbOCPsbe5OBJEEN8++hSnNiXD6vm+mFUTJ9mpitON7m42hr/+Jm6cODaWiWT+tNbv53cq0lBaPWSwghxPlDQsk54mhrJ6475kEdQ9R1Bnhmp5fL53WR4XXZ35TJ/oaMvq2GBweHxHU8huamZe3kZSYGrI49kBh9X8tOub5CCCHOPRJKzjoWUA00Aj6glAONtbx5KIijTU6t5SFxTjju4YU9hQPu1Sk7BfczleaaRR0sm9ZLdiDdYNiR5ALFwExk8TQhhBADSSg5q9gkptNGSQwUhUPN3fx6a8mY1hQ5WXq4IbIGXDq35xRLXQlkn2qVhBBCnMNk9s1ZpZqBgUTbFi/vzRtDINEMnUFzql09mpkF0VM8NwsJJEIIIYYjoeSs0kQykLgu1NXQHvaN4bxEi4fXdBOzbJQ7pvEfSoGhNEolAoypNF5Tc/2SjlOoezaJ/W6EEEKI9KT75qziP/HPpgZ0Wyv5mXEau0cfm2EozdySMLOLY0zLj3K8LcDzuwv6umjSz6zRGvIybSryYlS3BfCaLium9VCcbaU9FoYb7JoLXDGWJyiEEOI8Ji0lZ5VZgJn4Z6gHGuu5ZkZdmpVXh3bNuFrxbkOQHceDHG4JsOVY9oAxI8M3m5iG5kBTJqGYSUfYx4YDBWw6ko094JIjBxIFFI3t6QkhhDivSUvJWaUArRdhOXsgkI1pmMzZ/yx3Tb+cw8ymPezjWKt/mDEmicRQ1xmgrnPss156Ip4ha5/8aU8BjV0+rlnUQU7GaF1BJokwJYQQQoxMQslZRusZ/MefuqnMKWKdU0vnrDUsXWAyw+1Eo3C14idvldDYdbrTbTUKRdxJH3B21maztyHIF95zHN+w7yI/cCky9VcIIcRYSPfNWcYwFGW5WRzrCPJy7q0snu/BY4LfCwGvJtPn8qGLm5ODU0+eRqEpyoqjcdGjzNLxmiM96gGCp1gPIYQQ5xsJJWeh962owFSwekYIjzk0NJiGZkbhSNN2000RBq/hcNHMLm5c1obfq/F7Rg4kfq8zSk0zRnlcCCGEOEG6b85CxdkBsgJe/B4XY5jxHL40YaXfquk99ERNjrVlYDsK09Asrujl5uXt/Gl3AX/emzNoHEm62Tmay+d2jVLTdqAXaS0RQojJ0WU28Fz+v57papwyCSVnodZQjO6Ixd76ILOLo/gGtWiYxsAdgoeqKoixvKqXA40ZHG4OUFUQY9m0MB1hk86wyeziKMfb/ESs4fpmNLOLIlw0KzTKIFcXOILscSOEEGIsJJScNRygAeikqcvG51E01sdwZnSi84IojwfHBcdV/GlPPjE7Xc9cotvmQFMGO2qCLK0Ic8PSjmRrS7bf5e4LW9Akgs0r+/J463AuoPAYLrar8Jma1TO7WVwWprXHQ3GOPUq9u8fxNRBCCHEuk1ByVoiT2PMmDjjML1N8phj0mxvwbupAl5VDWQWhuI+3qgt4uzubnAwbr+HSEfbiDthUz1CKfQ2ZXDqnm+VVvSkDVT2mTmn5uGpBJ3Udfo63+1k+LcRNy9vZ35DJMzsL2XYsB1dDQdDmo5fX4/cOV/exrDgrhBBCSCg5S+wjsedNopvGa2o8hkavWA5vboDGenRjfWIhd9ePVVXExXPDPPaX8pRAAgqtNRlel4tm92Aamr31mbT3erh4djeeQb01HlOzZmY3x9uLaev1crApg9++XYSrT7TCdPSamCMOly4Yl1dACCHEuU9CyVmhgcGzZZRSkJtLrVFBsdOCT1lEtJ8/e67l5mVdHGvLwDRIWXkVEhN+I5aJx9T88PVyWnu8VBVEWTOzZ8hMHkNB0J+YYVPdFqCmPYA7aPysd9R3UOnJP10hhBDnJQklZ4X0TREuBr90byPmmuTSTSe5uK7J0Zcdrl7YgTN49XmAvpVHXnk3j+ZuL7ZrUNsRSNva4bpwtDVAcbZFZ3jwyq4J4bhBxDLJNtNND/aR2BlYCCGEGJ2sU3JWmMbgb5XTFxh6nQA2XtoowMGDRtET9fDcO4WU58bT7IujAMWOmqzkcvQx2+D53fnEbYXbd3jcVjR0+dhTm0Wmz0kbSPqnCj//Tn5y/5tUF57WsxZCCHF+kZaSs8J8Emt+dONqjWVrwnGTp7YP3OgudW6u7SiKs+MUZ1vsqg3iuArT1Nh94WLw/jhvV+fQ2OVn9YweMn0OB5oy2FWbTUEwTm8s/dRgQ4FSmtwMJ83U4Eog/3SetBBCiPOMhJKzgkliD5kO/rLvAPVdBoebMwbs8juURtEe9nL/2ibes7SdNw7l8OqBwSEhdVG0+k4fHb35eExNTzTx1mjp8WMqF4Uecj2v6XLLigqWVGQBIcAi8ZaaB8w57WcthBDi/CKh5CzR0BXl5Xe7OdQcZOjqqulobEfxyr589jdm0tbrTXnMQINHox0Dj0pMBfaYmg9d3Eh+0OYHr5XTGkpM53W0gUL3rVViYBqJlWTvXNPC3JIiYCayE7AQQojTJaHkLHCsNcTPNh4j220ntUukv6Wjf0BH6vTf2o4AtR2BlOMNBZdWaioKwzzfDTdX9BAKeQn6HeaWRDANcDW8Z0k7P9tUlnKl0tw4Vfkxgn6HFVW9ZAccYCeJVpIFE/HUhRBCnEcklExhsViMaDTKW/ubyHdbaCefgcFDAUG/zfJpvbzbEKAj7B+lRMXqEkVVlpfDXaBUL3MLYhiFsZSjDAXTB90HisZOHx+9rBEjZTiKCxwGZgPDrqAmhBDiPDRz5kyys7MxTROPx8PWrVtHPF5CyRSktaa+vp5wOAzAsnyXJ9uLcHRqt41GYRhw/ZIODjSVk37jvFTbmjRZPpf6iMYNJlpF0m3qF7OH3mkYUN3mZ1bx0MACPchCaUIIIQZ75ZVXKCoqGv1AZErwlNTR0UE4HEZrjdYaw1CDVmY9IRI3cFzoCPsYy1gTF3i7yUbHTLRS7O30D1lgzXFhW3U2RVlxrlrYzk3LWllQFsZ24IU9+bT1nMiyianADjD8BoBCCCHEWEhLyRTU2dmJHrDwh8dQBL0QsgYfqanKjxG3jWHWCUmvOw6LCk1aWvy8ojSZHpeZWRaOVniMxLiTK+Z2ceX8LiDRkrKiqpeGLh8/fauU3fVB1i3o6qsBKAJA5mk9ZyGEEGeXlpYW1qxZk7y9fv161q9fn3KMUoobbrgBpRSf+MQnhjw+mISSKUinSRhryjy8Vmvj6EQMUGi8puaGJe0EvC7ZAZuuyOAxHem7c/wmVGbBzhaT3toMftflJdtvMzPb4to5XZheMActTeLzaMpzY6ys6klZ8N5xoSu6lPzg6T5rIYQQZ5Pi4uJRx4i8/vrrVFZW0tzczPXXX8/ChQu58sorhz1eum+moKysoUuzlwcN3jtXs7g8THF2nEXlvcwtCfPkjiL+tDufqxd04jVdTszE0ZhKM3DPHENpZhVGuXh6jD9XO8RshWV5iLQHaG7I4u1Dubywd/hxIT4PXDG/i0XlvYkraNh2LJfeeM44PnshhBDnisrKSgBKSkq4/fbb2bx584jHSyiZgoqKivB4PCiVCBRKuWhcDnbYtIa8VOVH2dsQZG9DkMYuP5uO5vCHXYXcurKVheVhgn4bQ4GbbCXRzCgM84X31HDPRU2UFnbQFdcDHk+wXYOdNVkjdgVlB1xKc2y0ToSS9rCXshwZTyKEECJVb28vPT09yX+/8MILLF26dMRzpPtmCjJNk5kzZ9Ld/RbRqI+DrR4215qE4onumeYeL4PXJLFd+Mv+PC6a1c3BpozUgbFKk5fpkOFLjGh9bnfhsNe2XZWYkdN/6qDen/7bSiW+blzSjmG4SL4VQggxUFNTE7fffjsAtm3zwQ9+kBtvvHHEcySUTFGGYZCb20PcgFeOVqJ1aggZStEa8rL9eDbOoH1t0IrddUFuWtZOd8RDKOYZpgwoz42zpz6T1h4vVy/sSnuM1ifCSSKQ1AEzTvIZCiGEOJfNnj2bnTt3ntQ58uftFNYbL+LJ7cVjnlmjgJ6oCWj8HgdD6ZTHInGDzogHj5m+QIVm2bQQz+ws5LWD+bT0jHUxtL1AfIzHCiGEEOmN2lJSU1PDX/3VX9HU1IRSivXr1/OZz3yG9vZ27rnnHo4dO8bMmTP51a9+RX6+7Ao7np7blUdTd4Sx7nXjMV1mFUW4dlEnWQEHVyvers7ihT0FKAXZAQfDiOO4w5//0rsFLK3sZW5xmKYeL/lBG7NvmrDrnui2GXxuorVE9r8RQghx6kZtKfF4PHzzm99k7969bNy4ke985zvs3buXr3/961x77bUcPHiQa6+9lq9//euTUd/zSlOPi3sS64+U5ljcsqKd3EwH0wCvqblgeohbVrRSmhPDdhXZAYeZhVFgcMEKrRUfv6Ke9y5rY+m0MIvLw6A1dR0+2ns9NPd4hwk0LhA+5ecphBBCwBhCSXl5OatWrQIgOzubRYsWUVdXx1NPPcUDDzwAwAMPPMCTTz45oRU9H80vyWZsrSQAiivnd+Ed1DXj82iWVvbSGvLyzT9V8cvNxZTmpO9qUQqOtfrx9ZVhGuD1QHG2xXderuDZXYXDriwroUQIIcTpOqkxJceOHWP79u2sXbuWpqYmysvLASgrK6OpqSntOY899hhr1qxhzZo1tLS0nH6NzwNaa9qiYYpzvWn3pRlOQdBK07UCjqvIDrjEHYN9jUHePJyX9nzLMXhpXwFbjmWn3O/3aD55dT1xC0APM8alGRi8J44QQggxdmOefRMKhfjABz7Aww8/TE5O6mJZSilUut+GpC47O3A5WpHesZ5O/lhziIhtg4LMGRBu8uNG+r9Vg1/nE6u21nX6yM+0B+3iC6aCznDqtzrD6xCxzCHlWY5BbbufC2f2pEz/zc+0+Zt1jZhGujElACbQCZSe7FMWQgghgDG2lFiWxQc+8AHuv/9+7rjjDgBKS0tpaGgAoKGhgZKSkomr5XmiIxbh6eMHiLo2qu+Xv2sZuNH+KbwjN5u8eiAPy1UpLRlxW/HG4RwsJ/VbHbE8mMM0w6ycHhoSPAwDPEb6HYUTNOAbsX5CCCHESEYNJVprPvaxj7Fo0SI+//nPJ++/9dZbefzxxwF4/PHHue222yaulueJHW1NODp1JKnV4xk6JhUNfUvID5ze2xby8d+vl3O4JUDUUrSFPDz3TgF/2Z835Fqm4VIQTD+2JMvvnELtA8DQ6wghhBBjNWr3zRtvvMFPfvITli1bxsqVKwH46le/yhe/+EXuvvtufvCDHzBjxgx+9atfTXRdz3ldVmxI/tDOiRYSZboEimOYGYnQ4EQN7DYfDDimqdvHzzaWMnKriuaahR0UZdv8anMJzqDBq4ebAxRnpx+fMqQkDUoFgLWjXFMIIYQY2aih5PLLL0+7ay3ASy+9NO4VOp9ND+ZQHers2wk4wRN0sMOJ1pLMygjK1MmwYAZcjLIYVk0GBgyYPjxyIAHQWjG/NMKCsl72NqRuAPjqwVyWVoYJBpyU7hqlEtfov09riFoKv/cqDCWLAwshhDg9sqLrFLI0v4RMT+oqqmaGjTI1nqCDUjql9UIpUEbiscRU3XTjToauRwKKP7+bz6Yj2SwoC/ftLnxC1PLwX69UsGFfLvaAnhzHhYNNATrDJj1Rg86IwXc3VKIwT/OZCyGEELL3zZTiUYo7y+fyVmMje3s70K7C6vagbYURdNNGSGVAMGjRFRpuSfjhWk0Uz+8uGHBbpxwbt03ePJyLUoqrF3biarAdxXPvFNIV8ZAdsInEvaycXjDszCshhBDiZEgomSK6u7uTS/nPcqHSl8dfam3CsURLhxM3BucGALzK5eq5PTzdGhgyw2Z0icLKcqI0dfuHtKk4rkFdh4+4rahu8/PCngK6IonwE455mV2cxQ1Lyk7+yQohhBBpSCiZAuLxOE1NTWit0VpjGpBhKK6u8vDkIQsXcCImPsBB4/aFCQNN0OsyOyfO4opedtZkDyq5P2aM3JLR3DM0kEBihs7hlgy+9sehOwB/8up5FGT5T/apCiGEEMOSMSVTQFdXV9rBxEpBWbA/UChyehVL86P4DRe/4bIkP8p9s7vI8GjyMhxSx48k/u33uCm7BadzYjzKwF2FNT5Tp40zBUGfBBIhhBDjTlpKpgDbttPerwCfeSIs1LZlcO+FrVxX2ZtyXMxS1Hd6SW0RSfzbNFymF0Q52Jw55LF0VzSUi6FgTnGERRW9PL2jOGUxNo+huH6xdNkIIYQYfxJKpoCsrCxCoVBKa0lEuxxzozQHLLzaxAp5sV3F4aYA88qiyY33bEcRtwMcbg6kLTsc91DXqZhdFMEwYFp+jEPNGdR2+EkXTuaXRrhrTQsoaO/x8P4Lmnn1QD7tvV4MZTCrOEhZbvprCSGEEKdDQskUkJWVhdfnJxYLozDocC022j04GswsMDIdfHkW4boMfr2thLXzOrl0Tg/aUeyqCbLxcH5ynMlQikjcJC/T5pYV7QBcMqebb/6piriTeo7XdCnJsajt8PH4m2UYCrym5vJ5nby4Nx9Xaw43h3h0wyE+dsVsirMlnAghhBg/MqZkClBKMW3aNKKebnrdEG9GQziQbMhQBiiPxpefWBZ+85Ec/u/zVTz8YhUv7ysgbI08kFWj2NcYTKxrosDn0dyxugWP4eIxXEBjKI3tKDYezuanG8twtcJ2DSKWyYb9+SwqCwOJxdNitsufdjdM3AsihBDivCQtJVOExzTotn28dlThr4wPafdQKrG6a6xNoR2Dk13SffA42rklEW5b2Up31ENjl5e9DUFcbRB3+hdCO3GC5SiitolC0z/09Xh7+OSeoBBCCDEKaSmZMiwONduJtUaGyxtjWkZ+KFO5LB00ONY0IOBzcV2IWiaOO/itkDpotjtiUpRtJe/xe2QVVyGEEONLQsmU0Uhjlw9chRMxh7RsaBfi3cOt2jo8hSY/aHPNos6U++O24kBjJlurswnHR3sbaKYXxojbRl+ZUJjlIxS1Rj5NCCGEOAkSSqYAy3Fp742R5U9sNBNt8eNaCu2CdhKBxA6bWF0nH0o0mt6Yyb89V8V3Xq7gcHMAy1F0R0121GRhOwaLysN9Y0vSU8DCsjBdEU9fmVDTHuY7rxykLRQ7lacshBBCDCFjSs4gV2t+veU4+xp7AI2pTAylcR2DcG0mZsDF43XwuhDt9TF0c72xUESsRPZsDfl4YnMJi8t72d8UxHEVCyp7WTOrh+3Hs+mKgD2kGwfyMm02Hs4ZVHeIWS4v7GnkvrVDV3wVQgghTpaEkjPoiY3HONTSP9ZD4ejE4mUeI7EbsI4rKrNsjrRmJI9JuwFOisGPpx7ruAbv1GXjNV2CfoerF3bg92jWr6tn4+FsXtmfn3KOQrNqRg8vv5uf9kpHW0Mn+7SFEEJMkNyoy017J6cF+6EJKFO6b84Qy3YHBJITXG3gM12m5UdxXDUgkAxdQj49NcrjiVVer13UwaeuriMrkOi28RiagqCNz3Txmm6yDNPQrJ3djTHMO8VryltICCHE+JDfKGdISyg67GNhy8PR1sy+6bcDv2BsM29GPsZvuqyaEcLr6duB2AUXyMmwWVkVor+1RaFZWRXCa8LSyhDmoHEnHkNx4cyCMdRHCCGEGJ2EkjMkL8N3GmeP3hoykrDl4ZebyjnYlEF3JDG111AwvTDOtYs7+ZsrGvF7HAJelyvmdwHw3qXtTMuP4zEUfo+Bx1DML83mivklp/E8hBBCiBNkTMkZkun3UJEboL5r+BaTkQ0MJie3bgnAkVYfNR3FfOE9NQzsgfF5NPmZNu9bEaM8rwu/1yVuK3rjJjHbxDQUNywpY1ZRFvnB0wlWQgghRCoJJWfIkZYQzT39gURj+F3Q4MZPZrXWkw8j/TRQkR/D0TB4orHXo1lcYfFfL1Xh9fTiuIrWUGIXYoXL0dZeVs2QbhshhBDjS0LJGdAbs/nF5mpsFzyZNoHivnCiQDuKSGMGrjVwHMn4yA7YXL2gg7mlUaKWwd76DAw1tBtIa7AdL50RG1f7Ux8DDjfLjBshhBDjT0LJGbC7rgutQXlcAiVR1MCRPUoTrIhQ3hShNhokzOCdeE8tqGT6HD6xrp6A18U0IDvgcMU8KzH1WCf21unX3OPlF5sySGzUl1iTZOB1A14ZiiSEEGL8SSg5A8JxG9vV+PKsIRlDKTBNTdX0AlbiZ1+7y65Wp+/RsQxu1dxzYTNVBTFaQ15e2ZdPdVuAi2Z142r48958mrp93Le2Ga+p6Y0Z7K0PEncUc4oj1MRMXq3LRheF8YQ8xDt99M/E0Si8pmLt7MJxfkWEEEIICSWTzAGOMquomVcPZGOYOqWFYqC4VpimYkGBQXvUpTZ0YlG0RECAwYlGoVlS2cvC8ggAQX+MD65t4ldbiplWEOVHb5TTFTa5fVUrptLsb8zgN9uKE2NZtOLN9gBmhovyJiYj+3ItPEGbcG0mhqHRrmLZtDwumiWhRAghxPiTdvhJ4wJv0BM5xK+2BIHEfjY6zZYzGvDYBo7Wiam3+Sd25DWB6T6FxxjaarK8KsT7L2hNuc/n0bxnaQd76oL0RE0cbVCWG8d2Fb/dVoztGIml5b0uRsBNyTnKAMOj8QRtHNdgeVWA962oRA2XpIQQQojTIC0lk6YBCPHYqxV9e9Eo7F4Pbq6F4XNPjCtxIdrj5bUOjcJidalJnv9ECHAAKw4ZHojgJveqMXC5dWUbRpq8UJRl8W5DJpaTOLa520dTV+o4EtOffkM+ZYAZcDCiJgvKMtIeI4QQQowHaSmZFBo4SHvIIBQz6W+O8ARt6FslVTugYiaRlgCxNh+2C5YLWxod9rc7KaXVAyHbwKcNMgxNUMW5hM3EY+mDRThuELU8qL5On1cP5uLo1M4f10nf+qFdUA6U5FjMLy07nRdBCCGEGJGEkklxHOilI3yiYcqbHSdQHMP0JVojXEfRU5eB3ethYFxwNBzrTjNtF4hqWGiE+Xvj+1xjvoV16BBxOzVcxG3FawdzATD6unwau/xsOZaNMyDDOGET7Sr0oEsp4OLyMH6Py+NvVtPYFTmdF0IIIYQYloSSSXEE0JTmWH23Nf6CeCKM2Ao7bOJEjOTw1cGGm3PjAiG7AQ+JHSHDR+vYctBHzFZYtiJmK944lMOmIzkA3H5BK0VZcQylqWkP9HX9nFgVNlyfgRs3EuNcNPjQzDFcthzJ4XBLBsfbw/z360fp6I2Pz8sihBBCDCBjSiZFIoxkBRxyAhY9lokGYq0+rJ6+9VRPaiubE0Giixw6dC6/dG+lk1w4CC8f8pCXGWfd/C4undPNxbO7eac2i0y/w6euqael28P/f0NlsoxkqbZBuC4TZSYGvGpb0TZoho/turx5uJWbl1ecygshhBBCDEtaSiZFUfJfH7+yEb/SWCFPIpBolfhK/vIfPp14TRdT9U8NThzfShE/cu+mlQIsvFh4cbWiO+KlLDeO36vJ8GlWzeihNDuO1lCcY7P+yjq8ppP2etox0Hb6t4aroaFTunCEEEKMPwklk2Ih4EVrRVbA5X9cV4fd3RdIUgy/tLyhNK4GZ9A5GkVMBdCDvpWuVrx1KJefvFnKvz1XxbsNmfg8mpitsB0oy7X54ntruHR2F7OKwig1eJBs+nCkFJTmDF5lVgghhDh90n0zKTKBdSh1GMdtY1OrQrsnt9bHtPwoTd2+lMGpkAglfp9mWnaEK+d3kp9pU9fp58U9+eyozUoe9+T2Yp57xyVmGygFSypC3Ly8neuXdgKwryGD53cX0h010RrMoA0anIgnJTx5DMWlc4sQQgghxpu0lEyaAFovZlfNUrbVBJg/LcT7L2gmw+uMfiqJtUbsNEHGUJqKvBj3XtRMaY7FztosNh3JpjvaP4sn8eVqRcQycbXCcRV76oP8aktJspyF5RH+x7X1LJjTg/K5OGEPTszE8DugNKApzw3wwKWzKMzyD6mHEEIIcbqkpWSSaK15/I2jVLf3cusFIZaVh/B4FM/uGlurQ0vIx+LyMO82ZmI7/VlS4zE11y7sIG4rvvuXCqKWMWhWTXqOa1Dd5qej10N+0AY8HGyeyb5DFskuJEfhugpvXpzFVUHeP3PuqT59IYQQYlTSUjJJ/ry3ier2MBV58WQgAajMjzH61BtNTbufFVUhLpvTRdBv4zVdZhdHuf2CZgqyHF47mEs4biZXeB3LbsKmoekIe9DaQ0fvVfx6iz30PK2wOn2sKZLZNkKIc5frunRUd9B+pB3HHlsLthh/0lIyCVyt2XS0DYB5JWHME1vZcMOSDn74Whm2C+mDxInpv09sLuXi2V389WWNGAp21QZ5akcxn7+hlgNNmbhDBs6OzHYNSrIteqLw7K5G3GGykakMsg0Z3CqEODd11nSx64lduE7f/l8alty+mOKFxWe6aucdCSWToLM3jtu3VGrcMcB16U8m5blx7lzTwi83lww33yX5L8dVvHEojzcO5aUc8XZ1FgFP+iXmh6dZXN5L0O/Q0uPlSEsPw7euaDL98lYRQpx77LjNjp/twImlto7s/u0eLvn0xQRy5Q+yySTdN5PA5zGSDR6764JDwsf80gi5GTYnuYJa3/GaF/YUUJYXw2uOPZgo4D1L2lEqMfNmpN6eC2fm4zW7gTYSWwIKIcS5oXVfa9qPXq01DTsbJ79C5zn583cCdfTGeXZXHUdaepPv+Z6oh1fe9nLNBXGSG80oxR2ZL/N45FqcMXxLDEVfV0siSbgadhzP7ttwTzPaeBKFpiI/RjDgsrc+k9cP5aGH6fpZWAY3LHmXxKq0fe2arABkjIkQ4uxnRW1cd+gfdNrRWBHZUmOySSiZIDHb4fuvHSYSdwaEcI0ZcNkUKqfhlTrek7+fXN1FV0uEzc5FIwaS/jjg9xjE7HQtImqUdpYTjwZ9DneubgXgjYO5WE66BjNNZV6Auy88ilKDfzB3ADlA1tDThBBiitFaU7uljuo3q7HCFjkVOcy7YS45FTkUzM5HoYbsPWZ6TYrmyZpMk01CyQR5p6YTy3FPvM0NTWZ5GMOrUQqadBH/HSsh0piRZmXXocbWsdMfXdK1lpxYxj4r4JCXaQMQipmk4zU191zkS7PSa39tjgOLx1QrIYQ4kw6/fISaTTW4VuLzrLO6k20/epsL/2YNWSVZlK8sp3FnI46V6J42vQZ5M/PIn5V/Jqt9znAchzVr1lBZWckf/vCHEY+VUDJBmntiWM6JKBEoimL4EoEEEsu1m34XX36cePvYFyOz+6bIJHJ9orCcgM3C8jAKaO7104CL8micsEm82wcpi64pWkJeWkMe8jJsphdE2FOflSyrn8fQBH2NpI9DGvp2JhZCiKnMjtvUbKzBHdTC7NouR189xrI7l7LgpvkUzi2kfns92nEpW15G6ZJSlDq5GY0ivUceeYRFixbR3d096rESSiZIWW4Ar6n6gonGE3RQCjIxWOYJUqQ8aOBdw2EXYx+g6riaReUhjrRkErMVq2Z0c+PSDvrDg1LwWlMm29syMX0u3hyb3trMlGDiuIonNpbg92pM08XncbEco29KscZram5c2o5haNKHEhMoPfUXRwghJkm0M4oy0oQLDT31PQAopSheUETxAumuGW+1tbU8++yzfOlLX+Jb3/rWqMdLKJkgSyvzeGVfM7ZjJ3+te4DLvDl4USilUECvdXIzbhSam5e309LTw7O7CrlxaQdeM7WMy0vDHO3x0xk3AY0vN068I7U1pj3sTf571fQelFJUt/nJy7C5bF43M4uixOw8/J4yoJoTs24MIBsoO6l6CyHEmeDP8aOd9J+zmUWZk1ybc0tLSwtr1qxJ3l6/fj3r169POeazn/0s//7v/05PT8+YypRQMkF8HoOPXzmHP75Tx8GmHnRMUZHhxURhDGgS1MlxIGOTk+HgNTUzi2J8Yl096f4AMIB5OTG2tGaiDPAGbeIdfkzl9u0ynHrSztpsPn5FPbessJL3xW3Fi3syuXn5QpQqJBFMbBKzbqqQ2eRCiDMp2hWl+s3jdB3vJLMwkxmXzyC7LHvIcd6Al7IVpTTuakrpwjG8BrOunDmJNT73FBcXs3Xr1mEf/8Mf/kBJSQmrV69mw4YNYypTQskEcbTLjs56GjObCM7SeJWLobx4BvVRzsgxONblDtuBU5wBLZETtyOWwjQSIcaTfozqEEWZNgtndNMR9nCkJWNoXV14+3g21y7qwNVgGvD6wRx21vi5bK5NfrAU6a4RQpyMeG+cpt1NxEIx8mcWJGa5jNMYjXBbmM3f24JruWhX09MUouVAK8vvXkbh3MIhxy+4aQEev4farXW4tktGXgbz3zuf3Gm541Ifkd4bb7zB008/zR//+Eei0Sjd3d186EMf4qc//emw50goGWdaa/Z1tfGXxmOE7cQMl4ATZUloP0VWO57AdJzc5Wgz0Z1SmqnID0BbdLjyUm+X5cRxNSgXjGEaK1zgULcPAI/SrKsMMSvb4q3DOVS3BXCG7Das2Hw0h6OtAbL8Dg1dPqKWiccAGeclhDhZHdUd7PzZLrTWuLZL7eY6cipzWHn/Cgwz9YNLu5qmPU007GxEKai4oILiRcUjBphDfz6ME3dONDJrcC2Xd/+wj8s+c+mQcw3TYN4N85h73Vxcx8X0jvEvOnFavva1r/G1r30NgA0bNvAf//EfIwYSkFAy7p6tOciR7g76R5IUxNu5p/EZDBy82sHqPca+thbe8l2DrU2mZyuKAtAeTd+JMzis1LQHePjFabxvRRtzSiJoDV4zEV6UAkfDlpYMem0Dj9JcUtLLrOxEt8zi8l5efjcvWZahErsMx+3Eh0RLj4+WAd1+eZle8jJ94/nyCCHOcdrVvPPr3cnptQBO3KGrtov67fVMWzPtxLFas+vX79B+uH3AdN0uig+0suT9wy850FHdkfYDMx6KY0UsfGk+t3pbeqnf2YATcyheUETBnAKZXTMFSSgZR43hEEd6OrEH/LRc3/YaPh1PjuJ4wbmSd1iEFTEATXtU903uHfrDkW60iUYRjpv8ckspXtPFdhX5mRZLKsLMLelleqHFwrw4VVkWJQEH/4BBsLmZDjevaOOF3fncsKSDpZW9KAUdYQ9/2FlIdduJrh2vobhrzfTxe3GEEOeFnqZQMmAM5FouDTsaU0JJ5/HOlEAC4FgOzXubmb62iuzyoWNEALwZXuyInfaxdK0gdW/XceC5g4kN9zQ07mykYE4By+5eKsFkklx11VVcddVVox4noxXHUU1vN6520U6i5cLUNqXxlmTcaNN57GIxFidSfP9q8WaGhaFOBAhD6SErDJ6QKNFyDLRWtPf6eO1gHj/dWM6O40EK/A5VQTslkPRbMa2Xz15fy5LKXjxmYvxIUZbNB9c2U5R1YuXWoN9DSY5sRCWEODlKJVpA0j426DdO+5GOtAFGO5r2ox3DXmP6JVUY3tTCDNOgdEnpkFBiRaxEILHd5F95juXQdriN1gNtY3hGYjJJKBlHAdMk1u4nVB3E6vbgukbKomTHdWX6xeC1AlPjL4viybLwBC38pVG8eXHStVEqNF5z6MZ4lmOwYX/e0OJ1YjCrq2HjkWyUUgz+Y8I0NJfMObGwTWfEGvaDRQghhhMsCQ47BTfaHaNuWx3xcOIPIG+GF+VJ00psKryB4RvyK1dXUrmmEsNjYPpNDI9B/px8Ft68YMix7Uc70q5T4louTbtlw72pRrpvxlG8xyTe7QEUsTY/rq04HJjOnOhxTFwyVQSlT6zIWpYbRwONXV4Mj8aT4WAGnOTgUq01VprraMBy0g/U6ooM/ZZajuJHb5TS0OVnZlGUC6aH0oQSKMo+cbWgz5RmTSHESetpCA27J2isK8b+5w6w/7kDLLh5PqVLSzn88uEhrcJKKUoWlwx7DaUU82+Yx6wrZtLb0ksgN0AgN33Lrukxhq2PDHideiSUjJs4W4/W4rr9L6nC6vLzNDfyIeM35NNFla7DxGV6QYS7LmzBa2hQELcNnqrOptlKnOta4NoG0dYA6X6aPIbG1RpXD23oMgxN/6B0RWJH4Q3782joSvzAtvR48RhD/4qxHajtW+7eUHDF/OE/EIQQItIRoWFnA/GwRdG8QgrnFqKUItoRGXHppf5WlH1P7ye7vJ4FNy/g4HMHE8FEJ1pJlt+zHM8ILSX9vBle8qbnjXhM/uz0+9cYXoPyC2S386lGQsm42Us4zS7XETL4nns/VTRQoDop8Ib44MVh/J4TP7V+j8Ndc7t47N18erv9xNoS4cCjEsuVDWYPmdJ7gqsVj+4rZFZ2HK+hOR7y0Xw0m/6Y0hvzsKMmi+XTevH11cF1E2VuPpqN3+Nw6ZwCLppVcBqvhRDiXNb8bjN7frcXrTXa0TTuaCC3KpcV968YdnBqOj0NPRx5+QiX/cOl9NT1gFLkVuVgDLfewSkwPSYr7l3Ozid2AX3jXVyYcdkM8qpknZKpRkLJadBa0xuz8ZqKza0dRL25EE23Q69BDRXU6EourOxGER5SlkIzQ2veGbAc/Joyk00N9pDN8oZti+xja8XBbj9oyDHhPRf2smlvgLaexLf72V2FtIW8rJ3dTcDrcrQ1wLySCH91aROOC0VZPum6EUIMEQvFaNrdxOGXjqSsjupYLh3Vnex98l1mr5tF4dyCMQ8itaM2ncc6KZo3cfvO5M/M54p/uJzWA604lkPB7IJhu3vEmSWh5BQdbOrhDzvr6I07uFrjyczDmx8n3utJrF42zPLxmX5nyF41AKaCTFOjtWZ6QZyusEllFiydFmJfQxDL6Q8JI4UFjSfD7itPYypoPp6JKgoTswZ+qxUbj+Sy8Uhu8rzpBVHqOgMYKjGI9n0rullYnnPyL4wQ4pyjteadX71Dy77W4Y9xNE3vNNHybgvTL63Cm+mlYcfoA0m11sS6J37XcdNnUrpUVqae6iSUnILGrgi/3nq8bwfgBKvXg+sqglUR4p1enKiJ8rg4vYmBr/0B5VhrgEvmdKd030Bi595jrRk4WlHf5eU9i9uoLLJYmBln1fReXtyTR33X6Ml+9dwuwhhUZFpUeB1+ciyL1vZc/KZJKO2w2YTj7YnxKw4Ky4HfvV3DRy+fTVnu0GXphRDnJjtu07KvlXhvHG+Gl2hXNPHf7uiIgWQg13Y59no1K+9fQdH8IvY/f4B4d5q+7QFyKuUPIJEwaij56Ec/mtxUZ/fu3QA89NBDfO9736O4uBiAr371q9x0000TW9Mp5M1DrdhDprwpnKgJLgQKEz+AWkPo6MCXWFPdFuB4W4AZhdHkmI64rTjcnEFdZ2L9kjnFUVZMD+MxNErBzKIoD1zWxHdeqqQ7Nty3TLOyqocbZ4aAxMDVrceycRzYdMCBYXfXSdR9MNvRvHW4jdtXTUtzvBDiXNNV1832n2xHuzpl7RDDY6R01YyJCzt+uhNlKrQ9/KhXZSgKZhWk3UhPnJ9GDSUf+chH+PSnP81f/dVfpdz/uc99ji984QsTVrGprK03Puzgctc2MLxO378HPnKiteSJzcWsrOplZVUIDWw/ns2umiCgUEpz68rWIV08XlNzxfxOnttdiKsH7eugNJk+h2sXdQKJNUnijsGbh3PTjEcZGw10pBu5K4SYciKdEeK9FlnFQUzfyNNcu+q6qd1cQ6wnTtG8QipWVWB6TXb9YhdObOj6RycdSPppRgwkANMvnc7sq2adWvkirXpdyr84n5ukq31k3EscNZRceeWVHDt2bNwvfDabUZhJU1eEdOsDGT4nuYmeYUJwephIYwA3buAvjuHJsIm2+tl+PIvtx4f+dVCUZSV3AR5IKVg1I0R9p5936oJ9M3ASgcPVirLcGB1hL5bjcLglwGsH8uiJnnrvnGnArKLgKZ8vhJh4VsRi1y/fobuuO9Eq4WrmXDOb6Ren3yKifnsD+5/bn2wJ6arponZLHQvftzCxwd0kyirPYu61cyb1mmLqO+XfWv/1X//Fj3/8Y9asWcM3v/lN8vPTzwV/7LHHeOyxxwBoaWk51ctNKZfMKWL78c6UDadA4820Ezv39jVOGAqCpoG/PEpHXQBvlo1Sie6deBgcDFxMTgyIVThOYq2RwY63+Xl2VyHNPd6+VpTUFpBDzUGq27JwHD1iR016uq8dR/XVQuPzGKydNXQLcCHE1PHOr3fTVdOFdnVy/YDDLx8hszBI0bzUn1/Hcjjw/IGUrhnXdon1xGja0zTapL5xF24dOgtRiFOaDP53f/d3HD58mB07dlBeXs4//MM/DHvs+vXr2bp1K1u3bk2OQTnbZQe8rL9yDksqcuhfvVihMAMnwkSV4eN6bz6XeXK4LpDHNbNMfH3TbLOMXv7G+CkLAwcozGxjhnGcRRxgFtXMy6imNWriDsglTV1efrKxlOYeH6CwnMRmfgPlBDz81aWzuHBWIV7z5D9dls+JkpPpkOFzmV5q8aFLS8n0yzhoIaaqaHfsRCAZwLVcqt+sHnJ8qDH9Squu7dJd333q3TSnaLRuJnF+OqXfOqWlJ6ZVffzjH+eWW24ZtwqdLfKDPu5cMx2tNXUdEeo7w3TYrYRshaM1S8wgnr4Q0unadGibCsNHtRsj2w7x+2k3ETN8ONqkTRXS1WGxrnsTnrjFU9VXctesbrK9DhrFqwdysZ3BnyYnbnsMuHxRARV5GVTmZVCU5eOtI210jDD2ZTCf1+XKlb0oFH4zQHm2tJIIMZVZ4XjaPV0A4qGh48E8GZ4hAabfZAcSZSqmXSiD6MVQpxRKGhoaKC8vB+D3v/89S5cuHddKnU2UUhRlGkTbO8lTBo6ZidF3v9aanU4vDW48Mf4ksYoyTd7iRKZQ/d0lYOd7eT52NbPix4g4Bo8fyqM0wybocdnflsFIbau21mzsPsa2fTW8f8YCFpTn4PeaPL2jDnuYD6FBz4LtBzJ5z0W9FAQKyVKVhGI22QHvab8+QoiJERxhzFdmwdCp/MGiIJkFGYSae1MaWg2vgRN3ht1Eb9wpKFlUzMwrZkzO9cRZZdRQct9997FhwwZaW1uZNm0aX/nKV9iwYQM7duxAKcXMmTP57ne/Oxl1nZK01tTV1eG6ib80zL6gYbuaIz029f44pkeT7XEJWQa2VmitGLJgqgJfvsN7lmSyP9TLppYgTZG+UOB1IT5gsMqJqwMQKI5iaRfLcfnJtoPEunyYSuGMKZAkmIZJtGs2v9/RTtQ6iquhPDfAXWumk5Mh4USIiaS1puNYBw07G9Gupnx5GQVzCkZcWdnwGMx7z1wOPH8wZZwIQNuRdo5sOMLsq2an3L/8nuVs+9HbxHv7Wlk0zLx8BjUbaybkeQ2sq9aavKpcFt6ykMzCzLTHua5L7aZaarfW4VouRQuLmL1uFr6gb0LrJ6aOUUPJE088MeS+j33sYxNSmbNRNBpNBpKBPIYi2zS4ODvK6hkheuIG29sD7Ov0E3WGBgyloDg3TnbAYW2mQ4HP5fcH81CGxjA16cbFK69LRmkUtMLuNdEaop0e0BpnzB03CVprNuxvSQkydZ0RHn/zKJ++Zp4sOy/EBDr4p4PUvV2fDBct+1ooXVLC4tsWj3he5apKQk0harfUpbR+aFtz9C/HaNnfSunSUipWVWBHLLZ8fyt2JDEiVjsaDIiGYthppgKfLMOjcIeZAly8qIgFNy3AO0rr657f7aV1f2uyO6l+Wz2tB1q5+JNr8fhkjNv5QL7L46C/q2awgqDDgqpeIrbBL47mEXcUbt9aJINpF2aYmp3bqnBdRXFpD05TgLibfiyyAny5caLNAVyr75jTaH21HT2kIUZrCEVtjreHmVEo04OFmAih5hB12+pTxnW4lkvTnmamrZk26mqn4bbhd+UNNYYINYY4/OfDKE+ahcxcqN9Sf7pPAYDCuYW0Hmobcg1lKDLyM0cNJOG2cEogAdCuxgpbNOxspErGoJwXxm8rxvNUIJB+6Xfb1ZQXd+DzaDY2ZxLrCyQJif/25xjtgs82cI4V09WZSU93BscOF3FR0IPXSD8AzWMqrG4/btwArRJfnFi75GR4TEVlQSZpe3sUdEeGX55eCHF62g61pwxA1aaNPbOG+OwD1B57d9Tz/dn+Mf3Yj7aQ2elqPdSWdlyKMhQVK8tHPb+7vjvtwF3Xcuk81jkeVRRnAWkpOU1KKcrLy6mtq8N1NaahsBxNW9RlYV4XANW93mFXVnXCCt3rY4GVCepERnS1wmPAsoI4+zr9FGeYxF1NY69G09eyMUwrysmYXpDJXWums6+hi8auSMp+PgCuq6nMl/1vhJgI9aFtHOrdjFZzUHhwCzqIXf8qKA2Gy1FjN7GWfaws+jBKpf95r7poGk27myZ9Bs1g2tYYnr7B+2ZfXV1YdNtCMsbwGTLcrr3KVGQUymfQ+UJCyTjw+AL88UicaVkGfo+isdeloVezZJ4HyzEIR8zhX+m2TK6oNAg39d02XDKm9+DJTUzpCzqKWeEAKp6Y0+9oeKXGpjOmT6u7BjTTim0umBOiKX6ImaVFZB320B2xcfqacLymYnFFLgVB/+lcSIjznuNagMY0TgzYrA+9zc7WH+NMUwQ2zUGjiV39JvhPtExqXBp7t1OfsYTKrDVpy84ozECZKrl42pnk2pqi+YVMv2Q6ruOSNz0P0zu29Uhyq3Lx5/gJt4VTZwcZimmrKyeoxmKqkVByWlwawu/yh+ou3EqD4xri3V7ivYnZL8/uLKS200e5r56cnB4aAyXEzMRfA9oFJ2KyoMBLZVELx1qLcRzInN2NGbRONJoYmlhON5ldeZiuB9d2WZBvsKkx/cA00wCvoYiO2FSruXBhhKJcB8fQdMahO97Je1cXc6w+l70NXfg8BhfNLGDVjILxfcmEOINc7XCo808c73kdR8cpyVzKovz3E/DkDXuO7cbotZoJeHLxGkF6rWa8RsaI5/SL2O3sbPkpbdEDAOT7Z7Oi+MMEvcXs73gKR1sQgPi6jXjeWQi+oeuLODrO8Z7Xhw0ljTsbz3grSZJKtHjkz0y/wveIpyrFqgcuYM9v99JZ05lYMynHx+L3Lx62FUWceySUnIb22HZ+czSOpY3EFF8FvhyLzIDLGk829W0xPqF/TmYsAq0KE4c3c1ezLWcFTtQk2hxAF7mUl3Rx/GAR2mOnBpI+roZ4IEJ9fSbbmpyUjiDV939aJ2b8ZPpMLp1bxIt7GjFMzbxpUcoLbVwXjjf56O7OIjfLpiTfSRlw6+LS47RwxYIKrl9SNgmvnhCTb1vz92iJvIurE60RdaEtNITeJtc/g/LgBUzPvhSPEcDVDhG7ndrQJo50/RmFkQgQaBQeUC55vpmsLv04fjP9Dreutnmj/j+IOd30b/7QHjvMG/Xf4NqqfyVst5842Gvh5nUy3OAQVw/fDNJZ03Vaa4wYXmPIlOJTLstjULnm1Fs1/Fl+Vj1wAVbEwrVdfFk+mfl3npFQcsoivNkUwtKp8+eVAW7AIct0uSb6O3LpZuDYrYu73qa6axrHnBmYCsqzDDY0ZnMkt4e58Qy0q1CD9r4xDOhxbbY1OUM2ATQNxbLKXLqiFnOKs1g1owCF5pW9NVy6LErAr+nv3p1fFSPT4yfXH6Q52pXmOWm6rU4CHum/FeeennhDSiBJ0LjYdMQO0xU/TnX3a8zIuYIDHX/A0RbpJuNr4qChI3aUzY3f4YrKL6a9XlP4HSw3QupuVBpHx6nv3Uamp5Beuxnz4Ay8G1ej0dir9gwpx1Q+pmWtHfZ5GebpjS1TSmH4DdzYyMGkf8M/dCJ8eAIePH4P0e4oSiXWXlp460KySrJOqz4AXlkb6bwloeQUHetp4kC3jyAmFcqHVymatUVr3180TqyBIGEGDyb3aps1ehfHmcHsPEU4EGZvlw/H47JH9XJxms34HAfq2830uxIrxbyyHBaVn5g2uHvvTsqLLPy+E4EEwDQhbHdT3RKnrCixBd9ACoWp5C0hzk3d8VrUCBMOXW0Rtlt5t/13jGVbS41DyGqkJ15Ptq9iyOO9VsugAJTg6Di9VgsLC25jR+OP8W65AKWNxDT/V9cSv/otQIPHxcBHrn8GVdmXDCnHilo072mm41jHqHUdiWM5XPixNWz90bZhZ+h4MzysuH8FNZtqiXXFKJhbwLQLK/EGvPS29uLEHLLKsk47IAkhv4FOQdxxeOp4E2Wun5W+IEol5lZX6QAtrsXbbohM18JQashgVKUgSISrphmUZ2vecMM4OvFtiLuKmmYP04ptPH1jw1wNjgtHGtKvaGi7LnE7DhwlHK+lvsNhW0eQ3DySZaTStIc0xQWpgSVROcjzyxiSqUZrjaNjmMo37AwMMbpMTxGjjQ5Pv0zh8BQGUaebbIaGkiyzAm0bYGg87yzEs28OWF50STu+95RTPvMCwjrOMd2dPMdsKCPw+xuxZ1ejM2L4OyvQ7VUcnHuEmVfMwJ+VGHTeUd3Jzp/vRGt9+l0vGsIdYbwZXuI9Q8e0ACz5wBJyK3PJvSN3yGMjLXcvxMmSUHIKDnW3g61Z6Q/iGdAU4lVQjJdS7WWDJ4vZaf7aimsPdf6ZzJ7RQHlehNcO5iUfUx7NEUsT74GqrMSg1Y4o7DvmI2al71c1lMvSyt24bphMn8ucEphe2Ms7DVk4jhfTTIw3sZ2+kKKhu9dk58EMVi+IoVAk/qeYm7MQU8nOnVPJse5XOdDxByw3jMfwMy/vJmblXCP97H3CViuutgl6S0d9TbI85Wg9vgNCXWxyfekX9bL25aLMLDy752Eer0Q5fR+3jUUcfyJM+d9FqChcTrXeiB4QllQkA++ehUAiQkWJUre1jqbdTSy7aymeDA/bf7x92M31TsXeJ4dfD8Wb6aVglvyxIiaHhJKT0BWO8+bhVo5E2sglAzdN44XXUJTbfpqMOC9nX8JVXW/hwcZQiUDiBoJceEUhHm8Ew4APz+vkyeocjvf6Ei0uHmiIGjRET5Sp82zo9JFuENzqGSEUYYy+RdaUAp9Hs7yih51teVQ3+dlf7cdyFKYBM0pjtHaZ+D0mJcY8crJsFIosb7b8FT7F1PRs5N323+HoxF+vlhthf8czKAxm5V49qXXpjtcSsprJ9pal7aqYbCGrmW1Nj9FrtyTvyzDzKclcwuzc68kYNDNGa5c3Gv4dd9R5sye6NRX+xPiRYVpXTOVjVs41+Mysvmtoavbtp6OhiWBRDp3vOPgb1oFjovSJny2FwrEcjr9Vw4L3zierNEhPQ2jEWvWvbPr249tHqf+pSTtQViXGjiy+bdGwuxELMd4klIxRa0+M7792GMtxweuSMbQVEwBXaxxHganYFVhCTWcFa9hFFmE6s6ax/JJCfP4TrREeA943vYdH9+Xj9M/iGcRHnDyfRWc8j8HBZF7piUCSUg8U1V2ad6t9OHbiA9F24HC9H1BoDWW5mXikD3jKOtD5bDKQ9HN0nIOdz01aKLHdKJub/h9dsWoUBi4OBf65XFj6iZQ1NyZLU3gXhzpfoCN2lMFhoddu5mh3K7WhTVxe8UWC3qIB571DyG4ctlwTPy4OWd4Seqx6sv2rCXrnY7vd9Mb3EXeaAJvijEX0xOvwuNnMDK5jWv5qAOKRKK9//2XcbhMckxazOTFOxBlmwKaGxl0NdFR3EOrswF56GLesBRUKotpz8ByedaJl5QxQCkqWljJ73axhN88TYiJIKBmjF/Y2EOtbC0A54PoiwNC5866G404UTDB9DvWU87SuAA33LGjC748MOcdraC4tCfNmcyaOHhoSHNugN56+37Y3buK6iRk6Ayk0MQWB8ii9NZmcCDOJ/75naVkykNiuS8SxyPR4MaW1ZMqI2p1p74+7IVztYExCV9vett/SGT2a0sLQHj3Ivo6nWFJ414Rff6AjXS+xv+OZIUEtldvXovQUq0pObBxa17t1xLKXFd1HXe8WWiJ7CXhmkumdi1ImXjOfvIxL0NrFdjuY7ltAw8sR2g61c5Aeags2sfi2Rby7aRNupxfl9n1PbKOvS0YnukjTsKMOlmoleuufwWuBx03mLBXKwlM/+tLsJ8PwGRTOK6Rlb8uoCy8aPpPK1RUSSMSkk1AyRtVtYVAaf2EMb7bNe2Z28OLbmawt6VvtNDE0gwOdDh2ZFgagTPDlxYl3+kCrtK0gfaeysiBKr2Xwdnvqh4DSLm6n0bdM/dAC3jqUw8KyML4Bs3ZcDWHHoDHqQZkaM8PBiZz4Vitg+bQ8tNa83lTD9rbEX5CGUqwtrmRNUbmMWZgCgt4SQlbDkPszzIJJCSRaa2p7Nw/p8nCxqel5a9JCiattmsN72Nf+1Bi6XwA0rZH9KfeM9nrZOkp79CCgCfoWoLtNQq/GsGsSO+l65xkELs1m/2/rsNpIjucIt4bZ/pMd2K5Guakfp8OFkYGsFXsSC6aZ/Wmk7/6L38b43XsxxnF7Mjfu0l3bPewGogNpV4+6EaAQE0FCyRgFPCZmUS9mwEEpONjj546LavntlhJMJwOPmVhevtd1yRqQK/z5FqbPJTtqcLQ1wJySCJ5BnzNaw64dleSWuFxUVMy21jpM10IDmU4UeqGH9B8Qjd1+Xtybzw1L2nFJfKaFbYPfVefS/wlneFLnFAT9ifVNNrbUsb2tEbt/8J+Gt5pryTA9LC0oGadXTpyqxQV3sLX5sZRppYbysqjg9kmqgR520S4nzVTXcb+6djnc9SL7O55JtjqMlddMbVmsyrqE+tC2tLNrFCbHujacaIGJ+Qj9Jo6O9V3SAeuAi90IuluDmxo2XMcF99RColZAzA+Z0dQHAjFURgwi47tmUKwrNrZ6OZrumm4yi4P4gl75I0VMGgklY7R8ZjY7oh3J1VZ3dWTQ1RKgqcfHwvJuSnMssnu87KkPEm31EShKLLCEAjPDwbU11yzswOz72e7/Q8VxYc/OabS3ZRKJe7n1kunMdSJ0HfgNQSdEeayZP+sraKAEZ5hv19ZjOezt8DOzqpe4q2iKehjYquLETqQgr+ly/eJE68621oYTgaSPrV02ttRKKJkCSjKXsKbkE+zreJJeq5lMTxEL8m+lLLh8Uq6vlEGBfzbtscODH6EoY8GEXtvVDm/VP0xHfPC1R2fgZXbOdQBE7S5cbVMYmE9BYE5yufeBNE7K7JfIu11ouyQ1A7mguxkSSCDxC9zM0ji9bsqA1rHwbVkBmy/AXngIe9U7qb2s9pn7eNau5u0fb0eZCm+mlwU3zadkoXwmiIknoWSMppf6eee4wu37pIqHPRzu8PLJq+sIxw32NQQxDc37L2jhT7sL6TmeiZmZ+KvMDnsoLoigUMml3ZUC14WQ5SFeGsWvFfGYF9u26QkblBl+gpHjKOAiYwfb3OU4/SknjXC3j+NdDob3RMjwKE1hwKE3w6bNUeRm2lyzsIuF5WuxXY3lpl+Todee+L+CxdiUZC6mJHPxGbv+sqL7eKP+P3C1jYuNgRfT8E541019aAsd8SOndK7HCFAYmM9rdV+nO16LxkVhMi37YjqiR4Z0AZnKh696Lr0VTaDAaongSddANMxsYsNjoCwDpU/8POkRxpIMpOzEQFjP/jm4Re24M+rAURgNJSjrzK9qqh1NvCfOnt/txf9hP7lVw4zwF2KcSCgZo8JAZqLVA5hlBCjP8JOd51DdUMAf3s3A1QpXw67aLCryYnRHA9ihEx8ql8zpxusZunx8ltcmkGGhprnk2AF6enoAaCq7hdzO7Vi9R3gpbzV+N47TZuImWz1SP/By/V7MDi8RfxgzaKE1TMtQ3Dq3A8/87gFHLgQyMZUmy+ujxxo6aLAoIIPbREK2r4Krpn2Z6p7X6I7XkuubzoycK4bd72U4thvFcsMEzLwxTT2vC21l5O4ao++Xvh6y+qqjY7zZ8B/E3Z7kfRqHmp43CHpKiTodya4aU/nIic0l+mo53JIJ2WHcgnb0scqxzX7p+zF04m7yhvZHcQs60YEYZm3FiXAxdBHlE8XYHjx75xKvaER15uB748LRrz2JXMvl2OvHWHHfijNdFXGOk1AyRrEIOGGDdTlBgsrE9CjQJl7Ly+pSl00Nib+SLEdR3+kfcn52IH2rhO0qPIYmboJttOPqosQgNGXQmLuSV4KzkmNFgpURtAtWr4dYi5+BwaQ37uBENTrkg7bEVM13DUWJUcbl8+2+Y8uAROBQSnFV2Qyeqz2c0oXjUQbrymaMwysmzhUBTy4L8m85pXMd1+Kdtieo790KKEzlY3HBB6jKvhhIDKaN2O0opcjwnFiga7Rl3hfl30ZH7BiN4aHrdmg0ltub9rxeu5mVRQ/QHNmNq22mZa2l+ucxsEL4Nq4iftWbOHOq8e5ajHaN0btjdN+YEt133Yvfxpl9HFRi7InFdnwvXYbZXJxY9nmEBWONzjz8z12N6swdUyvLeFFGYpT+aJv6hdv/P/b+O1yW6zzvRH9rrarOvXNOJ+cIHBxkgAQJgEEMIkVKlm1JHkmmfa8018+M7DE9c/+4vvPca9p37LE982g8smSbIytRpmSSIEEiRwI4ODg4Ocedc+pcYa37R/XuvXt39977gIhEv3zw8OzqqlWrq7trvfV97/d+lZWDddTxbqNOSjaI4zdnaMvFiDYo1ArRlyUFA0nJ+WmfVDHr4foSWH5yArg2FaUt4VZYv0sBOS/Y6GuwEqqkjn/dS1XemgU4M5VGal4Vd0dPG167nuXBnXuqvqcdja2ElMVrE0PMOXnaIjEe6OynJ3Z7T8F1fHzg+Gkuzv2AscwJJJK+5H3sbPp8Tc+S0zN/wlj67VLKRBuXszN/TsRqJCQTnJj8I3J+0LslbrVxZ8dvkwx1o0QlsV+CEmG2Nn6a6wvPFclFebrRGL1ManyJ9fY+rCtbwVPozmm8z0ru3PSbpf1vem8E4451YZ3ah3f4PIXHXsR+6yByvCOonFuLJBR/erp/DH/LIFhF5lH8rTuP/JTId76I8NcmOMK1EfNNa+7znkBCy5YWZq/N1naJFdBUT93U8T6gTko2iHTBozMqsas4GxoDbTFJamGJQhhCuGgkHjagee1aA4f600TQJWLieILzk3EUWRKpCVwZJmXvIafiZAqzZKo8LWpHYMzGn6L8dayoNyUa2ZSo32w+zjBGM5W7yET2DLaM0Je8j4RdKWr0jcsro/8/ct5sqYrlxuLzzOavcn/371VUaLh+lrH0iQoNh28cLs/9kJQzimeWq05S7hg/HftXPNr//ylLvazGvpavI4SkP3k/1xaewjEeS8xAYtMY7me+cBODJvTivYE+o5iKkePt3PjTFF2/UyDSEBCf5j1xsrMZjAf2+V1YV7aiW+cQ+fAGhasGhMDbdgPsKqEQYdAdM6iJ9g2M9f5j0/2b6L+nj1f+1as191G2YvNDm9+/SdXxsUWdlGwQu7oaGBzN4WuDqkJM8iu6ayp8HEKEyQe3ygh89sAMz55vpj3psL0jTyqvODGY5C7/GP0zZzBCog0ULr/Mk3ydmXiCUCtlAREvq8hNhDdcGSmAgaYQvu+jVL2nTR2VMEZzfPIPmM5dwjcFBIrri89xsPVv0pe8p2zf8cxJCv5iWVmtNi6LzjBTuQsVgtyCn0IIBVXKitPueNUUjTYe49lTJOzuErFYCSls2qI7AQipGA/2/GPOz/4VU7lzSGEzkLifnc1f4PjE/8n05GAZIYHAO8R4MPjaLTY/2sObE/+Ohd5R7ORDiIUEQiuEa6PGN15pYgCTSLOmFYp4d3vuvJuYvTZDQ08D0pL4fiWpsuM2R/7OnUSb393y5DrqqIY6KdkgDvQ1cXpwuqx0EAJbeVfDVC7wL5FGs09c5FrHZrSUJKN5tjU47O/O47mKH55u5enzwdPXTnmNHnEWiQ/GR8XiWJ3d/E1zjD+aeRg3eAADwHcEuYlI0digElIE5mcQpG2UAFvCvibNzZs32bRpE5ZV/nE7vk/e90jYodKxdXy8MJ49VSIkUCyPNT6nZ/6UrvghLLnsWjxfuFnabyV84/DmxO/Tm7ibA62/ipKBsDNqt1CLQUtsXJOtHEu7zM2PEo+3Ua3SzBjNePY0WxoeQQhBzG7lrs6/W7Hf0c7/G6+e/3MK0lToOIw2DL0+zJhznFxIIGQnhU+/iLrVj/3mIcRtGJYt+acUPv8sjf4OFvU0Rq5IJ/kSOdSN/JBGSQDcnIcdtap/VALadrTVOwHX8b6hTkqKmCvkeWn8FkOZBULS4o7WTo609ZQWa1tJfrF/hpvjl9FNR5FCIYCM8XlTp7FaJSHt86n8K+zLX2J2sYkfdHwWrcL4BsayFk+fb8HTwQ3vQG+KL3nHkNPFp8itOxA79yAIpKj/dyZ4biTOmYUoQoC7aNe4vwcbw0mPv31nL8fPzzKX17RGYEujwlbg+z4zMzN0dnYCga38M6M3uLQwjUBgyUDcuq/5w3vjrOO9wUj6zapEAwNT2Yt0Jw6XNsWtDpQIVbV5N+iiOZnhjvbfAEAJm5jVQcodrti/oBeqT8g33Mw9gyhIjKz8wht8Ls19H9/3iVzdw+iJUYw2dB3qpP/ufqYuTXHjhRvk5vIY3c1aYUX3RCeWbAepsc0dOA+9gXfoAtaZ3ct28WtgqezXSE34iUcpIJB3z+N3TYLlIeYaCT/9EDih2/Yveb8glKB9dzuN/Y3YMRvfKWdwUkn6jvZ+QLOr4+OIOikB0q7Dn147g6MDJxBHO7w2OcJMIc9n+7YBsDg/zF/Op8nFtuJ5iySQaCCLBgF2UoMxTMg2+vwxvtv5RTyhML7kWkpyKx3CszQ4is6GAl84NIs8VnyiiicCQrIixWIBn+rLcDMbIu0ptCep7lGyVGdoePbCCCE3zK4WSUuk/CaYySxXIzw9ep3LCzP4JjjO8zXPjt4gYdtsSjS9a9e1jg8/lKguUNW4nJr5Y+J2Gw3hPgB6k3dzab527xmNy1j6Lfa3/jK2DEL9+aKIdUMwBFUryi93XvUF6vRu7MvbwbUwsSy3PAdRuFziHDdeuMnI8VGcjIN2l1MlawlUBSJwYi0SkNDL95D/2g/RTQuEX7x/3ekujS20gkwMAdjP34fqnMbvHsc+vwvh2NTyFnq/IeQKe3kT+KvYMZtNDwwghOCOXzvMyT85RSFVCPbVhl2f20VDT91uvo73D3VSApyYGcPVuuyZyjOaSwvTPNDZT9IO8fKtM2RkA1oGN7B0tZJFIbgS38ZgpA9XWMu5FwSeAbvVwcla3LU5hZIGevpgcR66e6jWGMcA+xo93pxRxf41qkb6RpBbtLm+CKC5Mq/Z1yrZ17b88cpix768760gJOXv943JkTop+ZihP3kf49mTVYmGp3O8Pv6/8djAP0MIiS2j3N/9e5ya+mPmnZtVxxNCUfBTJVJiyXDN8tzKg6tsMxB65iHkVOty9CKdqNhNe5r8fL5i+23BV6hrAxvgEJUmhiWCAqiJ9vdM1Bpri5KdfgeluQKaNjWx9VNbGTsxSm4+T8u2FvqO9GJFgvtErCXGfb97L+mJNF7Bo6GnAWXXtWh1vL+okxJgNJNCVwnzKiGZLWRJ2iEGRaxESNaCLy0ywqrafE9aGqUMibCPkmD6N2GGb0ENMymFYKuM0GQ3cb4pw4UFU9QM1oqYFOdg4NyMZlODIRESCCFoamoCIOu5SCEqSAnAoruxvhh1vPvwdJ4rc08ynDkGGHriR9nV/Atlmo53C0FPmWe4sfgcrs4Rlkly/mzVfbVxmclfoSm8iUVnmJBK8mDv/8CbE3/ARPZkxf4CQazoN3Jr8RVyNTodV4UvipGP5e+mmG5BzrSUpVPeSw8P68p2orEka/8SPrjIR3bm9gmJkIL+e/vY8dgOAJr6alfbCSFIdtUtAer44PCxJyVXFmYYy6WrvuZon5fGBmkITeC9C906DQLfhysTUba25wlZCu7/BEyOUyv3nU3HGdUF5o1PQ7+Dk5PkpiJQ4XlQ+fQ2mjHsCksaGhpobAxuRA12df8HAXV/kg8Ixmh+Ova/knbGSuWztxZfZDp3kYd7/8mGHFBvB2dn/oLh9Bul6EgtQrKE4fQxjk38PhKFxidpd7On5atMZE+x+nvbE78LKSwWCoOcm/1LanqzV4OWwffaWtGAcLbpdvrw/UwQCMRCEjcrOfy3DzD400Fmr99G+ul9gBBFo7N1Sv1LkGDH7Ho5bx0fGXw41VfvE9Kuw5PD19a8500VslxLzeEKharRMdVoKMzapIdiZIajuIsWq9vKGAPuogIkp4cTzGUtXA+ElIiuHkQxCmNM0BNHa8HUVBtvFRzO+1lS+GgBKqqJdeeC3HtwBFbCJdqdI9qdxU46gEFKQUtTI1u2bKGzs7PkIWFJyQMd/VgrFjpR3H5fR987uo51/GyYyl0g406W+XloPLLeNJO5c+/quRw/zVD6tZq6kNXwjMNo+jjauHgmjzYuC84wp6b/GFnlmWY0+xba+NxafKVmh+EKGMCThF94kNBL94GnSpERk1j5XV9xiFjDGvVnhHY1Z/7yLDs+swMrYiHU+xsZsaJrPytKS274zt2+q527/97d2JEPvo9OHXVsBB/rSMnlhZmKEt+aEBLfCDCauGVwjcDREqMhMxLDeAKMwACFGYlfUEQ7yoPARi+V7Er+6OVu7hxIsa8vTU+TW+oe7HmKhYVGUqkG5h3FqF4oe9YUAqRlsBMebsoi0pHHjvmlm5QKO1hxH3cyyuHN7RVlwAB3tnWTsEMcmxoh7bn0xpI80NlPc7juQ/BBYMEZrEoSfFNgoTBEZ+zAu3autDuBxK4wNKsFhY1fkczQ5LxZqoYwjGG+cLNofrbR35bggdb/kdQBD9/VNCQ/yUL0PAVvkev6WUykABlVqmAxGLA0uO+d3sF3fBZHF7nvd+/l7T8+SXqiejT1XYeAvV/Zw5nvnMV4VciYNmz79DbSE2mmr0xjhYNS3uxMZXm1Cin2/9I+pPpYP3vW8RHDx5qUuFqjq2gramFT0uXxnjRRK6AJN9M23zvfUiIkyxB4GQvtOkh7uStwqNFjp5J0RiULjuH8SCNv3GjgzoMzfHYgjQKU0szNtWCMZE4XqqtHJKiox46uAiN+0AO17LWIz30HWoiFa3+8Oxtb2dnYuuH3Xsd7h5jVViy1LV/8lQgRus3Gdxs512pb9rVQSUgCCERVQm8wCKHoih1mKndhQxEZgcSPpBi4b1dpWytdeI7HjTPn8XZdQw32IKeD76tpXsC59y0iP3p0w+/jtmHgylNXibXFSE+9T4SEIAqSGk2z98t7OPfd81X3mbkyzeG/dRgI+u68+C9eqrpfrDVaJyR1fOTwsSYlm5NNvDE1UtaQrhZawx5fGljEXvEb35xw6UCTrmFo5ucV0l5+IpUSdjZLwkrSFjNsaZS8MORy9mojs2mbO3uyJG3NnL1Ag9NIpIaWwBgwnuDGjI3dVHnTFxLSzhyO00YoVL3ks44PD7pihzkn/ktxAV9e6H3jcG7mO0znLnK4/dfeFdFrxGqkM3aIiexpNBsnJ6thfANGgtQlKZO62Yd9dTs3wrN0He4j0dJDyhteNypjtM+Jq3/Ova3/HQ29Qfnp/OA8J//0FGF9tKifEHh7L+EduAyWixzpBgHhZAgn4yKkKCsF3jBkkWBV0Wj4rs+Z75y9LVnMzwrtam68cGPNfdzc8vUsLBaqZbeC19IbS9HVUceHCR9rUtIZjbO9oZkrC7P464Sa72zNVaRxLQmdSZfrk5UiUwBhlY8ZQRIqWtRLIZjNBzU/ft4iPx3hraxiJgpCGFq9DNvtMDbFSpmVwxtwFkOoiIdlRMWpJSA9n8HBQTZv3lxK4bieRilRd2/9kEFJmwd6/iFvT/0nFgq3yqzVDT6T2TO8NflH3NP1O+9o/Iw7zZX5J5nNXyFmtbG18dOErSQ3F1/kHatIpSnTetgv3Y0a7kF4FnPMszCySGzr3eh7bq4/lla4aZ83n3+JvY8dgf4pLv7pNLoAQQ1aAOviDryBYWheRGSi6HAONytpGmik964+fM/nwn+9cHtvSYMVt3AzlQTN+AYn9eFa2KUl6dizXG5sx21qBXujje9+5VYddbzX+NiSEm0Mz4xc58pi0Da95i+7yDeai2W8q3HX5jTHbzbg+qt8C5RBRZbFeBLYb8VKgtPJrOaFIY+gW7hgfDGMTIUId+awYpoZy2XGuMvTKq5TRgvyk2GMJ/EyNqbNqSg/FkCPDKG1Zm5ujhRRfnh6lLmsgxKCOzY18/jeLqx6aPdDg7jdzoM9/4hj47/PZO5s2Wsaj5n8JXLeLNFiue1GkXYneWXkW3jGATRZb5q5wnUOtP4qD/V8k1fH/pfbSueUsOI7J2aaUEM9ZT1mtKuZ928E39tahWJLjbQdhZxpxmkf5QTPIU+0YPtHESyLM43lkf/C05AMPE/8Xdfxd12nMNWEfuUBBkKbcLMuUkm0d3uhDb/gI60qx71PVT8bhbAEkcYIvXctO6xaIYuug12Mnx4vm7+0JVs+seWDmGYddfxM+NiuSm9Nj3KxaCK2ZvqmyFeG0jbV7nUtcY+/cfck8bCHksEOTWH4ZL+iW9pEkbQJi3tUkk4ZwinA1Di4U5Kj4RD91rJYTxtBfrr86WaJcHh5SWYkRmYwhp8v3vyNIDsWQZmgS7oCQgiOWknCxdTPYjrLnx+7xWzGwZigL87bt+b4rydHgiGMIe/ncPy6R8mHATmvegmqxCLv1bBmrwFjDKem/nOxE+/yl9c3Dudm/wu2jLO/5VdQIoQlItyW/0YuDLkQGFBjHaCrmf8VUzyVLwSeJBRPGXPwd1/Hv+sMJpwPjls1nHPvW5DIBNtX/tc2j5Yup//sNCPHR9AbSMWuhvY04YYwokqjzQ8Ttn1yK3d/42ggbl2BXZ/fSffhbqQlSy6tuz6/i9btdc1YHR89fGwjJW/PjG9ISwKAhlfPNHPwoQLC1qWIieMJRudDvHE9iedLorYmU4CkrWi2LFpJBFU1xWZ5ngvTE2CKGpSQgAFLERGCK26QJzaewKxoxAfBv62wJu8u3YlXTK1gwWic+zYFNvSNQpWiMdoYptIenr/KvVUbLo4tMpYeZyj7JsYoLNlE1IqzrWEXYVUP+35QaI3uIOWOsVrIoPFJhLo2PI4xhhOTf8Rc4WrV112d4fnh/xdCCPri99IdP8zN1EtF75HaEPNJQi/fg1goCnCFxt13CZRhtXREjXZSpYAk8COB4JglWH5AVCTorqkKkqMHRqs/QgnQ3ZPISw1kpjMYrSHsgmPfVr8Zr+Bt3PvjA4CQgoH7B0q/7ZWQSrL7F3ax4/HteHmPUDz0oSdYdXw8kM/nefjhhykUCniex9e+9jX+6T/9p2se87ElJYXVRiJrIDseQRcUf/BiD5/cPcf2jhx5V/LS5UbOjixZXgsKngQMQ2nD8BWXRAju7FD0JIJoSGqhMkukhKBTSW66BLLDGvdR7VcSkiUsFARNstKHQAMnFheJ9rggwE1bxcZ+gvbWM7w19WqxI6pBiigtsU9xacHlQPMRhBBoo5nOTzCTnwSgNdJBe6TzXTfzqmMZ2visJiQSi20Nj5as2zeCmfylijTQahg8jIHhzOtIIfH8FW6hWsBsI0QKEM8FXz1XEf7xJ4MGc8XvokFin9tddXyZjaNO7cU/dAGkH4zhKUjFoWWx+jE3+hC+wj36Nvabd4CWAbmQtR8gjNQYDO6uS3gHL4LywZdYZ3Zjndu1IQfYapqSDxPadrVVJSQroWxVt4Wv40OFcDjMc889RyKRwHVdHnzwQT73uc9x77331jzmY0tK+mIN3EjPV31NESwLBtCOQBcUIFjMW3z/5OqeFqtFrkGDPAOkHMGCp+k0EiUErgPViIUGolLgaoOKeBVDGg3ugl3lXAHaouXbjDGkjc8pP4PT4Jc8UKTtYMc9mEvR0/kqsNz4zDcpZrPP0hn/KovuPA12E1cXL5B2UyXhZT5ziwVnju0Ne9a9QZbPR+PoNJaIoGS9GmgJvnZxdZawSiKEZDp3mZH0G5U7CsG2xs+sOVZuPsfC0AKhRJjmzU2MZ05t2CBNG5dbqZdLn7Mc6SL08t1BmbsBE3ZwHn4DOdMUkISVJegI8C26jrQx/tY0AEZosDycB45hYnms4wcxDSlQGnWrD924iHfnGbBXPRhohRrqRQ13UfjCMxS+8Czq6iasK1uQYx3o7oly0q4J+tUM9eDtvIZ36PzymErjHbwAnsK+tGND1+FDCQHhRJi9X9rzQc+kjjpuG0IIEongwd11XVzXXXft+NiSkk90b2L46gKeMaWlXgJ3qAQhIRjzHRqVxfC84EZNxVt1kqCkIZpwSKdDjETy7CCweLdC4LrVmnlBrhg69rMWftbHTngIDL4O0jnhVodwq4OzaOPMhkpjSAF3d5U/HXkYTvkZ5o1fngaSIEOa9s7TQGWkSJs8jp7C1VtIuQvM5Ca4tLDAvNuKwNAaXmRngyYTS5GwN9Y5dCz9Nmdn/wJXZ8FAT+IoB1p/5WNLTowxaONzYfa7DKZ/CgR+JHtbvsJ0/kpVIiFRzBQuVTVRM8Zw6YeXGDs1XgrZ21Gbhq8HRHqjak2DBg1ysDdIz6xMfXgW4WcfRLhrVHrEEwg5g9s+gX/wArpzOqjQAbym04ReuA81GqSf5GwT3h1nygfQwX9yqAvh2xilIZ7BO3IWNdyNfewOCp9/NoiCWDqIuPgSdW47Mp2gcPBiJcmxfbyDFz+SpCTWFiPREadlawtdB7vqEZA6Nozu7Az/09v/8X0513+YmuKuu+4q/f2Nb3yDb3zjG2X7+L7PkSNHuHr1Kr/zO7/DPffcs+aYH1tS0hKO8qub9vLK4DVmjUtSKLapKAmhMECTtBEGxrVHtQV8+WZfSTJ8I9ANPpFYnhya171FDqk48QZFPivKbuwGw4y/7BhhK0m718KXt7fx3PB5bmVhZR/AUIOLEIbCTISQNHx5ewirIn8sWDTV01NCgm1nqjYMBIE2BeJWkvHsEMdnNK7pIIgdwVShmdRMmv7EwoZIyWz+Gien/xP+iuqO0fRxfONwpOO31j3+5wlZd5ozM3/OdO7iCtOx4P+1cTk78x0aQwM1jzerPk/fuCwUbjF3dZHR0wtl7p+e65G5dQ26qzGINYiKBN07jrfrGvbFHSuOEBhfIm2BrhF8GX5rBCuqyO+6iu6eKn/R8nGPnEaNdqGTKdx7TwQEYikcufRdlIbC135E6MX7UDf78HdfBWXwt93COrWXyF9/Dm/7DUzrHGK2CXVtAJmPBcdGagi1I4XA0O0DbKJXC3bMxnf8iqofoQR7v7SHxv7ajfPqqOPDgPb2do4fP77mPkopTp48yfz8PF/5ylc4e/Ys+/fvr7n/x5aUALQlkjzcNcDMzExpmzEGIQSi6A3Sm5Rcma+2wNe4uYugFNhdsEGAFXWYNz4veotYCCItit6FOFFPofDoaC/Q3T6PN5TA9QUHejMcHtiKkg0s+BKxKpcuJNhJj8KszwNbFKvbchhjGNeFNf2esqaPuJlAruofYtC0R3YQtWJcmb+OZxIsEZLgdYu8jjKanaM73l9zfGM0txZf5sLcX5cREgCNy0T2FAU/Rfhddiv9sMLTeV4Z/Rc4OkMtQuAbh7y/UHR2LV/5NT5t0WXdxlj6JKem/6/guLCH+aJN+LkHkPNNAJjWWZz24arn2dn0ea4tPF0ZkSnYQZpkoh2dTGOEjzAr2LAv8f3KxT1Y8ANNht8xHQhWHQtCq1SvjoUJFyh8/jmw3eXqGU8iZpuw3t6Ld/8JTCKL88irhH/wKLp/FBPN4+25ihzsRc43YJ/fRTWoczvwD1yu2C5SiQ8lIQFo3tJMzx09nPrzUwHxKz6tbH5wU52Q1PFzh6amJh555BF+/OMf10nJWmhpaSGZTJLNZhFCIKVkeHQUjOHWoubizFqC2CIxEbpkM2/FXeJtDu5smHxOlj0JehjSIY/L7fPsTGo6owY/v0BnzOWxQ3kaQpqIMhhzBl+3MjcVIrsoiHQUUOGAZixFOHa236TD2l2RnxNC0Crt6sGdIha97TTZlwmLNEslEwKL3sTDbG88CMCc42KoFM8aJAvO2u3Tz878JcNrNH0LylvnPzakZDj1RoVbazW4fo6O6AEmc2fKrp0t4oxkjjOTu8x45lS5E6sFxD0Kj79E5L98AaElXt9okOZYBYHCljHu7/49Ls59j6nchWBO2QiRJz4Nro3wrYCsSoPfOoWabC8dvXJxD8p2TSnN420exL3/reC8S3x9xVfTfusQ3vYbwesrdSGWxjQtIFyb8A8eI/+1J4KKmt4Jwj94DG/LEKZjGt01gVxMUAv2yf2YeA69dWh5o6ewjx9c85p/kNj2yFZirTEe/r2HmLo0he9qWre3EG2q96Cq4+cDU1NT2LZNU1MTuVyOp59+mn/8j//xmsd87EkJwJSb56ezI8wUcvSGYmzxJa+MeEzlTMncbHlBqUyVWHGXSJsDYpk03JFQjC4OsZhOUEiEKRWsGGgIQWcseFQ857XwzAgoEfSw2duU5+GOLH/82jWyCxbR7iwyrCtKhCfjnTXfj6rxZGghiAlJ1giGc5+n0b7KQGySpN3A5sZP0r7iabw5HGY0X0lMJJrWSO2nuIKfYij16prW4hqfuL1aMPzzi0VnZEOi0+bIZu7s+E2G069zavpPWKrCKeh5zkz/KbWjc4DU6J7xkqtq0FK6fF8pFEqGaAz3c0/X7/L62L9hOn8J++19UAiXCIYwCnwQ2Sh+3whyuKcy2qB88IudfDGosU7Mhe14+y8tz2kJBkwkj2leCPQgq2HANKZhpgXr/E68gxcwYQfhW9hXt8DV9U3AhJGEXjtCIZnBNKQQiwnsk/tRY7V/J+82bidNtOn+AWKtQerJilh0H+p+L6dWRx0fCMbGxviN3/gNfN9Ha80v//Iv84UvfGHNYz72pORWeoHv3bpU8iy57C0gsw1MlwgJgAlEe1UMohAGGTIl0iGA+90Ud0x+G1eCmYWr+S28mLiPvAjjZyxCSY1odRhMw1gWNAJdjLRcmA/jz2eZSLtEOr0KQrIEX0DWaBKiXACnjWHMr1wA96sY/TKMxiAR3NIFzru7Saf2sbOhhbkZ2N20QH+8ASEE93Qc4cLCOfwyS06NJTz2N++seT1TzghSWDXb1isRYlvjY+9KH5ePChrCfahMZVpmJZQIsbv5SwghmMieoYx8aFCXtmFd2oZ76Dx6S5XUjDCYsIMRGjnUA4fPVexiMHRGDnLz1VsMHxvGLRwk1NWCHG+r6ukhslGcB44RHu6GVVGSle6tAgGFMNbpPZCN4N2zyutEgPvQMcRoZyBQtfyK1+V8AwKBOrcDnUwjRztqXqtaEL5F5MlP3fZxGz8Bawe7Qg7GCRV3rU1Oug52sv2x7e/u3Oqo40OIgwcP8vbbb9/WMR97UnJqdJB7VIKIEMxoj8t+jjMpB8+svDSiSEgM5XFpU2wK5iKAMIJ9GA6O/RXKOCW9x4HcJXZmr/Ov9d/FIcTNtGFXn8NwRpR1+NWeIJsN8Vahk0ivEzQLq3ZvK2476We4VyQRBH4nngniE5d0eRvzHTJCnwyjhChFUQZkmILRXNN5zs4HwsRLCzPsbmrjsd6tNIbb+PJADz8ZuULGbwKgyU7xi5vuxuBwce4pRtNvIYXFpuSDbGp4ECk0UasZXSN3ZIko+1q/Tl9ibfX1zxO08ci5sxXampWIqlbu6vwGDeE+AOYLN1m5+tmvHkUN9iJ8C+tWH07fWGWlidIY5eLtuI7Ih7GO3YF3V0AOlGWDMNzZ8VtcfWKIyfMT6KIwVg52r+pwvQIGTFOKymqx6vsL38K6uhXv8PnAwGwlbB/TMwGeDDxLljhQUVMiZ5uD+XghQq/fiW6aR8czyEy85nV7vyGEIJQM4WScwGit+BEJJZBK4nnBA8JahKTzQCd7f3Hv+zHdOur4SOJjTUrm5ubYbparV3qkpFPaPBvJks9UK/ddSUxA2potzRC2bEaMi4vhlPE50/erfHHqafrzo2VHbw3d5EbDJlTI58ycwF3x1FVYKJb6Ggg1laeCVsPoQPA6bzxecBfYpMIkUcwYlyHtVCROtqoI1qrBLCHYqiJc03kkQa+cThHCWUwzkpijt7GZzQ07+HsNO8i4iyhhE7GiaOPx0sg/I+tNlaIhF+b+mpn8y9zVeRdx22J74wGuzJ/GrJiJEiHu7/k9GkI9638wH1IYo8l401giTMTamBDxxOR/LJqY1X7EdnSqKIINELVayfvzAIjFOGowMBQD0MKA0uXc2BcgDN7R04R+9AgilUB6Iayb/fh9o9hRi/u/9GUujz7L5LlkaSwI0h4GjVmhD4Fli3j71aOBCHOjqQmpEek4Jjxf+ZoR2K8exd91PfAc0Qp1bRP2ifJSZ+HZyLlmCo+8GpQimw9HOazRBjfrcsevH8ZZdDDGUEgVuP7CDXzHR6xzO+2/r58dj22/LY+fOur4uOFjS0q01kxPT5ct1lIEZlG7Gi3eWNAYL7BuL4OAWE8WaQdRkqHRCNGEu1ztIhQIxffbH+e3h/+EcPEJeTzczmhnN7bwEAIWVjAHo8GZs0tPrDKsqWWaanTwHwTEJI/mkp+rsKZfCavGYmIX/VwfsBqIC4klJNoY0hNTLApFQ0NQ9htfUf47lnmbnDdblp7RxmMyN8liYZ6GcAM7mnqAEFfng1KxsGrgYNvf/EgTkonsGU5N/Wd8U8AYTVN4C0c6f4uwql0anXEnmcydXbfhnW8cBlOvljQ9Oxo/y/HJP0DjImaa0O1TgeZCg3f0dMn/AwjIiTQlx1XnF54HI1C3erHfuBPr+mZMvMAr5/4dBb1AKHIPIlMuMBZIdDgHhRAYWZS0Bt8ZNdxV3GeDC6kvMbEaQmipUbPNqFeO4rfNoEa7it/AKtAy6KnzIauckUqiXU3n/kCrcvavzqHd9dtVbH1kC1seXl8bU0cdH3d8bEmJ6y4vFCN+gYt+jhy65OYa7wMMFGZDuKlyoy9pBxoSY4CEg1+MXKzG1dgW9mWCMsXnWx/ASFnmhbnyn6EmB2c20FloR2KifsWYxgRW8c5sCCvpEW4KUjxGBz1zVLj60/ii8WkUlR/1ovEZkGHiQpXImSz+/8TEBIlEAinLJzGTu4JvKj0hBIK5IinxTIGCP4uSIYzRtEZ30Riu7cHxYUfKGeWtyT8sIxdzhWu8Mfa/8VDv/1j1yXf2xiznzryA3qVhAz5xWgdjz+Wvc272L0tOu3rTSJCuEYAw5YQEynUO9hJRNPgDI5hYHnVpK+4Db5aOLXz5aezXA7JCaW+Nbp5HzjYhC9FVw99mSwFhsE7vwTtyulzU6gvEVBvkIhS+8CymcQF/vAP7zUOIxWQl6ZEaE9mYI+37Cd/zGTk+wuUnr2DHLBaGq9vlr4SdsNn80Ob3fnJ11PFzgI9tExPLstDGMOwXOO1nyBVjHT7FyLgMgh7hVgcVK97shcFOuiVC4mdlYLtd5WFOI3CK/WgyIsbCGmZjQkCo0UPFgoXJXQyOKzNZ0+DnJYXpMEZL3IUQ6Vtx0jfjZAdj7IlE2CYjRCo+Upcb5gq+8Us+CNoYPGM452fpkaGK1E4wJ0GhUEk+onYLsgqXFUDEimCM4bWxNxhMXcXVWTyTZyR9jFdG/0VN8euHHTcWXqiYu0GT8aZYdCpFp4PnLvL2n7xF7pasFHXWQHtsDzlvltfH/y0Zb7Jk+Y4AbB0s8Kqye25NWBrdOYX78Bvlx1oa994T6Hhmxc4CNdWOKNye+NisSGWWRvItrBsDgaB1yRytGM1J+n1E9mYAjXV6D2q4G799qnJgAF+iOyfRTQvL16LmHN4fCCVAw9SlabIzWRaGFtc3zBVw8OsH6imbOurYID6WkZKc5/LS+C3CxQjJmk4kEsJNDtmcwm5wCbcET28CyE2HCTXUWGiFokNIMsldhLc/iph117yBCgG7uiE7LRhJSbKjUSJtBWTRn8TLWHgpCxn20Y4q3Qwlgnu6FJsshQZ2qihveWkmjYstUvRHf4TEZ5gTtPj3EjZtzJswI9LQ19SCn6oeajfGVERJAPoT93F1/scVN2MlLdqjbUznp8m4mbKFxOBT8FOMZU7Sm7iLDwK5uRwXf3iJuRtzCCno3N/Bzs/sxIqs/xPIetNUW30Ekrw/TyPLRnKeLnD5J1cQfhzdPblhEjGZuUDOnSs25Cs7yfqotU/N7QZ/8xDi3E5K/iNecB02qh0xGNx9l7AvbQ/EqkUybKSPtlzCTzyK1z+Ef+gi1omDhMcG2POFOzh55nnClz4VCMdLjfaWfxkrzx1+7iHcvmFkLopxLNBqY3MT+rY6BG8UK8WtG4G0JAP3D9A00PSuz6WOOn5e8bEgJcYYJvMZfBO4T3735kUc7a9b4bcEFdYkNpdbs/cKm3yzZDrpVtz8FdCvIuQ6P08WSFqNRNQQGderqRXZLMLstqLoDrijA14fhbHRGEpq7t+fZSLmMpZ0MUbg5xVeRtFgSe5pitAYkqXzAtxhxXnanacj9BqKAkJAgSnGrB8Agpbwdg5FthG1monHdzE7OV8xH8uyCIUq8w4Rq5F7uv5b3p76jxT8FKBJ2gnu7LgDKSSLTgptKp9sfVNg0Rmil/eflHh5jzf/8Dhuzg0qSrRh/MwE6YkMR//uXes+xbZFdzNbuFahDdHGq7CGH1s8AanAf8LbfW19UpELI3zJpDlLyh0upW3eUyiDd8c51FAPcrE8grda1LpEFyrIgDD4+y6jixbwcroFE8kjZ1pQ2aBixr60C/vaNgqfegV9dSunv3MGYVrKU0JaVT2HQGIKIexrW8l/9YeEXrkbOVGlTFiYwH1WF0mV8IPPU7KmgeA7wgYJSaQ5Qs8dPXTu7Sh5kdRRRx0bw889KZnKZfivty6R1x4Cgav9VZ1H1kertPAxLBgfq3hLncLloeYYL/qUakxUccwEkh6x5FcA12emyPoeiOWUzMp1MIFkp4oy7Dtc0jk8DFanILmo2NnhMeEb5oodhoUAK+pjRX0OWwkaq0QzQNAsJDE1USW1ZJgtXGG2cAUpbCQWexr/Ds7icuheKUVvb2/Nxbolso1P9f3PZL0ZpLCIWhngClAgbnUhxY0K3YkSIeL2+2dktRJjp8bwXb/sAze+ITudZWFoYd0n2U3JB7m5+AIFP1UiDUqE6E8+UFGFkzcpsDXGsyBa2/lWpGPYL92DnG0qeYw4mwfh8EJ5yqdgI1IJTDwL0Rr9XW4XS+JYr/bP39l5BdM7gZhsxbq2GfLhgCgUL6JRHkQcTMTB/cQboAWhJ8o9QgQC41klV1WdSCHyEXBl5X7VIlFGYoSPGu5BzrRWECODwT14DqEM6vJWhGehmxaQEx2I9bWn7wmEEhz5jTuJNH58fHjqqOPdxM81KfG05i9vXiDvv3MtgwL2W3GSRZMyzxhecRfIYhjUBdqEzXjxCXppKVlA84af4qhI0oTibT+DMWB8gZCmLFrSK0MckDFGjMMFnS2N4WKQDR6X8+VOshJDV9QwmhNYNfwlLCFotte3qtbGReNy3fkuD235f5LL5VBKEY1G140eCCGI223Fv5qAXgA6Yj4heZqc70IphSNQIkRP/Mi6c3ovkBpPV62QMBgyU5l1SYmtYjzU+0+4Ov8TJrKnsWSUrQ2P0Ju4B2M0E9kzTOcuEbEaSYY68fe/ihhuC1IUq4WpABpCP/kEIhtddlHNWtjnd+FvHoTWQDwpb/ZiH7sDoSX4Cr9/NBCtVnNFXX5TRdJRZfsSih+tPzCCuLwVoctLbk08i3/0dKBD6ZnAv/McYrQD6/omjO1ALoo11gUG1IUdWOd3IHIRMEEqyNgF8K3gbyOR800YDN6uq9inKnterKcLUSNdgUtttdfGuvD2XUYfPYl1bTNysHfjlUKrIG0ZNMd7hzIVaUu6DnTVCUkddfwM+LkmJTfS8/hVUgnrougB0YnNbjtWIiTGGApo0sXFdg4PWeMG6APnvSybVJhcVpKfDmP8oOQ41Fwg2uTRLC06hY0Sgstepbal2O2mYpstYW+DIZKu4d9gDFeuS/bu2IlWV9YUCkKgmXBZJJlsWXO/jUAKxQM9/5BT03/CdO4CAM3hbRxq/9tYMvwzj/9OkOxKMGHLCmIihCDevjFzrrBKsq/1a+xr/Vppm69dXhv71yy6w2jjltIS0cNtOMotlnhXrnByrBNRCFXqHqRBuKHSEbpvnELvjwk/9QnkbDNquBuOH8K9t4ZD4tKBvgoMykoTpVi9Q9nXyTt4ATXUE0RBfAsjg5437v3Hly3qi/9veidxeyeLByrE8/dhv34H6vqmkrurKf4PLcuIjtAqGFsE/XIqL4hfQYyWxvN7xmvqQwQCNdm+oj/PO4dQgl2f20kh5eC7HoOvDWH8GuxEgAoptKeJNkfRrkaFFf339NFzx0e37L2OOj4M+LkmJXnPK6tg2TBEUO0S80JELYkf2EthgONeemkXmrHwjSEiwkwYh8KqBWgRn/O5PLmJaJlrZqcX5R7bCizqi6vE6mPXmtxYDg7MNmI3V79Z+waUgDNX7+fgrimMyKCNV1OvEJCWVaFxY1h0htHGIxnqZTp3nqw3S2N4gJbwtjUjKRGriXu6fic4pzEoWdnY7/1E96Fubrx0s+wpWChBrDX2M3VjvTb/FPPOjdLfS+Qv608SPeDjlUq5yo8T2fLvwxK8ndcw7XPL+xddW51PvE74rz+L8C3Utc24d5+s9CpZIkAC7Bfuw7vjLKZ1vnjCGhGbiEPhS0+jrm1CjQfdgf2d1zHJbOW+KyF93MNnifzkkXLysZSG0VUs67VC3erHeeRVQs8+WJx3wJK8HTewL+6oPAaBNdKLjmc2KF416MYM4fsM7nNJuI2KYuMbug93l77XQffe02Sns2W8UtqSo791F0YbIo0R7NgH+92uo46fN/xck5K+ePJnKhk8P+8xOCXo7fZwlc+4dnFzCt+x6bUV03GPeTyEgTCSdqGYWlU6mlmwym5qyRDc220VXWSLYkJjiCHJrhPRWIKnBbqgKOTBsipN0wyw6BgMUW4O/iq7tv4lGa9G6SUAgqjVXPpr0Rnm2Pj/gauDG7JPASlsMAYhJA2hfu7t+m9Rcm0DDimsD4X3lRWxOPrbd3HpR5eZvT6LkIKuA53s+MyO2y7VzHvzODpDwu7k2uIzNffL+TPEVDtZv/K667bZqsf4229WLSE20TwmmUakkkFKyFMQWv6eiZkmwk99Avfuk/hbBjEtc4R/8kn8gRHce09UWtKvhOXh776Gv/ta7X1WQwLxHCgfUyQgSykTgcAUCZexPNwjp/C3DoLSiOlmTMsc+a8/EUR9XBvdMY281bv26VZYza/8Pa9O0+hkhvBXFgj7m3BvM2WrwqrsuxBriXHP37ubGy/dZPC1wSDKJgKR9OWnrnDgl/bXCUkddbwH+LkmJc3hKPua27kwN437DtI4fl4x78HCoCLc5lKYiwYurwZuAEhJvM9HWoYcGsdobCg1lhdAl23T2GaRcQ2Di5ptjQq5+slZCHbKCKd0dn0KZSDpKdCS9IIhFg82Lt1Q047mzfFlMe/kokNnfhHbqj1ye3S5F4dvXF4b+ze4OlO2T6nyxMBCYZAr8z9hd8sX15vthwbR5iiH/9ahd3y842d4a/IPmStcQxTrnKqZyK1ENUICYJoX8XsmUKOd5akPuf5CauLZFSZpgKewTxxEeDZiPnBqVdcHAjLZP7o2ISlCjLZjuqdW2NbLwMq+FlwFxuDcfxxdNHaTYx3Yr9+JyERBGLTROI++FERrimOZttlSRMfvHQepkcM92KeXv39GaHTfGLplHpGKo271LV8j6WMi+cCmXmjCJ5Y/T79hHufzL9Ac+xpCWIR2GZzLK1To60C7mlf/9asM3DdA3919CBH0s+nY3c7ga4PFyQURlfmb85z8s1Mc/a0Ppry9jjp+nvFzTUoAPt29hYF4I8+P3STjrW33XYIGL20VbeZBFIWF0c482pE4cyG0G5hDZYaixPtySDsI3seQuGgsBPdbSaJtCkuAp+Fwu2Iqp0uuqSvRo8KMmVmmtF30laUyymAgUbDYMt8AGPSSv1ZxPE8bnr7lkV+xDhljcenaV9m3888Qq/L5AkVIJjjU9rdL2yaz59Y1OdO4DKdf+0iRkneCmfxVbi2+iONnyHoz5LzZYgrsNp7Cq7VQAtxPvI6+uA3r8lbwFe6eS9CQrZ7uyUUgFQvEoruvIhaSmHgOMZ/EPnEANdGBsVxM8yJyrAOZCyILwgkFJKCajqM0P4F1cRs6lcDfdQM0iPF2TOf0cpWO1MviWVfBTBO0z6L7R0uCWt09QeHzzwVpJs/CtM5imhfKyY0EPIm6sA012QYLDah0YnkqtkPhc89j4jmwPPAU7l2nCT31MNge/tZbQTRJGdBgTu4vpY9kLkZQchP8Hf2khWwROKc9jAOyWeCP1b4ORhvyCwWuPnuN3FyOnZ8NOmEPvj4UpP1W7ZueSJOZymxYk1RHHXVsDOuSkt/8zd/kiSeeoKOjg7NnzwIwOzvLr/zKr3Dz5k02b97Md77zHZqbm9cZ6f3HXCHHW9NjQfdcvYFIiQe+L3EWLfAFkc4cQumSfbsQIG0fK5YjOxZFFwITs+xolPhAFgR4xRjFThUhLhSqSBhsBcoYGkOCnPaZNC4SQae0CQmJAVImTKN9lrgcYbTwKfRqf3IDval4cY0R2KHym+xQSuNVvE2B68VYSA3Q1HCruEXSHN5KX/JeeuN3laVhHD/NRsoP1hPPftRxbeEZLs89gW9+RqvzJa3ramIiDf7eq/h7rwZ/V9vHAL7AOrEPPTCKbpujP/IQ4z+eBtcqNaozQmNCLn7fKKEX7i8dbl3ahr/1FixFyVwZEAMtlkWnroX74FvYr9wVkBIB4efvxzSmgmhFOoYxAr3jBsb2UDf7wLVxP/E6qBXsVwKWh94yhHVlK6YxVf16WD7Ec6iRSkGoe8c5TDK9LLK1fbB8nC88U1lNpGVgvrYEz0LMN+I2TGOrNoQQRA5ZRA5ZQSO9Gz7ZsfXJpHY1I8dH2fKJLdhRm9xcrurPQUhBIVWok5I66niXsS4p+Tt/5+/wu7/7u/z6r/96adu3vvUtPv3pT/PNb36Tb33rW3zrW9/in//zf/6eTvR28fL4Ld6cHiv9bReD7rWC2V5GkJuIAYJwWx47Ud3oTBSlIJHWAtnRYH+jwcsorJjP/LwFEnrbwyVCsgQpBJMqz2UvV1p/zvhwUMbIC0MWQc7bjbYUXZFXGM1/qrRQSQ2N+RCxJedNpfFNeVZ9oWDwqlWgakW+0AzcKs7DYnfLl2mJbKvYtzW6Y10djsB6z8p78948OW+OuN1JSL2/xlPjmZNcX3iWvJ8i603xjmtDV2NlEc7tuK8KwDK4nzgGjgWFEIsvuOBa+P2jqNHuIE3TN4Z75xnEYhI5sVyJIueasI4dwrv7NPiC8FOfgFwE3TsBlo8Y6UB6Nvkv/xhv/+XSHE24gJxrQs41Lc+lWOEiLYm392r16IvtoxuL5cwLDZAPIQoiSDktvT9PIWarP8BYp3ejO6cwzSv6yVS7Lp5EXduEWCkWNgKxmGQh9yat0c8AEmEJjGfAh8IxjYoo/IK/7scqLEF2JktjXyMtW5tZHF2sqMQxviHRlagxQh111PFOsS4pefjhh7l582bZtu9973u88MILAPzGb/wGn/zkJz9UpGSukCsRkqRQHFbLPiMzxuOkl66odinMB+Wq0vZrEpKVWLJ/B8CA9iTG1zgLQdTBtLJssVrEova4rHMVMYaTernawWCz6O2k1T6FxCHkRLC1ojUXpilfHBtDyvK41pziQdNAg1G4YhodvYmUe9G6XIAnpU80MhP8G4uGUD/N4a1V31fC7qQvfg8jmWOrogTByqpEmIjVxM6mX1j7At0mfO3w9tR/YjJ3FomFxmdz8mH2tHz1Z+oboo3H1fmnGEy9ijYunbGD7G75UkV338tzP+LqwlPonzUyUgsCqhQ5bRxS02S2kXcXcY+cDzxL7n07iFb4EnVtM/bb+yrEn/bVbVi3BtANi4i5RgQSeW1z6XUdT0MhhGkPvh8Y8HZcI3T6QGmflY6r0eYIkfYtDOvTlboT1yoRGTHbROR7ny0Zw7kPvonumAJfYl3dTDWIfITwDx5Ft8wjM3F00wLenWcDYbAviqXOOtCvHD9cfiyC0LHDuJxkatMTJGcfwg41oqehcNojEomS9/JIa30vEuOZktdI3919jBwfxc25gc08QQVO/919hGIb6LRYRx113BbekaZkYmKC7u5uALq6upiYmKi57x/8wR/wB3/wBwBMTa1VAfLu4Y2pERRBX5j7rSQWorSwtWJxv93A8+5C+UHF8kQV3Zg3tfFX3PwFGA+yI7FSqeeNec3OFlEWLRnxCxtKehgMvokipUvBDtE/EyfhLt8ANTAez4GAN/wUB2WOtPVXxBoNanIrWkuWGJEUEA/7tDbMIEWS/sS97Gz+hTUX+gNtv0pbbDe3Fl9CG4+u6CGEtMh7szSFN9MVPxRU1ryLODvzHSZzgZ5FFzUbt1IvE7Pa6Is+wMzVWYw2tG5rwY5uvOrh+MS/Zzp/sSTUHUq/xlD6NcDQEOpjX8vXaQj1cnXhx+99w8DbJSSmaKB2bje4ivj2DhYe/ytMNLec4nAV6uoWQicO1j6taweRC2lY+QU0QuM8/jKsjGQY8Hdfx5zZt6IE1xQ5qSAzlSX3skI8mihqRorz0IBjoW70BSTGUEoviaxF6NkHcO45gX1mT6B1qXp5BAZQs4FfjproQD71MOLBS5jj29ANC4hUApmunjIRbojIG/eyyd7Eloc342Qccm15JrwJRt8erdCGVB1DCtp2tRFOBg8poViIu//+UW68dJOZKzPYUZuB+/rp3P/BuBPXUcfPO37mlUUIseYC941vfINvfOMbANx11/ujVvf8gFj0yRCS8vlJIQgZQbuwmVrRy8ROOhRmIhi9gYZfGpyFYGEUGIwRuIvlxmBnZ326GyGuVDFtdDsqDIHAxzMxEDDRmCM+bWFEcO8fbsiQCQcLqIthXL5MDA+pYM+27zI0+iDzqc0I4EBvK5/Zv4do6OjGzy4ESbuLmNVGxp3Eo8DmxL3YMspE9iyDqVdpDm+lMdy//mAbgG9cRtLHSmRkebvDlZmnuPLCGO7+c5hEBvlWEzsavsD2g3dWjDOTu8LFue+TdseIWe30J+9jZgUhCbD8iLzoDPPGxP/Ovpavs2HGsFr7YQBfIgd70JtHqvuBAORtcGwIuRDZgODagLy0ldCbh0vkYPZUDmvyEO6nXlvez/bxd1/BnNuFKNQ2pzO2i3BtWOErovvHMJFCuV5DAsqg+0dRg30YETivrfQI0Y4m/ONHiiXIQyCCKprQm4cQ2sKgy/vbAPiK8E/vXvdtr470CN/CvLwbYRQqs7ZTaigRYvNDm+g7GlTPhBNhQvEQJ//zyaqOvtUQ74iz9yt7yraFE2F2f37Xho6vo446fja8I1LS2dnJ2NgY3d3djI2N0dFRpVHWB4h9jW1cTc2VCU1XQiCICrm8PhmDLPpDeBkLWquUehqDLTRaSJqkYTxjsGSOFjnLlNeKQ/kN09PwYj5NZ0gSRpLBZ4+KMegX1u0TFhazTLn3sLT65W2fK53jeDpEQYmK9TOiZkpeJbadZeump4AgVfPowP+XkKrh/LoCjp9hOP0GOW8WJUJcX3gWU6RS84Wb3Fx4ASEE2vhoEzQ9a4vu4UjHbyNXON5O5SeYzo+jjaY53EZXtBcl1z6/r2t3UC74C/DQqyX/Dr9nnIv+fyQxGaOrY3dpv+ncJY5N/H6JgCw4t1icGa5cHFdBG5fri89hVnfn1aCubilVx/ibBvG2DENjuny/YgRBXdmC3jRKRV5gyfbd9gJvES2QQ13ovvG1edBUE6G3DpaRASN83PuquLlKg9c9hnVzU1WLdaM8vP2XsK5sgcVkqWJFNy0EVS6rYXnoxkWk0CBB+NXN0EKvH4HXq2iLqhjZvlPr96VzrQdpSe773XuxwuW3NKNN0PdoA5C2ZMdj21HW+uero4463hu8I1LypS99iW9/+9t885vf5Nvf/jZf/vKX3+153TaMMYzl0mRcl4ZQmN0yyrzx8IzGqiIQWVwZqheCwlwUih3zsuNRop355RJaAR2ZKR7vm6exs4mw9GGPxExN4pw8gZbwx/prjNMJ0mBFguZ70tJMr4iPKKBXhhnRaxETjWviFPxlohcVhgVpUWFwUoRvoihRqYUQQmDJ9ftwXJi7wvNjV8nrBizRTIt9mkbLLREdjRekNlYuNAamche4tfgyWxo/CcCN1GUWnDl08T1P5EaYd2bY03QIuYZIx5ZRIlYjOW+VqdhSVGJllYcALJ/zM39NV8c/KW0+P/tXFV18A1K1/oKUcccJywYKellgab96FDXYW/LIEGf34PdNVKdORiA9G3V1M/62W5UGaHLpzQDSoLsnkdf70duGqk/IgJpqr3B99TcPg13ZlRoB3v0n8O59G8bbiLxQdEy1PfAl/pbBwCBt6yD2scOom/2BMDQbCYzYVnuZuFbQPViYwLPkdlHFrdaogJCtjrq8axAwfWWGrlVpFakkkcYI+fn8modLS9LQ00Dzlg9fFWEddXycsO7d4Vd/9Ve57777uHTpEn19ffzRH/0R3/zmN3n66afZsWMHzzzzDN/85jffj7nWxKJT4D9eOcV3b17gJyPX+NPrZxkyDs0oHAz+Cq953xjmjcf8iidjY0A7AmH5CGXQBUlmMEZ2PEpuMkJuMsxcsoPWziYiIYmwbIRSiPZ2wrt2EREOX1M/xG4okBjIEGkvEGkvlMZewnmdY4+McrdKskWEajBCiaY8Z54zhgrVbDA6Ap+0t4nVK5UUNn3J+9eNFAxnFvnxyCR53QwoPBNnyrmLWXffmscBaOMwmHolmKOXZX4FIQlmZ3D8AnOFmTXHEUJwoPVvokSo9D4EEmHsmh4b+VC5jintjFXdb2ms9dAZO7y8/3yyjJBA0Sb9ej94VcYyAjHbhH3sDqzTeyAXDjiIprgQr4I0iHyNVMvS1zKWD0quVkA3L9Q2Q3NsQATkT/kQLuDc/yb5r/wY9/4TwWUNu7gPvEnh0y+B7SBvDATzWKmP8gXCCSGHegLH1jU6HdeCQAS9booITM8K5B9/cW3PlJ8BQgiMXz1F07m3diTXilokuhJs+1QPd3x1ApH+D5B7GnS65jF11FHHe4d1IyV/9md/VnX7s88++65P5p3i+4OXWXDyZU+xKeNz3uSwdJ4dKkq3DKExDPkFrurlp6bu/DhHF07SYKcYjnZztuWTyLkoI2mD9BVbk5JMQ455YGqmh+7u8dKxQlmYgS1w/gzD4W7CLW5Z1Y4xyxbwW2WEARnGE4Ywgq1WlEZtcdbPrmnFpQALhVNVkWLoCT9HTC0vyLL4kVoizK3FFxlafJXuxBEOtP5K1ajJK+M3wAgOps6xL30JieFCfAenG/bSYp+vMFxbDV1cRSeyV/GNUyGA1WhS7gKtkbWbpnXE9nJ/93/P1fmnyLgTNIW30GXu49jCvypvLldEWC73rMm400hhVRWqKhGhJbyV6fzFNVU9SlpsbXiU64vPIKdaqy6e1pVteHuuQiK7nKLQgtCrR0tP//bZ3Vjnd5L/5e8ve4FUSemIQgh1eQuiEMLvmgrcTgVF7w2N3z+K/cZhDKaU+pDzDfhulcgGQKQAvkJdHwjIlG8hfAXhVRE0AaZ9jvwXnyXyg0cJP/kpnLvfRvePAQY51EPo2B2BOVlDFrFYXva6cj5rQktMqICxfPzeMUwyReTF+zeUinknMNrQur216msNfY3LVTcrIaDrQBe7HktA6t+C5wEeuBcg/zw0/Peg2qqOWUcddbw3+Mg7ui44eWYLuaphdZ9AYHrWzzKkC4SRzBuvtDRtz1znMzMvYhkPASTzHrFwAtld/jTsmQSX/AzpdBXfDCkx3b28zN2VTpzFv/eqKAMyglXc4GNIGZ+T/jqNzwh66hy1krzozVN+Ap+onCTrdzPtHqJBXSeuxogoF4OPU3zS03iMpd8i505zf8/vVYw/Wyjwxamn6c+PYBejR00Lx9meu8Gbm8PYqnbYW2LTG78bx89ybeFJGiL3VOwjEIQ22B24MTzA4fZf59zsdxlOv86geRVrtguvZbIsJSJ8i52dnwdgJneZYxO/j28qxaNKhNje+Bl2NH+GF4b+36S98Yp9lmbZGTtAY3iA4fTreNEa79mAutGPf/DS0mFBlCS1sjeLRrfMBfoRQ9XmdHKyFevc7mI5r8JSGr9nHPfh14My22KKyrn3LUIv3VciAvJmH9xxNrgWK6plKHrniNkGxGKitL/96lHInsXfeR1sD7GQQMw2o0a7UDf7CEcc3HyIcPEcwVsKBtZ2IfAZ0e/sFiEQ4IQRDsgrlX447yakJdn+2DZC8epVPa3bWxBKVBjxSiXpOdwNmT8Cs/Iz98D4kP1rSP7d927iddRRRwU+8qTE0X6w+Nd4oI8guMdKEhUKQ9CV97rOc8nL8sjsq9grnq5zyd1Io1ltUiKAXSpwtlwZ/YAgbHyi/SjOfKiiMd4SoqgSIQFQQpBA0iFsJqsspiuxTUWICkkcRQYfgQtIbLGIq+MYJP2RZwCNFEVDtVVP+RqPBWeIRWeEhlB587PteoG+/GiJkADYxqfNmaU7q5hviKCNR0dsP1O5C8Gia1yUCBO329na+ClG0sdw/HGMcTFYiBXXz6Bpi2xcCP3W5B8ynbtYqsTxOsYQ2RjIQkAApKQpNsC1hacYTr9Oyhmt6roqkGxrfIz+5H2MZU6S9qqUrRuQE22E070o0YG1KcKDPf+Ys6G/ZPE1D+Op8tSPMPjbBsvHkBpvx3VCx+8IUhZK49735tIkIBULIitL0BB67sEgsrKUCvIkarQLfXMgaF5XhFVczEuREs8m/KNP497/JrpravkcBuzXjqBu9Jc0IAZTavynLmxH3uzDWmgqjd174Aabj17mtW8/WuQ1y99Pg8E0pHA+8QaR738G4a1IY/0MgtV3HQI693ey+cFNJDpqG5kpW3HoVw9y6s9OLxvEacO2T28l2RWDuVtVjjLgXnpv5l1HHXXUxEeelLSGYygR9JuphiNWkrhQxX4zwQ11i4yQI4utywmBlqEKQgKB8EYIiMczlR15DbyZaqEWIxFAcxVPD0tIWqTFpF+blCgIUj5ASAh2yQhdshmEYAHDCUfSEX4eKVZ0jK2RbnG1YS4/VkFK7lLZgIitQsh4HJL3ku64h2Sol4jVSMFPMZx6nZw3R2t0B52xg0ihcHUG37jMZJ+mKfowtmwIik5MgYSlCamNRUoy7jTT+UsVpcEmlqMtvJv+hvs4O/PnzBVuYPBJu7V1JIoos/nrXJ7/YfUdDKgL27BP7gejOPHmW0RaQhz9jXvY1PgAg195m8xPOtALIcDg23ncB48F3XFXQoLfNYW38yo6ngXHIvTcgziPvIZpWYCmNCzEkTPNiEIYsZComhoSnoW6shl/S5GUCJBTLRUkQGZihJ55iMKnXsFE8tC6iBztQt3sL2vu595zIiA4lg9G4O+/jHlrP/bFnYTjOXY8eA5laXZ+4gyXXzqA9kUxdVSMysy0Evn+4+jGeUQuirf7GqYhjRxrx7q8teRB8kEi0ZVg/1fX1z4BNG9q5qHfe5DZa7P4rk/L1pYgsmKW+uVUSaKKehfgOup4v/GRJyVSCD7Tu5UfDl3FW7W4RpE0lgjJMiwh6LUb+L96f5mvTzxBoxf06YhlrjPXfA9GlIeBhQApNR0dkxXn9zXkq4ToywfQrNYUe0aTW6dzcRgZCPiMZr+K0yCW26s3mQ7us3MMiRo9RiqgOT2bpT/plek+mhs68aUFelW0QdpEEgNEY8sdXMMqybamxypGbovuRs3/GN+kmcn+CCliRVP/Aju6/h8bnB9k3cmim+tqomaYLlxgeurChsdSIkMHl9mXsChouOb4TK304Bfg77yJGu5BjXeCD7mpAi/99V/g3PcW2nLhF0CkYwjfQjcsVi/hNUBjCvfek8HfnkT3TBF6+kEKX30SUYhg4jl0LI8c7kHebAKv1s+uvNzbhB2EV2VhlBrTPoN1ai9ew3nU9f6yaIbumA4IyZL2pKhr8e46gxrqpXXrRCli0LtvkKbuWUYv9OMVLIwvGbtYLC32bORcM96uq/h7rgT+JT0TeAcuEfn+owhnvcquGt0I3w1IaKuhIakFZSvad6/SNgkJoTvBOUE5MbEgfO/PPM066qjj9vAe1Oa9/9jW0ML9HX0V2/erWM1boo0grWJ8r/3x0rZIYZxY6jK+XikkNESjWbZuvYFtexizXFET/FtWr7AoHW14zU3hWHmccA7PCjw5DDCymgisgAA2yTCeMdx0UySFLDOBE0JgVa3IKZ24BG0UWb+Hq2nJU7f+B4ZSy+Zb2dY+tNCV2S+hoOeeFWP4jGVOcnH2BwylXsPTy14uTeHNdMQOFKtnQJssUKA9uo+m8Jbac1yBmXyW4WyUlNeKqVJSejsIC3g4YbMpJEkqSZstORKz2BRa9XVXPu4dZ9HJQH8jtILrXWWkyCSyQT+XWlMyLLuaAlga0zoLlk/kL75M+PuPE/mLLxH+L58n9PJRrKn26l4iloe//UbZNm/fpaCUduV+ysPbcQNCPrp1DjHdAkaU+byIXITQsw+grm0qc3BFGgpf/jHacjErKnviLWl2PHCBXZ88Q7SxXOcktMK6tH35O2754Av89lXl2zUuzXsFZSl67+pdf8eNIP5VsPqBEBAGbLC2Q/Tz7874ddRRx4bxkY+ULGE4u1j2d6NQtEq7qtusbwzj2gEhmbcambaaSHoZJIaL47NcX/Q41O3SGTe0tMyQSGQRArQWDI9009y0QCjkkstFmZppZptyuFajZ0pIwv4Wn7xML2d4PMXJWVHqKNyOYgofyxd05yP0h0Mkw4KQztM4/QIdxmGu7VOYVamlkAgjdT++HAqMrooQRmGZBjxSSGwa/f3cLOzEIBjL78aZ+j4xqxVLRvjp+P9KbJPiziGPokksMtSAOvI7iFCQp3d1jldH/xdy3iy+KaBEmPOzf8UDPf+IhB3oRe5s/28Yy5xgKPU6BkN/8j564neu27dGG8OPhq5wPTVfbA/zCQR5+iM/xpaZNY+thW1hhSUoM86zhGBPRDHkrChaFmBa5yh88SnkcDehl++p6rFRgZWrbRVary5tQ2RjgW5k6WS+KpERr2MysFI3wXaUj981uZy6WTpk13VEJoZ1YUcggPUl/qZhvCOnAdBbh2EuiTGalVEJmUpCKomcbUHe6sV95KfLpEoaZi5uRtx7BValPI0vmbhSZaHXkvBPHqHwuedRVzcV+86sTzkC2/ja1TrxjhhCCDLTWTCUesush3h7jD1f3kukYX0Png1BRKDhH4A3DP4UqC6wut+dseuoo47bws8NKVmdpm8VNpIgTaKWvC+EwNeaNJrrxbJgXyj+suMLNPgZ5qxG8k6Y3onrvO218ahuIBpb1hAsLjaQzcXI58pFddstixtu9aLdXY2GkCqXnPiWT0scFtKSMJBCE/Yku2cb6ekWSBnsb1SMubZPYLvDRROw8lC+j4vj3o22F7BlGgFIDDGzmSb/ELPqDRyxgCMn6VHdDPpNzHn7mPf2kJh5lbAMSEYqAi9uDxFzTOBVlmjn4ebtpfNcmv42GXestAz5poBvHE5OfZsHe/5R8dpKehJ30ZO4vVYCp2cnuJ6aX5F6U0CMscInGYj+EPIh5GwTOpmC5MY8M9otUdXJ1wAJKVhcufhJQGp03zju/ououcaK46oiGwn+a14Eq/yTt8/vqjAIK1W1tM7iPvoqrqdQN/sQThjdMYXumK5KcLytt/B2Xke4NiaWg8gq8tucwnvoONatSst/4Vmo8Q78yTZ053Sw0Ve4uTDnnz3I3k+fLsuwXD+2k/Rssoo3m4CFJNbbe7EubQ9KjTcAIzS6Zxw50r2KmBhCiRzh6ALzo82okKJtZxsTZycquvGWzcMSHP4bh2jZ1rKh8982rL7gvzrqqOMDw88FKbm+OMdQtlxbMa4LtAhFuwyRxzCnXWa0x4gpVEjaCnaUKTsKgBI+ToPNzGyUYwsKx+8j3DhFX3OBxUwYiWRRe1zyc8wbj4iQbJMReggx5jtB8UPx/isxNIUqjViVgM4IXJuHggVgaCuESCaDfcureyRuqJ98bIZIWiFkQEwMGoNLKPQ9HBSNoW000ovMdjMjX2NE/VWpVDRHmkj4FrHCp8j6fRgUlxZ7GYi+tfJEZMPFE7vDGKODKhr3CqPZ01Weiw0LhUFcncOW0dv5uMpwanaiQgsEAtc0w42tWJMNYEDfc7Lq8QJV4dqa05Cssm5KwDE1Fj3Lx991vdjMcB0IAnOzcCH4Y6V0QgOF2t1jvb2XA9+ViI+/+/ryCzWmJbREjrYHqR1VY6d8BBNxIB+pjEp4CjnevkxKLB+DYfLyAPPDHbRvHUNIw/SNLvKpWM2JCN9CXdtcM5JkCEqZl8iYQYPl4x4+R3ikPOogLQ/tWMzeigAa7Womz02S6EyQGq2tkZJKllnGZ6YzjJ0ax3d82na20ry5Gak2npHOzmS5+KNLzN+cRyhB98Eudjy+AxX64EW8ddTxccVHnpSMZlP8YPAy/qqbaQFDm7C54Ge5pQs1g80rCYD2ITsSJ+0nwMBVNCMpxeGOXv7iDZ9NDZKt7R5v+IulZbBgfE76GZoKNntmm7nYMY8ng66qa2UuhAG/oBAqKGmeiRYwoVCJdJQjqEO2/Zu4cisgyIpBJtRzGOEQQrG98QEy2QwpFunUj6GI4DDLlHqRrAzSAt3hl7iW/ZsA5HUSS0ZwdaVXijKq6PwG5J5cQ06hcf3Mz0RKKgkJhMQc/ZGnoM/H6zeBn0eNSVSzkb9e8GmxRFkZtm8Ms54hv1aGIJrfUPYGKHqJFAVGS/5oRiJmGwMn1homYTqR2biSS4BpWcDkw5COBURotXGaLwg/+QgiH66eJlE+Jryil1MhVNrPyUYYOVuu+Vm35LfqBTKYSB7vjnNYF3cgchH8zim8AxdQNwYCbqx8pAyIS8e2USYu97HyQmhPk55IB5tq6L+Np2nobQBg5MQol5+8jPY1GBg+NgxA26429nxxd03PkiU4WYc3//A4Xj54RDHaMHpyjMxUhiP/TZV+PnXUUcf7go+00NXTmicGr1QQEgALwSWd4+YahGQ1CtNhjCeKN16BBrIe3Ej59G0uMBlLc1lnK5ZBDczbHhaCXdONNOZtMOBrQcal8uHTgO2GsKN+ibgYCVmj0VWf5AW+JXDUFDfUt7mq/h0j1n/FE4vF4QxvT/8HZlLDxBnAIopAEKaVHv+LRHTwpCrxUKJon20Eav4QclVpp9SGgVkPc+mvgg3+BL22rPFFMbw08s+Yyl0k7YwXtQ23h50NratSLYaeyPNIUQh6t1i1CckygsTVEqZ9w/mcj2cMrjElQnIiu5Z3Lu+sUGTp1Fphv3YnkSc/HbiZrhaoSh8jfOR4++31kxGgO6cRC0nUmZ0w2Ywc7MZ+6kHsl4+iLuxAFELUtNIXxZ45AK7COrO7+n4VKP8eSuXR1j9VM1IiCxZironCZ14k/9Uf4d7/FmqwD+vcLqINGSLJLFJpmnunycwl0VVSQEabmoQEAb139RFOhnFzbkBIPF3x25q+PM3x//DWuvqUkbdGKxxejW9YHEuRGttoRVsdddTxbuMjHSn58fBV0l51gWkBww1dpdvvGvCyFqtXJgOMpgyJNhcRgekaC68RBldqQlrRtxgnE1rAF4aRRYvdLX7wRF18ChRGEsnH6bFyjK6owLmu83TJ8ic8Ywy+7WGEy3g8hectLj+ZF6dqm0YavQM0c7hicZLYtOr7GJEByWgQGeZ1gsZ8CDl/N+36TaYaDNIEBRZdi5pdEx6o5zC7voJQneyMpJnxNIuVawCeyfHG+L9FiRBKhDjU/ut0xvZXvUa+dhjLniTvzdIU3kxrZBd3t/dwdXGWtOvgGk1ELmCJ/JpRpipXn6hqJu8vlCIng65m7Go3DcN9uPNxcg0pvAMXgi6/70WVquXj7b6KdX0zAkHh8FmskR7kQhLduIh3+Dw6nCf81Cfwd9wE3OV0jKuQo53ovrHqKRpLY/rH8bunCf/VZxGujdAKg0bd7KtKFAxBhMl5+LWgE7BjYZ3bFYhmNwBpBYTZ9yTK0iQ75tn/yBlOZyPMj1a2DAjFJVzcgXVpW2BtXwiVUjm5xSXHW8H0jc4giig1ZnWqrHq3QxCansPd7PhMoHOavT6HqNGcEgNO2mHm2ixtO2qXDKfHU5W281AU3mZIdidrHltHHXW8d/jIkpKM63AtNffuDrrqpiiKlt9VWqpUPVQWyyZHGrKlFE5U20Tnk/hhByN9lG9hOUGovVkoRleMsWB8TrppDqkEUgTOrJ7tkk+kgqiNv6JktOjkmdDb6dKfQSBrPi2HTEvxEMER1Y+QMebnBR6SQ6Mu/oRDJiyIOYbI0nvVPvgFiP4ClvfveDABTy66NaNOvnHwjcPxif+TQ22/hhCCxtAAiVDQtTXtTvDT0X+Jb1x846KETTLUw31d/4BfHOjgjcnjTOWhyS6ghGSDhRgldMXvICwTXJp/AoOPdXoX6sweskVDMTXThBrupvDJVzFt80H+bJVAFU+hrm5GDfZgIgXMwDhe7whIE1S/uCqIcoSrdOoFTMs8uV96AjHdjLq8BefRl4NjITBruzaAcEKEnrsf78BFdMsCIhfGOr8LMdyJ8ze+X/sNimAQv38U++rW4iaJkR6gK6IvAgG+Rej5BypIwvow7P/McbxCiEImQmPXHE29MwgBBz5/nJf/8HMVR3Qf2sKtnw4F58ivropZebFkUGlT8WOjBikJIpdTF4fY8YkprMZHkEqsSSy1r8nNZoHapCTRlWT68kxltMQY4m3xGkfVUUcd7zU+sqRk0Q0WL99Uago2CgH0iRATnkteg4p5+FkLoQyRjjwqXLxhrbKWXw0JhBEIBb6vSa1YtLJGY4wgXCjXXXjakNGVT2pjxsXPpdjWZAhFPbTyMLjM519Gm3xFhKRbf65oVFYOjwxZMYwk6B6LgaTeRUQkQEB7O0yOGsYjrTixSWxtSK4QXLjhOAW9yPXFnzKTk4SLlGe9BI3B5+T0t1EihMEnYXfTl7iHodRPcXSGpZXHNwUWC8Ocnv5TxrMn8Y1Hs60RKPQ7+EyHU2/Qk7griJS4CuvMnvIuv0iMZ2Fd2IFMJ3E+8RqmcUUnWFcRfvJTiFS8eJyBmwNYtoO39womkUGOdyBGOnB+6cnKiMaSI2o8j4mM4/VOIuaSmNZiqbovETd7KTzyU0zHTLC/0siRLtRgH2rrHLVda4pQfmX1jfLACdUsva1OEtbH+OU+Dnz2RMV2K7Tqs5HQsaeD4WMjt2VMIqQk0ZEgNZ5CSkm0NUpmslYJuMT3FBOnztB7d4KWbXeueS6pJPE1bOcBeo/0MPjTwTJSIpQg2Z2sR0nqqOMDxEeWlDSHo/jvQMOwpN9sEIptRDk/IpgrBDf1QM5hiHblkCGzbgpBAvtUjD4ZRgJur+F61im7X04ZFw+DMqbkLGuMQRsYzLuYYramdC4Bk7aHmbLpjgzhNZ7GM3MUHy9LhEQYiwH/b1BNFjQrjjOjXiu9ZvBAW3TqZaM4IQxe1/OcjsxBkdSc64I94x63WizSkQKM/s+l/W+vgb3BN0HqbNEZ4vzsMNVWEY3LSObNsteqCVc3AtekGUq/Gvzh2Bjll5ESCBZoOd2KcEKoczvxjp4qCUfV1c0rCAksXWjhhrBPlVuZq1N78A9eXI60aIIUylLTQGUAH5PIQyYCkQIiE0NvHcJ0TAfHFU/jbxmEVAxn62jl0382gnV9APJhdPcUun0KObGqa60TLs729nNS0pIYbaroLwT5heXmkwvjzVx/YxeZ2SSRBpfmLc2kxlPYUZuBe/uZvTGH71b5LUqQskp3XiCcCHH3N46ifY0Qgtxcjtf+99drzlW7FpnZKOR/gmo6ysFfOcCpPztdMbZQgmhLlObNTWu+91AsxNHfvouLP7rE3I05pCXpPtjF9se3r3lcHXXU8d7iI0tKBIE5ll+rxLPWcSIgJu5EhLMezBeWGtoXjadCGmmvTUiWmvIdVHG6Zagk1Awh2B4LM+M6TOU1uiCRtuHVaIo77ThNWBgg5WlO6TQ65K84z0q3Vp948xPk7dmifmTpPSqWYhVJswuBVbEYGTQ5MYIRQY/kJXSbz5bvKwwd1mFucQ5TNHnRAs512++B5mKtz+jd8/3US80NY3nc+48TfuGBVWcyiGLZrHV1CyaZCezTtUTd6qsgMUvHmHABoRXCDSqjQmf34s814x48j2mdB08FXYFXQ2qs4X5MPId67RDOV38SpIFWwvYDgrOqqkaOdBJ64T4wItCPXN6KbkwhMstRDyN90HIDhEQTbU7jZiNoz8YgkUrSf3cfN1+9VfWIQjaM5ygWxpo5/aO70Z4CBIV0BJhj5+d30n808PS49epg1TGUpejc18H42Qn0CtIibcmmBzYF/y6W8MZaY4SSIZxU9XiRsj2S7Yugg8hTy9YWHvqHDzJ2epzxk2OkpzIIKeg60MX2R7eta9q3dM47f+2Odfero4463j98ZEnJKxNDOFXSHxvBZhlmV28oWOINXJj1OTdTpCaWqblOhvwCn5t+jjm7iRMNd9JtN1eYdClgqxdncGx5kcorw8s9aSIWCAxOKR9UXnUCPko49ITfJKSWbbxFUdLgSUNU9yOFTUR3FFMz5VAiy/ZonqaQhWPgRsFn1m0mbjaVLV4CiSJBwuwgJS6y4oV3H55E3erF3zL0/tR7CdC9E/jNc6i55uXt0Ty6aRGRjiM8FXiAjHQHEZKFOFV7tSg/aK7XOocc6ib00yMIz0aNdUDfGE7bXODKWrWhm6HZ20vmxQhaOtT8YlUp87V/egRv36Wgh40RqCtbUBe342+/gZfMIJ0oWvvY53et4UIbvB8hwC+EuPtXXiSXinLuJ3fhZCMMHRuuOaVCOs6xP/skvi/RZb16gnNd/tFleu/sQSpJtCVKfiFfeXZt2P7YdoSSjJ0cQ0iBMYaB+waqWsTv/dIeTv7ZqSo5Qo0VcunYMQJqU2mrFbboP9pXIkfVkJvPcenJy8xenUUqSeeBTnY8vh0r/JG99dVRx881PrK/zEsL0+/ouF4ZYo+KlTwsFLC3VRXJSRDdqLVwOjLED9sf5WvjT7DZe5GJ3l8uucUuQQhB3BJlC4XxgnJj1VV5415xJBE5TX/kKYSxUamdCB3Bj9zChOZRspMB/yslMWugvdDIFdbzUuTZ1PRdlMyUtjcqwUg+RK7KmqkIEdV9pOTFyhffTSiNv2nktg9biki9o1NaFsn9gtxPBUiDc/QtvC23AkGo1IEA1YiSgNXfOkjkx59CpJfTFgaNieUx7TMB0RkYId8/ilhoQAx2B6kYAbhWUOGyklxoEKk48/EzcDCBensXIh0v17HUgBjpxPnUq5jGVClF5B0+j+6ZwH7mQWhaBGlQC4l1xKtL6UKJkwtx9dW97P3MCbp3D3HrxA58x2eZlVRe6OWqmep4/f94g3hrjHhHnIWhhbJUirQlXQe6sKM2u39hF9sf3UZhsUCkKYKyFcYY8osFrJDCigS3odbtrdzxtw5z5emrpCfTYAxCaNq3jbHzoXMoS0HsS+tevyV4eY83//1x3JxbLNH3GTs1Rno8xV2/fdeGoil11PFRQzrRzsv3//3352Q/ePFdH/IjS0putzpjCTtVtMxUC8CSgr2tiguzGrthDbmhEHgoXm8+wmenX0RV00low0yuMkfvZ9WKRVbQLWz2WDFS2ueyzrFgfCQ+qtBJZPSXAhdTex60ROsJmu2DqJLcNMCSDmbp7TSGz6FEvsxB1hKCvsgs17I5MOWRFY2LKxbWv2jV4AfRDzHfgGlK4W8aXk5NrA44CILF9TY/s0C6+c4+aN84zGx5hnB/Cwm7h5w/FJCRsvRJcWzbB8sn9Ph13O/tCypaDJhIIaigWXovEqzTu7DO7AlcWc/tRrfO4XWPISIe/rabgdjVAE4I+/n7cB9+HQaGsC9ux379SLEiR9eOSHkKmU7gd0+XVwdZPrp9BtM+i5wKqkpuS0diJNM3u3CzIRYnVlrp146yxJrSZOdriz5zMzlyMzmkLZEhiRW1cDMuQgl67+xl+2PblqcftrDag9vNzLUZLnz/Im7WxRhDy7YW9v3iXuyoTcvWFu75e3cHBzkXIPck6BlQAxD7Alibqk2lKsZOjwUOsCu+QsY3ZKayLAwv0tS/wZYCddRRx/uGjywpaQyFmcpXupFWw8qFO4ZEm6DHy8onJVuCCvmEGr21n86FZDzUTkTnEYvn8Rr2l0iOMYFM89x0DbHmisVaAzGhiEpJq7R505vDtq4TGfpFhA7jJs/htD8HwgdhmDDTLJrz9K6IlgD4HqhisUjEGkLKynNrY4hY0+TcJKt7kCzKc9XeJGsyiFyY8I8+FZh2eTbGcrHeOkDh889BfA1J7G2soQIZlFir9a/lWihYsxTM7PppIwH5hmG8X74Fkw2YsINpnSs7h7zVg3Vmd9D7pWj+JadbsBcTyHwUfXYXumMGkYsEJmmWh1xsRLQXiO7wyI6GQayRcvQUIhVHxzOBcdxqSI1pm0VMtVW+thEIw+t/8ml8d30bdak0B7/wBrmFBKd/eBRTw6EWQLsaow0993az+cHNKFvVtHtPT2U4/RdnyjQms1dnOfmnpzj6W6v6JoX2BP+9Q6TG0mXnWYLBkJnK1ElJHXV8CPGRdHTNex7TGyQkUFxiDcSRvOwu8iN3jifdOU57abyiiNTxQcU24h4KSS+Dh2QxN8wZP0NKezhGM6FdXprIk3INVtwl3p+hYXOa7QMuu7ugSRUXMqBVBHxQiMAOfb8VodEtgFEY4QSERHosdRo0wiUnxkiJy8vvSwgKBcPooGB0SJDJJNBV9AVSKMbVOQpiGoOPQeOJLMPqr/FF+XWU2LRF1nb9tI8dRmSjCC+IvAjPRuTChN5YIRqsxmk2HPSQGGMgb0G+mu0+a39O7zCKJoRChDS6dwLTNldxDuvcrsqKHq0Q+TBG+shMHOvGAGq8o0T+dDKN57rkhjWFoyeCMavN3Reoy1uxXz6K6RuvXnvtK8jWtvSv8P4onyjGUCQkq7VMlQjH88SasjT3TrP1nku1x10axTdMX5zGjthr9p8Zen0I45Wf02hDeiJNeqpKSbA3DNkfQPZ74JULat2sy8S5CaYvT1et8El0JpB25VwEgnhbrGJ7HXXU8cHjIxkpeXL46obWHUVwy20QkgyazIo7vQaGtUPWaO61G1jAZSOFPJbxuNe5gBGSVONhFrTHK3qRGJIUGt0osLEIN7skpeQ+uzFoRBsVGELMaJezfoZ+Ve4dkSBCS1aQB/zYcNXEhREeKXmJBn938T245PIaIyTChBiaPEB70/XAu2LpfRpI+x6T3jWwrqFMDIGFx2LVxbElso155+ba13Wop0oXXIkc6SrqdYtdCa1yjcVGIyVyqgX7lSOITLBw6I5pnAffDHq/bATVzrMysrLkiLvyLXiK6OguMpcUKhcGT+F89sVAK1LcTxTC1c9X7M1jVlTCGOljGlOY5jnETAt6NooJ59bIlgjs03twD54PiOjqtVQDvkIN9RTfQrkviVlt81vl/Rt/NSGB8qiYKPaoMex9/ARCgLI1Pftuce21vTUmvuIybEA8mp3NBoRz9SykID+fJ9G+QseS+wnkniEQERvIvwKRhyD2JYaODXH16WslZ1chYPtj23EyDlbYomNfJ92Hu7j50o0yO3qhBLHWKI31KEkddXwo8ZEjJRnPYTCzMR3E0pI4X8PySwOzxmNRe0zhEm52qt/SjUEJgyXhwfY025t3MJffRX44yR5CtAsbATzpzoFlCLe4CAFHrCQhRFmaqE3aPCwbsVfniEyOuaYxIimXpjaPiRoLqyhW3Ghc8mKM2Y4nsNK7kPkOFvKdXLz1CDsHXkIIjRA+C9rjraxXWotWR0ZWYzZ/LegOvBZEDfa2NGdLQ6rYQM6Xwf6+Atuhis9bObIRQk8/UIrCAMiJdsJPPUzhy09tjNjUWps1IEEMdUFDBrOUahIGOdpJ7uVu3C8+jXAtwk89QuiJR/EPncfvGYeIg98zjri8BbGqXxDKUHjsRew37kROt4A0+H2juPecRN0YwH7jTgDsZx/AfeQ1TFNlb5Utkccxu/oZbT25bM5qCOq0jUAsJgm9dC+imEYJSKtZTm9puY7odR1BrNB07hgm1pShZ98gkcQyAVSrnW9roOtA57r7NG9qYmFoAeOvipZ4mmTXCsMzfwpyT7OyqskYl8kzVxi58FPmBvMVQZ6LT1wCEZQZX332Gge+vp+7fvsuLv3wErM3Amv6zv2d7PzsjrrItY46PqT4yJGSRcdBvgN/ktWQLD8sp43PsO9QfS02HGktcLgtS9LWRRGpRUMMtnbNcGW0nRZlYSF4zG7inJtlWDhEkcSErLj5KSEqKnbQDjPqONrSNHa8RUxsRghZESkRWDTpA6W/xuWzIB3chjPQAOGxLzIxv4XJ+X4aE2lyHS8wJ4Zv67po3HXTH/6mYdTN/tICCWCExu8fWSYDySXyYwJSsrrstQasy1thVU8UYSRko8jJNnTnO6u6ApC3+tADI1hXtqJGutEdM5h4BjnbjFxowFguaqYFOdQTWM67CdSrd2Msl/yv/ADvwEWsW30Yh2LvmYAUuHe/jWldwPn887CQJPrsI8iFRiLfexzhLEdXZCpJ+IefpvD4S5j2pZJvQWf0IPu6vgT9kFic4cLsOL5xgmtZCBF+8hFkptKhVCqNqeKtshpCinUb1GEk/Ydu0NA5X6apMkYwM9SxgatL7X40RThZh/nB+QpCIm1J96FuwskVkSi3XOtkDJz50V3MDnXgu2tEzAylVM7Z/3KOh/7hg9zxa3eUojN1MlJHHR9ufOQ0Jc3hSI1OurcHAzxuN9EmbC77OdwqT/8Cw/akz5EmF3IJctlYKcVjSdjRUGCbimAXyYctJHutGIJl0rP+RDSO9zaL5gT33HRoVha5BpuIvbliwk36DmKmv7SpSQeN74QI/nN6fkC2/z+T6/4BdneWNOuXn74TuHedxiQyGMvFCB38fyKLe/fJyp1VlVTEGhCLiTKyU/ZaegM6gFppItfCutlP5C+/gBrpQiBQk21YNzYhFxqCfXyFsT3URHtZ1EF3TgURn1ie/OMvoLsn0PEMumcc5/GX8LcOLZ+nMUP+088F3idOebpHIBC+hf36HeBY4Cs6onu5s+M3S/v0xe/BlvFlMXOsACG3il7E0NC9PkFTIVU1XVINF587hO9aKzr42hiiXPtp9QaLqzHy1mjN14wxnPzjk8zfqoxybnpgE7s+v3P1zAGB9gXX39jJS//+s0xd78Z3b+M5SsD8rfngn0LUCUkddXwE8JEjJRFlsb+pskvpbY+DxBaSw1ZiTfKwxU8yNtLP5GQHo6Pd3Ly5Gc8rClZFULWzEiEp2SzD5NE4a3aKCc7asHCSRf8Eh8Y8on6C+datZPUIWfdq+e5CoVeYvUssbBoqhw3No2MjzHinUCb6MwpOayDiUPjsc4G5187rOA8eo/Dln0D09royV4PunMZYVSpPjEC3zlU/aElO4ViIsY7AYXU1lI+YbUY45WXVq8fR3ROYyKr3sVRpc7OXyA8fRU60I/Jh5EQHYnJ10zeNieXwNw3XFJ7KuSbsn97J1vFf43Dy77LUzNoYzcnpb+PqTPmxAyNl6hEwNPVPYj71Gu7Oa9XfSxENPckNf97pmUaO/fljpFP3gH0Aop9BNv9PNG3ZhqoiGAVDU880nTuHiTalyc5kSY2l8D2f+cF5UmOpoCLN9Zm9OUd6OlMRsRFS4OXcSsIQOgjAmSfv4taJ7XiFEO+Ns9+7B601szdmmbo0jZffQBfPOuqoowIfufSNpzXzhQ0KHmtAATuLQlMF9KswF7xcRSnwNhkh7IUxKxYx15WMjXXT3z9MoRDCVMnj75ZRLASnvAxHrWTJEn/pidWyChhjobUildxHo9NLRLxBPl4Ao0k7Z6GiB4zPorxAu/4EEhuNQ1YMrT51CRlxk0Z9mLwYu/0LVAERCFmFhW8KqAvbsE8cDPw2DMjhbpzGlzdkDLYe/K23sM7uAi1KEROjPLzesapajOL06Is8wOJfD+C4KfJffCYovV3ZNM9XyMzakRbdvADK4O2/hP3aEUTRyVROtEM+TOjVoxXVN/bJfeieCUzz4oqNPrp7EnWrD6pFfWwPE80zczLL+AtBv574Jhv/oRPM6ksVmh1z6CK72wpMX+9BhTwau6dxPYtLtzrx7ziLdWVzpc6liLmb82u+55UQUmBIEu9/FOzl8XZ/YRet21sZPTHC3M05tGcIx3Pc+dWfEiqKj4U0TF3vZvh4JxNnpwL+oA1GBOkUQfUUktGGzHSVqhvZSCb3NWYHF1dEbm4TBprW6YHzbmFxLMXJ/3wS7QcPIsY37PjsdvqO1HabraOOOirxkSMlTw5f5VZ2cf0dV6GkwUSwS0VL1S9CCBpqqC8HVITKYJIgl4uScyRDE9UjNmk0PTLMDiKB5tJAPg+ZVFDCK0WYhkZNLAFGhbCjnUyHPwsmTTgbZZP7qxg0i+I8M+o1jFhRTYMDCDyyLMgLNd+vET5ZeROBqmxyd5sPnK3hHWxtepTzs98lN+JhnzhQ5tWBZxF65iEKX32yOLagMdTPojOydoO9alUwtk/hF57FOrUHmYlhbA+vfwSzqXZqACCtR7j373+d55/8I0IvH8XbewXdPRk4twoTEAGqd9KF4Hp5hwMdg795CDGfwDq/K9C3GEH4mYeqCnyNATncib+SlJiAXIlUDOt8eRmxUR7ejuuAIT1wEtnQiBrrYGGugH81A1srz+H6kvCOIfbuHOHME/dy9ZUDaD+QPNvSYEIOolC7VHgjsMIW7Xva2f7oNpRd/nsQQtCxp52OPe288e+OkZ5Is/8zbxFpyCCLn53R4GRtxk6OYXTlNV6rXHl+cIGpi1O07y7/PaXmNiPUBfBrRxyFFHQc6MDP+czemEX7ulSSfODr+wMX2PcY2tec/OOTgXPsClz58VUaexrrXYfrqOM28JEiJSm3wPVUjRD+OtgqwmxTUexVuWVjat8u7VoLGPDn15qIaM0hy5Q5xHrGcN7LUhCG+60kAkMhI1iYo2g9LzDSEE2oMnJgZAhDM5a77NTZZA4R9bsZUt8JCiSw8HFYEOeYVccDY7U14IiZ5YV/Ce8gAj5TuMzsxFUaQn2oSwPLZKQ0pIBCCDndgm6fRQmbg21/i5dHv7X2wILgmriAZZbnFnHw7jm1vN8GjNLmnVvYMQt59y0KuYuEXjmKfPE+EAYTcnHvOoVAoKNZRC5S7oyrfLxtNwISU4R3xwW8/ZcRc41Yl7eibgywOg/i7ryGd+fZshLs0vsS4G8dQl3dArml6xVU5XgHLoCnCP/o0cCATitUPop84whirgnvrjNlwxmpeXMsQmioDzPaUiI5AoKA2juNJCxNVwoe+O/uL+sHY4whM5nBd32S3cnSQt93dy83XjhLQ+d8iZAsTjZy8nv34NbylFkH2tWc/s4Z9v3SPrr2LVfwRJujNcv0hRIoW3HwbxygeVMzAKmxFDPXZoolwR2EYqF3NJ/bxdyNuVKEZCW0rxk5McruX9j1vsyjjjp+HvCRIiXzTgElJL65/fb2eRGQh2pit3njVSx8CiqrZJZekz77GnK8OmiR1zn2xCIkbEEan0s6y7QOFqlnzDzdMkRLKoa1Is0TipS7zPrkKTBDlC7EiqiNxCJs2oiYbrJMMjR+hEzz94mE5xG1ynJXQ6z8hw2sYaO/BgyajDuJzA9UjzYIA04IRZS9LV/B03k2JGbwFXK0Hd0/Vpt4bIBICSQLhaGgSaGVwrn/ONb5HVjndiNzFqFX7sa96zTkLayzy+ZwxvIofO45TDKzbOsuBNsbP4PB52boRdyO47iHzhP53mdKb8kfGME7crp2VZFrEf7xJ6EQWvYuEQY52wwYrGubEY5dJuoVnoV1cTvevsvl+hwBfkMWfWULskq1zW3ZzVdBvD1WRkiyM1lO/ukpCqlCqaJmz5f20Lm3g547ehg/eQFjgu/wpecPMnJuyfr9Z5iHgXPfPUe0IVLyEGnoSRJvjZGeLNeiSEuy58u76djTUWbUluxOfiBRCa9QQz9iqIie1FHHxwlDQ0P8+q//OhMTEwgh+MY3vsE/+Af/YM1jPlKkpCUUwdW3T0gAJrSDVvGqnlTDvlNxP20SFnZ2BBNuDqIY0i4xCS187urIsbPB4d8808dg0dxJRX1i3UWbehOMPaIdGnWs7EKbFQ9VGTHIqPo+7f4niJnKzqkgsPQ2Rt39ZPQAk1cOs3/XtwmHquTh14AkiV63GkcQUr04fvUyYs8UUANjyMm2Cm0FWmE6ZpFScX72u2izwZux5WEaMu9Yci3H27Au7MSyQrz6wL/CSDcYK+LgHbyIieUIHbszqHyZbcQkU2V+Yd7265hEZtnoLRfGuj7AYGGMOw7+Im29exhKvY6PRtynmX8d0AL3wPk1y5zVjX7wrbKITKm0eawTOd5ZeQ2DnZCTLeiBsaDiR+llF9ia3YA3eK0sWUqjGM8glUQowZ4vLVu5G21469sncFLl5PX8X58n0REn3hYnn4ri5MJMXu1h7FIft0VGRFG74lchrAYuP3WlZDcvhOCOX7+DCz+4yPTlaTAQb4+z50u7aeipIvL+gNC8ublqpETZko49P7sov446PqqwLIt/+S//JXfeeSepVIojR47w2GOPsXdvbTPGjxQpiVl2mf/k7cAD3vJS3GklGE1pzs9och7YYR/dAipEqZRXALvmTrFp8RgGyCT3kI0MIHWBxYaDzDkCKSFqawZaCgzORgCBn19+6s3P2YQaPIQyLIQd2rIRZPHmXcgDxkdjGFNPYISHI6bRxg2e9FdAoBj1dpLVHiYU3PjS2R5s60opfL4RaP7/7P13lGTXld4L/s45997wkd5nVmV5bwAUPAiAIAASBD27qbYUu1tNtfRaozfSvJk1oxmtp/Vm/pg1M2/1e09qPbHVakktqdWOpulBAiDhTcGU95VZVVnpffhrzpk/bmRkRkZkloEhshUfVy0iIm6ce+6NyDjf2fvb374ZHY7BDdbr5mto2d5J5nwWFpOIwKp4dbhH3sHYLnXcvm981mTupnvZrIQ1NIj1ymFEoCg9cBTDqh2rFRDsGMIc24coRRDZOP6BM2GkpOxQpjdfr5ALOdaB8/yDZVdaxclzp4m3JXBz2/HzHgaId0vyk8Vl47U1IObTFaFs1fOBhZxrxiRyGKFrDc8ChXViD3quBVwLk8wT7A0rsYItVxEndiJu889WSMHBrxwkN5Vj4doCsZawY++110dIdCXoO9xLZjxDUKrTPynQXH/rOjs/uZNoOsrpn9xJfiGOrnONa08g9CSpS0jKyI5XE2c7ZnPwKwfQvkYHuiqi81GBk3DY+ugWhl4YrvTaUbYk1Zumo0FKGvhvGD09PfT09ACQSqXYs2cP169f/9tDSiaKuXWLbG+ERRPwwkyBqRkLU951lvISCjHifQUsR6MQPCgsDiy8jiyLNNOLJ0gvnkALm0BYHBM76BvtwnMj7GoSzGQg54UVCABKQF/coBajDOcgYtsVQgKAga7r3+JK//7KznVRnqVN349BVXbXBoOLZjzwERh8N3x+bPIIzakhjLlB88DbwhoLhoGo7qNV38vsp76BuNyFutqHiZYIdl1Gd87c3ukEYepHy1UdfOtMa8W12iSxXzvCUiZPt82FotbVkIagbRY13onumsI0ZfHuOIn9zoFwULdMArUIHVNXLLLaM2QmsgRqRSDleoDACvUzveO1EZ7yPHXHDG46g+6eQhRiWKd2oka7w0ueacI/eBY1PMCyfevS7RCo2VbUbGs4h6bFCinx959DXdwMBbVGyuYGzK4cpdh03wDFPR288UdvErgB2tNIS3LlxSsMfmxz/fcaKJWjJ5sf2syJv8ygvVuLWtoxmyO/fRfnfnSe2YuzdY9xEvV1INKSSOuj62Aw+NAgzZuauf7WKH7Jp2tvJ537OpG3snNooIENhqmpKY4cWW6k+fWvf52vf/3rdY8dHh7mnXfe4d577113zA1FSnLe7ekhluAZw+LsMiEJIcAYSrMOqruIi2EqfxkjFKzSrkjjIbNnGOsc4JuzgrstRatj8dQg/OSqRzHu0SksdkdtTGcOYVw2ZSxmL0XQwiDLDMImR6o0TOfEMGP9NoEALUpctv6EJr2bFn0EiwTz+LzphjtHYwzeYriAem6SuYUttLdeoPwiHwA7WYYBSZQ+/bnwsRUQ7Boi2DX0/oxvachFIeKFZcbCVK+tS3brGCjEUONddFv3MScNQZk4yrkmgub6/Xy8O09gPfMYRhrUxc0E24fRm64jR3or44vZ5hqCAOFUSmnJTLuk6/xSJEZgv7OfUtcUECwTE1+hTu4g6J5Ebx4J0y3KYJozuB0zWG8dwD63HTXai+6cxb33bZy3DoIfGqnV1+ro5XsgDe69bxP92UN1buJNhJoMNA2EaY/zP7qAl19279W+RvuaiZOTdSMZTjyg7+AMuCdp376THU9s59wPz99S2NIv+dhxm+zE2mnEzWuRog2A5k3NNG9q/kVPo4EGPjR0dHRw9OjRGx6XzWb58pe/zB/8wR+QTq+fet1QpMR6j7sO3xdrqPkFurQ89pyof1s0MC2dcpLA8Iaf4Qm7BUvCwwNwYOc42UwTE3MRckuGnCkf1VXCnVryyDBoE/q9tuc0esVHYESJeXWMeXmMkneYq94+DAJHLCBz8/hWN3Y0T1f7cdKpZY8SOwBPfXDEpCU4QtrsZlR9j8KSbf1tpFvWha+IfO9xTHu4WBNxl/UUroV6Zy+yGEOOdSC9KLNWgF6xeKozOwi21PFtEUDLIkLb2Cd2g9TYbxym9OSLBLtXGI+tIxw2Eib22DRdD4jmwuPkXDORH34c745T6PZZRDaOfXwv6noP5rRH8Ys/hNgKEm0F+HeewLq4BREonHcPYDAUv/AjhBE4zz0AmVQVMQnLh4dCUnitZ7k7szRlXdLysUKY8ne7zociQUrJ3i/sqZTIzlycqUsoMmOZsD/QCmv6rp2j7PnEO0jLYkmW1H/oa6T77+atf/dW3Q699SCEwC/6NXqV5QOg787emxqrgQYa2BjwPI8vf/nL/Pqv/zpf+tKXbnj8hiIlaeu9lfgJtfbCI6zl10Yj3WjpIIPqBn2BUJxIVefCxrVLv4qQsi1sy9DUtICKFLmQj5fPCXZbcQUpAZSFp8HWkrTZzAJXVk0GYvZp9ssZCmoMKXyIA0stSHSZEZQZlif5QCMlUXq4av05Bm95zVsqNV552pIVEombbOC2Eq1dvYjWZgpjMcR3OnE//RwmnQ0jBY5PcO9xAj8so7Zfuhvraj9GGEzzYmi0Np8OIxNrkAsd6NBgrFzt4vz8Pky0SLBtmGDnEKZ1HmP7VY0AIcwqzW1StA55+BFghb5YzjcTef7B2pMZgRraVEm7rHzeNC8gZsLUjEBgDQ3g7zuP+9irRH70CEarShNDYSRiso3I2/uRwfK8ak3IDC0Dk8yPtldrPESYMuk70kfv4R5iLcteJlLJtcmEBiMNkVSEVKfH3ifeRcqAlYZ+JvvvuPrKV27awh7AjttEm6PYcTuM0qxCtCnasIJvoIG/RTDG8Du/8zvs2bOHf/JP/slNvWdDJTxH8hli76EAUkiwk17twiUMkebl3VtUCF7r/jxZlcAVNiVh4wnFi833MR5Zbk5mAK+83ZSy7OQoDK7UKFHvB98QT0JTb56Lmz5FNtlFwa5vzW4wSGlCQoIgam2iKXo/tmxf/uFe+v8bNEJ7r5iQz1YTEliuCCk/KWaaiPz1p7HfuAOxkLilsL5A0hrZxl2/cYSWweZQnJrKVVeeCMKQkBXgPfQmQf91il/6PqWnnqP0mWdxP/cTmG6uf96SjVhVuSJzcdRMK/ZbB3GeDYmF+/FXMHbYz2cJRkDPCY+eUz7J2Zu7KBFYyOll+3lbtmGrjvCvrRAFDE6iiLJ9rJO7UGe3Yb9+GN2ygL9lGO/QKURznvv/4QOkM7uqCMnSHVv9Ycxf72DbfeeRtkFFFNKSpHpS3PsP7mHbx7dWERKA7sPdVeW0NdBQypQYvN9F1tHqGG1Q8uS6otWV05W2ZO/n9yCEYMsjg8hVtvXSlmz9+JYbj9VAAw1sGLz88sv86Z/+Kc899xyHDx/m8OHD/OAHP1j3PRsqUqIQHJFJXtaZm2x2V3tUpD0kH162/EMvINJSwkos7wILwNtWnLf6fpUedxJHu4xFOnFlpGa8DmkjhKalZY6cpzi/mCqXA4vKFLxZBzC0d4HtaKRsIr0QsNCyCcRE/bkLcJkFJG2xx7FUC8a4LOjXV61HH/zOUsv8Gq8ImuxNLHhXsN/dj/BtrItbUBcH8Q+fwt97dqmv2rowaLLeBE6rwx2/eQc/Of1tfGsdEaUMcB95vUoYa5I5SOZqz2XAfvkIa0EEFnKqHTnRge6cpviF72O/cD9qohOBYClAsHpYJ+Vgx2xyk7Wl2QaDSWaxZSstsUcRwg4nEhFY+2bZs/8l7GgYhXv7W/ez8PbByk2SEx0gDO07u4i33UQDwqXrUBodiNB9NhVh92d30bKpZc3jt39iG5mxDIvXF9clFu/8heKBryqcWPXnYYSHvvMY/rzCurqGlbqAWHOMth1tDNzTX7me/rv7wcDQC8N4BQ8nbrP1sW30HOypvFUHGq/g4ZcCnISNHb09Y7YGGmjgF4eHHnrolqKpsMFIyUAkwX/SQzddgSONZlfuAkcWj+FLm7/s+iy+sIh2uETaXIwWCGXqruthel4wFula+UwFCugTDgkhSacXaG6e4+R8c1kvUg3tKaIxsB2IejPE85eJZy/ht7UTt7bhBlOwupwVQSDyxOztWKoFKWwK/hjvr5DjvcJgqRjCU4jFFDge3rZhTNscYq6JyPeexN80QnDH6XWnLYVNa3Rb5bGTjFIKoPJB1zr91xLOtcY3YF2v5/+yAr7Cfv1weA2UK2ceewnnhXvBd+oO7WZd/EJ90ywhDH1bpyjGH0eKFSlHBebuNqQUKKFZnGgmM91UNXlhJBjoORBW6rRsaaEwV7hh5ClwLS69GvqNFGYKnP3uOe77h/eumQ5RtuLIb93Fa//76+Qm1va8MVoydnqQzXedr3ltEhfvz4hwbwAAgkJJREFUoTeRz8SqIkMAya4EB75ygHhrLbESQjBw7wD99/Sj/bDyZ2megRdw/ofnGX13rHLNQgo69nSw93N7UM4HbxvfQAMN/OKwodI3We1TuIW8gBGCnbkh2vxFutwZeorjldeEBGnVJyT1IYgg6BIW3cLmsEriGU3PpiE6OycpaEVQp+eHEOC0FYlGDZ3Tz9J/7T/SNv0C0eJ1eq6/Q2cmIGoNENIcSdidx6I59hACh5i1GSlsjDGU/GvUNur7xWK2eB5DQNA3RvGLP8C/8wTB1mv4h05Reup51EgP67NIiS3jDKTurzwz2PwQwliok7vqN7SD6mZ76yEXx9wEjRULqVDDYSRysi10gD10ivVKpOtpMpTtsv3BUySszVh1q2kEMyasMJkdaUfXqfgByJYjMFs+NljjzVG/McIKgawxlBZLLF4bhvz3IPdN8C7UEDkdaPJTa0XBysf4hsxsX6jnKY/tG8OVkianARng7asmLNKSDD68pS4hqZqxCK3iVxKnU986zdix8arbbrRh6uwUp759et3xGmiggY2PDRUpWfDD1EsSySYVIYpkTLuMGa+uqZpBckEN0m/GCITCINZpyXZjtAiLI3a4mzbG0CQU37pW5LHeLM12PeVnCFtm6cu/STp7HGnC3fXSUVsvvUDuzl+n5OyhFIwjRaRMUiQxexBTPj7vnafgX6kZ+xeNpQXfv+vYsvMchL1spIf78VdZo98hAL3xO9nb9mVsuax5GEg9wOjcMXIndoEdEGwfAlXOofgKMZvGdM7V3mpfYb96B8HWa+i+closViD4wjPsnOwnO9XE5MW+sJxmFaqcV5GYIMw7Re97l8VoHpGLY53fhlxIV4t9V90NYyRDr+/G3msT6bBYXchlUPiEzSDtiItUGu2v0ldYguxElos/vUjLYAt3/+4RXvvD1ytplpv7Bge4k38FyTFCgchr4ByAxG9UUn65yRxC1e/euzwXyeSFFK6zk857T2OkZsTVzCylfCSQyiAtH+1bSCVp3dZK5+5bNw0rZUvMnJ+p3004MEyfn+b8j84jLUnnvi7SjUZ3DTTwtw4bipSIQNMjbA5bydB9VQg6pc1g4HHZuEwbj4DltXGPivNuah/HM/vwsfDGbZCG5KY8Yr0YUR1uIYHtannhFEIQRVIoOXxruImvbp+r2MtXz9mjZ/xtWjJnEWvscFPzI+j2rdiqdXkKxiBFlJx7FsfqJuue5gYhhw8MCgchLHyzzq66HvGQQHL9nfh44Tj2fIytTY+TsDsYz13k3PwPyM1M48gd2G8eQl3tJdgWEjJ1eRNWPkrhCz+pvpueQo51Yg0Noq724z7yOrp/DCxDkM7idF1guyXpPTDEmWeOUMpFMUqHrqqBqonIiMDCyqTI3PdOeNe1INgxTPSVe4jNbaWUczH+6s9ToP2QzJirgkgdjyBpPNIyjNi1bxnn/Iv7a47RvmH6wjTmrGHkzethYzppbilIZoKAdNc0y98ZF9wTEDkPdtggzopZ6xKSpb43JoDZM4OM7j9RU1klgPYodD0wSSF/Nx27O2jd2nJbVTTFhRLCEmtepwkM114fAQHX3hhh4J5+tj++/ZbP00ADDXx0saFIScp2OGQlUSt+8CwhSSmbu4zNAgFTxsdG0CsdFIIT+YDsSut2LXAXLJzmem6oJqxrKEj64jajuASEkZn9VoJmWa8ZGuxsKhG3NFtTWS4tpirVshJDojhL58wyIZmNC851WmQdQcwzbJ8OK3ZqxjUC5Vm4ZoK8ew5jiu/x7t0+drV8HktFOD79n279zTdYm7RxuZJ5iZHsG2xr+jwX5r8V2sUnFGgZOpxOdKImwqong8bbNFohJGI+FZqWXdiCdTGs3hCBhX30AKX+sfAcwLuFAEmASU7S/5UfEb+0mXOJWYRvE33uoap+RADS8untWaDiZCINyAD/Y29zuOWLvPGHR9dNo+g5g3s+wNmhEI5Alwze5YCoyRIZnOXqha1cfn2pMeBS1CGURwtpKqW9gRuQm86VUy/rCGdWvCZt6N9/lUhidWWXC6V3KqQkmo6uMR5IR9J9oJuZ89OUMi7Cs7FO7gqbBS71/DFgCTjY5RPd/QSorjXHuxnE22I3V81jws7C114foftAN8mu5Hs6bwMNNPDRwYYiJa7vlxeC6h9nG4GQghYkzcZi3gTMG5+4EeQWai/RW4hgpwKQZkXExBAVhva8ZHwuwaFmhwOBh2cUjlruLpw1Aa7RpIWFxiCFZlPcw1HgKJ/9rfPMFB18I0nJEsnRk2AEhWgPOTnB0U0KXd6Bepbg3T5DSgbEV16WARXYtLv3smgdI+O+u/6NWdIKfECVOKfn/pIPVn5kCHSJC3Pfxoiyf4Xj4++4jHVhS3XjOqXxD55ZehuRv3mybjpDZKoXKsPyBnzE12zacYXIbIxibIFE6yK5maZyygYQGjvisX33KEOF6viUkeDHZ9n+xDbOP3MeY3SVP8rKrr+Fn/n41zRWh6bwpgm/BzrKy+LJcEZmRYdgGRBL57GipXA8aYg358lONZGdblrzszUywI7Z9BzoZfbSHFbUYuAun87+WmEqCFbmk2YuzdZtjiekYMfj22nf1c7MheX2AdbxvYjFVEhMoiV60y67kj1EU7/6ngkJgB216b+nn2tvXKsThaqFDjSTZyYbpKSBBv4WYUOREltZ9RcgIdDGUEDzupehVC7I1YDdZOPOV5fyGi3Ij8Swm12suI+0QRcVYiHOiAe7WhXGGAIsXpspkmjx2WfFOepnyRAgy2NvdgQH2zUpRxJoUBJsaeiOhztU43l4nsXVLb+Plg7XxTfQsnr3qqUgq0/Skr0DLxq+ZrsR7FIUjUvM9JIXdZxKq2/Abd7RW8H6qaM2JbAFzAWG0q14lGTi2K/diRzvBAHBphG8e94Nu/zefQxiRazTO8G1Qx+Pe97FtC6U3wxES1Cs3fGb2IrIkib0B4mVQBi0gWFXc6i9wNkiHPz8K1x9fS/j5/oxWtK+ZZwdD51C2QFiVd89g0bJCK13CErRZ5FXusAIgr4xIj9+tCYN5A+7+EMydGFbmnSdbr9GK/LzSSCJkBqpNPmZJg599jW8osOlNx4hN5cPy+sCWTGVsw5Mc//Hv4ITX/EdNyWY++v6N1y2VtoS5GfzlQZy1XMxLFxf5NJzl/FLyxVGAoE1vAnn+iB9d/ay85NbqRHNvEdsf3wbsZYowy8O42Y9hBRYUQsv79WkmoQQWJEC5L8P/lWweiHyMKi1S6EbaKCBjzY2FCkJlKCERhlZlbP2jUGa0PY9v2rxdJo9gpIiKCxfqrA00Y4iKhoeqz2BOx1lsrxJf2cyYKZgONwlWEwXWDQw4S9UgvVLZ7jiauau27w6E+P3Hh1FiuVqHqMhM6s4lf08LQkLISBv1y8hDUQBy7NxvFjNa+aGQgJBOnIvcXsrAKVgjPnCyxjeW5+gW8HhmKKrbIYlgecy3s0RE88i8sPHoOiEQlMDangANdZJ8Ze+D8rgHziHf+BceHwuConqNJZ38DT22werGukZ5eMfLFdquApRjGIS+eWGfeVeOieKGg3MSI8dD59g1yMnl8cwhgW9+u4LElYHSbuLK4svIdJ5/P3nKq+6D7yJ89I9YbTECLA8nKiLV4xg9M1EmsoaDq0IyuTm5I/v4r5ff45BrjB+toXmrkuYiMt0HFq3H2Sw6VdQYpWHh4hA6rch88eE8aGlb6yBwo8wwSTz059k8uQaHjnAwrUF/GL97+um+zex9ZEtHwgZFkLQf6Sf/iPL3ielTIlX/tdXa0hJvDXLwK4fQ9EHfPAvQvEVSP8+WAPv+9waaKCBDx4bqiTYNQFveBlKGDyj8YwhMIYZ47GAplhnNy8k2E0rLK2FId6bR0U1QoS/q9I2RHoKLOX2AwNXFjUv5HJhHxBRvzBUI5j2YaFg8+9f7mZi0SbQoDWMj6V5+/hOVLmcUwiwSNS9Loldd3xDQEGMrXtP2uOfIW5vRQiJEJKI6qE9/ul133NLMIAv16yMbZKCXktgi/CfEoI+++YWK3V5ADxrVeWLgFIE59napnPWqZ3gVUcigl2X8Q6fxDguRmqMU8K74wTB9mHQAjk0gImVqkuIJaBF5dtyphjgmpDcAgTG4APH8kHlDUpEiFmt3N31ewDYKs7qPx+9eRT3M89j7Z/G33IN97636Tl8ifq4uXCSX3IoLCRp7XmbA0/8hE13XGBw7xXu3HSNzSJbS0iWYO+G5Ndq5ggupvAmF374Igsji2uetzBbqPu8chTdB7oqItgPA5FUhN2f2RV2CrbL/yzJoc9dQlBk2eMnAEqQ+8sPbW4NNNDA+4sNFSlx/YCYkDznzdMqLGwhmdMefTJCh7DXlAEqu9x51oQ280JVb/KEAKQh0uriZSy0p9AYSpF6YthqiPLue2Ixwr/5eR8fSyosJEEQNt2zLFjqI9ga3Mekeg4jVoTEjUWLPoLAoPEJXSjC5XJUfafWEn8FIlY/SsYRK0qJhJBIGSGqBikGw+tPfh0sVTDZgY3+yf24T75U5aAKEAHuTaia9uzbIxbXXA9vxVhbI5IBOyQU17yAyyWNnG+q1ossXQMidDbNxYmnAgq6FAqHr/eg0jmCHUNhCkNpkJpg70WCPRfBs8D2l7U5GnT/ONSz/Ld0SLYsTcnA8xmPflvSYgmy2nDV1bjlW98Z28u2pidojW6r3Ouu2P4wWrfi4xFAujVPf/8Mtn+GlBJk5mPw6r417rLmxvsCg9ECO+pVfReV1Bj/Z+AdAbsP/FEofBf8KyBSEHsSglFqTfnCKFBzzwSZyVvXYuhAY8c/fHfVnkM9tG1vY+rcNEYbOna1E/G+V//g4FpYMiQaRmsNNLDRsKFISRzJHSrJWZ3nunYxJnyuW9qkUGvuPaVtSA7mMDqMnNQlGgLstIed9ghKksLEap1CrcDWaPDzq374fEVQOYHALRm0DolJk9mL1iVm5GthhQmSFn0nrfpuDAFXrD8lbgYIKJETlzFi/dSNJdOIOrW4UthYqum2fdYE0GtLBiOSmC94fd9l3Dp3N6FEXY2PIwWH4oqj5UjD/QmLtBKVqqkdUjHoSK50Z7h2TldFSpYvImBHoYmdPdM8uwhFINh3HvvNQ9jH96Bb5xG5OEHbNP49x0My4qxagKWBqAt1TO1qIi7AFU9zxaXqY5bCZm/bL5G0O6uOV9Lhvu5/xBvj/5rAePRYmn0xUEIhOA+2QghItpYo3nmBK2/vwJSN0qQV0L5lHKk0C2NtRJJ5MjMpgmKE1d8xaWkSSxqaGhgo/BDkZ2DxD2ApZWfykPtzsLYT/olX3xejBV7p9ohFui9NdjxLuj+N5Xy4Px9OwqnuIjzngKmXYloyImyggQY2GjYUKWmxI+SF4JCV5IAJ8/12eaErmoA9Ms4ZnWc56B4itIxff+MkVvQ4UxFNpNVFlyQyommJBOxpKvLmdBy/PJjE4AcSe9HjHvEObcwxL/sR5gBaLIsOC3lINQHliEmLvoNmfYiAIooIAoXBUBCjeGKBBbHWAlQLP5jHENQs6tp4+Hr+psepuRfAtogkIgQvllyKAyN1q1Hng7D4eTEwFDU0K4EjBb4xTJerJ9otQWoFIQFQ5TTP9j2jjLx0ABPUITdG0t6aQwhBRAqK2hDsGEJkE1hndiCnWkErRPMCsW9/iuLjLy4LYFciEIhCDJPML0d6NBCokLCsQNruBwFZbwKBRAqLwx1frRCS4kKR3HQOv+gzf3UBaUnuP/jP0KnTpIt/hcCnwgTLlyMFbLvvPNFkgZGTg6S75uneeZ3m3tkKOdaBYPjoNobe2FOO6AmEChDCsP/JowSehbRqF18hgGAI8j8EVnfddUONRd34oWDqUk+d52+MzOgix//iBCYw7HxqZzVJ+LAReQCKP6f62i1w7v6QxN8NNNDA+40NRUpSiQQScI1mVLu4GNqERRrFeV2kZDQ7ZYxJ7ZElIIXisEpwQucrxmo3g7CbsE9+NEq8t8AvDS6QtDX9SY+3p2NkfcmWpMfIOZ/P8k2k0NgiwOcSvv86b9tfwxNL+hHB5JghmYJk2pR77QhUud+xQWPwmVIv3PL9KAVjBDqHkCmEWLIBD1BCUvSv131PVLVQDOZZS9MggUFHklKSUwWfwjr2GBp4NuNXYki6/N5eS3DVDQlAs5KoNQZQdgBPvAA/frSqIsVIH903gZ3OA5Ju2yJT8tHC4N95En//WcRiCpPII10b+/uPo6724aczNeZeIHB+9GhIaBaTiHxIULy955HjHVjntiF8G7ZMsPWBe0jHu5HCQeOSsnsRQqIDzalvnmbq3FRY/aIrQ3P11asc+PRJ2FKna98K9Oy+Tm4uzbb7z6BWzdEYQeA5pPubaBlsoTj5Bsn2aXp2jxBJlDj104Ps/cTxtQcPrrCmRiX+Jch/GxAYo/FLhmPfu4fAu71IifYN+OFf0vkfnifVkyLRFmfs+BjzV+aJt8bpOtDNwrV5ivNFUr1p2ne0fTAalNinIJgA7wzhT1kA1lZIfOH9P1cDDTTwoWBDkZJ5r8S1oMhpHXbA0YSB2rRQzJsAA0yYcNdkIbjbTqFE2Fn4unYZ0SXA0KsUCwGMGg/P6Pq7KgHaVbRlFRFpkAIGEj4DiQwA2kChME729ACuSJEyYyTNJIKAweBFLlifqhoum4G8HxDbsohtCex8DKUVOTPMjHwRT6wtOlwbhpn8M6QjdxK1NyMQFP1rHGx9gsmshb8qtC1QRFUXtkiS8WvLjG3gSFzRVtZ+OAL6bMmYp9csCF69fx9yNUPuUrJL4RmHAL+mD4wxITlr65vn+lPP47x2J2K2GVSAv30Yc+Q4CdkKVhuDzQcZmfkZOX8aMOD4mPY5AKJI0ALrzA6CrVcw8SJYQfjl0CosNy5FkSf3LJ97LsAa6QNU5QLMfIozl67hP/1nOE6cu7v+AV7e5/rbo4weGyHbfJ7g4euIkoM6vxU13VbhAUplEOtofwC8ks3kxR623X+m7uvTV/rY96WdpPvSMPuHrMy97XzoNIGnsJw6tNreA3oO6kbGAojcAZG7wb9MZjTHO/91Hr94a10714IONFdfvcrC1QXcvIv2NEIKhl4YRqqQzClHEWuJctdv3VXTw+c9Q1iQ+h0IpiEYB9XxvvilNNBAA784bCxSUipwSheqFsgAmDO1P9ZNIhSrKkLjs34VoV9Fyu8J6BOC/SbgTT/P5KoYijGgS6HUM2qFj8vWDhWUihbHhu/Ft8J+OghJNOLR3BlB4pFcMGQXIdw+l10+CxaFy02oPXOUUtlwLoHEy9cjJCs965dd1VJCoIUJm6EBBpeF0msslF4DQGKhc2PcETO8lafiLgsKg2bePc9KzxFBSOx8ICUhbUm0MeUUjiIA9kUVL2a9MGpyAywdErc6uKvzd3lt7P/Drkgo310q4zbl1JsF7IgqxtvnKH3m2XBaApSAnRGJEiVI/2Ns4GPR+7my+BJXFl+kFCyipE1fbBs70xd4xQ4wJZvIjx7HeTRPpD+BCcA9FeANR2rmKIIwZSaqnrMgE4fhDgpbr/LauW8Q+cFjSCeL+6mfEkSKoTTFQDA4gvX2fuyzOwAYenMHqY55nPjqFEoIrS2uHd+GW0xw4aX97HjoJEKIsuedYeT03ez90sdCQgIg20BPVt5vR/3yfQvvjyh/nogIxD4Nehoyf0RtCsdA8TWIPQr2ThI9ATp4kTWjKrcKAwtXFyguFitDLpXt6iD8joWOtHmGXhhmxxMfgCW8zoN7HPxLoDoh8hCothu/r4EGGvhIYkOREhA3sPCC/uIoD869QQrB+MBvUNvkxiAp+5wIyX4nztFgkUwQmswbA9Jo+kqCGaYZm4ryBz8ZoOQLutIuTx+cZaC1xKnjvZQ8p2r8gquwspBKOwR1LR4EuqQICgoVC4mQkkkEzipfEYESSQw+wjgYFjgUU3RYIcGSwJs5j+k6G2eNT1Rfp9k2PJqyueYGFLUgox3mg+Wy5+W7AQkJWQ2zGi4UA3ZFFVKEKg8JXHX9Vb4jAoGFEil8M1v3c0jb/RSDebSRvJJzuTOuSJRvVU4bLpUCDsYsIr7goaTN+ZLPjG+ICsH2qKLHlqxcZP0MLHy3DYYeJQK0bG1h22d343lZAv84SEh/PoFsTob9UwDrsKFkBxRfrqPHqGfC59vI690EW68iXtuNEDn6v/wTzgal5e+dAKwA/84TWJc3I1yHhbE2jv7Vxzjyyy/ixMI5mxX9GYXwGbzPx25uYnH0bq5fuhvbOkduMsvcSD/te7eT6l5RCRP/HGT/A9Ukw0bEngjJSjAJ9naIPgKyCVQrJH4Fcn9GdexKQ+EH4evOQZSj2P2ZXZz97rmQNNThJkIt9bu5CeKiqCIka8EEhvHj4+8/KdELsPD/BVMEPPDOQvFlSP0elH17GmiggY2FDUVKYtb6efCBwnU+N/Vj7HLkJFKaohTtqlG4CgRCaLq6xkkmcxwESoHkxyMJ3OtTPJ55gXzzHfxctTAUNJc9XMOy3z95qZtfvWuKbCbCaoW/MYJ8xpBKQxCEZ1oNIQzGkxBbZhS1BmmCwGTK/13ABtosgbOi9HZHRDGdr6+SOVlwORiziEvB9ohizNMov8j8GqKahRVMb8CRVaLUQGtOFlenbwyGAMfqICr6ybq1eodikOHoxDcw+GQ1vJD1iZaHXcoeXHc98CW9ymZShnchKUOSZACs7Qgg8APe/LdHcXNuZQGcuzzH0T9+i62PbUUqhdxkkGlRISQAwhbYg5Liy/WvezUMBnW1B+ulI8SKEfruvMg4a2iRtER3TqNGegFBIZvkrWOfZMfOt1H5EnasSKwpj7LKcSp9nt4tQ1x59QkmT1tAa/kfZKeHWRxZ4NCvHgrHdvZD8u9C/rugZ0C2QPxpcA6vPXl7D8VslNHTPZRyUVoHpunYMo5ULhSeBecgOtBYjkX/3X3kZwsIKfCLPgvXFjDGYMdsdn5qB4EbcPlnQ5QWS0hbov36BGZFGO6GuJ0GfTdE/vtgcixH/oLwX+6/QNM/a4hdG2hgA2JDkZKuWKIqqbESCvjY3GsVQgLQO/ZXjHV/gVK0l6C8oFvKRwcOvb2jxGIFZNlnJCYDPrc5w5jpwRrrYyo/w8XgLmrKgIHvvNPBXdH6P3hLbWiiMXBLdcqIjUDFy+F4DcXgOrW1u9UUwAdez/l0WpJNjiKhBF655qZe5GjcN0xlPAYjkkul99ZZ+HQxWCM6pSn5YySd/QgiGKrt8+dzVyEbg+ZM5RYEwDYnjIL4GIZLmmtCM2qW3zsTGF7K+jyYsDg/v4D8m7cozbp4ea/qgzfG4Jd8CrNhF2KrVyKc2s9EJgQiHlbJrkSYvqk+XiAgsOHyZkpK07vvCrPldW41hDSwUiwaQOm64sod97PF/zmJllxVkE5IsCIe6fYpZnLVlS/a18xeniM7kV3u42LvgfRmEPF1y8b8ks/EyQlmL48xde6RsKWOVkycG+BKS5a7vvwSSi5SXCzx1r97C6/goQONVJJ4W5w7v3YHQgqCUoAdtyvkofeOXowxFOYKvPGNNwncoCqb2LK5mcXRTPj8DSAtSffBD0Dr4a3ROVvPg8mGfi0NNNDAhsKGIiWLpeKaG7MAaPXmq55TQYH+63+Gq1L8e/Hr7PKOcVhdIJJKEtt+uKYiQAhDss3l24XHmMhrRH6ZZKw4KtRz1GVHhli56CYah+wiaL2q054w5f4l4Pt5FtzXbnjdBshoyLqaYVdzV9wiIurFYarvx+0QkhFXsytmIdEExnDNW3srHJhFFktvVEd6DFjv7Mc6sx3dMYP76Kvg+CFpTNhEJOVIjGBLBEY8XdMKRgMv5nzQ17HU2yi3m+DAMCZaRI11I6/1IIwkcAOEktgxG53xML6pipQszUe2CYK8We7qK3Xoa1Kq1ZuEEOhAkZlsZrBnknFP1/ISz0JOtlc9JeMCYRlK7e0IOV07qqiTTVw+JZmxDMnOBBSfh+IzZQ8OBbHHIPpkzc4/N53j6L97i8ALyg3slslL4FnkZpOMHN/C5vtaOfPtM1WpliAIyE5mufz8ZXZ+cifKqiU+QgjirXHu+d27ufyzIeavzhNJRxh8aBCjNae/XV+0C1QiLMpWxNvjbHl4y5rH3jZEJCQfNTCwltNtAw008JHGhiIlF2an1n09qxI0B5ma5wO/xOf5S1qZx9ZBaDke+GGP9xUQAhwxzbx2cBxFoU7jtCUUogHxvMCY0I1NCINSZU8SwLKgqzesuinkDcZA4IswzT+URjkuc63/ueJLgbFA1hdKLsEQko13Cz6PJRSWCC3x308MuZpux9CsHAq6tC7xCedUrdeQlzZhndmOCCxEJhUSAKDfkTgVQhKioJdFtnWhNP7d7+DfW34sCPUec81EnnkYy3JIdafo+Vo37/71u2HoaeXctMF4EFw3ICDS5OAWc5iijViTkCzjyjtbOdw/za6o5GxRE3r0gtQS+ZOHESu/HxZEDloYISnQtOaYibZFpq524h06FVrhS40a6SFy/A6izVEovQKFH1ExQsOHwk9BF8C4YbrCOQDOYU5/+wx+Yc27h/Ytxs8N0P+xB5gbOlVDokOtxwQ7P7lz3fsQb4uz/8vVrrSlTKmmF03V2MbQf3c/7Tvbad3a8sGkbyIPhuZxVdobBfYuELVNGhtooIGPPt4TKRkcHCSVSqGUwrIsjh49+n7Nqy4ywfKPjwQkAn/FL+1rzXfyidmXqlI4rrG4aAbZJYawlxxSM5ll7/cVMEHAWNbgC0m/bWPFYLpOCxAJ2Nqww/sB562nAYWU0NxeAmlhyg0DhYJ0c/jPGBgfCTvUJh2HRNpiLnBAaoSXxpl+hFLfN2/qPgQGphfiiJE0MlZEd8ysHza5BWjgtZzDtsQRbH0BzcgtvV8UIxXreJmLIyfb0V3TdFgCa9XClJQ3Fi7XGNbaAaZlnmDnMInpAxUPjPt/537e/NYbmDs8RNkYVc8bCs8G3P07R0j3pjn2Z8eYPu/d9K2aH+nAdxVbHEG/bTMXuFhWD4XLn+d8bgxjGZZyaNH7LaweicQnQX3xL0Dr5nHOt46h22crnirB4Ai6f4pk717I/hhqmil6UPoZlfCcdwZ/8WUyYwdveA0y0h1W86yB9YjFqgOh9DqUXgUCIs7d9B3p5vpb4/U7DfuGuaE5dj21PuF5T4g+CsF1cI8RflFMWIGT+PUP7pwNNNDAB4r3HCl5/vnnaW9vv/GB7wOaozHkAhxQcXplBAEU0Bz3c8wYn7PJnSSCEg8svovUHh6KF83ddDCDIzwCJHN2E1FdInXlMmbTFoQV3gKjNQQBV8YK4ITlog/1Sb53SZddXEMIICUFzbkJmhnBokjgRIgOLuLGPFQ2jeXX7tKMBicCxQLE4uFIiflPExRiyFInYaMWB+SSz/kai4UBcWI3J0/sAalxDJhYkdKTL0CiUHPsza7AUWsQQ0DJHyVqbeVC9hUguOXi0dXHOz+/H/fBN8hvn0JbBrmCmCSUoM0STPm3eBY7QO4f58jWr1Wl4O58+i7O/+QCk8PjBCVNc1szR76yi0RHmFOrTcUtoboHjXI8OraMkWjPYEcMRJ7EieynSyRBtUAb9B3ZxcmL75LP5lBdoqxn0UTI0CaG6t8bA0E6i3bcCiFxBNyVUjQrjcj+L6zfG2DpAtzQl4MD694maUv6j2xG2Yp0X5qFkYXqXj1S0Lm3c+0BViL778PqliXCVJhkx319NPX9Cqe+faZutU5+Nk8pWyKSvHFU6rYgJCR/E4KnIBgJyZfqbwhcG2hgA2NDpW/2tnSSm56lXdqVNEACxd1Wipf9RXI6QNstDG/+PRCaGddhdtHQmnuRk5EdvNB2PwaBRtI7M8GTmXNEt23HsQXB3AKcOUaL7IPWHUxqj/12gs9uU7wzETCS1QgD3ZakJekjY1neXvx7+NoiuWMOYYUNbrQ0dUWUiLB7cCIJygKNIZJoozQfivEMAiXi2FYfKecQYJjKf5caIZ+AoGsK+939oVU6QFbh/Pw+3E8/X3vTboqYKJLOXpRMkXfPU/CHa4SrNwu9fZjgWh9qKiSqwrOJ/OxBxk7OsvnvvFQ1F20MuyOKKX/tFMRaSLamsWPV6TflKPY8vZs97K6Ys61Ez6Ee5obnanb2Lf3TzI20A4LWTZMc/HQY8ZPWkmW8AWsAr+BRXMwSa45iRSz2bT/I9fw1ZoqT2GaRbeJ5IuTWXBOFgJzyEFJXuMG9cYuUEoTc6uabFVlOgea+HPMjyVCjtOo8Qgk693TSfagbgL2f31PRn2gvNDVzEg7bP7Htxifzr5VdU1emSTxEMErXzhkupiIU54v1r/nD6Cas2sN/DTTQwIbHeyIlQgiefPJJhBD8/b//9/n6179ec8w3vvENvvGNbwAwNbW+JuSGCIIqQrIECWyVUa66OUYm4nQkFbZ0aHVcNkVOY3vTDKkEUe2yaIUk4Hq0m++VbAaPN9HZ2h6GMnqO0DT9MkFeMLcY4du+y0AMHkwM0WJfx7XSnElsZ0QoZGkngRGolBdax5c32l60iO1WR0qMCQlJPAnxRFhmW0hMYQoWkAJhsNIu6dTTCCHwgwJT+e9Rv7aGipvpEoSRyLlmRC6GWYqWGEV78BBZeZki18pkYMnIbSkyEEZkUs5BbBWWpyad3WTcE7f80aSkoEkJCspn5vGXiHzzU8jS8n0oTrdw9tnD7HviGAaFNj4LgeGt/K0TEonFZqsEi/8LkAAZB+cg2HvJFM9yLfsmPoqu+GGicwPh/HpTdO7tYPJMO9Pnp9GeRloSBBz83AXwj5JbiJNuX6wRo5ri8wy/Yhh+LRISCi0ZOBJn25P3MBBrYcD/Gwgu39x9skGUQq64xRGklVhXb1FcjOIkS5UqsZXY+6l5jv55K37RRwcBQvhEEkUGDl2ldWCKRM8hIHSyjbfFeeAf38/EyUnyM3lSPSk693Qg1U00rvMvUz9y54J3kd47DjL84pWwdHgJAlLdKZy4c+PxG2iggQbKeE+k5KWXXqKvr4/JyUmeeOIJdu/ezcMPP1x1zNe//vUKWTly5Mh7OR3jmYWKS+tKSCFICsVk1qJoNtGRE2xLFBkY+Y9Yfg5pPJqQHMqe4Tudn2Qk2osWigmng92JFGaF4PWC/RCFiTDWIfF4LPhzWrLzRISHj8VDsy8y2vcrTLhpjBFIW7PyB1tbAcVEhmg+idAaYQx+YBFoQSwOmWCEqeiPCfwcWKB6NhFb/ASxgVJlcZov/hzWi1S4dSoLhAYvQrib9UEEzKvQOVTg0Jn8EoHO4ulZJBG0KWIIiFp9KJlYHgaDkkkCPY8QEaKqH1HupaONi8Kv2s9L4EjconWp6sUIirbkjcFrmHM7Vk6Q8bOb2P2FX+PE3L9kujhEcZUTr0LQbTvErRZGivMUTO3uWwD9tqZHXgdfUMpGOPfCAaaHx4BR/M0juHcfg4jH1fnXUWPdRF9+kHhTid1P72D/l/ayeD3DzOVZ7KhF1/4uLAkUvk9T5xpW/8bDUhfQwUEod/q99lYWJ/Kv2XR4CmMyN61TaRYCx7eIOwF7ompNQpKba+LUTw6Rm0nxwFefxYkXV0VgHKId9/HA/2EbM+enKIx9m2T7JC19M8vHlV4De2foewJYjnV7DfREivqSZAtkms0PbGZueD5MD+kwSmNFFPt/aV+dwRpooIEPEql0kY8/ce7DOdn/9f0f8j2Rkr6+PgA6Ozv54he/yBtvvFFDSt5PLHoudp2f/8AY5oyHTAQwB/1JSevsa1heBlleQi00GM0np3/GH/f9KgiBkgJvxS+9NoajU1QMyO8TR2ljtiKQtfDBQM/4d5lp/TpCQJCzatIjfsQlryboHDlLJFdkqO0xiiUD1gwLsW+xspNskLhKMfUtktZTgMDXi3h6Zu2bEAjU2Tohdysg2pKgYJarj3yxNI5AoLBVM7ZqXu8Wg1AkrBZcnaY5+kCljDYduQvhnaaZk1xyNZ6BiIBNjqTNWtEFWEDcMhzcNc2xKlISvuaJEqOFYVYX2XZagrviNlI4CHLssA3HCpLrK1ItUQH3xS2SVkgMAl/y5l88TCkfARM+J4cHiMy0UPrcT8J70jfOkV/5EfGEC+YZvIkkQ88dYX4sTfdeD93/FxC7xnIUqb7wRJQjFUJoEAbtW8yNtDJw8NItSRikFNxjO1iJUpW+ZiUCTzB6qo/MRDMA73znPu74wqsoK0BaEik1xB4HewcS6Nieh67L1BJZN6zmKZOS24azH/J/VefWSIgcQUrJHb95mMXriyxeXyTaFKVtR9vNRWEaaKCBBlbgtklJLpdDa00qlSKXy/HMM8/wz//5P38/51aDpmiMc/Oz9MtIpZJDm7DP7uWgxJLhhRCQzJ6rEJKViOoiaT/Dop3GACmxfAuybphmWcIBeQa7zhgqyNIUXcB2mjFa4c3HMek8S0OJwMf2cjTPXcb2c6R7PoXsnGHBPQ41lQqaQC/g61mUTDJfeIUb2WSqoQGM8hGBhREaoSD+WJzA2YznlfD1ao8Mg6/nKimatSAIaLab2Bq9n4sFjRDVbfSEs48+cZ1t0ZD4zAeaqBBV6TRjYOpSD1fe2Ua1oMXQ2j+Fzv1JzdpvC7gzbhE6nIcLqxRwMKaYCzT5cunwvmhoHLeEyQt9+K5VISQAQivIxpGjXdA3wQNNEkcsRxkUC+z/5M/IzSRJtmcxWpT7GtVVAgEQ+IqZK53se/ItOrePIoRhcbKZqctdNT2Rqu56+bWVxwgBqbRLjTnLivcEnsXoqc2V53IzaV7+d0/Q3D9Dqkux41NPgFkEbwisTYQRjLUmsawDqaezuSkIB1K/D9k/Bl0mvSIaus7KVPm6BE39TTT1r10O3UADDTRwI9w2KZmYmOCLX/wiAL7v82u/9mt86lOfusG73huS0Singzw5E7BFRbERzGifM0GegtF4mTB/PZLRbJX1zZOEAV9aSB3qUDLTgsKS26e9rOKQkQAQdU00NBKjHKLlooKIjlGctvGasyhLE80LbLeX0f7fYMvQv8KN5jHC4OsM9QmHwNc55ouv4+u5Oq8vQSGUwvvs88hLA9jjgzhNaQrb38LpeQDYTNQewBiPmfxPVljVw0z+WeL2LhLOdpSMU26jiywTB4NAYRiMSmaz7yLFwZqZGiQzZoA+cRqAZiXxVoksL726m2vHtqL9clVTeZRossD+T72FpV1sE1Tt6bstWVeQK4CdEUVRG6JS0IHNSp1NZjpF4NX5nAML57kHSO++gvz4qUqUYwnSCkh1LdRUhQsMWlcTCBDMX29ly93nsGMuY2cHwAjaBsfZeu+5Mrmo/Ux1INC+worU6bsj1qedp565E39Vis4Yydy1DpzEHCz8P1kSnRojGLt0L12bfVTNrXAwzl1cefkKV16+gl/wiTZH6djdQaw5SsvWVpIdidVvqg+rF5r+76AnQv2V6l7HCa6BBhpo4PZw26Rk69atHDt27P2cyw0Rt0PSMaRLDOnqULUJwFsMf5XfnQzY3XoHfXM/Q67YKQYIpp12LJFmy7xFyo2UO9+WVyEP2qRkRmuibSVOlnbx0MJbKLO8sHgqycimr2GseOU5KQUx28IpJVAFC2EkRoIISky37ceNheLTiOrCDSZYXWVhCAi0R6AXWW+5iqhuWmKPEJgc8oiDwCLrnqPVuR9RDtMIFAibpui9zBZ+uuIcJXLecXLeKZojD4LJ0sElOiIJMgwwyyZ8IsjCdzEk15oCICultZ6x8I2LkqGuxy04XHt3GzpYNhcRCBABvfuuYEU8Lr2yB6YS8LE3QmM1aVDUjzYIoNeW5U9HMPzmVgbvvlh5PdmWQdo+2rNWvU+AUWTPDlK8Yxi7rdr1s45FzfJ7xeq5GFr65hg718uFFw6ErBbghf3sePgkTrRE6+YpLDv8TI0BowXXjg2SbM3SNjhV99p0IJDK1LwWeIrZkfplutF0jj2PhqXaldkZQ2vXu5x65hD7nnwXITVSGcABaxNDr7Vx5ZWhSsVRcb7ItdeuIaRASEH3oW52P73r5iIoQoRkpIEGGmjgA8KGKgnG80mgyFHdj8VoKE5FWSIXxQD+dHovv5a6wkD+UiUekFcxMr1f5GNWikI8YKHgskW/TJc+DRgm5W6wH+Wkp3AjmmOR/RwuTZIqjmBMKLGd7HqKQMVr5ybAd1wK0UWcQpxIMY6RFqODdxK2mxfEne3kvDNos1Icq7BkMxn3TdbfP0sc1Y0QEkukyvPxMaaOuxtgyVaWnL1s2Yat2tGmQNEfYb70AgDzSKbkQWJ2mCqwhI0XZGgiwzUO15lBQLO4viIVAif1J9hjfoYdQGaqCaH0cqly5QNSzF3rINGS5drxrUjfIvL9x/D3XMSkM8wUo3BwovaWiup0yqY7LzN5qYvObeGxndtHufTqHlxPsro5IoRRhJHjW9jz8ROrnr81Kwu/pLjwwoEqsgVw4YX9aGPo3XMNqTSTF/vwS3ZITIzg4//g+2uOKZXBzTs4cbeS4jFacPz796yZ2unbewUhq9N/UoIV8fE9h9f+y6MMHL5ES98M8dYkwmzn6ivX65ubaYPRYffe9u1tdOzuuPkb0kADDTTwAWFDkRLPaLIEdAmbSRM6cwYaSjMOQSHUJOxvV2xpkggBF3mKl4rjtLmTZFWC0Ug3cWO4wzOMzgU85v8ZcWYr2pMefZxmfRXX+i3OGIGWkpPdn6evOIM/OUpJpHDiW+ruKgVgpAYBbiyP8iQGgVmxjkkRoT3+aTLuMUr+dQQ2cXsncXsbnp5htvDsOlev0cZF6xJCWLjBFIulN4ham8LoyCpIYQOClugjOFYPIkxOkDYGbfJYMkVgCmRLJ5EiQsTqJjA+J3iK5sIV/DN55MFkSKgkSBHQziUSYjm9JAjYLk8wFOzAL5wnOZ1E+3WaxwlNNJ0PoyjltI5caMJ57a7wcxWaS5t+yNZmv1KoDNS9zwtj7Vx9ZzuDd15EKE3XzmuMndmEX3JWvHN5hpnJZgJPouxwYTblhrdrkRLftSguxkm2L1aOmbzUQz3CaExImkZPDVbOV34Fy/EJXAsZq986QAiIJJadWwNP8ep//jhutg7hLSOSKpSjIDUzIRIvon1ZIUhSZiguLGDMx6m1xV2G9jSj74w2SEkDDTTwkcCGIiUL2kch2KFiHBQJXAzCFzxb9NECHhqQOBGNkgJHSAZMhPb4AD8vi1oBCiYgM/YyD3nXiDJfISShpCF05EyICySKA2SjLpdFidZ4D1NWL8bAegWViWSOBc8GAcVICW35NcukknGao/fXvNcWHViyFV8vsJaJVs47Qc47SUQN0By9D2MMpWCSZPnqjDYE4wbvaoBqkSQGduNY3cgVqR2DQclmACyRJB29i7x7CUd1sFg6Rt67yCgBVlsXzvcP03nEpal3jlYxUkVIwvEMaTnNbjHDVDzO0YErOG9vQxRiVZJRqTSbDl/m5I/vWnVF5YoWI7j6Xz7FwqNvMLhzgohWqEKMaLKAspYXYSEMUmkWx9s4/oOydbrQ5chCfZaRmWxi9lo77Vsmw8MlmECURZ91jp9o5vgP7sFyPA4+/SbprnmMlpg60YuKnEaYKrEtCHQgGTm1mS1HLta8rx4sJ6C1b4bxc1HqRn0wzEw00bFtDMuujnwIaVgYa+XQZ1/Hcpa/O06sEFYLrUNKAHTw3jpJN9BAAw28X9hQpCRhOxgMZ/w8gypKgKHVsvnUoMUbbpZ3lY8MBDowbJYR9qo4USTd0mFMh7vSXdkLPOi9gUWAAOatFM+1PsS1aC8Szc7cJe4qnKMlsYVTwHXtcpocm5sS5BYEpaIhEl29izfE4znaEzkW5psB8LUm4kWwCjZGarxoEa3WduwUSBLWLnyTo+gPYfBwVA+CCAX/LKCwRDOt8UeQIgJIOhKfwddZBLLi6inbBZEmi+zfuMQ7DiLj1QvS6uiDFDZxZyuzhRdwg3GWCJHfMsb+T03TY0mUqr9oLQ0lhaFT5dnfdoCLX7yCfrYTppuRUqKUYNt9p4i3ZGnfMsa1+UQYYVAas1J7ohWZn9/NlTPzLE62hGWvCLbed4ZNh0PbdmME05eXNQ1CauxYETe3dnTBjnq0bpquIiBSmbqW876rGDvXT+BZBJ7F29++n4d+6xnat0xw6dU9NbESqTStmyaYvdqF9quJhA4UC2PrVzutxrYHzjBztROvUGvLLhBMXuhj84OniGtQZafUwFOMn+/DKzrEmnKr5mcYPHKBoTd3ViJUqyFtSc/BnsrjueE5rr91Hd8N6NrXRde+zkZpbwMNNPChYUORkrgRxJBM4zMTZMOurQE0aY+MVGgkSwbeV3WJOJItVowmoRg14Q5578I5nHJJTVE4/HnXZ2n35tiZv8xYpItz8W3M2G185eq/IT7w6+yyWpjJGpyEJtaVwcQ8zEJr2CG2XHlh2z7d3ePlhTScU0Q7RN04ge3hRopoGdzA8l0QdQYRSNLRg2jjE+gFZvLPlF8PsFUzUsRWEAuFJVOhrb0I3UmFA8YyxB+t5+iy1pkVvp5nZYQmKg09UYMSqzrvlldmIWDO14x7GiWg11Z0ymGmW55C/pJkUO7EK2a4Jn/KueIwxy+nscbSKCNp7p0iM9VMsEqjYQLFwngrGFl57dKre4g35WnbMsvYue3k5lJLR9O98xqRZIHhozupF10AaOqZxQSy0mumcs2i+lp0AF7BZsdDJ9nz2Ltkp5u49OoeJi/10rvnGoN3n2f46A50EFYKSUszcOgyXTtGmb3aRbwlw86HT9DSN0PgK8ZOD+AWnBvqV1aWDUeTAff8dhcv/6v5uscGwJAbmq6p8nt1ILhydDtC1ie8m++6iHJ8rhzdQSkfC7+D5SaCylY0DzbTuT8U1g69MMTwS1cqGpT5oTlG3x7ljq8eRq6nDm6ggQYaeJ+woUhJPp/HXUpVUF5CjWZRCIJV5YkBcFmX2Gwccrk8uak4xpf8R/PLtDLPl8T3mE6182vj38HRLgKDxHAqsZOXmu9mwm6ia/x7eJu+Rk8SSk0LeI6HQeA1zWF5NrJo483aLMw5XB3aRmfnIkGbh1IQcWMElkchuVhNRNYgJqGos7pqZbbwAqZCFCxs1Y4QAmMCcu5Z8t4lQBOzt5Bw9i2naaRAdULxLZ/oHQJh35ieGFPdmTalBNqAWvVWIWDGC3inEFBcETq4WNLsjOTBAY3mgvcmGfcH7Iga9iYF/rYFLve+xvgLh2jrypOZaq4ziyWitwztWwy/cxfth3bj6kUGDr9CU/csLf2z2NFwzu2Dk5z44d2UcrGaEf2SXfd+ryQkEKZ1oullP5N01wIHn36DiYthwm7wyAU6towzfqEPDHRuHyPduQBAS980+z75Npbth/ob5dO3/0rVuepW4GgoZSN4xQiRpiYizXcQje6lbec5Zi/OVnXwNconsXuYvTFV8egRAuyoz/1/9+fkZuIEnkJIv+pcQsDAwWEGDo5D+vcp5tsZPz6Ol/do295Gy5YWhBCUMiWGX7hSlcoJPE1mLMP02embb9zXQAMNNPAesKFISVaCi8EOJK35CJYW2GKcK6n6JawlNNpzuTjqYMoSSo3FNG18w/xd/rvcn5AgV7XH3pO7wLjTwZzdTAoFxmDsgMD2qKxuAkpakxuKgy6buAWC8bEmVMalqb+ERFKI5+ppL28SgqSzl8XSm+VHKuzkawyzhedxgymWIhtZ9xRFf4T2+FNhxKQM92SAPShRHXLNkk9jDNqUWN1np6ANVp23jJYC3i0GNV15FFAyLl3mOBnRz6R7nIeSYAtRdi4V7EsYUo+coL2YYOj13fVmQ70blJ+LcfyvRpHmAt27JmkfrDaHS3fPc/9vPkt+Ps6Fl/YzN9JRGWd+rA3ftVCWX2OrsXrxXg1paVr6plmcbCLRukiyPcP29rM1xx349JthKfGK8dUK3YdXtMjPJ2nqnq88F/gS37W4/Poedj16AiMzePlryPzf0P7x/RTm9lOcLxAYDwzorikO3XuxQkiqroOAZFum5vlqBKDaiaajDD40WPPq3PAcQokaOVPgBkyenWqQkgYaaOBDwYaKyc4FHumizZ7JZrqzMTrzMZpyA0TWkGpoY7g0dQ6DwKmSXgpaWCBlcjU3wDE+B7NnaPHmme58HITA1BGsliZidfrlCYKcg1UM/VLW05CsxOourwBCSGL2dmJ2aNVu8PH0DJ6exg2mqV49NL5eYLH4FtqUQqKRN+iiZv6NyxT94brnCM8jUDKOJVtY+XWICVFrnmYMp0q1hASgxRLsjUr65Fl2i2c5HBnHElRZqVtCsDluKOSjbL33LNLyQ6EqIC0fIWrnaDB4eY+pc3kmzvdy4of3cOm1XauuAWaudHL0rx6uIiThAIJ3vn0/pVwM37XwS9ZNlwSHRMNw4aXda5bpAijLrFEVszSO4a2/fhDfjaI1ZGaSnPv5ft74rw+z+9HjWHaArQy2oJwKO4H1hZ9T/MQLePe+Temp53Eff5mEvX7J+NqCVgeiHwtdWNeAFV1jfyKo6cbcQAMNNPBBYUNFSiSweT5Z1ZBPYjGwkOBiaz5cRFdtfy+39mJlHSQCd8XC7OCh1whbRHWJuLC4tpQO0apmgdYFi/p5AQ2lWUqpm7PbXossAEihSEfuouBdBgLmCz8n6eynfvdgTd6/SCF7kebox4jE+kj/ZoTp0jkWiwWiyU3UW7SMMSyUXiMwWSpiAyCmUhg8VpIf14C3xnTnfMOlV/cwN9JOLJ1n98ePoaxaUqYNTF3qZs8jp2jpn+H66QEC16Zr+yilvMOFl/avEGWaVXdYoH2Lq29vp2/fFaKpsGGfDgRnnr1jTTFnfi7Fy//+cdJds1iOT6Jjnh33n7uhIakxUFyM0dwzHz7Wt2di6rk2ex47hrJKaKOIpQpsvec8kbgb+rqsggB65AizXQHeigBFQRtici1ypEB1QjBF+Bku9VdqgtgnIPLQunNs3doaRkpWQSpJ7+008WuggQYauA1sKFIScVf3Bw6RdOMoncev86NqhKA/AkPF6tV0kjYCFEt23UsIkKT8LEZFMeXeL9KzEIHCqKDCQ1Tcr09MjKRr4mdc6XryplI1oUbErNmXRKBoiT7MXPF5DB5esBBW29QlJgEGmC++RGfyy8ikoiV2D0LY1PfZMCyW3iqTnmUzt6g1gIgcRvCjquPrpXMqZ15IcvXdbZhAsTjRTOumSbp3j9S4p0oBW+68BAbSXfOku+arXo815Rl+azsLo20YvdK1ZOVNMcxc7aBv3zUAMtNptOVifBn2vqkLweJkM5vvvMT0cDfenZexo94NIyYXX95LMRtn2/3n6lbs3AhGgxMt0b17JDREJQj5gxUwcLh+Mz8hBH22pNeWzAWGd/I+RQNnigGHYqqq11AFsglS/xT0VQjGQXaCteWmXeKkktzxG4d59z8fQ/uh344JDDuf2kmqaz2H3wYaaKCB9w8bipR0xBJcY7buayqQ+PVC6AKasEjKgPmycLCbSXaJi1w0g+zhApYwCDQagSov9hl7ufRUCkE800QxkSlrSyDansebqRMOF5Br6b4F7cgSMQlLYOu95ljdRKx+Sv4IxWC0LIit05RnBUr+GDF7E7ZqxxBUbOiXsKQlyXsXqCYsAUX/Kr1mESF0VapDIui2BKN+9X0WgUSd3LWixFdy9Z3tdO0YDa3kV5xTALF0cc0Fvm3TNE7c5ehfP0Bb3xROvMT8aCv5StVNmA6xIz7GwKyvmUrNcvCrP8E3cO1iN/PP3oOok5mUCrp3j7D13nNMnO+hc+cYap20y+JkmoXxduzY6u679bF0TUYLMtNp7GiJWLqItGo9UZYEsWthyc22RcEDSZvnMh6jnkZicShuISqfvwXChuRvhfauchCswZua72qke9M89E8fZOHqAoEb0Ly5GSuyoX4iGmiggQ2ODfWL05SOYEmBXi3GMwazYGO15fClTcrPsCt3CTtwmVWbidHOoG1xvOTxcfECR8QxLIIwOSAE+Vg/WsbBK5Byw6qJQmKw6hzSSOLZJgJtyCwacgtr7MilYbpv721c3dosRgpF3NqKH+ToSHwaXy8wX3z5Bs379Ir312oCTB70osKOtOHZk1WvKTRxMnUjN3tjCl0ImPBNZdm3z22DS4NVx+Vm05x85k4OPHV0RbREsJR9qLeBX5K5Bq7igd98LvRHkQaBYeJiL2d+egdlz37aBicIfIEjYXtMVgSgbXsmuNL8PMN//QlWki1pBfQfGCbZGvbB6do5WknF1ItS+Z5k9monhz/3CumuhTXnvAStBce+ew/zY61oz0JIjZCG/Z98m46t4wB4RZsLL+1l8mJYwdO+bYIdD50kEl+b9EghsDF0WILZwCaWeBzR/DT4Q+E/mQbnIIhab5PbgZSSlsGW92WsBhpooIFbxYYiJZ7n0d4JUxMGzPKSk0VzyD2NtTDHgpXmsdmXyyW+Gs0ppqyrnJNP8jFnhruDY9iVXWY4Qqx4neFNv0vHzM+gXBlr6fzq0wPhIhGNCNzmIt5CpFoAKQyRvly4Jb8NrN9aXtAWexwhBLZqpiPxNAVviPnia9Q2+DNErJ7ymD5gV8Y1gSH/rId3WYMCK3gIs/0S/j3HKrzIAPOBJiqrq3aEgKiQHElISoGmBMSl4O0rm8nUIVUzV7rwSzZO2Wr9RpmEpZebukOytVK/0bl9jLmRdiYu9HLH515DWZpAh4LclRUplhAM9mQZ65winXJRdoCUhu7dI7T0zVSOW/0Rrbz3WoP2FZsOX0Za+qYyIOPnepkfbVvujqwVRsOpZ+7gY3/vx0ipOfpXD1FYiGPKKabJCz0sjvdy769/b92IjQCaVIKu9NNsTj9crgXeGv5bC8EcoEG2lo1QXCi+Ct4JEIlQ+Gpvv/GFNdBAAw18iNhQpERrTSQq6OkzFAuQcw1vTPugc3widoHE4khNUanCpzM4RTa9G2d+CKtO2sMYQSp/iVgx1CgYIJ65wFzrA8igRNP8W0SLo7hOGwvNRyDaQktcMu/kcWejEAiErYn25rFbSis26Ou6pVVhvS6t2ngEpkBURcrz1QghiVqDxKwxCv4VQmIiAEnKP0LxJYEpuFiDEmfH8liFl/2QkATltyCxLg4i91/AS4RiYQ28WwjoDwwHYvW/IhElWdqbb7rjEmeeO1QlNBUyoKV/ukJIbhYGBSKoIQKWHdB/cIjtD54iEvcIPEVWa5rqBAgMsP9zr9Mcuzn7dCEERhMaowmYHu4k1TF/S3OfOD9QX2grYGGsFe0rStlohZAAGC1xC4rpy3107RhZc2xL2Ozu/EdgDdx4IsEEZP8Egunw5LIJEr8Gub8APUNFQ+WdgdhTEPv4TV9jAw000MAHjQ1FSiKRSLijlRBLwPMTHoP6JI/ELzFfDJjV3fSJ8dr8vfHYOvfXjHIQU25NtxIGQTSex955BP3qiyzG9zLXdj+WO0P/9f+K0D6SgFhhhPTiSYa7fo2x+W6MscNIidQICVZ6yYBMrN/17RZgjMH1p4mXd7Vhtc6SeZagKXo/sWAbefc8Sqawrm7GfT4Wdio04A1p7H6FiIe9cdwzQY0Xhe6exnWqUwgBcM3VbHEMyToC4pXo2nmd7EyKa+9uC+3jtSDdOc/eT7x9axcrmigZTYRF6pE5qTRnnzvM4N3nwMiwL02d6JIAEpGg7hhrnlrC+PkeTv8k7M/z2H/3N7c0dSnXIEAmnPfiRDNBnWaFgRuQnU7RtaPOeyuTS98cITEuLP6vYFbYzetpyPxh+cFKQu5C4QcQuQ9krelcAw000MAvAhuKlEgp6ezsZHJykqwbIL0F9kbO8xf5h2gjzxPy52suQ9J4TFp76PGPsVokKjAktragLcHCpoeZ5E6kbdM19i1k2e01PE4jjGZuRpazNuWzaYkuCYqjcaL9i/hBBiliSBm5KV6yftpGE7G6K6/XLMBCELG6cFQn+LDwfKn68nwovukT+5hVVSm6EkHfONj1PVVmfE1SrZ+OEgK2P3CWzXdeIjuTJpIoEm/OEfgCHYiKh8dqcWvNJZs8EePVvReBJxk7O8D0UBeb7rhEU/dc+f11yAvLvWFuFlpDsjXD1vvOMH5mgOG3dhB4itb+aVoGpm/4Oe58+ASlbJTMdHP1XJSmqXsWNx9BWQGBVyvA9Ur2OuXGMky1LMG44J4EUwB7R1gGvAT3JJh64tmAetVXCAX+MDh71r+4BhpooIEPCRuKlAA0NTXhlCY5NjyE2ZTiW5NPsYUxPi+/j6qTmgmwGZf7yIheAmyG5YMM6pfKr0rAMBx/guhUB0OX2jFGoLUgEoMt+Ss1URUfhxzt1JYCC7z5CLnWn+DpOZrsjxFz+m+6LHjt125OnyKEwBsL6trhuScDrJYA52AE2STQ86uqZ4pO6EwrV0WQAokpRvAB31NEk+tXodhRr0q3IQRMnO+jY9sYqkx6Vl9qJfIjQLBcorv0vBBho7z8XJLrJwaJpguku+bXNSu7HQgByY5FvJLN8NEdDL2xE6MlI8e30tQ9y6HPvr7uOaPpAke+8iInfnCE2WsdCGlAwIGn3kRIaN8yjh31wmhJpaNwWHE1caGbrfeeWaNE2YLI3eF/+lcg86/L7K4cmYncC/EvhxegF6hflbXGvI0BuXYzwwYaaKCBDxsbjpQAcOGveTP1AL6wKBaiPK5erEtISiR42/67+ETRwkEYnxHVyZTcQZsZBiGYUTtBOZTOOyzbyBtMMkeQt1D65spBAYzxMLiAwdH9t2W09V4gbLHm+qNGM+w7+FMuPnoXE99rWV67hMa52o8+dKbG+cQEiov/9ZNcChTS8rnzi69UWaXD+lEeY2JcPb6FiQt97P/kUaw61rv13psJNL6BSDbG/GgHM1e6mLrcjdGSSHwRHUiUtZ5e5NaiJJWmeBpO/uhIlTYk8Czmx1oZOzNA3/6ra59RgFCGg0+/yczVDgJPYUe90EyuKUckWeLIL7/I2ecPMTPciQHaNk3Rvesq+YUkqq6gNgGp3waZDCeX+SMwxepDSi+B+zZEP1EuBVbUhsNsasNkIhxXbbqle1UFnYPSaxCMghqAyD0NktNAAw28J2xIUnIxUFR0G0DTGt4lV+V9uCTCMDVghAXGoEWEXHwzyU6FmYjglmyWFzJDfPs8KuYzJffSff0dlF4mPJKAuJwjZ9qoWvyExmkNSMWfAiMIcgqjgw+VmKgugbDBrNZnWobBfWewhM/mnhMUn36I/CkLPavp6B5hy10XWExYvJv3w0aHngW+IvLcg4iy94j2JVff2caBp96qGno+MDSrWnJhDFx4aSsikaN9cALl3Nhy3zeGN3I+eW14NGXjScG5nx1Er+gmnJ1JIeu4oK487+0iM9Ucil1XQfsWozcgJUsQEtoHpzAGho7uYOjN3Uxe7uXIL71IJFHi0GfewOjlY40JhdZSrp64hMgdYG8LH/rDdT7YMkweCj8E2QeyBfQsy6aAFqhucO6Bwt8QkhYNMgWp37t93VMwAYt/UE4XecBxKP4E0v8EVFtIovyLoOfB2hTOoYEGGmjgBtiQpKRopwlEGNqXjqYYxIlRW8I7oN9gXB1Cr7RXFwLfxDlb9Ljf8fC9MsEpw0q7qJiPUDDZs59oYY6WmSG0lIjAsCh6KZgUKFPuHR+mPWQkINqTD91TBYh4EJYt32SflfcDQgqSn3XIfsfFhC2UQUPr4QWuD9zPNS3DlbAPEv3hpCImRlQWcBA8lrIYmUxx6cX9iIlORFW/F0kxU2sWN1QKOBC3sFZcpzEwc6WD6ye24CQK9D19c4LXEwWfucAw6ITEIJYucOhzr2E5PvOjrVx9ZzulbIwrb21jyz0X1nBDfQ/EpE7vnQrW6X1TdygBmw9fYuTdrWhPhemcpddk9XH1w1sa/EsrHt9IuOuDvkL4J61BNIemas6RsMJGOBC9O0wBiTio/vf2xcz9ZTlqszR3LyQo+W9C4pdh8V+CzpRfN2DvheRXKxuEBhpooIF62JCkZKBjJ6IQ/qA2t2R4e/IAD8jXa36ybQp0BycYte6qet4g6TYtDF2o/V1WCW/5d1NIrm57hLH+I0Tzc2TmO1mc7ysPookMZMGTyKiPla7WAwgFQQmk8/5d981AtUvSX4vgjWiymTN0b27FTXUDsrym6Sq30wUxwLu6Fcd/ER3MMmFlEVPtqwgJgGHnwydrSNaBmMW5gk/7tc009cziFR0unupnEg/2n6MvVhYJ32D908Yw5hkMEBGiQiNb+mZCvUfbIj17rnHiB0do7p1bd7ylVMzNRqmWxkp1LKAcn8CrNZvr3Xfl5gZbha4d15m81Iv2BepW/9p0HrwLoaDVGmTN3FwVylE9U4DmfxYSE+8SFJ8LNSf2bog++t4IiVkiTDUtG8E7B9n/WI7WrIhoeaeh+CLEHr398zbQQAN/67HhSInruuTEJrrkPEV3mofnrrCo2tBGVizil6DwaeEKo6wkJeGqWvWTbALCRVugPYUJqjd0XiSBayUoLKRWvElgxX1UdO20hLQ/vCjJSgglsDeByY9Siu+r9EcWeBhqF1xNhKmgnZw3DbbG2n8W6+QuRFD+eghNS+8MqY7FmuuxpaDXkZx/awe5mSa8A2fwD55gqfuviirETbADAWyPSM6XNNOBZluZOFUs7pVBSJ+Dn3mDsdObyUw10bVzhEiiWDd1FPiy/J5lUjQ/1oxbiNA2MIVQhlI2QjRVrJxDCDj46Td55zv3g4bAX+5tlOpYuOE1rIa0ArY/dJqdj5y8wfdAYpArrOOXLmQh1JFEHwN7J0QfhuLz3KjFQPlqQkKj5yH/LSrpnGAMSq9D0/85TOHcFgSs2epAhdGYGoWSB6WXG6SkgQYaWBcfshTzvcEYw5WRa3i+zx1Wks/PvkyGPpS0kXUa1Bkh0LEEUmqk1CgVEIt5rN7hSQI2By9gmwz+bG1oYynv78+vcOoy1O3wunKuom7TvA8exhhK/iipyOFVC/ZyF+Cq4zG4erlqxj94hvaPvUOqY45oOkffvisc+PSbVSmIlWi1FJvvugCts/gHzoIVhOktZVgwAf5N5FOEEGyNKCSwENQ/XohQfHvh5b1cenU3r/7pJwhcu6bTshCgbA0YdBBe/+JEGiENbZumULZGSkMkWcToarbQ1D3PQ7/1DN17riJWeI9cem33LaeFNJDHvwEhsRDWToSIUT8940LxR5D538NIA4T9bW74p2vC8uH8d6huOhmAycL8/wsW/zV4l2/2cpYhBDh3UNt12got79dMM92akV4DDTSw8fHbv/3bdHZ2sn///ps6fkNFSkqlEp7nYwmB0B7p4igJNUNXcKLu8QJB1z0dJPyr5OYMkVhAskVz+kQfszOJynEahcRwj/cN8DTqqEZLxULLJq73H0Eag3s5ukJXYFBJF2mvvUrNu4KROZesiJKwYUvKkKoNUnwgMMbDVm0oGWel06tBUcnxVzzlffxgFi+YqrxfCNi/fxTrwNhNn7N75yhXrRyFVSZiE74hrw0puX7pM4TLbFRAKZTC1Cx5EPaPWWr8ZzQc/asHuPvvvIhUusYSP4x2GYyB8fP9bL33XFXVjpTUEBoAywnY9cgpku0Zrr27Db9kr9ufZgkrx1oyn0tKWDseUe7eHJy94diV/gcAegRiXwgjD3qS+mkdE/bFqT9ToAj+OchchsRXIXLgJuawAvEvhd2Ig4nl56y+sDzZv1x2j10JBfahWztHAw00sOHxta99jd///d/nq1/96k0dv6FIyXQxh6ksqOG/vuAoFqX6e7NEAjyPxLvfJ5HLAQbT0k4s9hSwREoM0ahGRjphzkEFuTA4rX1aZi7TPDuEMAaXJG9Gfw+DREV9IpsX15znVAHOLgg0MTDguoaFGcGhVkP6Q9CYSOkADqCxcPFZ6di5lMoJsCjSIoY4XzxeFUOJiPqEANYuARYCWvtmmC6ZqiXSAFeLmn3xGwscBXBf0ma4FDDra9osiVxxLmMgM5VmiVS1bppk35NvEfqZrOf1AtFUHlFHyLrW24SA/v1X6b+JipuVKGrDdGAYcTUzgeHh5Fp/Yg6ILjDXbmn8ED6UXgw1I7oEuT8H7yQhcSmnnBK/Flbi1HPLq4IHhW+Cs//Wco0yFlbaBFcgmAyra6xyeXHiN0I/FXQ4V5wwVRR78tYvtYEGGtjQePjhhxkeHr7p4zcUKRG2XdFHGGmRjw0QK1xhzVC2MZjXXgR/Re57dop+8R2uW78HAlrbNdGYhEIUz25BywiOF5YYC0CUd7+OLHJo52lUc4pjp3ooVxfXMQODSxmBrqJJYSLnUgbuaHt/Tb/WQzOjzNO7xquGg/L7AHSnFMMlmPQNEXuQQXVtzUU+YO0vTY+tuFDSNfv2uLU+aYBlshMXsHeNfjtCQNvgFD17rjI30sHBT79ZMWW7Efr2D99un0QgjNBceWcrAweHsRwfZQc1n78QAoThWGF5TrM+pOue19wmISlDT4N/NSQCyd8MS4ZLb4YkQedC0iJj4euV3khrjbUIlIDa6qp1IUQowLUGq5+3t0Dz/w2Kr4XztLeDc2dYAdRAAw38rcHU1BRHjhypPP7617/O17/+9fc05oYiJT3xFK8EF9mnEijtYZWjGvV0EkgJsTgUqkuFBWCbHL3BUQqJQWKxZpAWpdgAo72/BEJie/P0jP41tr8sbjQGEimXkakkIu2FmpI6XEgbKK0hJcnejD7xfUSa6yzSha5D2mxKlUXVFoIdUYsdIg0t/wNm9n+gXv7fGMObOZ/7k/XyUBZJBZsdxRW3WNWKMHWDDbhvDK42xG/QYwfCxnxb7jlPNFWoiGlvBrdc+bICOpC8+Zcfo7gY49q72+jaPkqqa47AU2y+83KVC+3iCj2MRJGua3cvQrOz4k+4cSRjLRhw3w1JhxAgE+C+BWXzPvx5yPxbiH22/HiEMGpRjxRLqCOAfk+QzRD/1Ps7ZgMNNPCRQkdHB0ePHn1fx9xQQlcpBHsz53itNEty6mUcd7qSyKmCUhBPQCQSNjVZBSGgm1N0xsZAllvNSwujIhhp4zptXO//1crPtyF0hz07vJOhS+1YaXfNclMpYK211fmQ73YgonRyDlnT68eng/N1es9koPBjhHM3q7WmxhhGPU10rWuI/x1o/h/ZFN/Gw0mbPVHFnqji4ymbDmftEIUxcLkUMOTqmxLEAjjxEpFkAWV9OFGnyUs9uLmww6+yAgyChbE2rh3bxsJYa9Wxl0oai/APa1/UpqUuGTKhf4h9cI0zSm78p7lUAVNG4QdUCEkFXiiSTf0+NP1fIPJxasmHDdEHG/4hDTTQwEcCGypSYgKPbbOvsyC30OafWNtKatdexKYteFfHkKPjyDpOmL6TxkPWz8EISSCjFGIDxArX8IlwSn2J3FQoHDR+/SoWCIfqixtGclSlcKQwbEp8cIuoMZqCP0zBu4RA0BTZw6TYxF75HAbFFEttaA1dnKZLXKo3ChR+BOn/B8a/ih+MYDAIYD6As0XDA3U0EnktOTH3M6aL/4G74opuW9ywid8ShBBsi1iMuMGaOpbV8EsWdsT70IzpFsebCTyL3r3D7HzkZLliRyCEZnakvXKcEIK7ExYFbYhJgS3C3jZ1EYxB8u/A/CUwK/VJIjQ2s7ZA6RXWrlixwpQIhIpf9yJ1oyDGD8uCVTvEPwsyAoWfEpKeoGyu9tlbuyENNNBAAx8QNhQpYWGYC/ITdAWn1z5GSERvP0Ipgo5BzPlz4AeVkmEtLKYjPfxFx4M0CYt7WXtP6qomxuQ2RtVdaLG8w3QnY6h4Zs1oyWAyTOOM5sOmbALYlDB0f0Ad4o0xzBVewA3GMOV0wExhFm0C4hHJlshxus0JXCKAyzmvg954PwT1RJwG3Fexmv9PlErnmMm9yLxfJBrZw8PRUWz/HVYSMs8YXsoWcc0wYJj0od1SWDdkCxJkO8Q+g8p/k82R+Zu8VogkXDq3j9/U8e8HYk1ZEm0L7Hz4ZE3PnfbNk1WPbSGwbyINhXssrHhp+h9Di3jvHcCByIMQvTc8xmTD4xCE5GRFdCT2FFg94J6C3J8BuTVOpMPUDoQMLvZJiH4c9ByIdKg7aaCBBhr4gPCrv/qr/OxnP2N6epr+/n7+xb/4F/zO7/zOmsdvKFKitWGefmatrUg/oN+8iVqRkzeAiMdBKYwxOJEiVzb/Bi1Tr5LIncMIi8vJPfw0vQ9fKGaACe3SLZ0aIaYWDieCp9EWrN7t+gsOJqchWV/AKQRsSxsGU+AF4KgwrYMxNWPdLIwJMPgIaufqBVO4wXiFkAAExkUCzUqghCTA4FDk9RzsbXsUvJdYE94p4GkikV30RnYtS2WDaVg4SSiKDDHiCgKzHDkacTVbHUVMrp3GqiD1u5D5w9B9dN1rX/qP8mf8IRvS9R+8Quf2sSrfkgpudy7eW5AJwHuX5RJtK9TJiPKfZfI3QX8OgpnQGt4fAgKw94FqAX8Msv+edaMp9j5wT4Zj2ntC7xI9D6oDxC0KWxtooIEGbhF/9md/dkvHbyhSYrQmEFEMigl1gB7/GJLlBU0AJpeFd9+CVBpRyNO2eJHJzqeY6ngcIwTPeHNV0sJ3ghwPC4s4y+Wn2hh8FrE6LXTewl8om6aJcPGIbsoik+KGq6MSqwSWt7GaGmPIuCfJuSfKYyZojt6PY3VWjikF45g67pqasGFeuw2qfO77kxay9E3WSj+FJ11DkavaIf3fh4Zc/mWQcRZFgoChqnO+lPXYElFsiyawKNYfCwnZ/xJan68zl6oUjQATCLQP8ib1JJUOwOXDb4fQCBFGZ+pJXm6fIJlydGTFY7zw3qpOsHeFT8um8B+AtaqpXfFF1hbKKpA9IcH0lnxQlsiLE74v+okwcvKLsB1uoIEGGqiDDUVKsjoJZLHJc9j/T1gUKhtVjUCU9Q9mYgwxERp/pYB49iIlq43J5F0Eqd6q3a0GXvQX2CIcdkZstBB4sSKe5RKVHkZD4YSP1RmAJbGaS2XTtA/rh9xQ8C6ytHAHJsNs4TnaE09jlW3CpYhQr2W9AuxVKabVotdaKHD2rf2y1QPp36s8bFr4OaOFKwQriIVPKF5tj26jjTPUJx1+WL56A9fb1eulVIbcXILiYoyWgWmEqO+bAqADKmXAS/1wgkDWpGBuFh/O2u1D4dllUrIeguvUv39OSDYKPwrHq/nMy0Sx+FzY0Tdy93uacQMNNNDA+4UNVX2TKUaQ+PQHb6JwqyYvyyI/A2gRQaPQCAygjEfcG6d/7hniQe3OPQCmTZZsepFC0yK+4yKkCO3lfUFkVwm728XpKK7r4vp+wxhD0R9Dm2q9gEGTd89VHsfsLcg1BC69q1nJurBAJMId9E2iL3k3alVprgASElrN+Ru8+/bIQeBaXD+9CW3W9j/xXVnjSyIkYETFev4ji5UuqWvBvwbBWj4nutwQ70blxm5Z9NpAAw008NHAhoqUWPlhIEqTuVqlJVkJjxgXY0/j+Bm2uM9WIgMhlZA8MvMKP+74OHrJP8KEWZnNrXZNbxcRmpog12tp/wHC4LNQfLXOK5qYgv3NdwIBDiVmk50cnfyTsvbEoDAcictyBciNIEH1hCWq9h7I/wD0GKjNEH0k1C+sAVtGeSjpcLLgMeWHkapeW7IvphAiICxBXcuiXXBznW+XEXiKsbMDxFKFdT+XsPdNLYyBxYlmWnrnbum8HyqMF07UvxhW6ciOMHKykngWfsiapM65m/De3sRnb3KhVkgvhN8BGX8fLuAjgGAc3NOhYZtz6D00H2yggQY+TGwoUqLzi+z0n6dIEynGKtGRJQRYvGP/JkW/mS3+z1FlQjIldnLRehyPBCLQfGr0GF28wfHEHs5H76Q3l6Cpc/4XcEXrwxgPs7LnSRkSm9boNiL+m1D4PhhNG4Yn2u5hQd0BQtHs9COyfxxqPyqdZDR1RZEiDul/Gjp/Zv4lYbhfh46h7muhjkR1174PQEjizjbukXVKjEUnmMna5yuvp8NOuDfAki7EdxWZqSaunxzgri+9elvpFKMlbi5y4wN/ocjD3P+x/N9lASwKSIBKQvRj4I3UvMuYsIBbl17lihdli6OQN4yWGFj4f1Pp+ht9FGJPl/NdbiiSNYuha6vavDH0J/nvQPElwu+7DB8nfhMia/nCNNBAAx8VbChSkom1skVeRwfVio4MnVxWj7AgBwBJWl8jra+jESyKfs5an62U9BoUE/IgaMVDiz/mrsWzvGX/Nu5sBKe9WFPm+4v8DVYyTtQapFhlEy5Q0mFzJFluSb+iwZz7Bi0RCxJfDJ9I/f2w7NcfBm+o3B9lNWxIfjXchef/kqrGbwRgAsh9u0pHUoPEL8PC/8yEV+RiKaCoDa2WZGdkisSanZQtiH8Rcn/KjdIMRgtmr7dx7d1tzFzpZPNdF0i2L6z72dR7zRg4/dPD7H383XXP99FCWQCLBxTDSpzcFeppmoQQ5daLsNkuMOFChy1Qot7R5S+6KRLe/zJZLb5Q7mPTD4v/W1n07AMKrG2Q+nsfbaM17xIUX2aZfJe/W7k/Bed/alQcNdDARxwbSlOSNwnGen8JSyw7V/rYIAQZ2R8mLYTFotjECfvvkBWdXFEPVHmMQFjuOy4PEOAQY4Ej3h8Tnx3H+OKW29N/0GiO3kfKOYQUCaSI0Ju4i4/1/PdEin9DbXjeCw23lqpnhABrMzh3lQnJasGjhNinwN4ZvidYoyuwX46CGA3eOSi+WrYtL0PnGCqVeDvvMxcYCgaue5oXswVyOrSQfyvn8YMFl+8vuLyZlxTjvwKRw5D4VZYjAWtj9monM1e6AEH3ruuo29D2+K5F26ZJ7OhaJbQbCTe+/qkg4O085OQ2iHwM4r8C9l6QXWDfSUhVVhNCF4rPQ+ZPwtQOpfIxbphOKq5TSv5RQOko1IkuglxRhdRAAw18VLGhIiXNQZZStJNMej9NC28DoPC4pD5BgLO8PRYCjc1F9SSuSNYdSxLgkiCOS5RF9i9+h9GJQ0wNHP6QrmY1lhaZ6j2tEJJkZC+pyD564v30xAegsF7PFB3ufldetz9E+FGvJiW6nN75BCE/VXWOAUQk1Bws/m+gM+W5GrC2Q/K3CbL/hbNFr2ZGPnCh4DOnDXm9fIUTnsvC5Df5eN9BlJ4rn3vtqiAhDXMj7UjLR/sW5jaEqjoQ6EDSf/DKLb93I2Kpm81V3yXqtXGw+cvhC9H7wv8PZmDhOHW/R3qxHEFZDS9M58UeubXJmGJIemX9v8X3F+uQNXN7wuoGGmjgw8OGIiXJmMWcARUsV6MIYFH01Y3XZ0Qvbfo8JZmq6Z5nEETIAKCxuLzjK2SbY+/Jz+K9QSApoanVOwgEURWnK1a2MfPWcbTFDjUiVQMkWPPHOpiC0jvgHATnHnDfoJog2OEuO/ufyhUdK37Y/YtQ/DF5f3bN2UwFBt+sPrvG0wXGFv6Mfk5Rf2e7YvoC7vj8q4yd6yeaLBBvyd6yxbxUhkh8/fP8bYIm7PossYhZdYTKsiUUgZrV90SCtXXt75hZJ9XmnYf8d8PqIdkcOse6x8C/UB66HZK/FkbvjA6/P3o+bCqoukFnQ5GvbL79P8DIXeC+Te13Soci7gYaaOAjjQ1FSixHIgo+0UJ1mkHiUa9zisJls36JabWdkIaEP3TSuGwKXq0IYQMrQralbLdtIMhbqKjPUre/94ugSEo4FCiRxKy69QJI2z0sePOYysIviKs43fE+mp1WxBKxWjLTqofoJ2rbF1uDYVWFXt2wDdCTkPuvYcfa5G+HqRo9SeXinUOh9Xnxx9RPFx0lIkTVqAJIK4E2oDG4dfhQYEpkiscgenO7Vzvmsenw0I0PbABjDK6GrDZIIRlI3V97kJAQ/2XI/SeWuwdbYVQs/gXIXCuT0JWw1/Y08S5A5o+oaDn0JOT/vPoYPQGLfwjpfwjZP10RddNlglQkbD/QBInfAHvLrV+8tT2cY2mJXEtAQOJXGpb6DTSwAbCxSIkdQ+RzWEEGjUSWW9716ne4Lo9Ua0eMxjY5zqjP44hJStE2ZFESjSyQbhnFKIuF2QHS89dQurpktTiSQAhQKY9Id/59mr1mkDdJi0nAMG52Mc4+ltI1URVnS3onpaDIXGkagJZIGzGRDdM1xdGwUVvsMYg8EvY8WV1JI1sh/kTtqYWE1D+EzL8pO6iu1lSUIJiEhf8f1e3tFVg7wwqfNctLA5zIXjoL7zDpG1otwZ0xC7FCXPla1mdeVzMTJSKkpMeNtCQN3DqEEEQk7IgmaGv5+0St5voHRg6Bag1N1IIZsHeE1TcyBcmvweK/YrliKwKqKywR1xlw3wojG/aO8DuS/y5r292vhFfWqyxS9Z2quAhr0DOQ+VeQ/B1wbjG6IUQovI7cV472OBC5I4y+NNDAfwOYKFj8zyfbb3zgRxQbipTMTMxgBzMYJIqAIkkccgwGL1KgmRm5A0GALrdnL4pmEIL49jmi8YWyeZahQBcF0c1C6xaSi2P0Xn2j6jy6pMAIIr259zGNI5mnnxYxCkCPOI+hmQV20hbtoDPWjTYBQgh64gOhKZg/DAt/SKVEN7geLgbpfwTxL0P+m1S6vaqusDJiLagOaPpn4J6A3H+kVsOx2vlzyfb8L9e9JpxDEHuKw+4pThdc9sasqmZ8Brg3afPTxYCgol+Q2DJGTyQGZuYm7l0DtwolYEesBRHdvv6B1gAk/26d5zdB8z8PUyF6PuxabO8NUy6Zf0tIKPywWsfaGvqC3BR0uQz8RkJdH7J/FEY+Ur8Lq8TqN4Q1EP5roIEGNhQ2FCnxS4tMLHbTK2yECRD45Oggziw7gx9RDF6hJFJctD5JSSylOAz5Cy2odIn41kyVA6hWNtl0DxO9hyq/kXo+QrQ3j91cQtxkf5Wbg2FlkkPi0y8v0N/8FXztc2nxHBlvAYFACMlAYgttpb+mOjeuw8e5b0LTP4bInRCMhpoR1XHjKQgBVh+3ZpEfsHaUJBpW78gElkxzML6AWVW+JABLOOxKdnM2O4zB0BU/wP62r6D0CGT/A9U7bBvfSCa8PK6BdkuQUhuqSOwjA3FDj5IbQCZCT5QlmKDcAHDld9INU34iVkefsvbMbs40T4ci7cKPIP7Zm511Aw00sIGxoUhJ2p5lkmauiSMURRO79fdxKJbVDwFJpoiZBUqsVPmHC7BYamS7Kluglc1cchtGQ7SQwCJCrn1t4eZ7QZsYrnpsTBYBXM6cI+stYsr/w2iuZi/SKq/Vpw9BuYJEOKFe5Fag2kC2gV69s711d1XwwzlAmMcvPrtGZEkzmLqbrR3/pPy26+AfCyuEkl8N01N6CmQ7c+4Ur2cXl+p7iAl4OOXcuONwA7UwXhjBWMv47mahM1B4JhSt1u3o7IERhPU+N0rhqFAzUqNXWQselF5rkJIGGvhvBBtqC2pHu9BYjMi72a6fXZJiAmHvm/Cxj6i3uOqldn2rnzf42sbP2njKw8j3uLusQbi8tnCVJFPLzxpwtY2X/ylZbz4kIyunhcGwVshal8uCbwPBVFlguBIidOv8/7d37sFxXXWe/5x7b99utd6yrYdlx44sx2PLLxI7HmA2YIITyA4JSczimaQ2MwbMZM0Wu1Ch2KmlEraoCrv8A7OG3fIyxWZnJ06xmQrenWATiswEAp44dhInxDiJhZXxK44eliW1+nXvPfvHuXq1ulsPy5La+n2quuzuvvf06dNH9/zu7/x+39+4z1MmTqVY3EfmdfNv2R2g8svRa615K5HkSrrbBDj2fdfEICR+DIm/NTEAtY+jIxs5lujDY8Q/Y5a4eSYeUyroPuj7HgRXERcVJODKfwn1b/oo/FtkwL4RVCXmsuIw3iOnoGKPUVfFZeSeaILLUKGq1YIgXHeUlFHiZT08FaeCLlSBLQWNQ7nuJFcFzet383sdFNgxn0h1Fj+aYbDqiinEN2ProCJONyvUseFXAq0IcOjQN+OkfsYqlV+QqoebGG8ohCSfMzLwU2XgfwK5i5QN7uawEJ8Tql66YNVD5V6wlhZozAsDZzES9Xkk4wNt0cMyBnUF7f1vkU2/jTE1PCBtMi76fwg6oDf5Cl7OuK9wLePDyflBcp8LBdBemIkyTVK/Cr0jkzDW/TNQ85+g9nGo+c8Q/1fGK4dr1GCr/h24a0xWTc1fQuzj4G6B+E6I/TH5HbdW8arVgiBcV5TU9k1gx4CAAItCd2w2GSqD8wzkuqy1YvD3lcRX9YXPMZX4UMOq2cpijP7F6HXvagJeB1nMO/ojNKqTxPQACeq4qNeRphK0RwXvE6ebQRaNfB6KZGS7ucv186XChouNc8PEHQj6If2S2TbxLzJ+7DxIH4Ga/2BiCLx3TQaGvcx88fgnYeBHjHfNO0ZzAiDxNPkWrgxRevQKbDIEOPTQRAM5nhqdAv8CWkXGGI5tMZvlroU1OnBWayOnXgo1WOYFWZPunTludGhiH5qaTLz3NsWE7XIONv+oqHGSxD5oHvmwaiD+ibGvueug/69GSdu7YYry3ZPvryAIJU1JGSWRuhvgvSQDqhGfKE6e/WsFtAa/oM9aRoKGMRaG3+8ycKICp8pHWzbRpQnsaJ7KwKP+P1WRrkIkWEy7vm3s92EQpTQWUKl7xhgllrJpjC+HZH0Bo0RPLrDQOwd9+xhT3yQvoUFhlZvFYUxH/8BUkPUvMLJARYxB5Kwyi0iQv/BelCQt6jdYBFzQbXg6UiDONqBmlLHiADe4FnbO4IsxMg10AvwEJC9B9g2ofHjyk9qqA84wqS00e+XV/bE4S02GWPqfjPFsr4DYNqlXIwgLiJLavunpDYa1kH7r3I9HFA8XHztULDH4TpRN/gEagxMo7YEOiOsuPpD9ETcHT5LtK8frjWFN4obxaq6xFhZRK0a1W4fKGWqFRzMnwv871JTdSNypwLWiLI42sK5mE5H086E6ZT5ccD8wcScSfwukKG6QOKY+TiGUBVVfMnEj1hKzrVP2Caj8i3CAhmII8pyqwFEelgpoUiepC1Oix+JB/99gcZmb4w42ELcKbdAJxSk2qcNMmeQvJp8pE/sI43/b8I9w+LNsIArlO6fY1zxYlVC2wwRAl31EDBJBWGCUlKfEVQOUlUVIDsKA1cSRyJdY5L9NNWeJqV7cWIruhtWko1VYfpbl515i9eDP0MoGBdlonFPq9jDDVZHpieEuTo4TQJ0Ys+0zEUvjN9AQN/EYVzKXOZ84Q8pPEGWApeq31Krz5kDlUBH/EGvVqJ9Dp42oVV5jQpl99sgfFO9AkDCiaEWJGgGtso8VP0y5xigpuyPPe5aR8M6+XrQJW/nESGOCHHOrEZsg4PqIxUcrI1zI5NPoLcZQetU1wGowaqSlgIoCVpE4EB9Sf28e9koov6/4FqCz3KirDv7YZPMQGF2S+KfNlpB31qSZR28DO3+gsyAIwmQpKaOkrK4WegeGs1cDFaGWDuqDU9hk0QmId3TRs7iVcyv+kLeq78HOpClPvE82Euc1r57OlMX6S2YJS18ox4p5OBXZMTZGce9IwGQMEguLSndEDr7araXarTXxGgNPhBkwEZOiW/HnoHJ+Cr+Hgo4sVQnl/3oSbpxi1lYkXMCUEajSGaNDoZyRNN+pEP2w2RqYwM2vrBpwPwSppwseU2YpVsWmMDUjm4yRljoK/unJnzcpYib+oZBRErkNgndH0rTnGp2Bmsdg8CeQOVb8WL8D+v4Kyh8wqqeFiG4Cd4NJ21Zx480As90iCIIwg5SUUaJjrsm60GYxrgwuDhskEDqUA49FnafpWrKOVLSKRNdiBnQ92d4IPTX9aDvgSixDbSoKWpH8fTWRG/qoiESwfRu/LInvhroLSlMRqSIbZMgEacCjnG4qeY/3aEMXuJe3sKh264g75ePfdFZA9Tcg6AJs46XI20g1BTMe7PwFCMe3UWbuar12xnoRQhVYPWCepn9tHgAo44Ep/5OpVXWdlBaGbeIbihgk00JnjOy+3zGz7QKQBu+twm9nf3kNPvNq8CB9jMn/aXtGtddeauobeb8HVW22UNz1I4cpy6gGC4IgXENKyiipKi+nqqabdFKjUdTpdqx82xs6YPGlTs6urCV7OYb2bDSajGMiTzw7AKVRlsZtSuBaLtGMi6UUKuESDAYEyscOHG5afRNBEHD67ZdZsfgwMTuB1hBol/cxsvYahzLb3D1aymJxrIG6aBGFVaUmVmC14iZdMnOcXMVTyvLUtylExYPQ919HFT/TjN8Sytn2yJ6C/h9A1SPmvaDHqMZaOdWHwUQCDz4TGjXFvCQ2xhAanzZ81Xi/m/k2hynB1OPks6GR6DCpzBk9CH3fZuS79prU8UibCTjVKYisN1lYQ16SqaKzpv3peOEEQVgwlJRRMphO4EQCamO9XE4vwsNFY4+X01YWWsVwvAjat9BoLsWTpuIvmsYbByhTCZStUQosz2bQNl4DOxshlqjECSJowPd93j3TS2+XQ+uSlGlewTL1Oo36d6SpwI2sIFL1Z2P7oLOQ7WA4bXbqgStGVExFjXAVvvGexHdCpGXybVjVUP2Xpny83wMEYfG0VJGTfPC7YPAQZF4MUzQDU/uk4oFw2yck/WuTLVFUx8IG+wbw/3mC42YRZ62RMC86DqVKFvyzTK3YYZ408eyJkaeZ30DmCKg6KPu4KXg3GW9d0AcDB0a8TfYNUPEn4nURBCEvJWWUOJYFgcapiLO4RpO40gpXXsh7bH/lGvSAjYfmQuUg3RVpLKVpiEF5BEZfhAPbHw4T8SNZBqt6Kb9Si9bwzLsvcwMxBjOVDAwupjLeiWUZz4KjstgMoMr+xdgPT5+AxJMMB78o1xQVm4ymyGiUbQIKh4JLVTz/QhD0QeofIfNbwDbGQ9ntI54NZUFkjdFh899jcoaBhvQvxh6bfQP695vMiGy7SR9O/SNjg1ZzsU28yaQ/dxZw1kLVF02QZv//CLexZj5IdkjnJvAUWlvYEX8GCzxOhmugTqy7jWcs6JpY+l0HoaLsZYbH13/XvFb9DbO9KAiCMIqSMkoqbJv+BASxCDWuoqIqQyq9lFj6PXR4tVda817TPQR2nBgBg9UZsvEMtRFNU1yzKMd7PE6HRIFWmqyToSetOJ+Ehrok8dYsb7TfxfqWw1SWv4/WxvNhld+HiqwaOd/vgsT/ZswWiU5D/3+Dmm9OzX2dft1UAtZ9gA2xD0PZp0bEr4JBSP69ET4bfaebvgjpF0zKbiSnSqzdGGqOnKf4ouUx/u5Zm/iU3scwGTQA6SJtOGbbp2wHpF4w8QqTFuLKZSq1ecKtIuzwnFEGh1pkXuv7a4hugapvQPDPMPB/QE+20u3kSKfb6Do9wJKWiwRZmwu/a6B53VnsyDwxzqZNxlQHjn28uGGRPQVBrsGnQ5XZY5BrzAuCsOApKaPEisR5UydZpGycQShLd5Ny41xo3kt88CwAyfgKtBUBNJ6TYXFUs6xhZBEYLSGvASvfnavSpCyPE34GDVzOQnPMI+1W8Orb9xGN9BNxUqhII1u25WylpI+S965bB5B+GbwOyL5lFuvYRyF6a37vR7YdEn/DiHETQOpF8C6Bu9EYF4knw4DZfIu1B/3/PZT5TpuYgLI7TUxA5R7o/xH4vx/qXM65EcL8pDztDh1fzBjBnB+93WhNeOdNzZ1xn2ObTCLdR3FPxVCVIz3q+VAKcJ423W2hV8k1P7Z32qTIBv2gL4PXbQ713jDH2yuNeq03k0aJIhY7RXObPyzCt3xjh1mTZ0iQb24J6y/FPlg4PiroJr/hmzHF/fzzDAd7Oy2muGShgdFpE8xM1nj9rJqZ+BKCIMwzrsooOXz4MF/+8pfxfZ/Pf/7zfP3rX5+pfuUlEWiyMZ+mGARLeim71E0y2oK2XAYrWvOeE20aW+dl9DVPa1BY6JwF0dfwejpFNpQ8MxVqFXaZjz8YIeNX4QXV3LypefwH6gQF9SEGD2I8BYFZiAf/zqSa5pPRTv6M8QGpngnq9E6bNvIuyjnHD6Wypo9A5g2o/rpRZvU7Cp/rtJoMjOw/FWl7ImxIPw/Zl/NUhLVMXIrdEsYa2BQ3SoYCdEc/L+Rt8CHz0qj2JvKu+OC3T3DMdDB9HJpvw/Ou5I2RIXzz+6ZfgOgHIX7/eIPCXkrBtHT/dE76tm00USofHhuzBJB9G/r/OnwSer7KPmFiWwRBuK6YtqKr7/vs3buXQ4cOcfLkSQ4cOMDJkydnsm/j6Boc4JZYFF2RRlmQrFxMJF24cmmQtfH7IwRB/gJ7tlJYOQGovoZ+D/pG2QOLY2Dbiqb6RTQ0VrLyxjr+8MMrqKjMuXiCEREj3xaNz0jt2yFCN3iQyNP5zvGvDZMN25pKZohvsixSvzZxFMW2brzToW7F1OTLxvcxW6BEvYbKR0bVVZmo3P1UGRqbEsycKTl84x3MvjH+LafFbBVO6t7HNzE+g8+OfVlnQoMkHT4ygGeM9ukUpBQEYV4zbaPk6NGjtLa20tLSguu67Nq1i4MHD85k38aRvjJAEM0Mh1QMVizBzV4sePM50Okw2F5DtrPQvreisayZRdF6bOUANhcTipOXFbYCW2nW1WpcS1HmlHNjcxPrNjSysqUON1rgQhtZFxapG22YuKAqyG8IOGGRvBzs5cz8bXU2zKiYKKbBNx6M8ge5NpUINCT/H2I0XC9kIPWb8S8rBVX/BmJ/ZLYriVB8TvuQeXnsS9lTBY71TJFJQRCuK6a9fXP+/HmWL18+/HzZsmW89NL4i8T+/fvZv38/AJ2dxe7+J8ZBhZV9R+i46TbqLp0g424cDnYFRTIZkEmar+ddieIuTua58dfURRfj2iMejw21Pu19nXSlLxGzB3GURV10CcsqVk6uGJyyTIBp5hikj4OKGPd2+lXIvsL4hdjPvz9e9snwgjzJGiWTwgoXh4kITEpz9AOm0F7y55jBC2XGZwKdRoyS64kC3i4VNRlk8U8bIyLxY4oaxTrnPV0oMFqH2ieCIFxPXPNA1z179rBnzx4AtmzZclVtLW9sZOBcD1nXG/aWaMumu2k52uvF6a3AceDK+SiZzMhX8xMO2csxIrUpIxeiTCxJU3zZGIMEwLVt1tY2ApNRKC2AsiG6zTyGsGrC2jCjL6S28arYi8e34TRB1b81miLeu5htjtyLuYW5+wzjS1S1qdrrLDXaIUEnY40I2xg7/e9M8AVccDeZ/5bdaTQpsm+b1OPkYWZku6XsjrAfpZ6JIkCkeEHH4cMmqNWEylOh+ibyzxEX3M2T654gCCXDtI2S5uZmzp49O/z83LlzNDfnCfycQVw3guPF8DJpdGiY6HDNTXdF8cqTpDqqzQ3UGKeGInOhEjtVQX2LhW3ZLIotIe5MQUb9anGWQcVD5k5RJwFt4k/K/7TIOcuN+xvMfnvfPoyRkQXcMJPm35vnyhmrthndAgP/KxQIU8ZDUvGnEFkBZfdC8u/I76kIDRJnVJqzVQ3RrSYwx+8OXewTGSYKM70Cxi0q0e0QWWkq0KZeYGz68UwU1ruGxfmGcG4GbzJbYfORoUymodTpyeIwPsg4DFCN3jrx6Va1SWlP/l/G/z6hFy/+6ZyXKyB+r6nlMxyT5Zpt0gmNHEEQSo1pGyVbt27lnXfe4cyZMzQ3N/PUU0/x5JNPzmTf8tJ20xrOXbzApe5z+K6Fl9UMZAaJxy2c/hpqa+KARTRqU1MTJ5nMkk571NbFWVJfgZU3B3iWcNebi2lwxZRkn4p4lLMcav6jCSr03zeqru4HCuueWJVQtdfoROgMWLUj2RFlf2TOT/7MiJpZNWbBUBFz9+m05k/NVMqozEZvgcxrYbtLTDBr0A3OatPP7BuAYwwjqwFSz0HmdZOmG/9kePcLxP/YjEk63NaKtIF/yWQHBcnQQ5Q1AZOxjxhFWO8CYTK3eegArAACH8iY7QJnpTF60q+bc/wewA/jemLg1Jmxc1ZA6ojpr9Zh+vQg+L2mLRwjEOf3AAmMcXcjVOyCyCLT1+Q/mM9Q5aCqzFhoZdrSl00Qs9LGy4RlvovuM9+PGOj3zXckCtbicE5Ugu4188SqNv3wz2HUZ6Pmu5BhxPCzgDgmEHQQE8NUa4TOhlO3bbDXGK0b/7T57dxNRqE18XSYHu6H7SkzVlaT6atlQ2Sj8fz554wXzu8y8yZ6i/nd1CSDoss+an7/1D9A9myY3V1r2ohtHZ95A6bPTosxhnXapMQ7N10PedWCIOQwbaPEcRz27dvHnXfeie/77N69m7a2tpnsW0GWNS1lWVOJVihV1vRLvFuVRql1SucU8AY5S6Hyz6feB6Ugsso8CuGuGfs8/i/NI28/VprHMMXufldPro+j256I+MeBaaaW2g3GQCk5cv5Oq/9i8qdaqyEyxd8hF2epKVcwpXOawMmTOi8IwnXFVcWU3HXXXdx1110z1RdBEARBEBYw1yLfUxAEQRAEYcqIUSIIgiAIwrxAjBJBEARBEOYFYpQIgiAIgjAvEKNEEARBEIR5gRglgiAIgiDMC8QoEQRBEARhXiBGiSAIgiAI8wIxSgRBEARBmBeIUSIIgiAIwrxAjBJBEARBEOYFYpQIgiAIgjAvEKNEEARBEIR5gRglgiAIgiDMC8QoEQRBEARhXiBGiSAIgiAI8wIxSgRBEARBuCYcPnyYNWvW0Nrayre//e0JjxejRBAEQRCEGcf3ffbu3cuhQ4c4efIkBw4c4OTJk0XPEaNEEARBEIQZ5+jRo7S2ttLS0oLruuzatYuDBw8WPceZpb4B0NHRwZYtW2asvc7OTpYsWTJj7ZUqMg4GGQeDjIOMwRAyDoa5HIeOjo5Z/by25St48s++NCuflUwmx6zpe/bsYc+ePcPPz58/z/Lly4efL1u2jJdeeqlom7NqlHR1dc1oe1u2bOHYsWMz2mYpIuNgkHEwyDjIGAwh42BYSONw+PDhue7CVSHbN4IgCIIgzDjNzc2cPXt2+Pm5c+dobm4ueo4YJYIgCIIgzDhbt27lnXfe4cyZM2QyGZ566inuvvvuoufM6vbNTDN672ohI+NgkHEwyDjIGAwh42CQcZgbHMdh37593Hnnnfi+z+7du2lrayt6jtJa61nqnyAIgiAIQkFk+0YQBEEQhHmBGCWCIAiCIMwLStYomap07fXKypUr2bBhA5s3b55RDZj5zu7du6mvr2f9+vXDr/X09LBjxw5Wr17Njh07uHz58hz28NqTbwwee+wxmpub2bx5M5s3b+anP/3pHPZwdjh79izbt29n3bp1tLW18b3vfQ9YePOh0DgspDmRSqW49dZb2bRpE21tbTz66KMAnDlzhm3bttHa2spnP/tZMpnMHPdUKIguQTzP0y0tLbq9vV2n02m9ceNG/eabb851t+aEFStW6M7Ozrnuxqzzwgsv6OPHj+u2trbh1x555BH9+OOPa621fvzxx/XXvva1uererJBvDB599FH9ne98Zw57NftcuHBBHz9+XGutdV9fn169erV+8803F9x8KDQOC2lOBEGg+/v7tdZaZzIZfeutt+ojR47oz3zmM/rAgQNaa62/+MUv6h/84Adz2U2hCCXpKZmOdK1wfXHbbbdRV1c35rWDBw/y0EMPAfDQQw/xk5/8ZA56NnvkG4OFSFNTEzfffDMAlZWVrF27lvPnzy+4+VBoHBYSSikqKioAyGazZLNZlFI8//zz7Ny5E1gYc6GUKUmjJJ907UL74xtCKcUdd9zBLbfcwv79++e6O3PKpUuXaGpqAqCxsZFLly7NcY/mhn379rFx40Z279593W9Z5NLR0cGrr77Ktm3bFvR8GD0OsLDmhO/7bN68mfr6enbs2MGqVauoqanBcYwCxkJeL0qBkjRKhBFefPFFXnnlFQ4dOsT3v/99fvnLX851l+YFSimUUnPdjVnn4Ycfpr29nddee42mpia++tWvznWXZo2BgQHuv/9+vvvd71JVVTXmvYU0H3LHYaHNCdu2ee211zh37hxHjx7l1KlTc90lYQqUpFEyHena65Wh711fX8+9997L0aNH57hHc0dDQwMXL14E4OLFi9TX189xj2afhoYGbNvGsiy+8IUvLJj5kM1muf/++3nggQe47777gIU5HwqNw0KcEzU1NWzfvp0jR47Q29uL53nAwl4vSoGSNEqmI117PZJIJOjv7x/+/3PPPTcmE2Ohcffdd/PEE08A8MQTT3DPPffMcY9mn6FFGOCZZ55ZEPNBa83nPvc51q5dy1e+8pXh1xfafCg0DgtpTnR2dtLb2wuYCrY///nPWbt2Ldu3b+fpp58GFsZcKGnmOtJ2ujz77LN69erVuqWlRX/rW9+a6+7MCe3t7Xrjxo1648aNet26dQtqHHbt2qUbGxu14zi6ublZ//CHP9RdXV36Yx/7mG5tbdW333677u7unutuXlPyjcGDDz6o169frzds2KA/9alP6QsXLsx1N685v/rVrzSgN2zYoDdt2qQ3bdqkn3322QU3HwqNw0KaEydOnNCbN2/WGzZs0G1tbfqb3/ym1tpcK7du3apXrVqld+7cqVOp1Bz3VCiEyMwLgiAIgjAvKMntG0EQBEEQrj/EKBEEQRAEYV4gRokgCIIgCPMCMUoEQRAEQZgXiFEiCIIgCMK8QIwSQRAEQRDmBWKUCIIgCIIwL/j/8551mJWRnsgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x216 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "network = Autoencoder(data_zoom, pruned=True)\n",
    "network.build_encoder()\n",
    "network.build_decoder()\n",
    "network.build_autoencoder()\n",
    "network.fit_data(epochs=20)\n",
    "network.plot_latent_dimension(\"Compressed AE without classifier\")\n",
    "network.plot_reco()\n",
    "network.plot_score(use_latent_classifier=False,model_name=\"Compressed AE without classifier\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeating the same procedure for the autoencoder with classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "encoder_input (InputLayer)      [(None, 196)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_12 (Dense)                (None, 32)           6304        encoder_input[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_13 (Dense)                (None, 16)           528         dense_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "encoder_output (Dense)          (None, 2)            34          dense_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_14 (Dense)                (None, 16)           48          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_16 (Dense)                (None, 16)           48          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_15 (Dense)                (None, 32)           544         dense_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_17 (Dense)                (None, 32)           544         dense_16[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "ecoder_output (Dense)           (None, 196)          6468        dense_15[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "classifier_output (Dense)       (None, 10)           330         dense_17[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 14,848\n",
      "Trainable params: 14,848\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "WARNING:tensorflow:`epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n",
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
      "Epoch 1/20\n",
      "  2/235 [..............................] - ETA: 1:01 - loss: 0.3597 - ecoder_output_loss: 0.1298 - classifier_output_loss: 2.2990 - ecoder_output_accuracy: 0.0137 - classifier_output_accuracy: 0.0723WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0030s vs `on_train_batch_end` time: 0.5282s). Check your callbacks.\n",
      "207/235 [=========================>....] - ETA: 0s - loss: 0.2706 - ecoder_output_loss: 0.0905 - classifier_output_loss: 1.8003 - ecoder_output_accuracy: 0.0074 - classifier_output_accuracy: 0.2697\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.18926, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.18926, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 5ms/step - loss: 0.2618 - ecoder_output_loss: 0.0880 - classifier_output_loss: 1.7378 - ecoder_output_accuracy: 0.0078 - classifier_output_accuracy: 0.2973 - val_loss: 0.1893 - val_ecoder_output_loss: 0.0674 - val_classifier_output_loss: 1.2185 - val_ecoder_output_accuracy: 0.0128 - val_classifier_output_accuracy: 0.5218\n",
      "Epoch 2/20\n",
      "203/235 [========================>.....] - ETA: 0s - loss: 0.1658 - ecoder_output_loss: 0.0638 - classifier_output_loss: 1.0204 - ecoder_output_accuracy: 0.0124 - classifier_output_accuracy: 0.6102\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.18926 to 0.14353, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.18926 to 0.14353, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1628 - ecoder_output_loss: 0.0629 - classifier_output_loss: 0.9994 - ecoder_output_accuracy: 0.0121 - classifier_output_accuracy: 0.6165 - val_loss: 0.1435 - val_ecoder_output_loss: 0.0563 - val_classifier_output_loss: 0.8720 - val_ecoder_output_accuracy: 0.0120 - val_classifier_output_accuracy: 0.6622\n",
      "Epoch 3/20\n",
      "225/235 [===========================>..] - ETA: 0s - loss: 0.1321 - ecoder_output_loss: 0.0520 - classifier_output_loss: 0.8007 - ecoder_output_accuracy: 0.0111 - classifier_output_accuracy: 0.6786\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.14353 to 0.12460, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.14353 to 0.12460, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1318 - ecoder_output_loss: 0.0519 - classifier_output_loss: 0.7988 - ecoder_output_accuracy: 0.0112 - classifier_output_accuracy: 0.6790 - val_loss: 0.1246 - val_ecoder_output_loss: 0.0477 - val_classifier_output_loss: 0.7695 - val_ecoder_output_accuracy: 0.0132 - val_classifier_output_accuracy: 0.6891\n",
      "Epoch 4/20\n",
      "222/235 [===========================>..] - ETA: 0s - loss: 0.1203 - ecoder_output_loss: 0.0471 - classifier_output_loss: 0.7328 - ecoder_output_accuracy: 0.0138 - classifier_output_accuracy: 0.7023\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.12460 to 0.11891, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.12460 to 0.11891, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1203 - ecoder_output_loss: 0.0470 - classifier_output_loss: 0.7328 - ecoder_output_accuracy: 0.0139 - classifier_output_accuracy: 0.7022 - val_loss: 0.1189 - val_ecoder_output_loss: 0.0452 - val_classifier_output_loss: 0.7375 - val_ecoder_output_accuracy: 0.0160 - val_classifier_output_accuracy: 0.6945\n",
      "Epoch 5/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.1154 - ecoder_output_loss: 0.0452 - classifier_output_loss: 0.7015 - ecoder_output_accuracy: 0.0182 - classifier_output_accuracy: 0.7172\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.11891 to 0.11552, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.11891 to 0.11552, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1154 - ecoder_output_loss: 0.0452 - classifier_output_loss: 0.7016 - ecoder_output_accuracy: 0.0182 - classifier_output_accuracy: 0.7173 - val_loss: 0.1155 - val_ecoder_output_loss: 0.0439 - val_classifier_output_loss: 0.7166 - val_ecoder_output_accuracy: 0.0185 - val_classifier_output_accuracy: 0.7117\n",
      "Epoch 6/20\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.1125 - ecoder_output_loss: 0.0444 - classifier_output_loss: 0.6812 - ecoder_output_accuracy: 0.0194 - classifier_output_accuracy: 0.7305\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.11552 to 0.11279, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.11552 to 0.11279, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1124 - ecoder_output_loss: 0.0444 - classifier_output_loss: 0.6801 - ecoder_output_accuracy: 0.0193 - classifier_output_accuracy: 0.7309 - val_loss: 0.1128 - val_ecoder_output_loss: 0.0433 - val_classifier_output_loss: 0.6946 - val_ecoder_output_accuracy: 0.0190 - val_classifier_output_accuracy: 0.7302\n",
      "Epoch 7/20\n",
      "212/235 [==========================>...] - ETA: 0s - loss: 0.1103 - ecoder_output_loss: 0.0438 - classifier_output_loss: 0.6652 - ecoder_output_accuracy: 0.0188 - classifier_output_accuracy: 0.7383\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.11279 to 0.11273, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.11279 to 0.11273, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1105 - ecoder_output_loss: 0.0438 - classifier_output_loss: 0.6667 - ecoder_output_accuracy: 0.0190 - classifier_output_accuracy: 0.7383 - val_loss: 0.1127 - val_ecoder_output_loss: 0.0433 - val_classifier_output_loss: 0.6943 - val_ecoder_output_accuracy: 0.0187 - val_classifier_output_accuracy: 0.7326\n",
      "Epoch 8/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.1085 - ecoder_output_loss: 0.0433 - classifier_output_loss: 0.6525 - ecoder_output_accuracy: 0.0193 - classifier_output_accuracy: 0.7462\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.11273 to 0.11068, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.11273 to 0.11068, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1085 - ecoder_output_loss: 0.0433 - classifier_output_loss: 0.6523 - ecoder_output_accuracy: 0.0193 - classifier_output_accuracy: 0.7461 - val_loss: 0.1107 - val_ecoder_output_loss: 0.0431 - val_classifier_output_loss: 0.6758 - val_ecoder_output_accuracy: 0.0208 - val_classifier_output_accuracy: 0.7331\n",
      "Epoch 9/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.1072 - ecoder_output_loss: 0.0429 - classifier_output_loss: 0.6430 - ecoder_output_accuracy: 0.0194 - classifier_output_accuracy: 0.7495\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.11068 to 0.10908, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.11068 to 0.10908, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1072 - ecoder_output_loss: 0.0429 - classifier_output_loss: 0.6426 - ecoder_output_accuracy: 0.0193 - classifier_output_accuracy: 0.7497 - val_loss: 0.1091 - val_ecoder_output_loss: 0.0423 - val_classifier_output_loss: 0.6678 - val_ecoder_output_accuracy: 0.0193 - val_classifier_output_accuracy: 0.7430\n",
      "Epoch 10/20\n",
      "226/235 [===========================>..] - ETA: 0s - loss: 0.1058 - ecoder_output_loss: 0.0424 - classifier_output_loss: 0.6336 - ecoder_output_accuracy: 0.0188 - classifier_output_accuracy: 0.7531\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.10908 to 0.10756, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.10908 to 0.10756, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_epoch10.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1059 - ecoder_output_loss: 0.0424 - classifier_output_loss: 0.6348 - ecoder_output_accuracy: 0.0187 - classifier_output_accuracy: 0.7529 - val_loss: 0.1076 - val_ecoder_output_loss: 0.0417 - val_classifier_output_loss: 0.6589 - val_ecoder_output_accuracy: 0.0196 - val_classifier_output_accuracy: 0.7376\n",
      "Epoch 11/20\n",
      "226/235 [===========================>..] - ETA: 0s - loss: 0.1051 - ecoder_output_loss: 0.0422 - classifier_output_loss: 0.6293 - ecoder_output_accuracy: 0.0197 - classifier_output_accuracy: 0.7556\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.10756 to 0.10689, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.10756 to 0.10689, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1049 - ecoder_output_loss: 0.0422 - classifier_output_loss: 0.6279 - ecoder_output_accuracy: 0.0197 - classifier_output_accuracy: 0.7559 - val_loss: 0.1069 - val_ecoder_output_loss: 0.0414 - val_classifier_output_loss: 0.6546 - val_ecoder_output_accuracy: 0.0205 - val_classifier_output_accuracy: 0.7437\n",
      "Epoch 12/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.1037 - ecoder_output_loss: 0.0417 - classifier_output_loss: 0.6198 - ecoder_output_accuracy: 0.0194 - classifier_output_accuracy: 0.7583\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.10689 to 0.10542, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.10689 to 0.10542, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1037 - ecoder_output_loss: 0.0417 - classifier_output_loss: 0.6194 - ecoder_output_accuracy: 0.0194 - classifier_output_accuracy: 0.7587 - val_loss: 0.1054 - val_ecoder_output_loss: 0.0409 - val_classifier_output_loss: 0.6456 - val_ecoder_output_accuracy: 0.0179 - val_classifier_output_accuracy: 0.7553\n",
      "Epoch 13/20\n",
      "219/235 [==========================>...] - ETA: 0s - loss: 0.1025 - ecoder_output_loss: 0.0413 - classifier_output_loss: 0.6116 - ecoder_output_accuracy: 0.0190 - classifier_output_accuracy: 0.7619\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.10542 to 0.10522, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.10542 to 0.10522, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1025 - ecoder_output_loss: 0.0413 - classifier_output_loss: 0.6115 - ecoder_output_accuracy: 0.0187 - classifier_output_accuracy: 0.7622 - val_loss: 0.1052 - val_ecoder_output_loss: 0.0409 - val_classifier_output_loss: 0.6432 - val_ecoder_output_accuracy: 0.0173 - val_classifier_output_accuracy: 0.7594\n",
      "Epoch 14/20\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.1017 - ecoder_output_loss: 0.0412 - classifier_output_loss: 0.6052 - ecoder_output_accuracy: 0.0196 - classifier_output_accuracy: 0.7659\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.10522 to 0.10458, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.10522 to 0.10458, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1018 - ecoder_output_loss: 0.0411 - classifier_output_loss: 0.6071 - ecoder_output_accuracy: 0.0197 - classifier_output_accuracy: 0.7649 - val_loss: 0.1046 - val_ecoder_output_loss: 0.0405 - val_classifier_output_loss: 0.6409 - val_ecoder_output_accuracy: 0.0189 - val_classifier_output_accuracy: 0.7566\n",
      "Epoch 15/20\n",
      "226/235 [===========================>..] - ETA: 0s - loss: 0.1010 - ecoder_output_loss: 0.0408 - classifier_output_loss: 0.6023 - ecoder_output_accuracy: 0.0189 - classifier_output_accuracy: 0.7653\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.10458 to 0.10449, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.10458 to 0.10449, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1009 - ecoder_output_loss: 0.0407 - classifier_output_loss: 0.6019 - ecoder_output_accuracy: 0.0188 - classifier_output_accuracy: 0.7657 - val_loss: 0.1045 - val_ecoder_output_loss: 0.0409 - val_classifier_output_loss: 0.6354 - val_ecoder_output_accuracy: 0.0173 - val_classifier_output_accuracy: 0.7583\n",
      "Epoch 16/20\n",
      "227/235 [===========================>..] - ETA: 0s - loss: 0.1005 - ecoder_output_loss: 0.0407 - classifier_output_loss: 0.5979 - ecoder_output_accuracy: 0.0195 - classifier_output_accuracy: 0.7677\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.10449 to 0.10316, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.10449 to 0.10316, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.1005 - ecoder_output_loss: 0.0407 - classifier_output_loss: 0.5979 - ecoder_output_accuracy: 0.0195 - classifier_output_accuracy: 0.7678 - val_loss: 0.1032 - val_ecoder_output_loss: 0.0404 - val_classifier_output_loss: 0.6273 - val_ecoder_output_accuracy: 0.0196 - val_classifier_output_accuracy: 0.7604\n",
      "Epoch 17/20\n",
      "225/235 [===========================>..] - ETA: 0s - loss: 0.0999 - ecoder_output_loss: 0.0403 - classifier_output_loss: 0.5955 - ecoder_output_accuracy: 0.0191 - classifier_output_accuracy: 0.7699\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.10316 to 0.10249, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.10316 to 0.10249, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0997 - ecoder_output_loss: 0.0403 - classifier_output_loss: 0.5939 - ecoder_output_accuracy: 0.0188 - classifier_output_accuracy: 0.7704 - val_loss: 0.1025 - val_ecoder_output_loss: 0.0399 - val_classifier_output_loss: 0.6256 - val_ecoder_output_accuracy: 0.0149 - val_classifier_output_accuracy: 0.7646\n",
      "Epoch 18/20\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.0989 - ecoder_output_loss: 0.0402 - classifier_output_loss: 0.5876 - ecoder_output_accuracy: 0.0186 - classifier_output_accuracy: 0.7730\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.10249 to 0.10237, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.10249 to 0.10237, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0989 - ecoder_output_loss: 0.0401 - classifier_output_loss: 0.5881 - ecoder_output_accuracy: 0.0185 - classifier_output_accuracy: 0.7727 - val_loss: 0.1024 - val_ecoder_output_loss: 0.0399 - val_classifier_output_loss: 0.6249 - val_ecoder_output_accuracy: 0.0153 - val_classifier_output_accuracy: 0.7650\n",
      "Epoch 19/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.0986 - ecoder_output_loss: 0.0400 - classifier_output_loss: 0.5857 - ecoder_output_accuracy: 0.0185 - classifier_output_accuracy: 0.7743\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.10237\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 0.10237\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.0987 - ecoder_output_loss: 0.0400 - classifier_output_loss: 0.5866 - ecoder_output_accuracy: 0.0185 - classifier_output_accuracy: 0.7740 - val_loss: 0.1024 - val_ecoder_output_loss: 0.0400 - val_classifier_output_loss: 0.6246 - val_ecoder_output_accuracy: 0.0185 - val_classifier_output_accuracy: 0.7665\n",
      "Epoch 20/20\n",
      "226/235 [===========================>..] - ETA: 0s - loss: 0.0980 - ecoder_output_loss: 0.0399 - classifier_output_loss: 0.5819 - ecoder_output_accuracy: 0.0186 - classifier_output_accuracy: 0.7748\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.10237 to 0.10173, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.10237 to 0.10173, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_epoch20.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 2ms/step - loss: 0.0980 - ecoder_output_loss: 0.0399 - classifier_output_loss: 0.5816 - ecoder_output_accuracy: 0.0186 - classifier_output_accuracy: 0.7752 - val_loss: 0.1017 - val_ecoder_output_loss: 0.0394 - val_classifier_output_loss: 0.6232 - val_ecoder_output_accuracy: 0.0175 - val_classifier_output_accuracy: 0.7646\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "network_with_classifier = Autoencoder(data_zoom, pruned=False)\n",
    "network_with_classifier.build_encoder()\n",
    "network_with_classifier.build_decoder()\n",
    "network_with_classifier.build_classifier()\n",
    "network_with_classifier.build_autoencoder(use_latent_classifier=True)\n",
    "network_with_classifier.fit_data( batch_size=256,epochs=20, use_latent_classifier=True)\n",
    "network_with_classifier.plot_latent_dimension(\"Autoencoder with classifier\")\n",
    "network_with_classifier.plot_score(use_latent_classifier=True,model_name=\"Autoencoder with classifier\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\loren\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow_model_optimization\\python\\core\\sparsity\\keras\\pruning_wrapper.py:212: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.add_weight` method instead.\n",
      "Model: \"functional_3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "encoder_input (InputLayer)      [(None, 64)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_10 (Dense)                (None, 32)           2080        encoder_input[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "prune_low_magnitude_dense_11 (P (None, 16)           1042        dense_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "encoder_output (Dense)          (None, 2)            34          prune_low_magnitude_dense_11[0][0\n",
      "__________________________________________________________________________________________________\n",
      "prune_low_magnitude_dense_12 (P (None, 16)           82          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "prune_low_magnitude_dense_14 (P (None, 16)           82          encoder_output[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_13 (Dense)                (None, 32)           544         prune_low_magnitude_dense_12[0][0\n",
      "__________________________________________________________________________________________________\n",
      "dense_15 (Dense)                (None, 32)           544         prune_low_magnitude_dense_14[0][0\n",
      "__________________________________________________________________________________________________\n",
      "ecoder_output (Dense)           (None, 64)           2112        dense_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "classifier_output (Dense)       (None, 10)           330         dense_15[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 6,850\n",
      "Trainable params: 6,268\n",
      "Non-trainable params: 582\n",
      "__________________________________________________________________________________________________\n",
      "WARNING:tensorflow:`epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n",
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
      "Epoch 1/20\n",
      "  2/235 [..............................] - ETA: 1:40 - loss: 0.3643 - ecoder_output_loss: 0.1345 - classifier_output_loss: 2.2987 - ecoder_output_accuracy: 0.0137 - classifier_output_accuracy: 0.1133WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0020s vs `on_train_batch_end` time: 0.8613s). Check your callbacks.\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.3018 - ecoder_output_loss: 0.1052 - classifier_output_loss: 1.9656 - ecoder_output_accuracy: 0.0181 - classifier_output_accuracy: 0.2021\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.24567, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.24567, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00001: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 1s 6ms/step - loss: 0.3003 - ecoder_output_loss: 0.1047 - classifier_output_loss: 1.9562 - ecoder_output_accuracy: 0.0182 - classifier_output_accuracy: 0.2042 - val_loss: 0.2457 - val_ecoder_output_loss: 0.0850 - val_classifier_output_loss: 1.6072 - val_ecoder_output_accuracy: 0.0186 - val_classifier_output_accuracy: 0.3619\n",
      "Epoch 2/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.2120 - ecoder_output_loss: 0.0784 - classifier_output_loss: 1.3355 - ecoder_output_accuracy: 0.0271 - classifier_output_accuracy: 0.4907\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.24567 to 0.17905, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.24567 to 0.17905, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00002: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.2115 - ecoder_output_loss: 0.0783 - classifier_output_loss: 1.3320 - ecoder_output_accuracy: 0.0271 - classifier_output_accuracy: 0.4926 - val_loss: 0.1791 - val_ecoder_output_loss: 0.0701 - val_classifier_output_loss: 1.0897 - val_ecoder_output_accuracy: 0.0277 - val_classifier_output_accuracy: 0.5894\n",
      "Epoch 3/20\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.1564 - ecoder_output_loss: 0.0624 - classifier_output_loss: 0.9399 - ecoder_output_accuracy: 0.0378 - classifier_output_accuracy: 0.6407\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.17905 to 0.14300, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.17905 to 0.14300, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00003: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1557 - ecoder_output_loss: 0.0621 - classifier_output_loss: 0.9361 - ecoder_output_accuracy: 0.0384 - classifier_output_accuracy: 0.6421 - val_loss: 0.1430 - val_ecoder_output_loss: 0.0547 - val_classifier_output_loss: 0.8831 - val_ecoder_output_accuracy: 0.0459 - val_classifier_output_accuracy: 0.6597\n",
      "Epoch 4/20\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.1326 - ecoder_output_loss: 0.0509 - classifier_output_loss: 0.8162 - ecoder_output_accuracy: 0.0469 - classifier_output_accuracy: 0.6811\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.14300 to 0.13012, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.14300 to 0.13012, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00004: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1325 - ecoder_output_loss: 0.0509 - classifier_output_loss: 0.8157 - ecoder_output_accuracy: 0.0469 - classifier_output_accuracy: 0.6814 - val_loss: 0.1301 - val_ecoder_output_loss: 0.0478 - val_classifier_output_loss: 0.8229 - val_ecoder_output_accuracy: 0.0455 - val_classifier_output_accuracy: 0.6854\n",
      "Epoch 5/20\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.1237 - ecoder_output_loss: 0.0468 - classifier_output_loss: 0.7698 - ecoder_output_accuracy: 0.0480 - classifier_output_accuracy: 0.6992\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.13012 to 0.12385, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.13012 to 0.12385, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00005: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1237 - ecoder_output_loss: 0.0467 - classifier_output_loss: 0.7695 - ecoder_output_accuracy: 0.0480 - classifier_output_accuracy: 0.6993 - val_loss: 0.1239 - val_ecoder_output_loss: 0.0452 - val_classifier_output_loss: 0.7869 - val_ecoder_output_accuracy: 0.0482 - val_classifier_output_accuracy: 0.6997\n",
      "Epoch 6/20\n",
      "200/235 [========================>.....] - ETA: 0s - loss: 0.1196 - ecoder_output_loss: 0.0452 - classifier_output_loss: 0.7444 - ecoder_output_accuracy: 0.0480 - classifier_output_accuracy: 0.7081\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.12385 to 0.12151, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.12385 to 0.12151, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00006: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1196 - ecoder_output_loss: 0.0451 - classifier_output_loss: 0.7452 - ecoder_output_accuracy: 0.0484 - classifier_output_accuracy: 0.7078 - val_loss: 0.1215 - val_ecoder_output_loss: 0.0449 - val_classifier_output_loss: 0.7662 - val_ecoder_output_accuracy: 0.0513 - val_classifier_output_accuracy: 0.7059\n",
      "Epoch 7/20\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.1168 - ecoder_output_loss: 0.0440 - classifier_output_loss: 0.7279 - ecoder_output_accuracy: 0.0458 - classifier_output_accuracy: 0.7149\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.12151 to 0.11907, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.12151 to 0.11907, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00007: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1168 - ecoder_output_loss: 0.0440 - classifier_output_loss: 0.7280 - ecoder_output_accuracy: 0.0459 - classifier_output_accuracy: 0.7150 - val_loss: 0.1191 - val_ecoder_output_loss: 0.0439 - val_classifier_output_loss: 0.7518 - val_ecoder_output_accuracy: 0.0503 - val_classifier_output_accuracy: 0.7129\n",
      "Epoch 8/20\n",
      "210/235 [=========================>....] - ETA: 0s - loss: 0.1150 - ecoder_output_loss: 0.0435 - classifier_output_loss: 0.7149 - ecoder_output_accuracy: 0.0443 - classifier_output_accuracy: 0.7201\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.11907 to 0.11737, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.11907 to 0.11737, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00008: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1151 - ecoder_output_loss: 0.0435 - classifier_output_loss: 0.7159 - ecoder_output_accuracy: 0.0443 - classifier_output_accuracy: 0.7195 - val_loss: 0.1174 - val_ecoder_output_loss: 0.0432 - val_classifier_output_loss: 0.7417 - val_ecoder_output_accuracy: 0.0431 - val_classifier_output_accuracy: 0.7158\n",
      "Epoch 9/20\n",
      "223/235 [===========================>..] - ETA: 0s - loss: 0.1378 - ecoder_output_loss: 0.0554 - classifier_output_loss: 0.8250 - ecoder_output_accuracy: 0.0401 - classifier_output_accuracy: 0.6881\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.11737\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.11737\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00009: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1378 - ecoder_output_loss: 0.0553 - classifier_output_loss: 0.8250 - ecoder_output_accuracy: 0.0402 - classifier_output_accuracy: 0.6884 - val_loss: 0.1356 - val_ecoder_output_loss: 0.0534 - val_classifier_output_loss: 0.8212 - val_ecoder_output_accuracy: 0.0400 - val_classifier_output_accuracy: 0.6976\n",
      "Epoch 10/20\n",
      "224/235 [===========================>..] - ETA: 0s - loss: 0.1241 - ecoder_output_loss: 0.0487 - classifier_output_loss: 0.7534 - ecoder_output_accuracy: 0.0446 - classifier_output_accuracy: 0.7125\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.11737\n",
      "\n",
      "Epoch 00010: val_loss did not improve from 0.11737\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00010: saving model to model/AE_model/model_2\\KERAS_check_model_epoch10.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1237 - ecoder_output_loss: 0.0486 - classifier_output_loss: 0.7516 - ecoder_output_accuracy: 0.0446 - classifier_output_accuracy: 0.7125 - val_loss: 0.1218 - val_ecoder_output_loss: 0.0455 - val_classifier_output_loss: 0.7635 - val_ecoder_output_accuracy: 0.0392 - val_classifier_output_accuracy: 0.7114\n",
      "Epoch 11/20\n",
      "230/235 [============================>.] - ETA: 0s - loss: 0.1170 - ecoder_output_loss: 0.0448 - classifier_output_loss: 0.7222 - ecoder_output_accuracy: 0.0436 - classifier_output_accuracy: 0.7210\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.11737\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 0.11737\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00011: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1170 - ecoder_output_loss: 0.0448 - classifier_output_loss: 0.7217 - ecoder_output_accuracy: 0.0436 - classifier_output_accuracy: 0.7210 - val_loss: 0.1191 - val_ecoder_output_loss: 0.0446 - val_classifier_output_loss: 0.7448 - val_ecoder_output_accuracy: 0.0409 - val_classifier_output_accuracy: 0.7142\n",
      "Epoch 12/20\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.1145 - ecoder_output_loss: 0.0438 - classifier_output_loss: 0.7067 - ecoder_output_accuracy: 0.0442 - classifier_output_accuracy: 0.7240\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.11737 to 0.11671, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.11737 to 0.11671, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00012: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1144 - ecoder_output_loss: 0.0438 - classifier_output_loss: 0.7063 - ecoder_output_accuracy: 0.0441 - classifier_output_accuracy: 0.7243 - val_loss: 0.1167 - val_ecoder_output_loss: 0.0433 - val_classifier_output_loss: 0.7340 - val_ecoder_output_accuracy: 0.0444 - val_classifier_output_accuracy: 0.7182\n",
      "Epoch 13/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.1128 - ecoder_output_loss: 0.0433 - classifier_output_loss: 0.6950 - ecoder_output_accuracy: 0.0450 - classifier_output_accuracy: 0.7274\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.11671 to 0.11548, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.11671 to 0.11548, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00013: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1129 - ecoder_output_loss: 0.0434 - classifier_output_loss: 0.6958 - ecoder_output_accuracy: 0.0450 - classifier_output_accuracy: 0.7273 - val_loss: 0.1155 - val_ecoder_output_loss: 0.0430 - val_classifier_output_loss: 0.7250 - val_ecoder_output_accuracy: 0.0453 - val_classifier_output_accuracy: 0.7228\n",
      "Epoch 14/20\n",
      "216/235 [==========================>...] - ETA: 0s - loss: 0.1121 - ecoder_output_loss: 0.0430 - classifier_output_loss: 0.6909 - ecoder_output_accuracy: 0.0461 - classifier_output_accuracy: 0.7317\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.11548 to 0.11465, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.11548 to 0.11465, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00014: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1120 - ecoder_output_loss: 0.0430 - classifier_output_loss: 0.6899 - ecoder_output_accuracy: 0.0457 - classifier_output_accuracy: 0.7315 - val_loss: 0.1147 - val_ecoder_output_loss: 0.0429 - val_classifier_output_loss: 0.7172 - val_ecoder_output_accuracy: 0.0459 - val_classifier_output_accuracy: 0.7248\n",
      "Epoch 15/20\n",
      "235/235 [==============================] - ETA: 0s - loss: 0.1111 - ecoder_output_loss: 0.0427 - classifier_output_loss: 0.6833 - ecoder_output_accuracy: 0.0454 - classifier_output_accuracy: 0.7321\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.11465 to 0.11366, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.11465 to 0.11366, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00015: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1111 - ecoder_output_loss: 0.0427 - classifier_output_loss: 0.6833 - ecoder_output_accuracy: 0.0454 - classifier_output_accuracy: 0.7321 - val_loss: 0.1137 - val_ecoder_output_loss: 0.0426 - val_classifier_output_loss: 0.7102 - val_ecoder_output_accuracy: 0.0436 - val_classifier_output_accuracy: 0.7269\n",
      "Epoch 16/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.1104 - ecoder_output_loss: 0.0425 - classifier_output_loss: 0.6789 - ecoder_output_accuracy: 0.0469 - classifier_output_accuracy: 0.7330\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.11366 to 0.11318, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.11366 to 0.11318, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00016: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1104 - ecoder_output_loss: 0.0425 - classifier_output_loss: 0.6787 - ecoder_output_accuracy: 0.0469 - classifier_output_accuracy: 0.7330 - val_loss: 0.1132 - val_ecoder_output_loss: 0.0422 - val_classifier_output_loss: 0.7094 - val_ecoder_output_accuracy: 0.0419 - val_classifier_output_accuracy: 0.7265\n",
      "Epoch 17/20\n",
      "233/235 [============================>.] - ETA: 0s - loss: 0.1096 - ecoder_output_loss: 0.0423 - classifier_output_loss: 0.6728 - ecoder_output_accuracy: 0.0460 - classifier_output_accuracy: 0.7355\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.11318 to 0.11276, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.11318 to 0.11276, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00017: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1095 - ecoder_output_loss: 0.0423 - classifier_output_loss: 0.6725 - ecoder_output_accuracy: 0.0460 - classifier_output_accuracy: 0.7356 - val_loss: 0.1128 - val_ecoder_output_loss: 0.0423 - val_classifier_output_loss: 0.7049 - val_ecoder_output_accuracy: 0.0426 - val_classifier_output_accuracy: 0.7267\n",
      "Epoch 18/20\n",
      "229/235 [============================>.] - ETA: 0s - loss: 0.1091 - ecoder_output_loss: 0.0421 - classifier_output_loss: 0.6701 - ecoder_output_accuracy: 0.0449 - classifier_output_accuracy: 0.7368\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.11276 to 0.11264, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.11276 to 0.11264, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00018: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1091 - ecoder_output_loss: 0.0421 - classifier_output_loss: 0.6697 - ecoder_output_accuracy: 0.0449 - classifier_output_accuracy: 0.7371 - val_loss: 0.1126 - val_ecoder_output_loss: 0.0420 - val_classifier_output_loss: 0.7066 - val_ecoder_output_accuracy: 0.0423 - val_classifier_output_accuracy: 0.7308\n",
      "Epoch 19/20\n",
      "228/235 [============================>.] - ETA: 0s - loss: 0.1085 - ecoder_output_loss: 0.0419 - classifier_output_loss: 0.6661 - ecoder_output_accuracy: 0.0453 - classifier_output_accuracy: 0.7376\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.11264 to 0.11248, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.11264 to 0.11248, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00019: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1085 - ecoder_output_loss: 0.0419 - classifier_output_loss: 0.6661 - ecoder_output_accuracy: 0.0455 - classifier_output_accuracy: 0.7380 - val_loss: 0.1125 - val_ecoder_output_loss: 0.0417 - val_classifier_output_loss: 0.7076 - val_ecoder_output_accuracy: 0.0419 - val_classifier_output_accuracy: 0.7280\n",
      "Epoch 20/20\n",
      "231/235 [============================>.] - ETA: 0s - loss: 0.1081 - ecoder_output_loss: 0.0417 - classifier_output_loss: 0.6640 - ecoder_output_accuracy: 0.0471 - classifier_output_accuracy: 0.7388\n",
      "***callbacks***\n",
      "saving losses to model/AE_model/model_2\\losses.log\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.11248 to 0.11145, saving model to model/AE_model/model_2\\KERAS_check_best_model.h5\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.11248 to 0.11145, saving model to model/AE_model/model_2\\KERAS_check_best_model_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_last_weights.h5\n",
      "\n",
      "Epoch 00020: saving model to model/AE_model/model_2\\KERAS_check_model_epoch20.h5\n",
      "\n",
      "***callbacks end***\n",
      "\n",
      "235/235 [==============================] - 0s 2ms/step - loss: 0.1081 - ecoder_output_loss: 0.0417 - classifier_output_loss: 0.6639 - ecoder_output_accuracy: 0.0469 - classifier_output_accuracy: 0.7390 - val_loss: 0.1115 - val_ecoder_output_loss: 0.0414 - val_classifier_output_loss: 0.7002 - val_ecoder_output_accuracy: 0.0348 - val_classifier_output_accuracy: 0.7271\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "network_with_classifier = Autoencoder(data_zoom, pruned=True)\n",
    "network_with_classifier.build_encoder()\n",
    "network_with_classifier.build_decoder()\n",
    "network_with_classifier.build_classifier()\n",
    "network_with_classifier.build_autoencoder(use_latent_classifier=True)\n",
    "network_with_classifier.fit_data( batch_size=256,epochs=20, use_latent_classifier=True)\n",
    "network_with_classifier.plot_latent_dimension(\"Autoencoder with classifier\")\n",
    "network_with_classifier.plot_score(use_latent_classifier=True,model_name=\"Autoencoder with classifier\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "789093bb736187c46b4a361b2c83e244a48ff9bb7ad40e66e5ffb40455c8edff"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}