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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sid-betalol/Document-Classification-Models/blob/main/DiT_finetuning_tobacco3482.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers[torch] -U\n",
        "!pip install datasets"
      ],
      "metadata": {
        "id": "2VXU12ouH5bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73a51e96-af45-4ffe-a540-081a03617b93"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: accelerate, transformers\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.35.2\n",
            "    Uninstalling transformers-4.35.2:\n",
            "      Successfully uninstalled transformers-4.35.2\n",
            "Successfully installed accelerate-0.26.1 transformers-4.37.2\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.16.1-py3-none-any.whl (507 kB)\n",
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            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
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            "INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.\n",
            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.16.1 dill-0.3.7 multiprocess-0.70.15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import AutoModelForImageClassification, AutoFeatureExtractor, TrainingArguments, Trainer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_recall_fscore_support\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from PIL import Image\n",
        "import io\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils.data import IterableDataset\n",
        "import os"
      ],
      "metadata": {
        "id": "5YAsq-7VfDkj"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BzTCkhPtAd56",
        "outputId": "92567e38-03f3-4236-9bf3-4db86ca72191"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "task = 'tobacco3482'"
      ],
      "metadata": {
        "id": "027c81c7Do4N"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "np.random.seed(42)"
      ],
      "metadata": {
        "id": "rWjSbGZ4AA07"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_labels = 10"
      ],
      "metadata": {
        "id": "T7XPPtXgACbO"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset('maveriq/tobacco3482')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281,
          "referenced_widgets": [
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        },
        "id": "fBvscZAgpB6J",
        "outputId": "dd2c5ab9-e9dd-4e2f-c5df-2717ede85ac2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/644 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/435M [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "cb9e4b8b7cbe4305878affebcf7ebc81"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "0932184b28044fe1b44a0fbe37ff7daa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b8e8629f1c4c4a1784e268ad78633552"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_extractor = AutoFeatureExtractor.from_pretrained(\"microsoft/dit-base\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138,
          "referenced_widgets": [
            "80325da60ca9439e95b39d115a67224f",
            "eda6150495724224a90d6338276912cb",
            "75938126b844424c90846092eefe01f9",
            "40d059dbc1c945129ab31ab538d945f0",
            "e5209422dd7b4f4db269d77d3feb706c",
            "873935ea760545db97482af3bb2cbfdb",
            "d60cd98652f447c3805c85e170ee7a12",
            "e2513e1d1e3749c79905a250aed023e3",
            "d62f733ae1dd41549e9dd715251aa96e",
            "e7577d16daf34a72838022cd7733b99a",
            "6039a763a08b46dcbf297e899d2c422e"
          ]
        },
        "id": "x4zEHMZypFXP",
        "outputId": "0330c2ef-f05e-43c2-93ae-0a3b67b12476"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/302 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "80325da60ca9439e95b39d115a67224f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/beit/feature_extraction_beit.py:28: FutureWarning: The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use BeitImageProcessor instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/models/beit/image_processing_beit.py:110: FutureWarning: The `reduce_labels` parameter is deprecated and will be removed in a future version. Please use `do_reduce_labels` instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, ds, feature_extractor, indices):\n",
        "        self.ds = ds.select(indices)  # Select the specified indices\n",
        "        self.feature_extractor = feature_extractor\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.ds)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = self.ds[idx]\n",
        "        if isinstance(item['image'], bytes):\n",
        "            # If it's bytes, convert it to an image\n",
        "            image = Image.open(io.BytesIO(item['image']))\n",
        "        else:\n",
        "            # If it's already an image, use it directly\n",
        "            image = item['image']\n",
        "        if image.mode != 'RGB':\n",
        "            image = image.convert('RGB')\n",
        "        pixel_values = self.feature_extractor(images=image, return_tensors=\"pt\").pixel_values.squeeze()\n",
        "        label = torch.tensor(item['label'])\n",
        "        return {\"pixel_values\": pixel_values, \"labels\": label}"
      ],
      "metadata": {
        "id": "AZlLOYapXmtD"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_indices, test_indices = train_test_split(list(range(len(dataset['train']))), test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "z1p73FripQBJ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = CustomDataset(dataset['train'], feature_extractor, train_indices)\n",
        "test_dataset = CustomDataset(dataset['train'], feature_extractor, test_indices)"
      ],
      "metadata": {
        "id": "SjpDy4vyXqxj"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForImageClassification.from_pretrained(\"microsoft/dit-base\", num_labels=num_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170,
          "referenced_widgets": [
            "fa3eb6313e1f414ab035815eaebbd715",
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            "b024c91a60a74853b76ad4e4835b5bee",
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            "59dfa2a83f78436aa3c7809009477895",
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            "5c47825ef44e4ed98217fe3f1a9bc811",
            "7e56475d7ff945e581c13dd7bcac8c95",
            "5f8bfd1bb5d54fb8bf8b065d925b32e3",
            "d13b78087ac6422191bf421a5d278cd9",
            "da03b9d5fc8f43d3b0d94054b683b794",
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            "73988357dada4816b88b7a27e150d097",
            "8b0180710c6848f5a2000083a466e291"
          ]
        },
        "id": "FubElu_HATLI",
        "outputId": "ecf27dfa-0586-4271-e09e-d10f7a64536e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.03k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fa3eb6313e1f414ab035815eaebbd715"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/369M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f8bfd1bb5d54fb8bf8b065d925b32e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
            "  return self.fget.__get__(instance, owner)()\n",
            "Some weights of BeitForImageClassification were not initialized from the model checkpoint at microsoft/dit-base and are newly initialized: ['beit.pooler.layernorm.bias', 'beit.pooler.layernorm.weight', 'classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    precision, recall, f1, _ = precision_recall_fscore_support(labels, predictions, average='weighted')\n",
        "    acc = accuracy_score(labels, predictions)\n",
        "    # Optionally print a classification report here or save it to a file\n",
        "    report = classification_report(labels, predictions, target_names=[f'Class {i}' for i in range(num_labels)], digits=4)\n",
        "    print(report)\n",
        "    return {\n",
        "        'accuracy': acc,\n",
        "        'f1': f1,\n",
        "        'precision': precision,\n",
        "        'recall': recall\n",
        "    }"
      ],
      "metadata": {
        "id": "jE54_7ivAWFQ"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir=f\"/content/drive/MyDrive/rvl-cdip-pp/DiT/{task}/results\",\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=8,\n",
        "    warmup_steps=500,\n",
        "    weight_decay=0.01,\n",
        "    logging_dir=f\"/content/drive/MyDrive/rvl-cdip-pp/DiT/{task}/logs\",\n",
        "    logging_steps=10,\n",
        "    evaluation_strategy=\"epoch\"\n",
        "    # Since we're aiming for 3 epochs, max_steps might not be necessary here\n",
        "    # but if you choose to use it, set max_steps=600\n",
        "    # max_steps=1000  # Optional based on the above explanation\n",
        ")"
      ],
      "metadata": {
        "id": "Dpca10QYAxCM"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=test_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "id": "dqM2a5kMA0TB"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "33bh-_Y5A2Mg",
        "outputId": "2ae689a2-c40d-4a26-b516-30f55899d240"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1047' max='1047' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1047/1047 22:41, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.476600</td>\n",
              "      <td>0.412966</td>\n",
              "      <td>0.879484</td>\n",
              "      <td>0.871696</td>\n",
              "      <td>0.879945</td>\n",
              "      <td>0.879484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.295500</td>\n",
              "      <td>0.434363</td>\n",
              "      <td>0.898135</td>\n",
              "      <td>0.896654</td>\n",
              "      <td>0.901967</td>\n",
              "      <td>0.898135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.297000</td>\n",
              "      <td>0.381761</td>\n",
              "      <td>0.934003</td>\n",
              "      <td>0.932343</td>\n",
              "      <td>0.934154</td>\n",
              "      <td>0.934003</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Class 0     0.9464    0.9298    0.9381        57\n",
            "     Class 1     0.9925    0.9852    0.9888       135\n",
            "     Class 2     0.8252    0.9659    0.8901        88\n",
            "     Class 3     0.8571    0.9836    0.9160       122\n",
            "     Class 4     0.9057    0.8807    0.8930       109\n",
            "     Class 5     0.9375    0.8824    0.9091        34\n",
            "     Class 6     0.9600    0.6667    0.7869        36\n",
            "     Class 7     0.6885    0.8750    0.7706        48\n",
            "     Class 8     0.8462    0.7333    0.7857        15\n",
            "     Class 9     0.7037    0.3585    0.4750        53\n",
            "\n",
            "    accuracy                         0.8795       697\n",
            "   macro avg     0.8663    0.8261    0.8353       697\n",
            "weighted avg     0.8799    0.8795    0.8717       697\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Class 0     0.9655    0.9825    0.9739        57\n",
            "     Class 1     1.0000    0.9852    0.9925       135\n",
            "     Class 2     0.8916    0.8409    0.8655        88\n",
            "     Class 3     0.9402    0.9016    0.9205       122\n",
            "     Class 4     0.7770    0.9908    0.8710       109\n",
            "     Class 5     0.9706    0.9706    0.9706        34\n",
            "     Class 6     0.9091    0.8333    0.8696        36\n",
            "     Class 7     0.8500    0.7083    0.7727        48\n",
            "     Class 8     0.8235    0.9333    0.8750        15\n",
            "     Class 9     0.7907    0.6415    0.7083        53\n",
            "\n",
            "    accuracy                         0.8981       697\n",
            "   macro avg     0.8918    0.8788    0.8820       697\n",
            "weighted avg     0.9020    0.8981    0.8967       697\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Class 0     0.9655    0.9825    0.9739        57\n",
            "     Class 1     1.0000    0.9926    0.9963       135\n",
            "     Class 2     0.8854    0.9659    0.9239        88\n",
            "     Class 3     0.9084    0.9754    0.9407       122\n",
            "     Class 4     0.9298    0.9725    0.9507       109\n",
            "     Class 5     1.0000    0.9706    0.9851        34\n",
            "     Class 6     1.0000    0.8056    0.8923        36\n",
            "     Class 7     0.8750    0.7292    0.7955        48\n",
            "     Class 8     1.0000    1.0000    1.0000        15\n",
            "     Class 9     0.8298    0.7358    0.7800        53\n",
            "\n",
            "    accuracy                         0.9340       697\n",
            "   macro avg     0.9394    0.9130    0.9238       697\n",
            "weighted avg     0.9342    0.9340    0.9323       697\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1047, training_loss=0.6896394507420439, metrics={'train_runtime': 1364.4875, 'train_samples_per_second': 6.123, 'train_steps_per_second': 0.767, 'total_flos': 6.475673349798912e+17, 'train_loss': 0.6896394507420439, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "iq-TfydXX9gK",
        "outputId": "3a73bde4-d2c9-4d78-a61c-3a85ff479727"
      },
      "execution_count": 17,
      "outputs": [
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='88' max='88' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [88/88 01:14]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Class 0     0.9655    0.9825    0.9739        57\n",
            "     Class 1     1.0000    0.9926    0.9963       135\n",
            "     Class 2     0.8854    0.9659    0.9239        88\n",
            "     Class 3     0.9084    0.9754    0.9407       122\n",
            "     Class 4     0.9298    0.9725    0.9507       109\n",
            "     Class 5     1.0000    0.9706    0.9851        34\n",
            "     Class 6     1.0000    0.8056    0.8923        36\n",
            "     Class 7     0.8750    0.7292    0.7955        48\n",
            "     Class 8     1.0000    1.0000    1.0000        15\n",
            "     Class 9     0.8298    0.7358    0.7800        53\n",
            "\n",
            "    accuracy                         0.9340       697\n",
            "   macro avg     0.9394    0.9130    0.9238       697\n",
            "weighted avg     0.9342    0.9340    0.9323       697\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.3817610740661621,\n",
              " 'eval_accuracy': 0.9340028694404591,\n",
              " 'eval_f1': 0.9323430154824087,\n",
              " 'eval_precision': 0.9341537697323402,\n",
              " 'eval_recall': 0.9340028694404591,\n",
              " 'eval_runtime': 76.1894,\n",
              " 'eval_samples_per_second': 9.148,\n",
              " 'eval_steps_per_second': 1.155,\n",
              " 'epoch': 3.0}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = trainer.predict(test_dataset)\n",
        "labels = predictions.label_ids\n",
        "preds = np.argmax(predictions.predictions, axis=-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "zNUo1lu-C__P",
        "outputId": "8d651aba-c1f4-4b98-e8f1-e29a3f7dfbee"
      },
      "execution_count": 18,
      "outputs": [
        {
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          "data": {
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Class 0     0.9655    0.9825    0.9739        57\n",
            "     Class 1     1.0000    0.9926    0.9963       135\n",
            "     Class 2     0.8854    0.9659    0.9239        88\n",
            "     Class 3     0.9084    0.9754    0.9407       122\n",
            "     Class 4     0.9298    0.9725    0.9507       109\n",
            "     Class 5     1.0000    0.9706    0.9851        34\n",
            "     Class 6     1.0000    0.8056    0.8923        36\n",
            "     Class 7     0.8750    0.7292    0.7955        48\n",
            "     Class 8     1.0000    1.0000    1.0000        15\n",
            "     Class 9     0.8298    0.7358    0.7800        53\n",
            "\n",
            "    accuracy                         0.9340       697\n",
            "   macro avg     0.9394    0.9130    0.9238       697\n",
            "weighted avg     0.9342    0.9340    0.9323       697\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(labels, preds, normalize='true')\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='.2f',\n",
        "            xticklabels=[f'Class {i}' for i in range(num_labels)],\n",
        "            yticklabels=[f'Class {i}' for i in range(num_labels)])\n",
        "plt.xticks(rotation=45)\n",
        "plt.yticks(rotation=45)\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "id": "gqoHTuMxDBih",
        "outputId": "c7bfa620-3eba-42aa-ef13-591d2524bfe0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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WrVJ0eEREREREVAKVGXmRSCTIzc3FsmXL4OHhgU2bNgEAnjx5gunTpyMsLAwvX75EYGCggiMlIiIiIpXFBfslUpmRF4FAAHV1dYhEImRlZQHIK2hMTU2xePFiODs74/fff8euXbsUHCkRERERERVFZYqX/JEXMzMzpKWlIT09HQCQm5uLGjVqYPbs2dDU1MSWLVsUHCkRERERqSyBUHGbElCOKOVAIBBATU0Nvr6+OH78OJYtWyZty83NRc2aNbF8+XIcPnwYly5dUnS4RERERET0DpVZ8wLk3W3MwcEBa9euxahRo6ClpYWvv/4aampqAAB1dXVYW1tDV1dXwZESEREREdG7VKp4EQrzBpq8vb2Rnp6OKVOm4L///sPgwYNhYWGB7du349WrV9DX11dsoERERESkmrhgv0QqVbzkq1KlCiZOnIj69etj/Pjx2LdvH7S0tJCVlYW9e/fC1NRU0SESEREREdE7PsniRSKRQCAQ4NGjR9DU1IShoWGR/dzd3dGyZUvcu3cPr169QqNGjVCrVq0KjpaIiIiI6A0lWTivKJ/cq5NfuOzfvx8DBgzAsWPH8OLFi2L7mpqaomXLlmjfvj0LFyIiIiKiSuyTK17yC5fBgwfD3d0drq6uhRbgSyQSaV8iIiIiIlIOn9y0sYcPH2LmzJlYsGABJkyYgOzsbKSnp+PChQswNDSEo6MjixYiIiIiqpz4d2qJPrmRF3V1dWhra8PMzAwpKSlYtGgRPvvsMwwYMACenp749ddfFR1iqfl90QYJ+2ci7cz3OPXTRDS3NS+2r7qaEIG+3XBt7wyknfkeF8KmoqurtUwfoVCA2f49cGPfN0g9/T2u7Z2B6SO6lncaZbZjexh6du2EFo7NMNjzC1y9cqXE/pERh9H78x5o4dgMHn3ccfrUSZnfSyQSrAkJRucObdHSyQ6jRgzHvXt3yzGDD8O8mXdJmDfzLkhZ8t4ZHobPuneCi7MdvL0GIP5qyXkfjTiCfu494eJshwF93XGmQN7Z2dkIXr4UA/q6o3VLR3Tr1A6zZgQgOelJeadRZqp6vVU1b6pYSl+85E8BS0pKQkZGBjQ1NSGRSBASEoJ69eohJiYGHh4eOHr0KGrXro2rV68qOOLS6d/VAd9/1RvzN0TAdehyXLn1CL+FjEINA50i+88d3Qu+fV0xecleOA78Hhv2nMPOxT6wb1xH2meKdyeM9GiNSUv2wGHAIswM+R2Th3bEmIHtKiqt9zpy+BCWLl4IvzFjseOXvbCyssZovxFISUkpsn9szF+YPm0K+vbrj52796Fjp874avxY3Lr1t7TPTxvXIzxsG2bOmYufw3dBS0sLo0eNQFZWVkWl9V7Mm3kz78KYt3LnHXHkEJYvWYRR/mOxfdceNGpshbF+vkgtJu+42L8wI2AKevfrj+2/7IVbpy6YPHEc/nmT96tXr5Bw4zp8/cZg+85fsXRFCO7dvYOvxo+pyLTeS1Wvt6rmXS7efep9RW5KQDmiLEb+4vwDBw7Aw8MDx48fR/Xq1REeHo6BAwdiyZIl2Lx5MyZMmAAnJyfo6OhIn/VS2U3w6oCf9kVh24GLSLjzBOMX7kbmq2wM+1/LIvt79XLG4s3HEHHuBu4+TMX6X88h4twNTBziJu3jYmeJ309ew5GzN3A/MQ17T1zB8Qt/o3mT4kd0Ktq2LT+hX/8B6NPXAw0aNsTMOd9CU1MT+/YUPWIW9vNWtG7bDsO/9EX9Bg0wbsJXsLG1xY7tPwPIe4+EbduKkX6j0bFTFzS2ssa8hYuRnJSEE8ePVWRqJWLezJt5F8a8lTvvsK2b0dfjC/Tu64H6DRrim9nfQlNLE/v3Fp339p+3wbVNWwzzGYH69RtgzPiJsLa1xc7wMACArq4uQtdvQrcePWFZrz7s7B0QMGMWbly/hsTERxWZWolU9Xqrat5U8ZTjL/liCAQC7Nu3D15eXujZsyeaNGkCAKhfvz78/f3h5+cHPT09ZGRkIDAwEFFRUfD09FRw1O9XRV0NjtZmOBH99tsHiUSCE9F/o2UzyyL3qVpFHa+ycmTaMrOy0dq+nvTnqCt30bFFIzQ0rwEAaNaoNlzt6yHy3A35J/EBsl+/xo3r1+Di2lraJhQK4eLSGlfiYorc50psLFxcXGXaWrdpiyuxsQCAhw8eQCRKRiuXt8fU1dVFMzv7Yo9Z0Zg382bezLugTyLv7Ly8C8YoFArRysUVV+Jii9znalysTH8AcG3dptj+AJD+4gUEAgF0dfXkEfZHU9nrraJ5k2Io9YL9Bw8eIDAwEPPmzcPEiRORk5ODrKwsREdHw9jYGDY2NggLC8Ovv/6Kv/76C5GRkWjcuLHczp+VlVVo6FIizoFA+HEvq7G+NtTV1ZCUKnuL56TUF7CyNClyn2NRNzFhcAecifkXtx+koGOLRujdsRnUCow0Ld1yAno6moj7JQC5YgnUhALMCT2MHUf++qh45SXtaRpyc3NhZGQk025kZIQ7d24XuY9IJIKRkXGh/qIU0ZvfJ+e1GRc+pkgkklfoH4V5M2+AeReFecv2V6a8n6bl5W34Tt6GRsa4e+dOkfvk5f1uTsZIKSanrKwsBK9Yih49P4OOTtFTqiuaql5vVc273HDBfomUeuQlJycH2tracHJyQlJSEpYuXYru3bujV69eGDlyJP744w906dIFzs7OOH78OBwdHeV6/oULF6J69eoyW07iRbmeo7SmLtuLf+8nI+6X6Xh+bjFWfN0PWw9EQyyWSPv072IPzx5OGD7zZ7gOWQ7fueH4arAbBn/WXCExExERfYjs7GwETP0KABA4a65CYyGiiqXUxYu+vj6Sk5MRGBgIW1tbREdHw93dHZGRkUhPT0d0dDRMTU0RGBiIBg0ayP38gYGBePbsmcymXqvFRx9X9PQlcnJyYWIo+3waE0NdPE4p+oGboqcvMWDaTzBqPx1W/5sH+/6L8DLjNe48ertQbsFEdyzdcgK/HI3FtX8TEX74MkLCT2La8M4fHbM8GOgbQE1NrdDivpSUFBgbGxe5j7GxMVJSRIX7v/k2x9g4b4pciqj0x6xozJt5A8y7KMz7nf5KlLe+QV7e7y7OT00p/G17vry8381JBKN3csrOzsb0qZOQ+OgR1v64sdKMugCqe71VNe9ywwX7JVKOKPH2rmLPnz9HTk4OXrx4AX19fZw7dw49e/ZEUFAQNmzYgMmTJ8PV1RVmZmYQi8UAyu9hlBoaGtDT05PZPnbKGABk5+QiJuEBOrZoJG0TCATo2KIRoq/eLXHfrNc5eJT8DOpqQvTpZIffT8ZLf6elUVVmJAYAcsUSCCvJ8GSVqlVhY9sEF6LOS9vEYjEuXDgPO/uiR83sHBxwISpKpi3q/DnYOTgAAOqYmcHYuAYuXHh7zPT0dFy9ElfsMSsa82bezJt5F/RJ5F0lL+/oC7J5R0dFwc7eoch9mtk7yPQHgAvnz8n0zy9c7t+/h3Xrf4K+vkF5hP/BVPZ6q2jepBhKseYl/65ihw8fRmhoKB4/fgwbGxv4+vqiXbt2CAgIgLp6XiqvX7/G3LlzER0djRUrVgAov+KlPK3afhLr5wzC5Rv/4dK1+xg3qAOqaVXF1gPRAIANcwfhUfJzzF5zEADQook5aptUR9zfD1GnRnV8M6o7hEIBlm89IT3moTPXEODTBf89TsP124/hYGWGCV4dsPW3aIXkWJShw3wwa0YAmjRpiqbN7PDzti3IzMxEn779AADfBH4NExNTTJw0BQAweIg3Rgwfii2bN6F9+w44cvgQrsXHY9bcIAB5137wUG+s/yEUFuYWqGNmhjUhwahhYoJOnbsoLM93MW/mzbyZ96eW92Dv4ZjzzXTYNmmKJs3ssP1N3v/rk5f3rBkBMDExwfiv8vL2GjIUI328sW3LJrRt54aIIwdx/do1zJyTl3d2dja+njwRCTeuI3jNOuSKc6XrIqpXr44qVaoqJtF3qOr1VtW8y4WSjIAoSqUuXvKLFoFAgP3798PLywsBAQEwMjLCyZMn4enpifDwcLRv3x4SiQTbtm3Dzp07ER8fj4iICDRq1Oj9J6mkdh+NhbG+Dmb79YCpkR6u/P0QvSf8iKTUdABA3ZoGEEvejqJoaFTBHP+eqFfHCOmZWYg4ewMjZm/Hs/RX0j6Tl+zFHP+eCA7wQA0DXSSKnmHjnvNYsCGywvMrTo+evZCWmoq1q1dBJEqGlbUN1v6wQTpt4HFiIoQF/lE7ODph4eKlWL1qJUJWLoe5hSVWhqxBo0Zvb8zgM2IkMjMzETR3Nl68eA5HJ2es/WEDNDQ0Kjy/4jBv5s28mXe+TyXv7j3y8g5dE4KUN3mvXre+QN6PZEb+7R2cMH/RUqxdvRKrg1fA3MISy4NXo+GbvJOTnuDkn3lfyHn27yNzrh83bUHzFq0qJrH3UNXrrap5U8UTSCQSyfu7VSyRSCQzn/HmzZvw8vLCyJEj4e/vjydPnsDZ2RkaGhpIS0vD3r170aFDB9y/fx8//vgjhg0bprDCRavFZIWcV9HSzi9XdAhERCQHueJK92dBhVATKt8sDfpwmpX463utDkEKO3fmydkKO3dpVbpxqZCQEHTq1AnXrl2TtkkkErRs2RJDhgzBf//9h3bt2qFXr17Ys2cPLC0tMXDgQBw9ehTm5uYICgpS6hEXIiIiIlJhQoHiNiVQ6YqX/v3748mTJxg/fjyuX78OALC2tsasWbOgo6ODBQsWwMHBAcHBwbC3t4eNjQ0yMjLg5+eHly9fKuX6FiIiIiIier9KUbzkz1zLzc1FrVq1EBcXh4SEBPj7+yM+Pu9uWbVr10ZmZibi4uJga2sLLS0tAICenh5CQkIQHR0NbW1tFi9EREREpLx4q+QSKTxKsVgMgUCA5ORkxMTEICoqCjVr1kRMTAxu376NMWPGSEdgtLS0YGtri127dmHXrl2YNGkSDh48CDc3t0//nt9ERERERCpOocuVxGIxhEIhrl+/jlGjRkFXVxfVqlVDWFgYTE1NcfnyZTg7O8Pf3x+hoaFo0qQJfH19kZqaiilTpsDQ0BD79++HhYWFItMgIiIiIqIKoLDiRSKRQCgU4tq1a2jbti3GjBkDPz8/mJmZQSgUIicnp1ABs3HjRri4uGDnzp1ITEyEjo4ODA0NFZUCEREREZF8cQlEiRQ2bUwgECA1NRX+/v7w9vbG/PnzYW5uDqFQCIlEAnV1dZkC5s6dOxg1ahSuXr2KKlWqwNzcnIULEREREZEKUeial8ePHyMxMREeHh4Qi8XS9vxF92pqapBIJDA1NcWlS5cQFRWF6dOn4/Xr14oKmYiIiIio/HDBfokUuuYlNjYW9+7dQ7t27SAQCKRrYPIJBAJkZGQgLi4Orq6uuH//Pp49e4aqVasqMGoiIiIiIlIEhZZYlpaWUFdXx549e/KCERYOZ9OmTZgzZw4yMjJgYmLCB1ASERER0adLIFDcpgQUWrxYWFhAT08PW7duxb1796Tt+c99AYC7d+/C2dlZ+lwXIiIiIiJSTQotXurUqYPQ0FBERERg1qxZ0ue55E8XmzFjBnbv3g0fHx8+fJKIiIiISMUpdM0LAPTp0wfBwcEYN24cLl68CFdXV2hqauLhw4eIiorCkSNH0LhxY0WHSURERERU/pRk4byiKPzVEQqF8PPzw9mzZ9G0aVPExMQgPj4eNjY2OHPmDBwdHRUdIhERERERVQIKH3nJ17JlS+zYsQNqamqKDoWIiIiISDG4VKJECh95Kajg3cYKLtonIiIiIiKqVMVLwUX5XKBPREREREQFVZppY0REREREKo8L9kvEV4eIiIiIiJQCR16IiIiIiCoLLp0oEUdeiIiIiIhIKXDkhYiIiIiosuCalxLx1SEiIiIiIqXA4oWIiIiIiJQCp40REREREVUWXLBfIhYvcpZ2frmiQ1AIg5YTFB2CQqRFr1J0CAohkSg6AsXg5wmpAjUh3+hEVHmxeCEiIiIiqiy4YL9EfHWIiIiIiEgpsHghIiIiIiKlwGljRERERESVBaeNlYivDhERERERKQWOvBARERERVRa8tWWJOPJCRERERERKgSMvRERERESVBde8lIivDhERERERKQUWL0REREREpBRUbtqYRCKBgAuhiIiIiKgy4t+pJVKpkRexWAyBQIDHjx8jKSlJ0eEQEREREVEZqEzxIpFIIBQKcePGDdStWxeDBw+GSCRSdFhERERERG8JhIrblIByRCkHAoEASUlJ8PPzQ9euXXHz5k0WMERERERESkRlihcAiI+PR7169TB79mxERETg+vXrLGCIiIiIiJSESi3Yd3Z2RpUqVeDi4gIAiIiIQPfu3TF48GD8/PPPqFGjBoC8tTFCoUrVdURERERUGXDBfolU6i/06tWro127dgDyChRbW1tERkbi+vXrGDJkCEQiEXJycrB69Wr89ttvCo6WiIiIiIgKUqmRl4LyR1ZsbGwQGRmJbt26YejQoTA1NcXPP/+MGzduKDhCIiIiIlI1fKRHyVS2eCnIxsYGBw8ehIODAwwMDBAdHY1GjRopOiwiIiIiIiqAxQuA7OxshIaGolq1ajh9+jRsbW0VHRIRERERqSCOvJTsk1zzIpFIAACPHj1Camrqe/tfu3YNf/31F/744w8WLkREREREldQnV7xIJBIIBALs378fAwYMwLFjx/DixYsS97GyskJERARatGhRQVESEREREVFZfXLFS37hMnjwYLi7u8PV1RW6uroyffJHZvJpaWlBX1+/AqMkIiIiIiqCQIGbEvjk1rw8fPgQM2fOxIIFCzBhwgRkZ2cjPT0dFy5cgKGhIRwdHTmXkIiIiIhICX1yxYu6ujq0tbVhZmaGlJQUrF27FseOHUN8fDyMjY2xYMECeHh4KDpMIiIiIqJC+CV7yZR+2lj+FLCkpCRkZGRAU1MTEokEISEhqFevHmJiYuDh4YGjR4+idu3auHr1qoIjLr0d28PQs2sntHBshsGeX+DqlSsl9o+MOIzen/dAC8dm8OjjjtOnTsr8XiKRYE1IMDp3aIuWTnYYNWI47t27W44ZfBi/Ae2Q8PscpJ1fhlNbJqN5E/Ni+6qrCxE4sgeu7Z+NtPPLcGFHALq2tpHpo1NNA0um9sPNg3ORem4p/vhpEpxtiz+moqjq9d4RHoae3TqhpVMzDBn0Ba5efX/efdx7oKVTM/TvWzjv40cj4T/yS3Ro0woOTa2QkFA5n9mkstebeTPvEjBv5l2QsuRNFUupi5f8xfkHDhyAh4cHjh8/jurVqyM8PBwDBw7EkiVLsHnzZkyYMAFOTk7Q0dGRPpyysjty+BCWLl4IvzFjseOXvbCyssZovxFISUkpsn9szF+YPm0K+vbrj52796Fjp874avxY3Lr1t7TPTxvXIzxsG2bOmYufw3dBS0sLo0eNQFZWVkWl9V79uzni+8l9Mf/HI3D1WoIrtx7itzVjUMNAp8j+c8d8Dl+P1pi8eDcc+y/Aht1nsXPpCNhbmUn7hM4ehE6trPDlrG1oPnARjkUl4GDoWNSuUb2i0novVb3eEYcPYdnihfAbPRbhv+xFYytrjPEbgdQS8g78egr69O2PHb/k5T1pwlj8UyDvzMwMODo5YeKkqRWVRpmp6vVm3sybeRfGvJU7b6p4Asm7q9eVzL59+zB06FAEBgbC09MT9evXL9QnIyMD3333HTZs2ICzZ8+icePG5RbPqxz5HGew5xdo0rQZZsycDQAQi8Xo1rkDBnkNxYiRowr1nzblK2RmZmL12h+kbUMGDYCVtTVmzQmCRCJBF7d28B7ug2E+IwAAL168QKf2rRE0fxF69vrso+I1aDnho/bPd2rLZFy+fh+Tvt8NIG/o9J/D3yJ0xyks3XysUP/bEd/h+42R+GHXaWlb+JIvkZmVjS9nboOmRhUkn16MLyavx5Ez16V9zoZNQ+TZ6/h27cGPijctetVH7Z9P2a63vP6rMWRQXt6B37zNu3uXvLy/9C2c99dv8g4pkPdQrwGwsrLGzDlBMn0fPnyAz7p3xo7d+2BtbfPuoT6IvEbyle16ywvzZt7Mm3nnU3TempV44YTuwC0KO/eLncMUdu7SUo5hiGI8ePAAgYGBmDdvHmbMmAFzc3NkZWXh9OnTuHEjb6pIWFgYhgwZgvDwcERGRpZr4SIv2a9f48b1a3BxbS1tEwqFcHFpjStxMUXucyU2Fi4urjJtrdu0xZXYWADAwwcPIBIlo5XL22Pq6uqimZ19scesaFXU1eBoUxcnLtyUtkkkEpy4cBMt7eoVuU/VKup4lZUt05aZlY3WDnlFrLqaEOrqanj1WraqfPXqtbSPoqnq9c7Ozsu7YIxCoRCtSso7LhatXGXzdm3dFlfiYsszVLlS2evNvKVtzJt5F8S831K2vEkxlLp4ycnJgba2NpycnJCUlISlS5eie/fu6NWrF0aOHIk//vgDXbp0gbOzM44fPw5HR0e5nj8rKwvPnz+X2eQxlJn2NA25ubkwMjKSaTcyMoJIJCpyH5FIBCMj48L9U0Rvfp+c12Zc+mNWNGN9bairqyEpVfa5PEmpL1DTSLfIfY6dv4EJQzqiQd0aEAgE6NTKCr072qOmcd6UsPSMLETF3UGgb3fUMtaDUCiAZ6/maGVXDzWN9co9p9JQ1eudlianvI0rT06lobLXm3nLtDPvwpj3O/2Zd15bJc67vAgEAoVtykCpixd9fX0kJycjMDAQtra2iI6Ohru7OyIjI5Geno7o6GiYmpoiMDAQDRo0kPv5Fy5ciOrVq8tsS75fKPfzUPGmLtmDf+8nI27PN3h+YTlWBPTH1gMXIBaLpX2+nLUNAoEAtyPn4VnUcoz17IBdEZchVu4Zk0REREQqpxLP+JOVvzj/+fPnqFatGjIzM6Gvr49z585h8+bN8PLygqenJwwMDCAQCGBmZib9A7a8KsnAwEBMnjxZNk41jY8+roG+AdTU1AotcktJSYGxsXGR+xgbGyMlRVS4/5tvNYyNa+S1iVJQo4aJTB8ra+uPjlkeRE9fIicnFyaGsqMsJoa6eJzyoph90jFgygZoVFWHUXVtPEp+hnkT/oc7D9++dnceiNBt5CpU06wKPR1NPBY9x7ZFw3HnQdGLCCuaql5vAwM55S0qvn9lpLLXm3nLtDPvwpj3O/2Zd15bJc67vCjLCIiiKMXIS37hcvjwYQwZMgStW7fGuHHjcPr0adSpUwcBAQEYM2YMDA0NkZ2djRkzZiA6Ohr9+/cHUH5vAg0NDejp6clsGhofX7xUqVoVNrZNcCHqvLRNLBbjwoXzsLMveuqbnYMDLkRFybRFnT8HOwcHAEAdMzMYG9fAhQtvj5meno6rV+KKPWZFy87JRcyN/9Cx5dt1SQKBAB1bWiH6yp0S9816nYNHyc+gri5En872+P1k4VtiZ7x6jcei59DX1UIXV+si+yiCql7vKlXy8o6+IJt3dEl52zsguqi87R3KM1S5UtnrzbylbcybeRfEvN9StrxJMSp18ZJ/IzSBQID9+/ejf//+aN68OYYNG4bMzEx4enri1KlTUFdXh0QiwdatW9G3b1+EhYUhIiICjRo1UnAGH27oMB/s2b0Lv+3bi9v//ot5QXORmZmJPn37AQC+CfwawSuWSfsPHuKNc2dPY8vmTbhz+1+ErgnBtfh4eHoNAZD3Gg4e6o31P4TizxPHcevvm5gZ+DVqmJigU+cuikixSKvC/oBP39YY/HlLWNUzxaoZA1BNqyq2/nYBALAhaAiCxrlL+7doaoHenexgWccIbRzr47fVoyEUCLB883Fpny6u1uja2gYWtQ3RqZUVjvw4Hn/fTcLW36IKnV9RVPV6D/V+k/f+vLznf5eXd+8+eXnPDPwaqwrk7fUm760F8r5+7W3eAPDs2VMkJNzA7X//BQDcu3MHCQk3pPOnKwOVvd7Mm3kzb+b9xqeSN1W8SjltTCQSwdjYWDpicvPmTQQFBWHZsmXw9/fHkydPsHDhQmhqaqJPnz7Yu3cvOnToADc3N/z9999YuXKlUhcuANCjZy+kpaZi7epVEImSYWVtg7U/bIDRm+HXx4mJEAre1p4Ojk5YuHgpVq9aiZCVy2FuYYmVIWvQqNHbUQyfESORmZmJoLmz8eLFczg6OWPtDxvkMlokL7sjY2BsoIPZo3vB1EgPV24+QO9xodJF/HVrGkAsfrtWRaNqFcwZ8znq1TFCekYWIs5ex4iZ2/AsPVPap7qOFoLGuaOOqT5Sn73E/hNxmLPmd+TkiAudX1FU9Xp379kLaWmpCC2Y97q3eScmJkIglM17wfdLsSZkJUKC8/JesWoNGhbI+88/TmDOzEDpzwHTJgEA/EaPw+ix4ysos5Kp6vVm3sybeTPvfJ9K3uWCs8ZKVOme8xISEoL169cjPDwcTZo0AQAkJCQgODgYS5YsQVpaGjp37gw3NzeMHTsWPj4+ePToEbZt24auXbtCLBYr9EGU8nrOi7KR13NelI28nvOibCrXfzUqDqchExF9Girzc16qe21T2LmfbR+qsHOXVqWbNta/f388efIE48ePx/XreQ8VtLa2xqxZs6Cjo4MFCxbAwcEBwcHBsLe3h42NDTIyMuDn54eXL19ykRMRERERKS3eKrlklaJ4yR/8yc3NRa1atRAXF4eEhAT4+/sjPj4eAFC7dm1kZmYiLi4Otra20NLSAgDo6ekhJCQE0dHR0NbWVpoXnoiIiIiIykbhxYtYLIZAIEBycjJiYmIQFRWFmjVrIiYmBrdv38aYMWOkIzBaWlqwtbXFrl27sGvXLkyaNAkHDx6Em5ubUt0qlYiIiIiIyk6hM/7y16dcv34do0aNgq6uLqpVq4awsDCYmpri8uXLcHZ2hr+/P0JDQ9GkSRP4+voiNTUVU6ZMgaGhIfbv3w8LCwtFpkFEREREJBecRVQyhRUvEokEQqEQ165dQ9u2bTFmzBj4+fnBzMwMQqEQOTk5hQqYjRs3wsXFBTt37kRiYiJ0dHRgaGioqBSIiIiIiKgCKWzamEAgQGpqKvz9/eHt7Y358+fD3NwcQqEQEokE6urqMgXMnTt3MGrUKFy9ehVVqlSBubk5CxciIiIi+qRwwX7JFLrm5fHjx0hMTISHhwfE4rfP3Mh/8dTU1CCRSGBqaopLly4hKioK06dPx+vXrxUVMhERERERKYhC17zExsbi3r17aNeuHQQCQaFntAgEAmRkZCAuLg6urq64f/8+nj17hqpVqyowaiIiIiIiUgSFjrxYWlpCXV0de/bsyQumiIdLbtq0CXPmzEFGRgZMTEzQqFGjig6TiIiIiKhCcNpYyRRavFhYWEBPTw9bt27FvXv3pO2SAo/vvnv3LpydnaXPdSEiIiIiItWk0OKlTp06CA0NRUREBGbNmiV9nkv+dLEZM2Zg9+7d8PHxUZpqkIiIiIjogwkUuCkBha55AYA+ffogODgY48aNw8WLF+Hq6gpNTU08fPgQUVFROHLkCBo3bqzoMImIiIiISMEUOvIC5K1z8fPzw9mzZ9G0aVPExMQgPj4eNjY2OHPmDBwdHRUdIhERERFRheCal5IpfOQlX8uWLbFjxw6oqakpOhQiIiIiIqqEFD7yUlDBu40VXLRPRERERERUaUZeAMgMVynL0BURERERkbzwb+CSVaqRFyIiIiIiouJUqpEXIiIiIiJVxpGXknHkhYiIiIiIlAKLFyIiIiIiUgqcNkZEREREVFlw1liJOPJCREREREQfZM2aNbC0tISmpiZatWqF6OjoEvuvXLkSVlZW0NLSQt26dTFp0iS8evWq1OfjyAsRERERUSWhTAv2d+7cicmTJ2PdunVo1aoVVq5cie7du+PmzZswMTEp1H/79u2YPn06Nm3ahNatW+Pvv//G8OHDIRAIsHz58lKdkyMvRERERERUZsuXL8fIkSPh4+MDW1tbrFu3DtWqVcOmTZuK7H/u3Dm0adMGXl5esLS0RLdu3TBo0KD3jtYUxOKFiIiIiKiSEAgECtuysrLw/PlzmS0rK6vIOF+/fo3Lly+jS5cu0jahUIguXbrg/PnzRe7TunVrXL58WVqs3L59G4cOHUKvXr1K/fpw2hjJRVr0KkWHoBAG7aYrOgSFSP5zoaJDUAihin7dI1SiKQxERPThFi5ciG+//Vambc6cOZg7d26hviKRCLm5uTA1NZVpNzU1RUJCQpHH9/LygkgkQtu2bSGRSJCTkwN/f3/MmDGj1DGq6EcxEREREREVFBgYiGfPnslsgYGBcjv+n3/+iQULFmDt2rX466+/sGfPHhw8eBDfffddqY/BkRciIiIiokpCkQv2NTQ0oKGhUaq+xsbGUFNTw5MnT2Tanzx5gpo1axa5z6xZszB06FD4+voCAJo1a4aXL19i1KhR+OabbyAsxRQHjrwQEREREVGZVK1aFc7Ozjh+/Li0TSwW4/jx43B1dS1yn4yMjEIFipqaGgBAIpGU6rwceSEiIiIiqiSU6VbJkydPxrBhw9C8eXO0bNkSK1euxMuXL+Hj4wMA8Pb2Rp06dbBwYd5aWXd3dyxfvhyOjo5o1aoV/vnnH8yaNQvu7u7SIuZ9WLwQEREREVGZDRw4EMnJyZg9ezYeP34MBwcHHDlyRLqI//79+zIjLTNnzoRAIMDMmTPx8OFD1KhRA+7u7pg/f36pzymQlHaMhkrlVY6iI6CKxLuNqRbebYyI6NOgWYm/vq/tt0dh5370Qz+Fnbu0KvGlIyIiIiJSMfy+qEQq+j0iEREREREpG468EBERERFVEsq0YF8ROPJCRERERERKQeVGXsRicakegENEREREVNE48lIylSpe8guX27dvY8eOHXjw4AHs7e3h5+en6NCIiIiIiOg9VKZ4yS9crl69ih49esDOzg7Z2dnYsGED7ty5g0WLFik6RCIiIiIiKoHKzJ8SCoW4f/8++vfvDy8vLxw+fBjHjh3Djh07sGnTJly9elXRIRIRERGRihMIBArblIHKFC8SiQR79uxB3bp1ERgYKG23s7ODhoYGcnL4dEkiIiIiospMZaaNCQQCtG/fHiKRCIaGhtL2hg0bQktLC0+ePFFgdERERERE4EMq30NlihcAcHJygpOTE4C8kZj84TGhUIhXr15J+x06dAgODg6oXbu2QuIkIiIiIqLCVGba2LsEAgFycnKQnZ0NoVAIXV1dAMA333yDzz//HLm5uQqOkIiIiIiIClKpkZd35Y+8SCQSaGhoYP78+QgODkZ0dDTq1q2r4OiIiIiISNUoy8J5RVHp4kVNTQ0AoKenB39/f9y6dQtnz55F8+bNFRwZERERERG965MsXvLXszx69AiampoyC/QLEovFSE9Px3///YcnT54gLi4OTZs2reBoiYiIiIjycOSlZJ/cmpf8wmX//v0YMGAAjh07hhcvXhTZVygUQk9PD5s2bUJ8fDwLFyIiIiKiSuyTG3nJL1wGDx6MWbNmwdXVVboYP1/BO40BQI8ePSo6TCIiIiKiQjjyUrJPrnh5+PAhZs6ciQULFmDChAnIzs5Geno6Lly4AENDQzg6OvJNQURERESkhD65aWPq6urQ1taGmZkZUlJSsGjRInz22WcYMGAAPD098euvvyo6xFLbsT0MPbt2QgvHZhjs+QWuXrlSYv/IiMPo/XkPtHBsBo8+7jh96qTM7yUSCdaEBKNzh7Zo6WSHUSOG4969u+WYwYdR1bz9PFyQsCcAaX9+h1MbxqC5rVmxfdXVhAj8sjOu/TINaX9+hwtbJ6KrS2OZPgl7ApB5flGhbcXU3uWdSpns2hGGz3t0gmtzO3h7DUD81ZKv99HII+j3v55wbW6HAf3cceb02+udnZ2NVSuWYkA/d7Rp6Yjundth9owAJCdVvofQ7gwPQ69undDKyQ5DB5Ui74gj6OveE62c7PBF38Lv8+NHIzF65Jdwa9MKjk2tcTPhRnmG/8FU9d8382beJWHeyp03VSylL14kEgkAICkpCRkZGdDU1IREIkFISAjq1auHmJgYeHh44OjRo6hduzauXr2q4IhL58jhQ1i6eCH8xozFjl/2wsrKGqP9RiAlJaXI/rExf2H6tCno268/du7eh46dOuOr8WNx69bf0j4/bVyP8LBtmDlnLn4O3wUtLS2MHjUCWVlZFZXWe6lq3v072+H7CZ9j/sZjcB0egiu3EvHbihGoYaBdZP+5ft3g26clJi//DY5eK7BhbxR2LhoK+8ZvH6za9svVsPxsnnTrNWEDAGDP8crzbyDyyCEsX7IIo/zHImznHjS2ssI4f1+kFnO942L/wjcBU9Cnb39s37UXbp26YMrEcfjnzfV+9eoVEm5ch6/fGITt/BVLl4fg7t07mDRhTEWm9V4Rhw9h2eJF8Bs9Ftt/yct7jF/xecfG/IXAr/PyDv8lL+/JE97mDQCZmZlwcHLGhElTKyqNMlPVf9/Mm3kz78I+lbzLg0AgUNimDASS/L/+lVD+2pUDBw5g8eLF+Prrr+Hu7o7bt28jMjISAoEAgwYNgp6eHgDA3d0dLVq0wOzZs8stplc58jnOYM8v0KRpM8yYmRerWCxGt84dMMhrKEaMHFWo/7QpXyEzMxOr1/4gbRsyaACsrK0xa04QJBIJuri1g/dwHwzzGQEAePHiBTq1b42g+YvQs9dn8gn8Iylb3gbtpn/U/vlObRiDyzceYNKy3wDk/Yfrn/3TEfrLOSzddrJQ/9u/zcD3W07gh1+jpG3hC4YgMysbX367s8hzLPnqc/RsY42mXyz96HiT/1z40ccAAG+vAWjStCkCZry93r26uWHgoCHwGVH4ek+fNgmZmRkIXv32eg8bPBBW1taYMevbIs9xLf4qvL2+wO8RJ1CrVu0i+5SWUE5f9wwdlJf39G/e5t2jixs8vYbgS9/CeQdMyct7VYH3ubfXQDS2ssbMObJ5P3r4AJ9174Idu/fCytpGLvEK5fSBpmz/vuWFeTNv5l358tasxAsn6n11UGHnvrOycryfSqLUIy8CgQD79u2Dl5cXevbsiSZNmgAA6tevD39/f/j5+UFPTw8ZGRkIDAxEVFQUPD09FRz1+2W/fo0b16/BxbW1tE0oFMLFpTWuxMUUuc+V2Fi4uLjKtLVu0xZXYmMBAA8fPIBIlIxWLm+Pqauri2Z29sUes6Kpat5V1NXgaFUHJy7+I22TSCQ4cfEftGxqUeQ+Vauq4dVr2Uo5Mysbre0tiz2HZ3dHbPn9ktzi/ljZ2a+RcOMaWrrIXu+WrVxxNS62yH2uxMWiVavWMm2urdvgSjH9ASA9/QUEAgF0dfXkEfZHy87Oe5+3eifvVi6uxeZxJS4WrVzLlndlo6r/vpk382ben27e5UagwE0JKHXx8uDBAwQGBmLevHmYMWMGzM3NkZWVhdOnT+PGjbz53mFhYRgyZAjCw8MRGRmJxo0bv+eoipf2NA25ubkwMjKSaTcyMoJIJCpyH5FIBCMj48L9U0Rvfp+c12Zc+mNWNFXN21i/GtTV1ZCUmi7TnpSajppGOkXuc+zCLUzwbIcGZkYQCATo1KIhers1QU0j3SL7/6+DLfR1NPHzwctyj/9DPU0r7nobF3ttUkQiGL7T39DIGCnF9M/KysKqFUvRvedn0NEp+rWsaGlv8n43D6MS8hAVkbeRcfH9KyNV/ffNvJk3wLyL8inkTYpRiQfN3i8nJwfa2tpwcnJCUlISNm3ahCNHjuDy5cuwt7fHd999hy5duuDu3btYsmQJGjRoINfzZ2VlFZp3KVHTgIaGhlzPQ/SuqSsOYO30fojbMQUSiQS3H6Zi68HLGPZ58yL7D/u8BSKi/kaiqOhnHn2KsrOzMX3qV5BIgMCZcxUdDhEREcmBUo+86OvrIzk5GYGBgbC1tUV0dDTc3d0RGRmJ9PR0REdHw9TUFIGBgXIvXABg4cKFqF69usy25PuPXwtgoG8ANTW1QovcUlJSYGxsXOQ+xsbGSEkRFe7/5lsNY+MaeW2i0h+zoqlq3qKnGcjJyYWJoezIgImhDh6npBezz0sMmL4NRp1mw6rf97D3XIaXGVm48zC1UF/zmvro1KIhNv92sVzi/1D6BsVdb1Gx18bI2LjQovbUFBGM3umfnZ2N6dMmITHxEdb+uLHSjLoAgMGbvN/NI6WIPPIZF5F3iqj4/pWRqv77Zt7MG2DeRfkU8i4vXLBfMqUpXvLvK/D8+XPk5OTgxYsX0NfXx7lz59CzZ08EBQVhw4YNmDx5MlxdXWFmZgaxWAyg/B72ExgYiGfPnsls0wICP/q4VapWhY1tE1yIOi9tE4vFuHDhPOzsHYvcx87BAReiomTaos6fg52DAwCgjpkZjI1r4MKFt8dMT0/H1StxxR6zoqlq3tk5uYi5+RAdmzeUtgkEAnRs3hDR8fdK3DfrdQ4eJT+HupoQfTo2xe+nrxfqM/Sz5khKS8fhcwlyj/1jVKlSFdY2TXDxguz1vnghCs3sHYrcx87eAdEF+gPAhahzsCvQP79w+e/ePYT++BP09Q3KI/wPVqXKm/f5O3lHX4iSyaMgO3sHREfJ5h11/lyx/SsjVf33zbyZN/P+dPMmxVCKaWP5dxU7fPgwQkND8fjxY9jY2MDX1xft2rVDQEAA1NXzUnn9+jXmzp2L6OhorFixAkD5FS8aGoWniMnrbmNDh/lg1owANGnSFE2b2eHnbVuQmZmJPn37AQC+CfwaJiammDhpCgBg8BBvjBg+FFs2b0L79h1w5PAhXIuPx6y5QQDyXoPBQ72x/odQWJhboI6ZGdaEBKOGiQk6de4in6DlQFXzXhV+ButnfYHLCQ9w6dp/GOfZFtU0q2Lr73lrVDbMHoBHyc8wOzQCANDCti5q19BD3K1E1Kmhh298u0AoEGD5z7J3JhMIBPD+zBlhh/5Cbq64wvN6nyHewzFn5nTY2OZd7+0/513v//XJu96zZwSghqkJxk/Mu96DBg/FyC+9sW3LJrRt74bIwwdx/do1fDM773pnZ2cjYMpEJNy4jpWr1yFXnCudN129enVUqVJVMYm+Y4j3cMz+ZjpsmzRF06Zv8+79Ju+ZgQEwMTHBhDfv80FDhmKkjze2bt6Edu3dEPEm7/z3OQA8e/YUjxMTkZSUBAC4e+cOgLzRqvxvMBVNVf99M2/mzbw/3bzLg7KMgChKpS5e8osWgUCA/fv3w8vLCwEBATAyMsLJkyfh6emJ8PBwtG/fHhKJBNu2bcPOnTsRHx+PiIgINGrUSNEpfLAePXshLTUVa1evgkiUDCtrG6z9YYN0msjjxEQIBW8HzhwcnbBw8VKsXrUSISuXw9zCEitD1qBRo7c3KPAZMRKZmZkImjsbL148h6OTM9b+sKFSrdFR1bx3H78CYwNtzPbtClMjXVy59Qi9J21CUlretLG6pvoQi9/e1VxDQx1z/LqhXm1DpGe+RsT5mxjx7U48S38lc9xOLRrCvJZBpbrLWEHdevRCWloq1q0NQYooGY2tbBASul66iPPx40cQCN/+R9zewQnzFy1FaMhKrFm1AubmllgWvBoN31zv5KQnOPnnCQDAoC/6yJzrh41b0LxFq4pJ7D2698zLO3R1Xt5W1jZYs259gff5IwgL5O3g6IQF3y/FmpCVWB28AuYWlli+6m3eAHDyjxOYM3OG9Ofp0yYDAPxGj4X/2PEVlFnJVPXfN/Nm3sz7082bKl6lfM6LSCQ75/3mzZvw8vLCyJEj4e/vjydPnsDZ2RkaGhpIS0vD3r170aFDB9y/fx8//vgjhg0bprDCRV4jL6Qc5PWcF2Ujr+e8KBt5PedF2cjrOS9ERJVFZX7OS8OphxV27n+W9lTYuUur0n0Uh4SEoFOnTrh27Zq0TSKRoGXLlhgyZAj+++8/tGvXDr169cKePXtgaWmJgQMH4ujRozA3N0dQUJBSj7gQEREREVHRKl3x0r9/fzx58gTjx4/H9et5i4+tra0xa9Ys6OjoYMGCBXBwcEBwcDDs7e1hY2ODjIwM+Pn54eXLl5wnSERERET0iaoUxUv+zLXc3FzUqlULcXFxSEhIgL+/P+Lj4wEAtWvXRmZmJuLi4mBrawstLS0AgJ6eHkJCQhAdHQ1tbW0WL0RERESktHir5JIpvHgRi8UQCARITk5GTEwMoqKiULNmTcTExOD27dsYM2aMdARGS0sLtra22LVrF3bt2oVJkybh4MGDcHNz++Tv+U1EREREpOoUulxJLBZDKBTi+vXrGDVqFHR1dVGtWjWEhYXB1NQUly9fhrOzM/z9/REaGoomTZrA19cXqampmDJlCgwNDbF//35YWFgoMg0iIiIiIrlQkgEQhVFY8SKRSCAUCnHt2jW0bdsWY8aMgZ+fH8zMzCAUCpGTk1OogNm4cSNcXFywc+dOJCYmQkdHB4aGhopKgYiIiIiIKpDCpo0JBAKkpqbC398f3t7emD9/PszNzSEUCiGRSKCuri5TwNy5cwejRo3C1atXUaVKFZibm7NwISIiIiJSIQpd8/L48WMkJibCw8MDYvHbp3/nLxhSU1ODRCKBqakpLl26hKioKEyfPh2vX79WVMhEREREROWGC/ZLptA1L7Gxsbh37x7atWsHgUAgXQOTTyAQICMjA3FxcXB1dcX9+/fx7NkzVK1aVYFRExERERGRIih05MXS0hLq6urYs2dPXjBFPL5606ZNmDNnDjIyMmBiYsIHUBIRERHRJ0sgUNymDBRavFhYWEBPTw9bt27FvXv3pO35z30BgLt378LZ2Vn6XBciIiIiIlJNCi1e6tSpg9DQUERERGDWrFnS57nkTxebMWMGdu/eDR8fH6WZh0dERERE9KGEQoHCNmWg0DUvANCnTx8EBwdj3LhxuHjxIlxdXaGpqYmHDx8iKioKR44cQePGjRUdJhERERERKZhCR16AvHUufn5+OHv2LJo2bYqYmBjEx8fDxsYGZ86cgaOjo6JDJCIiIiKiSkDhIy/5WrZsiR07dkBNTU3RoRARERERKQRXSpRM4SMvBRW821jBRftERERERESVZuQFgMyifC7QJyIiIiJVw7+BS1apRl6IiIiIiIiKw+KFiIiIiIiUQqWaNkZEREREpMo4a6xkHHkhIiIiIiKlwJEXIiIiIqJKggv2S8aRFyIiIiIiUgosXoiIiIiISClw2hgRERERUSXBaWMlY/FC9BHSTi9SdAgKYdBmmqJDUIiUM4sVHQJRucsVSxQdgkKoCfkHI5EyYPFCRERERFRJcOClZFzzQkRERERESoEjL0RERERElQTXvJSMIy9ERERERKQUWLwQEREREZFS4LQxIiIiIqJKgrPGSsaRFyIiIiIiUgoceSEiIiIiqiS4YL9kHHkhIiIiIiKlwOKFiIiIiIiUgspNGxOLxRAKhRCLxRCLxVBXV7mXgIiIiIgqKc4aK5lKjbzkFy43b97E2LFj0atXL0yePBmPHz9WdGhERERERPQeKlO85BcuV69eRbt27fD06VM4ODhgw4YNWLRokaLDIyIiIiKCQCBQ2KYMVGbOlFAoxN27d9GnTx+MGDECCxcuBADUqVMH8fHxyMrKgoaGhoKjJCIiIiKi4qjUyMuBAwfQvXt3BAQESNsTEhJw6dIluLi4YOjQodi1a5cCoyQiIiIiVSYQKG5TBipTvAiFQnh6emL48OHQ19cHACxYsAA//vgj+vbti4kTJ+K///5DcHAw/vvvP8UGS0REREREhajMtDGJRIIaNWqgRo0aAIC0tDRkZGTg0KFD6N69OwCgS5cuMDc3R3R0NOrWravIcImIiIiI6B0qU7y8uwjJwMAAM2fOhKamJgAgNzcX6enpcHZ2hqWlpQIiJCIiIiJVpywL5xVFZaaNFaXgAn01NTWEhYUBAMzMzBQVEhERERERFUNlRl6Kkl/ZxsbGYvfu3QgJCcHp06dhamqq4MiIiIiISBVx4KVkn2TxIpFIIBAI8OjRI2hqasLQ0LDYvomJiVi0aBGuX7+OU6dOwc7OrgIjJSIiIiKi0vrkpo3lFy779+/HgAEDcOzYMbx48aLY/rVq1cLChQtx5MgR2NvbV2CkRERERERUFp/cyEt+4TJ48GDMmjULrq6u0NXVlemTX+Dkq1evXkWHSURERERUCBfsl+yTK14ePnyImTNnYsGCBZgwYQKys7ORnp6OCxcuwNDQEI6OjnxTEBEREREpoU+ueFFXV4e2tjbMzMyQkpKCtWvX4tixY4iPj4exsTEWLFgADw8PRYdJRERERFQIv2MvmdKveZFIJACApKQkZGRkQFNTExKJBCEhIahXrx5iYmLg4eGBo0ePonbt2rh69aqCIy69HdvD0LNrJ7RwbIbBnl/g6pUrJfaPjDiM3p/3QAvHZvDo447Tp07K/F4ikWBNSDA6d2iLlk52GDViOO7du1uOGXwY5q1aefv1b42EvYFIO7UApzaOR3Pb4h8Qq64mROCILrj263SknVqACz9PQlcXK5k+QqEAs/2648beQKSeXIBrv07H9C+7lHcaZbYzPAy9unVCKyc7DB00APFXS77eRyOOoK97T7RyssMXfQtf7+NHIzF65Jdwa9MKjk2tcTPhRnmG/8FU9X2uqnnvDA/DZ907wcXZDt5epXuf93PvCRdnOwzo644zBfLOzs5G8PKlGNDXHa1bOqJbp3aYNSMAyUlPyjuNMlPV662qeVPFUuriJX/tyoEDB+Dh4YHjx4+jevXqCA8Px8CBA7FkyRJs3rwZEyZMgJOTE3R0dCAUKkfKRw4fwtLFC+E3Zix2/LIXVlbWGO03AikpKUX2j435C9OnTUHffv2xc/c+dOzUGV+NH4tbt/6W9vlp43qEh23DzDlz8XP4LmhpaWH0qBHIysqqqLTei3mrVt79u9jj+4numL/xKFyHrcSVfx7ht2Bf1DDQLrL/XP8e8O3jgsnL9sHRcyk27InCzu+Hwb5xbWmfKUM7YmQ/V0xauhcOnkswc81BTB7SAWMGtKmotN4r4vAhLFu8CH6jx2L7L3vQ2MoKY/x8kVrC9Q78egr69O2P8F/2wq1TF0yeMA7/FLjemZmZcHByxoRJUysqjTJT1fe5quYdceQQli9ZhFH+Y7F91x40amyFsSW8z+Ni/8KMgCno3a8/tue/zye+fZ+/evUKCTeuw9dvDLbv/BVLV4Tg3t07+Gr8mIpM671U9Xqrat7lQSAQKGxTBgJJ/tCFktq3bx+GDh2KwMBAeHp6on79+oX6ZGRk4LvvvsOGDRtw9uxZNG7cuNzieZUjn+MM9vwCTZo2w4yZswEAYrEY3Tp3wCCvoRgxclSh/tOmfIXMzEysXvuDtG3IoAGwsrbGrDlBkEgk6OLWDt7DfTDMZwQA4MWLF+jUvjWC5i9Cz16fySfwj8S8lSNvgzbTPmr/fKc2jsflG/9h0tJ9APL+g/3Pb98g9JezWLr1j0L9b/8+E99vPoEfdp+TtoUv8kbmq2x8OTccAPDrMh8kpaZj9Pxfiu3zoVLOLP6o/fMNHTQATZo2xfRv3l7vHl3c4Ok1BF/6Fr7eAVMmITMzA6sKXG9vr4FobGWNmXO+len76OEDfNa9C3bs3gsraxu5xCuU0weasr3P5UXZ8s4Vy+fPAm+vAbBtIvs+79nVDZ6DhsCnqPf51Dfv8zUF3ueDB8LKyhrfzP62UH8AuBZ/FUMHfYGDkSdQq1btIvuUlpqQ7/OPoWx5a1bihRNtlpxW2LnPTmunsHOXlnIMQxTjwYMHCAwMxLx58zBjxgyYm5sjKysLp0+fxo0beVMmwsLCMGTIEISHhyMyMrJcCxd5yX79GjeuX4OLa2tpm1AohItLa1yJiylynyuxsXBxcZVpa92mLa7ExgIAHj54AJEoGa1c3h5TV1cXzezsiz1mRWPeqpV3FXU1OFrXwYnoW9I2iUSCExdvoWUziyL3qVpVHa+ysmXaMl9lo7W9pfTnqCv30LF5QzSsawwAaNaoFlztLRF5PkH+SXyA7Oy8613w2giFQrRyccWVuNgi97kSF4tWBd4fAODauk2x/SsjVX2fq2zeH/A+vxoXK9MfeP/7PP3FCwgEAujq6skj7I+mstdbRfMmxajEdef75eTkQFtbG05OTkhKSsKmTZtw5MgRXL58Gfb29vjuu+/QpUsX3L17F0uWLEGDBg3kev6srKxCQ5cSNQ1oaGh81HHTnqYhNzcXRkZGMu1GRka4c+d2kfuIRCIYGRkX6i9KEb35fXJem3HhY4pEoo+KV16Yt2rlbayvDXV1NSSlpsu0J6Wmw8rCpMh9jkX9jQle7XEm9g5uP0hBxxYN0btjU6gVmA66dOsf0NPWQNyuacgVS6AmFGDOuiPYEVE5PuzS0vKut2Gh622Mu3fuFLmPSCQq3N/YGCmV5FqWhqq+z1U176fFvM8N3/M+L/w6Ff8+z8rKQvCKpejR8zPo6OjIJ/CPpKrXW1XzLi9KMntLYZR65EVfXx/JyckIDAyEra0toqOj4e7ujsjISKSnpyM6OhqmpqYIDAyUe+ECAAsXLkT16tVltiXfL5T7eYgoz9Tl+/HvfyLE7ZyG52cWYsXUPtj6+yWIC0xz6d/FDp49nDB89na4eq+Eb9BOfDW4Awb3clZg5EQkT9nZ2QiY+hUAIHDWXIXGQkQVS2lGXvIX5z9//hzVqlVDZmYm9PX1ce7cOWzevBleXl7w9PSEgYEBBAIBzMzMIBaLAZTfw34CAwMxefJk2TjVPm7UBQAM9A2gpqZWaJFbSkoKjI2Ni9zH2NgYKSmiwv3ffKthbFwjr02Ugho1TGT6WFlbf3TM8sC8VStv0dOXyMnJhYmh7DemJoY6eJz6oth9Bny9BRpV1WFUvRoeJT/HvLG9cOfR29duwfjPsXTrH/jlaBwA4Nq/j2Fe0wDThnVC2KHL5ZdQKRkY5F3vdxctp6SIYFTC9S7UX1R8/8pIVd/nqpq3fjHv89SUwt+258vL+/3/LrKzszF96iQkPnqEHzZurjSjLoDqXm9Vzbu8KMvCeUVRipGX/MLl8OHDGDJkCFq3bo1x48bh9OnTqFOnDgICAjBmzBgYGhoiOzsbM2bMQHR0NPr37w+g/N4EGhoa0NPTk9k+dsoYAFSpWhU2tk1wIeq8tE0sFuPChfOws3csch87BwdciIqSaYs6fw52Dg4AgDpmZjA2roELF94eMz09HVevxBV7zIrGvFUr7+ycXMQkPETHFg2lbQKBAB1bNET01Xsl7pv1OgePkp9DXU2IPh2b4fdT16S/09KsIjMSAwC5YjGEclqM+7GqVHlzvS/IXu/oC1Gws3coch87ewdEF3h/AG+udzH9KyNVfZ+rbN5v3ufR777Po4p/nzezd5DpDwAX3nmf5xcu9+/fw7r1P0Ff36A8wv9gKnu9VTRvUoxKXbzk3whNIBBg//796N+/P5o3b45hw4YhMzMTnp6eOHXqFNTV1SGRSLB161b07dsXYWFhiIiIQKNGjRScwYcbOswHe3bvwm/79uL2v/9iXtBcZGZmok/ffgCAbwK/RvCKZdL+g4d449zZ09iyeRPu3P4XoWtCcC0+Hp5eQwDkvYaDh3pj/Q+h+PPEcdz6+yZmBn6NGiYm6NS58jwDg3mrVt6rwk/Bp3crDO7lDCtLE6wK6IdqmlWx9feLAIANczwRNKantH+LJnXR260pLGsboo1DPfwW7AuhUIDl2/6U9jl0+gYCfDqhRxtrmNcywP86NMWEQe3x25/xFZxd8YZ4D8fe3b/gt/1513vBd3nXu3efvOs9MzAAqwpc70FDhuLc2TPYunkT7ty+jXVrQnD92jV4eg2W9nn27CluJtzAv//+CwC4e+cObibckM4brwxU9X2uqnkP9h6Ovb/+ggP79+L27bfv8/+9eZ/PmhGAkJVv8/YaMhTnz57Bti1v3udr897nAwflvc+zs7Px9eSJuH4tHvMXLUGuOBciUTJEomRkZ79WSI5FUdXrrap5U8WrlNPGRCIRjI2NpSMmN2/eRFBQEJYtWwZ/f388efIECxcuhKamJvr06YO9e/eiQ4cOcHNzw99//42VK1cqdeECAD169kJaairWrl4FkSgZVtY2WPvDBunw+ePERAgFb2tPB0cnLFy8FKtXrUTIyuUwt7DEypA1aNTo7d3VfEaMRGZmJoLmzsaLF8/h6OSMtT9skMtokbwwb9XKe/exOBjra2P2qO4wNdLFlb8fofdXG6SL+Oua6suMomhUrYI5/j1Qr7Yh0jNfI+JcAkbM3YFn6a+kfSYv24c5ft0RPK0fahjoIFH0HBv3RmHBxmMVnl9xuvfshbS0VISuDkHKm+u9Zt36Atf7kcxIkYOjExZ8vxRrQlZidfAKmFtYYvmq1WhY4Hqf/OME5sycIf15+rS8Ka1+o8fCf+z4CsqsZKr6PlfVvLv3yMs7dM3b9/nqd9/nBWZG2Ds4Yf6ipVi7usD7PPjt+zw56QlO/nkCAODZv4/MuX7ctAXNW7SqmMTeQ1Wvt6rmXR44baxkle45LyEhIVi/fj3Cw8PRpEkTAEBCQgKCg4OxZMkSpKWloXPnznBzc8PYsWPh4+ODR48eYdu2bejatSvEYrFCH0Qpr+e8EFVm8nrOi7KR13NelI28nvNCykFez3lRNvJ6zgsph8r8nJf2y88q7NynJleeBzoXp9JNG+vfvz+ePHmC8ePH4/r16wAAa2trzJo1Czo6OliwYAEcHBwQHBwMe3t72NjYICMjA35+fnj58iWrVSIiIiJSWgKB4jZlUCmKl/zBn9zcXNSqVQtxcXFISEiAv78/4uPz5qnXrl0bmZmZiIuLg62tLbS0tAAAenp6CAkJQXR0NLS1tVm8EBERERF9ohRevIjFYggEAiQnJyMmJgZRUVGoWbMmYmJicPv2bYwZM0Y6AqOlpQVbW1vs2rULu3btwqRJk3Dw4EG4ubkVeys+IiIiIiJlIRAIFLYpA4XO+Mtfn3L9+nWMGjUKurq6qFatGsLCwmBqaorLly/D2dkZ/v7+CA0NRZMmTeDr64vU1FRMmTIFhoaG2L9/PywsLBSZBhERERERVQCFFS8SiQRCoRDXrl1D27ZtMWbMGPj5+cHMzAxCoRA5OTmFCpiNGzfCxcUFO3fuRGJiInR0dGBoaKioFIiIiIiIqAIpbNqYQCBAamoq/P394e3tjfnz58Pc3BxCoRASiQTq6uoyBcydO3cwatQoXL16FVWqVIG5uTkLFyIiIiL6pHDBfskUuubl8ePHSExMhIeHB8RisbQ9f86dmpoaJBIJTE1NcenSJURFRWH69Ol4/bryPIyKiIiIiIgqhkLXvMTGxuLevXto164dBAJBoWe0CAQCZGRkIC4uDq6urrh//z6ePXuGqlWrKjBqIiIiIqLyoSwL5xVFoSMvlpaWUFdXx549e/KCKeLhkps2bcKcOXOQkZEBExMTNGrUqKLDJCIiIiKiSkChxYuFhQX09PSwdetW3Lt3T9qe/9wXALh79y6cnZ2lz3UhIiIiIiLVpNDipU6dOggNDUVERARmzZolfZ5L/nSxGTNmYPfu3fDx8eEQGhERERF98rhgv2QKXfMCAH369EFwcDDGjRuHixcvwtXVFZqamnj48CGioqJw5MgRNG7cWNFhEhERERGRgil05AXIW+fi5+eHs2fPomnTpoiJiUF8fDxsbGxw5swZODo6KjpEIiIiIqIKIRQIFLYpA4WPvORr2bIlduzYATU1NUWHQkRERERElVClKV4A2buNSSQSrnMhIiIiIpXCP39LpvBpYwUVLFZYuBARERERUUGVqnghIiIiIiIqTqWaNkZEREREpMo4+6hkHHkhIiIiIiKlwJEXIiIiIqJKQsiBlxJx5IWIiIiIiJQCixciIiIiIvoga9asgaWlJTQ1NdGqVStER0eX2P/p06cYO3YsatWqBQ0NDTRu3BiHDh0q9fk4bYyIiIiIqJJQpgX7O3fuxOTJk7Fu3Tq0atUKK1euRPfu3XHz5k2YmJgU6v/69Wt07doVJiYm2L17N+rUqYN79+5BX1+/1Odk8UJERERERGW2fPlyjBw5Ej4+PgCAdevW4eDBg9i0aROmT59eqP+mTZuQmpqKc+fOoUqVKgAAS0vLMp2T08aIiIiIiCoJgUBxW1ZWFp4/fy6zZWVlFRnn69evcfnyZXTp0kXaJhQK0aVLF5w/f77IfX777Te4urpi7NixMDU1RdOmTbFgwQLk5uaW+vXhyAvRR8gVSxQdgkKknV2i6BAUwqBtgKJDUIi0M98rOgSqQGq81ZFKEUtU83MM4Pu8KAsXLsS3334r0zZnzhzMnTu3UF+RSITc3FyYmprKtJuamiIhIaHI49++fRsnTpzA4MGDcejQIfzzzz8YM2YMsrOzMWfOnFLFyOKFiIiIiKiSECiwsAoMDMTkyZNl2jQ0NOR2fLFYDBMTE/z4449QU1ODs7MzHj58iCVLlrB4ISIiIiKi0tPQ0Ch1sWJsbAw1NTU8efJEpv3JkyeoWbNmkfvUqlULVapUgZqamrTNxsYGjx8/xuvXr1G1atX3npdrXoiIiIiIqEyqVq0KZ2dnHD9+XNomFotx/PhxuLq6FrlPmzZt8M8//0AsFkvb/v77b9SqVatUhQvA4oWIiIiIqNIQChS3ldXkyZOxfv16bNmyBTdu3MDo0aPx8uVL6d3HvL29ERgYKO0/evRopKamYuLEifj7779x8OBBLFiwAGPHji31OTltjIiIiIiIymzgwIFITk7G7Nmz8fjxYzg4OODIkSPSRfz379+HUPh2rKRu3bqIiIjApEmTYGdnhzp16mDixIkICCj9DXEEEonK3maiXLzKUXQEVJFU9W5jqno3It5tjIg+Nap6t7FqVSrv51jv9ZcUdu79I5sr7NylxWljRERERESkFFi8EBERERGRUuCaFyIiIiKiSkJQeWe0VQoqOfLCZT5ERERERMpH5UZexGIxhEIhRCIRsrKyUKdOHUWHREREREQEABBy6KVEKjfyIhQKcffuXTg6OmLFihW4e/euokMiIiIiIqJSULniBQBOnz6Nhw8fIjIyEuvXr8f9+/cVHRIREREREb2Hyk0bA4BWrVph2LBhcHZ2xvz58yEWizFp0iSYmJgoOjQiIiIiUmGcNVYylSxeAOCPP/7AunXrIBaLsWTJEujo6CA6Ohrm5uYICQlRdHhERERERPQOlSteJBIJGjduDFtbW/z777+YMGECtLW1MXHiRKipqWH79u2KDpGIiIiIVJSAQy8lUrk1L/lvCIFAgJMnTwIAoqKioK6uDk1NTVy8eJGL+ImIiIiIKiGVG3nJzc2FmpoamjdvDrFYjAkTJuDgwYOIi4vDoUOHMHnyZKirq2P69OlQV1e5l4eIiIiIFIgDLyVTub/O1dTUAAB2dnb44osvULNmTRw4cAAWFhYYPXo0hEIhOnXqxMKFiIiIiKiSUdm/0Fu0aIGgoCC4u7vD3t5e+vBKPz8/RYdGRERERERF+CSLF4lEAoFAgEePHkFTUxOGhoaF+pibm2PatGnQ0NAAkPfwSiIiIiIiRRJy3liJPrm/2PMLl/3792PAgAE4duwYXrx4UWTf/MKFiIiIiIgqv0+ueMkvXAYPHgx3d3e4urpCV1dXpo9EIlFQdERERERExRMocFMGn9y0sYcPH2LmzJlYsGABJkyYgOzsbKSnp+PChQswNDSEo6Mj759NRERERKSEPrniRV1dHdra2jAzM0NKSgrWrl2LY8eOIT4+HsbGxliwYAE8PDwUHSYREREREZWRXKaNPX36VB6H+SD5U8CSkpKQkZEBTU1NSCQShISEoF69eoiJiYGHhweOHj2K2rVr4+rVqwqLtax2bA9Dz66d0MKxGQZ7foGrV66U2D8y4jB6f94DLRybwaOPO06fOinze4lEgjUhwejcoS1aOtlh1IjhuHfvbjlm8GFUNe+d4WH4rHsnuDjbwdtrAOKvlpz30Ygj6OfeEy7OdhjQ1x1nCuSdnZ2N4OVLMaCvO1q3dES3Tu0wa0YAkpOelHcaZaaq19vPwxUJewOQdnIeTm0ci+a2ZsX2VVcTIvDLzri2+2uknZyHC9smoqtLY5k+CXsDkBn1faFtxdTe5Z1Kmajq9WbezLskn0reO8PD0KtbJ7RyssPQQaX7HOvr3hOtnOzwRd/CeR8/GonRI7+EW5tWcGxqjZsJN8oz/EpDIBAobFMGZS5evv/+e+zcuVP684ABA2BkZIQ6deogLi5OrsG9T/7i/AMHDsDDwwPHjx9H9erVER4ejoEDB2LJkiXYvHkzJkyYACcnJ+jo6CjNXcWOHD6EpYsXwm/MWOz4ZS+srKwx2m8EUlJSiuwfG/MXpk+bgr79+mPn7n3o2Kkzvho/Frdu/S3t89PG9QgP24aZc+bi5/Bd0NLSwuhRI5CVlVVRab2XquYdceQQli9ZhFH+Y7F91x40amyFsX6+SC0m77jYvzAjYAp69+uP7b/shVunLpg8cRz+eZP3q1evkHDjOnz9xmD7zl+xdEUI7t29g6/Gj6nItN5LVa93/y52+H7i55i/4Thch63ClVuJ+G3lCNQw0C6y/1z/7vDt0wqTl+2H46Dl2LD3AnYu8oZ949rSPm19VsOy13fSrdf49QCAPScqzxc2qnq9mTfzVoW8Iw4fwrLFi+A3eiy2/7IHja2sMKaEz7HYmL8Q+PUU9OnbH+H5n2MT3n6OAUBmZiYcnJwxYdLUikqDlIBAUsbV6/Xq1UNYWBhat26No0ePYsCAAdi5cyd27dqF+/fvIzIysrxiLdK+ffswdOhQBAYGwtPTE/Xr1y/UJyMjA9999x02bNiAs2fPonHjxkUcST5e5cjnOIM9v0CTps0wY+ZsAIBYLEa3zh0wyGsoRowcVaj/tClfITMzE6vX/iBtGzJoAKysrTFrThAkEgm6uLWD93AfDPMZAQB48eIFOrVvjaD5i9Cz12fyCfwjKVveuWL53PzB22sAbJs0xfRv3ubds6sbPAcNgY9v4bwDpk5CZmYGVq15m7f34IGwsrLGN7O/LfIc1+KvYuigL3Aw8gRq1apdZJ/SUhPK59sZZbveBm0DPmr/fKc2jsXl6w8wadl+AHnfsv2zPxChv5zD0m1/Fup/+8A3+H7zCfzw63lpW/jCIcjMysaXc3cW6g8AS75yR8821mj6xZKPjjftzPcffQxA+a63vDBv5l2Z8xbL6SZGQwcNQJOmsp9jPbq4wdNrCL4s6nNsypvPsQJ5e3sNRGMra8ycI/s59ujhA3zWvQt27N4LK2sbucRbrUrlHWUYvC1WYecOG+qgsHOXVpmHIR4/foy6desCAH7//XcMGDAA3bp1w9dff42LFy/KPcCSPHjwAIGBgZg3bx5mzJgBc3NzZGVl4fTp07hxI29oMSwsDEOGDEF4eDgiIyPLtXCRl+zXr3Hj+jW4uLaWtgmFQri4tMaVuJgi97kSGwsXF1eZttZt2uJKbCwA4OGDBxCJktHK5e0xdXV10czOvthjVjSVzTs7L++CMQqFQrRyccWVuNgi97kaFyvTHwBcW7cptj8ApL94AYFAAF1dPXmE/dFU9XpXUVeDo1UdnLh4S9omkUhw4uI/aNnMvMh9qlZVw6vXst+MZGZlo7W9ZbHn8OzhiC2/X5Jb3B9LVa8382beKpH3B3yOXYmLRSvXsn2OEQEfULwYGBjgv//+AwAcOXIEXbp0AZD34Zubmyvf6N4jJycH2tracHJyQlJSEpYuXYru3bujV69eGDlyJP744w906dIFzs7OOH78OBwdHeV6/qysLDx//lxmk8cQbtrTNOTm5sLIyEim3cjICCKRqMh9RCIRjIyMC/dPEb35fXJem3Hpj1nRVDXvp2l5eRu+k7ehkTFSUkrK+92cjJFSTE5ZWVkIXrEUPXp+Bh0dHfkE/pFU9Xob61eDuroaklLTZdqT0l6gppFukfsci/obEwa1Q4O6RhAIBOjUshF6uzVFTaOiC9H/dWgCfR1N/Hyw8hQvqnq9mTfzBlQg72I+x0r6XBKJRIX7GxffX5VwzUvJyly89OvXD15eXujatStSUlLQs2dPAEBMTAwaNmwo9wBLoq+vj+TkZAQGBsLW1hbR0dFwd3dHZGQk0tPTER0dDVNTUwQGBqJBgwZyP//ChQtRvXp1mW3J9wvlfh6ij5GdnY2AqV8BAAJnzVVoLPRhpq44gH//EyFux1Q8Pz0fK6b0xtbfL0FczLTFYe4tEBF1E4mioh/QS0REpKzKfKvkFStWwNLSEv/99x8WL14s/RY3MTERY8aU32Lg/MX5z58/R7Vq1ZCZmQl9fX2cO3cOmzdvhpeXFzw9PWFgYACBQAAzMzOIxWIAKLdKMjAwEJMnT5aNU03jo49roG8ANTW1Qov7UlJSYGxsXOQ+xsaFv6VPSUmB8Ztvc4yNa+S1iVJQo4aJTB8ra+uPjlkeVDVvfYO8vN9d1JiaUvjbuHx5eb/7Oolg9M7rlJ2djelTJyHx0SP8sHFzpRl1AVT3eoueZiAnJxcmhrLXwsRAF49Tii42RE9fYkDAVmhUVYdR9Wp4lPwc88b2xJ1HqYX6mtfUR6cWDeE5fVu5xP+hVPV6M2/mDahA3sV8jhX1uZTP2Ni4cH9R8f2J8pV55KVKlSqYOnUqgoODZaZhTZo0Cb6+vnINLl9+4XL48GEMGTIErVu3xrhx43D69GnUqVMHAQEBGDNmDAwNDZGdnY0ZM2YgOjoa/fv3B1B+xYuGhgb09PRkNg2Njy9eqlStChvbJrgQ9XZxrlgsxoUL52FnX/TUNzsHB1yIipJpizp/DnYODgCAOmZmMDaugQsX3h4zPT0dV6/EFXvMiqayeVfJyzv6gmze0VFRsLN3KHKfZvYOMv0B4ML5czL98wuX+/fvYd36n6Cvb1Ae4X8wVb3e2Tm5iLn5EB1bvB2pFggE6NiiIaKv3i9x36zXOXiU/BzqakL0cWuK309dK9Rn6OfNkZSWjsPnEuQe+8dQ1evNvJm3SuT95nPswrufYxeK/xyzs3dAdJTs51jUO59jqkogUNymDEo18vLbb7+V+oD/+9//PjiYd+UXLQKBAPv374eXlxcCAgJgZGSEkydPwtPTE+Hh4Wjfvj0kEgm2bduGnTt3Ij4+HhEREWjUqJHcYqloQ4f5YNaMADRp0hRNm9nh521bkJmZiT59+wEAvgn8GiYmppg4aQoAYPAQb4wYPhRbNm9C+/YdcOTwIVyLj8esuUEA8v44GjzUG+t/CIWFuQXqmJlhTUgwapiYoFPnLgrL812qmvdg7+GY88102DZpiibN7LD9Td7/65OX96wZATAxMcH4r/Ly9hoyFCN9vLFtyya0beeGiCMHcf3aNcyck5d3dnY2vp48EQk3riN4zTrkinOl86arV6+OKlWqKibRd6jq9V4VfhrrZw3A5RsPcOn6A4wb2BbVNKtg65s1KhtmD8Cj5OeYHXoEANCiSV3UrqGHuL8TUaeGHr7x7QqhUIDlP8s+E0EgEMD7s+YIO3QZubniCs/rfVT1ejNv5q0KeQ/xHo7Zbz7Hmja1w/af8/Lu/eZzbGZg3ufYhDd5D3rzObZ18ya0a++GiMN5n2P5eQPAs2dP8TgxEUlJSQCAu3fuAMhbG5M/IkWqp1TFS58+fUp1MIFAIJdF+yKRCMbGxtIRk5s3byIoKAjLli2Dv78/njx5goULF0JTUxN9+vTB3r170aFDB7i5ueHvv//GypUrlbpwAYAePXshLTUVa1evgkiUDCtrG6z9YYN0OPVxYiKEgrcDZw6OTli4eClWr1qJkJXLYW5hiZUha9Co0du7q/mMGInMzEwEzZ2NFy+ew9HJGWt/2CCX0SJ5UdW8u/fIyzt0TQhS3uS9et36Ank/grDAVyL2Dk6Yv2gp1q5eidXBK2BuYYnlwavR8E3eyUlPcPLPEwAAz/59ZM7146YtaN6iVcUk9h6qer13H7sCY31tzB7ZDaZGurhy6xF6T9okXcRft6a+zO1LNaqqY45fd9SrbYj0zNeIOJeAEd/uwLP0VzLH7dSiIcxrGWDLgcqzUL8gVb3ezJt5q0Le3Xv2QlpaKkJXv/0cW/Pu51iB2+w7ODphwfdLsSakwOfYqrefYwBw8o8TmDNzhvTn6dPypur7jR4L/7HjKyiziqcsC+cVpczPeSlvISEhWL9+PcLDw9GkSRMAQEJCAoKDg7FkyRKkpaWhc+fOcHNzw9ixY+Hj44NHjx5h27Zt6Nq1K8RisUIfRCmv57yQcpDXc16Ujbye86Js5PWcF2Ujr+e8EFHlI6/nvCibyvycF+/tVxR27q1edgo7d2l91F/5r169en+nMurfvz+ePHmC8ePH4/r16wAAa2trzJo1Czo6OliwYAEcHBwQHBwMe3t72NjYICMjA35+fnj58iWrVSIiIiKiT1SZi5fc3Fx89913qFOnDnR0dHD79m0AwKxZs7Bx48YPCiJ/8Cc3Nxe1atVCXFwcEhIS4O/vj/j4eABA7dq1kZmZibi4ONja2kJLSwsAoKenh5CQEERHR0NbW5vFCxEREREpLaFAcZsyKHPxMn/+fGzevBmLFy9G1apvF/02bdoUGzZsKHMAYrEYAoEAycnJiImJQVRUFGrWrImYmBjcvn0bY8aMkY7AaGlpwdbWFrt27cKuXbswadIkHDx4EG5ubsXegpCIiIiIiD4NZX7Oy9atW/Hjjz+ic+fO8Pf3l7bb29sjIaFst+bMX59y/fp1jBo1Crq6uqhWrRrCwsJgamqKy5cvw9nZGf7+/ggNDUWTJk3g6+uL1NRUTJkyBYaGhti/fz8sLCzKmgYRERERUaXDWUQlK3Px8vDhQzRs2LBQu1gsRnZ2dqmPI5FIIBQKce3aNbRt2xZjxoyBn58fzMzMIBQKkZOTU6iA2bhxI1xcXLBz504kJiZCR0cHhoaGZU2BiIiIiIiUUJmnjdna2uL06dOF2nfv3i3z0Mr3EQgESE1Nhb+/P7y9vTF//nyYm5tDKBRCIpFAXV1dpoC5c+cORo0ahatXr6JKlSowNzdn4UJEREREnxSBAjdlUOaRl9mzZ2PYsGF4+PAhxGIx9uzZg5s3b2Lr1q34/fffy3Ssx48fIzExER4eHjK3OM4fLlNTU4NEIoGpqSkuXboES0tLTJ8+HXv37pVZb0NERERERJ++MhcvvXv3xoEDBxAUFARtbW3Mnj0bTk5OOHDgALp27VqmY8XGxuLevXto164dBAJBoWe0CAQCZGRkIC4uDq6urrh//z6ePXvGwoWIiIiISAWVuXgBgHbt2uHo0aMffXJLS0uoq6tjz5498PDwKPLhkps2bcJvv/2Gffv2wcTEBCYmJh99XiIiIiKiykjIBfsl+qDiBQAuXbqEGzduAMhbB+Ps7FzmY1hYWEBPTw9bt25F8+bNpXcNk0gk0qljd+/ehbOzs/S5LkREREREpJrKXLw8ePAAgwYNwtmzZ6Gvrw8AePr0KVq3bo0dO3bAzMys1MeqU6cOQkND4eXlhVmzZmH69OmwtbWVThebN28edu/ejcjISN42joiIiIg+efyTt2RlLl58fX2RnZ2NGzduwMrKCgBw8+ZN+Pj4wNfXF0eOHCnT8fr06YPg4GCMGzcOFy9ehKurKzQ1NfHw4UNERUXhyJEjaNy4cVnDJCIiIiKiT0yZb5V88uRJhIaGSgsXALCyskJISAhOnTpV9gCEQvj5+eHs2bNo2rQpYmJiEB8fDxsbG5w5c6ZMt18mIiIiIqJPV5lHXurWrVvkwyhzc3NRu3btDw6kZcuW2LFjB9TU1D74GEREREREyoxLJUpW5pGXJUuWYPz48bh06ZK07dKlS5g4cSKWLl36ccEUuNuYRCL5qGMREREREdGnpVQjLwYGBjJV4MuXL9GqVSuoq+ftnpOTA3V1dXz55Zfo06fPBwdT8BysOomIiIhI1fBP4JKVqnhZuXJlOYdBRERERERUslIVL8OGDSvvOIiIiIiIVB4fUlmyD35IJQC8evUKr1+/lmnT09P7qICIiIiIiIiKUuYF+y9fvsS4ceNgYmICbW1tGBgYyGxERERERETloczFy9dff40TJ04gNDQUGhoa2LBhA7799lvUrl0bW7duLY8YiYiIiIhUgkCguE0ZlHna2IEDB7B161a4ubnBx8cH7dq1Q8OGDWFhYYGwsDAMHjy4POIkIiIiIiIVV+aRl9TUVNSvXx9A3vqW1NRUAEDbtm1x6tQp+UZHRERERKRCBAKBwjZlUObipX79+rhz5w4AwNraGrt27QKQNyKjr68v1+CIiIiIiIjylbl48fHxQVxcHABg+vTpWLNmDTQ1NTFp0iRMmzZN7gESEREREREBgEAikUg+5gD37t3D5cuX0bBhQ9jZ2ckrLqX14pVY0SEoRBX1MtfBRKQkDNoGKDoEhUg7872iQyCicqL5UQ8LKV/j995Q2LlD+too7Nyl9dGXzsLCAhYWFvKIhYiIiIiIqFilKl5WrVpV6gNOmDDhg4MhIiIiIlJlyrJwXlFKVbysWLGiVAcTCAQsXoiIiIiIqFyUqnjJv7sYERERERGVHyEHXkrEVdZERERERKQUWLwQEREREZFSqMQ3iiMiIiIiUi2cNlYyjrwQEREREZFS4MgLEREREVElwVsll+yDRl5Onz6NIUOGwNXVFQ8fPgQAbNu2DWfOnJFrcOVFLBYrOgQiIiIiIiqjMhcvv/76K7p37w4tLS3ExMQgKysLAPDs2TMsWLBA7gHKm1gshlAoxK1bt/Dnn38qOhwiIiIiIiqlMhcv8+bNw7p167B+/XpUqVJF2t6mTRv89ddfcg1O3vILl9jYWDg5OeHGjRuKDomIiIiISEooUNymDMpcvNy8eRPt27cv1F69enU8ffpUHjGVi/zCJS4uDm3btsWoUaMwevRoRYdFRERERESlVOYF+zVr1sQ///wDS0tLmfYzZ86gfv368opL7vKnirm4uOCrr77CwoULkZ2djf379yMxMREGBgb4/PPPoa+vr+hQiYiIiEhFcb1+ycpcvIwcORITJ07Epk2bIBAI8OjRI5w/fx5Tp07FrFmzyiNGuRCLxdi4cSN0dHTQuHFjAEDv3r2RmJiIjIwM3L17F506dcLs2bPh6uqq4GiJiIiIiOhdZS5epk+fDrFYjM6dOyMjIwPt27eHhoYGpk6divHjx5dHjHIhFAoxfvx4ZGZmYt26dZg5cyYcHBzwyy+/wNzcHP/99x969OiBxYsXY+/evYoOl4iIiIhUkJBDLyUSSCQSyYfs+Pr1a/zzzz9IT0+Hra0tdHR05B2bXEkkEulIUVBQEB4+fIjvv/8etra20j6nTp2Cm5sbYmJiYG9v/0HnefFKNW/DXEWdzzsl+lQZtA1QdAgKkXbme0WHQETlRLMSP+lw+qG/FXbuRb0aK+zcpfXBl65q1aoyf/hXdgKBABKJBLVr10ZQUBDi4uLQqFEjmT4vXrxA48aNUatWLQVFSURERERExSlz8dKxY8cSn/x54sSJjwqoPOUXMCYmJujatWuh3586dQpmZmbQ0NBQQHREREREpOo4l6VkZS5eHBwcZH7Ozs5GbGws4uPjMWzYMHnFVW6KKrxiY2Pxyy+/IDQ0FGfPnkX16tUVEBkREREREZWkzMXLihUrimyfO3cu0tPTPzogeSi4vkVTUxOGhobF9n306BHmzJmDhIQEnD59Gs2aNavASImIiIiI3uJ6/ZLJbWRqyJAh2LRpk7wO98HyC5f9+/djwIABOHbsGF68eFFs/9q1a2PZsmU4fvz4By/SJyIiIiKi8ie34uX8+fPQ1NSU1+E+WH7hMnjwYLi7u8PV1RW6uroyfQreYE0ikaBhw4YwMzOr6FCJiIiIiKgMyjxtrF+/fjI/SyQSJCYm4tKlS5XiIZUPHz7EzJkzsWDBAkyYMAHZ2dlIT0/HhQsXYGhoCEdHR5l1LyXdfICIiIiIqCLxOS8lK/PIS/Xq1WU2Q0NDuLm54dChQ5gzZ055xFgm6urq0NbWhpmZGVJSUrBo0SJ89tlnGDBgADw9PfHrr78qOsRS27UjDO49O6N1C3sMGzwQ8VevlNj/WOQRePTuhdYt7DHQ4384c/qkzO9/CF0Nj9690LaVEzq2bYUxo3wQfyWuPFP4IDu2h6Fn105o4dgMgz2/wNUrJecdGXEYvT/vgRaOzeDRxx2nT8nmLZFIsCYkGJ07tEVLJzuMGjEc9+7dLccMPgzzZt4l+VTy9vNwRcLeAKSdnIdTG8eiuW3xo97qakIEftkZ13Z/jbST83Bh20R0dZF9BkHC3gBkRn1faFsxtXd5p1Imqnq9mTfzLsmnkjdVrDIVL7m5ufDx8cHy5cvx008/4aeffsLGjRuxaNEidOvWrbxiLFH+FLCkpCRkZGRAU1MTEokEISEhqFevHmJiYuDh4YGjR4+idu3auHr1qkLiLKvII4ewYun3GOk3Fj/v+BWNrawwfvRIpKakFNk/LjYG30yfit59PRC2cw/cOnbG1K/G459bbx90ZGFhia8DZ2LHr/uxYfPPqFW7DsaO9kVaampFpfVeRw4fwtLFC+E3Zix2/LIXVlbWGO03AinF5B0b8xemT5uCvv36Y+fufejYqTO+Gj8Wtwrk/dPG9QgP24aZc+bi5/Bd0NLSwuhRI5CVlVVRab0X82beqpB3/y52+H7i55i/4Thch63ClVuJ+G3lCNQw0C6y/1z/7vDt0wqTl+2H46Dl2LD3AnYu8oZ949rSPm19VsOy13fSrdf49QCAPScqz3/rVfV6M2/mrQp5lweBQHGbMihT8aKmpoZu3brh6dOn5RRO2eQvzj9w4AA8PDxw/PhxVK9eHeHh4Rg4cCCWLFmCzZs3Y8KECXBycoKOjg6EQuW4e3bYti3o0+8L/K9PP9Rv0BCBM+dCU1MTv+3bU2T/HWFb4dq6LbyHj0C9+g0wetxEWNvYYNeO7dI+PXp9jlYurWFmVhcNGjbCpKnT8TI9Hbdu3ayotN5r25af0K//APTp64EGDRti5pxvoampiX17ih4xC/t5K1q3bYfhX/qifoMGGDfhK9jY2mLH9p8B5L1HwrZtxUi/0ejYqQsaW1lj3sLFSE5KwonjxyoytRIxb+atCnlPGNQOP+2PxraDl5BwNwnjv9+LzFfZGPZ5iyL7e/VwwuItfyDi/E3cfZSK9XuiEHE+ARO92kn7iJ6+xJPUdOnWq40N/v1PhNN/3a6otN5LVa8382beqpA3Vbwy/yXftGlT3L5dOT4UBAIB9u3bBy8vL/Ts2RNNmjQBANSvXx/+/v7w8/ODnp4eMjIyEBgYiKioKHh6eio46vfLzn6NhBvX0MrFVdomFArR0sUVV67EFrnPlStxaFmgPwC4tm6Lq8X0z85+jb2/7oKOri4aN7aWV+gfJfv1a9y4fg0urq2lbUKhEC4urXElLqbIfa7ExsLlnbxbt2mLK7GxAICHDx5AJEpGK5e3x9TV1UUzO/tij1nRmDfzVoW8q6irwdGqDk5cvCVtk0gkOHHxH7RsZl7kPlWrquHV6xyZtsysbLS2tyz2HJ49HLHl90tyi/tjqer1Zt7MWxXyJsUoc/Eyb948TJ06Fb///jsSExPx/Plzma0iPXjwAIGBgZg3bx5mzJgBc3NzZGVl4fTp07hx4wYAICwsDEOGDEF4eDgiIyPRuHHj9xy19LKysgrlL4+hzKdpT5GbmwtDIyOZdkMjI6SIREXukyISwdDI+L39T5/8A+1cnNG6hQO2b9uCNes2Qt/A4KNjloe0p2nIzc2F0Tt5GxkZQVRM3iKRCEbv5G1kZARRiujN75Pz2oxLf8yKxryZN/Dp522sXw3q6mpISpV9HlhS2gvUNNItcp9jUX9jwqB2aFDXCAKBAJ1aNkJvt6aoaaRXZP//dWgCfR1N/Hyw8hQvqnq9mTfzBj79vMuLUKC4TRmUungJCgrCy5cv0atXL8TFxeF///sfzMzMYGBgAAMDA+jr68Oggv8IzsnJgba2NpycnJCUlISlS5eie/fu6NWrF0aOHIk//vgDXbp0gbOzM44fPw5HR0e5nn/hwoWFbmCwbMkiuZ5D3pq3aIXtu/Zg09btcG3TFoHTJhW7joaISJGmrjiAf/8TIW7HVDw/PR8rpvTG1t8vQSyWFNl/mHsLRETdRKKo+Gd7ERGRcit18fLtt9/i5cuX+OOPP6TbiRMnpFv+zxVJX18fycnJCAwMhK2tLaKjo+Hu7o7IyEikp6cjOjoapqamCAwMRIMGDeR+/sDAQDx79kxmmzJt+kcfV99AH2pqaoWKitSUFBgZGxe5j5GxMVJTRO/tr1WtGuqaW6CZnQNmfzsfaupq2L+vctyBzUDfAGpqaoUW96WkpMC4mLyNjY2R8k7eKSkpMH7zbY6xcY28NlHpj1nRmDfzBj79vEVPM5CTkwsTQx2ZdhMDXTxOKbrYED19iQEBW2HUcRas+i6C/cCleJn5GnceFb7JiHlNfXRq0RCb918sl/g/lKpeb+bNvIFPP+/yIhQIFLYpg1IXL/l39erQoUOJW3nJP//z58+Rk5ODFy9eQF9fH+fOnUPPnj0RFBSEDRs2YPLkyXB1dYWZmRnEYjGA8nuWi4aGBvT09GQ2DQ2Njz5ulSpVYW3TBNEXoqRtYrEYFy9Ewc7Ooch97OzscbFAfwC4EHUOzYrp//a4Erx+/fpjQ5aLKlWrwsa2CS5EnZe2icViXLhwHnb2RY+a2Tk44EKUbN5R58/BzsEBAFDHzAzGxjVw4cLbY6anp+Pqlbhij1nRmDfzVoW8s3NyEXPzITq2aChtEwgE6NiiIaKv3i9x36zXOXiU/BzqakL0cWuK309dK9Rn6OfNkZSWjsPnEuQe+8dQ1evNvJm3KuRNilGmh1Qq6oGO+XcVO3z4MEJDQ/H48WPY2NjA19cX7dq1Q0BAANTV81J5/fo15s6di+joaKxYsUKhcX+MwUOHYe6sQNg2aYomTZth+89bkZmZCfc+fQEAs78JgImJKcZNnAwA8BzsjVEjvPHzlp/Qtn0HRBw5hOvXrmHGrG8BAJkZGdi04Qe0d+sIY+MaePr0KXbt2I7kpCfo0rW7wvJ819BhPpg1IwBNmjRF02Z2+HnbFmRmZqJP37yHo34T+DVMTEwxcdIUAMDgId4YMXwotmzehPbtO+DI4UO4Fh+PWXODAORd+8FDvbH+h1BYmFugjpkZ1oQEo4aJCTp17qKwPN/FvJm3KuS9Kvw01s8agMs3HuDS9QcYN7AtqmlWwdY3a1Q2zB6AR8nPMTv0CACgRZO6qF1DD3F/J6JODT1849sVQqEAy3+WfRaEQCCA92fNEXboMnJzxRWe1/uo6vVm3sxbFfIuD0r4Z2uFKlPx0rhx4/cWAqlyfGZIftEiEAiwf/9+eHl5ISAgAEZGRjh58iQ8PT0RHh6O9u3bQyKRYNu2bdi5cyfi4+MRERGBRo0ayS2WitatRy+kpaVh3dpVSBGJ0NjKBiFrf5Qubnv8OFHmts/2Do6Yv3AJ1q4OxpqQFahrboGlK0PQsFHeDQqEamq4e+c2fv9tH54+TUN1fX3YNmmG9T/9jAYNK8/r1KNnL6SlpmLt6lUQiZJhZW2DtT9skE5/e5yYCKHgbd4Ojk5YuHgpVq9aiZCVy2FuYYmVIWvQqNHbGzP4jBiJzMxMBM2djRcvnsPRyRlrf9ggl1EyeWHezFsV8t597AqM9bUxe2Q3mBrp4sqtR+g9aZN0EX/dmvoQS96uZ9Goqo45ft1Rr7Yh0jNfI+JcAkZ8uwPP0l/JHLdTi4Ywr2WALQcqz0L9glT1ejNv5q0KeVPFE0gkkqJXPr5DKBRi5cqVqF69eon9hg0b9tFBiUQimfmMN2/ehJeXF0aOHAl/f388efIEzs7O0NDQQFpaGvbu3YsOHTrg/v37+PHHHzFs2DCFFS4vXlW+b/0qQhV15Xh+DhGVnUHbAEWHoBBpZ75XdAhEVE40y/T1fcX67tg/Cjv3rC4N399Jwcp06Tw9PWFiYlJesQAAQkJCsH79eoSHh0uf2yKRSNCyZUsMGTIE//33Hzp37oxevXph7Nix8PHxwcCBA7Ft2zZ07doVQUFBSvMgSiIiIiKigpTllsWKUuq/8itq3Uj//v3x5MkTjB8/HtevXwcAWFtbY9asWdDR0cGCBQvg4OCA4OBg2Nvbw8bGBhkZGfDz88PLly+Vcn0LERERERG9X5nvNlYe8o+dm5uLWrVqIS4uDgkJCfD390d8fDwAoHbt2sjMzERcXBxsbW2hpaUFANDT00NISAiio6Ohra3N4oWIiIiIlJZAgf9TBqUuXsRicblMGROLxRAIBEhOTkZMTAyioqJQs2ZNxMTE4Pbt2xgzZox0BEZLSwu2trbYtWsXdu3ahUmTJuHgwYNwc3P75O/5TURERESk6hS6XEksFkMoFOL69esYNWoUdHV1Ua1aNYSFhcHU1BSXL1+Gs7Mz/P39ERoaiiZNmsDX1xepqamYMmUKDA0NsX//flhYWCgyDSIiIiIiqgAKK14kEgmEQiGuXbuGtm3bYsyYMfDz84OZmRmEQiFycnIKFTAbN26Ei4sLdu7cicTExP+3d99hUVxrGMDfXZAiRZpiFMUGAirVAiqxxx5RjGKX2LAmaoxiwxIlsSQitqgxxopdY2L32hVQQ8eugBWkWJAisHP/IKyuFEuAYdn3d599npvh7Mz3MrsrZ885M9DV1YWRkZFYEYiIiIiIihUX7BdNtMtySSQSJCcnw8vLC4MHD8aCBQtQs2ZNSKVSCIIAdXV1hQ7MvXv3MHLkSERERKBChQqoWbMmOy5ERERERCpE1GsKP3nyBI8fP4a7uztksjf3R8lbdK+mpgZBEGBqaoorV64gMDAQ06ZNw+vXr8UqmYiIiIioxEgl4j2UgahrXkJDQxEbGwtXV1dIJBL5Gpg8EokEaWlpCAsLg4uLC+Li4vD8+XNoaGiIWDUREREREYlB1JGXWrVqQV1dHXv37s0tpoCbS27YsAE+Pj5IS0tDlSpVYGFhUdplEhERERGVColEItpDGYjaeTE3N4e+vj42bdqE2NhY+fa37ykTExMDJycn+X1diIiIiIhINYnaealevTpWr16No0ePYtasWfL7ueRNF5s+fTp2794NT09PpekNEhERERFRyRB1zQsAuLm5wc/PD+PGjcPly5fh4uICLS0tPHz4EIGBgThy5AgsLS3FLpOIiIiIqMQpy8J5sYg68gLkrnMZNWoULly4gIYNGyIkJASRkZGwtrbG+fPn4eDgIHaJRERERERUBog+8pKnadOmCAgIgJqamtilEBERERGJgisliib6yMvb3r7a2NuL9omIiIiIiMpU5+XtRflcoE9ERERERG8rM9PGiIiIiIhUnZRf4BepTI28EBERERERFYYjL0REREREZQQvlVw0jrwQEREREZFSYOeFiIiIiKiMkEjEe3yKlStXolatWtDS0kKzZs0QHBz8Qc8LCAiARCKBm5vbRx2PnRciIiIiIvpoO3bswKRJk+Dj44N//vkHdnZ26NixIxISEop8XkxMDL777ju4urp+9DHZeSEiIiIioo/2888/Y8SIEfD09ISNjQ3WrFmDihUrYsOGDYU+JycnBwMGDMDcuXNRp06djz4mOy9ERERERGWEFBLRHpmZmXjx4oXCIzMzs8A6X79+jatXr6J9+/ZvapdK0b59e1y6dKnQfPPmzUOVKlUwbNiwT/r98GpjxayCOvuDquRVZrbYJYgiK1sQuwRR6GiqiV2CKFLO/yR2CaKoOWqn2CWIIu7XPmKXQKUoK1smdgmi0OLfawXy9fXF3LlzFbb5+Phgzpw5+domJiYiJycHpqamCttNTU1x/fr1Avd//vx5/PbbbwgNDf3kGtl5ISIiIiIqI8S8R6W3tzcmTZqksE1TU7NY9v3y5UsMGjQI69atg4mJySfvh50XIiIiIiKCpqbmB3dWTExMoKamhvj4eIXt8fHxqFq1ar72d+7cQUxMDLp37y7fJpPljvypq6vjxo0bqFu37nuPyzEzIiIiIiL6KBoaGnBycsLJkyfl22QyGU6ePAkXF5d87a2srBAREYHQ0FD548svv0SbNm0QGhqKGjVqfNBxOfJCRERERFRGSEWcNvaxJk2ahCFDhqBx48Zo2rQpli1bhlevXsHT0xMAMHjwYFSvXh2+vr7Q0tJCw4YNFZ5vYGAAAPm2F4WdFyIiIiIi+mh9+/bF06dPMXv2bDx58gT29vY4cuSIfBF/XFwcpNLineglEQRBNS8bVEIyVPPiUyqLVxtTLap6tTFVvYoirzZGqkBVrzamp1V2P9fWBsaKduyRzuaiHftDld0zR0RERERE9BZOGyMiIiIiKiPEvFSyMuDICxERERERKQWV7LzkXVOaiIiIiIiUh8p1XmQyGaRSKW7duoUtW7YgIyND7JKIiIiIiAAAUolEtIcyUKnOS17HJTw8HM2bN8fp06eRmJgodllERERERPQBVGrBvlQqxYMHD9CjRw8MHToUixcvBgAIggCJkvQ2iYiIiKj84p+kRVOpzgsA3LhxA1ZWVli8eDGysrIwffp0XLt2DTo6OmjTpg28vLzELpGIiIiIiAqgUtPGACA0NBSPHj0CAHTr1g1Xr16Fg4MD0tPTsW7dOkyYMEHkComIiIiIqCAqN/Li7OyMP//8Exs2bIAgCNi0aRPMzMyQkZEBf39/7N69GyEhIXBwcBC7VCIiIiJSMSo3svCRVO73U7NmTcTFxWHBggVIS0tD1apVAQBaWlrw9PTEjRs3cOXKFZGrJCIiIiKid6lU50UQBNSoUQNbtmxBYmIiIiIicObMGfnPDQ0N4ezsDFNTUxGrJCIiIiJVJZFIRHsoA5WaNiaRSCCTydCiRQsEBATA3d0dc+bMQWxsLJo2bYotW7YgLCwMdnZ2YpdKRERERETvUKnOC5B7uWQA6Ny5M86dO4cpU6bAx8cH2tra0NDQwKFDh2Bubi5ylURERESkipRj/EM85bLzknfflkePHkFLSwtGRkb52shkMjg5OWH//v149uwZ0tLSUKVKlQLbEhERERGR+Mrdmpe8jsuBAwfQp08fnDhxAi9fvszXTiqVQhAE6Ovro2bNmrCysmLHhYiIiIioDCt3nZe8jsuAAQPQvXt3uLi4QE9PT6GNIAjytkREREREZYVUIhHtoQzK3bSxhw8fYubMmVi4cCEmTJiArKwspKamIigoCEZGRnBwcGCnhYiIiIhICZW7zou6ujp0dHRgZmaGpKQkrFq1CidOnEBkZCRMTEywcOFCuLu7i10mEREREVE+/Iq9aEo/bSxvClhCQgLS0tKgpaUFQRDg7++P2rVrIyQkBO7u7jh+/DiqVauGiIgIkSv+cAHbtqJzh7Zo4tAIAzy+QkR4eJHtjx09jB7dOqGJQyO4u3XHubNnFH4uCAJW+vuhXauWaOpoi5HDhiI2NqYEE3waVc29Z8c29OraAa2dHTB8sAeiI4vO/b/jR+HRqxtaOztgYB83XDx/Nl+bmLt38P23Y9Hh82Zo27wxvh7YB08ePyqpCJ9k367t6NvjC3Ro6Qgvz364FlX0e/TUiaMY9FV3dGjpiKH9eiLwgmLutLQ0LFu8AL27tUMHVycM7vslDuzZUZIRPsnOgK3o3rkdmjexw5ABfREZUfT5PnHsCNx7dEHzJnbo6/4lzp9TfJ3/unoF3Ht0QctmjmjTshnGjPREZHhYSUb4JKr6/v66TT1c+akr4ta44/CMdnCoXfQay5HtLXBxQSfEru6FkMXdMK+vPTTV3/yT7Wxpgs3jWyJ8aXck/NYHnR2qlXSET6Kq51tVc6vq5xqVLqXuvOQtzj948CDc3d1x8uRJVKpUCdu3b0ffvn2xePFibNy4ERMmTICjoyN0dXXll0ou644cPoQli3wxasxYBOzah/r1rTB61DAkJSUV2D405B9MmzIZPXv1xo7d+9GmbTt8O34sbt26KW/z+2/rsH3rZsz0mYMt23dCW1sbo0cOQ2ZmZmnFei9VzX3i6GEs/3kRvh45Br9v24V6FvUxcewoJCcXnDsiLAQ+06ege49e2LhtNz5v3RbTJo3Hndu35G0e3I+D17BBMK9VGyvWbsSmHXvhOcILGpqapRXrvf53/DBWLluEIcNHY92mXahrUR/fTRiFlEJyR4aHYP6s79Hly55Yt3kXXFu1xYwpE3D3zpvcK5ctQvCl85gx1xebdvyJ3h6D4LdkIS6cPVVasd7r2JFD+GXJTxgxaiy2BOyBZf36GD96BJILeZ2HhYZgxrTv0KOnO7bu2IvWbdrhu2/H4/Zbr3Nz81r43nsmAvYcwPqNW/BZteoYO3o4UpKTSyvWe6nq+7tHkxqY29cOS/6MQvu5xxF1/xl2TPwcJnoFvxd7NauJmb1tseTPaLSceQQTN16GW9MamOHeSN6mooY6oh48w7Qt/5RWjI+mqudbVXOr6ucalT7l+Eu+EBKJBPv370f//v3RuXNnNGjQAABQp04deHl5YdSoUdDX10daWhq8vb0RGBgIDw8Pkav+MJv/+B29eveBW0931K1XDzN95kJLSwv79+4psP3WLZvQvKUrhn49HHXq1sW4Cd/C2sYGAdu2AMjt6G3dvAkjRo1Gm7btYVnfCj/4LsLThAT87+SJ0oxWJFXNHbD1D3zZsze69eiJ2nXq4fsZPtDU0sJfB/YW2H7nti1o5tISA4Z8jVp16mLkmAmob2WDPTu2ydv8unI5XFp8jrHffof6VtYwq1ETrq3awsjIuLRivdfObZvQza03unTviVp16mLytNnQ0tLCoYP7Cmy/O2ALmjq3QL9BX6NW7boY5jUellY22LfzTe6o8FB07NoDDk5N8Vm16viy51eoa1H/vSM6pWnr5j/g1usrfOnWC3Xq1oP3zDnQ0tLCn/sLPt8BWzfBpXlLDB46DLXr1MXocd/AytoaOwPe5O7UpRuaOTeHmVkN1K1ngYnfTcOr1FTcunWjtGK9l6q+v72+sMSWs3cRcCEGNx+/wJTNV5H+Ohv9WtYusH2TusYIvp2IvUFxuJ+UhtNR8dgXFKcwWvO/yCf4cV8kDoU8LK0YH01Vz7eq5lbVz7WSIJGI91AGSt15efDgAby9vfHDDz9g+vTpqFmzJjIzM3Hu3Dlcu3YNALB161YMHDgQ27dvx7Fjx2BpaSly1e+X9fo1rkVHwdmluXybVCqFs3NzhIeFFPic8NBQODu7KGxr3qIlwkNDAQAPHzxAYuJTNHN+s089PT00srUrdJ+lTWVzZ73GjWvRaNzsTQ6pVIomzZwLHR6PjAhFk2bOCtuaubRAZHgogNz7GF06fwY1zc3x7ZgR6NLOFcMHe+DMqZMlluNjZWVl4eb1aDg1eZNDKpXCqYkzoiIKzh0VEQanpornu4lzc4X2DWztceHsKTxNiIcgCPjnSjDux8WgSbPm7+5OFFlZr3H9WhSaOSue76bOLgj/9/y9Kzw8DE3feZ27NG+JiELaZ2W9xr49O6GrpwdLS6viKv0/UdX3dwU1KezMDXH2Wrx8myAAZ6MT0LhuwV8kXL6TBDtzQ3lnxdxEB+0afYYT4U9KpebioKrnW2Vzq+jnGolDqRfsZ2dnQ0dHB46OjkhISMCGDRtw5MgRXL16FXZ2dpg/fz7at2+PmJgYLF68GHXr1i3W42dmZuYbshXUNKH5H6flpDxLQU5ODoyNFf9hMzY2xr17dwt8TmJiIoyNTfK1T0xK/PfnT3O3meTfZ2Ji4n+qt7ioau5nz54hJycn34iIkZExYmPuFficpMREGL7zezI0NpZPS0hJTkJaWho2//4bRo4ZjzHfTELgxfOY/t03WLH2dzg4NSmZMB/h+b/n2/Cd3IZGxoiLLTh3clJiAe1NkJz85lx+8910LFk4B727tYOamjqkUgm+mz4Hdo6Niz/EJ3iW8u/5fuf8GRkbI+Ze4efb6J3XuZGxMZLeeQ2fO3MK06d+h4yMdJiYVMbKNb/BwNCweAN8IlV9fxvpaUBdTYqnLxT/rXj6IgP1PtMr8Dl7g+JgpKuJg9PaQAIJKqhLsfHUbfgdulYaJRcLVT3fqppbVT/XSgqvils0pR55MTAwwNOnT+Ht7Q0bGxsEBweje/fuOHbsGFJTUxEcHAxTU1N4e3sXe8cFAHx9fVGpUiWFx+KffIv9OEQfS/bvhSxcW7eBx8AhsKxvjcGeI9DCtRX27S57i9eL096dWxEdGY6FS1dg3aYdGPPNFCxbvABXgi+JXVqJa9ykGbbt3IsNm7bBpUVLeE+ZWOh8cyq7mtevjG+7WmHqln/Qft5xDF1xAe1tP8OkbjZil0ZU6vi5Ru9Sms5L3lXFXrx4gezsbLx8+RIGBga4ePEiOnfujHnz5mH9+vWYNGkSXFxcYGZmBplMBqDkerDe3t54/vy5wmPKVO//vF9DA0OoqanlW9yXlJQEExOTAp9jYmKCpKTE/O3//VbDxKRy7rbED99naVPV3AYGBlBTU8u3OD85OSnft1J5jE1MkPLO7yklKUn+bZ+BgQHU1NVRq45ip928dh3EP3lcjNV/ukr/nu93F+enFJHbyNikgPaJMDLKbZ+ZkYF1q/ww9tspaOHaGnUt6qNXn/5o274TdmzZWCI5PpaB4b/n+53zl5yUBONCXpPGJiZIfud1XlB77YoVUaOmORrZ2mP23AVQU1fDgf0Fz7Mvbar6/k5++RrZOTJU1lccka+sr4WE5xkFPmeaW0PsuhSLrefu4drD5zgU8hAL90ZgQhcrpZmTrqrnW1Vzq+rnWkmRivhQBkpRZ95VxQ4fPoyBAweiefPmGDduHM6dO4fq1atj6tSpGDNmDIyMjJCVlYXp06cjODgYvXv3BlBynRdNTU3o6+srPP7rlDEAqKChAWubBggKfPNNsUwmQ1DQJdjaORT4HFt7ewQFBipsC7x0Ebb29gCA6mZmMDGpjKCgN/tMTU1FRHhYofssbSqbu4IG6lvb4GrwmxwymQxXgoPQ0NauwOc0bGSPK8GKuYODLqGhrb18n9Y2DREXE6PQ5n5cLKp+VjYuqVqhQgVYWtng6uUg+TaZTIZ/rgShQaOCczdoZIerlxVzXwm6JG+fnZ2N7OxsSN65qqBUTQ0yQVbMCT5NhQoasLJugOAgxfN9OSgQtv+ev3fZ2trhcpBi7qDAi2hUSPs3+xXw+vXr/1pysVDV93dWjgxhsSlwtTaVb5NIAFfrKrhyp+Bvj7U11CATFLfl/LtBoiR3gFDV862yuVX0c43EUaY7L3mjLRKJBAcOHEDv3r3RuHFjDBkyBOnp6fDw8MDZs2ehrq4OQRCwadMm9OzZE1u3bsXRo0dhYWEhcoJPN2iIJ/bu3ok/9+/D3Tt38MO8OUhPT4dbz14AgBne38Pvl6Xy9gMGDsbFC+fwx8YNuHf3Dlav9EdUZCQ8+g8EkPs7HDBoMNb9uhqn/3cSt27ewEzv71G5ShW0bddejIgFUtXcHgOG4M99u3Ho4H7E3L2DxQvnISM9Hd2+7AkAmDfLG6v9f5G379N/IAIvXcC2zRsRc+8u1q9ZievRkXDv21/eZsBgT5w8dhgH9u7Cg7hY7A7YigtnT6PXV2Xnint9+g/G3wd248hfBxBz7w5+/mk+0tPT0bmbGwBggY831q58k7u3x0AEX7qAHVs3IjbmLn5fuxI3rkWhZ5/c3Dq6urB3bIw1y5ci5GowHj98gMN/7cfRQ3/CtVU7MSIWaMCgIdi/dxf++nM/7t29A98f5iI9PR3d3XLP9+wZU7HC72d5e48Bg3Hx4nls+eN3xNy7i19Xr0B0VBT6eOTmTk9Lw8rlvyAiPBSPHz3EtegozJ09A08T4tG+Q0dRMhZEVd/fa47dxMDP66Bvc3NYfKaHxQOdUFFTHQEXctcCrBjWFDN6vbkM8rGwxxjaui7cmtZATRMdtLIxxTS3hjgW9kg+JVRHUx0NaxigYQ0DAEBNE100rGGA6kYVSz1fYVT1fKtqblX9XKPSVyYX7CcmJsLExEQ+YnLjxg3MmzcPS5cuhZeXF+Lj4+Hr6wstLS24ublh3759aNWqFVq3bo2bN29i2bJlSt1xAYBOnbsgJTkZq1YsR2LiU9S3ssaqX9fLh1OfPH4MqeRN39PewRG+i5ZgxfJl8F/2M2qa18Iy/5WwsHhzdTXPYSOQnp6OeXNm4+XLF3BwdMKqX9cXy2hRcVHV3O07dsazlGSsW70CyUmJsKhvhZ9X/CqfPhX/5DGk0jffuDayc8DcBYuwdtVy/LpiGcxqmuPHn/1Rt96b132rtu3x/XQfbPp9HX5Z7Atz81pYsHgZ7BycSj1fYdp26IxnKSnYsDY3dz1LKyz2WyPPnRD/WOHeTA1tHTBr/k/4bY0/1q3yg1kNcyxYvBx16r7JPfuHJVi7ahl+mD0NL148R9Wq1TDcawJ6uPct9XyF+aJTF6SkpGDNquVISkyEZX1r+K9aK1+0++SJYm47ewcs8F2MVSv8sNL/F9SoaY4ly/xR79/XuVRNDTH37uKvP/fj2bMUVDIwgE2DRlj3+xaF14TYVPX9feDyfRjraeJ7t4aooq+FyPvP4PHLWfki/upGFeWdEgD4+a9oCBDg7dYQVQ21kfQyE8fCHmPh3jeX+7arZYj937eR//d8D3sAQMCFe5iw4XLpBHsPVT3fqppbVT/XSgIX7BdNIgiC8P5mpcff3x/r1q3D9u3b5fdtuX79Ovz8/LB48WKkpKSgXbt2aN26NcaOHQtPT088evQImzdvRocOHSCTyUS9EWVGtmiHJhG8ylTNE56VXaY+NkqNjqaa2CWIooJ6mR6kLzE1R+0UuwRRxP3aR+wSqBRlZZeNKbWlTU+r7H6u7Qx9JNqx+9iXjanlRSlzZ653796Ij4/H+PHjER0dDQCwsrLCrFmzoKuri4ULF8Le3h5+fn6ws7ODtbU10tLSMGrUKLx69Yq9VSIiIiJSWhIRH8qgTHRe8gZ/cnJy8NlnnyEsLAzXr1+Hl5cXIiMjAQDVqlVDeno6wsLCYGNjA21tbQCAvr4+/P39ERwcDB0dHXZeiIiIiIjKKdE7LzKZDBKJBE+fPkVISAgCAwNRtWpVhISE4O7duxgzZox8BEZbWxs2NjbYuXMndu7ciYkTJ+Lvv/9G69aty8zlAomIiIiIqGSIumA/b31KdHQ0Ro4cCT09PVSsWBFbt26Fqakprl69CicnJ3h5eWH16tVo0KABhg8fjuTkZEyePBlGRkY4cOAAzM3NxYxBRERERFQsOIuoaKJ1XgRBgFQqRVRUFFq2bIkxY8Zg1KhRMDMzg1QqRXZ2dr4OzG+//QZnZ2fs2LEDjx8/hq6uLoyMjMSKQEREREREpUi0aWMSiQTJycnw8vLC4MGDsWDBAtSsWRNSqRSCIEBdXV2hA3Pv3j2MHDkSERERqFChAmrWrMmOCxERERGVK+/e9b40H8pA1DqfPHmCx48fw93dHTLZm0v15Q2XqampQRAEmJqa4sqVKwgMDMS0adN4Z1UiIiIiIhUk6pqX0NBQxMbGwtXVFRKJJN89WiQSCdLS0hAWFgYXFxfExcXh+fPn0NDQELFqIiIiIiISg6gjL7Vq1YK6ujr27t2bW0wBN5fcsGEDfHx8kJaWhipVqsDConzfVZWIiIiIVJdEIhHtoQxE7byYm5tDX18fmzZtQmxsrHx73n1fACAmJgZOTk7y+7oQEREREZFqErXzUr16daxevRpHjx7FrFmz5PdzyZsuNn36dOzevRuenp5K0xskIiIiIvpU7971vjQfykDUNS8A4ObmBj8/P4wbNw6XL1+Gi4sLtLS08PDhQwQGBuLIkSOwtLQUu0wiIiIiIhKZ6FdFk0qlGDVqFC5cuICGDRsiJCQEkZGRsLa2xvnz5+Hg4CB2iUREREREpUIiEe+hDEQfecnTtGlTBAQEQE1NTexSiIiIiIioDBJ95OVtb19t7O1F+0RERERERGVm5AWAwqJ8LtAnIiIiIlUjVZql8+IoUyMvREREREREhSlTIy9ERERERKqMk4+KxpEXIiIiIiJSCuy8EBERERGRUuC0MSIiIiKiMkLCBftF4sgLEREREREpBY68EBERERGVEVywXzSOvBARERERkVLgyAsRERERURnBm1QWjZ0Xov9AJohdAZWmCuocrFYlcb/2EbsEUdjNOCp2CaIIW9BR7BJEkZj6WuwSRKGnpSV2CfSJ+C8xEREREREpBY68EBERERGVEVywXzSOvBARERERkVLgyAsRERERURnBkZeiceSFiIiIiIiUAjsvRERERESkFDhtjIiIiIiojJDwPi9F4sgLEREREREpBY68EBERERGVEVIOvBRJJUdeZDIZAODly5ciV0JERERERB9K5TovMpkMUqkUN27cwNdff427d++KXRIREREREYDcNS9i/U8ZqFTnJa/jEhYWBkdHR+zZswcHDhwQuywiIiIiIvoAKrPm5e2Oi4uLCyZMmABtbW3s2LED7u7uqFmzptglEhERERFREVRm5EUqleLq1av4/PPPMXHiRPz4449wcnJCdHQ0oqOjAbxZC0NEREREJAaJRLyHMlCJzosgCMjIyEDnzp3x9ddfY8GCBQCAbt26oUOHDvDx8cGrV68glarEr4OIiIiISCmpxF/rEokEWlpaCA8Pxy+//ALgzShLr169kJSUhLCwMIXtRERERESljQv2i6YSnRcgd/SlatWq8v/OG2Xx8PCAuro6Vq1apbCdiIiIiIjKFpX5S11SwES+nJwcqKmpYfr06bh48SLOnz8vQmVERERERPQhVKbzUhA1NTUAQOPGjSGTyXD27FmRKyIiIiIiVSaViPdQBirdecljY2ODnj17Yt26dcjMzIQgCGKXRERERERE7yiXnZe8zsejR4+QnJz8QW09PT1x+vRpaGpqFjjFjIiIiIiopHHBftHKXedFEARIJBIcOHAAffr0wYkTJ/Dy5ctC2+d1VGxtbWFubl5aZRIRERER0Ucqd52XvI7LgAED0L17d7i4uEBPT0+hDaeFEREREVFZxJtUFk1d7AKK28OHDzFz5kwsXLgQEyZMQFZWFlJTUxEUFAQjIyM4ODhwWhgRERERkRIqdyMv6urq0NHRgZmZGZKSkvDjjz+ia9eu6NOnDzw8PLBnzx6xS/xgAdu2onOHtmji0AgDPL5CRHh4ke2PHT2MHt06oYlDI7i7dce5s2cUfi4IAlb6+6Fdq5Zo6miLkcOGIjY2pgQTfBpVzb1n5zb07tYBbV0cMGKwB6Iji879v+NH0b9XN7R1ccDgPm64dD7/1fJi7t3B1Ilj0fHzZmjfojGGD+qDJ48flVSET7Jv13b07fEFOrR0hJdnP1yLiiiy/akTRzHoq+7o0NIRQ/v1ROAFxdxpaWlYtngBendrhw6uThjc90sc2LOjJCN8ElV9nTO3auXu71IDJ6d+jvAf2mPn2GZoZFap0LabRjbBjZ865nv8OtRR3mZc+7o4PLkFQua3Q7BPW/w+vDFsaxS+T7Go6vn+c08ABrt3Rvc2TfDNiAG4EV3453nM3duYP30SBrt3RqcWdti3Y0u+NgGbfsP4Yf3Rs70L+nZtjbnTvsX9MpibSpfSd17ypoAlJCQgLS0NWlpaEAQB/v7+qF27NkJCQuDu7o7jx4+jWrVqiIgo+g+jsuLI4UNYssgXo8aMRcCufahf3wqjRw1DUlJSge1DQ/7BtCmT0bNXb+zYvR9t2rbDt+PH4tatm/I2v/+2Dtu3bsZMnznYsn0ntLW1MXrkMGRmZpZWrPdS1dwnjx3Gip8XwXPkGPy2dRfqWdbHpHGjkJJccO6IsBDMnTEF3dx6YcO23XBt3Rbek8fj7u1b8jYP78dhzLBBMK9VG/5rN+KPgL0YOtwLmpqapRXrvf53/DBWLluEIcNHY92mXahrUR/fTSg8d2R4CObP+h5dvuyJdZt3wbVVW8yYMgF377zJvXLZIgRfOo8Zc32xacef6O0xCH5LFuLC2VOlFeu9VPV1ztyqlbuzbVV4d7PCypO30XP5JVx//BK/DXOCkY5Gge3Hbw5Fi/mn5I+uP59Hdo4MRyKeyNvEJKZh3oFr6P7LRfRfE4SHKenYMNwJhjoVSivWe6nq+T5z4gjW+S/BwK9HYcWGANSpVx8zJo3Gs5SCc2dmZqBqNTN8PXoCDI1NCmwTEXoF3Xv1xS9rN8N32a/Izs7GjIleyEhPK8koopOI+FAGSt15yVucf/DgQbi7u+PkyZOoVKkStm/fjr59+2Lx4sXYuHEjJkyYAEdHR+jq6kIqVY7Im//4Hb1694FbT3fUrVcPM33mQktLC/v3FjxytHXLJjRv6YqhXw9Hnbp1MW7Ct7C2sUHAttxvMgRBwNbNmzBi1Gi0adselvWt8IPvIjxNSMD/Tp4ozWhFUtXcAVv+QPeevdH1y56oXacepkz3gZaWFv46sLfA9ru2b0Ezl5boP/hr1KpdFyPGTICllQ327Nwmb7N21XK4tPgcY775DpZW1qheoyZatmoLQyPj0or1Xju3bUI3t97o0r0natWpi8nTZkNLSwuHDu4rsP3ugC1o6twC/Qbl5h7mNR6WVjbY91buqPBQdOzaAw5OTfFZter4sudXqGtR/70jOqVJVV/nzK1auT1dzbEz+AH2XnmEOwmv4LMvGhlZOXBvUr3A9s/Ts5CY+lr+aGFhgowsGY6Ex8vb/BX6GJduJ+NBcjpux7+C71/XoadVAfWr6hW4TzGo6vneu2MzOnXvhS+6usG8dl2MnzITmppaOPrX/gLb17duiBHjJqF1+86oUKHgDu2Cn1fji649UKtOPdSxqI/JM+YhIf4xbt24VoJJqKxTjr/kCyGRSLB//370798fnTt3RoMGDQAAderUgZeXF0aNGgV9fX2kpaXB29sbgYGB8PDwELnq98t6/RrXoqPg7NJcvk0qlcLZuTnCw0IKfE54aCicnV0UtjVv0RLhoaEAgIcPHiAx8SmaOb/Zp56eHhrZ2hW6z9KmsrmzXuPm9Wg0bvomh1QqReOmzoiKCCvwOZHhoWjczFlhWzOXFogMDwUAyGQyXDx/BjVqmmPS2BHo1t4VIwZ74OypkyWW42NlZWXh5vVoODV5k0MqlcKpSeG5oyLC4NRU8Xw3cW6u0L6BrT0unD2FpwnxEAQB/1wJxv24GDRp1vzd3YlCZV/nzC3fpgq5K6hJ0KC6Pi7eevOtuyAAF28nwaGmwQftw71xdfwd9hjpWTmFHqNvsxp4kZ6FG48Lv6poaVLV852VlYVbN67B4Z3Pc4fGzrj2ninQHyPtVSoAQE9fv9j2WRZJJRLRHspAqTsvDx48gLe3N3744QdMnz4dNWvWRGZmJs6dO4dr13J75Vu3bsXAgQOxfft2HDt2DJaWliJX/X4pz1KQk5MDY2PFb8iNjY2RmJhY4HMSExNh/M6wq7GxMRKTEv/9+dPcbSYfvs/Spqq5nz97hpycHBi9k9vI2BhJhdSYnJSYbwTF0MgYyf9OS0hJTkJ6Whq2bPwNzZq3xC8r1+LzNu0wY8o3CLl6uWSCfKTn/57vgnN8TG4TJCe/af/Nd9NRq3Zd9O7WDu2aO+D7b0bh2ykzYOfYuPhDfAJVfZ0zt2rlNqyoAXU1KZJSFac1Jb18DRO9gr9lf1sjs0qo/5kedgU/zPez1laV8c+8dgj/oQOGtjTH1+uvICUtq9hq/y9U9Xy/eJYCWU4ODN75fDYwMkZKcvHUKJPJsMZvEWxs7VGrjkWx7JOUk1JfbSw7Oxs6OjpwdHREQkICNmzYgCNHjuDq1auws7PD/Pnz0b59e8TExGDx4sWoW7dusR4/MzMz33xTQU2zTK0pINWUtxasZas26DtgCADAor41IsNDsX/PDjg4NRGzvBK1d+dWREeGY+HSFaha9TOEhVzFssULYFK5isLoFhGVXb2bVseNxy8R8eB5vp8F3UmGm98lGOpUQJ+mZlg2wA5frQhC8qvXIlRKpWXl0oWIuXsHS1dvFLsUEplSj7wYGBjg6dOn8Pb2ho2NDYKDg9G9e3ccO3YMqampCA4OhqmpKby9vYu94wIAvr6+qFSpksJj8U++/3m/hgaGUFNTy7e4LykpCSYmBS9qMzExQdI731YnJSXB5N9vc0xMKuduS/zwfZY2Vc1dycAAampq8lGTPMlJSTAupEYjY5N8i9pTkpPkoze5+1RHrTqKr3vz2nWQ8ORxMVb/6Sr9e74LzvExuRNhZJTbPjMjA+tW+WHst1PQwrU16lrUR68+/dG2fSfs2LKxRHJ8LFV9nTO3auVOSXuN7BwZjHUVv8wz1tNA4suiOxnaFdTQ1a4qdl9+UODP07NyEJeUhrC455ixOwrZMgG9C1lHU9pU9XzrGxhCqqaGZ+98Pj9LToKh0X+vceXShQi6eBaL/NehchXT/7y/so4L9oumNJ2XvG+SX7x4gezsbLx8+RIGBga4ePEiOnfujHnz5mH9+vWYNGkSXFxcYGZmBplMBgAldl8Xb29vPH/+XOExZar3f95vBQ0NWNs0QFDgJfk2mUyGoKBLsLVzKPA5tvb2CAoMVNgWeOkibO3tAQDVzcxgYlIZQUFv9pmamoqI8LBC91naVDZ3BQ1YWtng6uU3OWQyGa5eDkKDRnYFPqehrT2uBCvmvhx0CQ1t7eX7tG7QMN8lJe/HxsK0arVirf9TVahQ4d/cQfJtMpkM/1wpPHeDRnYKvycAuBJ0Sd4+Ozsb2dnZkLxzYQ6pmhpkgqyYE3walX2dM7d8myrkzsoREPXwBVzqGcm3SSSASz1jhMQ9K/K5nWxNoaEmxZ8hH/ZFi1QigYZ62fhzRlXPd4UKFWBR3xqhVxQ/z0OvBsG6oe0n71cQBKxcuhAXz/4PPy1fh6rVzIqjXFJyZePd/h55VxU7fPgwBg4ciObNm2PcuHE4d+4cqlevjqlTp2LMmDEwMjJCVlYWpk+fjuDgYPTu3RtAyXVeNDU1oa+vr/Aoriljg4Z4Yu/unfhz/z7cvXMHP8ybg/T0dLj17AUAmOH9Pfx+WSpvP2DgYFy8cA5/bNyAe3fvYPVKf0RFRsKj/0AAub+DAYMGY92vq3H6fydx6+YNzPT+HpWrVEHbdu2LpebioKq5PQYOwcF9u3H44H7E3LuDJb7zkJ6ejq5f9gQAzJ/tjTX+v8jbf9VvIIIuXsD2zRsRe+8ufvt1Ja5HR8K9T395m36DPHHy2GH8uXcXHtyPxZ4dW3Hx3Gn0/KrsXLSiT//B+PvAbhz56wBi7t3Bzz/NR3p6Ojp3cwMALPDxxtqVb3L39hiI4EsXsGPrRsTG3MXva1fixrUo9Pw3t46uLuwdG2PN8qUIuRqMxw8f4PBf+3H00J9wbdVOjIgFUtXXOXOrVu7fz8WiT1MzuDlWQ50qOpjT0wbaFdSw90ruOpaf+jTEpE751y70bmKGE9EJePbOOhbtCmqY2NECdjUroZqBFhpU18fC3g1gqq+pcDllsanq+e7VdxAOH9yL44f+RFzMXfgv+QEZGen4oqsbAGDx/BnYsNpP3j4rKwt3bl7HnZvXkZ2VhcSnCbhz8zoePYiTt1m5dCH+d+wQps75EdoVdZCclIjkpERkZmaUdrzSxaGXIpXpNS95nRaJRIIDBw6gf//+mDp1KoyNjXHmzBl4eHhg+/bt+PzzzyEIAjZv3owdO3YgMjISR48ehYWF8i7o6tS5C1KSk7FqxXIkJj5FfStrrPp1vXwa0ZPHjyGVvOl72js4wnfREqxYvgz+y35GTfNaWOa/EhYWby5Q4DlsBNLT0zFvzmy8fPkCDo5OWPXr+jK1RkdVc7f7ojOepSRj/ZoVSE5KRD1LKyz1/1U+fSr+yWOFq4A0snOAz4JFWLd6OdauXAazmubwXeqPOvXevOZbtW2P76b7YMvv67BsiS9qmtfCD4uWwc7BqdTzFaZth854lpKCDWvf5F7st0aeOyH+scLlzRvaOmDW/J/w2xp/rFvlB7Ma5liweDnq1H2Te/YPS7B21TL8MHsaXrx4jqpVq2G41wT0cO9b6vkKo6qvc+ZWrdyHw5/ASEcDE76oh8p6mrj26AWGb7iKpNTcaWOfGWhDJig+p7ZJRTSubQjP9Vfy7S9HEFCnig56OtnDUEcDz9JeI+L+CwxYE4zb8a9KI9IHUdXz3ap9Jzx/loLN61chJTkRdSzq44elq+QXWUmIfwLJW7mTEhMw1vPN5/Ke7X9gz/Y/0MihMRav+A0A8Ne+nQCA78cNUzjWpOnz8EXXHiUdicooiZA3H6sMSUxMVJjHeePGDfTv3x8jRoyAl5cX4uPj4eTkBE1NTaSkpGDfvn1o1aoV4uLisHbtWgwZMkS0jktGtiiHJZG8VNETnpNT5j42SoVBGboRHlFJsZtxVOwSRBG2oKPYJYji8bNyPopRiNomWmKXUKigO/kvVFFamtWtJNqxP1SZmzbm7++Ptm3bIioqSr5NEAQ0bdoUAwcOxP379+Hq6oouXbpg7969qFWrFvr27Yvjx4+jZs2amDdvnlKPuBARERERUcHKXOeld+/eiI+Px/jx4xEdHQ0AsLKywqxZs6Crq4uFCxfC3t4efn5+sLOzg7W1NdLS0jBq1Ci8evWqxNa3EBERERGRuMpE5yVv5lpOTg4+++wzhIWF4fr16/Dy8kJkZCQAoFq1akhPT0dYWBhsbGygra0NANDX14e/vz+Cg4Oho6PDzgsRERERKS2JRLyHMhC98yKTySCRSPD06VOEhIQgMDAQVatWRUhICO7evYsxY8bIR2C0tbVhY2ODnTt3YufOnZg4cSL+/vtvtG7dusxc65yIiIiIiEqGqFcbk8lkkEqliI6OxsiRI6Gnp4eKFSti69atMDU1xdWrV+Hk5AQvLy+sXr0aDRo0wPDhw5GcnIzJkyfDyMgIBw4cgLm5uZgxiIiIiIiKhZIMgIhGtM6LIAiQSqWIiopCy5YtMWbMGIwaNQpmZmaQSqXIzs7O14H57bff4OzsjB07duDx48fQ1dWFkZHR+w9GRERERERKT7RpYxKJBMnJyfDy8sLgwYOxYMEC1KxZE1KpFIIgQF1dXaEDc+/ePYwcORIRERGoUKECatasyY4LEREREZEKEXXNy5MnT/D48WO4u7tDJpPJt+ctuldTU4MgCDA1NcWVK1cQGBiIadOm4fXr12KVTERERERUct69631pPpSAqGteQkNDERsbC1dXV0gkEvkamDwSiQRpaWkICwuDi4sL4uLi8Pz5c2hoaIhYNRERERERiUHUkZdatWpBXV0de/fuzS1Gmr+cDRs2wMfHB2lpaahSpQpvQElERERE5ZZExP8pA1E7L+bm5tDX18emTZsQGxsr35533xcAiImJgZOTk/y+LkREREREpJpE7bxUr14dq1evxtGjRzFr1iz5/VzypotNnz4du3fvhqenJ28+SURERETlHm9SWTRR17wAgJubG/z8/DBu3DhcvnwZLi4u0NLSwsOHDxEYGIgjR47A0tJS7DKJiIiIiEhkoo68ALnrXEaNGoULFy6gYcOGCAkJQWRkJKytrXH+/Hk4ODiIXSIREREREZUBoo+85GnatCkCAgKgpqYmdilERERERKJQktlbohF95OVtb19t7O1F+0RERERERGVm5AWAwqJ8LtAnIiIiIpXDP4GLVKZGXoiIiIiIiArDzgsRERERESmFMjVtjIiIiIhIlSnLne7FwpEXIiIiIiJSChx5ISIiIiIqI3jNqqJx5IWIiIiIiD7JypUrUatWLWhpaaFZs2YIDg4utO26devg6uoKQ0NDGBoaon379kW2Lwg7L0RERERE9NF27NiBSZMmwcfHB//88w/s7OzQsWNHJCQkFNj+9OnT6NevH06dOoVLly6hRo0a+OKLL/Dw4cMPPqZE4N0gi1VGttgVUGl6qaInPCdHNT82DHQqiF0CUYmzm3FU7BJEEbago9gliOLxswyxSxBFbRMtsUsoVFjcS9GObWWqgczMTIVtmpqa0NTULLB9s2bN0KRJE6xYsQIAIJPJUKNGDYwfPx7Tpk177/FycnJgaGiIFStWYPDgwR9UI9e8EP0Help8CxFR+aKqf8QbNhkndgmiSLm8QuwSqAzx9fXF3LlzFbb5+Phgzpw5+dq+fv0aV69ehbe3t3ybVCpF+/btcenSpQ86XlpaGrKysmBkZPTBNfIvLyIiIiKiskLEBfve3t6YNGmSwrbCRl0SExORk5MDU1NThe2mpqa4fv36Bx1v6tSpqFatGtq3b//BNbLzQkRERERERU4RK24//vgjAgICcPr0aWhpffg0PnZeiIiIiIjKCGW5SaWJiQnU1NQQHx+vsD0+Ph5Vq1Yt8rlLlizBjz/+iBMnTsDW1vajjsurjRERERER0UfR0NCAk5MTTp48Kd8mk8lw8uRJuLi4FPq8RYsWYf78+Thy5AgaN2780cflyAsREREREX20SZMmYciQIWjcuDGaNm2KZcuW4dWrV/D09AQADB48GNWrV4evry8A4KeffsLs2bOxbds21KpVC0+ePAEA6OrqQldX94OOyc4LEREREVEZIVGOWWMAgL59++Lp06eYPXs2njx5Ant7exw5ckS+iD8uLg5S6ZuJXqtXr8br16/Ru3dvhf0UdkWzgvA+L8VMRW/7QUREpNR4qWTVUpbvdBDxIFW0Yzcy+7DRDzGV4VNHRERERKRalGjgRRRcsE9EREREREpBJTsvnClHRERERKR8VG7amEwmg1QqRWpqKtLS0qCjowMdHR2xyyIiIiIi4ryx91CpkZe8jktUVBTc3d3h6uoKNzc37N69W+zSiIiIiIjoPVSq8yKVSnHt2jW4urrCysoKc+bMgSAI2LRpE6eSEREREZHoJCL+Txmo1LSxjIwMzJkzBwMGDICfnx8AoHr16liyZAkePXoEHR0dGBgYiFskEREREREVSKVGXrS0tJCcnIyKFSvKtx06dAhXrlxBs2bN0K1bN4wbp5rXeSciIiIi8Ukk4j2UgcqMvAiCgPT0dOjp6SE8PBw//fQTkpOT4e/vj19//RW1atVCUFAQAgICEBAQAA8PD7FLJiIiIiKit6jMyItEIkHFihWxYMECaGtr4/r16zh8+DB+/vlnDBo0CK6urhgxYgSeP3+OqKgoscslIiIiIqJ3qMzIC5B7tTFra2ts27YNWlpaaNOmjcIUMj09PVhaWsLIyAhA7miNRFnG0IiIiIhI6fEvz6KpzMgLkHu1MQDQ1NRERkYGBEHA5cuX8fTpU6SkpGDu3Lm4evUqvvzySwBgx4WIiIiIqAxRqZGXPBKJBFpaWpg5cya6du2Kv/76C0ZGRkhJScHhw4dRt25dsUskIiIiIlXE786LVC47L3nTvR49egQtLS35NLC3yWQytG/fHpcvX8aJEydQuXJlfP755zA3NxehYiIiIiIiep9y13nJ67gcOHAAixcvxoQJE9C5c2fo6ekptJNKpZDJZLC1tYWtra1I1RIRERER0Ycqd2te8jouAwYMQPfu3eHi4pKv4yIIAoA3a2CIiIiIiMqC/Pe9L73/KYNyN/Ly8OFDzJw5EwsXLsSECROQlZWF1NRUBAUFwcjICA4ODlyIT0RERESkhMpd50VdXR06OjowMzNDUlISVq1ahRMnTiAyMhImJiZYuHAh3N3dxS6TiIiIiCgffsdeNKWfN5U3BSwhIQFpaWnQ0tKCIAjw9/dH7dq1ERISAnd3dxw/fhzVqlVDRESEyBV/uIBtW9G5Q1s0cWiEAR5fISI8vMj2x44eRo9undDEoRHc3brj3NkzCj8XBAEr/f3QrlVLNHW0xchhQxEbG1OCCT4NczN3UZibud/G3Mxd1nK3cKyL3ctG4e6xBUgPWYHurd+/rtbVyQIXt03Fs6BfEHnABwO7N8vXZlSfz3H977lICfwFZzd9h8YNyt4FhlTxfFPpU+rOS97i/IMHD8Ld3R0nT55EpUqVsH37dvTt2xeLFy/Gxo0bMWHCBDg6OkJXV1dp1rkcOXwISxb5YtSYsQjYtQ/161th9KhhSEpKKrB9aMg/mDZlMnr26o0du/ejTdt2+Hb8WNy6dVPe5vff1mH71s2Y6TMHW7bvhLa2NkaPHIbMzMzSivVezM3czJ0fczM3c+dShtw62pqIuPkQ3/ru+KD25tWMsc/fC2ev3EQzjx+xYtsprJ7dH+1drOVten/hiJ8m98SCXw/Dpf9PCL/5EH+uGovKhrolFeOjqer5LgkSER/KQCLkDV0oqf3792PQoEHw9vaGh4cH6tSpk69NWloa5s+fj/Xr1+PChQuwtLQssXoysotnPwM8vkKDho0wfeZsALmXdv6iXSv06z8Iw0aMzNd+yuRvkZ6ejhWrfpVvG9ivD+pbWWGWzzwIgoD2rV0xeKgnhngOAwC8fPkSbT9vjnkLfkTnLl2Lp/D/iLmZm7mZOw9zM3dp5jZsMu4/Pb8g6SEr0GfiWhw8XfgIxA8TeqCTawM0/mqhfNumHz1RSVcbPcatAgCc3fQdrkbFYuJPuwDkXpzo9pH5WB1wBkt+P/6faky5vOI/PT+Psp1vrTK8cOLmkzTRjm1ZtaJox/5QyjEMUYgHDx7A29sbP/zwA6ZPn46aNWsiMzMT586dw7Vr1wAAW7duxcCBA7F9+3YcO3asRDsuxSXr9Wtci46Cs0tz+TapVApn5+YIDwsp8DnhoaFwdnZR2Na8RUuEh4YCAB4+eIDExKdo5vxmn3p6emhka1foPksbczM3czP325j7DeZWjtyfopldbZwKuqGw7fjFa2hmWxsAUEFdDQ7WNfC/t9oIgoD/Bd1A03/biI3nm0qTUndesrOzoaOjA0dHRyQkJGDJkiXo2LEjunTpghEjRuDUqVNo3749nJyccPLkSTg4OBTr8TMzM/HixQuFR3EMZaY8S0FOTg6MjY0VthsbGyMxMbHA5yQmJsLY2CR/+6TEf3/+NHebyYfvs7QxN3MDzF0Q5n6nPXPnbmPu9+5TGZga6yM++aXCtoTkF6ikpw0tzQowMdSFuroaEt5tk/QCVY31S7PUQvF8FzPOGyuSUndeDAwM8PTpU3h7e8PGxgbBwcHo3r07jh07htTUVAQHB8PU1BTe3t6oW7dusR/f19cXlSpVUngs/sm32I9DRERERERK1HnJW5rz4sULZGdn4+XLlzAwMMDFixfRuXNnzJs3D+vXr8ekSZPg4uICMzMzyGQyACix+7p4e3vj+fPnCo8pU73/834NDQyhpqaWb5FbUlISTExMCnyOiYkJkpIS87f/91sNE5PKudsSP3yfpY25mRtg7oIw9zvtmTt3G3O/d5/KID7pBUyNFG+mXcVIH89fpiMjMwuJKanIzs5BlXfbGOvjSdKL0iy1UDzfxYs3qSyaUnRe8q4qdvjwYQwcOBDNmzfHuHHjcO7cOVSvXh1Tp07FmDFjYGRkhKysLEyfPh3BwcHo3bs3gJLrvGhqakJfX1/hoamp+Z/3W0FDA9Y2DRAUeEm+TSaTISjoEmztCp76Zmtvj6DAQIVtgZcuwtbeHgBQ3cwMJiaVERT0Zp+pqamICA8rdJ+ljbmZm7mZ+23M/QZzK0fuTxEUdg+tm9ZX2NbO2QpB4fcAAFnZOQi5dh9tmr1pI5FI0KapJYL/bSM2nm8qTWW685I32iKRSHDgwAH07t0bjRs3xpAhQ5Ceng4PDw+cPXsW6urqEAQBmzZtQs+ePbF161YcPXoUFhYWIif4dIOGeGLv7p34c/8+3L1zBz/Mm4P09HS49ewFAJjh/T38flkqbz9g4GBcvHAOf2zcgHt372D1Sn9ERUbCo/9AALm/wwGDBmPdr6tx+n8ncevmDcz0/h6Vq1RB23btxYhYIOZmbuZm7jzMzdzKmFtHWwO2ltVha1kdAFCrujFsLaujRlVDAMC88V9i/fxB8vbrdp9HbTNjLPimByxrmWLkV65w7+AA/62n5G2Wb/kfPHs2x4DuzVC/timWT++Litqa2HRA8Y9/Manq+abSVyYvFJeYmAgTExP5iMmNGzcwb948LF26FF5eXoiPj4evry+0tLTg5uaGffv2oVWrVmjdujVu3ryJZcuWKXXHBQA6de6ClORkrFqxHImJT1Hfyhqrfl0P43+HSp88fgyp5E3f097BEb6LlmDF8mXwX/YzaprXwjL/lbCweHN1Nc9hI5Ceno55c2bj5csXcHB0wqpf1xfLaFFxYW7mZm7mzsPczK2MuR1tzHFs/Tfy/170nTsAYPOfgRjpswVVTfRRo6qR/Oexj5LQc/waLPquF8b2b42H8c8wet42nLh0Td5m97F/YGKoi9mju8LUWA/hNx6ix9iV+Rbxi0lVz3dJKKEJQ+VGmbvPi7+/P9atW4ft27ejQYMGAIDr16/Dz88PixcvRkpKCtq1a4fWrVtj7Nix8PT0xKNHj7B582Z06NABMplM1BtRFtd9XoiIiKj0lMR9XpRBcd3nRdmU5fu83E5IF+3Y9apoi3bsD1Xmpo317t0b8fHxGD9+PKKjowEAVlZWmDVrFnR1dbFw4ULY29vDz88PdnZ2sLa2RlpaGkaNGoVXr16V2PoWIiIiIqKSxislF61MdF7yBn9ycnLw2WefISwsDNevX4eXlxciIyMBANWqVUN6ejrCwsJgY2MDbe3cnqG+vj78/f0RHBwMHR0ddl6IiIiIiMop0TsvMpkMEokET58+RUhICAIDA1G1alWEhITg7t27GDNmjHwERltbGzY2Nti5cyd27tyJiRMn4u+//0br1q3L/WXziIiIiEgFcOilSKLO+MtbnxIdHY2RI0dCT08PFStWxNatW2FqaoqrV6/CyckJXl5eWL16NRo0aIDhw4cjOTkZkydPhpGREQ4cOABzc3MxYxARERERUSkQbcF+3r1boqKi0LJlS4wZMwajRo2CmZkZpFIpsrOzoa6ujvj4eDg5OaF27dr47bffYGlpiaysLDx+/Bi6urowMjJ6/8FKERfsExERKR8u2FctZXnB/p2n4i3Yr1u57C/YF/VqY8nJyejRowccHR3h5+cn357XsXm3A1OvXj34+/ujUaNGYpX8Xuy8EBERKR92XlRLWe683H2aIdqx61TWEu3YH0rUNS9PnjzB48eP4e7uDplMJt+et+heTU0NgiDA1NQUV65cQWBgIKZNm4bXr1+LVTIREREREYlE1H5naGgoYmNj4erqColEku8eLRKJBGlpaQgLC4OLiwvi4uLw/PlzaGhoiFg1EREREVHJ4IVziybqyEutWrWgrq6OvXv35hZTwM0lN2zYAB8fH6SlpaFKlSqwsLAo7TKJiIiIiKgMELXzYm5uDn19fWzatAmxsbHy7W8vw4mJiYGTk5P8vi5ERERERKSaRO28VK9eHatXr8bRo0cxa9Ys+f1c8qaLTZ8+Hbt374anpydvPklERERE5R5v81I00a+14ObmBj8/P4wbNw6XL1+Gi4sLtLS08PDhQwQGBuLIkSOwtLQUu0wiIiIiIhKZqCMvQO46l1GjRuHChQto2LAhQkJCEBkZCWtra5w/fx4ODg5il0hEREREVDo49FIk0Ude8jRt2hQBAQFQU1MTuxQiIiIiIiqDykznBVC82ljejSqJiIiIiFSFRFmGQEQi+rSxt73dWWHHhYiIiIiI3lamOi9ERERERESFKVPTxoiIiIiIVBknHxWNIy9ERERERKQUOPJCRERERFRGcOClaBx5ISIiIiIipcDOCxERERERKQVOGyMiIiIiKiO4YL9oHHkhIiIiIiKlwJEXIiIiIqIyg0MvRZEIgiCIXUR58uq1av461aSq+UZ7npYldgmi0KqgJnYJokjNzBa7BFEY62qIXQKVoswsmdgliEKzgmpORmnkfUTsEkRxa3EnsUso1IOU16Id28yw7H/ec+SFiIiIiKiM4JqXoqnm1wxERERERKR02HkhIiIiIiKlwGljRERERERlBGeNFY0jL0REREREpBQ48kJEREREVEZwwX7ROPJCRERERERKgZ0XIiIiIiJSCpw2RkRERERURki4ZL9IHHkhIiIiIiKloHIjLzKZDFIp+2xEREREVAZx4KVIKtV5yeu4XL9+HWvWrMHjx49haWmJrl27wtnZWezyiIiIiIioCCo1BCGVShEdHQ0XFxfEx8fD3NwcO3fuxLBhw7BkyRKxyyMiIiIioiKoVOclLS0NU6dOxbBhw7B9+3YsWrQIV65cwbNnz+Dr64vp06eLXSIRERERqTCJiA9loFKdF3V1dSQlJcHGxgYA8OrVK+jp6aFdu3ZwdHTEqVOnsHfvXpGrJCIiIiKigqhU5yU9PR1PnjzBnTt3AAA6Ojp49OgRgoOD4enpCQDYsWOHmCUSERERkQqTSMR7KAOVWbAvCAIqVaqE+fPnY9CgQbh58ybMzc2xdu1a9O3bF/3790flypXh6emJx48fw9TUlFclIyIiIiIqQ1Sm8yL5tzs5YMAA6Orq4ueff0ZOTg58fHwwefJkAEBcXByMjIxgYmLCjgsRERERlTrepLJoKtN5eVuPHj3QpUsXSCQSqKu/+RVERESgdu3ayMrKQoUKFUSskIiIiIiI3qWSnRcACp2TqKgorFu3Dhs2bMD58+dRsWJFESsjIiIiIqKClMvOiyAIkEgkePToEbS0tGBkZFRo27S0NJw/fx43btzAuXPnYGtrW4qVEhERERG9hbPGilTuFnbkdVwOHDiAPn364MSJE3j58mWh7StWrIj+/fsjICAAdnZ2pVgpERERERF9jHLXecnruAwYMADdu3eHi4sL9PT0FNoIgqDw33p6eqhUqVJplklERERElA9vUlm0cjdt7OHDh5g5cyYWLlyICRMmICsrC6mpqQgKCoKRkREcHBzkVx4jIiIiIiLlUe46L+rq6tDR0YGZmRmSkpKwatUqnDhxApGRkTAxMcHChQvh7u4udplERERERPSRlH7aWN4UsISEBKSlpUFLSwuCIMDf3x+1a9dGSEgI3N3dcfz4cVSrVg0REREiV/zhdmzfiq4d28LZyRaD+/dBZER4ke2PHz2CXt07w9nJFn16dsf5s2fkP8vKyoLfz0vQp2d3NG/qgC/aumLW9Kl4mhBf0jE+WsC2rejcoS2aODTCAI+vEBFedO5jRw+jR7dOaOLQCO5u3XHurdxA7mtkpb8f2rVqiaaOthg5bChiY2NKMMGn2btzO/p8+QXat3DEqKH9EB1V9Gv11ImjGNi7O9q3cMQQj564dOGsws/T0tLwy6IFcO/aDu1bOmFQny9xYM+OkozwSXYFbEWPzu3QsqkdPAf2RdR7Xucnjh3BV25d0LKpHfr1/hIXzp0ptK3vD3PQ1N4a27f8Udxl/2f7d21HP7eO6OjqhDFf98e195zv0yePYkif7ujo6oRh/Xsi8J3znZyUiJ/mzcBXXdui8+dNMPUbLzyIiy3JCJ9EVd/fqppbVd/fqnq+BzSviVPerRC5sAN2j3eGbY3Cp+Rv8WqKW4s75Xus+9qxwPbzetng1uJOGNrSvKTKLzPevet9aT6UgVJ3XvIW5x88eBDu7u44efIkKlWqhO3bt6Nv375YvHgxNm7ciAkTJsDR0RG6urpKc/PJo0cO4efFP2Kk11hs27kXFpb1MXbUcCQnJRXYPiz0H0yfOhk9evXGtl370Lpte0z6Zhxu37oJAMjIyMD1a9EYPmoMtu3YgyW/+CM25h6+HT+mNGO915HDh7BkkS9GjRmLgF37UL++FUaPGoakQnKHhvyDaVMmo2ev3tixez/atG2Hb8ePxa1/cwPA77+tw/atmzHTZw62bN8JbW1tjB45DJmZmaUV671OHjuMlcsWYejw0Vi/eRfqWdTHd+NHISW54NwRYSGYN/N7dO3RE+u37IJrq7aY8d0E3L19S95m5S+LEHzpPGbO88XmnX/iK49BWLZ4Ic6fOVVasd7r+NFDWLb0JwwfNRabtu+BhWV9TBgzAsmF5A4PDcEs7+/wpZs7NgfsRas27TBl4njcuX0zX9tT/zuOyPAwVK5cpaRjfLRTx49gtd9iDB7mhV//2Im69Swx9ZvCz3dkeCh+mDUVnbv3wtpNu9Di87aY/f03uHcn93wLgoDZ33+DRw8fYP7i5fh1806YVv0M340fgfT0tNKMViRVfX+ram5VfX+r6vnuYlcV07tbYcXx23BbdhHXHr3EhuGNYaSjUWD7sX+EwGXe/+SPzkvOIztHhsPh+b9U7dCwCuzNDfDkeUZJxyAloBx/yRdCIpFg//796N+/Pzp37owGDRoAAOrUqQMvLy+MGjUK+vr6SEtLg7e3NwIDA+Hh4SFy1R9m66aN6On+FXr0dEeduvUwY/ZcaGlr4cC+PQW237ZlM1xatMQQz2GoU6cuxoz/BlY2NtixfSuA3IsSrF63AV906oxatevA1s4eU6fPwrXoKDx+/Kg0oxVp8x+/o1fvPnDr6Y669ephps9caGlpYf/egnNv3bIJzVu6YujXw1Gnbl2Mm/AtrG1sELBtC4DcP+q2bt6EEaNGo03b9rCsb4UffBfhaUIC/nfyRGlGK9LObZvQza03unzZE7Xq1MVk79nQ0tLC33/uK7D97oAtaOrSAv0GfY1ateti+OjxsLSywd5d2+RtIsND0alrDzg4NcVn1arjy15foa5FfVyLLjujj9s2/wG3Xl+hu1sv1KlbD9NmzoGWlhYO7t9bYPuAbZvg3LwlBg0dhtp16sJr7DewsrbGzoBtCu0S4uOx9McFmLdwkcKNaMuKXds3oUsPd3Tunnu+J06bDU0tbRw+WPD53rtjC5o6t4DHIE+Y166Dr73Gw6K+Dfbv2g4AeHA/FtGR4fh26ixY2TRETfPa+HbqLLzOzMT/jh0uzWhFUtX3t6rmVtX3t6qe768/r4UdQfex58pD3E54hdl7o5CelYPeTasX2P55ehYSX76WP1paGCMjS4bDYU8U2pnqa2J2DxtM2haO7ByhwH2VNxIR/6cMlLrz8uDBA3h7e+OHH37A9OnTUbNmTWRmZuLcuXO4du0aAGDr1q0YOHAgtm/fjmPHjsHS0lLkqt8vK+s1rkVHoZlzc/k2qVSKZs4uCA8LLfA5EWGhCu0BwKV5i0LbA0Dqy5eQSCTQ09MvjrL/s6zXubmdXRRzOzs3R3hYSIHPCQ8NhbOzi8K25i1aIjw0FADw8MEDJCY+Vfjd6OnpoZGtXaH7LG1ZWVm4eT0ajZs6y7dJpVI4NXVGVERYgc+JigiDUxPF3E2dmyu0b2hrjwtnT+FpQjwEQcA/V4JxPy4GTZo1f3d3osjKeo3r16LQpNmbHFKpFE2auSAiPLTA50SEh6FpM8Xczi4tFdrLZDL4zJyKgUO+Rt16FiVR+n+Sd76d3j3fTZwRXcj5jo4Ig2MTZ4VtTd4631mvXwMANDQ0FfZZoUIFRIb9U9wRPonKvr9VNbeqvr9V9HxXUJOgQXV9XLz1ZnRJEICLt5LgYG7wQfvo3dQMf4U+RnpWjnybRAIs7meL9Wfu4XZ8anGXTUpKqTsv2dnZ0NHRgaOjIxISErBkyRJ07NgRXbp0wYgRI3Dq1Cm0b98eTk5OOHnyJBwcHIr1+JmZmXjx4oXCoziGcJ+lpCAnJwdGxsYK242MTZCUlFjgcxITE2H8TntjYxMkJRbcPjMzE36/LEGnzl2hq6v7n2suDinPcnPnz2GMxEJy5OY2yd/+399TYuLT3G0mH77P0vb839yGRu+cbyNjJBdyvpOTEvO9PgyNTBTafzNlOszr1IV713Zo6+KAKRNGYeL3M2Dv2Lj4Q3yCZynPCnmdGxf6uk1KTITRO+fbyNgYyW+13/T7eqirqaFv/0HFX3QxeP4sBbICzrehkXGh02mSkxILbJ/y7/muWas2qlT9DOtXLcPLF8+RlZWF7Zt+w9OE+EJ/l6VNVd/fqppbVd/fqnq+DXU0oK4mRWLqa4XtSamZqKynWciz3rCtUQn1P9PDruAHCttHtq6DHJmAP86XvfV7JYlrXoqm1J0XAwMDPH36FN7e3rCxsUFwcDC6d++OY8eOITU1FcHBwTA1NYW3tzfq1q1b7Mf39fVFpUqVFB5LFvkW+3GKW1ZWFqZ+9y0AwHvWHFFroZKzZ8dWREeEw3fpCqzfvANjvp2CXxYtwJWgS2KXVmKuRUchYNtmzJ7nq1KXRFdXr4B5P/6CB3Gx6NGhJTq3aoLQq5fR1KUlpFLV+T1Q+aaq729V8FVTM1x//BLh95/LtzWoro8hruaYuqPsTHWmsqHsTRYtRN7i/BcvXqBixYpIT0+HgYEBLl68iI0bN6J///7w8PCAoaEhJBIJzMzMIJPJAKDEPuS8vb0xadIkhW3ZkoIXpn0MA0NDqKmp5Vucn5yU/9uZPCYmJvkWAyYlJcLYRLF9VlYWpn03EY8fPcKvv20sM6MuAGBokJs7f44kmJgUlTsxf/t/f08mJpVztyUmKSzsTEpKQn0rq+Is/5NV+jf3u4u1k5OT8n0LmcfI2CTf6yMl+c23lpkZGVi3yg8LFvvBpWUrAEBdi/q4ffM6ArZsRON3pmaIwcDQoJDXeVK+120eYxOTfKNRyUlJMPq3feg/V5CSnIQvO7eV/zwnJwd+Py9CwNZNOHD4ZDGn+HiVDAwhLeB8pyQnweid0ZU8RsYmBbY3fOv1YWndAOu27EZq6ktkZ2XBwNAIY77uj/pWNsUf4hOo6vtbVXOr6vtbVc93yqvXyM6RwURX8W8gY11NPH1Z9IwU7Qpq6GpXFX7Hbitsb1LbEMY6GjgzvZV8m7qaFNO6W2GIay208S38SnRUvinFyEtex+Xw4cMYOHAgmjdvjnHjxuHcuXOoXr06pk6dijFjxsDIyAhZWVmYPn06goOD0bt3bwAl13nR1NSEvr6+wkNT8/3Do+9ToYIGrG0aIPitb8hlMhmCAwNha2df4HMa2dkrtAeAoEsXFdrndVzi4mKxZt3vMDAw/M+1FqcKGrm5gwIVcwcFXYKtXcFT/mzt7REUGKiwLfDSRdja2wMAqpuZwcSkMoLe+t2kpqYiIjys0H2WtgoVKsDSygZXLwfJt8lkMvxzOQgNGtkV+JwGjezwz2XF3JeDLsnbZ2dnIzs7GxKJ4ltcKlWDTJAVc4JPU6GCBqysG+By8JscMpkMV4ID0cjWvsDnNLK1U2gPAEGBF+XtO3f7Ett27ceWHXvlj8qVq2DgkK+xfPX6koryUfLO9z/5zncgbAo53zaN7PDPlSCFbVeCLxX4+tDV1YOBoREexMXi5rUoNP+8bb42YlDZ97eq5lbV97eKnu+sHAFRD1/Apd6bL2AkEqB5PWOExD4r8rmd7apCQ12KA/8oXjxo/z+P0O3nC/jyl4vyx5PnGVh/+h6+Xn+lJGKQkijTnZe8e7hIJBIcOHAAvXv3RuPGjTFkyBCkp6fDw8MDZ8+ehbq6OgRBwKZNm9CzZ09s3boVR48ehYVF2VvM96EGDB6KfXt24eCBfbh79w4Wzp+D9PR0fOnWCwAwa/pU+C9bKm/ff+AgXLpwHpv/2IB7d+9izSp/REdFoW+/AQByOy7fT/oG0VGRWPDjYuTIcpCY+BSJiU+RlfW6wBrEMGiIJ/bu3ok/9+/D3Tt38MO83NxuPXNzz/D+Hn6/vMk9YOBgXLxwDn9s3IB7d+9g9Up/REVGwqP/QAC5r50BgwZj3a+rcfp/J3Hr5g3M9P4elatUQdt27cWIWKA+/Qfjr/27cfivA4i5dwdLf5yP9PR0dOnuBgBY4OONX1f8Im/f22Mggi5dQMCWjYiNuYsNa1fixrUo9PqqPwBAR1cX9o6NsXr5UoRcDcajhw9w+OB+HD30Jz5v3U6MiAXqP2gIDuzdhb/+3I97d+/gpwVzkZ6ejm49egIAfGZOxcrlP8vbe/QfjEsXz2Prpt8Rc+8u1q5egWvRUejjkZvbwMAQdetZKjzU1dVhbGwC81q1RclYkK/6DcbfB/bg6N8HEHvvLpb9NB8ZGeno1M0NAOA7ZzrWrVwmb9+r70BcvnQBO7f+gbiYu9i4bhVuXouC21f95G1OnzyK0KuX8ejhfVw48z9MmTASLT5viybOZeMCDYDqvr9VNbeqvr9V9XxvOBuDvs3M0NOpGupW0cG8Xg2graGGPZcfAgAWeTTC5M75L5rUu0l1HI9KwLO0LIXtz9KycCs+VeGRnSMg8WUm7j19VSqZqGwqk9PGEhMTYWJiIh8xuXHjBubNm4elS5fCy8sL8fHx8PX1hZaWFtzc3LBv3z60atUKrVu3xs2bN7Fs2TKl7rgAQMdOXZCSnIzVK/2RlPgU9a2ssWLNOvlw+5PHjyB9a0TJzt4RC35cglUrlmGF3y+oaV4LP/utQD2L3A+KpwnxOHP6fwAAj95uCsdau+EPNG7SrHSCvUenzrm5V61YjsR/c6/6df1buR9D+tZogr2DI3wXLcGK5cvgv+xn1DSvhWX+K2Fh8eYD0nPYCKSnp2PenNl4+fIFHBydsOrX9cUySlZc2n3RGc+epWDDryuQnJSIepZWWLJ8jXwaWPyTxwqjKI3sHDD7h5+wfrU/1q3yg1kNcyxYshx13rr6js+CJVi7chnmz5qGFy+eo2rVahgxegJ6uPct9XyF6dCxC1JSUrB29XIkJSbCsr41/FatlU+PjH/nfNvaO2D+wsVYs9IPq/x/QY2a5lj8iz/q1iv7VxF8W5sOnfDsWTJ+X7sSKUmJqGtphZ+WvTnfCfGPFdaqNLS1x4z5P2LDmhX4bbUfqtcwx7xFfqhd9835Tk5MxOpli3Onn5lUxhedu2PQMK9Sz1YUVX1/q2puVX1/q+r5PhT2BEY6GvimowUq62ni2qMXGLb+CpL+XcRfzUAbwjtXOq5dWQdN6hhh6NrLIlRcdnFJV9EkgvDuS0lc/v7+WLduHbZv3y6/b8v169fh5+eHxYsXIyUlBe3atUPr1q0xduxYeHp64tGjR9i8eTM6dOgAmUwm6o0oX70uU7/OUqOmoouCn7/zTZGq0KqgJnYJokjNzBa7BFEY6/73tXykPDKzysbU0tKmWaFMT0YpMY28j4hdgihuLe4kdgmFepae8/5GJcRAu+z/+17m3qm9e/dGfHw8xo8fj+joaACAlZUVZs2aBV1dXSxcuBD29vbw8/ODnZ0drK2tkZaWhlGjRuHVq1e8AgkRERERUTlVJjoveYM/OTk5+OyzzxAWFobr16/Dy8sLkZGRAIBq1aohPT0dYWFhsLGxgba2NgBAX18f/v7+CA4Oho6ODjsvRERERKS08t/3vvT+pwxE77zIZDJIJBI8ffoUISEhCAwMRNWqVRESEoK7d+9izJgx8hEYbW1t2NjYYOfOndi5cycmTpyIv//+G61bty70EoRERERERFQ+iLpgP299SnR0NEaOHAk9PT1UrFgRW7duhampKa5evQonJyd4eXlh9erVaNCgAYYPH47k5GRMnjwZRkZGOHDgAMzNzcWMQURERERULDiJqGiidV4EQYBUKkVUVBRatmyJMWPGYNSoUTAzM4NUKkV2dna+Dsxvv/0GZ2dn7NixA48fP4auri6MjIzEikBERERERKVItGljEokEycnJ8PLywuDBg7FgwQLUrFkTUqkUgiBAXV1doQNz7949jBw5EhEREahQoQJq1qzJjgsRERERlSsSER/KQNQ1L0+ePMHjx4/h7u4OmezNpRnzFt2rqalBEASYmpriypUrCAwMxLRp0/D6ddm5qSIREREREZUOUde8hIaGIjY2Fq6urpBIJPnu0SKRSJCWloawsDC4uLggLi4Oz58/h4YG7zlARERERKRqRB15qVWrFtTV1bF3797cYgq4ueSGDRvg4+ODtLQ0VKlSBRYWFvnaEBERERGVC5w3ViRROy/m5ubQ19fHpk2bEBsbK9+ed98XAIiJiYGTk5P8vi5ERERERKSaRO28VK9eHatXr8bRo0cxa9Ys+f1c8qaLTZ8+Hbt374anpydvPklERERE5R5vUlk0Ude8AICbmxv8/Pwwbtw4XL58GS4uLtDS0sLDhw8RGBiII0eOwNLSUuwyiYiIiIhIZKKOvAC561xGjRqFCxcuoGHDhggJCUFkZCSsra1x/vx5ODg4iF0iERERERGVAaKPvORp2rQpAgICoKamJnYpRERERESi4EqJook+8vK2t6829vaifSIiIiIiojIz8gJAYVE+F+gTERERkarhX8BFK1MjL0RERERERIUpUyMvREREREQqjUMvReLICxERERERKQV2XoiIiIiISClw2hgRERERURmhLHe6FwtHXoiIiIiI6JOsXLkStWrVgpaWFpo1a4bg4OAi2+/atQtWVlbQ0tJCo0aNcOjQoY86HjsvRERERERlhEQi3uNj7dixA5MmTYKPjw/++ecf2NnZoWPHjkhISCiw/cWLF9GvXz8MGzYMISEhcHNzg5ubGyIjIz/89yPwbpDF6tVr1fx1qklVc4jzeVqW2CWIQquCmtgliCI1M1vsEkRhrKshdglUijKzZGKXIArNCqr5fW4j7yNilyCKW4s7iV1CoTJE/KdG6yMXlDRr1gxNmjTBihUrAAAymQw1atTA+PHjMW3atHzt+/bti1evXuGvv/6Sb3N2doa9vT3WrFnzQcdUzXcqEREREREpyMzMxIsXLxQemZmZBbZ9/fo1rl69ivbt28u3SaVStG/fHpcuXSrwOZcuXVJoDwAdO3YstH2BBCoXMjIyBB8fHyEjI0PsUkoVczO3KmBu5lYFzM3cJD4fHx8BgMLDx8enwLYPHz4UAAgXL15U2D5lyhShadOmBT6nQoUKwrZt2xS2rVy5UqhSpcoH18hpY+XEixcvUKlSJTx//hz6+vpil1NqmJu5VQFzM7cqYG7mJvFlZmbmG2nR1NSEpqZmvraPHj1C9erVcfHiRbi4uMi3f//99zhz5gyCgoLyPUdDQwN//PEH+vXrJ9+2atUqzJ07F/Hx8R9UIy+VTEREREREhXZUCmJiYgI1NbV8nY74+HhUrVq1wOdUrVr1o9oXhGteiIiIiIjoo2hoaMDJyQknT56Ub5PJZDh58qTCSMzbXFxcFNoDwPHjxwttXxCOvBARERER0UebNGkShgwZgsaNG6Np06ZYtmwZXr16BU9PTwDA4MGDUb16dfj6+gIAvvnmG7Rq1QpLly5F165dERAQgCtXrmDt2rUffEx2XsoJTU1N+Pj4fPBQX3nB3MytCpibuVUBczM3KZ++ffvi6dOnmD17Np48eQJ7e3scOXIEpqamAIC4uDhIpW8mejVv3hzbtm3DzJkzMX36dFhYWGD//v1o2LDhBx+TC/aJiIiIiEgpcM0LEREREREpBXZeiIiIiIhIKbDzQkRERERESoGdFyIiIiIiUgrsvBARlSOqeg0WmUwmdgmiUPXcL1++FLmS0qWq72+it7HzomJU9R86Vf3AV9XznZdbJpMhOztb5GpKj0wmg0QiQWJiIh4+fCh2OaVGJpNBKpXi1q1bOH36tNjllJq3c2/ZsgUZGRlil1Qq8nLfuHEDX3/9Ne7evSt2SaUi7/2dmpqKhIQEvHr1SuySSoWq/jtGhWPnRYW8/YE/YcIE9OvXD3PnzkVkZKTYpZWovA/8J0+eICEhQexySk3e+b579y4WLlyIMWPG4NdffxW7rBL39ut87Nix6NKlCyZNmoQnT56IXVqJk0qliImJgYODA3755RfExMSIXVKJyzvfoaGhcHR0xLVr18QuqVTk5Q4PD0fz5s1x+vRpJCYmil1WicvLHRYWBkdHR+zZswcHDhwQu6wSl5c7KioK7u7ucHV1hZubG3bv3i12aSUqL/f169fx7bffom/fvpg1axYCAwPFLo1ExM6LCpFKpbh27RqaNm2KmJgYaGpqYtWqVRgzZgyWL18udnklQhAEee4aNWpgwIABKvUPfEREBFxdXXHu3DncvHkT48ePx7Rp08Qur8S8m/vZs2ewt7fH+vXr8eOPP4pdXqk4d+4cHj58iGPHjmHdunWIi4sTu6QS8/Yfsi1btsTIkSMxevRoscsqFVKpFA8ePECPHj0wdOhQrF+/HmZmZuV6lPnt8+3i4oLx48fDx8cHO3bsKNevc+DNv9+urq6wsrLCnDlzIAgCNm3aVK7PuVQqRXR0NFxcXBAfHw9zc3Ps3LkTw4YNw5IlS8Quj8QikEqQyWRCVlaWMGzYMMHT01O+/cmTJ8LQoUOFpk2bCgsXLhSxwpITHx8vuLq6Cp07dxZq1KghfPHFF8LTp0/FLqvExcbGCpaWlsJ3330n37Znzx6hcuXKQnh4uIiVlax79+4JderUEaZNmybftmzZMmH48OFCRkaGiJWVjhs3bghDhw4V/P39hapVqwrTpk0T4uPjxS6rxNy8eVPQ0tKSn+/Xr18Lu3btEpYvXy5s3rxZSElJEbfAEnTixAmhU6dOgiDk5v7uu++Erl27Cn369BFWr14tcnUl48qVK4K+vr4wffp0QRAE4eDBg4Kenp5w+PBhQRAEIScnR8zySkx6errQp08fYdy4cfJtZ86cEbp37y48ePCg3L7OX716JXTr1k2YPHmyfNuLFy+EatWqCUZGRoK3t7eI1ZFYOPKiIiQSCdTV1ZGYmIjMzEwAuaMSpqamWLRoEZycnPDXX39h586dIlda/CIjI1G7dm3Mnj0bR48eRXR0dLkfgREEAXv37kWNGjXg7e0t325rawtNTc1yuw5EJpPh4MGD6NixI6ZOnSrffv36dVy5cgXOzs4YNGhQuXydv+3UqVMYMWIEvL29sWXLFqxbtw49evTA+PHjxS6tWMlkMvz222/Q1dWFpaUlAKBHjx5YsGABVqxYgWHDhqFfv364dOmSyJWWjNDQUDx69AgA0K1bN1y9ehUODg5IT0/HunXrMGHCBJErLD6CICAjIwOdO3fG119/jQULFgDIzd2hQwf4+Pjg1atXkErL5581WlpaSE5ORsWKFeXbDh06hCtXrqBZs2bo1q0bxo0bJ2KFJUNdXR1JSUmwsbEBALx69Qp6enpo164dHB0dcerUKezdu1fkKqm0lc93OeUjCAJycnJgZmaGlJQUpKamAgBycnJQuXJlzJ49G1paWvjjjz9ErrT4OTk5Yfjw4XB2doa1tbVCB+bp06fyduVpUaBEIsHnn38OZ2dnGBkZybfXq1cP2traiI+PF7G6kiOVSuHh4YGhQ4fCwMAAALBw4UKsXbsWPXv2xDfffIP79+/Dz88P9+/fF7fYEiAIAiwtLWFjY4M7d+5gwoQJmDNnDnx9fXH69Gl06tRJ7BKLlVQqxfjx49G/f3+sWbMG1atXh0Qiwa5duxAREYHo6Gjcvn0bixYtErvUEuHs7AwDAwNs2LBBPoVo/vz52LlzJzw8PBAUFISQkBCxyywWEokEWlpaCA8Pxy+//ALgzWd2r169kJSUhLCwMIXt5YUgCEhLS4Oenh7Cw8Px008/YerUqVi+fDl++uknbN++HW5ubggMDERAQIDY5Rar9PR0PHnyBHfu3AEA6Ojo4NGjRwgODoanpycAYMeOHWKWSGIQcdSHRBASEiJoaGgIc+bMkW/Lzs4WBEEQQkNDBYlEIly+fFms8kpc3pSC6OhowczMTD6FLCsrS/Dz8xMOHDggcoUlQyaTyf9//fr1hX379sn/+++//xYePnwoQlXF7+2cgiAIycnJwowZM4QjR47It92/f1+QSCTC7t27S7u8UtOlSxdh1apVgiAIwvDhw4VKlSoJVapUEXx8fIR79+6JW1wxyjvfDx8+FEaNGiV069ZNiIqKUmhz5swZQSKRCKGhoWKUWKLi4uKEWrVqCXXq1BFatGghZGVlyX/29OlToVKlSsLatWtFrLB4vfv+zpOdnS3Ur19fGDBgQClXVLqio6OFnj17CkOHDhUaNWqkMDXw2bNnQr169YSZM2eKWGHxyjvfW7ZsESQSidC7d29h8uTJgp6enjB8+HBBEATh2LFjQvXq1YVHjx6V2ymDlB9HXlSITCaDvb09Vq1ahXnz5sm/jVRTUwOQOzxrZWUFPT09McssUXlTCqytrXHs2DFER0dj0KBBGD58OCZNmgRra2uRKywZEokE2dnZyMrKglQqlZ/jGTNmoFu3bsjJyRG5wuIhkUgU/tvQ0BAzZ85Ex44dAeSONKampsLJyQm1atUSocKSlXceGzduDJlMhgkTJuDvv/9GWFgY5syZg59++glbtmwpN9MGJRIJBEFAtWrVMG/ePEyYMAEWFhYKbV6+fAlLS0t89tlnIlVZMgRBQI0aNbBlyxYkJiYiIiICZ86ckf/c0NAQzs7OMDU1FbHK4vXu+xvIfc2rqalh+vTpuHjxIs6fPy9CZSVPJpPB2toa27Ztw++//w5jY2OFKWR6enqwtLSUj7QL5WARf975HjBgAPbt24eEhATcvXsXPj4+WLduHQAgLi4ORkZGMDExKbdTBik/dbELoNKT98YePHgwUlNTMXnyZNy/fx8DBgyAubk5tm3bhoyMDPl0m/LO2toaf//9N+zt7WFoaIjg4OB8f/iUJ3n/EAiCAE1NTSxYsAB+fn4IDg5GjRo1RK6u5Ghqasr/v5qaGrZu3QoAMDMzE6ukEpP3RYStrS2++uorVK1aFQcPHoS5uTlGjx4NqVSKtm3bQl29/Hz053VgqlSpgg4dOuT7+dmzZ2FmZqbwOigPJBIJZDIZWrRogYCAALi7u2POnDmIjY1F06ZNsWXLFoSFhcHOzk7sUktU3ms+r8N+9uxZtGzZUuSqil/ev9+amprIyMiAIAi4fPkyOnfuDHV1dSxbtgxXr16VXzm0oI6eMuvRowe6dOkiX7+bJyIiArVr10ZWVhYqVKggYoVUmiRCeeie0yc5ePAgxo8fj5ycHGhrayMzMxP79u2Do6Oj2KWViqysLEyYMAGbN29GcHCwfEFgedesWTO8evUKt27dwoULF9C4cWOxSyoVoaGh2L17N/z9/XHu3DnY2tqKXVKJiYuLw6ZNm9C9e3fY2dnJLzGrSkJDQ7Fr1y74+/vjwoULaNSokdgllairV69iypQpuHXrFrS1taGhoYHNmzfDwcFB7NJKzcSJE7F//35cv34dGhoa5e4P+LedOHECXbt2RbVq1WBkZISUlBTs2bNHZc53VFQU1q1bhw0bNuD8+fPl+vOc8mPnpRwSBAESiQSPHj2ClpaWwoLtd8XHxyM2NhYZGRmwsLBQ6qkVH5MbyP3jZtSoUVixYgWaNGlSSlUWvw/NLZPJkJqaCisrK8THxyMsLAwNGzYs5WqLz8ec78ePH2PixImIjo7G5s2blfrb6A/NnZmZWa5GGz7mfD969AijR4/G9evXsXPnznJ/vvM6py9evMCzZ8+QlpaGKlWqvPczsCz7mPOd1zY8PByVKlWCubl5KVZavD7mfIeHh+PEiROoXLkyPv/883KfO09aWho2b96M/fv348cff1Tq9zd9olJdYUMlLm+B2/79+4UWLVoIO3bsEF68eFFk2/LgY3LnSUtLU/pr439K7sOHDwvR0dGlUV6J+ZTcd+/eVfoLE3xK7vLgU3LfunVLuH//fmmUV2L4ec7XeWG5y9Pi9E853y9evBCePXtWGuVRGcTOSzm0f/9+QUdHR/jxxx+FuLi4fD8vT//IvY25mfttzF2+fEzu8vQ74Plm7rcxNxE7L+XOgwcPhIYNGwp+fn6CIOTedfnly5fCiRMnhH/++Ufk6koOczM3czN3ecPczM3c5Tc3fbryc8kZApB7uWMdHR2YmZkhKSkJq1atwokTJxAZGQkTExMsXLgQ7u7uYpdZ7JibuZmbucsb5mZu5i6/uenTqdblZ8oh4d/rLSQkJCAtLQ1aWloQBAH+/v6oXbs2QkJC4O7ujuPHj6NatWqIiIgQueLiwdzMzdzMzdzMrcyYW7VyU/HhyIsSE/69OsfBgwexaNEifP/99+jevTu2b9+OY8eOwcPDA/369YO+vj4AQFdXt1xcLpW5mZu5mZu5mVuZMbdq5aZiVhpz06jk7Nu3T9DV1RUWLFgg3Llzp8A2r169EqZNmyaYmJgIN27cKOUKSwZzM/e7mJu5lR1zM/e7mLv85Kbiw86LErt//75gZWUlLFu2TBAEQcjKyhIyMjKEs2fPyi+Fu2XLFqFnz56Cubl5uVn4xtzMzdzMzdzMrcyYW7VyU/HitDEllp2dDR0dHTg6OiIhIQEbNmzAkSNHcPXqVdjZ2WH+/Plo3749YmJisHjxYtStW1fskosFczM3czM3czO3MmNu1cpNxUsiCP+unCKl8+zZM9jZ2aFGjRq4fv06Pv/8c7Ro0QLNmzfH6NGj0a9fP0ydOlV+N97ygrmZm7mZm7nLB+ZmblXITcWLIy9KQvh3kduLFy9QsWJFpKenw8DAABcvXsTGjRvRv39/eHh4wNDQEBKJBGZmZpDJZAAAiUQicvWfjrmZm7mZm7mZm7mVj6rmplIgzmw1+hh5d5Y9dOiQ0L17d6FJkybC4MGDhbNnzwqCkDtnNE9mZqbg7e0tVK5cWbh586Yo9RYX5mZu5mZuQWBu5lZezK1aual0sPNShuW9+QVBEPbv3y9UrFhRmDt3rrBixQrhq6++EqpVqyacOXNG3vaPP/4QunTpItSsWVOpF7kxN3MzN3PntWVu5lZGzK1aual0sfNSBj19+lThv69fvy44OjoKq1evFgRBEJ48eSJUr15dqFOnjmBoaCicPn1aEARBiI2NFWbMmKG031wwdy7mZm7mZm7mZm5loqq5SRzsvJQxy5cvFxo1aiRERkbKt127dk3w8vISXr58KcTFxQkWFhbCiBEjhNDQUMHBwUEwNTUVjh07JgiCIOTk5IhV+n/C3MzN3MzN3MwtCMytbFQ1N4mHnZcy5tGjR0KVKlWENm3aCFFRUfLtDx8+FARBELy8vISvvvpKSEtLEwRBEPr37y/o6ekJtWvXFlJTUxWGbJUJczO3IDA3czM3czO3slHV3CQeXoeuDBD+vVp1Tk4OPvvsM4SFheH69evw8vJCZGQkAKBatWpIT09HWFgYbGxsoK2tDQDQ19eHv78/goODoaOjo1RX6GBu5mZu5mZu5mZu5ib6KGL1mihX3nBpQkKCcPnyZeHSpUuCILyZH+rq6qrwTcawYcMEa2trYceOHcK3334r1KhRQ4iJiRGl9v+CuZlbEJibuXMxN3Mzt/JQ1dxUdrDzIqK8D4CoqCihRYsWQqdOnYRevXoJ6enpgiAofhDkzSW9dOmS0LNnT8HMzEywtbVVyqtzMDdzMzdzMzdzMzdzE30Kdl5EkjfHMzIyUjAwMBCmT58uxMbGyj8Y8q6BnvdB0LJlS+HGjRuCIAjC69evhdjYWCEpKUmc4v8D5mZu5mZu5mZu5mZuok/FzouIkpKShJYtWwoTJkxQ2J73AfHuB0GrVq2E8PDwUq+zuDE3cwsCczM3czO38mJu1cpNZQs7LyKKiooS6tatK5w5c6bASwXKZDL5B8Ljx48FTU1NoUuXLkJmZmZpl1qsmJu538bczM3cyou5mftt5TU3lS3qYl8wQJWFhoYiNjYWrq6ukEgkkMlkkErfXABOIpEgLS0NYWFhcHFxQVxcHJ4/fw4NDQ0Rq/7vmJu5mZu5mZu5lRlzq1ZuKlt4qWQR1apVC+rq6ti7dy8AKHwA5NmwYQN8fHyQlpaGKlWqwMLCorTLLHbMzdzvYm7mVnbMzdzvYu7yk5vKFnZeRGRubg59fX1s2rQJsbGx8u3Cv9dPB4CYmBg4OTnJr49eHjA3cwPMDTA3cys/5mZuoPznpjJGrPlqlGvPnj2CpqamMGjQIIXror969Urw9vYWzM3N5VfrKE+Ym7kFgbmZu3xhbuYWBOYur7mp7JAIwlvdZSp1MpkM69atw7hx41CvXj24uLhAS0sLDx8+RGBgII4cOQIHBwexyyx2zM3czM3c5Q1zMzdzl9/cVHaw81JGBAcHY/Hixbh9+zb09PTQvHlzDBs2rNzPFWVu5mbu8ou5mZu5yy9VzU3iY+elDMnJyYGamprYZZQ65lYtzK1amFu1MLdqUdXcJC4u2C9D3r5qhyr1KZmbuVUBczO3KmBu5iYqaRx5ISIiIiIipcCRFyIiIiIiUgrsvBARERERkVJg54WIiIiIiJQCOy9ERERERKQU2HkhIiIiIiKlwM4LEREREREpBXZeiIiIiIhIKbDzQkSkhIYOHQo3Nzf5f7du3Rrffvttqddx+vRpSCQSPHv2rNA2EokE+/fv/+B9zpkzB/b29v+prpiYGEgkEoSGhv6n/RARUdnCzgsRUTEZOnQoJBIJJBIJNDQ0UK9ePcybNw/Z2dklfuy9e/di/vz5H9T2QzocREREZZG62AUQEZUnnTp1wu+//47MzEwcOnQIY8eORYUKFeDt7Z2v7evXr6GhoVEsxzUyMiqW/RAREZVlHHkhIipGmpqaqFq1KszNzTF69Gi0b98ef/75J4A3U70WLFiAatWqoX79+gCA+/fvo0+fPjAwMICRkRF69OiBmJgY+T5zcnIwadIkGBgYwNjYGN9//z0EQVA47rvTxjIzMzF16lTUqFEDmpqaqFevHn777TfExMSgTZs2AABDQ0NIJBIMHToUACCTyeDr64vatWtDW1sbdnZ22L17t8JxDh06BEtLS2hra6NNmzYKdX6oqVOnwtLSEhUrVkSdOnUwa9YsZGVl5Wv366+/okaNGqhYsSL69OmD58+fK/x8/fr1sLa2hpaWFqysrLBq1apCj5mSkoIBAwagcuXK0NbWhoWFBX7//fePrp2IiMTFkRciohKkra2NpKQk+X+fPHkS+vr6OH78OAAgKysLHTt2hIuLC86dOwd1dXX88MMP6NSpE8LDw6GhoYGlS5di48aN2LBhA6ytrbF06VLs27cPbdu2LfS4gwcPxqVLl7B8+XLY2dnh3r17SExMRI0aNbBnzx64u7vjxo0b0NfXh7a2NgDA19cXW7ZswZo1a2BhYYGzZ89i4MCBqFy5Mlq1aoX79++jV69eGDt2LEaOHIkrV65g8uTJH/070dPTw8aNG1GtWjVERERgxIgR0NPTw/fffy9vc/v2bezcuRMHDx7EixcvMGzYMIwZMwZbt24FAGzduhWzZ8/GihUr4ODggJCQEIwYMQI6OjoYMmRIvmPOmjUL0dHROHz4MExMTHD79m2kp6d/dO1ERCQygYiIisWQIUOEHj16CIIgCDKZTDh+/LigqakpfPfdd/Kfm5qaCpmZmfLnbN68Wahfv74gk8nk2zIzMwVtbW3h6NGjgiAIwmeffSYsWrRI/vOsrCzBzMxMfixBEIRWrVoJ33zzjSAIgnDjxg0BgHD8+PEC6zx16pQAQEhJSZFvy8jIECpWrChcvHhRoe2wYcOEfv36CYIgCN7e3oKNjY3Cz6dOnZpvX+8CIOzbt6/Qny9evFhwcnKS/7ePj4+gpqYmPHjwQL7t8OHDglQqFR4/fiwIgiDUrVtX2LZtm8J+5s+fL7i4uAiCIAj37t0TAAghISGCIAhC9+7dBU9Pz0JrICIi5cCRFyKiYvTXX39BV1cXWVlZkMlk6N+/P+bMmSP/eaNGjRTWuYSFheH27dvQ09NT2E9GRgbu3LmD58+f4/Hjx2jWrJn8Z+rq6mjcuHG+qWN5QkNDoaamhlatWn1w3bdv30ZaWho6dOigsP3169dwcHAAAFy7dk2hDgBwcXH54GPk2bFjB5YvX447d+4gNTUV2dnZ0NfXV2hTs2ZNVK9eXeE4MpkMN27cgJ6eHu7cuYNhw4ZhxIgR8jbZ2dmoVKlSgcccPXo03N3d8c8//+CLL76Am5sbmjdv/tG1ExGRuNh5ISIqRm3atMHq1auhoaGBatWqQV1d8WNWR0dH4b9TU1Ph5OQknw71tsqVK39SDXnTwD5GamoqAODvv/9W6DQAuet4isulS5cwYMAAzJ07Fx07dkSlSpUQEBCApUuXfnSt69aty9eZUlNTK/A5nTt3RmxsLA4dOoTjx4+jXbt2GDt2LJYsWfLpYYiIqNSx80JEVIx0dHRQr169D27v6OiIHTt2oEqVKvlGH/J89tlnCAoKwueffw4gd4Th6tWrcHR0LLB9o0aNIJPJcObMGbRv3z7fz/NGfnJycuTbbGxsoKmpibi4uEJHbKytreUXH8gTGBj4/pBvuXjxIszNzTFjxgz5ttjY2Hzt4uLi8OjRI1SrVk1+HKlUivr168PU1BTVqlXD3bt3MWDAgA8+duXKlTFkyBAMGTIErq6umDJlCjsvRERKhlcbIyIS0YABA2BiYoIePXrg3LlzuHfvHk6fPo0JEybgwYMHAIBvvvkGP/74I/bv34/r169jzJgxRd6jpVatWhgyZAi+/vpr7N+/X77PnTt3AgDMzc0hkUjw119/4enTp0hNTYWenh6+++47TJw4EX/88Qfu3LmDf/75B/7+/vjjjz8AAF5eXrh16xamTJmCGzduYNu2bdi4ceNH5bWwsEBcXBwCAgJw584dLF++HPv27cvXTktLC0OGDEFYWBjOnTuHCRMmoE+fPqhatSoAYO7cufD19cXy5ctx8+ZNRERE4Pfff8fPP/9c4HFnz56NAwcO4Pbt24iKisJff/0Fa2vrj6qdiIjEx84LEZGIKlasiLNnz6JmzZro1asXrK2tMWzYMGRkZMhHYiZPnoxBgwZhyJAhcHFxgZ6eHnr27FnkflevXo3evXtjzJgxsLKywogRI/Dq1SsAQPXq1TF37lxMmzYNpqamGDduHABg/vz5mDVrFnx9fWFtbY1OnTrh77//Ru3atQHkrkPZs2cP9u/fDzs7O6xZswYLFy78qLxffvklJk6ciHHjxsHe3h4XL17EXVo2gAAAAMpJREFUrFmz8rWrV68eevXqhS5duuCLL76Ara2twqWQhw8fjvXr1+P3339Ho0aN0KpVK2zcuFFe67s0NDTg7e0NW1tbfP7551BTU0NAQMBH1U5EROKTCIWt+CQiIiIiIipDOPJCRERERERKgZ0XIiIiIiJSCuy8EBERERGRUmDnhYiIiIiIlAI7L0REREREpBTYeSEiIiIiIqXAzgsRERERESkFdl6IiIiIiEgpsPNCRERERERKgZ0XIiIiIiJSCuy8EBERERGRUvg/kQK40xNQl18AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nilBdUJ0ueYl"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}