bird.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "height": 1000
        },
        "id": "pJ9K_tV4hSoL",
        "outputId": "fda4517e-7f3d-4b70-9dcb-1fa3f8693ee4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7d3adadd-70d9-424d-9295-b00fd9b8bc64\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7d3adadd-70d9-424d-9295-b00fd9b8bc64\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  inflating: /content/Bird_Dataset/train/TURQUOISE MOTMOT/043.jpg  \n",
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          ]
        }
      ],
      "source": [
        "#@title Run to Prepare Data { display-mode: \"form\" }\n",
        "# import gdown\n",
        "# gdown.download(\" https://drive.google.com/uc?id=1tKGq6FBACRv2razgZxKi_64nWhpHjrU4\",\"birds\", True)\n",
        "# ! unzip birds\n",
        "!pip install -q kaggle\n",
        "from google.colab import files\n",
        "import os.path\n",
        "if (os.path.exists(\"kaggle.json\")):\n",
        "  print(\"Token exists\")\n",
        "else:\n",
        "  files.upload()\n",
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! kaggle datasets download gpiosenka/100-bird-species\n",
        "!unzip 100-bird-species.zip -d /content/Bird_Dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow.keras as keras\n",
        "import tensorflow.keras.layers as layers\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, GlobalAvgPool2D, Dropout, BatchNormalization\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import PIL\n",
        "import pathlib\n",
        "import math"
      ],
      "metadata": {
        "id": "_tO48XVU95Uj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T99ZhfbOpmag",
        "outputId": "09ebf470-6200-4de7-cca6-7229e81c0fd2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "47332\n"
          ]
        }
      ],
      "source": [
        "train_dir = pathlib.Path('./Bird_Dataset/train')\n",
        "val_dir = pathlib.Path('./Bird_Dataset/valid')\n",
        "test_dir = pathlib.Path('./Bird_Dataset/test')\n",
        "\n",
        "train_image_count = len(list(train_dir.glob('*/*.jpg')))\n",
        "print(train_image_count)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 32\n",
        "image_width = 224\n",
        "image_height = 224\n",
        "\n",
        "#creates a complete dataset from directory structure ready to use\n",
        "train_ds = keras.preprocessing.image_dataset_from_directory(\n",
        "    train_dir,\n",
        "    label_mode='categorical',\n",
        "    batch_size = batch_size,\n",
        "    image_size = (image_width, image_height)\n",
        ")\n",
        "print(train_ds)\n",
        "val_ds = keras.preprocessing.image_dataset_from_directory(\n",
        "    val_dir,\n",
        "    label_mode='categorical',\n",
        "    batch_size = batch_size,\n",
        "    image_size = (image_width, image_height)\n",
        ")\n",
        "\n",
        "test_ds = keras.preprocessing.image_dataset_from_directory(\n",
        "    test_dir,\n",
        "    label_mode='categorical',\n",
        "    batch_size = batch_size,\n",
        "    image_size = (image_width, image_height)\n",
        ")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dXK4OARbBagm",
        "outputId": "3bc5a8d9-0165-4041-ee3e-4db95edecd66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 47332 files belonging to 325 classes.\n",
            "<BatchDataset shapes: ((None, 224, 224, 3), (None, 325)), types: (tf.float32, tf.float32)>\n",
            "Found 1625 files belonging to 325 classes.\n",
            "Found 1625 files belonging to 325 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = train_ds.class_names\n",
        "print(len(class_names))"
      ],
      "metadata": {
        "id": "U8NtHiGsB9Sc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b37c11a-d9c9-4bb9-e9b3-4bd6035e6420"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "325\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "\n",
        "for images, labels in train_ds.take(1):\n",
        "    for i in range(9):\n",
        "        ax = plt.subplot(3, 3, i+1)\n",
        "        plt.imshow(images[i].numpy().astype('uint8'))\n",
        "        plt.title(class_names[labels[i]])\n",
        "        plt.axis('off')"
      ],
      "metadata": {
        "id": "0Z1vrkU_CCLq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "94de517c-632e-400b-81f0-e669137163c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-a7c5b49dcb2f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'uint8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_names\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'off'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m__index__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1055\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1056\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__index__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1057\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__index__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1058\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1059\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__bool__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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89hzjco5MJH88xcq+umCwRohBcOISSJCVGJPjXJkq+WLvE/hUfAsplzEghUOsxWNo+4AdF4zHFrU5u13PzcUd9abjm7/9jrpq+P30t+RFQcCnJFMNRgSvA56lCdQVVaxJOcdei40mC9wu14QA09NjirwgikVMjsVCH/DbLX7dUK1bPEpzWHPw8BhnFfUe2yd4FGfpNKLWJQEUwUvEyb7uM+QKRjg6O2X3eM37N+fsNltkcUhmLTETxhOHKSfcLTdU6wpjYDwtODo+5PHDxzgynChoYLBF6R6HHMtoxGnKKXAOrEmok93XR2zyp+IxmgrBqTRghuSdoeAY036Ha1lJiobIAGAqpv8F5CApukqwg8ZI6HvevXjLq7/6Cu0ixWTK7HjB+GDKwdkRk8MpNjMEC2rB2GTdi6zEiU3whQTOHp8wm+SUeUYxs0QXKMQwynP61vPym1c8zTp+dfprTJYOIZCKihaHFYeTnCwfY2yBNTahZSEQfU/QHkiHaxKGhBYOnykRIZYFFBYnjraNdLGnKAuyLGM+m7Ndb7m6uOLh7FFKRM3ABNBIiD1BlT1NAlWMpnwswd6KFUF6T7+u2W4bptMTytGEYCxGHNIr7XZHv1wSlg3dTqlaz+X5DQ+rmqPTA6rNEl9vWRwsGB0f0BsZvIRjj5cQUyTp7l8AV+YcPTzh8bOH6GrH7eqKPC/BFeByRqOCbdNgJVXPgyhZ5vC1Z3Wxxh4uMBoSxC4mVdERjAUbIR/qHREIYhJ9aHgOcchNrDVIBIdNIRTJYDojWDfkIgoEUO/JMoexcq8wXVVxe3Xzo7L5s1CQfXoWY6p651ik7Yl1y+3FEs81fGspi4zZfML8YM787JDJyYLZ6RHlYoFzqdDV9w199IhE7MgwdbNUr3CeEHucs2TTMb2BNgb6qPggGOcwJJqHCUomJYWdkLsRMTo0QIw9RgLet4j2SOgTEhICfdslwS3LBP06SxRLr0oIgaCeYix88Wef8uTxMfPxjOubG25urjh+fgqZHUIon64dh2xCTCJYkmoWYgzOZWSSUKZ62zIez3GMCZXHzIQwhKG2j8i2IfPKervh6npL00HjA+v1ksPFhK5a44wSnz1mfLQAEpTtJAygWYJCTTRYOwioRIL3TA8nPHr+kO5mSaxqYi4sDhbcXG25urkA5zg8XmArz2635d3bC0JXU07GnH78EcXpbAixwBhLCMnQZM6Qa/oPL4mGA6lYHGPEGYuViDUZe5KnShzKZ4qzDhMDRi2+7Wirmr7rcJMZZI4sy6g3O1598/KXoSBpDRXrCIWxxLqDPpDbjOADoYt0Xctd1VMta27Ob3CFZXIwY3Z8xNmzj3jw7DGRQLAeXMSIYqwSgyIkOob6SC+BNg8cPTvhwcePUGfwQXExI0PwjadvN2yrW/rG09Qtoe/pu5bDxZRRmVHt1jhrGJdjjIG+61it1rx4+RKvSl6OmMwXnD18yMHREeVsRJFnPH54yNnhjMwVNLFifb3Fx4BTkOjR4MkQ1Cd0KorBmAxrLc5mFPkI5yyFsSg90QUmhw4ztaxWO7pNSygt4gxOLDbPiHlGzARXCLQtu/WGLLeE3ZpqdcfhYkZ7dEhoOrJJQRjCHyOJCiKSUEWViFFFNIIFN3IUi5IsWzAtTrF5wWR0xKb6lt1uR547CpdRbbYsl1vuQo2JgcNFRHnHQh6QnR7QA4gkj0AimloMiiEzJsGJAw/PxICRhEw5DGrAe0VsQrTUGDIRtO2Ibcfy8oa762u6vmM8mjKbzZlMxtxd33Lx5h1N3f+oVP58FGQo/jhrCI3n4v05ReaQ6QRpeoIKEgJN07LpaqwoRe5oVy3Lt3e8+eNLivmYJ5895cEnj5kcjvHS0ZmIBsGEiDWQOQdR+OjpU9qmJ4+Om++uub1dU91u0aanb2pyIxS5Y1yUPHhwxmwxo8wO0d7jxJAdPEghWfD0oUeykkdnU373+BmX1zf88esXXH/5HbtXFxwcH3P2+IxHHz1hlmcEcfiu58F8Tp4ZShTte4xN9BlUcVi8CGIceT4iszmZK8iyPFlQhLwcMzoo2C1vqDY7Xn/7mtFiyoNPn9KrIK6ASQmZcDTJGV1vuH1/iyscjx6dMS5y/vhXf4VIZLNacXV+wdlnzwf6zICk3bcaJNg48cYStNr4jsnJAsKYwmWYIqetlVY6ynHJelPx8t01f/zmPW3vcVnEOEFDqn63oebR+DeUBwu8pgKqDPUOEXMPw6fvBjUByQSjgomChMQayIzDGEmEUQzSe5xXzl9f8u3XL9htVlRtQ9/D6ckZoyKj7yqaXctm2/6oWP5sFETjvlgkQ+VZKMcloa/JnSUAQZSiyBPhTiNBBe08mQqxrenrli/vlpy/+I6Hzx7y6FePKY7GCAbfBK4uL/G7hrCL9JtAs225tRYfwNqChZQUowluPEfo2W2XxGrN+5drvtnsEAy71YayKFksDrEDR2k8GZPljqPFAYXLeTA/5Omf/3t4lOu7JV9+84K7795z990FxlrK0YjJdIo4SxE77HqHHWcYq2SZJYhAUdKj4DJGxQRnLM7mdF2PZCYhXWJp6orzb16yudvx+vU5D5884ujxCfl4QiOeYCNaOkwxYewyooXp0YjT42NyEQ5O5/iuIx/nFKPivlod+Z4DligfBtVUs4ka6GLC+KITxGWJNKohhcsOtm3N6/Nrvnn1nvO7NSqG0Tjjgc3AGHy1I9Jgek9mDRoFaxwaQ8q7jCB2D9EqGjyKx+U2JdcIXdtTbRuMGpwz5Jmh7z31asvp7AgaaNYtV5d3rHZr2jayXdWMC0uRG/ouUDXxR+XyZ6EgqTAIxhrEprrE4uyY269e8eDshLvbNXXbpXh0NCYodF03NEOZgbCYrE4uObruuf3qHd31hoPHJ1hXUu9qxi5nWiyQ0pCVGeWTktw5rMnIXEGMHX1fcfn+nJev3nJx9Z6qrmn7Dh/S/oos48FoxJcvv0t7dgn+dM6ymM/4+OkzHp4+IHctb9+94+rmhs5HLq9vWW42ZHnBfD5jMZnz/NkzxoVjWht2yzUmS9QVg7KqLth2DcVoTH5ywmgyZzQeoV1NVhYJGrWWfrlEtzXNakNXN6kNwPdYY+/JiTHExLydlIzdIfgJzmVI5xkfjGkay9nzRxw/PaNziifcJ80yEDj9niSoSusDYWg+CzFgLEOlOjK2GeV4wrerd9zWNbWxUJa4PGe+GPP44UOenS5YX77BRWFcFEDqqbHG3DdVeVFEQ2ou6xo2V0t81zI/nDAuR4zGU8QHvvvqW+rdjiITDmZjQies1x3hJNBsA+tVy+t3V9xVK3yv3K12zEc5D04W9F1PjL8EmFdIBzhUQaMzjBcz3uNZrW9YzGZMRjmbzZZIwGQFZZYUww+NOSKCdY7MWSbjMW1TsburuL76hrYPqArTcsS4LBEjTCYTnDU4ETKXDWzRAAT+9m/+L96+O2e72+GDIi4b9qbM5xnfvn5LVTfEmCjXoyJjOhkz8fDP/vlfEWMgcxmH8wPyLMPajDwvqOsrNlXFtt6iZ0r34hu6riPEyGhUUmSO6aikHI14ff6Wuu/pe890POHs5JQ8zwhGiRZGszFHx4dIiEzzgo2zLCYjJnmGBD/wtCDRduJQUzFI7pBM6RRKl/H41x8TYqCYTQi50uMJGu/5cAk6TUYshER58THiVYkDhwoVTFAKMVgciMOHyGQy5rEbk49bppMxn370gD95csQ09tQCxaikLHNqDViTJRhZJBX/RBGTiqjVbsfy6op6uWF5bjl9cEz+9BFllnE8n/Dq8pxtt8HWIxwLtHXcXN5xfVvx5vyS99dL6tgmDxgbJAZOjuZ0ff8DkuTfvX4eCrLnOBkLGKJEDh6ccfTkAc3lkuvlNceLQ548OCaqoeo9u12FsY7gEjU+/W4i5HVtQ1YUbOqKpm3oQsC5gl3dcn2zZLVZsd5taKotBJ9gW01tm8eHC+5ul6njThNFwbkcSEUlZy31bkPje7A5AvguYzae8fbdBVXbYDJDnmdsm5rPPv4V1lh83/GH3/+Wum+4Xd5iM8Nys0yUGuO4W61YzGdUu4qDk2PqXlluKvq2Q4Pg9A5UKUYFm2qLyYTr8YjZZMJoOuLBySnPP3pOFxqk74ldTzSJXrJfTQhkVtJ+UFoRsqMZjuRhutCjJlXq/XAuaOJlhT3tXxNh1GtKlCOJUq0DFd/7wHZXMy5LNDbkuWU+nZFnlo8fHrAYCd2qRq0yOV4guU3HZhJ7Igzcq9T2EcjEkTvLbDrB1B0aPKMsp/cto3HBgweHXL5KlJiyMHS1J0ZLva25uL5iVVfUIeI1ETP9wL0L+87N3v+oZP4sFCTxFAUhJWN98FCWPPv9F9y+ecf6/TXv314QNxUuy5ktjhjl+X2smhc5Ygxd8IQY8QE0eI7GI662K158+4LVpgIceT4hG43wahiPD1OhzAq7usLHjsrD5PCI3bZCYkRDCjliiEQCm80a33eEEPAScFhCFJqqZbOraH2PGsjyntxalssVn33yCbe3N7x5+y2/+fxz/vDF51SbHW9fvWa9XGJzQ06Ob3fgpviu4WA+ZVdvqaqe9W6Ls3mCKusGr4Gw81jJmE4ybq5umRY1h4sFh2eHtF0grnaMphOC9ahV+tQ3kOgqIRKd0LuUQ9jkL9AQMUM9IYreM4593HdYKjF1BwAWM3hQIRXwoiYBz8cFB8czZtOMoEK0IzbrJcfHGZOxw2vOvHjE4fMHdFZRO7goEcLAyDZD7cNoILdCtVuzvHrH8XSKdC1WZzhrODua8enzhzgLxWjK6+82vD5/z6aHt5fv2bQ1IWFjKQqISucjb99fcryYY90vgmqi9+4VawjR0BKJmWH68RNmD05xZcHNly9YLTdc3NwyG085PTlhWo6Q0CEqmBiIwWOjYG1OU1e8fPEt1W53T0rUGGh2OwIBr8qkzJGozMeGXa1I6Oh9xGjqmwhRCZF7ynVWWLrgk6DF1E/eRsOq3rGta/rgCTGQOUvuHC9fveL46IhHj894/Zcv+V/+4i/43W9/z3/8H/5HPDk74dXLb3j55iV/+P1n1HVLV3u89xSjgtnzJ7w9f0/T9uQ28uT5E7ZVxa6uubvbsdvuODk6phxNWd3e0mw3rOsl5WLGQi2682SzHC0SD6l0QxJuTeqAHCx1kKHNxZhEK1c7ULksEkFjIO7pPhHiwA9DU55ih4Qea/EGjp6eUBYCwVOUOR5hV01YPJozLkvc6QGzGLGTnNZEvPrU9Snuvm8lBo8zKSHPnGOxmKILx8z13L1/QzGb0E9KTiY5v//i1/Qd7HrD+flrtk3NzbZi225pQ0+eF3RNi/gALkHX26rh+OgIY38JOQiJd4NEQlS8BpoQUWOxxjAdT3j8u8+xVtAXr4nXd/Sh4+LyHL84YDGdUo4KMlVC36Mh4qVju9lSljlHbo4rSzKXU9cddVMDwnwyRmLiFkkmzEcT1ssNISiNetq2pfeBMMCMxiQlswa63g9dfymZ7ILHayQMzOAQPF0MrNTzL/74N/zZF79hPh3jGs92s+Uv/te/4FcfP+HjT5/y2efPuLq85PHpY0blhNXNLavlLXerNf/Wn37K1c2Sq+slV+cvOTo94/DghK7aENuazfIGXAZG2DY17Z0nq2psMEynC6TracXTowngyCA/GGNyh5cBdRoEfz8pxRiLU0H3vb/Dtxg1we37fnyNWE0QsKjgQ8BYwU2Ew9EJziQ4N0hkHCaIywgux4pNdPnQ44n0Q4FYhhbjPeFQSP0zuRFOz+aU/oTu6obt7Za7yw3WFByJMskcGpTVbcVqtaNpe+7u7uiDxzrLycEBq+tb1Pepz0SFPMvJ8hL1v4Q6iCb6RCRZ7d53BAVrCqx1BAEzm3D0xaeUizGXf/uC+mZNFoToOy6uLhiNS44ODtO0DzEsNxu6vuH0ZIF1qWFpMh4ngmKecomb6xtWyxW7qk70aoHjwznBR5wtuLy9Y7XdUXtPJJI7x+FkAr5gu9tRx4B1OcY6ZvM5IkLd1KgOTt1a+tBzs1xyeXnFrz/7NUiimIsIbdtzc7tmOh7x5NHTNBlF4NnzR/DkjE1Vo+J4+uQp7y+uuF2tePX6Lc0259/9B/8Gbzjt4e4AACAASURBVN++pa6b+yYjsY626XCu4Juvv2UymTGZzynHJePZmImJ3F0vsWIoR0e0LhBkID7GMLCFFR97MnFJKTSmVuAhBPqeGJh6LZxNDC0dFKrXDnURcgGTIWrxUZEsH2gsPoVjkYFCk6axBO0QLHbo/RGTqDtD9zV5blmcnPDy7Zr1umX39S3rpbK5DBxNxvRbz+X7O1ZXa/A9FsjzEtNXGJQsF44enrDdbKl3DUShrTo0/hIUZIASVSMxpGklmbNkWYGo0oeOGBrURuzhiIefP+Pu69dMokWCsq12IIZquyL6SAhC27V0PnB2fMx+2EPfdazulvewrO9DsoJiaNueQMTZVL09mo85XIw4P3/PtqnpI3Q+4ExgcXyItZaw3aZhEAjVZkvuMqLztO3Qy24M1o6wruBuU+PDFYLgfeohn05GzCcls+mIm+mEyXjMfDGn84HQBw6PjqjrhsPZjLLMON7OmU9HvHv3jlHpUO1QApnLiaoUecb17ZY8L/C9Z71es6tbirKgf9Vw9vCEyXzG7ZsLPjo6wBrBG70nDFoxGFX6qHSS+j9igD5GVAKo4IwhU4HOp151MXibknZI4Vcf94MtNLGP7X6egBmSfKXXmMLcmAiKIUQyo3Rti3MOsYZOI8YZjDVYifgeCDmFybm7vWG3vePmvOJoWlJIRlcDsWEy7nhSTnhx2SGacXmxZJTBydExoyLn1fYtISqr9YY8l79HKNP6eSjIsEJQRA15ViB5ytt836Pao0Ta0KOZMH9ywnQ8pn1/Q9YGRnWObzu6qqXpPdu6oaobqq7nIkQWiwVv3rzh/P05fdfz5MlDptMJZ8cn+FiyYUtZFlRtB0QkBs6O5vzpb37N2zeveHfxnl3dsmnSuJ2iEM4eHjOtxjRtS9f2qLacLObMnpzRdR1V0xDFgVhcXtA2Ne+u7hiVqfZyfLggzwx5UaAKu13Fer1hW1WoWA6Pjzj/8isenJ7inKUsc7rOcnQ4I4Zj/o//83/HZRkhCiEoo/EEl1nmsykhBEajnKbpGY9yEKWYTnjz6jUPHj3i4UeP2by/wR1OkDIVO31MHScyVKVDVLoQiD71jWCTwEv0uM6j65a27SG3ZIsx+ShP+UOIFLYYcrbEkQJLblyitGukJxDE0w9dmyYanE9k1YyhRz1ExKZ+lV49WQisbzdkruBoAcY2nF9d0K4r2FkO5jMODk/4/N98Rh0a3t1WvP6nf42RjK6NTPKC3bpmvdkOwEOkahqw+Y/K5M9GQRJZUbHWJbw/BJqmTqN5+jbx/q1D8hnBOEYPZyCW3eu3OCtMy4IeIXcOj9APzNzed7x99wYl8Mknz5nNZsxmCXacjCfEECjLnBACxybD9x5Cx2hUQvR88uwJTx6ecbNccXW3YlO31F3k2fPnTMoRbdcOrb/JEh4uDmjaDutyXDHizfkFl1e3XHUdNisJarBZkXAf41Asbed5cHKMiuJDYLfb0rYdZVHw+tVrZtMJx8fHHB+fYN2G3geePXvO23fvuLm55sHZY0ZljnWWy+2aoiho20ieF4zLnOOTU0IMNPWOuqrZrneMEfq6JT+cIJOCbJQlYGTIR0IIhC4MM7WGyjYBEyK6aRk3wm7Vcbm+Q2Y5jz9+yGQ2YbetIQ+MZiOCSewHEQNBUR+G9uDUUiwhYr2h2zRsb3ZEb2hbT1bkTKYFi6MZmESOrLc7tqsNzaZjc7OkF6GrK2JbceASP+7Zp2c8+5MTbu7uWNUteZbhnEndh1G5vV5S1dtU01Glajt6/QXAvDAkfNaSuRyDIQy0hf3YUCUxWY0rMFlOMMLoaYH3PcsXL7HWkpcZaoTC51jf8ujoMV3bkTmHAH3fEwWarmO9aXl/eU3wnq5vKYuC3OZ8+umv+JPPPiP6jq7ZUfuAkHNy8pCjk0dMFgu6GLm9W6W+96ygaVs22x1VtePm+jblVMZQlmM26y3dbkff1Li8IM9ztlXNZrPGGcFJZJQ7qqpiPB6T5zm5szSbNe3O4lxG2/asNjXrbUuW5ZSjBcfHicSYZQXrzYbDowWTyYizkyOiKlXVUNU7bm/SoLiPPvqIRw8ecnl9w83FDZvlmvF0zJN8TN83WJPRWuhtTMl2TE1r9+0Sqqg1GBVsELJgKSi4eHsBM+HhowWuyLl58YYyK1mcHjB7MCefl/S9srm9xVcds+MFbpzjo8F1yuV37zl/8Y7dXUPXKtttzXQ6JR9Z5kdTfvXFc45O5vRbz3q55uZqw+XbK8rxhKauOVuUPH50yGhqKQ4c29jw3dtz/vjH94BFTYfYoe+EApEc6xTEEHxH1fwiuFg6dBFajBiC94TeD8ihAzEDFdxgbY6xli56OlWyByeMuprd5Q1ZGHo4LVhnWK+XOFfg+0DXtTRtx2a7Y7XZ0HYeIwaNAWPg008+4U9+86dMxiV/8+XXVHVNU1W0bcuoKDk+OKQsC+rXF1Rdg3U5vVdWqx2bqiKSFND3PaFvmY5HfPHF57De0jUVk9xRdQ2basu2brHWMioLYtfSlzmjUU0fIicnR4AynUzIsoLxdAYmo+sDq80O2BF8z3QyZjadMJ99yrcvv+P8/C2ffPwxHz19ynq94WChrNdbtlXFanlHVe2YTKb0TUf0iu86tss1sQ+cffQoVfsnGZoJcSAlGmMRG9i3x2c4MgxO0pwtH5Wqb3n+6COOTg+QTri9uONwvED6wLgw2MzQV57N6yt2yx1aB6ZPzqh2De++Pefbv/6KPFjqbZPmlrWeq+U5i/mcZl3TN57xtOSTh6dsd4Hzq6s0ZCMGFkcLfv3rMw5mBVu/oybNFP7u7Q3fvbxiV43wwYMJiFiMzTAmojYhjTaTX44HkaFrLQRP9D2+S3lHVuRkeaKgBE09I13XEkOH7xscHne6wDpozq/plztW2w23t0uaTrGmwPs0wLpte26WK1abCh9JkzFCZDIueXN+zWbzzzg+PqZuGuq2w4glz0tmB0dcb1p2b69Zb5YYZxnPZizXO/pAitUH3lLX9kTvWRyNuFtvmB/OycuMGCJFOeHr717RdR5xGW0fcDajnMyIxrFcb1muVpydHLGYTJnnI1QMZVEwnxdUVcXB4YJqs8E5oa7T6M6njx9zeHCQqvCLAx6cnWGdZbHY8e3LV1RVw3azIXM5ohH1gaaumc3nXF9dEzWyqI/pnXD00SOiQJ8boh0672yajOKwqZ8/ph6RcjHlV3/6Gc9+84R8nNGFQNt6tDRor7z++jX+peJbJe6UDMf2akvVwZcvXnP99hatPdVuxXw2JUpgMS1R3w28MtjdNGxvG8IqEHqDK8fkRWC3rZktFhw8OMCaiG89rU1jTb3J6dXQ+BQxGGNpg8d2beK6WYsxkbZvUfNLoJoM0xR1mC4YNOBJtOeiGKV+ZBW6rqUNDX27xvs+vddEilHBKD/F2pw2X7LZ9SzvXrFeV5h8hIhhPpsnwW8SutXH1E9Q5AXzg0Pmswk+Ri7utgme7YWonvnhnDcXV1xfXab5vzFwcjznblWxrWs6H5PiDjOnMiuMJyO21ZYTnePygpPpIV3d0XaB333xOd+9veDV2/dpIr3LUQx3yzVdVxG8593lDWU5YjIqOTiYczifcjifMR4V5M5zfHSIMUJZGO7ulvRdy3q9Js8LXr9+y3Q65tHjx8znU2aTMXmWs1ytQZWu73AOMmfouhabZayWG+5uV8wXCzZXSz778z9w61sqCfQxEQbdkID7DnIkhbLTgqfzp5SlI7MGV1oePDxld73F9zn//Ku/4d3NNZmbcTo/4dPHT5jEim/+xZfs2h5todpuMRqomgqvkdF0ihql73t2qxaX59gsJ2pJxHB4dIqGFTF4VusNd3XN008/YqGexmUIkWe/eU6UKav/7Tu251ucKTEEokmTUqaTMcZ0dKsN8AvwIPuBxCEm2NVYoSiL+7E0MUb6vqP3LSH0iVSoAzwpDozFZo7s0DF1U6pVj5gXtO2a0FeMRiOKMk99G7Kf+pG+HxzMOT4+YHV3w922SSiOpvh7OpkQNXB1c5WgztBijKVuPZtNhY8h5UXDuJxRWXJ2ekyZW87fvuHlyzdMJ2NG5QhrLHXdcHO3YrmtMC7Hdx2dQussYiI+Cp1Xdo2nCjV1F6jaDlWlaWqOFzOMJCh8MhlT1zXrzZrbuzs22x2+X1HXDXmeUTcNDx4+4MGDM776+hu22y19F+iDpyhLcuuom5a+75nM5vRdg3Y9RZaxen1Bm4M9mmILk3hYQ+dflllal4q5RW5xhcXj8ZqT58LJ2QE351fkoxJxBU3IuNm07LY3zCZz4s172uDRKFRNg5gURvcxcHF1xaJPtaUuBlabFePZmJHAqzcv+ejxR5RuTowwHhuWyxvuNjs+mhSIOFoNiA9MDyd8/mczbqvIxcVf4YxJ9RWTJuWnmRQRMxQof2z9LBQEZJiWqGkoWOYwJgMVut7jfUffp2FvRoSimJDZiA8R6yzOGEZ5idWObrek3nXYvASX4YOyqWoeiqFpuvvJJc6l4c6z6ZgYOjarWzpv6KNQ2AyDcnx0wGa95vruZhj0ZjmYL9hVW25vb4e/VGA4OFiQZxlnp8fk1lBtNtxe3yIm4zarCVGZjNLf2TAuB2np2ibVXCxsd2v6vicoGJemoBOEThSte84vb1lMx9zerphejjBEDhZzFosZTdPQ9p4HZw/xPtD3Pbe3t7x9+45dtePg4IDRaMx02rPb1RRlCRJZHCzI65bb5ZKq2mEQbq9v4PqWzW7L8bMnVFXD9PEpvrR0NhAMxMLA3GHV4nOGkUCGOgSCBOaPDni0fUxuCv4w+z19/jXfvLjC2Iz319fMxoag0HaKczl57oihxwhMJzNCH5hMRmx2d+zqmqBK33esVity4xhlBfODgnI0h9U1rsjoxdNoRxsUi+CMJ5/nPP5oQlm27FaBrJyQlznOWfq2om+W+KbF6E9INRGR74ANKY3zqvoPROQI+J+Bj4HvgP9CVe9+9EIKgh04QvuBbZEQFO/T+BshkhrMCjJXYqMQg9LHkJqHgsPvGm7e3lKtG2xeEtwwnSNG9n9rJEYhqoGYrEmZF2mQnEga09lD39eURcFqvcIYYbNb42NMVf2u5eHJCb6v8WKw0aCxT30S1QZvYLm8o6prXGHpgM5H2tiQtamfpChyfNdgjELsMaIJmu0jdevpusS9wlpQw2bbUNWe0Hcs5h2GyHh+iA8GxTGdzKnqmvFozMnxKaenp0ymE5arO25vb9lsa6IaRuWIoIGua3j3f1P3Zj2WZeeZ3rPWnveZ48SQkZGVY2XNrCKr2M0WSbkly+7uu5YF2zeGYaD9C3xj69pX/RcMAwa6bciAYUDdlt0tmRKtFklRlEVRZA2sOasqKzLGM++zpzX5Yu2IIuBmSQ01geIGApkZGXEyMmKvvdb3fe/7vGcn9Hp9nOi8NdoSBxHWGVRZM3/8hDBLKbaK8b0DyMDEkkZqogQ/WUw6iJ+VNEbQBBY5TDh67g6xcdxQoHEsL1YETmJtQ6MkQZCTphmr1ZKqbkiSCFXX9LP+9QMsTRMGozFSRrSN5ujwiGK1pZf0KCrDjf0Bh+EuuwdTlNUY4VvUSEnr/EB370bC/n7M/HRO0wjC1CNiG9PStjXCCdK4/7m35r+PHeTXnXOXP/Pn3wb+yDn3T7vwzt8G/rvPfQXhGU+e/IdXzlrl/Qddu1GGCWEofZtOxIR47Y+qC6xq0e2WZj7n/PgES4CQIUmaoooK5yxREJCnGctggwi8nEISUJYFwzzpOFfa+xzwSJhNsWU07BNcYzEDWt0ihCXPEurWEoSCJInYFgVWNeRZTJJGxHHodVxGe4dgJ6ven47IkpA8grZpQIDRGhFIrDU0ddNRAD2Zo2orVKu9ocsY5LZmPMo5v5xTFAVSCsqyQgaSOI7ZVhVHRzdZrpaUVUWcZCQa1ustMgj9SEP6uUCSxGyKDRhDnqZooxkOBrSqRdeSOIwoL2Zsmi1HLz/svq8ChO7osA5tHaGIOg1ViMMhe54+GRrJaJIzGcWUq4os8QPEMArZllvmiwX7B/tY63xtoRWXl5csn5ywu7/PeDCgbRrOTs/p9XLqtmZbb8mjDMKEG3eeJt8Z02DQnXzdCYt2neohlkwPhjh5Qlv7jBCtIqyusfhh4SCNPvfW/EVkFP5j4J91v/9nwG/+dZ9wFYblrNfoGG06CLPvNEVRTBSlSJEgCXBGoUyNQ/viyzSoZsPl6THVdst8viIII7I4JY5DX69YQy9PuHm4y3iUE4VeEPfxJ59Q1Q070wmjQUIcePGidZq2rsjikP3JkH7iWU4C6A367O3usDvqc3N/ijWasiy5nM2p6prxaMRg0CNLI6KADvQmMEqjVc292zd58bkHPLh7mzgKqesaazW9LCIOvORCCIe1ikE/o9fLiCL/LEuyFIuX16yLkvPLBYvVBmMdjVJetFgW5H2vO9uWNU3TkuU5/cGAJE1p2hattZd9KE0YBtRNSd2ULBYz4jjE6Jbzk1OEtVSzNZ++9QhZWqKOTGjoqIxS+BtPXDMfURIqp6nahifHT1DNFuksm1WB1o5tWVLWBb1+7iEUQcJyVTBbrllsCmTgk78W8zmr5ZJer4eUkrLasiqWlGVJ01h2D24TZn26iRki8MldDkGjHUEYcffBXdI8RAiHUS3WtDgs070Dwiim108/99782+4gDvi/hSd+/Q9drNqBc+6k+/tT4ODfuih+JqNwerDXiRWvtKOiO2bJa/CXs11+iGtwTnkKHwKHQgTehbjebqjrmrZtGQx6pGlCo1qwlrPLMx7ev0veBIAXs1WV75YUxZbnn71LHgsi21IrQas9IHpv3Gd/lDFfr5ktN2zrhtPTU7784oseltD6fy+JImpj2awqBg9H3LnleHJ6zmJdE0Yx1vkZT5ZkxEHIaDwmiWKW6xVV29IbDHDWonoRyngi+Xg8IEkSqrJBKUsgPfkjSSJU06CNZbPZYoxiOp2S5QnWOp6cnJLMY0bDMc45kjQlTXOKbUmaJmy3G+qm5vj4mNFoxHq99hPuDqy2WCwQSFrjqM7OEWFEnKZcPnrC0TNP4RLh5yVcTxH9qMRZj+2x4BScPj7l7PiUSHgwXpwkvjmhDXGUUpuWvJdjtGG9XoOw9Pp9giAgiGLSFEajMc4JVKtJ09zPxRAsFkveeusdnkmPEANwkT95BEGINr7zRhBw86mb3L3/FG+/cXLNI6urEmcMaRRz++Ytvvc5N/jfdoF80zl3LITYB74lhHj7Z//SOee6xfP/u342o/Dec8+4K/YtV7VGh5O7iiGAK/l1C7QYYQmMh4AFkSB0MUm/x3rzcUfXk4xHI7RqCAQoq5Gh5HC8y84wZ9pfc3ZZcDFbUW4roijmlRefY3eQU9SK5XrrC/VhQr+fMRokDHspZxcLlDYEruGZ+0ecz+aczdfefehC6rLkzR+/xWtf/RJRJIjPLilKhbKCYX/E/s4Ouq4YHOxSVVvC2NdJxlgePrjL/PKU9brAIsnSmKIoqCtNVbXcuXuHKJZoVXN5fo7RlrbVjEf97qlaoZrGS3W05fLyksFozHKxYrVac/PWLWaXlzhrGQ6HnmRpDE3ToLViOBr6+dMV9lN0MA1tcEXNVl8yTyKm9w4gijzk2jN4uksgLAgjmJ2c86M//0vOj88RNmYy2mdbN8ggAmGw1pFmAYvlkuFwyP7BPlVZoXTLfD6nqRXD4ZgnT07J8x5zpTm6dURZlaxWGz/bimE1q9jp9zB6i5V+95CB71hpB4Nxn69941WKzQ8IRMZkMuH9d9+l3C45OrzBnadufO4N/rdaIM654+7XcyHE7+Ljn8+EEIfOuRMhxCFw/jd4HbTywOQr3IzrpO+uWxhXBiWfPqi9y816G6UTAqKIuN9DOYNVlrKsybKIYZ4BlqqxnJ5fMOo/xXQ0Ig0ikiAmCgSL9YaPP37MnZv7PPf8C8wXC9brNQJH21ToVvD0/TtMZgt2x0MEMOpFDHoJxo4othUbYVGBIElyWu04OT3hwYMj8jRgPiuYFzVBYDC6oa4M5+enFFXFZr1GtS0CSZ7lDG7dYr64ZF2UCClQYYgSGoFhs55z994d5vOKJEloMQRBgiCgaVrCMKBtVh4gMR5SFFuqpqUotozHEz9gtQZjLEop/E7t81fAwx2sswgRoI2mrlviNCMKc9azBYQhSjUI6Rjd3CEYxB7kFkqstt50ZRyb+Yo//5M/4/LTC5qiJc8ytLUY51ivVuzsTBBCEMYJVdWyWq8ZDUfIICBwnnucJCnrzYZiu0UQsL+3S1mWOBzL5ZIoiljNVxw/TpkcDSDC/x/8cxUbCELpA4SeerDPr/7Ga2yWiqZSPH7seO5LD3lw+x79LPvFLBAhRA+QzrlN9/t/APz3wP8B/FfAP+1+/Zd/gxfDIT29EI21LZYWOjS9tZ+hWUzX5cD51FJHF24TCAbTCXE/5+LijLY1NE3A4XTiOboOFssNn3x6wm9881eo1kviMPJDs0iwWK/59nf+lC+/9CKvfeXLXJw9wVlF0zTUdYWUkgcP7jE7O2O9mKOamicnp8RJ7omPvZSmrbCRJI4Tst6A/mDAII3Jo5hosaJpDG2zZWsjqpMtVkC/lzObrVjNFzx5fMzto33uPHXE2cU5i+WWLE38/EI1xKGk2q7ZblY0TY01kjTtkSQRZbllPB6y3ZZ+NxiPcPgO1WA4BCGYzWbUde0Xh/PHl/Pzc5IkIc9z2rYhzRKaVqO1T61KkpCq3pLnPZpGUS9WnL73MbPzcx688jxRFiGNIHK+sXD25Jx3/uonLE5m0HrqfZb3SLKMWmvfwVPK6720oWk1qlVkrcJYXzcgBGEUc2MyBSRVVZOkKUWxwWKIkxiwPH78mNI2PHj5NnIousQxjx8VwiuSHYq4H/PwxadYXjacPbng1a+9yKuvfInJYHQdZvrzrr/NDnIA/G6Hkg+B33HO/b4Q4v8F/jchxH8NfAz853+TFwuDAG18bLN1PgLY7+9XsQdXEWgBkpAoiLwXQ3iYciwDksOb7Bzd4OyjU1abNalJefreU+xLAReXbLSh2NRsW8vO3oGnagaGIPQDsNPLOd//i78kjBNeeOZpBv2ctq1pmprFYkZdV9R1zdn5OdpKVqVv7968uU8SSfI0YLFpWZeaN999l6OjKYc7I/IoIY4inpxfUtWKoqx8IIyEJE6YjofM7YrNckF094jJZEwYCQIxZ1M0CARRJFhv5uS9iLAbnm63FUJIermPTqtrL2bMshytLb1ejyRJWC6X7O3tYa2m3+9RlhV13ZA0NVEUkaYJxmhG4xFNU1PXtRdFpiFp4nm2VVXQ7w0oy5L1yQWH8S1O3/qQ1lkm0ykCOD0+4fTTY4r5kn6QUThDlmXIyHeQlNHEcYgUlrIsyPpD4jgmiVPiJCVNMpRuSZOc1WpFsdmys7NDVTUU24LlasnO7g5pFlNsC/r9Ab1kQF0oRuMEETksoZfIN62vYIVDBIogidm7OWY47mPtLbJYYqVC/KLmIM65D4FX/i3vnwG/8e/yWt6PjkdPyoTACbTpqMk/80GB9Gh+KUJC54NdrHBI11G/45CjB0/xwY9/ijYt682Ws9mSo4Md2qoidJZGGf70z/6cl158lnu3D3DCT+/DwLcwF8s13/vud/n0k4/5x7/5m0z2Dyk2SxBweXbG6cUli23DplI0CrCW3X0/xxgMBJW2qKrFGMd6teXe4Q2sEwzynDiIKaym2DZsy5IsT7h7e5ckSkjjkOFoyGo9J81hujPm9GR2PYmv6oy8FyOlIApD0jSlrhRtW6N1Ri/PUVoBAdPplFa1gMAYQxRFxLE3VW23pd95nZfWZ3nmJe7OorXfMX08mc8MWW9WWAd5mlGWG/+aynD55JToMqG1jtkn50RJgm5aXKHIREQSRrRhhBYSbTSh050yW2KUIs9yEJJe3sc5QZL65kXbNohd75Mx1qKMoT8YUBUF/cEAbQ2b5YJelhIGIYvZnLdef5tvHH0FgQ8u9RES3U0j/F0krx6kvQRsAOguW+Tzry/IJN15j4ATBEhCmRK5+DozBOgsCT5qDAfSCG/GEV1vGFCu4eDuAYO9AWV5iS4tx2czdnfG7O/tkoaC5bqk1YYf/fh1ohieuX+Pft7j4nxGnMYMBxnbomK92fA//87vcPPoJl/76qvkqedVWSForGBdKZTxffJN2XL7qUPC9YrW+UiKqqx5990P2Onn3L99RFzWOOcFkxZJ1WiMExjjwz93piPCUKJ0TVlu6ec5APP5jMlkwmCQo0zDYrnyJPs4JssThoM+aRJjuil6GIY0Tc1o5Cf+zjmGwyHg5y6+be5oW+llJr0ebdtQVg3WWbIsQxvHdLpD27ZsyjU4Q1GsMNYRJxlhKGnKElMq4iyn3G4xqUY4iEWECf0AMU5ihO3CQ7VhOpkipfSelG3pvf91w2g0IctyjFIEQch4PCZNU8qmpmkajLXk/T5hHNKohouLGb08p21awiRmfr5E1xIZOGTsTWoyiDv1rusWjr3mp3GVk84VGPznX1+IBXKF+xdX/mgHV8Cy7iNwznx27HIOaXw9YqXGWeN3ANHiQsXTL9/n4uTSPz2c4J0PHvGlZ2+zf3hAkm+YLVZsqy3vvPM+hwcHHN66jZQxUZIQRyFV6hla67Lh08fHCGP56qsvMRyNuHv3HlE+YvvOB2xmS0IZ8smTM5Ksx82jfa8dOrugrS1lUfL4+JTReEiS90izjCja4KoaIQSz+ZIfvf4WL73wDDu7O0QhFJs126Lkg80jpjsHlGWDdQYpHIN+jmo1dWuIo4gbB3vUdYm1GiEcUShxGIJQsljOuXv3Llpr0jShKDZMJmOWqzWGkOneHov5nDRNiaKQ9XpJ27b0+32iKKAst2itccab2KpyTZyktG3NYDDCBpAEiYfqtS1lsUVrT5URwiK7vHdjKPdfBAAAIABJREFULWiJDCENUwbjCQu55Hy+ZjKZ4iOyPckllAFpL/Uq607/FUUxQkhUo4nTiHVZkPf7pGmO1ZqyLglUwOX5msP7OwQy9ANL62MxfHvH4qyGrl4VTnZt7eCv48b9QgaF/86Xw2Gt91d/lnCqcE5jncLYBmNajK3RunuzDdY1nS9bEUiDkAYCw96tXSb7Y9IkAhyt1nz46TFhnrO7N2V3PGCYpTRlxZ/8m+9yOd/w1N2nOTy6xf7BPtOdEaN+zu5owO54zGaz4S9/9GPOTs+4desmzz+8xytfep6dnRFKa1ZFxes/fZfReEKeJhzt77A7GrAzHLEtaz558oRWa6IkIM1CIo+nRYYRrYJ33v+I45NzlHb0kj660ahWc3k5J88ztsUGawzj8YhBv08UREgh6OUpWRqhlK+ThHCEUnJ0dESSJMxmM6y1lGVJFEXgLGmSMpvNSNOUvNfzaVNRRJ7nrNcrPj1+jLWWuq4py4r1YsV6tUYGEdpo+n1fzJfllsYq5usl2hqCQCIDQZREKKshkCjdEgYB48GQXpaTRCm4gJ3dG7z00pcZj8fkea9TDvimS1lW3aJO/WAzSRgNx+xMd1HKMB7vMByMKDZbz1rWLUYL3nnzfZIwJxBRd2yy19gm5/xk32fMOwLp4RMBAaH8/En6F2IHAYexGocfhEnh4WU+TKlLnfVAGrqoHZ8w5CzC+vGithonLMpoepMBD7/0gMX5ClXWBDLhfLbm+3/xY155/lmme7tEkSeQFEXJD//iLzCvvMqNmzfIez3mZ2fE0QXLxYqo1bRWIJxlW2554/XXOT4+4fHZjNm6xrjAb2qN4ff/4Nv8h3//a+xNJiySFYOBompKNpsNjW4ZjYdsig1VVrONa1QboS0+Mnm54cbeHkWxIYlCRsMh88XS22bznIvzC6qqASST0YBaKbIsxtqWum7o93ssFguMtWy3Bbdu3fKDOynJspTHjx+zO52iTMndu3eYz2cMRyN/vJuMQTg2xQYh/EAWB/P53Hf6RAfnw1GXFWHoi+Q479Eb9FFKsy2Krl7oezqjM11t439ycZx0SWARvf6Y/X5O01SsFkuKbel1dZ0RTHQ23TTJsdYx3hnz3PMvcHp6xltvvsl2taU/6DEcZqxWc6RzOAUnn16wd2fXQ7Sd6LhfPkpB4CEqwlkvaOxmNn/d9YVYIO7qzfkiS3TK2Y5ehvGbNT4HPeh2FgNGdDl3/pxpDCAijFM89ewd3nvnQ07fPkeKnMAlWB1xfDLn5ZceMN0fMz8/ZTFb0NYtf/rd73D7/n1efe0rTHb30dr4WOnthjDK2b9xwPHxCRcXc+aLNcW2odWgjZ8eOwfL9ZbXf/o+f+e1l5ne2KfcFFTb0J/lNxtwjigIGaQ5biKIgpKyaRHCs79Ozk6Zjkf0Bn0CGXDzxiHGGNYb7/VYr1Y455ju7tHrYA/OOfqDPkmSUVYVzjmePDkmCCTT6ZTVaoW1hslkxGq9RGlDmKSkWQIOzs8vWK1XlGWJDH1HK45Tym3J7v4+xWYDQlButl4wGnpQxGAwQCBRbUuW5ai2JRESVbckSYrWLVGUorSj0Zppv4clRKmWQMJ4NCaM9nh4/yGz2SXvfvg+m0IxW15ilebw8JD96S4A48mI+w/vcv/+PbIo5Tt/8m+4nJ0gQ0tZFVzO52ybDQ+ev8v4cISIPmN8+btIIFzgU5JFgHSOer1BNy3jyeRz780vxBHrKvrZOofWita0GNvi0Djp6eFh4DO8pYy9JkvECGIEEbgQYwOQEU4Engyehrz2q18ln+Zsmw1RJP2xoW749OScKO4xGe8yGe+Q93tkvYTz81O+990/Yb1eEccxvV6PPOsRBiGr1ZpGKRyCKEq87D4QxNIihUEID3X+8KPH/PF3/oyiUQRZTj4a0RuOPE1dCOIkJEtjelnCeJhzeDDlxuGuH7ZZy6bYULcNcZairEGEAaPJhMFgQBRFDIZDf3SRglDAcDAgz3MEjr29KePhCK0Ui/ncq6CFuK4nvLhRkiYxcRyTZRmDwYAw8F2utm0BWK1WjCdjkjTh5q0j+v1+V+T6AWPdNLRKeUlIENC2LVmWdeANSyC9WFSIkDTNvJGt1QyHI3Z3p6RJAl0Qzng85tkXnucb/8E36Q16FEWB04ZitSJNAg53d1idn/Hp++8y7mV8/Vf+Hl/7u19jd3cPhEApSyhDiuWGwArSIALrlQnW+ZGz7QoNITpfiJOsl2vOTk5p6vpz780vxg7i3HVWOc5grMEGPrzSdSpaKfw3XIrQ7yIi6I5fQReB3CmDghAjAzSG3nTMc6+9yNs/fANVtsRxRN20PPromEBIXnj6LuOxj1yQcYhqW5qy4O233uJgb4/Z5Yyyqmm1pmq8EzGJU/LBkBsypKxqiqqirNouVtmgjeT8csX3vv9Dnr5/j5sHu6RZ5OFrusVqg3A+rFQGwvvqo8TfWFGMVg2bzQYZhJ1ILyCOY3Z3/dM0CEMGg74XHCrt6wVjCKWkqmtA0Os6YMVmTZL6wtonPvmFkUQp9CKapiEIQoqiIIwCdnd3kVJcOyfjOEZrr6FK0hRjNJvNliiBvNfvKIySqm6IOop9kgUobZAyQMrIDzKzAb1+nyiOieMEBCzXS3KdswhjoiTi/v07RL/xa/yL+Zz5yRnVdkW7mbMzGPLOT9/hB3/8Lf6b//a3ufnUA15+5UVWmxnf+/4lZa0YZD2Eszz+4CMevnwH7cAZRxyH/pju6AiYfpAog4DBdEqYJrj4l6IG4TOcJV2PXre+9SskQoQ+g06CFLGXa7sAifXmIuELdG1ARmCbbiobCx6+/Ayb5ZLZh6dENkIrhQ0kH378KUJbnrl/hzTvYaTvAqlakEQJJyennJ1dMl8saY3P4qjaljAMfeHY7zPIUwZ1ympbYhA0yqK0xRjL5eUC1SjaquZgf0oahwRhzGg0IYkLlGqQAUTOEYYhyhgf9WYt8+WSxXLNjRs3SLoiend3h93dXVarFc460jSllS1x4k1ATaMYDgYEQUDTPeWVatmZ7hCGAev1Gmvttfgvdn7XLoqCfr9PGAVo7SkxQjTIwBNVVqsVWimiOMZWljCOut3eD/5kR3DZbDYkSY6QkjRNUVphjCNJfJZHEES0rSKMLHEcEUjBer2iKRsCIcnTiHv37/Jbv/Wf8M//x/+J9eIcdXnKp7MlulHoWPC7/+v/wn/2X/4TsuGIr371K7z7/ts8eXJCXTcIZzh+9Ji2UOSjDC0Mznh/iBO+9+411T5LPh70SPp5FxHx868vzALxl0CICHdVtGvfLvRFm0Vah2cNB8judOinpR4LFEchSkvSFIrtBo1BpJYvfe1V/qr6AeuTJVEcopXBGnj0+JhWt9y5c8R4sk+9WVI6wAq0cVzOl6y3NY02KOPh1eOdlOcfPs8gj2mLDXVT0uun3vuBoCxb6lojZYi0jtOTJ9T1lslkTIAgzxJ2RkOM1V1dAtoYBBKZJtRtgNaeV/vkyRPP8IpjlGrY29vj8PCQqtqSJAmBgKqqcFaj2pqdnTHjyZiqrJkvloD30oRBQK/njy9FUTAa7VCWG+qm6dq8EbP5JXkv99ZlHPPZgrZVJFlGkmXo1u+SWltkINHGMsr7KK3Z1AVBZ05z1qEdlLUCERJGKXt7+x4AjiRKIoQUtG1NgKNYb7DG8vpPfkxpKp59+lm+/o2v8y9+559THX9KX1nu3LlLFVi+/+0/ZHrjBr/2D/8RcRzy5a98ifc//IDNfE0/zkhI+eidx7zy9VdANtSq8j6eLhdaSoE1yotZhe+6yV+WBfKZnEQSCE+7M6btdFheloHUXn3iExvxjFjRSeJByIA4yglkhLMBVVmgXUOQJzx46Xnebd6kXmw7F5ylFfDR8QnrbcH927fZ2xnTNg3z2YKirJBRAoHBaIGyBqUNF7M1P/yr17l1Y4/D3QnjnSm5KrFd0m2dxGgNiJAoitFGoduG5WJNmiRepLeXMJ5MiaKIYrNBKIkxLc75pCXrHHVdkyUJUkryPEMIwWbjXyNLU6IwJB9PMIMh26pksVj6yfd6jRQBSRxTK8+d9SI/D4Or6gJBQN20GO2DOhtr6fcHHqqGf9ru7e9zenqG0pobBwc8+uCDTtPmMN3cwlpH3TRUZUWapgSBxDhBGEbkvQG6202tdURhipOdRttZhBSs1xt0K9CqZVVURL2IUT7ky6+8zP/1v4dUVnHZlJwcf0AmIHCW3/9X/5K9m/u89Mor7IwH3HrqJq9fzEmSBKvh7Tc+4NbTdxnsZYDzc7MOjI7z8mTnLE6GGOt+uRaIXyS+J+5J6gLntO9aWYuxDSJQXVhOgHGCwHnpCULiWouQ/kiWhjkihboVODTDgz1uPbjH7NETNhcLVOv1OSBZbWp++tP3WR5MONifouwSKyT90YhKC2pTImyAtQqQXMxXGGO4e/cuo50hVpUEGHTboFvNerXBOP/UMiL1T08ZEycZMgiZLzcc7HtmcBTFlNuSup6RpymbbUmWpFgHUSAZjUZEcUBdVqxXS6QU7E13OTg4II4iiqbpaJEjNpvNtY87imO08zdwHCc0betnIUhWqyUHNw4Jw5jlcokIu7iDSrBYzAmDmMlkh73dPUQYsF6tiaKI5bIgSVIQ0vvWnUVrTRTHJGlKkmY+PgE6lW+IMaYr+qOulhTXgkqlNUo7Li7OiSL46Zs/ZWeww8svvMCvfOPrfPuTRyyc4pP5OXsiYJT1aDcr/ujbf8B4Z8De/gH/4D/6dcb5gIsnl4RBxOXJJa//8Cd89ZsvkyQByikEvpa1Xf6JHyuoLgLi8/tUX4gF4gWPvlB3zuCE7zgIeaWbEd73jQZh8VE2pjtXCqTTCOFhB8L6+GMpPPxao7EyQldweOcusnLYUlGLktp2Jh4L1jiaFhbrEmRIqz0BJQg7y2+Xv22cI4hiGgM/eec9nrl/iztHB+SRhLYGa0iikFYZqlYjwphVUXZBLQJlYLOt0ScX3DjY9am1XcTbtmwwgKi97GMymTAc9sE50jhGNSlNXXdCxS3WWvJej7LxEvTBaESS5hRlSasUURRxfn5JkiQYY0iSBOcUeR5wdnbKYDCkbmpikRDF/uvo5R7Nul6v6fX6NEozXyzYmYw73q632SYIFssFed4nz3sYo/3PrJtlKaOvJBF+Mm4tvf6AQAQM+n2klFycn2NUSy/PaNuKUAQ8evSIl196iQf3HvDn+ZDlZkMWGkIXYoTACnjrzTe4/+Ae3/jmr7I7GaN1y6fHjxn0Box3d3j83occHgx47qWHCKuviZpY/MMUMHT6vV8GqQmAQGKdFw76TkOIICSQnjDufQrOd7ctWGxX0YuO/+oLdukkVnjUqMM7zJy1iChBYzm4eQdqy+XJE6RqUFoRRjHOWRbrgp29Ka6qiKII65SfTocCqcBq28nABZVrePTJMRcX5/B3XuPZB3dI8xinGzIriK1F1i1F1ZClPtFKW41uPeVxvlTUTc3+dEwSRR6pEwdM8iFyVRAGkuGgTxxFKNUSSkna79M2DdZqttstZVkSRBHK+ri4fn9AGIYMBiOqqqJuG/Lciw77/T6r1QpvnQkIGklVVUgZkKYpVe3lL4PBgCRJcbScX87JsgyjfeailLLbyQ3bsmQ43r3Wy6Vpjr3y7nTzHimDa6BekqTEkVdg+8GvVxsHMsJqQxTIa+X0p6dPeOGVV9h/8DSX1ZZRE5GKiMFoyKxaUdU1b/zkLb7x936Vut2yO53Q6orFokS1Ww6jA37wnT/jheceEkchQkJjFBhHEoUYB1gv5w/+mkHHF2SB+Ahga7y0xAnpB4ZXrV0pcNb4Y4DzAzxLlwUm/GIRgReeCQdSBNdZ2xZwVhBHOfO6INKOvYObWK2ZXZ75fEJjCcIQGUhOzy/Z35sQSsdmsyIKIIkDdFek46DV2stBnEErzXf/7Ic8OTnlyy88Qy+PCKOIQDiktQhlcI3xRwrjqNuWi4tLennmbyCtONjd6eQeGdo5H+xpNFJC29QMBwPCIODi4pw4iajqirb1hicnpac7as1yvWF/f5/hwO8IIgjQenM9r8iyzA/qgoB+P6fX82C7bVXSti1Sel2UkD5iLuxyTKbTKUorlFLkvT7IFiED6rqi1xsA3neC6HRQzhHHEU2rcM6SpjG9XtrNSgR1XXvItZcRkmQxo0GPpq5olOLjTz5m/ytf4T/+T3+LUgpGC+9Nf//xx1jpSOOETz455uT4lHv3HjDIUtJY0FQ1toViFnKQHfDTH73B7WefIpnknp2GIA1jlPaqDR9P8ksAjoOrY5Z/OglpsVZ0nYfIF+IiuM7h7rYQnOtc7PJKqvLZBNVY0y2SLuAFQZhkVOsFSRxzcOOQuixwxtIohdIKIRPaVvvYNWfJsoTJaIgQFVJonK1x1i+8WvkgGGMc603Ju+89othsuHPrBnkek8SS7baiqhVta3EEVE3LeltSFAWD0ZBWaS7nW9qm5MaNPaIkRlUVWean5Eq1pLEn0G/KEqUUm22BtRatS6ztpBRx4vNRqoq21dy8CcPhkFD4NN/NZuNVscbT1Y2x3f9zjevoHkmSkCQehF3VDVKGjEZjimJD3stp65r+YEBZVijTHW+tY7vdevsuXo7i29zmepFIebXz+Ha2Nh5SHscxTVPjhGNblbgo8Q8Gpzl+/JibBwc8/9prpKMxf/ivf59v/eG3EHFCKCDNctIk5Tt/8h3u3bmHBIrNiigQCCex5ZZ6vubRG+/RNBUPX32WbJgShBKrtY/JE94Buey6fT/v+sIsECec7yzo1nOsAuWHZ2g/QQ8DpE0w1ltukf6b4TCeN+t8t6s7WV6zE4X1/W8lDa4HegObpqGXxuweHqHMY2hqryVSLdpKzudz+mlIBOyOJ0gXsg0bYhlQ1jWlqsldgFYOZ5zP40h9GtZqVaB1yrYsqKuSqmlplaWsDeBxn7u7U5IwROAX58WiJskzmlbT6/fZHe5cT8ItsN4WXF7OUUoRhAHb0g8E4yTxSlfVift6fQbDMSAxBlarDUkSI4Vkuy0YjYdYZ6maliAKKbclcZaRZRlhFFM3DXWjSNKM6e4eZ2dnxHHEar0izzNCEtrNhrznpS1VrYiTxM8XhB9UxnGKs4Iw8s5AGXrbQtM2NI0n3HtyZkQYg91q5M4I3SoiGWNq7WuniwsGkx0On7rH13/9N2is4tGHH3LeEfmTJOTy/Jjv/vG3+OTTYw5299lsNsRBSIClXi9Z533qd5/g4oCvfO1ZhITWOpT0mSOmdFwc/xIskKvorytwg7Heg+2cz5SDpGNm+fOksD7Dwl2puJxDdpkPDt3tOF3/zvl4MisFIpPEkxzZGNpGE7g+I7PPx+++SxhItPEYc6U1YZihty2T4QghAsLNliSJyJuIWiUoY3xjwHgySpIk7E13CKTn3vbSjtZRlGy3FdZF3s8toJclWKNQ3XneaMVsviTY2+fO3gFWa6IopigWRFHUFcw9VuvVtTo1zlKiOGY4HFI3Lc56Med0ugtI0iSjSZVX+cqAOE7814HrgnccYRLT7/eviYxVVZOmGWVV88477xIEkvFogNKKTeHdgQiB68xVk8nEU+DDkM16S5r2cJ0gMZC+oRGGvu7weS81oXWkSepnE1r4Y17YeDCdNQzMCIFFW1hvCiZRwv2Hz3B0uM8H73/A7/2f/4qzsyeEQlMVC37yF3/OZluS5H3uHB0SYdmdjLmcz9isltw7vEE1K0hFSCtaWjRG4LlrWnB2cvlz70v4giwQ3M/aaiXOCpzxi8Y4A3j6YCictx5ydWzCz0Oc6OYiAsQVZYvPLLvWt49lFBD0YlxssVmESAMmg5TFesH88TG9NKeqa9pa0e+P2BrLqtiwt7+HiEKquvZ+aqW6bo71HC9rcdYRB5DnGU3th3d5krIJQpI4RilHlPlc9eHA+8id1RjtI8mqqmK+WPLRx4/Z39tjZ2cX1UlJlPJeBoSgqRt6vR790dDjSo0hTVPCMPK+jaryC2YMrVIYa0niBJNYTxO0ziOU8Galg4PcAyqEdx/2B0OWyzV379ymrEqc8U/0qtMs9fIeDqiqmrxnKcqSPO910hKJ0gbhDELGJGFIlmVkeU4cJyRZ6mHkIgAdQBB6uX6qaFVLqzySNQpDyrLmyfEJUnr35Kg/5pUvv8rZ5Yxv/eEfML845aUHD1kdH0Nbs6k2CFMyGQ5oq4TbR3c4u7zgw5++wYPnHrCdV0S7ES60aGuRGEIh2azXn3trfjEWCHSegsD7rZ3DaF80AthOyo70WqzP3m+u6nS/cJy47pB8Ji3w1EbvWgQRSyrTYoTBhppQK/Yf3kE1Nc1iQ56k1KrhvQ8fcevoBkjYbgsmI59KZa3BKgPO0ugGIUPqpkVrQ5ZGpHFIs1VeTBiETCc7ZHVN03q3nwwEeRpjrCKJQoSLkVJ6rKkxbDYbojAkDn3XptqWhGHIYrlAKUuvl1/bYcfjMXGccHp2Rhh5ndmgPwIhuZjNfeHf6xNHIVmeo7XqoA0tIGkbRbktWS3X9AZ9is2aMJBMpxOSJCZOIj7++GP6gxHjyQ6z2Zw0TViu1v7fq2sCGVwZOknSFL31tZIQAVkYkSSZRzMFsZebNIogciRRjFY1qmlQxvtJsiT17WzV4qxlvVrzdvEO050d2r0pURTw6qtfoT/s8Qf/+vd4/4NHPH24x/17N5ldnnB5foaooZEBl21LmMaMh33KxZo//aPv8fBXHjK6v4NAe5pkknFjb+dz78svxAK5oq3bTo4cSIfFXB+PfEXhF4PfKYLPIA6IruMlEcIinMN0HC2uYpoR3S7VOcmkQAlL6wxxJCCPufHMfU7eep9mviEMQ6q65exyzp1bN3BlQblZMRj0idMU3fpjkbYtzgnqtsUhMNphjSaOI6x1xKmXafRbRds2OLyGKk9j0myAlJL1ekOS+um5V75GzOcL8jRFBiO2pQczpGmOMSVKa5IsI4oilPKdpZ2dCXUnmzfWsLe3e31cElJSNy1JHCPDkDCOUcZ339Is90LEJPEDv8gX1NpqimJNq/xQNggCjPF23O122wHEE4qiIE4ytKmJk8S3oyO/k4RxhMMH8cTxVdNBE0Ye62OFI8sj6lYThpJWadI8xuiWOAogDHChpdxWbMo1YmYYD/sMh0Oee/Y5+nnGj37wPX7ygz/l+OQTnjqccvupm1SLBWo7YzTqYVJJbzDm3XfeQwSW00c5o6MBQai7WtUy7CWfe29+IRaIv66yur0kWUsPNaOTknsh45VfhOvF4X0jV1IVH0VghbtmaRkcwjms9rWLsJ6A0gqJlYLGWlwAvZ0Rk7u3OG8eoWtFGMYsVmtuHu5zuH/A6vKM9XLBaDIhjGL6gxHWGLQ19Jy3xVdl5VUNowHL5QplDJPJGKU1RvtJthAQBiFKa3TnCRdB6BdUnKCNpWnVtQw7jmOiOGFbVp3q2Q/rVqsVYQdviFPvTV8uVwRhgNEKKQTWGJqqolWK+XxGGIVMp1MiYwjCiKosuby4JAgCqqqiP+jTNA3DyRilFFL6+YUxput8tf79gcelGuMV1EEgCaOIKI5o29b/bJwgTVPSNLsmljRte02sSZKYIJSEfViuVyRphJEaJy1SOvJeRF8m7Mox69WKj0/eQ5sjTp6c0DSKOI549oWXWcwu+ODtN3jjvQ/ZG2Y8vHUIuqHanjPZuc35+aeoZku5mrN6ktLObhPsetiEw9Af/JKEePrW4BXRT/oZwbWPGDyg0XZFu48UFsLvBrYLmhT4EB46OQFXUmfrsMbhjEVoPz6JZUQjNC2WVgqsNgyODhjVLYuPnmCbhoCA09Mz7t7YZ2d3l/nMMZvPcEISJyl5kvmUWhEQRiFpDuW2ZDQcIKOA2WyO0g1hGJOlPYJAEMexdwYqn6Pudx4DQmCtDw0KgoBGK45PTxiPRh6k3k3Dr5A/Unp/SxzHSBybtTdGBc779qPQzz2apsZqhcRrssIgwjl3XbcYY4miCCcgzTKM8125qqrIe32kBK01ZelbxVcJTdY6wiQgjCKSOL6WCkVhRGOUZ/8KQRj6jBcn8FnyQdiBEwQyDtEoZApOtKR5RChTrDFdPrpBhobIadys4f/5zh/x8XvHqMZinSFJJLvjPoe379HWu8wvjvnB22/y/L0jRr2MdnvB7mCA2gSU60uopnz45iMefO1FROxw0jA+GH7uffmFWCB+GtsdhbpLSuEXSRcV7D/Q+raukAjnrg33sksOohPaSWTnbbc4K7oIBT9bcdYinAQZkMUZ1oJuvcJz6xy9w33qTUXx5IwAaKqWjx4f89TNfXqjMS4MqbYl203B+dklQRBdewyCMCBP/VR+ZzpFaUtZVaRZhnD+h7pcrXFW0B8MWC1XtK2mURoZBKQpIARRFLFYzHHO0ba+tZtkGSIM0ErjcNdP6rqu0brt0oETcJbZ7ILBYES/36ff72OdZbFceRWu1azXGw7zQ8LIt8+19urjIAiJo4Q0y72Qr/OirFYrjLFUVcNg5JGlWmmixPtYom6XE0KglD+qISNw+EWoNEYbAhmQRFFH0tRorZCxppdJnBC+NrABxcpnvQ/HOdqVpJnk8Kk9Nos1H33wEWESkPdynK25cXOHs5MTnnn5eQ6Ovsm3v/17fHh+yo22x8EkA11y+3BErRzz2RllGNA7OGT69JBWKIL+L8UO0j3qcZ7g0XmIpRQ44wM83XU9ojvJSWeG4cpQFXTHLEcg/FHEWAfGgPVuOOc8idy3cQRhEJElKaUyGG19nl0WM71zROQcyycnKKWZrzb0hwOSKKbXHxKHEXneI04q6kZRNQ2NasmCDKUt5xeXzOZXLdqC7bam18tJ0wREiHWGy9mccluhtelk5xlK+TTWQPqOUhCGxEnMwcGB114V1scHhAHWxtR1db2rjMYThsMh89kCRIC1lqoDwEVJQpplTHYmFIV3F7ZXpieUAAAgAElEQVSt15pZa8nzHufnF2hjGE8mrItNN1vJyfOc2Wx2LUi01hJFIZOdHebLVXeEiq8HgW3TYqwjzdJOf4b3rrSqo5WYazBDGAZI3bAqzhiMMgIZ09RwcbJgU1Q88+w9sqEEYtI04Pa925wdn3F5fsGv/trfZTzO2Bn1ePfd9+lNRoz2R/zmP/kv+OD1N3n7+z9gWa04mgYYsyJJBqhWc2My5cn7x0RDR76XIaNfYIDOv8/LGG/B7BpO3udNF7Dabd9eKOoQ6K7RG3Alcgyk5Kr9649l/khmnPafJ65wDwDCp+ZaTRTGhGFL3ZSEUUjrHPkgZXhzj6YsKBcryqrl/HKB1Q07wx674xHOWYIoomk1aatoO5ynUYokSSnLkvlsyXq98bVU4HVM0+kUYyzj8ZhWaT8MdB5YoI1GyAAbhD762jk2RUEcx53yN+hccg7dYTzbVlNsSyY7U4qipOo4U/PFkjjNcfjWa1FuscDxp59y9+5d2laRZRlt6+klQkqatuXi4oLRZExdN5ycnPLss8+S93tst1vSzNcjSisa5eOT67pBBgVJknX2W4VShjD2sQLOOd91FNC0Dbatr2F2USB5/fU3eefdn3B484DRcIokQ6mQx49PKNctg2lGmEHaC+nFMV9+5Ut8+OG73L5/QJRDlgY8zO9ipKRxDukSnv/qa9w8vMmHf/XHnBw/Zpz0iQU4G3DyyftMDu+zPSvoTQYoaT73vvxCLRARXM03PpOJiE7Z6xz+eCQ7E/4VVLwDFxhru/anwBiPBDXOYoXwui3Z1SOmO3ohOmiY9F0nlUBHkt8KSzJK2Ll9iLCOYragn2Xs700pi/X/R92b/Fqepnden3f6DWe8Y4wZOdVcTrcp29gStAAhtdQgpN7Su0aIFYg9bOAPQEJISOwQ9IZeI2ipJYQAIQHdYGzcrrKrcqyMOe507jnnN70Ti+c9N8rYFdWyhZR1pFRmnoi498bvvMPzfJ/vwBebr2jbiuVyTVVZXNvQ96MM+BZz9rsdbd0w9hNGG2JKpAhoUyIIZsSYC/xp7krIEAJVpamcoapmdF1XbEBdoWcsCmMAbm9vy8Eh7vb7rme1OiKj6bqB+WLJ7XbP/QcrWhSLxcToJyIwes/JyRlGS2kzDAPzxQKUou8H9sXtfrvdse86Zq3MSpx724Tf3Gw4OTlDa0NMuSgRNcY4wFC5CoUihCBwsLUleDVhnGGcJkal+OzL51xej2x3L2iaW2bNipgMN7c7Xt1sGOIAJvH48T0+fvSAR+dH/N4//3tUjaHPO5St0euK4DMqQHc9kELg5IPH/Oj0b/FP/4//jc3Tp3TbSxo3Y38ZWLRLNlbTnh7TPPi1QLFU8WSR6yPnLAv6cCHwFgoGir/R4T2BdGMWj8mUE35KxBhkRyjJrTugX5FIKreJ0tw1lqZVTL2nmzwREQSplDl7+BibFFdXV8Q0cf/8jKGLXF9tePb1CzCa2WpN08yw1pILzdxqzWq1JJPpBpHSxhRxzlJXFufkxhtHse3xk2c+X3B6di46j92Wtm3wk+fzzz9nvV6zXq9YLsUfV2uDVDCK+WJ1x4E6Ojpit+8Z+g7vI8+eP2ez3XFycsJisebhQ4W1NZvNrejTa7EznaYRY61AvUaz3e44Oztj3+2pq5rlYoEtjN5+GHFK45wtN9TE8ckxo48YDDWGxtVYo5jPW1LKEEIJ3JE8eZ+gHwLt7Jj79xw319dsdhPX+w1d7wkTzJZLuiGy3V2xubrGesAnnrhTgvG4ZUWKLVEnJiZC9oQc2HU3tHPNYn3Oj/7lv82f/sE/4dmP/5ih72k13Lz6nCenZ2xeXmKOHr5zZf7KDaKU+i+BfwN4nXP+pLz3l+YQKsFe/zPgXwc64O/lnP/gV32PjKgBU4achMVqCl1EK0NS6ReuCyDlgnIciibZACFmYkyEmAkBUgzo7KmbRko2rVG66E4OG64ABNY6VG3wcZIkIhTb/cjL5xd88v57vH7pmaLn7P59hv0cnRU6a/bjxJuLK2K6RAFt07Cazzg5PuKorog54OpK+iG4uwHGceBQHqYU8SJDJGcYxhGllAzhjFA86rpmHCdAyIoHx5KqqpnGEesquv0OY6vCvJW50W7f0bZzUoLLy2vmc4nFns9lcHh9c13CimDoe6x1oKpfcHyf8NOEn6SErGuBnMXtfWI2m2Pnc0Axec+8XaICtG1DM29JuZAXxUkBWxmiAu0cFQptLRpL42Zsxg2bfs80gdMtra7RVmTBKo/EkLjdjvzZn35JPc9854cfs1wdMaRITjtCitimZlUdY5uaRMWkFB/81u9Sz1s+/z//MSYr1NSxffM5i/lH5M27bX/+WW6Q/wr4z4G//wvv/bIcwn8N+E755/eB/6L8+1e+UpRa0ChpvEOhh4iTibyfSMLazVGE+MgprAojNaRMiImctUSDpUhQQnOWuGaN0VY0JymJDpxCbDQKN3O0piXtJ3IS+PXV6wvGm2v+ud/6hGfPvuYnP/ucDx8/4OTsDGtr3NBj9nuGsojGYeQ6eJTWrJZLzu8/YN/tmcapCMLyHfo0jpMcBkazXC44OT2i22/leSTZAMfHx5IFUmYQXWH1VnWD1ro8DzC2ImcpvWIGbWwZvi3oup52NmdR0puUVuz7jto5Zu3srk8Y9UTKb5WA4zgKfeQXLH66oRf4eJo4Pj4RThWK4AOVrTHaULWmsIZFImCsZRxFBBZiRlXgmsxqbVmdKG5vetHCmxajoKkkb2QYdyQVsLXjZL7k9PSMbr9nHHvqXaKZv+LBex+jnSWpikxFyhNZB4L1ZD8StOfW95x+94fM12f8+H/9H9HTLcvhgqPpnNXNX5NqknP+X5RSH/5/3v47wL9S/vu/Bv4nZIP8HeDvZymq/3el1NEhTOdd3+NwORitS3MtDxcVy3tCYhSprQRwpkNTj7hWeJ+ISQJUlCrBMLrCJ/CAQzaHMxqVPPHwZ+8I9JlEwNQGFyqSB5SiqhtevH5D++nXfPvbH/Hy+dc8ffGC73/rI8lU30mATzVNdPuOmC0xRd5cXnJ1fcNqvWI2mwlj1ug7Xcbl5SW3txu0ElOFlDKztmYcJuE6DZ7ZbEYIgb7viTkVRoBY/xjztlkfR880BfbdQF3XnJ7fo+8HSamazchKs1qtePr8Gacnp9xubmlnDShZvBSQAKBtWxaLFSlnrq6ErrJcLsVNpejRbVVR1Q2QSVFuqqOjNV03FXFXJXQTW5OzYooe7Qw55uKI6akbSG7i4+8/wlnDzz9/zSzWRGDWzCRYKA+MfkQrxdH6Hsa2jMMtUy9xdMv5sZBYTYVNmcplUq7xeGAkaQgx46qWkGFxds4Pfv9f4PlPfsyLr59yPt6y3L/5622QX/L6ZTmEj4Gvf+H3PS3v/YUN8osZhUfnJxASVHIapYJapZQxyr4dFOYDu0q0CEqpu8FWLEn3Siu0EjsHoxw6l4k8stEqZzDK4CdPiIXGUqzx0eLK0bYzgtfMlwt0XTEG+PSrF4xR8b1vf0C/ecXV9hZnNHXjyKlh0BmrWoZpxCdTHD4Sm80t11c31JXlO9/6mKPjI0GE6oqxqhjHiSfvPWaxKIuwMoWcKP3ROI50XUfMicVcHE4OHrbi5D5xenrGs+cvuHfvPtY51kdHXFx9wfn9B2y2O8iK12/ecHR0xPHxMdZYPvroQ2KMPH/+NQoKkbAuoqnEfrdlvV7hrKPvy/eapKS6urnh6PiEbt9LbEHdsNvtcK5l1rbk7ItT/IwQE1MIMoPJER9GnBUAxRnN8qTmN3/7O3KjP9twfLzm4b2H9LtbbvdbLje3XG+2HJ+eMXkJ5KmMxZF4/73HVI1jVBqnq0JwjbjoyFRE3WBVj8o7zDSw21yTzJzf/Jf+FvuL13z6x3+IO/3/eVD4rhzCX/Hn7jIKn3z7g2yUgZhJqmhDyskubF7xXiJlkpIpOglSUsQyGUdryRchY5XGKQM5k7IlIC57OYi/VeWslFBJMjQ45PGp4piiElXbcv7wHm9+/hxT1+Rkub7u+errVzx5uOZm17FaNMQw0rSOptJ0nYY+o3wEJclIGsljPzs7p5nNef3mkhgju61Ao6uVGDjvdjtCENg3g5QkZWJ+KIGmkk47ny85Pz8FYLffs911/PCHn+C9Z7le40OkqhpizDx9+pTf/M2/wfXNpvQuiceP3+P+/Qc8f/5MWLhGeEkxRl6/fs3p+T3qpuHm5oZ7Z+copZjN58UURLFeHaGUYbFY0c7m7LuOpp5TVQ1N05CyxZgKWxn8GGnqhjF40YFYi2sTKib665Gmrjg7X/Pg0Sk/+dPPOVk/xFnF4nQN2VPXFU3VsO9v0W2NVZ71UcV8palnELUnqozG4JSDbIjJklNmVJ6sDVYr+u2OYeOJSjFVS05/8ICjDz/i+VefvXOd/lU3yC/LIXwGPPmF3/deee9XvISynik5eToX2roMCRVywKNBJQ1JrusYZCNltGiglYg4a6XRMTJ1IyGAqRzaarLRxfRMleRZgVYpU3cV4RDajEnM1zMefvCI188uGDaeFOHFizf03YbvfecJw5TJATbX1zglg826ajA2o/SEAipXCQLkLNc3G/bdnuBD4SVB3czYbHeMoyBZvsClB1dD7w/QqEU5xWwmw7uLy0tOT0/5/ve/zxdffMXF5SXjONLMFtzuthyfnHC73/Pbv/07DOMkcGzOd3LT680NwzCglGQUbjY3zBcLrKuKXU9mHCa2tztR+qWEq2tm8wUhRvb7vehknGW9OiLkjKsc/TBirGGcBqZ4jbUN2gpa1vsd2kRCmBg2e6arPaenRwxmwdWrS6ytGP3E7X7DelHjQ4efIqvVjJ/9/HN+97f+Bk2acbRSvPfxKbOVZYgjHtHmKDRWg3WWUGTIRlmSrjDtEXWQeUzKFT457OyER99evnNl/lU3yC/LIfxvgX9PKfUPkOZ886v6j8MrH5i5KhU+FeKmmGWolxUYJQ15jkrmGUmwYK01Rim0SjilqRKE/cjlVy949uw195884sGHj8EYppyQrahKMxqEHp8SZCMOJlqRcoBKcf/JPe6/d85nmy/Y3I6s1wteXVxzcXHBtz/6gG9/9D7NzNLvN4RxZOhvGSePq2R2Ef3E7eaaiEhFybk4Q4qeZN91xCjS4dFP1FV99/cP3hfDg1piTb04h1xdXXF8IjfI06cS5Wysw4dI13fMZjO0FQrMs+cvsNbiqpq6aYlJiI4vXj5DK8XlxRvWx2uWyxXXNzcM48jpWUtKiXv3DVM/4KoaHSV2e7/fU9W1OJ5MoTCpBXGLMUiug7JkFYp4ygilxBqi8mQ1MQ0dV8+eEy/2+JtbNtdbXr54yepoyWy55Oi44d7Jkqoa+eynz1gulpyuVqQpUVnH2cM5H/3gPaq5pu8DJgu8b3SFVpAtYDK1Fi+BWLW48znt0Rk5TlQGWtdQ1Y5g/5qadKXUf4M05GdKqafAf1Q2xl+WQ/gPEYj3UwTm/bf+mTYH8hcUeoiUGFofbpTybzJooZVYo0g+kJUE2islU3eFQmfw246bZ6/RY2S42fPp7Z9xcv+MuhIZaExJkB+thf0axeFCI2YRAMoooorU64aPv/8Rl6/e8PLnL/BXO4lowPLl00silh9890PcrGXYb9HVnrzd4seJ1XJB9BJcP44R72XRaGSDVzPRkhsrUlxtjNDYpwlnDNViyTCOUgKmJF5ZTtxCbm4kGqGqKvb7XgiEGdbHxwzDhK3kyl0slkX6amibGUPfS39WwAJjhZYyFpTs5PiUqQz3lFJUTSPujYC1lspaVOF9ZUbqpmW339O2QsaMEXxIuKopZn9grMZnCdPs/R7XVhyfHaNsS7/fc3F1wdmDE+69/wG6UpyctBy1FZXqePllps6Zjx++T44atObek/uYecWYAyhL6Cb63UTTrogaqnmFMhmnFFY7gnF4shzCgSJ9UBgs87b5622QnPPf/SW/9BdyCAt69e/+qq/5F14KQpYyJxXauvQZWSxaynwgqYQx0sgbY8m6UN+tNNjkgMFw/WbDyy+ek6aMzprNzQ3JB2IMmMZCgYNjCtJ75ISYX5deJUcygawCqnbc/+ABn/zub6CN5+r1BVM/Mmvn4Coutjs+e/GMH37/YyY10Vgk92/yRD+yHwdQhZKhJXtPId8zhsDJ8XGJS9tzdXlZYgXAaqGgz2YzjDW8ePWKjNjyGGPpx5G+72XmMUWadkbK0qtpYxjGgaOjE25ubxmnCWsqlsslpMz29pbVWiDf1WpF3/fyMRQxmtFiz6qUZn10jLEVwzCAUsXeh8Jr0xjrmM0WaG3u6D11Iym/vrjhZ5uxrWYXtkxK2A1Hj845/9aMPHmudjtyUzM/WZJNwOgJU2U++uAen/1fPyUOe6gyPiaC8TTrOQORKUbAYbKDMaNUw26/Z54SixPHkD1aawkcSoEcE0lbMpExRVLXYWaLdy7Nb8YkPR+a8bdcKeFTSbOV0WWgFgSnN3LqhiByQmGMKHKEHBK7zY40RK7e3KAWc+4/eCAnoLMEMtaU0JsYy8CwuKkoafi1ypJtpxSRQLtc8hu/8wMePTrm4ukLPvvJV1xd91DB8YNjtn7H16+f8sHjB2zeXDDcbNEgYiTfMk6i7UgxFksJxdFaypq2aeh2O+azGccffywS2rrmdiM69K7r2O/3gj65itvbW87Pz8lDLuGcGoVHaUW/33N1dclidQQomZlMEzEmGiOs3dPTU6rKcnZ2StftC/lQHM6NtVxf33C2XHP//gznHIvZjKsbCebx3pOVYtbOmLyn1QfoXdzcDZBNpB8HsrJkFKa2GCZCCux9T9CBmA1qZjGrChsrVjOLN4o+DKicqFwmAMTEer2g28KLqwuGEDBV5M2bC87mp8RKS8eoLLWZ860n3+Orpy+4uHxBUxtxb1cZV2Y/msQ4FdsorZiimPu96/XN2CCFoHgwbtD6EHcgSFSMsSAwQhbRCqE4HGpeKJaSYjJXVw0qi8v4R7/xfdSyIpRNgLalZtZ34iOtxcQgpIjKGa0UMUxi5uYqlNO42nD66JRHD884v3eff/KP/4h6ueLjH3zItrumaSypsTQnS4a+Y391iys0eG0sdV3JZF8p5vMFy+WKnBK73Zb1csU0jvhJPKv22x3r1YqbzYamaViuVlxeX9H1PX0/iLAqJY7WJ2w2GwBC8Oz3OzLQ9QP3HzzmwYMHvHz1mna+4Oz8nNvNlp/+9Gc8fHiflIRKn5IMBBeLBdZY5ssFR8dH7LZdmdxnZjPhhbmqFraxrfD9eBfak5IkDXvvJVdl0WCrlmEKYBVdHhinAVXLoWaNIRro4kSVNdkoYpbPNCtDBPpJMW126Mqi7ESME01d008dL5+9YfXwGJ8CGsXMVWQdef3qBUPfCRrqM/W8Kr0JkBPK2YIGBnwOpXJ5d8zUN2ODAM5UYgepbSFhaTIao0sUW0pgBD+n0CxSQY4ASHKDaGNYHC+4XFc0DeiFYv1gSZ8jgUxOEgIqm8ShoyWiUUYTQ4dBpuyJhCsOjT57NA6cJdWW+9+7z988+3181Ew6065PGUfPRcxU9QzWS47rirjd0XcTQ0zEKaIzzOcLnBPR1IOHD7i8uODF69cYramso21buq4rA8DpLlnq/vk9Xrx6SUqw3/eMU+Ds9AGvXl/w+Ml7d+6ITRFwDeOAD4Hl6ohmNufp02dcvr5gtljw+MmHbDbXzBYrun6HU4rRe1BR9DMxM18si0+vxB0cn5wxTaImjCmzXKzZdT3WNQL19h3WyOFWz2bgGrwbCFVkOw0MuUMnjXEa6yy9j+TcY7Km0o4pBLLR4kkWNSlFtNVMBGytuXf/mHY+58unt9zcbNhte5gZlBZXFTW3PL38jBQN5w+PMG3CmbFEPSt0iGStMaairVts12GNgezfuS6/ERtEocpVLZysXOqmA4M3lz7kTh2YwTlNZYQfn1V5CMYQUqY5WfP+J99jv9uj5jUjkWQRljAHHbt8TclC1GAsxjpyLGm6ZLKV2UjIgUpZoawbjcqK2fGS/ZjZjx0+ZkLO7GNgQlPPV2h6XDLc3rzEGYdyAT9OTMHjqpopBG63e8YpYkxNU0vEgTIytExA1dR0fce+67B1zcnpOUsfePnqDQ8fPsI6x/2HD1kfHXFzfc3p6Sl106KNw1QS1rk+OWccR25vtzx4+JApBC4uLmmait1uK8E8SqbmcmMIE8FaC2hilnz6EKfyswkH1DnHrAEfE7aqMKMX+1A/kSdPVkIgHPyIzxMhB3TMVK6QOpXCZ3GtyUrjTSZkj0ELuTSCa2ecPXrEzatrpv0169mK03snPHz/EbqqSAqmEFFppNaK+UmL0Q47B2UiyfcoIwGeLkEIEa0S2Wi0SUzdgP0V3qPfiA0CudwEwmHPZXOkYqcjvX+JO8iKFEs0m9Jv3RVNFr8jlTFOYY6XzJczSVtVU9lEIkcV/99cJK4Oa6woELW7c1DJlKtXicjKx4BDE6eETop+iGBqajfDD5LPkX2kHyPbfU+4uuW8nVHPluyvLhjHAWM0/TSIXHfWSnglmrqZoxS0rWW72xUDt/HOT7edzfAhsLu84vjkhPc/+ICsNJHM8ekpPkS5ZRU0TUvKmt2uo2paJu9JKfPg4UNiEDcPV1R9s9m8eGKJInG9nrHZ3BJjZD/tadtWotmCL51aInmJPQgx4FyLspYwTjRNSwyBpDTKwBAHBnr61JP0hMrC9K2sLTC65JajFRMJryJJ5zubWaUcMcNifcr+ZuTxt5ayOewj5mdLUm0IOQp0HzLaRJraol0i6JHKgE2ZED1kzebrK3a7DrRidbaiPVmgrGJ7279zZX5DNkjx5k1JnEEKzQQQi1FUIS6CMlYaxpjQqpRLMlgX+rpS+LKosbqIdazkCCawlAZQZZJKxOC5M8lWknOIKnMZLZ5XWmuyVofrhP2uo7sdMC4zDiOxn4iXW/ztjnHfg9ZcX17x1c0Nn3z3u5ycn3N9pRj6PSFHokr4FJkv1+x3A8ZWGJ25unrNajWjamrJHAeOjo/p+p7dfk9MmdF7WtdIXFuSuGylLdqJCfS+65nNl6yO1sSkcFXFNHqaZka33xdH95G2ae5mLFKe9XgfODu/h9GOkMtGCKkMBGVGlZL0Gilm8QPwEetqfBS4QznNpDyTHuljj9cDmYjR4lDvrCEXb/WU5OZVRQkqitLMmIX5u73t+PL/+YI3z1/zwfc/4uHRY5TNUCkCEbRBTP4VvR8JeGprqZKGoIW5SyaPgYsvv2JzuUVZRZ0eslhYTHb80R/88TtX5jdmgxycEDMUxOqQdMtbcAvRkkddbHwoD7eYHUhZJugXRpFikepmIbdV2qKSIqtIVoqgxAQ7TyNV5XDa4nXEFNtvnUMp8TIhRizQb3u++vQrdjcd82qBthVWK+bZMSpFVoEQIpVOpKbm2cuXPLh3yuromNl8JvqKuscYRwjyfTWKcRqom5p+6EXCmjOr5Yr9fofWEqvWzub4kFhXNbZuSMNAzqHIVyv6qUNHCQJtZktOTs/Zdj0xCNARYqRtJSatbmq0loYeKMbUM5SxDP3IME60VYOT/Acx6s6CJDYF1s0hyeaYPIpMUJ5JTYx5YDvtSVZuBXHId1R1hbYyJDUG5AoRHY8t3ssgh1JQGWMtV5tL3ly/4bH9gFEFrNbE6En6LTNCG42PkZwCaZpAVRgsUVmsht3thtubS8Z9oPceVyt0W6OaNZ//9Mt3rsxvyAbJ+IJUyWYRbtDBKfFOB4ICJBxSKdkcGTF1yCmIVFebt1NxDk1YxiSDzQaTJXtvSGJBo4wmarE41aok6R7SUBOELCVK1Ak/weblFa1uMDXU2pJjwvcj19evGXaXNK3Basv9+6c402C1I3nPbrcrzi2GruuZt3P6QQh+KEvTivvidjfQaEtVOSEPKk1egI+Zvu9IGS6vrzk6OWMqsGtCgTKElFjOZrTtnGHyEndgKpqmYb6YF/pKpG5aMXIu9j+T96A1/TBxdCybsC4O+6OfWDRLjKoYQ6Bpl8Iu9ukO7QsEcIqQPF3csfcdQfuCEEqUhasbibRQSp51FqmBHFoaQkbFjI7FwMEodOP44W9/wnsfv8/Dj99DVY6kknBWU2bo9lhXobSU2TFBmAIGRV1pvEklPTjgVYTKMY6Rn37xkpfXO04evE8Ovwaa9Fzo1geeFKWVPpD0lBJG7iFurbY1OUViHPFhQqkkPUyS0kyjyDFhlMYYoT3qpHHJUmV3F8AzEUglViEp0bULciXZiCUWlZAzwScYPdvLaxZ1y67rwAz0XUftDEb1ZDpCsJIu5Vp+9Ds/oqlapn3PxZsLnj97Rs6e4Du6oWcxX1BVmpOTYy4vrmlmLc9ePmOxkEU4TRNN3bI6OsKmtxqP5epI0pxKM+2nyDj2PF4/4eXLl6DM3S0x+YStK2wlElhtsxhX9/2d9f8U/F1WoXUVwyDwcgyBQManRNO2+JAxGJyuxFGfwBQCSUfGPHE1XbOPnZRnOeJcjUK8tVxd31UHpEQiiB4nZlpTEX2gwmIDRBUYYiBXhsW9BcvzJaaqCDGSkuSrjOPIi+fP+O73voeuJAMGLf7N0xDp4kCwFTkq2vWCR9/9gC9/8gqaBd0t7F7uuLz+kkr9OriaKIUveXoHnbjRppw2EoIDBmLCajFkiFmRONTCFPNmocQ7JQbKzmgynpw1lappmfNgcYqPI09vXjBpRSQhTMMiqsrS7+jisaVVJkUhDPpu4Or1G6aqwfsBN3OQdkW+qrAOrre3BN1iVM9XNzf84Nv3WC6WJFezOjtns7lk6vdUFm5vrnly7ww/ebKKKFNxdHwK2uLjSEigbeLFy9fcv/8QYyumbsKHSErgfRT1ZEy08zkvXzxnHEfmixkowxQDxr61EBLRk0VHASCakgy1PFoJtUQbrKuYL+aoolc3zoIzjD7cNfzBe7TTIhPW1CQAACAASURBVHG1gdtxx3ba0uWOoEP53ASCb5uGtq4KAyKVw0d8AjJyY0Q/YvqMi4r1akFuNM/7K6JOBC0HnVYR71MhmIpTzdnJCUaBn0ZCknQAp2uSQvIZs2HsByqbmd8/Rn1xxf6yw82OSDTc3mzEKukdr2/EBjlshFjQqpykbKqd5WAbqpXMKpSSX9dkyfsmkXJgimORdWiJSlCq/H7hWsVCUGzbFhcUVus7TlLSlFItAqLliAlIMrjSugzVYmDc78ndnhgnpj6xmDnmi4p9GLnVwOkZ7eyE5ekDXg57hi8/5fHZGc28wi0alq3Ddz397Q0ff/ch2+2GWdOwPj6m70aOjk5IKbFcO9artZgejBNvLq+YLRa4piVrzeg9q6NjJj8xN5b9bouzjmAnPv/ic6q6YX10QsojKYth3TRNTFOgqhrqpsFaW6xALcZEtNGSaVg3hBgxTjyApwKP2trSd3u004wp0Mc918Mt+9gz4fEqvoXctZF03bbFGoVSsXiZHRgKhQWhM74fWbsFtYIUAmPW5MaSTCRHYRaXlUIm4yqDMzWzpmIaJwK5wO9ZgocMkDMhS2blfuqZu5on7z/kzcvP8FNEJUNta36VUOMbsUFSikUnDlkj1MQUiClSGXMHYaachdlbJu5GKSpbERIlQEdORh89PgWsMjgrM4U+TujYsRn2KDyBQFYRa6yYOBRs+ZB9KIiwyH6NVhjrZT7hRzb7PdplzudrmqbG60SylpP3PmJSNcYuQBs8mWvf4a9fMq8q1u2K5WLFYr7ENTNeX7zBYcj9yLxpsQ6skYb5dr9lATTzBa5JYPbYqibHTDtfiBl2SmhrmXzJ6chiVqEQzfvV1SXzxRHHJ2eE4O/cK1OWRXyQ1voQ0cXDqu970EbKu6ZBhQRGoFsfRpgrumnPpr+l83uG7PE6iuYmpTKsVdRtSztrsRoqIk7LITjFLBF5xbEm+oDOmawTtnFsw8DWJ4ZKfM0cGlVIrMpptFIiDRhG0pQ4OjoR3U8SIBo1iV+AMQx+KBUFOKVZn52yar5CJ4hKEnmnNL5zbX4jNkguFu3Be5mKKl2GgpGkE1ofOFlSD2eZV2EVMhQjyelZSqGYAimCchUma5JRZKXYxo6vLp+iVGBIPcEi+DCy2bLKgvnn4vmbkNuq0hhXk7Q0mMHLgq6dk6DO5YLjB/cZXSslQ8gygNIRDEw6otRETnsWizOcWdIujmnaBVcvn7Hf37K9vWC1WNLWLSHB2b17XN9ssHbg9PSU+XIlEuEQGLynalsZ0llbDNsGiX6zlug9zlW08zmLhUQtWGvuGArGmgKVC2rUtg3jOJIzjD6Qo0cZxTh5NBZtNUPo2cc9Q+y43l0xxRGfvWSnJxGIacSWtG4acZW3UFtFBbjiIeAqy5BgShGds5RPteY29UzZ412kz4ExyWemo6aKCp8jKch6yFpsnPpxYBaiwPaVEbJp9JJzH6Rs1ihqrZk2exoyeeiYNhOVWeDHnkn9GkzSUSJgmrzIA40W+nuOHowpOohD2m0oQTnSpxhtyNmhUxLj6ih6daUUkogrXliYRLKwTROQ0AZ0OUFzBqssPomYKIXicGiMDK9K1LRSBlVpMApjwPuBfUo0qyOUq8nWkXPC2hKUWVXEHIkp4lXCT3v+7Kuf0aiWuZvRaoc9WtLOKlLXE3xg3/c4J362uq5x1pU8c0M/dAw+MnV7TipL42pqa6irGRd9zzBNhBxRpjTegKtbYtbMm5bYbUWDrjLWyUg8pSTJTK4mxyQKyNqSTWKKgX7q8ZP0GTfdDUPs8fhimlEk0AqIiazSXRZIpZE8FxJWG3aXW4bNwHK1YnWyYjCS8uSUlLdTjiiTiRp8CUgCiDkRtEgUQoHcNVC1NUZbUBFrjEQmaCnXD3O0ECONrdje7vj6p09Z6Tndvmez63jv8Rn1NLK92r5zaX4jNkgGyBpnK4kViF6mqcaJM0kIKH24WRI6J4GCs5guaG3RKhVFYSyER0Blke9mmQCrw/BQlRjglMRuKGZ8iiUQpwwfkXIuKRC/X4M1FhwoA3XliHFkMwQRIhmDbRzNTAuvytUSNeAnuqEj5qmUkJk+9OyHjtpU5Ckws5Y8jZzN5zTKUlvDttuzPD6m3+4libZu2Xc7ZrOGmZVG+ni5Ik9RIqNPTkgpst0ZmZ2gqJoKVxsur66o6prlainiLCVl6MGoz4cg+fJao2tDn3qhuPie3dDTjXuGOBB1BiOS6JjEUCOnjMrQ1g3aKZq2wmordhgCS9L3ni/+7ClvvrzgZL3mO598B7Ow6FphTCKogKoMUUV8VoScyFmIqkFFfIH5D7TClDPojLIibKtKP5mRlDBlDMqAyolpGvE3HT/+k884qU7YbQPXu4HlMPDe++9xvJrzj/7nX742vxEbBEpTrMSMIZZAFQmYFFOFg0bkjpKCBOugNdbYO/pE1AZdYFrpW2IZPEo5gipRbTkJ/aGwenOm2PxHufaNEZTl7ntJw1e3M6ZqD8Dtdo+erZit19jFQnTbOeGcgXwYfloWdkEmljiCSbB8ErfdDpVgChqdE+PmmjxOPDo9xRrF5vYWncHUNVSOer6QPMS6JYXEy5evcUrTrioRDkXF0E84UzGMPRdvnmFdwppa6Dm5QmlH1oa3MrREMpEuy03Rh5Gr/TVDnBiDcMdSDmDgjgeEoHtaaZRVOOtoZ3O0EXl0ikL7kWdnZdbhLbfXE1dvXvPsxY7FsmW5rHnw8IjqyHL83gmZYr4hBsrkmKS3gbs4B3QJSQrSa1XaYFLEaUv2iTRMog9y4gWAUnLQ6gafK7b9xNWmw3/5JYbIJ9/91jvX5Tdjg2TIWRc/K9EVaC0O7T4KXl7WKW/zDBU5h0KBp8QGWEwSszbx3pX4tUPG9yF0Ryw7ZWKbUoRcDLLvbqNCZiwQhwJQQttoZjOmuqLbbYXqsTC4dkbTzmS4WAad+e7nTRijUFi0qcg+SzKVUSiriCmzDx6nNUkbYtC82e9YaIOrWlRM7MeO7CemkvVtvKcyYkk0jSNqitS2pp2tGPsdw9DjXMPUb9nv9zhnOT29hzKZYfIY60Rplz37qeOiv2Y37Ol8T0iBMUyo0qeIP4B4dyn99plobbGVE3NsJ+lRilT6yXLDKAVBE7vEfjMxDJqYNF0/cHPR09SWixdv+NG/+ENM1Bgr5nMaQEmoUC4VXMqZmDM6y2cTSpJuNoWZnYWx+/mf/JgHTx6zvn9KSFEGo/MZP/zRb7J5ueFq11EvK3b9jq+e/pyp371zaX4zNgiHQXmZoquCVpUrWiuDKu7tKOFcKZ1FF5ISOQdQDcYYXM6ENBLTVJSCopXQppRISjaH90IJcVVboGNV9O0l6zALPVIpsFYLoc9V1PMZaLmBKlfjFguqIl11RmrikBNZi5GE4pD7IbmLtWswyjCMkxDtpiCs1BiJgK4Nr66u+ckXX/L4wSOO1isSa9r1KaZqSGMoxtYwTBPNfElVOyrXMIsGo+/jw8TN5pqj8zO0sZyfP2Y/DbjGki14ZOFcb695efOaPg0EHfAmEI3Qb1RRd+qSHy/hRQmnDa6yuMrh6kooJ2j5/JISGJIEyVO7mioqnn3xgotXG3Y7j65qko/0/SCT/13m7OcXLE9W1C0YLWnFFBMPo6xEVPy5xSKbNGVhdU0xYCO4kInjxPbqiuXpGlMZJgJee84/POXswTG96TkNx4xdYPdmw5tfmxjoAt8K01aqzVDM4bJS6CwevApdzMoSRufCRDmUM7pEhsltlFW6uw10uTnI0sgZa4mJUn+XZJKMoDHF+kfIpqpQYCQUMynDFDy1Fm9at5hhK1GstU1FP3RCkNRSZhltpZwpwhyjFXUrNJJ+mujtRNjuxN+rQNPXmw1fffklz7/+mntnJ7z34YecPIm0iwUzW2FqR9M2aOdAS5ZgZRuMtthK4eOIah2jn0jZcNPtWaw1kcgQRm53O4Zx4na/ZzfuSRaC9SQrtzNWkYOc3imLRsRYgdTbtr7TxR/UnAfZMoVdrVWmNYZqimxfX7B9c4MfvTB/U6AfOnZ9h0fTh8D//Yd/yvJozre//xhtIppETknKy0Ip8hRIIEvpBYeGXERPUw5YY3j85DGb3YYQRqhaMTDPiZ6O2ari27/1hJwtFTN+/qfP+Nk//fSd6/IbskHkpE6p+GIhmDllJgIZ73PRPWtiDLhKyhNT8PUQJyQzJEPWxXU83s1MKGRIrQWGzVncUFLWwhK++0koflpZgkOVFu211kQUSdsygIzMZw25Eqw4Ro9VYhpx58pyOAyRGXJW4qiSUsZoTVPyAa0x3G62+GEiDCM3V5dit5o9280l292aeXxAtw/EdsU4esxeY12NdQ1N65jVM0xSBDx9nhhtYsyRWSOL5LK7ZH+zYzt1jCnhlGQVxhJLF8ckSVdGtB6mElPWlALGihG3MZq6ljSpQ193CD1SKaNVJFtZxDYYLr54zubrS/yYmYYObRxKW2bzBbuuh5zQumFzE/mDP/yMj773MSH2KKMwKBn6Jvl6CYQ9ESPmUL8izzrkxJgTdeVY3T+hPqqxlaILcksnlelTJPoB1UJOgZBG5ueO+vjt1/rLXt+QDZLLtFzQpYOZgnAWy/S1uJyQxYBajK2RDxUl3KlSBhwy8n7xlSQAUUqTYSDlfEdMpGzI0hoWEoQ0ozkJ8qS0wtmKZrFkNp8zqzTtvMFrId1pY5j8iC7U+wMD2Vkn8HFOKC1qvRRFG6GVAq1ZzOaYrOjYcXGzJQ4Ds6amtZnFosE50ewPMdHngHFOZhlKEUMgjxmVLQZN1oGByO3Uk1Tm+uoVseiudZ3p1YTXwi8zTrhniUytG6Y0EZNA7XVbU1cWaxTGKGzlikOlHDIpHZ4zkOWgSimUG8Vgs+bm5TVqACWTWBTgrGVKYrqtk2eaMkrXPHu+4csvXvLhd86pMFxdXNH7wMn9U2SjCkX+MDMTqKZ8xkqTFHTRM3OGxi1IMquXzawU0VjGnLDKFhf4keak5pPf+z7//T/4737pyvxGbBCllDR0ShOhaDykH4kFhdIlkzDndNdwH/oJpeQBGq3LHMKSgyVlL7RqJIZNbhO5qfSBtlJQGXnc0tRL4E6+u00go7JIgG0toZmNTZJ5kaEyDqwlxFB8tuRraGXRVmGKhJTDz1oo+lkp0cBrQ1vXtMcWxokvp4mKTFNZ1suZ8JNyxFYtqq0wdUtlLQSFpSaEzM1Vj5963CKxzwMDcsqrnBj6iRwSC1ehtbi2+JTIylE1DSQxfWtUi3EGraFunJQ3B/hcKXFgTEl6NQRxPKxR8fpKpdzKKG04Ojnh+vY1YHHa0GjD6mjNxeUtxiia+QyfG/b7gTTBT/74p5wsK+aN4frLV3z67Cm//6/+TezcQhaiosqHJrQgi6V3RJdSqwAtKShksi4/X0YRsyH6jNYVAY91MD+bv3NtfnM2iLZSJilVHnYs+eHm7TWulGiVy0Z4y/YtSVMFvVIIymKyI6RJyjBEH6JKj6HRxbDtENKj3t4duSA2hbCYigTXKM1suebWOabuBlPN8WOgthVDzjKk0xQdigcNUxju4haEDSBwMSV1yVVNIUXK5He+nAmZLyUWiznr1RKjlJDv5jM2+73YpjrHyy9e8Gd/9DM2t4E0wdn5gh/89gc0ZzOGLDQaEyImaq7fbGmto1nUoCeyFrMEOzNoI3nmphhp5xTEeyx5UKWMTfL3z0V743MiZo22VgZ4SaQIOYspQ8iJx995nzR6ptue0+OWl5c7utsLcphoGsPq5IgxWfbDDpJne33DZ3/yU47mFct2xv2zc6HSFBjeFheVEEuepZahoFGyGfKhIFcK5yqGaZBZDJEQwRhXci+ldPY5on5dTBsk51wegLnzxxL3kZi4m4+o0k3nfLhi1R2vR96PpEhJbrKMw56oNeowRCrf70Ca06U/kfKqyHcPGnid5cSi9D/ZiFlzVZGMo27neO0gHqJ8sqgckZMUFcvUv0yGs5jVaWOFLatFNptyJEWPykKjqdua0O2Lm6LHZKhczVRiIfZDT+gGwuhJU2K/2ROmRIoD52/WPDxpyGoihAGnLMfzGWaRSdtM5SzLo5pcKaJJuFmFNu5uOJpJoJW43xuZMRmUiJmiaDGclj4wZyUMa2dIaEwqBwoQDITWcvLkjO3Pn/GeWYKWIZ0zUGvY3lyBa5jVClKibRw5RxarJW3b8MGjU5JT+DKNTwW9ylpuEXH4f2tWLn8HEKaEQiTWh89ZlaawKE+LUvXdpj/fkA1y0KAbrUkYVAwiFCpugofFbIwpg8K3FMyURGuekNpYldvlcBMZU2pnJXOPg+HAYSZyKLMO8W2poGiatxunsg6d5XRs6oqqbhm1Fd+umKlNxZi9nMoHZ8iyWeX/i4MKutx+SmYoUWSjbzXykWQUR/fPuNrd4n3A2hqdFYbMfLlkMAZCoh4yXVAs2iXzNqFbjTGJ18/fcPL4iPn9mhhH7JhYNDV2dFy+uaXfjNy/f49UTwSbUbYsvPJYcxIaTsoF7o0Z5wyLtsHvRrbX10zes93f0q5XrM5OZfpdaD1KCeQblKKPiXa9YGsiRo2892DFvPeM3rHrPKZtcKslb64vWS9anjw+A0YWj+5hrAGnmAwSX1dmV3ITJ5kjKe6IitqUzzIK4TSWclpl6SNTmc+gtJTXWomuTv0aNOly6pShnDosIn4hcKY8eC2wnipNPb+wyFVWEpWgDSmLxae1DucbSCMpSwqr1McAB1brAXVKqBSl9znQLmRECUHmMj6MzCsZPG6D4uZmi24XxH5gvpzRhwmfZFNEDSZH+VrWYpQDivBLiXQ1poRKgZwkd0Mpja4qjh6csbt4Seg8QxeIaQDesLx/H13VBDXx4sefc/n5G0IwHB2dYLVi7G/ZXd3w80+/5Pc++F1MvcKEyDyvGDa3bG6uaPNSREJ1YEwRoysShikI0zfHSM7SbBvrJNAoZWI/8fM//AK/6bB4XJtJY8DYGevzE/Z6YpcCIWV0Mqgs8gRdW4xt8Uwoozg+X4N2+GxYP3qAO1rRhQlrDDNnidEz6iy8rMPiz0oCTg8S2ywLN5XS+LDGY1ZU2WB1VdxSBMFMyYM5SLL/PEwc01+zxPolEWz/MfDvAIf0kf8w5/wPy6/9B8C/jYQH/vs553/0K7+H7JK7xvjQ6P3CDyEbKKU76ocsbNFEG6UhmzvKsyoy3Zwz1lVkxIVDUZpypcoQUN1NaeXCVThjSSWGGZC7WBXBjzKgZNgVgZm1OGfZX13SWkNVO2IMxEPZpjVGO1K5pYTWHwsiA+owwc+Fvl/4ZsvjY2ZHa8bda7Zdx0xl0IEwDtjZTAwdJolyFgdDQCtOzo9ZHJ1z/uEpbduQbRK2czZo9xZadtqhVI3SHqWhn3zpxwRMMLkY7BW4KIbI2Ac2l7fEmwmjPUenFZWd6F69YtbWuKMGi4QYba6vZXg6X5CsY3Z+zuADpq1ZP7xHzJpsK/KsITpLpRwGMcDW2TEliTSIKd5FzSkln0k24pUGQn3nwKrgMEQW7+CcFdELv04VhWWM4kWQy/o5fN13vf6qEWwA/2nO+T/5cwtdqR8C/ybwG8Aj4H9QSn035/zurF0oEO4BXuUtXMvba/AwSc9ZUKiDoQLFxCEmoZcoI/2EzpQyS2Yib6cd8jrEvuXi+M5hsZabQ5GLPiUSckZbiXqzTYW2Gmdh3O/48mef0txsePytjySXL4qpndxSiKndgcOkFSnJxgwpMnmPtTJ4kz5F4WYLzp98yLPLDfuxp5k7ausYd7ecHJ9gK8dyPuclsDpecnLvAfO2xrrAh996CDMY1FjKyoh2ngePF5jwPm+utrx5/oajB3Nm6wYfPDr9v9S9SaylaXrn9XunbzjznW9EZETkPJWrbLfB7rYE6kFi2hg2LFGLDQtYIPWC3rdaYgNSr5BALBoJBEiwYIsQQmJwW8Z2lV1VWVVZGZmRGeOdz/gN78Dieb8b4caOctvQyjpSSqkbce898Z13eJ7nP0lJG1XKh4mc0n2QXkprRT0as7e3x9OzZ2w3G5KOFEDfeS6fPGExfw9CxKBodztiUtTliI2C+mCf4/kMVTk6JzkwGEsvZsLoW4cT8SgzriJGT2ErYh8wUZFMoI19vqHjkN0qw46kMtAMnY+kKHysrg8kLdM3H2LG0YYyTfD/v/IG+XMi2P681+8A/21KqQUeKaU+B34T+L/e+Dsgj18zssbQd+S9PlBMcn1724IoiRkmqezIiPiuBiHhDQ9HaZNBwTyRklI09zRS43rfiVl28LKAU0QbOXli8oC4p6sUsVVFQrFZreiDojEOtmMkwaqg14l22JAx94YqZ72nwZwbIGRmgDBi5YYDW9RUi33mJ6csnz1h1zdMzATVNpiupy5HfLHacHR8wIff+YQ79+/irOLy4iUhBQpboehJwVNYg3GeyV7BrLhH9c01/+v//n8yXoz49Fff4eTBPj529CnTOUyeRqGyJ5kSQDEFkpLqft107J5tqNKcvXlNs14CAeMsoQkcHRzgMdlQz8shNXIkp2mVYDA6IpOvkA89FH74qEPCmAKjDKUzxLbHxw5VWFTwt/LqEMXhnyx/GFgVPiYMGp/tniWfUr1WWQhSjxFg902vv0oP8h8opf4d4PeBv5dSukLi1n73tb8zRLD9v16vR7DNDvcJ3hNiLzqNIXY1s3fFWFoYvSkMeegwRBUMnrpktdwAJsUobvBDD0MKtyeizXJceaYyJhwkuEkhbNEBaxHPfGIAnSLaOUKMdG2HsiWLvX0efvwx1WhESAlrnMiHc0xcTPF2U8shkH2Bs1jLe49noPAL/8nWY+and7m6OONyec3+3oQqedRmQ0oFRVGwN5uxt5ig2OL7wGJvjCkc0Rh2bY8ygURP10Y6rbA+UZcTUqt5/NkTVN8ym3yP8V5F53t2viMZK1SbDAD6KDECXeigjBRzjd5oLs83TK1mPK4YOUsfe6IxssGMRkVF13dYa9mGkIXMkKwRtnQePJCpP0lLWWoy+GmUwSpxoek2W6KJuLIiZeVjTBEV/C1YGTN2ZSJZMxQp65H8WcZQQgjC8CX3unlK+qbXX3aD/GfAP0Ae4z8A/hPg3/1n+QGvR7DdefdhitHfljfq9ZIKgIGBm5HyBF0nD8e51xBeZQjDVZod4YU6/wrriDFgC5trWn87GrxFh/NIV7JH5Do3ymZzMwEXh0Aa1Ufq2YxyNmUyX9Abkz8EydHzXrL6JPtQAE7yJCaEIE6EmZ0qpEqfBw5iOqFHExZ37nH+aCXmAwQqEqvLGy5eXvJk94QXLx5zcvcYazUP3n6X5mbHxc2SXnn2j0eMagjJ8c2LNWHVs1177hy9RWgCu+uWz3/0BR/+xvu4wqBTjw8yjTL55k5RALhkDO988pB7d0745qsLfvyDn9KFljiusbMpAU3vM3XFiPulHO5CYY8xQRCA1BknCxcZnAxTtKQSVhlKo9FBRrOh6+nbnmpaoZTBqMER02Qv4LzAtUwGtYooa0EJ4bHvWwgK6xxdL6lfglUFwcV48xXyl9ogKaUXw/8rpf4LYMDq/1IRbAopdwxaFrNR9L2RZjaXJfn8AUS30WUn9KGuFGOGeDu9MsYSQqYmaHU7Ju57ZDSYhOouzZpmSMZNyGK+RdmVlF0Gi3QjEYvCuIJAQ1KaXd/TqezZkRTJ502pNY3vCXEIzYl434OS0JpB+ZaG6UoeN4e8UZIrmBwcsb58wfnVJUeLBfPZMYWaMR6N+PLnn/P8ac+Tx4+pxiPWK09MJcvNTkzctnPefvcQTMHN1Q5aBdFxuH/E6vqKPqxZXm7YrTrc/girjPDNYrZMygs8AW3wFEZTzUve/+QBs/mM85cvmZzMqA4X+KTwIeBDfDXBSvLEQkwyJvcx+/xKVmTIpZUApwmrtRjDbbfENnBxfsnB3iEH8zlm5GiMqDNTLpX0gNrfUovkv0Sm42uDRqGs+J+RRFattcKnTnrQ9P/DDfJPRTv/W8Cf5P//n4D/Rin1nyJN+gfA7/3FfiYMTbTKpU6MQ2//6pTVSkwBKEspufItI+msPpsuQwpZ7JSi1Ph5euGsyzVrluWmVz68McnJFGMCH+h9j61cRpDzRo1eJJ9FyToEttstejxiSBrRRhaCCmRJcMBHD1rR5mx06yQeOcVI34db4+6BQhOD0GZsWWAWC/bu3efm0YaL5TXV+UtMpXn3nbd5+fwJ1xcvabctu13km+Kc6fSElIRx++zL59w9mVJMar5+9ph7Jw/ZbLZsl2uuri4xLlCnGVaX+BZSzJqZbFCNEvOMpCBqy873BB8pTM30ZML4dIpyGlU52pSIwWc/5YDWoq8BWZxJya1JSkRtSCEIMTUL0bRy6BxHsbm5Jqwann3+iHiwpnzwALzD7k2y2XSQhj4fPEYbIgmjhFUdQ5CDh4TVVmQIJChSRt+jTPJSpG2bN67Lv2wE299USv2a/Fa+BP49gJTSD5VS/z3wI6Tn+/f/IhMs1GsxBvmx3hLRBmZabrCHEstoURwOjRcMvk8i1Uy335Zum/sYMkr+2hhZFoS6JURqBdEnCu1EXqtFWahSrnu1pCoVVSmTMqPwvRfduHHiMWyQficlkREH+VCNMRSFk3SrEDOtSeU+Sb/2nuQ5uLJAlZZ6b4/25ohts+Z6tWKuF4xmU959521+uLrm8mpJ0iXXVyusPSBFhSsSN9s1y+WGaKDeg717Fls5ttseXSpsaSnGFa4Sc2yRrXo5VLSGIFTxpDVBJWKUCZxXgd4kkjMYJ7rxiEFpJ7eGjtlGVGg/twMKOZYyKGwxidw7JqF8KBlbTBczVusdR7MFNA0vHj+mOJ5zMCvJYR9YrfH5cyEElMkM8CTj7AHk1Qn5PWiULcQL2OQD2HsK81f0xfpzItj+yzf8/X8I/MNf9HP/jO9jKKFS/kda6/KJ9ArYUcPWyQt24GUZo7BWJJOuMQAAIABJREFUplUp8BrOIQh4iIHge2L0t4lKw2hQp1cj4yHt1liDU9D6/taPyxgDSnhaxmXZaugJbYtvGqpqlDfgEBOQsBhMSnjvZTjgHL1vs87eUBSvnP2G2Gdj8zgyyXExPTgkNA392TecXV+SGGHLEccnR3w5n/HlN49RdoRrtviXLzA4ylLRE3j05XM+nD3k7U/vUtWJ0WzObrvm5qbElJGH792nC4FnTy+oFmNSJaCtzC0UMUlMdMwZJzEovNK0eMBjyXavSTJeiDK5klz7IADsMCBJ0gNGJV9TQTgQOkrvGXSkM5FyVLJ/55i98YzLp+d0wTOdjKSBV8PgI93SR7q2pRhVmb0QSSoPZpIceCpBWYozoyZmZaLGmgLjfiniD7gF7MQBUDThJgE5AkXYsFkHTUIbi07DtCpLQvN0y1qLIm+WKJoGqy1RBWLyYi+USWuvU02MUlgMlSsx2tDEFh9jFjMNEQ2C8BpnMFYmJk4rQtdhEqRMjBw2CQlMMPhETsqSnkOaS1EwDim31oruZBjNa21kQleMmBydsGw3vHz6ktLe4EPi6OQtPvzoI9rQ8PJ8SSSw3t6gkqEODldrXr5Yc28XKOYFuxiwNNx99xjfQTFW7J/uc7XacLPaYCc1OgI6ZY6azlw0JJIO8D5inAJX0IcdkIT1W9QoLMaIxagPw4BDTvGBxkIOSApRBhQWi1EGHXU2yYhs6RmPCiGUXlsO9k+Y7M+JEp2CUsNhhngjtx2uKuS95GGL2AQ6LJrQ9Wzbnmo8xqhsJ9X3bDdbjg8O37guvzUbBJ1w2ok8VoDSbL2fKYkZABmQcClvXtXuMtuWDzEq6S/00DQng0mKgsTF2RlomC/maKvyZCnXpkpM24pksLln6Un4KOZmWrj4Qo6rKwJJYIMYib1oKSIRMuaiACJoDEZZjI75FgOUys4rHmPkxtP5aynTtH0SRSMxMZpM4Pgu12cXnF1fsR8D5c2I8eKQv/Y3fpvv/8Ef8/LpDSYVkomoPGU9InaB9fWWyX6NqzUdCVfDO995C2MTQXlwsHc6QdcKdETl/A+rHQqF7yPKGZQDowLRJvokDF6tFCH0EhuByt4AhpgMkZBLrVxiDThETJkGIgseFDH7DsQQ8SmwU0aSKBYj3HxEZ6W0ttGKkzuRIANCyqJER4W2Gou4o2zbLUWeVl69OGd1c8Mn3/0UXcgzjl3g6598TnXwS2D7IyNYKXlk2tNnGoFHKXmLKl/7w1AupoRO8U+dxkOZJvTxSNJJ8sK9z8i4JXbyAcRpygvZC40k5lY9JZq+zfQNOf004gesYoQYCEYiGIKB4HtoGmnkk4xIB+vUlPEEaSSduDxGhb0FJD06GQrnGGYwDHoUg4xM0TL9sZZqvuDw4ds83f2ILrV07YZR3EMXjvliztMvXxBDYtN0FN4wm1ha37K6vuLYLwhRkTKNXZUpuwsmdKmYmpoO4TMZ5DT3oUVFwBh6Hwkp4HQihFaUmH0gGcV2s8UlA1Zl3y1JIZbeI8sMjJS+KT/jGAJJI70O4lAvrNsMTuLRRjM6GBNtAh2JPuLbFqp88CH1hckeXwpQUWTaRSk9oo8eW1rG0xp0EifMEMAFpnXJ9vrqjWvzW7FBxMYl0xpk0ChaFyWMWIaamAzcxeH7Xn3/AOoNwJPJKDi5CY4xoq3hzlt3abLJmJhDcCvWEjP3SAsQIyaCQ8huorUeTCUUJudckEQlJ2xTKaEGWjYaVJQG3A6MYKSZbIlSlimNkXno7Sk7CMREyhtE+WgMtnDMDvfYHh+yfvKCyq6otjcspnc4PNgXA2wdKWyk7xqWK0M0XnQyoSUkJWUL0BCJ2RzD5DG3SVLji5BLorFDVmqqKOBml7luoffUOJpVI5O/kFA6EKNGG0GvVTLCaojpdkiiTDbky816TLKhVAp5ECLArdYKq8E6RUgt3ltigC55jCpIyZCiImYL2ZiS9DIxkbRGW0eKEW881UHNzMygtERlhNozMjz45AO2VzdvXJtv7lD+Ob1UBBfARSiUQcMtuDdIX7XWt44jQ40+UDYGUdPtutT6dmR6m30OoMUztigL6XGsWNcoI7LTNCRMGUtA0XmPTtKXaNRQAKNIOGNxrsgCIui7RgYHGd8AbikRosazaOskutk6CiNAlzXSUyWCPAgRtRN9zMFAiNOiFp+nclyzf+8OZlSx2i25vHhG2DacHByzt3+Ej4G9+Qx8JPYCci7mc4pCyqUUFSEluhTpiHQEuthKJrySUi/GLDVOiYiX01kblJYEWh8SKigKLE5bTGElqTaKAXjIUzqlDClPt0Ic/MiGz0rgb4mfCKADKjcrMcZbwLKPXjayl9xDnMInT4hJxtJav4JAAAjYGCn6RG0tRWGxtSMVEFKHip4SYX+70ZjFW3ffuDa/FRsk+MDF4zOun13RLhtUUJlSIlw0WfCW18mGA9oulI34T03BAiH67NUrPKJIkJLJGox75ZOl1WuPIMUc+CKxCJ5ce1tD0oaolNApEJWjLUuMlcXbN60YRA9s3WEhKEGM/YDcKgnwcdYxKgtsPhBuuUL5VkoxYjW4QtwkU6ZVGFcw3T9g784pG9+w3S25uTxDJfj1v/Yb1HVNs91BiFRuBGi6piX6INEOSdYjUaY8RmvpAHRA0aOCx4aEiQqC3CqFrQApx0JS9G2kXbVsb1bE1KGsjHpD8rfmeykpjHEURYU2BVq7bN+kb2ksWsuQI0ZPjC0hineJVoIRpcwwVigcGpfAGbnRQ+huJ5wqYyGD+6Vqe/zVFWVMMmUzA5u3J/YthI6n33zFxeUFmDcXUd+KEmu32vLDf/InFHXB5GDM3XfucHT/UK5+I+WLD68mF8NL+DX+9sbQmcskr1entyJl5qdQIZ1z9L5jkIc2XZOb9SjNeYoEPNppVGFQfcia9JRLB0g5S8OMJCBmu1kxjUFqbXjtvUqQZAyC9Zicumq1xegK0V5kt5OUABHzWKOoaiOiMR/ltDSJoBXKOaaHh6wvL+hubmjbNavVFQdHp7zzzgP6zY4/fHGO78XgQaFkmqQ1IQZxX9EukzgjyWQDPaVwSaE6eR/GaGIykk2YwMdEaCGse9JyR9esOby3J5y2lGRyl4Emhc5NvJU4CjRN3GWe3DCuV7mmyvhUdrokeHEuyai81ZbUBQiJ0diRVE+vIjF2EuhDwuTPvXYlroCm6bHeE2+JqhkMjp7OR65uzolXivli9sa1+a3YIMFHVs+X4BTLmxu2uzVHp4fY0tLGAULXovsIkl+YKbK3mIYZSpVEHu3K6HQgHQ56AOHviF1m17eEASDL5UwK0tC7GMFE2t0GvCC2yolzoifIh14U+G0nvk1Nl4NAuT0hrdYZXU6EADpoNJbCFZACWltC7Gj7VsqRKHaazhkZOefpRes9GkPfe6IW+K2azdi7e8JZs+Z6+ZKgFdV8ykcfv8tPfvAj6lGJUopRPWJcj7DGkJWqKJUw2mBVQYGCpNG1Ydu2+M7z9NHX1OWYcjqimBb0Jsh0Liq016gm0d9s2C0vYH+CG42xzorJNHmAoofyVwnCbQ1FTHR9kwE96YeUtqiY8lRcUVqHFYsBUko4bdAebl5uuHp2yfsfv0s9t9KbGI0m4pTCKnGNqbSlMInZwnITI9YWBKPQUWg+rYaeyP2P3kHHhLZvFt1+KzaIAowy7HYNttIspgsKV9ITMklwmMNrtHaZ1Ghu0WfvhW0L3PYtIVtTkhSucPnPcoqUAms0vs9STMij3nzK972UGUlQ9CYmotZED1HJbHHQlXsfckkkOI1RipAbep3HxhGZzCRlAJuHCI6UPM6Wgo+kiM0DAGs0KiTCTkbeFkVIIc/wxa4olon5yRG75SW7pxdMlefm6iXz6TEff/IRP/vJI0ECXEFVTKjdhISiYyealBQwCZqrDWdffc3DD99mNq45376kjhG92bFerpndP4JSvL+UN/hVxDWB5dUlN2dnGGMZX3eM5jPKoxnKvgpelVG3jL2NFkGYMS6zrMFpizMlbdcQ8+GgQ6S0lVgVJUVsE5//7OdcP11i+8TZuORwciS59zrhsNSZFh9UpN/2fPHZF9TWcvrRO4BCD2Vv9PRaS/6h0jgUv4jo8e3YIFrlzIfIYn/Be++9gzEanydaWokWg9dAPTLwNjgpDuAQ+cYeKO7OuGHYntm9QG6OnbX40KOSxqfspBQjhMBUWZwHHyNYxy7/XKnERNgjkxj52cYoiAHvO3E4vGUHx4wm5+GCCvjQi4grCBGwqmq6dicOhlrIjgSghy70KKtJ5rZwIWRhVzEasX/nDi9utjRdQ0orTHIcLk75W3/nb/F//O4fonAsr1oS5xyeHlHPxvjUyG0bPV9/8Yjm5Q2pDYwPptTjkr3pjLjpKbQhdr24LJKIGw/bxOr6AmMSpStYjA4IrUNvE6kN6NrKqDuIc/6QP2mt9G2lq4QkqmT6VxhNTB29XD2YaPEbj28ioU88efyMx59/jWojk8JwdaE4YI7WkrlugmIxmlKXjuvlDS9eXPHVz5+TApzvej747gfUE/mchf+ViMqQtAwNjFJ/5pocXt+KDVKUBbOjEQfVHr/52/8CeydzutTLwiLdRj0nJae1DxHSK+RcWLj5JM+cHqVe5RzGNIBvKWMUkn7qnEQPh9BlYzpxmU8xsGpbzM6jsUxOTun6ji6KiEsrhVESPdx0HZPggUKYulbqcIU4oBuVf28KqMxnEtTcSB65b9FayWRNCQ7k+4AKBt9Euj5gxwqsgJnJJ1QKWKsgKmYHR7QnO26enaHilsqNCann8HSfk9Mjdo3n4uWS9UZz/nLDyb05p/cXFA6CbxiPHZQOv9pxtlnz8IO3qcdj+tCyXa0oi4Kth9j16Ab6bkcfdxzcOWA+X6BtxZNvXjBfTDg8rLGZBfzKkSZ7nCEDAukHcuCO1mIMkQ0CiYrtquGLP/k5JlRU5YTLszXKFyTfYKZw74M7mMoQrZKqIgqLW55ZBB84f3lN18GmD7RNx8HRhJh6Du8cUs1GtL1HWyPJYuaXgGpSjyu+95vfZTQaMZqV2FJLiErvM/Mqo7HZtEya78GJZKCnyMxdDaZySf7MZ0fFSPZ3ylpnHck4yZA3ocRlUCmSKwnGEunw645JH6iweBVokXJIpYhzJc45drsdZVGSgsfa0W0jr1LK+ulsZUpgSPGNxFvfrxg6OZFLR/IBbQy7ZUe3DkLOU4aoPdqIFDglYWwED8Y5FkeHLM/PWV5dMB1N6fyG8bTivffv88MffcGzpy84vnNICg3Xl1e0u2N+5dMHtKrl5HTB2a5n83INPorjY1WxutxyudkxWm9QdgYtxG3DZn1NvVdTHswJq8jl+ZoXywt2qWFf3aMwVtKgyCPvIDJo4woZpOSNYrRD64oUG7Rx6CDS4/X5iosX14SdpnRrYh/QCTZNx3hxSn0wwVtFkIqYPrYs1zfsokLFjlGlWW2uQY3YrRt+9sdf8GxSUY0c7dbz0a9+RFQdiUQy0P8CSeG3YoNoq/noV9/HJM315SWb5RpblahdwpZarD+zx5HkfBhSUrRtlxNwb6uoPCHRGY8It1OtZCDk09sgvr7OaElyCj0oGd5qY/EamgDFqII+cfb8OfP5HpURcpxXkdRHQcGLknEh7u6+aZjt74HXqA66LjCuC4Lq6XSiF4dZFAEfOmySbjQBfZPlscJvYXl2g1EOO6rwHlRUuXYe/qG9UDGSppoUHN494vHyhs531FVJWTgevn1K0275wQ9+zNmzQF2PSSrx5OdnfPTgHqOZw40Sq1GBHznuHJyyWffQJ8r9KWFzTRsVbhcxQXN2dU7SntHiAEYlrnIspmP++ntvoWNiuj+ljx1t0wj7F2FS6/zvVJjbSX1RFIQAKloUJWiPM5rDkwPKUcH5xTWqUKgAXfT44LlZL7lZrrFujHKWaBOaSJ962l2DS55RPcKaxHa7YyvprJiU6Joe4ku+8/HH6Eo4bjFq+rZ749r8VmyQpBItHZuLG57+7CuUqzk+OuXi5UtGd/ew8xKnNDHnoQ+3hta5Zk8pA1Py9IVKPmg7siwz5Bsj+12RxELGGpsJggIeDRPHEOX2CaGjubxgrJXo1q2GwlAUlhJDp6AoLIWzpN2OcVB88dkjJnrK9WbH4nBCWSdGBxNaKzr5puvJhZawh1GECL6N2BRZnq/46vPHVJMaU1akQvHwk3tZTSfov9VJmAPKU9aOo3snXF/esLxZkzzMRxMa3/Hg4SFPvq64uWnYJU1RlPRN4mc//ZLv/YtvU5UV7a6jjx27tqOJjssX59z/6AHTkwNi72DXs1pfsWmuObp7hC6cxCLYgvGswJkClxJ9atjsluzaRrwArBxAEYeP7pb1a6xCmUTsOpwqZUysPF61lLViflCyuzDUaLZtS9s3+NRyfXXO8vyYk8Wc3iTQCZUMwUe87yicQYXE3nROu94S+ogtS5IW1eluuWN3vWK0L9iKVQVPv7l849r8VmyQmGDnhaZx/uQFCsvm5TVd7LkKSz7Y/5gmtGAE4R3APaUG1igonbKbYoI84UIh4ZUMwKKUN0plz63co2il6XpprkMSrpaUZz2Jnm2z5PK8x5AkyLO0hFFJ7LdYFbEmspjWXGxXhK6h3e44nB9DDHzzzSV7ezVH45rxoqBXHpym82KHmbI5hDYy608h8uSLL7l89pT90xOunj5jvjfBPFgQdIeua3QldqUuJUa2QDtHqxxv3b/PF8uf8uWXP2VvscdktiD0Dd/99AN+8EefiaouehajCc4mms2awpacnV3Tbpf4HrY7zf6dt7h6vobColF0bctqdcFkMWK0GGNKS1IarJXNYgyha2naLU2zRRtNPaol/EgNNqtJBFgJrC3wg1AsCbWmLEtBMFPicH/Brr5GNZ5OdSjt0VE+5/29OZNJzYZGyJCxQ0WN71o2m8TTZ+eEoHDlkI7cEQMU2mGN5ez8Oe8dn/KTn3zO8d5Dzr+4eOPa/FZsEGGuJqqqZlKN+PLzr5gs5izuHLJ3sE/vO6FhILfAsEmsEX/dpNKtkjBmf1yJQNCvaUleCZIG3UeM4lxujCPsWjCZyJj1gZEIBtrY8vJ6RZmENOmqgm5dSDJs6Eh9Q+h3CNYf6fues4trHj+/5LrZ8TbHRH3BW+4QNxKsZDBFG5wjrbbiUbUNXC1vGE1qHjx8i4Ptlr35iJsXT+nTjvJwn9oeUWrNwha4qOiCptQFR/sLVgczrp5fc372lOPTO6xXivfffUizWfPi+ZLtWk7a8XjEl48es793zLYLnN49xUbNs2dPOL73Dk47druWbbPEloG6rtg72cPUDp0Tc5UytG1HcB58Qxd7klZYl726kviVaWOz9HlAvYVXpZTGFIZ+11BoQ1AWDRzt73OuH9H4NVYHNA1aRxbzGdP5iIRHIVT6RIQoYaDzyYLztiVGfZv54owBHwgm8taD+3zwnQcsDmuOzo/5vf/t93HxlwAoBHFFNE5xfPeUzXKDqUv2T484PDmkU17Sl7RGUiRlGpviq5w8xStLHxCqRrjlQg3OJoN0V0iHAiaKZVBRlDJR8a/8s2ICjGF8eMDN8+f4LuAUhNZjooh2rHC0GdcVRAit5G4/e/acL756wSbAdr3jaK6YT0tGhwVuXNIlwUYY3rLRmNJQMuX973xC2LScnJ5QWsPXjz9nt1szP5yinUETmLiamRnx+OePeH615INP3mExrjjen3P21VNuri94/uwbZtMZ41HBxx9/QFk85SefPWLXtVyuliwOZuhqxMHdOxwcT3jyxWN2fctut6UeTdndLAmxoy4K9vb2GE/HhNoSk5FbwHta39P5hFVRUHonpVTvA0o5lLJIhF4ipZ7SjajKir5PWFPS950YRqgESpwcd13L4d6Mm3BFf9PgtGc0GfHw3buYSsw0rDGEbJs00FNUgpvlkrbrhbtWaArraHcts/mIT3/9IaM9jU+B8WiOigU6uTeuzG/HBkkSKN+0HWnsuPP+A3axZ/HWEV6LV24CCHkiFAesI7u/K8mmEB27XONJie5gmHQNDn3p1ow6833y6NFlpZ+xQn33Mcq83BTUhye40Qy/aeg2W9q2lwDQ5NGxZzqZU1c1u5sNu9WayahmZzcEH7i83rC9WXHnu2+jg6ZyFU3vsUpnVaGQ7frYE7Nacf/0iLDaEkJDGyM+tRzcO0VVmmo+QSmD8YrHj5+zuUncvFjC2z2TyZjjxT5flTXLq0t+/KPv893v/RrHR/fpFjNO7vR8+fQbPvjeu4wODKqAtoscvH2ENgo33+Phh/vU0ym77Zrgd7S7HeCYTmaMJnO6Qvql0EfarhMj6ZQEQDUSV6eNGH2nZIlJKDwaOcxcYei6HnBEIr1vGFU2l7oFpdU8W2/BwXQxpmlbitIyP17w8a+8T6oMbfIoVaGSJUbx4/XdhiZuGe9VqCJQa80Hv/IOdT3m8RdfcXJnSn1QsOo7Sl0wXxzy1oP7PP3y/I1L81uyQWSk60PH9O4Re3c1Td+h6kJIaTkUM/bC8jTG4n2g7wPj8fiWVjJwsgaFnmjMsxFCLrWMUXif9eCJfAIhPzP2WU+gM2vYEDWkUlNWE9zYY5qWbtvSb1b06xtKV+LKmqbt2W1bZmPNfDJlUwWxMe09bUoY7UjZDwsnFBIzbFanBetJohOhD1ydnRO7HU27Ye90DzOu0ZXD2AKbDN/8/AmPfvwN+7Njmk3i/OkVbx0dsj89pi7GbNuOplnx8uU3jEcOax2jcc3+4QFRgXUlXfL4LHxpdhFr59R7I/pmyWp5wW6z4frimqqpSDPLsSmFgRAa2nZHYS1tkKRhgKoeEWJ2ccmKRMgiHh0pSksInr7xWDem61tUCrnU1TgsVjnGszlff/2EMoF1jtRHOt+xarYUk5Ho2n1EqwLQWGfQVQ/A/Q/u8tXTbzg5POLtT+/T+8j8ZMxs5kS/EyD6lonVHBxPOT9bvnFpfis2SEImEhRWTiitUWWdbw5xH49aYtV0lAdxawc0sELTK5R9MIB4/WsS3fa6z2vEmYEWT1b4SQqrIoqemaHB1GAlD9AWJanswCoUHWoX2LUd+weHLOaW1XKNNmPm0wm10+heUPPz80t+/Jnnw9G72EUlQiFtUN5TklH9bUe7btldLulu1rw4e8JoPuZkPIG6xBiNDRYTNBdPr5hNj+ljQaLmpz99yvJmjYuJ4PN4UwsYuLw5487JXZrNisV0jqXAbxJBa4haRGSNwuoxKkCzuaZvNhyfHPP4m8fsVMeCU0C8pZbLNcl32NFIYtcyrcSY4tY4fFC3DUI4Yw1Ga9qmx5oaSDTtjhhatK4pVYmzFoPl9K0HHOzt8+ynX7BdfcWkLpnd3adJSpza84aM+YCLSWNHE4yOOFvwW3/zNyiLiobIql9zfLyPKxV9DlwidkRrqMc12237xrX5Ldkg0Cuy7FUy58iW92ZAwnWWVGakzNoa58TlZHBgFGPodEv/uM0KTOm2HBuo8Tpbjw6s0hTklPdKmgKnLSEFQs4pDL7PmgiFMuBGDh1rUr9hMp9R1SOur3ZcnF1weFiymM349e9+hHUlF+cXrFZL4BDrpoSgiZnSnyt0umbL7nLD7mrN+uqSzeaGXdtzNN+nHM8wlcHGgNp4Xjx+wX61hw8lT1/esNwJ4a57smZsEvuzCcuLG0aFotutWV0lKqOwSeF3DauzG1Rfk6x4VI3Kms57+tCzXl2w25zx5MljfvzTnzBaTLj77lvsHR9yvbqhoadtGgqtCL5DG01PwCpza8YG+vbGRmmsEVPwrvWoaMRYLgXadovNwK1S0tzHBMFair0Fdz94n5dPLnnr3gNOP33I1jX0dLcG1HoQqJHooscqIHYUi5oQoOsNdjxm7RtwFptBSoXl4rxhtzFMql8STXqfR7UpgooiLUtJciASoha7leRmbpMzov32XZ/15XljDP67ZFf4P9Wkq1sE29yOi/PPRAuIqBTGObk9stECIdB2jbBgrSYlix3V7C4S5xeXEBRnZ1dcnV/SbltOj97i5HiPxf53OTu/ZLfdsdif0297QicKQ6cU9C27bs3qcs3Ny2uuX16CibiJ4/j+PQ7v3ZWwTpW4evKE5sUNu+sOq0asdlvOXl5wud4AirtHexweTTg+3ePF+WM2mzV17bAKrs5+ymi0hyLRbFtubrZoZyiKERsjpUrnG16cfUPf7zi8f4eHs33GRwtsWeBtoGmWBASANa6UIUhGogdLT5KwcgffAEXEFULI7PogXCw5xjEaCjfk0ifaIGYaZO3GzgfWMbAlkcoS4zQ+GFLoUTonimWv3kii9QmnBs2kHGRKeZRJdCGCsuANbae4ftGh1gVHhwdvXJffjg2SFCmPYVMXcErSUL3yeDwpBHHiTppXxs+i8TB5Hh9iLxuBfK1nlq7W8rWUEn3fY4y9xUtCNqYbvLWs0bhMIgzpVZyXGAwYnBlunYRXmqQcTRf46ukTnn71gq6Vceh6dcOXuzWz+R71dMFkrDk52icaTb+6oQuJ0pVolfDNhuvrK87Obri8WHF9dUM9HXEwOuDD9x5QT0acnp7y7OtHvHz8FLfxuFTgChEmrTdrlus1CsuycvSLiulkwuH+Hhc352y3O0o3Rqua69WKejrDasv2asP5i+cy7DCO0WTMrl9jRoZf+fSvY8qaXd+xSw3rdosVrSx931MVIhQbbmsh/gVK5XJoxyCXFnpMiB19B9aI6be1mq5tJanKeyiEmt8m+Zw1URxrnGN0sE/vNJvU4mxJgSH1O8n8SJK10scA1iGJUUlY0SkRuwaNp+16qCpSEIDYGEc1txL/UPRvXJrfjg2CaEKU2I9hnJwiwl2Vh03wpKRRyopw6TXnd2MM2qRbL95hlDtsIm3Ae7EZtTZb5Sehz7+y/SHb1GisMnQxyzqTAiWG1kPwZwgehcXYgr6LdL3otrW2VPWYm+Ulu90aHxtkY2Z6AAAgAElEQVT0+hzlDHUxZTqbM57MKHwkbtds2obt5hqf4PLsOcrVXG2uWMeGaq8WKnb0LK+ueP71U0xUhLZBq57G73j0+IybTaLZ7bDG0rWGo4P3iL5Ha4ls3mx3aNPya7/2Xb7/w++T+sjp/bu42SFuPAEVUc7Rxx4VLKf371EdnrJud+xSw67vSC4Rtchptc2DBSMM6MHzanCUSbmkNVm2aF3BbttgdAWInibGSN/n7EiVy1ylBH/KvDgFuKrgO7/xPRnpukjjO1wq0LqQ/kMJtiIiT4eyluA7Ykik3uNQVMWI1XaNUQUgTGNlGso9RT2JzMwvQYjn0Gw7rXNQjVzBWg168gGv0GJQbEy2k4HBbEEr8eSNqiSEDlSPjz3altLPhEjpnIiScvjjqw7gFRFSo3HKEXoxjhB/9MiQ4Jeil/fmsp+wLbl7+hAVxEtW6YI7D9/jy6dfcHGzwukWXWtWZs3lzXNm9Rjdp9ub7OpmxfX1ivPLFXfvPaQuS2bzCXvzEV///BHj6QRjDdOywpQF69gSkme16/ni0Wds4oRRMeZgUfEv/fb3ePftYx4/+jHL5TmbpsFrS20Vz5bXuMUeajIhzMaMDvexRzMg0IaOLniO65rJdCasBi3Rzy5Z+t6LoEkpQkhEFehCx2BVJFFyEuOglRLEPLsqXrw8p6wnQqcJUlZt2x29b9BKEokjFWiTPa0EoE0qYQqNKcVnTBi7SXx2tUEjCz5ph1HCpUMpvEoknbCF0It6nUhGo0wkpJaYFLGXgU+vFMu/asLUP5eXQhRvWe02ZJorIyTEiATXQJ6YEG71HiSR2hqn8uTGYFUpLu9IyGMXI4V1Ahhqbr2rMtwLpFsbfJ0AZfBNS9O2lKP69q8OLuRaCWXbaMN0PqOOhm8efcnZ5TmLowPu339AOZ1y9vQ555cvOLx/RNvv2N4sWbU7XITUBpqm4d6dt3j/3ff44Q8+Q8eeg2nN/p199k72mO4tKCYjyqpkbAvCbMYXq0v2xgWLoNn77Gv8dc/9kwX/2r/6t5lMHY8e/4yNb5jcO8X6CKZkPD+A2ZiDowNwjlA6tk1D0zb0sSfEjhAjwRWYCG3f03QNxiTqSmTBQs8Jr0VTpDz5Ax2zelDlHEgVcaW7vaWJ2TTaAURSDlNNyeOTJpIZ1vnHGquJ2RPYZ9azSqIfERvZkPlbFq0cRjf0WdYsJbj4k9lCZU+CmBdSJKEJXqxOQzI08ZdAUQjkEyD/N+i5MzUkZtq6SXKKK8QMQSmTEUMAmbHbZHDJ4IKYBQQi2iTk8et8rulXWhNgUFlJFonHak1VOkF5rb41c+7DqwgEgpycIQZ2XYsblZhpxbq5IfqGu4dvMZvsc9y9BSPNtt8x3e7oV2tMDNiU+NH3v489e8GdwxPeeXCHy5sleyd7HLx7n9HJAaqs6BT4qqBNBl3XtAq6mBjVFf/K3/6X+cnPX/Dbv/03qEeOx08fU85GnDy8A1VJnwxtJ5yvGx/QfQuhp8rWn1GLQjHkZ9h1LU2zE1FR1uGY3JuRVZESLyFPbfAjG15D/qM2wnLu2g7nCimlQo9RFSp5gm+lXMtcOEHZRYYLUm5DvMVHBFAVmfCgaRdjcPIYxqHQ2YVG3pM2mhBaNEZk2VEMBFPSOY1Y2NHqtff/Z73+IubV95H4tRNZSfznKaV/pJTaB/474G3EwPrfTildKSn+/xHwbwBb4O+mlP7gF/2eFMXtWw8hngivSpQgWm6XKAGdRiuUigKuaalr+y5glUaHnotnF5w/u6AuLQ8/vMd0XLPqeprk0a7Io+EcwSWWZbd9i84N5mRSE/CgovheRdmaqDx1IZJ8ZLG/x0w5qsmMpU5cN0u+/PJzPv14D5TCTWbstIxDi6Kkns1xRjEuHMVsgmo7fKVQs5LRaM7e6Sknbz9gGxNtxmtCHwkqUVrD/PQeyxffcLHa8OEnv8G7n/4aXeh59PWXTE8OMJOaxlm8srQp4bO8VjkIoUcrw6bZUbgC7SzQybRQa5KGpu9wzgrTgIT3PVVVCYCpFEN+PUieiTYQlQIiQQW0E1aCj4nORzBWmApFgVESW7FbL+mDZ7G3wGkjQTo5kx3E5EJpccZsdz3b7QaVApPJPlU1lTG9SpRWNl+MktWudED1wubWSj7ZrmlwthL9ewLIoTk6EZXYvf6VNgji0v73Ukp/oJSaAv+3Uup/Bv4u8L+klP5jpdTfB/4+8B8B/zoSe/AB8FtI2M5v/cINku+G4aUQ0dNgAhcgxxDoTHOWEyfl77XWYrVCh8DP/vgzzr6+pHSW3eqGD773IdVsJO4ixGxcFsVtMac8kUe9Sal8oiaK0tJ56TleudVxS3pUClxREj3U0wV337UsXz7jannDi5ePOX34AV9dXRJLg7LkjRjRzhKN5eTttymFkkx52DFazPFW0ztL6L3U9EbhU6AP4GzB3Xc/YD0Z8/KbJ0xme6yawOMXzxmfnhJLxzpr5JVF+jOdo6+1JiGod5cCTSdKxqoqCbGV3HNjM5Y0YEevaDrWWrz38v71K/5SiEKXEemBREanCLtdg0JT2IKqLNBa0bUd3geKssjNtqYoCjEFJwKemCIhZpsipdhstrRNg9Oart0xqme4zDKW6ASbSy1wVpSbhoTvdgQvuh3nxM5GRcknQWWbVxVx6s09yC/0xUopPRtugJTSCvgxEqv2O8A/zn/tHwP/Zv7/3wH+qySv3wUWSqk7v+j3yIoTEmIMEkGgksqTJolLC4DkimdXvgh97+mDPFjftVjg/sk9Jm7Motpjd9nw8x/+BEtkVBkZBMYelQluKJOn9YC2KOOIytAnWfzG2QyAeYiB6Hu898Q4JFdZorKYokYXNdX8AKqKr559QYoN8/E463gRD63CgTEka+m1pdUWxlPKo2P6oqLVlgZhExACZQrUvmOaFFNdQizwjDi69x5rr9gmxf6Dd6gPTohuRNIFPgrCbJRGx4ghYlQUT9rkCaHHhx4fWkLosNZg8/g6pZAlxQqbdTJN09xuFq3EQWa4RV65WCqMSYzrghR6iJGyKKjKipgiu91OxupZamCswVpDWRYUhUUb0bb0fsN6fclqfcH1zUu6fo2xCePkJkQrrMnhOEkRfcoeASrLsA3WlDhTEzpwriJE+f0SeyADHZPAJgX9/4c9SA7z/HXgnwAnr4XoPEdKMJDN8/Vr3zbkFD577Wu8nlE4P9y71TCTw2dCL1FpEVDWELo+25GaXBSpjI5rAjLqK5WmxqEixD6RrCI0kdgGTPCUhUUVFp+g91HixXLa0eBUEmPCuUKcEq3CJnF3t2rwrJX3kFLARoUyBh0llN66EaaOVIfHNJfP+OyzP+ajT77H5U2LV4akreAHSRGC+Nf6gUDp8/SGJDHPKWK6nrhaw3ZH0yvs9JB6vEf0BXpac42CqqIcT7DO0gVFwtDFVhgAMZIQYzavJdcDbblNt0TRdg2jqsAjfZ/VKlNt8i0Nt3W6pAXHW9O94TW43teFQxMpnaEsanqf6PuO1ouhnLMOH3x2xjeUhRMdvpbR+Xa7YtdsaNqt9C2AKwrKosA3AVs4tMnUFSW3iNWGLglupbSisiNC1+F0iU6JEBphaVt9Czo7qyUaO/pcdv35r7/wBlFKTYD/AfgPU0rL1x9QSikp9Yt+1Z9+vZ5RePfdB0lpRfAB1Seuzy6ZT2YY60hG5axrQWeFOKUhj2hvhVEaCmXpli1XF5c0bUMIigkFv/7OR0zKCZ2SW6MPWeOuMq6ltZijedFQa/sq0MY4h4nSUIYg0dLGCO3eKA2tRwcPSlMVFV5BWReQIuvrKx4/+gnzo1NuUkLpgoSSFCqlaXykKCwY4ZtpMhU/BSE5Xi/pb27YLa/pg2Jfl0xHM8zREVfbFW0Ua9R9ItZoylGFTQU2Wlrf4H2HTMN1vnnJo9nMSkhaVC/RC9YQEFDWudvobXGNsYRsoJ1y9F0cpAQMiViCirfNBjD40NF1QciBeZQq4GzMjosGYyCmnrZtaNoNbbul63YoJfEWCU1VVXg/0IlMjs7LIHBQVGWFih2db3IvBCiNswWogO8NPniSFusf6yTSIQWFxvKLlu1faIMopRyyOf7rlNL/mL/8YohiyyXUy/z1f/acQjX0HHB9ec0f/d7vczDd49Nf/Q6jgwldRrytlXDNlB3Ih6mHUYpCaYqo+aM//BFff/kEbR0+BTZNy+eff009H9GEHlM5qmmBUZY2JLQt6FPExxaXnEg4O4+1it73ogPXsiC0fmVEoFR25AApv1SOAbAKHy1uuk+72/Ly/BnVeMRi74TrxuMmIzGo1lLe9Sm7uIvLQ168Mi2LyuDmC8x0ii1rKKdcJU+IsEMsVXXo2bVbohInczKgWRqHKxRNm8S6B1DxlXZCAFiZRHnfUziNKxwxDHZK+tb0ECXuJMbKIh1sVZXOEQ5JUoiDDzRNT1KBhBG71SjS5qIo6bsepWRY4IqSkFraXUPve0HdtdCNnLOApixHGFMSQ8QUjrKsZbkEwaiefvWUGAMnD08xlcaHkDd5JJmE0QVl/f+0dyYxdqVZXv99071vjsER6bCd6RycrhxoNUmpKFqiuhEgMdQGsUG9gRbqZSOxYdHQm14CEiyQ2IDoDQtabFqwAIlBSAhRaroKcs60XZm200M6wmFHxIuId4dvYnG+sE0qM7KrKj2kiGNZ8XRfxLsnbtxzv/Od8z//f03oA/P5PtpFcvZoq0lR04VA7Y4Pga/dg5Sq1L8CPso5/9NH3vr3wG+U178B/LtHjv8tJfYrwN4jqdiXW6Z0VwOT4Zgz66fZ2drm04+vsHvvvsitlc1iivGIAhxRpIXKaGpjoE9s3thk594OvfcEnWm855NPb/DjH73PJx/cpNnu6Hc7ajTjyjGwGqsytVGY6FF9S0XCcSR3IERtWmthBddCQSqzbLE08zOh7fC+I+HRFoaTGeO10xz6yM79bZ6bzhhmjQoyyOVDKrCLGhBGQp+ipCMKolEM1tYZnjrNeP0seTxlPwRa35JCB9lD8mhEPjuknkikDz2+9yQfqLRhNBiIMI8qTI1JUpojpMGRTHaIXo4XrRXRl9fCTpKPRFQlLTziQTZGmntGKZy1NK0nZS3jBFnmCeKDHkoBluaEM5CiwPHbZp/QLzAqUTlNXUm/qq6HODckeoVRNTlaajcQpIPW4BNpEbh7c0uILop2pFEaV3QMa1sxcmP6eeT+zT3aex11HmCiJXaK3fuH9McjTf5YK8ifBf4m8J5S6u1y7B8A/xD4t0qp3wSuA3+jvPcfkBLvT5Ey79/+2jMoJbmtESCgRnPv7jY+eLyL/NL6W4QQcOVGhYTKRvJXZXDGygqiLbPhMlbtsrd/wGTV4X2gHk65e+eA0QDG7oDxYJXhKUeXZbR25DQ+ZhYHc2pVEYNiET1Ygx3UknKUm0QrS9aJnAV6YbXGqIr9g11iPZC82RpUUtSzU0zWzjHf2WTz2nWWVtb4fH+OWZkJZ1cGnbIQLhuI2RJTKGlPwmiHj1Lq3e86YQHJiZwDOXZy46FEAyM6YVpHqjWJTOx66tpALePByRZ+WnKRf0AqgqW6FlOQJ3aS1SyVflTKwl5Z15X8SKn0WS2wkZQizg7Lc8uB8vTeP0AfWCOinb7rqayidoa+OyCkTnQcK4MpPFeVs5CtBEeUvoVWFZPJBKMtOQl/72efXOWj//0x0QfWz62ycfFsSVUVRgupxcAY+nnHp+9+yq3rn7Px/Aobq29IQxPou0zf/YLMijnn/8Gj3aD/1/7il3x/Bn7r6z73Cz8jWJwgVYp6VLHfzrlw9gJvvvkaGuG5zbroWZQxS5sjca8FN0GPaq799BZbW3N81vjQs333LuPxDI2hawIqJza3dnnplQ3CfuTGjessr51isjajb3tWxiOmgwmfb23z6ZUrnHnlPJOqkn2HgtZ7ep+KNICgRH0MRO/puw6iw6Qy/IRs/mfr6+x2B1y7cZUXFCTvSUnhZlNwmqAiNitMOlKcsiQlRYeu6/F9ou072Zhqg9UKgyUnTR86FBBTT4pFbFTyVUARUiZ1PYNhJUjprhU4eZKbXEhG5N+RSperLKEPQCkeaE2OWZADRp7Q1li87wVJWxhF1DQQkwyiGV3kpJX0q2IQ4unFwR7TjSVSbvCxI5OpnJF9QUn3rLVYM8QnQ0gZlYRLa2Jr8n7AKMtgWGGyQWVF03Tcvnab0+dOM5jWeCUinTIVIU3e+e49LIp27tm+eZ/zF56nGlv6lR5bfwuI447+TNZaiInTL53h/Osv88ZbbzAcD1DO0sYCOzBO5tcT6K7j0tsfktoRxlg2b26zs9sTlaOurcxIG1Fe1RqiTjS+49rVW7jc8dln11le3eS1ty4yXq4ZWsupyZTYJVamS8wmy8RMybFF6zDmLCKhJV1BKVrfYCwc7h8yrpbIPksObBLVeMh44wx3L1/mkyuXOL1xnr4/ZFCNQFuSLTivDCSNdo6YAjFkDg7nsg8C6R5j5YlqDc4aohfOJ3Ig4UXBVRoupDJuHLOi6wPWOcZDje8PELBhUc1SD/EEMQp/rbWFc5hSZs8ZbbOMJBsJEKUUh/M9RsMplatwRnHYtlhTFdSs8J0laZQTvWdleUQfRPJMWOeNBIi1ZCXKt7UbEpMBZclEjFLoEBlg+Oza5xjGhKXAsJpw+swZmvYGe3cPuPSTS7xw4QWWz52CUrL20TMa17z1vV/i+sd36H3i3t0D1tZ63ECztrJGp74FaF6lhNUw+iCYnLri1TdfZ7Q0lTH0orIUgiepTFQKqxV1PUSnihtXt+k9aCzGDGUwKEYp2wK7812GVY1JLdHD9SvXqGLPa6++TD2EtcEIbQR/Ne/uoRIsj6fSJzGlgYh0XbPIUBEL3iflWIZ/Gm7f3eHVlZnskUwillFgN6ypl8bs3rxDfy3xp7//q8y99Bt8CASdwBi0qURFyUeaphE+2ZLLK4puvEp0fUDlzHA0lP2PAtFEyegiwCltCgm8mBK+6RmPBoxGQw4WDUeSdgohDgd5TDVtw3S6JJQ9hRJUGxmGCiEwrsYPENFN09L3gdFkSMoDIfGTXTv0JSWOhuB7BrVCWehiwllXsHcWaw3OCPeytRUZS+eFg8Boh44KFRR3bmxz5cOb+M6ysrpMSp520VBXQu5378YWh7v7/Ln1v4DvPabS2Dpj6Dl75jSb1w7Y29vFx8gnV24zHg8Zrw5ZOb107L35bARIYfcQMZyMRvPcmTNkXZgHMzhX42MSepyc0ToxrC3PnznN1Q/ukbNjMB5TDTLdnmc0mtL1PSn3uKoW1hIfha0vG4bVkIGGUe1EEySAUZb9wwW7hw2JTFVVdPhSySnl3ZRIWjbvMUVC8DTNIb3vWT+1KpWuUokqs8CgNdNTp9jdvE/wnuw7Tq+usDCZefKgLD6VChZF88ToMrdyVCJND0rPMQv8XynLYFiLjoZWDxStCgqz9BfkdQgC8xhUFd53eC/s6QIolBXkaCzZ+56qcnR9V1C56oGuu6ulbBpjYjgaYYxmaXla9Fuk8hX6XobBGs/Ijhgsjdjv79GmhrquRdVWW7QWnZQYhGfAmgF9n4XxMJZeR9bolLl66Tr7O3t0XabvOqxzdO0+A4NUt0IQmMmB55OrlxlNa86eX2P3YE5tZsyWp2zd28PHwN7ePtvb27yQN5iORsfem89EgEjJUTEajUT0RWtuXr+OXV9jOKolVbJGiABipDaGycAwBHLf44zGugHT6Yw+9NjWUI8G8kQtEtB9E5hOpywtrbC3vc1B3xKI1LMpZlSz6DoOu55FTpjZhOWqIikRgDmasz4iVhD8ESJREGXVk/QkylBWNZBuOUdCnAY7WmLpuQ3uX7/Fx5ffY/3MBude/w6qmnF3Pqf1HmMjrpbavLVGYBtZPhukMqSPmqMJQuxJyTIcDEtlrOiTczSv/3DrKBzAmWQio+GQRV4URnyQlSY/qG41zYLV1VW6riXHVGDoEHx4wG2ccsZV0muoRzWLgwatLF0IZEQ6Ymdzm2a/ZeP8Gr09xNSFTT1lgYtoiwKCjxhdkVPhOcsiMpSjojIV9+/e4fPPbkOfmQyGOJPpuoZu0VGPR5A1s6UZyiT+6H/+iDtbW2QV+eD/VPS+YTKeoc2Mza1dFq00jFemUy5/9Cntt2EmHaVQVS0PWyWUlJc/vkSzv88bb75W5jAyla1QqUflHhUdORlmoyWm9RSvRhzMF3T9AYNKY1THeJA5NVnhcL+naTt27t2lLzT42UEcV+TlMTvZM+8O6GJmMJ2hrEgmHI326vLUVsqirJSZc4hk/xC7NRgM8drIU9Y6jqYbMVlgkcYyXdtARWgW+9xf7OJvXcPOlmkztL2nVokcYtE7SSiVqQeOrhNgpNYyJwOit5EBHz06O1xVC24ryAb7aOU9IrEQuQYhtRuPRwTvaJtO+hpf+HOE2BNij9KlDpXkc0IMeN+W1SQSFFS1AiXpHUqOV8ZyuHPAxx98ADmx/uKYRE9la1RWImhaZn9ypJCAD/B9IgZEwVYL/EVnOJjPCW1HlTR19Dy3NCSbmo+vHFCZGWg4/+o5xjPFH/7oHXJSrCxvsLszJzNkZ7HA5wX7C2haxcIoum6f1aURe7vNsbfmsxEgSCYSY8Rq0QN87TuvoXNmf3ef8WwkOthRYA5aSVrg7BDfbzMYVhzuHRBi5OzpFdY2xjSLfZzOPLcy4523P6XTkjPfv3mNl145z/LaGLta0TiBZzfJMhiN0FVFVtJ0EqlhRcwamZCS/UdInqQkEJIOoA2jcc2+9+QkVSKOqjKmQhmNGVr0aMTK0oxm9z7ZQK4tB+0+fZLR3hAyCalG5RxFLRYR1JE5i3KhKGW1ch7vRb7BuQooUgAcBcZRCVIBhpSkc13XlfRLchYOY1LpVwS0UrRtgzlK8wrPsdKZrm8Akc921lAPHE27kABMEYOQURzsN0Qy5158jk61DEc1RjnBT0UprR5RNFXVAKMdXZnXAC0rl7PYrFhanmBqMAFOLU/5zovnub21w8pkysbpNQJ7uKnn7Cvr/Kl4katXtphO1qncEI0h6cStO3fwfUMfBC3QtAsygfnht4H2J2dikI1nnyNWK8698DykyM72XQbDiq5t6XzHdDqiqi0mOz69/Bm3b22iXWa2XLG8vMzSpOb1N85COmDoEil07O1OWJob9g6GdNlz8bXnefHCOXIdCFYgFj5pxroiR03UCrk0khtrlQk5IKzsHjLU1YCsPXqoWNQVRBnVdbaicgMZ0DlqZiahN1XGgoGqXqXtDlmEnj4EeYIr8UPpCqWPIDWpoGcVtnYyzVc2/koJDkywXVkAhQjU3KhE3/tCu5rJ+SGwMMZE3/eMRhXD0YDDw0YwcElK6EJ9FOi6lslEMF7RJ6Cj7ztGo/pBc1EbqAeO+f4cwwSQbVdo5RpdeP0Cs1OOXEWUM1RmKIK21srvmxWuGmB0xeGhxwfRdNFKyr3OGRYHh6yfWeH8a2d4/dzLTHDoUFNZx3Rc41zD+ZfWOLVRE+2Cl197jo0zGywOYL66T/KGys04XES2d2+RVcdh2+JDS0gN47o+9t58JgJEkYXRqwiq9ClhtKBJZytLbG1uUlWOwaCmmS9oYmDPBy6/fZn51gJXjRnPxtzb2yTmEVGtMawDozpireMHv/rLXL2xx/Vbd7HTEWunl1mEA1ZOrRGt47DxOOdEM1Ah2hOmqM8aTSSig2i5VzisElHIoBXKamxdkZtE5aqCPBbhyZRLv0ELXEXpxMJ7DroDYuzIKgl95hFQs0C+hXy7TI+Vm7aqHCFEWR3KGEDOZQ+hpRHX9R3WCErW+76ACAtFTuH/SvqhbF1dD2jbToKNh8NDAiURxvzKORZ9Kz+bAzHKAJIxGaOL5krMMtBUztsW5OxsZYKqW0aTIUZZ2Yu5IrmdQRdJPR/BJ0lFU4LKOrKGrm8JvsE5y8U/+RKnp6doNxd8/M417m7vc9gdcP7CChcvnoVRw2E/BwvTlVWmk4oXzy7TzCO3b7esr6+zszig2b6DQ5E6R9dF1mbTY+/NZyJA4GgCVsqpSiSKZA9gNMtry4wGQ8iJe3fu4g8Ooe0499yLXH7vJwTdE29uAZnhwNCrhl++uMbspVWyExqY+XyT5XXhWoo64UNHtooUA92iQeuKRejBGkE+lilCgVkIIZDWiujLHsEJbsgThBanWaCSbHaFAUKhsnSRjdIkIr1vaNp9+tCAkolGY6URJ0XWWLBmPJy246EufF1VKDzBC/ZLGxkWkwAQzq/et4xGIyaTEX3by6hyacSKyee1bYtzjsFgIKsN8UHVS2VhhenbjtFk9ACtS05E76kmUyBijSh2OetEztpouk66+G6koQoYqxiYSlgQs34QzCiLUg4fYP+wJWUrfABapv9iDrRdi1KRJnoOW084iNz+aIsrl2/TBUVVG95970N2D2/xZ/78WyRnaDu4v7vDyIw5f3rE0ukZly/fAjKz2RLjpiGpQGUTVc6sr3wbaH/ggTKq1ZKDkwTyXQ8cphLqUa0MZ184xyBGlgYDPnrnNlQDDptA7wOVc6jg+OCjG1SxZVpZzHDAdKS4ePEC+9ToYU1PQFWaiKFrG7QBN3SgHcoagX4DOskzVWlNpihYOYuyNSF3ZI3k/gNHGzw6aUwhoMAIP5Q1Bk3G9wu6bp8QWhkVVRRhS03wAqnP6qGC1hG5HRSsV/BCbl1XAHIjK/WgA02WkdQYI8F7xuOJ9GN8YWcpHXaVZaVKSVKt4XBIzqIAC1LKjlGRYiLb/CBIjxjxgw9lbxOpnMycGGWorKHtelTWVHWNdh5Uy3Q8YqAcaEtEE5USBpKsIVuapiMkES7KWUQ+dYLYB+EhM4FB5VgZr3P/6pyPL91g77BDVQNGoxmHzT3ubG6KrLc1hJDYunuf1YEnrlg2793mcH/Ozm5LjJnajdmLe9TG8erzG7z+0gv8wULx0bkAAARiSURBVDH35TMRIJkCzNWQYkQV/I7RVrasClCZbASmnHLg3s4WO/P7JAvRUKAXEdVFUsxs3d7jQ3WNUFlePLvG6TNn0YMhOJnHIGt8F2m7jvF0Qq8l51fGFQhFlI1poQZCqzJYJSjblESJNiuhwIkpEDxUKWIpvMDlt4sh0PdCRm2tRqkaCFgL1gmlTUiKEFXpdZSVCFW0UGSF6bqOwWAo/ZnOcxRI6sGEnKxcMUS6pqGqKiHhfqROpY0ufQfhuKqqqoAwHxUmekiHFGMqTT1d+h8P2SlRsq+x1hXiOFUGwxy2Fom62lWYJHM2SScRII2K2srMumTWlqwMTjucq8ghEBtBJgt1OWxv7vHuTz5id75PwOH7jkHXMtKaN//Ed7CV5jD2uHrCcxtrrNgJtXNc/uwa77//EfcXluxqmgjWOsZDx8ZzK5xeO17+QB2Njz5NU0rdBQ6B46m2n5yt8ez4Aif+HGffhC8v5pzXv+yNZyJAAJRSP845f+9p+wHPli9w4s9x9rh9+dp5kBM7sf+f7SRATuzEjrFnKUD+xdN24BF7lnyBE3+Os8fqyzOzBzmxE3sW7VlaQU7sxJ45OwmQEzuxY+ypB4hS6q8opS4ppX5aKEyfhg/XlFLvKaXeVkr9uBxbVUr9Z6XUlfJ15TGe//eUUltKqfcfOfal5y9sMf+sXK93lVLffQK+/K5S6la5Pm8rpX74yHt/v/hySSn1l79JX8rnv6CU+m9KqQ+VUh8opf5uOf5krs+jYphP+j+iqPIJ8ApQAe8Abz4FP64Ba1849o+B3y6vfxv4R4/x/L8GfBd4/+vOjzDG/EcEv/4rwB8+AV9+F/h7X/K9b5a/WQ28XP6W5hv25wzw3fJ6Clwu530i1+dpryDfB36ac/4059wDv49w+z4L9lXcw9+45Zz/O3D/j3n+n5/7+Of35avsrwG/n3Pucs5XEaqn739TvhR/nhw39JfY0w6Qr+LxfdKWgf+klPpJ4QyGr+YeflL2s3IfP277OyVl+b1H0s0n6ssvyA39c9nTDpBnxX6Qc/4uIt3wW0qpX3v0zSxr91Orhz/t8yMSFheAtxAS8n/ypB34Ijf0o+89zuvztAPkZ+fxfQyWc75Vvm4Bf4CkCZtHS/MXuIeflH3V+Z/4Ncs5b+acYxaKlX/JwzTqifhyHDd0ef+xXZ+nHSB/BFxUSr2slKqAX0e4fZ+YKaXGSoSBUEqNgb8EvM9Xcw8/KfvmuI9/QftCDv/Xketz5MuvK6VqpdTLiGjS//qGz/34uaGPs8dVmfkZqhQ/RCoTnwC/8xTO/wpSiXkH+ODIB+AU8F+BK8B/AVYfow//BkldPJIz/+ZXnR+pzvzzcr3eA773BHz51+Vc75Yb8Mwj3/87xZdLwF99DNfmB0j69C7wdvn/wyd1fU6gJid2YsfY006xTuzEnmk7CZATO7Fj7CRATuzEjrGTADmxEzvGTgLkxE7sGDsJkBM7sWPsJEBO7MSOsf8Le0FZmTO68LcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hr1jsQUxiXqc",
        "outputId": "3d1fe714-95f4-4aaa-adc9-98ab86f05874",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-6eb77a7cb3fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mcreate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_width\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mimage_height\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;31m#Input layer, each neuron gets exactly 1 image (batch size of 32 images)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"relu\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'same'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaxPool2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpool_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'image_width' is not defined"
          ]
        }
      ],
      "source": [
        "def create_model(input_shape=(image_width,image_height,3)):\n",
        "    model = Sequential()\n",
        "    #Input layer, each neuron gets exactly 1 image (batch size of 32 images)\n",
        "    model.add(layers.Conv2D(32, (3, 3), activation=\"relu\", input_shape=input_shape, padding='same'))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2, 2)))\n",
        "\n",
        "    model.add(layers.Conv2D(64, (3, 3), activation=\"relu\", padding='same'))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2, 2)))\n",
        "\n",
        "    model.add(layers.Conv2D(128, (3, 3), activation=\"relu\", padding='same'))\n",
        "    model.add(layers.MaxPool2D(pool_size=(3, 3)))\n",
        "    \n",
        "    model.add(layers.Flatten())\n",
        "    model.add(layers.Dense(120, activation='relu'))\n",
        "    model.add(layers.Dense(60, activation='relu'))\n",
        "    model.add(layers.Dropout(rate=0.2))\n",
        "    model.add(layers.Dense(325, activation='softmax'))\n",
        "\n",
        "    return model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g-xO1r9_wJIT"
      },
      "outputs": [],
      "source": [
        "def compile_model(model):\n",
        "  model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=\"accuracy\")\n",
        "  return model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_training_curves(history):\n",
        "    # Training curves\n",
        "    import matplotlib.pyplot as plt\n",
        "\n",
        "    history_dict = history.history\n",
        "    loss_values = history_dict['loss']\n",
        "    val_loss_values = history_dict['val_loss']\n",
        "\n",
        "    epochs_x = range(1, len(loss_values) + 1)\n",
        "    plt.figure(figsize=(10,10))\n",
        "    plt.subplot(2,1,1)\n",
        "    plt.plot(epochs_x, loss_values, 'bo', label='Training loss')\n",
        "    plt.plot(epochs_x, val_loss_values, 'b', label='Validation loss')\n",
        "    plt.title('Training and validation Loss and Accuracy')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "    plt.subplot(2,1,2)\n",
        "    acc_values = history_dict['accuracy']\n",
        "    val_acc_values = history_dict['val_accuracy']\n",
        "    plt.plot(epochs_x, acc_values, 'bo', label='Training acc')\n",
        "    plt.plot(epochs_x, val_acc_values, 'b', label='Validation acc')\n",
        "    #plt.title('Training and validation accuracy')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Acc')\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "GCwuLRML-e2P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9O1t48WkwOrS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9dc6079-f133-40cc-8322-660944074e66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "1480/1480 [==============================] - 157s 99ms/step - loss: 5.7311 - accuracy: 0.0213 - val_loss: 4.9139 - val_accuracy: 0.0578\n",
            "Epoch 2/2\n",
            "1480/1480 [==============================] - 144s 97ms/step - loss: 4.5488 - accuracy: 0.0972 - val_loss: 3.6531 - val_accuracy: 0.2111\n",
            "INFO:tensorflow:Assets written to: tf_cnn_bird.model/assets\n"
          ]
        }
      ],
      "source": [
        "model = create_model((image_width, image_height,3))\n",
        "model = compile_model(model)\n",
        "\n",
        "history = model.fit(train_ds, validation_data=val_ds, shuffle=True, epochs=20)\n",
        "model.save(\"tf_cnn_bird.model\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_training_curves(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "IneYy7odAh9B",
        "outputId": "9de95369-fafd-40cc-e340-0bc7b1b1fbaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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njviaIUOGMDWY3Ddv3jyaNWtGo0aN2LhxI927d+eOO+6gT58+rFu3jk2bNtGyZUuuu+46vv/977NixYpKfw9lUXATERGppcaNgylToF07MPPfp0yp+tGj22+/nZ/97GdkZWVVeg8ZwAknnMBDDz3E+eefT+/evcnIyKBx48blvmbixIksX76cHj16cOedd/Lkk08CcP/993PWWWfRo0cP6tWrxwUXXMC8efPo2bMnWVlZPPfcc/zoRz+q9PdQFotdfVGTZWdnu5ycnKjLEBERqXLvvvsuXbt2jbqMyO3atYv09HScc9x000106tSJW265Jeqyvqa082Vmy51zpa6Loh43ERERqXEeeeQRMjMz6datG9u3b+eGG26IuqRKoatKRUREpMa55ZZbErKH7Xipx01EREQkSSi4iYiIiCQJBTcRERGRJKHgJiIiIpIkFNxERESk0owYMYLXX3/9sLb777+fCRMmlPma4cOHE1u268ILL2Tbtm1f22bixIlMnjy53GNPnz6dtWvXHvr5l7/8JbNmzTqa8ks1b968I96kvroouImIiEilGTt2LNOmTTusbdq0aRW60TvAjBkzOPHEE4/p2CWD27333suoUaOOaV+JSsFNREREKs2YMWP45z//yf79+wHIy8vj008/ZciQIUyYMIHs7Gy6devGPffcU+rr27dvz5dffgnApEmTOOOMMxg8eDDr168/tM0jjzxCnz596NmzJ9/4xjfYs2cPixcv5pVXXuGnP/0pmZmZbNy4kfHjx/Piiy8CMHv2bLKysujevTvXXnst+/btO3S8e+65h169etG9e3fWrVtX7vvbunUrl156KT169KB///6sWrUKgPnz55OZmUlmZiZZWVns3LmTzZs3M3ToUDIzMznrrLNYuHDh8X24aB03ERGRGuvHP4bc3MrdZ2Ym3H9/2c83adKEvn378tprr3HJJZcwbdo0vvnNb2JmTJo0iSZNmnDgwAHOPvtsVq1aRY8ePUrdz/Lly5k2bRq5ubkUFxfTq1cvevfuDcDll1/OddddB8DPf/5zHnvsMX74wx8yevRoLr74YsaMGXPYvgoLCxk/fjyzZ8/mjDPO4Oqrr+bhhx/mxz/+MQDNmjVjxYoVPPTQQ0yePJlHH320zPd3zz33kJWVxfTp05kzZw5XX301ubm5TJ48mT//+c8MGjSIXbt2kZqaypQpUzjvvPO4++67OXDgAHv27Dmaj7pU6nETERGRShU/XBo/TPr888/Tq1cvsrKyWLNmzWHDmiUtXLiQyy67jLS0NBo1asTo0aMPPffOO+8wZMgQunfvztSpU1mzZk259axfv54OHTpwxhlnAHDNNdewYMGCQ89ffvnlAPTu3fvQjenLsmjRIq666ioARo4cSUFBATt27GDQoEHceuutPPDAA2zbto26devSp08fHn/8cSZOnMjq1avJyMgod98VoR43ERGRGqq8nrGqdMkll3DLLbewYsUK9uzZQ+/evfnwww+ZPHkyy5Yt46STTmL8+PEUFhYe0/7Hjx/P9OnT6dmzJ0888QTz5s07rnobNGgAQEpKyjHf9P7OO+/koosuYsaMGQwaNIjXX3+doUOHsmDBAv75z38yfvx4br31Vq6++urjqlU9biIiIlKp0tPTGTFiBNdee+2h3rYdO3bQsGFDGjduzOeff85rr71W7j6GDh3K9OnT2bt3Lzt37uTVV1899NzOnTs55ZRTKCoqYurUqYfaMzIy2Llz59f21blzZ/Ly8tiwYQMATz/9NMOGDTum9zZkyJBDx5w3bx7NmjWjUaNGbNy4ke7du3PHHXfQp08f1q1bx6ZNm2jZsiXXXXcd3//+91mxYsUxHTOeetxERESk0o0dO5bLLrvs0JBpz549ycrKokuXLrRp04ZBgwaV+/pevXpx5ZVX0rNnT1q0aEGfPn0OPXfffffRr18/mjdvTr9+/Q6FtW9961tcd911PPDAA4cuSgBITU3l8ccf54orrqC4uJg+ffpw4403HtP7mjhxItdeey09evQgLS2NJ598EvBLnsydO5c6derQrVs3LrjgAqZNm8bvf/976tWrR3p6Ok899dQxHTOeOeeOeyeVyczygJ3AAaDYOZdd4vnhwMvAh0HT351z95a3z+zsbBdbH0ZERKQme/fdd+natWvUZUgFlXa+zGx5yfwTk6g9biOcc1+W8/xC51xirIQnIiIiUk00x01EREQkSSRicHPAv81suZldX8Y2A8xspZm9ZmbdStvAzK43sxwzy9myZUvVVSsiIpJgEm0alJTuWM5TIga3wc65XsAFwE1mNrTE8yuAds65nsCfgOml7cQ5N8U5l+2cy27evHnVViwiIpIgUlNTKSgoUHhLcM45CgoKSE1NParXJdwcN+fcJ8H3L8zsH0BfYEHc8zviHs8ws4fMrNkR5sSJiIjUCq1btyY/Px+NNiW+1NRUWrdufVSvSajgZmYNgTrOuZ3B43OBe0tsczLwuXPOmVlffK9hQfVXKyIiknjq1atHhw4doi5DqkhCBTegJfAPMwNf27POuX+Z2Y0Azrm/AGOACWZWDOwFvuXUHywiIiK1QEIFN+fcB0DPUtr/Evf4QeDB6qxLREREJBEk4sUJIiIiIlIKBTcRERGRJKHgJiIiIpIkFNxEREREkoSCm4iIiEiSUHATERERSRIKbiIiIiJJQsFNREREJEkouImIiIgkCQU3ERERkSSh4CYiIiKSJBTcRERERJKEgpuIiIhIklBwExEREUkSCm4iIiIiSULBTURERCRJKLiJiIiIJAkFNxEREZEkoeAmIiIikiQU3ERERESShIKbiIiISJJQcBMRERFJEgpuIiIiIklCwU1EREQkSSi4iYiIiCQJBTcRERGRJKHgJiIiIpIkFNxEREREkoSCm4iIiEiSUHATERERSRIKbiIiIiJJQsFNREREJEkouImIiIgkCQU3ERERkSSh4CYiIiKSJBTcRERERJKEgpuIiIhIklBwExEREUkSCm4iIiIiSULBTURERCRJKLiJiIiIJAkFNxEREZEkoeAmIiIikiQU3ERERESShIKbiIiISJJIuOBmZnlmttrMcs0sp5TnzcweMLMNZrbKzHpFUaeIiIhIdasbdQFlGOGc+7KM5y4AOgVf/YCHg+8iIiIiNVrC9bhVwCXAU85bApxoZqdEXZSIiIhIVUvE4OaAf5vZcjO7vpTnWwEfx/2cH7QdxsyuN7McM8vZsmVLFZUqIiIiUn0SMbgNds71wg+J3mRmQ49lJ865Kc65bOdcdvPmzSu3QhEREZEIJFxwc859Enz/AvgH0LfEJp8AbeJ+bh20iYiIiNRoCRXczKyhmWXEHgPnAu+U2OwV4Org6tL+wHbn3OZqLlVERESk2iXaVaUtgX+YGfjannXO/cvMbgRwzv0FmAFcCGwA9gDfjahWERERkWqVUMHNOfcB0LOU9r/EPXbATdVZl4iIiEgiSKihUhEREREpm4KbiIiISJJQcKsEu3fDwYNRVyEiIiI1nYJbJfj1r6FFC7jySnj0UcjLi7oiERERqYkS6uKEZDV0KOTnw8yZ8Pzzvu300+Gcc/zXiBFw4onR1igiIiLJz/xFmjVbdna2y8nJqfLjOAfvvusD3MyZMG+eH0atUwf69AmDXP/+UL9+lZcjIiIiScjMljvnskt9TsGt6uzfD2+9FQa5pUv9XLiGDWHYsDDInXkm+KXrREREpLZTcIsouJW0bZvvhYsFufff9+2nnAKjRvkQN2qU/1lERERqJwW3BAluJW3a5APcrFn+q6DAt591Vhjihg3zPXQiIiJSOyi4JWhwi3fwIOTmhkFu4ULYtw/q1YOBA8Nh1d69ISUl6mpFRESkqii4JUFwK2nvXli0KBxWzc317SeeCCNHhkHutNOirVNEREQqV3nBTcuBJKgTTgjDGcCWLTB7dhjk/v53396hQzg/buRIaNo0uppFRESkaqnHLQk5B++954dUZ86EuXNhxw5/ZWrv3mGQGzQIGjSIuloRERE5GhoqrWHBraTiYr/USCzILVni2044wS8OHLvQoUcPLTsiIiKS6BTcanhwK2nnTr/sSCzIvfuub2/R4vBlR1q3jrRMERERKYWCWy0LbiXl54chbtYs+OIL396lSziPbvhwyMiItEwRERFBwa3WB7d4zsHq1eFFDgsW+CtY69aFfv3CINe3r28TERGR6qXgpuBWpsJCePPNMMgtX+7DXaNGvhcuFuTOOEPz40RERKqDgpuCW4UVFMCcOeHQ6ocf+vY2bcK5caNGQfPm0dYpIiJSUym4Kbgds40bw7lxs2f7+60CZGaGvXGDB/srWEVEROT4RRLczKwhsNc5d9DMzgC6AK8554qq5IDlUHCrHAcO+KHUWJB74w0oKvJrxQ0eHAa5zEyoUyfqakVERJJTVMFtOTAEOAl4A1gG7HfOjauSA5ZDwa1q7N7tL26IBbnVq31706Zw9tlhkGvXLto6RUREkklUt7wy59weM/se8JBz7ndmlluFx5Nq1rAhXHCB/wL47LNwbtzMmfD88769U6dw/bgRI/z9VkVEROToVWlwM7MBwDjge0FbShUeTyJ28snwne/4L+dg7dowyD31FDz8sB9C7ds3DHL9+0P9+lFXLiIikhyqcqh0GPAT4A3n3G/NrCPwY+fczVVywHJoqDR6+/f7W3HFgtzSpXDwoO+1iy07MmoUnHmmlh0REZHaLfKrSs2sDpDunNtR5QcrhYJb4tm2DebODefHvf++bz/11MNvy3XyydHWKSIiUt2iujjhWeBG4AD+woRGwP84535fJQcsh4Jb4tu0KZwbN3u2X08O4Kyzwoschg71PXQiIiI1WVTBLdc5l2lm44BewJ3Acudcjyo5YDkU3JLLwYOQmxsGuUWLYN8+qFcPBg4Mg1zv3pCiWZMiIlLDRBXc1gCZwLPAg865+Wa20jnXs0oOWA4Ft+S2d68Pb7Eglxtcm3zSSTByZDi0etpp0dYpIiJSGaJaDuR/gTxgJbDAzNoBkcxxk+R2wglhLxvAF1/44dTYhQ4vveTbO3QItxs5Epo0ia5mERGRqlCtt7wys7rOueJqO2BAPW41l3Pw3nvhRQ5z58KOHf7K1N69wyA3cKC/w4OIiEiii2qotDFwDzA0aJoP3Ouc214lByyHglvtUVzslxqJBbklS3zbCSf4ixtiQa57dy07IiIiiSmq4PYS8A7wZNB0FdDTOXd5lRywHAputdeOHTB/fjg/bt06396iRTg37pxzoFWraOsUERGJifSq0iO1VQcFN4nJzw/nxs2a5efLAXTtGga54cMhIyPSMkVEpBYrL7jVqcLj7jWzwXFFDAL2VuHxRI6odWsYPx6mToXNm2HlSpg8Gdq2hUcfhdGj/UUNQ4bAvffC4sV+qFVERCQRVGWPW0/gKaBx0PQVcI1zblWVHLAc6nGTiigs9EEt1iO3fLm/+KFRIxgxIhxW7dRJ8+NERKTqRHrLKzNrBOCc22FmP3bO3V+lByyFgpsci4ICmDMnnB+Xl+fb27QJQ9zZZ0Pz5pGWKSIiNUzk9yqNK+Qj51zbajtgQMFNKsPGjWGImzPH328VIDMzDHKDB/srWEVERI5VIgW3j51zbartgAEFN6lsBw74odRYkFu8GIqK/FpxgweHQS4zE+pU5UxSERGpcRIpuKnHTWqk3bthwYIwyL3zjm9v1swPp55zjr9qtV27aOsUEZHEV61XlZrZTjPbUcrXTuDUyj6eSCJo2BAuuAD+8AdYvRo+/RSefhouvNAHuu9/H9q3hzPOgJtugunTw6FWERFJbFOn+v+H16njv0+dGl0t1drjFhX1uEmUnIO1a8O14+bN8z10depA377hsGq/flC/ftTViohIvKlT4frrYc+esC0tDaZMgXHjquaYCTNUGhUFN0kk+/f7W3HFgtzSpXDwIKSnw7BhYZDr2lXLjoiIRK19e9i06evt7dqFqw1UNgU3BTdJYNu2wdy54fy4DRt8+6mnhndzGDUKTj452jpFRGqjOnX8yElJZv4f3VVBwU3BTZJIXl64CPDs2X49OYDu3cMQN3Son1cnIiJVSz1uEVBwk2R18CDk5oa9cYsWwb59fi7cwN7UCQsAACAASURBVIFhkOvdG1JSoq5WRKTm0Ry3IzCzFCAH+MQ5d3GJ58YDvwc+CZoedM49eqR9KrhJTbF3LyxcGPbI5eb69pNOgpEjw/lxHTtGW6eISE0ydSrcfTd89JG/t/WkSVUX2iD5gtutQDbQqIzglu2c+8+j2aeCm9RUX3zhh1NjPXL5+b69Q4cwxI0cCU2aRFuniIhUXLWu43Y8zKw1cBFwxF40EYEWLWDsWPjrX/2/BNetgz/9yc+H+9vf4Ior/CLAffrAXXf5iyD27Yu6ahEROVYJ1eNmZi8C/w1kALeV0eP238AW4D3gFufcx2Xs63rgeoC2bdv23lTazEKRGqy42C81EuuNW7LE36orLc1f3BC7YrV7dy07IiKSSJJiqNTMLgYudM79wMyGU3pwawrscs7tM7MbgCudcyOPtG8NlYrAjh0wf34Y5Nat8+0tWx6+7EirVtHWKSJS2yVLcPtv4CqgGEgFGgF/d859p4ztU4CtzrnGR9q3gpvI1+Xnhxc5zJrl58uBX/g3Nj9u2DDIyIi2ThGR2iYpglu8cnrcTnHObQ4eXwbc4Zzrf6T9KbiJlO/gQX+P1ViIW7DAX8Faty707x8GuT59fJuIiFSdpA5uZnYvkOOceyXolRuN75XbCkxwzq070v4U3ESOTmEhLF4cDquuWOFXDm/UCEaMCINcp06aHyciUtmSLrhVNgU3keNTUABz5oRBLrZaeNu24dy4s8+G5s0jLVNEpEZQcFNwE6k0zsEHH4Qhbs4cf79VgKysMMgNHgwnnBBtrSIiyUjBTcFNpMocOADLl4dBbvFiKCqC1FQf3mJBLjPT36xZRETKp+Cm4CZSbXbt8rfligW5d97x7c2a+eHU2Py4tm2jrVNEJFEpuCm4iURm8+bDlx3ZvNm3d+oUhrgRI6DxERf2ERGpHRTcFNxEEoJzsHZt2Bs3fz7s3g0pKdC3b7gQcP/+UK9e1NWKiERDwU3BTSQh7d/vb8UVC3LLlvk15dLTYfjwcH5c165adkREag8FNwU3kaTw1Vcwb14Y5DZs8O2tWoW9cWefDSefHGmZIiJVSsFNwU0kKeXlhfPjZs/268kBdO8ezo8bMgQaNoy0TBGRSqXgpuAmkvQOHoS33w6D3KJFsG8f1K8PAweGQa5XLz9nTkQkWSm4KbiJ1Dh79vjwFhtWXbnSt590kh9OjQ2tduwYbZ0iIkervOCm20WLSFJKS4Nzz/VfAF984YdTY0HuxRd9e8eO4UUOI0dCkybR1SwicrzU4yYiNY5z8N57YYibOxd27vRXpmZnh0Fu4EBo0CDqakVEDqehUgU3kVqtqMgvNRILckuW+Ft1paXB0KHh/LizztKyIyISPQU3BTcRibNjh192JHahw7p1vr1ly3Bu3KhRfhkSEZHqpuCm4CYi5fj4Yx/iYl9ffOHbzzwzDHLDhkFGRrR1ikjtoOCm4CYiFXTwIKxeHQ6rLlgAhYVQty4MGBAGuT59fJuISGVTcFNwE5FjVFgIixeHQW7FCn/xQ+PGMGJEOKzaqZPmx4lI5VBwU3ATkUpSUABz5oRBLi/Pt7dtG17kMHIkNG8eaZkiksQU3BTcRKQKOAcbN4YXOcyZA9u2+eeyssIgN3gwpKZGW6uIJA8FNwU3EakGBw5ATk4Y5BYv9kuRpKb68BYLcj17Qp06UVcrIolKwU3BTUQisGuXv7hh5kwf5t55x7c3b374bbnato22ThFJLApuCm4ikgA2bw5742bN8j8DnHFGeJHDiBH+wgcRqb0U3BTcRCTBOAdr14YXOcyfD7t3Q0oK9O0bBrn+/aFevairFZHqpOCm4CYiCW7/fn8rrliQW7bMrymXng7Dh4fz47p00bIjIjWdgpuCm4gkma++grlzw6HVDRt8e6tWh9+Wq2XLaOsUkcqn4KbgJiJJLi8vnBs3e7ZfTw6gR48wyA0dCmlpkZYpIpVAwU3BTURqkIMH4e23wyC3aBHs2wf168OgQWFvXK9efs6ciCQXBTcFNxGpwfbs8eEtNj9u5Urf3qSJv4tDLMh17BhtnSJSMeUFN90iWUQkyaWlwbnn+i+Azz8//LZcL77o2zt2PPy2XCedFF3NInJs1OMmIlKDOQfr14cXOcydCzt3+js39O4dBrkBA6BBg6irFRHQUKmCm4hIoKgIli4Ng9ySJf5WXWlpMGxYeKHDWWdp2RGRqCi4KbiJiJRqxw6YNy+80GHdOt9+8sk+xMWC3KmnRlqmSK2i4KbgJiJSIR9/fPhtubZs8e1nnhle5DBsGGRkRFunSE2m4KbgJiJy1A4ehNWrw4scFiyAwkKoW9fPiYvNj8vO9m0iUjkU3BTcRESOW2EhvPFG2CO3YoW/+KFxYxgxIgxyp5+u+XEix0PBTcFNRKTSffmlX3YkFuTy8nx7u3bh3Lizz4ZmzSItUyTpKLgpuImIVCnnYOPGcG7cnDmwbZvvecvKCoPc4MGQmhp1tSKJTcFNwU1EpFoVF8Py5WGQW7zYL0WSmgpDhoQXOvTs6deUE5GQgpuCm4hIpHbt8hc3xC50WLPGtzdv7odTY/Pj2rSJtk6RRKDgpuAmIpJQNm8O58bNnAmffebbzzgjDHHDh/sLH0RqGwU3BTcRkYTlnO+BiwW5+fNh925ISYG+fcMg168f1KsXdbUiVU/BTcFNRCRp7N8Pb74Zzo9btsyvKZeR4XvhYhc6dOmiZUekZlJwU3ATEUlaX30Fc+eGQW7DBt/eqlV4kcOoUdCyZbR1ilQWBTcFNxGRGiMvL5wbN3s2bN3q23v0CIdVhwyBtLRIyxQ5ZgpuCm4iIjXSwYPw9tthkFu0yA+11q8PgwaFQS4ry8+ZE0kGSRfczCwFyAE+cc5dXOK5BsBTQG+gALjSOZdX3v4U3EREaoc9e3x4iwW5lSt9e5MmMHJkGOQ6dIi2TpHylBfcEvW2wD8C3gUalfLc94CvnHOnm9m3gN8CV1ZncSIikpjS0uDcc/0XwOef++HU2BWrL77o2087LbzIYeRIOOmk6GoWORoJ1+NmZq2BJ4FJwK2l9Li9Dkx0zr1pZnWBz4Dmrpw3oh43ERFxDtavDy9ymDsXdu70d27Izg6D3IAB0KBB1NVKbZZUQ6Vm9iLw30AGcFspwe0d4HznXH7w80agn3PuyxLbXQ9cD9C2bdvemzZtqo7yRUQkSRQVwdKl4bDqW2/BgQO+127YsHBYtVs3LTsi1au84JZQd4gzs4uBL5xzy493X865Kc65bOdcdvPmzSuhOhERqUnq1fMXMEycCG+84a9OfflluPZa+OADuPVW6N4dTj0VrroKnnoKPv006qqltku0OW6DgNFmdiGQCjQys2ecc9+J2+YToA2QHwyVNsZfpCAiInLMGjWC0aP9F8DHH4dz415/HZ55xrefeWbYGzdsGKSnR1ez1D4JN1QaY2bDKX2o9Cagu3PuxuDihMudc98sb1+a4yYiIsfj4EFYtSoMcgsWQGGh77UbMCCcH5edDXUTrUtEkk5SzXGLiQ9uZnYvkOOce8XMUoGngSxgK/At59wH5e1LwU1ERCpTYaEfXo1d6LBihb/4oXFjf5VqLMidfrrmx8nRS8rgVpkU3EREpCp9+SXMmRNe6BC7Hq5du3BYdeRIaNYs2jolOSi4KbiJiEg1cQ42bgxD3Jw5sH2773nLygqD3KBBkJoadbWSiBTcFNxERCQixcWwfHkY5N580y9Fkprq76kaC3I9evg15UQU3BTcREQkQeza5S9uiAW5NWt8e/PmcPbZYZBr0ybaOiU6Cm4KbiIikqA+/dRf4BC7YvWzz3x7587hRQ4jRvjlSqR2UHBTcBMRkSTgnO+Bi12tOm8e7NkDKSnQr58PcaNG+cf16kVdrVQVBTcFNxERSUL79/s5cbFh1Zwcv6ZcRgYMHx4Oq3burGVHahIFNwU3ERGpAb76CubODYPcxo2+vXXrcFh11Cho0SLaOuX4KLgpuImISA304Yfh3LjZs/39VsFfoRrrjRsyBNLSoq1Tjo6Cm4KbiIjUcAcOQG5u2Bu3aJEfaq1fHwYPDnvksrL8nDlJXApuCm4iIlLL7NkDCxeGPXIrV/r2Jk3CZUdGjYIOHaKtU76uvOCmW+GKiIjUQGlpcN55/gvg88/9cGqsR+6FF3z7aaeFw6ojRsBJJ0VXsxyZetxERERqGedg/fowxM2bBzt3+js3ZGeHQW7AAD/UKtVLQ6UKbiIiImUqKoKlS8Mg99Zbfs5cWhoMGxYGuW7dtOxIdVBwU3ATERGpsO3bYf78MMitX+/bTz758GVHTj012jprKgU3BTcREZFj9vHH4UUOs2bBli2+vVu3MMQNGwbp6dHWWVMouCm4iYiIVIqDB2HVqjDELVgAhYX+FlwDBoTDqr17Q11dAnlMFNwU3ERERKpEYSG88UY4rPr22/7ih8aNYeTIMMiddprmx1WUgpuCm4iISLX48kuYMycMcps2+fZ27cIQd/bZ0LRptHUmMgU3BTcREZFq55y/n2osxM2Z4y98MPN3cIgFuUGDIDU16moTh4KbgpuIiEjkioth+fIwyL35pl+KJDUVhg4Nr1jt0cOvKVdbKbgpuImIiCScXbv8siOxK1bXrPHtzZsfvuxImzbR1lndFNwU3ERERBLep58evuzIZ5/59s6dw2HV4cOhUaNIy6xyCm4KbiIiIknFOd8DFxtWnT8f9uyBlBTo1y8Mcn37+qVIahIFNwU3ERGRpLZvHyxZEga5nBy/plxGhu+FiwW5zp2Tf9kRBTcFNxERkRrlq69g7twwyG3c6Ntbtw7nxo0aBS1aRFvnsVBwU3ATERGp0T78MJwfN3s2bN3q23v2DHvjBg+GtLRo66wIBTcFNxERkVrjwAF/B4dYkFu0CPbvhwYN/JpxsSCXlZWYy44ouCm4iYiI1Fp79sDCheGw6qpVvr1JE38Xh1iQa98+0jIPKS+46favIiIiUqOlpcF55/kvgM8/98OpsSD3wgu+/bTTwhA3ciSceGJ0NZdFPW4iIiJSazkH69eHIW7uXL8wcJ060KdPuBDwgAFQv3711KShUgU3ERERqYCiInjrrXB+3Ftv+TlzDRvCsGHwu99Bt25VW4OGSkVEREQqoF49f/Xp4MEwcSJs3w7z5oVBLj092voU3ERERETK0LgxXHKJ/0oECXgRrIiIiIiURsFNREREJEkouImIiIgkCQU3ERERkSSh4CYiIiKSJBTcRERERJKEgpuIiIhIklBwExEREUkSCm4iIiIiSaJW3KvUzLYAm6r4MM2AL6v4GHL0dF4Sj85JYtJ5STw6J4mnus5JO+dc89KeqBXBrTqYWU5ZN4SV6Oi8JB6dk8Sk85J4dE4STyKcEw2VioiIiCQJBTcRERGRJKHgVnmmRF2AlErnJfHonCQmnZfEo3OSeCI/J5rjJiIiIpIk1OMmIiIikiQU3ERERESShILbUTCzv5rZF2b2ThnPm5k9YGYbzGyVmfWq7hprowqcl3HB+VhtZovNrGd111jbHOmcxG3Xx8yKzWxMddVWm1XkvJjZcDPLNbM1Zja/OuurjSrw/6/GZvaqma0Mzsl3q7vG2sbM2pjZXDNbG3zmPyplm8j+3iu4HZ0ngPPLef4CoFPwdT3wcDXUJEc+Lx8Cw5xz3YH7SIDJpbXAE5R/TjCzFOC3wL+royABjnBezOxE4CFgtHOuG3BFNdVVmz1B+f+t3ASsdc71BIYD/8/M6ldDXbVZMfAT59yZQH/gJjM7s8Q2kf29V3A7Cs65BcDWcja5BHjKeUuAE83slOqprvY60nlxzi12zn0V/LgEaF0thdViFfhvBeCHwEvAF1VfkUCFzsu3gb875z4Ktte5qWIVOCcOyDAzA9KDbYuro7bayjm32Tm3Ini8E3gXaFVis8j+3iu4Va5WwMdxP+fz9ZMt0foe8FrURdR2ZtYKuAz1SieaM4CTzGyemS03s6ujLkh4EOgKfAqsBn7knDsYbUm1h5m1B7KAt0o8Fdnf+7rVcRCRRGBmI/DBbXDUtQj3A3c45w76jgRJEHWB3sDZwAnAm2a2xDn3XrRl1WrnAbnASOA0YKaZLXTO7Yi2rJrPzNLxowI/TqTPW8Gtcn0CtIn7uXXQJhEzsx7Ao8AFzrmCqOsRsoFpQWhrBlxoZsXOuenRllXr5QMFzrndwG4zWwD0BBTcovNd4DfOL7q6wcw+BLoAS6Mtq2Yzs3r40DbVOff3UjaJ7O+9hkor1yvA1cHVJv2B7c65zVEXVduZWVvg78BV6jlIDM65Ds659s659sCLwA8U2hLCy8BgM6trZmlAP/z8HonOR/geUMysJdAZ+CDSimq4YD7hY8C7zrk/lLFZZH/v1eN2FMzsb/irepqZWT5wD1APwDn3F2AGcCGwAdiD/5eSVLEKnJdfAk2Bh4IenmLnXHY01dYOFTgnEoEjnRfn3Ltm9i9gFXAQeNQ5V+6SLnJ8KvDfyn3AE2a2GjD8FIMvIyq3thgEXAWsNrPcoO0uoC1E//det7wSERERSRIaKhURERFJEgpuIiIiIklCwU1EREQkSSi4iYiIiCQJBTcRERGRJKHgJiK1kpkdMLPcuK87K3Hf7c1My2iISKXTOm4iUlvtdc5lRl2EiMjRUI+biEgcM8szs9+Z2WozW2pmpwft7c1sjpmtMrPZwR05MLOWZvYPM1sZfA0MdpViZo+Y2Roz+7eZnRBsf7OZrQ32My2itykiSUrBTURqqxNKDJVeGffcdudcd+BB4P6g7U/Ak865HsBU4IGg/QFgvnOuJ9ALWBO0dwL+7JzrBmwDvhG03wlkBfu5sarenIjUTLpzgojUSma2yzmXXkp7HjDSOfdBcKPpz5xzTc3sS+AU51xR0L7ZOdfMzLYArZ1z++L20R6Y6ZzrFPx8B1DPOfer4JZSu4DpwHTn3K4qfqsiUoOox01E5OtcGY+Pxr64xwcI5xRfBPwZ3zu3zMw011hEKkzBTUTk666M+/5m8Hgx8K3g8ThgYfB4NjABwMxSzKxxWTs1szpAG+fcXOAOoDHwtV4/EZGy6F96IlJbnWBmuXE//8s5F1sS5CQzW4XvNRsbtP0QeNzMfgpsAb4btP8ImGJm38P3rE0ANpdxzBTgmSDcGfCAc25bpb0jEanxNMdNRCROMMct2zn3ZdS1iIiUpKFSERERkSShHjcRERGRJKEeNxEREZEkoeAmIiIikiQU3ERERESShIKbiIiISJJQcBMRERFJEgpuIiIiIklCwU1EREQkSSi4iYiIiCQJBTcRERGRJKHgJiIiIpIkFNxEREREkoSCm4iIiEiSUHATERERSRIKbiIiIiJJQsFNREREJEkouImIiIgkCQU3ERERkSRRN+oCqkOzZs1c+/btoy5DRERE5IiWL1/+pXOueWnP1Yrg1r59e3JycqIuQ0REROSIzGxTWc9pqFREREQkSSi4iYiIiCQJBTcRERGRJFEr5riVpqioiPz8fAoLC6MuRcqQmppK69atqVevXtSliIiIJIRaG9zy8/PJyMigffv2mFnU5UgJzjkKCgrIz8+nQ4cOUZcjIiKSEGrtUGlhYSFNmzZVaEtQZkbTpk3VIyoiIhKn1gY3QKEtwen8iIiIHK5WBzcRERGRinIu6goU3Cps6lRo3x7q1PHfp049vv0VFBSQmZlJZmYmJ598Mq1atTr08/79+8t9bU5ODjfffPMRjzFw4MDjK1JERER49134+c/h9NMhPz/aWmrtxQlHY+pUuP562LPH/7xpk/8ZYNy4Y9tn06ZNyc3NBWDixImkp6dz2223HXq+uLiYunVLPz3Z2dlkZ2cf8RiLFy8+tuJERERquc2b4W9/8xlgxQrfcTNqFGzbBq1bR1eXetwq4O67w9AWs2ePb69M48eP58Ybb6Rfv37cfvvtLF26lAEDBpCVlcXAgQNZv349APPmzePiiy8GfOi79tprGT58OB07duSBBx44tL/09PRD2w8fPpwxY8bQpUsXxo0bhwv6e2fMmEGXLl3o3bs3N99886H9xsvLy2PIkCH06tWLXr16HRYIf/vb39K9e3d69uzJnXfeCcCGDRsYNWoUPXv2pFevXmzcuLFyPygREZEqsGMHPPkknHOOD2c/+QmYwR//CJ98Aq+/DmedFW2N6nGrgI8+Orr245Gfn8/ixYtJSUlhx44dLFy4kLp16zJr1izuuusuXnrppa+9Zt26dcydO5edO3fSuXNnJkyY8LW1z95++23WrFnDqaeeyqBBg3jjjTfIzs7mhhtuYMGCBXTo0IGxY8eWWlOLFi2YOXMmqampvP/++4wdO5acnBxee+01Xn75Zd566y3S0tLYunUrAOPGjePOO+/ksssuo7CwkIMHD1b+ByUiIlIJ9u/3gWzqVHj5ZSgshA4d4K67/Khaly5RV3g4BbcKaNvWD4+W1l7ZrrjiClJSUgDYvn0711xzDe+//z5mRlFRUamvueiii2jQoAENGjSgRYsWfP7557Qu0Y/bt2/fQ22ZmZnk5eWRnp5Ox44dD62TNnbsWKZMmfK1/RcVFfGf//mf5ObmkpKSwnvvvQfArFmz+O53v0taWhoATZo0YefOnXzyySdcdtllgF9EV0REJJE4B2++6cPac89BQQE0bQrXXuvD2oABvqctESm4VcCkSYfPcQNIS/Ptla1hw4aHHv/iF79gxIgR/OMf/yAvL4/hw4eX+poGDRocepySkkJxcfExbVOWP/7xj7Rs2ZKVK1dy8OBBhTEREUlK69b5sPbss/DBB5CaCpdcAt/5Dpx7LtSvH3WFR6Y5bhUwbhxMmQLt2vkE3q6d//lYL0yoqO3bt9OqVSsAnnjiiUrff+fOnfnggw/Iy8sD4LnnniuzjlNOOYU6derw9NNPc+DAAQDOOeccHn/8cfYEiXbr1q1kZGTQunVrpk+fDsC+ffsOPS8iIlLdPvsM7r8fsrOha1f49a/htNPgiSfg889h2jS4+OLkCG2g4FZh48ZBXh4cPOi/V3VoA7j99tv52c9+RlZW1lH1kFXUCSecwEMPPcT5559P7969ycjIoHHjxl/b7gc/+AFPPvkkPXv2ZN26dYd6Bc8//3xGjx5NdnY2mZmZTJ48GYCnn36aBx54gB49ejBw4EA+++yzSq9dRESkLDt3wlNPwXnnQatWcMstfnj0D3/wy3n8+99wzTXQqFHUlR49c4mwmlwVy87Odjk5OYe1vfvuu3Tt2jWiihLHrl27SE9PxznHTTfdRKdOnbjllluiLusQnScREamIoiIfyJ55xl9ksHevX3d13Dj/lUx/SsxsuXOu1HW/NMetlnvkkUd48skn2b9/P1lZWdxwww1RlyQiIlIhzsGSJeFFBl9+CU2a+N6073wHBg5M3IsMjpWCWy13yy23JFQPm4iIyJG8954Pa1OnwsaN/iKD0aN9z9r55yfPfLVjUaVz3MzsfDNbb2YbzOzOUp6/1czWmtkqM5ttZu3inrvGzN4Pvq6Ja+9tZquDfT5guhO5iIhIjff55/A//wN9+0LnznDffX4o9PHH/XPPPefDW00ObVCFPW5mlgL8GTgHyAeWmdkrzrm1cZu9DWQ75/aY2QTgd8CVZtYEuAfIBhywPHjtV8DDwHXAW8AM4Hzgtap6HyIiIhKNXbtg+nQ/b23WLDhwADIzYfJk+Na3/IUHtU1VDpX2BTY45z4AMLNpwCXAoeDmnJsbt/0S4DvB4/OAmc65rcFrZwLnm9k8oJFzbknQ/hRwKQpuIiIiNUJxMcyc6cPa9Ol+DdV27eD22/1QaLduUVcYraoMbq2Aj+N+zgf6lbP99wgDWGmvbRV85ZfS/jVmdj1wPUDbqrjFgYiIiFQK52DpUj9nbdo02LIFTjoJrrrKh7VBg/xN3iVB1nEzs+/gh0V/X1n7dM5Ncc5lO+eymzdvXlm7rTQjRozg9ddfP6zt/vvvZ8KECWW+Zvjw4cSWNbnwwgvZtm3b17aZOHHiofXUyjJ9+nTWrg1HrH/5y18ya9asoylfRETkuL3/PkycCGecAf37+8Xthw3zPW2bN8Nf/gJDhii0xavKj+IToE3cz62DtsOY2SjgbmC0c27fEV77SfC43H0mg7FjxzJt2rTD2qZNm1bmjd5LmjFjBieeeOIxHbtkcLv33nsZNWrUMe1LRETkaHzxBfzpT9Cvnw9s994LbdrAY4/5iwxeeMHfhiruTo0SpyqHSpcBncysAz5cfQv4dvwGZpYF/C9wvnPui7inXgd+bWYnBT+fC/zMObfVzHaYWX/8xQlXA3863kJ//GPIzT3evRwuM9PfYqMsY8aM4ec//zn79++nfv365OXl8emnnzJkyBAmTJjAsmXL2Lt3L2PGjOG//uu/vvb69u3bk5OTQ7NmzZg0aRJPPvkkLVq0oE2bNvTu3Rvwa7RNmTKF/fv3c/rpp/P000+Tm5vLK6+8wvz58/nVr37FSy+9xH333cfFF1/MmDFjmD17NrfddhvFxcX06dOHhx9+mAYNGtC+fXuuueYaXn31VYqKinjhhRfo0qXLYTXl5eVx1VVXsXv3bgAefPBBBg4cCMBvf/tbnnnmGerUqcMFF1zAb37zGzZs2MCNN97Ili1bSElJ4YUXXuC0006rpDMgIiKJYvduvyjuM8/4RXIPHICePeF3v4OxY6F16yPvQ7wq63FzzhUD/4kPYe8Czzvn1pjZvWY2Otjs90A68IKZ5ZrZK8FrtwL34cPfMuDe2IUKwA+AR4ENwEaS9MKEJk2a0LdvX157zZc/bdo0vvnNb2JmTJo0iZycHFatWsX8+fNZtWpVmftZvnw506ZNIzc3lxkzZrBs2bJDz11++eUsW7aMlStX0rVrVx577DEGDhzI6NGj+f3vf09ubu5hQamwsJDx48fz3HPPsXr1aoqLi3n44YcPPd+sWTNWrFjBhAkTSh2ObdGiBTNnzmTFihU899xz3HzzsndP9wAAIABJREFUzQC89tprvPzyy7z11lusXLmS22+/HYBx48Zx0003sXLlShYvXswpp5xyfB+qiIgkjOJi+Ne//Dy1li39XLV33oGf/hRWr/YdJj/9qULb0arSBXidczPwS3bEt/0y7nGZ43POub8Cfy2lPQc4qxLLLLdnrCrFhksvueQSpk2bxmOPPQbA888/z5QpUyguLmbz5s2sXbuWHj16lLqPhQsXctlll5GWlgbA6NGjDz33zjvv8POf/5xt27axa9cuzjvvvHLrWb9+Pf+/vTsPj6o8/z/+vgkgCIhsKoIBVMCliuAA1gWkSkEUEEVAU6vWllqrbf22/tSv11ctaqvV2pZKbemCrQwEBMFQBVwoat1KQBAFtWxCAJVFAQ1byP374xnIGAMEyOTMZD6v65ormXPOnNzjCPnwnOc+T7t27ejQoQMA11xzDaNGjeInP/kJEIIgwJlnnslTTz31ldfv3LmTm266ifnz55OTk8MHH3wAwAsvvMB11123p8amTZuyZcsWVq9ezaBBgwCoV69e5f6jiYhI2nKHwsIwspafHy6LHnkkXHVVWMng3HM1X+1QaeWECA0cOJBbbrmFefPmUVxczJlnnsny5ct5+OGHmTNnDk2aNOHaa69l27ZtB3X+a6+9lqlTp9KpUycef/xxZs+efUj1HpaYcJCTk1Phove/+c1vOProo1mwYAGlpaUKYyIiWWLp0tAROnZsaDioWxf69w+jbP36ab5aVVLujVDDhg3p1asX3/nOd/Y0JWzevJkGDRrQuHFjPv744z2XUvemR48eTJ06la1bt7JlyxamTZu2Z9+WLVto2bIlO3fuJB6P79neqFEjtmzZ8pVzdezYkRUrVrBkyRIAnnjiCXr27Fnp97Np0yZatmxJrVq1eOKJJ9i1axcAvXv3ZsyYMRQXFwOwceNGGjVqROvWrZk6dSoA27dv37NfRETS37p18Oij8PWvw4knwt13hxvi/uUvoclg0iQYNEihraopuEXsyiuvZMGCBXuCW6dOnejcuTMnnXQSV111Feecc84+X9+lSxeGDh1Kp06duOiii+jateuefffeey/du3fnnHPO+VIjwbBhw3jooYfo3LkzS5cu3bO9Xr16jBkzhiuuuILTTjuNWrVqccMNN1T6vdx44438/e9/p1OnTrz33ns0aNAAgL59+zJgwABisRhnnHHGnvlxTzzxBCNHjuT000/n7LPP5qOPPqr0zxIRkepXXAzjx8PFF0PLlnDzzaHx4MEHYeVK+Ne/4Prrw+VRSQ1z96hrSLlYLOa773+22+LFizn55JMjqkgqS5+TiEi0Skpg1qxwGfSpp0JQa906zFvLy4O9TMGWQ2Bmc909VtE+zXETERGRL3GHuXPDvLXx48Olz8aNw6078vKgRw81GURFwU1EREQAWLYshLV4HN5/PzQZXHxx6Ajt1w/Ucxa9rA5u7o6ZRV2G7EU2XMYXEYna+vUwcWK4FPr662Fbz57w05/C4MFhzVBJH1kb3OrVq8eGDRto1qyZwlsacnc2bNigW4qIiKRAcTFMmxbC2owZYR7bqafCL38Z5q7l5kZdoexN1ga31q1bU1RUxLp166IuRfaiXr16tNYttUVEqsSuXaHJIB6HyZPh88/D7TtuuaWsyUDjGOkva4NbnTp1aNeuXdRliIiIpIw7vPVW2UoGa9fCEUfAkCFh3lqPHpCTE3WVciCyNriJiIjUVMuXw7hxIbC99x7UqROaC771rdBsUL9+1BXKwVJwExERqQE2bIAnnwxh7dVXw7bzzoOf/ASuuAKaNo22PqkaCm4iIiIZauvW0GQQj8P06bBzJ5xyCvziF+Gea23bRl2hVDUFNxERkQyyaxfMnh1G1iZPhi1b4Nhj4Uc/CpdCO3VSk0FNpuAmIiKS5txh/vyylQzWrIFGjcJ91vLy4Pzz1WSQLRTcRERE0tSKFaHJIB6HRYugdu3QZJCXB/37q8kgGym4iYiIpJGNG8uaDP7977DtnHPgscdCk0GzZtHWJ9FScBMREYnYtm3wz3+GsPbss6HJ4KST4L77wkoGuu2o7KbgJiIiEoFdu+Cll8Jl0EmTYPNmOOYYuPnmcCm0c2c1GchXKbiJiIhUE3d4++0wsjZ+PKxeDQ0bwuWXh47QXr3UZCD7puAmIiKSYitXlq1k8O67ocmgb1/49a9Dk8Hhh0ddoWSKWqk8uZn1NbP3zWyJmd1ewf4eZjbPzErMbHDS9l5mNj/psc3MLk3se9zMliftOyOV70FERORgfPop/PnP0LMntGkDd9wBjRvDqFFhzdBp02DoUIU2OTApG3EzsxxgFNAbKALmmFmBuy9KOmwlcC3ws+TXuvu/gDMS52kKLAGeSzrkVneflKraRUREDsa2bfDMM2He2jPPwI4d0LEj3HtvaDI4/vioK5RMl8pLpd2AJe6+DMDM8oGBwJ7g5u4rEvtK93GewcB0dy9OXakiIiIHp7QUXn45XAadNAk2bYKjj4Ybbwzz1rp0UZOBVJ1UBrdWwKqk50VA94M4zzDgkXLb7jezu4AXgdvdfXv5F5nZcGA4QG5u7kH8WBERkb1buDCEtXHjoKgoNBlcdlnoCP3GN8I8NpGqltb/W5lZS+A0YGbS5juAj4C6wGjgNmBE+de6++jEfmKxmKe8WBERqfFWrQrdoGPHhuBWuzb06QMPPQQDBmi+mqReKoPbauC4pOetE9sOxBBgirvv3L3B3dcmvt1uZmMoNz9ORESkKn32WbgEGo+H+665w1lnwaOPwpAh0KJF1BVKNkllcJsDtDezdoTANgy46gDPcSVhhG0PM2vp7mvNzIBLgXeqolgREZHdtm8PKxiMHRtWNNixAzp0gHvuCZdCTzgh6golW6UsuLl7iZndRLjMmQP8zd3fNbMRQKG7F5hZV2AK0ATob2Y/d/dTAcysLWHE7qVyp46bWQvAgPnADal6DyIikj1KS+GVV8LI2pNPhpG2o46CH/wghLVYTE0GEj1zr/nTv2KxmBcWFkZdhoiIpKF33ilrMli1Cho0gEGDQli78EI1GUj1M7O57h6raJ/+dxQRkaxTVBSaDOJxWLAgLDP1zW/CAw/AwIEhvImkIwU3ERHJCps2weTJYXRt9uzQZNC9O4wcGVYwOOqoqCsU2T8FNxERqbG2b4fp08PI2rRp4fmJJ8Ldd4eVDNq3j7pCkQOj4CYiIjVKaSm8+moIaxMnhjVDW7SA4cPDSgZdu6rJQDKXgpuIiNQIixaVNRl8+GG4Ge6ll4awduGFUKdO1BWKHDoFNxERyVhr1pStZDB/PtSqFZoM7rsvhLaGDaOuUKRqKbiJiEhG2bw5NBnE4zBrVmgy6NoVfve70GRw9NFRVyiSOgpuIiKS9nbsgBkzQlgrKIBt28LqBf/3f+F+ax06RF2hSPVQcBMRkbTkDq+9Fi6DTpwIGzdC8+bw3e+GsNa9u5oMJPsouImISFpZvDiMrMXjsGIF1K9f1mTQu7eaDCS7KbiJiEjk1q4tW8lg3rzQZHDhhTBiRAhtjRpFXaFIelBwExGRSGzeDFOmhEuhs2aF+6/FYvCb38CwYXDMMVFXKJJ+FNxERKTa7NgBM2eGkbWnnw5NBu3awZ13hnlrHTtGXaFIelNwExGRlHKH118PYW3CBNiwAZo1g+98J8xbO+ssNRmIVJaCm4iIpMR774WwNm4cLFsG9eqF+Wp5edCnj5oMRA6GgpuIiFSZjz6C/Pwwb23u3NBkcMEFYVH3QYPUZCByqBTcRETkkGzZEpoM4nF44YXQZNClCzzySGgyaNky6gpFag4FNxEROWA7d8Jzz4WwNnUqbN0KbdvCHXeES6Ennxx1hSI1k4KbiIhUiju8+Wa4DDphAqxfD02bwjXXhCaDs89Wk4FIqim4iYjIPn3wQdlKBkuXhiaDAQPCyFrfvlC3btQVimSPWqk8uZn1NbP3zWyJmd1ewf4eZjbPzErMbHC5fbvMbH7iUZC0vZ2ZvZk45wQz018ZIiJV7OOPYeRI6NYt3Fvt3nvDpdAxY8K+CRNCeFNoE6leKRtxM7McYBTQGygC5phZgbsvSjpsJXAt8LMKTrHV3c+oYPuDwG/cPd/M/ghcDzxWpcWLiGShzz8P89XicXj+edi1Czp3hocfDk0GrVpFXaGIpPJSaTdgibsvAzCzfGAgsCe4ufuKxL7SypzQzAz4BnBVYtPfgXtQcBMROSglJSGkjR0bQltxMbRpA7fdFi6FnnJK1BWKSLJUBrdWwKqk50VA9wN4fT0zKwRKgAfcfSrQDPjM3UuSzlnhvwHNbDgwHCA3N/cASxcRqbnc4T//CSNr+fmwbh00aQJXXx3C2jnnhPuviUj6SefmhDbuvtrMjgdmmdlCYFNlX+zuo4HRALFYzFNUo4hIxvjvf8uaDJYsgcMOg/79Q0do377huYikt1QGt9XAcUnPWye2VYq7r058XWZms4HOwGTgSDOrnRh1O6Bziohkm08+CY0EY8eGUTYz6NUr3G/t8suhceOoKxSRA5HK4DYHaG9m7Qjhahhlc9P2ycyaAMXuvt3MmgPnAL9ydzezfwGDgXzgGuDplFQvIpKhvvgCnn46hLXnngtNBp06wUMPhSaD1q2jrlBEDlbKgpu7l5jZTcBMIAf4m7u/a2YjgEJ3LzCzrsAUoAnQ38x+7u6nAicDf0o0LdQizHHb3dRwG5BvZvcBbwF/TdV7EBHJFCUlYbmpeDwsP/XFF5CbC7feGuatfe1rUVcoIlXB3Gv+9K9YLOaFhYVRlyEiUqXcobAwjKzl54fLokceCUOGhLB27rlqMhDJRGY2191jFe1L5+YEERGpwNKlYWRt7NjQcFC3bmgyyMuDfv3UZCBSkym4iYhkgHXrYOLEENbeeCM0GfTsGe63dvnlYaRNRGo+BTcRkTRVXByaDOJxmDkzzGM7/XR48EG48ko47rj9n0NEahYFNxGRNFJSArNmhZG1KVPCMlStW8NPfxouhZ52WtQVikiUFNxERCLmDnPnhpG18ePDIu6NG4dbd3zrW3DeeWoyEJFAwU1EJCLLlsG4cWF07f33Q5PBxReHsNavH9SrF3WFIpJuFNxERKrR+vWhySAeh9deC9t69gyXQgcPDmuGiojsjYKbiEiKFRfDtGlhZG3GjDCP7dRT4YEHQpNBbm7UFYpIplBwExFJgV27QpNBPA6TJ4cmg1at4JZbQpPB6aeHW3qIiBwIBTcRkSriDm+9VbaSwdq1cMQRYSWDb30LevSAnJyoqxSRTKbgJiJyiJYvL2syeO89qFMnNBnk5cEll6jJQESqjoKbiMhB2LABnnwyhLVXXw3bzjsvXAodPBiaNo22PhGpmRTcREQqaevW0GQQj8P06bBzJ5xyCvziF3DVVdCmTdQVikhNp+AmIrIPu3bB7NlhZG3yZNiyBY49Fn70ozBvrVMnNRmISPVRcBMRKccd5s8vW8lgzRpo1ChcAs3Lg/PPV5OBiERDwU1EJOHDD8uaDBYtCk0GF10URtYuuQTq14+6QhHJdgpuIpLVNm4MTQbxOLzySth27rnw2GNwxRXQrFm09YmIJFNwE5Gss20b/POfYWTt2WdDk8HJJ8P994cmg7Zto65QRKRiCm4ikhVKS+Gll0JYmzQJNm+Gli3h5pvDpdAzzlCTgYikPwU3Eamx3OHtt8Nl0HHjYPXq0GRw2WUhrPXqpSYDEcksCm4iUuOsXBmCWjwO77wDtWuHJoNf/xr694fDD4+6QhGRg1MrlSc3s75m9r6ZLTGz2yvY38PM5plZiZkNTtp+hpm9bmbvmtnbZjY0ad/jZrbczOYnHmek8j2ISGb49FP485+hZ89wI9w77gjrhP7hD2HN0IICGDpUoU1EMlvKRtzMLAcYBfQGioA5Zlbg7ouSDlsJXAv8rNzLi4Fvu/t/zexYYK6ZzXT3zxL7b3X3SamqXUQyw7Zt8MwzYWTtmWdgxw7o2BHuvTc0GRx/fNQViohUrVReKu0GLHH3ZQBmlg8MBPYEN3dfkdhXmvxCd/8g6fs1ZvYJ0AL4DBHJaqWl8PLLZU0GmzbBMcfAD38Ybo7bpYuaDESk5kplcGsFrEp6XgR0P9CTmFk3oC6wNGnz/WZ2F/AicLu7b6/gdcOB4QC5ubkH+mNFJM0sXBjC2rhxUFQEDRt+ucmgtmbsikgWSOu/6sysJfAEcI277x6VuwP4iBDmRgO3ASPKv9bdRyf2E4vFvFoKFpEqtWpVWHJq7NgQ3GrXhj594KGHYMAAzVcTkeyTyuC2Gjgu6XnrxLZKMbMjgGeAO939jd3b3X1t4tvtZjaGr86PE5EM9tln4RJoPB7uu+YOZ50Fjz4KQ4ZAixZRVygiEp1UBrc5QHsza0cIbMOAqyrzQjOrC0wB/lG+CcHMWrr7WjMz4FLgnaotW0Sq2/btYQWDsWPDigY7dkCHDnDPPWHe2gknRF2hiEh62G9wM7MGwNbdlyrNrBZQz92L9/U6dy8xs5uAmUAO8Dd3f9fMRgCF7l5gZl0JAa0J0N/Mfu7upwJDgB5AMzO7NnHKa919PhA3sxaAAfOBGw78bYtI1EpL4d//DmHtySfDSNtRR8EPfhDCWiymJgMRkfLMfd/Tv8zsDeBCd/888bwh8Jy7n10N9VWJWCzmhYWFUZchIoQb4u5eyWDlSmjQAAYNCk0GF1ygJgMRETOb6+6xivZV5q/IertDG4C7f25mmhIsIpVWVBSaDOJxWLAgLDPVpw/88pcwcGAIbyIisn+VCW5fmFkXd58HYGZnAltTW5aIZLpNm2Dy5HApdPbs0GTQvTv8/vehyeCoo6KuUEQk81QmuP0EeNLM1hDmlR0DDN33S0QkG+3YAdOnh7A2bVpoOjjxRLj77rCSQfv2UVcoIpLZ9hvc3H2OmZ0EdExset/dd6a2LBHJFKWl8Oqr4TLoxIlhzdAWLWD48DBvrWtXNRmIiFSVynSV/hCIu/s7iedNzOxKd/9DyqsTkbS1aFHZSgYffhhuhjtoUOgIvfBCqFMn6gpFRGqeylwq/Z67j9r9xN0/NbPvAQpuIllmzZqylQzmzw9NBr17w/33hyaDhg2jrlBEpGarTHDLMTPzxH1DzCyHsNyUiGSBzZtDk0E8DrNmhSaDbt3gd7+DoUPh6KOjrlBEJHtUJrjNACaY2Z8Sz78PTE9dSSIStR07YMaMENYKCmDbtrB6wV13hSaDDh2irlBEJDtVJrjdBgynbIWCtwmdpSJSg7jDa6+Fy6ATJ8LGjdC8OXz3u2HeWvfuajIQEYlaZbpKS83sTeAEwlJUzYHJqS5MRKrH4sVhZC0ehxUroH59uPTS0BHau7eaDERE0sleg5uZdQCuTDzWAxMA3L1X9ZQmIqmydm3ZSgbz5kGtWiGkjRgRQlujRlFXKCIiFdnXiNt7wCvAJe6+BMDMbqmWqkSkym3eDFOmhEuhs2aF+6/FYvDb34Ymg2M0AUJEJO3tK7hdBgwD/mVmM4B8wsoJIpIhdu6EmTNDWHv66dBkcPzxcOedYd5ax477P4eIiKSPvQY3d58KTDWzBsBAwtJXR5nZY8AUd3+ummoUkQPgDq+/Hi6DTpgAGzZAs2Zw/fUhrJ11lpoMREQyVWWaE74AxgHjzKwJcAWh01TBTSSNvPdeCGvjxsGyZVCvXpivlpcHffqoyUBEpCaozO1A9nD3T4HRiYeIROyjjyA/PwS2wsLQZHDBBWFR90GD1GQgIlLTHFBwE5HobdkCU6eGeWsvvBCaDLp0gUcegWHDoGXLqCsUEZFUUXATyQA7d8Jzz4WRtalTYetWaNsW7rgjXAo9+eSoKxQRkeqg4CaSptzhzTfDyNqECbB+PTRtCtdeG8La2WeryUBEJNsouImkmQ8+KFvJYOnS0GQwYEBYyaBPH6hbN+oKRUQkKrVSeXIz62tm75vZEjO7vYL9PcxsnpmVmNngcvuuMbP/Jh7XJG0/08wWJs450kxjDpL5Pv4YRo6Ebt3CvdXuvRfatYMxY8K+CROgf3+FNhGRbJeyETczywFGAb2BImCOmRW4+6Kkw1YC1wI/K/fapsDdQAxwYG7itZ8CjwHfA94EngX6AtNT9T5EUuXzz8N8tXgcnn8edu2Czp3h4YdDk0GrVlFXKCIi6SaVl0q7AUvcfRmAmeUTbuS7J7i5+4rEvtJyr+0DPO/uGxP7nwf6mtls4Ah3fyOx/R/ApSi4SYYoKQkhbezYENqKi6FNG7jttjBv7ZRToq5QRETSWSqDWytgVdLzIqD7Iby2VeJRVMH2rzCz4cBwgNzc3Er+WJGq5w7/+U8YWcvPh3XroEkTuPrqMG/t7LPD/ddERET2p8b+unD30e4ec/dYixYtoi5HstB//wv33AMdOoRlpkaPhvPPDyNtH30Ef/wjnHuuQpuISLqLx8MtmGrVCl/j8ehqSeWI22rguKTnrRPbKvva88u9dnZie+uDPKdIyn3ySWgkiMfDrTzMoFcv+N//hcsug8aNo65QREQORDwOw4eHqS0AH34YnkOY4lLdUvlv/TlAezNrZ2Z1gWFAQSVfOxP4ppk1SayP+k1gpruvBTab2VmJbtJvA0+noniRyvrii7A+aL9+cOyx8KMfwfbt8NBDsGoVvPgiXHedQpuISCa6886y0LZbcXHYHoWUjbi5e4mZ3UQIYTnA39z9XTMbARS6e4GZdQWmAE2A/mb2c3c/1d03mtm9hPAHMGJ3owJwI/A4UJ/QlKDGBKl2JSVhual4HKZMCeEtNxduvTX8C+xrX4u6QhERqQorVx7Y9lQzd4/mJ1ejWCzmhYWFUZchGc49LOQej8P48eGy6JFHwpAhIaxpvpqISM3Ttm24PFpemzawYkVqfqaZzXX3WEX7tHKCyH4sXVq2ksEHH8Bhh8Ell4SO0IsuCs9FRKRmuv/+L89xAzj88LA9CgpuIhVYtw4mTgz3W3vjjdBkcP758P/+H1x+eRhpExGRmm93A8Kdd4bLo7m5IbRF0ZgACm4iexQXw9NPh5G1mTPDPLbTT4df/QquvBJat97/OUREpObJy4suqJWn4CZZraQEZs0KI2tTpoRlqI47Dn760/CH9LTToq5QRESkjIKbZB13mDu3rMng44/DrTqGDQvz1s47T00GIiKSnhTcJGssWxbutzZ2LLz/PtStCxdfHMJav35Qr17UFYqIiOybgpvUaOvXhyaDeBxeey1s69kzXAodPDisGSoiIpIpFNykxikuhmnTwsjajBlhHtvXvgYPPBCaDHJzo65QRETk4Ci4SY2wa1doMojHYfLk0GTQqhXccku4FHr66VFXKCIicugU3CRjucNbb4WRtfx8WLsWjjgChg4NHaE9ekBOTtRVioiIVB0FN8k4y5eHJoN4HBYvhjp1ypoMLr5YTQYiIlJzKbhJRtiwAZ58Moyuvfpq2HbeefCnP4Umg6ZNo61PRESkOii4SdrauhX++c8Q1qZPh5074ZRT4Be/gKuuCgv8ioiIZBMFN0kru3bB7NnhMuikSbBlCxx7LPz4x2HeWqdOYd1QERGRbKTgJpFzhwULwsja+PGwZk1oMhg8OMxb69lTTQYiIiKg4CYR+vDDspUMFi0KTQb9+oWRtUsugfr1o65QREQkvSi4SbXauDE0GcTj8MorYdu558If/xhG2Jo1i7Y+ERGRdKbgJim3bVtZk8Gzz4Ymg5NPhvvvD00GbdtGXaGIiEhmUHCTlCgthZdeCmFt0iTYvBlatoSbbw7z1s44Q00GIiIiB0rBTaqMO7z9drgMOm4crF4NjRrB5ZeHeWu9eqnJQERE5FAouMkhW7mybCWDd96B2rXhoovgkUegf381GYiIiFSVlAY3M+sL/A7IAf7i7g+U238Y8A/gTGADMNTdV5hZHnBr0qGnA13cfb6ZzQZaAlsT+77p7p+k8n3IV336abgEOnYsvPxy2HbOOfCHP8AVV0Dz5tHWJyIiUhOlLLiZWQ4wCugNFAFzzKzA3RclHXY98Km7n2hmw4AHCeEtDsQT5zkNmOru85Nel+fuhamqXSq2bRs880wYWXvmGdixA046Ce67LzQZtGsXdYUiIiI1WypH3LoBS9x9GYCZ5QMDgeTgNhC4J/H9JOBRMzN396RjrgTyU1in7ENpaRhRi8fDbTw2bYJjjoEf/jDMW+vSRU0GIiIi1SWVwa0VsCrpeRHQfW/HuHuJmW0CmgHrk44ZSgh4ycaY2S5gMnBfuaAHgJkNB4YD5ObmHsLbyE4LF5atZLBqFTRsCJddFjpCe/UK89hERESkeqX1r18z6w4Uu/s7SZvz3H21mTUiBLerCfPkvsTdRwOjAWKx2FeCnXzVqlUhqI0dG4Jb7drQpw/86lcwYAAcfnjUFYqIiGS3VAa31cBxSc9bJ7ZVdEyRmdUGGhOaFHYbBoxPfoG7r0583WJm4wiXZL8S3KRyPvsMJk8OYe2ll8ItPb7+dXj0URgyBFq0iLpCERER2S2VwW0O0N7M2hEC2jDgqnLHFADXAK8Dg4FZuy97mlktYAhw3u6DE+HuSHdfb2Z1gEuAF1L4Hmqk7dvDCgbxeFjRYPt26NABfv7z0GRwwglRVygiIiIVSVlwS8xZuwmYSbgdyN/c/V0zGwEUunsB8FfgCTNbAmwkhLvdegCrdjc3JBwGzEyEthxCaPtzqt5DTVJaCv/+dxhZe/LJMNJ29NFwww1h3tqZZ6rJQEREJN1ZBfP6a5xYLOaFhdl595B33ilbyWDlSmjQIDQZ5OXBBReoyUBERCTdmNlcd49VtE+/tmugoqLQZBCPw4IFYZmpPn3ggQdCk0GDBlFXKCIiIgdDwa2G2LSprMlg9uzQZNC9O/z+96HJ4Kijoq5QREREDpWCWwbbsQOmTw9hbdq00GTQvj3cfXe4FHriiVFXKCIiIlVJwS3DlJbCq6+Gy6ATJ4Y1Q1u0gO9/P4S1rl3VZCAiIlJTKbhliEV0fIuGAAAMYklEQVSLwsjauHHw4YfhZriDBoWO0AsvVJOBiIhINtCv+zS2Zk3ZSgbz54cmg29+E+6/HwYODMtQiYiISPZQcEszmzeHJoN4HGbNCk0G3brB734HQ4eGe6+JiIhIdlJwSwM7dsCMGSGsFRTAtm1h9YK77grz1tq3j7pCERERSQcKbhFxh9deC5dBJ06EjRtDk8F3vxvmrXXrpiYDERER+TIFt2q2eHHZSgbLl0P9+qHJIC8PeveGOnWirlBERETSlYJbNVi7FvLzw+javHlQq1YIaSNGwKWXqslAREREKkfBLUW2bIGnngqjay++GO6/1rUr/Pa3ocngmGOirlBEREQyjYJbFdq5E2bODCNrBQWwdSscfzzceWe4FNqxY9QVioiISCZTcKsChYUwZgxMmAAbNkCzZvCd74SwdtZZajIQERGRqqHgVgXGjw/BbeDAENb69FGTgYiIiFQ9c/eoa0i5WCzmhYWFKTv/+vVw2GHQqFHKfoSIiIhkCTOb6+6xivZpxK0KNG8edQUiIiKSDWpFXYCIiIiIVI6Cm4iIiEiGUHATERERyRAKbiIiIiIZIqXBzcz6mtn7ZrbEzG6vYP9hZjYhsf9NM2ub2N7WzLaa2fzE449JrznTzBYmXjPSTHdJExERkeyQsuBmZjnAKOAi4BTgSjM7pdxh1wOfuvuJwG+AB5P2LXX3MxKPG5K2PwZ8D2ifePRN1XsQERERSSepHHHrBixx92XuvgPIBwaWO2Yg8PfE95OAC/Y1gmZmLYEj3P0NDzeg+wdwadWXLiIiIpJ+UhncWgGrkp4XJbZVeIy7lwCbgGaJfe3M7C0ze8nMzks6vmg/5wTAzIabWaGZFa5bt+7Q3omIiIhIGkjX5oS1QK67dwb+BxhnZkccyAncfbS7x9w91qJFi5QUKSIiIlKdUhncVgPHJT1vndhW4TFmVhtoDGxw9+3uvgHA3ecCS4EOieNb7+ecIiIiIjVSKoPbHKC9mbUzs7rAMKCg3DEFwDWJ7wcDs9zdzaxForkBMzue0ISwzN3XApvN7KzEXLhvA0+n8D2IiIiIpI2UrVXq7iVmdhMwE8gB/ubu75rZCKDQ3QuAvwJPmNkSYCMh3AH0AEaY2U6gFLjB3Tcm9t0IPA7UB6YnHiIiIiI1noXmzJotFot5YWFh1GWIiIiI7JeZzXX3WEX70rU5QURERETKUXATERERyRAKbiIiIiIZQsFNREREJEMouImIiIhkCAU3ERERkQyh4CYiIiKSIRTcRERERDKEgpuIiIhIhlBwExEREckQCm4iIiIiGULBTURERCRDKLiJiIiIZAgFNxEREZEMoeAmIiIikiEU3EREREQyhIKbiIiISIZQcBMRERHJEApuIiIiIhlCwU1EREQkQ6Q0uJlZXzN738yWmNntFew/zMwmJPa/aWZtE9t7m9lcM1uY+PqNpNfMTpxzfuJxVCrfg4iIiEi6qJ2qE5tZDjAK6A0UAXPMrMDdFyUddj3wqbufaGbDgAeBocB6oL+7rzGzrwEzgVZJr8tz98JU1S4iIiKSjlI54tYNWOLuy9x9B5APDCx3zEDg74nvJwEXmJm5+1vuviax/V2gvpkdlsJaRURERNJeKoNbK2BV0vMivjxq9qVj3L0E2AQ0K3fM5cA8d9+etG1M4jLp/5mZVfTDzWy4mRWaWeG6desO5X2IiIiIpIW0bk4ws1MJl0+/n7Q5z91PA85LPK6u6LXuPtrdY+4ea9GiReqLFREREUmxVAa31cBxSc9bJ7ZVeIyZ1QYaAxsSz1sDU4Bvu/vS3S9w99WJr1uAcYRLspGJx6FtW6hVK3yNx6OsRkRERGqyVAa3OUB7M2tnZnWBYUBBuWMKgGsS3w8GZrm7m9mRwDPA7e7+6u6Dzay2mTVPfF8HuAR4J4XvYZ/icRg+HD78ENzD1+HDFd5EREQkNVIW3BJz1m4idIQuBia6+7tmNsLMBiQO+yvQzMyWAP8D7L5lyE3AicBd5W77cRgw08zeBuYTRuz+nKr3sD933gnFxV/eVlwctouIiIhUNXP3qGtIuVgs5oWFVX/3kFq1wkhbeWZQWlrlP05ERESygJnNdfdYRfvSujkh3eXmHth2ERERkUOh4HYI7r8fDj/8y9sOPzxsFxEREalqCm6HIC8PRo+GNm3C5dE2bcLzvLyoKxMREZGaKGVLXmWLvDwFNREREakeGnETERERyRAKbiIiIiIZQsFNREREJEMouImIiIhkCAU3ERERkQyh4CYiIiKSIbJiySszWwd8mOIf0xxYn+KfIQdOn0v60WeSnvS5pB99Jumnuj6TNu7eoqIdWRHcqoOZFe5tXTGJjj6X9KPPJD3pc0k/+kzSTzp8JrpUKiIiIpIhFNxEREREMoSCW9UZHXUBUiF9LulHn0l60ueSfvSZpJ/IPxPNcRMRERHJEBpxExEREckQCm4iIiIiGULB7QCY2d/M7BMze2cv+83MRprZEjN728y6VHeN2agSn0te4vNYaGavmVmn6q4x2+zvM0k6rquZlZjZ4OqqLZtV5nMxs/PNbL6ZvWtmL1VnfdmoEn9/NTazaWa2IPGZXFfdNWYbMzvOzP5lZosS/81/XMExkf2+V3A7MI8Dffex/yKgfeIxHHisGmqS/X8uy4Ge7n4acC9pMLk0CzzOvj8TzCwHeBB4rjoKEmA/n4uZHQn8ARjg7qcCV1RTXdnscfb9Z+WHwCJ37wScD/zazOpWQ13ZrAT4qbufApwF/NDMTil3TGS/7xXcDoC7vwxs3MchA4F/ePAGcKSZtaye6rLX/j4Xd3/N3T9NPH0DaF0thWWxSvxZAbgZmAx8kvqKBCr1uVwFPOXuKxPH67NJsUp8Jg40MjMDGiaOLamO2rKVu69193mJ77cAi4FW5Q6L7Pe9glvVagWsSnpexFc/bInW9cD0qIvIdmbWChiERqXTTQegiZnNNrO5ZvbtqAsSHgVOBtYAC4Efu3tptCVlDzNrC3QG3iy3K7Lf97Wr44eIpAMz60UIbudGXYvwW+A2dy8NAwmSJmoDZwIXAPWB183sDXf/INqyslofYD7wDeAE4Hkze8XdN0dbVs1nZg0JVwV+kk7/vRXcqtZq4Lik560T2yRiZnY68BfgInffEHU9QgzIT4S25kA/Mytx96nRlpX1ioAN7v4F8IWZvQx0AhTconMd8ICHm64uMbPlwEnAf6Itq2YzszqE0BZ396cqOCSy3/e6VFq1CoBvJ7pNzgI2ufvaqIvKdmaWCzwFXK2Rg/Tg7u3cva27twUmATcqtKWFp4Fzzay2mR0OdCfM75HorCSMgGJmRwMdgWWRVlTDJeYT/hVY7O6P7OWwyH7fa8TtAJjZeEJXT3MzKwLuBuoAuPsfgWeBfsASoJjwLyVJsUp8LncBzYA/JEZ4Stw9Fk212aESn4lEYH+fi7svNrMZwNtAKfAXd9/nLV3k0FTiz8q9wONmthAwwhSD9RGVmy3OAa4GFprZ/MS2/wVyIfrf91rySkRERCRD6FKpiIiISIZQcBMRERHJEApuIiIiIhlCwU1EREQkQyi4iYiIiGQIBTcRyUpmtsvM5ic9bq/Cc7c1M91GQ0SqnO7jJiLZaqu7nxF1ESIiB0IjbiIiScxshZn9yswWmtl/zOzExPa2ZjbLzN42sxcTK3JgZkeb2RQzW5B4nJ04VY6Z/dnM3jWz58ysfuL4H5nZosR58iN6myKSoRTcRCRb1S93qXRo0r5N7n4a8Cjw28S23wN/d/fTgTgwMrF9JPCSu3cCugDvJra3B0a5+6nAZ8Dlie23A50T57khVW9ORGomrZwgIlnJzD5394YVbF8BfMPdlyUWmv7I3ZuZ2XqgpbvvTGxf6+7NzWwd0Nrdtyedoy3wvLu3Tzy/Dajj7vcllpT6HJgKTHX3z1P8VkWkBtGIm4jIV/levj8Q25O+30XZnOKLgVGE0bk5Zqa5xiJSaQpuIiJfNTTp6+uJ718DhiW+zwNeSXz/IvADADPLMbPGezupmdUCjnP3fwG3AY2Br4z6iYjsjf6lJyLZqr6ZzU96PsPdd98SpImZvU0YNbsyse1mYIyZ3QqsA65LbP8xMNrMrieMrP0AWLuXn5kDjE2EOwNGuvtnVfaORKTG0xw3EZEkiTluMXdfH3UtIiLl6VKpiIiISIbQiJuIiIhIhtCIm4iIiEiGUHATERERyRAKbiIiIiIZQsFNREREJEMouImIiIhkiP8PNTb4jLWoN9cAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluation = model.evaluate(test_ds)\n",
        "probabilities = model.predict(test_ds)\n",
        "finalLabels = probabilities.argmax(axis=-1)\n",
        "for i in range(len(class_names)):\n",
        "  print(sorted(class_names)[finalLabels[i]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fUe9XZezTBPF",
        "outputId": "daeeccbc-238e-40d9-8964-e0d2cf5caffc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "51/51 [==============================] - 3s 45ms/step - loss: 3.6212 - accuracy: 0.2105\n",
            "HARPY EAGLE\n",
            "RED FACED CORMORANT\n",
            "AMERICAN AVOCET\n",
            "EURASIAN GOLDEN ORIOLE\n",
            "PUFFIN\n",
            "QUETZAL\n",
            "MARABOU STORK\n",
            "NORTHERN GANNET\n",
            "BROWN THRASHER\n",
            "HORNED SUNGEM\n",
            "SWINHOES PHEASANT\n",
            "SRI LANKA BLUE MAGPIE\n",
            "GROVED BILLED ANI\n",
            "CAPE GLOSSY STARLING\n",
            "BANDED BROADBILL\n",
            "OSTRICH\n",
            "CURL CRESTED ARACURI\n",
            "MALACHITE KINGFISHER\n",
            "MANGROVE CUCKOO\n",
            "EMPEROR PENGUIN\n",
            "GREEN BROADBILL\n",
            "RAZORBILL\n",
            "EASTERN MEADOWLARK\n",
            "SPANGLED COTINGA\n",
            "CAPE GLOSSY STARLING\n",
            "SPOON BILED SANDPIPER\n",
            "BLACK FRANCOLIN\n",
            "BLACK TAIL CRAKE\n",
            "MIKADO  PHEASANT\n",
            "COLLARED ARACARI\n",
            "LAZULI BUNTING\n",
            "PATAGONIAN SIERRA FINCH\n",
            "GOLD WING WARBLER\n",
            "RED HONEY CREEPER\n",
            "INCA TERN\n",
            "RING-NECKED PHEASANT\n",
            "IWI\n",
            "KAKAPO\n",
            "SCARLET CROWNED FRUIT DOVE\n",
            "YELLOW CACIQUE\n",
            "COCKATOO\n",
            "PYGMY KINGFISHER\n",
            "MIKADO  PHEASANT\n",
            "EMPEROR PENGUIN\n",
            "CALIFORNIA QUAIL\n",
            "KOOKABURRA\n",
            "TRUMPTER SWAN\n",
            "PEACOCK\n",
            "OSTRICH\n",
            "MARABOU STORK\n",
            "BLACK VULTURE\n",
            "GOLDEN CHEEKED WARBLER\n",
            "GAMBELS QUAIL\n",
            "ARARIPE MANAKIN\n",
            "WILD TURKEY\n",
            "RAINBOW LORIKEET\n",
            "RED HEADED WOODPECKER\n",
            "BANDED PITA\n",
            "ROSY FACED LOVEBIRD\n",
            "GRAY CATBIRD\n",
            "PAINTED BUNTIG\n",
            "GREAT POTOO\n",
            "OYSTER CATCHER\n",
            "TOWNSENDS WARBLER\n",
            "YELLOW CACIQUE\n",
            "HOUSE FINCH\n",
            "MOURNING DOVE\n",
            "EMPEROR PENGUIN\n",
            "AMERICAN PIPIT\n",
            "GOLDEN CHLOROPHONIA\n",
            "BLACK TAIL CRAKE\n",
            "RING-NECKED PHEASANT\n",
            "HARLEQUIN DUCK\n",
            "AFRICAN CROWNED CRANE\n",
            "GRAY PARTRIDGE\n",
            "COUCHS KINGBIRD\n",
            "PAINTED BUNTIG\n",
            "STRIPPED MANAKIN\n",
            "CROW\n",
            "BLUE THROATED TOUCANET\n",
            "BLUE HERON\n",
            "GREAT POTOO\n",
            "GREAT GRAY OWL\n",
            "OYSTER CATCHER\n",
            "CROW\n",
            "SCARLET IBIS\n",
            "LAZULI BUNTING\n",
            "TASMANIAN HEN\n",
            "SPOON BILED SANDPIPER\n",
            "SPOON BILED SANDPIPER\n",
            "BLACK BAZA\n",
            "NORTHERN JACANA\n",
            "RED BELLIED PITTA\n",
            "TRUMPTER SWAN\n",
            "BLUE COAU\n",
            "PAINTED BUNTIG\n",
            "YELLOW CACIQUE\n",
            "OSPREY\n",
            "GROVED BILLED ANI\n",
            "HARPY EAGLE\n",
            "PINK ROBIN\n",
            "AMERICAN AVOCET\n",
            "SNOWY EGRET\n",
            "SPANGLED COTINGA\n",
            "SPOON BILED SANDPIPER\n",
            "IWI\n",
            "MALAGASY WHITE EYE\n",
            "RED BEARDED BEE EATER\n",
            "AFRICAN FIREFINCH\n",
            "KOOKABURRA\n",
            "COMMON POORWILL\n",
            "BLACK FRANCOLIN\n",
            "STORK BILLED KINGFISHER\n",
            "INDIGO BUNTING\n",
            "NORTHERN FULMAR\n",
            "WHITE TAILED TROPIC\n",
            "GRAY KINGBIRD\n",
            "SPLENDID WREN\n",
            "SCARLET CROWNED FRUIT DOVE\n",
            "COCKATOO\n",
            "KAKAPO\n",
            "COCK OF THE  ROCK\n",
            "AMERICAN GOLDFINCH\n",
            "BLUE HERON\n",
            "AMERICAN AVOCET\n",
            "DOUBLE BARRED FINCH\n",
            "BOBOLINK\n",
            "HOODED MERGANSER\n",
            "RED FODY\n",
            "TEAL DUCK\n",
            "BLONDE CRESTED WOODPECKER\n",
            "KOOKABURRA\n",
            "PEACOCK\n",
            "TASMANIAN HEN\n",
            "BLONDE CRESTED WOODPECKER\n",
            "PYGMY KINGFISHER\n",
            "PEACOCK\n",
            "GUINEAFOWL\n",
            "TOUCHAN\n",
            "WALL CREAPER\n",
            "HIMALAYAN MONAL\n",
            "NORTHERN GANNET\n",
            "YELLOW HEADED BLACKBIRD\n",
            "REGENT BOWERBIRD\n",
            "PEACOCK\n",
            "MARABOU STORK\n",
            "HOUSE FINCH\n",
            "SNOWY OWL\n",
            "CHIPPING SPARROW\n",
            "HORNED LARK\n",
            "SCARLET IBIS\n",
            "BRANDT CORMARANT\n",
            "IWI\n",
            "HOUSE FINCH\n",
            "GOLDEN CHEEKED WARBLER\n",
            "BULWERS PHEASANT\n",
            "PAINTED BUNTIG\n",
            "COCKATOO\n",
            "BEARDED BARBET\n",
            "AMERICAN COOT\n",
            "INCA TERN\n",
            "WOOD DUCK\n",
            "OSTRICH\n",
            "NORTHERN GANNET\n",
            "WHITE TAILED TROPIC\n",
            "SPANGLED COTINGA\n",
            "VARIED THRUSH\n",
            "PYGMY KINGFISHER\n",
            "AMERICAN AVOCET\n",
            "PURPLE GALLINULE\n",
            "BLACK FRANCOLIN\n",
            "HORNED LARK\n",
            "RED BROWED FINCH\n",
            "RAINBOW LORIKEET\n",
            "MARABOU STORK\n",
            "NORTHERN CARDINAL\n",
            "RED BEARDED BEE EATER\n",
            "CANARY\n",
            "TAKAHE\n",
            "VERMILION FLYCATHER\n",
            "ORIENTAL BAY OWL\n",
            "CALIFORNIA CONDOR\n",
            "GRAY KINGBIRD\n",
            "D-ARNAUDS BARBET\n",
            "DOUBLE BRESTED CORMARANT\n",
            "MIKADO  PHEASANT\n",
            "SCARLET IBIS\n",
            "AFRICAN FIREFINCH\n",
            "MYNA\n",
            "SPOON BILED SANDPIPER\n",
            "YELLOW CACIQUE\n",
            "ELEGANT TROGON\n",
            "YELLOW CACIQUE\n",
            "STRAWBERRY FINCH\n",
            "RAINBOW LORIKEET\n",
            "GROVED BILLED ANI\n",
            "SHOEBILL\n",
            "STRIPPED MANAKIN\n",
            "GOLD WING WARBLER\n",
            "BOBOLINK\n",
            "SNOWY EGRET\n",
            "SORA\n",
            "UMBRELLA BIRD\n",
            "HOUSE FINCH\n",
            "ROYAL FLYCATCHER\n",
            "COCKATOO\n",
            "GUINEA TURACO\n",
            "RAZORBILL\n",
            "AMERICAN PIPIT\n",
            "HIMALAYAN MONAL\n",
            "BLACK VULTURE\n",
            "COCKATOO\n",
            "VIOLET GREEN SWALLOW\n",
            "SPOONBILL\n",
            "BORNEAN BRISTLEHEAD\n",
            "VICTORIA CROWNED PIGEON\n",
            "CASPIAN TERN\n",
            "MARABOU STORK\n",
            "TOUCHAN\n",
            "BLACK-NECKED GREBE\n",
            "AMERICAN BITTERN\n",
            "BORNEAN BRISTLEHEAD\n",
            "MARABOU STORK\n",
            "MALACHITE KINGFISHER\n",
            "SCARLET IBIS\n",
            "RED BEARDED BEE EATER\n",
            "TOWNSENDS WARBLER\n",
            "CALIFORNIA CONDOR\n",
            "SWINHOES PHEASANT\n",
            "PALILA\n",
            "HORNBILL\n",
            "TRUMPTER SWAN\n",
            "COCKATOO\n",
            "FAIRY BLUEBIRD\n",
            "GREAT GRAY OWL\n",
            "KAKAPO\n",
            "RED FODY\n",
            "D-ARNAUDS BARBET\n",
            "TIT MOUSE\n",
            "CARMINE BEE-EATER\n",
            "MAGPIE GOOSE\n",
            "HIMALAYAN MONAL\n",
            "EURASIAN GOLDEN ORIOLE\n",
            "SRI LANKA BLUE MAGPIE\n",
            "CUBAN TROGON\n",
            "SORA\n",
            "WHITE THROATED BEE EATER\n",
            "GROVED BILLED ANI\n",
            "HARPY EAGLE\n",
            "CASPIAN TERN\n",
            "OSTRICH\n",
            "RUFUOS MOTMOT\n",
            "ROSY FACED LOVEBIRD\n",
            "SRI LANKA BLUE MAGPIE\n",
            "CASPIAN TERN\n",
            "SCARLET IBIS\n",
            "VICTORIA CROWNED PIGEON\n",
            "RED BEARDED BEE EATER\n",
            "NORTHERN CARDINAL\n",
            "COMMON GRACKLE\n",
            "RUFUOS MOTMOT\n",
            "WALL CREAPER\n",
            "SNOWY OWL\n",
            "GRAY KINGBIRD\n",
            "BLACK THROATED BUSHTIT\n",
            "BARN OWL\n",
            "ENGGANO MYNA\n",
            "SCARLET CROWNED FRUIT DOVE\n",
            "NICOBAR PIGEON\n",
            "UMBRELLA BIRD\n",
            "COCKATOO\n",
            "QUETZAL\n",
            "NORTHERN CARDINAL\n",
            "CALIFORNIA QUAIL\n",
            "SCARLET IBIS\n",
            "RUFUOS MOTMOT\n",
            "HARPY EAGLE\n",
            "SATYR TRAGOPAN\n",
            "CURL CRESTED ARACURI\n",
            "CASPIAN TERN\n",
            "BARN OWL\n",
            "CERULEAN WARBLER\n",
            "SUPERB STARLING\n",
            "AMERICAN GOLDFINCH\n",
            "SNOWY EGRET\n",
            "RUFUOS MOTMOT\n",
            "GOLDEN CHEEKED WARBLER\n",
            "COCK OF THE  ROCK\n",
            "PARADISE TANAGER\n",
            "KING VULTURE\n",
            "AMERICAN AVOCET\n",
            "MARABOU STORK\n",
            "TURQUOISE MOTMOT\n",
            "ARARIPE MANAKIN\n",
            "RED BELLIED PITTA\n",
            "RED BELLIED PITTA\n",
            "HELMET VANGA\n",
            "SCARLET IBIS\n",
            "SCARLET CROWNED FRUIT DOVE\n",
            "RED BEARDED BEE EATER\n",
            "NICOBAR PIGEON\n",
            "LAZULI BUNTING\n",
            "GOULDIAN FINCH\n",
            "SCARLET IBIS\n",
            "HORNBILL\n",
            "BALI STARLING\n",
            "EMPEROR PENGUIN\n",
            "OYSTER CATCHER\n",
            "GREY PLOVER\n",
            "BLACK BAZA\n",
            "MALEO\n",
            "BLACK THROATED BUSHTIT\n",
            "UMBRELLA BIRD\n",
            "HOUSE SPARROW\n",
            "CRESTED CARACARA\n",
            "STRIPPED MANAKIN\n",
            "HARPY EAGLE\n",
            "GOLDEN CHEEKED WARBLER\n",
            "MARABOU STORK\n",
            "OSPREY\n",
            "GRAY CATBIRD\n",
            "WILSONS BIRD OF PARADISE\n",
            "AMERICAN AVOCET\n",
            "KILLDEAR\n",
            "EMPEROR PENGUIN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datagen = ImageDataGenerator(rescale=1/255)\n",
        "\n",
        "train_gen = datagen.flow_from_directory(train_dir, target_size=(224, 224))\n",
        "val_gen = datagen.flow_from_directory(val_dir, target_size=(224,224))\n",
        "test_gen = datagen.flow_from_directory(test_dir, target_size=(224,224))\n",
        "\n",
        "train_groups = len(train_gen)\n",
        "val_groups = len(val_gen)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ksoaVk0o8B7a",
        "outputId": "d4857e91-55e9-403a-e983-7b036449ba81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 47332 images belonging to 325 classes.\n",
            "Found 1625 images belonging to 325 classes.\n",
            "Found 1625 images belonging to 325 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_modelv2(input_shape=(image_width,image_height,3)):\n",
        "    model = Sequential()\n",
        "    #Input layer, each neuron gets exactly 1 image (batch size of 32 images)\n",
        "\n",
        "    model.add(layers.Conv2D(64, (3, 3), activation=\"relu\", input_shape=input_shape, padding='same'))\n",
        "    #model.add(BatchNormalization())\n",
        "\n",
        "    model.add(layers.Conv2D(64, (3, 3), activation=\"relu\", input_shape=input_shape, padding='same'))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2, 2)))\n",
        "    \n",
        "    model.add(layers.Conv2D(64, (3, 3), activation=\"relu\", padding='same'))\n",
        "    #model.add(layers.MaxPool2D(pool_size=(2, 2)))\n",
        "    model.add(BatchNormalization())\n",
        "\n",
        "    model.add(layers.Conv2D(64, (3, 3), activation=\"relu\", padding='same'))\n",
        "    model.add(layers.MaxPool2D(pool_size=(2, 2)))\n",
        "    \n",
        "  \n",
        "    model.add(layers.Conv2D(64, (3, 3), activation=\"relu\", padding='same'))\n",
        "    #model.add(layers.MaxPool2D(pool_size=(2, 2)))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Dropout(0.3))\n",
        "\n",
        "    model.add(layers.Flatten())\n",
        "    model.add(Dropout(0.35))\n",
        "    #model.add(layers.Dense(120, activation='relu'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(layers.Dense(60, activation='relu'))\n",
        "    model.add(layers.Dropout(rate=0.3))\n",
        "    model.add(layers.Dense(325, activation='softmax'))\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "kmjZKd3q8JfL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelv2 = create_modelv2((image_width, image_height,3))\n",
        "modelv2 = compile_model(modelv2)\n",
        "\n",
        "#SGD optimizer preformed worse than adam optimizer!!!!\n",
        "history2 = modelv2.fit(train_gen,\n",
        "                      validation_data=val_gen, shuffle=True, epochs=40, verbose=1)\n",
        "\n",
        "model.save(\"tf_cnn_bird2.model\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "fiIkWRMr8NsN",
        "outputId": "e455f6a2-4074-437d-c54e-5af77843573a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "  19/1480 [..............................] - ETA: 7:52 - loss: 8.5954 - accuracy: 0.0000e+00"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-e1f2633c145e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#SGD optimizer preformed worse than adam optimizer!!!!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m history2 = modelv2.fit(train_gen,\n\u001b[0;32m----> 6\u001b[0;31m                       validation_data=val_gen, shuffle=True, epochs=40, verbose=1)\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"tf_cnn_bird2.model\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1219\u001b[0m               \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp_logs\u001b[0m  \u001b[0;31m# No error, now safe to assign to logs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1220\u001b[0m               \u001b[0mend_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1221\u001b[0;31m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1222\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    434\u001b[0m     \"\"\"\n\u001b[1;32m    435\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_call_train_batch_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m    293\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_begin_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_end_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m       raise ValueError(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_end_hook\u001b[0;34m(self, mode, batch, logs)\u001b[0m\n\u001b[1;32m    314\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhook_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_batches_for_timing_check\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook_helper\u001b[0;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[1;32m    352\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m       \u001b[0mhook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m       \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_timing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1030\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1032\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_update_progbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1033\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1102\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1103\u001b[0m       \u001b[0;31m# Only block async when verbose = 1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1104\u001b[0;31m       \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_to_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1105\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m    552\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    555\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m    867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    870\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    870\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36m_to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    548\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m       \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    551\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1147\u001b[0m     \"\"\"\n\u001b[1;32m   1148\u001b[0m     \u001b[0;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1149\u001b[0;31m     \u001b[0mmaybe_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1150\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1113\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1114\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1115\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1116\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1117\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "import shutil\n",
        "testModel = load_model(\"tf_cnn_bird.model\")"
      ],
      "metadata": {
        "id": "3IO_oIaOQ1z9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "bf027daa-1c27-430f-fdb5-a5399cab7ff8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-27a2e98ff120>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshutil\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtestModel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"tf_cnn_bird.model\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/saving/save.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile, options)\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'No file or directory found at {filepath}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    210\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0msaving_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_hdf5_filepath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mh5py\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m             raise ImportError(\n",
            "\u001b[0;31mOSError\u001b[0m: No file or directory found at tf_cnn_bird.model"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "newFiles = files.upload()\n",
        "!mkdir ./newTests\n",
        "!mkdir ./newTests/uploadedImages\n",
        "for name in newFiles.keys():\n",
        "  shutil.move(name, \"./newTests/uploadedImages/\" + name)"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "jOFNrLlNb10q",
        "outputId": "debd1ee1-7022-43bc-d644-05671f9f3d48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a9d1dc53-ffdb-474c-9d40-2c13711aea81\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-a9d1dc53-ffdb-474c-9d40-2c13711aea81\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving test.jpg to test.jpg\n",
            "mkdir: cannot create directory ‘./newTests’: File exists\n",
            "mkdir: cannot create directory ‘./newTests/uploadedImages’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded_tests = keras.preprocessing.image_dataset_from_directory(\n",
        "    pathlib.Path(\"./newTests\"),\n",
        "    label_mode='categorical',\n",
        "    batch_size = batch_size,\n",
        "    image_size = (image_width, image_height)\n",
        ")\n",
        "uploadProb = model.predict(uploaded_tests)\n",
        "uploadLabels = uploadProb.argmax(axis=-1)\n",
        "for i in range(len(uploadLabels)):\n",
        "  print(sorted(class_names)[uploadLabels[i]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KmsOLgbnfCKZ",
        "outputId": "b2e668c1-5ad2-4fac-a402-6767104203db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2 files belonging to 2 classes.\n",
            "OSTRICH\n",
            "TRUMPTER SWAN\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "bird.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}