112. Random_forest_with_inceptionv3.ipynb

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    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kEfWkpzVY_g9",
        "outputId": "5801d2dd-47c7-4313-af14-7ee55d7c541b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: gdown\n",
            "  Attempting uninstall: gdown\n",
            "    Found existing installation: gdown 4.4.0\n",
            "    Uninstalling gdown-4.4.0:\n",
            "      Successfully uninstalled gdown-4.4.0\n",
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            "/usr/local/lib/python3.8/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1-Vm4Y4YTPSH_zM3xSTzB9RQvJqiVFwaw\n",
            "To: /content/Tumor.zip\n",
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          ]
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      ],
      "source": [
        "!pip install --upgrade --no-cache-dir gdown\n",
        "!gdown --id 1-Vm4Y4YTPSH_zM3xSTzB9RQvJqiVFwaw"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install segmentation-models\n",
        "!pip install tensorflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y3FkRaBvZIdj",
        "outputId": "e78ede5d-68c8-4f8c-ba22-a484ec0e2c9f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install patool\n",
        "import patoolib\n",
        "patoolib.extract_archive('Tumor.zip')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "3aljWbTyZKDJ",
        "outputId": "94614336-30d8-46a0-c43b-d2745e865514"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting patool\n",
            "  Downloading patool-1.12-py2.py3-none-any.whl (77 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.5/77.5 KB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: patool\n",
            "Successfully installed patool-1.12\n",
            "patool: Extracting Tumor.zip ...\n",
            "patool: running /usr/bin/7z x -o./Unpack_xn6qf61_ -- Tumor.zip\n",
            "patool: ... Tumor.zip extracted to `Tumor'.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Tumor'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "import cv2\n",
        "\n",
        "from keras.models import Model, Sequential\n",
        "from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "import os\n",
        "import seaborn as sns\n",
        "from keras.applications.vgg16 import VGG16\n",
        "# from keras.applications.resnet import ResNet34\n",
        "from keras.applications.inception_v3 import InceptionV3"
      ],
      "metadata": {
        "id": "8AiuvzCkZMnM"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SIZE = 128\n",
        "\n",
        "train_images = []\n",
        "train_labels = [] \n",
        "\n",
        "for directory_path in glob.glob(\"/content/Tumor/Training/*\"):\n",
        "    label = directory_path.split(\"/\")[-1]\n",
        "    print(label)\n",
        "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
        "        #print(img_path)\n",
        "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)       \n",
        "        img = cv2.resize(img, (SIZE, SIZE))\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
        "        train_images.append(img)\n",
        "        train_labels.append(label)\n",
        "print(len(train_images))        \n",
        "train_images = np.array(train_images)\n",
        "train_labels = np.array(train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NL9omPsdZOVS",
        "outputId": "04c3bc69-65f0-4948-a41e-7b333052abb1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pituitary\n",
            "notumor\n",
            "meningioma\n",
            "glioma\n",
            "5712\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_images = []\n",
        "test_labels = [] \n",
        "for directory_path in glob.glob(\"/content/Tumor/Testing/*\"):\n",
        "    fruit_label = directory_path.split(\"/\")[-1]\n",
        "    print(fruit_label)\n",
        "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
        "        #print(img_path)\n",
        "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n",
        "        img = cv2.resize(img, (SIZE, SIZE))\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
        "        test_images.append(img)\n",
        "        test_labels.append(fruit_label)\n",
        "print(len(test_images))            \n",
        "test_images = np.array(test_images)\n",
        "test_labels = np.array(test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98MLeOozZPqM",
        "outputId": "0c7e8e4d-5a50-46b4-d95f-3852697174e8"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pituitary\n",
            "notumor\n",
            "meningioma\n",
            "glioma\n",
            "1311\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "le.fit(test_labels)\n",
        "test_labels_encoded = le.transform(test_labels)\n",
        "le.fit(train_labels)\n",
        "train_labels_encoded = le.transform(train_labels)\n"
      ],
      "metadata": {
        "id": "Nn0QPVdyZRXo"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, y_train, x_test, y_test = train_images, train_labels_encoded, test_images, test_labels_encoded"
      ],
      "metadata": {
        "id": "HZWsDQs0ZS0Y"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ],
      "metadata": {
        "id": "-BslgEawZUc4"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import to_categorical\n",
        "y_train_one_hot = to_categorical(y_train)\n",
        "y_test_one_hot = to_categorical(y_test)"
      ],
      "metadata": {
        "id": "kZJYyW6_ZWdu"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inception_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(SIZE, SIZE, 3))\n",
        "for layer in inception_model.layers:\n",
        "\tlayer.trainable = False\n",
        "inception_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlJm8htRZYQ-",
        "outputId": "23343525-0d05-41b0-bb17-ccc95060d318"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87910968/87910968 [==============================] - 0s 0us/step\n",
            "Model: \"inception_v3\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 128, 128, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv2d (Conv2D)                (None, 63, 63, 32)   864         ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization (BatchNorm  (None, 63, 63, 32)  96          ['conv2d[0][0]']                 \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " activation (Activation)        (None, 63, 63, 32)   0           ['batch_normalization[0][0]']    \n",
            "                                                                                                  \n",
            " conv2d_1 (Conv2D)              (None, 61, 61, 32)   9216        ['activation[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_1 (BatchNo  (None, 61, 61, 32)  96          ['conv2d_1[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_1 (Activation)      (None, 61, 61, 32)   0           ['batch_normalization_1[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)              (None, 61, 61, 64)   18432       ['activation_1[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_2 (BatchNo  (None, 61, 61, 64)  192         ['conv2d_2[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_2 (Activation)      (None, 61, 61, 64)   0           ['batch_normalization_2[0][0]']  \n",
            "                                                                                                  \n",
            " max_pooling2d (MaxPooling2D)   (None, 30, 30, 64)   0           ['activation_2[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_3 (Conv2D)              (None, 30, 30, 80)   5120        ['max_pooling2d[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_3 (BatchNo  (None, 30, 30, 80)  240         ['conv2d_3[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_3 (Activation)      (None, 30, 30, 80)   0           ['batch_normalization_3[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_4 (Conv2D)              (None, 28, 28, 192)  138240      ['activation_3[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_4 (BatchNo  (None, 28, 28, 192)  576        ['conv2d_4[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_4 (Activation)      (None, 28, 28, 192)  0           ['batch_normalization_4[0][0]']  \n",
            "                                                                                                  \n",
            " max_pooling2d_1 (MaxPooling2D)  (None, 13, 13, 192)  0          ['activation_4[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)              (None, 13, 13, 64)   12288       ['max_pooling2d_1[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_8 (BatchNo  (None, 13, 13, 64)  192         ['conv2d_8[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_8 (Activation)      (None, 13, 13, 64)   0           ['batch_normalization_8[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)              (None, 13, 13, 48)   9216        ['max_pooling2d_1[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)              (None, 13, 13, 96)   55296       ['activation_8[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_6 (BatchNo  (None, 13, 13, 48)  144         ['conv2d_6[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " batch_normalization_9 (BatchNo  (None, 13, 13, 96)  288         ['conv2d_9[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_6 (Activation)      (None, 13, 13, 48)   0           ['batch_normalization_6[0][0]']  \n",
            "                                                                                                  \n",
            " activation_9 (Activation)      (None, 13, 13, 96)   0           ['batch_normalization_9[0][0]']  \n",
            "                                                                                                  \n",
            " average_pooling2d (AveragePool  (None, 13, 13, 192)  0          ['max_pooling2d_1[0][0]']        \n",
            " ing2D)                                                                                           \n",
            "                                                                                                  \n",
            " conv2d_5 (Conv2D)              (None, 13, 13, 64)   12288       ['max_pooling2d_1[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)              (None, 13, 13, 64)   76800       ['activation_6[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)             (None, 13, 13, 96)   82944       ['activation_9[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)             (None, 13, 13, 32)   6144        ['average_pooling2d[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_5 (BatchNo  (None, 13, 13, 64)  192         ['conv2d_5[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " batch_normalization_7 (BatchNo  (None, 13, 13, 64)  192         ['conv2d_7[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " batch_normalization_10 (BatchN  (None, 13, 13, 96)  288         ['conv2d_10[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_11 (BatchN  (None, 13, 13, 32)  96          ['conv2d_11[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_5 (Activation)      (None, 13, 13, 64)   0           ['batch_normalization_5[0][0]']  \n",
            "                                                                                                  \n",
            " activation_7 (Activation)      (None, 13, 13, 64)   0           ['batch_normalization_7[0][0]']  \n",
            "                                                                                                  \n",
            " activation_10 (Activation)     (None, 13, 13, 96)   0           ['batch_normalization_10[0][0]'] \n",
            "                                                                                                  \n",
            " activation_11 (Activation)     (None, 13, 13, 32)   0           ['batch_normalization_11[0][0]'] \n",
            "                                                                                                  \n",
            " mixed0 (Concatenate)           (None, 13, 13, 256)  0           ['activation_5[0][0]',           \n",
            "                                                                  'activation_7[0][0]',           \n",
            "                                                                  'activation_10[0][0]',          \n",
            "                                                                  'activation_11[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_15 (Conv2D)             (None, 13, 13, 64)   16384       ['mixed0[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_15 (BatchN  (None, 13, 13, 64)  192         ['conv2d_15[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_15 (Activation)     (None, 13, 13, 64)   0           ['batch_normalization_15[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)             (None, 13, 13, 48)   12288       ['mixed0[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_16 (Conv2D)             (None, 13, 13, 96)   55296       ['activation_15[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_13 (BatchN  (None, 13, 13, 48)  144         ['conv2d_13[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_16 (BatchN  (None, 13, 13, 96)  288         ['conv2d_16[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_13 (Activation)     (None, 13, 13, 48)   0           ['batch_normalization_13[0][0]'] \n",
            "                                                                                                  \n",
            " activation_16 (Activation)     (None, 13, 13, 96)   0           ['batch_normalization_16[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_1 (AveragePo  (None, 13, 13, 256)  0          ['mixed0[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)             (None, 13, 13, 64)   16384       ['mixed0[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_14 (Conv2D)             (None, 13, 13, 64)   76800       ['activation_13[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_17 (Conv2D)             (None, 13, 13, 96)   82944       ['activation_16[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)             (None, 13, 13, 64)   16384       ['average_pooling2d_1[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_12 (BatchN  (None, 13, 13, 64)  192         ['conv2d_12[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_14 (BatchN  (None, 13, 13, 64)  192         ['conv2d_14[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_17 (BatchN  (None, 13, 13, 96)  288         ['conv2d_17[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_18 (BatchN  (None, 13, 13, 64)  192         ['conv2d_18[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_12 (Activation)     (None, 13, 13, 64)   0           ['batch_normalization_12[0][0]'] \n",
            "                                                                                                  \n",
            " activation_14 (Activation)     (None, 13, 13, 64)   0           ['batch_normalization_14[0][0]'] \n",
            "                                                                                                  \n",
            " activation_17 (Activation)     (None, 13, 13, 96)   0           ['batch_normalization_17[0][0]'] \n",
            "                                                                                                  \n",
            " activation_18 (Activation)     (None, 13, 13, 64)   0           ['batch_normalization_18[0][0]'] \n",
            "                                                                                                  \n",
            " mixed1 (Concatenate)           (None, 13, 13, 288)  0           ['activation_12[0][0]',          \n",
            "                                                                  'activation_14[0][0]',          \n",
            "                                                                  'activation_17[0][0]',          \n",
            "                                                                  'activation_18[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_22 (Conv2D)             (None, 13, 13, 64)   18432       ['mixed1[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_22 (BatchN  (None, 13, 13, 64)  192         ['conv2d_22[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_22 (Activation)     (None, 13, 13, 64)   0           ['batch_normalization_22[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_20 (Conv2D)             (None, 13, 13, 48)   13824       ['mixed1[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_23 (Conv2D)             (None, 13, 13, 96)   55296       ['activation_22[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_20 (BatchN  (None, 13, 13, 48)  144         ['conv2d_20[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_23 (BatchN  (None, 13, 13, 96)  288         ['conv2d_23[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_20 (Activation)     (None, 13, 13, 48)   0           ['batch_normalization_20[0][0]'] \n",
            "                                                                                                  \n",
            " activation_23 (Activation)     (None, 13, 13, 96)   0           ['batch_normalization_23[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_2 (AveragePo  (None, 13, 13, 288)  0          ['mixed1[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_19 (Conv2D)             (None, 13, 13, 64)   18432       ['mixed1[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_21 (Conv2D)             (None, 13, 13, 64)   76800       ['activation_20[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_24 (Conv2D)             (None, 13, 13, 96)   82944       ['activation_23[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_25 (Conv2D)             (None, 13, 13, 64)   18432       ['average_pooling2d_2[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_19 (BatchN  (None, 13, 13, 64)  192         ['conv2d_19[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_21 (BatchN  (None, 13, 13, 64)  192         ['conv2d_21[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_24 (BatchN  (None, 13, 13, 96)  288         ['conv2d_24[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_25 (BatchN  (None, 13, 13, 64)  192         ['conv2d_25[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_19 (Activation)     (None, 13, 13, 64)   0           ['batch_normalization_19[0][0]'] \n",
            "                                                                                                  \n",
            " activation_21 (Activation)     (None, 13, 13, 64)   0           ['batch_normalization_21[0][0]'] \n",
            "                                                                                                  \n",
            " activation_24 (Activation)     (None, 13, 13, 96)   0           ['batch_normalization_24[0][0]'] \n",
            "                                                                                                  \n",
            " activation_25 (Activation)     (None, 13, 13, 64)   0           ['batch_normalization_25[0][0]'] \n",
            "                                                                                                  \n",
            " mixed2 (Concatenate)           (None, 13, 13, 288)  0           ['activation_19[0][0]',          \n",
            "                                                                  'activation_21[0][0]',          \n",
            "                                                                  'activation_24[0][0]',          \n",
            "                                                                  'activation_25[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_27 (Conv2D)             (None, 13, 13, 64)   18432       ['mixed2[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_27 (BatchN  (None, 13, 13, 64)  192         ['conv2d_27[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_27 (Activation)     (None, 13, 13, 64)   0           ['batch_normalization_27[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_28 (Conv2D)             (None, 13, 13, 96)   55296       ['activation_27[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_28 (BatchN  (None, 13, 13, 96)  288         ['conv2d_28[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_28 (Activation)     (None, 13, 13, 96)   0           ['batch_normalization_28[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_26 (Conv2D)             (None, 6, 6, 384)    995328      ['mixed2[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_29 (Conv2D)             (None, 6, 6, 96)     82944       ['activation_28[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_26 (BatchN  (None, 6, 6, 384)   1152        ['conv2d_26[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_29 (BatchN  (None, 6, 6, 96)    288         ['conv2d_29[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_26 (Activation)     (None, 6, 6, 384)    0           ['batch_normalization_26[0][0]'] \n",
            "                                                                                                  \n",
            " activation_29 (Activation)     (None, 6, 6, 96)     0           ['batch_normalization_29[0][0]'] \n",
            "                                                                                                  \n",
            " max_pooling2d_2 (MaxPooling2D)  (None, 6, 6, 288)   0           ['mixed2[0][0]']                 \n",
            "                                                                                                  \n",
            " mixed3 (Concatenate)           (None, 6, 6, 768)    0           ['activation_26[0][0]',          \n",
            "                                                                  'activation_29[0][0]',          \n",
            "                                                                  'max_pooling2d_2[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_34 (Conv2D)             (None, 6, 6, 128)    98304       ['mixed3[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_34 (BatchN  (None, 6, 6, 128)   384         ['conv2d_34[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_34 (Activation)     (None, 6, 6, 128)    0           ['batch_normalization_34[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_35 (Conv2D)             (None, 6, 6, 128)    114688      ['activation_34[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_35 (BatchN  (None, 6, 6, 128)   384         ['conv2d_35[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_35 (Activation)     (None, 6, 6, 128)    0           ['batch_normalization_35[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_31 (Conv2D)             (None, 6, 6, 128)    98304       ['mixed3[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_36 (Conv2D)             (None, 6, 6, 128)    114688      ['activation_35[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_31 (BatchN  (None, 6, 6, 128)   384         ['conv2d_31[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_36 (BatchN  (None, 6, 6, 128)   384         ['conv2d_36[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_31 (Activation)     (None, 6, 6, 128)    0           ['batch_normalization_31[0][0]'] \n",
            "                                                                                                  \n",
            " activation_36 (Activation)     (None, 6, 6, 128)    0           ['batch_normalization_36[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_32 (Conv2D)             (None, 6, 6, 128)    114688      ['activation_31[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_37 (Conv2D)             (None, 6, 6, 128)    114688      ['activation_36[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_32 (BatchN  (None, 6, 6, 128)   384         ['conv2d_32[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_37 (BatchN  (None, 6, 6, 128)   384         ['conv2d_37[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_32 (Activation)     (None, 6, 6, 128)    0           ['batch_normalization_32[0][0]'] \n",
            "                                                                                                  \n",
            " activation_37 (Activation)     (None, 6, 6, 128)    0           ['batch_normalization_37[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_3 (AveragePo  (None, 6, 6, 768)   0           ['mixed3[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_30 (Conv2D)             (None, 6, 6, 192)    147456      ['mixed3[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_33 (Conv2D)             (None, 6, 6, 192)    172032      ['activation_32[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_38 (Conv2D)             (None, 6, 6, 192)    172032      ['activation_37[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_39 (Conv2D)             (None, 6, 6, 192)    147456      ['average_pooling2d_3[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_30 (BatchN  (None, 6, 6, 192)   576         ['conv2d_30[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_33 (BatchN  (None, 6, 6, 192)   576         ['conv2d_33[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_38 (BatchN  (None, 6, 6, 192)   576         ['conv2d_38[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_39 (BatchN  (None, 6, 6, 192)   576         ['conv2d_39[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_30 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_30[0][0]'] \n",
            "                                                                                                  \n",
            " activation_33 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_33[0][0]'] \n",
            "                                                                                                  \n",
            " activation_38 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_38[0][0]'] \n",
            "                                                                                                  \n",
            " activation_39 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_39[0][0]'] \n",
            "                                                                                                  \n",
            " mixed4 (Concatenate)           (None, 6, 6, 768)    0           ['activation_30[0][0]',          \n",
            "                                                                  'activation_33[0][0]',          \n",
            "                                                                  'activation_38[0][0]',          \n",
            "                                                                  'activation_39[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_44 (Conv2D)             (None, 6, 6, 160)    122880      ['mixed4[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_44 (BatchN  (None, 6, 6, 160)   480         ['conv2d_44[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_44 (Activation)     (None, 6, 6, 160)    0           ['batch_normalization_44[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_45 (Conv2D)             (None, 6, 6, 160)    179200      ['activation_44[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_45 (BatchN  (None, 6, 6, 160)   480         ['conv2d_45[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_45 (Activation)     (None, 6, 6, 160)    0           ['batch_normalization_45[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_41 (Conv2D)             (None, 6, 6, 160)    122880      ['mixed4[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_46 (Conv2D)             (None, 6, 6, 160)    179200      ['activation_45[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_41 (BatchN  (None, 6, 6, 160)   480         ['conv2d_41[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_46 (BatchN  (None, 6, 6, 160)   480         ['conv2d_46[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_41 (Activation)     (None, 6, 6, 160)    0           ['batch_normalization_41[0][0]'] \n",
            "                                                                                                  \n",
            " activation_46 (Activation)     (None, 6, 6, 160)    0           ['batch_normalization_46[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_42 (Conv2D)             (None, 6, 6, 160)    179200      ['activation_41[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_47 (Conv2D)             (None, 6, 6, 160)    179200      ['activation_46[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_42 (BatchN  (None, 6, 6, 160)   480         ['conv2d_42[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_47 (BatchN  (None, 6, 6, 160)   480         ['conv2d_47[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_42 (Activation)     (None, 6, 6, 160)    0           ['batch_normalization_42[0][0]'] \n",
            "                                                                                                  \n",
            " activation_47 (Activation)     (None, 6, 6, 160)    0           ['batch_normalization_47[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_4 (AveragePo  (None, 6, 6, 768)   0           ['mixed4[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_40 (Conv2D)             (None, 6, 6, 192)    147456      ['mixed4[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_43 (Conv2D)             (None, 6, 6, 192)    215040      ['activation_42[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_48 (Conv2D)             (None, 6, 6, 192)    215040      ['activation_47[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_49 (Conv2D)             (None, 6, 6, 192)    147456      ['average_pooling2d_4[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_40 (BatchN  (None, 6, 6, 192)   576         ['conv2d_40[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_43 (BatchN  (None, 6, 6, 192)   576         ['conv2d_43[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_48 (BatchN  (None, 6, 6, 192)   576         ['conv2d_48[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_49 (BatchN  (None, 6, 6, 192)   576         ['conv2d_49[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_40 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_40[0][0]'] \n",
            "                                                                                                  \n",
            " activation_43 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_43[0][0]'] \n",
            "                                                                                                  \n",
            " activation_48 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_48[0][0]'] \n",
            "                                                                                                  \n",
            " activation_49 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_49[0][0]'] \n",
            "                                                                                                  \n",
            " mixed5 (Concatenate)           (None, 6, 6, 768)    0           ['activation_40[0][0]',          \n",
            "                                                                  'activation_43[0][0]',          \n",
            "                                                                  'activation_48[0][0]',          \n",
            "                                                                  'activation_49[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_54 (Conv2D)             (None, 6, 6, 160)    122880      ['mixed5[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_54 (BatchN  (None, 6, 6, 160)   480         ['conv2d_54[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_54 (Activation)     (None, 6, 6, 160)    0           ['batch_normalization_54[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_55 (Conv2D)             (None, 6, 6, 160)    179200      ['activation_54[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_55 (BatchN  (None, 6, 6, 160)   480         ['conv2d_55[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_55 (Activation)     (None, 6, 6, 160)    0           ['batch_normalization_55[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_51 (Conv2D)             (None, 6, 6, 160)    122880      ['mixed5[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_56 (Conv2D)             (None, 6, 6, 160)    179200      ['activation_55[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_51 (BatchN  (None, 6, 6, 160)   480         ['conv2d_51[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_56 (BatchN  (None, 6, 6, 160)   480         ['conv2d_56[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_51 (Activation)     (None, 6, 6, 160)    0           ['batch_normalization_51[0][0]'] \n",
            "                                                                                                  \n",
            " activation_56 (Activation)     (None, 6, 6, 160)    0           ['batch_normalization_56[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_52 (Conv2D)             (None, 6, 6, 160)    179200      ['activation_51[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_57 (Conv2D)             (None, 6, 6, 160)    179200      ['activation_56[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_52 (BatchN  (None, 6, 6, 160)   480         ['conv2d_52[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_57 (BatchN  (None, 6, 6, 160)   480         ['conv2d_57[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_52 (Activation)     (None, 6, 6, 160)    0           ['batch_normalization_52[0][0]'] \n",
            "                                                                                                  \n",
            " activation_57 (Activation)     (None, 6, 6, 160)    0           ['batch_normalization_57[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_5 (AveragePo  (None, 6, 6, 768)   0           ['mixed5[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_50 (Conv2D)             (None, 6, 6, 192)    147456      ['mixed5[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_53 (Conv2D)             (None, 6, 6, 192)    215040      ['activation_52[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_58 (Conv2D)             (None, 6, 6, 192)    215040      ['activation_57[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_59 (Conv2D)             (None, 6, 6, 192)    147456      ['average_pooling2d_5[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_50 (BatchN  (None, 6, 6, 192)   576         ['conv2d_50[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_53 (BatchN  (None, 6, 6, 192)   576         ['conv2d_53[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_58 (BatchN  (None, 6, 6, 192)   576         ['conv2d_58[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_59 (BatchN  (None, 6, 6, 192)   576         ['conv2d_59[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_50 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_50[0][0]'] \n",
            "                                                                                                  \n",
            " activation_53 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_53[0][0]'] \n",
            "                                                                                                  \n",
            " activation_58 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_58[0][0]'] \n",
            "                                                                                                  \n",
            " activation_59 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_59[0][0]'] \n",
            "                                                                                                  \n",
            " mixed6 (Concatenate)           (None, 6, 6, 768)    0           ['activation_50[0][0]',          \n",
            "                                                                  'activation_53[0][0]',          \n",
            "                                                                  'activation_58[0][0]',          \n",
            "                                                                  'activation_59[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_64 (Conv2D)             (None, 6, 6, 192)    147456      ['mixed6[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_64 (BatchN  (None, 6, 6, 192)   576         ['conv2d_64[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_64 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_64[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_65 (Conv2D)             (None, 6, 6, 192)    258048      ['activation_64[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_65 (BatchN  (None, 6, 6, 192)   576         ['conv2d_65[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_65 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_65[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_61 (Conv2D)             (None, 6, 6, 192)    147456      ['mixed6[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_66 (Conv2D)             (None, 6, 6, 192)    258048      ['activation_65[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_61 (BatchN  (None, 6, 6, 192)   576         ['conv2d_61[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_66 (BatchN  (None, 6, 6, 192)   576         ['conv2d_66[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_61 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_61[0][0]'] \n",
            "                                                                                                  \n",
            " activation_66 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_66[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_62 (Conv2D)             (None, 6, 6, 192)    258048      ['activation_61[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_67 (Conv2D)             (None, 6, 6, 192)    258048      ['activation_66[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_62 (BatchN  (None, 6, 6, 192)   576         ['conv2d_62[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_67 (BatchN  (None, 6, 6, 192)   576         ['conv2d_67[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_62 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_62[0][0]'] \n",
            "                                                                                                  \n",
            " activation_67 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_67[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_6 (AveragePo  (None, 6, 6, 768)   0           ['mixed6[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_60 (Conv2D)             (None, 6, 6, 192)    147456      ['mixed6[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_63 (Conv2D)             (None, 6, 6, 192)    258048      ['activation_62[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_68 (Conv2D)             (None, 6, 6, 192)    258048      ['activation_67[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_69 (Conv2D)             (None, 6, 6, 192)    147456      ['average_pooling2d_6[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_60 (BatchN  (None, 6, 6, 192)   576         ['conv2d_60[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_63 (BatchN  (None, 6, 6, 192)   576         ['conv2d_63[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_68 (BatchN  (None, 6, 6, 192)   576         ['conv2d_68[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_69 (BatchN  (None, 6, 6, 192)   576         ['conv2d_69[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_60 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_60[0][0]'] \n",
            "                                                                                                  \n",
            " activation_63 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_63[0][0]'] \n",
            "                                                                                                  \n",
            " activation_68 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_68[0][0]'] \n",
            "                                                                                                  \n",
            " activation_69 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_69[0][0]'] \n",
            "                                                                                                  \n",
            " mixed7 (Concatenate)           (None, 6, 6, 768)    0           ['activation_60[0][0]',          \n",
            "                                                                  'activation_63[0][0]',          \n",
            "                                                                  'activation_68[0][0]',          \n",
            "                                                                  'activation_69[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_72 (Conv2D)             (None, 6, 6, 192)    147456      ['mixed7[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_72 (BatchN  (None, 6, 6, 192)   576         ['conv2d_72[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_72 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_72[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_73 (Conv2D)             (None, 6, 6, 192)    258048      ['activation_72[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_73 (BatchN  (None, 6, 6, 192)   576         ['conv2d_73[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_73 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_73[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_70 (Conv2D)             (None, 6, 6, 192)    147456      ['mixed7[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_74 (Conv2D)             (None, 6, 6, 192)    258048      ['activation_73[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_70 (BatchN  (None, 6, 6, 192)   576         ['conv2d_70[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_74 (BatchN  (None, 6, 6, 192)   576         ['conv2d_74[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_70 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_70[0][0]'] \n",
            "                                                                                                  \n",
            " activation_74 (Activation)     (None, 6, 6, 192)    0           ['batch_normalization_74[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_71 (Conv2D)             (None, 2, 2, 320)    552960      ['activation_70[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_75 (Conv2D)             (None, 2, 2, 192)    331776      ['activation_74[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_71 (BatchN  (None, 2, 2, 320)   960         ['conv2d_71[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_75 (BatchN  (None, 2, 2, 192)   576         ['conv2d_75[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_71 (Activation)     (None, 2, 2, 320)    0           ['batch_normalization_71[0][0]'] \n",
            "                                                                                                  \n",
            " activation_75 (Activation)     (None, 2, 2, 192)    0           ['batch_normalization_75[0][0]'] \n",
            "                                                                                                  \n",
            " max_pooling2d_3 (MaxPooling2D)  (None, 2, 2, 768)   0           ['mixed7[0][0]']                 \n",
            "                                                                                                  \n",
            " mixed8 (Concatenate)           (None, 2, 2, 1280)   0           ['activation_71[0][0]',          \n",
            "                                                                  'activation_75[0][0]',          \n",
            "                                                                  'max_pooling2d_3[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_80 (Conv2D)             (None, 2, 2, 448)    573440      ['mixed8[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_80 (BatchN  (None, 2, 2, 448)   1344        ['conv2d_80[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_80 (Activation)     (None, 2, 2, 448)    0           ['batch_normalization_80[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_77 (Conv2D)             (None, 2, 2, 384)    491520      ['mixed8[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_81 (Conv2D)             (None, 2, 2, 384)    1548288     ['activation_80[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_77 (BatchN  (None, 2, 2, 384)   1152        ['conv2d_77[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_81 (BatchN  (None, 2, 2, 384)   1152        ['conv2d_81[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_77 (Activation)     (None, 2, 2, 384)    0           ['batch_normalization_77[0][0]'] \n",
            "                                                                                                  \n",
            " activation_81 (Activation)     (None, 2, 2, 384)    0           ['batch_normalization_81[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_78 (Conv2D)             (None, 2, 2, 384)    442368      ['activation_77[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_79 (Conv2D)             (None, 2, 2, 384)    442368      ['activation_77[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_82 (Conv2D)             (None, 2, 2, 384)    442368      ['activation_81[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_83 (Conv2D)             (None, 2, 2, 384)    442368      ['activation_81[0][0]']          \n",
            "                                                                                                  \n",
            " average_pooling2d_7 (AveragePo  (None, 2, 2, 1280)  0           ['mixed8[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_76 (Conv2D)             (None, 2, 2, 320)    409600      ['mixed8[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_78 (BatchN  (None, 2, 2, 384)   1152        ['conv2d_78[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_79 (BatchN  (None, 2, 2, 384)   1152        ['conv2d_79[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_82 (BatchN  (None, 2, 2, 384)   1152        ['conv2d_82[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_83 (BatchN  (None, 2, 2, 384)   1152        ['conv2d_83[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_84 (Conv2D)             (None, 2, 2, 192)    245760      ['average_pooling2d_7[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_76 (BatchN  (None, 2, 2, 320)   960         ['conv2d_76[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_78 (Activation)     (None, 2, 2, 384)    0           ['batch_normalization_78[0][0]'] \n",
            "                                                                                                  \n",
            " activation_79 (Activation)     (None, 2, 2, 384)    0           ['batch_normalization_79[0][0]'] \n",
            "                                                                                                  \n",
            " activation_82 (Activation)     (None, 2, 2, 384)    0           ['batch_normalization_82[0][0]'] \n",
            "                                                                                                  \n",
            " activation_83 (Activation)     (None, 2, 2, 384)    0           ['batch_normalization_83[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_84 (BatchN  (None, 2, 2, 192)   576         ['conv2d_84[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_76 (Activation)     (None, 2, 2, 320)    0           ['batch_normalization_76[0][0]'] \n",
            "                                                                                                  \n",
            " mixed9_0 (Concatenate)         (None, 2, 2, 768)    0           ['activation_78[0][0]',          \n",
            "                                                                  'activation_79[0][0]']          \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 2, 2, 768)    0           ['activation_82[0][0]',          \n",
            "                                                                  'activation_83[0][0]']          \n",
            "                                                                                                  \n",
            " activation_84 (Activation)     (None, 2, 2, 192)    0           ['batch_normalization_84[0][0]'] \n",
            "                                                                                                  \n",
            " mixed9 (Concatenate)           (None, 2, 2, 2048)   0           ['activation_76[0][0]',          \n",
            "                                                                  'mixed9_0[0][0]',               \n",
            "                                                                  'concatenate[0][0]',            \n",
            "                                                                  'activation_84[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_89 (Conv2D)             (None, 2, 2, 448)    917504      ['mixed9[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_89 (BatchN  (None, 2, 2, 448)   1344        ['conv2d_89[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_89 (Activation)     (None, 2, 2, 448)    0           ['batch_normalization_89[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_86 (Conv2D)             (None, 2, 2, 384)    786432      ['mixed9[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_90 (Conv2D)             (None, 2, 2, 384)    1548288     ['activation_89[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_86 (BatchN  (None, 2, 2, 384)   1152        ['conv2d_86[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_90 (BatchN  (None, 2, 2, 384)   1152        ['conv2d_90[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_86 (Activation)     (None, 2, 2, 384)    0           ['batch_normalization_86[0][0]'] \n",
            "                                                                                                  \n",
            " activation_90 (Activation)     (None, 2, 2, 384)    0           ['batch_normalization_90[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_87 (Conv2D)             (None, 2, 2, 384)    442368      ['activation_86[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_88 (Conv2D)             (None, 2, 2, 384)    442368      ['activation_86[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_91 (Conv2D)             (None, 2, 2, 384)    442368      ['activation_90[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_92 (Conv2D)             (None, 2, 2, 384)    442368      ['activation_90[0][0]']          \n",
            "                                                                                                  \n",
            " average_pooling2d_8 (AveragePo  (None, 2, 2, 2048)  0           ['mixed9[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_85 (Conv2D)             (None, 2, 2, 320)    655360      ['mixed9[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_87 (BatchN  (None, 2, 2, 384)   1152        ['conv2d_87[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_88 (BatchN  (None, 2, 2, 384)   1152        ['conv2d_88[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_91 (BatchN  (None, 2, 2, 384)   1152        ['conv2d_91[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_92 (BatchN  (None, 2, 2, 384)   1152        ['conv2d_92[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_93 (Conv2D)             (None, 2, 2, 192)    393216      ['average_pooling2d_8[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_85 (BatchN  (None, 2, 2, 320)   960         ['conv2d_85[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_87 (Activation)     (None, 2, 2, 384)    0           ['batch_normalization_87[0][0]'] \n",
            "                                                                                                  \n",
            " activation_88 (Activation)     (None, 2, 2, 384)    0           ['batch_normalization_88[0][0]'] \n",
            "                                                                                                  \n",
            " activation_91 (Activation)     (None, 2, 2, 384)    0           ['batch_normalization_91[0][0]'] \n",
            "                                                                                                  \n",
            " activation_92 (Activation)     (None, 2, 2, 384)    0           ['batch_normalization_92[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_93 (BatchN  (None, 2, 2, 192)   576         ['conv2d_93[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_85 (Activation)     (None, 2, 2, 320)    0           ['batch_normalization_85[0][0]'] \n",
            "                                                                                                  \n",
            " mixed9_1 (Concatenate)         (None, 2, 2, 768)    0           ['activation_87[0][0]',          \n",
            "                                                                  'activation_88[0][0]']          \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate)    (None, 2, 2, 768)    0           ['activation_91[0][0]',          \n",
            "                                                                  'activation_92[0][0]']          \n",
            "                                                                                                  \n",
            " activation_93 (Activation)     (None, 2, 2, 192)    0           ['batch_normalization_93[0][0]'] \n",
            "                                                                                                  \n",
            " mixed10 (Concatenate)          (None, 2, 2, 2048)   0           ['activation_85[0][0]',          \n",
            "                                                                  'mixed9_1[0][0]',               \n",
            "                                                                  'concatenate_1[0][0]',          \n",
            "                                                                  'activation_93[0][0]']          \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 21,802,784\n",
            "Trainable params: 0\n",
            "Non-trainable params: 21,802,784\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_extractor=inception_model.predict(x_train)\n",
        "\n",
        "features = feature_extractor.reshape(feature_extractor.shape[0], -1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBzJw5RjZdN_",
        "outputId": "98ec0a18-65eb-40fe-c508-29df5cab37de"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "179/179 [==============================] - 13s 26ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_for_RF = features"
      ],
      "metadata": {
        "id": "u5XwJ10RZjNI"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "RF_model = RandomForestClassifier(n_estimators = 50, random_state = 42)"
      ],
      "metadata": {
        "id": "-eFvdOWEZlVk"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RF_model.fit(X_for_RF, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TjFF5gJAZnyt",
        "outputId": "bc9f2058-7127-4a52-8c85-837062779d67"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=50, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_feature = inception_model.predict(x_test)\n",
        "X_test_features = X_test_feature.reshape(X_test_feature.shape[0], -1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CDclVWzdZpi7",
        "outputId": "2988bc66-b990-4317-de5d-1b976a154700"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "41/41 [==============================] - 2s 44ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Now predict using the trained RF model. \n",
        "prediction_RF = RF_model.predict(X_test_features)\n",
        "#Inverse le transform to get original label back. \n",
        "prediction_RF = le.inverse_transform(prediction_RF)"
      ],
      "metadata": {
        "id": "FmFdjBbqZrXd"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print (\"Accuracy = \", metrics.accuracy_score(test_labels, prediction_RF))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66hA12q_Z1B0",
        "outputId": "d81c645f-bd0f-44f0-fcad-e3eaf89b43e0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy =  0.8871090770404272\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt   \n",
        "from sklearn import metrics\n",
        "import pandas as pd\n",
        "from sklearn.metrics import classification_report\n",
        "ax= plt.subplot()\n",
        "cm = confusion_matrix(test_labels,  prediction_RF)\n",
        "#print(cm)\n",
        "\n",
        "sns.heatmap(cm, annot=True,cmap=\"Blues\",fmt='g')\n",
        "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
        "ax.set_title('Confusion Matrix'); \n",
        "ax.xaxis.set_ticklabels([\"Pituitary\", \"Glioma\", \"Notumor\",\"Meningioma\"]); \n",
        "ax.yaxis.set_ticklabels([\"Pituitary\", \"Glioma\", \"Notumor\",\"Meningioma\"]);\n",
        "d = {'Expected': test_labels, 'Predicted':  prediction_RF}\n",
        "results = pd.DataFrame(data=d)\n",
        "print(classification_report(results['Expected'], results['Predicted']))\n",
        "     "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "tKFDsB6_Z2dK",
        "outputId": "8b2d1057-3019-49a2-ab52-1264e62ff14f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma       0.91      0.75      0.82       300\n",
            "  meningioma       0.78      0.81      0.79       306\n",
            "     notumor       0.99      1.00      0.99       405\n",
            "   pituitary       0.85      0.95      0.89       300\n",
            "\n",
            "    accuracy                           0.89      1311\n",
            "   macro avg       0.88      0.88      0.88      1311\n",
            "weighted avg       0.89      0.89      0.89      1311\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n=np.random.randint(0, x_test.shape[0])\n",
        "img = x_test[n]\n",
        "plt.imshow(img)\n",
        "input_img = np.expand_dims(img, axis=0) #Expand dims so the input is (num images, x, y, c)\n",
        "input_img_feature=inception_model.predict(input_img)\n",
        "input_img_features=input_img_feature.reshape(input_img_feature.shape[0], -1)\n",
        "prediction_RF = RF_model.predict(input_img_features)[0] \n",
        "prediction_RF = le.inverse_transform([prediction_RF])  #Reverse the label encoder to original name\n",
        "print(\"The prediction for this image is: \", prediction_RF)\n",
        "print(\"The actual label for this image is: \", test_labels[n])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "qHdfLO0YZ4iH",
        "outputId": "7ffb8ee0-cb56-413f-adaa-698798c6ebf0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 552ms/step\n",
            "The prediction for this image is:  ['notumor']\n",
            "The actual label for this image is:  notumor\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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CCAaDIhT0fFAbNZtNdDqdU9YELR4tBMyUYX3seDzGYDBwmKp8Rm2p9ft9EUZ+vx+TyQTdblfuNxwOMRwO0W63hSGj0Sh8Pp98x2vQpfF6vSIsLMtCu92Gx+NBPB6X56lWq2i1Wmi3247GImZeg1sehI46jMdjBAIBABABwtZmfE8zccfNatT9J/RcuWlvc21MJeNmPZh/TxtuluzPlBDggplAmPatAWf8fNbQAkWbwbwfh5uw0MfoRdbHm0xMAqOJq4lFx8/JtJZlOczHYrGIbDaLubk5BINB+P1+xONxJBIJBAIBRz8+fe1+v49gMIhcLodYLIbBYOAwT2kZ8Ll7vZ4QIF0Kmui9Xg/RaBTRaFS0d6vVknel9u31eggEAkgmk+h0OhiNRhJy1Pekb0/hEwqF0O/30e12hVnS6bSse71el7VmSJFgZiQSkegGf8/NzYnl0mw20Wq10Gg00O/30W63T2lR07I0AV2a+XrNzEjEWQrBdA+nDcuyxEJxcwPc6HLa9cj0ppthKh63cWGEAHDazAamS7GzJsTNLJplUUy753n8K7fPteYxr6+tDDJ3IBBAOp1GOp1GLpcTbR0OhxEOhx2+MoUaBQkXXyPMNJO73a5oXD4DBaJG/tvttjA916DX64k5D5xgCPTV6brwHjyOWpTMadu2AH5aMOiQI5lORx7Yk4BYiT6erlE0GoVt2+j3+w7XqNfrwePxYDgcintiWndu6+emtbX1OMv9JL240Z4bbUxjfn2t89K423HnxQYujBBwM8eB027CWaaNeYxmRL2Yphk2bZJ0w1FTmwBwaAp9L22taP+SzEdCXlxcRCaTwerqqlynWCyKr05mIPJOMI358yT4yWSCTqeDRqOBdruNer2Ofr+PRqOBbreLdrvteC8yI/1o4Dg0mE6nRZiUy2Ux32lC8/0SiQQmkwk2NjYcgoHX4TwPBgN4vV4Ui0UMBgM0Gg0RapyDaDSKXC4neACfT7trnNPBYIBmswmPx4NUKoVCoYDJZIJms4l4PC6uAT+r1+sol8s4PDwU7EFjIW6RqGlum8aU+JkpXHQeghsT838zp4NWpPksbiC3xmVM2nULL57lRlxIIQC4A2raLTAl6HmEgx6z8AT9HKYpaGqCaZpFuxR6EXw+H0KhEDKZjKD4iUQC0WhUfHRNSNR2NO/L5bJDk4/HY5TLZQwGA2H2Tqfj0OJmWG1aQ08m92jQ0LIs1Go18fljsRi8Xi9qtRpGoxEGg4EDsKXfz3dvNptiYhPAY8iQWj4cDqNSqSASiUg4kuDceDwW1ygSiTiInEKGeQa0qPg80WgUoVAIXq9XBESv10Ov13PkLbitrRYEdN9mWanazeBwY2AzFdwtB8TNOtDHcV109EMrN31vNxo0x4UWAqa5wwkiY7o1j3S7ppv5z//NRdD3A5yZgtMElJ5wfQ0zWQSAZOotLS0hnU4jEAgI4h8OhxEIBISBx+MxwuEwCoUC/H4/+v0+7t27J4vfarXQ6XSwsbGBdruNRqMh6bl8Bt1dl1bLYDCAz+dDOBx2hCXJpGSO1dVV+Hw+VKtVEV7xeBx+vx97e3siWMi8FEy6XXez2US/30e5XJbPaNJ3u11YliVCwefzIZlMyhzxHdLpNCKRCOLxuMw334XvW6vVkEgk5J38fr8kMTH5ybZtVKtVHB4eihDgGmv60tfXIVZNoyagzKHRe7fkH8433TM3i2eaBcHv6Boy41KD0G6t7qbRKMdnshfhxx0+n88m8ms+tJbInEgujE5AmSUEpg298Ka7YLoh07oaz7o3tTrDW6VSSZJncrkcotEokskk/H6/Q4Npouv3++j1emg0Guh0Otja2kK/3xfgi99p0JTmN3vr0S+mtdFsNoVZmW9QqVROWQzhcBgA0Gw2HSY5GZj306Ywn5vWBK9HoabDjrQEIpGIrDE1t477x+NxhEIhmStGEZikxPXRcX+v14tMJuNIMGLmYrPZxMHBAQ4PD9FoNASj0GAhmYmf6doUTX+mBaE7KJmlxvpHX5/nmqFsM/fEPE5v1DKNljXdNhqNH9m2/YJJqxfGEgA+vklvmub8bNb/Z93LxBBmWRpnXZcINjV9Pp8XIUATn5qK5q/2JUejERqNBsrlsiTKVCoVDAYDQdhpZpuhPybp8Dqj0UiSith+ut1uIxKJIJlMOpptknG0MNCgIQAJq5kuhWWdRrw1Q2grjBYDrRyNUWgXDIAkCHE+9fXoUvAcuihE+XU8n4JtNBqh1+sJIzEkaq7jtCrWWXSjLQb9v2mR8jPzxzzGpCvTejWjXm4Kbda4EELANMVMN8AETLQZR/NK78HmZlKZ4IuJLwCnfSsSNACHn2v6/7wOr0HCzGQyyOfzyOVyCIfDiMViiMViSKfTglhrX5v+6t7eHhqNBra3t1Gr1VCr1Rxhr2Aw6EgeYkgxHA5jb28PlUpFQMTr168jl8shn89je3sb5XIZP/zhDzEYDFCv15HL5eD1enHz5k1Y1nFu/eHhIY6OjjAcDhEIBLC+vo5Op4NqtYorV65ISK/T6YiZb1kWDg8PYVkWUqmUWCs7OzsSdlxaWsKtW7ekkKndbqNWq+HBgwc4ODhAtVqV3ATOi9frlZoEug+2bSOZTEotQyQSweLiorglDD/qUGg8HkcsFkOpVEKn08Hh4SFyuZxYV41GA1tbW2IJuIWhSUPEB3RbNLP60aQnk/FNnEC7azrxiMfTnXHr1ejGF/r3LCsWuCBCADit1d00vBuz8js36TpN4gJOX9/M2tPjPN2JtPAh8SaTSWQyGaRSKaRSKbEAiOhSuLBtNVF8CoFarYadnR10u11J/gkEAuIXa3eDWrXdbqNUKuHKlStYWlpCMplEsViUXIBkMgmfz4cvfvGLKJfLePTokQi6y5cvw+/3o9VqIZ1OY25uTgRBrVYTwiQYqH3/Xq+Hfr+PVqslc8Zw3Y0bNyTJKZPJYG5uTiwWpkBHIhHs7u6iXC478iwajQaq1aokCdGMZvIRU5Up0DToaFkWOp2OuFraJfF4PIhEInLNZDIJAIjFYmKNucXWTcxgGnNpWjWVxrS/tbXghglMO9/NWnWzSmaNCyMEAHdBQBOPWlO/tFt4zzSttLTVJqbZXFJbE/p5KO1p/lKzmM9NhvT5fIjFYrh8+TLi8Tji8TgymYycb1mW5MpT03U6HRwcHIj/f/fuXVSrVezu7orvmUgkEIvFcOXKFTSbTWxtbck7MkOv1+vhxRdfxGuvvYZkMinakNV58Xgc+XweX/3qV9FsNrGxsYHvfe972NzcxAsvvIBoNCqMPxgM8N5772FnZwff/e53JYOxUqlIGjDnigys4/G5XA7FYhGvv/46lpeX4fP5HPiFditisRi2trZweHiIbDaLyWSCg4MDvPvuu3jzzTclZHp4eCiCstVqwePxIJ/PYzgcYn9/XwTw2toa/H4/6vW6ZF3SneGaM78AAHK5nFgQOrpiMjnnm5mRJr2atMNz3BSIeR6xB40JuDGwPk+7RG4mvxtA6DY+sRCwLGsJwP8GYA6ADeAbtm3/a8uyMgD+DwArAB4B+Jpt29UzruVA0s2X0FVwWhK7mWD6PF6bn2ntr6+lP3N7Ln5vAjn63iy4WV1dRSQSES1HtJvot876G4/HglRvbW2h0+mg0+lgf38fo9EIuVzOIfBGoxE6nQ7y+Tw+//nPI5/PIxAI4G//9m8RDofx3HPPYX19HalUCpVKRaILTESin7+xsSHJOqurqwKgtdtt0aaxWAzPP/88rl+/juXlZTx48ADvvfceHjx44MhbCAQCEn9nok4sFsPCwgKWl5cRDocxHo/F1O/1euK+fPDBBwiHw8jlckilUgAgdQLPP/+8FEM9ePAAtVpNfPpwOCwRlKOjI0dSFQAcHBxIEhYFA10Q5lSMx2NEo1Fxz5iaTFxid3dXhKdWQiadkUbc+iC4MZ6p8XmcLlLSFi6PpSVm27bQkekauNHvWZYF8OksgRGA/9K27Tcty4oD+JFlWf8PgP8MwN/Ztv2vLMv6YwB/DOBfnHWxaQLAZFbg9MYePE4PN1Nulnvg9ixuLoS2SHTCEav55ubmhBgZ9uOi6dTffr+PwWCASqWCSqWC/f19tNttdLtdtFoteL1epFIphxAj4+VyOTz11FNYWVlBMBjE22+/jWQyiRdffFEAR6byUjiRWQeDAcrlsoQI8/k8EomEmPej0ciBXUwmE2QyGViWhbt37+Lw8BCtVktcm3A47IhMhEIh5PN5FAoFFAoFMcW73a5EKjhX9N15XjQalTThxcVFSU/e39+Xe2paoGVBP55lzM1mUyouKXx1MxQCnxQyHo8HoVAInU7nVBhVA6W8xlk+9jQ6NAFD0ppORNP0rY8zwT+d7mwKDbfhRufy3Xl8hvMMy7L+GsCffvTzS7Zt71qWNQ/g27ZtX5t1LkOExvVOHcfJ1/Xq04Yb07s8s/jxWtpqgaNzEnhNmmHD4VCaeZRKJaTTaSGsubk5R7jJsizEYjG0Wi2Uy2VUKhW0220cHByg0Whgf39fnpfn0VROJpN49dVXsby8jCtXrqDf76NSqSAej8Pn8+Hhw4cIBoNYWlrCYDDAYDAQgcN8ftu2RfDU63VEo1EUi0VhToYBfT4f8vk8MpkMNjc3MRqNkMlk0Gg0cHBwgL/5m7/BvXv3sLW1JQxFIbOwsIDV1VX87u/+rrgh8/PziEQiiMViqNfr2N/fl1Dfzs4O/H4/CoWCmOG2fRwmXFpaEoFoWcf1Ebdv38ajR49w+/ZtaZUGQOYjGo0iHA6LxUNLRZdGLy0tCQPxcxYtUUgMBgNUq1V0u11UKhVUq1XJi9CFVWY16zRa00w6KwOVTE93g2FPE6/SoLYGpmcpPsuyUK/XP7sQoWVZKwCeBfD/AZizbXv3o6/2cOwuuJ3zdQBf5wOaY5Y/5OYvnSUFzcWa5suZ95kGCBJcikajwvws/aWQoinaarXEdCPYRY3K+L8uOMrn84hGoygUCnKPpaUlzM3NCaClhVI2mxV0meYsM+90aS1Di27ulxaodLVochM8i8fjKJVKqFar2NnZOWUFraysYHl5WbIKTQJl7QPNagJ+BPl0fQDNdjJxOBzG4uKiYCAUXo1GQxgeOAn9AZBW44PBAKFQCOPxca9DpluT0aj1dbZiLBZDIBCQOggmJLEmQWewzrJKz/Lr3ejU/NscH0dxa1qeNj61ELAsKwbg3wH4z23bbhgTYFuW5Xp327a/AeAbwLElcI77nGmOuZlFJFQz68skfr2oPE5bCSQGCgafz4fFxUVB4AFIPrtlWahUKigUCojH4zg6OpL2Wc1mE9VqFZubm2K2mljFrVu3sL6+jtdff11ATGqe7e1tBxDp9/tRLBbFp9cZY+PxGO12G6FQCOFwGLVaDd1uV4AwAOKnc7C+nwKL9y8UClhcXMSHH36Ifr+PDz/8ULRWJpNBJpPBb/zGb4jVwOw/ABI98Hg8DowkFouh0WjgwYMHiEajIvAsy5IsRZ/PJ3jAU089hZs3b+K1117DD37wA3zwwQd48OABRqORgH+dTge1Wg0+nw/FYhHNZhPlchn5fB5+vx+VSgXhcFiAU50qzeQlr9crrhAAJBIJzM/P45133sHBwYFocloGFEJaw5+lxKZZD7wG/9a0rl2EacrJTRi54Vh6fCohYFmWH8cC4H+3bfv/+ujjfcuy5pU7cHCea53Hn3EL12lmJaNwstxCf6aPNm3BOHRHXAqEXC6HeDyO+fl5Ad40WEOknjF7avvd3V0J+emoA5/1xo0bWFtbwzPPPINUKiUar9PpnEqRJojFOWHoDIDUygMnOMZkctLOS2t6mr/6uvw+mUwKeElCL5VKmEwmeOutt9DtdhEMBnH58mUsLCwgFosJY3Et6I7Q19fvQbM6kUg4BK3P50M8HpcoAK0EChLLsnDp0iXEYjFxJarVKra3t7G/vy9JUZVKRZqj1Ot1dDodNJtNRKNR9Pt9ySugv8/cCyYXAZCsRJ/Ph+XlZSQSCTx+/NhRAq5pVJvsZm6Jm5DQ/r8Z7dLH0fUxcQCTT8gP57FmOT5NdMAC8G8A3LFt+39UX/3fAP4ZgH/10e+//qT34NBmJeC+84tb9ZaeCGr6aVzesz8AACAASURBVCCKXkwtqbmQjG37/X5ks1lkMhkUCgUhagoCMiNNVZrfvV4P29vbjqw8mtI8/9atW3jppZewsLAA4BjlrtfrqNfrQoxkNJr3BCp1nruJHJPw6COTyQnUsbuOycDxeBzD4RBbW1tifRQKBWG+TqeDUCiE9fV1XL582dGijPel6U9ty7m2bVuEQCwWk4xBrh/7IrAZicfjEbPd6/WiVCphfn4eN27cQLVaxTvvvCM1ChR29XpdUHfmMOjwIAuMKASIE7AOwuM57nXAe87PzyOVSuHw8FDm0C25zfyfVgIFrHa9dHq0SY9uwsK0IDj05/oZZlkAHJ/GEngJwD8F8I5lWW9/9Nl/g2Pm/z8ty/rnADYAfO08F9Na2818cTO59CBBUfJpbagJm0NLZ/Mz/T/9xHA4jFQqJeGsSCSCSCQCAOKHM4mGP8zw297eRqfTkXx7+rWWZWF1dRXFYhHXr1/HysoKfD4f9vf3MZlMRICweo5JOGQgCgEzwwwA4vG43IfuAJmQyD5RcaL81NREwxnTz2az6Pf7uHv3LtbW1uDz+ZDNZlEqlXDjxg0BRavVqrgu1KiVSgW2bcucJZNJ7O3todVqSRQFgDBaNpt1pD9rk1hvKkIhx2zL9fV1hMNhLC0tiRv09ttvo9VqodlsipAm8+7u7gowWCqVHJGAdruNXC4nuQZ0H5ib8Mwzz6BSqeDBgwcivBhZ0C6lqYHp0gKnd9jSGlsLCfKFDoebDO5mzZKHPlMhYNv2/wtg2h1++ZNck5LSfHitpadJwmngntv3WgBMAwv5P01BxpRZ9kszV1eb6USkdrstcWaG/nRIkUyxtLSEUqmEtbU1xGIxAJCOODq0yIoxXXhiujk6U0+b3XwXamkmKemCGd0izLadG4MQVNPP7/V6EY1GUSqVHJ2NCSTy2bQLR0uE82auPTEG1jYQ5ScDkz4o/ICT9uiZTAa2bUsxEvGXRqOBWq0m4KO2GMnAbsVQujsSBQTpgSHVeDzuyuicdzPUZwKIbvSozzPBR03LmnZnubUaY5g2LkTGIF9YF56Yw2RWE0ChCad9YbfMK56rd/LRwBtwEqIjoYfDYRSLRREETNI5OjoSpqTvyU6777//Pjqdjmy4wefjM/zWb/0Wbt68KQk10WgU1WpVogc+nw8rKyuilamh2VBD5xyEQiHpJxCLxZBKpaQ4hmh2IBAQ7Z9IJATtjsVi0vVH4x6WdVwDwLlgtIJ1DzTNl5aWUC6Xsbe3J12EOKe0YnQ/QSbpaHSe66HTfe/cuSNzdenSJUQiEfT7fQfwR8uF67O4uCgmPQB85StfQavVQrVaxZ//+Z/jJz/5iZRCc/2azSbu3buHcDiMhYUFiYLcv38ftm1jZWUF9XodR0dHyGQy4pYlk0lcu3YNu7u7ODo6kvXX7oFWClQWbrSr6c3kCW0dkL51PgEAwUx4Xc4nP6NAmzYuhBDQppM5WXqY/pH+XCP3OqXTBAY5MW4dWMyiEdu2HU09qJGYumpuqUUAipofgIP5Y7EYcrkcFhcXcfXqVczPzwOAaCOv1yv566PRCHt7e+J20N+nQGDVHBmNgoh+tQ4hcj7IaNTKtGiYfw8A3W5XtC+Zmu5QPB5Ho9FAvV6XazG0SSHDnoQ6M5JWzXg8ll6GGtAl0wJAvV4XAcPkH64Xw4K2bYuLE4vFHFiMbnTCa2ezWbz44ovI5/N499135XkYymQ2Y7ValbmnG9hut+U5OHSOAsOzFMI6ddo0+Tk0LqC1vptFqvEwk/nd6FZbgG4Ao9u4EEIAcE4GzVpzmOCdfjFKYQ2+kAg1jkAhoJtJkiB1rJnXZvovowCsaqOG5vPSknj06JEICYa9Go0GACCVSuHGjRt47bXXUCqVEA6HJeTHHv3cPaher+Ott95CqVSSUBuBMk3AZGaGx4hL6L75/CGzacuA70RsodVqSaZjrVYTRJ61A5ubm9jZ2XEIRMuypBai3+9LJyJeF4CUI9dqNVkTCiT2ABgOh2JdsdU6cZderyd9ECzLkpRsmv+MojDiwWPoz//yL/8yDg8P8cEHH4iQoNZn45OjoyP0ej10Oh1cvnwZwWAQ5XJZGq+SucLhsMNNicVi0tqtXC4DcO4rYQKiWkDooZmcP5q+OW+aB4CTIjSdzXqWm6DHhRECmsF1Iov+oSnkVmppSkPTLKOpyWu4SVsSmMYAVldXpd03zTEKAGYDMrOM++0xe+/o6Aj7+/u4efMm5ufn8eKLL0qcnJpXN+Bk+2wCkM8++6wINWp/VslR67JZSDQaxfr6umhnltHSHNf+vN66i8KL5cs7Ozu4du0a1tfXUSgUJG2XZng0GhV3iP44mSifz4ugZdiO5vmNGzeEQFutlnREJg5B64Ran+eFQiERRgxzsgeDJnKuCQUABRDnmW7d7/3e7+HOnTv45je/KXQ1Pz8P27bFSmEyF7M/GfVgxiXzOyikotGonNfr9aTAys2t1Yxr0qBmeq6VDgPzOH0N0vcsrf+ZhQh/2sP02/UwkVZt6rudo6WkPpYaR1sE+pr83OfzIRKJIJFIiHamb6V3v6FQ4a46jUZDtuXSlsP8/DxWVlZw5coViR/TfKUlon1m4BgJz+fzQlR8B62RtFZh/wC6BNrXpiAxw4DAifap1+uo1WqoVqui/elyAJBoB01sXovPoTWfjmDweZi1p7c00yAj14fX0kVKFHbASY9GHSamNcbjWGPAaw0GA7EKnn76aanipNVEC0mvCxuz8ju+jwYkKcACgYA0XqXrpqNVJn0DpzedmTa0e6xpddowwcdpoLceF6K9mNfrtUnc/NFhPc3w1NSmSaW/138Dp2Ou2j0g+MLr0ofMZrPS/pvAFgGiSCQiCHa9Xpf4/2QyQSwWE3T61q1beOaZZ3D9+nUxXckAOpTGdFZ9jWAwKAlDjDRYloWFhQXRRhQynC9m17VaLQlxMXGp2+0im82KVqNFRV/2+9//PobDIbLZLFZWVrC0tCRaudlsyoYn+/v7qNVq+PDDD5FKpfD000/L3BJP0K7dgwcPJOGHTUW4Jtx0hIU+Xq8X+/v7gnvQ4tnf30ev15NjGbunEGIhFjstM8wYCoWEiVKplGAI3W4X5XIZf//3f487d+7ge9/7HmzbxvLyssw1BW82m0UikUA2m5Xmqkxf7vf7yGQy0hBmOBxK67K9vT1p406hrGlZY2Bu9KkH51ILSm056PO1RUzhyGt+prUDn3ZoF4D/TxtnTRiP0b+1cOG5OhzD+7MclaajDgPqxBxqnGq1KqEnLhA1y9zcHEqlElKplCDKWvBQgGktxuehKUkTWPt51F7UoLRuKMwYUqS5qc1ordUZUWFWInfrZdhLd/HRRS/sOsS5cvM9dcktBU+5XJZ9BTOZDILBoLgsevA5OS8a39B5/MCJL0xhxs/4o9d+NBpJpIb5CJ/73OcQj8fR7/dRrValiIrzaNu27L+oG5YSl2H7Nlp0tAyYD0FXUbuv5vvquZtG91pwaCtXfz+NT6Z9r8eFEALAiX/DYYIqgLNd0izzn7/JTLqJp96EQ0tkj8cjTTcJGOmNPlkzH4/HZQusx48fOzIK6Y/ncjl89atfdTCjDl+S0OjzkYiZxENip3uhkXfm5esNQzUz8Xia0bVazbHHH5+DzTqPjo5Qr9fR6/UQiURQKBQAANVqVQQbM+wGgwE++OADbG9v40tf+hJSqZSDWbVApRZfW1vDcDjEgwcP0O12cefOHTz77LOOLcW0VReLxSQUSgGVTqfh9XpxeHgoVlA0GkUgEBArikJMl2/r2L7OjgwGg0gkEnjppZcQDAbx7LPP4t1338Wf/umfitCLRqPSB4H4xOLiIsLhMH7yk58gFothfn5e0q95T75DMBiU0JzeUk0DeJqmNa2bLgLXQVu5boLAtHxN/pk2LowQOI9bYgoEDfjNAkS0OWUmcDBxJxqNSsvqRCIhviAjAYw+sBcAQTz63bzH9evXsbCwgGw267A+WHarrQ5tjVDz8F78jkg2mYouks6z5zU0kERBSUuEZjA1pplBp5OBdAr0eDyWTUh0yTEFkzY/GSvXW5nxmqurqyiXy3j8+LEg8MwYpI/u8/lQqVSkkIegG4/nHGkLia4NswlNM9u0AHU/AWIsTH3+wz/8Q7z99tv48Y9/jHK5LPUOk8lESsApKHVhk050mp+fl0Slubk5xGIxHB4eSmk151k/nzm0wNCmv1ZeukqTNG7bJ1m1ulGsmThmjgsjBKbFVN1AQH7u5k/pwXN03oGZVOHxHJe0klmISHPSWeyj0VwChHw2MoLX68XKyopk0el7EywiQwMnGZL8TINi5vuyBp/aTIOKpjtFxqZGYz68mTWn51EPgn8kJN3sRJc8m1EcgmpMrw0EAnK/ubk52Y1oMBig0+kgmUwKU3Me6VtTmwYCARG4dLd0Fx6CgBr0dBsmnfT7fVlnv9+Pubk5rK6uSqUmOzNxjrlhCbENzouZe8Eq0tFoJL0lCbQy6xNwFsOZpjvpk89t/tbKzeQHU+idR7leGCHAoaXjNM3Ol+Xk6xfXQzMSBQZN6MlkIjsBMRU4n88DgJTS0t/kua1WS0ClTqeDubk5MdEvX76MxcVFfPnLX0Y6nXbcm9qXsXL6pvS9/X4/UqkU6vW6MJDX65VMQBIPmVNbCbqUNZFInEL/vd7jHoZ7e3uCFxChr9frEoVg5ILanEzKXZLI+DSRmRAUj8fR7XblPMs6rrIcjUbiUliWhQ8//BDD4RDxeFxAvBdeeEEAPO4jkM/n5Vr8IUZD8FSvL5N4xuOxWCcaUadAZKiUdDKZHG9T1mw2pW5iOBzi5ZdfxksvvYQ/+7M/w507d/Dw4UNxJeiKxeNxcd1olXG+2emIacwUEuVyWZSHbqtOjIm4jSkUNICshbw5tDIyv5+FNwAXSAi4+fX83Dxull/k9rnbtcgQjL3rUBpNampcTiKjAZoQmbe+tLSEy5cvSzMKLYkpvDTB0kQjKq4z5KhtQ6GQQyuYYT4AovEJWvF71h+YaLJmBJ5LHxk42ciC5j+vzWfT1pLX65XYuQbHKGRoHdDCoRvEYhvOqybwRCKBfr8v80xLQpvAOhzJ8zR4yu8ptPi/pjHtOnG96B7E43Fcv34dk8kEOzs7ogjoNjHdmpaMTlRrt9uC1xC7IUDLyBHnQtOqG+1qN0DTrhtTm+efBQbqceGEgBn2I1FyQrTpSbPZzQQ0wREyBz/nYicSCdFCJGZmnjFtloRaLpextbUlJna9XkexWMTnP/95vPzyy7h69aoAbfSHNfJPs5cCRzPlZDKRrLvHjx9LyTJ9yeFwiH6/LwAfBYdlWcLATLf1eDx4+PAhhsOhWDfMMKSpqi0F9hoksMaKx3K5LOXD1LIUAMvLyxiPx9KdeDKZIJ1OSwIN14l4gt7yjAlSdI+4f8BkMsHCwgJGoxHu3r2Ler0u2tfNCuI5FFSsldC4h84d4Br4fD5HVaV2RzqdDizLwu/8zu/gF3/xF/Hee+9JEhGf+e7du8jn80ilUuIC8d6j0UgKzSjY6XJ4PB48evRIWqXzXG1lke51PoJp3p+Hwc1qxZnHnnnEf8Ch48saDTWtBJ1eyQk5CzXlIvPaTOllqCscDkumly7SoGahX0wNAgDFYhHr6+v4hV/4BaTTacEJ6OeaWkhHB7S2IhHzOxIP8QJdp68jA7wuEfXBYCAbjNDsp9lpWZYj0YmD5ikFLs/RYUc+q87EpFYcj8fSnENvC6ZLnTUTaKHV7/cRCASQSqXw5ptv4sMPP8TVq1dFOOtwIdeblhrnjIJB4xR6dyT+cD3oKvCZfD6fRBYoSJg6nUql8Nprr+H999/HG2+8IcdQGDBXg3kCwEl7M4/HI3khTLLKZrM4OjoSnMnEc2gd6SiLW6TAHCYAys/OiwtcGCFgAiImg2smN48xTSU3iakJhUKAyDBTQ5ncYZqTDLWR+Mg08/PzWF9fxwsvvCCFQ1oI8FokRGrgyWQiRElipGbSLgK/18g3Q4BE1qm9eb2trS3cu3cPzz33nGhcXpcxa60ddKoyhQAtCu1Xm5aYrpvgnopacPb7fdkpmEKN68FkK7bzTiaTuH//Pr75zW/i4OAAS0tLePXVVyX6oIUQhTQFD4WOtgq1b0zhr/1wHQUh8MhzaXHZ9vEeiK+++ioGgwG++93vIhQKOTaMbTQayOfziMfjIhCJ+di2Le9Jzc99KNhpygSs+Z4mU5vguMnY+nu3FOKzBMGFEQIa3dTZYHrwM77seHzSjVUn2/B6WpNwEliYQtOVBENfjwtGq4F7AZBQaKGQQJaWluTePEcLKh1DZ0op6wu83uPNQdn2ipqJi1av10UgcBchugx6jiqVCra3t/Gtb30LhUIBt27dcjQ84ZxRE5KB+ay81mg0kl2ACGJqH5ZhPGpEpstSyzMzkAAYNSw7+tq2LZrWtm1Jta3X61hbW8Ov/Mqv4I033sDW1hbi8TiWl5fx/PPPY2dnR8BaXo/v1ul0hPm0JUBXhU1KuR7MIqTLR/og3sD3ZULU+vo6tra2cOPGDRwcHKDT6Ygls7Ozg1gs5tj4VGtw7ftz78hoNIqFhQVxN7kuukBICwc+t1kVqqNpZuUgBaam+1kuxNmZBP+Bhn5YUwBMM/FnHQOcmIP6eJqRzO2nANHJJPpZyMQayGLcvVAoIJFIOLSrltw6HEgASQsFM/WVviWvoZ+HwByFgn6ncrmMw8NDVKtVeDwe2W4McDY94Xvp0KrpivB+1Kg6D4CEPRqNxIKgW8Jn13Nh1gKQUSgg+F2n00EsFsPi4qKEFcn4LKUmI5sWCd9Nr59eM231aCbiOexqTOCP5/N6fv/xFueLi4uS3ky3h/kLbIHG+dM1CFrgMlGJnak1IEp6c6Nnk7bdrINZfDDrc+ACWQIa8TYBQu2H6pfXSTr8W6PlOomDg35kMpkUs7ZWq4n/ysQQfU+dlAMACwsLKJVKkklXrVZlYWgxEHgMBAJSXUagTae0sqEmtQJwEkMmwVNosfYfgLgmg8EA3/nOdzAYDHD16lXk83kH0Mb50SYhmZ/mKs1guhZk2Gg0Ku9MS6lWq+Hg4AD7+/vI5/N4+umnheg514VCQRiN6cLNZlMYgWtKYbK1tQW/34/Lly/jt3/7t7G3t4dvf/vb8Hg8mJubE+CWezNwDcmQBE9p7jPJJ5fLOQBIy7JQr9cd2pPza7oJ1KQEgn/1V38Vk8lxLQQjHOyUvLm5ifX1dXi9x2Xm7MRE4c1rJhIJERLFYhGVSkVoghmhXB/TFTaFGWkZON1KTK+5tgamjQsjBAjWmD4M/SJt/mgwRQsGwJlVqDP0eB43wtCoMrUB/XgdlmKWHf1rSvNQKCRdfri4WttSO1Ij6Diw1tI0Y7kNFwBHb3u+E+eAQi4QCEgTUuA4B4E9CvQcasRZYw4ac6FQ0MASrSC9BsCJe6O3WCe4yFReNijVCT58D23NaOCQzxIKhZBKpbC+vo5QKITt7W2srKxIGJOAozafAYhFwnklLsFIgGYGugz8nxqegoXChRYJaSCVSmFubk6KnHSORLfbFTDWtm3ZDFULcr32dEvpnujhhnWZoV7tPpvhRr1meo2njQshBLSf44aG8nstvfUkaUlIqUnfl9qJ5yWTSaRSKTFlI5GIAHoaOOO9uHFILpeT8BMX7+joCD7f8f4DugiHzEehwrg/22rxOamlAoEAMpnMKXeAzKMLZSaTiZjHDx8+lAYfqVQKly9fliiGvoYGyzjfnA8SIF0jLWwJcOlQF+eWbcwymQyazSZ6vR4KhYKAZ2zywRx+Hd1guPDo6EhCfxSaPp8P6XQaX/ziF1GtVnHv3j3kcjmEQiHkcjk0m02xCIgJAZA9D3U8v91uSzl4t9uVYiQ+A+ea2aLJZFJyRnq9njQTpeDNZrPw+Xy4e/cugOPiKG6SynqGZDKJTqeDVqslBWi0KrQFEolEJL2aliDnXQtFMrh2rXg9XlMnSPEa/D0LC+C4EEJAI+JaGOiJ0C+kE1M4sTqBBzjxX2nyMwxI5tQda7gIOpGGDULT6bSjKMfj8eDSpUtYXFwULcwcdwJW9HOpKakNGWLku5Ixh8MhKpWKPDeJTicecZ6INlerVSkbpgvAnYFHo5EAjMQSdEEL51Oj7bQ+aMpqd2I4PNk/kD37rl27Jg03WVnI3X1oQRFF93g80gbM7/eLZtWMwsE1pbDNZDIydyT4WCzmsK4oLLXVZVmWRFCazaZDSdA05/PpRCtahK1WC6PRSJqhBAIBme9CoYCNjQ18//vfl4Yq3LaMViTDuhwU4pzXYDDowDq0K2DSMWnEtJJJP2YW5ccdF0YIuMX89ff80RrSREXdzgFOXA3d6VYLFGoOfR6RZ0YRgBMzMpPJIJ1OSzEMiVm/A90GLdx0iIqalxYBY8l8R96LC61NQR5PhDuTyQhQpYFHClcSOQtvtEvF+aDpzp6I2j2jRUATnqYvfVIyDaseJ5OTHga6uQp/GEbToVi+o143HaVhTgGf0UwWc3MbdRRBY0T6PalM+K4UiswZYW+CcDgsiVTcA/KHP/yhaGhaGrQytHAiPWiQkOvCXo58z2lD40Ru9K3p3uQDrVDdxoURApxM/dDav9b+P8E1ptjati0LzusBJxuXjkYjkf4EmahtWNnl8Xik0SQZZjwei6QncHXz5k3pfjs/P49YLIZisSgMQVOXef0MMVJr0czs9XqSMw846wB0OzMNOBKp7nQ60plYtyfX+xMwa06X+7L9GXsGULPSAtC4CcFI5h5MJhPR7t1uV7Iab9y4gfn5eUknJs7SaDTg9XrFStFMpkG9WCyGubk5eR8yNZvMeDweuS/z8YPBoLwLBQtdLW1p0c2gCQ2c5OJbliX+OF2Wg4MDtFot2agEOGY+VpcypOz1evHFL34Rzz//PP7qr/4K3/nOd6RBKq0Bdpq2LEt2qu73+8jlcoJJDYdDaRKrKyg1w5IHdFUnhSFp/5Nofz0uhBCYhgPo3/pFzbCXDplooEmDKZTmJBBqWo/HI3v0afON57PxZyaTwfz8PLLZrCQZJRIJSVrRSCyZiwRDBuB7aO1O85A4hNbO2ucjyMjtuOmL6tCk7rJLPERbOBx0FdgRGThpo6WTm7TFwM5KerMNaj++E9+r1WqJ8KvVasLsdC34vMQYyDB0g/j+/N6kAW39adBRhzU1DqKBUFomGqdgubjuHs1zKLCI19D0DoVCKBQKuHbtGsrlMt566y3ZDp5zpkutKZj18zL7k+FI06rV72wC5bOAPpOXzM/McSGEAOBsFKJNf42A0rfWlX1kAv6vgTBqOWoK9tHXDB6Px9FutwWkoYTmAi0vL2N+fh5PPfWU4ArpdFoiBDTpCBpykfkdtQyf2ec72fOOWo4Mr9OXaTnEYjHZuJPaam9vDx988AGAk4aVOt6ui3T0ttw01ZkdyYQYAp0ApHkKABE8Xq8X7XYb+/v7srVavV6Xxh8UDKz029jYEO29tbWFYDCIz33ucw7wj0K41+thc3NTQD1iNbRkKAQpvLnGOlpCgWKCwDrDEjh2H7lvQq1Wk8pCdoieTI7rH7i/AOefnY65vpzXfD6PV199Faurq9je3sbW1paUDRPIZN4DAEexFQFIbrfGtGPt9mn3ktfQ4CyHFhCzPps2fhq7EnsBvAFg27btr1qWtQrgLwFkAfwIwD+1bXsw6xpuko3aSvttOnGGLoG2AjRYyMny+/3I5XIS0yfSzeYgLA8mUXI/OwCi7ZPJJJLJpANcbLVa6PV6wmS8Nstu9XvRAiEQ1ev1HEypNRpdFzK2DrVNJhNUKhXs7+9jY2MD2WwWqVRKdi5ixh6r/YbDoTA6O+JqyyidTkvyTr1ed+Qw0NICgEwmg62tLalLqNVq2N/fRyqVks47gUBA7h8KhSTsRhOfzOnznXT5oevFbEjTz6VWZw5+uVyWZBvmQpBGdOiQ2p69CAn4UQhRW7OpKl0UYj3JZBK7u7uyVRqzJennU2C0Wi0kk0lcuXIFzzzzDCKRCN59912ZU7oG1WpVOjlxT0ZWr7IgjOvDc/hDetbKUONOwOmeBCZvnTV+GhmDfwTgjvr/fwDwP9m2vQ6gCuCff5KLupmkHNrkMxlfh7MoUdlkUgsUANJrnuEZEqrWomw9zloDtp2ipqN0p2Tm8fSrCVDpDStIhFrDaRdBI9W0IviuzWZTBAFDgSQoovok6kAgIGY5cyF4D9bFa2yEIU4eQ6Zi0lClUpEt1qvVKqrVqpT8er1emY9AICBbtmezWclgZCye+A4tCKLvJqJOi40Ck5YHP9Ndhck0XAvLsmTdOD/a9AaOa/8PDg5krXO5nDSY5ZyQZph+TMyHOA2xoWvXrmFlZUXejX0gut2uRBWoAHTBEsOKmUxGWtrpZCXt7pKhScPaapgGqp9HCHzarckXAXwFwH8P4L+wjp/gPwLwTz465H8F8N8C+F/OuI4DHdWINHCilbSGN9NCdaxZAz9M79VENx6Psbu7KwvFZJdGoyFgSzqdxvz8vKDIZCISG68/HA6xsbEhz8HWZCyooTlN96HVagmBsmKP76ELVICT2D2TlUajETY3N/H48WM8fvwY6XQa6XRa5oJ+J8GoYDCIK1euSHqrjqPTP97Z2UG5XEY+nxffl0K12WxiNBpJ+KtUKiGXy4lQ2d/fx9tvv41isSi5FwwHMj4fiUQwGo2kLRmBx2AwiI2NDYlc0I3Rmo/ro3Pwe70ednZ2xErb3d09NccEc0ulkuAoFBZMd2632+LaLC4uCmBs28ddhFKplKyNDhFzdDodbG5uot/vI5lM4pVXXsHc3Bzefvtt1Go1NJtNZLNZscps25Y+BAwh0trjmjB3gVmN3FSGgoi8QSGmez9oPtLXBT77fQf+ZwD/NYD4R/9nAdRs22bgcgvAgtuJlmV9HcDXzYd0AzWAkxCJCRLqoTECfjzlhgAAIABJREFU4KTppk7WIMNRw+qttjSDc3LZKaZarYr1oRNsKBjo4+mwFjUWn5mLxuP5vwmA6gQR7RPSbGcHIjIjNbHun88oCsNZnJvx+GTL7tFoJGj40tKSVFMCp8OoOi7PVmWtVguVSkWsHroFnANaQR6PxxGC1OYsNSeJlu9u5inQEuJ3pBuep6NDdK8I1Gl/m7s06d2a+B3BQuAEa+EzakCZkSDOAaM2hUIBpVJJqk6Zg8BSb7oiOs1arzvplf9PA3bd6H4WT2hLwW18YiFgWdZXARzYtv0jy7J+6eOeb9v2NwB8AwB8Pp9NoiOxkljItIPBwMFgLtdzmEc+n0/8ZS4uS24JQE0mE9lVx+fzyYYTzGYrl8vY399HIpGQkBRNRK/3uG9eNpvFpUuXcOXKFczPz0tMu1wuy/4FNAWpBVl0RGbVvy3rZDt0gps+nw/ValUadVJ7Hx4eSu1BPB5HqVSS1tmVSgXD4RCZTEY0L62h3d1daYb56NEjNBoNvP7668hkMgAgvffpEhD8297eFsHCsOH+/r7kvfO75eVlCWHRj65UKkgmk5ibm3Og6Hxv/jARSGMUZELdmMTjOa49YAPSTCYjz0mBsLGxAeAkPVsn7OhCp0ajIRakbjVvWRba7bYAuHwGumpM8BoMBlhdXUU+n8drr72G7373u3j48CEajQZs28bVq1cl+7DVakk4kNehpUP3jI1YdPTLLTrCZyb9A87EIR11+kyEAICXAPyWZVm/ASAEIAHgXwNIWZbl+8gaWASwfdaFtB/KBXZDN83ogQZEKES0b61j5sx1B44XjX0CtfvArMGVlRUUCgVJEyWxhsNhzM3NCXJMkIo+r45M0Eqg/zwej5FKpRzakISsU4v5joyjM/5OzcLS49HouOw3FAqh0+mIBqEFQIuFlYVaoC4vL+PevXu4ffs25ubmsLKygmQyCa/XK8VUNLEty5JNSGu1msTp6W9zXomfEIDVIVif73jTEOZ1UOPPz8/D5zve4ZdhVzZn2d/fFyHInAEyFd0dtocHIGFUzdy0FBhbJzObYBvxGlqFGnAkeMnIFPebYF4AU6M5L6VSCaVSCfPz86I06vU6JpOJQ1AR3KTAZ56Hx+NBPp9Hq9USt0D/AKc3JzWBaBMHOCuk+ImFgG3b/xLAv/yIKX8JwH9l2/Z/alnWvwXwn+A4QvDPAPz1ea7HRdFZZW5mjhYAXBidJaYtBY2uk1C5yJVKxWEOUgjQj7516xauXbsmGvOdd95BJpPBwsICMpmMgG4096j5yODUjs1mE+VyGbZtC6PRt/R4PJJQQl9c5xQQLCNxMHmJQuDo6AjAcVSAxEQG49/lclncFOCYWVZXV7G1tYXbt2/ja1/7Gl544QUkk0nZTJTzQQLj9mTcMp2oNrf2oi/O7Eo+gzbRmaBEzIU1F7rPI8NqTLmmMGUvgo2NDdF0+Xxeuv/QTdHNWDi/FAJMwNJmP+mEgCTpQdMFlYpuB3Z0dCQ7Gev8klAohFKphMXFRSwtLeHevXvodDqoVCrSwu3Ro0fSfi4ej0s4kkKUGNT+/r4UqH3Eb7IuJs1rd498Y/DqzGzEzyJP4F8A+EvLsv47AG8B+DdnnaA1OX+0VNMvSl9KS0VqLZ0s4vP5cOnSJSHK0WgksW2CQkR2KbEzmQxWVlbwa7/2aygWi8hkMqhWq8hms7h586YjZZbuBX1EpvFSSpOpGf5hrB04MTN5Pa1ttB8/HA4FtWe+PTVZoVAQHOP73/++NOBgfz+zGMm2bQHfGo0Grly5gj/5kz9BoVCQ7cXo4xIFp2vBz5kaTCBVJ7mwa7FlWVKHQIL1eDxYXV11rCvBUYLAxBKoAWu1mswtLaijoyOxyAjwkn6YV0BhMB6PJWtRb1LC+dPFXMw+ZciUFZG6dwM1vW3bAggmEgmkUikkk0lhvkQigUuXLuHZZ59Ft9vF4eEhOp2OCMlarSYWGt0fCtTd3V3JImSLNbZ5p7Iwoyeah4CTQiNtLczCDICfkhCwbfvbAL790d8PALz4Ka93SghoF0GDVm5mDheeDSkowZnpRmYlMdBEjcfjyOfzWFlZET+aDL+8vCwhJW2NaABHN7/UmXd6ay8OCgENcvFH1xvoxBjLsqQNGHsB6MYUOj+e2ow/zD+gMAqHw7h586ZDQHJeAIggYYYiXSdmCtJys20bjUZDmnESINRAGHMByKymJcefyWQiey/yfwp+zehkap1gpsFQrUhokdACILJOq5GhS6L4nD8WhNFlY/iT0RLgJBJEvIX3Y5v2bDYr+AbXh5vV0o3QORP8TdxBZzpq3990EUxe4dDnzRoXImOQRMEF0jFQfs/fJArgJMWVBMtzqVXG4+O692vXrmF/fx+PHz9Go9FAo9FAPB4XX5Mm7vr6Oq5fv46VlRUB4jS4xcwuCiRmh5H5qM3JtIwT8z3YVgs4AXXIzHrRtStEDIGJSH/wB3+Ao6Mj3L9/H1evXkWxWMTa2hos6zi/oVwuo9FoCMPTdWAodDKZiDmaz+dFKN69e1fwEE341WoVb7zxhqOIiMRLbbOxsYFmsynJNvF4XKwavgdbqB0eHkrJLkFWWgUMWbK+gfNYqVRkrsjIulR5MpmIa8Y1IAPT3WDyDxvGMLTHjEwKUx23p0Arl8uoVqt4//33Ua1W8eDBA8RiMeRyORFUXGeWcvt8PnzhC19Ao9HA3/3d34mFxK3p79y5g9FohMPDQ9nwVW+5TkFK4WPSPDEnWp1mZAlw7sI0a1wIIcBh5o1zMvhbp/ya3wMneAE1pE7+YV44NTUlPZl1MBggk8mIv8/cAEpn1szr59D3p0bXmpsJRXxeHc/Vz2tqJV1uCpwk97AMNxKJ4KmnnpKyX84Z8wFarZZsgELtREHFwW4/JDLNQHqHpUAggFKp5LAsaBqzKItYQq1WE22n6ymoDVutlnQYIpDHZ2PVHgB5FloLzWYT4/EYV65cwWAwQKPRkOtzaG1IZJ19CbWQpgtH4cCiIN6La0sB1Wg0UK1Wsbe3J2nTyWQSuVwOS0tLAvDROtEpwrQ+mVxGbIAZlQSOSZfEQfRmNZwnrRjd6J40YNKXiSm4jQshBPiABNOI5gInZp5Gq/WL6WQKMpReYMuysLe3J74V0WPWEdDv7vf7mJ+fR7FYFJ+SsWSPx4ODgwOHhjZxCaL8uq6B/rROEXaT2HQBmGtAQIhJJuPxWBj+wYMHWF1dxUsvvYS33noLBwcHsokoAbxarSYNQKm9NXDGeWUoajKZIJFISEIMrSC//7hD7nPPPSeCgUlHrMW4dOkSbt++jc3NTRweHsLr9cqOvHz2yWQiTTq5l4BlnRTzxGIxqdxjFEFn6L355psYDAb48pe/jHa7jUePHglTcFArUugwG1RbJaQJCgQmM9EC01EDVhTev39fMCS2Srt69ar4/WRyy3Lu56AbyBLLabfbuHTpklganI/BYCB7YGqwOJlMSs2Idus0s1OBmMlzjGrpXIpp40IIAeBEimnzxWQWDhIXfVBKSzIfEWVKeYJrRIX5/WBwvK89+9Nxeym9YQaZlz687tPPOgEKADI5kWemgHJBmO/Olt40W+n/Mz2VfjOjCKPRcV17OBzGD37wA3g8HtHAdGsIQr377rt4+PAhEokEisUiVlZWUC6Xsbu7K2a4Dt1Rw1NgsOtxPp+Hx+NBp9PBhx9+6ADr2GjF4zmuwLt06RKWl5fl2dlHADgp56Y1owuacrkcbNsWjQ1A/GECoY1GA0tLS/B4PLh3756rT0w/nYAqsRXODzd/5XuS4RmWZL7IW2+9hfv372NjYwNra2vIZrNYXV1FLpcTP54gncdzXMugn2MwGKBeryMej+PWrVt4+PChhCBJf7pQitYY6YfAcTwed+BKFGxa+5M/tDLSOI1pJc0aF0YIAO4+jH5B4CSSoMEuEjOvwaQOhuuoxchcZD76lronAQmfWWiUpBQCLDoCIDsPaymscxfMdFNdyMJ3MsOCOrRGAqD7QsulVqthY2NDYvWMnLRaLezt7eHhw4doNpuySw6TlcyQGAmIrgetIoarKLC63S4ajQaOjo7g9/tl+y3gONFmfn4e6XRa8iEoWHRUhxmDOiTIXoRM+PF4PNIjkZmaPp8PS0tLCAQCePTokVRh8vnJQOzPoCMGOrJEF48CQWM4xJS2trbw7rvv4vbt20JLTz31FFKpFNLptFh629vb8Pl8sh+jBmO5TTkrP1ksRhBYu1M6Tdvj8cixDBNSaU0z//UwwXOOnylgUCOZZHIzBMKXJDNzIvk5K87YTHR+fh7D4RB3797F0dERDg8PxcxkrQAXNBqNiqk6NzfnABjJDMz55gKRgZiWSvQegFgXlPr09Ri1MLsAkVDG4zGy2Sy8Xq9If2owv9+Pp59+Gnt7e3jrrbdkC/RUKiXXI3P95Cc/QbPZlNyE5eVlMa8vX76Mvb09vPnmm7J5BoVWPp+Xopv3338f5XJZfNpqtYqXX34ZxWIRGxsb2NrawhtvvIFr165JzN/n84lmZwUe8RHOJ0Omm5ub8HiOG4hcvnwZCwsL6HQ6uH//Pv7iL/4Cn//85/HKK6+IYAGOa0Sy2SyAYwvp3r176Pf7SKfTIsAZhWHj1eFwiEKhIBEfrt/BwQGOjo6wuLiIlZUV/P7v/z5+/dd/HTs7O1hbW5OWaO12GwcHB+L/P/fcc9Ilma4kMR1aoMFgEC+++CLK5TLef/99bG1tye5D4XBY5pwdkXUG4tNPPy0p2cCJu6ijRDoqYIbN9d/AyZZk08aFEAKm+QKcbqMMnK4f0N/TvKb21a3EdOKHWZeuXQpKaWojnX1GhmfeP5mWGIROUAFO6hY0iEh3gKnLOgxmmrHawgAg9yKKTOJuNpsSgmJ/BG61zp1x+O58T1pKTFTRc0xhxSIbAo0Erjgv1LLMkajX68hkMg6wS9dG8N4sxdYhVF0VuLe3h16vh6WlJRSLRaTTaRGI7GNIYcY14ZwSWOM66vAhB9OhSRv8nu5BOp3GZDKRtmLapyfdJBIJRyye78q15juxsnNtbQ2DwQCPHj0SazKbzUpGKEFRfqcBUuIZmo4137j9rT/TeNm0cWGEABdC+3kAHOYWiY+AmX5Jbgihy39JxDQD8/m8AIzavCLDMnegUCjg4OBAXACeQ+m8tLSEUCgk1Wxzc3OiEXhsOByWRJK7d+9iMBhgaWlJ8AkyGpFl7auzMo4Zg+FwWJJMmCcfjUaxtbUlGoogHWvkv/SlLyGbzTry8ikEGap65ZVXsLOzI+nAHDs7Ozg4OBCg8fHjxxIlqFQqqNVq4hevra2h1Wphc3NT8iGazSaWlpYkTZggVzabxdLSEu7fv49yuSxWkw65/vjHP8Z4PMYf/dEfSfiOz/jmm28iEong6OgIa2trEgEZDAbY2dnB/Pw8EomElDfrnATmOWxubsLr9YqWj0aj0huC80QXsN/vY29vTwQhw5Da9SPNUVhpt44JSF/5ylcQjUbxox/9SFwfhnXZVIT3Zl0BszKZos71IV+YeQMUfHro57zwQsDNn9EPbaLqGh+gtmSSBUN/lmVJthaRfsayCVpxkug3svadPiZj6HQ1EomElO9qLafDhcDJTr8M9xFg1M9OrUUtphtV8j0ZpaAwYhybBMxUXwJivV4PKysrWFhYQCqVEgahpUFi93iOy32ZwcbttGi6692BfD4fPve5z4kJW6lU0O/3USwWxU3Y2tpCtVoVjCOfz0sdgrZwut2uow8CNXyj0cC9e/ewvb0tJdDs7ru1tYVMJoO5uTk89dRTCAaD0tqcyUy2bYtA4f6H+t0BSHtzvj9w0naMNESz3uPxYGtrC7ZtSxo1wT12SWZGImmV92V6Nt1Xn8+H+fl5lEolFItFR54DE6k474zccAMbszLSzALUQ2MgbrzyMxEdMCWVZnT+1mCWNulootLfpw9UrVbFN2fmGxtfahdES229cUa32xWCZfVfPp93lHsSDNLmGrPGuLgM/WmfjXgGCUu3FCMzUUjQTGRKMRuU0rKh1mo0GlhYWJD+/DQtaY5zA1DeI5VKSb9CMirfnXPr8/mwtrYmOAtzJkqlkgOYo19KS8XrPd6Nh4k4RPzJuHwOHYsHgJdffln2KGw0Gnj8+LE0+7h27Zr43bpV/GQyEdeHwKbX63UkajGiQ4uIQogWZq/Xw9HRkXSRYlovw7PxeFxcAyZgMcnH7/eLhqeCIcZiWRaKxSLy+TxyuZw0sWHyFYUAGdi2jxOPdMm1pnMADq3vxjfmz89EiND0XXSoAzi9DRlw8rIa4dWhGAASm63X69L2isyncwASiQRyuZxoJnYD9nq9KBaL4j7QvGdZMZOMyMhE8fnDZ9TttQku6oxB5rUTKc5ms/B4PHLN0Wgk7o7GPajFvF4vUqkUVlZWRJMBkAYlxBE4J0z7pSC4du2aED0ZloAdj6HWLxaLkpdAN+zSpUsCTjIhh0KDc8TsRbYgs21brs9QK810y7KwubmJ0WgkAm00GmFxcVGEFQu7Go0GxuOxI/OSxVScQ9b8M7KgE4moQACI+W1ZlpQXJ5NJmWcqF0aOFhYWHGtCBqYwAuAICedyOUfhUSAQkBZtpGkAIog5n+yubCYDmcNNAGj+mjYuhBBwCwOan5t+vAZgaEaZklOHEnmeTmcFTpBXjb5SIFFTUUp3Oh3BCnSGIEEubfrqWnEOnUHGZ6W1wPfWwpC597yHBjR1I06Nj1Dq61ZbOo/fvBdLlilkdeGRmXDCkKxlWWK5UHARTOVcUpBr5NokTq4r3R6GBm3blt2RmU0IwNHajRl5vIYGGXl/M7NUM4Jb2E0/t6Yx0pOeC4YdeU8WQwFw5BJoxabdRiogRlS0gNGam9fXvGLyiUn7H3dcCCEAOFOGTd+GwJluoqCZCID0AmBZr5miS8YhQs28c5psk8lEwCNqI4/Hg/fffx/9fh/5fB5bW1v44IMPJOd8fX1dCkgSiYQQMxmDVXd8L7az0qWyBKJY/UftxVJhbc5S63Q6HRwdHUkCEdN/6ZcywsDcB2ojCpJ0Oo1EIoFsNou9vT1xNVqtlmy4Sc1Kf1cLIlpMLJPlc2mQczAYyNbg9PGj0SgKhYKY05pxd3d3cXR0JOXCen1WV1elw3O9XsfGxoYIPoYGmSPAcCoAsWx0kxBGXrS5TUGsLbZUKuUIwbEPpTm3dFMXFxcFIGRTFoYU2SyE5euTyUS6O9FKSyQS0t6u2+2KVaI7NWmm15EyrSC1MND//0wAg+agBKVw0BlTnHjtS5E49Y8pVUkQDMtwMZm+ubu7KxqDPie3sGLDCAJofr9fynl1hR7NOKYMMySls750nYDWEPxN7a8Xl8eWy2UxeZnfbtbDkxF4PUYqarUahsMh9vf3JVWVAohhOjKo9jvJCFoIUPDYti29AOjzd7tdyTXQrhytIG2FAc6NWaldWc+g+/7t7Oyg2WwKCAnAofk17ZBmOOcaLOP7UGBqXIX4DS0Y1m9oWtJmOwUlgT4ew0pPv9+P/f197O3tOZra8jia+5FIRIQtsRxiBrphjK46NSMF2krgOMuFAC6IENDExkHGpklO89c8ViO9lJjavKcZS4uCqaKs9mJc27IsqYZjFx+a0PRxS6US0um05HNzl9xqtSoAD12FWq0mdfhm3JqmpC4Z1nUJJFxaFiQW27axt7cne/QRqGLfBGpnthujC8P+d+PxGNVqFVtbWxImZA49ADnn8PBQ8uQ1AEvG8fv9IlCIAcTjcfH3GX1gajZw0iORfRx0W3Wa9pwXfsZoD3CMZXCHYNYekElJI7o+wrZPuiVrF4tan0JHCyjd14BCgCXQ/J4KCIBDkDJ7kN8zcc2yLNy9exebm5vY29sT5UWaYAYlMRtTCDBMzDXQFiE/Ix+YoUC33AK3cSGEAHAS4tDAFiefmlQzkfbpuOCWddxLnkkrGpRi8wav1ytIumVZ0ieg2Wzi3r17iMfjSCaTKBQKyOVyWFhYkNCg3mV2MBjgvffek8XUmtrjOW4RRfCQjMFMN7omZBAi3vpYvj9wkqhEjUVty4Vn7JsEPplMsLe3B+A4RZimp2VZsqUWW7DzPqzuy2az0i359u3bjjJXboHW6/Wwvb0N2z5uVFIqlZDJZFAulzGZHLfRyuVyKBQKov0JbtLi4BzyGfh+1LzUssBxq3P9rBoL4powmUdnkOrkIX5OOiJzUyAQACWWw2fSTUpJb2QsXfbLHAIWAZEWms0mvvWtb+HOnTuOfBauP2me4UAzYsV5oZtLetORKM6TDhOSDmYBghwXRghwaGkNOF/M9Hm4KHpx6NdpwIxmFVM1WU1IKUzklbvv2LaNfD4vSTiMywMnG0sOBgNJ4KFWA45jvIxHa1APOME9+Ox0DbiQNPf4t7aMdE6Ddhn4PDQ/eRwJjMVVekdkEqK2QCh8CBLyPLozdKPo5pTLZWFCnavBaItuIa4Td2juAycmMd+P5+vrUmBqX18DgNri43daeHLOKaR1pqJZ+KXxIR05MM1sHq/BZzOSZVkn+zns7e1JVifXQLcw4/vwHjp3hfSo3Vztlmh3kkP/PeszjgslBKahnmQUEjZ/az+VOfIEg9gAlD4wE2pYUswqrr29Pdl4gvn1Xq8XTz/9NL7whS/g6OgIo9FIzPrx+Hgrbu4+RPOY3XeYHqsXkj63SeT8ze7ExCnMCIMWNEyK0aY0CZ9x8+FwKGE3XbhDDIGgF5OQJpMJCoWCuCi6WKler8smGyyKabfbeOedd5DP53Hjxg1sbGzg7t27aDQaKJVK+M3f/E15JroBlUpF3pdFW3y2cDiMwWAgtQ70kWu1mnQpjkQiou11ARHXIJFICGMyP0AXjGkTnjgQrRS6crqQSSsWrhl/000k0KyBUTY+JRbDfA6WUnOOeQ1qfOYbMP+AuQ8UBAyF0uXVWl7zjck7bsrTHBdCCOhQjCkItO9jSkoyBglWS3O9eJxImoU0RWkxUJq3Wi0EAsf7D3LjUWbZ0cKgFNZWBnACOtK05LPr0lU+B59ZayOtFai1dMhNm3emX6nNUu3D8zezEnlvMh8xB51WTFOdqLxlWXjnnXekqInHUyvRpx4MBshms8jn846Grnx33UZd5zjwXQiQUShx3nie9sXdEG/9t7Yw3LQ451mHlrXVoLUs51jTHz/T7heFjq4s5LOTlujT67Cl7hpEXIvn6VZo5AkNhOuQI783mZ1zNmtcGCFAQjell45Rk4loymvzl2ZroVBwhIK0Jh4MBtJkgmAgM824o0wwGMQzzzwjFWgMF9ZqNUdOAAuIAEiySiQSkcovMif7z1Fo8TsSnNbGJBzG3HXFWbVaFbSbiDFdAWpFbZbqHZyYiMRsymazKbgAiZeuECv8AoEAXnnlFfz/1L1bbGT5de737WKRbF6ryCJZvDfZN3bPjHpGo57R6OKRZUsjRTZsWQiMJHDgc+JAD05OgjwdvzkPefBDgNhAgBMYudgGokj2mYOxfNHIUMOWxiPNWKOee083u3m/FFlF1oXFO1m181D9W1y1u9jdR7IPWhsgms2q2rX3f6//unzrW2sNDw/rr//6r40FSCiUTCbvm5Dz5JNPamhoyIA1T7ChMKdSqdj3snmampqsJNo3F2lqqrEPfU0GBxsCmfGvE+KgYFgfFCJKCSXIM8USUyzl19QrfB/acC0o8lwuZ2uLV3n27FnNz88bKaxSqVitCpOZJNV5e1Qv+j3Bd4FvsPZe6flMCWvwIC9AekyUQKPDp2F8nCuprkbfu8tYRDTl7u6uofU8NNxlyB0+/u7s7LRGEh0dHfb5nZ0di7tbW1sNvfWFHTyErq7aMCYvgFK9ZwOajHXmwZIX5r1gEZ60g2ARC0syBdnd3W0tzTivvz5cTsBWT1jBhaZBSRjWcvCTk5P62te+ZqFVJpPRwsKCTUAmFGtpaTHmINdAxR8xMPfvrScbE0UCYMfGYi0b4SlsGNbUGxAU+/HxsbHzkCnCO2QLl95jN1yvb4qCbIHke3yG+8QTPTo6svcVCgVbi3Q6rY6ODuXz+fsMHsqzUCios7PTFLPHBnymKeoZ+Ov0JK2HHY+dEuDCvcvkhZ040L+OEmCx2HyeoSfJMAMemk+f8HChEBNvk2HwLDE+x/f7XDTotnd1o24reXAwBA/YER8HQVBX44Cg837Pk+C7SRN6cMwLhs+pe9ALC48HhaLs7+9Xb2+vPvnJT1pIVCwWlcvlTMjAXzo6OpRKpWzKE/RknpUPDbwS8IIdhicEHJ8VipZke+FGsXnX2Id4/t8oL8ETqDwwieyE4UmbcS8rXKMHBVEAfsPiKYA/AE4nEgkrTPIyz7MhfY0y96Ch3+BenqLPm3X4uQkHuGhvWVlALyA+HPDADb+zkWGpMayTakJaPzU313rZ7+zsqFgsmsYdHx/XyMiIueJ0B/ZuJg+UoiQfD1arVbOK9LMDAERg6JKLcqpWa01HUqmUMQHph+Djvp6eHh0cHCibzda50Hg8CBDXWCqVFIahfT+9C3EzvfVjbcMwtLFizc3NWlxcVGtrq5599lltbm5qaWnJCo4uXryo8fFxnT9/XufPn1c6nTaiFP0EJNVdL5ubZ+7jblqeU6BFGTK5e64PRYg3trW1ZWXic3NzWlpa0sTEhLq7uzU+Pq5SqaRcLmfPTJINqsUbI6wknvcWHdlAwWPlATQJA7zHSgm0JOtqhWKlpyDn4PtRdBgVniEgKeGDVO8JNTpQzlGg8LTjsVEC/mKjCKf/3Wu6KBAWPUdHR4eh+Dw8qX4yDT88ZCyXByFJU6EMfAzvwxZcw2i+mutGqH0BDxu0ra3NOhslk8k6kogkswqeF0+qD4HyOIoHEz2Ah8D6GBlr6L0wsjFY8aamWgUjAt7T06NUKqXe3l6bsExPBe/Gs/kalcX6WJbDI+ZRufD8AB9q8Vp7e7sNEKFvgyQDeD2oKsnanPm18pmZMAwbDv2IngdkN75yAAAgAElEQVTZ9MAhnkaUtXiarPNMfc7fey/R7/CHP8dpm/5ByuCxUALcnN/YUUTUo6AsjM/h+uYZbLa+vj61trZa33qUgCRrvsH38eAB3UD6aSaxublpM/goaOnr66tDrrHyYRhavT2g1vHxsVWQ7e/vG6+ccVTJZFJ37tzR/Py8Ll++rM7OTkvzhWGtoAargbAw/IIwCW+qs7PTevQzg4D7wRthPaD/wrwDKCUddXRUa8+WSCQ0Pj6uqakpVSoVDQwMmCdAHLuxsWEKhbX0/RJ41n6SMkoQjwU3mDZuKCqwC569zzTgnk9NTenZZ5/V9evXtbGxoXK5rL6+Pg0ODpqbzfPe2dnR4OCgEolEHajMGhEGeu9DOhltx4HiBx8BpKZhKx4juI338nx4UqlULJxjXcCDvDfsQ52HgX7RcPq042dSAkEQJCX9n5KekhRK+m8k3Zb0LUkTkuYl/WYYhoVHOZ9Ht3EnvdZlwyH03nr5SiyagSKAfgNEaaTeErGJffoQAWDWvO9R59l9nuAC4YYUkCeqkOceHBy0+DmTyej69etG7+3v77fJxQgdlOe9vT3zHBBMBMevE/8HnIL2yjWwETxrDSvkgbZKpdY8g/qEVCplNRR+vNru7u59qStAOdaUMIn3+U3gN1osFjN3WpJ9nmIhvzGw9sjM0dGRJicnbdDJ9va2stmshYBhWCv3LZfLVnAFELi+vq7+/n4NDg4aZ8MbJw4yRqw7CrdardYRmDzuwLriRfopxygKlIWnYvuGM9JJs9p7+6/OOHpPwF/vw5TFw1GDBx9/JOnVMAwvS3pa0keSfk/S9TAML0q6fu//j3RErb5ffO8KohHJ+SNAbDZSXuT4AY8IA3xaLaoEePgeWKHhCNWDvgKQjUp6r7293TYM8R0HrjkEknQ6rXg8rtXVVf3FX/yF1tfXja6MEkBg2fhsAn6XZArOu/NYWDwHzrW/v29CCnoeVQJ8hyTjT7Ahenp6lE6nLUVIXEuhVSMlQN9+PBCuz7vPxLFYSzYBWQkfm3slR7yNZ3N4eKjx8XHjONCKnewJ64Z8cN3Hx8daW1tTtVqjTvf19amvr089PT3WIIbvpbGLTzdG+RMg/R77AXjd3d01hQxAzRr7ykfGtOPBSCel8NFsiFcGfv/48O6046f2BIIgSEh6UdK/uvfFh5IOgyD4dUm/eO9tf6rajMJ/+6BzITAeTffxDw8YC+vdJR8PtbS0WJza1dVlqSw/2458NBWDCDcbxWcEAHOOj2uz+vAE/KaX6gkoTL+lfJbOvwwh6evr08S9MWcLCwt6+eWX1dzcrN/6rd/StWvXdOnSpTqvhfia+JKipGKxWGf9q9WqjfPmXgBOcaVxDSGu+PJYNr1vOkI4wvjzmZkZ+3symVRra6uVO6Mcj46OlMvlbA1Ic3Ien2L15d48T5SDb79NvQLudrlcNrca9D6bzZqnwLmmpqbuA+bovERVJYxQ8vmFQkH/8A//oIsXL5oyR+mzVrA8KRWvVCq2HhQ8STIuCWlmBpOSam5qajKCGqElHkyhULCBN6yJp1D77IoHCRvhaVHcJXr8LOHApKScpP8nCIKnJf1E0v8oKR2GYebee9YkpRt9OAiCr0v6Ohd7WtyCtfZAideEHB5LwJrwGZ+28VYSofIehB8fhZUkBuN6JN2nXbl+7+bykKDIUlra0tKi7e1tG3Hd399vCDt15b7KjXOh3REGtDwtrfxn8Jh8dWIjl9ADmNEY0n/u6OhI29vbRismhkfZRFO2PmTzysCvHd/j1wuB956gD+NYTx9XVyoVozajXIIgME4FeA3namqqdWJCsUL/ZTgtk5EB9PAGfS2Il40wPCn59XLHfWFU2Kzcowd5vQdBbwpCLC9fHlj0YUD0aCSbpx0/ixKIS3pW0r8Jw/DNIAj+SBHXPwzDMAiChlcQhuEfS/pjSYrH46F0f5GMVI+IYqEg+gC24PY1NTUpn88bgQSQjF56tNCSpN7eXoVhqEKhYFo+m80qlUpZatArFiitxWKxboAlVhZWHuk6OOBYLcp/4/G4NjY2ND09rUwmo2vXrmloaEgXLlywoR6ejEKbK5QXltRX4JHiY6wXHkilUlEmk7GwBaGmglKSWWcKXHwDDu7fb0DWtlwuS6pvUQ7BprOzU+VyWZubm8b7P3v2rEqlklZXV9XR0VEHaLJ5KR/23o0vxOJZIRNkEAhHfP+B4F56r6WlxRqS7O3t6b333lMikdDly5ct1Pjggw/qxoenUikbGHr58mVLURYKBW1vb9eFXfRNoEU5YCrksiCoVRPChPSTsulZyaDUo6MjdXV16YUXXlA2m9Xi4qJVe7JWtGzjO/w6eKXgKxAfBg7+LEpgWdJyGIZv3vv/v1dNCawHQTAUhmEmCIIhSdlHORmay2cK0JZSvQYkzo7G7aDtCAbxMG2u5+fnLb7zSobz07BifX3d5tR5tyser3UuQjh9TwDO5WsAojFZPB63Sb9Ys/Hxccti+Fr46ENjk3miC1YfAaYfAMBT1IICvHneggcL/fWjgPwzqFarpmSwuL7VF2lUXF6sdRiGVmBFiOIPFDPXJcl4Ar5QywNs4ACEOxQ94fqTx5dkWMjxca1nIUj+3bt3tbq6atfK/VcqFSUSCQMLvTdEOMUzZQYEsoDBaGtrqwutyGR47AUsAtlKJBLq6OhQOp024trGxoa2trZsbbw34kMUnznw4TRy+C+iBMIwXAuCYCkIgqkwDG9L+mVJN+/9/LakP7j3718+7FxcrLf6/M2XlN773rpYVjpp8c1YaTZGd3e3urq6bDR5LpermzqMG4vnAbFkcXFRk5OT1rab7wX4W19fN6KQZyryUKOKAy1OM47Z2VkdHx+ro6NDFy5cMByBEMUXrfDw9vf3rXMPlXk+rqxWq0qn02a5EXrQf74f99gj92EYWq8Dj7ZDnhkYGDABBLTj9Xw+b6ETxB7iY+4fvIJniALz4RIbhHunt2ChUDAcxytYP1MC954UI0zMCxcuWCMTvJSzZ88aSPyTn/xEb775pr7yla9Y1oIQIZ1O6+DgQO+8844pOcI1j3PgCcAvgfzV0dGh2dnZulbkpGHZvH698VgSiYSGhoY0NjamIAj0j//4j5ZJ8B4XDFYfWiHPfl9F8bVGx8/KE/g3kv7fIAhaJM1K+teqZRz+PAiC35G0IOk3H+VEXKRP4WHRojfDzQMMIkjVatVKSuPxuD71qU8pnU5rYmLCioMQHG9VsMLb29va2dmx1tOkgqrVqhW9kO/m+tionuMPb92DQbjZlNXSkTiVSplQgRz7cdU+b40lQumxDkxYBqTDQoJkk2by49h9vp1wAYvFfWHxKLElzZZIJGxTJ5NJuzbPeuMZ+Pbi0km2oVqtmsLm/hBizsHcB87LRpVOsAyUnO/pR/MYP4nat6LHqHz2s5/V5cuX9frrr6uzs1Nf/vKXbd3phPzpT3/aOlYTOg0NDeng4MDWYG9vT7Ozs4rHa3MTKYvOZDLK5XLGv6DJCnUEGJVCoWAzMWKxmN5//33LqHAPntDkcano5vaK8lGPn0kJhGH4jqRrDV765f/Yc2GN2aDcHO6e1554CaTpeB9ChHuXTqc1NDSk3t5eCw2wjl6L4qKR0mOsNC2fJZmVetD13FsTSTJXluIV6aQj0u7urk3LJaUIWn9wcGBFTx4999/twSU2GwrTeyHe/fRgKpbDe1+eN++BLZQEa0rWA/cWRck5vFfHc8Fq8RobzXMRPL8fxUt6jTX1oWHU3eV95O1RXmwa3+eB842NjWloaEjf+c53TFmhzElXdnV1aWdnp66rFAaBdQL/ARTEIHl+BHUhhE2kV5ErPLf9/X1tbm5qa2tLxWKxDuPwXrLf/FxLI/AwCiw2Oh4LxqCkOgGPxuwIIG4QAuRdb5qELC0t6cqVK5qcnDTW2NLSks2BI2YllUOFF6mY4+NjLSws2NQbAEAwAMaX8/ABHxFkhn6Q1wZFb21t1ejoqNbX11UqlfSJT3xCFy9eNOAHYE46yXTAIPM/hEd+nLePOX1M29RUmwLU1dWlsbEx69EHB8GXsfq6DawVsSgptdHRUQPo8I76+vq0uLho3YZaW1s1NjZm1o1Nurq6ataQeN8TX9jgcBfAfVCSvmjMbzS8OIDReDxu7nNPT4/JFgqFUIEeiLFYTIODg9re3tZPfvITTU5OamJiwmZJ8PzxeKrVqoGDY2Nj5uKPj49bXF8oFJTL5awkOJutwWL5fN7CFT/6nfDnjTfesPvu7u5WMpmsy1R5ijfKIxrvR5WF9HM2mvy0VJZPPXl3yOfnsR5YcJh4bMxMJmO9BKSThh9U7bG4x8e1Vt+gtT5+5UD5cD5SZd5yRTn+xO9gGjxAn1LDumOFPCBHzpo18p6Ib5WNwJArx2X21+Y9IG9p+S4PBrJOUbQeENN7GHwGxeozDH4sG+dtdPjmoFhQXynIWnnlARbDmvpr814Anzs8PFQ2mzXGH92Stra2VCqV6orKuru76+o44JJwXg+qSie9G4IgqBuVzoGnynWznt6Ke6+oUSo8Kv/+8BvfZ49+LjyB0w4WBQH3KLd0kpfHMyiVStrc3FRbW5uNnn7zzTc1ODios2fPWptsSCA9PT0WewZBYPHdxYsXDYD0LDjIKtVqVRcuXFAYhha3+aEk3mWkTyH3QIVYsVg0YAgLQAaiEf6B8HqXl++PkopKpZKRpySZJfEoNSGEd8296+/da0mW9qPDcVQpdXd3a39/X7du3TJLS6zPmHifYuPwa0uFJW3MKU8G+cdLYZPD3CuVSnYdYB+sJ9aa71hfX9fNmzeNCn737l0dHR1Z92UwlHg8romJiTrWIBkDwi3O7anCfC+AJildSebRoNRJAR8fH1vK0hPBvDKX6qtq2dheWfgwzPNrfi6UAFYyCmhwg1EU1ANgHiPAevtFZ4owlFmYWVgb6aT1GK5mLpfTzMyMzp8/b/EbcXt/f7+am5vN68jn8/c9HB4IMWRLS4tt/GKxqB/+8Ie6c+eONZoYHh42gI2GqLADGVoC8ox7y8MdGhrS8fGxDV31r3vKKesUnR7EusF5wDshjh0ZGdHOzo6y2axu376t3d1dQ8WxsICuQRDY/aJAvHVjTTyGAvkJK8t3oxzoAsXnQNjhKHiGJaxKlBnvxdsjFEwkEtrY2NDu7q6++tWv1oGsjF0LghqHYXd3V++++67GxsYMLD08PFSpVLJwxeMDYD0ofHgAGAsUCF4qCg8MQ5JlWXy46q/HF8+xjsgx+0Y6mSf5oOOxUQJRy8C/p6U5fIjgwSVPjMHa9/X12d/RxNvb2yb0CJtXMqVSSSsrKxodHTVXFgXR19entrY2Q2+hFKPIOFBcuLOQh3Z2djQzM6P19XW9+OKLSiaT6u3tte7Fvtc+mxuvhGGdQXCStybGD4KgruAEpeXDEjwm1orN6kuVOW88Hrfmmfv7+yoWi1pbW7MZgKyxX0d+9znzMAzNA/Ob2StygFQ8Iv/M8SbIDviaEawosgDIFoYn9GhJFg75uoTV1VVtbm7qpZdeUltbm1ZWVixrwyY6OjoyivfAwIDNJsSL8vwKvt9nm/x0a54L1t3zTfBU2MSEqsiEVw5M2vZhCIfPennw9EHHY6EEvKsvyeIp6aR2Xzq5MV+i6uMjrEC5XNb29rYGBwfNfeMhoVGh8SYSCQ0ODqq1tVVbW1uKxWLq6+vT+vq61tfXbejI8PCwWRYaYNB2zHe2YeERfklKJBKqVqv6wQ9+oNnZWYVhqC9+8Yt64okn9OlPf9pamaG1WY9isWhViNHYEIVTqVSM1NPZ2VnXFBVFyeY6e/asEomEksmk8vm87t69a+W0nZ2dZt24bryCDz74wDb8c889Vxd6HR8f21AW0mUM4/SpWMDUvb09w0jowOsbbRweHhpzr7293dx5ZEE6aeqKq0383NbWZrTro6MjdXd3q1gsanNzU52dnabwEomErly5orGxMVN+Ozs7isfjxhWh+zLeIm3T4C14XgebjPASQtXs7KwWFhY0OztrvSJ6e3vteftO0J4PAzblZRlFA5iL0vWZIhQyR1RBnHY8FkqAw6et+L8HOHxs7uMhFsO7ZCwwm4W/8x2+qtCTX7DcpAnz+bzlnn11oD+PB8C8GyadzL8jxKAkdnJyUhcuXDCarJ8642mheDMe1ONeAdfIrGC5YU16AA0X1jcgRbDIOvAZBMqXGcfjcSPkeBCWUIXwA++Dz2DZuW5fjIP1h1IL3kNzF+/2e3ngfFFw0Vs9rsM/e4DQWCxmSoEsCdcJEOq5/mw6lDOeCFgAoSLfTXel+fl5LSws2LOF2AS12KeAPbjpCWjcs7+vKDDM0QgofJTjsVAC/uGhWRFAD2oAzlCSCgnF56AlWa7dx4vk4BOJRF01Wy6X0+DgoIaHh22CDl10K5WKVlZWtLu7q1gsZm23SUex2XB3sVSHh4fK5/NWVDI/P6+trS3Nzs6qvb1dL7zwgj71qU9pampKa2trdXFcEAR1PAHccawuSsgrI7/pe3p61NXVZUpsZWXFNtTa2ppyuZwpG1qsbW9v6/Lly3ZOBJuR3r29vRoYGNDIyIhmZma0u7urZ599VisrK5qenjZ2H+So1dVV4+CT8mOK0cjISF2HpXK5rGw2q0Qioa6uLvMAKKLBM+JevYcjnfAQPB4Dak/Ksq2tra5lfFtbm7WBo+qRgizKuO/evWuDY4nzPRdCkvEkfNfmbDartbU1ra6u6pVXXtHa2pqVjCPfpDHxflGekMTS6bSlopFvOA4oP7zZRpmzaJodD/W047FQAhxcONqav3Fw095aSfWpRUAutHsQ1NqKg5ijkYmJURBBUBvRxeZraWkx7vbe3p5GR0cNlCF9hFXwU3g82Yl0FJVpW1tbdX0BwjA0ZdRoMAVxP4AZ1w567/kNuMOshxcKFGBHR4dZIeJwhApr3N3drd7eXvX09Oidd96xKcAo3lKppIODA83MzGhnZ0f9/f3GeZdqQj44OGiAK24rKcPopm5urg12JeQBVPPuLR4bg1boyuw3hyd/ET4Qm7PmWGxJ9mzwWsgMkRLEECwtLZknyNoii3RtIptTKpX0+uuvK5/Pa3NzUzs7O3ZN3nC0t7crm80aGC2pTqZ9aItx8cbQg+GspfegH/Rao+OxUgJsHCx89MKxkN5Fle7v08amOzw8VGtrq7q6ulQqleoAM0/AQMAB2Kjxbm9v1+3btw0D4IeUHEyyVCp133mxGiga4jvqBHgvxTgII/Ggxy4QVsIcuOkAh7irCLoPQSCk7O3tGcDpuf40sSQm7u7u1tjYmEZHR/X2229b2zCQfxTPnTt31NzcrHQ6bdkASVZTz/ejBKjMDIL6fv2UGYNbUJDjnyeeHufzFhKAjd9REqQsMQ54kVCWUaykG/Ew2Zhc0/Lysvr6+izV6oHGQqFQ1+1ob29Pr7/+eh3jFLfdKwHWp1qtWgclQj++A4OHEvCKHXn3fRh9SCzVj2P7uVECHi2WTlpm+foA6LUIt/cc2HQsfDwe1/r6ulpbW43+29vba8Mfuru761zJIKgVg9AAor293ZqTHB0d6e7du8rlclpbW9MPfvADY35NTEzol37pl4y3LslSY1istbU1tbS06MKFC9rY2NArr7yiw8NDXb58WZOTk4ZbkLYkJsbDOD6udb1BMRCbgklItQ3FvEHppOJvbGxMpVLJhpeQkmLNqbADIGTC8fb2tm7cuKHZ2VlrLDo1NaXl5WXrdwAJ59VXX9WHH36or371qxodHdXU1JQ2NzeVy+XsnmgACu4iyZqQgOYD+OGm46WgINhQtOJirfHiBgYGzKuoVqu24cMwVD6f1/7+vm7evKl8Pq9MJqPPf/7zunLlSl3vAHo5Dg0NWQ9HMh4of9KgIyMjamlpsdbqQRCov79fh4eH2tjYsNTy9va2gZ14LoSOeEd+GAqKsrOz08bT+TF43DMGwBtBzxNALsAeTjseCyWA++LZVd5ljLo1UYDQp5qkk6GhWHg+Q3znFw0lQ2oHS+wVA+kxABpabx8cHFhWwceJfBc/TBEaHR1VEATW14AyXFKIfhahd4c9M8+TRrgv7yZ6qwBhhXw178Oa+foErj2RSGhvb89Q8mw2a+EKrjb1/1zT2tqabt26Zd19aM5BegyQlnCGDcdBWOWtubdihFge5EM22Dy+0xCy5LECrCig3ebmZl3FJOuCQiINzAwK8BgO+B++bsMPfZFk90N6l4yRT4P6kJZn57sgI+ce5G20X047PHZ02vHYKAGP3Ho0lHiITYpwI0x83tN7yc+m02mdOXPGyB8QaXjY8Xhc/f39Vus+Pj5uJA7GeU9MTOj4+Fjvv/++4vG4NQahfv/MmTM2grq1tVXnzp0zq0Q9+eTkpJqbm/WJT3zC3GXCnWKxaG54JpPR/v6+NjY2bNP4+QV+4/j2W15Zgj7zOxabzQvoRwdl1hokvaurS7dv39Zbb71l8xp2d3d169YtvfHGG7p8+bKGhoYkyfr34Zq//vrr2tzc1NWrV9Xb26vh4WH94Ac/UKlUMg+ts7NTmUxG29vbpiy2trbU399vrdahFxMqoNSoZpRUx9KTZFgQ3aFhcBLWDQ8PW+2HR+YBD4nBOTejxPv6+iTVT5FGAYM3NDc3686dO5qZmdHt27dVrdYqJKNhTWtrq6V9Ufw0m6WCtbOzU88884zNdyB1ioLAKPgsEQYy+jdfc/OgzMFjoQQ86uutNIcHTHz6MPoe7w6B1vo++IBi3svwaTJcPUKFcrls7ap9Ec/FixcVBIGmp6dNGHwTUvLoAF3klHkopOOwVt61k2SI/tmzZ21TeC/AM9MId6STzAobm9gWT4A8PGtTqVQsg+EtMAIrnfTQz+fzmp2d1ejoqFVlMpEIpHt1dVWdnZ0qFApWPosCA79AGHF9JZmnlEwmLXULF8SP9EYhcG08Z9aZz/q6AULEQqGgWCxmlFwyKoQAHkPCwPD5aAja2tqq7e1ty5R4z8mDrcgT2SgwL54d1+vlGKvvwWDWyXsOvNdnAHzmAjkBBP65UAKnxTceLPTMKak+c+A1Ib/7UdaAZ4A//rs8bTUWi2lgYEBra2va3NysyzgAJH35y19WKpXS7OyspJoQDw8Pa2BgwB7Y7u6ujfNmcyK8vmUWDxr3LgxDTU9Pq6urS1/5yldMQXh2GAAhvPr9/X07v88K4N6HYY16u76+Xkc5ZbPT7ovQgolIWL39/X2tra3p3Xff1VNPPaWRkRF1dHTUMQjJGARBYC3EBgYGbKZePp+vW2PSdrFYjarb39+vgYGBurw4zMTe3l61trYqlUrZ8/UpNkBAgFH4HKQnUVYos+7u7rr6gkwmo3K5bEQlwiQMD5aazdzZ2alcLqfXXntNN2/e1NLSkoaGhuxZ4alyf5SNe6Xii9m87JIqJN1KmttnvyTVeX/eiLJ38KaimYVGx2OhBPxG4/AXHb1RNr9XFLhBfjHYPKOjo8pkMuZmk0FASyNA9NcfHh62TV8qlewcYVgbAlIsFtXW1qbx8XG1t7fbgyKWJJXG/ZDVgP2HZxCL1WivbW1tSiaTVm++tbVVl0JD8ECLPU0WgcLCsC4+nGpvb9fExIRtQJB+6uVhC0L+6e3t1dNPP62enh6trKxoa2vLFOLy8rLS6bTS6bTK5bLef/99ra+vW6YBvMArWhQJG4JJRR783d/fV6lUsqIbPAE8NOJ/Ngxri/dAdoMwzROX6OuA0qXEnA21tbUlqTY5eXBw0Jp/oGDAljw209/fr89//vN1KT2yWp6oRCdhNjfnQQF5g0CvBjILvvLTswK9AYsevOaB1UbEIn88FkpAun/mfDQsiIKAbPpo6sOzvNgsdPn1CsS7SIAw+XzePAdmGiJcyWTSWn2DOdCqzAN49JFnU0KlRaGAgEN7ZaPjvnsKMilE0ktRj4BwAtcaxNkfuPwUA2EdvSfgXf+9vT11dnZaGy0wEjCEYrFo1NX9/X2trq4a1sKa+biUH+o2KDTyz9FbTjgCgGpkP6JxLz9wLghvpJOUGGvqeyKiQOgQJclSj5CUent763pXeDCPTYgRoJ6AHgtwEXxlJi3Kscr++jw7kZCRtLB35X0twIOsujeGUSN52vFYKAEEolHJJIsQ9Qz8v96VrlarhtriAt66dUstLS26dOmS7ty5o3K5bJbVT5nB+sPV7ujo0MLCgmKxmD72sY9pa2tL+Xxei4uLkmodi1OplC5duqTh4WElEglls1ljwvEgL168qNbWVuVyOWPKQc7p6uoyzgEu4Be/+EVJ0p07dzQwMKB0Om2NPTzpiW44hAE0wWBTSTWhIG/twTY2AkDi2NiYmpubtbGxYUpncHBQQ0NDWlpaMiwBLnu1WqMNZzIZS03iVY2Pj1tXXNbAhyhYJ7yf/v7+++JmSWbZyfsDaDJCLgxD8wQ4qtWq7t69q62tLS0uLmp4eFgXLlywYqJUKmWbD9BzenpakuqqH8vlsjEwW1tb1d3dbR5fuVzW3t6eVY92dHSop6dH+/v7WlxcNMvb29tr6VYIWyi+nZ0dpdNpvfjii3rnnXd0+/ZtCx2pNM3lcnVYSjST4D0CDxayf1CsPxdKgOM0b8C/7l0i/97oOcgoHB4e1vUFxPKOj4/bHMD19XULBeLxuNFF+/r6tLy8bLEVViGfz6utrU1nz561mYGQcLAYfhPg5kFn9eEMgk9cfXR0pGQyKUlWRHR0dGT5b0IGXHqfFvTVdV5IfLrU9ycg8+D5/D61hkdA7QJZGN/wFDeb74dtR0zK5qfvnwdAowqLe+YZEj5wPXhW3soBxEGzbWpqMmVdLpe1sbFhSlKS8S8IXfyaQqGmDBgjhMIlW+LXFOXIpvPyR9qS58o984yOj4+tA9Pe3p5lR/h8tLagkVfM/0/bM5zrQcdjqQSibj9/x1WOujx81r9GLEmTCDR4tVprRiVIhwMAACAASURBVPqlL31J6XRag4OD+qu/+iu9//77Fofm83mNjY0pnU5rYWHBKtGYKUAtwNTUlFKplEZGRpTP542X39raav0BiMkJRdiA/GBBVlZWLKTo7++XJGuNls1mdfXqVWOucZ1YCTa/xwLYMGQEfNdbKh9xfaHIciD4fX19ViFIjLqzs1PXJguPRzphC6LEcHPj8bjOnj2rnZ0dFQoFe248T/oFSLI8vSQNDg5qdHRUS0tLdUw/vgvlGY/XGnyiyOA3AEi+9957tk5TU1Pq6+tTU1OtCWtXV5dSqZQKhYKWl5eNE8FsCTILq6ur+vGPf6xsNquhoSHLaDDQFaWIYj44ODA8J5fL2fOHVBYENRbl97//fWWzWZVKJQ0PDxvQ6xVhtIrUZ7eiyqDR8XMBDEr1bZM4oimRaMagkZvjvQUoq4VCQalUylpnV6tVvfXWW+rs7FQikdDq6qok1XXGnZqa0nPPPaeDgwPNzc3p7bffViwWU09Pjw4Pa0NI8vm85X3ZVH6TExd6dx0LQYqIDTA8PGwPCiSZ99ErH2uLcEmqi1vpV4jm9+4j1WmAUp5Pz3sRYNaQllug1JLs+7e3t827IFeOV0Fcu7+/bwU5eFA0OalUTga3SiezAfDG4EUwURplTthBOhaa8dHRkdbX15XP51WtVpVMJjUxMWGKkLJvujnt7e1pcHCwrlaAORN4Za2trRoZGVFnZ6eGh4f1i7/4iyqVSvre975n947SouMSbntnZ2cdJd3LOfgEXAaPbRCi4jHyN2TJ1174/eH3hXTSGPdRjsdCCSCkfsNz+I3eyOpzNFIWWMetrS2lUikbQ72zs6MPP/zQ6JqgzjC7SPldvXpVS0tLisfjeu2116xF+Pr6ulm1Uqmk7e1tYwYCFHqGn0e6pRMeOTG4Z6ahtWOx2kANNhDWATQdKwqe4MFSD46yviDMbFiUgK9uk+qBVT/UFeH2aL5vv+aVCyy5w8NDayuOZ7axsSFJppT4HZcZlx4uBWxM0pee/+DHtx8dHdnUHhTyuXPn7PxbW1sql8t6++2363j9hEgULnHfu7u7Jgcot/Pnz2tvb0+vvPKKVldXtbW1ZROWYJBCAALI9VaYTUqsDj4knTSklWSgJPKD0vT7hbVAEfgwhP3wMA+A47FQAtL9eABH9Eb8+zwgiMvEhvNpNNxG+v23tLTo7t279rne3l6zJsR/3/zmN/Wd73zHkPHe3l5JtZ4DuIpLS0va2NjQu+++q2effVZnz5611ltXrlxRc3Otq22xWDRsAIFG6Pv7+8015GET75O/x5MAzecBw4dno1D96BHmjY0N21zwChBGQheEnPU4PDxUoVAwrgTFNzDuEHJPviLGRZE1Nzcb5RovJZlMWmdfrDqWGkWCEoAdSIoOy8kmQ3FQLcrkqHK5rIsXL1q/QEBanvNnPvMZy+IAWuK5TU5OKp/PK5/PW9NRnxY8d+6c2tra9NJLL+m9997Tyy+/bHUDKFa8mGiWJOrK83+IQ4QXKHyUgC8m81kKT4jy+4HnEU0j/lyEAw/Le0on6Q8PDEaRUp+a8sKOe4rLLp24Zr5RBF4J5aBUeYEO4yaibBhd1t3dbUhsb29vXVsvUoQoKngKxOGECl4wPKhHx5+mpiaj7OJ2IwjVaq1gBiDPp7g8eIh1YQ1QAnw311Yuly0d6ElFKADAtZ6enjpQk3Xm3lFYPtcPmOZxHw9Mek8PRcF9sIY+BYuh8M1fcJt59ngQbFAqSynIYT3gfVDl52NvUokDAwMGBvPsvDeEfPj03mmyiofmKyvZ+NG18AokCgTyeiOPwP+90fHYKAHv7vgLJhbiIWHtsSAsAkJGoYt3IUnJlUolq+SCQ47lJe8vyXq4cU3Nzc06d+6cVZ9hObB0m5ub+u53v6vj42N9+tOf1rlz53Tx4kWLC7HG9Nxvbm42Kw5Qtre3Z64l7jSZiaOjI0s9Qa9NJpNGfSVtSO37wMCAbUR48bRDk2S89e7ubmviUSwWDWW/deuWbt26penpaS0vLxt2MjY2puHhYUtZnj9/Xp///Of1xhtv6M6dO9Zvf2FhQclk0rgGXL8HBCXZ86NQJggCU3Z9fX33YRT+eeDpgQ0MDQ1Z73/aiZNypXUbRCl6+nsaMNeTTqc1Pj6utbU1YxASu9OglUGio6OjdX+nBgTLznUkEgnzMD3vACMARwHsBcyCHhMAlNEqQk8nZtOztt4IRfdU9PiZlEAQBP+TpP9WUijpfdXGkA1J+qaklGrjyv/rMAzvZ7Gccnj6pNdy0RiXf0/LDkgnVkU6sba4nnDfDw4O7MFBEiENhBYnZkTDY8lSqZRVpZE2S6VS6unpMWAm6jlwDXDVsXa8TmGTJxwdHh6qs7NTsVhMs7OzRrOlxJeNAxWXclisCTRdflCo3D8W8ujoSCsrK/roo4/04x//WDs7O2puro0eGxgY0NjYmHp6eowQlUwm9Qu/8Avm8hcKBUPZcb+phwe05Nn4eghPcsJqe6osaTWUxt7eXl17OCePdVkHNhzhBbE4aU7kB0R+enraejUAaEoyI8E1A5Z6HAoiF2EVr3syUNRb5V/W3w9OxeIjBz7tG93Q3KMHEj1g6P9tdPzUSiAIghFJ/4OkJ8Iw3AuC4M8l/ReSviLpfwvD8JtBEPwfkn5H0r972Plw8TwY4sEPNnejdIi/YQSD+Nr/jTiZeJWNmkqllEqlbIT58vKyuYI8SNxIGnBUKhWrFMRtPj4+NtopzDFYcJKsBReUYjT7mTNn1N/fb/RaOgp7JBkvZXp6WolEQtvb23r66aeVSqUMvMQzoY8CriVKD3ce5UBdPhbs6OhI8/Pzevvtt/Xaa6+ZAiNVODExoVQqZSnF3t5efeELX7CBn9///vdVKBQ0Nzen3t5eNTc3Gx6C1+L5BjxrlIgnt0B7BRsJgsAsLtV23iNAGYRhaOCsBy5pKoNbv729ba3YBgYGtLOzo3feeceaz7700kvq7++378QYxGIxwwy4Piw7YCtWnHDNpz3514caKAG6EaG8UAK8B8/Wb2zOx/X5feP26r8oJhCX1BYEwZGkdkkZSb8k6b+69/qfSvqf9QhKQKpvGgrN12t6bsyHAdJJb3WvCf1iHR8fm6ACUFGMUiqVtL6+rkKhYLx/2owRi+7t7Wl1ddVqCLAEq6urRhoqFApWUJNIJKwTDUIXhqEJWCqVqmOBsSHxGFKplPWpxxJfunTJCEheoLa2tmycFrnq4+NjFYtFSbINFIvFDGCDuNPU1GS9/aanp3Xnzh1961vf0srKisrlsiYnJzUwMKBr165Zz0BAwrm5OQVBjRI7OTlp3tTm5qb+/u//3ph0nmzEehISQeelsevOzo7dC9gB1yzJ1gM6M2Ehz4qSZbo0gZ8AjMZiMatKbG9vV39/vzExK5WKent7tba2punpac3Nzdnz6urqUm9vr5aXl3V4eKi1tTVlMhmtra0plUqpv79fi4uL6uzstMYxmUzGwkrfCg1lR+uxoaEhxWIxFQoFkyu6MEmqwyuiWQGP9XgMAeUv1afMH7SJf6ojDMOVIAj+V0mLkvYk/Z1q7n8xDEOYJ8uSRhp9PgiCr0v6uqT7XJfo7/e+777Y8JTz1v3uAbYwrFXTAaqhOLCICCYgH/la4nKm6mDlQZ37+/sNDWaQJN+DoHLNnqGGq04MCAIPyizJvBamAVOIRPzIeXFFEQxCDVxFzodVYd3pNjQzM6Nbt27po48+Mu+B5qKDg4OWDUBhFQoFSSdNVPr6+jQxMaHm5mZlMhmtr69rbm5OU1NTBq7y/QgxLr2kukpID4Kx/v6zHon379vf3zeQkhZuyAGALGsFHRmPKQgCC1ngQLS0tGhwcNB4B6SD4SNADor2MsQTQfZ4prAHfZjS0dFhYSOeqg+FuPZohoyQif97fsBp++a042cJB3ok/bqkSUlFSX8h6cuP+vkwDP9Y0h9LUjweD7lY7yZxE/z44hp+2Mi4l7SmwsJ4TnUQBBbnArDQA/7o6EgbGxsaGBjQxz/+cS0uLmp5edlcMOlkIASgYjabVTwe17lz5yw2n5ubUzab1ezsrJ599lk988wzWltbM3bdhQsX9MlPflJbW1vWbhuFAyhIXJhOpw2Uk2qWkDZhPnZkHdrb2y2uxDUkw5FIJNTX12dpRzbOa6+9pg8++EDf/va3bdrN5OSkzp07py996Uvq7+83pVIqlfTBBx+YEFL5h0IdHx/XyMiIRkdHdfPmTf3hH/6hfvd3f1dTU1PWrlw6GapCVoINeE+uLEXJxmVT0YshmUwauAl+A4BMoVQsFjMlzbSho6Mjm5zU19eno6MjywLRg5EyadLGPT09hsHQgfj69es6PDxUb2+v9vb2bOx9c3OzVlZWVCgU6obbMHw0DGvVpeVyWalUyrglvjM2cu72mf3rlYLf7I2MJ0owylVodPws4cAXJM2FYZi79+X/QdJnJCWDIIjf8wZGJa086gm91vKgYPSIpkf833CJsNac07tNxIS4sywUBUT5fN6AJKx41J1tbW21ZhLZbNZiOSwrxTk7OzuG5sKd53voPITVxmIXCgUDG2kEioVn42AtKpWKgYxgIVybdJIiYpMQBuTzed28eVM3btzQ9PS0uckjIyOamprSE088YQw66gM8tVhSneBSUBSPxzUwMGB04tnZWW1tbenKlSumjPAAPEjoY25PfvH4gW/CwfPyaTpAV9LAEIJQmsTtnl/iU3JsQAqCmFQEqcpfP59nTZExirXo7kzPRLxRz+jEI0EJ+Hje10g0iu89BuIB8NP2yoOOn0UJLEp6IQiCdtXCgV+W9Jakv5f0n6uWIfhtSX/5KCfj5nxcH31NOnEJozRjLDYccRqIePdXksWZm5ubFrPyQEHtp6enTTD8D7XpDPjwI6LoWQcwtLq6qpmZGbW1tenatWtKp9OanJw0d5OQwbPxcHOXl5dVKpWsvh4ALBaLmfXhM9VqVRsbG4Y7UPRDtyFiYV4nXz4/P68/+ZM/0czMjHK5nNW9X7t2TZ/61Kd07do1zc/PmwvsN6GkOkKLVMNlstmsOjs7denSJaVSKT311FP6sz/7MxUKBf3Gb/yGzp8/b81KWAfCgmj9A/GzJGNEAo5KMtCP6U6+Bj+dTpuiY2Oh1KEj4xEiayh80scAoHgcUq3fQLVaq0QsFouqVCqWNSLc9AqcugzwDY9lIb/t7e3a2NgwsBl5J0SQTjIpUbDQg5Io92j24GGhgPSzYQJvBkHw7yXdkHQs6W3V3Pu/kfTNIAj+l3t/+78e5XxeqKPpwXvfV6f9vIb0Fp+cM2Ahn2OD4doWi0UFQaCenh6jD6MsyuWyenp6rDSYWnpPhCH1w/cRj/q+Bb29vRocHNTExIQ1rjw4ONDKyoppfvj78PFzuZyFCCikxcVFjY6OmhVqaWnRyMiIAYFMW4Yfj4BjBVtbW22WQCwW06uvvqr33ntPy8vLqlQqSiQSeuqppzQ+Pq7Pfe5zisfjWlxctK7CEGjowhyLxQzYYtArvQ92dnY0PT1tFXQvvfSS9U3MZDK6fv26JiYm1N/fr8nJyftSl3TV9SEf3+mJUWARhFWsZWdnp9UnQF6KpmnJFBEqgg8kk0nbNHhthF2+CpBw0MfxvuEp4SU4gfcSee7Dw8Nqbm42DMOnDPkezwj0Xguv+ewH68L1+330L4YJ3Luw35f0+5E/z0p6/j/yPHWWPsqKkurBj+iN+WyAB2S8dyDJLBDkGZBzlIVUyySAQjOIU5LVjkN3bW5urltkH04QC/b09BiwBndAkhFzEEDIIsVi0ZpTcE7CDT99CYQcMJEqP3LYnmvA7zRKOTw81M2bN3X37l0VCgWbcHThwgVNTk7q4sWLWltbM7IMIRMKB0VMqtWvMZuMdttdXV26dOmSKpWKvve972l1dVVzc3Mql8saHR216c6+fgEqMvfv0fWo50f+n81AmIYy5Lpo7MmaIGOeLMSzQIZ4nrjlUKFx4b119/gTGxIrDwcBchvfT00Icxi5Jp8BIIUdxce4Ru89+9c5138SJfDPdaB5Jd3nBXhQDyUg3Y8HeM+AzyAIgCTE5wgum4JmDgsLC5afxoNgaCUNJYvFovr7+9Xd3a1SqWSVbLi55LA//vGP6+mnn9bVq1c1NDRkQyfy+byVrJIXB9UndUmb8NXVVbP8WLutrS3t7OxoY2OjTjiYMZDNZo0ZB5OQ6UA+fmZtrly5oosXL+qFF15QU1OT3nrrrTowtVqtGqtRkikemqFsbm4aeMdGwyNYW1uz1Oczzzyj5557Ti0tLfqbv/kb/fCHP1R7e7umpqb01FNP2Xpzj7lczkA7BrYQEjBodGdnR7lcTkFQmzIF0Ys1g9oM7oNCJO3Lps5kMnVKHJCWdeju7lZfX58pfkDMjY0N+67e3l7jEJB+/LVf+zUNDg5qbm5O09PTev311424lc/ndXBwYA1bvNfheQkcyLbfJ1wPRs7za/w+8NmpRsdjoQSk+7nP0v19BaLv90rBv5/3+jhKUp0nQXzvY0OPtCMUCB/0Ub/gvh5ekrnG7e3tOnfunEZGRqy7DJYMd5LJuRTKeJ4A9+e79W5sbNSNIiMOxr1MJpPmYkqq6+FXqVTMOgZBoNHRUXPvz58/r7GxsTrPgZQWlhhXWDrpWIP19NmaRs8NKw25B54GvRHgLPi1p34BoC5qILzSx2Ki0EnZefCUZwiG4S024SFryjOKfkc0K8Xm4/p4vkdHRxofH9fly5dNHmhJx7pBA4bT4IHJqDxH//UhQ6OsgP+d6/feRKPjsVACPiPggRpvHXDv8RpOEzqO03LjkswriMfjWllZURiGVsFFHh/yCSkoPyoLYaNKLZfLGe//8PBQQ0ND+sIXvmDDKokHGWt94cIF3bp1S+VyWSsrK6aQiCGZ7zc1NWUu5ebmZh2PXpIWFxe1vr5uab1r166ZpaMjEUzApaUlK3z51V/9VeXzeS0sLNhgzvn5eaMQszF8ERKbh80JbwB3FwWJp4HAsxkLhYLd31NPPaVr167ps5/9rDo7O03h+qYca2tramtrUzqdtmfIswCQA5dpaWnR0NBQ3bi14+PaHEGeE9YVYJB1Ah/g2TNXESozBUFcF9eJa4+8+BDiueee09e//nV961vf0j/90z/prbfesvMxnCWTyZhiZv2QWY/2e/n3ygClEzWQvlgp6kmfdjwWSoAjarm9W+OVwKMcUY0Z1aaSDCBkI/T29poQIyAXLlwwRhspM9JWlLSy6ZqbmzU4OGhDT7AoKA9y+Fj9wcFB63WIZ8N9QmJBQNhY3mLQ/45cNAJF9iAMQ2scSpffWCxmbcCPj0/Gm3lwigMLAq+A9GoYhtYMg1AEq4cl5jw+pgaolGphxXvvvWfpOJ4X1Gq8MeJpPr+3t2dFTWQ0guBk6CqMSo+gY/n5Xv/8j4+P1dXVVQesRYE6Po/35Ps1gg/4KseZmRl9+9vf1o9//GMbKBP1UFAkENJOO9jAURnGy8EjZY38s4sSh047HislIJ1e8MDD9JvlYQeC4LUj/4ZhaDl9UO90Om3AFsLLTDpouLTXOjg4qBt5TV7+iSeeUF9fn7nLkiyrAH9gd3fXeOuMrSbc4JpwTxFUn2PGY0okEoYj+BJYXHCplsrr7Oy0AabVam06Dn0DyEYkEon7Sl7ZFEyxAQTDrcbKELJAdyUk8R4doYnn++/t7amjo0ODg4N2LtKZeHsg8HhjsCUlGQUbAJVrJIzx1tDTs6UaPsJGhMjEs0RRcd2Afr4qkVARwo9XAvPz89rZ2dGdO3cMZI52dfaKIBrWRg9vHKPPBmA7Kt+8/iiK4LFQAl5YfIrGCwWuoucIeIXh431P5OBBeo2KVmaTwsIDMT4+Prb87vXr1+ssEWg2LiXVhygGUpGSLMdO+EDTzIODA62vr9tDxU0Fm4CSKp2085JUF3tKMiYdQzpWV1e1ublp1+9dZ/Lmh4eHunv3rnkMAGdsAPAJlAfpQNYUKzk2Nqbd3V0tLS0ZwSadTpuV8i22EVzvWvPM9vb2LCSTdN8I8CA4YRNC/mlpaTGefTabrSsI8/UVfg6jL+Nl3fFuyuWy/Y2+CShMT5fe3NzU6uqqVlZWrJnI2bNndf78ec3MzBigeHh4qOXlZdv4KAEKvACYUdbRELjR4RUA60cog9z6/eC7Nj1MGTwWSsAfjVD/Rl7Bwz7Ppo2+FvU0cDklWf027l88HjeEu1qtWmNOn7OmhBfLwnkBe9D23tLxXh4iig6QyY8cJ56NKjGEQDpxufEEpJPY0GdKEEBAKn/4DYLHwXl8+gsmnt/kNPNgs+DJ+HbXHqTyQum9KOlEsTWKfz1jEcUMSg6giBL37rwPSaLyxNr4+/PPCmNBEVqhULCycchEYAIYF4/GIx+kOQkL8Ry8zEY3eTQsPk1B+O+KyrmX9dOOx0YJRNHX6E34/KuvHmt0NHJneTBeSUgnm5VmE9To+wKTo6PaVCCGa0TR5s7OTiPWIPzM4yOUgH/Q0dFhJaoHBwc2vw7BOj4+rqv5Z+NSjko775aWFm1sbNi4M9YLT8R7EkzdAYhiU8ViMWWzWW1tbdXVUvgCnY6ODj377LMaGBjQ0NCQbty4ofn5ef3t3/6t4vG4jSKHFry/v6/Z2VkjRLE5qdHI5/PGy/fNW6IEGH9Eabf+OsmQYFkh7oDboHS9AkZGAEGRN5q5ek5AIpHQ0VGtQens7KxmZ2ctK8BreFhcG8NuKElnehVty+GccB3RtDj/eiwgCoRLqrP+Ua6Az4Q87HgslMBpOIAH9zw7EKsV9Rp4PQqk+EX2YKFHYMOwRtyoVqvWcCQej1uGAK4BQ0PQ6r4ijc1P1xrCB+rvUVx+zpy3yHgOZ86cqYtffQjkFRwptzNnzti9+fgRwUco/Ln8uhDX04eQa/W1DBQodXd3a3BwUGFY48kz4KOtrU1Xr141nAMrCgOQDsq+aQhcBZ8C9OAvhUlsTP5PShFwDMXrgUHppISYWJxzerlCSfI3vsN3Yt7f31c+n9f6+rqy2axZdypKK5WKhQHlcrmOhCTJGoZQI4AcRFN9Xv69fEe9l6jsR+Xee1o+TD7teCyUgHQS1/tiCX+zPo/vOdb+81F3qtH7o1oXgapUan0Fj4+PjZ4ryXj5tARnDiEpJkC9trY27e/vK5vNmnBAJOG99DQAHCRM4F4RbNxcOgZhmaPvJX3oLaUH7nzY4DEVgDaPSdC9d2hoyDwPXieNKUl9fX11WMTS0pIpuaeeesrakCWTSaXTac3NzRkXAysMxkPdP0oPj8C71rTdIuUIJsAzQwZ8iMLzpiSaNeR5g1uQjkMZU2DElCZIVxCflpeXtbKyYi3Ke3p6zMvs7u62iUyAheAJ1JPQTCYMQ0sZojCimzQqt14R+HDN7xOepZdrvu+xVwIIdTQW9TFRVCue5jX493mU2v9Ez8/mgkm2sbFhYQKNRqg1R3B9DBqL1ebYt7e3a3p6WvPz8/rRj36kj33sY3riiScsVi+VSlYTgLBByd3a2rJCETYEVFaAKjYP7ENCCAScJh24zWwkQFIIStwzbjXUZa6HFmK+9x5WDvbjc889p+npaf3oRz/SnTt3bGDLxMSErly5Uofse97AxMSEisWidVQ+PDy06U+EWqyp9wJQolT2sdmJ6VHO3d3dVjvAdGIAW08S8sqBmgHWgXkVNBD58MMP9dprr6lUKplS7+rq0sTEhIGOhHXeY2REGnUYfoaDZwB6jwx5RYa9YYvKcSPMgGfd6O+nHY+FEpDuBz+iN8hxWujQKM0SLTDyn4suFO+jAAc3Es4+wBSFJaDdzc0nrcdQFDTpGB0dVRCcDNxEaUgnqL/P8foWUtBL2fBYLJ/68orTU309HhKtTAMTYA24Pp9Lp54eLv3BwYH11YfDMDo6ao1FeD2fz1vOn+/iXnChe3p67P7JNHhQ0ocx0kmohvXmvrzc4AURi3tPAqXq1ycqR9ynL9ohw5HL5ZTJZLS0tGQKAGUJrgGtHO8Cj4zny0g0lLYPzaJU39MOHwZGZdm/znui3sXPhRLwD0i6P8aPHtGFi1o+vxE4v3S/lo26VfQagAhE6ok2Xj09PVYj39fXp4GBAb3wwgva3t7W1taWMpmMcrmcrl+/bsAg/eoHBgbqru34+FjZbNaUCwNS4K2zgYIgMNIQTUTz+bxtLmoiuFbATOjCWHnfnAMriGUiC7G5ualkMmmDOHAxmeDE5n3yySeNnXft2jVduXJFn/zkJ9XV1aWNjQ1ls1krfOrp6dHo6KiFBIODgzo4ONCNGzfMElarVfMO8GyopsRDYkaD51HwXGnndnBwYIVJFPmQ/gzD0NK0nlDFgBTSiLFYrZoxm83qlVdeMXkATLx8+bLNrsjlctra2tLQ0JCCILCJST6dWywWLbOAwvXdhaJZAk+H9unuqKxG8Y3opv+58wR8rtMrAL8w0v2ECI5GLpN/f/T3KODoz4GQlEolY6aR0+Y6KUqhm5B0MkjizJkzWltb0+Liom7cuGHDJpPJpCH7uPZsWuJy4kcsKRaHjjjgCjDWsHSkOj3o53/3ZCM2PcoAzALB2t7e1ubmplU+cs/8fnh4qLm5OVMYY2NjunDhgmEh5XJZkoypSK4ca8ymhmXYqMaDzA+W3TPj8EgIBSSZd4Mh4N58RiD6nL0nxftQVHAutra2JEnJZNJqRFh3QGIYk2SK8GS2t7fNK/DxOtcZ/Z3r8l5QFP/y/0Y9Av+3KDbWyJByPBZKAFfPPziEIAoW+s9EQwCPGPOeB4EpWEP/N96L9iZN1tzcbCOjz507p0QioVwup9nZWd26dcvm2hGzVioVvffee7p7966uXr2q0dFRvfjii+ru7rYCItJZEHb4npmZGZVKJRWLRUvNXb58Wc3NtfZV/t48Ul4qlUyIicPZ4FCXCStQVri4eBZwI0qlkp5//nnziHhG1FS8b5NW0AAAIABJREFU+uqr2t3d1fj4uK5evarnn3/eaLUbGxvq6urSyMiIeQ+5XM7CFqwhdfn5fF6S6oT/8LDW85+1lGSNYiA5NTefzCfEC4rFYvYaSs33iuA6WCMASuY+kgbO5XJaXFy0rsRjY2NaXFy0XpSVSsWmV9PIxacHW1pa9KMf/Uibm5t1pcoobB8KsO4+TPDZk+jB5vbU4UahAa89LNx4LJSAdwEbFUxI9W68d4P8ovK6Bxp5v69O88rAf5f/Dtxx+s+l02mlUimrwNvb29PIyIg1zDg4OFCxWFQ6nVZra6uWlpa0u7urYrGo5eVluz6AN3LigERNTU3KZrPGMOzs7NTExITF6plMRsfHx1pdXa3LAMDn91atUjmZ84f77mNUyD/QXOlPwHOg9dn777+vrq4u64gryRqxfu1rX9Pm5qYWFhasrBicZHR01ITceykIpG+2KdWHgihlrCCbPAgCa85BDQLXD4iHAgRoZLqzdDKl2QPPXV1dtkasC+HgzZs3tbS0pPHxcVUqFa2urprCxuWPpp6xurlcTqVSyRQ9mZWop8o9Rq28V/BRT9VzKvz+abRffFr5QcdjowTYdNEYpxFi6kke0XDBv9cvArGwB6F4T/R7vVYtl8uqVCrGzx8ZGdHc3Jz29/et8IiNe3h4aI1KEomEbSbm+ZEWAugjNkSz00CEDjnMNiyXy6aMSqWSZQKwHD7dxD17Vxivymc0uHcsM9dNJ5z9/X0r1PFNQkmJXrt2zRqPgEHwbBjZxg9eBIoh2qDEPzuPhQD4YSA8IYdcOxwJf34/LdmHQtGiIjaTXwdqQ5aXl7W+vq6rV6+qWCzaSPKOjg6jGXsCG3KHR7a8vGxkKMIXvBMvp94riMqv/w5vHH3IcFroGz3Xg47HQglIJ5ofIfXsPqkeC/A0XJ8v5mFgHUDrYco1IsxIJ+6VVz5YDrTv4uKi5eYLhYJ2d3c1Pz9vG4021Xfu3FFLS4umpqbU1NRkLLm9vT194xvf0Gc+8xkrj+U6GawZBLU5howr29zcVLFYNDBQksbHx+3eYRaurKyYMuF6UQxsBCwdPAE20fDwsDXMIO5mk9IslHsi993a2mqsRybyhmGokZERHRwc6NatWwYMjo6OKplM6uLFixZ6gJSvrq7q6OjIXH7wBklWzg0NOQxD82C8BfYy4p/v7u6uPvroIwNFYVrirtOZiZ4SrN1PfvITvfHGGzY9GUVGDQLfRzcoiGKQxRYXF204CZvV8zjYxN6bRYbw5HwY4MMG6URJknIFUI0enhkZ9Sjue+/p2/I//eE1YCMPgCPKJfA//vNsFs+ai/IP/LkbxVUoGIZCABT5um9PYIHlx7y7VCqlXC6n/f19ZTIZzczM6J133jGk3mt40mgoJFxJn9uPFhAR83oSTJRjHwVTvUVhc/OddOahbJrY2Qsj3hQCnsvltLq6aj0MPQeiEWmJMeFQoQlduC7Ydr6azwPH3gD47I9H1f0z5rkAwka9w3i81uZ9fn5ei4uLWllZsWcKP8PLCx6VlzlCxOgodw/6RdF6b9UbGSZ/X1FgL+oJn7aXHpYZkB4jJRDVVD51hKvmLTM3RszsaZI8eI8w+6YVKAV+WGTO4Qk7aGbSeZI0MDBgAs9PX1+fqtWqDaE8c+aMJiYmNDg4qNdee81Gfb/66qv67ne/q7GxMaVSKV2+fFk9PT0aGhqy+Xcwy1AEWDBiYbwfOg/19PTY/RE3E1sjwNJJLT1rAIqOJ1MsFnX79m1NTExofHxcXV1d6urqUnd3t30Gt5vKvkQiob/7u7/TjRs3NDIyoomJCT3zzDPWKAWrVq3WGJDk3fEeuCeUHYoOQhDnAAsgfocz4LEFsB/uibSefy7j4+MWpiUSCSuhnp2d1Te+8Q1lMhlls1k988wzOnPmjBYXF+26PHgonXiMkmzWAGXoyJjf2N6FB+vw06XhTRA2oCA9UO7lllCLwysKb1yir0WPx0YJSLLYzy8eG9OTRvwNPchj8BkGfy4siNfk/vBVWX7THB4eKp/PmxtGf3usH8h0tVrVysqKzf7r7++30ACBxhXd29uzHnrt7e3q7Ow0kIs4mJiyWj2ZeoSV5Z79NXCPWEA2BiEO4JJvFgIuMDIyoiAItL6+bpuO2Ygg9oCHy8vLun79ujo6OvS5z33O8u2kxg4PD+tYkvQCIM4nRAN3CcPQpgz7zI1XAv65euWNJ4aRYIMhS8lk0hQl8lIul7W9va1bt25pfn7eCrJ4HReeeYwUljFToqOjw9ZvY2PD7ttnohpZYQ+E83m/YX14i9L11+RllHWIyn9UOTz2SoCb8+w5FsL3upPuz4k2umle926xT6dxIEReuXjvA4+Az0JPpWovkUjYOWAPdnR0aHt7W5lMRsPDw9ZPLxaLGa7ABqHghfQofABIK2QzfOkxHY5A86P355UAqUHcfW9BeB/ZBa59dHRUm5ubFhMTTwNwUTexs7OjhYUFfe9739Ov/Mqv6Pnnnzf6bblcto2bz+e1vb1tPQOCoDZgFGDz8PDQNh5eANYP4M6zD7kvzwCk/oDYnbAPJVCpVGzeIESjIAgMvb9x44aWl5etr4QPH2nRNjw8bOAp8T51DWSIfJfj01xwn2VByXiFEfVOUdr+fNGwwXvR0dDDh0+nHY+FEpDq6/ol1Wm8aDzFZm2EqEonms97Ed7yN9KaLLzPq/rMARZEqrl+MNmSyaSGhoZMYKMVfcVi0Sw3DMTR0VHt7e1pfX1dHR0disVimpycNK0Pd51cMZx075KXy2V1d3cb5ZXNDekIgSaMIL6HqBOGtWGmNMe4dOmSAW8M6lxeXraNzGZ99dVXtbq6qrW1NXN/9/b2FASBbUAsLlbU58F9GpPn7Z8trrF0Ut8AiOljfr9JqtWqXQMYCf0fWUNCRmpBjo+PbWbirVu3rHszYRnrB7aTTCYNu+jp6bHv3tzctNkUPjRpFMMjF36jcl/euDXCsLxc8rdG4KjHex7k7frjsVECUr2bzuEX9UEgSDS9F439o69Fj+jnGn0HB52Ctre3bdN7NxRgLQxDy4/j/iGksP7g3GezWSUSCQPEcL+xqCgBLE0U8JPUkIKKS+83P9aUDUL8zvWh0BB6PlOpVKwd+J07dwxjIbQhrPDKk03b1tZWB9JFrSXPnVy/D8OQDe7HezweRPVUXUl14Y8vyOL+y+WyIfl0Umpvb1dbW5uy2awODw+VSqWsoSnxO41DUIy+81JUnvy1e1nyMnva53hP1JpHjVkjOX1UBSA9JkrAb9go6Sf6Pv7lgXuSjL/pKIASzdP6gY1ROmcUlPQPAwtdrVaVyWR0dFSbVswmQ0gTiYSNK6cegM66GxsbGhkZ0ZkzZ3Tnzh2tra3pzp07mpqa0tmzZzU1NWXAGwfKYX193cp+uU9ChoGBAS0tLalUKtWlptra2myoaaVSUTabrdtgYAC4/txnV1eXCT3j177whS+oVCrp5ZdfViaT0fz8vJaXl3X79m0jFfkBLJ7gg6tPytO3jUNR9fb2qqWlRcVi8T5MZm9vT2fOnLHmrABn+/v72tzctFSdB9foDQD7j9Bmb29PN2/e1MLCgsrlsjo7O/X8889rfX3d5kJAfCqVSrpx44aef/55q3solUpaWVlRPp+3xqi+JgCLHQUHkTM/hdmHuz409efDCHiOjD+iMuqZpP57Gx0PVQJBEPzfkn5VUjYMw6fu/a1X0rckTUial/SbYRgWgto3/ZGkr0jalfSvwjC88bDviN6MF1C/OGhDX2LKYkStN9ZSOuF6e/6Bxx68tvWf518WmO/3wBTkEijDLDbuN/TcarWqvr4+lcvlulLa/v5+q8JD+JLJpHp7e9XX13ff+gBGbm1taXt72zZpEARGTgHExP0lhMA7Qbj8PW1tbVmc7jsqIYCsGfc0OTkpSVpaWlKhUNDi4qLNUMBz8Q1ACFuiz8jjOih+CDc07gAEpa9AI3fXewd8t5cNMhP+2a2srGh1dVXJZFKtra3a2NiwaknmCDJGnolBVAxub29bc1Nk0cuhNxZR3Mlb9igeFZV1vy5+f/i/NeJNRHkFp3nR0qN5An8i6X+X9Gfub78n6XoYhn8QBMHv3fv/v5X0n0m6eO/nk5L+3b1/H3pEbwKyg48jPRIPwMZCeRqlj+ljsZhN4OFvHpH1SK4nWPhcdFSb+6Yc5XJZmUzGgDWEgJJd+thL0tjYmJaXl7W2tqZCoaDj42NNTU2pWCyaVQVQjMViGh8ft/uRZEqjXC4rl8upq6tLsVjMugFxTliMZ86c0ejoqDEO6TJM7p/3SjWcAyCSMmJAS7rpIvAtLS168sknFQSB3n77ba2vr5tiwnWGosxz6ezstKyIV6xR95Xnms1mlUqlrFMym9THxSg7LCoeHeAlmENTU5M2NjZsfRkRNzMzo9XVVb3wwgsKgkAzMzMWQjz55JOWBuYchICMi4M+jJLlurg2qhe9Eo266Q8LFaKZAbwbH9pFPQ0fipFO9dmE6PFQJRCG4Q+CIJiI/PnXJf3ivd//VNI/qKYEfl3Sn4W1J/tGEATJIAiGwjDMPOx70Fge9EGLsjmx3NBHPcOw0cF5fNoGTwBvIgoE8rmo9vSa2VelHR0dGU10a2tLIyMjtplg2KHI2tvbNTAwoEqlolwup1wuZ00oP/axj2ljY0PlcllvvfWWZmZmtLGxoaGhIUvbgdBjjXjQW1tbampqUjKZtNcHBgasYcji4qLeffddvfjii5ZVAKy894zNUsMUrFQqxojz2MDKykod0aenp8dShu+8847S6bTGx8d1fHxsnoFPYbFxmQ+IcvHtu8MwVDKZVBiGyuVyOnv2rCkCmn5INYFnuEoQBMaE5P+ca2dnRzs7OwZkEsq1tLRoYmJCa2tr9uyZHUncTyo1DEObe7i4uGjKzMsV14Q8eZmJepa813tHUcvP9zcKcb1haIR9+exPVNFEj58WE0i7jb0mKX3v9xFJS+59y/f+dp8SCILg65K+Lp1sMO8q+Zv0f/Mb2f/497rvsM3jP+e/y7tPKB2pcbbBa1gOUG4IPqDIlcrJlBw/Ypq5hYywLpVK1sWY5qPZbFblctmEj9gcKxkFvLA2vlOvxw12d3eVy+XqCm583IjylWTNNLD43qKQhqNUWJKNWJNqrdhaWlp09uzZunCMjcEzZaP67/SApCTrnASdmvvDCnLdcCOgiPuce2trq2EPEIR8J+fe3l61t7cbCYwms0NDQ3WpRNYKRqBvExbFpLyHE03N8bqXu0axupc19ocHWxudIxpa+f3yzxEOPPAIwzAMguDhVQr3f+6PVRtlrubm5hDXnZgwqvV8isi7aGxur0g8H8CDI1J9K7GoIomCgA+49joN7KmwAIXDw8O6d28GRq2trSkIavUBU1NT2t7e1uLiojH1Ll26pCtXrtiIqnfffVe5XE7Ly8tKpVLWPisMa12HNzc3tb+/b7x8ylx7eno0MDCgIAisAQqxNaEUw1Ag55TLZbPKeEpkDsrlss1XAKBiDQBBe3p6dP78efX395sHQTUloCJrSo9FlAegbXNzs4UUAwMDWl1dVSaTseadeAcoEZQiqVqeK0orlUpZGpRUHgDt4OCgZmZmNDc3p/HxcXsmR0dH2t7eNree3oV7e3uan5+3qdF8N5sL5RXdbH6z8v/TwkwfxyOryLdnuxKa8DeUtVSfDo8auNOOn1YJrOPmB0EwJCl77+8rksbc+0bv/e2BRyPtFl04/o0uavR3r50bgYWc5zTN2Gjzn/be6AMEnJNq1Yc0BOEcntziabee4kvHYzYGhTaAmgznQAjgK0QLUNjstLem350kKzby/QR8Xz3vDXnrKskm/foW62QCOB9W3itmn8Lz1xmPxy27AqgYBCcju6QTXMgLe3QT4bZ7cBZuwvb2tpF6yCxQjdnZ2ane3l6rlES5ACzSI6FYLFp7ML+JG8lK1PJ6mfayHJXFqOxF3f7ocznt+x4ks42On1YJfFvSb0v6g3v//qX7+38fBME3VQMES4+CB0j3dwaS6oso/GuNbpBN2CjuR/AagSNRBeM1daMHHX0QbGquC1osmweOvy/04f14CUyuXV1d1cLCgoaHh3XmzBkNDg6qUChYFZ9UAxe5XzYbGxBXnrmIx8fHmp+fV3d3tyYmJizORXEwe7FardFqsXqeNAVRB06Bj9UprsEaERrAnCPWj8Vi1lzDg2SHh4fWwpzaAQ+w8Qy5DpiP/ABcYvm7u7vV1tZmw1Wgai8vLyufz2t3d1epVEo7Ozu6ffu2TQ+6cOGCVTdGUXXGny8sLJhcRWWjkWHy8uFl1ntQjcJMb9G9VUeB+5AYWfTl6NH98CjK4FFShP+faiBgXxAEy5J+X7XN/+dBEPyOpAVJv3nv7X+rWnrwrmopwn/90CtwhydcNPIKeDCw906Lp/AAPLLK/6Ma2Gta//uD4iivKBq919NTy+WyLl26ZFOPo5gE99XW1qbx8XENDQ1ZWytSa6Ojo+bWkr9ubm624h7WLRaLWQoxCAJzg1OplM6dO2ddgsihY9UQSngCKBxPncbqE9pQLUdHYjIN/N+XSR8fH5sHAy0Xq01GBcXgy4Xpy0iYdeHCBZOBYrGofD6vmzdvKpFI6DOf+UxdeFKpVPTRRx/pgw8+0BtvvKFSqSRJNrbs7Nmz6u/vr+sP4FObzJuYm5uz1GujUDMqF6xbI7ngdY81nCZrUQPIZ7wMR4Hs6OH/dhp4Lj1aduC/POWlX27w3lDSf/ewc0aP6E35mPPeee3vsVjsPiUQXQCfq42eN3p4LIJzRV206Of4WyNU1ls58v9jY2Pm/jc6Nxuwp6dH8XjcaMmQY1KplFZWVoxfwBAQrh3PA8qwL2um4Gh4eNjSgj5txEaH2kuPAk988mlaEHyQch9icA4vrFCJKeCBH0EY4LECLD9hAZ7T+vq6YQi4+Ci4Dz74QP39/Xr66afrqNtHR0eamZnRrVu39MEHH5gXuLW1pZ6eHg0ODlr/Q8agcf1kXEqlkmUOotY7KqNR+fOclEZH1Dvw4aI/p1cC/txe7k/bB6eFGdHjsWAMSvcPYYhuLhYiSv3kaORB8OC95m2kXHwo4TemXzzvOTzIxfLxKL9/+OGHOnPmjNLptDo6OpRMJutIRFhn7n9iYkJ7e3uWQbh586ZSqZSV9pbLZd2+fVtNTbWedhP3Sn8vX75sLbPu3Lmj9fV14yk0NTWpVCppZ2fH2m5BwKG6D08CJN5TlQkHpNqGLxaL1uV4e3vb/tba2lpXdch6V6u15i7r6+vmrtOBGOUCxyAWq1Vj8nw//PBD5XI5zczMqFKp9fbj/ra3tzU9Pa3333/faL9kBFg/P+fh/Pnz9gwIH2ggGo/HratQJpMxfMeHmchLow3nQwBv4RvF/9HPegwLTwn58OeIGspG5+IcGKRoliJ6PDZKQDpdo3GwWD5Pz+eiblcjgNGfp9H3Pur/H3beKM6ws7NjbrxP0/mUl2e4+UaZbEKfHgyCwOLjeDyuubk5HR4emnsNKg//nc9Jslp96SRz4sMJrwAIC3z5LWkyYvhqtWpjvKghYCIynyH04Dq8lwHRhr9xPUdHR8aHKJVK2tzctDh5b2/PCFd8z87OjikBSnrpGISyZH3gZjDTgXshJYmCI7UbxaIaWeDTNpn/+4MstzdQUU/zYXJ2GvLvPbLHXgn42Nz/LXp4RFSqX2C/eI8SBvjzNNKU0XQN72+kWaN4QtSTwDItLy+ru7tb+/v7lrJLp9N1YKEka1pBbHt8fKw333xTKysrSiaTGh4e1mc/+1mrZ3/55ZfV1NSkJ598UmfPnlU6ndYPf/hDlctlNTc3a2xsTOVy2RiDKBZouOTZw/CkDgHLKakup7+0tKTV1VXrQ+jLnguFgmUI4PF7kJBzd3Z21oURR0dH1raMMCqTydj8gs3NTSvPRvGFYWi1GJIsw0FI4DGMZDKpkZERG5cGSDswMKDe3l7lcjkVi0UtLCyoVCqpVCo1BPr85vQy5t1+nxXh8OzURrIW9YB9qjqqgLxssg7eI0YGOQ99JP4lUoT/7MdpVtS/fpp1j7r4UWXxKN8d1Zo+VPAHm6HRg/SHR2/9g6T4BMsOb72lpUX9/f3/f3tXG2PncZWfWTu7a3fX6297nbo4VqxUCYqSKEKtQKSiQEtUFSHxo1UlWhVUgfjBxw/UqD8QP/qjgCpAAkpF+RDqJ6VAFKkqpVRClWhIo0BJ/EHcxMbu2uu11+tdr7OO7R1+3PvMPvfxmfdeJ9nr2/oeabXvfd95Z857ZuacZ86cmcGWLVvw0ksvlQ5DlLB///5yus+1a9dw7NixMutAVMCTeU+cOFGOVL9x4waeffZZrK6u4tFHHy1bo9M6bty4sXj/c87lxCVaaADF+UgYzo1WaT0pO8YTcHqPh3py2DEyMlJiDTTSc2RkpAx/Tp8+jeXl5eIcvXTpUnEY0sJzq2/uqkQ5Xb58uez1uLq6it27d2NycrIMicbGxooivOuuu8puQKdOnSpbzKv1V1J4rwqB/LPD0n/i7cENlBNlqON9bXuRjyoaMus1FUS3IezAKAGSd0je6wXWA2tTKA7RvHOTVOhq9fmualFabEcYruE9OEPL5FiVSoALfMbHx3H33XfjLW95S1mMowd2HDx4sByLNTc3h2PHjpUzAzn3vbi4iFOnTpUYhdXV1dKRXnzxxTIfzyPWGHzEKbzV1dWO3Z0WFhaQc+uEHkLyo0eP4siRI9i0aROuXr2K5eXlEoTEcw+IBGi1OXU5MTGBnTt3dswcsG556u+RI0dw8eJFzM7OlhDo6enporBWVlbK6U+bNm3C/v37C2I5efJk2dR148aNmJ6eLkoAaFnOXbt2FcTDjUB4aKpu8KHkVl/bjk6nUgmw/ZKICqPFPNqJVTEq6qzx40pAV10qv92MYerVWq4nbdy4MTPiq0kDO8yPgoGiKT+Odz2GXd9zYlle6Qrb1RlEa+TQTHnXCuZBI/ScHzp0qCABhvgyio5RgDlnHDlypKxhp1NvYWEBQCuQZ3p6usDcnFux/hcvXsT58+fLJiRcmLNt2zYcOHAAu3btKnCZKGFkZKSMi+fn53H06FE888wzeOGFF0r8vcp5ZGQE999/P6anp/HQQw/h3nvvxaFDh8p27K+88gomJiawa9euMkvBMN4LFy7g5ZdfLvP5TH/mzBmcPn0ae/fuLQuJOENAuc/Pz5fty5iGSoljf11rwNOjL1y4UPwKPD49pbUQ86j+1UEd1XFkmSPLXfNfeeyA+r90wxJVPFoW25+jZv4tLS09m3N+1Nv6wCEB7fQk/tZOFI3JXVk0kU/vMV/nxR02vO95ubMnGtbot7GyuOHmpUuXykpAnSlg2Vzsw2W1umuu5js6OlqO/8q5tRCHkYhnz57F9evXcfbs2XLoKGH46uoqJiYmSgTjhg0biiU+ffo0XnrpJRw7dgyLi4vFQnHMyQVd8/Pz2LBhAy5cuIDdu3d3LHIiIqClZkfnjMHMzAzm5uYKOmDYMR2kvmELz2a8cuVKcVZyT8ft27eXICMf4nExEVcCEu0AnaG/URuKIH3kn/L0TRS1v6gdavuJ7teGzr3QwCCBqampm8b2JH4UYWQEuXQ3VqC5AlJKxQrT6UVnGZ/xfd+UQRsCLSZXDXJszXR8pt9BiMbGSacaFxuNjY3hgQceKEE7um9+SqmEwOrmnFyxd+bMmY59Czds2IDt27eX1YzqBXenk3ZYPSZ8dXW1xCcsLS1h27Zt2LJlCx555JES/Xf58mUsLy/jueeew9WrVzE5OYkHH3wQDz/8MN761rdi8+bNmJubw9GjR/Gtb32rBD0xuvKVV17pOOF5cnIS9913X7H63Kfw5MmTZZaBIch79+4tG41Q3kQz3Gk5pbVj2w8fPlyQFGci1LJzKMd61GPq2AmJDtx7zzpnHmxX0dDQjZYPgZlWA4+UP32myMH7jKabn58ffCTgXlIgnmJxxRVpxxp5JURlqWPHrX9Utubl1iRSZpofy9E9DLnQZcuWLWUczyAbdu6JiYmO1ZRcQ08Hl85p616FnHpjo+O0G2cKUkplj37Cbjr/iBS4gQqVAL91z549WFpaKr6JlBLOnz+PsbExLC4udlh78kT+NMox54yZmZmiGDi7wpmHnHNRUoT/XDFJmabUWn/ARVGM/afzVM8SUB+So04dGjQNG719ad3X0GaELGvotNbG9Xnk86rx3fENg4IEuICDjZKkFaNLT3V8HsGfSCCRIH2YoWWpo8W1LRED0OkI1LP0VNs7+RCCnZK8jI+PY9++fdi9ezf27t3bcQQXG73urEuozI7GSMLZ2dmSP2E4nY6cz+fegfwG1oHyvXXr1nLeAo9IY30QmtOr/8wzz3TsqkPSBUQcumh9UAGxTugPoS+DU4tjY2Ml2o+RhJxO1fBe7vy8sLCAw4cPl1WXyo9aakWPrpwj4xKFoXfrxI5Uo3aqSLfJqPmQmO/WjOXi4mKIBAZGCXDP+mhuP4qacgF6p1Y/AZ97nkr+3POPfAgsR8edun1VBAvVcnr5mi+DetgRtm7dis2bN2Pv3r3FCUlLyam40dHREhVIn8ONGzewvLxcpr/U8ZVSKl5xOioZQcdIO22Qio4I1bmPIA/O4KYkKquRkc7dnTRYh8MZyobfwjK50Ik+EPKonnz1TVAxrqys4OTJkyWWYWFhoeMw2ghqK/FbXRlG9daEVpvG57UO7r4M7xc1P4QqIi2bfwPvGHRhaYfXNLVnDtlqmrtWllr86N1o6shDPSM4qUqpBtWUd6a5fv16CcdlpN3U1BR27NhRGiaXG9PC6ko6WjBCcW4Iwu9gh+Xvffv2lVDmxcVFLC4ulnxyzmXZ8pUrV4pTj6iEsxREZ+Pj4zchKu6DwFWV3D8w51yUU84ZO3bsKB5xKh4qAS493rRpE5aWlsraBW0P9BmoD0E96zq7423/WVDpAAAVLUlEQVQnIp3+07ZRa6/62xFBBP/5zMv0tthtmKv5qAGjzJtoYJSAk+5K043citaisnxapSkPtWJA5xBEeeQQgdaVjYzQlM4nPvOyak4dvktYPDs7WzbC3LJlC3bu3Fk6wfz8fAmcoS9BhyWcYuNSYe6SfPXqVczOzuLcuXN4/PHHMTY2hhMnTpTw23vvvRc557KXXs65BAlpsBAtOwOCNm/eXKYw6bjj6kGuD+BsBNDqvHp4i0ZbqkLlzryXL19GzmunDjOMmNGFRCWUI/c8VD+H+mBq7azJipPcSRflGSG/JmI71RBz5amGiGuGTNt9RAOjBJqcI/6cafS6ZsFreXoFKayPKt7T17S8ptWOr+QIpjaGUyhHGE+nX0prpx5xE086BxXmLi8vlw7DzsdzE2/cuIF9+/aV+eiUUgkk4oyED3+IHnTlHb+JsfoTExOYm5srzjyuhaCcycfk5GThk34NbfQqa6ZZXV0tMyBUQleuXMG5c+ewsLBQ0BOw5qR1peKdpYbQtI4caUbpehlaR21UkajKtJZvE9qN+kE3vgZGCTjVKsepWwWSvONppWoDJKRUvwLQ2dijoYSHfBI1aB58X52GOv4lImBHYIMlIgCAhYUFXLp0qWyUOTo6iv3795eViVw4wx1+zpw5g/Hx8dKhN23ahMcee6xsADI5OYmZmZniMNu+fTump6exZ88ejIy0dszVU4T0jMSpqSkcPHgQJ06cwOzsbNkGbGpqCs8//zzm5uaKAtKtuyjPHTt2FOTEaUOduqN8afG45dnly5cxMzNTNmLRw0wVBVJu3Ksx51w2QYmm43xMzfJ1Ordbu1OfkPqMNL8mouGgwuNQRvNQJzQNBN+LjOkPpBKoQbMoXeSUqWlITadp3ZOvMIv31SkWVSobgEK1WhARO6g6DZ0ftVLKj+bPBjc3N1dOMuIJOno8GTsCF9YcPny4KAYet8UzDKampkoUH2MfdINWDfUl1OaGJ1QQ3MbrwIEDJWafm6yoo21+fr5YdpWhdsjV1dZpxisrK2VdwNLSUtnxWDudyp2y1E6i6fQ+y9NVjC7raNgWtbEI+TWRokAagm5DlRoqifKt/VYaGCVQc5w0Cd3TdvtdE4Q6y/iOKgReu8NGYaXutwegw3mn5dOi6nOFqHw3pdRxYhJJx3dUBBcvXiy/GTrLhTyMnqOlvHbtGo4fP47x8fESTchpw9HRUWzdurXspcexN6MUV1dXy5l7d911V4n2I18c43Nj0enp6cI/d++hYmVenFrUZdW0+lQaly5dKrEHPMmZyoK+D04vuyy97hRxRc8pxwiSu/GISJVMRPp+zUrrdmG1oYEbjm6K4QdKCfgH6R9hMjeeUE+vBul009hKUaVp+eokdL4UsrJzM63uecB0fK7n/rEMPao7svxspLqohJ1EFc3y8nKJwBsZaZ2yww1PGVM/NTWF0dFRnD9/vmNN/6uvvlqCfFJqxRWwY9HRqXP8vK/frEqOji0AZfaA4cN08HFcT9RBxUb/Auub0Z3aFljvipZq7ck7iTpptfP7vYhYlradyJB4B9bnepaGti1vO/qeKiy2CVUkrqSi+xENjBIAbu5kvKcfEHVWv+5VAUTIQynS/FG8QO0basOayFLx3Vrj0Qr1cpQ0cAlYO0dgZWWl+BDYOXUv/8iqcOqOnV87vCoB/XYqNHYMWmqevacKgqiCjj46DF0JRLzxd00uUUf29IrmorqpDSu7KQmlpnaged4KMU9FM1E+jlZrNDBKQGG0WkKFhapFfYwH9BAeWakMNl61CK7Z6axhIyZk5D1acXYo7dy0OuwwiloI/ZmfHsCh89O+NqL2TTrDwQ7F4BnmOzMzU55xzQI7oDsxdbysyEvLIJrQmHpOcdLnQQRDWakDlN+nayrUwut3akdVhaxtJoLQ2rHd+evpm7zz6vDToQ1/+4YgLNPbq08b1wyd31PFqP6QyCDy28gTnbJOA6MEXKOTokANUgTl/Dnz9PdrQo8gl/LCBqzaWCujCSW4s9C/x4cxvYwffXzrDdjf1TJ1PtqhY1QP+r7LTIcKLh9Px3c1z2717OSNXv9uxTB4fjVZKc86RHQk4YixGx/d5vCdP80rGv5EAXHd8h8oJVDT+gBustTRWM+1ZdRQorTe6FWrkwhh1Q9AZxS3rKJ3OYJ6bq1UEXgH7LUj87dCcr7vHnKd6qTlVq+3Wnm16N7QokAsVRz6DSoPX1jldRFZOq83UjQ7o1Y9mk5zGev7+qczIV4HOi7XPJW0jqPyvFx3VEbf60MX5hn5Q7xcR3cRDZQSqJE3+ibPrQrJhxTuEY7K1Qog1I3gvWtw5h91VNXONauuvzUfjydQvnX6UqPhOO2m7/kcuncyLdMtnVu7Wn3pMMYtIuG3B+7UOgzlq+WoYnMl6tN92slcETDc2mUcGRwllu/DtZrxcQWqvJGnpgVKTkwT7YDl8vPvaaKBUQIRNQkmgqVOrtFrnU87F/OOGrtbnSi95qvveGPnfx1CRJ2BnVoP4vQyFbn4tti65tx5q1kozb+GaiLl6d+qVr2mTJg+gtg1JBd1Mpd9t2cMxokMh3+r80BevQ5qsvSyI0WsaWp8k9Tvo/cj48Y21tSXBk4J9KIRgZs7r8NITacnyJLUIx6Rjm+BNRityEAtbxOfnPf1CgM6oTzJ4bg3WJLyohuaACg7EUXj7ogPt876bhQAEyk0zZ/RhSsrK2WWoNap9bdPv6kfhc81KMvH3nyHCtCJfPrpS3xG30btXZWXwvHaMEnl5EONWpuNyq399/qMfErdqBkntBj9q5TSuZTS83LvD1JKR1NK300p/WNKaas8eyKldDyldCyl9K6euEDdkkTPSW45mE6feRn+WxuY59mk5Z2PGr81ftxaaiPykNMIxiv5UMTzVxiueapFcR4ifiMfgb6rsmJnVUXSBEtdHo4gavURQfEmufMeZaayi9qYlxPd74ZGI2RHUsVZ+1MZeZ5N6T3/GnVVAgD+BsC77d7XAfxozvlBAP8L4Ik2k/cDeB+AB9rv/FlK6ZbiKKOK9OdKNSE5qUOMFgWI94mPpncc0nqFqGWgFarBMKYlxNdrPuP7rgT0+1iWN2JVbMyb/3nNP1U8CnUd3Xh9aOfh1Kbyef369YICgLVAom6kjj21zDqLEcnW5eZ1xjT8NnX0+hZjTUbIlWc35eA8uG+KU8u3ogh0yryGQn2quAmt9nIW4b+nlA7YvX+Rn98G8Ivt658H8IWc81UAL6eUjgP4MQD/0a0crwSfonMobPx0CKoWqcUGqxXouwmzMXnHirS3Wj+3xFqxnr86vaLGqlBYZVKzgLpCj/lo6CnziTzfzIfpo7lzrQOvH+alU3/eUZi/KkwlVy5evn9HVC9s7PqudnhXclp/Pq1Wm33SPJXXKPQ7kpe3o1q9apm8VoWt6ys8L1d6yluN3gifwIcBfLF9fTdaSoF0un3vJkopfQTAR8isMqmVrHA1goNM75ab9zUNx24MZnHBqKYHOr3cmiYa29XK8zLUy69jyQjReLk16xbNlqgvg0SL4PPo2nl7hcRqGd3SaKNXZev1p/8d3dQUBtNEnSpq6JG8lE8lrS9XyJq3x2W4MnJ+FG35/Sj/qC+4UnR0U2sf0SyL0+tSAimljwG4DuCzt/puzvnTAD4NtLYXi6Z/gLgjBnx43jd1zJQ6pwp1f8AIetU6Ww3yaxShfkfEi+avvDU1PDqqCB81Go9pIwumvHu8OoAOKOx8qiKhVXGLT9Tk9RRFOHrn1shPXa2ofKtDUS01hzY+tRnVu8/A6PdFv6MZG7fWkUL0+9qWvI2yDC0rUtx8Xzew0XJrvDTx5fSalUBK6UMA3gPgnXmthO8D2C/J3ty+d0vUZBUVxtU6mP7uZmX9Od91i6D/oykiz7OJIs3sPDoPUad2XlUB1OQSlRV9T1Oj0bRuPSM4zHI0jfPJ76lthRXl7dDaFXjtO6I2pc9c1jUk0S1f58U7p79Ta09AJ/rolf9e6hJ4jUogpfRuAL8D4LGc8xV59CSAz6WUPglgH4BDAP7zVvJmo+I4jkdM1SCiQyElhfVqQQjfFNJGcM+vaf1pgTk2047gU0veCGjF+VutsP7l3LkBp/JKR5yGMKulVxkp0qn5KSKFUuNJ8/PpOn3Pf2v9RM5OblzCrcGU9Lcqi+ggTj7TcmvXXncqX3Uce6So56d16MMvyl2VXW2WRevZKWon+r7LR/NtcgoCPSiBlNLnAbwDwM6U0mkAv4vWbMAYgK+3mfl2zvlXc84vpJS+BOAwWsOEX885N+9yiJsbX7vc0IrXLLvDqsh6RoLzSvPnzmeTU4YQV/Py4YE6qvwZ83z11VdLx9bvcmXgkZAcjuiQRSFv1Mk9FsCHBvxuh+QeBqyKNLL6jlbYOVzWkSWrWb6oI6nMeV2DzZqWz3xKTf0/ER+1zug8Rddq7NwQ1b5XlZciOOdJjRy/q0a9zA68P7j9mYb0Hwfw8W75Bu91fJQ7zKLGUWt4FJKOU/39JljlY0K3qszPwzfVGjNACYjDPLXiIyUAtJbr8p5aPVUEKiOVnVt97QguC/Vl1Bq2/3cve5PS9rxICv19SXJNAahzU8uslUulqGs6vP51WjSynCpvfd8pkpWm82saBEUgtbxVDlFMiP73eu9GAxExqBaUpNNaPm2l4yN+rEJstcaRdtZrd7QAnYdSeGOK3tXIMXZgbVDuYKNTUhueOtLIo0a1KX+Ep26VR0bWNgeJhiWRNdPZEv32yGHF9M6nO9hUzj7Wr3VwWsVa54oasyrPCH2ozJ0PrdualaSi93p03jS9fmPEs25dxraqiKzGh5bjCKAJkdTQhdJAKIGIVJBe0U6R5qtZeV47qtBya+WxMfk8sfIQzWa4pXbrXbNQTTC5Bm/VukRQ3HnSe46anO+aPP07ojKi/Jq+x/lxiuqmlm9ETcYhyrcpTVMn9Dz8ea2um6jWFyJ59YIIBkIJqCUkObxugjiEpGqZauWQFAJG1pb31AfgUW+sXOXdp6m8kn2YEnX0iJ+aDBTO6z067HzsrnySn5xzx5Qp/1xB9WJtvMPwW3zZq0P5yBcR/Y4cX55GUZJ+Z202ISKtryaI3oulVb7d0RvlGbVvLUdjTYigoiFBrzQQSsAtBSmylkB8DFk3be3l1axsZD2pkGqKwjuLp9H/jhaarJ0rqNq3kIdaRKArj2hM2Yv8XInyXk0pq2wiObjcXWk2WXJ9Rzu7vqPOVy+7V9K8avw0ISBtNz68jL6hG4JlPtHzqB91QxXAgCgBoL7DSqSJHVb30oC9Y2hgSGSBSXqAhZeteft8uWtu3vd5cFUy/i3qNPLvUD4ckeg9tzwuM/V/aIPXMrQuXJb+m/m7Q8630XJU4gqvG6rTSL2a/DSYy5WNWs+IVFY1I8VyavephDQMXfP2b60pADeAkR8rMmSapqlvDJQSUIocZlEH6MVikNhoeF3TvArvdfyv7zGta3HfRxC4eZopmg6qRS46Rfd9n3ptgLpbsDrwoulHt6JqwZqUbaTY2Fh916PIirlMa044pnVUwXiFKL/IidwranS5eMdyvvg/ypc8MbZDFYHK19ELSR3NbtD02pU8331dU4T9oJoVrsEsva6hBKeo4ps6msNS5ZX/+Uwr1aG+N/yaVal1xqZv4jNFDE3Wxg/ViBqM/q7xGUF3Jf1unRnxRhxN4XaL1dC0miaawfDveS2kaCqatm76H/Hs7cUNWdMQUNtwN9TQSzsvvL0eAb1RlFKaA7AM4Pzt5gXATgz5UBry0Uk/yHz8SM55l98cCCUAACml7+Tg7PQhH0M+hnysLx+9bCoypCEN6YeYhkpgSEO6w2mQlMCnbzcDbRry0UlDPjrph46PgfEJDGlIQ7o9NEhIYEhDGtJtoKESGNKQ7nAaCCWQUnp3ap1TcDyl9NE+lbk/pfTNlNLhlNILKaXfaN/fnlL6ekrpxfb/bX3iZ0NK6bmU0lPt3/eklJ5uy+SLKaXRPvCwNaX05dQ6U+JISuntt0MeKaXfatfJ8ymlz6eUxvsljxSfsxHKILXoT9o8fTel9Mg68/GGn/cBII5U6+cfgA0AvgfgIIBRAP8N4P4+lDsN4JH29SRa5yfcD+D3AXy0ff+jAD7RJzn8NoDPAXiq/ftLAN7Xvv4UgF/rAw9/C+BX2tejALb2Wx5o7U79MoBNIocP9UseAH4SwCMAnpd7oQwAPA7gqwASgLcBeHqd+fhZABvb158QPu5v95sxAPe0+9OGnsta74bVw8e+HcDX5PcTAJ64DXz8M4CfAXAMwHT73jSAY30o+80AvgHgpwA81W5U56XCO2S0TjxMtTtfsvt9lUdbCZwCsB2tsPanALyrn/IAcMA6XygDAH8B4P1RuvXgw579AoDPtq87+gyArwF4e6/lDMJwgJVOqp5VsF6UUjoA4GEATwPYk3M+0350FsCePrDwR2ht3MoA+B0AFnLOXMnUD5ncA2AOwF+3hyV/mVJ6E/osj5zz9wH8IYD/A3AGwCUAz6L/8lCqyeB2tt0Po4VCXjcfg6AEbiullCYA/AOA38w5L+qz3FKr6zqHmlJ6D4BzOedn17OcHmgjWvDzz3POD6O1lqPDP9MneWxD6ySre9DasfpNuPkYvNtG/ZBBN0qv47yPiAZBCbwhZxW8Fkop3YWWAvhszvkr7duzKaXp9vNpAOfWmY0fB/DelNIJAF9Aa0jwxwC2ppS4yrMfMjkN4HTO+en27y+jpRT6LY+fBvByznku53wNwFfQklG/5aFUk0Hf225aO+/jA22F9Lr5GAQl8AyAQ23v7yhaB5o+ud6FptZazM8AOJJz/qQ8ehLAB9vXH0TLV7BulHN+Iuf85pzzAbS+/d9yzh8A8E2snfHYDz7OAjiVUrqvfeudaG0d31d5oDUMeFtKaXO7jshHX+VhVJPBkwB+qT1L8DYAl2TY8IZTWjvv47355vM+3pdSGksp3YNbPe9jPZ08t+AAeRwt7/z3AHysT2X+BFqw7rsA/qv99zha4/FvAHgRwL8C2N5HObwDa7MDB9sVeRzA3wMY60P5DwH4Tlsm/wRg2+2QB4DfA3AUwPMA/g4tr3df5AHg82j5Iq6hhY5+uSYDtBy4f9put/8D4NF15uM4WmN/ttdPSfqPtfk4BuDnbqWsYdjwkIZ0h9MgDAeGNKQh3UYaKoEhDekOp6ESGNKQ7nAaKoEhDekOp6ESGNKQ7nAaKoEhDekOp6ESGNKQ7nD6fzsFFyxxA8ybAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7KmohE9HZ_mI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}