113. Random_forest_with_resnet50.ipynb

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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8T5VyTlHaOmq",
        "outputId": "dc5c7454-8522-4eb3-832c-8507f1e81589"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: gdown in /usr/local/lib/python3.8/dist-packages (4.4.0)\n",
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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",
            "100% 156M/156M [00:02<00:00, 63.2MB/s]\n"
          ]
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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": "oH_oPOcFaadJ",
        "outputId": "1efd05c3-6d50-4cb1-bb79-61fd1b9fe2ed"
      },
      "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": "xkBrrlJiavBJ",
        "outputId": "f23cc370-700a-492f-aa99-89de86d67c6a"
      },
      "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[31m3.3 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_l2wewvni -- 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.inception_v3 import InceptionV3\n",
        "from tensorflow.keras.applications import ResNet50\n"
      ],
      "metadata": {
        "id": "_w4r9l5AaagW"
      },
      "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": "t_rd1lKwa06A",
        "outputId": "250b02da-126e-4b42-c1fc-7ece60b3a8f7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "notumor\n",
            "glioma\n",
            "meningioma\n",
            "pituitary\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": "XkAmH4PUbToG",
        "outputId": "3751319e-c7b8-4395-b16f-525392340382"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "notumor\n",
            "glioma\n",
            "meningioma\n",
            "pituitary\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": "UuUETpPEbVGg"
      },
      "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": "6UoqUMO-bWQM"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ],
      "metadata": {
        "id": "Ruo1i-sTbYMJ"
      },
      "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": "wEM2f-02bZrG"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "resnet_model = ResNet50(weights='imagenet', include_top=False, input_shape=(SIZE, SIZE, 3))\n",
        "for layer in resnet_model.layers:\n",
        "\tlayer.trainable = False\n",
        "resnet_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ys1_OJribbD5",
        "outputId": "cd293981-715a-4038-acf1-d647072a3706"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94765736/94765736 [==============================] - 1s 0us/step\n",
            "Model: \"resnet50\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 128, 128, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv1_pad (ZeroPadding2D)      (None, 134, 134, 3)  0           ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " conv1_conv (Conv2D)            (None, 64, 64, 64)   9472        ['conv1_pad[0][0]']              \n",
            "                                                                                                  \n",
            " conv1_bn (BatchNormalization)  (None, 64, 64, 64)   256         ['conv1_conv[0][0]']             \n",
            "                                                                                                  \n",
            " conv1_relu (Activation)        (None, 64, 64, 64)   0           ['conv1_bn[0][0]']               \n",
            "                                                                                                  \n",
            " pool1_pad (ZeroPadding2D)      (None, 66, 66, 64)   0           ['conv1_relu[0][0]']             \n",
            "                                                                                                  \n",
            " pool1_pool (MaxPooling2D)      (None, 32, 32, 64)   0           ['pool1_pad[0][0]']              \n",
            "                                                                                                  \n",
            " conv2_block1_1_conv (Conv2D)   (None, 32, 32, 64)   4160        ['pool1_pool[0][0]']             \n",
            "                                                                                                  \n",
            " conv2_block1_1_bn (BatchNormal  (None, 32, 32, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_1_relu (Activatio  (None, 32, 32, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_2_conv (Conv2D)   (None, 32, 32, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block1_2_bn (BatchNormal  (None, 32, 32, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_2_relu (Activatio  (None, 32, 32, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_0_conv (Conv2D)   (None, 32, 32, 256)  16640       ['pool1_pool[0][0]']             \n",
            "                                                                                                  \n",
            " conv2_block1_3_conv (Conv2D)   (None, 32, 32, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block1_0_bn (BatchNormal  (None, 32, 32, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_3_bn (BatchNormal  (None, 32, 32, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block1_add (Add)         (None, 32, 32, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
            "                                                                  'conv2_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block1_out (Activation)  (None, 32, 32, 256)  0           ['conv2_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block2_1_conv (Conv2D)   (None, 32, 32, 64)   16448       ['conv2_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block2_1_bn (BatchNormal  (None, 32, 32, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_1_relu (Activatio  (None, 32, 32, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_2_conv (Conv2D)   (None, 32, 32, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_2_bn (BatchNormal  (None, 32, 32, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_2_relu (Activatio  (None, 32, 32, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_3_conv (Conv2D)   (None, 32, 32, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block2_3_bn (BatchNormal  (None, 32, 32, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block2_add (Add)         (None, 32, 32, 256)  0           ['conv2_block1_out[0][0]',       \n",
            "                                                                  'conv2_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block2_out (Activation)  (None, 32, 32, 256)  0           ['conv2_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block3_1_conv (Conv2D)   (None, 32, 32, 64)   16448       ['conv2_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv2_block3_1_bn (BatchNormal  (None, 32, 32, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_1_relu (Activatio  (None, 32, 32, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_2_conv (Conv2D)   (None, 32, 32, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_2_bn (BatchNormal  (None, 32, 32, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_2_relu (Activatio  (None, 32, 32, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_3_conv (Conv2D)   (None, 32, 32, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv2_block3_3_bn (BatchNormal  (None, 32, 32, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv2_block3_add (Add)         (None, 32, 32, 256)  0           ['conv2_block2_out[0][0]',       \n",
            "                                                                  'conv2_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv2_block3_out (Activation)  (None, 32, 32, 256)  0           ['conv2_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_1_conv (Conv2D)   (None, 16, 16, 128)  32896       ['conv2_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_1_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_1_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_2_conv (Conv2D)   (None, 16, 16, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block1_2_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_2_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_0_conv (Conv2D)   (None, 16, 16, 512)  131584      ['conv2_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block1_3_conv (Conv2D)   (None, 16, 16, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block1_0_bn (BatchNormal  (None, 16, 16, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_3_bn (BatchNormal  (None, 16, 16, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block1_add (Add)         (None, 16, 16, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
            "                                                                  'conv3_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block1_out (Activation)  (None, 16, 16, 512)  0           ['conv3_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block2_1_conv (Conv2D)   (None, 16, 16, 128)  65664       ['conv3_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block2_1_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_1_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_2_conv (Conv2D)   (None, 16, 16, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_2_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_2_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_3_conv (Conv2D)   (None, 16, 16, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block2_3_bn (BatchNormal  (None, 16, 16, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block2_add (Add)         (None, 16, 16, 512)  0           ['conv3_block1_out[0][0]',       \n",
            "                                                                  'conv3_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block2_out (Activation)  (None, 16, 16, 512)  0           ['conv3_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block3_1_conv (Conv2D)   (None, 16, 16, 128)  65664       ['conv3_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block3_1_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_1_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_2_conv (Conv2D)   (None, 16, 16, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_2_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_2_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_3_conv (Conv2D)   (None, 16, 16, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block3_3_bn (BatchNormal  (None, 16, 16, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block3_add (Add)         (None, 16, 16, 512)  0           ['conv3_block2_out[0][0]',       \n",
            "                                                                  'conv3_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block3_out (Activation)  (None, 16, 16, 512)  0           ['conv3_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block4_1_conv (Conv2D)   (None, 16, 16, 128)  65664       ['conv3_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv3_block4_1_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_1_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_2_conv (Conv2D)   (None, 16, 16, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_2_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_2_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_3_conv (Conv2D)   (None, 16, 16, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv3_block4_3_bn (BatchNormal  (None, 16, 16, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv3_block4_add (Add)         (None, 16, 16, 512)  0           ['conv3_block3_out[0][0]',       \n",
            "                                                                  'conv3_block4_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv3_block4_out (Activation)  (None, 16, 16, 512)  0           ['conv3_block4_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block1_1_conv (Conv2D)   (None, 8, 8, 256)    131328      ['conv3_block4_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block1_1_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_1_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_2_conv (Conv2D)   (None, 8, 8, 256)    590080      ['conv4_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block1_2_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_2_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_0_conv (Conv2D)   (None, 8, 8, 1024)   525312      ['conv3_block4_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block1_3_conv (Conv2D)   (None, 8, 8, 1024)   263168      ['conv4_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block1_0_bn (BatchNormal  (None, 8, 8, 1024)  4096        ['conv4_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_3_bn (BatchNormal  (None, 8, 8, 1024)  4096        ['conv4_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block1_add (Add)         (None, 8, 8, 1024)   0           ['conv4_block1_0_bn[0][0]',      \n",
            "                                                                  'conv4_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block1_out (Activation)  (None, 8, 8, 1024)   0           ['conv4_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block2_1_conv (Conv2D)   (None, 8, 8, 256)    262400      ['conv4_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block2_1_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_1_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_2_conv (Conv2D)   (None, 8, 8, 256)    590080      ['conv4_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block2_2_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_2_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_3_conv (Conv2D)   (None, 8, 8, 1024)   263168      ['conv4_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block2_3_bn (BatchNormal  (None, 8, 8, 1024)  4096        ['conv4_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block2_add (Add)         (None, 8, 8, 1024)   0           ['conv4_block1_out[0][0]',       \n",
            "                                                                  'conv4_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block2_out (Activation)  (None, 8, 8, 1024)   0           ['conv4_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block3_1_conv (Conv2D)   (None, 8, 8, 256)    262400      ['conv4_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block3_1_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_1_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_2_conv (Conv2D)   (None, 8, 8, 256)    590080      ['conv4_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block3_2_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_2_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_3_conv (Conv2D)   (None, 8, 8, 1024)   263168      ['conv4_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block3_3_bn (BatchNormal  (None, 8, 8, 1024)  4096        ['conv4_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block3_add (Add)         (None, 8, 8, 1024)   0           ['conv4_block2_out[0][0]',       \n",
            "                                                                  'conv4_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block3_out (Activation)  (None, 8, 8, 1024)   0           ['conv4_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block4_1_conv (Conv2D)   (None, 8, 8, 256)    262400      ['conv4_block3_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block4_1_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block4_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_1_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block4_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_2_conv (Conv2D)   (None, 8, 8, 256)    590080      ['conv4_block4_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block4_2_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block4_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_2_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block4_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_3_conv (Conv2D)   (None, 8, 8, 1024)   263168      ['conv4_block4_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block4_3_bn (BatchNormal  (None, 8, 8, 1024)  4096        ['conv4_block4_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block4_add (Add)         (None, 8, 8, 1024)   0           ['conv4_block3_out[0][0]',       \n",
            "                                                                  'conv4_block4_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block4_out (Activation)  (None, 8, 8, 1024)   0           ['conv4_block4_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block5_1_conv (Conv2D)   (None, 8, 8, 256)    262400      ['conv4_block4_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block5_1_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block5_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_1_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block5_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_2_conv (Conv2D)   (None, 8, 8, 256)    590080      ['conv4_block5_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block5_2_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block5_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_2_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block5_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_3_conv (Conv2D)   (None, 8, 8, 1024)   263168      ['conv4_block5_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block5_3_bn (BatchNormal  (None, 8, 8, 1024)  4096        ['conv4_block5_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block5_add (Add)         (None, 8, 8, 1024)   0           ['conv4_block4_out[0][0]',       \n",
            "                                                                  'conv4_block5_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block5_out (Activation)  (None, 8, 8, 1024)   0           ['conv4_block5_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block6_1_conv (Conv2D)   (None, 8, 8, 256)    262400      ['conv4_block5_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv4_block6_1_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block6_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_1_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block6_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_2_conv (Conv2D)   (None, 8, 8, 256)    590080      ['conv4_block6_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block6_2_bn (BatchNormal  (None, 8, 8, 256)   1024        ['conv4_block6_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_2_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block6_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_3_conv (Conv2D)   (None, 8, 8, 1024)   263168      ['conv4_block6_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv4_block6_3_bn (BatchNormal  (None, 8, 8, 1024)  4096        ['conv4_block6_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv4_block6_add (Add)         (None, 8, 8, 1024)   0           ['conv4_block5_out[0][0]',       \n",
            "                                                                  'conv4_block6_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv4_block6_out (Activation)  (None, 8, 8, 1024)   0           ['conv4_block6_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block1_1_conv (Conv2D)   (None, 4, 4, 512)    524800      ['conv4_block6_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block1_1_bn (BatchNormal  (None, 4, 4, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_1_relu (Activatio  (None, 4, 4, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_2_conv (Conv2D)   (None, 4, 4, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block1_2_bn (BatchNormal  (None, 4, 4, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_2_relu (Activatio  (None, 4, 4, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_0_conv (Conv2D)   (None, 4, 4, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block1_3_conv (Conv2D)   (None, 4, 4, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block1_0_bn (BatchNormal  (None, 4, 4, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_3_bn (BatchNormal  (None, 4, 4, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block1_add (Add)         (None, 4, 4, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
            "                                                                  'conv5_block1_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block1_out (Activation)  (None, 4, 4, 2048)   0           ['conv5_block1_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block2_1_conv (Conv2D)   (None, 4, 4, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block2_1_bn (BatchNormal  (None, 4, 4, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_1_relu (Activatio  (None, 4, 4, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_2_conv (Conv2D)   (None, 4, 4, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_2_bn (BatchNormal  (None, 4, 4, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_2_relu (Activatio  (None, 4, 4, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_3_conv (Conv2D)   (None, 4, 4, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block2_3_bn (BatchNormal  (None, 4, 4, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block2_add (Add)         (None, 4, 4, 2048)   0           ['conv5_block1_out[0][0]',       \n",
            "                                                                  'conv5_block2_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block2_out (Activation)  (None, 4, 4, 2048)   0           ['conv5_block2_add[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block3_1_conv (Conv2D)   (None, 4, 4, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
            "                                                                                                  \n",
            " conv5_block3_1_bn (BatchNormal  (None, 4, 4, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_1_relu (Activatio  (None, 4, 4, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_2_conv (Conv2D)   (None, 4, 4, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_2_bn (BatchNormal  (None, 4, 4, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_2_relu (Activatio  (None, 4, 4, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_3_conv (Conv2D)   (None, 4, 4, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
            "                                                                                                  \n",
            " conv5_block3_3_bn (BatchNormal  (None, 4, 4, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " conv5_block3_add (Add)         (None, 4, 4, 2048)   0           ['conv5_block2_out[0][0]',       \n",
            "                                                                  'conv5_block3_3_bn[0][0]']      \n",
            "                                                                                                  \n",
            " conv5_block3_out (Activation)  (None, 4, 4, 2048)   0           ['conv5_block3_add[0][0]']       \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 23,587,712\n",
            "Trainable params: 0\n",
            "Non-trainable params: 23,587,712\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_extractor=resnet_model.predict(x_train)\n",
        "\n",
        "features = feature_extractor.reshape(feature_extractor.shape[0], -1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcNFiGU-bhX1",
        "outputId": "5f3ecaf6-5498-4c3e-9579-e2dc4468eaae"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "179/179 [==============================] - 303s 2s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_for_RF = features"
      ],
      "metadata": {
        "id": "_C7Uy3rDblDE"
      },
      "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": "xIC4nY-5bmJg"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RF_model.fit(X_for_RF, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wpBwfqvCbnc0",
        "outputId": "625f7cb2-5ea1-47dc-a247-45cebb91ccbb"
      },
      "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 = resnet_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": "0KUlQitDbo0G",
        "outputId": "5dfe4f1a-d843-4c4a-88b8-8881ec09c277"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "41/41 [==============================] - 70s 2s/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": "sD4ZhO-TbqFR"
      },
      "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": "O-pyaDDqbr1h",
        "outputId": "196776b7-710b-4d37-fbc1-61d0b89924db"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy =  0.9046529366895499\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": "xi9Yka_lbtM3",
        "outputId": "12c1c7b7-fd4e-411a-c5dc-2eef3c5f9489"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma       0.89      0.76      0.82       300\n",
            "  meningioma       0.82      0.87      0.84       306\n",
            "     notumor       0.99      1.00      0.99       405\n",
            "   pituitary       0.90      0.97      0.93       300\n",
            "\n",
            "    accuracy                           0.90      1311\n",
            "   macro avg       0.90      0.90      0.90      1311\n",
            "weighted avg       0.91      0.90      0.90      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=resnet_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": "avzDE69fdPkV",
        "outputId": "b92c1294-7274-4164-a0e4-b5f41fb4c63c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 88ms/step\n",
            "The prediction for this image is:  ['meningioma']\n",
            "The actual label for this image is:  meningioma\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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eQTQahWmaaDQauHjxIm7cuIH19XWhDjPDoAHCbreLdrst2QHdYYhueyqVQjgcxunTp/GpT30Kv/RLv4RAICAVie12WzIY7XZ7oCkKx+7mzZvY2trC5cuXhfOwsLCAra0tzMzMDLjx1mdFpTZMMWi5n/N1WBi8H3KgPYG9yDCQTz/YO/3Ofl2LPi8Zf8lkUtx95v3JAGw2m+LqO51OAQM5Kfv9Pur1OtbW1pDNZvH222/j3XffxcWLF8VSW88PQOJ7cghcLhdGR0eRTCYRDocRiUQwMTGBQqGAer2ORCIhHAMei+Sjfr8Pr9eLcDiMZDIJn88HwzDQbDaxtbWFSCSCZDIpCxMA3G63LHZ6IESwuVg1xZnhQalUElc/mUxicnISU1NTCIVCiMViA1wODWS2221ROOFwGKZpIhAICIaRTCYxMjKCjY0N1Ot1VKvVAWt/uzD0fs2X/TrunlLVh57A/oh1Etntdjz33HN49tlnYRgG3G43Tp48Cb/fj1AoJIs0lUoJeKbz/YyzFxYWcO3aNfzxH/8x3nvvPQHWNOmH1le7sewj8Nhjj0n+/tSpU4jH45icnEQwGEQsFpPS3lKphGq1irW1NXGbC4UCut0uXC4X0uk0jh07JiHNN77xDdy8eRMXLlxAOp1GKBTCysqKfJ7jEIvFhM9QrVYFzWfDEfIQWMIci8XQ6XQwNzcnrvtnPvMZPPnkk/jd3/1dUUoECVkv0Wq1hETEoqmNjQ2srq5ifn4ePp8PpmlidnYWi4uLeOutt2QMdWu0h0XuxTN5ZD2BgyRc/JFIBE8++SQmJyfh8/mkpVc0GpU6e13HTyVAqu/W1hY2Nzfx6quvIpPJYH19HcvLy5I1ACBWlOekdSRPgOnHo0ePwuv1wuv1ihXXNF7tkvPaCcDR/WaZb6VSQSaTgd/vx4kTJ+RY5XIZ2WwWxWIR/X4ffr8fwE4BEd1sAngUejLkO3S7XSFDaTzk2rVrqFQqcLvdmJ6exvPPPy9ViIz1qfiIafT7fQSDQaEnk5I8NjYmimNlZQVbW1vy7B52uVdFdqgE9klogUdGRjA6Ooqf+7mfE/c3lUqJ5WV8TtebpBt6BjabDZlMBhcuXMAf/dEfoVKpCJ+eC52lwTwvi4MCgQCi0ShCoRBGR0cRDodx8uRJwSKcTicASAVevV4XK93tdmG325FIJISxZ7PZUKvVsLW1JYCbzWZDOBzGk08+KQvq1VdfxdzcHAqFgngn9CY0wxHYnqgME4hX+Hw+GaubN2+i3W4LDtLv9zE7O4vLly/j7bffxqc//WmcOXMGiURCQD+Sl4BtIHJjY2OgLiIej+PSpUvodDqYmJhALBZDIpHAP//zP0sq8mGT/QxZDsOBfRKSfj7+8Y8jHo9jYmICExMTGB8fh8vlEmtNrr9mRnKBz8/PY3FxEX/913+NhYUFzM3NSTxPIK1cLos3MD09jdHRUcERer0eRkdHkUgkcPLkSWkrRqLP+vq6IPLMWExOTiIajQpDs16vC+C2tLSEarWKQqEAv9+PaDSKaDQKp9OJUqmETCaDd999V8ag3W5L1SHTjPQ2qtUq6vX6QE0ACT8a1LTb7eh0OtKTkOEPrVw8HsfJkyfx/PPP48yZM/j0pz89UGzU7XZRrVbRarVQLpcHCE3NZhOXLl1CoVCQ1GE2m8XFixcf2bJkS0r7YIcD9wsk2e3vezneMLYiLXggEJB4OxwOY2RkRF4jm42ZABJr6AG0221UKhXMz8/jypUrePvtt6XlFgFDzQpkKXEqlUI6nRYwr9frYWxsDKlUSpQDj8+KvkqlIk1IuDg0GYr3o++ZOXOCeN1uF1euXEEmk8H8/LyAjVR2vB4A4n0wfGFDEAASBumKQoYY+Xxezql5BZlMBtlsFg6HA41GAx/5yEck86LZjaQz87VgMIhOpyNkLKfTiVgsBrvdjvn5eVEiD1r2Yx3oYwybt1Y5MEpgPz0Bfazd/r6X41m/bxiGLMYzZ85gfHxcrNqP/diPwePxoNlsDrTWJuONJJpWq4WZmRm8/PLLeO+997CwsIBerwe/349UKiVxdKlUQq/Xg9frxfT0NF544QXJ56+ursLj8eDEiROYnp7GkSNHcP36dRQKBSwtLcHlconLTbff5XINpN62trZEmfE+e70eSqUSFhcX4fP5xHLXajW89dZbaDabEs8zFDIMQ3AAxvkMAZgdqdVq6PV6woKk0un3+2g0GmLx2VMhn8/Lgua1vf7663jrrbewtLSEZ555Bp/73OekRJqA4enTp1Gv11Eul6XHIRuxBINBbG1toVgsolAoIJvN4vr16w+8Y9F+rINh8/RWcmCUwJ3KnWrK+4n28tg2mw0+nw8TExNIJBKIRCKIRqOIRCLSyYdgH8lFdrtdYlkuppmZGczMzKBYLIo7TWINm3KSQDQ1NYVUKiW8AebiA4EAxsfH4XA4UCwWsb6+Lq27PR4PfD6fWGiShWjBvV4vOp2OXButPav/6MEEAgFB9OkxkPyksxW6DoHjRS+Gng0bmeoaBdM0pQ5Ad0liqNJqtcQzYf3C3NwcDMPA1772NTz22GM4deqUkK/oRbBFGz0LAEilUoJVpFIpAMDy8rKAonc7F+71/b1+fj/koVMCdzpA95vMAWy7s9FoFOfPn0c0GkUwGMSZM2cQDAYRCAQE/NP5fgJVb775Jq5cuYLf//3fRy6XQ7fbRTQaRTKZRDQaFQ+BCPrp06eRTqfxwgsvSB//9fV11Go1HDt2DKlUCmfPnsXa2hrm5+dx9epVNJtNJBIJBINBTExMiOVvtVrw+/1IJpPikusWY+VyGeVyGaFQCKFQSNKYDocDKysrqNfr0prM7/dLGABgoIyZY0TrTOEiBXbCBOb4GZbQqpumKcpIewNUPJcvX8a1a9fw+uuv43Of+xyOHDkCv98P0zRFqXq9XlEwrVYLHo8H4XBY+ALHjx+H2+3GzMyMZBfudi7c6/t7/fx+yD0rAcMwJgH8PwBGAZgAvmia5p8ZhhEF8J8BHAVwE8Avm6ZZ2PulHixxuVx47rnnMDY2hkQiIUw8TlRSYEkAIjB448YN/PCHP8R3vvMdXL9+XVJfpNUCOxYJgNTlp9NpeL1erKysoNFooFgswjRNhEIhnDt3DtFoFKVSSXLjyWQSXq8XZ86cEXKQpgCTlkziDr0VXafAXHypVBroA8AiKLINdeERMJjD1rsVAZDY3jRNyQCQoqwXeCgUkroHFpYxfcnSZS5ww9iunvz7v/973Lx5E7/1W7+FI0eOSMqw3+/D4/FIFqXVaqFSqQj5aG5uDrFYDC+88AIuX76Mq1evfpBT6YHLXjyBLoDPmab5Q8MwAgDeNgzjWwD+DYB/Mk3zTwzD+DyAzwP493u/1AcvnNxMa508eRKJREIsJhlqug8BLRwn6uLiIr773e/iu9/9LpaXl+F2u+H3+xGJRMT1LxaL6Ha78Hq9SCQSOH78uMTqmUxGdvIJBoPw+/0YGxuTAh127Z2YmBAknZOfnYK4yHWjD60MWBqcz+cld8824uQ8sJV5sViUxasBWGCQ/qo/Q8+IioM1D8wOkD9BsE4XTxFz4OKmV9FsNgVT+dSnPoVAIIBUKiWZEb2VGs9Fz2h1dVUamxAbeFi3QrsXuWclYJrmOoD19/+uGIZxFcA4gF8E8JPvf+zLAL6DR0QJUD760Y9icnISqVQKyWQSJ06ckDJgdvfhwmOMvri4iD/8wz/E6uoq1tbWAGynFZ966ikA27n7zc1N5PN5uFwujI2N4ZOf/KQszvX1ddTrdYlzg8Eg0um0tCDv9/vY2NgQktDTTz+NYDAIm82GSqWCtbU18S7oyudyOfEIEomEpCAZr7tcLsEUms0mstms1P7rtuXEDXSWgkJcgBkA3S2H3YN8Pp9QhTWOwDCjVqsJL8LpdCIcDqNcLqPVakmTFQDY2trCxsYGfud3fgfnz5/Hyy+/LJuyZLNZNJtNqaCMxWIClBqGgXw+j9nZWUxPT8PpdOKtt95CsVj8AGbTg5d9wQQMwzgK4CkAbwIYfV9BAEAG2+HCsO98FsBn7/QcB4HW6Xa74fV6EYlEEIlEEA6HZZLpHw1+NRoNvPvuu5idncW1a9dkGy6fzyd9/5nbJooej8cRjUaFGMQiGV2iy14DJCCRqUerTmBSLyCGG6Totlot8Vq4GNgshB4CAcF+vy/lykT+qeiGddLRQKGmM2uPgWEB05W6eInf0aXUenszr9cLp9MpXoTeFm19fR0jIyN45ZVXMD09jbNnzw6AjRpc7Ha7CAQCknmJRCJoNBrw+Xx3XH78sMuelYBhGH4AXwPwu6Zpli15dNMwjKE+lWmaXwTwxfePcVu/60ErAABIJBI4ffo0UqkUQqEQjh07JsAY6cC0kH6/H7lcDhsbG3jppZdw4cIF1Ot1RCIRHD9+HH6/HzabDTMzM8LfTyaTOHr0KB577DE4HA5kMhl5j2448+C9Xg/pdBonTpyQc05PT0thUqvVkloALmRa7NXVVbHWpBQzZ95qteB2uyWtODIygmAwiLW1NelPwGwFQT0i6lwwzIg4HA4BNpk+pJBzQCyAY0gGoO5q5PF4JDyhQmBB0DvvvAMAwotwOp3Y2NjA/Pw8fuM3fgO//uu/jj/4gz8Qr4i1EQDkfkZHR4UtyZLlK1euoN1uI5fLPfJhwZ6UgGEYI9hWAP/JNM2/ff/lDcMw0qZprhuGkQaQ3etFPmgh2YSuN4FA3YufMSfz7svLy3jttdfw+uuvY3FxEb1eD+FwWFh3+Xxe4neXy4Xx8XFMTk5K3z2WzGpWIeNonm90dBTj4+OyoEmmId+fvPqRkRHJv5PDT2vK3Yh1RR/7CNJrIagWiUTEO+FOROxMrNN3XOy6a5HuF0FcgJkJKideK70KXj+VAjdVZWcf0zQxMTEhTVFjsRhisRgACLfijTfewEsvvYRf/dVfld6Nekcmr9crfyeTSSlseuyxxxCJRPDKK6888t7AXrIDBoAvAbhqmub/qd76ewC/BuBP3v/9d3u6wn2Uew0pWFjDn1gsJptjADv9/2i5Go0GFhcX8corr+ArX/kKAAiqzg1CSVlttVrScvzYsWMYHR3F4uLiQEdh3XQEgHgaiUQCo6Oj2NzclEagGxsbyOVyqFQqMAxDFi5rAQAI2EbvhSk0LjwqJxKLyHcIhUISQhAvILhHtF6Dg7oun0qAoQzHikqBJB3WAHALdB0GMSxhGGGaJtLpNEqlEtbW1gSkNU1TWpG/++67mJubwzPPPCPpQ/YvoCLMZrMwTRPxeBy1Wg2FQgGnTp1CIBDAG2+88SMl23uVBxHa3sqbuefaAcMwfhzAqwDeA8AR+j1s4wL/BcARAIvYThHmb3OsA107EA6H8eKLLyKRSCCVSkl9O9F51uIbhoG1tTVcuXIFf/qnf4pMJoOtrS1MT0+LF7C5uYmFhQXZjvvjH/+48Ao42XQ3IS5uxs2maeLIkSP4yEc+gscffxyjo6NYX19HLpfD7Oys9Nkj4YjpsUAggKNHjyIUCiEcDotXoAuamK5j1SAXscvlEg+B4cDa2hqq1ap0LS4WiwOpUD5PknTIzadHY4376RHQ6hLQ1McFdpp60luZnJxEq9XC4uIiSqWSWHG73Y7Z2VnU63XUajU8+eSTOHv2LL7whS8gGAxKCKMbmrTbbZRKJRQKBfzgBz9AJpPB8vIybty4gQsXLjyYybePsu+1A6ZpvgZgt5X74r0e937KvWhg9uEjWBcOhyU+52/y47vdLq5du4ZLly5hZmYGDodDyDaceMzxu91u+Hw+oRQbhiGuNdtv0fWmaH49C5O63a6U+ZIGW6lUBmrlbTYb/H6/0IX9fr8w8HSXXgADKUOd3qMF5wLXqU/+6Pf5XQ0K6udgNT78jv7RzU55LgrTmLynUCgkm48woxGJRAAAtVoN169fR6PRwNzcnKRPeXy9OzNTtmRZptNpFAoFOJ1OCU/uRA4CkH2n8tAxBvcid/tQbDYbpqenkU6npShnfHxc9gkYHR0VSmqpVEI+n8dLL72E+fl5tFotnDhxAufOncP09DRarRa+/OUvC2/+9OnTspDZHouoPSm8RN81HTcQCODIkSN46qmnUCwWkc/nZStuQuIAACAASURBVOtubiACAKurqwAgGQSSjTRqzzGhx2Cz2SS0seIEROEJzPl8PrleLkQen+66DhOo0LQrT14AkX82JOl2uwICsgUbsIMFsFcAKw4BIBaLIZ/Pw2634/r16wiFQvjIRz6C1dVVGd+FhQV8/vOfx4svvojf+73fG5gTup16t9vFsWPHEIvFMDs7i0QigbGxMWSz2YF+CPs51x6kfKiUwN0KC1nY946EINaps8TXMAxcuHAB77zzjsSXR44cweTkJMbGxrC4uCg753CHHrboYqMQXY/AtCGr4UiRZXeiVCol1YH8PmP5drst9Fg2+WQhDvPpjLO58BnKcIFpK84fLnzSf/kd1hFwYxCi/QT+uLCsbcZ0ahDY2VqOC56NSYAdarHmGlBZ0SNgrcPY2BjK5TLq9ToymQy63a5UcnY6HSwtLeHKlSv43ve+h6NHjyKRSAgj0efzoV6vCxja6/UwMjKCSCSCkydPSmjxMC3wOxHb7T/y4RLtqhqGIdY/Ho8LN4B4AFF60zTxne98B3/1V3+Fzc1NOJ1OTE9P48SJE5iYmMB7772H73//+8LZP378uKTYWGdPZJ2ZAbICCcKxO/ATTzyBqakpQfm5GScr/orFIkqlkgBrbHEeCoXkWrnvn1YCXq93YCFz4XE8WBpNa8wF3Gg0BgqPyDNgFkP3EKTV11Regp5ssEK3nHsyanyB16w5CvQuGo0GgsEgjh8/LsVXCwsLaDQaSKfTUkOxtLSEd955B1//+texsrIiXkiv15P9HtntiFyLZDKJxx9/XLITj5ocegK7yNTUFKampjA6OopQKIR4PC4Ln3nwTqeDq1ev4qtf/Spee+015PN5jI6OSl+BhYUFvPbaa1hdXYVhGHj88cdl4tPakz9PNp/2CLgXQ6PREKtF110DW1x81WpVLDAA8RaazaYsVlpkLb1eD9VqVVJ+ZPURqQcg71UqFbRaLcTjcSEP6fiXwB/vT4OOXPzEE7TC5XF8Pp/8b937gN6TqQqS6I1Rkdjtdpw6dQq1Wg3Ly8uSKWFIxIYnX/va1yTbcO7cOdkDkQ1ZK5UKer0exsfHhTlIBTM/P/9IpQ0PjCdwr1mKYSDTvZ5TL8BIJILJyUkEAgFhCnLCcRHV63WsrKzg1VdfxebmJkZGRsRjAIBcLoe5uTm0Wi04nU5Eo9GB/nu6f722vPp66Or6/X5pVmIYhiwSphKpRBg+6FiXSsK6OzPdb4YQDCN0DE6wj9kKLkwWH+lr19fN13gMHVroxiXW50jiE7/PMdL9GAHIuPB+WbHJbsPhcBgOh0M6GNN7SSaTcLlcWFlZwdzcHK5evSqKiUqLoRO7NZM0FYvFBhrDftByq3m+F0LTgfEE7nVQ9/IwrN9lBVsgEMDo6Kj0o6MrTRotY+E33ngDb731FhYWFvCJT3wC09PT8Hq9WF9fx1e/+lXhBkxOTor7zyIhKhdr4Q0VEWm63KacTT1Y/svdfDKZDGZmZtBut3Hq1ClZgAQn6/W6/Pj9ftm4k4sqm82i0+kIlz6bzSIej/8Id4A/zIhwPMhOJM7AychFRKVBRcCUIz0ppiGtuAQA6YLM9mf0jDqdjtQsMG3KUIaKw+124+mnn8bS0hKuXbuGxcVFBAIBvPjii5KtefXVV/FP//RPeOaZZ6TQis+X270lEgn0ej3kcjmcOnUKyWQSs7OzKBaLA+XRH4Tcaq7vZR0cGCXwQcqw9A0nr9vtxpEjR2TisUJQewEE7y5cuCDdaNiXf319HVtbWzAMQ6rtmOfWE55WixZPW9JWqzXAaEskEtJAlJOdMb/NZpPW4ez0wx6AjM91XE5LytCA4BrxAbYJI3ZAwJHXQ2VAj0Hz9615f+158N7pxej3WHxEPr9mYnLsGdqwsEl7FsBgqzJgZ+MVYinkOKytrcHtdiOVSmFtbQ3lchnf//73UalU8PTTT4uHwa3SW62WEKU4nhMTE3A6nbh586ac62GWD6USuNVDCwaDePbZZxEOh+F2u5FOpxGNRqWNFpVAs9nE3/7t32Jzc1Pi1UAggK9//esoFArSICQejw8U6pClRstGF5uFOQBQKpXQ7/fh8/mQSCTw2GOP4fjx40gkEnA6nahWq7JlmMfjwRNPPIFCoYBLly5hdHRUsgdsDkIMQaPqzBww186y3HQ6LQqFnY4LhYL0E6hWq0L8aTab0jKNVthu3941iaEFgT0qLPYGpOLUfAV6OAxHSMYiQalQKIhiZSZEhwq6sSkVBMHCubk5lEolfO9738OpU6fwsz/7s1hcXMTGxgb+4i/+As899xyeeeYZNBoNlMtl8dSy2SzcbjcmJyelEvGjH/0obt68KUrgYZcPpRIYJna7HWNjY0in05IWSqVSQtxxuVwCCL7++uu4evUq8vk8nE4njh07hhs3bmBhYUEahKbTaQEAmSbTNQYEzhhz09J1Oh1RNvF4HEeOHMG5c+ekW06tVkM+n8fKygpKpRI6nQ6OHz8uhCFaS/bXI2mJRCQ2HwUglp9ueK/XQzabFZeaC75SqYjiYmaEoJrP50MulxtgNeo+fda2Y1QEukBIb0uuuQBswEoOAa+dXAjiCsQ1eA5N6qEVP3LkCMrlMlZXV5HJZPD9738fqVQKk5OTePvttzE3N4fXXnsNExMTSKVSko5MJpMol8vI5/NStJTP5yXMKxaLsifiwyoHBhi8lewF9NDHuNVxbDbbwHZh7BTMBUkLCgCXLl3CG2+8gWq1CofDgXQ6jY2NDbz11luSsmO6iTEsLS4nP2Nt3dgDwI+w4BKJBMbHx2UzTjbOzOVyKBQKqFQqsnjoVRiGgXQ6jbGxMektSBdcd/BhyMJt0RwOh+TYCSbq/f646MPhMAKBgGAMdMM1S1GDrPo3MLiLsR5bXVxE76HZbAqoqbMyVCB8ttYsBYU1EvF4HMlkUujIs7OzCAQCOH/+vGAhFy5cwNbWlnQrYtESSVDEZNg0NpVKwev17nlu7kX2Y208FJ7AfsRctzuGw+HA2bNnMTExIT372DeQpbXsV//aa6/hBz/4AbrdLsrlMi5cuCAbYh47dkxANSoAPeGpAHR8zL9pAW227f0JWPDCnXy73S5mZmaQyWRw8+ZNyZdnMhmUy2VJ6bE7UbvdljLiWCwmHAfiErSsvV4P5XJZKg1JiSYPgKg5lQDZhFRUbKFOhamLhbSwNoGgIUMEp9MpuX7tJVC42Bm2aA9KMxNp/ZlmZI8G5v3dbjeefPJJ5HI5XL9+HTdv3oTdbsfHPvYxFItFvPTSS/jt3/5tnD9/Xq6RHZUajYa0K9/a2sLo6CiefvpptFotZLMPrlB2P9bGQ+EJ3G9hnMkuvGwUwkVG5LpQKODq1avIZrMSk3e7XWGRcaKRcKIbWGjuuy6x1cCZ1upUBAwTqFByuRy2trZQKpUkzGAtf7/fl5iW8TzTm6wdAPAjDVB10xFrh2SyAcml5/3REjNToCejBjqt9QB8jfdozYxQdNZEey4UnUkZ9h7vRzMSWX1JD4bhAa+DrcW4GYkeL/aNYGs5v98v7FH2anhY5aHwBO63TE1NYXx8XIg80WhU3Gguin6/j4sXL+Lll1/GzZs3ZZJzccbjcbGKwGDen+66tlzW/3UqjYAXiUS1Wg0ejweNRgPXr18XRcAJSUSb6HepVEIqlYLP50M0GhXqMwBh1nGRM4RgazS29+aOPdy2nIQp4gXNZhNOpxOJREKuk+AgQUidEdFFRhwbFk5p5uIwBUlh3QHHnTiLtR6i1WrBMAzpeUBviwotGo3izJkzWFxcxMzMDJ577jkZ829/+9uYm5vDF77wBZw7d068k7GxMdy4cUOauRAkHRsbQ7vdxuXLlz/wlOF+yYdeCRiGIZV+rA3QFh3YRsHZynt1dXVgZ2DuO0DgTLvk/Iw1/814lwtEo9n6byoQTmZOQIJj3FVId+/tdrsCGLJ3INOIVEa8Bh6bC7fVaiGXywklNxgMyj1wp2RaX4/HI/eZSCSQTqeFZUfPwGqlGQ7o/L51XDRISaWhx8rq/u4WegCQ71Dhkf7s8Xgk9QhgAMzt9XpYXFzE0tKSsER5jazA5H1wz8dut4u5ubl9VwLDMI77IR9qJUDrQMCHIYFVCdTrdczOzmJhYQGZTEa+S2LRxMSETGjy37WioNDiafBMM+eGKQFW9BFcHBsbAwAUCgVRAtya3OPxSC0Cq/CYxuPi02605vKzOCabzUoGgKw78vX1vXAB0QuiEiAVmvejFy49DF04pHEQPg9+jmGLLqbSioLHtL6mz0dlp7MFfAbsB7m5uSnFRysrK1heXsbi4iLS6bQs8nq9LgqTLMxQKIRkMimcBu29fBA41n7Jh1oJhMNhTE1NSVPPsbExhEIhQbo50cvlMr7+9a9jdnZWvut2u/GJT3xioPUXAFm44XBY8u3aEnGCkLTDRUiwi5kBu90+0N6LhByGKSweImGHoQuR+0qlIhOWOwszLibSztjfZrPJ1ukk5mj6Md17hg8kEXGRHX2/WUk2m0W5XJaFywUPDOIEvE+9DyJDGt47sRQqBopWFLTIVMg8jzUTMTIyMkBookxNTSEej2NmZgb9fh+pVArlchkbGxt4+eWX8dprr+HP//zP5fuaiMUyZ+7JcPbsWWSzWczMzNyv6Xrf5EOtBFwuF2KxmBCB6AHQUtDtrlaruHnzpmySSQAtHo/DMAxUKhUAg5s/ahxAa/TdwC7rZ2id6V5z4TCdZq0D0OQfegjMn7daLWlWQiYgQwhrmo4EHk1f1nl5KjYSfOgiM+QwDEOyDPQ8AAwc08oi5KIHBus3+FntNVjH0SrDxnUYeEggl96GHgePx4ObN2/CZrNhc3MToVBowIOgAqW3ODIygmg0KrjCfqTtPkj5UCsB0kfZOJQpoXA4LJ11MpkMFhcXB2I+7ujDJpUaeAJ2Cn84wYjiD0PDNXVWg16NRgNra2s4efIk7Ha7ZCz6/b7k5nkNP/7jPy49/0qlEsrlMhYWFmRrbtJm3W63TFx2Ima58lNPPSV1CsQfuInp0tKSKEluXV4sFhGJRHD27Fnh3P/0T/801tbW8K1vfUuUQ7FYlFCEVpkcBHoaOu+v/yYIqUMoChc1+RPac7BmYtjzQGMa2n0Ph8OCBTidTpw9exZXr17F5uYmvvnNb+Kpp57CT/zETwhzMRaLScgTCAQQCoWQTqdFqT1s8qFUAiSBcOPQaDSKWCwmKTAu5n6/j6WlJdy8eXMgD80Hb6X7Ahhq0TTqra+B59CxrFYkrA5kCo9uO1uf69bgpmnKBh5E9J1OpyD8mUxGwgNWOVIIDGoGni7GMU1zoHRZVx6yIQczBXa7HefPnxdGJLdm46YmOsfPhc4qS51q1NkVq3dAD4U4gQYc+V0dog0TPebcHZoFW2zt3mq18L3vfQ9+vx/PP/+8XAcVpd1uh9/vRywWk23f4/G44DIHSW7lnRx4JWAFWe4EdLECRNbvMOXDjkGjo6MYGxuTzrz8Tq/Xw5UrV3D58uWBCRWJRBCPx6XZJYEn7eLSImkkXLfWorXSrbr5wwlM15uuvN1ul/ZiTOdxslYqFRQKBdRqNTSbTaTTaXS7XelEvLCwIIU/k5OTopwYWjCtRpCx399ueEIPptFoSHt0AJJCLJVKkndny2+fz4d8Po98Po9arYZarYa5uTlUKhXU63UEg0FJRbKBiqYTa+6Cfp4668Dr1PgAr5WKiwuV+IsVgAUgacNud2d7duIulUoF3/jGNxCPx/Erv/Ir8h1+nmEA04WNRgNHjhzB2tranpSAnr93AzLqcIvfH/a6VQ68ErAOwJ0MyO2+43A4MDk5KTxxUkMZd9vtdukZ+Oabb8ruwDwW2WS0groSUMef/J+TlW45mWwEtPQEpnLgItzY2MDCwgJOnDgBt9stIYnD4ZCed6woNIzt7bQ2NjYQj8fhdrsxPj4u7ne5XJZuwlREbMHNZpps2U1PhB5BtVqVwiju6utyuaRy0TC2yVQMjchUpOv9+OOPI5fLYWlpSchNnJQE3HTvAV0LoHEFHZfrPgVW8FBjEp1OZ2hdgwZFWdjFdDBDOIYJ//iP/4innnoKo6OjopTJ0WCDEY/Hg1OnTqHdbstWc/ciwzCku/ne3a6Zh5fmdI/CCcTGEyzP1XRVm217n7zNzU2srKxgfX19wCXlj+5+Y83zW5FoLgx6GnqiWvn1VBy08LTmwE5/QNYKlEollEolKfet1WooFouiGLivgOYDWBuEMvvA7zAFxvvja5qfzw1JdOhQr9elQpDKkgBaMpmUkmgqPipWnX7U46GbmmirxvHTCkIDf3oc9Xf4twZUNRgYDodhs9kEVAW2FU8ul8PFixdRq9UEx+DY6cInp9OJWCwmdRgaBD3IcuA9gf2WeDwuPPpYLIbR0VGZzOxz53K5sLm5iffeew/lcllcU/bvZ4xvbapJF9TqbnLhcnLptl1cVG63W1xyxpzNZhP5fB7Xr19HKpWSnXZcLheCwSCCwaCkDYvFIq5evSpddKhElpaWhO1Hggxr5PVk1VRg1t4TPSf/gdkQWk96LixtpvIgaUgDpqa50zdwdnYW2WxWtlcnCMswgFRo3UxUe1X8X4dVrK0gIAvsYAP0AAg6agAWgGAqpmkOYD30SK5du4Yvf/nL+NjHPobp6WlRACQScV5x7GKxGCYmJrC+vi5l1wdZHgklMCxu2g1LCAQCAgiSKkuATXfQzWazuHHjhrj8AMRd5oK3Iv3Aj5KDdEXdbixB6/VqK8Z4vVAoDNwPLTCLW+r1+oB1p+LSvfB4r8MacOjz0TrS5dbno3XWG4DyPnUsbiU96aajZO1RKVrHgeNodW85trul/azP2vosrBZ52DMj0KqJSa1WS8KsbDaLYDAo3Aaenzs3U0HGYjHkcrmhSuBWcb51Xt3J5/Yqj4QSGDYYu02ORCKByclJ6QXIklj2zWdV2HvvvYdXXnkF5XJZvu/3+zE+Pi7WkRaOixuAkIdo2ZhPJ5ilQwlNedXH0BOcSmB5eRnVahWnT58WoK5SqaBUKsnGpQS1fD6f1ASw6Eij8mNjY+h2uygWiwPgJSc843mKw+FArVZDtVpFIBAYqB5kmNHr9bC+vj7APyBoCewskkwmA7vdjmg0KtkMtkfXi1zvuMQxshKC9HPVPARNQtLeBUFc/T09X+gd2Ww2ien5rCqVCi5fvox4PI6f+qmfGtiNudvtildmGAbC4TCOHj2KpaUl4ZDcbm4Oe+9OP7dX2Y9die0A3gKwaprmZwzDOAbgKwBiAN4G8D+aptne63n2qvnouuoKMJ1jBratZrFYxOXLlwdy6A6HQ0IIegs6HNDFQNYfxu/AjjW1pq+oSKweg7bYXAS08rVaTSjFLKSJx+OyOFZWVsQ7YMxKQDMcDsM0Tamk494FtNT0GNxutxBjuMkKd+cha5DKhc/G4XAMbKmmx4UxM7cMi0ajohj0ePF5UUny+dBz0ZwLvZB57zy3PpbO4FjnE8/NcIEch263K2MDbPeRsNvtePbZZwVQ1b0H+v0+YrGYHIv1JAQ5H4TcybrZD0/gdwBcBRB8////A8Cfmqb5FcMw/i8A/xbAf9zrSfaq+TgRWFpLRFpPIsazCwsL2NzcRKPRkMIRAj7Wwhed76bomFO7xZrsou9LKyIdKujOuiTaMPbVm4vQirFYCAC2traklwGPy4VDajB3K2b9AXEIXrfuhUi8hAqCfQaIcVBhkXXIDID2lkZGtrc5bzabKBaLSKVSAwpEK4FhKS0NuvKZ8ns6NNCdhejlUFFbF6ROMWplS89FH//GjRswDAO1Wk08IN47WZTctLVerw+0l78XItF+uPx38v29bk0+AeDnAPwxgP/F2D7jfwPgX7//kS8D+N+xD0pgr0Igh+4/+9fpGLleryOXy+Hq1avY2NgYKOk9efLkwEQBMOBa6kmr3Xm6o1yMnJQ8J2N2egBcVDrM0H0BODEJUHJXonq9jvX19YFyXpfLJWy4druNlZUV4fezfRp3H15bWxOvhy3RyPvnQqFrzNQhFzCVAV1iMgorlQrGxsbgcDhkOzC2OatWq7hy5YoUYbFnAgFKnQ7cjTgEDPZm0J+jaAVMb8T6PWI8vHZacVp6vraysiJlw8ePH8eRI0ck5GJtAsOcYrGIY8eOwePx4OLFi7ckLu0m++ny30r26gl8AcD/BoABZAxA0TRNMj1WAIwP+6JhGJ8F8Nk9nv+OhbRXhgOkz2orQdYXd/DhpCFoyAU9DJDaDXTS+MCwVKBWHjr+5aThOVkDQMVEq8ve+IZhSGWhLvjhBKcV7Ha7KBQKcm+c4LTsBLwIOmqiDTMcumMxr9dm2yk3pheh43rr3gZkNjLkGDYuuwGAemytY251863vWXEAfWx9DVSwwWAQ1Wp1YIOXjY0NRKNRHD9+XJ4TPTUNNIdCITSbzQ9sMd+r3LMSMAzjMwCypmm+bRjGT97t903T/CKAL75/rPueSCXddmJiAhMTE4hEIoJwEwVfXV3F0tLSwMaT4XAYwWBQWG2c+LcTxv4aQNJ0WWCQ3qr79mvEnEDbysoKms2mdDzixLLb7bKzMXkDpmkObHPGCazbehUKBelqTACTMTCzJpFIRDwRa1qNW3T5fD7pP6D3UeCmHcViUUqUeV/lchnFYhE2m02AVGZrdHMVK0hmXchaydEb0eCfZh1SsfI56oIgimmaoryIs/h8PqytrUknqU6ng4sXL8LtduPjH/+4PLdwOCxKsFqtolwuY2JiYgDwPKiyF0/geQC/YBjGfwfAjW1M4M8AhA3DcLzvDUwAWN37Zd67cMKQEaZjelpTWr1sNotsNiutuziZdT7YCurp81jTYtYUFz+jWWsUq0XiNdMysUSXi4G0VSoxTnxaMx3/83j0fqj8qIy46Khw+Dopzcwi6IXD7kLsV6DDKr4WCARkAUWjURSLRckg8DkQ5OTis1pv65gMe7bWMIz/D1PYmuylnwmFz5AEK614yX2Yn5/H6OgoarWaKB1iNfQUOdZOpxN+v18yLwdR7lkJmKb5HwD8BwB43xP4X03T/BXDMP5fAP8K2xmCXwPwd/twnXsWhgN6EWsl0Gq1kMlkkMlkUCwW5YFx8xEubK1EeAxgx93UuXcdv2rAT59fhwhaefDa+LlyuTwweZlvZ0hgBQD5ORZEGYYhVYJspsH4mOfUm5Ry0nY6HeTzecmfU6lwQTCDohVbq9WSvoZMLU5NTaFWqyGXy4kS4KKpVqvweDxiSa2goBVvsT4Dvq5Rerr0VLr6eXN8tNfF93kvkUgENtt2Z2KyI1mMNTc3J01UGCrynvib32Hnas4xPd4HRe4HT+DfA/iKYRh/BOAdAF+6D+e4a2G7LGp4IrdOp1OwgEwmI01ESZrRzUbptmurbXVRtfXhhNKFMXoSAzuTV1saLkQW2dDqMh7ltTMHTV4ALdLIyAjq9Tpstu2uSdFoFE6nUwC9RqMhOEA+nxckmyAfrzEcDsPr9Q400qRbzEpEWv5+v49yuQy3243R0VHY7faB9Fq1WsXW1haWlpYEjAQgY6qrJbUC5P8ca2DHtdcsQj4vYhJUtlZQUYtO2fK7VKz0/orFIlwuF1KpFDKZDOr1OrLZLFZXV7GwsICxsTGhQhND4HFyuRy8Xi9OnDgh+0To+zkosi9KwDTN7wD4zvt/3wDwzH4cdz9Eu9bU2lyUdIk7nY6012LPfav7qyebtc2VtXBITzire8vf/FvzCG4l2nVmaMPUnnbldX6cBUIejwderxebm5sCzpFslMvlUC6XpZUY79lutw/U/PNaWVjD8+o0JtOALMjhWNlsNhlf3RNRj6kem91AV2sqcNiztj4D68LncaycAR5bjwE/oynWpmnK2G1ubiIajQ6kIdl0leAoNz8pFAq3fcZ3KruN0W7jcjvP45FgDN5ObLbtctFQKCQuLQtkDMOQ6rZSqSRdcdgzgNx1TlbNTNNlx9rFpHXTk1aHIXpiaqtlBRJ1Mw6CesViUWipi4uLyGQymJ6eRrfbRT6fR6lUQq1Ww/HjxxGJRHD06FFRWteuXRPGXrvdFg+IHYwCgQBOnjwpnkY+n0cul5OS5ZGREVEChUIBwWAQExMTsoiZJpyamkKz2UStVhNi0szMDDY2NgYWJ394bPIX6AloS27NluiKQx1WARhI+fH5ayHYyUU6bC/FYrEo2RJuiU4WIVmYb7/9NhKJBE6cOCFZgGQyiWq1KvdDEtfW1ta+zedhC/pWi/x2nscjrwT4UHXsrJFnuscsgOFkIK+Ai5fegxUgtGpZLggNPHGBULTnoEkk+rtWZJyvsXSYlpisR23ZbDabKDE2KSWI12g0RAmw4o/XpMOdXq+Her0+oPD0/VPJUUEwdGIxDnv1raysoFAoYHNzU/oeWoFZFm3Re7B6UcM8qGGYDP/na1bsQCsePcY6W6PvTytyYijsn9hqtbCxsYFisShKlIqbYxqLxdDv96UPoQ5VdpO94AX3+t0DowT24s7cTrQl5Q8fPFmC1PycJD6fb6DPPmmn2tXUgJNG7QEMeAmcTNpCaSoxMLiHHuNzCpUAsB2Tc6NR9tAHdsgvtKqsliTFl+XG3CiFIZDmPfR62/sP6jCHxCYem9fDe2IczO3MqGDW19dx9epVXL9+XVqecbFo5cnmqKxYpFXWis8KxOrXdIZDg4MUeg4UjrfuYqTTjLw28icomjrNsJEbmnJPSgDC5ej3+5iYmEAoFML6+rrwIdgMZTe51Ty/XRjw0IcDe3FnbndcbcWGKQTW7efzeVQqFfkOO8gAEAuprYv1bz0B+d5u125NM/LaaC14LLqstDD0BlZXV4X9SHZgOp0Wpt7i4iJyuRwmJydlodNis2OQFeAkABmNRoUjwOsiDhCLxaTsORwOI5FICEfeZrNJlmVubg4XLlyQSkEqXsbYrOOgEjEMQ2jaAAYWt64J0OGVdRyJS2jl+2fk9AAAIABJREFUwfd1bQb/15kI3if/t57HbrfLhqTz8/NSZ8IOSiws0waG/RjpmY2Pj2NjY+OeNzC92zDgTj9zYJSAdcHcqfXXANNug6QfDGNs/ZA1MMjYjk0irFZRL1KeX09EbYV2uy6+bhVelz4er0W3JmNpcSAQkA1HDMOQv0kGqtVqglZToZAWrK2ovg+CgcFgUCwjqbH9fl+yBbVabWBLL+bJ2+02tra2sLGxgfX1dVG6urMvwTfiMyyKouW1FgcNmxfDlIAOM/R7+hnSo+Pz5Pf0s9Pf42/D2CY0afo0+yhwUTNkJGbB8zBsI1dimBLYbf7ebh3cbv7fiRwYJTBsodzN93b7PAtr6HLqhpZE2zc3N2VTTzIFNWOObrJeuPpvHmvYpLNmDqzurO7WAwzW0TO3rMuA2UGIC7Lb7QpwxVBCpyNbrZaUF6dSKRjGNueA7i6VBv83DEOKiNhmy2azwe/3C3syFApJVaXuO8AWZLOzs9J7kOFJIpFAt9tFIpEY8Gp4jXphU/Si1PG8HlOd4tN8DL3QNTVc7/egx5rHJT6kFYP2MGgYiAmsrq5ieXkZx44dE8CZ1GgqWo/HI81huYXcbvP4Tl+3vr8Xb/nAKIG9xv27iWEYYm10KyhOMFogLjYA4tpq4gy7D+n4Uj8Aq3utP2NFsDl5tVXTca8+NrDT6IMTVdcStFotxONxmdTkDrDnAZWMzbazFRkxhXw+P8ByozA8qlQqEmrooqZeryeEI81tYDzMiR8IBGQMeb8kZ+3mIVndcMpumIBVdlPU1ueh398NZNTH4fPVIQM9yEqlIoqNll+no4kl7EaGetByYJTA/VAAwM4+esyV6+461sIcxonRaFQsGzvP+v1+NBqNgQaY2vXXeWWNE+j702CUrmojKm8V7VJy0lAJcFMU7oRDZuPS0hLW1tYkNKC1B4Dp6WkYhoF0Oo3FxUW89dZbMgYaMKWnsLW1hX6/L+3LeU4SiYhPsKipVCqh3W5jdHRUJj8p2MViUcaBSkO3WdOxt8ZerOXYDGt0yzGr+6+5C7spCq34NEjJEEm3iaOi09wRfqZUKkmcTzal2+2WVuvcrowt3Q5iHcGBUQJaboeC3o04HA7ZW48xLBc2FwAXKCcR6cV8zwpMaY2v37fGrvo3421dQKTjf5JytLKwAo8ABlxeLkp2HeL5qLDYK4BlxvQWOEmff/55rK2toVAoIJ/Pi8eRSCQQj8dx5MgRGIYhFGBu2MoquU6ng9XVVVQqFdRqNRQKBdntiBwEusXcHYmViXrceF901/keWYRW625NM/LZWN17rRw4tjozYcVetNLQCluPvTW8s14TwwXdm4DYBz0hZgkOihxIJbAbwHcvYrdv9+r3+XwD3gCwk76zKgFqdCuzjZOT1kFnBazXaHU7adU0GKWPqwuANHC52zgwjWUY2ySWZrMpOwax/JlVgSyPpqt++vRpJBIJnDlzBpFIBJubm9JPsdvtIh6Py07L9BBYO6Bj7FqthvX1daytrWFrawvFYlG8KTIT+Qy4KSqBR46jfk4EEfW9W0MVYKdZix5njQ1o996avbFyOxiu6IUMDCoBfs6quPSz1cqcyo7PVTewoRLQLdcftBxIJbCfYrfbpWyYQCAru9rtNjY2NqRRB7U30W7rAqcbyYXAB09mmZ40VgYhsBOnc8Lyc6ZpSpNKTlT9fT2JaWWI1tPq3rx5E5cuXRoA29gpSLPs+v0+5ubmpE4inU7j1KlTOH36NAAI3dXKXCyXy1hcXEShUBAPQ4cqzFpwjEi24ThycWhCkl5Y1jCA/Q30OGsFbGUD6vCBrw2r46ACIC6k430NAnLMmdbksXlNvEZmCEji8nq9A+EFG5MQWE0mk6jX61heXh4aAj4IeWiUwL2GA/qBAzsdgIHtxctsAMkeAH4kdhvmOt7pdWnvQYNa+jhaUVjPxfd1WGHNLGiSE8+nY1p9LVQQhmFIKtDr9UoXZRJnOMm5WUmxWMTW1pYoAYYfRMx1lyV93fqedgPkrN+h0hqW/tvte7s9kzuZM7uBuZocpq9BK2uyMakEtDC8oVJlRWE4HMbq6gOtsB+Qh0YJ3KsCAAYbfGj3sdlsYmtrCx6PB6Ojo2J96QYDO9ZE4wMa/NOvDVvowKAHwImhaboa/LJSXbVwwVM0Ym1Vcrw23U/ASo7J5XKoVCpwOp04fvw4gsEgUqmUWMO1tTWUy2UsLS0N7GzMEIv3zNesMTRDA9M0pYGJNZ623h9ZhTptqGsTrC65Ppb183xNpw+t9G0ruGgNG1j+qxmdRP1JvS4Wi1haWoJhGDh69KiEQvSAiEX5/X5MTU3B7XZjdnb2wPQXeGiUwL2ILkxh91yCY7TOdDutC89qna2lrTqO15ZHH0PHndqa6EXPz2mswJp50EpFKwlNWtLKx3o9VjKTXoC8d/YeLBaLsjjYZo2Zk2HH4zGs7rb+PJUXFZQObagIrR149H3eamytOIzO0lgVhP49bOytop+ZngsERev1upyDuy/pZ0MFqMlcWlkeFHkklQAnAN0wAoLMCmjmH1Nc2kIAP7pbMB8ohdgBPQpthfi+vh4NBNKa6+Mz527FA/i+lcXGharv13peYCdNpie0/gzPub6+PnBcfT7dp1CPC0XvoaAXgVXxMabmWLFfQqPREJIT3+Pz0fUZ1uc7zI23KoXd/tceiVYM+odi9a7cbveAZ0glQIo0713XpwCQ/S6tSvRBy0OjBO4FE6ACYL87t9stxyLdVR9fP3yi1cBObp6VZHrXWj50TU22xpDAj1KIOXm0F6LLY63xML9HYEpjGFakfNjisJKNeA36mmmV9RgMs7TWhWNVPMOUDEMD8he63S42Njbgdrul9bju2quVie7MrBXyMI/HqiCs3p1Wrhw7K4CrPSorX8FqLHiuYrGIUCgEANIK3jpunD/s9nRQ5KFQAveiAABIqozUVg348cGy245euJqmaxVaRlpHYHADzFu5rlqsi8n6OevktdYVWCczP3e78+vYmu/pRWD1OKzH2O0era68xmP4Wf5wsbfbbVkUulGoFZAbZsF3UwK7XZ/1Xqzjax0/6xhw7K0Kn8JSbes1cx4x/Uk8Qd/H7eRe5/+dykOhBO51AKLRqFBluc0YF+/169dx7do1XLt2TarYTHM7957JZJBMJqVjL7CT/61WqwL6UCGwR722YLxuneIaBkBZWW3DsAn9W18nj2Hd5ZfHH3Y87d5rr4CTmpNWj7leXDwu04EABuJ5LnL9XU5i1haUy2X0ej1MT0/D6/VKXwPG0aQW69CHC4r/6/HldexmyTXvQpOPdFhomuZAhoOv8z6ZPux2u0gmk7Db7RJCAZBeDTQqrVZLUtOkT5PLwWIqNqy5ndxvDOGhUAL3IgRiSNTQE8Fms8meenqCUdszt6557tacs2YMMt4dZom0Fh9mZfQ1WZXI7bIOFCsYpc8zDEkf5u5qkFErHN6v9Zr1OYeFOhpb6Ha7sl06efb6GrUC5ULWykN7EbeK6TV/QI/Nbi6/HjurorT+zzmiS9ABCOuUOxLxGjl36DnQe+DfVkX7IOWRVQIApOKNnV856A6HA2NjY1haWpI0Dmm7vV5voAZeKwROBj3R9GQ3TVOaiQA722HdDhHWZBqtTNjclJMYGGxCQrG60XqhWOvyd3OVdSpMCy07P6/r8DVxh1aVeAXjfrIW5+fnxbozU0MFoWN8npNZBKtypCdjtfA8hrUwS48P/9bekMYS+D2dbuU4GoYxUIHK+cINSbgnJOeXzWYTfIMLn9+jUToouMAjqwSsA04trNFc9stja21afFbIacvD3YXpeuv3tBuuU2DU/tpi6cUK7NQM8Jp5nfo+9OLVWIIGzvSksrrSeoGyTwJJQNZ4WHMYrB6Mjud53cM28SA9ma4+42WXy4VoNIqTJ08iFouJO8xuRlZFqV136zjwerTbzn6AXNQsnuL1WvkUPIY1E6TPpz+rX9PXwPCMLE1+jtdGz4cdo/hMDj2B+yicULTwVAI63tVtoPiQaC2t4QOwg8rrGNTqSurFYo2hrWGCXtTW72irZl2I1hy71VpZQwqK/p5ORw4D32jhrVRoLiytWPS4apyBW3IxvAIg+w6OjY0hnU6jXC5LoZG+TgqvQy8q68LhcyNFmV4Ir4PjqNOcPLY+j75H/TnrZ4aFZASTCW5au1Rrz07Ps714AvsJFj6SSgDYfliRSATJZBLpdFp2FSafe3l5WZo7sNceJzfLjbkngWnuNNQEdiaqbtulQTIKFxkng3Y3tUKxKhJ6FrrxpjUroHv06xhbKx/9PQBS0EPXX3sQTNEVCgXZYffs2bPSTotKg+PEXYm4557P55Pz9Ho9RKNRHD16FABkKzR6SWw6SkupFRrHj2GULqyyhjMav6E1Nk1TqOCseAyHwwO7KGvFoJ8Nj6u9ORb+0FjQs+BYaq+MgCCzAHyNtRZsCKPnwL3KfnoRj6wSAHbQX1q0fr8vbtv6+jrK5fJA/MqHTQKRjve1Fh9m+SlWMGsYEMVr4/86vLBaGaubPOwz1s9qRWI9h/VarednhZv2PNhqjPXzuqOu9h60a02Pg5kCVhHSurO+nkrSCgwOuy+Nb+jx1d/V8b8Oaaxjudv4U6z4yjDyl36ePD93X2LPAwCSFhx23QdBHlklwIXAydtqtUQbF4tFvPnmm7JHHvvx0YosLCzA5/MhFouJddETkK+REGJ1J/VC1EAeJ4J2863FP1YNry2GdeJZP6M9C6a22LhDE110IxNtmRwOB5LJpPTN4+adTzzxhNBkL1y4gHK5DGBnu3cNgvE83NdgY2NDNkyhx8T7YZpT4xBWpUmPwGbb2ZFJE4o0KKo9KNM0pbOyds15bi5SzWLUIRMtva69GPZ8eI0cz1KphFAoBL/fL1vHxWIx2Gw2GTcqwUeiitAwjDCAvwJwHoAJ4H8CMAvgPwM4CuAmgF82TXP/tl+5A6EHwBQhXXlSfFutFlwul5A7dE84YLD+fzcEmYtcW/hhcZqeWNp6WhXHsCyCXuzabbWSk6zglmmawijUk19/R2ckaPW4WCKRCGKxGEzTHOizwC5GPFYgEMDk5KTsYsQ+DRxPvs5ioN28ET0u/Fvn73nNur+DHhc9/hxHYCc7Yx1b61jo9K8VJ9HPmOfR3Zr4fW52u7CwAMMwZM7xWdMgcNw9Ho/sdvWgZa+ewJ8B+KZpmv/KMAwnAC+A3wPwT6Zp/olhGJ8H8Hls7094z3K3IAgnNFNRfCB011qtlqR7qMGpBLQFsoJHnLC7sfesk1xbaGAHtR8Wr98uzLAueG3dhhVAMSWq41vrQtBuuF4U7MTEyj/dp5DNRQEgEolgamoK9XodhUJhoEqz1Wohn89LulXvwzDM9d4tHNKf1W3frW66Rvut46Kfk/UcWgnoFK1uUabHfpgSsNlsogSWl5cFh4jFYoJB8Ydhkt4H827n937LPSsBwzBCAF4A8G8AwDTNNoC2YRi/COAn3//Yl7G9R+GelMDdDhAfWqvVkpp4Isd8mNyua7dz6MnP96nRre65zmnzdXoYuo02+Qo+n0+sgI6fTXOn4oyTk5OYr/Pzeis1HXJoKwcM9u8fFiIw7cn/GQZks1lxiQlQaqvFtm1PPPEE3G43YrEY1tfXJVTSY2el2OpFxevWICrHj9fGe9K7DFtDIw3OaeXKcbTm5XUdgt7Hkd/RTEodRgAYeK6sE+DvsbExjI+PI51OC+M0m83KXGs0GjL3mDV5kAoA2JsncAzAJoC/NgzjCQBvA/gdAKOmaZJPmQEwOuzLhmF8FsBn93D+W4rV/eQPkdthTSv0d60xNj+jLfGtRMe4VveTx7ICQ9ZjW4Eka2w6LEblaxrYGnb9vEbt1fB9nVoDIKEUi6h0lsTlciEWi6HX62Fra0sUhxX7sHo5VrF6Mvr6rfdtDSd4LVaLOsyz0t6U9oysn9Wehf5fzystxDZobNhTUJdZ67BAg6sPWvaiBBwAPgLg35mm+aZhGH+GbddfxDRN0zCMoVC2aZpfBPBFANjtM3sV9hGgpWm1WgiFQtJjQLcV06w4h8OBQCCAWCwmvfm45x8/T+ttde/5Q9CL22xxp6NOpyNdfLVw81GtfKxpKFouegaagqpdarvdLlZfk5s0yw3Y6anP4wE/arWBHVIOz82fWq2GpaUlpNNpHD9+HCsrKwLEUoYtSt679kC0Befi4fVoRcBzc+9C7hhFgpfdbhfvj6QlkpJoBLQitOIpVo+OIVWn05G2c7rClFu865QySWgbGxuSMjQMQ/Z2pGf1KCiBFQArpmm++f7/X8W2EtgwDCNtmua6YRhpANm9XuTdiiayWJlZ+n/t5ukFxB2Mk8kkMpkMDMMQJcCYkBOHBURc4ETee73ewF4HBLb48LnVNTc10bGtTnlZrSHdWE5OKyDIyW3tI6BjaR3GWGNxK0ioRSs89mtcXV2FaZoIBoOoVCqStx/2feuz2M2qU+noMeHfOpvQ6XTg8XiEhERmovW+tJejQxr9Pv/nPXJjVeb2e70eUqkU3G43rl+/Ls+N7cUASKkwsFNgpUM0ekecPw99itA0zYxhGMuGYUybpjkL4EUAV97/+TUAf/L+77/bywXqhznMlRz2OmNJPZGsLrS2+vr4bBGdSCQwMTGBxcVFmRSUWq0m7bDtdjtCoRBKpRJ6vZ5w4nu9njQy4aLRqS12QOakZQt07YZbQwnG9aT+8vPapdUgFK/DSrrhZ/Xk11bZSorS4Bivo9lsolgsYn5+HrVaDYFAALlcDvV6fWDsh7nkWqygGxeOfna8L01sYmdk7oUYiUSwtraGWq0m59Uemg51NAZDZUoDQCUQCoVgmiYKhYKM87FjxxCNRvG1r31N9lFg6pnHZLv6er0uCkvfJ+eBtXr0TmTYXLdiIsN+3072mh34dwD+k7GdGbgB4NcB2AD8F8Mw/i2ARQC/vJcTaKt9q/e1sJsw9xjQXWvo+nJ3Hb2JJCeKYRjSN47EIeZ4OSE5ybhXPSclQ4BUKiXanqWymk1IliL3t9Ptt4ah2ZxEuj0aQTNt9fmjuQBWz0LLsMVqXTi8b+1C8/9er4dMJoONjQ0JOah0mNfnmGkvh+fWrr9+PhwvPh89FlrZkaOwvLws7b6sHoi+D92MhePmdDpFubDgiV7c1NQUisUiMpkMCoWCpJmpOHi97CxENqbH4xmI/7nBrbXD1d14A8PmutWzGvb7drInJWCa5gUATw9568W9HHeXc935Tb0/AXUJp1UJEFnnlmPWhdBoNFAoFMSF14AXP8OHSEqqJqy43W7pz2fde49xebc7uI+gTtvxHDzn/9/em8ZWmqXnYc+595K8+8KtWFVkV1X3dPf0TISRR4bTwgiKYHmZGcSWAliBFCORbRmDBA6cOD9iDfRDCRADUWxYiZFAyiBSLAeyxrKsZaAk0MiKDOuHZ5S2JrN193RVTVV1F4v75d1X3vvlx+Vz+Hwvv8udLLKLL0CQvPdbzne+827Puxw+v5rZ6psqY6oPT4axYUTrEtl7AggJI30PVjDw+TkmO04rPKJcgCiLwPrpel/9m0k6Niqg47bPYoW5rg/iCbFYDNPT036HJrZZV1BZTXzNPFW8SDci0b0bDxMCR1nzx+GLcXRlMgaP86AE/nq9ntfQqVQKU1NT2NraQq1W8y+TL43aAIAHnJaXl73Jr0yj/hw/V3+32+3i2bNn+zSfAnhkHMbXs9msb3zCphvK/GrGktTk12MV/OMYdfGrS2GxBx6vTKnPphEPAH6OuWchP9dYun2PKrBYf0BMRVF+FWSKdWjnp42NDZ+fz4IxK1hUuLKuQAWEdv2l9q7Vav6aU1NTeP311/Hs2TO/fyOvxxwTPle9XvegLa003o9CgHkE7LMwjo6y5k8rAICR6X7lyfpWnHAA/kVpbT/bRau/rKg4/URNIIpaWFF/q5msAoBjsQucQoXxdQtcjvMB+bcdl2VgayEACPnbeg/VrpxHdRcIqqlQ1OuqC6FhMOta6HwofqGgpB1X1A/fLcdlLYio+dN3YN+ZjpNj0E5SURaVzu1wOPQCUZWMjoNzSjf1MtCVsQQOIvui6X8B8D4cJz+VSvlkHedG2XEM76j/zXoD3bLMmrl6f2WEKK0K7IUBmWbK4weDAVqtlkeXo+7Da9AP1jJfXWB2rNrcg9qJyUJc7PSF9VzOI5mBG2zkcjmftMNn4jm2eEfLam2PBD6L1ndoNibJMrbFGCgAoiwXO3/W7SIpwMr7EODb2NhALpfzc8dn1/Fw7L1eD5VKxQPHjJRo+zVaYNPT076Q7XnTh0IIWGJfN26pnclkkMvlkMvlfMguk8mEXpSGoJgBBoRDa1wkXDQsLuHiUuZTU1vz86P8fP7Q+tCcd46BzEHNTO1stRKtGroIWpprNT7/1ogA50L/57ywVZutiLOhRz6/xuf1Xnocx2TzE1TIRc2T+uXOuciIBK/J74MgCKH2tvOTCl+dP95zdnbWh3YZFuT7JfZDoUdgdjgchkDalZUVH71SwXjeZK0wpQ+lEGBqLieaW5OzjqDT6fg9A2i6kagpdQFZYMwKAUvUGPQHtUBGzWgeq4tbq/H0/ipglDnI3Oo363i5qG2RjHUZrBBQq4L3pgAgE2k+PAWhPv/U1JSPjOi9STZjT+9lrRrOhT633k/zQlSz80ctLP2tgpLCIpFIhCI6fDelUgm5XG7felDT346JOA6VkVpG1qI8T1IhbelDKQSmpqZ8iJAbPlSrVayuruLBgwfY2NjwC9/21ovH417aa2GJovCKN/Bc9bHpG3IBsq0UcQhlCoJM6XTaZ5/xOio0bHELtRX/J1lGUhPdWgJqwShzagyb1gf/Z9Ybr098hZEWLja7IalaBCSLIfB/XlcFlIKqaglpRMYKSQoKMjNBSN6P7hetNo6ZOwnx3Q4Go76Tt27d8gCyChC6V/1+H5VKBWtra3jy5Invbk3h3u12vZKh5XIZ6EMpBCiF+YKdGxUTVSoVLC8vY3NzMwRCUZKTokx2XcBWm0dJWQuyjSM15zU/395f72PBL3s9tQ4OOs66BTxuXIhSzXl+R+2n17IAm7ouUZaTkgVGo0g/ty6Wko4rSgjpfOlvPY7CXXsO6L251ihkbK2AXtMKz2shcM4Uj482zcxms4jFYmg2m1hZWcGTJ09QLpe9WTc5OYlut+vDhgBCcX0Cg/xfF4G+aGCPSVVTEPl3zoUqCkm8F9NPe70ecrmc/473tBunWJOexEXIY7V81TJrlIBQZtD8eH5GK4SakzF0bYWmW3SpRWTvpXNB4aNZgUpRVpe1ytSiodvGazGfX8FZPpeGDWn5cH7p1ikAqYKCyiaXyyGdTqPVaiGZTCKTyezDcVRopNPp0C5Fz5M+lEKA/qhmA+ZyOSwsLOAjH/mI32KbDEjcQCMIlOZRGlOle9SipgmqTKrmdpRVAeylO3PhRPndUeOgOatuhsanrY+tGskKMQXIeLya2/ybC1vTrrXnon22cZZRlCDj51Ga25r/1kqiAFTrw2I+OibFDsZFE9QatO+c78yClQBCrcajXJTDrEQd52EW1GnoQykE6KdpyilDWzs7OyiVSvja177m8wcmJyc9dkApzagBX6oCZ9pkI2rxAns4giL1JGUIXSCaZaZJNOPcDs14U2FlhYwyBbCnTenP68JVQaBMRGHCZ1chYN0Em4Skz21dlIOsBM63Nfl1/hU05T0t4EjBwLmwgt3Okb4XFaZ2bvnO2DhEm5jQSonCa7iudN4PovMUAMCHVAi0223UajWv6Znbza236/U6pqamfCUcgR2azYVCwdcXJJPJUOMJLj6agVxcCvrpYtOMQ6uBeU9gtNgJJNFMZWGKZjOqJlYQDtiv5XRxa7NVksb4bfRAGdb6tOraKPH+dAcUqIsKd1phoH9TwKlwIZjGbFC+W5LiFFFWgiYkcRw2G9Qyqrormqat9yR4SFes0+mgWq36d0cwUp9Pm8E8b/pQCgEivLqQ+KM+M1+I9qQng0eFkqLMR/1bTU81tRXs4ne6KPRva4YrgGS1K8dDwWNJx2oXYZRGBvY3TY26nj5nlCZXTEJJBaEVinqMPluUhcP3dJAmtSZ0lOnN76O0fNT7tmPlNSgEuN6YaNbv90M9GKKufxnoQykEGo0GNjY2vEYl4s4tylkFRqwgFov5HXIB+M0wstmsXyia/EHtoe2hmJhE4GlqasrvchS1yIH9ab9sh0amthYAgS0KLC46FQKqSVUzWosE2N8bkWPieerb27Gq0KOgYjadxum1jReJY1PBSyaymtkKQP6wwlBr861w1jnWEHDU3PN/bYqi16M1pUlMXBcsFhsMBn53p3a77YUUC9boetn5fN70oRICNBk1ESPKRyUj22IcgnK6Yw5fFK9hmUYtDC4i9ZWtFWA1itVmNucACKP4mvev3+liH6dNo8xu1VBRmv+gz/V/CiOr6e2x+rkdj85JlAVlNWkU6TV1DqOI49DaAwD78kcOYtZMJoPp6WkvPDQPQS0Ivd9lYX7Sh0oIsCsQMwPJUET+Ofk0s3mO9gEIglG/erYqZ4UYE34A+NRe1WBaamxDWOpSAOGkFl0YqnGA8J4EPI/WShRzqD9Liso+1BwJazbrNVXQWRNcBRUXvQXRNFzHY9XfV63K+aDFo8LUMhH/HucO2OfRcVtBojkiqkQajYbX7AcJn1KphKWlJXz3u99Fr9fzTWRsSFPn5LJgAaQPlRBIJpNYXFzE4uIilpaWsLS0hJs3b/rmFizUqVarPn5Nk5LmcqfTwdraGm7fvo1SqeTNc4Z/bHqoJviQqazfyIWjCSuWmaiRba691ZoEmiikCCSyGQaZUTcbVQa2ITYb7hyHnqumt5iA3ofj1rwC3kdN63FkQ5o8V+eRn1tcwV5bhZet31ArQ6MyfKfqlhAEtoKc77TX66FUKiEej2N6ehrZbDb0zhTOxOgiAAAgAElEQVRI5vNcdO3AQXQ5RoGDY6H6XRTgo3779PS03wHGbiyqfjeTXBgZAPY63dRqNdy4cSNUfEMmsCE1ZZiDwCplHgWKovxuy2Aa+9YFxR4E9DkZ8uv1el4gKFMoUEqKGq8dQ5Q5S4bS9F5r2SjDKCMfJAT0nlYDW4uCn1vfPcrk1jFbYFYFjwoLGxngdXScvC4bnfK3jf6oVXMUYXhcOoh/DqNLIwQOeoCDfDz+zy2xXnrpJSQSCWxtbeG3f/u3cePGDXz605/2GV3NZtOXexJ4o0YtFAro9/tYXV3dVwaqL5yuBl0FjShon0Abn1c/10YTlJE0bbjX66HVavm8fIYNCWpmMhncvn0buVwOt2/fRq1WQ6PR8Kh0JpNBvV7H9vZ26Bm4KDWsCewBkLqoVIhSgHU6HQ8GksYJFFoEVoBE4QeahcnvtFZCXTp7zyg3gAJShYbWXCig2e12Q0IAgH/HdA0V97BFQN1uF48fP0Y8Hsfs7KwfE8+ja6qA81nRaa53aYTAUSlK4vGFsFKLLZy4FdZbb72F2dlZzM/PAxjVitfrdZ8uzNpx9hVgGFG1lm0pRbNaNYlqKEXDVaNYLWez2axFYIt5VMsy1bjRaHiwk81J+Bzc5IRJS8pcnEtaQFbb8lmjtCZBVB3XuHd0EBhGprTHWg1PvCBqXPa8cZ9Z94c/ukW9nq/uDdeKWjX6TtnBivMdZanovB4nWei86coJgXESj00ci8Ui8vk80uk0Njc3sbW1hZ//+Z/H3bt38cYbb2BhYQGLi4tYXV1Fs9nExsYGSqWSrzxsNpuhnX3IIKzuo3ah5kgkEn5vAzIUNYc2zOQitiarYgNMhmHrMW3RxUQVNTOHw1FdwtOnTzExMeF3w2Xa9HA4xIMHD5BOp1EoFFCr1XyrNQAhS4fCg4JPGYYgnf5oOFBNZ2suj8snUCbRPRdsUtG4klsNFUYJZQD7hB4QvZWZ1o3QqtMmIqwOtAlKvDarVAkYE1/i/DFsyPNZrXhZAMIrJwSiiC+VGz2yd983v/lNbGxsYHl52fvzqVQK+XzeYwGsNGTNAD/ny1LNYRdqVB85YG9hK5ZA7clrc9y66LWZiAXr7GK2eEG/30e9Xg+V3bItODstkaEVmyCDK0LOZ7D+tQoDvT+PVzNZrRj1gfmenHPegrFCwc5lPp/3mZvdbheNRsNnhFowMmpfAQK3av0QUFXBbLsa8bpkdD4T08851iDY2+RFw89aik4hACB0rctAHwohAOxpjlQqhUajgXq97nsHbG1tefT84x//uG/uwEXBhcQ201zc1jy0pFpIw21ceCRaFFpowgVnAaSouD2vbc1mK0ColYGRf9rpdNBsNjE9PR0SQNyNWYUBr81n4n2tCc17K6ahwo7Pyf91TnhNWjzsEBzljqiwYWeoVCqFZrPp072t+c/5oGDR/BCdN16f1haVgL5PnWP7vEwa0vwCCj4FbimQibtwTVBIH7S2LpI+NEKAL6dUKvntx7e3t303V2IAjOeyvVSj0fDtxLgQyFSdTgfz8/NwzmFlZWWff6mgoKLAGusH4IuAFGegpaB+rtYn8FpWwylyTaZn6Em1GwCfJdnv97G8vOw7A+XzeS+oqtWq37ZLTWmSxTEUKLWVimq6s7MunzebzaJQKPjNQhiO3d7exv3790NuB6M8mUwG2WzWWzRvv/12iEGZ/8F3QKam4KEwUgGZyWS8lWSbimiHZrWY2FE4l8t5IccmIbw231kikcDS0hLefPNNX1i0vLyM4XAYygC9DhGeISnDMFkI2Ns5lgt1MBh4cKdarfodYymhdbGrEFDzzt7Xakcla97ac+y5qnUsyKgmqgof/m/P4fdc5EEQeMCKLpOCmePI5jLoeKI0mV5TrQieRyuEc6rddfj8mlzFz2nRcN9BamKNGNjyZf1tsRQ+m46X9x+HXdAN4A5VtEZ0/VBoM1TI++iccJ2OI77L49JJzwM+BEKADz45Oek7Cff7fZTL5X0+13A4xMbGBgD4SIFzzgM1ujtNvV7H2tqaT8zJ5/NeK2h1IgBvWWi5Mc1hTTzRHAEuTl5DcQJqdo0MkDk0vEfhoQ0vNG2a92FVG8NdzWYTyWQS2WwWAPb1SlSXQvEP7TKs/q0CoNaspqCtVqu+bz+rDD/+8Y/7a/E5KaAYyqUW1noEzhcrPZ1zPuymrgcZj63b+D44LwrWAWEkX/s6UGgtLCygWCyiXq9jZWUF5XLZb0PGJiKsUt3a2vJjYUiZG9umUilvSYxbz8elk54HnFIIOOf+DoC/CSAA8E2MtiG7CeCLAGYw2q78Pw6CoDf2ImdAsVgMxWIRhULBp21qAYeM1zM0uw2z1TdDaFwQCqplMhncuDHaYZ1gWpSA4T348vmjvqC1ArTKTLWJgngAQqaqgly6cJXx1N2gSa7alc/B7xQL4PNQmKmZr58D4fZifAZF6avVaiiRibkLExMTaLfb+6wRzi0ZxGIkmsdAK4E4A9+j5iFEXYNzyGtoZMdaaBxbPB5HrVZDr9dDo9HwuIoCt3RJHzx4gN/6rd/ySukTn/iEHw+Li8rlst/a7qzopNbAiYWAc+42gL8N4GNBELSdc78O4McBfBbAzwdB8EXn3C8C+CkAv3DS+xyFYrEYSqWS9yNp/urL52LL5XIolUp+E1FW+alGA+BxhW6365uWEujRjkQW0LMLCdjzLzVjzJraNnzI8B8XJ7WymrZqJfAa/LGgJkOMXPQAPHPSxFYm07mLwgF4f90lmeOi20HGYbKTvqt0Oo1GowHnnC+6oV/OcdDs5/tT64pzZJOWbPRG3w3HzDnVOeQa0M1M9Pt4PI7t7W0EQYBKpeJbkvFanLeJiQm88847+OM//mNMT0/j1q1beO211zA1NeWFW6vVwsbGxoG7D52Enpc7kACQcs71AaQBrAD4swD+o93vfwXAf4NzFgIAPCi2trYWypsnZTIZFAoFfPSjH8Wrr76Kl156Ce+++y7eeecdD2LZ8NdwOPTlwF//+teRzWZRLBZ9aiiPUZNfi35sbJ9MaMOOXNwWhCOYRHOSYU4CaCQVRNofkSAbk1io8ZWheByZjxbCzs6OD8txkWtUg9aNBUuBvQ69Ohf1et3vHkxTXIWiYhvKsBpq5bvh9SnU+ZwAQg1W1KqyGIq15NRCU+BW3wUjE3our9tsNgGM3JmlpSXMz89jYWEBpVIJpVLJC2a+w2q16ndQthbrWdJRrIPTbE2+7Jz7BwDeB9AG8GWMzP9KEASMjz0FcDvqfOfc5wB87jiDPYjIjGzmoOEpYJQlOD8/792GGzduYH193e8EpIuS4wH2fE9lPtVQagaTWXafL6RZ7bNZba7goIYb+UNtrIkqvI/FICiU+D2Pt2Cd/q0mvQUo9XP1yXn9qPfG90EsQedOoyIqdEnWxFbmVLcJ2Ot1qG6VXtNeN+pz+06inodWEJ8jyo0BRvhKoVDA3bt3cePGjdCOTbTuaNU553z+RhAEftu705KuexWu4+g07kAJwI8AuAegAuCfA/j0MQb6BQBf2L1WcFoBUKvV0Ol0kM1m0ev1sLm56Rfa5OQkXnvtNXzqU59CLDbaxPLll1/G0tISfuzHfgxf/vKXcf/+fbz22muRyC3H1mg00Gq1fFKR1QhcpIlEAsViEbVazbcyY0iSGYb0Lxml2J0TAHsMxqzDer3uE2bIUPSZuTipwQnM6Wfso8jF2263IzMduRi1TTufx4KFZAb2cmQOBBczn2dhYQHOOV+aS3M7FouFohRaj0+mpuClr6+WAo/hZypc+A4ZBuR8BEG4tFuBV/u+GV6061ItI649Ch5uU//KK69gfn4+lFXJXa7ff/99PH782Be65fN5P6ZvfOMbaDQaJ+SC/et13P+WTuMO/DkAj4Ig2Ni90W8C+BSAonMusWsNLAJYPsU99lHUiwmCUeXf9vY2NjY2vKk1GIzqwZeWlnypJzeCXFtb80CVNWmj/Hreh9YGdzHiZwqcqSlsLQt1B4BwPFpBKD0H2Gv7bbP4xmlwm/xDxrEujx2PAohRmprn8LsorUvLgqavWhp6HetCKR6iIG2Uy2A7EHEu1LRW3IX3ttfUObbWjwoduz70mhSUQRCENmehAKIl2e12vdv1Pd/zPUin07h7967f53F1dRVBEHjX4qLoNELgfQBvOufSGLkDPwzgLQB/COCvYBQh+EkAv3PaQSqNMz03NzeRyWTw3nvv4dmzZ6hWqxgMBsjlcvjEJz6BYrGIXq+H2dlZfxxfHgtseP3D0jpZyceFQFNQtWGr1fLmMLC3wGgSclHp37p46DdzTASimOzC56amIdFnpRXE9ul8Vo5HhZadXy58LmyOm4II2GunzevZFGFqfc1OVECO80VwT7cVp+ZUhuN4KSxoHfAd6P4Nmq5rTWPG9LXEnNe1z6+ukgp1HQ/fgfYK4BwxEUmzN3u9HuLxOD7zmc/g3r17+IEf+AE8efIEjx8/xrvvvuvxm7NwC45Kp8EEvuqc+w0AfwJgB8DXMDLv/08AX3TO/Xe7n/3SWQz0kLH4sMt7772Hzc1ND2xNTk6iXq/73Pr3338fQRB4U3x6etoDiY1Gw+9Jf5AQIOJdqVR8XT+z24DR4mN0wob/OF4yO2P3NPMVgNOKRi4sNeV1M1UyAO/DuDl7J/DYiYkJFAqFkDlsswStlRI138AeEGk3H6UASSQSWFtb8/cmMEihxTlqt9vequJYaMIzjq9+uLpAFNi26EsZWWsHgP2KhHPLhCCdG80d4XNrTgiJ49EtxnjPcrmMlZUVfOc738Hq6iqq1SoePnyIZ8+e4a233grtm5nJZEJr5CLoVNGBIAh+FsDPmo+/C+DPnOa6J6F+v+93GaL/Sf+SCD+TPDqdjq841Fx67RB70AtgD0IAoSYSRKn7/T5qtVoIcaYWAvZyzRUAtO4EsQz1W6nxnHOh7cVVe9FXZsYaEW2GDNXtUFdAP1PTmcdEZbpFnQ/sMRPnRZuBakhUk66IE2jEgPOhVoRiAWqlaGjTjm8c+q7Pat0ivb49nriA/RxAKF+BY6tUKr6QbXt7G61WC8vLy+j3+1hfX8crr7yC27dve4zlounKZwySqMkfPHjggSn+Xltb8x2FGo2GX3D5fB61Wi0E8kRhAQdRp9PB6uoqNjc3MTEx4cFFYhI0FbkoiJZzzBrLZtILU541PEbGoIDZ3Nz0gCMXZSaT8UVQzIZjvkO/30cqlfJdk63ZS9NfY+RkIM0GJFPQWlELhhZJo9HA1NQUkskk7ty540ubm80mtre3sb6+jm63i6WlpVAxTrPZDAkBNorRZh604jRtmCg7O/7q1md0v4Bw92Q13VUIECfS7E/FKIIgvKkI/X1aAFNTU77YaX19HdVqFV//+tfx7NkzPHjwwCcavfXWW14IbmxsYGlpyQOjF01XRggcxTwaDAah/QaobRqNhvfLNBWWgAwXs25qcdTYLX1YLobt7W3fE0BBO1tlFgWuAdFdd1UrW61K7cjzpqamkEqlQnn23GqdQiAKPVaNaEOJeo9x7oG6CPxtE4/UNKeFoECmAoSqaTU/n6TvR+fRhml1fOpucE41hKtWig2j6jUoVJV0r0jet1aroVwuo1wue7CaLicxK2CUos5+BWcRHTguXRkhcBT/iCCTntPr9bC2thap4Sk0stksJiYmUCqV/EtWsO2oNBwO8ejRIwBhM71QKHjtQbCMzDU3N+d713MhM4OOKbnEM7jw2EGJTK29AJjQRKZRII01AhSEUQsdQCj/X60VfQ8WWFRzmuHIwWCAra0tLwTJdNPT04jH49ja2vLjo5ChlcZ8f2DEJGyGouE9MpHiBWRGNet1fAQfaVFQ8ycSCWSzWS+ouI7S6XRo7fCZ2GWIwiOTySAWG9U80KJYXl7GysoK1tfXvRuwtbWFRqMRwltarRa2t7fx9OnTCwcFgSskBE5CRzHtFRWmhI8KH0WdFyWY9Bx9yeoj6uKlG8FmJ1xMLPNl6jIZnd2DFJWnVUAm16pIzbVXtJvPrPOkC53f83jNwNNztZWWalnryqi1Qi1cKBS8tUaiKa6uCom5CZoYxLEoiKvhVH0GXkNxF4LAzPHQ56Og0DkC9vJF9F1XKhUkEgmk02ns7Oz4QiJ2quKPLT3m9QiQ2uYuF0EfaiFgKYpxFYSiL66fjaOjvqThcBhZLUZieIwJSESHB4MBarUa2u22T3phGI3oM5mJjMOyYa0bUPOczGaz7KwQoBViqyLtc/P+dh7VRVKXhffmmPP5PAaDgcdPou6h99ZjFCMAEAJ01a+3qdtqXanFw2vq++Vx9tmazWZo9ynnHGq1GqampjA9PY3BYODNf/YetD/6jM1mMyQAzoMOUmgvlBCwjMuQjN0ghAvlPO4ZRaxNf/LkCZaXl72W7/V6PuLABqoExDRZhdYBwTeGSIFRXgE1MJlNiVpRtS8ZNyqUptpc/7cuhYbR1LLS+zcaDX88r0lwr9lsolgsYnp6OoTjEB9Q98oysdZ1aMYkj7G5FdZSoitjLSc+T7PZ9IK9VCrh9u3beO+99+Ccw507d5BIJPw7GA6HHhfQMmo7r+ftAhxk2b5QQsBaAmpC6wKwWXlneU/7HX+riUqEnL48GZwxbL0eTVpqWN3zTk1wCz5qCE7DhnrdqL913HYsUZ/z+S2j8d5R99D54Pjsefb9KCahiUBqLUVFBKLcNxsq1AgBhQoFTjKZ9EVMzM/QvQgp2DqdTijZatxaeB70QgkBu5gnJiaQy+V89IAde0ulkvf7zvqeBxE1MH+4pVoqlfIVfWre05xNJpPY2dnBxsZGKNYO7FkTFAIUKhoWY1KOgpJRdREE7nhvZRTV6LrjE7/XbsZ0DzRDkMIqmUx6Td3r9bC+vu4ZivNCMJQmPYE4goWcx1wuF/ocCHcbJpNGFY7x+XgfZvwxXEnK5XK4ceOGf0dcT0T5g2BUQdlut89MsZw1vVBCgKQay8a+ASCfz/uGI+dJvL8yHZmE5c3cQ8Fm9Q2HQ58RqeXD+oxWC/IeGkIF9kxxmw5LbWpNSQso8jMtBiLjaZqygpgch1pAnA8FGIfDIbLZrAfder1eKLeeJrc2N+HzMVNRXTydZ01oUoyEFtXOzo7PK6F2p/vB41utFsrlMubn55FOp30eRqfTQbvd9hmbvN61ELhkpIuOgFIQBCgWixcSr3XO+UWjoJe6APxRIvOxlbhutW5NWv3Ne/Ia1Jqq6cnwUW6MmuRqSSgDAwhpX51jRfSB/Ui9cy70rLxGsVhEJpPBzMwMtre3/dbxAPa1euPYhsOhr9ePxWL7IhD9ft+3BNP5pKXDRKuNjQ0vrLRnJS21er2OZ8+e4aWXXkKhUPDvs9VqodlsenCQc2IF52WgF1II0HebnJxEu932m48wb4ChufOmIAhC9yEj6C5KqpnJuJp1yLJiZTBqKv5tmZrMqtiHmuRWGOh1SNaKUEGmhU8UEsxTUCZn5l8ymfSaUrsqU9tubGxgfX3d93y8ffu2T7rRjVo4Z1odqglI6gaoC0EQTxOUdD5VAKigzmazvlM1N6dZXV1FvV5HEASYm5tDMplEoVDwguEy0qURAgcBaIedBxzf96bp1u/3kU6nkcvlUC6XPfBzVtGBw0g1NBcpfX0F84BwKyybJ6BmLucyioEVHFS/WC0Imx2o1oR1F1SQWIFjwUEbluQxrL5UJJ4Ui8U8qNbr9ZDP51EsFkOgH5lTQVCO0UY/eA+tXWAfRAuiWnBRhWA8Hvet0/k9BTJdBuIbmUzGd3ay0Znj0GEg80kti0sjBE76ACc5b2JiAnfv3sVwOESj0fC94FjqWSgULrSmm8/AFuGMr+t+f9pHMJlM+q7KDDlZhmaSiyLiPJ9ugO01QMtE6xF4DonXsRYKsCekGI5TQUBLQxlSW4JxERMspGVAa4HuUafTwdtvv+39c14vnU77MTFj8tmzZyG3hdqYz87nUzPfCl0rNPnMbCMWi8WwtbWF+fl5v69Cv9/3Xa2HwyHu3r2LRCKB7e3tE2ej2rEd57vD6NIIgfMmLlCapHyp2vuekpw9+g+73ln6dKo1bRhLNZ39XMt3bbKLYgJWO/Ne1kqIytZTv9+ONQroUqFgQ7JRZN0OnQcyuVpFaj3xODKXCi9uSUdhQoBP3RRrfdjnGfd8ExMTfmcn5iu0Wi2sra2h1WqFIhhMAiuVStje3j6VNXAe9MIIAVImk/FaVOu3GQ3gfgNagxBFZykA1HynmUrNrGmkajEwrKdhMp6nzTa0aYnNFbBEoaguhgoQRe5JykyKL0TdQzUvoyA2NKdCi8KQgppjoIWk/Rfok/P6yWQSs7OzaLfbqNfrqFaroVTfw4T8uPfE81OpFBYXF/0YuDlKrVbzSU6slmQm4d27d32U4TJFCV4oIeCcQ6lUQi6Xw87ODorFIl555RW/axH3FmDjkSg6bwtAtR3bads6fmoYm2qrpr8F/8bd2wJgGmojKRZgNbW1EqxFQmIyUxQ+YS0KFSRRrdCZRcnt5ykANbGI33H8Z4XxMILBfRT4LiqVCoIg8GZ/q9XyVloqlcJwOEqTnp6exnA4yiK8KNzpMLoSQuAsGI/nc3PLarWKZDKJxcVF33SkUCig1Wqdm+91EFkhAOzlLVhGpvazi1vdBQUax5ntvK9aItr4xDKpFQyWcaM+jwIQLehmr0Omt7381KLRWgQ+t+3MRGGgyVGnJeIW8Xg8FNHY2toCABQKBV8SzMawtF6461O320WlUjkTIXAWvHElhMBZMR4XnXMOMzMzPs5LYCydTqNQKGBhYQEbGxuhXX/OkxhCU01Gc9ma30SjmYRCkI9zZMtoCRgSbFSGVDBMmc9GJchQtEx4PF0P3ouans/Cv4Fw1EGvQRBQcwnoc1MoMaNPdxvSXgAAQt2GeR43N3HOncj8H0e8V7lc3te9ivtcZrNZpFIpBEGADz74AL1ez7eum56exrNnz86kYOgseONKCIGzJK1gm5ycRK/X8/F4Nd+0uuw8kzpUC+sY1de32jMqLKgRApKOO0r7qrVwGDgWZQ2MA9H0cwu+RYUeo+ZEz4+yQqwVZKMWvI4NgZ4FUahqJIXJRRqyTKfTvtOTukIUiHbenxe9MEKAL4DZW7Ozs5iYmECr1UKxWMTk5CQ++OADNBoNn6xzkWPTrDmCXESZ1aykGdrpdJBKpUL98ZnxRnOYAgNAyKpQJmLrM/rsql21OpDXsALEkmVO3jcqeUmFl57L73ge/X4LdtJaArBvOzKd17Ms0aUV1mq1fINUvqtarYZGo+F3jQqCADMzM3j99ddRqVTw+PFjNBoN1Go1b01cRFLaYXRlhQAluxbbBEHgCzzGSdhqtQoAoRfBDj00b3Uf+YsiugQ2HAbA96vXuL0CeLQKNHqgGIMeY++pZrwydxSAp98Be5l0UaE7Cxbae5L5bbTC4h9Wg2v2HuP+LH6ywlRdDwtKRo3rqKTuozY44XvhvavVqncVEokEbt265QVIlKsXdZ/ztEJJV1YIkJhIwi41QHhLbRIns1ar+a491H7ci4ACRUNSF0Ua/mIIjAueabA8Btjff5DETEMtdOHxZAQVNNZcjsol4Of6vd47yu2IKsm2wk2zHu35vLdNRuKc0OJhOy+NdCj2oLiFzfrTex6HOK+pVCrksqkwphAARv0HstksFhYWsLq6GupfcRBd1Pq78kKgVCqhWCxiYWEBQTDqOFyv19FoNLC5ubkPEGJ8nd16JyYm8OzZM9RqtVCnmShf+byIzMQFRPM/nU6H4uBc4Jqyqs/FpBhlJA2tqSZnGNGa/GoFqMDhfNgUXQ2VkfH1PF7TChZ+Z7UzTX09ns/A+/C3ui86RmVKFUaWqbRHA03zcTiFEpVFIpFAtVpFvV73eIC2VSNOUC6XfZs41jzwGS4DXVkhwBc6OTnp94GndKY2rVQq+4QAGY4bjkxMTHgGy+fzIY153pI4CnikBlPXQEtcAUT6vkCY6RS1H2cGqwugNM6cV82sAkhdD/XdgegsQTteC0paIaHVhpZJj5KFOI60RoNg32HnqcVGv58bieo8UEgxk5DnakXhZQAFgSssBEiU9Kzqmp2d9RuBbmxshCrTSIPBACsrK5ifn8ft27fRarW8n8bQEiX7eZCNBvA5VKA5N9rEk8/GzUS0SYi9hprsWoCkve3UhNdFa39UiKi/rUKAixzYy2IMgsBnLsZie52btEDICiRmb8bjcb/NWqPR8Pv3kcnYWIUVnxolYedgBQ2BgxOFaDmoxaUCaNw52WzW15t0u12/X4K6HaxIDYIAGxsbPl2dnYq1XuF506Hol3Pul51z6865b8ln086533fO3d/9Xdr93Dnn/pFz7oFz7hvOuU+e6+B3NX8ul/O79hJZn5ycxI0bNzA/P79P0ykAptozqhPsRZGCa9QiauIqc6oAUPNdNTbPi2rSMY7IkGRmNbWtbz/ufH5vLZgooFF9eL0/wUYyfDqd9r0g+TzEgtQ0t59pZGKctcUkHm35Ps564fjZ63FcpEQF0GAw8JGD7e1t3+jkKG7HWdJB9zoKBP6PsX/L8Z8G8AdBELwK4A92/weAzwB4dffncwB+4Zhj3Ud28LqQYrEYcrkcZmZmMDc3h3Q67ctCJycnce/ePdy5c2df7BiAXyzqZzMX3x47bgwHjfcox1jtSwFAjabPyb913CQ1lTW8x+fitamheC07BoJu6m+rQNLr6rl8Jv4ok6qZraSIvSYPsUcAAN9glbgPY+5kYA1vshMTU3q1O3OUEOD5U1NTyGazvgoxSmhY4j3U/+d19Tn57NxcZHV1NdR78KD1cZR1FHWePZ//H/Q8h7oDQRD8a+fcXfPxjwD4od2/fwXAvwLwd3c//yfBaARfcc4VnXM3gyBYOeJz7KMof5W/4/G4N5/JzNxarFKp4M6dO5idncXq6qpfcNynkJuIptNpv7kFM9Hos9DJGYYAACAASURBVOm9DhrTuPEd5xl10wwL4jGLjwxGZrUZeEC49p8NO1TAaNiQ19Mx22o9PUaRcE0vjtKIjGro9RRBJx6j9wmC8EYnCpjaObX31HvoHEUBvDpn6qJwS7UoXEAtMbZ90x/uN6Dj47W4izXn5TBr0wqso9JJ1itwckzghjD2KoAbu3/fBvCBHPd097N9QsA59zmMrIUTEbUc97wjMZmm3W77pJ9CoRCaeAoBtupWt4DfX4SpZs1jpsQyTZaLkUwQZdarpQCEwTZGAKg5ea7VElGaQkFAdUc4VzZ0x/HRClCf16Yy63X1fxUCKlx0vNby4GcqCHScNkmJ36u7wM/VIhlHXHfqevCZ9b3pu1BhqJ2KLwudGhgMgiBwzh2bY4Ig+AJGW5njJOezU/CtW7ewtLSESqWCarWKJ0+eABi9rKdPn4aKPAaDAe7cueO36uIC4QvKZrNot9unavxwEqKGXlhYwM2bN30688rKigfGstmsBy4BeGRamUiLigaDgReQZC4+p8ayaeorM2gvfy5qLl71sy1jD4ejDMTFxUW/zyOr6XTX4Uwm40OLCi7yWgBCNQyW2VXo8RhNkCJIyZCfljbHYqOOxsVi0af1EtDjvcbhJpxvdhW+ceMGnjx5gmq1GgJTSVHCMsrKeN50UiGwRjPfOXcTwPru58sAluS4xd3PzpzUP9akFMayh8Mhtra2/MLVMBM3KdW0VBaqaMnreRMXL3MC5ubmMD8/72P9bN2tZrx25qHm09ChXjsI9jruUhjyOiTVWmQSdUn4t0395eJWc5p+NtF267pRCHDMKoCsuW7BOd5LBZ8FLPVYdQt4TR0HrS42BlXz/bB3ls1mMTU15bMVKRg06qLWlYY79VmiSL876nGnpZMKgS8B+EkA//3u79+Rz/9z59wXAfy7AKqnwQMOIroCAEJbXhUKBVSrVbTbbTx9+hQAMDs765NBKpUKarUaOp2O3/qaWWfsOXjQJhFnSc6NmooWi0XcvXsXL7/8Mubn5/Ho0SMEwajrsTINNaduRaaanUAcn8U5F9rPUM11YH9DEGCvjZlqRAoe3VRUG4NQaOZyOWSzWeRyudB24hwbhQSTZXh/va42D1EBSKJ7ZPspcJxBsBe3J8aj2AHHwe9TqRRarRYGg4HvZXgQxWIxLCwsIJVK4enTp74r9fT0tE83jxJK9r0ftCaOe9xp6VAh4Jz7NYxAwFnn3FMAP4sR8/+6c+6nADwB8B/uHv5/AfgsgAcAWgD++lkN1Eo+xs7ph3GnV5YDx+NxdLtdtNttLC8vo9PpYGtrC7du3fJ79nFjD80o6/f7qFarh3YWOi0RUCoUCr7zMbPiWDSkvie1i1o+FsTSODdBMYs7kInsYqM21OYc9ntFw9X6YhShVqt5q8o558Nu/X7ft/9m1IPWmQovCjRgf0szjk39fc3V57yw2zDniueS+TOZjI8IDAajJqOVSsW7gQf567RQuH62tra8AHFu1DNxZmYGjUbjQntUHkRHsRiOEh34iTFf/XDEsQGAv3Wk0R2T7IPQEqAQYFYWWzvlcjk45/yWz91uF7VaDbdu3fJgIOPDXCzUPrQMjkInNcuYccYtxqlRu92uZ34uukQiEWJ4NdMt2s8fbSEOhNuLWwbjc6iZb49Rk9aauprazB6NfA+kbrfrgTH64XQFFNij5tbQLYnMpi6fFuxQGERhB0wRpimfyWT8LlPb29v7wsNRxGvSWqrX6yHBkUgkPAh9FkLgLEz+o5x/ZTMG+TKoFWgCciFOTk7ij/7oj7C6uoonT56E+u9ls1m8/vrrPobL3AJufnkcPOCkL0l9WmrRnZ3RltZkluFw6BeuzZ/XJiFcnOqDK2LPv3UzU2UgKzz0XF5by42DINy7kG7K3Nwc4vE42u22L5nlmFQrKz5B853Ao86nhhPpNtCq0M+Hw6Hf3VkBUGpnIvcMKdOV2tnZ8fsEHhUI5jgUT6rX69je3sbExITvNsz3eRps6SxN/oPoygoBYC8kxdRPhsTq9Trq9TqWl5exubnp47SxWCyUjMOMw1qttq+W/ryJTKgmLhlEi5i4eGkNKDMpyGe1oGp2YK+uX7WtXkcFgf6v19PIA/8n42qbciZeEYegyU9SF0avq9ZHFMCmrg7HyB4HOjZaAJpJSGHATkCce92o5DDSDEyOiRYdBSpduVQq5TdGOeja1gp6HnRlhYBqqGQyiVdffdV3DP6TP/kTvPvuu9je3t4XfqIft76+jsXFRXzyk5/0u8YsLy+jXC5fyPi5SUW5XPaZa+xrwIWrWWx0d7hbD2sbrNbWiAEFABc//fd6ve61ovr0nFO1EnhdNfspbFmIxW3CeC36+Jx7Ne+VaKWoAFam4A+BTKLxmudPIartxYgVMVEKGFWbcr+Gdrvtc/jr9fqRBT9TmNW6mpubC3UWzmQyfschth/XkKt9foKJfMcXpYSUrqwQICZAExbYswxYuWUlaxAEfoORZrOJTCaDer3u49mVSiW0z91503A49Eg5q8uoRTT8qSnN/C6VSnkAU5NcqNU0UYcMmc/nPdZg8/kVQOS1NJzGYxS5B+Bz79PpdMispyuj6c8Wa9CeB9Tkil2w6IYMXq/XQ0VMPJdpw2ohKJ7CMRKL0DDoccLBpVLJA3/sScHM04WFBfR6PdTr9dAOzzq3HK/iNLrVnKaLXyRdWSGgKK1qe2qoTqezD1gJgiC0H1wul0OlUkGz2fQA0WGAzlmANUoahaAJDeyBYFy01Pzsjcg4dbvdDvn5yiTKsMPh0O+5EIvF0G63UavV/DOp6U8igykD0x3hnNOCSafT3rfudDqe+TQUSAuD49VIgXVfqNE5Xs3zsBaQukuaYkxFwW3DWWtgLZuj0vT0NObn531PgEql4ueU29kvLy/7udQ6Bs6zbnZDC4ZzTsv1NDjCSejKCgEisAyzNRqNfX7mYUQNmU6nMTMz43GFg+g8wRpqUnbVteW8zHQjOk4/XMNkVlOrtl9fXw8xlOIAVljq30T2LWhHJqMA0g7Iem0gvIGpZvDRklO3Y2Jiwm/1zdTvwWDgzXfdvEOFQLFY9FYBkXr66FQY6+vrqFarvpjnOFQoFPyGJgB8XgQbvOzs7KBUKnlMSguYOF5GJ9LptLfIKJC4V8JFYwNXVggwnEaLwJquR70GzWltRsJFrUIlivnPyipQlFtj/AqOUaswCQgIuz/A/hx6IBza00Vvj9Ox2GejJqfG5mcspgH2ErbI4GpqqzDgtS0Qqc/ElHCG9XjfZDLpu0PzeqptVdsTU2E+BoUPtwsnaHccIghIa1ErFxUYpOZXLa+WCYVAEATeEjwOFhC17g5ai4et0ysrBLrdLra3txEEgd+NhgtDkeiDqF6v4+HDh5iZmfE9Blm+Sv/MptkqnZVVMI4ZlXEUbefCownOvRM0OmCLYYJgr35AidZGp9Px52pCEK0OzeOnoEomk5ibm0O9Xke5XPYpyjYcyHsTq4iKUlDwzc3NIZ/PY2lpCZ1OB81mExsbGx7TYDIYr1soFAAA7XbbW0b0/+fm5vzcEnfZ2tpCo9E4ttmtOIImU9EVePjwIRqNhk8B13kPglEbe+YQcH7b7TZWV1f9GLVm5aC1FfXdcY9XurJCgFKdoJ6mqR715SpDMU9fUWXnXGTT0sOuqSEtMgBw/P3vNGMQgN/OigzKrja8Lhk86qUruGfDjPybvwmq6aLnNTS/gT6+AlrU5HptG+JTzaRCK5FIYHp62u8VyQhAp9NBt9v1qH8ul/PXzuVy/pn4o/fkeNfX11Gr1Q5E61Vo2vdARcPrT01N+R4Hg8EA6+vrXhhrVETdId1JmfgJheFZNLM5qWV6aYTAcc0ZJmRsb2+jUqn4JB9Flw8jmp3sK8CEk2w26/2z49QRaCIJsFcnz9ZgxxECFFAEnmjxaCy8Xq9ja2vL5w9oiysdkwKHBKeozceFrpSRWVzFZ9LOQ61WyyfcqMnb7/dDeQJq+qvFxvGl02mk02ksLi4ikUhga2vL36vZbKLT6aBQKCCVSiGbzfpnYiIVsQnOi4692+3i4cOH+3aVihJG/FyJa4INYJ1zSKfTuHPnDuLxODqdDr773e+iVqv5CIy6TgR2meREHIBWC7MsD2Piw9yAK+8OHNecYZpqo9HwoTJOOhfkYYKAYRwyDgttBoOBtxCohcYxMMN1WpwTBEEoCYUtzjSEdhARm8hkMr5rks4DLR8CZHxWZkqSyXmOBQCZ7adaHYAfN/EVm5CjoFU2m0UikQhZIuo6schHhQCtBmpU+vNTU1MolUq+5qPf7/stvFXocZwq8FifoC5co9FAt9v1m31UKhVsb2/vwwCi1oj+z2fJZDK4ceOGBymZbp5KpbC9ve1/Go2GtyaJ39isTlorfB/MbWGG4UF0XDfgqMdcGiEQhVAfJhV3dnbQarV8U0ou6sNirWryajyeFYQqBLSc1y4Ynk/NpD41sFfVlsvlfHbiYQ1MaU0QhGLvAzJtt9tFp9Pxi0aZlVuX6RZqJC1z5f35Nxcu/9cIA60rnQMCYMCeH6vWEseq4TqeT6uFP7R2crmcN+2Zx6FtzjjfmgJsewryObkGNjc3UalU/Gah4+Y7CrzUbFRWm5J5OQYm+JTLZR+utS4cwUG7sYyGPGmtRFmcR7UOTgNSXxohYB/gqA+0vLyMnZ0dzM7OotfrYX193QOGh92LHYrZsIN1BN1uFzMzM16DNxoNrKyshNDvWGxUtpzP53Hz5k2f7MNNMLPZrBcqNFs/9alP4enTp3j77bdDeIH64AC8NtfCIhar1Go1bwFRG62srISq73Z2dkJtxdQv5THUSsr4/I5j45goXOgysayZgphI+8zMjDeP6Wrw3ooFUNAyxPbaa6/5Z2q1Wmg2m1hbW0O73Uan0/HlyXfu3PGum27G2uv1sLW15YXP8vIyqtWq7ycRRSq0owQ8NXmxWPS+//vvv49YLIabN2+i2+3iwYMHePz4sW/+MhwOQ9uQAXuWYiqVwuzsrJ/TR48eYWpqCvfu3fPnKf5yVD44zBU4Cl0aIXBSScZNQ5hVt7W1deQyYC5imrEEoxTsYrIK04m1tp5lqbyOFikVCgXvXszOziKXyyGVSqHX66FSqfj70tdWaU4AjEkuALyPznvTLGX+ADMKyXjWhObzWtJQJEmr9dSXV2bR7cdsSFILhBRg1HdM4WKr+Gz6MN2pmZkZn3zEzT5Ykcj0a15XAUAlWm5RURB1lzTTkN2HFHTlGmFzGq06pXbv9/v7sj61p2I+n/fVjGp1PQ+6NELgpJJsbW0N5XLZp2tubGwcOdmCVYSa3cZ9/+hDvvrqqwBG2Vz1eh39ft9vOz03N4fhcIhqtepxidnZWRSLRbz88sveRPze7/1e5PN5bG9vI5/P49atW96X1kw/zsPc3BxisZhPcWaFI4WAhguHw6EHnba3t/21+L3GqqPmmcdouE7Ndg2xESAk0EWfnIxFBtasS0XKFXxzbtTwpFarYXl52SfdzM3NYXJyMuRi/eAP/iAWFxfx4MEDPHnyBF/72te8G0IhTBdwHChM4TkzM+MFACtO1X2hG0asolAoeGFbKBQwMTHh9w3odDp+XagGpwXIpCXNP6Ggfv3110NFV1qVedF0aYSA0mEoqBI1T7lcjjSnxhGlM31bMg79bJp3tVoNyWQSS0tL2NzcxHA49GGsXC4XCo8FQeDNOi4kRdmZ366AJO/H/HhgT8sOh0MfX6afq369+uKqYZlNSCGhEQLOGc9RBNteR4FBm5pMhmceAYWRfU9qPah2TSQSPr04l8uh0+n4jlCcAzLMW2+9hXfeecfH+Pl8qlltlqQlrUPgMUwsYr1IIpFAPp9HsVj01ZzauEbzJmgJHLTemDeRz+cRBIEHFIMg8NmbrDZ8XgIAuKRC4DgoqJpfwP5dbQ+6B000MhzNUSLU3DsuHo/jxo0bCIIA9XrdV80x9VP9aTbWpEvAxBZtfgGE9z0A9moeaHYD8AunUCiEtrIio0eVP5N5uWApZHQOo4Sl+sgk1jEouq1gHe/Be2qxEEnNezIuUfYgGGVCzs7OYmVlBa1Wy4N46XTaP8+3v/1tz3gU3Nr7XyMS46xAnqOmNzW0CoFsNouZmRlffq47VGtqswrhceuNioTCTCM3jx8/RhAE3m18nnQphcBp6KgCgOmkRHpVi2gyTblcRr/f923A7ty540G6fD7vGaPdbvvtqAj00P//4IMPkEwm8dGPftTfk4xF5mIUIghGRU7JZBJvvPGGNym3t7e9T9vv97G5uek1IXMFOGaG86wfqs9IxuV8HJRubWPrwJ7loGEtCgTND6BAKpVKmJ2dxUsvvYTV1VUf3Uin0yiVSkgkEiiVSnj33Xd9xEfv55zz27NpyFFrKhjO5R6UKuiYqMPPFbegW7GwsODxFQKr3J6OzVLoIuimogeVADOiQzeTeMvi4iLi8ThyuRzW1tYOXbPnSVdGCBwEHB4XT3Au3KlYs/x4LxKZSev56RPSctDzlbEBeJeCbcT1fvZHzXfG0emXU4sSLKSgUvMc2IsscMGp+WsjANaCsNgBmS0q54BkF78KGBUsGobj/bXCjj448w90TjQngEQMg/OSy+V84g0z8piAQ4HB48n4tFIIsjIpiy3eGQLM5/M+8sF3pyHpw1xQ4joUWvF4HPl83j/faZD9s6ArIwTOcqKoVZjkQ21JzaWar9lsotvt4v79+yiVSj4ffTgcYnNz04f/uOC4mGdmZvy2WSsrK4jHR5ttAvCaXrML1axn8QxdETJ8MpnEvXv3kMvlkEgksLm56YFNXuPmzZuYn5/Ht7/9bT8mPhsFSlQNvR6nKcbUmhZRV6Gnrb6AcLMQMiDHur6+7hH3mZkZb+VQq77xxhv7EnJyuVwIW1FLi01WKpWKt5qYInz//n2P2XB+aXERBEwkEn4Lu5mZGaytrWF9fd2/q6dPn+LNN9/Ej/7oj+K9997zz6Gun42uKA2Hoz4I3NMinU4jl8uhVCqh2Wzi4cOHz70p6ZURAmdJZAhgL9lFwSbW6BMbAOBzAIA9IIrMXCgUUCwW/UJlcgnjwxor5/3pjgDwhS1keDIHUW/NO2cijQJXqVQKzjm/GcvMzAwePHjg972zz65CQBlXNb9qfQUL9Yf+ueITatFECZrhcOi779y8edP3DCiXy6hWqz78SReBVoDiFRoq1RRoalqGD9lOXpOc+O4KhYI/v1QqIRaL+Q1DdY4GgwFWV1fxla98BSsrK76Tko3ojBMCCuLy/XU6HbTbbb9ZDvs6PC96IYUAAM9k9CN7vZ5fgAp6aTZetVr1wODExIRftOxszCQW9jhgMkwymdzX8IK7Aznn0G63sba25j+bm5vzZivNWm3nzVx6ujTNZhOpVAovvfQS5ufnUSgUfGxbzXf9PU7z6/dWUPA7ChYWWzGkyPwM6waQKGgKhQIWFhZw7949FAoFzM3N4dGjR1heXvaVeJolaTMX1RrhMzKJiVbCzs4OisUigBEewLkfDAZIJpPevGfYsNvt4oMPPvBuDLGOwWCAJ0+e+LbksVgM3/d937dvHAcRBSS3x+PcbW5u4v79+8cuaT5rujJC4CiTfZxr0XdkqGpnZ8cnmXAhaOsnLradnR3frooJKSsrKz7KQM3D0BO3RwuCwO8vMDk5iWq16vMAGFYkIz19+jTkH5PRG42GL4Lpdru+ZPYjH/mI91d5P8baq9UqgGh/np8DewlCFI7W77eCgeeQwXhMMpnE4uKif57V1VV0u11MTExgZmYGd+/exa1bt1AsFpHP5zExMYFGo4FMJoPFxUU8fvwYg8EAlUrFuwRavMTxFgoFOOewsbGBarXq8zrI7AQaZ2Zm8Morr3i/vFwuewuKz7K6uuqzInkfIva06ubm5rxyYKkwsYvDMAEKqUql4v9eXl72Yz5qWPu86EoIgbMUACROvPrkmnVHl0G1pGUETeklg9N/ptRn1IDXpGlKzcb8BN6DyTYUUmQE1bYcJ90PFt7w836/79tqUQjoM4wDQcdRlADg51zEah3k83mv+Vj5yOy4UqnkY+804VWIUFjx+UjWPeG7YZoxi7x2dnZCjUXY/5DYgJY8c9wU3OquaaNVArJ855oFeNQ5pAXHHJStrS2faXjY+eex/pWuhBA46wnQ+C9NNTU9CRpls9lQ3zwuCmpxZtq1Wi2/qMiEDx488KEh3osamsky7H5DDUqtT8HAOoRYLIbZ2VnMzc0hmUyi0Whge3s7lI2WzWYxPT2NRqPhAcVyuewzKKNMewAhgQSEQ37qvkSRzQ0gkJdOp302HjMds9msz7grl8tYW1vD1taWt2q40G/evOnLu+nfqyDWd9DtdvHo0aNQmjhDrExcIgpPYrUh3xkAVKtVL0DpkpE5GQWgVcgEL0YVbOn2OGJfQloPx2l1ft7Rg6NsQ/bLAP59AOtBEPw7u5/9fQB/CUAPwEMAfz0Igsrud58H8FMABgD+dhAEv3dOYz8xaVhOwzz6Umz4RqvwWC3GIhEg3CuA7gYXFu+l2ojMyzbj1J4WZCOopJtrMJGG2o4CSO/J4pvjkC5Iay0cZbGyJwBdo4mJCW+2M3Q5GAzQaDQ81qGCRt8JtaaGUzUHv1aroVar+SiBFeLAXjEVAVKGDm0bNLUA+Nx6Tc61fq/WyEHAoM6h1qmosL0KIcJ/DOB/BvBP5LPfB/D5IAh2nHM/B+DzAP6uc+5jAH4cwMcB3ALwL51zrwVBcGk2ZOeLpATXDDhbvELiC+T5tApo1k5NTYU2/QT20l7V/Gd6KP3JZDKJhYUF5PN5LC4uolwuo1arhYQRE2cqlQri8TiKxaLX+ja3f2NjwwOda2trqNVqIRN73HwoncT05DnsCUA/mrgGc+ZZ38Baj1gs5rELMhTdh9XVVZ/Hz3GyZ2AikcDa2hpWVlZ8MRbrGrQgixWGtBpYskvSfRMpTKmpiXVQoGjHIl5buxcfRUgC+1O2n7cAAI62F+G/ds7dNZ99Wf79CoC/svv3jwD4YhAEXQCPnHMPAPwZAP/mTEZ7xkR/jxoCCIfM+J19wVwo1P6anKOaS5ti8JoqYAaDAcrlskfM2TWH4CTDkNT8FqeggCFASe1K3OAwUq1mP+fPURa3RlKYHcjztdnLcDiqmmSRFfMostmsZ+R6ve6rOOnza6IUW30Trbdp171ezwtnugJBEPgsT81hIPOT2TW1Wy1C+vAM++r86LMfly6DAADOBhP4GwD+2e7ftzESCqSnu5/tI+fc5wB87gzuf2yyL5ELgEQJrYuEn+v5XDSKBWiGn80o0wxFCoh6vY7JyUk0m01MT08jm80C2MtFoAlMC4KCRwUMO+mw56L60Uch1UjjFuZh1gTnIQiCUKoy8+f5DPF43FsHdIXYp4Apv9rJye2GU/n8nU4H5XLZR1bs+6BgjMfjvicjBTR9fhUCKpStS0hi+TLTlnlPm3F6VGvgLOgsrYhTCQHn3M8A2AHwq8c9NwiCLwD4wu51Lmz2mAvA4p9areYX5O64vE/PZCCafWQ83bRUw410G2hW0qe0zKxVfjRnY7EYNjY2fEaZahdqMiabMPGEacv0renH0lQ9bHHaGD7pKKmwUddptVpYXV1FpVLx1XgMA3LR8m9aMI1GA2tra/uEHAW0Yh7lchlbW1t49OiRn89SqeRrKZiVqbtVN5tNX/0Zj8f9/hS01PQ9krHj8XhoDwgKounp6VCxWqFQ8C7KWTQKPQ6dpRVxYiHgnPtrGAGGPxzsPf0ygCU5bHH3s+dGVmKqBQAgtBWWPU8XBnP/uXDUxNViFQ2nWfNdLQMucGBk1nP3GWpOfkfSnXPJ8Ozgw3OspQKEtyQ/bJ7ss9skoqPMcxAEPqrBebYlvByrVk3yeGpvy6AUsPTrCT6S+XTvCd6PIVrFanSeeA8rAMe9Y45Xw6EEeMcx5WXx+w+iEwkB59ynAfzXAP69IAha8tWXAPxT59w/xAgYfBXAH596lKegg14ANY8tpAHCPeZisRhyuVzIBCURT7B5+YyRc2MKxokpGJhwNDExgVarhcePH3vQj9tcp1KpUAktTWkuRrZbJxbBMJZmOqpFchBFzQHn7igCJAgCvxMPtyVn05BkMolSqeSfQYUhBSzHzV2EGCHgjkMM2yqoSwuAewyQUYkFPHv2zAO2PJfCR4FeWmjK7HyPPI9RHbp8jMywtoF9DixddgEAHC1E+GsAfgjArHPuKYCfxSgaMAXg93cf8itBEPynQRB82zn36wDexshN+FvPOzJgJbFqOV0UfPn8ThlHw1O0CFSDqH9pU1z5vcaa6ZJoM0o2nmC7KoYgrUnP9GGmDlOIEYfgeDmW42akKeMf1bxl/QIzHJk0RUZjuS2ZR/PyqbmpUemeES/h/+p/A+GdlbR2QNOX2fuQpcLsf2jTfaNCw7QweG0APudfk5hY3XgcDOay0VGiAz8R8fEvHXD83wPw904zqIhrnlii6qLWa5C5aP5pgwhdHBYIYqYbgFBTEP2tkQFgL3ecuf40IxOJhNfy1CZqbRA9Zw8BLlT25uNzHeX5TzJvR3UhUqkU8vk8MpmMD32yKzKZUIuy1A1g+JCYjGZPMu2aVZWat6EuVxDsFVoxlZkgogrlqF5+XAMkzQTk2uDvdrvtC574PQFIO8/HWbPP22V4YTIGeQ1qIzIffU3bHVfP40KxSSWqPdQdAPZyC+zWUkSrG40GEomEX7wMi2mUgtpI8xd0w44oDX/RKDUA75Kw2afiEGQqAqzcTZlZe/F43IOh6XTaC1hWWLLV92AwwLNnz9But32bcs36pHVGoJaMS8uE75hzpJaFNhdhXwG1sChcaKFpQhRBQwUweY+j0vN2Ga6EEDhLiorr2n5xCu6pNcBzNXGEv/XHnq/Hc3Eqys8FaHMSNLuM2kILmngvKwxOs6hOIkCY5syoibol2g+Bz6dNNugS8Bz2hPXqGwAABylJREFUUbSVnMQI+D3nwrph+pvzpMLWCnp9V8BecxOdX/7W6Iy+CxtqvGr0wgkBDQFRYzH9VAtZqJnr9bo34RUbiGo0af/mAmK9OrfroqkM7F9Aurg4Dl209EnVBOW2WxdNvD/DlSS1ZujaMFefgChbthMv6Pf7PlGITMk8fmYNsgqPfwMI4QqaJ0CriT/MpVChou4Fj2eR0+bm5r78EFovdDMmJiZClYtXlV44IUCitqBmjsoM1OPInGxioaAisKe1VTtTw9P3pRYZF87j/Q4i1Uo0aVkWqyGsk2SwnYbGuSFqRenYFUPRWn/OjYYHNTRKZqVmJ5aglhQFA9uR6e4+Gv1RK0JdLQ1Z2jRfjt9+r0lgV40uvRBQcyzK3DoOqGJBJS4YFQIWNCIxdq+JLyoM+KMJQUSWu90unHOhxhc6/oOeSYWE3pfVieyQq2Y2mcyG+g6bNz3ezntUlGXcGO2ca+QCgDerdb7tnFCQkiFJ7MfAKrxsNusTifSd8therxdKL7ZulX7GDsgEJ4kX8G+OU60eizXZDU/Okk77zsbRpRcCutAO+v6opGAQMD47ji97cnIyBOgB2Oe/a1yeY2KHIl5HAabDXo7FFizxMw1N0mXh8zATcZxlEXV//czOuz1+HBrOHAjV6jpmFZh2Aetx6ucrBsDvWZ2oSVQkann2AFRLIApH4bgpNHRjWj1GlYBe4yRg4EnotO9sHF16IUA6izCK+t9RIKAeZ0FB/h9lskb59XoPey+9Dz/XZzzoOS2opa6Kmtwaj9drH0SnnWOOwxZY2WeMAufGuRPqZul8EZEft90YsLeTUpSJrvfieNmYhQLARoM0T0GfI0qJnMV6vYh3BgDueQBK+wbh3AaAJoDN5z0WALO4HofS9TjCdJXHcScIgjn74aUQAgDgnHsrCII/fT2O63Fcj+Nix3F1cx2v6Zqu6UzoWghc0zW94HSZhMAXnvcAdul6HGG6HkeYPnTjuDSYwDVd0zU9H7pMlsA1XdM1PQe6FgLXdE0vOF0KIeCc+7Rz7jvOuQfOuZ++oHsuOef+0Dn3tnPu2865/2L382nn3O875+7v/i5d0HjizrmvOed+d/f/e865r+7OyT9zzk1ewBiKzrnfcM6965x7xzn3/c9jPpxzf2f3nXzLOfdrzrnkRc2Hc+6XnXPrzrlvyWeRc+BG9I92x/QN59wnz3kcf3/33XzDOfdbzrmifPf53XF8xzn3F491M5vGedE/AOIYbWDyMoBJAF8H8LELuO9NAJ/c/TsH4D0AHwPwPwD46d3PfxrAz13QPPxXAP4pgN/d/f/XAfz47t+/COA/u4Ax/AqAv7n79ySA4kXPB0bdqR8BSMk8/LWLmg8APwjgkwC+JZ9FzgGAzwL4vwE4AG8C+Oo5j+MvAEjs/v1zMo6P7fLNFIB7u/wUP/K9znthHeFhvx/A78n/n8doY5OLHsfvAPjzAL4D4ObuZzcBfOcC7r0I4A8A/FkAv7u7qDblhYfm6JzGUNhlPmc+v9D52BUCHwCYxiit/XcB/MWLnA8Adw3zRc4BgP8VwE9EHXce4zDf/QcAfnX37xDPAPg9AN9/1PtcBneAL500dq+C8yI32lzlTwH4KoAbQRCs7H61CuDGBQzhf8SocSuT0GcAVIIgYGXMRczJPQAbAP73Xbfkf3POZXDB8xEEwTKAfwDgfQArAKoA/i0ufj6Uxs3B81y7fwMjK+TU47gMQuC5knMuC+BfAPgvgyCo6XfBSKyeawzVOcd9Hv/ted7nCJTAyPz8hSAI/hRGtRwhfOaC5qOE0U5W9zDqWJ0B8OnzvOdx6CLm4DByp9jvI4ougxB4bnsVOOcmMBIAvxoEwW/ufrzmnLu5+/1NAOvnPIxPAfjLzrnHAL6IkUvwPwEoOudY5XkRc/IUwNMgCL66+/9vYCQULno+/hyAR0EQbARB0AfwmxjN0UXPh9K4Objwtev29vv4q7sC6dTjuAxC4P8F8Oou+juJ0YamXzrvm7pR/eUvAXgnCIJ/KF99CcBP7v79kxhhBedGQRB8PgiCxSAI7mL07P9PEAR/FcAfYm+Px4sYxyqAD5xzr+9+9MMYtY6/0PnAyA140zmX3n1HHMeFzoehcXPwJQD/yW6U4E0AVXEbzpzc3n4ffznYv9/Hjzvnppxz93Dc/T7OE+Q5BgDyWYzQ+YcAfuaC7vkDGJl13wDw/+3+fBYjf/wPANwH8C8BTF/gPPwQ9qIDL+++yAcA/jmAqQu4//cCeGt3Tn4bQOl5zAeA/xbAuwC+BeD/wAj1vpD5APBrGGERfYyso58aNwcYAbj/y+66/SaAP33O43iAke/P9fqLcvzP7I7jOwA+c5x7XacNX9M1veB0GdyBa7qma3qOdC0ErumaXnC6FgLXdE0vOF0LgWu6phecroXANV3TC07XQuCarukFp2shcE3X9ILT/w/8vElzqP/UVAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uneH4_KfdRel"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}