116. Classification_with_XGBoost.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RyGZaBMu1LWS",
        "outputId": "318f8dca-84ef-415e-dba1-b8c9acf1709f"
      },
      "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 segmentation-models\n",
            "  Downloading segmentation_models-1.0.1-py3-none-any.whl (33 kB)\n",
            "Collecting keras-applications<=1.0.8,>=1.0.7\n",
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            "Collecting efficientnet==1.0.0\n",
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            "Installing collected packages: keras-applications, image-classifiers, efficientnet, segmentation-models\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow) (3.2.2)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ],
      "source": [
        "!pip install segmentation-models\n",
        "!pip install tensorflow\n",
        "!pip install xgboost  "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade --no-cache-dir gdown\n",
        "!gdown --id 1-Vm4Y4YTPSH_zM3xSTzB9RQvJqiVFwaw"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XA2gClQj1eEr",
        "outputId": "b08b592c-f306-4540-ca83-a12c418e0d63"
      },
      "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",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.8/dist-packages (4.4.0)\n",
            "Collecting gdown\n",
            "  Downloading gdown-4.6.0-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.8/dist-packages (from gdown) (2.25.1)\n",
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            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "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",
            "Successfully installed gdown-4.6.0\n",
            "/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 [00:01<00:00, 144MB/s]\n"
          ]
        }
      ]
    },
    {
      "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": "O1wEXWVi1jlY",
        "outputId": "0fe159a6-8112-43d2-bfa4-51eb65adfd96"
      },
      "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[31m11.4 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_0y077ry0 -- 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 tensorflow.keras.models import Model, Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "import os\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "NN6AOAUA1l11"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SIZE = 64\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": "oMJWY79I1sb2",
        "outputId": "df2db782-7082-4085-deef-b429d5ff1f72"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pituitary\n",
            "glioma\n",
            "notumor\n",
            "meningioma\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": "xcslS8kH1uGh",
        "outputId": "6855695a-a742-4ee5-9921-b331f0bc49eb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pituitary\n",
            "glioma\n",
            "notumor\n",
            "meningioma\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",
        "x_train, y_train, x_test, y_test = train_images, train_labels_encoded, test_images, test_labels_encoded\n",
        "\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ],
      "metadata": {
        "id": "pRd5e3dK1v0U"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "y_train_one_hot = to_categorical(y_train)\n",
        "y_test_one_hot = to_categorical(y_test)\n",
        "     "
      ],
      "metadata": {
        "id": "YJWVCa4k1xj5"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "activation = 'sigmoid'\n",
        "\n",
        "feature_extractor = Sequential()\n",
        "feature_extractor.add(Conv2D(32, 3, activation = activation, padding = 'same', input_shape = (SIZE, SIZE, 3)))\n",
        "feature_extractor.add(BatchNormalization())\n",
        "\n",
        "feature_extractor.add(Conv2D(32, 3, activation = activation, padding = 'same', kernel_initializer = 'he_uniform'))\n",
        "feature_extractor.add(BatchNormalization())\n",
        "feature_extractor.add(MaxPooling2D())\n",
        "\n",
        "feature_extractor.add(Conv2D(64, 3, activation = activation, padding = 'same', kernel_initializer = 'he_uniform'))\n",
        "feature_extractor.add(BatchNormalization())\n",
        "\n",
        "feature_extractor.add(Conv2D(64, 3, activation = activation, padding = 'same', kernel_initializer = 'he_uniform'))\n",
        "feature_extractor.add(BatchNormalization())\n",
        "feature_extractor.add(MaxPooling2D())\n",
        "\n",
        "feature_extractor.add(Flatten())"
      ],
      "metadata": {
        "id": "-cQ2SIwr1zZq"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_no = 4\n",
        "x = feature_extractor.output  \n",
        "x = Dense(128, activation = activation, kernel_initializer = 'he_uniform')(x)\n",
        "prediction_layer = Dense(class_no, activation = 'softmax')(x)\n",
        "cnn_model = Model(inputs=feature_extractor.input, outputs=prediction_layer)\n",
        "cnn_model.compile(optimizer='rmsprop',loss = 'categorical_crossentropy', metrics = ['accuracy'])\n",
        "print(cnn_model.summary()) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8lPt-svs16LE",
        "outputId": "3cbc065f-777e-43f9-ee6f-2c791b121785"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_input (InputLayer)   [(None, 64, 64, 3)]       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 64, 64, 32)        896       \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 64, 64, 32)       128       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 64, 64, 32)        9248      \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 64, 64, 32)       128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 32, 32, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 32, 32, 64)        18496     \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 32, 32, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 32, 32, 64)        36928     \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 32, 32, 64)       256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 16, 16, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 16384)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               2097280   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4)                 516       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,164,132\n",
            "Trainable params: 2,163,748\n",
            "Non-trainable params: 384\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_for_xg = feature_extractor.predict(x_train) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JAutMizL2M9Y",
        "outputId": "8b284100-10d6-4521-9cad-c1ed5bb5e8b4"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "179/179 [==============================] - 7s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "model3 = xgb.XGBClassifier()\n",
        "\n",
        "model3.fit(X_for_xg, y_train)"
      ],
      "metadata": {
        "id": "hYdffne72QwT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43dab9a4-b795-4cfb-fd6b-bbfbcc2a6523"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(objective='multi:softprob')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_feature = feature_extractor.predict(x_test)\n",
        "#Now predict using the trained RF model. \n",
        "prediction_RF = model3.predict(X_test_feature)\n",
        "#Inverse le transform to get original label back. \n",
        "prediction_RF = le.inverse_transform(prediction_RF)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dOdFHM6EB_v_",
        "outputId": "39fecfbf-5e71-430d-a60f-1e52e8b18d42"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "41/41 [==============================] - 0s 8ms/step\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",
        "print (\"Accuracy = \", metrics.accuracy_score(test_labels, prediction_RF)) \n",
        "ax= plt.subplot()\n",
        "cm = confusion_matrix(test_labels, prediction_RF)\n",
        "#print(cm)\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']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "yLnuTO70COVq",
        "outputId": "a7a7cafc-ed76-468c-eb7f-a5309e2575df"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy =  0.9206712433257056\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma       0.92      0.82      0.87       300\n",
            "  meningioma       0.83      0.87      0.85       306\n",
            "     notumor       0.97      0.99      0.98       405\n",
            "   pituitary       0.94      0.99      0.96       300\n",
            "\n",
            "    accuracy                           0.92      1311\n",
            "   macro avg       0.92      0.91      0.91      1311\n",
            "weighted avg       0.92      0.92      0.92      1311\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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mtn85xxvwt1TyiHta1+Ylj8PHg4HA7uWf4ZxzmRFHG3dcamyMZ+ij+AwVdAd0zrmMUQpbhsXdVJI4kUUesAtR327nnMsquVTjjnv0yCEJj9cDs4maS5xzLqvk5eXOJFNxB+57zOzdxARJexGNJnLOuayRSzXuuN9ibk0yzTnnMquut3FL2gPYE2gr6ZyEp1oAPjORcy7r5FKNO66mkgZEK93UA5onpC8HjogpT+ecq7I6H7jN7C3gLUmjfPSkcy4XKK+OB25JN5nZWcBt5UwydWgc+TrnXFXV+Ro38ED4+Z8Kj3LOuSzhgRumSzoL2BaYCoxMZV5u55yraR64YTRQCLxNtJ5ad6IV351zLit54IbuZtYTQNJI4KOY8nHOufTInbgdW+AuLHlgZutz6Z3MOVc3+ZB36CVpeXgsoHHYF9FEgT4ft3Muq+RSBTOuftw+OtI5l1tyJ27HPsmUc87lhFyqcedOo45zzsVIUtJbJddpJOkjSZ9Imi7pipC+taQPJc2Q9JikBiG9YdifEZ7vUllZPXA75xzpC9zAWmB/M+sF9Ab6SdqdaKX3G81sW2AJcFI4/iRgSUi/MRxXoaxtKnlsyC6ZLkLW6H/Lu5UfVEe8eubemS5C1vjZXBJ1WvWbOdI1V0lY/Hdl2K0fNgP2B44N6aOBy4E7iRaXuTyk/49oqhCF65TJa9zOOUdqNW5JQyVNSNiGlrpWvqTJRIvGvAZ8DSxNGEE+F+gYHncE5kDUfRpYBmxWUVmztsbtnHM1KZUvJ81sBDCigueLgN6SWgFPA7+odgETeI3bOecAKfktWWa2FHgT2ANoJamkstwJmBcezwM6R2VQPaAlsLii63rgds450tqrpG2oaSOpMfBb4DOiAF6ykMwJwLPh8ZiwT3j+jYrat8GbSpxzDoC89C2k0AEYLSmfqHL8uJk9L+lT4FFJ/wQ+BkaG40cCD0iaAfwI/L6yDDxwO+ccqTWBVMTMpgA7lZE+E9itjPSfgCNTycMDt3POkdYad+w8cDvnHOmrcdcED9zOOUduzVXigds55/Aat3PO5RxfSME553KM17gTSOpBtFhwo5I0M7s/7nydcy4V3sYdSBoG9CUK3C8Srfj+DuCB2zmXVXIobsc+5P0I4DfAAjMbAvQiGofvnHNZJY3zcccu7qaSNWZWLGm9pBZEUxx2jjlP55xLWRbE46TFHbgnhMlW7gYmEk0u/n7MeTrnXMp85GRgZn8ND++S9DLQIozjd865rJINTSDJqoleJTsCXUrykrStmT0Vd77OOZeKHIrbsfcquRfYEZgOFIdkAzxwO+eyite4N9rdzLrHnIdzzlVbDsXt2AP3+5K6m9mnMefjnHPV4l9ObnQ/UfBeAKwFRLR6/Y4x5+uccynxppKNRgLHA1PZ2Madla4cdgnvjnuL1m3a8MiTYzakP/7Ig/zvsUfIy8tjr31+zelnn5fBUsanXfOGXDZge9o0rY8Bz06ez+MTvwPgiD5bcESfLSgy472vf+T2sbNo36Ihj568C9/8uAaA6d8t59pXZ2TwFcTjissu4Z1xY2ndpg2PP/UcADffcB3j3nqT+vXr06lTZ4YN/xfNW7TIcEnjNzzhXjwW7sWdt93MuLFvoLw82rRuw7Arr6Ztu3YZLmnV5FLgjnvk5PdmNsbMZpnZNyVbzHlWycGHDuKmO0ZskjZh/IeMG/sGDz7+NI8+9RzHnTAkQ6WLX1GxccubMzl25EROeWAyg/tsQZfNmtBny5bs220zjr9vIseNnMjDH83dcM7cpT9xwqhJnDBqUq0M2gCHDDyMW+/c9PfiV7vvyWNPjuHR/z3Lllt14b6RI8o5u3Y5eOBh3FLqXhx/4kk88r9nefjxp9l7377c8987MlS66kvXKu+SOkt6U9KnkqZLOjOkXy5pnqTJYTso4ZyLJM2Q9IWkAysra9w17o8lPQw8R9RUAkA2dgfcaedd+G7evE3Snnr8Uf445GQaNGgAQJs2m2WiaDVi8ap1LF61DoDV64qYvXg1bZs3YGCvDjzwwRwKi6JFp5esLsxkMWtcn513/dnvxe577rXhcc8de/H6/71a08XKiLLuRbNmzTY8XvPTmpz6gq+0NNa41wPnmtkkSc2BiZJeC8/daGb/KZVvd6IFgncAtgD+T9J2ZlZUXgZxB+7GRAH7dwlpOdMd8NtvZjN50kTuuu1mGjRsyBlnn0/3Hj0zXazYtW/RkO0KmjH9uxWc1rcrvTq35M/7dmHd+mJufXMmny1YCcAWLRsx+sQ+rFq7nv++PZtP5i7PcMlr3phnnuK3B/bPdDEy6o5bb+KF556lWbNm3HXP6EwXp8rSuFjwfGB+eLxC0mdAxwpOGQg8amZrgVlhtffdqGCUeaxNJWY2pIztT+UdL2mopAmSJowaeXecRUtKUVERy5cvY+QDj3L6Wedx8d/PwcwyXaxYNa6fx9WDunPT61+zel0R+XmiRaN6nPzAZG4bO4t/Dox6dy5etY7D7vyQE0ZN4uY3ZnLFIb+kSYP8DJe+Zo28+y7y8/PpP+CQTBclo/56+lm88Oqb9BtwCI8/+lCmi1NleXlKekuMVWEbWtY1JXUhWvH9w5B0mqQpku6V1DqkdQTmJJw2l4oDfbyBW1InSU9LWhS2JyV1Ku94MxthZruY2S4nnnRKnEVLSruC9vT9zW+RxA49dyQvL4+lS5Zkulixyc8T/xrUnVc+XcRbXy4G4PsVaxn75Q8AfDp/BcVmtGpcn8IiY/lP6wH4YuFK5i1dw5ZtGmes7DXtuWef5p1xY/nn1dfl1Jdacep/0MG8kcPNRnlS0ltirArbz77okNQMeBI4y8yWA3cC2wC9iWrk11e5rKkcLKl1GMKerPuAMUTtNlsQtXXfl0qemfTr/fZn4viPgKjZpLCwkFatW1dyVu66pP92fLN4NY+O39iOOe6rxey8ZSsAOrduTP38PJauKaRV4/qUdHvdomUjOrduzHdLf8pEsWvce+++zf2jRnLDzXfQqHHdebMqy7ffzN7w+K0336DL1l0zV5hqSteXk9G1VJ8oaD9U8p2emS00syIzKyaaeG+3cPg8Np01tVNIK1elbdySxgKHhmMnAoskvWtm51RefNqaWWKgHiXprCTOq3GXXngekyZ8xNKlSzn4d/sx9NTTOOSww/nnsEs5ZvCh1K9fn2FX/qvW1q527NiC/j0KmLFoJaNP7APAXeNm8dyUBVxy0HY8+KedWV9UzJUvfAFA784tOWWfrVhfZJgZ177y1YYaeG1y8QXnMjH8Xhz0274MPfU0Rt17N4Xr1vG3v5wEQI+evbj4H5dntqA14JKEezEg3It33xnHN7NnkZeXR/sOW3DRpZdnuphVlq6/bUUXGgl8ZmY3JKR3CO3fAIOAaeHxGOBhSTcQVXC7AR9VmEdlbbaSPjaznSSdDHQ2s2GSpiQziEbS60Q17EdC0jHAEDP7TWXnLl1TVLsbk1PQ/5Z3M12ErPHqmXtnughZw/9ANmrRqPrDHvvf+WHSt/SlU39Vbn6S9gbeZtPxKxcTxb/eRP91s4E/lwRySZcAfyLqkXKWmb1UUf7J9CqpJ6kDcBRwSRLHJ/oTcCtwYyjse0Dt7QztnMtZ6RrybmbvEI0SL+3FCs65Crgq2TySCdzDgVeAd8xsvKSuwFfJXDwMtjk02cI451ymqMxYm50qDdxm9gTwRML+TGBwRedI+ruZXSvpVsr4RGdmZ1ShrM45F5scmmOq/MBdXtAtUUnw/Sz8nFDFcjnnXI3KpY4HFdW4qxx0zey58DN3h1E55+qUHIrb5Qfu0kFXUhMzW53MRSU9R8W1dW/3ds5llbwcitzJ9OPeg6hPYjNgS0m9iLqx/LWC0/5TRlpJIM+du+OcqzNq20IKNwEHEnUSx8w+kbRvJee0AjqZ2e0Akj4C2hIF7wuqXlznnItHDlW4k5sd0MzmlGq4L3e6weDvRNMUlmgA7AI0JRqQ80RZJznnXKbUqqYSYI6kPQEL4+/PZGOvkfI0MLPE2a7eMbPFwGJJTatYVueci03uhO3kAvdfgJuJphn8jmgwzt8qOWeTmZjM7LSE3bapFNA552pCbekOCICZ/QAcl+J1P5R0ipltMqm2pD9TyeQpzjmXCTn03WRSvUq6EtW4dyf6cvF94OwwgrI8ZwPPSDoWmBTSdgYaAodVq8TOOReD2tar5GHgdqJpCCH60vER4FflnWBmi4A9Je1PtI4awAtm9kY1yuqcc7GpVU0lQBMzeyBh/0FJ5ydz8RCoPVg757JeDlW4K5yrpE14+JKkC4FHiZpKjqaC6Qmdcy4X1ZYa90SiQF3yav6c8JwBF8VVKOecq2m5E7Yrnqtk65osiHPOZVJ+DrWVJDVyUlIPoDvQqCTNzO6Pq1DOOVfTcqmppNJV3iUNI1p+7FZgP+BafFUb51wtk65V3iV1lvSmpE8lTZd0ZkhvI+k1SV+Fn61DuiTdImmGpCmS+lRW1koDN3AE8BtggZkNAXoBLZM4zznnckaelPRWifXAuWbWnWj8y98kdQcuBF43s27A62EfoD/Ryu7dgKHAnZWWNYnXs8bMioH1kloAi4DOSZznnHM5I101bjObb2aTwuMVRHM7dQQGAiXrHIxm42DEgcD9FvkAaBUWaC9XMm3cEyS1Au4m6mmykmj0ZKwa1suPO4uc8cY5lc2iW3e02e20yg+qI5aMvy3TRahVUmnjljSUqHZcYoSZjSjjuC7ATsCHQIGZzQ9PLQAKwuOOQOKkfHND2nzKkcxcJSULJtwl6WWghZlNqew855zLJfkpBO4QpH8WqBNJagY8CZxlZssT3xjMzCSVu0pYZSoagFNuA7mkPiUfBZxzrjZIZ2/AMAX2k8BDZvZUSF4oqYOZzQ9NIYtC+jw2bX7uFNLKVVGN+/oKnjNg/wpL7pxzOSRdgVtR1Xok8JmZ3ZDw1BjgBODf4eezCemnSXqUaA6oZQlNKmWqaADOftUou3PO5ZQ09uPeCzgemCppcki7mChgPy7pJOAb4Kjw3IvAQcAMYDUwpLIMkhqA45xztV26atxm9g7lj6D/TRnHG5UvTrMJD9zOOUctXCzYOedqu3o5FLmTGfIuSX+QdFnY31LSbkmcly/p7HQU0jnn4pauATg1IZmRk3cAewDHhP0VRCviVMjMihLOcc65rJbGIe+xS6ap5Fdm1kfSxwBmtkRSgySv/66k24DHgFUlid4H3DmXbbIgHictmcBdKCmfqO82ktoCxUlev3f4OTwhzfuAO+eyTg5Nx51U4L4FeBpoJ+kqotkCL03m4t4X3DmXK2rVQgpm9pCkiUT9DwUcZmafJXNxSS2BYUDJLElvAcPNbFkVy+ucc7HIobidVK+SLYlG8zxHNDRzVUhLxr1EX2YeFbblwH1VK6pzzsVHKfzLtGSaSl5g46LBjYCtgS+AHZI4dxszG5ywf0XCEFDnnMsauVTjTqappGfifpg18K/lHF7aGkl7hyGgSNoLWJNyKZ1zLma1KnCXZmaTJP0qycNPBUaHtm4BPwInppqnc87FLZcWC640cEs6J2E3D+gDfJfMxc1sMtArLHmGmS2vSiGdcy5u+ckMR8wSydS4myc8Xk/U5v1kMhcPS579EegC1Ct5RzOzM1IqpXPOxSwbRkQmq8LAHQbeNDez86p4/ReBD4CpJD9oxznnalytaOOWVM/M1ocvFKuqkZmdU/lhzjmXWTlU4a6wxv0RUXv2ZEljgCfYdL6Rp8o7McEDkk4BngfWJpz7Y9WK65xz8cjLgv7ZyUqmOb4RsJhofpGDgUPCz2SsA64D3gcmhm1C6sWsWQvmz+fkIcdz+KEHcfjAATz0wOhMFylj6vK9yMsT7z9yAU/e/BcAttpiM8bdfx7Tnh3GA/8eQv16+QDs1Wcb3nv4AlaMv5lBB/Su6JK1yrtvj+PQAQdycL/fMvLuChc8zwm1ZVrXdqFHyTSiNuppwPTwc1qS1z8X2NbMupjZ1mHrWq0S14D8evmce/6FPDXmRR54+DEee/Rhvv56RqaLlRF1+V6cdux+fDFr4Yb9q84cyK0PvUmPgVewZMUaThy0BwBz5i9h6LAHeOzlrK+TpE1RURH/umo4d9x1D0+PeYGXX3yer2fk9u5MBOsAABpUSURBVO9FvTwlvVVG0r2SFkmalpB2uaR5kiaH7aCE5y6SNEPSF5IOrOz6FQXufKBZ2JonPC7ZklGy+GVOadu2Hb/sHg0Mbdq0GV27dmXRwoWVnFU71dV70bFdK/rtvQP3Pf3ehrRf77odT/3fxwA89NyHHNK3FwDfzv+RaV99R3GxZaSsmTBt6hQ6d96KTp07U79BA/odNICxb76e6WJVS5pr3KOAfmWk32hmvcP2YpSvugO/JxqN3g+4I3QMKVdFbdzzzWx4Bc8nYxVRG/mbbNrGnTPdAefNm8vnn31Gzx17ZbooGVeX7sV15w/mkpufoVmTRgBs1qopy1asoago6hw1b+EStmjXMpNFzKhFCxfSvkP7DfvtCgqYOmVKBktUfensDmhm4yR1SfLwgcCjZrYWmCVpBrAbURNzmSqqcafjVTwDXAW8x8Y27onlZigNlTRB0oSR92S+zWz16lWcd/YZnH/BxTRrluyHjNqpLt2L/vv0YNGPK/j4szmZLoqrQanUuBNjVdiGJpnNaZKmhKaU1iGtI5D4yzY3pJWrohr3z5aRT5WZpfRNlpmNAEYArCkko587CwsLOfesMzhowCH85re/y2RRMq6u3Ys9enfl4F/3pN/eO9CwQX1aNG3Ef84/gpbNG5Ofn0dRUTEdC1rz3aK6Oztxu4ICFsxfsGF/0cKFFBQUZLBE1ZfKwMnEWJWCO4EriSbtuxK4HvhTitcAKihrOrrsSZolaWbprbrXjZuZccVll7B1164cf8KQTBcno+rivbjs1jFs2+8f/GLAMP544X2MHf8lQy4ZzbgJX3L4ATsBcNwhv+L5sbndNFAdO/Toybffzmbu3DkUrlvHyy++wK/3y+2FreJec9LMFppZkZkVA3cTNYcAzAM6JxzaKaSVK+VJplK0S8LjRsCRQJuY86y2yR9P5PnnnqVbt+04avBAAE4/8xz22ffXGS5ZzfN7sdElNz/LA/8ewrC/HswnX8xh1DNRE+TO3bfksRtOoVWLJhy0b08u/csAdj7iqgyXNl716tXjoksu49ShJ1NcXMRhgwaz7bbdMl2saol7yLukDmY2P+wOYmPvvDHAw5JuALYAuhGNoyn/WmY12yIhaaKZ7VzZcZluKnHZqc1up2W6CFljyfjbMl2ErNGoXvW/k3to4tykY85xO3eqMD9JjwB9gc2BhUQrgfUlWofXgNnAn0sCuaRLiJpN1gNnmdlLFV0/1hp3mLu7RB5RDTzuWr5zzqUsnRVuMzumjOSRFRx/FVFHjqTEHUSvT3i8HphFtISZc85llVo1H3c1nWRmm3wZKWnrmPN0zrmU5dB03LGX9X9JpjnnXEbF3asknWKpcUv6BdHwzZaSDk94qgVR7xLnnMsq3lQC2xPNINiKaDbBEiuAU2LK0znnqiyXmkpiCdxm9izwrKQ9zKzc8fbOOZctcqnGHfebzBxJT4fpDRdJelJSp5jzdM65lCmFLdPiDtz3EY0K2iJsz4U055zLKvlS0lumxR2425nZfWa2PmyjgLYx5+mccymrLSvgpMMPkv4gKT9sfyBaBs0557KKUviXaXEH7j8RjZRcAMwHjgDqxhRzzrmckks17lhHTprZN8ChcebhnHPpkEurvMc1AOeyCp42M7syjnydc66qsqEmnay4atyrykhrCpwEbEa0+oNzzmWNbBjKnqy4BuBsmBVQUnPgTKK27UfZdMZA55zLCnm5E7fja+OW1AY4BzgOGA30MbMlceXnnHPVkQ29RZIVVxv3dcDhRItp9jSzlXHk45xz6ZJDLSWxdQc8l2ik5KXAd5KWh22FpOUx5emcc1VW5/txm1memTU2s+Zm1iJha25mLeLI0znnqiNPyW+VkXRvmJ9pWkJaG0mvSfoq/Gwd0iXpFkkzJE0pteRj2WWtzgt1zrnaIs0LKYwC+pVKuxB43cy6Aa+HfYD+RCu7dwOGAndWWtYkX5NzztVq6Zwd0MzGAT+WSh5I1FGD8POwhPT7LfIB0EpSh4quH/uK65IGEK2Gs2HlGzMbHne+rnZaMv62TBcha/S86OVMFyFrfHVd6cpt6mqgH3eBmc0PjxcABeFxR2BOwnFzQ9p8yhFrjVvSXcDRwOlEb1RHAlvFmadzzlVFKjVuSUMlTUjYhqaSl5kZYFUta9w17j3NbEdJU8zsCknXAy/FnKdzzqUuhQq3mY0g6u6cioWSOpjZ/NAUsiikzwM6JxzXKaSVK+427jXh52pJWwCFQIVtN845lwk1sMr7GOCE8PgE4NmE9D+G3iW7A8sSmlTKFHeN+3lJrYDrgElEHw3uiTlP55xLWTpbuCU9AvQFNpc0FxgG/Bt4XNJJwDdEU14DvAgcBMwAVpPE1NdxT+taMpnUk5KeBxqZ2bI483TOuSpJY+Q2s2PKeeo3ZRxrwN9SuX6sgVtSPjAA6FKSlyTM7IY483XOuVRlw4jIZMXdVPIc8BMwFSiOOS/nnKuyXJqrJO7A3cnMdow5D+ecq7Ycitux9yp5SdLvYs7DOeeqTVLSW6bFXeP+AHhaUh5RV0ARtcX7RFPOuaySBfE4aXEH7huAPYCp4ZtT55zLSjkUt2MP3HOAaR60nXNZL4cid9yBeyYwVtJLwNqSRO8O6JzLNt4dcKNZYWsQNuecy0rexh2Y2RUAkpqFfV970jmXlXIpcMc9rWsPSR8D04HpkiZK2iHOPJ1zripyac3JuJtKRgDnmNmbAJL6AncDe8acr3POpSSXatxxB+6mJUEbwMzGSmoac57OOZeyHIrb8fcqkfQP4IGw/weinibOOZddcihyxz3k/U9AW+CpsLUNac45l1VqYCGFtIm7V8kS4Iw483DOuXTIfDhOXiyBW9JNZnaWpOcoY0FMMzs0jnydc67Kcihyx1XjLmnT/k9M14/VgvnzufTiv/Pj4sUgMfiIozju+BMqP7EW8nuxqXffHsc1/76K4qJiBg0+kpNOSWlx75zTvmUjrvt9TzZv3hAz47EP5zL6nW/4RYfmDB+8A00a5DNvyRrOffgTVq4tAmD7Ds24cnAPmjXMp9jg8FveZ9367J+OPxu6+SUrlsBtZhPDz7fiuH7c8uvlc+75F/LL7juwatVKjjlqMLvvuRfbbLNtpotW4/xebFRUVMS/rhrOf+++j4KCAo49+gj67rc/22xbe+9FUbFx9fNf8Om85TRtmM/TZ+7Ju1/+wFVH9uCa5z/no5lLOGLXjpzcd2tuemUG+XniP8f04vxHpvD5/BW0alKf9UXZH7Qht7oDxj0AZ6qkKaW2tyXdKGmzOPOujrZt2/HL7tE4oaZNm9G1a1cWLVyY4VJlht+LjaZNnULnzlvRqXNn6jdoQL+DBjD2zdczXaxYfb9iLZ/OWw7AqrVFfL1oJQUtG7H15k34aOYSAN75cjEH9mwPwN7bbcYX81fw+fwVACxdXUhxjkwxpxS2Sq8lzQ7xb7KkCSGtjaTXJH0VfraualljX0gBeAE4LmzPAROABcComPNOi3nz5vL5Z5/Rc8demS5KxtX1e7Fo4ULad2i/Yb9dQQEL69CbWMfWjem+RQs++XYpXy1cyQE7tAOgf6/2tG/ZCICtN2+KmXHvybvwzJl7cErfrTNZ5JTEsJDCfmbW28x2CfsXAq+bWTfg9bBfJXH34z7AzPok7E+VNMnM+kj6Q8x5V9vq1as47+wzOP+Ci2nWrFmmi5NRfi/qtiYN8rntj725asznrFxbxEWPT+Mfh/2Svx2wDa9/uojC0BySny923ro1g29+nzWFRdz/592YNncZ78/4McOvoHI10FQyEOgbHo8GxgIXVOVCcde48yXtVrIjaVcgP+yuL32wpKGSJkiaMPKeETEXrWKFhYWce9YZHDTgEH7z27q9+prfi0i7ggIWzF+wYX/RwoUUFBRksEQ1o16euO2POzHm4/m8Oi36hDHz+1UMuXsCg25+n+c/ns+3i1cDsGDpT4yfuYQlqwv5qbCYtz7/nh065saCV6k0lSTGqrCV/pbagFfD/EwlzxWY2fzweAFQ5V+euGvcJwP3htkBBSwHTg7D3q8ufbCZjSCa34Q1hT/vRlhTzIwrLruErbt25fgThmSqGFnB78VGO/Toybffzmbu3DkUtCvg5Rdf4Orrrs90sWL3r6N68PWildw3bvaGtDZNG/DjqnVI8NcDtuHRD+YA8PaXP3BK361pVD+PwiJj166tGTXumwyVPEUp1LgTY1U59jazeZLaAa9J+rzU+SapyjEu7gE444GeklqG/WUJTz8eZ97VMfnjiTz/3LN067YdRw0eCMDpZ57DPvv+OsMlq3l+LzaqV68eF11yGacOPZni4iIOGzSYbbftlulixWrnLq0YtHNHPp+/gjFnR3PDXf/Sl3TZvCnH7bklAK9OXcj/xs8DYPma9dz79myeOmMPDHjr8+8Z+/n3mSp+StLZHdDM5oWfiyQ9DewGLJTUwczmS+oALKrq9RXnqmKSGgKDgS4kvEmY2fDKzs1kjdtlr1zqshW3nhe9nOkiZI2vrutX7d+Mb39cm3TM2bJNw3LzCy0KeWa2Ijx+DRgO/AZYbGb/lnQh0MbM/l6VssbdVPIssAyYSMLSZc45l23y0lcpKACeDr1P6gEPm9nLksYDj0s6CfgGOKqqGcQduDuZWb+Y83DOuTRIT+Q2s5nAz/rMmtliolp3tcXdq+Q9ST1jzsM556pNSn7LtLhr3HsDJ0qaRdRUIqIvVHeMOV/nnEtJFsTjpMUduPvHfH3nnEuLbKhJJyuuaV1bmNlyYEUc13fOuXRLYSh7xsVV434YOJioN4mx6acQA7rGlK9zzlVJ7oTt+KZ1PTj8zJ0ZZpxzdVoOVbhjb+NGUkdgKzYdgDMu7nydcy4VdX4hhRKSrgGOBj4FikKyAR64nXPZJXfiduw17sOA7c3MR00657JaDsXt2AP3TKA+PtzdOZfl8nKokTvuwL0amCzpdRKCt5mdEXO+zjmXkhyK27EH7jFhc845lyZxz8c9WlJjYEsz+yLOvJxzrjpyqcYd9yrvhwCTgZfDfm9JXgN3zmUdpfAv0+KeHfByopUflgKY2WR81KRzLgv57IAbFZrZslJzABTHnKdzzqUsGwJysuIO3NMlHUu02ns34AzgvZjzdM65lGVDE0iy4m4qOR3Ygagr4CNEq7yfFXOezjmXMm8qCcxsNXBJ2JxzLmulMx5L6gfcDOQD95jZv9N4+djm466w54iZHRpHvs45V2VpityS8oHbgd8Cc4HxksaY2afpySG+GvcewByi5pEPya1pAJxzdVAah7zvBswIiwYj6VFgINFke2kRV+BuT/RucwxwLPAC8IiZTU/2Ao3rZ0ewlzTUzEZkuhzZwO/FRtlwL766rl8ms98gG+5FOjSql3zMkTQUGJqQNCLhHnQkqriWmAv8qvol3CiWLyfNrMjMXjazE4DdgRnAWEmnxZFfzIZWfkid4fdiI78XG9W5e2FmI8xsl4StRt+4YvtyUlJDYABRrbsLcAvwdFz5OedclpgHdE7Y7xTS0iauLyfvB3oALwJXmNm0OPJxzrksNB7oJmlrooD9e6Im47SJq8b9B2AVcCZwRsLISQFmZi1iyjcOOd92l0Z+Lzbye7GR34sEZrY+NAu/QtQd8N5Uvt9LhswsnddzzjkXs7hHTjrnnEszD9zOOZdjal3gllQkabKkaZKekNRE0i6SbgnP95W0ZxLX2ULS/8Lj3pIOirvscZNUIOlhSTMlTZT0vqRB4Z48H445VNKFmS5rOkkySdcn7J8n6fJKzjlMUvfYC5dG4XU+mLBfT9L3Jf+3Vbzmi5JaVfHcDX93Lr1qXeAG1phZbzPrAawD/mJmExLWuewLVBq4zew7Mzsi7PYGUgrckuKeeTElir4hfgYYZ2ZdzWxnom+7OyUeZ2Zj0j2vQhZYCxwuafMUzjkMqNHAnYbfmVVAj7DqFESD4KrVDc3MDjKzpVU8N/HvzqVRbQzcid4Gti2pUUrqAvwFODvUyveRNEpSSYBG0srws0uotTcAhgNHh3OOlrRbqK1+LOk9SduHc06UNEbSG8Drku6XdFjCtR+SNLDmXv4m9gfWmdldJQlm9o2Z3Zp4UHgNt4XHXSS9IWmKpNclbRnSR0m6U9IHofbeV9K9kj6TNCrhWndKmiBpuqQrauZllmk9Uc+Hs0s/UdZrDJ/IDgWuC//n20gaK2mXcM7mkmaHxydKekbSa5JmSzpN0jnhd+MDSW3Ccb3D/hRJT0tqHdLHSrpJ0gSiXljV9SLR+AmIxlA8kvBam4b/p49C+QYmvIanJL0s6StJ1yacMzu83i7h//fu8P/5askbhKRdw+uaLOk6SdNCeuInuTbhPk0J92HHkH65pNGS3pb0jaTDJV0raWooT/1w3GWSxoe/yRGhIlJn1drAHWov/YGpJWlmNhu4C7gx1Mrfruw6ZrYOuAx4LJzzGPA5sI+Z7RSe+1fCKX2AI8zs18BI4MRQnpZENf0Xqv/qqmQHYFKK59wKjDazHYGHiAZRlWhNNCfN2UQLQt8Y8ugpqXc45hIz2wXYEfh1yR9rhtwOHBf+HxL97DWa2XtEr+n88H/+dSXX7gEcDuwKXAWsDr8b7wN/DMfcD1wQ8pkKDEs4v0EYfXc91fco8HtJjYju+4cJz10CvGFmuwH7Eb0xNQ3P9QaOBnoSVVISB5CU6AbcbmY7EK1qNTik3wf82cx6A0XllOsK4OPw+i8muh8ltiGqWBwKPAi8aWY9gTVsfBO6zcx2DZ+kGwMHV34raq/aGLgbS5oMTAC+JQqe6dYSeCLULEoCVonXzOxHADN7i6gjflui2s+TZrY+hvKkTNLtkj6RNL6Cw/YAHg6PHwD2TnjuOYv6kk4FFprZVDMrBqYTjZQFOErSJOBjonuUsTZjM1tOFCxKf3Sv6DUm600zW2Fm3wPLgOdC+lSgS3izaBV+HwBGA/smnP9YFfIsk5lNIbr/xxDVvhP9Drgw/H2MBRoBW4bnXjezZWb2E9FkSFuVcflZYflBgIlEr60V0NzM3g/pD5dxHkT39YFQxjeAzSSVjOd4ycwKie5XPmGN2rDfJTzeT9KHkqYSBfnEv7k6J6vaYdNkTXjn36CST1XrCW9gkvKABknkcSXRH+sgRc0vYxOeW1Xq2PuJBiT9HhiSxLXjMp2NNSTM7G+K2nwnVPF6a8PP4oTHJfv1FI0aOw/Y1cyWhCaURlXMK11uIvrUcV8Vzt3we8LPX0fp1594b5L5Gyv9O1NdY4D/EH2fs1lCuoDBZvZF4sGSfsWmr6GIsstd+pjGZRxTFWsBzKxYUqFtHFxS8rvUCLgD2MXM5ij6YjnTv0sZVRtr3JVZATRP2J8N7BweHwrUT+Kclmz80ufESvIbRVj1J53z8VbBG0AjSacmpDWp5Jz3iN5wAI4j+s4gWS2IAtIySQVEzVYZFT4JPQ6clJBc3mus6PfkCFJgZsuAJZL2CUnHA29VcEp13Us01cTUUumvAKeXtA9L2qm6GYUvLleE4A8b72VpbxPdXyT1BX4In4KSURKkf5DUjBTvf21UFwP3c8Cg8EXKPsDdRO2vnxB9bC6r9vMm0D2cczRwLXC1pI+ppEZlZguBz6haLS9tQi3mMKLXOkvSR0Qf2S+o4LTTgSGSphAFm6S/PDOzT4iaSD4n+vj8blXLnmbXA4m9S8p7jY8C54cv8bYhqsGeGv7PU+mdUuIEojblKUTtycOr+gIqY2ZzzaysbnhXElVMpkiaHvbT4STg7tAE05Souai0y4Gdw+v/N9H9SEp4c7gbmEb05lNR816d4EPeYyapCVFbXZ9Q83KuVpHUzMxKemNdCHQws3T0kHHlqIs17hoj6QCi2vatHrRdLTYgfBqdBuwD/DPTBartvMbtnHM5xmvczjmXYzxwO+dcjvHA7ZxzOcYDt/sZlTHDYjWutWEuGEn3qIIZ95TkzI1lnDdbZUwgVV56qWNWppjX5ZLOS7WMzqWTB25Xlp/NsJj4pKo4i52ZnVzJIKS+JDFzo3N1nQduV5nEGRbfljQG+FRSfpgJbnyY8e3PEE0fK+k2SV9I+j+gXcmFtOkMe/0kTQrzpbyusmdubCvpyZDHeEl7hXM3UzQ73XRJ9xAN5a6QopnpJoZzhpZ67saQ/nqYVwZFMwK+HM55W9IvyrjmGZI+Da//0ardXudSVxvnKnFpoo0zLJZM+tMH6GFms0LwW2Zmu0pqCLwr6VVgJ2B7ogmlCogmLLq31HXbEo2E2zdcq42Z/SjpLmClmf0nHPcw0UyO7yiaUvYV4JdEM+u9Y2bDJQ1g0yHs5flTyKMxMF7Sk2a2mGik3wQzO1vSZeHapxFNA/sXM/sqDOe+g2hyo0QXAlub2VpVcbEB56rCA7crS8kMixDVuEcSNWF8ZGazQvrvgB21cS7zlkTTfu4LPGJmRcB3iuYmL213ogUdZsGGOUTKcgDRVAMl+y3CXBX7Ek2jipm9IGlJEq/pDEmDwuPOoayLiSYyKpmd70HgqZDHnkQzQJac37CMa04BHpL0DNEiFc7VCA/crizlzbCYOI+LgNPN7JVSx6Vzibc8YPcw1WjpsiQtTGp0ALCHma2WNJbyZ5ezkO/S0vegDAOI3kQOAS6R1DNbpu11tZu3cbuqeoVo0qWSFUq2UzQp/ziiifjzJXUgmrC/tA+AfRVN/YrCKjH8fEa+V4kmgSIcVxJIxwHHhrT+RIs6VKQlsCQE7V8Q1fhL5LFxtrljiZpglgOzJB0Z8pCkXokXVDQFcGcze5Nooq6WQLNKyuFcWnjgdlV1D1H79aQwR8V/iT7BPQ18FZ67n2gVmE2EBQeGEjVLfMLGporSMzeeAewSvvz7lI29W64gCvzTiZpMvq2krC8Tzev8GdHMdB8kPLcK2C28hv3ZOGvfccBJoXzTgdJLzuUDDyqa2P9jopVzqrQ2o3Op8rlKnHMux3iN2znncowHbuecyzEeuJ1zLsd44HbOuRzjgds553KMB27nnMsxHridcy7H/D+nQxFkR86xLgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HlgQX_uHCS33"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}