MURA_DenseNet_v1_5.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MURA-DenseNet-v1.5.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VPz1RlDzUQL0"
      },
      "source": [
        "# MURA-Densenet-v1.5-Humerus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zL4-xYNJQNMb"
      },
      "source": [
        "!pip install focal-loss\n",
        "!pip install keras-radam"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkjRfywGQWRn"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "import random\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "import keras.backend as K\n",
        "import matplotlib.pyplot as plt\n",
        "import keras\n",
        "import keras.backend as K\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Dense, Conv2D, Conv3D, DepthwiseConv2D, SeparableConv2D, Conv3DTranspose\n",
        "from keras.layers import Flatten, MaxPool2D, AvgPool2D, GlobalAvgPool2D, UpSampling2D, BatchNormalization\n",
        "from keras.layers import Concatenate, Add, Dropout, ReLU, Lambda, Activation, LeakyReLU, PReLU\n",
        "from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, Callback\n",
        "from keras.preprocessing.image import ImageDataGenerator, load_img#, image\n",
        "from keras import regularizers\n",
        "from IPython.display import SVG\n",
        "from keras.utils.vis_utils import model_to_dot\n",
        "from keras.applications.densenet import DenseNet169, DenseNet121, preprocess_input\n",
        "from time import time\n",
        "\n",
        "\n",
        "from focal_loss import BinaryFocalLoss\n",
        "from keras_radam import RAdam\n",
        "\n",
        "import datetime"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SE2tMLbKQZsU",
        "outputId": "b57164ec-35e9-43be-bbe9-555a68e6301e"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txkV3l09Qdij"
      },
      "source": [
        "!unzip '/content/drive/My Drive/Dataset/MURA-v1.1.zip' "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sa-ji_TQmmV"
      },
      "source": [
        "def listdir_nohidden(path):\n",
        "    '''\n",
        "    Utility function to find the list of files in a directory excluding the hidden files.\n",
        "    Args:\n",
        "        path: contains the path of the directory containing the images\n",
        "\n",
        "    '''\n",
        "    for f in os.listdir(path):\n",
        "        if not f.startswith('.'):\n",
        "            yield f"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4iU6lPsjQ4Qy"
      },
      "source": [
        "def create_images_metadata_csv(category,study_types):\n",
        "    '''\n",
        "    This function creates a csv file containing the path of images, label.\n",
        "    Args:\n",
        "        category: train or valid, depending on which csv is needed\n",
        "        study_types: the type of the body part in MURA Dataset, eg:  XR_SHOULDER\n",
        "    '''\n",
        "    image_data = {}\n",
        "    study_label = {'positive': 1, 'negative': 0}\n",
        "    #study_types = ['XR_ELBOW','XR_FINGER','XR_FOREARM','XR_HAND','XR_HUMERUS','XR_SHOULDER','XR_WRIST']\n",
        "    #study_types = ['XR_ELBOW']\n",
        "    i = 0\n",
        "    image_data[category] = pd.DataFrame(columns=['Path','Count', 'Label'])\n",
        "    for study_type in study_types: # Iterate throught every study types\n",
        "        DATA_DIR = 'MURA-v1.1/%s/%s/' % (category, study_type)\n",
        "        patients = list(os.walk(DATA_DIR))[0][1]  # list of patient folder names\n",
        "        for patient in tqdm(patients):  # for each patient folder\n",
        "            for study in os.listdir(DATA_DIR + patient):  # for each study in that patient folder\n",
        "                if(study != '.DS_Store'):\n",
        "                    label = study_label[study.split('_')[1]]  # get label 0 or 1\n",
        "                    path = DATA_DIR + patient + '/' + study + '/'  # path to this study\n",
        "                    for j in range(len(list(listdir_nohidden(path)))):\n",
        "                        image_path = path + 'image%s.png' % (j + 1)\n",
        "                        image_data[category].loc[i] = [image_path,1, label]  # add new row\n",
        "                        i += 1\n",
        "    image_data[category].to_csv(category+\"_image_data.csv\",index = None, header=False)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x0KgZTeDQ7ib"
      },
      "source": [
        "def getImageLabels(dataframe):\n",
        "  '''\n",
        "  This function is to get the labels of the images from the dataframe\n",
        "  Args:\n",
        "      dataframe: pandas dataframe containing the labels of the images\n",
        "\n",
        "  '''\n",
        "  labels = []\n",
        "  for i, data in tqdm(dataframe.iterrows()):\n",
        "#        img = cv2.imread(data['Path'])\n",
        "#         #random rotation\n",
        "#         angle = random.randint(-30,30)\n",
        "#         M = cv2.getRotationMatrix2D((img_width/2,img_height/2),angle,1)\n",
        "#         img = cv2.warpAffine(img,M,(img_width,img_height))\n",
        "      #resize\n",
        "#        img = cv2.resize(img,(img_width,img_height))    \n",
        "#        img = img[...,::-1].astype(np.float32)\n",
        "#        images.append(img)\n",
        "      labels.append(data['Label'])\n",
        "#    images = np.asarray(images).astype('float32') \n",
        "  #normalization\n",
        "#    mean = np.mean(images[:, :, :])\n",
        "#    std = np.std(images[:, :, :])\n",
        "#    images[:, :, :] = (images[:, :, :] - mean) / std\n",
        "  labels = np.asarray(labels)\n",
        "  return labels"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLbE7a2NQ-rO"
      },
      "source": [
        "def getImage(dataframe, size):\n",
        "    '''\n",
        "    Function to process the images\n",
        "    Args:\n",
        "        dataframe: contains the path to the images in the directory\n",
        "        size: contains the value to which the shape of the image will resized\n",
        "    '''\n",
        "    Images = []\n",
        "    for i, data in tqdm(dataframe.iterrows()):\n",
        "      try:\n",
        "        image = cv2.imread(data['Path'])#cv2.IMREAD_GRAYSCALE)\n",
        "        image = cv2.resize(image,(size,size))\n",
        "        #image = randome_rotation_flip(image,size)\n",
        "        Images.append(image)\n",
        "\n",
        "      except Exception as e:\n",
        "        print(str(e))\n",
        "\n",
        "    Images = np.asarray(Images).astype('float32')\n",
        "\n",
        "    mean = np.mean(Images)\t\t\t#normalization\n",
        "    std = np.std(Images)\n",
        "    Images = (Images - mean) / std\n",
        "    \n",
        "    #if K.image_data_format() == \"channels_first\":\n",
        "    #  Images = np.expand_dims(Images,axis=3)\t\t   #Extended dimension 1\n",
        "    #if K.image_data_format() == \"channels_last\":\n",
        "    #  Images = np.expand_dims(Images,axis=3)             #Extended dimension 3(usebackend tensorflow:aixs=3; theano:axixs=1) \n",
        "    return Images\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DfIyvgFREaz"
      },
      "source": [
        "def build_model():\n",
        "    base_model = DenseNet169(input_shape=(None, None,3),\n",
        "                             weights='imagenet',\n",
        "                             include_top=False,\n",
        "                             pooling='avg')\n",
        "#     i = 0\n",
        "#     total_layers = len(base_model.layers)\n",
        "#     for layer in base_model.layers:\n",
        "#         if(i <= total_layers//2):\n",
        "#             layer.trainable = True\n",
        "#             i = i+1\n",
        "\n",
        "    x = base_model.output\n",
        "\n",
        "    predictions = Dense(n_classes,activation='sigmoid')(x)\n",
        "    model = Model(inputs=base_model.input, outputs=predictions)\n",
        "    return model"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XSGVIWqDUCfr"
      },
      "source": [
        "### Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S-rZwni8Rizb",
        "outputId": "daeec073-70f9-47dc-bea4-3cd5a32214f7"
      },
      "source": [
        "###################\n",
        "# Data processing #\n",
        "###################\n",
        "\n",
        "img_width, img_height = 224, 224\n",
        "#Keras ImageDataGenerator to load, transform the images of the dataset\n",
        "#BASE_DATA_DIR = 'data/'\n",
        "IMG_DATA_DIR = 'MURA-v1.1/'\n",
        "train_data_dir = IMG_DATA_DIR + 'train/XR_HUMERUS'\n",
        "valid_data_dir = IMG_DATA_DIR + 'valid/XR_HUMERUS'\n",
        "\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rotation_range=30,\n",
        "    horizontal_flip=True\n",
        ")\n",
        "\n",
        "test_datagen = ImageDataGenerator(\n",
        "    rotation_range=30,\n",
        "    horizontal_flip=True\n",
        "\n",
        ")\n",
        "study_types = ['XR_HUMERUS']\n",
        "\n",
        "#TRAIN DATA\n",
        "create_images_metadata_csv('train',study_types)\n",
        "#VALID DATA\n",
        "create_images_metadata_csv('valid',study_types)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 587/587 [00:03<00:00, 171.70it/s]\n",
            "100%|██████████| 132/132 [00:00<00:00, 170.19it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJztmkXwRn6Z"
      },
      "source": [
        "valid_image_df = pd.read_csv('valid_image_data.csv', names=['Path','Count', 'Label'])\n",
        "train_image_df = pd.read_csv('train_image_data.csv', names=['Path', 'Count','Label'])\n",
        "\n",
        "dd={}\n",
        "\n",
        "dd['train'] = train_image_df\n",
        "dd['valid'] = valid_image_df"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aujYyGwQRr7H",
        "outputId": "4f03a2ee-6d78-4432-b7a5-b55b7fdca40e"
      },
      "source": [
        "train_image_labels = getImageLabels(train_image_df)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1272it [00:00, 14854.78it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9z7Onjv-Ruoa",
        "outputId": "a3cac163-b1d9-4fae-9477-62f9f133e2dd"
      },
      "source": [
        "train_images = getImage(train_image_df, size = 224)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1272it [00:04, 289.37it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-vjDo-M65Bvp",
        "outputId": "3845a879-4a83-4c45-b508-67f30a739f52"
      },
      "source": [
        "train_images.shape\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1272, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TxiT79kRyGc"
      },
      "source": [
        "train_datagen.fit(train_images,augment=True)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCSwarbzR0rB"
      },
      "source": [
        "train_generator = train_datagen.flow(\n",
        "    x=train_images,\n",
        "    y=train_image_labels,\n",
        "    batch_size = 32\n",
        ")"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_L6c6SP2R624",
        "outputId": "e3947a84-ec71-4b09-df34-8e0d2b9b6cea"
      },
      "source": [
        "valid_image_labels = getImageLabels(valid_image_df)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "288it [00:00, 10784.68it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OLvYk_lhR_Dv",
        "outputId": "da9b74a2-98ec-4b0e-eb0e-83f3ab96767c"
      },
      "source": [
        "valid_images = getImage(valid_image_df, size = 224)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "288it [00:01, 244.03it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sM_-sFxfSDK9"
      },
      "source": [
        "test_datagen.fit(valid_images,augment=True)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTdsSZXaSF-l"
      },
      "source": [
        "validation_generator = test_datagen.flow(\n",
        "    x=valid_images,\n",
        "    y=valid_image_labels,\n",
        "    batch_size = 1\n",
        ")"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rc2WUD9ATuot"
      },
      "source": [
        "### DenseNet Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1rXUaJnSIQ8",
        "outputId": "5d4b8d1d-d006-432e-f4b7-88921cc46062"
      },
      "source": [
        "###################\n",
        "# Construct model #\n",
        "###################\n",
        "\n",
        "\n",
        "\n",
        "#model parameters for training\n",
        "#K.set_learning_phase(1)\n",
        "nb_train_samples = len(train_images)\n",
        "nb_validation_samples = len(valid_images)\n",
        "epochs = 10\n",
        "batch_size = 32\n",
        "steps_per_epoch = nb_train_samples//batch_size\n",
        "print(steps_per_epoch)\n",
        "n_classes = 1"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "39\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkqM6sfnSL_x"
      },
      "source": [
        "#Building a model\n",
        "input_shape = (224, 224, 1)\n",
        "#model = densenet(input_shape, n_classes)\n",
        "model = build_model()\n",
        "# Build optimizer\n",
        "#opt = keras.optimizers.Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=0.1)\n",
        "\n",
        "model.compile(loss=BinaryFocalLoss(gamma = 2), optimizer='adam', metrics=['acc', 'mse'])"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFinSZrRSd4v"
      },
      "source": [
        "#callbacks for early stopping incase of reduced learning rate, loss unimprovement\n",
        "early_stop = EarlyStopping(monitor='val_loss', patience=8, verbose=1, min_delta=1e-4)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=1, verbose=1, min_lr=0.0001)\n",
        "callbacks_list = [early_stop, reduce_lr]"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2VTC2AISlZC",
        "outputId": "7dfc2a39-21fe-4d64-fd63-708d84202ed1"
      },
      "source": [
        "#train the module\n",
        "model_history = model.fit(\n",
        "    train_generator,\n",
        "    epochs=epochs,\n",
        "    workers=0,\n",
        "    use_multiprocessing=False,  \n",
        "    steps_per_epoch = nb_train_samples//batch_size,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=nb_validation_samples //batch_size,\n",
        "    callbacks=callbacks_list\n",
        ")"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "39/39 [==============================] - 44s 783ms/step - loss: 0.3461 - acc: 0.5539 - mse: 0.2773 - val_loss: 2.8770 - val_acc: 0.2222 - val_mse: 0.6044\n",
            "Epoch 2/10\n",
            "39/39 [==============================] - 28s 715ms/step - loss: 0.1566 - acc: 0.6405 - mse: 0.2204 - val_loss: 4.2358 - val_acc: 0.5556 - val_mse: 0.4246\n",
            "\n",
            "Epoch 00002: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "Epoch 3/10\n",
            "39/39 [==============================] - 28s 709ms/step - loss: 0.1421 - acc: 0.7298 - mse: 0.1997 - val_loss: 0.3653 - val_acc: 0.6667 - val_mse: 0.2364\n",
            "Epoch 4/10\n",
            "39/39 [==============================] - 28s 715ms/step - loss: 0.1314 - acc: 0.7520 - mse: 0.1917 - val_loss: 0.1736 - val_acc: 0.3333 - val_mse: 0.2128\n",
            "Epoch 5/10\n",
            "39/39 [==============================] - 28s 715ms/step - loss: 0.1195 - acc: 0.7954 - mse: 0.1781 - val_loss: 0.0474 - val_acc: 1.0000 - val_mse: 0.1010\n",
            "Epoch 6/10\n",
            "39/39 [==============================] - 28s 712ms/step - loss: 0.1192 - acc: 0.8008 - mse: 0.1757 - val_loss: 0.1475 - val_acc: 0.7778 - val_mse: 0.2119\n",
            "\n",
            "Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.0001.\n",
            "Epoch 7/10\n",
            "39/39 [==============================] - 28s 714ms/step - loss: 0.1071 - acc: 0.8100 - mse: 0.1657 - val_loss: 0.2439 - val_acc: 0.3333 - val_mse: 0.2802\n",
            "Epoch 8/10\n",
            "39/39 [==============================] - 28s 713ms/step - loss: 0.1121 - acc: 0.8187 - mse: 0.1659 - val_loss: 0.1015 - val_acc: 0.7778 - val_mse: 0.1641\n",
            "Epoch 9/10\n",
            "39/39 [==============================] - 28s 714ms/step - loss: 0.1055 - acc: 0.8169 - mse: 0.1618 - val_loss: 0.1182 - val_acc: 0.6667 - val_mse: 0.1814\n",
            "Epoch 10/10\n",
            "39/39 [==============================] - 28s 712ms/step - loss: 0.1070 - acc: 0.8216 - mse: 0.1591 - val_loss: 0.0770 - val_acc: 0.8889 - val_mse: 0.1340\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Mf2ajBiPu0N"
      },
      "source": [
        "model.save(\"MURA-DenseNet-v1.5-Humerus.h5\")"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbXYRSxySn2w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53f6ad03-40af-4d86-d987-9889eb2f768a"
      },
      "source": [
        "ev = model.evaluate_generator(validation_generator, steps=nb_validation_samples,  workers=0, use_multiprocessing=False)\n",
        "ev[1]"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1877: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
            "  warnings.warn('`Model.evaluate_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8333333134651184"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5t1WEOfJTqiE"
      },
      "source": [
        "### Performance Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3-jQN-wwQsmX",
        "outputId": "2a5eef5b-834a-4a8b-9dbf-c2ce5c4933db"
      },
      "source": [
        "#Now we have trained our model, we can see the metrics during the training proccess\n",
        "plt.figure(0)\n",
        "plt.plot(model_history.history['acc'],'r')\n",
        "plt.plot(model_history.history['val_acc'],'g')\n",
        "plt.xticks(np.arange(0, 10, 1))\n",
        "plt.rcParams['figure.figsize'] = (8, 6)\n",
        "plt.xlabel(\"Num of Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Training Accuracy vs Validation Accuracy\")\n",
        "plt.legend(['train','validation'])\n",
        " \n",
        "plt.figure(1)\n",
        "plt.plot(model_history.history['loss'],'r')\n",
        "plt.plot(model_history.history['val_loss'],'g')\n",
        "plt.xticks(np.arange(0, 10, 1))\n",
        "plt.rcParams['figure.figsize'] = (8, 6)\n",
        "plt.xlabel(\"Num of Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Training Loss vs Validation Loss\")\n",
        "plt.legend(['train','validation'])\n",
        "\n",
        "plt.figure(2)\n",
        "plt.plot(model_history.history['mse'],'r')\n",
        "plt.plot(model_history.history['val_mse'],'g')\n",
        "plt.xticks(np.arange(0, 10, 1))\n",
        "plt.rcParams['figure.figsize'] = (8, 6)\n",
        "plt.xlabel(\"Num of Epochs\")\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"Training Loss vs Validation Loss\")\n",
        "plt.legend(['train','validation'])\n",
        " \n",
        "plt.show()\n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECcQeXQsRz4g"
      },
      "source": [
        "#pred = model.predict_generator(validation_generator, steps=1, batch_size=1, use_multiprocessing=False, max_queue_size=25, verbose=1)\n",
        "validation_generator.reset()\n",
        "#pred = model.predict_generator(validation_generator,steps=nb_validation_samples)\n",
        "pred_batch = model.predict_on_batch(valid_images)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cEvuq_CfR_ti"
      },
      "source": [
        "predictions = []\n",
        "for p in pred_batch:\n",
        "    if(p > 0.5):\n",
        "        predictions+=[1]\n",
        "    else:\n",
        "        predictions+=[0]"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O8meoRTbSD_S"
      },
      "source": [
        "error = np.sum(np.not_equal(predictions, valid_image_labels)) / valid_image_labels.shape[0]  \n",
        "pred = predictions\n"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JASSNiwATgQR"
      },
      "source": [
        "### Confusion Matric"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 841
        },
        "id": "F95xLw23SNwZ",
        "outputId": "c3aac5fe-9896-44f1-a686-6d74c2136b69"
      },
      "source": [
        "print('Confusion Matrix')\n",
        "from sklearn.metrics import confusion_matrix, classification_report, cohen_kappa_score\n",
        "import seaborn as sn\n",
        "cm = confusion_matrix( pred ,valid_image_labels)\n",
        "plt.figure(figsize = (30,20))\n",
        "sn.set(font_scale=1.4) #for label size\n",
        "sn.heatmap(cm, annot=True, annot_kws={\"size\": 20},cmap=\"YlGnBu\") # font size\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RqhrvxQiS-SE"
      },
      "source": [
        "### Kappa score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRJV6FhcSQnx",
        "outputId": "7bed5bbc-643e-4029-ec21-6b2473b299aa"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report, cohen_kappa_score\n",
        "cohen_kappa_score(valid_image_labels, pred)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6587347649448636"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zf5Q9YhvSaYO",
        "outputId": "92555bcf-d44b-4599-f94a-d1ff4e3b18ea"
      },
      "source": [
        "print()\n",
        "print('Classification Report')\n",
        "print(classification_report(valid_image_labels, pred, target_names=[\"0\",\"1\"]))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.87      0.84       148\n",
            "           1       0.85      0.79      0.82       140\n",
            "\n",
            "    accuracy                           0.83       288\n",
            "   macro avg       0.83      0.83      0.83       288\n",
            "weighted avg       0.83      0.83      0.83       288\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xG2ozPVsTCBz"
      },
      "source": [
        "### ROC score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLx_MV7pS2sd"
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "fpr_keras, tpr_keras, thresholds_keras = roc_curve(valid_image_labels, pred_batch)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "crergcMnTOIH"
      },
      "source": [
        "from sklearn.metrics import auc\n",
        "auc_keras = auc(fpr_keras, tpr_keras)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "id": "_D71aDlDTUMj",
        "outputId": "e9b871ca-5615-43ff-9536-e21a1ee994d2"
      },
      "source": [
        "plt.figure(1)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.title('ROC curve')\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n",
        "\n",
        "plt.figure(2)\n",
        "plt.xlim(0.0, 0.2)\n",
        "plt.ylim(0.65, 0.9)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.title('ROC curve (zoomed in at top left)')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vrjPAKwqINEd8fBzmzAmBjo4uli//Dk5OXwgdSelYCIg+gFcWEGkefX0DODg0xcKFS1C3rqXQcSoFCwERERGA27cTkZh4E4MGuaFbt+7o2tVRLecKlIaFgOj/lLUSIRGpL5lMhh07orFy5VKYm1vAxaU/DAwMNKoMALzKgKgIVyIk0jwZGRnw8/sWS5cuQqdOXbBjx08wMDAQOpYgOEJA9A7OFyDSHLm5ufD0/BrPn6dh+vQZ8PAYrnGjAu9iISAiIo0ik8kgEolgaGiIMWO+RbNmLeDg0EToWILjKQMiItIYKSnJGD16OH77LQEAMHjwEJaB/8NCQEREGuH48aNwd/8Kd+7cxuvXuULHqXJ4yoCIiNTa69evERERjj17YtC0aXMsXrwc1tY2QseqclgISGOUdFnhu3iJIZF6OnEiHnv2xMDLyxuTJk2Grq6e0JGqJBYC0hjvLkNcEl5iSKQ+ZDIZHj16CBub+ujbtx8aNPgETZs2FzpWlcZCQBqFlxUSqT+JRIL582fh99/PYO/eA6hb15JlQA4sBEREpDauXr2MoKAApKU9w8SJk1G7dh2hI6kMFgJSCx+aHwBwjgCROpPJZNi0aT3WrFmF2rXrYNOmbWjZ8lOhY6kUXnZIaqGkZYf/G+cIEKkvkUiElJRkODv3xq5dsSwDH4EjBKTS3o4MvP32z/kBRJrlt98SUKtWLTg4NEVQ0Czo6Oho9PLDFcERAlJp75YBfvsn0hz5+XmIiAjHpEnjsGHDOgCArq4uy0AFcISAqqzyzAvgyACR5njw4D6CgwOQmHgT7u7D4O8/XehIaoGFgKqsD60bAHBeAJGmuX07EaNHD4eOji6WL18NJydnoSOpDRYCqtL47Z+I3mVnZ48BAwbDy2s06tSpK3QctcI5BEREVKXdunUTY8d6ITMzE7q6upg+fQbLgBKwEBARUZUkk8mwbVsURozwwIMH95Ga+kToSGqNpwxIMKVNGtTV00Z+XiEXEiLSYBkZGZgzJxhnzpxG9+5OmDt3IWrUMBU6llpjISDB8GZDRFSaFSuW4Pz53xEUNBPu7p68nLASsBCQoEqaNGhuboy0tCyBEhGRUAoKCpCVlQVTU1NMnfofeHp6wcGhidCxNAYLARERCS4lJRnBwYEQiUTYtGkbzMzMYGZmJnQsjcJCQErzoYWFOEeAiADg2LEjmDdvFqTSQsycOQ9aWpzvLgQWAlIazhEgorK8fv0aERHh2LMnBs2atUB4eASsrW2EjqWxWAjoo8k7AsCFhYioJIWFBbhw4Ry8vLwxadJk6OrqCR1Jowk6LnP//n14e3ujdevW6NChA0JDQ5Gbm/vB/V69eoVly5bB2dkZrVq1Qq9evbB69Wrk5eVVQmp660O3HOYIABH9N5lMhsOHD+D169eoXt0Iu3btx9Sp01gGqgDBRggkEglGjhwJS0tLREZGIiMjA2FhYcjIyMCKFSvK3Hfu3Lk4duwYpk6dCnt7e1y7dg2rVq2CRCJBSEhIJf0EBHBpYSKSn0TyEvPnz8axY0cwffoMDB06AoaG1YSORf9HsEIQExMDiUSC2NjYopmk2traCAwMhI+PD+zt7Uvcr6CgAHFxcRgzZgxGjBgBAOjQoQNSUlJw4MABFgIioiroypVLCA4ORFraM0yZMg3u7p5CR6L/Itgpg4SEBHTo0KHYZSW9e/eGnp4eEhISSt1PJpOhsLAQxsbGxbaLxWLIZDKl5SUioo/zyy/74e09Atra2ti8eQdGjfLmlQRVkGD/i9y9excNGzYstk1PTw82Nja4d+9eqfvp6upiwIABiI6OxtWrV5GTk4Pz589j9+7d8PRk4yQiqmpatfoU/foNwM6d+9CiRUuh41ApBJ1DIBaL39suFovx8uXLMvedP38+5syZgyFDhhRtGzVqFCZNmqTwnFT61QRcR4CISnPmzGlcvHgWU6cGo379TzBv3iKhI9EHqORlhxERETh9+jQWLFiABg0a4MqVK/j+++9Rq1YtjB07tlzvVbOmYv+gmZsbf/hFKuZS0nM8TsvGJ/VMim23szKBY2srpfzM6ngcKxuPYcXxGJbfmzdvEBYWhh9//BFNmjSBvr4MJibvf/kj+VXW76FghUAsFkMikby3XSKRwNbWttT97ty5g02bNmHNmjXo2bMnAKBdu3YoKCjAqlWrMHToUBgZyf9HPj09G1KpYuYeqNMa/O+OCrwdCfB3a1XiaxX9M6vTcRQKj2HF8RiW34MH9xEU5I9btxLh4eGJhQvnIysrn8exAhT5e6ilJSrzS7Bgcwjs7Oxw9+7dYtvy8vLw8OHDMgvB33//DQBo0qT4DS+aNm2KvLw8pKaWvlAOye/dNQa4ngARfUhBQQEmTPBGSkoyVqz4HkFBs2BgYCB0LCoHwUYIunXrhrVr1yIzMxOmpv/e4zo+Ph55eXlwdHQsdb969eoBAG7evAlLS8ui7Tdu3IBIJCq2jSqGawwQ0Ye8epUDAwND6OjoYMGCxbC0rIc6deoKHYs+gmAjBB4eHjA2NoaPjw/OnDmD2NhYhIaGwsXFpdjVByEhIWjatGnR4+bNm6Nly5aYM2cOYmJicO7cOfzwww9Yv349Bg8eDENDQyF+HCIijZOYeAPu7oOwZcsGAECbNm1ZBlSYoHMIoqKisGDBAvj6+kJfXx+urq6YNm1asddJpVIUFhYWPdbW1sa6desQGRmJ9evX4/nz56hbty5Gjx6N8ePHV/aPQUSkcWQyGbZti0JkZATMzMzQsuWnQkciBRDJNHw1H04qLNni7ZcAQJBTBup0HIXCY1hxPIYly8jIwOzZwfjtt9Po3t0Jc+cuRI0apiW+lsew4ipzUqFKXnZIRETCePjwPv788yKCgmbC3d0TIpFI6EikICwERERUpvz8fFy4cA5dunTDp5+2weHDx0sdFSDVxcWkiYioVMnJj+HtPQKTJo1DUtJfAMAyoKY4QkDFvF2QiMsSE1F8fBzmzZsFQIbw8AjY2zcWOhIpEQsBFfNuGeBiRESaa8mSRdixYyuaN2+J8PAIWFlZCx2JlIyFgEpcppgLEhFpNltbW4waNQYTJ06Grq6u0HGoErAQULFRAY4MEGkmmUyGvXt3wcjIGH36uOLrrz2EjkSVjIVAg3zoNsYcFSDSTBLJS8yfPwvHjh2Fs3Nv9OnjKnQkEgALgQYpbbIgRwWINNfly5cQEhKItLRnmDJlGkaO/EboSCQQFgIN8N9XDnAkgIgA4P79exgzZgTq1rXE5s070KJFS6EjkYDKXQiys7Nx9epVpKeno1OnTqhVq5YycpEC8coBInpXXl4e9PT00KCBLWbNmg9n594wMuJlxpquXAsTrVu3Dl27doW3tzemT5+OpKQkAP+ubd2qVSvs3LlTKSGp4t6ODHT/tJ7QUYhIQAkJJ9Gv3xe4fTsRADBw4GCWAQJQjkKwc+dOrFy5Ev369cOKFSvw7j2RzMzM0LNnT8TFxSklJBERVUxeXh6WLFkEP78JqFGjBgwMDISORFWM3KcMoqOj0adPH4SGhiIzM/O955s0aYKoqCiFhqPy+dBVBESkmR48+AfTpwfg9u1EeHh4YurU/0BfX1/oWFTFyF0IHj16BC8vr1KfNzExwcuXLxUSij4OryIgopIcPPgrnjxJxooV36NHj55Cx6EqSu5CYGJigvT09FKfT0pKgrm5uUJC0cfjVQREBAA5OdlISUmGvX1jjB07AV9/7Q4LC34xoNLJXQgcHR2xe/duDBs27L3nbt++jT179sDNzU2h4ah0JZ0e4KkBIgKAxMQbmD49APn5efjllyPQ09NjGaAPkntS4ZQpUyASidCvXz9ERERAJBJh7969mDp1Ktzc3GBubg4fHx9lZqV3vD098C6eGiDSbFKpFNHRmzFy5FDk5b3BokVLoaenJ3QsUhFyjxCYm5tj7969WLFiBY4ePQqZTIYDBw6gevXq6N+/PwIDA2FqyntkKxqXGyYiebx6lYNp06bi7NkE9OjhjLlzF8DEpIbQsUiFlGthIjMzM4SGhiI0NBQZGRmQSqUwMzODlla5ljOgcuBEQSKSh4GBIfT19REcPBtDhgyFSCQSOhKpGLkLQXBwMDw8PNCqVSsA/5aDd127dg07d+5EWFiYYhMSRwKIqET5+fnYtGk9vvzyK9Sta4mIiFUsAvTR5P5qv3//fjx8+LDU5x8/fozY2FiFhCIiorIlJz+Gt/cIrF37HY4dOwIALANUIQq7uVFmZiYnrxARVYKjR+Mwf/4sADIsXrwcvXu7CB2J1ECZheDixYu4cOFC0eP4+Hg8ePDgvddJJBIcOnQIDg4Oik9IRERFfv55H+bMCUGLFq0QHh6BevWshI5EaqLMQnDhwgWsXr0awL9DUUePHsXRo0dLfK29vT1mzJih+IRERASpVAotLS04O/dCevpzjBjxDXR1dYWORWqkzEIwZswYeHp6QiaToVOnTpg3bx569epV7DUikQiGhoZcF5uISAlkMhn27InBL7/sx4YNW1G9uhFGjx4ndCxSQ2UWAgMDg6I7Yh0/fhxmZmYwNDSslGCaiKsPEtG7JJKXmDdvJo4fj0fHjp3x+nUu71JISiP3pMJ69eopMweh5DUHuN4AkWa6fPkSQkICkZb2DFOmTMPIkd9wzRdSqnJdZXDnzh1ER0fj5s2byMrKglQqLfa8SCTCsWPHFBpQ03DNASKSyWRYunQRtLW1sXnzDrRo0VLoSKQB5C4Ef/zxB0aPHg1jY2O0aNECiYmJ6NChA968eYMrV66gYcOGaN68uTKzEhGptdTUVBgaGkIsFmPZskiIxSYwMuIpQ6occo8/RUZGwtLSEnFxcVi0aBEAYPz48di5cye2b9+Op0+fwtXVVWlBiYjUWULCSbi7D8CSJQsBAJaW9VgGqFLJXQhu3LiBr7/+GsbGxkXnsd6eMmjTpg3c3NwQGRmpnJRERGoqLy8PS5Ysgp/fBFhY1IG393ihI5GGkrsQiEQiiMViAEC1atUAAC9evCh6/pNPPkFSUpKC4xERqa/Hjx9h5EgP7NixFUOHjkB09C588omt0LFIQ8k9h8DKygqPHj0CAOjp6cHKygpnz54tOk3wxx9/oEYN3mqzvN691JCXGBJpFl1dPbx+nYvIyDVwdHQSOg5pOLlHCDp37ozDhw8XPR4yZAj27dsHLy8vjBw5Ej///DP69++vlJDq7O2lhgAvMSTSBDk52di8eQOkUilq166NvXsPsAxQlSD3CMG3334LV1dX5OfnQ1dXF2PHjkVhYSGOHDkCLS0tTJo0CePH89zXx+ClhkSa4ebN6wgKCkBy8mO0bt0Gn37aBtra2kLHIgJQjkJgYmICExOToscikQgTJkzAhAkTlBKMiEhdSKVSbN8ehcjI5ahVqxY2bozGp5/ySwBVLXKdMsjNzUWTJk2wdu1aZechIlI7CxbMQUTEYnTt6ohdu/ajdevPhI5E9B65RggMDQ1hZmYGY2NjZechIlIbMpkMIpEI/fsPROPGTTBkyFCIRCKhYxGVSO5JhX379sXhw4ffW66YiIiKy8/Px6pVEYiMjAAAtG79Gdzdh7EMUJUm9xwCZ2dnnD9/HsOGDYObmxusra1LvOtWy5Zcc5uINFdy8mMEBwfg2rWrGDTIrWiUgKiqk7sQjBo1quifr1y58t4v+Ntf+lu3biksHBGRKjl6NA7z588CIMPixcvRu7eL0JGI5CZ3IQgLC1NmDiIilZaamopZs6ajUSMHhIdHoF49K6EjEZWL3IXgq6++UmYOIiKVlJr6FLVr10Ht2rWxfv0WNG3aHLq6ukLHIio3uScVEhHR/5PJZNi9eyf69++FI0cOAQBatWrNMkAqS+4RAiIi+tfLly8wb94snDgRj86du6Jdu/ZCRyKqMBYCIqJyuHz5EoKDA/D8+XP4+/8Hw4ePKrolPJEqE7QQ3L9/H6Ghobh06RL09fXh6uqKwMBAGBoafnDfrKwsrFq1CkeOHEFGRgYsLCwwYMAATJ48uRKSE5GmSkt7Bl1dXURF7UCzZi2EjkOkMIIVAolEgpEjR8LS0hKRkZHIyMhAWFgYMjIysGLFijL3ffXqFYYPHw6RSIRp06bBwsICjx49wtOnTyspPRFpktTUVPY3VZ8AACAASURBVNy8eQ1OTl+gV68+cHTsAX19faFjESmUYIUgJiYGEokEsbGxMDMzAwBoa2sjMDAQPj4+sLe3L3Xf9evXIysrC7/++iuqV68OAGjfnufwiEjxEhJOYvbsYMhkMrRv3xHVqxuxDJBaKteJr8zMTKxYsQIeHh7o1asXLl++XLR99erVuHv3rtzvlZCQgA4dOhSVAQDo3bs39PT0kJCQUOa+P/30E77++uuiMkBEpGhv3rzB4sUL4ec3AbVr10VU1E5Ur24kdCwipZG7EDx+/BgDBgzA5s2bUVBQgEePHuH169cAAFNTUxw6dAjbt2+X+4Pv3r2Lhg0bFtump6cHGxsb3Lt3r8wcaWlpMDU1xbfffosWLVqgbdu2+M9//oOXL1/K/flERKXJz8/DwIEDsXNnNIYOHYHo6F1o0MBW6FhESiX3KYOlS5dCJpPh4MGDqF69Ojp16lTs+Z49e+LYsWNyf7BEIoFYLH5vu1gsLvMP+/PnzwEAS5YsgZOTE3744QckJycjIiIC6enp2Lhxo9wZiIhKoqurB1dXV3h7fwtHRyeh4xBVCrkLwblz5+Dt7Q1ra2tkZma+97yVlVWlTOp7e7fF+vXrY9myZUX3VDA2NsbkyZNx7dq1ct1gqWZNxQ4BmpuX7xbRunraH7WfuuPxqDgew/LJzs5GSEgIhg4dio4dO2LSpElCR1IL/D2suMo6hnIXgjdv3pT4jf4tiURSrmtxxWIxJBJJie9ja1v60JyJiQkAoGPHjsVusNSxY0cAQFJSUrkKQXp6NqRSmdyvL4u5uTHS0rLKtU9+XiEAlHs/dfYxx5GK4zEsn5s3ryMoKADJyY9hb98EDRs25zFUAB7DilPkMdTSEpX5JVjuv+D29va4ePFiqc8fP34cTZs2lTuYnZ3de5MQ8/Ly8PDhwzILgbW1NfT09Ep9/s2bN3JnICLNJpVKsXXrJnh5DUN+fj42boyGh8dwoWMRCULuQuDl5YXDhw9j7dq1Ref4pVIp7t69i4CAAFy9ehXffPON3B/crVs3nD9/vtjph/j4eOTl5cHR0bHU/fT09NC5c2f8/vvvkMn+/5v92bNnAQDNmzeXOwMRabb4+CNYvnwJunXrjl279qN168+EjkQkGLlPGfTv3x9PnjzBqlWrsGrVKgDAmDFjAABaWlqYNm0anJzkn3zj4eGBbdu2wcfHBz4+PkhPT0d4eDhcXFyKXX0QEhKC2NhYJCYmFm2bNGkSPDw84O/vj0GDBiElJQXLly9Hly5dynW6gIg0k0TyEmKxCb74ojf09L5H9+5OxU5BEmmici1MNG7cOPTv3x9HjhzBgwcPIJVKYWNjg169esHa2rpcHywWixEVFYUFCxbA19e3aOniadOmFXudVCpFYWFhsW3NmzfHhg0bEBERAR8fHxgZGcHFxQWBgYHlykBEmiU/Px9r167Cvn17EBOzH3Xq1EWPHj2FjkVUJYhk7467ayChJxUu3n4JADDds41CMqgDTkSqOB7D9yUnP0ZQUACuX7+KQYPcMG1aSJn3TeExrDgew4qrzEmFco8QODs7w9XVFX379oWDg4NCwhERVYYjRw4hNHQ2ABGWLFmJXr36CB2JqMqRe1KhlZUVNmzYgK+++gqurq74/vvv8c8//ygzGxGRQpw+fRK2tnbYtWs/ywBRKeQeIdiyZQsyMjJw+PBhxMXF4fvvv8fq1avh4OAAV1dXuLi4wNLSUplZiYjklpT0F3R0dPHJJ7aYNWsedHR0oaurK3QsoiqrXDc3MjMzg6enJ6Kjo3Hq1ClMnz4denp6WLZsGXr27AkPDw9l5SQikotMJsOuXTvg6emGJUsWAgAMDauxDBB9QLkKwbssLCwwatQo7Ny5E6GhoahWrRquXr2qyGxEROXy8uULBAT4ISxsPtq1a4+FC5cIHYlIZZTrssN3Xbx4EYcOHcLRo0eRkZEBsViMwYMHKzIbEZHcHjz4B+PHj8bz588REDAdnp5e5VpOnUjTlasQXLlyBYcOHUJcXBzS0tJQrVo1ODk5wdXVFV26dIGOzkf3CyKiCqlbtx6aN2+Bb74Zi2bNWggdh0jlyP0XvEePHnj69Cn09fXRrVs3uLq6onv37tDX11dmPiKiUqWmpuK775Zj+vSZMDY2xrJlq4SORKSy5C4EjRs3xtSpU9GzZ09Ur15dmZmIiD7o9OkTmDMnBG/e5GHAgEFo16690JGIVJrchWDdunXKzEFEJJe8vDysWLEUO3dGw8GhKcLDl6FBg9LvkEpE8uFJfyJSKUuXhmHPnp0YNmwkpkwJLPN26EQkv1ILgYODA7S0tHDlyhXo6enBwcHhg3cDE4lExe5KSESkCDKZDG/evIGBgQHGjBmPrl27oVu3HkLHIlIrpRaCiRMnQiQSFV058PYxEVFlys7OxoIFc/DiRSbWrNmA2rXroHbtOkLHIlI7pRYCX1/fMh8TESnbjRvXERTkjydPUvDtt5Og4TdnJVIquVftWL16Ne7cuVPq80lJSVi9erVCQhGRZpNKpdiyZSNGjRqKwsJCbNgQjbFjJ0BbW1voaERqq1yF4K+//ir1+aSkJHz//fcKCUVEmi0nJwcxMdvg6NgDu3btR+vWbYSORKT2FHaVQU5ODlcqJKIKuXz5TzRv3gLGxsaIjt6FWrXMOXeJqJKU+Rf89u3buH37dtHjP/74A4WFhe+97uXLl9i5cydsbXktMBGVX35+PtasWYXNm3/E5MmB+OabMTA3txA6FpFGKbMQHDt2rGhegEgkwq5du7Br164SX2tiYoIlS3hnMSIqn+TkxwgKCsD161cxePAQeHh4Ch2JSCOVWQiGDBmC7t27QyaTwc3NDX5+fujWrVux14hEIhgaGsLGxoanDIioXM6cOY3g4AAAIixZshK9evUROhKRxirzL7iFhQUsLP4dttu6dSvs7OxQs2bNSglGROqvTp06aNKkGebOXYh69ayEjkOk0eS+yuDzzz9nGSCiCrtz5y/88MO/VyTZ2zfGjz9GsQwQVQGljhAEBwdDJBIhNDQU2traCA4O/uCbiUQiLFq0SKEBiUg9yGQy7N69ExER4RCLTeDm5gEzM37JIKoqSi0EFy5cgEgkglQqhba2Ni5cuPDBN+PlQfI5dSUZF26mAgAePsuGjYWRwImIlOvlyxeYN28WTpyIR+fO3RAaGsYyQFTFlFoITpw4UeZj+ngXbqYWFQEbCyO0b1Zb6EhESiOVSuHtPQL3799HQMB0eHp6QUtL7rOVRFRJeFmAQGwsjDDdk6uvkfoqLCyElpYWtLS04Ofnj1q1zNG0aXOhYxFRKeSu6c+fP3/v1sZ3797F7NmzMWXKFMTHxys8HBGpptTUpxg3zgsxMdsAAN269WAZIKri5C4E8+fPx8KFC4sev3jxAsOHD8e+fftw5swZ+Pn54eTJk0oJSUSq49SpExgyZAASExMhFtcQOg4RyUnuQnD58mV07dq16PEvv/wCiUSCffv24fz582jTpg02btyolJBEVPW9efMG4eGhmDLFB3Xr1kNMzF64uvYXOhYRyUnuQvDixQuYm5sXPT558iTatm2LRo0aQVdXFy4uLkhKSlJKSCKq+m7evI7du3fC09MLW7fGoH79T4SORETlIPekQhMTE6SlpQEAcnNzcenSJUycOLHoeZFIhLy8PMUnJKIqSyaT4a+/bsPBoQnatGmL/fsPsggQqSi5C8Fnn32GHTt2wM7ODmfOnEFeXh569uxZ9Pw///xTtMwxEam/7OxsLFgwB0ePHsa2bbvRtGlzlgEiFSb3KYOAgADo6enB19cXu3fvxqhRo2BnZwfg38uLjhw5gs8//1xpQYmo6rh+/Ro8PL5CfHwcJkzwQ+PGTYSOREQVJPcIgY2NDeLi4vD333/DyMgIVlb/v/Z4bm4uZs+eDQcHB6WEJKKqY9u2KKxcuRTm5hbYsCEarVtzPQ0idVCuhYl0dHRK/KNvZGQEZ2dnhYUioqqrsLAA3bs7YfbsUIjFJkLHISIFKVchyM/Px+7du3H69GkkJycDAOrVq4cePXrg66+/hq6urlJCEpGwzp07i4KCAnTt6oiRI0cD4L1LiNSN3IXg5cuXGDVqFG7duoVatWqhfv36AIDExEQkJCRgz5492Lx5M0xM+I2BSF3k5+djzZpV2Lz5R7Ru/Rm6dOnGIkCkpuQuBBEREUhKSkJYWBgGDBhQdHMSmUyGn3/+GTNnzsTy5csxb948pYUlosqTnPwYQUEBuH79KgYPHoLAwGCWASI1JnchOHHiBDw9PfHVV18V2y4SiTBw4EDcunULBw8eZCEgUgPJyY/h7j4QgAhLlqxEr159hI5EREomdyGQSCSwsbEp9XkbGxtIJBKFhCIiYchkMohEIlha1oOX1xi4uPRDvXpWH96RiFSe3OsQ1K9fH8ePH4dMJnvvOZlMhmPHjhXNKyAi1fPXX7fh6fk17t27C5FIhLFjv2UZINIgcheCYcOG4ffff8eYMWNw+vRpPHjwAA8ePMCpU6cwZswYnD9/HsOHD1dmViJSAplMhpiYbRgxYgjS0p5BInkpdCQiEoDcpwyGDh2KzMxMrF27Fr///nvRdplMBl1dXfj5+cHd3V0pIYlIOV68yMTcuTNx6tRxdOniiPnzw2BmZiZ0LCISQLnWIfDx8YGHhwd+//13pKSkAPh3HYJOnTrB1NRUKQGJSHm2b9+K335LQEBAEIYP9+JVBEQarFyFAADMzMzQr18/ZWQhokpQWFiIZ89SUbeuJcaM+RbOzr3RuDGXHSfSdOUuBOfOncOpU6eKrVTYvXt3dOzYUeHhiEixnj59ghkzpuHp06f46adfYWhoyDJARADKManw1atXGD9+PEaPHo2oqChcvHgRFy9eRFRUFEaPHo2xY8ciJyenXB9+//59eHt7o3Xr1ujQoQNCQ0ORm5tbrveIj49H48aNOWpB9AEnTx6Hu/tAJCYmYsIEXxgaGgodiYiqELkLQXh4OE6fPo0JEybg3LlzuHDhAi5cuIBz587h22+/xZkzZ7B48WK5P1gikWDkyJHIyclBZGQkgoKCcODAAYSEhMj9Hrm5uVi0aBFq1aol9z5EmiY/Pw/h4aGYOnUiLC3rISZmH/r1GyB0LCKqYuQ+ZRAXF4chQ4bAz8+v2HZTU1NMnjwZz58/R1xcHObPny/X+8XExEAikSA2NrZoVrO2tjYCAwPh4+MDe3v7D77HmjVrYGVlhXr16uHGjRvy/ihEGkVLSxt//52E4cO94OcXAD09PaEjEVEVJPcIgVQqLfHWx285ODiUuGhRaRISEtChQ4dilzj17t0benp6SEhI+OD+d+/eRXR0NGbNmiX3ZxJpCplMhgMHfsbz52nQ1tbG2rUbERgYzDJARKWSuxA4Ojri1KlTpT5/6tQpdOvWTe4Pvnv3Lho2bFhsm56eHmxsbHDv3r0P7j9//nx8/fXXaNSokdyfSaQJsrKyMGnSJMycOR07dkQDAG9NTkQfJPcpAx8fH0ydOhXjx4+Hp6dn0TLF9+/fx/bt2/Hs2TMEBQUhPT292H41a9Ys8f0kEgnEYvF728ViMV6+LHultIMHD+LOnTv47rvv5I1PpBGuX7+GoCB/PH36BBMnTsHo0WOFjkREKkLuQuDq6goAuHPnzntD+m9PFZQ00//WrVsVyfee7OxshIeHw9/fv8RCUV41axopINX/Mzc3/uBrdPW05X6tpuKxKb8jR45g3LhxqF27Nvbu3Yt27doJHUnl8few4ngMK66yjqHchWDixIkKXcVMLBaXeHdEiUQCW1vbUvdbt24datSogS+++KJo//z8fEilUkgkEhgYGJTrPGl6ejakUvnnPpTF3NwYaWlZH3xdfl4hAMj1Wk0k73Gk4uzsmuKrr9zg6zsFdnZWPIYVxN/DiuMxrDhFHkMtLVGZX4LlLgS+vr4KCfSWnZ0d7t69W2xbXl4eHj58iEGDBpW6371793Dnzh20b9/+vefatWuH4OBgjBo1SqFZiaqq33//Dbt2bceyZZEwMamBGTPmCB2JiFRUuVcqVJRu3bph7dq1yMzMLLoPQnx8PPLy8uDo6FjqflOmTIGXl1exbevXr8c///yDsLAw3oKZNEJ+fh5Wr45EVNRG2NnZIyMjA7Vr1xE6FhGpMMEKgYeHB7Zt2wYfHx/4+PggPT0d4eHhcHFxKXb1QUhICGJjY5GYmAgAJV5VsH//fqSmppY4akCkbh4/foSgoADcuHENbm4eCAgIgoGBgdCxiEjFCVYIxGIxoqKisGDBAvj6+kJfXx+urq6YNm1asddJpVIUFhYKlJKoapHJZAgJCcSDB/exbFkknJ17Cx2JiNSESFae1YTUkBCTChdvvwQAmO7ZRiGfq244Eel9ubmvAACGhtVw795dGBgYwNKyXqmv5zGsOB7DiuMxrLjKnFQo98JEVHGnriRj8fZLePgsW+gopEL++us2hg37GkuWLAIA2NralVkGiIg+BgtBJbpwMxUPn2XDxsII7ZvVFjoOVXEymQwxMdswYsQQZGdnoW9f3tGTiJSnXHMI8vLy8PPPP+P8+fPIyMjAtGnT0LRpU0gkEpw4cQIdOnRAnTqc6fyuU1eSceFmKgAUlQGeKqAPefEiE3PnzsSpU8fRtasj5s0LK3bfDyIiRZO7EGRmZsLLywt37txBrVq1kJ6eXrTEsJGRESIjI5GUlPTepEBN9+6oAEcGSF7Z2dm4evUyAgOD4ek5UqGLghERlUTuQrBs2TKkpKRgx44daNCgATp16lT0nJaWFnr16oWEhAQWghJwVIDkUVBQgPj4OPTp4worK2scPBiPatWqCx2LiDSE3HMITp48iREjRqBNmzYlflupX78+njx5otBwRJri6dMnGDduFIKDA3Hu3FkAYBkgokoldyHIzs5G3bp1S30+Ly+P6wUQfYSTJ4/D3X0gbt1KxIIFi9GpUxehIxGRBpK7ENSvXx83btwo9fnffvsN9vb2CglFpCnWrFmFqVMnwtKyHmJi9qFfvwFCRyIiDSV3IRgyZAj27duHX375peh2xyKRCLm5uVi2bBnOnj0LDw8PpQUlUketW3+G4cO9EBUVg/r1Gwgdh4g0mNyTCkeMGIGkpCT85z//QfXq/57bnDp1KiQSCQoLC+Hp6VnmXQqJ6N+1BWJj9+LFi0x8881YdOzYGR07dhY6FhFR+dYhmD9/PgYOHIjDhw/jwYMHkEqlsLGxgYuLC9q2bausjERqISsrCwsWzMGRI4fQsWNnjBw5Gtra2kLHIiIC8BE3N2rTpg3atOEldETlcf36VQQFBeDp0yfw9Z2KUaPGsAwQUZUi2N0OiTRFRkYGxo71gqmpGTZt2oZWrVoLHYmI6D1yFwInJ6cPrpYmEolw7NixCociUgevXuWgWrXqMDMzw+LFy9G6dVuIxWKhYxERlUjuQvD555+/VwgKCwuRkpKCS5cuwd7eHk2bNlV4QCJV9PvvZzBrVjBmzpyHHj16wtHRSehIRERlkrsQhIeHl/rc7du34e3tjf79+yskFJGqys/Pw3ffrcTWrZvQsKE9rK1thI5ERCQXhdz+2MHBAe7u7li2bJki3o5IJT169BCjRnli69ZNcHPzwLZte9CwIRfrIiLVoLBJhTVr1sTff/+tqLcjUjmXLv2Bhw8fYNmySDg79xY6DhFRuSikEGRmZmLv3r2oU6eOIt6OSGXk5r7CrVuJaNOmLb788it07dodZmZmQsciIio3uQvByJEjS9yelZWFe/fuIT8/H0uWLFFYMKKq7vbtWwgK8kda2jMcOnQcJiY1WAaISGXJXQje3r/gXSKRCFZWVujYsSMGDx4MOzs7hYYjqopkMhliYrZh+fIlqFGjBlas+B4mJjWEjkVEVCFyF4Lo6Ghl5iBSCQUFBQgMnIxTp46ja1dHzJsXxlEBIlILcl1lkJubi5EjR2Lv3r3KzkNUpeno6MDGxgaBgcFYtWodywARqQ25CoGhoSFu3ryJwsJCZechqnIKCgqwdu13uH79KgDA3386hg/3+uDKnUREqkTudQjatWuHP/74Q5lZiKqcp0+fYOxYL/zww/c4deqE0HGIiJRG7kIwa9YsXL16FYsXL8ajR48glUqVmYtIcCdOHMOQIQPx11+3sHDhUvj6ThU6EhGR0pQ5qTA2NhZt27aFlZUV+vbtC5lMhi1btmDLli3Q0tKCjk7x3UUiEa5cuaLUwESV4cyZ0/D3n4SmTZshPHw5bGzqCx2JiEipyiwEwcHBWLJkCaysrODi4sJzpqT28vPzoauri06duiAoaBYGD3aDrq6e0LGIiJSuzELw7toDZd3ciEjVyWQyxMbuxcaNPyAqaidq1qwFDw9PoWMREVUahd3LgEhVZWVlYcGCOThy5BDat+9Y4iJcRETq7oOFgKcJSJ1du3YFQUEBSE19Cl/fqfjmm7HQ0lLITUCJiFTKBwtBcHAwZsyYIdebcVIhqZqtWzdBJpNh06ZtaNWqtdBxiIgE88FC0KpVK1hbW1dGFqJK8fx5GvLz81G3riVmzw4FIIJYLBY6FhGRoD5YCNzd3dG/f//KyEKkdGfPnsHMmdNhZ9cQGzZshVhsInQkIqIqgSdLSSPk5+dh+fIlmDhxLGrWrIng4NlCRyIiqlJ4lQGpvdTUVPj7T8LNm9fh5uaBgIAgGBgYCB2LiKhKYSEgtScWG0NbWxvLlkXC2bm30HGIiKqkMgvB7du3KysHkUK9epWDjRvXY8yY8TA0rIaoqJ28hJaIqAycQ0Bq5/btWxg6dDA2bVqP8+fPAeB6GkREH8JCQGpDJpNhx46tGDFiCF69eoX167egR4+eQsciIlIJnENAauO771Zg06b1cHTsgblzF8HU1FToSEREKoOFQAlOXUnGhZupAICHz7JhY2EkcCL1JpVKoaWlhUGD3GBhYQF3d0+eIiAiKieeMlCCCzdT8fBZNgDAxsII7ZvVFjiReiooKMCaNaswbdpkyGQyWFlZw8NjOMsAEdFH4AiBkthYGGG6ZxuhY6itJ09SEBIyDZcv/4n+/QciPz8fenp6QsciIlJZLASkck6ciMfcuTNRWFiAhQuXwtWVS2sTEVUUCwGplFevcrBw4TxYWVlj8eLlsLa2EToSEZFaELQQ3L9/H6Ghobh06RL09fXh6uqKwMBAGBoalrpPdnY2Nm/ejISEBPzzzz/Q0dFBs2bN4O/vj2bNmlVieqpMjx49hKVlPVSrVh3r12+BjY0NdHV5ioCISFEEm1QokUgwcuRI5OTkIDIyEkFBQThw4ABCQkLK3C8lJQW7du1Cp06dsGLFCoSFhUEqlcLDwwM3b96spPRUWWQyGfbt2wM3ty+xdesmAICdXUOWASIiBRNshCAmJgYSiQSxsbEwMzMDAGhrayMwMBA+Pj6wt7cvcT8rKyvEx8cXG0Xo1KkTevbsiW3btiEsLKxS8pPyZWVlITR0No4ePYz27TuiX78BQkciIlJbgo0QJCQkoEOHDkVlAAB69+4NPT09JCQklLpftWrV3juloK+vDzs7Ozx79kxpealy3bhxHe7uA3H8+FH4+flj7dqNMDe3EDoWEZHaEqwQ3L17Fw0bNiy2TU9PDzY2Nrh371653uvVq1e4desWbG1tFRmRBCSTyaCjo4PNm7dj9Ohx0NLikhlERMok6BwCsVj83naxWIyXL1+W671WrlyJ3NxcDB8+XFHxSABpac/w008xAIAWLVpi376DaNnyU4FTERFpBpW/7PDXX39FVFQUZs+ejfr165d7/5o1FbussLm5MXT1tIv+meRz4sQJTJkyBa9evcKAAa6wtLQUOpLK4+9fxfEYVhyPYcVV1jEUrBCIxWJIJJL3tkskErmH/s+ePYvg4GB4e3vD09Pzo3Kkp2dDKpV91L7/zdzcGGlpWcjPKwQApKVlKeR91Vl+fh5WrVqO6OgtsLdvhPXro2BpacljV0Fvfxfp4/EYVhyPYcUp8hhqaYnK/BIs2CkDOzs73L17t9i2vLw8PHz4UK5CcO3aNUyaNAl9+/bFtGnTlBWTlEgmk+Hbb70RHb0FQ4YMQ3T0btjZNfzwjkREpHCCjRB069YNa9euRWZmZtFtauPj45GXlwdHR8cy97179y7Gjh2LNm3aYNGiRbyZjYoSiUQYNGgIPD1HwsnpC6HjEBFpNMFGCDw8PGBsbAwfHx+cOXMGsbGxCA0NhYuLS7GrD0JCQtC0adOix+np6fD29oauri7GjBmDmzdv4sqVK7hy5QoSExOF+FGoHF69ysGsWUGIjd0LAHB17c8yQERUBQg6hyAqKgoLFiyAr69v0dLF/z38L5VKUVhYWPT477//xpMnTwAAo0aNKvbaevXq4cSJE0rPTh/n9u1ETJ/uj0ePHqJBg0+EjkNERO8QyWQyxcyoU1HKmFS4ePslAODtj/+PTCbDjh3RWLlyKUxNzbBo0VK0bft5qa/nRKSK4zGsOB7DiuMxrDiNmFRImuPq1ctYunQROnXqgl27YsssA0REJAyVX4eAqq60tGcwN7fAp5+2wY8/RqFt2885AZSIqIriCAEpXEFBAdasWQVXV2ckJt4AALRr155lgIioCuMIASnUkycpCA4OxJUrl/Dll19x8iARkYpgISCFOX78KObOnQmptBCLFi2Fi0t/oSMREZGcWAhIYe7c+QvW1jZYvHg5rK1thI5DRETlwEJAFfL330mQSF6iTZu2GDt2AsaMGQ9dXT2hYxERUTlxUiF9FJlMhp9+2oXhw90QHh4KqVQKHR0dlgEiIhXFEQIqN4lEgtDQ2YiPj0P79p2wcOFiaGmxWxIRqTIWAiqXtLRnGDnSA2lpzzB5cgC8vLxZBoiI1AALAZVLrVrmcHTsAVfXL9GiRSuh4xARkYLwqx19UFraM/j7++Lx40cQiUQICprFMkBEpGZYCKhMv/2WgCFDBuL3388gKemO0HGIGIyiLQAAIABJREFUiEhJeMqASpSfn4dVq5YjOnoLGjVqjPDw5bC1tRM6FhERKQkLAZVo8+YNiI7eAnf3YfD3nw59fX2hIxERkRKxEFAxWVlZMDY2xogRo9C0aXN06dJN6EhERFQJOIeAAACvXuVg1qwgeHl5IDc3F4aG1VgGiIg0CAsB4fbtRAwdOhgHD/4CZ+fe0NXVFToSERFVMp4y0GAymQzbt29FZOQymJqaYf36LWjb9nOhYxERkQA4QqDBCgoKcPjwAXTq1AW7d8eyDBARaTCOEGigP/74H9jbN4KJSQ2sXbsBxsZiiEQioWMREZGAOEKgQQoKCrB69UqMHeuFdeu+BwCIxSYsA0RExBECTZGSkozg4EBcvXoZAwcOhp/fVKEjERFRFcJCoAH++ON/MHXqJEilhQgLW4a+ffsJHYmIiKoYFgIN8Mkntmjdug2mTQuBtbWN0HGIiKgK4hwCNfX330lYsGAOCgsLUbNmLaxatY5lgIiISsURAgU5dSUZl5KeIz+vEA+fZcPGwkiQHDKZDHv37sayZWGoVq06RowYhfr1PxEkCxERqQ6OECjIhZup+Cf5JQDAxsII7ZvVrvQMEokE06ZNwYIFc/Dpp22we3csywAREcmFIwQK9Ek9E/i7tRLs8wMCfHH58p+YPDkQXl6joaXFvkdERPJhIVBxUqkUhYUF0NXVw5QpgZBKpWjRQrhSQkREqolfIVVYWtozTJjgjeXLlwIAmjVrwTJAREQfhYVARf32WwKGDBmIq1cvo1GjxkLHISIiFcdTBiomPz8Pq1YtR3T0FjRq1Bjh4ctha2sndCyicsvNzUF29gsUFhYIHaVEz55pQSqVCh1DpfEYVlx5jqG2tg6MjGrA0LD6R30WC4GKSUlJwZ49u+DuPgz+/tOhr68vdCSicsvNzUFWViZq1DCHrq5elbyfho6OFgoK+MesIngMK07eYyiTyZCfn4cXL9IA4KNKAQuBivjzz4to06Yt6tdvgJ9/PozatesIHYnoo2Vnv0CNGubQ02OhJVIEkUgEPT191Khhjpcvn39UIeAcgiouJycbM2dOh7f3CCQknAQAlgFSeW+vjCEixdLV1fvo03AcIajCEhNvICgoAI8fP8L48RPRuXM3oSMRKUxVPE1ApOoq8u8VC0EV9dNPMQgP/9/27jyupvz/A/irXdttEaMaaVOW24JWCjVjqctXQ0jFSFMoLcQg87VlGUMakikkuwaRZYrJjGVIYcyI7OIrSanUbd/u+f3RrztztWq7F+/n4+Hx0Od8zjnv8+52z/uez+ecuxaqqqrYsWMPzMwshB0SIYSQjxgNGYgoVVU12NjY4siReCoGCCGEdDoqCETI9espiIs7AgCwt/8SYWERUFZWEXJUhJCmREdHYdQo2wbtu3ZFwsbGDEeOHBJCVO8nNfUaxo8fjcrKCmGH0qXKykqxceM6cDhfYNQoW3z77XxkZ79q1brnziXA3X0K7OysMWnSOOzZs6vBrYE3bqRg5cplmDJlAmxszLB584Ymt5eX9wbr1q3CuHGjYG8/FC4uE3H8+FH+8r/++hMczhcoLS1p28G2EhUEIqCmpgbh4WGYPdsDsbEHUF1dDYDGWAn5EEVHR2HPnl3w85uPKVNchR1Oi6KiIjBliitkZLoJO5QutXLlMly5chmBgYuwatV65OW9QUDAXFRUNF8YJSaeQUjIcpiZWeCHH8Lg6DgeMTE7sWPHdoF+KSnX8OTJY5iaDoaCgmKT28vLy8Ps2R7Izn6FhQuXYNOmrZg61RW1tbX8PoMGDUHv3n0QG3uwfQfdAppDIGSvXmVh6dKFuH37Lzg5TcLixcsgJSUl7LAIIW0QE7MTMTE74esbiKlT3dq1rZqaGoiJiUFCQqKDomvor7/+xNOnj7Fp05Z2b6sr4u0o6el3kZx8BRs3/ghraxsAgK6uPqZOnYCEhNOYOHFyk+vu3r0TI0bYITBwIQDA3NwKxcXF+Pnng5g61RUqKqoAAF/fAPj5zQcA3Lp1s8nt/fTTFnTvroawsAhIStadkgcPNmvQb9y4CYiM3Iavv/bk9+toVBAIEZfLhavrJFRXV2P9+k1wcBgn7JAIIW20d280oqOj4OMTgGnT3AWW5eXlITIyHCkpV1FWVgZ9fQP4+gbCxMSU38fZeTyGDrWBhoYm4uKOICfnNY4ePYXKygpER+/AnTu3UVhYCHV1dYwdy8G0adMFTgyHDu3DqVMnkJubA1lZWejq6iMoaAm0tZv+CvTExDMwNR0MVdXu/LbKygps374VN26kIifnNZSVVWBmZgFf3wCwWEotxvvZZ71w7lwCDh8+gBcvnkNBQRGjRo3B7NnzIC1dd6tpQUE+oqIicOvWn8jPz0OPHj1gazsSXl5zuuRKRUrKVSgoKMDScii/rVevXjAyMkFKytUmC4LCwkJkZ2fBzW2GQLuFhRWOHYtFauo1jB3LAYBWfdtsaWkJfv/9PJYs+W+LJ/nhw+2wceM6XLt2Bba2I1vcdltQQSAEtbW1kJCQAIvFQmDgIgwZYo7evbWEHRYhpI3274/Bzp0/Ye5cP7i6ThdYVlxcjLlzPSEjIw1//yCwWEo4ffoE5s/3xYEDR6Chocnve+nSBaira2DevEBISUlDQUEBWVkv0bu3FkaNGgM5OXlkZDxBdPQOcLlc+PoGAADOnv0FUVER+OabORg40AhlZaW4cycNpaWlzcZ940YqOJz/CLRVVlaipqYG33wzF6qqqsjNzcWBAzEICvLDzp37BPo2Fu/Ro7HYti0Mzs5T4ecXgMzMTERFbUd5eTm+/XYZAKCoqAgKCorw85sPRUVFZGVlIiZmF16/zsaaNU2PtQN1758MwzTbp6UrFc+fP4OWlnaDk7aOjh6uX7/W5Hri4nXDuPWFTT0pKUn+dt/Hw4cPUF1dDUlJScyb5427d9OgoKCAL78ci7lz/QSeRMtisaCtrYsbN1KpIPhYPHnyGEuXBiEoaAmsrIbCyWmSsEMiRCRcvZONK2nZQtu/jbE6hhmpv/d65eXliIqKgIPDOLi5fd1g+ZEjh8DlFuLQoTh0764GoO4T5YwZU7Fv324sWfJfft/q6mps3rwNcnJy/LbBg834l5AZhoGxsSl4PAa7d0fBx8cfYmJiuHfvLvT0+mL6dI9/jsdmRLNx5+Xl4c2bXOjp6Qu0s1hKWLQomP9zTU0NdHR04OHhhocPH8DQsF+T8ZaVlWLnzp8wdaobfHz8ISkpjiFDLKGgoIiQkOWYPt0D6uoa0NHR5V9OBwAjIxMoKSkjOHgRiooKoaSk3GTcU6c64fXr5l8npqaDsW3bjiaXFxdzGx3XV1RUBJfLbXI9FksJSkpKuHcvHY6O4/nt9+6lAwC43KJm43pXQUE+AGDDhrXgcP6DWbO88eTJI+zYsR3V1VUCvwcA6NvXgL+vzkAFQRdhGAZxcT9j48b1kJdXoAmDhHwkZGRk0L//QPz2WxIcHcdj0KAhAstv3EjF4MFmUFJSRk3NP0+QGzLEHH//fUug7+DBQwSKAaDuE/uBA3vw66+JyM3N4U86BupOKN27q8HAoB9OnDiGrVtDMXy4HQYONGpxLlJ+fh4ANHon09mzv+DIkUPIzMxEeXkZvz0z838CBcG78d69ewdlZaX44ovR/3+sdc/hHzLEArW1tXj06AHU1TXAMAyOHj2MU6dO4NWrV6iqqvzXPjKbLQg2bAhDdXVVs8f2bg470ldfTcahQ/thbGwKK6uhSE9Pw9GjhwG0bpjg33i8uisdQ4aYISAgCEBdAVhTU4vIyHB4es5Gz549+P2VlJT5v7fOQAVBF+Byi7B69XKcP38O1tbDsGbNBv4nBUJInWFGbfuELmzi4uLYsGEz/P3nYvHiBdi6NRL9+vXnLy8sfIu7d9MwcqRVg3UVFBQEflZR6d6gz08/hePUqRPw8PgG/fr1h4KCIm7cSMWOHdtRVVV3YnR0HI/y8nKcOnUcR44chpycPMaMcYSPjz9kZWUbjbv+JPzuI6QvXbqANWtWYPx4J3h5zQWLpYySkmIEBfnx99dUvIWFbwEAnp6Ccyjq5eS8BlB31WTbth8xbdp0DBliDkVFRTx//gzr1q0SKA4ao62t06ohg+YoKrL4sfxbcXExWCxWs+tOnz4TWVkvERLyXzAMA1lZOfj4+CM09Pv3fl9XVKy7SvHus2bMzMzB4/Hw/PkzgYJAWloalZXN56c9hFoQPH/+HCEhIbh16xZkZGTA4XCwcOHCJl/A/xYfH4/IyEhkZWVBS0sLvr6+cHR07IKo39/587/i4sXfEBi4CDNmeLx3FUkIEW3y8goIDQ2Hr+83WLjQDxERu9CnjzaAupOPhYU1vL3nNljv3feCxs5jFy6cx4QJEwWGA/7++68G25k82QWTJ7sgLy8PFy+ex/btWyEnJ4e5c/0ajbl+gmBJSXGD/enrG2Dx4u/4bQ8e3G90G+/Gq6hYdzJdu/YHfPZZL0hIiKO29p/783v2/Oz/9/EbbGyGw8fHn78sNzen0X28qyOGDLS1dXDjRioYhhEoHp4/z+D/3poiI9MNK1asgb9/EAoK8qGhoYnnzzMAAGy2UauOoZ6Ojm6zy989+dcVLEpN9G4/oRUEXC4XM2bMgIaGBrZs2YKCggKsX78eBQUFCAsLa3bds2fPYvHixfD29sawYcNw/vx5LFiwAPLy8hgxovlxs65SW1uLZ88yoK/fF1995QxT08HQ1dUTdliEkE6irKyMsLAIzJ3rifnzfbF9+y706qUOc3NLnD37C7S0tNt0KbuyslLg8j/DMEhKOttkfzU1NTg7u+DChd/4J6rG9OqlDikpqQYP43l3fwCQlJTYqliNjEwgKyuLnJwcjBhh3+RX91ZWVjS4MvHrr00f0791xJCBldUwxMTsRGrqNVhZ1d1pkJPzGmlpfyMgYGGr4lBRUYGKSt1wS1zcEWhqfo7Bg81btW69Xr3Uoaenj+vXUzBp0lR++40bqRAXF0ffvoYC/bOzX0FLq8977eN9CK0giI2NBZfLRXx8PFRV6+7blJCQwMKFC+Hj44O+ffs2ue6WLVswduxYBAXVjblYWVkhIyMD4eHhIlEQ5ObmYNmyb3H/fjpOnjyL7t3VqBgg5BPQs+dn+PHH7fDx+QaBgb746addcHFxw/nz5zBvnhcmT56GXr3UweUW4cGD+5CSksKsWd7NbtPc3BKnTh1Hnz7aUFVVxalT8SguFvxU/8MPa6GgoIiBA43AYrFw795dpKffwbx5gU1uV0ZGBoaG/fHgwb0G+9u8eQOio6NgbGyKmzev48qVy606fgUFBXh5zUVkZDjevMmFubk5xMQkkJ39CsnJf2DBgsXo2fMzmJtb4siRwzh6NBZ9+mjjwoXzePz4Uav28e4kyLYYOJCNoUNt8P33IZg3LxDy8vLYtSsKn33WS2CyYGLiGXz/fQh+/HE7f27ItWtXkZWVCR0dPXC5Rfj117NITU3G5s3bBO5seP06G/fv100ArKioQFZWFi5cOA8AsLa2QbdudbdXenn5YOnSIGzevAG2tiPw+PFj7N69A+PHO0FNTXAI4uHDe5g2TfAulo4ktILg8uXLsLKy4hcDADBmzBgEBwfj8uXLTRYEmZmZyMjIwPz58wXax40bh6VLl6KgoEBgm13t8uWLWLFiKcrLK7BkyXcC9/cSQj5+vXtrYfPmbfDzm4358+chPDwKUVF1tyVGRUWgsPAtlJVVYGjYD87OU1vc3oIF32LjxvXYujUUUlLSGD16LOzsvsDq1f/cnWBkZILTp+Nx5sxJVFRUQF1dHXPmzIOzs0uz27az+wKxsQfB4/H4wxcTJkxEdvYrnDx5HLGxBzFkiBnWrNkAD4/WPWhpyhRX9OjRE7GxB3H8+FFISEhAXV0dlpZD+UMKM2d6oaioCDExO8Hj8WBjMxzffhuM+fN9W7WPjrBy5Vps2/YjQkM3oLq6CoMHmyEk5Hv+iRqouxrz7m2OkpIS+OWXU8jMfAFxcQmw2cbYvn0X+vUbILD9W7duYt26VfyfU1OTkZqaDAA4evQU1NU1AAA2NsOxatV67N27C6dPx0NZWQVTp7o1KBTv3buLoqIijBz5RYfnop4Y09LsjE5ibW2NSZMmYeFCwcszHA4HpqamWLt2baPrXbp0Cd7e3khISICe3j+futPS0jB58mQcPHgQZmYNn/LUlPz8Ev5Mz/b4/uCfeJX1EpcPLYaBgSE2bAhrcXyINK5HD0W8eVPcckfSJFHP4evX/0OvXp136bMjNHW5+2NSWFiIiRM52LRpS6NPx2uvTyGHna0+h1u3huLJk8fYujWyxXWa+vsSFxdD9+4Kjazx//tqV6TtwOVyG53NyWKxUFTU9L2c9cveXVdJSUlgeWs1l5z3wbHRxaFDqfDw8MB3330nUGWS99ejR9PP/iatI8o5zM0Vh6Sk6E+u/RBibA81NVU4O0/Bzz8fgIVF53yr6seew65QXl6KX345jU2bwlqVT3Fx8Tb9/X/ytx121BUCoz4q2LFpEfLzS1FcXI3i4uqWVyKNEvVPtx8CUc8hj8cT+U+On8qnWze3rxEXdwSlpWUd/tjgTyWHnUlSUhxZWa/g7T0XRkaDWpVPHo/X6N+/yF4hYLFYjT4RisvlQle36Uvt9VcCuFwuevT45/7M+isD9cuFgW4nJIR8aJSUlFuc2EiES1+/L/T1m55o31GEdgbT09PD06dPBdqqqqrw4sWLZguC+mUZGYK309Rvq7l1CSGEENI4oRUEw4cPR0pKCt6+fctvS0pKQlVVVbO3Dvbu3Ru6urpISEgQaD9z5gyMjIyEeocBIYQQ8qESWkHg4uICRUVF+Pj44I8//kB8fDxCQkLg6OgIff1/7jMNDg7GgAGCt3P4+/sjMTERYWFhSE1Nxbp163D16lX4+TX+RC5CiOgR0g1OhHzU2vN3JdQ5BHv37sWaNWvg5+fHf3TxokWLBPrxeDzU1tYKtDk4OKCiogKRkZGIjo6GlpYWQkNDReKhRISQlklISKK6ugrS0jItdyaEtFp1dRUkJNp2ahfacwhERUfdZQCI/szuDwXlsf1EPYfl5aUoLn4LZeUekJKSFslv/6QZ8u1HOWy/1uaQYRhUV1ehsPANFBVVICsr36CPyN5lQAj5dNW/WRUV5aG2tqaF3sIhLi4OHo9OZu1BOWy/98mhhIRkk8VAa1BBQAgRCllZ+Ta/cXUFUb/K8iGgHLZfV+aQbpwnhBBCCBUEhBBCCKGCgBBCCCGggoAQQgghoEmFEBfv2NudOnp7nyrKY/tRDtuPcth+lMP266gctrSdT/45BIQQQgihIQNCCCGEgAoCQgghhIAKAkIIIYSACgJCCCGEgAoCQgghhIAKAkIIIYSACgJCCCGEgAoCQgghhIAKAkIIIYSACoJWef78OTw9PTFo0CBYWVkhJCQE5eXlrVo3Pj4eY8eOhZGRETgcDhISEjo5WtHUlhyWlJQgPDwckydPhpmZGaysrODp6Yn09PQuilr0tOe1WC8pKQmGhoYYN25cJ0Up2tqTw+LiYqxduxbDhw8Hm82Gvb09tmzZ0skRi5625rCsrAybNm3Cl19+CRMTE4wePRrbtm1DVVVVF0QtWv73v/9h+fLlmDBhAgYMGPBef4+ddV755L/LoCVcLhczZsyAhoYGtmzZgoKCAqxfvx4FBQUICwtrdt2zZ89i8eLF8Pb2xrBhw3D+/HksWLAA8vLyGDFiRBcdgfC1NYevXr3Czz//jEmTJsHf3x81NTXYt28fXFxcEBsbi4EDB3bhUQhfe16L9crLy7Fu3Tqoqal1crSiqT05LCsrg7u7O8TExLBo0SL07NkTmZmZeP36dRdFLxrak8OVK1fi/PnzmD9/Pvr27Yu0tDRs3boVXC4XwcHBXXQEouHx48e4dOkSTExMwOPx0NpvEejU8wpDmhUVFcWYmJgw+fn5/LZTp04xBgYGzKNHj5pdd+zYsYy/v79Am4eHBzNp0qROiVVUtTWHpaWlTFlZmUBbRUUFM2zYMGbJkiWdFq+oas9rsd6mTZsYd3d3ZvHixQyHw+msUEVWe3IYFhbG2NnZMSUlJZ0dpkhraw6rq6sZIyMjZsuWLQLtK1asYKytrTstXlFVW1vL///7/D125nmFhgxacPnyZVhZWUFVVZXfNmbMGEhLS+Py5ctNrpeZmYmMjAxwOByB9nHjxuHOnTsoKCjotJhFTVtzKCcnB1lZWYE2GRkZ6OnpITc3t9PiFVVtzWO9p0+fYv/+/fjvf//bmWGKtPbk8NixY3B2doa8vHxnhynS2ppDhmFQW1sLRUVFgXYWi9XqT8cfE3Hx9z/9dvZ5hQqCFjx9+hT6+voCbdLS0tDS0kJGRkaT69Uv09PTE2iv31Zz635s2prDxpSVleH+/fvQ1dXtyBA/CO3N4+rVq+Hs7AwDA4POClHktTWHL1++xJs3b6CiooI5c+bAyMgIZmZm+Pbbb1FUVNTZYYuUtuZQSkoKEyZMwP79+3H79m2UlpYiJSUFR44cgZubW2eH/VHo7PMKzSFoAZfLBYvFatDOYrGafSOoX/buukpKSgLLPwVtzWFjfvzxR5SXl8Pd3b2jwvtgtCePv/zyCx49eoTw8PDOCu+D0NYc5uXlAQB++OEH2NvbIyoqCllZWQgNDUV+fj6io6M7LWZR057X4erVq7FixQpMmTKF3zZz5kzMmzevw+P8GHX2eYUKAvLBOH36NPbu3Yvly5ejT58+wg7ng1FSUoLvv/8eCxYsaPSNnLSMx+MBAPr06YNNmzZBTEwMAKCoqIiAgACkpaXB2NhYmCF+EEJDQ3Hp0iWsWbMG2tra+PvvvxEREQE1NTV4eXkJO7xPHg0ZtIDFYoHL5TZo53K5/KqsMfXL3l23voJrbt2PTVtz+G9Xr17F0qVL4enp+cleXmxrHiMjI6GsrIxRo0aBy+WCy+WiuroaPB4PXC73k7rlq71/z9bW1vxioP5noG7G+KeirTl89OgRdu/ejVWrVmHy5MkwNzeHl5cXZs+eja1bt6KkpKQzw/4odPZ5hQqCFujp6eHp06cCbVVVVXjx4kWz49j1y94d06nf1qc0Bt7WHNZLS0vDvHnz4ODggEWLFnVWmCKvrXnMyMjAo0ePYGlpCXNzc5ibm+PMmTN4+vQpzM3NcejQoc4OXWS0NYe9e/eGtLR0k8srKys7LEZR19YcPnnyBADQv39/gfYBAwagqqoKOTk5HR/sR6azzytUELRg+PDhSElJwdu3b/ltSUlJqKqqavaez969e0NXV7fBAyPOnDkDIyMjgRm6H7u25hCoe6F7eXlh8ODBWLduncCns09NW/MYGBiIffv2CfyzsbGBpqYm9u3bh7Fjx3ZF+CKhrTmUlpbGsGHDkJycLDAj/urVqwAANpvdeUGLmLbmUFNTEwAaPFjs7t27EBMTg4aGRucE/BHp9PNKu29c/MgVFRUxtra2jIuLC3P58mXmxIkTjKWlJRMYGCjQb+nSpUz//v0F2hISEhhDQ0Nm8+bNTEpKCrN27VrG0NCQuXjxYlcegtC1NYd5eXnMiBEjmGHDhjHJycnMX3/9xf+Xnp7e1YchdO15Lb7rU30OQXtyeOfOHWbgwIFMYGAgc/nyZSY2NpaxsLBgZs2a1ZWHIHRtzWFNTQ3j7OzMWFtbM4cPH2aSk5OZyMhIxtjYmAkODu7qwxC6srIyJjExkUlMTGTc3d2ZESNG8H9++fIlwzBdf16hSYUtYLFY2Lt3L9asWQM/Pz/IyMiAw+E0uHTN4/FQW1sr0Obg4ICKigpERkYiOjoaWlpaCA0N/aSeUgi0PYdPnjxBdnY2gLqZyP+mqamJ33//vdNjFyXteS2SOu3JIZvNxq5duxAaGgofHx8oKCjA0dERCxcu7MpDELq25lBCQgKRkZHYsmULduzYgby8PKirq2PWrFmYPXt2Vx+G0OXn5yMgIECgrf7n9evXY+LEiV1+XhFjmE/wiRCEEEIIEUBzCAghhBBCBQEhhBBCqCAghBBCCKggIIQQQgioICCEEEIIqCAghBBCCKggIETkvHz5EoaGhjh+/LiwQ+l04eHhMDQ0bFXfTykvhAgDFQSEdJDjx4/D0NCw0X+rV68WdngfjIMHD36UJ/3Tp09jz549wg6DkCbRkwoJ6WB+fn7o3bu3QJuOjo6QohFtc+fOhbe3t0Db4cOHoaKigokTJwq0a2pqIi0tDZKSH+bb1pkzZ/D48eMGT90kRFR8mH9ZhIgwGxsbmJqaCjuMD4KkpGSrT/BiYmKQkZHp5Ihah2EYVFZWolu3bsIOhZAOQ0MGhHSRrKwsrFq1Cg4ODjAxMYGZmRlmz56Nhw8ftrhuaWkpNmzYAHt7e7DZbFhZWWH69Om4ceOGQL+0tDR4eXlhyJAhMDY2xrRp05CSktLi9uvH53fs2IH9+/fD3t4exsbGcHFxQVpaWoP+Dx484H8LpampKaZPn46bN28K9KmpqcH27dsxZswYGBsbw8LCApMnT8avv/7K7/PuHAJ7e3s8fvwY169f5w+32NvbC8RYP5xw9uxZGBoa4tq1aw3iO3nyJAwNDZGamspve/bsGQICAmBpaQkjIyM4OTnh7NmzLeYGAAwNDbF8+XIkJCRg/PjxMDIy4n/j3PHjxzFz5kwMGzYMbDYbo0ePRlRUFHg8Hn/96dOn4+LFi8jKyhIYSqrHMAz279/P37a1tTWCg4NRUFDQqvgI6Qh0hYCQDlZcXNzgjVxVVRV37tzBzZs3MWbMGGhoaCA3NxexsbGYPn06zpw5g549eza5zZUrVyIxMRFubm7Q19cHl8vF7du38eDBA5ibmwMArl+/Dk9PT/Tv3x++vr6QlJTEyZMn4enpid27d8PS0rLF2E+fPg0ulwtXV1fweDwcPHgQM2fOxIkTJ9CnTx8AdV9J7erqCjk5OXh6ekJGRgZHjx5+JBR7AAAJPklEQVTFzJkzERMTw49n27ZtiIyMhLOzM4yNjVFWVob79+8jLS0No0ePbnT/wcHBCAkJgZycHObMmQMAkJeXb7TvyJEjIS8vj4SEBFhbWwssS0xMRM+ePfmxPH36FC4uLlBTU4Onpyfk5eWRlJSEgIAA/PDDD5gwYUKLubl58ybOnTsHd3d3qKmp8b97/uDBg9DT08OIESMgLS2NlJQUbN68GcXFxfwvPpozZw6Ki4vx+vVrLF26tMG2V6xYgbi4ODg5OcHNzQ3Z2dk4cOAA7ty5g2PHjonMlRHykWv39yUSQhiGYZi4uDjGwMCg0X8lJSVMeXl5g3VevHjBsNlsJiIigt+WmZnJGBgYMHFxcfw2MzMzZtWqVU3um8fjMWPGjGG+/vprhsfj8dsrKysZR0dHZurUqc3GXr9PNpvNZGZm8tszMjKYAQMGMEFBQfw2X19fZuDAgcyzZ8/4bfn5+YyFhQXz1Vdf8dsmTJjAeHt7N7vfrVu3MgYGBgJtHA6HcXd3bzLGf+clKCiIsbCwYKqrq/ltRUVFzMCBA5k1a9bw2zw8PBhHR8cGvwMPDw/G1tZWIGeNMTAwYAwNDZl79+41WFZWVtag7bvvvmNMTU2ZyspKfpu3tzdjZ2fXoO+ff/7JGBgYMCdOnBBov3HjBmNgYMDExsY2GxshHYWGDAjpYN999x1iYmIE/nXr1k1gvLm8vBxv376FvLw8dHR0kJ6e3uw2FRUVcfv2beTk5DS6/MGDB3j27BnGjRuHt2/foqCgAAUFBSgpKcHQoUNx+/ZtlJeXtxi7nZ0dPv/8c/7POjo6sLGxwaVLlwAAtbW1uHLlCuzs7KCtrc3vp6qqiokTJyI9PR15eXn8mB8/foxnz561uN+24nA4KCwsRHJyMr8tKSkJ1dXV4HA4AMBf7uDggLKyMn5uCgoKYGtri5ycnFbFOGjQIPTv379Bu6ysLIC63BQVFaGgoADm5uYoKytDRkZGi9tNTEyEnJwcbG1tBWLT1dWFmpqawLAHIZ2JhgwI6WBGRkaNTiqsrKzEli1bcOrUKbx580ZgmbKycrPbXLRoEZYsWYKRI0eif//+sLW1xYQJE/iXretPaMuWLWtyG4WFhfyTV1P+fZL/d9vFixfB5XJRWVmJ8vLyRu+aqI8lKysLampq8Pf3h6+vL8aOHQt9fX3Y2Nhg3LhxMDIyajaG92FjYwMlJSUkJCRg+PDhAICEhARoamryfwcvXrwAwzAIDw9HeHh4o9upPwE3R0tLq9H2mzdvIiwsDLdv30Z1dbXAsuLi4haP4fnz5ygrK8PQoUMbXZ6fn9/iNgjpCFQQENJFQkJCEBcXB3d3dwwaNAgsFgvi4uJYt24dGIZpdl0HBweYmZnht99+w9WrV7F//35ER0dj/fr1GD9+PH/9oKAgsNnsRrehqqra4cfUHHNzcyQlJeHChQu4cuUK4uPjsXfvXgQFBcHLy6tD9iElJYVRo0bh3LlzqKqqQklJCVJSUuDh4cHvUz+5b+bMmRgxYkSj2+nbt2+L+2psHD8zMxMeHh7Q1tbG0qVLoaGhARkZGaSnp2PTpk0CEwubwuPxoKysjLCwsEaXs1isFrdBSEeggoCQLnL27Fk4OTk1+BRfVFQEFRWVFtfv0aMHXFxc4OLiAi6XiylTpiA8PBzjx4/nP/dAXl6+yU+arfH8+fNG21gsFlgsFmprayErK9voJfb6y+Oampr8NiUlJTg5OcHJyQkVFRXw8vJCeHg4Zs2aBQkJiUZjEBMTe6+YORwOjh07hj/++ANv3rxBTU0Nf7gAAD83EhIS7cpNY3777TdUVVUhMjJS4LhfvnzZoG9Tx6WlpYXk5GSYmJg0OYGSkK5AcwgI6SISEhINrgScOXMGubm5za5XW1vb4NIzi8XC559/Di6XCwBgs9no06cP9uzZg5KSkgbbaO3taxcuXBA4mT179gxXrlzhX46XkJCAra0tLly4gBcvXvD7FRYWIj4+Hmw2G2pqagCAt2/fCmy7W7du0NXVRWVlJSoqKpqMQVZWFkVFRa2KFwAsLS2hpqaGhIQEJCQkQFdXV2Csv3v37rC0tMTRo0cbnYPRnlv76ouaf/9eq6qqcODAgQZ9ZWVlweVyG7wGHB0dwePxEBER0WCd+nkJhHQFukJASBext7dHfHw8FBQU0LdvX9y/fx+JiYkNnmr4rtLSUgwfPhyjR49Gv379oKCggFu3buGPP/6Au7s7AEBcXBxr167FN998Aw6Hg0mTJqFXr17Izc3F9evX+fe5t0RbWxtubm5wc3MDj8fDgQMHIC0tjXnz5vH7BAYG4sqVK3B1dYWrqytkZGRw5MgRFBcXY8mSJfx+jo6OMDc3B5vNhoqKCh4+fIhjx47Bzs6u2U/CbDYbhw4dwrZt26CtrQ05OTn+swgaIyEhgTFjxuD48eOorKyEj49Pgz4rV67EtGnT8J///AeTJ0+GlpYW8vPzcfv2bTx9+hRJSUkt5qYxNjY2kJKSwpw5czB16lRUVVXh5MmTEBdv+FmLzWYjISEBa9euhYmJCcTFxcHhcGBubg43NzdER0fj4cOHsLW1hZSUFF68eIFz587B39+/wVMbCekMVBAQ0kWWLVsGSUlJJCQkoKysDGw2Gzt37sTGjRubXa9bt25wdXVFcnIyfv/9d9TU1ODzzz/H4sWLMWPGDH4/c3Nz/Pzzz9i+fTsOHTqEkpIS9OjRA0ZGRnB2dm5VjOPHj4esrCxiYmLw5s0bDBgwAMHBwQKTCPX09HD48GGEhoZi586dYBgGbDYba9as4d/3DwBff/01fv/9d6SkpKCiogLq6urw8vJqcf6Ar68vsrOzERMTg5KSEmhqajZbEAB1xcfBgwf5/3+Xrq4u4uLiEBERgfj4eBQWFkJFRQX9+vVDQEBAq3LTGB0dHURERCAsLAwbN26EiooKnJycYGFhgVmzZgn0dXV1xaNHj3D69GkcOHAADMPwhzaWL1+OAQMGIDY2FmFhYZCQkICGhgYcHBxgZWXV5vgIeR9iTEuzmQghH72XL1/iiy++QFBQUIPvFiCEfBpoDgEhhBBCqCAghBBCCBUEhBBCCAHNISCEEEII6AoBIYQQQkAFASGEEEJABQEhhBBCQAUBIYQQQkAFASGEEEJABQEhhBBCAPwf+cLQA0b5P8MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_eq7rlNGTXdl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}