Air_Pollution_detection_using_Inception_V3(Transfer_Learning)Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MpHg45O9JsPL"
   },
   "source": [
    "## Transfer Learning Inception V3 using Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hHQYN8seyUJu"
   },
   "source": [
    "## **Step-1: Importing Required Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "waLSjY8oJsPN"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\onkar\\anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1771: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
      "  warnings.warn('An interactive session is already active. This can '\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.compat.v1 import ConfigProto\n",
    "from tensorflow.compat.v1 import InteractiveSession\n",
    "\n",
    "config = ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.5\n",
    "config.gpu_options.allow_growth = True\n",
    "session = InteractiveSession(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "G25Ac4UWJsPO"
   },
   "outputs": [],
   "source": [
    "# import the libraries as shown below\n",
    "\n",
    "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "#from keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications.inception_v3 import preprocess_input\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img\n",
    "from tensorflow.keras.models import Sequential\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "#import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i25_5AbwKYIu",
    "outputId": "646c08ea-f2d0-4a2e-962b-0a9bad7d9986"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'google.colab'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-d5df0069828e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'/content/drive'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'google.colab'"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oMrmcwINyv6U"
   },
   "source": [
    "## **Step-2: Assigning Datasets path**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "SVV4ogywJsPP"
   },
   "outputs": [],
   "source": [
    "# re-size all the images to this\n",
    "IMAGE_SIZE = [224, 224]\n",
    "\n",
    "#train_path = 'D/content/drive/MyDrive/Air_Pollution_Dataset/train'\n",
    "#valid_path = '/content/drive/MyDrive/Air_Pollution_Dataset/Validate'\n",
    "\n",
    "train_path = 'C:/Users/onkar/Desktop/LY P/Input_dataset-20230125T202411Z-001/Input_dataset/train'\n",
    "valid_path = 'C:/Users/onkar/Desktop/LY P/Input_dataset-20230125T202411Z-001/Input_dataset/validate'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PFwzQeahzDKb"
   },
   "source": [
    "## **Step-3: Importing Inception v3 Layers and adding preprocessing layer to the input layer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FanrOEuUJsPP",
    "outputId": "932ba14c-fd76-47a5-e711-07f19c02eadd"
   },
   "outputs": [],
   "source": [
    "\n",
    "# Here we will be using imagenet weights\n",
    "\n",
    "inception = InceptionV3(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "-cYDvV2hJsPP"
   },
   "outputs": [],
   "source": [
    "# don't train existing weights\n",
    "for layer in inception.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "VRKXGZZvJsPP"
   },
   "outputs": [],
   "source": [
    "  # useful for getting number of output classes\n",
    "folders = glob('C:/Users/onkar/Desktop/LY P/Input_dataset-20230125T202411Z-001/Input_dataset/train/*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "XBlcFdajJsPQ"
   },
   "outputs": [],
   "source": [
    "# our layers\n",
    "x = Flatten()(inception.output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xu62Pq2SzhJA"
   },
   "source": [
    "# **Step-4: Creating the Model object**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "diGX2gkFJsPQ"
   },
   "outputs": [],
   "source": [
    "prediction = Dense(len(folders), activation='softmax')(x)\n",
    "\n",
    "# create a model object\n",
    "model = Model(inputs=inception.input, outputs=prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mlTNr3UGJsPR",
    "outputId": "57625581-f208-4c95-e270-b27b470b0eb3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_2 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv2d_94 (Conv2D)             (None, 111, 111, 32  864         ['input_2[0][0]']                \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_94 (BatchN  (None, 111, 111, 32  96         ['conv2d_94[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_94 (Activation)     (None, 111, 111, 32  0           ['batch_normalization_94[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_95 (Conv2D)             (None, 109, 109, 32  9216        ['activation_94[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_95 (BatchN  (None, 109, 109, 32  96         ['conv2d_95[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_95 (Activation)     (None, 109, 109, 32  0           ['batch_normalization_95[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_96 (Conv2D)             (None, 109, 109, 64  18432       ['activation_95[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_96 (BatchN  (None, 109, 109, 64  192        ['conv2d_96[0][0]']              \n",
      " ormalization)                  )                                                                 \n",
      "                                                                                                  \n",
      " activation_96 (Activation)     (None, 109, 109, 64  0           ['batch_normalization_96[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d_4 (MaxPooling2D)  (None, 54, 54, 64)  0           ['activation_96[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_97 (Conv2D)             (None, 54, 54, 80)   5120        ['max_pooling2d_4[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_97 (BatchN  (None, 54, 54, 80)  240         ['conv2d_97[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_97 (Activation)     (None, 54, 54, 80)   0           ['batch_normalization_97[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_98 (Conv2D)             (None, 52, 52, 192)  138240      ['activation_97[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_98 (BatchN  (None, 52, 52, 192)  576        ['conv2d_98[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_98 (Activation)     (None, 52, 52, 192)  0           ['batch_normalization_98[0][0]'] \n",
      "                                                                                                  \n",
      " max_pooling2d_5 (MaxPooling2D)  (None, 25, 25, 192)  0          ['activation_98[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_102 (Conv2D)            (None, 25, 25, 64)   12288       ['max_pooling2d_5[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_102 (Batch  (None, 25, 25, 64)  192         ['conv2d_102[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_102 (Activation)    (None, 25, 25, 64)   0           ['batch_normalization_102[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_100 (Conv2D)            (None, 25, 25, 48)   9216        ['max_pooling2d_5[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_103 (Conv2D)            (None, 25, 25, 96)   55296       ['activation_102[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_100 (Batch  (None, 25, 25, 48)  144         ['conv2d_100[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_103 (Batch  (None, 25, 25, 96)  288         ['conv2d_103[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_100 (Activation)    (None, 25, 25, 48)   0           ['batch_normalization_100[0][0]']\n",
      "                                                                                                  \n",
      " activation_103 (Activation)    (None, 25, 25, 96)   0           ['batch_normalization_103[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_9 (AveragePo  (None, 25, 25, 192)  0          ['max_pooling2d_5[0][0]']        \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_99 (Conv2D)             (None, 25, 25, 64)   12288       ['max_pooling2d_5[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_101 (Conv2D)            (None, 25, 25, 64)   76800       ['activation_100[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_104 (Conv2D)            (None, 25, 25, 96)   82944       ['activation_103[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_105 (Conv2D)            (None, 25, 25, 32)   6144        ['average_pooling2d_9[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_99 (BatchN  (None, 25, 25, 64)  192         ['conv2d_99[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_101 (Batch  (None, 25, 25, 64)  192         ['conv2d_101[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_104 (Batch  (None, 25, 25, 96)  288         ['conv2d_104[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_105 (Batch  (None, 25, 25, 32)  96          ['conv2d_105[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_99 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_99[0][0]'] \n",
      "                                                                                                  \n",
      " activation_101 (Activation)    (None, 25, 25, 64)   0           ['batch_normalization_101[0][0]']\n",
      "                                                                                                  \n",
      " activation_104 (Activation)    (None, 25, 25, 96)   0           ['batch_normalization_104[0][0]']\n",
      "                                                                                                  \n",
      " activation_105 (Activation)    (None, 25, 25, 32)   0           ['batch_normalization_105[0][0]']\n",
      "                                                                                                  \n",
      " mixed0 (Concatenate)           (None, 25, 25, 256)  0           ['activation_99[0][0]',          \n",
      "                                                                  'activation_101[0][0]',         \n",
      "                                                                  'activation_104[0][0]',         \n",
      "                                                                  'activation_105[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_109 (Conv2D)            (None, 25, 25, 64)   16384       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_109 (Batch  (None, 25, 25, 64)  192         ['conv2d_109[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_109 (Activation)    (None, 25, 25, 64)   0           ['batch_normalization_109[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_107 (Conv2D)            (None, 25, 25, 48)   12288       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_110 (Conv2D)            (None, 25, 25, 96)   55296       ['activation_109[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_107 (Batch  (None, 25, 25, 48)  144         ['conv2d_107[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_110 (Batch  (None, 25, 25, 96)  288         ['conv2d_110[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_107 (Activation)    (None, 25, 25, 48)   0           ['batch_normalization_107[0][0]']\n",
      "                                                                                                  \n",
      " activation_110 (Activation)    (None, 25, 25, 96)   0           ['batch_normalization_110[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_10 (AverageP  (None, 25, 25, 256)  0          ['mixed0[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_106 (Conv2D)            (None, 25, 25, 64)   16384       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_108 (Conv2D)            (None, 25, 25, 64)   76800       ['activation_107[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_111 (Conv2D)            (None, 25, 25, 96)   82944       ['activation_110[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_112 (Conv2D)            (None, 25, 25, 64)   16384       ['average_pooling2d_10[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_106 (Batch  (None, 25, 25, 64)  192         ['conv2d_106[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_108 (Batch  (None, 25, 25, 64)  192         ['conv2d_108[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_111 (Batch  (None, 25, 25, 96)  288         ['conv2d_111[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_112 (Batch  (None, 25, 25, 64)  192         ['conv2d_112[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_106 (Activation)    (None, 25, 25, 64)   0           ['batch_normalization_106[0][0]']\n",
      "                                                                                                  \n",
      " activation_108 (Activation)    (None, 25, 25, 64)   0           ['batch_normalization_108[0][0]']\n",
      "                                                                                                  \n",
      " activation_111 (Activation)    (None, 25, 25, 96)   0           ['batch_normalization_111[0][0]']\n",
      "                                                                                                  \n",
      " activation_112 (Activation)    (None, 25, 25, 64)   0           ['batch_normalization_112[0][0]']\n",
      "                                                                                                  \n",
      " mixed1 (Concatenate)           (None, 25, 25, 288)  0           ['activation_106[0][0]',         \n",
      "                                                                  'activation_108[0][0]',         \n",
      "                                                                  'activation_111[0][0]',         \n",
      "                                                                  'activation_112[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_116 (Conv2D)            (None, 25, 25, 64)   18432       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_116 (Batch  (None, 25, 25, 64)  192         ['conv2d_116[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_116 (Activation)    (None, 25, 25, 64)   0           ['batch_normalization_116[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_114 (Conv2D)            (None, 25, 25, 48)   13824       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_117 (Conv2D)            (None, 25, 25, 96)   55296       ['activation_116[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_114 (Batch  (None, 25, 25, 48)  144         ['conv2d_114[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_117 (Batch  (None, 25, 25, 96)  288         ['conv2d_117[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_114 (Activation)    (None, 25, 25, 48)   0           ['batch_normalization_114[0][0]']\n",
      "                                                                                                  \n",
      " activation_117 (Activation)    (None, 25, 25, 96)   0           ['batch_normalization_117[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_11 (AverageP  (None, 25, 25, 288)  0          ['mixed1[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_113 (Conv2D)            (None, 25, 25, 64)   18432       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_115 (Conv2D)            (None, 25, 25, 64)   76800       ['activation_114[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_118 (Conv2D)            (None, 25, 25, 96)   82944       ['activation_117[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_119 (Conv2D)            (None, 25, 25, 64)   18432       ['average_pooling2d_11[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_113 (Batch  (None, 25, 25, 64)  192         ['conv2d_113[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_115 (Batch  (None, 25, 25, 64)  192         ['conv2d_115[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_118 (Batch  (None, 25, 25, 96)  288         ['conv2d_118[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_119 (Batch  (None, 25, 25, 64)  192         ['conv2d_119[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_113 (Activation)    (None, 25, 25, 64)   0           ['batch_normalization_113[0][0]']\n",
      "                                                                                                  \n",
      " activation_115 (Activation)    (None, 25, 25, 64)   0           ['batch_normalization_115[0][0]']\n",
      "                                                                                                  \n",
      " activation_118 (Activation)    (None, 25, 25, 96)   0           ['batch_normalization_118[0][0]']\n",
      "                                                                                                  \n",
      " activation_119 (Activation)    (None, 25, 25, 64)   0           ['batch_normalization_119[0][0]']\n",
      "                                                                                                  \n",
      " mixed2 (Concatenate)           (None, 25, 25, 288)  0           ['activation_113[0][0]',         \n",
      "                                                                  'activation_115[0][0]',         \n",
      "                                                                  'activation_118[0][0]',         \n",
      "                                                                  'activation_119[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_121 (Conv2D)            (None, 25, 25, 64)   18432       ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_121 (Batch  (None, 25, 25, 64)  192         ['conv2d_121[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_121 (Activation)    (None, 25, 25, 64)   0           ['batch_normalization_121[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_122 (Conv2D)            (None, 25, 25, 96)   55296       ['activation_121[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_122 (Batch  (None, 25, 25, 96)  288         ['conv2d_122[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_122 (Activation)    (None, 25, 25, 96)   0           ['batch_normalization_122[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_120 (Conv2D)            (None, 12, 12, 384)  995328      ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_123 (Conv2D)            (None, 12, 12, 96)   82944       ['activation_122[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_120 (Batch  (None, 12, 12, 384)  1152       ['conv2d_120[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_123 (Batch  (None, 12, 12, 96)  288         ['conv2d_123[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_120 (Activation)    (None, 12, 12, 384)  0           ['batch_normalization_120[0][0]']\n",
      "                                                                                                  \n",
      " activation_123 (Activation)    (None, 12, 12, 96)   0           ['batch_normalization_123[0][0]']\n",
      "                                                                                                  \n",
      " max_pooling2d_6 (MaxPooling2D)  (None, 12, 12, 288)  0          ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " mixed3 (Concatenate)           (None, 12, 12, 768)  0           ['activation_120[0][0]',         \n",
      "                                                                  'activation_123[0][0]',         \n",
      "                                                                  'max_pooling2d_6[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_128 (Conv2D)            (None, 12, 12, 128)  98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_128 (Batch  (None, 12, 12, 128)  384        ['conv2d_128[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_128 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_128[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_129 (Conv2D)            (None, 12, 12, 128)  114688      ['activation_128[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_129 (Batch  (None, 12, 12, 128)  384        ['conv2d_129[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_129 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_129[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_125 (Conv2D)            (None, 12, 12, 128)  98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_130 (Conv2D)            (None, 12, 12, 128)  114688      ['activation_129[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_125 (Batch  (None, 12, 12, 128)  384        ['conv2d_125[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_130 (Batch  (None, 12, 12, 128)  384        ['conv2d_130[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_125 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_125[0][0]']\n",
      "                                                                                                  \n",
      " activation_130 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_130[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_126 (Conv2D)            (None, 12, 12, 128)  114688      ['activation_125[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_131 (Conv2D)            (None, 12, 12, 128)  114688      ['activation_130[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_126 (Batch  (None, 12, 12, 128)  384        ['conv2d_126[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_131 (Batch  (None, 12, 12, 128)  384        ['conv2d_131[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_126 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_126[0][0]']\n",
      "                                                                                                  \n",
      " activation_131 (Activation)    (None, 12, 12, 128)  0           ['batch_normalization_131[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_12 (AverageP  (None, 12, 12, 768)  0          ['mixed3[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_124 (Conv2D)            (None, 12, 12, 192)  147456      ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_127 (Conv2D)            (None, 12, 12, 192)  172032      ['activation_126[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_132 (Conv2D)            (None, 12, 12, 192)  172032      ['activation_131[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_133 (Conv2D)            (None, 12, 12, 192)  147456      ['average_pooling2d_12[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_124 (Batch  (None, 12, 12, 192)  576        ['conv2d_124[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_127 (Batch  (None, 12, 12, 192)  576        ['conv2d_127[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_132 (Batch  (None, 12, 12, 192)  576        ['conv2d_132[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_133 (Batch  (None, 12, 12, 192)  576        ['conv2d_133[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_124 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_124[0][0]']\n",
      "                                                                                                  \n",
      " activation_127 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_127[0][0]']\n",
      "                                                                                                  \n",
      " activation_132 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_132[0][0]']\n",
      "                                                                                                  \n",
      " activation_133 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_133[0][0]']\n",
      "                                                                                                  \n",
      " mixed4 (Concatenate)           (None, 12, 12, 768)  0           ['activation_124[0][0]',         \n",
      "                                                                  'activation_127[0][0]',         \n",
      "                                                                  'activation_132[0][0]',         \n",
      "                                                                  'activation_133[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_138 (Conv2D)            (None, 12, 12, 160)  122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_138 (Batch  (None, 12, 12, 160)  480        ['conv2d_138[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_138 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_138[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_139 (Conv2D)            (None, 12, 12, 160)  179200      ['activation_138[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_139 (Batch  (None, 12, 12, 160)  480        ['conv2d_139[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_139 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_139[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_135 (Conv2D)            (None, 12, 12, 160)  122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_140 (Conv2D)            (None, 12, 12, 160)  179200      ['activation_139[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_135 (Batch  (None, 12, 12, 160)  480        ['conv2d_135[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_140 (Batch  (None, 12, 12, 160)  480        ['conv2d_140[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_135 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_135[0][0]']\n",
      "                                                                                                  \n",
      " activation_140 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_140[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_136 (Conv2D)            (None, 12, 12, 160)  179200      ['activation_135[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_141 (Conv2D)            (None, 12, 12, 160)  179200      ['activation_140[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_136 (Batch  (None, 12, 12, 160)  480        ['conv2d_136[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_141 (Batch  (None, 12, 12, 160)  480        ['conv2d_141[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_136 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_136[0][0]']\n",
      "                                                                                                  \n",
      " activation_141 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_141[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_13 (AverageP  (None, 12, 12, 768)  0          ['mixed4[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_134 (Conv2D)            (None, 12, 12, 192)  147456      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_137 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_136[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_142 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_141[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_143 (Conv2D)            (None, 12, 12, 192)  147456      ['average_pooling2d_13[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_134 (Batch  (None, 12, 12, 192)  576        ['conv2d_134[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_137 (Batch  (None, 12, 12, 192)  576        ['conv2d_137[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_142 (Batch  (None, 12, 12, 192)  576        ['conv2d_142[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_143 (Batch  (None, 12, 12, 192)  576        ['conv2d_143[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_134 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_134[0][0]']\n",
      "                                                                                                  \n",
      " activation_137 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_137[0][0]']\n",
      "                                                                                                  \n",
      " activation_142 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_142[0][0]']\n",
      "                                                                                                  \n",
      " activation_143 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_143[0][0]']\n",
      "                                                                                                  \n",
      " mixed5 (Concatenate)           (None, 12, 12, 768)  0           ['activation_134[0][0]',         \n",
      "                                                                  'activation_137[0][0]',         \n",
      "                                                                  'activation_142[0][0]',         \n",
      "                                                                  'activation_143[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_148 (Conv2D)            (None, 12, 12, 160)  122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_148 (Batch  (None, 12, 12, 160)  480        ['conv2d_148[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_148 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_148[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_149 (Conv2D)            (None, 12, 12, 160)  179200      ['activation_148[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_149 (Batch  (None, 12, 12, 160)  480        ['conv2d_149[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_149 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_149[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_145 (Conv2D)            (None, 12, 12, 160)  122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_150 (Conv2D)            (None, 12, 12, 160)  179200      ['activation_149[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_145 (Batch  (None, 12, 12, 160)  480        ['conv2d_145[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_150 (Batch  (None, 12, 12, 160)  480        ['conv2d_150[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_145 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_145[0][0]']\n",
      "                                                                                                  \n",
      " activation_150 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_150[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_146 (Conv2D)            (None, 12, 12, 160)  179200      ['activation_145[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_151 (Conv2D)            (None, 12, 12, 160)  179200      ['activation_150[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_146 (Batch  (None, 12, 12, 160)  480        ['conv2d_146[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_151 (Batch  (None, 12, 12, 160)  480        ['conv2d_151[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_146 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_146[0][0]']\n",
      "                                                                                                  \n",
      " activation_151 (Activation)    (None, 12, 12, 160)  0           ['batch_normalization_151[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_14 (AverageP  (None, 12, 12, 768)  0          ['mixed5[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_144 (Conv2D)            (None, 12, 12, 192)  147456      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_147 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_146[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_152 (Conv2D)            (None, 12, 12, 192)  215040      ['activation_151[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_153 (Conv2D)            (None, 12, 12, 192)  147456      ['average_pooling2d_14[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_144 (Batch  (None, 12, 12, 192)  576        ['conv2d_144[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_147 (Batch  (None, 12, 12, 192)  576        ['conv2d_147[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_152 (Batch  (None, 12, 12, 192)  576        ['conv2d_152[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_153 (Batch  (None, 12, 12, 192)  576        ['conv2d_153[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_144 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_144[0][0]']\n",
      "                                                                                                  \n",
      " activation_147 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_147[0][0]']\n",
      "                                                                                                  \n",
      " activation_152 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_152[0][0]']\n",
      "                                                                                                  \n",
      " activation_153 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_153[0][0]']\n",
      "                                                                                                  \n",
      " mixed6 (Concatenate)           (None, 12, 12, 768)  0           ['activation_144[0][0]',         \n",
      "                                                                  'activation_147[0][0]',         \n",
      "                                                                  'activation_152[0][0]',         \n",
      "                                                                  'activation_153[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_158 (Conv2D)            (None, 12, 12, 192)  147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_158 (Batch  (None, 12, 12, 192)  576        ['conv2d_158[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_158 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_158[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_159 (Conv2D)            (None, 12, 12, 192)  258048      ['activation_158[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_159 (Batch  (None, 12, 12, 192)  576        ['conv2d_159[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_159 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_159[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_155 (Conv2D)            (None, 12, 12, 192)  147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_160 (Conv2D)            (None, 12, 12, 192)  258048      ['activation_159[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_155 (Batch  (None, 12, 12, 192)  576        ['conv2d_155[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_160 (Batch  (None, 12, 12, 192)  576        ['conv2d_160[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_155 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_155[0][0]']\n",
      "                                                                                                  \n",
      " activation_160 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_160[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_156 (Conv2D)            (None, 12, 12, 192)  258048      ['activation_155[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_161 (Conv2D)            (None, 12, 12, 192)  258048      ['activation_160[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_156 (Batch  (None, 12, 12, 192)  576        ['conv2d_156[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_161 (Batch  (None, 12, 12, 192)  576        ['conv2d_161[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_156 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_156[0][0]']\n",
      "                                                                                                  \n",
      " activation_161 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_161[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_15 (AverageP  (None, 12, 12, 768)  0          ['mixed6[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_154 (Conv2D)            (None, 12, 12, 192)  147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_157 (Conv2D)            (None, 12, 12, 192)  258048      ['activation_156[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_162 (Conv2D)            (None, 12, 12, 192)  258048      ['activation_161[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_163 (Conv2D)            (None, 12, 12, 192)  147456      ['average_pooling2d_15[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_154 (Batch  (None, 12, 12, 192)  576        ['conv2d_154[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_157 (Batch  (None, 12, 12, 192)  576        ['conv2d_157[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_162 (Batch  (None, 12, 12, 192)  576        ['conv2d_162[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_163 (Batch  (None, 12, 12, 192)  576        ['conv2d_163[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_154 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_154[0][0]']\n",
      "                                                                                                  \n",
      " activation_157 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_157[0][0]']\n",
      "                                                                                                  \n",
      " activation_162 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_162[0][0]']\n",
      "                                                                                                  \n",
      " activation_163 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_163[0][0]']\n",
      "                                                                                                  \n",
      " mixed7 (Concatenate)           (None, 12, 12, 768)  0           ['activation_154[0][0]',         \n",
      "                                                                  'activation_157[0][0]',         \n",
      "                                                                  'activation_162[0][0]',         \n",
      "                                                                  'activation_163[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_166 (Conv2D)            (None, 12, 12, 192)  147456      ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_166 (Batch  (None, 12, 12, 192)  576        ['conv2d_166[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_166 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_166[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_167 (Conv2D)            (None, 12, 12, 192)  258048      ['activation_166[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_167 (Batch  (None, 12, 12, 192)  576        ['conv2d_167[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_167 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_167[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_164 (Conv2D)            (None, 12, 12, 192)  147456      ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_168 (Conv2D)            (None, 12, 12, 192)  258048      ['activation_167[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_164 (Batch  (None, 12, 12, 192)  576        ['conv2d_164[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_168 (Batch  (None, 12, 12, 192)  576        ['conv2d_168[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_164 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_164[0][0]']\n",
      "                                                                                                  \n",
      " activation_168 (Activation)    (None, 12, 12, 192)  0           ['batch_normalization_168[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_165 (Conv2D)            (None, 5, 5, 320)    552960      ['activation_164[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_169 (Conv2D)            (None, 5, 5, 192)    331776      ['activation_168[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_165 (Batch  (None, 5, 5, 320)   960         ['conv2d_165[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_169 (Batch  (None, 5, 5, 192)   576         ['conv2d_169[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_165 (Activation)    (None, 5, 5, 320)    0           ['batch_normalization_165[0][0]']\n",
      "                                                                                                  \n",
      " activation_169 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_169[0][0]']\n",
      "                                                                                                  \n",
      " max_pooling2d_7 (MaxPooling2D)  (None, 5, 5, 768)   0           ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " mixed8 (Concatenate)           (None, 5, 5, 1280)   0           ['activation_165[0][0]',         \n",
      "                                                                  'activation_169[0][0]',         \n",
      "                                                                  'max_pooling2d_7[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_174 (Conv2D)            (None, 5, 5, 448)    573440      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_174 (Batch  (None, 5, 5, 448)   1344        ['conv2d_174[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_174 (Activation)    (None, 5, 5, 448)    0           ['batch_normalization_174[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_171 (Conv2D)            (None, 5, 5, 384)    491520      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_175 (Conv2D)            (None, 5, 5, 384)    1548288     ['activation_174[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_171 (Batch  (None, 5, 5, 384)   1152        ['conv2d_171[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_175 (Batch  (None, 5, 5, 384)   1152        ['conv2d_175[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_171 (Activation)    (None, 5, 5, 384)    0           ['batch_normalization_171[0][0]']\n",
      "                                                                                                  \n",
      " activation_175 (Activation)    (None, 5, 5, 384)    0           ['batch_normalization_175[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_172 (Conv2D)            (None, 5, 5, 384)    442368      ['activation_171[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_173 (Conv2D)            (None, 5, 5, 384)    442368      ['activation_171[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_176 (Conv2D)            (None, 5, 5, 384)    442368      ['activation_175[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_177 (Conv2D)            (None, 5, 5, 384)    442368      ['activation_175[0][0]']         \n",
      "                                                                                                  \n",
      " average_pooling2d_16 (AverageP  (None, 5, 5, 1280)  0           ['mixed8[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_170 (Conv2D)            (None, 5, 5, 320)    409600      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_172 (Batch  (None, 5, 5, 384)   1152        ['conv2d_172[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_173 (Batch  (None, 5, 5, 384)   1152        ['conv2d_173[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_176 (Batch  (None, 5, 5, 384)   1152        ['conv2d_176[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_177 (Batch  (None, 5, 5, 384)   1152        ['conv2d_177[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " conv2d_178 (Conv2D)            (None, 5, 5, 192)    245760      ['average_pooling2d_16[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_170 (Batch  (None, 5, 5, 320)   960         ['conv2d_170[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_172 (Activation)    (None, 5, 5, 384)    0           ['batch_normalization_172[0][0]']\n",
      "                                                                                                  \n",
      " activation_173 (Activation)    (None, 5, 5, 384)    0           ['batch_normalization_173[0][0]']\n",
      "                                                                                                  \n",
      " activation_176 (Activation)    (None, 5, 5, 384)    0           ['batch_normalization_176[0][0]']\n",
      "                                                                                                  \n",
      " activation_177 (Activation)    (None, 5, 5, 384)    0           ['batch_normalization_177[0][0]']\n",
      "                                                                                                  \n",
      " batch_normalization_178 (Batch  (None, 5, 5, 192)   576         ['conv2d_178[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_170 (Activation)    (None, 5, 5, 320)    0           ['batch_normalization_170[0][0]']\n",
      "                                                                                                  \n",
      " mixed9_0 (Concatenate)         (None, 5, 5, 768)    0           ['activation_172[0][0]',         \n",
      "                                                                  'activation_173[0][0]']         \n",
      "                                                                                                  \n",
      " concatenate_2 (Concatenate)    (None, 5, 5, 768)    0           ['activation_176[0][0]',         \n",
      "                                                                  'activation_177[0][0]']         \n",
      "                                                                                                  \n",
      " activation_178 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_178[0][0]']\n",
      "                                                                                                  \n",
      " mixed9 (Concatenate)           (None, 5, 5, 2048)   0           ['activation_170[0][0]',         \n",
      "                                                                  'mixed9_0[0][0]',               \n",
      "                                                                  'concatenate_2[0][0]',          \n",
      "                                                                  'activation_178[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_183 (Conv2D)            (None, 5, 5, 448)    917504      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_183 (Batch  (None, 5, 5, 448)   1344        ['conv2d_183[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_183 (Activation)    (None, 5, 5, 448)    0           ['batch_normalization_183[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_180 (Conv2D)            (None, 5, 5, 384)    786432      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_184 (Conv2D)            (None, 5, 5, 384)    1548288     ['activation_183[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_180 (Batch  (None, 5, 5, 384)   1152        ['conv2d_180[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_184 (Batch  (None, 5, 5, 384)   1152        ['conv2d_184[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_180 (Activation)    (None, 5, 5, 384)    0           ['batch_normalization_180[0][0]']\n",
      "                                                                                                  \n",
      " activation_184 (Activation)    (None, 5, 5, 384)    0           ['batch_normalization_184[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_181 (Conv2D)            (None, 5, 5, 384)    442368      ['activation_180[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_182 (Conv2D)            (None, 5, 5, 384)    442368      ['activation_180[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_185 (Conv2D)            (None, 5, 5, 384)    442368      ['activation_184[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_186 (Conv2D)            (None, 5, 5, 384)    442368      ['activation_184[0][0]']         \n",
      "                                                                                                  \n",
      " average_pooling2d_17 (AverageP  (None, 5, 5, 2048)  0           ['mixed9[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_179 (Conv2D)            (None, 5, 5, 320)    655360      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_181 (Batch  (None, 5, 5, 384)   1152        ['conv2d_181[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_182 (Batch  (None, 5, 5, 384)   1152        ['conv2d_182[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_185 (Batch  (None, 5, 5, 384)   1152        ['conv2d_185[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_186 (Batch  (None, 5, 5, 384)   1152        ['conv2d_186[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " conv2d_187 (Conv2D)            (None, 5, 5, 192)    393216      ['average_pooling2d_17[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_179 (Batch  (None, 5, 5, 320)   960         ['conv2d_179[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_181 (Activation)    (None, 5, 5, 384)    0           ['batch_normalization_181[0][0]']\n",
      "                                                                                                  \n",
      " activation_182 (Activation)    (None, 5, 5, 384)    0           ['batch_normalization_182[0][0]']\n",
      "                                                                                                  \n",
      " activation_185 (Activation)    (None, 5, 5, 384)    0           ['batch_normalization_185[0][0]']\n",
      "                                                                                                  \n",
      " activation_186 (Activation)    (None, 5, 5, 384)    0           ['batch_normalization_186[0][0]']\n",
      "                                                                                                  \n",
      " batch_normalization_187 (Batch  (None, 5, 5, 192)   576         ['conv2d_187[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_179 (Activation)    (None, 5, 5, 320)    0           ['batch_normalization_179[0][0]']\n",
      "                                                                                                  \n",
      " mixed9_1 (Concatenate)         (None, 5, 5, 768)    0           ['activation_181[0][0]',         \n",
      "                                                                  'activation_182[0][0]']         \n",
      "                                                                                                  \n",
      " concatenate_3 (Concatenate)    (None, 5, 5, 768)    0           ['activation_185[0][0]',         \n",
      "                                                                  'activation_186[0][0]']         \n",
      "                                                                                                  \n",
      " activation_187 (Activation)    (None, 5, 5, 192)    0           ['batch_normalization_187[0][0]']\n",
      "                                                                                                  \n",
      " mixed10 (Concatenate)          (None, 5, 5, 2048)   0           ['activation_179[0][0]',         \n",
      "                                                                  'mixed9_1[0][0]',               \n",
      "                                                                  'concatenate_3[0][0]',          \n",
      "                                                                  'activation_187[0][0]']         \n",
      "                                                                                                  \n",
      " flatten_1 (Flatten)            (None, 51200)        0           ['mixed10[0][0]']                \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 1702)         87144102    ['flatten_1[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total params: 108,946,886\n",
      "Trainable params: 87,144,102\n",
      "Non-trainable params: 21,802,784\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# view the structure of the model\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mI7Oz4rzz1fU"
   },
   "source": [
    "# **Step-5: Assigning Cost and Optimization method to the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "noAvEMlxJsPS"
   },
   "outputs": [],
   "source": [
    "# tell the model what cost and optimization method to use\n",
    "model.compile(\n",
    "  loss='categorical_crossentropy',\n",
    "  optimizer='adam',\n",
    "  metrics=['accuracy']\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k2E3GT3Z0Kyk"
   },
   "source": [
    "# **Step-6: Performing Preprocessing on the input Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "VRuJx32FJsPS"
   },
   "outputs": [],
   "source": [
    "# Use the Image Data Generator to import the images from the dataset\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
    "                                   shear_range = 0.2,\n",
    "                                   zoom_range = 0.2,\n",
    "                                   horizontal_flip = True)\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u4_T7As6JsPT",
    "outputId": "8de805c7-8e6a-4561-e656-7295f2a2a732"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 6805 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "training_set = train_datagen.flow_from_directory('C:/Users/onkar/Desktop/LY P/Input_dataset-20230125T202411Z-001/Input_dataset/train',\n",
    "                                                 target_size = (224, 224),\n",
    "                                                 batch_size = 32,\n",
    "                                                 class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PQV8jgvYJsPT",
    "outputId": "be7f8c95-3b0b-4f20-b1ec-bfeea6ca83c6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2445 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "test_set = test_datagen.flow_from_directory('/content/drive/MyDrive/Air_Pollution_Dataset/Validate',\n",
    "                                            target_size = (224, 224),\n",
    "                                            batch_size = 32,\n",
    "                                            class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PKPXHM8G0jxD"
   },
   "source": [
    "# **Step-7: Fit the Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "493WlXYXJsPU",
    "outputId": "571f294a-57c0-41af-ff40-d1c64f1dc892",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1972: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "213/213 [==============================] - 1522s 7s/step - loss: 0.6459 - accuracy: 0.9453 - val_loss: 0.0069 - val_accuracy: 0.9988\n",
      "Epoch 2/10\n",
      "213/213 [==============================] - 107s 504ms/step - loss: 0.0047 - accuracy: 0.9981 - val_loss: 3.9689e-04 - val_accuracy: 1.0000\n",
      "Epoch 3/10\n",
      "213/213 [==============================] - 106s 499ms/step - loss: 0.0970 - accuracy: 0.9855 - val_loss: 0.0333 - val_accuracy: 0.9951\n",
      "Epoch 4/10\n",
      "213/213 [==============================] - 106s 498ms/step - loss: 0.0529 - accuracy: 0.9929 - val_loss: 0.0096 - val_accuracy: 0.9988\n",
      "Epoch 5/10\n",
      "213/213 [==============================] - 105s 494ms/step - loss: 0.0110 - accuracy: 0.9975 - val_loss: 0.0273 - val_accuracy: 0.9951\n",
      "Epoch 6/10\n",
      "213/213 [==============================] - 107s 502ms/step - loss: 0.0255 - accuracy: 0.9972 - val_loss: 2.9895e-05 - val_accuracy: 1.0000\n",
      "Epoch 7/10\n",
      "213/213 [==============================] - 109s 513ms/step - loss: 0.0317 - accuracy: 0.9957 - val_loss: 0.0723 - val_accuracy: 0.9550\n",
      "Epoch 8/10\n",
      "213/213 [==============================] - 109s 513ms/step - loss: 0.0084 - accuracy: 0.9985 - val_loss: 7.4886e-05 - val_accuracy: 1.0000\n",
      "Epoch 9/10\n",
      "213/213 [==============================] - 109s 511ms/step - loss: 0.0079 - accuracy: 0.9988 - val_loss: 0.0256 - val_accuracy: 0.9951\n",
      "Epoch 10/10\n",
      "213/213 [==============================] - 109s 511ms/step - loss: 0.0300 - accuracy: 0.9965 - val_loss: 1.6401e-07 - val_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "r = model.fit_generator(\n",
    "  training_set,\n",
    "  validation_data=test_set,\n",
    "  epochs=10,\n",
    "  steps_per_epoch=len(training_set),\n",
    "  validation_steps=len(test_set)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qJBU6eIpJsPU"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "id": "3rLH3pfqJsPV",
    "outputId": "0056360b-0ddd-46f0-f4f9-7770673664d4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the loss\n",
    "plt.plot(r.history['loss'], label='train loss')\n",
    "plt.plot(r.history['val_loss'], label='val loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('LossVal_loss')\n",
    "\n",
    "# plot the accuracy\n",
    "plt.plot(r.history['accuracy'], label='train acc')\n",
    "plt.plot(r.history['val_accuracy'], label='val acc')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('AccVal_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OKAII6udJsPV"
   },
   "outputs": [],
   "source": [
    "# save it as a h5 file\n",
    "\n",
    "\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "model.save('model_inception.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kjYo2LckJsPV"
   },
   "outputs": [],
   "source": [
    "\n",
    "y_pred = model.predict(test_set)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HAC3DGTHJsPW",
    "outputId": "f805ef67-c7b1-4041-8de9-ff38ff6608b7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0000000e+00, 2.2542253e-20, 2.2843137e-14, 2.0930392e-37],\n",
       "       [1.0000000e+00, 0.0000000e+00, 6.3773278e-22, 9.4818261e-25],\n",
       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 1.0000000e+00],\n",
       "       ...,\n",
       "       [0.0000000e+00, 9.0824435e-25, 1.2668066e-31, 1.0000000e+00],\n",
       "       [1.0000000e+00, 1.7591634e-15, 1.3245913e-17, 6.1878869e-30],\n",
       "       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 0.0000000e+00]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TqRA64zHJsPW"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "y_pred = np.argmax(y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0Jf18lSOJsPW",
    "outputId": "633259f9-ca2d-44f1-8434-ee1f973af67b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 3, ..., 3, 0, 1])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KRgolRvGJsPX"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pZvcasMGJsPX"
   },
   "outputs": [],
   "source": [
    "model=load_model('/content/model_inception.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EbZX3KKUYzFt"
   },
   "outputs": [],
   "source": [
    "# Load the weights fromt the fine-tuned model\n",
    "#model=load_model('/content/drive/MyDrive/Air_Pollution_Dataset/Model/model_inception.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RYIZ-0eNY9Ma"
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import img_to_array,load_img\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "%matplotlib inline\n",
    "def pred(img_path):    \n",
    "    img = load_img(img_path,target_size = (224,224)) #Load the image and set the target size to the size of input of our model\n",
    "    x = img_to_array(img) #Convert the image to array\n",
    "    x = np.expand_dims(x,axis=0) #Convert the array to the form (1,x,y,z) \n",
    "    x = preprocess_input(x) # Use the preprocess input function o subtract the mean of all the images\n",
    "    p = np.argmax(model.predict(x)) # Store the argmax of the predictions\n",
    "    if p==0:     # P=0 for basal,P=1 for melanoma , P=2 for squamous\n",
    "        print(\"Sorry!! You've enetered Irrelevent Data\")\n",
    "    elif p==1:\n",
    "        print(\"Moderate Pollution\")\n",
    "    elif p==2:\n",
    "        print(\"No Pollution\")\n",
    "    elif p==3:\n",
    "        print(\"Severe Pollution\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kgCnUmpos2vu"
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 256
    },
    "id": "uF-414Z_bIeq",
    "outputId": "1dcd2bab-58f1-4e8e-a844-ce69e7c8dbb7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moderate Pollution\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred('/content/drive/MyDrive/Air_Pollution_Dataset/Validate/Moderate Pollution/7.jpg')\n",
    "z = plt.imread('/content/drive/MyDrive/Air_Pollution_Dataset/Validate/Moderate Pollution/7.jpg')\n",
    "plt.imshow(z);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "dMjFzevoY9nS",
    "outputId": "31a1360e-e101-4f5d-cf40-3bfa355dd607"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Severe Pollution\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred('/content/drive/MyDrive/Air_Pollution_Dataset/Validate/Severe Pollution/12.jpg')\n",
    "z = plt.imread('/content/drive/MyDrive/Air_Pollution_Dataset/Validate/Severe Pollution/12.jpg')\n",
    "plt.imshow(z);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "fR7LoZ_rB8vG",
    "outputId": "112879ca-766d-4df9-bf66-395146b52a2a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Pollution\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred('/content/drive/MyDrive/Air_Pollution_Dataset/Validate/No Pollution/71.jpg')\n",
    "z = plt.imread('/content/drive/MyDrive/Air_Pollution_Dataset/Validate/No Pollution/71.jpg')\n",
    "plt.imshow(z);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "yc3rDXc_shc0",
    "outputId": "dd4edbd4-8b85-49a4-a9d2-753433dc4179"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorry!! You've enetered Irrelevent Data\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred('/content/drive/MyDrive/Air_Pollution_Dataset/Validate/Irrelevant data/21.jpg')\n",
    "z = plt.imread('/content/drive/MyDrive/Air_Pollution_Dataset/Validate/Irrelevant data/21.jpg')\n",
    "plt.imshow(z);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 257
    },
    "id": "eggFojojm0kp",
    "outputId": "67a81887-0cee-4c40-edbe-397d8c0d3533"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorry!! You've enetered Irrelevent Data\n"
     ]
    },
    {
     "data": {
      "image/png": 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/23Hqgm8YBt29vYyNjuI6DigCTVVRFQ1VU3EdB6ko9K9ZxfDFSzS1ttIbj3Ji8AK6oVMqlatNY9+vPnCKUn0orArReBPZzFy1X1pV0TUDRXjYrk9LIkEqlaK1rY255Cy261Vjy6NxdF0loGvoZoCZ6UkcV5JINKMoCq7romka0vdRNYVCLkfZcghHwuRzeYSi0tHRRrlskU0n8Rp1U4P7ALHAxF+/bh0Vq0JTUxNnz55bFO5oGAYb1q9HVVXOnT8/HyFTwTD0+jb79+/nQx/6ED/5yU/48pe/jOdVx/Lous6b3/xmdF2nUqlw/vx5Ll26hBCCXbteT09HJ4efe4HUmZO4gFBUpO/Vj7tt2zZOnDhx0y37K7lnRH/nzp309PSwZ88eDh8+TKFQqK+Lx+NEIhHa29uZnZ1d1JufSCR4/vnnyeVyTE5OLjrm3r17eeGFF9i1axexWKzuH/O8y62BdRs28pa3/hI//N53uTA4gKYHePSX30o8GmVyKk1fa5iRfI7eRDtd7d30dLRhFy227tqL52Z56cVDrFrVjaFHURSDeJOBhs7I1BjDI2O0t7ejSB/b8zA0A9sqUSiWiMai6LqG6/l0dPfg2hYSgWVZdHd3IRSFSqmEbuh09HQi7TLJTImNa3s5eeoc6zduJBxQmRAqrcEguiZoamrCKpcQikIwGCInxOWWS4MG9wlCUUin53hw505mZ2eZmpqur2tpSdDR2YHv+7S1tTE2NoY33yKoWfq+77N+/Xp6eno4duzYIkNSURQqlQrr16/nxRdfrC9vjscJKDCWLrHrwQ2YXVGenZ0gn7scdXM9LqSmpiZ6e3spl8sMDQ3VXdLXcox7RvQPHDjAjh07OHr06BIXTSaTwbKseuzqQoaGhnjkkUdwXZeBgYFF60ZGRnjwwQcRQiwayKEtyHVRLpWYmZ6iXKmGc7q2RUAPc/L4KcLRGOfHioQjGhfOD2IEA6TnUtgeFM/Pgm+jhAxKRZsic6gojE1mUD0TPRYmOTtDk5uoNieFoOyW8PyqRZ9KJgGBY1vogTB2KY8rJa7jEQoFcVwfx6oQCobJziVRlOrIz6GRcdo62rHLRdLpEgEzgKap5DIZjGCEYj6H0AMIoeA3BL/BfUipVOLd734CXdMWjfIFSCarA/k0VWV2dhYAVVUWiWlvby9f/epX6erqWjSOwHVdTp48iaIorFq1apEWnTpzBnv1Wjat76OnNc6ZyiUKpcWDx17NrbOwDN3d3Zw7d47Vq1cTCFTDza+10rirQzbD4bCsDU9+LawUmlcLffJ9v95Eg1oHSqk+sGfRPopCR1srU9Mzr6l/Vyjzfv9bcflr937+2GYwgGNZBEIhysViw7hvcN8huezTh2r6i5qxt1w/IFR1QBGCaDSMrmv15bqu4/vVVCnXEjUkpUTVNMKJbiw1weqQTXJukrm5zJLzLiyLlJJisbjE3aMoCnv27KFQqA70O3v27BJ9Gxoaujfj9G+W6F8vdd/+fPTO3XuFGjRocKtQhMAw9CWW/o2wMC0UXFsQ0Uqiv3r1aizLIplM4nnesi2Eq4n+PePeuZ3UbrBpGpjmHS5MgwYN7nlqdcbNyEc6Ojr6mgYV3huiLyWmuDzjiwO413H5tHnfXWFBJsgbLccSbtACkIBvtqFIG+zs9T0M9bDRRQW5xvMKnEgXnqqj23nUcvqmPIjXgyIlTW61mWrhk1e4A+l5JQEVYpokbQtcCTfnlbyxstR/3aE0xQIwNai41xnMfgXNzc20trYyMjKypH/tF5Ga61hV1fpAUt/3SSaTtyz08rWOIr83RF8IdgV0IoqGgseQ43DWkYRjzXQ0h5icmUP63ryfHCyrUtdF0zR57LHH2L17N6dPn65mzVM1Nm/eSC6ZJZVJVZtIAIrCli1bGb0wSDzRysT4KNYCn93WQoFfy2TQgIuaxlc6OggnWmiOxSjm5sgVSyiqxubNm7gwOIhQdGIBg6nUHAIf25nPziklvZqGueotjLZ2Yh/6G7BdOtesxc9nSHQ0MTw0AQik64EiFr1AnT70C49xQ2HH9izfOdKEpgdY27+G1OwsZauM67goqkZrWwuVbJbZbK5+LQutD1Bq30SlbTVrf/jnCLvExge2USnMMZtK43oehmESCBjYrouhKJTK1fjhWnTCxs0PUC7MMT0zh1BU4k1NxIMhRsfHaO/oIZ2awXYsKpWlL37Ig/cEWtj2nsf50dPf49u5KXQzwNo1q5mdnaRi+QQCBroRwlQlM+ksq3u7cCQU82US8RBTqSxWbg5fUfB9SXNTM7lcGs8XqGo1tbbtuAQCBqXS0pxKAQX2tjgEVEhagkNpDdUIkkiEMMwAyek0EllPzywQROJN9HS3cnFwGNv1qkaIEFiVChIIxZvo7WxhdHSClkQLuXSaiuMQikTwXId8vrCkHCBpMX16Iz7HUxoSMM0AQoDnS9ra2wkogtHJyaqbQVGxymV8KVF0g7amOOlcDjF/b7RAiI5YhNm5FBXLQVNVypUKpmnS2dbGpdHRZd2VnRHB4xsN/ucRC09CrK2D/q5Ozp8/S3dfH7qmMHppFCMUximXsW2LRGsXqdmJevrx7u5uPvCBD2CaJtPT03zhC1/AQ9DZmiCTy+O7DghlPqmbRAgFy7Lo37iRbGqKYqECQkFIUFSBYzvVFONCYAbCSLeMJ1SioRDFfJ6KY2Hbt3cSlVAohKZp9ZTLgUCAUqlEZ2cnb3rTm/B9n2AwyNe//nVKlsOOzVuYHr9EulBACAUUjb72dmZTs5RtC0XRsB2bNavWMjM7gREMUZhLYzm37nvdE6IvgZf6wvjaFrYqA8jUGEyZ9K/fxKY1HWwte0TCUWaLGQJInnnmAPlSVWw6OzvZsGEDk5OTbN68uZqQSaisX7+F2FaDim8hPY1QUPDUt76FHozwxre8jdZghHPDA/z8pZ/XX5I9uRzv1jTYuZPxH/yAb7a1Uak47H7s9aRmZkg0xxgbm0IRLpu2bacj0YohFbSwzsjweX7+8vH6dwoHK/zGzs/yafkgHKnW3M2d3Wx/8CFU02LLlodQfZtSwWYuk+RnLx+s75trf5ANOry9coR/6+5AHrVp6ewmGDB50+v3Q8AllyvR1BJH8SWnX3n5suj7Pk1jP8Pq20ilo4di1xailw7R17eGeGwj6VSGkKniYCB0BUXxMHyFQjZPZ1cf2XKW88eP0dvXTcjcwFypQETXcVSN5mCU7t5O2ttXYzklZtPTHHvlEBXbXZSOWgpY//hjdGzbjPutbwPQ2tWLXcyzdfvrCQQN8nPTXJpK09ISYiaZprOjg2y5TLlsg2+TaOskkGhh47ZNZOfmUAkiRBE10I4R9PCyWU4OTrB9xwZ+9twBsvnKojLYPvx0RkdSbUH6QMAIs+N122hr72BidILO1nZKnkNIC5KaGmEkOUd7Rxdr123Ckw6ioqOF4Nmf/BhVNyjbPt1tPWzd8RABTUG4klK5wEQyST6bYXDwIp7n1RPWSUBTBLvbXAIanEyDJwV79+0nEAgghIOma4RUg4dclxmrSHMgzKmDr5C1HPJWha6uHtZueoBowCTR3koqlyemBNEUBVv3KGaLHH3pJV7/1v0EKiWmpqZwfR93QeACQhB/3Rs4XUnhMwBIIolWOtrb6e1bhaZaXBiZZf+jjxEJmIyPT2IqHuki5DLTddHftm0bBw8e5LnnnuMP/uAPSCQSpAol9rzhjWgI8vksZrQJShZKRKK6GseOHyEeirFp3XpMw6Rol2gLtlDy8thoBD0PT9VQVIO55BjtnT2UCnkMoTKanuToK0fxrgjEuJWsXr2ajRs38u1vf3uRxS2EIJ1OEwgE8OfH5Ci6wYa169mweT2FVJpQczOFSolWPYKPShGLUrnMwJEj7Nyxm7KfB1Xy8o9+Uhf9W9FauCdEH6oejYe052mlxBDVgRLJiQtkUtM8uHkToxMzlCqzFIoVnAUjjmZnZ0kkEpw9e5bBwcGqxayq/PyFZ2lta8bxPBzbI6hr5IplxoYvYueyTEhIZhdnWzwVDvNiOo328ssMNjVhKQpuucDPnv8Z4VCY0bFRTFUllU6jmlHsYh7pC4rlEoXc4qRQgyWNf5ztxZrJQKX68EwMnsOenkQKj3g8Tm9XJ2fPDwKLa/2KV+YrSgStKCiOGyBtklMTRIL9nB08SaFcQjVNmuYiVIplZtOXzy0E4FbofOFvUawiDiFAcvHieQw8hKbOZyn00M0QCBC+xCpbFEsusaYA06kkuaNlLFvQ3t7ExUweLRQC3ydo6oyOzlT3Uz2amhI4nsfM9HT9WpZVhaeefYYNPz/IMbcECJIT46xa1YepawwNnKFSsShXXDLSQdM0xkdHSRcK+CjkUlmMiEVrPMSJk8fxJQgX9ICOoWRI5zIYiiSVLnLsqEU42owR8JiZnqA2BMNU4aFmF0VA0YWjcxq2VeDIoaNEmhIENJ2x4VEqnkPYjFAuZcmWyoyNjdG/bi1mOEpCNxi+OEouX8TxcgRDESZmJhhLTSFtB1M1KVk2QvgoQqW9vY1sJrsopUFrSysiYjPmhfGp5mWXvs+FwbO0tsSZmk6Ba2OaYVzVZahoUczlmMvnEZpOySpTTJaYlYKp1Ay244Pl4NguWizChtWrcTyXSxeHcIslWjpacSo2M8nUomfqkmxm6ML5ushkpif42cgQ2x7eTXJyBM+XXBhwwHFA03ErBcxI6yJROnfuHG95y1uYnZ0lHo+TyWTwfJ8Tx4/hWRZS1WlqaiYciBK0HEZGZ7k0Ok6XD5nSHMKX9K/fxMXhM4xNzaAYJgFFULFddNMkoKvMTqdwfA8hFWxZob21jbJVIpVeHAlzqxgYGGBwcHCJi8X3fVpbW/F9vz4RjmfbPP/iC7R0tFIu5InEY5RthwlXYjkerqayceMGbMfi2IlDCEOCBxX3cgW2UgeyMj/I80Yqu3smekdRFVQBzKdDXjB+Ck1VqU4c5C3rdg8EAsRiMVKp1GuzCKSs9yvI+Z8b9ulLCaYCnkSsUCRNVRdbZAvOfeUScQN+WCfUhlZOI+S1XhOBqgi81+hTlFKiKiqGqlFx7UWDSlRVw/NuR5NdEtclLaZkoqTM17vXfg11w0D63nxuphstQfUeV8OGZX0+AlXV5q+JXDRQ8HoRioqhq1jLhB5fWQ4lEEJaFZBL05SsNEFHLW35Qpqbm7FtGyHEogGUCzEME89zlv1ugUBgkXv2budy6nalPjNbLd/9qyIEAdNY1gUKK0fvxONx3vWud2EYBocOHeL48eNL9v2FiN7xPZ+VHv/lhHEhlUrl5gxtFmLFMlz/oQTYV3+yV/peS6XpxioevTR7nXvI6rR8rxEhBL70qbiXJzipcXsEH0CQdQTZG5xe1bkJWR0F1ZzwVwrnzboG0vewrFevlAQgK8snALvajEzLGVDXkubYtlfu4L3VKQhuNrVr4Pt+fR6La0bKFQX/auzevZsjR44wNDTEBz7wAc6cOXP3zpx1I9zNLZEGDRo0uNksjL2vuZEWGkblcpn29vb6xCnXmn6hxl0t+q7rkkwm73QxGjS4r1lpxGqDm4+UEm9+Tu7p6WnS6fSSnPoHDx5k37597Nq1i6effvq6XdZ3teg3uHlIKRdFrzT4xUZRFCKRyLJzOl8N3/fr04bWxL5medZCdtX53FQN8b92aimdryXGPhKJ8PjjjzMwMMC//Mu/LFlv2zYHDhy44bI0RP86iEajCCGoVCr33Ew9juPcc/7SBjeOoig0NzfXBfpa8TyPcrm8IBWJvcjSr+Wp0vVqBN19K/xS0u84bK5U8IAjwSAzmrYksEPTNHbseIievjU8sG0rI5cu8r2nvkX2Kv7/WgVxvffuWrknRb/2wMHKFqwmJbsrFbo8D0NW0y2VheCsYTDe3FzdRtPq6ZQVRaG7uxvP85icnCQejyOlXNQL39/fTyKRYGBggPHx8Vv7JRs0uAXU5p3IZDKLMssuR20CEID29nYCgQAjIyP1yUEMw7gdRb5hNO1ykrRXa+k2NTXV3SRCiKtG34RCIba3tvIfT52ir78fa2SE88kkf97RQXGB6CuKwtse/RUefN0ukkMXyaTSdGzYyP5fezff+cqXcO+Q4XjPib5hGKxdu5QfIIcAACAASURBVLbebK1UKvV80gvZbNv83cwMQYBoFOZv4nHD4C/e8AZet3cv2WyWAwcOUCwWCQaDfOhDH2Jubo6vfvWr7N69G13X+dGPfkQikWBqaorh4WGGh4evq6f8bqQ236mqqriuWx2w5jj1yR9M06RSqRAIBOjp6QEglUphWVZ9eaFQwJxPTKSqKlJKHnzwQY4ePYrnefT09DAzM4NlWei6Xh9RXLtvq1evrs+DcPToUebm5ohEIkgp6erqYnp6mmKxSCgUwrbtehlN06RcLmOaJpZlYRjG/IAn/74Y9r8csVgMXdfxPA8hxIoRNJqmsWHDBi5dusS6des4e/bsii1WKeUiV8QnPvEJ2tvb+djHPlZ39VxvB+LtRAjB7t27sW27Hk558eJFhoaGlmyrqiqPPfYYxWIR3/fRNI0nn3xy2eMqisLWrVsxz5yhLRQi9thj+KUSPf/wD0Q9j+ICd1p7Ryf79r2Zcr7AyNAlkqfOUomaCFUl3NRMdmZ62XNc+T1uNvec6Ash0HW9Lh4rCXCL5xGQEqHr8NnPwte/Dt/8Jq2ex/jFi/zeH/4hY2Nj7N69m2Qyieu6OI5DIpHgIx/5CJFIhEAgwPbt22ltbeXf/u3fFk2KcK+i6zrveMc7KBaLxGIxAoEApmnWX2LP82hqaiKfzzMzM0NXVxeZTIZIJFJv2iuKwtNPP82ePXvo6OgAqm6AcDhMW1sbnucRCoUIhUK4rkupVKJQKDA0NMTb3vY2HMepbyOlpLe3l29/+9s88cQTuK5bjw3/9re/zW/+5m8yNTVFf38/lmVRLBbxPI9AIFBvpbW2tvKd73yHsbGxO3lp7xiFQoG9e/diGAbPPffcitvpuk6xWCSTydDc3HzV6UcXGlGRSATDMNA0bdFkQ77vX3efwe2i5obdsWMHpmly8OBB1q1bt6zoA1iWVX++r+Z3rz1357JZTqdS+H/7t3j5PEdUlfQV7pi21laCRhCUAmO5Mjg227dsQAuEiQt48RpEf7noxdp9q7W0FrbIroV7TvQ9zyObzdYftpUeWleI6iAm34cf/QheeQWkxBOCuWyWycnJ+sQDLS0t5HI5SqUSmqah6zqqqjI+Ps6mTZvIZDL1zq17nZoFV5sf1HEcJicn6ejoYGJigq6uLlKpFKlUCk3TmJiYIBAIUCwWmZmZYdWqVYRCIVRVJRaLoaoqo6OjGIaBoigEAgEMw6BYLHLp0iUSiUS9aT01NVWvpKenpwkGg6iqSjKZRFEUgsFgPdZ5ZGQE27bJZrPE43FSqRS5XI7p6Wl6e3sJh8P1gTFzc3NMT7/6C/SLimma5HI5pJSEQqEVXROVSgUhBFu2bKGjo4NisbjiAKqFFmY4HObFF19EVVXi8Xhd9O9WK7+GruscP34cVVUxTZOzZ88uu52UEtu2KZfL9eRpKxGNRgkGg4hEgie3bOFrAwMUNI0xXce+4noUigWKhSyaa9HS2ko5n2fzmi4uBWYpjd74COKHHnqIcLg6EfzIyAhDQ0PXFaRxz4m+7/vkcrn6A7dSPukLus5h06TbddHme8AtTeOZYJCilBw6dIjp6Wl2797Nyy+/zKpVqxgdHUVKyZo1a5iZmaGjo4Ovfe1rbN26lVQqteQc9yKu6/Kd73wHqDZVF/oxa9dR07S6u8dxnPpEM1JK+vv72bRpE3Nzc3zta1+rz8tb26+Wd2ThpNM1N5LnefzjP/5jvSwLIxqklBw5coQjR47UKybP8/jmN79Zt/5rVtjhw4dRVbVeNs/z7uvIJMdxOH36dN01sRJSSgYGBlAUhXPnzrFmzRpmZ2eXfX9q90ZKyfT0NKdOnSIUCjEyMlLf5m618qGqE5OTk/Xr4TjOVVuCyWSybu1fTfQrlQqWZfHoo49y/PhxRg2Dygotg7HRMQ4eP0FTMEhfa4REfyeGoiDzFQZODV7T91iuYi0UCpw9e5a+vj6ampqu+z7c1WkYTNOU3d3dN7azlJjzPyrVoeaeEJSEwLvLLZRbgW3bjeid+4haYMKNRO/UWg2+76/oPtU0DVVV73pr/1axXAqK5Yg3Jehat4MpP8HruwPI0ixj06OcPr1yqyMUCvEnf/InXLhwgX/6p39aUpHv3buXWCyG53kMDw8zNDS0pCwXL168+WkYhBB9wBeADqqa+nkp5WeEEAngX4E1wDDwfinlnKg+HZ8B3gmUgA9LKQ/f6PmvoYBYQnB/du0tZWF8dYNffBRFWdIZey3UjEAhBIqiLGph1ZZrmlZP+HW/cq0DorKZNLkjzyJUkx+e1hFeGc+99kCQ5a7x0aNH6wEVN3KPX4t7xwX+i5TysBAiChwSQvwQ+DDwYynlfxdC/Cnwp8CfAO8ANsz/vB74/+Z/N7gNaJp21aZ/g188Xi0kczkWtvxrwr+c++B+FvzrRfo+0i8D198vuJwnpta/eKP9jDesAlLKSWBy/u+8EOIM0AM8Aeyf3+yfgJ9SFf0ngC/I6rf4uRCiSQjRNX+cFXmts8Q0aHC/civfnbvZLfyLxF0bsimEWAM8CLwEdCwQ8imq7h+oVgijC3Ybm1+2SPSFEB8FPgrVJuq9NvK1QYMGDV4LoVDolh7/NXe/CyEiwNeA/yylXBQrNm/VX5dJIKX8vJRyl5Ry190cHdCgQYMGdyOJ/m1XXf+aVFUIoVMV/C9KKb8+v3haCNE1v74LmJlfPg70Ldi9d35ZgwYNGjS4HlQNNH3ZSZy6tr/5qru+lugdAfxP4IyU8tMLVj0J/Afgv8///taC5Z8QQnyFagdu9tX8+Q0aNGhwP7Ok70QIjJ370X7lCYg4mCM/I//V7+Fmrr1T97X49PcBHwROCCGOzi/7P6iK/b8JIX4PuAS8f37dd6mGaw5SDdn83Rs9saIot9zv1aDB/UK5XF4xBFEIQSAQqIf7SinrI1dX2v5WRvbU0oWsxPWc/2rHupbj1Pa9lZ3aV5ZB6d6E89j/TjkoCLUWiW97kJbIIBf+8sQ1TzH5WqJ3nmflefp+aZntJfDxGz3fQh544AE+97nPNUIQGzR4jUgp+eM//mN+/vOfL1nX1tbGxz/+cR555JG6keW6LqdOneKv//qvOXnyZH3bWloOXddveTinbdvkcrlFFZVhGESj0evShFoFVigUFgl3OBwmFAqtONK1lqPKdd36j+M4tyRa6soKpRRbS2ZGJXTpBQz/EPKj/Zg9PigCvGtT/XtSNQ3DYNWqVYtSLDdocDdTz0i5wnspqab/FqI6xf31WI8LU2iIBf/XjsySZZfxpU8wGFyyPBKJ8JnPfIb9+/cvSk8MsHbtWnbu3MkHP/hBzp8/X8/df7uMMNM0SSQSpFIpfN9H13Wam5uvu7IRQhAOh1EUpZ5PKBKJEIlErmnf28GV5/EKWZxCGXc6iV8ZIP+TGVKvjCGvUfDhHhX9Bg3uJWp5hSqVCmvXrKVYKNLS2kqlYlOpFGhra+Pw0SNopkFrc4LOzk4mJiZob2+nUCjU526IxWJMTk6yc+fOeiK648ePY5pm3crs6+llYnyc173udWi6xrGjx2jvaEdRVMZGx+jvX4frVnMWBQIBwrHIshXMvn37ePOb34wQAtd1uXDhAoFAgHw+z5YtW+jr6+N3fud3+LM/+zMCgUBd8NetW1efc6JSqSzKB1QLv65NEFLLm1XLn1RL9107p67r9e8VCARYu3YtJ06cwHVdVFUlGAxSLBYJh8N1cdy2bRuapnHu3Dk8z6vnkapZ7bVzwOXMmsFgsG7t1xKZVSc/2cHY2BirV6+uf59arijbtut/13JLLUc8Hq+ntbjRZ2chweljGBNHsWKrScnHyH1/lND0Ja5nrH1D9Bs0uMVIKTlw4AClUpnzXeeZS8/NW6vtnDl7jN179vDCSz9HUVW2b93KpUvD9Pf309nZycULF5mYGCcYDFIpV3h41y6+9c0nuTg0xJ49uymVirS0tHDgwAE2P/AA50+fASDR3Mz09DSKonBx8AKDg4Ns2vQAX/7il8jn8wSCYd78pjex48GtywrS+vXr6+I4NTXF4cOHWbNmDcVikS1btiCEYOPGjfVUDTX6+/txXZcnnniCUqlUz7wqhGBgYAApJWvXrqVSqRAMBpmYmGDbtm08+eST9Pf309bWVrfim5ub666cYrHImjVriMViPPPMMwD1yqT2WwhBT08PgUCAN7zhDUgpKZVKdYGupf8uFoskEgl++MMfcunSJaDqnqolC4SqN+Gxxx5jbGyMRCLBhg0bmJmZoaenh2KxSD6fZ2JigtnZWdLpNJlMZtnsprFYjLVr13LkyJEbenaW+PTtAm0vfZpKy2aELzGyQ6jW9WXsbIh+gwa3gVrq6bHRMQzDxHFczp17gdbWBC88/yKRSIRypUJHWwfDw0PEYjGOHT2Ka9ng+azvX8v5c+cZH5vk0sgYqqpz+PAxNm3agGEY+L5PxbIQSNLJFEeOHCESDlMsl7Edh0KxyMTUJGXLYi6TQckXmZiaoivVsWwn7vj4eL0yyGazbN68mUwmQzweB6oV2djYGL7vL7JyU6lUfV/TNAkEAkxOTuK6LpVKhXw+T3d3d31Cno6Ojnpq7La2Njo7O+tpzuPxOHNzc8zMzBAOhzl16hSlUql+rlq5F/rSi8Ui8XicQqFANpslmUzS0tJCNBrFdV2KxSIXL15E1/VFM38tTDcuhMDzPE6ePEkwGGR8fLw+beTIyAiapmFZ1qLsriv58w3DWFTmm4HilAhNHVpxvVu5evqNhug3aHAb2L9/PyOXRgjoAVy3OoFMOp2ipaWFbDaHaRgIRcEMmDzyyCPE43EUIYiGwkgpCQaDRCNRMoUia9evQ1U1SqUioVCA5uYmHnnkEayKRUsiQaolSU9XN5FwmKmZGSKRCJs2bSKVSvHLv/woqXQaz/OJhMO47vJRO88++ywnTpxgx44dbN68eXG/wfzsXF/+8peBarrhmm/86NGjSCmZnZ1d9rirV6/mxz/+Mdlstj7Rei1p2CuvvMLBgwcBFq2rCfGVc/XWcs8Ui0UMw0BKyQsvvLDIOl5Y5oWfjx8/Xhdqy7Lq4l1rgViWxVNPPVXf98oy1Dpwbduu/70c4+Pjrym77Y3MlXvp509ddX1D9O9CFja2G2mt7n1qrpD29nbKxXJ9QE3f6j5A0jcfHiiEQFEVNL06t2tbWxu+6+F5Loqq0tPXh0Rg1Scrl+h6NZFeS0sLruvi+z5r1vajCgVNVUl0tFcTfgG9q/qQEjq6OvClRADhyPJRKul0mk984hN88pOfZNeuXfWpMT3PY2hoiE9/+tMcPny4vmxubo54PF53tazkwx4eHl70eWEr48oQyivX1XBdd1H0jmVZZLNZotFovaK4kiuX1c5Vm6inRs1FEwgEluy78BhXlnWl7/ta05nHYjHa29vrldqVld+V7h8pJS0PvZPTRw+ueMyG6N9x5IL/QUioS/18sEdD+O99dF2npaUFmVh+wpKFL/LCtMU1twNc9jsvFMNaFsyF7gkAtTYJyoLjLEctk+ZyDA4O8vu///u0trbWXSGe55FOp5dkeHQch2QyeVsieJazqsvlMpVK5bqs4lro5ZXLstks+Xz+VScnWThX8K2K1a+lsr6e62qEoldd3xD9u4BauJ6HREcAgtorqtAQ/l8EbqSZDlWhX7ifoihLBODKSgMuPy+vJkX1UNIVqE1zea3cyRnMatNy3gwWVrZ3kuXujQasFj4BJJekQuE61aEh+ncBQlb/UyyL9PAoVq5CdE0fwdZmQMy/wQ3pv1e5HTHdK53j1c7cSJF8d7M0DQO8TXN5VFUQmmDCLfBPbpi0e+1p1Bqif4ep3VIvV2Lo//kcpacP4HmSqYe30vF7/wtdux5CLjPkpkGDW4mUC563ZR+8+WeyVtnUKx1x+QBUW7HVh7z2pF8hYpLL210D9/vkLZWYxqnVAWLNv81kIMyjLd9k5+kpfnL02lPQN0T/jrH4IZ/5/rOEhsaIOQ6l1CTez1MkU1O0/MNn0SNX99HdcuSrtTJerev5ta2/3M0hQQoQcpmt7m8xuOlIQCoIRUEVCigqmmogFA1FNdD0AJoexgjGMEMxRLAZocWRohpt5JaLeHYOu5LBKafwnByek8d3Sniei/QdpPQAH6SP9CV+3Tde/ZHMf55/PDRDn3eTKYC45ikLg8HgVWeZqnaig+/LRdtX+zsEnrfYzaNp2iI3UrUPRkFRwHFurXvLV+BAfwRFNemeHuIfTijM+Ql8OXnNb0BD9O8osura8QX2ufNEZIlMdhRLk5DP4V8copzNo0ci3AlRkxKEkEjhV8WW+WLIK0tzhWgvqSNeY6VQz14gESgIFk7TIFY4ZoPXQu0Kq4AUCqpQEYqGUExUI4Sqh9HMOHqsFTPWhxHpQ4v14asmnutj5/JYuSlkYQSpGMiSgkQi8FEUG9+TeJ5ESgG+QAiJkJKqxksk/nwDQS563jQjyHve824U6TF4YYhsJo3tSPK5GaLN3YQNgRGKUspnaW1t4diRk/z6u5/gxJkz5LJpWlrbKJdcQgEFRRUcPnyYLbv2s6FN5fzoLAjJ/re+haef/C6BSDs9HQbZvMXk1AxN0SCnBoZ56769FEolBs6cJN6zji3rujHDbcjCJP/+5PeuOfHZjRDIeYjJEic6v8d6+pidTLH+4TWcSadwrGuz9huif8cQl4VUSnTpU3JdzuZTmEIhqhooXqRufYjb7NKviaznSQ4fTHFhoAA1uRXKou18/AUBR9V/l5vtIF9tvbhsSS1aL6pLqtdJQeKxYT08/HBnvXPzPm/t3zIEoprEa6HXppocCCkFPgqoBpoRRjWbUSPdhJu72bQ2iiIlx05myHgK0i3g2RnQc+AV8HwNIV2kX41Qkr6PlNX7X8s9BMp863LxJO0CgWuXyXkaCdPnDbt207Omi5FLSU4c+T4bd/4Ss5PT7N77eiJWkeFUnmJ2ikR7J7/7xtczNjJKV+8qRKrAmZkLBEyT8fER9u17Az1xnTWbMlieTyAc4+2PvZNYUy/xWJnRySRdHS2MTCSZSX6Dh3btply2SURdvv/iAJs29zM1OcnObevQFQXbu7kdwAujg4QH/UdKFBIjvOIUae7oYG56Buc6ZhhsiP7dgnSwrTJlu4JqBskW8sSbW/Ac9/qmHruZRUIwOWXx8Y8cJBwvEwxEEEJFVQykqFUAXLbKqNndyqIYVH/BN6i+0spla2iZ9WJ+bp/q37UYaRXfC5OcGuVzX8iwc+c2bsLEbw1WQMxXuPVKmNrgJh+kxJfg+wLX0/FkEESUjtY4v/uOOCFD4S9KCkfyBWQuUL13Yv4YsupGkT5UQ9TmQ0slKELMtwYEfm3befH3fB9l3hI4+LNnMfwy6zZu48Bzz7Jq9WouDk+RLr1I1NR4/kAJO59meq7A7HSaH//oBxw83EIpn6Wnp4dcMslUJo2qquSKFs98/xvYnkIiopOzFLpa41iuwOQUFTdPOpMnFI4SNhVmU2l++MMfUCpVKBeTuKUCJ48cJWDGePlnkzg3WfCXICWqDfEph5ycIsMMqri+czZE/25AAL5PSEh2KAFMTydvGjjpPJeePkDLH/yHO1Y0xxYIbY7/7U9b2bFjM4pQ0RWNeVsQkEhxRbigXCjGV6yXl0X92tZT9+BkUwq/+4GLpNPXl2ukwY0gUOYt7Nq4AaFcFn+BqPqxpUCRgPQxdI9QEAxDkkgI8CWapqDUQk6FQBEKKApSVVBRwZfVvgOqYZJVP76PIqphy8p8OGptgJkAZseq+XLGJ34CUE/xPD55eNlvMps6Uf/7yNGlOXCymeyiz6df5crURg3XGB648Cp7vDauNg5AwVviTvK9q/crNET/jlN1kGBbqEJBVXRc4aOj4Ds25YHBeWv3zvgxqnaWQmtzC6v7Vs1b6re5DPO+LUP1QdGR/sJIkIZ/59Yg6xa+nI8fk1KCkPPuGB8pPSQuUlp4XplCuUzJkThCUCiWEVgoio2mOHjSw5M1P33VMq3nuqHqz6978/zL7kCEgCvGEoTCYRQhMAMRYhGd2eQcjuMACr193dXcP76PqupYlRKev7xg3gvkcrl6wji4Isppha81Mv0PVz1mQ/TvJPN+UonEA6TtoAVMXOmgC0G6YNOyc2MtYOUOsrSj9XZJrZyP2BEsGK3c0PlbjpTgC4mCREgfX1ajbBR8ED7ggXRwnCJKaQ7MWaJGC7omMVVJc7SM7yaplOdwnTJSOvjSrlcYotbCq+XWUari7nv+fFeXuNxfo1y+4YoZ5Lf/1/9Ee1DDy6u4cRvdVqmUKkxnC2zdsJpksYLhVDBDMb72pb/j1IWx238BbxKe51018mg5uh7eA6+8sOL6hujfYap9lCqdv/Mepj6bIXfiFKonoL2Vnvf8Kj3v/dW7RuPuVDnqFuCiUjSs/FuNL32EnG9nymqHq5Qe0nORro1rF5AVE0VLohQD2OkoyfEEwZBOPjOBXR7Ds9K4dh7XLYP0ABtfethOmXKpCEiCgcD8sathOtHmNrbteB3DQxcYHx5CiFofT9XVc2HgLJPSoj28AeFUmBpN0d+9io6mMAMXLqKHwhRTMyQ6+li36QFGplLki9cnnHcLK7l1AoEAhmFQKBSWjBwOtXRd9ZgN0b/TzFuxyuYtuP/pw1zs7cOTHpve9Ab69uxG6AZC3n7rVsiF9v3dIa6Syy9BNX3Aq6cRaHBjiGVad+AjfQ8PB5QyIdXkgf4mjGgTk7NzHD9ygv96dohY3CBfyVLOT+E6SXwni/RKSN/Cdso4lSLlUoFKpYyhG4SCJhKBapqEjCB//n/9n6Tn5li/9vf54z/+L8yOj9aLIe0KB576bvWjqPr0a8/AcsnIahk8f5HY/vAOdvzHN+LEJO7LWZ7+u28tTt/8KjGjjfCHO0ztdVJRsbQAqbZmtr77cZ5+6SUmkmmkorLcUKTbVTZR/6Pa3L5T8lrtzKvFdlxrZpkGN8z8YLiFnavS9/Clh/RdpGfTF/UIVKaYuHQBtzxLKT9OevYiQ4MnmZu8iF1O4tt5bDeP4xRx3By2W6Bil3A8t/pMqQqqGcAIR3jvb32Qt//Gb9DUFOdv/ubzZHN52jo6QCiLKvZax+bCJHO1RHS1ZQs//yIRDofZ/vtvRN8Q4afWC+TWT7B3367rOkbD0r/TLIxfnw9Z2/TAZsanpnjp5Zf59Xe/ez5+7g7I7RV9RnfOnpbVQWJSogpQVIWGvXLrEYhqSK70qV5vH6SHFAq+r1DIzrGnL8KJ4Sy2GkaoARAqnu9h6i7S9SiXsjh2FqFkkbKC61l4vodQBGrA5IHtD7H3kf28+x2/zNHjJ/n/2XvvKMuu67zzd8699+VQ4VWuzrkbDTSIRAIkBZGSSYqkaFmmRAWOZI6k4dJoPGtMW7Y80qzRLGnNSB7LmmVbaSRZliULoClxxAgxITVAoHOjc1d15VyvXk43nTN/3PteVTcajVQNgmLvtbqrXr2bwz77fPvb3z506AAnT5/lt/6v32R8ep67jryDe47cw988/jieE8gUtztqdXd3s7i4CARwR6vV6ujwp1IpKpVKR4G03dqwq6urI8vc1dXF+973Pubn5zl27NjbVodo43ElEglWUhV2r2n+/b+dYn/E48VL4zynNa3XOOO94/TfjiagK5sln88HH+/AF0DA9pBoZDiVv2NvgWkNG4rxCBO6aJ/5iuL8YpGoadFyymivCcIkGk0SMQX1Ro1GfQXlV9E4oFx810dLg0S2j//uk59k5/YdLCyvELFMKtUqhjR46plnOXH2JY4cPsKRu++mUCgyumULU9fGEEKwf/9+arUa8XicgYEBlFIdvX+lFNVqld7eXlw36AXc7tVrGEan7+6VK1e466672L59O8PDw1y6dOmm7Q7fblapVOgpJ5m98CXuf3YS+aEDPFytMyoE4xvaVt7K7jj9t7F1QIzvOG69cd96w/8bl7h9x3ddAlfoO2W4b5W1q0DDeZ7WGqkFiCCh62qDs7M1+vpzqHqTaCrGlu070J7m6tUzrOQX0MrGtETYoUsioxl+4uMfp1xrsWPnDlbzBR687x6e/PaLHD9/kVPnLjI6vIV7j9zNti3DPPPiMWpreWanZ4BA52ZqagrDMMjn8/i+TzqdxrZtIpFIpwl6qVQiFot1mrHHYrFOs3UIWDFXrlxhdHSUUqlEvX7rFoNvFzMMA45V6VncTj43R1+rxaTW5DfIb7/afOWO038b2sYWb+JtEdW2S+O/M3tv71YhQ1EuteGv3+lr8/fbZAjvSBGInAUUWoUMOz24nkOrUQEtUZ7DwvQl5uYmaTTrnWW1MOkfHOUX/smnGBkd4szFK3z4A/dx7OQp+gcGePyJr1Atldm6dZADO/aR6+0GNH/12c9y/vSLrMwtoBUI08D3/Q6k07aVlZU3dG7VapXHH3/8bQvr3Mzq9TqP/9lfIYAXXZcDL7zAiUiEogylK17D63DH6d+xN2btwgH9VjjdwNkopRG6ndS74+xvp+lwcJXhdVbKR4pAdbPNitG+j6dtlleb2J6mtdIK6JxaBwlfabJ91y4+9KGPkK9UOHz4IH/1+a/wwR94L0dPneHAjp1844Wnybeu8K8+9ascO32WVtPmb770ZY5+7UvgO2jXQfmgMTC0REiD/sFB7HoFzBgRU6KUB8KgVMjjq0Als43vwzqLp+PchWCgr4+VsI9vu8XixuBKKfWyv7ergt8OpoErlsXlGztqaSjOXL7lunec/quZ7vz3+vzMrQLR67575Y1ubOb8RqL9V9O2vNV6waHdcBJBWSZhRdnr3OobN0lwDXwt8DFDmQbFBn7RHdt0E2HtoF7XQNqggyeFQbNVx3Fq+D6hVo7GD6RZyQ0OYyZ7+MVf/DRbBnpZzBcZn1rgH//QD/L82XNcHrtGdyLGow+8k68+v8iVq1eZujbOiVOnWc2XadabKN/GkgK0RIb6MlorpGEFMxApE2sLawAAIABJREFUyQ0OMtLfh1KaEyePs337ToQwSCYT1Op1UokY9aaNXSsyubBGbzpGw5ccOXyQF06cYfeu7URicaqVCpl0glq9QSye4MrF84zu3EdMAlLiug6V4hpTs/PfuVuywW41O5k79c1brvumnb4QwgBOAPNa648IIXYAjwG9wEngk1prRwgRBf4cuA9YA35caz31Zve/+aav/yE2POmvw9o+euOqndu0YVsirDhtU+SCdUUIp4owjyZ43SHGm6ji7RzedZcirMIUOmzlKIIpfmeZG1n9m++MVTupeN0AeMfp3x4LntzAwV8PMUopMQwD0zRIWoFzrbZslJCYkTi9A8P8we/9LrMLK3zxqef5N//qlxDmLF/8xjOcv3SZgb4+9m3fzqG9e3j8K18j7fbyG//Hb9ATNVHSwC0WyGRzpCOKUsPFrq9huz7xSAQBuHaDpuMz0JOmWlxjotFAuy6NRoNisYjj+mRSSaq1Go1knHrTJmJoWvUqi40qhhXh8vg1WraN47Qolqt4ngvaZ2V1lWxXD0pp7GYTbQj6+nPMzRWv58J/B+3V4Kjc7iPMzMy84vebEen/z8AlIBN+/i3g32mtHxNC/AHw3wO/H/4saq13CyE+ES7345uw/9tg7YsqmcsXqdRrSEC2tUdusWbgu0XomHSHXy6FDIqJEB0tGdnWIgkd18TSCiuNBhcnZ5hdWWOx2uDi7HxATgwqkzbu5BaHr0O1gnYkrK/bz8vO9YaHKFg6oEUuzWrqjmRqfpXc+HygtiuDbQmtyKST5HpzoQrvxs6+m2wCpAYpQcowx3DH4d9GW38HNG0FzPXvIhGB70ukkCRjUWpK0pMbJJnpQVgZ6nWb/bu2kkol+fxXvsVTx8/xvgfv4fj5C/zQ+97LyXPn+cpTz/HN546jSqukrBQffORhevft5tTRJ1laq/P+9zxMYmiALz72e7x0YaGz93wIy0xNvlzorO3sXqmrr9KgHJvp6WmENLh8+SruDX11V1aC7VevXkJKg6mpSRqvUwrhrbBQxeVlNnTXI/CtL7ziem/K6QshRoEPA78J/DMRhAPvA34yXOQ/A/87gdP/WPg7wOeA/yCEEPptmUUJLqcCfudvPs9jX3keQRRPhpKvr2IyDLMlHnuHB3jw3nsYm5zlxKUxPCQKgdKahNfAKOURKhCdSqcydHV182f/7RfpSaYgk+K3//yxUM62c1gEkysRilUFtv61oCkMtBHH6h5EGBKlHKKFPFHtoVGdDkVShjMJrRCyvb1gwFI6uAJO8V6K00f4F7/+O2S6o6h2PYEArV2G+rr4v//Xf81DR+5a7+K+yaav+02/TZLbf79NIAL8fgMvK5OIMDzcy2xB42kX33ewXQcrnmXH4BBWMkGub5Bo9zYaNjx94jKnrkySTSaJptIYlsl9h/bzzeeO8fWjJ1nKr+FrD8f3aBbynL18mWyjhlKaWCzGxMw4hSuXqDV8Esl4MOveRNPKx32V2i2l/Ledwx+KRvjhvixZKXiyWOVkrcnr0ZR7s5H+7wK/DLT7+fUCJa11e+icA0bC30eAWQCttSeEKIfL5zduUAjxC8AvQDCNfOtNdH5qoOX6VJouWhj4hkC96nMXSM4KYEt3kj/8F59mx8gg+UaLn/6N3+O5S+NIbaEReHYTa+IlnGqRnq4sMpslpYcxFsehK0vMTiGnp6hUKpiGQTyRoFapEotGiUSjVMplYrEYhmFSq1ZIpVIIIWnW64iuLbiRHMIC4dZg/BSNWoFkOonrujiOTTqdpl6roX2f7p5uKpUyhjRJJNLUKmWEUEhvO0YzSnO5hFcsYrdcUok0UhhUq2XKvV3883//p3zpd3+drmzytt6Z9YnOHYd/+y3A6IWA7lScPTsH2bV1iGQ8QqFi88KpSzQ8G6U0rutipYZwUj088sBe0JpvPX2UaCJNJp7Acxya5TJfe+ZFtDY4ff4qNb+Fwsfw60ScOq5b4/mzx1BnXgxUNhGYhoFSCtfzMaQknc1imt/bRXkiavLxAyN8MLcdO5vmUfcl/rdTi5yYtdeXeZVtvGGnL4T4CLCitT4phHj0jW7nRtNa/xHwRwCWZX1HZwHrUXQbJpGvyd20J8LDvTm29uWIAD2JGNu2DvHtS9dCmEfjC0HEsjBjSZQRYa3apFSfwtCSpdU1RLGAEJKoFSEajWJIg3gshjRNlAAzGsWMxVBKYUajaMNASEkyYqJNgY8NOorQEtOII60EhoiipYERjWKIKBEThKVAWUSsoEmK75uYRgwrYqDVl0jzBIbRxNcgTUVUegh8MnELjCjlegPnVTS836xpCNvrqbAi947dTmv3OOjtybB9pJ9IxGJ+uRB+Jxkc6GUhv0bLdUjHDVZW82SUYn6xC6e0yHBPnuMnPZarir7+NAuLqxTLDTylcfHojxv4rSZuqxw0AcmkA5FxpXAch650Gsd1aDQdDEu9DWpV3h6mcnG+/sgIjdjPs9gs8CP79rNbfosTsydefeXQ3kyk/wjww0KIHwJiBJj+/wN0CSHMMNofBdrp7nlgCzAnhDCBLEFC921qug0m4AsQwnodyIUGobg8t8jXjp3gPfffw5nZeV48eTZAu4VGKo0v47h9hzG0RwOfbHeWTDrF8twMKJ82YCNzMoSWfKQIEr8asND4OjhGicAJ17CAhtTYZjBbMo0kzcHtCL0FJ1xWAK22UJWU2O08QNjCDsAXbbxG44XXQyCp+QqlAnhISYuYkQ7R/NvnjEXYS9XHRQiDDey7O3a7TAhWihVWiq9UqSqJRmO4Th2cCxRL8MVrz75sqdUNOUUz/Oc02ltoE8ECUoCQkmjMpOX6aAyi8TjRTT2p7y4LuXLrwWbT40ISpPcUPZdn+K9nfFZqJoZp4XtuZ51b2Rt2+lrrXwF+BSCM9P+51vqnhBD/DfjHBAyenwH+NlzlC+Hnb4fff+vtiee/3NYv+mvxMOu3qWh7/Nx/+EtGer/AUqlGpeEjO12lNEpapHbvo6erm+mZKTI7t9Pb28OiSGJKI9DZWF0jnc0iBJTrJbq7slQqLXxXMTTYz1qxhO04bBkaYnF5FXzFlpEBphbnMcIaJldKoiMH6BvIMTO3QCqZJBqJUigW6e/vo1arUa83SCTiCCGoN5r09+WoVqvYtk1/f47l1VU8z2Oov5/FpRVMw2SgP8ficp6UjN0gCreZnjikrYqgxZ7SCinX6YR3krm3x/r6+ujr6/tOH0bHtNZMTk5i2/arL/z32ESxiXpygvP7K7zLzbK2MMvM4hpC+695G7eDp/8vgceEEL8BnAb+JPz7nwD/RQgxDhSAT9yGfd8W07yxGiQBVG2fy/MlEAJBqJipAS0xhE/cMhno7aZayONUqyQG+vBsm1gqTX9fH8vLS2SSMXw05ZqmJ5NAeVCq1BnoH8C2XVzbYXR0lFqtiuvYHDi0h/nVeXxHhTMBUMolZplI7TDcv5VEIkGpsErclGT6c0xOTrJjywgtu8Xk1DSZVAyJz2qrTl9PlsLqEp7vMDrQx9ryMsm4xXCul7XVVaR2byPOviGNq9t0NbGBnXTHbod94hOf4J995jOosALWNE36B/pDFtr11/2tgF5s2+ZjH/sYYxNTPPr970fUS7x46jQtz8OUkpZtc+jueynmlzhw6DCXzp5gqVDjwfvv4eSJM0gp2L7rEF5znun5Ejt37mBuZgbHdTBNi1arRXduiIfuu5uTx1+gWm8Fr6lSCGngeS6WZeKrEOwVYDsulmGCgB2796CcFiNbt/D80edwHHdTzvtllSgazOOLcHGVs8pg/45ttHwH73X05t0Up6+1fgp4Kvx9AnjwJsu0gI9vxv7eatOi08OZDpd+w/8vn1AFSbCghF2gN/SM1azz52WbxROPB7LFUuC4Lp7WNFyHsckJPKBSrWG7gYZIIb9Gy5ZoLRifnKBaqaKU4sy5l2g0G8Qsk2e//RwIH8sQpKwYVc8DpZienAStmZ+bxzItjHB7WoPUgrErVzGkRGrN0vxiIFTl+0xNTdMKNUymJyawhKBZrTEzPY1UGvnaqr/ftCkFQpid26C5g/PeLpOG5NlTl/nrJ05j2gsYQjOydRcf/eC7ObBjtFPlurS8xOVLl3n00Udvci9uoALfrFrwdcz1hRD4rk1dG+weGOFnP/UAiWgEz2vwF3/xZwyOjrBlaCsjPT1kH2hyZqLOT/3UT7Jjy110d0XIDQ2i9BpXruUZGhwkbkaxWw1cFFMXL7PmSu46eIDB0S3s2jJEo9WkXquR6OpldXGJWCJGKpllaX6OrnSEv/jcV/nUp36WqOHTcDy6EjEwLXaO9vOf/vyzt616VwC65tLE5czFKy+Tz3ebtVuuf6ci9xWtDSuo9cKj0Nt02vZ1/tiGdNoWUt2EREpCDH79e6lBhJWt0hABxtm0SSZTVMsNMAwisRjJaIxGvUkylYQWONUW/d3dLBRaNNwmmWwXtVoNlKZSq9KVSbJvy1ZSlsZ3bbSQxAyDSLybxWqFC2NXQQhsz8UJB5Fmy6adqvZ9H+UHdMhWo4GvNQpNuVJFhbLGe7bvYKmwxvzCAvv27mZyZhbVeuuKVgxDhvDvdwUy+F1sgm8cvcTVyxeoL8/hC0H8/BSDW7dxYMcoWmvK5TJjV69gWmYAu92Q09FotPKRMnAzMyt5xhZLhOkZDCGRQtOTjHNo6yCmlGitaNkuruej0SRiUcwNYmJoTSm/yoqTIN3fj2ll8N0qnuczMz1NJhZj584tLFzOs3V0J9968ihbB7Zw9tJp7sskqDtNtowMUyqVmc/P0JXLYllR6rUqTdeg7riYhmBlZYVCqYinJb0ySsQw8HyP1YV5rl6d4p0P3UWrWqRULhOVPvPLedKxGK7XpN5svDKJfrPuTkda4uXfjX3rsVuue8fpc7MKt/WIxVCCiNZgCrBMgj6eHloFqoFoEzBAqLAoSqOlQEgz0AyXRvDwa915CKTWEHLvXd9HGRJXa2bnl5DmGq6vcV2NI30UUG+18JRGmCbbRrbis0a9Oc+uXTtoteoUCkUO7dtLobBKtVLBVi4R08TXmgW7QTxSg1iMRCKJXauSjSfYtmULExNTZDIZUpkUV8fH8cMDlAL2btuO63uMz86GV0QQtUyUUti2g2VZgQjX65hWvllTSqP0W5O83ajb0v78PTWr0OBFNYWWS7x7mFLTxo5EceX6/b548SIvHj8BQnD4rsNks1m01niei+e6nD/+dUpT13jkR3+eRCrDn3zzDH97bhkrk8T3FZXZKRrKY/doD5//zE/Sm4yzUq5SrztMLC5RXFpi//ZhDh08cN2hXTn9AleAbx19ml27D7CyME6x0qRQOQfA6dOnUL6HkJc7kspKKZ599uh1mjqdeywlfligdf7MMXzfxzAkSrWfAYlSAW20XePy4vGj+L7PH//Bf9xwZIJ2eLcZ2co3mvI0Y4lbf/+Gtvr3xGq1WkezXr9s7hlUzvq1OlGtEFKF1bhB9OuH2u4hmQaHDRWLGhTBKOz7XocN0/ZWUqswJvIpVcqcvnARW4MrBWiFNgyqzSaVRg0tBfliCSnBjJhIK4YKJRGk0BiWRErBcP8AS0sLjK0thmXzQeNppcHTa6A0WgukZRGPRBkdGmb62iTJSJTR4WEuj43h6+D8UNDVnUIpzfjsTIey6vuKpfwqtXoNrTWr+TxN2yb6ljAodSC8pdoJq9vrgJVStFpB045YLHZ9zUjnWWlXy93qePSG/9trrQ8k69t544NZew+bmuMQmoL2yHs+790zSqxY5VK5ggqDbs/zOH36NLVajVgsRiqdAqDVqtGym8xMjXPsqSdwF6fZ9673sXXvERqOT9dgP9ld+/DrTdKL1yhVC6SXVlhdnCezbRtrhTr1ls35sxcY6Y7z1FNPsXfP7g0DriDXP4ByW6wVSly5fA4hDTKpFJVaAGv4vodpmnihI5emxdaBfibDal2lFPFUmu50kpWVlc5yQkgS8RiVai2Ugm5b8P56/vrf/PD329GZq16vc+LECaanp2/p+G9sD9m2PY/+GGMXzr7iet/TTv/YsWP89m//9k0iuHb6RNFyBftcD+kVaFfBmr7C0BotAiy8YUjGY33UjKBGLXCcocSCEAEYHVbpQsBACeIBha01K4U1lBYgjKAy1iBYVkk8rTA6vkFSqTXwtEJpRa1SxhACrRXNRp2oZUFYZesjkEp0mk3Trq3UAt91sZstDCHwfR+72QpeEteh3XzacRykNGjzZAAc5TO3vIQM9YiuTU6gtKC/K83ttdCthZMlIUXYH3eTtr5hQy3boVyp8MJzz/HNb30T1/U4dOgQP/4TnyCWSiOEwBTr9RpSis4gK8IE8w1HjUAgRXgC7ZkD0DkJ0V5ed66/bdtoHVSmBhbeic4u1o+5fV/DD5tzTQDTkGBIlDBIpZLEGnXatVGtVotarcq18XF+6qd/GkMGo0E8niYWS2LuirBy+BFiu+4iN7QdAE9rmvUK1soC2vGoI2laGSopg0x3D4ZpMtCT4sLFJQwjyXve8y6mro3TbDZJJILoVUZi/Mwn/wkGmrnZSSq1Jk2vRVcqg2GZLC0u0tPdze6dO7k0Ns7S9Dheqp/vv38fM/Nr1GolKmuLmFsO8dH77+Xp554kkUhSrtukE1lSCfC0QblSIBVPsLhSoDsVxYxEsT3B7Nh5zlwc25yL/Armui6f+9znADBvUNE0pKYrqVAKSg0D2jpdG3sDG7d269/TTt/zPJrN5q2n7RriAL4Tfl7H9wU6fNnjeCRQbWa/DlcMf3QSndcHdmhkUAOgFYggD2DJFhFToYlgqyimVHi+jxIST2tMJAnTwNKKhGUREQJfKCxTYoZSCg6hI9NhW0Et14NRP8wliEATSChF1DRDaQWB1CIYZKSBsILHwwq9rR8mtH0RnJCEzkzndiosaxHkQHxAaK8zp9oMtGVjo/VarcZnP/+3fPvYCYZ7u+jt6aJUrfPNJ5/i4uQUFRGj5WsiltXRookaFolYDCkF0UgEaRgYpoVpmpimiTQE6WScTDTAhQ3TQkuJaZpYloERjRCPm8SlJhWxiEiTs6dOceLECYSQfOSjP8z+3bsxrYCLLQyJNAwsARZVvOYsAoWWKaLZfQhpborjF8BH7t2LnS9hKoHb0nzsobu4b2dQYJ9MJfn0pz+N47qkUkGU336PhDBIp7v4gR/5qfBzMFK8d+8wk6uXcFcmQUD/thFyQvPA1hxd6TRSCLoySXbtGGHf3u10Z1Msx+PXOX0A5Xk8+fQzPPDAYbK9fUQySbpSvXQnJGuVEi+dOofnKLbu2EVfwuS5c2OUXMlo/wDDDx7mT/7wjxjcKqhUK9z/yKPEfY96s4JrdJOwWkzNzfN9930fly9e4WMPP0JhZYmlpXmMxCAZs3nbnf6tbNeQ4N/+nIU0Y8zY+3CVwd89t8iXnpp7zdv4nnb6bbslXvuyScAG2kHHr4uXcas6S+kw5ag3+Pz2eh3nH9iAJfhgrsmP3X2VsfJOPj85zEpyCxOz89i+j2maJJNJihPjGKZFJBqjWKpgWREM06JQKAWpAqE3DEDtCDGIKgWQSKVotlo07RaZriy1egPX89ChO9UCUtkM07Oz6HbKVNDRPtEhpKE2HPxbQZ/ssDU3GVtvtVq0Wi3m5uY4dvw45y5cpjE8wMd/9GM0HQ8hDaqey8Xxq6zVPaQVQ0sD5XloJFqDIVT4aJhh8K5R2kTpgKibjjr0ZaMYBijfJaIVMQOUlWaq4IDXImL4bE1FKCzOsm3bNjKZDF/8whf4SqqHs2NzaGkhhUcmFecjDx7k/XubpCNXkFRxjSyRQ7+KiPSwGV5fCsFH33UfH37nO8K5XvD8GKYMJ62CTCYL3Px+BPpIG7o5ac3H3nWYj77zrvWF2s+OAKMNnwnJ0EBfZxs7d+7EMIzO4Kxcm//yF/+J/FqRmdlxzFiS3sEh9g318tLlccrlEulkgmMnTxKxLFqNKqVqndXFOXK5PqrVEsVCkeXKk6xdOsvi8gq9Pd3sv+dBtveV+K9ffoJmy+abX/8GjUaTr3z5a8QiklqtTiSexm2W3/S1fTMWT2bYec99+DLCFsCSitn5Al9+BajnZnbH6W+CGRqUaDePBmFaGGak8317PNh4U0QIH2nfx/c9QGP7LcZrgscvpVhrapYqRUpOhEgkSqtex7UdXjh7Gtt2aDkux0+fpWk7tDyXE2dfwvZ8dLu7VHtqEaJKHaKp0MyvrFBrNPGAsalJ4vFEAC+F62k0Z89fxEejhO7AS0FUr6+X1EfiE+H2ijAE56I3eToRJB09arUapVKJiclJisUyvqewonEEBoZQ2LaLGY3itpoMJLvo7e5DtRr8gz0WO3sNDNUkamqi8TgyGqfhOlxaafDUeRfHNclksxyIzvOe0VUs2cQyICoEUQ1Vv84fXhniWiVKobDASqPE4QMHePTRR4GgK5RI9vDE8yfoHx4NlFm1y4njR3l4uIdcVwEDB0wLsal3QSClpNVo0Gw2SaVSnT60N7JSPM+jUqnQ1dV1ywFZigAKgwC39n2fTCYQ53VdF9/3AzgrhB2llKTTAXTYbnuIVqyGebjV/BqwxvLiPJdPr2PuN+uj1Wo2KRaLnc92foVyPliyUCgwNT0L2sdtY/mrNzuD77yAgNY+zy6P8IX8e4nLFhmrRZd3KfjyTmP0t9o2vAlSojfSzLjxPREhfivRUgZMHuVTRHK0meF5uxsUCOWg18phIlXhO4plu4ARDi7FYinAuYVmNXygA4aoDGcffuCcwwGg7TMbjkOzkEcCdtOj2GwGLUlESD7VgkKpHODnQnbokeHcoaPo137GCo0Gf/uN5/m5f/iDWKbxsrPdFGvnTDd5QqGUwvM8HMehUa/TqNbJpLM0Gi0MI4KhNPFYHBGNkrUsTENw5dxZumSTuNGkrwzdMZ/e3iyDuS1gQLlVwGlKZob2UKjBsRefo39Lk/SWLG5zGa1dbG0ihIVn5qgtOSg/yY5cllqx1WnqrXyfSCSCxMHyajQKi2RSERpFm1U/j+tqTNlA+O3Z2ObPtmZmZnj22WfZvXs3iUSCZrPJ4cOHuXr1KqZporUmHo9z9OhRDh061FnPsiwsy0JrTalUIhKJ8MADD7C4uMjg4CBHjx7l3LlzHDhwgJ6eno5WfTKZxPM8JiYm2LlzJ0eOHOk4/rbF4/FAiC1sfC6lxLZt4vE4nufheV6nJ66UcgPsJHAcB9M08X2/M8MXYV7M91VnvbZprTtJ27eD1RoOZ68UGJVfZl90nC9WP0Ju8QaZjFd5DO44/U0wQRAFS72eaBOdb9aXuS7JpwnhFLlBuVNiIJEEyVwhLLTyOvRORKBt7rPBrQrVEccSbU0eDQoV6NUAWrQfWqNzMFoHceHGI2xvU4eaO0GDFxnGj+tLdhR5dBC9uY7Hb/3nr+DUG/wPP/lRYpa5aXDPxuHDV8GLaxjt5PImwBhSEolESCQSdHV1o3yfeCTG4bvvYWxiiu6eLjLZLNV6lR/50Hs5e+4ipt/Lhx59J5nFM1T1Espo0rQt3JqBtiKU7QxuPMXi+UkKDcEjDz2EbozxxGSdfXveTV+uGylgsVDm4tgcW3dsZWfPIC8ce46udJJqtcqlS5doNBo4jsM7H36EbDJFy3eZnpjB8j3uGomD8sJckUL47mttkfqaTQjBtm3bOH78OKVSibGxMbTW9PT0MDExEbBg4nH27t2L7/s8++yz5HI5Wq0Wtm1jWRaZTAbTNCkWi+zZs4crV64QiUTwfZ93v/vdjI2NUSwWKZVKlMtlenp66O7uptlscuLECXbu3Hmd029DPr7vI4Qgl8uxsrLC4uIie/fupdlsMjs7y/79+6nVarRaLaLRKFpr+vv7aTQatFrBwNpoNNBa02q1WFtbwzAMkskkkUgE27bJZDKsrq4yPT29iVf1zdnkgssf/cdvBJN25aHMKXCCpu7r/KZbPwV3nP5mWycKvvWF7xRXsF5gEUHQbWlGEyu0PMl8K03v8AjSkFQqATaplUZphRA+A7keFlfXQPtIIJlIo5Wm2WpiSkFXVzfFQiGAbQAlwuZ3neif9XxDG6On3TZjPUnbbsYSsERUG/lHCREOLIqyEvz2557GFVH+l5/+EKax+VGn72++lr6UklgshhCCbDYLwqBYqTO7uEw0HoGFBarlMrt3bufIkfsQmCSj1/j2C8ewGmXuu+8e9h/czkqrzrlqk+eeOkGzZbNj1zCCCpbfoFXMc3FqFe06xLeOMjD8Dqq1GlemL/LE6RMoijz0SAJ8F7vVZHFxkatXr+L7Pj09PfT1DfDJj/8jtu/aw1889uccf/45lPI3MDY0yEiQxN1ki0ajfPSjHyUSiZDP55menuLQoYNs3boVwzACQT/TZOvWrUgpA2aaUmgNkUikQ2lUSpHJZPjABz6AYRh0d3djWRYHDx5ECMGlS5cYHh4mk8l0OPRa6+CebLB2UZgQgkQi0XH+bTMMg66urs5nIQSVShAJJ5OBrDhAuVwmmUzSDLXyRQgpZTIZPC/o87u2tsba2nce0tloWoMfFlYC4AUO/zoSsb41jfSO07+dpq//9eauSoSOWDBoGPxwV5Wfv+ckZwpDPD6xlwnfx0ezc+c2Tp+9iJbBsn25BB9537186evPc3DXbuqNBsP9o8wvLDK1ME9XKkkyleHgthE8pahX6gz15njx3EWKduvmRyLXY4VAS12TlZK+eAUhkqzYMRqORyolcZygcMyKmGhXYQhBpWnzjbNX+J9+4gMB3W9TTaKFwjA3N5ErpSQajWJZFolEAm1EWSoUaZ2/SjIRIZpIUF4rMDY5j4fJ2dPHGejNMTszj3Ycth2QxHM76E1EuHJpnEuTeXxf4VtLaEPgCY+GW0MB2jQ5/dIFZhcXcV0nELRzvYDFI3y055Dq7qZYLBKPx7Esi0arxUvnXyLb1cWhg/vp7w0StRJF5+IpAAAgAElEQVSBVkHfYIlECYuwpdmmWdBoR3ac6MhQH4NZA1VbIZPKIc3IevAiJZenZtg1Mkg6kXw5pVZsIP9q3WH8tPMEDz744MZFudWcZX5+vU9te+BxXZeXXnoJpRRCCJaWljoDQhueWVxcBIJ73h6MNhbfKaXI5/OdfEIbAvpusPa11cDUt798y2XvOP03ai97FjYWZwVUyE7y9CardNzrhu5XK57HE4UEl188SMuPMts0qYsSWirWisX1KkIB9+zZD9UmD+8/yGqlxEBvltW1BXp60uD0kuvuwoxYoBQtw2BkqI/FuUUG+noozIUvjV4HoYSUGFaoayMESmm6/Dof7m7xibtO0JT7+OOrO7lYj3Pf3QdxbA9faEb60/zow0fwpcGn/8//N5gevNHmvK9kbcxMgxBeODi9ecffdliGYWAYBtFo0BnMRTK/VAiokEJgCUnEgK994ymW56d5+KGHEMLAisa5eOUqa6UVrKhBtdKg3mphRaP0DfSztLSItCx6+weYmJommUxx8uxL+MoLI2SLWMSiN5NhfOwaaEE0Gu1AIkpp4skEY2NXmZud4+izz3L12tWAzYLGExKXNJbpY8Uym4vtAGtrayyvrGBZJtFIhFx3nHhmELTGaRaJmrkg8lSKp8+e52vPn2D3yDA//gPvoScbROwXz51nYHiIXC7XYYEBLBZrrJRqRISmL2WxWnfxEWzpS5NNJGm/LW1cfqOlUmlazTrSjECoumoYMVqtJloH3PZ2RL/R2g5cyqBxerX6co2a9nq3o+jqdlvb8VdXXrk/Ltxx+m/K1smbOqDutamMnoOnVEhxfHWYR4Y8+G4J+7tb/NiuRVZq2/jqlEG1bwtGPE4kGuPFk6eC5JMWXJ3K8/zqKqMjQ5QrZWL5GsOD/Zy9fI19W7YyMb+MLXRQSm5IIobBQC7H5Oq10ClvODYBAgWeB2ErRLSmri2+XZJMnrgXIWHWzrNGgqeOnSSbDjT0VxZNfuVj34+KRuhOx0BsvvRtO2eCBqGNTY30245fiKD/rhICx4gEcgPaD1tfCrT2iSbSeDpCqdIi29NPvVJiaTXP7OJCJxEohEE0lsDxFU3bJhZPkUpleecj7+HFF4+hjCgyksQ0TaKWxeGD+7n70AGeefoZTGGR682xsDhPtVrl7sNHuHT5AkMDgyilWFpaoL8vh2M3cTzF+ekIC4Uke/fdxbaRhxEkg6u1OekOpmamWVlbJRoJnGsydoBE2gABhpUMqqOlyalLF/jrbz1DpFygkElihPfH9z1mFhf462ee58Mf/hiHtvQRM4O80lePjfHXn32MKBbZhEFdJqiuTfM/fvpT/NDD7wBgrVRgaX6eXC5HV1d3eL8kuYFBWpU1HBklm0ywdXQIjcGZ0yfozfUTjcawIhbNRgPLsvCUwqmXWVytkElGaNg++/bu5srYNQb7+4jGEzTqNZKJOI7rYVkWk9fGGRjdhqEVkWgE23GplwssruRvfrG+i+yO03/ddn0UK0NGSbvZeZDQ1Qg/cKDtnrqv9A4K2U6aCnylka0mK0VFwYa6cplfWcKMJxGyjbRr0JrpuTk0cGFyCh3SKqcW8ihhMFO4fN3+tBEc15W5PD4SiUmHzhnuHQUewdS4TS9tAVeUyXi1B42PjwfCReJQaDaQQrAsFJ/89X+Hj+Da7Crd+0Y3lbuz8TyU2nyOPqxvM4AkZACeiFAdNZA/DRLIZoQjR+4LSKqmQTwex3Ya6wJwGrSviUYiVKo1hoZH0UqwuLSE47okkimMaECPFQikYVCtuxw7cZ5CxSYWy2BaEQYGBjDNCIViAcdx6e7uIRoKj83MzWBFIsyvVfk3v/8M/bl+fvAf9PNP79uFkG1e1+ZYq9XCdz2wIgGLxV0f0Nv1J6VSkS8/9SzNhTl6d+/nY/ffQypiAWAYJtmBIYrHzvGv//Ion3zXdn7i0SMYhkm/0cCZPgXpHi4sjJKLXuPciSf5c7/C1z/bA4bBwtI8lUqVob4cn/nML3f2nUzEqZfBaTZoSkGl3kQ7NrZjE08ksG2XZCJOQ0hSyQTFcpVEIoHrrlKpuURjKWy7het6dHd34fiaaCxOOp1mfmGBeDJFJBIhlc4Qk5pYIkGhUMCxrE28ut85u+P0Q7tpgdaNeF4Ha1xX3mynOIM0aCiuRrucPvQZG7MsN25OteEdTRnBNyo5vlXvQ0mB4/ngNlGlGqA6HGe9YQMGAROnjecJ3S4S2rCfkB7k67aT38DOaZ/qhuvQxl3b59E+W4FAKgOJhxAE6oda8tJ0PjgGITfV4d9oSmm0djefERpGxlqDUJKY0BiGxlUKwwiE80xpMDc3h287GDqY17muEypM6kBczzCCJ0ArIhEDw9igOynAsCIkEnGsSBwrEhR4lVoeWrtkB7dhosl09TB2dYFiscjIyCjCMElns9TrVSYmr5FOZvB9n2bTRUmTR37gB1ko5vG0wmpzbjfJtm3dRn6tSKnaJJNK0HI9fN8NJY4bWNEElXKJnlgMa+9e3nP3fnYODSLDaF4IwTvvPgStFn/y9Wf5/756gZxR5wff+26UFWHL7oM0WxorFSER20o0keHe++9n6sJLDAwNMXBoLwNbdvHC331lg0aW4sK5l8LfNdVyicWFdYz/wvnznX3fDI+3fbDtAucrRaRpceHCBerNVpjDCtaZmZkNqKanT2BaUbqzKVbza98V+P5rCYnuOH14RUZI0DrQCGGbIBkkOg4wdILrQH54xTVahzTMtifm5RTqjnMNWfgIgS8F2hBI0wrgBi1Qng+h/qW+2cb0+likxIYdrp8FQquO0xasR6U3DCEhL//6ddvn3P5NCpBa0tvbw5bRURbnFymuBpr8arOx/A1HJwCtwoFos6J9ff3vWkvwXR48cgAtbC5fvsT+/QdoNOq06g12bd/OpfOXSCaSdHf3MDkxwc5d26nWyriuS29vPxMT10gkEzRbNp7r0tfTw+LCPH25HI1mi0KhwODQMCsreYpNGz+U4Tb0CqM93SzOL9FoNIjHYkEggqDVtOnOZohYO1jLl4BAtltIxbETx+nr6wuam2zy5R8dGWZyZp5nzi9z744opVqTbL2B16qR7e0DBEP9g9y7ZZS68jm4cxvRWAwR6sUIIZDS4F0PPUCyp5cnn36G7bleBJoPPHyE9967H8dpUC6tkUz18Eu/NI7wFcMDOZqeT8ZKon0/0KLaeKteg/N9tWWCmYtD3b3+bzf+dJ0WK6s3Jz58t9r3tNO/++67+bVf+7VXdCJaa/7yied44ewlFBLXDCpvpR9G1Fpj4OMT6NLokPTY2ZoIqnVv9viJdlQmgmSu1kHzZ1cpcEMnq30Qgbga6FCnh07LRS1EZ6Bhg2NGhAJvoUTAutNuz05UCO1I1mcuKvy8fuzXQUSE/HytUUKRn52idPk8pgQVrqbjaYQefm0X/3VYkAAMBSGEum0Sx56vQGpiMQOEwdaRAe676wATE5NcKxXAbdKTjTLQ183O3Xspri2zZWQQ101hmIJoNMVafp7hkSGi8QQJM0pXJsvR55rs3rOLUqlMvFjg4MF9HD9xibItgxIMrVDKwXFdqtUazUaLbDZLPBYlGUsglEb7HulknIH+IRYXlwFFrV6hUFjjgQceCOSC2excruD+e+9mz846iUSUVDLO8vIK8VgC0wgqzqPxOA888jD1YolUKoMRj3eOYqM89T17d3F4945OUjYqCQQCSdOf68dxHLLpJH/3xJfCCvIgzyKERCtFJBK52QHesTdg39NOf3BwkMHBwZt/qQM45LOXFpi+OAdGHGQ0YCsYflD8hAatsLSH0hEEHkGxlEBKE1Oa4KkgAhZtjjshZBJCP0KE+vwqaLpimBiGCQiUEijlbDykADYKufNGSJkjhBR83wuiK9MIdE+0QCkf5fnBEBBgMgGzT2uklqgOLU3Qng+0C8dAhto9AX1T46OFRmqHntVp9vTFWJq5hhaarVt3cHapiOUd2GwSSceTdSC4N+j0Xyn2a2/N9YLmHYsrBWIxi+mZPBHrAiDwHMFavky52MC1V6nWPGzbZa1QYXFlHttp0dc/hOubeJ7B8vQ8Uil27dyB5/vYnkfdsRGhJrsQAtvxkNJEK42hfeq1Mqt5h5HhwaAwy3XYsmUEy4wwNn6NVtNmdCv09g6QzWbxPJtEIsn999+/PhBu0sU/efIkv//7v3/D1RPcFAblFeDR12Faa7Zv337T91EIwYsvvvi248x/t9r3tNO/sVHGy74PQHd8KTB0AJP4IYwiAEPLQHVSW0h0AMcgg+FAGCBNhPCCriQSUIHapRAi6JzVjtRDGQYIZgyiXdCi23MHCZ2UcIibS4FhtcW9QCuFr3wQEgwDLYxgBoFGiaCQRxgG0jKDIUcDCrQKhiEpBFsMTVQIaE/PtUArKGlF3tVobYQDl0RoxerqEh4uSsPy8ipKd+FsMld8oyk/mBW9mUD/Oscv1v8mCLRbar5gbmwxvCeC1fOTxKTBgdEhmg0HIRM0mopaI4/t+Jw6ewFf+0Fv4ekVDMNkabWIROG5Div5NSrlEg3HpdFs0nJarBWL1Os+ricxTYlQisN37SEdkyzMjHHx4kVc1w1ondJkcHCQB+6/n0q1zqkzL5HOZolEk9SbLR4+cm9HfnkzZ0DPPPMMTz/99KZtbzNsswvzvlfte9rpv9YHyBCCpGkgystUF8YRvgW5YYxcwFkW6I7kQbBhjdA6iOhCHD3A0AMd+LbXEqKtwRPMDLToSLZ1NhXGV+sfCFOwKoAjRIjlauhsw/c1WvjINq8/3JevFJ7jrM84tMAgYBBZUUF2cYr+hMnoaD+u72BFDObm5rFbikj3Xlw3QJxMTGKxGHZpDU+bgKTa9HB7k2xIcmy6BeOkCDp2vZ71XuNyyvdRyHAmFSRnldZ0ZbOMjg4htU290aBcrqLxGc0MIJUmGY8yMzeHbdscOnwPp86cJV8oYhgmfbkh+vuHuHz1Cp5SeJ6NkAajIzuoTeXxlUJrn4lrV3BbZQztkk7EOs+mUj6e5zM+eY3e3ADxVDdYKRpOi5mlAq4yaDqbzynv7e9nZGgII9SuMW7Qknqj1i6egkCf5+bLuGjloYW5PrvVmosXLnYqaO/YG7fvaaf/aiYAqQ36u3sY6e1l4dQUfWYNK5Lmcr2M3z+EUB5aBHr4pg6KmqQAodwQ/5YYuo3bhy8y60nZwPkG+LpGYCgJsi2NHNYlhU5ah/+jDaQR8KUxBEFNpgHhS6JFIK4mVNDaERXyjURAQTSECUIEMwrt42uN6fjs3jpERDU4e+YElWqVgeERtm3dguvHWa6bgcIjGqk0fqvO4d27Gbt2BQODvXv28uxMMVD4vA33AXQwyK3/4XXbqx2X7yukgJH+LHt3bWNufpGJ6QUsA5Tf5J0P3cvF8WscP3uePbuGePcj7+DJv/s6uewQmYSJkY2zbcc2nj95mobjkkrHyXT3EYtGOXd5Att1EcJg977d/MD738+f/unn8F2DVsvn0IH9uK0yKwuzOHYzjAsUCkkqk+aBdz5C0/E5emGe1kIRyzJYM3v42zMTvFdbfHCTZ1jv/f738elPfxqTwDnH4nESicSbmmUpFbRSDJrzaIxolBeuzRGV4FeKwaMtBNpZIt0FTy5fYHd0N7a2WfZWKP9mifHxSbZt28rKyiqjg0PkCysUS+uCY9K02L9vP9VyPhjAPRdfQ73lEpWKrtwQJj6VagUtAnkTyzLJpNJ09XRTLK7R1dXD3NQkkWQafI/hkVHq9QrxeJL8WpFsOsH4+Biu9xYUcG2EMzeJPXTH6b+aCUFXVxfZTJYFBfnCGq3aKmrbESIStm8dJZVKoHxFb1eOb588RbPVCoibQoAMVCnbidpO05H25nWbCRQKimlF8LIrOjo5bWcfLimlEbw4wkAIyXr5ffAQtrcfPC8GQhoBNhJGykKG848wmSwIolzLsliYnGdpaQmEYHJyglxvL1a6C8KZiyaAoHwU45MT+J6P0oqrV6+gEv0YG7pKbcrlD3/q8D8h2wno22BCE7EUj3zfA2zp78V19vDHf/Y5IpaJp3wuj11lcSWPj0ILqNVreMonGovi41NYK/PSubPYTouW49BcK/DM0aNIaeFpEMLCtOJMTuZ5/PEvBo1XElEcp8HZs2fRfgNDu6QS8esOq1Ao8tWvfBUrkaXiKMqlKlv7s3zfw+/mzOoaE406m03deXF6gaVvvEjDsbGsKCv5PLZjb8K9FaSTSR7ZNcr7d47yK59/loqVoEfbWIZJ0/MQWvOz78mhDI/3H34fK7VVHrv0WUAgrSj/9DP/kuMvHON9dx/hybNPkY5neeyxx8CI4FsJPvmTn6RWXaKlNDtHRxi7Ns5s3mUw5pLqGyUioWZ7lJcmefbMVUZH+nn4ngcQUSASpzA+if2Ow+S2H+Tc0a+y78iDXL4yxpGDe5hcKDLcm+QvVxdZXqu82slurr1M2+KN2R2n/yqmEdiOja+CHq2O6xAEyEE0Ho3FqVXrJJMJao36uhMlSHyiAx53m/fepnTe+Pa0Hb8OyfRCiw78owm6WrUdtZbBYBJUz8q27yYAJYLBpjOPMARCm0Gf3vZx+BsiBwUiTDQHz1PIDFIqKPaRBlKaCBGWpyPwpESaJr7tdRRCpdB4EfM2ojvB8RkyKLt/fWuuD5k3szYE5roOKAupLQyg6bQQvoPymvT29VIorhKPx3jogftAtTj90jlyA4MsF/IkMxGS2UGsiCTXlSAVi7G2ViSVSVKuOijXDDpmWRGkEWduLk9XNgPKART33HM3drPIwswUsYhFs1kPeeOQzWT5wAc+QMWFy5/9JpFMgl/95Z/nPXft47lrCzzx3LE3cV1vbntH+vmZRx/gd166yoqj0KkkEdG+km/CRABDtlJJnvu7L7O1XufZzG6SUUFDRfAMC9+3mVir0N3v43ouvvLwWY+qZ8enueuuvSyVa0RjMXbt3IEQki2jo8ysFCmXini+wlUuJ89d4eCBvRjWCoYwwG9SLNe4OJ6nvHSBWl2ze/tOWk6V1cVl/n/23jvKsuuu9/zsfcKNlau6qruqgzoptlJLsiVZDpJt2TJgbGwwD3i2CR7mvQGGxQRm1noP3prHzJsHDAOLwQSbhwGBjAPGlgO2LFm2ZOVgdVDnVDmHG0/Ye88f+5xbt6qrg7qrpW67vmt1V9U5+5yz775n//Zv/8L39/TeYzzw5jfj+S2Y6hybtmxlfHyMyekZZmfnmJmeYfNALy3F/Osj9JtoWuz4nX+xlDNhTeifBakgnhobpzI1SxjFdHX1kPNaOaDsdvXgoWO2AITRSAROYkJpTnwSSTSNSaqPOKm2r00ijFPNvEmvbdpHm8Z/Aowl2tLCIKRJYvCX6MM2B0BgzUkm4RExi5H61reQhHTaaroAKB3TPzBAqTxLuVJhfX8/LcUC5VjbhSoxTKGBKGTrtm0cPXYUgWD79p0MD83TINtfNSzmM1g/teRCnHlnFPgsirGgHhHEim8++j0297WxobeX6669jgMHDvPC8y/gO7ak4sbNWwmDOqdOnWJz/wDZjKGzO4vnF8jlO5Is0FmOH4+45Zab2bf/JPsPjaHxqdbqhGFIHClmZufJ+D4qDjlw8FUkAapep5jPLSb2CcHc3Byf+6fPUXdyhJFPwcvQ2dpG1nFY19VOS74t9RytGnZ0dXL3VQNcv64LozSu59FSLCJXgUjPGMPszCxDm3v51j98HVfFjFUlSioco9hYn8dMKPqv6ue7+x5BOYKr23fypHgcHdb51Kf+1AYyCAeE5ovmq1Rrdfbu34+Qkk9/5q8JahUMgjiO8DNZ4jBEG4HvCWIFWmliFRLFii9+cY56rYxSmjBW/OPoIJVyOUm+s6GiURwzdPwYlWqVl5//PlNTr08k0fJ3PZUTDSPqBUy3NaF/FiSxL2zdPMDGdRt4cuQ44+NjOHoed0sPCEl7eyv1xLnkuC5zc3ONCSgA4Qik5zQxWAJYU08cRSijFgWxtNWKGiGbRhNGzRmoKSNnDFohlINBohBLVdlkV6AhCdnUCCOtw9ZZTJwxxtbd1RgcBMdPHKSrmOemW25Ba43rSE4NDnGiPo1s244QAWCQSKTwOXT0KForhNa8/OpedFv/qpsZ0vHSRlszmTSNZ5wr+up8kfoMqkFE3QRMlirMzE8jhcveV/bR09XLNTu3sWnjeh7+8lcYPHmUG2/ezQ9e2Y9Rw6xf38uJU8MU8jnWbwgpFvNE9ToSTcZ1IFJoI4iNQWIFaL7QYd+VWOG5DusH1uG5molTp6jX6zY+PelcR3cn97/rPZBt4Y8e/FeqszX+7M8+y7vuuYnHD5yivXfdqke1SCFwpEtXSxGtNJ5n6/42O3Rf6zMbGqoxVJF09a5nOt+GqimEtjtIZQQjuQ783nZ+8c0f5Pi+52nvu4piaxefkX8NGMqV04nSGs/QmsmJ8SXHarV60++nXzM9tbRM1vj44vVBuJi9NTY2BkDpfD/wJcBpI75U3zsvrAn9syAdz0ptgVj3kC20kunpwVVZ5lrbqDmCm2+8kclx+zJU6gFz83MJE2YqSgAh0M12fEiMP7rBa78or5M4eWMTppZelUbymCSjUKNjwCzTBpa9GanJRSiF0mniVrMObdDCYbR1OxOOy9HZxaghldlE5Eq7xTYGoUEJSXXH7eQyDqkxyxUCNb+Aci4BFYNp/vSXYFFJrBZBGNoi9EoitKE8O4MOSkyMLDA7dhyBQKmY1o52gnqEMRnmF0JaW2KKxW6mp6YYn9jPT/7kA3R0FpmaHmOgfwODgwvIk7OQ7LCuvfZqurt7+M53HieOIjwks3NzxEGJqF4n7/tLtLlSqcQX/uUrhE6O8kIdZXJ8/6UTfP+Vw7jFdn7yxzpJSTVWU/QrrYjCiFq1SiaTIZfLYwTkMpkLiuhpRCQlccYnDr7K+FOPYa6/E+H5FD0Hz3WIYhfXyxMFowgCEM6iIiVEI1ErCCwXkO/7FItF5ubmGrTJVyJL5muBoEm+pBPkPKfGRQl9IUQ78CnghuSRvwgcBD4LbAFOAD9tjJkV9hv/Y+ABoAp8zBjz4sU8/9IjSUySBj8r0cbSuuZlCxNhgNGGvfv3EQUBYRQRxrElTUtJyxLDizYmsXokmmmSzYs2uDo1MdjAeZ0Yya3jVyeO3cUtdeN7TbVcw2nTPSHtTD7BUkFwJntgjGBEFkAbnEYIoMEGaDoYvfgMAWzevg2HmLBapX/DeuphxPQLL3MpplpD4GsS/8JqO3JtubwwCDEKpBKgNToMMHGMQRMR29B9NFJqwrCKFi5GSFpaW+hZ14njOoyMnGB4dJDt2/p5x9vfip/xmZ6dJTaK2IAxisOHjzI/VyaOk12elOTyeaq6ToxAa92g0DYGOjo6ufut7ySQLv/tH75Ed28n6zd0MTh4kut29PKmLb1Wx2hSNlZlVAz42Qy+Z82TfsZHComUFx8vbzDkfI8betuJeztwPI++qZME2sdFc2P3NXjeAEJWkWIxtFMIwa5du6hWq2itqdVqxHFMT08PxWKRtra2BrXy1NRUQzv/YUWzKogw52Xmu1hN/4+BbxhjPiSE8IE88L8D3zbG/BchxG8Dvw38r8B7gR3JvzcBn0x+XuYwtLW0Mjs9RywcKtqjXDM4PXkipZmamAFhdfI4oda18yFZg1UMwmCkZdy0QjixJDdU8tSxupTH2zpuZbKQJ0K3cZloare4AABocbrGd8ZXoUlJEEnOgTSaRpgRIll4jKWARiCE4fiJU2RzGTCCyfIQswvzxHisbuxOU++NIVKWuTF15J6JVOsC7g4IgiDCxBqpdUKPYHAwKK0bC7fWCrQiDEMiFaNMzMT4IH29LYyPn8J1bSWuffv2Ua2U2L7taoIwxJEST2ukcAiCiKPHjiYaqx1P3/cgm0M5TaaLRIZXq1WefuY5FgJFUJ6nZWADLZ7H9Rs3smvrZno72xvL/mpp+8YYHNelXi0TR3ViLcnmC9TCgHzG0ktc6H3Bfnd33/EmvnHr7sRnZTh14FXKM7O4roPnSY6+9DL1KKK9KUtXa90ohtLZ2Ukul2NmZobZ2dlGHQIhRKOO7o8KRNP/58IFC30hRBvwVuBjAMaYEAiFEO8H3p40+wzwHazQfz/wt8Z+608LIdqFEOuNMaMX2odLDQMYCaPDU7Tm2giyReoDtxMgiUjlYjIzDVYDbbD1aYRRYCJkDMJxEdJJtu02e9bg0OyGbTw0gUwycTEqOawQSdavWXKJSa39YBYTvBqhm2eRiwLLD2RvqJJFafnLY+/tJr9ro6lUa1SqdZsMljzLkWn13EsBg7VMhUt6tzr2fOt/rkcRsQFlJMq41OoBd99zNy+++BLXX38tR48epaerk2Ixz3w9JBY2wqmtrZ1SaR4/65PxfNpa2qjMTxFHCt/NUC6XcaSgq71Ia2sb8wsLZPPt+F6GoaExtIZyqUxcr6CTgIDkI9txdx0GNvVjvBzHTw7huQ5BPWRycoJYGAa2rEubr9qSa5KoEYHB9XwbfKBswl/WzzTaXCiCWo2JkVGk4ySLuKG9u4tMPo8R0u4oHIe8lDjSpVwqN4R4WjlrZGRkST+a34UrgRHzjcLFaPpXAZPAfxNC3AS8APwG0NskyMeA3uT3fmCw6fqh5NgSoS+E+ATwCeC0ijlvBASCyYUFBit18LII4YPr2LBIYQNZpNAIFG5s0GGMFVAGoWUjVFMmlalAoHRsk690athZ9oKm4ZnGloMzQqKEbWUZ0xtVbEmNQ1ZDX7zecH7JsYLFRaHZv3DmS60j1WiNVmbZqnLpBL4QCQ2DWKRhWC1HLlgTXBiFWE4iiCNNqVJj45ZNHD0xxDXX38jsfIVtO7axbl03f/WZf8ZxMrjS0L+xH88Df8LWIw6CiIVylXy+yKZNm4nD7+MLwY3XXsv6vm727n2Fvg0bcLwCI8MTuK7Dpp5d9A0AACAASURBVI2bCeuzDB4+YiOyEvOOZcWwAQKz5XGCekSpVEKjbSJRR576svKXqyH80/KBxdaOxviuJg1CtVJhfmYGz/MS34BpsHIK18NJau+6rkNpYZ4gjNBqqea+XLCvCfrzw8UIfRe4Ffg1Y8wzQog/xppyGjDGGCFeWziHMeYvgb8E8DzvDf8Wbaiig5A+eHY7jhRJLHzimMIgkDjSID3XVhTSyfl0kigFOkh2BNbxaYxBpwlVS5+a/C+QXg4cgZE28l8bByHcZZM6EfpY+bvEvHOOEUwjRBouB8E57fLGWIewDmNErJY96BKZdwCjBVImLuZVnuDGaKqVMqjYarVGUqnD5778HepBzFe++TSlSonByVfwPAlOkawrieqzPPPcM+RzOW7YdTNdHR10dBQJok2AYnB4mDfffhvj43OYesj4yCmq5UmcqI1IC5AOlVqdvfsPYaIFPB3jJ2GRIln8K9UKuXpgFQAjyedz9HXnuO36bezccRvbNg2wPEx4NRArhQ5DMAopbDnJ5pqyaR8vBHZRUWgtSSuX1et1vvr1b7Jz5w5uveVGhPFxhG830kpx6ZSKyxtLdjDNJy5wDlyM0B8ChowxzyR/fx4r9MdTs40QYj0wkZwfBjY2XT+QHLu8YRyMsaYZI+RiKKQ92dTORluksTFCkCRUkZikLalaek7IlNUyDa1Z/oXag1b7liiMFeYCMIsROEmr9BavwZi/ApK2+kzXJb4FS8gsGmRvNPICVti1rBoESoGQ8ar4KpcsGsIKuGqlRNF32Lyhn3w2x/DwCSbmSkTGpTo5T6wUxDGOgLvvuhPfFRzc/xI7rxrA83ziMODIscP4GY+Drx6kf/06NmwIKM2FbFi/kUqlysZNfcxND7J1cz+zdQ8VRziOw+7dd1AvT3Lk1ZetDyVxzArpsL53AwNbtzExU0JryOdbmZ6d5tj+Uer1dvp6B1bVnp/C9zzCMERKiUDZ2HdjuZ2k61Io5C/q/iZJFrTFgay20b+uj87WNrQyOFknKXzu4Lqr66S+UrFkBJYtwOeLCxb6xpgxIcSgEOJqY8xB4D5gf/Lvo8B/SX7+S3LJl4H/QQjxENaBO3852/MB6zQ1AiUcIumCkEhkU90s26hRnCSpHiVQaCFA6OSswTggXK+hwytlo3OMMKfVEk+XAi1t+ZQ0kSq1/zcHazX/thoT/3xeH0sqvehDgFTgi9egfLz2nYGVhUlJR3HBik6DBlhrTRRHxEpTKs0TRzXe+uabuelGS1X8+S98kYVKGR0nC7hjjWtCRwT1EhF1sm7IDdduo14PGRoa4b63302pXOPVfUdpbe/hzrvv4snvvUS5WmJ0bBRtptmxcwv1oMroqC05WavXeeqpZzBqge7WLEGlbN8BpckX8hw6epyh6Qq1yBDEIc+/tBc/61NWLiN7hxi4Zoq3plu21zakZ4VSCsdxCIIA3/NQCJRStBQKF33vMAxYmJu191WK2fkyUWzY3N9Hb0c7jhQNExMmRsXxj6qif040dl/pgXNMjIuN3vk14MEkcucY8HGsH/GfhBC/BJwEfjpp+zVsuOYRbMjmxy/y2a8bGkJmxdm0xK24GGUDS2wmWqnEvt+0MidcPCvdcXlgj11W0upIYkWtZ9GyfimjFhKqB2MaDscLm4uvRTLZZbCRYpAI/Ivlby+Xy8zMzlApV5ieniKq1RgfGmZ2fTtSZhBxlZ7WPOVQs2PnTiampxgbGqRYyJPzFI7UENd5+YWn6e8fwMRVvvmNrxBGmrBeZWRkiC/985eoVmI2DgwgZcDMTMjI0AgdHZ0UO9eDiVBaoUWWWhzhtG1gYEs7cRAS1QN6enrZv28fxydOgeMSG8N8uYwbZVEtbdQCj0p8KQqoWH+S4zhJrQfr64hUTBRFRFFEsVi0u4AL+B6iKKJcKZPxfFwp8B3BI49+j7nxMd60eSObbtvNrttvxXEctDCEcbRmsz8HGt/COb6PixL6xpiXgdtWOHXfCm0N8O8v5nlvFAxWCBsB2oikCPrpWMnyZv2ciZNVpzoyCX0vqfQ6g+BMnbQaYQQyzUQ95xxb7aT8xm0Tige7NZHm0i4vS5FSVa+ecz8MQxbm5hkfG+PEqVMcO3aUzrZ2skDWh6s3r+fY8Aw/de87eeB97+XEiWN8/nMPsXlLP5g6C7MT9HXcTHlhAaVjetZ109LaQld3Nzt2lgkjjRSGbCZLtVqhXJkll80S64iJyTFm5kps7l/PkeFRgjgC4XLs5ATHT02AsnvEg8fH6O3q5APvfC/7Dh1g38GDSGNwhSaLwjEhOgouuojJcsRxTJCYdhzXQQqJA3gJLUEmkyGKIpYu+SuFAKygnBhjhbnSxFLhCJc4ChkfGqQwM8fs3ALFjjZe8V36N27C8zJMzsyc5rBew4VhLSN3OZpNvSKJFjEGqRRaaJwVbNciCZOUiRauEYkpwvoAZGLkSYUmyX2XqGdi6fRoaO2medtm2+glat3p+4+zTf2VFpczFW5f2l4sa5I6stPjy8+vDlJvhUCilUE62BoCq/Agx3HwPN8KMq3xpMvE2BjBtq1oFdPR3s69O2/AOAJXGBwjyGc85qbGWNfTyvH9+ylIKwx7tm9n/UAv33/yOfbs2c/Y5ATZQhsuAh1HxFFIJuMT1SrU6nWklBSLhre8652MfPnrtHd2Ih3D+MQ4G9b3EwYxI2PTxAbe92M/xi/8zIfYs+8V/uAP/wgwxEGdKJhmU76FoT0/4OjRG9i2bUsS8XbxTvUvfOVhHn3+xXQSnKN18wu5+GzRUHE4LafC1Gu49RpS2mQvrRQjI6N0RooJIfnm9BBzsaalrQ3PsxnKU5NTF/x51rCINaF/Dlgt2+CGIUorHGlFuF6y1bTb60bpdJVy5wsc6SCS+qUSkywAFlrHGK0WHaLNSBkxhdOYUg1L+rLV4fR9gjl9DjYJ5SXUzjQ1XUFJs9FAS70F0sjE5yBB6yR2KF3EVn8Lnn5mpQ1C6sTxd3EQQlAoFOjr66W1pUgcBvT29PDy6Ahfevhh+nr7aW3r5qa+zezZs5+H//VR5ss1br62n4wrII6YHR7jbdfvYiCT40gEhw4epLRQIQoUbW0d3P/2e/mXr38VFQRcf91O+jcM8Ph3H0egueeet/HSD/ZRq82Tzzls2byebF4x0O/R0dbG7KxmdHKOyEScGj5GUJtl8OhB3nLHbtb19jIxOsSbbr+J2+/YzbETx3nuuWcpFnKs37Ce1XDpTo2PMTQ4uETBSIuZpMqLlLJJkNt3xBiDFJKuri66urs5evQIruuybds29u7di1IKIQTrujrxha3Yli7irhCEnsOk46C0wXVdatUqdVnHALGKL+ozXYk4W5jshZq71oT+cggIg4BarY7GUimnhcaN0sRKLNW+oRHPktrwZWrbF4ArFssPQtLSXq0D+xKfWYbZ2reYJI4Za1YRZlGbE4BjFnn008cmybMNx/Hi01lxkbHVv1i8T3oZ1jx1mqafLEqnb+hXW9cXjYVEs+hHuKg7Jlqn53m0tbXTUmwBBF/55r+S6+pi647tbN+0nWuvv4nvPfkMB44eZWy2hBIuXrbAwsIYfX3tXHfbbkZiGJkvkW9rZ8dVVzE4PE2hTSIPnWTdp75IodehIiUH9nTzyrMe2Y4ArQ2HDx+kVqsChiAMMSLC8x0WxmYpFHy0cdBGIQR8+4mn+cGeVwkqFe7cfQuTU5NsHOjjjttvx/ddrt6xnZeee5qZ2ZlE6F/8GJk4xoQBi8mGy+KyjAEpMdo0OdZtJI7Wmv6enbS1tXLtu97J5OQU2WyG4Xye2dlZm3MRG8aq0WJfE2qSRuQSyw1HhmLWxXHWInhSXGgy2prQb0I6cA999rP8n7/3exgcpk0eN9OCi8AVNPjjl9rtZSKSHci3I7p60EKgACNsLL5JqJWT5WFRKp92txTWYBIHNTTOokouAOE0XWuS3YKlevAQONr2RqVOZGGSbb8gk8lQrdfwPB8VWwZBy2kj0MIlVipx1DZ3JbXcJw5D4dhQO60Qr6NVH0ODuvpiaWZSQSakwBWutcNffTX7j51kdGKGQnGeg99+lIOHDjGv6oiONhwj2TcyT2fbOp48WkbRRSGXxfgxaiHGPTRB++YdbPBd7luQ3P7iy4zu2MTYDXfy/Lc+SGUWtl5ziu4el+9893GklyHjO+D6HDg2QS6XpVrJMFeJiZRBZluJVUgl1gRTVTwh8DJZCm0FJqemmZ6dY+vWTYyMDHHkyBHuvfcd6ae76KHO5PK4ucJ5jfFK8kY7Lk4mx+GTpxAGevr6KLS1ITzf7toUBEG4eIOE6mPlyBOrpeQzEofVKdv4w4bX4s9ZE/rLIIRgbnaWw4cP27/xbLiesfTD9hhL5pUm8fIicdZvx+vsJBA2vA+lbbKWtNuDVPNevJNopLynsILV/pRaNxYKKSSIRWHeuEMyUaSB23fdQFFCIZ9jvlwil8ty4tQpNm7cSHl+nvbOdsbHh2ltbyeKI1zHZWhwmGJrBzXtcOTUIJFSjXvbWKB0IsbJUVu2UKban2juf7PBaBXQiFaCKE6XQhvKl2ZsX6gDszGCErLZLL/wcz/P/Q/8GFHiRA21AaVRSlFThlqlThArwjgmikJUHFOrhcQqJgwilFKYKCA7PcP1Y99jA4qbhqbZ07qd6cnrCaM6M1Nb8HOvMjMzgxGSp598kl3XX0fdOMSxwXT22DBhFdPWXiCOFXGsUSpGxRETk8P897/0UZ5/7mn+63/9v7jnLXdx8tQJNm7axNZt21ZnzIEgqGOEpL29HUc20SmnVdcSxHHMzMw0URwhhMTzM4T1Gq8eOEg2e5K56WkQglMnTjA3t8hAa7RGqEWeVwDHseYi13WtEpOakLROdqfZVft8P8pYE/orwNCseccN5SNlMT5dxKROK4EjY3ASB64BEVnNmSQBJY0v1w3ZmBhIlmQ4Jm3T08n2wpHgZ3yCeohWylbQAoRJKNwknBgawjMa33EJotAmDcURR08MUavVyM3MIyRMzg4ThRHFQoFKJSCMZomlxDExlu9/UYN3lrB8GoywVBOLrlyHNDHtUiTQ2GdYBtKG/2I1HtO8PRZQLBYpFG0MespXKJMVOBVPMqlXvKQDxpqeNGCExKnH1Ht6OPp//B7B++/jt953P5/v93n62ef58L/ZTWvbRo4dPUK5XOWuN+/m13/zt2hpydmiOsY66lVCgx0r+27ESlmbdlChWMhx2007eeTbG3j55Ze59tqd3H///eQL+VWL4vEcuzON6zXUWe5njMFzBG7ChCnROL5nTYtBnZa8FdRRUKOQS4X2GaLfUk2n6XGp3yBdANZw8VgT+k1YabIs1UVo0tZXuB4B0rEcPVpgVNwkPEXDxm5SXvGm8M2mm4BYJFQzxuBIK4A817Br6yYGR6cZm5ppmiCLfDTjs9M4Op1AMlml0k4bmC83ae6GiVINgYRKIuyFSXT7pn4v+ZkQtGHNWrkMfOJ9b2f71i08+PDjeDpeVat+uts486ivHlKiuvSJDR9H03g1tmCNtib5agVO2tOcS+att1BvK7Dpbe/kLff0sPcHMVMLU/z0z76Z7q4eTp4Y5tjx43zsox9hXWcOT6Y5GKlg8xr9aIYW3Y2+ffADH+SDH/hAQuK3ushms7ju2cWDNoYgivAyr5MG/iPEmnkpsSb0VwWGNC9XCGEdXIBWEi0SbVGKpNRfQ07b6xqhNMLWvZUSHHeR5MrYwiUYjZHg+R6d7R2MT8/iYCOGtBA4QtHSWkAphcTGQDsIXNemsZfKZaIo5S8RLJKrWdbP1rY2MBBHEX7GIYwCPNdDCIHr+szNzdHS0kIcR5TLJUs1jMN77riJ3/65H6cgXa4f6OP3P/X3jTFJn3BREOnoLkZENd9z0T/y2u55HoeWnBPLDwDNGoDdwYl0bUc4jk1oclxckSWMSvgZSa6Qxc9myeXzZHNZ1q3rScwnMlEGVojEanxEgTBL9xmi8T2uLj7ykY/wbz/6MepRhCMkQRzjuZK87zbsj0cmJvgf/+GzTNUrtHg+1dgyoEZasC6foxyGaAR1FZC1sbZkHQeMYD4MaMvmwSjmg4Cs61DXyRZYWPNiRyaL73jkHUlsNJPfegSnXD6N/yeTzRLU641IlyWUykLgubZGdKwUy+E4ThJRJMn4LkEY4boucRwvcY66yT1U072ldDBGXXBm+BuFNaG/akhNMosFza1rykY5CCkSQbA4S1OzSSNgQUqkIxGOuzibDQhlQMUIY9BhRNb3yWR8rrvmasamJhkeGcWVgpt2XU8UR+jY0FIoQhQTx4qW1hZe2bOH4dExlpDAJQ/wfZ/bb9tNEIQYpSgWC4yMjRDHMR0d7QS1AGEM1127Eykl33nqKVRs+6Ms9aV1BJsIw/KEnVWAsVFLcQwIgYPGaTAgLd8mncUEkO6MloUZroQly8q5s+EaUVPpOmAcFyPBGgWhVIrxM3YRFtLu9oQQSCETSm5hNf3TBH8i5E9z+jSfN0tOrQa6Ojvx29p4Ys+rbOtbx3wMG9ta2L6pv/GUqJDjgTftphyEDBSLzEYRrlHUlWHvxDjXdPXQmc9SRVOvhLTns+wdG2N9extGaabDkFv71lGJIoRwqamIrBDUo5hqFHFidpre1lauam0jVIoHH30MgN13v4v+fJ1j4/MoZXjHO+7lka9+iXzreto7XarlOqPjU7QVsxwbHuPnP/aLTB/ez7N79nLtDbcwOzVEFEYESrJlYAOnTg7iuHm2b2plz0SNe3Zu5oUf7CWOA9xMliPHj/Pxj/131BZG+f5zP6BWKdG9ro9Cro0onKZcruP4ORZmJim2dRLUKjiuxHd99u7dQxhfXjuUNaG/KkgcsulfQtvi5UYhjcZIgXAEypEgZGOaSkD6HioWCG0wjsTItLB6el+ZZABrBBqhoDQ3h0RTr5ZQYRWHEKXhpZdfxHO9Rhk7ozT5XI6gHhIEAQqFwW1sLFI7gdIRTz31BI7r4jguURiQy+cJ44ih4UGyXoaFhXle+cELOMI6FQGQhm89u5ff/esvs2ugny889lgSXbHKjlyRuJONoLTQyfPPlSkUDEJIHEcmlLwCxxFIV+BIO25COCTDiZTJXkwaHJdG9SchjN1cSYFotLXjsthGJEI3iSlHNnKWGiSyjbVUWsOYcYilIdZ1YqUoVUMcv4aQLs2i2bI5LRfs5xi/0043maQubJRPgwF6O9p5243X09NSoJjN2fFAp2swBsGXDxxCGUUGl9DE1gchDaUwZHB+jtiAKxxCpXEdw0IQUZiYxHcEkTK8MjLC9s5ORstlZuo1pJFoYjACheGl8QmyjkesYgQGT7rcfecdrMvDtkqFegzFXCfve9/7aG/dRFwsMz9Wpre7k5HRcUYf/BTHhkfI4fKe9z1Ad08PkhuYni+zsa2DY6OjbLtmF/2969n7wiPkWjwyrss9b7uXTEbiiDoH/+IELfk87R07+ZWb34SqVhmZHqElvx7Xm2OhYnfQxdZWulo7KJdnePq5Pdxx4w0MnzzC+Gxllb6V1cGa0F9V2Lj6OIrRCkgcUIsCIt0ZL89fTcwCjUSMVIIkuwcpQEjQCtf3aW1vZXBynHwmQ0lKdm7fTrlSIYoj5ufmuWrLFiYmJwniiIwsMFsu0d3RSXtnJ+VSnZmFBeIkRK6vZx1SwNj4KOt6eymVSpSrFdo72omiiPmFEu0b2ylVSsyXFti6ZRPzwQwG2Lp1KwcOHuAvvvYornEQRvLWnesvwbBKjHDYvdvh6w9v4Hd+9+hivoP2UMZDGw/wAQ9jnIZCH2uTmOJtapwg2XUJW0tVCJAiLXEZgQhtHDkRwjEgFFrHyffiYvAS7bx54bALhZRZjPHR0qU/Ps4HT7Tx//3ncT75yRc4dFRy3/sySJJqWelnu1yZI5PFLOtKSrUapXpAS84nn5guHcclVjEz9RrX9XRzbVcPtbBms9GFYKxUYUNLC0Gs2NjexuzCPK7jUNYRSgtuXN/PQq3M8dkFBtpb6c1lKUUK4UBOeuyZmGRXbzfScZmu1/n2kcN0GpCuy2P/+iUULsWMpBI5DHS3ERqJrw4xH5dYmC3TUmwhl5HMlQIO/eAlVKXCra7guedeoJjPkPV9npmapRZGdK3r46DrMnTqMBM1xRdenKNjYCu9bTlm5map16o888JzBOUZhJdDK82P/8SP8/yT32NofIh6ZEArpOPR2dFFaX6KkfEZ6qUFKvXwjf0eV8Ca0F8VJGYbo8FotNJoY+3xJtEOm7fp6TRPfasCgxIs6siiKRY5iSeXwtI55FsKTJ4cIe9ncB2HrO/jSklPVxezc3NU3TLFXI7ipk22dBySudFJNvVtIFYxXW0ewYnjzJdLCAQtuQKOFEwYgSdc2optBLU6xVye8nwZYwSO65PJ5glUTG/vRqamKwRhSM7L4HouJlTW0GJCWlr8VcmYXQqBxOOtbxf8/TWbqda6E6FuF1VtNJgkaU7bSBed5i4YgdaW1RQENhjGgImJ4xil1GLwlDENXmltMpY11ZAwosYorVHaUnAYY7l70vsaY4vKaBMTC4f2eUP7n1W54cYq11znce+7Q66+fhv5TAaZEjmtIo/QakMg8ATkyqOITAHT0oMrXRzPQcUxMuH8NxgG50s4QhCFIY7rIaVgeGGB2aCGFILJehnHSMIwRrqCsWqZwfICGEMlCBkuz5N1PJQBjUJpxVRQYaiUIdKamXq9YWJTYZ1XXtm7pK97T+v9Upw8dACAoeHBlRvs33faofHZlzjQ9Pe3v/m1xu+O5zM0eIKR4SGi+HQ/QYrBk8fP0bM3BmtC/yyQgCN9tHBANPPtpJM10cCFZdkxxtZARWlrokmjdRKKWCGTKBSjE2Gv0EolgsdgPA9tDFIvRgxprUFrpFKYesCzj30XQ0yoDXuembQhco4g0kkNVwNH5l8g1gaJtuYIpTjw7BO28IvMUvIyGCFwlWLk1b0gYlwD86eO2jhprRnev4dQxUhjGDm0j1jFaGN4+ruPIbS1Px95/lmMjhHZDA4xu7b08j/90k/jumkcyyqg4fh2kMLQt6EIpmXlhulyuiKvxeIB02QIWdpsqa0/bZeG2TY7VdOSmEuX8MXvzUx08IPPGd59/xbu/4lbcZPdhXCscmDZhFKNP73u8tH649o8euR52sI55lUX+WAEWS9Rz7WgQo+cXADVjUAwXq4wVimR2OEWh6RUpnlMPemwq6uLuzZsJDCGhw8dJEqoU+3wNjlGgKnqcOPaNBcF4bB54yYWqiFvufMWRsfHcXDpXdfFi889jSx0c+3WDXz/6WeIohghHeI4ahRLdx1b9S4OQ7rWb6QrJzg2NAYG3vzmN/HC80/jZIp0FXOMTc0QhiGu6xBrybVXb8P3M2QyBeZnx7j+tjcTjg7y6qlTV5Qzd03or4C77rqT//SffhetJY++cpIDEzP2ZcHBoLHWRQnCIXZ9EBoHBdpBOQ71tCp9+vLrRIToECnM4i7AYLVUbU0tSggc10XryDr2MPacVghivOlRKoMncN0SnqcJQpsTkBeGfBgSeFm0lIT1kKyfwZEGHURkfZdYK8KwjujqR219E0q4ZMrz1A+/TCau0ZLPE8QhxhhymQz1eh3X8ch5lv8k53tIaajX6hT8AmhDLajjdnYjdlzPdVdt4Pf/l1/lhk0bVte43EDTgtt074YsaBbqif29+bol7t4z9m2xHoDdkS2a31KbfnMmsJTLs0NNEvFhEK515LpuFkemxdzTaB9rx3dMKu40l5vWr5FE5DCeAJnHSFC+QDl5tOMQOwZf5rm6ex11lWR2s8LXbmjktnhScu9VW+nO56lEEUfmpq0PoDE0phEZlI5Vw/euNSXXwQiPnTuv5dTYPDfu2snW624gbzxa8j7lqRHi1gGu2TnAzut301HME+uIhekFOnuKnBoZI18s0prN8+m//CTveNd72NbjMxe46HrA5qvWsX3rAMXODrSWFLNFquVZYq35/D9+jvf8+E+xqa+bqdEZAl3Cbe/ErVT57IOf4dXjZ9hFXAKsGFr+GvIz1oT+MhhjuP3227j99ttQRjL+p//IU994Euk6pCpfGk9tEJZXRwogybw1GqO0LYwiDI6xWbVGm4YilBAdNLSXBuFmFGMilcYAgV1asEw7Cq3KaFXCAzLSI6wHyLzDdTXN/1xy+GRbzHezAhVVcTMSHSmmJydoLRYpFFoQsSZyJNIIVJJlnI+qCBmBUQhVxwV8kyEIy7gZj4zIEukKPlkcJKEqIwAtNEpVcSmQzxf4k9/4FW7evL4pSX6Vpf4ZXuiVDi8lll7pujP37VzO1HPPq0RfF4vaq3Wcm4bwMwkltUr8DMsXsssCbh53/Q040qEVGoLYZg/Y36/Shq//+m8k7zKLG60Vb2i/FZE0rFerfPSet5DL5Oy8SFdbDcbYLOgwjJmYmaGrswOlFL/6xPc5NncKo20Qw3cf+1eOT8M73/QmjszPU65VMXIBLV2UjhgbHWVmYYJirot8lAejicKAwZFR5msxnqpyajLCC2pM1WIKLYJcSycZzzA5VeLksREGBtpQEYRBjZODgwyeOIIMFLFTp2P9RkaOnWL71mvoWL+ZkaFjnDg1sqpfw/nWJV6jYbgILI6dSUw6YHQyOQ2g02BMG4YllUIYFyMkRmnQsc3Exca/p5tXgbB1c8Ui6ZpIlBzVIC9Ltv5SLkaFpNqgEJiMQ3tvJxiJUoZCazeBMIzVQ3R9iGHZSlBoIdveR2hAxTHxbIVQtpBt7UW2ZwjcQkJsBTgOud4+jLQp/m6LndKxlBTabSZkpA25VpsKH2tFoa0XjMFF0NnlUHdcso5gU08H7pLEpR9VJFpqErbbTGJnmps0mYMuP4kPaZHy5I/06GIDY99vQ4znuEQmtjvApja1apXnnnuWG2+8ifaODkjGwwAZx7GmzVhRC+oIKcjlsuSzucXohhWpQQAAIABJREFUM2MY2NCHkJIoMbNoHfDMc9+nVC5x6rj1mf3tkVcbARPGHOSlZ59AacvGmi4mUkqU0similz/8OCDKW8c2pBU67IfVzexKgphK4Z9/sG/WaJRCykahHPA0vyAyxhrQn9FLKosb7/1WjK5Ijhp+E1ik29qmZIRGDQiKZSCEI2JLhGLFa8wjfaL60vqSUzu2Rzq09j+CwSW4Kw5S1hJQT7SxJ/8c3bu3M49992LcZNlJg757J/8IVtvuZsb3nI30nEABy1ctBC4RiMJrYmJlMjMLGZ4Nts6Ul8EpqF9pBybHZkcuWyGVNj9KMN+bfb7c4W078NymXmFLIxnKsWT7lym5id4+fgL7N60myeOP8lP7P5gU2QajI6M8OlPf5Jf/dXf4K677iJ10Igkyxwt0AKMVriOhxQCP5OxG+eGtyPdDevGkVJpYbEfmAbNeYMuJbYhxc0u1lQgN+dnLT92rnD6tH3zruRS41JUC1sT+qchEXCJo+2n77qZD99906UxU68KFIQxR7/2DT5wx43c//EP4HkeBkkUhTz/ub/hZ952Mx/7+IdxHBsjblZbMCeMoc3EDZfnWL0esKY5Le1ib+sNpJWSF5EurqtZ7WrVsULXmlQTPMen4OeZmpuhzW8ljqIln2dg00Z+/w/+hLbWtiUacjqXHMdJ6k0IfN8nk80m5i/byi4gSzshpaS/vz8hepuxzlnXxfd96vV6g68/k8mQyWSYn5+nuYDLlVhycXmfL/adWRP6KyLZviW2xuZX77KZosnEMMJghMT1PYxO+PmxttPmeBWBdT00QhNXS9ikk+kyNlW8fmiqkCwljhAovch0JC5fzeGsMA2Hcwq7vHcUWrjrmjso10tsdLagzGJBIEdKwiii2NaK65xOh2wMuK5DtVohCEKiKKJWq+G6LmEYksvlKBQKp5Gsaa3xfZ9sNsuWLVsatAtRFCGlJAxDstlso15CGIYNSoVarcbhw4evGDPMmXCuhetci8Ka0D8HLltNrNEtm7HrY30JJk0rJUnzl9KGhDYiRy6Ng/UyHaU3AKnjNuFaQlkzn2my2F0haBYuI5MzjM4uUC1NEykN1Nm6YR19XV24wkPHFXBy1Ks1YqMQSFQcY4SDLwUdXT3QZPoxRmOMREoHz/MabJpCWK3/bIJtZmYGIQT1ep1cLkelUsH3fdatW8f8/DxBEDA1NUVLSwuFQoFarYZSikwmc0Vq+q8V5/qMa0L/iobAuoUVQhhLtwwYsxhi6EjHeqnM4hVwbhfi+Z4/E5afP5c5ezXOn61PF3O+eQxWarN8jAwGLYRdgJddca5+XC4YHBzk03/1V2RyGd773gc4NTzB//Pw8yAk+UKOciXgZ97Wyoc29YAxBPUyQa3C7NQUKEN9YR6nvQutYkJVo7WjE3eZ1q7imIzn43kevu+fk9UzxezsLGCFfzOGhoaSYu2L7YQQDRv/GizWhP6VjmTrLR2F1JYELbWcCmHtpppkzy1sbkDiGksvX2KDb44pOZ/zsJLQe+POn0sov9bzKy165kznm803QtiiO+bKNCV0dLRz86234rou+XyBA2Nl2vM+47Nl5uZLxFpxYnS24c/x/By1WoBwHIyETFvRhjMLhWoE8i4ZIEsuKGyQg9aaMAwbppdmzd/zvKaeCQY2baJaLtPe3koQxnaxmZtHOi5F3yeKI5Syb2rKoglWA96wYYDZ6THCOOVuErYOcBJMYTPoDY7j2mxro86adXslYk3oX+EQgJJgMj5GK2JiPJwkpM4WntBGo9Foo2iUZm+OFmq638UKzZXaXEqhfLmdJ6V/NtIKfa2vyKCmlpZWbrttNyCI44hDe18gqHnk4nmc6jxSGFqdaxp+Ckc6eK4HJOG9xsVREUrbjebpjP9WIIdhyOzsLJOTk7iua6uPGUOhYIvZSCnxPI+urq7GdTgOPd3dZPJ5dvT1sefFZ+np66d/wzoyThZEjHQzGFUH4XFg38vkWruZGhtCG0km47J+01X0dnfguVANDTlXcOjQAVq7++nrbCPUESpWVEvz7D9w+PUb+NcBa0L/CkdaDF215IhHp1jYd4jQtZqR0ga/VsOMjFDadwBHysU4kuaQ62X3vJw099d+3iwTMGlo6eJfzVek4bOrdt7YEFtRDyCMbFLelWDPWQZjFrVyKR0+/uEfI8IjCAMWKrO0FtrZ0N25ZDXL5Qqs3zCAimOUVvh+BgzUdQ3puEsXz0TpcF2XRx55hC/888NcNdBLZAQz48NUlaSj6NPR3c/U2DH+3a/9VuPamclJ5oSgvbXI0OApjIqpzE8SBRWb/YxGI8EovEyOWj1keu4EAPNz06g4IlevceLYtA1jlh7SxMzMLaBkhqhaYmDTAKPDI1Qr5Us91K871oT+lQ4DQki6fvL99PzOH/Lqr/wmWREi0QTSRZ48wPy+/bz88LfJJjZmRygWCwD+MOKMBpjzOH9xS5BIMmwFUNnSzbqtAwlV81k7fFlDSMH2zRsbfEPTCwXacu0IIIqihgAXAlzPQwiJJwSZbMZuKNVicEEKa1ExKKWsCUbDpi3byRgI+rp5+fBxNm/IMx+71CvVJbb6arkEQDmJ108xHdTO+VlqNUtzPD58asUonqnxUaaAoeGhH1pfwJrQv8IhsMVYsm97O9v/bgcT05MYJwahyceK6n/4j0xvvoquj34UJ+Nbq6pcZc77NTQgGzsNQ5/rs2HrTtyG+/bKHPNmHikhBflcjjgpltO8I7DhzQYjrSJSi2uN6+Vpi6No2OuVUniex+jIMUIlqc7OMTU2QlzP4WZKjEzOrfpnOlfY5g+rwIeLFPpCiN8Efhn7Ru8BPg6sBx4CuoAXgF8wxoRCiAzwt8BuYBr4GWPMiYt5/hqAxD4vpKTnqo30btnYOKNjRUdbL93rt3DDrXciM95Z7rOG1YBcYfuUcnFeaXZ9Y8xSUS0AI8j7BUxjIVvqnG3shIxYQiXSCGVtGh9jDHEcs2nTZjZu6CJSAimg2NnOzo72xnj19PSwbt26S/MhfwRxwUJfCNEP/DpwnTGmJoT4J+AjwAPAHxljHhJC/DnwS8Ank5+zxpjtQoiPAP838DMX/QnW0KjmZ60ITfZlKXF8D43CCFt5yzTaXGES6EqAWfrr0sinK2u89+3by0P/+OAZz7/WVLzl7Y3WSUayjdK59dbdy8jull7x1FNPNUI113BxuFjzjgvkhBARkAdGgXuBf5Oc/wzwu1ih//7kd4DPA38qhBDmRyFb4lKiaX4sTVm3IZvScTCA0hq3Ie6vLAF0xWIxE6n5hz11mav9j3/nOzz+ne+80d1YwyXABQt9Y8ywEOIPgFNADfgm1pwzZ4xJDWJDQH/yez8wmFwbCyHmsSagqeb7CiE+AXwCOC0Few3ngUSWpPH4Qkpe2bOXhx76LL7vN8b0Yuham483GAeX8cicjVem+ZpznV/pWRfyt9NclP48+7RS+8WauSufO1M/lr/LQ0NDjcpflyPuu+8+3vOe9yzTtxuGqsYuJuUVskgWt8YR01R8xkY12SpnKfuoLS+KUWgVosMFjKqA1o09aQqlDX/xN19jfPL10/abOXt+mHAx5p0OrPZ+FTAHfA54z8V2yBjzl8BfAnie98M34pcAZxOeN910Ew899BAPPvjgEsF3Pi/zuYRS8/nmPmitz4vgqrlNc7szXdPc9/Pp20rXL+/X+YzHSs86U9GKM7VdDq017373uy9bxWbXrl387E99kOrjj2GiCPf664lOnoDWVsTQIKKlldxd92B0TPmxRyBfxG9pRY+Oogo5TKGIi8DZ0E9tbBg3rBNNTODf/RY4/Crh4DC5G67DZLPEL78Mbgw7dhK89BKxL8ju2oGQJLkmEEQRD33xMSZmK3zggx9CBCWmazV6utdTnRnmiWf38cD997HnpWeZmFkgjBWetMyZKgzI5vMEQQAmZsd1N3PTtdv51re+Samu2LapjxOnRomigNa2HjpaPcYmZth5y63s/953mKnW8fwiH/rw+1mYHuPZF/cQ1kNaW9vo6Chw8MgJ3v2ud/LtRx7hplt3s2njJl5++jGOj0wThcFlV1XrYsw77wSOG2MmAYQQXwTuBtqFEG6i7Q8Aac2zYWAjMCSEcIE2rEN3DZcAqYD75V/+ZR544AGy2exrNik0C7AzCdHmn2e7z/k8Y/l9z3a8+V/z88+1mC1fMJrveT5EXMv7ez67oeZ+Ne8SMpkMuVzunM98oxDPzTL+Z3+EyeWpdffiHj+M3n41JqhTGDwFv+3T21Jk6v/9feQv/jv8L3yW+bk5uOtOcvv2IV/di//BjzDyyot0jI8R3XEXmalJyg9/mew77yPzH/4OffudVF56mUJUY3bLRoovv0T9g++lY2udbM4HuVjnGAPCcblmx04mx07R29FGT0cv5bECz/7gGD3r1nH3ve/luh1bmC9XmJsvkfULVGpziEjRs76Df/ncZzl4+BBXb+3jHe+6n0JLK72drUzPzNLd08vo0ATGrbB54zXMh2WOPvs0N2zfzqmTE1y9YwuDGZ+P7r6bHtHCoZMv4eZc9r96iPbuTgY2byMnFJH02LSxj5rOMjZykjC6vDJ6L0bonwLeLITIY8079wHPA48BH8JG8HwU+Jek/ZeTv59Kzj+6Zs+/9Mjlcmzfvr3x99mE90o4n69ouZB+LUL4bNdejngt43HFIwwh4xPetBvV1Y04dhg3CokjRdDWTte27Xj5DK1vfTvBnhegXkNLic74+J7H9NYddHzxnzA33UzLz/0c9S98AbVjO5mBAbruup25h7/K5LEjFD2PemUW955byWYNzqHjcM8uW9M56YrSiZFIxTzzxON89/tPcM+73sWhcC+uA9qEzEzPUavOszcoMzE5Q6kesK6jGyECstkiwy+d5MiJYcJYcPLkIB29W0BV2Tc6iudnmZqY5OCRQQauWkc+M87I1Di1IODEsRNEseTxx77F088d5Of/7S9QrY3x1HNPsWXbdpSKOXzoCD3dnRw9eYK3bN7GwbFpXO+1K1qvBy7Gpv+MEOLzwItADLyENct8FXhICPGfk2OfTi75NPB3QogjwAw20mcNlxBnsztfzD0uxfPeqMnxWheby3ESXyoY1yO8/iY2fOLfk5md4tjxozhdndS7e8idOEHG86gdO0Z1aoKxjl66Nl+FPzVObWqG2rpe9L3vovSNr1JpbaP0/AtMej7ihu30vfgK43/+50zdci1dG/qozs9TmGvBn1wgmFxgpDXPxkglvFG2CI1OuHRMHPLtxx8D4NGvfW1Jf//+7//mvD/bE08+Czy74rkXnl/5mscet+3/6s//tHFs/4HjADz9xBOW8kRrTv793513P94IXFT0jjHmd4DfWXb4GHDHCm3rwIcv5nlrWMNq40dJiL9W+Bs2sPV/+494nofobGfbf/o9ZFJFTWiQrgvdXWSu20WfsLnISimCMGR8eAjfEUTX3Uy751ER4EUR06PjmKvvZGp8HCeIcfCoqYjIm6Ovy0fedjX9cYAUUK+FaGFwPQ+dchoJSW93N6Ex1OZmqZ8hicrxMvR2dzAyOgYICoU81UplxSz0XD5PsdjK/OzkRZlirhSe/rWM3DWsYQ0rQlfKlP75C+ieHtx6gKqVcbrXoz1BMD6Kg8S0taNHR2xxk/4B1OAgtVhhalW6fvbnKBmHSigII4VSklB7PP6tp7l96zVszvbT6mdYKC1QKi0Q+zVOYBipVXGMpttz2JQ1kM/YGtRaI70MP/8LHyN0JAsnThBnBE6+ldaMx/ToBF0b1vPow19At27kYx94J48++X1cv8hdu29i38HDaGkotneRcwQjR0/St2Uj+ZZ2yv9/e+8dJslxHXj+Ik1l+a6uau/HYgxmBnZgSABckgJBC9FJ5OqjWUpH7Z10J93t3S512k/SOZ3W3krcXS65uxQpiqJIHZdHrgQSljADDMwA432b6WlbXdXlXdq4P6q6p8diBuDM9Azy9331VXZUZOTLyOyXkS9evFfIkMnnCKkhorE4k6OH+Okzz1/vS3BV8JW+j89NhicldctFeDYXcx1RVZVAIHDRNiRQeXEXx44doSe4ndknn2Rg23ac//pjRLVELWAQXsxid3Thui7FvgH0Z55C+9W/Tzi7wMLENDEhqCGoqxI0Fc8VLJTyrNmymeENtxCyPQrHFvj50T1sXBMgpisE1W42bd5BQ4JXSGPO7kJzJZ50lk8ln1kg58Gmnh7KGhRKBQr5DL1dm+ntjxEMGmzYvpVsqcE9d9+J40IuX6SrI4kXjpCen8F0LTau30Hf+i5OToyjaQH6B3pQ7AimmSGVuHlXAPtK38fnJiNXs/jffnaQyMldBGq5C9a59dZb+fSnL21t1deO0Pd0g9rkJH3xdqpHj1FMJtF7ezHmpslu2UFicQEZDKN6guhgP9pre8j3DiA6ughFIuh1l4B0W579CvFgiDX9A7gBqGkK7pZONqx5hNLoc5hWg2DcIBAIYrkKlgxgOR6G7TbTTkqJZzX4/g9/gCvhKVVF1xRM20HggXycYDhEqVDg+OT3MC2bWDSCpimYptPyaHNxXA+BRBGPEwoHqdcbaKrAdlw0VcV2nIs+LG8GfKXv43OT4UnJYtXELBQJVgsXjJVQrVbftJ3g2ltIfu7X0W2LtniCfDpNTyKB59q4tRpu3SS0dh2ulDiWRbwjRaWQJ6LpzM+mMes2NelR8zykouBJQdGDRaX57Vgetu3hKBLXcynX6jyVPUb29Cy21FgnG7w7aNOouHieRHoeIKnWaheVuVprnpfdsvXn89Ylz7FcKV3y95sRX+n7+NykSCFwtQBCughvaYJSgqI1P29C/cBeRv/9Vwlu2szCkYO0tbdTKZepmw0CQqEcjJL83X/E8MZbWusXPIKxKJ7n0dbezvTMPBOTU8Q6+lq5ZQROtcbs5BQOGpoWxgZ0RdIRNNCDITZVcixkp7Ati654GBlO4srmWuClsXcikaBWq2FZZyt0TdNu6uiYvyh8pe/jcxMiEZQ7N+IEExi4tM8fQinMNAM/r7sfb/0db9qGVywR6u0jNTLM3KlRgt095MdGqYdChIoF6iMbCEYiLIdfaEVbEBJ0FQZ624kGJZmZeeq1Ip2dcQZ7HHILEwSDCsIF13QxGzV03UUPhFjblWQ4lWhmeVtK/6kqeK2Uh5qmsWXLFmZmZlAUhVgsRq1WO2vxoW3byxm3LMtCURSmpqYolW68Uf25iwGDwWBzZTFc1iLBC7E614D7+Pi8PYQAoRKfP4iqCLyNDyDVZmhtGYxBMHbp3ZGE772PznvuY8p26Pn4p1hIJDE+99/Aez6A/sXfILp1E7ZlwsrYO1IiPRfPc0C6xKJhhtb00jfURTozT62aJxoVxNo0NNFAERWiEWiLRxFCxXVcXMdBOi7C8cB2cU0Lu15Htuz66XQa13Xp6OigXq8Ti8VIpVIIIWg0GkQiEVRVRVEUTNNEURSCweDV7e9rQE9PD8PDwxcN9XG5sZz8kb6Pz82GbKZtjGRPEirP4QYM6Ho/Ug8iXPvN9wdAYE2dpv7i84w8/AEKk9OkJJgH36Bv6xaKJyYI3XsPvSNrmxmwpMS2XapVC8exkJgEcDg0VeDYidOcOH4Ss1RiYSGHMHQMDRy7QVDX+cJHt6EIB89zcR0XIXRc12y6aNJMsiudZvA213UZGxsDYGZmBiklwWCQaDTK4uLieaPfpTAdN8Pi/6GhIY4ePfqm9d7sXH2l7+Nz0yER0gXRzLDgoaAoWnN0b9fBCMNlKH9ZKFB6dRfjpRJhTSNumVQnTqG89grptiTZjkGOjZRZqDvMzikU0zUWApBNqvRYFv/LZklbqo1ypcbxRYP2xFaKIwlma4LOGJTzZailOT05RW93D0FhIdQQLoBVQbom4CEAxzrjfqrpAQK6hq7rmGbz4ZDNZhFCoCoKbmuR1IXiRqmajiIktn3j2f7379/P0NAQlUrlbT3EfKXv43OToTomeq1AsXsLVjCOmRyhTUr0rQ8jGxVkvAdyp4G+i7YhkYhoFHPTNpJbthFbmObAlEdlYDtthooeATvWzYaQzo5UEKXXo17VmMvVKVo2qu0Ssk12T9i8OqWgRxKUHJtyrYFd85jKSwQVYlSYnF0kZgDt/XhusBlcrV4Hr/lgUgQgXZqvMApbb7uTamaKUDxFMpVi9MgB6o4gkUxgKBqq1lwMJj2HgBFkcnyUYKydUj7L+s3bMMsZKg2PWDRCyNComU4zIqcrUXCYnF5g/boRGqaFKgRCEUxOjNGwru+DwjRNTp48+bbb8ZW+j89NhqYq7NAWyaTW4AwNopsl4uWjKP1bkHonYmGUbmlesg2BwNi4iY1//C85WXL48ZTDwlSDd40Y3LMuSlhzUQI6oWBo2XPHjQpGOhVc18Z1AlhWnaOlPLNuG2uiddLzBeqZcRTXpZKrIW2TQKDArMizvifAX/7oO5QaAsc2URQIqBJFUWnUKnzqAzubUglBpVxEorCYSWM7Doqm41gOhq5i6CEcu0Y4EgXPQqgGhqFTKZdwPdlMqu56BEJRIsEAiqoQN6LowmEmnaUnlcAIlmlvb8c0LXRVpVKvoqkqzRBjq5OluD+Xg6/0fXxuMuKxGP/Dlz7X9KkRrRzKAEsJzmXfZXl7uELwSj1Apq7w4Q06Q7dHCQZCF0n220yK7kHLrORRLFY5/szPKZ+aZK5nCwgHe/4A2DXCZg3LLFJ2PUqd/VTtIDMLZT78yV9h34svMLxuPclEmIYb4vien7NYruO1JonHjh1ZPmoztk6TUj5D88FwdiC9laaQU+PNkXI0FmdusoLrnZ3HYXZ6CiklL730ErG2dqTToFypXfc5gUgkwvbt21EUhT179mCaJvF4nI0bN3Lo0CEMw6BcLl+W4veVvo/PTYYQAlVVL6vuxZWZ5ETZxqrAr64L4VYW0fVkyytoRa4sKZsPEq/luSNdkC5CSGqmSW5qHLeYx4hPYys2QglitCUJS49GNY/0GqhqiEatgec6uKbNhjX9NDwHIV10TeIpghMZjeql11kty73ylC52fpXyhd03l+p7nkcxvzrSfQgh+MQnPsGHP/xhwuEwTz/9NF/96lcZHBzEcRzWrl3L/Px8Myrpzar0p6en+cM//MNVm3XIx+dGQUrJiRMnzi8Hig1JXIIqFGzXQSjqBfMrC2Qz9r0QTSd92fz0doT40z/4LKVcDrNSplQqUSosYlkmjmVTrwkc12agI0AiZhALCX74/W+hKgqKoqAoAqGoOI7Fl+7fytFDKu/E1OhSSsbGxjh58iSKojA3N4eUkvn5eTZt2sTJkyfRdR3XvbwIoTek0s9ms3zrW9+63mL4+Ny0LC4uUpsZZfd4gw47gmuV0eaLCHH+QEviIT2vlSbTRboOrmthm0Ucs4bq1tAxiYUswnoQ2xJIV8fzgriui9VosFis8vADd1I3LcLhKEEjiFAFnvSwLQtVVW9Ij5tfFC+99BIvvfTSWWWLi4u89NJLSCmvyKYvrret6lLoui47Ojqutxg+Pu84NE1D03QcT6KrzRj6Fwzicx4rzSvnuEyeU768Jc/+WyzNPawolEgc27lgPPybGV3X0bTm2HzJsnE5yn1iYuJ1KeVdF/rthhzp+/j4XF26VYWhgP4mtSTIMwpaxmKow8MXmei9OK7r4rguAV1vumYKBaSL7YJrO4TCITxPsn//PmqXCLZ2M6OqKtu3bwfg8OHDy3GHhBB0dnaiqipzc3OX1Zav9H18fM7j4+0p/lH/APKssXVr5K5pCNdteQWdyWPr9fWh/8n/jRKPn9WW7TRDIwuaQeA0VUVVzswOHD16jL3799ETNyjMnKBrZAv1uaNkjWFcKXn4Q49gqBqf+vgnOHb0KOFoG4bqkS+WAcGaDes5PTqKGggRDqoUiuXlYwuhsn7DBmqlPDPz6bNPUgjWrV/P+OgoUkq6ewfp6mjj1PhJylWLcChIrV4HQFE0Usk2MtlFugaHaGQXaO/uRVc8xidO48nmO0ooHKJ+FR5MoVCIer1OZ2cnmzdvZv/+/QCMjIzQ3d1NLpe7uZW+UFSGhofBtcgXS2iaQaq9jYX0HKVag66OFOn0Ap2d3eQXF3C85gVRVQ0joFKrN85rM55IkmyLMDU1fZYb1+USiUSo1+sYkQh2vYbjNCdV2hJJ6tXicho2TdMJBTXKlTpCUQkZ+gXlAejs7qEtGmF2Zppa43y/aqEohA2DauvGXELRdGIhg2K5AsDgmnWYpQwLixcPOBWNxjEbFWznxkj55nN1EUKgiKYj5hJuKkV9aAgUQXTvXpx4O4pjo5ab4YyVhQycOIG6s5ktVSKwLJvsYqFp4lEEKAoCUAIK3W1tKEJhJjPHv/pPf4Z0BeXTCwxsXMP0sVF6NqyHhM5T2UN8+ZFPLEvy3g9+nOGoRcHVsB2PbZtu5dUXnybWNkQw5WF4Aabn0sRCOo/97Fm+8MXPUc3lOD2/iOlZdMXjTM+m6e/tIBQLMXk6S2bqOKPTFR5+/30cO9mPUCLcvW0TR0YnUIRLKBTHtApogQR6W5jnf/B9PvzpTxJQVSYnp9BcDS0g6B3o5U//zf+DompULiN89eXiui7RaBQpJfl8czpb13VSqRRSSiKRyGW3dUMqfS0c59GHP8B0vkh/d4xKVZBfnOA9D9zFd378NL/3B7/PH//R/8X/+k/+KV//6r/GSMTQEx10BkPML0zQsDXK+QyxZAdYDY6dHOdXP/NZ5qYnCYeCBIIRFhZz9HZ2MjszRWdPH3MzU3R091Er5WhLdjIxdpJYLI5QNSzbZvuO7fT3DZK3GzTSs7z68quUag0+9xv/Hfue/TGZCjiNCsMbNtLdLnniqVeId/XRE43TsG1yuXkUI4omLUbHTiGBD330w0yMnuLeO7ez/9g4C3PTpLr7KS0uEEt2YloNBru6OXzsMMFwlLa2djLzUzS0GB9jjsVhAAAgAElEQVTZeRuHpyapFqv88qO/yvO7fkoiVSYUipBJZ+jqSlGq1NBVj5oFv/S+h3nqsb/i9PyNF4nQ52pxRuELCUq9jlouorsSLxzGC4eRYQOlPN5043Rc3F27UO++CyEUavU62cUimXwBy3bQAwE8z0PXNE4sjKEkAjy67d2UhImSiJIIJQgFowRTSdpucdB7kvS1dVJcLC0rUDUQYv1gJ2FDQxazVGyNSrXGlm23ETdSlLVFpscXuHPHNrLZRYIBlUq5iAgEuXXbVmxF4cT+/dyxfTuu62IrJpu3bKU9Inhl32PMz82R7BqkvzNBqVKnpytJNJHk8MEj9K/pZmRgI2W3hud5VCtlHnvqGR559FOsa+/jqed/ACEDVdNZMzTEoSNH+EUNoRqNBvPz8yiKwvT0NNB8EBw7dgzgklnQzuWGVPpCQCLVSa5WoVgqYxgdbNpyK+OHmq88xcUan/jkx6lUSzzykQ9z4OQYG7ZtJDO5yINbhpmemKCz+0GCQUG57DA9/Z/Zt38/O7ZuYnj9etrDYayAwfT+N7jv/vuIxeLMzU1TKebpHXoQTUi+duwId7/773HH9k0Uc1kcRcGueeTMBpFIFw/s3Mbuo/NEVYc773k3ECRbyJHPzbNu/e3ckSkwcscDBMuChjVDsP191PIzWF6UTesmeG73SwSDYQb6eqhWazz00HtRZZ5ApIeZ8XH6RtaSXpwnrBps3rEDTRN0tieZPHWC//eJ3XR0dXN3VzsaYWrZArfdfR+HDx/lzh13EtE10ulTDG3Ygm1ZNEqLpAv2lZpifW5y3KDRTH4iQG/Y4DgEp2aaikwK9NwiDkncWAQZjSMsC3H0JFYmzePz4xQWcqyJ9WG6UK3VcS2HYDhIOGjQE+zg8Pwk3879HUEh6F27noQRpxbPU280CHe1Q6FCuWBRD8WpW3Uk4DkW/+kb/w5PKgR0BccVRIIBXASK52F5Do7toioKuq6ymCvw53/+HaxGHd0I4rkOjYbJuo238p77b+P7P/gBjtRwzBpWo8qP//YZUHQiQR3LdhGKinQsTNtF0xTCkSiWZVEuFPirv/wulXKFhW/+RwKqoFwpoRsHqdaqHDhy5E1698qQUi4r+yU8z6NSqVxxWzek0vdcl32vvczfPv0kd917P72dJi88t4dNG7cgXYc9r7zI0JphXtzzGmFFpb+niyMHDtHf1svp06eIxjvJ5+ap1GtYpgoIOju7ME2bXGEGKxEjXSgzd/wkayNh6qZDrVIiFGsnn8mQKxRomBYnRk+hNIqkUu3MFCpoUsfVLMZOTNOVcBgcGOD5Z56ge2gTcS3L0VNz3L71Fk5PT7Bmww7q5RLlbIP0wgna+jzCAR3VK/Pz55+j0rCYmhznuaeeoq13HZ8eHqFcNnEqCwgd5tILaLpkcvwEycF1HHz9Fe5/17s4fvQYrm2xkM0yV1ggHulEVh2cCBQqNaRwKJTLnBg9Ql0JYlfyaEaUZLJt2QTl4wNgrRmBgIEIGthTk7j9g8hUOx4KjpAEx8Yx161F8UAGA5BMop6aJrB3H5Nqnu8893d8fP272T5wK67j4OLhuQ6W6WG7Jm46z0uvv8wD9z2Ikq1wam6cdVu2EIuGUCvgmgpqw2M41A2thVnSc6lW6wig1oqgWa00zVEX82pJz59v697/xssc3PsynpSoqkorxwuNhoWUJpVS88HhtVYzey0Po0ql1nRLlWC3zKf5XPZMw/VLh7dYDdywLptLfqlCiOULvlS25Nq05A6mqiqO46CqKtLzmk9vz132Mmi2o6CqCo7joGlay+e4eVMpioLruigr9mveYKK1QHHFa/DKm0+I5vGEgqKA63qttptteXJpFaNstdF0jVvaf6Xv7ZJMQihIrzUCkV7L3a3pz6xqGp7rLvvtLrfb8rbzPImmqc1coyv7TkpURfGzDvks89/39vE/br4F6UmEEYRGFRmMgnQQdQupqAjHglAYGdCRtgNBHaVuwfAQc5//Nb7x7I/42fHXeHTrQ2zt3ogmNDy7QS47yvxCjuxMjrGx09x+x9387NXnCAymuLO/g/cmx3FR+Mb+AE5dR6+YfPE3P8c3/s3XOHbsBJ/++59j8shrnJyYZmDrbcQaFYb6+zgxPUuxVESR4NgWBOJsGGgjW25w8uhh2lIdqIpKtC1OrViiPZXi2JFDfPKzv0Y0aFAv1Ai3R5G2yd/87U/5wmc/xZ7Dk/RQZzSbJdnVT9BzOT13Cs8DoYeoF7OcHJ+8atdhpcvmlXBTumx6K8Knrlw6vfJ7iSVltrxizW0pt5X+wdLDaU1irlR+Swqyuf+5+8nW4sOLPDiXQ7t6LB16qW3Pc86pen4bK8/jjEze2efQkqNZdKZsZf+sqHLeuS23f5kLO3zeOSilKm5vL144hKt3oJgWEhd7fSeBxUVErQaahjQiSAFqo4FSXECeNBGzC+xIjDCTTPPk6Gu0GVFkvkJ1Zg9WpYyIbWB4eB3bt9/NfHaOVEccW1eYSGd4LgtawCDensJtF9jpRZSWZ6hmGAyPDNIdDTIz90OiyQ5u697ALVu20XXiOPGeLtYMDXLwlef422dPsPOeB8jVC6QMhe6NW2hvS5BItoHj0dWR4uv//s9IpVLMjI/RmRqkuzuJ2ygjFY3hgVvoHlqPmquSKpxmIDXAzPQMd9+3lYWSJKnppAtZ5r/7PTw8qtWr7066NJhbOcBVVfW81JGX4oZV+j4+PlcfJxmDaBuOriPVAMbEGGowjNUfQizmUAIaVnsSXFCtGtGZGajVCR04SGrTZm6PDFGfzvPiM08Rqpv0pdrYcMtD9A5vJhaNE42G0QM7+diHPoYRDOC4NrZt47nNt1lV1VAUhe6uDr7O13DMOj9/6ucodg1V1Th1aB+Tr5QZ3HeARCrJ2NNPkUq2U1xMM5cu8+zzCpF4jP1Hj5MsO6wf7KBUszGrNdraE0zNpHnyZ49x9MgxNm3ahhFwcTwVq1LiO9/5FkpYw61apLpT7JrdTVs4QmO/SSQcopArgaqzfsMtSGlz8ODBt+T5dyXs3LmTTCbDLbfcwsTEBL29vYRCIXbt2kWxWLysNnyl7+Pjc0GEBOP4GNKTBCR4CihSos5OowgVJ6CjODZBqeIGNNxIpGnOBCJHD9N1106G+kf46Y9/yqYtG9l213b6Bobo7OykPZEikYgRiYRQNQ1N0VA0peUwtPTWKZa/LatlK5eS11/ZdZ6sswsLy9ujK8p37T4TumAhv5djB88/z+Kh5qTrkSP7zyp/be9rV9RfV5MlU20ul6Ojo2M5qmY0GiUUCl1RW2+q9IUQ3wQ+AixIKW9tlSWB7wMjwCngV6SUedE0TP8p8CGgBnxRSvlGa58vAP+01ez/KaX89hVJerZU9PT1YtXrGEGDzEKGtvYUtlkjGm+jmFtED4YpF/K4UhIIhhCeg2ldPFuQUDTa26JU6xa6KqlW68uLThLJFMKzqZkOmvCo1hq0JzvAs9GNENVykYBh0DAd4pEg6Uz2osfxufr09/cDzXR6SxiGgeO4BHQN23FRFYF5gVfiDRs2EAqFMAwDIQR79+7Ftm0MI4j0PPRAANsysewL30vBYJCBgQFGR5uqRwhBKBTCtm1ctzk5KAHPcwkGQziOjarqSOnSOGcthtIKPAZNc91Kc18oFMIyTRRVXY5nr2o6uqZh2ybW20z4ISRIXaN+/900oglU20TP5UAq2IkYnhFBJmOE9h7A7WjHjkQRgRDqY3+HZtvo8/OopyeJxeN87AN/j67uDpLJboyAIODVEVYB89Qspm0zenICy1HJBEKIYAIVB92IEevoob8nQcyQWJaJfRmOBleSGvFGSaO4MgPY2NhY0+1V13Ech0wmg6ZpVK9gTcDljPS/Bfxb4C9WlH0FeFpK+SdCiK+0/v4nwAeBDa3PPcDXgHtaD4k/BO6iaV1+XQjxEynlWwqap6ga69dvJLcwhxoIY9aqxNraSMb6iCbamTl9ivXr1rHr+RfoGRwiGovhuS7RcIhKtYauqUgUPLvGkePjrFszxPh0mvvuv5eZdBFDmAhNx3ZdTh45TCgYoqezDxmIUctNcWJ8mmA4xNDgLTTqFbxUO4FwkErVorejnVzuJWzXt5FfazRNo7+/n8HBQarVKvPz87iuy/CmHXzil+7h5b3HefC+u5hbyBFW4c+//W3MFdcpEAhgGAZHjhxhYGCAoaEhAoEAnlD5yMc/TUTa1JQg5ekjPP7Cq+cdPxAIsG7duuWQt6dOnWLd1jv5+MMPMFsoM3vyBF1tMbTuIaYOvcxdD7wPq1Ilkoiz+4Vnef7FV85qr7e3l6GhIUzTpKenhwMHDjA9PU2ye5jf+MKnefm5Z7n9gfdx4I09hHSF3rUb6InF+Ml//T4Hj556W30pBQjHwTg5jqKpKAiwHYSUKKUI2B6uoaJnCqjFPAYKUldRW3NGiu2gvPgiM1s20KY30Kw8hXQV6dkUDRVNC9I+l2HCdvjqt/4/IrKXshHGbF+P6pWIxHoZvPVu7r9riO5giUatSK1WQ1VVduzYQbFYJJ/Pk0gkKBSai78qlQojIyOYpkm53FyV29bWRqVSwXEchoaGsCyLbDaLbdukUilqtRrhcJjx8fG31V9XC1VViUajOI7TTA0pJbquYxgGlmUtO5nEYjGklJim+aa5Et5U6UspnxdCjJxT/Cjwntb2t4FnaSr9R4G/kM1H08tCiIQQordV90kpZQ5ACPEk8Ajwvcs79fPJZDJEwmFCRoCFYIRIKISUHplMlq6eblzPQ9cUjEgcz7Fpa2/HaVSIxuIYAY2FhQzJtjjSszk9PYOUCtlCmbZoANMSxGNRiqUFKtU6A2s7Mc0asbCKnkhi6POkUl2U84uEYzHy+RwN10EoBo7roPgO79eFrVu30tvbywsvvMDIyAjbtm1j3759DA4PYcQ6SQXHsYUg0RbDrlpomsBcMXhUVZW2tja2bNmyvALS8zw8x8KybTJTR0kObWbf4fOTU4dCIe6++25s2+bAgQOsXbuWyclJrFoVG5Wevl60RoNUvI3k8BCDCY1wsp2CaRLQVfL5wnltLnlX1et13vWud5FIJPjBD36A65oUyya37ribwd4uyv2drLt1Jy/ueprejZsplS886rvnnnsol8tYlkUkElleyn8xpJR46TQ2EkOCJpuJVUQGbMCQEtUDryiYMRTmDbhFEUQ80JH0ZufRa/04nkej3sByLZAWugjiWi4N1yRr29jBMIFIO50Bg8BgikrRZnAozrvvTtKRdMnOZTEbtWWPs2AwSCqVwvM8HMehs7OTaDTKoUOHqFQqJJNJ1qxZQ7VaxfM8kskkJ06coKOjA9d1iUQihEIhisUiPT09VzRKvtasW7eOdevWIYRgz549hEIhtm3bhpSSo0eP4rouO3fupFarsbjYjP+vqirHjx+/aJtv1abfLaVccn6dB7pb2/3A1Ip6062yi5WfhxDiy8CXgYvGy/dcm1PjY63XbY1IJMbY6Akc20bVdWzLRIjmE7B8aN9Su2iqitNyo5E008o5rodTqwGC11955UxdTcNxbDwJ4yeOAR7m8ZO0JRIIYPT4EcBDIpaPB4LTmorp+P7u14MlBakoCtlsllQqBcDe3btwSxnm0xkyhRwLhQptIY2aefZ1qtfrvP766zz44IPMz8/z+OOPN111NZ1cZp58qcLMG6+ykCufd+xNmzZhmibpdJq77rqLp556CiklucU0e159mWxukczcPL19gxR/kiEUT+A5zQQiyWT7sh/4SorFIlNTU2QyGb7+9a+TzWZxHId6pcT+fW9g1qv88Ed/QyzexkuvH2ZudppysYIQF06gsnfv3mVvtzcbDQopeSEZ4CdrIpQFaKrgE6NlfjgUphEJ4EjYVHJ4z1SNH2+MczKm4LmSmC1J1R3+8eESPbU6ei6DHVeomy62I5FOjYCwCRgRXNdlZiGLpQo2buihYjq0D8eZnpoDdwGFOo4jsG0L27bwpMRxHN544w00rZkYfcm7TlXV5YVKuVyOqammunFdF8/zaDQavPzyywDLyUZc111uY7XiOA6WZZHL5ZbPsVQqEQ6HEUIwNDTE7OwssViMjo4O3njjDTo7Oy/Z5tueyJVSSiHEL6zXpJTfAL4BTT/9i9UzzWa8Gsu2znKVcpYTCTS/VyYWONcP/SyvR+RZv9srbLaNxpnYNoV8bqnGORItHc/3db9eTE9P4zgOvb29eJ7H2NgYANVSnt27m//wE2/yFm9ZFocOHWJ+fv6MC7Bj8+Lzz15yv6NHj9JoNFBVlXQ6Tb0VD6lSzLFr15mJx8VW3BRm3zw4VrlcXjZTnD59+oyMjRov735x+e+Vc0gvPP/cJc/tcvGEZCooaNQt4qakGjPoNiVxy6WqSCKmieMKpgxJ/3wZs6LxSNpiX0InZtkYnoetqjQ0geM0QyLXqzXMegkVm7AHZqWCGJthoFxHS6fJaxr12Tksq4EeMMhlTTyvue7Ftu1lF2jbbnr4mKa5/P99rpu2aZ6/SGrJPLS0Bgea4Q1WMxMTE8zMzJw1p7N79+5WfgH7rHkrIQSu65LJZC7Z5ltV+mkhRK+Ucq5lvlmaOp8BBlfUG2iVzXDGHLRU/uxbPLaPzwXJZrNks29vEt3zPGZnZ694vyXl4brussK/kbF0hfcXbB4sWERsDyFrRF34/WNlXNHMGatJ2cxHi8QGYh48kLdQgICEUtRg0XEIqh6qGqBareLYJmZdxRU6s3i8ULdZCBko0TiKGqYjGqGUN9EDBqmONhr1DJVKGatlzxZCYd3GTdi1MvFEioChM3t6gkKlQSQWI9By6G9OZHsEAgaZhXm0QIhGvcbw2g041TzFmkUgoGMEdCzbQxWyuVjTc8nkCvR0dWI7HgKJqqmk5+dwrsM8nZTyvMHqygeAfRGHgkvxVpX+T4AvAH/S+v7xivLfFkL8Nc2J3GLrwfA48MdCiPZWvYeB33uLx/bx8bnK/NV9QZ74TBtR0yPekEQakKhJEjWPeF3S1tpO1D3iNUm04VEzIWiB4UpwIa8K6raFKiTS0Fohhy1cW6NaLINq4FkNHKvBxMQRgkaEammGuanjJDvTPPdzh3J5Edeqs2ZkfVPRteIlCASlYo6Onj5UVSUYiTPQ301QD2NbNQKhGLgNPKFj1kq4wsAy60gJuqbS3tFDb6oNRQUHjaDqMTWfpTsZp27DLZs2UymX0fUANbNGpZCjWL3xH+ZweS6b36M5Su8QQkzT9ML5E+AHQohfByaBX2lVf4ymu+YoTZfNfwAgpcwJIf4PYMnx9X9fmtR9q7yZTdLnnUNHRwd33HHHNTteOp1mbm6OTCazqu3Bl8uFzqEYVsj3aiAFQjSj6stWClw4kydF8yTCA80Fw5YYtiRsSyImfOpkB+1jLrV6GQeJ45powsXzXArlMv1D3SQjYUYn59ACIe7f3EdGKnSGh5kpSazKJOvXbePZx39IQDeaZhnPY3J8FNex8TzJ1HTTvOG6HsXF9IqwKR4g0TQNyzTxZDN67OzUKRzHIt7ukJ4eR7byAQgpsV2X2dMqpm2ze/dLJJKd4NTIFcqr3gx0JVyO985nL/LT+y5QVwK/dZF2vgl884qkuwR+UnSfJYrFIrt3775mx3McB9d1l71rbnRW2rhXctf+EGY1waF+gxH3EPWQimF6lNsCBGp2M26TrqA6kmpUA8fD9iRe2SYdVdFGtrBz42bGT+zhsZ8+wfzcPJGgQWBdBMtyqJSL5BfzBIJhcvkSAg3bsUlPzlK1XOheQ6lRpiMZZeb0OFZrHs9coYDPDlVyvvnlXNPI0lxgPrtwXl04E8qkVqtRq129mDpvh3g8vhw/33VdstnsZefHhRt4Re7lZn73uflxXfeKJil93hwpIFFSGQ0YaDWDmOXQO1lBkWAZKpPrYvSfriHxqAdVkguCaNnG0gUB2yPbFaI0WWS+v8z6296D+sJeStXTmHWHdLFONBREVQVqZ4quuEt6bJTn9ryEVDzwPMxajZ8/M0soeojCYoaOaBzLjw8FwJe+9CXe//73Y1kWQgg+//nPL0/4Xw43rNL38fG5eige7L6jguNaOIbksNaGJH4mrYoQZDsDSEABPCFaph+JFNC+4HL0+Vc5FRlnaE0ft27bSrlWJaHWSBgBHAQN0+Ge99xLvHc9j+/PkK46uOUqHZrNg4MO+47YFE+9htJoEDICVC6SYe6dRjAYxHVdTNOkWCwSj8eX1yRcDjeF0r/UcnUfH58rR31sBu+NHApgvIX9LQv25BQUobH/gE4woFE2PcxqBc+yQQhUTSPW2UHWeZ1itoRbLaE4dRaDCs+fCNPw2rAciwaCRsP0I8G2WAqsduzYMUzTZGBgANM0L9tz7YaNpy8BRSiEw804Kcsx9FtLkWu1mq/8fW54hJRXdWTm0ExWfi6KopyXmGRlfoml5f9uK3/DeXIvt7nytyuLi6MuxxU6Ez79ZphDuRJ0XUdVz15sZxgGqVSK9vZ2HnroISYmJti7dy/z8/PLdU6dOnVzxdOXgDQiBDoHCDUWz0tiEgwG0TSNUql01k0rhEI8HmtOBLUSnCAErueB9AiGIiBdbMtGKALbdlEUULUAhhHAbNQxjCCVShlV0xEIQuEQruugKiqm2UBTNcpvIYWZj8+FeG+jwS9fpTABEvjLaJRXg8HzfhscHKTRqLOYWyRoGDiOg+M4JFMdDPQPYNs2ExMTZDIL2Laz/H+mqhrtbXE0VcF0bKQrcVcobtturjBdmjw+9yGwNDkeCoUYGhqiUCzg2PbK1BcYAQOhCMxGo+l5syJwmhEMETJ0SqXy8irnNwuspmo6sWgEpxWIr1QuoQeCqArYTjPb12oaQJqmydzcHI7j8Nprr1GpVK5obcgNqfQBqpveS0DWYS63PJYQSymihEDTdcLhCJXKmQmO9o4ufud3fpvM5Awu0NMVZ/z0JPmGjlqdY3DjHQQNlWqpQC4zy55DMwz0eNyy7ZeIhxpYahAqDU6fOk7Pum0c2/MS9z74EOPzc/QZOouWRywg+N5ffpfF8tVPqOBz89Pluqy5ihnNkhdRZq7rUC6X0FSNRHs7mcwCCFAELC5myOfzLC42w0IsvQGoqoqu69y1YweLpQKeBFXTWBrhNxom+XyOmZmZ5SQgy55DUi4r8ICuMzIywgc+8DBjY6O8+uqr2HYrI1wgxJd/87cpZE9xej6HJlS6u+OkM0WOHHidO97zUUKVBbJVk2IxS2d3L91dg8xPHQdVwxMqnmuTz+U5ePAgnoR1d97Lo/fsoOioRBWFp576IZvueB8dEY2CFLz6zE8ZHZ9aVVE5pZRks1lqtdoVLwi8IZW+NCI0hm/HSh9hfuP7cQJRUvlxQnOHEZ6LkBLZMYw+fDfKru/jVZtL34Wi4FgW0bZ2HMfCFC4777mddNZEuENIEaRRWmC+5jB16CBSpOjrGyIWDiHtIq8fOMGGjk7WbNxGR2cHcv0ws3MzzKQLtLUHmcuV6Nm+g1BAv8495HPTouvN+7uVJlNoKiyn9gTpgVDEsiP9UmpNkE2XHEUgLxCi4Fx27NjOxls2oAiFzq4u9u7dy969e5mbnaNSqVAul3Edt6XsFfp6+/jUpz/FY4/9HdOZBVzXIxaLUqvXsBoN8vkCQlGQ0iOVSuI4No2GSaPRwLGdZWWqKs238Y0b1rHzrrt46MF3MzY2zvx80wcfIegZHEK4FRJdnYQDMYr1KtvvuJ+AsDCCBnbR5b4HHyLZ1c6R1/dgBOLcffdOirZFbyLOnmOz3LbB5fRMloGeNuyATr1SxgslmZo4zuRsjtvu00EqHBs/iaJqdA4M0xkWHDlx6upd2wugaRqBQOCivy+tyDWMy595uSGVvhtK4AXjZIfvQbXreIpOaPN7CB74L4jjzwMgE73I2z6KcvTFZaVfLuR44oknWEwvIIWgVi+S7OyhlMsi1SBBBWwJdcumnstS92aJh2HXC2/QaJTI1xysbbeSeXEX5VKBcDSOlC5Vy8UeHiCXz5NLz7NQ9M07PlcBNUjnb/0WbnYO6TlIL4SxZhDhNHBNBy+/iIjEmnlso20owsWrFiHWiSpcPNemsf8ApWeehTcJKTCyZph/+N/+Jvv27qdcKrNmzVoOHDiIZVmY9Qa2baMIBVVTkFLQMOvs27cXVVUIh0KYpkm9VmkmVIlGUFSBqizFvRFYtkm9VqfeMCiXK1itXBfRaJSRtcO0xeOYpknfQC+333kbP3vsiWZwNdvk63/2z9GMILFolPziApG2NvZEXmPm1Bje4TGKmQXG5rO4Zo1QOMziYp6gESAcMiiUSgxvvoOXjx0hl50ll50llsqyx6pSKhQZGz2J6bo8/fSTxIIKM9kinlmjWq1x6Yg2V4elORQ4M6dyKS7nTWRVT+QKIcrAxWOErj46gBspg8qNJO+NJCvcWPLeSLKCL+/lMCylvGC4zdU+0j9+sRno1YgQYo8v79XhRpIVbix5byRZwZf37eLHMvDx8fF5B+ErfR8fH593EKtd6X/jegtwhfjyXj1uJFnhxpL3RpIVfHnfFqt6ItfHx8fH5xfLah/p+/j4+Pj8Alm1Sl8I8YgQ4rgQYlQI8ZVVIM+gEOLnQogjQojDQojfaZX/kRBiRgixr/X50Ip9fq8l/3EhxAeug8ynhBAHW3LtaZUlhRBPCiFOtr7bW+VCCPFnLXkPCCGuXVaS5vFvWdGH+4QQJSHE766W/hVCfFMIsSCEOLSi7Ir7UgjxhVb9k0KIL1xjef+FEOJYS6YfCSESrfIRIUR9RR//hxX73Nm6h0Zb5/QLD35zEVmv+LpfK51xEXm/v0LWU0KIfa3y69q3F2RlQKPV8gFUYAxYCwSA/cCW6yxTL3BHazsGnAC2AH8E/M8XqL+lJbcBrGmdj3qNZT4FdJxT9s+Br7S2vwL8s9b2h4Cf0kxIdy/wynW+/vPA8GrpX3hXk64AAAP2SURBVOBB4A7g0FvtSyAJjLe+21vb7ddQ3ocBrbX9z1bIO7Ky3jntvNo6B9E6pw9eI1mv6LpfS51xIXnP+f1fAX+wGvr2Qp/VOtLfCYxKKcellBbw18Cj11MgKeWclPKN1nYZOAr0X2KXR4G/llKaUsoJmikkd159Sd+UR4Fvt7a/DfzyivK/kE1eBhKimfT+evA+YExKeanURde0f6WUzwPnpvi80r78APCklDInpcwDTwKPXCt5pZRPSCmXAvm8DAxcqo2WzHEp5cuyqaX+gjPneFVlvQQXu+7XTGdcSt7WaP1XgO9dqo1r1bcXYrUq/X5gasXf01xawV5ThBAjwO3AK62i3269Mn9TnEn+vhrOQQJPCCFeF0J8uVXWLaWca23PA92t7dUg7xKf4ex/mtXav1fal6tB5iW+RHN0ucQaIcReIcRzQogHWmX9NGVc4lrLeyXXfbX07QNAWkp5ckXZqurb1ar0Vy1CiCjwQ+B3pZQl4GvAOuA2YI7mq91q4d1SyjuADwK/JYR4cOWPrRHGqnLfEkIEgI8Bf9MqWs39u8xq7MuLIYT4fZqh9L/bKpoDhqSUtwP/E/BXQoj49ZKvxQ1x3S/AZzl7wLLq+na1Kv0ZYHDF3wOtsuuKEEKnqfC/K6X8LwBSyrSU0pVSesB/5IyJ4bqfg5RypvW9APyoJVt6yWzT+l7KEH3d5W3xQeANKWUaVnf/cuV9ed1lFkJ8EfgI8GutBxUtU8lia/t1mrbxjS3ZVpqArpm8b+G6r4a+1YBPAN9fKluNfbtalf5rwAYhxJrWyO8zwE+up0AtW91/Bo5KKf/1ivKVdu+PA0sz+j8BPiOEMIQQa4ANNCdurpW8ESFEbGmb5iTeoZZcS14jXwB+vELez7c8T+4FiitMF9eSs0ZKq7V/V8hwJX35OPCwEKK9Za54uFV2TRBCPAL8Y+BjUsraivJOIYTa2l5Lsy/HWzKXhBD3tu7/z684x6st65Ve99WgM94PHJNSLpttVmPfXvWZ4rf6oekBcYLmk/H3V4E876b5+n4A2Nf6fAj4DnCwVf4ToHfFPr/fkv8412hmfsWx19L0YNgPHF7qQyAFPA2cBJ4Ckq1yAfy7lrwHgbuuQx9HgEWgbUXZquhfmg+iOcCmaX/99bfSlzRt6aOtzz+4xvKO0rR7L92//6FV95Ote2Qf8Abw0RXt3EVT4Y4B/5bWgs5rIOsVX/drpTMuJG+r/FvAPzyn7nXt2wt9/BW5Pj4+Pu8gVqt5x8fHx8fnKuArfR8fH593EL7S9/Hx8XkH4St9Hx8fn3cQvtL38fHxeQfhK30fHx+fdxC+0vfx8fF5B+ErfR8fH593EP8/OTmP7j3P49AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred('/content/2021-10-01.png')\n",
    "z = plt.imread('/content/2021-10-01.png')\n",
    "plt.imshow(z);"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Air Pollution detection using Inception V3(Transfer Learning)Model.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}