GAP_cnn_tf2.ipynb

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      "name": "GAP_cnn_tf2.ipynb",
      "provenance": [],
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/raqueeb/TensorFlow2/blob/master/GAP_cnn_tf2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CkGh79ZDOG31",
        "colab_type": "text"
      },
      "source": [
        "## ‘কনভলিউশনাল নিউরাল নেটওয়ার্ক’ এবং গ্লোবাল অ্যাভারেজ পুলিং"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBd60ARu5iiB",
        "colab_type": "text"
      },
      "source": [
        "আমরা যখন ‘কনভলিউশনাল নিউরাল নেটওয়ার্ক’ নিয়ে কাজ করলাম, তখন একটা জিনিস বোঝা গেল আমাদের শেষ লেয়ারগুলোতে আমরা ‘ফুললি কানেক্টেড লেয়ার’গুলোর কিছু ভ্যারাইটি পেয়েছি। মানে শেষ লেয়ারে আমরা একটা স্ট্যান্ডার্ড নিউরাল নেটওয়ার্ককে যোগ করে দিতে পেরেছি। ‘সিএনএন’ অথবা কনভলিউশনাল নিউরাল নেটওয়ার্কের যে কয়েকটা এলিমেন্ট আছে সেগুলো নিয়ে আমরা দেখিয়েছি আগের চ্যাপ্টারগুলোতে। আপনি যদি ভালোভাবে দেখেন তাহলে মনে হবে এই কনভলিউশন মানে একটা ‘ইমেজ প্রসেসর’ - এর পরে একটা ‘স্ট্যান্ডার্ড নিউরাল নেটওয়ার্ক ক্লাসিফায়ার’কে যোগ করে দিয়েছি। আমাদের এই কনভলিউশনাল নিউরাল নেটওয়ার্ক বেশ কয়েকটা কনভলিউশনাল লেয়ার দিয়ে শুরু হয় যার মধ্যে ‘কার্নাল কনভলিউশন’ এবং ‘ম্যাক্স কুলিং’ অনেকগুলো ফিচার ম্যাপ তৈরি করে দেয় যেটা ছবির বিভিন্ন কম্পোনেন্ট এর রিপ্রেজেন্টেশন নিয়ে আসে। \n",
        "\n",
        "সবশেষে এই ‘ফুললি কানেক্টেড লেয়ার’ ওই ফিচার ম্যাপগুলোকে এমনভাবে ইন্টারপ্রেট করে যাতে তারা দরকার মতো ‘ক্যাটেগরি’ প্রেডিকশন করতে পারে। ভালো কথা, জিনিসটা এভাবেই চলছিল অনেকদিন - তবে এই ডিপ লার্নিং রিসার্চের যুগে এই জিনিসটাকে পাশ কাটিয়ে চলে আসে আরেকটা কনসেপ্ট যেটাকে আমরা বলছি ‘গ্লোবাল অ্যাভারেজ পুলিং’। এই ‘গ্লোবাল এভারেজ পুলিং’ করতে গেলে আমাদেরকে একটা ‘সিএনএন’ নেটওয়ার্ক তৈরি করে দেখাতে হবে। এরপর, আমরা সেখানে কিছুটা এডজাস্টমেন্ট করব তখনই বুঝে যাবেন কেন দরকার পড়ছে এই ‘গ্লোবাল অ্যাভারেজ পুলিং’। দুটো ‘প্রি-ট্রেইনড’ উদাহরণ দেখি। প্রথমটা VGG-16 মডেল। এটার একটা ছবি দেখি। \n",
        "<img src=\"https://raw.githubusercontent.com/raqueeb/deep_learning_book/master/assets/vgg.png\"> চিত্রঃ VGG-16 মডেল\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKtffSO-Sy8c",
        "colab_type": "code",
        "outputId": "ad8ba61f-c8be-4bd7-8a5e-05f1963b4865",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "try:\n",
        "  # শুধুমাত্র টেন্সর-ফ্লো ২.x ব্যবহার করবো \n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "\n",
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gnioTikw5LSx",
        "colab_type": "text"
      },
      "source": [
        "## প্রি-ট্রেইনড মডেল VGG16 এর লেয়ার"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6KePwncpRcwW",
        "colab_type": "code",
        "outputId": "c0b2c6a4-d042-47f4-cb78-60c95a3dd29e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        }
      },
      "source": [
        "# from tensorflow.keras import layers\n",
        "\n",
        "from tensorflow.keras.applications.vgg16 import VGG16; VGG16().summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              102764544 \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "predictions (Dense)          (None, 1000)              4097000   \n",
            "=================================================================\n",
            "Total params: 138,357,544\n",
            "Trainable params: 138,357,544\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Rcr0FwU4jOc",
        "colab_type": "text"
      },
      "source": [
        "## প্রি-ট্রেইনড মডেল ResNet-50 এর লেয়ার\n",
        "\n",
        "কি দেখলাম? অনেকগুুলো কনভলিউশন নেটওয়ার্ক, ম্যাক্স পুলিং, আর শেষে তিনটা ডেন্স কানেক্টেড লেয়ার। বাকি আর কিছু বলছিনা এখন। দেখি আরেকটা নেটওয়ার্ক। \n",
        "\n",
        "পরেরটা ResNet-50, কি পার্থক্য দেখলাম?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QC01bvx665rq",
        "colab_type": "code",
        "outputId": "e3fbaf1d-0db6-4087-9051-7e6105ee1f44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from tensorflow.keras.applications.resnet50 import ResNet50; ResNet50().summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"resnet50\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_4 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          \n",
            "                                                                 conv2_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           \n",
            "                                                                 conv2_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          \n",
            "                                                                 conv3_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           \n",
            "                                                                 conv3_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          \n",
            "                                                                 conv4_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           \n",
            "                                                                 conv4_block6_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          \n",
            "                                                                 conv5_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "avg_pool (GlobalAveragePooling2 (None, 2048)         0           conv5_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "probs (Dense)                   (None, 1000)         2049000     avg_pool[0][0]                   \n",
            "==================================================================================================\n",
            "Total params: 25,636,712\n",
            "Trainable params: 25,583,592\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5lA4HADZ7O9A",
        "colab_type": "text"
      },
      "source": [
        "কি দেখলেন? শেষ আউটপুট লেয়ারের আগে একটা নতুন এন্ট্রি 'avg_pool (GlobalAveragePooling2D)', চলুন আলাপ করি। শুরুতেই আরেকটা ডেটাসেট, সিএনএন দিয়ে।  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y3gF-hFNBtKg",
        "colab_type": "text"
      },
      "source": [
        "এই নতুন নেটওয়ার্ক তৈরি করতে আজকে আমরা ব্যবহার করব আরেকটা নতুন ডাটাসেট,যা বেশিরভাগ ডিপ লার্নিং এক্সপার্ট ব্যবহার করেছেন তার শেখার শুরুতে। বেড়াল এবং কুকুরের ছবিকে ঠিকমতো ক্লাসিফাই করার জন্য এই ডাটাসেটটা ব্যবহার শুরু হয় সেই ২০১৩ সালে। এর শুরুটা হয় একটা ক্যাগল মেশিন লার্নিং কম্পিটিশনে। এই কাজের পেছনে ছিল মাইক্রোসফট এবং পেট ফাইন্ডার বলে একটা কোম্পানির পার্টনারশিপ। আমরা যে ডাটাসেটটা ব্যবহার করব সেটা প্রায় ৩০ লক্ষ ছবির একটা ম্যানুয়াল ‘লেবেলড’ ডাটাবেজ। মানুষ নিজে হাতে করেছে। তবে আমরা সেটার একটা ‘সাবসেট’ ব্যবহার করব যার জন্য এত ছবি না হলেও চলে। শুরুতে আমরা একটা স্ট্যান্ডার্ড ‘সিএনএন’ তৈরি করলেও তার শেষে গ্লোবাল অ্যাভারেজ পুলিং অংশটুকু জুড়ে দেব আপনাদের বোঝার জন্য। আমাদেরকে একটা ছবি দিয়ে বলতে হবে সেটা আসলে একটা বিড়ালের না কুকুরের ছবি? এবং সেটার অ্যাক্যুরেসি সাধারণ ‘সিএনএন’  থেকে কতো ভালো?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyuDd_3a_wYl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# লোড করে নেই হেল্পার লাইব্রেরি, টেন্সর-ফ্লো ডেটাসেট এপিআই\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "import tensorflow_datasets as tfds\n",
        "import matplotlib.pylab as plt\n",
        "import datetime as dt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQjvvHB8DBs5",
        "colab_type": "text"
      },
      "source": [
        "## গ্লোবাল অ্যাভারেজ পুলিং\n",
        "\n",
        "আমরা যখন এক লেয়ার থেকে আরেক লেয়ারে ডাটা পাঠাই, তখন লেয়ার ভিত্তিক ক্যালকুলেশনে যদি এমন কিছু করতে পারি যাতে সামনের দিকের ডাটাগুলো কমে আসে। এটা ভালো হবে না? এমনিতেই একেকটা নিউরাল নেটওয়ার্ককে ট্রেইন করতে যে সময় লাগে, তার পাশাপাশি সেই কম্পিউটিং রিসোর্স জোগাড় করা যেরকম কষ্টকর - সেই দিক থেকে বিবেচনা করতে গেলে ‘গ্লোবাল অ্যাভারেজ পুলিং’ আগের লেয়ারের প্রতিটা ফিচার ম্যাপের আউটপুটের গড়কে ক্যালকুলেট করে পাঠায় সামনের লেয়ারে। এই সহজ কাজটা আমাদের ডাটা ফ্লোকে অনেকটাই কমিয়ে নিয়ে আসে আমাদের শেষ ক্লাসিফিকেশন লেয়ারের জন্য। এতে ট্রেনিং দেবার মতো আর কোন প্যারামিটার থাকেনা, কিছুটা ম্যাক্স পুলিং এর মতো। আমরা এখানে একটা ছবি দেখতে পারি।\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/raqueeb/deep_learning_book/master/assets/gap1.png\"> চিত্রঃ ‘গ্লোবাল অ্যাভারেজ পুলিং’ লেয়ার চলে আসছে আউটপুটের আগের লেয়ারে"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3R-EYlZuO9qQ",
        "colab_type": "text"
      },
      "source": [
        "আপনারা দেখুন - সবশেষ লেয়ারে যেখানে আমরা জিনিসপত্র ক্লাসিফাই করছি তার আগেই এই গ্লোবাল এভারেজ পুলিং লেয়ারটা দেখতে পাচ্ছি। সেটা থেকে আউটপুট চলে যাচ্ছে আগের মত সফটম্যাক্স লেয়ারে। আমাদের এই আর্কিটেকচারে ৬৪টা গড় ক্যালকুলেশন আসছে সেই ৬৪ এবং ৭ x ৭ চ্যানেল থেকে - যেটা আমরা দেখছি দ্বিতীয় কনভলিউশনাল লেয়ারের আউটপুট থেকে আসছে। আমাদের এই ‘গ্যাপ’ মানে ‘গ্লোবাল অ্যাভারেজ পুলিং’ লেয়ার আগের ৭ x ৭, ৬৪ ডাইমেনশন থেকে ১ x ১, ৬৪ ডাইমেনশনে যা আসলে আসছে ৭ x ৭ চ্যানেলের গড় আউটপুট থেকে। অনেকে বলতে পারেন শেষের ‘ফুললি কানেক্টেড লেয়ারগুলো থেকে গ্লোবাল অ্যাভারেজ পুলিং লেয়ার কেন ভালো? \n",
        "\n",
        "১. সবচেয়ে বড় কথা হচ্ছে আমরা মডেলের শেষ লেয়ারগুলোতে ট্রেনিং করার মত প্যারামিটারগুলোকে বাদ দিয়ে দিচ্ছি। একটা ‘ফুললি কানেক্টেড’ অথবা ‘ডেন্স’ লেয়ারে প্রচুর প্যারামিটার থাকে সেখানে আমাদের আগের ছবিতে একটা ৭ x ৭, ৬৪ ডাইমেনশনের ‘সিএনএন’ আউটপুটকে প্ল্যান করে সেটাকে ঢুকিয়ে দেয়া হচ্ছে ৫০০ নোডের একটা ডেন্স লেয়ারে যেটা প্রায় ১৫ কোটি ‘ওয়েট’ বের করে দিচ্ছে আমাদের ট্রেনিংয়ের জন্য। এটা একটা বিশাল কাজের লোড। এই জিনিসগুলোকে বাদ দেয়াতে আমাদের ট্রেনিং স্পিড বেড়ে যাবে অনেক গুন। \n",
        "\n",
        "২. যেহেতু আমরা এতগুলো ট্রেনিং দেবার মতো প্যারামিটারগুলোকে ফেলে দিচ্ছি সেটা অনেকটাই কমিয়ে দেবে ‘ওভার ফিটিং’। সেখানে আমরা ‘ফুললি কানেক্টেড’ লেয়ারে ড্রপ আউট ব্যবহার করা যেতো। ‘ড্রপ আউট’ নিয়ে আলোচনা করা যাবে সামনে। \n",
        "\n",
        "৩. ‘গ্লোবাল অ্যাভারেজ পুলিং’ নিয়ে প্রচুর আলাপ হয়েছে রিসার্চে। দেখা গেছে আমরা যদি এই ‘ফুললি কানেক্টেড ক্লাসিফিকেশন লেয়ার ফেলে দেই তাহলে ফিচার ম্যাপগুলো খুব কাছাকাছি কাজ করতে পারে তাদের ক্লাসিফিকেশন ক্যাটেগরি বানানোর জন্য। কিছুটা ডাইরেক্ট কানেকশন। এখানে আমরা বলতে পারি আমাদের প্রতিটা ফিচার ম্যাপ একেকটা ক্যাটেগরির ‘কনফিডেন্স ম্যাপ’ হিসেবে দাঁড়িয়ে গেছে।\n",
        "\n",
        "৪. সবচেয়ে বড় কথা হচ্ছে ফিচার ম্যাপগুলোর গড় অপারেশন আমাদের মডেলকে আরো শক্তিশালী করে তুলছে ডাটার ভেতরের ইন্টারনাল ট্রান্সলেশন কাছাকাছি হবার কারণে। যতক্ষণ দরকারি ফিচারগুলোকে যোগ করা বা এক্টিভেট করা হচ্ছে ফিচার ম্যাপে, সেই জিনিসগুলো চলে আসছে সেই গড় অপারেশনে।\n",
        "\n",
        "আমাদের একটা সাধারণ ‘কনভলিউশনাল নিউরাল নেটওয়ার্ক’ এবং ‘গ্লোবাল এভারেজ পুলিং’ এর মধ্যে লস এবং অ্যাক্যুরেসি বের করে দেখব কোনটা ভালো কাজ করে।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IlKXX2DuJK6D",
        "colab_type": "code",
        "outputId": "028bb0f5-fceb-45f9-a2df-56c3732dd710",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# দরকার টেন্সর-ফ্লো কলব্যাক ফিচার\n",
        "%load_ext tensorboard"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The tensorboard extension is already loaded. To reload it, use:\n",
            "  %reload_ext tensorboard\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_bx27_1DrkS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.callbacks import TensorBoard\n",
        "log_dir='log/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M59ag156CDoc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# আগের সব ট্রেনিং এর লগ ফেলে দিচ্ছি \n",
        "!rm -rf log/ "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ENeoSPCJRSVm",
        "colab_type": "text"
      },
      "source": [
        "## বিড়াল এবং কুকুরের ডাটাসেটের উপর ‘গ্লোবাল এভারেজ পুলিং’\n",
        "\n",
        "শুরুতেই আমরা সাহায্য নেব ‘টেন্সর-ফ্লো’ ডাটাসেট এপিআই থেকে। কল করছি ডাটাসেট অবজেক্টের ভেতর থেকে। তবে, এটাকে দরকার মতো তিনটা টুপলে ভাগ করছি। ৮০, ১০, ১০ যার থেকে ৮০% ট্রেনিং ১০% ভ্যালিডেশন আর বাকি ১০% টেস্টসেট হিসেবে রাখবো। শুরুতে আমরা ডাটা সেটের ইনফরমেশন দেখি। আমাদের স্প্লিট অবজেক্ট ডাটাসেট লোডারকে বলছে কিভাবে ডাটাসেটকে সে তিন ভাগে ভাগ করবে। আমরা তাদেরকে ‘সুপারভাইজড লার্নিং’ মডেলের জন্য ব্যবহার করবো কিনা সেখানে ‘হ্যাঁ’ করে দিচ্ছি। এখানে আমরা ডাটা নিয়ে আসছি লেবেলসহ। কি ডাটা নিয়ে এলাম সেটা দেখি একটু?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJM3-v4s_1Yf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "split = (80, 10, 10)\n",
        "cat_dog_spl = tfds.Split.TRAIN.subsplit(weighted=split)\n",
        "\n",
        "(cat_train, cat_valid, cat_test), info = tfds.load('cats_vs_dogs', split=list(cat_dog_spl), with_info=True, as_supervised=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMeuSM1pRxH5",
        "colab_type": "code",
        "outputId": "930d6bb0-ee11-4944-a1f4-2f3d7b990924",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "source": [
        "# ডেটাসেটের ইনফো পড়ে দেখি \n",
        "\n",
        "print(info)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tfds.core.DatasetInfo(\n",
            "    name='cats_vs_dogs',\n",
            "    version=2.0.1,\n",
            "    description='A large set of images of cats and dogs.There are 1738 corrupted images that are dropped.',\n",
            "    urls=['https://www.microsoft.com/en-us/download/details.aspx?id=54765'],\n",
            "    features=FeaturesDict({\n",
            "        'image': Image(shape=(None, None, 3), dtype=tf.uint8),\n",
            "        'image/filename': Text(shape=(), dtype=tf.string),\n",
            "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),\n",
            "    }),\n",
            "    total_num_examples=23262,\n",
            "    splits={\n",
            "        'train': 23262,\n",
            "    },\n",
            "    supervised_keys=('image', 'label'),\n",
            "    citation=\"\"\"@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,\n",
            "    author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},\n",
            "    title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},\n",
            "    booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},\n",
            "    year = {2007},\n",
            "    month = {October},\n",
            "    publisher = {Association for Computing Machinery, Inc.},\n",
            "    url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},\n",
            "    edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},\n",
            "    }\"\"\",\n",
            "    redistribution_info=,\n",
            ")\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c8Rl4kG42AXm",
        "colab_type": "code",
        "outputId": "833f778c-b0af-4228-bb92-73a71d5d1b87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "source": [
        "# for image, label in cat_train.take(2):\n",
        "for image, label in cat_test.take(2):\n",
        "  plt.figure()\n",
        "  plt.imshow(image)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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USiMx1iKE90UJVeJkRW8EFyuftGedoiwLRKGwvabv+4065NZaJpMJutWsVqu0\nkNbrdVrEbdMlDjoajVJ/o1SJTtXoTtBaM51OWS0uN/KO4r0jwcXCLrFPOWCQwI3MaerTVaoryGR8\ndnxG35sgQUvPLNZrfvbjn7I4X/Bb3/sul5eX7N44QDtDXQ5q35UsghDWlL9b13VXJFXO8Pq+T/Uo\nc9QvMkfcJvTvx0aAkAiVRfLAhgYgFZSVQkifGW5Mj+nCjimuA+mo6zGFKlPp69dpbwxxXUdE3q+x\n+ZtBhXMhbCkMcBhk61zQ4YbrxNYXmUFOuRTzAMpAZGVZ0bZ+8ffG0eqO5brl977/b/N//ot/Qdd3\nuK6lKL1tUVUVTgp08EHldkKULilBL0jbuPDiQh6NRjjn0hY50VbK1TUppfeDBfSu73uqqmK5XFJV\nFV1n0jhGqVBVFaPRiNV6scFcIhFLKalKb/N1XZeuMcYHFo/rCUoqVF0muPzyYsF8NsHiI+9X/9eK\n7/yV7+K0Ybw/pw/xlTnQFMc5SvSo6ubgVI4O5trHddJp01UTY0UHwiRmpFsXcugGUCMywqZpMaan\n7wtssLls0DCcr1SOtWsE7Tcv5QS27J7831tQa34u90XkHMxau3Fdfs/BCPYtv0dU/2AIt5pOp5xd\nnGOE5GR1QVWOebm4CHtEVaybS7quRaqSi4sLRKHSPVRQr2w/MIzYD/ALS/eeqKqqYr1eU4RdR6J6\nKKWkaZpMunjis8Yk1TISSFVVtG1LXVfpOXnuUnQax++wmY2dZ9/mEm80GlEWZVLjlstlkkid0azO\nzyiExBnLj//0h9x//13KpyW333vAdDajrusUERJbVMOjr23bbowth/i35y7OX3pHNmsRxqgNbwd7\n8CKqvNGcgIhGhqwE5e8ng2qOlCG9xzuS+ywA+Fe1N4K4ctviqjqwGd4zXCOH8mlCoF2AyZ2jYDPl\nxKUEO596b+xgJ0WGmVTDuGkevg7F+nLJfP8mP3/0SxprWaxafvCDP+Fb3/qIi4sz1t0Fo0mJ1jAa\njXzahhi4rsAL0qiKxcUUJ7au63QuLuqqqjx6GFS3qM6Mx2NGoxEXFxe+dnvgvm3bMp1OU6R3JLSu\n6zbUzb7v6YwnsCqojN6g94vOWZOIKUL4kVmt12tGoxGz2Yy+75lMJjRNw6pv6bqODpBnjvV6zWKx\n4M4795kf7vt7hXvk9krfG+q6oCzrYG8OcxKDdiPhbNhaW8S1DYD4DAeSe0c6QAoK5TMVRNAU6rpm\nMhp5SW2kNyespR5VYF0qDCSLOmQ2GPq+pWma117XbwRxAT7iWmwOlhfrA1gBV+0yKT1sLkPBZ+GE\nj9nIJJcHwUNkLQ4rOpwTvvJ2P2X/AAAgAElEQVSRv5M/JaSHbfGb5/UdtFZx/OwlvZxQCENVegTw\nF58/ZDaboUrvsdf9EovfV0ypEmf9zo7WWLTx6pyxDt36EKaq9n6nw4M9VqtVkiRxQUeiiyBG3NYm\nqoQuRFaAoCjKQAwe5EAJlo1XYURRol0oDgP01oHztTuEKnHGIlAY7VJgbs7oItgSM5ujndYsfZ91\nv8YGBNQqRTEeMx7v0C172os1TXFJrxTTvTmd81C9tgbXKzSCFs10p6IoRJBgGoQJ6fXDvlrW9rhQ\nXMhlwQN5jKVwEh80r4jFd2QZyp5bg7UaZzRY4bUKqXDOgrFoG+xLVSKVQCW7WaNEATiqakIlvmER\nGtG4z3XqxI0ypOjX8Y5v338TBYycMIeIUyIJTki0hc5oFsuG82VDUVeU5VAlqG3bFIqktfZ2l3MU\nqgogwdaGCxmyBSSkMIIPMMS35SpbtCnG43FaUG3bMgpooTGGnZ0dFotFuq6u6qQWdV2XYPeu79nd\n38Vo7dHHsqJQfpvS6XQKeKdzJKjoZ/PIZIjZjG4NM+RerdfrdF1838vLS9btgmpccXh4yIP33mXv\n8ACretZtg6CkUw07O1PW0lKPwi6OGJQUaOO39lFqU538qjXg39n7wZxzlJUKoVR5PtiAJo+C5PLP\ndYmxFFIlR3w83/fae+7k65PMG0FckEmhKwbjb15bMN0hg6aHrUvjZAyORytAS4WzsGhaFss1jba+\nJLQxySkphGBnZ4e+71NsXlWPPbjQGbRugt+kSAhYBAhy9S8CFDE2MH+/8Xi8kQC5Xq99jYyq4uLi\nwu9m4lzymUWiHY1GjLJrczClqiqOj48Zj0bedgv7UkXCimpmdN4WRcF6vU7qpnO+9IKxPaVUjMYj\nnBQsl8sEDnRdx/n5eYC5eyol0eues9MzlFLMZjPKsqScVF7a3brFrtjH2SLUX3T0vUBQBPhfXrHB\nXgW/e/NCIFXcoMHQNBqlvM9KqWLDBZBHuW/nBopkJigsgt4YwID5htlcXrXxXdn2ZSix6RvJWx5C\nsz3Q0f+zSVgeZRwkiufOOF/nQpYVTio63bFYrum0RogIVfsA2YjOxYUbQYcBdRSB4EwynhFDIZd4\nfV3XiTNG4CC+kzGGw8NDHj16lCD1xWLBeDxmtVql9+pCnOBqtaKq/GJVSiXCatuWnZ0dTk9PU1Zy\nzq3rsgJjU/nm2N+qqhiPxylMqSgKeu1tt5hvt14vaZpVyEsbxrvv+9SvZrli7bx6t7pcMRqNWJ75\nCJHp7pji9m3a1Qr0nKX2tp/BMNvZxRhNWQ7oos942GS829qAkgqEdxcoFTOY/faxSpWJ0cTxgSHT\nQYbCRG3bMh1PcMEH1vZ9it3s+56u/8YVBR1aDpU651KA7Kug2Ng2YfpNYttEBP0+ueEMhYpGtsRY\nR4dl2fS01u/8kXv240RsR1VEjh7VWI/iZZtiM0RFVFWVwAdf7cmrVhEhjGreyckJ4/E4+cRms1lS\n1w4ODlgvl+zs7KTIjZOTEw4ODhLAERd4lI6j0Yh101BWBTK4Aqy1SEhEtV6vmc/nHgXsOuq6TpH3\nRekhfd35yJFR6QGTUqpE+FGKxzlctU3qgzEG3XmwhqqiXQhWoyVmb4+Ls3MWzZobtw+Zz/bQ1kBW\nys6P82Z992vXAAYXyjREu8taR9cZpHEJlY0qbwR0mqahpPbobbBPbZBqsihxGK/mrlff7PCnba97\nEs9clU7b18Xf5IR4HRjiz/lgRa/uhVJeQrBYd1yuVuhw3ODABchWllek56B2ebXI2aiCqGRDdb3e\nMLwvLy+ZzWYsl0vms51ERNGpOp/POTk54fDwMBFLlJC5Tyjew1rL/v5+InylVKpDEZnCarVivrtL\nbzpMls4h3RAtEqVgHPv1ep1sE6ksXdfQNV7CRCe9FcUVv128t7ESVVS+3IAxFEKijGO9atG9RbfH\nWAvV6Qve/eBddO84Pj5lNp/6oGq3WfP/dRisL+ngK29VVY02HVp3GLNZIi2X4l3XIQqVQKN83FRZ\nooSX+k3TIOTrmya/kriEEP8I+H3gyDn32+HYAfA/Ae8DvwT+jnPupfBv/t8A/xGwAv6ec+5fv3Zv\nuKpTy0w6+Ntfb9Bu+8hy4sqT+fz1Pj7MIlO4Tt8bLtuey2ZNqw1SFb6EerDNrHNY028k2uXRA0UZ\nN7sWwf5QCVmL0ilO2HQ6TbB6XVYpWRFgOp3y4sULZrNZsqdiftPOzg7T6RStNRdnZ97/FOwjYzx3\nraqKsqiCPdEm5HF3d5flcslkNub4+NgjnUJiA+FHwo4ugPHYp5JEdW/VrjBdv+EIBp8h0ITipF3X\nQeZWcVLgWkFntE8EhQF9LMdcXi45ffmCb/2lj/js5484uH3IaDz2NlkJRmksBr9puBtKDGQtmQbO\n18YQgtD/EdZppPMqdN+1CRiKRFQUFVJ23L07CU7kPiXjlkEdN8Jnk9d1zXg8omlXr72WX8fd/N8B\nf2vr2D8E/plz7tvAPwv/BvgPgW+Hzx8A/+3rdCIyow2JJfLAzZAL5TQhIQIhN4s8RkmR7Kzw8X4N\n/08hQj1yZ3wWcKEwsqCxEl2NWWuwxm/eYw04K3zEtChQosAi6LRBW+cdiwhkUaKtQxtHrz3oX1Yj\neq2RqkAbvxdW06zRume9XqF1j9aeQM/PLzk/v2Q2m/Pgwbv4nUckOzu71EXN7myXdtVSFzWTesLB\n7gGmM1SjCdp6BtH2ht44ZvM9jBMY4yiKCqMtbdMlbmxMz3rZMJ/t4gwsFiuWTcuyaemMxVnLbDql\nKksK5ZPmnPW11a0TCFXSaR+1r6oaQ4xHdBSFRJUSg6EzPkNgf75LpQrqIiKBPmess4bL9SUaTed6\nPv/8c549/ZJffPozTp4+4ckvnnD85Qkvjo5ZLS5xzoSa9zYEEMc1IXyya0gh8pqFV/21tjgrguRv\nkEowritGlUIhqFRFKUtqNUIawUjVjIsR0kkUyqO+QVPxUlnw8uUZq+VfoM3lnPs/hBDvbx3+28C/\nF77/98D/DvyX4fj/4Dxb+7+FEHtCiLvOuaev05ltVPCrVAC/reLVwMzh35kkc8ITsABrBUJVIBTO\nKYx1GHyayrrpMHbY9QLYUBNicf+8RQDAWj1ExYvtgqNi8EEBRVHStj4dJI8YiBHz0+nUc8+y3EAJ\nozHe9z3j8XgDDIlhUlFqRGYzGo0oqyKpQX3v1azIvZumSe8awZMcVge/hZG1fr/qIfJhiCyPKGjT\ntUOYV5B66/U62ZqxzJtSijq4Erq+ozeapm+8pNYtN2/dYzQaURQFO5MpdisDIoJbznknfWKuwuEY\noi26rsE6zWg0ZlqNPNgX0sG0DlkMrqfrmo1o9zg+26BHHhT9Ou03tbluZwTzDLgdvt8Hvsh+9zgc\nu0JcQog/wEs3RmWxARBs/M55zzrEXTni7hrpPklapegOfK4X8V8CcCG3SEiM8CqathaKimbV0GrN\nqutpdZsIJqpk0SuvSpVSwePmaMPz/WKRQtJ1PaPRGJDMZvOgHhbUdRF8ZB7MqKoRe/Mxy6XPv9rb\n2+P09DQt/IuLi6TOGWO4uPBhVw8ePOCLJ1+mKIm4GICkBhpjmE6n/j10FxalROt2g0Ci2uptw95v\nAetcitWMkeNDwHQoPVYNPr8c/Fhn2QAnzYuwE4t3Z/jCQ34TiHXTeFXbaWol6daeyM9fnvGtDz5m\nZzpD2BCyhfA7PErQcph3pQrv9JVhjTivFmrt7ayyLJmOpr7uSKNBQTkaAZZ6UrNaL6GwjApfJjuq\n7H3f0/TeP2ic9dsyCRFUw/FrE8n/a0DDOeeEEK9v5Q3X/SHwhwDzyciFY/n5DfjdOVK1jEiEG1Wc\nQhPCx2LIFKUhsdJX7xWhQlNnARTrtqdrNKt1S29N2qYqT+IDklSIfcnrl0cCK4sapQocMBr5oNvx\naMzickU9KjcCd6Nf6+7du5wen6SFvl6vmUwm6Rlt27JarVLIUVmWrFarJF1u377Nz3/+85TKkqvJ\nfow8ete0TYD+dWIOQvjy1zDsO1WWCqkURmuKwktDobxOXcoS3fWMRiPa9Tq5JYA0NjEbWkoZwrME\nXXDgtn2X/ILOOXamM1rtofJ+sWA2myAdTOoRF2fnlGXJbD7FScF4PkGHRFBnNzfjyNeLksI7e4VP\nDYmS2BhH2/fshzqMq/WCy8tzXp69QEqoyjHn55fs7Oxw48aNNPdCCJ9h3ZmNcX3d9psS1/Oo7gkh\n7gJH4fiXwDvZ7x6EY6/VcrUwEpZMTuS8WI3Xq68pjRCaDHsLK5wTGCu9fWAFGkVvBW3bsGobeq1p\nes+hOqMpg4Ecg2ijQe+38Nzc/zeCGpE4vNqgGI8rppMdgMTpq9KHO43qCe++8z6ff/45L07PcM6x\nu7vL5eUlFxcX3Lt3j4cPH6Z9oqJ6GOHs8XjMeOyr5Eap0fc9h4eHnJ6eIqXk7OyMmzdvIiVJ6i4W\nC0Yj73aIx6IrIMLSUUIBG2pq13WhQOoQ5e+s3ZBmnsF4R3DTNPTGgzoRvYzO9MRAQ/0PZ3RiGlYb\ncIa2dzx+/BhZCG7eu8O9B3fZv3HIZDbZICprLc5Gf2bh0cy+YTweM5vNUoqMcwZZKs4uz3j69Ck/\n/LM/5enTLzk/Pw/qs6+M9df/+l/nxu1bOBxFXW3k2I3HY35dGfKbEtf/CvxnwH8d/v4v2fF/IIT4\nx8BfA85f197aTv2AWNk1on4xjduXKftVLypV5avuCuk3VLDQOUtvYN21LFdL2r6hC/4sL7ksSlUb\npY2ttUnNant9RVJGLto2nqvv7u5zebFktDvB2mHDt5s3b3J8fMxkMuH8/JwPPviApmnQWnN+fr5R\nUiwSkHSeKKrKI4ox3vDo6IjTs5cpyHc8HlMUBcvlkr29Pe+sXS4xZpB2sSBNEwCOCP/nmcy9GbKj\n499Vsw6QtTdWuq6jLAqM08mmi4G9zmhkcBmUssThpbUJQbuj4DiPxJxHj6jCI5W6a1iuOuZ7e9y4\ndcitwxuUqsBktfG3AS8f9tUDDUqJVKGpabrAABVdv+T09JR/9a/+Faenp9R1zfvvfeSlauft2r29\nveT2yP2t0dm/wRxeo70OFP8/4sGLG0KIx8B/hSeq/1kI8Z8DnwN/J/z8n+Bh+Id4KP7vv04nooMw\nDtZmBxQIHw3tK/GE4jNf4W9wAloDTvo9ujpT0GjDuulYtC0ah7E9TnkiVVLS9j3adFhZpAlzzqUQ\nIB+p7SWoUtVGoqIUBXt7s1SW6+7du5yenrK7u8ve3h6PHz+maTrm8z3m83kgWp3Uj/v33+Hk5IT1\nek3TNNy+fRshBId7+3z22Wdpr62nT596vX82hbOXiTs/e/YMYwx3795NC2M6nbJYXHB2dpYksDF+\nG57IOLTuaBqdGESeqRyBByEci0XHdDwJNQBDUmhwJi+XywHCr0pQ3i2htabXPVIO4V5RvRZCsG46\ndnZ2GNUlF2cvKVXl5033VM4zpLZpeP70GfW4Yu/wAFFI9vb2QPkiNDHZFHy42GQy9dLXkCRl32t+\n8Yuf8uiLn3Nxccbd+/d48M47KOVLhPtx6KmqgvFoSlnUWKcHH189QcmSyaSgLBUvX754nSUNvB5a\n+Hdfceo/uOa3DvgvXvvpw5WpZqBjcCLbVFDT4xeGsLmcL27hiW7LwZiQLGUxzhdJMc6ijaM1+N1F\nMOg+2gZ+oc1GU9p2QNtii2hc9JF5ZK5LC3E88nF5faspZMlsMsVZzeHBnkf4hOPWzUNuHO5ycXGB\nkpbVauXLm4XqQl88+pL9/X0uzhcsFy3vvfuht7faNe+8/wF1XXN+fk6rDdVY8ezZEaPxlKKsWSzX\n7B/c8DbeZMbx8TFd37BcL5BSsru/y+XjS+pRSW8E2rYIJ1iufbCucRpVSA9Xi8pnFsjgrnAGKX10\nfymkD6QVkqIs6EUshe0L8VS1B52kBeEc1vrCOetmQVF4ZlSVI287O8eNg31msxnj8Zj9vUOePX/i\n87GqgnXfIRYvWS3OmI1qDuYP2JvtUAhF32tKSqra567hHGVRUJWKohzh6Gm7NRZv5x0dHfHo8SOK\nasq9B4fM5/MB1Q0rThU1xkLXW4QokEim45p23WAtCCVxTrNuG9r+Gxdb+JsE5UYEcfP6lO6OxFjQ\nQNv3NJ2m7Vp6h698FAJUY2RCtJ2i5MyN1xQ5neDYocRZtFn2dw+p6zohdMvl0sPTTYNzAiVrPvzW\nJ3z55ZdYI6kmI3QfiVXz9OlTZrMZv/M7v8NPfvIT6rrm5dkpd+/eZTqdcnl5yfn5OXfv3iXWGTw6\nOuLg4IDz83Nf3iwkWK4bk0lHk97Dw+Heibuzs5NsSS9BS8qiykoVuLSrSNP3CD3Ywrl9VlUlZBW4\nYMhyMMBOXbG/f+hh/3WHlF5Cnp2d8fLlSwDW6xXT2TiEJ4XKXKZgb28fKX1ZhOPjY+6Ma8xq5ZlC\ncJdEhM9am5BT5xzL5ZKTkxMePXrEfD5nOttJEno70kNJPwdd14UQsB3vCpCKzjq0NZydXbJcXfx/\nAmj8hTYHG4v6dZsl2mR+W9RQZxXwxNX1mnXfcbFqsLJEFd6pK5zdKBaTh9jEnLK2bVNIT5qUIDWl\nLLAGClWhtWF//wAlBHVd8vKlh9Kn0ykPHtzj9PQl9+99C4CuM9y//y7OOZ48ecJstsNyuWB3d5dP\nPvmEf/7P/zlnZ2csFgvKskwb1l1eXjIajfi93/u9IXBXlezvezX09u27rFYrzs8vmUxmAKmMmtYd\nOztzlssFWhtGo0mC+n2eWJ2g82i8IyzOmSC1Q7lrJEWI5NBGp8UdQ4JyOygyqb436NbvqXX37l2U\nLKnrEUop9vb2ePHiBU3TbPjpwGsLFxcXKASffPwxt27dYudgj53dXZz02kl0X+SRL0JI2q7l9OQF\nT58+DePmwY2YBHvdGovqedM0nJycUJYFVndMRmOvDeG8Gqwqvnk1NNxQOAQ2w5di4O6waU12GaHW\ne0ghUcoPsHWgnQzIYE9nHX3f4FTBcrUC57hx4wbT6ZSLiwsmkwmLhVejhBuM11xFjAG4vh6D9yG1\nbRcMcsftOzeSkb5YLNBas1wuOTw8ZL1e88EHH3BxccFyuUQIwSeffEJZKtq24fHjx3znO9/hJz/5\nSUrfGI/HrNYL1us1ZVlyeHjI7u4uP/zhD71ztrnkww8/TDGEP/7xj7l9+w7OhfT7rkHJkoNbBxwd\nPaOq6gQeNLpL3Dvme8VFqrVGKpE5zB3amJAh4Odlb2+Pi4uLDC3cDLiOzKgwgnt37tP3huPjY0b1\nhNVqnYCQ6HxeLhcJWfTVphw4w8nJCT/84Q95fnTEnXfu81H1HerJmN3dXdq2ZW9vLyGfVVVxcbnk\n8vKSH/zgz1BKMZ/Pmc1mvl+ZYz+usfg9puPE8XDO0TQNdVkhlcQQUoC61y8I6tfsG9K2UaB4LD+X\nN++TEunj69n57w5F19tQc04iixLjHG3Y5T4GqErpDeSiKNjd3R2i2LOFlxdLiQCHBzkaxuMpZVl7\n34gzHB0/5+LyHIdltV6yXC18TlNV8PDhTzk+fs54XFOWg91mjGF3d5dPP/2UyWTCy5cvk3M4Rmco\npTg+Pubx48fUdc27776bEjbbtuXi4oK7d+8Gdc0H8QrhVb+XL86Zzebcv/8OxliMsTRNi1JFyHly\n7O3tA0MYWQQ18vJjUV2M6OZqtXpl7Ys4VnVdc3Jykq47PT3l5cuXNE2TShBEdM4Yk+4ZkVNrLYvF\nguVyiXOOi4sLFgvPcPb29hIz01pzfHzMZ599xh/90R8lB3peTeu60LrY1zziJAbyxnVSVRWjqmJ/\nd5fbN26zO9t97TX9Zkiua8KX4vd4TlwnufIsfSFwAoyzGOcwTtF0Peu+Y9V2rLqGZdswHc+YjidM\nJpO0U0dM8BuPx+iuTXZbVJ9i+NFq7YM7RyMPlfe9T78/Pz/nwf07xM2/wU/Uo0ePePLkCft7fsfG\n2WxG16+5vLzk8PCQO4c3OT2xPHz4kGfPnvH973+ff/kv/yUPHjxgZ2eHqi4SdB3V1K7rECj+3X/n\nb3B8fEzfafb3Dr1UXKyZzWa89/47acEWRcHhjX0ePXoUbB/BxcUFAHt7e8xmvpJv27YsFpdBennn\nrkcNQyC1UhhrMdrQGx02Dwxzx+Ym8EkLUaAKldTcYloRa/PHiBKPYNas1ouN7WvLQmGl5PLykv2D\ng0RwUeK1bZuiaM7OzvjTP/1TFsuW6XSH3d1d5vP5hh0vlUruhDz/zqOiRfInGmNYLpfcuvGOf5es\n7mT8zeu2N0ZykRGS/+fmXwi2mSBFUpCdSaihFRgLbee3XnVO+MUg/c6IRSGZTCYJgTs6OvIgg7W0\n7Zp8/+Po/Dw7O+Ps7MJHFuzsBFXDZ9W2jeeel8sLikoxnY4pCr/D/LpdsF4vWS7Pefz4EavVgufP\nn3L37m3293f59NM/55NPPkmL4eHDh8znc548ecJ0OmW9XnNycsLjx485Pj7mVthWVQjBD3/4w5Sb\ntFxdIhW8/8G7FKXk8eMv0Lrj/Pwl2nT89Kc/ZbVa8PHHH7Gzs8tHH32cfDqLxSJJ6CiVhzomWcEg\nBKUqGNejlBcVbbQ8/CxGrWitcQxO6agS6+BXbNuW2WzGdDrl2bNnWUGcIqhmwketK8WLFy/4/PPP\nmU2mKVMgukhOTk747LPPsNZy9+5dDg4OUhb3EHQAmyWwh40fcqd0VBXX6zVKlj4TfbXGGUtdVoyq\nmkJ8w7ZtBbBhTykrN8sVi1DnwgFCCr/nsBBY6WsUChyFcyhZ+a05VYXuLa1r0ralUhb0bYtC+QKd\nizWmt9R1ybiuYDbFOk1ZlPR963c7cY6u1UhZMBrVlNWISdDfu94wnXoim+3sc3i4z2p9jipaVm3I\n+ZGW3nQ4Z7C6ZTKZsVpcoJTiy0dfMBqNuHPnDv/0n/5TPv74Y5RS/PKXv8Ray/379zk6OvIJjKvW\nAwHVmJPjFxwcHPD8+XPefec+SjqOnj/hwTv3WK/XfPaLT9G6YzL18YpffPFF4t6j0YiTo2MWFytM\nb3n25Dnz+ZyqKCmVr0N4cvqcup6wWFiadoWSoLWhrsc+BAi/EbpUBRjLtB5h+g5ViIQ8wpAGorse\np/z14+mIrtVoZ9DNivlsh7719s2dW3d8DUapEEpwudA0aOq6RFtYNi3nLy9wbR92uVGs1muOjo54\n/Pgxq9WKW7duIaWf+xgHKGVEMA1KqKzg67CHgBTSlz/HVwKTVYU2lnXfU00mrM8vGFc1wvpdaeSv\ngRa+MZIr14cHzrl5LG/Rhxz/prAda1KxlqhPRw4WUbw8yjk+G4LjMaSclMWY8XhKUVRMJjPmszll\nWXL//n0ODg44Ozvj8vISIQSnp6fJRorGcNN0KYI9L2kdVZnpdMrR0QkPHjxAKcXnn3+eQJX5fJ5s\nwpcvX/LJJ58k2zBurBALxxweHvLZZ5+xWnkQpWkanj9/ztHRUYqc6Puepmn48ssv+e53v8t8PuP3\nf//3uXXrFkJkVXhVjdFQ11Nm0z1G9YyyGKN7u1GnPtpD0VGblxSP9leE46uQKT2uR8zn8+SuaJom\npdtPJhN2dny4WCxZkGcw933P0dERT548SeFo6/WaR48e0fc9e3t7gZhkckekYjOZNM4/19lh8Rpr\nLScnJ96POR4nGzG+++u2N0ZyKQQSgRJyQ0wLBsKz1mYQhk/kFqG4rlSSXhs6a9DW0PU6RSWsGp8+\nMZ5MvLQZz3DOcXl5np4jpc9MFrHakPPl1nZ2p3z00cdYq1m3Swop2ZlNuHP7t31xmabHOs3Nm36R\nnx6f4FQoftJbjBgAklh3fVT7NI3FuuHo5ATjHOPplNFkwsnJCZ9/8QXz+RytNbPZjD/7sz9LNQN/\n8YtfcPfuXbquZb47o9ct3/72h/zoRz9MkfOL5TnOuVSybXd3l6bpUarkBz/413zyySf8yZ/8CR98\n8AHn55eB60s+/cnPuFz4a63S1NWE3bmiadc0wccToz18tLumCmnweSmFFOFRKKSQtOsGofwuItOR\n92fFylORYHstGY1rquDO0NoXm4kRHTEEbL1e0xrvF9zd3d1IswG/+GMCqVKbVb6uS1vZyKYIDLwo\nCo6P/X5k92/fZjSquVgu0lZJr9veCOISgbA8Auur5Urps4Rl5p8oMq7h8L+NkfHWWlBeX++tuTJw\nk4mPrH758iWmMyker+t8NduqrrhxeIujoxcYY3jvvff54osvuXfvAQ8fPvTGsPA1F3Z398EJ7t69\nS12POTw8BCvYm+8yHo3oLzqK4Cy12m1wxRcvXnDrpg+g7fuev/L97/NHf/RHfO973+PP//zPcU54\n8GM65s9+8AM+/PBDDwuH8taRs1Z1wcOHD3HOcHLiAQ+H5eLynKbxRWDadk1d11xcnIVF6vvwox/9\nCCkLfvaznzGf7/Ho88cpf2q+s8dieYExhtlsxzuZVyvm83lCDONezQN0T9oYItq+xhgKFXK+lKCo\nKpyQlMojcDuzCWdnZzTNKtlQPiHWcPPmTZbLJeCjWap62AGz73uOj57jnLsCWjjnkqSMoVsR0YTN\n4kdxTuBqYdG4XowxPH36lP2DXWazGeeXF6G+x+u1N4K4wOvyMHAVGaRWvqFCXmzVCm9kS+dVw84a\nOmtZNWt6Y9F6gIVj/lVRlr6oi/ScbTaboZQn5K5vePbsGdPpDsYYfvbwp3z04bc5Pn5K37eAxdmG\nQih02zEej7k8P+fux7cppffPjMoR7z14l+PjI9brNZeX51RFyWw2S6pTTLl/+fIlOzs7/PEf/zGH\nh4c8f/6cW7duJbvr6bNn/O7v/i5Pn/q456Zp0n3m8zk//dlPEMLX46jrkrZrgpprU6q+ECIF53qn\nr6PvvaoqheLg4AOqcsRoNE5SUiqoRyWXl+ch0bHBYVmvu40FOOSzQR9CguJewpHo2raltKEAaUxF\nCb6sZr1MmQcRnIjgQgArLOIAACAASURBVF5W26Nz/j1/+tOf0mAQZZFcFDn8H9XAmFhqTJ9U3hTN\n79wW0PFqN5AQgqbvOHnxAqGUL7GwHfv6Fe2NIK7IRa6zrbZ9ErFJR4DnMzgVQHq388Z11ttBUimW\nyyV3bt4JC2+o5iOlRFtN2y1RsuTOnRs8efoFfe/TOnZ2plgt8be1gVC87+XBgznO+VqGk+mI9957\nj5cvX/Dpp5+mKkyx6tNqtUqxhQbBd37rtzk7O+P09JSu63jvgw/p+54bN25xcXFBLC7z4MEDhBCc\nnZ2xXF1SVQWLxYKbNw85PjkKm901LJeX9NpvDtFr70PamU+TreLrU9RMxtNUd2M89g7kzx99hpTe\n9vQlyQSq8E55KYvEIGJumVLKAxoBMYxZzalsdxs20Qsbrdu+ZYW3KU9PjzGmTxnBnjANzsF63ae4\nRQAXwsw+++wzyp0JH//WX0o18/NwJimH2pC+qSCVVMp3i7/9Kn9qvK9nBp5II5PLy/n9qvZGEBeE\n+hZieNFclMe2HX/opPCIjLUgLEpKlJIY0yUpF6+JRVeKokgZvd4ID4iiKJiMR3S65+Ytbz8JVPJl\ndeuGg/0dOt0zm0zZnR/S95pC1EhXsli85PDwPdbLFQeHe5RK8fjxI9rWe/UjbL6/v89kPOF8ccmd\ng0POXpwwm81oVjWjquCLzz/zEQhryZ2bh8znu5RlyZMnT3DOcuPGDeY7ezz54hF14esZzqY7PH/u\nVSWLSFH9kVkVqsJob0f2vWFnZ8TefE5d7fD+ex/w5PGXFFhMu2LZ+UrCTkJZKooiRK0EDWA6nXJ6\nejqUUXMWHSou5Ztx/z/UvdmvbFuW3vWbc64uVvSxu9PdPu+92d+sLqtsU7hQwYNLmHpA4s1lEMIl\nAxIgHrD4C/yEBAghISEayUKAyqhAQgLbUCoXpiy5cKqysrK5mbfd55zdRh+xYnVz8jDXnLF23HNu\nnovL6OSUts4+e0fEjlhrjjnG+MY3vlFVFSIUSG1FhYpdgVQhVVUwnd4gJUgZ+k3fppq5911V9t6F\nUeg/jwsh2yG//VcSRTFxYofYOXqWNab96x6mC3f34N1SkJSy+dv7n7lG1xdZL4VxCQGh52w1Igf+\ns7dPmNbESQG1UgTaau+JSqN1iRSGmgIhWqNZ65ogClEC0iSmLGtb0zKG2Wy2p76UJUfHZ3xyfkUY\nJWzXG6IgQBo7u4qqIAkCTo8mDPpH9NIRJ8MzSl3TG/bYbFZ0wojl7YyiKkBoZCgZDyeIhkW+3RVE\niUaqkNl8zvEkZHq9Yb1c8tZbbxEIw3Q65d133yUIbFv59fU1SZI2eVTO9PqWbtSjqG2j5GaXYRAE\nYUKcJCxWU7pJjDCSutRMp3OSMKGuDKPTCePumE6Q8KXX3ibbbDG7Jdl2zv3RxPa67TLyOifPM+ra\ndifEnT4qDEEI0t7AgjMapC4JRYhAUDXQupQSozWVqUEKyrzCYKiriqreEUUJuglZ24VZZwywB0b2\nBmIN65VXHlHVJRQ0eWDdMGdChAIZGKsYpUDXdl61ksp2MbvD2YA2+9eWLdD8kLFRN7r6QWQNM4x/\n1riF3EVwXnRJXSOMRkg7gCFUAaGsCVVAWe95ZLbXZ785jRE+0R0MBp7h/eD+Q9abHUdHE26un1ql\n2aJmOEzo9UZ2zq+uuJ4u+OjTJ4RBzFff/Rpf/urXiXTC408+JtA2P7q8vmzKAcLzEMMowpQ1Vqa5\n8hy99957j+9973ssFhapOz09bXKQgqdPH3uY/9GjhygFQWDDn2yd+cK21qecP3lM1AirhMrmHWHH\nAg1pbBkpr7zykOXqht5ozHR+Y3PA0xHdMuTi6Zw0CtgWW7brjLJqeH5Kosm8FPVqtUJrTbfbpdiu\n0A1Xsa2aLKWkynNUGCDlXS3J3W4HTYnkrqeqmvuyL8fkeY42e+Dh+vqaydkJSTPIwvWLOSTThXPP\n2lvu+/Z7PDSu9uNtfdXcOQC+yP58KYzLKvhYD/aZ0K/VVmKdmCWT2nCw8qNeKqGRKuKVB69wOf2e\nb21oaxa6U6mqtK/E23yqT5qmfPzxx3S6XYoi9yfnyckJg8GYItfc3E6RSqN2NtENYsMffu+H/N4f\nfZdRp8Nf/o2/xPL2kmynUYEg21o56SiK6aYDiqomiAy6hm63jzHCa78/fPjQMyUcKjhf3JCmKWf3\nHvD48WM2W9uxfHlzS6Qi+sMBciMa73ZJmnTYbJd04pTxcGKH1kUdO2ih0yXtdOj2Qo7vfYnLp3PO\nP73hZnrLcnuDYEckQ7qdiDgJOTkdsysL5ouVDwFdW4xrCp3P54RCY5rBB1XZeK66mb0VhT5EtYYn\nvBGaFqzfJtRa7YvEh3NCCLSpfG0P9kMsLKskanKkkDAKPAzfDjHdTmp7xXZ+L8zzQ0Qh73q1w/35\neeulMC673OYXBx/i8KQwuIRKWml2ampqoXj02uv0Tx5gvvsD8u1+uIHtyl1zfHzcaE7sdc2dN6uq\nivFkyO10bpWDAnj48CGBiFgtrTLu8XHA8ckEQcDFdEM6fEjv6G3qsuLqJ9/h//qDf8hrDydoY1Vq\nB4MBRa0b/Tub9+22O4ypiIVgPJ7w5Pyc73//+/R6PW5vb3n06BFpmnJze8Vut+Xk5ITlcunlok9O\nTphOp8g4ZVcWvijd73apy5Jhf8R6aT0LtdUC6cTWc4/6I2a3O/7+H3yHODkiKwcYdcrw9AhjVpjs\nmizPqEr7NTk9Q6qQjz85JwkNvV7Pk2K73S7X19fkmyW13tOgHFLorr2rGTqhVLfxa63veCwnetqW\ni/O5WDM6yJGWLa+zbArHTiQnJEliX9tyOZcrXcBdb+XeH1hgrJ3rt8NCNybWRTpfpIj8cjA0xGeB\ni2edEIc1DYywsDuS195+l6+89y1Gx6cYEd650cYYrxnhqv4WERN3wojlcklZ5lS19SCLxYI8z/mN\n3/gX+dLbbxKrgvXskk8/+oDZ1S3XV0vWWcLX3vuL/I///e+SJCnX15doXXktvHfeeaeZ9hgjZUC/\n3yeOE68N2O/3/bCEV155hSiKuLi44Oz0PicnZ9zcTHn69NJ7usvLa46PT9HYIvG2UXaKw4hhf0Aa\nW213arshup0u/dSWH5QM+c4f/Snj4UO+9a1/jt/4y7/Fu+/9s7zxlV/h/mtfI4lTxoMhJ0djrh4/\nYTWdE8iQ0WAMwGKx8COTplNbD9RYNeJaays4gyXJPu98d/e5zaTXWntGRptVoxvtDcem0S2DdMZ3\nyOpp/5225sWzGBnP+2qHfodMjy+yXg7jAsvHE9oSZ5svbSr2nsqO/9l/GWopEXHM5P5DvvnLf57e\nyQPS0Qn98Yk/9XxcLfdzhAF/WrbrKpvNBtPIJ1uaTECadvnJTz7kzVffwWQhD49eJ6gkSsNiNmW5\nm/H7/+Dv89u//W+SbQs+/vhjvve973Jzc2NPzdoOq95sMp94V1VF2ulZmbKGApQkiW/We/DggSXq\nntxn0B9z/94jvvOPv8toeEQn6bFcWB3DLMu8XmAURfTTLkfjCXFgpdPund7zndHT6ZRf/IVf4Otf\n/TIYyXK7Y3zyiEUhyGTCk9ma+XzJbDqlHyd8+5vf5L2vfI2wlpwdnXjxz6IoPFroGhzd4eR+9rzV\nFqdph+ruywn2uFDS0Y7aEy4dhcyhr87QXGnAGZjzMG1jdNSnNmn3UKm5nQO6sUmwFyJqT8f8aeul\nMC5HcYLPqu76xxgLWtiBgdbQKg290Zhv/sIvkQ6PiNI+27LglddeI0ltfC6E8A11m83G9205So1r\n4zDGEIbN+NCmLWK9XrNer3j//fc5OjoiVAPKnSGUkm6iSKOKzfwTrp7+gE8++iHG5HzpS1/CGNN4\nvZJyV7Jcr/yNciejU3NSSnmFJxcOuQbJDz74gPl8znA45pd/+c8hhGAwGFiFp7TDaDxmNBqxWCx8\ns+Nut2MwGFhSbhQhhQVlfvNf+pf5a7/910nTDkkkePzh9/ndv/1fYfJrlrfnFNkcpUsGXUuzKqpm\n8Hgg/PtxHqQoCt9jJQIFKrCCn1JQNSKaRn42jwkC2YAOVqfDf7XqnHvqkt30LooAV16QSOHY7Pva\nqDWKZ3upQy/3074ODdB5rS/qvV6anEvWArRteNQYpLCjU2tRInWAqAXKRI2kFxRUjI9HfPVb36J3\neh+dDpviZsCwFxIlITLC6p/3BgRSIkXAcrVGKHDTBsuytLWMukApQadJwiUKISW7ssDogv/lf/uf\n2KocUdQ8eHTExflTjM5g9SGpUKTjmrNTyYcffUpRGqJOl0f3XyXtdZktF6jCeqzi9pYkSZjeXvP4\n/GOC0BZQ48Qq+7qcKkmsUu7Z2SNbGC5L4iTg8uoagKo0nBwfc3NzRSeKSZKI2WxGEAS8/vrrCGE4\nP38CCL78znv89X/730MDt7M1Ub2jF295evND8k2PihC1XdKNK4bDAetdxqaqEfMZeZVTFBkagwoD\namMnlggpqI2m3DWqw8JuQlPZYQY0IaMQAoQtiQSh7alyw9yRrnO8oqxtaFjWe89GMyDcmBonX21Z\n7yGw5xQK0XgmDEbspyYrN99a12DMZw5xjyKafS7mQDBvkPJuwfkLodn/xFbxZ7DaH/pZyxgbKJZV\nhRE2z4rTlLe/9k3eevcrjCZHRIGdn6zrnE5sNRqca28vp1YL+yTVnXx1vdd2d2Flnuecnp7ywx+/\nz+PLKz765DHdXp/ecECaJpRFRlVYGs9HH35CHMfcu3ePV1991ZJz11YjI8s2JEnCycmJ98ppmjIa\njXjrLav2NBgMvJLtdrtttN0t6LJYWDqSa0kHPIPDMcRda/vN1TWPz8/JtmuqouCDn/yYIs8QAjpp\nn0oLVputbekoMqpihcLqzwcqYrlccXx8TJjEHlBwHcmHYZRjn7haoQcsDjh9TtW3TT1qI7hOqzEI\nLOLn/t9ejgXS1vI/5AU+7/v2XjoUMz18TtvT/ZOsl8Rz7d1xfUCMNFrYaRvUiDCkEIK4P+Yv/gv/\nPPdee5cwTYi7PebLGXW5QeqCOFI+flfC0nniMEQFylN3XCjowjXPPdOKKIyom2FpWbbh8ZNPmByd\nkO1K6nLHP/7++yij0TWsV1tkGNFNYtv2H1lA4ebmmuVyQVlXFOc1Dx8+ZLmcs9k8RQjB66+9yWq1\nYj6fs1gs6Pf7vP/++z6HOToas16vCVTKzc2tN7zZbGbZ76sMoytGowGL5coTb5fLBf20y3yR04li\n+r0huhb8Z//pf8xf+a1/g7e//E2+/8cFpgzJtjeoqKAroSwVkojb6YLxeExelSxWa+up2BtCmxoE\njcx4U6wXQiCVnY4ipe0MbwMTNK9T1zVCWmlqGpWpMAhIkoTtZoeQVmJhl2+bssRedsDlUsYYTxbe\nAxqmzTnwxuEOAmdYn0k5Doys/TgVPJ+O99PWS2Jc+5vw2Z9D4CBbIRFS8ZX3vsnxo9fpDMdWy9vY\nYdCmqhCm9hoIYRhiavb66OwTb8CHFXfbvgVVZW+YBQxCNpsVSMHo6B6hSMgXNeiabhpzdv8+dWUI\n4z6rzZpYSktJGvWYz+eoIACBrQmFIUqFLJcr5osp4/GYi4sLHjx4wNOnT5lMJj4fnE6naF0RhbZB\n8/Lykv4gbQ28s/vo4uKC+6dnrFcLP7guSTq8OnyV+XzKcDimyGu+850/4g/+wR+yzjS6yjDlms3m\nhiAAbWqEVKggYTAYkXR7ZHnOdDqlqAukCDBm38eltfYRgPu5aaZ1SkEjdW1sc2trI7c3uzE2fBMG\nqDVFtiMQkiSKWK7Xtgug3DWGjEd+D72eKx671UaaDwvH/gBteUwX0h6utqG1P8PPXBEZWi784FRR\n2FMnr0pEEvHu177Oa+98md74CNHMuaWuAYNUQBCiwmhf3zB7LXSjGxnl1rV0IYA7rQIVe9Jop2Nl\nx4Q0bLdrENd2EuRgQF1WCCQyiun2U+pK8ubpGU8vn3J274QgtsO9y6qyrTBl3hjwjl6vy3w+9SpP\nWmuGwyFnZ2dcX18znU4ZDPqsVguEEL6uVNV2w7vwqZsmmEYl6frqwjM70jAhiTp04oQ82wIBQkg6\nccBg1CWJT9iu51w8rajrEiEty0MEKUfHY4qy5NPH52R5bhFcBW6KizMs134SRc1MaNNiYYh9bag9\nsMKBAwBKyMbLNBw+FaCEpK720x/3B95nvVCbhXEHDGscy4sWez/PE9k9+eKPP1wvh3GJlpZc/dmx\nqKW2HLVv/7lv89qXv0ZndILqdAmExJgSITVRFFBVHZKeoDs+8eibMHeb4eI4pjZ3hUpcyCGEoq7c\nDC3b66UCR33RFJs5iypnrUJfswqMQhlFJ4wByXg8Zja/ZfrYghLr7Yow6PpNJpCcnPS8kpPLyWaz\nGYvFgtXKisSsVgs/cHu9XnuJsLqurejN5JTlYoYxNUcjixp2u12WsxlaWci82+1yO71uCMh2sois\nC3QZAzWvv/VlwIIBjx9/SqwCfvyTD229T+cNwFD4jeogb4ceSil9lGB01XgR5bVQAnV3IPydLx+/\nCQ8s2deP6aUp293Oo4dFsfNkgDiO/d92r9sOFd3ADve75265dq5lnv+7n3njancbHy4pBUJFDI+O\nuf/qmyTdAVHHntgSMNj5vEEQEEQhUamJknjfndrA06FSKBlaLQS9nzkF+ya6ILDwrq3HlE1cH5Pn\ndjNFcUBNzWqT00m6bNZbW+sZK3r9DpW2c7fWmyWj0YTb22svuulqN71un5ubK87OzpjP51RVxfe+\n9729wtR23bRkzBkMelxcPmE0nPD06RPGE6vXl6YpYaiIoz5xHEKtqY1mtphD01bTScdIIel2LF2p\nrqxWY7adESY9wl2FjBKurm5YrTaEYcDHH39Mlu+sii4SoSTSyCYEdAeQ8J7L/n8PCNlN3gIxDmpE\n7fYQo11Y5+qQyns35H4YoC2RhFS6pj8cIAJ14Lns1FEX9BxSnJ675z4HsDgEOA5/96LrpTAuAClq\nNAIhFTQ3MRACgaHTP+Ktb/wynZM3SYY9lNTEYaNLCKAU0oREgSGXOaHQVHVJECrqsgZhwzylFFVd\nILQmFBINFl6uG/Z2XSCN7WStK9sfXdWGKLahW94MrU6TDkLXJKEkDhRxpLhdz+n3+1xeXDc3VmEI\nyHcVaS8liRNk01eUJAmr1QKtK4JAEscBWhfWc9YlVs7MAJKi2JHtVrz9zhvM53PAxv2r5ZzBYIDW\nik7aIcl6LBYLNtmWQTdhvd003iMkTlKEgdPTe2y3QzbbLSKpSNIOi9sbTDdESkGlC9I0Ju33yHIL\nCAVh3LRZ7HOUNqKGtAwNCVYwSDa5GRpTmSbPNejaoOuqAZCshLRbxhiEMr5dZLPdUOscO14XhJRU\nZc3R2Rlp2muM0ZZTLN2pBmGhd2MaUMxAIPc5otV7v1tTc8tLwzVwvWqBHxambxnVz14nssDoAIkh\n0DVRczIarRndu883fulXOH3tS3SHQ1QoEVTUVWVpNmbfr+VoNA6SdigS7NkBDu1qezUA3Yz6dPN7\nbX5wFwBxMLEw1qPudlYiYLVa0e0NePz4sQ9Z5vM5UkqGw6EVJW0gdAtGWFg6SRI7jbERtnGwe1EU\nd+S2t9utn4Jia2AJcRx7Vd/Ly0vPxBCihxAgg4gkSVAIer0BSimub28o8maAXVlweX7Dg1ce8ac/\n+D7GaN566y1WqxW7svAMlqq5Fu0pmtbD2iF8RTOpsm6kFYI4RAo7V9iF2G65a+A8UrtoC3iKk7sW\nQtnWlbwoiNMO9+/f96hoO1S1G38PPOyRzD1BV7c4gz/Nqx2GlW308It4rpeizmUF0pq6BQIa5Kk3\nGPKlr3+Tew8fEXcSglC2AI/Pdo+2k9j2qBx3ejnovf0cV6uxHbrhHQqPg+vb7AFnPK6u42o808Wc\nrMiZLuZcXF+RVyVGCpKuRfgcUdgNDGirJTm2t9OIcKifE/as65qrqyu2jZaFm9N1cXEBgJvN1ev1\nMEawWq3Z7nZobUiSDioImc8W7LKc4XDIeruxZN9+n8tGksAYO3DOdU63W+b7/f4dxoLjaDpFJHft\nXejrQrrD5RgObWHNQ0aEEILNLqPStknVYJtix+MxcSe5w0ds3//2ahsscCc/exHjOOSwus/2RZBC\neEmMC0AJixYZoZGBopSC0dl9zl59g6A3IOyktrpvaqjvEjLdchehneC2jcrVWg5jagdsuOW+dwbk\nTqz2/OHdbufVh5arFbs8ByEoqwoVBEil6A8G1FoznU49+94NrXZD6dbr9R3ddcetWy6XXuq5LEtf\nXHYF1ziOvUDL8fGxP83feOMNxkdHlha1WpJXJVpAknaIOwmfnH9qi+NVyYcff8RqtSLt9aiMpt/v\nU5al79dyh4eToKvr2g5eOKgJHXoRXyNqcf6cQTipN3ewuXvlaE9OHk4qC4xkhf3cRyfH/uBr17xc\nXfCQ4tS+t+33dlgkbr/f9s88ANUCw9oqUy+yXoqwsOFJY4wmjCM2Vc3r73yNL33jG3TGE+Ju34aA\nwraMO+madmHzkAHdvtFp2vVzgN0F2m63aKz8ta25NIk2e4DDjd9Ryt2Uho0QhMRB2IybGdBLEm4X\nSz/T2IU9Dvmry9pru7vwzcmsuffoPGp7JrJD49pk1ddee42rqys/HaQoCs7Pzzk5OaEoLHHY/q2S\nbpIiVcjV5Q337t2jLkvitGPfU2NMmyxjs9kQxaE3nCiKrDxdUaAbQ3AbzV0/ZxS65aHsNbPTZiwD\nqr4TIbjXqWsb+reFaNxBAhB3EgywKy1pN+11GY1Glq/4nDnY2nwxr/KsQnL758/zcD9zxgUGISuk\nNGRG8+Vf+EUevvN1hvdeIel2IQjQpiYwTQOXq2W0MFR3wZ2HunfvHm+++SYfffARbtBCVWmur6/9\nxnXMASmlH4gdyqQ5aSvaEsj2VLUhE9ogtLEcOyHYbrdk+Q4VBqw2ayuegqGsbR0rkHvv6QzJaqQn\nflO5CY17paZ9UNHv98nznA8//JDHjx8jhPD0LtfoOZvNfD1MKImuWmyGhinx8aefcnQ85vr2pjEw\nW5jtDfqs1ys/GG66mHtPo5vH+MJvs7kcBO9iH+fttamtIi93D7xn6Va45wRB4BW67LjZmiCJKTcb\n4k7CW2+9RW84uNNpLOXddiHTgBEvvOPMZ/dO+/vnhZA/k8ZlBRkkb737VV57+x2i0QQVJ6AcQiUR\n7dBN3JUGcKdqEASoKGR8dMQbb71FWdiJj2mnw/R66nOnvAnj2vQZC1bYYnRdWgoPuE0gMaYBReqm\noQ/BdDZDC9hmO19Hs8m/pNfrcnp6iilrz6tzbHgnNe3CPimln8rhGgNVI1DqQA5nfI4Bv91u/eu6\nMUOz2Yxaa6uCpTUqCtnc3HBhLhGBrZkB9Ho95sslo4mlWSkpmc0WXrKs2Fpjl43nQey1Fx2Y0Q6b\noMlPtLZqrew5he2Qu10EbgMOLsyraztD2ZQ1VW14+43XGYybiSZ8lt3e/tviC5hX20ikuFuQ/rzc\n7IsY10/NuYQQrwgh/k8hxJ8KIb4nhPh3mp9PhBB/RwjxfvPvuPm5EEL8J0KIHwsh/lgI8fM//W0Y\naiWouwNO3/wGyeA+48GYbhAQGoPQNRKBkcp/7REijev3EsLqSyRJTNRP6Y4GnDy4Z+N1bTlyDvlq\nS7m5E9Ym5DlaW+a9CyVNw/KoKjsrNy8L4k6CSiKW2YaiKn04WFU1UoQMB2OOj+4hCKkNjCZHTI5P\nOL13HyMkKowIotgy9oOIMIwb2pUiSVILy5c53U7MYnZLICEOFUoYqmJHXZR2CHetEdow6g8od9YL\nDgdjXnv1DVRoa3+jkzG7OkdLEJFiV+Z8+uQcqQSBkiRxxGQyYXR8jJaSxXLN0dGR/UxVQaBsOG4H\n4hVWEUqYhvNXYUzpe/F8vlJpMBrZQOQYjZLCzqAzmlAqIhUQNyF2J4oJpaKfdpGEFGXNq2+8ycnZ\nQ2qjkCpGGmlFY7VBGMvykE2ByyK4d/uymv1ot5g2/nnCWOAskMoyRbgbFh7qGt4xGPniMMWLeK4K\n+PeNMf+PEKIP/JEQ4u8A/yrw94wxf1MI8TeAvwH8B8BfAt5uvn4Z+M+bf5+7NDA8PuPd936RB6++\nStIdIuO9FsJPO0WedZrUde1P8/n1jDAMGY/HLGYznzM4xM4tl2843pxLvB1K6DiHgNeJd+TR1dJC\n791OhzCMGA4GSGHQdUmZF+TKAiBVUTIejhj2B8znc7L1hk5k0b/xeEye5xwfH7Pb7YjigIuLC7Is\n4+nTp34mlRPbOTs7817Eaawfn52S7Qo+/fRT8sJOASnLvJnEGPDqo1fZbjICFYIRXF/d+M8aRKGX\nQ1gsZ2htJ3A69LKdO7WvszHaEwEcBUk0m76N2DqP4Fr722CBy7dGoxHDk3skvRQZhVRlSdTo4rv7\nYMNB0SC33CH0ttfh323n6G2v6bQ93F5yz7MHMLTZwC+CNrr1IgPHnwJPm+9XQojvAw+B3wR+rXnY\nfwP8Hta4fhP4b419t38ohBgJIe43r/PMJYOAt77yDY7O7hN2UmSgbCFVgwzC57piX+1/xu9dPO4Z\n2dqwXc/8/5VSFI0Hs8XChgun9xMUXTjm2AhJknq4eU9c3echTnDlwYMHGGObA9frLXEce0RvtbK9\nWZ1OhyRJ6IQ2D3Mh36A/sURVXbHd5mTZlt0uIwwDisICIqPRiCROPQDi3osQwjZpFvZ7JzttxTcr\nJpMxT55cNNStbjNszpDnFo2MmqmUy6Udl2Trcqs7OhRtPUAHThhTW4VkIWj6FZsNumfQt3MvF266\nzS2l9GCPlJL+YICMQmoBQWT76pIkIWyYN23Uz+lrtGc/P2uPuN+1kUz3fynVZ4zveU2RX0RD4wvl\nXEKI14GfA/4hcNYymAvgrPn+IfBp62nnzc+ea1xx0uH44atE3SFBGNt8CkDs4/VDdMhx0lxM7yvx\nQtw5LT1qV2tfrqNv2gAAIABJREFUwwFa+uR70q59nv0baZp6SQDAI3fu9HQ/E8K2RygBSsCg32W5\nWNiidVkzGAy4d3JKqAKKvKDc2aFt0sDJ5IjlYuZhfQd23N7e2omRa6ulrnWNapRfrd6ihbRdaWAw\nGNDpdKyu+fER7qRdLBbc3NwwGPRIU2uMWZb5PjA3+M9dJ+fBwLIl3KRH5+XbBXn37yGEHShFHMV3\nVHkPwQytNVLsD782Wqq1DfsiqdCBJKsrXzR3RXd7EMmmdWXvgQ4P2XZ42D4E2z9rfx73mHavmDVa\n7hjfi64XDiCFED3gd4B/1xizbP+u8VIv/lft6/01IcQ/EkL8o11ekQ6PiHtDtFR+YuTnhYOHhT73\n+PZp6G6sq99orb0MsruprvbkNoorED9LWdW9n/bp6byaEgZhDPk2o9xlZOsNuioJlbKbJQiJw4jJ\naGzpUwaenD+mLgqqPGc5myG05vbqitV8zu3tjX9fTmdjPB57hNGNQxqNRkRRxGq14sGDByyXtiQg\npR1Je//+fe/FHj9+gtYwGk0YDEY8ePCIOO6g9X7wgPucDkBxaOizWAqHuYnzZk5Ny9WjXHHcbWJ3\nf9y9cgCOqyvKJi8KhfKG1e/3iTvJZ+5FO+T7vPV5EPvhfjrcQ+266Z+5+pOwfdW/A/wtY8zfbn58\nKYS43/z+PnDV/Pwx8Err6Y+anx1+oP/CGPOLxphfHI3HyE4fEXdAKqRwLIrPvA//IV1Iclipd/G7\nYwK4kCaKIt/e4W5s+6RqC620T+n2cqpRrufKvXYYOoDFDo2zIWREmqbIpvFv7XqUGvWkq6srBsMe\nnU6H09NTRqORR9/6/T79Qdf2MwWCpBMRRpYXGcUBk8mo0UO0f6PbtRMXt9st9+7d8/OAHRPEwdvO\nOJ03cmpOaZpyfHx6Z5M7Y3PhMXDnwGobyOG1bG/Www7mdojmrmGbnaG1psoLik1GsdsRqYBuJ90P\nDj8oPj8L4n/Wah8Mh/f2cB+11+HnfRbz5HnrRdBCAfyXwPeNMf9R61f/M/BXm+//KvC7rZ//VoMa\n/gqw+Lx8C+xsraih9AhhJ69/3gVrx8YH73VvZNxVba3r2ouqtG+of54BXdUeTn+W52qHke2N1+l0\nKGtLtG2HTnEcMxqNODoec3R0RFEUTKdTnj59SpzYVnaEZjDskXQiqrqAABabBbe3t2htB267EoMT\n03GafU6ardvtcnR0xP3799ksV+yyDF3XrBZLZrdTkiTm9vamGVsbsMsKNusMXUMcWTZ+lm28wlMY\nhsxmM7JsP73+rkFZ4mwYKqTY15fa96WqKvKy8IeJew13/aIoIgoTojDxIqOurBAIaUds1JoAQRJG\ndm5bK58TQnh08kUxhud5r8P91I5s2t62vWdeZL1IzvUXgL8CfFcI8Z3mZ/8h8DeB/0EI8a8DHwP/\nSvO7/xX4DeDHwBb4117onZgaJQQS24YAoI28c+Ha42Lg2RfJbfhQKkKpCBr2dVmWlHWFCAMLYzcb\nIFSBBwMkwusZigZSbudkWtucS2s397fwJFalYrQIkEFCp9NFVzWiYWancczV1ZVlxEcKUwuE1sxv\nb9Gm4vj4mPlqyXKz5vLqCmMMeV0QNWpJRVGQbXOCIIJuwPX1Lbus5t69e17qWkqrL2GZHFDs1kg0\nSmhEXZFvNojTY25nV8SdLkIoiqpkNBliKMmyLWW1o9Z3WSFVpQGNFDV1VXr9YymlZWfoNpvcXusa\nqwplVXXdgSMoy4pOFKMChdABUiowoEtBFHR8yF4UBaExtgO9qElQpCpEGmzXgAjQ1AQqQOsaKSGQ\nUB8o5x56q33z5YEoqPjs81xUYlti9kDOnykUb4z5A3hude7Xn/F4A/xbL/wOfurfb9Nr7iaed0Z3\ntS6UKwjb1gcDzeT5MI58PtDmju1hV0HSyJ058MIhgm2tcGOMH+fpknHZ5Acu7Do5PvZh6GKx4OTk\nhB/96EdMp1P/+lJKur0On3zyCZtdxmw28wIsQRwQBBKlLFHYjk6N/EEwnU49E+X09JTFYsFisbC6\nG6GkLAvKsqAodpSlIIwUf/Inf8wrr77F7e0to9HEtr6sFwyHfU+vWq/Xnr7lQjApLWHa/azd7n/3\n8NkDEz5vaanZOuqUMcYPQojjmF6v5xsw931iApQlO99eX1PWFce7HfHAFtBVP0V4/ROa3rEvwtHY\nr3a60f5M+1xunxt+EUDjJWFovPjaG9azuWQeOZKCOO0wmUxA21b57WrLx/lH1GXlSaXw2QTbfbXj\n8Hah2QEZsN8wrptaCIFq5kTdv3+f9XqNMYbr62u01n6YgQs7HcO9jdyVZUnUiRooPLvDO3T/Bipm\nMpmw2WxYLBYMBgP6/X4DaOwaI7BARrbboPOKMAz56KMPee+9n+fB/Ud897vfZTKZsFhMefzkU5Sy\ng+naaKu9BppA7VFBB048i3lxeB/8wHiznzhpr1t0B6xxhgX7CKUyGrm2cm4qDDk+PqYTRiRBSGAE\ndWGl9PZ1q/9ve6ptXO7w8PdSSV9Lc/f/RdfLb1xi761s0G2/Di+mu7FOqbXTtQTYIAgJlE3QL801\nJ2enXOtLe8I3XD8HUFRVRYi5MwvKXXiXlDtjOITy0zT1A+6OxxOG/QGLxcIa0jbzbfwu3Dw+Prba\n6KbyeZVSigcPHnBxcXHHI2w2G8IgZrFY2Lm8zeNcITvPc66vrxkM7FD03XqD1hWr1QqERilbcNVC\nM56ckucZ3/nOd1BKMZ9Puby8IEkSFouFv5ZtfqOjV7UTf4cGtr2U+2x32+734IXWdpxPXddUxnpg\npwfS7hiIVYAMA39IltqO2V1OZ2TZhurslKOjMWESo5VV6tEYT49rAyfu6/NYF36rtdBLB8zsc0z5\nmQPlp62XyrheBPV51uNdnO5OnfF4TFGVTcjRpxt3ybKMo8kJ/3czj8tpQWRZRqfT8YXDvGFi2wka\ngd9E7uJmWeaHX7vN5sLHtBlorpS600hZZDtWq5VH3QaDgTcS3RSyI2FDvHWjClzUBVm29aETZn+z\nAStKc33twyrnHWtdkiQR222FkIaisJ6oLHNqDLfT68bIgwaiD6h1SdVwI2GPlraT+Xbxtb1h23mw\nawlx6GtRFJbqpRSqQXCF3rcFOYS2vWmNMWx2GWHd6GQIQZkXUGtiGRD3EuqqIluvGB1NGJ1MCGTT\ndiLs9W2HrO41D1e7MK71fnCDzanvth+1lXz/qRWR/6mtzw1jTevfu19Of8FdPC+aUpUIKZHGIKT0\n4pa12bOw3UqSxJ+4ZVkigz2C2I6xnWfTWt/RmHenZLfb5eTkhCRJuLi4YNDr+25jZ6xKKY6OjjwI\nIqUdrbrb7Sh17aH6IAgQgbBJem03Y7bNGQ4HTCYTS66Vtqt5u7UDu91UlW5vwtXVU9abpd/8QtiO\n6yCOGtWoLtttxnq9Ic8DsmzTGMn+1G5P9QiCEKMLksQ2K7rwtW1M7qstHhNFkZ9p7UPCWvu6mx1O\nIVmtVt5zR1GEbhtdc1+01jyVkjiNGWVjlBIcnZ7Y62oUutZ+3E/7nj2v8NsGNXwq8YzH2oP1bl3t\nRdfLYVzN+qKeq73ciVPXNXEYUdTWI8hYsdtaVkSSJHzpnXf4JPiQst6r7ro8QymFNuZOmAh4aLkN\n4TtvFoahRQQ7HbppynQ2o9Prcn17w6BnYfJit6Xb64CRfrjAYr2ygIepEIEilqFX1DXGeJaKB1Jq\nODs7AwTn54/tmNk0bQab29dyg+kMbnyOYLHY0eulSClJ44TtzkrMdbspeb7zcLYFWSo/XKEd7oVh\nyHaz9Z6z7WXcQeWMp/17rTWIwOvD29C+qW2F0s8jM0KTlyVKK6vMVe8L9UEgkQJqXbKczRkwoOr1\nkMZqXURB6COHQ8i/bVzPQgQPQbC2BJxbDjBxz/+ZCwsNVvwTLKxqDH7onUMI2/Hz4WrHx9DApg0s\nq2tjUbZAE6ddtFIkvS6DiQ0dN6s1RtiaSl1bffM2uOGaEi3yWCOFpKpqBNBPuwQCxsMhIq+YXt8w\nOJ6w3Kzpj0coDavFjLQT2gmYCrJGn8IIiVYCVExZ2OEPqICoEVWM45Ait+KfdW3oxCnFrqCuDFVe\nU9RbTk9PQVhPmjRD6wwV65UhDq13TKKYPCvQpibbbul0+pR5BjInCKEoqr1IDxqlJFq7rgH7fZ5X\nnh1RliVVo5eBAGXwfVZVVVGXlfdeQZA2c5MldQVVZcnUGkmpa8JEsdsWaKMp6gKFoqIiqAFtD7ti\nV/ryStBR6LogjCQGzdPHjynLgrTXBSmRoUAoRRgmjbFUjWG5/Hwvs33XAPcerF37lC76EQIpJAbz\nhZoyXwrjcoxqIVpw+zNAi8P1vGKyW1JKUPviL+wHjx8dHdHvdpndTrm5uWE5XzQXsd0cWd0RVDHY\n9pMwsN4qimwBVGvNNtuilSDZ5QRGIJUkDgMCIdmVFUHaY7lcsStqoihGqpDaBOiyQOD4idK373c6\nHaqi4ObmxoMuhgojah4+ugdYIKOXdhkMBpYNgmC9tBLZdW2Rt07HTpasanzvWBAEVI3HLssSFdr8\nyBVz3WG1p0PZ5kuwh5Br23DX0+U4n2leFPuuAfdeRqNRkyPvyNeZ1250HQhKKZK0Q5kX7JpyiFKK\nqrD/T8qcwtRM5zPe+7mfY71e0+33iOIYEYBpIbuuXueaU/2e4C4BWYi7tat2iNhm+7v396LrpTAu\n16JgjasxmhegKraN61kezYaJ+0S7XUNxF73btWBH3sDXVUs6y3UpZ5mFwyu91+2Lgj0pVWtNmMQM\n+wP6ndTmUI1QiwhD8tqwXC7tuFgElZEUuwJ2OcKUdOIUTECcdEDXdJIuo+GI2e2UTidlu90QhJKq\nKuh2O4SRoNpZ+W6MYTGd0ut0yNZrimzX1PGq5oRu2PrNcPKitEYkmlKEEIKybrwNe/aEq8W1D652\nicJtwHbR3dW53P1wm7wsSy9vIKX0tUMhLJHY5XI+rKtbhq2l76QWQlDp/Sje1WpFb9BvlKIUSRRT\nNjofWuuGfvbsecfu7zvPdbhv/IHcCgOfFzk9b70cxoXje7XhUpcI3221bxcs/bOf8aEd4OBurjuJ\nXS3lMKF1pNE2ZcfF4S4skcYhSba4K1DEkc11JqMjXn/9dauG2xswXcwRQnA7m1HW1uMhFMbAerVB\nBGEzZaSiKCrG4zESQRQErFYZm03esFWaucmrLVVlw0UoWC6WPs9MkgSEpj/ocnN7ZfPAQDYATOXp\nUhbpjL0nDOO9RLWrz7WZKfu8C0xVYqSx1CS55wGW+u71dB4M8KpSWms7wrahbjnZu11mD6GzszOL\nEm42vuW/Dag45Nb9bLlcEkQWJNput5R1xWgyYVdmqGYuAOxzLwcsHeZZ7fvv3nvbO7Whefd5v8h6\nSYzr+euQqvI8ysqzlvOGbaN0mwfwqF+n00FXtYWym985IMHd1DzPEdKe6GFgJ5pYOo5BCM1qY1Wc\n8s0Wk5eMBwM+/PQTy0SoBUiFkpDlJcfjMXlZMjk6YrlcEkcR3W4PJSSRCpoJ9jV1naEbdeBARXTS\nmKdPbT1rPBgyn89t3a0J1crcknA3uwytTaN2FfiDotfr2ekoxgp5upYVsGFS0hieO+2ddw8DSbHV\nd1SbpJTEDRvl8H45zxSo2KOCDvhwEgXr9ZqqtvnU9fW1f92iKFiW1Z1DT2urKFxVFRpNjTWwTz/9\nFKTg6OTY9sj1+hgh7hi6A2QOe+++CI3pRWpkz1o/E8YlxGdbCg5Pnmf93hmXe5y7SI7ImySJbaLc\nbv3/q0ap1nur5uQKgoBa203V6XTs3282hPMgm+WKNIwtGyTbeSa8NAKFBCk5nYw5Pj1hsdrw6OFD\nLqKE3Tajm/ZJOx12my27ndUDrOodRVkBkrOzM25ubghUjNRu8oeF2fv9LkFgwZfNatnoCermtN3X\nnZIkIS+aUoIxFJVtBYmS2Nb66n245yB3q9SkPhO6PUtIx11nZygOwbTEYEsCdpFDEATkzVyzJEnu\ndH7HcXjnEHX31+aDgjqzw/iiJIFmCN9oMrGep3XvHKr7rJy8bSzPSdn949oG+TMYFoJt9n+22z00\noJ9mWNCasFHtmwDL0upOREFAkCTMbqfsthlVUfpQqV3jcaGkhZwji44J2xVb5LmFg5vaTKlrgjjC\nUHE7n1NWFemwT7beECuJEAopIO1GFLuMb33z64wmx6w3Bf/Mn/8L6LpmOp0yfv01fvKTn/DBh+/T\niTTb1RIpA8R4zOnpGffOHvB//N2/S9oJiJvWEHsqCzqdLqYBRWxjZY7VurA5Sl0ZkjilEAWlri2t\nSClk0IRecs+scHICqulHy3cZVWlDYlcon8/nz9xsopky2e/3vaE52QWXD8Gede7YMs7r5eTYYZCG\nXOVNmBsQBrHtvE5ixpMJX/rKO4yPJhydnDAej1FJ4P+eOxjdPTwEIl7UuNz1cOvzZj4frpfEuDTa\n5D5e92M9AXUwKQPaISKtf+/ywwxQa83O1VKaG7fbZiQoim2Oqg1UNXVeEKuADEEQJFAayrL2tKE4\naciiTbhS5E0rhpJUpmJXlWgjKRCUdc3o9JQPPv4IE0aYpGC1urXGaQJEFNLrDtBa8/iTc5a3C0IC\n0v6A3WbN7fSGn3zwI6So7RC76QIlJLObW7Q2hDLk1TdeZbdboVTIpijopn2yWmOqmkIIoiSkqoSX\n6JZNviCDkHuTI/I85+L6CiGbjgBpwZKy3O7LEI2AS1XauldZa4SyPXLZrkKIGhUkCGGRP6UsXzKO\n7URIowXjowmTyYQPP/zQIpJV2aolCYKGYymEHZqnjUEqhZIxurZeqixKkjgmUJa+FcZdy9rQBkFA\nXlZsdzsW2zV6XXlRISn3alr2AHbd627P7I3L5ZjOC985vOW+K/l5fX7PWy+JcdnVThqfh/61E+12\nvN/OyVxOAfaidJMORbYjUSFJGJFnS8+Od02B6/XaokTNyFBZlQTNkG0n3xwI6U9RpwTlcoq8qJnP\n59wbH2NqjS4rrm+uCZSgE8VMp3MCZVs4nMpuUVQIofhb/91/3bDXS7Ldil6vR5oknJ2MQCqyfAdC\nsVqtmxCqbJL2Zm50w1Wcz+ecnZ3x9NzKtmVbSw6eTCwDfr1ec3NzQ5IknJ6eUjYefb3dANq2goi9\n6q0xljPpxwS1Qj73fVHkFEVOENhOgcHAQu0YycXFBR988AHD4dAfbi48NKYmivYNrW0p8bjVmZAk\niZfN1lpb2YLRkCov+PGPf8zZ/VOMMXQ6CVEn8h7SqSu7kN4ctKO0vVGbrX9YA3vWHnzR9dIYVxu+\nbVNN2i77MA5/1mv4iyYFCkWompoGgjgIMa0eHyfE6TbS0dERq2YsUBRFFNWe+R4Etg/MtZGATc5X\nqxUqsNMQjydHtvitNSfHE9b5hizbUitFJ+kCVjVKSuWHZZTVfk7wk6efUBQ7drshr7/+OucXl2zz\nHUYLys2ak+Mj0rTDfJ5jjGQ0GlLUFRg7zTKKIvL51Htqx5l0G8Yy4FdkWcbt0ycekMjLomFQaH9C\nO6aG5SWWPqpwdb39MLsOURSgVEiSJERR5OXdzh9/wmg04vb21r8n51mqqsAVdd09cLSozc7mYkEc\neUK0McYOtxCC5XzBZrPi2GiSNCbppiyXS3qi54nAbprMYbTzrPXZut7nExZedL0kxnVXbOYu9N48\n4pmxvbjj7aBBq+racwSVUtS6so2YwnYcV1XlW0E2m41PgIMgYLXeorUmUhGxkmy3W9KmZd7VeNzG\nC8OQzWbDze0VNTZ5fvr0KfdPz+gmHUStMWXFfLuh0+nw8OErfPvb3+Z3fud3qMJmWIEoCVTEer1B\nBQZV29c5Pz8n7VuV2c1yxRuvvU7aTah1SRhIfzoXWUW32/Xvb3o7IwxtYTZNU08mdpB9ltkWj9PT\nU4SULBYLNIayzBEC79G/9a1v8f7773uwp6oq72U2m403rjgOGY1GaL2nLFVVxdOnT9ntdh4Gd60l\nQriG1H0k0gaN4jimFNo/18HzdV3T7Xah1kRhSLfXRQpBkedUeeHvK+CjC/fa9oD57D46rHe5/dc+\npHUrr3ef70XXS2Jcz/ZI9gPevRjtk8glyod1L8fCruuaqiiZT6dcPblgfjvn9vKKotix2Wz8JrGA\nReBfwzHmK117pEtKiToooO52O/r9PovVklLXXF1d8sa9hywXczqdDg9OTskHQ5bZhul0yo9+9CO+\n+93vMhgMePXVV5hOb5jOrjCmJulEGDNgwwaQ5FVNZAxFtuPk3hndvjWU7XpFkkTIwEqgbfMdVakJ\nY9uSst1u6XY6DIcjlFLMZnOMoRmwcO5LE1EnYdFIqElpRXmobU7R7/eZTqfcu3ePJ0+e+CmVURQx\nm82sPAH7PCTLMrrdfmMEe97heDz2BXUprdqUa4oMQ4VSgfeOzii11my3NgyNktgDHUZApWuSpngv\ntCGNE/rdHr1ulzAMGfR6JI1knXteuy+rDVC1kb+2kT+rXto2wC+yXhLj2q9DN3540rjlPnCbV9iO\nm1fbDbqs2Cw2XDx5yuz6htVizXI2J69yH46MRiN/OrvQSmtNZTRlXvlT27ELXIFTKeXzNFtHqb2x\nx0FIXVaM+gMYSZLt+s6pd3t7zfvv/5AoDmzvWadLJ+lyfHSvaWvZMV8u6Q/HfPsXf54n5+f0kphi\n1xB7qYlUB2PwHcSihZDZDbrl/v37pGnKYDBgtVqRJAm3tzfeS7vh5fmuaPQw7IHktBBdPcvD+A3B\nGSyVyRZ3JWmaeIWoqrKe/dHDV7m5vfL3zYEBrtXGmNq2wdRWfm69XvvHxHHamrkceeN0EUYc2ntU\n7HKfHyqxn33dFhAC13y5H9jh3pO7H062vL3vnrXnnodMP2+9NMbVrpTfZU/sH/Msw3N1K6tj0agX\nxTGqKjFGQq2RCFarFbdXN7alIwp8HaeqqgblsqpHu9xOoOynXRxjIwxDooby5MIDV68pigKhJGkn\nps4LVKzsGCQJRmh2Wc7jpxd86c03+fDDD3zuUuqa5XJ5p8gdBAHbbca33vt53v3qVzCh4uL8U/Ld\njjSyRe3RaMSTJ08Io4TlckV3YLuP+8Mhy+WSsKFk2amTugl911xcPLU9VrucdGIpWpvt1l5HC62i\nQju2VqmQ8fiIi4sLlFKkSUKn0/FTXVz9yx5QliE0Go1Yr9e88soDAH7w/R8RhNKHg8bU/r51u12q\nqkap0EcMvV7PDyLMigwpAlQQkDfXOQwCwjgm7aSkSYdal77o7GpuRVHYgo7WvnC9DxHxxnm42n1s\n7SJ6e9/9zBeR27Fuu3gIe/krdyLZU8k0uURMGNqPUlUVq+WS7aZgeTtjdnnNT/70B2xWa2SgLHS+\nsTfR3RSHPCqlSGKLWmU7K/9clgbQ9Hop24WVnnbTIZ3HHAyHaCMotCSvNYWuKbQhXy4ZjkdMjk/4\nzp981+YZpmoYHzZpV9i5X0VecXl5jVKK8/Nz/u7f+98ZjscM+32iIKBOO00+pRgfTbi+XTCZTDzh\nNs8yqqKwI4ZubhiPxyyXCxaLma3v6RJBxNnJmF1ZEEWSfv+ExXRGbGx3dSGgyGu66YhuPCBbf8hk\nMGY6vWExmyMRdDupbelx0y+b0LmqNFJa+e0osgz9UpcEsQUr8iL3hd1tviVSSRNtCKSMGtCkkbnW\nBqk0gbSy1fZ+S0xdsl1v0M0I3m21oxI1/WGfs4f3kaWmm8YMe0P/t9y/rkCtW7QqpSRCSnQzbFGa\npqxTa2SjQKXN3abLL+K5Xprhd+2ibfvLhwItvfB24uyMrh0Xl2VJtt4wu73l9vbWe6ftduv7qVwC\n60IfY4xnbrS5bO71VquV5x86VKv9vDRNOTo6otfrcXJy4kPWoigodhmPHjykm6ZWLkwqO6ZFW8PV\nuqKsclRgqKqCMAwa7QbJyckJ2+2Wy8tLz9DPssxvmPV6zXxu5zE/ePDAs0WWy+UdrYuqsuOMhsMx\nVaXZ7YoGSi/8xtuuNwyGtiWl10/pd3usVitCqTz62O48dp4sDEPm8zmr1Yr1es35+bm/dm2KmQMu\n4jj2KKDW2oebju0SRQlSBoBsOg86SBlQ14btLiPb5uzyEmPswXR5eWX5i70BoQoxtUEiUUIhjEBX\n+jP7rB0d3ZUluLuepbf4ouul8lzAM9z2HsU5bGJrf2D3vUvOO3FiG+mUsj1bxrBrpjO6uNzBte51\n2921ZV1RN8buc45N7pn1cRx7Vrkxxg8rEGCnj2BnGU/nM2S4/0xKhZRl3oACGmFKwJ2wNuzMdyVF\nUfs80A5aWBMpG94Mh0Pq+ZrFYuFJqu0Cp3tv0+ktm83aGvzpEcLA06dPCaOQMFBMpzcU5Y7AWFQ2\njkOKbMdGBnz84UdWiarIyfOMJEkJhCTPdiRJwv3TM7Is4/z83IdgQWBzyDS1mvoGc0eIx4MTDRjk\nnuOQPUeX2jbhqjN+t8GDIECoCIKAUteUpaYT2D6y9WLFaDT2NbK2ER3WRA/Tizvk5SaaaT/2i3os\nv5e/8DP+f17uQrRDuGehis7rFUXB7OaW+eXc9mpdXQPY0KQsyCrbN+VQq3bM7jxgt9tlk20Rzf9r\nY0Vi0rBDntu5wsvlkuFwaG+kFNRaMxqNSDsdQqlYZyvquqYTJ9TGkUbh/ukZl5eXFmVczAjjoCmS\nNshn03FsjEBjpaSVaJgicYzWFbPZjKI0/nOFYegH4J2fn1NUFaORVeUNQ8Xx8THaVNxc3WJKQ20q\ndFU2JQWJrA1RFNDrdigLw+nxGfPbKeNhn5tijZIdaLQwnPfabDb0+33OpCDLd76L2nmhMAxRkULK\nvh0O0UDyzntRC8/ndPcALKqpZHgHvQuD0AMbRVGwy3PitEOvm6JCiRRWX1+pkGyU8fDhQztPOTA+\nyoD95Jv2PGprePuOc2dobVS4/bv2POeftl5643rRE8OFIPP5nE8++pjHP/6U5e2MPNtR5HuP4yZD\ntgeMu5sfK40JAAAgAElEQVTrNAcdOiVxSqt2WqXQomEhDPzflVKiopCqtqfsoN9HV7U/wYtyhxIA\niiSOEQbefftdGx6FMXEn5OOPP6Y/GPHKK6/6ZsbdbkdRVVRFwTbLENKe9nHcjAtKej60ckI7VWVr\nXnqz4fHjx9y/f480tZoe22xNmnQ5PjrhenpLpW2tLs8yAhWwy7dsy5x+d8Djx4+JAslycQtCE0UB\n3XToSxIGQVXY/Ge5WbErch9quwPLGMNusyNNEx8KtvPo4WBAlmXe47uoASDtRIBgOBw3I5Ii+n2L\nKG6LkqTXRyq4vLbjkaqq4gc/+AG7Yk/IdvW9+/fv86u/+qu8/eW3SVJLh3LR0fPQv31Ov++ccGHr\nzyC38PnrRY3L1S/8BdN7wq7VqjDs1jkqjDDNHGQXAjoBUAcORFHEdrmgbF4TY298JKxBugHhDm00\n+Y7BcMxwOGS9XrNZrtBV7Tt/O52Y5XJNv99HSssMmYyPefTwVa5uLxkOJ0zGxwgUSkoEkiiUdPuK\nxWzWGIBqcqjKew7A551FUfgWlEePHjEcDtluNywWFuIeDoeYGrrdvlWBWs4oyrxpz7cCN1oIS6O6\nnVtCc10QBXvVYscy73SsB3dai7BvzXFQt9uE7vq7nK99v5ymvcv7nKdy99zpMS4WC5bLpc/bdoUt\nLue5paWFgaQsa+IwQStNVVQsixV1qbm9nvKDP/0h6bDDN977Br/+67/Om2++aXPh5r0fiokeNoW6\n9/ssJPHz1s+Acblk9PnJpOtULcuK7WrD9PqG1XTeIEs1082GMIoIkxikoKr2TZQu1HRtD84LxJ0E\n0YQBtdFURUngbm4jG10ZjW7IqAZJHEWEQcTJyQl5tvMD6YQQ9HrWy3SSLvP5gqLc+bE41xc3JGcx\nadIlSmJWqw2r1S3FYoswFuY21Oiyoih2xHEHpJ0f7OpuYRjy4MEDpJRcXV01irwhDlGt65qz0/vM\nljNkYMVrdvMd/V5CnHaZTm958+23eXr+mN2uQCJI4pRAOV13O4gujCJ63Q5GV6y3O3q9HjTDJlar\nFUXhmCwVdaO10e8Pybcb6qLch4xqn9+EDZ8TGk+BIs9tU2WYxFRzy3ucTqcsso0fyu6bReO9yKrr\nInbcQscIyW63/P7v/T5/8sd/wi/90i/xa7/2a0wmE1+gBosxuee02TjO2Nph4ousl8S4rIiIjXXv\nAhVK7MmSgqDRARAYUaOFsdSdqma1WLOazrl+fM3N+TVlURAGAbWULDdr1oWF3x00C3cTWVdkHY/H\ntriabakbxvZyvSIOI/KmDhN3U7ZlTtB08kqpiGVMGnVJOpbFEAQBg9EIpSTTG5sP7LICKQKOjiYo\nJdluNxSbLWkSkm2WlPnWG/mwGwOxP9V1bdA1BCoBI9FlRTex84sHEyvXtlwukWHAkyePm81hIes3\n33rdDjooNv8vdW8aZFt21fn99t5nvEPeHN/LV6/GV4MQkySQRKloEAhEE5YA424smsFENBi3DeEe\nHA3G0dh0B9Hh9gC4wyPhDhv7CzgIOqBBfDA2docnMCCgkUpVquFVvSnnOw9n2Hv7w9773HPzvZKe\nHBDxdCIyMvPmzXvPPWevvdb6r//6L05HpxiPniZRDLVkOJliTcTtm3fdhhDF9AYDBEERWDMdn4OU\nGCM4PZ+5LmY0y8UYjYsSSl2i0ZSV8wiKCIFAl5o87bmNzFioBMtqhhpIrK4bvmIcx2A0MkkpTM3Z\n+Bx94cK8yWTCZDYhiiTFarYRqmm9rlOFtpaAAgYghBqstlwcD/mfP/k7fPLXf5sf+qEf4kMf+hA2\nFxjcdEyNQWCIWiQFIS3G1hgNcZw89Kp+RIzr4Y4muTRu/q7GUBtNVboh4NaHakopirJksVg0SJTR\nxsmpybWSbjvvCupFjlgr/Y7bZzp1U+5XiyXz5czP+ipI8wxjapI04cr+VXZ6bnB3WbaY+R4QSVPX\njHhxcdHkhaHtXnrZrpCjZVlGVVWOiNrr+YKr4/G5orNo/rfX6zVcwOFwyNbWFjKO/P+5hs7JdMTF\nxQWLhQsPZ5OxuybWEkUSa3XTUGlMTZbGLlc5vOJg8sUSrQXxzo4LozybYTRxuvTLqqS265ofrEGo\nKDApyhIbu1A98W0ri2LFZDLz3qfwZQv3ObUvHAf9kuFw2EgVhCbQticJ5ZP2Ztl+vA2OBC8uhOBX\nfuVXmM1mvPjhF9naGTRakoDbuIXFognBk5QPL04DX2LGBQG1sVjcQOvAyLS1AyJmE1drCbBqWVeg\nJBiBqUyTlAshmgKokK73Kc0z1zIvBPWyBukaA43fmSeLBYmfmFiuCio/9XC5nHNxfIoQiitXrrC7\nu8NkMm0g+tTX5vr9fiOoGRSPiuWcqqqchnytOD6554jDSdIYe2Ca93o9Sr9ppFnMfDElTVOGo3Oe\neuopRqMRR7fvIoVF1zVgsNowm4xdK0fm2AoS93nRhpmf3SWEQPlBEp0sd9LWei2LUJmq+byzxbyZ\nZKJX2knTWdsk/8G4QqvKyn/mKIoo6goRKbrdbhOOSSkbDcYoipgvC8qqbDY5aJOC15Nn2lxBa20T\nJoaQP0Qp7XwpbHrh59/6rd9ishzz8Y9/HCn94D0T5Nisl/1bz3T7Yo5HqojcvmjtGsSD4HdYqzP1\nez2nVrt0kLCp3GyoStdYKZoYPE5TVmVJlCRESUKlNfPlkkprag+lhzaNLMuaTtqGr6ZgtpiiIoGK\nBJESzGcTju7doaxW1HqFMXXT/xXQR6019+7d4/T0tAFZAiQcRc5jlOWKolhSlqumi3gymTCfz5t8\nZD6fO0Rya4tOxzUq5nlKv9/FmJrFYsb+/q6vcZ15pjzMF1OqunDzmEsnBJpEkuVi5lo/jCZNY7Is\nYavXBQxprIhiSZbG9Le6bO/uuI1ISaaLOScnJyxXK0pdN1FAWOzN/VISFbtrb6Dpel4ul0Rxyqqs\nWRYVVigmswWVtsyXBauyaK75eDzeKOoHOlMI+QKfc5NH6LiPod4VcutAbwrQfKBm/V//+//JP//d\nf47VBsxaK9/gNuw2L/RLsp8rHPex4lvGZr3OuNE4MczSGdBsPOXN129y961bTEYjwN9IP8w773YY\nT90ub1j3jYWLHFjfF6MR5WKJEILFaonB3YDC5xAycg1809mMbiiU1o6AqnD1sfFk6Mf9pETKacoL\na5sJJW0127yTUiwcfB4S7xCebm9vs5i7+V9hQELY2aNYomrnmYO4S4Diy7JkOhpRrQqGizn7B7uU\nyxWpl7JOlNNrXyxcV0Any1HemwVhVCXc4PIsSYkyhdWG6XKJUJLTC9eb1d/aampswaiC5wg7fZo4\nsKXSNZFUaGtJlCLr9zg+O2d/f79BabPM5ZdugymbPCzA4usw+35kD9hgwQfWR1gHwTADyBGQzIDm\nxirnk7/xSQ6vXOX5dz1HFUNd1ahYeraM55LaLy40fGSNK3gu1QoNLh+xUg0cvFgsODo6YnI2opvl\nxFlK7AfezTxqN53PmkY6rTWmckl0pWtqo4mIODg4cAVRaxq1XVta+oMe5yOnSTFfTEkTxwuMogij\nJUW5Qkg4O7sgS3vkeY+T4zOee/4GS98zFnQptHZ1sKouUMKSeYGYkHtJrzW4teXqMpPJBKVUoxNv\nbM1weOohcafmNJtNWk2OCWkas1zWHB8fk2VuCqXr6wrFckOaxKRZQha7TUDGEXnqKFZX9vdJkoTz\n01Om8zk6dn1c08Uci9uQNJtc0DZDJBh8lDjSc7vBsixLdnZ2mPlmVWstW3FMmmWuVcgX3SeTyUao\n96C6VDCoEP61ZdHazwne5/LrCCGYjqYMBgN++5OfZP/qD7CX7WOkRrF+nlIKrOSygOjnOx5J42pX\nxWUL2VsL7WvAsSKSLKMoSoYnZwgr2dnZYTmbY+2ayhRJSVFVpLEf7uY7bsMNCeKRFxcXjM4cF3F7\nd4ft3V3Oz89J05Tp2CXf0sJitmQ2cyz8TuqYAyqSWOMRSFtz7doh9hBOj0/Y2XGDv8/OzryH0VSV\nMwJLRZJkaG05Pz8j8UDJ3eMjkrjT5Auz2Yyj05NGo7CqlhweHnJ8fLwhfpkkCYVnX2S9jMmkbNjm\nZVlS1BXlcgXG0slz0sjB4P1+n9o68CGSLic6Pz+nt7WFShOG8xlG+N3eMxwSP0wwDOhz98828HVD\ng/JhttXOwNI852J0Tr83cPll6pSgFotF04gaaEwhp4LQjr8pcxa8WpsmFSKD4FmDd23WlC8Ghw0h\nqP6ubq34tV/9NT7xA5+gv+saTK0SWKspihJrvjjtwkfCuKzdLBa3cy5d+4ZJ66Ylhr9XRlPVFirN\n8GTE7GLG4mJO6Rv+jHUJbRKlSBQSF+vvbe9xMb5o+o+EEJSrokH10jRG+mR4NpmghMAYS55mbuyr\nlCSJZVVUGFOgDc2NnUwLBv0+aZryuVc/00gCdHrXWayWfNV73sNyueRP/+TP+Nqveg+DwQ6vv/46\n3W7OyckJVs0oSscuTzo5RVkilAClSPvdRgtjuVyC1SAFVx+7TjdLOT4+ZjIe0h9scff8hG7eYV7i\nGB4+b8v7fayusFLQyXMG/S2yOGnYFnm/j4kk8+WS127ebIi/WmtErLD1OucpyxJT1cSRKxDjUc80\ny1npFUZbB2n7xY+xjZqUtZZer4OxJVJJFotl49UcGmi5uDhjuVw0/E1339dSbqHXqy0a2u7pg/UE\nG/f/oR8sEK9b/MPIkKQ5wsKtN27zz37tn/H9f/37MMIwX00ol6ElKXMDNR7yeCSMq32EnSF8cCnW\n2uPt2FtawFjmsxnT6dQl616YQihJJ+k0MXVIZDudDqPxuJGxDjJfYWxPVVX0fNjW/j9jDAY2hFPC\neYYxq6FOcz4cMps5IZnd3V2cBNkF168/weuvv8ZTTz3NE088wcHBAbdv3+XLv/wrGY+H9HpbvPTS\nS7z2+qucn587TYmBbFr1Z7MZFxcXVA7rB2gMrTKa7lafvJNyenHuw8SZW2z1mr93dO8eeZ41n6Oq\nKhTC9Ur57uuATG5tbTWdBNP5jGLuit6ldkwLFUXMZjOKak14DchiyC0DGAAudw1eZLFYUBvdoIlt\nNV0hRMOACb1ebcL25XCvjSi2GSKBTQLrYeFtBsjGd+GmqZjaUpWam2+8xb07R+xe3UEJB464wvUX\nhxh+wQBSCJEJIX5fCPEnQohPCyH+vn/8GSHE7wkhXhNC/IoQIvGPp/731/zfn37os2FTHKTNdH8Q\niXI1X3B2dsbt27cbOpCMFKWuGY5GTGczyqoi73SckhOws7PTwNxtCBfczZkvl5S1k+uqjaGsa6xw\nyq9tHYgATISmydpoz4OLQTrtjfPhBVVVMRqNGI+Hvj61IO+kjMYXHBwcsLO9y5Urh55KZUjijO3B\nLv1+3+3upmY+nzIcnlNVBdPpmOVyTifNGiSx0jWT2ZTxbMrFcNgk9AFYCIu43+87gADh1H09a0VG\niqIqiZKYTq/LslhxdHLM6fmZg9219vU9t+HMPXwfCMN5njdgQXuBt0P8KIo2OJBtYwy/O5ZN1aB7\n4f/aR/v121SkAKqE923Xu8Lv7fpj20gCGKKUcjlxbfnMv/gMmUqbzSa81p83/akAPmKtnQkhYuD/\nEEL8NvB3gJ+31v6yEOK/Bn4Y+K/896G19jkhxPcC/wj4xMOeUPjQzYf3IW64SIEZfXFxwcXZiNc/\n+yr37hy5iR21kzoWUhIp4WZ0oZgML5rQoS5qFgsnQtPr9Zr2hhDaPfb4dY6Pjzk7O2tCEikl4+mk\ngYADpSn0QYVzG+zuuHnLnkE+nU6pqoobN25Q1zW9nlOLcrSljOXy7WZumBCCk5MjinJJWa0oihXG\nls1r7+9uI73R7u3tsVwUnJ2fuPlciyn37t0DnOwztRMvresa6UnHYfe2tfVNiNLrFTrkTEhJvVgw\nHA43CuzBQJPMScgtS8da0cYwGo2otFlLDbTg6nZO2yy2KFrrFLY4ewGIaBtNMMqQH4XFH9ZCiGKC\npwwRRjCEtgcN/9P2cu1SD1pjZUQsIkzt0oPXP/MaH/yaD7B/fYcoi30XRepG4T7k8QWNy7qzmPlf\nY/9lgY8A3+cf/yXgZ3DG9V3+Z4BfBf5zIYSw/38aYths6Q83IYQLwzPfDOnbzqVUbnFJwWDgkuX5\nfN68xnK5bGpMeZ4zGo3u6xd6+eWXN7xTuJEhpg+JcK/Xa4bOlXVFpTVRsu7QrYqS2hiWRcGbb75O\nr7fVfJ7FwvVEpWnKq5/7DL3uFnGi2BoMiGLL+fkJZbVE2LWX7mQ5s4kz1uH5BccnZ2ijOR8OWSxn\nPj/zgxCkxBjfAaDWKki6rFCtHTuEaoGMa4A4dRMrwzQRFz3IBmiovCZ+4kcdXYxmGw2mYaEHwwpR\nR5BhSJKE1crVI8M9Ojk5oShcj1uapk2EEK6XUqo5zxBqhnvWPtqIYFgra2YGjQ5i2BRD7hi62kN4\nW60KpmPFqy+/yvbVr3X1UmsbOYiHPR4q5xJCKOAPgeeA/wJ4HRhZawP//jZw3f98HbjlL0wthBgD\ne8DZpdf8UeBHAfav7L/jezd0FL8rVVXFfD7n9PiE4XDIyhuMVIoozYisJd/qoauS5coRcMu6oii8\nsKSHjwM0HVolisIVL60AqRxaFkJNFUduqka1ZiAEBC6cY8cjTqlnzQNrIRXPWC+KAqzLoy4uLnj2\n2Wc5PT1hNHLt+595ecRg0Ofs3MHsi5l7f0eKHTed0kmSUJaG8XTEZDZDKEdetsYgIkWv08VqV8S2\ntSYNiX9ZuTGodo3ohc1msVoSps0EKL1NFwpCMla6x0bjsQNWZNIs2KCvEboMgpcIvVir1appTl2V\nBScnJ01n+fpzThuvFDxhMNw2cyU0urb37FBmsdY2zwvGmCRZ473C+bS9mTGGWtQoEaOkopNmvPLy\nq7zw3ufpDxwvcrFYNaKmD3M8lHFZh32/VwixDfxT4Mse+h3e+TV/EfhFgGeff/YdvVrDTraGxXTm\ncpnTc47u3OPk6JTFfEVVFEjhwp+iLDm5d0SeJU0rRrjA165dYzIas1jMyPPcwfL+xrkuV4moHe9w\nMZs3MtBlWToVXX9zHQ2nbuSSpZSsqookSZh7vUCJC60WszmLqqSXdzg5OaLb7bMqFtQG7h7fZTS6\nYHd724cbhpPzI7Is4+zshDRd176CUU+nU58jSobDITKK3DxgIYjSlF7eIfY9a91uF1PVpD5vKMSy\nmXhv/MKqqgqkYDGdkKZZwywJu3g4dG156+Ytsm4H6T3JsixI1FrZaeoL9UDj2QP3MSz01WrFYOB6\nwwLlKdT4FosFaZo2oWo75A7XWYh182OgOIUj5JbBOOM4bnikAbSaz+cbObcQjmkTDiHAeK83mUy4\n8/YdXvqGl7h7dMzOzh78RbWcWGtHQojfBT4EbAshIu+9Hgfu+KfdAZ4AbgshImAAnH+h13ajeNax\neNiR6qqiMDVlXbGqKobnY47eOuLs9inT8QIpEzo9118UmAw7g22iJOHs7Ixu3sHUmt3dXY5u33II\n23yODru5NSyLFSp29RtqgwUkgrpyN7n2UxWRYgMoCAuwrmtMXVNpTawip6YkXBhq0AgFhSmxEUxX\nkyZ0uXnLAQPDydl9uUB7Rw75Q3jczN37G2modU2WpWSx0+rD13wi6QrseZI2AEHW7ThjS1PXkhJF\n1FWFLSukVIxnzjiqxdyxTcajRlIN6yZyTmZzlkWBlIo46WD0WkgT1qH3bDZjsXLaj9Vi7riCSoI1\nDP3ssuAFQoQAeCNIWp41pAKJByJsg+IGIwkGFgYZxrFT/53P5xuIZVVppBRoXRFFQTnKaXMYYylk\nidGWJIoZzpz25B/933/CC898Bb2tPaazeaPC/DDHw6CFB95jIYTIgY8CLwO/C/xV/7QfAn7d//wb\n/nf83//Xh8m3Qmzu32fNMxSOSKkrzWw0YzKctIqxdaOeGy5yUJe9uLggkmt12OO799ja2mKxWPim\nRa/JEJRYLU3xMtysoEXR5rG1c7CQB4bcoo1uhpsadt/QmNdmEARIv1Em8uFJW3Kg2WTqdTG1jYZF\n0Tr/CJ66DRa0lYKXy2XjScLfw2uFulF4jWDMIVcKxtCmI4WwKmwyATgJrxeQwPBzKH1YH5a2BYba\nn7kdigZPdfketA06bEzhelprG8LzlStXHtg9HJDPy2TjNiJYliVHR0fcvHmT27dvc3Fx0dCqHuZ4\nGM91Dfgln3dJ4H+y1v6mEOIzwC8LIX4W+BTwT/zz/wnwPwohXgMugO99mBNpG1T7gxYrV2uZzWac\nHp9x59ZtxhdjxpMZtV2jiqGDOFysK/sHLoRSEbOJGw06m0y9aE1ElqQNUwPwRmwwvlAZFlpYONau\nR7oGzxUS72CMzfPq4PH8bKw4arhy0+m0SfjD84yxRNE6GW8vmvU1aSNc7vltcZfLMDQ+LLVmHRFI\nKZG+3QMl0VjXMYAL+6JW5BDyo7CJRSprPluY1VXpmqooN0oa681gzYAI97SNBLcLvmHDugyRSykb\nAnV4PSe7vZ5Z3UYNQ64VOsT7/X4TUj/oCIBVAD8EDgFWwokxKqXY2tri937v93jpIx8m7eRNKvAw\nx8OghX8KvO8Bj78BfPABj6+A73noM/BH++K3d/IA/SYi5tbrb3L31t0185r1qM3hcNioy3Y6HVbL\ngp3BNmdnZxRFwd7uLpBhKsdaLwkD5urWIjeNBwg7efsGWrFGpMJzw7m384SwC+/t7TGZjDbqOGEB\nhOeGnbmuaxaLJXEcBng7A4I1ND0YhGmSrkQQBhS0C6fNYg5wtgdvQk0qDBlvG27YrdvnFjRFQvnB\naD+gwegGJe10OizM5kbQllQrq7V4aJsfGK5ZG/Bos+nDhmWMG/caogwHRM2a823D9wFRdDojafNz\niCra97NdtwrXL9zj8NzIe2Snpe9yyLSTk+YPb1yPVMtJOzwIP6dJznQ84+Ybb3F858gxFJAYs/6f\nsPC01pycnPD22283r9vNcwZbW8wnU+qi5ODggBs3btDLXau4UopBv99M6Ag1nm63u1HRD+cE64p/\nG3pubw7hb+3/C19tzwRsSKN1u65Z0oWi6/do5xTt0LW9aMMRziOEm+F9y7J0ZQlcHUVIiTbGjVoS\nAuFRwnD+wbsEIwvnqrVuvrfzxPb1aXuSy5+3vaDD+bbzzMsLfTAYNOheyKnbz22/RntzbK+l8JwQ\nModCdTsEbCOjYZMIhhYK8W2A52GOR4b+1I7Fw263XC4ZjWeMji84Pz2jXK4QRqBN3bSEhwsR0CAh\nHH3mxCu/upAG+v0+SRJxfO/I3QxB0xqfZCk9j1ytWghVWFzgw08pmnNs11nahhPCyCiK/OTFNaug\nrt08sMViwe7urssLvdJTkiRu3Kvvng4d0uG9w7iiUI4IxtX2VuEclFJYEzwSJK3ZV9LTnkJu1M4V\n27lWWFBpmm6Ix4Tzqeua8dgN5gvXvc16aV+Ty0f4e3tDagNZYSEH5kww8NVqRV2XG7leO4IIBhEe\nC+fQrr1dft9w/4qiQHjt/bIsSfzEziBcenp6yu7BO5eMHnQ8Mp4LNgc7B4M5P7rglc+8wt1bd5Ei\nahSDDIL9vQN2d/aagmzIKwLBNcTpSsaURc3p6TkyirGe3W2MYTAY+JBssSHz1U544X5B/jDiZv0c\nh2QFz+eMpJVXGIE1grKoiaO0qQW1i6KhhtWuEYWjrRLc9oBtBkM7fAqHk7J2HqrW2o1rDV3EWIwA\nKwVWCoeWCgFiTRcKjIRYuuJqN++Aqal1iZTr3Cqcf5uR0fZc7et32euEe6+1pjaaOFaELuDYD5nY\n2dnZ4HsCG14nvK4Q91OdwuuHDavdbBk20RC1BP2N8Dk6nQ661AzPL77o9fxIeC4BKGuodcVqWTKb\nzZktF1ycj/jcH3+2mdEU5RlGSopixaDfZzJZNP1MUbTegaMoQkWSxx9/nLIsOTk5Ie11qbAsq5o4\nU9RSEOd+0LW/kIOdHebzebNQ2jtXu+AINPlO8A7G+gReSVbFDKks2mgQFiVTtLbE8VqcUtcCwbp7\nNrxWCENgM9xRvnct5GjhsZB7wTpBF21n4YGLZhzSzF1LW+umnd1ajRZgIunKHpUbvZR4RsJ0MidX\nqR8c6IwTL9qysjV1AUkUIZOIauk2HGEttXURRiIVcawoViuqqiQyljxyhWqjNVLh5jM7NQZsvQQp\nkSrCSEV3sMNkNCbPuswXY9I0eHVDmjrv5gxLNZ673aoSrlWIbsImHjaGKErAuO9K+Vy/qrEqQpfO\niF97+XU++MEXv/REQS2WStcUq4rJaMJqtWIymnBy9x7Hx8cb9aSQrLodfD2VIooVQkRMJk78pS7r\n5n+D5NjVq1c5OztlWc4Zj8ckfmB3myoVLnw7YQ5qsWGm7xdD3tz4nNbx1uatJkFrN5P7y/nJ5RCm\nfbT/L5zrZZJzuFZhpw7liFD7Cp8neC/Zyjestd6gFVKl2EihdYXGog1Yad1EF2Wo6xVYi65KytWK\nTppxdWeHa1eucuPGDd79ZS8QRRF54u6frlydSkaKoi4ofX5YW8N8MuViMuWP/vTPmCwLtjo96izj\nrAljHV3JWtEYlOvzuv/6hOt4OTxte/62iE37mtd1zXg64bHDawjg9OiYJ5996qHv96NhXMbN7y2W\njlJULArOT04ZnY8a9aNgWOGGl2VJ1unR6bhYfDgc0ut1fNFziZQRpddkv3nrrQ02QFWtNe7Oz119\nO+iUh9DKmPV0w5DfwNo7XL5Zl4mk7SPkhcYYrl692sDDSik35pX7W23a//tOR/Bs4bwfFJKFMDIk\n8oFepJQCJTHWYrFI/Dk2/w9Bp18lKZOVywWlwi9usLUlNhpWS6qiZNDt8f4Pvp+vf/FDPHvjBnmU\nkMauWJ97hDP2XMckcmx8KSVlXXjV4pBrVxyfXzAfzXnlrTtIhKOW5Rm1LjfoUeF7uGcPuj7tsLT9\nnJ8oYFIAACAASURBVHWuuq5TBoMLbPhExEznM7J+l9PTUx7zI5Ie5ngkjCvcxLKuuTi94O7te7x9\n8y3HXq9EQ1kJF7CpXyhJlnVQkeTK1T0n9bWcc3Cwx3g6Q8WS3f0dOj1HrxEqxOnrXCd4qjAALyBr\n4QYGmDgs3LbxrBe+mxLfju/D3117+BpSPz8/J8/zpqAaYrjLoWDYPdvQdAgZw2MhPGyjlMEw22hi\neE5VVSg/FCLogrQPqVSjWZLGCcXSw9pSoOIYY2uWkzkRhkGaYUXNd3/0O/mqL3sXzz77LNvdPrFU\npJEzdtVJ3DXHqSqB+xkA2xoK73vWZOzRV5FwuDfhU3/0p3z61Te5mEwcEdrXrlwk0fGIn2tmlXKz\nT+syUtnesO5vX1qHkpdrccbrihRFwb27d7n++LWHXtWPhHEZa9DauDaJUjO6GFIsCqSVaGu4cuVK\nAzKERRjHMVbCdDZhe3ub0eiCunbEzzt3b5GkjgrzyiuvNP1IoYUkS+LmAgdQIVzYOI4bguz9N2Qd\ngrWRsXBcLoCCN0bWiq0BGWyS7xbCGJ4fXjPc4GDclxdLYF9cLnjX9Vr3b7MQ/fnb1K21ja5xQAoB\nyqIgKSuksexlCd/00kt87C9/G9euXKUv3FBAcAbdSbPmGpGqBuSx2qN6AdWstRtgoTWLxbJp1Ox0\nOthUgpK8+NKH+PXf+V2EdPclM+58uh3fQiMdb7TWNVGcoE1F06MEG8bW3nhg04O1a5dtD2etJc+d\nhEKe5yghObp77x2v3+XjkTAugPlqyeh0yCuvvML4fOTIk6wZ6GEmb1j43W6XytQ888wzvPbaq2jt\nSKHL1ZwojhlNhgB0ep1m118WTvRxMZs2VJ3L7O02+tXuRG5f/AA8QAi/XFIebl4btnaw+Dqe36xN\nbe6wbdg/fLWLq+06SzjvAKGH9w3/H1Cx8LrGGISSaF/fedBh6xrh/1/5DvDFYgF1zde/+CLf/69+\nLy/ceIZeFJPFEdJCnvXc63kZNaukY7uEkoSfMyYtTroMwKzD50rXoFzrx3SxYLpYkm4bVN7l9OKc\nyXxG5skBcrDFxBOXg4Bn+5o4Y9qE9cN9bDNC2nms28DUxu+wNr7AVBmNRuR5yn6x+9Br+pEwLlMb\nivGKo1vHTMczlIgwpWVZrIj7OaWuKCcjptOx341rdswOw/GE23dvNTUXdzhBkVi5jxaaFyPpxqli\nodPrusUXxy408YRcFUcgnd7ewpNKkyRB2Pb4UbtRZ3KhqsDYeuOmQovmIwzWBIBBYUyNkNapCVnP\nKrOga3CTFiVSSKcqrNeSYBv5l3Wjc9z7GcAgUAgk2rosyulx+AFwVqBLixEJJd6zGU2MRGmLUJKl\ngEHexZZLxGzKtf19fvLv/wzPPf0Uz1x7nDhx7SJRnKCSFBW5AXVI6TT4jW6iAYcuGDfgL1yPygNQ\nShDlClPXJICIY+KyJM4c2DO8d4/JbMHv/y+/w0tf/RX88Wc/y6DbZVXXxHFCnneZz1fkecdvtq40\n4IxGIgR++MWENE0IAjpl6dS8Kj+Aou3RlYzRWCLliQRRgtGglaA0hsf2D9Da8tlPv/rQ6/qRMC6L\n5Y3XXuett24xvhgT+4uR5znj+bxpZ5/N3EysslohfWNgyJU2Y22xUe8p/NC7y0XGdijV7/cbyB/Y\nGCfUDh/bMG7wQMbUBDm7BwEQ1jqF4JA7uccAYb1xrf/XeBHhkI+F822TZa21SI+YrQ/HWoF1UXtd\nOpDNa0ohiI1ES4OQEcZohBLEUjLIchajEU89dsjf+Dd/jO/49m+jF8dO21BEqDhCJilEEUQxCIUt\nC4QUoCQydu+nrUUhXP4mfKhpLFoAwsHtUtAo7obygvLlFGUleZrxXR//Dv7Df/yfcWV3h9l8gpVu\n6mRd1xweHnJx4VoE67p259C63saYpndsHfa7MbCuJGIQ0qO1rLUP28CQMQYZuVrXfD7nscNrLOyM\nhz0eCeOqyorj42PKVeFkjvV60WznMU8++aSbYdXvu7qTrXn77bep6nUI1O7lccXCtQ4DrMOUYGQh\nVOz4Svzlqj5sInjh9dvhQ0CWHCS8Nqw2VL9+ziZwcbmI2v55HTKujeoydcrYzdypjYhdzs2aYqqx\nSFtjsCgh0LamtpokzdB1Tb9a8fUf+jp++t/7KZ7YPyBNEnppzqpYEmU5VgpIMmdYHmO0InJeSkmE\nkkhrMVWNRWD9xEph/VOi2O0H1rpher5gLSWYWiOVAz50UVN5TZLHHnuMl994E52koAxJltPr9Tg7\nO2t0Odymsb6G7eJxuxs6RB2u63h97RxwYRumSVgbaZo61a+qIk8zzs7OSKKHN5lHwriM1kgcxcdU\nhslk2jASFsWSo6MjDg8PeeONV11bgXUfvKxWTXEwFPfCBQzT44NRtZP7YJBBjyPkdW0aUQjpLnup\nQD8K79UOAdvG9SC4PhybSNbaENvMAyFsM4KnXfBsaD+tDegyQhbOpf15HQoHlYf+hRUkxpClKeV0\nwbufe4Zf/E9/jr3dXTp5l36vh65d0T3vDSDLnHeQTqZMe+qZTHK0Mc4LKek8FtKHwQ7AsP4zC7Me\noaqsccO8vW59FEdYo7GhpX8xbwZNOI35mq/+qvdy9+i4qdONx8MHhuLhWgbJcPcEiZKSijX1Cx8l\nWGHReq021c7lZpMph4eHaK3Z393l1q1bD72uHwnjKoqC8TDIQCesvIJumA5YFAXHx8cY48QxLb4P\nyaqG5xe8UGB9q1Z3absuEkKssDNVVdUoG7UXZuDttft92kBG8ILrkE1veAtYAxttIKS9k7rHNz3Z\n2lOtvesmbBwQrbUXbTMRQttH29DDSNW4k7BYLbm6u089mfHkYJcf/v4f4OMf/ctcO7yCSARxmmOF\nwIoYmSkypSCKMI0uoKMoCaUQUmDjGGFdrQzlwk9dBwlrgYi816g1MnZQv1ISW3u+onUMF7Rpwu+w\nWSql+NjHPsZ/89//EjuDLc7Pz1kul42Ed3uKJf4zt1HcNhHAWkcFS5OcWgemRo21a/34cE+CHmIU\nRcSpoi5LBn7izeerO14+HgnjMsawWiyxVjBbroiUn+A4m9LvOUg95EMuf1kv/nbYVHqhGmc0xX2L\nPSy6tuFEUdQgT1rr+zxgCCHbrSihIBueZ60mijc9UBv2bnu1z+fRwm65RhPFRkjZNn4p1wTcy97z\nMuTctLRM5mQRqMWS7/q2b+en/u2/xdXtbRSCpN/3wIQbai5UhFESoRTIBCskRlpAIUSNlP79pMSE\nvEoIRG1QQiDw6KaQCITzegHbsNIJ51gPeOBoUOEz7uzsMJ5NefLJJ7l54jbVw8NDTs5HjR7jZR5j\n2+uE6xQ2MYfYSuo6lBtcicBtfjHSqQIipWw6DoI+RyfNsdqwmM05Pj1hsLPz0Ov6kTAuKQR72zuM\npjOsgiTNqI0mlbjh2r6oGwxGKgc4rIo1kBG8UPAulxddmx/YbicwxjTGE7pM2zF6Qyit643H2wAD\ngDHrG32ZUNo+2otfCLGJSbSeE16jDby0/w5seNbw+GXjCueaJAm5Unziw9/M3/y3fpxOnnL16gFR\nJ4NIUlYVSdQBIbBSUSscgVc5EEBY5UM9T7FCuIjWOoQOKbzOCCgRESURoB2SaS0YVzQOvEdtDNpq\npAVpjStgC+FkFkq36UVGNMThe/fuEWfd5tq4mmWgZ0lg01sHLxQ8XOQ9ehNFGHBbgm0IvQFYARpP\npoxgsD1gPHHSehcXD0/gfSSMSxvL2WjKqlorMZ2fn1MUBVtbHZIk8+InjtmAlVSlJY3dLltVZROe\nRVKQpgmVXnsNFwLmvmXBuMUiRFNUDl4vTJsHNlpYhHCDxtvKvO2CtmtsjHydbN16Img1EsrIqTMJ\ngTUOOkcoQmE6nGcjgGOgEIbEKhQKtHGeQziYLdM1hbUYBJW2pHHmalRWEUWGWEjMakWC4Js/8EF+\n4u/8bfZ2drl6sIeKU0gijFLYSGGkQHQccwVwoZtoLXgMWhcgggG7MFBY4ehb/uOE0zPCIJTAFmvD\nt7pq8i+3qThU12qNKWqENUjjQJzRcIgWkpu3bnM+HBJnfoTSqnDKt/56d7LctQwlrm2nqIq1qFBV\nI6UiTRPSJG91IZRUlSGKJXXtdQ6Bbmt4e5IkdDsd13A5XdAbbCE6KePpmOHx6KHX9SNhXBYPz/r8\nIIAQeZ6jTcWqcB7J1ZoEUkYkSQQteDuEBO0EPsTbIdRrh2sBNQz1DtjMXcL/htcOjHTYDPPC79YK\nL4DSRhRDzrXO29agRdQ8L3jQcC6uiAuFNlgs1vhQS0gQAg2U0qABbUGbmiSNMIWmXi3Z2+kyGQ75\nuve+lx/6a9/Px77lW1EW0p0BGA0+j7JYEOHzW7Roszmk2xqCd5b++niEz31AXF2Q9a8OfnfPEUmM\nKVf3ySEITw5QuL46pHY5l7/2SZIwnS2IhOTOrdsOqVWKpJORxhm9TndDys2lCwIZyda9kj4iiSiK\nqunwDpuqRTchYJ72iCM36SXyZSCjLUkn5fHnHuPNt26S9VzzbJR+iY1tDdrtgS0+n88bmS1j3fxe\nV9yTrbwkLGq3UJUKNJd1A107tGoDE1KqDbDgcjgVQqngTYIRtlHEy0hhyNEeVPC9XP0PP4fcLWwE\nIfwMRyYUSkiEEuiQLwJGCRZKEoq0vaxDYg2Wmq6Eb/vg+/mRH/kRrh4c0ut06PR7gMCUJTJPcXUv\ni4pirJKAQFy6Du1zDUZ1eVO5/JnAIW+qCfFqqrpGtwCD9bVQrjQgNcZqJ/WGBiOJlSLPU2pd8tZb\nb1EXJVYIert7dPMe8+mM3d1d0jTlxo0bTkO/qhBmXR5RnkQQAJ4kSZtGS7BUFR4Ec82R1tDo8gdg\nbLFYMBSSp556ij/59J8hlKBa/fkK1PyFH2VZ8vbbN7HWNjJbaepnTK1WbgcSDolqE1KTZN0BC+ua\nUlVVRMkamAieIUCzbXh9A6r2Na5gwO0mxAcBFJeNK3yWdjh5+fmXi8Hthdxe2EIIPzbVFWWBhlYk\nLVQSIgMpsCUkzGZ82zd+M9/x7R/lw1//IbJuBxUlaCFcXSpNmZ+f0e92nEeSCqSbN2XCi0sfyErR\niPcI7zGbmXPtnK5VCIZWUcEbLEoRpxkyiolafErADR6XYI1wHwSglmDdRjMaDhldDFnOZiRJwkJr\nFrMZb7z6BlEUcePGDfr9vu/2dt4oSmQj7Saly5+CoQBEUdIgto7hHxFFbj6ZEAqlYr82DFtb207u\nfDZhuXQCR3eP7tLJ/5wVd//iD4e4FUWB9IPf4lixWi3WeuxGE8dps0DDeFWXhOZ+FpP04cI2RbXa\nGI8awg2HFq7ljC+jTKGiH9o4QhIckt42EhV+bkO/AY2EzaJy+Hu7GNw25PBYMExweu8Gd5NUHDGf\nOsHR1WzKQZ4SlYZ/6Zs/wrd+wzfw0te8j628S5YnyF4PVASR12sXjluV7+xgFCCCNACeOKyc58fc\n55WttRuzi9shtKudeWPBG7+ncoEFpZBKrY2u7e39g0JYRBpDWSCKurkuu9vbXL16wGC7z63TM2Te\n5eT42E/ITDg9PeWJJ54AaCSwL0ZnTT4cRXFzL7TWWCNcGBlFjP2kmySJUTImz7pNG9ALL7xAXdec\nnZ05w7WawtdDD69c5ez0+KFX9SNhXCGkCgxvY5zeRFmWxImbB1VXpgkFq6pyxmVARYo0yVDSifwr\nuV6kQXcheIt1Q+E6zwloXDvnCSFlO2xs10zaQpR1XW8UK9u7c1hI4fXbKGDb8NbQ+6acmg0FUgHL\n2Yx+lhNpSKKYba35d3/i3+Glr/0geZqyu71DstV1XirO8L0e1Fpjfe4nlHTghRAYhAMoWL9H88X9\nIKaIVJNvOeNjnXv55wt87hweVK2mUoGrRYXrUgHCOM+KRUQxwoLSCZ08x+qK1WKOqSuUkuDnP8dR\nhhBuyubJyQmTycTlQr6k0o5OHLM/Z2try3ExpSRNcrrdGmNqP4/Nte30eltkWc7rr79Jr9djNnNy\nfnkSc3B4gFCSsiw4uv0lVkTGupZxACEVRrs8B2HQOkDpliTJmrxGKcWVg/0mJBiPx82iny9mxGm0\noYHRDsPCa4Qcp83daz+3/bwgiNk2kIAchpzpcl2t3dZw+W8h9GvnWJfDRYNtGiiiKEJqy3B8xje9\n9wP8+L/2V/jA13yAbt4j296CJIE09h3CmTMkYcBD1kK5TmMRKVw85/IeV4B1xWFaId475Vb3naPn\nHUm7NrDm81yaHxwoWxaLFBYrFEiDES7nk0I5sIX1xJKDgwNOJlOqukbK2OvSa27fvk2e5+zs7FCW\nZaPeG0om7bFQVVUhiNnZGTCZjJAiQluNMQ642du/0sg7dLtdJhPXDd/v9wHTDH8XWOSl6/L5jkfC\nuCwu9k6ShMW89Du3RBBjjHPJceRiXTeEzKk5bW9vI4TrUg0TO+q6JktzhPdOWeJh3PmcTtalEEvy\n1JE/lXAGmOX5uiBrHKPe1JpO5qcmGouQgihxjAAlo4YZIpTrOaNdu/JH8G5VvWwY7EGaAGGQIsK6\nHN4hbECE9ax4QV1btKyxpqaTZnzg6af4ez/2C9x48nG2nzzAComKY4hijFCu3SOJMPh2msQtVGvW\nBtx0GguXdglf68FKV9D1knXWMyuMtS5iMBKpLdIahNVYYUFalI2az+482/q+au+4rLUepnf5orEW\nHTY9aUGAtK6GhgDinEgVPPPYk5i6pgZ6/QHSxBSLkriTcfXq1aa7PISCSRyzXM7JsxxdV+xs77p1\noSLHnK8K4kgyLpZ0u10ApuMZh1euNRupMU6ZeLkokCJGRBpRWDqdnCyKGef9h17Xj4T6k7VBXEQ0\nH1oI0Wj0uXYBV6dI05Rut4tSijt37vDGG28wnU4bb5LneVNcbf+8tbXlAQua14d1wbENJgTybxzH\nzeypttRZyNPaoeQ7sS7A61coNr+kRKoWKwOLlqB9wl3XNVEsMauSfpyQVJqf/rs/wVe863m2BwNs\nnCLjDKIEVIxVEUJGbkEoifBFYKTABkBCbg4VfOAR8it7yct6Ai6+dUYg8W/iUY0HfOkadI0wGum/\nwCKM3tiMIqncBFHfumKwJHnWyJxZa5ivlqRRylNPPcX29jYHBwfN/b569WqjAhzC/HUf15qgvVot\nmM/nbG1tNXlav9/n5OSE1WrB0dFdtra2SJKEq1evkuc5USQbcnAURTz+1JMPva4fCeNq16RClTzE\nwmHwQViEy6VTexoMBm7Or5+QEcLD0ELf6/UacKDT6dDv97HWrrlorBV2g3GFvCoYVzgvx4GDSHpo\n3Lq8J5KKNE6QbBJnH/T5wuuH12xCTAfaIYRTsJKRY4kLBcv5jJ045cbgCv/4Z/4hTx4+Rmk1cm8X\nEaWIvAtpDlHqjCxKIIob6bCQIz0IubzPuIJNsEYlXcjoQjhhKywVWvgOYqXQQmGEdMaLxAjpPKD/\nihDuy+INSlPNJxSLqbOjVl2yyd+EQKYJKomJ0oRnn30WlcTEaQKR4s69uw1xN89zlsslk8mkASmW\nS9dZIYVq2k60Xt/vAE4Fatt0OqWua1arFU8//XSjB//0008zGAyoitLPdQaZJCzK++UR3ul4JMLC\nsPCCkmsYTKe1dkVU6yhPriO5z9bWFvfu3aOTdxtWRWhL11o7rTmP7KVpSp7nnJ+fN14vjsPUi6oR\n4QxEzRA4Xa5pgaPsNEVIu24DaYMm4fO0j3a+FQw6vIcMM7GEoC5dY2eMRAlLKiX/3X/8CwxQvPvZ\nZ9nZ2UVHEisNWqXIOHUUHuG8k/CCL8j15yAADyFnavc93XcfNv8QWOxCgKb0oavEKAfZW6H89+DN\nxOYL2xp0BVVF7UO3PO8QdzogE0BgbY22BmE0IjDlhUbbmihSPPvsMyR/8PtYTyApFkv2ruwxHJ7T\n6/UI3T1RJBmOHJpqDRxeP+Sxxx7nzTffIoo0k8mkiWTGY9d0G37Pssz1Do7HbG/vOvKC1ty8eZPB\ndsZiOee97/4KPvWpTz30moZHxLgCuheS+9B9G+Lp4NXCYp7NZhjjtDVmsxmj0ahhzDfIoFyLPoap\nhRAa4tZ1rWBU7f4fIUQjoRzOzxhD4nc8jKWT5c3ul0QxhvtbHpr2FLFu428zQkJIVBmNFE56O0Ey\nH4754Pvfz1/7nn+F/U7Ota1dIvzAgH5OaR37AxGaEyFKnRcRUiFsC+8TwpFvQz70juEr4EfehumT\nUrlCr60duKRUhPHsCulbNnRLNEdYMHjpOSEpxxN06boUrHTTOFESdI01Ai0kBu1yWixKeu+uFDJJ\nWA1HPHH1kEQKx4Fk3TWQ53kzFjcAQ0nsIp4XXniB1apkNHJDCB+79jife+2Vpncv5Fa9Xo/z83Oq\nqmJ/f78BRcKg+H6/z2I1Y3/vgNHFBREwPj974JV70PFIGFegw7hK+npIQZuwGowqz53UWq/X4+jo\niLquOTg4YDweNyFfkiRIte6BCjOQ27Joobgcwk9YU2+CZkcYPxQMQwKZV6Gy1hL7buWiKDbUlC7X\nwdqhWbsuBm5Qua5rJJJiUdJVGX/vx/423/6t30qtF0SlptvNSTo5RE4OTSmFxgl0CodS++tksFZu\nQOTNj5ecygMPGeB57+lCi761CBkjpEIJ4biAAqhrotq0IHcXYSzmM6qqopt0iOOUPIogjaCq3GcA\nhJVESEwIqfWaAkWtsWWBNJZrB/t85bu/nP/3M59FyBXdwTY333qzCdfrumYwGHAxPCdSMYPBDqPR\nhHe9692O3VG5DTRNU6c9WZZ0Oh0mk0mzced53oyWmkwmvPe972W1dG1Peb+HlTFvvPYa8/NT+snD\n68U/EjkXOJpKv99vPBRsTnIPizHArEGaLE1Tjo6OAJrJhbCe1SSEYHd3tyk6Axv5WRClAbcrBtSx\n44mbwRCCx0mSpPm/OI7Z3t6+L89qxFdaPWLh/9vDG7Qx1BjyNMWUJRmC//I/+Tl+4Du/m6629FEc\n7O6SDXrITkrt75aSCmGNI7sCSlgEPrQy7zyc7fMalrsw6/zHG5auaqqyBisdzK81UhpnKIspq9MT\nitMTyoszlsNzxmenaF3R2+oS9TqIPIcoQmtDHUVUCCqEC12FcPIAPteVIb9V0oWkRtONUz70Ne9H\nGkNVlJyenhLHMaenp0yn06ZM0u12m+gkDJJ/9sbzPPfcc1xcDBkMBnQ6nSbqkVJydHTElStXGrg+\nDH8/PT3l4OCAg4MDkixjNJ1wcvcOKYb3PfvMF1zL4XgkPFeYwhgWfRi1Wte10zivKtLUaTgsVyvK\nquL69esIu/YQITEN6CJ6LbAf5vSGmlVoMxgOh027SfBGW1vbANy7d8xgMGA2W2CM7zmrC/KeG8y9\n1d8m9cMT3nj9JrFyr62kwATnITVx4sMZq7C6YrlYkPZztHLMAjsq6FSCv/vXf5x/4/t+kExYpqMx\n8+EZpl5xePUApEBlMaWuSL0YTXPrpEKYVl4nlatvhUvbUCE8ax3d1LY2uI6ONYuNnO6FrZfI1Qq1\nXKFqDVUfdEW5WrHyXjrvdMmefAyQjWfM/XsZ4c5SRA4pUWb9XsYY0EGFyqDRRFZgqxKq2jVWZhmd\nnT3iRcGTh4d844sv8Uev3+RoeMJwes6qnFOWK/I8Z3d3m9r06KQ9Dg6usre355tqK9Is5hs//PX8\nwR/8frPRBrh9MBi4qEMX7O/vY+qSfmeLYjLh0//iTzC24tbxbZbjEfux5K985Jv4ro9+C7/wm7/5\nUMv60TAuu4kczefzZrevzTrODgIxUkpGoxG727tuLrBcz8E1xg2Yi+LN1v72sO5Axm2PkQleMgjx\nB/1CIVy7SSB9rooCJTSL6YLnbjzPbLrgmadu8Natm+DBiaAbGEI1YyRQY7FkXVdjiyTY5YrtKOU/\n+qn/gO/+yEeZnZ1ydH6GSmL29vaZjc+g0iRpjhByDTMbg4oV1oCrsq9DLdgMR5olbZuik2fDu2Ju\n82wBIgKBRVQVsixhscQMR+iyoshmRFlO1uuS7O8CriiNlA0F6gG3lfv+YqyH811LjsUZttPBEI0k\ngNbazU4zmjROuH7lkP/tj/+Y+XzOeOgky/d2dlksVpyfn7O/f4U8c54pjmNms1mjsRFFkieeeILZ\nbMadO3eakL+JPga7rJYljz/5GPPpArRlaVe88eabrPQSs1py7eoB3/iX/hJPP33joZf1o2FcwoVS\nk4lTd+p2uw3T2RCGqpVova7cg2ukTNOU2WzG3t4eo9GoARkQ667jNpjgkD3RKErBGoQIbI2Qq4X3\n6nQ6jMfD5nkBkQQXzh4eHnJydtyEKW2wRAq3hVfSYJVTFU6FJC8sXRnzfd/5cT7w7nfx9quv0E9z\ndrf3iP2AtchYbGUQ0tWTgo67shYpJNq6XioZ+WZGYZBWYVrCmJeXtxWyATW0Bz4cd9Bg7JJMC8Si\ngMmU5d17nN25y/4T18mefJwoySBWjg0iBBqBfGeN0fsOxxH1VK9LBumQ0zV4hNZYUyOEJU9Sju7d\nQxkYnZ85KL0oyfOcbrfPfL5kNpuRxK40c3x87OcCnHHz5k1GoxFhpG9oUwmbdZ7nZHmXqtJARKfn\n0OjPvfkaNpKkNcSR4j1f/dU898LzqC+1TmQsDVF2OHRinoFkaxD+YtB0C7tQYJe6dMYQoNPL9CWg\nkb4GGs/XnjkVRVHj1UJoGCD99jjWMArUeSb3t1C87nQ6PPfcc7z88svMljO6fcfElpHAaE0iJZW0\nICWRtWRa8OKTz/EzP/GTbGUxV9KMKO+jVIrq9CFOoCjI4pzp5Jys0i40jATaAlGCtbVT8vUECzAI\nK1rf3XGf57Dr50tbO+kz66bMyMUC5kv0xYTz2/cY7G/zxEtf57xd2kFb4SpXWqA9i35zpMoXPkIh\nXqn70/26qrBaE+G8qvCtSIW1pEpi5wtuXL3GycUZ/f1duv0eWlukillMZg6AyHOMMdy+fRshVV9u\nywAAIABJREFUBAcHB9R1zc7OTgNinJ6eAnD9+nUODg6YjRc8/fgTHB0dMZ6N0WjOLk5ZzqZ0l2MG\nWcbf+JEfZvvKFYj+AuhPwqnv/wFwx1r7cSHEM8AvA3vAHwI/aK0thRAp8D8AXwucA5+w1t78fK8t\nlfTewQl7BmY6tOtNjsERjHC5XFIV7jmh2t6uTYULGnQx2hocbZGYthZhaBdpj2oNHjVJEqLE9Vyt\nViuqrCKJXJgZamX9fh+VKEaToUP0KuOUraRAakusFFkcc7XX4Sd//G/x9OE1siwlSTo43UEBWe7I\nt1ECdUo9G6OrCpWnSCzWSmypESkIJbHa+CZFiTEaoSHojAkh0K26mrUWaSTCurxT6hpdFahYQVEw\nfeMmF/eOiVXCztWrxLtbLOoKG0fkpUHFCVgFxnV8gwJbYt5hvW3U+0KhP0mc59Rm43nGWpR0M5sp\nS0TeQZoZW1s9jFR84nv+Kk8//y7+6Sd/g/FkRF0VzOdzitLQzbtkcY6tNRcXF+R5ztnZWYP+tUkI\ngQEUZmhnWcarn36F22/fIk4jojRiOR8zOT/Flgu2k4j3ftnzjhGSxO8YAj9wXT/0M+FvAi+3fv9H\nwM9ba58DhsAP+8d/GBj6x3/eP+/zHxbG43EDo4c8Jwy1C0MY2sYRPFhYOIFYa60lyzK2trYa2eM2\nqTNMbQwT4tvtH2FXDe8BNGFi+3uSJMznc2azWePNer0eL774IsvlsmF3hMK4jCWZUiSFYS9K+dmf\n+mne/WUv0O8PSLZ2oNODvIvpdrFpAkkEWQadLlGvy6r2xiAkAoWpfe3JuladwKRQQiBxC9ehbZ7T\nZ9xjtq6xuoS6hHIFukIZTXl8xPlrn6MTRTz11V/JY+/5cvKnnkDu7iG3d0gHO2hbs1rOKWYTqsWM\nerHErpYNuttGed/xNhvPwRTCEXRbYXbT3CqdUpSejlkupkgFvV6H/e0B73n3V/DuF55n0OkhjGW+\nXHDl2qEfyqCbyGY6nbK/v89isXAisl64aD6fc+fOHeq65l3velfTNVFVBXfv3ub07B6JhJuvfZak\nLugJw1434we/7xPQ71MZg/kidAsfyriEEI8DHwP+W/+7AD4C/Kp/yi8B/7L/+bv87/i/f4t4RyKb\nv+isQzZY15uC1wkG1G7bd4xlJ8vWDuuAxv1PJhO01o3yavBo4flB9al9emFaZNvYwq7X7nUKBeJu\nt9uwskOYGXqIjHETHaM0IdKwHSf83D/4hzx3/XE6vS6im6GjHJN2sN0u9HqYPMGmKSZPqaOYdLCF\niCPXeq1ihPWDv3XpWvatResKjMGYMKzBYG34vfWzqRG2BlNCscAc32X02U+zvHOLve0t1N42JhJU\ncUQZR5RIUpkS2RiRRcjcfTeRwCiLluY+4/pCNDCE2CT6+qO53sa4frt6Xc5QUjLo9dnf3uaD7/ta\nunmK1Zrd3V1GoxGdTodnnnwGY1x6cX5+3pQ9hsMhVVUxHA5ZrVauL+vwkNFoxNbWFp/61KdYLGcU\n5ZzR6JzT41us5hPmZ8ccZDk/+L3fw/ve/z7QFSJOEOrPXxT0F4CfAAIleA8YWWsD3fo2cN3/fB24\nBWCtrYUQY//8dyxtW2vJkxyMpS5Lsk6OSmK6e9ssJzOiWLI16HB0dMTO9jZb/S66rjFa0O32m0Jw\nWRXknQyEpqoKv9gls9lkoyGyrjc1DEN4GFDHsNO1hyBUlUYmkqqGXrfLarGk3+myWM3dAog0n33l\nz3jiqcd5+ZXPAkGSQHF+d0hXlPzsP/j3efz6Dk88dh0ZbWHjjpMYiyMn9SwFtRAujJSKKO4AhnIy\nBxGD1SglENagl2BM4YqysaTWK1+gtS40NEF7wz2m6hrKEqZTZtMxq/mCcjGn3+uRpDnFeEm6tQ2R\nwiYJRkmUkU4J11pIYpI8ASxGrInKUjqV3fbvQvppJtaFe3jvGkkJQjuVKC28jj9ERmC1RWhH9o3S\nFJSgmNfIKAZpWc4n5KrihScf58bV66xWJXdvvk3a7fHK2SmD/hZprDg5u+Da008wLqa8dfQ2+7v7\nGDRR2kEpwfXr1zk9vsuzz9zgj//w/2GxWDCaL4g7EbKcI4oRO6LkI1/3Pn7sR/91dp95Afr/H3Vv\n1mNZlp7nPWutPe8zxJwRkWMNWUNXd1d3F9nNwS01acuURbUtgbRhwjBs+IKw4Qvf2DAEX/jGf8A3\nvhCgKwO2AdmiSFiGKEsEAYkUhyZ7rq6u6so5M+bhTHtaky/WPicjiyaZDZtA9S4UMiPz5MmMiP3t\ntdb3ve/zboBStNMJxWj8kiXzEsUlhPjbwLH3/k+EEF976Xf+y9/314FfB0jisPcVQnB8PMVJwThP\n2N7cJN7a5ujoiEXdMioHRDJezaLms5rNzc1VlvFSBtO27UpfuFwRly335dnqamEtFovVFnKpN1u2\n7Zd/Pk4TtDE4Z1fnQWttyASr5kg7ZHdnhw/v32eQDdDW4Fz49wyKITdGEZ99/XXWy5I0ThBRDnGG\nUwS5jwywFuWfE2qRQJmHlSv4csAHX1TkBEIb8A6JCrZ8H6wsK4mgDR/7psZ0OtCngMHmNoPdGJQI\nnIuqCl3UuiMaj0hUhIkilJPBMewl3uvwb5AydPWuaBQ/aQ798yRW3rlA1fUeSYSXfUNmqZJ3y6aU\nABW49MZYYgRSKUxdY5uOL737eT569Ai6GU0f/DWdewZJRiIVD370MXGeEUcRi9mUYZ5TL+bs7e0h\npeS1N+4yn1yijWFRVViniY2hjCSJ0Xzu7hv82q/8B7z65puwsQ9C4dqWssyBl2+PvszK9fPAvyuE\n+FtABoyA/xFYE0JE/ep1A3jav/4pcBN4IoSIgDGhsfHiF9r7vw/8fYBBUXohBJeLCVGZUOQZiXVc\nPHyCrsONX6oYaz1lljOdzcmLwUrlXlVVMFBGcnWuurrNXG7VPnmWWrpXl9aGFZ1IPAfGrED+/kV7\nyXIQmSQJZTmknla0ecvt3ZvU04rLuoM+WKAYlFzf3OTW5nVGKkf44MGyEmScIGR0hecXEGRCACoY\nGJ10aBcGra63Cwvvg0pCAzrY9JcqVrdUgFiL7b1cURyj8hTSFNPf/AFkmiCLlFxbzKLCdh1xmiJ8\nMGtKBKJPh2F54/fX1aL6pJkSQKCCVQxWfi4hl00Yj+qlVs6acEb0HmeX3jhJnGS0rgGpiBKJ8wvK\nsuQrP/0e3/vwAxbvv8+8a1FFgbOeNCmZXJ5QloMwj3Serp5RlEPKPOX6tR3OJ+dEccLp5ZSqM0yb\nhrUyYRBLtqTkpz7zNr/y9a/z1uffhbSELAFt0aYlIihNXvb6S4vLe//3gL/XfzG/BvzX3vv/SAjx\nD4FfJXQM/xPgN/s/8lv9x/+6//3f8X++2QkAax3TyQUiFqRK0JyfkYqIW+N17n7pdYyHg+MzDuIK\nn+bU1tNow+bm5mobd3p6jIpeBH5eTYO82jG68rmttofLofFSc1gUxaootdYURRHS7nnuMF66Xeum\nocgGtA7qyYzN7WtcVHMa1yHjhKlpuf3GXeK0YEmbZZRQd45ChG1emDUolKMftGowNXRzbLUgHg8B\nhVqmorg2aPD6JoEQor+LBY1ryfKcKM9D1zEO8TjGg0ivyMBWiSsekThiIXBX9JcC+s5jQL0FFkWv\n9+xlS0LKlXX/6oNnqaaHUFgrrsay+EzPI8Fj24ZYBEGvdC6MX6whjWKyvMBqAyLAYss0I4tjfu3v\nfJ0/+dYfM4gTLi8n+LTkbDHltJ4SNdOgyImDpvDw4pBXbtxB6wUqjvjjb/wpne3wWpNnEQPf8sbW\ndf6b/+zX+dybb5Lv34SyQAuJ1BoVRaRpEB8kw8FfVjKr6//LnOu/Bf43IcT/AHwT+Af9r/8D4H8W\nQvwIOAf+w7/sjaSSpFGMsx3Tw2O+9t5P8zPvfI5b2zuo2HB+dsn1jW1+94+/TW0soigp19eZnJ0x\nGo2CmzjL6HTbk36ek56WWOJlQSy3elf1icut4PLmWP7ZZUjDyv/Tby+df05p7bou2OcHGVp5hmtj\njo4PKPMUnMNYg8Pxw49+wKSdU25s05oahQVpkf3T3dY1Ks3AaJwxYCzUDfPJKbZraOcKUcsAF5WC\n1tXYviuYJAl4R1c3VG3D2vY6QsnQkYtjiFOkEsTI8C0Xy1Wnb9nLoI3yWRGKVSqUfA5Mdd6hZN9Z\nFT33Q/BccPvJ7+fybNv2mVk9X1IK8D4IdJ02CB8G2bGQYcuLwPdCZiUC80NFCuV9GHcoiWtbXLNg\nZ23Ez733RX7vm99C4EMiqTkH0dE5R5aH3YjVDjkYcHJ5ypOjA9JihLEdrusoI0tXN+yMh/zyL3yN\ndz//2fC1dCboleMEJR22nqN1R5anodP6ktePVVze+98Ffrf/+T3gy/8vr2mAf//HeV/nHKNBSX3W\n8t//l/8VP/e5zzFIUooowomOyayiqjVpsc4fvP8DfnR8zKyt2d3eWbXIjTFXaE/dC82IJVrragEt\nVRjLZsjyTHbVOPkcONm//krX0lpLLFWv1hggpUA3NbubJadVRdy2lNIRJTG2cxw+eczR0RG39q6H\nLZ7WDJTCT865f/8+9SK0jZdKkkhIEiTZMGeUlL2jMu4Fr5IoHXJ1wCQjSe4cI2upmymVd+RKIJMM\nHycsA8RWmcRCLOGPsLT6RzE4h+hDHpzoz1b2SnrKlbPWn3OPrL7G8HwTKfodpeix1rL3cnljMV2H\n7G0swgdFhNENumrBebqeFSisDa5mb8kk/MLPf4V53fBH73+IjgtKVdGqFt1YlHDESUTVdWjbcXxZ\nkWUFi8szurZloCSZNWxkgl/+6l/nl776NVSaIDbWAIkRwSRq+/N1CMwTROov/vyvXp8KhUbU2+n3\ndvf4Nz//HuM0YTjIsN6grWR9f4Onz47ZHo/ZGBRk5wIRPY8xvXXrFo8fPwQRnuLLaJll0V1tD1dV\ntSqkZQFmWbZqaqy2ROJ5TtOy9b4MyI6iCGSYIzkXVoxmvuD62gY3hyO+9nd/lUg6Hh0/4fj4hB/9\n4COq+YQffvd7fOXdLxJ7+OBf/h54y7W9ba5vlLBR4qVARUGgjIpwIkGoAHAJgogEISK8UMgi6iEY\nAtM0+P58J52lGKQ0ncb2zmTiFLeExfj+RhX0gJZlR1wgXBR8Wv0Llts6J8Vz7+WV89Xy46vX1Y+v\nFtby3GV7CZQ2LcqFYpdxDMaEFc1orGuxyxQU5ymzhLZpmC2mjNIS2zY407KzscE7b93lux/do7Oe\npJ0xihzDzQ1EnLFoNUeTCXVt0RFUrsUaT9QLhUfC8Dd//mf51b/xS+zdfp2FnhJF0EpBmsa99ExA\n1Lvk5Yuf+196X7/0K/8KL28tQ+/57/6L/5z9QU6eZqi8DGqFIsUsal59fchlW3FwdsTh7IKJkDx5\n8oi7d+9SliXGQFkMOD07QQiP1h1ChBlQHKveuxOA/UtApRegrcFUC5J+T728eTrznG3o+rZ8kgZg\njRQRUaJoq5rt7R201mysbbCZ52yVQ37xZ77C9b1ryDTCy5Cy6F3oYp4dPCFJEt64eweZxCGlUfUy\n+jhAZoQK1nlHjOyRZjZQBVfqCxk9N12qpAirDP2M1uckzuGcxysRWvjL8yWstnvLlXjVplASqXrU\n2wueMImPxAo3HSFWjA3p5MotHo58/RnMeoTv42qdQ+FwxoIzxAgQMnRDrcG2NbZtQuC86VhMpkgE\ns9kUZ5ZnpxglBT96+B3iNEHEEY+fPeKPfv9fYS4O+dpXfoav/dTf4LVXX2G8tcusMfzWP/sdvvn+\nD3l8dMbU1lxU5wxSwahq+Nmbb/Dr//F/yue+8mWyvZuYeU2RlZjWIDLVd1ajsD0HVJoT5eM/o4n8\ni65PRXEpKXnrlVcp0pQsj8PZI4l7GVBKlDmwip3ta7z55pt8897H3HvyFKOWgMeEnZ0dhPAYq6mq\n6QuswKsH7bIsw1P9CuDTOfeC3ApYccTbtn1BSZ8kCV3bERfFyt4yGI3xcRye8LGiHA4ohiWyyEK3\nqanBS3IdCkwohSxTiGK8jPpiEoik/7kUIFSwafTb1KgPnluJjL3rV52w9Kza7wKEXzI7lluzpUSq\nf80ntmwvc/0Zhbt/LitcrkzL9xZChG2fCzDXILcSSGPCVtSH/23XYHVwCEg8XVMzuTjvI3vmpHFC\nWWR0XUPTaA6ePSWNBUZ6BqMBn3/ni1gjeHj/Hn/33/s6W2sjiqLgYtEyby758pfeJYoTnv5f/xR5\nfMo7t3a4ub/Ouzdu8XOfeZef/uv/Fmxvg3CoIkN0LVGSoY1F5AqvNUIoXNOQxRnWucAJecnrU1Fc\nRZbxlS98AYUnihOIFdZbFtUc1yyIHAjbkeclN6/fYn20hu7uk4wz6rrm6OiImzdvc3R00EuTQliD\n6O31TdMQRcvQabNSy0spybJspchYRrPCi27iFUz0SjhD27bkSSguj6MY5HhtIFLk4yFyUEIceBY+\nzxFOQgppXoTVJxI9BkoEXrsU+Ej19KPl37/0bH0ic6tvYV91N3/yCrb7K0qJpaj5EziCl72Wr5ZX\nd4GeMEfr/3NiSY0K1hVrTWgOON9jAWy/EnhcPQ8Q0a7hYj5DNzV1VdG2NWVSkChJXU1pmzCeqKqK\na9eusbm2Tt11CJWyv7HDjeu3kQRhr5KG+/fvczGbQ5SSCyjosOcHfP2LX+bzn/sMdz/7GhuDAXfv\nvA7lgK7TobhMh7IWbIeMFLbtUF6CM5imIclSnLMrZsfLXJ+K4hpkGUnXkUUxnfAkeY4qBoyiCOjx\nXU2LP7tAn5yxPlwHo6nrBbPZjPW1rZXN4P79CqXiF7Ruo9FoJfb13pP0YXBLmOhSwLn8NQi+rhBb\n9KKCPoqiHgUmWEyfI93atuX2jRs8OTzksm1YUxJVDtAelCrxnQk3pgzGQi+DH8pHYtXSXm5VlzyO\n2AXMWGjT9/6pK6vUskA+WSjLDt7VwlsVmfiz56aXvZaFs+x/hDd+8ffpdYyuM2C74I62BjyYxRyh\nLdJ5rK6o6wUXl2ecn5+TpkHvGQuH9MHgmmYx1jnKfMyN27fC0D8rWU+Cu1mkJQMhQLfUkwuOHhxw\nfHzO5uYm3ltGGwO23nuXd/Y3efPWXV556zOYZsG0aol2doNLIcoQrqZdLFDDAbZuEGWGjHxQtNQ1\nqtP4tkbmxY+13H8qiiuOFFtrI05OTtjZ2UK0DaZpQCmUiIhVgkAx2Nzk88MhP7x3n+x3/wWnbUWk\nEiaTCXfvvk2a5mxubqJ1S9vPv5aKjKUc6qpCHEKIHrwYoAC8sKotUWum1yk2Vc362hpdHFgbAfUs\nmV5O2MgzPBKVFzgZVAgGUEmCIxzgvegzB2BFovLwnF575WuzfL27ugVcdvr665NNhauFc7VrGn79\nuVLlk9dV4e1yS/38jVyvuRWYtluBS501IWy8/zPe2H6n4ZCtpu0qUhVhmwZdLTBNi7eOpp4zm1/0\nWcPBIyX7JlXef0+EEERpRprnPe5NQVEisgznJCLLwBnapuXekwdUFxMQMaPRiEGRkyUK3TXcffUG\ncm0NUsm8tkSjIS4OQfHKWExVo4SErsM5SyxlWGGdwzY1Yqkx1i2m+/9XofFXfgnvsaajE56nh0dk\nPTJtMBhiY4Fp27Ctaiwdjlfv3CEWoT0qRQTS8vjx48AEp+8wExIzvAe8WEXBIjy6n1UsVQzLLeDy\n15Y34rIYl2cyqeI+LCL4zrY3NpnNZty4cSMATJXANEH4ixUB1Nnj2lZQTURwxYvwEHRXH4WfXAVw\nLxTWatFYtsU/uSr9GNePu3ot3cI4v5pv+V5twTJ/zHuksQjjwwpWNyRG43xHNbkEHGZJ1LIa29me\nVzIgSkKDJs0TnAmeOi+g7hxqtBkQAEsRQGeQRRl2L9NLDp485OLoiPXBBmu7YzY2N+naBTLPSRMF\nsqTLU7SQGEcgNScpMkpgoZHG4KzB1RqRKxAe17bIztPZmrwY4nSDsNGP08/4dBSXVJLPvPMmbac5\nPL5EzSvKNGNxMUEWJWlSEkVpEHB2NVmS88brr/Hog29ibIfXnovzCTeu31qpoOH5DbgE3wghMFZj\nnCZN0xfOVctZ17IFvzyXFUWxKrC4t4UrIVnM55RlyWQyCV6ypmN9PKSratK4h3QS2v1RCsJdmfWE\n3gRWBAfxsjHwwnlGhBmU74twKYa1S46j8y+sxFevv6jYls2d5VnyZS/nHL4PBV/KlISiV4mYXnYo\nkdpBZ6DRuMkEvO21nGFVmFcLhIJIRGxu7JAWOUkWISNFlMXhk41SmtkMGSUMrm9gmxZDoAnHbYNf\nzDn/+BFPnj7Fac3GqOTLd14l2tpBlkPa+Yxic5/24oymahlvb6CaFhYdW8kIZBK+jnqO7DRNvaAo\ncqyuiZI+PceDbTqSLMMJjYwyrO4QP2kNDSEEZVZQxoprn73FydkZs7MzdDXDzidQDNAyQhNBGtNW\nF3hvieOMshgiZcTF+TEXl2fcunWH+WLKYjohi7OVILcsSy4uLvDSE/WoY/phpvM+bAsE4bxmHVme\nYYQhTdKVSgNgPFqjrWvSUUjcGA0lXWvJhgVJkfPs3gVVU4cun3GkaYzrOhxLuZDC4fEK0A7pr+Qd\nS/l8uNufXyQ95tn0Z61+tXWYkNzYPxQUy0xihxJR8EV5h/Ahg0vQB+gtA8UB56+czXzo2NHLqZyx\nK+t9aNOLgOFwvSzKO1xjEN5iTcisxgq80QjrcYsZ1mnaNpxzbddiTIfQmkwlrG1tYfrmUBRHBGi+\nAGfQXYNSCXFeYM97+4gLM8uz80um02lP6fIMN9YZjQYk6+OAH9AdaZZCa/AmIklHeFuiVMyifkY2\nGmGaCWkygEUDnSEyBmwg8yrtEUaDdnTzKXKQBreAc4Fz737CiLsOT9toNsdrUI64sbkD1YzJ0wM+\n/tH3eXz/HkIojI/QcUS+tRZ0f8b3WABJpFIePHjQR8SU2K4ly7KVCbNZhugpQAmiKPARl+qNVR6X\ngywNhZQmGQJBlgbw/9nJMabryLMQ4dk0gcWwu7tNXkQ8OzqkbgNKm65BZjlYh/QCEcnVSuT880R5\nv2SvC/GCmjzMjXz/b+pPa33EqlKEdrbpw/ggzKV6+MsyJhYlETJkMHshUFJeSWLw+KWkyQVF+rK7\nSj+bwj0/Z7l+eQ2rHqHwPUihkLKXcNkO0RqUtuiuxVtLkgdW4KgYB7lYE4yrUZ6xxNkTx8G8KfrU\nFxEHd3TToqTCdg2XF2fEkaQzLZvbG5yfnxNJRZIGDB5eQBvgQiJW4KBrGrJyhIgLzMlpEA4Ih1QS\nvZgjqg5bNaSjLarLS4r1Mb7PCNOLOiREKxXOYtagnSXt742XuT4VxYUPcxviOLDP4wiinOHWLm9F\njur6TUxn+fDBI37vm3/C9+/f41lbvwCSWd9aByK6rqMsS7I44tGjR2xsbDCfz1eWlsn0AhmpF85W\ny8IzxhCp55q6pRnyar7XaDSiWjTs7+9zfHzK/v4+Z2dn3HnlbT783ndYS1OUkpiuI4qTsALEYfCK\nCBZ77yzShTmqt5Zl/vDzLYfH9zE6Ui2zuzxg8aE6EYC9Qq9aHQaWWzYhUDJ6LpFaFm/PI3TOISPV\n20CCqXMJiFp6wa5e0l95Kx8eEN57nNbgLFIpIi/QnUaZ8JRXeRZEzeUwzNqU7aN5w2coVO9iQNIZ\nTdLfuHEaQRyjpzOW57TwPXArqNBoMGQ2mWA9+EJTzyrSIsU4Q1IUmKYlS1KSsgyErKUr2zriKMFU\nFa7TtHVDZ07DbFX2q7h1VNWC9fEYh6XrgnMiTZPADnnJ61MBBW07jTZBGY9uwHs6r3DZAJmkFMWA\nJIm4ubvLL3z1q/zSL/07nJ6dr3R4EPR+t2/fJsvC7GsymbC1tbUKZVgmW+zv3aDIy1WTI0tz0iSj\nLAaUxYCtrR2kjFhb26AoBqytbaxWuSzLyLKMoihWuGNrLcPhkI9++BHVPLiRL85PEc5AVeHmM2zd\nhlVGB1qTMCG5fqXzW+LG3BXVOP0OURvkUudmbRio1guoWpg1qEqjKo3QFmEcwjgiL1AqAgeu6QLi\nGoFrOmg1tBrfdNh5ja9aZGsQ+vkWcXmWW8E6lxGnUpGoKNhdljRebRE2nLPcosFXLU73DBIRE2Ul\nTipEXoCKGaytsbZ9LaRJ5mmYtPSZXCCwpusdARolYTGfYkzHYDAgibM+hC48QHd3rmE7HRocnQ38\nXtNCW9PO5iRZCgKq2ZSoKIikIk5S9GSGaTpOj45RUhKnYRSDdXSLislFoEVRFBA5rG3x8jl75WWv\nT8XK1bQth2dneO1IRmPSOEaqEFIQxSlOW5I4pshTMpORlyUOGRoDUpKmEfP5fNV239jY4N7ZKV3X\n0TTNCy31JZV3f3+f09NTiqJ4YRVbxg0tjZPPQ9GGeBvmYMv2//b2Nbz3PHv2DGsaZKS4nE3CoNFo\n9KIhSmJEoqDtQhejA9O1RINBcP22XegoShn8WULg+n8rgPehQ4dw0FliIfBSIZou3FRROK945/CR\n7DWIEl/ViDhGeo9bLJBpEp6k3gY51xXKsJSq71zS/51Xu5HhAeD6WR+yt+ioCG81Qilcq0Ozo1e6\ny0jhAJXn4ALQJ0pyVL/6NdoQJYIoXj5cDEp4XF2FQbwAqhlSSdq6WY1COqNBBut/XdfMLi7J0jSw\nCYUkdUHZ4nSPt6sXSCuCw9lptDUo55HAs4MDdNv1DSuP9QavLV1bM5tesre/HwbKRUpm+gd4koB9\n+c7sp6a4LmYzBnFGdnFCVmYhaM3FyCjBZxa6llp4tG5DN09FJMkyiyliY6MHhIqIy8n5Cle9BJNA\nr74fjZjMZ0ync7KsYGNji7pu0dqwubmFMY6maUjTHO8FXWe4cWOXk5MT1scDmqZhNFzj9PSUtR5K\nutxaOufQRgcqUbUg9zK0pyMVnvIqdPxEn/yBNYi+NY2UYfviffgxCqFy1iwZ9/04XURsAXvrAAAg\nAElEQVR446EzqM5CE+ZMHo+xYAmvFwAmhMz5SCLank/in3NArvJJXigowWpbKMKhKzRSvEd62XcN\n+zyzOIa2wdgwH/IioD4tHpnG2NqRFmX4vJIMhcS1dZjfaUNbL1AmRTpwusN3HebyEifCjG5Y5ngZ\nHnqTyQzfN4CcsWFlQlCZjr2da1AOiU1Hu5ijohQpIrq6Jh9tMjk6YrwV5mnPnj2jaQKtNx8NiLLA\nvlRC0lY1sRSoWAIW0wZNZ9iwGlDJS9/Xn4riSrKcx8+OGQlJW13QtjNGwzGjcoxHrdDWkfRsrm/w\nzfvfwjpWSOKqarh9+zZnZxMilWArzd61HQaDwSoNoyxDE2KZhLLMblrOrZYs+SwtiKPALbx79y7f\n+c53OD0559XX7jAeZhweHrG2tkbTNCtlfRyHb6RSMfXikt/+7d9m91eGbGcD8qIMC4/qkHEU5EfW\ngIzBh2JUiQMVtGydvRqO7oO+ULu+aSFxXmPbDtFoXNUGL5QUyOR5iHgINPAI58IqKOKQLZwmBO2v\nRCixOnNZa9HWElm16hz65Wq+HBz3K53svW5K9AkoTmOWLm0Zsrh8CDMLrt0oDmkpvUgXpbAirNKp\nDyp+1zZIa+kWFV5rjDdop9HOo43DArN51Yf7eSKpMBhcHFMOB6RRRBcLnJVE0QhNx2CtxCwqkiji\n9PEDfGy5nF5ycnRMW4dI1p29XbwUnE8nKASnR0eMBkOu7e6FrWqaIJBEMkI4i28Nuv4zpvo/9/pU\nFFeZ5exdv8FxNedGsc7hwQlFUmKSBqlSWmEwkUC4lDyWXE4vsLFB4WlbTZ6tcXZasb6+w2Ryzt7e\nNtVixtHxQQg1u/0qWlsGgxFJUnCzzHny5AnVrMJpx6gcrbZhxliGwwF5kXJw+JSt7Q2apmI6vWRU\n7qO7QJna29vjwYMHWBuaHm+/8Q7f+vY30EnM+/fvMe0aNsoRuuvwJhSiTBLaqiLLc2gbyBKkM6FD\n0FiEcyhj8MagbIY1Sz1jWFlUHOONwbVdUKYrELEMK6KxxNYRpzEQ4meXGcRSeLx0uK5FRGngdfQd\nv9CCFyjRmymlAmuxHqRbOpwD+VcpidCOyDmkAmNbpI9wXuGlR8YKocDVc2IpMN2cOEpAC3AqbF91\njdQ11nYwHCA7S2fmAU6Ep/OG7nIRCl8KZk0DsWLeNcRpzo2NfSaX55RFRp6nfXMKElVCNgySJhdD\nZ4lEAPfMmwvQCbSS2bxhZ2eL9fV1tIeL01OGecHhs2ekcUw2zGm6iiwpodYoVeC9xrQVzrZ0bfPS\n9/WnoriSJOGV23e49+EPqRpNJDxd26KTGC8MprNhRuPBI7j/8AEGS5qmdK3h1s1d1tc2sVaTZRnP\nnj3DGc1wOFyFRSsVrxiDzjmGwyGbm5s8e/aM2Wy2YiSO1jbDOSsSjMdjkiTi2bNnrK2tIaVkZ2eH\n73//+6v8sOvXr3NxMQkyrThmrl1Pbp3RjTaCUUR5pBUreVC3CJ6yuInpTEdifADF6HCmiKUEJDII\nDUPCh9aB4OQcwgSnrEpiPBbdGVQctmoSj1s2JJREqVBo4BAynM/csstqnruIJQLXdmGV8T4UsbiS\nhknIa8YalLU43WKalkSFLbxKFSKKsFWYUdG1GDRRJEEm0DS4rsXS4WxA2+n5HOFCksqimgWXtYpY\ndA2xj5kvKrJBSacdIlIrx3gcpcTx86DEttW9HUBgplM8lnrREcWC04tTWquxVjCdTrl9+zZ5lmA8\nXJ6e0jYVT+8/JEsS9l9/PexwIhUAq0IgrKapKqaTE5y3YR76ktenorhwDuXD/0mkkA7m0xkxkihK\n8D0vHiU5PD7h0dER2nva+ZzhYJO2bVnfGDOfzzk9nbC+vs7k4gJjghZwd3eX8Xidi4ugZVtbW2M6\nneL9c3T12toaRVHQ6nDmGq8NmUwmvPLKbfb391edyaX+bikEBlhfX8eZELsqZGCQP3jwgC+99lYY\n7kpw2tJoTbekBCcxttWItoPM4uomrFxKhWQQXWN4ntXsnMN0dvX1QgmEi7Fokiynqhpcrw6P4yic\n2aQA4RDWBm5HJDDarpT93rgVck4KEQ7rtleGWLvye4FAeh0SxHWHns2J8ETWIbOYmNDM8E5jte2H\n0CAU6LoGsyB2Cu803gedZ5LmiEgiRczZ4Xk45xY5TX/GNMB4c4tWdzSzOeP1NdZG60ghKQYl6+tj\nOl0xrxZY68mdxU/POTs9ZTwo0d4wnTY8PT7DJxFVveCVV16h6zpmkylKeOr5jHq+YGNtjTTPQ1Jl\nHNG1LUkWxN1dWzOfz5gtqgDU+UlTxYNnczygfPstpufndE1NNZszHgzDlkUleG+ZLubM6prL2ZyO\noHa3xrCoLpnOzsizku2dTdbXx/zh00N2bu+ytPLX9YInT57wpS99iR/96Efs7Ozw9OnT8B49ILRt\nW15/42329/d4/OQhSZJwcHDAYDDg448/pp4vuHPnDnfv3uX3f//32d/f5+IioKvruqUcDZmfp3TT\nC7q65uDkiL1r10jyAWojJ7cW34TkFKIYV82hUhAp6rbBGENZlkGxYAyqh7hIIYijqAd/OoT3KBlj\nuxbrwgC+2NnqB7kGcLgm2De8gKSHorou2EKUUuAsCvBarxQqzoVhtJQS1bMGTa8AiaUFEYavqtWo\nSEGnwznEB3e2F5K2aygHI+JBjmsvQyfOORbVDOcM2hpEIomSmGpRcXx0QFPVgei78JSjEYu2C1nG\nnaaa1Wxv7rA+3sBbS1qGs/FkMuHDj95nbW2NmzdvY4yhml5iujmtVbTW8sGDe9TacPPWHYYbikdP\nHoNx+DaMRnbX17nz2ptERS+HEhLdGdrO8OTwiMlkwmLeIoRkfVyQFzFFXrz0Xf2pKC7Z8+vW19dw\nXYNOY06PT9DOoqxDKYFUEcZBFAXctek0FZ4kCXtvYzqELMiyjOl0ytZWKJ5lGsnZ2Rnee54+e8i1\na9dWBWaMYWNjg7qu2dra4vz8HO8dN2/epG1bokj2ZyvLYDBAKUVVVWxsbGCtDYD/xZzbr9/h23/8\nR1jdkUUKZy0Hh09ZLBZs7+6zu7sHaRoiVJ0DbZjVFUUW0bYLprZFRYo8j+m8xliNMBbVxxLpuiXO\nUkQUTIhG2GCP8uHAbxYzoihBek9dzQKxKEpWqY+hIGVYLa4IYCMCT75rF2Rphm7a0BXst4NxktC1\n9bIh30NufCgsrem8QyVpiFldVKsA+FZ3pNkAuhpjauJYMa8aNAZTe5yomFxccDmd0MyndFYzHI2h\nbjHOM1u0JCoiz0uGw3EYsDsX0j6d4dGjBwglGY5HOO+p64qT40OyPOF8csnx+QVnk0vu3HmVi4sJ\nTVOhJEReIJDsX99jrQ8xtD5gI6qqYjqbsVgsePjkMUmWsrF+jUFRoiIXpF7+J00VL8BajSoyrl3f\n5/zsjLRa0DhHIoO6TZvQOOD8HIkkVkkvCG2oqjknp4cIIdjd3Wc6NXgv+niZBXBO3YQghqdPn3Ig\nTlbqi+3tbb7xjW8QRRH379/nyz/z8xweHnDv3j3u3LmDUoLJZBKSB1XE7u4u3/3ud7l79+7KwKcu\nAkkqyxOOn855dXcb5wyT6TSgx84UXVszGAwosnBuOD0+Cgjs3W0ePnmElDIER0QyhAdoQ2SeRx8B\nNHX1fMAci5DHJRRlWeKajs63AaFGwFdbY0jSFC8VMhKgRM+p6WdnVmM6jUKQqgjfNMQiFNuS2djO\nZlitiWSE9za8Xni8Dd1DIxXpYIRtqufiaK+RcZ+L3Fm8UlxenjOvFkRZTpwXTBYLDo6OmE0u8LYm\nimNmizntdEqeDUnTnLRIKIsB3kPTdNSzmkkzYzK/5HJyzs2be3ghmMxnTC4rZouK1ltOpkdM5guy\nIufBo4esD9dZKwYIIdheX2M8HOGsDtwOEcICz07OeXYYGmBeCjY2t4MvzAUPX9NMMaYh+TFY8Z+K\n4lJKolJBa1vSOCEfDxn2BCcbqx5yKQJnLsm5sX+dDz/6PkKEgeLxySFaW44Oj/s84yFbm9vs7l3j\nd37nn6OU6NMl216rF60SBo+Pj1eSmjt37qwSTawNNpauC9Kos7MzPvPmW7z//vtsbGxw+/ZtPvjg\nAyaTCaenp3x470Ok7lA4Xn3lDj/75a9w80bYNs7nc6rFJWdHYSXz/ZlnkBdcnBxTFAVlmdNMZjw+\nPV+xPIiCYDhJkpVaYkURFjDIC1KV0LURs/NLqnmFtpp4LSPPS8rhGKtUUOA7EaAvPrTfnQmmxXgp\nDLYWb8IQO0C4w9xJWhtWwM7hnSCNM7TtMBKiJKbcuIapNTItKNIcpENFjs40LKqOrtFUizlPnj1D\nRRHzs3PqzmK1JpESlcTsbG0zGg1ARog4oUwGlFmBtcuMNU+jF3z08CGPnj2gLDN2rm0BhNVmOufy\nYkGSpZyfXnJZz1nbWGNrYzPIzlrD9Z3r/REjfP4nJ2dYqzk7O+VickmaZ9y6dYs4TVnUVb8jEDjv\nSJOYlAw6T/uT1i1ESJJe2CnilDLKiJOc6XRKRIRzhlRFVPUE39UkOMZFwfFiEm44obB4mnrB+cUJ\n68MBHsOTJw/Y2tpgvpgyn8/Z3d0NwdJrQ7z0HBwfobXm2s5eaHZ4ycXpGc5bXn/lVR4/fszBk6dB\nl+gtR8fPOL84W0mi8jxHm4aHj+4xX1ywLiTXhgM++5m3yNdGJPmIDZ+xNrSsbW/QmRZtDHmWcHp4\ngG0Mg3LMvG4w1lOkBdt7+7TG0tYz2vkZZyentIuWNI6xNvA8mtri4xhtapLIoOqWG9f34fycKE6p\nvKOxmtnlJZEUZAiGaUiEsflzolWaJ/gkwdrAexSRIpIyEJmsD6oO57HzhnbRUAwGkCQ4I4nTDOMs\notYhqtbRh+h52qbGOcPlIow6Ds8Omc1D80e3QUF/Y3sbvKXMY6T0FHFCkuQIFTPeu4XWjsR7dF1x\nfnnC/Qcf8+DJx6yXQ165/QpIqCvDwckhl9MJ2WDA2Uzz0Ucf8/Ybb1LmG5wfz7Ct5vVXX+Wymvcr\nUEPTNFxcXNDWDa/cvskrb75FVVWsbYyDmiTLaHrtatu2dLqmSBOytAiOg5e8Ph3FhSBNeolSz7rI\n4hiUxNQWbzuU1ciyZM1arm1t0n78gwCRSROquuViOmFztEFXN0ynExZ1HTqAZcZkesH6+jpCCPb2\n9kBGpGnK0dHJKoz87CwMB8/PzxmNh8znc4bD4cqpvLOzw8nJIWVZsL29jdaa8/Nzjk8OqOsFocFX\n88abX2BzfQOlYrrO0NQtggAHlSoi7bnwa+UQOYxQMmG4uQVRFvbHSU7uIR+NYGvItdt3lg7JcFZL\n034706uzoyikPbYtO6MR3js2imHPMbPotmZ6eIRDEClF5BRxFIUGh7fUi/AkVirCiaB8iaSCyOHa\nDqsN1nmK3WtBcW8caT7ECUEcC5QXeGuCakOB1h2z2bxfFc45OztDd01QdOiOrfUNxqMha4NheGhm\ngewUjKtBAaJbQ2sMzlqODo94+Og+T548IR+OKDc3aaRCJTEfPvioh6Bu8t37D5jNK27dvs2DixM+\nevqYIs/Z377G48sz4l5AMJlMyNKUa9eu8epO+JzSMscKSdMZqqYDHE3b9EZaj5RQtQ1Uz82eL3N9\nKopruVdPi4Kuz9mSUpJmGZEwOKcQLkVpR641n3ntVfI/+Jd0CLq6IZIRUgY+/HweEgabtuXDDz9k\nf3+f8/Nzuq5ja2sL7z15OeTi4oL9/RscHh6SxIHKe3JyQt4fcp0L86qbN2/y4MEDJpPJSskxmVxw\ncbHG+cUp0+klUkEZKba2NvnpL73H/v4NZrMZvtY4bcJ8xnvqeYV3htmixjYhxyvJC1SzQMUpVgiS\nbIB1y5wwjYhUSLePI0wECoPMItDPlfCuapCDEm0DadibDhHFECfE5YDNta1gW29a2sll8FcJQRql\nFKO85x8CxmKbhkXVIHxQzadlQTyIWEwrIIiXnQcZp0FNYTqECGDWyfkFSsHl5SUffPA+QoZAwDIv\nGBYlWRqzvbHJoCyQIu63aAYRWXRncYFCivCWs9MjTs5O+c4Pf8jB8RGbm1uU4w0OnOODJw/56P49\n3v/wfe49fEBjNIPRmKoLofWDougd0p4kijB1i9CGne1t9nZCIPntm7fI7mWURcFGWoYmVD8SypKY\nYVmQRIFurOIodFOVo6p+wraF3gfyUtavRKL3PXnviUmxXoVZTZkjJpfkShFbHeYwLnQT86Kgnlak\noxFPnz4lL8tV6qP3njzPg+i2zzjuuo5nz56tZleDwYDHj59w6/oNFosFN29eZzAYUFUVe3t7PHn6\nCIRjPp+ysR4aFtPpNGw16hm5cKxt7PDaq6/yxhtvkJYFyjgujo6Is4SmXuC1QfW2hzSKiVSE0w1t\n7UgyQxylaD8NkpskWdFooz4QEG+x0iCcQ6i89zQIZJaFh1IfKOFNg3CWOMkQRZgPogL6Lcm2MG2H\nNYaq1dj5InysDWuDIVGSUK6VkOeh4JqGdr6g3NgMZzPvoWlxjcZ2Gm1qFtWM2WxCVc0xVvPxxx+j\nlOD2q7fw3rM+GoZzYw9tjfMcbwRkGUK3tF2FUBFCKapFwwcf/CkPnz7kYlFxcHHOaGuThXdMj475\n3//JP+b49IS8LCFWZMM16sklBkGS5SglyIqCtp9B1lojE4W2mpk3PP7B97DWMh4Msdqwt7fHz3zu\nXbbG64yKnDIvkEnMZLEIq7EM7Mosy4hVsDS97PWpKC4lVS+WTUMyiFKBhkSvBvcq5FcRZlabo5JB\nHHGBIIsTkrzAmufhd0jBtWvXePToEdvb26t2/HA4fCEf96tf/Tm+853vrIIXlr6t8XjM2dkZGxsb\nPHx4f4W8TtOYpjZo0/Ktb/8pWnekmQJnyJKI69vbvP766yRxYB56vWBtPAzD26ZD101QhEcJaTHo\nA8ktVd3S1hWd6hiNRjhnsFWHNC7MtwgiX4WnswYRp/i4De7oKKCqpQWcxWtLrARt22DbjlR3yNEY\npEemEUQJcZ4SW9sXr0ZrjbAOt+ioFovQ9JnPQkHLEN/jTUgm6eoFGMt8OglpnhHMFlO6rqGua54+\nfcpwOOTOnTvEsSKNE1QkQlHJQDdGgJMKZcPcy0nZJzxaZrMFH378I5wSHF1c8N2PP+Loj/6Q88sJ\nTnssZpUgKa1CO4vw8N7nv8AXv/Qua2ujFdsfwip6enzCvKk4PT1l/v771JMpC6eJYsXD4wMO/+9D\nrl+7xr/x5Z9hy1tmiznb4zFJJFFCMV9UOBs6t3GavfR9/akoLqkka+Mxs/mc4fZmT6ANZkBMh5EC\nlURY7xkOS7bX19keljy5rEmimHpRkRblqm1t2o75fM7Ozg6bm0HOdHh4SNM03Lx5k2fPnrG/v8+D\nBw/Y29vjB+//kK2tLZRSZFlGXmQcHFwCMOvnHsYY5ovJas4mpexhoS1CQK4kb73+GmmSk6YpTddx\neXTIzmiMMwLbNuHJnedESUqUpQA47SmKMJ8LwQHdqjMY9HgetMF2LmRwSYH0HaatqI0nyvKgAI/C\nDC1xhB9NS9MsmF9OSasF6do4rHRJ1otSU0RkAxfEhnAEEoOsKkynSZMk2FlUIFsxb3FaY9uGajGn\nrRbgPW3dcH52wsnJCUVR8Nl33mFrays4xbuOTjd0bcOwKCmKjPPzc4oyR4tA0zo+eYaKArZuNp3T\n1IbR9g4Pnx2w99pr/E//8H+FSJFGMbrucEBRFOxc2+HNN++yubPNrTu32VjbJEmC4r8zehXAsba2\nxttvv41XEfNqwd/uQuJoPV9wenLC97//fep6wcdPn3H0T/9PPvfm23zuzTdJkog4iiiSOMTeSrGK\n433Z61NRXN55UIrhjetMDg8QSUSSZ8RJguqhLnUbwttSayljxU997rM8+t7HHJ+dYqWka1u8CzOp\n9dGY6XTKzZs3cc5RVRXXr19nPA4dybIsWSwWpGnOeDzGe8/5+TnGWE5OTuh0y61bNzDGsL+/z/Xr\n1/nn/+K3cc5gjOTJk8d9GmWLRzMeDXjvrbv84l/7a6zv7uJMh1IRw7JE6xaHJU9zorwIFGEp6RSA\nJIkywEPbBt6FDL4hYwy+7BXq3iO9IlFhRNDMZxTa0hoLdUuHQCU5xnlUlKCi8JQtfQTKUZ8dcfLk\nPgZBlI4oBmHLbCWo/oax3pFkKVGuiLJg26dt8Y1BeImrDOCJjKNIFKZxXE4uODk9JUkSvvj5z7O7\nu4cQgvPzSxbTBVkakccpRZYSq4jLy0vatuXZwVMeHZ8wWcxxXnN+cRxmfiIhy3Jcssbv/tE3+Fff\n+AOynTHr62PyOOKVvX1ee+dt9m/fpBwOVjTkLMuw1mIc5HkBXYfWOmQYK7BeI1tDJMPnlsUJxY2b\nmFdf46ff+yl8kdB2HT/89rf59h9+g//lH/8Gf+trv8A7b7yFkBFlWdBpTdPMQrPnJa9PRXEJQTjY\nNhXj3W26xQLbtOi6BR9A/cMoZSoFLQ6VZdwclHx1Z51/PTnmxBhEPGJycUk5HDBvZ4zSEbptudSa\n89NTrm1vk8YxdVNzdnHJe++9x8HBEaenp3gsUsY41yFFIMOen5/iBBwdH5CUCTJVtB6UcNSTS0bD\nEu9asBVxXvI3v/ZVPvPeuzBeRzpPKgTp+gaYFtt1tFVN3TYUIkZ4Q2JE6OhJBVKiogxsCIJwUiLz\nGClUgM7ICKFSyEuUc2SjFmYXpITABtqWdjHHti3CzkmNwipI8pTOtOTjnDTPqLWhaSzTkxNM3dLO\n5sRKEQsVdK+DgEJQUUyelygZ44QkURG+1Uznc07Ojzk6P+w9dYKt0Trbmzts7uzQtQ0Xk0s607F1\nbYd0WCKTlHY2o2tqWu15+PSQ09NjvHJksWQy7djev0s8KGk03Hv0iP/jn/wGx+dnyGHKr/3ar7Gx\nscFgUFCmGTKJiZI+HirtZ3/GIWVMFCuaqkVbs+o4R1GEd544CV3oyWRCkeVYAR2OqmuIohBG/9kv\nfJEvvPslvvftb/Fb/+g3+Pj4iF/+ua8hYsd4MES3HclfQSbyX+nle9oQUqPwJFEMcRaIpz64YW3d\nopRi3rUB8GI9O4nile0tzp8dUtcNURQO9HVruLZ2DSEEx8fHq2jXk5MTjLPEacZ8Psday8FBQGAb\nbVZ8eSlhOCrxQnB+EXHv3r2eMb8AJNa3SGdJAdtp/s4v/iLvvPMODAqIIlzAJCFFkB+pckhRtmB0\nQIYZgbAdaEGc9Fu/3jqvpMQai20Dl17IEDIulQLtIEqQRQyuxVqJihOiPCcaFuE9vcDNO5qmYlFX\nCAl23pKkEaXKyFNP4wVOJvgiBLmtIpQiFXxdKqGpO6yt8T6cl2SI7WJvb4+d3W062zGZTRinAaEw\nvbxkVs2IsoSN7S3y8SjsarF0RvPNb/4pSgRWxvrWNvcefIT1jvHmFg/Pzvln/+i3uPfkKZO6xsWW\nzna8++67fPGLX2Q6nQbicZbhsFd4/vnq586F0LxlTBT0yG8PzjpqE1JAkyShampS70jShFL1qv80\nQ/gwlL71yqv821//Or/1m7/J7OScX/prv8Bn7t5lNBxjdPv/tHemMZJdVx3/nbfXq6Wre7pn3OOZ\n8awZb7GJd+MIPBGgxEEYKYmTfMABRYoEX4BPcYSExLfAByQiIWwLIiVgwDFgxYqwrbFjiBDECyZe\nguOZweOZ8Sw9nu6qrvWt9/Lh3io3kZe2kkl1m/pLpbrv3ldVp1+/8+65557zP+u+rzeEcglCZ6VF\nKVBr1I3Hy9KeUZQ4YlPec+Ohcl2f7dt3kHa7bMtS9orL8U5GogtyVTA/v5U0TVlaWkJrTbvdHiuT\nF/gkecazzz1Dc2YW3w85deoU+/btY35hjoVmk1arxdLSEm+uXGB1dRXXN9m3fipoMSZX1mnR8Fx+\n//N3c+MtN7Ntz+UQVcmyAjeM0ZSIEyBiZiOpGEaiKKphYvNSyAvUcGhSarIcB8EXU+BbK4c0KTA+\nGod8kBDWYsgwBdh8jLs98hA/slxpBaVWlAuaCKGSFug0p2i1yNIML9W4aUYcxujIIxUHt1LBDQOj\nvOKY79YKAs9kSxelMWX7A8rcUKWlaUmy2qMRxgyKgpMr52k0GjQv3Ua1Xrf5ZQVZ0uf8uSXOnDiF\nKo2Z1uqsklzImNu5m7Q0JK/feOg+VjptWoMBUTWmWq/zqU98imuvvdZUc7RKYZxK4ZhJ2dCr+WNH\n1NvRe4+Ur1qtkltCn9H6dlQPe64+g8pLwiginp2jaCpqszPs3Lub00de44HvPMxtH7mB2268ifpm\nC9wFmF28xDzZezadwfcMHZgfgNJ4rken38NzXUrPZ35hkdbZN/DbbbM+KUpUUZoi0/0++OafsrKy\nYoqP64KsyMyTTxx832dpaYlarUGz2SDLMvbu3U3aG7DSusBya5nuoA+WhHp+fp6k1SEpU7I8pVmv\nsG9xkT2X7uTAVR9GVxug3qoZppVQiJjAWKc09GoOKBxcxwXXRXkFThgSuK5J90gz1CAxCZa5ptCK\nIi3xXIVWBflqG8cRwxaQKZyoAZkiT4d4UWgydSkpdEEJOL6H6/sE1QpYIpfOyVM4uqRaqRH5EWA4\nEctC4VY8o0hFZq2G0mQP65zSVu1UStFptXFx6XR6zO3azsL2S3A8D5UmNlBYMxgOOP76EU6dOMmW\n2VniWoVTp8/SmGuy0KgxdALIC777xGH6RcpqMmBmSx0FfPazn+Gyy/ZQrVbHD9osy8ZOihGnRmlL\nO432RUclfEd5e8C4f1Sat9vtEgQBW7ZsGStmkeVmg9x+X7vbYXbrPNnSOQ5ccxXbd+7gm/feh1KK\nm6+/ft339IZQLqVKVLuF43lEYWj+QY7hbVCZJs8S8qyD55pp3vdDvChmdmEb8coq2/2QN/PziGuI\nI3EgVzn9Th/Hd+gOTDJkUDGevqQ/pCxz5ue3MhyYio79fpfl5WX6q21by8vF9yxfZ3IAAAtCSURB\nVF2yrGQ47DOMQvIixfc0SbfPzl0H+KWP3c6Hrr0aoopJRMxLcEHKzBDD6MJsLWhtbr6yNAmBIojv\noT2fXAq8EV1aJcSJI8LSKDQ5kGboIkWrApUNyfIMyc2MVPFnwAnwiwISBZ5G8pSwGplrWOZox6Xw\nHcrApMg3gj0M+wPanT6VrCQKY/Aj3KIg7bVNzKGAKyYxUBUZruPSXl5BPJder0eOoTjbu2sX4SWz\nZtZ0XRwnMkQ8rsO5M2dZubDM7FyTJM1Y7fbYc/AgrX6fwvE5evoN/ubvH6TX67C82qbRnOGqq6/k\npltv5tLtu4jj2njWGpV7AsZ0eqPZa6REZVmOkz9HNHojd/xwOKTimL3N0BbhWF5eJggC0jTlkrl5\n8jRDHIdSKbMtg1CrVvHCCo2tAXfedRf/8dT3OL+yydL80ZpsmBBYz5XG5OWVgkkR1wrPtZx4CMrx\n8KIqfq3BsVNvcEZBq7MKpSKMQ7I8BdeEU3meN15TlGVpi4jnYzPDDzy63S5pmtLrzRnOcGUUShxB\nKcP41GmvEAPNuEojjPj4zbew65LtzO3YaePtFI4ruFqDo8zmr+cg2iQJQmRp0xTi+ijL2wcuuU0H\nQWsk9I05rB0IBCIfUTGiSxzXwctTU+9rmNDtD8k6fZRycJUi9Fxc0UTDCPwA4hDxwA08lGczdSvg\nusJsXKW40GbQukCIAy6EzRh8IU8Sw+9hyyrpUlHkKeeXlgkrMbv37yOo1swD0PfRurB8HTmiFOdO\nnuTka8dxI4fVTpc0zTlw5Yc5dvIEuePxxBNP8vDhR00so+fxa79+J/v27+fSHYv4YYiIPy4Wv7Yy\nKGBJUd0x1+TaGtae446VajQ2+txodhvNYCO+xyAIGCbJuORvGIbGYshyItcnzTNqcZX9l19BNY45\n/Nij676t16VcIvI60AWTOqS1vkFE5oAHgd3A68BdWuuWGIP3z4A7gAHwm1rr59/t+x3HwXNN7JlW\nGuUI2nfxQh/PjaBIyYYZrk18LJMCx6uw1M85duYcr/X66EpMxfcI/NIQPWoxfA6UJhEvGxo2KG0u\ndKUS0u2uEsc1RuVei6Jg2O/TbretKzfH9QTP98FR7J+pc+i6G9npV1iI57hm1wEy7SIFOCoxtNGm\njg4UBVKNyXtdvNiDpEAryJQmjKvkuVk/ONq1bE//lyFKK4XyXUOXZk2hoshQroMbxXgzBVUlVHMx\nrL5ZBmUOyYAzx4/SSxJm5xeoN2eJZpqErvUs+j64UKR9vLkYbz6Gfofe8gXUiTPkZQE45BqUFkQM\nT2CRZVxx8CDewpzduHboDvpUVGlIQRWoouT1o8dYPnOWhZlZvv/y84T1OnFc5d+f+0+cuMbj//o9\nnn3hBfrZgEZc54qrDnLo0CET+VC6qFTAL8dKY9KKzLopSRLCSmDJe2S8hhplDqiiHGcOjPgrR0o6\n4hxcO1YUhVlCzDTAEwaDHn6aUAlCXA21ICLwTDpR1feoNmf5zN2/wWNPPf3TUy6LQ1rrC2uO7wGe\n1Fp/VUTuscdfBj4BHLCvm4G/sO/vDMfBqzUYceS5I1pnrSHPKQqF4/mmrm9W4oaaYTaglWUMrQu5\nl6QMeh3KvEpUrZAmheFCEAiCCM9xyYcZ+D6+HzIYJJa5yawjkiRh6bxhZe2lfUpdmMBgV6DIqYhP\nmsC5QUpzcYHdl+wwxCz9PtmgRz2Kjfw2Y9h1XfDMolnwQFycosBVCqdUOEWGdg2ltTGBTVE4PeJu\n8BzDmSGODYzNCRBUqSzLk/kdQxRT4kQhFAKhx/Yrr6GzsoJKMo6/coSF2Sbz8/NQrcBMzV7v2DLR\nAo2I0Itx6j1UlpKs9ugvt8kLTao1bljh4A3XQ60KZYbdfCMOA9y8oCxM+NSZM2dorS5Tma3x6qs/\nYuu2eYZ5Qa8okNk57vv6X9JLhvSyNiLCwQOXc/svfoxSmYgNzzdlljzxyMvU0ErnJnM7CEx2hC4d\nfLcCygHlkOQZyaCPKgrDceF59Hp9PM9hOBwgYtZnw6Q3rrVmdtMdut1V/MBUpymzHM9y7yd5Qq5K\nFMqUjfUckjInqlfHM+F68JOYhXcCt9v2N4B/wSjXncA3tZHi+yLSFJFFrfXZd/4qMU9VEVRm7GRZ\nU6oGwHF9SpWjxSw6h+mAF1/5oXmyFwV5luGJ4X5vbplD5ymlQKlMedNSgycOvutRIOPyQd1ud1zp\nJIoiOq22WSSHJjnQ0d64iuPJ82/ywkP/wCcP3U69VqNardJOe9Rnmzih2QoIQsMKqwNBleW4WLdT\nFGRJaoqJW3NFRgG6NsxoVPxuVI4HS0s9jjof1Y1yHBD/LU5BewlLMTeNW/FpbN1K2Vol3r2HPOlz\n+sTrSODSWFxEooCwPoMXRTZo18XHQVPi+r4h27GRH+JHeHENag1UkaIdQbThkadU4GiKJOXs2bOs\n2PXIkaNH8UKfwHMRP2Dnjsv42l8/wHMvvcSHLt9PWab86h2f5Nbbfp75hQUGg4QgCKjGVbIiN4xU\ndi01cq0XRYHnu3S73XEFGmUfYv1+n0qlgmPPq8TRuL7aSLl0Vo5nrSwzdApB4DAYdsFe3xHhqWP5\nR8pSjZm3PM/Dt17G9ULWo4kichxoYfbt79Na3y8iba11044L0NJaN0XkO8BXtdb/ZseeBL6stX7u\nx77zS8CX7OHVwMvrlnrymAcuvOdZGwObSVbYHPJeprVeeK+T1jtzfVRrfVpEtgKHReRHawe11lpE\n1j9fms/cD9wPICLPaa1veD+fnyQ2k7ybSVbYfPK+G9ZFFKW1Pm3fzwMPAzcBSyKyCGDfz9vTTwM7\n13x8h+2bYor/V3hP5RKRqojUR23gVzAm3CPAF+xpXwC+bduPAHeLwS3A6ruvt6aY4oOJ9ZiF24CH\nbUiJB/yt1voxEXkW+JaIfBE4Adxlz/9njBv+GMYV/1vr+I3736/gE8ZmknczyQqbT953xLocGlNM\nMcX7x4YofjfFFB9ETFy5ROTjIvKqiByzm9GTlufrInJeRF5e0zcnIodF5Kh9n7X9IiJfs7K/KCLX\nTUDenSLylIj8t4j8UER+d6PKLCKRiDwjIi9YWf/I9u8RkaetTA+KSGD7Q3t8zI7v/lnJ+lPBKP5q\nEi8M9+T/AHuBAHgBuHLCMv0CcB3w8pq+PwHuse17gD+27TuARzGhJbcAT09A3kXgOtuuA0eAKzei\nzPY3a7btA09bGb4FfM723wv8tm3/DnCvbX8OeHCS98b7/nsn+uNwK/D4muOvAF+Z+EUx8ZJrletV\nYNG2F4FXbfs+4PNvd94EZf828MsbXWYgBp7HhMZdALwfvyeAx4Fbbduz58mk74/1viZtFl4KnFpz\n/Ibt22jYpt/aTjiH8aDCBpPfmk0fwcwIG1JmEXFF5AeYfdHDGMulrbUu3kaesax2fBXY8rOS9SfF\npJVr00Gbx+iGc7GKSA34R+D3tNadtWMbSWatdam1/jlMcMFNwOUTFumiYdLKtVmiOTZ0NIqI+BjF\nekBr/U+2e0PLrLVuA09hzMCmyJiEfa08Y1nt+Ayw/mzFCWPSyvUscMB6iwLMovWRCcv0dtiw0Sg2\naPqvgFe01n+6ZmjDySwiCyIyCvauYNaGr2CU7NPvIOvob/g08F07C28OTHrRh/FeHcHY3n+wAeT5\nO+AsJsn+DeCLGDv/SeAo8AQwZ88V4M+t7C8BN0xA3o9iTL4XgR/Y1x0bUWbgGuC/rKwvA39o+/cC\nz2Cieh4CQtsf2eNjdnzvpO+P9/OaRmhMMcVFwqTNwimm+MBiqlxTTHGRMFWuKaa4SJgq1xRTXCRM\nlWuKKS4Spso1xRQXCVPlmmKKi4Spck0xxUXC/wK38gRqCNcelwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KzHFC3B3RtWS5X4d9OxxqE7qnWMhd5csvn3E8TgzOrzc7l4RpzifnQilNetTK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k2fBK1aq5UQRdVNZob3JmThM2tOy2ir16a3AUpMolaxYFjQskV/AEUszs+mn/vYvR4t34oGkB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8PuQZU/gUeEoXrAisFkBkyh5YGzApGCznuapuN8VYcdMcehUtXdu3dZnp9yfn6qcaFa2IJ8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\nV3RFD4iuGOoVXdEVXdEDov8P5ZqhgmRCQ04AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cF-s4fRzv6eZ",
        "colab_type": "text"
      },
      "source": [
        "একটা জিনিস বোঝা গেল যে, আমাদের এখানে যে দুটো ছবি দেখছি, সেগুলোর সাইজ একরকম নয়। আমাদের নতুন মডেলে ডাটা ফিড করানোর আগে ইমেজ পিক্সেল ভ্যালুগুলোকে আমরা এক রকম করে নিয়ে আসবো। তার পাশাপাশি আগের মত ছবির পিক্সেল ভ্যালুগুলোকে নর্মালাইজ করে নেব যাতে এটা ০ থেকে ১ এর মধ্যে থাকে। আমাদের এই কাজে ইমেজগুলোকে ১০০, ১০০ পিক্সেল সাইজ করে নেব tf.image.resize থেকে। ছবির সাইজ ছোট করে ফেলার কারণে অ্যাকুরেসি কমলেও ট্রেনিং এর স্পিড বাড়বে। এখানে tf.cast(image, tf.float32)ব্যবহার করছি তার দরকারি ডাটা টাইপে কনভার্ট করে সেটাকে নর্মালাইজ করব ২৫৫ দিয়ে ভাগ দিয়ে। আমাদের দরকার মতো ডাটা সেটকে ‘শাফল’ করে নেই যাতে ঠিকমতো ব্যাচ হিসেবে ট্রেইন করানো যায়। আমাদের এখানে ব্যাচ সাইজ হবে ৬৪ করে। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgW5Klkk_2Fo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "IMAGE_SIZE = 100\n",
        "def pre_process_image(image, label):\n",
        "  image = tf.cast(image, tf.float32)\n",
        "  image = image / 255.0\n",
        "  image = tf.image.resize(image, (IMAGE_SIZE, IMAGE_SIZE))\n",
        "  return image, label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4xKlcei_4hZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "TRAIN_BATCH_SIZE = 64\n",
        "cat_train = cat_train.map(pre_process_image).shuffle(1000).repeat().batch(TRAIN_BATCH_SIZE)\n",
        "cat_valid = cat_valid.map(pre_process_image).repeat().batch(1000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60QmXJb3xYT4",
        "colab_type": "text"
      },
      "source": [
        "আমরা যেহেতু টেন্সর-ফ্লো ২.x এর জন্য ‘কেরাস’ এপিআই ব্যবহার করছি, সে কারণে আমরা একটা নতুন কনসেপ্ট দেখাতে চাই এখানে। জিনিসটাকে ‘কনভলিউশন নিউরাল নেটওয়ার্কে’ ব্যবহার করতে দেখেছি অনেক জায়গায়। একই জিনিস, লেখার ধারণা কিছুটা ভিন্ন। আমরা এটাকে বলছি একটা “হেড মডেল” যার মধ্যে স্ট্যান্ডার্ড কনভলিউশন, ম্যাক্স পুলিং অপারেশন চলবে, শেষে আমরা বলছি কনভলিউশন এবং ম্যাক্স পুলিং এর পাশাপাশি ‘নরমালাইজেশন’ এবং ‘অ্যাক্টিভেশন’ ফাংশন হিসেবে রেল্যু থাকবে। দেখুন, এটা কিন্তু আমাদের মডেলের শুরুর অংশ, যাকে হেড বলেছি।\n",
        "\n",
        "আমরা এখানে ব্যবহার করছি সিকুয়েন্সিয়াল মডেল, আগের মতো।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ElBQi7KzABvq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "head = tf.keras.Sequential()\n",
        "head.add(layers.Conv2D(32, (3, 3), input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)))\n",
        "head.add(layers.BatchNormalization())\n",
        "head.add(layers.Activation('relu'))\n",
        "head.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "head.add(layers.Conv2D(32, (3, 3)))\n",
        "head.add(layers.BatchNormalization())\n",
        "head.add(layers.Activation('relu'))\n",
        "head.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "head.add(layers.Conv2D(64, (3, 3)))\n",
        "head.add(layers.BatchNormalization())\n",
        "head.add(layers.Activation('relu'))\n",
        "head.add(layers.MaxPooling2D(pool_size=(2, 2)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fl0H2hP03SAK",
        "colab_type": "text"
      },
      "source": [
        "## ‘স্ট্যান্ডার্ড ফুললি কানেক্টেড ক্লাসিফায়ার’ যোগ করছি শেষে\n",
        "\n",
        "এখন আমাদের নেটওয়ার্কের শেষ অংশ মানে ‘ব্যাক এন্ড’ যোগ করতে হবে যাতে ঠিকমতো ক্লাসিফিকেশন করতে পারি। কি দরকার শুরুতে? ফ্ল্যাটেন, আর শেষে ১ নিউরন মানে হয় বিড়াল নাহলে কুকুর। শুরুতেই আমাদের ‘স্ট্যান্ডার্ড ফুললি কানেক্টেড ক্লাসিফায়ার’ যোগ করছি। এই মুহূর্তে আমরা ‘ড্রপআউট’ নিয়ে চিন্তা করছি না। আবারো বলছি, যেহেতু শেষ লেয়ারে মাত্র দুটো ক্লাস বিড়াল অথবা কুকুর সে কারনে আমাদের শেষ আউটপুট লেয়ার যেটা ‘ডেন্স’ অথবা ‘ফুললি কানেক্টেড’ যার মধ্যে একটা ‘সিগময়েড অ্যাক্টিভেশন’ ফাংশন থাকবে শেষ নোড এ। আমাদের কাজকে ভালো অ্যাক্যুরেসি দেবার জন্য আউটপুট ক্লাসিফিকেশনে দুটো ১০০ করে নোড যুক্ত করেছি সেই ডেন্স লেয়ারে। এখন আমাদের হেড মডেল এবং স্ট্যান্ডার্ড ক্লাসিফায়ারকে (ব্যাক এন্ড) যোগ করার পালা।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oC7voX-xAhqk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "standard_classifier = tf.keras.Sequential()\n",
        "standard_classifier.add(layers.Flatten())\n",
        "standard_classifier.add(layers.BatchNormalization())\n",
        "standard_classifier.add(layers.Dense(100))\n",
        "standard_classifier.add(layers.Activation('relu'))\n",
        "standard_classifier.add(layers.BatchNormalization())\n",
        "standard_classifier.add(layers.Dense(100))\n",
        "standard_classifier.add(layers.Activation('relu'))\n",
        "standard_classifier.add(layers.Dense(1))\n",
        "standard_classifier.add(layers.Activation('sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kq-MIk25H0EL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "standard_classifier_with_do = tf.keras.Sequential()\n",
        "standard_classifier_with_do.add(layers.Flatten())\n",
        "standard_classifier_with_do.add(layers.BatchNormalization())\n",
        "standard_classifier_with_do.add(layers.Dense(100))\n",
        "standard_classifier_with_do.add(layers.Activation('relu'))\n",
        "standard_classifier_with_do.add(layers.Dropout(0.5))\n",
        "standard_classifier_with_do.add(layers.BatchNormalization())\n",
        "standard_classifier_with_do.add(layers.Dense(100))\n",
        "standard_classifier_with_do.add(layers.Activation('relu'))\n",
        "standard_classifier_with_do.add(layers.Dense(1))\n",
        "standard_classifier_with_do.add(layers.Activation('sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ogrkida-AvHt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "average_pool = tf.keras.Sequential()\n",
        "average_pool.add(layers.AveragePooling2D())\n",
        "average_pool.add(layers.Flatten())\n",
        "average_pool.add(layers.Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VrhWvzS83e6I",
        "colab_type": "text"
      },
      "source": [
        "## টেন্সর-ফ্লো ‘কল ব্যাক’\n",
        "\n",
        "আমাদের মডেল ভালো করছে কিনা সেটা দেখার জন্য টেন্সর-ফ্লো ‘কল ব্যাক’ ফিচার অ্যাড করেছি। এখন আমাদের এই স্ট্যান্ডার্ড মডেলকে ট্রেইন করার পালা। আমাদের লসে ব্যবহার করছি ‘বাইনারি এনট্রপি’ কারণ আমাদের এখানে দুটো বাইনারি ক্লাস।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkILQOt2RUmS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "callbacks = [tf.keras.callbacks.TensorBoard(log_dir='log/{}'.format(dt.datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")))]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IK_QshcQORci",
        "colab_type": "text"
      },
      "source": [
        "#### হেড এবং ব্যাকএড যোগ করছি\n",
        "\n",
        "এখন আমরা গ্লোবাল অ্যাভারেজ পুলিং এর অংশ যোগ করি। এরপরে আমরা বলছি পুল মডেলটা কি? দেখুন এখানে মডেল হেড এবং এভারেজ পুল যোগ করে দিয়েছি। এখন দেখি তাদের অ্যাকুরেসি রেজাল্ট কেমন? দুটোকে আমরা পাশাপাশি দেখতে পারছি। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orNV6J8JBwnc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pool_model = tf.keras.Sequential([\n",
        "    head, \n",
        "    average_pool\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rs8kbREsB2l2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pool_model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "08ordANFWv-c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "callbacks = [tf.keras.callbacks.TensorBoard(log_dir='log/{}'.format(dt.datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")))]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mtZvuhxQB5Jr",
        "colab_type": "code",
        "outputId": "5ce28da5-a9a1-48e9-b57b-56eb88be917e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        }
      },
      "source": [
        "pool_model.fit(cat_train, steps_per_epoch = 23262//TRAIN_BATCH_SIZE, epochs=7, \n",
        "               validation_data=cat_valid, validation_steps=10, callbacks=callbacks)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train for 363 steps, validate for 10 steps\n",
            "Epoch 1/7\n",
            "  1/363 [..............................] - ETA: 22:23 - loss: 0.8463 - accuracy: 0.4531WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.475070). Check your callbacks.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.475070). Check your callbacks.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "363/363 [==============================] - 110s 303ms/step - loss: 0.5331 - accuracy: 0.7295 - val_loss: 0.6724 - val_accuracy: 0.5919\n",
            "Epoch 2/7\n",
            "363/363 [==============================] - 111s 305ms/step - loss: 0.4168 - accuracy: 0.8107 - val_loss: 0.4806 - val_accuracy: 0.7724\n",
            "Epoch 3/7\n",
            "363/363 [==============================] - 111s 305ms/step - loss: 0.3683 - accuracy: 0.8376 - val_loss: 0.5664 - val_accuracy: 0.7423\n",
            "Epoch 4/7\n",
            "363/363 [==============================] - 110s 303ms/step - loss: 0.3454 - accuracy: 0.8521 - val_loss: 0.3705 - val_accuracy: 0.8421\n",
            "Epoch 5/7\n",
            "363/363 [==============================] - 113s 312ms/step - loss: 0.3116 - accuracy: 0.8653 - val_loss: 0.7058 - val_accuracy: 0.7020\n",
            "Epoch 6/7\n",
            "363/363 [==============================] - 115s 317ms/step - loss: 0.2859 - accuracy: 0.8796 - val_loss: 0.4707 - val_accuracy: 0.8017\n",
            "Epoch 7/7\n",
            "363/363 [==============================] - 112s 308ms/step - loss: 0.2697 - accuracy: 0.8855 - val_loss: 0.3756 - val_accuracy: 0.8392\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f57b9bbc2e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3BTvt6tVz429",
        "colab_type": "text"
      },
      "source": [
        "এখানে কি দেখছি? আমাদের ট্রেনিং অ্যাকুরেসি ভালো হলেও এটা ‘ওভার ফিটিং’ এর মধ্যে চলে যাচ্ছে যখন আমরা ভ্যালিডেশন ডাটাসেট ব্যবহার করছি। ভ্যালিডেশন ডাটাসেট এর অ্যাকুরেসি ৮০তে আটকে থাকলেও যেহেতু ‘লস’ বাড়ছে, যা সিগন্যাল দিচ্ছে একটা ‘ওভার ফিটিং’ এর হিসেব।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-HWApO0Covx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "standard_model = tf.keras.Sequential([\n",
        "    head, \n",
        "    standard_classifier\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zLZgAbLhMmIm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "standard_model.compile(optimizer=tf.keras.optimizers.Adam(),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A79o49VyVzqL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "callbacks = [tf.keras.callbacks.TensorBoard(log_dir='log/{}'.format(dt.datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")))]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTOAmtEu0__V",
        "colab_type": "text"
      },
      "source": [
        "আমরা দেখতে পাচ্ছি স্ট্যান্ডার্ড ‘ফুললি কানেক্টেড নেটওয়ার্ক’ থেকে গ্লোবাল অ্যাভারেজ পুলিং মডেল ভালো করছে ৭ নাম্বার ইপক থেকে। যদিও ট্রেনিং অ্যাকুরেসি ‘ফুললি কানেক্টেড মডেল’ থেকে কম তবে সেটা ‘ওভার ফিটিং’ এর জন্য। তবে সেটাকে কমিয়ে আনা হয়েছে ‘গ্যাপ’ মডেলে। এখানে একটু ড্রপ আউট নিয়ে আলাপ করি। "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jPXUfutBMpU9",
        "colab_type": "code",
        "outputId": "de99b7c4-52da-4601-db75-ee8b4e3e302c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        }
      },
      "source": [
        "standard_model.fit(cat_train, steps_per_epoch = 23262//TRAIN_BATCH_SIZE, epochs=10, validation_data=cat_valid, validation_steps=10, callbacks=callbacks)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train for 363 steps, validate for 10 steps\n",
            "Epoch 1/10\n",
            "  1/363 [..............................] - ETA: 24:23 - loss: 0.7721 - accuracy: 0.5469WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.929957). Check your callbacks.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.929957). Check your callbacks.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "363/363 [==============================] - 114s 314ms/step - loss: 0.3241 - accuracy: 0.8577 - val_loss: 0.4319 - val_accuracy: 0.8124\n",
            "Epoch 2/10\n",
            "363/363 [==============================] - 116s 320ms/step - loss: 0.1897 - accuracy: 0.9227 - val_loss: 1.1670 - val_accuracy: 0.6750\n",
            "Epoch 3/10\n",
            "363/363 [==============================] - 113s 310ms/step - loss: 0.1159 - accuracy: 0.9549 - val_loss: 0.5938 - val_accuracy: 0.8165\n",
            "Epoch 4/10\n",
            "363/363 [==============================] - 114s 315ms/step - loss: 0.0790 - accuracy: 0.9693 - val_loss: 0.5316 - val_accuracy: 0.8480\n",
            "Epoch 5/10\n",
            "363/363 [==============================] - 114s 315ms/step - loss: 0.0630 - accuracy: 0.9765 - val_loss: 0.6797 - val_accuracy: 0.8180\n",
            "Epoch 6/10\n",
            "363/363 [==============================] - 113s 312ms/step - loss: 0.0430 - accuracy: 0.9844 - val_loss: 0.5791 - val_accuracy: 0.8404\n",
            "Epoch 7/10\n",
            "363/363 [==============================] - 112s 309ms/step - loss: 0.0436 - accuracy: 0.9839 - val_loss: 0.6394 - val_accuracy: 0.8444\n",
            "Epoch 8/10\n",
            "363/363 [==============================] - 112s 307ms/step - loss: 0.0322 - accuracy: 0.9889 - val_loss: 0.6894 - val_accuracy: 0.8105\n",
            "Epoch 9/10\n",
            "363/363 [==============================] - 116s 320ms/step - loss: 0.0366 - accuracy: 0.9868 - val_loss: 0.5386 - val_accuracy: 0.8485\n",
            "Epoch 10/10\n",
            "363/363 [==============================] - 113s 311ms/step - loss: 0.0277 - accuracy: 0.9904 - val_loss: 0.8477 - val_accuracy: 0.8287\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f57b8e96d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z1SIvCC_2HCD",
        "colab_type": "text"
      },
      "source": [
        "## ‘ড্রপ আউট’\n",
        "\n",
        "আমরা দেখতে পাচ্ছি স্ট্যান্ডার্ড ‘ফুললি কানেক্টেড নেটওয়ার্ক’ থেকে গ্লোবাল অ্যাভারেজ পুলিং মডেল ভালো করছে ৭ নাম্বার ইপক থেকে। যদিও ট্রেনিং অ্যাকুরেসি ‘ফুললি কানেক্টেড মডেল’ থেকে কম তবে সেটা ‘ওভার ফিটিং’ এর জন্য। তবে সেটাকে কমিয়ে আনা হয়েছে ‘গ্যাপ’ মডেলে। এখানে একটু ড্রপ আউট নিয়ে আলাপ করি। \n",
        "\n",
        "আমাদের মডেলে যখন ‘ওভার ফিটিং’ হয়, তখন বিশেষ করে নিউরাল নেটওয়ার্কে রেগুলারাইজেশন টেকনিক (লস কম আর অ্যাক্যুরেসি বাড়ানো) হিসেবে ‘ড্রপ আউট’ ব্যবহার করা হয়। ‘ড্রপ আউট’ হচ্ছে আমাদের মডেল ঠিক মতো কাজ করছে কিনা সেটা দেখার জন্য সেটার নিউরনগুলোর মধ্যে যে সংযোগ আছে সেগুলোর মধ্যে কিছু কিছু জায়গায় দৈবচয়নের ভিত্তিতে সংযোগ বিচ্ছিন্ন করে দেওয়া হয়। ফলে মডেলকে যদি ভালো পারফর্ম করতে হয় তখন সেই সংযোগ ছাড়াই নিউরনগুলো ঠিকমতো আউটপুট দিতে পারছে কিনা সেটা দেখা যায় শেষে। মডেল ঠিকমতো জেনারালাইজ করতে পারছে কিনা সে কারণে কিছু কিছু নিউরন বন্ধ করে দিলেও যদি মডেলের ‘নেট গেইন’ ভালো থাকে তাহলে বোঝা যায় যে মডেল ঠিকমতো জেনারেলাইজড হয়েছে, মানে ট্রেনিং ডাটার সাথে একদম মিলে যায়নি, মানে ওভার ফিটিং কম।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHSeWkggH8ir",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "standard_model_with_do = tf.keras.Sequential([\n",
        "    head, \n",
        "    standard_classifier_with_do\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBy1Kq4XII6V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "standard_model_with_do.compile(optimizer=tf.keras.optimizers.Adam(),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYLZkYZOW3Lp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "callbacks = [tf.keras.callbacks.TensorBoard(log_dir='log/{}'.format(dt.datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")))]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23XflV2r2zWW",
        "colab_type": "text"
      },
      "source": [
        "আমাদের ফাইনাল মডেলে ০.৫ এর একটা ‘ড্রপ আউট’ লেয়ার যোগ করছি ওভার ফিটিং থেকে বাঁচার জন্য।"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MCCIGV0cIM1J",
        "colab_type": "code",
        "outputId": "57f80ab3-de54-4603-94db-5efd3b482aee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        }
      },
      "source": [
        "standard_model_with_do.fit(cat_train, steps_per_epoch = 23262//TRAIN_BATCH_SIZE, epochs=7, validation_data=cat_valid, validation_steps=10,\n",
        "                           callbacks=callbacks)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train for 363 steps, validate for 10 steps\n",
            "Epoch 1/7\n",
            "  1/363 [..............................] - ETA: 27:03 - loss: 0.7907 - accuracy: 0.5781WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (1.172179). Check your callbacks.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (1.172179). Check your callbacks.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "363/363 [==============================] - 119s 327ms/step - loss: 0.3032 - accuracy: 0.8693 - val_loss: 0.5006 - val_accuracy: 0.8005\n",
            "Epoch 2/7\n",
            "363/363 [==============================] - 117s 322ms/step - loss: 0.1877 - accuracy: 0.9238 - val_loss: 0.3590 - val_accuracy: 0.8603\n",
            "Epoch 3/7\n",
            "363/363 [==============================] - 108s 298ms/step - loss: 0.1361 - accuracy: 0.9459 - val_loss: 0.5233 - val_accuracy: 0.8333\n",
            "Epoch 4/7\n",
            "363/363 [==============================] - 111s 304ms/step - loss: 0.1127 - accuracy: 0.9576 - val_loss: 0.4749 - val_accuracy: 0.8522\n",
            "Epoch 5/7\n",
            "363/363 [==============================] - 114s 315ms/step - loss: 0.0915 - accuracy: 0.9647 - val_loss: 0.4062 - val_accuracy: 0.8687\n",
            "Epoch 6/7\n",
            "363/363 [==============================] - 111s 307ms/step - loss: 0.0768 - accuracy: 0.9709 - val_loss: 0.4679 - val_accuracy: 0.8535\n",
            "Epoch 7/7\n",
            "363/363 [==============================] - 112s 308ms/step - loss: 0.0689 - accuracy: 0.9731 - val_loss: 0.5361 - val_accuracy: 0.8565\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f57b7bad940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_3OHgQCM-B_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorboard --logdir log/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4AGqD5m-oLT",
        "colab_type": "text"
      },
      "source": [
        "<img src=\"https://raw.githubusercontent.com/raqueeb/deep_learning_book/master/assets/ten10.PNG\"> চিত্রঃ স্ট্যান্ডার্ড কনভল্যুশনাল নিউরাল নেটওয়ার্ক / 'গ্যাপ' মডেলের অ্যাক্যুরেসি এবং লস \n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/raqueeb/deep_learning_book/master/assets/ten12.PNG\"> চিত্রঃ ট্রেনিং এবং ভ্যালিডেশন ফাইলের হিসেব \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMgzXns531U-",
        "colab_type": "text"
      },
      "source": [
        "শেষে আমরা এই তিন মডেল থেকে কি দেখলাম? এই তিনটা মডেলে কিন্তু একই ‘কনভলিউশনাল ফ্রন্ট এন্ড’ ছিল, তবে আমরা দেখছি সবচেয়ে ভালো ভ্যালিডেশন অ্যাকুরেসি এসেছে ৭ ইপক ট্রেনিংএর পরে আমাদের ‘গ্যাপ’ মডেলে। যদিও ‘ড্রপ আউট’ কিছুটা মডেলকে বুষ্ট দিয়েছে ভ্যালিডেশন অ্যাকুরেসিতে তবে আমরা যেটা বুঝেছি, স্ট্যান্ডার্ড ‘সিএনএন’ এর সাথে ‘গ্লোবাল এভারেজ পুলিং’ ভালো কাজ করে। "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XAFhyTN1P0q_",
        "colab_type": "text"
      },
      "source": [
        "## গ্যাপ মডেলকে (সেভ) স্টোর করি"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDgvL6I0Bz5G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pool_model.save('gap_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ekwT0tPQAzi",
        "colab_type": "text"
      },
      "source": [
        "## নতুন একটা ইমেজকে প্রেডিকশন করতে পারবো?\n",
        "\n",
        "ব্যাচ নিয়ে একটা সমস্যা হতে পারে। একটা ছবি কি পারবে?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9tB2Yv5Ii07",
        "colab_type": "code",
        "outputId": "351eb410-1bd0-4f23-81b0-3ea922b3c7b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        }
      },
      "source": [
        "# নতুন একটা ইমেজ প্রেডিকশন করি\n",
        "from tensorflow.keras.preprocessing.image import load_img\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "from tensorflow.keras.models import load_model\n",
        "\n",
        "img_path = 'sample_image.jpg'\n",
        "img = image.load_img(img_path, target_size=(200, 200))\n",
        "img"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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ikXAiFh0fGzkyfygSDi5ev1YqFxzXOnjogKYp7XZTU/RysXLl0uV0KpFKRmnc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\nxxLKCFDUz3YG/78AHyl/0MlbLEAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=200x200 at 0x7F57A95D2208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAXycn20KRNA",
        "colab_type": "code",
        "outputId": "ecb84eaf-5505-4edc-f02f-085baa83dba6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# ইমেজটাকে লোড করি\n",
        "def load_image(filename):\n",
        "\t# ইন্টারনেট থেকে sample_image.jpg নামালাম\n",
        "\timg = load_img(filename, target_size=(100, 100))\n",
        "\t# অ্যারেতে কনভার্ট করলাম\n",
        "\timg = img_to_array(img)\n",
        "\t# মডেলের জন্য রি-শেপিং \n",
        "\timg = img.reshape(1, 100, 100, 3)\n",
        "\treturn img\n",
        "\n",
        "# লোড করার পর প্রেডিক্ট করি\n",
        "def run_example():\n",
        "\t# ইমেজ লোড করি\n",
        "\timg = load_image('sample_image.jpg')\n",
        "\t# মডেল লোড করি\n",
        "\tmodel = load_model('gap_model.h5')\n",
        "\t# ক্লাস প্রেডিক্ট করি\n",
        "\tresult = model.predict(img)\n",
        "\tprint(result[0])\n",
        " \n",
        "# run_example() চালাই\n",
        "run_example()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.9999999]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5s8BZnAuJWuM",
        "colab_type": "text"
      },
      "source": [
        "বলতে হবে - কি ভুল হলো?"
      ]
    }
  ]
}