Ehsan1997/assg1acv-inrn.ipynb

## assg1acv-inrn.ipynb
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  "metadata": {
    "colab": {
      "name": "Assg1ACV-inrn.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNYMJGLgvufK/rQ6ceeZ0hq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/Ehsan1997/36de5990dfa0749081f10897a9e63c21/assg1acv-inrn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5lhhps7b73Mc",
        "colab_type": "text"
      },
      "source": [
        "# **Assignment # 1 - ACV (Advance Computer Vision)**\n",
        "## Author: *Muhammad Ehsan ul Haq*\n",
        "## Instructor: *Dr. Moazzam Faraz*\n",
        "\n",
        "### Implement:\n",
        "\n",
        "\n",
        "1.   [Resnet - Click to Open The Notebook](https://colab.research.google.com/drive/1968MMaWEyZirpycwCv_OCVfm07evDJWH)\n",
        "2.   Inception-Resnet-v2 - Implemented on this notebook\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDdszCCL8ROQ",
        "colab_type": "text"
      },
      "source": [
        "# Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hmp3z6VS7yn_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import datetime\n",
        "\n",
        "from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPool2D, GlobalAveragePooling2D, BatchNormalization, Activation\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau, LearningRateScheduler\n",
        "\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3gyV_U_8avii",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow.keras.backend as K\n",
        "config = tf.ConfigProto()\n",
        "config.gpu_options.allow_growth = True\n",
        "sess = tf.Session(config=config)\n",
        "K.set_session(sess)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N2CtZXeqGGzF",
        "colab_type": "text"
      },
      "source": [
        "# Tensorboard setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDprXQPmGJfz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%load_ext tensorboard"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZQ6liivcGfos",
        "colab_type": "text"
      },
      "source": [
        "# Implementing Modules or Blocks for use in Model Creation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vH2civ6L-jf_",
        "colab_type": "text"
      },
      "source": [
        "## Inception Resnet v1 Implementation\n",
        "[Inception-v4 Paper: Szegedy, Christian, et al. \"Inception-v4, inception-resnet and the impact of residual connections on learning.\" Thirty-first AAAI conference on artificial intelligence. 2017.](https://arxiv.org/pdf/1602.07261.pdf)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FcEltAtD-iMF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class InceptionResnetBlock(tf.keras.Model):\n",
        "  def __init__(self, filters, strides=1):\n",
        "    super().__init__(name='')\n",
        "\n",
        "    self.strides = strides\n",
        "\n",
        "    self.conv2d_1 = tf.keras.layers.Conv2D(filters, (1,1), strides=self.strides)\n",
        "    self.bn_1 = tf.keras.layers.BatchNormalization()\n",
        "\n",
        "    self.conv2d_3_1 = tf.keras.layers.Conv2D(filters, (1,1), strides=self.strides)\n",
        "    self.conv2d_3_2 = tf.keras.layers.Conv2D(filters, (3,3), padding='same')\n",
        "    self.bn_3 = tf.keras.layers.BatchNormalization()\n",
        "\n",
        "    self.conv2d_5_1 = tf.keras.layers.Conv2D(filters, (1,1), strides=self.strides)\n",
        "    self.conv2d_5_2 = tf.keras.layers.Conv2D(filters, (3,3), padding='same')\n",
        "    self.conv2d_5_3 = tf.keras.layers.Conv2D(filters, (3,3), padding='same')\n",
        "    self.bn_5 = tf.keras.layers.BatchNormalization()\n",
        "\n",
        "    self.concat = tf.keras.layers.Concatenate()\n",
        "\n",
        "    self.conv2d_depth = tf.keras.layers.Conv2D(filters, (1,1))\n",
        "\n",
        "    self.conv2d_spat = tf.keras.layers.Conv2D(filters, (1,1), strides=self.strides)\n",
        "\n",
        "  def call(self, input_tensor, training=False):\n",
        "    x_1 = self.conv2d_1(input_tensor)\n",
        "    x_1 = self.bn_1(x_1, training=training)\n",
        "    x_1 = tf.nn.relu(x_1)\n",
        "\n",
        "    x_3 = self.conv2d_3_1(input_tensor)\n",
        "    x_3 = tf.nn.relu(x_3)\n",
        "    x_3 = self.conv2d_3_2(x_3)\n",
        "    x_3 = self.bn_3(x_3, training=training)\n",
        "    x_3 = tf.nn.relu(x_3)\n",
        "\n",
        "    x_5 = self.conv2d_5_1(input_tensor)\n",
        "    x_5 = tf.nn.relu(x_5)\n",
        "    x_5 = self.conv2d_5_2(x_5)\n",
        "    x_5 = tf.nn.relu(x_5)\n",
        "    x_5 = self.conv2d_5_3(x_5)\n",
        "    x_5 = self.bn_5(x_5, training=training)\n",
        "    x_5 = tf.nn.relu(x_5) \n",
        "\n",
        "    x = self.concat([x_1, x_3, x_5])\n",
        "    x = self.conv2d_depth(x)\n",
        "    x = tf.nn.relu(x)\n",
        "\n",
        "    if self.strides != 1:\n",
        "      input_tensor = self.conv2d_spat(input_tensor)\n",
        "      input_tensor = tf.nn.relu(input_tensor)\n",
        "\n",
        "    x += input_tensor\n",
        "\n",
        "    return x\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90NRH5psQRMM",
        "colab_type": "text"
      },
      "source": [
        "# Data Loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ja1wGvVX9mQg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "2604a33c-8d48-48cd-ffb4-5f4518e1ca2d"
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar100.load_data()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\n",
            "169009152/169001437 [==============================] - 2s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69AjgypKS95j",
        "colab_type": "text"
      },
      "source": [
        "# Callbacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3eR54MKFUu3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Tensorboard callback\n",
        "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUmvVDLgQLgP",
        "colab_type": "text"
      },
      "source": [
        "# Model Architecture Formation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MS7wmaVQFK0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "ca7babd6-6111-4ed1-d18e-d7049e6a4dab"
      },
      "source": [
        "model = Sequential([\n",
        "\n",
        "                    Conv2D(16, (3,3), activation='relu', padding='same', input_shape=(32,32,3)),\n",
        "                    InceptionResnetBlock(16),\n",
        "                    InceptionResnetBlock(32, 2),\n",
        "                    InceptionResnetBlock(64, 2)\n",
        "])\n",
        "\n",
        "model.add(GlobalAveragePooling2D())\n",
        "model.add(Dense(128))\n",
        "model.add(Dense(100, activation='softmax'))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnm9cuEXhGyv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = tf.keras.optimizers.Nadam(learning_rate=0.001)\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWm4ZB42_EBf",
        "colab_type": "code",
        "outputId": "ceeb4705-87cc-423e-e34b-1b7d70f1b55c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 32, 32, 16)        448       \n",
            "_________________________________________________________________\n",
            "inception_resnet_block (Ince (None, 32, 32, 16)        8752      \n",
            "_________________________________________________________________\n",
            "inception_resnet_block_1 (In (None, 16, 16, 32)        33408     \n",
            "_________________________________________________________________\n",
            "inception_resnet_block_2 (In (None, 8, 8, 64)          132352    \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               8320      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 100)               12900     \n",
            "=================================================================\n",
            "Total params: 196,180\n",
            "Trainable params: 195,508\n",
            "Non-trainable params: 672\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLrakvBds6XB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "8c663b26-6f7c-4e60-c392-81be30563fe1"
      },
      "source": [
        "# Plot model graph\n",
        "tf.keras.utils.plot_model(model, show_shapes=True, show_layer_names=True, to_file='model.png')\n",
        "from IPython.display import Image\n",
        "Image(retina=True, filename='model.png')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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qlTZYTnLnzp3dKu6TVMfe19e3yXGp62PxR0TURu+88w6io6MRFRWFXr16Ydy4\ncfDw8EBGRgbkcrm639KlSzF+/HjMmTMHAwcOxObNm9WX1/z8/NRTtixZsgSOjo7w8fHB1KlTUVZW\nBuDxfVi+vr6wsLDAmDFjMGDAAJw/f17jHrm2xtC3gIAAZGdnq+fxe1Jzc8/V1NSgpKQEx48fb7KP\nTCbDCy+8gAULFsDFxQXW1tYICQmBh4cHLl++jCFDhrQYpzvGfdJXX30FFxcXDB06tNW5UNfDe/6I\niNpIIpHgrbfewltvvdVsPwcHB6Snpzdo3759u8a/hw8fjvz8/Ab9rK2tmzwr1h4xpkyZotepXgDg\nzTffRHx8PI4dO6aeOkelf//+TT6FmpSUhBdffLHFCZObK5ZUhgwZ0upCrDvEVSktLcUXX3yBLVu2\nQCKRtPp91PXwzB8RURfR3IMQXVF1dTXS0tKQk5OjftDA29sbUVFRiIqKarD8WFPq6uqQkpKCqqoq\nhIWFdWTK3TpuZGQkhg0bhvDwcACPz0QWFRXh0qVL6onAqXtg8UdERHpRVlaGyZMnY8CAAfjjH/+o\nbl+3bh1CQkIQFhbW7MMfKhkZGTh27BhSU1NbXBmkPXWnuLt27cKNGzdw5swZ9dyJx48fh4uLC8aM\nGYPTp0+3SxwyDBLBBfwMikQiQUJCQoMlmoioc4SEhAAAjh49qudMfrV+/Xr87W9/Q01NDTw8PLBj\nxw4EBwfrOy21xMREhIaGtvt6sKqHFd599912HZc0HT9+HDdv3sSaNWtgYmLSrmMb4s8T4Sjv+SMi\nMnDR0dGNTsjb3U2aNAmTJk3SdxrdXmBgIAIDA/WdBnUiXvYlIiIiMiIs/oiIiIiMCIs/IiIiIiPC\n4o+IiIjIiPCBDwP05Zdf6jsFIqOlmkQ5MTFRz5l0Har/s3jM6Lfu3LkDV1dXfadBv8GpXgwMZ1Un\nIqLuJDg4mFO9GBZO9WJoWIsTdbyOmpeOiKgr4D1/REREREaExR8RERGREWHxR0RERGREWPwRERER\nGREWf0RERERGhMUfERERkRFh8UdERERkRFj8ERERERkRFn9ERERERoTFHxEREZERYfFHREREZERY\n/BEREREZERZ/REREREaExR8RERGREWHxR0RERGREWPwRERERGREWf0RERERGhMUfERERkRFh8UdE\nRERkRFj8ERERERkRFn9ERERERoTFHxEREZERYfFHREREZERY/BEREREZEUkcW68AACAASURBVBZ/\nREREREaExR8RERGREWHxR0RERGREWPwRERERGREWf0RERERGhMUfERERkRFh8UdERERkRFj8ERER\nERkRFn9ERERERsRU3wkQEXWkO3fu4PXXX0ddXZ26rby8HNbW1njxxRc1+g4cOBAffPBBJ2dIRNS5\nWPwRUbfm6uqKn376CXl5eQ22/etf/9L499ixYzsrLSIiveFlXyLq9l577TVIpdIW+4WFhXVCNkRE\n+sXij4i6vVdffRW1tbXN9nn66afh4+PTSRkREekPiz8i6va8vLwwdOhQSCSSRrdLpVK8/vrrnZwV\nEZF+sPgjIqPw2muvwcTEpNFttbW1CAkJ6eSMiIj0g8UfERmFOXPmoL6+vkF7jx49MGrUKHh4eHR+\nUkREesDij4iMgrOzM1544QX06KH5316PHj3w2muv6SkrIqLOx+KPiIzG73//+wZtQgjMnDlTD9kQ\nEekHiz8iMhrBwcEa9/2ZmJjgpZdegqOjox6zIiLqXCz+iMho2NvbY+LEieoCUAiBefPm6TkrIqLO\nxeKPiIzKvHnz1A9+SKVSzJgxQ88ZERF1LhZ/RGRUpk+fDnNzcwDAK6+8AisrKz1nRETUuVj8EZFR\nkcvl6rN9vORLRMZIIoQQ+k6CDFNISAiSkpL0nQYREWkpISEBs2fP1ncaZJiOmuo7AzJso0aNwsqV\nK/WdBhmJL7/8ErGxsUhISOjQOHV1dUhISMDcuXM7NE5nCQ0NxYoVK+Dn56fvVMgAhIaG6jsFMnAs\n/qhZrq6u/OuROlVsbGynfOeCgoIgk8k6PE5nCA0NhZ+fH39WCQCLP2oZ7/kjIqPUXQo/IiJtsfgj\nIiIiMiIs/oiIiIiMCIs/IiIiIiPC4o+IiIjIiLD4I6Ju58yZM7C1tcXJkyf1nUqXdO7cOaxbtw71\n9fUICgqCu7s7ZDIZXFxcEBgYiMzMTK3HjImJwaBBg2BhYQG5XI5BgwYhIiIClZWVGv2ioqLg4+MD\nGxsbmJubw9vbG2vWrMGDBw902hdDjnvixAnExMSgrq5OpxhEumLxR0TdDueu190777yDPXv2YP36\n9aivr8fFixdx6NAhlJWV4dKlS6iursbYsWNRVFSk1bgXL17EwoULUVBQgOLiYmzevBkxMTEIDg7W\n6Jeeno5ly5YhPz8f9+7dQ3R0NGJjYxESEqLT/hhy3OnTp0Mmk2HChAmoqKjQKQ6RTgRRE4KDg0Vw\ncLC+0yAjkpCQILrbf0sKhUL4+fl1aAwAIiEhoc3jbNu2TQwYMEBUV1cLIYRQKpVi2rRpGn2uXr0q\nAIitW7dqNXZQUJB6XJWQkBABQBQVFanbAgICRG1trUa/2bNnCwCioKBAq5hdIa4QQoSHhws/Pz+h\nVCq1jtOY9vo+ULeVyDN/REQd6MCBAygpKdF3Gi3Kzc1FREQENm3apJ4D0dTUtMGlc09PTwBAXl6e\nVuMnJyc3mFvRxcUFADQurZ46dQomJiYa/Xr16gUAUCgUWsXsCnEBIDIyEjdu3EBsbKzWcYh0weKP\niLqVS5cuwd3dHRKJBHv37gUAxMfHQy6Xw9LSEsePH8eUKVNgY2MDV1dXHD58WP3ePXv2QCaTwdHR\nEYsXL4azszNkMhn8/f1x5coVdb/w8HCYmZmhT58+6rY33ngDcrkcEokE9+7dAwCsWLECq1evRl5e\nHiQSCby9vQEAZ8+ehY2NDbZu3doZh6RV9uzZAyEEpk+f3my/6upqAICNjU2bY+bk5MDOzg59+/Zt\ntl9hYSEsLCzQr1+/Nsc0xLj29vYYN24cYmNjecsCdQoWf0TUrYwePRr/+c9/NNqWLl2KlStXorq6\nGtbW1khISEBeXh48PT2xcOFCKJVKAI+Luvnz50OhUGD58uXIz8/HtWvXUFtbi4kTJ+L27dsAHhdK\nv11Kbd++fdi0aZNGW2xsLF555RV4eXlBCIHc3FwAUN/gX19f3yHHQBenT5/GwIEDYWlp2Wy/q1ev\nAnh8nHWhVCpRWFiIvXv34ty5c4iLi4OZmVmT/RUKBdLT07Fw4cJm+3X1uMOHD0dhYSG++eYbnWMR\ntRbX9iUio+Lv76++HBcWFoaLFy+ioKAAXl5e6j6mpqYYPHgwAMDHxwfx8fEYOXIkPv74Y2zcuLHN\nOQQEBDR42lSfHj58iB9//BHTpk1rsk9xcTHOnz+Pt99+G35+fi2eIWyKm5sbiouL0bNnT2zfvr3F\ndWijo6Ph7OyMLVu26BSvq8Tt378/ACArKwvDhg1rU0yilvDMHxEZLdUZGNWZv6aMGDEClpaW+O67\n7zojrU5XUlICIUSzZ/38/PywfPlyzJgxA6mpqZBKpTrFun37NkpKSnDo0CF88sknGD58eJP3RCYn\nJyMxMRFpaWmwtrbWKV5Xias69sXFxW2KR9QaLP6IiFrB3Nwcd+/e1XcaHeLRo0cAHu9jUxwdHZGe\nno64uDjY2trqHEsqlaJ3796YNGkSjhw5guzsbERHRzfod+TIEbz77rvIyMiAh4eHzvG6SlwLCwsA\nv34WRB2Jl32JiFqgVCpRUVEBV1dXfafSIVSFR3OTDffu3Rt2dnbtGtfb2xsmJibIzs7WaI+Li0Na\nWhrS09NhZWXVrjENMS4A1NTUAPj1syDqSDzzR0TUgoyMDAghMGrUKHWbqalpi5eLuwpHR0dIJBLc\nv3+/yT4nT55UT1WirdLSUsydO7dBe05ODurq6uDm5gbg8eTca9euRVZWFlJSUtpcgBl63Cepjr2T\nk1ObYhO1Bos/IqLfqK+vR3l5OWpra5GZmYkVK1bA3d0d8+fPV/fx9vZGWVkZUlJSoFQqcffuXfz0\n008NxnJwcEBRURHy8/NRVVUFpVKJ1NRUg5rqxdLSEp6enrhz506j23Nzc+Hk5NTowwphYWFwcnLC\ntWvXmhxfLpfj888/R3p6OiorK6FUKnH9+nW8/vrrkMvlWLVqFQDg5s2b2L59O/bv3w+pVAqJRKLx\n2rlzZ7eK+yTVsff19W1yXKL2wuKPiLqVvXv3YuTIkQCAtWvXIjAwEPHx8di9ezcAYOjQofjhhx+w\nf/9+rF69GgAwefJk5OTkqMd49OgRfH19YWFhgTFjxmDAgAE4f/68xj1xS5cuxfjx4zFnzhwMHDgQ\nmzdvVl+y8/PzU08Ls2TJEjg6OsLHxwdTp05FWVlZpxwHbQUEBCA7O1s9j9+Tmpt7rqamBiUlJTh+\n/HiTfWQyGV544QUsWLAALi4usLa2RkhICDw8PHD58mUMGTKkxTjdMe6TvvrqK7i4uGDo0KGtzoVI\nZ/paW4QMH5d3o85mCMu7LVq0SDg4OOg1B22hHZbzysnJEaampuLTTz/V6n11dXVizJgx4sCBA22K\nr63uFPfevXtCJpOJnTt3tst47fF9oG6Ny7sREf1Wcw8+dFfe3t6IiopCVFRUg+XHmlJXV4eUlBRU\nVVUhLCysgzPsvnEjIyMxbNgwhIeHt9uYRM1h8UdGISoqCj4+PrCxsYG5uTm8vb2xZs2aFn/JLViw\nANbW1pBIJLhx44bO8W/duoU333wTTz/9NKytrWFqagpbW1sMGDAAAQEB+PLLL3Ueu7205hgdO3YM\nnp6eDe6JMjMzg6OjI1588UXs2LED5eXletwT0tW6desQEhKCsLCwZh/+UMnIyMCxY8eQmpra4sog\n7ak7xd21axdu3LiBM2fO6Dx3IpHW9H3ukQxXd7rsO27cOLFv3z5RWloqKisrRUJCgpBKpWLy5Mkt\nvvfw4cMCgLh+/bpOsT/66CMhlUrF2LFjxdmzZ0V5ebl49OiRyMvLE0eOHBH+/v7igw8+0Gns9qTN\nMfLy8hK2trZCCCHq6+tFeXm5OH/+vJg/f76QSCTC2dlZfPXVV1rnoO/LvuvWrRNmZmYCgPDw8BBH\njx7VWy7aQDtf5ktLSxNr165tt/GocSkpKSI6OlrU1ta267jt/X2gbieR8/yRUbCyssKiRYtgYmIC\nAJg9ezaOHTuGxMRE3L59u9GpF9rD5cuXsWjRIowbNw5paWkwNf31R87T0xOenp6ws7PTeNhAX3Q9\nRhKJBHZ2dnjxxRfx4osvIiAgAKGhoQgICMD333/fpgmBO1t0dHSjE/Aam0mTJmHSpEn6TqPbCwwM\nRGBgoL7TICPEy75kFE6dOqUualR69eoF4PEC7s2RSCQ6x92yZQvq6uqwbds2jcLvSS+//DKWLVum\nc4z20pZj9KTg4GDMnz8fJSUleP/999s1RyIiajsWf9TuPv30U4wYMQIymQxyuRweHh7YvHkzgMdT\nKuzatQuDBw+Gubk57O3tMWPGDI01U+Pj4yGXy2FpaYnjx49jypQpsLGxgaurKw4fPqzuN3jwYEgk\nEvTo0QPPPfecukBZs2YNbG1tIZPJ8I9//KPJPAsLC2FhYYF+/fqp24QQ2LFjBwYOHAhzc3PY2tri\n7bffbvDes2fPtjhPW01NDb744gv07NkTzz//fKuPn6Efo9ZQzYeXmpqq1fuIiKgT6Pm6MxkwXe75\n2717twAgtm3bJkpLS0VZWZn44IMPxKuvviqEEGLjxo3CzMxMfPrpp6KiokJkZmaKZ599VvTq1Uv8\n/PPP6nE2bNggAIgvvvhC3L9/X5SUlIgxY8YIuVwuampqhBBC1NbWCg8PD+Hu7t7gnpmVK1eK3bt3\nN5nnw4cPhbW1tQgPD9do37Bhg5BIJOJvf/ubKC8vFwqFQuzbt6/BPX+nTp0S1tbWIioqqskY33//\nvQAgRo0a1foD2AWOkRCa9/w1prKyUgAQbm5uWu27vu/566rAe7zoCfw+UAsS+b8sNUnb4q+mpkbY\n2dmJ8ePHa7TX1taK2NhYoVAohJWVlQgLC9PYfvXqVQFAo5BSFTbV1dXqNlURlpubq25TFZuJiYnq\ntocPHwp3d3dx//79JnPdsGGDGDBggKisrFS3KRQKYWlpKSZOnKjRV9cHPr7++msBQLz00kutfo+h\nHyOVloo/IYSQSCTCzs6u2T6/xeJPN/xlT0/i94FawAc+qP1kZmaioqICL7/8ska7iYkJli9fjq+/\n/hoPHjzAiBEjNLaPHDkSZmZmuHLlSrPjm5mZAYDGeqoLFixAZGQkYmNjERISAgA4ePAgZsyYARsb\nm0bHSU5ORmJiIj7//HNYW1ur23Nzc6FQKDBhwoTW73QzVOuDanO/XHZ2tkEfo9Z6+PAhhBBNjt+S\nxMREnd5nzAxhuiAi6hpY/FG7qaysBADY2dk1ur2iogIAGl003c7ODlVVVVrHtLKywp///Gfs2LED\nV69exfPPP4/33nsPSUlJjfY/cuQIdu3ahYyMDDz11FMa21Rra/bu3VvrPBrj4eEBmUyG77//vtXv\nMfRj1FqqfR40aJBO729sDVlqXmxsLGJjY/WdBhF1AXzgg9qNqlC4d+9eo9tVRWFjBUxFRQVcXV11\nihseHg6pVIrdu3fjwoULcHNzg5eXV4N+cXFxOHjwINLT0xstamQyGQDgl19+0SmP3zI3N8fLL7+M\ne/fu4d///neT/crKyrBgwQIAhn+MWuvs2bMAgClTpuj0fiEEX1q8ACAhIUHvefBlGC+ilrD4o3bj\n4eEBBwcHfP75541uHzJkCKysrPD1119rtF+5cgU1NTV47rnndIrr6uqK2bNnIykpCREREVixYoXG\ndiEE1q5di6ysLKSkpDR6Vk2VX48ePfCvf/1LpzwaExkZCXNzc6xatQrV1dWN9vnf//6nngbG0I9R\na/z888/YvXs3XF1d8cc//lHncYiIqGOw+KN2Y25ujvXr1+PChQsIDw9HYWEh6uvrUVVVhZs3b0Im\nk2H16tVITk7GwYMHUVlZiaysLCxZsgTOzs5YtGiRzrFXr16N2tpalJeX43e/+53Gtps3b2L79u3Y\nv38/pFJpg6XJdu7cCeDx5d5Zs2YhKSkJBw4cQGVlJTIzM/Hhhx82iJeamtriVC8AMGzYMHz22Wf4\n3//+hzFjxuDMmTO4f/8+lEolfvzxR+zfvx9/+tOf1Ms6GfoxepIQAg8ePEB9fT2EELh79y4SEhLw\nwgsvwMTEBCkpKTrf80dERB1IEDVB1+Xd9u7dK3x9fYVMJhMymUwMHz5c7Nu3TwjxeCmwHTt2iP79\n+wupVCrs7e1FUFCQuHXrlvr9+/btE5aWlgKA6N+/v8jLyxMffvihsLGxEQBE3759xffff98g7vjx\n48VHH33UoD0rK0sAaPK1Y8cOdd+qqiqxYMEC0bNnT2FlZSVGjx4tNm7cKAAIV1dX8c033wghhDhz\n5oywtrYWW7ZsadUxKSgoEG+99Zbw9fUVVlZWwsTERNjZ2Ynhw4eLP/3pT+Lf//63uq8hH6MTJ06I\noUOHCktLS2FmZiZ69OghAKif7H3++edFVFSUKC0tbdVx+S0+7asb8OlOegK/D9SCRIkQvEGAGqd6\nMvTo0aN6zoSMRWJiIkJDQ3nfkpYkEgkSEhIwe/ZsfadCBoDfB2rBUV72JSIiIjIiLP6IiIiIjAiL\nPyIiI3bu3DmsW7cO9fX1CAoKgru7O2QyGVxcXBAYGIjMzEytx4yJicGgQYNgYWEBuVyOQYMGISIi\nQj0XqEpUVBR8fHxgY2MDc3NzeHt7Y82aNXjw4IFO+6KvuCr19fXYvXs3/P39m+yjVCoRHR0Nb29v\nmJmZwc7ODkOGDEF+fj4A4MSJE4iJiUFdXV2bciFqDos/IiIj9c4772DPnj1Yv3496uvrcfHiRRw6\ndAhlZWW4dOkSqqurMXbsWBQVFWk17sWLF7Fw4UIUFBSguLgYmzdvRkxMDIKDgzX6paenY9myZcjP\nz8e9e/cQHR2tsRKNtvQVFwBycnIwduxYrFq1qtlVfUJDQ/HPf/4Tn332GRQKBb799lt4eXmpC8/p\n06dDJpNhwoQJ6knfidqdnp84IQOm69O+RLoyhKd9FQqF8PPz61IxoMPTndu2bRMDBgxQrw2tVCrF\ntGnTNPqo1pTeunWrVmMHBQVprDkthBAhISECgCgqKlK3BQQEiNraWo1+s2fPFgBEQUGBVjH1GffG\njRti5syZ4uDBg2LYsGHimWeeabTf4cOHhUQiEZmZmS2OGR4eLvz8/IRSqdQ6H12+D2RUEnnmj4jo\nCQcOHEBJSUmXj9Gc3NxcREREYNOmTeqVbUxNTXHy5EmNfp6engCAvLw8rcZPTk5Wj6vi4uICABqX\nVk+dOgUTExONfr169QKg3ZrY+o77zDPP4NixY3j11Vdhbm7eZL/33nsPzz77LHx9fVscMzIyEjdu\n3OCSfdQhWPwRUZcmhMCuXbswePBgmJubw97eHjNmzMB3332n7hMeHg4zMzP06dNH3fbGG29ALpdD\nIpGolyRcsWIFVq9ejby8PEgkEnh7e2PPnj2QyWRwdHTE4sWL4ezsDJlMBn9/f1y5cqVdYgCPl8Rr\nzcTh7WHPnj0QQmD69OnN9lOtStMek3Xn5OTAzs4Offv2bbZfYWEhLCws0K9fvzbH1Gfc36qpqcHl\ny5cxbNiwVvW3t7fHuHHjEBsby6mPqN2x+COiLi0yMhLr1q3Dhg0bUFJSggsXLuD27dsYM2YMiouL\nATwudn4759m+ffuwadMmjbbY2Fi88sor8PLyghACubm5CA8Px/z586FQKLB8+XLk5+fj2rVrqK2t\nxcSJE3H79u02xwCgvsG/vr6+/Q5OE06fPo2BAwfC0tKy2X5Xr14FAIwePVqnOEqlEoWFhdi7dy/O\nnTuHuLg4mJmZNdlfoVAgPT0dCxcubLafocZtTlFREWpqavDf//4X48ePV/8RMXjwYOzbt6/RAm/4\n8OEoLCzEN9980yE5kfFi8UdEXVZ1dTV27dqFmTNnYt68ebC1tYWvry/ef/993Lt3r9Gl+XRlamqq\nPrvo4+OD+Ph4VFVV4eOPP26X8QMCAlBZWYmIiIh2Ga8pDx8+xI8//ggvL68m+xQXF+PIkSNYvnw5\n/Pz8WjxD2BQ3Nze4uroiMjIS27dvR2hoaLP9o6Oj4ezsjC1btugUT99xm6O67Ny7d29s3boV2dnZ\nKC4uxowZM7Bs2TIcOnSowXv69+8PAMjKyuqwvMg4sfgjoi4rOzsbDx48wIgRIzTaR44cCTMzM43L\nsu1txIgRsLS01Li83BWUlJRACNHsWT8/Pz8sX74cM2bMQGpqqnrtaW3dvn0bJSUlOHToED755BMM\nHz68yXsdk5OTkZiYiLS0NFhbW+sUT99xm6O6F/Dpp5+Gv78/HBwcYGtri02bNsHW1rbRP1RUn5Hq\nDDZRe2HxR0RdlmoqDCsrqwbb7OzsUFVV1aHxzc3Ncffu3Q6N0d4ePXoEAM0+mODo6Ij09HTExcXB\n1tZW51hSqRS9e/fGpEmTcOTIEWRnZyM6OrpBvyNHjuDdd99FRkYGPDw8dI6n77jNcXZ2BgD1vZ8q\nZmZm6Nu3b6MP1VhYWAD49TMjai+m+k6AiEhXdnZ2ANBokVdRUQFXV9cOi61UKjs8RkdQFRTNTSLc\nu3dv9bFtL97e3jAxMUF2drZGe1xcHNLS0pCent5oEd9V4/6WlZUV+vfvj5s3bzbYVltb22iRXVNT\nA+DXz4yovfDMHxF1WUOGDIGVlRW+/vprjfYrV66gpqYGzz33nLrN1NQUSqWy3WJnZGRACIFRo0Z1\nWIyO4OjoCIlEgvv37zfZ5+TJk+opUrRVWlqKuXPnNmjPyclBXV0d3NzcADx+Snvt2rXIyspCSkpK\nmwswfcXVRmhoKK5fv44ffvhB3aZQKPDTTz81Ov2L6jNycnLqtBzJOLD4I6IuSyaTYfXq1UhOTsbB\ngwdRWVmJrKwsLFmyBM7Ozli0aJG6r7e3N8rKypCSkgKlUom7d+/ip59+ajCmg4MDioqKkJ+fj6qq\nKnUxV19fj/LyctTW1iIzMxMrVqyAu7s75s+f3y4xUlNTO2WqF0tLS3h6euLOnTuNbs/NzYWTk1Oj\nD0mEhYXByckJ165da3J8uVyOzz//HOnp6aisrIRSqcT169fx+uuvQy6XY9WqVQCAmzdvYvv27di/\nfz+kUikkEonGa+fOnV0irjZWrVqFvn37Yv78+SgoKEBpaSnWrl2L6upq/OUvf2nQX/UZtWZeQCJt\nsPgjoi7tnXfeQXR0NKKiotCrVy+MGzcOHh4eyMjIgFwuV/dbunQpxo8fjzlz5mDgwIHYvHmz+nKa\nn5+fesqWJUuWwNHRET4+Ppg6dSrKysoAPL7vytfXFxYWFhgzZgwGDBiA8+fPa9w719YYnSUgIADZ\n2dnqefye1NyccjU1NSgpKcHx48eb7COTyfDCCy9gwYIFcHFxgbW1NUJCQuDh4YHLly9jyJAhLcbp\nSnEB4PLlyxg9ejSeeuopXLlyBd988w2cnZ3xwgsv4MKFC+p+9vb2uHjxIlxdXTFs2DC4uLjg6tWr\nOH36dKPz/3311VdwcXHB0KFDW50zUavoZWER6hK4vBt1NkNY3q0xixYtEg4ODvpOo0nQcjmvnJwc\nYWpqKj799FOt4tTV1YkxY8aIAwcOaJtimxhbXCGEuHfvnpDJZGLnzp1av1fb7wMZHS7vRkTUGs09\nINHVeHt7IyoqClFRURrLnjWnrq4OKSkpqKqqQlhYWAdnaLxxVSIjIzFs2DCEh4d3emzq/lj8EREZ\noXXr1iEkJARhYWHNPvyhkpGRgWPHjiE1NbXFlUHak7HFBYBdu3bhxo0bOHPmjM5zLBI1h8UfEVEz\n1q9fj48//hj3799Hv379kJSUpO+U2s3WrVsRHh6Obdu2tdh3woQJ+OyzzzTWLu4Mxhb3+PHj+OWX\nX5CRkQF7e/tOjU3Gg/P8ERE1Izo6utEJgruLSZMmYdKkSfpOg/6/wMBABAYG6jsN6uZ45o+IiIjI\niLD4IyIiIjIiLP6IiIiIjAiLPyIiIiIjwgc+qFmXL19GSEiIvtMgI6FazorfOe3t3r0bR48e1Xca\nRNQFsPijJvn5+ek7BTIyrq6uCA4O7vA4P//8M65fv44pU6Z0eKzO0BnHjLqO4OBguLm56TsNMmAS\nIbRY6JCIqBtITExEaGioVuu8EhF1E0d5zx8RERGREWHxR0RERGREWPwRERERGREWf0RERERGhMUf\nERERkRFh8UdERERkRFj8ERERERkRFn9ERERERoTFHxEREZERYfFHREREZERY/BEREREZERZ/RERE\nREaExR8RERGREWHxR0RERGREWPwRERERGREWf0RERERGhMUfERERkRFh8UdERERkRFj8ERERERkR\nFn9ERERERoTFHxEREZERYfFHREREZERY/BEREREZERZ/REREREaExR8RERGREWHxR0RERGREWPwR\nERERGREWf0RERERGhMUfERERkRFh8UdERERkRFj8ERERERkRFn9ERERERsRU3wkQEXUkpVKJBw8e\naLQ9fPgQAFBeXq7RLpFIYGdn12m5ERHpA4s/IurWysrK4OLigrq6ugbbHBwcNP49fvx4pKend1Zq\nRER6wcu+RNStOTk5YezYsejRo/n/7iQSCebMmdNJWRER6Q+LPyLq9n7/+9+32MfExAQzZ87shGyI\niPSLxR8RdXuzZs2CqWnTd7mYmJhg8uTJ6NmzZydmRUSkHyz+iKjbs7GxwZQpU5osAIUQmDdvXidn\nRUSkHyz+iMgozJs3r9GHPgDAzMwM06ZN6+SMiIj0g8UfERmFadOmwdLSskG7VCpFUFAQ5HK5HrIi\nIup8LP6IyCjIZDLMnDkTUqlUo12pVOLVV1/VU1ZERJ2PxR8RGY25c+dCqVRqtNnY2GDixIl6yoiI\nqPOx+CMio/HSSy9pTOwslUoxZ84cmJmZ6TErIqLOxeKPiIyGqakp5syZo770q1QqMXfuXD1nRUTU\nuVj8EZFRmTNnjvrSr5OTE0aPHq3njIiIOheLPyIyKv7+/nBxcQEAvPbaay0u+0ZE1N00PeV9O7lz\n5w7+85//dHQYIqJWGzlyJAoLC9GzZ08kJibqOx0iIrXZs2d3eAyJPfCxYgAAIABJREFUEEJ0ZIDE\nxESEhoZ2ZAgiIiKibqGDyzIAONrhZ/5UOmFniIhaLSkpCcHBwfpOo9sICQkBABw9elTPmXQdqpMj\n/P1IQOeeLOPNLkRklFj4EZGxYvFHREREZERY/BEREREZERZ/REREREaExR8RERGREWHxR0RERGRE\nWPwREZHBOHPmDGxtbXHy5El9p2Lwzp07h3Xr1qG+vh5BQUFwd3eHTCaDi4sLAgMDkZmZqfWYMTEx\nGDRoECwsLCCXyzFo0CBERESgsrJSo19UVBR8fHxgY2MDc3NzeHt7Y82aNXjw4IFO+6KvuCr19fXY\nvXs3/P39m+yjVCoRHR0Nb29vmJmZwc7ODkOGDEF+fj4A4MSJE4iJiUFdXV2bcukMLP6IiMhgcM67\n1nnnnXewZ88erF+/HvX19bh48SIOHTqEsrIyXLp0CdXV1Rg7diyKioq0GvfixYtYuHAhCgoKUFxc\njM2bNyMmJqbB1Ejp6elYtmwZ8vPzce/ePURHRyM2NlY936O29BUXAHJycjB27FisWrUKCoWiyX6h\noaH45z//ic8++wwKhQLffvstvLy81IXn9OnTIZPJMGHCBFRUVOicT6cQHSwhIUF0QhgiItKj4OBg\nERwcrO802pVCoRB+fn4dNr6uvx+3bdsmBgwYIKqrq4UQQiiVSjFt2jSNPlevXhUAxNatW7UaOygo\nSD2uSkhIiAAgioqK1G0BAQGitrZWo9/s2bMFAFFQUKBVTH3GvXHjhpg5c6Y4ePCgGDZsmHjmmWca\n7Xf48GEhkUhEZmZmi2OGh4cLPz8/oVQqtcqlE+ulRJ75IyIiasSBAwdQUlKi7zQ05ObmIiIiAps2\nbYJMJgMAmJqaNrhM7unpCQDIy8vTavzk5GT1uCouLi4AoHFp9dSpUzAxMdHo16tXLwBo9uyZocV9\n5plncOzYMbz66qswNzdvst97772HZ599Fr6+vi2OGRkZiRs3biA2NlbrfDoLiz8iIjIIly5dgru7\nOyQSCfbu3QsAiI+Ph1wuh6WlJY4fP44pU6bAxsYGrq6uOHz4sPq9e/bsgUwmg6OjIxYvXgxnZ2fI\nZDL4+/vjypUr6n7h4eEwMzNDnz591G1vvPEG5HI5/h97dxoV1ZX1DfxfMhVjAREQQUDAWZyiHUGN\nD4+tSYc4R0VNlPSKr0O70Gg6BqNGSQC1baVFTGKWy06bOOujcSC6bCRqd1DjhMHEAIoDdABHUFCq\nYL8fbCqWQEExVAH1/63lB2+de/a+k7Wte+85CoUCt2/fBgDMnTsX8+fPR1ZWFhQKBQIDAwEA3377\nLZycnBATE2OMXVLJ2rVrISIYMWKE3nYlJSUAACcnp3rHzMjIgLOzM3x9ffW2y8nJga2tLdq3b1/v\nmKaM+7zS0lKkpqaiV69etWrv4uKCwYMHIz4+vsk+xsDij4iImoSBAwfi3//+t86yWbNm4d1330VJ\nSQkcHR2xfft2ZGVlwd/fH9OmTYNarQbwtKiLiIhAcXEx5syZg+zsbJw7dw4ajQZDhw7FzZs3ATwt\nnsaPH68TIzExEcuWLdNZFh8fj+HDhyMgIAAigszMTADQPsxfXl7eKPugJgcPHkSnTp1gZ2ent93p\n06cBPN2ndaFWq5GTk4N169bh6NGjSEhIgLW1dbXti4uLkZycjGnTpult11Tj6pObm4vS0lKcPXsW\noaGh2v9YdOnSBYmJiVUWeL1790ZOTg4uXrzYKDnVl6WpEyAiIqqNkJAQ7a3B8PBwnDhxAjdu3EBA\nQIC2jaWlJbp06QIA6Nq1K9avX49+/fph06ZNWLJkSb1zCAsLq/QGqrE8evQI165dw+uvv15tm7y8\nPBw7dgx//vOfERwcXOMvhNVp164d8vLy8MILL2DlypWYMGGC3vaxsbHw9PTEJ598Uqd4po6rT8Vt\nZzc3NyxduhSdO3eGhYUFli9fjtmzZ8PZ2RmTJ0/WWadDhw4AgEuXLtX6F0Nj4i9/RETU7FT8ylPx\ny191+vbtCzs7O/z888/GSKtR5efnQ0T0/uoXHByMOXPmYNSoUUhKSoKVlVWdYt28eRP5+fnYsmUL\nvvzyS/Tu3bva5x/37NmDHTt24PDhw3B0dKxTPFPH1afiWcBu3bohJCQErq6uUKlUWLZsGVQqFTZs\n2FBpnYpjlJeX12h51QeLPyIiatFsbGxQUFBg6jTq7fHjxwCg98UEd3d3JCcnIyEhASqVqs6xrKys\n4ObmhmHDhmHbtm1IT09HbGxspXbbtm3D8uXLkZKSAj8/vzrHM3VcfTw9PQFA+zxoBWtra/j6+lb5\nUo2trS2A345ZU8PbvkRE1GKp1Wrcv38f3t7epk6l3ioKCn2DCLu5ucHZ2blB4wYGBsLCwgLp6ek6\nyxMSEnD48GEkJyfDwcGhQWOaMu7zHBwc0KFDB1y+fLnSZxqNpsoiu7S0FMBvx6yp4S9/RETUYqWk\npEBE0L9/f+0yS0vLGm8XN0Xu7u5QKBR48OBBtW3279+vHSLFUHfu3MGkSZMqLc/IyEBZWRnatWsH\n4OlA3AsWLMClS5ewd+/eehdgpopriAkTJuD8+fO4evWqdllxcTGuX79e5fAvFcfIw8PDaDkagsUf\nERG1GOXl5bh37x40Gg3S0tIwd+5c+Pj4ICIiQtsmMDAQd+/exd69e6FWq1FQUIDr169X6svV1RW5\nubnIzs5GUVER1Go1kpKSTDbUi52dHfz9/XHr1q0qP8/MzISHh0eVL0mEh4fDw8MD586dq7Z/e3t7\nHDlyBMnJySgsLIRarcb58+cxdepU2NvbY968eQCAy5cvY+XKlfjiiy9gZWUFhUKh82fVqlXNIq4h\n5s2bB19fX0RERODGjRu4c+cOFixYgJKSEnzwwQeV2lcco9qMC2gKLP6IiKhJWLduHfr16wcAWLBg\nAUaOHIn169djzZo1AIAePXrg6tWr+OKLLzB//nwAwKuvvoqMjAxtH48fP0ZQUBBsbW0xaNAgdOzY\nEceOHdN5Tm7WrFkIDQ3FxIkT0alTJ3z88cfa23PBwcHaYWFmzpwJd3d3dO3aFa+99hru3r1rlP2g\nT1hYGNLT07Xj+D1L35hypaWlyM/Px759+6pto1QqMWDAALzzzjvw8vKCo6Mjxo0bBz8/P6SmpqJ7\n9+41xmlOcQEgNTUVAwcORNu2bXHq1ClcvHgRnp6eGDBgAI4fP65t5+LighMnTsDb2xu9evWCl5cX\nTp8+jYMHD1b5Nu+ZM2fg5eWFHj161Dpno2rsOUQ4vRsRUcvXFKZ3mz59uri6upo0B0PU5fsxIyND\nLC0tZfPmzQatV1ZWJoMGDZKNGzcatF59mVtcEZHbt2+LUqmUVatWGbQep3cjIiKqA30vQ7QEgYGB\niI6ORnR0tM60Z/qUlZVh7969KCoqQnh4eCNnaL5xKyxduhS9evVCZGSk0WPXVpMs/g4dOgSVSlVp\nrsKmrrnm3Vz069cPFhYWNQ6YuWrVKu2D0Z999lmD5hAXFweVSgWFQoELFy7Uu7/U1FR06dIFrVq1\ngkKhgIeHR6MOVtrQGvuc3717N/z9/Ss926NUKtG+fXv88Y9/xLVr1xoldkOrblssLS3RunVr/P73\nv8eePXt01mlO5zIZT1RUFMaNG4fw8HC9L39USElJwe7du5GUlFTjzCANydziAsDq1atx4cIFHDp0\nqM5jLBpDkyz+pInOhVeT5pp3c3HmzBmEhobW2O69996rNEVUQ4mKisLnn3/eYP31798fP/30E4YN\nGwYAuHLlChYtWtRg/Te2xj7nx44di6tXryIgIAAqlQoigrKyMty4cQPR0dHYvn07+vfvjzt37jRq\nHg2hqm0RERQUFGD79u3IycnB2LFjsX37du06zelcNrWFCxdi06ZNePDgAdq3b49du3aZOqVGFRMT\ng8jISMTFxdXYdsiQIfj666915jM2BnOLu2/fPjx58gQpKSlwcXExamxDNcniLywsDA8ePMDw4cNN\nnUq1SkpKEBISorOsOeTdFFS17wyhUCgaMBuqraZyzrdq1Qru7u546623MHv2bOTn5+Po0aNGi19b\ntT3PXVxcMGTIEPztb38DAOzYsaOxU2uRYmNj8eTJE4gIrl27hjfeeMPUKTW6YcOGYfny5aZOg/5r\n5MiRiIqKgoWFhalTqVGTLP6ag40bN1Y75QzpV99915R/Sm/JmuI5HxgYCAD49ddfTZxJZYbur4pZ\nCu7fv99IGRERPdXkir+TJ0/Cx8cHCoUC69atAwCsX78e9vb2sLOzw759+/CHP/wBTk5O8Pb2xtat\nWyv1sXnzZvTt2xdKpRL29vbw8/PDxx9/DODpg6BLliyBj48PbG1t0aNHD+1tlrVr10KpVMLd3R0z\nZsyAp6cnlEolQkJCcOrUKW3/c+fOxfz585GVlQWFQoHAwMAq8wae3hZbvXo1unTpAhsbG7i4uGDU\nqFE680waun01WblyJezs7ODo6Ij8/HzMnz8fXl5euHLlit7tB4DvvvsOv/vd72BnZwcnJycEBQWh\nsLDQoBz1xahq3xkqMzMTnTt3hr29vXY4h5MnT9a4Xm2ORQV959Dz8vLy4OfnB0tLS7z66qva5d9+\n+22dxwPjOV87FUN89OzZU2d5czzP09LSAACDBw+usa2xz2UiamEa+33iury6fPPmTQEgCQkJ2mUf\nfvihAJB//vOf8uDBA8nPz5dBgwaJvb29lJaWatutWbNGAEhcXJzcuXNH7t69K59//rlMnjxZRETe\ne+89sbGxkV27dsm9e/dk4cKF0qpVKzlz5oyIPB0qwN7eXi5fviyPHz+W9PR06devnzg6OsqNGze0\nccaOHSsBAQE15r1kyRKxtraWzZs3y/379yUtLU369OkjrVu3ll9//dXg7autiv7mzJkjCQkJMmbM\nGPnpp5/0bv/Dhw/FyclJVqxYISUlJfLrr7/KmDFjpKCgwKAca9rHVe272hoyZIj4+/vLtWvXRK1W\ny48//igvvfSSKJVK+eWXX7TtMjIyBIB8+umn2mW1PRY1nUNbt24VAHL+/HkRESktLZWxY8fKvn37\ndHI9cOCAODo6SnR0dI3b9corrwgAuXfvnnYZz/nfBAQEiEql0v793r178ve//13s7OwkLCys0v5s\nyuf589tSXFwsSUlJ4uvrK8OGDZOHDx/qtG8K53JtNIWhXpobDoVGzzLmUC/NrvgrKSnRLktMTBQA\nkpmZKSJP/+FydnaW0NBQnf40Go3Ex8dLSUmJ2NnZSXh4uPaz4uJisbGxkVmzZonI0y/CZ/9hFhE5\nc+aMAJBly5Zpl9Xmi7C4uFgcHBx04omInD59WgDoFAW12T5DVNVfTdv/448/CgA5cOBArft8Psfa\n7OP6Fn89e/bUWZaWliYA5L333tMue/4Ls7bHoqZzSET3C1OtVsvEiRMlKSmpTttTQV/xx3P+acEE\nQOePQqGQTz75pFKh2NTP86q2BYAEBQXJl19+KU+ePNFp31zOZRZ/hmPxR88yZvFnWf/fDk3H2toa\nALRzNKalpeH+/ft45ZVXdNpZWFhgzpw5uHjxIoqLi7WjhQNPJ11u06ZNlbdLKvTt2xd2dnZ621Ql\nPT0dDx8+RN++fXWW9+vXD9bW1jq31WqzffV15coVvdvv7+8Pd3d3vPnmm5gzZw4iIiK0zyHVNsea\nYjSGoKAgqFQq7W2zqtT2WNR0Dj2rrKwMkyZNQtu2bY12i8ycz3mVSqV9Hu7999/HX/7yF6hUqkrP\ngDaH8/zZbdFoNMjLy8ORI0cQGRmJ2NhYnDx5Eq1bt65y3aZ8LqempmLcuHF1Xt/cVEwBxn1GAKqd\ntq8xNLln/uqjsLAQAODs7Fzl548ePQIALFq0SGecrevXr6O4uFhv3zY2NigoKDAon4p/3KuafNrZ\n2RlFRUUG9VdfNW2/ra0tkpOTMXDgQMTExMDf3x/h4eFVTiNU1xiNxcrKSm+RXNtjUdM59KzZs2cj\nIyMDn332GS5fvlyXtOvNXM/5xYsXo02bNli4cKF2Kq4Kze08t7S0hJeXF95++22sWrUKV65c0Tt8\nR0s9l4nIeJr1L3/Pa9u2LQDg9u3bVX7u5uYGAFizZg3mzp1b637VajXu378Pb29vg/Kp+Ee3qi+8\nuvRXX7XZ/m7dumH//v0oKCjA6tWrsXz5cnTr1g2LFy9usBgNTaPR4O7du/Dx8am2TW2PRU3n0LPG\njx+Pt956C927d8eUKVOQmpoKS0vjXlLmes47Ojpi+fLliIiIwKxZs3QGmW7O53nFJPD6CrCmfC73\n798fO3fuNHg9c7Vjxw5MmDCB+4wA/HY+GEOL+uXPz88Prq6uOHLkSJWft2vXDkql0uDR7FNSUiAi\n6N+/v0Hrde/eHQ4ODvjhhx90lp86dQqlpaV48cUXDeqvvmra/tzcXO2XjpubG+Li4tCnTx+Dfgmo\n6z6uj2PHjqG8vBx9+vSptk1tj0VN59CzQkND0bp1a2zYsAFnz541ycwc5nzOT5kyBS+99BIOHDig\nMzZecz7Pz549CwDo1KlTtW1a6rlMRMbTooo/GxsbLFy4EMePH0dkZCRycnJQXl6OoqIiXL58GUql\nEm+//Ta2bt2K9evXo7CwEGVlZbh16xb+85//aPspLy/HvXv3oNFokJaWhrlz58LHxwcRERHaNq6u\nrsjNzUV2djaKioqqvOWoVCoxf/587NmzB1999RUKCwtx6dIlzJw5E56enpg+fboxdotOPvq2Pzc3\nFzNmzMDPP/+M0tJSnD9/HtevXzeoAKjNPq7NvtOntLQUDx48gEajwblz5xAZGQlfX1+d41NVXrU5\nFjWdQ1UZMWIEIiIiEBMTo/3yBoCkpKQ6D/VSW+Z8zisUCqxduxYKhQKRkZG4d++eNofmcJ6XlJSg\nvLwcIoLc3Fxs2rQJixYtQuvWrfHuu+/qjW3sc5mIWpjGfqXE0LdXEhISpE2bNgJA7OzsZMSIEZKY\nmCh2dnYCQDp06CBZWVmyYcMGcXJyEgDi6+urM8zHunXrJCgoSJRKpSiVSundu7ckJiaKiMiTJ09k\nwYIF4uPjI5aWluLm5iZjx46V9PR0EXn65qOVlZV4eXmJpaWlODk5yahRoyQrK0snz3Pnzomvr6/Y\n2trKwIEDZdGiRZXyFhEpLy+Xv/zlL9KhQwexsrISFxcXGT16tFy5ckXbl6HbV5MVK1aIra2tAJB2\n7drJ5s2btZ/p2/7s7GwJCQkRFxcXsbCwkLZt28qHH34oGo3GoBxr2sfP77tnh6aoyaZNmyQ0NFTc\n3d3F0tJSXnjhBZk4caJcv35d2+avf/2reHh4CACxt7eXMWPG1PpYVKjuHNq9e7e4uLgIAPHz85P8\n/HwpLCyUdu3aCQBxcHCQf/zjHyIicujQIXF0dJRPPvmk2u1JTU2Vbt26SatWrQSAtGnTRmJiYnjO\n/3f7/vWvf0nHjh21b8S2bdtWZsyYoZNXRESEABBnZ2eJi4urcZtNdZ5/+umn1b7pa2NjIx06dJBZ\ns2bpDK/TVM7l2uDbvobj2770LGO+7asQadzJOSvuYTdymAYzY8YM7Ny5s1nMFUrUEHjOU0OoeGOV\nz6/VXnP7fqTGZcTzYWeLuu3bUMrKykydApFR8ZwnIjIfLP6aiZ9//llnSInq/oSHh5s6VYO01O0i\nImpsR48eRVRUFMrLyzF69Gj4+PhAqVTCy8sLI0eO1Dv2aXVWrFiBzp07w9bWFvb29ujcuTMWL16s\nHTqoQnR0NLp27QonJyfY2NggMDAQ77//Ph4+fFinbTFV3Arl5eVYs2YNQkJCqm2jVqsRGxuLwMBA\nWFtbw9nZGd27d0d2djYA4JtvvsGKFSuax3+mG/vGcnN6piEqKkqsra21z8Ds3LnT1CkRNSqe89RQ\n+Myf4erz/bhkyRIZPny4FBYWilqtlhdeeEFOnDghjx49kqtXr8rQoUNFpVJJTk6OQf2GhYXJqlWr\nJD8/X4qKimTHjh1iZWUlQ4cO1Wk3ePBgSUxMlDt37khhYaFs375drKys5NVXX63T9pgqrojIL7/8\nIgMGDBAAlWaQetbo0aOlU6dOkpqaKmq1WnJzc2XEiBFy6dIlbZv4+HgZPHiwzmxNtWX207sREVHz\n0hSKv+LiYgkODm42Mer6/RgXFycdO3bUTkGoVqvl9ddf12lTMd1fTEyMQX2PHj1aZ2pDEZFx48YJ\nAMnNzdUuCwsLE41Go9Nu/PjxAkDnpaWmHvfChQsyZswY+eqrr6RXr17VFn9bt24VhUIhaWlpNfYZ\nGRkpwcHBolarDcrFmMUfb/sSEVGLsHHjRuTn5zf7GPpkZmZi8eLFWLZsGZRKJYCns8Q8O9A5APj7\n+wMAsrKyDOp/z5492n4reHl5AYDOrdUDBw7AwsJCp13FlIR1mc3JVHF79uyJ3bt3Y/LkybCxsam2\n3aeffoo+ffpoB2LXZ+nSpbhw4QLi4+MNzsdYWPwREZFJiAhWr16NLl26wMbGBi4uLhg1apTO/MiR\nkZGwtrZGmzZttMv+9Kc/wd7eHgqFQjuDydy5czF//nxkZWVBoVAgMDAQa9euhVKphLu7O2bMmAFP\nT08olUqEhITozDNdnxgA8O233zb6mJ4V1q5dCxHBiBEj9LarmK7Qycmp3jEzMjLg7OwMX19fve1y\ncnJga2uL9u3b1zumKeM+r7S0FKmpqejVq1et2ru4uGDw4MGIj49vsm9ys/gjIiKTWLp0KaKiovDh\nhx8iPz8fx48fx82bNzFo0CDk5eUBeFrsjB8/Xme9xMRELFu2TGdZfHw8hg8fjoCAAIgIMjMzERkZ\niYiICBQXF2POnDnIzs7GuXPnoNFoMHToUO280PWJAfz2tnx5eXnD7ZxqHDx4EJ06dYKdnZ3edqdP\nnwYADBw4sE5x1Go1cnJysG7dOhw9ehQJCQmwtrautn1xcTGSk5Mxbdo0ve2aalx9cnNzUVpairNn\nzyI0NFT7n4guXbogMTGxygKvd+/eyMnJwcWLFxslp/pi8UdEREZXUlKC1atXY8yYMXjzzTehUqkQ\nFBSEzz77DLdv38aGDRsaLJalpaX218WuXbti/fr1KCoqwqZNmxqk/7CwMBQWFtZ6bui6evToEa5d\nu4aAgIBq2+Tl5WHbtm2YM2cOgoODa/yFsDrt2rWDt7c3li5dipUrV9Y452xsbCw8PT3rPTWgqeLq\nU3Hb2c3NDTExMUhPT0deXh5GjRqF2bNnY8uWLZXW6dChAwDg0qVLjZZXfbD4IyIio0tPT8fDhw/R\nt29fneX9+vWDtbW1zm3Zhta3b1/Y2dnp3F5uDvLz8yEien/1Cw4Oxpw5czBq1CgkJSXBysqqTrFu\n3ryJ/Px8bNmyBV9++SV69+5d7bOOe/bswY4dO3D48GE4OjrWKZ6p4+pT8Sxgt27dEBISAldXV6hU\nKixbtgwqlarK/6hUHKOKX7CbGhZ/RERkdPfv3wcAODg4VPrM2dkZRUVFjRrfxsYGBQUFjRqjoT1+\n/BgA9L6Y4O7ujuTkZCQkJEClUtU5lpWVFdzc3DBs2DBs27YN6enpiI2NrdRu27ZtWL58OVJSUuDn\n51fneKaOq4+npycAaJ/9rGBtbQ1fX98qX6qxtbUF8Nsxa2osTZ0AERGZH2dnZwCossi7f/8+vL29\nGy22Wq1u9BiNoaKg0DeIsJubm3bfNpTAwEBYWFggPT1dZ3lCQgIOHz6M5OTkKov45hr3eQ4ODujQ\noQMuX75c6TONRlNlkV1aWgrgt2PW1PCXPyIiMrru3bvDwcEBP/zwg87yU6dOobS0FC+++KJ2maWl\nJdRqdYPFTklJgYigf//+jRajMbi7u0OhUODBgwfVttm/f792iBRD3blzB5MmTaq0PCMjA2VlZWjX\nrh2Ap29pL1iwAJcuXcLevXvrXYCZKq4hJkyYgPPnz+Pq1avaZcXFxbh+/XqVw79UHCMPDw+j5WgI\nFn9ERGR0SqUS8+fPx549e/DVV1+hsLAQly5dwsyZM+Hp6Ynp06dr2wYGBuLu3bvYu3cv1Go1CgoK\ncP369Up9urq6Ijc3F9nZ2SgqKtIWc+Xl5bh37x40Gg3S0tIwd+5c+Pj4ICIiokFiJCUlGWWoFzs7\nO/j7++PWrVtVfp6ZmQkPD48qX5IIDw+Hh4cHzp07V23/9vb2OHLkCJKTk1FYWAi1Wo3z589j6tSp\nsLe3x7x58wAAly9fxsqVK/HFF1/Aysqq0nScq1atahZxDTFv3jz4+voiIiICN27cwJ07d7BgwQKU\nlJTggw8+qNS+4hjVZlxAU2DxR0REJvHRRx8hNjYW0dHRaN26NQYPHgw/Pz+kpKTA3t5e227WrFkI\nDQ3FxIkT0alTJ3z88cfa22nBwcHaIVtmzpwJd3d3dO3aFa+99hru3r0L4OlzV0FBQbC1tcWgQYPQ\nsWNHHDt2TOfZufrGMJawsDCkp6drx/F7lr4x5UpLS5Gfn499+/ZV20apVGLAgAF455134OXlBUdH\nR4wbNw5+fn5ITU1F9+7da4zTnOICQGpqKgYOHIi2bdvi1KlTuHjxIjw9PTFgwAAcP35c287FxQUn\nTpyAt7c3evXqBS8vL5w+fRoHDx6scvy/M2fOwMvLCz169Kh1zkbV2HOIcHo3IqKWrylM71aV6dOn\ni6urq6nTqFJdvh8zMjLE0tJSNm/ebNB6ZWVlMmjQINm4caNB69WXucUVEbl9+7YolUpZtWqVQetx\nejciIqIGou8FieYmMDAQ0dHRiI6O1pn2TJ+ysjLs3bsXRUVFCA8Pb+QMzTduhaVLl6JXr16IjIw0\neuzaYvFHRETUjERFRWHcuHEIDw/X+/JHhZSUFOzevRtJSUk1zgzSkMwtLgCsXr0aFy5cwKFDh+o8\nxqIxsPgjIqIWaeHChdi0aRMePHiA9u3bY9euXaZOqcHExMQgMjIScXFxNbYdMmQIvv76a525i43B\n3OLu27cPT548QUpKClxcXIwa21Ac54+IiFqk2NjYKgcIbil/7ROIAAAgAElEQVSGDRuGYcOGmToN\n+q+RI0di5MiRpk6jVvjLHxEREZEZYfFHREREZEZY/BERERGZERZ/RERERGaExR8RERGRGTHa274K\nhcJYoYiIyET4b73huM/I2Bq9+AsJCcH27dsbOwwRUa19//33iI+P579NRGSWFCIGzJRMRNQC7Nix\nAxMmTDBoongiohZiJ5/5IyIiIjIjLP6IiIiIzAiLPyIiIiIzwuKPiIiIyIyw+CMiIiIyIyz+iIiI\niMwIiz8iIiIiM8Lij4iIiMiMsPgjIiIiMiMs/oiIiIjMCIs/IiIiIjPC4o+IiIjIjLD4IyIiIjIj\nLP6IiIiIzAiLPyIiIiIzwuKPiIiIyIyw+CMiIiIyIyz+iIiIiMwIiz8iIiIiM8Lij4iIiMiMsPgj\nIiIiMiMs/oiIiIjMCIs/IiIiIjPC4o+IiIjIjLD4IyIiIjIjLP6IiIiIzAiLPyIiIiIzwuKPiIiI\nyIyw+CMiIiIyIyz+iIiIiMwIiz8iIiIiM8Lij4iIiMiMWJo6ASKixlRQUID/+7//01n2ww8/AAA2\nbNigs9zR0RETJ040Wm5ERKagEBExdRJERI3lyZMncHd3x8OHD2FhYQEAqPhnT6FQaNup1WpMnToV\nf//7302RJhGRsezkbV8iatFsbGzwxhtvwNLSEmq1Gmq1GhqNBhqNRvt3tVoNAJg0aZKJsyUianws\n/oioxZs0aRJKS0v1tnF2dsb//u//GikjIiLTYfFHRC1eaGgo3Nzcqv3cysoKb775Jiwt+Rg0EbV8\nLP6IqMVr1aoVJk+eDCsrqyo/V6vVfNGDiMwGiz8iMgsTJ07UPtv3vLZt2yI4ONjIGRERmQaLPyIy\nC7/73e/g6+tbabm1tTWmTp2q8+YvEVFLxuKPiMzGW2+9VenWb2lpKW/5EpFZYfFHRGZj8uTJlW79\nBgYGIigoyEQZEREZH4s/IjIbnTt3RteuXbW3eK2srPD222+bOCsiIuNi8UdEZmXKlCnamT40Gg1v\n+RKR2WHxR0RmZeLEiSgrKwMA9OnTB+3btzdxRkRExsXij4jMio+PD1566SUAwNSpU02cDRGR8dV5\nOPvvv/8eq1evbshciIiM4smTJ1AoFDhy5AiOHz9u6nSIiAy2c+fOOq9b51/+bt68iV27dtU5MBGR\nqXh7e8PDwwNKpdLUqZid1NRUpKammjqNZuXWrVv8viWthjgf6j2RZX0qTyIiU8nMzERgYKCp0zA7\n48aNA8DvDkPs2LEDEyZM4D4jAL+dD/XBZ/6IyCyx8CMic8Xij4iIiMiMsPgjIiIiMiMs/oiIiIjM\nCIs/IiIiIjPC4o+IiJqdQ4cOQaVSYf/+/aZOpck7evQooqKiUF5ejtGjR8PHxwdKpRJeXl4YOXIk\n0tLSDO5zxYoV6Ny5M2xtbWFvb4/OnTtj8eLFKCws1GkXHR2Nrl27wsnJCTY2NggMDMT777+Phw8f\n1mlbTBW3Qnl5OdasWYOQkJBq26jVasTGxiIwMBDW1tZwdnZG9+7dkZ2dDQD45ptvsGLFCu1MQ6bA\n4o+IiJodETF1Cs3CRx99hLVr12LhwoUoLy/HiRMnsGXLFty9excnT55ESUkJXn75ZeTm5hrU74kT\nJzBt2jTcuHEDeXl5+Pjjj7FixQq88cYbOu2Sk5Mxe/ZsZGdn4/bt24iNjUV8fLx2yB9DmSouAGRk\nZODll1/GvHnzUFxcXG27CRMm4B//+Ae+/vprFBcX46effkJAQIC28BwxYgSUSiWGDBmC+/fv1zmf\nepE62r59u9RjdSIiMkNvvPGGvPHGG6ZOo0EVFxdLcHBwo/Vf1+/buLg46dixo5SUlIiIiFqtltdf\nf12nzenTpwWAxMTEGNT36NGjtf1WGDdunACQ3Nxc7bKwsDDRaDQ67caPHy8A5MaNGwbFNGXcCxcu\nyJgxY+Srr76SXr16Sc+ePatst3XrVlEoFJKWllZjn5GRkRIcHCxqtdqgXBqg/trBX/6IiIjqYePG\njcjPzzd1GjoyMzOxePFiLFu2TDuTjaWlZaXb5P7+/gCArKwsg/rfs2dPpRlyvLy8AEDn1uqBAwdg\nYWGh065169YAoPfXs6YWt2fPnti9ezcmT54MGxubatt9+umn6NOnD4KCgmrsc+nSpbhw4QLi4+MN\nzqe+WPwREVGzcvLkSfj4+EChUGDdunUAgPXr18Pe3h52dnbYt28f/vCHP8DJyQne3t7YunWrdt21\na9dCqVTC3d0dM2bMgKenJ5RKJUJCQnDq1Cltu8jISFhbW6NNmzbaZX/6059gb28PhUKB27dvAwDm\nzp2L+fPnIysrCwqFQjt4+LfffgsnJyfExMQYY5dUsnbtWogIRowYobddSUkJAMDJyaneMTMyMuDs\n7AxfX1+97XJycmBra4v27dvXO6Yp4z6vtLQUqamp6NWrV63au7i4YPDgwYiPjzf6Ywws/oiIqFkZ\nOHAg/v3vf+ssmzVrFt59912UlJTA0dER27dvR1ZWFvz9/TFt2jSo1WoAT4u6iIgIFBcXY86cOcjO\nzsa5c+eg0WgwdOhQ3Lx5E8DT4mn8+PE6MRITE7Fs2TKdZfHx8Rg+fDgCAgIgIsjMzAQA7cP85eXl\njbIPanLw4EF06tQJdnZ2etudPn0awNN9WhdqtRo5OTlYt24djh49ioSEBFhbW1fbvri4GMnJyZg2\nbZredk01rj65ubkoLS3F2bNnERoaqv2PRZcuXZCYmFhlgde7d2/k5OTg4sWLjZJTdeo9ty8REVFT\nEhISor01GB4ejhMnTuDGjRsICAjQtrG0tESXLl0AAF27dsX69evRr18/bNq0CUuWLKl3DmFhYZXe\nQDWWR48e4dq1a3j99derbZOXl4djx47hz3/+M4KDg2v8hbA67dq1Q15eHl544QWsXLmyxjlnY2Nj\n4enpiU8++aRO8UwdV5+K285ubm5YunQpOnfuDAsLCyxfvhyzZ8+Gs7MzJk+erLNOhw4dAACXLl2q\n9S+GDYG//BERUYtV8StPxS9/1enbty/s7Ozw888/GyOtRpWfnw8R0furX3BwMObMmYNRo0YhKSkJ\nVlZWdYp18+ZN5OfnY8uWLfjyyy/Ru3fvap9/3LNnD3bs2IHDhw/D0dGxTvFMHVefimcBu3XrhpCQ\nELi6ukKlUmHZsmVQqVTYsGFDpXUqjlFeXl6j5VUVFn9ERER4+uVdUFBg6jTq7fHjxwCg98UEd3d3\nJCcnIyEhASqVqs6xrKys4ObmhmHDhmHbtm1IT09HbGxspXbbtm3D8uXLkZKSAj8/vzrHM3VcfTw9\nPQFA+zxoBWtra/j6+lb5Uo2trS2A346ZsfC2LxERmT21Wo379+/D29vb1KnUW0VBoW8QYTc3Nzg7\nOzdo3MDAQFhYWCA9PV1neUJCAg4fPozk5GQ4ODg0aExTxn2eg4MDOnTogMuXL1f6TKPRVFlkl5aW\nAvjtmBkLf/kjIiKzl5KSAhFB//79tcssLS1rvF3cFLm7u0OhUODBgwfVttm/f792iBRD3blzB5Mm\nTaq0PCMjA2VlZWjXrh2ApwNxL1iwAJcuXcLevXvrXYCZKq4hJkyYgPPnz+Pq1avaZcXFxbh+/XqV\nw79UHCMPDw+j5Qiw+CMiIjNUXl6Oe/fuQaPRIC0tDXPnzoWPjw8iIiK0bQIDA3H37l3s3bsXarUa\nBQUFuH79eqW+XF1dkZubi+zsbBQVFUGtViMpKclkQ73Y2dnB398ft27dqvLzzMxMeHh4VPmSRHh4\nODw8PHDu3Llq+7e3t8eRI0eQnJyMwsJCqNVqnD9/HlOnToW9vT3mzZsHALh8+TJWrlyJL774AlZW\nVlAoFDp/Vq1a1SziGmLevHnw9fVFREQEbty4gTt37mDBggUoKSnBBx98UKl9xTGqzbiADYnFHxER\nNSvr1q1Dv379AAALFizAyJEjsX79eqxZswYA0KNHD1y9ehVffPEF5s+fDwB49dVXkZGRoe3j8ePH\nCAoKgq2tLQYNGoSOHTvi2LFjOs/JzZo1C6GhoZg4cSI6deqEjz/+WHt7Ljg4WDsszMyZM+Hu7o6u\nXbvitddew927d42yH/QJCwtDenq6dhy/Z+kbU660tBT5+fnYt29ftW2USiUGDBiAd955B15eXnB0\ndMS4cePg5+eH1NRUdO/evcY4zSkuAKSmpmLgwIFo27YtTp06hYsXL8LT0xMDBgzA8ePHte1cXFxw\n4sQJeHt7o1evXvDy8sLp06dx8ODBKt/mPXPmDLy8vNCjR49a59wg6jo3CKd3IyIiQzWF6d2mT58u\nrq6uJs3BEHX5vs3IyBBLS0vZvHmzQeuVlZXJoEGDZOPGjQatV1/mFldE5Pbt26JUKmXVqlUGrcfp\n3YiIiOpA38sQLUFgYCCio6MRHR2tM+2ZPmVlZdi7dy+KiooQHh7eyBmab9wKS5cuRa9evRAZGWn0\n2Cz+iIiIWqCoqCiMGzcO4eHhel/+qJCSkoLdu3cjKSmpxplBGpK5xQWA1atX48KFCzh06FCdx1is\nD6MWf4cOHYJKpao0sXRT11zzbi769esHCwuLGkc3X7VqlfYtts8++6xBc4iLi4NKpYJCocCFCxca\ntO/y8nKsWbMGISEhde4jNTUVXbp0QatWraBQKODh4dGoI9U3tMa+hnbv3g1/f/9KD3YrlUq0b98e\nf/zjH3Ht2rVGid3QqtsWS0tLtG7dGr///e+xZ88enXWa67VhCgsXLsSmTZvw4MEDtG/fHrt27TJ1\nSo0qJiYGkZGRiIuLq7HtkCFD8PXXX+vMZ2wM5hZ33759ePLkCVJSUuDi4mLU2BWMWvyJkScubijN\nNe/m4syZMwgNDa2x3XvvvVdpPs+GEhUVhc8//7zB+83IyMDLL7+MefPmobi4uM799O/fHz/99BOG\nDRsGALhy5QoWLVrUUGk2usa+hsaOHYurV68iICAAKpUKIoKysjLcuHED0dHR2L59O/r37487d+40\nah4NoaptEREUFBRg+/btyMnJwdixY7F9+3btOs3x2jCV2NhYPHnyBCKCa9eu4Y033jB1So1u2LBh\nWL58uanToP8aOXIkoqKiYGFhYbIcjFr8hYWF4cGDBxg+fLgxwxqkpKSk0i80zSHvpqCqfWcIhULR\ngNmY3sWLF/HBBx9g5syZRp2z0dSayjXUqlUruLu746233sLs2bORn5+Po0ePGi1+bdX2unFxccGQ\nIUPwt7/9DQCwY8eOxk6NiFooPvP3nI0bN1Y7PyDpV999Z4rnHhpTz549sXv3bkyePFnvNEstTVO8\nhgIDAwEAv/76q4kzqczQ/VUxRdX9+/cbKSMiaumMVvydPHkSPj4+UCgUWLduHQBg/fr1sLe3h52d\nHfbt24c//OEPcHJygre3N7Zu3Vqpj82bN6Nv375QKpWwt7eHn58fPv74YwBP39pZsmQJfHx8YGtr\nix49emhvi6xduxZKpRLu7u6YMWMGPD09oVQqERISglOnTmn7nzt3LubPn4+srCwoFAoEBgZWmTfw\n9DbW6tWr0aVLF9jY2MDFxQWjRo3SmRTc0O2rycqVK2FnZwdHR0fk5+dj/vz58PLywpUrV/RuPwB8\n9913+N3vfgc7Ozs4OTkhKCgIhYWFBuWoL0ZV+85QmZmZ6Ny5M+zt7bVjb508ebLG9WpzLCroO4ee\nl5eXBz8/P1haWuLVV181eHtq69tvv63zYLC8hmqnYny3nj176ixvjtdNWloaAGDw4ME1tm3u1wYR\nNZK6DhJTl3Fmbt68KQAkISFBu+zDDz8UAPLPf/5THjx4IPn5+TJo0CCxt7eX0tJSbbs1a9YIAImL\ni5M7d+7I3bt35fPPP5fJkyeLiMh7770nNjY2smvXLrl3754sXLhQWrVqJWfOnBGRp+M62dvby+XL\nl+Xx48eSnp4u/fr1E0dHR7lx44Y2ztixYyUgIKDGvJcsWSLW1tayefNmuX//vqSlpUmfPn2kdevW\n8uuvvxq8fbVV0d+cOXMkISFBxowZIz/99JPe7X/48KE4OTnJihUrpKSkRH799VcZM2aMFBQUGJRj\nTfu4qn1XW0OGDBF/f3+5du2aqNVq+fHHH+Wll14SpVIpv/zyi7ZdRkaGAJBPP/1Uu6y2x6Kmc2jr\n1q0CQM6fPy8iIqWlpTJ27FjZt29fnbbpWS+99JL07Nmzys8OHDggjo6OEh0dXWM/r7zyigCQe/fu\naZfxGvpNQECAqFQq7d/v3bsnf//738XOzk7CwsIq7c+mfN08vy3FxcWSlJQkvr6+MmzYMHn48KFO\n++ZybTSFcf6aG46rS89qiHH+mkzxV1JSol2WmJgoACQzM1NEnv5D4+zsLKGhoTr9aTQaiY+Pl5KS\nErGzs5Pw8HDtZ8XFxWJjYyOzZs0SkadfXM/+QyoicubMGQEgy5Yt0y6rzRdXcXGxODg46MQTETl9\n+rQA0PkSr832GaKq/mra/h9//FEAyIEDB2rd5/M51mYf17f4e744SktLEwDy3nvvaZc9/wVX22NR\n0zkkovsFp1arZeLEiZKUlFSn7XmevuLPEPqKP15DTwsmADp/FAqFfPLJJ5UKxaZ+3VS1LQAkKChI\nvvzyS3ny5IlO++ZybbD4MxyLP3pWix3k2draGgC0E2qnpaXh/v37eOWVV3TaWVhYYM6cObhy5QqK\ni4u1U7sAgK2tLdq0aVPl7Y0Kffv2hZ2dnd42VUlPT8fDhw/Rt29fneX9+vWDtbW1zm2w2mxffdW0\n/f7+/nB3d8ebb76JpUuXIjs7u8Y+n8+xrvu4PoKCgqBSqbS3uapS22NR0zn0rLKyMkyaNAnu7u7N\n9paWOV9Dz74h++c//xkiApVKVemZ0uZw3Ty7LWq1Grdu3cK7776LyMhI9OjRA7dv36523aZ8beza\ntavSUDb8U/2fijl4TZ0H/zSNP1XNyWwoy3r3YASFhYUAAGdn5yo/f/ToEQBg0aJFlYa/8PT01Nu3\njY0NCgoKDMqn4kFrBweHSp85OzujqKjIoP7qq6btt7W1RXJyMj744APExMQgOjoa48ePx6ZNm7Tz\nVNY3RmOxsrLSWyTX9ljUdA49a/bs2Xj8+DG++eYb/L//9//QtWvXuqTepJjrNbR48WJs3rwZCxcu\nxMiRI9GuXTvtZ83turG0tISXlxfefvttlJWVYdq0aYiLi8Nf//rXKts35Wujf//+ePfdd+u0rjn6\n/vvvER8fr/M8KpmvivOhPppF8de2bVsAqPZ/uW5ubgCANWvWYO7cubXuV61W4/79+/D29jYon4p/\nJKv6gqpLf/VVm+3v1q0b9u/fj4KCAqxevRrLly9Ht27dsHjx4gaL0dA0Gg3u3r0LHx+fatvU9ljU\ndA49a/z48XjrrbfQvXt3TJkyBampqbC0bBaXSrXM9RpydHTE8uXLERERgVmzZukMMt2cr5ugoCAA\nwOXLl6tt05SvDW9vb4wfP97g9cxZfHw89xlp1bf4a5K3fZ/n5+cHV1dXHDlypMrP27VrB6VSafDo\n8ykpKRAR9O/f36D1unfvDgcHB/zwww86y0+dOoXS0lK8+OKLBvVXXzVtf25urvZLws3NDXFxcejT\np4/eLw5DYzSGY8eOoby8HH369Km2TW2PRU3n0LNCQ0PRunVrbNiwAWfPnm1WM2lUx5yvoSlTpuCl\nl17CgQMHdMbGa87XzdmzZwEAnTp1qrYNrw0iqk6zKP5sbGywcOFCHD9+HJGRkcjJyUF5eTmKiopw\n+fJlKJVKvP3229i6dSvWr1+PwsJClJWV4datW/jPf/6j7ae8vBz37t2DRqNBWloa5s6dCx8fH0RE\nRGjbuLq6Ijc3F9nZ2SgqKqrylqNSqcT8+fOxZ88efPXVVygsLMSlS5cwc+ZMeHp6Yvr06cbYLTr5\n6Nv+3NxczJgxAz///DNKS0tx/vx5XL9+3aAv7Nrs49rsO31KS0vx4MEDaDQanDt3DpGRkfD19dU5\nPlXlVZtjUdM5VJURI0YgIiICMTEx2i/bxpCUlFTnoV5qy5yvIYVCgbVr10KhUCAyMhL37t3T5tAc\nrpuSkhKUl5dDRJCbm4tNmzZh0aJFaN26td5bpy3h2iCiRmKst00SEhKkTZs2AkDs7OxkxIgRkpiY\nKHZ2dgJAOnToIFlZWbJhwwZxcnISAOLr66szzMe6deskKChIlEqlKJVK6d27tyQmJoqIyJMnT2TB\nggXi4+MjlpaW4ubmJmPHjpX09HQRefqmopWVlXh5eYmlpaU4OTnJqFGjJCsrSyfPc+fOia+vr9ja\n2srAgQNl0aJFlfIWESkvL5e//OUv0qFDB7GyshIXFxcZPXq0XLlyRduXodtXkxUrVoitra0AkHbt\n2snmzZu1n+nb/uzsbAkJCREXFxexsLCQtm3byocffigajcagHGvax8/vu2eHkqjJpk2bJDQ0VNzd\n3cXS0lJeeOEFmThxoly/fl3b5q9//at4eHgIALG3t5cxY8bU+lhUqO4c2r17t7i4uAgA8fPzk/z8\nfCksLJR27doJAHFwcJB//OMftd4eEZHvv/9eBgwYIJ6ento3Ndu0aSMhISHy3XffadsdOnRIHB0d\n5ZNPPqm2r9TUVOnWrZu0atVK209MTAyvof9u37/+9S/p2LGjdj+3bdtWZsyYoZNXRESEABBnZ2eJ\ni4urcZtNdd18+umn1b7pa2NjIx06dJBZs2bpDK/TnK4Nvu1rOL7tS89qiLd9FSJ1m3Rzx44dmDBh\nQrOZ93bGjBnYuXNns5jbk6gp4jVEDWHcuHEAgJ07d5o4k+ajuX3fUuNqgPNhZ7O47dtQysrKTJ0C\nUbPGa4iIqPkzq+KvKfr5559rNa5PeHi4qVM1SEvcrpa4TUTU8h09ehRRUVEoLy/H6NGj4ePjA6VS\nCS8vL4wcOVLvWKo1KS8vx5o1axASElJtG7VajdjYWAQGBsLa2hrOzs7o3r17rcbObCpxV6xYgc6d\nO8PW1hb29vbo3LkzFi9erB0qqUJ0dDS6du0KJycn2NjYIDAwEO+//z4ePnyobfPNN99gxYoVpv3P\ntAnvORtNVFSUWFtba59Z2blzp6lTImpWeA1RQ+Ezf4arz/ftkiVLZPjw4VJYWChqtVpeeOEFOXHi\nhDx69EiuXr0qQ4cOFZVKJTk5OQb3/csvv8iAAQMEgN4ZjEaPHi2dOnWS1NRUUavVkpubKyNGjJBL\nly7VaZtMETcsLExWrVol+fn5UlRUJDt27BArKysZOnSoTrvBgwdLYmKi3LlzRwoLC2X79u1iZWUl\nr776qk67+Ph4GTx4sM5sTbXV7KZ3IyIi89YUir/i4mIJDg5uNjHq+n0bFxcnHTt21E5BqFar5fXX\nX9dpUzHdX0xMjEF9X7hwQcaMGSNfffWV9OrVq9oibOvWraJQKCQtLc3g/JtS3NGjR+tM5SgiMm7c\nOAEgubm52mVhYWGi0Wh02o0fP14A6LykJSISGRkpwcHBolarDcqlxU7vRkRE1Fg2btyI/Pz8Zh9D\nn8zMTCxevBjLli2DUqkE8HSWmGcHOgcAf39/AEBWVpZB/ffs2RO7d+/G5MmTYWNjU227Tz/9FH36\n9NEOTF5fpoq7Z88e7X6s4OXlBQA6t3QPHDgACwsLnXatW7cGABQXF+ssX7p0KS5cuFDvAZvrgsUf\nERE1aSKC1atXo0uXLrCxsYGLiwtGjRqlMz9yZGQkrK2t0aZNG+2yP/3pT7C3t4dCodDOYDJ37lzM\nnz8fWVlZUCgUCAwMxNq1a6FUKuHu7o4ZM2bA09MTSqUSISEhOvNM1ycGAHz77beNPqZnhbVr10JE\nMGLECL3tSkpKAABOTk4NnkNpaSlSU1PRq1evBu+7KcTNyMiAs7MzfH199bbLycmBra0t2rdvr7Pc\nxcUFgwcPRnx8vNHf5GbxR0RETdrSpUsRFRWFDz/8EPn5+Th+/Dhu3ryJQYMGIS8vD8DTYuf56c8S\nExOxbNkynWXx8fEYPnw4AgICICLIzMxEZGQkIiIiUFxcjDlz5iA7Oxvnzp2DRqPB0KFDcfPmzXrH\nAH57W768vLzhdk41Dh48iE6dOsHOzk5vu9OnTwMABg4c2OA55ObmorS0FGfPnkVoaKi2qO7SpQsS\nExMbreBpzLhqtRo5OTlYt24djh49ioSEBFhbW1fbvri4GMnJyZg2bVqV7Xr37o2cnBxcvHixzjnV\nBYs/IiJqskpKSrB69WqMGTMGb775JlQqFYKCgvDZZ5/h9u3b2LBhQ4PFsrS01P662LVrV6xfvx5F\nRUXYtGlTg/QfFhaGwsLCWs8NXVePHj3CtWvXEBAQUG2bvLw8bNu2DXPmzEFwcHCNvxDWRcXtUDc3\nN8TExCA9PR15eXkYNWoUZs+ejS1btjR4zMaO265dO3h7e2Pp0qVYuXIlJkyYoLd9bGwsPD09q50K\nsUOHDgCAS5cu1TmnumDxR0RETVZ6ejoePnyIvn376izv168frK2tdW7LNrS+ffvCzs5O5/Zyc5Cf\nnw8R0furX3BwMObMmYNRo0YhKSkJVlZWDZ5HxTN53bp1Q0hICFxdXaFSqbBs2TKoVKoGLdyNFffm\nzZvIz8/Hli1b8OWXX6J3797VPtu5Z88e7NixA4cPH4ajo2OVbSqOUcUv2MZiadRoREREBrh//z4A\nwMHBodJnzs7OKCoqatT4NjY2KCgoaNQYDe3x48cAoPeFCHd3d2zcuBHdunVrtDw8PT0BQPssZAVr\na2v4+voa/JJJU4hrZWUFNzc3DBs2DO3bt0fHjh0RGxtb6aWNbdu2YfXq1UhJSUHbtm2r7c/W1hbA\nb8fMWFj8ERFRk+Xs7AwAVRZ59+/fh7e3d6PFVqvVjR6jMVQUFPoGEXZzc9Pu28bi4OCADh064PLl\ny5U+02g0UKlUzTpuYGAgLCwskJ6errM8ISEBhw8fRnJycpX/aXlWaWkpgN+OmbHwti8RETVZ3bt3\nh4ODA3744Qed5adOnUJpaSlefPFF7TJLS0uo1eoGi9+kmhkAACAASURBVJ2SkgIRQf/+/RstRmNw\nd3eHQqHAgwcPqm2zf/9+7VAljWnChAk4f/48rl69ql1WXFyM69evN9gwLI0d986dO5g0aVKl5RkZ\nGSgrK0O7du0APH0rfcGCBbh06RL27t1bY+EHQHuMPDw8DMqpvlj8ERFRk6VUKjF//nzs2bMHX331\nFQoLC3Hp0iXMnDkTnp6emD59urZtYGAg7t69i71790KtVqOgoADXr1+v1Kerqytyc3ORnZ2NoqIi\nbTFXXl6Oe/fuQaPRIC0tDXPnzoWPjw8iIiIaJEZSUpJRhnqxs7ODv78/bt26VeXnmZmZ8PDwqPJl\nhfDwcHh4eODcuXMNksu8efPg6+uLiIgI3LhxA3fu3MGCBQtQUlKCDz74oFnEtbe3x5EjR5CcnIzC\nwkKo1WqcP38eU6dOhb29PebNmwcAuHz5MlauXIkvvvgCVlZWlab+XLVqVaW+K45RYxbCVWHxR0RE\nTdpHH32E2NhYREdHo3Xr1hg8eDD8/PyQkpICe3t7bbtZs2YhNDQUEydORKdOnfDxxx9rb6cFBwdr\nh2yZOXMm3N3d0bVrV7z22mu4e/cugKfPXQUFBcHW1haDBg1Cx44dcezYMZ1n5+obw1jCwsKQnp6u\nHcfvWfqGOiktLUV+fj727dunt//U1FQMHDgQbdu2xalTp3Dx4kV4enpiwIABOH78uLadi4sLTpw4\nAW9vb/Tq1QteXl44ffo0Dh48qDMOX1OOq1QqMWDAALzzzjvw8vKCo6Mjxo0bBz8/P6SmpqJ79+4A\n9O/X6pw5cwZeXl7o0aOHwevWS13nBuH0bkREZKimML1bVaZPny6urq6mTqNKdfm+zcjIEEtLS9m8\nebNB65WVlcmgQYNk48aNBq1XX+YWV0Tk9u3bolQqZdWqVQatx+ndiIiIGoi+FySam8DAQERHRyM6\nOlpn+jF9ysrKsHfvXhQVFSE8PLyRMzTfuBWWLl2KXr16ITIy0uixWfwRERG1QFFRURg3bhzCw8P1\nvvxRISUlBbt370ZSUlKNM4M0JHOLCwCrV6/GhQsXcOjQoUYZY7EmLP6IiMisLVy4EJs2bcKDBw/Q\nvn177Nq1y9QpNZiYmBhERkYiLi6uxrZDhgzB119/rTN3sTGYW9x9+/bhyZMnSElJgYuLi1FjV+A4\nf0REZNZiY2MRGxtr6jQazbBhwzBs2DBTp0H/NXLkSIwcOdKkOfCXPyIiIiIzwuKPiIiIyIyw+CMi\nIiIyIyz+iIiIiMxIvV/42LFjR0PkQUREZqBiOit+d9Te999/D4D7jJ6qOB/qQyFSh/lI8PQkrGpe\nQCIiIiJqXHUs3wBgZ52LPyKi5qriP6/854+IzNBOPvNHREREZEZY/BERERGZERZ/RERERGaExR8R\nERGRGWHxR0RERGRGWPwRERERmREWf0RERERmhMUfERERkRlh8UdERERkRlj8EREREZkRFn9ERERE\nZoTFHxEREZEZYfFHREREZEZY/BERERGZERZ/RERERGaExR8RERGRGWHxR0RERGRGWPwRERERmREW\nf0RERERmhMUfERERkRlh8UdERERkRlj8EREREZkRFn9EREREZoTFHxEREZEZYfFHREREZEZY/BER\nERGZERZ/RERERGaExR8RERGRGWHxR0RERGRGWPwRERERmREWf0RERERmhMUfERERkRlh8UdERERk\nRixNnQARUWO6desWpk6dirKyMu2ye/fuwdHREf/zP/+j07ZTp074/PPPjZwhEZFxsfgjohbN29sb\n169fR1ZWVqXPvvvuO52/v/zyy8ZKi4jIZHjbl4havClTpsDKyqrGduHh4UbIhojItFj8EVGLN3ny\nZGg0Gr1tunXrhq5duxopIyIi02HxR0QtXkBAAHr06AGFQlHl51ZWVpg6daqRsyIiMg0Wf0RkFqZM\nmQILC4sqP9NoNBg3bpyRMyIiMg0Wf0RkFiZOnIjy8vJKy1u1aoX+/fvDz8/P+EkREZkAiz8iMgue\nnp4YMGAAWrXS/WevVatWmDJliomyIiIyPhZ/RGQ23nrrrUrLRARjxowxQTZERKbB4o+IzMYbb7yh\n89yfhYUFfv/738Pd3d2EWRERGReLPyIyGy4uLhg6dKi2ABQRvPnmmybOiojIuFj8EZFZefPNN7Uv\nflhZWWHUqFEmzoiIyLhY/BGRWRkxYgRsbGwAAMOHD4eDg4OJMyIiMi4Wf0RkVuzt7bW/9vGWLxGZ\nI4WISF1W3LFjByZMmNDQ+RARERFRDepYvgHATsv6Bt++fXt9uyAiMqqysjJs374dkyZNMnUqZmfN\nmjUAgHfffdfEmTQf33//PeLj4/l9SwB+Ox/qo97F3/jx4+vbBRGR0Y0ePRpKpdLUaZidnTt3AuB3\nh6Hi4+O5z0irvsUfn/kjIrPEwo+IzBWLPyIiIiIzwuKPiIiIyIyw+CMiIiIyIyz+iIiIiMwIiz8i\nImp2Dh06BJVKhf3795s6lSbv6NGjiIqKQnl5OUaPHg0fHx8olUp4eXlh5MiRSEtLq3Pf5eXlWLNm\nDUJCQqpto1arERsbi8DAQFhbW8PZ2Rndu3dHdnZ2s4m7YsUKdO7cGba2trC3t0fnzp2xePFiFBYW\n6rSLjo5G165d4eTkBBsbGwQGBuL999/Hw4cPtW2++eYbrFixAmVlZQbn0VBY/BERUbNTjwFuzcpH\nH32EtWvXYuHChSgvL8eJEyewZcsW3L17FydPnkRJSQlefvll5ObmGtx3RkYGXn75ZcybNw/FxcXV\ntpsw4f+zd+9xUVVrH8B/o8MwDDADKCCCIBfvomRaQXqMY1lKKpooVue8dCpR8wWSjJQswcDMUo4I\ndjQPnTQVUF/whvWactQ6XvIShGWC4o0EFeQig8zA8/7hy+TIMMzAwAzO8/18+MO9117r2XvPmnnc\nl7Vm4quvvsLXX3+N2tpa/PLLL/D29lZLiEy93SNHjuDNN9/ElStXUFpaimXLlmHFihWYPn26WrmD\nBw9i/vz5KC4uxq1bt5CYmIikpCSEhISoykyePBlisRjjxo3DnTt39I7FIKiN0tPTqR2bM8YYM0PT\np0+n6dOnGzsMg6qtrSV/f/8Oq7+tv7fLly+n/v37k1wuJyIihUJBL774olqZEydOEABKSEjQq+6z\nZ8/StGnTaPPmzeTn50fDhw/XWG7r1q0kEAgoLy9P7/hNqd2pU6eqjmOTkJAQAkAlJSWqZUFBQaRU\nKtXKzZgxgwDQlStX1JZHRESQv78/KRQKvWIxQP6VwVf+GGOMsXbYuHEjysrKjB2GmsLCQixZsgRx\ncXGqMS2FQmGz2+ReXl4AgKKiIr3qHz58OHbs2IFXXnkFlpaWLZZbt24dRowYAV9fXz33wLTa3blz\nZ7OxQV1dXQFA7Urinj170L17d7VyPXv2BIBmVymXLl2Ks2fPtnvA5rbg5I8xxliXcvToUbi7u0Mg\nEGDt2rUAgNTUVFhbW0MikSA7OxsTJkyAVCqFm5sbtm7dqtp2zZo1EIvFcHJywpw5c+Di4gKxWIyA\ngAAcP35cVS4iIgIikQi9evVSLXvrrbdgbW0NgUCAW7duAQCioqIQHR2NoqIiCAQC+Pj4AAD2798P\nqVSKhISEzjgkzaxZswZEhMmTJ2stJ5fLAQBSqdTgMdTX1+PYsWPw8/MzeN2m0O6FCxdgZ2cHDw8P\nreWuX78OKysreHp6qi23t7fH2LFjkZSU1OmPMXDyxxhjrEsZPXo0fvjhB7Vl8+bNw9tvvw25XA5b\nW1ukp6ejqKgIXl5eePPNN6FQKADcT+rCwsJQW1uLyMhIFBcX4/Tp01AqlXjuuedw9epVAPeTp4en\nU0tJSUFcXJzasqSkJEyaNAne3t4gIhQWFgKA6mH+xsbGDjkGrdm7dy8GDBgAiUSitdyJEycA3D+m\nhlZSUoL6+nqcOnUKgYGBqkR70KBBSElJ6bCEpyPbVSgUuH79OtauXYsDBw4gOTkZIpGoxfK1tbU4\nePAg3nzzTY3lHnvsMVy/fh0//fRTm2NqC07+GGOMPVICAgIglUrh6OiI0NBQ3L17F1euXFErIxQK\nMWjQIFhaWmLw4MFITU1FdXU10tLSDBJDUFAQqqqqsGTJEoPUp4+7d+/i0qVL8Pb2brFMaWkptm3b\nhsjISPj7+7d6hbAtmm6HOjo6IiEhAQUFBSgtLUVwcDDmz5+PLVu2GLzNjm63T58+cHNzw9KlS/HJ\nJ59g5syZWssnJibCxcUFH330kcb1/fr1AwDk5+e3Oaa24OSPMcbYI6vpakvTlb+WjBw5EhKJBL/+\n+mtnhNWhysrKQERar/r5+/sjMjISwcHByMnJgYWFhcHjaHomb8iQIQgICICDgwNkMhni4uIgk8mw\nfv16g7fZ0e1evXoVZWVl2LJlC/71r3/hsccea/F5z507dyIjIwPffPMNbG1tNZZpOkelpaVtjqkt\nhJ3aGmOMMWaiLC0tcfPmTWOH0W51dXUAoPWFCCcnJ2zcuBFDhgzpsDhcXFwAQPV8ZBORSAQPDw+9\nXzIxhXYtLCzg6OiI8ePHw9PTE/3791cN5/Kgbdu2YdWqVcjNzUXv3r1brM/KygrAH+ess3Dyxxhj\nzOwpFArcuXMHbm5uxg6l3ZoSCm2DCDs6OsLOzq5D47CxsUG/fv1w7ty5ZuuUSiVkMlmXbtfHxwfd\nu3dHQUGB2vLk5GR88803OHjwIGxsbLTWUV9fD+CPc9ZZ+LYvY4wxs5ebmwsiwlNPPaVaJhQKW71d\nbIqcnJwgEAhQWVnZYpndu3erhirpSDNnzsSZM2dw8eJF1bLa2lpcvnzZYMOwdHS7t2/fxssvv9xs\n+YULF9DQ0IA+ffoAuD/weExMDPLz85GVldVq4gdAdY6cnZ31iqm9OPljjDFmdhobG1FRUQGlUom8\nvDxERUXB3d0dYWFhqjI+Pj4oLy9HVlYWFAoFbt68icuXLzery8HBASUlJSguLkZ1dTUUCgVycnKM\nNtSLRCKBl5cXrl27pnF9YWEhnJ2dNb6sEBoaCmdnZ5w+fdogsSxYsAAeHh4ICwvDlStXcPv2bcTE\nxEAul+O9997rEu1aW1vj22+/xcGDB1FVVQWFQoEzZ87gv/7rv2BtbY0FCxYAAM6dO4dPPvkEGzZs\ngIWFBQQCgdrfp59+2qzupnPUkYmwJpz8McYY61LWrl2LUaNGAQBiYmIwZcoUpKamYvXq1QCAYcOG\n4eLFi9iwYQOio6MBAC+88AIuXLigqqOurg6+vr6wsrLCmDFj0L9/fxw6dEjtObl58+YhMDAQs2bN\nwoABA7Bs2TLV7Tl/f3/VsDBz586Fk5MTBg8ejIkTJ6K8vLxTjoM2QUFBKCgoUI3j9yBtQ53U19ej\nrKwM2dnZWus/duwYRo8ejd69e+P48eP46aef4OLigqeffhqHDx9WlbO3t8eRI0fg5uYGPz8/uLq6\n4sSJE9i7d6/aOHym3K5YLMbTTz+NN954A66urrC1tUVISAj69u2LY8eOYejQoQDaNuXgyZMn4erq\nimHDhum9bbu0dW4Qnt6NMcaYvkxherfw8HBycHAwagz6aMvv7YULF0goFNKmTZv02q6hoYHGjBlD\nGzdu1Gu79jK3domIbt26RWKxmD799FO9tuPp3RhjjLE20PYyxKPAx8cH8fHxiI+PV5t+TJuGhgZk\nZWWhuroaoaGhHRyh+bbbZOnSpfDz80NERESnt92pyd++ffsgk8mazS1o6rpq3F3FqFGj0L1791an\n4vn0009VDzJ//vnnBo1h+fLlkMlkEAgEOHv2rEHqjI+Px+DBgyGVSmFpaQkfHx+8++67On8RP+jY\nsWMYNGgQunXrBoFAAGdn5xYHDTVFHd2HduzYAS8vr2bP2IjFYnh6euJvf/sbLl261CFtG1pL+yIU\nCtGzZ088++yz2Llzp9o2Xa1vsM6xaNEihISEIDQ0VOvLH01yc3OxY8cO5OTktDoziCGZW7sAsGrV\nKpw9exb79u3rkDEWW9XWa4Ztuey4Z88ekkqltGvXrrY2axRdNe6uZNy4cTR8+PBWy124cIEA0Lp1\n6wwew9atWwkAnTlzxiD1jR07llJSUuj27dtUVVVF6enpZGFhQS+88EKb63z++ecJAFVUVBgkxs7S\nWX3I29ubZDIZEd2/nVNaWkpfffUVSSQScnJyolu3bnVo+4b04L4QEZWXl9OBAwdo4MCBBIC2bdum\nVr6r9A1j3/ZdtGgRiUQiAkB9+/alzMxMo8Wiq/be5vvmm28oJibGgBGx9sjKyqLExERSKpVt2r7L\n3fYNCgpCZWUlJk2a1JnN6kUulyMgIEBtWVeI2xRoOnb6EAgEBozG+GxsbBAeHg4HBwfY2tpixowZ\nmDp1Kvbv3696UPxRZCp9qFu3bnBycsJf/vIXzJ8/H2VlZThw4ECnta8rXfuNvb09xo0bh7///e8A\ngIyMjI4O7ZGUmJiIe/fugYhw6dIlTJ8+3dghdbjx48fj448/NnYY7P9NmTIFixYtQvfu3Y0WAw/y\n/JCNGze2OFUL0669x84ol7470J49e5ot69mzJ4D74009qkyxD/n4+AAAbty4YeRImtP3ePXt2xcA\ncOfOnQ6KiDH2qOu0K39Hjx6Fu7s7BAIB1q5dCwBITU2FtbU1JBIJsrOzMWHCBEilUri5uWHr1q3N\n6ti0aRNGjhwJsVgMa2tr9O3bF8uWLQNw/8HNDz74AO7u7rCyssKwYcOQnp4OAFizZg3EYjGcnJww\nZ84cuLi4QCwWIyAgAMePH1fVHxUVhejoaBQVFUEgEMDHx0dj3MD9V7pXrVqlmhjc3t4ewcHBavNC\n6rt/rfnkk08gkUhga2uLsrIyREdHw9XVFefPn9e6/wDw73//G0888QQkEgmkUil8fX1RVVWlV4za\n2tB07PRVWFiIgQMHwtraWjX8wtGjR1vdTpdz0UTbZ+hhpaWl6Nu3L4RCIV544QW990eT69evw8rK\nCp6enqpl+/fvb/N4YNyHdNM0xMfw4cPVlnfFfpOXlwcAGDt2bKtlu1LfYIx1orbeMG7LPeerV68S\nAEpOTlYti42NJQD03XffUWVlJZWVldGYMWPI2tqa6uvrVeVWr15NAGj58uV0+/ZtKi8vp3/84x/0\nyiuvEBHRO++8Q5aWlrR9+3aqqKigxYsXU7du3ejkyZNEdP/Vfmtrazp37hzV1dVRQUEBjRo1imxt\nbenKlSuqdl566SXy9vZuNe4PPviARCIRbdq0ie7cuUN5eXk0YsQI6tmzJ924cUPv/dNVU32RkZGU\nnJxM06ZNo19++UXr/tfU1JBUKqUVK1aQXC6nGzdu0LRp0+jmzZt6xdjaMdZ07HQ1btw48vLyokuX\nLpFCoaCff/6ZnnzySRKLxfTbb7+pyml6rknXc9HaZ+jh55rq6+vppZdeouzs7Dbt08Pu3r1Ltra2\nFBERobZ8z549ZGtrS/Hx8a3WoemZP+5Df3j4ObmKigr68ssvSSKRUFBQULPjacr95uF9qa2tpZyc\nHPLw8KDx48dTTU2NWvmu0jeM/cxfV8RDq7EHGeKZP5NJ/uRyuWpZSkoKAaDCwkIiuv9FY2dnR4GB\ngWr1KZVKSkpKIrlcThKJhEJDQ1XramtrydLSkubNm0dE93+4HvwiJSI6efIkAaC4uDjVMl1+uGpr\na8nGxkatPSKiEydOEAC1H3Fd9k8fmuprbf9//vlnAkB79uzRuc6HY9TlGLc3+Xv4hY+8vDwCQO+8\n845q2cM/cLqei9Y+Q0TqP3AKhYJmzZpFOTk5bdofTWJjY6l///5UVVXV5jq0JX/ch+4nTADU/gQC\nAX300UfNEkVT7zea9gUA+fr60r/+9S+6d++eWvmu0jc4+dMfJ3/sQYZI/kzymT+RSAQAqjkV8/Ly\ncOfOHTz//PNq5bp3747IyEj89NNPqK2tVY2yDdyfJLlXr14ab280GTlyJCQSidYymhQUFKCmpgYj\nR45UWz5q1CiIRCK122C67F97nT9/Xuv+e3l5wcnJCa+++ioiIyMRFhamem5I1xhba6Mj+Pr6QiaT\nqW5zaaLruWjtM/SghoYGvPzyy+jdu7fBbmnt3LkTGRkZ+Pbbb2Fra2uQOrUx5z4kk8lUz8O9++67\nWLlyJWQyWbNnSrtCv3lwX5RKJUpLS/Htt98iIiICiYmJOHr0qOo50oeZct+4du0av7Cih//85z8A\n+CUfdl/T56E9TDL5e1hVVRUAwM7OTuP6u3fvAgDef/99vP/++2rrXFxctNZtaWmJmzdv6hVP05ex\npkmb7ezsUF1drVd97dXa/ltZWeHgwYN47733kJCQgPj4eMyYMQNpaWmqqYra20ZHsbCw0Jok63ou\nWvsMPWj+/Pmoq6vDrl27MHv2bAwePLgtoats27YNq1atQm5uLnr37t2uutrKXPvQkiVLsGnTJixe\nvBhTpkxRTcAOdL1+IxQK4erqitdeew0NDQ148803sXz5cnz22Wcay5ty3zh27JjGeWWZdnzMmKF0\niRk+mn4wb926pXG9o6MjAGD16tUgIrU/bRmyQqHAnTt34Obmplc8TV+Smn6g2lJfe+my/0OGDMHu\n3btRUlKCmJgYpKena5xkuj1tGJpSqUR5eTnc3d1bLKPruWjtM/SgGTNm4H//939hZ2eHv/71r1Aq\nlW0JHwCQnJyMzZs34+DBg0ZL/ADz7UO2trb4+OOPUV1djXnz5qmt68r9pmkS+HPnzrVYxpT7xvTp\n05sdD/5r+a/pBSFjx8F/pvH34EtpbdUlkr++ffvCwcEB3377rcb1ffr0gVgs1nv0+dzcXBARnnrq\nKb22Gzp0KGxsbPDjjz+qLT9+/Djq6+vx+OOP61Vfe7W2/yUlJaofCUdHRyxfvhwjRozQ+sOhbxsd\n4dChQ2hsbMSIESNaLKPruWjtM/SgwMBA9OzZE+vXr8epU6faNJMGESEmJgb5+fnIysrSePWlM5lz\nH/rrX/+KJ598Env27FG7bdaV+82pU6cAAAMGDGixjKn2DcaY8XWJ5M/S0hKLFy/G4cOHERERgevX\nr6OxsRHV1dU4d+4cxGIxXnvtNWzduhWpqamoqqpCQ0MDrl27ht9//11VT2NjIyoqKqBUKpGXl4eo\nqCi4u7sjLCxMVcbBwQElJSUoLi5GdXW1xluOYrEY0dHR2LlzJzZv3oyqqirk5+dj7ty5cHFxQXh4\neGccFrV4tO1/SUkJ5syZg19//RX19fU4c+YMLl++rNcPti7HWJdjp019fT0qKyuhVCpx+vRpRERE\nwMPDQ+38aIpLl3PR2mdIk8mTJyMsLAwJCQmqH1tdnTt3Dp988gk2bNgACwuLZlN1PXj1KCcnp81D\nvejKnPuQQCDAmjVrIBAIEBERgYqKClUMXaHfyOVyNDY2gohQUlKCtLQ0vP/+++jZsyfefvttrW2b\nYt9gjJkAaiN93zZJTk6mXr16EQCSSCQ0efJkSklJIYlEQgCoX79+VFRUROvXryepVEoAyMPDQ22Y\nj7Vr15Kvry+JxWISi8X02GOPUUpKChER3bt3j2JiYsjd3Z2EQiE5OjrSSy+9RAUFBUR0/01FCwsL\ncnV1JaFQSFKplIKDg6moqEgtztOnT5OHhwdZWVnR6NGj6f33328WNxFRY2MjrVy5kvr160cWFhZk\nb29PU6dOpfPnz6vq0nf/WrNixQqysrIiANSnTx/atGmTap22/S8uLqaAgACyt7en7t27U+/evSk2\nNpaUSqVeMbZ2jB8+dg8OJdGatLQ0CgwMJCcnJxIKhdSjRw+aNWsWXb58WVXms88+I2dnZwJA1tbW\nNG3aNJ3PRZOWPkM7duwge3t71ZRPZWVlVFVVRX369CEAZGNjQ1999ZXO+5Ofn6/xTc2mv5UrV6rK\n7tu3j2xtbemjjz5qsb5jx47RkCFDqFu3bgSAevXqRQkJCdyH/n//vv/+e+rfv7/q+Pbu3ZvmzJmj\nFldYWBgBIDs7O1q+fHmr+2ysfrNu3boW3/S1tLSkfv360bx589SG1+lKfYPf9tUfv+3LHmSIt30F\nRERtSRozMjIwc+ZMtHHzTjdnzhxkZmbi9u3bxg6FsS6J+xAzhJCQEABAZmamkSPpOrra7y3rWAb4\nPGR2idu+htLQ0GDsEBjr0rgPMcZY12dWyZ8p+vXXX5s9D6bpLzQ01Nih6uVR3K9HcZ8YY4yZH7NI\n/hYvXoy0tDRUVlbC09MT27dvN3ZIKgMHDtTp1e5t27YZO1S9PIr79Sjuk65MuQ8xxgzjwIEDWLRo\nERobGzF16lS4u7tDLBbD1dUVU6ZM0TrgvjZbtmzBqFGjYGtrCw8PD7z22mu4ceNGm+M0dH0KhQKJ\niYnw8fGBSCSCnZ0dhg4diuLi4ha3qaurw8CBA9XG79y1axdWrFjRJe6QmEXyl5iYiHv37oGIcOnS\nJUyfPt3YITHWpXAfYuzR9uGHH2LNmjVYvHgxGhsbceTIEWzZsgXl5eU4evQo5HI5/vSnP6GkpESv\netPT0/HKK68gJCQE165dQ3Z2Ng4fPowJEya0aZxIQ9cH3B88+6uvvsLXX3+N2tpa/PLLL/D29kZN\nTU2L28TGxuL8+fNqyyZPngyxWIxx48apBlk3VWaR/DHGGGNN5HI5AgICunwbhvLxxx9j27ZtyMjI\nUE0/6e/vj9GjR0MikcDT0xMJCQmorKzEl19+qVfd//jHP9C7d28sXLgQMpkMfn5+WLBgAc6ePdvq\nNI6dUd+2bduQlZWFzMxMPPnkkxAKhXBxcUF2drbatIwP+uGHH/Dzzz9rXBcZGYnhw4dj4sSJ7Zog\noKNx8scYY8ysbNy4EWVlZV2+DUMoLCzEkiVLEBcXB7FYDOD+VIK7d+9WK+fl5QUAKCoq0qv+q1ev\nwsXFBQKBQLWsaZrFy5cv6x2voetbt24dRowYoZo1pzVyuRwLFy5EUlJSi2WWLl2Ks2fPai1jbJz8\nMcYYM2lEhFWrVmHQoEGwtLSEvb09goOD8euv0yWBcQAAIABJREFUv6rKREREQCQSoVevXqplb731\nFqytrSEQCFTT10VFRSE6OhpFRUUQCATw8fHBmjVrIBaL4eTkhDlz5sDFxQVisRgBAQFqV5Pa0wYA\n7N+/v8MHdNfXmjVrQESYPHmy1nJyuRwAIJVK9arfy8urWRLc9HxeU0JprPrq6+tx7Ngx+Pn56bxN\nbGws3nrrLdXUjZrY29tj7NixSEpKMtnheTj5Y4wxZtKWLl2KRYsWITY2FmVlZTh8+DCuXr2KMWPG\noLS0FMD9JGbGjBlq26WkpCAuLk5tWVJSEiZNmgRvb28QEQoLCxEREYGwsDDU1tYiMjISxcXFOH36\nNJRKJZ577jlcvXq13W0AfwyV1NjYaLiD00579+7FgAEDIJFItJY7ceIEAGD06NF61b948WLcuHED\nycnJqK6uRkFBAZKSkvD888/rPS2koesrKSlBfX09Tp06hcDAQFXSP2jQIKSkpDRL3L7//nsUFRXh\n5ZdfbrXuxx57DNevX8dPP/2kV0ydhZM/xhhjJksul2PVqlWYNm0aXn31VchkMvj6+uLzzz/HrVu3\nsH79eoO1JRQKVVcXBw8ejNTUVFRXVyMtLc0g9QcFBaGqqgpLliwxSH3tdffuXVy6dAne3t4tlikt\nLcW2bdsQGRkJf3//Vq8QPmzs2LGIiYlBREQEpFIphg4diurqanzxxRdtitmQ9TW90OHo6IiEhAQU\nFBSgtLQUwcHBmD9/PrZs2aIqK5fLERUVhdTUVJ3q7tevHwAgPz9f77g6Ayd/jDHGTFZBQQFqamow\ncuRIteWjRo2CSCRq00P+uho5ciQkEona7eVHSVlZGYhI61U/f39/REZGIjg4GDk5ObCwsNCrjdjY\nWKxfvx7fffcdampqcPHiRQQEBMDf3191RdVY9VlaWgIAhgwZgoCAADg4OEAmkyEuLg4ymUztPxaL\nFy/G7Nmz4erqqlPdTce06cq0qeHkjzHGmMlqGjLDxsam2To7OztUV1d3aPuWlpa4efNmh7ZhLHV1\ndQD+SII0cXJywsGDB5GcnAyZTKZX/b///jtWrFiB2bNn489//jOsra3h6emJDRs2oKSkBCtXrjRq\nfS4uLgCgelaziUgkgoeHh+rllqNHjyI/Px9vvPGGznVbWVkB+OMYmxpO/hhjjJksOzs7ANCY5N25\ncwdubm4d1rZCoejwNoypKUHRNiixo6Oj6hzo68KFC2hoaEDv3r3VlkulUjg4OKCgoMCo9dnY2KBf\nv344d+5cs3VKpVKV7G7cuBHfffcdunXrpprJqemFj4SEBAgEAvz4449q29fX1wP44xibGk7+GGOM\nmayhQ4fCxsam2Y/r8ePHUV9fj8cff1y1TCgUQqFQGKzt3NxcEJHaiwSGbsOYnJycIBAIUFlZ2WKZ\n3bt363yr82FNSfPvv/+utry6uhrl5eWqIVqMVR9wf4DnM2fO4OLFi6pltbW1uHz5smr4l7S0tGYz\nOTVdDY6NjQURNXssoemYOjs76x1TZ+DkjzHGmMkSi8WIjo7Gzp07sXnzZlRVVSE/Px9z586Fi4sL\nwsPDVWV9fHxQXl6OrKwsKBQK3Lx5U+PYbw4ODigpKUFxcTGqq6tVyVxjYyMqKiqgVCqRl5eHqKgo\nuLu7IywszCBt5OTkmNRQLxKJBF5eXrh27ZrG9YWFhXB2dsbMmTObrQsNDYWzszNOnz7dYv2enp4I\nDAzEhg0bcPjwYcjlcly9elV1zl5//XWj1gcACxYsgIeHB8LCwnDlyhXcvn0bMTExkMvleO+997Ru\nq03TMdV1/MDOxskfY4wxk/bhhx8iMTER8fHx6NmzJ8aOHYu+ffsiNzcX1tbWqnLz5s1DYGAgZs2a\nhQEDBmDZsmWq224PvhAwd+5cODk5YfDgwZg4cSLKy8sB3H8+y9fXF1ZWVhgzZgz69++PQ4cOqT0T\n1942TE1QUBAKCgpU4/g9SNsYdfX19SgrK0N2dnaLZQQCATIzMxEaGorXX38d9vb2GDx4MK5cuYId\nO3ZgzJgxRq0PuD8m35EjR+Dm5gY/Pz+4urrixIkT2Lt3r17j/z3s5MmTcHV1xbBhw9pcR4eiNkpP\nT6d2bM4YY8wMTZ8+naZPn27sMJoJDw8nBwcHY4ehUUf+3l64cIGEQiFt2rRJr+0aGhpozJgxtHHj\nRoPEYer16ePWrVskFovp008/7ZD6DfB5yOArf4wxxhi0v/jwqPLx8UF8fDzi4+NV4961pqGhAVlZ\nWaiurkZoaGi7YzD1+vS1dOlS+Pn5ISIiotPb1hUnf4wxxpgZW7RoEUJCQhAaGqr15Y8mubm52LFj\nB3JyclqdGUQXpl6fPlatWoWzZ89i3759eo+J2Jk4+WOMMWbWFi9ejLS0NFRWVsLT0xPbt283dkid\nLiEhAREREVi+fHmrZceNG4evv/5abY7j9jD1+nSVnZ2Ne/fuITc3F/b29p3atr6Exg6AMcYYM6bE\nxEQkJiYaOwyjGz9+PMaPH2/sMLqsKVOmYMqUKcYOQyd85Y8xxhhjzIxw8scYY4wxZkY4+WOMMcYY\nMyOc/DHGGGOMmZF2v/AREhJiiDgYY4yZgWPHjgHg3w59NE0VxseMAWhxOj59CIi0zN+ixX/+8x+s\nWrWq3QEwxlhnu3HjBs6cOYMJEyYYOxTGGGuTzMzMNm/a5uSPMca6qoyMDMycOVPr3KWMMfaIyuRn\n/hhjjDHGzAgnf4wxxhhjZoSTP8YYY4wxM8LJH2OMMcaYGeHkjzHGGGPMjHDyxxhjjDFmRjj5Y4wx\nxhgzI5z8McYYY4yZEU7+GGOMMcbMCCd/jDHGGGNmhJM/xhhjjDEzwskfY4wxxpgZ4eSPMcYYY8yM\ncPLHGGOMMWZGOPljjDHGGDMjnPwxxhhjjJkRTv4YY4wxxswIJ3+MMcYYY2aEkz/GGGOMMTPCyR9j\njDHGmBnh5I8xxhhjzIxw8scYY4wxZkY4+WOMMcYYMyOc/DHGGGOMmRFO/hhjjDHGzAgnf4wxxhhj\nZoSTP8YYY4wxM8LJH2OMMcaYGeHkjzHGGGPMjHDyxxhjjDFmRjj5Y4wxxhgzI5z8McYYY4yZEaGx\nA2CMsY6kUChQU1Ojtuzu3bsAgIqKCrXlAoEAdnZ2nRYbY4wZAyd/jLFHWnl5OVxdXdHQ0NBsnYOD\ng9q/AwMDcfDgwc4KjTHGjIJv+zLGHmnOzs7405/+hG7dtH/dCQQCzJo1q5OiYowx4+HkjzH2yPvL\nX/7Sapnu3btj2rRpnRANY4wZFyd/jLFH3ksvvQShsOWnXLp3744XXngBPXr06MSoGGPMODj5Y4w9\n8qRSKSZMmNBiAkhEePXVVzs5KsYYMw5O/hhjZuHVV1/V+NIHAIhEIrz44oudHBFjjBkHJ3+MMbPw\n4osvQiKRNFtuYWGBqVOnwtra2ghRMcZY5+PkjzFmFsRiMaZNmwYLCwu15QqFAq+88oqRomKMsc7H\nyR9jzGy8/PLLUCgUasukUimee+45I0XEGGOdj5M/xpjZePbZZ9UGdrawsMCsWbMgEomMGBVjjHUu\nTv4YY2ZDKBRi1qxZqlu/CoUCL7/8spGjYoyxzsXJH2PMrMyaNUt169fZ2RmjR482ckSMMda5OPlj\njJmVgIAAuLq6AgD++te/tjrtG2OMPWqajXh67do1/PDDD8aIhTHGOsWoUaNw/fp19OjRAxkZGcYO\nhzHGOsyMGTOaLRMQET24ICMjAzNnzuy0oBhjjDHGWMd4KM0DgMwWJ7vUUJgxxh4Z27dvx/Tp040d\nBuskISEhAIDMzEwjR9J1NF0M4nyga9J2MY8fdmGMmSVO/Bhj5oqTP8YYY4wxM8LJH2OMMcaYGeHk\njzHGGGPMjHDyxxhjjDFmRjj5Y4wxxhgzI5z8McYYYzrat28fZDIZdu/ebexQTN6BAwewaNEiNDY2\nYurUqXB3d4dYLIarqyumTJmCvLy8NtW7ZcsWjBo1Cra2tvDw8MBrr72GGzdutDlOQ9enUCiQmJgI\nHx8fiEQi2NnZYejQoSguLm5xm7q6OgwcOBDvv/++atmuXbuwYsUKNDQ0tDmWlnDyxxhjjOmIx7zT\nzYcffog1a9Zg8eLFaGxsxJEjR7BlyxaUl5fj6NGjkMvl+NOf/oSSkhK96k1PT8crr7yCkJAQXLt2\nDdnZ2Th8+DAmTJgApVKpd5yGrg8AZs6cia+++gpff/01amtr8csvv8Db2xs1NTUtbhMbG4vz58+r\nLZs8eTLEYjHGjRuHO3futCmWlnDyxxhjjOkoKCgIlZWVmDRpkrFDgVwuR0BAgLHDaObjjz/Gtm3b\nkJGRAVtbWwCAv78/Ro8eDYlEAk9PTyQkJKCyshJffvmlXnX/4x//QO/evbFw4ULIZDL4+flhwYIF\nOHv2LI4fP653rIaub9u2bcjKykJmZiaefPJJCIVCuLi4IDs7G0OHDtW4zQ8//ICff/5Z47rIyEgM\nHz4cEydObHMyqgknf4wxxlgXtHHjRpSVlRk7DDWFhYVYsmQJ4uLiIBaLAQBCobDZbXIvLy8AQFFR\nkV71X716FS4uLhAIBKplffr0AQBcvnxZ73gNXd+6deswYsQI+Pr66lReLpdj4cKFSEpKarHM0qVL\ncfbsWa1l9MXJH2OMMaaDo0ePwt3dHQKBAGvXrgUApKamwtraGhKJBNnZ2ZgwYQKkUinc3NywdetW\n1bZr1qyBWCyGk5MT5syZAxcXF4jFYgQEBKhdYYqIiIBIJEKvXr1Uy9566y1YW1tDIBDg1q1bAICo\nqChER0ejqKgIAoEAPj4+AID9+/dDKpUiISGhMw5JM2vWrAERYfLkyVrLyeVyAIBUKtWrfi8vr2YJ\nb9PzeU0JpbHqq6+vx7Fjx+Dn56fzNrGxsXjrrbfg6OjYYhl7e3uMHTsWSUlJBnvsgJM/xhhjTAej\nR4/GDz/8oLZs3rx5ePvttyGXy2Fra4v09HQUFRXBy8sLb775JhQKBYD7SV1YWBhqa2sRGRmJ4uJi\nnD59GkqlEs899xyuXr0K4H7yNGPGDLU2UlJSEBcXp7YsKSkJkyZNgre3N4gIhYWFAKB6OaCxsbFD\njkFr9u7diwEDBkAikWgtd+LECQD3j6k+Fi9ejBs3biA5ORnV1dUoKChAUlISnn/+eTz11FN6x2vI\n+kpKSlBfX49Tp04hMDBQleAPGjQIKSkpzRK377//HkVFRXj55Zdbrfuxxx7D9evX8dNPP+kVU0s4\n+WOMMcYMICAgAFKpFI6OjggNDcXdu3dx5coVtTJCoRCDBg2CpaUlBg8ejNTUVFRXVyMtLc0gMQQF\nBaGqqgpLliwxSH36uHv3Li5dugRvb+8Wy5SWlmLbtm2IjIyEv79/q1cIHzZ27FjExMQgIiICUqkU\nQ4cORXV1Nb744os2xWzI+ppe6HB0dERCQgIKCgpQWlqK4OBgzJ8/H1u2bFGVlcvliIqKQmpqqk51\n9+vXDwCQn5+vd1yacPLHGGOMGZhIJAIA1ZW/lowcORISiQS//vprZ4TVocrKykBEWq/6+fv7IzIy\nEsHBwcjJyYGFhYVebcTGxmL9+vX47rvvUFNTg4sXLyIgIAD+/v6qq6fGqs/S0hIAMGTIEAQEBMDB\nwQEymQxxcXGQyWRYv369quzixYsxe/ZsuLq66lR30zEtLS3VK6aWcPLHGGOMGZGlpSVu3rxp7DDa\nra6uDsAfSZAmTk5OOHjwIJKTkyGTyfSq//fff8eKFSswe/Zs/PnPf4a1tTU8PT2xYcMGlJSUYOXK\nlUatz8XFBQBUz2U2EYlE8PDwUL3ccvToUeTn5+ONN97QuW4rKysAfxzj9uLkjzHGGDMShUKBO3fu\nwM3NzdihtFtTgqJtUGJHR0fY2dm1qf4LFy6goaEBvXv3VlsulUrh4OCAgoICo9ZnY2ODfv364dy5\nc83WKZVKVbK7ceNGfPfdd+jWrRsEAgEEAoHqhY+EhAQIBAL8+OOPatvX19cD+OMYtxcnf4wxxpiR\n5ObmgojUXi4QCoWt3i42RU5OThAIBKisrGyxzO7du3W+1fmwpgT5999/V1teXV2N8vJy1RAtxqoP\nuD/A85kzZ3Dx4kXVstraWly+fFk1/EtaWhqISO2v6cpvbGwsiAgjR45Uq7fpmDo7O+sdkyac/DHG\nGGOdpLGxERUVFVAqlcjLy0NUVBTc3d0RFhamKuPj44Py8nJkZWVBoVDg5s2bGsecc3BwQElJCYqL\ni1FdXQ2FQoGcnByjDfUikUjg5eWFa9euaVxfWFgIZ2dnzJw5s9m60NBQODs74/Tp0y3W7+npicDA\nQGzYsAGHDx+GXC7H1atXER4eDgB4/fXXjVofACxYsAAeHh4ICwvDlStXcPv2bcTExEAul+O9997T\nuq02TcdU1/EDW8PJH2OMMaaDtWvXYtSoUQCAmJgYTJkyBampqVi9ejUAYNiwYbh48SI2bNiA6Oho\nAMALL7yACxcuqOqoq6uDr68vrKysMGbMGPTv3x+HDh1Se05u3rx5CAwMxKxZszBgwAAsW7ZMdbvv\nwRcR5s6dCycnJwwePBgTJ05EeXl5pxwHbYKCglBQUKAax+9B2saoq6+vR1lZGbKzs1ssIxAIkJmZ\nidDQULz++uuwt7fH4MGDceXKFezYsQNjxowxan3A/TH5jhw5Ajc3N/j5+cHV1RUnTpzA3r179Rr/\n72EnT56Eq6srhg0b1uY61NBD0tPTScNixhhjrMuaPn06TZ8+3agxhIeHk4ODg1Fj0Edb8oELFy6Q\nUCikTZs26bVdQ0MDjRkzhjZu3KjXdl21Pn3cunWLxGIxffrpp3ptp+X8ZfCVP8YYY6yTaHsZ4lHg\n4+OD+Ph4xMfHq8a9a01DQwOysrJQXV2N0NDQdsdg6vXpa+nSpfDz80NERITB6uy05O/TTz9VPQz6\n+eef67XtqFGj0L1793ZdMm1vDMw0tHQO9+3bB5lM1mz+SEOKj4/H4MGDIZVKYWlpCR8fH7z77rut\nfsG98cYbsLW1hUAgwNmzZ9vc/m+//Yb//u//xpAhQyCVSiESieDo6IiBAwdi2rRp+J//+R9V2a7U\n37Zs2QKBQGCSE9Qb0o4dO+Dl5aV6u08gEMDCwgKurq545ZVX8Msvv3RY26bebzQdG4FAAJFIBCcn\nJzzzzDNYuXIlKioqOixOZjiLFi1CSEgIQkNDtb780SQ3Nxc7duxATk5OqzOD6MLU69PHqlWrcPbs\nWezbt0/vMRG10uMyYbtduHCBANC6dev03nbcuHE0fPhwo8bATIOmc7hnzx6SSqW0a9euDmt37Nix\nlJKSQrdv36aqqipKT08nCwsLeuGFF1rdduvWrQSAzpw506a209LSSCQS0ejRo2n//v1UUVFBdXV1\nVFRURLt376agoCAKDw9X26ar9LegoCDy9vYmAHThwoV2t2nqvL29SSaTERFRTU0N7dq1i9zd3cnG\nxoZ+/fXXDmu3K/SbB49NY2MjVVRU0KFDhygsLIwEAgG5uLjQyZMn2xSHsW/7Llq0iEQiEQGgvn37\nUmZmptFi0VV784FvvvmGYmJiDBiRecnKyqLExERSKpVt2v6Rue0rEAiMHQIzUUFBQaisrMSkSZM6\nrA0bGxuEh4fDwcEBtra2mDFjBqZOnYr9+/e3aWR5XR07dgxvvPEGAgICcOjQITz//POws7ODpaUl\nvLy88OKLL2LNmjUGb7cz+tvt27dx7tw51bylX331VYe3aUqsra0xadIk/P3vf0dNTQ2Sk5M7tX1T\n7jcCgQB2dnZ45plnkJaWhoyMDJSWlqpi7moSExNx7949EBEuXbqE6dOnGzukDjd+/Hh8/PHHxg6j\ny5oyZQoWLVqE7t27G7zuDkv+iAiZmZlq05m0l0EveZqJjjgP5kDTcduzZ0+zTtizZ08A98dx0qY9\niVRCQgIaGhqwfPlyCIVCjWW8vLwM/ihDZ/S3jIwMBAUFYfLkyRCLxdi0aZPWNwJNiSH71hNPPAEA\n+Pnnn9tdlzEZut88aPr06QgLC0NZWRk/tsNYOxkk+WtoaEBiYiIGDBgAKysr9OzZE56enkhMTMSM\nGTO0bktEWLVqlWqia3t7ewQHB2uc57CwsBADBw6EtbW16jX5o0ePqpU5cuQIBg8eDJlMBrFYDF9f\nX3zzzTeG2E2tdQ8aNAgCgQDdunXD448/rvpSe/fdd1Xlv/zySwD3j9cHH3wAd3d3WFlZYdiwYUhP\nTwcAfPLJJ5BIJLC1tUVZWRmio6Ph6uqK8+fPt7pvup4Hbe3ras2aNRCLxXBycsKcOXPg4uICsViM\ngIAAHD9+XK2srudYn8/Cg44ePQp3d3cIBAKsXbsWAJCamgpra2tIJBJkZ2djwoQJkEqlcHNzw9at\nW9W2b8/n9/r167CysoKnp6fafqxcuRIDBgyApaUlZDIZFi5c2Gzb/fv3tzoeV319PQ4cOAAHBwe1\nQWDbytT625YtWzBt2jTY2tpi/PjxKC4uxpEjR9TKmEPfUiqVANSnxTK3fqOLprHwcnJy9NqOMfYQ\nPe4RtyghIYG6d+9O2dnZVFtbS6dOnSJnZ2d65pln1Mppeubkgw8+IJFIRJs2baI7d+5QXl4ejRgx\ngnr27Ek3btxQlRs3bhx5eXnRpUuXSKFQ0M8//0xPPvkkicVi+u2331TlMjMzaenSpVReXk63b9+m\np556inr06KE1Bl1pq1upVFLfvn3J3d292f35t99+m1avXq369zvvvEOWlpa0fft2qqiooMWLF1O3\nbt1Uz7LExsYSAIqMjKTk5GSaNm0a/fLLL63um67nobX2dRUeHk7W1tZ07tw5qquro4KCAho1ahTZ\n2trSlStXVOV0Pce6ltN0Dq9evUoAKDk5WbWs6Th+9913VFlZSWVlZTRmzBiytram+vp6vY/bw+7e\nvUu2trYUERGhtjw2NpYEAgF99tlnVFFRQbW1tZSSktLsmb89e/aQra0txcfHt9jGb7/9RgDoqaee\n0hqLJqbe3y5fvkyOjo6q/rJp0yYCQK+//rpauUetbz34XFuTpn1fuHCh3ufqUek3LR2bB1VVVREA\n6tOnj9Y2NDH2M39dEQ/91rVpe+bPIMnfqFGj6IknnlBbNnv2bOrWrRvdu3dPtezhL5/a2lqysbGh\n0NBQtW1PnDhBANR+FDU9gJ6Xl0cA6J133mkxtsTERAJAZWVlGmNoj4frXr16NQGgjIwMVZm7d++S\nu7s7VVZWEhGRXC4niUSits+1tbVkaWlJ8+bNI6I/vnzlcrle7etyHnRpX1fh4eHNvqhPnjxJACgu\nLk5Vty7nWJ/Pgr4/Yg8ex6YkrLCwULVM18/vw2JjY6l///5UVVWlWlZbW0sSiYSee+45tbJtfeHj\nxx9/JAD07LPP6rUdken3t+XLl9Nrr72m+ndlZSVZWlqSVCql2tpatbKPUt96+IWP7du3k7OzMzk5\nOdG1a9dU25lTv9F0bFoiEAjIzs5OaxlNOPnTHyd/XZu25E/zA0R6qqurg1gsVlvW0NAACwsLrQ8q\nFhQUoKamptkcdqNGjYJIJGp2+/Bhvr6+kMlkyMvLa7FM03NLHTG20sN1v/HGG1i6dCmSkpIQEhIC\nANi8eTOCg4MhlUoBAOfPn0dtbS2GDh2qqsfKygq9evVq9TZNa+3rch4M2b4mI0eOhEQiUdWl6zlu\n72dBVyKRCADU5s1sy+d3586dyMjIwLfffgtbW1vV8sLCQtTW1mLcuHEGidfGxgYAcPfuXY3rMzIy\nEBMTg+LiYgDAwIED8e9//xtOTk7Nyppaf9uyZQsSExNV/5ZKpRg/fjx2796N7OxstfG0HrW+VVlZ\nCYFAgO7du6NXr16YOHEiPvzwQ9Wcp+bWb3R19+5dEJHqnOvr2LFjqs8Pa13TlGJ8zLqmlqbZAwz0\nzN/EiRNx6tQpZGdnQy6X48cff0RWVhZefPFFrcnfnTt3APzxA/cgOzs7VFdXt9q2hYWF2hfS3r17\n8cwzz8DR0RGWlpZ4991327BHmrVWt42NDWbPno0ffvgBJ06cAACsW7dObWDGph/x999/X208q8uX\nL7f68HNr7etyHtrTvq4sLS1Vk1Treo4N8VloK30/v9u2bcPHH3+M3Nxc9O3bV21dU2dzdHQ0SGwe\nHh6wtLREYWGhxvUzZszApUuX4OHhAWdnZ/zyyy8aEz/AtPrbzz//jPz8fEyaNEntc9g03tzDb/0+\nan1LJpOBiKBUKnHt2jX885//hIeHh2q9ufUbXf32228A7v8nhzHWdga58rd06VKcOnUKYWFhqKmp\ngYuLC2bMmNHqxNJ2dnYAoPEL6s6dO3Bzc9O6vVKpRHl5Odzd3QEAV65cwdSpUzFt2jT885//RO/e\nvZGcnGyQBFDXuiMiIpCUlITVq1dj7ty56NOnD7y9vVXrm5KC1atXIyoqyqDt63Ie2tq+rhQKhdq5\n0/Uct/ez0B76fH6Tk5PxzTff4ODBgxp/cJuuhNy7d88gsYnFYjz77LPYu3cvjh071q6XPkypv339\n9deYNWsWtmzZora8oqICrq6u+Pbbb3Hjxg306tVLtc6c+pa59Rtd7d+/HwAwYcKENm3/1FNPITMz\ns83tm5uMjAzMnDmTj1kX1XT+NDFI8ldQUICioiLcvHmzxaEoNBk6dChsbGzw448/qi0/fvw46uvr\n8fjjj2vd/tChQ2hsbMSIESMAAPn5+VAoFJg3bx68vLwAGG6sMl3rdnNzw4wZM5Ceno6SkhJ8+OGH\nauv79OkDsVis90wPurSvy3loa/u6ys3NBRGpkhRdz3F7PwvtoctxIyK89957qKioQFZWVovlhg4d\nim7duuHf//435s6da5D44uLi8O2332LhwoU4ePBgm4dgMZX+RkTYtm0bNm/e3Gydvb09QkJC8NVX\nX2HLli1YsGCBap059S1z6ze6uHHjBlavXg03Nzf87W9/a3M9jDED3fadP38+3N3ddZ7Hr4lYLEZ0\ndDR27tyJzZs3o6qqCvn5+Zg7dy5cXFzICRqaAAAgAElEQVQQHh6uVr6+vh6VlZVQKpU4ffo0IiIi\n4OHhoXr9v+mKxIEDB1BXV4cLFy4Y7JkXfeqOjo6GUqlERUUF/vznPzfb59deew1bt25Famoqqqqq\n0NDQgGvXruH3339vV/u6nIe2tt+SxsZGVFRUQKlUIi8vD1FRUXB3d1edE13Psb6fBUPS5bidO3cO\nn3zyCTZs2AALC4tm01B9+umnAO5f/XnppZewfft2bNy4EVVVVcjLy9M4FlxOTk6rQ70AwOOPP45N\nmzbh1KlTeOaZZ7B//378/vvvUCqVuHz5MjZt2oTy8vJW99NU+tsPP/wAqVSKp59+WuP6pqRZ04DP\n5tK3zK3fPIiIUFNTg8bGRhARbt68ifT0dDz99NPo3r07srKy2vzMH2Ps/+nxdkiLDh48SD169CAA\nqj8LCwsaNGgQ7dixg4iIPvvsM3J2diYAZG1tTdOmTSOi+1P4rFy5kvr160cWFhZkb29PU6dOpfPn\nz6u1kZaWRoGBgeTk5ERCoZB69OhBs2bNosuXL6uVi4mJIQcHB7Kzs6OQkBBau3YtASBvb2+KiorS\nGIOutNX94NAmRESBgYH0xRdfaKzn3r17FBMTQ+7u7iQUCsnR0ZFeeuklKigooBUrVpCVlZVqOINN\nmzbp3L4u56G19vURHh5OFhYW5OrqSkKhkKRSKQUHB1NRUZFaOV3PsS7lNH2OkpOTqVevXgSAJBIJ\nTZ48mVJSUkgikRAA6tevHxUVFdH69etJKpUSAPLw8FANWaLLccvPz1db//DfypUrVTFWV1fTG2+8\nQT169CAbGxsaPXo0ffDBBwSA3Nzc6KeffiIion379pGtrS199NFHOh3vS5cuUVRUFA0ZMoSsra1J\nLBaTp6cnjRkzht577z06fPiw1uOkz7noqP72+uuvk7W1NQmFQho+fDidPn1arb5ly5aRi4uL6ri6\nurpSSkqKWpmu2re+//576t+/v2p7FxcXCgkJafF8m1O/2bVrFw0bNowkEgmJRCLq1q0bAVC92fvE\nE09QfHw83b59u8Xj1Rp+21d//LZv19bhQ72kpKRQVFSU2rJ79+7R22+/TZaWls2GbWAdo7PPQ3h4\nODk4OBi0TmPgzy9rDX9Gmutqx4STP/1x8te1dehQLzdu3EBERESz51xEIhHc3d2hUCigUChgZWXV\n3qaYFsY6Dx0xhE5n4s8vaw1/RprjY8JY19buZ/6srKxgYWGBjRs3orS0FAqFAiUlJfjiiy/wwQcf\nIDQ01GSfz/j111+bPYOi6e/B8cZMlSHOw6N0PHTVlT+/rHPwZ6Q5PibMGA4cOIBFixahsbERU6dO\nhbu7O8RiMVxdXTFlyhStY5Bqo1AokJiYCB8fH4hEItjZ2WHo0KGq8VM1qaurw8CBA/H++++rlu3a\ntQsrVqzoGhdF9LhM2KLDhw/Ts88+S1KplLp3704ymYwCAgIoJSWFFApFGy9YMn115nlYtGgRiUQi\nAkB9+/alzMxMg9bfmfjzy1rDn5Hmutox4du++jOl274ffPABTZo0iaqqqkihUFCPHj3oyJEjdPfu\nXbp48SI999xzJJPJ6Pr163rXPXXqVBowYAAdO3aMFAoFlZSU0OTJkyk/P7/FbRYsWEAAKDY2Vm15\nUlISjR07lioqKvSOw9A6/Jk/xhhjzJSZQvJXW1tL/v7+XaYNU8kHli9fTv3791dNOahQKOjFF19U\nK9M0pWFCQoJedW/dupUEAgHl5eXpvM33339P48eP15j8ERFFRESQv7+/0f8TpC35M8hQL4wxxhjT\nbuPGjSgrK+vybXSmwsJCLFmyBHFxcapB9IVCoWo2oCZNY3QWFRXpVf+6deswYsQI+Pr66lReLpdj\n4cKFSEpKarHM0qVLcfbsWa1ljI2TP8YYY0wDIsKqVaswaNAgWFpawt7eHsHBwWpzNUdEREAkEqnN\nRvPWW2/B2toaAoEAt27dAgBERUUhOjoaRUVFEAgE8PHxwZo1ayAWi+Hk5IQ5c+bAxcUFYrEYAQEB\namNNtqcN4P7MKLqMKWqK1qxZAyLC5MmTtZaTy+UAoNezpvX19Th27Bj8/Px03iY2NhZvvfWW1ik8\n7e3tMXbsWCQlJYGIdK67M3HyxxhjjGmwdOlSLFq0CLGxsSgrK8Phw4dx9epVjBkzBqWlpQDuJycz\nZsxQ2y4lJQVxcXFqy5KSkjBp0iR4e3uDiFBYWIiIiAiEhYWhtrYWkZGRKC4uxunTp6FUKvHcc8/h\n6tWr7W4D+GNUhsbGRsMdnE6yd+9eDBgwABKJRGu5pjm/R48erXPdJSUlqK+vx6lTpxAYGKhKvgcN\nGoSUlJRmidv333+PoqIivPzyy63W/dhjj+H69ev46aefdI6nM3HyxxhjjD1ELpdj1apVmDZtGl59\n9VXIZDL4+vri888/x61btzTO2tNWQqFQdXVx8ODBSE1NRXV1NdLS0gxSf1BQEKqqqrBkyRKD1NdZ\n7t69i0uXLqnN4f2w0tJSbNu2DZGRkfD392/1CuGDmmancXR0REJCAgoKClBaWorg4GDMnz9fbe5x\nuVyOqKgopKam6lR3v379ANyfPtIUcfLHGGOMPaSgoAA1NTUYOXKk2vJRo0ZBJBIZbOpQTUaOHAmJ\nRKJ2e9kclZWVgYi0XvXz9/dHZGQkgoODkZOTo9fc55aWlgCAIUOGICAgAA4ODpDJZIiLi4NMJlNL\n8BcvXozZs2fD1dVVp7qbYm66Qmxq2j3IM2OMMfaouXPnDgDAxsam2To7OztUV1d3aPuWlpa4efNm\nh7Zh6urq6gD8kaRp4uTkhI0bN2LIkCF61+/i4gIAqmcmm4hEInh4eKheHjl69Cjy8/OxatUqnetu\nGuC8aR9MDV/5Y4wxxh5iZ2cHABqTvDt37sDNza3D2lYoFB3eRlfQlEBpGzTZ0dFRda70ZWNjg379\n+uHcuXPN1imVSshkMgD336D+7rvv0K1bN9VkB00vfCQkJEAgEODHH39U276+vl5tH0wNJ3+MMcbY\nQ4YOHQobG5tmP+rHjx9HfX09Hn/8cdUyoVAIhUJhsLZzc3NBRHjqqac6rI2uwMnJCQKBAJWVlS2W\n2b17t863YjWZOXMmzpw5g4sXL6qW1dbW4vLly6rhX9LS0kBEan9NV2VjY2NBRM0eD2iK2dnZuc2x\ndSRO/hhjjLGHiMViREdHY+fOndi8eTOqqqqQn5+PuXPnwsXFBeHh4aqyPj4+KC8vR1ZWFhQKBW7e\nvInLly83q9PBwQElJSUoLi5GdXW1KplrbGxERUUFlEol8vLyEBUVBXd3d4SFhRmkjZycnC451ItE\nIoGXlxeuXbumcX1hYSGcnZ0xc+bMZutCQ0Ph7OyM06dPa21jwYIF8PDwQFhYGK5cuYLbt28jJiYG\ncrkc7733Xptjb4pZ1/EDOxsnf4wxxpgGH374IRITExEfH4+ePXti7Nix6Nu3L3Jzc2Ftba0qN2/e\nPAQGBmLWrFkYMGAAli1bprrd5+/vrxqyZe7cuXBycsLgwYMxceJElJeXA7j/XJivry+srKwwZswY\n9O/fH4cOHVJ71q29bXRVQUFBKCgoUI3j9yBtY+jV19ejrKwM2dnZWuu3t7fHkSNH4ObmBj8/P7i6\nuuLEiRPYu3evXuP/PezkyZNwdXXFsGHD2lxHRxLQQ0cvIyMDM2fONNmBCRljjDF9hYSEAAAyMzON\nHIm6OXPmIDMzE7dv3zZ2KM2YQj5QWFiIQYMGIS0tDa+++qrO2zU2NuKZZ55BWFgY/va3v3VghM3d\nvn0bbm5u+OijjxAdHd2pbT9Iy/nL5Ct/jDHGmBFpe6HB3Pn4+CA+Ph7x8fGqcfla09DQgKysLFRX\nVyM0NLSDI2xu6dKl8PPzQ0RERKe3rStO/hhjjDFmshYtWoSQkBCEhoZqffmjSW5uLnbs2IGcnJxW\nZwYxtFWrVuHs2bPYt2+fXmMOdjZO/hhjjDEjWLx4MdLS0lBZWQlPT09s377d2CGZrISEBERERGD5\n8uWtlh03bhy+/vprtbmQO0N2djbu3buH3Nxc2Nvbd2rb+uJBnhljjDEjSExMRGJiorHD6DLGjx+P\n8ePHGzuMFk2ZMgVTpkwxdhg64St/jDHGGGNmhJM/xhhjjDEzwskfY4wxxpgZ4eSPMcYYY8yMcPLH\nGGOMMWZGWnzbVyAQdGYcjDHGWIfj3zb98TF79DRL/gICApCenm6MWBhjrFP85z//QVJSEn/XMcbM\nUrO5fRlj7FFnCnOWMsaYkfDcvowxxhhj5oSTP8YYY4wxM8LJH2OMMcaYGeHkjzHGGGPMjHDyxxhj\njDFmRjj5Y4wxxhgzI5z8McYYY4yZEU7+GGOMMcbMCCd/jDHGGGNmhJM/xhhjjDEzwskfY4wxxpgZ\n4eSPMcYYY8yMcPLHGGOMMWZGOPljjDHGGDMjnPwxxhhjjJkRTv4YY4wxxswIJ3+MMcYYY2aEkz/G\nGGOMMTPCyR9jjDHGmBnh5I8xxhhjzIxw8scYY4wxZkY4+WOMMcYYMyOc/DHGGGOMmRFO/hhjjDHG\nzAgnf4wxxhhjZoSTP8YYY4wxM8LJH2OMMcaYGeHk7//au/eoqKu9f+Dvr9yG4W4BooRyURJFraMl\niLd89BxlKZogeDlFlnmpBV5WB8VMJUHNlrAwOaX54HrWSRmQHtAUO6sMzSe1fAw0fCzBMBEFFOU6\nymX27w9/M8c5XGQEZmDm/Vpr/nB/93fvz/frTH7a3+/em4iIiMiEMPkjIiIiMiFM/oiIiIhMCJM/\nIiIiIhPC5I+IiIjIhJgbOgAiou5UUVGB//7v/9YqO3/+PABgz549WuV2dnaYP3++3mIjIjIESQgh\nDB0EEVF3efjwIVxcXFBbWwszMzMAgPo/e5Ikaeo1Njbi9ddfx/79+w0RJhGRvmTwsS8RGTUrKyuE\nhobC3NwcjY2NaGxsRFNTE5qamjR/bmxsBAAsWLDAwNESEXU/Jn9EZPQWLFiAhoaGdus4OjrilVde\n0VNERESGw+SPiIze5MmT4ezs3OZxCwsLLFq0CObmfA2aiIwfkz8iMnp9+vTBwoULYWFh0erxxsZG\nTvQgIpPB5I+ITML8+fM17/b9u/79+yMgIEDPERERGQaTPyIyCS+99BIGDhzYotzS0hKvv/661sxf\nIiJjxuSPiEzGX//61xaPfhsaGvjIl4hMCpM/IjIZCxcubPHo18fHB/7+/gaKiIhI/5j8EZHJeP75\n5+Hn56d5xGthYYE33njDwFEREekXkz8iMimvvfaaZqePpqYmPvIlIpPD5I+ITMr8+fPR3NwMAHjx\nxRfh6elp4IiIiPSLyR8RmRQPDw+8/PLLAIDXX3/dwNEQEekfl7OnXm/nzp04c+aMocOgXuThw4eQ\nJAn//Oc/cerUKUOHQ73I6tWruSYk9Xoc+aNe78yZMzh79qyhwyA9OHToEEpKSjrdjru7O1xdXSGT\nybogqp7t7Nmz/H10kUOHDuHGjRuGDoOo0zjyR0Zh7NixyMjIMHQY1M0kScKqVaswb968TrdVWFgI\nHx+fLoiqZwsLCwMA/j66ABcCJ2PBkT8iMkmmkPgREbWGyR8RERGRCWHyR0RERGRCmPwRERERmRAm\nf0REREQmhMkfEZmcY8eOwcHBAUeOHDF0KL3SN998g3Xr1kGlUmHOnDnw8PCATCbDgAEDEBISgosX\nLz5Vu42NjUhISICPjw8sLS3h6OiI4cOHo7i4uM1zHjx4gOeffx7vv/++puzw4cPYvn27ZicXItLG\n5I+ITI4QwtAh9FobN25EcnIyYmNjoVKp8P333+PAgQOorKzE6dOnoVQqMWHCBJSWlurcdnh4OP7r\nv/4LX3zxBerr6/F///d/8Pb2Rm1tbZvnrF+/Hr/++qtW2axZsyCTyTBlyhTcv39f5ziIjB2TPyIy\nOcHBwaiqqsLMmTMNHQqUSiUCAwMNHUaHbNu2DWlpaUhPT4ednR0AICAgAEFBQZDL5fD09ER8fDyq\nqqqwf/9+ndpOS0tDVlYWMjIy8PLLL8Pc3Bxubm7Izs7G8OHDWz3nhx9+wC+//NLqsejoaIwcORIz\nZsxAU1OTTrEQGTsmf0REBrRv3z6Ul5cbOownKiwsxIYNG7B582bNzijm5uYtHp17eXkBAIqKinRq\n/+9//ztefPFF+Pv7d6i+UqnEe++9h6SkpDbrbNq0CXl5ee3WITJFTP6IyKScPn0aHh4ekCQJn3zy\nCQAgJSUFNjY2kMvlyM7OxvTp02Fvbw93d3ccPHhQc25ycjJkMhlcXFywbNkyuLm5QSaTITAwEOfO\nndPUi4qKgqWlJfr166cpe+edd2BjYwNJknDnzh0AwMqVK7FmzRoUFRVBkiTNwtPHjx+Hvb094uPj\n9XFLOiQ5ORlCCMyaNavdekqlEgBgb2/f4bYbGhpw9uxZjBo1qsPnrF+/Hu+88w6cnZ3brOPk5ISJ\nEyciKSmJj/qJHsPkj4hMSlBQEH744QetshUrVmDVqlVQKpWws7ODQqFAUVERvLy8sGTJEjQ2NgJ4\nlNRFRkaivr4e0dHRKC4uxoULF9DU1ISpU6dq9n1NTk5usQXd7t27sXnzZq2ypKQkzJw5E97e3hBC\noLCwEAA0ExVUKlW33IOncfToUfj6+kIul7db78cffwTw6D53VGlpKRoaGvC///u/mDx5siapHjp0\nKHbv3t0icfuf//kfFBUVYcGCBU9s+4UXXsDNmzeRn5/f4XiIjB2TPyKixwQGBsLe3h7Ozs6IiIhA\nXV0d/vjjD6065ubmGDp0KKysrODn54eUlBTU1NQgNTW1S2IIDg5GdXU1NmzY0CXtdVZdXR1+//13\neHt7t1mnrKwMaWlpiI6ORkBAwBNHCB+nntDh7OyM+Ph4FBQUoKysDLNnz8a7776LAwcOaOoqlUqs\nXLkSKSkpHWp78ODBAIBLly51OB4iY8fkj4ioDZaWlgCgGflry+jRoyGXy3HlyhV9hKV35eXlEEK0\nO+oXEBCA6OhozJ49Gzk5ObCwsOhw+1ZWVgCAYcOGITAwEH379oWDgwM2b94MBwcH7NmzR1M3NjYW\nb7/9NgYMGNChttUxl5WVdTgeImNnbugAiIiMgZWVFSoqKgwdRrd48OABgH8laa1xcXHBvn37MGzY\nMJ3bd3NzAwDNu5BqlpaWGDhwoGbyyOnTp3Hp0iXs3Lmzw21bW1sD+Nc1EBFH/oiIOq2xsRH379+H\nu7u7oUPpFuoEqr1Fk52dneHo6PhU7dva2mLw4MG4fPlyi2NNTU1wcHAA8Ghm9Lfffos+ffpAkiRI\nkqSZ8BEfHw9JknD+/Hmt8xsaGrSugYiY/BERdVpubi6EEBg7dqymzNzc/ImPi3sLFxcXSJKEqqqq\nNuscOXKkw49iWxMeHo6ff/4Z165d05TV19fj+vXrmuVfUlNTIYTQ+qhHW9evXw8hBEaPHq3Vrjpm\nV1fXp46NyNgw+SMi0pFKpcK9e/fQ1NSEixcvYuXKlfDw8EBkZKSmjo+PDyorK5GVlYXGxkZUVFTg\n+vXrLdrq27cvSktLUVxcjJqaGjQ2NiInJ6dHLfUil8vh5eWFkpKSVo8XFhbC1dUV4eHhLY5FRETA\n1dUVFy5caLeP1atXY+DAgYiMjMQff/yBu3fvIiYmBkqlEmvXrn3q2NUxd3T9QCJTwOSPiEzKJ598\ngjFjxgAAYmJiEBISgpSUFCQmJgIARowYgWvXrmHv3r1Ys2YNAOAvf/kLrl69qmnjwYMH8Pf3h7W1\nNcaPH48hQ4bgu+++03onbsWKFZg8eTLmz58PX19ffPjhh5pHjwEBAZplYZYvXw4XFxf4+flhxowZ\nqKys1Mt90FVwcDAKCgo06/g9rr019BoaGlBeXo7s7Ox223dycsL3338Pd3d3jBo1CgMGDMCPP/6I\no0eP6rT+37/76aefMGDAAIwYMeKp2yAyNpLgypfUy4WFhQEAMjIyDBwJdTdJkqBQKFqsoadPy5Yt\nQ0ZGBu7evWuwGHTRVb+PwsJCDB06FKmpqVi0aFGHz1OpVJg0aRIiIyOxePHiTsWgq7t378Ld3R1b\ntmzRJPKd0RO+f0RdIIMjf0REOmpv4oOx8vHxQVxcHOLi4jTr8j1Jc3MzsrKyUFNTg4iIiG6OsKVN\nmzZh1KhRiIqK0nvfRD0Zkz8iIuqQdevWISwsDBEREe1O/lDLzc1FZmYmcnJynrgzSFfbuXMn8vLy\ncOzYMZ3WHCQyBUz+iAC89dZbsLOzgyRJyMvLM3Q4OsvMzISXl5dm+Qv1x9LSEi4uLpg0aRJ27NiB\ne/fuGTrUXi02NhapqamoqqqCp6cnDh06ZOiQ9C4+Ph5RUVHYunXrE+tOmTIFX3zxhdYex/qQnZ2N\nhw8fIjc3F05OTnrtm6g3YPJHBODzzz/H3r17DR3GU5s7dy6uXbsGb29vODg4QAgBlUqF8vJypKen\nw9PTEzExMRg2bFiLddCo4xISEvDw4UMIIfD7778jNDTU0CEZxLRp07Bt2zZDh9GmkJAQrFu3DmZm\nZoYOhahHYvJHZKQkSYKjoyMmTZqE1NRUpKeno6ysDMHBwR16ZEdERMaJyR/R/ydJkqFD6FahoaGI\njIxEeXk5Pv30U0OHQ0REBsLkj0ySEAI7duyAr68vrKys4ODggPfee69FvebmZnzwwQfw8PCAtbU1\nRowYAYVCAQBISUmBjY0N5HI5srOzMX36dNjb28Pd3R0HDx7UaufkyZN46aWXIJfLYW9vD39/f1RX\nVz+xDwA4fvx4ly34q16EOCcnp0ddIxER6Q+TPzJJGzZsQExMDJYuXYqysjLcvn271V0E1q5di48+\n+giJiYm4desWZs6ciQULFuD8+fNYsWIFVq1aBaVSCTs7OygUChQVFcHLywtLlizRbO1VV1eHWbNm\nITQ0FJWVlbh69SqGDBmi2XO0vT6Afy0rolKpOn3d6sVyH99CqydcIxER6ZEg6uVCQ0NFaGhoh+vX\n19cLuVwupk6dqlV+8OBBAUD8/PPPQgghlEqlkMvlIiIiQutcKysrsWLFCiGEEOvXrxcAhFKp1NTZ\nvXu3ACAKCwuFEEL88ssvAoD46quvWsTSkT504e3tLRwcHNqtI0mScHR07JXXCEAoFAqdzjF1uv4+\nqG38/pGRSDc3WNZJZCCFhYWor6/HlClT2q3366+/or6+HsOHD9eUWVtbo1+/frhy5Uqb51laWgKA\nZlTMy8sLLi4uWLRoEaKjoxEZGYlBgwZ1qo+nVVdXByEE7O3tO9W/Ia8xPDy81T1kqX3G/k4rEXUc\nkz8yOeqN3p2dndutV1dXBwB4//338f7772sdc3Nz63B/1tbWOHHiBNauXYv4+HjExcVh3rx5SE1N\n7bI+Ouq3334DADz//PMAeuc1rly5EgEBATqfZ6rUexavWrXKwJH0fvyfDjIWTP7I5MhkMgDAw4cP\n262nTg4TExOxcuXKTvU5bNgwHDlyBBUVFdi5cye2bduGYcOGaba86oo+OuL48eMAgOnTpwPondcY\nEBDAvVV1oN7Tl/es85j8kbHghA8yOcOHD0efPn1w8uTJdus999xzkMlknd7xo7S0FJcvXwbwKNna\nunUrXnzxRVy+fLnL+uiI27dvIzExEe7u7li8eDEA47tGIiJ6MiZ/ZHKcnZ0xd+5cHDp0CPv27UN1\ndTUuXryIPXv2aNWTyWR44403cPDgQaSkpKC6uhrNzc0oKSnBrVu3OtxfaWkpli1bhitXrqChoQE/\n//wzrl+/jrFjx3aoj5ycHJ2WehFCoLa2FiqVCkIIVFRUQKFQYNy4cTAzM0NWVpbmnb+eco1ERKRH\nBp5xQtRpTzObsaamRrz11lvimWeeEba2tiIoKEh88MEHAoBwd3cX+fn5QgghHj58KGJiYoSHh4cw\nNzcXzs7OYu7cuaKgoEDs3r1byOVyAUAMHjxYFBUViT179gh7e3sBQAwcOFD89ttvori4WAQGBgon\nJydhZmYm+vfvL9avXy+ampqe2IcQQhw7dkzY2dmJLVu2tHk9hw8fFiNGjBByuVxYWlqKPn36CACa\nmb0vvfSSiIuLE3fv3m1xbk+4xo4CZ1vqjLN9uw6/f2Qk0iUhhDBc6knUeWFhYQD+9W4TGS9JkqBQ\nKPj+mg74++g6/P6RkcjgY18iIiIiE8Lkj4iI2vTNN99g3bp1UKlUmDNnDjw8PCCTyTBgwACEhITg\n4sWLT922SqVCYmIiAgMDWz0eFxcHPz8/2Nvbw8rKCj4+Pvjb3/6G2traFnUPHDiAMWPGwM7ODgMH\nDsQbb7yB27dva44fPnwY27dv1+yYQ2TKmPwREVGrNm7ciOTkZMTGxkKlUuH777/HgQMHUFlZidOn\nT0OpVGLChAkoLS3Vue2rV69iwoQJWL16Nerr61utc+LECbz77rsoLi7GnTt3kJCQgKSkJM2jbDWF\nQoGFCxciLCwMJSUlyM7OxqlTpzB9+nQ0NTUBAGbNmgWZTIYpU6bg/v37ut8MIiPC5I+IqIOUSmWb\no1S9qY+O2LZtG9LS0pCeng47OzsAj9ZYDAoKglwuh6enJ+Lj41FVVYX9+/fr1HZ+fj7Wrl2L5cuX\na/abbo2trS2WLl2Kvn37ws7ODvPmzcOcOXNw/Phx3LhxQ1Pvs88+Q//+/fHee+/BwcEBo0aNwurV\nq5GXl4dz585p6kVHR2PkyJGYMWOGJikkMkVM/oiIOmjfvn0oLy/v9X08SWFhITZs2IDNmzdrFkU3\nNzfHkSNHtOp5eXkBAIqKinRqf+TIkcjMzMTChQthZWXVZr2vvvoKZmZmWmXPPvssAGiNFt64cQNu\nbm5aW9g999xzAIDr169rnb9p0ybk5eUhKSlJp5iJjAmTPyIyWkII7Ny5E0OHDoWVlRWcnJwwe/Zs\nrT2Fo6KiYGlpiX79+mnK3nnnHdjY2ECSJNy5cwfAo23l1qxZg6KiIkiSBB8fHyQnJ0Mmk8HFxQXL\nli2Dm5sbZDIZAgMDtUacOtMH8CCtglgAACAASURBVGhnFl3Weuys5ORkCCEwa9asdusplUoA0Kwb\nqQ83b96EtbU1PD09NWVeXl4tEmb1+37qBFXNyckJEydORFJSErjYBZkqJn9EZLQ2bdqEdevWYf36\n9SgvL8epU6dw48YNjB8/HmVlZQAeJTr/vnTH7t27sXnzZq2ypKQkzJw5E97e3hBCoLCwEFFRUYiM\njER9fT2io6NRXFyMCxcuoKmpCVOnTtU8muxMHwA0kxRUKlXX3Zx2HD16FL6+vpDL5e3W+/HHHwEA\nQUFB+ggL9fX1OHHiBJYsWQJLS0tNeWxsLG7fvo1du3ahpqYGBQUFSEpKwp///GeMHTu2RTsvvPAC\nbt68ifz8fL3ETdTTMPkjIqOkVCqxc+dOvPrqq1i0aBEcHBzg7++PTz/9FHfu3Gmxo0tnmJuba0YX\n/fz8kJKSgpqaGqSmpnZJ+8HBwaiursaGDRu6pL321NXV4ffff4e3t3ebdcrKypCWlobo6GgEBAQ8\ncYSwqyQkJMDNzQ1btmzRKp84cSJiYmIQFRUFe3t7DB8+HDU1Nfj8889bbWfw4MEAgEuXLnV7zEQ9\nEZM/IjJKBQUFqK2txejRo7XKx4wZA0tLS63Hsl1t9OjRkMvlWo+Xe4vy8nIIIdod9QsICEB0dDRm\nz56NnJwcWFhYdHtcX375JdLT0/H1119rJqCorV+/Hnv27MG3336L2tpaXLt2DYGBgQgICNCaGKKm\nvjb16C+RqWHyR0RGSb2ch62tbYtjjo6OqKmp6db+raysUFFR0a19dIcHDx4AQLsTMVxcXHDixAns\n2rULDg4O3R5TWloatm3bhtzcXAwaNEjr2K1bt7B9+3a8/fbbeOWVV2BjYwNPT0/s3bsXpaWl2LFj\nR4v2rK2tAfzrWolMjbmhAyAi6g6Ojo4A0GqSd//+fbi7u3db342Njd3eR3dRJ0btLYbs7Oysub/d\nbdeuXfj6669x4sSJVhP5q1evorm5Gf3799cqt7e3R9++fVFQUNDinIaGBgD/ulYiU8Pkj4iM0vDh\nw2Fra4vz589rlZ87dw4NDQ3405/+pCkzNzdHY2Njl/Wdm5sLIYTWZIOu7qO7uLi4QJIkVFVVtVnn\n35d86Q5CCKxduxb37t1DVlYWzM1b/+dKnWDfunVLq7ympgaVlZWaJV8ep742V1fXLo6aqHfgY18i\nMkoymQxr1qzBl19+iX/84x+orq7GpUuXsHz5cri5uWHp0qWauj4+PqisrERWVhYaGxtRUVHRYn04\nAOjbty9KS0tRXFyMmpoaTTKnUqlw7949NDU14eLFi1i5ciU8PDwQGRnZJX3k5OTobakXuVwOLy8v\nlJSUtHq8sLAQrq6uCA8Pb3EsIiICrq6uuHDhQqfjuHz5Mj766CPs3bsXFhYWkCRJ6/Pxxx8DADw9\nPTF58mTs3bsXp06dglKpxI0bNzR/v2+++WaLttXX5u/v3+k4iXojJn9EZLQ2btyIhIQExMXF4dln\nn8XEiRMxaNAg5ObmwsbGRlNvxYoVmDx5MubPnw9fX198+OGHmkeCj08aWL58OVxcXODn54cZM2ag\nsrISwKN3x/z9/WFtbY3x48djyJAh+O6777Tem+tsH/oUHByMgoICzTp+j2tvbbyGhgaUl5cjOzu7\n3fbPnj2LoKAg9O/fH+fOnUN+fj7c3Nwwbtw4nDp16on9PE6SJGRkZCAiIgJvvvkmnJyc4Ofnhz/+\n+AOZmZkYP358i3N++uknDBgwACNGjOhQH0TGRhJc5ZJ6OfU+nxkZGQaOhLqbJElQKBQt1swzpGXL\nliEjIwN37941dCiteprfR2FhIYYOHYrU1FQsWrSow+epVCpMmjQJkZGRWLx4sc6x6sPdu3fh7u6O\nLVu2YM2aNTqd2xO/f0RPIYMjf0REndTe5IjeyMfHB3FxcYiLi0NtbW2HzmlubkZWVhZqamoQERHR\nzRE+vU2bNmHUqFGIiooydChEBsPkj4iIWli3bh3CwsIQERHR7uQPtdzcXGRmZiInJ+eJO4MYys6d\nO5GXl4djx47pZW1Cop6KyR8R0VOKjY1Famoqqqqq4OnpiUOHDhk6pC4VHx+PqKgobN269Yl1p0yZ\ngi+++EJr/+KeJDs7Gw8fPkRubi6cnJwMHQ6RQXGpFyKip5SQkICEhARDh9Gtpk2bhmnTphk6jE4L\nCQlBSEiIocMg6hE48kdERERkQpj8EREREZkQJn9EREREJoTJHxEREZEJ4YQPMgolJSVIT083dBik\nB2fOnDF0CL2Keisz/j6ISI07fFCvFxYWZnRLbBBRz8QdPsgIZDD5IyKTk56ejvDw8A7vH0tEZES4\nvRsRERGRKWHyR0RERGRCmPwRERERmRAmf0REREQmhMkfERERkQlh8kdERERkQpj8EREREZkQJn9E\nREREJoTJHxEREZEJYfJHREREZEKY/BERERGZECZ/RERERCaEyR8RERGRCWHyR0RERGRCmPwRERER\nmRAmf0REREQmhMkfERERkQlh8kdERERkQpj8EREREZkQJn9EREREJoTJHxEREZEJYfJHREREZEKY\n/BERERGZECZ/RERERCaEyR8RERGRCWHyR0RERGRCmPwRERERmRAmf0REREQmhMkfERERkQlh8kdE\nRERkQpj8EREREZkQJn9EREREJoTJHxEREZEJMTd0AERE3amkpASvv/46mpubNWX37t2DnZ0dJk2a\npFXX19cXn332mZ4jJCLSLyZ/RGTU3N3dcf36dRQVFbU4dvLkSa0/T5gwQV9hEREZDB/7EpHRe+21\n12BhYfHEehEREXqIhojIsJj8EZHRW7hwIZqamtqtM2zYMPj5+ekpIiIiw2HyR0RGz9vbGyNGjIAk\nSa0et7CwwOuvv67nqIiIDIPJHxGZhNdeew1mZmatHmtqakJYWJieIyIiMgwmf0RkEubPnw+VStWi\nvE+fPhg7diwGDRqk/6CIiAyAyR8RmQQ3NzeMGzcOffpo/2evT58+eO211wwUFRGR/jH5IyKT8de/\n/rVFmRACr776qgGiISIyDCZ/RGQyQkNDtd77MzMzw3/8x3/AxcXFgFEREekXkz8iMhlOTk6YOnWq\nJgEUQmDRokUGjoqISL+Y/BGRSVm0aJFm4oeFhQVmz55t4IiIiPSLyR8RmZRZs2bBysoKADBz5kzY\n2toaOCIiIv1i8kdEJsXGxkYz2sdHvkRkiiQhhDB0EEStCQsLw6FDhwwdBhGRzhQKBebNm2foMIha\nk2Fu6AiI2jN27FisWrXK0GFQLxMeHo6VK1ciICCg1ePNzc1QKBRYsGCBniPruRITEwGAv7cuEB4e\nbugQiNrF5I96NHd3d/7fM+ksPDwcAQEB7X535syZA5lMpseoeraMjAwA4O+tCzD5o56O7/wRkUli\n4kdEporJHxEREZEJYfJHREREZEKY/BERERGZECZ/RERERCaEyR8RURuOHTsGBwcHHDlyxNCh9Hjf\nfPMN1q1bB5VKhTlz5sDDwwMymQwDBgxASEgILl68+NRtq1QqJCYmIjAwsNXjcXFx8PPzg729Pays\nrODj44O//e1vqK2tbVH3wIEDGDNmDOzs7DBw4EC88cYbuH37tub44cOHsX37djQ3Nz91vEQ9HZM/\nIqI2cA38jtm4cSOSk5MRGxsLlUqF77//HgcOHEBlZSVOnz4NpVKJCRMmoLS0VOe2r169igkTJmD1\n6tWor69vtc6JEyfw7rvvori4GHfu3EFCQgKSkpIQFhamVU+hUGDhwoUICwtDSUkJsrOzcerUKUyf\nPh1NTU0AHm3/J5PJMGXKFNy/f1/3m0HUCzD5IyJqQ3BwMKqqqjBz5kxDhwKlUtnmyJchbdu2DWlp\naUhPT4ednR0AICAgAEFBQZDL5fD09ER8fDyqqqqwf/9+ndrOz8/H2rVrsXz5cowaNarNera2tli6\ndCn69u0LOzs7zJs3D3PmzMHx48dx48YNTb3PPvsM/fv3x3vvvQcHBweMGjUKq1evRl5eHs6dO6ep\nFx0djZEjR2LGjBmapJDImDD5IyLqBfbt24fy8nJDh6GlsLAQGzZswObNmzXrJpqbm7d4TO7l5QUA\nKCoq0qn9kSNHIjMzEwsXLoSVlVWb9b766iuYmZlplT377LMAoDVaeOPGDbi5uUGSJE3Zc889BwC4\nfv261vmbNm1CXl4ekpKSdIqZqDdg8kdE1IrTp0/Dw8MDkiThk08+AQCkpKTAxsYGcrkc2dnZmD59\nOuzt7eHu7o6DBw9qzk1OToZMJoOLiwuWLVsGNzc3yGQyBAYGao0wRUVFwdLSEv369dOUvfPOO7Cx\nsYEkSbhz5w4AYOXKlVizZg2KioogSRJ8fHwAAMePH4e9vT3i4+P1cUtaSE5OhhACs2bNareeUqkE\nANjb2+sjLADAzZs3YW1tDU9PT02Zl5dXiwRa/b6fOkFVc3JywsSJE5GUlMTH/2R0mPwREbUiKCgI\nP/zwg1bZihUrsGrVKiiVStjZ2UGhUKCoqAheXl5YsmQJGhsbATxK6iIjI1FfX4/o6GgUFxfjwoUL\naGpqwtSpUzWPIpOTk1tsp7Z7925s3rxZqywpKQkzZ86Et7c3hBAoLCwEAM2kBJVK1S334EmOHj0K\nX19fyOXyduv9+OOPAB7dU32or6/HiRMnsGTJElhaWmrKY2Njcfv2bezatQs1NTUoKChAUlIS/vzn\nP2Ps2LEt2nnhhRdw8+ZN5Ofn6yVuIn1h8kdE9BQCAwNhb28PZ2dnREREoK6uDn/88YdWHXNzcwwd\nOhRWVlbw8/NDSkoKampqkJqa2iUxBAcHo7q6Ghs2bOiS9nRRV1eH33//Hd7e3m3WKSsrQ1paGqKj\noxEQEPDEEcKukpCQADc3N2zZskWrfOLEiYiJiUFUVBTs7e0xfPhw1NTU4PPPP2+1ncGDBwMALl26\n1O0xE+kTkz8iok5Sjy6pR/7aMnr0aMjlcly5ckUfYXWr8vJyCCHaHfULCAhAdHQ0Zs+ejZycHFhY\nWHR7XF9++SXS09Px9ddfayagqK1fvx579uzBt99+i9raWly7dg2BgYEICAjQmhiipr62srKybo+b\nSJ+Y/BER6ZGVlRUqKioMHUanPXjwAADanYjh4uKCEydOYNeuXXBwcOj2mNLS0rBt2zbk5uZi0KBB\nWsdu3bqF7du34+2338Yrr7wCGxsbeHp6Yu/evSgtLcWOHTtatGdtbQ3gX9dKZCzMDR0AEZGpaGxs\nxP379+Hu7m7oUDpNnRi1txiys7MzHB0d9RLPrl278PXXX+PEiROwtbVtcfzq1atobm5G//79tcrt\n7e3Rt29fFBQUtDinoaEBwL+ulchYMPkjItKT3NxcCCG0JheYm5s/8XFxT+Ti4gJJklBVVdVmHX3s\njCKEwNq1a3Hv3j1kZWXB3Lz1f9bUCfetW7e0ymtqalBZWalZ8uVx6mtzdXXt4qiJDIuPfYmIuolK\npcK9e/fQ1NSEixcvYuXKlfDw8EBkZKSmjo+PDyorK5GVlYXGxkZUVFS0WHMOAPr27YvS0lIUFxej\npqYGjY2NyMnJMdhSL3K5HF5eXigpKWn1eGFhIVxdXREeHt7iWEREBFxdXXHhwoVOx3H58mV89NFH\n2Lt3LywsLCBJktbn448/BgB4enpi8uTJ2Lt3L06dOgWlUokbN25g6dKlAIA333yzRdvqa/P39+90\nnEQ9CZM/IqJWfPLJJxgzZgwAICYmBiEhIUhJSUFiYiIAYMSIEbh27Rr27t2LNWvWAAD+8pe/4OrV\nq5o2Hjx4AH9/f1hbW2P8+PEYMmQIvvvuO6335FasWIHJkydj/vz58PX1xYcffqh5zPj4RITly5fD\nxcUFfn5+mDFjBiorK/VyH9oTHByMgoICzTp+j2tvbbyGhgaUl5cjOzu73fbPnj2LoKAg9O/fH+fO\nnUN+fj7c3Nwwbtw4nDp16on9PE6SJGRkZCAiIgJvvvkmnJyc4Ofnhz/++AOZmZkYP358i3N++ukn\nDBgwACNGjOhQH0S9hSS4eiX1UOp9OTMyMgwcCfU2kiRBoVC0WENPn5YtW4aMjAzcvXvXYDHo4ml+\nb4WFhRg6dChSU1OxaNGiDp+nUqkwadIkREZGYvHixTrHqg93796Fu7s7tmzZoknuO6onfP+I2pHB\nkT8iom7S3mQIY+Dj44O4uDjExcWhtra2Q+c0NzcjKysLNTU1iIiI6OYIn96mTZswatQoREVFGToU\noi7H5I+M2ltvvQU7OztIkoS8vDxDh9MpKpUKiYmJCAwMfOo2MjMz4eXl1eK9KEtLS7i4uGDSpEnY\nsWMH7t2714WRkzFbt24dwsLCEBER0e7kD7Xc3FxkZmYiJyfniTuDGMrOnTuRl5eHY8eO6WVtQiJ9\nY/JHRu3zzz/H3r17DR1Gp129ehUTJkzA6tWrtTaq19XcuXNx7do1eHt7w8HBAUIIqFQqlJeXIz09\nHZ6enoiJicGwYcNw/vz5LrwC0xIbG4vU1FRUVVXB09MThw4dMnRI3So+Ph5RUVHYunXrE+tOmTIF\nX3zxhdZ+xj1JdnY2Hj58iNzcXDg5ORk6HKJuwaVeiHq4/Px8xMXFYfny5airq+vyTeYlSYKjoyMm\nTZqESZMmITg4GOHh4QgODsZvv/2ml8V5jU1CQgISEhIMHYZeTZs2DdOmTTN0GJ0WEhKCkJAQQ4dB\n1K048kdGT5IkQ4fQKSNHjkRmZiYWLlzY7m4KXSU0NBSRkZEoLy/Hp59+2u39ERGRfjH5I6MihMCO\nHTvg6+sLKysrODg44L333mtRr7m5GR988AE8PDxgbW2NESNGQKFQAABSUlJgY2MDuVyO7OxsTJ8+\nHfb29nB3d8fBgwe12jl58iReeuklyOVy2Nvbw9/fH9XV1U/sozscP368y9Z8U69Dl5OToykzxntG\nRGSKmPyRUdmwYQNiYmKwdOlSlJWV4fbt21i7dm2LemvXrsVHH32ExMRE3Lp1CzNnzsSCBQtw/vx5\nrFixAqtWrYJSqYSdnR0UCgWKiorg5eWFJUuWaHZjqKurw6xZsxAaGorKykpcvXoVQ4YM0WwJ1V4f\n3UE9s1SlUnW6rVGjRgEArl27pikzxntGRGSSBFEPFRoaKkJDQztcv76+XsjlcjF16lSt8oMHDwoA\n4ueffxZCCKFUKoVcLhcRERFa51pZWYkVK1YIIYRYv369ACCUSqWmzu7duwUAUVhYKIQQ4pdffhEA\nxFdffdUilo708TRefvllMXLkyKc+X83b21s4ODi0W0eSJOHo6CiE6H33DIBQKBQ6nWPqdP29Udv4\n/aMeLp0TPshoFBYWor6+HlOmTGm33q+//or6+noMHz5cU2ZtbY1+/frhypUrbZ5naWkJAJpRLC8v\nL7i4uGDRokWIjo5GZGQkBg0a1Kk+egr1xBJ7e3sAvfOenTlzRudzTJl6K7P09HQDR0JE3Y3JHxkN\n9T9ezs7O7darq6sDALz//vt4//33tY65ubl1uD9ra2ucOHECa9euRXx8POLi4jBv3jykpqZ2WR+G\n8ttvvwEAnn/+eQC9854lJSUhKSlJ5/NMXWt78RKRceE7f2Q0ZDIZAODhw4ft1lMnh4mJiRBCaH10\nHS0aNmwYjhw5gtLSUsTExEChUODjjz/u0j4M4fjx4wCA6dOnA+id90yhULRoh5+2P6GhoQgNDTV4\nHMbwIerpmPyR0Rg+fDj69OmDkydPtlvvueeeg0wm6/SOH6Wlpbh8+TKAR8nR1q1b8eKLL+Ly5ctd\n1och3L59G4mJiXB3d9fsu8p7RkRkPJj8kdFwdnbG3LlzcejQIezbtw/V1dW4ePEi9uzZo1VPJpPh\njTfewMGDB5GSkoLq6mo0NzejpKQEt27d6nB/paWlWLZsGa5cuYKGhgb8/PPPuH79OsaOHdtlfegi\nJydHp6VehBCora2FSqWCEAIVFRVQKBQYN24czMzMkJWVpXnnz1jvGRGRSRJEPdTTzD6sqakRb731\nlnjmmWeEra2tCAoKEh988IEAINzd3UV+fr4QQoiHDx+KmJgY4eHhIczNzYWzs7OYO3euKCgoELt3\n7xZyuVwAEIMHDxZFRUViz549wt7eXgAQAwcOFL/99psoLi4WgYGBwsnJSZiZmYn+/fuL9evXi6am\npif2oYszZ86IcePGCTc3NwFAABD9+vUTgYGB4uTJk5p6x44dE3Z2dmLLli1ttnX48GExYsQIIZfL\nhaWlpejTp48AoJnZ+9JLL4m4uDhx9+7dFuf2pnsGzrbUGWf7dh1+/6iHS5eE4AsK1DOFhYUBADIy\nMgwcCfU2kiRBoVBg3rx5hg6l1+Dvrevw+0c9XAYf+xIRERGZECZ/RHp25coVSJL0xE9ERIShQyUi\nIiPE5I9Iz55//vkOLReRlpZm6FCJOuybb77BunXroFKpMGfOHHh4eEAmk2HAgAEICQnBxYsXn7pt\nlUqFxMREBAYGtlnn9OnTGDduHORyOdzc3BATE9Pqsk9Pqnf48GFs375ds10ikTFi8kdERJ2yceNG\nJCcnIzY2FiqVCt9//z0OHDiAyspKnD59GkqlEhMmTEBpaanObV+9ehUTJkzA6tWrUV9f32qdgoIC\nTJs2DVOmTEFFRQW+/PJL/Od//ieWL1+uc71Zs2ZBJpNhypQpuH//vs7xEvUGTP6IiLqYUqlsd5Sq\nt/TREdu2bUNaWhrS09NhZ2cHAAgICEBQUBDkcjk8PT0RHx+Pqqoq7N+/X6e28/PzsXbtWixfvhyj\nRo1qs96HH36Ifv36YfPmzbCxsUFAQABiYmKwf/9+ra0BO1ovOjoaI0eOxIwZM9DU1KTbDSHqBZj8\nERF1sX379qG8vLzX9/EkhYWF2LBhAzZv3qzZYcfc3BxHjhzRqufl5QUAKCoq0qn9kSNHIjMzEwsX\nLoSVlVWrdZqamnD06FFMnDgRkiRpyqdPnw4hBLKzs3Wqp7Zp0ybk5eVxi0AySkz+iMjkCSGwc+dO\nDB06FFZWVnBycsLs2bO1RoOioqJgaWmJfv36acreeecd2NjYQJIk3LlzBwCwcuVKrFmzBkVFRZAk\nCT4+PkhOToZMJoOLiwuWLVsGNzc3yGQyBAYG4ty5c13SB/BoWz5dFvrurOTkZAghMGvWrHbrKZVK\nANAsGt6Vrl27htraWnh4eGiVe3t7A4DmXcOO1lNzcnLCxIkTkZSUxC3byOgw+SMik7dp0yasW7cO\n69evR3l5OU6dOoUbN25g/PjxKCsrA/Ao0fn3ddt2796NzZs3a5UlJSVh5syZ8Pb2hhAChYWFiIqK\nQmRkJOrr6xEdHY3i4mJcuHABTU1NmDp1Km7cuNHpPgBoJimoVKquuzntOHr0KHx9fSGXy9ut9+OP\nPwIAgoKCujyG27dvA4DmkbOaTCaDtbW15u+vo/Ue98ILL+DmzZvIz8/v8riJDInJHxGZNKVSiZ07\nd+LVV1/FokWL4ODgAH9/f3z66ae4c+dOi+0BO8Pc3Fwzuujn54eUlBTU1NQgNTW1S9oPDg5GdXU1\nNmzY0CXttaeurg6///67ZuSsNWVlZUhLS0N0dDQCAgKeOEL4NNQzdc3MzFocs7Cw0Iw6drTe4wYP\nHgwAuHTpUpfFS9QTmBs6ACIiQyooKEBtbS1Gjx6tVT5mzBhYWlpqPZbtaqNHj4ZcLtd6vNxblJeX\nQwjR7qhfQEAA6urqMG/ePGzZsgUWFhZdHof6XcPWJmY0NDTA2tpap3qPU19ba6OCRL0Zkz8iMmnq\n5TxsbW1bHHN0dERNTU239m9lZYWKiopu7aM7PHjwAADanIgBAC4uLti3bx+GDRvWbXGo34+srq7W\nKq+vr8eDBw/g5uamU73HqRNC9bUSGQs+9iUik+bo6AgArSZ59+/fh7u7e7f13djY2O19dBd1YtTe\nYsjOzs6a+9tdPD09YWdnh+vXr2uVq9+DHDFihE71HtfQ0AAArY4KEvVmHPkjIpM2fPhw2Nra4vz5\n81rl586dQ0NDA/70pz9pyszNzdHY2Nhlfefm5kIIgbFjx3ZbH93FxcUFkiShqqqqzTr/vuRLdzA3\nN8eMGTNw6tQpqFQq9OnzaEwjJycHkiRp3jPsaL3Hqa/N1dW126+DSJ848kdEJk0mk2HNmjX48ssv\n8Y9//APV1dW4dOkSli9fDjc3NyxdulRT18fHB5WVlcjKykJjYyMqKipajCQBQN++fVFaWori4mLU\n1NRokjmVSoV79+6hqakJFy9exMqVK+Hh4YHIyMgu6SMnJ0dvS73I5XJ4eXmhpKSk1eOFhYVwdXVF\neHh4i2MRERFwdXXFhQsXuiSWDRs2oKysDBs3bkRdXR3OnDmDHTt2IDIyEr6+vjrXU1Nfm7+/f5fE\nSdRTMPkjIpO3ceNGJCQkIC4uDs8++ywmTpyIQYMGITc3FzY2Npp6K1aswOTJkzF//nz4+vriww8/\n1DwSDAgI0CzZsnz5cri4uMDPzw8zZsxAZWUlgEfvjvn7+8Pa2hrjx4/HkCFD8N1332m9N9fZPvQp\nODgYBQUFrc6UbW9tvIaGBpSXl7dYWPnfnT17FkFBQejfvz/OnTuH/Px8uLm5Ydy4cTh16pSm3rBh\nw/D111/jn//8J5555hnMnTsXixcvxt///net9jpaT+2nn37CgAEDWn0kTNSbSYKrV1IPFRYWBgDI\nyMgwcCTU20iSBIVC0WLNPENatmwZMjIycPfuXUOH0qqn+b0VFhZi6NChSE1NxaJFizp8nkqlwqRJ\nkxAZGYnFixfrHKs+3L17F+7u7tiyZQvWrFmj07k98ftH9JgMjvwREelJe5MjeiMfHx/ExcUhLi4O\ntbW1HTqnubkZWVlZqKmpQURERDdH+PQ2bdqEUaNGISoqytChEHU5Jn9ERPTU1q1bh7CwMERERLQ7\n+UMtNzcXmZmZyMnJeeLOIIayc+dO5OXl4dixY92yNiGRoTH5IyLqZrGxsUhNTUVVVRU8PT1x6NAh\nQ4fUpeLj4xEVFYWtW7c+b1pSKwAAANxJREFUse6UKVPwxRdfaO1f3JNkZ2fj4cOHyM3NhZOTk6HD\nIeoWXOqFiKibJSQkICEhwdBhdKtp06Zh2rRphg6j00JCQhASEmLoMIi6FUf+iIiIiEwIkz8iIiIi\nE8Lkj4iIiMiEMPkjIiIiMiGc8EE92tmzZzWLzxLpIjExkQuE6+Ds2bMAwN8bkQlg8kc9VkBAgKFD\noF4qNDTU0CH0OmPHjjV0CEYjNDQUzz33nKHDIGoTt3cjIiIiMh3c3o2IiIjIlDD5IyIiIjIhTP6I\niIiITAiTPyIiIiIT8v8AzNkdszAb1WkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 319,
              "height": 424
            }
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1PCx7nfC6Ol",
        "colab_type": "text"
      },
      "source": [
        "### Inception-ResNet-v1 Module Squential Implementation\n",
        "*Note: This is the same as the sub-classing implementation above*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GwPA15lttWW8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_tensor = tf.keras.layers.Input((32,32,3))\n",
        "x_1 = tf.keras.layers.Conv2D(16, (1,1))(input_tensor)\n",
        "x_1 = tf.keras.layers.BatchNormalization()(x_1)\n",
        "x_1 = tf.nn.relu(x_1)\n",
        "\n",
        "x_3 = tf.keras.layers.Conv2D(16, (1,1))(input_tensor)\n",
        "x_3 = tf.nn.relu(x_3)\n",
        "x_3 = tf.keras.layers.Conv2D(16, (3,3), padding='same')(x_3)\n",
        "x_3 = tf.keras.layers.BatchNormalization()(x_3)\n",
        "x_3 = tf.nn.relu(x_3)\n",
        "\n",
        "x_5 = tf.keras.layers.Conv2D(16, (1,1))(input_tensor)\n",
        "x_5 = tf.nn.relu(x_5)\n",
        "x_5 = tf.keras.layers.Conv2D(16, (3,3), padding='same')(x_5)\n",
        "x_5 = tf.nn.relu(x_5)\n",
        "x_5 = tf.keras.layers.Conv2D(16, (5,5), padding='same')(x_5)\n",
        "x_5 = tf.keras.layers.BatchNormalization()(x_5)\n",
        "x_5 = tf.nn.relu(x_5)\n",
        "\n",
        "x = tf.keras.layers.Concatenate()([x_1, x_3, x_5])\n",
        "x = tf.keras.layers.Conv2D(16, (1,1))(x)\n",
        "x = tf.nn.relu(x)\n",
        "\n",
        "t_input_tensor = tf.keras.layers.Conv2D(16, (1,1))(input_tensor)\n",
        "t_input_tensor = tf.nn.relu(t_input_tensor)\n",
        "\n",
        "out = x + t_input_tensor\n",
        "\n",
        "incep_resnet = tf.keras.Model(inputs = input_tensor, outputs = out)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVHkEA7Tu437",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 972
        },
        "outputId": "8aa425d3-379a-4da4-9ece-585da50b620e"
      },
      "source": [
        "incep_resnet.summary()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 32, 32, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 32, 32, 16)   64          input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Relu_3 (TensorFlowO [(None, 32, 32, 16)] 0           conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 32, 32, 16)   64          input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 32, 32, 16)   2320        tf_op_layer_Relu_3[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Relu_1 (TensorFlowO [(None, 32, 32, 16)] 0           conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Relu_4 (TensorFlowO [(None, 32, 32, 16)] 0           conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 32, 32, 16)   64          input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, 32, 32, 16)   2320        tf_op_layer_Relu_1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 32, 32, 16)   6416        tf_op_layer_Relu_4[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 32, 32, 16)   64          conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 32, 32, 16)   64          conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 32, 32, 16)   64          conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Relu (TensorFlowOpL [(None, 32, 32, 16)] 0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Relu_2 (TensorFlowO [(None, 32, 32, 16)] 0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Relu_5 (TensorFlowO [(None, 32, 32, 16)] 0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_3 (Concatenate)     (None, 32, 32, 48)   0           tf_op_layer_Relu[0][0]           \n",
            "                                                                 tf_op_layer_Relu_2[0][0]         \n",
            "                                                                 tf_op_layer_Relu_5[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 32, 32, 16)   784         concatenate_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 32, 32, 16)   64          input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Relu_6 (TensorFlowO [(None, 32, 32, 16)] 0           conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Relu_7 (TensorFlowO [(None, 32, 32, 16)] 0           conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_add (TensorFlowOpLa [(None, 32, 32, 16)] 0           tf_op_layer_Relu_6[0][0]         \n",
            "                                                                 tf_op_layer_Relu_7[0][0]         \n",
            "==================================================================================================\n",
            "Total params: 12,288\n",
            "Trainable params: 12,192\n",
            "Non-trainable params: 96\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MaOGT-8vwEh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "outputId": "0cfae0c3-4dbb-4772-a6db-42a7cd7e360f"
      },
      "source": [
        "# Plot model graph\n",
        "tf.keras.utils.plot_model(incep_resnet, show_shapes=True, show_layer_names=True, to_file='model.png')\n",
        "from IPython.display import Image\n",
        "Image(retina=True, filename='model.png')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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+sjl++PBhVVZWKiQkRJJktVr1+9//XmfOnNHKlStveyVkQ5/3SlX3vk2bNrc1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\nO6b8/HxlZGQoJCTE3qXeEF8PjU/VSlv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RGXL1+uMI9//vkHANCsWbO7/l3VPXat+XglIqoO1k+mY031U01j/cT6ydKOVyKqXXRrn3t7e5d5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qlw9OhRxMTEoG3btkhPT8eECRPQtWtXXLlyBfXr1y/3eo8ePYrx48fj3XffhZWVFfbv34+R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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 950,
              "height": 645
            }
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8D_v7guwAQf5",
        "colab_type": "text"
      },
      "source": [
        "# Data Feeding to the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AfeHTAuUgu-j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 128\n",
        "train_data = ImageDataGenerator(rescale=1/255, validation_split=0.1)\n",
        "train_gen = train_data.flow(X_train, y_train, batch_size=batch_size, subset='training')\n",
        "val_gen = train_data.flow(X_train, y_train, batch_size=batch_size, subset='validation')\n",
        "test_gen = ImageDataGenerator(rescale=1/255).flow(X_test, y_test, shuffle=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DymbjI1VFE5z",
        "colab_type": "code",
        "outputId": "892cc9e6-859a-4bf4-8e9a-c519d6d46d78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        }
      },
      "source": [
        "history = model.fit_generator(train_gen, steps_per_epoch=len(X_train)/batch_size, epochs=10, validation_data=val_gen, callbacks=[tensorboard_callback])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "  1/390 [..............................] - ETA: 1:11:57 - loss: 4.6046 - acc: 0.0000e+00WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.261856). Check your callbacks.\n",
            "389/390 [============================>.] - ETA: 0s - loss: 4.1742 - acc: 0.0572Epoch 1/10\n",
            "391/390 [==============================] - 32s 82ms/step - loss: 4.1722 - acc: 0.0573 - val_loss: 3.8620 - val_acc: 0.0946\n",
            "Epoch 2/10\n",
            "390/390 [============================>.] - ETA: 0s - loss: 3.7121 - acc: 0.1197Epoch 1/10\n",
            "391/390 [==============================] - 21s 54ms/step - loss: 3.7116 - acc: 0.1198 - val_loss: 3.6228 - val_acc: 0.1348\n",
            "Epoch 3/10\n",
            "389/390 [============================>.] - ETA: 0s - loss: 3.4455 - acc: 0.1695Epoch 1/10\n",
            "391/390 [==============================] - 21s 54ms/step - loss: 3.4442 - acc: 0.1696 - val_loss: 3.3349 - val_acc: 0.1904\n",
            "Epoch 4/10\n",
            "390/390 [============================>.] - ETA: 0s - loss: 3.2018 - acc: 0.2157Epoch 1/10\n",
            "391/390 [==============================] - 21s 55ms/step - loss: 3.2020 - acc: 0.2157 - val_loss: 3.2000 - val_acc: 0.2066\n",
            "Epoch 5/10\n",
            "389/390 [============================>.] - ETA: 0s - loss: 3.0139 - acc: 0.2541Epoch 1/10\n",
            "391/390 [==============================] - 22s 55ms/step - loss: 3.0130 - acc: 0.2542 - val_loss: 3.0651 - val_acc: 0.2330\n",
            "Epoch 6/10\n",
            "390/390 [============================>.] - ETA: 0s - loss: 2.8762 - acc: 0.2821Epoch 1/10\n",
            "391/390 [==============================] - 22s 55ms/step - loss: 2.8765 - acc: 0.2820 - val_loss: 2.9201 - val_acc: 0.2658\n",
            "Epoch 7/10\n",
            "389/390 [============================>.] - ETA: 0s - loss: 2.7597 - acc: 0.3022Epoch 1/10\n",
            "391/390 [==============================] - 22s 56ms/step - loss: 2.7604 - acc: 0.3021 - val_loss: 2.8366 - val_acc: 0.2908\n",
            "Epoch 8/10\n",
            "389/390 [============================>.] - ETA: 0s - loss: 2.6590 - acc: 0.3241Epoch 1/10\n",
            "391/390 [==============================] - 22s 56ms/step - loss: 2.6580 - acc: 0.3244 - val_loss: 2.7835 - val_acc: 0.3044\n",
            "Epoch 9/10\n",
            "389/390 [============================>.] - ETA: 0s - loss: 2.5512 - acc: 0.3440Epoch 1/10\n",
            "391/390 [==============================] - 21s 54ms/step - loss: 2.5510 - acc: 0.3441 - val_loss: 2.6790 - val_acc: 0.3252\n",
            "Epoch 10/10\n",
            "390/390 [============================>.] - ETA: 0s - loss: 2.4409 - acc: 0.3682Epoch 1/10\n",
            "391/390 [==============================] - 21s 54ms/step - loss: 2.4406 - acc: 0.3683 - val_loss: 2.6501 - val_acc: 0.3282\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0LwWWswQ_GL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5ed15b31-a3db-4e30-9516-9e03fa1fc579"
      },
      "source": [
        "%tensorboard --logdir logs"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Reusing TensorBoard on port 6006 (pid 185), started 0:06:28 ago. (Use '!kill 185' to kill it.)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div id=\"root\"></div>\n",
              "    <script>\n",
              "      (function() {\n",
              "        window.TENSORBOARD_ENV = window.TENSORBOARD_ENV || {};\n",
              "        window.TENSORBOARD_ENV[\"IN_COLAB\"] = true;\n",
              "        document.querySelector(\"base\").href = \"https://localhost:6006\";\n",
              "        function fixUpTensorboard(root) {\n",
              "          const tftb = root.querySelector(\"tf-tensorboard\");\n",
              "          // Disable the fragment manipulation behavior in Colab. Not\n",
              "          // only is the behavior not useful (as the iframe's location\n",
              "          // is not visible to the user), it causes TensorBoard's usage\n",
              "          // of `window.replace` to navigate away from the page and to\n",
              "          // the `localhost:<port>` URL specified by the base URI, which\n",
              "          // in turn causes the frame to (likely) crash.\n",
              "          tftb.removeAttribute(\"use-hash\");\n",
              "        }\n",
              "        function executeAllScripts(root) {\n",
              "          // When `script` elements are inserted into the DOM by\n",
              "          // assigning to an element's `innerHTML`, the scripts are not\n",
              "          // executed. Thus, we manually re-insert these scripts so that\n",
              "          // TensorBoard can initialize itself.\n",
              "          for (const script of root.querySelectorAll(\"script\")) {\n",
              "            const newScript = document.createElement(\"script\");\n",
              "            newScript.type = script.type;\n",
              "            newScript.textContent = script.textContent;\n",
              "            root.appendChild(newScript);\n",
              "            script.remove();\n",
              "          }\n",
              "        }\n",
              "        function setHeight(root, height) {\n",
              "          // We set the height dynamically after the TensorBoard UI has\n",
              "          // been initialized. This avoids an intermediate state in\n",
              "          // which the container plus the UI become taller than the\n",
              "          // final width and cause the Colab output frame to be\n",
              "          // permanently resized, eventually leading to an empty\n",
              "          // vertical gap below the TensorBoard UI. It's not clear\n",
              "          // exactly what causes this problematic intermediate state,\n",
              "          // but setting the height late seems to fix it.\n",
              "          root.style.height = `${height}px`;\n",
              "        }\n",
              "        const root = document.getElementById(\"root\");\n",
              "        fetch(\".\")\n",
              "          .then((x) => x.text())\n",
              "          .then((html) => void (root.innerHTML = html))\n",
              "          .then(() => fixUpTensorboard(root))\n",
              "          .then(() => executeAllScripts(root))\n",
              "          .then(() => setHeight(root, 800));\n",
              "      })();\n",
              "    </script>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Dyjq76ybycx",
        "colab_type": "code",
        "outputId": "0180d971-38b9-4a34-c70e-e4e8cc1ddff3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.evaluate_generator(test_gen)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.3483095595630976, 0.4171]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BchlB3A16RD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "39cb4eda-2bc5-49c2-e76b-79984f7ca2a5"
      },
      "source": [
        "# Confusion Matrix\n",
        "pred = model.predict(X_test).argmax(axis=1)\n",
        "y_true = y_test\n",
        "cm = confusion_matrix(pred, y_true)\n",
        "\n",
        "cm_norm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "range_max = cm.shape[0]\n",
        "df_cm = pd.DataFrame(np.array(cm * 100, dtype='uint8'), range(range_max),\n",
        "                      range(range_max))\n",
        "\n",
        "    #############\n",
        "plt.figure(figsize = (25,25))\n",
        "sn.set(font_scale=1.6)#for label size\n",
        "cf_hm = sn.heatmap(df_cm, fmt='g', cmap='PuBuGn', cbar_kws={'label': 'Accuracy %'})# font size\n",
        "plt.xlabel('\\nPredicted Labels')\n",
        "plt.ylabel('Actual Labels\\n')\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in true_divide\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p06ZYvHhxrFu3rtBVAAAAAADq1G47+F27dm2cffbZsXjx4kJXAQAAAACoU6k9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\n/uH+rpo8eXI8/fTT0bt37+j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58ye2uQPEN39imztAfI89ImpcfOS9gHHjxiE2Nhb+/v7IzMysc0xmZib8/f0RFxeH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\nQmCC\n",
            "text/plain": [
              "<Figure size 1800x1800 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnNsZNb1DbTA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "fe62e0b9-27dd-411b-8f56-ff51efea8a6b"
      },
      "source": [
        "# Epochs vs Accuracy\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4YmJm4OXVOo82CoLQMKNJ4uIvBRy7mMXpq7lU6k04dbdiwpCeDB/gQR8P+3Y/DmnRJJOY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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uyTSN1K9ppH7uaTNDmEXT6DrH43fjI8ToiplYuYm31u3H1sLP7AghxNVIyLQj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\nPN4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9Ip_xOuDbCt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DjcD4fMoIfjt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Clear memory\n",
        "K.clear_session()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8zbOW1mtGXr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}