Notebook_5_CNNs_from_scratch.ipynb

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  "metadata": {
    "colab": {
      "name": "Notebook_5_CNNs_from_scratch",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mY5WOZWrFI91",
        "colab_type": "text"
      },
      "source": [
        "# Neural Networks for Data Science Applications\n",
        "## Lab session 5: Building CNNs from scratch\n",
        "\n",
        "**Contents of the lab session:**\n",
        "+ Build flexible image pipelines in TF using tf.data.\n",
        "+ Defining a CNN architecture from scratch.\n",
        "+ Make code faster using `tf.function`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rnT0_RqFjcJr",
        "colab_type": "code",
        "outputId": "b5355bea-fe51-4eb5-865e-0cf9b740f993",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "# Remember to enable a GPU on Colab by:\n",
        "# Runtime >> Change runtime type >> Hardware accelerator (before starting the VM).\n",
        "!pip install -q tensorflow-gpu==2.0.0"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 380.8MB 44kB/s \n",
            "\u001b[K     |████████████████████████████████| 3.8MB 44.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 450kB 45.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 81kB 8.8MB/s \n",
            "\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorboard<1.16.0,>=1.15.0, but you'll have tensorboard 2.0.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorflow-estimator==1.15.1, but you'll have tensorflow-estimator 2.0.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorboard 2.0.1 has requirement grpcio>=1.24.3, but you'll have grpcio 1.15.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement google-auth~=1.4.0, but you'll have google-auth 1.7.1 which is incompatible.\u001b[0m\n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u3xJbtVsFsOj",
        "colab_type": "text"
      },
      "source": [
        "## About the task for this lab session"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANUF1kb7FjSZ",
        "colab_type": "text"
      },
      "source": [
        "The data can be downloaded as **Dataset 1** from here:\n",
        "https://github.com/SullyChen/driving-datasets\n",
        "\n",
        "Direct Drive link:\n",
        "https://drive.google.com/file/d/0B-KJCaaF7elleG1RbzVPZWV4Tlk/view?usp=sharing\n",
        "\n",
        "For each image (taken from a front-facing camera in a car), we want to predict the steering direction.)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nEuwwee9F5DR",
        "colab_type": "text"
      },
      "source": [
        "### Step 0 (ONLY COLAB): Load the Drive file\n",
        "This part is only needed in a Colab environment for downloading the Drive file.\n",
        "\n",
        "*Note*: the download code can be found in Colab from the left menu, under Code Snippets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xilk9BqHl5KG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# After this, restart the runtime to avoid authentication issues\n",
        "!pip install -q -U PyDrive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "goMDxq-hkNpP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import PyDrive and associated libraries.\n",
        "# This only needs to be done once per notebook.\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# Authenticate and create the PyDrive client.\n",
        "# This only needs to be done once per notebook.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8O3QToxPkjbn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Download the file based on its file ID.\n",
        "# https://drive.google.com/file/d/0B-KJCaaF7elleG1RbzVPZWV4Tlk/view\n",
        "file_id = '0B-KJCaaF7elleG1RbzVPZWV4Tlk'\n",
        "downloaded = drive.CreateFile({'id': file_id})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LpjGMw7ioNfh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "downloaded.GetContentFile('driving_dataset.zip')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96X6DCWqpFFz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip -q driving_dataset.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AS0D0WtpY4m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Explore the content! (Also from the left menu.)\n",
        "# You can see that image information is provided in a single .txt file,\n",
        "# making difficult the use of the ImageDataGenerator from tf.keras. \n",
        "# For this reason, we'll make a more flexible pipeline with tf.data.\n",
        "!ls driving_dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cy5FuAagGwpl",
        "colab_type": "text"
      },
      "source": [
        "### Step 1: Load the CSV file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhRtM-rMp00M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "files = pd.read_csv('driving_dataset/data.txt', \n",
        "                    header=None, sep=' ')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A19KBQmOqEGj",
        "colab_type": "code",
        "outputId": "f6bef716-0686-4600-9d98-85da3459acf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "# First column is the file name, second column is the steering angle\n",
        "files.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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      "cell_type": "code",
      "metadata": {
        "id": "L1KhZeCaqnji",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "paths = files.iloc[:, 0]\n",
        "labels = files.iloc[:, 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67lfquFcrGpE",
        "colab_type": "code",
        "outputId": "35549887-f3d2-432f-a6d6-2df757b463a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# Plot the distribution of labels (note some form of unbalance)\n",
        "import matplotlib.pyplot as plt\n",
        "_ = plt.hist(labels)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
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kT7WfS1s9SW5JMprkkSQXdm2zpY1/KsmWrvo7kjzatrklSWb7ICVJJ9bPmcGXgA0TatuA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\nQJKEYSBJwjCQJGEYSJIwDCRJwP8HRNgjwM/GeGkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qYlW1RRrq5hp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Some angles are definitely strange, we'll remove them\n",
        "good_idx = labels < 180"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNwJLwVXrfCL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "paths = paths.loc[good_idx]\n",
        "labels = labels.loc[good_idx]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnqCeq7-sHYf",
        "colab_type": "code",
        "outputId": "f549573e-0295-4bc1-e71f-daaf34f78680",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "paths.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45187,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0ytky6tHIFF",
        "colab_type": "text"
      },
      "source": [
        "### Step 2: Define the tf.data pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kkKg9A4JHtlG",
        "colab_type": "text"
      },
      "source": [
        "We could also use the Keras interface `flow_from_dataframe`, but because our data preprocessing pipeline is more complicated this time, we show how to do it entirely using the 'tf.data' module.\n",
        "\n",
        "A full guide can be found here: https://www.tensorflow.org/tutorials/load_data/images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0KnSMassqPT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmox0i1Iswt0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "paths = tf.constant(paths.values)\n",
        "labels = tf.constant(labels.values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11NqgJIHs-8V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = tf.data.Dataset.from_tensor_slices((paths, labels))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9xGUcGItTzR",
        "colab_type": "code",
        "outputId": "640b2092-a65e-46ff-d785-239cc32fd938",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "for xb, yb in dataset.batch(4).take(1):\n",
        "  print(xb)\n",
        "  print(yb)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor([b'0.jpg' b'1.jpg' b'2.jpg' b'3.jpg'], shape=(4,), dtype=string)\n",
            "tf.Tensor([0. 0. 0. 0.], shape=(4,), dtype=float64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNX2XinwtiLJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Preprocess the dataset in order to load each image:\n",
        "# After applying this function, each pair (path, label) is converted to (image, label)\n",
        "def preprocess(path, label):\n",
        "  \n",
        "  full_path = './driving_dataset' + '/' + path # This is the actual path\n",
        "  image = tf.io.read_file(full_path)\n",
        "  image = tf.io.decode_image(image, channels=3) # We specify the number of channels to avoid b/w images having just one channel\n",
        "  image = tf.image.resize_with_crop_or_pad(image, 196, 455) # We crop or pad to a specific dimension\n",
        "  image = tf.cast(image, tf.float32) / 255.0 # Normalize the image to [0.0, 1.0]\n",
        "\n",
        "  label = label / 180.0 # We also normalize the label to [-1.0, 1.0]\n",
        "  return image, label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOVkfEBoPvZZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def augment(image, label):\n",
        "  # Another preprocessing function to randomly change the brightness of the input images\n",
        "  image = tf.image.random_brightness(image, 0.05)\n",
        "  return image, label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jMd9IhX2uT2U",
        "colab_type": "code",
        "outputId": "f8994cbb-04c2-4b47-cd4f-9f3db852312a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# Try the full pipeline\n",
        "for xb, yb in dataset.map(preprocess).map(augment).batch(1).take(1):\n",
        "  plt.imshow(xb[0].numpy())\n",
        "  print(yb)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor([0.], shape=(1,), dtype=float64)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S6p7RHKbN/B5QqZJ89siBbORbwasIpLext/qlrHVIntdSQJg1XTmk4vz5TrnH\nOxtDhn0pwDEr/FYBWA75loMti4TzUKrvDcthm42ip+kSTi0N8/Ui3hQx8xlFIZKpu0We5ShKdDan\nb8c9VzUVSsDtmIrLKRbqTdBTpRuskVbd31ulzf1KL33Sa+UqNp9hAyXdGKh68dA3lj8pvumQ4z4F\nSuI3WoA4IMPnE02CyYjY/QwG5kDvK+s2EZdBVpE1aNTQeDfM1nyW/fvr2QpqSNsY4kKuyhccwR39\nln1q71tqHuvRjJsyHuVoDb2FwfqiutkthZQNsW8DXZhNuZJAZSbOn7X+VNqY4Gdh5/jnWrZ6meh9\nakjAeK2LskJF2ztlZI+1iLWGMN/S57uPbl8FVR1FX4r2deamyE6bnbsv0FeCt/sT3qJVnK/HZy+8\nv/Sg8GMhqO0m8FuFXsZkfRN0DUSU88wWqqv4Ag6OykROGFSLsjtZXsQUCt0jabKSnZKn3qqjiAO7\nRPDWnR2Y9r32NWAxRPz9sziL4ta8zzm0yWxJ9d7BwVQ1rPYmo9ob3slK61yTbUbESK9N8k+CwHnm\nNZF/jDLGR50zmehkHzWMvJvkxsadxfwZEKpgbLYKGhwoNR9yl84oq6nddLwp3aFGWdNEhQDk0ZSa\nB76C/aHmV6cH1zU9P9bgaW4qDrqVEGZDsIQiLG8IXv1G7p4YKmjqSQ2NaEVbu0ZHLzSS9hz97Gwi\naRxfqXhlTIOmUeBwV0GOXatGXTREfqlgxbnVySP6omyxvdmlSMGn2VjQhN9yUG7QA7Apzhgwz+IT\nuLBihUzZyi38nWngZc0VpWKTEq4aKOqNCFld7+kd09PV464aHfNW/aWb5wsHwFd8qlLa1NRpNM6C\nGWRssmpkmUq1b0dewEVKLWciIh2btN5fUazw7nshPsFKIVDKdu7qTTMj+uBVkDSFMOEG1nRuYopg\nul01Ct7MlG8vBCYi2BG2m7fjch+lqybkLw7g3ipSiq2BeERtg7eUIrc0zxtqF3ljyTCVsXdBknPT\nV/jUGND4qKVoldSHn9TAXfjdckLPg2keVmjqcYyktXqczSG8OhQWUCP5rCbmTAiCCYpLWRjKcLqN\n9A0DvVJr8Pd+FXu5qzr5DhbExR9DMM5+9CJMAValMi/3pb3jMqjtysQjThliTH1apkp3VwMufP6W\nXpvr+WZIWFEXZhcWpzL+Nmx4uiaoFJyDq62HqrSNipN9XRPgPSaKKUFbbVyzR9MOQYGeckKOemYN\nRKdiwEtZrxus649a1d+EcTec00+6P2g26219RlV7Ca/dwVVGOfgp7bo3rBpj7M37niRxLc4345oI\n/UgdrMODWD8olzcsNptmRcL0jzIAACAASURBVLNa+OrVinaPkNm91I9yNLPCzt2n2ZRvjM3qJFNR\ntA588dzPFyYqUleb4xo1CN2vtgZms6lTFasOrwziCGO5CNEnmzaVlccQY+U22HuyRnXhbhwx5GhL\nQpc7oUrEZxdMt0YAa6mC0otHn/Tm1S4Ba9qFQZep8Okwx+0N760I2xNcznR5E96UObnVHdL9zgy+\nEHXT3dBczZ96p+I4XxgP9Ua5o0k3ln/q2HdKOeNTiKQKQh3KnTbSuQYrFRTVKG3mEN0RrdoBSFPu\nvoSf3wctL6DxpcZwr9YDI7c0S+xQJKd1fgx+rQi9KFVo+p64HSXqtiC2FC7VQ6IvZXjTJsB8nJvs\nDXVIjx2pgjFvqaOOJThvzSmuTsnvBsbDgqMX6Yojy8D3vW+aI6YFA5fe7y52Qo50VyoCdh+eJShL\njkDa8hK+TThXf3AAZsnyEyzwdo5yvBftxrYPDoqaArlcrULFMXSHv7H7yU0q2yiG/JDDb5J9CVYq\nfxtC+YMzAuKNIPh4JhFLPERtQbbWipD7GCemtX2bbG33JiOGeF0cdY1EUwZXnjLxaR3QWVgvvFUU\neJwq1vt4l5PB1Eeq3agILntStXnEglThWqVzxqJts/k9hJF5sitwPyZCb1W68zOxJOt9brU2kXP8\nM4nc3Z2d/aVBkw0pFyq2GB2rRSPDpOg0vGRYzCXr67vYZIw+xkoXNttQV6t5lvu0HdgiBs0U147h\nU68JFU5UNmvUHSBdu2a9qX7KeHUomnJnb+lXq+7eD2PcRnLoki8DcjIK7u9iiU81hzfqlcFNKis6\n9CFm6Y2XqC5BV0slXwZMt0NMsE6PGiduydkdvVWPMkcVemk1Ed7MaYydM2RilojTutUiAydwJ0F3\n9Ksags/+2TfObdPuQACBz/dWuwV1YqQ/G4BZ+DyT+30cdzWtAil2lt8ktmEsyifDQpG2ioTr1dLB\nhiAzYvrFfzpCEYlFeDDKvSHeb13RdAp0Vw+RcVjTUYFb6+4Ia58PmKmPlv7OCAQa8am7nzX6ktnc\nPAWMkoUhc28I5wU1iYuIiFeB2/N+Rhh0sAa+BEEk2ffvTMfV2/kh5+79GWTI4AseuhK8tAY3rBim\nKHOC0hdBzR4ZqdVkWvYiyoOSRr9KpPhdY2DAl30z6aIygngMBCbN/uHxyp4Kw2pBi3+jtQcKpmit\n2auJEod2ONQ8L8KGk5AKaU3bDLGYJcWV3xLXHJ7Mydz4dgUb03zO0PmgxddZ10ByNX8/sFzkZPYu\nnuPeE7M/zEfNsxVcmSnY8OxRxX1doz8cvwnjbgi/8sdJevB7S8qdroMftoTv8cGP5WSBc7IzqTD6\nUHVeVRJ1cufGKhS6i1g2XkG2sf3E23m3EiEXkLWp3iyaA8hKnhMB3sVOy6V+2SvBVUbudcAdobZa\nD1T2QEcpaeUYspxGVz7aaeZg5rRvUTGMDvVxQwMBz9qKUEZBRG+6imcCvXCMH76wgvfnNDELe/EM\nALtKUWUZez9fkXS7JKGPOCg39uswy/DXmNB24yERvapdh5vYBTkFXCogihceT8MxVYAAH+9PFYtE\nTG8VHZApBOa0UI9yU0p6V+S6T++OHtmdC/+N0O+Z7am8BHqgDQ85wlYP7xXnVCgKz2xunfUtP9QB\nNv+UdrbZQENNWOPHvSJqpmYWZK2JLA8qf9bn+oBzs/YW8WngR/V0d1Jsty/2VFmfDW9iryhaDaq6\nG/ZnEFAUx/Rb4g4GJgsqBJedh4ppKLX1EC1y3AkBhXMJ/6DdIWWQH4c03XmTq+2EPUT2F9Q0xvJ2\nliOys1o69A6eHXicoxzR2TKH5njhxI9zUfkx32n027Fl18swHq/1736qQ2esgUoKD0X49IEKrhJf\nS1mMGbBw094pb45DBlWO5EnvzeGHes6kYauw1QoMUl0xs5rlB2aJ25ukqA2Ps6lL6e1yyVJ3bvyH\nzsjaBn2Q7VxTrLYWrG4qnF7ws73jPn1ynn+fInjMHM2S596yKX5SdkpRiKpkc4I27M/AuGOt1NC0\ncXCjK8h2nlvluWrdMdIx1EEuvIQJ16gKDnno3EgaeYB1KVgJGV41CzKOklM5gfcpjHArPqbd6Wkn\n7s5HbrYz8IdNcdHAC67quVvCVjk6VzPozTE9rq+8pseF1AhKMW9iaxoz+a2PTZYfVG2ez02v4Km4\nlAMgpQYR9jqQQi86jGWwu5ROu3BAQBKwvAktSU6Log4ZmVXGQnDNckaRMhJNB6Ywpq93qXpqs7um\n/7oOIFcPcevsO9abyP7Wk9scNp+sSFj7RDtu6jg5mnIb8k0xe/ByNQ1uwnOtoAMqhNXfWz0KcTfm\nAxvp2ZmrwVtVjh761qvP0Qkmu5oQwXrw6yCZNhNxcx/qb6/+5rBZUDIItg6pwgvujEPqmnzhXX3z\ntH3DMcKJK4eIoycKDHzWpW+Ax+Q0brngCrUSkOzUp4hriDfriSgV1V/TwwXADsEfDZj/EJy0N6nS\naNxGG4xTO6Zmwz/nZrqUgtrDjSgSteUkT2DRq3E7MG9yO1mKSK8snEVvXhmmHXd2dJMjn1Gs21SP\nupy+oXsHmG6jsaD7ItanQKFrMtdgHLpEAOFNhWBO+bwaAYfUXXvOd6yFVypguEr9bsywnH77Vawl\nMjdraiP2xbLESa7c6B6cxRmqBF/R4hdjqxFgObDUgTNg73cFpiuoTJ7Pd94OqX6u63b6ksTmCzv9\n9fHbMO40hE815J3gK5KohDUEyc9b7PRhxXkXZtQQn0udGw0kE9spbfmw301TfmOpTlYSg0PGeMCy\nafNpn/iuNvP0qkDZQO/N9hIx1Wv4s1FMNCgcvb/bFJrUHSX6L2GZ1+t02u8bc8CVKmOfpGMmweI8\nTp7VMHBVzuFX+3lphZstZRFDSs1BicFUjRjGoSUD5IZQPrXhETFl8Dba/3k2EhG/YBvzUKn3FD9l\n1kBqd7fAcWDc0MPMwaZic4pr1AlQM3i17t2tCCnWYN83dHYb5s8MRQhLgo8CZzo+7hII82pjMSTf\na+VNWdNLg+yClqTsV6sEwWoqIrpmjdT/qKmd+JSC39CT8VlYpL8vKZ3GqTs2eHu8onEAol+tHW4F\nlaWIyrR+6ftf9R3eo02Hu9CmuSEzNagKlwOrSszXGEUTTOVqtOfj0H0Fbbphaq31et8evubwYNdW\nbyUYUni6TRozH9fZs8TWbPAp7CkTSW7RKiTSDlfG5jcMJYny5/OJmYegM3dnDdekOhI5nlvbjo3g\n4SGVynNgRMGb2vsWOhOEqqnFjQkoW+M8/YbK2vA4cDteZLbO1aVblYSOc+09nUJ7uoQGZyzJfedx\ndeeLmO48qEvKm+pLcM8yXQrjylJ9MtYwJ07d/FZ2Q5r80fGbMO6NkVPF5ganO9VOrcXy5Dh0UMOD\nOA/YG0uwjdpjHtIy//7jom2qylK3A1kEP/s1laGF1wfpxmOpa+STwjkB7dOwz/L8ZrOWwVWc9mBn\n8WSxDpfe/AN2zuGNhIlcKiXZ6jpG/WB0TmrtUrS8NM2jksncImxHIQE6hNbJtRXFrHKoJ1dd7GNJ\nRjYFWE82vmQcaZWUu+tGnE6f7n7wicU3x2DIwm0V4aZDhGCT3Zd6ebRjV6o/+MhAly/aVK5dEwmq\n+hHdmjQHo7I+L994VWT2QCETbd88gx10q6tj3Cmry5C2JzHEJnYXuUxTpvTJpoCHnMXG1Ce8hdWo\ng57WIA5+gVffVZbY9NgxPSF6qmpbxho3FcHVhbtzHqEbu7pJuxs66WITNXlygoOXwmeJMKnBwq3t\nxVOoL7nIV2nuhU2rKnfkr3ErsFwKFW5+imnY2Kqb8EX4tEgYZ7hGpoodukSlmrsnfqykc3oGWbDi\njoQ/2GUjt7xhQbWtvmWDHxsocQ4RQbuM4xpVWbMnWDmw5WpVYY1zEuacltpTGFUhts0kA31VNFtz\na/7vZ1YmHq0yBP8ko3pC7z8ZKIbgF04sk2UfeAg+23MBSETQ2epP03q9zVWBuBNh5HuKX5t53oFL\nseHQTXCXqxfS8yOprVqVbQtbKp5ckTxzU2187Aeei+CAeMePxNbBx/vmShXNuemqy1XKEnZvjvOQ\n/HPgtz82fhPGHTNymvwQ6q5IF+HJ4xyxW30QcYKJ0MvrJlmnoCRhoU5tNz5d6AIOs1DLWYTlZql/\nw9WuoofZwJat68IYh5M9cqyp8oxk1QfL5iahW4YVN7wi2Ru9Jhq9+3jwIhEPf3A3zmI+h9vAqtSU\nro9XFLLMXvds+mzGdafGnSKVX7JNm6KNLYPKHMQ5FAB750R6pbsfmR6RPn3cfSDiuiODz94vd1tZ\nayPK1VCsdWuN0ZKcAT39cMLvrpBDTs86/NpQ4RBQgqxU/aqD1QhqmticZYv9IgqXNPz69urFg/iP\nRlDXfbG0TKG+3OsSkRfcwYsHUTah5lg2mUfb9I4xFaG5q0GF2XAmtl6/z6sP/hjEwfGtpYLxyZiU\n0N1SzQkqmPtwXXyKlXGX4cvJONj0QL9rFqypaYKl7om63IGyub1JxcaOmmwJVZkCpbjbEAyWn0at\nekXCuglLihrnJMJet46lFRZLEW9uPHp6uSCivtQ6OxuarbYhr6eofZ13ljONyiQ7VvFVDsFtrogX\nvyvAZTN049O4uG4kvxUPkflZoNTAioGstlRvXcbdKsHu7HYytAgnWpj3RmclLVFZmOOH7AH0SCjV\nctr6mr2TBK5boLhrWja6EnFeP5mnRxFTmNZbz/tcwdNu9dlB2CHOoRe2554BnjTPP2pWfxPG3b4Q\nUF022LVKyB0mbWFY57kT0w4Y4qwwKi9VoTYvAqtNTe59uusZuhygqxXNto3CZfqo94UuNFAhRpbu\nPrSlPuHWxbF0iGs6Kd54oQy0Dp4QBRvd7jzEu6nV9Nh4KRz8xhXVI+cm+KTBV8+ZbhXm2JIaxGml\n0mPgJeHq6d09kU9eL/zf5qKFe13sVgIMtipToya8Wj9F2csGHjNj2/N104zNxRC26/O5mfrisNQD\nu0qYqiGHcWPBwp4Vwb60/SZ4YHo76nmVHFqMtjmmjF97ZO5rxUS+DiEKg5+bqxVxL6X7dZfHTFps\nX3DwGa9IvmCYOhhoRY7AoATvndOSurJRUjXwy9x0pZ79d/n/5+fUeM3D7tuVxp/Zl6KXcTZOYTZN\n7OxeY5mFT/jsrsJVlqBkwdjXNXUZY9Dd5fgKtXZABXpmPdClyNC7MV624KOYEvobyrJpx1ChQCa7\n1DDPYEXIEdtkG0OGeh8ERQ55rquETX2ESn9HTwaZytBveMSNIZ73ZHdFugrUykNhiUuMIbXNrLnH\nyBV13l8KrnutGPhm4Dt3eO65j9fU80V9eIoOI3MTy8eZ6mzd9SI5QYiFq937zROskniAhJp6lpj2\nvg3Ri23FVcWxDvZ26oLTT13d2U9iuC6fnlVhzM1vcrrx59A4zAzikGdXv6AH6ziwbj7++q841sHi\nIbZ6sDY/RkVRDrZG6r5xVAre8WBbsveTH/HQbUe9ZMhK0WT4geVTipuFsLHL0IULwdvjJ8qK5/4Q\nyZmLfamTnJmxHGqkUq++62FcSMZX5soK3KEvpYvT/IdUJAg+73V+IddU9ZZdcBTBobTsUp/oMFOp\ndApOufrSYfTPNrJ1a65T7UQVeTdvj2Oyk6KuNfDQJf28w2rJP22aGiVTxXg6T7t4c0VLUT646BCV\n0cBWW4UwrAO2FA4vmedEvTKcgkLOEBGYXXRuypwjprXEGMs2Eer1HC7EDh6HpJDtob5EYxRfjblG\nsVOgRlRj0H2uKbMxFrd99zi+YNPFMUUyNVGlJLkn1imnJqvNMjmXi5LDG0fmpGSatl9Qz7XHZfQY\nS2BPrYA7anHN8EhbNz05008JPjM7Gtv5Ke8cg6buuq57dG8n1+KSQL10+hIWjhvrEJzmBLVVkKdC\nr+Pz3mJXM7Lu4U2quZZNgz/nsMWiWahiXJWnn43CYsOxDM7iiaTBkcZpFxa6jJpeIsTtzohU2u+z\nr7L0LOKUyOJ6GtTJEY3Zz+rJ7w1XDKtTqgVxFAxS6rVEsPyBW0rY0K1q9lQTQlsjV+QaxxusOOdy\n9udLAJCmVsPhi2W69DoN3AR01pmYJSxY68HHu/bEs4oV4lDWaD6tm9jwsJiOoroE6NHnFFs6FRsz\nwWYRKr667kDpj4zfhHFvwNYhSCA3ZReVUkvEKS1ptfMs8Fy8bcmh2sDPiRZwbKtIwLC5BeniCPVN\n1sI7ezceTXQKszycDD2wY+n33EP6a0ZlcJoMT6DWtlMlmPWhSk0EIdX0MLcxEo06BtZlvC1ptvvG\nE1utRo8wrmcSHNO+W5FIdo8uGootpYaAipmb+mNUKuWPmIq4wdzvKNktXg2LzE3S4x54pOf+02Ya\nm90wAi84wU3Vu+FODRFlFFlP7Dhom4jbpK/3ZshR08XSe1OpYiy3qWPoJYxfHQSUEhccPJSi1w1F\nBR9XD+xxwxYNroKSHqVNVrFbipPjiNHxPwe+UtR9v0dOq+Rln9kcgIcYgexLBxR1EpTqXB0ZezB7\n2+rR0ymZrbmPxFOO20g5N0QMKuqDDLh2jrZamH6bqWUwCZUDGThu50hAk7MOwMm+uKt098W0pW7s\nXFrXWGTFZI41NQ82ijL1wr9hDGUx6i0kbPw5TlS3A5G6A9UGIgW1z6iC1Tb97JuYi0cMXXje7RjC\n9KHxY65rTNddAWaqLg+1i04bot6Fyyc2CjTxKb17uPNbAjrXYvaTTDmQg5q7fVXgZ6VqVuMHZon7\nUzaimv1UVacfdudKNMaaoCpc87MaMj2hc5GuqL9NmVaswPvOGJtrYJOqpA9/QVDXe4EtdqK7Gva0\n4fCmeytLyIuIZHdQ00hMzrTAS8q+VHDmoX5aqh3443b1N2HcjSnl38lhSjvfn09inS8powXCA7eM\nwzoWzeadn4V7O7r1vBTNGkyvb2FguuPAWR4YRXyhmmtgBPXjHqZ/iJ278KLHAIH0yU6qt42agwg+\nsBARvOHKp6LsIW+g5lKJOVChORvNEcHzY6sv9mDJk1wIahnicvUx8d4nTivkpCGa3P0qpOKl/PDX\nHuiuz8uBfcm4dA5UNcRgqWhKCCbYfROVOdbBvoqwgEF5zKfAZ26Xcp9uj8UUQPXIt6Qvn4B3Il8f\n9csNiRg9d9DWHZX0tGwep6XvXi+VoWpFVG169yzRF5cDleOfLGDwWp+CtK7p+1EDB45j86lDqPki\nxmebBil1Q8+xBX8Y6mSJ9asfzi2/5CarGUcWDBeAYIF5jmFr5MByonfvIZioftaaCRzuniPF515t\n9GyqNroAYtao6jV/D3/BU/eNQX5/gMIOeX9uSG8ChZITW0sXfawW1CgSWrLMnsxBF6yoyVsVCmJe\nhWM9z/zzBqqmXtyZzquyLmeMP4Iqu5rsa2Ss981RgujaS90o/cKYG7ZAAUuXcGvjU71j6A5kPnvj\nVya5m58eb1rfPRyWOZUjcfMDequHVakgCm8eR8ClfbL9hl+1p+MrHPXFIvv0MRJ0e01ApyI0SsEh\nbtSeKwhHBLHCoErVwX9k/CaMO92cfy1Mzx/OX++LZW8ce2FsenS6Dw6eFO8H5HrSXpKOoQjjfHtQ\neyRv1YQdLFOfFmJSsVSDp481EI0BhDoZ2snbSqISrkE1zXTbCw8dYneIKWxJReZZUkDYHa3nwRE/\nQVxi3XlSKXnTLjmr8AP3xhPKix8/BVe7Wiy0LrYIO1iiZiZ6QtAUkuSZqay+euEdNzWqDGV6srR/\nXtUHNhdYTyQcKnMOoUMihKcrYvvcZ4pN83dwP9kfUjaY6R5azHB76Pv7dMC0nu6CrQ5/1ezhSqDV\nb6WMHkPhjiJSZs4zJ5FjknruLmxN4Y81H3c9AZKS3QogSNYSMVXJaKODLEEQETb94SdSLGVJy2Mk\nZqdaFbSK1nRI7+hVcxfZXdiS0sOseaSudCwzcjm3G9pTTahdJvxUz+JuZHbLRFFpvAuykVEIZXP2\nJKunBbXaMhwPsBPN6TGqm0oV3L1iUmHxhkr4JVlQkVhgeiZp7A7i1OUrYtlthARLzstVr9E8JRUe\nTJz5HF6N05hsV9fhuclPLA45r5p17ENzGedniGMpU5MwMzh8eBJT88QsqAyODt0/bCfdiVfyUQ52\nUKduWVtD3vvzfW46E3SXJDw+G8hVn3jpwuoV4MfBfibPXLgdVDTbNnZsjoS+HNt399YSDJwXKxa7\nkw8uRTwfxp4ov7OIVl1Gc3Hd8sqPxOMNWipAWBSb4NCF68/n7AMoe+DnQfZm758Jh5Xg+/FHzepv\nwrjrjKqdqEWzGUjBnWMgkqxm5Q1LXLy3MOtlob7uKpKTlw0jIvG718YWrhvNwCKS77X3q8QckyfV\nVXal5kClsuau+8osRHzaKDc61Oiq4/+l7l1+bcuyM6/fGHPOtfbe55x7b8SNjHBmOit5FKiQjCwk\nq4SgGgVIdOlVlwZS/QtUm1b9C7iBBA0k6JSghUBI1aGBUEEJl+wyrnS+I+Nx477OOXuvNR9j0Bhz\nn5tIdljIGKWPlMp43X1ea8/HN77v983moij8zSVTR2PMNKlM3457ejrR+aRERVdnoE1N4ypqc7Ka\npuotPsLKKdcRXCxqQpyOdbooVEKvu56MIyIdp8nr6TB87eGl7tNPi/t0vcyZxiz1GEJIVxObEG6a\near2OYQj3viSrxKG4LNQnGuFnlzDSZNs5zZtfxpc+jhK4pqnc0ln8QXTHxx6rqYUThwG2udAXKLI\n2J+WG+YQUPHsU5KxuSHNgSyhe18dC0/BsxCoYcis1LN5AZKnLITTQg4CJpYUxyNmrvGMpGl1xMIR\nkzQzcLqGc0TnoSJOqfq06SnxDF6TwnFjipvMVYaMr8bRbPMmNt8rV9OAB1M8JIwoBFHxCOukuMVy\nfYquz9PUx4UcJ3QImWguDzKbo3wOZdMTKdLm5WKWg1xdSXO3UmYzEnOQOGFbcQmLzVLDl8ogNmM0\n3D8uAzONuVLqSLo6TeKQFRJWNH3pfF/L6HEbncP4iALH5pQ1sMN2hTdJpEdtZja6CWqRpE1mT90B\ntsocEOf5vflMYs8Djxxic7reRgidXC2erUQ8Q/E9F8QIIdcHtNl1YD1O7JaeSn0y+vS1XRn+yROM\nRFYjZ9DlrwE4DAxSvEk6Tlqjaq95tKokSVEjl3xepzrLXIBkNjgJyhCCwnaVK2yezD2hFva0lFMk\n8IwZPrD4nKpon3bJ66LYIfSvD+jPIQHgwucDxFxIVcFmatR9kh2Zy4pOFEbgPYWCe4/uszxP0dN9\nk3ReyTxSlAENC0nBXEOamq8LcKUnhvPEP/w8ZRY/aOh8MK/i/uFi6H6F/kss+CJhMfSJMWX+vK/S\nD6E1jhbXcV3CUTC8xZtX+HDShQ8nUgscrxDSh7hPyWYOTLGwvV1/J9fP6WA2fdISi7w/SVLxc43X\n+WB/vf6unk6XGkPla8PQNak4pmsEjV6o+DxzcUQxaR9CLDLRCjKirm6G3Ji/gajRmAsyXN/JH/7+\n137eV+nLbULeZJ2f4+ockqf/MQ85UcbxZHaKl4e47VgsYi7xDOg8LUoKnG1yD92YwFPnEvbQ6AFW\nJOWn7yO+jjj9RnnGh4Hz9bBwdRVdpcGrMqAwv36d773J8596uVx/XBqOrDHC9ZGuluC5QOM+dW1C\n1pgvHjMWx8dEAc//9rpBuV830njP+LDQs0xmxqWFhm5rbDDzedEnJAUYSp4W08wg5UxjrtsW36Mm\nI6dlDr6D799agAltoo1FF65o4whaTtSyFxJxy0yJqIsUAbWYw8zZi7tTlkBLD+306bILV9yCiuGj\nYvbXYHFXCKrc8TYsbFQ0N3Q16jCyz1b0ZccihsmxxylLNLEnaAJ1AoTiAREszaRaUtSj0MGd6OMc\nk1wxlGSQ8xKe8Hykz7aVlHUKpRY9rZPkNywcFEtW6qhzQUmYZfBlbio9TsoWNsS8bsGktoRXRdOK\ns9OtPRVjWDMsUhU8ceXnyer68Bng4pMvMZOYDuIeiATipJXz1dV9BZVF+UNacgzVDDJLFEx4SCpX\nKuEY14Jmn0x7jyg2jrcxQyKK0TCfw6p0jNOxX2sIQ6ccPWSzlK7X9vEUHAluzIe+0zQdNDGhmEPj\naV3TOWRywiInC7HIEgNDnb7wIoFuCC/wnClkxXRh+JhviBjE9hYn/5RS6JhMmx3zZKvTWTUi/QyA\nKCWHDNXHVeqwmA3Y1V0at5Are7vP21YhJKrg0w/ccgzCPXSxkuL5FXPWMnc3kdDPNbG5Yihowa2h\nNp3XFpKDJIG1IaOGna5FQfltPs2eXntCPDASfRIvl2U8UQavgD6XQMvKdQYwV3EXj41vbgY2Oe82\ncc/qUQCvxCbOWGLjJtKqqiHPdCvzBjUbBETI6yQmAqOlyHBkghw5Ag2suUXoyAZtSJRON8FtkIuT\nlkSzwCC37DPHkjhqjoYxE9qu7H2QbwRSpVGBAr3QhnBbHJUzrkKxkG5C4+t47uyj4SRSTnQp1Fqh\nKzmNsE/6eEINZ+8svkADJNESGBXNO43YfDQ5bkqzYOBUueC2UNuge6d5pjejlMJHd7egxm7O3fHm\nW9fV34jFPYZNg9YvFNbwV7vjdeewHqmXPbzNZQ4VtEQUP+zMFJnuih59gxjkkuiWWZZgIHeX8IoS\nwRu9LmYjLH90oSboNb6ejJGL0ccI+9419TYcs3jDBcsjKIuSEz4XMjRohY6E/cwTo80wlQhNgjyX\nsgSpz0e4OCy0txikxHQ8GCHXtGZ4lk2Nq8ngyjoP5IA/SQWBkB2hJ84FO/ztkVzttcbCK+lJxojw\njJNGOGmW5ThP8CNuIDP2Hw6a0KVTKjGQZkKvwnP2gVwpV3rjXBymBqYuqCk+hMwskGbM0x8wr/FR\nRh3ywrA2iflTSZinIvFYlFWIRiaJ3/M1i2AeV/4r5vd6d7lWC/rTzSR+Ti7TBlgSo8c/w4SU82Tj\n5BhGKgxpT/Hzp6fZxzAAYgAAIABJREFUY7ApU1+3Ge0XLBxDE+KW1hIJYLmeeufpOU1vtoYrpeiR\n1iO8l0qmjcq6hhCdJKGWSRoLNTjrGgAqUuKoK2mPN0opwpXyKZbxrLg6mR7MnBE/p+Oh0LpyXBPM\nMhpsUgtF4KCzdSzoRSmFa0rzzGf0wZKm738U4mYZvzOVMBg0m6iRHOnsUiLJazUzhrDcZAI61ijl\niPUPw/TraX7NcUs4rKco50lGWpXe9hjuEyEg36BojjLqWUejV60fOKwrrXdSPjB6TD+rP6IKS4o5\nVQ7GNKMYlz1QDJpAh9On1KU5BeLk0smSY/NNQu2NOkZsLL1yOCrny3ucRMkry3pLyYdwcEmj7Tvv\n3lxoI6SuZY1ZjHajbg+87Q989c0XU8j78z9+Yxb3vBYMY0iwX4oecN+gjaewjXqAtcaIIAUamnMM\nXmSyw0s4XhCyJ7T7tG1p+M4drqTwKMvIgdTUGO4588QgFR/hUR4W1rvrCUOfmCBxghnXOjQVZBbY\npus9latdcU7vLbRSVw0njsTQVhPR6yrCZUoLA6NMfcJdJo1w9mtOFwYzhBGSSpx3k9vTYlFmwCel\neBMOwoI1fm0xiZaYeNOXSehTD5qhXIWQeXiJ38X0JEv8Pq6GMJsnd/H4Uyph1ftQZs3TMMuvbBNm\nMnH+WWUu2lgMdQ0EI0/pBSaGebo3koQffG43H3T16Zqy6+kIn81JLb5GgoRo889EBsZBYyCYrs4Z\nFcjMABoxwLY+o/5xFU86gzMespDORp64/vPklpqeRHJKWIog1HjyzKd5u5pBmWmVNBtc6oakOVo3\nx4fRGKw5SI3Xr71j8Yb22Hx6MyqzZEWhtcHwznE90PpOG0JeAhVhNpG8fVBraMKjTyeKy5QiMy5G\nPVfcB6UUMGN4LM69RnBuUWFr0auqHunNa0Arpajla6PP/MfcJG2P21wKXG/vFUkdEadWhxFSaUoh\nJZn1GAIPo/cWSdwknC+VdV0572fWNUeeYYRN1sw5t42nNBUR8KvnCkXi5+yJsTvWdwTjuBSyCm/f\nvqN7JR8EP96w1R16Y5w7p/XA7fM77u/vef32LQ/vdp7f3nE6Fl6//hxNa2ycbeG4Fr74/B1dz7z4\n6CXn8ze8f3zAqjNqAMeePT9wXD/m/bsz9+d73l0uHA4n+qVSObONjSE7/+cf/9G3rqu/EYs7JHJW\ntDgDpfcD1geZX+8YXViG4JKpAmNRdoHRt0DOijBU8RwxbO2DRWLwQguPu0gUUlyn8UqcZrcSEWOV\nAFW5XDeDq55/HYLypKMJjo9CTgmnRltSng/MmIEXnbY9iSKKGKg6zeqUDGP4Naa2nlNi2K9tIJIJ\n/G4sHGmGOa6DO2shD4VH2LgiYgOZG/r1lScgWZGU2FoNzVah5ODdeJ+qsYTtUmY6D5nVZxY3k0zQ\nJ8Pqx1xIr5S+q6URsqWnOYjMMNW1yNrdSVdOCZEi1Klf55RgQruqj6n3Gu46Fy6df8aRMYd6KnE7\n8D5nFnMwJ4rLtYRb0DEiQOKxIECckF1l1uZdZwzzpjTmYHvqxZr2GAhnn6jfBP2ANaMKUOrUWSE2\n4+C9F2J+5GNwyIUkhkmKAJ1PAWrKFr13NFQ5mENoN8NUGH3He9wUZQzIhb1GtZwucRNEOlqdOgpI\npgPdGzuxSA4P7tLDwwM5JdQatTmSCkk7WECwxsTovqvxPaWUsGZ4n7mLNRw41jslKb13dr/aeGHT\nDydK9UrSWGbq6BNGprRWWQ4rrW6kJGzbxvG4cvfslsfzezQdAhHSxtPtxjy4TDYSluDu7o7z+cL5\nMcow7h/e8tHdJ5S0YM34qr3Heufm5pZXr76OzSPnMA4AP/rTn1CWE3cvnvPm3StyEZaSeHh14f/4\nX/+Ah4eNv/13/m1evf+K/+uP/jjcK5740Rc/Y10Lt0vh7c9+wbNnt5w+jsU95YW3F+eLX37B2Dc+\ner5Qe+N4vOGj4ycsaaF6ox865/2M0zgdCgVI7cS1nSrnhW07owrn6pRlJWuhjcpuOyY76dj5to+/\n1OIuIj8B7glLdHf33xORj4H/BviXgJ8Af8/d33zr6yDQEz5PeS4B2nHXQPSm0HlHnw4aIh2Hwx5A\n6xlaSJOlLeAdk+tDHUPOMZtvQtf1eVI0sliQ4Dx8te7zNA7TvyuoREckVJAR+GA9zlN5BC/8GvyZ\nPBq3Fo4Lwo0ScKR4rfD6CqKFIBcOnnwdEjeD5HPhYp54588rvh55Gt7FqTfY2rFQTF+9hBNHnBkq\nmeiGyaYZBEI0TSeQzsV0WA+NVBNKpBaTygSUSGjdAupB3YvFb/rh/erf5QnlYNOWOubAU+S6MUBJ\nHvF7E/zqAJqLRJJYJOMQG86GqzRg8zYW+YHrRhEOmKujGvOnE7CoM7yTpzMmbHnBZokh4DXMHRtV\nTmFHNYNuMmWncA6Fvi6oRvjEGDQKSeL50zm7SITlU1FGAks+KxMDL329CQIwDM082VB7D2lIlfn5\nDOsVzcEeGWNQSgrjwd5wRkiAKnTfEe/BQCHmBxFigloj3DVqJ5VEEmOMHZfYVNq+zwRnXDTaiFah\ncJsKRuLlix9wOT/w9vVb7o4HRm+sh2NgeVNhjMG2bez7zqHcYdZoYydlZ1kiif3u7SN7H+znR25u\nj+Ss/MnXf0LOSh1G7wtC4nw+8+z2GUvOPD4+4DQu552tG+/uH5G88vNf/Jjz41s++/RjFlnZz5X9\nUnl//w0iwv3DGV1io7//6hvevb7ncrmQSqB1NSc++s4n9HohJ8EfjRc33+Gyb/yzH/0+no1Pnr+k\ntZiTuDWaNy4bnNbMvj1QX29RAtQGRznyWy9fxI3QLzwvd7gJu200Ao/89qtXlDXPUh8hFSH7Hql0\nhP1iUyI2chZGfYyMggcWZG9Cvlm+dX3+/+Lk/u+5+6tf+/t/APzP7v4PReQfzL//T7/9JWZc3kaw\njsXjAWwjOhvntXNMS5BCPHkaCmp4cGfkOM2hUC6MYYzkYWOyHm9eJ9wC2qc+PPtCRxAmr58rLF8x\ncHfLc9c2ZnxvgpVkLq4z5DJPoAEWIixO85+rGJ3ZvKQhGURyPD+xwkkx4MukGOJ4sMWvCFp3CbQC\noIs+6bIyEz1xIp0DZWIRxqOZyOdmyZOzJwocRDPpaVOcnvpf52/4dBSYP7E3nhw0zIAZ04+Nz1ml\nzSEvH6x6KWxiIT30WODV540qfn7Xk+qVox4LM7+2qV15LfLkvhCJxVOmjo99KCxPItEsVeL+Y2aT\ngXN1pkCf0luSMhWnYIso4d4xc8xmiQIyCZgWSWdikxb8iSIYs4i5sKvOWU2UsowxG7pGnxmC/IF1\nMjc0M6OPyughCYkOigijOng0VO3bFjetGTxDBnvb2FVYlskesi3khelBdxO8O2X6qEtJPF7OdO90\n2wM3kaJC8by95fb2RDNnPdzgLrx+9YasSlkPfPXFK+rlwvnxnkt6z/v3b3n37p5cCqqZh8cLYwxq\nrexnj893UG5uFy6XC7/61a/4xc+/ZF2OCPDw8J7Pf/kLvvv9T9jamfcPj7x7F32rSQtWG702zucz\nd88OPHv2jK9ev41bRyl89tlzjmvmlz/6E96/Ps+DV6aOM9/57BMue6Ma3B1uYevc3Nxxe/sMJRLC\nu3Uu9w/krJwvG8/LiXdvXiE5kQ6DZoNvXv8KkYLKAkm5PFzQsqJtp/YdtYUuMZspUmeAzhDt1Dp5\nTHqg9koflUMWat2x2d2byyAtcQtqIgyOiCTGMNaUqb0TSU6wGmnmen+Npv3ZH38Vssx/BPzd+df/\nJfCP+QsWd8fxxaBA6s7CHomspWCeSaNHnd5M99U9Fv8Y2l3thlDS1W9qZA3m+SKK9xhiIQHbsmEM\nVbqEv5Q2gtGelOV4iBq5Nhi+sSyZug96jcCSrnm6QSzYKTnhLW4H4Uce4Hu8tvhTCs1TC53Xw7K2\npkQegzZ2poaCpViEFSGZxektFUR7OE5iGWE4FApJenQvNgOfYQ2fpzQlXBXw1OiSNUGPxU3dpkQV\nt5jQamOYGO1IMq1sTpr+Z0+EVVLkqZs0yJlMHZsPMpIISI/B8GQBBw/fQ7v1Nn8u+rRYd4nsQZK5\n6JvTp3w2d514WiSIg08L++Ty+JDQwU1IYlSPrIHK5NInxceYjGzBxqTqmZIsdGWZb/iGzpNaYIdl\ncUiDVh8m+yXTLi20XzU07zPped3y5tcriVIWSlpQG2z9kTfv3lLKys3NDUmUy+XC5bKFC2Lf2ffG\nutwGSsEqD28uuAt/8If/O/f395yOt/z2Zz/k8f6Bn/38Jzyc31BOK57h6zevAaW1SDW2bWe0wegC\nA1qttNbCwVUm2wbQccP3v/+DyDssO59/+Qtefuc79BFdqasuOB2zzt7j1hAD3fCZj9FC93ewmT8Q\njRQpzI3e4uctIpRSIpXtH+yVP/3x5+xtw2jkdYnxB45bY1mV4/EO6cbYHnl+E3MeFYfHr7m8ixvq\nsRxIx4Xqg2VZONdvcJwDJx7ef0NaVjKP4ZGvnd6itY0DXC4NzcK9P+JLdOjWajAyJkLvZxBlma6n\nusX3091xG6hGBuLCe8oxUUcj9RwSooF4nQiSTqfhnmnbwHTnoMrlcVCb0CXhq1N7DIa3hz5vRAtj\nhJU0D6O/OX/rQvyXXdwd+B8l7tj/ubv/PvCZu/9q/vsvgM/+ohcRYLEag810jAdujUZyITN65yYp\nVY3mjpSEoIw+0LSQfY03pkh43YnrpMzWoTixB9CLElHxahfyesB0wWsnF5sa+UaWRBelS2wQssKy\nBlDqypQYrbMumeRX49wRpZEylOVAHZ20FsJu0QmK5UIZ0LLgo1IUlqQMUSgJmUGINjRwoQWQNXj1\n1ijHHPVfPd6Qko+QYC8dIVFItH2D4pgmam/kVHCr8zQplHyKH/q0R6oqQ/sMugwasEhhjJmI1BE5\npwrmjcVL6NRMSFeKEuAi8oH3Mu0MLkJeDB87S4rBm5piTSgpFoty1Ej6Dses0FOLApWuoBkkrKJm\nHSYuQLrFSYh5axr7/J5CnrLuFBG2fY90a83ICl5jFjGIYd7eO0pnWRYOyw344O3b92ytcvv8GXNE\nTLLCfq58+eoXWN74+KMXqKz86F/8lFrPLGvicFRu7m7IWblcdu7fn6m18/nPX3FY77i5uePzz3/O\nVt9SSmbfK49vHxAG6/HA42Xjiy+/nu+qwXbpsUB753S6pfdOrRu3t7e4C/9E/ylriRlOKgLnmGvU\nNp5so6MLx/wcyXGbSSK4Va7Y7KgVTNgI0mmv96BOr8bLly+nPdKD9zQTvm6dJcVhKH7maZa+x202\nAWpXvzaorOEp9x65C79mHpx1VfqIIBYimCoFYSnHeGkP6Sod59eLRYm3E4ukjwmpW6IyksEQqNJB\nCku6+tMdTzrLzkMJiPLysJVKUgaNtCxoEjQPYMG8ckoZ8Yz1A72F8+q4BpsqG8jIdBIdpaRO80o+\nGp4qB1mweiLXhHY45sxIiToqKheEhX5ISMmIDzIPpO6IHyAXWlnZpVNWi+7jrjzWwbKu0BvHv+KC\n7L/j7r8UkU+B/0lE/vmv/0t3d/mQrPl/fIjI3wf+PsD3PvseS0t0j2Z7L4U+DJEDdTQOp4Xzdo5B\nD0pJjvZKyUJzC5COjYlRF2wGg7rEyYuupBrMi220ebsZWH9EJRrNzTqDgdcULBcdpLzFgzciBHHK\nAn1jr+Ffv6zXE+KO9ghtoFAfNqgLIh3Tna4bUgzxzKiZfcTuXb2Tc3huUzrgm5FMkX6VFjKX9j7a\nXhS2ieIdcxCseVIpRuPqKfE0iCBWQd3orc8BUqBF9/MlnNmjcWAN18KhcLZHtIB1o0jYxbrdU0iM\nbmw+OJUj/dIxopc1S2ZrlZamMBKCOx89+4jeO2XN3L97y9gry3pC0sL92/ecnj1Hl0xtg1Uztg+2\n8865d2QJ73TblXYZbPU+JJGsPD5UHt8/ApBuMj/9+c+4XHZ8m3MJzbz58jXSGp8+X/nq9Su20Xjc\nnL03uhuFjFtluPH24cK2v0fMOSw3qBqX0ch5IaXEKWdySshIWBq0USF7FDeM0KFNNoZ3jBJefvmQ\nXhQSY1dyPgBgUhkWLhJxIxMlGGj46G0GyUaDsiRON4fwxLuxlkxZFsz3qGFTYeuhh9PjeYghbJtz\nIwdfAls9T8d92jev/n0fDtLJ6YJEuDPuh/Mk3S3QCenqc5+v25vOcm1B8zWoMxOjIvP2Gs8QUsGj\niq6UEs+eGX6t+hOiVUmYc4QlJM2x06d9slsg85o4ts6hep/J396je3beEq70z1BQA/ucdKKpe7yn\nuuTouxWnlJCjdpn8KBXE5tBVToiG13+ogoat+JQ1wGK6YCPB8Og9XsKVsx6XOGR6xYuxHBZ8d57J\nieZgq9AOlV0H1mHpL0mWKccjx1U56kJ/nElzS7xPBmVQqHy8BL222IFn4wX/+FsW57/U4u7uv5z/\n/5WI/CPgbwNfish33f1XIvJd4Ks/58/+PvD7AP/m3/od33sj54KIxlVIE2PsJB0wr9XD54Mwrpps\nnBQ1OTknqo3pypj6pRC2QZ1c8OlQQQm9dBijG6NNYJGASY8rlixRyDEMH1GCK9VZxMg4PcHDfgYP\nVsdhsq5HH4zWyT2HbV1j0WntEbPOqs/C9lXCn3w5N7RktvNOsRzJVq+oLrS2k+daISLhphAgZdrY\nuVLruscJrNYt4P/qJBrWHGSlWpxUv9kfKClhvbEU5bF18MzwzsjOVit2MdwaLz56Dn2j+Y6Z8frx\ngYf0yNiMT15+ynm7kKRxOB2pe+W87+yXjXf3b/ln93/I5eERTbCuiWNZKJ748us3/MEf/iE3z5+R\nVHjx4pbT3cqC8u7Ne/7Fj3+CSefu7o7XX76L4AeDd+f3SIK6DXzA8XSiFedwWhERHl+/o2hhOZ4o\nllmT8vrzjZ6M7oNtxMLm7lT/cENatZDXu+l/T2gy1jyZOeZsW50YYMc08NEiiezrtMjuDAmeTNwk\nJ1AFx61jVil6wiw84eSJFMgJH0SAzTKBEfBZpyjkpYC32Ezk15Kn0zKbi9JbyF/XQfIUN2Mmch1y\nawRkWp1cH5vzHgNm/25sCGM6rfTX358xHyH+TIxyAuMpOhPYMotMrjgGIi2t070VXJtwuARGo8yZ\nljzNp4L9w5xpGNfKQ/NwhQFkv0pw0YQFsKZMlilWPs2I8rQFZ/qYBTEqiDS0G1kKDCWnNbIrw6Z0\nKFieWPD5uxfKkzyopcy+YGERgtueM6ILAyeXWH8aIest6RA01lFoEi44EqxLQXtnaKYfF45ZWDxT\n30zbLce49fqCYxSEowq9JKps5GSgHbPGKpnF/op47iJyA6i738+//g+B/wz474H/GPiH8///u7/o\ntUycjUEaivhCN3BaXMnp1B1UD8F4JtgR1zYi03BjWB+IluBSu09EJvMKCkMikIGGIjoq4AnkEBVn\n3ol2lTdoPjDagV4LWo6ceyNZoauRZ6WEi7GUjPsanHExkjYSTkYgG3WGldo2SCwIwsUqPYevvFeB\nkWkPkd4zq7g4G5d4aEVI3WnNWNcVdajNqPsFTQPzLYaxHNEcw5YlC0Ubh3VFEdplsBxuuVw21nLL\ntt/T+sb7x42sN3zzzRu+fPUr1uOJh/szt+kZz559xD//pz/m629+QvGdb9694ov376EI9693lmXh\n/nLPos6b1+/wtATFROGyn6EJvcYp7+Wnd5h1tsczh8NzHh43Xrz4ONCs4ugKvW5hS+qD3Xa++uJX\niB8oniPdqBHFfnY8hZd6dA7lwP2rd6yHxCJGb4/0BGMIuzne4udMUtoMjMXFYgEfmA1qj7Tq4XCI\nNvvRIyW5V1RLNNt7p/eKLhLJSJeJNW7kEnV23Uf4sGUyVriQy3Q10cAHrTVKimFnzEA0ZgEzTR3r\nojN6RzyqHOkDZMoKc8iftdAbcTMq18V4WjafFtn0wRU1wsGEE4vXlWEAJCkhOdg1X8CHYbyGgCUy\nJm8lWpPCDfYBPezTky+kyfpxVK7tSS3eC56J1qpMMGyuTqfQ1FXixK2T2CgCgwhNhal2Mn5U0Flb\neKMRCCTPSs5pQU3i2ExQt8ysNkz4Fp0MeiGSp1LicJSDlz8WiQ4ZNbqEm0JRdA+5KC0LqSwsKIxO\nSnF461Lj96oJHwdAWFkRM0bvJAswnDJIRZCUaAqajyRGdNKug9yEpRcSBwZHvBlQKenCArSuqB0x\nNrIoy3JAxrcv33+Zk/tnwD+aVq4M/Nfu/j+IyP8G/Lci8p8APwX+3l/0QiaDckq0tpOScJTQoJJ3\nqgqlHHl3FtLScamsJVMvFRNlm5zklFJMnz2gWNmULCWCMmWn1kukDLtDFWp/BM/U/czL5y/IC7w7\nn8n5yN3xEx4vzuXywN3HN3z++mcMW2EYp9y5u7khp8KrL16z3BQuj+85ZkfzgX13fvLzV3zy8Qse\n3l14eHhgWYx337yiPu4UF754/w4phdoHX77+klrPtNbIY2WMwXoss1YOpBnnbeP+fOHtw8Z6vMH7\noO/vOayZZc0cj8/J+cDlcs/hGCjay/2ZXG4Q1tkms5NyuDgA+l7B4/NpaiwlBrXnTTkcbiLxxz2a\nRrhAGshacOvsFiGyzYzCAN/IWsLSWnLglEsipwPt8kBZlGMR3B44HaH7K8Qz58eNvB8QbHq8NSL0\nCfq+ob6Ek6YbXRp7ClJjs0HaLtwsGWQDwoLXamzyuHHSJWBnLpD7HLRHiEddGcNQLaQstP1xOoIM\n252bQwEs/P82gzMaun/OGWojF+VSa6jyqZAtwmuSBrkYOQ963cl6AJShEU1XuTqrwjqrEjOMCONV\nljVspG6dUqK8o6ihSRh9xQlHxXIo0/GTggPj+mRlJQnm8+cpQlmY/22erPuwCqXk0Xo2jqwpfPku\nA9GOu2FeMI7hkkkaOWI1hsbw1EWwnsHS06Kb8JAy1DAx1Jwssbg7BbdEN38qQHdrMAu+RTJLlqge\nNAtcgwirRjH0wHlecsiG83kZ4gwP2ebqAhseocRdBVeNPtflQkmJrgFIQws+KiMTifF0YHVnIWF5\n0Kc7KtstLRceLpWlFE55QXKFZPS8o8UYPkI6kgPZjaIDaxU347jchAVWE4t1CsLWMu9fJZaTQHYO\ny8rNcsT3jbxcOPIOk0h+36dKGYWbntgsYfKcchB2r7D+FQ1U3f1Pgd/9M/75N8B/8P/ytWguNHeG\nVdrDIwtKGo1xe2Q9LDw83nPqgA56ltiFc+Kbt1+GDFKiSxQVSorSjtF3hle6btSqLHlFuzHqIJ8U\nT/D67Tf88R/9AafTCRMocmS//ClvX73nBz/8hDdvlB//7E/Zq9Pa4Gc//RGXxzOtDb768i2uG6Kd\nQ1bevN+5Pw+W48dY3zjmhW9efcVHL56T6uB2PXJ3PNEU3jzecxmN0/MjOWf28462PRapttOtx5vY\ny5PF6nA4cDocqfuZu+MdKsHvtX6h9YGocdm2OG2VHFkBq4y+kUvEvEdzApWlqLWgLQrI6KEDlwN4\nJedBXka8xnCKLDTClidZWQ4F64NeBsOjcMLM0JxZJGQHhrAcjkDIbN0MzUJPA6dzvDmQfIlYvQZv\ne+igu3FYM8UzbsIw45CXwAtYdLYmb5DC0RCM/BIFxylCUcGXCYeTdAGNE2gWwqaGoWWimnPCe5xG\ncw62fspC8qvz3iJFqzGb0aS00ViWA8OhN0NyDMc1C0YUWKQUi3MWZV0zw2YBjWgM+jzKM0InTKQl\nhoRjDJKE5DTMyNnIonQVkMSyBlL4yqRRS/N1jKSRIu0eFsTwWgekK8JxYb0LySIxVLApXySPm3C0\naoF4IVNQT5FzwAK/jT0lTiXz5IEXmXZUSTPANmZ6N4e7yhe6BnVRptvNpowVJ+WQXkWUVZWk0WSG\nRPTfe5s3Hhgyq+2y4091j2GLtsn4MU9RHONhda0eYDwlTvsuceNuDLy2yMnM1LOnmafok5s/OxhG\nGiwKQwai12CcUFzxrvTRQwrOiSzBT+oMzJ0dJ6cysxANa4OcVkaP20ZZBUpnWStsid1gSwPdHWUh\nWUbWAtmprVKO+7euq78RCdVhztfvK5KEx4d3+Ludv/Hyu7x5c+YP/vCPeXd5pO7Cv/Nv/R5vX73m\n/M1bvv7qS87bPXonHA5R3Ft75eF8xofx8M0D5/OZ5bDy2Q++z1dvz9Tm1Lc7v/jRz7n9aOHt+S3v\nzu+5vb1FXfnil685jiPKIOfOs2eZURvvHx/I6wqaOTx7FoUFY5AFVDPneqGlA4fjieW4sO2dfHOg\ntnd85wfPWf2W9NBRlMfLhi+Q8uD2mKhbLORqIa63fmYkwaUzqJwtT/6EoK1T/EIqndMxNsLeG26N\nnJ3qwdfQHHppoH0HJzGWImBCPsZJ0oaQcnTU9t3JJaOp0DxxKoVSnIdxT8uKeSLVm7i2b8aaC7II\neJ0+8CWuyAqeDW0JOowGXia3pM02qCzsujM6YImFE54Gw4zm4CXwe7k6xRNGZpmefM2hifYRC2ZS\nj42vHRAOFI+kpfed43BcU7xxtOBTU386RQqTuT3LWXyZJEEhLRGKWnUu7iPTVGMQ78H7WeUQc5v5\nWk6f+rHjfgIZeM6zmm/WGSZA+iw8yagJWRbGcJobudQZVCmMPSSeXCZ4TaYmL0aAZCO34KaxAAMh\nz9T4Xl0JXDCBwZ61csfDidY7vQ+KlimBAG5IChkjp0gum8+EsUl0r+qVGvpBV3eNYpAk8gSkE6J9\nKRAT+uSsGrOMRBWcLSQiicQyEtZfQUiu6LD4flXiuXCh9s5imW1UDmlls0bOho06xdCEpygOaeK0\npohmLAd2w3qj5CNOpo0IF6ouwY5vjvYINHkZ6BK1eKKdoSClYanRJJracs704ZQcrh5vZ0xWhhiX\nHuE/WhT27ZIgL3iJPoGWL+hyQWoCXyjEwLVphB7Pi7NUpVrnrZ05rUsE8FJsFCId1spFXn/ruip/\nUQ/f/x8fz++imWEEAAAgAElEQVTu/N/93b9LG/COxxl3rigNufOwPT0aS7nD22AZIL3TGVy8c1jX\nOBWVzL5fSDJ4flRMdh62C5ZvSX0hETrVDki5hHd+RA9nH05OR9I2+S25gxfEhMMShQyPrdHXE52K\ntcrHp+ekURlSOS8NHzGEbfXMy08/YukNr8bj241HTeT1wM1yy2cvP+Zxu+ft5Q2ffud79K1CG7x8\neUO3zutv3pHXhdPplt/67Ht889Xn7Oed56dP2NrGy88+5uevfgYE5GnpAzsLRz6O8IieqakGJVIF\nmkdwqwnHfJqDsp2zbqhGf2MT4XGvlCIUA01C1W0WLgt9u6EvnXVKBiZGUqVuLeBrpgyJirFVhDSE\nsY3YqGb1nfWwq1Vz1nTEzXjz1VcsL1ZOtzeUVOilM+gctbKWBZWVMZzjslJ98PC4hZVyVpyN1BgW\nqdzmUbxwSImlxw1ANQfSYEb8xxg4ykhCm1KASoZLpdfBuh5wGQx27tYVb51RnR4janQJK6X3QWYF\nlDEapEAHmEGvKTABEr5cs/B+a+lo3rEWgbE8Jn7XYUhGDxXRDtZp+4HhCgySjOleic0johmVa/do\nkZs4DSq4X54kPZ2cnKtjbowxhXV90sz7cNCCzg3U5ErnnPKIxkEhT0lJdKH1uBk5RsoDhSeXihh0\nCxqoO6SSuCKTzX32Ijh4NJUNs0mCjFtFInIjZaxxa1OwErcFfHCznBCH1ndMQQ6J5D0gc65IKk88\neVomeUZJDJ3p7ElCTdlgfaBbDJMTCz4SWRSRHaZrSfyGIdBLzF3AKHKI4XztSIr3jBOFHrFRL0QX\ngPP85sRDrRF+usCzZaFkZ7cHalsYFgqC9MzCCT0J4/CeU8tczjv3y0aVBWmJWzkiy4bknbU0jlb5\nL/6r/+WfuPvv/Vnr6m/Eyd3dyKNR62BZNJrSMwwa3pyDrSQCYrQclTJ186wHVJW1LIzubO3Mft65\nucmc1gOP552Ht+9oeef54SXf/c6n/OmPf8r9fuHlb93yvU+/Tzs33j7e8+nLj9m3jXePb/jeb3+f\nfFz5+pvG5eGR3/rep6SidIR3rbL1e5Ajz/IdqwmH28I5PwbpTzJ9u7CuK6kKayqMT4z2bOFwumH1\nhe3Nxs3Nysv8MevpyHb/yCEnymqIrDy/e0ZrHReobeP2eODZ4UD2hWVRtm3j5cuX7K2RxDgtg/V0\nIN1nhhe8ZNoS7HlX4fFhJ0uGsbDIDKO4kNaw6C3mWFFy2UgpsZYDxqBIC5mgOS0ttMVYeienU9AE\nU8gvMiZITYyxKHclkYZz0IW3j2f6BEndrAfu7u54+3BP30NT/cFvfUzPg+qNJJlNKs12btKCuFLk\nxL7Hv1uScnsDKgVtYRcdqSES1Mrm0YJ1yIUyOs3a04KlV3cIwvBEd+PRGkKhiJJ0wXIM4gYe5cdJ\nGEMxMXLKsTCVwNvGO34mdzWBRtYghqMZ1SvpMopahgSXPQZ2IySZa4rWeMJJiMeJXaaGHL2ZPncI\niFvXZOOIgzDLrKMHYEzAmUkE/q5+iiuvyC1cNOEcmxmGqXGHZTIwEEg0ag0LyqYNBw2qJchTGC+C\neS1cNTOMNqsNcIu5RtwCJst+GCKDpSSchnmPkpZJ79Rp29SSoU8XkAciWiSDxa0tcMsCPYiUOUWA\nr7U25SFYZsm7An0y3Gddwky1p1kOk0B08tMhi1A85gcOiAQXVSzRSeytUcoaw1jvMXDNCXms6Agl\nIWcQNbqfydlIydEe1kxrcWNIXugCljqKoVXQKuyiYeRx4YaVXI6YasyApp/fBvgofNvHb8Tirkko\nt507hJKUZ9/5Dvfne168/C5f/PRLfBMOUvjh3/oBH318y9uvXvHmV295++6R73//B9wcTxyPB8oa\nFWt9u/C9Tz4h58znX39Bfn6EnqA7v/O7f5PjixvOj+/Zzzv9Inz/X/4h5pU0Ot98/Uvy8cj7x84P\n/9VPsW5sj6+De47j4z23rJSS0W0Fz5RD4U4bmneg4mkle8bXO7RkchGOhzPL0rB24cXdHa06u2pI\nHKc1EMPLgdGNZVGWNVNKYe9CkhuOZUW6RrfsurCXRikXUjZWIqKcT4XHh42tbTx78QnrcaGOBvqK\n4/GGXO4QFjQZWgyhRFz//pGUw5qnemKQqBh+7FHZZj1OO8vKsUURwmN/BCbaoYeLpMugLyOcDH2g\no/JsOYIuPD5eWBOsxbh7EXqymsOjY6XQfGFkR4ZQxsLRC1IV7SfcKlmcVC6UdUFTwjphZUUR9jj5\nGdAKxYVlgazO8IqJ00ZYB9McQDqJXINpgzlrSthCaOoCFKXXDjOWP9IHKFgCNHvkC1KJ+c8wQOkW\ndXoyj8tmARgO7nq0giW/LoIWIbdJ1wxUgCEyT/3EQqnJadaZcbypmceCEavPhkuw/o0WHnORaDOa\n/vcr9uJ6cnYLBITKTHhLCb08Wk7mZlAi2QoxjB6xsDBRzvLEUJpNTT7oPkgpSIpmsXEMC8eOeMg1\n11uBOTwVyicN66RLIJp1Vj66RBpZNVw43oNhQ0Kt0HfwRTGdl5IU/nofAy3R4OXE8NrF8BKkyt47\nbkvo8WPKWjJQdXJWlhGJV/NGT4aloKdCotsg4WEc8EhVRzZqibn9LiE3aXCycgJhR2aJfe2RqqZE\nI1vNhSKNtVe8eSCUTSg+OFm0Mp0ZpLXRc42cADrzKH/+x2/E4n57d+L3/v1/g+VwQjWzyjNGd5aj\n4r9ziEHCMFo6ozmTPv3XsH+9kVfl4mcOhyX0tHUNal03Ug+O8g//lb9JTYZnZ9jOrSvDFm5PN/EA\nD2G7vCdZp5D5re/9DR73nSQV296z5sIb61xqnDpOS4r6r7bTrbILPLzvHHzjuGSw8ELXXrC8MGpl\niHKsYGrc6IkXOSGrsKeM6IF89yyGnIeEZ+f5M2OMxpILhZW2d2xALidsFHI5so0LN7cZ8mDwiGuH\nUXjZQS1O6QCajN/+4XdDZ7aF3gIpoHKhWQsGj9ywuiPd6JbosrCP4HyL7IgOejZqFw47MIzb2TYz\nqpMsYFGeoJWO1g3tEkNYX9m6c/PiRBo7tW3cLUe8O946N8+fcSlRKrzZmUym9cDUlr5gDwVdCiYN\nocYBNRUkB8s8c4Q9YuDLZMrIGCzJaZI4jwxeOB4q+M7YFUa4K45pYfgcOlJnVDoWXRlOKUs8NzSg\nI55oNdALWizebB5VeLgzujJckGIkCUcJOisWCe27W/D9ZeyzeCJ83o7QxkRRW8Ilg8bcpe8jaJYx\nReUKT4uFV1E5xgKmHrJOGBTnIgwwcQCiT+0/7nlCycYcUo9pSRxYj2S4kGJhcmfN8fyTIsDmCoMR\niACZORKPcrEwLEWlYM5QcgS6rF/7VBXv4XrrGjOU1kLCURfyGqXlNpPQSkIzoHBxZVc4pBUlhfx3\naJzHBcM53byI2cno1Mhk0y2xD0PG4HkaJHVGSjx6RooyXHnuQhodN2WvNww3shmaGiMPmoa0hji3\nNytZE70LqzyjDCdZhRxFJdiKjB4SDSeWIiy6c1suSD5Qzdm9U3tHTHnWn0dJ+9FhJA49DNUtK3tq\nmBxJ3km7UvxIsxGUzvRXDw77S38IsKxKySMg983jGtxD8xPppEVZyhGVBasrp9sMUlmzAJ1cjrRq\nlOWAD0EbLD0cGfdjo1MDVHTZGG2hWcD2MzPa28BrJAVvjgcWLez9LeKVw5ooKXGuld0SfWQw43y5\nYCWFS2c4o3aEsLVFS/s95XhLMiVZZkkLh3JDkkggqmakLKhk1vUY6bulk/OGSJzctRuH08KyHNm2\n6Y/VlVUmalgcTRktirJG2YAVfDZwhec4rrxoCi66GimtHCWz+wjvvye0pCcUwCEJRaNVakij9p2E\nRotV1ugz7U5eBG+zpKRIBGxGOCF8hJ4acCuiLEVyQMY0eOrDnN5DmxcJ6S1KEgpLKuw7pMk1X1KC\npAzCyy4JcKHN07FbRuci5SLRmGUF9zwLHHzCysI+K2MOvpLhNptYZ6+pDY3gEwbJseS06rjHwI7u\nyBr/bRtOnviLkA98WidndkOuzpa468RfXK1788g5Q0pu0aGaMxGuMcVGDD8n942UNLRaD05PhJE+\nVEQOswmbi88VWJ5g+1SPsgslhX++B9Yaom84OKk1rJg5h2/crv/NNdwUZSjuUaIeQ1tBpZBSeNm7\nB51VZKApkSSztwYpfuZ9FoNQgrVfJq3TzSI01KN2cxqCKEtskLXV0MZTlFsYRvWQ5QzC6jpTUX3y\n9F0iU5B0uqJKuLYifBXEzeRrSJBAE2W401Vm72p+0tADC34h5wVG5G+uBeG6xOZGbVgauC4seou1\nHQoM3xk+aOKwDNZSEDFurHCRna5QNLHKyq7C8E41o7aGdONkiVUWVI3dG3v/ayDL5FT46HhDzoWc\nTrR6IKfQg9ebKHWIgusDSU5cMhyXeEOodDxH7D7nPMsBAhequTFG43YB94K4IVq4GFGOa51ROlJG\ndE0aMHbaMEYzmgSj3IiKvVU72+MjjM5wIfUjeEOS0S34G0oi5QUpzuFZBX1A/MTRbmZxxZldThFK\nKAEQMzxY8L5DD653Siki3iW433vtlHITGqdVOjuineJOkYGaUVugiM2vA7MIwCQEH52OR6putKe6\nOcdRdZrNgZMk8Jm4HABxsvFOpIalhlOjO9512ioNUo1YO8KQCIaod7wpSqGZ03oPCJo5rgVLQk4x\n7G11YHnF2Bl9QA+LoY8R/mutdGoUtngijaBLkpSzddz+b+re5ceyJUvz+q21zGzvc9wjbuR9ZFZV\nZlV2NVUCISQQUtUAITWglpB6xr8E4p9CQkLdEowYQUPzqEb1zKy8z4hw93P2NrO1GCzzyFFnS4jB\nrSvlIG9Gusdx39tsPb7v9xmbVoROyOTonT7Suh460KIMF4p1RCfeOxqZX6qaS9Jsw1NXHz2Xe6pO\nH8FZwAuYr2ATAR+FOfMw89mX7HAgphCJTQbLz7vc1OFpnrEKKoU5Uq6XCsKKWmq3h+bFDUqxnUAS\nze95ERWpqCa/JWIS83Wcssw9SdXKsRk3imniPSxrYYmgRsr+mL4u8twr5WA+9e7TEx1gbEwRrBhl\nLedfKZsjEq5VNOMKY6wlqwrThCFOnQOtaUY7ZiqLMlTcMZKsaVZSZCmD2H/bmZgU3DRRQ9HZ7IFx\nGFIN6onPdbGQ+nKJmnuMWjK/NDoXex07paQXDXQ+UVTYys5xF0wak4lfNLsen8yb0btyaYAMzIOo\nINIpl53zNunutJq7Q52wjcHZUo1W73cOZhYK1pilZqyfBbVU5DixmiKAY0bO2sW4c0tlTiRqwqsz\nXXE9UyKKcbv/AzjcTZSfPn4FUlG5cNpMXnh9wNp9VRiFmDVdbNFp+kzRwcUKU3eG53IHu4Pc6AVI\n9ddq0/LtDKBdhN7vTHMojnRDrRLVsH7HcEozwgq99wzX9mDfKlF2RjgjjHg6KDTMBkmYlxWjdnJ9\n3GB/zOWfNq6iiWNtG2afg1REg23PXVhRQWxLKWNtJHkkRwplzWAl0vqtmknoppYzWWrOIWvhUMm/\nW3FMFuWwz6TskbbuZhckKueZGGQXPmVghkANltklPlVqpe15iAWggcWCMMVMrbGcTO8ZTEHDNZeS\n21JkjCEU2RC2lMpNUJmID4obm8AxY7H6NyJ2XJRSOpX4pG7BJjImTYQx4exJLCwGRZzpG92V0UDa\nwEbP6njWNNqwOPpLe1+KJlZAT6oNiAvdhV4npjlHbjI5PYNMSlFchU7Qp+BBKix8S8ZMDExTTlhk\no89JxKr87MSiMyO7gjGTGipqxOsMvTiE564gUmXSFjN+VCW84F6wc0dnIn2tSi7PY+IKeHZWXQ5E\nBkpjnMKcntRTJrM7c1n6zTbGHKBBr8md0Sicrkw3AmOiYAXXoFbAkjFz6pbzflHOyCxWhQTfhWOx\nI9MZDnNriCg2g+KTJABk5a/FuOlMSJ4KZTS2YlTLMd0YOb7a3lRsntg0Ygo+d6xULg026ZzzRGQS\n4hQlc3wdtvhICcP7I5zGPJy9PhAluMdB3VIllpLbG00N3DjHhVYyJW2eI2Wml6TW2llSuirgeqA6\naJeC2QV0J6RQy8negnt/YjSY/oKq8fSy4aMg+sjRlLBC8RtU6PLMxySDUOyg6cHDdeOhbvzwdEfM\naNuFtt1/57n6ozjcAayWvN18Uk1XIlmSGF9j8nJEM9nKGje8RqG5oDNbPa0BlpjYMcuqRhW1CmrL\nuQdFcvsuCrxalEsqIuaceQNfhLorEQdVSioJLBNlYkzKrFw1sDDCLWP9DLQ1tusGdgVtVDUeN6Xt\njSlCbQ9oqdmybml4EXJMo+pg6Q4UGj7LCkFI56lqkhhtWayVQJbOvItSxCEqU09U1uFbs6XOgAr5\nFEOoI+fAAmj89nCX9bNPxkeOL0zSwcgylXisVPkQnL40CfKp6mdVmq/LuVKyMjJLk5BLOl+RSKaL\npanEh6yRQx4mOle8tMLQVC+gyTd3F0YoZkFV0DhwSmIYpGEo47cu/qVmYa3FyO+xxgul1Gy5p2ZA\nuY/sPlwQqVTm+j3BGZ6ZvMvwUpe0UMPXulWQpfGeYwGgVhC3rAvWYxnJTBY2IDnxEkKR7DSXJgar\nguJrdJV/NmfsiXlGPXk5EjmbZ1XwtSSzpdc1opnZ+U1fB2rBVZDxesG8ZhXkcrMHBC3/nDuihjMQ\nW+EzMUHK+sxBWcXG6yJalhqoJCObG69qHHu1zGbuqipuBtyoLVG/5cx3yWRVyiNpn2XLi7K2DJU+\nRnYhZfUok54XnAoaiTbOXAKnrxhDmxsxC8V0JWjl4v2c+eeqaM7yO5S2gU3OW8otKZk1zJxMJDMY\nbFCt4AtpPBRmZIj9LU42gkHgrhRWCpdWAkUbnGVSJY1qolnIuK+OuHZMFJmD4Z1SJUNrdHD5t5yp\nP47DXYJpR4K3AvAt56eAe8erQCVnyfjikmzLppxzzxCYzZCS9nf1dVi7JohLGiCca45/2Tb6zIc2\ndKZoLDJswGdn+GS3kxGgdkG8MXvgpcE46MfJtQ6aH/nijx0dLeVt176WK1dq3WjFeLgqZSsMcqyA\nFrRkF5Gz15rzU02naXBBYmdqHihqkojdlSM5Fkwpj+3lCNSUbRVJsp5EHoy23KMmssYosTqjdAU6\nOetN1+HMuaeRapqIJN+JUEwYQSpMyAc42+nXz9QoCMKeSgmbxMy80aIL9KaDLgdTHNWJiaf8Lc3p\nKdFEOX3J4ArZEalhsi1ekHJKx83ygNZJ2AAGOjrqsPdHSlTcN560Mz8dfqtjEGeGEDOjSkyuCM4Z\nNbn99gqsc0oIJvccc5WUJGbObI4FTQbUzDONRXfsUxf6Nk1eOa9dIebheUlHUHTlwS73au5HUpst\n5AEhysrp9U87FKsD8QybdgBfIc5TFywvYwZzwp4LzyKK95TjqQFmiXZmBdxIQfxMfK4KpzuzCFWN\ncJbjN2WBKq9xlKn9xqFodpN9OW7VDLHnpDJq0EaO+nQUpBWmdLSu6L6Y1FKo4QvMVQmbuAyUmfr3\nIdSxM+mUNul+pqnKfwsTVKmICuc8KZZjx64nUwou0M1pVOYwtDilDqp1zm641iwOJpRR0TkZl0xl\nOwrUzWia5rlZBDcHm6imSo/DsEi/h9U7WOEeE9eCtx0BtrJBUeo2qGMQ2gnpPGqjzUxlOqNSbEfK\nIOzEp609w8FlF25z4POOn/8A1DIhwRTQvUDJakxDUR9gFV/J7LvsyFwzYbkgOG5JzlOUq+xUtqyE\nPNipRDGqZXzeJKiMlHFJBk0ATLlnq+oFtvJpHDG5MefBtl0QKucxqWfnHA/s52BMaNzRGMh9mZik\nYJcrfQ4eLo/Z0m2BXQ4Qo8gDag+UVpOzzQGyqh6v2U0sE8krc0TXES5L4WBFsmpYFnANQxFiZCjA\niteAFQguIp/yJ5HGGAN3pW0rYGEm0AjSHFJKslU8jDl0HcyG+0mJTljuCKbNlA6SaUUmmQ503POz\nhU60OTIHwcmIzvSBXmylPln+DvtgdIjYUVFUheLKGZ1DT06bFMkKM63jQdn21I3Pk7pomTMa3ipG\nxZ93fE6mHej1iVZ2JDL9ZkYu3Lyk2UQjqc6j5+eNGLSWwR8jjMOFUjamn4QqyhWLSotJ4Ybq5LZl\n/KK4Iudr5Fx2STEzPrEjUJyyDaIHzB28LZt+0Fpq3cdKqhoxUelMrclwGZXyykO3FUeZtzROcvT3\nkjuqKZFdSAhwTRREhSI3Jk5HOT1DbLQWXCalOK1k8MwMo3ilRMVc2YdkbgIbyLnej463/P3LuTHc\nIIytJjYYh1kaPQa1O59ZQUyYdTJapXulS8kBpAwupTO94soySRWwxrTEQhADqYEM4+iFkMJWCz5q\nVvk6uWoyb5q0NM6JI00wdXwqz0/2aZQyzXAeaM2IuFNLjj5z37LRzOin8awdPjOwgG5cxzX15kW4\n2XNe6BSwKxY7+6hIfITZefP2C74fd8Iam99S2gxsLZf+Y4L4G24vgvYcQ12LcPFnSi08ywYtU8GG\nvmXEE7UWrAqP8sXvPFd/FId7qglkOfLyoRCVbP+kpCzKBanZRovX1UKO5UDMWDqhZIwYgTNz5CB5\ncOWs1RlBQomsUW3xsxPVz5hGaTln791T5mWN8DutNITkgGvVnG/fB+IVC2V7aIgrMypTNors1LJj\nNikF2p4W9OkbbdsorWWK+0q9r0VRbzlnX/W0Sy6sMmEpKxQzxdRwP3jFu6qAotRqyHxtfVd49qqu\nJCrOBDLDVUyW4mEt/cQ+aatTaiPgeYWoGKhR5bIUCAltGszF8MhxQGqe10Uka4TCitlbizEtS+nB\nityTgqgh5ogv7olIqkUwDgw0wV26/vfsVzKZSiVTfSIyALqfECOfqa6aWml9zVU1WL9npKClYtpB\nghG5jBZ1bAWW+5rfBEGp+xoX9Rw5zJT/qFRUDK1nVssO3hOjHOvrxMqOhTzsVYKhTvT89xqvub95\n2RlpstIgx3yaI64RGZguCOGTcGN6pM5egvAMtD7dE9zmBiVDnX12xgjM8hJIrXyh2QYjnayiJdk4\n4074pEhFfMHTRLJLI+f+QwKLTM/SZZCa59pNXQp44plj8XA0NP87wWTkO72e6yDn7DECZMtxYj2g\nVKRU7qMzNVHbsb6XtJ1SKv1MN65Jjivc83lzqUwpOUo8HKsFjYpFYbNCa4V7Ce6nEzfWkvnMZ3Q5\nj5mGKdSSOcLR09CX3Y5nFKYtJdQQHuob+l0YvYDtuajvTikbQzqbbcxT8us6nFOYJ5heiOl4Mdzy\nnX173Qhxnk+hPu5IOOcERmVTwwKi/wOQQioG+hmmDfXAxjVn4gUomcQTYZTWmC50b4yaYba178xe\n8mVdY4ZiULRzlkGfHZNtAZwqxhvGGPnBPe3eIgWjcIwGOFu5UKpyHllBMF9SCXAZ1Paec9wYY3DZ\nd2LUBJdFku1Sn3IBr1yvabwoZdEOQ9jrTuhl6YhTF6tUqjasvEKdlmpltcb6ygpZxpjUOVs+uJLx\ngnmAZuB0TiCyCpyR7ruIpPP1c+GShVQPAa8uQAFqyUzZ3g+qZRB1kGk8zSpzGi6TER2fBxG5ONXF\nIAlyHgrZaicc6pXQeWGwGPkoM05mGD4WqEo7iYD1lDGKUC2Th3TN9oMAE0oMmIkd8DhXgInSKOkY\n/Ezw6KmZtwfCBh6dOnembpwz2+cMmDCkKbY5pQtlNrgp2MQsMv1rNtQLfZ7sG5+s+wPNPAHxXGg6\nCI5aoVlZB1pO/IcPZCoee6pu1Kl1ZiW+giXgddTjmO5rlPRExFhQMCNmBr/UslNDOL0nGqA0joBz\n1DSDtUpMo147funM7jlioy68dkp6d3FO3RlT6HfYHmGLk9rzABeBowvOYL4ewghYQ6djAdfa0qSj\ngdfFr3dnr1mcTWk8H0ophjWBuGPivH0oOTLzwOKK9exOz5LKrnMGdatMPzKK8oQWFddnQNnN0lDU\nKl2c7rk/mDOQrVFcqF2Ie8Ll3uzGpgPloA6h6duMbbwcEDv9ONnLBRtG24zDnmj15DpyTh9XWx4Y\n0LGjNbupR1Haabhs3B53ZrnSuXMxx8+TEoXKxkNNLlWcxhnw9s0j4/s7d56Qd8rhE2Hn5sIYJ6Vs\nOG9ybLUL7UOjHUoTIK6/81z9URzu4combxL0RKeUVK+oBWwd1UiGxhodYIbVHdUgSoXSUscqR6og\nxGGreaAMJ7xALAmYplGitgZeEDlQSzu+n0Z4Sg9VjfCJu1D0mk60UFp9oJRC7wd9BLPmf2SSagfJ\nLE8zWAE8oAWpG8tHnctQS4hQqXnDZyhzGkGsCGJrLjtTSgfJqn4dGbHm3b+14PHb6LlFRozISMCB\noroccyO1/GlkyQP4VYOddnLLL1lWzihKoIw8jpfNPiukUlaXMZOnPX0SpMXeCapsYD05HjPnz+FJ\nThTJX9N0IWrDMHy+5MEomdMRUiix2OQiuI2cIGsg4zUkWxMetmbLxa6MCKwGKdGceMnKV3DEQXVQ\nndynLJ14qKYBKFI9Ij15IaL5/zGz1HJbA+3JibeRev1I5o7LwgUsFpFJ/g7mSNu8ieYlKD0TwMqk\nNMmZMVnZZtuaCpJc/haUB0x7jhNMkJJjmFzOOqJrGYvkYt8SI9y04Tox6/lekOMWjQZSEOmggyGB\nr4yBmE6oZ0bxzPlzvIZCyCAk6C3HLqpCmVlQNU3JcBdhlJbLe/FUgWFE2fEQZnGinmkUW0KH6SwJ\npLJpENMpXPAzzUBqT2jNC7VZzTFOW3Lf2rKDrpVZnDKykncXDikpoy6TtmVCWdUrJSSlnPPCpg1r\nzuk9f5dNcYTSJ2in2J0hyiFOWOb3bqEIG+4bvZOyRgEu+XzaFBqBR083eJ8py4wduwjik2MmM8j9\nmX0X9nLhdk7wQd2T+2SXBihywLU+4Pd8zq4tKLVzL9//znP1R3G4iyi1vmGYgCXvQ1BaVdg8o6+m\n0dqOjk7UzZAAACAASURBVGDEUpYYDG1Q0y1p7jRAXw9PkcxFtQst5mImLbYDQvhrmzwJUUIzpzAx\no0op6exTjOM4mJ5ZqEUUtUbcxnoRZY2SEhUbLlStaMuHLDQXvSq5RFPRNLpIjlKK5Istq8K2mkk6\nmFJfaVSwLN2RFElJ9QeAuxKebb9GytuKr/QqTymYapL8Ss3KKy+6FVy9YvIgX+RAsBBWstoa7eTL\nmowQQVSptaThRwUZr8vQubqCZK5EQPiAyEzcsah6armEy+VhSwTt2TNsGklZnadbUyXNMGfRPPwR\npCkSvoKPXg+ISndLZK0lt92ipqLEeg4EpCOlp1rI9ZM8NhedsdyqngayqWm4winhTEnw1QjJEAgX\nMqrJ0GNDV47oqK/YgfUzVFvhMmssFpaXjZIdHaQCwpaiKeVLIGnhz4pegLGCwQOrlT4Gc0UbCoJ4\n5IgIXd/XM2rxVdkRFZGanYpn5q2VV0JoruUXPBJ3ZQrYSkhy8hkfAVFKKr3I+ZctSuWQ7GR6ylVQ\nCW7HkV2FKHVLhViUQNf3S4WWrucxw0zGmKAFSk1l27al1yFmLkwNrObYso87Q7JjdBVqzW5p+vIs\nyMBt5mhPEkMsoxMBKnt6IGqOwRBDrFCUpUZLPr9OZZQEq4UKeKO0QpTG5KCYEhaMqPiErcQrt5PY\nhE0uiFzpR76TYUGpfGIz9acbRqFIpdWCFSg11f9mxvHy6qGpPF4L2+Y8Hd8hb/8B6NzLtnH96pd4\nUWQLtIOcubzzdmEu7Kzuyj6cMit+VFyNecnwWyuBTWVjT7lRpBEIDW7ek6s8BtjAKwvsdEF00OcV\nwaiFZMhMx7vgNcFZ9+OZx/0NM5yzv+QIxIXaCmOm/KkHKBO1QVXHXbFSs7ooupbAhuKUBZVSDbZK\nOhm1YrrnfLzFSiHz1LaviDgraRxhGq6vs0VFpeI9Z4AaYDNNWYgwWC5Myfi8EskCnx6EkT33nJl4\nFDlAUHKJHTOXeqgi1VMuPtffgZYGkemYGraWrOkMTA6Jz6CLJH+FvCDq4nqUlfITariUVNu0hpN6\ncgtHh+Qojsm0QLsg0rBZOffbeuHBzj0ZJVOYdkc3JRhYVyx2xk2QVhjSic1Q6dmLDOdk5O+lC1f2\nDF/wwdTO7Kujcc8OT4VuheiytNgpz5RZCL+m6UU8L5aSF7LHUhWJU0uhz1TmwGT6jTHz0qFCkwwP\nZyazW1SoIsyzgFSqDHxOxgi0KFIXPtckQ0qmp3jAEwS2XQy0cpufr3SzxvSTqpOyd0pNh2vMQokX\nWoWq0PtbgsIsd6YeoD3zi09FZ8Niw7QT3NDdCQ1uvRF6yd+lC1WCLQZqD1lkqIOPlfa0I3pJ01sc\nmLVEXo+CtULUO0e7M4QVLSmJhj6DiXJo5yzC9Bu2dd69+Ql+KuecRFSQjTcPV55H4h0umxJx5XZM\nHh+EqzoeHbEX+gm3kX6DTSutFOTICnwSWC28UeO8TqIax00Zz0CBcXnm0gZbqdhZuf+wEyfsexZ6\nLgPfN/xo9BdFR+VsJ5cL3I4fuH0YvDwP4kigmRTn7du37OWBr//yG1ozbk/fYJeNcyibveXv7+9x\nfeHxC+HjU/zuc/X/53P6/9M/Isb+5h1elbA79ajYBhJ3TrtTNTXwpdbkonjiCVxgXO54yXbNbvty\nsSoiz7n4NE2XpOz06KAHA6c2WwtGBdmAlEB6ZBiAGGhLjXLxhlnLsQbC9HQPxlRqb1nJui2ZHTSO\nXKYoaEk++OwFkVShtD3liEouDt39UxWamuFCkJeG2tLVxpLQSc4tu79CodKMmNr1lcEq+edW+Z9V\n+evi00iNMpJys0j0Vmrgg6qp5/cQwn+bjuSSsjwnC1Y8l2VpGY9lxU5JZEyDOGklDUPhWd1KrNY4\nZMG1XqvmPOC7bjiZ1LxZwrdGjbXsm+jSMxsF9Z1OocuAlhV1GWTW6XIWsxZwRCp3UEFrRWcjfLCV\nTtFYIS2V4tCRdPgulYsGuSjV/LynpuZcNKhrfhROogNUcLGVJrUcpi6wOqT0bVak3JEYjHkmp4is\nhN2CGK9PRjYFak4swNlUSXmjLqTvUom5p2R1zuSbZCfZl/N10AkczfFYyXW9SknZpbOe21imt4nJ\nnoqV0NxnKUhRtBqlGPMOzILpG4iehlYrmZaqAzOwEMqEGPns6TbZHgDSBSuq+fk8U5FSYX9ylhMr\ng1ZPNjVGd6oG4/ZCILhdGECV1IzX2CljUSqnZfej2a3u3pCp1MV1FxE4nFvP79mb4ufMrsxfoHam\nNvrYGIfjITwM4aEY5zcfiL1xjI3+fMdagdHhPJB2wY7Chw8/MM/JuDe2ZkidTF6Ip53zvfPyPPnm\n19/S2kk/V5FYjOEnKsHLvPF//fWd3//qj/jNr7/my3dv+erdA9KCzy4PXFvF46e0TTj6B/7DX/4H\n/Hf89//Gc/VHcbirKm274i1h+eJrPi1GJdUuNjUTlkpS3ZqnRfheTrwUqlXktKWkSSOKlqTgWSiF\nRkwhatriedVXi1A0F7JhlWPeyfnt0h2L8XBJBnSIgJ+0aJgo53GgxZgj04GyMg+q5ry1c19GDuA1\nMxOotSwTSLakwqC0moegyyL8ZXWY5iZJJYcsMqGsg5WU2LHwsfkya1bDpGnGQhkjR0daLC3vPpBl\nc05M66pTFtnQrORLL6kzNlttK/k5Z2QyTdY2ihVNFVEYwyWxAFEyQGfmRfGqVfGlzU9tf6bJT1UM\no69MUZlKtbyUBnekDDCoFEpUVArWlfAVaBzpxlUJLnvjmCd9AsXSLSm25p/JplkifIoIymBG4olN\nnDnO1HPPZMTkRrgyJQ9oH45EqkbMhOgQn8YsukZ/6+ej6TKsNS+afia0sK1Rj4jRYxmEiEWfzN1Q\n0QV4U6hV6H2Cpf1f3ShqS4IXZAsW6Z3YMiv2FM/LPyIPUQWRTtuSiokHmeubgeqlVGpKMDjXChi1\nHCEaaMuRojjMU3BP1RWRVvBSLYuecDZzthD2ksEoY3UXj1sKAs4Qzp7jnNw1L8Jimatahqaybs2B\nzBwT8lpgzAk9Wfh9Ou/7C4olgkETEDj8lggTzQQ1Lbngff/+B47nk7I1eNiY3dlbJeZHTs3O4mVc\niZ4/36fxHT+5PvC//B//MycO9Q0qW/48LTjPDxQ1LuXCw5u3jO60z7/is4cv+ckXXxJl8OYXX/H8\nfvJHf/THBD8Q/sSby0bQ0WocMnj//Xf8/Xe/oQ9o5crjn//H7EXZdPLZ52/4+HQi8sAxjNYaRQbn\n/R+AWiYiZ4tbqexl46wrM1GhxJkDtOUuVEszjFq+zFfZEdkodqFvOaf1AmrXlOrpICwfhK1eufEM\nODpyvuwK9OWo60KTypRc6s6RC8M5O+d84dSObkoZKyxCnKGdWXKGFp4RX+GGqVN3IZotOZhQLZIL\nEb5GILogT4U+sluIknFwupyTI3I0o2Rkl0rAsrgLE/f8mgBDBDWjUNOAQs5gm2X48BlrnmlKsVgV\nexonmpRklY9gdjAq9fqKXx1wWjI8Ii/JOQN0IV6rEmMgKmy14n7DrDJ1Ukgkat1LVjVjpCvTUjXQ\nn280M6wZ8xTmoTQ1qgnDD/YNuilDlWOmy0/EKSXHR4TkOIyAMnIfE1C1g8LpB22rzFO5Xq68/3gk\nLK4Y/hLs9crsoEOAF/YmHHMm9Ko6x5hpqGvpf3yrTqmNiMmYzrVlmMg57kRt6XBszhBLK7wNtpYd\njpUK4wa3SdNKGiLvGZE397zwNSFqo2eHM25HCj/D6bdsDbdt47wfGRs54dIcb8FtdL5/fqa1nW0v\nzDHREzbNbOI5OzMaJg8wSzpTfaQSRRrnCPqEKRkW3xfuODSYt2emH4QL9yNJrXo3LISwA+xkzhNT\np2xJ1OSsfBg/8PTywsvLM28spcmnCx/OSohyuYJYEiI5E4FgbePf+5N/n8eHjcfHt7x58xX7wxWR\n4OGqPFw3HttPUNt4ioMvPv8Z58tJFcXvJ3Nxfs75gpnwww8/UMyYZ6cu5PCIwdOY3LoyhvNoTpE7\nYYOPougSUZR4hlD+s3/2HxErH0A+/fxI34wGncEZHcR4uQdBSanzVG4vg/bWmPGeTSeE8vLDB6iB\nq/DkgUfl9/af0e8DLZXH7YEqGSpSb4V3unOfBWk7VQU5Ff+3nN4/isPdFWgnglH8gj0aQ+CppwJC\nU8691AaCDlmpMEFFMBo6U8NcbC2n6nJ7SkN7RyJN8U3SaJHqgGzZ0zCvSAlCE7sqKKdEyqzCaZrS\nxVEDN6d7UNwSa0rO/ef0VBfMbAOzZJK0LKNrdHIszjOET0Tbqvg13bERSMlFaWZppspCIl8MW/r1\nYKUL6UjlgQZtuRuZlgoLmVh4qjBUUN/T4aoO0ikheMq2IcaiAqYTNtniBZ9nskpEKa1wHJ3QBRgY\n+XeYLxnmfI6Rbkty6Uw/6Ste7zxPxkiJnJkhfS2PsWynh2K6ISWT5bkfCaCqqVRJlUzPRZwpR39J\nrrs7ZwiVvByqJVK2mDPmncu2c88scJ7PG0Rl3x8oBD3uHOdtaZWVfbtw70cGXqug1hnzROtGqcbp\nN/ZrAq3mKHBXRgwoE90H50iW+bjf0WK5/ZECLxlKYVu+bqVdMhDdSkoJtVDKxu35zsPDW+7j4Lnf\nGXPyYCWlolMwaxz9ztFvGajiMOagj87EOXwwwql9cPbJvJ9YaC6pCyADkY2X+wsM42Hf8Rg0zRFD\nuxhzwl/9+i8pbadtV87z5H678eGbb/FxpvzWoOnOXi/84me/z5vrhdt58PZx5/HxEdPg8fLIF+9+\nny9/8Yc4k+tD5U9++YdoCOeA4xyIBtctF6Nbqct+X+hnYGVDS6f7ybcf7liFYGJ+studcnxgzsKl\nFj5895fMPvKZDFJsIYXn5ydqS0OfjTM169aYChZKKx1dHomL5mzf8QTihTP8zAIlsqsOK7hNRixk\nh5IFnuS/i/kIkRiLkEQxGDe65W6oyEDmiZ8w7p9hZV8AsW8wTh5HcJ4wZqbPybZDKDMmYgXM1uhL\nqMWYxu/850dxuEeA2pLaAVGdSaeWQpsVPNNzcnabFEld895GyhYhmBbrUIVikTPKmEjmtq9te74k\naYHKscfoA0USgBWFQaTLsyQiVjTRreZGxoMm33t/uKIzpWTDHTQlkFvUvJAskQBuGRJRVCjimSfw\nyrahApIxYZahF1IsTTACVWVJJAMfkyn2SfVDTNRLyvUkqG0t05YpJscyGckm66CZGuADlzVXz/uN\nmCtOreRoJH9e+XN4Zd/MOZizp5IkcklsUajAKTO5PZI4XR85yzdNR6m7M+fisyxDTCmGTjjPk/Po\nyJ7tbjCZy6VpVhlzptQzVvpSDLSkHK6EZXVre/oJvKRjOJytFLrnwWcKdW8cN/BbpyvczwMpuQvY\nS0tu/QugwhidtjtSlHl2breTtgXHcSdc6acisdF7x1rw8vLyaSz39vLAeQ5672y15RJ4M57HCyLK\n+w9PjDHo55237x75+uuveff2HcfL4P3H76hb4R6CzMHT8ZHHy5WnpzsvRy7pIJlLMVPS+5e/+ktu\n/c7+5oH7fe00VPm9L76iSOHdZ1e++PJLHh52kD3HQzT+i//8n/DZ4xsQZ3+olJYz66dxo9SNKco4\nBv080XHmYp+TZ/+IebJxPn73A8dxo0d2mlos0c7aKNJSslyUugfv378np3tK2wONyRwFn0nd1LL2\nFQJzHAQHLpOHx0LQ06vixlaSDeXR6P2OSLC1pfaaICQrpm6pUKsPV86XJ677zu1M8QBaMDxdzqIw\n+ifpaVGlxEEx4ZRKHalfn5r4DJmG2Qr4jjS7eSgzSoLdUJSSJq95o1ruNIrKqmTnQkNrgtNKYVOl\nkJTTPskuzxrDFR0HZpbqmp7vhbQc2/2uf34Uh7uiXLgAzlEP9OERjY037Gw3JfrJEGXWhOkUU9Qz\nCb3qSpZR2EWAmeYJdCFchb438DzMZJz5BLgjkksp35ODPWQCF9TT/NOiZ1SWdQob8pL5rdE6yMTP\ngyY7NQKpC5vrwq4bOgOtZEtrS6amisiWQDRyqfWarqOWJph06/ZEstqrQNGQpT3O8AdPXnREShF7\nWZr2O6qdsnVGrCXp8DWfTWmcaCck7dKHCKdOquX3mD55vj2jWhLkNgArzMW9LgLSZroPY1C2nfNM\n8JmJc6l5mXpfenzznLPPSa2G2b5MWkBMxuzUS6qDODoimlr2CLgIhyS8CavE4YzzwBbFUX3ypj3w\n2Ap9PnPdrzx/vPNym1xs5/70xLff/T23eXBYAttuH+70v7/x/fffcht36rsLz8/PHC8Hf/z7v8R9\n8M2373l6eeZnv/gpQ07+9f/zf/Mnf/qnRBf+7m/+hnm88MW7R263W2KLdYBU3l6/5B/9499HyuCD\nn7x9+AmfvX3gy8/f0OxtGsmas18azS7MGDy+2RLbXJRf/+pv+fkf/jHfvP+ec56EPy7J6eDSoFjD\nqSCd2vL5l1nY6p6kVAJKw+OSPok42bZYzu3ELEQMrE5aa/iEWnbGSLJgzHvuaULZXeFMRk8Bqk6i\n5DM//eCtGD6Vcw4ev6xcaPiouGc1q6rYkifv0hZETdjYEQm0Be2xI9JgvGOMhtmWC/g6sUgSa7UN\nAe79BHbCgj6CmMaB4KrIVrm2wnm7Z2dfUoY5Z4CkNPk4Dtq7t9x6p9eGjOV1qQHT0RmEXJnqjNHZ\nLjV/njPQo4FAuT5mBxxBtRVYDoyxI57x3N2c2GBvMPogptD0Ic1nmW2GlopWQfU9VguFyjEeqCL0\na/BcJk/uyITWnVmV635JFAsHnz8mprokpPp3nqs/isOdCDRKasbLZOKMMdlrGpQyzV2w5WSrImly\nUeGw1GurgOUOCzPJ6lNSmzroRGypB58FI9UnSsaaNfVPyfbEIJbBwuaJiDPVF8YAbCXpgHBGzwUl\n6UJ9ZY/PFdyQipmZZhbJfNXwBA+ZpJuv2vx02L1Wtq/5kCqCj5F0xUikgiyTCzFQSU06nvpvkZ3R\n70ScuKbq5jWgwr2nKcfuqeeOAqOlGiQy6kAiwwIgbfOiOaP3gDkGUwcmuTicR+ec93QCimTgxhox\nWH3g6eWZh+3KXpXZB30dIlWFIEOJz/NkRtDPO7eXO7fb93z3w/f8+jd/z/fvPzJnMHq2qOcxmN6X\ncnPy/vv3eHf6cXKRDHMoRZG6c7vdEAaPj1fCCh/c2a8VCfjFZ7/HL//xz/nJl5+xXSufvXnHcR/c\nj8nl7ZXdNlrLSvHuzu38J1wedkwKtw9PMDo+vseaoNeNp3NwP4Kmb5jxxGWv1J5L+lqMrU7meObo\nwd52ns5nSvzAtld+9ZuPvHl4ROagqfDN178iRKk4VStzBrUUdkkrf26oU7a5b1coqQCpOyDJ6w/x\nRSctXDZnDsFiy8ANc1qLfM4ikRRGpZSCtMmcyRpqu3COE9Wyvq8i7LlPkEuaxmpJ858+ES4ct44U\no9ZUM3mustkWkVRl48K+GPCe41IVdFOoQZEU7SbGokIMGBNhcGlb/p1VKWUiVZiaJNYeG0Q6Z+tC\nBeQoI5B7IjFsC6bf2Wqhemrpkcmwkk7uSGielElop6ozXDG9opbvxfR0cesMmm6pq2dyWlbb4oZ5\nZglI6VlEdaXPB4iaLm8boDVHLJrHMyIw0sfRy879PFOUILoosYG0VJapD/CS3bnZJ/Phv+mfH8Xh\nLmshGBEMGZSSTBAEjpE2emKyRRofZHFX1FJVok6qUXxJDzUPgBEjT3vmSkbKBZAS1C3100pfDBah\nzBXEQC4LbSYUaYgmhsA0pW+RJqNt2+jdkZmIgKJ5wOdneeW4rA+paRTqnp0HKkT0/LqynIHumGX6\nkbvn3E0KS7KOaSWi00fHNdU8jKCfg0zZgTEcjcGUiWrhWjesZIWCKqVtaOTPdYwkRtZlEQ8VLo9v\nViCEcJ6d8wS/n/z1r/6an/z0S960xuyD3/zmN/zrv/grRISXlzvHcfDxwxPffPMdz0eSIGMtOj0G\n/byt7gV++rMvAfj+/XtKq7SWtu8//Xf+mP165d27d7z78ivEKl99/lWSD915++bhU2X45vEnbCUz\nMMfHJ0a/c/QXXsZAiqEe3O8vTFHO1tCS3cTx/oVS4Ow31CZ93KnbBq3QOTlvd+o9X4xpSmsbL0+3\nT5dgUWHfL0RxRoVaDLtW5t3Y6xuKTbbds6MMp1rJUIYykufv+SyPcfL48BYfk2YbZopbpijVVjJf\ndi6KpeZCfZKcHDNj9EnRWBd/ik5sXfwmEHMyRvJ6RJUIz5QzTabMHEmOrKXkTsODbbvi7vTZ093q\nub+ySLe4L/cnakxXTAseBybw9rLjPjCVdIFrspT2i61dVKFGxZowCzkeNNBSPhkPbaQ5yxeBUpmY\ntsQrlLKUSwuqNyQjDsnzYds2NDzd0Z5hIFacUowYypyNao3oWUA6gasm7pslb62CUjPMBqGURvN0\nW5/nmX4OUSQTBhh5JeQZBphtzHmAHbTiybi/5ah49jtdzlTKmXGM4KHlz7gVo7UNP3dqUW79OQPh\nxVZ600DUWGAsxH2pzv4BHO4eMxcVdNSD83yi1sYxhbYrJ5MWhX4455g0hTMG42XwIJc0mSjMdmUT\nRSPw8xk0l3HPv/keivHm4ZE5TtDCc5yfuOwP5ZLAoDH54vKO7p2hLzx89hbvzvuPg7unDnlsJ3u9\nEAz8eOb5+MilXXj57juO3pHaeH5+IXrnPN6jqjzfbvztX/06F47SF9vZud/vfP3115znSYoBU+Z0\nv58cx0HKg/bc/mtwvW5YzWq87VtK52bn8bJTTfniZ3/Mw5vPePfmwq9+8x23+zN7Ub784k1+j1K5\nFBJnYMoxkqX98ek9l8vG+48f+OzNm1QXVOWyP2R1oMFPvrgSMTLcl+BnP/8Dfv6Pfr64P+l+jBV3\np0UYc3KO5PT33mlb4f7yvEZTsYxQ60JfYSjSM4kmRDlmyuIkRtIogTHOvAg1eD6+5uW05O3MRCKE\nAiUBaa3B1SwPEZmJikDwNzt3OvFwYfqNLUpKTAeYFKSBDahTMe3MeVAiYVUmQjUYfWfOXBLTDi5V\n0DMPEaNSLicwcC/08RZZppk4nVoqrg+ginmOWRzQshPxnrbp0j57igIEDivrsFmeZINCzmxVc7Fd\n6yXHkER2klvNV99KohUEdLN8x1ShGl4D9zse2akdfaBq3M/Ial6Nsudsud+fKVtC9lDwMwh3LvUx\n58Dk2M9koI1c+FshBtRojMhZPOaoBcdwqu30PglJp/dmjUlnRnCxnT5PJopLoez5nBzP+ZmtlTTr\naTC7ZgU+J3OctCK4H7QCMzqijX40fBjRn6gtn6GXGTyUWCKHwTkTaU2rjAlPL3d2bxxyUFruBirG\ny/3O9fEBPQePvvH88QnRyTkafSaioljNZfTzB15u79kvRrBRNuXl6Pz677/hapWPP7znX/zz/wm/\nn1z3z/mjP/kT/sv/6p/x57/8d/n27/6G6+dXhmdx8cOHj9xe0q/x3Q9f80e/+MXvPFd/FId7zDXL\nHmt8MIJ2KXz44SMv4wN/+PNfoL7xFJ0hwRxZfVRx7rdnTCYRwtO3TzDviAfj6Ua9ZM7oHB/4+off\n0Hvn29888y//1V/w/njJb34OdKR2fp43/Hhch+ZHnj9+R4jRHr7g22+/JmLy8Ljx+c8+5+FS2Zvg\n1hhjcq0XnOTFX64PtFr4oz/4ir/4i78gQnj32c/48otHXvoL718+UGvlF3/4GX/25/8J1QpPH54p\nYjw9vdBaOlXdnbC8oNSglfJp2Xi97olPnXkwqgSqV8bILuAPfu9LkC8gOupjmY0q434SCc3n5oNB\nqhaOc9D2KyHClz/9iojIxWmu/FFLXkqtuTsopaTpRVLvfI6USooHdcmXE007EZ+YFEpbXw9Hpy+w\nWFBEmBK4NXSSPJuelmtRTbTDKxogvzCweDMCPjwJjpJKIZmDIjle00V5VCmZBqSL2UPn0g2dadqi\nGFOCYqAkUtcsL0IdwrZVPI5cWkuuzJQCC2XxYLmnCQDZCDYc44xCLU6tOz46xImaJubBA9OR83gG\n5VIRnTk+DFsYZmNEtuLbtSIkfmLi7LUhqig7pyd1sxRhxjNSxvIqGFozJjEEIgozgdAUbYgMGktP\nrspxnmyXfRUDCpFS1rZXfE7UNaW3pmna4kAa2RmUhZ7WIGbQe+dx2/Excw9QjiyQ5E6rO+f9PdeH\nn/ByOnVrRDgxUtMe0dNpjjFFOF9+QAps9pgjUj+oEsThqBbOORgeqO2MOShqzHu+41UG23lmQErd\n6Dzzctz46c9+zmcmnB/eo+fkLIXn3nnfT/qLs9kDL/2Jb3/4DV+9+5xC0jefv/2Gb3/9N7x8fOJq\nV/73f/UvaXulXXa2bePP/uzP+PLdTzmOg//xX/wP/PVf/Z98+PADrhtle+TD7RnXg/N4xqzyeP0p\n5xw8/N6XPCvUx3d88+E76qVwuz+j/WSooHVH6yA0+OIP/oCPz8fvPFd/FIf7d999y3/7X/83hDh2\nVaRkks3t6eTNOyfGpB+Nx89/j7/91d8hw2nl5O3jI8Fkv1aGw//6v/01Tx++xwLmhwMdnX/6T/9T\nHr+c7G+yGjrPjYfHt+h25bJVmOkA3JsS447Pz5LpPJ5R/pCB8cPzwZdffLW4Dz2t3zp5eFM4I40U\nb+uVgXCOycPbn9DPOz98eOarn/0BozsqGyGV6/WR9rCtrETl+/dPVE0WepixXS/UkksTK5Ksbc9l\na62FiKxwbC0vS0mjlUQybcwsW7iYdO8oGZlWRfCpbCVfjpy7Tvo8KZvw/HTj8XLNdJ/lSNVSU3FT\nlFJzTp7jpFizRaW9Gp4iK3pWNVnVKEU+ta0UoZJAq1ha4wxSya7LCG6+8LEBZcqa2zqeW5LcU8jy\nFev/y9y7xVqa5nd5z3v8Tmutfd7Vdezu6Z7u6emZnvEwNsYKibElxwYkKxZK5FiEYCS4CFHgJnFy\nJLOUJwAAIABJREFUwwXBchIlEsISEUoQoAjsCCyDZAVkQ8AeYjuZsWc8Y895pruruw679mEdv8N7\nzMW7emQFeybBIvKWSrV71a69u1bVevf3/f+/3/OoEvMRiaj9PswqsdaSQ4GXZanJFAm0FgVcppT4\nxtcrDH1NyKJcqSaw+4SUC7FU0SlMGaUEKe4pK3Xx/igHZI1MgqQEUurCYld7jpCU+FhejOx3NkW3\nlylFi306hLJ/meIe07bX+RV8RonByiQLwwRfPKWIcmBS/v5zfg+klmkqQxLFmCT3kmylC+BLSIlQ\nipBKwSmn8mfSSuNdKNHNEGnqmhAjQipSKgenQKKNwY+ujCCTIO3TZEJJpmkscVofMaYqUcsBYqZI\nOXQxqcWUUSnTVhX9zZpsND5lUgg8uH2PsZ/YrJZMw8D5+TnaQF0dMAVPGApZtVI1xITQgvVux9NH\nF7z97jtcrZakFEjB0+825BBJKbFcLokB+iSRdaI96Hjf8y9gXE+TE2IQeFVTLeZss2NeHzOfHzJv\nWqaqARfQVtMYydnRAiEX6Lt3kV7y+quvMKUSf/UpUtctwzBAyrz84h0evfV5WmuZnCSOI9J7ks10\ns7Y4ZaeIMpbtsOWN7/oYLgWSFiTh9y4Fgc8FWlZ3NZDLHtCFb3qu/r443K2SvP+lB+VgqWBu6uIr\nFIqp3uG9p9ULfKo5ODji1/7PX8fKzM3NE9p5h/cKPTvgzgsf4tc+80mG7UBeRuYp8Ylf+GW+79/7\nA0QLQgpq24DtaKGkBlJiNXloK4xM2KqCJDBhQVYalzMHypLGshFXwRVuiYHRJlRSLJRmZosa0JMI\nwlIlSaMKq70C2qpFqmK1z1KgTYU0JeMeQsAqXYQCKVDVGkhkURjeSmgqXWGN2s/yM5GxzEdTRmBI\nrnCmS9sxIeixwpaikg/Ucs84GYAk0DmjQiSlCMLRHrfEnDDWok05EIoGSZeZrwxIaUkUBLOx4j3/\nE0brMtvcRx5VhmwzCFO45Psik0xlaRzDe2gE9smaAl7SDKhY8BJJFolISBYVgZRL2kNmUO9JLcoi\n2FjIOmBSguTwfiKIQLkGN5iokCqWq3jJvicgoE24qPF7dIJMuQCyFGhtEGGiqgRZTxSWVYdLmaBK\nM9fahhQnIpFdJZCURElli9dBZaiICGURwhB9AZvprkKQCCmiVBkLSQxJ2L1TNNMaS0wjkZFWHKC0\nRYeA0pkk/F6Grgpr306QJqboqaJk2kTa2ZzKWnyI37jrCm7k7OyEdrZAKM2UPTEEGtNw8c4jhsGx\nulmy2WxQRvPs5pph6lltN2zH4g2d1Q3bZ5c4V8o2RyfHHBwdYLoD7t6/xzSMnC00U14TxEDVnOKF\nJE2O4XpZWECm4bDTxBS5ePcdXCrlQyUqnn71aWES+dLefOvdR0xiQ84FfqZcYbJrXxad0mqikEw+\nkoTh5Pi54jzIkLIjpcDoJmbrJVIrjs+OiXv5fKMEOtW0QsLQMkaDqCraOtCgkH6HCIIze4Cubbko\nwtPNddmRoBCUtnrlK2hqVC62tCL+8BzLOR/6to/w2c98lu7AMuUJOUrGqNBtwkdHqqYiIUmZy0df\nIbz0CnGWkGqCbFjGkZhLo1nshtKZqBVi+D06VIUQfxP448BFzvlD+8eOgZ8GXgDeBP79nPONKLaH\nvwr8UaAH/uOc8699q68hgdZkogJhJGnqwQVMU7EeJuq2JqWBPEZSP+Lcmr4uBiXviozYCsfmes2z\nhxecn50xqC0TA6s8sckRkkGm8hcKAp8iPsV9XromaVNwB6qQGjEGLxWVkvjeMZu1RJ8IecDKBAaS\ncsUxKipyKkZ1ayvImtrWiNFjTPkGEnKg1jVZGJS1oEo+trJVOeglpUhiTEmd6BJHjPurKqkKyjUl\nQwh72UEqy99MIuuMVZYUcvFpypJKiKEs8gBk9ChTdH4xRuS+6GMbyzR6jNForcsdlFKI/WhBCEFd\nlZZvTj1a5SKKVgpE6R+oXP4ec7EwkHMmirKQlpR9rim923LA7g/394BlSYg9F2gPFFOlsp/2Gfui\n2ttLJUQm8x47Zs/W2f/Icu/L1XvOTiqM+qxC+XNRIWLpS6CL8Nuowu3X0pQGLMWh6vZ3RFJXReZB\nmXmH6PeCij3wSSYyE0qWq34/+H30lrLkVxWSRBxGlLGo0RZDVnLUtQEliezR0mQ6q5m1HcO4JpHp\nqhatavphjRtHBrdhtR6ZpqKG3O6WjMOa9eaG7B05Buq2QduOmMoITeQSoa2aOdbWICR2blASzueH\nPH/7Puvra/phKs+pFJzdOqWZN2V/MhV2/8HBnGHcknyg67qi+tMSL2qUrnF1y6zLCOK+BV6uyF0c\naFTpm4TkWa6eFXZRVQxEWhmyhxin8lppDSn25e7UlIM6ZgiUhFfpYEBMnqwz6Amj0/4iIGN0xTCU\nxag0Ct1UJYZZJ1LyVELSRE3ystieXIkeBx9RnSj/zlvL5C1SG8ZsSyIPhRVjCWyQiaIYvmzS6FDS\nOgnw4wg5MOwKzuLs+JjdOCCEoR935DASthGhDUFLlNZYL7GpQfbw+d94k4NZx2q1Y+OWJBmYxojY\nlZSfyx49/N6v3P8W8JPA3/ltj/0Y8E9zzj8hhPix/X//F8APAO/f//iDwF/f//xN365vrvmNX/4U\ntq7p5nOO5QKjJUGt2S4E6Ipp2LAbPKv1luPbHcKNaDPDaIs0C7JuqY4yL7/+gM3NM4TeUM0aPvTG\n62hp6UyJfKlmAdWceVVagkW2K/bFHE1Ned/v2eZaGqzS5eCMHhYtWI3TCWUdwjmqJLBR4vaIhJkB\nnQxeaZDFPVpphdAGYWtsSBhbEUwxtuScSMLTKcrtXwK/16VVUqCFLpYlI9FZYmnxg2fwA1oLvJio\npCINmUxAaskslsVrsjVDcKSUqIwgZFdioFbhfSIbw5QjzdwSXaQ1swK9IpLdhG7muJQRcShFjKrZ\nX2mCCR4pI8ooBh9KmkdIotB456hUjVTT3pBU8vxCVWw2A03V4IOjagM5S9wUcdMeo0ChkyspqLMj\nikCQgiw9ITjStOeFIPbxyCLcOD855923nyKUJFWa+fECFxNf/63P8frr7yu3yoPg8vKS1fUNi9MZ\nz65uuF4vsaZm0R7w1S98BSEjUXmm4LG6Yl7PuLp8wjQNSKn5yIc/wGZ7zeKkZhwcVmusamjmRzxb\nrmgOO/CRrq4Yb9a07cCs7chihDAxjprJ7WirSBczPsMwQdU8t1/AZ25uygjG2sSuHymo6RohE9Ys\nuHV+TKNbQBLUc1SyJE+8khhRUjpZNQRP2bfECSVLpDJE0KZiG7dlj4JmNQ1UsxrVtsS9ykqpkgip\nrOK4O0KMZWxjF0e0usJ7h7YFMJeUJUtNW0kQDiUK4bNLGh0dbSMYS3UNowxMa1IMtHpGZWOhsepM\nJRdM4w4tPdNESYtFQDRII5hyInpN8jUZyTAMxCqiTcmSq5ixsmW3yQjVILUg7xx6mIipJ5sVjUhY\nKRA+kntFMAte/o7vQdalh7GwEtEYbNXiJ01lLG8++TJCwcnBIaQtLkR6J4m+JoXMNE2YvfWLlNHV\nIVolzo4OuX32gJvrNcoadkPPYZPoR8eoGhyC1s5ok6HSikYppuGCD77yYWSKPHfak5UlC03I4F2J\nQ+dUCoX89z/5r3+455x/UQjxwv/j4R8Evnv//t8G/vn+cP9B4O/kEsD8FSHEoRDids758Tf7Gkop\nNrstxrlCrJvVGGOpGk1aaBKOptWFwCVqdAisd0NRr+2XQVZLpHI0rWS7LnJckR2PH73NB149QwmQ\nlO+2qlRE9xnX4uuUe5StrhtCiNiuJk2RSmmUKVt30yiUreg9TCmitS7cFh8RwVDLmpg8Vu0XiHvk\ncJYZZYtZKRGQKmGswUvQRpfRSC7lIElGW02QhTth2M+yY4k/GaX2MuMyXw+pMF2Sj4XMmFNpErpU\nQFmWb4xAosvI/Ww/+oyL5UUspMDHSG0tfkwFPiWhrS0eX66wTCmb9S4RQ8QISasTRmV245akNUNw\nuBA5PL1DbVrWz55x594xKQWcfi8hI9GqQcrSrPW5px9G5gddAVKRuby55mtvvgk+M+zWXC+XDGMp\n7/S7DdPOsRsHqsoS/cTJyRHL5RopNW+8/jFiTkxbx8Nnjzg8PePg/JTL9RIloWuOOL91l7PzO9xs\nrrj3YMGxO2c+PyBNifOjszL2yCNTclS6QibNsLtHXZcxl5aZk7MZg19ydnprL1oxuKy4datlSAPS\nWJTSHBycYHWFAprZAS4UGbvQFqkGsiqN3k6XOGSZs3u0AGNqhNnzIZJE7uf6ypo9iqMQQFF7kqko\nxZ6MwmeBzHtTVRJlLJTiN3ogMcZyP6QkOcrCB0qpGNBi2dkUfWMpAkpVY+vyWp3iQHIgs9nHE8ti\nnRzxLqBlZDv2CGGQ0RN94PhkzvL6CW13TEyKO7cfMI09OI1PJSTw5OkzZvMF8/kMH7bE3Y43v/42\nq5sbht0WROTBC/eZnGa4Kkyq7bRDNhmfJprWcu/8DiHA8ck5PicmN9CcLNDyhnG3Qo47iOWunRQR\nY0OqyuepWkOMkWqSGFuzHTwiCq4vH1N3DVVlGPqpvGb3ikCVC0jOGFNAcUJALG1sWfKR2LahOThg\n/Xigaw8Zesc0JiYBqTLIkKiyxGpLv9txtXzGen0XneDO7WM2u8DTyxuE1Ki67N367Q5R/5spMd36\nbQf2E+DW/v27wMPf9nHv7B/7poe7RLKNPUZ6xBCw4xKpIpu05f53fYTTW7fZra7pV1u6ZBj7iAmF\n6me1wmRFKwybvGHWaC4JIAKH8w6rRqwuV+i2mbNTESkSIZSKegoOpTuS17gpMihP2zYQBSYJ0tgT\nCVRdhxeBSSlcEigqVPAkEfERrGpQsaQ/SgVaYVMgxoCTmVRLIpFGN4S4Y+N6ojkg7b2ZkojZZ1d7\ndkyiJC900FSymOXZi0RCDEzTVLK2MSK1JLgJvZ9XWyEx1bzIAmRAxlRGP6KkbXIuHzNFiakNVVuR\nhg3ZRx68+D6Wbs3kBzbbG242zwiTQ8bSMnz70RofElZphFvipp7dsEW3NVloVssdj55syDFzfHhI\nbTXWSk5O5jSmpaoaAKq24ujokO7wAKlrAoKqq9HWcHrnFg9eeIBEEqfIbhwYXYnw5QjJB5JJ9G5g\nvdsQ3IiWplCRec8u5RnHEWksuIytIkpKhm1Eq5oQAvV8wRR2dG2Liz0hR3Rr9qUWQys0OSZiSszO\nDkqfIYKtBHnyzOpDBA0xlh2D8JGZKfuWmItftRENeQw0lSW6kZDBVGVBq2RV8uC5IKtFiX4X6qRJ\nSCPAaLQ3GKPxOYFRBBFKLtpHtNjP3XOGCEaXGXQMZUcUM+RYoo6ampxciZpOhWOfHXSLQ0IITH5E\naMV8NidOEwe2LNgdkKVl2g3sNlseP32bRXdIzpld6FEic7nekUKgX21xw0DwkYPDU+4d3eHrX/8K\nogkc3Z5xdLgBWXHxloEUgMwUIz4npCoe5VnbstrdcHFzwTruOLp9j7umYeg3dPMKMQTaukMLTU4B\n05Y7Zm0atNq7e2XGqAbVVBgty53yoIjLAaZcklg5IbJB5IBPA95buna+ZwdFJj9iZELXuiSfJkix\nwociA5JxRHtfOEFCscMVb+4IKbl9BHYg4ti6kaxn7PoJSQe0+CmipadSGulg7B29u0J1NS+cDdTS\n8vUvPcTFUkoTyhHzRCIyXzR7gu3v/vZ7XqjmnLMozNj/T29CiD8L/FmAxlaM1xv6nHkmBG9WgSgc\njsAnf/YJra3QIdLKrsw6YyYYzZQjVdcRg4InhoHIZrNh3AUmBxfbNSMVws6QbYeqK2Y+MW5ueLT2\njH5i1ij08hJjKpKWuGh59MgTg+bMzOg3l2S5AquLsHoQ9MuM85mbsOH65pL19ZL3ndzj61/9ItO4\no6skL99/mXcePWTrJ262juXmmspKOmloDwO6a7jcZHS34MMf/TDPv/w8rZ7Re8913mHbriRrNpFO\naFQWVE0NVrOdBipdl7scBErkoiC0rqQzMkhhSxlDgDQWa9uSmkmGLBJJCXTbsu0DSpUXendU8/b0\nGCcyWI0+us385AQrQTkPSnP0fktdl0Mi7gruNIpSyojRk5PHxYHkyrJZphoXAtqkfYqktD+FLFc3\nQQkg4EJikpE8SvKwKhweBEFovM1QF86NHwuoy2VPrBJHiwPoO5KLe4VgIpLwYaCuKqYYSEJSWU2a\nIrVWkBIyJbQEq03h1dPghcdqxW63o1MWqTLjOOKch74Aw2Rl2U0DZIvOhm0PRhrsdkTVBpcDg48I\nX6rluyljqwphamxXilBX1zf43tOqlmebHSEnvEiItuZmuSRNkQ+/8T4mP/L02TUMgdPTUx49e8TZ\n2Qm2rnj+/gscHh7SHCy4c/suNgMx0OeEyE1BRpixpMFCcYpO3hHcxLjrsVoQZWY7jrhx4sBYbh2d\nkEg4HWDW4JShUhU2leW+mdUYD8fPv4HIpb+giYRx5Pn3JO4Beu/RQhPHhCfxwdsztNZsBlc6HmIk\nZIXCYoKgFgqRHT57wmi4WG6JqaeeHOvlijsvvEqQI81hw3Q18ehrl1Qnnm5W89LdBxhRKKt10zE7\nOiCLxETkxXvPl2jBlLh5eMHjh4+ZJkmOAYFHykDIgkkoLp++zXn1GtpApQPZJqa0RdqMFpbtGKjr\nhmF0dLogpofscFPptCghqMJm/5woNmEkaclqGLnZLvnqk3c4Fcd0ITLXhsckqmxxPfhsmbyglhEl\nKq6fLJn/kSNUzKhg0WNhy8TgmGIgyLLnGdI3P2P/dQ/3p++NW4QQt4GL/ePvAvd/28fd2z/2r7zl\nnP8G8DcAjmbzXKkya81KMIjMFDPDFKlqwRgDesokOUAO1Fbi0UzAsHWFva0qxlQATj4GfM6oWIor\nP/dzP8/nvvh5hMg8f3KOki3PvfxBpuCZhhvoxzJzbyruv/Qibz98wumtF/jHn/o8FYHMEl1XJaky\nJPJgULomLwztvOZAKYYnDzkSGWc1WmfG7RUnRwvScgmNpNGHhH7H3FpqWyFty2gCb775Fj/6p34E\nl3u26y0+SZrZvDTZRC6AqwyzqiKKwqDpZha9ly9LKSEmKl0x6oRuLYri7JSyImdB3As+hJA0qWTN\no85EKbHtnNxkfApkk0tkUdhiNsKipCYysegsoyt8mylMhZTZ1LgpoHUNqSxkJRrpPMJC6BMiZiqt\nyMqAfA9KVoo6trJFPK5LKxBZLD5asqdZltnoe8mYEgBMCBFprEUFQdy/mIQxhOAKW38PX/KxzOK1\nFOzWA1Yq/OTQWRUxiyhJl7bq6AdPjjAGx9D3+9vsxHa7hayIo6PfjbQHB3zmc59CychmveTFlz7I\ns8fP+I4PvELfe5wM7Pqei3ffYlw7zo+fI0bP3Qe3OLlTM/UDRlV0hy13bz3geH5MTIn12BOtKgKV\nINFyizaKV1+pipxESr6jLbHHuq6LWF0WF8jV8qakO6TAiYQbpzImMaXzoaJhCiWuiBTYriKFEts8\nmc8YQmbedgQ3YqyiqgqOOISEjI6cynOeSSgFLoFIJWqbs8e7EfYzYQKMY4nXDv1IoCelQOzhqw/f\nZbm5RknHa+97HaJG+oJoto0lOIfXoI0kjAXHfHOx5L/+S3+FXqyQWvC+5z5ArU750b/w55AaGqVo\njcLIvSJPa5LM5Dixub5gdJ5JGoIbqIQgifIcFShd8TsoIsSA304Macf1+gmract2HNCqww2J+w9e\nJoYt09bRmh0x9Dx89wmLpqGpK3L0uGmNkJnDozOktZimYtF0bENC2horLAbFtFsTokMoMLoqKTpp\nCUz4BE17yKw7oSJx2FW40DOOI1/+8pf55U99kuvdiqglH/v2j/8bOdz/EfCngJ/Y//wPf9vjf14I\n8VOURerqW83bAaTM6M5TCD0JgsDEmqZqyNoTRk+MkmQKw2UIHp8kISfSOKKzQSlHTJK+3+DjHoOb\nBNNuYpCK01t3yTnTtTNEEFy+8xhtFTpPHFcGkIQAs07zoY++iksNX9GCaYRWdeS+WHZkdCWXnjNh\n2DC5K9quImx2kMpVwURmOUSmVDbnWieG9TMqq9mOA8OmJrvEzc4jcmRzdQFyQNPSipogNDl7SI7U\naHzSbGSiM5paBaQMCOlJ2hCUKnHKKKDpCpUvZnTyKCn2/9BDiW1YCWMshwUgRUTEQPag6wjZUYkW\nP5RsvG0rJlwRjLtILYtVKSEKDEwUAp93HiPADQOZSCNtoSUqSZKZhGIzOLQ138jJRw+kwBQHaq05\nPFpQtzMqacgp0WaJRuB8IBuFY+TJ1bKgGTy88/Bt5vM515dXvPvwMVpZpikyDAPGKHyeGL0rIyRt\naGaG7WbFt73+7VhhUGqGDz0uupI3J9F2DVdXl3SLA4SIHJ4ccVcbvMs8evcZ3VkRoHznd30XVavB\ngB8jr37wZWoR6KfMWTdjs13zyiuvIRH0my1Gd6WIJkcOzg8JucDRhrzm8a60dhEaI1sEZe5e2Qol\nBRpFMhUhRnTdkBJ4FM55WqsK1RCJp9BAffZ7CqlGRUVIcQ+KiyQ/kWTcs0kEOQpEjBgt6Kc1m/WS\nedshnf5GUXCSBdSW8Tg34WOkj8VtOrktWowl+hoMyQFB0HtIasK7CGkFoshb6nng3vGMw7rCuBuE\nqkiqKngDnTiea5SRxCyo5if43Y66PuL5515hx5pkMpNsuFyuGX3AasWGEaksgx9IPRzM5mSR6NoK\nhoFKJKa4oxIRmwIhehJ7o1guQhaNYLvt6ZuB3S4z7xZYUXPv9IDrpwPvf+0e735tSfKCMGqGasX5\n+SEPv/aEiycPySLQHDSsVlcMw8Tnv/QWq94xRg9KIG3Nxz72cQ6fO6E2Bqcn8qwueGabgZGkKmI7\nQzYtj9eRv/s//wwmDNhq4PB9C4bo2W4GaMqorx8HHn7xS9/0XBXfCj4jhPh7lOXpKfAU+EvAzwL/\nK/AAeIsShbzeRyF/Evh+ShTyT+ecP/mtDveT+SJ/7/e8UXgPveSA0jgbUmat9qJYoWmmQCs0VYaV\nVWzzQJI7Yu8Jk8cFycSE8xGZFFaU6FU/DsQsCpJ3z4VvhWVmLYdtzfNHLRWS7DTtR85JM4vINav1\njqxB1x1nzRmttsjkuHz6rNDqxh0heZSEU2twfmBIO9YzIB4ir6s9N8PxG5//DWSKNMFjuwOcsjwe\nHJNb8vqH38fLrz7g1p2XGceKWh1zcrpAVQlbZbbbLdfLJbY5op1VCAIa2O7GkrsWnjB4Nm6AqKhi\nTfZrsLB0Hnm1LVe1ncXvighC2Yydz1gtNxzO5phFRZKCOtfMm5ajowPqQ8OUMkZL7sw6Do/n7KLE\nh4QUkcZEdv2KJBWzg0OMMoyrnkV9jJSSKU3MFnN2ux0xRoyxuFiakHKfixe5gMVCdCSK9SmFSNo5\nrCyM+KwEYwI0VMYioyCK0tKNk6OpZwyjI0uFn3ZIFQlyLFxsIQg7j4slPuo2ZfFcGsCOmAIuBPrB\nIY1mGLdYa9n2G0LMaKkJ3uO9KwWoHIlDX2KmIlKpsoj3XuyvpiX9NCJVixamZPS9KLCsmSfaiNVF\nBWlkpLG+3PnkjkxNbWpCgPm8I+3HZ92iQ9cKqzPZJ1rbIFzxe2Zg8mWhaZQkhC29C0QhGaeSTY97\n7WPdtVipECFRWVtGNjKjG0NGMg0Og6ZKGjdOoBXD1BNSpN9tERm894SY8ERCiuxhhSy3xTZjTIVU\nkVpBnQJaO+q2ISvL5eUFadrQhJ7YX5K05OTlj7I4fLCPE2usUXjv+eJX3iZEya998jOE9cB2uQSR\n8EYidMfDr30ZWwmiDEyr6xJ+MB3G1tysV9TdjHuHxwzesRaBl08NP/ojfwyRv0AIjnF0+CixukwJ\nduIVNu6A3gm+9JUv8ta7T9hsEwt7n5i3bK4GtLRkPzFf3DA7sLgUebrakDP4aUQkhQ/leRPRILJC\nm4ZoFadnB3zsA3fpgiIMkbcvnoHtaeYahGXyNaK9zRTh8OCYv/XX/nI5HCfHf/Zf/kUevvsu2+Wa\nuJlYrVbcuX8Pt+35J//ylz6Vc/4dL+H/36Rlfvh3+aXv/R0+NgP/ybf6nL/Tm/eekDzCK2wzozKK\nNAzMpCFIRUYyqxpmWiN8ZBKBrAxRVTgtEEHgoy/0SEpKAFmk1CGVOksOCZczWsIUM7XUxCjQpqbV\nFlLixXv3uQ47khMcPDhlEhGnFMZntptrQtyh54rkI52t8dFASuz6CUQiGVVkzphSJ4+RJxeXpaAl\nFDKnYoRZdMgxIpTi+PiUm9WWf/Z//ENkannx/DWWy2tSnDiYg5CO7/3+7+N6dc3kylilFRIlDCZl\nblY3EBPX1zd89jO/RZc7jg4t7dEBV2Ng/Vvv8Bf+0z/Hw80jbq57tNaYOpLrCnn/Nl1VE60hZcGB\nbahMKTJF6ei02L+gRy6vepLuULpEPLfDDW1bONdKFkrl0dEBYQtQePUpRIyWtLZQDttuxnYaCM6z\n/yiiC3sefQFcxeQJBPxUzDcpg4+S2lqMVqSYCMGx3Tm0kBzozJgGpt4xTQMHh7PChfeF0TMODoRh\nGh1KNqSYyHlP1ySgRMSYjA8Ts6ZlGHc0umZwDkWRePSh3+egB4xJkCBGiRSFzZ0ikCMplrsYLQQi\nQaM0ISa0tWDLxYeOGhkCtd7H7rIgRPAiEf2IiBmRapwf8DnRti0iCHbTmkoo+nFAjQlpLbpucGMR\nTWSZ0HlC+IGcoasXRTyhStNXZ0EtLHVnSZPn7PwOyhqeXDxmtS2/B6Vp6xntvKN3A6o1DFOPE45+\nGIlT5OrZo7InCIEnFyvWF2vefnyNnc1oZw0iTQzLS9ZP3+Fm+azsxZLgj/yh7+S1l+6iGCCNiCj4\n6mc/wy/96t8H0ZYld1VawdfXPVV7xHI78P4XX8BWGpUK4ltpzQvPnZFxuDwxP57tv/FEhKojfksM\nAAAgAElEQVS59dwxUQhO6pqUM2ackDIyDqG00pNHS4MSFVlsQQmmYeLpu+9wuRoQORGnkafvXqCf\nm4McODialQV/FBQJfGK1XTE6X4ioo6My7b5DElChLMZDcqQI41ZSvG4JaxS1UDgPYQiYxqKEQOap\nzO7zjr/2Ez/O9eUTPv2FL3OxvCa7gIkSHTMH1YzdzQY3uG96pv6+aKgKMjIklIrIKkPtETHTWWhF\nwEvJlCW1SWidyDohphGdIipCDrYc5nsgVcqJjMYrhdKCREHkim8IHzK2XdDHhPCRh6sNh7aimgTx\n81/i9MEtuvaA5bRFN6qortwOXUe2wpUXjJFYD53VZJ8JPpblpdasJ4eWUB8q3OhIbSYzMkVPEAqj\nNMmPBAS7rUfKlpvNNad3XiB7yXK9xLuEkTXH7RGLM8mw23J0foRt6oIEGBPTFCBGFp0lTNAYi8ow\nTRMhwDBM3Ln9GtefvsSqGZ1O9NzQKkuMPUbMGHwg2pYYK9p2hsg9zldMQ+bl971IyCPCNmSlsZVk\ntYOm6YqMowo0c4uwFPJdyMTgOTw8YdsPVKLh6tkjjFFUpiZm+OrX32SMHjcFrm8uGftV0cVlycuv\nvlyuiGNEyyIEEbgiNsmazc2abS6EQS8TPo0ombl89FWsMqSQkbbi4vGamEa6rsFPPUYZfIamrkhB\nIYwmJg8ikJyD4KmNosLgXaKpagiCumvwYSifq4rEsEZXCe8iwUGrF/Rus0+ZGHLej5ymYpyqraWf\nRpSsyEEjVoWjYxmJU4+1AuNhtmgYk6BXAaNVQV/7HXNbgRbUvuVwfkR79xw/balFphoL9TMm2Mpy\nYRRGj8WRwg4VI/3Fjn70/Iv//RdwQ4+ICa0tl1dLJp+5vt6WRevY0/uIVIbRu4JMVpIgXOlC7G/u\nQ4K7t885toX0mYXCZYWKAtV2BNfjdpZa63KH0SwwtqZ3npP5Cb/487/Aaw/+BKqdCnSsKqiN/mJN\ns6jx4YowThwtDjhtLTu/4WCuqI5S8RMMiVpUeD+gzQ5tImcHDXla0fcbbJVoFufELDg+PeX+YcWj\nR0+oveKF03OEGBDBwCQQDpCSqAokLU2JR28/xHnF0+snTIPj7LAlySukFkzeoWSFjwHbKdZhAmFI\n467ERSWQPUpLvJfFspYjRkms1sRpRHhLjhGZIrXNBKcJU6JpitJRcYW2EuHWfPpTXyL4iFARkyH7\nyCyCtpYhRkDz6oc+zCc++7sPRn5fHO5Igak0ViVqKely+d9StWSQFiMqcICacGHHTFkO5w3bKdAP\nGZWB6PF+YPSJySeM2dfeYyZHSRBFJquEQebMcneDMpI+9GQOkScHzNqOx7sr7FcGhlXg3uEpi4OO\n1IAVFhcm9HHESsHkdti2JSmHthmlDTl12Ky4mzyyk+yMZ7wZsbsGwkRTZJgYI0m1xQ0969Fx+95L\n6OtD7HLLLq5x9VOSzFRdi7prufvSAyodOT89KMRF58kuUNeZrMDmRIg16vCErz2Z4ydLdDVWzgkh\ncGG2vJ1uqGdzNp/852TlsXKHOVRU9Yw7t/4wuT1lcJmwGdkud3zxs5/lCz//0+h6xKmGX/zNLUk4\n/t0/+oPEWJ6DWmU2/Y4xJc5u3y3uUiG4fPQEgWa1mTg5s2w2KwQ1R8fP8fDRU+ZHp3g/0TaKWycz\ncpDkLPnNT3+Ndt5S1zVxGOi0pRapSMiFYlIOZRXatkhqpvXA7Kji3v1zBLDdjFxvJlIWWFHhtp5a\nz6h1TUiKujtgmEp8LCTPOMLl5ZLlcs1m3bPr12x3V1xdXVGrmrArd4DXmxXvPn2EICKyp4/lKldk\nSd3Bg/v3cKPk4ZOHxBw4e+42KRju373LwUyQY8K7ia9+8Uv4ceKF0/My6nrwHLausdUVTb3gpVfe\nT9fVZOUZ/chi3jKbzahaS11H3NWGH/6hP8Hp4RF5k2hsS4pQWc3ObaitZJYUSVhENWeXFKqp2PYr\nurYm+YTJA0pKsggFk2syi4NMthmtLK6fUGlGForRKqgMQiiWT65pqpYUIvNKovxUEMBSM5OW5mTB\nViXWYkTVHVlEjg4W3L7VMCVP0pJv/7bvZn4+I7ieGDXbaYNqlvzJP/MRlss1tw7PSrBeWNa7Fl3V\nbKeJLz16wrXcwXxOvxmZzSo+9NIbHB3PqGaWEC4IcQJV07saHxQhZBw33HrlRQ58YpoUv/DpLyNC\nYrVa8eTRMy6e3mDqAVsrXDykag+o2o7JewylwxLVhDASHxRJRbwc+PAbL/Prn/wM/Vaxmq44PpqR\ntwJVNSAl2e8QKpV+SRLIaDmu5sTJIRuB0pFEzzTsmLIm6hlZjByKgJWG4BOpH7m6WrHNGbcrd4M0\ngtwUKfsUBj79W5/6psfq74/DPYOuNIWKH/EhIbQiaYXUFoFFmIxSGRlBxOLLDL6gWLMqt2cxlxmr\nUmV0kXPY89nlXqxSuCep4KSYgsNakG2FOZzx8msfoPc9b775EOk9223P5HrETJEnhdSKw64lxYQU\nBpdLmafwTYoRiWzIQrAZdghbxN1npydcmi9giehEqSB7R6UFLzx/m5vlFVcXz/jQB9/gudtnvP+1\nc1AaXWmSGjiZz3ny5ILJaRCJxcERtJAiaAMHpqfvE/7iCT/w/d+Pc5qLr9+wXm1QtWISa378f/jL\n3JrV/MAbLzK6NVl6msGxW235ud/8Wf7JL/8WuwmEh9unL/DKi/d5cDogLSit6IdITIGf+qm/z2q5\noZKZFx/cpp3NuF5tuFht6DdrDmcdd89PeevNd6jsnO/53o+TwsDhwRm1hMN2xtnRGUkEou/BB24u\nr0gRni13YBRN0yBTZLxZYnPJhPucSJVkioHNbsuv/Oqv7yvoDpEG3DRxfHLOwyfP8N5Ta0F0E4u2\n4/6duwxuYvDFMSqlpO4Kwa9tF0QXWXSHxOTpZorTs2NOj88IPUhheEFIzh49xRjFyekB6/6mpE7q\nGu+v0VJx0D7Hd9YwuB3b3YCx5e6mtRGdFVoq3vjg66XQtVsTgqOZt0jVYUyFkZqQJbshoCqBqiyb\ncYuPAeMc1tZM1zvmuuPYHnA5PSNZibWauRLU9j12UU0zf45tyNy9excvIuLmGdZahvWOqV8Txh5t\nEt3ZITFNSB1RRrCYHSAjrC/XhJg5PX+OmBJ+Chw/Z5mGEe/9/q6kwtZlEbzIlnoxZ3Jbck4oq5DC\nUrWWyiSMiGAFVW159+ljHtxtCZNH2A49D8Q8cnRSKK/KNDifMHVGqIIJUEkTnEdYx+VqwzQtEOYF\nsmpZbx3t/BjbCJxPKDEvWBGKsKff3bDrHZ/7wkO26x4RKBayyRLMKT6vWffFaxymwNObR3RNi+93\n1FWL3yVklKjcMJvN+Q//oz/N//J3/zrGGNq25S/+2I/zD37mp3n7848JUlNVFXOgn3piyFS2QhoF\nGpKcQGuyitRtQxp3IDKu9MBI2RZVX5iKFEQoshtxITI7PKQ9mPFsu2I39qUfYb65RPX3xeEuENTa\nkHRGBOhlRugMNmG1LfS5BNKAxkCy+CvICZSOSBNQdSgsESGQWSBzgUZFEsjyazInwCGSJInSKhPK\nFDVbPePrX/4SH/2Oj3Pr/B7/6Gf+N86OO0La0Q8DZmxxXvDOzVMqIbh3/zaL4wonMlpJDowiThKf\nJM9Gx2/85udZXa3pqjlzM8ePkaaztEbjpabRllefP+BP/pkfoe1m3Ln3EmeL+3z+i1/gf/rb/yNX\n6yWf+MQvceu0ox9WJAFRNuQcEEIzN/dIvsWnjJsuyd7xb33nh/m2j3+MVz/4BmevHTGOPZvNhv/q\nP//z3L51xCx7/tk//gc43zAgcOseMUmeXSyZzY6pFjPIFVlb1sGzGsGplmnSBbuaMkcn5yAq0jhg\ndE1KxY2JFJycn6DJSF3hXEDkgdWzC7TyMKxZPnnGJz7xaxye3cW2AltJtJCslxuapmE9jky5FFqM\nKVyfuWxREoQqiy+fIu2s4ZVXXy5V8abD9TuapmE3Dnz3d//bJALnd05IPrNbj5weHiOrRJaRkDWL\no0OqpmaaehaLBdFHVlc9XTdnPu+orCkN0bpBUKiX3he88Ha3IjOyWq25uLomxuOyrHYKqTLKtlid\n94LmCYNCUkEQ3DxbYyykFpSuQJvCVaFCGcmYoJY1MmeIAWkkUSn8MHG97tGhYl7NWegGedjy7/zQ\n9/FLv/gvOPeCSh4wZXicK176jo/zQ//BD/P6H3z9X32xTRPEHaPf8Oj6kpv1FevVUz77uU+zWW7o\nV1s+8gdu85uf+wLZK64vnlIlgfITjcwMUnAzZJKCrBIuTTgp6XRddhwhsNr0dFYSRaZSBeGR9gC5\nxcECkRxWGIYgGcN8P4MPrJ2mCXOSgKyLqB5RcX74MkeHL9B0HdtXA9eXE0l1TNGidYeWXWEAiZFZ\n1ZCniWdXS95962usV09ZrreMoiF4Seq3xGzwIeOCR1iFkDV4cGHAxUTvbjDK4NxQoHx+olbw5SdP\n+G/+yn/Lg/fdRYmBatbyEz/xV5mmiVl1gNQNxtZU0hCDZMyeerFg3rV0BxXUI1PItLph2mSMqxEF\n9AFZkl1HlqC1ol9fgnP4oadqTxizJU2RzZiIWIbNiOzqb36ufqu0zP8fb7cPj/MP/7E/hOoCOa6o\nZx0eg081tV/sGSIjqs3EnBh3kWHTMw4T216zHR1jCjy5uSA6mKaIzhKpMsYoxiFgiOTgSdGjdM2k\ndJFX2IbTk0OOD2ecHnfcf/UWTy6WDDuB1ZFZowkbhwgGoyueDc9AilJFV7FUt3VG5YkwzMmxoV4c\nEoSjOxaE4JgvjvmVf/pL+N2A2zkuUbgUmXWCGQJhDWsvuLU4InvHya1zfvXXP4WSklfObnOrXXB+\neMxHP/oGD56/R9udcXb3DzMaw1urzH/3N3+M1qz42qf/L8ZhKBIJIIRQKv+6qO0IHikLNnfX97Ar\nsK3u/2buzcMsPcvzzt+7fNvZa6+u3iV1a5fQhpCEwBgDARvjYMeGAWzA9jWO4y1esB2SMONtnLFj\nX+GaTDxesE1w8ALYLAIks2gXAoEE2rpbvai36trr1Fm+9V3mj++ouRwnTK5JnEvnn+6qrq7l1Hee\n73nv537uXxvuuON24k6P17/pBwgJOXvkGOe+8WVyDa7R4q4HH+U3f+OX+NMPfoydzRGytLS04tLL\n9vPa138bz51b5eOfvItAeXqtPfT7Q6qq5MbDi5TpOt3QcvbcBqVvMrAJM3vajNIBJ548SeBCRsMB\n+w8vUbkcJ+DC8jqb6ylXH7qcS/YsEEkDyTQXNjM2tkvGGQSxQpDTasaYXCLFDOvbZxlmaxw42GZx\nsUMSh1RpSaPlKbIxzeYirYUZZmamCF2FICSII+YWpygzg6fevm1FDXqtaU4cOc7hSy5ludzGaI+h\nQqgaOi6FxhpTO2Tycb01bC1VtkWW7+AcZHmLQb+oh4EiR6gcF1TkZUYjTGhHs0RRgyRJmJueIwoV\ngaoIozpG2OuYvKg75K7ssHNkma997vNEbPGKd34f73//v+PO3hwmiVkdGh4+PmJYVVzY3uD2m15G\nkY/YXLvAeFBRlQ3+4K//Hc9fOAJlyb9+76/VcQYISpchVe0VbzWa4CCwmqlmm10zM/iqJMsFxgtG\n2qBdBb4ibER4V7IwM83c4gLHzzyPbwRICha6AfvbAi0tOrCIoMvC3DSxzijHFVYEmKSJ8SVFbrBV\nA2skQsYcP7PBdpriUczPLuBbEiNLRJZS9AOyqksYxzibsba2gtaaKFSUZc7m+jq2cgy31+qdDq/I\nXc2RDYStiWpSEEWqNkE4R7ozrsEv1KFpQtUcVkltl4yTOiu/MhnF0KNkXMuT7Yi4HWFdiZQNVCno\nuhDbhLX+Grvmp5BljdW89NAiU0mTKFZsbW+zeX5ElleIaQ2hppu2kCIklQ493MFXhsoJ1itDWhVg\nIJMOKw3WFQgX8uyT3/j/75b5n/EQUtBotECmKNVA1dBCrBUoGddsVO/xzuKtJY5j8jQlSiLysl5C\ncEVFrCIGZYpwNammFYWUVU6iAmxRkYQREFEUBVrVeDlvQYuAOEwQBnrNKc6XG1BJdFjThaxRSAlG\nlrQSjRe61nW1RgtFGCmMl7VclDtG/U1cYDl2+iTjbExmHbJSULl6jTgMacaa0XidQEbkw4zMwUqV\n4yvD+dX12tYVBKysbkFbsLOV8aUHn0DqGvwRdu7CRLOsl2POu6NUxRla1DqfDuo8eOvA2QpbTcAW\nTteOHUGdFNis89BFs+Kzn3uY737jK/j85z7H+rl1puOEni8QQUigBIuzc/z6//YbXHboejKZUdmM\nuNHDWfjUJz/LPQ98ib0H9gMe60GImJXzy1QHZ5EopFTMTM9S+IR0R3Di5BmsK4gaCdXQ0e50QXmU\nEIRasjA9S0PX9kMUKKmI2jEtozh19hzjkUblAmNHSOFIoh7rK1sgApbm97O1foYsHRDFimYQUY5r\nQtezTz3G8vYmu3fvZrE7xeLiLgpT0U+HzHR7FEXGxto6R54+wl9+8D9xw7XXoxw8c/YIVeBQgaSS\nIFWA1jXI2AlbJ3ZqWXN2q4K4pagqS3/bonWI8JY8K8lGA7ysM8ZtoJjfk9BI6uLejBRJQyGFoDQF\nUauHI8I4ST4uGWYDiqwEK2m0Wwz7KUppbBSymWdsW0jLgnGe0213OHbqOSIhwDhkGOEtnFk7T1YZ\nfvvf/BZB2Kqz71Udqle5CmsgFhFSCZxULPd3GFQlSilGA8uupd1spn2KdKcu7jvQmW5y9fw+GtoS\nY8m9wwuLt6qGYGMJZE0NkwgCFeOVwFuJlhHGCBARGzslqxvbeEJ0PEdv13wd0yAcKZaispBVaBKG\ngzFN4NClu+m1Q7a2ttja7LNy4TxlWeKdwUwiqZEKKSbrnFLWeThYpKqvS+EVVaDqaAvqjPy67bFI\nVcdZq0ZYzyliQZEZjBMY72hLjU8LonaEDkLiJKZrNFVkSZpLBMoRaEGiQwISirJCKldnSfk65lc7\nRxgI4qbEoijKirjTphpnDLZ2SIucytbwFRfI2m6pDOJbh0K+SIq7ULRa0yAToqCFNAVGazIX42QT\nJSJcnlP5lFYzor89JGkukucpnZbFVxZNgrUe3UuorEVWklYUUDiohMWLGs4rETSTFplLsWaEVwlB\nEmMspJnh/i9+DRHWFr5G1AIVIZsBRhuCCKIdj9AJpQFvJ9AC45FxSNyUmKBCMvGXyxZVZSltxfbO\nFktzCwRGEFVj8jxDBCX9rGS6M0ciBYFUjIY5pY9BOJKkSSlTqt09rA/5nu99N1v9VW6++Wbe9JY7\n6yFzCK/93h/mwso6+dYm3tbBVC/g7ITwSFdfsgIxyfOQaCkpyRFKoaKI3bstb3jta/m//8NnCZyg\nu0tRqh2UaROJDtWooBcuYIcOjcYnAe2pNgbFoJDs3n8QLxxBUAOmrRkz35knCkK8DPFaErQamAyk\nr+g1ZhmkO5QyJeoojKkonSWMdM2VDQS6IdBtxXq6DWXJbddfweGb9/GGN/9jDu67iampKS6/sknw\nX7iKH3/sab74pS/y8KMPs3HieUrjCbUE7ZibmUX6iJ2Rw5w7h4piFvZexaHL9vDQAw8igh7dud38\n+E//LC+98Qamej1a0zUHNW416S3sIU1LgljSCRpEUUC/GDFOc7a3+5jBmHy8xXhUsL5tKc2Y3GSk\no022trZATgKqhOIp9Sxa1TbQILRUVYlxFp10UXELJ2IUPQZb21Rrq1w+v4+ZMCRsBngXsm/PYdby\nMTuB4vGjRyl0h7BZh7jZWDMqKxrNLpubm/zIj72DVlMxGjt2SosuQfga5r3nkj0cP3mMVrdLJSZF\nMQCtm9gg4N0//k+ZnpnjyHPHuLDyPNOdAFnlbJw+g1aOpWlNFAWcOWvwYQwefCnA6hrYjkHGIUWe\nMpVM1aFiOmA9DcgNrG722RxpXDJNECZErR6FKCiFoZoUU1cFCDfN1o6lKIfsn55GMWb59HOsrW6i\nVEQ2LjGT05SjBvQoZVET+IlSIZWrcM5jrYFJpLXQClMLQYTBCzM7gXUaqQVjqRhnOcVOn3ajV7++\ncJjxEKwjTXM6iyGBzsmKkkE/R8ca7+ts+KEztHoJcbeW4ISo46bDMEZ5g7IVRpdkpqLUnpGB3GVU\nMRRFVVupqedFrnLooF4Q/JZ19cUgyxw6cKn/wL9/P4UYs9E/jxYV0kcUhcKjaMQR3WaL1RRy6+hv\nr1PkA9LRkL179pDnOaNxxtZOTqPZodNpU442WTl3npWzFzh99Bz9nU16Uy2kh2GW4jVYIQmSDljH\n297yJtpdwzdOnGCcOtbOrXHJJfvQoa7X2L0nVo42Gm/aFKmg1Zghnk4oZEU41WR7ZZlx2ufEyime\nO3EcbRqEIqLT6rJRnSdqNrCFRIkCoQ2Fq0iKBp3WFFGzSWfXLKbyXHnwRloqQgnJd73ptVx51WXf\nfLImBf09P/d+zp5+nqzoc2z5MUxRUpVlrf9afzHz2l3Mn6gvDGvtxZucNfWmZNRu0lAx66sX+E8f\n/mNWNjY5ffYY2xe+RtKIMDbhgYc2eMkNV3J+ZchOf0ioJfO9kJm5WXzUZGo6qdP8Ksfl+y4lUBoV\nxRRVyumz59gZ1jbV1Y11nn3qaUbjPs6W7Nu1m6IwmKxAujGjvKDyMCpzpISrDh/iq489Rj5OEaag\n0ZS891++hw/+8T08/szTWFEgPDRbml9+73t43y//BjOdGbQK2Swz3vmj7+ZvP/8JynFJnpU0m01i\nGVOOSjY2+hTWMDXXpTOdYApHmlfML87y27/zq7zjB3+O3XuXuPN1N/L6V97AT/z4T7C8nhOpBg0V\n0IublEGdw/Pud/8IjcDyzBNPcM9HPsuVV15JVpXkYUGeZ+wMR+xsg5OSucUu090G2uZ0wpA4DLDe\nErQKVKCIogZBuEh/WPLPfvKnyAw88eQT3PaSm/jGo1/ns5/4GK46w2UH9lOUhtbcHnSry8FLDlHk\nnpmpHrPdJiJd55pbbmQjHXNuZZ1TZy+QRNN1mmkYsrZynGYzQekQiyAvKpQOyMqq7rJVVXvCtWbf\n3kOsb/Yx1hMnAdIYQqnQIsaKgKe/ei+hdHzj8acZFA4lJRFwzUIDqQrK0KDDJjpsMzN3kIFxDEc5\n1dhS+HFtyQ2miWSI956k12R7tIWtKjqNJjPT82gRs2d6H4PhFudOfJXHv/wYWjUZ5pY0L5ChZmc4\nRGk5eR1UIGuaWQlEYYLNinr139l6gKuCSWEXExu1IAnCCaBG4sKQsBlxYuUcVVVx00uu58LJdfqb\nfbJhn92zU5RphpYKFQnAISqPDhsIoWqATF4RByFX3ngtcS/CqxTlhpw4PWI8trQiRdRWRL5mQeTe\nsT4e8VP//CfIy00Gq2vYyuAFjIb17wMgzyP+93/1L1/csszpM2d414/8OC6swdRRoHEVpJlnYXGO\nmV6XV995J509V9Kdmecl1x/g2msP1lxLZ9jc2GBzY5snn32Opd37SYucp7+2ztXXX8vV11zH2UPr\nKOko8jH3ffGLtGeajNKcJImpvECIMd1eglAZ48KwsrkN1AT3MIygLPFFSagjApXgqibNVodLDl9L\nMJvQr8a4IODcyWWyTPDQw48yPzdDHAf4qmI03mB+3zQ5lmICEnBOkaiItu4S6SZVKdBE3PqyW7jj\nxjvZO7+HpX3dv/tEGfid3/4D+qOUe+7+NK12wGC8wTAfYgqDmoRx1XGu35ykW1tT6//zG7nWuk4D\nTAuclLQ6s7zl7e9karbJO9/1NpzW5Caj1eoilefU6ZOIcIZmt8t4Z0DSaZB0Etq9GXbvW+S5o8f5\n6qPf4PH4cdrtJroRM7e0yFRvlne86x1csm8KDTx14hxbW8tkox1+41d+HS0aRI0mw52MtBJUxtDs\ndIiiiNPPXyBJeqigSSRKuu2Quz7zab7x7FHipItVnlgHhJHgbz5+FwbJ2DqoMsI45utfexykZ1il\nCB1QGsv8fJutbINjT32Dj971aT780Q9zbvUY2JpSf/bMSRphwC23vYxBnrKytcHtN9/IN778GBvW\nc81lNxOETXZ2dpi7bC+6lYD0dKdi7rj9BvonV0hLQ6hCUkZMzXUZjMZEYYRREEQKoT3e146O0lh0\nKAmjJlEcoFWMLWG+N8sXPvM5ysBz+KoreerZJ7n/4Xu58aXX0gm7BMbjRchzqylrqyucP3uOm65/\nGSvnLpCuK56871OcPXOKaLGHjBOmpjpkaUVVFKTDDeJQIEWBMSWVC+p0S2rwd9yISOKkxg7iWDm/\nTBy1kTh8VRIHEa6o8A2JI2ZUecpxv0Y9OoWtLBkOFydUzqM6PXRQF/cq6HLl9ddy6tQpBufXmGnN\nsbnTZ33gqLwjSRIslrVz52kmEQvdKdrNJnHQrHnJlDz1+ON4C+OipLAC4zy+skzNTJGWKdoqynIy\nrJSCF6INnfMIO5FevK+B33i8qleMxARej/P1bCXLQXqm44iw06S/eoEITTeKmY4XMBS4SFPrcTVB\nypWgbUUQCdrdHlvjFVQiWN8Z0tGSRjuiqlKGaUHpNCEQOon0EltWNKKIwwcv5ysPfYUrr9lNkaYc\nOXKEYZqytOsAvU6XIk8ZDf9hIn//hz6kVsRJk9KVRCqqw6oigW6E5Maz3k/5i498BiMexHtBtrnJ\nvkvnuOVlN/DzP/eTHNx/GQf3e26+6Y6Ln/O6K6/mmSee4MSJUzy3fK6G9Ho4fNVNZM5y7LkTlLZi\naqZDEre4/+FHuOSSS0izJnmesb68zNUHL0UXAlt5qqIiiBrs7EgWo1na8SxjY3jnW78DgPf/0d08\n/dRJVi88Tzvs4TMIEo+OFUp7fFOgjCSMPEnYxFYOUQWQK17xqtczrkquPHSIJA659ea/63L4sz++\nj/Ew5bHHv8SzJ55jZfMCTQ1bW+tYbxilFmctsZ7MJbzHUB9NhZvg7FyNJavBDQ6hNeW15QcAACAA\nSURBVEGkUV7iKiiKMVoqprrTqErxJ7/3Yd7y9lfhzZBAKXTg0DRQKmKUZzSm2rTmOpw8e4JT9z5E\n3OpRZR6fBdz0mhvZSQdsbQ+5vLObKy65hMP7pi7+PO//nX9L3Pa87jWv4Bfe8zOcP73FM8dO8Nd3\nf5rxcMje3UsoKpQXDMeOMOmxPdqilBHehqTrGaa1SNiYh1Kh4wSpS77y9WcJpg8wSlNQFfS36W9v\n0mk12djYwpiK2+68kVPHjiNUwb/6+Z8mnJrh7W97K8tbp3n4Cw9w8OBBHrzvXgbnN1naNYVbL7nt\nJbfyq+97D5detocUSTuQDDe3OXT5JTS6bfYevoT9ly7g0jWEK7EixwmB9fVikNIh3/7qV/HAfV8h\n9wU33X4lR556GmSIJaq1V+8xhaYVdpE2JNsx+PGIk+e3mTqwQDkYMVpbJyxyRssnOXxwjPUVRnRQ\nBVx3YD9z84ucPn6OJJkhbMbceet38MSRI7z08usYmwwVKBpxjilKhC8QVuEqj4pDijKktJPZgRAU\nRUCVWTrNqIZPmBBsRBQFGLOJLVOaSUSr1eRzDzzGgT0L5DsBp554GqlaBGFE3GygZmdRyqEbDWan\np6kKSWlChjtDtIWl+Tm8hv0HD9Ga20OZlVxYX8GIId/3xu9ga2WF4yfOkOdDHnnwfkZrQ0qb1nny\nKqK0lspWqEBSliVpatGxxghbE8Gtw07kU2llHT9PvRntvcBP4Ota64unXCnqLVmFIqokdivFZ9v4\nUOPjCFNofFnihMVpjw4jvI6Zmp/i1HPH6cgEU1qEchQaLr3pynr25S1KRwgfo2WE9mOkcqi43l5t\nijrQLwwV2+c3SS9ssHHqOfLhNo1Wh1C2WVveZLSxTRQ6rAu/ZV19UcgycRT7uaUlhJIkgabbimk3\nk5plGYHzHuMdo6yOrS6HI1SjtgH1pGRGNWkKyU033Mh3v/WfcODmm0H8/fvW9qjk7s89yrPPHuXJ\n555kPB4z253Hj3KSwNBJKuYPTNHtTXH29DKJqTCULPe36ZGQ+AYwzTvf9ZNceusuvvz1lN/7/f/A\nY098hdHwAqPhCtpW2LKCoODnf+5n6E51+OinP0lpB4RI/FBx+dU3cuCyQ8wv7eMd3/fGv/d9fujP\nH6LoV9x/7/2srKyBUGRYLrgNXDCkKkaEQ09gY0w6ZKt/hApDt9sFZL0lae2EEv/Nv8tJNy+EqAes\nahLTW1iMqB0E0tXdTCADOs3a1nfldVdx5PQ5BBGXXH2Y9fV1Tj9zkvnZkIXFaYIgoDRt2o0FXnnb\n6/ieN72O/nCLS6/cR0PC17+2ybnjFzhzdos/+/BHUV1FP30e43bwo5JussDa9jq/9ge/zOL8DCeO\nH2NreRMlNJsrm+zas0Q80+bQYo/5Xo89i7voTO/9e89bbuGBBx6gPxxgMGgB3XbCp/7mI3zq458g\nDGOsSgBJJEO6UjLOBuzsDLnz1m9jb7fNybUtLmytMRyt81u//+v84r94L/nAMBV0QVhuf+XLWB3l\n3H/fIwijiOOAbreLEpqt0TphItk9N0Ov0SEbZaxtDhhlaT1A9XViZOpypqamaEUdIgFRLEgamule\nByklzWYDEStcECB1xCf+4z1M92YYVSPiIOYVtxzmtr2b6AR82ORCOgOBwPgQL5Zot2dotZs0GoY8\nN+Q6RUiLzgxfuf9JPvxXn6RS8KEP/l/oULBRpSyv7yAIKNOCSDfRUtBMPFGo0EKibIS1FQJDpD0n\nnj7H773/D0na82Q25Jfe9zZ0MOZDf/bHNNr7GOceJwKCKkF7TRS2KcQIg2dsNLPtHuPtLRa7LZJ2\ni2a7wcbmMqWBt7/jLbz5jXdCnnLyvkf5i48+TFoM2NhYwVaO7cpQBg6pPcYYDIrCevLCYp2uWaPO\n14SoCQjEqhL8JD6gjg3DK1+zi5VCqzrsToiaWyAVeO/Q1iJ9SGpKrLAo51Fl7cAZVYZcFogQrK1Y\nXFykynLMesnOeIyVnkzVsRXKwcHFQyzMLNFoTbHn4CF+9J/9IId2zfATP/BdOF9RjWtgThQKVtfB\nSE0VGrJigC0doWpRqgoRGFRYEZqIv/nMfS9uWUYI6qOTFqhQEMUvAI0lGofTIJSgpQNc5ejGUwyr\nlF6vR0skxEZwYNcS7V7Cl7/0EI989Su89cd+8u99nalWyD/+njv5zjfcyfv/nz/kwto6zWCKs8fP\n4igoTE6RjqHVxuYlQtdLUmVlKYQiGxX8L2/5Ti69ZRfnThp+5H99N2k6oCxHOHZQ2oCwmFGO0JKP\n/OXHuLC2SnO+x/59M+zqzdBd6PKm7/xeXvaKO0mSv/v9ZanjIx/5FI8/dZp0Y0gxHrJn9yzT89OI\nRkTrsnmuvelyDhzYh1urGK6mPPrQg3zwQ78DqiRLBxcL+ws3be/dxWLOC+zJyb9ZYyYf4/ESvJAI\naRBo4kaDVtSmFQfYoeEt3/82vvHkszzyxGM0khZB1EGHLYxL6DYbfP/r3853vv7NNAM4eXyVT37y\nLjY+NCAbCYp+xXRjgfWNMVXpCEXI5mofx5jAWRJV8oW/vYctfZbxaIvF2Wkip3jmqaMI4yjTMf3h\nOp/8k3ugLGkEbS4/fA3GOEzuGLuK7XSbvBpx7dXX1Hn2UUx/e5uyzMEa9i8usbm5zeYoQ+mQ1GSM\nqhJnauTf1NQUqxcucGFljRzDen+Luz9zD2eeO0M3mUIu7GdtY50Pf/gT3PSq2zh01WEe/NwjRFrQ\nTZqEcYMk6eEDx8ZgQDbOMVlFkTls4XHeIjQ04yaRj4ip811K6esgY29xfoswDChMTizaWFMR9TSv\nfNXLWT6zwrHHj3Lg4H6ePvY0t+7bg/OOyljSosRXEt1oUNiCpjaELYkNarJSIBxVWSFMwEMPf41G\nq0fUm+Jf/otf45ff9wvYwCKFxxtDp9VEWonWEiFTrHMoFSKVwouKOFKMBzm//X/+e/YuHaQxPc36\nZsUffeBP+OEffTNBLOv4XWfI8pxQtZmb34MUMes7hihSyLCWQH7wB9+JHWxx9Mhx8jylLSI20xF/\n/ad/zptfegMrjzzAQ3fdzcnHzpGZjMrmFM6QB00yVbA92mJ+fh4ZNQDBBI9c84ipC7D3Fu8Fwmkc\nHolACAlSYUX1TQnGAM7hvCdsJAgt8N4QxpJi6EjaPYSqXzMtH6NkyLSUjMSYsKHpNGIiHSK8ROzW\nTC/MQwBTs02uOHgIScANV9zCzMwMQdKkOx3ypS8/Sbp6llffcQf3PPB5Vvub7FqYB2doN9r0UzPZ\n74mxvsQYSwFU1jDdaeHH33qJ6UXRuSdx4hcWdqMDSRR6du2dAulRMiIStc+0cIZGPEMrahBUFd3F\nCKslzd4+ep0uiws9pjuSxYW9aNVkevpS9h3Y/d/09Y+dXuXMubPce/99nD99HpNndbCTLImjgPV+\nzuaoZHOrz2i7HvJorfFKEMSCIFasb63jbQGVxVc1AaeZtFCRwAQ1ICSWGpcV/MD3/HOm5/YRNBos\ndbsU41HdFcUlTz/9dcbpgCLbYWunT9Bss7B7ienZKd7wpu+F5jxR0uPQUkI5Bivh9W/9BS48/1V0\ncR5TZRPK1DcLvBAvSDNcfFt46nx66s9RD2AFUmicE4SBZDoouXRugd279rCcS5a3hrR2LRJ2pvnK\n40eJTUQcCdo9wR033M7exX288bXfyVWHp6Hznz3JFfDCcDeqnWBIqDkvNUz6xOmzDIcbbG2sUgxS\njj7zPI9/+XHG4yFhUvv28xLCoI2ICmRgJjeyAiklnfYUd3/6bsbjkrnZBbJszK2334q3JcP1PisX\nVlk3Oc57BNBNYnpTsxRVySjNiYMmxo4RwhOGMf1hibeOhoV+lfHtb3gNSVhH9wohGA4zXFmQjoaU\nxjIqDKWfEH6sw5uqzrCxNRpcS0/lKvC6vqHaAh026ijkQNBIPEqAEjHOhTRaCT7ytJsR3U6LZuwR\n1rKzcpoblmICWWFExMfvXyZqzrK6uc2td97B/Pwcc7M9TDHACgmB49RzF/jQH97F7CW7WVxaIFQN\nTj1zmudPH+GHfuzNRFGEs4rdS3vrPB0MjabG4xgPdtg1s4DwUFaGf/2r/5aFzn7yvOTGV17LOPcc\nf+ZhXv6yaxmvr2K3IgaDisJbTDeh2e1hKkk2HFAUFVo3EBjaScLayTMI41manUMZxxhLN2mwd5Ax\n7StG4z7no5DCW6wSZGGAb3SIlCSvckohUHGIMWUNGnc1FMY4KCuHE3XzWDOSQZQG5+tdDyHqLCAB\naC9QUtbvU3U8hU4ChmVKQ3eQVpIbS25hOk6IpaQRRrhpT9KOUUagqoQgbJFKSdwJ2bUwzXh1HTs0\nBER0p2ZozjaZXprhE5/8a7qppiUlI1YgVrz01ldx2+23Ys2A3/yF3yCM22xlFecH63Wooof5g/tZ\nWFxk95558pWKD/zp7724O3fvQcu4ptcDRgeIgDp9kAau9AQKkiQiiiCIA6Zm9xA2W+zZdwnSC3Yt\nLrJrcYY9+/ai44RINP+bv/7h/Qsc3r/ATbe8hL/863t48N4H2F5fJekE2MoQtCLmZlosXbHEvV/8\nPKYoiXWMqgTDUYbLPKPC0NAN0nwEQICmyEtsaggiSSgSpNM04xmOHj3Nde1dSJlz1bWHydKEhx55\nGJ0PMeMMPxxQlGm9KBUERCIh8i2yrYLbr9uFddAIgDY4Be2eZz02iKomMikhcE5gJp15beuqvb3O\nvXBRC4TwmPqEisdQR98YHJqsEow9HDl/Ft1NCDsLmLWc7/2+13LtDddx1TXXMiNjHDVkRVKnR6j/\n2own+LtvCgWDbEgnSbBUZLbkwnAZWxic0Izzgu5sj72HD3LsuaNUvqKUChdC7sY0lWQ8zrBC4WWF\nVpL++XPouEEv7lIUGTrwPPXsE0zN78KYklQLXOlABAgUiBgZhzhpuPKy61hf7dPfcvSmu0RRwMCe\npcxz9l5+Ka+/8grSfEwUwdaoT1FUjHeymsUpPcN8B+9CcOAmnmQhPEYJhHd46+tdRMFEzwW0RgYe\nAoFXNWBdeY+WHi1LrBOEPiECAl9bhr3wlECmLUEMtiw4+dxpFvdUDHYyPvqxvwFZa7FFOsBay3js\nmJnu0m63WJpu04kFVTFE6pL9B/byp7//FwjqmImpqSnW+xv0Zpsgas+9Eo5EeZIgQAYBi/MzDDc3\n0WGADj3tCFrtgHQ0pBE3cYkAIdnMclRD0+gGLExNsbGjsFZjioCdtYyoOc3Pvu9nmG7BvXd/ivMn\nTrC0a5E9iwssf/4BdoqQLJphU2fk0hK0QmxjGuMSmi2F79duLyvAKoHQEiUlSgVoJ5CyrGHpwlHJ\nEinqLsbjJzzaemlJIWpojJBYZxBasLM1YLS8QxgFBFMxM50lwqykLTRCGrywjGyOyBzGW6RUxL4J\nMuC1r3k1P/Su70YD733vr7A2WMXYDDleYzuDE2eeI0kihPBErQaN1l5EELKxnfPZe77E6177crYU\nJKFgmFpEoImUREpBoOsAu42VMWIov2Vde1EUd4BQaVAgZD0HCaRCeInWCTLSRGHI9FRIu9OgEQY0\n2lPMzS9yYM9eep0eV111DUL+9/04U6HmXT/wBm68/kaWz57iLz7+F2yurbA024PAU5kh3/GaV9Bu\ndblwdplHv/gQeFMDteMYaTVBEGCFqYeWEjQGjMdHCQbN9nDEsQtHuO9PHuGyy6/i8/d+nJ2dbUZV\nymLUZFe3y+X7Z4hMyC0vv523v/vH6tZDAxU89tVnGI1GfP2xJ/nYxz/N2c0NVlZOEM+3CMu8BvZO\nOhLn3N/784XiDnWXclGPl1B7JRQqVOTWkvqAwhq+fuw0L3/lXn7z/3gft73uTpw0JDU3qz7mMrmQ\n/k5hf6FNL5hgloFah8ptymAwYG5qin5+Dqxnc2dAVQ1Ihzk2N4Sxphs00auazXEfmxXkxgIOU6ao\nTY8OagtfZsaEkaa/vk1lDDrSiEhRlSUuy7Db2xR5hSlLtJb16nlVQiNiNN5BhJKtnZVa0mgFXH/9\n1SzsXmBmtgV4inHK6vNniZRga3OdrKq3gLWoeaZ5kdWOJO/AKqg8EonXAiFqC6r0vr4ehK5/B4h6\ngUZ6hDRIHYEQdUa7KYmTEDXZUVDe46scJbsEUYPBuKSiw7AcEYcJP/xD380ol3zu/i+zcWELqRQO\niTE1nzWKJNYoxlXGeNRnfr6DwNJsKbY3h+xanCbUMYPBgHQ4Yv++BRqtJuubOwgn8cYyNd1httdF\nBZq17QE+H5LmOY888giXHTpIo9HCEzIc5ti0QugIHScEUZs4ahEGkl6vTV7A9jhHxwlbaUFv1z5c\neYbeYpuTpyrWR9v07BTjWFO6gFw4BhQQh5RKYFwd1zy/Zy+Ogqw/xLk6W15aWztOJjn+Soi6iGtd\nM4iVxAlz8f1KyIuSZTihOCkt8N6yd/8Se3YvoYWkVE2oAnzpWFrYw2Nff5R+f5tsOGL59Fnmdy9i\nrKPUMRvZgOPHnuE9P/0Ilxzcz+b2FirWSGcZDHYocaRVhvUlkYoZlmOmwjaF9WyvrhMHMafPrZKK\ninY3ZrrV5Ir5K9ja3qDf38Z5g61yDB6Tf2u3zItClmkkiT+0/zBSC6K2Yu/SDO1mm0i0STqzjMdj\n5mbbXHH1JWitmZ5dZN/SLqZ7Peb3Xvo//PsxxrG2tc1zp07RH+wQxJ7ls2coRinaSdKsIIwDSl8R\n6gDjLIU1BF5BWdJqSqampjFliZcGLx33Pn6Ce+9/kATLd731Nbz8zm9jaeowR778FM8ePcrjR57k\nyFOnwBik3WJ7fZ0oUFiZUNkYoUKUzAn0pFijMa4eKJVVQai5qC9a7xETyaVeZKqHRFC7ZgCkl0hX\nLwyJwNdJgWiEikjzMTfd+lLe8pZ384s/+z72zM3Rifv80q/8NFfdfBP9nTFFlpPoCFMYsmGBmRSv\nQAqanYCiLFlc2MvmxohWO2Grf5qs8Dgr8E7R7iRUxQjnUtZWVlm9cIEDe3bR6U3THwwRYYzXgrDZ\n5cF7H+HP/+ivSLMK6wqMzfBOIzyEypF5sAioHNffdCnNXoOKkJNPnsKkJfPTXdq9Kbz3dKeaxM2A\ndq9DJASdZou4nZDMhgTW0Ay65BVs7oxYP30SW5XsDHPGQ4PDU+IY74xRotZ48zLDiZrq42uOEqbK\n68LvHTIwNQ7RQxRLrFA4BcaUBEISa1X/DrQiiBO8dUShJpH1IppWEXFD0Gi12BykFHkd7bx3tkWa\nrjHVaZL2K3ygELrBx+56uB4aSk0pckSo2b/rANkoY5hmKFEwNz9LEMVsra0RJ5q1jWVGA0u7GTHY\nLlhamsFiSdO8hkEjaEYB3k/A0EIjRRMrJD60KAXOa5SqP2a600arEHRA6Fo0kpCobYjjkPG4YLxj\nyWIImm1uesmtzPdSLpw9xokjKwxTS1YYFjo9Yq8YjUY8v36OQMtJ5n8D7yVxJLGuoHD+4hzJ2dpP\n/sICUqCTSZCgwlORFjlC1vhKKepdECklQgcTlYA6rmKcoYVE65D9l1/JoWuv4OqrDqCFJhtVDEcp\n6WhMQ2pcNaA/HHD3PV9AxS12hkN0qGgGCXEcE0+1uOaa6+g1u3zmo5/FCEtlMwb9NdbOrLO5sU0y\n18BrqIi47WW3c+O1V9KYtjzw0P1kqUXqiCwt6q15PGEAwpe4KuKuT939X5VlXhTFPY4Tf3DPJcSR\npt0J2bM4Q7czhxRN9h44RLfXpNFU7D+4iFYhjbDLjTfeCMm3tgL99z4csDEasrZ5gUAEjPsDnn7i\ncYSsUXMDU+GlRQLp5hhZKVQFMTlxw7J46CCGmgn6E7/4K8RRkyobcvbc8/UnF+piN7S6vsbc3B4E\nElNmVGWO9xYhAvAKkAhl8dRDIOk0HokX4HyJFAZvPU5M1uNdffEKX5+EXtDdLXWx114grcIJh9UW\nRd3BCJUQByH9fp+/vfvzfO6LD/OBD/wJr7zzJbz6H11P1A7rQZUNETbEWofNHUEQoKVASV8HhSmJ\n15qdnYowUAS6oL+1g/cSW9YvuJWVFX73d3+Hubk5cCUrK5t8x7ffyBve+I/ozHQZpzmd7ixf++pT\n/NkH/4p05PECPAXO1NetEyWVnSRk6pBbbrqMpBXSWVjiSw9+FXLLfLeFCEJUoEkakmYjZn5+Fi00\n83NzzMxMYX3F0aef4dizz4MO6Y/GxEENga4lFjUByriLJ6EXziMAyHpugPC15j55vhG1dKCErIPq\nhLgYPyC8JBR19470BFEdGyEniEExWTyTVT1Ey4HKBRjnaMYC6XOqosBUCq8UhYVR7slzgy3razOK\nW4RRTbvyRhDquHaFSImrSmTgsCpD6YSycGgZIXyFnxRFJeqTRaQFWOoFuaCBNTVJyylLECogwnto\nhCFJEqGjsIbpDPJ6MziodW/vBN56bKxAS3YtLHLTDYf42mOPsjMuqUqJViG9doAGlpeXOb+8ipwY\n1eOoVcOiba2be2nq59rWF3u73cRaS1YWSBK8F4RhSBxKlBKkWT1TkRIq50EoSmtBCJIgQEuFSD1M\nyFo+8UilOHf2NHleksRd9u/fj/cW5Q1h4C8OcIdFOQGTC7QArQTt1gxV7jn7/DJ7F/eQVzlZkbF8\n4Qz5cFzvOkhBJTxON1CB5trrruCaWw5QmYyjTx2j2WgxHqaMNkcESmGMwVYlUjV44L77X9yaOwik\nDgmDiHbcYLq3QJy0mJvZQ6PZpNlu0e0kBDrBWsfyuQvceMc/bGGHWkyYb7WZbrWx3tKPt1l89TSn\nzz7Pzjgl39ik2W7U3XAmoKgn7PlwxDjb4rETxymNZ7DdZ74zy/r6OkWR0Y7bgENIT2kKMAXddocy\nH9UDQqHwDpxQKCzCufoiEA6Eo8Z+e/Av/PpqDzuTI6aYDJGYRCB75y5KJl7W77e2Li6eF4okgEfZ\nCus97ThiY2WZV778Fv7jn36AT9/9BZYOtpmZD1A6oig1SvawtsbwhUKCKwm0IAxqeaF0njyVVJUF\nP8Z4S1mWSBOSjnLOn1tlbnYP3lnanTazxnDk6GnOnv8Ab/4n383a2gbnzq+TZhVS+3roN3E0CA8W\nS+5NHcHsJcLA0aeO8tI7XspT33iWAI0ONVmWIT2YsqCsPOtraxw/fpJGlGCreugQKEmoQoocKptT\nYSkLO5GyQPqy7sTx2BecSFLiJxHSL9xE8TV4mcmJybp6YOeoXUzSS6yweG+RXmEDiXB1Z2+rCoHC\nCzHR6D1QEXhVz0gCiVAKrKLCo1SCTBKCWDHMC0rh2DFjTFGhEHSiFkmjQ+XG5GVGpzXFVHcX/cEO\njUYDaQy9qSZR05PljihqsrW5jVKK0lT1D0OGpCJQgFGUxkEY4azEuZqIJbVChwnCeZIwQkUe600N\n+dAW4y2ukngnJx22wXtF3IgYbK3y6btO0GwlOCHpTbcJgoAkEYwG25xbPY0UCc5Nhv+2wlPWn0s4\npK5TYI1zCCBNU6ytcEKSV2PCMKYsHa/+tlextrbCyRMnKIoMZy0SSeksZTkJabEl2gvIZF3cVYCr\nKpRSdNrTtFsCM8mDr80IjlC5yT6JB13jQakERnmE9FQ5aB/TjhuMRgOMLciKlKIoJsN3UAT1vLEq\nqZzj6WeOcun1B1nadZC//eS95KMhSRTTCJpsDccMxyPKwtBq9b5l/XpRdO4zM/P+h9/5Y8xNz7Br\nYYlXvfI2VjfWSBoBV1x97X/Jsv4P//Cws7XFZn+d0WiItVDkFVEzwGlD4Uq2dsZYC8oH7KztsH1+\nmVgLTqwcx1tYG+aM05ITR45RjUbkeUo64a7WL34LMgGvcZUH0jrS10ucq7cXrbS17unUZNvOIqRD\nO4cXErzCoesjpp8U/wmEum4l67peZ5/XgyfvPdJ5tIzq04Fykw09CLwgDAK8qdBRiCkM/+a3fpd/\n+lM/y+75ab7/bXfU/NLWDKeX+8hAYXxKrxcRhwHTvS6veuVriIOQssjoX6gIVItnTh1lK1+nn25x\n7KmTTIWzfOHuLzDVadGZamKd45qrb+JvP3sv49GIgwd7jIYpTtadigw8+UhTlRbvDK1ml2E2Yqfo\nE5OAEWgR0uxEDMdDsqJgsbtApxVjVUFzZgEVRZT5+OK2onQWyaQDreohaGVdPVzWBmOqyZGfi7MJ\nqLcQgVofd0wcF3X3LWWdYfJCT++9rxF43k9QfDWsuv4/ijAIaulMukmQlUSpcEKnqoevQlmM8JRS\nIWSIDgPioA6ukwgazRYuEFTeI1SXbtChE7doxRqpQEWOwtay0riKajnPZsjRAEoDRf29FlXJOCvI\nK09pDKVNEVQ1jL3w2FLh8FSBgYmuXVmPUBJvHcaWBFKSxCHG1dHNVSkJVEiAJpDq4twnCmsYtm5G\n6GaIcYYsHeCqkjxLwUGZ5RRFhXQNcAbvLVoppKrQQd11OwJ8pSmKCmc8WZECjsJUBGGIUopGEhEq\nQ6fTQU1uMLZydVGXAked8ulFjfv0dgLZkBKLReqYwhm0rm8oL9zJJQGhC5CT05r3CikUOgxqZoR3\n9aBWTULTlGM43GGcDUnLCu/rrdRQeaQX5MZihQbVoDu7h13zcwSUnDn2NcIwxPsa51jaEmvrTPhj\nx469uGWZm2++2T/22P8nR/t/ysPmBpygKAoql6I0FKagKh1pnlFIQ1aNGaY1SMFkJQ3V4kufeoiy\nv8mjjzzI2e1lhllO1JrGIXG+ROt6rTvNx1hTT7mFdHgnLx77YQACvHkhk05gpUJOZBnrfZ3iOBm0\n+Uk77mXNZhVO4JFY8U33Cs7X/t5JVsYLxf3/pe7NYmXbrvO8b8xmrarazTnn3nM79uooiZLpiJFk\nIZIcyoYcP8hwGj8kL0lgwdJDAr/kKUEABzCcOICblwRGlER2Whu2AzdRAhuOIskRZZmUKVMWKbG9\nl+Ltz7mn2buatdacc4w8jFm1D0XqUo6igFnAxd2ndu2qVbXmGnOMf/zj/+PRcC3kOwAAIABJREFU\ncpAGUrDoAkpZR8Tc67HozFO3bvPw6ppZjbu3Lrl1e+DDf+DDjOs1P/+xX2RzMXJ5e8PDhw95+eXX\nEDK375xDm1nHwK18h3F1zrZVxstEC0qoK175/Ou8+eprfMd3fAPTMpNS4t4rjzkcIldXW55+ZgV4\n4AgBJAbG1VOYBqpWnn32We48d5t0Ftne27JiZEgrSJEmgSGOlOsJrTMl7ym4Nsfu0RWmSikz0dNu\nSinM80yMmdoaTQvKjZSD6U2vIqZwknOgZ+6iRnBrzS7YJicc+MjIOAaU46Edoc8pIaJIaITkvOUg\nA2N2vBhVhNmz+TwS4kAcEpIqMTgFabPZsN44RHZx9iyrYWRMmfFsZJ0jq+g6+Nd1JozvZN7PRAoy\nPQRcmOrx1QQE5vnAYdq5mfiysJQdaotTV9UHwJCJtvRJaPWKpdYFrBGlm5gn17+XYUWURDR6gPLN\ncT24ONdBqlMmrXK4viKHjDWlFaVMM4fDAVpyaKcfMTRiUrfJM8FKYJoqWo3SFq+Y+nfvkJhw63JN\nSoHVauP+t0vze0Pds7eYongPC+nm7WKYFkIcusmLQLeBlO4RHNU3WNduGjzoJ4dLzRpBGqv1wOHg\n3sXbrftDHEolZJdbPh8HogYOy0yzSLWBcf0s73jhOXIoHO6/jFkjr89ZWqWqQ7NpiPzKP/3E1zss\n8/VzxFFAHSM1S4QAWSpNIa0GrnZbaq2MaWCQgXi+4dc+9kk+8nf+PvP9V5mnHUMynl6tud4uLK0S\nM9R19OzAXBLXmmPlKTgtz1R9LPrIB5WEJ4PxlGlCVyvt2aR0zN0zPFATTMzhfPPXCSKkGEnJ2QAm\nnAY3mvprOTLRg5dpT0yMnBNvPXpAHkeSGo+vtlxcPMuvfOLXmOfC+myk7QuvX72G2sDl5lnGcc3t\npy64OFtxloRBA7XC/PAR24dbdsvE7mHhlRdfc6Pg1YphNbJer/mW934rP/tz/4SLizNEYBwzBBjX\nK2IUGsJqdcY4rDk/Pz8F0btPXZIZGOLazZhNmfdKjCMS4MHyJqEdaK0SIiCC1UArC9ba6bO3VlBr\nEEBr5Yhl3QyEPTHtGwImgqqh4gEkBLnB2qHLLLsTmBknL1K/hAHDewUhOnupmXWKaiM289cDBF8L\nVvp1FfWAqurNQnN5DBGhTT0DpSEtQmtOt4yw2ax4tFtAfQN64V3vZFkmhxjVWSZKIw7eNJ3ngoQL\nilYeP75iWaLrvFuG2L+XII59iyImSFUOuz0huXOZEKhSSH2giCDugjYrSyvUCDXAw8ePGYKrg07T\nQo4DtMA4nNPq4kwkegWrXZcH95Gd50Ip1SVGcnL4KwR3K+oQ3m47sT5bQfIkqDXIZFqbfBgO7dcj\nEFKgUbvOTAOdiSFhfTMAxz0FqFr7tfZBKFeL7PeiGSnDNLm14zTNNIUQBkSM2Cu1cbVhtAHTHYt5\nNanLzLI/kNeBy/NbXF9fU6ZGxfsDWGCeytvGsq+P4G6AzhASfkO9PX/zqx3T3PjcZz7ndlwiHHZX\nnF9s+MB3fucpI/rtnMihPqTR2M4Lb95/QBRhPkyYClUb28OWsl+IDZbHjYdXV7z8mfv83vd9K3J5\ni/uvfYkXHz+m7gLj5TltTExaub88ZGkCMSM1ojQkBCw4Jx2JhLhBAswHJceREASjp/Eo6RQgfOGi\ndiIjOt/Xy0yCoFVJMZBTZn22OWWQdVoIyaf1milFjVYc8xMVmijaFlIEnStVBG0V6kDTysuvfok3\n77/hg06SyXmkVW/khlgJSXlxjgRJ5LESY0J7/HTza+OwU5YlsMzKRz/2KTabDQ8fPeD81gZQt78b\nn6Zo484zTxGHwO07Z6zPR9bDGTYlxALrzUgYA2t8A2tV2e1fIedMPUzMB5+Uenh9j4ZirTFfL0iF\n2hYP7tUzUO2BeNbeyI6BI11UBOc/i/imejRiJyASyDEQsK+gmx5ZG4Jg/Vr5lfReR7COQasRREkp\nohIQfAhHzEhBPCPEG5JREqKCLAs593Uw+20TYsCisZSJSKJOIzUmxrWQkm/qqzywiJDyiqsyo81Q\nC9y6fYYQefe7P8gXPvMS8zxzdlaZ68y8VOJ4B8XQ2kj1gPXscXc4QBRCGinTgcPVnsOhoVVRKQyT\n3sBgY+wb282UtIoPp2WJNAUhMubbaG1IWHnVwIIFv4etBYzB174Z8+wSCyklhs3Ac889w26349Gj\nR9y9+xRvvnGfmBJlNiqNIVbEIpHklbEFJA1o6ZRdMapVl50G0nBG04JJAhKY0JpThxWl2dLXSCKJ\neK+hNIIMhJCxapTWAEHigNZGWRopGhFBTagaSAYQEXUp8ShXbB/N0NZc5sE37qVB7MmAqicib3N8\nXQT3YwbgOGX+Wk//qsdqjKxWI7Iaaa3xez/0VSzGfusz4LBsmSaXH5gOC1NpTLuFUgpl8qnPhrGf\n9giKTQ2b3VHpHd/+Xl7ULf/0pz/BxbOX3Hn+GeZt4f3f9A5KNq608uBXPoodFDtUtBYv77RQKRiB\nlDKlzZyvxJ1bolFr8xLvFAASZk4JM6neTBKjVc8Club86YgPbqQuDmbWGIaB7dY9LkPIxBiotYE6\nPBS8SsUYMR08EMlCDIaaEWNlvVoBK1QrMQRCXtMsYDEgtvgGUgubYUXr2HKzASNCVKcvJqFwRRiE\nnJPr4h8K52d3GIOgsfn8eAyMQ2Y9Ru48dYthyKzOR0QgrjOhKENSCI2yKEsxprKwv76iYcyLocUz\ntMUarc6Oqzf1z2yQgtPStckNAyb0rFrt1MQzgygdShPvXbido+sIhiCdhnqk5fU5+O7dq30AKQSH\nCyKulxREiEEwaV8pEYGd3jup83KMytSlJc5XiWgKy0S0EUVoMUDas2ghlkCgUOOaISkpg0kmxhXB\nIrokqgpBMhljZs/6fGR32FJDpgUhZ8XmiMgMc0XCwLRcMy3XDCmylMJ+tyApk6JSZkNr8GRIcL59\n801SaYjefM7WC5wogUzCVBAMC8o4CE8//w72+z3b3RVVqlearWGhi36pB+NlaagKrRmr1cDlpTcZ\n9/s9d5+6w/u/+Zv4+Mc/Tqm9Z2Ke7YuJzx3EAW0zsW/kIXRmWhi8eS3uW+yJZ6Bp4LBfCGKkLEQS\n2+325ID23DNPU+rCrYs1tXozvtZCa82VWwVSNE/sVAghuzRxVI5DhoHIOidy9EHDQ9sy68JinrFH\n8+/J7O3dOr4ugruEIxYBHuB/c+buI+o3xLOvntl/87e875/rfUs98OjRI1qrvPXwPuv1muuHW5al\nUptwmCYPwEthKY2lVeayEAPYotTDQhPj6nDN5j3P8K/++B9nkwY+9ov/hDe+eJ93fcO7OXvmkr/3\nf32EnM6YtNFUaO1AGnvJL5kQB8yE1eAlZ864NWBeUVslpERMgTK57EEpDh8MQ8JqI6bIVMspO5em\nJAmnfx8bgKqKdAux7WHfzQsiZg4LhSC+4FIiQMf0XXxMomdOw5DczBZnhYgEsOTOZdGIIXGxvo0F\nYaKy3twlxgStOeXMAi+80BhyYJr2rqcdXdHvzq1LLBfGVeZMNqw356zWwvnZmhgMyV49iRWCCGoL\nNJhmv3nmZWG3u/LKrRq1GK25Y06rFa0dL9UezJtRq68pEXFcWwXVo3RD6NhrOD2ntUbM4ZSlI+JB\nrG8OQIdgjhjMTSX6m9tbZh6YzBqGEpv05ysaKoJniUogmFKlUXsgbNmlDKJB0w4zRaHKNTkIuhir\nAIhyMGUYIyQlj1vXm7CE1gETQSIQjN3hit3+wFQUtcCA0zTbMiMVhpV7ug6bM+q8sMlr4sWGpfl3\neXY2cpA9j8OOqSwYLkwn4nS/PieHASHH7pHatV36Y0RDbeG55+/y+mv3qG3h0Cam+XDqewgR1dKp\nX9Lxb2FZKlePtzQ1QkjcunWL6+1jhjGxnwxrhdBWJJFOh4wgjp8PaUAk9ntCfC3HTOn9sWZKQzgf\nNzx7d2QcR9abkTuXF0QJJ4jl4aO30Hrghefeyb179/i1T32a1jqLra+p4JiW/189ySA1LBgxBawA\nWrDW3KUqNorOVAskEiEKEuXEzf+tjq+L4F5r4bXXXkUiDClSFmGeF3KI1ObTf0qituOgzUBMkMX5\nvqoVCca4PuPi4jmWeuDx4x3r1RlvvvkmMPOO59/Jxz/+ceapECwwz7NPByZhWvZd2N+omjGSY1rL\nDlVl2lU311Unp6kdcTahmjEfCmerkUeHmetYOXvn83zwW76ZMRWmZeJdzz/HFz73BrcvL5nOJqbl\nQFuMUCOxNwJbhfXwAkomjAPPPrPmcNhi7SFTu0Jth473XScjZarAUgo5DRSttN5kzRJIJuRxRHoT\n9c6t25RWWa1WnhUGIbTCaliTQmQ+BGYpnj01hxSUgkVhIGMaGVYXnF2OrM8qEgDL5OyTfiJCTgNE\nIWXI6RwkozFzHp4iS2A9NEiZmJOzWlaesVzPM0X8d3m9gXAg2MxTsqY1f461Sp1n7BDQ1pDF+xi7\ntrCbCw8fXVGrOiPBCq1V9rZwNCtpFQTXK9fiQbhVzwTjqeHpqKtqTySMJ4KR9lkBToYngDfYehPO\nA7dv2EeYTFvvjcgTvHcz9IkkRdVf4zhZadZbrRqciy5GI9PMaFVRq5gJ02wU/HwGBWvNGRpVYUgO\ny5Yr17YZM2GMpE1kWL1GjpFVSIieQUzUUCmhOutFFs7PRubdBHNlAC6fumQ/HTwp2QQOV2tadXPz\n0q4ZpBEHQ2thHY2EQQ3UFtBYUFtQE1KvmIIFFvFmeUqpb6y9t9A34I/8ws8xhLVTZ6OdKKsp+Li/\naTtRejW4A1jMwqPrLSlmzlaXfOIT/wwv05TVaqDMlTwboRZUYFbtrJaG6XK6p83M7fZEvNokEwNA\n45q3ECohZrDk1W/w6m1p1YtOU1L8ZP97T0haa91Zz6+1QzyGhUYkEGNiHB368cpk6swcpQCSM6sQ\nSUFIORCCV2Nvd3xdBPeyLDx4eOV0g+MuLokyKWXyCU9JBWSgSaPpTAyVIcK0EiT0cnq38MVX3yAE\nLweX5RUO2wOH64lXXnyTeZ6obc8yT0jswXmCufQsIES0KbUeqEWpwcWIdnVC3e69a1LgbvXVaBOU\nBR8GqgXNC+k8ey0ehVp2vPuZFT/643+UWRKtJf63n/p5PveZ15nLBWfnt5mZuXxqhdZzsp2h04pa\nEr/n238fn/z1f0DQmcfbN4AHzoKJAclr3vOud7EsC7vrK98wmgcWDXSiO1ht7HY70pAZhoHLYU3V\nxlNPPcX1do81Zdo77xpxXY7es8WsIEGIObA+z8QhenkqRoiJOPjmMc8zKWdi9IZtCwPr1ZmPhGsl\nEMgJJM+E3NgMK+ridmiXt1bUbISUqTPIsCbLBpkKq3Vkv59pS0OmwK7fJLIs5NSY2sKuVKZlRmtD\nFaIFqnn57LKvPnEs0mjNaHrDXVdTOvKCPYFfnuIw4Ql4pEM0pxhtPhtwwo9vGqnBBLl5Ir37fWJQ\nnA6zU71q2hk2KihKh5gJIszRoSLpqL0YaGs+iRwCGn34SYPTK+vcyNGgFmqozCtncXDYsyIyDAN1\nWBFKIyzKMEBOBRBqC4gZozQaE63Co4dbHxRLlWVZaBYdEgyB8/GM6+1DNmeZMjc0BXblMZVIk4Eh\nJKTFnjCIb0JqEANiwtKxbQOsOqc850ytyq7s+s/dDVX6NK8lgo2YQIy1z34U8rgmdQ2WMQ1IOOf6\n+jHgFVzT4NdbXFDsxHoSOwVecMKDGqgYhBnRyhACQkWtEkwpMnsCY07VVcyhqqWRiCzeQ/Xqum9e\nxAKmmLXuiCZEMsscCeL9m9YqhOBa8RhSCiFlhAwhYdE3HlVYlv8fNFTV4MGDw4lGpu3g/OM5M08e\n84fRqGFBJRBTwrQyocyznKhyu/kxeTUyz4XlMFHmhXk/UycjoNQ6YxTWq8z9+/cACCER0oia43Yx\nDNSqlKVx3XZINJoE2lJJaWAl0ZugFmnaMAtsNhvG0JAxwagsh2v00HjppZc4WwlvfOlF/ss/+5/y\nk3/1r7PKl3z3h76H55/Z8X3f/Uf4xm9+P888d87v/4HVzcVYcBXFBIw/yuuvwRdf+SwXF9d87rOf\nZbvb8ZP/3V/jrXuPiBJo897Pp2eVVRWvP4RYlEM7IMvMxdk5QRPf973fwyc+/2nuz6VjxqFzkB0L\nVlUIsFqtGPNAjNldrwAjevCT4DxjgzxsyOOKI5h9++I2z9x9ju12S9kWpHlWmFaRNLjhsCQhkxgv\nV9ReGk/LgpAYYiJnzyJLPVDmhsxwaNXPbSlMh5nFClttzFox5yt2GMPhLq2V1gOD66l7du+YtkNK\ntbNfYu/6iQV/vmknRXTlwD4ZZuaNz2rqVQRHhoycoBjHdm8YNsfjFMg5wgtH2IcTD94zd58cPto1\nFymut2ROszKMVnCZbKB17qzg0Ftt6hOu4jh9IUALRDHKZNQi1CLkuCFZRGa3nPPGr1Kb008JRowB\nCSOtNNR8YCeNmWSZy/ML3vfe5/jMZz8FNjElh/D+4B/4QT7/G6/y8mtveXIGiCnNhOZNHocoejA1\nMxo+Ud2aUyWHYSCl7GbXpgwp+kbb/05OypogWRjXK556+jZD9KlUmpIHY73JzPPE/bdcDru4shuA\nK0hyc42tzx80DTQLzp/RAmospTIOqRuJe6Negk+fCW6SU1tjiIlg2bHzEGhqxOx8dhXXGFLAWkSt\nEcxoi7m6ZewJhy1Uq30Wovo0eUhEcb0qLykFz+J+6+Prguf+Dd/wzfYf/6k/zzi6o3wrW+f/6kjS\nlWOMQWG9ZxgSosk5vtEDjAVnn5S2oFopy8y9V17h8uyc3/jSS/zn/9mfRYDLi0uW2WlrKQSaKdUq\nm82KH/kjf5jv/OAHGPMZ86KUIugghEEIsZBTIBrE1shjohZlbkKSkdaMaTczDJEiE/v2Fm23Qg8H\nclSGMbF57t289uYjlu2Bn/0HP0ObE8tuzdWjx4xDpTR3ttcG83JgtRqYponNrTMszDTdYzYzxIFp\naUj0aU2sEGNDQmKqblNmOTI2OI8D58OKugqEnLi1vuA7n34Pf+kn/iv+2J/4d/jYJ3+V9dkGrQWp\nhbPVmvHiDKKPaoeMZwsGsbnJdRxyxwaNmB1DFFacjQmhMI6GLBvG4RahOzjFJKzOxhse+OCmw2CM\nISNsKBOYdhrffiItMxonrnZXTPsKS2LfDs5UOTRagdkaOyaWtneKaGmIRkpTl31ohlajHjF0tc6+\n6OxHSz1oK9gCagQZnW2kisQjs0MAZ8tICoTIDQe+Z9sxDn4OKEcZWeDLAv5JA+WJx0IvE7wi858k\nxD730MXnwpH/br0h6c1qLCFd8CoERVLENFHFSBK4EG9ktug+oqBcpqf9/HIlrkd3ipKIjA2lEsZM\nCCtSyAwCSUCrsdsXtAUsGGV3zXm+IMcBawckVHJQtE6UunC1zDRJmAxMh4MnEBZ4/f4D7j18xP4w\nU5fCmDPDeqBq8b7QrGjX1BnXA8OwQVJEy9KvgV8nbwgHSm2UqqRV7JuRV2XPPvM0t84vePONV7l3\n7x7vfve7efhoR+mSBYinKWjXWELIAmqeGKlE58+boXXqWXafRQnJIaBewWt1IkGtSh58w9aSyckF\n4qpVl+Iww5JPUmsLlEmcsCCQdUDiQMqNOAiqC3NnZzk12ZPeJLDeDIyj+76GJPzCz/8OeO4i8pPA\njwBvmtl39sf+E+BPAPf60/4jM/vf++/+Q+BHgQb8STP7+1/rPYKCVjighN3EeH7Gfqkk84GFEMFC\nYDy/w7wcOBsGZx5gzLXQyoKI0XSH1cbF6oyf+vmP8X/+9M+ShsitO7cJQdnPC2E4I8bKmBL7ZSbn\nESPwN//G3+b1N17hh//wD9MiSBjJOTFmI8pMnQvPPPs8tSYOc2E/XaNL4Gr3iIePtrx+/y2eevqM\nMFam+RHLVjnLI2hltTlnevPXaQZf+PxLHA47tts95aBIgl1tIJWw8abLSiOqMzkHluZB3axhLdFM\nO1fdSzLBtW1a11eJIToeiTDrQrDAxeqSWitX28d85N6n+J5/5Q95OTwG8mZg/3AiizjVLkZMIhXY\njGPHAIz1ZsXUGiUKMQbCACn1AIMv+CCRGJTx4hK1QBgCOfhGKm1xQkA8YsnROeK1om3nOvJt9gGi\nWpnmiaYzh0P1SmyeOE2CamO7LF62hka2Y0MzU4tn09YWlxvpWbs349Rv0B4ogkinvHmj2TnYCkeK\nYntiyjTQhbwi0zSdHgNvxIoehdqO2bcf1rNyU2c0mdoJghHx6y/izA01OclG+O0Dpj4o5Lx6OOb/\nEn0IyCVVDLPY6U4+0m4YZWjONdSA1EAImd1hZhgGBhJaKs2UJRhJO3QyV9Yrxep0dBelmaJRaB0f\njzZQl4otiqJobayZoWevNSQseKU3pIguBdXGkDK3zy6wahwW56Uvy0JTdRo7CSQRU+b8/JxhPZCG\nSNUBVe9HaEkEiyzbPe3QTQKadOpkIA7+/c/LxG67sN7c4uGjHdaUoN6UDifuRm9eA9XzdlQbGprP\nopihzcX4sIiIzybUOlFb18qmB30LsARUKyGoa/lYpwh3XB8KlZ56R/dwdbpzw+oBzQnUNYrO49Al\nu31DOTKFWjOmbiS02oxvG1d/O7DMXwH+C+C//02P/0Uz+3NPPiAiHwD+TeA7gHcA/4eIvN/s7QmZ\nqpUhwLjJoJUvvfEFgiRaDXzgW76Ry1tnPP/8XXQ98t53v4uXPv1ZXv7SSyz7Ldt5pmhjd3XN5fo2\nX/jsb/BX/vL/6NzZQcAW1jGSUiYxMBOICTTCOGTHtvbCanXORz/6K3zxxc9ytl7zL37Xd3Hn9l2E\ngRY3HKaJT/zyq5Tg+JmiTNNEkEhDeLw9cP9wjbaZVZoZJHLv6gE0pejLpHHF9f7A9e6K3X5LqQuE\niklFcaEtwVX1iO6paFZdDjYkBOe6xgRG6cMS/vzU6ZIPr66pxW+2FL3bUlpFl44z18bqIpFGYwiZ\nu+fv5f5bb5Ji49btSy4vz0hRSGlgHEdijIx5YEyZtM5sy4HHs7M1BgIXSYhDRsJAk+IZjTSWMDoN\nM1RCWHnTqSkxCEGFaTfRog9kCCvmQ0KrEeUxrTXKPHHY7Z3H3DPk1uxkndZaoza3AzQ7yhlbf05n\nn6hj0toccw9dyhW6eJUKSuVoFnIMyCLmFLOOj99wsj17mqdG6wybEJzt4PFYO1h/HFzq2fqT67z5\ne4nR4RyAoySz0/BMewO3Q0Hufujfj5lTY0MItKo0vFG79EBiAdabDFpQMaqO7rCkEbWKaCKkgpaJ\npUEeIKSIhkRpdJYIJK0khFYVCwHJidmgiFeII5GaF1QULd4orDpQ6kwhMllhng/ktOGHf+hHePzw\nHm/df4P587/KeGfD6g68+qWFskCtEZVIiIFEI+VASoLMSl0OVNEOgzjkV3qvwYXjeg8iOOdeQ4AY\nWWolKOSUPBhrYynOXdd6E4qeFNQ7TrNa69pNIp2VEzhKZEMgiWCUfv3SqXIzhSbWzUEi1qE9wamb\nrmWeTz7GYUgQmpMbJJE7u82sEVNmIMCYQHyKGQss6hOLJs5x303K2x1fM7ib2T8Ukfd9ref1448C\nf83MZuBFEfkc8L3AP3q7P9ps1vzQD/5L3Hn+LhcXwkf+8S9ydbVlt1349Gc+xTwfiDnw3Hvew+fu\n3mXeXVGXHRaMZdlRS6OWhV/4pY/yd//O32O93lDqDCyYFMwyy1IZhxXzsjCMI57fKVJn0uCaENM0\n8eC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/jzLdY4zwSx/9JNdzYS4F0UDKA8MQ2awCtuwJVWmGv1fwXlYSo07Fm9QSqCxuqzd7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\nheNq3K/jOq7jOt7C8f8BKawB3gSsrkQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5ntvJjOyqv7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split the dataset\n",
        "test_dataset = dataset.take(5000)\n",
        "train_dataset = dataset.skip(5000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPIUhSy8JeQ0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Setup the train / test iterators\n",
        "train_dataset = train_dataset.map(preprocess).map(augment).shuffle(1000).batch(32)\n",
        "test_dataset = test_dataset.map(preprocess).batch(32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-DWBS2XJrLa",
        "colab_type": "text"
      },
      "source": [
        "### Step 3: Define our convolutional architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzS4kBk3zMvd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras import layers, regularizers, initializers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8jS1VHhy0s90",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "images = layers.Input(shape=(196, 455, 3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTKcrQNr1FbK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def add_block(x, n_filters, strides=2):\n",
        "    # Convolutional block with custom initialization and regularization\n",
        "    x = layers.Conv2D(n_filters, 5, padding='same', strides=strides, \n",
        "                      kernel_regularizer=regularizers.l2(0.001),\n",
        "                      kernel_initializer=initializers.glorot_normal)(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.Activation('relu')(x)\n",
        "\n",
        "    return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6W6ZoRvj3gym",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = add_block(images, 32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "351wYvce31pf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_blocks = 3\n",
        "for i in range(1, n_blocks + 1):\n",
        "  x = add_block(x, 64 * i)\n",
        "  # TODO: experiment adding more blocks and residual connections\n",
        "  #x = add_block(x, 64 * i, strides=1) + x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8LrpK1O4UFN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Final part with regression block\n",
        "x = layers.Flatten()(x)\n",
        "x = layers.Dropout(0.4)(x)\n",
        "x = layers.Dense(50, activation='relu')(x)\n",
        "x = layers.Dropout(0.4)(x)\n",
        "x = layers.Dense(1, activation='tanh')(x) # The tanh ensures the outputs are properly normalized in [-1.0, 1.0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oH_eEn_H6O3k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = Model(inputs=[images], outputs=[x])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0uOqQfC06bVT",
        "colab_type": "code",
        "outputId": "8c61bd3d-0396-458d-b6d2-64d60b471c34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 780
        }
      },
      "source": [
        "net.summary()"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_5 (InputLayer)         [(None, 196, 455, 3)]     0         \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 98, 228, 32)       2432      \n",
            "_________________________________________________________________\n",
            "batch_normalization_22 (Batc (None, 98, 228, 32)       128       \n",
            "_________________________________________________________________\n",
            "activation_22 (Activation)   (None, 98, 228, 32)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_23 (Conv2D)           (None, 49, 114, 64)       51264     \n",
            "_________________________________________________________________\n",
            "batch_normalization_23 (Batc (None, 49, 114, 64)       256       \n",
            "_________________________________________________________________\n",
            "activation_23 (Activation)   (None, 49, 114, 64)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_24 (Conv2D)           (None, 25, 57, 128)       204928    \n",
            "_________________________________________________________________\n",
            "batch_normalization_24 (Batc (None, 25, 57, 128)       512       \n",
            "_________________________________________________________________\n",
            "activation_24 (Activation)   (None, 25, 57, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_25 (Conv2D)           (None, 13, 29, 192)       614592    \n",
            "_________________________________________________________________\n",
            "batch_normalization_25 (Batc (None, 13, 29, 192)       768       \n",
            "_________________________________________________________________\n",
            "activation_25 (Activation)   (None, 13, 29, 192)       0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 72384)             0         \n",
            "_________________________________________________________________\n",
            "dropout_10 (Dropout)         (None, 72384)             0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 50)                3619250   \n",
            "_________________________________________________________________\n",
            "dropout_11 (Dropout)         (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 4,494,181\n",
            "Trainable params: 4,493,349\n",
            "Non-trainable params: 832\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwvNmrEl8hGd",
        "colab_type": "code",
        "outputId": "b0e3dc57-bac8-4070-d701-a60d7354b980",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# When we enable training, the output changes every single time\n",
        "net(xb, training=True)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=30887, shape=(1, 1), dtype=float32, numpy=array([[0.95246494]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5O66VAbYKX_0",
        "colab_type": "text"
      },
      "source": [
        "### Step 4: Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCC_F5C97wme",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras import losses, optimizers, metrics"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGYsm8yT7vPe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Because this is a regression task, we select the mean-squared error as loss function\n",
        "net.compile(loss=losses.MeanSquaredError(), optimizer=optimizers.RMSprop(0.001))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J-Eeq8FT6kQn",
        "colab_type": "code",
        "outputId": "66ead34a-5c4f-45f2-f527-2bd2445c29f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "history = net.fit(train_dataset, epochs=2)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "1256/1256 [==============================] - 218s 173ms/step - loss: 0.5389\n",
            "Epoch 2/2\n",
            "1256/1256 [==============================] - 214s 171ms/step - loss: 0.0196\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4CXK9--UoVP",
        "colab_type": "code",
        "outputId": "3ba6e08c-9238-41fd-e34a-31a8e7c1153c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# Test the final error\n",
        "net.evaluate(test_dataset)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "157/157 [==============================] - 16s 100ms/step - loss: 0.0294\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.029351789060873306"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZYpP_r3zKwXr",
        "colab_type": "text"
      },
      "source": [
        "### Step 5: Make inference faster with tf.function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hdGOfo3gXAOh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@tf.function\n",
        "def predict(x):\n",
        "  return net(x, training=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhPtMl9mXVcj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for xb, _ in test_dataset.take(1):\n",
        "  _"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QkBKgUPnXGZt",
        "colab_type": "code",
        "outputId": "f25c220a-9171-43e3-dcae-c4c5815e8162",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# See the difference with and without tf.function\n",
        "%timeit -r 10 -n 5 predict(xb)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5 loops, best of 10: 34.2 ms per loop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1CmGZn4gK20y",
        "colab_type": "text"
      },
      "source": [
        "**TODO**\n",
        "+ Experiment with different architectures! Remove the residual connections, add more blocks...\n",
        "+ Play with different data augmentation strategies.\n",
        "+ Try to visualize the results on different images.\n",
        "+ Also test with different optimizers: in this example we have used RMSProp because it seems to work better, but you can try fine-tuning other optimizers.\n",
        "+ You can also try a simplified classification problem with {go left, go straight, go right}."
      ]
    }
  ]
}