ML_01_FendyHendriyanto.ipynb

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  "metadata": {
    "colab": {
      "name": "ML_01_FendyHendriyanto.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fendy07/FMNIST-DeepLearning/blob/master/ML_01_FendyHendriyanto.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Fashion MNIST Classification Using CNN and RNN-LSTM Algorithm**"
      ],
      "metadata": {
        "id": "RSUDBZgr7v7n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q visualkeras"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oLtVc4dQAFcl",
        "outputId": "fef30bbd-707a-491b-8ad5-8b5ea4ed705c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/993.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.6/993.0 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m573.4/993.0 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m983.0/993.0 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m993.0/993.0 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4IrIKw7CztT"
      },
      "source": [
        "# 1) **Exploring The Dataset**\n",
        "## 1.1) **Importing Libraries**\n",
        "\n",
        "Memasukan Library yang akan digunakan dalam proses Deep Learning mulai dari memasukan data sampai proses evaluasi model data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQzO4V5nWbGj"
      },
      "source": [
        "# Ignore warnings :\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "# Handle table-like data and matrices :\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "# Visualization\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "# Modelling Helpers :\n",
        "from sklearn.preprocessing import Normalizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "# Deep Learning Libraries\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.optimizers import Adam, RMSprop\n",
        "from tensorflow.keras.models import Sequential, load_model\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Dense, Dropout, Flatten"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAAqDQnHDEtN"
      },
      "source": [
        "## 1.2) **Mengekstraksi Dataset**\n",
        "\n",
        "a. Menyambungkan data dari Google Drive\n",
        "\n",
        "b. Memasukan data csv dengan Pandas\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOH5NFt_eSaO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9044202-5066-45a3-a020-bbd36533fcaa"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfYIV7X4f7ZQ"
      },
      "source": [
        "mnist_train = pd.read_csv('/content/drive/MyDrive/Proyek/Computer Vision/Fashion-MNIST/Dataset/fashion-mnist_train.csv')\n",
        "mnist_test = pd.read_csv('/content/drive/MyDrive/Proyek/Computer Vision/Fashion-MNIST/Dataset/fashion-mnist_test.csv')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9usKv-5E8Zh"
      },
      "source": [
        "## 1.3) Combine Dataset\n",
        "\n",
        "Kombinasi data yang awalnya terpisah dari data train dan data test sehingga yang awalnya berjumlah 60000 pada data train dan 10000 pada data test. Sehingga, keseluruhan data berjumlah 70000 dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGkr7lD0zNhT"
      },
      "source": [
        "combine = [mnist_train, mnist_test]\n",
        "fmnist = pd.concat(combine)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fmnist"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "GpdUByI2AirM",
        "outputId": "f9072ce4-fbc0-48d1-ea62-ea1c94b863cc"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>162</td>\n",
              "      <td>191</td>\n",
              "      <td>214</td>\n",
              "      <td>163</td>\n",
              "      <td>146</td>\n",
              "      <td>165</td>\n",
              "      <td>79</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>114</td>\n",
              "      <td>183</td>\n",
              "      <td>112</td>\n",
              "      <td>55</td>\n",
              "      <td>23</td>\n",
              "      <td>72</td>\n",
              "      <td>102</td>\n",
              "      <td>165</td>\n",
              "      <td>160</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>188</td>\n",
              "      <td>163</td>\n",
              "      <td>93</td>\n",
              "      <td>...</td>\n",
              "      <td>171</td>\n",
              "      <td>249</td>\n",
              "      <td>207</td>\n",
              "      <td>197</td>\n",
              "      <td>202</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>21</td>\n",
              "      <td>25</td>\n",
              "      <td>69</td>\n",
              "      <td>52</td>\n",
              "      <td>45</td>\n",
              "      <td>74</td>\n",
              "      <td>39</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>68</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>187</td>\n",
              "      <td>189</td>\n",
              "      <td>...</td>\n",
              "      <td>230</td>\n",
              "      <td>237</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>68</td>\n",
              "      <td>116</td>\n",
              "      <td>112</td>\n",
              "      <td>136</td>\n",
              "      <td>147</td>\n",
              "      <td>144</td>\n",
              "      <td>121</td>\n",
              "      <td>102</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>93</td>\n",
              "      <td>72</td>\n",
              "      <td>58</td>\n",
              "      <td>55</td>\n",
              "      <td>63</td>\n",
              "      <td>66</td>\n",
              "      <td>89</td>\n",
              "      <td>159</td>\n",
              "      <td>120</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>67</td>\n",
              "      <td>125</td>\n",
              "      <td>151</td>\n",
              "      <td>168</td>\n",
              "      <td>...</td>\n",
              "      <td>112</td>\n",
              "      <td>115</td>\n",
              "      <td>117</td>\n",
              "      <td>117</td>\n",
              "      <td>113</td>\n",
              "      <td>146</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>76</td>\n",
              "      <td>54</td>\n",
              "      <td>55</td>\n",
              "      <td>51</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>55</td>\n",
              "      <td>53</td>\n",
              "      <td>44</td>\n",
              "      <td>38</td>\n",
              "      <td>37</td>\n",
              "      <td>32</td>\n",
              "      <td>23</td>\n",
              "      <td>14</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>76</td>\n",
              "      <td>36</td>\n",
              "      <td>110</td>\n",
              "      <td>170</td>\n",
              "      <td>78</td>\n",
              "      <td>136</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>68</td>\n",
              "      <td>...</td>\n",
              "      <td>94</td>\n",
              "      <td>84</td>\n",
              "      <td>70</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>152</td>\n",
              "      <td>65</td>\n",
              "      <td>110</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>57</td>\n",
              "      <td>89</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>52</td>\n",
              "      <td>23</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>152</td>\n",
              "      <td>184</td>\n",
              "      <td>158</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>219</td>\n",
              "      <td>223</td>\n",
              "      <td>226</td>\n",
              "      <td>239</td>\n",
              "      <td>247</td>\n",
              "      <td>241</td>\n",
              "      <td>224</td>\n",
              "      <td>213</td>\n",
              "      <td>144</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>44</td>\n",
              "      <td>232</td>\n",
              "      <td>199</td>\n",
              "      <td>200</td>\n",
              "      <td>190</td>\n",
              "      <td>180</td>\n",
              "      <td>179</td>\n",
              "      <td>185</td>\n",
              "      <td>189</td>\n",
              "      <td>191</td>\n",
              "      <td>185</td>\n",
              "      <td>179</td>\n",
              "      <td>178</td>\n",
              "      <td>175</td>\n",
              "      <td>172</td>\n",
              "      <td>172</td>\n",
              "      <td>182</td>\n",
              "      <td>199</td>\n",
              "      <td>222</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>131</td>\n",
              "      <td>192</td>\n",
              "      <td>79</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>13</td>\n",
              "      <td>26</td>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>119</td>\n",
              "      <td>103</td>\n",
              "      <td>99</td>\n",
              "      <td>102</td>\n",
              "      <td>101</td>\n",
              "      <td>104</td>\n",
              "      <td>119</td>\n",
              "      <td>118</td>\n",
              "      <td>122</td>\n",
              "      <td>130</td>\n",
              "      <td>129</td>\n",
              "      <td>127</td>\n",
              "      <td>166</td>\n",
              "      <td>128</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>211</td>\n",
              "      <td>212</td>\n",
              "      <td>212</td>\n",
              "      <td>225</td>\n",
              "      <td>...</td>\n",
              "      <td>217</td>\n",
              "      <td>206</td>\n",
              "      <td>211</td>\n",
              "      <td>217</td>\n",
              "      <td>219</td>\n",
              "      <td>216</td>\n",
              "      <td>249</td>\n",
              "      <td>222</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>58</td>\n",
              "      <td>94</td>\n",
              "      <td>104</td>\n",
              "      <td>109</td>\n",
              "      <td>113</td>\n",
              "      <td>105</td>\n",
              "      <td>92</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>152</td>\n",
              "      <td>120</td>\n",
              "      <td>111</td>\n",
              "      <td>95</td>\n",
              "      <td>75</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70000 rows × 785 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-afa0f615-58c8-4c50-bfc7-296a5fb27d84')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-db9758dd-efaf-4357-8201-711f50af46cc\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-db9758dd-efaf-4357-8201-711f50af46cc')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-db9758dd-efaf-4357-8201-711f50af46cc button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-afa0f615-58c8-4c50-bfc7-296a5fb27d84 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-afa0f615-58c8-4c50-bfc7-296a5fb27d84');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CKHs1FwrFpqY"
      },
      "source": [
        "## 1.4) Check Data NaN\n",
        "\n",
        "Memeriksa data NaN secara keseluruhan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JqposnAFFo4w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "956925d1-85c4-451d-a0ba-8ec54cecd697"
      },
      "source": [
        "fmnist.isnull().any().sum()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKFCH8AuFEDp"
      },
      "source": [
        "# 2) Visualizing the Dataset\n",
        "## 2.1) Melihat Bentuk data beserta label"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PWCtZrImxdJx"
      },
      "source": [
        "x_data = fmnist.copy()\n",
        "y_data = x_data.pop('label').values\n",
        "x_data = np.array(x_data).reshape(-1, 28, 28) / 255.0"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GrQghQY6wFD7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "outputId": "7f2f957e-13cc-47ab-8bb5-d4d5fba4243e"
      },
      "source": [
        "labeldict = {\n",
        "    0: 'T-shirt/top',\n",
        "    1: 'Trouser',\n",
        "    2: 'Pullover',\n",
        "    3: 'Dress',\n",
        "    4: 'Coat',\n",
        "    5: 'Sandal',\n",
        "    6: 'Shirt',\n",
        "    7: 'Sneaker',\n",
        "    8: 'Bag',\n",
        "    9: 'Ankle Boot'\n",
        "}\n",
        "\n",
        "plt.figure(figsize=(15, 15))\n",
        "for i in range(20):\n",
        "  plt.subplot(6,5,i+1)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.grid(False)\n",
        "  plt.imshow(x_data[i], cmap='Reds')\n",
        "  plt.xlabel(labeldict[y_data[i]])\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "svWvyjSCFil3"
      },
      "source": [
        "# 3) Data PreProcessing\n",
        "## 3.1) Membagi data menjadi 2 bagian yaitu data train dan data test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VvPcWtJOBLnL"
      },
      "source": [
        "x_fmnist = fmnist.drop(['label'], axis=1).values\n",
        "y_fmnist = fmnist['label'].values"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "86G167yCG9yL"
      },
      "source": [
        "Memeriksa jumlah data train dan data test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6LNHRbI2pbq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb4a4129-9da2-48c2-cad6-650b7b21502b"
      },
      "source": [
        "pers_test = 0.2\n",
        "X_train, X_test, y_train, y_test = train_test_split(x_fmnist, y_fmnist, test_size= pers_test, random_state=25)\n",
        "\n",
        "print(\"Data training (image) shape: {shape}\".format(shape=X_train.shape))\n",
        "print(\"Data testing (image) shape: {shape}\".format(shape=X_test.shape))\n",
        "print(\"Data training (label) shape: {shape}\".format(shape=y_train.shape))\n",
        "print(\"Data testing (label) shape: {shape}\".format(shape=y_test.shape))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data training (image) shape: (56000, 784)\n",
            "Data testing (image) shape: (14000, 784)\n",
            "Data training (label) shape: (56000,)\n",
            "Data testing (label) shape: (14000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Czb0ImmhHJPj"
      },
      "source": [
        "## 3.2) **Membentuk Ulang Data Citra**\n",
        "\n",
        "Data gambar diubah menjadi ukuran 28x28 pixel dengan cara `reshape` atau membentuk ulang menyesuaikan dengan data piksel gambar dan citra negatif dengan parameter `-1`\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ovGLt0rEAjWD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02c7d4dd-fae8-4609-d31b-41ced758b631"
      },
      "source": [
        "# Reshape the Image\n",
        "x_n = 28\n",
        "x_train_pic = X_train.reshape((-1, x_n, x_n, 1))\n",
        "x_test_pic = X_test.reshape((-1, x_n, x_n, 1))\n",
        "print(x_train_pic.shape, x_test_pic.shape)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(56000, 28, 28, 1) (14000, 28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIcU_gtgHSsZ"
      },
      "source": [
        "## 3.3) Normalisasi Data\n",
        "\n",
        "Nilai pixel disimpan dalam bentuk angka (Integer) dalam nilai antara  0 sampai 255, kisaran yang dapat ditawarkan oleh single 8-bit byte.  Dibutuhkan algoritma optimasi bekerja cepat. Disini, Menggunakan Zero Mean dan Variance Unit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "caQY7rdHnOxY"
      },
      "source": [
        "# Normalisasi\n",
        "x_train_pic = x_train_pic.astype(\"float32\")/255\n",
        "x_test_pic = x_test_pic.astype(\"float32\")/255"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b7Jb8IbkHhVg"
      },
      "source": [
        "## 3.4) One Hot Encoding\n",
        "Label diberikan sebagai bilangan bulat antara 0-9. Kita perlu one hot encode.\n",
        "\n",
        "Misalnya,\n",
        "\n",
        "8 [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]\n",
        "\n",
        "5 [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-O0qxK8nzuY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5709a49-93df-417f-e1f4-ef5870ad2d2c"
      },
      "source": [
        "y_train = to_categorical(y_train, num_classes=10)\n",
        "y_test = to_categorical(y_test, num_classes=10)\n",
        "y_test"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 1.],\n",
              "       [0., 1., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 1., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bpeow7Y4IrAa"
      },
      "source": [
        "# 4) Melatih sebuah model Convolutional Neural Network\n",
        "## 4.1) Membangun sebuah Model ConvNet\n",
        "Langkah:\n",
        "\n",
        "1) Pada awalnya, menggunakan Sequential Keras API yang hanya setumpuk linear lapisan. Kami menambahkan satu lapisan pada satu waktu mulai dari input.\n",
        "\n",
        "2) Selanjutnya, menambahkan Lapisan Konvolusional, yang merupakan blok Bangunan dari ConvNets. Lapisan Konvolusional memiliki seperangkat Filter independen yang kedalamannya sama dengan input dan dimensi lain dapat diatur secara manual. Filter-filter ini ketika mengkonvolusi yang berlebihan dari masukan Gambar menghasilkan feature map.\n",
        "\n",
        "3) Selanjutnya, menambahkan **Pooling Layers**, yang digunakan untuk Pengurangan Dimensi atau DownSampling. Ini digunakan di mana memiliki banyak fitur Input. Ini mengurangi jumlah Parameter dan daya komputasi yang dibutuhkan secara drastis, sehingga mengurangi Overfitting. Lapisan ini bersama dengan lapisan Konvolusional dapat mempelajari lebih banyak fitur Kompleks Gambar\n",
        "\n",
        "4) Kami menambahkan Normalisasi Batch di mana kami mencapai Zero mean dan Variance one. Ini mengurangi pencilan dan memaksa jaringan untuk mempelajari fitur dengan cara terdistribusi, tidak terlalu bergantung pada Bobot Khusus dan membuat model ini lebih baik.\n",
        "\n",
        "5) Untuk menghindari Overfitting, kami menambahkan **Dropout**. Ini secara acak turun beberapa persen dari neuron, dan dengan demikian bobot akan diselaraskan kembali. Neuron yang tersisa mempelajari lebih banyak fitur dan ini mengurangi ketergantungan pada salah satu Neuron. DropOut adalah Teknik Regularisasi, yang Menghukum Parameter. Secara umum, kami menetapkan **DropOutRate** antara 0,2 - 0,5.\n",
        "\n",
        "6) Akhirnya kami menambahkan layer Ratakan untuk memetakan input ke vektor 1D. Kami kemudian menambahkan Layers sepenuhnya terhubung setelah beberapa lapisan convolutional / pooling. Ini menggabungkan semua Fitur dari Lapisan Sebelumnya.\n",
        "\n",
        "7) Terakhir, kami menambahkan **Layer Output**. Ini memiliki unit yang sama dengan jumlah kelas yang akan diidentifikasi. Di sini, kami menggunakan fungsi 'sigmoid' jika itu adalah Klasifikasi Biner sebaliknya fungsi aktivasi 'softmax' dalam kasus Klasifikasi Multi-Kelas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cEX6k4_q664-"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, kernel_size=3, padding='same', activation='relu', input_shape=(28,28,1)))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(rate=0.2))\n",
        "model.add(Conv2D(64, kernel_size=5, padding='same', activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides= 2))\n",
        "model.add(Dropout(rate=0.2))\n",
        "model.add(Conv2D(128, kernel_size=3, padding='same', activation='relu'))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8CNxofUyMPJc"
      },
      "source": [
        "## 4.3) Model Summary\n",
        "\n",
        "Rangkuman model yang dibuat berisi layer atau lapisan output dan parameter."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NTjT4gDHMOeY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4255cbd-c34b-4425-a4fd-c5f9bd589afd"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 28, 28, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 14, 14, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 14, 14, 32)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 14, 14, 64)        51264     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 7, 7, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 7, 7, 64)          0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 7, 7, 128)         73856     \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 6272)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               3211776   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                5130      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,342,346\n",
            "Trainable params: 3,342,346\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from keras.utils import plot_model\n",
        "\n",
        "tf.keras.utils.plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "u9-CoMprCGwr",
        "outputId": "c64f11e8-dd09-414a-cac1-c462d004e3ae"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import visualkeras\n",
        "\n",
        "visualkeras.layered_view(model, legend=True, to_file='output.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "dWst7urjCjQp",
        "outputId": "6c0379f4-a2fd-4c51-d0f6-11f2abb951b4"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGBA size=744x186>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U4phnwj0MDcd"
      },
      "source": [
        "## 4.2) Compiling the Model\n",
        "\n",
        "1) Kita perlu menyusun sebuah model. Kita harus menemtukan pengoptimalkan yang diperlukan oleh model. Kami memiliki banyak pilihan seperti Adam, RMSprop dll.\n",
        "2) Selanjutnya, kita membutuhkan spesifikasi fungsi Loss untuk Jaringan Syaraf Tiruan yang kita minimalkan. Untuk klasifikasi biner menggunakan \"binary_crossentropy\" dan untuk klasifikasi multi kelas menggunakan \"categorical_crossentropy\".\n",
        "3) Akhirnya, kita menentukan metrik untuk mengevaluasi model yang kita buat."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZF6Mu0K8zSl"
      },
      "source": [
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iEnbss9sMw8E"
      },
      "source": [
        "## 4.5) Data Augmentation\n",
        "\n",
        "Augmentasi data gambar digunakan untuk memanipulasi data dengan cara merotasi gambar,memperbesar gambar, memotong gambar, membalikan gambar tanpa menghilangkan inti dari data tersebut."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3l_hoW2VoT-o"
      },
      "source": [
        "# Data Augmentation\n",
        "datagen = ImageDataGenerator(\n",
        "        rotation_range = 8,       # randomly rotate images in the range (degrees, 0 to 180)\n",
        "        zoom_range = 0.2,         # Randomly zoom image\n",
        "        shear_range = 0.3,        # shear angle in counter-clockwise direction in degrees\n",
        "        width_shift_range = 0.08,   # randomly shift images horizontally (fraction of total width)\n",
        "        height_shift_range = 0.08,  # randomly shift images vertically (fraction of total height)\n",
        "        vertical_flip = True)       # randomly flip images"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehlsDuyLpyRh"
      },
      "source": [
        "datagen.fit(x_train_pic)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKkrimJEM4Gz"
      },
      "source": [
        "## 4.6) Fitting the Model\n",
        "\n",
        "Membuat pemasangan model sesuai batch_size dan epoch yang dipilih."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Og5iqfLxp6jg"
      },
      "source": [
        "# Fitting the Model\n",
        "BATCH_SIZE = 512\n",
        "EPOCHS = 50"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_V8xjaxsqCon",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a57138bb-37d7-48b3-9eaa-a34272e38229"
      },
      "source": [
        "# Fit the Model data\n",
        "history = model.fit(datagen.flow(x_train_pic, y_train, batch_size = BATCH_SIZE),\n",
        "                              epochs = EPOCHS,\n",
        "                              validation_data = (x_test_pic, y_test),\n",
        "                              verbose=2,\n",
        "                              steps_per_epoch = x_train_pic.shape[0] // BATCH_SIZE)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "109/109 - 32s - loss: 0.9429 - accuracy: 0.6389 - val_loss: 0.5700 - val_accuracy: 0.7828 - 32s/epoch - 298ms/step\n",
            "Epoch 2/50\n",
            "109/109 - 18s - loss: 0.6378 - accuracy: 0.7579 - val_loss: 0.4842 - val_accuracy: 0.8178 - 18s/epoch - 162ms/step\n",
            "Epoch 3/50\n",
            "109/109 - 19s - loss: 0.5643 - accuracy: 0.7862 - val_loss: 0.4663 - val_accuracy: 0.8214 - 19s/epoch - 174ms/step\n",
            "Epoch 4/50\n",
            "109/109 - 18s - loss: 0.5251 - accuracy: 0.8016 - val_loss: 0.4531 - val_accuracy: 0.8256 - 18s/epoch - 165ms/step\n",
            "Epoch 5/50\n",
            "109/109 - 20s - loss: 0.4870 - accuracy: 0.8175 - val_loss: 0.3840 - val_accuracy: 0.8618 - 20s/epoch - 182ms/step\n",
            "Epoch 6/50\n",
            "109/109 - 18s - loss: 0.4570 - accuracy: 0.8282 - val_loss: 0.3751 - val_accuracy: 0.8619 - 18s/epoch - 163ms/step\n",
            "Epoch 7/50\n",
            "109/109 - 17s - loss: 0.4379 - accuracy: 0.8362 - val_loss: 0.3560 - val_accuracy: 0.8659 - 17s/epoch - 159ms/step\n",
            "Epoch 8/50\n",
            "109/109 - 19s - loss: 0.4196 - accuracy: 0.8436 - val_loss: 0.3677 - val_accuracy: 0.8576 - 19s/epoch - 174ms/step\n",
            "Epoch 9/50\n",
            "109/109 - 19s - loss: 0.4045 - accuracy: 0.8478 - val_loss: 0.3770 - val_accuracy: 0.8586 - 19s/epoch - 175ms/step\n",
            "Epoch 10/50\n",
            "109/109 - 18s - loss: 0.3906 - accuracy: 0.8543 - val_loss: 0.3359 - val_accuracy: 0.8758 - 18s/epoch - 164ms/step\n",
            "Epoch 11/50\n",
            "109/109 - 18s - loss: 0.3835 - accuracy: 0.8572 - val_loss: 0.3216 - val_accuracy: 0.8785 - 18s/epoch - 164ms/step\n",
            "Epoch 12/50\n",
            "109/109 - 17s - loss: 0.3692 - accuracy: 0.8611 - val_loss: 0.3198 - val_accuracy: 0.8820 - 17s/epoch - 156ms/step\n",
            "Epoch 13/50\n",
            "109/109 - 17s - loss: 0.3655 - accuracy: 0.8638 - val_loss: 0.3031 - val_accuracy: 0.8866 - 17s/epoch - 159ms/step\n",
            "Epoch 14/50\n",
            "109/109 - 20s - loss: 0.3596 - accuracy: 0.8664 - val_loss: 0.2956 - val_accuracy: 0.8890 - 20s/epoch - 182ms/step\n",
            "Epoch 15/50\n",
            "109/109 - 17s - loss: 0.3502 - accuracy: 0.8677 - val_loss: 0.2931 - val_accuracy: 0.8898 - 17s/epoch - 157ms/step\n",
            "Epoch 16/50\n",
            "109/109 - 19s - loss: 0.3481 - accuracy: 0.8689 - val_loss: 0.2886 - val_accuracy: 0.8914 - 19s/epoch - 178ms/step\n",
            "Epoch 17/50\n",
            "109/109 - 17s - loss: 0.3377 - accuracy: 0.8734 - val_loss: 0.2727 - val_accuracy: 0.8989 - 17s/epoch - 157ms/step\n",
            "Epoch 18/50\n",
            "109/109 - 17s - loss: 0.3379 - accuracy: 0.8738 - val_loss: 0.2765 - val_accuracy: 0.8986 - 17s/epoch - 159ms/step\n",
            "Epoch 19/50\n",
            "109/109 - 19s - loss: 0.3291 - accuracy: 0.8760 - val_loss: 0.2845 - val_accuracy: 0.8951 - 19s/epoch - 171ms/step\n",
            "Epoch 20/50\n",
            "109/109 - 17s - loss: 0.3220 - accuracy: 0.8804 - val_loss: 0.2922 - val_accuracy: 0.8913 - 17s/epoch - 158ms/step\n",
            "Epoch 21/50\n",
            "109/109 - 18s - loss: 0.3155 - accuracy: 0.8818 - val_loss: 0.2632 - val_accuracy: 0.9021 - 18s/epoch - 164ms/step\n",
            "Epoch 22/50\n",
            "109/109 - 17s - loss: 0.3090 - accuracy: 0.8847 - val_loss: 0.2797 - val_accuracy: 0.8962 - 17s/epoch - 160ms/step\n",
            "Epoch 23/50\n",
            "109/109 - 19s - loss: 0.3116 - accuracy: 0.8839 - val_loss: 0.2812 - val_accuracy: 0.8936 - 19s/epoch - 179ms/step\n",
            "Epoch 24/50\n",
            "109/109 - 18s - loss: 0.3073 - accuracy: 0.8855 - val_loss: 0.2764 - val_accuracy: 0.9000 - 18s/epoch - 166ms/step\n",
            "Epoch 25/50\n",
            "109/109 - 17s - loss: 0.2994 - accuracy: 0.8870 - val_loss: 0.2632 - val_accuracy: 0.9029 - 17s/epoch - 160ms/step\n",
            "Epoch 26/50\n",
            "109/109 - 19s - loss: 0.2991 - accuracy: 0.8881 - val_loss: 0.2802 - val_accuracy: 0.8994 - 19s/epoch - 173ms/step\n",
            "Epoch 27/50\n",
            "109/109 - 18s - loss: 0.2956 - accuracy: 0.8893 - val_loss: 0.2645 - val_accuracy: 0.9041 - 18s/epoch - 162ms/step\n",
            "Epoch 28/50\n",
            "109/109 - 20s - loss: 0.2956 - accuracy: 0.8883 - val_loss: 0.2713 - val_accuracy: 0.9014 - 20s/epoch - 182ms/step\n",
            "Epoch 29/50\n",
            "109/109 - 18s - loss: 0.2882 - accuracy: 0.8921 - val_loss: 0.2806 - val_accuracy: 0.8975 - 18s/epoch - 168ms/step\n",
            "Epoch 30/50\n",
            "109/109 - 19s - loss: 0.2872 - accuracy: 0.8926 - val_loss: 0.2513 - val_accuracy: 0.9074 - 19s/epoch - 176ms/step\n",
            "Epoch 31/50\n",
            "109/109 - 17s - loss: 0.2847 - accuracy: 0.8930 - val_loss: 0.2616 - val_accuracy: 0.9049 - 17s/epoch - 160ms/step\n",
            "Epoch 32/50\n",
            "109/109 - 19s - loss: 0.2806 - accuracy: 0.8931 - val_loss: 0.2937 - val_accuracy: 0.8944 - 19s/epoch - 178ms/step\n",
            "Epoch 33/50\n",
            "109/109 - 18s - loss: 0.2792 - accuracy: 0.8951 - val_loss: 0.2916 - val_accuracy: 0.8956 - 18s/epoch - 163ms/step\n",
            "Epoch 34/50\n",
            "109/109 - 18s - loss: 0.2773 - accuracy: 0.8957 - val_loss: 0.2536 - val_accuracy: 0.9064 - 18s/epoch - 164ms/step\n",
            "Epoch 35/50\n",
            "109/109 - 18s - loss: 0.2788 - accuracy: 0.8959 - val_loss: 0.2367 - val_accuracy: 0.9136 - 18s/epoch - 163ms/step\n",
            "Epoch 36/50\n",
            "109/109 - 20s - loss: 0.2695 - accuracy: 0.8991 - val_loss: 0.2495 - val_accuracy: 0.9111 - 20s/epoch - 180ms/step\n",
            "Epoch 37/50\n",
            "109/109 - 18s - loss: 0.2693 - accuracy: 0.9000 - val_loss: 0.2758 - val_accuracy: 0.8967 - 18s/epoch - 166ms/step\n",
            "Epoch 38/50\n",
            "109/109 - 19s - loss: 0.2684 - accuracy: 0.9001 - val_loss: 0.2430 - val_accuracy: 0.9094 - 19s/epoch - 179ms/step\n",
            "Epoch 39/50\n",
            "109/109 - 18s - loss: 0.2642 - accuracy: 0.9010 - val_loss: 0.2529 - val_accuracy: 0.9072 - 18s/epoch - 165ms/step\n",
            "Epoch 40/50\n",
            "109/109 - 18s - loss: 0.2602 - accuracy: 0.9015 - val_loss: 0.2470 - val_accuracy: 0.9098 - 18s/epoch - 168ms/step\n",
            "Epoch 41/50\n",
            "109/109 - 18s - loss: 0.2623 - accuracy: 0.9014 - val_loss: 0.2725 - val_accuracy: 0.9007 - 18s/epoch - 165ms/step\n",
            "Epoch 42/50\n",
            "109/109 - 18s - loss: 0.2596 - accuracy: 0.9027 - val_loss: 0.2660 - val_accuracy: 0.9027 - 18s/epoch - 168ms/step\n",
            "Epoch 43/50\n",
            "109/109 - 18s - loss: 0.2584 - accuracy: 0.9032 - val_loss: 0.2539 - val_accuracy: 0.9073 - 18s/epoch - 169ms/step\n",
            "Epoch 44/50\n",
            "109/109 - 18s - loss: 0.2558 - accuracy: 0.9038 - val_loss: 0.2379 - val_accuracy: 0.9174 - 18s/epoch - 163ms/step\n",
            "Epoch 45/50\n",
            "109/109 - 19s - loss: 0.2555 - accuracy: 0.9036 - val_loss: 0.2430 - val_accuracy: 0.9135 - 19s/epoch - 177ms/step\n",
            "Epoch 46/50\n",
            "109/109 - 25s - loss: 0.2526 - accuracy: 0.9046 - val_loss: 0.2536 - val_accuracy: 0.9093 - 25s/epoch - 227ms/step\n",
            "Epoch 47/50\n",
            "109/109 - 23s - loss: 0.2504 - accuracy: 0.9058 - val_loss: 0.2353 - val_accuracy: 0.9177 - 23s/epoch - 210ms/step\n",
            "Epoch 48/50\n",
            "109/109 - 20s - loss: 0.2495 - accuracy: 0.9047 - val_loss: 0.2499 - val_accuracy: 0.9102 - 20s/epoch - 185ms/step\n",
            "Epoch 49/50\n",
            "109/109 - 21s - loss: 0.2495 - accuracy: 0.9058 - val_loss: 0.2716 - val_accuracy: 0.9070 - 21s/epoch - 193ms/step\n",
            "Epoch 50/50\n",
            "109/109 - 18s - loss: 0.2452 - accuracy: 0.9072 - val_loss: 0.2635 - val_accuracy: 0.9049 - 18s/epoch - 163ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QlIXeq0-M9jK"
      },
      "source": [
        "# 5) Evaluating the Model\n",
        "\n",
        "Mengevaluasi model data menggunakan tes akurasi dan tes loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5qyjyrw56wn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c5823a8-7306-48d9-9ee3-031d4e728bae"
      },
      "source": [
        "score = model.evaluate(x_test_pic, y_test)\n",
        "\n",
        "print('Loss: {:.4f}'.format(score[0]))\n",
        "print('Accuracy: {:.4f}'.format(score[1]))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "438/438 [==============================] - 1s 3ms/step - loss: 0.2635 - accuracy: 0.9049\n",
            "Loss: 0.2635\n",
            "Accuracy: 0.9049\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yUUxkgDLNB8b"
      },
      "source": [
        "## 5.1) Plotting the Training and Validation Curves\n",
        "\n",
        "Menampilkan plot data train dan test yang dihasilkan dari sebuah metode yang digunakan."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(EPOCHS)\n",
        "\n",
        "plt.figure(figsize=(14, 5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "YAIl9EYtGvCs",
        "outputId": "742d00bd-7834-4e95-abaf-fad1072690de"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": 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cRf29SE5O5qeffmLfvn3UrVvXeWvUqBGnT59mxowZLvvdKoh/NonMld9/iw8++CCvvPIKd9xxB9988w0LFy5k0aJFhIaGFulzHzx4MKmpqcyZM8e52thNN91EcHBwoccSERHX0LWYrsUK4nK+FnOlypUrs2HDBn788UdnP6EePXrk6b3UqVMn9uzZw9SpU2nSpAmffPIJV111FZ988kmJxSnlixovy2Vv6dKlJCQkMGvWLDp16uTcvm/fvlKM6qzKlSvj4+PD7t27z3stv23/tHnzZnbu3Mlnn33G4MGDnduL03G/Zs2aLFmyhNTU1DzfIO3YsaNQ4wwcOJD58+fz888/M2PGDIKCgujVq5fz9e+++47atWsza9asPGW9+U0vKkjMALt27aJ27drO7SdOnDjvG5nvvvuOa6+9lilTpuTZnpiYSKVKlZzPCzNdqWbNmixevJiUlJQ83yDllqDnxldcs2bNIiMjgw8++CBPrGD+fJ599lmWL19Ox44diY6OZsGCBZw8efKC1TzR0dE4HA62bt160eaKFSpUOG9Fj6ysLI4ePVrg2L/77juGDBnCm2++6dyWkZFx3rjR0dFs2bLlkuM1adKEFi1a8OWXX1K9enViY2N59913CxyPiIiUDF2LFZ6uxUxl8VqsoLFs2rQJh8ORp5onv1i8vLzo1asXvXr1wuFwcP/99/Phhx/y3HPPOSvJKlasyLBhwxg2bBipqal06tSJ559/nhEjRpTYe5LyQ5U8ctnLzdKfm5XPysri/fffL62Q8rDZbHTt2pU5c+Zw5MgR5/bdu3efN3f4QsdD3vdnGEaepRcLq2fPnuTk5PDBBx84t9nt9kL/A7p37974+fnx/vvv8/PPP3Prrbfi4+Nz0dhXr17NypUrCx1z165d8fT05N13380z3qRJk87b12aznfctzbfffsvhw4fzbPP39wco0HKlPXv2xG6389///jfP9rfeeguLxVLgOf2XMn36dGrXrs19993Hbbfdluf22GOPERAQ4Jyy1bdvXwzD4IUXXjhvnNz337t3b6xWKy+++OJ535yd+xlFR0fnmdMP8NFHH12wkic/+X3u77777nlj9O3bl40bNzJ79uwLxp1r0KBBLFy4kEmTJhEaGuqyz1lERFxH12KFp2sxU1m8FiuInj17cuzYMWbOnOnclpOTw7vvvktAQIBzKl9CQkKe46xWK82aNQMgMzMz330CAgKoU6eO83WRwlIlj1z22rdvT4UKFRgyZAgPPfQQFouFL774okRLMS/l+eefZ+HChXTo0IFRo0Y5/0A1adKEDRs2XPTYBg0aEB0dzWOPPcbhw4cJCgri+++/L9Z84l69etGhQweeeuop9u/fT6NGjZg1a1ah50gHBATQu3dv51zwfy5rfdNNNzFr1iz69OnDjTfeyL59+5g8eTKNGjUiNTW1UOcKCwvjscceY8KECdx000307NmTv/76i59//vm8ipebbrqJF198kWHDhtG+fXs2b97Ml19+medbJzATGyEhIUyePJnAwED8/f1p27Ztvv1mevXqxbXXXsszzzzD/v37ad68OQsXLuSHH37gkUceydPYr6iOHDnCr7/+el5DwVze3t5069aNb7/9lnfeeYdrr72WQYMG8c4777Br1y66d++Ow+Hgjz/+4Nprr2X06NHUqVOHZ555hpdeeolrrrmGW2+9FW9vb9auXUu1atWYMGECACNGjOC+++6jb9++XH/99WzcuJEFCxac99lezE033cQXX3xBcHAwjRo1YuXKlSxevPi8ZUoff/xxvvvuO26//XbuvvtuWrZsycmTJ/nxxx+ZPHkyzZs3d+47YMAAnnjiCWbPns2oUaMuuYyuiIiUPF2LFZ6uxUxl7VrsXEuWLCEjI+O87b179+aee+7hww8/ZOjQoaxbt46oqCi+++47li9fzqRJk5yVRiNGjODkyZP83//9H9WrV+fAgQO8++67xMTEOPv3NGrUiC5dutCyZUsqVqzIn3/+yXfffcfo0aNd+n7kClICK3iJFNqFlu1s3LhxvvsvX77cuPrqqw1fX1+jWrVqxhNPPOFcgvnXX3917nehZTvzWyKRfywjeaFlOx944IHzjv3nstOGYRhLliwxWrRoYXh5eRnR0dHGJ598YvzrX/8yfHx8LvApnLV161aja9euRkBAgFGpUiVj5MiRzmUgz11ycsiQIYa/v/95x+cXe0JCgjFo0CAjKCjICA4ONgYNGmT89ddfBV62M9fcuXMNwKhatWq+S3S/+uqrRs2aNQ1vb2+jRYsWxv/+97/zfg6GcellOw3DMOx2u/HCCy8YVatWNXx9fY0uXboYW7ZsOe/zzsjIMP71r3859+vQoYOxcuVKo3Pnzuctv/3DDz8YjRo1ci6hmvve84sxJSXFePTRR41q1aoZnp6eRt26dY3XX389zzKiue+loL8X53rzzTcNwFiyZMkF95k2bZoBGD/88INhGObSqK+//rrRoEEDw8vLywgLCzN69OhhrFu3Ls9xU6dONVq0aGF4e3sbFSpUMDp37mwsWrTI+brdbjeefPJJo1KlSoafn5/RrVs3Y/fu3RdcQn3t2rXnxXbq1Clj2LBhRqVKlYyAgACjW7duxvbt2/N93wkJCcbo0aONiIgIw8vLy6hevboxZMgQIz4+/rxxe/bsaQDGihUrLvi5iIiIa+laLC9di5nK+7WYYZz9nbzQ7YsvvjAMwzDi4uKc1z1eXl5G06ZNz/u5fffdd8YNN9xgVK5c2fDy8jJq1Khh3HvvvcbRo0ed+7z88stGmzZtjJCQEMPX19do0KCB8corrxhZWVkXjVPkQiyGUYZS7CJXmN69e2vJRJFL6NOnD5s3by5Q3wQREZHC0LWYiJQ36skjUkLS09PzPN+1axfz5s2jS5cupROQyGXg6NGjzJ07l0GDBpV2KCIicpnTtZiIXAlUySNSQqpWrcrQoUOpXbs2Bw4c4IMPPiAzM5O//vqLunXrlnZ4ImXKvn37WL58OZ988glr165lz549VKlSpbTDEhGRy5iuxUTkSqDGyyIlpHv37nz11VccO3YMb29v2rVrx6uvvqqLCpF8/PbbbwwbNowaNWrw2WefKcEjIiLFpmsxEbkSqJJHRERERERERKQcUE8eEREREREREZFyQEkeEREREREREZFyoFz05HE4HBw5coTAwEAsFktphyMiIiIXYRgGKSkpVKtWDatV3zeVFl0/iYiIXB4Kc+1ULpI8R44cITIysrTDEBERkUI4ePAg1atXL+0wrli6fhIREbm8FOTaqVwkeQIDAwHzDQcFBZVyNCIiInIxycnJREZGOv9+S+nQ9ZOIiMjloTDXTuUiyZNbYhwUFKSLFBERkcuEpgiVLl0/iYiIXF4Kcu2kifAiIiIiIiIiIuWAkjwiIiIiIiIiIuWAkjwiIiIiIiIiIuVAuejJIyIiIiIiIlIS7HY72dnZpR2GlDOenp7YbLZij6Mkj4iIiIiIiMglGIbBsWPHSExMLO1QpJwKCQmhSpUqxVqcQkkeERERERERkUvITfBUrlwZPz8/rRIpLmMYBqdPn+b48eMAVK1atchjKckjIiIiIiIichF2u92Z4AkNDS3tcKQc8vX1BeD48eNUrly5yFO31HhZRERERERE5CJye/D4+fmVciRSnuX+fhWn55OSPCIiIiIiIiIFoCla4k6u+P1SkkdEREREREREpBxQkkdERERERERECiQqKopJkyYVeP+lS5disVi0KlkJUZJHREREREREpJyxWCwXvT3//PNFGnft2rXcc889Bd6/ffv2HD16lODg4CKdr6CUTDJpdS0RERERERGRcubo0aPOxzNnzmTcuHHs2LHDuS0gIMD52DAM7HY7Hh6XThGEhYUVKg4vLy+qVKlSqGOk6FTJIyIicrk4dQCy00s7CpGLik/NZMXueP6KPVXaoYiIXNGqVKnivAUHB2OxWJzPt2/fTmBgID///DMtW7bE29ubZcuWsWfPHm655RbCw8MJCAigdevWLF68OM+4/5yuZbFY+OSTT+jTpw9+fn7UrVuXH3/80fn6Pytspk2bRkhICAsWLKBhw4YEBATQvXv3PEmpnJwcHnroIUJCQggNDeXJJ59kyJAh9O7du8ifx6lTpxg8eDAVKlTAz8+PHj16sGvXLufrBw4coFevXlSoUAF/f38aN27MvHnznMcOHDiQsLAwfH19qVu3Lp9++mmRY3EnJXlERESKau0n8EEHWP0R5GS591ybvoG3m8P7V0PSYfeeS6QYlu2KZ8Anq3lj4Y5L7ywichkzDIPTWTklfjMMw2Xv4amnnuLf//4327Zto1mzZqSmptKzZ0+WLFnCX3/9Rffu3enVqxexsbEXHeeFF17gjjvuYNOmTfTs2ZOBAwdy8uTJC+5/+vRp3njjDb744gt+//13YmNjeeyxx5yvv/baa3z55Zd8+umnLF++nOTkZObMmVOs9zp06FD+/PNPfvzxR1auXIlhGPTs2dO5XPkDDzxAZmYmv//+O5s3b+a1115zVjs999xzbN26lZ9//plt27bxwQcfUKlSpWLF4y6ariUiIlIUsath3hNg2OHnx2HVe/B/z0HjW8Hq4u9Q9i+DHx4ADDi1Hz7rBcPmQaBKn6XsCfI1Ly9TMnJKORIREfdKz7bTaNyCEj/v1he74eflmn/Kv/jii1x//fXO5xUrVqR58+bO5y+99BKzZ8/mxx9/ZPTo0RccZ+jQofTv3x+AV199lXfeeYc1a9bQvXv3fPfPzs5m8uTJREdHAzB69GhefPFF5+vvvvsuY8eOpU+fPgD897//dVbVFMWuXbv48ccfWb58Oe3btwfgyy+/JDIykjlz5nD77bcTGxtL3759adq0KQC1a9d2Hh8bG0uLFi1o1aoVYFYzlVWq5BERESmsjCSYNcJM8NRoB/6VzeTL98Ph4y6w51fXnSt+F3w9EOxZULcbBEfCyT3w+S2QFu+684i4SKCPJwDJ6dmlHImIiFxKbtIiV2pqKo899hgNGzYkJCSEgIAAtm3bdslKnmbNmjkf+/v7ExQUxPHjxy+4v5+fnzPBA1C1alXn/klJScTFxdGmTRvn6zabjZYtWxbqvZ1r27ZteHh40LZtW+e20NBQ6tevz7Zt2wB46KGHePnll+nQoQPjx49n06ZNzn1HjRrF119/TUxMDE888QQrVqwocizupkoeERGRwjAM+N8YSIyFkJow4BuwWGHVB7D8bTi6Eb7oDbWvha7PQ7WYop8rLR6+vA0yEqF6a7jjM0g5Cp/eCCe2m4meIT+BX0XXvDcRFwg6k+RRJY+IlHe+nja2vtitVM7rKv7+/nmeP/bYYyxatIg33niDOnXq4Ovry2233UZW1sWnpXt6euZ5brFYcDgchdrfldPQimLEiBF069aNuXPnsnDhQiZMmMCbb77Jgw8+SI8ePThw4ADz5s1j0aJFXHfddTzwwAO88cYbpRpzflTJIyJyJcpMhcyU0o7i8rRpJmz5Diw26DsFfILAOwA6Pw4Pb4C2o8DqCXt/hY86w3fD4eS+wp8nOx2+utOsEKoQBXd+BZ6+ULE2DPnRrB6K2wJf9IH0RNe+R5FiCPQxv0NMzsgu9Qt2ERF3slgs+Hl5lPjNYrG47T0tX76coUOH0qdPH5o2bUqVKlXYv3+/286Xn+DgYMLDw1m7dq1zm91uZ/369UUes2HDhuTk5LB69WrntoSEBHbs2EGjRo2c2yIjI7nvvvuYNWsW//rXv/j444+dr4WFhTFkyBCmT5/OpEmT+Oijj4ocjzupkkdE5EpzbItZaZKeCHW6QpO+UL+HmaiQizu5F+b+y3zcZSxEts77un8l6PFvuPo++OUV2PyNmRDa+gO0vRc6PwE+wZc+j8MBs+6BQ2vBJwQGfgcB5yxXWqmumeiZdiMc3WBW+wyaDd6BrnqnIkUW5Gt+O5ttN8jMceDjwm+cRUTEverWrcusWbPo1asXFouF55577qIVOe7y4IMPMmHCBOrUqUODBg149913OXXqVIESXJs3byYw8Ow1kcVioXnz5txyyy2MHDmSDz/8kMDAQJ566ikiIiK45ZZbAHjkkUfo0aMH9erV49SpU/z66680bNgQgHHjxtGyZUsaN25MZmYm//vf/5yvlTWq5BERuZIc22I27U07AY5s2Pmz2Vvm9TrwzRAzGaEluvNnz4bvR0BWKtRoD9eMufC+FaKg78dw7+8QfZ35Wa/8L7xzFaybBg77xc+1eDxs+9GsCLrzSzOp80+VG8LgH8wk0KG18OXtkJVWjDco4hr+XjasZ67B1ZdHROTyMnHiRCpUqED79u3p1asX3bp146qrrirxOJ588kn69+/P4MGDadeuHQEBAXTr1g0fH59LHtupUydatGjhvOX28vn0009p2bIlN910E+3atcMwDObNm+ecOma323nggQdo2LAh3bt3p169erz//vsAeHl5MXbsWJo1a0anTp2w2Wx8/fXX7vsAisFilIM62uTkZIKDg0lKSiIoKKi0wxERKZtyEzzpJ6HaVdDzddi5ALZ8bzbyzeUVAA1uNCt8al8LHl6lF3NZsuRF+ONNsxLnvuUQElnwY3cthgVjIX6n+bxKU+j+GkR1OH/ftVNg7pkE0q0fQ7M7Lj72kb/gs1sgMwlqdTJ7BHn6Fjy2UqC/22WDO38OzV9YSFJ6NovHdKZOZVUJisjlLyMjg3379lGrVq0CJRrEtRwOBw0bNuSOO+7gpZdeKu1w3OZCv2eF+ZutSh4RkSvBPxM8g2ZD9Vbwf8/Ag+vgnt+g/UPmyk1ZqWbfmRl3wPtttYITwL4/4I+J5uNe7xQuwQNQtyuMWgHd/w3ewXBsM0zrCd8ONRs459q1COY9Zj6+9plLJ3gAqrWAu743k3P7foeZd0FOZuHiE3Gxc/vyiIiIFNaBAwf4+OOP2blzJ5s3b2bUqFHs27ePAQMGlHZoZZ6SPCIi5V1+CR7fkLOvWyzmClA3vAQPb4Lhi6DtfeBb0exBs3xSKQX+D/ZsiF1V8k2GT580++NgQItB0Lh30caxecLVo+Ch9dDqbnNFrr9nw39bw6+vwsE1ZtLHcEDMQOj0eMHHjmwNA78FTz/YvRg+7w17fzNXAhMpBVphS0REisNqtTJt2jRat25Nhw4d2Lx5M4sXLy6zfXDKEjVeFhEpzy6V4PknqxUi25i36Otgxu2w5hOzyiegctFi2LUIfnkZIlqaDZ6jrgHPApY5OxxwcDVs/tZMiKSfhEr1YcRic1UrdzMM+OkhSDkCoXXMSpzi8q8EN71lJnp+fgoOLIPfXjNvYE65ummSmXwrjJrtof/XZgVW7Ar4/GYIb2omlpreBh7exY9dpICclTzqySMiIkUQGRnJ8uXLSzuMy5IqeUREyqvCJnj+qe71ENEKctJh+dtFiyEzBX54wFwB6s8p5ipQ/6ltTinaMOPCU8Hi/obFz8PbzeHT7uax6SfN1+J3wOz7zASQu63/DLb9ZDZA7vuJa1cgq9IUhv4P7vgcgmuY28IawB1fFL0PUu3O5rSw1iPMqp64zfDD/fBWE1j6GqSecF38IheRu8KWKnlERERKlip5RESKIzMFTh2Ayo3MKpiyorgJHjArSa4dC9P7wtpPoP2DEFilcGMsewtS48zVpqL/D3b8DClHzcTJtp8AC0S2NSt8araH/cvMqp3jW8+O4RUIDXtBs9vB0x8+uwl2zIXf/wNdnipcPIVxYqdZaQNw3Tiz942rWSzQ6Baoe4M5zapmh8L/nP4pNBpufNPs6bP+M1j9kVmJtPRVs3F0szvg6vshvNFFh3E4DA6cPE1EiC9eHmXod1suC+rJIyIiUjqU5BERKarsdJjaw6yWCKwGjW6Gxn2gepvSTfi4IsGTK/o68/0cWgPLJkGPQkxXOrUfVvzXfNztVXPFrhsnmlU9O+bDjnlwbBMcXGXezmXzMhMfTW+Het3yrhZ101tmddDSCVClGTToWbT3djHb55kNkHPSoXYXaDfa9ec4l6evmchyJb+K0PFRM/atP8DK9+DIevjrC/NWs6P52dXthr1iNHtPpLLlSBKbDyWz5UgSW48kk5qZw4+jO9CseohrY5Ny72xPHiV5RERESpKSPCIiRTV/rJngAbNSYvVk81aaCZ/Eg65L8MDZap4v+sCfU6HDwxBUtWDHLhoH9kyo1Rnq9zw7XrUW5u3asWa8O+ebFT6H/oSqzcxKk4a9wLdC/uO2uAuOboQ1H5kNkUf+AmH1iv4ez5V8BH5+4kyVEVChFvSeXCaqtE6lZbEvIQ1/Lw9qhvrh42kr2IE2T7MnT5O+cHA12cvfw2Pn/7AcWGb2A1rwNAeNKvxhj2GJowVrHA3JPnN54OVh5fCpdCV5pNCCnD15NF1LRESkJCnJIyJSFH/PhnWfAhaz2S0G/D3HrE7JL+HT5DZzBSR3cjjM/ivpJ80Kl+ImeHLVvhZqtIPYleb0q57/ufQx+5eZ1SMWK3SfcOEmwiGR0GakeSuMbq+afXsOLIev+5uJHp/gwo1xLocd1k6BJS9CVgpYbOb0tM5Pgpdf0cctJLvD4NCp0+w5kcqe42nm/YlU9pxI42RaVp59qwX7UDPUn6hK/tSq5EfUmcc1KvphdxjsT0hjX3wa++PT2Bd/mn3xqexPOM3JtDuJ4Dq62f7kWutftLVuI8pyjLs95nM388m0+hFfuT3W+t2o1OImPEMKmNQTOcfZnjyq5BERESlJSvKISPlzcp9ZBeKKBMeFxv/xIfNxx0ehfnfzcf0ekJMJe37JP+Fz45tmQ1x3+XMK7PsdPHzh9mmue/8WC3QZa67WtO5Ts5onOOLC+zvsMP9ML5uWQyG8sWviOJfNE27/DD7qDAm7zYqeO78qWsXNsc3w08NweJ35vHprc3WrKk1cGnIuh8MgLiWDffFpHEg4fSYJk8b+hDT2x58my37hhtJVgnxIy8ohJSOHI0kZHEnKYOXehDz7WCyXXjk9J6g628Ma4BHxAMlhVlraNxJ+7DcsuxbinXaciGOL4dhi+O1xs5Ippr8r3rpcQc725FElj4iISElSkkdEyo+MZFg83pxWZPM2+43EDDQrUWwu+t9dThZ8dzdkJkPk1WZz23N5eJvJnnMTPhtmwLYfYcEzENXJdVOLzpWwx5weBXD9i2bzXVeq1clsCnxgOSybaCasLmTDl2bixDv4/M/HlQLC4M4vYWp3c8rX0gnwf4U4X1YaLP232avGsIN3EHQdDy3vBqsVwzBIzcwhITWLhLQsElIzOZmW+ziLhLRMElKzSMnMwctmwdvDhpeHFW8Pq/M+d5sFOHjqNPvjT3PgZBoZ2RdO5Hh7WKlVyZ/oygFEhwUQHeZPdFgAtcP88fPywDAMTp3Odlbp7E9IY/+ZZNH++DRSMs1/VFf09yIq1I9alQKoVcm8jzpT8ePv/c//HuoDd5jVYEc3wK6F5md65C+o3qqwPxkR9eQRESlHunTpQkxMDJMmTQIgKiqKRx55hEceeeSCx1gsFmbPnk3v3r2LdW5XjXMlUZJHREpfVhps+R4OrjGX7W7Qq/AVGbsXw48PQ/Ih87k905xS9fdsCAg3+7w0H3DJFYUuackLZvNanxBzSe2LJY9yEz71upsrVO1ZArNGwojFZiWKqzjsMGcUZJ82kzHuqBayWODap2HajbD+c7OCKbj6+ftlJJtTngC6PAn+lVwfy7mqtYBeb8Pse83Vtqo0NafHXYDDYXD46FFObZpLrY0TCcw4AsCGwC58Hnwf+9cGkfT77ySlZ5N4OpscxyVKYorIw2ohsqIfNUPNpEutSv7UDPUjOiyAiBBfrNYLTG/DvNip6O9FRX8vWtbM27fIMAxOpmXhYbUS7FeE3zGrFSKuMm9dnjKXXHf3z1Cc3nvvPV5//XWOHTtG8+bNeffdd2nTpk2++2ZnZzNhwgQ+++wzDh8+TP369Xnttdfo3r17CUedv8AzSR715BERKT29evUiOzub+fPnn/faH3/8QadOndi4cSPNmjUr1Lhr167F39/fVWEC8PzzzzNnzhw2bNiQZ/vRo0epUOECfRpdZNq0aTzyyCMkJia69TwlpUhJHndchBRmTBEpJocD1nwIfpXMhqwX6pfibsc2w5+fwqZvzD4oYK76E9YQOj1mNi62XqK5bPopWPAsbJhuPg+pCTe/a05V2jDDXI47NQ5WvGveqsaY1T1NbzNXHyqMnQtg5ZnVonp/YPaTKQiLBW55D96/2qyS+P11M2HiKiv/CwdXm0uN3/K++5oER3WEqGtg/x/mUtw3vXX+Pn+8AWknILQOtC5kn52ian6n2Yh51ftmsqtSXajckJSMbHYcS2H7kVOk7l1NhSN/UC/tT5qxi0iLmbw5ZFRiXPZQfjlxFZxwAInnDe/nZaOivxehAd6E+nuZt9zHAV4E+niSY3eQmeMgK8dBZo6dzByH85aV48DucBAR4ktUJX+iQv2JqOCLp831PyeLxUJogLfrBgwIc91YclEzZ85kzJgxTJ48mbZt2zJp0iS6devGjh07qFy58nn7P/vss0yfPp2PP/6YBg0asGDBAvr06cOKFSto0aJFKbyDvIJ8zUtMVfKIiJSe4cOH07dvXw4dOkT16nm/nPv0009p1apVoRM8AGFhJXd9UKVKlRI7V3lR6CSPOy5CCjumiBTTmo/O9kzZ84v5j3VPn5I5d9Zp+HuWmdw5/OfZ7RVrm1UoW2bDiW3w/XBzKk2nx8ymxflVzGyfB/97FFKPARZoex9c9xx4nflmoWpzuP4lc+rJxq/M6SdHN5i3BU9D495mr5mCTG1KOgyz7zMft72v8Mt2B1U1P+fvhsHvb0Cd613TiDluK/zysvm4x78Lnngqqmufhk97wPovzGqekBpnXzu5F1Z9YD6+4RXw8CrQkHaHwfGUDI4lZZCQmuWcEnUyLZOTadln7s1tiaezMQwDTw8rXjYrnjZzapSP9Qb+7bGM5lmbOPrhrYz1eIxqqX9zjXUTN1v/Jshy2jzZmXxmrDWSLSFdWF1tMA0Cgmjr60mInyfBvl4En3kc4udJiK8Xvl4FXMVKpBgmTpzIyJEjGTZsGACTJ09m7ty5TJ06laeeeuq8/b/44gueeeYZevY0/180atQoFi9ezJtvvsn06dNLNPb8OCt51JNHRKTU3HTTTYSFhTFt2jSeffZZ5/bU1FS+/fZbXn/9dRISEhg9ejS///47p06dIjo6mqeffpr+/S/cj++f07V27drF8OHDWbNmDbVr1+btt98+75gnn3yS2bNnc+jQIapUqcLAgQMZN24cnp6eTJs2jRdeeAEwv7ACMwk1dOjQ86Zrbd68mYcffpiVK1fi5+dH3759mThxIgEBAQAMHTqUxMREOnbsyJtvvklWVhZ33nknkyZNwtOzaJX0sbGxPPjggyxZsgSr1Ur37t159913CQ8PB2Djxo088sgj/Pnnn1gsFurWrcuHH35Iq1atOHDgAKNHj2bZsmVkZWURFRXF66+/7vz77Q6FTvK44yKksGOKSDHE74bFz599vnEGnNgO/aZfvJluccX9fbZqJzPJ3Gb1gAY3QathZq8aqxW6vmAmoVa+Bwm7zGk4S/8N1/zLrNiweUJagrnM9ZbvzHFC65iVMjWuPv+8Hl7Q8CbzlhYPm78z3/PRjWaVz9+zoeUw6PwEBFwgqWzPMadZpZ88kzh6sWifQZNbzaXCN38Ds++B+5adTUgVhT3b/HzsWeaUsJiBRR+roGq2h9pdYO9S+P0NjF5vk55tJ9tu4DvvabzsWWTU6MLx0GvIPpGK3WGQbXeQke0gLjmDo0kZHE1MN++TzPvjKZnYCzstKst+3qah3M9P3s9S3X6EafYxcM7f8XSPIE6Gt8dW9zpCm3WjRsWa1ADc9+dVpOCysrJYt24dY8eOdW6zWq107dqVlStX5ntMZmYmPj55k/O+vr4sW7bsgufJzMwkMzPT+Tw5ObmYkV9Y7hLqqZk52B0GtotMQRQRuWwZhjldvqR5+hWoEt/Dw4PBgwczbdo0nnnmGWcC5dtvv8Vut9O/f39SU1Np2bIlTz75JEFBQcydO5dBgwYRHR1doJk1DoeDW2+9lfDwcFavXk1SUlK+vXoCAwOZNm0a1apVY/PmzYwcOZLAwECeeOIJ+vXrx5YtW5g/fz6LFy8GIDj4/FVT09LS6NatG+3atWPt2rUcP36cESNGMHr0aKZNm+bc79dff6Vq1ar8+uuv7N69m379+hETE8PIkYWvMnc4HNxyyy0EBATw22+/kZOTwwMPPEC/fv1YunQpAAMHDqRFixZ88MEH2Gw2NmzY4EwoPfDAA2RlZfH777/j7+/P1q1bnQkpdylUkscdFyFFHbOkLlJEypXc3i056VCrM3R8xGwifGS9uUrRHV9AzXauPWdOlllts+Gcb5YrRMFVQ6DFXecnVnxDzIRL2/tg7SfmVKRT++DH0fDbf8zeOuumwel4c3nu9g+ZvUM8fS8di38luPo+83Zkg1kBs3sRrP3YnNrV/kFoPxq8A/Me9/t/zIbDXoFw26dmr52i6vm6OdbJvbDwObhpYtHH+v0NOLbJXEms19tum3aXmpnDwZOnzdupdPDoz3CWkrN+Oj3/bMXOrFDaWf/mK6+fyTGs3LSrJ7vfWFqoc9isFsIDvakU6O3sN1PRz4uKAeb0qIr+Z7dbLZB9ZnpUtt1MImXlOMiyOzgYX4XwXwdis2dij2iFZ72uEP1/+FaNIeJSU/9ESkl8fDx2u935jWCu8PBwtm/fnu8x3bp1Y+LEiXTq1Ino6GiWLFnCrFmzsNvPT4DmmjBhgvObUnfLreQBSM3IKVqPKBGRsi77NLxareTP+/SRAn9RePfdd/P666/z22+/0aVLF8Cskunbty/BwcEEBwfz2GOPOfd/8MEHWbBgAd98802BkjyLFy9m+/btLFiwgGrVzM/i1VdfpUePHnn2O7eSKCoqiscee4yvv/6aJ554Al9fXwICAvDw8Ljo9KwZM2aQkZHB559/7uwJ9N///pdevXrx2muvOf+OVqhQgf/+97/YbDYaNGjAjTfeyJIlS4qU5FmyZAmbN29m3759REaaFfOff/45jRs3Zu3atbRu3ZrY2Fgef/xxGjRoAEDdunWdx8fGxtK3b1+aNm0KQO3atQsdQ2EVKsnjjouQooxZkhcpIi6xe4mZRLB6mMkIT78z977m/6Bzt/mEmFOW3DV1auV/4dCaM71b3jOn9tyzFL6+C+I2w2c3QY//QKu7XZMwyEiCmYNg329mQqbBjWbVTO1rL903xicIrhkDbe81V8ta/g4kxZo9XwAqN4Jb/gsRLYsWW7UYuOs7c8nxRePNRNdv/zYTS52fNJf+9vCCvb+ZySWAXpOKv2qVbwj0fh8+v8Vc8rx+D7PZdGEd+cvs7QPmSleBrpuvbBgGa/efYuqyfazel8Cp0//sqRFIXc+mdLJtZrjje8YygnEeXwDwpaMrsbYa+FkteFgteNiseFgteNqshAd5UzXEl6pBPlQN8aVasA9Vgn2oFuJLpQBv13zTX78yXLUVrDas/0zWiZQjb7/9NiNHjqRBgwZYLBaio6MZNmwYU6dOveAxY8eOZcyYMc7nycnJzgtWV/PysOLjaSUj20FyRraSPCIipaRBgwa0b9+eqVOn0qVLF3bv3s0ff/zBiy+alel2u51XX32Vb775hsOHD5OVlUVmZiZ+fn4FGn/btm1ERkY6EzwA7dqd/6XxzJkzeeedd9izZw+pqank5OQQFBRUqPeybds2mjdvnqfpc4cOHXA4HOzYscOZU2jcuDE229kv96pWrcrmzZsLda5zzxkZGZnn72WjRo0ICQlh27ZttG7dmjFjxjBixAi++OILunbtyu233050tPlvhoceeohRo0axcOFCunbtSt++fYvUB6kw3L66VlEuQi6lJC9SRIpt2//gm8HmEs0FEVDFrChpNax4U3n+6fh2+OUV83H3V8/2bqkQBcMXwA+jzV45c8eYPWt6vlG8ipXkI/Dl7RC3BTz94Y7PipbM8PI3P4/WI2DdZ+ZUq3o9zARQceLLVasTjPwFtv5grgp1cg/8/Dises+cIvbLy4ABLQaZzZpdoXYXuPp+s1HwDw/AqJXgH1rw47MzzP5Aht1sTt2kr0vCyspxMG/zUaYs28fmw0l5Xgvx8ySygh+RFX2JrOBHmvUJWDWIOzz/oE/HpnitisXwCWHIQx8ypLANrV3NN6R0zy9SSJUqVcJmsxEXF5dne1xc3AW/0QwLC2POnDlkZGSQkJBAtWrVeOqppy76DaG3tzfe3i5szH0JQT6eZGRnkqzmyyJSXnn6mVU1pXHeQhg+fDgPPvgg7733Hp9++inR0dF07twZgNdff523336bSZMm0bRpU/z9/XnkkUfIyspyWbgrV65k4MCBvPDCC3Tr1o3g4GC+/vpr3nzzTZed41z/7L1jsVhwOBxuOReYK4MNGDCAuXPn8vPPPzN+/Hi+/vpr+vTpw4gRI+jWrRtz585l4cKFTJgwgTfffJMHH3zQbfEUKsnjjouQooxZ0hcpIkW2a7HZaNewQ8NeENkWstPN0s6s0+Z9dvrZbSe2Q8pRWPiMuXrR1fdDm5HF/0erPQfm3GcuK173BjNhcS4vf7htqtlvZskL5hLZx7eZ07eCqhb+fHFb4cvbIPkw+FeGgd+alTPF4el7dqqVq1ksZhPmBmeWB1/6bzi1H3488z/fsAZmhZMrXTfOrPCK3wH/ewTu+Lzg1VO/vmL+rvhXhp7F/+OYeDqLGWti+WzFfuKSzamw3h5Wbr2qOv1aRxId5p9n6oWpIcRfj2X3IrxWvQuApcvYwq9YJiJ4eXnRsmVLlixZ4mws6XA4WLJkCaNHj77osT4+PkRERJCdnc3333/PHXfcUQIRF0ygjwfHUzK1jLqIlF8Wi2u/lHWTO+64g4cffpgZM2bw+eefM2rUKGd/nuXLl3PLLbdw1113Aebfn507d9KoUaMCjd2wYUMOHjzI0aNHqVrV/HfDqlWr8uyzYsUKatasyTPPPOPcduDAgTz7eHl5XXTKce65pk2bRlpamrOaZ/ny5VitVurXr1+geAsr9/0dPHjQWViydetWEhMT83xG9erVo169ejz66KP079+fTz/9lD59+gAQGRnJfffdx3333cfYsWP5+OOPy06Sxx0XIcUZU6RM2/c7zBxoNsVt1Bv6Tsl/hahz5WTBpq9h2Vtmz5ZfX4YV75hVLO0eMHvKFMWyt8zpPT7B0Oud/JMJFovZo6dKE7NPz6G18FEXuP1Ts9luQe373Zz+lZkEoXXhru+hQs2ixV3SbJ7Qejg062euErX8zMoAt30KXoX7xuSSPH3h1o/gk+tg24+waabZWPpSYleZS8GD2YenMBVA/7D3RCpTl+/j+3WHSc82/6iGBXoz+OqaDLy6JhX9L7E6VpexZk8jgEr1zc9ORIpkzJgxDBkyhFatWtGmTRsmTZpEWlqac1GKwYMHExERwYQJEwBYvXo1hw8fJiYmhsOHD/P888/jcDh44oknSvNt5BHkayaHtYy6iEjpCggIoF+/fowdO5bk5GSGDh3qfK1u3bp89913rFixggoVKjBx4kTi4uIKnOTp2rUr9erVY8iQIbz++uskJyfnSebkniM2Npavv/6a1q1bM3fuXGbPnp1nn6ioKPbt28eGDRuoXr06gYGB5xV2DBw4kPHjxzNkyBCef/55Tpw4wYMPPsigQYPOa/9SWHa7nQ0bNuTZ5u3tTdeuXWnatCkDBw5k0qRJ5OTkcP/999O5c2datWpFeno6jz/+OLfddhu1atXi0KFDrF27lr59zUr7Rx55hB49elCvXj1OnTrFr7/+SsOGDYsV66UUerqWOy5CLjWmyGUndhXMuBNyMsypRbd+fOkED5g9YK4aDM0HwNY5ZjXP8a2wbKKZdGg51Jy6VJhVsI5tht9eMx/3eP3SlTl1usLIX+HrgeZS5p/2gCpNodmd5nSli/V+2fSt2djZkQ2RV0P/ry7Pyg7vAOj8OFw9ykzSues9VIsxEyW/vATzHoeaHfJfAj0zFeJ3mtU7v70GGGQ16c+xsC4kHUoiMd1cWjwp3bwlns4iPdtuNiM+05A4K8dsVJz7PD3LzvZjKc5TNKwaxIiOtbipeVW8PQrYoLh6S3O62LafoMdrZpJMRIqkX79+nDhxgnHjxnHs2DFiYmKYP3++86I1NjYW6zm9zDIyMnj22WfZu3cvAQEB9OzZky+++IKQkJBSegfn0zLqIiJlx/Dhw5kyZQo9e/bM0z8n929Jt27d8PPz45577qF3794kJSVdZLSzrFYrs2fPZvjw4bRp04aoqCjeeecdunfv7tzn5ptv5tFHH2X06NFkZmZy44038txzz/H888879+nbty+zZs3i2muvJTEx0bmE+rn8/PxYsGABDz/8MK1bt86zhHpxpaam0qJFizzboqOj2b17Nz/88AMPPvggnTp1yrOEOoDNZiMhIYHBgwcTFxdHpUqVuPXWW509hO12Ow888ACHDh0iKCiI7t2789ZbbxU73ouxGIZRyHVrzQ7Wr7/+uvMi5J133qFt27YAdOnShaioKOcSZr/99hujRo3KcxHy73//O88v1qXGvJTk5GSCg4NJSkoqdPMmEZc7vN5sqpuZbDYY7v910RspOxyw82dzFaUj681tVk9oOQQ6PQGBl8hY52TBx9eafXEa3GQuk17QKUGZqeYy5Zu+MZM2YDZPrt3FTPg0vOlseaphwPJJZ5dmb3izmdhyVwPp8sSeA9N6wsHVEHWNuTz7ie3mdLkTO8xEW2JsnkOOGKF0y3yNFIpXXWSxwHUNKnN3x1q0qx3qLNstXPzZkJ4IAWHFikWuLPq7XTa4++cwesZ6/rfpKON7NWJYh1ouH19EpCRlZGSwb98+atWqdd7q0SKucqHfs8L8zS5Skqes0cWilBnHtsC0GyEj0azKGPida6b5GAbs/RV+fxMOLDO3efqZy4x3ePjCPXt+edlcgcm3Ijyw+vzlygvi9EmzIfPGmebKXLk8/c0+Q83ugB3zzFWpAK5+AG54+dKrZ8lZJ/fCBx0hO+2Cu5wwgtjlqM4OI5Jp9m4cMKrg62kjxM+TYF/zFuLnSYivF8F+nvh62vDysOLtYcXLw4qX7cz9mceeHlaiKwVQI9TF09BECkB/t8sGd/8cxs7azFdrYnm0az0e7lr30geIiJRhSvJISXBFksftq2uJXDFO7DQreDISIaIVDJjpuj4uFgtE/5952/eH2Rz50FpzGtefU81eOm3uzXu+w+vhjzOlizdNLFqCB8ypSq1HmLeEPWZlz6aZcGqf2T9o09e5QUK3V8zeQYLdYZCakUNyRjbJGdmkZuRgdxjkOAzshoHdfuaxwyDH4U1E02e5av0zpNqC2JYTwTZ7BLuNCHY6qrPLiCAiojqd6obRqV4Y34b5E+zrWfBpVSIipSDI17zMVE8eERGRkqMkj4grnNwLn98Mp+OhSjOz2bB3oHvOVesaGL7IrJ5Z8pI5nWfx87BqMnR+wuzp47CbvXEMOzS+1eyb4gqh0XDtWOjylJlk2vi1WeWTnQF9PnDdecqIbLuDI4npJKefSdakmwmblIycM49znNty98l9LSWzsD0oamLjM+yYiZuK/l50qluJ/vXD6FgnjLBArSgoIpeXIGdPHiV5RERESoqSPCLFdWo/fHaLufR5WEMYNKf4S55fisViLvddrzts/tZcUjsxFuaOMVdeCm98dontG4u/xHa+549sY966/9tcmt1dSa1SkJlj5+s1B/nvr7s5kZJZrLF8PK0E+XgS4O2Bp82K1WrBw2rBdu69zYLVYsHbw0pMZAid61WmcbUgrNYi9MgRESkjgnxyK3nUeFlERKSkKMkjUhj2bLOJ8aE/zdvhPyFht/laxWgY/EOxlrQuNKvNXHa7cR9Y9xn8/h9zGtWpfebrvd52/+pWHl7mrRzIsTuYtf4wby/ZxeHEdMBM0oT4ehHk60GQjydBvp4E+XicufckyNeDQJ+zj3P3CfTxINDHQ1OqROSKFahKHhERkRKnJI/IhWSmQvIRM6lzeJ05PenoRnNZ9H+KaAl3fH7p1a7cxcMb2t4DMQNg9Qew5hNo0hca9CydeC4zDofB/zYfZdKineyNN5sfVw705sHr6tKvVSReHmoiLSJSWGd78qiSR0TKD4fDUdohSDnmit8vJXnkypWwx6zCST4MyUfNhE7KEfM++Yi5BHp+fILNxsrVW5n3ES1LtnrnYrwDoNPj5u0Kk5qZw57jqew+nsq++DR8vWxEVvSjRkU/Iiv4UtHf67wlwg3DYPG247y5cAfbj6UAUMHPk/u71GFQu5r4eKoKR0SkqJyVPOmq5BGRy5+XlxdWq5UjR44QFhaGl9f515YiRWUYBllZWZw4cQKr1YqXV9FnSijJI1cOw4Bjm2Hbj7D1R4jfceljvIOgYi2o3vpsYqditJYHL0Un07LYfSaZs+t4CruPp7LneCpHkvKpsDqH/5mkT2RFPyIr+FEtxIf/bTrKhoOJAAR6ezCyU23u7liLAG/9r1FEpLhyGy+rkkdEygOr1UqtWrU4evQoR44cKe1wpJzy8/OjRo0aWIvx7039S0bKN4fDnGq17QfY9pPZJDmX1RMqN4CgCAiqZt4Cq53zuCr4BJVa6FeyjGw7BxJOs/dEKnvj09h7Io298WaFTuLpC38jXCnAi+iwAKIrB5CRbefgydMcPJnOseQM0rLsbD+W4qzYyeXraWNohyju7VSbEL/y0VtIRKQsCDzTeDk5IxvDMPSNt4hc9ry8vKhRowY5OTnY7fbSDkfKGZvNhoeHR7H/XirJI+WPYUDsSvh7Nmz7nzkFK5eHD9TpCg1vhnrd3L8KllzS8ZQMNsQmsvFQIpsPJ7P3RCqHE9MxjAsfExHiS53KAdSpHEDdM/d1KgdcMEmTkW3ncGI6sSdPn0n8mMmfmqF+DL+mFpUDfdz07kRErlxBvmYlT7bdIDPHoSmwIlIuWCwWPD098fT0LO1QRPKlJI+UL0mHYe6/YOfPZ7d5BZoJnYa9oO714OVfevFd4dIyc9hyOIkNB82kzobYxAtOswr08aB2WAC1K/mbt7AAalXyp1Ylf3y9CvcPBR9Pm1nhExbgirchIiIF4O9lw2oBh2H25VGSR0RExP2U5JHyweGAP6fA4hcgK8WcitX0dmh0C9TuAp6q1ChpOXYHO+JS2HgwiU2HEtlwMJGdcSk4/lGhY7FAvcqBNI8Mpln1EOqFB1I7zJ/QfBoli4jI5cNisRDo40lSejbJGTlU1gxoERERt1OSRy5/x7fBjw/BoTXm8+pt4OZ3oHLD0o3rMmYYBusOnOKbPw9yNCmD8CAfwoO8qRx45j7Ih/AgH8ICvPHysGIYBrEnT5sVOmeSOluOJJGRff4SgFWDfYiJDKF5ZAjNq4fQtHqwGh2LiJRTgT4eZ5I8WmFLRESkJOhfVnL5ys6AP96EZW+BI9ucltV1PLQartWviijxdBaz1h/mqzWx7DqeWqBjQv29sBtGvg2RA308aF49xFmlExMZQniQqqpERK4U5gpb6VphS0REpIQoySOXpwMrzOqdhF3m8/o9oecbEBxRunFdhgzDYO3+U3y1Jpa5m4+SlWNW3/h62ujVvCqtalbkRGomx5MziEvOJC4lg+PJmRxPySDbbpCQlgWAl81Ko2pBNK8ebFbpRIZQK9Qfq1VTrkRErlTOFbbSVckjIiJSEpTkkctLWjz88jKs+9R8HhAOPf5j9t5R/5ZCiU/NZM5fZtXOnhNpzu2NqgYxoG0NbompRqDPhVcNcDgMTp3O4nhKJnaHQb3wQLw8VEElIiJn5a6wpUoeERGRkqEkj1wekg7Dindh3TTISTe3XTUErn8BfCuUamhlnWEYHDqVzt9Hktl6NJmtR5LZdjSZw4npzn38vGzcElON/m1q0DQiuEANj61WC6EB3oQGeLszfBERuYw5K3nUk0dERKREKMkjZdvJvbBsEmyYYfbdAajWAm54GaI6lmpoZVWO3cG8Lcf4K/YUW88kdi70DWrTiGD6t6nBzTHV1PxYRERcLuhMRaima4mIiJQM/atOyqbj2+CPibDlOzDOrNBUswNc8y+I/j9NzbqA1XsTGPfD3+yIS8mz3dNmoV54II2qBtGoWhCNqgbRsFqQ8+JbRETEHYLOVPJoupaIiEjJUJJHypYjf8Hvb8D2/53dVud6M7lTs13pxVXGxSVn8Oq8bfyw4QgAIX6e9GkRQeNqwTSqGkSdygHqlyMiIiUutyePpmuJiIiUDCV5pPSlJ8Lfs2HjV3Bw9ZmNFmjYy0zuVIspxeDKtmy7g0+X7+PtxbtIy7JjsUD/NjV4/Ib6VPD3Ku3wRETkCpdbMapKHhERkZKhJI+UDocd9vwKG2fA9rmQk2Fut9ig6W3QcQxUblC6MZZxy3fHM/7Hv9l9PBWAmMgQXrqlCU2rB5dyZCIiIiYtoS4iIlKylOSRknV8u5nY2TgTUo+d3R7WEGIGQLM7ILBK6cV3GTiSmM4rc7cxd/NRAEL9vXiyRwNuu6o6Vqt6FYmISNmhJdRFRERKlpI8UjIOrIQFT8OR9We3+VaEprdDTH+oGqNmyheRlJ7N0h3HWbg1jiXb4sjIdmC1wOB2UTx6fT2CfdVAWUREyh4toS4iIlKylOQR98vJgm8GQ9pxsHpA3W5mYqduN/BQ35gLOZqUzuKtcSzcGsfKPQnkOAzna22iKvLCLY1pWDWoFCMUERG5OPXkERERKVlK8oj7bf3BTPAEVoV7/4CAsNKOqMwxDIPTWXYOnjrNkm3HWfj3MTYeSsqzT53KAdzQKJzrG4UTExmCRZVPIiJSxuVW8qRm5mB3GNg0rVhERMStlOQR91vzkXnf6u4rNsGzbFc88/8+SnJ6DikZ2aRm5pCSkXszn59TqAOYs9euqlHBmdipHRZQOsGLiIgUUaDP2enEqRk5BPtperGIiIg7Kckj7nXkLzi0BqyecNWQ0o6mxB1LyuCl/211Nkm+FG8PK+2jQ7mhcRWua1iZyoE+bo5QRETEfbw8rPh4WsnIdpCcka0kj4iIiJspySPuteZj875xHwgML91YSlCO3cFnKw8wceEO0rLsWC3Qr3Uk0WEBBPl4EuDjQaCPB4E+ngR4exB05rGPp1XTsEREpFwJ8vEkIztTzZdFRERKgJI84j5pCbD5O/Nxm3tKN5YStD72FM/O3sLWo8kAtKgRwsu9m9C4WnApRyYiIlLyAn08OJ6SSXK6mi+LiIi4m5I84j7rPwN7JlRrAdVblXY0bpd4OovX5u/g67WxGAYE+3ryZPcG3Nk6EqsaTYqIyBUqyDd3hS1V8oiIiLibkjziHvYc+HOq+bjNPWYX4XLKMAy+X3+YCfO2kZCWBUDfq6rzdM8GhAZ4l3J0IiIipSu3+XKyllEXERFxOyV5xD12/gxJB8EvFBrfWtrRuExSeja7j6ey+3gKu+JS2XU8lZ1xKRxNygCgbuUAXu7dhLa1Q0s5UhERkbIh6Mwy6qrkERERcT8lecQ9cpdNv2oIeF5+K0QlZ2Sz81gK24+lsPt4KrvOJHWOp2Tmu7+vp42Hu9ZleMdaeNqsJRytiIhI2eWs5FFPHhEREbdTkkdc7/h22Pc7WKzQ6u7SjuaisnIc7I1PZceZhM6OM7fDiekXPKZqsA91KgdQp3IAdSsHUjc8gAZVAp0XsSIiInJWkK8qeUREREqKkjxXsoQ9EFgFvPxdO25uFU/9nhAS6dqxXeBoUjo/bjjC3M1H2XY0mWy7ke9+VYJ8qF8lkPpVAs8kdAKIrmwugS4iIiIFE+TsyaMkj4iIiLspyXMlOrETFj8PO+ZCpXpw9wLwq+iasTOSYOPX5uO297pmTBdISs9m/pajzP7rMKv3ncQ4J68T6O3hTOY0qBJIvfBAGlQJIthPyRwREZHiOtuTR9O1RERE3E1JnitJShz89m9Y9xkYdnNb/E74eiAMmu2a3jkbZkB2GoQ1hKhrij9eMWTm2Pl1+wnm/HWYX3YcJyvH4XytTVRFbmlRjc71wogI8cVSjlf/EhERKU2BquQREREpMeoQeyXITIWlr8E7LcxlzQ27OZWq35fgHQSxK+CH+8HhuPRYF+NwwJqPzcdtRpbKsumGYbBm30me+n4TrV9ezH3T1zH/72Nk5TioFx7AE93rs+zJa/nmvnYMbFuT6hX8lOAREZEy4b333iMqKgofHx/atm3LmjVrLrr/pEmTqF+/Pr6+vkRGRvLoo4+SkZFRQtEW3NmePKrkERERcTdV8pRn9hzYMB1+fRVS48xtES3h+pcgqoP53DsApveFLd9DSA3o+nzRz7fnFzi5x0wcNetX7PAL40BCGrPWH2bWX4c4ePJs0+SqwT7c3Lwat8RE0LBqoBI6IiJSJs2cOZMxY8YwefJk2rZty6RJk+jWrRs7duygcuXK5+0/Y8YMnnrqKaZOnUr79u3ZuXMnQ4cOxWKxMHHixFJ4Bxd2dnUtVfKIiIi4m5I8l6PsDMhJN5M4jmywZ4Mjx7y3Z5nbEmNh6b/hxHbzmApRcN14aNwnb4VN7S5w87swZxQse8tM9BR1RazchssxA83kkZslpWczd9NRZq0/xJ8HTjm3B3h70LNpFfq0qE7bWhWxWpXYERGRsm3ixImMHDmSYcOGATB58mTmzp3L1KlTeeqpp87bf8WKFXTo0IEBAwYAEBUVRf/+/Vm9enWJxl0QZxsvq5JHRETE3ZTkudz8PRu+H2EmdQrCtwJ0ftJM3Hh4579PzIAzSaEJMPdfEFQd6t1QuLhO7oVdC83HbUYW7thCcDgMlu48zvfrD7Noa5yzz47VAh3rhtH3qghuaFQFXy+b22IQERFxpaysLNatW8fYsWOd26xWK127dmXlypX5HtO+fXumT5/OmjVraNOmDXv37mXevHkMGjTogufJzMwkMzPT+Tw5Odl1b+IiAn3OLqFuGIaqakVERNxISZ7LzZbv8yZ4rB5g9QSbp/nY5mk+9/SBBjdBx0fBN+TS43Z+0kz0bPgSvh0Kw+ZBtZiCx7V2CmBAna4QGl2491RAv+88wb9/3s7Wo2cvSuuHB9K3ZQS3xEQQHuSCxtEiIiIlLD4+HrvdTnh4eJ7t4eHhbN++Pd9jBgwYQHx8PB07dsQwDHJycrjvvvt4+umnL3ieCRMm8MILL7g09oII8jUrebLtBhnZDn0RIyIi4kZK8lxujmww7wf/CLU6ua65scUCN02C5MOwdynMuANGLDanb11KVhr89YX5uI3rl03fcjiJf/+8nWW74wFzyfPbWlWn71XVaVwtSN8IiojIFWfp0qW8+uqrvP/++7Rt25bdu3fz8MMP89JLL/Hcc8/le8zYsWMZM2aM83lycjKRkZFuj9Xfy4bVAg7DrOZRkkdERMR9lOS5nKTFQ9JB83G1Fq5fvcrDC+74HKb2gON/w5e3w90LLl0JtOkbyEgy+/7U6eqycA6ePM2bC3cwZ8MRALxsVga1q8noa+tQwd/LZecREREpTZUqVcJmsxEXF5dne1xcHFWqVMn3mOeee45BgwYxYsQIAJo2bUpaWhr33HMPzzzzDFbr+Quoent74+19ganbbmSxWAj08SQpPZvkjGwqq/JWRETEbZTkuZzkVvGE1gWfIPecwycYBn4Dn3Q1mzbPvAvumgVWGyQfgVP74OQ+swdP7uP4XeaxrUdCPheVhXUqLYv3ft3N5ysPkGU3e+7cElONx26oT2RFv2KPLyIiUpZ4eXnRsmVLlixZQu/evQFwOBwsWbKE0aNH53vM6dOnz0vk2GxmhYxhGG6NtygCfTzOJHnUfFlERMSdlOS5nBz9y7wvTK+cogiuDgO+gU97wP4/YFITSD9lrtx1IYHVoMXAYp32dFYOn604wPtLd5Ny5iKwQ51QxvZoSJOI4GKNLSIiUpaNGTOGIUOG0KpVK9q0acOkSZNIS0tzrrY1ePBgIiIimDBhAgC9evVi4sSJtGjRwjld67nnnqNXr17OZE9ZYq6wla5l1EVERNxMSZ7LSW4lT7UW7j9X1WZw+2dmb57UM+XjVk+zR0/FWlChVt77irUvvHrXRRiGwYaDiXzz50F+2niU1EwzudOwahBP9WhAp7qV1HNHRETKvX79+nHixAnGjRvHsWPHiImJYf78+c5mzLGxsXkqd5599lksFgvPPvsshw8fJiwsjF69evHKK6+U1lu4qCDf3BW2VMkjIiLiThajLNb0FlJycjLBwcEkJSURFOSmaUxlwcTGkHwIhs6DqA4lc864vyHthJnMCa5uTttygZNpWcz+6zDfrD3IjrgU5/aoUD8euq4uvWMisFqV3BERKY+umL/bZVxJ/hxGfv4ni7bG8UqfJgxsW9Ot5xIRESlvCvM3W5U8l4vUE2aCB4tZZVNSwhu7bCiHw2DZ7nhm/nmQRX/HOfvteHtY6dm0Kv1aR9K2VkVV7oiIiJQz5nQtVfKIiIi4m5I8l4ujG8z7SnXBO7BUQymK79Yd4q1FOzmcmO7c1iQiiH6tIrk5JoJgX89SjE5ERETcKdDHvORUTx4RERH3UpLncnEkt+lyCfTjcSHDMHhr0U7e+WU3AEE+HvRuEcEdrSLVTFlEROQKEeSrSh4REZGSUKT1rt977z2ioqLw8fGhbdu2rFmz5qL7T5o0ifr16+Pr60tkZCSPPvooGRkZzteff/55LBZLnluDBg2KElr5ldt0uWpMaUZRKNl2B09+v8mZ4Bl9bR3WPNOVF29pogSPiIjIFSQot5InQ5U8IiIi7lToSp6ZM2cyZswYJk+eTNu2bZk0aRLdunVjx44dVK5c+bz9Z8yYwVNPPcXUqVNp3749O3fuZOjQoVgsFiZOnOjcr3HjxixevPhsYB4qMsrjMqvkOZ2VwwNfrufXHSewWuCVPk3p36ZGaYclIiIipUA9eUREREpGoTMpEydOZOTIkQwbNgyAyZMnM3fuXKZOncpTTz113v4rVqygQ4cODBgwAICoqCj69+/P6tWr8wbi4UGVKlWK8h7Kv5Q4SDkCWKBK09KO5pLiUzMZPm0tGw8l4eNp5b/9r6Jro/DSDktERERKiXryiIiIlIxCTdfKyspi3bp1dO3a9ewAVitdu3Zl5cqV+R7Tvn171q1b55zStXfvXubNm0fPnj3z7Ldr1y6qVatG7dq1GThwILGxsYV9L+VXbtPlsPrgHVCqoVzKgYQ0bvtgBRsPJVHBz5MZI69WgkdEROQKp548IiIiJaNQlTzx8fHY7XbCw/P+oz08PJzt27fne8yAAQOIj4+nY8eOGIZBTk4O9913H08//bRzn7Zt2zJt2jTq16/P0aNHeeGFF7jmmmvYsmULgYHnrySVmZlJZmam83lycnJh3sblJ3eqVhnvx7PpUCJ3T1tLfGoW1Sv48tndbYgOK9tJKREREXG/QPXkERERKRFFarxcGEuXLuXVV1/l/fffZ/369cyaNYu5c+fy0ksvOffp0aMHt99+O82aNaNbt27MmzePxMREvvnmm3zHnDBhAsHBwc5bZGSku99G6cptulyG+/Es3XGcOz9aRXxqFo2qBjFrVHsleERERARQTx4REZGSUqhKnkqVKmGz2YiLi8uzPS4u7oL9dJ577jkGDRrEiBEjAGjatClpaWncc889PPPMM1it5+eZQkJCqFevHrt37853zLFjxzJmzBjn8+Tk5PKd6CnDTZcdDoOv1sYy/oe/yXEYdKxTiQ/uuorAMxdzIiIiIrmVPKmZOdgdBjarpZQjEhERKZ8KVcnj5eVFy5YtWbJkiXObw+FgyZIltGvXLt9jTp8+fV4ix2azAWAYRr7HpKamsmfPHqpWrZrv697e3gQFBeW5lVvJRyH1GFisZa7p8oaDifSdvIJnZm8hx2HQp0UEU4e2VoJHRERE8jj32iBV1TwiIiJuU+jVtcaMGcOQIUNo1aoVbdq0YdKkSaSlpTlX2xo8eDARERFMmDABgF69ejFx4kRatGhB27Zt2b17N8899xy9evVyJnsee+wxevXqRc2aNTly5Ajjx4/HZrPRv39/F77Vy5Sz6XID8PIr1VByHU/O4LX5O/h+/SEA/LxsPHxdXUZeUxurvpkTERGRf/DysOLjaSUj20FyRjbBfvpCSERExB0KneTp168fJ06cYNy4cRw7doyYmBjmz5/vbMYcGxubp3Ln2WefxWKx8Oyzz3L48GHCwsLo1asXr7zyinOfQ4cO0b9/fxISEggLC6Njx46sWrWKsLAwF7zFy1xuP54y0HQ5I9vO1OX7eO+X3aRl2QHoe1V1nuhen/Agn1KOTkRERMqyIB9PMrIz1XxZRETEjSzGheZMXUaSk5MJDg4mKSmp/E3d+vIO2LUAerwObe8plRAMw2DR1jhenruN2JOnAYiJDOH5mxsTExlSKjGJiMjlq1z/3b6MlPTP4bo3l7LnRBpfjbyadtGhbj+fiIhIeVGYv9mFruSREmQYZ6drVYsplRB2xqXw4k9bWbY7HoDKgd481aMBvWMiNDVLRERECizI15yipUoeERER91GSpyxLOQqpcWCxQXiTEj/98t3x3D1tLZk5DrxsVkZ2qsX9Xerg761fGxERESmcQC2jLiIi4nb613pZltuPpxSaLq/am8Dwz8wET4c6oUzo04waoWWj8bOIiIhcfoLOLKOenK5KHhEREXdRkqcsO/KXeV+tRYme9s/9J7l72loysh10qR/Gh4Na4u1hK9EYREREpHxRJY+IiIj7WS+9i5SaUujHs+FgIkM/XcvpLDvX1K3E5LuU4BEREZHiC/I9U8mjnjwiIiJuoyRPWWUYJV7Js/lQEoOmrCY1M4era1fko0Gt8PFUgkdERESKL8hZyaMkj4iIiLsoyVNWJR+BtBNnmi43dvvpth5J5q4pq0nJyKF1VAWmDGmNr5cSPCIiIuIaZ3vyaLqWiIiIuyjJU1blVvFUbgSevm491Y5jKdw1ZTVJ6dm0qBHCp8PaaAUtERERcSlnT55MVfKIiIi4i5I8ZZVzqlZzt55m9/FUBn6yipNpWTSrHsy0YW0IUIJHREREXMzZk0eVPCIiIm6jJE9Z5Wy67L5+PPvi0xjw8SriU7NoVDWIz+9uQ7Cvp9vOJyIiIleuQPXkERERcTslecqic5suV3VPkufgydMM+HgVx1MyaVAlkOkj2hLi5+WWc4mIiIjkNl5O1hLqIiIibqMkT1mUdAhOJ4DVwy1Nl+NTMxk8dQ1HkzKoUzmA6SPaUtFfCR4RERFxn8AzjZdTMrIxDKOUoxERESmflOQpi5xNlxuCp49Lh07NzGHYp2vZF59GRIgv04e3pVKAt0vPISIiIvJPQWemhGfbDTKyHaUcjYiISPmkJE9Z5KZ+PJk5du794k82H06ior8Xnw9vQ5Vg1yaRRERERPLj72XDajEfqy+PiIiIeyjJUxY5+/HEuGxIu8Pg0ZkbWL47AX8vG9OGtSY6LMBl44uIiIhcjMVicTZfTlaSR0RExC2U5ClrDAOObDAfu6iSxzAMxv2whXmbj+Fls/LR4FY0qx7ikrFFRERECsq5jLqaL4uIiLiFkjxlTWIspJ8Eq6fLmi5PWryLL1fHYrHAW/1i6FCnkkvGFRERESmMQO8zlTzpquQRERFxByV5yprcfjzhjcCj+A2RP1+5n7eX7ALgxVuacGOzqsUeU0RERKQocit5UlTJIyIi4hZK8pQ1LuzH8+PGI4z/8W8AHu1aj0FX1yz2mCIiIiJFpZ48IiIi7qUkT1njon48v+88wb++2YBhwJB2NXnoujrFj01ERESkGILOJHlUySMiIuIeSvKUJYZxtpKnWkyRh9l6JJn7pq8j227Qq3k1xvdqjMVicU2MIiIiIkUU6HOm8bJ68oiIiLiFkjxlSeIByEgEmxdUblSkITJz7Dw6cwOns+x0rFOJN29vjtWqBI+IiIiUviBfVfKIiIi4k5I8ZUluFU/lojddnrR4FzviUgj19+LtO2Pw8tCPWERERMqGoNxKHvXkERERcQtlAMqS2FXmfRH78ayPPcWHv+0B4NVbmxIaUPzVuURERERcJbcnj6ZriYiIuIeSPGVBwh74agCsnmw+j2xb6CHSs+w89s1GHAbc2iKCbo2ruDhIERERcbf33nuPqKgofHx8aNu2LWvWrLngvl26dMFisZx3u/HGG0sw4sLJ7cmj6VoiIiLu4VHaAVzR0hPh99dh9YfgyAaLDdqMhGZ3FHqo/yzYzt74NKoE+TC+V2PXxyoiIiJuNXPmTMaMGcPkyZNp27YtkyZNolu3buzYsYPKlSuft/+sWbPIyspyPk9ISKB58+bcfvvtJRl2oeT25NF0LREREfdQJU9psOfAmo/hnRaw8r9mgqfuDXD/SujxGlhthRpuxZ54Pl2+H4DXbmtGsJ+nG4IWERERd5o4cSIjR45k2LBhNGrUiMmTJ+Pn58fUqVPz3b9ixYpUqVLFeVu0aBF+fn5lOsmjSh4RERH3UiVPSdu9GBY8Aye2m8/DGkC3V6BO1yINl5qZw+PfbgJgQNsadK4X5qpIRUREpIRkZWWxbt06xo4d69xmtVrp2rUrK1euLNAYU6ZM4c4778Tf399dYRabevKIiIi4l5I8JeX4dlj0HOxaaD73rQj/9wxcNRRsRf8xvDJ3K4cT04ms6MvTPRu6JlYREREpUfHx8djtdsLDw/NsDw8PZ/v27Zc8fs2aNWzZsoUpU6ZccJ/MzEwyMzOdz5OTk4secBHlVvKkZdnJsTvwsKmoXERExJWU5HG349vh9//AllmAAVZPaHsvdHocfEOKNfSvO47z1ZqDWCzw+m3NCfDWj1NERORKNGXKFJo2bUqbNm0uuM+ECRN44YUXSjCq8wX6nJ1SnpqZQ4ifVylGIyIiUv7o6xN3Ob4dvrsb3r8atnwPGNCwFzyw2pyeVcwET+LpLJ78zpymdXeHWlxdO7T4MYuIiEipqFSpEjabjbi4uDzb4+LiqFLl4itmpqWl8fXXXzN8+PCL7jd27FiSkpKct4MHDxY77sLy8rDi42lefqovj4iIiOup9MPVjm+D3/4Df88GDHNbw17Q+Umo0tRlp3n+x785npJJ7TB/Hu9W32XjioiISMnz8vKiZcuWLFmyhN69ewPgcDhYsmQJo0ePvuix3377LZmZmdx1110X3c/b2xtvb29XhVxkQT6eZGRnkpSeTWRpByMiIlLOKMnjKiWU3AH4efNR5mw4gtUCE++IwcezcKtxiYiISNkzZswYhgwZQqtWrWjTpg2TJk0iLS2NYcOGATB48GAiIiKYMGFCnuOmTJlC7969CQ29PKp6A308OJ6SqUoeERERN1CSxxUWPw/LJuHu5A5AfGomz8zZAsD9XeoQExni8nOIiIhIyevXrx8nTpxg3LhxHDt2jJiYGObPn+9sxhwbG4vVmnem/Y4dO1i2bBkLFy4sjZCLJMj3zApbGVphS0RExNWU5CmutHhY9pb52I3JnVxvLtzJybQsGlQJ5KHr6rrtPCIiIlLyRo8efcHpWUuXLj1vW/369TEMw81RuVZu82VV8oiIiLiekjzFdXi9eV+pHvSb7tZTnUjJ5Pv1hwB48ZYmeHmob7aIiIhcXoLOLKOenK5KHhEREVdTlqC4jpxJ8lS7yu2n+mzFfrJyHLSoEULrqApuP5+IiIiIq6mSR0RExH2U5Cmuw+vM+wj3JnnSMnP4YtUBAO7tVBuLxeLW84mIiIi4Q5DvmUoe9eQRERFxOSV5isMwzk7XcnMlz8y1B0lKz6ZWJX+ub1TFrecSERERcZcgZyWPkjwiIiKupiRPcSQdhNPxYPVwa7PlbLuDKcv2ATDimlrYrKriERERkcvT2Z48mq4lIiLiakryFEfuVK3wxuDp47bTzNt8lMOJ6YT6e9H3qupuO4+IiIiIu+UuoZ6SqUoeERERV1OSpzhKYKqWYRh8+NteAIa2j8LH0+a2c4mIiIi4W6AqeURERNxGSZ7iOPKXeR/R0m2nWL47ga1Hk/H1tHHX1TXddh4RERGRkqCePCIiIu6jJE9ROeznJHncV8nz4e97AOjXOpIK/l5uO4+IiIhISchdQj1ZS6iLiIi4nJI8RRW/C7JSwdMPKtV3yyn+PpLEH7visVktDO9Yyy3nEBERESlJuUuop2RkYxhGKUcjIiJSvijJU1RHzvTjqRoDNg+3nOLj381ePD2bViWyop9bziEiIiJSknIrebLtBhnZjlKORkREpHxRkqeoclfWctNUrUOnTvPTpqMA3NuptlvOISIiIlLS/L1sWC3m42T15REREXEpJXmKyrmyVgu3DD912X7sDoMOdUJpEhHslnOIiIiIlDSLxeKs5lHzZREREddSkqcocjIhbov52A0rayWdzubrtbEA3NMp2uXji4iIiJSm3L48SVpGXURExKWU5CmKuC1gzwLfilAhyuXDT199gNNZdhpUCaRT3UouH19ERESkNAV6q5JHRETEHZTkKYpzp2pZLC4dOiPbzqfL9wNwb+faWFw8voiIiEhpy63k0TLqIiIirqUkT1Ec+cu8d8NUrdl/HSY+NZNqwT7c1Kyay8cXERERKW3qySMiIuIeRUryvPfee0RFReHj40Pbtm1Zs2bNRfefNGkS9evXx9fXl8jISB599FEyMjKKNWapyq3kcfHKWg6H4Vw2/e6OtfC0KQcnIiIi5U/QmSRPsnryiIiIuFShswgzZ85kzJgxjB8/nvXr19O8eXO6devG8ePH891/xowZPPXUU4wfP55t27YxZcoUZs6cydNPP13kMUtVZgqc2G4+rubaJM+ibXHsjU8j0MeDO9vUcOnYIiIiImVFoI85XUuVPCIiIq5V6CTPxIkTGTlyJMOGDaNRo0ZMnjwZPz8/pk6dmu/+K1asoEOHDgwYMICoqChuuOEG+vfvn6dSp7BjlqqjGwEDgqpDYLjLhjUMg8m/7QHgrqtrEuDt4bKxRURERMqSIN8zlTxK8oiIiLhUoZI8WVlZrFu3jq5du54dwGqla9eurFy5Mt9j2rdvz7p165xJnb179zJv3jx69uxZ5DFLlXOqVguXDrtq70n+ik3Ey8PKsPZRLh1bREREpCwJclbyaLqWiIiIKxWqXCQ+Ph673U54eN4KlvDwcLZv357vMQMGDCA+Pp6OHTtiGAY5OTncd999zulaRRkzMzOTzMxM5/Pk5OTCvI3iObzOvHfxVK33l+4G4I5W1akc5OPSsUVERETKkrM9eVTJIyIi4kpu7+y7dOlSXn31Vd5//33Wr1/PrFmzmDt3Li+99FKRx5wwYQLBwcHOW2RkpAsjvoQjuZU8rltZa9OhRP7YFY/NauHeTtEuG1dERESkLApUJY+IiIhbFKqSp1KlSthsNuLi4vJsj4uLo0qVKvke89xzzzFo0CBGjBgBQNOmTUlLS+Oee+7hmWeeKdKYY8eOZcyYMc7nycnJJZPoSYuHxFjzcbUYlw37/q9mL56bm1cjsqKfy8YVERERKYvUk0dERMQ9ClXJ4+XlRcuWLVmyZIlzm8PhYMmSJbRr1y7fY06fPo3Vmvc0NpsNMJsNF2VMb29vgoKC8txKRG4/ntC64BPskiF3H09h/t/HABjVRVU8IiIiUv7lTtc6mZaFYRilHI2IiEj5UeglnMaMGcOQIUNo1aoVbdq0YdKkSaSlpTFs2DAABg8eTEREBBMmTACgV69eTJw4kRYtWtC2bVt2797Nc889R69evZzJnkuNWWY4p2q5rh/PB0v3AnBDo3DqhQe6bFwRERGRsqpO5QA8bRbiU7M4eDKdGqGqZBYREXGFQid5+vXrx4kTJxg3bhzHjh0jJiaG+fPnOxsnx8bG5qncefbZZ7FYLDz77LMcPnyYsLAwevXqxSuvvFLgMcuMw67tx3Po1Gl+2HAYgPuvreOSMUVERETKOl8vGzGRIazdf4qVe+OpEVqjtEMSEREpFyxGOaiRTU5OJjg4mKSkJPdN3TIMeL0OnI6H4YshsnWxhxz/wxY+W3mADnVC+XLE1S4IUkREpOwrkb/bckml/XN4c+EO3v1lN31aRPBWv5gSP7+IiMjlojB/s92+ula5kXTQTPBYPaBK02IPdyIlk6/XHgTggS6q4hEREZErS7vaoQCs3JOgvjwiIiIuoiRPQeVO1QpvDJ4+xR5u6vJ9ZOY4iIkMoV10aLHHExEREbmctKhRAS+blWPJGRxIOF3a4YiIiJQLSvIU1OF15n214jddTkrPZvrKAwDc3yUai8VS7DFFRERELie5fXkAVu5NKN1gREREygkleQrqyF/mvQtW1pq+6gApmTnUCw+ga8My1lxaREREpIRcfaaaeZWSPCIiIi6hJE9BOBxwZIP5uJgra6Vn2Zm6bB8A93epg9WqKh4RERG5Ml1duyJgJnnUl0dERKT4lOQpiIRdkJUCnn5QqX6xhpq5NpaEtCwiK/pyU7OqLgpQRERE5PJzVY0KeHlYiUvOZF98WmmHIyIictlTkqcgcvvxVG0ONo8iD5OV4+Cj3/cCcG+naDxs+vhFRETkyuXjaaPFmb48q/aeLN1gREREygFlGQoid2WtYk7V+mHDYY4kZRAW6M1tLau7IDARERGRy1vuKqNqviwiIlJ8SvIUxJEzSZ5qLYo8hN1h8MFvewAY0bEWPp42V0QmIiIiclm7uvbZ5svqyyMiIlI8SvJcSk4WHNtsPi7GyloL/z7G3hNpBPl4MPDqmi4KTkREROTyFhMZgpeHlRMpmew5ob48IiIixaEkz6XEbQF7FvhWgAq1ijzMx3+YvXiGdqhFgHfR+/qIiIiIlCc+njZa1qgAaCl1ERGR4lKS51KcU7WuAkvRljtPPJ3FXwcTARjYtoaLAhMREZHy5L333iMqKgofHx/atm3LmjVrLrp/YmIiDzzwAFWrVsXb25t69eoxb968EorWtXKnbKkvj4iISPEoyXMpzqbLRZ+qtWrvSQwD6lQOIDzIx0WBiYiISHkxc+ZMxowZw/jx41m/fj3NmzenW7duHD9+PN/9s7KyuP7669m/fz/fffcdO3bs4OOPPyYiIqKEI3eN3ObLq9WXR0REpFg0b+hSXLCyVm7pcfszFzAiIiIi55o4cSIjR45k2LBhAEyePJm5c+cydepUnnrqqfP2nzp1KidPnmTFihV4enoCEBUVVZIhu1TzyGC8PazEp2ax50QqdSoHlnZIIiIilyVV8lxMZgqc2G4+rlb0Sp4Ve+IBaFdbSR4RERHJKysri3Xr1tG1a1fnNqvVSteuXVm5cmW+x/z444+0a9eOBx54gPDwcJo0acKrr76K3W6/4HkyMzNJTk7OcysrvD1stIoy+/Ks3KMpWyIiIkWlJM/FHN0IGBAUAYHhRRriREomO+NSgbPzzUVERERyxcfHY7fbCQ/Pe60RHh7OsWPH8j1m7969fPfdd9jtdubNm8dzzz3Hm2++ycsvv3zB80yYMIHg4GDnLTIy0qXvo7iurpW7lPrJUo5ERETk8qUkz8XUaAf3r4LeHxR5iNwGgo2qBlHB38tVkYmIiMgVzOFwULlyZT766CNatmxJv379eOaZZ5g8efIFjxk7dixJSUnO28GDB0sw4kvL7cuzSn15REREikw9eS7GaoPKDc1bEa08M1VL/XhEREQkP5UqVcJmsxEXF5dne1xcHFWqVMn3mKpVq+Lp6YnNZnNua9iwIceOHSMrKwsvr/O/WPL29sbb29u1wbtQs+oh+HhaSUjLYtfxVOqFqy+PiIhIYamSx81WnJlX3r6OkjwiIiJyPi8vL1q2bMmSJUuc2xwOB0uWLKFdu3b5HtOhQwd2796Nw+Fwbtu5cydVq1bNN8FzOfDysNKqZkVAfXlERESKSkkeNzqcmM6BhNPYrBZaR1Us7XBERESkjBozZgwff/wxn332Gdu2bWPUqFGkpaU5V9saPHgwY8eOde4/atQoTp48ycMPP8zOnTuZO3cur776Kg888EBpvQWXOHfKloiIiBSepmu5Ue63UM2qBxPo41nK0YiIiEhZ1a9fP06cOMG4ceM4duwYMTExzJ8/39mMOTY2Fqv17HdzkZGRLFiwgEcffZRmzZoRERHBww8/zJNPPllab8Elrq5tfim2am8CDoeB1Wop5YhEREQuL0ryuJGWThcREZGCGj16NKNHj873taVLl563rV27dqxatcrNUZWsZtVD8PW0cep0NjuPp9CgSlBphyQiInJZ0XQtNzEMw1nJ0z66UilHIyIiIlL2edqstIqqAMAq9eUREREpNCV53GR/wmmOJmXgZbPSsmaF0g5HRERE5LKQ25dnpfryiIiIFJqSPG6SO1WrRY0QfL1sl9hbRERERACuPjPNffW+kzgcRilHIyIicnlRksdNVmiqloiIiEihNY0Ixs/LRuLpbLYfSyntcERERC4rSvK4gWEYznnk7euo6bKIiIhIQXnarLSOOrvKloiIiBSckjxusDMulYS0LHw9bTSvHlLa4YiIiIhcVnKnbKkvj4iISOEoyeMGuf14WkVVwMtDH7GIiIhIYeQ2X16jvjwiIiKFogyEG6gfj4iIiFyxdi6EKd1g3hNFHqJJtSACvD1ISs9m69FkFwYnIiJSvinJ42J2h+GcP94+Wv14RERE5ArjyIaDq2D/H0UewsNmpXVUBUB9eURERApDSR4X+/tIEikZOQT6eNC4WlBphyMiIiJSsiJamffHt0FG0atwcvvyrNp70hVRiYiIXBGU5HGxlWemarWtFYqHTR+viIiIXGECwyE4EjDgyF9FHiY3ybN6XwJZOQ4XBSciIlK+KQvhYrn9eNppqpaIiIhcqSJamveH/yzyEI2rBVE50JuUjBx+2HDYRYGJiIiUb0ryuFBWjoO1+82SYvXjERERkStW9TNTtg6tK/IQHjYrwzvWAmDyb3u0ypaIiEgBKMnjQpsOJXI6y05Ffy/qhweWdjgiIiIipaN6a/P+8J9gFD05M/DqmgT5eLDnRBoLtx5zUXAiIiLll5I8LuScqlU7FKvVUsrRiIiIiJSSqs3B6gGpcZB0sMjDBHh7MKR9FADvL92DUYyEkYiIyJVASR4XWrEnHlA/HhEREbnCefpCeGPz8aGi9+UBGNo+Ch9PK5sOJTm/UBMREZH8KcnjIhnZdtbHJgLqxyMiIiLiXEr9cNH78gCEBnhzZ+saALy/dHdxoxIRESnXlORxkfUHTpGV4yA8yJtalfxLOxwRERGR0pXbl6eYlTwAIzvVxsNqYfnuBDYeTCz2eCIiIuWVkjwukls+3D66EhaL+vGIiIjIFS53ha2jG8CeXayhIkJ8uSUmAlA1j4iIyMUoyeMi6scjIiIico6K0eATDDkZELel2MPd17k2AAv+jmP38ZRijyciIlIeKcnjAqmZOWw8lASoH4+IiIgIAFYrRLQ0H7tgylbd8EBuaBQOwOTf9hZ7PBERkfJISR4XWLvvJHaHQY2KflSv4Ffa4YiIiIiUDS5qvpzr/mvrADDnr8McTkx3yZgiIiLliZI8LpA7VUtVPCIiIiLncGHzZYCYyBDaR4eS4zD4+HdV84iIiPyTkjwusHKv2XRZ/XhEREREzpE7XSthF6SfcsmQo7pEA/D12lhOpmW5ZEwREZHyQkmeYsqxO/j7SDIAraMqlnI0IiIiImWIfyhUqGU+dtGUrY51KtE0IpiMbAfTlu9zyZgiIiLlhZI8xZSSkYNhmI/DAr1LNxgRERGRsiZ3KfVDrknyWCwW7j9TzTNtxX5SM3NcMq6IiEh5oCRPMaVkmBcWvp42PG36OEVERETyyO3Lc9g1fXkAujWuQu0wf5Izcpix+oDLxhUREbncKStRTMkZ2QAE+XqUciQiIiIiZVDuCluH/sRZ/lxMVquF+zqZ1Tyf/LGPzBy7S8YVERG53CnJU0zJ6WeSPD6epRyJiIiISBlUpQnYvCD9JJx03YpYvVtEUDXYh+Mpmcxaf9hl44qIiFzOlOQppuQz07WCfJXkERERETmPhzdUaWY+dlHzZQAvDysjrqkNwIe/7cHucE2VkIiIyOWsSEme9957j6ioKHx8fGjbti1r1qy54L5dunTBYrGcd7vxxhud+wwdOvS817t3716U0Epc7nStQB9N1xIRERHJV25fnkOu68sDcGfrSCr4ebI/4TRzNx916dgiIiKXo0IneWbOnMmYMWMYP34869evp3nz5nTr1o3jx4/nu/+sWbM4evSo87ZlyxZsNhu33357nv26d++eZ7+vvvqqaO+ohGm6loiIiMgl5K6w5cLmywD+3h7c3cFcov29X3bjUDWPiIhc4Qqd5Jk4cSIjR45k2LBhNGrUiMmTJ+Pn58fUqVPz3b9ixYpUqVLFeVu0aBF+fn7nJXm8vb3z7FehQoWivaMSluKcrqVKHhEREZF8RbQ0749thpxMlw49uH0Ugd4e7IhLYdG2OJeOLSIicrkpVJInKyuLdevW0bVr17MDWK107dqVlStXFmiMKVOmcOedd+Lv759n+9KlS6lcuTL169dn1KhRJCQkXHCMzMxMkpOT89xKy9npWqrkEREREclXhSjwCwV7Fhzd5NKhg309GdI+CoB3f9mF4aIVvERERC5HhUryxMfHY7fbCQ8Pz7M9PDycY8eOXfL4NWvWsGXLFkaMGJFne/fu3fn8889ZsmQJr732Gr/99hs9evTAbs9/OcwJEyYQHBzsvEVGRhbmbbhUcvqZSh4leURERETyZ7GcXUrdxVO2AO7uWAtfTxtbDiezdOcJl48vIiJyuSjR1bWmTJlC06ZNadOmTZ7td955JzfffDNNmzald+/e/O9//2Pt2rUsXbo033HGjh1LUlKS83bw4MESiD5/uZU8mq4lIiIichFuar4MUNHfi7uurgHAu0tUzSMiIleuQiV5KlWqhM1mIy4u73znuLg4qlSpctFj09LS+Prrrxk+fPglz1O7dm0qVarE7t27833d29uboKCgPLfSkqLpWiIiIiKXVv1MXx43VPIAjLymNl4eVtbHJrJy74Wn/YuIiJRnhUryeHl50bJlS5YsWeLc5nA4WLJkCe3atbvosd9++y2ZmZncddddlzzPoUOHSEhIoGrVqoUJr1Scna6lSh4RERGRC6p2lXl/aj+kxbt8+MpBPtzZ2pzC/99f8v+iUEREpLwr9HStMWPG8PHHH/PZZ5+xbds2Ro0aRVpaGsOGDQNg8ODBjB079rzjpkyZQu/evQkNDc2zPTU1lccff5xVq1axf/9+lixZwi233EKdOnXo1q1bEd9WyTk7XUuVPCIiIlI87733HlFRUfj4+NC2bVvWrFlzwX2nTZuGxWLJc/Px8SnBaAvJNwQq1TMfu2HKFsC9naPxtFlYsSeBdQdOuuUcIiIiZVmhkzz9+vXjjTfeYNy4ccTExLBhwwbmz5/vbMYcGxvL0aNH8xyzY8cOli1blu9ULZvNxqZNm7j55pupV68ew4cPp2XLlvzxxx94e3sX8W2VHOcS6qrkERERkWKYOXMmY8aMYfz48axfv57mzZvTrVs3jh8/fsFjgoKCOHr0qPN24MCBEoy4CHL78rhpylZEiC99r6oOqJpHRESuTEXKTIwePZrRo0fn+1p+zZLr169/wQZ4vr6+LFiwoChhlDqHw3D25NHqWiIiIlIcEydOZOTIkc7q6MmTJzN37lymTp3KU089le8xFovlkn0Ry5SIlrDhS7dV8gCM6hLNN38e5NcdJ9h8KImm1YPddi4REZGypkRX1ypv0rJycJzJXWm6loiIiBRVVlYW69ato2vXrs5tVquVrl27snLlygsel5qaSs2aNYmMjOSWW27h77//Lolwi6567jLq68HhcMspaob6c0tMBAD//XWXW84hIiJSVinJUwy5U7U8bRa8PfRRioiISNHEx8djt9ud099zhYeHc+zYsXyPqV+/PlOnTuWHH35g+vTpOBwO2rdvz6FDh/LdPzMzk+Tk5Dy3Ele5MXj4QmYSJLhvOtX9XaKxWGDB33HsOJbitvOIiIiUNcpMFEPyOVO1LBZLKUcjIiIiV5J27doxePBgYmJi6Ny5M7NmzSIsLIwPP/ww3/0nTJhAcHCw8xYZGVnCEQM2D6jWwnx8aK3bTlM3PJAeTcxpbO/9qt48IiJy5VCSpxicy6drqpaIiIgUQ6VKlbDZbMTFxeXZHhcXV+CeO56enrRo0YLdu/NPaowdO5akpCTn7eDBg8WOu0iqtzTv3dR8OdcD19YB4H+bjrD3RKpbzyUiIlJWKMlTDLlNlwO1spaIiIgUg5eXFy1btmTJkiXObQ6HgyVLltCuXbsCjWG329m8eTNVq1bN93Vvb2+CgoLy3EpFxJm+PG5svgzQuFow1zWojMOAD5buceu5REREygoleYohWStriYiIiIuMGTOGjz/+mM8++4xt27YxatQo0tLSnKttDR48mLFjxzr3f/HFF1m4cCF79+5l/fr13HXXXRw4cIARI0aU1lsomNzmy3F/Q9Zpt55q9P+Z1Tyz/zrMwZPuPZeIiEhZoBKUYjg7XUsfo4iIiBRPv379OHHiBOPGjePYsWPExMQwf/58ZzPm2NhYrNaz38+dOnWKkSNHcuzYMSpUqEDLli1ZsWIFjRo1Kq23UDBBERBQBVKPwdGNULNglUpF0aJGBa6pW4k/dsUz+bc9vNKnqdvOJSIiUhYoO1EMzula3qrkERERkeIbPXo0o0ePzve1pUuX5nn+1ltv8dZbb5VAVC5msZjVPNv/ZzZfdmOSB2D0tXX4Y1c83/55iNH/V4eqwb5uPZ+IiEhp0nStYkjOUCWPiIiISKHlTtlyc/NlgLa1Q2kTVZEsu4MJ87a7/XwiIiKlSUmeYkhOV08eERERkUKr3tq837UYYle7/XTP3dQIqwV+3HiEP3adcPv5RERESouSPMWQkqEl1EVEREQKrUY7iLoGstPgiz6w7w+3nq5p9WAGt4sC4Lk5W8jItrv1fCIiIqVFSZ5iSNYS6iIiIiKFZ7XBgG+g9rVmoufL22D3kksfVwz/uqEelQO92Z9wmve1pLqIiJRTSvIUg6ZriYiIiBSRlx/0/xrq3gA5GfDVnbBjvttOF+jjyfhejQGYvHQPe06kuu1cIiIipUVJnmJI1nQtERERkaLz9IF+X0KDm8CeBTPvgq0/uu10PZtWoXO9MLLsDp6bswXDMNx2LhERkdKgJE8xpGi6loiIiEjxeHjB7dOgSV9wZMO3Q2Hzd245lcVi4cVbGuPtYWXFngR+2HDELecREREpLUryFJFhGCSnq5JHREREpNhsnnDrx9B8ABh2mDUSNsxwy6lqhvrz4P/VAeDluVtJOp3tlvOIiIiUBiV5iigzx0GW3QFAkCp5RERERIrHaoNb3oOrhoDhgDn3w5+fuuVU93SKpk7lAOJTs/jPgu1uOYeIiEhpUJKniHJX1rJYwN9LSR4RERGRYrNaodfb0OZewID/PQIr3wcX987x8rDycu8mAMxYE8v62FMuHV9ERKS0KMlTRLlTtQK9PbBaLaUcjYiIiEg5YbFAj9eg/YPm8wVj4dshcPqkS09zde1Q+l5VHcOAZ2ZvIedMhbaIiMjlTEmeIsqt5FE/HhEREREXs1jg+pfMm9UDtv4AkzvCvj9cepqnezYg2NeTbUeTmbZiv0vHFhERKQ1K8hRRypnl0wN9lOQRERERcTmLBTo8BMMXQcVoSD4Mn/WCReMhJ8slpwgN8GZsjwYATFy0kyOJ6S4ZV0REpLQoyVNEyelnKnnUdFlERETEfSKugnt/h6sGAwYsnwRTrof43S4Z/o5WkbSsWYHTWXZe/GmrS8YUEREpLUryFJGma4mIiIiUEO8AuPlduONz8AmBoxvgw2tg3WfFbspstVp4pU8TbFYL8/8+xo8bj7gkZBERkdKgJE8RnZ2upUoeERERkRLR6BYYtQJqdYLs0/DTQ/DNoGI3ZW5QJYiR19QG4F/fbOCX7XGuiFZERKTEKclTRGena6mSR0RERKTEBEfAoB/g+hfB6gnbfoIPO0NGUrGGfeyGetzUrCrZdoP7pq9n2a54FwUsIiJScpTkKSJN1xIREREpJVYrdHgYRiyGwKqQFAs7FxZrSA+blbf6xXBDo3CychyM+Hwtq/cmuChgERGRkqEkTxHlTtdS42URERGRUlItBprebj7eu7TYw3narLw7oAVd6oeRke3g7mlrWXfgVLHHFRERKSlK8hSRpmuJiIiIlAG1u5j3e38tdhNmAG8PG5PvakmHOqGkZdkZ+ukaNh8q3lQwERGRkqIkTxEl51by+KqSR0RERKTU1GwPNm9IPgwJrllW3cfTxseDW/H/7d13eBRl18Dh3+4mu+mNkkYKvZNIIJEOAkZRBCsgVYoNEES+F3hVig0U5KUKihRBkaI0RWmhSO9BehMChCSQhPS+O98fAwuRAElIsiGc+7rmypbZmWeeRDOcnOecYH83kjNy6DlvLyejkork2EIIIURxkiBPISXfrMnjKJk8QgghhBCWY20LviHq4/Nbiuywdnor5r3RmEAfFxLSsunx/V7OXUsusuMLIYQQxUGCPIWUlH6rJo8EeYQQQgghLKpKG/VrEdTluZODwYof+gZTz9uJuNQsXp+zl4uxqUV6DiGEEKIoSZCnkG5315LlWkIIIYQQFnWrLs/F7WDMKdJDO9tas6hvCLU8HLmWnMnrc/ZwOT6tSM8hhBBCFBUJ8hRCttFEWpYRkEweIYQQQgiL8wwAW1fITIKrh4r88K72ehb1C6FqBXuuJmbQ9TsJ9AghhCidJMhTCCkZt/9C5CAt1IUQQgghLEurg8ot1cdFWJfnThUcDSwe8CRVytsTmZBO1+/2EBEnS7eEEEKULhLkKYRbS7Xs9DqsdTKFQgghhBAWV0x1ee7k7mTDkjefpEqF24EeqdEjhBCiNJEIRSFI0WUhhBBCiFLmVl2eK/sgs/i6YFW8GeipVtGBqMQMuny3m3+upxTb+YQQQoiCkCBPIdxuny5LtYQQQgghSgW3yuDqD6YciNhVrKeq6KgGemq4OxCTlEnX7/Zw7poEeoQQQlieBHkK4XZnLcnkEUIIIYQoNW5l8xRTXZ47lXcw8POAJ81dt7p+t4ezMcWXQSSEEELkhwR5CuH2ci3J5BFCCCGEKDVKoC7Pnco5qMWY63g6EZuSSbc5ezgdLYEeIYQQliNBnkJIMi/XkkweIYQQQohSo3JLQAPXT0JSVImc0s1ez+IBIdT1ciI2JYtuc/ZwMiqpRM4thBBC/JsEeQoh6WYLdSdbyeQRQgghhCg17NzAK1B9fGFbiZ3WxU7P4v5P0qCSM/GpWbw+Zw/HIhNL7PxCCCHELRLkKYSk9Js1eSSTRwghhBCidCnBujx3crazZlG/EAJ8XLiRlk2Xb3ez/nh0iY5BCCGEkCBPISTfzOSR5VpCCCGEEKXMnXV5FKVET+1sa82ifsE0rVqO1Cwjby06yNRNZzGZSnYcQgghHl8S5CmE2921ZLmWEEIIIUSp4hMCVjaQEg3XT5X46Z1srPmhbzB9mvoD8L9NZxi4+BCpmTklPhYhhBCPHwnyFIIs1xJCCCGEKKWsbcCvqfq4hJdsmYeg0zL2hbp8+XJ9rHUa/jwWzcuzdnE5Ps0i4xFCCPH4kCBPIdxeriWZPEIIIYQQpc6tujwl1Er9Xro09mXJm09S3sHAqehkXpixg93n4yw6JiGEEGWbBHkK4fZyLcnkEUIIIYQodW7V5bm4A3KyLDqUID831gxqRn1vZ26kZdNz7l4W7b6IUsL1goQQQjweJMhTCLJcSwghhBDFYebMmfj7+2NjY0NISAj79u3L1+eWLFmCRqOhc+fOxTvAR4V7PbArD9mpEHnA0qPBy8WW5W83oVOgFzkmhY9XH+e/K4+RlWOy9NCEEEKUMRLkKSCTSSHlZuE8J1muJYQQQogisnTpUoYNG8aYMWM4dOgQAQEBhIaGcu3atft+7uLFiwwfPpwWLVqU0EgfAVotVGmlPrZQXZ5/s7HWMaVLICOfrYVGAz/vu0S3OXuISky39NCEEEKUIRLkKaDUrBxudcGU5VpCCCGEKCqTJ09mwIABvPHGG9SpU4fZs2djZ2fHvHnz7vkZo9FI9+7dGTduHFWqVCnB0T4CSkldnjtpNBreblWVeb0b42hjxcGIGzw3bQfbzly39NCEEEKUERLkKaCkm0WX9TotBiuZPiGEEEI8vKysLA4ePEi7du3Mr2m1Wtq1a8fu3bvv+blPPvmEihUr0q9fvweeIzMzk6SkpFxbmXarLk/kQchItOxY/qVNrYr8Prg5db2ciE/Nos/8fXy94TRGk9TpEUII8XAkSlFAt+rxONpYodFoLDwaIYQQQpQFsbGxGI1G3N3dc73u7u5OdHR0np/ZsWMHc+fOZc6cOfk6x/jx43F2djZvPj4+Dz3uUs3FB9yqgmJUCzCXMn7l7Pn1nab0eNIXRYHpm8/R/fs9XEvKsPTQhBBCPMIKFeQpSFHA1q1bo9Fo7tqee+458z6KojB69Gg8PT2xtbWlXbt2nD17tjBDK3a32qfLUi0hhBBCWEpycjI9e/Zkzpw5lC9fPl+fGTVqFImJiebt8uXLxTzKUqDqzWyeUlKX599srHV81rk+U7sGYq/XseefeDpM28Guc7GWHpoQQohHVIGDPAUtCrhixQqioqLM27Fjx9DpdLz66qvmfb766iumTZvG7Nmz2bt3L/b29oSGhpKRUfr+knG7s5YUXRZCCCFE0Shfvjw6nY6YmJhcr8fExODh4XHX/ufPn+fixYt07NgRKysrrKysWLhwIWvWrMHKyorz58/f9RmDwYCTk1OurcwrhXV58tIp0Js1g5tTy8OR2JRMeszdy7Sws7J8SwghRIEVOMhT0KKAbm5ueHh4mLeNGzdiZ2dnDvIoisKUKVP46KOP6NSpEw0aNGDhwoVcvXqVVatWPdTFFYekjJtBHsnkEUIIIUQR0ev1BAUFERYWZn7NZDIRFhZGkyZN7tq/Vq1aHD16lPDwcPP2wgsv0KZNG8LDw8v+Uqz88m8BGi3EnYXEK5YezX1VreDAyneb0aWRDyYFJm88Q5/5+4hNybT00IQQQjxCChTkKWxRwDvNnTuXrl27Ym9vD8CFCxeIjo7OdUxnZ2dCQkLueUxLFg68tVzLUTJ5hBBCCFGEhg0bxpw5c/jhhx84efIk77zzDqmpqbzxxhsA9OrVi1GjRgFgY2NDvXr1cm0uLi44OjpSr1499Hq9JS+l9LB1Aa+G6uNSns0DYKvX8eUrDfj61QBsrXVsPxtL26+3MXbNcY5fLV3Fo4UQQpROBYpU3K8o4KlTpx74+X379nHs2DHmzp1rfu1WMcGCFBocP34848aNK8jQi8zt5VqSySOEEEKIotOlSxeuX7/O6NGjiY6OJjAwkHXr1pnvkS5duoRWKz0zCqxqG4g8oNbleaKHpUeTLy8HVaJ+JWcGLT7EmZgUFuy6yIJdF6nr5USXxj68EOCFi50E8oQQQtytRNNR5s6dS/369QkODn6o44waNYphw4aZnyclJZVYWrIs1xJCCCFEcRk0aBCDBg3K872tW7fe97MLFiwo+gGVBVVaw18T1UwekwkekUBZDXdH/hzSku1nr7P8wBU2nIjm+NUkRq8+zmdrTxJa14PXGlWiWdXyaLXS8VUIIYSqQEGeghYFvFNqaipLlizhk08+yfX6rc/FxMTg6emZ65iBgYF5HstgMGAwGAoy9CJjXq5lkOVaQgghhBClXqVgsLaDtFgI/wkCuz8ygR6dVkPrmhVpXbMi8alZrA6PZOn+y5yKTua3I1f57chVvF1s6dLYh7daVcFgpbP0kIUQQlhYgX7DFbQo4J2WL19OZmYmPXrkTpOtXLkyHh4euY6ZlJTE3r17H3hMS5BMHiGEEEKIR4iVHmp2UB+vGQTfhED4z2DMtuy4CsjNXs8bzSrz55AW/D64Ob2a+OFkY0VkQjqTN57h1dm7uRyfZulhCiGEsLAC/xmjIEUB7zR37lw6d+5MuXLlcr2u0WgYOnQon332GWvWrOHo0aP06tULLy8vOnfuXLirKkZJ6Womj5OtZPIIIYQQQjwSOk6BViPAxhliz8Cqt2F6Q9g/F7IzLD26AtFoNNTzduaTTvXY92E7Jr8WgIudNX9fSeT56TvYcuqapYcohBDCggocqShMUcDTp0+zY8cONmzYkOcx//Of/5Camsqbb75JQkICzZs3Z926ddjY2BTikopX8s1MHkeDZPIIIYQQQjwSDI7Q5r/QZBAcmAu7Z0LCJVg7DLZ9BU0HQdAbYHAomfEoCkSFg115cCl8XUkbax0vNaxEcGU3Bv50iCNXEnljwX7ee6oaQ9rVQCe1eoQQ4rGjURRFsfQgHlZSUhLOzs4kJibi5ORUrOdqM2krF2JTWfZWE4IruxXruYQQQoiyqCR/b4t7e6y/D1lpcHgR7JwKSZHqa7Zu8OQ70HQwWNsWz3kVRS0AvXUCXN4DDh7w3mHQ2z30oTNzjHz2+0kW7YkAoEX18kzpEkg5B8vUsRRCCFF0CvI7+9GoOleKmFuoy3ItIYQQQohHk94OQt6C98LhhengVgXS42HL57C4C2SnF+35bgV35j8LizqrAR6AlGg4srhITmGw0vFp53pM7RqIrbWO7WdjeX76Dg5G3CiS4wshhHg0SJCnABRFud1dy0aWawkhhBBCPNKs9NCwFww6AC99D3oHuLANlvWCnKyHP76iwD/bYH4HWNgJLu0GnQFC3oaW/6fus/sbtbV7EekU6M3qQc2oUsGeqMQMuny7m/k7L1AGkveFEELkgwR5CiAzx0SWUf0l7GQjmTxCCCGEEGWCVgcNXoXXl4KVLZzdAL+8UfgOXLmCOy/ApV1qcCf4LRgSDs9+Cc2GqoWg48/DmT+L8mqo4e7ImkHNea6BJzkmhXG/nWDg4kNsOX2N2JTMIj2XEEKI0kUiFQVwa6mWVgP2epk6IYQQQogyxb85dPtZXbJ16ndY+Ra8NEcNAuVXzHH44z8QsUN9rjNAUB9oPhScvG7vZ3BQiz3vnAK7ZkCt54rwQsDBYMWMbk/QyM+Vz9ee5I+j0fxxNBoAL2cb6ldypkElF+p5O1Pf2xk3e32Rnl8IIYRlSKSiAJJuLtVyMFihlW4FQgghhBBlT9U20GURLOkOx35VgzSdZoL2AQnw2Rnw10Q1aGPKAZ3+ZnDn/dzBnTuFvAW7Z6iZPpEHwTuoSC9Fo9HwRrPKPOHrysJdF/k7MpHz11O4mpjB1cQM1h+PMe9bydWWBpWcaV2jIu3quEvQRwghHlES5CmApIxbRZelHo8QQgghRJlVIxRemQfL+6iFka308PwU0Nzjj3wXtsNvQ9SlVwC1nleXZDlXuv95nLyg/qtw5Gc1m+fV+UV5FWaBPi4EdgkEICUzh+ORiRyNTOTvK+rXC7GpXLmRzpUb6fxxNBrtCgiu7MYzdT14uq4HXi7F1G1MCCFEkZMgTwGYO2tJ0WUhhBBCiLKtzgvw0newYgAcXABWNvDMhNyBnvQbsOFjtR07qC3RO0xUP5tfTQaqQZ4TqyHhErj4Full/JuDwYqQKuUIqVLO/FpSRjbHIhPZf+EGG05Ec/xqEnv+iWfPP/GM/e0EAT4uhNZ155m6HlSp4FCs4xNCCPFwJMhTAEnmzloybUIIIYQQZV79V8CYBavegb2zwcoA7cap7x1fCX+OgNRr6vOgN6DdWLB1Kdg5POpDldZqi/U9s+GZL4pu/PnkZGNN06rlaVq1PEPaVedyfBrrj0ez/ng0ByJucORyAkcuJ/DVutPUcHfg9WBfugb7YmNdgFpFQgghSoREKwogWZZrCSGEEEI8XgJfh5wM+P192DkVTEaIOwdn1qnvl68BHaeCX9PCn6PJYDXIc+gHaPWfggeKipiPmx39W1Shf4sqXEvOYNOJa6w7Hs2uc7GciUlh7G8nmL3tH95tU5UujX0wWEmwRwghSgsJ8hRAUrqaySPLtYQQQgghHiON+kJOJqwbqRZKBtBaQ4th0OIDNcPnYVRrCxVqw/WTaqCn2ZCHH3MRqehow+shvrwe4ktiejZrjlxl1pZzXE3MYPTq48zaep6BbarxWiMf9FYPKE4thBCi2Mn/iQvgVuFlWa4lhBBCCPGYefKdm0u1NOATAm9vhzb/ffgAD6h1fpoMVB/v/RaM2Q9/zGLgbGtNzyf92PJ/rfm0cz08nGyISszgo1XHaDNpK4v3XiIrx2TpYQohxGNNgjwFIMu1hBBCCCEeY82HwogL0Hc9VKxdtMdu8BrYV4SkSLXeTylmsNLR80k/tv5fa8a9UJeKjgYiE9L578qjPPX1Vpbsu0R6ltHSwxRCiMeSpKQUwO3lWjJtQgghhBCPJVvX4jmulQGC34Qtn8Gu6Wpr9Xu1bC8lbKx19G7qT5fGPizee4lvtp7nyo10Rq44ysgVR3G1s8bLxRZPZ1u8XWzUxy7qY09nWzycbNBqS/c1CiHEo0aiFQVwa7mW1OQRQgghhBBFrnE/2P41RP8NF7dD5ZaWHlG+2Fjr6Nu8Mt2CfflpbwRztv9DTFImN9KyuZGWzfGrSXl+zsfNlhHP1OK5+p5oSnlASwghHhUS5CmA5Jst1J1sZdqEEEIIIUQRs3ODJ7rD/u9h14xHJshzi61eR/8WVejXvDJJGTlEJaZzNSGdyIQMohLUx1cTMriamE50YgaX49MZtPgw8/0u8vHzdQj0cbH0JQghxCNPohUFkJQumTxCCCGEEKIYPfku7J8LZ9fD9dNQoaalR1RgGo0GZ1trnG2tqeXhlOc+aVk5fPfXP3y77R8ORtyg88yddA704v+eqYW3i20Jj1gIIcoOKbxcAElSeFkIIYQQQhSnclWh1nPq41vt2ssgO70VQ9vVYMvw1rwSVAmNBlaFX+WpSVv5esNpUjNzLD1EIYR4JEmQpwBuLdeSFupCCCGEEKLYNBmkfj2yFFKuWXYsxczD2YZJrwbw26DmhFR2IzPHxPTN52g9aStL91/CaFIsPUQhhHikSLQin7KNJtJutoKU5VpCCCGEEKLY+D4J3kEQeVCtz9Pmv5YeUbGr5+3MkjefZMOJGMb/cZKLcWmM+PUo4/88hZVWi9FkwmhSMClgNCkYFQWTSSHHpOBsa83rIb70bVaZCo4GS1+KEEJYlGTy5NOtLB6QTB4hhBBCCFGMNJrb2Tz7voOoI5YdTwnRaDSE1vVgw/ut+Oi52jjZWJGQlk1sitqpKykjh5TMHNKzjWTlmMi5meWTmJ7NrK3naf7lZkavPsbl+DQLX4kQQliORCvyKflmPR47vQ4rncTGhBBCCCFEMar9ArjXg5hj8H17eG4SNOxl6VGVCL2Vlv4tqtA12JeLsanotBqstBq0Wg06jQbdHY+1Wjh8KYFvtp7nyOUEFu6O4Ke9l+gU4MU7ratS3d3R0pcjhBAlSoI8+ZSUfrN9uizVEkIIIYQQxU1nBX1+h5XvwJk/Yc1guLwXOkwC68ej+5SDwYp63s4P3C+0rgdP13Fn9/k4vtl6nh3nYllxOJIVhyN5uo4777apJu3ZhRCPDUlJyafbnbUkLiaEEEIIIUqArSt0XQxtR4NGC4d/hLntIf4fS4+s1NFoNDStVp4f+4ewemAzQuu6A7DhRAydZ+6k+/d7WHcsiqwck4VHKoQQxUuCPPl0a7mWo2TyCCGEEEKIkqLVQosPoOdKsCsP0Ufh29Zw6g9Lj6zUCvBx4duejdj4fkteauiNTqth57k43v7xEE3Gh/H52hOcu5Zs6WEKIUSxkCBPPt1eriWZPEIIIYQQooRVaQ1vb4dKwZCZCEu6waZxYMx54EcfV9XdHZn8WiDb/q81b7eqSgVHA3GpWczZfoF2k//ipW92snT/JVIzZQ6FEGWHBHny6fZyLcnkEUIIIYQQFuDkBX3WQsg76vMdk2FRZ0i5ZtFhlXaVXO0Y+Wwtdo18ijm9GtGutjs6rYZDlxIY8etRgj/fxIhf/uZgxA0URbH0cIUQ4qFIWko+JaXfWq4lUyaEEEIIISzESg/PTgCfYFg9CC5uh2lPgGtlcHQHB487vt7cHNzVr1YGS4/eoqx1WtrXcad9HXeuJWXw66FIlh24zIXYVJYeuMzSA5fxL2fHC4HedAr0omoFB0sPWQghCkwiFvmUlCHdtYQQQgghRClR7yVwrwvLesH1UxBzVN3uxdpebcMe+HrJjbEUq+hkwzutq/J2qyrsv3iDpfsv88fRKC7GpTEt7CzTws5S39uZToFePN/ACw9nG0sPWQgh8kWCPPkky7WEEEIIIUSpUqEmvL0Trp+E5BhIiYbkm1tKdO7XslNh1Tvq4+bvg0Zj6dGXChqNhuDKbgRXduOTTnXZdDKGVYcj+etsLEcjEzkamcjnf5zkycrl6BToxbP1PHG2k38PCCFKLwny5NOtwsuyXEsIIYQQQpQaOivwqK9u92IywaYxsGsahI2DlBgIHa927hJm9gYrOgV60ynQm7iUTP44Fs2a8Ej2X7zB7n/i2P1PHB+vPkbrmhXpFOhF21ru2Op1lh62EELkIhGLfLrVQl2WawkhhBBCiEeKVgtPf6rW5Vn/X9g7Ww30vPjtY1+n517KORjo+aQfPZ/048qNNH47EsXq8EhORSez8UQMG0/EYK/X8XRdD14I9KJ5tfJY6yRoJoSwPAny5JO5Jo8s1xJCCCGEEI+iJgPVIswr34bjKyE1Frr+BDbOlh5ZqVbJ1Y53WlflndZVOR2dzJojkawOv8qVG+msPBzJysORuNnrea6+Jy8EehHk64pWq0FRFG6kZRMRl0pEXBoX7/h6KS4NvZWWhr6uBPm50tjfjdqejlhJoEgI8ZA0ShnoE5iUlISzszOJiYk4OTkVyzmaTdhMZEI6K95tSkNf12I5hxBCCPE4KInf2+LB5PvwGPtnKyzpAVnJ4F4Puv8CTp6WHtUjRVEUDl1KYE14JGuPRhGbkmV+z9vFFjd7PRfjUkm++Yfi/LDT6wj0caGRnysNb26yikAIAQX7nS2ZPPkky7WEEEXNaDSSnZ1t6WEIUeSsra3R6aRORWHMnDmTiRMnEh0dTUBAANOnTyc4ODjPfVesWMEXX3zBuXPnyM7Opnr16nzwwQf07NmzhEctHjlVWsMba+HHVyDmGMx9GnqugPLVLT2yR4ZGoyHIT83C+fj5Ouw6H8fq8KusPx5NZEI6kQnp5n09nW3wK2eHn5s9fuXt8C9nj6+bHckZORyMiOdAxA0ORdwgKSOHXefj2HU+7uY5oL63M91DfOkU6I2Ntfx/VQjxYJLJkw8mk0LVD/9AUWDfh22p6CgtFIUQhacoCtHR0SQkJFh6KEIUGxcXFzw8PNDk0cFHMkjytnTpUnr16sXs2bMJCQlhypQpLF++nNOnT1OxYsW79t+6dSs3btygVq1a6PV6fv/9dz744APWrl1LaGjoA88n3wfBjYuw6CWIPw+2btB9OVRqZOlRPdIyso3sOBuLSVHwL68Gc/ITnDGZFM5eS+FARDwHL97gQMQNLsWnmd93sbOma2Nfejbxw9vFtjgvQQhRChXkd7YEefJz/IxsGozdAMCpT5+RKLoQ4qFERUWRkJBAxYoVsbOzy/MfwUI8qhRFIS0tjWvXruHi4oKn591LQCS4kLeQkBAaN27MjBkzADCZTPj4+DB48GBGjhyZr2M0bNiQ5557jk8//fSB+8r3QQBqXZ6fXoWrh8DKVm2v3rAnOHlZemSPvWtJGawKj+SHXRHmzCCtBp6u40GfZv6EVHaTewghHhOyXKuI3VpLq9dpMVhJMTQhROEZjUZzgKdcuXKWHo4QxcLWVv0r87Vr16hYsaIs3cqHrKwsDh48yKhRo8yvabVa2rVrx+7dux/4eUVR2Lx5M6dPn+bLL78szqGKssa+PPT+DZb3gXMbYesXsO1LqPksNOoLVdpIq3ULqehkw5stq9KveRXCTsawYNdFdp2PY93xaNYdj6aWhyN9mvrTKdBbWrkLIcwkyJMPSek36/HYWkm0XAjxUG7V4LGzs7PwSIQoXrd+xrOzsyXIkw+xsbEYjUbc3d1zve7u7s6pU6fu+bnExES8vb3JzMxEp9PxzTff0L59+zz3zczMJDMz0/w8KSmpaAYvHn0GB+i2BI6vgAPz4dIuOPW7urn6Q1AfCOwBDhUsPdLHkk6r4em6Hjxd14PT0cn8sPsiKw+p7dxHrjjK6NXHMVhrsdZp0Wk1WGk1//qqxcZaS9UKDtTxclI3Tydc7PSWvjQhRDGQIE8+mIM8UnRZCFFEJGAsyjr5GS8Zjo6OhIeHk5KSQlhYGMOGDaNKlSq0bt36rn3Hjx/PuHHjSn6Q4tGgs4IGr6nbtZNqsOfIErVuz6axsPlzqN0RGr0Bvk3V/UWJq+nhyBcv1mdEaC2WHbjMwj0XuRyfTpbR9MDPHrmSyIrDkebn3i625oBPHS8nans4YWdQg/J3FvRQuP1Eg4Zy9nq0Wvl/vBCllfzfOR9uLddytJHpEkIIIUTRK1++PDqdjpiYmFyvx8TE4OHhcc/PabVaqlWrBkBgYCAnT55k/PjxeQZ5Ro0axbBhw8zPk5KS8PHxKZoLEGVLxdrQ4StoN/Zmds88iDyoPj6+AjQ6cPEFt8rgVgVcb351q6xm/lhLYeDi5mxnzYCWVejbvDJXE9LJMSnkGE3kmBSMJuXmVxM5RvV5cmYOp6OTOX41kRNRSVyOTzd3Adt4IubBJ7xDJVdbugX78mpQJSo6WbghjaJAWhzYuEjgUYib5L+EfEi61T7dVjJ5hBCiqPj7+zN06FCGDh2ar/23bt1KmzZtuHHjBi4uLsU6NiFKml6vJygoiLCwMDp37gyohZfDwsIYNGhQvo9jMplyLcm6k8FgwGAwFMVwxeNCbwdP9FC3qCNqds/RXyArGW5cULfzm+/+nKMXeDYAv6bg1ww8A0BXyu+jU2Ph4g6o3h709pYeTb7ptBp83PK3BDy07u2AcWJ6NiejkjhxNYkTN7+evZZMtvHunjx3JmYqCly5kc7E9aeZvPEM7WpXpFuwLy2rV7BMds+6kbB3Nmi04OgJTt7g7H3za6Xbz138wV5qIYrHgwR58kGWawkhHmcPWnYzZswYxo4dW+Dj7t+/H3v7/N9IN23alKioKJydnQt8rsKqVasWFy5cICIi4r7ZFEIUhWHDhtG7d28aNWpEcHAwU6ZMITU1lTfeeAOAXr164e3tzfjx4wF1+VWjRo2oWrUqmZmZ/PHHHyxatIhZs2ZZ8jJEWeUZAB2nwHOTITlKDfDE/wPxF+54fBEyEyH5qrqdWad+1toOfILVgI9vE7VNe2nI9lEUuHIA9s+B4yvBmAXVQ+H1pbkjG2WQs601T1Ypx5NVChb4SM8y8sfRKBbvu8TBiBusPx7D+uMxeLvY0i3Yh1cb+eBeUtk9V8Nh77fqY8UESZHqduUe+3ecBkG9S2ZsQliQBHnyIUmWawkhHmNRUVHmx0uXLmX06NGcPn3a/JqDg4P5saIoGI1GrKwe/P/LChUKVsBTr9eXaKBlx44dpKen88orr/DDDz8wYsSIEjt3XrKzs7G2lj82lGVdunTh+vXrjB49mujoaAIDA1m3bp25GPOlS5fQ3tHlKDU1lXfffZcrV65ga2tLrVq1+PHHH+nSpYulLkE8DrRaNTPC2Rv8m+d+T1Eg/QbEnYMr+yFiF0TsVF/7Z6u6AWitwTsI6r8CjfuXfEAlOx2O/Qr75kBUeO73zq5Xl6TVe7lkx/SIsNXreDmoEi8HVeJ0dDI/77vEikNXiExIZ9KGM/xv01na1qpIixoVcLKxwtHGCkcb61xfHfRWD5/1oyhqFg+K+r16+nM1wJN45ebXSEi6on5NuASp12DLF9CgC1hbeImZEMVM+iHmQ7Is1xJCPMY8PDzMm7OzMxqNxvz81KlTODo68ueffxIUFITBYGDHjh2cP3+eTp064e7ujoODA40bN2bTpk25juvv78+UKVPMzzUaDd9//z0vvvgidnZ2VK9enTVr1pjf37p1KxqNhoSEBAAWLFiAi4sL69evp3bt2jg4OPDMM8/kCkrl5OTw3nvv4eLiQrly5RgxYgS9e/c2L4e5n7lz5/L666/Ts2dP5s2bd9f7V65coVu3bri5uWFvb0+jRo3Yu3ev+f3ffvuNxo0bY2NjQ/ny5XnxxRdzXeuqVatyHc/FxYUFCxYAcPHiRTQaDUuXLqVVq1bY2Njw008/ERcXR7du3fD29sbOzo769evz888/5zqOyWTiq6++olq1ahgMBnx9ffn8888BeOqpp+5a+nP9+nX0ej1hYWEPnBNR/AYNGkRERASZmZns3buXkJAQ83tbt241/4wAfPbZZ5w9e5b09HTi4+PZtWuXBHiEZWk0YOemZu00GQhdf4L/+wfe3QPPfa3+Y9zBA0zZcHkP/DEcVr0LOVklM774C7DhI5hcG1YPVAM8OgMEvA4DNkPrUep+f46AtPiSGdMjrKaHI2NfqMu+D9sx+bUAGvu7YjQpbDgRw8erjjFkSTh9Fxzg1dm7eWbKdppN2EyDsRuo+uEf1B+znjaTttJr3j4+WnWU7/46z7pj0Zy4mkRKZs6DT358BVzaDVa20P4TcPJUM8TqdlZ/9p75Al5bCAPC4P3j4FQJUqLh8KJinxchLE1SU/IhKV39H42TZPIIIYqBoiikZxtL/Ly21roi64A0cuRIJk2aRJUqVXB1deXy5ct06NCBzz//HIPBwMKFC+nYsSOnT5/G19f3nscZN24cX331FRMnTmT69Ol0796diIgI3Nzc8tw/LS2NSZMmsWjRIrRaLT169GD48OH89NNPAHz55Zf89NNPzJ8/n9q1azN16lRWrVpFmzZt7ns9ycnJLF++nL1791KrVi0SExPZvn07LVq0ACAlJYVWrVrh7e3NmjVr8PDw4NChQ5hManeTtWvX8uKLL/Lhhx+ycOFCsrKy+OOPPwo1r19//TVPPPEENjY2ZGRkEBQUxIgRI3BycmLt2rX07NmTqlWrEhwcDKjFdefMmcP//vc/mjdvTlRUlLkFd//+/Rk0aBBff/21uTbLjz/+iLe3N0899VSBxyeEEA+k1aqFnCvWVrN2FEVd3nV8FWz+DI4shsTL0GUR2LoW/flNJvhnM+z9Ds5ugFudopx9oXFfeKLX7Vot7vXg2AqIPQ0bPobOM4t+PGWQjbWOlxpW4qWGlTgbk8zyg1e4GJtKckYOyZnZJGfkkJKRQ3JGDllGE4oCyZk5JGfmcCE2Nc9jlrPX41vOjsrl7Qn0ceEJH1dqeTpirdNCVhpsHKPu2Px9tfbO/VjpoflQNai4Ywo07K2+JkQZJVGLfLhVeNlRavIIIYpBeraROqPXl/h5T3wSip2+aH4NfPLJJ7Rv39783M3NjYCAAPPzTz/9lJUrV7JmzZr7FpHt06cP3bp1A+CLL75g2rRp7Nu3j2eeeSbP/bOzs5k9ezZVq1YF1CyITz75xPz+9OnTGTVqlDmLZsaMGfkKtixZsoTq1atTt25dALp27crcuXPNQZ7Fixdz/fp19u/fbw5A3epwBPD555/TtWvXXO2q75yP/Bo6dCgvvfRSrteGDx9ufjx48GDWr1/PsmXLCA4OJjk5malTpzJjxgx691brDlStWpXmzdUlFS+99BKDBg1i9erVvPbaa4CaEdWnTx9peS6EKBkajdqJq8Uw8GgAy/vAxe0w92l4fZnaoasoZKbAkZ9h33cQe+b261XbQvAAqP40aHW5P2NlgBemw7xQCP9RbSdfpVXRjOcxUd3dkf92qH3P9zOyjWrwJyOba8mZXIpLIyI+lUvx6VyKS+VSfBo30rKJS80iLjWLw5cSWHFIbftusNLSoJIzg7S/0irxMjmO3lg1HZy/gT3RE/6apC7hOvKz1OYRZZoEefLhVgt1J1uZLiGEyEujRo1yPU9JSWHs2LGsXbuWqKgocnJySE9P59KlS/c9ToMGDcyP7e3tcXJy4tq1a/fc387OzhzgAfD09DTvn5iYSExMjDnDBUCn0xEUFGTOuLmXefPm0aNHD/PzHj160KpVK6ZPn46joyPh4eE88cQT98wwCg8PZ8CAAfc9R378e16NRiNffPEFy5YtIzIykqysLDIzM7GzUzurnDx5kszMTNq2bZvn8WxsbMzLz1577TUOHTrEsWPHci2LE0KIElO9HfRdB4tfUwMx37eDbkvAp3Hhj3kjQg3sHFqkFoEG0DuqHcIa94fy1e7/ed8QaNwP9n8Pvw2Bd3cXvEh0yjW4fgpSr0NqHKTFqt27bn299djGGfxbQOWW6uZQsXDXnJ/xnP4Dar+gLqcrTgmX4cBcKF8DAl+/620bax021joqOBqoUsEhz8LPSRnZXIpL41J8Gqeikwm/nED4pRskZeRw5eI5gg0LQQND4l7mwKRd1Pd2oaKTATc7PW72eW821jbQ7D1Y/1/YMRkCu0vLdVFmyU92PphbqEsmjxCiGNha6zjxSahFzltU/t0la/jw4WzcuJFJkyZRrVo1bG1teeWVV8jKun/dhX8XFtZoNPcNyOS1v6Lc3f61IE6cOMGePXvYt29frmLLRqORJUuWMGDAAGxt73/D/6D38xpndnb2Xfv9e14nTpzI1KlTmTJlCvXr18fe3p6hQ4ea5/VB5wV1yVZgYCBXrlxh/vz5PPXUU/j5+T3wc0IIUSw86kH/MDXQE/03/PA8vPitWlslvxRFLfC8Z5YazFBu/t5wqwLBb6nBBhun/B+v7Rg49Ye6rGzrBGg/7sGfueXv5bD6XbVT14OkxaldyQ79oD6vUBsq3wz6+DUrmoBM7FlY9KK6JG7nNOi+HMpVffDnCirhsho8ObRIrbmEBtyqqkGzAnKysaaetzP1vJ3pUN8TAJNJ4UJcKtarBmAbmcUxq7qsy3oSY1ImMUkxDzymvV6Hj0MVlmqdcb5xkVU/TiXGvzPuTjZUdDRQ0clAeQcD6dlG4lOzuJGaTVxqJjdSs4hPy1a/pmaRkJ5Fg0ou9GtemfIOhgJfmxAlQYI8+XCrhbos1xJCFAeNRlNky6ZKi507d9KnTx/zMqmUlBQuXrxYomNwdnbG3d2d/fv307JlS0AN1Bw6dIjAwMB7fm7u3Lm0bNmSmTNz12KYP38+c+fOZcCAATRo0IDvv/+e+Pj4PLN5GjRoQFhYmLn19b9VqFAhV4Hos2fPkpaW9sBr2rlzJ506dTJnGZlMJs6cOUOdOnUAqF69Ora2toSFhdG/f/88j1G/fn0aNWrEnDlzWLx4MTNmzHjgeYUQolg5ecIbf8Kv/eHMn7C8NyR8Ak3fu3fnrbR4uHoYrh6C46sh5ujt96q0gSffgWrt1ZpABWXjpBaKXtINdk1XC0Z7Nrj/ZxQFdk2DjaPV5y5+aq0Yu3JgXx7syt/8esfzpEi48Bdc2AbRR+H6SXXb9x2gUc9Z41loNgT0dgW/jquH4ceX1WASQPx5+L4tdPnx7s5ohZV4BbZPhkMLbwZ3AAd3SIlRM6He+qtI6t9otRqqph2FyD8ADfX6zeJouboci0ziVHQScSlZ3EhTl3jdCsjEp6qvZRsVUrOMnIqH2bpnGWG9hPrn5zDsZA1MhehDtPNcHAt2XqRXEz8GtKxSuGBPahxkpYCr/JFFFL2y9a+KYiLLtYQQomCqV6/OihUr6NixIxqNho8//viBS6SKw+DBgxk/fjzVqlWjVq1aTJ8+nRs3btyz/kx2djaLFi3ik08+oV69erne69+/P5MnT+b48eN069aNL774gs6dOzN+/Hg8PT05fPgwXl5eNGnShDFjxtC2bVuqVq1K165dycnJ4Y8//jBnBj311FPMmDGDJk2aYDQaGTFiRL7ao1evXp1ffvmFXbt24erqyuTJk4mJiTEHeWxsbBgxYgT/+c9/0Ov1NGvWjOvXr3P8+HH69euX61oGDRqEvb19rq5fQghhMQYHtRvXulGw71s1WBJ/ATpMguw0tRPW1cPqFnkIEiJyf97KFgK6QsjbULHWw4+nVgeo0wlOrIbf3lOzjf5dw+cWk/H2uAGeHAhPf/bgAJNHPahxM5M3LR4u7rgZ9PlLLf4cdUTdjq+Al78HzwLUdvtnGyx5XQ0keAZA59mwZjBEHoCFneGFaXkup8q3vII7lVtCq5Fqke0ZjdSA1a5p0HL4/Y+VHyYTrLuZXduwF3gGYAcEV3YjuPK9M54URSE5M4e4lCyuJ2cSG1eDzHV/UDUnijFVz7KeplxLziQmKYPkjBystBpc7fW42elxtbemnL0BV3vrm8/1GKx0LNl/ib+vJPLtX/+wcHcEPZv48WZBgj2psTC7ufo9f+uvovl5FeIOErV4AEVRZLmWEEIU0OTJk+nbty9NmzalfPnyjBgxgqSkpBIfx4gRI4iOjqZXr17odDrefPNNQkND0enyvlFfs2YNcXFxeQY+ateuTe3atZk7dy6TJ09mw4YNfPDBB3To0IGcnBzq1Kljzv5p3bo1y5cv59NPP2XChAk4OTmZs4kAvv76a9544w1atGiBl5cXU6dO5eDBgw+8no8++oh//vmH0NBQ7OzsePPNN+ncuTOJiYnmfT7++GOsrKwYPXo0V69exdPTk7fffjvXcbp168bQoUPp1q0bNjY2+ZpLIYQodloddPhKXWa1biQcnA8nf1Pr1+TFrSp4PaG2bK//atHXm3n2Kzi/VQ0s7Z2ttub+t+wMWDEATt6sbfb059D03g0G7snODeq8oG4AydFwfjOEfaLWK5rTFtqOhiaDHhw8OrEGfu2nLhnzbwFdF6vZSX1+h5Vvw4lVsOodiDsPbT4sWLZT4hXY8T81uHNrSZp/C2g9Mnd20DMT1HnZ9hXUffHhl4iF/6QGvAxO8NTH+f6YRqPBycYaJxtrKpe3h8pukDIItn5B7+zl9H57qPn6M3OM6HXaBzYi6Bbsw9bT15my6QxHriTy3V//sCi/wR5FUTOckm9m8274CHr8ku/rKTYZSfDXV2qr+bovgqO7pUdUOqXGgs5aradVimmUQhQvmDlzJhMnTiQ6OpqAgACmT5+eq7DlvyUkJPDhhx+yYsUK4uPj8fPzY8qUKXTo0AGAsWPH5upAAlCzZk1zy9cHSUpKwtnZmcTERJycCrDeNh/Ss4zUHr0OgKNjn5YlW0KIh5KRkcGFCxeoXLmy/OPaAkwmE7Vr1+a1117j008/tfRwLObixYtUrVqV/fv307Bhw2I5x/1+1ovz97bIP/k+iFLt1B9qoCL75lJWF181oHNr8wwonpbr/3bwBzWTx9pOLcLs6n/7vbR4WNIdLu0CnR46z4L6rxTt+dPi1QycU7+rz6u0VrNynDzvPd7fh6p1iWo9Dy/PBes7/h9sMsGWz2H7JPV53RfVcd+vuHROJpxZr3alOrsBTOoqB/xbQKsRah2hf1MU+PElNVDl3wJ6/3bvpXcPkpEE04Mg9ZqaIZXfjlr3kp4AU+pDZpK6dK12x0IdRlEUNdgTdpYjlxMAsLHW0vNJPzoGeGGt06LTatBqNOi0GnQaDQ4nFuMW9gGKVv03pcaUDT1+hWrtHu6aHoaiwC991YwxAI0WKrdSA6e1ny/1AY0Sc+UA/PACKEa1817IO+Bep8ROX5Df2QXO5Fm6dCnDhg1j9uzZhISEMGXKFEJDQzl9+jQVK95dET4rK4v27dtTsWJFfvnlF7y9vYmIiMDFxSXXfnXr1mXTpk23B2ZVOpKMkm9m8Wg1YF/GamYIIURZFxERwYYNG2jVqhWZmZnMmDGDCxcu8PrrD5Gi/gjLzs4mLi6Ojz76iCeffLLYAjxCCPHQanWAwYfUZUvu9dQ6NpbQsBf8vQwidsDvw9R/kGs0aqHhH19Wx2dwVpea5RXseFh2bmog4tAP6pKwf7bCrCZqq/c7gxOKombYhN38w/kTPeH5KXd3kNJqoe3HambNmvfg+Er1Wrr9nLu7l6Koy+KOLIZjv0L6jdvv+TVXM3fud70aDTw3Gb5pAhe3qwGiwi4P2z5JDfC4VVULaT8sWxcIflM97rav1GBYIQJQGo2GNrUq0rpmBbaeuc6UTWqwZ872C8zZfuGu/f01UazV/xc0MD7zFSpqEuhv9SdnFg2hm3YSNgYDDgYr7Aw69ateRzkHA4GVXAjyd6VKefsHZhkVyuEf1QCP1go8Gqh1rv7Zom6/vw81nlYDPtVDcwcMHyc3IuDnrpCdqj4/tFDdKrdS639VDy1c/a9iUuCoxeTJkxkwYIC5mOTs2bNZu3Yt8+bNY+TIkXftP2/ePOLj49m1a5e53oC/v//dA7GywsPDo6DDKXa3lmo52lij1RbDf1RCCCGKjVarZcGCBQwfPhxFUahXrx6bNm2idu3alh6aRezcuZM2bdpQo0YNfvmlFKSHCyHE/Th53jtjpaRoNNBxKsxqCufD4OhytebMT6+qS24cvdTlNu51i3cMQX3Ujlu/9lOXLS3tAQ17wzPj1XpEGz+G3TcL6Td/X+0Qdr+AQODranbUku5qnZ45baH7MnU51N9L4cgSNYB1i6MnNOgCAd3yX0PGrbIaDNo0Rm1dXv3pggfr4s7D7m/Ux8+ML5IizgA8+a7ajS36bzi7UQ1kFJJGo6FNzYq0rlGBbWeuM2vreS7GpWI0gUlRMJoUtKZspjILe00mu011+N70HA5KGi/rtlNDc4XQrI0sTm+b5/EX770EgKudNUF+rjT0c6WRnxsNKjlj87CdUq+fgT//oz5+6iP1Zyf+HzWw9/dy9Wfg5G/qZnBSA4uN+4F30MOd91GSkQiLu0DqdfCoD+0/vb2U9MI2dXOrotYDC3wdDI6WHnHBlmtlZWVhZ2fHL7/8QufOnc2v9+7dm4SEBFavXn3XZzp06ICbmxt2dnasXr2aChUq8PrrrzNixAhzTYSxY8cyceJEnJ2dsbGxoUmTJowfPx5fX998jas4040PRtzg5Vm7qORqy44RTxXpsYUQjx9ZriUeF7Jcq/ST74MQBfDXJNj8qbpEzGRUl/pUqK0GeJwrldw4crJgy2dqO3QUKFdNzXQ6sUp9v6DLmWLPqe3r48+DlY26NIub/zy0slWX6wR0U5eJ3avw9P0Ys+G7Nmr3swZd4KXvCvb5n7vB6T/U5Uzdfyn8kq+8bPhI7Z5WqTH021i0x/63LeNh2wQ16+vdXeBciawcEzm7Z2EX9l9ybMpx/JUtJCv2pGblkJqZQ2qWkcgb6RyKuMGRKwlk5uRuYGGl1VDX25lGfq4E+rgQ6ONCJVfb/Gf75GSq3daij6oZKT1X5c5GURSIOaYGNo/+CklXbr6hUQMabT8GvX2RTE+pZcxR//s4H6YGOvuHgbO3+l7CJdg3R82yy7hZG9HgpGbRBQ9Qg5xFqNiWa8XGxmI0GnF3z12Iyd3d/Z71c/755x82b95M9+7d+eOPPzh37hzvvvsu2dnZjBkzBoCQkBAWLFhAzZo1iYqKYty4cbRo0YJjx47h6Hh3JCwzM5PMzMxcF1xckqXoshBCCCGEEI+3ZkPg2Aq4dlx97tdMXaJVEnWB7mSlh/afQNW2ahHluHPqptFBpxkFXxJVvhr036RmBkXsVF/za6YGdup0Ugs2PwydtZoJ9X1bNUMooCtUzecfzs9vVgM8WisI/aLogzBNBqv/SL+yX10GV7VN0R7/lsv71KLGAM9PNgcF9VZa9E3fhPD5WMWdJeDCPGg/Ls9DZOWYOH41kYMRNzgYcYMDETe4npzJkcsJ5npAAOXs9QT6uBBwM+gTUMkFZ7u7/x2bnmUka+2HOEcfJVPvymL3UUT8fhIXO7VAtV85eyqXs8fZo76avdJ2LFzeAwfmqUGfvbPU780L09QAYFmkKPDn/6kBHms76LbkdoAH1Ey4pz9V61Id+Rn2fgtxZ2HPTNjzDbzxJ/g1scjQi73IjMlkomLFinz33XfodDqCgoKIjIxk4sSJ5iDPs88+a96/QYMGhISE4Ofnx7Jly3K1fL1l/PjxdxVqLi5J0j5dCCGEEEKIx5vOGjrPVJc3VW4Fz//PsvVJqrSCd3bC2g/U4MzzU9Q6RoVh56ZmcZxdr2YFFXEGApWCIOQttUPZ7+/DO7tBb3fv/bPSYPdM2DlFfR78JlSoWbRjArWDVFAfdVx/TSyeIE9mstplTDFB/dfuLsyts1azr37uogYGGr2Ru7j3TXorLU/4uvKEryv9W6hFn6/cSDcHfY5cSeBkVBJxqVmEnbpG2Klr5s9WKW9PbS8n0jJziErMIDopgycy9jFfPweAt1P6s2VrPBB/13ld7KzxL2ePfzk7/MuXx7/KOGpU7EC1vR+hT4iAhZ1QnuiFJvSzslegec8sNaiFBl7+HrwC897P4KBm7jTqpwYm93wDsWfVDDELKVDkonz58uh0OmJiYnK9HhMTc896Op6enlhbW+dqV1u7dm2io6PJyspCr797XaWLiws1atTg3LlzeR5z1KhRDBs2zPw8KSkJHx+fglxKviWl367JI4QQQgghhHhMeT0B7x8v3mU9BWHnBq/OVzMOHnZMVvpCd5nKl6c+UmuY3LgI277MO2PFZFKzfTZ/CkmR6ms+IWqmRHFpNkT9h3zETri4E/ybFe3x141Ur9nZB56blPc+NULVwOGFbbBpLLy64IGH1Wg0+LjZ4eNmR+cn1OySjGwjJ6KSCL+UwJErCYRfTiAiLo1/YlP5JzbV/NkK3GCSYTYAv1p3JKtSO152sqWCo4H41EwuxqVxMTaVa8mZJKRlE56mHus2A/Z8wgirJfSy2ojm8EKuHf6dqTbvcNyxGS521lRwMOBf3h6/cnb4l7PHt5zdo7Uy5tQfah0pUINwtZ578Ge0WqjeTt0yku4uel6CCnRmvV5PUFAQYWFh5po8JpOJsLAwBg0alOdnmjVrxuLFizGZTGhvrvE7c+YMnp6eeQZ4AFJSUjh//jw9e/bM832DwYDBYCjI0Ast+VYmz6P0QymEEEIIIYQoeqUlwHOn0jimfzM4QodJsKSbWgen/qvgUe/2+xe2w4YP1aLSAM6+0G4M1H2peLsWOXnBEz3UQM9fE4s2yHNijdq5Cg28+O29M100GnU52uzmarezkHfAN6TAp7Ox1tHQ15WGvreXEN5IzSL8SgJnopNxsbPGw8lAo+39sL+SjOJej5cHzOVlq7z/XZ2WlcPF2DQi4lK5EJdKRGwaF+JSiUvJJDHdwKfpffk980kmWM+hijaazzM+Z3VqU8Zl9yKeu5f5lbPX43sz6ONXzg53JxustDfby2tvt5m/9Vir1WBnraNyeXsqOBqKp7NYXq6GqwXOUaBRX2gysODHeNhljg+pwOGlYcOG0bt3bxo1akRwcDBTpkwhNTXV3G2rV69eeHt7M378eADeeecdZsyYwZAhQxg8eDBnz57liy++4L333jMfc/jw4XTs2BE/Pz+uXr3KmDFj0Ol0dOvWrYgus/BuddeS5VpCCCGEEEIIUUi1OkDtF+DkGvjtPbXYcdx52Dgazvyp7mNwghbD1EBHSS2HazZUbYf9zxa4cgAqNXr4YyZFqdcI0Hzog4NHHvWgYU91HOtHQb9NRRLccrXX06ZmRdrUrKi+sGMKXNkO1nZoXpkP9wjwANjprajj5UQdr7wDFoqikJ79NInJfYn760vc/v6OTrpdhNqeZHOVEWzSNiEiTg0SxaZkEZeqbocvJRT4OhxtrKhawUHdKtpTrYIDVSs64Ouowfr6cbXbV2EKg/9b0tWbrdLT1NpRz371aARR/6XAkYsuXbpw/fp1Ro8eTXR0NIGBgaxbt85cjPnSpUvmjB0AHx8f1q9fz/vvv0+DBg3w9vZmyJAhjBhxO+3uypUrdOvWjbi4OCpUqEDz5s3Zs2cPFSpUKIJLfDi3lmtJJo8QQgghhBBCPIRnv1KLHEcehIWdIGIXKEa1cHSjvmrL9YK2WX9Yrn7QoCuE/6gWs+65ElweohSIyQSr3oH0G+AZAK3/m7/PtflILe4deVBtYd7g1fvvf+MibP5cXebl3QjqvKAu/bpXMfArB9WlcADPfgkVauT7kvKi0Wiw01thV84NXvwSgl+D1YOwuXaCDqdG0qHVCHh1FGg0pGTmEBGXSkRcGhfjUrkUl0ZsSiZGk0KOScGkKOQYb341KZhMCkZFISk9hys30kjOyCH88p3LxhRe0O5mpPXPeGniOGDbjNXVPsPDzQlvF1u8XW3xcrHF3dGAlS6fwbLMFLVVenIUVKilLpvTPZoxgAK1UC+tirMF6OCfD/Pbkat89Fxt+reoUqTHFkI8fh7nFuqtW7cmMDCQKVOmAODv78/QoUMZOnToPT+j0WhYuXKleYlwYRXVcUT+SQv10k++D0IIi9j/vVow+pYaz6odwx4y6PBQEq/A3FC1TbiTtxroKWyx5z2zYd0ItQX9W38V7Lr+mqQGYpwqwaD9eReoTo1V99v/PZiyc7+ntVLr+9TuCLWeB4ebSRMZSfBtCzUwVPdFeGV+8WSo5GSp4981TX3eoKvages+GUMPkpFtJCIujfPXUzh3LYXsS/vpEDmV2jm5u3tvMAYxMHsI2Xfksei0GjycbPByscHZ1hp7gxV2eivs9TrsDVbYG3TY6a1wtFZocvB9Kl7dTJahHAfaLyfDoVKe4/F0tqWGuyM6bclm+BRbC/XHkbmFuu2jGcUTQoiH1bFjR7Kzs1m3bt1d723fvp2WLVty5MgRGjRoUKDj7t+/H3t7+6IaJgBjx45l1apVhIeH53o9KioKV9eSaXObnp6Ot7c3Wq2WyMjIEqshJ4QQQjwSgvqqy6ISr0Cr/0DllpYekdrWvN96WPQSxJ6GeaHQ/ZeCLd0yZqvBl78mqs+f/rTggasmA+HgAki8rLbibvl/t9/LSoXd38DOqZCVrL5WpQ08+Y6a/XNiDVw/qbb8Ph8Ga4eBb1M14HN5z80C0L5qJ7biWoJkpVevu1w1tZPa30vU73PXH++dYfQANtY6ano4UtMuGc5OgoglACjW9iQ1HkyklR81dwzhaQ6yynk24x1HEpFoJCohgxyTQmRCOpEJ6fc8fnkSmamfSkXtKTIUa7olvcfhX6KB6Ht+xtFgRaCvC4383Gjs70qgrwt2+tITWik9IymlZLmWEOJx169fP15++WWuXLlCpUq5/6oxf/58GjVqVOAAD1CiS3Lv1QGyOPz666/UrVsXRVFYtWoVXbp0KbFz/5uiKBiNRqys5Ne9EEKIUkKrhRdnW3oUd3OuBH3XwU+vQuQB+KEjdFkE1do9+LPXz8DKN+HqYfX5Ez2gcf+Cj8HaFtqNVQv/bv8fPNFL7aJ2aKHalSzlZpdrzwBoN+522/caodDmv2rr7pNr1IBPVDhE7FA3UJfEvfw92LoUfFwFFdRbnc9lvdXzf98eui8Ht8oFP1ZWGuyeATv+p9bKAQh4HU3b0Tg7eeIM4OsOS16nbsoufvScAX0XYdQZuJ6cSWRCGlcTMkjJzCE1M4e0LCOpWerjCgl/0+vyx7ga40jT2DLJaQQmfSMCbp7630ueTIrCheupJGfmsP1sLNvPxgJqxlAdTyeC/Fxp5O9KIz83PJwtl7FfjKXKy4Ykc3ctuUEWQjyenn/+eSpUqMCCBQtyvZ6SksLy5cvp168fcXFxdOvWDW9vb+zs7Khfvz4///zzfY/r7+9vXroFcPbsWVq2bImNjQ116tRh48aNd31mxIgR1KhRAzs7O6pUqcLHH39MdrYajF+wYAHjxo3jyJEjaDQaNBqNecwajYZVq1aZj3P06FGeeuopbG1tKVeuHG+++SYpKSnm9/v06UPnzp2ZNGkSnp6elCtXjoEDB5rPdT9z586lR48e9OjRg7lz5971/vHjx3n++edxcnLC0dGRFi1acP78efP78+bNo27duhgMBjw9Pc3dKy9evIhGo8mVpZSQkIBGo2Hr1q0AbN26FY1Gw59//klQUBAGg4EdO3Zw/vx5OnXqhLu7Ow4ODjRu3JhNmzblGldmZiYjRozAx8cHg8FAtWrVmDt3LoqiUK1aNSZNyt36NTw8HI1Gw7lz5x44J0IIIcQjwc4Neq1Wi+5mp8HirnD0l3vvbzLB3u/UpVBXD6sdtF6eC51mFj5bpt7Lao2d7FRYMQBmhqhZOSkx4OoPr8yDAVtvB3juVL46tPgA3toGQ4+qXbt8m4BGqy6JK0TXrkKr1lbNjnKqBHFn4ft2agZXfimKOvczGsOWz9Xvh08IDNgML84CJ8/c53p9qbpE7uwGWNodnTETD2cbgvzc6BjgRbdgX/q3qMJ7basz6plafOa9jyGXhuBqjIPyNbEbuJ3Rw95n9aDm5m3Nv7bfB7fgyJin+X1wc8a9UJeOAV54OdtgNCkcjUxkwa6LDFp8mCfHh3EsMrHo5zSfJHLxALJcSwhR7BTl9l8mSpK1Xb5uQKysrOjVqxcLFizgww8/NLewXL58OUajkW7dupGSkkJQUBAjRozAycmJtWvX0rNnT6pWrUpwcPADz2EymXjppZdwd3dn7969JCYm5lmrx9HRkQULFuDl5cXRo0cZMGAAjo6O/Oc//6FLly4cO3aMdevWmQMYzs53tytNTU0lNDSUJk2asH//fq5du0b//v0ZNGhQrkDWli1b8PT0ZMuWLZw7d44uXboQGBjIgAED7nkd58+fZ/fu3axYsQJFUXj//feJiIjAz88PgMjISFq2bEnr1q3ZvHkzTk5O7Ny5k5wc9Q8Ks2bNYtiwYUyYMIFnn32WxMREdu7c+cD5+7eRI0cyadIkqlSpgqurK5cvX6ZDhw58/vnnGAwGFi5cSMeOHTl9+jS+vr6A2h1z9+7dTJs2jYCAAC5cuEBsbCwajYa+ffsyf/58hg8fbj7H/PnzadmyJdWqVSvw+IQQQohSy+AA3ZbCqrfVAsi/9oe0OAh5K/d+SVdh1btqVy5Ql051/kZty/4wNBp4ZjzMba8WVQawKw+tRkBQH3VJVH64+KrLvwrTAryouNeF/ptg8WsQ/TcseA5emqMWic5LWrw6n+c2w/nNkHxVfd3ZB9qPg7ov3fvetUpr6L5MLZ58bpPaJavbz2p21J2y02HtcLXQNkCdTmpQzuCYr0uy0mmp5+1MPW9nejf1B+BqQjoHIm5w8GI8ByJuEBGXRk2P/B2vOEiQ5wGS0m9l8kiQRwhRTLLT4IuHvCEojP9eBX3+auL07duXiRMnsm3bNlq3bg2o/8h/+eWXcXZ2xtnZOVcAYPDgwaxfv55ly5blK8izadMmTp06xfr16/HyUufiiy++4Nlnn82130cffWR+7O/vz/Dhw1myZAn/+c9/sLW1xcHBASsrq/suz1q8eDEZGRksXLjQXBNoxowZdOzYkS+//NLcLdLV1ZUZM2ag0+moVasWzz33HGFhYfcN8sybN49nn33WXP8nNDSU+fPnM3bsWABmzpyJs7MzS5Yswdpa/b1So8bt9fqfffYZH3zwAUOGDDG/1rhx4wfO37998skntG/f3vzczc2NgIAA8/NPP/2UlStXsmbNGgYNGsSZM2dYtmwZGzdupF07NS29SpXbzQb69OnD6NGj2bdvH8HBwWRnZ7N48eK7snuEEEKIMsFKDy99D3blYN938Od/1ILHbf6rBhmO/qJm12Qkqtkj7T9Rl2cVQdtzAHyCIfgtOLocgt+EpoPyHYQodZw84Y0/4Ze+cHY9LOsFT3+mBp9MRriy/2Ydoc0QeYhci6T0DmqL+6aD7g7W5KVyS3VZ2E+vqcGixV2g25LbBawTLsHSHhB1RM1uajcWmr730DWKvFxsecHFlhcC1HvYjGwj1vnt6lUMJMhzH9lGE+nZRgAcZbmWEOIxVqtWLZo2bcq8efNo3bo1586dY/v27XzyyScAGI1GvvjiC5YtW0ZkZCRZWVlkZmZiZ5dHV4g8nDx5Eh8fH3OAB6BJkyZ37bd06VKmTZvG+fPnSUlJIScnp8BdgU6ePElAQECuos/NmjXDZDJx+vRpc5Cnbt266HQ68z6enp4cPXr0nsc1Go388MMPTJ061fxajx49GD58OKNHj0ar1RIeHk6LFi3MAZ47Xbt2jatXr9K2bdsCXU9eGjXKXSgyJSWFsWPHsnbtWqKiosjJySE9PZ1Lly4B6tIrnU5Hq1at8jyel5cXzz33HPPmzSM4OJjffvuNzMxMXn31Ae1dhRBCiEeVVqu2fLevoC4X+usrdclUVoqa4QPg1RBe+k5dJlXUOnylbmWBwQG6Lla7ju3/HjZ8CCdWwfXTkJmUe9+KddTlctXaqkvN8hPcuZN/c+jxi1pb6cI2NYvo9aVweS/80g/S49Xg3Svz1OyfYmBjrXvwTsVIIhf3kXyzHg9IkEcIUYys7dSsGkuctwD69evH4MGDmTlzJvPnz6dq1armoMDEiROZOnUqU6ZMoX79+tjb2zN06FCysrKKbLi7d++me/fujBs3jtDQUHNGzNdff11k57jTvwMxGo0Gk8l0z/3Xr19PZGTkXYWWjUYjYWFhtG/fHlvbe9+o3O89AO3Nvw4qyu2/cN2rRtC/u5YNHz6cjRs3MmnSJKpVq4atrS2vvPKK+fvzoHMD9O/fn549e/K///2P+fPn06VLl3wH8YQQQohHkkajdgCzK6e2fT/0w83XdWrnq5bDQScrPvJFZwUdJoFrZdjwkZrBA2rXrSpt1KBO1acefrkbgF9T6PEr/PgKXNwO37WGuHOgmMDrCXhtEbj4PPx5SimJXNzHrc5a9nodVhZMtxJClHEaTb6XTVnSa6+9xpAhQ1i8eDELFy7knXfeMdfn2blzJ506daJHjx6AWmPnzJkz1KlTJ1/Hrl27NpcvXyYqKgpPT7WQ3p49e3Lts2vXLvz8/Pjwww/Nr0VEROTaR6/XYzQaH3iuBQsWkJqaag6G7Ny5E61WS82aNfM13rzMnTuXrl275hofwOeff87cuXNp3749DRo04IcffiA7O/uuIJKjoyP+/v6EhYXRps3dxRRvdSOLioriiSeeALirVfy97Ny5kz59+vDiiy8CambPxYsXze/Xr18fk8nEtm3bzMu1/q1Dhw7Y29sza9Ys1q1bx19//ZWvcwshhBCPvMb91EDPyrfUrlEvfgeVgiw9qkePRqMuvXKvA1F/Q+UW4BkI2mLIfPF9EnqugEUvQewZ9bUnekCHr8Hacp2vSoJELu7DpCjU93amtmfBlgIIIURZ5ODgQJcuXRg1ahRRUVH06dPH/F716tXZuHEju3bt4uTJk7z11lvExMTk+9jt2rWjRo0a9O7dmyNHjrB9+/a7giXVq1fn0qVLLFmyhPPnzzNt2jRWrlyZax9/f38uXLhAeHg4sbGxZGZm3nWu7t27Y2NjQ+/evTl27Bhbtmxh8ODB9OzZ07xUq6CuX7/Ob7/9Ru/evalXr16urVevXqxatYr4+HgGDRpEUlISXbt25cCBA5w9e5ZFixZx+vRpAMaOHcvXX3/NtGnTOHv2LIcOHWL69OmAmm3z5JNPMmHCBE6ePMm2bdty1Si6n+rVq7NixQrCw8M5cuQIr7/+eq6sJH9/f3r37k3fvn1ZtWoVFy5cYOvWrSxbtsy8j06no0+fPowaNYrq1avnuZxOCCGEKLPqdob/OwcD90mA52FVfQqaDwXvoOIJ8NziE3yzW1pbeGE6vDCjzAd4QII891WlggO/DW7OL+80tfRQhBCiVOjXrx83btwgNDQ0V/2cjz76iIYNGxIaGkrr1q3x8PCgc+fO+T6uVqtl5cqVpKenExwcTP/+/fn8889z7fPCCy/w/vvvM2jQIAIDA9m1axcff/xxrn1efvllnnnmGdq0aUOFChXybONuZ2fH+vXriY+Pp3Hjxrzyyiu0bduWGTNmFGwy7nCriHNe9XTatm2Lra0tP/74I+XKlWPz5s2kpKTQqlUrgoKCmDNnjjmrp3fv3kyZMoVvvvmGunXr8vzzz3P27FnzsebNm0dOTg5BQUEMHTqUzz77LF/jmzx5Mq6urjRt2pSOHTsSGhpKw4YNc+0za9YsXnnlFd59911q1arFgAEDSE1NzbVPv379yMrK4o033ijoFAkhhBCPPoNj8QYlRNGrFKRm9DTs9dAFlh8VGuXOxf2PqKSkJJydnUlMTCxwAU4hhChJGRkZXLhwgcqVK2NjU/b/kiDKlu3bt9O2bVsuX778wKyn+/2sy+/t0kG+D0IIIcSjoSC/s6UmjxBCCCHuKzMzk+vXrzN27FheffXVQi9rE0IIIYQQxUuWawkhhBDivn7++Wf8/PxISEjgq6/KSDtXIYQQQogySII8QgghhLivPn36YDQaOXjwIN7e3pYejhBCCCGEuAcJ8gghhBBCCCGEEEKUARLkEUIIIYQQQgghhCgDJMgjhBAWYDKZLD0EIYqV/IwLIYQQQpQ86a4lhBAlSK/Xo9VquXr1KhUqVECv16PRaCw9LCGKjKIoZGVlcf36dbRaLXq93tJDEkIIIYR4bEiQRwghSpBWq6Vy5cpERUVx9epVSw9HiGJjZ2eHr68vWq0kDQshhBBClBQJ8gghRAnT6/X4+vqSk5OD0Wi09HCEKHI6nQ4rKyvJUhNCCCGEKGES5BFCCAvQaDRYW1tjbW1t6aEIIYQQQgghygjJoRZCCCGEKCVmzpyJv78/NjY2hISEsG/fvnvuO2fOHFq0aIGrqyuurq60a9fuvvsLIYQQouyTII8QQgghRCmwdOlShg0bxpgxYzh06BABAQGEhoZy7dq1PPffunUr3bp1Y8uWLezevRsfHx+efvppIiMjS3jkQgghhCgtNIqiKJYexMNKSkrC2dmZxMREnJycLD0cIYQQQtyH/N7OW0hICI0bN2bGjBmA2obex8eHwYMHM3LkyAd+3mg04urqyowZM+jVq9cD95fvgxBCCPFoKMjv7DJRk+dWnCopKcnCIxFCCCHEg9z6fV0G/s5UZLKysjh48CCjRo0yv6bVamnXrh27d+/O1zHS0tLIzs7Gzc0tz/czMzPJzMw0P09MTATk/kkIIYQo7Qpy71QmgjzJyckA+Pj4WHgkQgghhMiv5ORknJ2dLT2MUiE2Nhaj0Yi7u3uu193d3Tl16lS+jjFixAi8vLxo165dnu+PHz+ecePG3fW63D8JIYQQj4b83DuViSCPl5cXly9fxtHRscjbtSYlJeHj48Ply5cllbmYyVyXLJnvkiNzXbJkvktOYedaURSSk5Px8vIqxtE9XiZMmMCSJUvYunUrNjY2ee4zatQohg0bZn5uMpmIj4+nXLlycv/0CJO5Llky3yVH5rpkyXyXnMLMdUHuncpEkEer1VKpUqViPYeTk5P8sJcQmeuSJfNdcmSuS5bMd8kpzFxLBk9u5cuXR6fTERMTk+v1mJgYPDw87vvZSZMmMWHCBDZt2kSDBg3uuZ/BYMBgMOR6zcXFpdBjzg/577DkyFyXLJnvkiNzXbJkvktOQec6v/dO0l1LCCGEEMLC9Ho9QUFBhIWFmV8zmUyEhYXRpEmTe37uq6++4tNPP2XdunU0atSoJIYqhBBCiFKsTGTyCCGEEEI86oYNG0bv3r1p1KgRwcHBTJkyhdTUVN544w0AevXqhbe3N+PHjwfgyy+/ZPTo0SxevBh/f3+io6MBcHBwwMHBwWLXIYQQQgjLkSDPAxgMBsaMGXNXerMoejLXJUvmu+TIXJcsme+SI3NdtLp06cL169cZPXo00dHRBAYGsm7dOnMx5kuXLqHV3k7CnjVrFllZWbzyyiu5jjNmzBjGjh1bkkO/i/xslByZ65Il811yZK5Llsx3ySnuudYo0r9UCCGEEEIIIYQQ4pEnNXmEEEIIIYQQQgghygAJ8gghhBBCCCGEEEKUARLkEUIIIYQQQgghhCgDJMgjhBBCCCGEEEIIUQZIkOc+Zs6cib+/PzY2NoSEhLBv3z5LD6lM+Ouvv+jYsSNeXl5oNBpWrVqV631FURg9ejSenp7Y2trSrl07zp49a5nBPuLGjx9P48aNcXR0pGLFinTu3JnTp0/n2icjI4OBAwdSrlw5HBwcePnll4mJibHQiB9ts2bNokGDBjg5OeHk5ESTJk34888/ze/LXBefCRMmoNFoGDp0qPk1me+iM3bsWDQaTa6tVq1a5vdlrsWd5P6peMj9U8mR+6eSJfdPliP3T8XLUvdPEuS5h6VLlzJs2DDGjBnDoUOHCAgIIDQ0lGvXrll6aI+81NRUAgICmDlzZp7vf/XVV0ybNo3Zs2ezd+9e7O3tCQ0NJSMjo4RH+ujbtm0bAwcOZM+ePWzcuJHs7GyefvppUlNTzfu8//77/Pbbbyxfvpxt27Zx9epVXnrpJQuO+tFVqVIlJkyYwMGDBzlw4ABPPfUUnTp14vjx44DMdXHZv38/3377LQ0aNMj1usx30apbty5RUVHmbceOHeb3ZK7FLXL/VHzk/qnkyP1TyZL7J8uQ+6eSYZH7J0XkKTg4WBk4cKD5udFoVLy8vJTx48dbcFRlD6CsXLnS/NxkMikeHh7KxIkTza8lJCQoBoNB+fnnny0wwrLl2rVrCqBs27ZNURR1bq2trZXly5eb9zl58qQCKLt377bUMMsUV1dX5fvvv5e5LibJyclK9erVlY0bNyqtWrVShgwZoiiK/GwXtTFjxigBAQF5vidzLe4k908lQ+6fSpbcP5U8uX8qXnL/VDIsdf8kmTx5yMrK4uDBg7Rr1878mlarpV27duzevduCIyv7Lly4QHR0dK65d3Z2JiQkROa+CCQmJgLg5uYGwMGDB8nOzs4137Vq1cLX11fm+yEZjUaWLFlCamoqTZo0kbkuJgMHDuS5557LNa8gP9vF4ezZs3h5eVGlShW6d+/OpUuXAJlrcZvcP1mO3D8VL7l/Kjly/1Qy5P6p5Fji/snqoT5dRsXGxmI0GnF3d8/1uru7O6dOnbLQqB4P0dHRAHnO/a33ROGYTCaGDh1Ks2bNqFevHqDOt16vx8XFJde+Mt+Fd/ToUZo0aUJGRgYODg6sXLmSOnXqEB4eLnNdxJYsWcKhQ4fYv3//Xe/Jz3bRCgkJYcGCBdSsWZOoqCjGjRtHixYtOHbsmMy1MJP7J8uR+6fiI/dPJUPun0qO3D+VHEvdP0mQR4jHxMCBAzl27FiudaCi6NWsWZPw8HASExP55Zdf6N27N9u2bbP0sMqcy5cvM2TIEDZu3IiNjY2lh1PmPfvss+bHDRo0ICQkBD8/P5YtW4atra0FRyaEEMVL7p9Khtw/lQy5fypZlrp/kuVaeShfvjw6ne6uytYxMTF4eHhYaFSPh1vzK3NftAYNGsTvv//Oli1bqFSpkvl1Dw8PsrKySEhIyLW/zHfh6fV6qlWrRlBQEOPHjycgIICpU6fKXBexgwcPcu3aNRo2bIiVlRVWVlZs27aNadOmYWVlhbu7u8x3MXJxcaFGjRqcO3dOfraFmdw/WY7cPxUPuX8qOXL/VDLk/smySur+SYI8edDr9QQFBREWFmZ+zWQyERYWRpMmTSw4srKvcuXKeHh45Jr7pKQk9u7dK3NfCIqiMGjQIFauXMnmzZupXLlyrveDgoKwtrbONd+nT5/m0qVLMt9FxGQykZmZKXNdxNq2bcvRo0cJDw83b40aNaJ79+7mxzLfxSclJYXz58/j6ekpP9vCTO6fLEfun4qW3D9Zntw/FQ+5f7KsErt/eqiyzWXYkiVLFIPBoCxYsEA5ceKE8uabbyouLi5KdHS0pYf2yEtOTlYOHz6sHD58WAGUyZMnK4cPH1YiIiIURVGUCRMmKC4uLsrq1auVv//+W+nUqZNSuXJlJT093cIjf/S88847irOzs7J161YlKirKvKWlpZn3efvttxVfX19l8+bNyoEDB5QmTZooTZo0seCoH10jR45Utm3bply4cEH5+++/lZEjRyoajUbZsGGDoigy18Xtzu4QiiLzXZQ++OADZevWrcqFCxeUnTt3Ku3atVPKly+vXLt2TVEUmWtxm9w/FR+5fyo5cv9UsuT+ybLk/qn4WOr+SYI89zF9+nTF19dX0ev1SnBwsLJnzx5LD6lM2LJliwLctfXu3VtRFLUN6Mcff6y4u7srBoNBadu2rXL69GnLDvoRldc8A8r8+fPN+6Snpyvvvvuu4urqqtjZ2SkvvviiEhUVZblBP8L69u2r+Pn5KXq9XqlQoYLStm1b8w2KoshcF7d/36TIfBedLl26KJ6enoper1e8vb2VLl26KOfOnTO/L3Mt7iT3T8VD7p9Kjtw/lSy5f7IsuX8qPpa6f9IoiqI8XC6QEEIIIYQQQgghhLA0qckjhBBCCCGEEEIIUQZIkEcIIYQQQgghhBCiDJAgjxBCCCGEEEIIIUQZIEEeIYQQQgghhBBCiDJAgjxCCCGEEEIIIYQQZYAEeYQQQgghhBBCCCHKAAnyCCGEEEIIIYQQQpQBEuQRQgghhBBCCCGEKAMkyCOEEEIIIYQQQghRBkiQRwghhBBCCCGEEKIMkCCPEEIIIYQQQgghRBkgQR4hhBBCCCGEEEKIMuD/ARIRk+QJunbqAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qRHvHjDNMN7"
      },
      "source": [
        "**Kurva Pelatihan dan Validasi sudah dekat, kita dapat menyimpulkan bahwa Model tidak Kelebihan Data.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ULQT4B2qNRAL"
      },
      "source": [
        "# 6) Confusion Matrix\n",
        "\n",
        "Confusion Matrix digunakan untuk mencari performa dalam memprediksi hasil data dengan data aktual."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fp_11aTa6bj1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "outputId": "e54bd14c-488a-4b2c-b579-52f883df9034"
      },
      "source": [
        "# Get Model Predictions for test data\n",
        "class_names = ['T-shirt/top',\n",
        "               'Trouser',\n",
        "               'Pullover',\n",
        "               'Dress',\n",
        "               'Coat',\n",
        "               'Sandal',\n",
        "               'Shirt',\n",
        "               'Sneaker',\n",
        "               'Bag',\n",
        "               'Ankle boot']\n",
        "\n",
        "predicted_classes = model.predict(x_test_pic)\n",
        "Y_pred_classes = np.argmax(predicted_classes, axis=1)\n",
        "Y_true = np.argmax(y_test, axis = 1)\n",
        "\n",
        "# Confusion Matrix\n",
        "cm = confusion_matrix(Y_true, Y_pred_classes)\n",
        "\n",
        "plt.figure(figsize=(10, 7))\n",
        "sns.heatmap(cm, annot=True, fmt='d')\n",
        "plt.title(\"CNN Model Prediction Confusion Matrix\")\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "438/438 [==============================] - 1s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2IbtAHGDNivG"
      },
      "source": [
        "# 7) Visualization of Predicted Classes\n",
        "## 7.1) Correctly Predicted Classes\n",
        "\n",
        "Visualisasi hasil data prediksi yang benar dalam sebuah label data berdasarkan kelas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "94kpN6hI7FlV"
      },
      "source": [
        "# Correctly Predicted Classes\n",
        "correct = []\n",
        "for i in range(len(y_test)):\n",
        "  if(Y_pred_classes[i] == Y_true[i]):\n",
        "    correct.append(i)\n",
        "  if(len(correct) == 4):\n",
        "    break"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UsPeDkfg7HTt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        },
        "outputId": "2b2fe5dc-e226-498f-b629-213770bacfda"
      },
      "source": [
        "fig, ax = plt.subplots(2,2, figsize=(12,6))\n",
        "fig.set_size_inches(10,10)\n",
        "\n",
        "ax[0,0].imshow(X_test[correct[0]].reshape(28,28), cmap='Reds')\n",
        "ax[0,0].set_title(\"Predicted Label : \" + str(labeldict[Y_pred_classes[correct[0]]]) + \"\\n\"+\"Actual Label : \" +\n",
        "                 str(labeldict[Y_true[correct[0]]]))\n",
        "\n",
        "ax[0,1].imshow(X_test[correct[1]].reshape(28,28), cmap='Reds')\n",
        "ax[0,1].set_title(\"Predicted Label : \" + str(labeldict[Y_pred_classes[correct[1]]]) + \"\\n\"+\"Actual Label : \" +\n",
        "                 str(labeldict[Y_true[correct[1]]]))\n",
        "\n",
        "ax[1,0].imshow(X_test[correct[2]].reshape(28,28), cmap='Reds')\n",
        "ax[1,0].set_title(\"Predicted Label : \" + str(labeldict[Y_pred_classes[correct[2]]]) + \"\\n\"+\"Actual Label : \" +\n",
        "                 str(labeldict[Y_true[correct[2]]]))\n",
        "\n",
        "ax[1,1].imshow(X_test[correct[3]].reshape(28,28), cmap='Reds')\n",
        "ax[1,1].set_title(\"Predicted Label : \" + str(labeldict[Y_pred_classes[correct[3]]]) + \"\\n\"+\"Actual Label : \" +\n",
        "                 str(labeldict[Y_true[correct[3]]]))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Predicted Label : Dress\\nActual Label : Dress')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QrCs8Mb5Npg7"
      },
      "source": [
        "## 7.2) Incorrectly Predicted Classes\n",
        "\n",
        "Visualisasi dari hasil prediksi data yang salah dari label data berdasarkan kelas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4P45E2Y7gNk"
      },
      "source": [
        "# Incorrectly Predicted Classes\n",
        "incorrect = []\n",
        "for i in range(len(y_test)):\n",
        "  if(not Y_pred_classes[i] == Y_true[i]):\n",
        "    incorrect.append(i)\n",
        "  if(len(incorrect) == 4):\n",
        "    break"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtnK3q117q9s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        },
        "outputId": "b56ab420-d456-4fa3-fae6-ee8e33c4c6d0"
      },
      "source": [
        "fig, ax = plt.subplots(2,2, figsize=(12,6))\n",
        "fig.set_size_inches(10,10)\n",
        "\n",
        "ax[0,0].imshow(X_test[incorrect[0]].reshape(28,28), cmap='Reds')\n",
        "ax[0,0].set_title(\"Predicted Label : \" + str(labeldict[Y_pred_classes[incorrect[0]]]) + \"\\n\"+\"Actual Label : \" +\n",
        "                 str(labeldict[Y_true[incorrect[0]]]))\n",
        "\n",
        "ax[0,1].imshow(X_test[incorrect[1]].reshape(28,28), cmap='Reds')\n",
        "ax[0,1].set_title(\"Predicted Label : \" + str(labeldict[Y_pred_classes[incorrect[1]]]) + \"\\n\"+\"Actual Label : \" +\n",
        "                 str(labeldict[Y_true[incorrect[1]]]))\n",
        "\n",
        "ax[1,0].imshow(X_test[incorrect[2]].reshape(28,28), cmap='Reds')\n",
        "ax[1,0].set_title(\"Predicted Label : \" + str(labeldict[Y_pred_classes[incorrect[2]]]) + \"\\n\"+\"Actual Label : \" +\n",
        "                 str(labeldict[Y_true[incorrect[2]]]))\n",
        "\n",
        "ax[1,1].imshow(X_test[incorrect[3]].reshape(28,28), cmap='Reds')\n",
        "ax[1,1].set_title(\"Predicted Label : \" + str(labeldict[Y_pred_classes[incorrect[3]]]) + \"\\n\"+\"Actual Label : \" +\n",
        "                 str(labeldict[Y_true[incorrect[3]]]))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Predicted Label : Shirt\\nActual Label : T-shirt/top')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ilP0RpvNuUV"
      },
      "source": [
        "# 8) Classification Report\n",
        "\n",
        "Classification Report digunakan untuk menampilkan Presisi, Recall dan F1-Score dan Nilai Support."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0bstnUMD7xCO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6166423b-39c5-444e-f868-ebd1ea8d5085"
      },
      "source": [
        "print(classification_report(Y_true, Y_pred_classes, target_names = class_names))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            " T-shirt/top       0.87      0.83      0.85      1389\n",
            "     Trouser       0.99      0.99      0.99      1455\n",
            "    Pullover       0.90      0.85      0.88      1378\n",
            "       Dress       0.96      0.81      0.88      1387\n",
            "        Coat       0.86      0.85      0.86      1435\n",
            "      Sandal       0.98      0.99      0.99      1364\n",
            "       Shirt       0.66      0.82      0.73      1427\n",
            "     Sneaker       0.98      0.93      0.95      1343\n",
            "         Bag       0.97      0.99      0.98      1401\n",
            "  Ankle boot       0.95      0.98      0.96      1421\n",
            "\n",
            "    accuracy                           0.90     14000\n",
            "   macro avg       0.91      0.90      0.91     14000\n",
            "weighted avg       0.91      0.90      0.91     14000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5TFQkqkN9xz"
      },
      "source": [
        "**Lihatlah Presisi shirt, kita dapat melihat bahwa model kami memperkirakan kurang dari 80% Kaos dengan benar dari total gambar yang diprediksi shirt. Kami menyimpulkan hal yang sama dari matriks kebingungan, di mana kami melihat bahwa banyak T-shirt salah diklasifikasikan sebagai T-Shirt.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5G0pqO2Sw89"
      },
      "source": [
        "# 9.) **Rekapitulasi Data**\n",
        "\n",
        "Rekapitulasi digunakan untuk menampilkan hasil evaluasi model data dengan variasi parameter yang digunakan."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zt-ZsOnWSwrm"
      },
      "source": [
        "evaluation = pd.DataFrame({'Model': [],\n",
        "                           'Data_Train': [],\n",
        "                           'Data_Test': [],\n",
        "                           'Epochs': [],\n",
        "                           'Batch_size':[],\n",
        "                           'Loss': [],\n",
        "                           'Accuracy': []\n",
        "                           })"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tr0KXso8TVO1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "e99f2652-dc7a-4200-ea32-c62f83a90e4b"
      },
      "source": [
        "r = evaluation.shape[0]\n",
        "evaluation.loc[r] = ['CNN', str(1 - pers_test), str(pers_test), EPOCHS, BATCH_SIZE, score[0], score[1]]\n",
        "evaluation"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Model Data_Train Data_Test  Epochs  Batch_size      Loss  Accuracy\n",
              "0   CNN        0.8       0.2      50         512  0.263525  0.904857"
            ],
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              "      <div>\n",
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              "      <th>Model</th>\n",
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              "      <th>Epochs</th>\n",
              "      <th>Batch_size</th>\n",
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              "      <th>0</th>\n",
              "      <td>CNN</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>50</td>\n",
              "      <td>512</td>\n",
              "      <td>0.263525</td>\n",
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              "              title=\"Suggest charts.\"\n",
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              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-8be6c69b-6a55-4744-9d4f-660072057535 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c5a950a3-16b8-47db-8a82-34b09c312d38 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c5a950a3-16b8-47db-8a82-34b09c312d38');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iVmQJ8-gjBW3"
      },
      "source": [
        "# **Recurrent Neural Network**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l4CWBJW724dm"
      },
      "source": [
        "## **Import Library**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IkW6HCW13DG7"
      },
      "source": [
        "from keras.models import Model\n",
        "from keras.layers import Input, Dense, TimeDistributed\n",
        "from keras.layers import LSTM"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfOfFixR3GL6"
      },
      "source": [
        "## **Inisialisasi Parameter**\n",
        "\n",
        "Inisialisasi parameter mendefinisikan parameter yang akan digunakan untuk melatih model data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uCZEB-7A3PRo"
      },
      "source": [
        "# Training parameters.\n",
        "batch_size = 32\n",
        "num_classes = 10\n",
        "epochs = 10\n",
        "\n",
        "# Embedding dimensions.\n",
        "row_hidden = 128\n",
        "col_hidden = 128\n",
        "\n",
        "# Converts class vectors to binary class matrices.\n",
        "row, col, pixel = x_train_pic.shape[1:]\n",
        "\n",
        "# 4D input.\n",
        "x = Input(shape=(row, col, pixel))"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ukj_IXi53VB_"
      },
      "source": [
        "Encode Data\n",
        "\n",
        "Encode Data digunakan untuk mengubah data baris dan kolom menggunakan metode LSTM (Long-Short Time Memory)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I1FCkj323jnQ"
      },
      "source": [
        "# Encodes a row of pixels using TimeDistributed Wrapper.\n",
        "encoded_rows = TimeDistributed(LSTM(row_hidden))(x)\n",
        "# Encodes columns of encoded rows.\n",
        "encoded_columns = LSTM(col_hidden)(encoded_rows)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T09yoyZy3cPt"
      },
      "source": [
        "Membangun Model Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6rpKfBt3xNl"
      },
      "source": [
        "# Final predictions and model.\n",
        "prediction = Dense(num_classes, activation='softmax')(encoded_columns)\n",
        "model = Model(x, prediction)\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='RMSprop',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vwqcdLYJ3qS5"
      },
      "source": [
        "## **Melatih Model Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxMn9kOe36zh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd6d5058-1ca9-488b-8c88-0ec7508450bc"
      },
      "source": [
        "# Fitting the model\n",
        "model.fit(x_train_pic, y_train,\n",
        "          batch_size = batch_size,\n",
        "          epochs = epochs,\n",
        "          validation_data = (x_test_pic, y_test),\n",
        "          shuffle = False)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1750/1750 [==============================] - 32s 16ms/step - loss: 0.7409 - accuracy: 0.7262 - val_loss: 0.5105 - val_accuracy: 0.8133\n",
            "Epoch 2/10\n",
            "1750/1750 [==============================] - 27s 16ms/step - loss: 0.4575 - accuracy: 0.8317 - val_loss: 0.4399 - val_accuracy: 0.8391\n",
            "Epoch 3/10\n",
            "1750/1750 [==============================] - 20s 12ms/step - loss: 0.3842 - accuracy: 0.8585 - val_loss: 0.3818 - val_accuracy: 0.8584\n",
            "Epoch 4/10\n",
            "1750/1750 [==============================] - 23s 13ms/step - loss: 0.3461 - accuracy: 0.8701 - val_loss: 0.3585 - val_accuracy: 0.8702\n",
            "Epoch 5/10\n",
            "1750/1750 [==============================] - 21s 12ms/step - loss: 0.3186 - accuracy: 0.8805 - val_loss: 0.3544 - val_accuracy: 0.8700\n",
            "Epoch 6/10\n",
            "1750/1750 [==============================] - 20s 11ms/step - loss: 0.2952 - accuracy: 0.8898 - val_loss: 0.3288 - val_accuracy: 0.8808\n",
            "Epoch 7/10\n",
            "1750/1750 [==============================] - 21s 12ms/step - loss: 0.2788 - accuracy: 0.8954 - val_loss: 0.3223 - val_accuracy: 0.8832\n",
            "Epoch 8/10\n",
            "1750/1750 [==============================] - 20s 11ms/step - loss: 0.2641 - accuracy: 0.8999 - val_loss: 0.3020 - val_accuracy: 0.8921\n",
            "Epoch 9/10\n",
            "1750/1750 [==============================] - 21s 12ms/step - loss: 0.2508 - accuracy: 0.9046 - val_loss: 0.3274 - val_accuracy: 0.8844\n",
            "Epoch 10/10\n",
            "1750/1750 [==============================] - 21s 12ms/step - loss: 0.2409 - accuracy: 0.9094 - val_loss: 0.3111 - val_accuracy: 0.8882\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7e01804b31c0>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UnIv2V0432cj"
      },
      "source": [
        "## **Evaluasi Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-GKRbvJiGlX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e9978c0-c6b0-40a3-aac6-e69c01107627"
      },
      "source": [
        "# Evaluation.\n",
        "scores = model.evaluate(x_test_pic, y_test)\n",
        "print('Test loss:', scores[0])\n",
        "print('Test accuracy:', scores[1])"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "438/438 [==============================] - 3s 6ms/step - loss: 0.3111 - accuracy: 0.8882\n",
            "Test loss: 0.3111443817615509\n",
            "Test accuracy: 0.8882142901420593\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get Model Predictions for test data\n",
        "class_names = ['T-shirt/top',\n",
        "               'Trouser',\n",
        "               'Pullover',\n",
        "               'Dress',\n",
        "               'Coat',\n",
        "               'Sandal',\n",
        "               'Shirt',\n",
        "               'Sneaker',\n",
        "               'Bag',\n",
        "               'Ankle boot']\n",
        "\n",
        "predicted_classes = model.predict(x_test_pic)\n",
        "Y_pred_classes = np.argmax(predicted_classes, axis=1)\n",
        "Y_true = np.argmax(y_test, axis = 1)\n",
        "print(classification_report(Y_true, Y_pred_classes, target_names=class_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVHCZ-gqK817",
        "outputId": "f4e3eff1-7422-4ed0-d294-6ccac42f29d5"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "438/438 [==============================] - 3s 5ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " T-shirt/top       0.83      0.87      0.85      1389\n",
            "     Trouser       0.99      0.97      0.98      1455\n",
            "    Pullover       0.72      0.90      0.80      1378\n",
            "       Dress       0.85      0.95      0.90      1387\n",
            "        Coat       0.87      0.70      0.77      1435\n",
            "      Sandal       0.99      0.97      0.98      1364\n",
            "       Shirt       0.80      0.66      0.72      1427\n",
            "     Sneaker       0.97      0.91      0.94      1343\n",
            "         Bag       0.98      0.97      0.97      1401\n",
            "  Ankle boot       0.92      0.98      0.95      1421\n",
            "\n",
            "    accuracy                           0.89     14000\n",
            "   macro avg       0.89      0.89      0.89     14000\n",
            "weighted avg       0.89      0.89      0.89     14000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sD2zII6iohNW"
      },
      "source": [
        "## **Confusion Matrix**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JC0qT48rf4sy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "outputId": "bf174184-4e73-4024-8969-82f1d8ac3478"
      },
      "source": [
        "# Confusion Matrix\n",
        "cm = confusion_matrix(Y_true, Y_pred_classes)\n",
        "\n",
        "plt.figure(figsize=(10, 7))\n",
        "sns.heatmap(cm, annot=True, fmt='d')\n",
        "plt.title(\"RNN-LSTM Model Prediction Confusion Matrix\")\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = {0 : \"T-shirt/top\",\n",
        "          1: \"Trouser\",\n",
        "          2: \"Pullover\",\n",
        "          3: \"Dress\",\n",
        "          4: \"Coat\",\n",
        "          5: \"Sandal\",\n",
        "          6: \"Shirt\",\n",
        "          7: \"Sneaker\",\n",
        "          8: \"Bag\",\n",
        "          9: \"Ankle Boot\"}\n",
        "\n",
        "y_pred = model.predict(x_test_pic)\n",
        "X_test__ = X_test.reshape(X_test.shape[0], 28, 28)\n",
        "\n",
        "fig, axis = plt.subplots(4, 4, figsize=(12, 14))\n",
        "for i, ax in enumerate(axis.flat):\n",
        "    ax.imshow(X_test__[i], cmap='Reds')\n",
        "    ax.set(title = f\"Real Class is {labels[y_test[i].argmax()]}\\nPredict Class is {labels[y_pred[i].argmax()]}\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 999
        },
        "id": "i2U9MtoDL5Im",
        "outputId": "e4945db6-20fc-4631-8172-f0b933d39ec2"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "438/438 [==============================] - 2s 5ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1400 with 16 Axes>"
            ],
            "image/png": 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YNGkSTpw4Uez++Pv7Y/z48fjyyy/h7u6O8PBwLF682KTtc+bMGRw7dgxt27bF+fPnDY/Q0FBs3Lix0APh2rVrG/2df8rPg9ck7tmzB5MnT8bkyZOLvA4aAG7fvo3ExER8/vnnBd4XgwcPBmDaDU/c3d0RFhaGsLAwdO/eHdOmTcOXX36JP/74w3BHw/wBf2GndzZo0ADx8fFIS0vD7du3kZ6eLp1Pr9fj6tWrRfapquE+oOLsA0xd//3KIi9CQkLQu3dvzJo1C+7u7njuueewfPlyZGVlGeaJjo5GUlISPD09C7xvUlNTC+wfHuw3lS3mfcXK+wEDBuC3335DQkICtm/fjhdffBHHjh3DM888g8zMTKN5Tf3cl/WTzBfztmLlrUpqaioAFPjiobAcjI6OxtatWwu8pmFhYQD+95o+ym1TlirMILpVq1YICwtD7969sX79ejRu3Bgvvvii4cXNv2B94sSJhl8MH3zUrVsXAPDOO+9g/Pjx6NChA1auXIlt27YhKioKjRo1KtEdHoOCgnDu3DlkZ2eX6LmtXLkSgwYNQkBAAL766its3boVUVFReOqpp4z64+npiePHj2P9+vV49tlnsWvXLnTt2hUDBw40zNOhQwdcuHABX3/9NRo3bowvv/wSjz32mGGgVxwffvghTpw4gWnTpiEjIwNvvPEGGjVqhGvXrhX5fABg3LhxCAwMNDw+/PBDZGZmYu3atQWW0Wq1ha5LCGH0d6NGjVC/fn18++23iImJKfI55G+/l19+Wfq+kN0IpSidOnUCAMM3a1S2uA+oOPsAUzzMXTxlpfLy8vIKzLdmzRrs378fo0aNMtxgMDg42Oh94+npKX3PzJ49u9T6TcXHvK+Yee/k5ITOnTtj1apVGDhwIC5cuFDgWmdTP/cLwzw0b8zbipm3hTl58iQAGF6PfIXloF6vR+fOnaWvae/evQE8+m1TZsrvTHLT5F9f8eA1bfnXA8ybN08IIURsbKwAIKZOnVrkOps1ayY6duxYYLqPj4/RdQ+mXl/xzjvvCABi9erVRT8hUfD6iueee07UqVPH6JpgIYRo06aN0bVAD8rLyxPDhw8XAER0dHSh86SkpIgWLVoIHx8fZZ8evL6iMPv27RMAxPTp06Xz6PV64efnJzp27CgiIyMLPJo2bSo6depkmL+o1/f+/uRfE3316lXh6+sr/P39xfXr1wv0AfddE52bmyscHR3FgAEDlM9f5f5rse93+/ZtAUA8/fTTQoh7124CEP/5z38KzPv0008Ld3d3Q5/s7OxEv379Csz32muvCQsLC5GUlCSEEGLNmjW8Jpr7AOl6zHEf8CDZNdEjR440mlacvMi/djIhIcFovjfffNPomujCrFq1SgAQX3zxhRBCiBEjRgitVlvkvRGEkO8LqPQx732l66kIeX+/RYsWCQDiu+++M2rzwes3C9vusmui33///QLtHD58mNdElzPmra90Peaat0VdE92lSxeh0WiMrnOWfRY2bNhQPPnkk8r2ClOa2+ZRqjC/RD8oNDQUrVq1woIFC5CZmQlPT0+EhoZi2bJluHnzZoH57y+XpNVqC3zTGRkZabj+orhee+01VK9eHRMmTDC6BXy+uLg45d2j87+Nvb9PBw8exP79+43mu3PnjtHfFhYWaNq0KQAYTk98cB4HBwfUrVvX6PRFUyQnJyM3N9doWpMmTWBhYaFc1759+3Dp0iUMHjwYffr0KfB44YUXsGvXLty4caNY/blfzZo1sWPHDmRkZKBz584FnvP9tFotevfujbVr1xq+TbufKWW0ZDZs2AAAaNasGYB71242b94cK1asQGJiomG+kydPYvv27ejWrZuhT126dMEvv/xiVIonNjYWq1evRrt27QxF7e3t7QHAaH10D/cB5rkPKKni5EVAQAAA47NA0tLSDNUA8iUkJBR4nfPvnJr/HPr164e8vDy8/fbbBfqUm5vL3DMzzHvzzPv09PQC/c63ZcsWAIVf6lSa+Hlpvpi35pm3RXn33Xexfft2vPDCCwgMDCxy/n79+mH//v3Ytm1bgVhiYqKhj49y25SlClviCgAmTZqEvn37IiIiAq+99hoWL16Mdu3aoUmTJhg2bBjq1KmD2NhY7N+/H9euXTPUlOvRowdmz56NwYMHo02bNvj777+xatUqo5vZFIerqyvWrVuHbt26oXnz5nj55ZcRHBwMADh69Ci+++47PPnkk9Lle/TogZ9++gm9evVC9+7dERMTg6VLl6Jhw4aGU1+AewXr7969i6eeego1a9bE5cuXsWjRIjRv3txQ9qhhw4YIDQ1FcHAw3NzccPjwYaxZswajRo0q1nPauXMnRo0ahb59+6JevXrIzc3Ft99+axiUyqxatQparRbdu3cvNP7ss89i+vTp+P777zF+/Phi9el+devWxfbt2xEaGorw8HDs3LnTcID9oHfffRe7du1C69atMWzYMDRs2BB3797F0aNHsWPHDty9e7fI9q5fv244TT07Oxt//fUXli1bBnd3d4wePdow3/vvv4+uXbviySefxCuvvGIoceXs7IyZM2ca5pszZw6ioqLQrl07jBgxApaWlli2bBmysrLw3nvvGeZr3rw5tFot5s+fj6SkJOh0Ojz11FOF1tysirgPML99wMMwNS+6dOmC2rVr45VXXsGkSZOg1Wrx9ddfw8PDA1euXDHMt2LFCnz22Wfo1asXAgICkJKSgi+++AJOTk6GL7VCQkIwfPhwzJs3D8ePH0eXLl1gZWWF6OhoREZGYuHChSUuhUJlg3lvfnmfnp6ONm3a4IknnsDTTz+NWrVqITExET///DN+++039OzZEy1atChWX4orICAALi4uWLp0KRwdHWFvb4/WrVvz+mkzwbw1v7zNl5ubazjGzczMxOXLl7F+/XqcOHECHTt2xOeff25SPyZNmoT169ejR48eGDRoEIKDg5GWloa///4ba9aswaVLl+Du7v5It02ZKrffwE0kOzVEiHs//wcEBIiAgADDbdIvXLgg/v3vfwtvb29hZWUlfHx8RI8ePcSaNWsMy2VmZooJEyaI6tWrC1tbW9G2bVuxf//+AqdsmHpqSL4bN26IcePGiXr16gkbGxthZ2cngoODxdy5cw2nIQpR8NQQvV4v3nnnHeHr6yt0Op1o0aKF2LhxY4HTmNasWSO6dOkiPD09hbW1tahdu7YYPny4uHnzpmGeOXPmiFatWgkXFxdha2srgoKCxNy5c0V2dray7w+eGnLx4kUxZMgQERAQIGxsbISbm5vo2LGj2LFjh3Qd2dnZolq1aqJ9+/bKtvz9/UWLFi2EECU7nft+Bw8eNJS/yT8dEw+UuBLi3qlDI0eOFLVq1RJWVlbC29tbdOrUSXz++efKvua3i/tKW1lYWAhPT08xYMAAcf78+QLz79ixQ7Rt21bY2toKJycn8cwzz4jTp08XmO/o0aMiPDxcODg4CDs7O9GxY0fxxx9/FJjviy++EHXq1BFarbZKntrNfYCvYT5z3wcUxtTTufOZmhdHjhwRrVu3NmyHjz76qECJq6NHj4oBAwaI2rVrC51OJzw9PUWPHj3E4cOHC6zv888/F8HBwcLW1lY4OjqKJk2aiP/85z/ixo0bhnl4Ovejw7z3NcxXEfI+JydHfPHFF6Jnz56G52NnZydatGgh3n//faOSQGV1OrcQQvzyyy+iYcOGwtLSkqd2lwPmra9hvoqQt0Lcy7H7j3Ht7OyEn5+f6N27t1izZk2B8mJCqD8LU1JSxNSpU0XdunWFtbW1cHd3F23atBEffPCB4XmV5bZ5lDRCmHAHByIiIiIiIiKqOHfnJiIiIiIiIipvHEQTERERERERmYiDaCIiIiIiIiITcRBNREREREREZCIOoomIiIiIiIhMxEE0ERERERERkYmq7CDaz88PgwYNMvy9e/duaDQa7N69u9z69CBz7BMAhIaGIjQ0tLy7IRUREQGNRoPDhw8r55s5cyY0Gs0j6hWZI+4HSs7c9wNEhWHOl1x55HxoaCgaN25c5HyXLl2CRqNBRERE2XeKzAJzueT4+V06ymUQnT/IyX/Y2NigXr16GDVqFGJjY8ujSyW2efNmzJw5s9jLrVu3Dl27doW7uzusra1Ro0YN9OvXDzt37iz9TlYAZ86cMbwXEhMTy7s7JfLg+1qj0cDT0xMdO3bEli1byrz9zz77rEIdQHA/wP1AaGhogZwp7FGSbUvmhznPnM93+/ZtjBkzBkFBQbC1tYWnpydatWqFyZMnIzU1tczbX716NRYsWFDm7VRWzGXm8v2Sk5Mxa9YsNGvWDA4ODrC1tUXjxo0xefJk3Lhxo8za/eOPPzBz5sxyGzdYlkur/2/27Nnw9/dHZmYmfv/9dyxZsgSbN2/GyZMnYWdn90j70qFDB2RkZMDa2rpYy23evBmLFy82OQGFEBgyZAgiIiLQokULjB8/Ht7e3rh58ybWrVuHTp06Yd++fWjTpk0JnsWjsX379lJf58qVK+Ht7Y2EhASsWbMGQ4cOLfU2HpX897UQArGxsYiIiEC3bt2wYcMG9OjRo8za/eyzz+Du7m70zWxFwP1A1d0PTJ8+3SjXDx06hE8++QTTpk1DgwYNDNObNm360G2R+WDOV92cB4C7d++iZcuWSE5OxpAhQxAUFIQ7d+7gxIkTWLJkCV5//XU4ODgUa52+vr7IyMiAlZWVSfOvXr0aJ0+exNixY0vwDCgfc7lq5zIAXLx4EWFhYbhy5Qr69u2LV199FdbW1jhx4gS++uorrFu3DufOnSu19u73xx9/YNasWRg0aBBcXFzKpA2Vch1Ed+3aFS1btgQADB06FNWqVcNHH32EX375BQMGDCh0mbS0NNjb25d6XywsLGBjY1Pq633Qhx9+iIiICIwdOxYfffSR0enE06dPx7fffgtLy3J9WYpU3B1UUYQQWL16NV588UXExMRg1apVFXoQff/7GgBeeeUVeHl54bvvvivTQXRFxf1A1d0PdO7c2ehvGxsbfPLJJ+jcubPyVLOyev3LWkXtd2ljzlfdnAeAr776CleuXCl0oJGcnFyidvJ/DS0Kc7B0MZerdi7n5ubi+eefR2xsLHbv3o127doZxefOnYv58+eXSlvmyKyuiX7qqacAADExMQCAQYMGwcHBARcuXEC3bt3g6OiIl156CQCg1+uxYMECNGrUCDY2NvDy8sLw4cORkJBgtE4hBObMmYOaNWvCzs4OHTt2xKlTpwq0Lbtu4eDBg+jWrRtcXV1hb2+Ppk2bYuHChYb+LV68GACMTmuRycjIwLx58xAUFIQPPvig0Hn/9a9/oVWrVtJ1/Pbbb+jbty9q164NnU6HWrVqYdy4ccjIyDCa79atWxg8eDBq1qwJnU6H6tWr47nnnsOlS5cM8xw+fBjh4eFwd3eHra0t/P39MWTIEGnb+Qq7lmLRokVo1KgR7Ozs4OrqipYtW2L16tVFrgsA9u3bh0uXLqF///7o378/9u7di2vXrhWYz8/PDz169MDvv/+OVq1awcbGBnXq1ME333xTZBsJCQlo1aoVatasibNnzyrnXblyJYKDg2Fraws3Nzf0798fV69eNem5FMbFxQW2trYFdqppaWmYMGECatWqBZ1Oh/r16+ODDz6AEMJovtzcXLz99tsICAiATqeDn58fpk2bhqysLMM8fn5+OHXqFPbs2WN4H1bU6124H6ia+wGZ/HsXnD59Gi+++CJcXV0NH9Sm5AYA6SnhD15Tl5OTg1mzZiEwMBA2NjaoVq0a2rVrh6ioKKPl/vnnH/Tp0wdubm6wsbFBy5YtsX79eqN58k933LNnD0aMGAFPT0/UrFnzobZFZcWcr1o5f+HCBWi1WjzxxBMFYk5OToUOhE6fPo2OHTvCzs4OPj4+eO+994zihV0TLXsfhYaGYtOmTbh8+bLhtfPz8yvy+VPRmMtVK5fXrl2Lv/76C9OnTy8wgAbu5fPcuXONpkVGRhqOsd3d3fHyyy/j+vXrRvOcOHECgwYNQp06dWBjYwNvb28MGTIEd+7cMcwzc+ZMTJo0CQDg7+9veO3u3z5lzay+Krlw4QIAoFq1aoZpubm5CA8PR7t27fDBBx8YTg8ZPnw4IiIiMHjwYLzxxhuIiYnBp59+imPHjmHfvn2GU3pmzJiBOXPmoFu3bujWrRuOHj2KLl26IDs7u8j+REVFoUePHqhevTrGjBkDb29vnDlzBhs3bsSYMWMwfPhw3LhxA1FRUfj222+LXN/vv/+Ou3fvYuzYsdBqtSXZRIiMjER6ejpef/11VKtWDX/++ScWLVqEa9euITIy0jBf7969cerUKYwePRp+fn6Ii4tDVFQUrly5Yvi7S5cu8PDwwJQpU+Di4oJLly7hp59+KnafvvjiC7zxxhvo06cPxowZg8zMTJw4cQIHDx7Eiy++WOTyq1atQkBAAB5//HE0btwYdnZ2+O677wzJcb/z58+jT58+eOWVVzBw4EB8/fXXGDRoEIKDg9GoUaNC1x8fH4/OnTvj7t272LNnDwICAqR9mTt3Lt58803069cPQ4cOxe3bt7Fo0SJ06NABx44dM+l0kaSkJMTHx0MIgbi4OCxatAipqal4+eWXDfMIIfDss89i165deOWVV9C8eXNs27YNkyZNwvXr1/Hxxx8b5h06dChWrFiBPn36YMKECTh48CDmzZuHM2fOYN26dQCABQsWYPTo0XBwcMD06dMBAF5eXkX21RxxP1C0yrgfKErfvn0RGBiId955x/BFkym5URwzZ87EvHnzMHToULRq1QrJyck4fPgwjh49avjV/NSpU2jbti18fHwwZcoU2Nvb48cff0TPnj2xdu1a9OrVy2idI0aMgIeHB2bMmIG0tLSH3g6VEXO+aJUp5319fZGXl4dvv/0WAwcOLLKdhIQEPP3003j++efRr18/rFmzBpMnT0aTJk3QtWtX5bKFvY+8vb2RlJSEa9euGT5ri3v6OBWOuVy0ypTL+V8e/+tf/zKpnfzX+/HHH8e8efMQGxuLhQsXYt++fUbH2FFRUbh48SIGDx4Mb29vnDp1Cp9//jlOnTqFAwcOQKPR4Pnnn8e5c+fw3Xff4eOPP4a7uzsAwMPDo9jPv8REOVi+fLkAIHbs2CFu374trl69Kr7//ntRrVo1YWtrK65duyaEEGLgwIECgJgyZYrR8r/99psAIFatWmU0fevWrUbT4+LihLW1tejevbvQ6/WG+aZNmyYAiIEDBxqm7dq1SwAQu3btEkIIkZubK/z9/YWvr69ISEgwauf+dY0cOVKYuhkXLlwoAIh169aZNP+DfRJCiPT09ALzzZs3T2g0GnH58mUhhBAJCQkCgHj//fel6163bp0AIA4dOmRSX+4XEhIiQkJCDH8/99xzolGjRsVejxBCZGdni2rVqonp06cbpr344ouiWbNmBeb19fUVAMTevXsN0+Li4oROpxMTJkwwTMt/fx06dEjcvHlTNGrUSNSpU0dcunTJaH1vvfWW0Wt36dIlodVqxdy5c43m+/vvv4WlpWWB6Q/Kb/fBh06nExEREUbz/vzzzwKAmDNnjtH0Pn36CI1GI86fPy+EEOL48eMCgBg6dKjRfBMnThQAxM6dOw3TGjVqZPS6mDvuB9aZNH9V2A/ki4yMLPBc8/N0wIABRvMWJzcAiLfeeqtAe76+vkavf7NmzUT37t2VfezUqZNo0qSJyMzMNEzT6/WiTZs2IjAw0DAt//3drl07kZubq1xnVcGcX2fS/JU952/duiU8PDwEABEUFCRee+01sXr1apGYmFhomwDEN998Y5iWlZUlvL29Re/evQ3TYmJiBACxfPlywzTZ+0gIIbp37y58fX2L3Xe6h7m8zqT5K3sut2jRQjg7O5s0b3Z2tvD09BSNGzcWGRkZhukbN24UAMSMGTMM0wrbRt99912BMcD7778vAIiYmJhi9700lOvp3GFhYfDw8ECtWrXQv39/ODg4YN26dfDx8TGa7/XXXzf6OzIyEs7OzujcuTPi4+MNj+DgYDg4OGDXrl0AgB07diA7OxujR482OuXClBtJHDt2DDExMRg7dmyBXx9LWhYpOTkZAODo6Fii5QHA1tbW8P+0tDTEx8ejTZs2EELg2LFjhnmsra2xe/fuAqfF5Mt/Ths3bkROTk6J+5O/rmvXruHQoUPFXnbLli24c+eO0bUzAwYMwF9//VXo6ToNGzZE+/btDX97eHigfv36uHjxYoF5r127hpCQEOTk5GDv3r3w9fVV9uWnn36CXq9Hv379jN5X3t7eCAwMNLyvirJ48WJERUUhKioKK1euRMeOHTF06FCjbwc3b94MrVaLN954w2jZCRMmQAhhuJv35s2bAQDjx48vMB8AbNq0yaQ+mTPuB4qvsu0HTPHaa68Z/V0WueHi4oJTp04hOjq60Pjdu3exc+dO9OvXDykpKYb33J07dxAeHo7o6OgCp6UNGzasxL9YVFbM+eKrTDnv5eWFv/76C6+99hoSEhKwdOlSvPjii/D09MTbb79d4JImBwcHozO5rK2t0apVq0I/9wvz4PuISg9zufgqUy4nJyebvC0OHz6MuLg4jBgxwuiSje7duyMoKMjoM/v+bZSZmYn4+HjD5R9Hjx4tVh/LUrkOovMHG7t27cLp06dx8eJFhIeHG81jaWlZ4Dqy6OhoJCUlwdPTEx4eHkaP1NRUxMXFAQAuX74MAAgMDDRa3sPDA66ursq+5Z+SYkp9QlM5OTkBAFJSUkq8jitXrmDQoEFwc3ODg4MDPDw8EBISAuDeacQAoNPpMH/+fGzZsgVeXl7o0KED3nvvPdy6dcuwnpCQEPTu3RuzZs2Cu7s7nnvuOSxfvrzAtYSmmDx5MhwcHNCqVSsEBgZi5MiR2Ldvn0nLrly5Ev7+/tDpdDh//jzOnz+PgIAA2NnZYdWqVQXmr127doFprq6uhe5k/vWvfyEuLg579uwpsEMvTHR0NIQQCAwMLPC+OnPmjOF9VZRWrVohLCwMYWFheOmll7Bp0yY0bNgQo0aNMpx+dPnyZdSoUaPAzif/jsT5793Lly/DwsICdevWNZrP29sbLi4uhvkqMu4Hiq+y7QdM4e/vb/R3WeTG7NmzkZiYiHr16qFJkyaYNGkSTpw4YYifP38eQgi8+eabBd5zb731FgAU2E882G9izpdEZcv56tWrY8mSJbh58ybOnj2LTz75xHDZw1dffWU0b82aNQsMemSf+w8q7H1EpYe5XHyVKZednJxM3hb5r2X9+vULxIKCgow+s+/evYsxY8bAy8sLtra28PDwMHyW5m8jc1Cu10S3atXK6C7GhdHpdLCwMB7r6/V6eHp6FjrIAh7x+fDFEBQUBAD4+++/0bNnz2Ivn5eXZ7i2d/LkyQgKCoK9vT2uX7+OQYMGQa/XG+YdO3YsnnnmGfz888/Ytm0b3nzzTcybNw87d+5EixYtoNFosGbNGhw4cAAbNmzAtm3bMGTIEHz44Yc4cOBAsa4PatCgAc6ePYuNGzdi69atWLt2LT777DPMmDEDs2bNki6XnJyMDRs2IDMzs8AOErhXgmLu3LlGH56yX3Qe/OYaAJ5//nl88803WLhwIebNm1fk89Dr9dBoNNiyZUuh7ZT0mikLCwt07NgRCxcuRHR0tPTabZWSfmtaEXA/UDyVbT9gqvu/mb7fw+RGXl6e0d8dOnTAhQsX8Msvv2D79u348ssv8fHHH2Pp0qUYOnSoYdtOnDixwIFivgcH9bJ+V2XM+eKpzDmv0WhQr1491KtXD927d0dgYGCBCh3F+dx/UGHvIyo9zOXiqWy5HBQUhGPHjuHq1auoVatWsbeHTL9+/fDHH39g0qRJaN68ORwcHKDX6/H0008bbaPyZlY3FjNVQEAAduzYgbZt2yoPUPJP342OjkadOnUM02/fvl3kN5j5N586efIkwsLCpPMV5wCuXbt2cHV1xXfffYdp06YV+xS/v//+G+fOncOKFSvw73//2zD9wTvH5gsICMCECRMwYcIEREdHo3nz5vjwww+xcuVKwzxPPPEEnnjiCcydOxerV6/GSy+9hO+//77YJabs7e3xwgsv4IUXXkB2djaef/55zJ07F1OnTpWWHPjpp5+QmZmJJUuWGG4IkO/s2bP473//i3379hV6xz9TjB49GnXr1sWMGTPg7OyMKVOmKOcPCAiAEAL+/v6oV69eidqUyc3NBQCkpqYCuPfe3LFjB1JSUox+jf7nn38M8fx/9Xo9oqOjjermxsbGIjEx0egU9co80C4M9wOVYz9QUsXJDVdXVyQmJhotn52djZs3bxZYr5ubGwYPHozBgwcjNTUVHTp0wMyZMzF06FDD+8fKykr5fqCywZyv3Dlfp04duLq6FpqXpa2qfV6aG+Zy5cjlZ555Bt999x1WrlyJqVOnKtef/1qePXvWcBf3fGfPnjXEExIS8Ouvv2LWrFmYMWOGYZ7CLrMq7zyukF/P9evXD3l5eXj77bcLxHJzcw0HS2FhYbCyssKiRYuMvrFcsGBBkW089thj8Pf3x4IFCwocfN2/rvxadw/OUxg7OztMnjwZZ86cweTJkwv9FnXlypX4888/C10+P1nvX04IYbhVf7709HRkZmYaTQsICICjo6PhNI+EhIQC7Tdv3hwAin0qyP23nAfuXa/UsGFDCCGU12msXLkSderUwWuvvYY+ffoYPSZOnAgHBwfpt5SmevPNNzFx4kRMnToVS5YsUc77/PPPQ6vVYtasWQW2jRCiwPM0VU5ODrZv3w5ra2vDwX63bt2Ql5eHTz/91Gjejz/+GBqNxnDH0W7dugEo+J796KOPANy7liSfvb29Se/DyoL7gcqxHyip4uRGQEAA9u7dazTf559/XuCX6Aefg4ODA+rWrWvYFp6enggNDcWyZcsKPdC/fft2yZ4MmYQ5Xzly/uDBg4Xeqf7PP//EnTt3Cj3ds7TZ29ub1WmhVQ1zuXLkcp8+fdCkSRPMnTsX+/fvLxBPSUkxVIxp2bIlPD09sXTpUqP+bdmyBWfOnDF8Zhe2jYDCX/PivHZloUL+Eh0SEoLhw4dj3rx5OH78OLp06QIrKytER0cjMjISCxcuRJ8+feDh4YGJEydi3rx56NGjB7p164Zjx45hy5YtBX75fJCFhQWWLFmCZ555Bs2bN8fgwYNRvXp1/PPPPzh16hS2bdsGAAgODgYAvPHGGwgPD4dWq0X//v2l6500aRJOnTqFDz/8ELt27UKfPn3g7e2NW7du4eeff8aff/6JP/74o9Blg4KCEBAQgIkTJ+L69etwcnLC2rVrC3wbd+7cOXTq1An9+vVDw4YNYWlpiXXr1iE2NtbQtxUrVuCzzz5Dr169EBAQgJSUFHzxxRdwcnIyHJyaqkuXLvD29kbbtm3h5eWFM2fO4NNPP0X37t2lNxy4ceMGdu3aVeDGWvl0Oh3Cw8MRGRmJTz75xFDqoCTef/99JCUlYeTIkXB0dDS6Qcn9AgICMGfOHEydOhWXLl1Cz5494ejoiJiYGKxbtw6vvvoqJk6cWGR7W7ZsMfyiHBcXh9WrVyM6OhpTpkwxXE/zzDPPoGPHjpg+fTouXbqEZs2aYfv27fjll18wduxYwzeozZo1w8CBA/H5558jMTERISEh+PPPP7FixQr07NkTHTt2NLQbHByMJUuWYM6cOahbty48PT0LfNtXmXA/UPH3Aw+jOLkxdOhQvPbaa+jduzc6d+6Mv/76C9u2bSvw+jds2BChoaEIDg6Gm5sbDh8+jDVr1mDUqFGGeRYvXox27dqhSZMmGDZsGOrUqYPY2Fjs378f165dw19//VXqz5XuYc5Xjpz/9ttvsWrVKvTq1QvBwcGwtrbGmTNn8PXXX8PGxgbTpk0rVj9KIjg4GD/88APGjx+Pxx9/HA4ODnjmmWfKvF26h7lcOXLZysoKP/30E8LCwtChQwf069cPbdu2hZWVFU6dOoXVq1fD1dUVc+fOhZWVFebPn4/BgwcjJCQEAwYMMJS48vPzw7hx4wDcu846/xrwnJwc+Pj4YPv27Yba4/fLf+2mT5+O/v37w8rKCs8884xhcF3myuq23yr3lyBSGThwoLC3t5fGP//8cxEcHCxsbW2Fo6OjaNKkifjPf/4jbty4YZgnLy9PzJo1S1SvXl3Y2tqK0NBQcfLkyQKlTQq7Db0QQvz++++ic+fOwtHRUdjb24umTZuKRYsWGeK5ubli9OjRwsPDQ2g0GpNvk79mzRrRpUsX4ebmJiwtLUX16tXFCy+8IHbv3q3s0+nTp0VYWJhwcHAQ7u7uYtiwYeKvv/4yKu0QHx8vRo4cKYKCgoS9vb1wdnYWrVu3Fj/++KNhPUePHhUDBgwQtWvXFjqdTnh6eooePXqIw4cPF9n3B2+Nv2zZMtGhQwdRrVo1odPpREBAgJg0aZJISkqSruPDDz8UAMSvv/4qnSciIkIAEL/88osQ4l45msLKzzzYn8LeX3l5eWLAgAHC0tJS/Pzzz0KIgiWu8q1du1a0a9dO2NvbC3t7exEUFCRGjhwpzp49K+3r/e3e/7CxsRHNmzcXS5YsMSqpIIQQKSkpYty4caJGjRrCyspKBAYGivfff7/AfDk5OWLWrFnC399fWFlZiVq1aompU6caldgR4l7ZkO7duwtHR0cBwOzLXXE/wP3Ag1Qlrm7fvl1gflNzIy8vT0yePFm4u7sLOzs7ER4eLs6fP1/g9Z8zZ45o1aqVcHFxEba2tiIoKEjMnTtXZGdnG63vwoUL4t///rfw9vYWVlZWwsfHR/To0UOsWbPGMI+p7++qhDnPnBdCiBMnTohJkyaJxx57zGg79O3bVxw9erRAm4WV3hk4cKBRiSpZiSvZ+yg1NVW8+OKLwsXFRQBguatiYi4zl++XkJAgZsyYIZo0aSLs7OyEjY2NaNy4sZg6daq4efOm0bw//PCDaNGihdDpdMLNzU289NJLhpJo+a5duyZ69eolXFxchLOzs+jbt6+4ceOGQCHlKt9++23h4+MjLCwsHnm5K40QJtyZgYiIiIiIiIgq5jXRREREREREROWBg2giIiIiIiIiE3EQTURERERERGQiDqKJiIiIiIiITMRBNBEREREREZGJzK5OtF6vx40bN+Do6AiNRlPe3SEyW0IIpKSkoEaNGrCwML/vw5jLREUz9zwGmMtEpjD3XGYeExWtWHlcVrWzPv30U+Hr6yt0Op1o1aqVOHjwoEnLXb16tUCtXT744EP+uHr1almlcYnzmLnMBx/Fe5RlHgvBXOaDj0f1MNdcZh7zwYfpD1PyuEx+if7hhx8wfvx4LF26FK1bt8aCBQsQHh6Os2fPwtPTU7mso6MjAODquVNw+v//E1FBySkpqFWvkSFnStvD5DHAXDZF+mv9lHHrWortLIQ8lpcnj+XmqDul1UpD+vQsaSz7drI05rB8nbrNKqys8xhgLpuDjJH9pbFDuy9KY21ebCWNaYJbK9sUV2OksZOfbZXGGo/sKo1pX/mvss2q/AunuedyRctjofqMQ8nfa/rLp+RtXjypXvjWDfl6T/0tX2+OXhrLvpWkbDLzTqo0lpIi/zz3aiB/P+ie7aZsU9NYsd+pWV8es5AfPwi94rikiGVVVO+TkrxHipPHZTKI/uijjzBs2DAMHjwYALB06VJs2rQJX3/9NaZMmaJcNv8JOzk6wsnJqSy6R1SplNVBy8Pk8f39Yi7LWVqpd8HWOit5UDmIVpyCVNRZhqpBdK78QzBb8Vwc+PoXqSwHH8zl8melyA97xSmDTop9gMbORtmmsLGWxhxUbSqW0xbx+lflQXQ+c83lipbHZTaIdrCXt2lnq17YVidfr7U8V4VG9dmpHjxaWSriWvl6nRT7HF0R+w6NYhtpFO+dyjCILs6ypX7RRnZ2No4cOYKwsLD/NWJhgbCwMOzfv7/A/FlZWUhOTjZ6EFH5Km4eA8xlInPEXCaqHHh8TWReSn0QHR8fj7y8PHh5eRlN9/Lywq1btwrMP2/ePDg7OxsetWrVKu0uEVExFTePAeYykTliLhNVDjy+JjIv5X77wKlTpyIpKcnwuHr1anl3iYhKgLlMVDkwl4kqPuYxUdkq9Wui3d3dodVqERsbazQ9NjYW3t7eBebX6XTQ6eTXGBDRo1fcPAaYy0TmiLlMVDnw+JrIvJT6INra2hrBwcH49ddf0bNnTwD3atP9+uuvGDVqVGk3R0RlgHlcevSxl6Sx8WuOK5fVaeQnC2UrbqYRYCPftVsWcbOMlDz5XUTjcuQ3BslR9GdJbrayTY2l/EZG9HCqYi6n9g9Xxm06PSmNZf9xRBq7eFD+S96JxDRlm6rT/uwUN/ma8FGUNJYltinbfMxBPoCysZDvB668s0Yauzv7B2WbPoqbK3Ua1UUa03hXl8b0J/5Stmn1wXfKeGVR1XL5YW4KlffjQnkwN1fe5pOdlesVNvIbj1nkKD7n6sjvaG2zbaOyTdtuIdKYm+pmXJ7ynIJPHWWb+k2KnFJ8MaN9cZw0prFT38xO5CleF22Z3Af7oZVJr8aPH4+BAweiZcuWaNWqFRYsWIC0tDTD3QSJyPwxj4kqB+YyUeXAXCYyH2UyiH7hhRdw+/ZtzJgxA7du3ULz5s2xdevWAjdDICLzxTwmqhyYy0SVA3OZyHyU2e/jo0aNqpSnlxBVJcxjosqBuUxUOTCXicxDud+dm4iIiIiIiKii4CCaiIiIiIiIyEQcRBMRERERERGZyDzvGU5EVEnof1wqjVWzVJSnABDqYieNZerlpagae8tLSWgt1WVDzlxPkcZU5bF2KEr86Hf9qGxT2/llZZyoOM4duamM//HrN9JYE3sbacxGUYrqMQ9HZZsZGfLyLVfTs6SxZ6rZS2M6RX8A4EaWvOSOs2LfY6com+PvIN8+AJCdLS+Dt3WRvCTXwZQMaWxwXQ9lm+piPWTOhF7+ftGoyjcBEOlJ8qCDPB81DR+Xr/Pob8o2UTtAHlOUsUKG/PNR82Q7dZvxsdKQUJXrUpTj0nj4qNv0ryuPWcv3AfrT+6UxbUt16UFVGSuhKKFZnvhLNBEREREREZGJOIgmIiIiIiIiMhEH0UREREREREQm4iCaiIiIiIiIyEQcRBMRERERERGZiINoIiIiIiIiIhNxEE1ERERERERkItaJJiIqQ5n7jkljHlbqOphJufIamvE58lhjxTrT0nKUbZ5R1Kz1s7GSxjL18jqO4s8/lG2CdaKpFNVvWUMZv7j3ojTm5SyvgWptJf/dIeZ2urLNFoGu0ljuRXnN9/0p8vXmFlE61dtavn95PNBNGjt7UV5/Ny9P3aiTk7U0ZpMl709Knnx/Vis0UNkmVWCakv+Wpz+2Sx7MkX/OiZjT8uW81PsOjaM8j0Wqom51ZqY8lpygbFOkKWpM+9SSL+glj4kzh5Rtwlm+f0DSXXnsxmV5m4qa4EDRdcGly2k0JVquNPCXaCIiIiIiIiITcRBNREREREREZCIOoomIiIiIiIhMxEE0ERERERERkYk4iCYiIiIiIiIyEQfRRERERERERCZiiSsiojJ061y8NOauKJkDAFpF6QZVSalsRfmr3Bx5OR0AsCxhtQgnrfy55F6+qW6zZE0SFcomvL0ynr3ngjSWmJItjXl72Epjqvc/AMTdzpDGrmXLy/HYWcgTspZOXk4KABJyc6Wx+DvykjvuLvIyX1pFfwAgT7Ffupshf54ZiuUsXnxF2SZVXKryREIUUcPtTpw8Fh8rj1nJSzXi5jVlk8LGXh60VcSsU+QxJ3nZLADAXUVJqeq15bGE2/KYo4u6TVU5KkWuwtFZHktS9AcAXL3VcTPEX6KJiIiIiIiITMRBNBEREREREZGJOIgmIiIiIiIiMhEH0UREREREREQm4iCaiIiIiIiIyEQcRBMRERERERGZqNQri8ycOROzZs0ymla/fn38888/pd1UlVXUbf9LWjJAuZzqdvcA8uaPkQddXOSxbHk5EViry3doHJ3kQc/q8piToj/2ihgAWMn7pLF1kMZEsrxEgbaJuhxLeWEul44L8enSmK2F+ntMAXm+qgpV/XYzSRpLzlPvP2wUJWxyFfsPZ0v5c7lz6oayTR9llB5GVcxji2cGKeNOb34rjVkrSlXdvZsljbm66JRturnJy0bdSZav191Kfph2NUvx+VkEezt5mZ8cRRm8ZEUJMABwsJev10axv/NWPE/tY2HKNquKiprLquNHjYVWvuAddbkpqJZt0FweO3Ncvsqe6nJq+gt/yYN2imNS+1RpSJw7rWwTTvL1ajwUn56K7S4y5P0BAOQpjk1s5KX+oJPH9DcuKpvUKkpcqcYn5alMynM2atQIO3bs+F8jlqwCSlQRMZeJKj7mMVHlwFwmMh9lkn2Wlpbw9q54RbOJyBhzmajiYx4TVQ7MZSLzUSbXREdHR6NGjRqoU6cOXnrpJVy5ckU6b1ZWFpKTk40eRGQemMtEFV9x8hhgLhOZK34mE5mPUh9Et27dGhEREdi6dSuWLFmCmJgYtG/fHikpKYXOP2/ePDg7OxsetWrVKu0uEVEJMJeJKr7i5jHAXCYyR/xMJjIvpT6I7tq1K/r27YumTZsiPDwcmzdvRmJiIn788cdC5586dSqSkpIMj6tXr5Z2l4ioBJjLRBVfcfMYYC4TmSN+JhOZlzK/I4GLiwvq1auH8+fPFxrX6XTQ6dR3tSSi8sdcJqr4ispjgLlMVBHwM5mofJX5IDo1NRUXLlzAv/71r7Juqsp4qFu9C0VhHI2iXECG/NQ/AIj+bp80VqOm/Pb8Vy/JS/HcTc9Rtmmp2AzJefJb+9tr5c+zqFMzchQlfurWcJTGDl9JlMae+3uPsk2N4rb/jxJzuWTylGXl1Mvm6OXLZujluZyhSPPUIkpcBdiU7GNB9TwtFGWz6NGqCnmsqaYummatSLzknFxpzN1OXuIwW1EWClCXl6zpZS+NxcXJS+TFZatLT/at7yGNXbohL3Hj42knjWUX0WaGItdVJbmCazgr10sFVZRcVpaxUtBfOKGeIfGONCRuKO77oPgCMW/FR0V0Sp7nmmbB8uVy5O99ce26skmLtv7yZf8+KI+dOCaNabr1UrYpThySL2ut+GKmbkN57HYRJctU/Slhed6yVuqnc0+cOBF79uzBpUuX8Mcff6BXr17QarUYMGBAaTdFRGWIuUxU8TGPiSoH5jKReSn1X6KvXbuGAQMG4M6dO/Dw8EC7du1w4MABeHjIvxElIvPDXCaq+JjHRJUDc5nIvJT6IPr7778v7VUSUTlgLhNVfMxjosqBuUxkXsqkTjQRERERERFRZcRBNBEREREREZGJOIgmIiIiIiIiMhEH0UREREREREQmKvM60VQyZVYTTbFeFf2Fv5RxVxd53Titk400VjtA/j2OR3Kmsk33V56TxjK3yWsvp15LlMbs3OX1OgEgNzlDGlO9Lp3by+v8mUsdaCobVoq6qUk56nzUKt5Tjlp57mQo6kt7WKm/O7VQtJmt2H9kKtp08HRQtkn0KIX9ulIa2/LUS9JYNcX7P6eIXE5OzpHG7O3lh2I2OnmN3TRFzdqiuDrIa16npsj7amWlrvl7N12+rE6xL/Q/JK9LS+avpMesIidLHjvyh7JNi07yY8C4oW9IYzpFTrksnahsU1w9J4/9tlMa09StJ41ZPNVZ3eZFeZvIk9dt1zRsLG+zThNlm3k/fSfvj7V836Fx95SvNDdX2abIk8c1WvMcrvKXaCIiIiIiIiITcRBNREREREREZCIOoomIiIiIiIhMxEE0ERERERERkYk4iCYiIiIiIiIyEQfRRERERERERCYyz3uG00OVsVKVGoCFujyFdJ2/b1fGbezlt7zPS5WXMLh9I0Ua8w0NVLap6dBd3h8fX2ksZvR8acxD2aJ622ot5d9JJV+6K42pi2pRVZatKGHjoni/XUjJlsbciyhx5aSV73usNfJlrRXla2x83ZVtEj1KGv+m0pil4rM3OVNegsXbzVbZZoZi2dg78tKJDRvJP5V8EtKUbf51JUkaq24r/8x2cpTH4u6qS0+qOCiOP0R6sjSmsXMqcZv0iAhFuTWN/HXX//q9fLlLl9RNZsuPLTMz5aWfXOp7SWMaV0WJJgDiQJQ86K74nFN8PkIv7ysAaNyqyYP+8tJZ4pdIeZNOLso2s67GS2Nx5+QxvzGz5W1u+lbZpv7gZmlM2+ZZ5bLlhb9EExEREREREZmIg2giIiIiIiIiE3EQTURERERERGQiDqKJiIiIiIiITMRBNBEREREREZGJOIgmIiIiIiIiMhFLXFVGijJMGouSfW+Se/y0Mp6VniON2TjqpDEnJ3kpjdgDMco2vW0+kMZyb8tLe5xNSpfGbGzUJcDs7OQpY6GXb3dVyRCq3CwgL22RpXjPAICNoixGdIY85550kuecqhQVAJxV5LKDVr7/qKWT58ba748o2+z/kTJMVKpEzAlpLE/1+alY59U78s8VAKhmayWNJebKy19djUmQxopIZThayj/PLqbJS1XVyJGXK0otohyPk1be5s1s+fPU714jjWm7DVG2SWVPWToVgKak5VMvnJWvs2FDdZsePvL1KvqbfSNRGtOlyUutAVCWidXUUZSbunhOvs4L55VN5t2Ilca0N67L+9PhKXksoLGyTZVkRQlNFXHrpnqGRPm+DixxRURERERERFSxcRBNREREREREZCIOoomIiIiIiIhMxEE0ERERERERkYk4iCYiIiIiIiIyEQfRRERERERERCYqdomrvXv34v3338eRI0dw8+ZNrFu3Dj179jTEhRB466238MUXXyAxMRFt27bFkiVLEBgYWJr9pjIgsuUlL64fuapc1iuwmjSWlyxfr0uzWtJY1g3F7e4BnPrpuDTm6+8sjT03voc0dnb5TmWbjopSVRpFvZHrN1KlMedceUkhANBYykujlBTz+NFxU5R+OpMuzw0AcNDKl72uKBcz+vGa0lhOrrx8DQBsP3hFGqtvJ38vxmXLS990rCHPR3o4zOUSsLGXhvKgLuUjU1S5OitreWkclyx5nsckyfcRiUXksl5R5sdWUe4yUy9fr30Jy2QCQJZivShi+1V2lTmP845EyYO3b0tDOX+dUa7XMi5OGsvMkn8eZWfKj7lEnPzzDwDSftwkjdn37CRf78WL0pjGR16qCwAsu3SRr3ffXmks++f10ph1T2WT0Pm4yWMn5SW39Ht+kcY0j7dRN5pwR77eK/L3gkXtBur1lqFi7w3T0tLQrFkzLF68uND4e++9h08++QRLly7FwYMHYW9vj/DwcGRmqg8WiejRYR4TVQ7MZaKKj3lMVPEU+5forl27omvXroXGhBBYsGAB/vvf/+K5554DAHzzzTfw8vLCzz//jP79+z9cb4moVDCPiSoH5jJRxcc8Jqp4SvWa6JiYGNy6dQthYWGGac7OzmjdujX2799f6DJZWVlITk42ehBR+SlJHgPMZSJzw1wmqviYx0TmqVQH0bdu3QIAeHl5GU338vIyxB40b948ODs7Gx61asmvkSWisleSPAaYy0TmhrlMVPExj4nMU7nfnXvq1KlISkoyPK5eVd/AiojME3OZqHJgLhNVfMxjorJVqoNob29vAEBsrPGd22JjYw2xB+l0Ojg5ORk9iKj8lCSPAeYykblhLhNVfMxjIvNUqoNof39/eHt749dffzVMS05OxsGDB/Hkk0+WZlNEVEaYx0SVA3OZqOJjHhOZp2LfnTs1NRXnz583/B0TE4Pjx4/Dzc0NtWvXxtixYzFnzhwEBgbC398fb775JmrUqGFU747KlqaEdRz1G76UxhISs5TL+rjI627mJmXIYwlp0pjdf6cp2/SbPksa0zropDFN08ekMZ31HmWbeXnyOpdWVvJ0Sk6T1/TVnz+qbFMb1FoZLwnm8aPj7CSvLa5Xl0KHvPK4mkOvUGksfdsfJVwr4KiV17rdo6h5PezTN0vcJqkxl0tA8T5WyVWUMlbVXQbu3WG5JGrayT/LVDEAuJ2ZLY1VU3xeOdrL68FfVdStBgA3S/m2VW4jNw/leiu7Sp3Hdg7ymKJEV8p5eT1iAHD/aLk05rbxaWns6Hn5B2+Xui2Ubdr4usuDPr7SkDj2lzSmKWrf4CuvBZ753U/SmE2b5vL+nDyubFLTtJk0ZvdbtHzBhHh5zMlF2aa4K68ZLr78QBqzmP2Vcr1lqdiD6MOHD6Njx46Gv8ePHw8AGDhwICIiIvCf//wHaWlpePXVV5GYmIh27dph69atsLGxKb1eE9FDYR4TVQ7MZaKKj3lMVPEUexAdGhqq/EZVo9Fg9uzZmD179kN1jIjKDvOYqHJgLhNVfMxjooqn3O/OTURERERERFRRcBBNREREREREZCIOoomIiIiIiIhMxEE0ERERERERkYmKfWMxejSU5TCKuB2+qsSVPu6yNJb2jfxW+QF1nJRtWj0VIo1lLl8rjemefkoa03jWVrbp0DtMGhP3lYp4UNbK76WxxCR5SRAAcHWVlxSxcrWTxmp45Uhj2R+8o2zT9stflHEyb1pLeT6qSuYAgFZTsiJXFs/+Wxqzu3lTvXDUOWkoWy/vcJaQl3+DzlbdJtGjlK0u0ySjs5DnoyI1AACeHvIcSEiRf+5YauVtOjnLy+cBwLVr8tKUWRp5h30c5evVFFHiSquV7++U+zPFtqXypynhZxEAaCzkZc9ST16Vxpyb1FSuN21IP2ns4+Pyz7n/dpaXjBKxl5RtXv8jRhqr/ZKbNJabmC6NWetuKdtEXp40dPeyvFxXjSYp6vWqKMp1nbyWrFisrnydRex3NQFB8lhID+Wy5YW/RBMRERERERGZiINoIiIiIiIiIhNxEE1ERERERERkIg6iiYiIiIiIiEzEQTQRERERERGRiTiIJiIiIiIiIjKR2Za4EkKoyzyVwMPcov+RU5SLUZULAACRlyuN6RfNksZyUuW3n7ev46ls06LzC9KY9ZYd0tjuqV9JYx36Hla2mZcqL99x5aC8lJeqZEjdeq7KNkWuvNRA9t00aUxnLf++6uTOC8o2H1dGydw5OVqVeFk7Rbk6FY2Xvzzo5VXC3hRRxkpBU6NOidskKm0atxrSmJXiOMFKUb4pPktexhAA7tyVf17ZWcsPxe6my9ebkiH/rAcAN0v5scKNbPl6HW5nSGP2im0AAGmKz0g9FMd0uernQhWXfs8macyhfzf5go7OyvXefVd+/Kh6rzl8vkIay1v+gbJN99qKPtnYS0Nn9smPSRs/XV/ZppWb/DPb2VVRPtJOXnZVXL2mbFPj6iGNBXk5SGP6rRuksZzb6pJb1g3kxy3Zq1ZLY+VZBpa/RBMRERERERGZiINoIiIiIiIiIhNxEE1ERERERERkIg6iiYiIiIiIiEzEQTQRERERERGRiTiIJiIiIiIiIjKR2Za40mg0j7QkVWmX08qneg6qUlQabclfGnE9WhrTJ8lvMW+pk5fi0Wery09kjB0qjV07cVMaaxVcXRrTeMhvsQ8Asb8fkMbsbOXbz95eHrN0VpQLAJCluEW/VlH6w9LWWhpLzEtStqmPOVH49FR5SS0yH1au8rIXOUXsd5QlYRQ0lvJc1jg6lWidAGAJRfkfxb7OwqdeidskKn3yvMrWy2OZivJNDkWVfkpTl8CSsbaQ51WR+w9F3EZRPk91PJRbRJsaxT4it2wOs8gM5B3fKQ9evSoNiVq1pDGNu7oco5Oj/LjK30ZRWtJafpyXd0neVwC4cjZeGmtYS16qytVFJ41pq6tLyIoz8nKvt2+lSmP2KYqSUor9CgBobOVlrDzruEljG74/Io096S9fDgA8u3eWxnYs3i6NPaNca9niL9FEREREREREJuIgmoiIiIiIiMhEHEQTERERERERmYiDaCIiIiIiIiITcRBNREREREREZCIOoomIiIiIiIhMxEE0ERERERERkYmKXYx47969eP/993HkyBHcvHkT69atQ8+ePQ3xQYMGYcWKFUbLhIeHY+vWrQ/d2bJU0prUD1NfuqS1oHOXvqme4dIlacjCXV6nLfvkdWnMuogamFk3EuTrzZLX1ky/ky6NWZ2/omzTzk6+/YSi1qeqbm9eapayTStHG2ksIyFDGrNWvE+si3jviX+OFj49PVO5nEplzWNzZBPgLY0lbjurXDZRUZe2xGzUtdBVVG9VyxLuQ+nhMJeLT3/+uDSWoddLY1rFe9zRXlGXFoCDgzx+7VaaNGZjqZXGqleTfx4BwGXFelXc3eX7iOxY+Wc2AAhFDW7lHsLGroheVW4VPo8TbktDmp4DpDGxb4d8nXr159/du/JjoMccFJ9zimNvrWc1ZZt3Us9IY/oLf0ljJ2/JazbXVtVzBoC4m9JQUnK2fLkceW16jZ063zTVqktjts+GSWONo3+QxuJuy4+RAcC7x2Bp7Iml8vXmndgjjWmbhijbfFjF/iU6LS0NzZo1w+LFi6XzPP3007h586bh8d133z1UJ4modDGPiSoH5jJRxcc8Jqp4iv1TaNeuXdG1a1flPDqdDt7e8l9f7peVlYWsrP/9+pecnFzcLhFRMZV2HgPMZaLywFwmqviYx0QVT5lcE7179254enqifv36eP3113Hnzh3pvPPmzYOzs7PhUatWrbLoEhEVU3HyGGAuE5kr5jJRxcc8JjIvpT6Ifvrpp/HNN9/g119/xfz587Fnzx507doVeXmFX9swdepUJCUlGR5Xr14t7S4RUTEVN48B5jKROWIuE1V8zGMi81OyO1sp9O/f3/D/Jk2aoGnTpggICMDu3bvRqVOnAvPrdDrodLrS7gYRPYTi5jHAXCYyR8xlooqPeUxkfsq8xFWdOnXg7u6O8+fPl3VTRFRGmMdElQNzmajiYx4Tlb9S/yX6QdeuXcOdO3dQvbr8dumFEXo9RGGlJlRlVIS8NMX/L6xqUbGY/LuGkpbGAgB93GV57KcvpbHsg8eV67Wu7yeNidg4aczGVX7L+9wkdVkLh3aNpbEGz/aXxi4NniBv87T8tv4AYGkpf12srRWvmZW8ZIjWzlrZZl66vJyAjaP8G18LnTzVrDVJyjZh5yAJyJ9HaStpHhMAf39pKFfsVi5a8gJ6Co7OZbFWOBVRBo/MA3MZ0Lh6SmOulvJ9tU7x2XEnVVFqBuqSjNYW8ty5lSlfb1as+pjHSfG5cytVXv4mPl5RrlGrPubJVlQlslU8T9jIS09SQeaWx9qOL0hjuZ+/JY2lbv5DGnOc0ETZpq2t/P2dkSfPDZEYq4ipj8ecbeRtajxrS2PNa8k/dzUdOirbhG+QNGRjEylfzsNDHrt7V9mk/uLfiqB829or9nOeHiUvr/n7Ofn1/92WfSKNaReXbYmrYg+iU1NTjb75iomJwfHjx+Hm5gY3NzfMmjULvXv3hre3Ny5cuID//Oc/qFu3LsLDw0u140RUcsxjosqBuUxU8TGPiSqeYg+iDx8+jI4d//etyfjx4wEAAwcOxJIlS3DixAmsWLECiYmJqFGjBrp06YK3336b12UQmRHmMVHlwFwmqviYx0QVT7EH0aGhoRBCfpLhtm3bHqpDRFT2mMdElQNzmajiYx4TVTy8iI2IiIiIiIjIRBxEExEREREREZmIg2giIiIiIiIiE5V5iatSp7hmRGPx6Er+5BOZqcq4fv1X0ljevn3SmOWYadKYrunjyjYzP1sqbzM1UxrLTc2SxqycbNRtnrwojdnYb5DGLK3k3+OoyhcAQG6u/Db7li7ycl0aRSme7LhkZZtW1WTlpgCNpeL9pyi3kFtEISNNg8Jfb02K+r1H5kHj4CSNZeqLeO1LuzMAUMO3xItqFD1SVJUjMisaZ3nplxzFMUZOjrx+k7J8E4Dbt+Vlo1TlGvMUnw+xOfIyVQBQXbED8VCU8rqdLV+vt636Rla5ebnymOr4TVfy8jdU/oTidb/+9RZpzOeZlvJ1/ikvfwUAp67Iy1F1/Ele9kjE35DG9Nny5wEAtxRlTps6uEhjCQny42unBV8r23T8+D1p7EyCvPxswOloaUyfoS7Jp1OUnNO07yaNpXz4ozTmWldRcguAft96aezZLfKyv9om7ZXrLUs87CEiIiIiIiIyEQfRRERERERERCbiIJqIiIiIiIjIRBxEExEREREREZmIg2giIiIiIiIiE3EQTURERERERGQisy1xpbGwgKaQkhFCUSJBpCYo1ymuy2/3LqL/li9497Y85uKmbvPYEWlM+9Ig+YJ28rI4Yqe8XAAA5N5Nk8YsbOQveVaWvHyHtap8E4Ckq4nS2O0Le6Qxdz8XacxCZ6VsUxMrL2+QclNeqkqrKHGVnq4ub6C/e0cas7CQ1xPJypZv20YBLso2LTwLL0lkYaMux0VmwspaGlKV0wFQRPGzktG4epd4Wb2iv9nyKm5EZkUkx0tj2Yr3uHwvDui06oJ02bny9aYrSmfZKEpnVbNWf0ZmK0orqtLVy0a+z8pQfJYB6lJfyYotKBJilesl85b3yRRpzOfFjtKYpmlwidtM/GyHPHjmmDzmIy/zqM9Ul41Tvb9FaqI0lpolP7a08nBUtqkS6CgvDZeXIi+rp2vTQr1ijfx5aqoHFNmvwsQcuaaMBw1Q7B++lZfuxXsscUVERERERERk9jiIJiIiIiIiIjIRB9FEREREREREJuIgmoiIiIiIiMhEHEQTERERERERmYiDaCIiIiIiIiITcRBNREREREREZCKzrRMt7t6EyEkpMD1v9SL5QomJ6pU6yWsvw0pRb1EVu3xR3Wb16tKQ2LhWGks7ESONZSTIa78BgM5OXTtSRlXnOPmGvCYzALj4yetlq+runTt2Sxq7lJGlbLO2Tl7LMl0vr4Jpr5XXvFZtA0Bdp9zGRr7eVEX96cAlM5VtUgXn7lniRdMUdV5LSuPoWuJlUxV5RVRRiHPHpTFrjfwzwFIRu5Olri8b4Okgjd1NyJTGbmbL1+uhqOcMANaK30kuZ8o/X9108kNDq4f4jFQuefOqcr1UvkSe/BgGAMRVeQ1gTcMG8gXdvOQxrfpY1lFxLCdux8n74+QijVlVr6Zs09X2kjyYJ8/VHEVe6AJqKNvE1WhpKDdX/pmsrAWdpT6+zl22UBqzniWP5SmOWfyaycdDAGDRqb80lr1xgzRWngNZ/hJNREREREREZCIOoomIiIiIiIhMxEE0ERERERERkYk4iCYiIiIiIiIyEQfRRERERERERCbiIJqIiIiIiIjIRMW6M/i8efPw008/4Z9//oGtrS3atGmD+fPno379+oZ5MjMzMWHCBHz//ffIyspCeHg4PvvsM3h5KW5jX4i8n75EXiHlG27/uFu6jFtj9W3itSkFS2YZqMpY5eXJQ0lpyjahKAeRfUteNkpky9u0dbJRNqnPlS8r8uS32VdxqGavjMedi5fGfrwoj4U4y9f7/Ol9yjZFgrw81tete0pj7bydpbGsLPm2u0f+eubkyLdtuuK2/9o2zxbRZul7lLlc5Xn4lHjRMikoZakui6OSrCin4WrJ72QfNeZxCWXKy0SqyjBptfJoUqb6s+NWvKJNRaN2FvK8upimLndZ3Uqe6y6W8sO/i2nykls1FaUlASBbUcpHtT8TF+VlfKoCc89l/Q/yskYAlMe6SE6Wx6L/lseKKMN0OStbHnR0lIY0DR+Xxm5/8LWyzUup8txo7CQvj+VoKS/HBUXOAAB86khDmYpjy+w/T0hjukH/UjZp9ezL0pjISpfGcnMVpbyCGyrbRMpd+XoT5GMt63T5+0tjpyhtXAqKddSzZ88ejBw5EgcOHEBUVBRycnLQpUsXpKX978mNGzcOGzZsQGRkJPbs2YMbN27g+eefL/WOE1HJMZeJKj7mMVHlwFwmqniK9Uv01q1bjf6OiIiAp6cnjhw5gg4dOiApKQlfffUVVq9ejaeeegoAsHz5cjRo0AAHDhzAE088UWCdWVlZyLrv26Zk1TdWRFQqmMtEFV9Z5DHAXCZ61PiZTFTxPNT5d0lJ905HdnNzAwAcOXIEOTk5CAsLM8wTFBSE2rVrY//+/YWuY968eXB2djY8atWq9TBdIqISYC4TVXylkccAc5movPEzmcj8lXgQrdfrMXbsWLRt2xaNGzcGANy6dQvW1tZwcXExmtfLywu3bhV+/erUqVORlJRkeFy9erWkXSKiEmAuE1V8pZXHAHOZqDzxM5moYijW6dz3GzlyJE6ePInff//9oTqg0+mg0+keah1EVHLMZaKKr7TyGGAuE5UnfiYTVQwl+iV61KhR2LhxI3bt2oWaNWsapnt7eyM7OxuJiYlG88fGxsLb2/uhOkpEpY+5TFTxMY+JKgfmMlHFUaxfooUQGD16NNatW4fdu3fD39/fKB4cHAwrKyv8+uuv6N27NwDg7NmzuHLlCp588snidWzom7B0Knhrco/UVOkyKVGHleuMuy0vB1EjwE2+oOr286raFEXITZb3R2sn//ZQn52rXrGivxpFGRqXhtWlsfO/XVQ2+eO1RGlsxtp50pi20wDlelVUJa4a2snLgFkqypTordTfK1koyjgkJMvLLbTr3VS53kftUeZyVWdR3b/omSQU1SJKzt6lxIvmKvYt3taKEh5UJpjHJZP36w5pLEtVokkvj6lKRgFAql5eAstFK88dVeUgB4065/SQ91eVy/Za+edglmIbAOpDItWn663I36SxmuOUTVYK5p7L2hcnFBGXx/J2/ygP2irKp96WH+MBQCPFcZ7GVV5uCnYO0lBikqJsFoDG3vKSSfqoH6Sxy5nycl3NcnKUbUKxb1FVrdVYKfYPF88qm9Rf+Eca0/YfI43l5MhLbuWcPq9sU2sh769QlPLK+3KuNGb5xnxlmw+rWIPokSNHYvXq1fjll1/g6OhouA7D2dkZtra2cHZ2xiuvvILx48fDzc0NTk5OGD16NJ588knpXUCJ6NFjLhNVfMxjosqBuUxU8RRrEL1kyRIAQGhoqNH05cuXY9CgQQCAjz/+GBYWFujdu7dRMXgiMh/MZaKKj3lMVDkwl4kqnmKfzl0UGxsbLF68GIsXLy5xp4iobDGXiSo+5jFR5cBcJqp4HqpONBEREREREVFVwkE0ERERERERkYk4iCYiIiIiIiIyUbGuiTYHlv9ZKI25/ke9rJPiNvtpHy6Rxq5cSJDG7qSrb4evs5B/T+HsYCWNVa8hX05jqS5rkZogL50VfylZGtu++4I01tLBVtnmzFunpDGNjaKEwUMQ0X9JY9/EJUlj/1asM7uI65K0ivodqkUtBg5XrpcqL001nxIvqypD41rEfkBGo9gnFSVTUd7Gy0q+PyMyJyknr0ljjoryTqoSV0JRTgoA3HXy/MjNVZRvUazWx8Fa2aZG8XmVmCIvueWpk683M1e+HADkKPZZdoptqyqNQxWbNrRfmay3TWhvaSzve/lYQZMiP6a/mig/fgaAVk28pDGLZ1+RxtymfClfqb6I937yXWkoNU+ej1nX5MtZN2utbFJzUV7iSn9Rfuzd/MXH5evs/bKyTeTJS33ZjpeX1bJo2kG93jLEX6KJiIiIiIiITMRBNBEREREREZGJOIgmIiIiIiIiMhEH0UREREREREQm4iCaiIiIiIiIyEQcRBMRERERERGZiINoIiIiIiIiIhOZbZ1oIQREIfUGVXUPi6KqVeekiDVWrFMkxirb1EcfkwdP/CkNpURGSWPRZ+4o29Rq5dsooKG7NDb+q1nydbbtqWxTpbDXMd/DvJ4WbXpIY4u/S5cvWKehPJYmry8NABoPRc1fC3ndXgu/Jsr1EhUmV1Ejtrp1yepEP4wcRX/cbFknmsyDyM1Wxv++kCiNuVrJD4sUH2W4kS2vcQoA7jby2svWily+k5EpjeWkqGtT17bVSWOKwwTcyJRvPx9bdW3qtCz5dnDUyp/n3QT58/RTbXg83HEElQ7VcZ7Kw7x2GgcXeTBdfgwojvwmjdWv4ahs075xLWksb/UCaex4qvz93baIOtEWbt7SmLOlPKcuX0qWxhpnqetho06QNCQ2/CCNWbz8mjxWv5W6TQVtNcWxdzniL9FEREREREREJuIgmoiIiIiIiMhEHEQTERERERERmYiDaCIiIiIiIiITcRBNREREREREZCIOoomIiIiIiIhMZLYlrjQaTYUoW6Bx8VLGtY8/LQ8qYi6vzJDGWhbZK/NSVq+jxt5FGtM++2qZtElUmnq4OSjj/6RnSWPu1mWz+37KxU4ai86Ql75xdZWX0yF6lDSW6jJMjzWXl4zZfuiaNNbQzV4aq56rLvGWqCj95OUkz516tjbSWFFF7lxd5Ou1SpV/Lmdly0vuWKpqYwHwVJTAOpsiL6vTuaGHNFYRjgWrupK+Rg9TAlV/6W95MCVF3qa1/D166Waass0aLi7SmObJzoolV8j7k5OnbFPEXZXGbLTy30KTMnPlK716UdkmqnnKY17ycY+4ESNfrogSV0Kv3g4loVGUni0N/CWaiIiIiIiIyEQcRBMRERERERGZiINoIiIiIiIiIhNxEE1ERERERERkIg6iiYiIiIiIiEzEQTQRERERERGRiYpVI2XevHn46aef8M8//8DW1hZt2rTB/PnzUb9+fcM8oaGh2LNnj9Fyw4cPx9KlS0unx0T00JjL5sFRUZ4CAKwt5CU+LMuo7Iutok0LyGPV/NzKojukwDwuGaefd0pjjz8hL8Oy69JdaayWTl1Wy9FSXmrFzk5dHktGVcIKAFJT5WW1snPkZax01vL9UkKGomwOgNPpmdJYcwdbacw1ap9yvZVdlc1lVVkjrXqIIq6eUyyrKG2UJ29T9ZkLAHB0lMdSE6Shmjr5c9FYFVGG6cI/iu7I9zvnY5Pl6/TyUbfpXkMa0mSky2O16qnXq1DW5ajKQrF+id6zZw9GjhyJAwcOICoqCjk5OejSpQvS0ozrqg0bNgw3b940PN57771S7TQRPRzmMlHFxzwmqhyYy0QVT7F+id66davR3xEREfD09MSRI0fQoUMHw3Q7Ozt4e3uXTg+JqNQxl4kqPuYxUeXAXCaqeB7qmuikpCQAgJub8Wl8q1atgru7Oxo3boypU6ciPV3+039WVhaSk5ONHkT0aDGXiSq+0shjgLlMVN74mUxk/or1S/T99Ho9xo4di7Zt26Jx48aG6S+++CJ8fX1Ro0YNnDhxApMnT8bZs2fx008/FbqeefPmYdasWSXtBhE9JOYyUcVXWnkMMJeJyhM/k4kqhhIPokeOHImTJ0/i999/N5r+6quvGv7fpEkTVK9eHZ06dcKFCxcQEBBQYD1Tp07F+PHjDX8nJyejVq1aJe0WERUTc5mo4iutPAaYy0TliZ/JRBVDiQbRo0aNwsaNG7F3717UrFlTOW/r1q0BAOfPny80yXU6HXQ69R0miahsMJeJKr7SzGOAuUxUXviZTFRxFGsQLYTA6NGjsW7dOuzevRv+/v5FLnP8+HEAQPXq1UvUQSIqfcxl85CWJy8zAwBXs+TlZE6nZ0tj/UrcI+C6os0rilh2fIo0Zv8Q/SE55nHp8z3wpzTW79/dpbGNv0Yr13tGUfopNzFNGnOylN+6Rq8qYQPgTq58/5KoiDlo5WV+AmzU5bheai9/Dzqu3aFctiqrsrn8EKUaNdXrSGPC8ndpLD3qgDSmU+QbAGiqucuDZ45LQzsS5deu90iW7xsAQHPjujTm5CjPxzvXFccXsfJ1FkX8vkce9FZ98dNCvV5FuTNzLX9VrEH0yJEjsXr1avzyyy9wdHTErVu3AADOzs6wtbXFhQsXsHr1anTr1g3VqlXDiRMnMG7cOHTo0AFNmzYtkydARMXHXCaq+JjHRJUDc5mo4inWIHrJkiUA7hV8v9/y5csxaNAgWFtbY8eOHViwYAHS0tJQq1Yt9O7dG//9739LrcNE9PCYy0QVH/OYqHJgLhNVPMU+nVulVq1a2LNH8TM/EZkF5jJRxcc8JqocmMtEFc9D1YkmIiIiIiIiqko4iCYiIiIiIiIyEQfRRERERERERCbiIJqIiIiIiIjIRMW6sRgREZWeLoPbKuMtdp2Sxjz6hZZyb+4ZOvk5aSx973FpzH72m2XQGyLzYffNJmmsqNrsuZNelMZO/HxSGruSkSWN2VqofwcJ9bGTxvyebiyNWQwdJ4/Vaa5sk+hR0VST18fW1PaTxrT2/0hj/nWc1Y3WUtSm3rdLGhvq7SqNWTaup2xS41NLGnNrESuN1YpJkMbE0SPqNl9sLo+FdpLHfBsq11vZ8JdoIiIiIiIiIhNxEE1ERERERERkIg6iiYiIiIiIiEzEQTQRERERERGRiTiIJiIiIiIiIjKR2d2dWwgBAEhOSSnnnhCZt/wcyc8Zc8NcLlpuVo4ynpKXJ43pMrOlMcvk5JL3SbHe9Fx5f/LS0qUx7UP0p7Iz9zwGmMulITdbnuuper00li7kMcViAICUPPkMyYp9j0VqmjzGXJYy91w2xzwWevlnisZCq142JVUa02dkSmOZObnSWI7iMw4ARFqGPJYl/+xU5Xiy4jMXADSK55KXLX8uqn1HsmJ/BAAaxec50uX90SheEwtb9b7jYd4Lpak4eawRZpbt165dQ61a8tu5E5Gxq1evombNmuXdjQKYy0SmM9c8BpjLRMVhrrnMPCYynSl5bHaDaL1ejxs3bsDR0REajQbJycmoVasWrl69Cicnp/Luntnh9lGrzNtHCIGUlBTUqFEDFkXUCy0PzOXi4fZRq6zbx9zzGDDO5ZSUlEr5OpSWyvo+LU2VdRuZey7zM7l4uH3UKuv2KU4em93p3BYWFoWO/J2cnCrVi1TauH3UKuv2cXZ2Lu8uSDGXS4bbR60ybh9zzmPAOJc1Gg2Ayvk6lCZun6JVxm1kzrnMz+SS4fZRq4zbx9Q8Nr+vyoiIiIiIiIjMFAfRRERERERERCYy+0G0TqfDW2+9BZ1OV95dMUvcPmrcPuaDr4Uat48at4954Ougxu1TNG4j88DXQY3bR43bxwxvLEZERERERERkrsz+l2giIiIiIiIic8FBNBEREREREZGJOIgmIiIiIiIiMhEH0UREREREREQmMutB9OLFi+Hn5wcbGxu0bt0af/75Z3l3qdzs3bsXzzzzDGrUqAGNRoOff/7ZKC6EwIwZM1C9enXY2toiLCwM0dHR5dPZcjBv3jw8/vjjcHR0hKenJ3r27ImzZ88azZOZmYmRI0eiWrVqcHBwQO/evREbG1tOPa5amMv/w1yWYx6bP+byPcxjNeayeWMe/w9zWY55rGa2g+gffvgB48ePx1tvvYWjR4+iWbNmCA8PR1xcXHl3rVykpaWhWbNmWLx4caHx9957D5988gmWLl2KgwcPwt7eHuHh4cjMzHzEPS0fe/bswciRI3HgwAFERUUhJycHXbp0QVpammGecePGYcOGDYiMjMSePXtw48YNPP/88+XY66qBuWyMuSzHPDZvzOX/YR6rMZfNF/PYGHNZjnlcBGGmWrVqJUaOHGn4Oy8vT9SoUUPMmzevHHtlHgCIdevWGf7W6/XC29tbvP/++4ZpiYmJQqfTie+++64celj+4uLiBACxZ88eIcS97WFlZSUiIyMN85w5c0YAEPv37y+vblYJzGU55rIa89i8MJcLxzwuGnPZfDCP5ZjLasxjY2b5S3R2djaOHDmCsLAwwzQLCwuEhYVh//795dgz8xQTE4Nbt24ZbS9nZ2e0bt26ym6vpKQkAICbmxsA4MiRI8jJyTHaRkFBQahdu3aV3UaPAnO5eJjLxpjH5oO5bDrmcUHMZfPAPC4e5rIx5rExsxxEx8fHIy8vD15eXkbTvby8cOvWrXLqlfnK3ybcXvfo9XqMHTsWbdu2RePGjQHc20bW1tZwcXExmreqbqNHhblcPMzl/2EemxfmsumYx8aYy+aDeVw8zOX/YR4XZFneHSAqbSNHjsTJkyfx+++/l3dXiKiEmMdElQNzmajiYx4XZJa/RLu7u0Or1Ra4u1tsbCy8vb3LqVfmK3+bcHsBo0aNwsaNG7Fr1y7UrFnTMN3b2xvZ2dlITEw0mr8qbqNHiblcPMzle5jH5oe5bDrm8f8wl80L87h4mMv3MI8LZ5aDaGtrawQHB+PXX381TNPr9fj111/x5JNPlmPPzJO/vz+8vb2NtldycjIOHjxYZbaXEAKjRo3CunXrsHPnTvj7+xvFg4ODYWVlZbSNzp49iytXrlSZbVQemMvFU9VzmXlsvpjLpqvqeQwwl80V87h4qnouM4+LUL73NZP7/vvvhU6nExEREeL06dPi1VdfFS4uLuLWrVvl3bVykZKSIo4dOyaOHTsmAIiPPvpIHDt2TFy+fFkIIcS7774rXFxcxC+//CJOnDghnnvuOeHv7y8yMjLKueePxuuvvy6cnZ3F7t27xc2bNw2P9PR0wzyvvfaaqF27tti5c6c4fPiwePLJJ8WTTz5Zjr2uGpjLxpjLcsxj88Zc/h/msRpz2Xwxj40xl+WYx2pmO4gWQohFixaJ2rVrC2tra9GqVStx4MCB8u5Sudm1a5cAUOAxcOBAIcS92/C/+eabwsvLS+h0OtGpUydx9uzZ8u30I1TYtgEgli9fbpgnIyNDjBgxQri6ugo7OzvRq1cvcfPmzfLrdBXCXP4f5rIc89j8MZfvYR6rMZfNG/P4f5jLcsxjNY0QQpT+79tERERERERElY9ZXhNNREREREREZI44iCYiIiIiIiIyEQfRRERERERERCbiIJqIiIiIiIjIRBxEExEREREREZmIg2giIiIiIiIiE3EQTURERERERGQiDqKJiIiIiIiITMRBNBEREREREZGJOIgmIiIiIiIiMhEH0UREREREREQm4iCaiIiIiIiIyEQcRBMRERERERGZiINoIiIiIiIiIhNxEP2IXbp0CRqNBhEREaW63tDQUISGhpbqOh/W7t27odFosHv37vLuChEA5p85ioiIgEajweHDh4uc1xy3M1Vu3GcQVQzMVXrUquQgOv+gLf9haWkJHx8fDBo0CNevXy/v7hmJjY3FxIkTERQUBDs7O9jb2yM4OBhz5sxBYmJieXfvkRk0aJDRa+bg4IA6deqgT58+WLt2LfR6fXl3kUzE/Ks48j+oTXmYm9OnT2PmzJm4dOmSdJ4NGzbAwsICt27dwo0bNzBz5kwcP378kfWRTMN9RsXz4Ge2paUlatWqhf79++P06dPl3T0qI8zViofH1yVnWd4dKE+zZ8+Gv78/MjMzceDAAUREROD333/HyZMnYWNjU97dw6FDh9CtWzekpqbi5ZdfRnBwMADg8OHDePfdd7F3715s3769nHsp16FDB2RkZMDa2rpU1qfT6fDll18CADIyMnD58mVs2LABffr0QWhoKH755Rc4OTmVSltU9ph/Zas08q9Bgwb49ttvjaZNnToVDg4OmD59+sN2sdiKs71Pnz6NWbNmITQ0FH5+foXOs2nTJgQHB8Pb2xuHDx/GrFmz4Ofnh+bNm5dOh6lUcZ9RtsryMzs3NxcXLlzA0qVLsXXrVpw+fRo1atQolXbI/DBXyxaPr81DlR5Ed+3aFS1btgQADB06FO7u7pg/fz7Wr1+Pfv36lWvfEhMT0atXL2i1Whw7dgxBQUFG8blz5+KLL74op96ZxsLColR3lpaWlnj55ZeNps2ZMwfvvvsupk6dimHDhuGHH36QLi+EQGZmJmxtbUutT1RyzL+yVRr55+XlVSDn3n33Xbi7uxeY/iiYcsCQmZlp8oHF5s2bMWTIkIftFj0i3GeUrUfxmf3EE0+gR48e2LRpE4YNG1ZqbZF5Ya6WLR5fm4cqeTq3TPv27QEAFy5cMJr+zz//oE+fPnBzc4ONjQ1atmyJ9evXG81z9+5dTJw4EU2aNIGDgwOcnJzQtWtX/PXXXyXqy7Jly3D9+nV89NFHBRIcuHdw+9///le6fHZ2NmbMmIHg4GA4OzvD3t4e7du3x65duwrM+/333yM4OBiOjo5wcnJCkyZNsHDhQkM8JycHs2bNQmBgIGxsbFCtWjW0a9cOUVFRyudQ2DUb0dHR6N27N7y9vWFjY4OaNWuif//+SEpKMmGrFG7KlCno0qULIiMjce7cOcN0Pz8/9OjRA9u2bUPLli1ha2uLZcuWAbi3Ex07dixq1aoFnU6HunXrYv78+QVOWymrbUMFMf8qZv4Vpajnly8rKwvjx4+Hh4cH7O3t0atXL9y+fdtongevTct/jt9//z3++9//wsfHB3Z2dvjkk0/Qt29fAEDHjh0Np6ndvy3+/vtvXL16Fd27d8fu3bvx+OOPAwAGDx5smP/+a+siIyMRHBwMW1tbw5cID56eOGjQIDg4OODixYsIDw+Hvb09atSogdmzZ0MI8ZBbkh7EfUbF22d4e3sDuHfQnq84r8Xly5fx7LPPwt7eHp6enhg3bhy2bdvG60PNHHO14uUqwOProlTpX6IflH/tnKurq2HaqVOn0LZtW/j4+GDKlCmwt7fHjz/+iJ49e2Lt2rXo1asXAODixYv4+eef0bdvX/j7+yM2NhbLli1DSEhIiU5bWr9+PWxtbdGnT58SPZfk5GR8+eWXGDBgAIYNG4aUlBR89dVXCA8Px59//mk4XTEqKgoDBgxAp06dMH/+fADAmTNnsG/fPowZMwYAMHPmTMybNw9Dhw5Fq1atkJycjMOHD+Po0aPo3LmzyX3Kzs5GeHg4srKyMHr0aHh7e+P69evYuHEjEhMT4ezsXKLnCgD/+te/sH37dkRFRaFevXqG6WfPnsWAAQMwfPhwDBs2DPXr10d6ejpCQkJw/fp1DB8+HLVr18Yff/yBqVOn4ubNm1iwYMEj3zbE/KvI+SdjyvPLN3r0aLi6uuKtt97CpUuXsGDBAowaNUr57Xe+t99+G9bW1pg4cSKysrLQpUsXvPHGG/jkk08wbdo0NGjQAAAM/wL3foX29PREy5YtERcXh9mzZ2PGjBl49dVXDQd8bdq0AXDvOr/Bgwfj8ccfx7x58xAbG4uFCxdi3759OHbsGFxcXAzrzcvLw9NPP40nnngC7733HrZu3Yq33noLubm5mD179kNtTzLGfYb57zPi4+MB3MuLixcvYvLkyahWrRp69OhhmMfU1yItLQ1PPfUUbt68iTFjxsDb2xurV68udPBC5oW5av65KsPjawVRBS1fvlwAEDt27BC3b98WV69eFWvWrBEeHh5Cp9OJq1evGubt1KmTaNKkicjMzDRM0+v1ok2bNiIwMNAwLTMzU+Tl5Rm1ExMTI3Q6nZg9e7bRNABi+fLlyj66urqKZs2amfycQkJCREhIiOHv3NxckZWVZTRPQkKC8PLyEkOGDDFMGzNmjHBychK5ubnSdTdr1kx0797d5L7k27VrlwAgdu3aJYQQ4tixYwKAiIyMLPa6Bg4cKOzt7aXx/HWPGzfOMM3X11cAEFu3bjWa9+233xb29vbi3LlzRtOnTJkitFqtuHLlihCibLdNVcb8q3j5d79GjRoZPVcVU55f/vshLCxM6PV6w/Rx48YJrVYrEhMTDdMe3M75z7FOnToiPT3daL2RkZFGz/9B7du3FwMHDjT8fejQoULfG9nZ2cLT01M0btxYZGRkGKZv3LhRABAzZswwTBs4cKAAIEaPHm2YptfrRffu3YW1tbW4ffu2dDuQHPcZFW+fkZ8LDz58fHzEkSNHjOY19bX48MMPBQDx888/G6ZlZGSIoKAgZa7To8NcrZi5yuPrkqnSp3OHhYXBw8MDtWrVQp8+fWBvb4/169ejZs2aAO6dQrJz507069cPKSkpiI+PR3x8PO7cuYPw8HBER0cbTufT6XSwsLi3OfPy8nDnzh04ODigfv36OHr0aLH7lpycDEdHxxI/N61Wa7guUK/X4+7du8jNzUXLli2N+uPi4oK0tDTl6REuLi44deoUoqOjS9wfAIZvwrZt24b09PSHWteDHBwcAAApKSlG0/39/REeHm40LTIyEu3bt4erq6vhNY2Pj0dYWBjy8vKwd+9eAI9221RFzL/Kk38ypjy/fK+++qrRXb7bt2+PvLw8XL58uchlBw4cWKxrsRITE7F//3507969yHkPHz6MuLg4jBgxwugatO7duyMoKAibNm0qsMyoUaMM/9doNBg1ahSys7OxY8cOk/tIBXGfUbH2GTY2NoiKikJUVBS2bduGZcuWwcHBAd26dTM6NdTU12Lr1q3w8fHBs88+a9QGr602P8zVipWrKjy+lqvSg+jFixcjKioKa9asQbdu3RAfHw+dTmeInz9/HkIIvPnmm/Dw8DB6vPXWWwCAuLg4APcS6eOPP0ZgYCB0Oh3c3d3h4eGBEydOlOh6BCcnpwJv2OJasWIFmjZtariWwMPDA5s2bTLqz4gRI1CvXj107doVNWvWxJAhQ7B161aj9cyePRuJiYmoV68emjRpgkmTJuHEiRPF7o+/vz/Gjx+PL7/8Eu7u7ggPD8fixYtL5XrM1NRUACiwY/T39y8wb3R0NLZu3VrgNQ0LCwPwv9f0UW6bqoj5V3ny7+7du7h165bhkb9OU55fvtq1axv9nX/aX0JCQpHtF5bnKtu2bQMAdOnSpch58wfx9evXLxALCgoqMMi3sLBAnTp1jKblnwKnKrdFReM+o2LtM7RaLcLCwhAWFoYuXbrg1VdfxY4dO5CUlISpU6ca5jP1tbh8+TICAgIKlNSrW7dusZ8blS3masXKVRUeX8tV6UF0q1atEBYWht69e2P9+vVo3LgxXnzxRcMbJv8i+IkTJxq+TX3wkb/zfueddzB+/Hh06NABK1euxLZt2xAVFYVGjRqVqMZaUFAQzp07h+zs7BI9t5UrV2LQoEEICAjAV199ha1btyIqKgpPPfWUUX88PT1x/PhxrF+/Hs8++yx27dqFrl27YuDAgYZ5OnTogAsXLuDrr79G48aN8eWXX+Kxxx4z3A6/OD788EOcOHEC06ZNQ0ZGBt544w00atQI165dK9HzzHfy5EkABT9MC/t1Sq/Xo3PnztLXtHfv3gAe/bapaph/lSf/nn/+eVSvXt3wyL+myZTnl0+r1Ra6bmHCDbmKe0fQzZs3o23btmVyHTiVHe4zKv4+o2bNmqhfv77hFymg9F8LKn/M1Yqfq/l4fK1QrieTl5P8azYOHTpkND3/GoN58+YJIYSIjY0VAMTUqVOLXGezZs1Ex44dC0z38fExupbC1Gs23nnnHQFArF69uugnJApes/Hcc8+JOnXqGF1jKIQQbdq0Eb6+vtL15OXlieHDhwsAIjo6utB5UlJSRIsWLYSPj4+yTw9es1GYffv2CQBi+vTpynUVdc1Gly5dhEajMboOw9fXt9DrKRo2bCiefPJJZXuFKc1tU5Ux/3yl6zHX/LtfYddEHz58WERFRRkep06dKnTZwp5fUe+H+/svuya6sOvA1qxZU+jz1+v1wtPTU7z33nsFnkNh740//vhDABCfffZZgTYaNGgggoODDX/nXwd69uxZo/m2bNkiAIjvvvuuwDqoaNxn+ErXY677DNVndqNGjYSNjY3hb1Nfi86dOwsfH58C2yj/WmleE13+mKu+0vVUxFwVgsfXKlX6l+gHhYaGolWrVliwYAEyMzPh6emJ0NBQLFu2DDdv3iww//3lV7RabYFfTCIjIwuUQDHVa6+9hurVq2PChAlG1w7li4uLw5w5c6TL5/+qc3+fDh48iP379xvNd+fOHaO/LSws0LRpUwD3Ss4UNo+DgwPq1q1riJsqOTkZubm5RtOaNGkCCwuLYq/rfu+++y62b9+OF154AYGBgUXO369fP+zfv99wSuf9EhMTDX18lNuGmH9Axcw/AAgODjacthkWFoaGDRsW2vfCnl9Zsbe3B3Avp+936NAhxMXFFbgeWjZ/y5Yt4enpiaVLlxr1ecuWLThz5kyh11V/+umnhv8LIfDpp5/CysoKnTp1epinRA/gPqPi7TPOnTuHs2fPolmzZoZppr4W4eHhuH79ulEJpMzMTLOv6UvMVaDi5SrA4+uisMTVAyZNmoS+ffsiIiICr732GhYvXox27dqhSZMmGDZsGOrUqYPY2Fjs378f165dM9Sp69GjB2bPno3BgwejTZs2+Pvvv7Fq1aoC18aZytXVFevWrUO3bt3QvHlzvPzyywgODgYAHD16FN999x2efPJJ6fI9evTATz/9hF69eqF79+6IiYnB0qVL0bBhQ8PpNAAwdOhQ3L17F0899RRq1qyJy5cvY9GiRWjevLmhHEzDhg0RGhqK4OBguLm54fDhw1izZo3RzXNMsXPnTowaNQp9+/ZFvXr1kJubi2+//RZardZwiodKbm4uVq5cCeDeB+fly5exfv16nDhxAh07dsTnn39uUj8mTZqE9evXo0ePHhg0aBCCg4ORlpaGv//+G2vWrMGlS5fg7u7+SLcN3cP8M9/8KwlTnl9Zad68ObRaLebPn4+kpCTodDo89dRT2LRpE/z8/AwD/XwBAQFwcXHB0qVL4ejoCHt7e7Ru3Rr+/v6YP38+Bg8ejJCQEAwYMMBQ4srPzw/jxo0zWo+NjQ22bt2KgQMHonXr1tiyZQs2bdqEadOmwcPDo0yfc1XEfYb57jPu/8zW6/W4dOkSli5dCr1eb7juNf+5m/JaDB8+HJ9++ikGDBiAMWPGoHr16li1apXhhn8PXitN5oW5WjFylcfXxVBeP4GXJ9npJkLcO6UgICBABAQEGG69fuHCBfHvf/9beHt7CysrK+Hj4yN69Ogh1qxZY1guMzNTTJgwQVSvXl3Y2tqKtm3biv379xc4DcTU003y3bhxQ4wbN07Uq1dP2NjYCDs7OxEcHCzmzp0rkpKSDPM92I5erxfvvPOO8PX1FTqdTrRo0UJs3LhRDBw40Oh0kzVr1oguXboIT09PYW1tLWrXri2GDx8ubt68aZhnzpw5olWrVsLFxUXY2tqKoKAgMXfuXJGdna3s+4Onm1y8eFEMGTJEBAQECBsbG+Hm5iY6duwoduzYUeR2eLBchp2dnfDz8xO9e/cWa9asKVD+QAj56SZC3DstZOrUqaJu3brC2tpauLu7izZt2ogPPvjA8LzKcttUZcw/X8N8FSX/7lecElemPL+yOp1bCCG++OILUadOHaHVag3ratmypRgxYkSh8//yyy+iYcOGwtLSssD75IcffhAtWrQQOp1OuLm5iZdeeklcu3bNaPn80+IuXLggunTpIuzs7ISXl5d46623Ct1HkWm4z/A1zFdR9hmFlbhycnISnTp1KrC8qa9Ffp+6d+8ubG1thYeHh5gwYYJYu3atACAOHDhQZL+obDFXfQ3zVdRc5fG16TRCmHDXFiIiogouNjYW1atXx8aNG9GtW7dSX/+gQYOwZs0ao18jiKhsLViwAOPGjcO1a9fg4+NT3t0hoiqC10QTEVGVkJSUhBkzZqBjx47l3RUiKoGMjAyjvzMzM7Fs2TIEBgZyAE1EjxSviSYioiqhXr16mDlzZnl3g4hK6Pnnn0ft2rXRvHlzJCUlYeXKlfjnn3+watWq8u4aEVUxHEQTERERkdkLDw/Hl19+iVWrViEvLw8NGzbE999/jxdeeKG8u0ZEVQyviSYiIiIiIiIyEa+JJiIiIiIiIjIRB9FEREREREREJuIgupT5+flh0KBBhr93794NjUaD3bt3l1ufHmSOfQKA0NBQhIaGlnc3qAJj/pVcRck/Pz8/9OjRo8j5zHU7k3nhPqPkKso+gyoH5mrJMVfLRqUaREdERECj0RgeNjY2qFevHkaNGoXY2Njy7l6xbN68uUR3kV23bh26du0Kd3d3WFtbo0aNGujXrx927txZ+p00U5cuXTJ6H1hZWcHd3R1t2rTBtGnTcOXKlfLuYqXE/GP+hYaGGr0HZA9zvEP2Z599hoiICOU8wcHBGDFiBABg9erVWLBgQdl3rBLjPoP7DKDgZ7ZGo4GTkxOaN2+OTz/9FHl5eeXdxSqPucpcBXh8/aBKeXfu2bNnw9/fH5mZmfj999+xZMkSbN68GSdPnoSdnd0j7UuHDh2QkZEBa2vrYi23efNmLF682OREF0JgyJAhiIiIQIsWLTB+/Hh4e3vj5s2bWLduHTp16oR9+/ahTZs2JXgWj8b27dtLdX0DBgxAt27doNfrkZCQgEOHDmHBggVYuHAhvvrqK/Tv379U26N7mH9VN/+mT5+OoUOHGv4+dOgQPvnkE0ybNg0NGjQwTG/atOlDt1WU4r72n332Gdzd3Y1+6bjfzZs3cezYMcyePRvAvUH0yZMnMXbs2FLqcdXFfUbV3WfcL/8zG7hX033z5s0YPXo0Ll++jPfff79U26KSYa4yVwEeX+erlIPorl27omXLlgCAoUOHolq1avjoo4/wyy+/YMCAAYUuk5aWBnt7+1Lvi4WFBWxsbEp9vQ/68MMPERERgbFjx+Kjjz6CRqMxxKZPn45vv/0Wlpbm/XIXd0dYlMceewwvv/yy0bTLly+jS5cuGDhwIBo0aIBmzZpJly+r90Rlx/yruvnXuXNno79tbGzwySefoHPnzo/8VDJTX/v09HSTDv62bNkCGxsbPPXUU6XRPboP9xlVd59xvwc/s0eMGIHWrVtj9erVHESbCeYqcxXg8XW+SnU6t0z+QU9MTAwAYNCgQXBwcMCFCxfQrVs3ODo64qWXXgIA6PV6LFiwAI0aNYKNjQ28vLwwfPhwJCQkGK1TCIE5c+agZs2asLOzQ8eOHXHq1KkCbcuujzh48CC6desGV1dX2Nvbo2nTpli4cKGhf4sXLwYAo9MmZDIyMjBv3jwEBQXhgw8+KHTef/3rX2jVqpV0Hb/99hv69u2L2rVrQ6fToVatWhg3bhwyMjKM5rt16xYGDx6MmjVrQqfToXr16njuuedw6dIlwzyHDx9GeHg43N3dYWtrC39/fwwZMkTadr7CrtlYtGgRGjVqBDs7O7i6uqJly5ZYvXp1keuS8fX1RUREBLKzs/Hee+8ZpuefqrRnzx6MGDECnp6eqFmzpiG+ZcsWtG/fHvb29nB0dET37t0LvN5luW0qMuYf889Upjy/fL///jtatWoFGxsb1KlTB998841RvLDXPjQ0FI0bN8aRI0fQoUMH2NnZYdq0afDz88OpU6ewZ88ew+v94LbYtGkTOnbsCFtbW4SGhmLTpk24fPmyYX4/Pz/DvHFxcXjllVfg5eUFGxsbNGvWDCtWrDBaX/5pcR988AE+/vhj+Pr6wtbWFiEhITh58uTDbsoKjfsM7jOAe9vSy8urwADll19+Qffu3VGjRg3odDoEBATg7bffLvS078WLF6NOnTqwtbVFq1at8Ntvv/H60FLEXGWu5quKx9fm/dVJKblw4QIAoFq1aoZpubm5CA8PR7t27fDBBx8YfokYPnw4IiIiMHjwYLzxxhuIiYnBp59+imPHjmHfvn2wsrICAMyYMQNz5sxBt27d0K1bNxw9ehRdunRBdnZ2kf2JiopCjx49UL16dYwZMwbe3t44c+YMNm7ciDFjxmD48OG4ceMGoqKi8O233xa5vt9//x13797F2LFjodVqS7KJEBkZifT0dLz++uuoVq0a/vzzTyxatAjXrl1DZGSkYb7evXvj1KlTGD16NPz8/BAXF4eoqChcuXLF8HeXLl3g4eGBKVOmwMXFBZcuXcJPP/1U7D598cUXeOONN9CnTx+MGTMGmZmZOHHiBA4ePIgXX3yxRM8TAJ588kkEBAQgKiqqQGzEiBHw8PDAjBkzkJaWBgD49ttvMXDgQISHh2P+/PlIT0/HkiVL0K5dOxw7dsxw8Pwot01FwvwrWlXKP5Winl++8+fPo0+fPnjllVcwcOBAfP311xg0aBCCg4PRqFEjZRt37txB165d0b9/f7z88svw8vJCaGgoRo8eDQcHB0yfPh0A4OXlZVgmJycHO3bswDvvvAPg3q8PSUlJuHbtGj7++GMAgIODA4B7B12hoaE4f/48Ro0aBX9/f0RGRmLQoEFITEzEmDFjjPrzzTffICUlBSNHjkRmZiYWLlyIp556Cn///bdRH6oS7jOKVhn3Genp6YiPjwcAJCcnY8uWLdi6dSumTp1qNF9ERAQcHBwwfvx4ODg4YOfOnZgxYwaSk5ONfrFesmQJRo0ahfbt22PcuHG4dOkSevbsCVdXV6ODeCo55mrRKmOuylS542tRiSxfvlwAEDt27BC3b98WV69eFd9//72oVq2asLW1FdeuXRNCCDFw4EABQEyZMsVo+d9++00AEKtWrTKavnXrVqPpcXFxwtraWnTv3l3o9XrDfNOmTRMAxMCBAw3Tdu3aJQCIXbt2CSGEyM3NFf7+/sLX11ckJCQYtXP/ukaOHClMfXkWLlwoAIh169aZNP+DfRJCiPT09ALzzZs3T2g0GnH58mUhhBAJCQkCgHj//fel6163bp0AIA4dOmRSX+4XEhIiQkJCDH8/99xzolGjRsVeT0xMTJH9fO655wQAkZSUJIT433unXbt2Ijc31zBfSkqKcHFxEcOGDTNa/tatW8LZ2dkwvay3TUXA/Ftn0vyVPf/uFxkZWeC5ypjy/IQQwtfXVwAQe/fuNUyLi4sTOp1OTJgwwTCtsO0cEhIiAIilS5cWWG+jRo2Mnv/9fv31VwFAxMTEGKZ1795d+Pr6Fph3wYIFAoBYuXKlYVp2drZ48sknhYODg0hOThZC/G8/dX9uCCHEwYMHBQAxbtw45XaoDLjPWGfS/JV9n5GfC4U9Xn/9daPtLEThz3348OHCzs5OZGZmCiGEyMrKEtWqVROPP/64yMnJMcwXEREhAEhznQrHXF1n0vxVJVd5fH1PpTydOywsDB4eHqhVqxb69+8PBwcHrFu3Dj4+Pkbzvf7660Z/R0ZGwtnZGZ07d0Z8fLzhERwcDAcHB+zatQsAsGPHDmRnZ2P06NFGp3aYcoOZY8eOISYmBmPHjoWLi4tRTHVKiUpycjIAwNHRsUTLA4Ctra3h/2lpaYiPj0ebNm0ghMCxY8cM81hbW2P37t0FTr/Jl/+cNm7ciJycnBL3J39d165dw6FDhx5qPYXJ/9UoJSXFaPqwYcOMvm2MiopCYmIiBgwYYPSe0Gq1aN26teE98ai3jTlj/hVfVcu/wpjy/PI1bNgQ7du3N/zt4eGB+vXr4+LFi0W2o9PpMHjw4GL1bfPmzWjYsKHRr+Gqeb29vY2uD7SyssIbb7yB1NRU7Nmzx2j+nj17GuVGq1at0Lp1a2zevLlYfazIuM8ovsq4z3j11VcRFRWFqKgorF27FiNHjsSyZcswfvx4o/nuf+4pKSmIj49H+/btkZ6ejn/++QfAvVM779y5g2HDhhmdDv7SSy/B1dW1RP0j5mpJVMZcValKx9eVchC9ePFiREVFYdeuXTh9+jQuXryI8PBwo3ksLS0LnM4THR2NpKQkeHp6wsPDw+iRmpqKuLg4APcungeAwMBAo+U9PDyK3Dnnn/rSuHHjh3qO93NycgJQ8A1bHFeuXMGgQYPg5uYGBwcHeHh4ICQkBMC9u2QC9w4+58+fjy1btsDLywsdOnTAe++9h1u3bhnWExISgt69e2PWrFlwd3fHc889h+XLlyMrK6vYfZo8eTIcHBzQqlUrBAYGYuTIkdi3b1+Jn+P9UlNTARTcMfr7+xv9HR0dDeDedT8Pvie2b99ueE886m1jzph/xVeV8i87Oxu3bt0yeuTl5Zn0/PLVrl27wDRXV9ciB98A4OPjU+ybrGzatAndu3c3ad7Lly8jMDAQFhbGH6/5dyfPf//me/B9DAD16tUr9Drwyor7jOKrjPuMwMBAhIWFISwsDM8//zw+/fRTjBgxAgsWLMDff/9tmO/UqVPo1asXnJ2d4eTkBA8PD8NNjvKfe/5rXrduXaM2LC0tTfoyjArHXC2+ypirKlXp+LpSXhPdqlUrw90DZXQ6XYGDHL1eD09PT6xatarQZTw8PEqtj6UpKCgIAPD333+jZ8+exV4+Ly8PnTt3xt27dzF58mQEBQXB3t4e169fx6BBg6DX6w3zjh07Fs888wx+/vlnbNu2DW+++SbmzZuHnTt3okWLFtBoNFizZg0OHDiADRs2YNu2bRgyZAg+/PBDHDhwwPANlSkaNGiAs2fPYuPGjdi6dSvWrl2Lzz77DDNmzMCsWbOK/Tzvd/LkSXh6ehp2kPnu/8YQgOG5f/vtt/D29i6wnvu/4X6U28acMf+Kp6rl3x9//IGOHTsaTYuJiYGfn1+Rzy+f7No0IUSR7T+Y40WJiYnBP//8gyVLlhRrOTId9xnFU5X2GZ06dcKnn36KvXv3okmTJkhMTERISAicnJwwe/ZsBAQEwMbGBkePHsXkyZONnjuVPuZq8VSlXM1XpY6vH+nJ42Us/7z7os6JHzhwoLC3ty8wfcSIEUKr1RZ6/cL9Vq9eLQCIrVu3Gk2Pi4sr8pqNQ4cOCQDi448/VrYxatQok6/ZSEtLE66urqJBgwZG1xvIPNinY8eOCQBixYoVRvNt375dABDLly+XruvcuXPCzs5OvPTSS9J5Vq1aJQCIL774QtmvB6/ZeFBWVpbo3r270Gq1IiMjQzpfUdds/PHHHwKAePnllw3TZO+dH3/8UQAQ27ZtU/a9MKW5bSoC5h/z70GFXRN99+5dERUVZfSQra+w5+fr6yu6d+9eZP9l10TLrgNr3Lhxoc9/0aJFwtnZ2ei6SiGE6NGjR6HXRHfp0kV4e3uLvLw8o+nff/+9ACA2bNgghPjffmrAgAEF1tG6dWtRv379QvtZmXCfwX2GEOrP7Px9yLvvviuE+N+1j3v27DGa7/PPPzfaRvv27RMAxOeff240X05OjnB1deU10cXEXGWuCsHj6wdVytO5S6pfv37Iy8vD22+/XSCWm5uLxMREAPeuCbGyssKiRYuMfvlYsGBBkW089thj8Pf3x4IFCwzry3f/uvLrpz04T2Hs7OwwefJknDlzBpMnTy7015iVK1fizz//LHT5/F917l9OCGEoCZAvPT0dmZmZRtMCAgLg6OhoOGUiISGhQPvNmzcHgGKfVnHnzh2jv62trdGwYUMIIUp8zcPly5cxaNAgWFtbY9KkSUXOHx4eDicnJ7zzzjuFtnn79m3g/9i78/CmyrQN4Heatune0p1CKaVsgixaBQEVEKSyjQiIoM4AbqiAIuLCOOI+HXcUEBx1wA1RUHAHAVkUAWVTQUBAyt5CC9335P3+4Gs0tO+TpS1N0vt3XbmUPDnnvFmec/I2yblx/h8bb8T+8/7+A85+7brqK5tVl4CAAIfuX30KDg6u8fn+6quvMGDAgGoxO8HBwdav4v3VoEGDkJmZiQ8//NB6XWVlJWbNmoWQkBDr1/iqLFu2DMeOHbP++8cff8TmzZsxcODAWt4j78d9hvfvMz7//HMAsGbO1nTfy8vL8dprr9ksd8kllyAqKgpvvPEGKisrrde///77Dv3sg+oWe9X7e7Uxvr/2yq9zu6p3796YMGEC0tPTsWPHDgwYMAB+fn7Yt28fFi9ejFdeeQUjR45ETEwMpk2bhvT0dAwZMgSDBg3C9u3b8fXXXyM6Olrcho+PD+bOnYuhQ4eia9euGD9+PJo2bYo9e/Zg165dWLFiBQAgNTUVAHDPPfcgLS0NRqMRo0eP1q73gQcewK5du/Diiy9izZo1GDlyJOLj45GZmYlly5bhxx9/xA8//FDjsu3bt0dKSgqmTZuGY8eOISwsDB9//HG1A83vv/+Ofv36YdSoUejQoQN8fX2xdOlSZGVlWcf29ttv47XXXsN1112HlJQUFBQU4I033kBYWBgGDRrk8HMBAAMGDEB8fDx69eqFuLg47N69G7Nnz8bgwYMdOsnDtm3b8N5778FisSA3Nxc//fQTPv74YxgMBrz77rvo3Lmz3XWEhYVh7ty5+Pvf/46LL74Yo0ePRkxMDA4fPowvv/wSvXr1wuzZs8/7Y+ON2H/e1X/OcuT+1afU1FTMnTsXTz/9NFq3bo3Y2Fj06NEDa9aswbx582q8/YcffoipU6fi0ksvRUhICIYOHYo77rgDr7/+OsaNG4etW7eiZcuWWLJkCTZs2ICZM2dWe+xat26Nyy+/HHfddRfKysowc+ZMREVF4cEHH6z3++zpuM/wrn1G1TEbOPsb1NWrV+Pjjz9Gz549MWDAAABAz5490aRJE4wdOxb33HOP9Xh+7htof39/PP7445g8eTKuuuoqjBo1ChkZGViwYAFSUlJcPtEUuYa96p292ujfX9fbZ9wNoLZfN6ny3//+V6WmpqrAwEAVGhqqOnXqpB588EF1/Phx623MZrN64oknVNOmTVVgYKDq06eP2rlzp0pKShK/blLl+++/V1dffbUKDQ1VwcHBqnPnzmrWrFnWemVlpZo8ebKKiYlRBoPB4a+eLFmyRA0YMEBFRkYqX19f1bRpU3XDDTeotWvXimP67bffVP/+/VVISIiKjo5Wt99+u/r5559tvm6SnZ2tJk6cqNq3b6+Cg4NVeHi46t69u/roo4+s69m2bZsaM2aMatGihTKZTCo2NlYNGTJEbdmyxe7Yz/26yeuvv66uvPJKFRUVpUwmk0pJSVEPPPCA9bT5OufGZfj6+qrIyEjVvXt3NX36dGukwF/Ze+2sWbNGpaWlqfDwcBUQEKBSUlLUuHHjrPervh8bT8D+Y/+dy5mIK0fun1L193XuzMxMNXjwYBUaGqrw/xE4X3zxhTIYDCorK6va7QsLC9WNN96oIiIiFACbr3ZnZWWp8ePHq+joaOXv7686depU7Wt7f/1a3IsvvqgSExOVyWRSV1xxhfr555/tPl7egPsM7jOUqjniytfXV7Vq1Uo98MADqqCgwOb2GzZsUJdddpkKDAxUCQkJ6sEHH1QrVqyo8Xl79dVXVVJSkjKZTKpbt25qw4YNKjU1VV1zzTV27x/9ib3KXlWK76/PZVDKgTOxEBERNTJ33303tmzZov2qXm1kZGQgOTkZzz//PKZNm1bn6yei6iwWC2JiYjB8+HC88cYbDT0cIvJg/Do3ERFRDbp27YqhQ4c29DCIyAWlpaUwmUw2X91+5513cPr0afTp06fhBkZEXoGTaCIiohrccccdDT0EInLRpk2bcN999+H6669HVFQUtm3bhrfeegsXXnghrr/++oYeHhF5OE6iiYiIiMirtGzZEomJiXj11Vdx+vRpREZG4h//+Af+85//wN/fv6GHR0Qejr+JJiIiIiIiInIQc6KJiIiIiIiIHMRJNBEREREREZGD6u030XPmzMHzzz+PzMxMdOnSBbNmzUK3bt3sLmexWHD8+HGEhobanFGRiGwppVBQUICEhAT4+NTP38Nc7WOAvUzkiPPRxwB7mai+uXsvs4+J7HOqj+sjfHrRokXK399f/e9//1O7du1St99+u4qIiFBZWVl2lz1y5IhNkDcvvPAiX44cOVIfbVyrPmYv88KLc5f66mP2Mi+8nN+Lu/Yy+5gXXhy/ONLH9XJise7du+PSSy/F7NmzAZz961diYiImT56Mhx9+WFw2Ly8PEREROPL7LoSFhtb10BqFwvHXaWvfbT6srRmh/8tkYliAuM22D/9dv95hE8RlyTX5BQVIbNsRubm5CA8Pr/P116aPAffsZWl31xB/ma9840mxvublz7S1lk2CtLUWlyVpa8Zgk7jNg6t/19ZKy8za2oUrP9HWDE3ixG02ZvXdx4B39jKRu3H3XmYfE9nnTB/X+de5y8vLsXXrVkyfPt16nY+PD/r374+NGzdWu31ZWRnKysqs/y4oKAAAhIWGIiwsrK6H1yj4+Omf1iCD/qsJ0iQ6xM5XGsIC9ZNsI5/HelUfkz9n+xjwjF52u0l0gByzEiz0XYhRXwvz1+8DjP5+4jal9fr66B+/sNAQbc3gJs+/O6uv15+39jKRu3KXXmYfE7nOkT6u8x9tZGdnw2w2Iy7O9pOHuLg4ZGZmVrt9eno6wsPDrZfExMS6HhIROcnZPgbYy0TuiL1M5B34/prIvTT42bmnT5+OvLw86+XIkSMNPSQicgF7mcg7sJeJPB/7mKh+1fnXuaOjo2E0GpGVlWVzfVZWFuLj46vd3mQywWSSf6NHROeXs30MsJeJ3BF7mcg78P01kXup80m0v78/UlNTsXr1agwbNgzA2RMfrF69GpMmTarrzXkt87ol2tqeiU+Ly67IzNfWWgbon/JA4beQP2UXitucNe5ZbW3SY29paxc8N1lbMw65Vdwm1R9v7eP6+q1a5Qv3aWv3PqbvZTPk8zreGhehre3NLtLWNn/ys7Z2qLRS3GaHIP3vtMuF35TPTbxUW5vQLFLcZue3HtPWjFeMEJd1lbv9Pr6+eGsvEzU27OW6kTekj7a2ZscJcdkhY3toa77PLHBpPMpikW9QWaYtmefqj53fvKA/MWnv3q3ETQa/95U8JgJQTznRU6dOxdixY3HJJZegW7dumDlzJoqKijB+/Pj62BwR1QP2MZF3YC8TeQf2MpH7qJdJ9A033IBTp05hxowZyMzMRNeuXbF8+fJqJ0MgIvfFPibyDuxlIu/AXiZyH/UyiQaASZMm8eslRB6OfUzkHdjLRN6BvUzkHhr87NxEREREREREnoKTaCIiIiIiIiIHcRJNRERERERE5KB6+000nWXZ+6O2tqzfzdraTwX6U9pH+Mp/+2jmb9TWis36U+nnV9o5zb7gstAAbe3rrDxt7d0bn9SvM+w5cZvDdqzW1gzRzbU1ZdZH/BiMbInGam/Xi8X650fOaGsHhNioFib9a8rXTpLSW1m52lq4Ud/nHYP9tLVgIcoOANbnlWpr0X76ZdsH6qOx3juRK26zfOAUbe3ysIe1tRF3XqWt+c54XdymN8VYERHRnyqfulNbe2jNfm3t4hA5V/uj+Ru0tUFbr9DWQu/+u36l/vI2f7nnRW1tV0GxtuYrHOOe/GyXuM1nnr5Lv95/zRWXbUz4STQRERERERGRgziJJiIiIiIiInIQJ9FEREREREREDuIkmoiIiIiIiMhBnEQTEREREREROYiTaCIiIiIiIiIHcRJNRERERERE5CCG4tazwgf1GafbC/VZ0J2C9ZmrFqXEbZZa9HUpCdpfyJTzsROpKmwSTYXc6mZChm5epVncZuW/p2lrfi8t0taYBd14rU7qoK0tzs4Xl00J0GcvXxCkr0kq5VZGlK++d8qF/cCvReXaWpCP6387lcYbZNTvJJID5J6T8iwPl+kzuCc9+6W2Nrt0rLzNf7+trSnhsWW+NBGRe1vx37XaWjN//fEo3CgfH6V3pek/HNbWwn78t7aWKLwPBoBis/54FCu8vzYZ9PelnZ33LF+//q22NvRf4qKNCj+JJiIiIiIiInIQJ9FEREREREREDuIkmoiIiIiIiMhBnEQTEREREREROYiTaCIiIiIiIiIHcRJNRERERERE5CBm/dQzS7k+nqVbaIC2liUs51OLiJUAe1lVGlIEDQCUCpEwUixOvhBjlRoSKG7TLESEuRY4RN7AvG2VtibFWF0cYhLXm1epD4iTXuMnhF7uFSa/xm9oF6Ot/XvHcW1Niq/4rVgffwXIj0O7QH1tWU6htmYvwqPEon9sQ4XIkWQhdmzFu5vFbQ7WJ44wxoqIyIOtzyvR1kKEOMYiKa8VcgTWZWH69/S+wiGl3M42g4SPOwOEGCspBtPfziFuU36ptjZUXrRR4SfRRERERERERA7iJJqIiIiIiIjIQZxEExERERERETmIk2giIiIiIiIiB3ESTUREREREROQgTqKJiIiIiIiIHMSIq3pWXlKhrYUajdraPrN+uQR/+W8fUtyOFI9lEU6HX2rnFPzS6fL9ffTjzRVigyqE8QDA0S2HtbWUcv3p+Q3++hgC8nwVc2e7tFyxWX696V+pgEF4rUb46l//e0vkuKldu7O0tWg//f5DirHqHS7Hao164U5tbdqEV7Q1KTbEHmnJYmHfEyhE9v2Qr484AYBBp0/oxxPZVFyWGp75nf/oi02bi8sar765jkdDROebMuvjI/PM+vjUOH9/be1gqf69NwB0DdbHPIYI8VdSjJW9Q2eQ8B5aSmM8LcRr2nNaeG+uSgr04wkMdXmbnqjOP4l+/PHHYTAYbC7t27ev680QUT1jLxN5PvYxkXdgLxO5l3r5JLpjx45YtWrVnxvx5QfeRJ6IvUzk+djHRN6BvUzkPuql+3x9fREfH+/QbcvKylBWVmb9d35+fn0MiYhcwF4m8nzO9DHAXiZyVzwmE7mPejmx2L59+5CQkIBWrVrhpptuwuHD+t+upqenIzw83HpJTEysjyERkQvYy0Sez5k+BtjLRO6Kx2Qi91Hnk+ju3btjwYIFWL58OebOnYuDBw/iiiuuQEFBzT9Enz59OvLy8qyXI0eO1PWQiMgF7GUiz+dsHwPsZSJ3xGMykXup869zDxw40Pr/nTt3Rvfu3ZGUlISPPvoIt956a7Xbm0wmmEz6s90RUcNgLxN5Pmf7GGAvE7kjHpOJ3Eu9n5EgIiICbdu2xf79++t7U25JSmmqFIolFv3p5X0M8tMmRVVJp9mXlpNiswDAVzhHv7TeIuF+nq6UT88fJ52C/8QBbc2Q1FFcL9XMU3r52+V7tLVAISrC107MhKvRcYFSBoUdUrJciD7hCllCSsfo3zaI21zdpbe2Ju0/UoL1sSGFZikgDKgUQq7KhP2Hr/DYSvF5AGB+dYZ+vY+/IS7ryc53H69r2UGsn6pwLYalVDh2xPr5icv2/tsybS3/t+MujcfPJB+XjYH6MZXm6uPYfvsjV1tLSZDjZI5mFmlrHbvGaWuBF7fV1ioy5Men8I9T2tqBDP1vck9V6Hda6/PkuLo4Ie7vupZR2prJpF8uvp/8PsH3qf+J9fPBU47J9UUd/NWl5QKEaMRTFfpoLECOXpX2SVKCpp231+JcwSzUfi3S91SXEP3x2h7L1lXamvHy61xeryeql99E/1VhYSEOHDiApk2Zu0nkydjLRJ6PfUzkHdjLRA2rzifR06ZNw7p165CRkYEffvgB1113HYxGI8aMGVPXmyKiesReJvJ87GMi78BeJnIvdf517qNHj2LMmDHIyclBTEwMLr/8cmzatAkxMTF1vSkiqkfsZSLPxz4m8g7sZSL3UueT6EWLFtX1KomoAbCXiTwf+5jIO7CXidxLvf8mmoiIiIiIiMhbcBJNRERERERE5CBOoomIiIiIiIgcVO850d5OlZeK9c0HTmtrUsZpjJB7eKhUCIEFEGzU/21E+quJEJ0HOXEVKBZC8PaV68fbMUifVRftK2d9Bgfr6yozQ78gc6K92menC7W1Zv76XZ6917iULVki5CebhOXs5a+HCvnrh8r0+brDokK0tbxRw8VtLsku0NY6CP16WshllrLiATlnW4jDFh8/f2mHBuCHBT9oa1c+Li7aaKnKCqjK8mrXl0+5UbtMh1YRLm+vyVVd9MU4fc5x2drN4npPbTuiX7ZMnxMbFRskrtdVQU3DtbVLhJqlpPpz8Vfh8cJ+IFO/n8z7apu2FhJuEreZn6cfU/P4YG0tRnjcO8TKediu2n4kV1vLe3O9uOzNT9XxYMhpavcWl5aTcpcvFI5xAHBSyJFOEI5j/kJNTqaWc6SLzPrj7kVCFnSp8J4FkMeLP/bqa5eLq/U6/CSaiIiIiIiIyEGcRBMRERERERE5iJNoIiIiIiIiIgdxEk1ERERERETkIE6iiYiIiIiIiBzESTQRERERERGRgxhxVUvq6B6xnmvWn7y+pUkfFXFpuP7U9P89dkbc5qWh+vUWC6fD94H+lPa+cloMQoRYrR1F+segX/sY/TaFeB8AWPnbKW1t+I/f6RfsPlhcL7k/y8lDLi0XKrxOcyrkkKtsKdrCpA9iMgirlaKdAKBcSKGQeu54uT7+atZPR8VtJpqEGDBhPGYhNsTO7kNc1lVhwuMDAAtP5WlrV9b1YLycRYhdbDJmgLisIb6ZtqYyDugXzNM/f6YLWorbbDpaiHkrKdbXSkv0tYBAcZuuUr8L7zECAsRlDS1aamuBecL7iFJ9dKchPELcZv2EUclUXq62ZmiZoq0lZR7Tr/T33+Vt5mZVv65AHxtGdU8dO+zScikB+vfXaTd3E5ed/Ooaba2VsN4yi/6NgL04xiAf/bFsU4F+n/TYP4dpa2vmfCNuc1OBfh8g9Vtjw0+iiYiIiIiIiBzESTQRERERERGRgziJJiIiIiIiInIQJ9FEREREREREDuIkmoiIiIiIiMhBnEQTEREREREROYgRV7WktqwT61nl+licnk318RTNuyXpt/nuj+I2A4TT5RfrhyP+RaWy7hNoAADBSVHamn+ntuKy4Xu/0hdPnHB1SOQB1Mbldb7OrAp9LBQAjI+L0Na+yyvS1sqE+KZgOxFXEiluKsZPH7kVaCdO4w8hrkha1CTclxJpsHbGdELYh1YKj21Tf/nwlqW/m6RTWX72cg6/9q1cXqVar4+MQXi4y+sVt/njxnpZb70QojClKCrATjyWi9TJk3W+TgDy/RTizOwp/3m3tuYT4Kdf7rgcJRriW0Ocka9+fVQPXHxdpP29u77YTB+5BwAKrkc56lTUIuIxW4jmNCTo78uVQzqI6/14wWZ9sZBRblX4STQRERERERGRgziJJiIiIiIiInIQJ9FEREREREREDuIkmoiIiIiIiMhBnEQTEREREREROYiTaCIiIiIiIiIHMeKqtiIixXKpEO0SGamPuPKf+k/9St8dJm5T+suIvxAlI6XQWOycgt/Vv8b4RYVoa8Zx08Rltzz9sbZ22S+HtLX6CU2h80kdPqitBfvoX4120p1El7z3b23t4Mj7tbXNBWXaWrDJ9b9j+guLFpj1sRf2tlhsFiK5hIWlx/agEJsFAC+N7KqtndyTpa09uzNTW/O381xLEVjmnd9ra8YLL5dX7M3MFUBlDc9llv45MsTGiatUcUJdinAK0B8/7UU/iXFK0nobC+nxsxc7Zu+xd0UtnhO/Ji6u107EVU1RbzX2BtUbc8ZRl5bzmTxDWyuZNsnV4cDsYlSVvcXKhRtIkbaly9fql3tYmGMAwILR2pI6eUpethFx+h3c+vXrMXToUCQkJMBgMGDZsmU2daUUZsyYgaZNmyIwMBD9+/fHvn376mq8RFQH2MdE3oG9TOT52MdEnsfpSXRRURG6dOmCOXPm1Fh/7rnn8Oqrr2LevHnYvHkzgoODkZaWhtL6+OskEbmEfUzkHdjLRJ6PfUzkeZz+OvfAgQMxcODAGmtKKcycORP/+te/cO211wIA3nnnHcTFxWHZsmUYPbr61wPKyspQVvbn1x3z8/OdHRIROamu+xhgLxM1BPYykedjHxN5njo9sdjBgweRmZmJ/v37W68LDw9H9+7dsXHjxhqXSU9PR3h4uPWSmJhYl0MiIie50scAe5nI3bCXiTwf+5jIPdXpJDoz8+xJXuLOOVFIXFyctXau6dOnIy8vz3o5cuRIXQ6JiJzkSh8D7GUid8NeJvJ87GMi99TgZ+c2mUwwSWfKJCKPwF4m8g7sZSLPxz4mql91+kl0fHw8ACDrnLiLrKwsa42I3Bv7mMg7sJeJPB/7mMg91ekn0cnJyYiPj8fq1avRtWtXAGdPZLB582bcdddddbkp95GXK5aPlVdqa2Ej+uoXDA5zcUCAr0GfG1cuhEFXCll1tcnXlZTs0+eLhkU2FZc9UqZ/bH1M+pe2qinf8f8ZfP3FbTYGntDH6mCGtiZls0u9YddRfTa1pMSiz2yutBMQKVWlfg0SsrJPV5rFbYYY9ctK440QlrPHP06fPRt5usildQbZGc+BUv3+Qy15W7+gB+VE13kvG/0AX7/q1wvZwcrOMRJ5efqavUxiqh+1yeD2EuYSOfNZnan+3kUVuravsscTjskNIefXYy4tZwgM1db2bTkuLhssHFsrhOOjdOz0s/MGW1o21s+orZ3ap89zTmreVtymJGtzhrbW3OW1eianJ9GFhYXYv3+/9d8HDx7Ejh07EBkZiRYtWmDKlCl4+umn0aZNGyQnJ+PRRx9FQkIChg0bVpfjJqJaYB8TeQf2MpHnYx8TeR6nJ9FbtmxB375/foI6depUAMDYsWOxYMECPPjggygqKsIdd9yB3NxcXH755Vi+fDkCpL9sEtF5xT4m8g7sZSLPxz4m8jxOT6L79OkDJXy1wGAw4Mknn8STTz5Zq4ERUf1hHxN5B/YykedjHxN5njo9sRgRERERERGRN+MkmoiIiIiIiMhBnEQTEREREREROahOI64aI7XrF7FugP7U9T5/+4e2Zvn6A5fHJJ0sX4r48RUWtBdxJZ6+X9jmN1uOamsj5U0iUYixqswv0S9YLkR0MOLKIxiaRGhryQH618UhIdbIrhB9LIb0GpdqPnYityxCXwULTbm3RB/jZu8vpyZhvdJ49EFe9lnK9HEyv/+Wra0pIQTsYKkcUSPtPyDEkjVqZcVAaQ2voLIy7SKG8AhxlUqKuJLilHhCpfrTSGKsakPl51S/rqi4AUbSeB0/4drjbfn1O21tR54cU9YuqIaIv/8nhUcahWO9NE8AgDKlPx4lmvQRVzuPF+iX2/uTuE3JiUz9Y9TYIq74STQRERERERGRgziJJiIiIiIiInIQJ9FEREREREREDuIkmoiIiIiIiMhBnEQTEREREREROYiTaCIiIiIiIiIHMeKqtsr1UTKAHBuFonxt6cjrX7g4IKBCiKGRSKEuFjurzDfrb3BBkD42alWuPqJgRA0REn8VatT/DejnfWe0td5CXAB5Bt9H52lr0x7VL2fevVFbMwRHiNv0aXGBtnao7GltrVLoHX870XHlUkSesJy03nI7zSytVwqNsrdeiaVMHz12yYFftbWL3npSWzNcNUzcpk9yZ7vjonMER9QY9Vb2xwntIgGdusjrlKKqGLVEtSW9vkwmbckYqI8yAgCU1RCjWdN1VG++Pq2PcJKo9au1tQN2ohG7ButfM2XCe+8gH/2R1d6Rs0I4tkb46qdxGaX6+Ynapn8vZM+uYv1++VKX1+qZ+Ek0ERERERERkYM4iSYiIiIiIiJyECfRRERERERERA7iJJqIiIiIiIjIQZxEExERERERETmIk2giIiIiIiIiBzHiqpZUiRzBkRKgj0lQW9Zpa19m6OOd2gbqI6MAoFw4zb6/jz77prQ2ETXCNqP8jK6tc/l7Yv3alGhtrazMrK2pwlxtzV7MEXk24wU96mW9B4VYjAhf/d8qfQxyxpXFIsSxCctKrWx3m0LNT1g2SIics6c8M1dbEwJqYLx1hsvbJOcZfP1g8K1+/Mncc0q7TMs0O7E/jLGihiLFX9mTd7r6dcWMuDqf/nVAiGkKjdSWLJv0EbKn/v2ZuM1AIaoqq0z/PiDEX79cbUJXQ4Tj7s5ifcSV4dIrxPXOLXxKXyzOszuuxoKfRBMRERERERE5iJNoIiIiIiIiIgdxEk1ERERERETkIE6iiYiIiIiIiBzESTQRERERERGRgziJJiIiIiIiInIQJ9FEREREREREDnI6J3r9+vV4/vnnsXXrVpw4cQJLly7FsGHDrPVx48bh7bfftlkmLS0Ny5cvr/Vg3ZEhwCTW2wiZzupUlrZ2RMg5Tg6Qn7Zisz4k1tUsaF85WlbMuXM1fbpkyZdiPTe3TFv74qQ+x256Ua6LI/Ient7HSsglhxJejULussFXn+luj9RWAUI2e6V0P9yQWRivlBVvz5H9NWSu/r+OwnLKXCkU7YxHyPuEQV8z2MnZPt/coZfzC/R5pHaFh+trecwjJTd1Jqf6dSWuZ567Qx97GkNkU9cWPLDH5W2ahOOGdDyXDhu1eRvg6tFIbfleXu8lafpicISLW/U+Tn8SXVRUhC5dumDOnDna21xzzTU4ceKE9fLBBx/UapBEVLfYx0Tegb1M5PnYx0Sex+lPogcOHIiBAweKtzGZTIiPj3d5UERUv9jHRN6BvUzk+djHRJ6nXn4TvXbtWsTGxqJdu3a46667kJNTw9de/l9ZWRny8/NtLkTU8JzpY4C9TOSu2MtEno99TORe6nwSfc011+Cdd97B6tWr8eyzz2LdunUYOHAgzOaaf+Obnp6O8PBw6yUxMbGuh0RETnK2jwH2MpE7Yi8TeT72MZH7cfrr3PaMHj3a+v+dOnVC586dkZKSgrVr16Jfv37Vbj99+nRMnTrV+u/8/Hw2OlEDc7aPAfYykTtiLxN5PvYxkfup94irVq1aITo6Gvv376+xbjKZEBYWZnMhIvdir48B9jKRJ2AvE3k+9jFRw6vzT6LPdfToUeTk5KBpUxdPRe/mSvafEOulQqQO7PyeRSfCV/7bR3aF/us9UhSVvRgriTQiV6NvDvyijwADgI5D9eE3u9//SVtTJ4/oV9qqq71hNUru1sdizJDBqC0plwMhZGYhyE2KuCp3MXKuoUiPXrlwV0KN8j5r95libU2KuDIY9YcwMQYN7hdVdb7URy+bTPqesysgQF9jxFXDkJ6TUtdjnFxmkqNE62O9PkI8KQCovDPVryvVx27WNXc7JjcEVVmhrUmRlWqPPuIqySRHXRYJX58vc/F4XlGLjCtpk5eE6F/flt9cj/ly9XH3Rk5PogsLC23+8nXw4EHs2LEDkZGRiIyMxBNPPIERI0YgPj4eBw4cwIMPPojWrVsjLU3IHCOi84p9TOQd2MtEno99TOR5nJ5Eb9myBX379rX+u+r3FmPHjsXcuXPxyy+/4O2330Zubi4SEhIwYMAAPPXUUzDV118Sichp7GMi78BeJvJ87GMiz+P0JLpPnz7i1+RWrFhRqwERUf1jHxN5B/YykedjHxN5nno/sRgRERERERGRt+AkmoiIiIiIiMhBnEQTEREREREROajeI668nTFIPqlDoI/+7xQluw5pa4VmfRhVsZ3T6FcK5SAhbkciRWOd3aZ+o4VmfU16fPLLKuWNxsVpS+VSZEBolLxeohqonGPaWjN//a40UOg5qVfdka8QC1Uk7LPC7ERcrcrVR1yNtD+smik7ey0hCo2cU1amj31R+36XFw4Pd22jtYlakiKcGgtXHz97j520Xlcf9waI1bKUlMs3qCl+rdTOMlS3hIhDScmBTG3N3ltk/Z5OPp4Lb4PtxjFKRzI5tlZ/Z4r3yPG8UsBbY4uxkvCTaCIiIiIiIiIHcRJNRERERERE5CBOoomIiIiIiIgcxEk0ERERERERkYM4iSYiIiIiIiJyECfRRERERERERA5ixFVt2Tk1fYSvPkYlP7NAW0sw6ZcLsHMO/lKhLp4qX7gv9iOupKq+mBygfwkeLC0Tt9kjJERbKxdiwAx+0sn7yauJsUfy3xQtW7/V1vKFeKdQO/FOnsTefkAn2M5j0ETYT1oy/9DWfOJb6VdqZ9+sLPp7YxCi96i6sFD9PrXiRI64rH+nLtqa+vVn/YJSNFZtIpHqK06pTD6eucRePJhJjuDUkh6DBoibshuNJd3Pkydd2qRvpP79BYCaH4eyCpe2Ra4xCBFOksO7T2lrfnbWKb1PlqJezULNXtKltKxFWFqaKhw9WihuM8LOmOgsvlMgIiIiIiIichAn0UREREREREQO4iSaiIiIiIiIyEGcRBMRERERERE5iJNoIiIiIiIiIgdxEk1ERERERETkIE6iiYiIiIiIiBzEnOha2rPxiFj/saBEW6s4ps938xWy6kqFDGR7dV8hN076i4o0HgDwF8rlYq6etE55m4agYG1tS6E+k3Pc1x9paz5tLxG3SR7OxVxJAFDrVmtrhWbXeq7cXkCkB5EypPUp0GflV+qXVis+1C84drq+Zicn2uBF+d0NrazcrK35XztEXFb9uFFftJeDrB2QnUxmoS7lWvsE+GlrxkB9VjYA+b5IOchSLnNenrxNiZStbC+X2VX1lTHtYh62ITxCW5OeawDI/GpbtesKzPo+IPdxLF/f/9J7WUDOkQ6UgpkF9t4G+AjbrBDe70tjPVwg9+KFdsZEZ/FdBBEREREREZGDOIkmIiIiIiIichAn0UREREREREQO4iSaiIiIiIiIyEGcRBMRERERERE5iJNoIiIiIiIiIgc5FXGVnp6OTz75BHv27EFgYCB69uyJZ599Fu3atbPeprS0FPfffz8WLVqEsrIypKWl4bXXXkNcXFydD94dXLzvF7ku1PKH9tXWvln/h7bWzF8OjHE1xkqKm7IXFyOd2V/aZqSvvrqvpELe5vA7tbV5/3hYXLaxa6y9bPCxF7akt+vj7dpagNAAYl95kUphH2GyEy1mFkI+ij5Zqa2FCRFXBl85osYbuEsfm6RjUkCguKyhTVttTeXlCuvVxzAZYu3cN2FM/m30sZQSVeZe8U12uRqrVQvi8yI8JyrvjLxiabxCTf2qf/+W/9txcZMxFydWu85UXgns3C8up+MuvdwYFFrqJ4pMWqv0NqDCzvtr6T1EpY++KB11y+xskxzj1CfR69atw8SJE7Fp0yasXLkSFRUVGDBgAIqKiqy3ue+++/D5559j8eLFWLduHY4fP47hw4fX+cCJyHXsZSLPxz4m8g7sZSLP49Qn0cuXL7f594IFCxAbG4utW7fiyiuvRF5eHt566y0sXLgQV111FQBg/vz5uOCCC7Bp0yZcdtlldTdyInIZe5nI87GPibwDe5nI89TqN9F5eXkAgMjISADA1q1bUVFRgf79+1tv0759e7Ro0QIbN26scR1lZWXIz8+3uRDR+cVeJvJ8ddHHAHuZqKHxmEzk/lyeRFssFkyZMgW9evXChRdeCADIzMyEv78/IiIibG4bFxeHzMzMGteTnp6O8PBw6yUxsfrvTIio/rCXiTxfXfUxwF4makg8JhN5Bpcn0RMnTsTOnTuxaNGiWg1g+vTpyMvLs16OHDlSq/URkXPYy0Ser676GGAvEzUkHpOJPINTv4muMmnSJHzxxRdYv349mjdvbr0+Pj4e5eXlyM3NtflrWVZWFuLj42tcl8lkgqm+zj5JRCL2MpHnq8s+BtjLRA2Fx2Qiz+HUJFophcmTJ2Pp0qVYu3YtkpOTbeqpqanw8/PD6tWrMWLECADA3r17cfjwYfTo0aPuRu0llNmirUmntNcvdVa5Rb+wrxA1I8X02CONySKcSl+KxSkW7gcAqGz9X1UNIRHiso2dN/eyEl5TBjtRS5IFx/QxKyFG/Xqltqq08xr3JNK+xd6uJdyoj0hauPGQtqYPubOvvl4n59P57mNVWQFVWV7t+lNn9DFCB259RlznVXf00dbMmae0tcozRdpa8Yk8cZtlpfowmlPZ+oirskr9ke5UhRzJWCb0ekapftn9Qm1MTJi4zQ6tIrS1U6f091OKLIuKDRK3mXOyWKxrxyO8hjp2lSOcAlNcjHgKD9eWIu8bLy5qHHp7tetM+fnA+y1cGoo3H5PdTUU9HXel9961OaKI76GF8KxyYTmLxd5Mghzh1CR64sSJWLhwIT799FOEhoZaf4cRHh6OwMBAhIeH49Zbb8XUqVMRGRmJsLAwTJ48GT169OCZA4ncCHuZyPOxj4m8A3uZyPM4NYmeO3cuAKBPnz4218+fPx/jxo0DALz88svw8fHBiBEjbMLgich9sJeJPB/7mMg7sJeJPI/TX+e2JyAgAHPmzMGcOXNcHhQR1S/2MpHnYx8TeQf2MpHnqVVONBEREREREVFjwkk0ERERERERkYM4iSYiIiIiIiJykEs50fQnZa4U6waj/iEOSI7WL/h9hrZk7+z8rsZYSSe8l6Ko7I1JWq+/UPO1lwkQGGrnBjWTnjPp+SIPoYRXnEEf3WJPxyA/bS2rQh+ZI0XbeEaQ0p+kvYD0F1npMQCACF/90lJMh6oo09YMfnbyUS365wzcD9TI/NoMmAOq77W7XJWiXcZSUj0Sy0aZ/jk0tkzU1irP7NGvUoiwAoDfjuZra72uaKmtmRKaiOsVtRCij4THwBCrj28y9B8hbtIQpI/AijqTpV8uVLifIfJjEJqxS188sk9bStqzU1vzGXKjuE1DjP51goBg/XK+0jsQ8lZ5Qrysv508RilSSlKbUC1pTK7GdZ0W3rOQ4/hJNBEREREREZGDOIkmIiIiIiIichAn0UREREREREQO4iSaiIiIiIiIyEGcRBMRERERERE5iJNoIiIiIiIiIgdxEk1ERERERETkIIZh1pbB9b9DGKP1eYtSLrOUuwzImXLSshY7WdASabxSbrX06EnLAYBByH+UF/S0dF5yipT/66PPiVanT4irPVWh7x4fIfFZerXVJjtS6uWG+OtopXBn7GW+m4R91sFSfa67Zftqbc3YbZC8UeZEO833nnT4hlXPHm6IR0vapr0jQ/O6HIiHMkTo86drw9i+u74o1a6u+7EQ1aRUyFYOsJMTLeUyS+9ZzcJ7ZOnYCQCBwphcTXuWHgNyHD+JJiIiIiIiInIQJ9FEREREREREDuIkmoiIiIiIiMhBnEQTEREREREROYiTaCIiIiIiIiIHcRJNRERERERE5CDmeDQgQ4cLtbUg40ptTYqTAoAisz78RjpVvnR6fruxWkb9suXCeAvN+lqwnagBBIbaGZWGRbg3QgQSeQgXo9pUznGxLr2Om/jq/x5ZLkRJ2Atbk+IrpFeq9AjY62VXSe1q737aixXROinHkomkiCsiIvJK9iKlJK4eNaRN2ntPbxCOoBUuvt+xFyFLjuEn0UREREREREQO4iSaiIiIiIiIyEGcRBMRERERERE5iJNoIiIiIiIiIgdxEk1ERERERETkIE6iiYiIiIiIiBzkVMRVeno6PvnkE+zZsweBgYHo2bMnnn32WbRr1856mz59+mDdunU2y02YMAHz5s2rmxG7HdfPle/T/3ptLcBnplCzc2p6IW6nVIjbKRZqvnY2KUUGSOOtFB6/liY/cZsG/wB5UDo+/NuRV/eyi9ENqrxMrJ8or9TWLEq/K/UXXm72Yib8pNg5IdpCWqu0ztowC+MpsxPDcaKkQlsrESLpVOZR+wPTMXj+fsCr+5ioEWEvnz8XBpm0tZxK/XEeAPyF42ewEPXqIywXYpSPRaFGfaBlgVkfuiVFZ3UQHgNynFPvItatW4eJEydi06ZNWLlyJSoqKjBgwAAUFRXZ3O7222/HiRMnrJfnnnuuTgdNRLXDXibyfOxjIu/AXibyPE59Er18+XKbfy9YsACxsbHYunUrrrzySuv1QUFBiI+Pr5sRElGdYy8TeT72MZF3YC8TeZ5afZ8tLy8PABAZGWlz/fvvv4/o6GhceOGFmD59OoqLi7XrKCsrQ35+vs2FiM4v9jKR56uLPgbYy0QNjcdkIvfn1CfRf2WxWDBlyhT06tULF154ofX6G2+8EUlJSUhISMAvv/yChx56CHv37sUnn3xS43rS09PxxBNPuDoMIqol9jKR56urPgbYy0QNicdkIs/g8iR64sSJ2LlzJ77//nub6++44w7r/3fq1AlNmzZFv379cODAAaSkpFRbz/Tp0zF16lTrv/Pz85GYmOjqsIjISexlIs9XV30MsJeJGhKPyUSewaVJ9KRJk/DFF19g/fr1aN68uXjb7t27AwD2799fY5ObTCaYTDxLHFFDYC8Teb667GOAvUzUUHhMJvIcTk2ilVKYPHkyli5dirVr1yI5OdnuMjt27AAANG3a1KUBuj070S0SQ1Qzbc1HCKk5UCKfgj9UOM1+gVk/XimKysdOrFaRWR9DY1b6Zf8o1d+XfULsDQCMFKsCL4i2qS2v7mUf175gY+zYU6zPuusKbe2XZTu1tfey8rQ1Kb6pMRkRHaqt9R11kbbme9tjrm/U19/1Zd2EV/cxUSPCXj5/pFio34rLxWXDhTiq/aX696xnKvXH+nyhBsjxsykB+vc7ecL78gBGvdYJp95tTpw4EQsXLsSnn36K0NBQZGZmAgDCw8MRGBiIAwcOYOHChRg0aBCioqLwyy+/4L777sOVV16Jzp0718sdICLnsZeJPB/7mMg7sJeJPI9Tk+i5c+cCOBv4/lfz58/HuHHj4O/vj1WrVmHmzJkoKipCYmIiRowYgX/96191NmAiqj32MpHnYx8TeQf2MpHncfrr3JLExESsW7euVgMiovrHXibyfOxjIu/AXibyPPxSPBEREREREZGDOIkmIiIiIiIichAn0UREREREREQO4iSaiIiIiIiIyEGuBarSn3yM9bLaB+7qra39/tnP4rKlpfoMvBOl+gy8QiE7z2iQc6LDffWPQ5S/n7Z2U1ygthY3Y5K4TVcZ7NwX8mwGIf9QOnmLvdeF3wsfaGupL+iXSxXX6jpVXqIvFhdIS8or9g/Q14QMbkNAsLze88zeiXqk1wkREXmnEcd+19au/e9j4rKGC4Q4sdzT2tLqiS9pay2jgsRtJvdtox9PiHDcjYjQlnynvihukxzDdxFEREREREREDuIkmoiIiIiIiMhBnEQTEREREREROYiTaCIiIiIiIiIHcRJNRERERERE5CC3Ozt31RlV8wuks8u6D7tngHXxTNCVZRXaWqHFIi5bKtSLlWs1I+T74WvR103CeAqEM4IHFgtnHwZgzM8X696uqkfsvQYbijv2cm3Ozu1uVHmpvlhSKC0pr9hfv++BQTg7d7m+lxtCfe2b65q79zHgnr1M5G7cvZfZx/ZVlpaJdUORlIqhPyYXCe+DC83ye/r88kr9eIS5AoQ0Ht9G/v5Z4kwfG5SbdfvRo0eRmJjY0MMg8hhHjhxB8+bNG3oY1bCXiRznrn0MsJeJnOGuvcw+JnKcI33sdpNoi8WC48ePIzQ0FAaDAfn5+UhMTMSRI0cQFhbW0MNzO3x8ZN78+CilUFBQgISEBPi4YeYte9k5fHxk3vr4uHsfA7a9XFBQ4JXPQ13x1tdpXfLWx8jde5nHZOfw8ZF56+PjTB+73de5fXx8apz5h4WFedWTVNf4+Mi89fEJDw9v6CFosZddw8dH5o2Pjzv3MWDby1Vfg/fG56Eu8fGxzxsfI3fuZR6TXcPHR+aNj4+jfex+fyojIiIiIiIiclOcRBMRERERERE5yO0n0SaTCY899hhMJlNDD8Ut8fGR8fFxH3wuZHx8ZHx83AOfBxkfH/v4GLkHPg8yPj4yPj5ueGIxIiIiIiIiInfl9p9EExEREREREbkLTqKJiIiIiIiIHMRJNBEREREREZGDOIkmIiIiIiIicpBbT6LnzJmDli1bIiAgAN27d8ePP/7Y0ENqMOvXr8fQoUORkJAAg8GAZcuW2dSVUpgxYwaaNm2KwMBA9O/fH/v27WuYwTaA9PR0XHrppQgNDUVsbCyGDRuGvXv32tymtLQUEydORFRUFEJCQjBixAhkZWU10IgbF/byn9jLeuxj98dePot9LGMvuzf28Z/Yy3rsY5nbTqI//PBDTJ06FY899hi2bduGLl26IC0tDSdPnmzooTWIoqIidOnSBXPmzKmx/txzz+HVV1/FvHnzsHnzZgQHByMtLQ2lpaXneaQNY926dZg4cSI2bdqElStXoqKiAgMGDEBRUZH1Nvfddx8+//xzLF68GOvWrcPx48cxfPjwBhx148BetsVe1mMfuzf28p/YxzL2svtiH9tiL+uxj+1Qbqpbt25q4sSJ1n+bzWaVkJCg0tPTG3BU7gGAWrp0qfXfFotFxcfHq+eff956XW5urjKZTOqDDz5ogBE2vJMnTyoAat26dUqps4+Hn5+fWrx4sfU2u3fvVgDUxo0bG2qYjQJ7WY+9LGMfuxf2cs3Yx/axl90H+1iPvSxjH9tyy0+iy8vLsXXrVvTv3996nY+PD/r374+NGzc24Mjc08GDB5GZmWnzeIWHh6N79+6N9vHKy8sDAERGRgIAtm7dioqKCpvHqH379mjRokWjfYzOB/ayc9jLttjH7oO97Dj2cXXsZffAPnYOe9kW+9iWW06is7OzYTabERcXZ3N9XFwcMjMzG2hU7qvqMeHjdZbFYsGUKVPQq1cvXHjhhQDOPkb+/v6IiIiwuW1jfYzOF/ayc9jLf2Ifuxf2suPYx7bYy+6Dfewc9vKf2MfV+Tb0AIjq2sSJE7Fz5058//33DT0UInIR+5jIO7CXiTwf+7g6t/wkOjo6GkajsdrZ3bKyshAfH99Ao3JfVY8JHy9g0qRJ+OKLL7BmzRo0b97cen18fDzKy8uRm5trc/vG+BidT+xl57CXz2Ifux/2suPYx39iL7sX9rFz2MtnsY9r5paTaH9/f6SmpmL16tXW6ywWC1avXo0ePXo04MjcU3JyMuLj420er/z8fGzevLnRPF5KKUyaNAlLly7Ft99+i+TkZJt6amoq/Pz8bB6jvXv34vDhw43mMWoI7GXnNPZeZh+7L/ay4xp7HwPsZXfFPnZOY+9l9rEdDXteM71FixYpk8mkFixYoH777Td1xx13qIiICJWZmdnQQ2sQBQUFavv27Wr79u0KgHrppZfU9u3b1aFDh5RSSv3nP/9RERER6tNPP1W//PKLuvbaa1VycrIqKSlp4JGfH3fddZcKDw9Xa9euVSdOnLBeiouLrbe58847VYsWLdS3336rtmzZonr06KF69OjRgKNuHNjLttjLeuxj98Ze/hP7WMZedl/sY1vsZT32scxtJ9FKKTVr1izVokUL5e/vr7p166Y2bdrU0ENqMGvWrFEAql3Gjh2rlDp7Gv5HH31UxcXFKZPJpPr166f27t3bsIM+j2p6bACo+fPnW29TUlKi7r77btWkSRMVFBSkrrvuOnXixImGG3Qjwl7+E3tZj33s/tjLZ7GPZexl98Y+/hN7WY99LDMopVTdf75NRERERERE5H3c8jfRRERERERERO6Ik2giIiIiIiIiB3ESTUREREREROQgTqKJiIiIiIiIHMRJNBEREREREZGDOIkmIiIiIiIichAn0UREREREREQO4iSaiIiIiIiIyEGcRBMRERERERE5iJNoIiIiIiIiIgdxEk1ERERERETkIE6iiYiIiIiIiBzESTQRERERERGRgziJJiIiIiIiInIQJ9FuJiMjAwaDAQsWLKjT9fbp0wd9+vSp03XW1tq1a2EwGLB27dqGHgpRrbF3z882lyxZYve248aNQ8uWLet/UOS22I/ea8GCBTAYDNiyZUtDD4XOE/aze6hpbI35eMtJdA2qdtBVF19fXzRr1gzjxo3DsWPHGnp4NrKysjBt2jS0b98eQUFBCA4ORmpqKp5++mnk5uY29PDOm3HjxlV7zhITEzF69Gj89ttvDT08Ok/Yu57p888/R+/evREbG4ugoCC0atUKo0aNwvLly+t928ePH8fjjz+OHTt21Pu2Ghv2o2dqyH4k98V+9jznvjcOCwtDly5d8OKLL6KsrKyhh+fxfBt6AO7sySefRHJyMkpLS7Fp0yYsWLAA33//PXbu3ImAgICGHh5++uknDBo0CIWFhbj55puRmpoKANiyZQv+85//YP369fjmm28aeJR6V155JUpKSuDv718n6zOZTHjzzTcBAJWVlThw4ADmzZuH5cuX47fffkNCQkKdbIfcH3u3ftVl777wwgt44IEH0Lt3b0yfPh1BQUHYv38/Vq1ahUWLFuGaa65xep1vvPEGLBaLQ7c9fvw4nnjiCbRs2RJdu3Z1eltkH/uxfrl7P5J3YT/Xr/p8b5ybm4uPP/4Y06ZNw08//YRFixbVyTYaK06iBQMHDsQll1wCALjtttsQHR2NZ599Fp999hlGjRrVoGPLzc3FddddB6PRiO3bt6N9+/Y29WeeeQZvvPFGA43OMT4+PnW6w/X19cXNN99sc91ll12GIUOG4Msvv8Ttt99eZ9si98berV911buVlZV46qmncPXVV9f4pubkyZMurdfPz8+hbTs60abaYT/WL3fvR09jsVhQXl7uFhNCd8R+rl/1/d747rvvRvfu3fHhhx/ipZde8uoPmJRSKC0tRWBgYL2sn1/ndsIVV1wBADhw4IDN9Xv27MHIkSMRGRmJgIAAXHLJJfjss89sbnP69GlMmzYNnTp1QkhICMLCwjBw4ED8/PPPLo3l9ddfx7Fjx/DSSy9V20kAQFxcHP71r39ply8vL8eMGTOQmpqK8PBwBAcH44orrsCaNWuq3XbRokVITU1FaGgowsLC0KlTJ7zyyivWekVFBZ544gm0adMGAQEBiIqKwuWXX46VK1eK96Gm31bs27cPI0aMQHx8PAICAtC8eXOMHj0aeXl5Djwq1cXHxwM4uxOp4sxzcejQIfztb39DcHAwYmNjcd9992HFihVu+3sVqhl71z17Nzs7G/n5+ejVq1eN9djY2GrXWSwWPPPMM2jevDkCAgLQr18/7N+/3+Y25/5Gq+r3dC+88AJmzpyJlJQUmEwmvPbaa7j00ksBAOPHj7d+5a2uf3dHttiPnt+PVdv86KOP7PYjAGzevBnXXHMNwsPDERQUhN69e2PDhg02tzl06BDuvvtutGvXDoGBgYiKisL111+PjIwM8f4DwJkzZ9CtWzc0b94ce/fuBQCUlZXhscceQ+vWrWEymZCYmIgHH3yw2tdYDQYDJk2ahPfffx8dO3aEyWTiV9edwH52z37W8fHxsf4OvKq3DAYDHn/88Wq3bdmyJcaNG+f0NoqKinD//fcjMTERJpMJ7dq1wwsvvACllPU2F154Ifr27VttWYvFgmbNmmHkyJE2182cORMdO3ZEQEAA4uLiMGHCBJw5c6baeIcMGYIVK1bgkksuQWBgIF5//XWnx+8ofhLthKoXW5MmTazX7dq1C7169UKzZs3w8MMPIzg4GB999BGGDRuGjz/+GNdddx0A4I8//sCyZctw/fXXIzk5GVlZWXj99dfRu3dvl75q/NlnnyEwMNDmReaM/Px8vPnmmxgzZgxuv/12FBQU4K233kJaWhp+/PFH69caV65ciTFjxqBfv3549tlnAQC7d+/Ghg0bcO+99wIAHn/8caSnp+O2225Dt27dkJ+fjy1btmDbtm24+uqrHR5TeXk50tLSUFZWhsmTJyM+Ph7Hjh3DF198gdzcXISHh9tdR3Z2NgDAbDbjjz/+wEMPPYSoqCgMGTLEehtHn4uioiJcddVVOHHiBO69917Ex8dj4cKFNe5Myb2xd92zd2NjYxEYGIjPP/8ckydPRmRkpN1t/ec//4GPjw+mTZuGvLw8PPfcc7jpppuwefNmu8vOnz8fpaWluOOOO2AymXDdddehoKAAM2bMwB133GF9M9izZ0+H7zs5j/3YuPrx22+/xcCBA5GamorHHnsMPj4+mD9/Pq666ip899136NatG4CzX8P94YcfMHr0aDRv3hwZGRmYO3cu+vTpg99++w1BQUE1jiE7OxtXX301Tp8+jXXr1iElJQUWiwV/+9vf8P333+OOO+7ABRdcgF9//RUvv/wyfv/9dyxbtsxmHd9++y0++ugjTJo0CdHR0Y32REmuYD+7Zz9Lqv7gERUV5fSy9iil8Le//Q1r1qzBrbfeiq5du2LFihV44IEHcOzYMbz88ssAgBtuuAGPP/44MjMzrR94AcD333+P48ePY/To0dbrJkyYgAULFmD8+PG45557cPDgQcyePRvbt2/Hhg0bbL59tnfvXowZMwYTJkzA7bffjnbt2tX5ffzrnaVzzJ8/XwFQq1atUqdOnVJHjhxRS5YsUTExMcpkMqkjR45Yb9uvXz/VqVMnVVpaar3OYrGonj17qjZt2livKy0tVWaz2WY7Bw8eVCaTST355JM21wFQ8+fPF8fYpEkT1aVLF4fvU+/evVXv3r2t/66srFRlZWU2tzlz5oyKi4tTt9xyi/W6e++9V4WFhanKykrturt06aIGDx7s8FiqrFmzRgFQa9asUUoptX37dgVALV682Ol1jR07VgGodmnWrJnaunWrzW0dfS5efPFFBUAtW7bMel1JSYlq3769zbjJfbB3Pa93Z8yYoQCo4OBgNXDgQPXMM89U69m/bvOCCy6wuf+vvPKKAqB+/fVX63Vjx45VSUlJ1n9XPTdhYWHq5MmTNuv96aefHHreyHnsR/ajxWJRbdq0UWlpacpisVhvV1xcrJKTk9XVV19tc925Nm7cqACod955x3pd1evqp59+UidOnFAdO3ZUrVq1UhkZGdbbvPvuu8rHx0d99913NuubN2+eAqA2bNhgvQ6A8vHxUbt27XLmoWp02M+e189jx45VwcHB6tSpU+rUqVNq//796t///rcyGAyqc+fO1tsBUI899li15ZOSktTYsWO1Y6vaxl+Pt8uWLVMA1NNPP22zrpEjRyqDwaD279+vlFJq7969CoCaNWuWze3uvvtuFRISYt0ffPfddwqAev/9921ut3z58mrXJyUlKQBq+fLlDj0+tcWvcwv69++PmJgYJCYmYuTIkQgODsZnn32G5s2bAzj7NZRvv/0Wo0aNQkFBAbKzs5GdnY2cnBykpaVh37591jMWmkwm+PicfbjNZjNycnIQEhKCdu3aYdu2bU6PLT8/H6GhoS7fN6PRaD1pgcViwenTp1FZWYlLLrnEZjwREREoKioSv34SERGBXbt2Yd++fS6PB4D1r2krVqxAcXGx08sHBARg5cqVWLlyJVasWIHXX38dISEhGDRoEH7//Xfr7Rx9LpYvX45mzZrhb3/7m802+Ntq98fe9ZzefeKJJ7Bw4UJcdNFFWLFiBR555BGkpqbi4osvxu7du6vdfvz48TYnXKn69PiPP/6wu60RI0YgJibGqfFR7bEfG28/7tixA/v27cONN96InJwc63NbVFSEfv36Yf369dZzE/z1d4sVFRXIyclB69atERERUeNze/ToUfTu3RsVFRVYv349kpKSrLXFixfjggsuQPv27a3bzM7OxlVXXQUA1b5R1rt3b3To0MGpx6qxYj97Tj8DZ79VGRMTg5iYGLRu3Rr//Oc/0aNHDyxdurRW49L56quvYDQacc8999hcf//990Mpha+//hoA0LZtW3Tt2hUffvih9TZmsxlLlizB0KFDrfuDxYsXIzw8HFdffbVNL6empiIkJKRaLycnJyMtLa1e7tu5OIkWzJkzBytXrsSSJUswaNAgZGdnw2QyWev79++HUgqPPvqo9QVadXnssccA/HkiDovFgpdffhlt2rSByWRCdHQ0YmJi8Msvv7j0m4awsDAUFBTU6v69/fbb6Ny5s/W3GjExMfjyyy9txnP33Xejbdu2GDhwIJo3b45bbrml2m+FnnzySeTm5qJt27bo1KkTHnjgAfzyyy9Ojyc5ORlTp07Fm2++iejoaKSlpWHOnDkOPz5GoxH9+/dH//79MWDAANxxxx1YtWoV8vLyMH36dOvtHH0uDh06hJSUFBgMBpvttG7d2un7RucXe9ezenfMmDH47rvvcObMGXzzzTe48cYbsX37dgwdOhSlpaU2t23RooXNv6u+Qnjub6N046Tzj/3YePuxagIxduzYas/tm2++ibKyMuu4SkpKMGPGDOvvKKue29zc3BrH/ve//x0nT57EunXr0KxZM5vavn37sGvXrmrbbNu2LYDqJ0njvsFx7GfP6ue/fsC0fv16HDlyBBs2bECrVq2cHosjDh06hISEhGp/zLjgggus9So33HADNmzYYP2jytq1a3Hy5EnccMMN1tvs27cPeXl5iI2NrfZ6KiwsbNBe5m+iBd26dbOegXDYsGG4/PLLceONN2Lv3r0ICQmx/vV02rRp2r96VE24/v3vf+PRRx/FLbfcgqeeegqRkZHw8fHBlClTXDpDbPv27bFjxw6Ul5e7dBr89957D+PGjcOwYcPwwAMPIDY2FkajEenp6TYnh4iNjcWOHTuwYsUKfP311/j6668xf/58/OMf/8Dbb78N4Ozp+A8cOIBPP/0U33zzDd588028/PLLmDdvHm677TanxvXiiy9i3Lhx1nXdc889SE9Px6ZNm6x/5XRG8+bN0a5dO6xfv956XV0/F+R+2Lue2bthYWG4+uqrcfXVV8PPzw9vv/02Nm/ejN69e1tvYzQaa1xW/eWEJTr1dYZOkrEfG28/Vj0nzz//vDZCLiQkBAAwefJkzJ8/H1OmTEGPHj0QHh4Og8GA0aNH1/jcDh8+HO+88w5eeeUVpKen29QsFgs6deqEl156qcZtJiYm2vyb+wbHsZ89q5+rPmByhdlsdmk5R91www2YPn06Fi9ejClTpuCjjz5CeHi4TZSexWJBbGws3n///RrXce63y85rL5+XL417mL/+3uavqn4LkJ6erpRSKisrSwFQ06dPt7vOLl26qL59+1a7vlmzZja/x3D0dx///ve/FQC1cOFC+3dIVf/dx7XXXqtatWpl8xslpZTq2bOnzW8bzmU2m9WECRMUALVv374ab1NQUKAuuugi1axZM3FMNf224lwbNmxQANQjjzwirqvqdx816dixowoICLD+29Hn4uqrr1bNmjWr9hhV/Vaav4l2P+zdJO163LV3dWbNmqUAqA8++MBmm+f+Lqymx133m+jnn3++2na2bNnC30TXE/ZjknY9jaUff/zxRwVAvf7663a3ER4ersaPH29zXUlJiTIajTa/y/zr6+rJJ5+0eS1VGTRoUI3H75oAUBMnTrR7u8aO/ZykXY+79rP03vivmjRpou69916b68rKyqr1niO/ib7jjjuU0WhU+fn5NuvbtGlTjb+B7tatm7rssstURUWFio6OttmeUmd/I200Gms8Z8K5kpKSXPoduqv4dW4n9OnTB926dcPMmTNRWlqK2NhY9OnTB6+//jpOnDhR7fanTp2y/r/RaKz2ScnixYutX2Fw1p133ommTZvi/vvvt/m9b5WTJ0/i6aef1i5f9dfjv45p8+bN2Lhxo83tcnJybP7t4+ODzp07A4A1JuLc24SEhKB169bVYiTsyc/PR2Vlpc11nTp1go+Pj9PrqvL7779j79696NKli/U6R5+LtLQ0HDt2zCaSobS01O0zBqk69q579m5xcXG1cVep+t1UvZ5ZE0BwcDCAs/midH6wHxtPP6ampiIlJQUvvPACCgsLq9XtPbezZs0SPw179NFHMW3aNEyfPh1z5861Xj9q1CgcO3asxuN1SUkJioqKnLofpMd+ds9+dkZKSorNNzYB4L///a9Ln0QPGjQIZrMZs2fPtrn+5ZdfhsFgwMCBA22uv+GGG7Bp0yb873//Q3Z2ts1XuYGzvWw2m/HUU09V21ZlZWWDHrv5dW4nPfDAA7j++uuxYMEC3HnnnZgzZw4uv/xydOrUCbfffjtatWqFrKwsbNy4EUePHrVm3Q0ZMgRPPvkkxo8fj549e+LXX3/F+++/7/JvEpo0aYKlS5di0KBB6Nq1K26++WakpqYCALZt24YPPvgAPXr00C4/ZMgQfPLJJ7juuuswePBgHDx4EPPmzUOHDh1sDnS33XYbTp8+jauuugrNmzfHoUOHMGvWLHTt2tX6+4YOHTqgT58+SE1NRWRkJLZs2YIlS5Zg0qRJTt2nb7/9FpMmTcL111+Ptm3borKyEu+++y6MRiNGjBhhd/nKykq89957AM5+/SMjIwPz5s2DxWKx/g6n6r478lxMmDABs2fPxpgxY3DvvfeiadOmeP/99xEQEAAA1X4rTe6Nvet+vVtcXIyePXvisssuwzXXXIPExETk5uZi2bJl+O677zBs2DBcdNFFTo3FWSkpKYiIiMC8efMQGhqK4OBgdO/enb+RrGfsx8bRjz4+PnjzzTcxcOBAdOzYEePHj0ezZs1w7NgxrFmzBmFhYfj888+tj+W7776L8PBwdOjQARs3bsSqVavsxvA8//zzyMvLw8SJExEaGoqbb74Zf//73/HRRx/hzjvvxJo1a9CrVy+YzWbs2bMHH330kTVHluoG+9n9+tkZt912G+68806MGDECV199NX7++WesWLEC0dHRTq9r6NCh6Nu3Lx555BFkZGSgS5cu+Oabb/Dpp59iypQpSElJsbn9qFGjMG3aNEybNg2RkZHVvnreu3dvTJgwAenp6dixYwcGDBgAPz8/7Nu3D4sXL8Yrr7zicqRZrZ23z7w9iO4rK0qd/cpGSkqKSklJsZ7a/sCBA+of//iHio+PV35+fqpZs2ZqyJAhasmSJdblSktL1f3336+aNm2qAgMDVa9evdTGjRurfZXE0a+sVDl+/Li67777VNu2bVVAQIAKCgpSqamp6plnnlF5eXnW2527HYvFov7973+rpKQkZTKZ1EUXXaS++OKLal/LWLJkiRowYICKjY1V/v7+qkWLFmrChAnqxIkT1ts8/fTTqlu3bioiIkIFBgaq9u3bq2eeeUaVl5eLYz/3ayF//PGHuuWWW1RKSooKCAhQkZGRqm/fvmrVqlV2H4eaIq7CwsJUv379qi3v6HNRNabBgwerwMBAFRMTo+6//3718ccfKwBq06ZNdsdF5xd7N8l6O0/o3YqKCvXGG2+oYcOGWe9PUFCQuuiii9Tzzz9vEzVSX1/nVkqpTz/9VHXo0EH5+vryq911iP2YZL1dY+5Hpc7G9AwfPlxFRUUpk8mkkpKS1KhRo9Tq1auttzlz5owaP368io6OViEhISotLU3t2bOnWsxOTa8rs9msxowZo3x9fa2xlOXl5erZZ59VHTt2VCaTSTVp0kSlpqaqJ554wuY5Bb/O7RD2c5L1dp7Qz0o5/nVus9msHnroIRUdHa2CgoJUWlqa2r9/v0sRV0qd/er6fffdpxISEpSfn59q06aNev7557U/r+jVq5cCoG677TbtGP/73/+q1NRUFRgYqEJDQ1WnTp3Ugw8+qI4fP269zfn+OrdBKQfOxkJEVjNnzsR9992Ho0ePVjsjKBEREREReTdOookEJSUlNmf6Ky0txUUXXQSz2Vzj722IiIiIiMi78TfRRILhw4ejRYsW6Nq1K/Ly8vDee+9hz5492lPtExERERGRd+MkmkiQlpaGN998E++//z7MZjM6dOiARYsWVTt7IBERERERNQ78OjcRERERERGRg5gTTUREREREROQgTqKJiIiIiIiIHMRJ9HnUsmVLjBs3zvrvtWvXwmAwYO3atQ02pnO545gAoE+fPujTp09DD4MaKfau6xqid/v06YMLL7zQ7u0yMjJgMBiwYMGC+h8U2cU+c527HyMXLFgAg8GALVu2iLd7/PHHYTAYztOoyJNxf+E6HpfrRqOZRFftwKsuAQEBaNu2LSZNmoSsrKyGHp5TvvrqKzz++ONOL7d06VIMHDgQ0dHR8Pf3R0JCAkaNGoVvv/227gfppqqa86+XsLAwdO3aFbNnz4bZbG7oIdI52Lvs3SqnTp3Cvffei/bt2yMwMBCxsbHo1q0bHnroIRQWFtb79hcuXIiZM2fW+3YaAvuMfXau3bt3W18Lubm5DT0cl5z7ujYYDIiNjUXfvn3x9ddf1/v2X3vtNY+YDDiL+wvuL6o05uNyozs795NPPonk5GSUlpbi+++/x9y5c/HVV19h586dCAoKOq9jufLKK1FSUgJ/f3+nlvvqq68wZ84ch5teKYVbbrkFCxYswEUXXYSpU6ciPj4eJ06cwNKlS9GvXz9s2LABPXv2dOFenB/ffPNNna5vzJgxGDRoEAAgLy8PX331FSZPnoxDhw7h+eefr9NtUd1g7zbu3j19+jQuueQS5Ofn45ZbbkH79u2Rk5ODX375BXPnzsVdd92FkJAQp9aZlJSEkpIS+Pn5OXT7hQsXYufOnZgyZYoL98AzsM8ad5/91XvvvYf4+HicOXMGS5YswW233Vbn2zhfql7XSilkZWVhwYIFGDRoED7//HMMGTKk3rb72muvITo62uYTU2/C/UXj3l809uNyo5tEDxw4EJdccgkA4LbbbkNUVBReeuklfPrppxgzZkyNyxQVFSE4OLjOx+Lj44OAgIA6X++5XnzxRSxYsABTpkzBSy+9ZPNVqUceeQTvvvsufH3d+6Xg7E7Rnosvvhg333yz9d933303unfvjoULF3IS7abYu427d9966y0cPny4xjcn+fn5Lm2n6hMUe+rrdeSO2GeNu8+qKKWwcOFC3HjjjTh48CDef/99j55E//V1DQC33nor4uLi8MEHH9TrJNrbcX/RuPcXjf243Gi+zq1z1VVXAQAOHjwIABg3bhxCQkJw4MABDBo0CKGhobjpppsAABaLBTNnzkTHjh0REBCAuLg4TJgwAWfOnLFZp1IKTz/9NJo3b46goCD07dsXu3btqrZt3W8lNm/ejEGDBqFJkyYIDg5G586d8corr1jHN2fOHACw+SqNTklJCdLT09G+fXu88MILNd7273//O7p166Zdx3fffYfrr78eLVq0gMlkQmJiIu677z6UlJTY3C4zMxPjx49H8+bNYTKZ0LRpU1x77bXIyMiw3mbLli1IS0tDdHQ0AgMDkZycjFtuuUW77So1/X5j1qxZ6NixI4KCgtCkSRNccsklWLhwod111cRgMCAuLq7aju/TTz/F4MGDkZCQAJPJhJSUFDz11FM1fu17zpw5aNWqFQIDA9GtWzd89913bv87NU/G3m1cvXvgwAEYjUZcdtll1WphYWE1HnR/++039O3bF0FBQWjWrBmee+45m3pNv73SvY769OmDL7/8EocOHbI+dy1btrR7/z0d+6xx9VmVDRs2ICMjA6NHj8bo0aOxfv16HD16tNrtWrZsiSFDhuD7779Ht27dEBAQgFatWuGdd96xu40zZ86gW7duaN68Ofbu3Sve9r333kNqaioCAwMRGRmJ0aNH48iRIw7dl5pEREQgMDCw2jG/qKgI999/PxITE2EymdCuXTu88MILODcNtrKyEk899RRSUlJgMpnQsmVL/POf/0RZWZn1Ni1btsSuXbuwbt066+vQ298PcH/RuPYXjf247N5/KjkPDhw4AACIioqyXldZWYm0tDRcfvnleOGFF6xfSZkwYQIWLFiA8ePH45577sHBgwcxe/ZsbN++HRs2bLB+9WDGjBl4+umnMWjQIAwaNAjbtm3DgAEDUF5ebnc8K1euxJAhQ9C0aVPce++9iI+Px+7du/HFF1/g3nvvxYQJE3D8+HGsXLkS7777rt31ff/99zh9+jSmTJkCo9HoykOExYsXo7i4GHfddReioqLw448/YtasWTh69CgWL15svd2IESOwa9cuTJ48GS1btsTJkyexcuVKHD582PrvAQMGICYmBg8//DAiIiKQkZGBTz75xOkxvfHGG7jnnnswcuRI3HvvvSgtLcUvv/yCzZs348Ybb7S7fHFxMbKzswGc/WvZ119/jeXLl2P69Ok2t1uwYAFCQkIwdepUhISE4Ntvv8WMGTOQn59v84n13LlzMWnSJFxxxRW47777kJGRgWHDhqFJkyZo3ry50/eP7GPv2udNvZuUlASz2Yx3330XY8eOtbudM2fO4JprrsHw4cMxatQoLFmyBA899BA6deqEgQMHisvW9DqKj49HXl4ejh49ipdffhkAnP6amidin9nnTX1W5f3330dKSgouvfRSXHjhhQgKCsIHH3yABx54oNpt9+/fj5EjR+LWW2/F2LFj8b///Q/jxo1DamoqOnbsWOP6s7OzcfXVV+P06dNYt24dUlJStGN55pln8Oijj2LUqFG47bbbcOrUKcyaNQtXXnkltm/fjoiICLv3Jy8vD9nZ2VBK4eTJk5g1axYKCwttvpGmlMLf/vY3rFmzBrfeeiu6du2KFStW4IEHHsCxY8esfQ+c/dT17bffxsiRI3H//fdj8+bNSE9Px+7du7F06VIAwMyZMzF58mSEhITgkUceAQDExcXZHasn4/7CPm/aXzT647JqJObPn68AqFWrVqlTp06pI0eOqEWLFqmoqCgVGBiojh49qpRSauzYsQqAevjhh22W/+677xQA9f7779tcv3z5cpvrT548qfz9/dXgwYOVxWKx3u6f//ynAqDGjh1rvW7NmjUKgFqzZo1SSqnKykqVnJyskpKS1JkzZ2y289d1TZw4UTn61L3yyisKgFq6dKlDtz93TEopVVxcXO126enpymAwqEOHDimllDpz5owCoJ5//nntupcuXaoAqJ9++smhsfxV7969Ve/eva3/vvbaa1XHjh2dXs/BgwcVgBovd911l83jrFTN933ChAkqKChIlZaWKqWUKisrU1FRUerSSy9VFRUV1tstWLBAAbAZNzmPvbvUodt7e+9mZmaqmJgYBUC1b99e3XnnnWrhwoUqNze3xm0CUO+88471urKyMhUfH69GjBhhva5qfzB//nzrdbrXkVJKDR48WCUlJTk9dk/APlvq0O29vc+UUqq8vFxFRUWpRx55xHrdjTfeqLp06VLttklJSQqAWr9+vfW6kydPKpPJpO6//37rdVWvr59++kmdOHFCdezYUbVq1UplZGTYrO+xxx6zee4yMjKU0WhUzzzzjM3tfv31V+Xr61vt+nNVbffci8lkUgsWLLC57bJlyxQA9fTTT9tcP3LkSGUwGNT+/fuVUkrt2LFDAVC33Xabze2mTZumAKhvv/3Wel3Hjh298j0A9xdLHbq9t+8vGvtxudF9nbt///6IiYlBYmIiRo8ejZCQECxduhTNmjWzud1dd91l8+/FixcjPDwcV199NbKzs62X1NRUhISEYM2aNQCAVatWoby8HJMnT7b5mocjP3jfvn07Dh48iClTplT7y6qrkQ/5+fkAgNDQUJeWB4DAwEDr/xcVFSE7Oxs9e/aEUgrbt2+33sbf3x9r166t9lWcKlX36YsvvkBFRYXL46la19GjR/HTTz+5tPwdd9yBlStXYuXKlfj4448xceJEvP7665g6darN7f563wsKCpCdnY0rrrgCxcXF2LNnD4CzX6fJycnB7bffbvPVsJtuuglNmjRxaXxUHXvXed7Uu3Fxcfj5559x55134syZM5g3bx5uvPFGxMbG4qmnnqr2dcuQkBCbT5n8/f3RrVs3/PHHHw5t79zXUWPBPnOeN/UZAHz99dfIycmx+U3rmDFj8PPPP9f4NdoOHTrgiiuusP47JiYG7dq1q7HXjh49it69e6OiogLr169HUlKSOJZPPvkEFosFo0aNsnldxcfHo02bNtbXlT1z5syxHvPfe+899O3bF7fddpvNp3ZfffUVjEYj7rnnHptl77//fiilrGfz/uqrrwCg2vuF+++/HwDw5ZdfOjQmb8D9hfO8aX/R2I/LjW4SXbUjXbNmDX777Tf88ccfSEtLs7mNr69vta/g7tu3D3l5eYiNjUVMTIzNpbCwECdPngQAHDp0CADQpk0bm+VjYmLsTqiqvgbjSI6ao8LCwgCcnQC66vDhwxg3bhwiIyMREhKCmJgY9O7dG8DZr0gBgMlkwrPPPouvv/4acXFxuPLKK/Hcc88hMzPTup7evXtjxIgReOKJJxAdHY1rr70W8+fPt/kNkaMeeughhISEoFu3bmjTpg0mTpyIDRs2OLx8mzZt0L9/f/Tv3x/Dhw/H7Nmzcffdd2PmzJn49ddfrbfbtWsXrrvuOoSHhyMsLAwxMTHWHUDVfa96zlu3bm2zDV9f30bxm8nzhb3rPG/r3aZNm2Lu3Lk4ceIE9u7di1dffRUxMTGYMWMG3nrrLZvbNm/evNobpSZNmmjfkPxVTa+jxoJ95jxv67P33nsPycnJMJlM2L9/P/bv34+UlBQEBQXh/fffr3b7Fi1aVLtO12t///vfcfLkSaxbt67aRKsm+/btg1IKbdq0qfa62r17t/V1ZU+3bt2sx/ybbroJX375JTp06IBJkyZZvxZ86NAhJCQkVJsgXXDBBdZ61X99fHyqHfPj4+MRERFhvV1jwP2F87xtf9GYj8uN7jfR3bp1szlDY01MJhN8fGz/vmCxWBAbG1vjAQQ429DuqH379gCAX3/9FcOGDXN6ebPZbP3d0kMPPYT27dsjODgYx44dw7hx42CxWKy3nTJlCoYOHYply5ZhxYoVePTRR5Geno5vv/0WF110EQwGA5YsWYJNmzbh888/x4oVK3DLLbfgxRdfxKZNm5z6HcMFF1yAvXv34osvvsDy5cvx8ccf47XXXsOMGTPwxBNPOH0/AaBfv36YPXs21q9fj06dOiE3Nxe9e/dGWFgYnnzySaSkpCAgIADbtm3DQw89ZHPfqf6xd53jzb1rMBjQtm1btG3bFoMHD0abNm2qnT1Y93u1c/8yXpOaXkeNBfvMOd7WZ/n5+fj8889RWlpabeICnI2TeeaZZ2zeCDvTa8OHD8c777yDV155Benp6Xbvh8VigcFgwNdff13jdlz9/aOPjw/69u2LV155Bfv27dP+dlvi6qeZ3oT7C+d42/7irxrjcbnRTaJdlZKSglWrVqFXr142X8U4V9VXk/bt24dWrVpZrz916pTdv7RUnVhj586d6N+/v/Z2zuy4L7/8cjRp0gQffPAB/vnPfzp9IoRff/0Vv//+O95++2384x//sF6/cuXKGm+fkpKC+++/H/fffz/27duHrl274sUXX8R7771nvc1ll12Gyy67DM888wwWLlyIm266CYsWLXI6PiM4OBg33HADbrjhBpSXl2P48OF45plnMH36dJdiDiorKwHAGg6/du1a5OTk4JNPPsGVV15pvV3VWSerVD3n+/fvR9++fW3Wl5GRgc6dOzs9Fqo77F3v7t1WrVqhSZMmOHHihFPLuYJvmvXYZ97RZ5988glKS0sxd+5cREdH29T27t2Lf/3rX9iwYQMuv/xyp8ZSZfLkyWjdujVmzJiB8PBwPPzww+LtU1JSoJRCcnIy2rZt69I2dc495iclJWHVqlUoKCiw+TS66qdbVa/dpKQkWCwW7Nu3z/opNQBkZWUhNzfX5ivq3GfUjPsL79hf6DSW47J7Tend2KhRo2A2m/HUU09Vq1VWViI3NxfA2d+H+Pn5YdasWTZ/WZk5c6bdbVx88cVITk7GzJkzreur8td1VeWinXubmgQFBeGhhx7C7t278dBDD9X415733nsPP/74Y43LV+0g/rqcUsoaD1CluLgYpaWlNtelpKQgNDTU+tWSM2fOVNt+165dAcDpr5/k5OTY/Nvf3x8dOnSAUsrl34Z8/vnnAIAuXboAqPm+l5eX47XXXrNZ7pJLLkFUVBTeeOMN60EZOHt2U0e+okL1i73rHb27efNmFBUVVbv+xx9/RE5ODtq1a+fUOFwRHBxs/bod2WKfeUefvffee2jVqhXuvPNOjBw50uYybdo0hISEaD89dNSjjz6KadOmYfr06Zg7d6542+HDh8NoNOKJJ56o9tgopardT0dVVFTgm2++gb+/v3UiPGjQIJjNZsyePdvmti+//DIMBoP17MGDBg0CUP01+9JLLwEABg8ebL0uODjYoddhY8P9hXfsLxr7cZmfRDuod+/emDBhAtLT07Fjxw4MGDAAfn5+2LdvHxYvXoxXXnkFI0eORExMDKZNm4b09HQMGTIEgwYNwvbt2/H1119X+6vuuXx8fDB37lwMHToUXbt2xfjx49G0aVPs2bMHu3btwooVKwAAqampAIB77rkHaWlpMBqNGD16tHa9DzzwAHbt2oUXX3wRa9aswciRIxEfH4/MzEwsW7YMP/74I3744Ycal23fvj1SUlIwbdo0HDt2DGFhYfj444+rTQ5///139OvXD6NGjUKHDh3g6+uLpUuXIisryzq2t99+G6+99hquu+46pKSkoKCgAG+88QbCwsKsByVHDRgwAPHx8ejVqxfi4uKwe/duzJ49G4MHD3bohA/btm2z/iWvoKAAq1evxscff4yePXtiwIABAICePXuiSZMmGDt2LO655x4YDAa8++671XZa/v7+ePzxxzF58mRcddVVGDVqFDIyMrBgwQKkpKTwL9ENjL3rHb377rvv4v3338d1112H1NRU+Pv7Y/fu3fjf//6HgIAA/POf/3RqHK5ITU3Fhx9+iKlTp+LSSy9FSEgIhg4dWu/b9QTsM8/vs+PHj2PNmjXVTqxVxWQyIS0tDYsXL8arr75qjSByxfPPP4+8vDxMnDgRoaGhNicb+quUlBQ8/fTTmD59ujU6MjQ0FAcPHsTSpUtxxx13YNq0aXa39/XXX1s/UT558iQWLlyIffv24eGHH7b+znXo0KHo27cvHnnkEWRkZKBLly745ptv8Omnn2LKlCnWTza7dOmCsWPH4r///a/1Z18//vgj3n77bQwbNszmG2mpqamYO3cunn76abRu3RqxsbHWLOXGjPsLz99fADwuN7qIK3ungh87dqwKDg7W1v/73/+q1NRUFRgYqEJDQ1WnTp3Ugw8+qI4fP269jdlsVk888YRq2rSpCgwMVH369FE7d+5USUlJ4un4q3z//ffq6quvVqGhoSo4OFh17txZzZo1y1qvrKxUkydPVjExMcpgMDh8av4lS5aoAQMGqMjISOXr66uaNm2qbrjhBrV27VpxTL/99pvq37+/CgkJUdHR0er2229XP//8s80p6LOzs9XEiRNV+/btVXBwsAoPD1fdu3dXH330kXU927ZtU2PGjFEtWrRQJpNJxcbGqiFDhqgtW7bYHfu5p+N//fXX1ZVXXqmioqKUyWRSKSkp6oEHHlB5eXniemqKuPL19VWtWrVSDzzwgCooKLC5/YYNG9Rll12mAgMDVUJCgnrwwQfVihUranzeXn31VZWUlKRMJpPq1q2b2rBhg0pNTVXXXHON3ftHeuxd9q5SSv3yyy/qgQceUBdffLHN43D99derbdu2VdtmTXEdY8eOtYnC0EVp6F5HhYWF6sYbb1QREREKgFfFXbHP2GcvvviiAqBWr16tvU1VdOOnn36qlDobcTV48GC746np9WU2m9WYMWOUr6+vWrZsmVKqesRVlY8//lhdfvnlKjg4WAUHB6v27duriRMnqr1792rH+tft/vUSEBCgunbtqubOnVst1rKgoEDdd999KiEhQfn5+ak2bdqo559/vtrtKioq1BNPPKGSk5OVn5+fSkxMVNOnT7dGX1bJzMxUgwcPVqGhoQpeFHnJ/QX3F0rxuGxQyoFfcxOR0ywWC2JiYjB8+HC88cYbDT0cIiIiIiKqA/xNNFEdKC0trfY173feeQenT59Gnz59GmZQRERERERU5/hJNFEdWLt2Le677z5cf/31iIqKwrZt2/DWW2/hggsuwNatW+Hv79/QQyQiIiIiojrAE4sR1YGWLVsiMTERr776Kk6fPo3IyEj84x//wH/+8x9OoImIiIiIvAg/iSYiIiIiIiJyEH8TTUREREREROQgTqKJiIiIiIiIHOR2v4m2WCw4fvw4QkNDYTAYGno4RG5LKYWCggIkJCTAx8f9/h7GXiayz937GGAvEznC3XuZfUxkn1N9XF8B1LNnz1ZJSUnKZDKpbt26qc2bNzu03JEjRxQAXnjhxcHLkSNH6quNXe5j9jIvvDh3qc8+Voq9zAsv5+virr3MPuaFF8cvjvRxvXwS/eGHH2Lq1KmYN28eunfvjpkzZyItLQ179+5FbGysuGxoaCgA4MjvuxD2//9PdUdZLNqaQfiLS+W8x+QV5+drS74Pvmx3XDVRds55Vx9/SW2Ibboqv6AAiW07WnumrtWmj4HG1cvS66Y2rxmVe1JbW91zsLbWtmmIthYWbhK3WVZSoa0dPlakraWueE9b82naWtymROxJ6XF3w0+CalLffQywl4nOB3fvZfZx/ar83zPamqF9J/2CfnYSXP7Yqy0Zb7jX3rDISc70cb2cnbt79+649NJLMXv2bABnv0KSmJiIyZMn4+GHH7a5bVlZGcrKyqz/zs/PR2JiIvJOHEZYWFhdD63Rc3kS/epD8orz8rQl30fn2R1XTTiJluXn5yO8aQvk5eXVS68408dA4+7leptEn8nU1r7p0ldba5+g3/mHR8iT6NJi/SQ642ihttbtu0+0NZ9mbcVtSrx+El3PfQywl4nOB3frZfbx+VU571FtzdCxq35Be5Po/bu1JePND9oZFTnLmT6u83cZ5eXl2Lp1K/r37//nRnx80L9/f2zcuLHa7dPT0xEeHm69JCYm1vWQiMhJzvYxwF4mckfsZSLvwPfXRO6lzifR2dnZMJvNiIuLs7k+Li4OmZnVP1WZPn068vLyrJcjR47U9ZCIyEnO9jHAXiZyR+xlIu/A99dE7qXBz85tMplgMslfLyQi98deJvIO7GUiz8c+JqpfdT6Jjo6OhtFoRFZWls31WVlZiI+Pr+vNUQ2UxaytGXyM2prl4C/amvG2f4rbtOyq+WuBAGBet0S/3t4j9StV+t9vAwAM+vviKnf6zXNDcvc+ro/fIEt9A8i9U1+vm4JxN+q3KSz3w+Ez2tqZP+S+CvDRrznaV/8YmF+coa35vLRI3KZEfGyFmjJXyitugOezIbh7LxORY9jLDc+86XNt7bVH39fWxvfZpK35hgaK23z3y13a2q2xTbU144C/i+ul2qvzr3P7+/sjNTUVq1evtl5nsViwevVq9OjRo643R0T1gH1M5B3Yy0Tegb1M5F7q5evcU6dOxdixY3HJJZegW7dumDlzJoqKijB+/Pj62BwR1QP2MZF3YC8TeQf2MpH7qJdJ9A033IBTp05hxowZyMzMRNeuXbF8+fJqJ0MgIvfFPibyDuxlIu/AXiZyH/V2YrFJkyZh0qRJ9bV6IjoP2MdE3oG9TOQd2MtE7qHOfxNNRERERERE5K04iSYiIiIiIiJyUIPnRJPzpHgfQI7ikVTOfFpb83/lI3FZ46XXaGsV992gX06IuHL1fgD2HyPtNr0o2sab1cfzVJvXm+WQPoKi4jl99NP+dQfE9fr66v/O2Tw8QFtr5adfLr+gQtxmhVkfgXWmUh8b9f3H+oi8gKUXitu8eHB7bc33lju1NePF/bU1FQaQJAAAnuFJREFUg9H1w1t9RKgREdH5oUoL9bXj+/W101naGgCojz/Q1tKaRWhrX2zI0NZOlMvxmoOb69erflivrZnLy/UrbdlO3KZPC/0x2RAWLS7bmPCTaCIiIiIiIiIHcRJNRERERERE5CBOoomIiIiIiIgcxEk0ERERERERkYM4iSYiIiIiIiJyECfRRERERERERA7iJJqIiIiIiIjIQcyJ9kRKn+MKADDo827Nv6zTr1bIh60NQ8skbc388xptzdilr7heZdGP1+DDvw81Vq5m/Fr2bRXXW/LEo9ra0Z2Z2prRR7/NZolh4jb9Y/X1X77L0NZOluuzoKP85N1+dLhJW/Mr0vdVYmKItuZrlLOVC3cf19ZKJj2irRmN/9LWYq7tIW7Td9rL2hqzoImIGpZ590b5Bru3a0sq56S2ZggM0q8zvrm4SUOXrtpaSnP9sjueXaqtHSzVH68BILl3a/14WrfRL+jvr6/t2iJu0yLVffRzDETHakvGvjeI2/REnGkQEREREREROYiTaCIiIiIiIiIHcRJNRERERERE5CBOoomIiIiIiIgcxEk0ERERERERkYM4iSYiIiIiIiJyECOuPJLr8Svq+2+0Nd8b/+HyeiWGS3pqa+r7lfoF7URcgTE0jZar8WaqvFRbyxx/t7hNHyGqqvkF+liHyrwSba2ksEzcpvnoaW2tU49E/XL5+vsJ4X7YY0qM1NbKjujHWpKrfwwAwGTSR2Y0SdLHfEmP7a//XS1us3PiTG3NeMMUbc3VCDUiIrJl3rBMWyt/63/isv5XXa6tGZq31C9YUqyvCdFY9hiS9VFUI9Nv0deC9PGQAICQUH2tolxfKxWOu1JMlT1l+vWqdau0tcrN34ur9X14lstDaij8JJqIiIiIiIjIQZxEExERERERETmIk2giIiIiIiIiB3ESTUREREREROQgTqKJiIiIiIiIHMRJNBEREREREZGDGHHlgaQIH3vMv+3V1kx3P6OtSbEugBztYuw1TFsre+8d/XKVwqn7ARh8/bU1xtB4ORefw/L7btLWiosrxWWjogL0wwnw09fsxFhJysv1UV6+QrxTXo4+wqNJ83Bxmz4m/X0p/eOUtibFdQUE6dcJQHw+y04VaGuVlfrHJ7mlPhoLALJf+1BbixMirrj/ICJynBLey+2f+KS2ljK6l7xeIarKcPKEfkGjEO/kp39fCQAwm/W1pDba0v4Z87S1fcLxGgAGrVukrak1n+kXDAzW16THAJDvpyQ+Xls6tmCFuGjzgWu0NaO9yNsGUuefRD/++OMwGAw2l/bt29f1ZoionrGXiTwf+5jIO7CXidxLvXwS3bFjR6xa9Wfgtq8vP/Am8kTsZSLPxz4m8g7sZSL3US/d5+vri3jhI30i8gzsZSLPxz4m8g7sZSL3US8nFtu3bx8SEhLQqlUr3HTTTTh8+LD2tmVlZcjPz7e5EJF7YC8TeT5n+hhgLxO5Kx6TidxHnU+iu3fvjgULFmD58uWYO3cuDh48iCuuuAIFBTWfHCY9PR3h4eHWS2JiYl0PiYhcwF4m8nzO9jHAXiZyRzwmE7mXOp9EDxw4ENdffz06d+6MtLQ0fPXVV8jNzcVHH31U4+2nT5+OvLw86+XIkSN1PSQicgF7mcjzOdvHAHuZyB3xmEzkXur9jAQRERFo27Yt9u/fX2PdZDLBZDLV9zBqpSHiklRlhX6bvnJcjHnHt/plfVwbb33dT2N0hLZmWblQXnbgOH3Roj89v/LRn9qf8TV67tTLrj5PlhJ9X5WUyBFXUt2vRB/hUVpQqq35+8sxE35NgrQ1VSqMx1f/99HKXDlOw9Q8Ulszhuif3+BgfTSIpUiOq/MV7mf2gRxtLShIfwg7eUofAQYAJpP+sbcc2aOt+SR69tlw7fUx4BnH5frSWOIRVZl+P2D54XNtzdj3hvoYjshexCak56wWkaDuzp2OyRLL8ne1NaP0ntTPTjSi8FV2FRurrRnChJhHHxejnQDg9EltKeWajtpaUo7+W0EAoLKP64tSjJUU1yW8RwYAVOiP2SorU79cmT7qMqad/jkBAPWZPnYSjSXi6lyFhYU4cOAAmjZtWt+bIqJ6xF4m8nzsYyLvwF4malh1PomeNm0a1q1bh4yMDPzwww+47rrrYDQaMWbMmLreFBHVI/YykedjHxN5B/YykXup869zHz16FGPGjEFOTg5iYmJw+eWXY9OmTYiJianrTRFRPWIvE3k+9jGRd2AvE7mXOp9EL1q0qK5XSUQNgL1M5PnYx0Tegb1M5F6894wLRERERERERHWMk2giIiIiIiIiB3ESTUREREREROSges+J9gaelg2pNupzon3at3VtnfZyGgXi45eUpC2Zl38lrlfKiTYY+dKm6ipO6fMYfYxyn+cX6DOmg04XaWsB4YHamjJbxG1CqFsq9TmP/v76v48afOVsalUh5EcKmZ6WfH0+pLLI91PaZrMrWmtr3y/eoa01j9Q/7gAQGKB/HNT29foFPTwnmlxXmwzp+sifVjnH5Hqxfn9nefMFbe3Ap9u0tdYPHhW36XPtrdqaIThCXFa7nL3Hpx7eo5k/nCnWiz74rNp1+cI+uTFTe3dpayEhQha0r533cRX6YzKMwnFOqgn5yHaXLSrU11q21JZ8mwn3AwDKSvQ1KQu6vkjHcyGX3N77nZLt+7S1ULuDahj8JJqIiIiIiIjIQZxEExERERERETmIk2giIiIiIiIiB3ESTUREREREROQgTqKJiIiIiIiIHMRJNBEREREREZGDmANUS3ajn8S6EHnhK5z2346Kn37R1vwffMS1lVpqEd0gxE359B+urRV/ulZcrd/pE9qaIbKptqak+1KbKC/GarkFZa7U1nKzhSgqk/z8HTilX9ZXiMdqdnGUtmYuLBW3WXQiX1srKdXfTykSJqFzorjN0sM5+qLQH8UF+oirkJhgcZsVZ4TnpWWMtlYoRGaI0SkAck7rH/vITDk6iLyX1Dv1FvUoMG9Ypi/ai7cp0kdcle49oq216t9Ov9znK8VNmv7Yry8GBWlLhrYd9Mu11I8HAHBwt74mxPGUfVw9pqqKf1c5yi70zXeqXacKCoELLxOXa5QyM7WlgADhuFsiRDsBUOX6aCiDj4ufEdrrKSniSnhvaQjUv/bhZyfi6lSWXNeR4rrs3E+VfUpfLNIfr+GvX68xJEDcZkWOEBHmpvhJNBEREREREZGDOIkmIiIiIiIichAn0UREREREREQO4iSaiIiIiIiIyEGcRBMRERERERE5iJNoIiIiIiIiIgcxk6eW7MZWuBhrURszl/2qrT08+wKX1llf8U0+LTtpa8cO5YrLtv3hK23NOORWbc3gI0QUkOcrcy2KKjTSJK72t5P6uJgD2cXaWsuEaG2tfIsQBwM5UicsVIiSCNDHO5UePS1u01ysj8UoK9ZHcUgxVpZy1yPyjqz5XVurFB6fyko5jqiyQh99gwL9c02Nl6sxVfaoolx9sVwfHVcya664Xp9A/T7ClNBEW8v58aC4Xkl5tj5is6hIH8sX2U7f56tX6WsAMPDm7tpa8e6j2trKn/S1Ee98KW6zJgZ/fSRhY1Z+Uv+4BCTpj49ilBKAP77Vvy5SWqdoa6pEf7y2x+AvvE8wC8c5KRpLqgFAsRD9ZAqUl9XJPyOW1bHj2lpphj7+KvCiNtqa7wWtxW2W/qbfprviJ9FEREREREREDuIkmoiIiIiIiMhBnEQTEREREREROYiTaCIiIiIiIiIHcRJNRERERERE5CBOoomIiIiIiIgcxIireqYs+hgVtX+bfkEhhkkdkeMebmwbo61Zfv1OWzM015+aHkV2ohuE0/4bQiP14zmlj5goLZVjcSxrV+m3efGV2pra97N+ubYXiduUIst84lvJy9L5UZSnLZVLsUbQx68AQKgQQ/F5jj6C4qpCfUyHuVzepsmk36bFoo9wMpfqo6ikGgCUlurHFBwWoB+PsJwxRI4P8wnUR3JVZJdqaycr9NssLpEf27Bw/ZjKtu3W1njQpLpm/vcUba0iSx9F4x8bJq7XJ1QfO1eyR3/sDYnXr9c3Qr9OAKjI0cfDRTYV9gPC/iynQn4vsPMT/TE9oWmQtnbtP0eK65UoS/Ux1XQdAeUncrW14Aub6xeMiBDX+/K+k9ra7BMn9AuGhuprduKmlL8+Ng4B+uNjbRh8hM87y/THR4nas0feplG/zTU/6vcd1yRGaWvGtu3FbdqLpXRHTn8SvX79egwdOhQJCQkwGAxYtmyZTV0phRkzZqBp06YIDAxE//79sW/fvroaLxHVAfYxkXdgLxN5PvYxkedxehJdVFSELl26YM6cOTXWn3vuObz66quYN28eNm/ejODgYKSlpaG01LW/lhBR3WMfE3kH9jKR52MfE3kep7+ZNnDgQAwcOLDGmlIKM2fOxL/+9S9ce+21AIB33nkHcXFxWLZsGUaPHl270RJRnWAfE3kH9jKR52MfE3meOj2x2MGDB5GZmYn+/ftbrwsPD0f37t2xcePGGpcpKytDfn6+zYWIGo4rfQywl4ncDXuZyPOxj4ncU51OojMzMwEAcXFxNtfHxcVZa+dKT09HeHi49ZKYmFiXQyIiJ7nSxwB7mcjdsJeJPB/7mMg9NXjE1fTp05GXl2e9HDlypKGHREQuYC8TeQf2MpHnYx8T1a86nUTHx8cDALKysmyuz8rKstbOZTKZEBYWZnMhoobjSh8D7GUid8NeJvJ87GMi91SnkZfJycmIj4/H6tWr0bVrVwBAfn4+Nm/ejLvuuqsuN+U29nS5WKzHxQZqawYhczg7u0Rbk7NugYRmIdra4UmP67eZo9+mPSEh+pzX4GB9zeijfwyaJch5lGe2ZGhr/rfcoq0ZfPU5gGWFZeI2w9o31dYC5n0iLuspPL2PLYf3amtKaJ3MLPn1r4QIw17h+nzIop36TMXKSrmXA2P0vSzlOOYeztXWYi9NErdpytbnvGbtPaWt+Rj1vRwpjBUAKkvKtbXTefqePFGuz2UtE3KrASAmWv+clWXpfzso75Xci6f3shKaTjp+NsQ2zT8tl9e7+F39eocO19ZKnnhJWwvrdYG4zcyVv2prBYX6njuZr++5EOH4CQAX9NLvX87s1+8/Zv6qz/W9yE7O/Pen9fusIRH6XN+o6FhxvbKaXgv185r09D7Oy9e/1kKaROgXrKgQ1/ts39b6onTWcinPuUx+D+iyWmRIKzuPg5ZwXyzl8vHRp0m4ttalub7mEyj0qp+QsQ2g1M4x2x05PYkuLCzE/v37rf8+ePAgduzYgcjISLRo0QJTpkzB008/jTZt2iA5ORmPPvooEhISMGzYsLocNxHVAvuYyDuwl4k8H/uYyPM4PYnesmUL+vbta/331KlTAQBjx47FggUL8OCDD6KoqAh33HEHcnNzcfnll2P58uUIqMVfYYiobrGPibwDe5nI87GPiTyP05PoPn362P2605NPPoknn3yyVgMjovrDPibyDuxlIs/HPibyPA1+dm4iIiIiIiIiT8FJNBEREREREZGDOIkmIiIiIiIiclCdRlw1Ri26Joj1PT8c1tZad4jW1lr218dElGfmittUFfrYl8RubbW1hNN52pqlVD7FvhQbZRTiKSpzi7W1ssM54jYhxGP5x+lPwS8JvjBRrBtuvM2l9dJ5tGurtiT95qzcLMdN5VTq4xcOCP0xsHmktmY+lC1u8/g+fQ/EN9dnfvoIfx6tyCkUt1mZp4/6kuLq/Pz0G5UirADAR+jlprFB2ppvrv6+5NuJy/AN08cP5hzV7wv1zybVOaFfhcQ5OY8OgEFoEDHGatcP+uXC5FdG3rYMbS3g1AJtLbCl/n3Cjvc2i9ts3kwfyNYsKUK/4KFcbcloJ67OIvR6XFpXbW2cEGW38MgZcZspgfr9UlCg/m2u+n2PuF5JTa8h6XXVmBUKcWoQcq7NP+8U1xtyyyhtTe0Xoi4zs7Q1gxS5BQBm/fvrBmER3reY9O+9pYhMAIBR/56++e1DtLXK7b9oa76B+mM5APj567epKvWvIYOvHJ1Vn9jxRERERERERA7iJJqIiIiIiIjIQZxEExERERERETmIk2giIiIiIiIiB3ESTUREREREROQgTqKJiIiIiIiIHMSIKwco4RTy5sJScdngYP1DnHtSiJoxnNCW/GP00TYAIAX1VJ7QR+ZIUVQ+fvpTzwOApVwfJ2Mu1EdXSHEiAUn6aA8AMBfr16uEuCKDEKcDf/lU+YbAELFODU8dPqStSa+3nAo5Eik5UN8fAy5upq2d2p2prfn5yn/HjE8I1db84/UxblJ8hRJ6FQDKhH4NDAvQ1ixl+pgvY6h+OQA48rt+v9RSeGxDD+gjwnyF5xoASnKKtLXfT+j3zS3FtXo3pVTNMXFSpJSd50HicmRQLbYpRamo9+ZpaxXZ+eJ6w669Qlsr/kYfVfXbrlPaWttWcpSjf6Q+4sovRr9vaSG9FwjW1wD5vULpzoPaWpMI/XqnxOr3AQAQnBSlrVWe0fd54Xf6CKUIcYvkDItZv38wBOtfh+YC+f21b0ycvvjjRv16i/U97ttE3CRQIcS9CrFQCNAfA+3t5+TAPj2Dnz76Df5CzZ5Ol2hL5vU/amu+FXLUpVF6by7sl8GIKyIiIiIiIiL3x0k0ERERERERkYM4iSYiIiIiIiJyECfRRERERERERA7iJJqIiIiIiIjIQZxEExERERERETmIEVcOUCf2a2vSqfIBoHlH/Sn4T/6uj2cpyinWb7NIH0EDAAZf/Wn2ffz1T7khpxaxIBYhwkA6bb0QRaIqpbAuOVbLVX7R+rgFADAkd67zbVLdOr1qh7YWGKjvjSIhyg4A9hbr4za6PjhJWzsx+RltzRQux8VI8XqVp/UxTPmnhFiXIiGiA0BMtGsxVmYhVg529llhofqICv84fYzPb8L+99KQIHGb/v7610ItEpK8msFgqDkmrgEeMFUhvKYM8ucD5lce0hfz9VFVhq4X69e5bLm4zdMfrtHWgoL0cTMXDWqvrZUfPyNu0ydQ31dlh09rawWn9e8/IpL1cVIAUPiHPpIrKE4fzxnRJlZb87+glbhNdfKktmYM0D+2hzbp4xDD8/T3AwAM4TFivbFRufrnICYmUL+gxaxfp3RMAQA/IdooWB/vZigskdcrkWKsfIUplVm6n/oaAEB6byKtV1qnvW2G6XvVEK6PnxUjNKXnC0CTVsK+pUSIBA5ouOhZfhJNRERERERE5CBOoomIiIiIiIgcxEk0ERERERERkYM4iSYiIiIiIiJyECfRRERERERERA7iJJqIiIiIiIjIQZxEExERERERETnI6Zzo9evX4/nnn8fWrVtx4sQJLF26FMOGDbPWx40bh7fffttmmbS0NCxfLmcoujO1e4u2FtK9nbis5Uyetla+K0tbi4zTZ9yJOWwAyor1Wa5SKq3BqP+bio+QqQoABj+hLmRISzmABqOcPSqNV6pJ45Eyts/W9ZmTnsSb+/jkSX0GZNOm+uzgQCnPHMCxMiFXMTpBWyou1vdrQhs5czUwJV5bO7J6j7aWnaN/DLoOu1DcZuGvR7Q1i9A7QgmmCDmz2adQn/eMpk21pbGxEdpauZIzRqUM3Qppn1Wuzws3+OsztuuLu/ey+YfP5BucFvJ4c3O0pfK1G7Q131D5eag4rc9RLz+pz4n2+fkPbe3UEf1yABCTqM9dXbb5sLaWv/24tvaPy5LEbUr8hMzm8GB9b/gIucsAEHaBvl+l43Jlgb6vzIePidv0f+5/2prld/37t6LVU4TltorbNF56jVh3lrv3sT2WEwe0tYh2+uMYyoT9qfQ+DoAhSn/cVULOMbKy9TWT9C7ZDh/pfadrWc8A5Gxqab2urhOAISZOW1MV+uO1pVK4LwFCXjgAU+tm+m0Kue2GJsLrq545/Ul0UVERunTpgjlz5mhvc8011+DEiRPWywcffFCrQRJR3WIfE3kH9jKR52MfE3kepz+JHjhwIAYOHCjexmQyIT6+4f4yQEQy9jGRd2AvE3k+9jGR56mX30SvXbsWsbGxaNeuHe666y7k5Oi/klVWVob8/HybCxE1PGf6GGAvE7kr9jKR52MfE7mXOp9EX3PNNXjnnXewevVqPPvss1i3bh0GDhwIs+Y7/+np6QgPD7deEhMT63pIROQkZ/sYYC8TuSP2MpHnYx8TuR+nv85tz+jRo63/36lTJ3Tu3BkpKSlYu3Yt+vXrV+3206dPx9SpU63/zs/PZ6MTNTBn+xhgLxO5I/YykedjHxO5n3qPuGrVqhWio6Oxf//+GusmkwlhYWE2FyJyL/b6GGAvE3kC9jKR52MfEzW8Ov8k+lxHjx5FTk4OmgoxJe5u9S1PaWuXtosUl/UVol3MZn2MijFIHzGRk10sbtMirPdMbpm2Vlqq/1qQn58c/yOpqNCPx1eIsfL1k//GI8XtJLbRPy+VxfrHwFykj1sAzkPDuClv6GMA8AvRR98ESPEUAAqFODYUF2pL2WX6yLmYY/Jv1CrO6Hu9SbQ+LsIg3JXTWw+J2wyOC9WvV4iyOypE9jWL1a8TAAIDhc7K1seRdOgYra2tEKKBAKCiUN/rwUK0ijr2u7ZmSO4sbtMd1EcvV856WFsr37JTXNY/taO2ZgjWRz1aSvV9ZbETw+QXo389+ifojx1Z6/dqa0076iNhAMC/jf4TwGty9ZF0uXlC/JudGMjKPP3+wzdc/94kIFm4L0rIsgMAP/1jn79R//gFt9e/Hitz5fc8xvde1BcL9fvm0gphn35QP1YAQB1HXDnL7Y7Jp/X7f9iJqtLx8bfzjiu0ib7m6u+/7f2hoUz//lEkxVjZi5sKC9fWVIncG1pCnwIAhBgrg59+fuLqOgGIY1JH9MddtOzk2njqgNNzgsLCQpu/fB08eBA7duxAZGQkIiMj8cQTT2DEiBGIj4/HgQMH8OCDD6J169ZIS0ur04ETkevYx0Tegb1M5PnYx0Sex+lJ9JYtW9C3b1/rv6t+bzF27FjMnTsXv/zyC95++23k5uYiISEBAwYMwFNPPQVTbQLMiahOsY+JvAN7mcjzsY+JPI/Tk+g+ffpACV/nWbFiRa0GRET1j31M5B3Yy0Sej31M5Hnq/cRiRERERERERN6Ck2giIiIiIiIiB3ESTUREREREROSgxprY45R+r9yjrRW9vVhcNmjkYG2t+J9vaWtKiNORfjcDALHtYrQ1c54+SqNCiH6qrJS36eurj9oQkqighKLRTnyHUYg/8DHpT5VflKmPvAgoqxS3Se6vtEyIkhBeb8F2Iq6OlevXawjRR1CE+OjjK0Ij9TFVAFBaoO9JQ6V+HxERrj/ZTF6+HDPRJF5/X8pO5GprsTHCfZF2AvZU6KOMpAhBf4Od+B9hn1Yu7GNVwRlxvd5MWcxQlup9YN6zT7uMFNcIAPmffa+tmYQoKiluzVwgRxWWZuhj03xM+uNKSBP9a7wiW39cAYADP23U1mKE3omO0sfylZzRH8/tCRD2ZxL/HheJ9aIvvtPWzMJ+QIqxspTI+6yy9T9pa76h+sfvwovkWDJywtEMfU16P2vR13zs7DsMIULErBBxZQzTR+dBGA8AoFJ4jxgoHAPtvL8QSfFYrgrQ9wUgR2cZwvXRkj6+QlxXuZ2IK2lMRzLkZRsIP4kmIiIiIiIichAn0UREREREREQO4iSaiIiIiIiIyEGcRBMRERERERE5iJNoIiIiIiIiIgdxEk1ERERERETkIEZcOcB43V362pLP5IVbtdeWTpbro1vaCassL5dPwV9xUn9q/zIh/qe4WH/q/qAg+aUiJdj4CEkzZUJMj1Ly33h8fPT3xVKpr0n3xZzvemQInT+qWP8aDzDpIxZKheibCjvRcaOiw7Q1Q1SCtuYnNEClneiWE5n6mIkWiSHaWsahAm0tsbkQ7wGgYNcxbc03UB85IsXKnToox0JFxuijqiA8L6Z2LbQ147oD4jZNgfr9gBiOlZMprtebWfZthSWk+uvHt18/7TLmtWvEdYZ2aq6tlWfmaWtSvJmqsBMJo/Q9IMUu5mfq+yo0UngNA2h5gT4WJvuIfn9WWKh/nyBFRgGARaqf0e8Lyw7kamttyuUYyOwTQoRkgH7f7Ce8b7EX6wnhuG0wCu8jpBg8PzleiWyp3/doa74x+thEQ7A+xk6KegUAQ0iEvmgUopbC9Mdyu3FSUt1Pfwx0eZ2A+Fo0VOr3D0qIh0RUlLxNYVlDkPBeSIieRQ3xiDaEiDCVnysv20D4STQRERERERGRgziJJiIiIiIiInIQJ9FEREREREREDuIkmoiIiIiIiMhBnEQTEREREREROYiTaCIiIiIiIiIHcRJNRERERERE5CDmRDtAlRZpa75N5MxVy4dva2uFFn0GXkGWPo9SynMGgNy8Mm3NR8is9fPT/01FzJsEUCnmPeuX8/fXb9NoFNNaxfVKwdXGAH2Wn0+wSdwmuQeVlaGtlZTq+0N6TZXZeY1f2EbIlvTX5xvmVurHYy/zPa9Mn9VoitOPx3hMn9UadlGSuM0iISfaJ1DfO0XCPiu6ZYS8TWHZgg36vOemL/1LW1v17FfiNjvn6veTldLOxU3zKs+Lw/uBoIBqV5ct+1K7iOn2W+R1+ujzXAMO6LNn4aM/dqiTWeImjYX6/jBnZWtrTVL0Wc9+scL+AYBByECNCz2prVWc0veGvfxki5CXLb2PsFj0vVGRo3/sACAwUP98hsbpM4HNBfrc6sDkWHGbxhD9cdsQWP31+ueA9ONBRbm4TTqHlJFsEt5X1VMet6VEeB8cI4xVype2R8pWFh4DOyno8mvRV7gvwhzDrsOH9TUf/dTRGCr0mz328rLdED+JJiIiIiIiInIQJ9FEREREREREDuIkmoiIiIiIiMhBnEQTEREREREROYiTaCIiIiIiIiIHcRJNRERERERE5CCnIq7S09PxySefYM+ePQgMDETPnj3x7LPPol27dtbblJaW4v7778eiRYtQVlaGtLQ0vPbaa4iLi6vzwbsDY5AciVR2JEdba+avPzV9eYUUGSWfEL+sTH+aeKMQceVv0p/a31xh9yT8WhazflmzECvk5ytHXPn568dr8te/tM3F+rgAVS7Hh3kLj+9lIQohLEwfmWH0079m/IXeAIDw7m30xYAgbSlUiMwoKtJHYgBAl/ZR2lrh0VxtraJS31dSZA4AGIzC31bN+v2SGJFXJMfFBIXr43/OnNJHDBoS22lrXULk6BRpP9lCGI86fFBc7/l0vvvYp8dA+NQUC/TRx9plMqaki+tsfpk+cs3YQf/8SlE0H73wmbjNSF/9shG++mNHiwR9pGVYqdzLEPYvSoiiKhOOV0HxcqyWj0n/HsOvoERbCwkX4ngq5Ria4DB9xM3+X/VRXu16tNDW/Kc/IW5THd2nLx7JEBYU3mdlHhe3Wdc8/pgsEaKfkH9GWzI2kV/fkrLj+vUGtmrp8nrFCCzhfYmqTdxUpfD4mYRIKSkyys54zPlCtG9IhLYmPmeF8nsPMSbNTTn1SfS6deswceJEbNq0CStXrkRFRQUGDBiAoqI/H+z77rsPn3/+ORYvXox169bh+PHjGD58eJ0PnIhcx14m8nzsYyLvwF4m8jxOfRK9fPlym38vWLAAsbGx2Lp1K6688krk5eXhrbfewsKFC3HVVVcBAObPn48LLrgAmzZtwmWXXVZ3Iycil7GXiTwf+5jIO7CXiTxPrX4TnZeXBwCIjIwEAGzduhUVFRXo37+/9Tbt27dHixYtsHHjxhrXUVZWhvz8fJsLEZ1f7GUiz1cXfQywl4kaGo/JRO7P5Um0xWLBlClT0KtXL1x44YUAgMzMTPj7+yMiIsLmtnFxccjMzKxxPenp6QgPD7deEhMTXR0SEbmAvUzk+eqqjwH2MlFD4jGZyDO4PImeOHEidu7ciUWLFtVqANOnT0deXp71cuTIkVqtj4icw14m8nx11ccAe5moIfGYTOQZnPpNdJVJkybhiy++wPr169G8eXPr9fHx8SgvL0dubq7NX8uysrIQHx9f47pMJhNMJvkM10RUP9jLRJ6vLvsYYC8TNRQek4k8h1OTaKUUJk+ejKVLl2Lt2rVITk62qaempsLPzw+rV6/GiBEjAAB79+7F4cOH0aNHj7ob9XlmCNDHWqgKORIpX4ihKRfinaTom1Ih/goAfIXolkohbipciOCoqJS3aTDol/U16mulpfqxVtiJHPIX7oupsExbKy/RP7bGEjmKx1t4fC8L0SQx0fp4Ih8//S6vWIhvAgBDxwv1w8k+qq0VCDETfgVyLE5cS31Mk/TWKD5O/xhk/ZYlbjM6UR9RYQzVr1eKlbMXHWcM0cd0GLKFiKu4ZG0tRIrqgryPjYoSYkNK9NFA59v57mNDSBMYQqu/PoLmf65dplWFfl8MAJYv/qetqT+E6KLcXHG9EuHQgT3Fpdrajt/1z/2O7cfEbV4pxKZJRzqTcByMzpB/4xojxP1FC/vJwNgaYsz+X0Db5toaAKBU//i1iw7R1vyi9DXLmy+JmzSf1j8OUqSlMUj/+PgOHixus655/DFZiify0e+LVY4+BtbQsqXLwyk5VaitBVYKxyNhrAAAfyE6UYq/qi9SjJXEzv20CH0jio3VltQp+b2HISpav2xJsWvjqWdOTaInTpyIhQsX4tNPP0VoaKj1dxjh4eEIDAxEeHg4br31VkydOhWRkZEICwvD5MmT0aNHD545kMiNsJeJPB/7mMg7sJeJPI9Tk+i5c+cCAPr06WNz/fz58zFu3DgAwMsvvwwfHx+MGDHCJgyeiNwHe5nI87GPibwDe5nI8zj9dW57AgICMGfOHMyZM8flQRFR/WIvE3k+9jGRd2AvE3meWuVEExERERERETUmnEQTEREREREROYiTaCIiIiIiIiIHuZQTTX8yCJE5gByjIgXqhIToT6MvxUIBgK+f/m8jlcLp8H2EKCqDWY6bsghxXRZhUSkaS4rjAgBIUV5CVFVAqBCn49sAEQXkNFVwRls7la2PoYlPqMUu78JL9OPZvUVbk6Ls2qY2FTdpEGKaTh3O049HaJ3YVk3EbZaf1kdKBQTpg7UKj+vHExgiZ5X6Rrr4vBj1y10Qpo/wAYCCUn3Miclf2A/48rDpDIOf/Nwbr7urzrd541NyXVn0R1+VfURfOypEbmWfkDcaLfS6KUhbMkToY19g57E1+As9EKDfJnz16zX4ClFGdkidoyqFSB2znYg8o35MtRkv1ZEQfXwZzuiP5YjRZ9nbU2LnfbJ+QTsRhmH6CEi3I0RuGcIiXF6tEvrRkNRKv9zP2+QVRwjvTYTovIbET6KJiIiIiIiIHMRJNBEREREREZGDOIkmIiIiIiIichAn0UREREREREQO4iSaiIiIiIiIyEGcRBMRERERERE5iJNoIiIiIiIiIgcx8LKWDGlDxPrxj3Zoa2YhzPXMmTJtrULInQUAP1/930aMwfpamZC7LOU522O2M179Nl1fb3GxPscuLFifgWm0ky1L7sEQEqGtNYmQs1O1y9nJ/zV26autVf73MW2tfZw+IzOghZABC6B493H9NoUcdSmfPim1vbhNtWGntmbO02doBgTrs+19AuTHtuyYPiv0RFaxttZC2Em06ipncH+3IUNb8/MT/r5ci30huQeDj/75NcQm6ReUalQrBl/9/gNSjdxDTo62ZGinP+ZU/LJbWzNNGChuUsor9vMV9tNS1rO9PGIhY14kZTYL+yMAgItZ5+J6/eSeMvgLx+yiXP1yF3bT1syrV4nbNLYV3puc0L8Xakj8JJqIiIiIiIjIQZxEExERERERETmIk2giIiIiIiIiB3ESTURERERERP/X3n3HR1Gt/wP/bDbJpiekBwgQEAgdDIIIAkqJFC8gioIF5KqogCKKil5RsOTaUUSwXeB6wQICKl9E6SgCShOp0msSIJBCerLn9we/rK7Jc7YkIbubz/v12tcL9pmZc2Z2npk52Z15yE4cRBMRERERERHZiYNoIiIiIiIiIjuxxFUlGXsN18av6f25GPv2O/nR/sgvEkNeNiqs6Irm6MpfFRbJJa6UphyX7bjcYW9j9ZTOMmv6U3xJLmHg10hfcohcgyGyrhjz9ZfLQej208pULirZukuMxTSPEmPFFy7pl6s5DujWJSZaLtWW/8tebZu5mXIZq6CoQDHmE+InL9RGmTtjkGbeAxfEkCqRS3nZPFBqeGvK4OlKlRAR1Ual2blizJgvlykszsgRY36R8do2zSf3izGTSVNSKjBYjClbJa50SuVraOjKTdkqcaU7z2koTTkuQ7F8bQEABs1YQV2SS1IaGrQUY+Y8fZtGL8251dY2qiGu2SsiIiIiIiIiF8RBNBEREREREZGdOIgmIiIiIiIishMH0URERERERER24iCaiIiIiIiIyE4cRBMRERERERHZyaESVykpKVi8eDH2798Pf39/XHfddXj11VfRvHlzyzQ9e/bE+vXrreYbM2YMZs+eXTU9djM+kSFiLNxbfpz7ueISMRZo41Hv29KyxVicr68Ya1w3SIzpSkYBgC5crCmd5VWJMjTO0lXb8QqVSx94ErfPZU3pBt8o+TNM25suxiqzJ3o3TxBjO977QYy179XE6TYNmppcfn7yod2gOe4AQECwXN7JrMllGOQyHD7h8rEFAApOyWWs9uXJJUc6e8vlzGytZ4S3ZhvpSu9VpgRKFXP7PCYiAO6fy0Z/+doShYXyfAGacoI2qPXfyMs1aq6TgzTXedmZ+kZ1JQ595PORjq4UFQAYnCzvpCvlBf8A/bya7aeO7hFjXnWbijFzkTyuAQCVmSEHXbS0pEOfzPr16zF27Fhs3rwZK1euRHFxMfr27YvcXOv6cPfffz9SU1Mtr9dee61KO01ElcNcJnJ/zGMiz8BcJnI/Dn0TvWLFCqv/z507F9HR0di2bRu6d+9ueT8gIACxsbFV00MiqnLMZSL3xzwm8gzMZSL3U6l7orOysgAA4eHhVu/Pnz8fkZGRaN26NSZPnoy8vDxxGYWFhcjOzrZ6EdGVxVwmcn9VkccAc5mopvGcTOT6HPom+q/MZjMmTJiArl27onXr1pb3R4wYgYYNG6Ju3brYtWsXnnrqKRw4cACLFy+ucDkpKSmYOnWqs90gokpiLhO5v6rKY4C5TFSTeE4mcg9OD6LHjh2L3bt346effrJ6/4EHHrD8u02bNoiLi0OvXr1w+PBhNGlS/iE6kydPxsSJEy3/z87ORnx8vLPdIiIHMZeJ3F9V5THAXCaqSTwnE7kHpwbR48aNw7Jly7BhwwbUr19fO23nzp0BAIcOHaowyU0mE0wm55/OR0TOYy4Tub+qzGOAuUxUU3hOJnIfDg2ilVIYP348lixZgnXr1iEhQS7rUmbnzp0AgLi4OKc66O7GzVgrxgZqyr7E+cqPyr9Qon9MvLem9E1WqTzvvP1y+Z/GfvpH9+dr6kb5acpY6coKGTXrAQClmrpabcICxdjZs/liLLR17bhnyN1z2Xx8nxgrzS8SY35+cpkE70qUW/N+/C0x1in8JTFWsGqjdrl+dcPEWIS6KMbOnZf3cXVOjgGAr4/8qIygIPk4oLtUK87M1USBwER5n7pnzlztvJLSPLmsCgB4e8vraQyVy38UbJX3PX0hr6rn7nlMRJe5ey4XpmaKMa+MS2KsJFP/fAYddVEujXj0mHwt1+bgfnmhNp4XAV25KV2pKt18Nso3KWfbxDk55O+vbTPvjzQxFnxIPgei62AxVKTZRwDA9/BhMaaK5BKaNcmhQfTYsWOxYMECfP311wgODkZa2uWNHBoaCn9/fxw+fBgLFixA//79ERERgV27duGxxx5D9+7d0bZt22pZASJyHHOZyP0xj4k8A3OZyP04NIieNWsWgMsF3/9qzpw5GDVqFHx9fbFq1SpMnz4dubm5iI+Px9ChQ/Gvf/2ryjpMRJXHXCZyf8xjIs/AXCZyPw7/nFsnPj4e69evr1SHiKj6MZeJ3B/zmMgzMJeJ3E+l6kQTERERERER1SYcRBMRERERERHZiYNoIiIiIiIiIjtxEE1ERERERERkJ4ceLEaOe/+gXCf60kNjxJgqlWu/6WIAYC4uFWOhd/YTYwNHTJQXarSxqxRpas/qHpihW66XjTbN8nrCS1N3L+usHAuL0bdJLsGrUUsx5t++qTyf71ExFhwTXKk+SYz3yk9PDbzX+eXKlYyBCE1M5efoF6zLyRJNrUaT3CODreNHNQgYeKM2HnhokRjzMsn1sH07tnG6T0REnsjv6Wfk4KWLYsjXP8jpNo1jXxFjbes1kGfMk+tW62pP25SbK8dCQuSYj3y+AQCUaq51NXWiDRFR8nzR+triwc3kayyvXndo5xWX+e8XtHFDcB05aKOWdk3hN9FEREREREREduIgmoiIiIiIiMhOHEQTERERERER2YmDaCIiIiIiIiI7cRBNREREREREZCeXezq3+v9Pcs7OsfEEWTdhzpGfAnipRH7iXqWezq1ZriG/UIx5ZWu2ua2n6xY7+XRu3RO4q+vp3JrPBF66Zx4DBh95+11pZTmidNu3BlVnLivNZ1haKD9BurC4RIzp8gYAVHa27Y65AVWg2f8Bfd6Vap7OXag57tTAkzVLNcc6ALikOY5mF8n7iXdBkRxzYh9x9TwGPO+8TFQdXD2XqzOPSy/lycFczfWhWf4uz2jjeKo0T6Y25xfIM2rODUpzfLepUDOvbrk2rum1cd3TuXXbIE/zmQBAgTyvbqxg8JXXszRXs48AMBh85aDmGsKriq/NHMljg3KxbD916hTi4+NruhtEbuPkyZOoX79+TXejHOYykf1cNY8B5jKRI1w1l5nHRPazJ49dbhBtNptx5swZBAcHw2AwIDs7G/Hx8Th58iRCdHXWailuHz1P3j5KKeTk5KBu3brw8nK9OzOYy47h9tHz1O3j6nkMWOdyTk6OR34OVcVT99Oq5KnbyNVzmedkx3D76Hnq9nEkj13u59xeXl4VjvxDQkI86kOqatw+ep66fUJDQ2u6CyLmsnO4ffQ8cfu4ch4D1rlsMBgAeObnUJW4fWzzxG3kyrnMc7JzuH30PHH72JvHrvenMiIiIiIiIiIXxUE0ERERERERkZ1cfhBtMpnw/PPPw2Qy1XRXXBK3jx63j+vgZ6HH7aPH7eMa+DnocfvYxm3kGvg56HH76HH7uOCDxYiIiIiIiIhclct/E01ERERERETkKjiIJiIiIiIiIrITB9FEREREREREduIgmoiIiIiIiMhOLj2InjlzJho1agQ/Pz907twZv/zyS013qcZs2LABN998M+rWrQuDwYClS5daxZVSmDJlCuLi4uDv74/evXvj4MGDNdPZGpCSkoJrrrkGwcHBiI6OxuDBg3HgwAGraQoKCjB27FhEREQgKCgIQ4cORXp6eg31uHZhLv+JuSxjHrs+5vJlzGM95rJrYx7/ibksYx7ruewg+osvvsDEiRPx/PPPY/v27WjXrh2Sk5Nx9uzZmu5ajcjNzUW7du0wc+bMCuOvvfYa3n33XcyePRtbtmxBYGAgkpOTUVBQcIV7WjPWr1+PsWPHYvPmzVi5ciWKi4vRt29f5ObmWqZ57LHH8O2332LhwoVYv349zpw5g1tuuaUGe107MJetMZdlzGPXxlz+E/NYj7nsupjH1pjLMuaxDcpFderUSY0dO9by/9LSUlW3bl2VkpJSg71yDQDUkiVLLP83m80qNjZWvf7665b3MjMzlclkUp999lkN9LDmnT17VgFQ69evV0pd3h4+Pj5q4cKFlmn27dunAKhNmzbVVDdrBeayjLmsxzx2LczlijGPbWMuuw7msYy5rMc8tuaS30QXFRVh27Zt6N27t+U9Ly8v9O7dG5s2barBnrmmo0ePIi0tzWp7hYaGonPnzrV2e2VlZQEAwsPDAQDbtm1DcXGx1TZKTExEgwYNau02uhKYy45hLltjHrsO5rL9mMflMZddA/PYMcxla8xjay45iD5//jxKS0sRExNj9X5MTAzS0tJqqFeuq2ybcHtdZjabMWHCBHTt2hWtW7cGcHkb+fr6IiwszGra2rqNrhTmsmOYy39iHrsW5rL9mMfWmMuug3nsGObyn5jH5XnXdAeIqtrYsWOxe/du/PTTTzXdFSJyEvOYyDMwl4ncH/O4PJf8JjoyMhJGo7Hc093S09MRGxtbQ71yXWXbhNsLGDduHJYtW4a1a9eifv36lvdjY2NRVFSEzMxMq+lr4za6kpjLjmEuX8Y8dj3MZfsxj//EXHYtzGPHMJcvYx5XzCUH0b6+vkhKSsLq1ast75nNZqxevRpdunSpwZ65poSEBMTGxlptr+zsbGzZsqXWbC+lFMaNG4clS5ZgzZo1SEhIsIonJSXBx8fHahsdOHAAJ06cqDXbqCYwlx1T23OZeey6mMv2q+15DDCXXRXz2DG1PZeZxzbU7HPNZJ9//rkymUxq7ty5au/eveqBBx5QYWFhKi0traa7ViNycnLUjh071I4dOxQA9dZbb6kdO3ao48ePK6WU+ve//63CwsLU119/rXbt2qUGDRqkEhISVH5+fg33/Mp46KGHVGhoqFq3bp1KTU21vPLy8izTPPjgg6pBgwZqzZo1auvWrapLly6qS5cuNdjr2oG5bI25LGMeuzbm8p+Yx3rMZdfFPLbGXJYxj/VcdhCtlFIzZsxQDRo0UL6+vqpTp05q8+bNNd2lGrN27VoFoNxr5MiRSqnLj+F/7rnnVExMjDKZTKpXr17qwIEDNdvpK6iibQNAzZkzxzJNfn6+evjhh1WdOnVUQECAGjJkiEpNTa25TtcizOU/MZdlzGPXx1y+jHmsx1x2bczjPzGXZcxjPYNSSlX999tEREREREREnscl74kmIiIiIiIickUcRBMRERERERHZiYNoIiIiIiIiIjtxEE1ERERERERkJw6iiYiIiIiIiOzEQTQRERERERGRnTiIJiIiIiIiIrITB9FEREREREREduIgmoiIiIiIiMhOHEQTERERERER2YmDaCIiIiIiIiI7cRBNREREREREZCcOoomIiIiIiIjsxEE0ERERERERkZ04iCYiIiIiIiKyEwfR1ezYsWMwGAyYO3dulS63Z8+e6NmzZ5Uus7LWrVsHg8GAdevW1XRXqsWoUaPQqFEjp+Zt1KgRRo0aVaX9oerH/CXyLMxpz8FzMtnCfKfqVCsG0XPnzoXBYLC8vL29Ua9ePYwaNQqnT5+u6e5ZSU9PxxNPPIHExEQEBAQgMDAQSUlJeOmll5CZmVnT3buivv32W/To0QPR0dEICAhA48aNMWzYMKxYsaKmu0ZXEPPXfZSdxO15Ue3FnHZPPCeTM5jv7mvdunW45ZZbEBsbC19fX0RHR+Pmm2/G4sWLq7XdV155BUuXLq3WNqqCd0134EqaNm0aEhISUFBQgM2bN2Pu3Ln46aefsHv3bvj5+dV09/Drr7+if//+uHTpEu666y4kJSUBALZu3Yp///vf2LBhA3744Yca7qWse/fuyM/Ph6+vb6WX9cYbb2DSpEno0aMHJk+ejICAABw6dAirVq3C559/jptuuqkKekzuhPlbvaoif1u0aIFPP/3U6r3JkycjKCgIzz77bGW7SB6GOV29eE4mV8J8r15Vme8A8Pzzz2PatGlo2rQpxowZg4YNGyIjIwPLly/H0KFDMX/+fIwYMaJK2vq7V155BbfeeisGDx5cLcuvKrVqEN2vXz907NgRAHDfffchMjISr776Kr755hsMGzasRvuWmZmJIUOGwGg0YseOHUhMTLSKv/zyy/joo49qqHf28fLyqpIDYUlJCV588UX06dOnwgPW2bNnK90GuR/mb/WqivyNiYnBXXfdZfXev//9b0RGRpZ7/6/MZjOKiopc4kLKEUopFBQUwN/fv6a74paY09WL52RyJcz36lVV+Q4AixYtwrRp03DrrbdiwYIF8PHxscQmTZqE77//HsXFxVXSljurFT/nllx//fUAgMOHD1u9v3//ftx6660IDw+Hn58fOnbsiG+++cZqmgsXLuCJJ55AmzZtEBQUhJCQEPTr1w+//fabU3354IMPcPr0abz11lvlkhe4fHH6r3/9S5y/qKgIU6ZMQVJSEkJDQxEYGIjrr78ea9euLTft559/jqSkJAQHByMkJARt2rTBO++8Y4kXFxdj6tSpaNq0Kfz8/BAREYFu3bph5cqV2nWo6H6MgwcPYujQoYiNjYWfnx/q16+PO+64A1lZWeJyzp8/j+zsbHTt2rXCeHR0tMPrXXZfzBtvvIEPP/wQTZo0gclkwjXXXINff/21XBtLly5F69at4efnh9atW2PJkiUV9uWNN97Addddh4iICPj7+yMpKQmLFi3SbSaqIsxf18xfexgMBowbNw7z589Hq1atYDKZLD8J3bFjB/r164eQkBAEBQWhV69e2Lx5s9X8L7zwQoU/CS/72eCxY8cs723duhXJycmIjIyEv78/EhISMHr0aKv5zGYzpk+fjlatWsHPzw8xMTEYM2YMLl68aDVdo0aNMHDgQHz//ffo2LEj/P398cEHH1RqW9CfmNOumdM8J1N1YL67Zr4DwHPPPYfw8HD85z//sRpAl0lOTsbAgQMt/z979iz++c9/IiYmBn5+fmjXrh3mzZtXbj578tNgMCA3Nxfz5s2z3ALgqs8vqFXfRP9d2YVWnTp1LO/t2bMHXbt2Rb169fD0008jMDAQX375JQYPHoyvvvoKQ4YMAQAcOXIES5cuxW233YaEhASkp6fjgw8+QI8ePbB3717UrVvXob5888038Pf3x6233urUumRnZ+Pjjz/G8OHDcf/99yMnJweffPIJkpOT8csvv6B9+/YAgJUrV2L48OHo1asXXn31VQDAvn37sHHjRjz66KMALl+gpqSk4L777kOnTp2QnZ2NrVu3Yvv27ejTp4/dfSoqKkJycjIKCwsxfvx4xMbG4vTp01i2bBkyMzMRGhpa4XzR0dHw9/fHt99+i/HjxyM8PLzS611mwYIFyMnJwZgxY2AwGPDaa6/hlltuwZEjRywHih9++AFDhw5Fy5YtkZKSgoyMDNx7772oX79+ufbfeecd/OMf/8Cdd96JoqIifP7557jtttuwbNkyDBgwwO5tRY5j/rpm/tprzZo1+PLLLzFu3DhERkaiUaNG2LNnD66//nqEhITgySefhI+PDz744AP07NkT69evR+fOnR1q4+zZs+jbty+ioqLw9NNPIywsDMeOHSt3P9eYMWMwd+5c3HvvvXjkkUdw9OhRvPfee9ixYwc2btxodRFx4MABDB8+HGPGjMH999+P5s2bV2o70J+Y066Z0zwnU3Vgvrtmvh88eBD79+/H6NGjERwcbLOd/Px89OzZE4cOHcK4ceOQkJCAhQsXYtSoUcjMzLSsF2Bffn766aeWdX/ggQcAAE2aNLF7va8oVQvMmTNHAVCrVq1S586dUydPnlSLFi1SUVFRymQyqZMnT1qm7dWrl2rTpo0qKCiwvGc2m9V1112nmjZtanmvoKBAlZaWWrVz9OhRZTKZ1LRp06zeA6DmzJmj7WOdOnVUu3bt7F6nHj16qB49elj+X1JSogoLC62muXjxooqJiVGjR4+2vPfoo4+qkJAQVVJSIi67Xbt2asCAAXb3pczatWsVALV27VqllFI7duxQANTChQsdXtaUKVMUABUYGKj69eunXn75ZbVt27Zy09m73mWfQ0REhLpw4YLl/a+//loBUN9++63lvfbt26u4uDiVmZlpee+HH35QAFTDhg2t2srLy7P6f1FRkWrdurW68cYbrd5v2LChGjlypN3rT39i/rpf/v5Vq1atrNZVKaUAKC8vL7Vnzx6r9wcPHqx8fX3V4cOHLe+dOXNGBQcHq+7du1vee/7551VFp6+yfeXo0aNKKaWWLFmiAKhff/1V7N+PP/6oAKj58+dbvb9ixYpy7zds2FABUCtWrLC53iRjTrtfTvOcTM5ivrtXvpfl4Ntvv23X9NOnT1cA1P/+9z/Le0VFRapLly4qKChIZWdnW963Nz8DAwPdIj9r1c+5e/fujaioKMTHx+PWW29FYGAgvvnmG8tfMy9cuIA1a9Zg2LBhyMnJwfnz53H+/HlkZGQgOTkZBw8etDxJ0GQywcvr8uYrLS1FRkYGgoKC0Lx5c2zfvt3hvmVnZ9v1Fx+J0Wi0PEzAbDbjwoULKCkpQceOHa36ExYWhtzcXO3PQsLCwrBnzx4cPHjQ6f4AsPyV6/vvv0deXp5D806dOhULFixAhw4d8P333+PZZ59FUlISrr76auzbt88ynb3rXeb222+3+qtn2c+Jjhw5AgBITU3Fzp07MXLkSKu/0vXp0wctW7Yst7y/3gt58eJFZGVl4frrr3dqHyA95q/75K89evToYZVTpaWl+OGHHzB48GA0btzY8n5cXBxGjBiBn376CdnZ2Q61ERYWBgBYtmyZeP/WwoULERoaij59+lj2mfPnzyMpKQlBQUHlfo6XkJCA5ORkh/pBFWNOu09O85xMlcV8d498LzvP2rs9li9fjtjYWAwfPtzyno+PDx555BFcunQJ69evt7zvaflZqwbRM2fOxMqVK7Fo0SL0798f58+fh8lkssQPHToEpRSee+45REVFWb2ef/55AH8+QMNsNuPtt99G06ZNYTKZEBkZiaioKOzatcup+wVDQkKQk5NTqfWbN28e2rZta7mHIioqCv/3f/9n1Z+HH34YzZo1Q79+/VC/fn2MHj26XHmKadOmITMzE82aNUObNm0wadIk7Nq1y+H+JCQkYOLEifj4448RGRmJ5ORkzJw50+7tM3z4cPz444+4ePEifvjhB4wYMQI7duzAzTffjIKCAofWu0yDBg2s/l928i679/H48eMAgKZNm5abt6KfbS5btgzXXnst/Pz8EB4ejqioKMyaNavS94xSecxf98pfe5b/V+fOnUNeXl6FedaiRQuYzWacPHnSoTZ69OiBoUOHYurUqYiMjMSgQYMwZ84cFBYWWqY5ePAgsrKyEB0dXW6/uXTpUrmHJv293+Q85rR75TTPyVQZzHf3yPeQkBAAsHt7HD9+HE2bNrX8UaNMixYtLPEynpaftWoQ3alTJ/Tu3RtDhw7FN998g9atW2PEiBG4dOkSgMtJCQBPPPEEVq5cWeHrqquuAnD58esTJ05E9+7d8b///Q/ff/89Vq5ciVatWlmW44jExET88ccfKCoqcmrd/ve//2HUqFFo0qQJPvnkE6xYsQIrV67EjTfeaNWf6Oho7Ny5E9988w3+8Y9/YO3atejXrx9GjhxpmaZ79+44fPgw/vOf/6B169b4+OOPcfXVV+Pjjz92uF9vvvkmdu3ahWeeeQb5+fl45JFH0KpVK5w6dcruZYSEhKBPnz6YP38+Ro4cicOHD2PLli0OrXcZo9FYYRtKKYfX7ccff8Q//vEP+Pn54f3338fy5cuxcuVKjBgxwqnlkR7z1z3zV1KZJ1pLdaZLS0vLTbdo0SJs2rQJ48aNw+nTpzF69GgkJSVZ7TfR0dHiPjNt2rQq6zdZY067Z07znEzOYL67R76XPVjt999/d7g9HY/Mzxr7IfkVVHY/xt/viyu7fyAlJUUppVR6eroCoCZPnmxzme3atVM33HBDuffr1atndZ+EvfdjvPLKKwqAWrBgge0VUuXvxxg0aJBq3LixMpvNVtNdd9115e4Z+qvS0lI1ZswYBUAdPHiwwmlycnJUhw4dVL169bR9+vv9GBXZuHGjAqCeffZZ7bIkM2bMUADUZ599ppSyf73LPofXX3+93DIBqOeff14pdfn+SwDq6aefLjddy5YtrZb56KOPKn9/f6t7d5RSasSIEeXu1+T9V85j/jYUl+MO+SvdEz127Fir90pKSlRAQIAaNmxYuWU8+OCDysvLS2VlZSmllHrnnXcUAHXx4kWr6Z577jmre6IrMn/+fAVAffTRR0oppR5++GFlNBrL3atVkYYNGzp1rxpZY043FJfjDjn9Vzwnky3M94biclw135s3b64iIiJUTk6OdjqllOrbt6+KjY0td4/6559/bvV8A0fyMygoyC3ys1Z9E/13PXv2RKdOnTB9+nQUFBQgOjoaPXv2xAcffIDU1NRy0587d87yb6PRWO4vJwsXLrTcr+GoBx98EHFxcXj88cfxxx9/lIufPXsWL730kjh/2V9z/9qnLVu2YNOmTVbTZWRkWP3fy8sLbdu2BQDLTxz/Pk1QUBCuuuoqq59A2iM7OxslJSVW77Vp0wZeXl7aZeXl5ZXrd5nvvvsOwJ8/47J3ve0VFxeH9u3bY968eVY/L1m5ciX27t1rNa3RaITBYLD69uvYsWNYunSpU22TY5i/rpm/zjIajejbty++/vprqxJV6enpWLBgAbp162b5mVnZkzo3bNhgma6sJMZfXbx4sdznXPaU1LJ1GDZsGEpLS/Hiiy+W61NJSQkyMzMru2pkJ+a0a+Y0z8lUHZjvrpnvwOVnIGRkZOC+++4rtwzg8hPzly1bBgDo378/0tLS8MUXX1jiJSUlmDFjBoKCgtCjRw8AjuVnYGCgW5x7a3WJK+By0fDbbrsNc+fOxYMPPoiZM2eiW7duaNOmDe6//340btwY6enp2LRpE06dOmWpQTdw4EBMmzYN9957L6677jr8/vvvmD9/vtUDcRxRp04dLFmyBP3790f79u1x1113ISkpCQCwfft2fPbZZ+jSpYs4/8CBA7F48WIMGTIEAwYMwNGjRzF79my0bNnS8lMZ4HKB+wsXLuDGG29E/fr1cfz4ccyYMQPt27e33L/QsmVL9OzZE0lJSQgPD8fWrVuxaNEijBs3zqF1WrNmDcaNG4fbbrsNzZo1Q0lJCT799FMYjUYMHTpUnC8vLw/XXXcdrr32Wtx0002Ij49HZmYmli5dih9//BGDBw9Ghw4dHFpvR6SkpGDAgAHo1q0bRo8ejQsXLmDGjBlo1aqV1TIHDBiAt956CzfddBNGjBiBs2fPYubMmbjqqqucun+FHMf8db38rYyXXnoJK1euRLdu3fDwww/D29sbH3zwAQoLC/Haa69Zpuvbty8aNGiAf/7zn5g0aRKMRiP+85//ICoqCidOnLBMN2/ePLz//vsYMmQImjRpgpycHHz00UcICQlB//79AVy+b3rMmDFISUnBzp070bdvX/j4+ODgwYNYuHAh3nnnHafLnpDjmNOul9M8J1N1Yb67Xr4Dlx/49/vvv+Pll1/Gjh07MHz4cDRs2BAZGRlYsWIFVq9ejQULFgAAHnjgAXzwwQcYNWoUtm3bhkaNGmHRokXYuHEjpk+fbnlAmSP5mZSUhFWrVuGtt95C3bp1kZCQ4HCJyyuihr4Bv6Kkn5IodfmnFE2aNFFNmjSxPHL+8OHD6p577lGxsbHKx8dH1atXTw0cOFAtWrTIMl9BQYF6/PHHVVxcnPL391ddu3ZVmzZtKvcTD3t/SlLmzJkz6rHHHlPNmjVTfn5+KiAgQCUlJamXX37Z8lNGpcr/lMRsNqtXXnlFNWzYUJlMJtWhQwe1bNkyNXLkSKufkixatEj17dtXRUdHK19fX9WgQQM1ZswYlZqaapnmpZdeUp06dVJhYWHK399fJSYmqpdfflkVFRVp+/73n5IcOXJEjR49WjVp0kT5+fmp8PBwdcMNN6hVq1Zpl1NcXKw++ugjNXjwYMv6BAQEqA4dOqjXX3/dqoyAvett70/Hynz11VeqRYsWymQyqZYtW6rFixeXW6ZSSn3yySeqadOmymQyqcTERDVnzpwKy+/wp2POY/42tEznDvn7d/b+nLvM9u3bVXJysgoKClIBAQHqhhtuUD///HO56bZt26Y6d+5s2Q5vvfVWuRJX27dvV8OHD1cNGjRQJpNJRUdHq4EDB6qtW7eWW96HH36okpKSlL+/vwoODlZt2rRRTz75pDpz5oxlGv6cu2owpxtapnOHnOY5mSqD+d7QMp075PtfrV69Wg0aNEhFR0crb29vFRUVpW6++Wb19ddfW02Xnp6u7r33XhUZGal8fX1VmzZtKtzm9ubn/v37Vffu3ZW/v78C4LK5alDKXe/mJiIiIiIiIrqyavU90URERERERESO4CCaiIiIiIiIyE4cRBMRERERERHZiYNoIiIiIiIiIjtxEE1ERERERERkJw6iK6FRo0YYNWqU5f/r1q2DwWDAunXraqxPf+eKfQKAnj17omfPnjXdjWpx7NgxGAwGzJ071+F5586dC4PBgGPHjlV5v6g85rDzPDmHyT0xn53nyfnMc7JnYr47z5Pz/Upy20F02YGt7OXn54dmzZph3LhxSE9Pr+nuOWT58uV44YUXHJ5vyZIl6NevHyIjI+Hr64u6deti2LBhWLNmTdV30oWdO3cOjz76KBITE+Hv74/o6Gh06tQJTz31lFWRe3ItzGHmcM+ePa32AenlzLalK4v5zHwuw3Oy52O+M9//Kjs7G1OnTkW7du0QFBQEf39/tG7dGk899RTOnDlTbe3+/PPPeOGFF5CZmVltbeh410irVWjatGlISEhAQUEBfvrpJ8yaNQvLly/H7t27ERAQcEX70r17d+Tn58PX19eh+ZYvX46ZM2fancRKKYwePRpz585Fhw4dMHHiRMTGxiI1NRVLlixBr169sHHjRlx33XVOrMWV8cMPP1TJci5cuICOHTsiOzsbo0ePRmJiIjIyMrBr1y7MmjULDz30EIKCgqqkLaoezOHam8PPPvss7rvvPsv/f/31V7z77rt45pln0KJFC8v7bdu2rXRbdGUwn2tvPgM8J9c2zPfane8AcOTIEfTu3RsnTpzAbbfdhgceeAC+vr7YtWsXPvnkEyxZsgR//PFHlbX3Vz///DOmTp2KUaNGISwsrFra0HH7QXS/fv3QsWNHAMB9992HiIgIvPXWW/j6668xfPjwCufJzc1FYGBglffFy8sLfn5+Vb7cv3vzzTcxd+5cTJgwAW+99RYMBoMl9uyzz+LTTz+Ft7drf7SOHuQkn3zyCU6cOFHhASs7O7vK2qHqwxyuvTncp08fq//7+fnh3XffRZ8+fbQ/Nauuz7+6uWu/HcF8rr35DPCcXNsw32t3vpeUlOCWW25Beno61q1bh27dulnFX375Zbz66qtV0pYrctufc0tuvPFGAMDRo0cBAKNGjUJQUBAOHz6M/v37Izg4GHfeeScAwGw2Y/r06WjVqhX8/PwQExODMWPG4OLFi1bLVErhpZdeQv369REQEIAbbrgBe/bsKde2dO/Dli1b0L9/f9SpUweBgYFo27Yt3nnnHUv/Zs6cCQBWP42R5OfnIyUlBYmJiXjjjTcqnPbuu+9Gp06dxGX8+OOPuO2229CgQQOYTCbEx8fjscceQ35+vtV0aWlpuPfee1G/fn2YTCbExcVh0KBBVvcGbd26FcnJyYiMjIS/vz8SEhIwevRose0yFd2PMWPGDLRq1QoBAQGoU6cOOnbsiAULFmiXc/jwYRiNRlx77bXlYiEhIVYHVHvXu2yfOX36NAYPHoygoCBERUXhiSeeQGlpqdW0mZmZGDVqFEJDQxEWFoaRI0dW+LOSXbt2YdSoUWjcuDH8/PwQGxuL0aNHIyMjw8aWqn2Yw7Urh2154YUXYDAYsHfvXowYMQJ16tSxnKhLSkrw4osvokmTJjCZTGjUqBGeeeYZFBYWWi1D+kn43++pKy4uxtSpU9G0aVP4+fkhIiIC3bp1w8qVK63m279/P2699VaEh4fDz88PHTt2xDfffGM1TdnPHdevX4+HH34Y0dHRqF+/fqW2hTtiPteufOY5uXZjvteufP/qq6/w22+/4dlnny03gAYu5/zLL79s9d7ChQuRlJQEf39/REZG4q677sLp06etprEnP1944QVMmjQJAJCQkGD57K7k8wtc+08lTjh8+DAAICIiwvJeSUkJkpOT0a1bN7zxxhuWn5iMGTMGc+fOxb333otHHnkER48exXvvvYcdO3Zg48aN8PHxAQBMmTIFL730Evr374/+/ftj+/bt6Nu3L4qKimz2Z+XKlRg4cCDi4uLw6KOPIjY2Fvv27cOyZcvw6KOPYsyYMThz5gxWrlyJTz/91ObyfvrpJ1y4cAETJkyA0Wh0ZhNh4cKFyMvLw0MPPYSIiAj88ssvmDFjBk6dOoWFCxdaphs6dCj27NmD8ePHo1GjRjh79ixWrlyJEydOWP7ft29fREVF4emnn0ZYWBiOHTuGxYsXO9ynjz76CI888ghuvfVWPProoygoKMCuXbuwZcsWjBgxQpyvYcOGKC0txaeffoqRI0dWyXoDQGlpKZKTk9G5c2e88cYbWLVqFd588000adIEDz30EIDLB/ZBgwbhp59+woMPPogWLVpgyZIlFfZj5cqVOHLkCO69917ExsZiz549+PDDD7Fnzx5s3rxZe9CubZjDtnlSDtvrtttuQ9OmTfHKK69AKQXg8jcf8+bNw6233orHH38cW7ZsQUpKCvbt24clS5Y43MYLL7yAlJQU3HfffejUqROys7OxdetWbN++3fKt+Z49e9C1a1fUq1cPTz/9NAIDA/Hll19i8ODB+OqrrzBkyBCrZT788MOIiorClClTkJubW+nt4G6Yz7Z5Uj7znFy7Md9t86R8L/vj8d13321XO2Wf9zXXXIOUlBSkp6fjnXfewcaNG7Fjxw7LT7Ltyc9bbrkFf/zxBz777DO8/fbbiIyMBABERUU5vP5OU25qzpw5CoBatWqVOnfunDp58qT6/PPPVUREhPL391enTp1SSik1cuRIBUA9/fTTVvP/+OOPCoCaP3++1fsrVqywev/s2bPK19dXDRgwQJnNZst0zzzzjAKgRo4caXlv7dq1CoBau3atUkqpkpISlZCQoBo2bKguXrxo1c5flzV27Fhl70fxzjvvKABqyZIldk3/9z4ppVReXl656VJSUpTBYFDHjx9XSil18eJFBUC9/vrr4rKXLFmiAKhff/3Vrr78VY8ePVSPHj0s/x80aJBq1aqVw8tJS0tTUVFRCoBKTExUDz74oFqwYIHKzMwsN609663Un/vMtGnTrKbt0KGDSkpKsvx/6dKlCoB67bXXLO+VlJSo66+/XgFQc+bM0bb92WefKQBqw4YNlvfK9uujR4/atf7ujDm8xK7pPT2H/2rhwoXl1vX5559XANTw4cOtpt25c6cCoO677z6r95944gkFQK1Zs8byHgD1/PPPl2uvYcOGVp9/u3bt1IABA7R97NWrl2rTpo0qKCiwvGc2m9V1112nmjZtanmvbP/u1q2bKikp0S7TEzCfl9g1vafnM8/JtQPzfYld03t6vnfo0EGFhobaNW1RUZGKjo5WrVu3Vvn5+Zb3ly1bpgCoKVOmWN6zNz9ff/31Gs1Pt/85d+/evREVFYX4+HjccccdCAoKwpIlS1CvXj2r6cr+Ullm4cKFCA0NRZ8+fXD+/HnLKykpCUFBQVi7di0AYNWqVSgqKsL48eOt/jI5YcIEm33bsWMHjh49igkTJpS74d3Zv3JmZ2cDAIKDg52aHwD8/f0t/87NzcX58+dx3XXXQSmFHTt2WKbx9fXFunXryv20pkzZOi1btgzFxcVO96dsWadOncKvv/7q0HwxMTH47bff8OCDD+LixYuYPXs2RowYgejoaLz44ouWb6wA+9b7rx588EGr/19//fU4cuSI5f/Lly+Ht7e31b5lNBoxfvz4csv6a9sFBQU4f/685edu27dvd2idPQ1z2HGelMP2+ns+Ll++HAAwceJEq/cff/xxAMD//d//OdxGWFgY9uzZg4MHD1YYv3DhAtasWYNhw4YhJyfHss9lZGQgOTkZBw8eLPeztPvvv9/pbyzcEfPZcZ6Uzzwn1y7Md8d5Ur5nZ2fbvS22bt2Ks2fP4uGHH7a6rWPAgAFITEy0Ome7S366/SB65syZWLlyJdauXYu9e/fiyJEjSE5OtprG29u73L1oBw8eRFZWFqKjoxEVFWX1unTpEs6ePQsAOH78OACgadOmVvNHRUWhTp062r6V/ayldevWlVrHvwoJCQEA5OTkOL2MEydOYNSoUQgPD7fcW9SjRw8AQFZWFgDAZDLh1VdfxXfffYeYmBh0794dr732GtLS0izL6dGjB4YOHYqpU6ciMjISgwYNwpw5c8rdj2iPp556CkFBQejUqROaNm2KsWPHYuPGjXbNGxcXh1mzZiE1NRUHDhzAu+++a/n55CeffOLQepfx8/Mr95OQOnXqWB3Mjh8/jri4uHJPGm3evHm5Pl64cAGPPvooYmJi4O/vj6ioKCQkJFTYdm3DHHacp+WwPcrypczx48fh5eWFq666yur92NhYhIWFWT53R0ybNg2ZmZlo1qwZ2rRpg0mTJmHXrl2W+KFDh6CUwnPPPVdun3v++ecBwLLfSf32dMxnx3laPvOcXHsw3x3nSfkeEhJi97Yo+ywrysfExESrc7a75Kfb3xPdqVMny5MBJSaTCV5e1n8vMJvNiI6Oxvz58yuc54r+pt4BiYmJAIDff/8dgwcPdnj+0tJS9OnTBxcuXMBTTz2FxMREBAYG4vTp0xg1ahTMZrNl2gkTJuDmm2/G0qVL8f333+O5555DSkoK1qxZgw4dOsBgMGDRokXYvHkzvv32W3z//fcYPXo03nzzTWzevNmhMhYtWrTAgQMHsGzZMqxYsQJfffUV3n//fUyZMgVTp061axkGgwHNmjVDs2bNMGDAADRt2hTz58/Hfffd59B6A6jyb46GDRuGn3/+GZMmTUL79u0RFBQEs9mMm266qVzbtQ1z2DGenMM6f/3L9F9V5t7Fvz+UqHv37jh8+DC+/vpr/PDDD/j444/x9ttvY/bs2bjvvvss2/aJJ54od6FY5u+Deqnfnor57BhPzmeekz0f890xnpbviYmJ2LFjB06ePIn4+HiHt4fEbfKzRn5EXgXK7sewdS/AyJEjVWBgYLn3H374YWU0Giv83f1fLViwQAFQK1assHr/7NmzNu/H+PXXXxUA9fbbb2vbGDdunN33Y+Tm5qo6deqoFi1a2HWf3d/7tGPHDgVAzZs3z2q6H374odw9Q3/3xx9/qICAAHXnnXeK08yfP18BUB999JG2X3+/H+PvCgsL1YABA5TRaLS6d8IRERERqnnz5kopx9Zb2mfK7s0s88ADDyhvb2+Vk5NjNd2XX35ptcwLFy4oAGrq1KlW0/3xxx/l7tWsTfdfMYeZw3+nuyf63LlzVtO+8sorCoDau3ev1ftpaWkKgHr88cct79WpU0c9+uij5fpnNBqtPv+/y8nJUR06dFD16tVTSimVnp6uAKjJkyfbXBd7929PwXxmPtvCc7LnYL4z35X68/N55ZVXtO0ppdTPP/+sAKj333+/XKxFixaW5xs4kp9vvPEG74muCcOGDUNpaSlefPHFcrGSkhJLSYTevXvDx8cHM2bMsLqXZ/r06TbbuPrqq5GQkIDp06eXK7Hw12WV1curqAzD3wUEBOCpp57Cvn378NRTT1ktp8z//vc//PLLLxXOX/bX3L/Op5SyPO6/TF5eHgoKCqzea9KkCYKDgy0/Fbl48WK59tu3bw8ADv+c5O9lJXx9fdGyZUsopbT3emzZsqXCJ97+8ssvyMjIsPxsxN71dkT//v1RUlKCWbNmWd4rLS3FjBkzrKarqG3Avn2IZMxhz8hhZ/Xv3x9A+c/xrbfeAnD5PqsyTZo0wYYNG6ym+/DDD8t9E/33dQgKCsJVV11l2RbR0dHo2bMnPvjgA6Smppbr07lz55xbGWI+e0g+85xM9mC+e0a+33rrrWjTpg1efvllbNq0qVw8JycHzz77LACgY8eOiI6OxuzZs636991332Hfvn2Wc7Yj+enIZ1cd3P7n3M7q0aMHxowZg5SUFOzcuRN9+/aFj48PDh48iIULF+Kdd97BrbfeaqlFmJKSgoEDB6J///7YsWMHvvvuO8vj1CVeXl6YNWsWbr75ZrRv3x733nsv4uLisH//fuzZswfff/89ACApKQkA8MgjjyA5ORlGoxF33HGHuNxJkyZhz549ePPNN7F27VrceuutiI2NRVpaGpYuXYpffvkFP//8c4XzJiYmokmTJnjiiSdw+vRphISE4Kuvvir34II//vgDvXr1wrBhw9CyZUt4e3tjyZIlSE9Pt/Rt3rx5eP/99zFkyBA0adIEOTk5+OijjxASEmK5wLVX3759ERsbi65duyImJgb79u3De++9hwEDBmgfWvDpp59i/vz5GDJkCJKSkuDr64t9+/bhP//5D/z8/PDMM884tN6OuPnmm9G1a1c8/fTTOHbsGFq2bInFixeXu18jJCTEcj9LcXEx6tWrhx9++MFSR5Gcwxz2jBx2Vrt27TBy5Eh8+OGHyMzMRI8ePfDLL79g3rx5GDx4MG644QbLtPfddx8efPBBDB06FH369MFvv/2G77//vtzn37JlS/Ts2RNJSUkIDw/H1q1bsWjRIowbN84yzcyZM9GtWze0adMG999/Pxo3boz09HRs2rQJp06dwm+//Vbl61obMJ89I595TiZ7MN89I999fHywePFi9O7dG927d8ewYcPQtWtX+Pj4YM+ePViwYAHq1KmDl19+GT4+Pnj11Vdx7733okePHhg+fLilxFWjRo3w2GOPAXAsP8s+u2effRZ33HEHfHx8cPPNN1sG19Wumr/prjaV/SlJmQ8//FAlJSUpf39/FRwcrNq0aaOefPJJdebMGcs0paWlaurUqSouLk75+/urnj17qt27d5crj1LRo+yVUuqnn35Sffr0UcHBwSowMFC1bdtWzZgxwxIvKSlR48ePV1FRUcpgMNj9s5JFixapvn37qvDwcOXt7a3i4uLU7bffrtatW6ft0969e1Xv3r1VUFCQioyMVPfff7/67bffrH5Kcv78eTV27FiVmJioAgMDVWhoqOrcubP68ssvLcvZvn27Gj58uGrQoIEymUwqOjpaDRw4UG3dutVm3//+U5IPPvhAde/eXUVERCiTyaSaNGmiJk2apLKysrTL2bVrl5o0aZK6+uqrrbbDbbfdprZv3241rT3rrZT9Px1TSqmMjAx19913q5CQEBUaGqruvvtuy891/rrMU6dOqSFDhqiwsDAVGhqqbrvtNnXmzJla/dMx5jBz+O8c+Tm3UkoVFxerqVOnqoSEBOXj46Pi4+PV5MmTrcpPKXX583/qqadUZGSkCggIUMnJyerQoUPlPv+XXnpJderUSYWFhSl/f3+VmJioXn75ZVVUVGS1vMOHD6t77rlHxcbGKh8fH1WvXj01cOBAtWjRIss0/Dl3xZjPnp3PPCfXDsx35vtfXbx4UU2ZMkW1adNGBQQEKD8/P9W6dWs1efJklZqaajXtF198oTp06KBMJpMKDw9Xd955p6UkWhl781MppV588UVVr1495eXldcVz1aBUBb9FICIiIiIiIqJyau090URERERERESO4iCaiIiIiIiIyE4cRBMRERERERHZiYNoIiIiIiIiIjtxEE1ERERERERkJw6iiYiIiIiIiOzkXdMd+Duz2YwzZ84gODgYBoOhprtD5LKUUsjJyUHdunXh5eV6fw9jLhPZ5up5DDCXiezh6rnMPCayzaE8rq4C1O+9955q2LChMplMqlOnTmrLli12zXfy5EkFgC+++LLzdfLkyepKY6fzmLnMF1+Ovaozj5ViLvPF15V6uWouM4/54sv+lz15XC3fRH/xxReYOHEiZs+ejc6dO2P69OlITk7GgQMHEB0drZ03ODgYAHDyjz0I+f//Js9VummZGFNrVmjnLTp9Towd/fWUGMvIKxZj129drW3T4C/vk8psluerhr9KZ+fkIL5ZK0vOVLXK5DFQvbmslBJjzv6FfXmLJG08yMsoxkJ85UOpl5fcH6ON3cLbR54gP79UjBWWyLEgP/1h38so91fJuzhKSuXPpLBY7g8A+Ho7lx8XCuVcvmHPr04tE7iyuVzdeQy4di4TeQpXz2Xm8WXVcf0AAOZTf8ixz2bLM8boPzfjHRPEmMHXz1a3KqTbBoB+O2jn1W1bF/x1RkUcyWODsrUlndC5c2dcc801eO+99wBc/glJfHw8xo8fj6efflo7b3Z2NkJDQ5GVegIhISFV3TVyMaU/LRFj6gd5gA0ARSfPirHDm0+IsfOaQXTPfZu1bRoC5H3yig+is7MRGtcAWVlZ1ZIrlcljS/+qKZer4yT4Tf3m2niwUR5Eh7rYILpAM4gOroFBdIGNQbTJyUF0hmYQ3fv4PqeWCVzhQXQ15zHg2rlM5ClcPZeZx5dV2yD6hHzOMf/3XXnG2Fjtco33PCnGDL7+NvtVEQ6iZY7kcZWvUVFREbZt24bevXv/2YiXF3r37o1NmzaVm76wsBDZ2dlWLyKqWY7mMcBcJnJFzGUiz8DrayLXUuWD6PPnz6O0tBQxMTFW78fExCAtLa3c9CkpKQgNDbW84uPjq7pLROQgR/MYYC4TuSLmMpFn4PU1kWup8e/WJ0+ejKysLMvr5MmTNd0lInICc5nIMzCXidwf85ioelX5g8UiIyNhNBqRnp5u9X56ejpiK/jdv8lkgslkqupu1GrVdb+HTslbj4uxcc994dQyn2lbVxuvO7CjGGvRorEYM/RMlmOae55tcZf7PezhaB4D7pHLpdtWirF8zX2wABCouSf6VH6hGCvW5KOXjXws1cwbb/IVY0n9E8VY1p4z2jbXHcwQY2ZNfwI0+3+AjZu/CzXbPshb3u5mza1Z5j+2atv0aiYfP+BB5V88NZeJapvadn1dXfftVuY6uGTKP8XYgo/Wi7EOYYFOt7nxmf+Ksbv7ys9yCfzfcjFWmW2g3e5OL9U9VflVv6+vL5KSkrB69Z9POTabzVi9ejW6dOlS1c0RUTVgHhN5BuYykWdgLhO5lmopcTVx4kSMHDkSHTt2RKdOnTB9+nTk5ubi3nvvrY7miKgaMI+JPANzmcgzMJeJXEe1DKJvv/12nDt3DlOmTEFaWhrat2+PFStWlHsYAhG5LuYxkWdgLhN5BuYykeuolkE0AIwbNw7jxo2rrsUT0RXAPCbyDMxlIs/AXCZyDZ7zJCQiIiIiIiKiasZBNBEREREREZGdqu3n3FSDzKVyzCh/5OYzB8XY8s43a5tsFOwnxmad/EWMGcLlMla6/gCAykiTlxscJi93yTwxduGZl7Vthq/aqI1LaqLsmCdzdpvlvvCKGPO3UaLMW9NkqKYMk052qSZXAQByo4cL5LJa+776TYz5eem3XaCmHFWI5vhh0PQ10Fu/bZ0tVeKjma8gZaq2zYA53zrVHyIisp/T1z82SlzpShHqlqu7tlRZ5/VtRkaKobs2LZGXe15TWrK0RNtkq/CKSxECgDoon+tL578uLzQ0XNumcaBcyov+xG+iiYiIiIiIiOzEQTQRERERERGRnTiIJiIiIiIiIrITB9FEREREREREduIgmoiIiIiIiMhOHEQTERERERER2YmDaCIiIiIiIiI7sU60J/JyrmatV92mYuymO6/Rzpu99Ygcu2eEGDu4L0OMfZx2Udumjq5mrU6IpkYuAPz7/Em5zch4eUZdvUPWpL1ijvxxQYylFetrNeoqOhdrPt8gTf3pSG8fbZvOltA0aeoym8362ptGo9yotyZWVGx2us2MkmIxZjLI65KlqbO9Z/1RbZv6IxoREVUFXc1mXQ3pyjCnHhZjO3sMFWPtPtPUVgZgCA0TY2rXJnm+FknyQv0CtW2av/mvHMzJkdts0EiMHZj8nrbNFtcmy8uNrC/PqPk8bX3S2prhLorfRBMRERERERHZiYNoIiIiIiIiIjtxEE1ERERERERkJw6iiYiIiIiIiOzEQTQRERERERGRnTiIJiIiIiIiIrITS1x5IG05AU0pGYOm3I5h+GhtmwEZ74gxn2uvFmMfr/1QjI1tEKFt87fMXDF2qVR+mH6ApkzP0QJ9mSPz5h/EmHHgP+UZSwrlmK+/tk1yTOn+LWLsSG6+GGtk8tUud3++/Bl2CQ4QY+ma0lmrMi9p22wVKPdJV8atsFAuN1Vko6TIyUK5v7q8ivOVS+sFeOlLVzTz9xNjBWZ5XcK85TZLqqdyChERVRFtWSMbJY9U5lkxljv2ATEWESGfb/JeeU3bpllzjejXUL5mLZg9X4x98otcOhUA/DTnzzs7aspNef0uhurG6ctqZd0tl6YN/fp7MWaoZdez/CaaiIiIiIiIyE4cRBMRERERERHZiYNoIiIiIiIiIjtxEE1ERERERERkJw6iiYiIiIiIiOzEQTQRERERERGRnVjiqrbxkkvCaGdrKpepAgCfIf8QY8b+cnmsek/8R4zty5bLEQHApmy55FAjP3nXDjDK2yCzRC6nAwCX3v+vGAvVlbgyyuXDqGqpbxaIsRDNZ68rGQUA1wTJZayu/vAZMVb67VIxlvnldm2b/Qe2FmPeV7cRY2tf/lKMedlYz+RmUWIsrGWcGFv+7R4xdlWwXFIEAFp9Ix8Hcp95Wowd2J4mxtKL5HJ+AFC6cakYM3YdrJ2XiIjsozRlCg1e8nd5qlhTGhTAjmt6i7GmbeTzWJ0oubxTaV6Rts0iTYmrgr2pYiw1LU+MdQ2Rry0AIOnOTmKs9EKWGDMGmMRYwfHz2jbNmlKXe6/uIsZabt8kxmyVv3J2P6lJVd6rF154AQaDweqVmJhY1c0QUTVjLhO5P+YxkWdgLhO5lmr5JrpVq1ZYtWrVn4148wtvInfEXCZyf8xjIs/AXCZyHdWSfd7e3oiNja2ORRPRFcRcJnJ/zGMiz8BcJnId1fIj84MHD6Ju3bpo3Lgx7rzzTpw4cUKctrCwENnZ2VYvInINzGUi9+dIHgPMZSJXxXMykeuo8kF0586dMXfuXKxYsQKzZs3C0aNHcf311yMnJ6fC6VNSUhAaGmp5xcfHV3WXiMgJzGUi9+doHgPMZSJXxHMykWup8kF0v379cNttt6Ft27ZITk7G8uXLkZmZiS+/rPhpsZMnT0ZWVpbldfLkyaruEhE5gblM5P4czWOAuUzkinhOJnIt1f5EgrCwMDRr1gyHDh2qMG4ymWAyyY9hp6qlLSege7x8QKh2uboyVgUP3iLGhsSFibH3bDyCf8ZD14ux8bN+FGN1vOVtEOmj/7vSxt/TxVh/zXwGo/s//MNdcnn3J+vEmI9BLu8UatKXITt8SS65tvPBFDGWVyrnVYyvvs29qyve1gBgXHdYjJ0slMs7Rfvo98XwpIZizJDYXF7uigNiLFtTFgQAMsaMF2N/HJVLeHhrPk+Tl76UV/HcuWLMk0tc2cpjwHVymYhk7nJOdlbpDLl0JAA0vipMjJnqh4uxkpwCMbZz3RFtmwn1g+U2TXIJzYQmYWKsIFdfVmvr/F/EWJ0QXzHm6yP3JyREf+0R3KqeGKuvuaYxr/5CjBn7jdK26aplrHSqvceXLl3C4cOHERcn1xclItfHXCZyf8xjIs/AXCaqWVU+iH7iiSewfv16HDt2DD///DOGDBkCo9GI4cOHV3VTRFSNmMtE7o95TOQZmMtErqXKf2N66tQpDB8+HBkZGYiKikK3bt2wefNmREVFVXVTRFSNmMtE7o95TOQZmMtErqXKB9Gff/55VS+SiGoAc5nI/TGPiTwDc5nItbjfXdxERERERERENYSDaCIiIiIiIiI7cRBNREREREREZCf3L15LVUg5PWfp73JdZu+ktmKs9ezF8kID62vbXP3lDnm5AXLtvIslco27UKP+70pniuT6u85Spfoaup5QY7qqqbxsMTb7VIYYe3OIvC/63TlM22b6mFfFWEaR/Bn6auoVB3nJdRwBoFCzr2YXloqxu26/WoylbjmmbXPlwp1izOS1S4yFaHLHT1M/EwC2H7ooxsK95XkTO8SKsf070rRtFp6W2/TTzknk2ZTSXAso+ZgEAAYbxzSn2sw6q585NFruj6aWPF0hzn4GNmpc6+ormy4VijGfiCAx1r5nY22bvg3lc07puQtirCQzT4yFN5eXCQARnTXXgEY530pz5DZLMi5p2zRozucl+fJ2V/t3ywvtp23SLfGbaCIiIiIiIiI7cRBNREREREREZCcOoomIiIiIiIjsxEE0ERERERERkZ04iCYiIiIiIiKyEwfRRERERERERHZi7Rz6C+dLQRjbXC/G/tPnXjF2j6b0waiYMG2bZk1JjK515BIGi89mibF4k1waCwD25cklrkpmPiPGvMe+Ii+0WC7FAABgiatyzMvmiLE+YQFiLHVvuhgLff1DbZulmv0tWFOGSVfwxd9XXw4mR1M6y99L/huosXNnMVZXU3ILAA4t/l2MRfj5iDFdX3Pz9aXh/DR90pWoCRjYU4wFH9KUzwNw+qRcJi1UOydRedoSTXC+1JKt5TpNU6pKW6bKoD9mqfOnxFjehAfE2Ber/hBj/jaOWbeN7CLGjM/NEGOGAGb6leDsvm+oE6GNX7okn1cK98olDuv1ainGVIFc+hAAkCeXjYJZzlVtyaisfG2TBl/5GtBLdw2hOXZ4BerLh+XuTxVjhZrymiUHjogxT7yS5TfRRERERERERHbiIJqIiIiIiIjIThxEExEREREREdmJg2giIiIiIiIiO3EQTURERERERGQnDqKJiIiIiIiI7OSJTxwnJxk0JXOUWS6HYWve0WmHxFjpf/8txlKL9GVxvr+YK8au0pTiidOUC7BVTOR8sfxofzhZxoElrJxQIJeE8NJ8DqcuyPMdOi/vTwAQ7iN/Tpklcnmn7jdcJcZMD9ynbXPLXc+KsVLN7vbzs//RLldHV8YqPb9IjPW6r7u8UM32AYCPZ68TY/c92FOMed35hBi7KlsuYQUA295Zro0TOcLZMj42OVuKyhZNqSpVcEmeL+eCdrGlH7/mVHfuubuTGCvWnOsBAP7+Ysi8RC5d6DV8ohir1LYlu2mvLaPjtPPmaEpcKSXHIo+fFWNmTalGAFAl8jWgWXN9aC6Q++Mdpi+tqm2zVN5+xiC5jFXRWf35MeOsXMrLS1Ny7vyu02KsvrZF98RvoomIiIiIiIjsxEE0ERERERERkZ04iCYiIiIiIiKyEwfRRERERERERHbiIJqIiIiIiIjIThxEExEREREREdmJtXXILroSVpXhNfAeMbbr0dnaee+IChFjsaF+YmzF6UwxdqJQX94gKVguGeD98MvaeeUZ9eUNqDx1WC6bdkhThqlrSIAY87JR0kRXxirfLBdH2//zCTHW9NJ72jZLNDXXAr3lnCzW9MdHU54CALI1OVCiNB0yarafjRJXeZr+Glq3lWe8JJfbMdQJ17ZZoCmtoiu7Ul3HQnINSrePa9gqcaVbrm5eXaklW31Vx3fLsY0r5Nju3+X+tGipbRMB8jE24G35nF743AQx9s3yvdomh94jXwtsfHauGDs1SS5/NXzfz9o2DaFR2jjZSbfvt75WO2tRiXyczimVy0IVZ8gl3AKaxmjb1JZb05SbKskpEGPGELlEmy1FaVliLKB9ghhLPSRfQwHA7nM5YqxdjJxvuXn60rSexuGrgQ0bNuDmm29G3bp1YTAYsHTpUqu4UgpTpkxBXFwc/P390bt3bxw8eLCq+ktEVYB5TOQZmMtE7o95TOR+HB5E5+bmol27dpg5c2aF8ddeew3vvvsuZs+ejS1btiAwMBDJyckoKJD/CkNEVxbzmMgzMJeJ3B/zmMj9OPxz7n79+qFfv34VxpRSmD59Ov71r39h0KBBAID//ve/iImJwdKlS3HHHXdUrrdEVCWYx0SegblM5P6Yx0Tup0pv7jp69CjS0tLQu3dvy3uhoaHo3LkzNm3aVOE8hYWFyM7OtnoRUc1xJo8B5jKRq2EuE7k/5jGRa6rSQXRaWhoAICbG+sb8mJgYS+zvUlJSEBoaannFx8dXZZeIyEHO5DHAXCZyNcxlIvfHPCZyTTX+mNHJkycjKyvL8jp58mRNd4mInMBcJvIMzGUi98c8JqpeVTqIjo2NBQCkp6dbvZ+enm6J/Z3JZEJISIjVi4hqjjN5DDCXiVwNc5nI/TGPiVxTldaJTkhIQGxsLFavXo327dsDALKzs7FlyxY89NBDVdmUx3O2pmSl2jTLdfV0tSptKXhgiBibMP9XMfZqjyba5f6656wYm3n4nBhLCpJrPf+eK9cYBoAnvqv4yZm2qKJ8MWbwdb5GYHVwhzzO235YjLUJlD9fX02N39SCQm2b/ZMTbXesAt/9sF+MnfnllHZeb02qFxXLNSl9DfJ65mtqWQJA8/hgMXb6jFwjc80Ha8VYkFH/91pdVJ1Ll4MmuSatuiTXuQSAbzS1Qq8/sUeMGRq10S7XlbhDLrua6jq/OrtcdeGMHMuUz4GX55VzRx38Q4wVnjwvxowZW7Rtpu6Qj2kNWso1330mPCXGhjb9QtsmNDWBO93UXIwVfycfm6H0x8kryZPz2PzdHDFW8n//p503wCRflx7Lks/n3qHyNVfpJf11gJe33GZpUYkYMwb4apero60j7yv3pyTtghg7eF5T79oGk2a7Z52Tr6FVif762uDt/DaqKQ4Poi9duoRDfynSffToUezcuRPh4eFo0KABJkyYgJdeeglNmzZFQkICnnvuOdStWxeDBw+uyn4TUSUwj4k8A3OZyP0xj4ncj8OD6K1bt+KGG26w/H/ixIkAgJEjR2Lu3Ll48sknkZubiwceeACZmZno1q0bVqxYAT8/v6rrNRFVCvOYyDMwl4ncH/OYyP04PIju2bOnzZ8aT5s2DdOmTatUx4io+jCPiTwDc5nI/TGPidxPjT+dm4iIiIiIiMhdcBBNREREREREZCcOoomIiIiIiIjsVKUlrsj1OVvGynxcLvkCAA+3TBZjN9UJFGP/vj5BjG34PU3b5rlieV1aacoJpGvmaxHgo23T2Km/Ni7y4cM/qpJ3uLxP9ZnxmBjLeHuuGNu5L0/bpk/TBnJQUzrLd6VcSsbkpS9706VzvBjzv+d2Mbby4dfFWKhRf9iPmS7fc1fnfbnE28+rD4mxYs29fgAwbuyN8rzbdouxz1/qKMa6NwrXtvnqM/8QY4YGLbXzUtXR3QeqLTOkKeNWmTJV1VVeUlfexbzgLXnGBo3l/tSVYwCAA7vkWESEGDJpag/rjnUAEB8eJMYupLwvxnw0pXoCW9bTtmlo2UJebsJVYqxHq2byQn15zr4S1N7fxdil/anaeUNC5XKWKks+n/vGhIqxvH1ySTkA8G8aI8bMGZocL5DLX6kQfTk1pSlLaQyRy3WZC4vFWKCNspPRXvJ1QnaOvJ5+fnIeq/P6kp6GWBvHMxfEb6KJiIiIiIiI7MRBNBEREREREZGdOIgmIiIiIiIishMH0URERERERER24iCaiIiIiIiIyE4cRBMRERERERHZiSWuXFSlSmk4WcZK56k2/bTx+2LriLGzRfJj9r/cIZcTyCjWP/Y/1Fv+G1CIUd5+p4vk5T7x/qPaNnWcLY2iLfFiY97a6vwfZ8XYhSflMioXC+R9sam/XC4DAH54f5UYK7TxGYrzlej38dN/nBdjjb5bLsYCjXKel0Df1/QJU8TYiTO5YqxYU46oVNnYhwPlkmXGIPlzaWCSY1mZhdomfZZvFWNxk507TtZWlSkLpT02Ovl3/socU6vreKsuyOV6DN0HiDGvRm2cb7NxWzmYmyXPlyUfd9T50/pGE5qKoTpXHZbnOySXyEOoXJIIAHDihBgqOLJFjJniI8WYIcBGm1Q1suT90BQVbGPmHDFyQXNuVUVyuSnvsABti16+8rBJW8bKLPfH1jHHbJaPZ17emuOnpoxVq8Zh2jYPHc0WY0Waa+jQELm8rEo7pm0TLHFFRERERERE5Lk4iCYiIiIiIiKyEwfRRERERERERHbiIJqIiIiIiIjIThxEExEREREREdmJg2giIiIiIiIiO7HElRvSPSofcL6M1dmeXcRYR02ZGQDIK5X7tCWnQIy1CPARY8Gax/MDQKHmsf+ZmvIG54rlEmDG2ydo29TSlPhRxXJpJYOvn/Nt1lL5+XIpid3ZchmmWF95fyu1UaUq2FvOK5NmX+zx9C1iLG/Fz9o2V2+Ty8nsXvq7GAvzlg/tITby6o9TctmQunX8xdjV3VuKsZ+/3q1tc/V7P4gxTbU61PGR1zOvUM5zADh4Ul7P2AJ5HzL4yeW4PJ1Symb5KGeWKXG2NGClSkRW03INQWFizHxGU/qpEgzemnIzweFizCs0Sl5ogxaV6ZJIV5qz9I2J+pnr15djRzSlxYb/01a3RBX1V7cOVDFzlnwc1pWiAgCfIPnaSXeWK80rEmPeYfrju9Jc6+qYdSW3KnFM1fVHV44rqLEmxwFcOHhBjIVpymQ2qqMpS5Yql6IDALTXh10Rv4kmIiIiIiIishMH0URERERERER24iCaiIiIiIiIyE4cRBMRERERERHZiYNoIiIiIiIiIjtxEE1ERERERERkJw6iiYiIiIiIiOzkcJ3oDRs24PXXX8e2bduQmpqKJUuWYPDgwZb4qFGjMG/ePKt5kpOTsWLFikp31tM4W4/S4OX83z5KZv1LjE359aQYuyc6VLvchnWDxNj2S/liTFcdL7VIX3Oxqb9c83ezpjb1uIaR2uU6S1uf29e52t3Vxd3zOFBTX9xbkzuBms/Iz0efVxcK5ZqVZt2eXCTXpLTleIFcX7xvnJyTze7sJsZ++3idts1dmtzp3LupGDMO/IcYM32zV9umj1Hefl6QP8/zmjqiATbqYQd6y3F3qgXtCrlcqfrJlZhXovLl2rMAgFL53KKr51ypGtKa2uMbB48XY92P7dEvV0Nbf9asqVuricGov2x09vPMHyUfP3wb19XOW3r8lBjz63eDGDO27WG7Y4KKzvfaawAbXCGPa4KuZjNsHMNNdeVzYOr6I2LMO9RfjNmqA63tr4bRT75msVUP26Cp9wxNf3Xr4h2uP8cdLpDXs2WASYx5+Wtq0586pm3THTk8GsvNzUW7du0wc+ZMcZqbbroJqampltdnn31WqU4SUdViHhN5BuYykftjHhO5H4e/ie7Xrx/69eunncZkMiE2NtbpThFR9WIeE3kG5jKR+2MeE7mfarknet26dYiOjkbz5s3x0EMPISMjQ5y2sLAQ2dnZVi8iqnmO5DHAXCZyVcxlIvfHPCZyLVU+iL7pppvw3//+F6tXr8arr76K9evXo1+/figV7kFKSUlBaGio5RUfH1/VXSIiBzmaxwBzmcgVMZeJ3B/zmMj1OPxzblvuuOMOy7/btGmDtm3bokmTJli3bh169epVbvrJkydj4sSJlv9nZ2cz0YlqmKN5DDCXiVwRc5nI/TGPiVxPtZe4aty4MSIjI3Ho0KEK4yaTCSEhIVYvInIttvIYYC4TuQPmMpH7Yx4T1bwq/yb6706dOoWMjAzExcVVd1OVoi3poGGr3JRuuc6WqlK5mdp4anKyGJu247QY+1e7emKs1KwrRgV8vDddjLUJlB95X2xjuTolTs7a4tPXnW5TFReKMfMv34mxgvc/EGPzV/2hbfOB9MO2O1bNXC2PdfujLpPbdW8kxkyj7ta2uWbUC/pOCX58a5kYK9UWeQOSNSU8Wuza4VR/kp7Wx/MSWomxHavlCzasThFDXjbK3vhqyljpPs9eU+4QY8fe/0bb5rITF8VYknZO91aZXDYYDFVekkpbhin7vBwrlcu/qUtZ2jYNAcHyvIH6co7ifLr1AGDev1WMLTgn97fbmYNizKuuXHLucqc01x/empI7JfK2tfX567bDT41bi7F1mXIJsKfuDtC2WXxOLmlmXvuTGPMbdL8Y05U6cwWudk52VslF+XPXlUsCALOm3FSEpmSlV5j8x4TSc/J5wRYvP3lIZdaUbLVVVkt3/jT4abaRl/Olcs8Vy/31NWi2rUkzrExN1bbpjhweRF+6dMnqL19Hjx7Fzp07ER4ejvDwcEydOhVDhw5FbGwsDh8+jCeffBJXXXUVkjUDOyK6spjHRJ6BuUzk/pjHRO7H4UH01q1bccMNfxavL7vfYuTIkZg1axZ27dqFefPmITMzE3Xr1kXfvn3x4osvwmSSi3MT0ZXFPCbyDMxlIvfHPCZyPw4Ponv27Kn9uc73339fqQ4RUfVjHhN5BuYykftjHhO5n2p/sBgRERERERGRp+AgmoiIiIiIiMhOHEQTERERERER2anaS1x5Bvk+FV3JIwAw+Dj30If0Hl3E2Ls7z2jnreMt/23k9f4txNj/bTwmxkw2HocfoWnzrObR/kU2yoLoXCyRlxvhbRRjB0c/JcaOXRyvbXNpxiUxFuUjt6krxxWjmQ8AVGmJ8L68/p7OXCpv0DTN/vb7j8fEWKNj07VtemnKMHlrqr4EafbFTE0ZCQBIXCuXx9KR9hkAMBj1h/2uaz8XYxtvkEtKBfvKy7VVFelCvlxSJ0STH4aQMDHmpSnvAWirf5BAKVXxfZuaUkrQlEMB9CWTSpfNFWNefW+Xl6kpYQUA6kKaPK+mxJXBX1MaqzBP3+avcqmloZGa5a76Sl7oPTbq1WlKbGrPvDaOETrne3UVYwfz5eulydPkY8vp+Wu0bda7b4AYM3hr1sVPXzqLql9pToEY8wkP0s5bcDJDjHUK1ny2muvO0gL5XAQARj+5NJyObrneYfr90KC5hvDyl/vjFao5Dgbo29RVn9Wdz71D5eWWpmtKFsI9B6T8JpqIiIiIiIjIThxEExEREREREdmJg2giIiIiIiIiO3EQTURERERERGQnDqKJiIiIiIiI7MRBNBEREREREZGd3PGJ4k6rsDSHHQxemhJEupgNZ7p1FmPLD8qPgr+rYbh2uYcv5oux6asOirFgo/w3lXAf/baL1JShySyRy2zkakoV2SpB46uJNw+QH/v/6TG5LIItrQN8xZifpsOXNOvZ2EbJBHXuRMXv58jltjxdeJRcRuGmArm806GLchma9MMXtG0aNXUdTJrPPqNI7k+JjWOSIaKeNi7OV4kSNV6N2ogxs6a/WYVyCY8AGyXyGkUHirF9aTlibO2k2WLM10ZppTBNWT6qmMFgqLgklUE+/qsSfckYeMvHP7X7dzFW9PNmeZF3j9I2qXZtFWOGBnvEmPGmkfIyL8plswCgcPMueV7NYSDv29ViLNhGiSuDZts6q3T/Fm3cx1feF0Z+MEmeMT9XDIWF+2vb9Op9ixzTHM90bJUsM5hYHsteupKLuutyg2ZfAoCsTLlkWvu+zcRYyblMeaGlmnJ9AMyaspQXU+VzlbdRvkYIjpXL6gHAibXydfvqUxfFWF1fOf8DbZyTOwb7iTHd8cqoma9YUyLWXfEqgoiIiIiIiMhOHEQTERERERER2YmDaCIiIiIiIiI7cRBNREREREREZCcOoomIiIiIiIjsxEE0ERERERERkZ04iCYiIiIiIiKyk0fVibZVB7rC+pZ/BuXlmuW6caVvPa5t8+u3/0+MrcyU6xC+NayDGJvxzW5tmybNujTzl+vG6ergpmlq3QL6Gsm6mK4/WTbq9f2UXaCNS9oGmsSYt43a1CWaXSxcUyv7piZ1xFidBH3db4NfxTV0DcXO1T33BEH3DBJjzfbKdV5Pz/tZjOXb2N+ubSZ/TmFvviTGNgwaJy/z6jhtm67mmhuaiLEfVv4hxq4d1EK7XO/RD4qxjGETxVip5phv6y/EQ3vJdUSpYqqkGKqkqHzAqKlHbKNmubZO7NXXiLH1j78vxm5M3KhtExfl2qrFW+V6zl597xJjhrBobZN+g/qKsRuvOyvPaNTU4DbLNWsBANnn5XkvpMqxvds0MXn7AEDQP7rL86aeEmMGP7m+bGByZ22b5m/ni7HcDfK6+DeJlReq6Q8AeI16pHw/Lsm1rms1TY4XF8vnXYO3vk50aYk8r8+NPcRYzpyvxJh3qL4mubokX3cWFcr5WKK5Djb4+mrb9PGVz2YFZnnb+mqu6Y8XVnAc/4shHeuLsZ275eOVwSj31Zyvb9Md8ZtoIiIiIiIiIjtxEE1ERERERERkJw6iiYiIiIiIiOzEQTQRERERERGRnTiIJiIiIiIiIrITB9FEREREREREdnKoxFVKSgoWL16M/fv3w9/fH9dddx1effVVNG/e3DJNQUEBHn/8cXz++ecoLCxEcnIy3n//fcTExDjUMVVcBFVcWO59g49cnkhbwsqGkhflEitrP14vxjKK9aWfonzk0h+v95fLvvz3//aKsRb++sfhl0B+5P0lTRmfEs2j8r1tbFtd6acSTXmDPE2Jjl255T//v+qhKUVw+/bvxdikpj3F2IiYUG2boSHyto+IkEtiBN8olywr2CGXBgIAQ1jFuWPw0pdi0LmSuVwdzL/8IsY++d+vYixSU4ZMX+AKOHHykhgLnjNbjBUqecmBt/Wz0apMV3rP4CX/fVQ3n615Td3lUjN+qw+KscMbjmjbDNz2rBg7W1QsxkI1JVB0JfsAwNRaLtflLq50Hpe+OQmlpvLntNzN+8V5jEHyOdsWn6hgMdYuIUyMXfo/fYkrs6Y0Tlq6XHqyoLl8HLeldb/mYkxpzssnNx0TY0dfXaxt84Lm+qSuST6XBWryqlkHTVkoAH5N64qx4nS5tNiO1YfEWESofh/KyZGPEeF15HljNOtZfFFfrirkngquXWyVHNNw93OyVlG+GCrR5KK5UH99nZeniRfIbRo1pVVtldXy0lx/R0TI5a9yczV9LdGvZ4hm/9eVsQrUlJsyaUpuAYBvdIgYiwzKFGNmTTnc0rxaXuJq/fr1GDt2LDZv3oyVK1eiuLgYffv2RW7unwebxx57DN9++y0WLlyI9evX48yZM7jllluqvONE5DzmMpH7Yx4TeQbmMpH7ceib6BUrVlj9f+7cuYiOjsa2bdvQvXt3ZGVl4ZNPPsGCBQtw4403AgDmzJmDFi1aYPPmzbj22murrudE5DTmMpH7Yx4TeQbmMpH7qdQ90VlZWQCA8PBwAMC2bdtQXFyM3r17W6ZJTExEgwYNsGnTpgqXUVhYiOzsbKsXEV1ZzGUi91cVeQwwl4lqGs/JRK7P6UG02WzGhAkT0LVrV7Ru3RoAkJaWBl9fX4SFhVlNGxMTg7S0tAqXk5KSgtDQUMsrPj7e2S4RkROYy0Tur6ryGGAuE9UknpOJ3IPTg+ixY8di9+7d+PzzzyvVgcmTJyMrK8vyOnnyZKWWR0SOYS4Tub+qymOAuUxUk3hOJnIPDt0TXWbcuHFYtmwZNmzYgPr161vej42NRVFRETIzM63+Wpaeno7Y2Iqf6GgymWAyOf/0TiJyHnOZyP1VZR4DzGWimsJzMpH7cGgQrZTC+PHjsWTJEqxbtw4JCQlW8aSkJPj4+GD16tUYOnQoAODAgQM4ceIEunTp4lDHDD6+2nJWzrjYp6sY+7/d6WKsvqYURHtNCQ4A2H1eLovz3c/HtfNKbJWbulAsl1k4p4nV8ZZ/mFCkKVMF6H/SkK8pnXW4QC5N8d4rI7Rtej/6mjYuydWU+GnaOko77/EDGWLMoCknYE47K8ZKsuVSDNXlSuZydTAEBYqxnZrSaNPv7iTGUrfp/0r/65ksMZb99W7tvBKvfnc5NR8AfTkVTZkqm2VYNPMarusrxnwMC8RYqaaMCQAcOJMjxppEBIix2Lry8XfLbjnnAGDrh2vF2LVPa2d1GVc6j72fmg7vkPKlT3SFAdUluawRACBf/uxVzgUxFuUtXx+obPk4DQBIPSaGQn3lUoVIO6Vfrk4TuaQlcuVtkHCvvI83adJO36ZQHhGoXElQZxk1+0J0995iLPaqCO1yVYl8TPOJ1uydmmsTU0P9tYAhulH59/zlz9EWdz8na+XJ505vzXWnuVC+PgSAnHy5nJI6f06eUXOtBhvXusZA+bhTVKQp16Vbro02i4vk/dtXU6oq0Fce4uXmyeW4AMAYLB8HdZ+ZLheNAfryvEpTCs3g63xJ1+rk0CB67NixWLBgAb7++msEBwdb7sMIDQ2Fv78/QkND8c9//hMTJ05EeHg4QkJCMH78eHTp0oVPDiRyIcxlIvfHPCbyDMxlIvfj0CB61qxZAICePXtavT9nzhyMGjUKAPD222/Dy8sLQ4cOtSoGT0Sug7lM5P6Yx0SegblM5H4c/jm3LX5+fpg5cyZmzpzpdKeIqHoxl4ncH/OYyDMwl4ncT6XqRBMRERERERHVJhxEExEREREREdmJg2giIiIiIiIiOzlVJ/pKKP3mQ5T6l3/E+srH3hPnuTYxUrvM7/fIJU98NOUeUovkx+wfSy/Stumvefy8rtxUgOYR/MdsPfa/VH7MfrBmuZdK5Xtywn30f285rCk1UKK512fWub1izBBQvpSKvUr+Pd6p+S6mySXJACA4RH5Ev18Def/77Vt5PZdm6MtivKyN1lJxcWJIt4/73H67GIstkUs0AYBvarYY89IcPwK8jGLMEC6vh03OlqjRlb+y1WRCWzGmIOd5QaG+rFax5hjRoGO8GDNNelaMBd50r7bN8FDWT70SDEF19BNo4oaoBlXcm/+vdbfqWS5p6faFq7Zvu4I9qR4GM7+Xqog5TS7nGpQgXzeVXMjVLreO5nrM0DBBjJnXyPuaMcD584KXUT4nBwf5yDPaOJcbNeMI3fV1qWYskK4ZfwCAsYNcPi/2sDyWKs2Tx0Te4UHaNqErhxjumiWumPFEREREREREduIgmoiIiIiIiMhOHEQTERERERER2YmDaCIiIiIiIiI7cRBNREREREREZCcOoomIiIiIiIjsxEE0ERERERERkZ1ctk60upgBlV++XtteTQ2ytF1p2mXqai8rXa01TUyuwnZZoWZeP03ttyKzpuaqJgYAclVaIE9TNy7MW94+B/P1tal16zL1glwjUEf3mQCAQVNbL2f1djEW4+P8bh+dGCPG0nedFmMLzsk1hnuFBTjdn1rLqNvLZfvGpoix9Fx9zfcIzX5TaJbzqn5YNdU3NDj5N1AbeaVVINdRv7aLXNO36Ly+/vrZAxli7PT2U2KswczXxVie5jMBgJBgTd1OIiKqGqnHxFDWoXNiLDtHf05u1ElTRz40XAzlZuaLsfC4MG2b5gL5Wtg3QK5bXXipULtcHd/wQDGmuwq4VCLXgj5ZWKJvtFFTMRSQuFuMpW48LMZikjSfFwDzoZ1izNiprnbemsJvoomIiIiIiIjsxEE0ERERERERkZ04iCYiIiIiIiKyEwfRRERERERERHbiIJqIiIiIiIjIThxEExEREREREdnJZUtcGQf9E8bgoHLvBzz7qThPqLe+7I1ZU9rloqb0k65QSomNajG6Nr01JZp0dKWoACCzRO5xuGbec8Xy4/B1MQCYnSuXodFRJXK5AIO38yVo1uyVyybEm+T9JLplrHa529fKj+/fl1cgxnTb3d+Lf8tylKFVBzF2bfBnYiwt1/kyE6WaXNdVnYuNqZ4SZgZn9xsnjzsAALN8HCjJzBNj3kHlyxX+la+mRN7pC3I5ktPf7BFjtip5+QXK5UiIiKhqGPvcJcbiNLHYC6na5RrC48RYyYyn5f7oyt1qxgIAoDRlo4o0JXiLijRjjEJ9CVnduVU3BjFprhFsXnfmyWUpvYaPFmPBv70gxry7dNY2aagvl9VyVbx6JyIiIiIiIrITB9FEREREREREduIgmoiIiIiIiMhOHEQTERERERER2YmDaCIiIiIiIiI7cRBNREREREREZCeHSlylpKRg8eLF2L9/P/z9/XHdddfh1VdfRfPmzS3T9OzZE+vXr7eab8yYMZg9e7ZDHTOERcMQElLu/TqaUkHbL8klhgAgRPNYe91ydX9p0M0HAEZNOZkLmrJRIZrlZmtKWAFArK9cwulwvvwo/dNFJWJs1rm92jadVZkyVjo+mu2uKwmgK2EFAGsyc8VYjGa7+2tK+PySI5fwAYBe2qhzrmQuVwdj18FizMvwlBjLKJb3cV2uAoCvJn5eU/ais43SezqqWC7JZfDRl42SZ3T+b6eGsBgxduRwlhjbni3nDQBE+MinIgU5YXV5frRAXzbkxg4J2rg7cPc8JqLLmMvl6UpY2Zw3JFSMmTRlTn0iypfWtVLBuKTMpRMXxFhBgXztUZQunzsBwDdWXpd/3lVfjBkaNRRjbXfYuKbPyRZDxv59xFidlXLMEzl0NbV+/XqMHTsWmzdvxsqVK1FcXIy+ffsiN9f6Aun+++9Hamqq5fXaa69VaaeJqHKYy0Tuj3lM5BmYy0Tux6FvolesWGH1/7lz5yI6Ohrbtm1D9+7dLe8HBAQgNja2anpIRFWOuUzk/pjHRJ6BuUzkfip1T3RW1uWfIISHh1u9P3/+fERGRqJ169aYPHky8vLyxGUUFhYiOzvb6kVEVxZzmcj9VUUeA8xloprGczKR63Pom+i/MpvNmDBhArp27YrWrVtb3h8xYgQaNmyIunXrYteuXXjqqadw4MABLF68uMLlpKSkYOrUqc52g4gqiblM5P6qKo8B5jJRTeI5mcg9OD2IHjt2LHbv3o2ffvrJ6v0HHnjA8u82bdogLi4OvXr1wuHDh9GkSZNyy5k8eTImTpxo+X92djbi4+Od7RYROYi5TOT+qiqPAeYyUU3iOZnIPTg1iB43bhyWLVuGDRs2oH59+clwANC5c2cAwKFDhypMcpPJBJPJyafMElGlMJeJ3F9V5jHAXCaqKTwnE7kPhwbRSimMHz8eS5Yswbp165CQYLtEyM6dOwEAcXHOP6r+r24/c1CM3bp9lXbeQ6OfFGOfH5cfTZ+qKf3kikKN8uP7s0rlUjwzN/5XjBkC5Mf6A5f3DXFeTRkaVSKXobFV/koVyaWh9uUXiTHdgwC22Cg3pSsD5q0rZ6YpgRSkKb1WXVwhl6tLgVkuAacrieTvpf8cdJ9vgJfcpm/dMO1ytWyU3XJukfplOpuTTZqHi7EjO/TlB8M0x6wizbHFT1M6rr5JU8sOgKH/YG3cHXhyHhPVJp6cy7rrQ+himvKGAGDwcq58ZG6ufI4L0pyLAKDwwAkxVlAoX+f5+8vDrZJM/TMqjAG+Yqz4glw+0ud8jhgrSrNRVis9VRuvDkpz7WawcX1WUxwaRI8dOxYLFizA119/jeDgYKSlpQEAQkND4e/vj8OHD2PBggXo378/IiIisGvXLjz22GPo3r072rZtWy0rQESOYy4TuT/mMZFnYC4TuR+HBtGzZs0CcLng+1/NmTMHo0aNgq+vL1atWoXp06cjNzcX8fHxGDp0KP71r39VWYeJqPKYy0Tuj3lM5BmYy0Tux+Gfc+vEx8dj/fr1leoQEVU/5jKR+2MeE3kG5jKR+3HNH5kTERERERERuSAOoomIiIiIiIjsxEE0ERERERERkZ04iCYiIiIiIiKyk0MPFnN1xqt7a+PNd24XY89r5lMFch0282/rtG2qzfKDIEr2HZJjFy7JyyyVa6kBgH/rRmLM+K+ZYszgY9IuV8dW7VlRJWq/GXz9xdg/6oWJsXoJdcRY7gV9vb7SUvnhH4GBcjodOZotxto9qN9vyTG3b/hSjKl9W+UZY+o532j6aTHk1XOo88v1qoFDtJO1N0O+WCrGhvy2QTuvIUqz7XX14jV179XFdG2bXkl9tHEiIqo87fWhs9eOtiRdL4bqXLVRjKkiuYY0AJjzi8RYWHSQvNxi+Vzl5a85x9mgqyFt8JbP5UVFcn8AwJBwlVP9qUytZ1etBa3jfj0mIiIiIiIiqiEcRBMRERERERHZiYNoIiIiIiIiIjtxEE1ERERERERkJw6iiYiIiIiIiOzkck/nVury04+zc3JquCd/UgXyE5vNufqnOasC+Ul+JUUlckzzJD/d0+8AoLhQfrqgMVvergafQu1yq4Myy+tpcPIpwQBwSbONskvkNvM0T/sF9E/nLi2RnzKp7Y9mHwEA7+yKn+xdliNlOeNqaiqXzZfkp+mrvAJ5Rhu5rKVZrpdm/Q1KfwiuzJMuneVsmypPXk9zbr62TYO//Jk5/XRuG5+nl5BXQOWOPY5y9TwGXPO8TORqXD2Xa1Mel16Sj/+FxfK1t4/muhwACjTXj6pEPncqzXxeJZWoUmOW9zWjZj0v2bjW9dFc0xg1586auGapao7ksUG5WLafOnUK8fHxNd0NIrdx8uRJ1K9fv6a7UQ5zmch+rprHAHOZyBGumsvMYyL72ZPHLjeINpvNOHPmDIKDg2EwGJCdnY34+HicPHkSISEhNd09l8Pto+fJ20cphZycHNStWxdeLvgXPuayY7h99Dx1+7h6HgPWuZyTk+ORn0NV8dT9tCp56jZy9VzmOdkx3D56nrp9HMljl/s5t5eXV4Uj/5CQEI/6kKoat4+ep26f0NDQmu6CiLnsHG4fPU/cPq6cx4B1LhsMl29b8cTPoSpx+9jmidvIlXOZ52TncPvoeeL2sTePXe9PZUREREREREQuioNoIiIiIiIiIju5/CDaZDLh+eefh8lkqumuuCRuHz1uH9fBz0KP20eP28c18HPQ4/axjdvINfBz0OP20eP2ccEHixERERERERG5Kpf/JpqIiIiIiIjIVXAQTURERERERGQnDqKJiIiIiIiI7MRBNBEREREREZGdOIgmIiIiIiIispNLD6JnzpyJRo0awc/PD507d8Yvv/xS012qMRs2bMDNN9+MunXrwmAwYOnSpVZxpRSmTJmCuLg4+Pv7o3fv3jh48GDNdLYGpKSk4JprrkFwcDCio6MxePBgHDhwwGqagoICjB07FhEREQgKCsLQoUORnp5eQz2uXZjLf2Iuy5jHro+5fBnzWI+57NqYx39iLsuYx3ouO4j+4osvMHHiRDz//PPYvn072rVrh+TkZJw9e7amu1YjcnNz0a5dO8ycObPC+GuvvYZ3330Xs2fPxpYtWxAYGIjk5GQUFBRc4Z7WjPXr12Ps2LHYvHkzVq5cieLiYvTt2xe5ubmWaR577DF8++23WLhwIdavX48zZ87glltuqcFe1w7MZWvMZRnz2LUxl//EPNZjLrsu5rE15rKMeWyDclGdOnVSY8eOtfy/tLRU1a1bV6WkpNRgr1wDALVkyRLL/81ms4qNjVWvv/665b3MzExlMpnUZ599VgM9rHlnz55VANT69euVUpe3h4+Pj1q4cKFlmn379ikAatOmTTXVzVqBuSxjLusxj10Lc7lizGPbmMuug3ksYy7rMY+tueQ30UVFRdi2bRt69+5tec/Lywu9e/fGpk2barBnruno0aNIS0uz2l6hoaHo3Llzrd1eWVlZAIDw8HAAwLZt21BcXGy1jRITE9GgQYNau42uBOayY5jL1pjHroO5bD/mcXnMZdfAPHYMc9ka89iaSw6iz58/j9LSUsTExFi9HxMTg7S0tBrqlesq2ybcXpeZzWZMmDABXbt2RevWrQFc3ka+vr4ICwuzmra2bqMrhbnsGObyn5jHroW5bD/msTXmsutgHjuGufwn5nF53jXdAaKqNnbsWOzevRs//fRTTXeFiJzEPCbyDMxlIvfHPC7PJb+JjoyMhNFoLPd0t/T0dMTGxtZQr1xX2Tbh9gLGjRuHZcuWYe3atahfv77l/djYWBQVFSEzM9Nq+tq4ja4k5rJjmMuXMY9dD3PZfszjPzGXXQvz2DHM5cuYxxVzyUG0r68vkpKSsHr1ast7ZrMZq1evRpcuXWqwZ64pISEBsbGxVtsrOzsbW7ZsqTXbSymFcePGYcmSJVizZg0SEhKs4klJSfDx8bHaRgcOHMCJEydqzTaqCcxlx9T2XGYeuy7msv1qex4DzGVXxTx2TG3PZeaxDTX7XDPZ559/rkwmk5o7d67au3eveuCBB1RYWJhKS0ur6a7ViJycHLVjxw61Y8cOBUC99dZbaseOHer48eNKKaX+/e9/q7CwMPX111+rXbt2qUGDBqmEhASVn59fwz2/Mh566CEVGhqq1q1bp1JTUy2vvLw8yzQPPvigatCggVqzZo3aunWr6tKli+rSpUsN9rp2YC5bYy7LmMeujbn8J+axHnPZdTGPrTGXZcxjPZcdRCul1IwZM1SDBg2Ur6+v6tSpk9q8eXNNd6nGrF27VgEo9xo5cqRS6vJj+J977jkVExOjTCaT6tWrlzpw4EDNdvoKqmjbAFBz5syxTJOfn68efvhhVadOHRUQEKCGDBmiUlNTa67TtQhz+U/MZRnz2PUxly9jHusxl10b8/hPzGUZ81jPoJRSVf/9NhEREREREZHnccl7oomIiIiIiIhcEQfRRERERERERHbiIJqIiIiIiIjIThxEExEREREREdmJg2giIiIiIiIiO3EQTURERERERGQnDqKJiIiIiIiI7MRBNBEREREREZGdOIgmIiIiIiIishMH0URERERERER24iCaiIiIiIiIyE7/D7vdKrJiMaafAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOvTAOdgmVzL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "cc99d21a-b987-41a8-80c4-ce5a3c245062"
      },
      "source": [
        "r = evaluation.shape[0]\n",
        "evaluation.loc[r] = ['RNN-LSTM', str(1 - pers_test), str(pers_test), epochs, batch_size, scores[0], scores[1]]\n",
        "evaluation"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Model Data_Train Data_Test  Epochs  Batch_size      Loss  Accuracy\n",
              "0       CNN        0.8       0.2      50         512  0.263525  0.904857\n",
              "1  RNN-LSTM        0.8       0.2      10          32  0.311144  0.888214"
            ],
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              "      <th></th>\n",
              "      <th>Model</th>\n",
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              "      <th>Epochs</th>\n",
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              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
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              "      <td>0.904857</td>\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2c8f96e9-db2b-4f3a-b1ad-a88122814a1c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2c8f96e9-db2b-4f3a-b1ad-a88122814a1c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FoWnF1UrnIQW"
      },
      "source": [
        "Memasukan hasil dari kinerja model data kedalam CSV dengan Pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARx6-A29f4wC"
      },
      "source": [
        "dict = {\"Model\" : evaluation.Model,\n",
        "        \"Data Train\" : evaluation.Data_Train,\n",
        "        \"Data Test\" : evaluation.Data_Test,\n",
        "        \"Epochs\" : evaluation.Epochs,\n",
        "        \"Batch Size\" : evaluation.Batch_size,\n",
        "        \"Loss\" : evaluation.Loss,\n",
        "        \"Accuracy\" : evaluation.Accuracy\n",
        "        }\n",
        "\n",
        "df = pd.DataFrame(dict)\n",
        "# Save to Drive for CSV data\n",
        "df.to_csv(r\"/content/drive/MyDrive/Proyek/Computer Vision/Fashion-MNIST/hasil_model.csv\", index=False)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model\n",
        "model.save('model-rnn.h5')\n",
        "print('Save Model Success!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WsCru0xFMJEm",
        "outputId": "33827368-dbf3-46a0-c3d1-4f5360bacf50"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Save Model Success!\n"
          ]
        }
      ]
    }
  ]
}