aziz0519/starbucks-finance-forecasting.ipynb Secret

## starbucks-finance-forecasting.ipynb
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        "<a href=\"https://colab.research.google.com/gist/aziz0519/c77fd334275914f132d1818198fd5544/starbucks-finance-forecasting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.metrics import mean_absolute_error, mean_squared_error\n",
        "import math\n",
        "import yfinance as yf\n",
        "import plotly.express as px\n",
        "from pandas.plotting import scatter_matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = yf.download('SBUX')\n",
        "data"
      ],
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          "height": 473
        },
        "id": "PrAB_MzN2Yyt",
        "outputId": "1853fb9e-ff27-4b51-f847-47287991a2e7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close     Volume\n",
              "Date                                                                        \n",
              "1992-06-26   0.328125   0.347656   0.320313   0.335938   0.267131  224358400\n",
              "1992-06-29   0.339844   0.367188   0.332031   0.359375   0.285767   58732800\n",
              "1992-06-30   0.367188   0.371094   0.343750   0.347656   0.276449   34777600\n",
              "1992-07-01   0.351563   0.359375   0.339844   0.355469   0.282661   18316800\n",
              "1992-07-02   0.359375   0.359375   0.347656   0.355469   0.282661   13996800\n",
              "...               ...        ...        ...        ...        ...        ...\n",
              "2023-06-23  99.650002  99.730003  97.519997  98.339996  98.339996   18765000\n",
              "2023-06-26  98.339996  98.769997  97.480003  98.230003  98.230003    6069000\n",
              "2023-06-27  98.389999  99.059998  97.730003  98.720001  98.720001    5034100\n",
              "2023-06-28  98.639999  98.639999  97.300003  98.610001  98.610001    6581900\n",
              "2023-06-29  98.610001  98.830002  97.980003  98.680000  98.680000    5111200\n",
              "\n",
              "[7809 rows x 6 columns]"
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              "      <th>1992-06-26</th>\n",
              "      <td>0.328125</td>\n",
              "      <td>0.347656</td>\n",
              "      <td>0.320313</td>\n",
              "      <td>0.335938</td>\n",
              "      <td>0.267131</td>\n",
              "      <td>224358400</td>\n",
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              "      <th>1992-06-29</th>\n",
              "      <td>0.339844</td>\n",
              "      <td>0.367188</td>\n",
              "      <td>0.332031</td>\n",
              "      <td>0.359375</td>\n",
              "      <td>0.285767</td>\n",
              "      <td>58732800</td>\n",
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              "      <th>1992-06-30</th>\n",
              "      <td>0.367188</td>\n",
              "      <td>0.371094</td>\n",
              "      <td>0.343750</td>\n",
              "      <td>0.347656</td>\n",
              "      <td>0.276449</td>\n",
              "      <td>34777600</td>\n",
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              "      <th>1992-07-01</th>\n",
              "      <td>0.351563</td>\n",
              "      <td>0.359375</td>\n",
              "      <td>0.339844</td>\n",
              "      <td>0.355469</td>\n",
              "      <td>0.282661</td>\n",
              "      <td>18316800</td>\n",
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              "    <tr>\n",
              "      <th>1992-07-02</th>\n",
              "      <td>0.359375</td>\n",
              "      <td>0.359375</td>\n",
              "      <td>0.347656</td>\n",
              "      <td>0.355469</td>\n",
              "      <td>0.282661</td>\n",
              "      <td>13996800</td>\n",
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              "      <th>...</th>\n",
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              "      <th>2023-06-23</th>\n",
              "      <td>99.650002</td>\n",
              "      <td>99.730003</td>\n",
              "      <td>97.519997</td>\n",
              "      <td>98.339996</td>\n",
              "      <td>98.339996</td>\n",
              "      <td>18765000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-06-26</th>\n",
              "      <td>98.339996</td>\n",
              "      <td>98.769997</td>\n",
              "      <td>97.480003</td>\n",
              "      <td>98.230003</td>\n",
              "      <td>98.230003</td>\n",
              "      <td>6069000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-06-27</th>\n",
              "      <td>98.389999</td>\n",
              "      <td>99.059998</td>\n",
              "      <td>97.730003</td>\n",
              "      <td>98.720001</td>\n",
              "      <td>98.720001</td>\n",
              "      <td>5034100</td>\n",
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              "      <th>2023-06-28</th>\n",
              "      <td>98.639999</td>\n",
              "      <td>98.639999</td>\n",
              "      <td>97.300003</td>\n",
              "      <td>98.610001</td>\n",
              "      <td>98.610001</td>\n",
              "      <td>6581900</td>\n",
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              "      <th>2023-06-29</th>\n",
              "      <td>98.610001</td>\n",
              "      <td>98.830002</td>\n",
              "      <td>97.980003</td>\n",
              "      <td>98.680000</td>\n",
              "      <td>98.680000</td>\n",
              "      <td>5111200</td>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "def plot_col(df):\n",
        "  num_cols = len(df.columns)\n",
        "  fig, ax = plt.subplots(num_cols, figsize=(10, 2.5*num_cols))\n",
        "  for i, column in enumerate(df.columns):\n",
        "    sns.lineplot(ax=ax[i], data=df, x=df.index, y=column)\n",
        "    ax[0].set_title('Starbucks Stock Prices 2010 - 2023')\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n",
        "\n",
        "plot_col(data)"
      ],
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          "height": 1000
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        "outputId": "806c8b7e-92d9-4d35-f03f-1eeb95571e28"
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1500 with 6 Axes>"
            ],
            "image/png": 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2YOTIkejbty9Wr15donYAuYF3enq6zrskIqpq2NNNRFTF5A0VLhgIFBbw/v777xg1ahQ+/fRTMS07O7tMKxFHRETo9JBGRkZCq9WKi2C5uroC0F/lOK9HOI+npyecnJxw6dKlEt132rRpqFWrFubOnQtnZ2e89957emWCg4Px1ltv4a233kJERASaNm2KTz/9FOvXry/FE+Z68udb2M82b9i3oVXFr127Bg8PD9jb28PW1tboz2sOwcHB0Gq1uHLlCpo2bVpoGSB3tfdu3bqVqM6yvKeHDx+iR48eUCqV2LdvnxgMF9SoUSPIZDKcOnUKw4YNE9NzcnJw7tw5nbSS8vf3h6enJ06dOqWXd+LEiUJ/Lnk2bNigM9Q7KCio2Htu27YN48ePx5AhQ7Bq1SqDZZo2bYrvv/8eV69e1RmJcPz4cTG/oOPHj2Pw4MFo2bIlNm3aVOiXMYbktd/QyBgioqqCPd1ERJVUWFiYwZWl8+YKFxzWbG9vbzCQtrKy0qtjxYoVZep9fjIAWLFiBQCgd+/eAHKHEnt4eODQoUM65b766iudc6lUikGDBuHvv/82GMwYeuY5c+bg7bffxqxZs/D111+L6ZmZmTrblgG5gZ2jo6PB4bcF7du3z2D6kz/fvBXen/z5+vr6omnTpvjpp5908i5duoR///1XHBJszOc1p0GDBkEqlWLhwoV6oyLy2tyzZ084OTlh0aJFUKlUenUkJCQAeLr3lJGRgT59+uD+/fv4559/9KY55HF2dka3bt2wfv16pKWliek///wz0tPTMXTo0OIf2oDnnnsO27dvx927d8W0ffv24caNG8XW2a5dO3Tr1k38FBd0Hzp0CMOHD0fHjh2xYcMGvbn0eQYOHAhra2udf1uCIGD16tXw9/fX2Sbs6tWr6Nu3L2rWrInt27cXOsohMTHR4O9i3jB+Q+sREBFVFezpJiKqpKZNm4bMzEwMHjwY9erVQ05ODo4ePYrffvsNNWvWxJgxY8SyLVq0wN69e/HZZ5/Bz88PgYGBCA0NRb9+/fDzzz/D2dkZDRo0QHh4OPbu3au3hVRJ3Lp1CwMGDECvXr0QHh6O9evX46WXXkJISIhYZvz48ViyZAnGjx+Pli1b4tChQ7hx44ZeXYsWLcK///6LTp06YeLEiahfvz5iY2OxefNmHD582OACT8uWLUNKSgqmTJkCR0dHvPzyy7hx4wa6du2KYcOGoUGDBpDJZPjzzz8RHx+P4cOHF/k8AwcORGBgIPr374/g4GBkZGRg7969+Pvvv9GqVSv0798fQO5Q7AYNGuC3335DnTp14ObmhkaNGqFRo0ZYtmwZevfujTZt2mDcuHHilmHOzs46e3kb63nNqVatWvjggw/EBeaGDBkChUKBkydPws/PD4sXL4aTkxO+/vprvPLKK2jevDmGDx8OT09P3LlzBzt27EC7du2wcuXKp3pPI0aMwIkTJzB27FhcvXpVZ29pBwcHDBo0SDz/+OOP0bZtW/HnfO/ePXz66afo0aMHevXqpVPvypUrkZycjJiYGADA33//jXv37gHI/bfn7OwMAHj//fexefNmdOnSBdOnT0d6ejqWLVuGxo0b6/wbfFq3b9/GgAEDIJFI8Pzzz+tNH2nSpIm4VV21atXwxhtvYNmyZVCpVGjVqhW2bt2K//77Dxs2bBAXQ0xLS0PPnj3x6NEjvPPOO9ixY4dOncHBwWjTpg2A3P3YV69ejUGDBiEoKAhpaWnYvXs39uzZg/79+xc6b5+IqEqw2LrpRERkUjt37hTGjh0r1KtXT3BwcBDkcrlQq1YtYdq0aUJ8fLxO2WvXrgkdO3YUbG1tBQDi9mGPHj0SxowZI3h4eAgODg5Cz549hWvXrgk1atTQ2WKs4HZVT8rbSurKlSvC888/Lzg6Ogqurq7C1KlThaysLJ2ymZmZwrhx4wRnZ2fB0dFRGDZsmPDgwQODW27dvn1bGDlypODp6SkoFAohKChImDJliqBUKgttk0ajEV588UVBJpMJW7duFRITE4UpU6YI9erVE+zt7QVnZ2chNDRU2LRpU7E/319++UUYPny4EBwcLNja2go2NjZCgwYNhA8++EBITU3VKXv06FGhRYsWglwu13uWvXv3Cu3atRNsbW0FJycnoX///sKVK1f07meM5y1M3pZhmzdvLvKZ895lQkJCoXlP+vHHH4VmzZoJCoVCcHV1FTp16iTs2bNH7/49e/YUnJ2dBRsbGyE4OFgYPXq0cOrUKUEQhKd6TzVq1NDZwqrgp0aNGnrl//vvP6Ft27aCjY2N4OnpKUyZMkXvfRZX761bt3TKXrp0SejRo4dgZ2cnuLi4CCNGjBDi4uKKbXtp5L3Dwj5P/vvRaDTCokWLhBo1aghyuVxo2LChsH79ep0yedv4FfYp+P8BJ0+eFIYOHSpUr15dUCgUgr29vdC8eXPhs88+09mujIioKpIIgoGxQERERERERET01Dinm4iIiIiIiMhEGHQTERERERERmQiDbiIiIiIiIiITYdBNREREREREZCIMuomIiIiIiIhMhEE3ERERERERkYnILN2A8kCr1SImJgaOjo6QSCSWbg4RERERERGVc4IgIC0tDX5+fpBKC+/PZtANICYmBgEBAZZuBhEREREREVUwd+/eRbVq1QrNZ9ANwNHREUDuD8vJycnCrSEiIiIiIqLyLjU1FQEBAWI8WRgG3YA4pNzJyYlBNxEREREREZVYcVOUuZAaERERERERkYkw6CYiIiIiIiIyEYsG3YcOHUL//v3h5+cHiUSCrVu3inkqlQozZ85E48aNYW9vDz8/P4wcORIxMTE6ddSsWRMSiUTns2TJEjM/CREREREREZE+iwbdGRkZCAkJwapVq/TyMjMzcebMGcyZMwdnzpzBli1bcP36dQwYMECv7MKFCxEbGyt+pk2bZo7mExERERERERXJogup9e7dG7179zaY5+zsjD179uikrVy5Eq1bt8adO3dQvXp1Md3R0RE+Pj4mbSsREREREVF5pNEKmPHbOVR3s8PbPetaujn0hAo1pzslJQUSiQQuLi466UuWLIG7uzuaNWuGZcuWQa1WW6aBREREREREZnboRgL+Oh+DlWGRlm4KGVBhtgzLzs7GzJkz8eKLL+ps6/X666+jefPmcHNzw9GjRzFr1izExsbis88+K7QupVIJpVIpnqemppq07URERERERKYS8SDN0k2gIlSIoFulUmHYsGEQBAFff/21Tt6bb74pHjdp0gRyuRyvvvoqFi9eDIVCYbC+xYsXY8GCBSZtMxERERERkTmkZKks3QQqQrkfXp4XcN++fRt79uzR6eU2JDQ0FGq1GtHR0YWWmTVrFlJSUsTP3bt3jdxqIiIiIiIi85BAIh6rNVoLtoQMKdc93XkBd0REBMLCwuDu7l7sNefOnYNUKoWXl1ehZRQKRaG94ERERERERBWJlTQ/6D53Nxkta7pZsDX0JIsG3enp6YiMzJ/sf+vWLZw7dw5ubm7w9fXF888/jzNnzmD79u3QaDSIi4sDALi5uUEulyM8PBzHjx9Hly5d4OjoiPDwcMyYMQMvv/wyXF1dLfVYREREREREZmNjbSUeX49PY9Bdzlg06D516hS6dOkinufNzx41ahTmz5+Pv/76CwDQtGlTnevCwsLQuXNnKBQK/Prrr5g/fz6USiUCAwMxY8YMnXneRERERERElVmWSiMep2WrIQgCcjRaKGRWRVxF5iIRBEGwdCMsLTU1Fc7OzkhJSSl2zjgREREREVF5oVRrUHf2LvF8SpdgeDnaYP7fl/H9yJboWt/bgq2r3EoaR5brOd1ERERERERUuLiUbJ3zVWFR4vHne28w6C4Hyv3q5URERERERFXdlZhUdP30AP44fU8nPSkjp9Br1JoqP6i5XGDQTUREREREVM6NXXsSUQkZeGvzeZ30R5mFB92FEQQBhyMSkZiuNFbzqAgMuomIiIiIiMq5uNT8YeRXY1PF46QMVaHXKNW5e3ZfvJeCPl/+h8MRibgRn4bunx/Cyz8cR+8v/zNdg0nEOd1EREREREQVyPW4NNT3zV2466uwyCLLpmSq0H/lYQDAyz8c18lLSGNPtzkw6CYiIiIiIqpA3vjtHGRWEtT3dcLNxIxCy/m52GDB9stmbFnZxKVk435yFlrUcLV0U0yCQTcREREREVEFM3XjWYRUc4afsw1iUrLxyjM1oFRrsOlU/kJrRyIfWrCFJffM4n0AgJ3TO4g9+JUJ53QTERERERGVY1EJ6QbTz99LQczjLcPGdwhEtwq4PZgg5K+wXnCuemXCoJuIiIiIiKgcO3snudgy9goZutX3xq8Tn8FPY1ubvlFG8igzfyE4T0eFBVtiOgy6iYiIiIiIyqm4lGxcK9ADbGNtOISTy6SQSiV4Jsgdvs42Jaq7sLrM6WGBbcuspBILtsR0OKebiIiIiIionGqzZB/yRmCHBrrh+K0kg+WcbKzFY3tFycI8hxKWM6VslVY81mqLKFiBWf6rDSIiIiIiIjKowJRnpGQZ3pN7YscgnXN/F1u8FFq92Lpz1OaLcuNTszFu7UkcuP5AJz1brRGPNQUfthJh0E1ERERERFQBpGWr0b6Wh3hex9sBAAwuoLZocGM0KGYlcLXWfEHuzD8uYN+1Bxi95qRO+p2HmeKx1oztMSfLjycgIiIiIiIiPSN/PKFzPqxlACZ3CcbEdafQrpYHhrYIwK2HGQip5mzw+h2vt8fhyES88sMJg/lqjfmC3APXEwymv7X5vHisqaRBN3u6iYiIiIiILEwQBLz7+3ks230NAKDWaHHohm6g2qmuJ6ytpFgzpjXGdwiCs501mga4QCIxvACZRCKBQmYlnu96o4NOfo5GC7XG/BOpu312EIB+kB3xwPDWaBUdg24iIiIiIiILO337ETaduodVYVFISFOi8ycH9MoU1qNdFEeb/MHN3o42aPJEHYnpOaWus7Rm/HZO5zzycXD9IC1bJ/1/u67hRCELxVVkDLqJiIiIiIgsLCohv5e31cd7ce9Rll6Zwnq0i1LPxxFTugTjtc7BcLWX43/PNYFfgS3FYlP072NMEfFp+PPsfYN5Mcn69x72TbhJ22MJnNNNRERERERkYfGpyuILlYFEIsE7PeuJ5/V9nXB0VlcMXHkY5++l4KEJe7rvJmXi7/MxBvPWHrmFdKXaZPcuTxh0ExERERERWdBXByLx2Z4bZr2nrTx3rneWSlNMybJJSFOiw9KwQvPn/33FYLqXo8Ik7bEkBt1EREREREQWtHTX9SLz+4f4YXTbmka9p508NxTMyjFN0P12gVXJS6P2423QKhOLzuk+dOgQ+vfvDz8/P0gkEmzdulUnXxAEzJ07F76+vrC1tUW3bt0QERGhUyYpKQkjRoyAk5MTXFxcMG7cOKSnV85V74iIiIiIqOpZ8WIztKjhatQ683q6M3NMM8T74BMrr0skwJDm/gbLBnnY49l6XgAAlbrybRtm0aA7IyMDISEhWLVqlcH8pUuXYvny5Vi9ejWOHz8Oe3t79OzZE9nZ+avcjRgxApcvX8aePXuwfft2HDp0CBMnTjTXIxAREREREZWZpfamtrN+HHSbYHi5IOg/08uhNfDZsKYGy2+d2g4vta4OADh3LxkT1p3C4YhEo7fLUiw6vLx3797o3bu3wTxBEPDFF19g9uzZGDhwIABg3bp18Pb2xtatWzF8+HBcvXoVu3btwsmTJ9GyZUsAwIoVK9CnTx988skn8PPzM9uzEBERERERlda2c4ZX9jY1e0VuKJhhgsXMlGr9vb9trA339zrayOBkYw1rWW5+jlqLPVfisedKPKKX9DV62yyh3G4ZduvWLcTFxaFbt25imrOzM0JDQxEenruMfHh4OFxcXMSAGwC6desGqVSK48ePm73NREREREREpXE5JrXI/Jrudia5r6udHACQlKEyet2Geu9tHvesbxgfqpOu1uSWlVuV29D0qZXbhdTi4uIAAN7e3jrp3t7eYl5cXBy8vLx08mUyGdzc3MQyhiiVSiiV+Uvyp6YW/YtORERERERkCm72cvH4xAddsffKA7z/50UAwIcDG6J/iGlG77rZWwMAHmUYf8uwO0mZBu6X+5ztannopOetni6X6e9BrtZoIasEwXjFf4IyWLx4MZydncVPQECApZtERERERERVTEKaEst2569c7uVoAweb/H7RznW94GInN3TpU3N9HASfup1k1P2ylWoNen/5n166n4ttkdfJraz00i7cTzFauyyp3AbdPj4+AID4+Hid9Pj4eDHPx8cHDx480MlXq9VISkoSyxgya9YspKSkiJ+7d+8aufVERERERFQeHY1MxCs/HMfp20no9cUhfH0gymJtGffTSb20gsOsFTLThWtuj4P5xPQc9Pz8kNHq3XXJ8IjjJ3u49drjoP/lwgd/XjJKmyyt3AbdgYGB8PHxwb59+8S01NRUHD9+HG3atAEAtGnTBsnJyTh9+rRYZv/+/dBqtQgNDdWrM49CoYCTk5POh4iIiIiIKr+Xvj+O/yIS8dzX4bgWl4b/7bpmkXZkqzS4cC+/J/fL4U0BAFbS/GHWclMG3QWC3PvJWUarN7OQfb8dFPk9+Mff7yoe923sCwDwN9AT/vHgRkZrlyVZdE53eno6IiMjxfNbt27h3LlzcHNzQ/Xq1fHGG2/go48+Qu3atREYGIg5c+bAz88PgwYNAgDUr18fvXr1woQJE7B69WqoVCpMnToVw4cP58rlRERERERUIimZKjjbWZv1nlvO6K5aPrBp7h7WBRdOy1t8zBQKBsFA7irm9oqnDw/vGpjP/SRvJxtEL+mLlCwVnG3zf+5rx7TC6DUn0TTABVuntHvqtpQXFg26T506hS5duojnb775JgBg1KhRWLt2Ld59911kZGRg4sSJSE5ORvv27bFr1y7Y2NiI12zYsAFTp05F165dIZVK8dxzz2H58uVmfxYiIiIiIqqYRv54HNumtjfrPdOV+auG/zyutXhc29sRq19uDolEYtKg2/aJusesOYlNk9o8db1JBRZmW/FiM6wKi8SHgwz3WBcMuIHcOeyVZZuwgiwadHfu3Nngxul5JBIJFi5ciIULFxZaxs3NDRs3bjRF84iIiIiIqApIyjT+Ct7FURfYVsvL0UYnr1cjX5Pf/8mA90R0klHqlUjyh8d3qutpstXXK5JyO6ebiIiIiIjIHBwURQ8tv5WYgSkbzuCSEVfTzlZpxWMPA4uImZqhrbgS05UGSpaOgyK/B93JxrxD9ssrBt1ERERERFRleDgo9NJa1HAp8pounxzAjoux6LfisNHaoS3Q0+1qom3BinP0vWdRz8dRPG/50V6ddpWFSpN7/dQutZ6qnsqEQTcREREREVUhuUHhzukd8Gb3OgCAG/HphZZWa7SF5j0Nf9f81bqlBVYsNyc/F1v8OvEZnbSMnNLt2Z2arcKhGwnQPA7WHz0eqm9toCe9quJPgoiIiIiIqgSlWoPE9Nyg0EoqwaEbCQCAE7eScOD6A4PX5JU3tmxV7tZa/ZqYfv52UZ6c2733ajx+Do8u8fXPf30UI388gV5f5O71ve1cDADjDFWvLBh0ExERERFRlfDdoZvisVQiwdm7yeL56DUnDV5zNCpRPDbW3Ov91+LFPboVMtOtUF4SBRc+A4AZv53HnG2XEfmg8N7/gvJGCUQ8SNdZJPtIgZ9bVcegm4iIiIiIqoQjkQ91zt3tiw+iD0fmB4+J6Tmo+d4OPHyKXtyjkYkYu/YU/jybu0+3jXX5DMkepGWX+prAWf+IxzN71TNmcyq08vmGiYiIiIiIjMxalh/+uNvL8dWI5oWWTctW4bX1p7HlzH29vBe/O1bqe+f1Av9zKVYn3dI93YUpYmdnAEBmjho3EwrvDX8myN3ILaq4LLpPNxERERERkblciUkFAMzuWx+u9nK0tHfDqx2D8M3jYecZSjXsFbkh0qf/3sDOS3EG60nKKN08b5VGiwErj8DGWgrHJ7bRKq893Zk5miLzX/z2GM7fK3wLNQutDVculc83TEREREREZETJmTni4l69G+cvXvZugWHQtxIzxOODjxdZM6R1oFup7n0yOglXY1Nx9k6yuHhbnuKCW0vJLGIV83SlusiAG9CfK16VMegmIiIiIqJK7/fT98RjlwIrdlsV6JLtt+Iwpmw8g8wcdZGLpilVpdtGTILCA9Bt5/SHr5tbpzqeemkZysK/DGj/v/3F1sme7nwMuomIiIiIqNJTqvMD5bwh5IbsuBCLd3+/gJPRjwAAdnIrrBvbWqdMaXunFUUMIX+9a+1S1WUKK15qppdWVE93cqaq2Dql7OkWcU43ERERERFVennzsMe1Dyy27PYL+YudfTm8GRr7O+vkh998+OQlRVJr9Fcl2zg+FDIrKVrUcC1VXabg9MQ8cyB/H/GyYsydj0E3ERERERFVarO2XMQvJ+4AMBxgFqVzXU/InnKsdJaBALa+rxNcS7BlmaUUHBlQ0O2HGQbTn8Se7nwcXk5ERERERJVaXsANAN5OCr18hazwsMjaSgqJRIKD73TGy89UL/ZeWQaGnuetml5QeQu4n+xxT8s2PLy8x+eHSlQfQ+58DLqJiIiIiKjSenKYtKGVx/e+2QmtauoP857UKVg8ruFuj8mdaxV5r50XY9Fw3i5sOnlXJ/3eo8zSNNki+jXx1TlfezTaYLnCesAHNvXDbxOfAQDM6FYHMiuGmnn4kyAiIiIiokprzZFo8XhGtzoI8nTQKxPgZocFAxrppU97VjfIllnl99/uvqy/h/eUjWegFYB3/7igk25oeHl5Y12GIHl025oAgPf71MOXw5shNMgd0Uv6Yno3yy8OV54w6CYiIiIiokrLukCgXFQw2MDPCbP71hfPvxrRXG+Vc3mBwPT9LRf16tDqr5cGIL+3fVz7QPRs6I1D73QpUdvNqeDPKc/RqMQir5k/oCGil/TFxI7BRZar6riQGhERERERVVp5Ae/wVgHFlh3fIQi2civkqLXo09hXL7/gkOkcTcn36s6b513XxxFz+jUo8XXmZKin+6XvjmPjhFC0DfYAAAhC/rcK9Xwczda2io5BNxERERERVVrZqtzg2MbaqkTlR4TWKDSv4CrmOU/Mbb7z0PC87bDrDxB2PQEAYFvCNlhCh9qeAHIXlSs4b/ul747juebV4GxrjfjUbDF93bjWenWQYQy6iYiIiIioUroWlyruqV3SoLsoBXuDVY97uh+kZuPnY7exYn+kTllBECCRSDBmzUkxrTwH3Z6OCpyf2wM2cinqzt6lk/fHmXv65R30V4Enwxh0ExERERFRpXPhXjIGrDwinttYP/1yVlYFerrz5m+/8/sFHLyRoFf2v4hEtAl210mzlZffoBsAnO1y9zBvWcMVp24/KrKshPtwl1i5X0itZs2akEgkep8pU6YAADp37qyXN2nSJAu3moiIiIiILOXcXd2AGzBOT7chhgJuALj9MAOtP95rljYY2zs961q6CZVKue/pPnnyJDSa/CX2L126hO7du2Po0KFi2oQJE7Bw4ULx3M7OzqxtJCIiIiKi8iEiPg2DVh3RS7eRGbe/USaV4FZiRqH5c7Zd1kur7a2/XVl55Odia+kmVCrlPuj29PTUOV+yZAmCg4PRqVMnMc3Ozg4+Pj7mbhoREREREZUzK8MiDaa72suNep9gTwdsPx9T4vKj29aEk421UdtgKsb+WVV1Zfq6JyMjA3PmzEHbtm1Rq1YtBAUF6XxMJScnB+vXr8fYsWN15hBs2LABHh4eaNSoEWbNmoXMTMMrB+ZRKpVITU3V+RARERERUcWWkqXCtnP5gXDBfbdbB7oZ5R559TSu5gx5KXrPp3ctfI/w8qa4UQHfvtLCTC2pHMrU0z1+/HgcPHgQr7zyCnx9fc02iX7r1q1ITk7G6NGjxbSXXnoJNWrUgJ+fHy5cuICZM2fi+vXr2LJlS6H1LF68GAsWLDBDi4mIiIiIyFxe+CZcPG7g64Rx7QNxPzkLjgoZfJ2NM2S6e31vnLiVBLVGq7O1VlHm9W9QoXqPZVZSvNg6AL+cuKuX9/WI5ujRkKOMS0MiFNzhvIRcXFywY8cOtGvXzhRtKlTPnj0hl8vx999/F1pm//796Nq1KyIjIxEcHGywjFKphFKpFM9TU1MREBCAlJQUODk5Gb3dRERERERkem0W70NsSu5e0pEf94bMyvjrRq89cgvz/76C5tVdcOZOcrHlHW1kuDi/p9HbYWpZORrUn7tLL33d2NboWMfTwBVVT2pqKpydnYuNI8v0W+jq6go3N+MMzyip27dvY+/evRg/fnyR5UJDQwEAkZGG53IAgEKhgJOTk86HiIiIiIgqJrVGiwylWux5frtHHZME3AAgfbxtWGEB91cjmuucV6Rh5QXJrAyPZk7JUpm5JRVfmX4TP/zwQ8ydO7fYudPGtGbNGnh5eaFv375Fljt37hwAwNfX1wytIiIiIiIiS9JoBTRZ8C8aztuNpIwcAEBDP2eT3S8zR1Nonru9HO1re+ikudpVnGHlBcmkhoPuRv6m+9lWVmWa0/3pp58iKioK3t7eqFmzJqytdVfhO3PmjFEal0er1WLNmjUYNWoUZLL8JkdFRWHjxo3o06cP3N3dceHCBcyYMQMdO3ZEkyZNjNoGIiIiIiIqf5buuqYXCHd4IvA1JkPB6Ns96qCOtyOaVneBoyI/XmlW3QUDmvqZrC2mVNi6XYEe9mZuScVXpqB70KBBRm5G0fbu3Ys7d+5g7NixOulyuRx79+7FF198gYyMDAQEBOC5557D7Nmzzdo+IiIiIiKyjG8O3dRLM9XQciB3q7Anda7rpdMDvG5sazzKzMHApv4ma4c5tajhiuRK9DzmVqage968ecZuR5F69OgBQ+u9BQQE4ODBg2ZtCxERERERlQ+J6Uq9tHn9G5j0nm1rueul2Vhb6ZxXtoXGxrSriX5NKmaPfXlQ5q+AkpOT8f3332PWrFlISkoCkDus/P79+0ZrHBERERER0ff/3UT9Obtw4PoDnfSWH+0Vj1sHusHTUYEhzauZtC1SA8Ougz0r95BrU86RrwrK1NN94cIFdOvWDc7OzoiOjsaECRPg5uaGLVu24M6dO1i3bp2x20lERERERFXURzuuAgBGrzmJFS82Q/8Q/V7X3yY+A7VWgLUJh5YDwJMht9xKWuj854ruv3e74GFGDudxP6Uy/Ua++eabGD16NCIiImBjYyOm9+nTB4cOHTJa44iIiIiIiAqa9stZpGWrkJyZI6Z1qO0BiURi8oAb0O/pztFoTX5PSwlws0PTABdLN6PCK9Nv5cmTJ/Hqq6/qpfv7+yMuLu6pG0VERERERFSYxvP/xcEbCeK5qedxF1RJO7XJhMoUdCsUCqSmpuql37hxA56elWvRACIiIiKiquJyTAriU7Mt3QwdGq3+gsqA7n7Z1d3MN/y5sg4lJ9MpU9A9YMAALFy4ECqVCkDuL96dO3cwc+ZMPPfcc0ZtIBERERERmV5UQjr6Lj+MHp+Xr+miWSqNwfRZWy4CAHo29IZcZvph5URlVabfzk8//RTp6enw8vJCVlYWOnXqhFq1asHR0REff/yxsdtIRERERFQhCYKAlh/twbDV4Qa3wC1P9l6JBwCkZKmgLaR32RLSs9UAACupBDteb6+Xn6E0HJQTlRdlWr3c2dkZe/bsweHDh3HhwgWkp6ejefPm6Natm7HbR0RERERUYf11PgaJ6TlITE/Ch9uvYq4Z5x6X1rrw2+JxcpYKbvZyC7YmX95wd08HhcGtq17vWtvcTSIqlTIF3Xnat2+P9u31v20iIiIiIiLgVmKGePzjkVv48cgtrHqpOfo28S30mqwcDaytJJCZYSVuALj3KBMaraAzlzsmOavMQXe2SgMrqfFWEs9rl7eTwmB+60A3o9ynrL4c3tSi96fyr8z/Evbt24d+/fohODgYwcHB6NevH/bu3Vv8hUREREREVcQvJ+7opU3ZeAaj15zA9bg0RD5Ix6X7KWLexXspqD93F2p9sBNn7zwyefuyVRq0/18YOi07AHWBIeVfHYgsU327L8eh3pxdqP3BTqMNp3/0eGswQ18CjGpTwyj3eBoDm/pbuglUzpUp6P7qq6/Qq1cvODo6Yvr06Zg+fTqcnJzQp08frFq1ythtJCIiIiKqcHLUWsSnKg3mHbiegBHfH0e3zw6i34rDyFCqodZo0X/lYbHM4K+OmryNPx2NNpj+z8U4qMuw//SrP58Wj5MzVWVtlo68LwPyes77PR4l4OdsgwUDGxnlHkSmVKbh5YsWLcLnn3+OqVOnimmvv/462rVrh0WLFmHKlClGayARERERUXmXnJkDpVoLFztrKGRWAICVYUX3Fiem5wfkg786ghvx6UZtkyAI0Aq5C5AVJiWr8MD42/9uYnLnWsXeJzYlC20W70e7Wu466TkaLY7dfIjh3x7D7L718dGOqwCAqwt7wVZuVcKnANSa3KBbZpX7HIuHNEaAmx0GN2MPM1UMZerpTk5ORq9evfTSe/TogZSUFANXEBERERFVTveTs9B04R6ELtqHl78/DgDYdzUey/dFiGWKiHsBoFQB9+7LcYiITyu2TYGz/kHw+/8gJVOFA9cfYOHfV5CuVItl1BotIh/o3nfas/lB9tJd11HzvR04cSupyHsNWHkEAHAk8qFOulorYPi3xwBADLgBoP7cXdh9Oa7IOvPsvxaPeX9dBgDsupR7jaONNWb2qoc63o4lqoPI0srU0z1gwAD8+eefeOedd3TSt23bhn79+hmlYUREREREFcG2c/fF45PRj3AqOgnjfjqlU+bQu11wOSYV/i626Lfi8JNVFEql0eosSPbzsduYs/USAODC/B6QW0lhY63fa9xuyX7xOGThv+KxjbUUXet7o1mAC2p9sFNMb1bdBRvHPwNbuRXq+zph8oYzYt6wb8IRvaRvoW1MSDM8hL6o4emv/nwav018BqFB7oWWAYCxa/N/juVoFzOiUilT0N2gQQN8/PHHOHDgANq0aQMAOHbsGI4cOYK33noLy5cvF8u+/vrrxmkpEREREVE5ZC3VHTz6/OpwnfMj7z0LfxdbVHO1AwD8924XdFgaVmh97/Ssi2W7rwMAOi4Nw8F3ukAuk+oE3ADQZP6/8HJUYNvUdvB1thXTzxSxANtXB6Lw1YEovfT+TfzEId+9GvoUen1B/16Ow8QCc7if9O2hm0Ve/8LjXvBLC3rCQaEfltR8b4fO+chysGgaUVmUKej+4Ycf4OrqiitXruDKlStiuouLC3744QfxXCKRMOgmIiIiokrt7qPMIvP9nG10zv1dbAspmWtKl1pi0B2bko2Nx29jUDN/nYA7z4M0JZ7/OhxH3ntWTAuPeqhXriju9nIMaOonnkulEvw+qY345UGAm257c9Ra3H6YYTDgdrGzRsfanvjrfAw2HNdfud2QCT+dwi8Tn9FJ23slXud8RGh1LOSiaVRBlSnovnXrFgAgMTERAODh4WG8FhERERERVSDrwm8XmS+R6E7olkolmNAhEN/9d0sn/dqHvQwOFZ//9xXM//uKXnqe+8lZSFeqxd7iNUduFVr2SQ4KGU7P6a6X3rKmG1a/3AKT1p/G3aQs7L8WjxY13KDVCuj++UEkpufoXbPvrU6wl8uw+dRdg/eSy6To18QXW87c10k/e1e/Z37nJd053/V8OH+bKq5SL6SWnJyMKVOmwMPDA97e3vD29oaHhwemTp2K5ORkEzSRiIiIiKh82nUptsj8d3rWNZj+Qd8GevOkCwbcK15sVmidH/Spr5d252Fub3tmjloMiAeE+OGLF5oW2b7nmhe+Arhclv9lwdi1pxCy4F80+3CPXsAd7GmPL4c3RbCnA3ycbdComrNOfofaHoj8uDcuze+JT4eG4PKCnjo98w39dMs/TFfijzP3dNJ6NirZkHdzydsffEgRPz+iPKXq6U5KSkKbNm1w//59jBgxAvXr5/6Dv3LlCtauXYt9+/bh6NGjcHV1NUljiYiIiIjKi1d+OI7/IhLF87+mthNX8gaA5tVdMKVL8VtuGdI/xA9aQcD0X8/p5U3oGISP/7mqk9Zn+X965Ua1rYnm1V3g7WSDB2nZYl2Dmvph67kYAMC8/g0LbUOwp0OJ2vrnlHZwsrEWz20LfHkwsk0NLBjQUKe3314hg71ChjWjW2HM2pNQqjVi3reHorDon2vieZCnPX6f1BZu9vIStcVc3u9bH13re6N1oJulm0IVQKmC7oULF0IulyMqKgre3t56eT169MDChQvx+eefG7WRRERERETliSAIOgE3kBtMFrTypeZPdY8+jX2xbPd13HuUJaZ1qJ07rTN6SV8IgoDAWf8Uen0DXydIJBK0CXbHg7RsyGVS+DjZ4KPBjbH1XAze6FYb0iL2Mqvhbo/aXg6IeFD4dmb2ciudgBvInded560edfWG1+dRWOcOulWq8lc5LxhwA8BfU9sbXGTN0hQyK3Ss42npZlAFUarh5Vu3bsUnn3yiF3ADgI+PD5YuXYo///zTaI2bP38+JBKJzqdevXpifnZ2NqZMmQJ3d3c4ODjgueeeQ3x8fBE1EhERERE9vZX7I3XOf5/UBnIr3T+t/YpZMA0AXn6mOgDDK4ZbW0lxeOaziF7SF7cW98Gfk9ti7ZjWYr5EItEZpv2kvNXIAcDL0QaH3+2CHa/nBrHRS/rijW51im3foiGN9dIWDGiI07O74cZHvXFhfk+9/Ho+TpjRrQ6WPt8EzrbWevl5FLLc9inVWmi0AhrN262Tv/rlFuUy4CYqrVL9FsfGxqJhw8KHoDRq1AhxcSXb6L6kGjZsiL1794rnMll+k2fMmIEdO3Zg8+bNcHZ2xtSpUzFkyBAcOXLEUFVERERERGX2/X838dWBKEgl0JnXfHlBT9grZIhPzRbTFg3WD1YNebtHXbQL9kD3BvqdWgVJJBI0q64/hdPfxRY3F/VBYroSrRftE9M7GeiF9XKy0UsrTquabhjbLhB3H2Xik6EhcLKRFdpzXdD0brWLLaOQ5X5JcScpE8Hv6/fYt6tV9B7eRBVFqYJuDw8PREdHo1q1agbzb926BTc3485rkMlk8PHR/+YvJSUFP/zwAzZu3Ihnn839hm/NmjWoX78+jh07hmeeeUbvGiIiIiKisvpox1WD6XnDygv2dNfyKtl8aBc7OXo39n2qdkmlEng52SB6SV9xb2tjLvA1t38Do9VVkI214UG3zaq7YM3oVnC0KbyXnKgiKdXw8p49e+KDDz5ATo7+FgFKpRJz5sxBr169jNY4AIiIiICfnx+CgoIwYsQI3LmTu9/f6dOnoVKp0K1bN7FsvXr1UL16dYSHhxdZp1KpRGpqqs6HiIiIiKi0wmflD++Wy0q9MZDR7XqjA5YMaYwBIX7FF7awaq52emndG3jj53GhcLErXwunET2NUi+k1rJlS9SuXRtTpkxBvXr1IAgCrl69iq+++gpKpRI///yz0RoXGhqKtWvXom7duoiNjcWCBQvQoUMHXLp0CXFxcZDL5XBxcdG5xtvbu9gh7osXL8aCBQuM1k4iIiIiqpyiEtLR9dODGBFaXS/vyS2/rAv0dAuCYPK2GVLPxwn1fJwscu/SsrG2QpCnPW4mZADI7eH+bmRLC7eKyPhKFXRXq1YN4eHhmDx5MmbNmiX+n4lEIkH37t2xcuVKBAQEGK1xvXv3Fo+bNGmC0NBQ1KhRA5s2bYKtbfELUxRm1qxZePPNN8Xz1NRUo7abiIiIiCqH2X9eAgBsOH5HJ71LXf0509ZW+XOdLRNyVzx5ATcALB9e+N7kRBVZqZcDDAwMxM6dO/Ho0SNEREQAAGrVqmX0udyGuLi4oE6dOoiMjET37t2Rk5OD5ORknd7u+Ph4g3PAC1IoFFAoFCZuLRERERFVZEejEnHxfopeulwmxepXWuilF1xgjKtul0ywpz2iHgfeAW76w82JKoMyTzxxdXVF69at0bp1a7ME3ACQnp6OqKgo+Pr6okWLFrC2tsa+ffmrNF6/fh137txBmzZtzNIeIiIiIqqczt9NxkvfHUe6Uq2TPrptTdz4qLe43dWTFg5siFc7BaGhX8UY4m1pX41ogZefqY4zc7pbuilEJiMRLDXhpATefvtt9O/fHzVq1EBMTAzmzZuHc+fO4cqVK/D09MRrr72Gf/75B2vXroWTkxOmTZsGADh69Gip7pOamgpnZ2ekpKTAyYn/B0lERERU1eWtAv6kW4v7lGjLLCKq/EoaR5brcS/37t3Diy++iIcPH8LT0xPt27fHsWPH4OmZO4fm888/h1QqxXPPPQelUomePXviq6++snCriYiIiKgiK7jfdp6lzzdB13peDLiJqNTKdU+3ubCnm4iIiIgAQK3RotYHO8Xzg+90hpVUYnB7KyKq2ipFTzcRERERkTnN++uyznkNd3sLtYSIKosyL6RGRERERFTZFFw47e+p7S3YEiKqLBh0ExEREVGV9uH2K5i68Qw0WgEKWe6fx+/0rIvG1Zwt3DIiqgw4vJyIiIiIygVBEDBh3SmoNALq+TpifPsgeDoqTHrPRxk5+OHwLQDA9guxYrrcin1TRGQcDLqJiIiIqFwInPWPeHzwRgK+OXgTNz7qDbmsbAFw3nrBRa04PvWXMwbTZVZcpZyIjINf4RERERGRxf1x+p7B9Dqzd+Kv8zF66anZKqg1WoPXKNUahCz4F4Gz/kHgrH8Qk5xlsGy6Uo0jkQ8N1hHs6VCK1hMRFY493URERERkMWfuPMKQr44WWeb1X87icEQClj4fAgBITFei49IwNPJ3xqZX2+iUvRGfhh6fH9JJa7tkv3j874yOqOPtiPN3kzFw1ZFC79km2L20j0JEZBCDbiIiIiKyGEMB93cjW+KzPTdwNTZVTNt06h7e6lEXdnIrrAqLRGaOBiduJSExXQkPh9x531qtoBdwP8lQfqCHPZY93wS1vByQo9FCJpXCmnO6ichIGHQTERERkVn9dT4GcSlZGNYyQC/v+ke9oJBZIS41G3O2XtLJC120T6/8V2FRmNu/AQDgf7uuienu9nJsmdwWnZYdKLY9f7zWFm728lI+BRFRyUiEvBUmqrDU1FQ4OzsjJSUFTk5Olm4OERERUaW172o8xv10Si+9aYALxrSriYFN/cW0k9FJ8HBQoMsnB4qsc+P4UPi72uoE2NuntUcj//wtv7JVGtSbs0vv2rzh5kREpVXSOJJBNxh0ExEREZlLx6VhuJOUqZcevaRvodfce5SJ9v8LK/E9+of4YcWLzfTSM3PUGPH9cZy9kwwAeP3ZWnizR90S10tEVFBJ40hOViEiIiIiswi7/sBgwD2kub+B0vmqudqV+B59m/gaDLgBwE4uw5+T28HRJneGZf8QvxLXS0RUVgy6iYiIiMjkEtKUGLPmpHgufbwNdp/GPvhsWNNir5/3eN62l2PuomnfjWwJdwPzsN/vU7/Yug690wW73uiA2hxWTkRmwOHl4PByIiIiIlN6/Zezenttjwitjo8HNy5VPVqtAGletA5AoxWw50o8mlV3QXxqNhr6OcOqQD4RkSmVNI7k6uVEREREZBIPUrOxZOc1vYA7yMMek7vUKnV90icCaiupBL0a+QAAvJ1syt5QIiITYtBNREREREaXrlSjtYEtvjZOCEXbYA8LtIiIyDI4p5uIiIioCpi77RKe/eQA0rJVZrnf25vO66VN6RLMgJuIqhwG3URERESVXI5ai3Xht3EzMQMdlpZ8662yupuUiV2X43TS3ulZF+/0rGfyexMRlTccXk5ERERUycWlZIvHyZkqCIIAicS4C45NXHcK/16J10s/P68HnG2tjXovIqKKhEE3ERERUSXXcZlu7/YvJ+5iSHN/CAKgFQTYK0r/J+HDdCU+/ucqtpy5X2iZ1oFuDLiJqMor18PLFy9ejFatWsHR0RFeXl4YNGgQrl+/rlOmc+fOkEgkOp9JkyZZqMVERERE5cvif67qpb3/50XUm7MLDeftQqdlB6DSaEtc35WYVDyzaB9afLS3yIAbAH4a07rU7SUiqmzKddB98OBBTJkyBceOHcOePXugUqnQo0cPZGRk6JSbMGECYmNjxc/SpUst1GIiIiKi8uWbQzfF4/n9G+jkaQUgMV2J2h/sxMCVhyEIQpF1XY5JQZ/l/yEuNbvQMp+/EIKIj3sjeklf2Mqtnq7xRESVQLkOunft2oXRo0ejYcOGCAkJwdq1a3Hnzh2cPn1ap5ydnR18fHzET1EbkxMRERFVFdvO5fdEfzm8KXo39i207Pl7Kdh2Lnc/7U2n7qLmezuw/ULueeSDNKzcH4G+yw/rXde5rqd47O9ii8HNqsHaqlz/iUlEZFYVak53SkoKAMDNzU0nfcOGDVi/fj18fHzQv39/zJkzB3Z2dpZoIhEREVG5MeO3c+LxgBA/SCQSDGtZDZtO3TNY/o3fziFHo8W7v18AAEzdeBbRiRn45N8bBsufndMdrvZyrNwfgd2X47FxQqjRn4GIqKKTCMWNIyontFotBgwYgOTkZBw+nP8t67fffosaNWrAz88PFy5cwMyZM9G6dWts2bKl0LqUSiWUSqV4npqaioCAAKSkpLCXnIiIiCosQRCw6dRdzPzjok76kGb++OyFpgAAlUaLv8/HIEulwcV7Kehc1xOT1p8p1X0uLegJe7mV0VdAJyKqSFJTU+Hs7FxsHFlherqnTJmCS5cu6QTcADBx4kTxuHHjxvD19UXXrl0RFRWF4OBgg3UtXrwYCxYsMGl7iYiIiMwpIj4N3T8/ZDBveOvq4rG1lRRDmlfLPXncMT2rdz0s3nmtRPf5e2p7OJRhtXMioqqqQky4mTp1KrZv346wsDBUq1atyLKhobn/9YiMjCy0zKxZs5CSkiJ+7t69a9T2EhEREZlTWraq0IB7SDN/tKzhWuT17g4KnfPx7QMRvaQvPBzkYppcJsWJD7qicTXnp28wEVEVUq6/phQEAdOmTcOff/6JAwcOIDAwsNhrzp07BwDw9S18oRCFQgGFQlFoPhEREVF598XeG/hib4TBvO3T2qOhn1OJh3/3beyLtzefF89n98td5XzXGx3R8qO9AABPBwW8HG2estVERFVPuQ66p0yZgo0bN2Lbtm1wdHREXFwcAMDZ2Rm2traIiorCxo0b0adPH7i7u+PChQuYMWMGOnbsiCZNmli49URERETGl6PWos7snYXm5y1uVhoFt/bq1dBHPPZwUODL4U1x7GYS5j2x3RgREZVMuV5IrbBvZ9esWYPRo0fj7t27ePnll3Hp0iVkZGQgICAAgwcPxuzZs0u1IFpJJ8ATERERmcOfZ+/hrU3noRWAVjVdsXlSW6Qr1ei0NAwPM3IMXiOTSnD1w15l3q7rv4gE/HH6HpY+HwK5rELMQCQisqiSxpHlOug2FwbdRERkShqtgLc2ncPWx3sgj25bE/MHNCz2uuM3HyIuNRvhUQ+RplTD38UW7/epb+rmkoU9SM1G60X7dNLqeDvgRnx6odeEBLhg25R2pm4aEREVUOlWLyciIqpoctRaZOao0XThHp30tUejMbptTaQr1ei34jAa+Dph44RQuNjlDwnOytHghW+P6dWp0mjxXPNqaOTvjBy1lj2SFVCOWovFO69izZFoAMDiIY2x61IchjT3h5ONNf46H6N3jaGA+5cJz6BNsDsyc9SwtbbSyyciovKBPd1gTzcRERmXSqNFuyX78SBNWeJrGvk74e+p7cWpVdGJGej8yYFir7OXW+H0nO6wYdBVYawLj8bcbZeLLWcvt0Jtb0ecu5tsMP/6R72gkPG9ExFZSknjSH49TkREZGQN5+42GHCveql5oddcup+KxvP/BQA8TFeWKOAGgIwcDerN2QWNtsp/h15uqTW5Ix7Cox7i3qPMEgXcAPDVyy0QUsT2XAy4iYgqBg4vJyKiSu3c3WTU9nKAvcI0/8m7HJOClCwVmgW4Qi6TIjVLhRyNVqfMCy0DsOS5xpBIJOhQpweaPA6un5SuVCMqIR3dPzuokx69pC8AYMyaE7CSSnAzMQM3EzJ0ygS//w++HN4UOy7E4qNBjQAJkKnUoKaHvRGflkrq4r0U9F95uMTlP38hBDN+O6+T1rG2BxQyKX4Kvw0A6FrPCz+MboX1x24j2NPBqO0lIiLT4fBycHg5EVFl9dWBSCzddR1AfuBqLCmZKoQsNBw85wmf9Sx8nW2LLKPRCgh+/59C878f2RLdGnjrpacr1bh4LwUjvj+Gojq5V7/cHL0a+RbZBjKe5MwctF60DzlqbbFl14xuhS71vPTS1RotZAVWIL9wLxnVXO3gVsptwIiIyLQ4vJyIiKqsmOQs1HxvhxhwA8B7f1ww6j2mbDxTbJniAm4AsJJKCv1CYM+MjgYDbgBwUMjQJtgdUYv6oKFf4f+hn7T+DOZuu4RslabYttDTG7TqSIkC7gEhfgYDbgA6ATcANKnmwoCbiKgC4/ByIiKqNJRqDerO3mUw79eTd6HRCpj2bG38b9c1zOheB7W88ofobjp5F+8+DsybVHPGtintxEXNnnQrMQOHIxOLbMt/73Yp41Pk2jg+FLW9HYstJ5FI8M0rLdD+f2EAcoPxljVdceB6glhmXfhtrAu/jS+HN8XxW0nYePyOmOfvYov7yVn44oWmGNTM/6naXJlEJ2bg3ytxiE9VYmLHIPx28i5+OhqNb0e2QIsabnrlBUFA/bm7kK3SDbgvzu8BRxtrsYwgAFKp4d8rIiKqnDi8HBxeTkRUWYz/6RT2Xo3XSXu2nhf2X3tQ6DVrRrfC94dv4kjkw0LL2FhL8d+7z8LTUYGbCel49tP8OdeLBjfGS6HVn7rt5+8mY2VYJF4KrY7aXg6o5mpXqutvP8zAjouxGNzMX+xhn/n7Bfx26m6p2/LdyJboXkgPe2Wm0Qr452IsGvk7o0sRC9k52cjww+hWqOPlCEcbGZou/Bep2WqdMvvf6oQgzrsmIqrUShpHMugGg24ioookKiEd1+PS0LuRj9gTrdEKeO7rozpbK33zSgv0aOANQQBGrTmB/yKK7pkuiZrudoh+mCmeD21RDcuGhjx1vaYkCAICZxU+Z7wwVxb2hJ28cg6IU2u0uJmYAXuFDDHJWWhVM7fnOnTRXsSnlnybt8L8PK41OtT2fOp6iIiofGPQXQoMuomIyr/41GxMWHcKF+6lAMjtbWxW3RWnbz9CulK3l/HGR70hl+nOi+27/D9cjkkVzx0VMqQ9cV3kx71hJZWUKEh9rnk1fDqsfAfcBX25NwKf770BAJjTrwHkVhI09HdG8+quyFZp8MPhW1i2+7redRfm94DT4+HRlcHDdCVafLRXJ83NXo6kjJynrrtzXU8sGNAQNdy5YjwRUVXAoLsUGHQTEZVfielKvLnpPA7dSCi+MIBrH/aCjbX+/sWZOWrsuRIPK6kE3ep7w8baCgv+vow1R6IB5PaM92zoAwDQagXcT86Cv4stJBIgKiED3Qps47X0+SYY1jLg6R+uHKr53g69tPBZz8LFNnchL1t5xdgbWqXRIiI+Hc521ohOzMDF+ylYsvNaia93VMhw4oNuCL+ZiG3nYvDJ0BB8czAKn/x7A61quuJk9COxrKejAic/6GaKxyAionKMQXcpMOgmIrIctUaL97ZcRH1fJwxtWQ39lh/GnaRM2MmtoBUEvYWp+jXxxfYLsXr1DAjxw6fDQmBtVbqNOXZdikNmjhpDmlcrtmxWjgYZOWp4OChKdY+K5NjNhxj+7bFC8w++0xneTjYGv9iwtOTMHAxYeQR3kjKLLwzATm6FzJz8Vd2bVHPGplfbFPlsgiCI0xq2X4jBD4dvYcP40Eo7FJ+IiArHoLsUGHQTEZlfSpYKIQuK3ue6oJ4NvfFOz7qo5eWIHLUW/1yMxcOMHPRv4gsvJxsTtrTqyVFrIZNKEFTE/uEFDQjxw+IhjbHmyC188m/uEPZfJjyDNsHupmymjnSlGo3m7S62nI+TDd7qUQfPt6gGiUQCrVbAzcR01HC3L/UXNkREVLUx6C4FBt1ERKajVGvw4+FoXI1NhcxKgi1n7pe6jj8nt0Wz6q4maB0VJStHg66fHkBMSnaZrj83tzsAoOnCPQCATa+2wcX7KRjWspq4jZYxqDVa1J2zCxqt/p803Rt442hkIjJyNPh9Uhu0rKm/3RcREVFZMOguBQbdRETGE52YgXXht/HjkVslvmZOvwboXt8b4TcT4WRjjd6NfVHwP0+F7ZdNpqdUa7Dv6gP8dS4Guy7HGa3e6x/1gkJW+iHqZ+88grWVFBfvp2DWlosAgDHtaurMzW/s7wx7uQzOdpVnATgiIip/GHSXAoNuIiorQRCgFQArqWWCwgdp2bj9MBNNqjlDIbNCjloLiQTQCkKZAhpD/otIwMR1p5Gl0mB23/oY3yEIQO5CVUq1FnO2XsKfZ0vfew0A/87oiDrejkZpJ5mPIAi4k5QJdwcFLt5LwYvf5c8BPz27G9ou2Q+lWltEDbmBcq+GPgj0sC/x9IBdl+Iwaf3pQvMb+Drhn+kdSvYQRERET4lBdykw6CaiotyIT0OPzw/ppH04qBEu30/BryfvimmhgW4Y2z5QXAG7oNiULPx9PgbPBLmjSTWXUt1/27n7+PpAFK7FpeGLF5ricGQifj99r0TXnpvbHS52cp20v87H4PVfzgIApBJgxYvN0b62B5xtc3sFIx+k48cjt3DhXjIu3U/Vq7M0bK2t8HbPuuhUxwMaLWAlBWp5MciuCprM343U7Pwt2f54rS3G/XQSyZkqnXKN/Z3x97T2hdaj0QoILuHc8sVDGuPF1tXL1mAiIqJSYtBdCgy6iSxHpdHiwPUE/HbyLno09Eb/Jn44EZ2ENkHuevssl4VWK2DWlov47VRucJzXsxqfmo37yVnwdFBg3E8nka3SIikjBxIJ8NGgRjh/N6VUw6NL6+dxrdHE3wWONjJIC/SSp2arcDQyEbW8HLByfySux6fjauzTBb7m1K2+N1rVdMUrbWpwNecqThAEnLmTjAa+TjrbjJ2MTsLQ1eE6ZY+89yw2nbyLjSfuICFNibHtAsV/f/1D/PD3+RiD91g8pDH6NvFF7y/+Q9PqLlj1UnPTPRAREdETGHSXAoNuIvPSagUo1Vo8ysxB2yX7Cy1346PexQbeqdkqOD2xIFPYtQcYs/akUdpaEq1ruqFTXU8s23291Nd6OCiw4sVmSFeqMWHdqVJdW9vLAV5OCvRu5IvZWy+J6d3qe+PYzYdIV6qLuLrkfJ1tsGVyW3g72mDqL2fwz8U4eDjI0T/ED02qOaORnzNqc4g4lVByZo64sFpZdK3nhe9HteQ8fyIisjgG3aXAoJtIn1YrIEejhUqjxcP0HHx1IBIP03OQmaPB4Ob+GNYywOB1eXOcIx6k4fjNJMz76zIAoG2wO4I9HVDb2wEr9kciIU1ZbBs61/XEe73roZ6Pk7g37qX7KXCxs0Z0YiZe/uG4WLaxvzPmD2iAfy/H45tDN43zQyhgxYvN0D/ED4IgIDYlGyejk5CZo9EZypquVOPVn0/hSORDg3W83aOOuJ1SaTTyd8K8/g3RsoYr0pVqqDQC3OzlxV7309Fo8edfUIfaHlg+vBlc7eVIyVTh7N1H+HxvBPo38cXdpEy42SvQq5EP6vowkCbTOHD9AUavKfkXY1IJcGaO/lQJIiIiS6pyQfeqVauwbNkyxMXFISQkBCtWrEDr1q1LdC2DbjIXrVbAnqvxSMlSoXNdT3g6KJCmVMNeLtNZiEup1uD2w0zUcLcrcjGsyAdpuJmQAQeFDMduPoRSo0VqlgrZKi3+PHsfzwS5YXLnWqjt7QB3ewWiEtJx+2EmlGoNqrvZoa6PI8KjHiIqIR2PMlVQqbU4fisJ0YkZSCtBL+ngZv74ZGgIDkUk4P6jLGi0gsEgryS+eKEp0rJViE9VYmVYZJnqKEzHOp5Y/XJzLN11HWuPRuvkDWtZDQsHNoKNde7P+WG6EpEP0tGypttTL46WrlTDQaE/xDpDqcYfZ+5BKpHgjzP3cPZOsk7+osGNoZBJUdPDDi1qcHsjqryyVRr0+PwQ7iRlYmSbGni/T31kqzT49eRdvNi6OqytJFgVFomQai7oYWCtBCIiIkuqUkH3b7/9hpEjR2L16tUIDQ3FF198gc2bN+P69evw8vIq9noG3fSkHLUW6Uo1nGxkkFnpD29Wa3Lzr8WlYefFWNxOykQTf2fcTMxAXEo2ujfwRlJGDgQAMqkEf569j9hi9rl1sbNGdTc7XItNQ44mf9VfDwc5UrPVyFFrMaipH2KSs/EgLRvRDzON/djFquXlgJjkLGTmaJ6qHl9nG3wyNARJGTnoUs8LciupzjDyqRvPYPuF2BLV1ay6i17QCuTuB9w60HDAmpqtwt2kTNTzcbLYquMFrT92G5djUjC3X0Odua9EVYFKozXqavtERETmUqWC7tDQULRq1QorV64EAGi1WgQEBGDatGl47733ir2+MgbdgiBAEHK3DdI+/t/889w0oUBeWrYaKVkqCIIAtVaARitArREgkQBSiQQCBGSrNLCxtoJGKyBHnTvsWGFtBXu5DAqZFA42MsikEmSpNFCqtLC2korDk9OVaqRmqaB8fF1WjgYZSg3c7K3hai9HplKDbLUGUokEjjYy2FhbwU5uBUcba2Qq1UjNViFdqYGDQgZrKwmsraSQWUkgk0qRkaOGlUQCmZUEOWotZFIprK0kSFeqEZOSjdQsFW4mZCAlKwdp2Wo8ysyBSiNAAkCp1uJBWjYkkKChvxPUGgFXY1Oh1ub+s7CXW0Gp1sLFTo4udT2Ro9EiNjkbJ6KTLPuCC5BJc39mjx6vCCyVAHZyWZHzeZtVd8Hth5lIysgBAEgkgKeDAq52cvRo6A0vJxsEedijno8jEtKVcLWTw/vxlj5KtQarD9zE53sND5P2d7FFdTc7TOocjE51PCEIufO3baytIAgCHmWqSjQ0Os9PR6OxMiwSz9b1gpWVBFqtgM51PaGwtkLH2p46QbNGK0Aq4Z7ORERERGR6VSbozsnJgZ2dHX7//XcMGjRITB81ahSSk5Oxbdu2YuuoCEH37K0XcScpC9k5mtygVq2BtZUUGUq1GCSrHgfJ2SoN0rKNs4ASFc3LUQFfZxvciE9HoIc9rjxeZfrZel6IfJAOiQRwsbVG60A3qDQCXgqtDgmAIE8HPEjLRlxKNjwcFEhIV+Lv8zG4eC8FHet4okdDbwR5OOD07Ue4+ygT1+PScO5uMnLUWoQGuiE+TYmQas5oV8sD9X2L/p1Va7S4FpcGlUaLIA8HONvlLjomCAIS03PgYmcNawO9+SURlZCOyAfpSM9W49l6XnCwkZW5LiIiIiKiiqSkcWSF388lMTERGo0G3t7eOune3t64du2awWuUSiWUyvxFnFJTy/92PCdvPcL1+DST1W9rbQV7RW6PtbWVBDIrKWRSCQQB0DzuHbe1zu31lUklUFhbAYKANKUaKo0WGo2AlCwVVBoBjo8DL60gwNpKCoUsd+iwVCKBh6MCWTlquNjJoZBJodLkbtNkbSWFtZUUEgmgVGmRrdYgJVOFbJUG9gqZ2PutVGuh1miRoxGg1mih1gqwk1tBrRGg1mrFgC9Ho4VUIkGQhz2cbK3h52wDb2cb2FpbwdVODrlMKpZ1s7dGfKoSielKOChkcHfIDaQdbWSIfJCO/yISYWtthSyVBnIrKdRaAQqZFIOa+cPF1lpnu6fS8HW2ha+zLQAgwM0Ozau76pVpE+yONnAv41vNJbOSopG/s166RCKBp6PiqeoO9nRAsKfDU9VBRERERFSZVfiguywWL16MBQsWWLoZpTKje21kKDWwlVvB1toK1la5AWtez6JMKoGVVCIGx4421pBJJZBKJJBIc4eISx8PFQd0zyUcjotaXoZXaW5SzQVNqrmYtzFERERERFRpVPig28PDA1ZWVoiPj9dJj4+Ph4+P4ZVOZ82ahTfffFM8T01NRUCA4e2PyotejXwt3QQiIiIiIiIqpQo/+VIul6NFixbYt2+fmKbVarFv3z60adPG4DUKhQJOTk46HyIiIiIiIiJjq/A93QDw5ptvYtSoUWjZsiVat26NL774AhkZGRgzZoylm0ZERERERERVWKUIul944QUkJCRg7ty5iIuLQ9OmTbFr1y69xdWIiIiIiIiIzKnCbxlmDBVhyzAiIiIiIiIqP6rMlmHGkPe9Q0XYOoyIiIiIiIgsLy9+LK4fm0E3gLS03P2vy/sK5kRERERERFS+pKWlwdnZudB8Di9H7mrnMTExcHR0NMt+1XlblN29e5fD2cs5vquKhe+r4uC7qjj4rioOvquKhe+r4uC7qjjM/a4EQUBaWhr8/PwglRa+MRh7ugFIpVJUq1bN7PfldmUVB99VxcL3VXHwXVUcfFcVB99VxcL3VXHwXVUc5nxXRfVw56nw+3QTERERERERlVcMuomIiIiIiIhMhEG3BSgUCsybNw8KhcLSTaFi8F1VLHxfFQffVcXBd1Vx8F1VLHxfFQffVcVRXt8VF1IjIiIiIiIiMhH2dBMRERERERGZCINuIiIiIiIiIhNh0E1ERERERERkIgy6iYiIiIiIiEyEQXcZHTp0CP3794efnx8kEgm2bt2qkx8fH4/Ro0fDz88PdnZ26NWrFyIiInTKREVFYfDgwfD09ISTkxOGDRuG+Ph4MT86Ohrjxo1DYGAgbG1tERwcjHnz5iEnJ8ccj1hpmONd5dmxYwdCQ0Nha2sLV1dXDBo0yIRPVvksXrwYrVq1gqOjI7y8vDBo0CBcv35dp0x2djamTJkCd3d3ODg44LnnntN7F3fu3EHfvn1hZ2cHLy8vvPPOO1Cr1TplDhw4gObNm0OhUKBWrVpYu3atqR+vUjHnu8pz5MgRyGQyNG3a1FSPVWmZ831t2LABISEhsLOzg6+vL8aOHYuHDx+a/BkrC2O9q9dffx0tWrSAQqEw+G/mwIEDGDhwIHx9fWFvb4+mTZtiw4YNpny0Ssdc7woABEHAJ598gjp16kChUMDf3x8ff/yxqR6t0jHGuzp//jxefPFFBAQEwNbWFvXr18eXX36pdy/+ffH0zPm+8pj6bwwG3WWUkZGBkJAQrFq1Si9PEAQMGjQIN2/exLZt23D27FnUqFED3bp1Q0ZGhnh9jx49IJFIsH//fhw5cgQ5OTno378/tFotAODatWvQarX45ptvcPnyZXz++edYvXo13n//fbM+a0VnjncFAH/88QdeeeUVjBkzBufPn8eRI0fw0ksvme05K4ODBw9iypQpOHbsGPbs2QOVSoUePXqI7wIAZsyYgb///hubN2/GwYMHERMTgyFDhoj5Go0Gffv2RU5ODo4ePYqffvoJa9euxdy5c8Uyt27dQt++fdGlSxecO3cOb7zxBsaPH4/du3eb9XkrMnO9qzzJyckYOXIkunbtapbnq2zM9b6OHDmCkSNHYty4cbh8+TI2b96MEydOYMKECWZ93orMGO8qz9ixY/HCCy8YvM/Ro0fRpEkT/PHHH7hw4QLGjBmDkSNHYvv27SZ7tsrGXO8KAKZPn47vv/8en3zyCa5du4a//voLrVu3NslzVUbGeFenT5+Gl5cX1q9fj8uXL+ODDz7ArFmzsHLlSrEM/74wDnO9rzxm+RtDoKcGQPjzzz/F8+vXrwsAhEuXLolpGo1G8PT0FL777jtBEARh9+7dglQqFVJSUsQyycnJgkQiEfbs2VPovZYuXSoEBgYa/yGqCFO9K5VKJfj7+wvff/+9eR6kinjw4IEAQDh48KAgCLk/d2tra2Hz5s1imatXrwoAhPDwcEEQBOGff/4RpFKpEBcXJ5b5+uuvBScnJ0GpVAqCIAjvvvuu0LBhQ517vfDCC0LPnj1N/UiVlqneVZ4XXnhBmD17tjBv3jwhJCTE9A9UyZnqfS1btkwICgrSudfy5csFf39/Uz9SpVWWd1VQaf7N9OnTRxgzZoxR2l0VmepdXblyRZDJZMK1a9dM1vaq5mnfVZ7JkycLXbp0Ec/594VpmOp95THH3xjs6TYBpVIJALCxsRHTpFIpFAoFDh8+LJaRSCQ6G7fb2NhAKpWKZQxJSUmBm5ubiVpe9RjrXZ05cwb379+HVCpFs2bN4Ovri969e+PSpUtmfJrKJyUlBQDE3/nTp09DpVKhW7duYpl69eqhevXqCA8PBwCEh4ejcePG8Pb2Fsv07NkTqampuHz5slimYB15ZfLqoNIz1bsCgDVr1uDmzZuYN2+eOR6lSjDV+2rTpg3u3r2Lf/75B4IgID4+Hr///jv69OljrkerdMryrp7mXvwbo+xM9a7+/vtvBAUFYfv27QgMDETNmjUxfvx4JCUlGfcBqhBjvasn/83w7wvTMNX7Asz3NwaDbhPIe+mzZs3Co0ePkJOTg//973+4d+8eYmNjAQDPPPMM7O3tMXPmTGRmZiIjIwNvv/02NBqNWOZJkZGRWLFiBV599VVzPk6lZqx3dfPmTQDA/PnzMXv2bGzfvh2urq7o3Lkz/6NYRlqtFm+88QbatWuHRo0aAQDi4uIgl8vh4uKiU9bb2xtxcXFimYJBQV5+Xl5RZVJTU5GVlWWKx6nUTPmuIiIi8N5772H9+vWQyWQmfpKqwZTvq127dtiwYQNeeOEFyOVy+Pj4wNnZ2eD0HipeWd9VWWzatAknT57EmDFjnqbJVZYp39XNmzdx+/ZtbN68GevWrcPatWtx+vRpPP/888Z8hCrDWO/q6NGj+O233zBx4kQxjX9fGJ8p35c5/8Zg0G0C1tbW2LJlC27cuAE3NzfY2dkhLCwMvXv3hlSa+yP39PTE5s2b8ffff8PBwQHOzs5ITk5G8+bNxTIF3b9/H7169cLQoUM5N86IjPWu8uZ2f/DBB3juuefQokULrFmzBhKJBJs3b7bY81VkU6ZMwaVLl/Drr79auilUDFO9K41Gg5deegkLFixAnTp1jFp3VWbKf1tXrlzB9OnTMXfuXJw+fRq7du1CdHQ0Jk2aZPR7VQXm+v/BsLAwjBkzBt999x0aNmxo0ntVVqZ8V1qtFkqlEuvWrUOHDh3QuXNn/PDDDwgLC9NbXIqKZ4x3denSJQwcOBDz5s1Djx49jNg6epKp3pe5/8Zgt4GJtGjRAufOnUNKSgpycnLg6emJ0NBQtGzZUizTo0cPREVFITExETKZDC4uLvDx8UFQUJBOXTExMejSpQvatm2Lb7/91tyPUukZ4135+voCABo0aCBeo1AoEBQUhDt37pj3gSqBqVOnYvv27Th06BCqVasmpvv4+CAnJwfJyck6327Gx8fDx8dHLHPixAmd+vJWsyxY5snVY+Pj4+Hk5ARbW1tTPFKlZcp3lZaWhlOnTuHs2bOYOnUqgNw/PgVBgEwmw7///otnn33WxE9YuZj639bixYvRrl07vPPOOwCAJk2awN7eHh06dMBHH30k/n8lFe9p3lVpHDx4EP3798fnn3+OkSNHGqPpVY6p35Wvry9kMplOYFC/fn0AuTsK1K1b9+kfooowxru6cuUKunbtiokTJ2L27Nk6efz7wrhM+b7M/jeGSWaKVzF4YnEuQ27cuCFIpVJh9+7dhZbZt2+fIJFIdBbKuHfvnlC7dm1h+PDhglqtNlaTqyxTvauUlBRBoVDoLKSWk5MjeHl5Cd98841R2l4VaLVaYcqUKYKfn59w48YNvfy8hTN+//13Me3atWsGF3uKj48Xy3zzzTeCk5OTkJ2dLQhC7kInjRo10qn7xRdf5EInpWCOd6XRaISLFy/qfF577TWhbt26wsWLF4X09HTTP2glYa5/W0OGDBGGDRumU/fRo0cFAML9+/dN8WiVjjHeVUFFLQwUFhYm2NvbCytXrjRa+6sSc72r3bt3CwCEyMhIMe3cuXMCAOH69evGeZhKzljv6tKlS4KXl5fwzjvvGLwP/74wDnO8L3P/jcGgu4zS0tKEs2fPCmfPnhUACJ999plw9uxZ4fbt24IgCMKmTZuEsLAwISoqSti6datQo0YNYciQITp1/Pjjj0J4eLgQGRkp/Pzzz4Kbm5vw5ptvivn37t0TatWqJXTt2lW4d++eEBsbK36o5MzxrgRBEKZPny74+/sLu3fvFq5duyaMGzdO8PLyEpKSksz2rBXda6+9Jjg7OwsHDhzQ+X3PzMwUy0yaNEmoXr26sH//fuHUqVNCmzZthDZt2oj5arVaaNSokdCjRw/h3Llzwq5duwRPT09h1qxZYpmbN28KdnZ2wjvvvCNcvXpVWLVqlWBlZSXs2rXLrM9bkZnrXT2Jq5eXjbne15o1awSZTCZ89dVXQlRUlHD48GGhZcuWQuvWrc36vBWZMd6VIAhCRESEcPbsWeHVV18V6tSpI/53MG+l+f379wt2dnbCrFmzdO7z8OFDsz5vRWaud6XRaITmzZsLHTt2FM6cOSOcOnVKCA0NFbp3727W563IjPGuLl68KHh6egovv/yyTh0PHjwQy/DvC+Mw1/t6kin/xmDQXUZhYWECAL3PqFGjBEEQhC+//FKoVq2aYG1tLVSvXl2YPXu23hY4M2fOFLy9vQVra2uhdu3awqeffipotVoxf82aNQbvwQEKpWOOdyUIuT3bb731luDl5SU4OjoK3bp109mKjIpX2O/7mjVrxDJZWVnC5MmTBVdXV8HOzk4YPHiw3hdR0dHRQu/evQVbW1vBw8NDeOuttwSVSqVTJiwsTGjatKkgl8uFoKAgnXtQ8cz5rgpi0F025nxfy5cvFxo0aCDY2toKvr6+wogRI4R79+6Z4zErBWO9q06dOhms59atW4IgCMKoUaMM5nfq1Ml8D1vBmetdCYIg3L9/XxgyZIjg4OAgeHt7C6NHj+YXJKVgjHc1b948g3XUqFFD5178++LpmfN9FWTKvzEkjx+MiIiIiIiIiIyMq5cTERERERERmQiDbiIiIiIiIiITYdBNREREREREZCIMuomIiIiIiIhMhEE3ERERERERkYkw6CYiIiIiIiIyEQbdRERERERERCbCoJuIiIiIiIjIRBh0ExERVQGjR4+GRCKBRCKBtbU1vL290b17d/z444/QarUlrmft2rVwcXExXUOJiIgqGQbdREREVUSvXr0QGxuL6Oho7Ny5E126dMH06dPRr18/qNVqSzePiIioUmLQTUREVEUoFAr4+PjA398fzZs3x/vvv49t27Zh586dWLt2LQDgs88+Q+PGjWFvb4+AgABMnjwZ6enpAIADBw5gzJgxSElJEXvN58+fDwBQKpV4++234e/vD3t7e4SGhuLAgQOWeVAiIqJyhEE3ERFRFfbss88iJCQEW7ZsAQBIpVIsX74cly9fxk8//YT9+/fj3XffBQC0bdsWX3zxBZycnBAbG4vY2Fi8/fbbAICpU6ciPDwcv/76Ky5cuIChQ4eiV69eiIiIsNizERERlQcSQRAESzeCiIiITGv06NFITk7G1q1b9fKGDx+OCxcu4MqVK3p5v//+OyZNmoTExEQAuXO633jjDSQnJ4tl7ty5g6CgINy5cwd+fn5ierdu3dC6dWssWrTI6M9DRERUUcgs3QAiIiKyLEEQIJFIAAB79+7F4sWLce3aNaSmpkKtViM7OxuZmZmws7MzeP3Fixeh0WhQp04dnXSlUgl3d3eTt5+IiKg8Y9BNRERUxV29ehWBgYGIjo5Gv3798Nprr+Hjjz+Gm5sbDh8+jHHjxiEnJ6fQoDs9PR1WVlY4ffo0rKysdPIcHBzM8QhERETlFoNuIiKiKmz//v24ePEiZsyYgdOnT0Or1eLTTz+FVJq77MumTZt0ysvlcmg0Gp20Zs2aQaPR4MGDB+jQoYPZ2k5ERFQRMOgmIiKqIpRKJeLi4qDRaBAfH49du3Zh8eLF6NevH0aOHIlLly5BpVJhxYoV6N+/P44cOYLVq1fr1FGzZk2kp6dj3759CAkJgZ2dHerUqYMRI0Zg5MiR+PTTT9GsWTMkJCRg3759aNKkCfr27WuhJyYiIrI8rl5ORERURezatQu+vr6oWbMmevXqhbCwMCxfvhzbtm2DlZUVQkJC8Nlnn+F///sfGjVqhA0bNmDx4sU6dbRt2xaTJk3CCy+8AE9PTyxduhQAsGbNGowcORJvvfUW6tati0GDBuHkyZOoXr26JR6ViIio3ODq5UREREREREQmwp5uIiIiIiIiIhNh0E1ERERERERkIgy6iYiIiIiIiEyEQTcRERERERGRiTDoJiIiIiIiIjIRBt1EREREREREJsKgm4iIiIiIiMhEGHQTERERERERmQiDbiIiIiIiIiITYdBNREREREREZCIMuomIiIiIiIhMhEE3ERERERERkYkw6CYiIiIiIiIyEQbdRERERERERCbCoJuIiIiIiIjIRBh0ExEREREREZkIg24iIiIiIiIiE2HQTURERERERGQiDLqJiIiIiIiITIRBNxEREREREZGJMOgmIiIiIiIiMhEG3UREREREREQmwqCbiIiIiIiIyEQYdBMRERERERGZCINuIiIiIiIiIhNh0E1ERERERERkIgy6iYiIiIiIiEyEQTcRERERERGRiTDoJiIiIiIiIjIRBt1EREREREREJsKgm4iIiIiIiMhEGHQTERERERERmYjM0g0oD7RaLWJiYuDo6AiJRGLp5hAREREREVE5JwgC0tLS4OfnB6m08P5sBt0AYmJiEBAQYOlmEBERERERUQVz9+5dVKtWrdB8Bt0AHB0dAeT+sJycnCzcGiIiIiIiIirvUlNTERAQIMaThWHQDYhDyp2cnBh0ExERERERUYkVN0WZC6kRERERERERmQiDbiIiIiIiIiITsWjQfejQIfTv3x9+fn6QSCTYunWrmKdSqTBz5kw0btwY9vb28PPzw8iRIxETE6NTR82aNSGRSHQ+S5YsMfOTEBEREREREemzaNCdkZGBkJAQrFq1Si8vMzMTZ86cwZw5c3DmzBls2bIF169fx4ABA/TKLly4ELGxseJn2rRp5mg+ERERERGRxWXmqDHz9ws4dvOhpZtCBlh0IbXevXujd+/eBvOcnZ2xZ88enbSVK1eidevWuHPnDqpXry6mOzo6wsfHx6RtJSIiIiIiKo+2nLmP307dxW+n7iJ6SV9LN4eeUKHmdKekpEAikcDFxUUnfcmSJXB3d0ezZs2wbNkyqNXqIutRKpVITU3V+RAREREREVVE1+IYz5RnFWbLsOzsbMycORMvvviizrZer7/+Opo3bw43NzccPXoUs2bNQmxsLD777LNC61q8eDEWLFhgjmYTERERERGZlEJmZekmUBEqRNCtUqkwbNgwCIKAr7/+WifvzTffFI+bNGkCuVyOV199FYsXL4ZCoTBY36xZs3Suy9vUnIiIiIiIqKIpuEt0tkoDG2sG4eVJuR9enhdw3759G3v27NHp5TYkNDQUarUa0dHRhZZRKBRwcnLS+RAREREREVVEtvL8IPuv8zFFlCRLKNc93XkBd0REBMLCwuDu7l7sNefOnYNUKoWXl5cZWkhERERERGRZgpB/nJ5d9PpWZH4WDbrT09MRGRkpnt+6dQvnzp2Dm5sbfH198fzzz+PMmTPYvn07NBoN4uLiAABubm6Qy+UIDw/H8ePH0aVLFzg6OiI8PBwzZszAyy+/DFdXV0s9FhERERERkdmsDMuPqbJUGhy7+RCnopPwWudasJJKiriSzMGiQfepU6fQpUsX8TxvnvWoUaMwf/58/PXXXwCApk2b6lwXFhaGzp07Q6FQ4Ndff8X8+fOhVCoRGBiIGTNm6MzXJiIiIiIiqqzuJmXqnGco1Rj+7TEAgL1ChjHtAi3RLCrAokF3586dIRQcC/GEovIAoHnz5jh27Jixm0VERERERFSuCIKA8KiHaOjnDGc7azE97Ynh5AeuJ4jHe67EM+guB8r9QmpERERERERV3VcHovDS98cx/+/LOuk5Gq3O+ZXY/D27i+nDJDNh0E1ERERERFTOLdt9HQDw59n7OiOCfz1xp9BrZFa587mTMnLwc3g0EtOVSMlSIfJBGgauPIxt5+6bttEEoJyvXk5ERERERES6dl+OQ69GvgCAX0/eLbScVJIbdE/ecBrHbiZh06l7uHg/Rcx/9/cLGNjU37SNJfZ0ExERERERlXf+Lrbi8aT1Z6B+Yli5IdZWueHesZtJAKATcAOAUl18HeZS3HpeFRmDbiIiIiIionLOw1Ghc372bjIAoL6vEwDgvd710CbIXadMxIM0fLj9ilna9zSmbDyDbp8dRLZKY+mmmASDbiIiIiIionIuJTNH53zo6nB8vucGoh6kAwC61vNC57qeOmVuP8zED4dvma2NZZGt0mDHhVhEJWTg/OMvEiobzukmIiIiIiIqx67HpSH6YaZe+pf7IsRjL0cbjGpbEw8zcqCQSbFif6Q5m1hmMclZ4nHewm+VDXu6iYiIiIiIyilBELD5VOGLpeWxlVvBxtoK7/epj+4NvEtUt4215cPBdGX+PuMlmKZeIVn+p0xEREREREQGvfrzaXxfYIh4s+ouBsvJZfmhnZW0ZD3G5WHtsgxl/jxutbZyRt0MuomIiIiIiMqpf6/Ei8cBbrY4eydZr0yLGq4653W8HUtUt1KtNeuq4feTs6DV6t4vMye/p7uSxtwMuomIiIiIiMqjVWG687LvJmUZLDft2Vo659ZWUkQt6lOie5hr27CdF2PRbsl+vL35vE56Rg57uomIiIiIiMjM0rJVWLb7uk5aoIc9Fg5sKJ4Pa1kNz9bzQrtaHnrXW0kluLW4D74e0VxMc1Tor6Ntrm26XttwBgCw5ex9nfSH6UrxWFsexrubAINuIiIiIiKiciY6UX+18t9efQZDWwSgga8TxrSriaXPh+DH0a1gbWU4rJNIJMgqEFRfXNATQR72OmWyVebvXZ7yOAAHgAV/5+8jrtYw6CYiIiIiIiITuR6XhtsPMyAIApKzcvTyvRxtYCu3wj/TO2Be/4YGatBXcHVwAJA8scaaOXq6n5w3vuNiLAD9tk1af9rkbbEE7tNNRERERERkYZEP0tHzi0NwtbPGc82r6axY/jQa+zvrnL/etTam/3pOPM9Wmzbo1mgFdPvsoMG8yAfpOudaAUhMV8LDQWHSNpkbe7qJiIiIiIgsKCFNia8eL5r2KFNlMOBuGuBSprqbVXfFxvGhODyzCwBgYFN/HHynM/ycbQAAWTmmDbrDrj3ArcQMvXSVRouULJVe+vtbLpq0PZbAnm4iIiIiIiILavXx3iLzvZ0UWD8+tMz1t31iobUa7vZwtZcjJiUbyQYCX2PYdSkO1+JSYfXkePbHWn28V29+OQBEP9QP0Cs6Bt1ERERERETl2F9T28PBwMrjT8PVTg4ASM7UnztuDMXNz07OVOGMgT3HA1ztTNIeS+LwciIiIiIionLM28nG6HU621kDyA1+zeXToSEG00e3rYkJHQIBADmayrdXN4NuIiIiIiIiC9FqLbNNluLxNmMqEwS5hp5pRGh1PNeimsHyM3vVQ8jjOes5agbdRnXo0CH0798ffn5+kEgk2Lp1q06+IAiYO3cufH19YWtri27duiEiIkKnTFJSEkaMGAEnJye4uLhg3LhxSE/XXQWPiIiIiIioPMrbPqsw/i62JrmvtRh0Gz/oN9RbrZBZGSxb19sRtnIrsT2nbz/C818fxU9Ho43eLkuxaNCdkZGBkJAQrFq1ymD+0qVLsXz5cqxevRrHjx+Hvb09evbsiezsbLHMiBEjcPnyZezZswfbt2/HoUOHMHHiRHM9AhERERERUZn9eyW+0LweDbyxcULZF1ArirUsd4EzU/R0Kw30Viusc0NPuZXUcLos93/VWgGnbj/CvL8uG71dlmLRhdR69+6N3r17G8wTBAFffPEFZs+ejYEDBwIA1q1bB29vb2zduhXDhw/H1atXsWvXLpw8eRItW7YEAKxYsQJ9+vTBJ598Aj8/P7M9CxERERERUWn5OufP1/5pbGvEp2bj3d8vAAAWDmwEH2fjz+cGAPvHC7OlZauNXrehIeJ5wfalBT1RZ/ZOMf3CvRQA+cPdC9JqBUilhlc/r0jK7ZzuW7duIS4uDt26dRPTnJ2dERoaivDwcABAeHg4XFxcxIAbALp16wapVIrjx48XWrdSqURqaqrOh4iIiIiIKr/41Gycvv3I0s0QyR4Hlc8EuaFTHU/xHAAUMtOFa+72uauXJ2UYf/Xyv87H6KVlqXL3A5cX8kzWBtIN7eNdEZXboDsuLg4A4O3trZPu7e0t5sXFxcHLy0snXyaTwc3NTSxjyOLFi+Hs7Cx+AgICjNx6IiIiIiIqj3p9cQjPfX0UJ6OTLN0UnIpOwlcHogAALWu4AQAKrkFWWIBqDG72CgDAn2fvY/flwmOn0nqUkYMPt1/RS7e1NjynO4+hxddiUrKM1i5LKrdBtynNmjULKSkp4ufu3buWbhIREREREZnBo8dbZG05cx8no5PwIC27mCtM5/nV4eLxrYcZAID07PzeXTt50YHq08jr6QaAV38uek/t0pi97ZLB9P4h+VN/lz7XRC9fI+gH3X2XHzZauyyp3AbdPj4+AID4eN2FBeLj48U8Hx8fPHjwQCdfrVYjKSlJLGOIQqGAk5OTzoeIiIiIiKqOX07cwdDV4Ri48ohF7q9+YgGzBr65MUlygSHVEonp5jMHedrrnO8tYkG30rApZJXyWl4O4vGwVvkjjUe3rQkAaBPkrnfNz+NaG6VNlmbRhdSKEhgYCB8fH+zbtw9NmzYFAKSmpuL48eN47bXXAABt2rRBcnIyTp8+jRYtWgAA9u/fD61Wi9BQ06zyR0RERERElUdsSjYepGbDy8k0C5YV5u8LuvOex7UPBAAMaxmAr8Ki0L2Bt6HLjKaGu27QvXD7FXQzwj0Lrod2bFZX7L4cZ/BZLi3oietxqWhe3RVA7hcMP49rjV9P3MWbPeog2NNB75qKyqJBd3p6OiIjI8XzW7du4dy5c3Bzc0P16tXxxhtv4KOPPkLt2rURGBiIOXPmwM/PD4MGDQIA1K9fH7169cKECROwevVqqFQqTJ06FcOHD+fK5UREREREVCKrwiKxYGAjs94zMS1/AbPlLzaDzeM5z34utjg3r3uxc6CN4bNhIXhz03kAMNowe2dba/HYy1GBUY97sp/koJChxeN57Hk61PZEh9qeRmlHeWLR4eWnTp1Cs2bN0KxZMwDAm2++iWbNmmHu3LkAgHfffRfTpk3DxIkT0apVK6Snp2PXrl2wscn/FmrDhg2oV68eunbtij59+qB9+/b49ttvLfI8RERERERU8ZhywbLCJKQrxeNqrrY6eXZymUmHlufp1yS/ozJbZZz9uvNGzU/sGFQptvsyBov2dHfu3BmCgQnzeSQSCRYuXIiFCxcWWsbNzQ0bN240RfOIiIiIiKiScba1FreimtgxCN8eugm1gZWzC9JqBRyOTETLmq6wkxsnhPJyVIjH5ujVNkQuk0Ihk0L5eF9tQRCeOthXa3PrMuV2ZxUNfxJERERERFRlWD+edLxzegdxSPeDVGVRlyDo/X8w8scTWBUWWWS50nBQ5Afv9XwcjVZvaa0bm79Y2ZSNZ0p9/dXYVKwKi0TO48B9XfhtAMD9R5Vjuy9jYNBNRERERERVRt5IW6lEgn8f70+942JsoeUzc9Ti8beHbhqtHZk5GgBAvya+ZhlKXpjQAquG/3MxDmHXHmBPKVYyH7DyMJbtvo55f13SGcW85ex9o7azImPQTUREREREVcbDjNwFzKQS4Fpcmph+vcBxQUkZ+Queda1nvBXFs1S5Qbcp9+IuizFrT2LCulN4kFqyhdVUmtxA+5cTd1HEzOEqjUE3ERERERFVCcdvPhSPn+xc7vnFIYPXfLk3QjzOfBwoPw2tVsCqsEgs230dAIw2R9zYHqQVPeTekBsP8r+4+HNyW2M2p0Jj0E1ERERERFXC/L+viMdWUim+eaVFsddsPn1PPD50IwE139uB6MSMMrfhz7P3xYAbgDiv3JL6NPbRS9MUs7icRisgXanWSev1xX/icVAl2mf7aTHoJiIiIiKiKuHeo0zxuKa7HbrU9Sqy/MnoJIPpnT85UKb7bz17H6sO6C7GVh6Gl3/+QlO9tLw554UZ99NJNJq3u9B87haWr3yOZSAiIiIiIjKRrVPaQSKRQC6T4I/X2uK5r48CyO29tXocLc7achG/nLhjlPupNVo0nLdb3JqrIKtyEJ0qZPqBf5ZKbaBkLo1WwIHrCUXWKbXg4nDlDXu6iYiIiIio0nuQlo207NxAspqrrZje0M9JPN52Ln/F7aIC7t6N9IdjF+X07UcGA24ASM1Wlaouc8lQFt7T/fovZ4u9nkF3PgbdRERERERU6X2+J39BNFc7uXhccE71m5vOY+SPJ5CSWXQgrNIYDqALU9SWYD5ONqWqy1yyihheXtQWa3kYc+dj0E1ERERERJWe3Co3CrSSSooc0n3oRgJCFv6rk/b5CyE65wW3ESsJQ7dbN7Y1pnQJxrCWAaWqy1wycgofXl4S7OnOx6CbiIiIiIgqvXuPsgAA07vWLtV1+9/qhL6N/XTSHhXTE/4kQ+U71vHEOz3rwV5RPpbZalfLXec86ym3RysHU9XLDQbdRERERERUqS3fF4F91x4AKP1q4UGeDpDLpJjYMQgNfHPnfz/KLF1P98EbD0pV3hJaVHfVOc9WGR5CH5uSVWgdz9bLXw2+qCH1VU35+FqFiIiIiIjIRD7bc0M89nOx1cvvWMcTh24UvRr3+33q425SJjosDUN2Ib3AGq2Aa3GpqOvtCJlVfv+mSl30ntflQcH2Aij0GV/+/nihdXw1ojmO30qC3EpaLlZlLy/Y001ERERERJXWhXvJOud1fRz1yqwZ3Qrz+zfQSz/wdmedc7ksN3wqbCXy7/+7ib7LD+PzvTd00o/eTNQ5H9Lcv7hmm53MSjdITstW4fjNh3rBd1RChni8+uXmAIAOtT3wdo86sLG2Qqc6nmgTrDtUvapj0E1ERERERJXW9//dEo+/faUFgj0d9MpYSSUY0qKaTpqDQoYa7nY6afLHvcGCAJy4laRXz+Kd1wAAq8KidNLvJuUOya7n44hVLzXHZ8Oalv5BTEx4ojP+lxN38cK3x/DO7xcKvaZXI19EL+mLn8eFYuqzpZsrX5Uw6CYiIiIiokorwC13OLncSooeDQvfX9vJxhpz++X3dh9571m9ecl5Pd1AyfaqftLotjXRt4lvqa8zh5Y1XA2m/30+BpfupyAiPg1abfkfJl8ecU43ERERERFVWurHgeLINjWKLTuqbU34udiivq8jnG2t9fILBt1qbcn36q7r7Yjr8WkIcLMrvrCFhAa5Y/24UEgkwIgn5m33W3EYALDj9fZiWsGfBRWNQTcREREREVVK9x5l4mpsGgDAxrr4VcutpBL0alR4b7iswOJgeb3gWq2A6/FpOvtSF1xEbOfFWFyPT9NLL4/a1/aA8OQ48wL6Lj8sHu+d0ckcTaoUGHQTEREREVGlo9Zo0f5/YeK5wgg9swWHm1s9Pl6+PwJf7I3QKacpMAz7tQ1nxGNZOQ+6gZJv9VXdvfz22pc3HBNARERERESVTq0Pduqcl6SnuzQcbHL7L58MuPNExKfht5N3dNIqypDspc83sXQTKpVy/9Zr1qwJiUSi95kyZQoAoHPnznp5kyZNsnCriYiIiIjIUsKuP9BLs7E2TujjoMgNtlsHuhW5sNiFeymY+cdFnTQPB4VR2mBq9X2cisw3NN+dClfuh5efPHkSGk3+3nCXLl1C9+7dMXToUDFtwoQJWLhwoXhuZ8ehDkREREREVdF/EQkYs+akXrrCSD3dr3UOxrLd16HWaHEwIqHQcvsNBP7uDnKjtMHUfJxtiswPe2L/cipauQ+6PT09dc6XLFmC4OBgdOqUP3Hfzs4OPj6FL3hARERERERVw/92XROPba2tkKXK7cDL66F+Wnlzw3PUWlyJSS203I4LsTrnb3SrDYXMuEPcTcXRpuiflZt9xfjyoLwo98PLC8rJycH69esxduxYnQn+GzZsgIeHBxo1aoRZs2YhMzPTgq0kIiIiIiJLEAQBl+7nB8Kz+tQTjzvU9jDKPfLmZZ+MflSqhdGmd61tlPubQ1Hz338a29qMLakcyn1Pd0Fbt25FcnIyRo8eLaa99NJLqFGjBvz8/HDhwgXMnDkT169fx5YtWwqtR6lUQqlUiuepqYV/Q0VERERERBXD7K2XxOP5/RtgRGgN7L4cB38XWzjaGGceslKVuz/3/eQsrD9+Wy9/br8GWLj9ik7ai60DSrwqeHmxflwoXv7huF66vbxi9NaXJxUq6P7hhx/Qu3dv+Pn5iWkTJ04Ujxs3bgxfX1907doVUVFRCA4ONljP4sWLsWDBApO3l4iIiIiIzOfc3WTxeHS7QADAhvHPGPUeBYde303K0smTSSVo5O+skzawqR8WD6l4q4E3ruZsMN1YX15UJRVmePnt27exd+9ejB8/vshyoaGhAIDIyMhCy8yaNQspKSni5+7du0ZtKxERERERmZcgCEjNVgEw7ZZX/UL8Cs3b/1Zn1PJy0ElrWcPVZG0xpcL2NbdXsKe7tMrU063RaLB27Vrs27cPDx48gFar1cnfv3+/URpX0Jo1a+Dl5YW+ffsWWe7cuXMAAF9f30LLKBQKKBQVY7l+IiIiIiIqnFqjxYR1pxB2PX8l8WBPhyKueDpFzeMOcLOFRCLBrjc6YPSPJ5GZo0b/IoL08kxuZTjo9nEqemVz0lemoHv69OlYu3Yt+vbti0aNGpl8foJWq8WaNWswatQoyGT5TY6KisLGjRvRp08fuLu748KFC5gxYwY6duyIJk0q3hAOIiIiIiIqnVlbLuoE3AAQUsjQaGOQGoh9lj7fBL7ONmJcVM/HCeGznoVaK8C6kOC1vJMW+HJhdNuaUGm06FjHE7IK+jyWVKag+9dff8WmTZvQp08fY7fHoL179+LOnTsYO3asTrpcLsfevXvxxRdfICMjAwEBAXjuuecwe/Zss7SLiIiIiIgsa/PpezrnwZ72Jg0MDXV0D2sZoJcmkUhgbVWxFk8rTJtgd/RsyC2ay6pMQbdcLketWrWM3ZZC9ejRA4Ig6KUHBATg4MGDZmsHERERERGVH/9cjNVLm96tjknvaainu7Kzl1eo9bfLnTJ9BfTWW2/hyy+/NBgIExERERERGdPbm8+j5ns78McTvdqTN5wRjyd1CsZLodXRr3HhazsZQ1WKuRcNboxXnqmBdrXcLd2UCq3EX1kMGTJE53z//v3YuXMnGjZsCGtr3WXji9ojm4iIiIiIqDR+fxxsv7X5PHZdjsM3L7fQmXMMAO/1rmeWtlS0/bafxkuh1S3dhEqhxEG3s7PuYgSDBw82emOIiIiIiIgK0mh1R9fuuRKP9cdvo3Wgm5j2fItq5m4WUYmVOOhes2aNKdtBRERERESkJzNHrZc2d9tlfDy4kXj+bq+65mwSUalwvXciIiIiIiq3rselGUy/lZABIHeOtZcj946m8qtMy9A1a9bM4FwGiUQCGxsb1KpVC6NHj0aXLl2euoFERERERGR6yZk5mPbLWTQLcMGbPcpPz/HGE3cMpn9/+BYAYHrX2uZsDlGplamnu1ev/7N339FRVH8bwJ/tyaZsem+00Ak99N4RRbAhCigWFFHBBooiNrArgl3hp8KroAgKAtKRDoHQWwIhgZBAEtKzfd4/lixZdjeNbLKbPJ9zcs7MnTt37uQG3e/eNgznz5+Hh4cH+vfvj/79+8PT0xPJycno0qULrly5gkGDBmH16tU1XV8iIiIiIpeh1hkQM3MtHvp+n9Pv/LN0Xyr+O5eFBVuSnKqucRE+5uOU+SOtrueV6GqxNkRVV62e7qysLLzwwgt4/fXXLdLfeecdXLx4Ef/++y/mzJmDt99+G3fddVeNVJSIiIiIyNUs3JIEANiZlIUBH2/HcwObYXSH8DqulW3f/3fefJxVqEWgl6IOa3OTVGIaYTu0dbDN63Xd073/tYF1+nxyftXq6V6+fDnGjRtnlf7AAw9g+fLlAIBx48bhzJkzt1c7IiIiIiIXpjUYzccXsorw/G+JePyng+Y0o1Go817l3GItrhdpcb34Zo/xpevF1S4vq1CD3GJtTVQNAFC6eLnYxvTWNdN6wUcpr7FnVUegp3N8OUHOq1pBt5ubG3bv3m2Vvnv3bri5mRYxMBqN5mMiIiIioobo1JV8q7SNJzPxyb9ncOl6MQZ8vA1P/JxgvpZbrEXz2esQM3MtTmdY31vTNHoD2r+1ER3e3miRnna9pFrlXcwuQud3NmHE5//V2JcJxhtR961Bt1IuQZtwla1balVD2rebqqdaw8unTZuGKVOmICEhAV26dAEAHDhwAN9//z1effVVAMCGDRvQvn37GqsoEREREZEruZxbgv/OZdm8tmBLEhbcGHqekm3qVc4u1KDTO5vMeYZ99p/NOcw1aeWhyzbTn/2/wxjVLrTKAeUXN94pPU+NfLUeOUVafPffeTzVtwki/ZQQBKHKZRpvBO+lt337cCe8+udxfHJfXJXKIaor1Qq6Z8+ejUaNGmHhwoX4+eefAQDNmzfHd999hwcffBAAMGXKFDz11FM1V1MiIiIiIhfy2cazlc573zd7sP9CTo0+P1+tQ4nWgGBv+6NPr+Ta79HedOoqpBIR2kf4wNfD/hDuMxkFGPrZDtzTKQK/J1wyp2v0Bgz8eBuMArBs380VyJ8b2AzTB8dW6h12JWXh570XAdwMvoe0DsGQ1iGVup/IGVQr6AaA8ePHY/z48Xavu7u7V7doIiIiIiKXUaDWofM7m6DRG/H8oGZ4flAssgo1WFEmAN3/6kAkXyvCuO/22iyjKgF3wsUchPm4I1Rl//N22V7zA68NQm6xFltOX8XEHjFwk0kAAGuOppt720s1CfRA8o39r0vnnrcK9cY/z/W2+6yhn+0AAIuAGwD0BsE8H7uszzefw72dIxDhqyz3PS9mF2H89/vM5/8cyyg3P5GzqnbQTUREREREwNqjV6DRmxZM+2zTOTzYNQp9Ptxqvt46zBtB3m4I8nbDv9P7YMinOypd9vUirUUv846z1zDhx/0AgP9e7g83mcTmKuNlh6mPXPAfrhZoAAASsQgPxkdBIZXgmWWHzXleHdECbcN90DHaBysPXcaslcfM107amJcOmL5sOJNRYLfu124805Ze72/F0/2a4NmBzcxfAtzqXGah3fuJXEmlF1Lz8/NDVpZpToqvry/8/Pzs/hARERERNRRKhWU/1oCPt0Otu7lq+bcTOpuPY4O9cPrtYeWWt/DBDubjDm9vhEZvAAD8dSTdHHADQO8PtiL+vU3IKrQMbtNyLFcev1om+H1n7Sm0emMDRi/aZZGnWbAXujfxh0IqwZiOldvS7KlfDuGer/fYvf7EzwftXgOAL7clo8Xr63Eo9brVteRrhXjsJ8v7vxzfsVL1InI2le7p/vTTT+Hl5WU+5ip9RERERESmodxlFWr0FufhPpbDwN1kEgxuFYyNJzOtynrv7ra4o12YRS/0ioOXMLBlEJ79v8NW+Y0CcO/Xe7D1xX7mtPXHKx6Gfexynvk4vpEf+jQLNJ8rpBI83C3aPJe6Vai3xb0lWgMOp17HziTbi8RF+ytxMbsYmfmWvxc3mdjiy4hSY7/ajQvzLBeMW7o31eL83k4RGNE2tML3qi1jOobbXYSO6FaVDronTpyI/Px8aDQajBkzxpF1IiIiIiJyGXP/Plnle76b0Bl6gxFNX1tnTkt6dzikEuuBqLNXHcfsVfbLupBVhGKtHkq56aP9qkRTMHhf5wgsP3jJ/o0AQlVu+O3J7lbpb49ugz6xgXj8p4M4eSUfF7KK0CjAA9eLtLj/2z04e8vQ77bhKnw5viP8POT4ee9FzF932uL6uXeHQyoWodGsf6yeZWtnsVv3CR/TMaLc96htn9zXHl1j/NAuwqeuq0IuoEpzun18fCrVw20wGKpdISIiIiIiV1HRAmjzx7S1e+3WALvs+bLH4/Hgd/tuvQWAafh52Z5wADidUYCOUb7QG4w4kW6ag90k0BPvjG6D2auOw10mQYnO+jP684Oa2a2fUn5zrnX/j7bZzffXMz0RqnI3zy3v1tjf4nrC7EGQ3Xi3NdN64VDqdTzYNQpjv96DI2m5iIuw3Gv7Sl4J/r1lFEB8I+ebwvpA16i6rgK5iCoF3Vu33lwQQhAEjBgxAt9//z3Cwys374OIiIiIqL549c9jFlth7Zo5AD3nbzGf75k1oNwVxsvTo0kAXhwSi4/+td52bGTbULzpeQJZhVpz2pgvd6N7Y3/sOZ9tThveJhRR/kq0DVch4eJ1vLXG1CN/b6cI88rq93exHzhGVrC6eKlbe3vbhN0cjv6/R7vC3/PmQm9twlVoE24Ksl8a0hwP/bDPvAgdAKxOvIznfk00nw9qGYwvxnWAWMypreS6qhR09+3b1+JcIpGgW7duaNy4cY1WioiIiIjI2ZUNuAHA85YF1SoTcD8/qBk+23QOX9lYJOzh7jHmoPvRno2wZPcFTOgeA5FIhIOzBwMA3l17Et/9dwEALAJuAAj3NT0/LtIHvko53lpzEgGeCrw+qhUu5hSjU7RvuXWL9HOHVCyC3ta+Xzf8+kQ3qzSpRIxXhrXAtQIN+jQLsHuvXGrq/dbeCLoFQbAIuAFgwbj2cJfbXt2cyFU49ZZhb775JubOnWuR1rx5c5w+bZojolar8cILL+DXX3+FRqPB0KFD8eWXXyI4OLguqktEREREDcQ7ayznca+Y0t0i6P5hYudbb7HpuYHNMLF7jMW2YKVU7jKkzB9pkdfb3fLj+z2dIs1Bd1nDWodAUqZ3OMpfiR0v9YevhwxebjIstzGP+1YikQjLp3THmC93W6SPj4/C2E4R8HaToWmQp817n+rXpMLyFTeCbo3eiORrhRj48XaL679P6W6ep07kypz+r7h169bYtOnmPoNS6c0qT58+HWvXrsWKFSugUqnwzDPPYMyYMdi1a5etooiIiIiIqu3Nv07gUOp1hKrcsOHEzTnHZ98Zbu61LdUqzPvW220SiUQ2A25bVEqZVVrzEC/8Mjkeh1Kv45ONN4eif3RfnFXeKP/KDRcvq0OkD3yUMuQW67B31kCEqNyqXIY9Cpnpd5ZTpLUKuPs3D0TnGOebx01UHbcddDt66zCpVIqQkBCr9Ly8PPzwww9YtmwZBgwYAABYvHgxWrZsib1796JbN+uhLkRERERE1bVkdwoA4OilPIv0sgH3v9P7IK9EV+253NXRq1kAejULwLMD7S+KVl0ikQiJbwyp8XKBm8Pxb13gLdLPHYsf6eqQZxLVhSoF3bduFaZWqzFlyhR4eHhYpK9cufL2a3bDuXPnEBYWBjc3N3Tv3h3z5s1DVFQUEhISoNPpMGjQIHPeFi1aICoqCnv27GHQTUREREQONbR1MOaMam2RFhvsVUe1cT3hPu4I8XZDRr7anNajiT/eGd2mDmtFVPOqFHSrVJbL+T/00EM1WplbxcfHY8mSJWjevDmuXLmCuXPnonfv3jh+/DgyMjIgl8vh4+NjcU9wcDAyMjLKLVej0UCj0ZjP8/PzHVF9IiIiInJxxy/nYeKP+60CwQBPBb55uHLztsk2kUiEKD+lOej+6N443NPJufbjJqoJVQq6Fy9e7Kh62DR8+HDzcbt27RAfH4/o6GgsX74c7u7VH7Izb948qwXaiIiIiIhuNf23RGQXafHU0kMW6YsndamjGtUveuPN7cIGt+JiyFQ/iSvO4jx8fHwQGxuLpKQkhISEQKvVIjc31yJPZmamzTngZc2aNQt5eXnmn7S0NAfWmoiIiIhckd5ghMHGdllbX+yHthEqG3dQVb04tDkAINBLAZW79UJxRPWBSwXdhYWFSE5ORmhoKDp16gSZTIbNmzebr585cwapqano3r38LRAUCgW8vb0tfoiIiIiISl3OLUHT19bhfFaRRfqglsFoFOBh5y6qqh5NApAyfyQOvDao4sxELsqptwx78cUXMWrUKERHRyM9PR1z5syBRCLBuHHjoFKpMHnyZMyYMQN+fn7w9vbGtGnT0L17dy6iRkRERES3Zf6601ZpA1oE4cvxHeugNkTkypw66L506RLGjRuH7OxsBAYGolevXti7dy8CAwMBAJ9++inEYjHGjh0LjUaDoUOH4ssvv6zjWhMRERGRK7taoMbfR9LN582CPLH8ye6V3k+biKgskSAI1hNVGpj8/HyoVCrk5eVxqDkRERFRA1ak0aP1nA3m85NvDYWbVAKxWFSHtSIiZ1TZONKpe7qJiIiIiGrTjOWJ5mOVuwxKOT8uE9HtcamF1IiIiIiIHMnb7eYK2j9M5D7cRHT7GHQTERERUYO2OvEy5qw+DgDmLcKeG9gMnWP86rJaRFRPcLwMERERETmN/Rdy4KGQoFCtR9sIlcOHd2fkqfHcr4kAgP/tuWhO557RRFRTGHQTERERkVN4/KeD2Hgy03zePNgLG6b3qXZ51wo0EAQBQd5udvNM/y3RZrpcygGhRFQz+F8TIiIiIqpze5KzLQJuADiTWYA5q4/jWoHGKn95G/BcL9JizJe70OXdTej63mYUa/U28xmNAvacz7Z5rXmIVxVqT0RkH7cMA7cMIyIiIqorGXlqfLjhDP44dKncfD9P7orezQIBAAVqHUYu2Ilujf3wwT1xFvlKtAa0fGO9zTK83aT475UBULnLkHS1EIM+2W4zX8tQb6x7rnc13oaIGhJuGUZERERETq/bvM1WaWfeGYZFW5KwYEuSOe3hH/Zj18wBCPdxx2ebziE1pxipOcV46642cJNJzPk+/veM3Wflq/WIm/svBrQIwpbTV83prcO8sWZaL4hE3IubiGoeh5cTERERUa26kFWE89cKkVOktbqW/N4IKKQSdGvib3Wt5/wtiJm5Fj/svGBOW5Fws4d8deJlfF/m2msjWkIusf64WzbgBoDFk7ow4CYih2FPNxERERHVmj8PX8L0344AAKb0bWJObxuuwrA2IZCITcFvjyYB+PuZXvD1kKHX+1vtlvf6quN4KD4K6WVWIQeAf57tjVZh3ni8T2MApkXVury7yer+hNmD4O+pqIlXIyKyiXO6wTndRERERLWl09sbkX1LD3eXGF+smNLD7j0HU3Jwz9d7Kv2MRQ92xMh2oVbpOUVafL09Gd/uOA8A+HFSZwxoEVzpcomIyuKcbiIiIiJyKpdzS6wCbgAVBr6dY/wq/YzHejWyGXADgJ+HHK+OaImeTQNw/HIe+jcPqnS5RETVxTndRERERORwgiCg5/wtVukTukfjqX5NbNxhadXUngjwVODOuDCEeLvhx0mdEe7jbpXv4e7RFZbVNzYQU/s35TxuIqoVHF4ODi8nIiIicqRFW5Pw4QbLVcU/vjcOYztF3Fa5BWodtp65hkb+Hli2PxWzRrSAt5vstsokIqosDi8nIiIiojqlNxgxffkR/H0k3SL97dFtMCou7LbL93KT4c4b5cyLaHvb5REROQKDbiIiIiKqcYIgoOlr66zSlz/ZHV0bVX6ONhGRq+OcbiIiIqIG4Oc9Kfhk41kYjLUzs/CDW4aTA6ZFzhhwE1FDw55uIiIionruWoEGr68+AQBYsPkcUuaPdOjzEtNy8dW2ZIu0z+5vj9Edwh36XCIiZ8Sgm4iIiKieO5Ge5/BnfLsjGe/9cxqNAjxwIavInL7/tYEI8nJz+POJiJwVh5cTERER1XOTFh+wOP9l70Vo9cbbLvfr7cmImbkW/57IwHv/nAYAi4D7zrgwBtxE1OA5ddA9b948dOnSBV5eXggKCsLo0aNx5ozl/KB+/fpBJBJZ/EyZMqWOakxERETkXPYkZ1ulzV51HLGz1+GJnw5ixm+JqMoOsmqdASsPXULMzLWYv84UaD/xc4JVvpHtQvHRvXHVrzgRUT3h1MPLt2/fjqlTp6JLly7Q6/V49dVXMWTIEJw8eRIeHh7mfI8//jjeeust87lSqayL6hIRERE5nY/+vdlhMWdUK8z9+6T5/N+TmQCAlYcvI9pfiW0vmjoz7DmZno8RC/4r93n+HnL8PDkercLs71lLRNSQOHXQvX79eovzJUuWICgoCAkJCejTp485XalUIiQkpLarR0REROTU/j2RgYSL1wEAC8Z1wJBWwRZBd1kXs4vxwYYzeGVYC1wtUGPr6asY0ioEvh5yLNuXisu5xVi0NdnqPpW7DHklOgBA0yBPbJrR13EvRETkgpx6ePmt8vJMi4D4+VluNbF06VIEBASgTZs2mDVrFoqLi+uiekRERERO5avtN4PkkW1D4SaTYNnj8fbzb0tGSlYRur67Ga/8cQwd3t6I/+1Owat/HrMKuPs1D8Sh1wfjyJwheKJPYzQL8sSaab0c9i5ERK5KJFRlEk8dMhqNuPPOO5Gbm4udO3ea07/99ltER0cjLCwMR48exSuvvIKuXbti5cqVdsvSaDTQaDTm8/z8fERGRiIvLw/e3hwKRURERK7rWoEGr/xxFFtOXzWnPdWvCV4Z1sIiX9LVAhy7nIc/D6djx9lrVXrG6beHwU0mqZH6EhG5qvz8fKhUqgrjSKceXl7W1KlTcfz4cYuAGwCeeOIJ83Hbtm0RGhqKgQMHIjk5GU2aNLFZ1rx58zB37lyH1peIiIioNhmNAp74+SA2nbpqdW1Qy2CrtKZBXmga5IW7O0Tg6+3J5kXRKpL4xmAG3EREVeASw8ufeeYZrFmzBlu3bkVERES5eePjTUOmkpKS7OaZNWsW8vLyzD9paWk1Wl8iIiKi2mQ0Cmjx+nqbAffQ1sHoFO1b7v3tI30szl8b0RIn5g61yrdiSnf4KOW3VVcioobGqXu6BUHAtGnT8Oeff2Lbtm1o1KhRhfckJiYCAEJDQ+3mUSgUUCgUNVVNIiIiolr39pqT+GHnBXSJ8cWBlOsW1/7v8W7o3sS/0mW1DVeZj0O83fBY70YQiUT446keGPvVbgCAl0KKLjF+9oogIiI7nDronjp1KpYtW4bVq1fDy8sLGRkZAACVSgV3d3ckJydj2bJlGDFiBPz9/XH06FFMnz4dffr0Qbt27eq49kREREQ1q0RrgEgEtHj95g4vtwbcye+NgERsf9svWzwUUiikYmj0RjwYH2XeNqxTtC8e6BKJXw+kYcuL/W67/kREDZFTL6Rmb5/IxYsXY9KkSUhLS8NDDz2E48ePo6ioCJGRkbj77rsxe/bsKi2IVtkJ8ERERES14b9z17DvfA7+OHQJk3s1wmO9G+Nqvhpd39tc/n0v90ekn7Jaz9ydlIXDabmY1CMGHgqn7pchInIKlY0jnTrori0MuomIyJEEQcCh1Fz8svcitp+9hlnDW+DezpEV3vf3kXQYBQEx/h74YecFKOUSzB/LkVz1XUaeGt3mWQbXPZr4Y3dytt17WoZ6Y91zvR1dNSIiKqPerV5ORETkikq0BrR8Y71F2ku/H8Wd7cMgEYnw1NJD2Hc+GwdnD4ZcenN90wK1DtP+77BVeTOGxCLQU2F3NBg5v9TsYrz4+xEkXS3E6PbhuKt9GDafvopO0b7o1tgPvydYL/BqK+De/lI/RPkpkXS1EBG+1evdJiIix2PQTUREVMMEQcDnm8/hs03n7OZpPtsyEP/rSDrGdgw3B9M5RVqb93V992YPaFykD14a0hy9mgXUQK2ptsxffwr7L+QAAH7cdQE/7rpgM1+naF/ojQKOpOXavB7lp4RIJEKzYC9HVZWIiGqAS2wZRkRE5EoGfrLdZsA9Pj7K7j0vrjiCJ39OAABo9Ab0/XBbhc85kpaLh37YhzdWH692Xan2CIKAEq0B/53LqlT+uXe2xpgO4RZp47pGonGABx7t2YijHYiIXAR7uomIiG7T1Xw1PN2kUMqluJJXgvPXiiyutwz1xuqpPSGXijF9cCw6v7PJZjn/nsxEWk4xvtyWbE5zl0lw6u1h0BuMaPraOpv3/bTnIn47kIb9rw2CwSjAz0MOncEIiUgEcRVXsaaakVOkxVO/JODgxeswGCtePqdjlA8Opeaaz0e2DUWbcBVahHjhdEY+TmcUYOGDHRHu4+7AWhMRkSNwITVwITUiovrqRHoeRi7Yia4xflg+pXuNli0IApbsTsHcv0/azbPlhb5oHOhZbjnFWj1avbHB7vXvJnTG4FbBVulH0nKx8WQmFm5NKrf8TTP6omlQ+XWgmnOtQIOP/z2DXw9Yz8u+1YJxHXBnXJj5XKs3Qi4Vw2AUqrzlFxER1b7KxpEcXk5ERPWORm/AW3+fxMgFOwEA+1NysOV0Zo0+4921p8oNuAFUGHADgFIuRcr8kTYD660v9rOZDpjmc784tDkSZg8qt/xBn2zHhawi8Dv22vHJxrOVCrgBYFS7UIvz0oX0GHATEdUvHF5ORET1yvf/ncc7a09ZpT+65CD+nd4HzYI8cSI9H7HBXharhV/NV2PkFztxrUCDZY/Ho0cT+4uTXbpejO932l78CgB8lDL88VSPKtX77g7h2Hjy5hcD793dFo0CPCq8z99TgbXP9jJ/wWBL/4+2AQB2zRyAk+n5eO3PY7haoMGglsGIi1Bhf0oOnh8Ui07RvlWqc31mMArILdbiyKVc9G4WiPfXncaZzAJ8cE87hKpsD/Feuu8i/m9/qkXa0TeHwNtNBgBIuloAfw8FjIJpCgDnZBMRNQwcXg4OLyciqi++3ZGM9/45XW6eh7tF4+e9FwEAnaN98fXDnbBoaxIW70qxyBfkpYBaZ4CbTIKn+zXBxB4xEIlEKNLo0XrOzeHgv0/pjs4xfgBgzl9dO89lITHtOoa0DkFsFVekXnnoEt5dewqLxndEt8b+AICBH29D8i3zyyvjn2d7o1VYw/z/YYFaB4lYhF7vb7W7gvxH98ZZrDS/KykL47/fZ5Hn/x7vhu5N/B1eXyIiqjuVjSMZdINBNxGRq7E153XO6uP4356L5vOXhjbH470bQyYR4fnfErE6Mf22n7v8ye6475s95vORbUOxaHzH2y7XUdQ6Aw6n5uLZXw/jWoGm0vfJJCKcfnt4vR/mrNEbUKDWI8BTAQCY9n+H8feR2/87WTGlO7rc+CKGiIjqLwbdVcCgm4jI+al1BsxZfQK/HTTNl/1uQmf4echxLrMAM1ces8ib/N4Iq4AxdvY6aPXGcp/x38v94aOUoe2b/1ZYn0YBHtj6Yr+qvUQdenppAv45lgHA1NvfOcYXzYK80DLUC6euFOD11ceRcPG6xT29mwXgp0e71qth0GqdASMX/GcxAuCT++IwY/mRGil/zbReaBOuqpGyiIjIuTHorgIG3UREzktvMGLu3yfNQ8IrsvOV/ojwVVqlq3UGfLfjPNLz1Hh1RAt4uclwz1e7cfBGoPnRvXG4p1MEANOK4jvOXkPTIC8Ua/XYlZSN99ffHLb+/KBmeH5QbA28nfOJmbnW4rx3swB8P7EzFNLqD5uvayVaA/4+ko6X/zhaqfwBnnK0DPXGvDFtodEbsSspC+O6RmHloUv4v/1p0BmMOJGeb3HPhXkj6tWXE0REVDEG3VXAoJuIqO4YjAIS064j2NsNEb5KJFzMwbnMQvy05yIm9YjB9zvP42xmYaXKOvPOsCoFh0UaPSb8uB/R/kp8cl/7CvPvTs6CXCI2z+GujxLTcjF60S6710+/Pey25q07ksEo4L9z16DWGTHll4RqlbH/tYEI8nKrVN7NpzJx6XoJ7uscCXe5c/5OiIjIcRh0VwGDbiKi2mcwCjiQkoMJP+yH1mAa9u3nIbe7eBUAzBnVCo/0bISrBWos3pWCIo0e3Rr7o0cTf3gopJBJuBNmTTEaBQz//D+cySyweb1FiBdOZ5iuLZ7UBf1bBOFEeh5mrTyGXk0D8MyAplDKa2+TFI3egOaz11cq75iO4fjwnjhIxCJczVdjd3I2hrcNcenefCIiqn0MuquAQTcRkWOlZhcjNacYISo3BHkr8NW2ZHy1LbnS9y8Y1wF3xoU5sIZkS4nWgCGfbUdaTkm17j//3ghoDUZ0n7cZI9uFom9sEMJ93Gt8ZfTKBtzfTehsd99zIiKiqmLQXQUMuomIao7xRg/2zJXHoNUbIZeKcSHL/rZVrUK90bOpP9qEq3A1X4MofyVU7jJIxCKczSxA98b+aBzoWYtvQGVdzC7CP8cy0CzIE/tTcvDtjvO3XeaE7tGYe2frKs+B1huM+GbHeXRv4g83qQTTf0vEY70bAQBe+t00X1vlLsP8MW3ho5Rzyy4iInIoBt1VwKCbiMi+zHw10nNLkFusQ7NgT4tFykq0BvyekIb1JzIgFYvRItQLfyRcRlZh5ban+vCedri3c6Sjqk4OklOkxaGL19EnNhArEtLw2p/HzdfWTOuFh37Yh9xiXbllLHqwI7o19oOfh7zSwffCLefw0b9ny82TMn9kpcoiIiK6XQy6q4BBNxGVJyNPjZEL/kN2kRbdGvvhrbvaoFGAB85kFOCOL3aa80X7K7FmWi94ucmsyijS6HHpegliApRVnjeq1hmQW6zDX0cu477Okdh4MhNvrTmJwS2DcfJKvnlebe9mATiRno+cIi1i/JXoGxuIuXe1sSrvWoEGb/51Aqcz8rHwwY5oGWr5373sQg0K1HpIxCIs3JJk3qKrlKdCikKNvtL1d5dJ8HD3aMT4e6BEZ8DItqFwl0mgUlr/nsg1FWn0KFDrEeilgEQsgsEooMmr/1jkubdTBFYkXLK6d1zXSMwb067c8l9ccQS/27j3VvPGtMW4rlFVqzwREVE1MeiuAgbdRHVLqzdi06lM9G4WYDNgvV0P/7AP/53LwuBWwXh9ZCtE+SthNArQGowoUOsxY3kivN1kyC3RwmAU8M3DnQEAc/8+gcvXS7DvQk6Vnxnjr8QDXaNwLrMQap0Ba49dMV97vHcjvDCkud0VoAVBgCAAWUUaHE3Lw2M/Hazei99wR7tQrDl6BTH+SmTma1CiM1jliQ32hFwqhkQsxpG03Go/y1MhxaCWQXi0VyO0vbFXMbdRapiKtXqsOpyO4W1C4OUmhfTGIncHU3Jwz9d7LPKeemsYzl0tQPK1QhxMuY5pA5rhnbUnodYZMLxNKF5YYXsP7V+f6IazmQVYnZiObo398NLQFg5/LyIiolIMuquAQTdR3dDqjXj59yNYlZhuda1bYz8se6wbxOKqB2xX8kqw4uAlfLLR9jDUX5/ohqd+ScD1Coa/OpJMIkKMvwdCVG6YM6oV1Dojftx5ASsPX65SOZN6xGD72WsWc6ZbhnrjbGYBDMaa+8/7uud6Q+Uuw9Rlh3A4NdecPrp9GAa3CsHIdqE19iyq39Q6A1q8XrlVxm1xl0lw8q2h/DKHiIjqHIPuKmDQTVQ+g1HA8ct5+HJbEpKuFmL+2HboUol9iq8VaHAo9ToEQUCzYC8Ee7shJasIc/46gYvZRcgqtL81FAB8P6EzBt2y0rDOYIREJML5rEI8/lMCJGIRmgV54uHu0egc7Ye957Mx4cf9t/W+trx+Rys82jMG+Wo9DqbkwGAU4KmQItzXHdH+HgCAhIs5GPvVnnLLCfdxx+Xcqq8E/fodrTC4ZTD+S7qG+Eb+aBpkubCYwSjAKAgWW2Y98O0e7D1/s5e+UYAHsgo0KNDosfiRLugXG4h1xzNwJqMAWYUa5BRpcfRSHhoHeuCeThEY2joECqmYwQ3VuMu5Jeg5f0uV7jnw2iAEeikcVCMiIqKqY9BdBQy6qTaVaA2QSUTmoZa3S28wQioRIyNPjUKNHjvPXUOIyh0lOj1ahnqjRcjNv2lBEFCo0cNdJjE/XxAEiEQiZOSpkZJdhF1JWTicmgut3gijIODgxesQiYBb/0txd4dwTOnbBM1DvHA1Xw25VIzcYh3e++cUjl3OQ16JDsVa62HM9kzqEQORCEi+VoQdZ6+Z0yN83eGpkOJsZgGq23Eb4u2GDdP7YNAn23GtwHqBr7+f6YWYACWMRmBV4mXsTMrCIz1iEBfpAw9F1fcZ1t8Ytn48PQ+tw1Tw85Cbf88AkJZTjOOX83AqowALNp+zuj8uQgWxWIQ2YSpM6hmDGH8PSKrR4w+YVhLX6I1wl3P/YXI++WodhnyyAxn56hvDw5tDbxAwb91pzL2zNXyVcny1PQmxwV54pGejuq4uERGRhQYXdC9atAgffvghMjIyEBcXhy+++AJdu3at1L0Muqm6NHoDLmYXQ2cwolhrgL+HHG4yCa4Xa+EhlyLIW4FTVwqw/cxVrDx8GQVqPfJKTEOamwZ5QmcwItzHHRn5akT4KhHgKUdGnhoFaj2KNHrEBHhALAKCvN2g0RnRLkIFf085Dl3MRcLFHBy5lAeRCJCIRNCXE5GGqdzg7S5DRr7avKJwgKcCISoFzl8rqlJwXFO6xvihc4wvhrQOQaSvO/w9b/Zgvb7qOH7ee/G2yre3H+/5a4XIKdIiMS0Xk3s1qvNeXEEQMHvVcWTkqTFvbFsEebnVaX2IaluhRg+jIMDbAes5EBEROVKDCrp/++03TJgwAV9//TXi4+Px2WefYcWKFThz5gyCgoIqvJ9Bd+UJgqnXTHqj162yvbWCIMAoAIVqPURiIKtAg7wSHQK9FHCXSaCQSSARiSARiyAViyzm8RqMAtQ6A2QSMWQSU3ApFYsqDJYEQcD1Yh2u5JUgq1CLlKwiXC/WQqM3Qi4RQywSoVCjQ2a+BlKJCM2CvKA3GJFZoEZWgRa5JVr4KuWI8lOicaAH8kp0CFW5o0RnwNbTV3EgJafC4dF1pWmQJ67mq5GvrvwK06XkEjH6xAYiRKVAuwgfbD97DU0CPDC0TQhahHjjQlYRnv/tMI5fzrdbxoAWQWgV6o1BrYIRF6GCSCRCem4JrhZocDVfjd7NAivseb10vRjXCjR49c/jOHXF9Kw24d7IL9Gjc4wvRBBh1ogWCPBUQKs34nqxFlfzNTAIAmKDPaGUV72HmoiIiIioshpU0B0fH48uXbpg4cKFAACj0YjIyEhMmzYNM2fOrPB+Vwi61x/PQGa+GvklOhRq9NAZTPM3jYJgnstpNAIGQYBWb4RGb4BUIoZaa4DhRsBrNN6SXzAFtKWBtEZvvHGvETqDEXqDETKpGDKJGGIRYDCaFsApu1WQu0xiWpVWLIKHQgq9UUCB2tRrodMboTMaoTMIVV7QSSQClDIJPBRS5BRprXpxS6+LxSJo9cabQ59FgAiA+EYAX5VtjW6Xv4cc2UW2g3ClXIJirQHjukYhwFOOFiHeSLtebP69XMkrwcn0fKRdL0GJ1oCO0b4YHx+F01cKcDjtOrR6I3YnZwMAgr0V8FRIEeCpQNtwFUa0C0V+iQ7Ngr3MPe2lLl0vRnahFueuFiIlqwhhPu4Y2DIIMokYp67kIy2nGI0DPRHsrUCwt5vd1bRt0RmM+HbHefyecAnuMgl8lDI81a8JujX2t5hXTERERERUHzWYoFur1UKpVOL333/H6NGjzekTJ05Ebm4uVq9ebXWPRqOBRnNzXmd+fj4iIyOdOuge+PE2JF8rqjiji3CTmYJ5jc4IrcHo0GcFeCrg5yFDhK/S/CWBwShALhVDIZUgVOWGC9lFyCrQwEcpQ7C3GwK9FJBLxEjPLcHJK/nmlaEDPBWQScRoFuyJ1mEqtAjxQtMgT4tgVaM3QCoWo0CtQ3quGrHBnjU2f5uIiIiIiJxDZYNulx9/mZWVBYPBgOBgy7mbwcHBOH36tM175s2bh7lz59ZG9WpM72aBaB7iBW83mXm/U4lIBLEIEItFpmOx6EYPL6CQSmAwCvBQSCAWicw9v6Y8pjnAohtpEjEgFYvhLpdALhFDLjX9SG/0Ipf2qotFIsilYgR6KiDA1FOeXWhaCRkw9YIDgK9Sbh4mLpPcLEt0437gZi81YOpt1xmMMBgF6I2mXnG9wYicYi30BtM8vwAvOXQGUz4RAKMAFGn0MAgCFFLTUHEAEGAaVl5aZoSvskq9tzVBITU9z0cph49SXqvPJiIiIiIi5+LyQXd1zJo1CzNmzDCfl/Z0O7M372xd11Wwyc/j9oNKU+BvHRgHeZe/oBS3jiEiIiIiImfn8kF3QEAAJBIJMjMzLdIzMzMREhJi8x6FQgGFggEbEREREREROZbLTzSVy+Xo1KkTNm/ebE4zGo3YvHkzunfvXoc1IyIiIiIioobO5Xu6AWDGjBmYOHEiOnfujK5du+Kzzz5DUVERHnnkkbquGhERERERETVg9SLovv/++3Ht2jW88cYbyMjIQPv27bF+/XqrxdWIiIiIiIiIapPLbxlWE1xhn24iIiIiIiJyHg1my7CaUPq9Q35+fh3XhIiIiIiIiFxBafxYUT82g+4ynH3bMCIiIiIiInItHF4O0zcTBQUFtfa80n3B09LSOJzdybGtXAvby3WwrVwH28p1sK1cC9vLdbCtXEddtZWXlxdEIpHd6+zpBiASierkH5C3tzf/4boItpVrYXu5DraV62BbuQ62lWthe7kOtpXrcLa2cvl9uomIiIiIiIicFYNuIiIiIiIiIgdh0F0HFAoF5syZA4VCUddVoQqwrVwL28t1sK1cB9vKdbCtXAvby3WwrVyHs7YVF1IjIiIiIiIichD2dBMRERERERE5CINuIiIiIiIiIgdh0E1ERERERETkIAy6q2nHjh0YNWoUwsLCIBKJsGrVKovrmZmZmDRpEsLCwqBUKjFs2DCcO3fOIk9ycjLuvvtuBAYGwtvbG/fddx8yMzPN11NSUjB58mQ0atQI7u7uaNKkCebMmQOtVlsbr1hv1EZblVq7di3i4+Ph7u4OX19fjB492oFvVv/MmzcPXbp0gZeXF4KCgjB69GicOXPGIo9arcbUqVPh7+8PT09PjB071qotUlNTMXLkSCiVSgQFBeGll16CXq+3yLNt2zZ07NgRCoUCTZs2xZIlSxz9evVKbbZVqV27dkEqlaJ9+/aOeq16qzbba+nSpYiLi4NSqURoaCgeffRRZGdnO/wd64uaaqtnn30WnTp1gkKhsPlvZtu2bbjrrrsQGhoKDw8PtG/fHkuXLnXkq9U7tdVWACAIAj766CPExsZCoVAgPDwc7777rqNerd6pibY6cuQIxo0bh8jISLi7u6Nly5b4/PPPrZ7Fzxe3rzbbq5SjP2Mw6K6moqIixMXFYdGiRVbXBEHA6NGjcf78eaxevRqHDx9GdHQ0Bg0ahKKiIvP9Q4YMgUgkwpYtW7Br1y5otVqMGjUKRqMRAHD69GkYjUZ88803OHHiBD799FN8/fXXePXVV2v1XV1dbbQVAPzxxx94+OGH8cgjj+DIkSPYtWsXHnzwwVp7z/pg+/btmDp1Kvbu3YuNGzdCp9NhyJAh5rYAgOnTp+Pvv//GihUrsH37dqSnp2PMmDHm6waDASNHjoRWq8Xu3bvxv//9D0uWLMEbb7xhznPhwgWMHDkS/fv3R2JiIp5//nk89thj2LBhQ62+ryurrbYqlZubiwkTJmDgwIG18n71TW21165duzBhwgRMnjwZJ06cwIoVK7B//348/vjjtfq+rqwm2qrUo48+ivvvv9/mc3bv3o127drhjz/+wNGjR/HII49gwoQJWLNmjcPerb6prbYCgOeeew7ff/89PvroI5w+fRp//fUXunbt6pD3qo9qoq0SEhIQFBSEX375BSdOnMBrr72GWbNmYeHCheY8/HxRM2qrvUrVymcMgW4bAOHPP/80n585c0YAIBw/ftycZjAYhMDAQOG7774TBEEQNmzYIIjFYiEvL8+cJzc3VxCJRMLGjRvtPuuDDz4QGjVqVPMv0UA4qq10Op0QHh4ufP/997XzIg3E1atXBQDC9u3bBUEw/d5lMpmwYsUKc55Tp04JAIQ9e/YIgiAI//zzjyAWi4WMjAxznq+++krw9vYWNBqNIAiC8PLLLwutW7e2eNb9998vDB061NGvVG85qq1K3X///cLs2bOFOXPmCHFxcY5/oXrOUe314YcfCo0bN7Z41oIFC4Tw8HBHv1K9VZ22Kqsq/2ZGjBghPPLIIzVS74bIUW118uRJQSqVCqdPn3ZY3Rua222rUk8//bTQv39/8zk/XziGo9qrVG18xmBPtwNoNBoAgJubmzlNLBZDoVBg586d5jwikchiDzk3NzeIxWJzHlvy8vLg5+fnoJo3PDXVVocOHcLly5chFovRoUMHhIaGYvjw4Th+/Hgtvk39k5eXBwDmv/mEhATodDoMGjTInKdFixaIiorCnj17AAB79uxB27ZtERwcbM4zdOhQ5Ofn48SJE+Y8ZcsozVNaBlWdo9oKABYvXozz589jzpw5tfEqDYKj2qt79+5IS0vDP//8A0EQkJmZid9//x0jRoyorVerd6rTVrfzLH7GqD5HtdXff/+Nxo0bY82aNWjUqBFiYmLw2GOPIScnp2ZfoAGpqba69d8MP184hqPaC6i9zxgMuh2gtNFnzZqF69evQ6vV4v3338elS5dw5coVAEC3bt3g4eGBV155BcXFxSgqKsKLL74Ig8FgznOrpKQkfPHFF3jyySdr83XqtZpqq/PnzwMA3nzzTcyePRtr1qyBr68v+vXrx/8pVpPRaMTzzz+Pnj17ok2bNgCAjIwMyOVy+Pj4WOQNDg5GRkaGOU/ZoKD0eum18vLk5+ejpKTEEa9Trzmyrc6dO4eZM2fil19+gVQqdfCbNAyObK+ePXti6dKluP/++yGXyxESEgKVSmVzeg9VrLptVR3Lly/HgQMH8Mgjj9xOlRssR7bV+fPncfHiRaxYsQI//fQTlixZgoSEBNxzzz01+QoNRk211e7du/Hbb7/hiSeeMKfx80XNc2R71eZnDAbdDiCTybBy5UqcPXsWfn5+UCqV2Lp1K4YPHw6x2PQrDwwMxIoVK/D333/D09MTKpUKubm56NixozlPWZcvX8awYcNw7733cm5cDaqptiqd2/3aa69h7Nix6NSpExYvXgyRSIQVK1bU2fu5sqlTp+L48eP49ddf67oqVAFHtZXBYMCDDz6IuXPnIjY2tkbLbsgc+W/r5MmTeO655/DGG28gISEB69evR0pKCqZMmVLjz2oIauu/g1u3bsUjjzyC7777Dq1bt3bos+orR7aV0WiERqPBTz/9hN69e6Nfv3744YcfsHXrVqvFpahiNdFWx48fx1133YU5c+ZgyJAhNVg7upWj2qu2P2Ow28BBOnXqhMTEROTl5UGr1SIwMBDx8fHo3LmzOc+QIUOQnJyMrKwsSKVS+Pj4ICQkBI0bN7YoKz09Hf3790ePHj3w7bff1var1Hs10VahoaEAgFatWpnvUSgUaNy4MVJTU2v3heqBZ555BmvWrMGOHTsQERFhTg8JCYFWq0Vubq7Ft5uZmZkICQkx59m/f79FeaWrWZbNc+vqsZmZmfD29oa7u7sjXqnecmRbFRQU4ODBgzh8+DCeeeYZAKYPn4IgQCqV4t9//8WAAQMc/Ib1i6P/bc2bNw89e/bESy+9BABo164dPDw80Lt3b7zzzjvm/1ZSxW6nrapi+/btGDVqFD799FNMmDChJqre4Di6rUJDQyGVSi0Cg5YtWwIw7SjQvHnz23+JBqIm2urkyZMYOHAgnnjiCcyePdviGj9f1CxHtletf8ZwyEzxBga3LM5ly9mzZwWxWCxs2LDBbp7NmzcLIpHIYqGMS5cuCc2aNRMeeOABQa/X11SVGyxHtVVeXp6gUCgsFlLTarVCUFCQ8M0339RI3RsCo9EoTJ06VQgLCxPOnj1rdb104Yzff//dnHb69Gmbiz1lZmaa83zzzTeCt7e3oFarBUEwLXTSpk0bi7LHjRvHhU6qoDbaymAwCMeOHbP4eeqpp4TmzZsLx44dEwoLCx3/ovVEbf3bGjNmjHDfffdZlL17924BgHD58mVHvFq9UxNtVVZ5CwNt3bpV8PDwEBYuXFhj9W9IaqutNmzYIAAQkpKSzGmJiYkCAOHMmTM18zL1XE211fHjx4WgoCDhpZdesvkcfr6oGbXRXrX9GYNBdzUVFBQIhw8fFg4fPiwAED755BPh8OHDwsWLFwVBEITly5cLW7duFZKTk4VVq1YJ0dHRwpgxYyzK+PHHH4U9e/YISUlJws8//yz4+fkJM2bMMF+/dOmS0LRpU2HgwIHCpUuXhCtXrph/qPJqo60EQRCee+45ITw8XNiwYYNw+vRpYfLkyUJQUJCQk5NTa+/q6p566ilBpVIJ27Zts/h7Ly4uNueZMmWKEBUVJWzZskU4ePCg0L17d6F79+7m63q9XmjTpo0wZMgQITExUVi/fr0QGBgozJo1y5zn/PnzglKpFF566SXh1KlTwqJFiwSJRCKsX7++Vt/XldVWW92Kq5dXT2211+LFiwWpVCp8+eWXQnJysrBz506hc+fOQteuXWv1fV1ZTbSVIAjCuXPnhMOHDwtPPvmkEBsba/7/YOlK81u2bBGUSqUwa9Ysi+dkZ2fX6vu6stpqK4PBIHTs2FHo06ePcOjQIeHgwYNCfHy8MHjw4Fp9X1dWE2117NgxITAwUHjooYcsyrh69ao5Dz9f1Izaaq9bOfIzBoPuatq6dasAwOpn4sSJgiAIwueffy5EREQIMplMiIqKEmbPnm21Bc4rr7wiBAcHCzKZTGjWrJnw8ccfC0aj0Xx98eLFNp/BAQpVUxttJQimnu0XXnhBCAoKEry8vIRBgwZZbEVGFbP397548WJznpKSEuHpp58WfH19BaVSKdx9991WX0SlpKQIw4cPF9zd3YWAgADhhRdeEHQ6nUWerVu3Cu3btxfkcrnQuHFji2dQxWqzrcpi0F09tdleCxYsEFq1aiW4u7sLoaGhwvjx44VLly7VxmvWCzXVVn379rVZzoULFwRBEISJEyfavN63b9/ae1kXV1ttJQiCcPnyZWHMmDGCp6enEBwcLEyaNIlfkFRBTbTVnDlzbJYRHR1t8Sx+vrh9tdleZTnyM4boxosRERERERERUQ3j6uVEREREREREDsKgm4iIiIiIiMhBGHQTEREREREROQiDbiIiIiIiIiIHYdBNRERERERE5CAMuomIiIiIiIgchEE3ERERERERkYMw6CYiIiIiIiJyEAbdRERERERERA7CoJuIiKgBmDRpEkQiEUQiEWQyGYKDgzF48GD8+OOPMBqNlS5nyZIl8PHxcVxFiYiI6hkG3URERA3EsGHDcOXKFaSkpGDdunXo378/nnvuOdxxxx3Q6/V1XT0iIqJ6iUE3ERFRA6FQKBASEoLw8HB07NgRr776KlavXo1169ZhyZIlAIBPPvkEbdu2hYeHByIjI/H000+jsLAQALBt2zY88sgjyMvLM/eav/nmmwAAjUaDF198EeHh4fDw8EB8fDy2bdtWNy9KRETkRBh0ExERNWADBgxAXFwcVq5cCQAQi8VYsGABTpw4gf/973/YsmULXn75ZQBAjx498Nlnn8Hb2xtXrlzBlStX8OKLLwIAnnnmGezZswe//vorjh49invvvRfDhg3DuXPn6uzdiIiInIFIEAShritBREREjjVp0iTk5uZi1apVVtceeOABHD16FCdPnrS69vvvv2PKlCnIysoCYJrT/fzzzyM3N9ecJzU1FY0bN0ZqairCwsLM6YMGDULXrl3x3nvv1fj7EBERuQppXVeAiIiI6pYgCBCJRACATZs2Yd68eTh9+jTy8/Oh1+uhVqtRXFwMpVJp8/5jx47BYDAgNjbWIl2j0cDf39/h9SciInJmDLqJiIgauFOnTqFRo0ZISUnBHXfcgaeeegrvvvsu/Pz8sHPnTkyePBlardZu0F1YWAiJRIKEhARIJBKLa56enrXxCkRERE6LQTcREVEDtmXLFhw7dgzTp09HQkICjEYjPv74Y4jFpmVfli9fbpFfLpfDYDBYpHXo0AEGgwFXr15F7969a63uREREroBBNxERUQOh0WiQkZEBg8GAzMxMrF+/HvPmzcMdd9yBCRMm4Pjx49DpdPjiiy8watQo7Nq1C19//bVFGTExMSgsLMTmzZsRFxcHpVKJ2NhYjB8/HhMmTMDHH3+MDh064Nq1a9i8eTPatWuHkSNH1tEbExER1T2uXk5ERNRArF+/HqGhoYiJicGwYcOwdetWLFiwAKtXr4ZEIkFcXBw++eQTvP/++2jTpg2WLl2KefPmWZTRo0cPTJkyBffffz8CAwPxwQcfAAAWL16MCRMm4IUXXkDz5s0xevRoHDhwAFFRUXXxqkRERE6Dq5cTEREREREROQh7uomIiIiIiIgchEE3ERERERERkYMw6CYiIiIiIiJyEAbdRERERERERA7CoJuIiIiIiIjIQRh0ExERERERETkIg24iIiIiIiIiB2HQTUREREREROQgDLqJiIiIiIiIHIRBNxEREREREZGDMOgmIiIiIiIichAG3UREREREREQOwqCbiIiIiIiIyEEYdBMRERERERE5CINuIiIiIiIiIgdh0E1ERERERETkIAy6iYiIiIiIiByEQTcRERERERGRgzDoJiIiIiIiInIQBt1EREREREREDsKgm4iIiIiIiMhBGHQTEREREREROQiDbiIiIiIiIiIHYdBNRERERERE5CAMuomIiIiIiIgchEE3ERERERERkYMw6CYiIiIiIiJyEAbdRERERERERA7CoJuIiIiIiIjIQRh0ExERERERETkIg24iIiIiIiIiB5HWdQWcgdFoRHp6Ory8vCASieq6OkREREREROTkBEFAQUEBwsLCIBbb789m0A0gPT0dkZGRdV0NIiIiIiIicjFpaWmIiIiwe51BNwAvLy8Apl+Wt7d3HdeGiIiIiIiInF1+fj4iIyPN8aQ9DLoB85Byb29vBt1ERERERERUaRVNUa7ThdR27NiBUaNGISwsDCKRCKtWrTJf0+l0eOWVV9C2bVt4eHggLCwMEyZMQHp6ukUZMTExEIlEFj/z58+v5TchIiIiIiIislanQXdRURHi4uKwaNEiq2vFxcU4dOgQXn/9dRw6dAgrV67EmTNncOedd1rlfeutt3DlyhXzz7Rp02qj+kRERERERETlqtPh5cOHD8fw4cNtXlOpVNi4caNF2sKFC9G1a1ekpqYiKirKnO7l5YWQkBCH1pWIiIiIiIioqlxqn+68vDyIRCL4+PhYpM+fPx/+/v7o0KEDPvzwQ+j1+rqpIBERERERUR24WqBGvlpX19UgG1xmITW1Wo1XXnkF48aNs1js7Nlnn0XHjh3h5+eH3bt3Y9asWbhy5Qo++eQTu2VpNBpoNBrzeX5+vkPrTkRERERE5CjZhRp0fXczVO4yHJkzpK6rQ7dwiaBbp9PhvvvugyAI+OqrryyuzZgxw3zcrl07yOVyPPnkk5g3bx4UCoXN8ubNm4e5c+c6tM5ERERERES14e8jpsWm80rY0+2MnH54eWnAffHiRWzcuLHCLb3i4+Oh1+uRkpJiN8+sWbOQl5dn/klLS6vhWhMREREREdWOK/nquq4ClcOpe7pLA+5z585h69at8Pf3r/CexMREiMViBAUF2c2jUCjs9oITERERERG5EhFu7hNtNAoQi8vfN5pqV50G3YWFhUhKSjKfX7hwAYmJifDz80NoaCjuueceHDp0CGvWrIHBYEBGRgYAwM/PD3K5HHv27MG+ffvQv39/eHl5Yc+ePZg+fToeeugh+Pr61tVrERERERER1RqjIJiPT2Xko3WYqg5rQ7eq06D74MGD6N+/v/m8dH72xIkT8eabb+Kvv/4CALRv397ivq1bt6Jfv35QKBT49ddf8eabb0Kj0aBRo0aYPn26xTxvIiIiIiKi+szb7WZYpzMI5eSkulCnQXe/fv0gCPb/KMq7BgAdO3bE3r17a7paRERERERELuOjf8+aj1OyitA+0qfuKkNWnH4hNSIiIiIiIrItJavI4rxAo8c/x65g1spjKNbq66hWVJZTL6RGRERERERE9t26TdjmU5nYduYaAEAsAt69u21dVIvKYE83ERERERGRk1PrDFideBnXCjQW6YUay97s0oAbAE5nFNRK3ah8DLqJiIiIiIic3Kt/HsNzvybivX9OWaTnFGnt3iOT2N46zGgUcOl6cY3Wj+xj0E1EREREROTkVh66DAD48/Bli/QDKTl275FJLMM9o1HAy78fQeNX/0Gv97fin2NXar6iZIVBNxERERERkQuZuvSQ+finPRft5vO6sZWY3mDEodTrOJCSg+UHL5mvv/nXCcdVksy4kBoREREREZGTi/JTIjXHNCR87bEr+EhrgLtcgmBvBTLzNTbvychT4+c9KXh9tSm4DvF2s7h+tcD2fbXt3bUncTqjAIsndYFUUv/6hevfGxEREREREdUzbjLL0G3u3ydw/lohxKKb87Z7NQ2wyHMoNdcccANARr7asZWsBkEQ8N1/F/DfuSwcSLle19VxCPZ0ExEREREROTG1zoCzmYUWab8eSMOvB9LM57tmDkBOoRY7F+6s7erdlnz1zdXXJWLbC7+5OvZ0ExERERERObFVtyyeZouHXIK2ESqkzB+J7yZ0rlS5zhDjll193Rnq4wgMuomIiIiIiJyYyl1WYR53ucR8fOvcbXsi/ZTVrlNNKSqzz7jBKNRhTRyHQTcREREREZGTuphdhO1nrwEAInzd7eZTSG8G3eJKRnk6vfG26lYTNHqD+bi+Bt2c001EREREROSk+n64zXzcOswbl66XWOX54J52FuexwV6VKltrqPugW627WQeDUD+DbvZ0ExERERERuYC953NspreLUFmcyyRinH9vRIXlaWqxp/tCVhHWH8+wSi/RsqebiIiIiIiInIBMIsaG5/tg6Gc7cH/nSIzvFoUSrQEtQryt8orFIpx5Zxi+2JyEhVuTbJanq8We7v4fbQMA+Chl2PfqQPNw+MW7L5jz1Negmz3dRERERERETigtp9ji/IN72qJ5iBdS5o/E+/e0Q7sIH8Q39rd7v0IqQXxjP/P5+ud7W1zX6I0QanlId26xDq3f2ADAFPTvSso2Xzt2Oa9W61JbGHQTERERERHVMUEQkJGnNgfBgiCg9wdbLfKEquwvpGZP2eHbMf4e6F4mSBcEoLjMdUfZk5xtca6/0aN97pa9xz/bdM7hdakLDLqJiIiIiIjq2KZTV9Ft3mbMX3caGXlqvPrnMas8lV0grawS3c2gWiEV4+P74tA3NtCclq/WVa/CVTDuu70207OLNFZpV/KsF4pzdQy6iYiIiIiI6tinG88CAL7ZcR7d5m3G/+1Ps8ojEYuqXG7ZnmyRSIQwH3f879Gu8PeQAwDyS/T2bq0Ru5OzbKZvOJGBArX1s6f8nODQ+tQFLqRGRERERERUh5KuFuLklXyHlG1v+Li3uwzZRVrklTi2p/uPhMs205+0E1zHBHg4sjp1gj3dREREREREdWjQJ9srzPNwt+hqlT2mQzhU7jKMiguzSPd2M/W/5jso6N57PhuzVh5FhG/V5qHLJfUvRK3TN9qxYwdGjRqFsLAwiEQirFq1yuK6IAh44403EBoaCnd3dwwaNAjnzllOrs/JycH48ePh7e0NHx8fTJ48GYWFlhPyiYiIiIiIXNV9nSPw9ug21brX10OOg7MHYcED7S3Svd1lABw3p/uBb/fi//an4fPNpvjNU2F/kPV9nSNwR7tQAHB4z3tdqNOgu6ioCHFxcVi0aJHN6x988AEWLFiAr7/+Gvv27YOHhweGDh0KtVptzjN+/HicOHECGzduxJo1a7Bjxw488cQTtfUKREREREREDvXayFa3db9MIoZIZDkf3Bx0OyDINdrYb/uu9mH4+5leNvO/d3db9G8eBAA4cikXT/58EElXC2q8XnWlTud0Dx8+HMOHD7d5TRAEfPbZZ5g9ezbuuusuAMBPP/2E4OBgrFq1Cg888ABOnTqF9evX48CBA+jcuTMA4IsvvsCIESPw0UcfISwszGbZREREREREzmDv+ewK86huBMg1ydvNVGaeAxZS0xqMVmkKqQRtI1RW6Y0DPCCViOEmkwAAMvM12HAiExtOZCJl/sgar1tdcNoB8xcuXEBGRgYGDRpkTlOpVIiPj8eePXsAAHv27IGPj4854AaAQYMGQSwWY9++fXbL1mg0yM/Pt/ghIiIiIiKqbRtOZNTJc73db8zpdsDwcr2Nnm6FzBR6dojysXmPm8xpQ9Pb5rRvlpFh+uMLDg62SA8ODjZfy8jIQFBQkMV1qVQKPz8/cx5b5s2bB5VKZf6JjIys4doTERERERFVzMvtZi92q1BvvDaipcX1uzuEO+S5KgcOL9fb6OkuXSBt5VM9EOl3c3G181lFAGDu6a6PnDbodqRZs2YhLy/P/JOWZr0HHhERERERkSMZjAIW3Fho7J5OEfjrmZ4wCjd7iZc9Fo8P72nnkGffHF5e80H39rPXrNJKg3yRSIS0nBKr6wqpdWh6NrN+zOt22qA7JCQEAJCZmWmRnpmZab4WEhKCq1evWlzX6/XIyckx57FFoVDA29vb4oeIiIiIiOq/fLUOm05mQqs3IiNPDYONodC15Z21J83Hl64XQyoR43+7U8xpzUO8IHXQFlqOXL38uV8TrdI6RfuWe09WocYqbdHWpJqqUp1y2qC7UaNGCAkJwebNm81p+fn52LdvH7p37w4A6N69O3Jzc5GQcHNj9S1btsBoNCI+Pr7W60xERERERM7tqV8S8NhPB3Hv17vRbd5mzFieWGd1WbwrxXwsEZtWF0/Pu7lTk9xG729NKe153ns+p0aD200nM22mx0X6mI/HdY0yH/duFgAA6BsbdOstNueGu6I6DboLCwuRmJiIxMREAKbF0xITE5GamgqRSITnn38e77zzDv766y8cO3YMEyZMQFhYGEaPHg0AaNmyJYYNG4bHH38c+/fvx65du/DMM8/ggQce4MrlRERERERkZVeSabXwI5fyAACrE9NtbnFV214bYdoWrEWIlznNQ+64zaa83W6W/eGGM9idlFUj5a6vxMJwr9/REr2bBaBTtC8Wje8IAHCXS1B2V7PhbULwwVjHDK2vbXW6ZdjBgwfRv39/8/mMGTMAABMnTsSSJUvw8ssvo6ioCE888QRyc3PRq1cvrF+/Hm5ubuZ7li5dimeeeQYDBw6EWCzG2LFjsWDBglp/FyIiIiIick1rj13BqLja7bQ7nHrdfDyyXShahZmmvH50bxzu+GInfJQyiMUie7ffNh+l3OL8jb9OYNOMvrddbtkvML6f0Bmv/nkMr99huc+4Ui7Fz5OtRybveKk//jl2BeO7RcNTUaehao2q0zfp168fBMH+t0oikQhvvfUW3nrrLbt5/Pz8sGzZMkdUj4iIiIiIGoAfd12og6A713x8T6cI83GbcBXWPdcbYSp3G3fVnEhfy/KTrhbWSLlKxc1VyHvHBmD/a4PKyX1LnfyUeLJvkxqphzOpP18fEBERERERVUNucc0vJlaRQo3efOx1S69uy1DHL/TsqAXaSt0ZFwaFtP5uA1YVTruQGhERERERUW2oaAXzV/88hi7vboJaZ6ixZxaUWTXckQumlefrhzpanJc3CrmyNDrTHt0tQr0qyNlwMOgmIiIiIqIGo3QBscYBHua0KD+l3fxFGj2W7UvFtQIN3l5z0m6+qiq7mneYj2OHktszrE0oJvWIMZ/vu5BT5TIy89VYfjDN/IWERm8KutnLfRODbiIiIiIiajBKe5W/fKgj3GWmwHBnOSt3Zxdqzcd/H0mvsXroDKbg1F0mQYCnosbKrapXhrUwH89fdxr/nbuGDZVYgbzU2K924+Xfj5r3HNfoTcG3oo56750RfxNERERERNQgqHUGZN0IoiUiEUrKDBcvDYJvlZpTbD7OV+tt5qmqnCItCm6U1bOpf42UWV3ucgl8laY9uxPTcvHwD/vx5M8JuJqvruBOk0vXSwAAv+xNBQBsOGHap1vu4DnjroS/CSIiIiIiahCm/d9h87GbzHL48z1f77F5zyNL9puPAzwVWH8847bmdl/MLkLHtzfijdUnAADHL+dXu6yact3GQnJZZXr4Kys1++YXFCev1P17OQsG3URERERE1CAcunhzb+wATwX6xgaaz4+k5VrkFQQBh1OvQ2e4ubhYVqEGU35JwBurj1e7DqsOWw5Rzyup/ZXTK0OtL/+LhXOZBfj3lmHofT7caj5WucscUi9XxKCbiIiIiIgaBO8bgWCrUG+4yyX44J52dvP+dSQdd3+52+a15QcvVev5W09fxe5ky/njk3rGVKssRyvWlB90D/50B574OcHu9Qfjo2q6Si6LQTcREREREdV7Gr0BF7KKAABf3dgqK9jbDX890xMAIBGLLIaNP/drot2yujX2q/LzT6Tn4ZElB6xWCHfWuc/FWvvz18vuMW6Pu5yrl5dyzhYmIiIiIiKqQb/uTzMfh6pubtHVNlwFwLRXd9zcf817VXeNsR9YB3u7VenZgiDgeTtBfE3sjX27HuvVyCqtpJx562//XfHWaWKR6LbqVJ8w6CYiIiIionqvWHsziJSX2c5KVCY41OiN+D3hEjR6A/anmHqkGwd6YPwtQ6XzqzgPe3dyNs5dLbR5TW+s+6D71REtrdKKyhle/tvBNLvXSokZc5tJ67oCREREREREjpZbbFqNe7KNXt2yTqTn42DKzQXX5t3dFlH+Sizdl2pOq+rWYeUtltY6TFWlshxBbCNCLm94eaXKZE+3GYNuIiIiIiKq1/JKdPhmx3kAMO9Jbc/WM1dxsczWV10b+VkNta7qiuPebtbP3Di9D/acz8bgVsFVKqu2lB0ZUJahkj3zjLlvYtBNRERERET1Wtzcf83HTQI9y81bNuAGTMPPlXIp5o1pix1nr2Hd8QyU2AlI7Tl5Jc/i3N9DjmbBXmgW7FWlcmqTvaD7+OU8m+m3Yk/3TZzTTURERERE9datPbPto3ys8nww1vbWYe+PbWs+Htc1CtMGNANgmvttS7FWj7VHr6BAbdkTXrpqeqnsIm2F9a5tHW75vZTYGV4+afH+SpXHoPsmBt1ERERERFRvnbtaYHFeduXyUvd1icRLQ5tbpY/tGGFxXroAm1Zvuxd4/rrTmLrsEF5cccQivaCKc8DrQrtwy7nlRXZ6uq8X2x5a/87oNvDzkAMAxnQIh4QrqZkx6CYiIiIionrrn6NXzMebZvSxm29SjxiLc3eZBNJb9tBWlAbdBts93T/tuQgA2HAi0yL91p7xt+9qXX6l60DZFd0BoLASXxR8fWO/82GtQ/BQt2hsntEXfz7dA5/c394RVXRZnNNNRERERET1lkRsCiZDvN3QNMj+HGoPhRR/P9MLoxbuBAAcmTPEKs/Nnm7bQbc96hsLsX0wth0GtAxCgKeiSvfXhluD7vUnMnAxuwjR/h527xnWJhRrpvVC40BTHl8POXxv9HbTTezpJiIiIiKiekt9Yyj4iLahFeZtG6HC6beHIWX+SKsgFADkN3q+jQKgt9PbfSujUcCl6yUATIG9MwbcACCxMQf70SUHLM7LftnQKMAUaLcJV0EpZ19uefjbISIiIiKieunvI+n4YecFAIC7vHL9jW4yid1rZQNxrcFoNfzcVr45f50wL6TmJnPePs9AbzertORrRdDqjZi96hgCPBWIi/QxX/vjqR61WDvX5rytfkNMTAxEIpHVz9SpUwEA/fr1s7o2ZcqUOq41ERERERHVpe//O49p/3fY3DtbE72xZYPp/+2+aHFt3Ld7zcfuZQL3n/fezKeQ2g/o69p9nSMwpkM4Pru/PXo3CzCnv/bnMSw/eAlfbkvGkz8nmNP9OIy80py+p/vAgQMwGG6unHf8+HEMHjwY9957rznt8ccfx1tvvWU+VyqVtVpHIiIiIiJyHklXC/HO2lMWae7l9GBXlrTMitzvrz+Np/o1wfHLeXh7zUnsu5BjvpZXYlrh+98TGRb3O3NPt0IqMS+AJhaL8N+5LADAioRLdVir+sHpg+7AwECL8/nz56NJkybo27evOU2pVCIkJKS2q0ZERERERE7mXGYBBn+6wyrdXX77QbeozLxn5Y3yXv79KE5eybfKu/NcFp4o0zMMAI0DPW+7DrWhSaD9xdOo6pz3qxYbtFotfvnlFzz66KMWf/BLly5FQEAA2rRpg1mzZqG4uLgOa0lERERERHVl7t8nbabL7My/rq4ATwUEQbAZcAPAmqPpFud9YgNdZki2r9I16ukqnL6nu6xVq1YhNzcXkyZNMqc9+OCDiI6ORlhYGI4ePYpXXnkFZ86cwcqVK+2Wo9FooNFozOf5+bb/oRARERERkWvZmZRlM71ztG+NlO8hl6BIa0DPpv7Ycvqq3Xy/HkizOP/24U418vza4OVWfpj446TOtVST+sGlerp/+OEHDB8+HGFhYea0J554AkOHDkXbtm0xfvx4/PTTT/jzzz+RnJxst5x58+ZBpVKZfyIjI2uj+kRERERE5EBJVwsszpc9Fm8+jgmomSHTT/dvCgAwGAXzquSVUd6q6M7GU2E/6O4bG4gBLYJrsTauz2V6ui9evIhNmzaV24MNAPHxpn9YSUlJaNKkic08s2bNwowZM8zn+fn5DLyJiIiIiFzcz3tMK4XHRfpg9dSeAIBP7ouDbw0O6y5dTE1vEFCiNVSQ2yRl/sgae35tENnYs7vUo70a1WJN6geXCboXL16MoKAgjBxZ/h9sYmIiACA0NNRuHoVCAYXCOTelJyIiIiKi6jmebpo2Or5rlDltTMeIGn1G6d7cOqOAjzeerTD/swOb1ejza8t3Ezrj8Z8OWqVXNPScrLnEb8xoNGLx4sWYOHEipNKbVU5OTsayZcswYsQI+Pv74+jRo5g+fTr69OmDdu3a1WGNiYiIiIiotuQWa/HFliQkXLwOAIj0c9wWwqXbfpVo9ZXK3zrM22F1caROdubAB3qy87KqXCLo3rRpE1JTU/Hoo49apMvlcmzatAmfffYZioqKEBkZibFjx2L27Nl1VFMiIiIiIqpNOUVadHx7o0Vahygfhz0vxNsNAJCRr7Z5feGDHfDMssMAAD8POQa1dM35z/b2FI/wda/lmri+agXdEyZMQP/+/dGnTx+786Zr0pAhQyAIglV6ZGQktm/f7vDnExERERGRc7o14G4W5OnQRcvCfExBZ3quddA9pFUw7mgXhpwiLQrUeky9seiaK3KT2v4dljffm2yrVtAtl8sxb948TJ48GeHh4ejbty/69euHvn37olkz15yzQERERERErkWts17IbLKDF/ryUcoAAAVqnXn7sDXTeiHQS4GAG0OvJ3SPcWgdaoNYfDO4fnZgMyikYvRvHlSHNXJd1doy7Pvvv8fZs2eRlpaGDz74AJ6envj444/RokULRETU7EIFRERERETUsO1KysLX25OhNxgt0nees9yT28tNiuFt7C+oXBNkpQupGQSU3Aj6AzwVCPZ2g0RcP3uBW4Z4YWr/pmjlovPT69ptzen29fWFv78/fH194ePjA6lUisDAwJqqGxEREREREab8koACtR7/nbuGpY91M6c/VmZ17T2zBkAhlUB1oyfaUUqDbgAw3pgBa2/+s6sb2TYUiWm56NucMd7tqNZfx6uvvooePXrA398fM2fOhFqtxsyZM5GRkYHDhw/XdB2JiIiIiKgBK1CbVgrflZSNmX8ctbneU6jKHX41uB+3PXKJdQgll9bPoHvhgx2w4+X+UMpdYv1tp1Wt3978+fMRGBiIOXPmYMyYMYiNja3pehEREREREVkF2L8eSINaZ8Bzg27GIJ8/0L7W6iOTWA8htxWI1wcikQg2XpeqqFpB9+HDh7F9+3Zs27YNH3/8MeRyuXkxtX79+jEIJyIiIiKiGlGktV4sbVViOo6n55vPB7eqvW25JGIRRCKg7HcB0noadFPNqNZfR1xcHJ599lmsXLkS165dwz///AO5XI6pU6eiZcuWNV1HIiIiIiJyMKNRwEcbzuDPw5fquioWcou1NtOTrhaaj2tz+LNIJLKY101UkWr9dQqCgMOHD2Pbtm3Ytm0bdu7cifz8fLRr1w59+/at6ToSEREREbmsnCItfNxlFlswOaN/T2Zg4dYkAMBdceFOU9/cYh0AINhbgXAfdxxKza3bCsE0nFyrN1ackQjVDLr9/PxQWFiIuLg49O3bF48//jh69+4NHx+fGq4eEREREZHrOn45D3d8sRMDWgThh4mdIRI5RyBry/vrz5iPc4q15j2n61peiSno9nGXY8G4Duj1/tY6rhEg5URnqoJqBd2//PILevfuDW9v7tNGRERERGTPx/+aAtktp6+i0ax/AAC7Zg5AuI97XVbLpsx8tcWxswTdpT3dKqUMEb5Kq+svDW1e21WCxIm/PCHnU63JCCNHjjQH3JcuXcKlS84174OIiIiIyBkopBKrtJ7zt2DbmavQ6K0XCAOA/Rdy8NKKI0jJKnJ09WA0CoiZuRYxM9eiuMyCZWUD8KrIKtQgZuZaPFFm/+zbVagxBd2eCtv9hVP7N62xZ1WWM49YIOdTraDbaDTirbfegkqlQnR0NKKjo+Hj44O3334bRiPnNhARERERGY0C1p/IsHlt0uIDmL/uNN76+yS+/++8Of3YpTzc980erEi4hKGf7XB4HRdsOWcz/Ze9qdUqb+YfxwAA/57MtLmXdnXojaZypDfmmJfdE3vZ4/E18oyq4jpqVBXVGl7+2muv4YcffsD8+fPRs2dPAMDOnTvx5ptvQq1W4913363RShIRERERuZo/D18u9/riXSnm44k9YnDgQg4e/H6fOU1TCwt1bT97zWb6ltNXkZGnRojKrcIyrhao8cA3e3F/l0hsOpVpTs8uMs0LNxgFlOgM6Pj2RsQGe+LvZ3pVqafYUBp035hHPe/utnjx9yP45L449GgSUOlyapK4TP03Tu9TJ3Ug11GtoPt///sfvv/+e9x5553mtHbt2iE8PBxPP/00g24iIiIialDScooxcfF+PNqzER7qFg0A+Hp7svm6XCKG1mA/iH7y5wRsOX21Us8qUOuglEshKWd1cUEQ8NAP+6Byl2HhuI64dL0EpzPyMaR1iEW+uAgfHLazGvhLvx/Bggc6wNdDXm594t/bDEEA5q07bZFuMAr4Ze9FzF513Jx2/HI+DqVeR6dovwre0kRnMOLzTabeeInY1L08tlMERrYLhZvMeuh+bSkbdDcO9KyzepBrqNbAiJycHLRo0cIqvUWLFsjJybntShERERERuZJX/zyG89eKMHvVcWTkqZGWU4xzZfaRPjB7EL5+qKPd+ysbcGfmq9H2zX/R5NV/YDTaH7499++T2JWUjX+OZeD53xLR58OteOLnBKw8dAnFWj0EQUDz2euwZHeK+R6xCPj43jjz+X/nstDh7Y146++T5dbJ3ihyvVGwCLhLLdl9Eccu5eFKXkn5Lwvg94RLyC4y7dOdmHbdnF6XATcAZBdpzMdOsrMaObFqBd1xcXFYuHChVfrChQvRrl27264UEREREZGr0OgN+O9clvl87Fe70fsDy22tVO4yDGsTij+f7lGlsg+m3OzQSriYg/j3NpvP+360Fa/+ecxq7vTVArVFMP3XkXTz8YzlR9DqjQ14ccVRi+Hr797dBmfeGY6xnSKsvhz4cdcFm3U7fjkPrd9Yb7fu128Ey7f6+0g6Ri3cie7ztljMZ7/Voq1JmLXymPm8UK23m7e2qXU3f3dcVI0qUq2g+4MPPsCPP/6IVq1aYfLkyZg8eTJatWqFJUuW4KOPPqrpOhIREREROa3dSdkW55dzLXtwj7wxxHzcIcoXZ98ZXm5521/qZz6+5+s9SM0uRpFGj7Ff7bHIl5ZTgmX7UjHnrxMW6SsOVryz0B+HLPPcGRcG2Y3VwYa1CUWLEC/ztZahtrcJvuOLnSjSWq7AXrbX944vdlZYj3fWnsJHG85YpW89fRUf3pK+4XnOnSbXVK2gu2/fvjh79izuvvtu5ObmIjc3F2PGjMGJEyfw888/13QdiYiIiIic1iNLDpR7XaWUWZyXXX37VgNaBCHa38Mi7cXfj6D1nA127/lpz0XoyswXT8spLrc+ZT3cLRop80fCy82yjuuf74OFD3YAAJy6km8uUxAEbD1zFXd/ucuqrMWTumDHy/0r/exSC7cmWaUlpuVanH/+QHsEeVe8qBuRM6rWQmoAEBYWZrVg2pEjR/DDDz/g22+/ve2KERERERE5u4y86u1n/eOkznh0ieVe1qfeGgZ3ufVc5f0XLNdMig32xNnMQou0ArUefjcWPDt2OQ8A8HS/Jlh+MA1ZhbaHeQNAm3DbvdgAICkzbLr3B1uhcpchr0RnM+9zA5uhf4sgAMCn98dh+m9HbOZrHOCB81lFaBrkiaQbc94jfN0t8qh1Bny+2XIrs1CVZR4iV1LtoJuIiIiIqCH7cecFvLXm5iJjzYI8LRZPA4AhrYJt3tu/eRDiG/lhX5mAumzAveWFvhjw8Xab9/7yWDy6vrvZIq3j2xvx9ug2+Hpbsnl4+4i2oRjRNhQzVx5FgKcC286YtgcL8XZDRr7py4KxHSPsvt+tq6PbC7iPvTnEoqc8xNsyQN75Sn8kXyuCm1SMNuEqXMkrQYjKHWO/3I0zmQUIKdOD/e+JDDzxc4LVM2KDuUI4uS5u605EREREVA1lA24A+OAeywWF/3iqB74cb3vFcpFIhF+f6Ga37MaBnkh8Y7DNa0FebkiZPxKHXx+MXk1v7lP9+qrjFvPJo/2VaBOuwpppvTG1f1Nz+lcPdYRIBAxrHQKpxH44UKIz2L1WlqdCavf8k/viEOGrRN/YQMQ39oeHQoqmQV7wVEjx0tDmAGAxNP7WgPvrhzpi04y+8FGWv20ZkTNz6qD7zTffhEgksvgpu1WZWq3G1KlT4e/vD09PT4wdOxaZmZl1WGMiIiIiaghunTf97MBm5oXISnWK9i03qK1o1WsfpRwp80ciZf5I7HipP2KDPfHO6Dbm674ecoyPj7J7f9ne587Rvvj8gfbYNKOPeTG3r8rZwgwA+sYG2r02om0IxsdH4cicIVbv0SbcGwGeciikYtzdIdxuGVKJ6T6dwbT6em6x5TD4zx9oj2FtQtE0iL3c5NqqNLx8zJgx5V7Pzc29nbrY1Lp1a2zatMl8LpXerPL06dOxdu1arFixAiqVCs888wzGjBmDXbusF3YgIiIiIqoJaTnFFluC/fNsb7QK88apK/nmtAe6RFaqrC/Hd8T7609j4bjyA+AofyX+nd7XKn1YmxA8O7AZFtwyB7p/c8uAWSQS4a72NwPgW78gsMVHKUfSu8PR9LV1AIDXRrTE0NYhiPJXlnufSCTCwdm2e+nLKq2D3mhEYlouRi+y/Aw/qKXtoflErqZKQbdKparw+oQJE26rQreSSqUICQmxSs/Ly8MPP/yAZcuWYcCAAQCAxYsXo2XLlti7dy+6dbM/XIeIiIiIqKo2nMjAL3svWuzJDQCtwkyLkUnLzIEe3ja0UmWWzruuLpFIhBmDYzFjcCwAIGbmWgD2t/mqKqlEjJT5IyEIQo3vRy2+Ud7ZzEKrgPv43KHwUHD5KaofqvSXvHjxYkfVw65z584hLCwMbm5u6N69O+bNm4eoqCgkJCRAp9Nh0KBB5rwtWrRAVFQU9uzZU27QrdFooNFozOf5+fl28xIRERERAcCTNhb4+unRrubjskPJZeKaDVArq2sjP+y/kFPusO7qqOmAGwCyCjU20/95trfVPHEiV+bUc7rj4+OxZMkSrF+/Hl999RUuXLiA3r17o6CgABkZGZDL5fDx8bG4Jzg4GBkZGeWWO2/ePKhUKvNPZGTlhv8QEREREZU6/fYw9Ckz77lsT7e4joLuXybHY9+rA9Es2KtOnl8VQ1tbj2b96dGu5pEDzuzZAaaF6Z7q16SOa0KuwKm/Qho+fLj5uF27doiPj0d0dDSWL18Od/fq79U3a9YszJgxw3yen5/PwJuIiIiIrBxJy8ULK47A38Ny9ey4SB+4ySz31C67xdat223VFrlUjOAyW3A5M7nUsv9v64v90CjAo45qUzXTB8firg7haOwi9aW65dRB9618fHwQGxuLpKQkDB48GFqtFrm5uRa93ZmZmTbngJelUCigUCgcXFsiIiIicmUGo4DfDqYh6Wohkm659sPEzlb5S1fjBm7OV6bKi6lggTZnIhKJ0CSQq6pT5Tj18PJbFRYWIjk5GaGhoejUqRNkMhk2b95svn7mzBmkpqaie/fudVhLIiIiInJ114u0aD57HZbtS7W6dv69EQjwtO7AkYpvfrSuo45ulzW6fZhD5o0TOQOn7ul+8cUXMWrUKERHRyM9PR1z5syBRCLBuHHjoFKpMHnyZMyYMQN+fn7w9vbGtGnT0L17d65cTkRERES35cN/z0BvFKzS/3u5v9352mWHlFvfSbb8O70P1h3LwON9GtV1VYgcxqmD7kuXLmHcuHHIzs5GYGAgevXqhb179yIw0LRgxaeffgqxWIyxY8dCo9Fg6NCh+PLLL+u41kRERETkygo1eps93N9N6IxIP/tDoGVlhpcLjLorJTbYC7EusOgb0e1w6qD7119/Lfe6m5sbFi1ahEWLFtVSjYiIiIiovvtme7LF+bMDmmLGkOYV3mfR082om4hucOqgm4iIiIioNl3NV+OLLTeXTUuZP7LS95ad082Qm4hKudRCakREREREjvTTnovm42E29pEuT9mebqON+eBE1DAx6CYiIiKiBk2jN0BvMAIArhaozelv3tm6rqpERPUIh5cTERERkdMRBKFWtpDSGYxoPns9AGByr0ZYfvASAGDagKYIUblVu9wmQdzDmYhMGHQTERERkVPQ6A3mABgApvRtgpnDWzj0meuOZ5iPf9h5wXzsq5RXq7z9rw5EkdZgcx9vImqYGHQTERERUZ27nFuCnvO3WKR9vT0ZnaN90Sc2EHJp1WZFHkjJwd9H0iEVi/H6HS1t9pobjQKe/b/DNu/3Ucqq9LxSQd7V7x0novqJQTcRERER1bkpPyfYTH/sp4MAgMQ3BsOnTO+z0ShALLY9/Lzfh1uRkl1sPv9xl6kH+672YfjgnnZQSCUAgBnLE+3WZ0CLoCrVn4jIHi6kRkRERER1Zue5LMTMXItjl/PMaUq5BDH+Sot87d/aaD7OK9ahx/wteP5X617qn/detAi4y1qdmI77vtkLrd6IZ5YdwqrEdPO1nk39EX3jmT9M7GwR4BMR3Q6RIAgNfj+D/Px8qFQq5OXlwdvbu66rQ0RERFSvFWn00BmMkEvFaPXGBotra6b1QptwFb7bcR7v/nPK4lqPJv4I9FIgp0iL/85lAQBOvz0MbjJTz3XCxesY+9XuKtdn/fO90SKEnwGJqGoqG0dyeDkRERER1RqN3oDWc0yB9ucPtLe63iZcBQB4tFcjuMklCPSUY8ovhwAAu5OzrfLP/OMoPnugAzR6g0XA/cqwFniqXxNcySvBicv58PWQ2wzIlz0Wz4CbiByKQTcRERER1Zo3Vp0wHz/3a6L5ePmT3RHgeXNIt0QswsPdoissb1ViOpoFe+HfExkW6U/0aQwACFW5I1TlDgCY2r8JFm1NNucZ1zUKPZoGVOs9iIgqi3O6iYiIiKhW5Kt1+O1gms1rXRv5oXGg7b2tf32iW7nlfrjhDI5cMs0Jd5dJkDJ/JCQ2Fll7aWgLXJg3wnw+qCUXSyMix2PQTURERES1ovPbm2ym7501sNz7ujX2t0p78kZP9q2+n9i53LJEIhE+f6A9Hu4Wjb6xgeXmJSKqCRxeTkREREQOpdUb8fG/Z6A1GM1pT/VrgiGtgtE4wBOqSuyJ/fVDHfHS70fxyX3t4echR4dIHxxIycGh1Fxznsd6NULPSgwXv6t9OO5qH16tdyEiqioG3URERETkMBq9Ac1nr7dKbxbkiQ5RvpUuZ1ibUAxrE2qR9tuT3bF070X0bxGEaH+P264rEZEjMOgmIiIiIof4eU8KXl99wiLtzrgw9G4WgLs73H5Ps0wixqSejW67HCIiR2LQTUREREQ1bu3RK1YBNwC8fVebSg0nJyKqLxh0ExERETUAh1KvQ6s3on2kD9xkEoc+S28wYuqyQxZpXRv5YdqApgy4iajBYdBNREREVM/lq3UY8+Vu83nK/JE1/oyDKTn4cMMZfDm+Izq9c3OV8u8mdMbAFkEQ29jCi4ioIeCWYURERET13OrDly3OrxVoaqTczHw1VhxMg1ZvxD1f78G+CzkWATcABtxE1OA5ddA9b948dOnSBV5eXggKCsLo0aNx5swZizz9+vWDSCSy+JkyZUod1ZiIiIjIueQWa63mVv+w8wJOXcnH4dTrOJNRUOUyL10vxjtrTiL+vc146fejiJ29zma+TTP6MuAmogZPJAiCUNeVsGfYsGF44IEH0KVLF+j1erz66qs4fvw4Tp48CQ8P07YQ/fr1Q2xsLN566y3zfUqlEt7e3pV+Tn5+PlQqFfLy8qp0HxEREZGzG/PlLou9rG059dYwyCQiSCXl98cYjQImLt6P/85lVfjcs+8Mh1zq1P07RES3pbJxpFPP6V6/3nJPxyVLliAoKAgJCQno06ePOV2pVCIkJKS2q0dERETk1Io0enPAHaZyw1P9mthcUbzlG6bPXGfeGQaF1P4iay+sOFJhwH1h3giIROzdJiIq5VJfP+bl5QEA/Pz8LNKXLl2KgIAAtGnTBrNmzUJxcXFdVI+IiIjIqfy896L5eO2zvXFXBXtjz/7zONQ6A2JmrkXMzLV4emkCAOCtv08iZuZa/HnL3PAOUT7YO2ug+XzZY/EMuImIbuHUw8vLMhqNuPPOO5Gbm4udO3ea07/99ltER0cjLCwMR48exSuvvIKuXbti5cqVdsvSaDTQaG4uIJKfn4/IyEgOLyciIqJ6ITW7GK+vPo7tZ68BABoFeGDri/0AmFYZn/jjfhRpDTbvbRzggfNZRebzYG8FMvMtF15rH+mDd0a3QZtwFQAgq1ADEQB/T0XNvwwRkZOq7PBylwm6n3rqKaxbtw47d+5ERESE3XxbtmzBwIEDkZSUhCZNmtjM8+abb2Lu3LlW6Qy6iYiIyJXtv5CD+77ZY5X+wT3tcF/nSIs0QRCQma9BkJcCfT7cikvXSyr9nM0v9EWTQM/bri8RkSurbNDtEsPLn3nmGaxZswZbt24tN+AGgPj4eABAUlKS3TyzZs1CXl6e+SctLa1G60tERERU2/4+km4z4AaAAS2CrNJEIhFCVG4Qi0V4uFu01fUpfa07LzpF++L028MYcBMRVYFTL6QmCAKmTZuGP//8E9u2bUOjRo0qvCcxMREAEBoaajePQqGAQsHhT0REROS6irV6/JFwCQNaBuP7/85j8a4Ui+tLH4tHmI87YvyVFc6zDrhlWPj/Hu2KvrGB2H8h22Ll86WPxcNNZn+hNSIisubUQffUqVOxbNkyrF69Gl5eXsjIyAAAqFQquLu7Izk5GcuWLcOIESPg7++Po0ePYvr06ejTpw/atWtXx7UnIiIicgyt3ohWb2wAAKvVyL+f0BmDWgVXqbw74kLxwoojAIDezQLQNzYQALD8ye5o+pppD+5xXSMZcBMRVYNTz+m2963s4sWLMWnSJKSlpeGhhx7C8ePHUVRUhMjISNx9992YPXs29+kmIiIil2U0CshX66Byl1l8HrpaoMbRtDw89tNBm/d9Ma4DRsWFVeuZdy3ciSOX8vD7lO7oHHNzp5iT6fnILFCjf3PrIepERA1ZvVtIzZEYdBMRUW24XqTF/pQchKnc0TZCVaV7E9NyAZhWjab6r9lr/0BnMH1E81HKkPjGEKRmF6PPh1vt3lOdHu6yijR6aPRG+HnIq10GEVFDUtk40qmHlxMREdUHR9JycdeiXeZzfw85El4fDAD4YvM5ZBdp8eadra3um7XyKP5vv+Vin2ffGQ651CXWQaVKMBoFiMWWI/tOpuebA24AyC3WIWbmWpv3Nw70wPlrRVj2WDx6NA24rbp4KKTw4JI3REQ1jkE3ERGRA2j0Bizdm4pNpzKxOznb4lp2kRarEy/juV8TzWl3dwhHXJle7OtFWquAGwBiZ5vm1/4yOR57zmfh8d6N4aNkz6QrOZx6HXd/ufu2yzn0+mD2ShMRuQAG3URERDUs6WoBBn2yo9w8ZQNuALhr0S6LIOrUlfxy73/oh30AgEVbk7F31kCEqNyqX2GqVe+vP12pfGM7RiCvRItNp67avO6rlNVktYiIyEE4Po2IiKgGCYJgM+D+6dGuOP32MHSM8rF7b8e3N8JoFHAgJQcPfr/PnP7e3W3hX06PZrd5m7Hq8OXbqjfVvIvZRUi+VojU7GLMWJ6IIZ9uxztrTmLv+ZxK3f/GqFbwdrcMrD+9Pw4A0DHKp8JtwIiIyDmwp5uIiOqtfLUOP++5iLs7hCPMx73Gy9fqjZj+WyLWHruCke1CMaRVMA6X2dMYAO7rHIEn+jRG0yAvAMDvU3rg2//OY8muFEzqGYM2YSpzrzUAbDyViSd/TjCf92zqjwfjo/BgfBQu55agQK1DkJcbRi74D1fy1OZ8z/+WiGOX8/DDzgt4uFs0ft57EaPiwrDggfYMzurAdzvO491/Tlmln80stDj/7+X+SLtejC4xfth+5hrcZBJMXXYIb49uA5W7DNMGNMPKQ6YvVDbN6IOmQV7o3jgA3u78CEdE5Cq4ejm4ejkRUX0V+9o6aA1GAMD590ZYLVh1O3YlZWF8md5oW1Lmj6xUWYu2JuHDDWes0u/tFIEP7mlnN2gu0ujxx6FLeOOWfZrL+vjeOIztFFGpetDt23H2Gib8uL9SeY++OQTebhUPEVfrDFBIxfzyhIjIyVQ2juTwciIiqlcEQcCX25IQM3OtOeAGgMav/gNdmXNb8tU6XM4tqdRzKgq4h7cJqVQ5ADC1f1Ob6TOHtyg30PJQSDGheww+u7+93TwvrDiCmJlr8UfCJQiCgCKNHvsv5CA1uxgGowCt3gh+/16xQo0eZzMLys1TojVYBdyP926Er8Z3xB9PdUfLUG+MaBuC9pE++GJch0oF3ADgJpMw4CYicmHs6QZ7uomI6osT6XkYuWCn3etKuQS/PBaPh77fh/7Ng/DJ/XFQSCW4kleCRxYfwOkMU1DVIsQL30/sjLwSHTwVUkT7e1iU8+7ak/juvwvm8/9e7o//zmXhbGYB2kWoIJeKMbJtaJUCpZEL/sOJdNPiaU/2bYznB8bCXS6p1L06gxFt5myARm/Ek30aY2ynCLhJJVZ7OqvcZRCLgOvFOqsyejcLwLQBzRDp545gL7caHRXgiv48fAnTfzsCAHi0ZyP8uMvU3q+OaIEn+jSxyCsIArKLtOj8ziaL9A/GtsN9XSJrp8JERFTrKhtHMugGg24iovrgSl4Jus/bYpH28b1x8PWQ4dElB+3eN6x1CNafyKiw/M8faI+72odbDFkHgBNzh8JDcfvza9U6Ay7nlqBJoGe1y8gr1sHTTQrJjYC5KkOdy5o2oCleGNK82vVwVUlXC/DokoOY0D0a76y1no99qzfuaAV/T7nVSvRxkT5YPbWng2pJRETOgkF3FTDoJiJyDTqDEY//dBAn0/Ox4+X+cJNJoNUbcfCW1b57NvXH0se6mc83nczEYz/ZD7xLfTGuA85lFmDBlqRK1WfHS/0R5a+s+ovUsoSL1zH2K9v7Qo9oG4L1xzNgvOXTwAf3tMOdcWFwk1Wut92VHL2Ui6V7U5GeV4IuMX54ul8THErNxX3f7LntsiP93LH9xf4NfqQAEVFDwKC7Chh0ExE5t+tFWnR6Z6NVYGjLw92i8fboNlbpn248i883nwNgCq5LtAa8/MdR8/W24Sr8Pa0XAOCvI+mY/lsifJVyhPm4IatAg/QyK4UDrhNwlzqZno9zVwvQJcYPoSo3m0Pf84p1iHvrX4u0r8Z3xPC2obVVTYe5XqTF//akYMvpqzh6Ka/K93eJ8cWBlOt2r7cK9cYDXSMxoXvMbdSSiIhcCYPuKmDQTUTkfPQGI/RGAf8cu4IZy49U6p7X72iFyb0aVfoZs1Yexf/tTwMAHH59MHzt7IUtCAJWJ6bjs01nUajRY/3zfRDgqaj0c1zJgI+24XxWkUXarOEtMKxNCHyUcqjcK7f4V13beuYqFmw+h/wSHXo3C8Sy/anQ6stfSA8wTUmI9leifaQPpBIxDEYBucVa+HsqsDspC4t3p+Dd0W0gAIh/bzMA02Jpr41s5eA3IiIiZ8OguwoYdBMR1Q2DUcC641fwzLLDAIAn+jTGhhMZuJhdbPeefa8OxGt/HsemU5kAAE+FFG3DVXjrrtZoFuxV5TqczSxAgKcCfnYC7obGYBQwa+VRnL9WhIMXrXt24yJUuL9LFO7tHIEruWoUavRoFXbz/53GG8MR6mJ49erEy1bzq+1ZNbUnQrzdcPeXu3AlT42XhjbHhO7R8KrkiuKAaR6+zmCs0j1ERFR/MOiuAgbdRES163qRFj/vvYhPNp6tVP4IX3cserAj4iJ9LNLVOkO9nHPsLMqOBKjIgnEd8Oz/HTafvz+2Le7vEgXANGxdqZBAJnHcTqX/HLuCp5ceKjfP2I4RmDEkFj7ushpZ/I6IiBo2Bt1VwKCbiKjmafQGXMlV4+jlPEz/LREGo4BgbwVahnpj25lrNu8J8lIgLtIHF7KKkHS1EADwULcovDmqNaQODNjIvl/2XsTsVcerde/qqT2hNwoWi7g90CUSb49uUyMBeG6xFip3GTR6Izq8tRElEjV1kAABAABJREFUOoP52sAWQXigaxTiG/th57ksXMwuxiM9Y/glDRER1RgG3VXAoJuIqPryinUQIEAQAAHA9/+dx7Yz13DySn6F9zYL8sTSx+MR5OXm+IrSbUnNLkaQtwJGQcATPyVgZ1LWbZWX+MZg+CirPqR/+9lryCnSYNPJq1h77IrV9fLm5hMREdWkysaRHFtFRFRJSVcLEOGrtOgp+z3hEn7ek4IHukZhZLtQeNfC3E5BEFCsNeB6sRaLtiZBIZVgVFwYInzdseFEBq4VaFCg1mP64NhyF70q1OjhWcEQW6NRQIs31psXoBrTIRyvDG8BiViEgynXcfJKPhbcWBG8MjpH+2JYmxAEeikwpFUI3OXsdXQVZVdq/+WxePNxWk4xPvr3DEq0Bkzu1Qjxjf2x8WQmHq9gi7ZPN55FtL8HBrQIQkyAR4XP1xuM+GDDGXy747zdPPGN/BhwExGR02FPN9jTTUQmh1OvQyIWoV2Ej0X64l0XMPfvkxZpjQM9cP6a5QrPpX6ZHI8eTfwhFouQklUED4UU6bklmLR4P8Z0jMDskS1tbtdkS75ah2+2J2PR1mQAQK+mAVXqYdwzawCUcil2J2WhQ5QvjIKAuxbtwrUCjTlPowAPPDewGVRKGYxGAbuTs/Hr/lQUaQ3llFy+p/s1QfMQLzQJ9ESrUG/uWdwAvbPmJL7feQEA8O7dbTA+PholWgNe+v0I1hy92UPdKdoXfzzVw+p+jd4AsUiEK7lq9Plwa6WeuWlGXzQN8qyZFyAiIqoAh5dXAYNuorpTojVg4uL9uHy9BPGN/NAy1BtrjqZjxpDm6BsbWO1yDUYBJToDft5zEe+vP21OH9o6GN883Bm7k7KwdF8qDqTk4GqZALS2xAZ7YnibUHRt5IeeTQMAmHqVt565iud/S0SXGD9sOX211utVFYFeCvh7yBHopcDzg2IR4CmHQipBsLei0l8qUMNzKPU6xny52yLtf492xVt/n0CynS+ybjV9UCyuFarxVL+mCPdxx/HLeQjyVnCaAhER1SoG3VXAoJuocnKKtBAEAX4e8moHVYIgYE9yNs5mFuBiTjEW70qxm7eiuZnXCjRIzy1BuwiVRX2GfbYDpzMKqlW/qmoR4oVfHouHu0yC1nM2VKuMaH8lruSpK7WH8IAWQSjU6DG0dQhGtA2Br1IOncGID9afga9ShtbhKoR4u+FsZgFe+v1ouWU92bcxvtl+HmIRYLTxf4LHezdCmI87mgZ5olfTAIhEIgiCgGsFGhgEASHebgyuqcrUOgNavL6+Wvd2ifHFnFGt0SZcVcO1IiIiqjoG3VXAoJvqoxKtAW4ycZWDoovZRcgt1mHfhWx4u8mQr9ahQK3HF1uSzHlEIuDk3GFW83EFQcC/JzMhl4hx8ko+PtxwxuJ6/+aBOJNRgPQ8daXqIhIBXz7YEYNaBePY5TxkFWiw53w2rhdpsSox3Sp/v+aBdlfFLk+Yyg2dYvxwLrPAKlhvGeoNX6UM749th0i/m3NaBUGw+t2qdQZsPX0V89adRmqOaZ/pUXFhaBeuwpFLuXj9jlYo0RrQ76Ntla6bSATMvbM1xsdHQ1LFIdrrj2dgyi8J6NbYD9MGNEOUnxIFaj1ahHhZDfcufR9BEHCtUIMADwWHhJPDqHUGfLD+DP63JwUGW9/43CJM5YZtL/WHXMoV7ImIyHk0uKB70aJF+PDDD5GRkYG4uDh88cUX6Nq1a6XuZdBNjlas1eNIWh6u5JVgd3I2DqVex9iOEbhWoIGbTAK5RIQxHSMQ5uOOlOwirD+egQtZReje2B+xIV4IU7kh0OvmkN20nGIkXyvEumMZSMkuQqdoX6TmFOP8tSJcyStBoUYPncH0T7tlqDdKtHq8MKQ5Dqbk4GxmIQQIaBTgAYlYBEEADtxID/BUIKuwakOtZwyORU6RFkt2p1T59+Iuk6BEZ8DT/ZrguUHNIBWLYTAKiJ29rspl2fL6Ha3QJNADnaJ94eUmw77z2bj/273m60/3a4KXhjavs95ao1HA0n0X8frqE+a0eWPa4u4O4RCJALmk6l+aELkSjd6AYZ/9hwtZRVg1tSfa37IPOwCkZBXB10Ne7qKAREREdaFBBd2//fYbJkyYgK+//hrx8fH47LPPsGLFCpw5cwZBQUEV3s+gu/7TGYwo0uhxtUCDIo0eRgEQi4CsQi2KtXoYBQGtw1Qo0RqQr9ZBbxBgMArQGYyI8FWiTbg3RCIRjEYBuSU6rDmajgK1HuevFSEzX42dSVmQiEUI8lJAIRXD000Kd5kEucU6XC/WVTmQrWsSsQgKqRhhPu6I8lNCbxSw4+w1vDysOU6m51ssgmSPSAQMbhkMncGIrWeuIb6RH4K93VCsNWBE2xCM6Rhh9941R9PxzLLDeDA+Cv+3PxW2/iullEvwcLdojOtqynM8PQ8BngocvZSHPs0CMH1wbLW2I6oLRRo9JGIR9w8mIiIiciENKuiOj49Hly5dsHDhQgCA0WhEZGQkpk2bhpkzZ1Z4vysE3XnFOhTr9CjRGqDWGaE1GCGCacsfwDRUT28UYDQK0BtNAWPpj+ncCINRgOLGh/rSVjcYjSjRGVCkMaBYq0eR1gCd3ohCjR4iEaCQSiAIAgyCKa+bVIK8Eh00eiPc5RJ4KqRQucsgEYsgFYug1hmgNZiepzcKFsFric4AncEIvUGAWm/AlVw1Iv2UCPJSIF+tQ7HWAJlEDI3eAA+5FDH+pi1k8tU65Kt1KNIYzM+SiEWQS8RQyMSAAIhEIlPPoFQMtc6AYo0BEokI568V4XqRFpkFapuBW21SucsQ46/EkUt5iPB1R5jKHRn5amTmqyEWiVCiM60ULRIBbcNNXwDklejsLvIlFgFebjJo9UYMbhWMmAAPRPi6w1cpR5SfEmqdAfPXncapjHzkFuvgo5ShXYQPWoR4IcLXHQdTruN6sRYyiRjucgmCvBSIi/BBn9hA+Cpl5faw7jh7De/9c8pqKLbKXYaP741DbLDpGTUxPDmnSIuL2UVoHaaCSARo9MYKt7kiIiIiInK0BhN0a7VaKJVK/P777xg9erQ5feLEicjNzcXq1aut7tFoNNBobgYy+fn5iIyMdOqge8BH23A+q3KrupJ9cqkYWr0RvkoZlHIplHKJRWBbuhpz6bzBvBIdLmYX2yyrfaQPusT4IurGlwNBXgpIxSL8efgyBrcKRnahFteLtWga5ImOUb6I8HUvN5At0uhRpNXDQy6FR5mgUhAEaPRGpOYUY9/5bLQOV6FpkGet7AddWVq9kXMtiYiIiKhBqWzQ7fLdRVlZWTAYDAgODrZIDw4OxunTp23eM2/ePMydO7c2qldjFDIJZBIR3KQSuMklkIlFEAB4KqQwCAI8FVJIb/QAm3qdxebj0l5oscjUE22K+0w9w9IbQ1o9FBJ4yKVQyqU3elBNfxo6g4DSOFEsEkFrMMLHXQY3mQTFWgNyS7QoVJuGa+sMRgiCqbdTKhFBJhFBIhab6+Uuk0AuFUMmMdXPTWYKeHOKtfBUSOAuk0AQAL1RwPViLfKKdRCLRfByk8LbTQa5VIxircHcm1/ae1668rIgmOYHKqQSKOUS89DwQC8FgrwUCFG5QSaxHRiWfvdkKyhW6wwo1JhGGXgopFBIxRZB8a0Gtgy2e608HgqpzXJFIlMbxQZ7ITbYq1plOxoDbiIiIiIi21w+6K6OWbNmYcaMGebz0p5uZ7ZmWq8qr1xMlVdeD7SbTMK5tkREREREVC0uH3QHBARAIpEgMzPTIj0zMxMhISE271EoFFAoFLVRvRrDgJuIiIiIiMj1uPyYULlcjk6dOmHz5s3mNKPRiM2bN6N79+51WDMiIiIiIiJq6Fy+pxsAZsyYgYkTJ6Jz587o2rUrPvvsMxQVFeGRRx6p66oRERERERFRA1Yvgu77778f165dwxtvvIGMjAy0b98e69evt1pcjYiIiIiIiKg2ufyWYTXBFfbpJiIiIiIiIufRYLYMqwml3zvk5+fXcU2IiIiIiIjIFZTGjxX1YzPoBlBQUAAATr9tGBERERERETmXgoICqFQqu9c5vBym1c7T09Ph5eVV7n7NNaV0X/C0tDQOZ3dybCvXwvZyHWwr18G2ch1sK9fC9nIdbCvXUdttJQgCCgoKEBYWBrHY/sZg7OkGIBaLERERUevP9fb25j9cF8G2ci1sL9fBtnIdbCvXwbZyLWwv18G2ch212Vbl9XCXcvl9uomIiIiIiIicFYNuIiIiIiIiIgdh0F0HFAoF5syZA4VCUddVoQqwrVwL28t1sK1cB9vKdbCtXAvby3WwrVyHs7YVF1IjIiIiIiIichD2dBMRERERERE5CINuIiIiIiIiIgdh0E1ERERERETkIAy6iYiIiIiIiByEQXc17dixA6NGjUJYWBhEIhFWrVplcT0zMxOTJk1CWFgYlEolhg0bhnPnzlnkSU5Oxt13343AwEB4e3vjvvvuQ2Zmpvl6SkoKJk+ejEaNGsHd3R1NmjTBnDlzoNVqa+MV643aaKtSa9euRXx8PNzd3eHr64vRo0c78M3qn3nz5qFLly7w8vJCUFAQRo8ejTNnzljkUavVmDp1Kvz9/eHp6YmxY8datUVqaipGjhwJpVKJoKAgvPTSS9Dr9RZ5tm3bho4dO0KhUKBp06ZYsmSJo1+vXqnNtiq1a9cuSKVStG/f3lGvVW/VZnstXboUcXFxUCqVCA0NxaOPPors7GyHv2N9UVNt9eyzz6JTp05QKBQ2/81s27YNd911F0JDQ+Hh4YH27dtj6dKljny1eqe22goABEHARx99hNjYWCgUCoSHh+Pdd9911KvVOzXRVkeOHMG4ceMQGRkJd3d3tGzZEp9//rnVs/j54vbVZnuVcvRnDAbd1VRUVIS4uDgsWrTI6pogCBg9ejTOnz+P1atX4/Dhw4iOjsagQYNQVFRkvn/IkCEQiUT/z959h0dR/H8Af9+lXHpCIJ1AEnoNPQLSe1MEFUGlShFQBEWMIiCiIF9BBFGsYMEfiiIoUqSEHmoIEHqAkEISanpyd7mb3x8hmxx3qVxJeb+e5x5vZ2d3ZzMJ3udm9jPYu3cvDh8+DJVKhSFDhkCr1QIALl26BK1Wi6+//hrnz5/HZ599hjVr1uDdd981671WduboKwD4888/8fLLL2PcuHE4c+YMDh8+jFGjRpntPquC/fv3Y9q0aTh69Ch27doFtVqNvn37Sn0BADNnzsQ///yDjRs3Yv/+/bh16xaGDRsm7ddoNBg0aBBUKhWOHDmCH3/8EevWrcO8efOkOjdu3MCgQYPQo0cPREZG4o033sArr7yCnTt3mvV+KzNz9VW+lJQUjB49Gr169TLL/VU15uqvw4cPY/To0ZgwYQLOnz+PjRs34vjx45g4caJZ77cyM0Zf5Rs/fjxGjBhh8DpHjhxBy5Yt8eeff+Ls2bMYN24cRo8eja1bt5rs3qoac/UVAMyYMQPfffcdPv30U1y6dAl///03OnToYJL7qoqM0VenTp2Cp6cnfvnlF5w/fx7vvfceQkND8cUXX0h1+PnCOMzVX/nM8hlD0GMDIP766y9p+/LlywKAiIqKkso0Go3w8PAQ3377rRBCiJ07dwq5XC5SU1OlOikpKUImk4ldu3YVea2lS5eKwMBA499ENWGqvlKr1cLPz09899135rmRauL27dsCgNi/f78QIu/nbmNjIzZu3CjVuXjxogAgwsPDhRBCbNu2TcjlcpGUlCTV+eqrr4SLi4tQKpVCCCHefvtt0axZM51rjRgxQvTr18/Ut1Rlmaqv8o0YMULMnTtXzJ8/XwQHB5v+hqo4U/XX//73PxEUFKRzrZUrVwo/Pz9T31KVVZ6+KqwsfzMDBw4U48aNM0q7qyNT9dWFCxeEtbW1uHTpksnaXt08bl/lmzp1qujRo4e0zc8XpmGq/spnjs8YHOk2AaVSCQCws7OTyuRyORQKBQ4dOiTVkclkOgu329nZQS6XS3UMSU1Nhbu7u4laXv0Yq68iIiKQkJAAuVyO1q1bw8fHBwMGDEBUVJQZ76bqSU1NBQDpd/7UqVNQq9Xo3bu3VKdx48aoU6cOwsPDAQDh4eFo0aIFvLy8pDr9+vVDWloazp8/L9UpfI78OvnnoLIzVV8BwNq1a3H9+nXMnz/fHLdSLZiqvzp27Ii4uDhs27YNQggkJyfjjz/+wMCBA811a1VOefrqca7FzxjlZ6q++ueffxAUFIStW7ciMDAQAQEBeOWVV3D//n3j3kA1Yqy+evRvhp8vTMNU/QWY7zMGg24TyO/00NBQPHjwACqVCp988gni4+ORmJgIAHjiiSfg6OiIOXPmICsrC5mZmXjrrbeg0WikOo+Kjo7GqlWrMHnyZHPeTpVmrL66fv06AGDBggWYO3cutm7diho1aqB79+78n2I5abVavPHGG+jcuTOaN28OAEhKSoKtrS3c3Nx06np5eSEpKUmqUzgoyN+fv6+4OmlpacjOzjbF7VRppuyrq1ev4p133sEvv/wCa2trE99J9WDK/urcuTPWr1+PESNGwNbWFt7e3nB1dTX4eA+VrLx9VR6///47Tpw4gXHjxj1Ok6stU/bV9evXcfPmTWzcuBE//fQT1q1bh1OnTuHZZ5815i1UG8bqqyNHjuC3337DpEmTpDJ+vjA+U/aXOT9jMOg2ARsbG2zatAlXrlyBu7s7HBwcEBYWhgEDBkAuz/uRe3h4YOPGjfjnn3/g5OQEV1dXpKSkoE2bNlKdwhISEtC/f38899xzfDbOiIzVV/nPdr/33nsYPnw42rZti7Vr10Imk2Hjxo0Wu7/KbNq0aYiKisKGDRss3RQqgan6SqPRYNSoUfjggw/QsGFDo567OjPl39aFCxcwY8YMzJs3D6dOncKOHTsQExODKVOmGP1a1YG5/h0MCwvDuHHj8O2336JZs2YmvVZVZcq+0mq1UCqV+Omnn9ClSxd0794d33//PcLCwvSSS1HJjNFXUVFRePrppzF//nz07dvXiK2jR5mqv8z9GYPDBibStm1bREZGIjU1FSqVCh4eHggJCUG7du2kOn379sW1a9dw9+5dWFtbw83NDd7e3ggKCtI5161bt9CjRw906tQJ33zzjblvpcozRl/5+PgAAJo2bSodo1AoEBQUhNjYWPPeUBUwffp0bN26FQcOHEDt2rWlcm9vb6hUKqSkpOh8u5mcnAxvb2+pzvHjx3XOl5/NsnCdR7PHJicnw8XFBfb29qa4pSrLlH2Vnp6OkydP4vTp05g+fTqAvA+fQghYW1vjv//+Q8+ePU18h1WLqf+2Fi9ejM6dO2P27NkAgJYtW8LR0RFdunTBokWLpH8rqWSP01dlsX//fgwZMgSfffYZRo8ebYymVzum7isfHx9YW1vrBAZNmjQBkLeiQKNGjR7/JqoJY/TVhQsX0KtXL0yaNAlz587V2cfPF8Zlyv4y+2cMkzwpXs3gkeRchly5ckXI5XKxc+fOIuvs2bNHyGQynUQZ8fHxokGDBuKFF14Qubm5xmpytWWqvkpNTRUKhUInkZpKpRKenp7i66+/NkrbqwOtViumTZsmfH19xZUrV/T25yfO+OOPP6SyS5cuGUz2lJycLNX5+uuvhYuLi8jJyRFC5CU6ad68uc65R44cyUQnZWCOvtJoNOLcuXM6r1dffVU0atRInDt3TmRkZJj+RqsIc/1tDRs2TDz//PM65z5y5IgAIBISEkxxa1WOMfqqsOISA4WFhQlHR0fxxRdfGK391Ym5+mrnzp0CgIiOjpbKIiMjBQBx+fJl49xMFWesvoqKihKenp5i9uzZBq/DzxfGYY7+MvdnDAbd5ZSeni5Onz4tTp8+LQCI5cuXi9OnT4ubN28KIYT4/fffRVhYmLh27ZrYvHmzqFu3rhg2bJjOOX744QcRHh4uoqOjxc8//yzc3d3FrFmzpP3x8fGifv36olevXiI+Pl4kJiZKLyo9c/SVEELMmDFD+Pn5iZ07d4pLly6JCRMmCE9PT3H//n2z3Wtl9+qrrwpXV1exb98+nd/3rKwsqc6UKVNEnTp1xN69e8XJkydFx44dRceOHaX9ubm5onnz5qJv374iMjJS7NixQ3h4eIjQ0FCpzvXr14WDg4OYPXu2uHjxoli9erWwsrISO3bsMOv9Vmbm6qtHMXt5+Zirv9auXSusra3Fl19+Ka5duyYOHTok2rVrJzp06GDW+63MjNFXQghx9epVcfr0aTF58mTRsGFD6f+D+Znm9+7dKxwcHERoaKjOde7du2fW+63MzNVXGo1GtGnTRnTt2lVERESIkydPipCQENGnTx+z3m9lZoy+OnfunPDw8BAvvfSSzjlu374t1eHnC+MwV389ypSfMRh0l1NYWJgAoPcaM2aMEEKIzz//XNSuXVvY2NiIOnXqiLlz5+otgTNnzhzh5eUlbGxsRIMGDcSyZcuEVquV9q9du9bgNThBoWzM0VdC5I1sv/nmm8LT01M4OzuL3r176yxFRiUr6vd97dq1Up3s7GwxdepUUaNGDeHg4CCeeeYZvS+iYmJixIABA4S9vb2oVauWePPNN4VardapExYWJlq1aiVsbW1FUFCQzjWoZObsq8IYdJePOftr5cqVomnTpsLe3l74+PiIF198UcTHx5vjNqsEY/VVt27dDJ7nxo0bQgghxowZY3B/t27dzHezlZy5+koIIRISEsSwYcOEk5OT8PLyEmPHjuUXJGVgjL6aP3++wXPUrVtX51r8fPH4zNlfhZnyM4bs4Y0RERERERERkZExezkRERERERGRiTDoJiIiIiIiIjIRBt1EREREREREJsKgm4iIiIiIiMhEGHQTERERERERmQiDbiIiIiIiIiITYdBNREREREREZCIMuomIiIiIiIhMhEE3ERFRNTB27FjIZDLIZDLY2NjAy8sLffr0wQ8//ACtVlvq86xbtw5ubm6maygREVEVw6CbiIiomujfvz8SExMRExOD7du3o0ePHpgxYwYGDx6M3NxcSzePiIioSmLQTUREVE0oFAp4e3vDz88Pbdq0wbvvvostW7Zg+/btWLduHQBg+fLlaNGiBRwdHeHv74+pU6ciIyMDALBv3z6MGzcOqamp0qj5ggULAABKpRJvvfUW/Pz84OjoiJCQEOzbt88yN0pERFSBMOgmIiKqxnr27Ing4GBs2rQJACCXy7Fy5UqcP38eP/74I/bu3Yu3334bANCpUyesWLECLi4uSExMRGJiIt566y0AwPTp0xEeHo4NGzbg7NmzeO6559C/f39cvXrVYvdGRERUEciEEMLSjSAiIiLTGjt2LFJSUrB582a9fS+88ALOnj2LCxcu6O37448/MGXKFNy9exdA3jPdb7zxBlJSUqQ6sbGxCAoKQmxsLHx9faXy3r17o0OHDvj444+Nfj9ERESVhbWlG0BERESWJYSATCYDAOzevRuLFy/GpUuXkJaWhtzcXOTk5CArKwsODg4Gjz937hw0Gg0aNmyoU65UKlGzZk2Tt5+IiKgiY9BNRERUzV28eBGBgYGIiYnB4MGD8eqrr+Kjjz6Cu7s7Dh06hAkTJkClUhUZdGdkZMDKygqnTp2ClZWVzj4nJydz3AIREVGFxaCbiIioGtu7dy/OnTuHmTNn4tSpU9BqtVi2bBnk8ry0L7///rtOfVtbW2g0Gp2y1q1bQ6PR4Pbt2+jSpYvZ2k5ERFQZMOgmIiKqJpRKJZKSkqDRaJCcnIwdO3Zg8eLFGDx4MEaPHo2oqCio1WqsWrUKQ4YMweHDh7FmzRqdcwQEBCAjIwN79uxBcHAwHBwc0LBhQ7z44osYPXo0li1bhtatW+POnTvYs2cPWrZsiUGDBlnojomIiCyP2cuJiIiqiR07dsDHxwcBAQHo378/wsLCsHLlSmzZsgVWVlYIDg7G8uXL8cknn6B58+ZYv349Fi9erHOOTp06YcqUKRgxYgQ8PDywdOlSAMDatWsxevRovPnmm2jUqBGGDh2KEydOoE6dOpa4VSIiogqD2cuJiIiIiIiITIQj3UREREREREQmwqCbiIiIiIiIyEQYdBMRERERERGZCINuIiIiIiIiIhNh0E1ERERERERkIgy6iYiIiIiIiEyEQTcRERERERGRiTDoJiIiIiIiIjIRBt1EREREREREJsKgm4iIiIiIiMhEGHQTERERERERmQiDbiIiIiIiIiITYdBNREREREREZCIMuomIiIiIiIhMhEE3ERERERERkYkw6CYiIiIiIiIyEQbdRERERERERCbCoJuIiIiIiIjIRBh0ExEREREREZkIg24iIiIiIiIiE2HQTURERERERGQiDLqJiIiIiIiITIRBNxEREREREZGJMOgmIiIiIiIiMhEG3UREREREREQmwqCbiIiIiIiIyEQYdBMRERERERGZCINuIiIiIiIiIhNh0E1ERERERERkIgy6iYiIiIiIiEyEQTcRERERERGRiVhbugEVgVarxa1bt+Ds7AyZTGbp5hAREREREVEFJ4RAeno6fH19IZcXPZ7NoBvArVu34O/vb+lmEBERERERUSUTFxeH2rVrF7mfQTcAZ2dnAHk/LBcXFwu3hoiIiIiIiCq6tLQ0+Pv7S/FkURh0A9KUchcXFwbdREREREREVGolPaLMRGpEREREREREJsKgm4iIiIiIiMhELBp0HzhwAEOGDIGvry9kMhk2b94s7VOr1ZgzZw5atGgBR0dH+Pr6YvTo0bh165bOOQICAiCTyXReS5YsMfOdEBEREREREemzaNCdmZmJ4OBgrF69Wm9fVlYWIiIi8P777yMiIgKbNm3C5cuX8dRTT+nVXbhwIRITE6XXa6+9Zo7mExERERERERXLoonUBgwYgAEDBhjc5+rqil27dumUffHFF+jQoQNiY2NRp04dqdzZ2Rne3t4mbSsREREREVFFFHsvC2/8dhpPBNXE2/0bW7o59IhK9Ux3amoqZDIZ3NzcdMqXLFmCmjVronXr1vjf//6H3NzcYs+jVCqRlpam8yIiIiIiIqqMfj8Zh4jYFHy575qlm0IGVJolw3JycjBnzhyMHDlSZ1mv119/HW3atIG7uzuOHDmC0NBQJCYmYvny5UWea/Hixfjggw/M0WwiIiIiIiKTUmm00nshRIlLWJF5VYqgW61W4/nnn4cQAl999ZXOvlmzZknvW7ZsCVtbW0yePBmLFy+GQqEweL7Q0FCd4/IXNSciIiIiIqps5IWCbGWuFnY2VhZsDT2qwgfd+QH3zZs3sXfvXp1RbkNCQkKQm5uLmJgYNGrUyGAdhUJRZEBORERERERUmVgVemiYQXfFU6Gf6c4PuK9evYrdu3ejZs2aJR4TGRkJuVwOT09PM7SQiIiIiIjIsoQoeK9UayzXEDLIoiPdGRkZiI6OlrZv3LiByMhIuLu7w8fHB88++ywiIiKwdetWaDQaJCUlAQDc3d1ha2uL8PBwHDt2DD169ICzszPCw8Mxc+ZMvPTSS6hRo4albouIiIiIiMhscrUFUXdaTi48i58cTGZm0aD75MmT6NGjh7Sd/5z1mDFjsGDBAvz9998AgFatWukcFxYWhu7du0OhUGDDhg1YsGABlEolAgMDMXPmTJ3ntYmIiIiIiKqybFXB6Hbs/UzU93SyYGvoURYNurt37w5ReC7EI4rbBwBt2rTB0aNHjd0sIiIiIiKiSkGVq8XPR29K2/EPshGVkIrIuBSMaO8PG6sK/URxtVDhE6kRERERERFVd2qNFpeT0tHUxwVyeUG28qu303XqzdtyHsG1XXEmPhW305WY1aehuZtKj+DXHkRERERERBXc8l1XMHjVIXz47wWd8oycXL26Z+JTAQBHou+apW1UPAbdREREREREFdxX+64BANYejtF5DHdTREKRx9jb5i0dFnc/CxuOx0Kt0SL+QRYu3ErDmv3XkJqtNm2jCQCnlxMREREREVUqD7LUcHe0BQD8djKuyHoK67yge/CqQ0jNViMyLgUbThTU33A8Fvtm9yjqcDISjnQTERERERFVIs9/HV6qegqbvHAvf0S7cMANADH3sozbMDKIQTcREREREVEF19CrYBmw6NsZpTrmYmIaDl2t+M91779yB7+fiCtx9arKikE3ERERERFRBZej1upsv/TdMcTey4LVw0zm4zoHoEcjD5061+9k4qXvjxV5ThsrWZH7zOVBpgpjfjiOt/88iyvJpfsyobJh0E1ERERERFSBpWapEXtfdyr4oei76Pq/MGi0eaPDb/RuiAVPNSvTeV3sbIzWxvJKTM2R3mcoq2ZiNwbdREREREREFdj+q3dKrGNnI0fdmo6IWTIIv74SUqrz5idjs6ScXI30Xls1Z5cz6CYiIiIiIqqoImIfYGMxGcrz2VoVhHauDqUbwdZUgGeoc9QFQXcFaI5JMOgmIiIiIiKqoIZ9eQQHHyZD693E02AdmQyQyQqez27s7VKqc6tytSVXMjFloWfVmUiNiIiIiIiILCa3iPnXGyY+obNtJZfhmdZ+JZ7PnEF39O10jPg6HEeidbOpFx7p5vRyIiIiIiIisphz8akGy33d7PXK3h3YBE8EuaO5X9Gj3mqN+YLu3ssP4NiN+xj13TFcu1OQpbzwM90CVTPqtrZ0A4iIiIiIiEjfkWu6o8IrR7ZGYC1HvPTdMbwzoDE0WoH0nFz4uzvoHevhrMCGSR2x7Vwipq6PMHh+S00v77VsP2KWDAIAfLztklReRWeXM+gmIiIiIiKqCNQaLWweJkS7lJSGUd/qrrHduX4tAMDet7qX+pyFp2/PH9IUH/xzQdpWmWmkOyEl22B5pjIXd9KV0nb07QzpHqsSTi8nIiIiIiKysDNxKWixYCe+2ncN8Q+y8Pnuq0Y5b3ahoHtc50Bc+rA/JncNAgCoNUInKDeFlCwVOi/Za3Df9TuZOtvz/z6PHVFJJm2PJTDoJiIiIiIisiCtVuDdv84hR63FJzsu4a2NZ7DdSMFnjlp3NNvOxgrvDGgMB1srAEBiao5RrlOU5buuFLnvbqZSr+y1/zM8Fb4yY9BNRERERERkQaO+O4rzt9Kk7aPX7xvt3ENa+gAAniw0bVsmk6GWkwIAcC9DP/A1hr9Ox+OLvVdxKSnd4P4dUYn47uB1vXK1puo92M1nuomIiIiIiCzImEH2ozxd7HBhYT/YWVvplOePdD86Em4sM387AwBwsTMcck75xfCI9tOtfE3SHkti0E1ERERERFSBvd2/Efo29S738Q62+mGf/cOgO9vEz3Sn5eSWqX4NB1sTtcRyLDq9/MCBAxgyZAh8fX0hk8mwefNmnf1CCMybNw8+Pj6wt7dH7969cfWqbkKB+/fv48UXX4SLiwvc3NwwYcIEZGRkgIiIiIiIqCqY2r0+6ns6GfWc9jamC7q1Wv0p4n5u9vhhbDuD9b96sY30PlNZtiC9MrBo0J2ZmYng4GCsXr3a4P6lS5di5cqVWLNmDY4dOwZHR0f069cPOTkFD/u/+OKLOH/+PHbt2oWtW7fiwIEDmDRpkrlugYiIiIiIqNyOXr9nketKQbfK+EGuoaXIBjT3Rs/GXnrlTX1cMKCFDxY+3QwAcDEpDQHv/ItvDlwzerssxaLTywcMGIABAwYY3CeEwIoVKzB37lw8/fTTAICffvoJXl5e2Lx5M1544QVcvHgRO3bswIkTJ9CuXd63JqtWrcLAgQPx6aefwte36j0PQEREREREVcc3B/STiZmDXf70cpXxR7qVufpBt6214fFehU1eef4U+KiEvIRyH2+7hEld6xm9bZZQYbOX37hxA0lJSejdu7dU5urqipCQEISHhwMAwsPD4ebmJgXcANC7d2/I5XIcO3ZM75xEREREREQViaezQnr/7+tPItjfTWf/0mdbmuS6BdPLjZ9ITWUg6FY8TOQ2d1ATnXLZw/862lrhUYamqVdGFTboTkrKW5fOy0t3CoKXl5e0LykpCZ6enjr7ra2t4e7uLtUxRKlUIi0tTedFRERERERVX5YqF3H3syzdDImrvY30voGns7TEFwAcf7cXnm/nb5Lr5mcvzzLB9PLjN/Szsdd0ykuQ9kqXIJ3yC4l5sZiDQn8SdlqO2uhts4QKG3Sb0uLFi+Hq6iq9/P1N84tMREREREQVy8hvjqLL0jBcSrL8wNuNu5n4+uH08pefqAtbazlEocFdhbX+6K+xuD3MEr5qbzR+PxFn1HMv+veCXllRY9bW8ryQ1NBI93/nk43ZLIupsEG3t3deSvzkZN0fdHJysrTP29sbt2/f1tmfm5uL+/fvS3UMCQ0NRWpqqvSKizPuLxkREREREVVMZ+JTAQA/hd/ExpNxFh31HvblYel9zL1MAMD5W6lSWf7zzqbg7lAwwv72n2eNdl4hBBJTc/TKh7X2k97nJ00DgHoejgB0R/zzzfs7ymjtsqQKG3QHBgbC29sbe/bskcrS0tJw7NgxdOzYEQDQsWNHpKSk4NSpU1KdvXv3QqvVIiQkpMhzKxQKuLi46LyIiIiIiKj6+PVYLGb/cRZdloZZrA0PsgqmT+dPuz4bXxB029mYbqTb3Umhs52cph8ol8ea/YYTwzkWmj4+umMAZvRqgA4B7ljzclsAMLgk2sG3exqlTZZm0aA7IyMDkZGRiIyMBJCXPC0yMhKxsbGQyWR44403sGjRIvz99984d+4cRo8eDV9fXwwdOhQA0KRJE/Tv3x8TJ07E8ePHcfjwYUyfPh0vvPACM5cTEREREVGpZFSAtaG7NfQAAIx/MtAs13N7ZGT5vb/OGeW8hYP34+/1wpRu9XD83V569Wb2aYjfp3SEj6s9AEAmk+HXiSGwtZbj11dCELNkEDycFXrHVUYWXTLs5MmT6NGjh7Q9a9YsAMCYMWOwbt06vP3228jMzMSkSZOQkpKCJ598Ejt27ICdnZ10zPr16zF9+nT06tULcrkcw4cPx8qVK81+L0REREREVDmduvlACnrNpfC09uk96kvvn2/njyvJ6ejawLTtkctkOtvHrusnPysPxcOlwRp5OcPT2Q7vDGhc6mM71auFK4sMLyldmVk06O7evTuEKDoNvEwmw8KFC7Fw4cIi67i7u+PXX381RfOIiIiIiKgaiEpINXvQvT0qUXr/dKuCWbq21nIsfLq5ya/fLqCGzna6kUb7c9R56373beZVQs3qo8I+001ERERERGQOJa0HffDqHczYcBoPMlVGu2b8g+yC61tgOWo7Gyvsn93d6OfNebjutymfR69sGHQTEREREVG11LZu3mhvcc90a7QCL39/HFsib2H0D8eNdu2GXs7SexsrWTE1TaduTUd0aVBL2i7PMmpqjVZnqrwyN2+kO3+aOTHoJiIiIiKiaqSWU9761LP7NUK7h0F34VHnRxUOyM8lpBZZr6wKL5EV5KGfudtcFg0tmMref8XBMh8/5edT6LI0DD8eiQFQkJFdwZFuCYNuIiIiIiKqFpJSc3A3I2+KeN+mXvjrdAIA4N9zichWaQwek55TsKxX/prSxqDMzZuGbe5nyR9Vt6buPc3eeAav/Hii2Nxbhe25dBsAMP/v8wCA/VfuAECpj68OGHQTEREREVG18Nr/RUjvZTLgdrpS2p72a4ShQ3AmrmB0u6mv62MHkxnKXHReshdvbTwDoOJNw954Kh67L95GRGxKmY8t/LO5lJRuxFZVbhWrh4mIiIiIiEzkRMwD6b3LI+tU7304YvuowsH4P2duITB0Gw5dvVvuNqzZdw0JKZZNolYa2hK+XBBCSJnK8739x1npvbeL3aOHVFsMuomIiIiIqFqQF8pX5uGkwM8TOhRZV6sVOHj1jsF9L31/rNxtiC2UdAwA/NwqZnBa1HT7fJN/PoXG7+/QKdt4Kl56/0IHf5O0qzJi0E1ERERERNWCtTwv/Nk/uztkMhmerF8LDrYFCb8KT49esecqXv7ecLbyxt7OBsuLkqvRYvqvEQh451/8feaWzj43B9syncsUdr7RVa8ss5iM7tkqDf67kFzsOe2ZSE3CoJuIiIiIiKq8iNgHUGnykpfVcMwLdGUyGXbN6ibVuV9oHe41+68Vea42D7Oel9bO88nYejbR4L7iliszl0YGvkQorl0HipgBUJhcZpll0CoiBt1ERERERFTl/d+xWOm9k6219N7PzV5633bRbuQ+DMyb+7oUea6cEqZePypTVXQAW1FD0/wvKAyZ/POpEo9n0F2AQTcREREREVV5Xg8Te7nYWUMuLzog/ONUPM7Gp0jZu7s29MD2GV106mSVMeguLkN54dH1ikSdW3TQXRqMuQsw6CYiIiIioipNCIFzCXlLf43tHFhs3Zv3s/DUF4el7Y+GNkfAI2tZZ6vLFnSn5eiPdHdpUAsA8Gzb2mU6l7kUN9JdGlbFfLFR3ViXXIWIiIiIiKjyem5NOE7ezFsurIaDTbF1v9qn+yy3v7uD3trcZQ26398cpVf27eh2uJWSjSAPpzKdy1zUGsNLhiWl5pTqeE4vL8CRbiIiIiIiqrKEEFLADQD+NRz06rjYFT8WKZPJsHhYC7QPyEug9uj61PkuJaVh9A/HcSYuRae8gad+YG1nY1VhA24AUBUxvXzt4RulOp4j3QUYdBMRERERUZX16DPTbQ1kHt83uwc61aupV340tJf0fmSHOpjZpyEA4Gx8qt7oNwC89N0xHLhyB+PWndApv3YnQ2e7Q4B76W/ATOp56E6hT8tRG6z39YHrBstb+LlifOdAeLvY4fh7vQzWqa4YdBMRERERUZW1fNcV6f1bfRtKy4UV5u5oixm9GuiVe7va6WwXXnv6z4gEvfp3M/IC/EcDfW2h+NzH1Q7vDWpSusab0aNfRtxJV2Ln+aRilw57tXs9AMCGSU/gn9eexLwhTREe2hOeznZFHlMdMegmIiIiIqIqq/AzyNN76gfW+UKCaqJXY09pe3a/Rnp17G0Lgu5NEfFFnsu6iKnVq0a2RnhoLwT7uxXXZIuwfSTD+taziZj88ylM/zWiyGPm9G+M6x8PxBNBBbMEZHyWWw+DbiIiIiIiqrIaejsDAIa18Sux7ndj2mFS1yCsfyUE03rU19tfeKTb2qroUOrRALaRV14bahoYZa8obK2sDJbvu3yn2OOKW36N8jB7ORERERERVVn5Sc+8XUqe8iyTyfDuwKKnfhce6bYqJtYsvC533P0sXE5OB1CxA9RHvygorNm8Heja0EOn/RVxtL6iYtBNRERERERVTo5ag+bzdyL34QPVhUepy6vwOfLPG3M3Eyv3XsWYjgHSvsIBbJelYdL7oqadVwSNH84IMCRTpcH2qCSdsv+bGGLqJlUZFX56eUBAAGQymd5r2rRpAIDu3bvr7ZsyZYqFW01ERERERJYihEDj93dIgTGgO0pdXoWD7vx1qEd+exSbIhLw9OrD0r7kNCWEEHoZzosbTba0p4J9MbtfI/w26QmM7RRQYn0HW47fllaF/0mdOHECGk3BOnhRUVHo06cPnnvuOals4sSJWLhwobTt4KC/9h4REREREVV9QgjM2BCpV64wwkh34ee4G3g6IT1HjcRCidoK23dZf+mw0kxxtxS5XCY9xx7/ILvYuo8uL0bFq/BBt4eHh872kiVLUK9ePXTr1k0qc3BwgLe3t7mbRkREREREFczvJ+Pw95lbeuXGmF4OANN71McXYdHI1Qp8d/BGkfU+2nZRr6ymk8IobTC1kKDi1xHfMKmjmVpSNTz2/IacHMPf7JiCSqXCL7/8gvHjx+ukol+/fj1q1aqF5s2bIzQ0FFlZWWZrExERERERVRxz/jxnsLyWk3Eyh9vZ5IVQOWoNUrPVRdaLvp2hs71rZldYVeBnuguzK+ELCg/nyvHlQUVRrqBbq9Xiww8/hJ+fH5ycnHD9+nUAwPvvv4/vv//eqA0sbPPmzUhJScHYsWOlslGjRuGXX35BWFgYQkND8fPPP+Oll14q9jxKpRJpaWk6LyIiIiIiqtyyVLk62x881Ux637WBx6PVyyX/WeYNJ+Lg5mBT6uMaeBWdqKyiKS7o/nlCBzO2pGoo1/TyRYsW4ccff8TSpUsxceJEqbx58+ZYsWIFJkyYYLQGFvb9999jwIAB8PX1lcomTZokvW/RogV8fHzQq1cvXLt2DfXq1TN4nsWLF+ODDz4wSRuJiIiIiMgy3tp4RnofXNsVYzoFQC4D3B0VRluu61JSwYDdz+E3S3XMqJA6Rrm2uRQ3Fd9JUeGfUK5wyjXS/dNPP+Gbb77Biy++CKtCi6gHBwfj0qVLRmtcYTdv3sTu3bvxyiuvFFsvJCQvdX10dHSRdUJDQ5Gamiq94uLijNpWIiIiIiIyv/Br96T3W6Y/CQB4uWMABrX0Mdo1CidTu5epKrF+t4Ye+PiZFka7vjlYyWUY3qa2wX0VOQN7RVWun1hCQgLq16+vV67VaqFWF/1cw+NYu3YtPD09MWjQoGLrRUZGAgB8fIr+w1IoFHBxcdF5ERERERFR5SWEwIOsvFjki1GtTXYdRTFBZ31PJ72y8U8GmqwtpjSzTwOD5TUd+Tx3WZUr6G7atCkOHjyoV/7HH3+gdWvj/4JrtVqsXbsWY8aMgbV1wXSGa9eu4cMPP8SpU6cQExODv//+G6NHj0bXrl3RsmVLo7eDiIiIiIgqnu8OXkdg6DZpu4mP6QbVipt6/dN43eeduzSohZDA4jOBV1RFTSP3cmHQXVblmpA/b948jBkzBgkJCdBqtdi0aRMuX76Mn376CVu3bjV2G7F7927ExsZi/PjxOuW2trbYvXs3VqxYgczMTPj7+2P48OGYO3eu0dtAREREREQVz/JdV7Byz1Wdsnoe+iPOxmIoyVgtJ1s83coPvm72OPJOT3Rashe1nGzx84QQk7XD1ByLCLoLryJFpSMTQojyHHjw4EEsXLgQZ86cQUZGBtq0aYN58+ahb9++xm6jyaWlpcHV1RWpqamcak5EREREVIkEvPOvzna3hh74cbzpMmxfSkpD/xW6s34vL+oPhXVBMB7/IAuu9jZwtit9dvOKKP9nO7pjXfx7NhETugRianf9x4yrq9LGkeVOPdelSxfs2rWrvIcTERERERGVSlqOGllKDbxd7XTK912+rVd3Rm/DzyIbi4+rvV6ZrZXuU7u1aziYtA3mNrFLED54qhlHucupXEF3XFwcZDIZatfOy2h3/Phx/Prrr2jatKnOEl5ERERERESP6/k14biUlI7vRrdD76ZeUvnYtSek9y1ru8LDSYFWtd1M2hZDK49V1WD0aGgv3M9Uwd+9an2JYG7lSqQ2atQohIWFAQCSkpLQu3dvHD9+HO+99x4WLlxo1AYSEREREVH1dikpHQDwyk8n8d3B6wbr/D39SXw/tr3R1uMuiryKBtiGeLvaoakvH799XOUKuqOiotChQ95zEr///jtatGiBI0eOYP369Vi3bp0x20dERERERNVYrkars73o34v4/tANJKXmSGXDWvuZrT1WJg7qqeop1/RytVoNhSIvVfzu3bvx1FNPAQAaN26MxMRE47WOiIiIiIiqtWy1Rq/sw60XcD4hVdr+4OlmZmtPNRroJiMp10h3s2bNsGbNGhw8eBC7du1C//79AQC3bt1CzZo1jdpAIiIiIiIyD422XAsbmVS2Sj/oBoBNpxOk9+bMEl6dppeTcZQr6P7kk0/w9ddfo3v37hg5ciSCg4MBAH///bc07ZyIiIiIiCqPqIRUNJ23A5/tumLppui4na60dBN0WDHopjIqV9DdvXt33L17F3fv3sUPP/wglU+aNAlr1qwxWuOIiIiIiCqzLFUuAt75F3P+OGvpppTog3/OQ5mrxed7rkJbgUa8Lz9MohZUyxGXPuyvt//J+rXM2h7G3FRW5V6n28rKCrm5uTh06BAAoFGjRggICDBWu4iIiIiIKr3BK/M+K/92Mg5yuQwDmnujS4NaFXKJqRMxD6T3qdlq1HC0tWBrCqgeJlIL8nCCnY2V3v7/PdfSrO2piH1HFVu5RrozMzMxfvx4+Pj4oGvXrujatSt8fX0xYcIEZGVlGbuNRERERESVkjK3IPP2/x2PxegfjiMwdJtUpsrV6mXnNrdrdzKQkJKtU3Yno/xTundfSMaZuJTHbFUBrcgbdbcyELm08neDj6u90a5FZArlCrpnzZqF/fv3459//kFKSgpSUlKwZcsW7N+/H2+++aax20hEREREVCkpbAx/3B75zVFcuJWGzp/sxcvfH5fKD0ffRcA7/6L38v24eS/T5O1LTstBr2X70XnJXp3yLZEJRRxRvH2Xb+OVn07i6dWHjdE8AJCmuhtKYDYk2Ndo1ymvM/P7WroJVMGVK+j+888/8f3332PAgAFwcXGBi4sLBg4ciG+//RZ//PGHsdtIRERERFTpXExMw/U7hgPn8Ov3MHDlQdxJVyL8+j1EJaQiMi4FL353DAAQfTsDMzZEmryN64/FGixfHXatXOf7bPdV6f2jI/jKXA2yVLllPmd+RnW5gfWxXwypU+bzGZurvfkyp1PlVK5nurOysuDl5aVX7unpyenlRERERFQtZalyYW9jJT3zO/nnU6U+dvCqQ3plkY85RfvUzfu4k65C/+beRdZR5RY9tX3PxWT8ey4Rz7apjU7FJCu7mJiGAZ8fRN2aDrh5ryAWSM1WIzE1B4NXHcKKEa3wxm+RAIA/pnREuwD3Ut2DEALXHn5xkT/S/fXLbfHOn2fx2YhWBp/xJqpoyjXS3bFjR8yfPx85OTlSWXZ2Nj744AN07NjRaI0jIiIiIqoMohJS0XLBf1iy/RKAvAA89n5BANqhlEFmYTZWhhN2xd7LQmqWuthj912+jeFfhWPKL6dw/U4GQjedxSs/nkSOumDN68tJ6XrTyBcPayG9n/DjSWyKSMCoh6PvRRnw+UEA0Am4AeBBllr6MiE/4AaAZ9eEF3u+wlaHRePnozfzzpepAgD0a+aNiPf7oHsjz1Kfh8iSyjXS/fnnn6Nfv36oXbu2tEb3mTNnYGdnh507dxq1gUREREREFV1+cPn1get4po0f5vx5Tmf/b5OfgFYAr//fafx7LrFU51RrBG6n5cDTxU4q++jfC/j24A0AwNWPBsDGQHYxrVZg7NoT0nbPZful90t3XEa7gBro2dgT/VYcKDjvM80xsn0dyOUyXE5Kx7ojMSW2b/+VO9gUEV/k/pQsVZH7+n12AEOCffByxwC42FkbzAh+N0OJT/8rWDP8UPRd6T0ziFNlIhNClGsRvqysLKxfvx6XLuV9m9ekSRO8+OKLsLevfNkD09LS4OrqitTUVLi4uFi6OURERERUiaTnqNFiwX/StrOdNdJzCp5dvrJoAGyt84LjHLUGf51OQOimc3rnyffOgMbSiDkAHJrTA35u9hi08hAuJKZJ5QprORp4OeH3yR3hYFswlrbtXCKmro8o0z1sn9EFTXzyPgfH3c9Cl6VhOvtjlgzSOybgnX+LPae3ix2S0nKKrQMAE7sE4r1BTXXKdkQlYcovutPzvxjVGoNbWj5xGqB774Z+NlQ9lDaOLNf0cgBwcHDAxIkTsWzZMixbtgyvvPJKpQy4iYiIiIgexzcHrutsFw64AUgBNwDY2VhhZIeik391qlcTk7sG6ZT9ezYR4dfv6QTcQN5yZFEJaXj6C91M4WfiU8rSfEzsEigF3ADg7+6AhU83k7Zb+bvp1M9Q5iIqIbXE85Ym4AYgjdwX9uHWCzrb7w5sXGECbqKyKvX08r///rvUJ33qqafK1RgiIiIiospEqxVYtTe6zMdN7BKoF2x+P6Yd2gW4602dXlxo1NuQq7czkKPWSEnFfjqS9wz0iHb++O1kXLHHDm9TW2+UGQBGdwyAp7MCU36JQGRcCsIu3Ya3qx2uJKcXmVV931vdUcPBFp/vuYofDusH0mVRt6aDztrhbevWeKzzGdvIDv74v+PF/2yJ8pU66B46dGip6slkMmg0mpIrEhERERFVYmqNFjM2nJa2n6xfS+e5YwB4tXs9g8e+N6gp3hvUVJqm7OmsQK8mBasDnXivN9p/tLvUbdkSmYAR7evgdnoOsh8mS+vVxBNJaTnYf+VOkcc93652kfsKr4s9bt2JIut1aVALz7atjYBajgAA60cSwH39clskpmSjXYA7hMjLqt7cz1VKqNbukYD6bHwKjly7p1PmqChXKiqT+fDp5hjRvg6a+/LRVCpZqX97tdqilxMgIiIiIqpuGry3XWd77uAm6L/ioLS9amRrDAku3ZToZo8Ebx7OCmye1hlDVx/Wqxs5rw9aLdylUzbnz3N6yds61quJkMCa+CMiHjF3M6Us4MH+bjjzcDmykKCaRbZJW8rMTz9PCNHZ7t/cW5pyP6VbPfRrprtkWYvargDyfj6v/d9pqAtdaFNEPGb9fkanvp2NHIEPA/qKwtpKrjftnqgoZXqme+/evWjatCnS0tL09qWmpqJZs2Y4ePCggSOJiIiIiKo2Bxvd8azBLX1KPObDp5vBSWGNaT3q6+1r5e+GJcNaYGQHf7Sp4wYAsJLL4OZgi5glg3D944HFntvZzgauDjaY8GQgRrT3l8pXjGgFAJg3WH9aeWFBHiUHunvf7KZX1rpQMPpyx7pFHutkl/fz0jwc3FNrtDoBd0MvJ1z6sD8i5/WFwprrcVPlVaage8WKFZg4caLBzGyurq6YPHkyli9fbrTGLViwADKZTOfVuHFjaX9OTg6mTZuGmjVrwsnJCcOHD0dycrLRrk9ERERElE+Vq0V6jhpCCL3M3Z8MbwE724KP1mM7BZRqWauXOwbg3IK+aFfEOt4vdKiDxcNa4teJT+D1Xg2wZVpnaZ9cLsPvkzsaPO6JIN3zNfdzxcYpHXH4nZ4IrOWImCWDMP7JwGLb1tDLGZ8+F6xX/lrP+oh4vw9uLB6IIA8nvf0ymQy/TXoC37zcFn5uRSdatpHn/bxyNQLX72TozRxYO64D7GyspGfViSqrMj0ccebMGXzyySdF7u/bty8+/fTTx25UYc2aNcPu3QXPs1hbFzR55syZ+Pfff7Fx40a4urpi+vTpGDZsGA4f1p+GQ0RERET0OF7+/hiO3bivV35j8UDIZDJkKAuylvdt6qVXryilCc7tbKwwq09DvfIOge74bnQ73MtU6kwvH9RCf5S9fRGBfXGGt/HDkei7OHbjPjZP6wwPZ0Wpjitu2no+K3nefV9KStdZSxwAPn+hVbEBO1FlUqagOzk5GTY2NkWfzNoad+4UnaihPKytreHt7a1Xnpqaiu+//x6//vorevbsCQBYu3YtmjRpgqNHj+KJJ54wajuIiIiIqHozFHA/17a2FDTbFVoaLLe0D0QbQe+HAf6I9nUQuuksjly7h2faFJ0grSxkMhmWP5yObmz3M1UGy90cbPB0Kz+TXJPIEso0vdzPzw9RUVFF7j979ix8fEp+dqUsrl69Cl9fXwQFBeHFF19EbGwsAODUqVNQq9Xo3bu3VLdx48aoU6cOwsPDiz2nUqlEWlqazouIiIiIqCwmdgnE4mEtpG1rq4KP1s52lsm2vXhYS+x7qzucKli2b0N83Oz0yt7u3win5vaxQGuITKdMQffAgQPx/vvvIydHf6H77OxszJ8/H4MHDzZa40JCQrBu3Trs2LEDX331FW7cuIEuXbogPT0dSUlJsLW1hZubm84xXl5eSEpKKva8ixcvhqurq/Ty9/cvtj4RERERVU/pOWq88E04Djyy7Jabgw3eHdhEJ9AGgDn9G+PFkDoWzWxdmunqFUGbOrpLha15qQ2mdq8vTTsnqipkQohSz31JTk5GmzZtYGVlhenTp6NRo0YAgEuXLmH16tXQaDSIiIiAl1fpn2Epi5SUFNStWxfLly+Hvb09xo0bB6VSqVOnQ4cO6NGjR7HPniuVSp3j0tLS4O/vj9TUVINJ4oiIiIioepr880nsPK+fqPfzF1pxCrQRFE5Id+3jgQy4qVJJS0uDq6triXFkmeadeHl54ciRI3j11VcRGhqK/HhdJpOhX79+WL16tckCbgBwc3NDw4YNER0djT59+kClUiElJUVntDs5OdngM+CFKRQKKBSlSwJBRERERNXTf+eTDAbca15qi/7Ni/+8SaXzes/6WLk3Gh2DajLgpiqrzA971K1bF9u2bcODBw8QHR0NIQQaNGiAGjVqlHzwY8rIyMC1a9fw8ssvo23btrCxscGePXswfPhwAMDly5cRGxuLjh0NL51ARERERFQauy8kY9LPp/TKh7XxY8BtRLP6NsIbvRtCzoCbqrByZ1ioUaMG2rdvb8y26HnrrbcwZMgQ1K1bF7du3cL8+fNhZWWFkSNHwtXVFRMmTMCsWbPg7u4OFxcXvPbaa+jYsSMzlxMRERFRuQkh8MpPJw3uW2Zg3Wp6PAy4qaqr0GkN4+PjMXLkSNy7dw8eHh548skncfToUXh4eAAAPvvsM8jlcgwfPhxKpRL9+vXDl19+aeFWExEREVFltu2cflLeN/s0xEtP1K00ScqIqOIoUyK1qqq0D8ATERERUdUWdz8LXZaGSdsfP9MCvm526N7I04KtIqKKyCSJ1IiIiIiIqrLhXx2R3vu42mFUSB0LtoaIqoIyrdNNRERERFSVtaztJr3f+2Z3i7WDiKoOBt1EREREVG1ptQIfbr2Avp/th1Yr4GyXNxH0vYFNYG9rZeHWEVFVwOnlRERERFQhCCGwOiwaV5Iz0LZuDYxo7w87G9MGvpeT0/H9oRsAgKB3t0nlNlZMmEZExsGgm4iIiIgsLkuVi6bzdkrbf5+5hc92X0HkvL7lPufvJ+Kg0mjx0hN1i6wz4PODBssdFPyYTETGwX9NiIiIiMjiPtt1Ra8sJUuNXsv2Ye7gpuje0KPUy3XtuZiMCT8WrLPdxMcFDb2coLC2gq11wdOVN+9lFnmOzvVrlaH1RERF45Jh4JJhRERERJZyMuY+nl0TXmK93k288N2YdtL2jqgkNPN1gb+7g06907EP8MyXRx49vOC4N7qgsbcLZmw4jS2RtwzWqeVki5Nz+5TyDoiouuKSYURERERUoQkhDAbch9/piWU7L2PT6QSpbPfFZCSn5UCp1uLPiHh8vucqAOD6xwMhl+eNgGerNMUG3ADQf4X+dPKxnQLw0hN14etmhyyVBrWcFI9zW0REOhh0ExEREZHZ5Gq0mP7rabT0d0W/Zt56+0+/3wc1HG0xqKWPTtANACEf79Grv/N8Ega08AEADPtKN+B+o3cDrNh9tdj2tKtbA+8NagIbq7xp5w62/HhMRMbF6eXg9HIiIiIic3n1l1PYHpWkV969kQeeae2Hp1v5SWXZKg1sreWoVyiruCFXPxqAEzH3MerbY1LZuQV94WxnI20/mqgt37WPB8JKzkzlRFR2pY0juU43EREREZmNoYDbzcEG68Z10Am4AcDe1gpWchkOzelR7DkbvLddJ+D+89VOOgE3kDeCHfF+H9SuYS+V/fv6kwy4icjkOH+GiIiIiMzir9PxBss/eKpZscfVruFgoMwe8Q+y9cpf7V4PbevWMHged0dbHJrTE4u3X0SWUoNmvq6laDUR0ePhSDcRERERmZRWK3Ay5j5m/nZGb1/n+jX1RrgNqefhqLO9/pUQg/VeeTKwxHOFDmiCD4c2L7EeEZExcKSbiIiIiEwqyMAz2dN71Mdb/RqV+hw/ju+ADcfjMLpTXXg62wEAfp7QAS9/fxzLngtG7Rr26BDoXuq1vImIzIWJ1MBEakRERESmsCUyATM2ROqVOyus8d+srvBxtdc/iIiokuA63URERERkMbH3sgwG3Dvf6IogD0dpiS4ioqqO/9oRERERVXFarcArP57EyG+O4kGmyizX7Pq/ML2y+UOaopG3MwNuIqpWONJNREREVMXdvJ+F3ReTAQCtP9yFmCWDTHIdIQRkMhlWh0VLZc18XSAEMG9IUzwRVNMk1yUiqsgYdBMRERFVcQev3tHZVuVqYWtt3NHmdot2426GUq98w6Qn9NbMJiKqThh0ExEREVVhuRot5m05r1P2+8k4vNDeH7laAblMVuYAXAiB5DQlpq4/hYjYFPi42hkMuFeMaMWAm4iqvQr9QM3ixYvRvn17ODs7w9PTE0OHDsXly5d16nTv3h0ymUznNWXKFAu1mIiIiKhiWXs4Rq9s7uYo1H9vOxq/vwMN525HapYat9NzSnW+i4lpGLP2BJ5YvAcRsSkAgMRU/WMDajpgaOuS198mIqrqKvRI9/79+zFt2jS0b98eubm5ePfdd9G3b19cuHABjo6OUr2JEydi4cKF0raDg4MlmktERERUoQgh8OvxWGl7eo/6+KLQ89b5ghf+BwA4/0E/OCqK/nj4/aEb+HDrhSL313S0xaKhzdGnqResmSyNiAhABQ+6d+zYobO9bt06eHp64tSpU+jatatU7uDgAG9vb3M3j4iIiKhC++7gDdy4mwkA2PNmN9SuYW8w6M738baL+OiZFvhw6wX8HH4Tq19sgz5NvfD3mVv4OTwGJ2Ie6B0ztJUvNkfeAgC82r0eBrTwMc3NEBFVUhU66H5UamoqAMDd3V2nfP369fjll1/g7e2NIUOG4P333y92tFupVEKpLHjuKC0tzTQNJiIiIrIArVYgPScXH227KJXV83ACAAxs4Y1t55IMHrf+WCxa+bvh+0M3AAATfzqJpcNb4u0/z+rVndq9Ht7u3xgAMCTYF6djUzC+c6Cxb4WIqNKTCSGEpRtRGlqtFk899RRSUlJw6NAhqfybb75B3bp14evri7Nnz2LOnDno0KEDNm3aVOS5FixYgA8++ECvPDU1FS4uLiZpPxEREZGpabQCk38+JS0Plm9850DMG9IUAJClysVfpxNQ190R/11IQuf6tTD551Nluk54aE/4uNobrd1ERJVRWloaXF1dS4wjK03Q/eqrr2L79u04dOgQateuXWS9vXv3olevXoiOjka9evUM1jE00u3v78+gm4iIiCqtuPtZ6LI0zOC+TVM7oU2dGkUe+8vRm5i7OapU1zkzvy9c7ZmRnIiotEF3pchwMX36dGzduhVhYWHFBtwAEBISAgCIji76eSWFQgEXFxedFxEREVFlIoSARps3dnLzXqbBgNvGSoZxnQOKDbgBIMjDUWd77bj2iFkySK/egdk9GHATEZVRhX6mWwiB1157DX/99Rf27duHwMCSnxOKjIwEAPj4MIkHERERVU1arUDQu9sM7nNWWCNiXh/YlCF7eDMfV+m9g60VejTyBAD8+WpHDP8qHADQq7En6tTkCjFERGVVoYPuadOm4ddff8WWLVvg7OyMpKS8pB+urq6wt7fHtWvX8Ouvv2LgwIGoWbMmzp49i5kzZ6Jr165o2bKlhVtPREREZFzKXA1upymLnEbupLDG0Xd7lSngBgAX+4KPhL2beEnv29Z1x9cvt0Xc/SxMeJJJ0oiIyqNCP9Mtk8kMlq9duxZjx45FXFwcXnrpJURFRSEzMxP+/v545plnMHfu3DJNGS/tXHwiIqLHlanMhYOtVZH/jyMCgPc3R+HnozcBAEuGtcALHeog5m4mei/fj1yt4Y9u+QG3UzHrbBdn8baL+O7QDZxb0BcOthV6XIaIqEKoconUTIlBNxERmZIyV4Ofw2/i6wPXcSddiWFt/LD8+VbSPgBQWFvpHbftXCK+OXAdttZy3E7LQZCHE74f044BexWgzNUY7HMAOBOXgqdXH9Ypq1vTATfvZRV5vkEtfbB6VBujtpGIiIpX2jiSX2MSERGZyJXkdKTn5GL4V0d0yjdFJGD+kGbYevYW3vsrL2P0ifd6w8NZIdVJSs3B1PUROsfF3MvCB/9cQPdGHuj+8JlbqnyORN/FqO+OAQCa+brA3dEWZ+NTMbJDHdR0tMWm0wl6xxgKuI+G9oK3qx3upCtRw4HJzYiIKiqOdIMj3UREZFypWWoEL/yvTMe42ttg16yu8HS2AwBEJaRi8KpDJR73Vt+GmNq9PuRyjn5XFl2W7kXc/ewS63Vt6IEDV+4Uud9QdnEiIjKfKrVkGBERUWWh1YoiA+4lw1oUeVxqthodPtoDIQSib2foBNzWxQTUn/53BZN/OQV+h15xZalycTstB7suJGPJ9kulCrgB4JuX2+qVff5CK7g72mJUSB1jN5OIiEyE08uJiKjK0moFTseloGVt1zJncy4NIQQ2RSTAzcEGrfzdYCWXISFFP6Aa2soXK15oDQDoWK8mhq4+jAdZagBALScF7mYopbrXHybLyufvbo+Db/eEEAK9lu+HWqPF6z0b4NuD13ElOQMAsOtCMgJDt+Hrl9ti3+XbWPBUM1xNzoCvmz3cHW2Nft9UssPRd/HiwynkxZncNQg2VnK0rVsDczdHSb8/3Rp6wM7GCjN7N8Rnu68AAKZ0q4enW/nhqWBfPtdPRFSJcHo5OL2ciKiqeufPs9hwIg4yGXBjsXGn4qZmqxH8QfFTyG8sHlhicFTcessA8H8Tn0DHejUN7jsXn4ohXxQ/BX3ra0+iuZ9rsXXIeC4npaPfigOlqnvknZ7wdbMvto5GK7DzfBLa1a0BTxc7YzSRiIiMhNPLiYio2kpKzUHAO/9iw4k4AIAQwI9HYox6jVd+PFFindKMRsrlMsQsGQQ/A8HXmfl9iwy4AaBFbVdcWTQAVsVMPx+86hDGrT2OO+kFo+naIpacosej1QpM/OlkifXkMuD1Xg1KDLgBwEouw8AWPgy4iYgqMY50gyPdRERVhSpXi+fWHMGZ+FSD+xcNbY4+Tb3wU3gMnmldG/U9nQDkjSYu+Pu8tC7yW30bYlqP+kUGzT+Fx2DelvPFtuXRbOQl+et0PGb+dkbaPjm3N2o5le74i4lpGPD5QQBAY29ntKztiv1X7iA5TalT75cJITh49Q6+PnBd7xxPBfvi8xdacdryQ5FxKVi09QIuJqbhr2mdsWL3FVxOSsf3Y9ojoJajXn2tVqDVwv+QlpMrldWt6YC9b3aXvhQRQvDnS0RUhXCd7jJg0E1EVPkJIRAYqj9N++Un6krBNAB4Oitwu9Co75qX2mDmb2eQrdYUe/5Dc3qgdg0H7Lt8G2PXFoxyn13QFy52xlmuKSElGzIA7o62sLMxvIZzUW7czcShq3fwVCs/uNrntWfo6sOIjEspczsWD2uBkR2qX6Ku1Gw1Tsc+QIdAdzSdt7PYut+83Ba1azggoJaDXl1nO2ucmtsHttacUEhEVJUx6C4DBt1ERJXH2fgUpGar0aWBh1SWqcxFs/m6gU/+s8xCCMz8LRKbI2899rUV1nIoc7XS9uRuQQgd0OSxz2sqQghoBTD/7yj8cjS2TMfun90ddWvqj+hWBbfTcxD/IBuNvJyRqxFwfbjG9fNfh+P4jfuPff4riwYw4CYiqgYYdJcBg24ioopv5/kkTP75lE5Zu7o1cPLmA50yB1srXFjYX+/4Tov34FZqDgAg2N8NZwyMAN9YPBAarUD997aX2J7Z/RphWo/6ZbgDy1q8/SK+3n8drfzd8FrP+mji4wJPZwWsreS4k67E4ei72BGVhB3nk3SOO/h2D/i7O1io1caVo9Yg4uYDjCpFVvHyOjW3N2qW8rEAIiKq3Bh0lwGDbiKiiuvCrTQMXHmwVHVb13HDhklPQGGtPzU7R63BnXQlHGyt4O5oC5lMhoB3/pX2/zGlI9oFuJeqDevGtUf3Rp5lvJPKocn7O/Sm2m+f0QX+7g6wt7EqNmlbRRJ3Pwt7L93G7fQcxNzNwrU7GbiUlF6qY5v4uOC5trUxumNdyGUyRN/JQH0PJyzcegHrjsTg11dCsP9KwbPxPRt74oex7U15O0REVAEx6C4DBt1ERJZzOy0H87acRy1nW2n96W8P3kA9D0fczVAhNVutU9/GSga1Rv9/XfMGN8X4JwPLfP2F/1xA7Rr2pTr2XoYSuVoBryqcSfpMXAqeXn24yP3/vv4kFNZWUhK6iuREzH08tya8VHXbB9SADDIcjymYTt6vmRe+fLFtqb9YiEpIxb/nEjGjV4MyP4NPRESVH4PuMmDQTURkfnH3s9BlaVip64/vHIjpPevD3dEWQghE387A1dsZ6NKgFpyNlMiMCggh0Gv5fly/k1mq+sff64X5W85je1Te9PQ9b3ZDPQ/zBeZHrt3FqG9LnjYukwGrRrbG4Ja+APIy3itzNfwdIiKiMmPQXQYMuomITCdXo8UL3xzF7XQlejTywL/nEtHQyxlHrt0r1fH2Nlb457XOqO/pbOKW0qNy1BqMX3ei1H31qHML+iIpNQd9PjsAAJjavR4ylbl4f3BTWFsZL9HYnXQl2n+02+C+j55pjvf+igIA/DP9SbSo7Wq06xIRUfXGoLsMGHQTERlPWZ7BBvKSoS17Phh13B0Qdvk26tZ0RGBNR8hk4JrGFUBKlgrHb9xH5/q1sCXyFt7965xRzhv90YByBd57LyWjlpMC/51Pxhdh0QB0l4H7YWw7PFnfAzZWMp3fH66RTURExsaguwwYdBNRaaRmqaWlhfLdzVDiboYSDTydLZJg6kpyOg5dvYsR7f3hqLBGYmo2hMhb2spYGZRf+CYcR6/nPfc6qIUPvhjVGgCg0mhx4VYanvnySLnO+0SQO74d3Y7TeishZa4GKVlqeDgpcCkpXfqS5cn6tbBuXHtM+vkU9l66Xew5pvWoBwdba7zQ3r/E31UhBJS5WszdHIU/TsUXWa+OuwMOvN2j7DdERERUDgy6y4BBN1HV9iBThXMJqejSoFaxI12qXC2s5DK94HlTRDxm/X5G2q7n4YjXezXA23+c1VmzGQC+fLENBjT31rvOsev3sHzXFUzuFoSejb1K3XYhBOb8eRa/n8wLNF7rWR/h1+7hclI6lLlaqDTaYo8/M78v7G2skJ6jlgKbY9fvYcQ3R6U67w9umhe021pBJpNhzf5r2Hw6odSZnovT2NsZz7T2Q8/GnnBzsIWDrRUcFdaPfV6q+J7+4hDOxKdK22fm98XGk3FY9O9FnXr1PByx583uesdrtXkfT7LUGjR/ZA32oqwa2RpDgn3L32giIqIyYNBdBgy6iSznQaYKS3dexvU7GXiyfi30aOyJ9cduYkq3eqhb0/Gxzq3RClwuNAoHAJ8Mb4ER7esgLUcNpVqLPyPisWT7Jfi52SMhJRsKazkWDW2O3ReTsfN88uPeXpH2z+6O1Gw1Ams56oz05qg12Hf5Nmyt5fj2wA2EXy/fs7TmVremAzycFOjW0AOd6tdCy9qusDHiM7tU+QghEP8gG35u9pAX+iLrSPRdvXWy973VHd0/3QcgbwaEfw0HbHw4oq2wlut9ueVgawU3exv8+3oXZKs1mPPnWTzXzh9PMeAmIiIzYtBdBgy6icwrU5mLHLUGSWk5GLTyUJH1zszrqzed+1FardD5QA8Ab/9xRhoZNofZ/RpBlavF53uulvlYexsrvDeoCb49eB0372WV+rjujTzwIEuN9wY2wbmEVHy49QIAwNXeBu8Pbooj1+5iU0RCmdtjyMgOdbDgqaYQAnjlx5M4FH0XA1t4Y1SHurC3laOZryuXS6JSU+Vq0XDu9nIf/2afhnitVwMjtoiIiKh8GHSXAYNuIn2pWWoICNzNUOFOuhIjvy2Yjjyzd0PM6G34Q++DTBVc7G1wKPouxvxwHADw8hN1cTs9BwNb+GD90ViddXGL46SwxuZpnVDf0xm5mryp3zvPJ8HPzQG/Ho/F/x2Pler2buKJbo088f7mqMe466L9N7MrGnrltSM1W41LSemo4+4Af3cHqU5Klgqjvj2GC4lpUpmznTXSc3IBAEuHt8SaA9dKvQRTPj83e2yZ3hm1nBRQa7QQArC1LnkU+eNtF/HNges6ZXVrOmB0xwCM7RQgTaO/nZ6DRVsvon2gO5wV1tBoBToEuuvcG5Ex3ctQou0iw9nGi3J5UX8orPnlDhERVRzVLuhevXo1/ve//yEpKQnBwcFYtWoVOnToUKpjGXSTOQiRF8DuupCMLFUuujX0QEAtRyQ8yIabgw3cHGwB5I0C3c9U4ertdLSr6w57W8MfMrNUudh1IW/6s8LaCpeT0rH/ym3UcXfAljO3IATQqV5N9GzsiScb1IKfmz3OJaTiSlI6ZDIZatewRzNfV5xLSEVSajbuZapwKyUb/11IRkqWulT3NLCFN1aNbIPfTsTlJfSKvovo2xnl+vlsmdYZR67dg0wGLNl+qVznKMp7A5tgbOcA/G/nZb0g9OuX26JvUy/IZDIIIZCl0uDmvSw09HIy6pJGhak1WpyJS4GrvQ3W7L+OPyN0R+V/mRCCuxlKNPJ2RhMf/ptEVdeDTBVaf7gLAPBW34aY2r0+ZDLgXqYKtZwUUOZq8Pr/ncbIDnXQvZGnhVtLRESkq1oF3b/99htGjx6NNWvWICQkBCtWrMDGjRtx+fJleHqW/D9pBt3VmxACQkCaoqzVCmSpNchU5sLV3sbgtNkctQa305S4kJiKbeeSoBUCNRxskZ6jhoezAmqNgMJGjttpStjZyHHsxv0yj27mU1jL0djbGbH3s+Djag9fN3tExqXgbobyse7bnOxtrOBsZy0t6RPs74a+Tb3g7miLYW38YGsl10k8dvT6PbxQKNFXcUaF1MGvx2J1yuxtrPDv608iyMPJ4DEpWSrcuJuJ1nVqlPOOjOtiYhpupWSjeyNPi2RAJ7KkO+lK2FrL4WrPLPZERFS5VKugOyQkBO3bt8cXX3wBANBqtfD398drr72Gd955p8Tjq1rQLYSAWiOQk6uBRiOQrdZArSnIymwlk0GZq4VcLoNWK6DWaJGWk4v7mUpotHnHp+XkwkoOOClsoH0YlKo1WthYyZCWnYs7GUq42tvASWENR4U1FNZy2FrLYSWXQanWQisENEJACAGtFshU5SJLpcH9TBUU1nJotAL3MlWwsZLBwdYaVnIZslUayGUyyGWAm4MNajopYCWXIUetQdz9LMhkee23lhf+r7xg26og67RGK5Cp1CApNTtv5PJ+Fm7ey4QyN29qrkab93PJVmmQk6uBlUwGD2cF5DIZ7mUqkaPOS9pjLZchVyvQyMsZgbUcoRECDzJVOHnzgSW7uFheLgq08HPF7ot5y/XY21ghW62R9jf2dkYtJwVO3ryPHLUWjrZWCPRwRA0HW9T3dIKTwhpt6tZAHXcHeLnYQS7LO0d+UJyr0WL6r6ex43ySwet7u9jh6da+eCmkrsHpyWVZK1cIge8O3sD2qES0rO0GH1c7OCqspdHftnUrRtBMRERERNVPtQm6VSoVHBwc8Mcff2Do0KFS+ZgxY5CSkoItW7aUeI7KEHRP/zUCMfcyocrVQivyRmO1QkCVq0W6Mheah9takTc9mcyjdg17xD/IBgA83642TsY8wPW7mRjW2g8372fhUmIagjycMCqkDlztbdCzsSfSctTwdLbD/UwV4u5nobGPMxJTcvDr8Vhcv5OBVv5ueOmJunC1t0HMvSwcjr4LW2s50rLViLmXCS9nO7g72aKuuyNa+LmWmGgMAO5nqnA/U4mgWk46ScfUGi1kQLmnUUclpOLItbuo5aRAn6ZecFJYlzqgJiIiIiKqzEobR1b6xVLv3r0LjUYDLy/ddW+9vLxw6ZLh50KVSiWUyoKpuWlpaQbrVSRXkzNwObl8a+baWMlgYyVHrlZIo+AKazm0QsBKnrfPWWENdydbWMvlyNVq4WpvAxlkSFfmSuexs5ZDrcnbZ29rhVyNQKYqF6nZaqhytcjVCijVWjjbWeeNWMvxcOQ6b/TawdYarvY2UGnyRldrOSmQqxXIfPilgYPCCkIAOWotbqfnIC1bDSu5DLbWcvi5OcDGKm/UWaMVD/+rRa6m8PbDlxDS6Hcddwc4Kqzh7WKHujUdYGdjhVytgOPDtYKdHo7SR9/JQKZSg1pOtnB3tIWnsx2srWRISs1BZFwKUrLVUKo1sLOxgo2VDHVrOiIk0L1cAWb+dHV3x7xrAUBALUe8O7CJXt3AWo4IrPV4y2Y9eq3CHndJp+Z+rmju5/pY5yAiIiIiqsoqfdBdHosXL8YHH3xg6WaUyfynmkKZq4WtlRzyh9Os5bK8oMnZzvrhdl4AqLCRw87GCgrrvLrWcplecFiWKb7VgaeLncFy/0eyUxMREREREZVFpQ+6a9WqBSsrKyQnJ+uUJycnw9vb2+AxoaGhmDVrlrSdlpYGf39/k7bzcXWqV8uo52PATUREREREZHqmWQ/HjGxtbdG2bVvs2bNHKtNqtdizZw86duxo8BiFQgEXFxedFxEREREREZGxVfqRbgCYNWsWxowZg3bt2qFDhw5YsWIFMjMzMW7cOEs3jYiIiIiIiKqxKhF0jxgxAnfu3MG8efOQlJSEVq1aYceOHXrJ1YiIiIiIiIjMqdIvGWYMlWHJMCIiIiIiIqo4qs2SYcaQ/71DZVg6jIiIiIiIiCwvP34saRybQTeA9PS89a8regZzIiIiIiIiqljS09Ph6upa5H5OL0detvNbt27B2dnZLEtp5S9RFhcXx+nsFRz7qnJhf1Ue7KvKg31VebCvKhf2V+XBvqo8zN1XQgikp6fD19cXcnnRC4NxpBuAXC5H7dq1zX5dLldWebCvKhf2V+XBvqo82FeVB/uqcmF/VR7sq8rDnH1V3Ah3vkq/TjcRERERERFRRcWgm4iIiIiIiMhEGHRbgEKhwPz586FQKCzdFCoB+6pyYX9VHuyryoN9VXmwryoX9lflwb6qPCpqXzGRGhEREREREZGJcKSbiIiIiIiIyEQYdBMRERERERGZCINuIiIiIiIiIhNh0E1ERERERERkIgy6y+nAgQMYMmQIfH19IZPJsHnzZp39ycnJGDt2LHx9feHg4ID+/fvj6tWrOnWuXbuGZ555Bh4eHnBxccHzzz+P5ORkaX9MTAwmTJiAwMBA2Nvbo169epg/fz5UKpU5brHKMEdf5fv3338REhICe3t71KhRA0OHDjXhnVU9ixcvRvv27eHs7AxPT08MHToUly9f1qmTk5ODadOmoWbNmnBycsLw4cP1+iI2NhaDBg2Cg4MDPD09MXv2bOTm5urU2bdvH9q0aQOFQoH69etj3bp1pr69KsWcfZXv8OHDsLa2RqtWrUx1W1WWOftr/fr1CA4OhoODA3x8fDB+/Hjcu3fP5PdYVRirr15//XW0bdsWCoXC4N/Mvn378PTTT8PHxweOjo5o1aoV1q9fb8pbq3LM1VcAIITAp59+ioYNG0KhUMDPzw8fffSRqW6tyjFGX505cwYjR46Ev78/7O3t0aRJE3z++ed61+Lni8dnzv7KZ+rPGAy6yykzMxPBwcFYvXq13j4hBIYOHYrr169jy5YtOH36NOrWrYvevXsjMzNTOr5v376QyWTYu3cvDh8+DJVKhSFDhkCr1QIALl26BK1Wi6+//hrnz5/HZ599hjVr1uDdd981671WduboKwD4888/8fLLL2PcuHE4c+YMDh8+jFGjRpntPquC/fv3Y9q0aTh69Ch27doFtVqNvn37Sn0BADNnzsQ///yDjRs3Yv/+/bh16xaGDRsm7ddoNBg0aBBUKhWOHDmCH3/8EevWrcO8efOkOjdu3MCgQYPQo0cPREZG4o033sArr7yCnTt3mvV+KzNz9VW+lJQUjB49Gr169TLL/VU15uqvw4cPY/To0ZgwYQLOnz+PjRs34vjx45g4caJZ77cyM0Zf5Rs/fjxGjBhh8DpHjhxBy5Yt8eeff+Ls2bMYN24cRo8eja1bt5rs3qoac/UVAMyYMQPfffcdPv30U1y6dAl///03OnToYJL7qoqM0VenTp2Cp6cnfvnlF5w/fx7vvfceQkND8cUXX0h1+PnCOMzVX/nM8hlD0GMDIP766y9p+/LlywKAiIqKkso0Go3w8PAQ3377rRBCiJ07dwq5XC5SU1OlOikpKUImk4ldu3YVea2lS5eKwMBA499ENWGqvlKr1cLPz09899135rmRauL27dsCgNi/f78QIu/nbmNjIzZu3CjVuXjxogAgwsPDhRBCbNu2TcjlcpGUlCTV+eqrr4SLi4tQKpVCCCHefvtt0axZM51rjRgxQvTr18/Ut1Rlmaqv8o0YMULMnTtXzJ8/XwQHB5v+hqo4U/XX//73PxEUFKRzrZUrVwo/Pz9T31KVVZ6+KqwsfzMDBw4U48aNM0q7qyNT9dWFCxeEtbW1uHTpksnaXt08bl/lmzp1qujRo4e0zc8XpmGq/spnjs8YHOk2AaVSCQCws7OTyuRyORQKBQ4dOiTVkclkOgu329nZQS6XS3UMSU1Nhbu7u4laXv0Yq68iIiKQkJAAuVyO1q1bw8fHBwMGDEBUVJQZ76bqSU1NBQDpd/7UqVNQq9Xo3bu3VKdx48aoU6cOwsPDAQDh4eFo0aIFvLy8pDr9+vVDWloazp8/L9UpfI78OvnnoLIzVV8BwNq1a3H9+nXMnz/fHLdSLZiqvzp27Ii4uDhs27YNQggkJyfjjz/+wMCBA811a1VOefrqca7FzxjlZ6q++ueffxAUFIStW7ciMDAQAQEBeOWVV3D//n3j3kA1Yqy+evRvhp8vTMNU/QWY7zMGg24TyO/00NBQPHjwACqVCp988gni4+ORmJgIAHjiiSfg6OiIOXPmICsrC5mZmXjrrbeg0WikOo+Kjo7GqlWrMHnyZHPeTpVmrL66fv06AGDBggWYO3cutm7diho1aqB79+78n2I5abVavPHGG+jcuTOaN28OAEhKSoKtrS3c3Nx06np5eSEpKUmqUzgoyN+fv6+4OmlpacjOzjbF7VRppuyrq1ev4p133sEvv/wCa2trE99J9WDK/urcuTPWr1+PESNGwNbWFt7e3nB1dTX4eA+VrLx9VR6///47Tpw4gXHjxj1Ok6stU/bV9evXcfPmTWzcuBE//fQT1q1bh1OnTuHZZ5815i1UG8bqqyNHjuC3337DpEmTpDJ+vjA+U/aXOT9jMOg2ARsbG2zatAlXrlyBu7s7HBwcEBYWhgEDBkAuz/uRe3h4YOPGjfjnn3/g5OQEV1dXpKSkoE2bNlKdwhISEtC/f38899xzfDbOiIzVV/nPdr/33nsYPnw42rZti7Vr10Imk2Hjxo0Wu7/KbNq0aYiKisKGDRss3RQqgan6SqPRYNSoUfjggw/QsGFDo567OjPl39aFCxcwY8YMzJs3D6dOncKOHTsQExODKVOmGP1a1YG5/h0MCwvDuHHj8O2336JZs2YmvVZVZcq+0mq1UCqV+Omnn9ClSxd0794d33//PcLCwvSSS1HJjNFXUVFRePrppzF//nz07dvXiK2jR5mqv8z9GYPDBibStm1bREZGIjU1FSqVCh4eHggJCUG7du2kOn379sW1a9dw9+5dWFtbw83NDd7e3ggKCtI5161bt9CjRw906tQJ33zzjblvpcozRl/5+PgAAJo2bSodo1AoEBQUhNjYWPPeUBUwffp0bN26FQcOHEDt2rWlcm9vb6hUKqSkpOh8u5mcnAxvb2+pzvHjx3XOl5/NsnCdR7PHJicnw8XFBfb29qa4pSrLlH2Vnp6OkydP4vTp05g+fTqAvA+fQghYW1vjv//+Q8+ePU18h1WLqf+2Fi9ejM6dO2P27NkAgJYtW8LR0RFdunTBokWLpH8rqWSP01dlsX//fgwZMgSfffYZRo8ebYymVzum7isfHx9YW1vrBAZNmjQBkLeiQKNGjR7/JqoJY/TVhQsX0KtXL0yaNAlz587V2cfPF8Zlyv4y+2cMkzwpXs3gkeRchly5ckXI5XKxc+fOIuvs2bNHyGQynUQZ8fHxokGDBuKFF14Qubm5xmpytWWqvkpNTRUKhUInkZpKpRKenp7i66+/NkrbqwOtViumTZsmfH19xZUrV/T25yfO+OOPP6SyS5cuGUz2lJycLNX5+uuvhYuLi8jJyRFC5CU6ad68uc65R44cyUQnZWCOvtJoNOLcuXM6r1dffVU0atRInDt3TmRkZJj+RqsIc/1tDRs2TDz//PM65z5y5IgAIBISEkxxa1WOMfqqsOISA4WFhQlHR0fxxRdfGK391Ym5+mrnzp0CgIiOjpbKIiMjBQBx+fJl49xMFWesvoqKihKenp5i9uzZBq/DzxfGYY7+MvdnDAbd5ZSeni5Onz4tTp8+LQCI5cuXi9OnT4ubN28KIYT4/fffRVhYmLh27ZrYvHmzqFu3rhg2bJjOOX744QcRHh4uoqOjxc8//yzc3d3FrFmzpP3x8fGifv36olevXiI+Pl4kJiZKLyo9c/SVEELMmDFD+Pn5iZ07d4pLly6JCRMmCE9PT3H//n2z3Wtl9+qrrwpXV1exb98+nd/3rKwsqc6UKVNEnTp1xN69e8XJkydFx44dRceOHaX9ubm5onnz5qJv374iMjJS7NixQ3h4eIjQ0FCpzvXr14WDg4OYPXu2uHjxoli9erWwsrISO3bsMOv9Vmbm6qtHMXt5+Zirv9auXSusra3Fl19+Ka5duyYOHTok2rVrJzp06GDW+63MjNFXQghx9epVcfr0aTF58mTRsGFD6f+D+Znm9+7dKxwcHERoaKjOde7du2fW+63MzNVXGo1GtGnTRnTt2lVERESIkydPipCQENGnTx+z3m9lZoy+OnfunPDw8BAvvfSSzjlu374t1eHnC+MwV389ypSfMRh0l1NYWJgAoPcaM2aMEEKIzz//XNSuXVvY2NiIOnXqiLlz5+otgTNnzhzh5eUlbGxsRIMGDcSyZcuEVquV9q9du9bgNThBoWzM0VdC5I1sv/nmm8LT01M4OzuL3r176yxFRiUr6vd97dq1Up3s7GwxdepUUaNGDeHg4CCeeeYZvS+iYmJixIABA4S9vb2oVauWePPNN4VardapExYWJlq1aiVsbW1FUFCQzjWoZObsq8IYdJePOftr5cqVomnTpsLe3l74+PiIF198UcTHx5vjNqsEY/VVt27dDJ7nxo0bQgghxowZY3B/t27dzHezlZy5+koIIRISEsSwYcOEk5OT8PLyEmPHjuUXJGVgjL6aP3++wXPUrVtX51r8fPH4zNlfhZnyM4bs4Y0RERERERERkZExezkRERERERGRiTDoJiIiIiIiIjIRBt1EREREREREJsKgm4iIiIiIiMhEGHQTERERERERmQiDbiIiIiIiIiITYdBNREREREREZCIMuomIiIiIiIhMhEE3ERFRNTB27FjIZDLIZDLY2NjAy8sLffr0wQ8//ACtVlvq86xbtw5ubm6maygREVEVw6CbiIiomujfvz8SExMRExOD7du3o0ePHpgxYwYGDx6M3NxcSzePiIioSmLQTUREVE0oFAp4e3vDz88Pbdq0wbvvvostW7Zg+/btWLduHQBg+fLlaNGiBRwdHeHv74+pU6ciIyMDALBv3z6MGzcOqamp0qj5ggULAABKpRJvvfUW/Pz84OjoiJCQEOzbt88yN0pERFSBMOgmIiKqxnr27Ing4GBs2rQJACCXy7Fy5UqcP38eP/74I/bu3Yu3334bANCpUyesWLECLi4uSExMRGJiIt566y0AwPTp0xEeHo4NGzbg7NmzeO6559C/f39cvXrVYvdGRERUEciEEMLSjSAiIiLTGjt2LFJSUrB582a9fS+88ALOnj2LCxcu6O37448/MGXKFNy9exdA3jPdb7zxBlJSUqQ6sbGxCAoKQmxsLHx9faXy3r17o0OHDvj444+Nfj9ERESVhbWlG0BERESWJYSATCYDAOzevRuLFy/GpUuXkJaWhtzcXOTk5CArKwsODg4Gjz937hw0Gg0aNmyoU65UKlGzZk2Tt5+IiKgiY9BNRERUzV28eBGBgYGIiYnB4MGD8eqrr+Kjjz6Cu7s7Dh06hAkTJkClUhUZdGdkZMDKygqnTp2ClZWVzj4nJydz3AIREVGFxaCbiIioGtu7dy/OnTuHmTNn4tSpU9BqtVi2bBnk8ry0L7///rtOfVtbW2g0Gp2y1q1bQ6PR4Pbt2+jSpYvZ2k5ERFQZMOgmIiKqJpRKJZKSkqDRaJCcnIwdO3Zg8eLFGDx4MEaPHo2oqCio1WqsWrUKQ4YMweHDh7FmzRqdcwQEBCAjIwN79uxBcHAwHBwc0LBhQ7z44osYPXo0li1bhtatW+POnTvYs2cPWrZsiUGDBlnojomIiCyP2cuJiIiqiR07dsDHxwcBAQHo378/wsLCsHLlSmzZsgVWVlYIDg7G8uXL8cknn6B58+ZYv349Fi9erHOOTp06YcqUKRgxYgQ8PDywdOlSAMDatWsxevRovPnmm2jUqBGGDh2KEydOoE6dOpa4VSIiogqD2cuJiIiIiIiITIQj3UREREREREQmwqCbiIiIiIiIyEQYdBMRERERERGZCINuIiIiIiIiIhNh0E1ERERERERkIgy6iYiIiIiIiEyEQTcRERERERGRiTDoJiIiIiIiIjIRBt1EREREREREJsKgm4iIiIiIiMhEGHQTERERERERmQiDbiIiIiIiIiITYdBNREREREREZCIMuomIiIiIiIhMhEE3ERERERERkYkw6CYiIiIiIiIyEQbdRERERERERCbCoJuIiIiIiIjIRBh0ExEREREREZkIg24iIiIiIiIiE2HQTURERERERGQiDLqJiIiIiIiITIRBNxEREREREZGJMOgmIiIiIiIiMhEG3UREREREREQmwqCbiIiIiIiIyEQYdBMRERERERGZCINuIiIiIiIiIhNh0E1ERERERERkIgy6iYiIiIiIiEyEQTcRERERERGRiVg06D5w4ACGDBkCX19fyGQybN68WdqnVqsxZ84ctGjRAo6OjvD19cXo0aNx69YtnXMEBARAJpPpvJYsWWLmOyEiIiIiIiLSZ23Ji2dmZiI4OBjjx4/HsGHDdPZlZWUhIiIC77//PoKDg/HgwQPMmDEDTz31FE6ePKlTd+HChZg4caK07ezsXKZ2aLVa3Lp1C87OzpDJZOW/ISIiIiIiIqoWhBBIT0+Hr68v5PKix7MtGnQPGDAAAwYMMLjP1dUVu3bt0in74osv0KFDB8TGxqJOnTpSubOzM7y9vcvdjlu3bsHf37/cxxMREREREVH1FBcXh9q1axe536JBd1mlpqZCJpPBzc1Np3zJkiX48MMPUadOHYwaNQozZ86EtXXRt6ZUKqFUKqVtIQSAvB+Wi4uLSdpOREREREREVUdaWhr8/f1LnGldaYLunJwczJkzByNHjtQJjF9//XW0adMG7u7uOHLkCEJDQ5GYmIjly5cXea7Fixfjgw8+0Ct3cXFh0E1ERERERESlVtIjyjKRP8xrYTKZDH/99ReGDh2qt0+tVmP48OGIj4/Hvn37ig2Mf/jhB0yePBkZGRlQKBQG6zw60p3/DUVqaiqDbiIiIiIiIipRWloaXF1dS4wjK/xIt1qtxvPPP4+bN29i7969JQbFISEhyM3NRUxMDBo1amSwjkKhKDIgJyIiIiIiIjKWCh105wfcV69eRVhYGGrWrFniMZGRkZDL5fD09DRDC4mIiIiIiIiKZtGgOyMjA9HR0dL2jRs3EBkZCXd3d/j4+ODZZ59FREQEtm7dCo1Gg6SkJACAu7s7bG1tER4ejmPHjqFHjx5wdnZGeHg4Zs6ciZdeegk1atSw1G0RERERERGZTZYqF+uOxKC5ryu6NvSwdHPoERZ9pnvfvn3o0aOHXvmYMWOwYMECBAYGGjwuLCwM3bt3R0REBKZOnYpLly5BqVQiMDAQL7/8MmbNmlWm6eOlnYtPRERERERU0Xx/6AY+3HoBMhlwY/EgSzen2qgUz3R3794dxcX8JX0f0KZNGxw9etTYzSIiIiIiIqo0ktNyAABC5MVQJWXTJvOSW7oBREREREREVH6FY+xcbYVYnIoKYdBNRERERERUiVkVirqVuVoLtoQMYdBNRERERERUickLBd05ao0FW0KGMOgmIiIiIiKqxNTagtHtLCWD7oqGQTcREREREVEllqspeI47MTXbgi0hQxh0ExERERERVVJCCHx/6Ia0feNuJn4/EYdJP53EnXSlBVtG+Sy6ZBgRERERERGV3427mTrbG07EIe5+Fu5lqmBnY4WVI1tbqGWlt/N8EhJTsjGmU0CVXO6MI91EREREREQVXHqOGv+dT4Lqkezkqdlqne3IuBTcy1QBAJIert/96Hlup+dgU0R8hUi6dj9Thck/n8KCfy7gYmK6pZtjEhzpJiIiIiIiquAW/H0Bf0bEo2NQTfzfpCek8tOxKUUeY2djBQA4ePUOfjsRh1l9GqLnsv3S/o0n43XOZQlJqQVfDGSrcy3YEtNh0E1ERERERFTB/RkRDwAIv34PQghpGvbCrReKPMbOOm9i88vfHwcAbD2bqLM//Po9UzS1TJS5BaPtWlFMxUqM08uJiIiIiIgqEUPTxg2xt7UycUseX466YLq8YNBNRERERERElta70BTx4myJvIVVe64Wud9abvmkZTmFRrpFFY26GXQTERERERFVcI29naX3mSqNlFTN1iovpPNyUWBIsK/ecct2XSnynE52ln/aWKmu+tPLLf9TJiIiIiIiomJlKHWTjE36+RQGtfCBSpM3PXvHjK6wspIhJUuFpNQcXL2dUeI5azkpTNJWQ07HPsCUX06hc/1amD+kGVztbQA8Mr0cVTPq5kg3ERERERFRBZaYmo34B9l65f+eK0iMZm9rBRc7G/w8IQQfD2tRqvOaczr3M18eQXKaEpsiEtBr2T6pfOvZW4UaZLbmmBWDbiIiIiIiogrs+I37JdZRWBeEdqUdwc4fJTe3uxl564jnarTYffG2VP7pf5f1RvSrAgbdREREREREFdS2c4n4ZPulEuvlLyEGAIG1HEt1blWueYLuuPtZBstv3M3U2Y6ITcHCf86bo0lmxaCbiIiIiIiogpq6PgK3UvOWCOvfzNtgnZUjW+uVTetRr8RzqzWmn8+dkqVCl6VhBvfdSVfqlf1+Mt7UTTI7JlIjIiIiIiKqBO5nqgyW1/PQH9l+rWcD2FlbQSYDPv3PcAZzc4x0f1ZE9vRslQZ3DdyPnU3VGxdm0E1ERERERFQJ3MlQ4tvR7TDxp5N4ob0/rK1kuJehQhNvF726djZWeK1XA/xzpiBR2UtP1MEvR2OlbVM+0737QjIS03JwKSnd4P6ey/Yh8eEIfmEj2vmbrE2WwqCbiIiIiIioghFCYO3hGJ2yNS+1RSNvZ8QsGVTq8+QUWgd70dAWmNajPmb9dgbh1+9BlavNW+vb2vijy6/8dBIApKXBHmUo4Aaq5lrdFh27P3DgAIYMGQJfX1/IZDJs3rxZZ78QAvPmzYOPjw/s7e3Ru3dvXL16VafO/fv38eKLL8LFxQVubm6YMGECMjJKXpOOiIiIiIioojp+4z4Wbr2gU9bI27nM58l5ZAq5j6s91r8SAlurvFAwOc1w8GssqdnqEus09ysYqc9UMXu5UWVmZiI4OBirV682uH/p0qVYuXIl1qxZg2PHjsHR0RH9+vVDTk7BL8aLL76I8+fPY9euXdi6dSsOHDiASZMmmesWiIiIiIiIHltKlgrv/XUOEbEPABT9/HZZyWUGyuQyeDjnLSt2J0M/mdnjMrT+t1wGzBvcVK/cz80em6d2xvQe9QHkfdkQ8M6/eOfPs0Zvl6VYNOgeMGAAFi1ahGeeeUZvnxACK1aswNy5c/H000+jZcuW+Omnn3Dr1i1pRPzixYvYsWMHvvvuO4SEhODJJ5/EqlWrsGHDBty6dUvvnERERERERBXRx9suYv2xWAz78gg+3XkZr66PMMp5h7WujXZ1a+Ctvg11yh0VVgCAHJXG0GGPxdCz4mM7BWL8k4F65TWdbGFtJZeWOYt/kA0A2HAizujtspQKmxruxo0bSEpKQu/evaUyV1dXhISEIDw8HAAQHh4ONzc3tGvXTqrTu3dvyOVyHDt2zOxtJiIiIiIiKqsNx2N1lsr6IizaaOe2t7XCH692wvSeDXTLbfKC7my1CYJuA1nRFUVkJVc8fJ7cwdZKb5+2ijzgXWGD7qSkJACAl5eXTrmXl5e0LykpCZ6enjr7ra2t4e7uLtUxRKlUIi0tTedFRERERERkCe9sOldinTruDka9pp2Zg+78Z8jPzOurU56/VreDQj/Hd1pOyc+DVwYVNug2pcWLF8PV1VV6+ftXvbT0RERERERUNWye1hlbX3/SqOfMH1nONsH08iQDydlqOdkCAFwddLOZ334YdDsaGOkOu3zb6G2zhAobdHt7ewMAkpOTdcqTk5Olfd7e3rh9W7cjcnNzcf/+famOIaGhoUhNTZVecXFV53kBIiIiIiKqWlr5u8HFzvDSW+Vl/zDIzTHBSPdH/17UK0tXGs5Knp9zzdnA/c3eWDWSqVXYoDswMBDe3t7Ys2ePVJaWloZjx46hY8eOAICOHTsiJSUFp06dkurs3bsXWq0WISEhRZ5boVDAxcVF50VERERERGRuCSnZFrmuKaeXH7l2T6/sqWBf6f2B2T2k98+2rQ0AaOjlpHdM2Fvdjd42S9CfOG9GGRkZiI4uSBJw48YNREZGwt3dHXXq1MEbb7yBRYsWoUGDBggMDMT7778PX19fDB06FADQpEkT9O/fHxMnTsSaNWugVqsxffp0vPDCC/D19S3iqkRERERERBXDyt1XLXLd/ERqWUaeXn70un7ADQC1axQ8k16npgMOvt0Dx2/cx9Ot8uI2mUyGr15sg1fXR+DjZ1pgZAd/yGQG1jurhCw60n3y5Em0bt0arVu3BgDMmjULrVu3xrx58wAAb7/9Nl577TVMmjQJ7du3R0ZGBnbs2AE7OzvpHOvXr0fjxo3Rq1cvDBw4EE8++SS++eYbi9wPERERERFRWRReXmtS1yCdffU9nfDrxKJn8D4OF/u86dxf7buGqIRUo5138+kE6f2qkXlx3tcvt9Wr5+/ugOFta8PaqiAkHdDCBzFLBmFUSJ0qE3ADFh7p7t69u8GF0/PJZDIsXLgQCxcuLLKOu7s7fv31V1M0j4iIiIiIyKTyE4wBwPSe9eHprMCih89E/zIhBN6udkUd+lhqOuZdV5mrxeBVhxCzZJBRzlvDseB+hgT7YkgwZyBX2Ge6iYiIiIiIqrIctQabI28BAII8HPWSpeWvYW0KNRxsdbaNlVAtf7mwKd3qGeV8VQGDbiIiIiIiIgt4d9M5aZ3qFn6uAIB7mSppv60Jg253R92g+/M9xnm2XJmbF7yb8guDyoY/CSIiIiIiIgvYVOj550bezgB0n4l2MLB2tbFYW+k+M/3VvmtGOW/+SLfChqFmPv4kiIiIiIio2oi7n4WDV+9Yuhl6xncOBAAMauEjlZkymVg9D/0luoxBmR90W5vuC4PKhkE3ERERERFVGz2X7cPL3x/H4ei7SM1SF5vY2VzaB9SQ1s2e2qM+Amo6YHqP+ia9pq+bPWb0avDY5/n2wHUEvPMvom9nAACU6ryg25RT4ysb/iSIiIiIiKjaUGvyguz5f59H8ML/MHdzlEXacS6+YJmuD4c2l967O9pi3+weeKtfI5O3YWafhmhXt4a0vTosuszn+GhbXqb13sv3AwB2nE8CANhaVZ0lvx4Xg24iIiIiIqp28kdm1x+LhbrQWtnm8veZgme3ZbBcgPrLKwXrgP9v5+XHOlfhWQOXkzIe61xVCYNuIiIiIiKq1q4kp5v9mh7OCum9gOWmuOdPa88X8M6/CHjnXykhWllsO5ckvc/JNc4SZFUBg24iIiIiIqrW9ly8bfZr1q3pKL2vb6KkZo/jRMz9Yvf/fiIOs36P1Cmb9muE9H5cpwATtKpysrZ0A4iIiIiIiCyppKRfg1cdRFRCGn6f3BEdAt2Ncs0cdd5I8BNB7rC2qnhjocWts63VCrz959lij/dytTN2kyqtite7REREREREZvQgU1XkPmWuBlEJaQCA578ON8r1lLkapGarAQD2NpZfWquhl/5Ie7oyt8j6cQ+ySjyn3ITLnVU2DLqJiIiIiKjasLMpCIEmdwsCANwvJuhOyy46+CyPuPtZaDR3B+ZtOf+wPZYPuv9v4hN6ZZnFBN0XE0t+Bt6KQbeEQTcREREREVULQgjkPFxH+p/pT2LXhWQAwMZT8VAWkfgrNbsgIO/dxPOxM52v3HNVZ9sSmdMfVdNJoVeWpSw6EdqUX06VeE7G3AUYdBMRERERUbXw+8k46b29rRzX72RK22/+fsbgMR9vuyS9333xNhq8tx1hl8qeeE2Vq0W2SoP0HN0R5PqezmU+lzmoHvPLAE4vL1CuoDslJQXfffcdQkNDcf9+Xla7iIgIJCQklHAkERERERGRZcz585z03uaR5GVbzyYaPGavgQB73LoTZbpuapYaDeduR5N5O7DjfJLOvsLT3SuSxx2BlzPmlpS5h8+ePYuGDRvik08+waeffoqUlBQAwKZNmxAaGmrs9hERERERERmdt6sdfhzfocj9p27eR8A7/xrcF+zvVqZrbYsyHNADQEqWukznMpeigu57GcpSHc+R7gJlDrpnzZqFsWPH4urVq7CzK0gDP3DgQBw4cMCojSMiIiIiIjIWF7u8FZNn92sEhbUVujaoJZUBec9855vyS4Te8fma+riU6bqPjqoXVlGDU1Wu4aB7c+StEo8d07Eu5BzqlpR5ne4TJ07g66+/1iv38/NDUlKSgSOIiIiIiIgs6066EmkPn6ce0d4fACCTybBrVjeEfLwHAHA3QwUP57ykYvU9nHAn3fCobraqbBnNi5tCrtFaPpGaISqNMFj+4dYLemVt69bAn692QlRCKk7G3MfojgEmbl3lUuaRboVCgbS0NL3yK1euwMPDwyiNIiIiIiIiMqYv90VL793sbaT3Xi4Fs3fbf7QbGcpcZKs0CL9+Tyof3zlQ51zZ6qIzexty/MZ9vbIlw1qgqY8LJnYNKtO5TKV1HTed7bTskqe973urO17vWR/fj2kHAGju54qxnQM5yv2IMgfdTz31FBYuXAi1Oq8TZDIZYmNjMWfOHAwfPtzoDSQiIiIiInpchdfDti5muvdfEfEYu/a4tP3j+A6Y2aeBTp0sVdmC7p/Cb+qVvdChDrbN6ILaNRzKdC5TafhIFvVbKdnYcDy22DXMA2o5YlbfRnBzsDV18yq1Mgfdy5YtQ0ZGBjw9PZGdnY1u3bqhfv36cHZ2xkcffWSKNhIRERERET2WjIdTy6f1qFdsvTX7r+NYoZHpbg094GCr+1RuThEj3UmpOVi87SLi7mfplPdopDsj2N2x4gWptta6oeF/F5LxzqZzmPBj2TK1k74yP9Pt6uqKXbt24fDhwzhz5gwyMjLQpk0b9O7d2xTtIyIiIiIieiyf7ryMn4/mjTbXKGFUNiElW6/MSi7D0Fa+UhKxoqaXT/75JM7Ep+LwtbvY+loXqTzs8h2dek8F+5ap/ebwaNCd73RsClbsvoJGXs7FzhCgopU56M7XuXNndO7cGQCkZcNMISAgADdv6k/HmDp1KlavXo3u3btj//79OvsmT56MNWvWmKxNRERERERUeXwRVvA8t4+rvd7+IcG++OeMflbuNS+1ld6veKE1RnaogxHfHEVUQhqyVLl6I+Bn4lMBAFEJ+jmwAODJ+rXQtm4NjH8y0OB+Syouw/qK3Vf1yvbP7m7C1lQtZf6q4pNPPsFvv/0mbT///POoWbMm/Pz8cObMGaM2DsjLlp6YmCi9du3aBQB47rnnpDoTJ07UqbN06VKjt4OIiIiIiCqf1EcSgrUPqKFXZ+ULrTDJQEKzfs28dLbtbQueC//cQCCaz+qRRGL+7nmB/uRuQZjZpyFcCyVyqyia+5VtGbS6NR1N1JKqp8xB95o1a+Dvn5dif9euXdi1axe2b9+OAQMGYPbs2UZvoIeHB7y9vaXX1q1bUa9ePXTr1k2q4+DgoFPHxaVsvzBERERERFQ1fbXvmvR+SLAvPAtlK88nk8kwumNdnbLO9WtC9sga2vaFkrGdv2V4NBsAFI9M1bazzjvu0WC8IhnUwgfzhzTFX1M7YXK3ipFRvaooc9CdlJQkBd1bt27F888/j759++Ltt9/GiROmfchepVLhl19+wfjx43X+ANavX49atWqhefPmCA0NRVZWVjFnAZRKJdLS0nReRERERERU9VxILPisv2pk6yLr1a7hgE1TO0nbv0wI0atTeKS7uAC6cND989GbuHo7AwBgLa+4z0TLZDKM6xyI1nVqoJGXc7F1f5/c0UytqhrK/Ex3jRo1EBcXB39/f+zYsQOLFi0CAAghoNGULXV+WW3evBkpKSkYO3asVDZq1CjUrVsXvr6+OHv2LObMmYPLly9j06ZNRZ5n8eLF+OCDD0zaViIiIiIisrzmvi44cOVOqZKXtalTA5c+7A+ZDHqj3IDuSHfhmPvRbOaFk5K9vzlKel9Z8pB1CHR/rP2kq8xB97BhwzBq1Cg0aNAA9+7dw4ABAwAAp0+fRv369Y3ewMK+//57DBgwAL6+BX8wkyZNkt63aNECPj4+6NWrF65du4Z69QwvBxAaGopZs2ZJ22lpadLoPRERERERVQ0arUD6w6XC8p+rLknh9bwfVXikO1crAABbIhMwY0OkTr3kNKXB413tK95SYYYU9zOY2r34JddIX5mD7s8++wwBAQGIi4vD0qVL4eTkBABITEzE1KlTjd7AfDdv3sTu3buLHcEGgJCQvGkg0dHRRQbdCoUCCoXC6G0kIiIiIqKKITVbjeAP/pO27YsJJEsr/9lsoGCt7UcD7nwHr97By98f1ynzcdV/nrwiKi7o7tXEq8h9ZFiZg24bGxu89dZbeuUzZ840SoOKsnbtWnh6emLQoEHF1ouMjAQA+Pj4mLQ9RERERERUcRUOuIHiA8nSkstl6NKgFg5evQsPJwWiElKLrPvh1gs628+2rQ1HRblXbDar4r6gsK0sc+QrkHL1+rVr17BixQpcvHgRANC0aVO88cYbCAoyTZY7rVaLtWvXYsyYMbC2LmjytWvX8Ouvv2LgwIGoWbMmzp49i5kzZ6Jr165o2bKlSdpCREREREQV2+bTCXplhaeGP47WdWrg4NW7UOZq8eORmCLrXUnO0Nn+37OVJz6xksvQraEH9l+5o7fP3pZBd1mV+Se2c+dONG3aFMePH0fLli3RsmVLHDt2DE2bNpXW0Da23bt3IzY2FuPHj9cpt7W1xe7du9G3b180btwYb775JoYPH45//vnHJO0gIiIiIqKK7XD0XbzxW6ReuTGmlwOA48Pg/ej1e6jpVPpHVg0lZqvI3h/cxGA51+cuuzKPdL/zzjuYOXMmlixZolc+Z84c9OnTx2iNy9e3b18IIfTK/f39sX//fqNfj4iIiIiIKqfpv0YYLK/n4WSU86flqAEAV29noGdjz1Id88qTgUa5tjk52BoOFW04vbzMyvwTu3jxIiZMmKBXPn78eFy4cMHAEURERERERKaXq9HiQZZa2l48rIX0PtjfzSjXuJuukt5/feB6ifUHtvDG3MFNjXJtc3Iw0nR8KsdIt4eHByIjI9GgQQOd8sjISHh6lu6bHiIiIiIiImOb8+c5ne0X2vujdR03OBYxalsedjZFj1u2rO2Ks/G6ydVGdahrtGubU+GR7v+b+ATiH2ThiaCaFmxR5VXm376JEydi0qRJuH79Ojp16gQAOHz4MD755BOdta+JiIiIiIjM6cDVgsRfMUvyVj1q7O1i1Gsoink2/JuX2+GJxXuk7aeCfRES5G7U65uLrbUcrvY2SM1Wo6mPCzrWY8BdXmUOut9//304Oztj2bJlCA0NBQD4+vpiwYIFeP31143eQCIiIiIiopIIIXAnXQkAWP58sMmuY2dd9Ei3l4sCp9/vgycW70FzP1esHNnaZO0wh31vdYdao4Wrg42lm1KpyYShDGWllJ6eDgBwdnY2WoMsIS0tDa6urkhNTYWLi3G/CSMiIiIiItP66N8L+PbgDWl775vdEGSkxGmP2nYuEVPX6yZr2zWzK2o6KeDuaAsASM9Rw97GCtZMOlallTaOfKyHGyp7sE1ERERERJXbvC1R+Cn8pk6ZqQJuAOhcr5ZeWT0PJ8jlBUuCOdtxZJgKlCrobt26danXlYuIMJyin4iIiIiIyNgeDbj7NPUy6fVkBgavCwfcRI8qVdA9dOhQEzeDiIiIiIiobM7Gp+iVze7XyKTXtCrlYCRRvlIF3fPnzzd1O4iIiIiIiAw6cOUONp9OwIKnm8Gl0NTtX4/FSu+fae0HD2cFGniabmo5AMgZdFMZlfrJ/gcPHmDVqlVIS0vT25eamlrkPiIiIiIioscx+ofj2HQ6AS0X/IddF5Kl8g0n4qT3n41ohXcHNin1Y7HlxZibyqrUQfcXX3yBAwcOGMzK5urqioMHD2LVqlVGbRwREREREVVvjy62NPGnk/j7zC2kZKmkso5B5ltD2orPb1MZlTro/vPPPzFlypQi90+ePBl//PGHURpFREREREQEAMpcrV7Z6/93Gp/vuSptLzPhutyP4vRyKqtSB93Xrl1DgwYNitzfoEEDXLt2zSiNIiIiIiIiAoBslcZg+drDMdJ7Xzd7M7UG4EA3lVWpg24rKyvcunWryP23bt2CXM7F34mIiIiIyHgylLmWboIOUz8zTlVPqaPk1q1bY/PmzUXu/+uvv9C6dWtjtImIiIiIiAgAcP5WKgDAw1mBy4v66+3v0qCWuZtEVCalDrqnT5+OZcuW4YsvvoBGUzDFQ6PRYNWqVfjss88wbdo0kzSSiIiIiIhMK1ulwfJdV3A1Od3STdGRkqUGANT3cILC2kpv//LnW5m5RbqOhvay6PWp4it10D18+HC8/fbbeP311+Hu7o7WrVujdevWcHd3xxtvvIFZs2bh2WefNWVbiYiIiIjIRDaeisPKPVfR57MDlm6KDs3D7OXOdtZ6+7o19ICHs8LcTdLh7Wpn0etTxaf/m1uMjz76CE8//TTWr1+P6OhoCCHQrVs3jBo1Ch06dDBVG4mIiIiIyMTmbTkvvc9S5cLBtkyhgslotXlBt6Glup5u5Wvu5hCVWZn/kjp06MAAm4iIiIioFIQQ+HDrRfRt5oUnzLiWdFkJIWBrJYdKk7c81+00JQJqlT3oTs1SY8Q34Wjm62q0ZbwextzSUl3OdtZIz8lLrja0lZ9RrkFkSkw3TkRERERkIov+vYgfDt/AC98cxau/nML/HY+FysC605YihMAzXx5GYOg2KeAGgINX75TrfIu3X8SlpHT8GREP8XBaeL5slaZcmciT0nIAAPKHI90bJj2Bfs28sGtmV6mMqCJj0E1EREREZCJrD9+Q3m+PSkLopnNoOHe7FJDG3svC3QylVEcIgYuJaUWuTW1sWyJv4XRsil75+4WmmpdEmavBoq0X8HN4DDaciJPKM5S5OHXzPgLe+Rdf7L2KJvN2oPn8nQi7fLvU5/7xSAy+2ncNAJCSpQIANPN1xdcvt0MDr/9n777DoyraNoDfu+mNhJKQ0Am99y5IExFEERReLChiQ2zYUT4RG1hRmqg0RVSKCIIUKSEIhBYSIEBCKmmk97a72Z3vj7DLtiSbsCWb3L/r4iI5dU5my3nOzDzjZfJxiGypTgfdH374ISQSic6/rl27ataXlZVh/vz5aNq0KTw9PTF9+nSkp6fbsMRERERERLf5eRlPstV+4T78FZaMUV8GYdaPpzXLn9x4Dvd99x+6fXAAPx6PtXj5rqUVVLruyLV0vPJ7WLWt3vd8cxzrTsQbBOq5xQpM/z4EAPDVv9c1y+dsPIfYzCJkaz1sMCYuswiL/759zP+is6rcnqiuqtNBNwD06NEDN2/e1Pw7ceKEZt2CBQuwZ88ebN++HcHBwUhNTcW0adNsWFoiIiIiogqhN3I1XaONWbD1IgAgOqMIszecRbt3/8Hx67cD3M/2Rdb63EIIDPj4EB5YdQLychVK5OVIySvV2Sa3WI6IlHydZR8/2EPz89yfz+Pvi6l4Yv1Zo+coV6qQlFOCxJwSo+uziysPqsd9HYwBnxzGuK+PGQ3qd4enYOzXwTrLVj/av9LjWdsLd3ewdRHIjtSNlIRVcHR0hL+/v8Hy/Px8rF+/Hr/99hvGjh0LANi4cSO6deuG06dPY+jQodYuKhERERE1UCqVQFBUBvq09kEzz4oprKZ/f8rk/bWD7epEphWgqYdLlVNl3fV5ELKL5cgulmPwZ4c1c13/OncIpBJgUPsmGPjpYShvZSlb8kAPPDG0LaRSCdIKyrA6qPpW9je3X8Su8NRK1z+0pvrrj80sxhPrzyJh2WSd5V8ciNL5/cMp3TG5d0C1x7OWdyZ2wf8GtUbbpu62LgrZAZOC7v79++PIkSNo3Lgx+vXrB4mk8oQFnp6e6NGjB9577z20bt36jgsYHR2NFi1awNXVFcOGDcPSpUvRpk0bhIaGQqFQYPz48Zptu3btijZt2iAkJIRBNxERERFZzdbzSVi48zK6NPfCwQWjEJ6Up7P+i+m9cTYhBztCk2t03MvJ+ejVylvz+8Pfn8L5G7kAgIOvjYJCqULPlt46+1y7WaDTqq0OuAHg8fVnAACOUokm4AaA3q28NUnJ5t4VaFLQXVXAfadKFbpj2vu09rHYuWpDIpGgXTMPWxeD7IRJQfeDDz4IF5eKJ2lTp06tcluZTIYjR47g8ccfR3BwcJXbVmfIkCHYtGkTunTpgps3b2LJkiUYOXIkIiIikJaWBmdnZ/j4+Ojs07x5c6SlpVVbRpnsdneXgoLKx7IQEREREVVn4c7LAICo9EJ8fywWnx+43TV8Yg9/zBjUGjMGtcazIwNx77fHqzzW/b0DsPfSTQDAlFUn8O+CUejg64kO7+3T2U59nJ0vDkf/No01yw9drT7HUblWwD2tf0v01Qpqm3g44895w7Fw5yVcTy+q2F6pgoO0IsfS5tM38H+7IgyO+fuzQ9GqsRtWHY3B1vNJBuvbNHFHU09nhCXmYUTHpjgZkw0A6KP1UEEIgS1nEpFTLNfZ19OlznfQJaqUSa/exYsXG/25MrGxsejRo0e121Xnvvvu0/zcu3dvDBkyBG3btsW2bdvg5uZW6+MuXboUS5YsuePyERERERFFpRXq/K4dcAPA2icGaH7u4u+F+KWT0H6hbgCt1ruVN1b8r58m6AaAreeSkF+qMLo9AExbcwpxn03StFRvPWcY8FZmxsBW+OJhw/m0B7RtjNfv6YIXfg0FAHR8f3+lx+jb2gdP39UewzpUzEPu4+Gksz7y44lwvBWwK1UCuSVyNPVwxv/tjsDvZ5N0etEu3HlZJwN6Y3cnDGjbGB39PE2+JqK6xiKJ1Dp06GCRLOI+Pj7o3LkzYmJi4O/vD7lcjry8PJ1t0tPTjY4B17Zw4ULk5+dr/iUlmf7BRERERESkrbqWa30SiQR3dWxmsHz5zD7Y8NQgSKUSOGjNP73+RHy13dLVQbkQQtO1/MuHe6N7QKMq95szon2l6xxMnAN71/wReKBPC83vjwy4PcR054vD4erkAEcHKRykEjg7StG8kSscHaSY0L3inr1cVTE/eLGsXCfgnje6A8I+mIB1Tw6qcngrUV1nUkt3deO4tV24cAEA4O3tXc2WNVdUVITY2Fg88cQTGDBgAJycnHDkyBFMnz4dABAVFYXExEQMGzasyuO4uLhoussTEREREdXW2ficKte3r2Tc76Y5gyArV6HH4oMAgABvVzzUr5Vmfeii8Xh60zlcMDKH9spZ/fDy72E6y/p9fAgjOzXTmVZrXLfmGNGxGX48HoecYjn+vlgxBvue7s01XdC7VRGUOzpUf///0YOGvVs7+nmifxsflMiV6N2y8phAffxypUBSTglGfhGks/618Z2qPT+RPTAp6NYex11WVoY1a9age/fumuD29OnTuHLlCl588UWzFu7NN9/ElClT0LZtW6SmpmLx4sVwcHDArFmz4O3tjblz5+L1119HkyZN0KhRI7z88ssYNmwYk6gRERERkcXN+zUU+yNu5xL6/dmhmPXT7Tm3J3RvjqXTehnd19FBCkeH251Ox3b101nv4+6Mn2YPxIBPDhvsO6VPC4zt6ofg65l4ccsFzXL9eax93Jwg9XDGhw/0wNXUAvx9MRXODlKsfrQ/XvrtAp4bFVjl9Q3v0BQd/TwRk1Gks/y+nv54e2JXeLo4VppBfeeLI6o8NgA4SiuuPzKt0CDgvvzhBLg4OlR7DCJ7UOMx3c888wxeeeUVfPzxxwbbmLubdnJyMmbNmoXs7Gz4+vrirrvuwunTp+Hr6wsAWL58OaRSKaZPnw6ZTIZ7770Xa9asMWsZiIiIiIiM0Q64AaCFj6vO798/PqDaLto7XxyO9Sfi8fJYw1bdpp4uiPn0PjhIJYjJKMK0Nafw/N0VgbKHiyMm9QrAlw/3xls7Lhns29LHTTPGGwC6t2iEv18aAf9GrnB2lOLH2QOrvT4XRwccWjAKs346jdNxOQhZOBYB3rXPq6Qvs8j4PN7fzOgDL1cno+uI7JFECCGq3+w2b29vnD9/Hp066X4wREdHY+DAgcjPzzdrAa2hoKAA3t7eyM/PR6NGVY97ISIiIqKGS33rrJ8I7blRgXhmZHsM/vQIAODxoW3wyVTjrdy1pVIJnUBaXZ7fzibi4JV0nbm+P5zSHU9VMV67LriaWoBJK/7TWRax5F5mKie7YWocWeNXtJubG06ePGkQdJ88eRKurq6V7EVEREREZN/e3H4RF5PyEK3X3Tr8g3vg7eaEEvntuaXv6xlg9vPrB9xARVK2x4a0xWND2kKlEnh1azhOxWRh+oBWRo5Qt3RvoRuk/P7sUAbcVC/V+FX92muvYd68ebhw4QIGDx4MADhz5gw2bNiA//u//zN7AYmIiIiI6gJjGcRnDmwNH3dnAICb0+0xyNrzYFuLVCrByln9IISwy2zfQwOb2LoIRBZR46D73XffRWBgIL777jv8+uuvAIBu3bph48aNmDFjhtkLSERERERUF30xvTce1mpR1m6JtuW80vYUcP/wxAA8vzkU70/qZlflJqqJGo/prkpERAR69uxprsNZDcd0ExEREZExsZlFGPd1MF4Z2xErjsZolg/v0BS/PWs4Y05UWiEKyhQY1I6ttkT1nalxpLTSNSYqLCzEjz/+iMGDB6NPnz53ejgiIiIiojpj/q0pubQDbgBYMauf0e27+Hsx4CYiHbUOuo8fP47Zs2cjICAAX331FcaOHYvTp09XvyMRERERUR1XplCi3bv/IDKt0GBd5McT0czT+PzURET6ajSmOy0tDZs2bcL69etRUFCAGTNmQCaTYdeuXejevbulykhEREREZFUzfzTemPTbs0PgqpUwjYioOia3dE+ZMgVdunTBpUuX8O233yI1NRUrV660ZNmIiIiIiKzuenohLiblGSz/eGpPDO/QzPoFIiK7ZnJL9/79+/HKK69g3rx5BnN0ExERERHVB0IITFh+XGfZzIGtsWx6L2bXJqJaMbml+8SJEygsLMSAAQMwZMgQrFq1CllZWZYsGxERERGRVc3/7YLO7wnLJuPzh3sz4CaiWjM56B46dCh++ukn3Lx5E88//zz++OMPtGjRAiqVCocOHUJhoWGSCSIiIiIie3Iju0Tz8+HX77ZhSYiovqhx9nIPDw88/fTTOHHiBC5fvow33ngDy5Ytg5+fHx544AFLlJGIiIiIyCJUKoEPdkfgrs+PQqkS6OTnCQB4ang7dLz1MxHRnbijebq7dOmCL774AsnJyfj999/NVSYiIiIiIqsIT87DLyE3kJxbig7v7cOu8FQAQLum7jYuGRHVFzWaMqwyDg4OmDp1KqZOnWqOwxERERFRA3Q+IQcPrw3R/L5u9kCM79681sc7ci0dCqXAxJ7+RterVALT1pwyus7b3anW5yUi0maWoJuIiIiI6E5EpOTrBNwA8Mwv5/HF9N6Y2MsfjVxND4IPRKThhV9DNb8/f3cgErKKMW90R/Rt7aNZvu18UqXHGNul9sE+EZE2iRBC2LoQtlZQUABvb2/k5+ejUaNGti4OERERUYORX6rAH2cTsXR/ZJXbvTmhM14ae3va2gMRaejRohFaN9HtBh4UlYE5G89VepwlD/TAQ/1boveH/1a6zdsTu+DF0R1NvAIiaqhMjSMZdINBNxEREZGttHv3H4NlY7r44kpqATIKZTrL/5w3HMm5JQhLzMOmUwkAgNjPJsFBWjGdV3xWMcZ8dazGZfjxiQGY0MN4F3QiosqYGkeyezkRERERWdXvZxPh5+WCbgGGN6kHXxuFLv5e2HkhGa9vu6izbvr3huOvVx2NwavjO6FIVq4TcE/o3hyPDW2Lj/deRbGsHA/1a4k1x2IN9t/2/DAMbt/kzi+KiKgSDLqJiIiIyGo+2nMVG07GG123YHxndPH3AgBM698KvVv5oKmHM/p9fKjS4y0/fB1xWUX4+2KqZpmHswN+eGIAJBIJ7taaazs+qxj7I9I0v780piMDbiKyOHYvB7uXExEREVmLse7kLX3ccPLdsZXuk1Ukw8BPDpt0fC9XR1xaPAESicTo+nKlCh3f3w8AiPx4IlydHEw6LhGRPnYvJyIiIqI65YPdEUaXf/lw7yr3a+bpYrDsrXu74MuDUQbLf507pNKAGwAcHaRIWDa5mpISEZmP1NYFICIiIqL6LaOwDC9uCcUvITc0y/rcmrrrs4d6YXjHZtUeY8kDPQAAXz/SB8ffGoMXR3fAg31b6Gzz27NDNMclIqor6nT38qVLl2Lnzp2IjIyEm5sbhg8fjs8//xxdunTRbDN69GgEBwfr7Pf8889j7dq1Jp+H3cuJiIiILCMlrxQjlh01WL5sWi/8b3CbGh0rr0QOH3dnnWVFsnJ4urDzJhFZX73oXh4cHIz58+dj0KBBKC8vx3vvvYcJEybg6tWr8PDw0Gz37LPP4qOPPtL87u7ubuxwRERERGQlSpVAh/f2GSyf0L052jXzwPQBrWp8TP2AGwADbiKq8+r0p9SBAwd0ft+0aRP8/PwQGhqKUaNGaZa7u7vD359zKxIRERFVRlauhKNUqpnT2tLe2nHR6PLVj/WHkwNHOBJRw2FXn3j5+fkAgCZNdKd22LJlC5o1a4aePXti4cKFKCkpqfI4MpkMBQUFOv+IiIiI6itZuRL9PjqEDu/tQ1BkhsXPFxSVgZ0XUnSWfTK1JxKWTWbATUQNTp1u6damUqnw2muvYcSIEejZs6dm+aOPPoq2bduiRYsWuHTpEt555x1ERUVh586dlR5r6dKlWLJkiTWKTURERGRzsRnFKJErAQBzNp2zSPbue5cfR1R6ocHys++Pg5+Xq9nPR0RkL+p0IjVt8+bNw/79+3HixAm0alX5GKCjR49i3LhxiImJQYcOHYxuI5PJIJPJNL8XFBSgdevWTKRGRERE9dKgTw8js/D2vc/el+9Cz5betT6eUiUQmVaA9/6KwMWkvEq3e3lsR7wxoUul64mI7Fm9SKSm9tJLL2Hv3r04fvx4lQE3AAwZMgQAqgy6XVxc4OJiON8jERERUX1z6Gq6TsANAPevPKH52dfLBUFvjgZQfVIylUpg8+kbWPz3lWrP2y2gEQNuIiLU8aBbCIGXX34Zf/31F44dO4b27dtXu094eDgAICAgwMKlIyIiIqrbhBD45tB1ze8B3q64mV+ms01moQw9Fx8EAFxZci88qgi85/58DkFRmZWu79zcExueGoRWjTmTDBGRWp0OuufPn4/ffvsNu3fvhpeXF9LS0gAA3t7ecHNzQ2xsLH777TdMmjQJTZs2xaVLl7BgwQKMGjUKvXv3tnHpiYiIiGxr+aHruHazImHs3pfvQqCvB7p/cLDS7edsOodfnh6Mrv9XMYOMl4sjwhdPwHO/nMcRIwnYnB2l+P6x/pj783kAwHf/68eAm4hIT50e0y2RGJ/SYuPGjXjqqaeQlJSExx9/HBERESguLkbr1q3x0EMPYdGiRTUam21qX3wiIiIie5CYXYLVQTHYej4JACCVAHFLK5KnrTkWgy8ORFW6b6+W3rickl/l8R/o0wKzh7XFwHYVM8oIISq9byMiqq9MjSPrdNBtLQy6iYiIqD44FZOFR9edMVj++j2d8cq4TgAqxmVfSS1AgI8rTkRn4d4e/nj7z0vYczHVpHOM7eqHn2YPtNp830REdVW9SqRGRERERJUTQqD9wn2Vrn98aFvNz1KpBL1aVWQun9qvJQBgfDc/naC7T2sfPDakDd7ecUnnON/O7KvZh4iITMOgm4iIiMgO/X0xFa/8HoYOvh6IzSw2WL/1uaFo4eMGv0YucHF0qPJYg251EwcAiQT4a95wSKUSZBfJ8fmBSABAV38vBtxERLXAoJuIiIjIjpTKlej2wQHN7/oB97bnh2Fw+yb6u1XJv5Gr5ucxXfwgvdV1fN7oDpBKgCPXMvDL3MF3UGoiooaLQTcRERFRHbPuvzh88s81AMAvTw/GqM6+yCgow/9+PI24LMNWbbX/3h6D1k1qnj1cKpXg+VGB+OF4HL77X1+ddc/f3QHP392hxsckIqIKTKQGJlIjIiLLU6kEwpJysSssFff18sfwDs2q3adMoYRKCCTnliIhqxhOjlKM6eJnhdKSpRWWKeDl6mR03ZXUfExecaJGxxvb1Q8bnhpkjqIREZGJmEiNiIjIxopl5ZBIgBe3XMCxqEzN8s2nbyDus0lIyi3B3V8eAwBc/+Q+ODtKNduUyMuNzqd88LVRaNvUHa5OVY/RpborKCoDczaeM7runu7NcSYu26TjHH3jbrRp4o7cEgUauxsP4ImIyPYYdBMREZlZfokCfT76t8ptAt/TzTR9/8r/8NuzQ9HM0wUAkJBVYnS/e789rvm5macLnhvVHnPvCuT0TXbkg90Rla47dDVd8/NjQ9pgy5nESrcN9PUEAPh6uZivcEREZHbsXg52LyciIvNRqgQ6vFf51E3ViftsEkITc/HI2pAa7ffPK3ehRwvvWp+XzKtUrsS/V9OQXlCGf6+kIzm3FMWychTKyk0+RvzSSei5+CCK5UrNsksfTsA3/17HsA5NcW8Pf0sUnYiITGRqHMmgGwy6iYjqq+wiGb49HI05I9ppWgXNKSqtUNPyPL6bH0Z28sWXB6NQpBdYtWvqjqNvjIZUKsH19EJMWH7c2OEAAK+O64TvjkRrfh/YtjF2zBsOIQR+PpWAczdy0djdCREpBQhPytPZt18bH4Ql5mFUZ18cv56Jaf1b4suH+7AV3IoUShUOXknDS7+FmbT94dfvho+7Exq5OuF0XDZUQmDRrgismNUP/ds0xs38UgxbehRA7ZOkERGRZTDorgEG3URE9YtKJfDnhWS8teOSZtnFDybA24zjXredT8LbWsc3JmHZZJOONfCTQ8gqkhtdd+SNu9GhkgcGSTkleHP7RZyJz6n02B18PbBpzmAGa1ayaNdl/Hq68i7h2mI/m8QHIkREdoxBdw0w6CYiqh+EEFj3Xzw+3XfN6Pp/F4xC68bu+C86EyM7+cLN+XYysv2Xb+LVreFo6eOGT6b2xIiOlWcXj0jJx/0rq84uPbVvC3z7v34ml/1/P4bgdNzt4Hl8t+b4afYASCTVB2Wrg2Lw5cGoarc7/tYYXE7Jx2f7rqFtU3ck5pSgRK5ETrEcK2f1w5Q+LUwub313I7sYO0KTEZtZhKXTeuPX0zcQlVaIDx/ogSYezgbbCyHw0JpTBr0P9r58FwJ9PeAglSA0IRftfT3Q2N0ZLo5Sk+qWiIjqLgbdNcCgm4jI/pUrVZj2/SlcSs6vcrsuzb0QlV6o+X3d7IH4/EAkojOKqtzv+Ftj0KapOw5EpOGFX0M1y796pA/u6dYcZ+KzkV0sx/AOTXEjuwSjOvvWqPwKpQqL/orAoWvpeGdiF8wc1MbkfVUqgXd3XsK288nYPHcw7urYDEIYJmsz1apH++H+3g0vAM8tluNETBZGdfZFnyVVJ8J7aUxHDAlsAm83Jzyw6qTB+q3PDcWQwKaWKioREdUBDLprgEE3EZF9EEJg8d9XAACLJneHk0NFS+Gp2Gw8tu6MzrbT+7fCpw/1BAD0+vAgFErzf91N798KX8/oY/bjmktusRxBURlY9188rt4sqNG+y6b1wv19WsDTpf5NdPLr6Rs4E5+DcqUKnZp74ZWxHRGVXljjubErY+6hDEREVDcx6K4BBt1ERHVb8PVMfLL3arWt0QBwX09/fP/4AIPlo74IQmJOxTRcQwOb6HTlVotfOglKlUDH9/dXe553JnbFvNEdTCh93fD9sVgsP3wd47v54d4e/mjfzANtm3jA290JN/NLcfhaBvaEp+Jsgu7fZcWsfnigHnQ7j8koxItbLuB6evWvodrq6u+FP+cNh0c9fFBBRESGGHTXAINuIiLbUakE4rKK4OvlCm83J1xIzEVcZjE8XRzh7CjBb2cScfhahknH+vjBHnh8aFujY2WFEJCVq+AolcDRQQoAeGzdaZyMyQYAnHx3LFr6uBk9bn6pAm9su4iknBLEZRXh6Buj621isifWn8F/0VlG193TvTl+mj3QyiUyXblShV3hqcgvVeDjvVdrdYy+rX1wT/fmeKBPC00dy8qVcHF0wInoLGw8GY9PH+oFlRAYvqwiq/iax/pjUq8As10HERHZBwbdNcCgm4jI+grKFJixNgSRaYXVb3xLu6bu2Dx3CFYdjcHW80kAgAndmyPQ1xP/G9Qa7Zp51Lgc+aUKODtIdZKqNWSlciViM4uwNjgWey/dNGmfoYFNsGnOYGwPTcb/7YqAn5cLDrw2ymjCMUtJzSvVBMGmCH5rNJp4OGPUF0HILVHg56cHY1C7xnB3Zis1ERGZhkF3DTDoJiKynDKFEg+vPYVimRK9Wnrj0NV0DA1sgqCoTJOP8cnUnpg1uA2nV7Ky0Bs5mP59SK33v7h4AlLzSnHfd/8BAHq19Ia/tytWPdoPLo53/pBDpRKQSiXILZaj38eHqt1+2bRe+N9g0xPUERERVYVBdw0w6CYiqr30gjKUqwQkABwdJNhyOhE7w5KRlFNq8jFmDW6DwGYeOBGThWEdmmJUJ1+4OEnhJJXCxUmK5o1cLXcBVKW4zCL8cS4JHX09sfpYDG5kl5jluBFL7q1xkrZypQqPrz+D3q18EJdZjMPX0g22GdmpGRbc0xl+Xi5o1biie3heiRwlciVaVDJ8gIiIqDYYdNcAg24iqoqsXImdF1Lw8d6rWP1ofwzr0BSuTg7IKZbj9W3hOBaVibl3tceQ9k0wvltzSCtpjS1TKGs9N69CqUJYYh76t/HB5ZR8JOWWwsPZAaE3cnE0MgOTegWgpY8b3th+EUBFMrFnRrbHgLZNDI6lUgmcjs9GUk4JJnT3R2O9LsBCCAgB5JUqcCk5D09tPKez/tEhbdDR1xOZRTIERWbUqHu4WmN3Jwxp3xSfT+/NLM92qFhWjuwiOZp6OuNmfhnu++44FEqB4R2a4uenB2PJniv49XRilccI8HaFh4sj3pvUFWO7Nq9y219CErDnYirOJeRWuV2/Nj7468URNb4eIiKi2mDQXQMMuolsR6USyCtV4GJSHvq29kFjD2eUyMvNOq5SnRhqYg9/LJzUFW2b3h73ezO/FJtDbqBdUw+E3siFv7crXhrbEZmFMnx+IBKHrqajRK6s8Tkn9w7A5F4B+OfyTcgUKqTkleLarSmb3pzQGfNGd6y0q3RhmQKOUikup+TjZn4pXv0jvFbXrebh7IBiE66hfxsfeLs51ajbtzFero7o36YxknJK8NjQthjRsSkCvN3g6eIICVDpQwmqX348HovP9kUCAAa2bYztLwxDcm4pHl9/xqC1PH7pJFxJLUB2sRz5pQoMC2yKt3dcRGMPZ4zu4odXfg8z6ZzH3hxdq3H9REREtcGguwYYdBNZl7xcBaVKILdEXmXio+uf3AdnR2mVxxJCGLQcbz2XiHf+vFzpPo8NaYMtZ6puhbOWsV398PLYjth5IQWbT9+o0b6eLo5o5OqI1PwyC5VOV8+WjRCRUvHgwM3JAaUKJVydpHh8SFvMuas9nBwkEALsCk5VuplfimFLTU94ZsyOF4bB2VGK3q18NMf0cXNmMjwiIrKqBhd0r169Gl9++SXS0tLQp08frFy5EoMHDzZpXwbdRLeV3moRDUvMhauzg6Yb6WtbwzXbzB7WFh9O6WG0xfJ8Qg5aNnbD72eTsOJINAJ9PdC/TWMkZBVjQo/m+PdKOs7fqLqLqLbfnhmCwe2bICq9EO2aemDFkWj4erkgPClPJ7Py5N4BaNfUHauDYmt/8VV4ZWxHzBzcBiGx2YhIycfWc0mY0KM5pvZtidFdfCGRSKoN9u+Ej7sT/m9ydwgA3m5OGN/NT+dhQ0JWMQDA3cUBfl6uEEKg/cJ9lR7vzQmd8dSI9vj+WAyOXKvoIt68kQsGtm2CUoUSXf298EDfFmji7oxGbk5wdWIwQ+az5cwNvP9XRI32+fulEejg68k5sImIqM5oUEH31q1bMXv2bKxduxZDhgzBt99+i+3btyMqKgp+fn7V7s+gm+6E+i1U2TjdMoUSmYUyrDkWg1aN3XH8eiYyC2WYMag1vN2ckFMsR7lS4N6ezdHEwxlXUwsQlVaIxJwSjOrsixbebvB2c0KAjyskqBhnm5hTgsTsEhyPzoQQFUHYzfxSFJSWo6xciYwCGVLyKpJYjezUDHd1bAYnBymyi2WIzyqGQinQpbkXfL1ckJBdjMvJ+UjOLYWvlwuu3iyAUmX6x8KnD/XE+hPxiMssvqO/4+ND2+DX04no38YHFxLz7uhY+nq0aIRvZvTFm9sv4nJKvs6650cFYsE9nZFbIoeDRIKQuGxEpxfhgb4t0Lm5V63OVyQrh6NUAoVSBQ9nR4OHEyl5pdhwIh7NPF3wX3QmTsVma9Y5SiV4Z2JXFJYp0LOlN7oFNEKrxm61GgeunpdaoVTBy5XjpqnuSckrxQit3i6b5w5GUw8XXE8vxN2dfRGRmo8n1p/Fx1N74omhbW1YUiIiIkMNKugeMmQIBg0ahFWrVgEAVCoVWrdujZdffhnvvvtutfsz6K45pUpAKqk80DSmXKlCWbkKpXIlyhQVrane7k7wcnFEmUIFWbkS6lejp6sjnBykmnMVycohkQAOEgmkEgmkUkAqkcBBIoGkknIIIZBbokBhmQK5JQrklsiRVSiDXKmCSiXg4uiAsnIlSuRKFJQq0MzTBe7ODsgrVSCvRAEAcHGUokyhxKB2TdDU0xkSiQR5JXJcSMxDdpEMV1ILEJ6UB6BiHKuLowMcpIAQQG6JHEIAKiFQgxi2TvF2c0J+qULze9um7jXOXNy2qTu8XB3hKJUiPCkP3m5O+N/g1ujg6wlfTxcMCWxy6+92uw4jUvIxbc0pyJWqKo/d0scNk3sH4LdbXcUDvF0RnVGEPq198P1j/SvNVJxbLEdsZhEGtjNMMmYLV1MLcCO7GBN6+HNKLGpwcovlcHCQoBEfDBERkZ1pMEG3XC6Hu7s7duzYgalTp2qWP/nkk8jLy8Pu3burPYY9BN2/n03EzfwyFJWVQ1auhIO0IvgskpUDqAjsHKUV4ylLFBVBpFIlUK4SWv+rUK6s+F13ner278rby50cJHB1coBS6yWiUgmUKpRQKCuWebo4wtvNCUIIKG8FlypVxc9KldD8rFKh0gDK2VEKebnuOokEaOzuDHm5SnONVZFKAAepBJJbgbhUAqgEUKqoeQIsS2rm6YximRKBvh5o5umCgjIFZAoVSuTlSM0r0/yNApt5wMlBCrlShYTsYjhIJCjXitwdpRV149fIBSM6NIO7iwOuphbA1ckBd3f2RYC3K0Jv5CIxpwRuTg4IS8qDm5MDWjdxQ+sm7vD1dEFUWiEyCmUI8HaFl6sjfNyd0cHXAx39vODp4ohmns5wdDAcTy2EwPHoLHx3+DoiUgsM6u7jB3tgcu8WaOzuVKvWWX3R6YUIS8zDXZ2aQf0XaObpDKVKmDXZGhERERFRTZgaR9r9HWtWVhaUSiWaN9edbqR58+aIjIw0uo9MJoNMJtP8XlBQYNEymsP6E/GIySiy6jnlSlSb8bhIVm5SUKzPzckBqltdX/WDNqCipTinWG7y8VQCUCkFAMNnSG5ODmjs7oRGbk7wdnPSJOZycpDC+VZgW1CqgKerIxylEjRyc0Ij14oHCXmlCkSnF6GsXAmZQgUhBNxdHNGuqTvaNPFAoK8HvFwd4e7siDZN3FEsL0dybinaNXVHkazi5/5tfNDB19OkAFSlEgYt9+rpm7KKKlrp/Ru5Gg2G9Y3rVvUUPLUlkUhwd2df3N3ZV1M+9XJL6NTcC51q2c2biIiIiMjW7D7oro2lS5diyZIlti5GjUzqFYCcYhk8XZzg6iTVtFZ7ujpCiIqu1ipREbC5OjrA280Jjg4SOEqlcJBK4CiVwMHh1v9SveVSya1tJXCQSuEolUAqlaBcqUKJ/HarekVAK4G7swPcnBxQrhLIKZajWF4xflV6q+u3g1QCByk0rc4VLdCAh7Mj3JwddOYpLpUrkVUkQyNXJ7i7OEAdtt3ML0OpQgknh4ry+Hq5AKgIxita1Cta0VWiovu56tYypaoiQFW3Crf0cas2+7W59W/TuNb7GktMJrnVhd6vjmaEtlSwTURERERUH9h90N2sWTM4ODggPT1dZ3l6ejr8/f2N7rNw4UK8/vrrmt8LCgrQunVri5bzTr1+T2dbF8EodTBcW27ODmjdxN1gubFlRERERERE9sa6TYAW4OzsjAEDBuDIkSOaZSqVCkeOHMGwYcOM7uPi4oJGjRrp/CMiIiIiIiIyN7tv6QaA119/HU8++SQGDhyIwYMH49tvv0VxcTHmzJlj66IRERERERFRA1Yvgu6ZM2ciMzMTH3zwAdLS0tC3b18cOHDAILkaERERERERkTXZ/ZRh5mAPU4YRERERERFR3dFgpgwzB/VzB3uYOoyIiIiIiIhsTx0/VteOzaAbQGFhIQDU+QzmREREREREVLcUFhbC29u70vXsXo6KbOepqanw8vKyypzD6inKkpKS2J29jmNd2RfWl/1gXdkP1pX9YF3ZF9aX/WBd2Q9r15UQAoWFhWjRogWk0sonBmNLNwCpVIpWrVpZ/bycrsx+sK7sC+vLfrCu7Afryn6wruwL68t+sK7shzXrqqoWbjW7n6ebiIiIiIiIqK5i0E1ERERERERkIQy6bcDFxQWLFy+Gi4uLrYtC1WBd2RfWl/1gXdkP1pX9YF3ZF9aX/WBd2Y+6WldMpEZERERERERkIWzpJiIiIiIiIrIQBt1EREREREREFsKgm4iIiIiIiMhCGHQTERERERERWQiD7lo6fvw4pkyZghYtWkAikWDXrl0669PT0/HUU0+hRYsWcHd3x8SJExEdHa2zTWxsLB566CH4+vqiUaNGmDFjBtLT0zXrExISMHfuXLRv3x5ubm7o0KEDFi9eDLlcbo1LrDesUVdq//zzD4YMGQI3Nzc0btwYU6dOteCV1T9Lly7FoEGD4OXlBT8/P0ydOhVRUVE625SVlWH+/Plo2rQpPD09MX36dIO6SExMxOTJk+Hu7g4/Pz+89dZbKC8v19nm2LFj6N+/P1xcXNCxY0ds2rTJ0pdXr1izrtROnjwJR0dH9O3b11KXVW9Zs762bNmCPn36wN3dHQEBAXj66aeRnZ1t8WusL8xVV6+88goGDBgAFxcXo++ZY8eO4cEHH0RAQAA8PDzQt29fbNmyxZKXVu9Yq64AQAiBr776Cp07d4aLiwtatmyJTz/91FKXVu+Yo64uXryIWbNmoXXr1nBzc0O3bt3w3XffGZyL9xd3zpr1pWbpewwG3bVUXFyMPn36YPXq1QbrhBCYOnUq4uLisHv3boSFhaFt27YYP348iouLNftPmDABEokER48excmTJyGXyzFlyhSoVCoAQGRkJFQqFX744QdcuXIFy5cvx9q1a/Hee+9Z9VrtnTXqCgD+/PNPPPHEE5gzZw4uXryIkydP4tFHH7XaddYHwcHBmD9/Pk6fPo1Dhw5BoVBgwoQJmroAgAULFmDPnj3Yvn07goODkZqaimnTpmnWK5VKTJ48GXK5HKdOncLPP/+MTZs24YMPPtBsEx8fj8mTJ2PMmDEIDw/Ha6+9hmeeeQYHDx606vXaM2vVlVpeXh5mz56NcePGWeX66htr1dfJkycxe/ZszJ07F1euXMH27dtx9uxZPPvss1a9XntmjrpSe/rppzFz5kyj5zl16hR69+6NP//8E5cuXcKcOXMwe/Zs7N2712LXVt9Yq64A4NVXX8W6devw1VdfITIyEn///TcGDx5skeuqj8xRV6GhofDz88Ovv/6KK1eu4P3338fChQuxatUqzTa8vzAPa9WXmlXuMQTdMQDir7/+0vweFRUlAIiIiAjNMqVSKXx9fcVPP/0khBDi4MGDQiqVivz8fM02eXl5QiKRiEOHDlV6ri+++EK0b9/e/BfRQFiqrhQKhWjZsqVYt26ddS6kgcjIyBAARHBwsBCi4u/u5OQktm/frtnm2rVrAoAICQkRQgixb98+IZVKRVpammab77//XjRq1EjIZDIhhBBvv/226NGjh865Zs6cKe69915LX1K9Zam6Ups5c6ZYtGiRWLx4sejTp4/lL6ies1R9ffnllyIwMFDnXCtWrBAtW7a09CXVW7WpK201ec9MmjRJzJkzxyzlbogsVVdXr14Vjo6OIjIy0mJlb2jutK7UXnzxRTFmzBjN77y/sAxL1ZeaNe4x2NJtATKZDADg6uqqWSaVSuHi4oITJ05otpFIJDoTt7u6ukIqlWq2MSY/Px9NmjSxUMkbHnPV1YULF5CSkgKpVIp+/fohICAA9913HyIiIqx4NfVPfn4+AGhe86GhoVAoFBg/frxmm65du6JNmzYICQkBAISEhKBXr15o3ry5Zpt7770XBQUFuHLlimYb7WOot1Efg2rOUnUFABs3bkRcXBwWL15sjUtpECxVX8OGDUNSUhL27dsHIQTS09OxY8cOTJo0yVqXVu/Upq7u5Fy8x6g9S9XVnj17EBgYiL1796J9+/Zo164dnnnmGeTk5Jj3AhoQc9WV/nuG9xeWYan6Aqx3j8Gg2wLUlb5w4ULk5uZCLpfj888/R3JyMm7evAkAGDp0KDw8PPDOO++gpKQExcXFePPNN6FUKjXb6IuJicHKlSvx/PPPW/Ny6jVz1VVcXBwA4MMPP8SiRYuwd+9eNG7cGKNHj+aXYi2pVCq89tprGDFiBHr27AkASEtLg7OzM3x8fHS2bd68OdLS0jTbaAcF6vXqdVVtU1BQgNLSUktcTr1mybqKjo7Gu+++i19//RWOjo4WvpKGwZL1NWLECGzZsgUzZ86Es7Mz/P394e3tbXR4D1WvtnVVG9u2bcO5c+cwZ86cOylyg2XJuoqLi8ONGzewfft2/PLLL9i0aRNCQ0Px8MMPm/MSGgxz1dWpU6ewdetWPPfcc5plvL8wP0vWlzXvMRh0W4CTkxN27tyJ69evo0mTJnB3d0dQUBDuu+8+SKUVf3JfX19s374de/bsgaenJ7y9vZGXl4f+/ftrttGWkpKCiRMn4pFHHuHYODMyV12px3a///77mD59OgYMGICNGzdCIpFg+/btNrs+ezZ//nxERETgjz/+sHVRqBqWqiulUolHH30US5YsQefOnc167IbMku+tq1ev4tVXX8UHH3yA0NBQHDhwAAkJCXjhhRfMfq6GwFqfg0FBQZgzZw5++ukn9OjRw6Lnqq8sWVcqlQoymQy//PILRo4cidGjR2P9+vUICgoySC5F1TNHXUVERODBBx/E4sWLMWHCBDOWjvRZqr6sfY/BZgMLGTBgAMLDw5Gfnw+5XA5fX18MGTIEAwcO1GwzYcIExMbGIisrC46OjvDx8YG/vz8CAwN1jpWamooxY8Zg+PDh+PHHH619KfWeOeoqICAAANC9e3fNPi4uLggMDERiYqJ1L6geeOmll7B3714cP34crVq10iz39/eHXC5HXl6eztPN9PR0+Pv7a7Y5e/aszvHU2Sy1t9HPHpueno5GjRrBzc3NEpdUb1myrgoLC3H+/HmEhYXhpZdeAlBx8ymEgKOjI/7991+MHTvWwldYv1j6vbV06VKMGDECb731FgCgd+/e8PDwwMiRI/HJJ59oPiupendSVzURHByMKVOmYPny5Zg9e7Y5it7gWLquAgIC4OjoqBMYdOvWDUDFjAJdunS584toIMxRV1evXsW4cePw3HPPYdGiRTrreH9hXpasL6vfY1hkpHgDA73kXMZcv35dSKVScfDgwUq3OXLkiJBIJDqJMpKTk0WnTp3E//73P1FeXm6uIjdYlqqr/Px84eLiopNITS6XCz8/P/HDDz+YpewNgUqlEvPnzxctWrQQ169fN1ivTpyxY8cOzbLIyEijyZ7S09M12/zwww+iUaNGoqysTAhRkeikZ8+eOseeNWsWE53UgDXqSqlUisuXL+v8mzdvnujSpYu4fPmyKCoqsvyF1hPWem9NmzZNzJgxQ+fYp06dEgBESkqKJS6t3jFHXWmrKjFQUFCQ8PDwEKtWrTJb+RsSa9XVwYMHBQARExOjWRYeHi4AiKioKPNcTD1nrrqKiIgQfn5+4q233jJ6Ht5fmIc16sva9xgMumupsLBQhIWFibCwMAFAfPPNNyIsLEzcuHFDCCHEtm3bRFBQkIiNjRW7du0Sbdu2FdOmTdM5xoYNG0RISIiIiYkRmzdvFk2aNBGvv/66Zn1ycrLo2LGjGDdunEhOThY3b97U/CPTWaOuhBDi1VdfFS1bthQHDx4UkZGRYu7cucLPz0/k5ORY7Vrt3bx584S3t7c4duyYzuu9pKREs80LL7wg2rRpI44ePSrOnz8vhg0bJoYNG6ZZX15eLnr27CkmTJggwsPDxYEDB4Svr69YuHChZpu4uDjh7u4u3nrrLXHt2jWxevVq4eDgIA4cOGDV67Vn1qorfcxeXjvWqq+NGzcKR0dHsWbNGhEbGytOnDghBg4cKAYPHmzV67Vn5qgrIYSIjo4WYWFh4vnnnxedO3fWfA+qM80fPXpUuLu7i4ULF+qcJzs726rXa8+sVVdKpVL0799fjBo1Sly4cEGcP39eDBkyRNxzzz1WvV57Zo66unz5svD19RWPP/64zjEyMjI02/D+wjysVV/6LHmPwaC7loKCggQAg39PPvmkEEKI7777TrRq1Uo4OTmJNm3aiEWLFhlMgfPOO++I5s2bCycnJ9GpUyfx9ddfC5VKpVm/ceNGo+dgB4WasUZdCVHRsv3GG28IPz8/4eXlJcaPH68zFRlVr7LX+8aNGzXblJaWihdffFE0btxYuLu7i4ceesjgQVRCQoK47777hJubm2jWrJl44403hEKh0NkmKChI9O3bVzg7O4vAwECdc1D1rFlX2hh0144162vFihWie/fuws3NTQQEBIjHHntMJCcnW+My6wVz1dXdd99t9Djx8fFCCCGefPJJo+vvvvtu612snbNWXQkhREpKipg2bZrw9PQUzZs3F0899RQfkNSAOepq8eLFRo/Rtm1bnXPx/uLOWbO+tFnyHkNy68KIiIiIiIiIyMyYvZyIiIiIiIjIQhh0ExEREREREVkIg24iIiIiIiIiC2HQTURERERERGQhDLqJiIiIiIiILIRBNxEREREREZGFMOgmIiIiIiIishAG3UREREREREQWwqCbiIiIiIiIyEIYdBMRETUATz31FCQSCSQSCZycnNC8eXPcc8892LBhA1QqlcnH2bRpE3x8fCxXUCIionqGQTcREVEDMXHiRNy8eRMJCQnYv38/xowZg1dffRX3338/ysvLbV08IiKieolBNxERUQPh4uICf39/tGzZEv3798d7772H3bt3Y//+/di0aRMA4JtvvkGvXr3g4eGB1q1b48UXX0RRUREA4NixY5gzZw7y8/M1reYffvghAEAmk+HNN99Ey5Yt4eHhgSFDhuDYsWO2uVAiIqI6hEE3ERFRAzZ27Fj06dMHO3fuBABIpVKsWLECV65cwc8//4yjR4/i7bffBgAMHz4c3377LRo1aoSbN2/i5s2bePPNNwEAL730EkJCQvDHH3/g0qVLeOSRRzBx4kRER0fb7NqIiIjqAokQQti6EERERGRZTz31FPLy8rBr1y6Ddf/73/9w6dIlXL161WDdjh078MILLyArKwtAxZju1157DXl5eZptEhMTERgYiMTERLRo0UKzfPz48Rg8eDA+++wzs18PERGRvXC0dQGIiIjItoQQkEgkAIDDhw9j6dKliIyMREFBAcrLy1FWVoaSkhK4u7sb3f/y5ctQKpXo3LmzznKZTIamTZtavPxERER1GYNuIiKiBu7atWto3749EhIScP/992PevHn49NNP0aRJE5w4cQJz586FXC6vNOguKiqCg4MDQkND4eDgoLPO09PTGpdARERUZzHoJiIiasCOHj2Ky5cvY8GCBQgNDYVKpcLXX38NqbQi7cu2bdt0tnd2doZSqdRZ1q9fPyiVSmRkZGDkyJFWKzsREZE9YNBNRETUQMhkMqSlpUGpVCI9PR0HDhzA0qVLcf/992P27NmIiIiAQqHAypUrMWXKFJw8eRJr167VOUa7du1QVFSEI0eOoE+fPnB3d0fnzp3x2GOPYfbs2fj666/Rr18/ZGZm4siRI+jduzcmT55soysmIiKyPWYvJyIiaiAOHDiAgIAAtGvXDhMnTkRQUBBWrFiB3bt3w8HBAX369ME333yDzz//HD179sSWLVuwdOlSnWMMHz4cL7zwAmbOnAlfX1988cUXAICNGzdi9uzZeOONN9ClSxdMnToV586dQ5s2bWxxqURERHUGs5cTERERERERWQhbuomIiIiIiIgshEE3ERERERERkYUw6CYiIiIiIiKyEAbdRERERERERBbCoJuIiIiIiIjIQhh0ExEREREREVkIg24iIiIiIiIiC2HQTURERERERGQhDLqJiIiIiIiILIRBNxEREREREZGFMOgmIiIiIiIishAG3UREREREREQWwqCbiIiIiIiIyEIYdBMRERERERFZCINuIiIiIiIiIgth0E1ERERERERkIQy6iYiIiIiIiCyEQTcRERERERGRhTDo1nL8+HFMmTIFLVq0gEQiwa5du2p8jIMHD2Lo0KHw8vKCr68vpk+fjoSEBLOXlYiIiIiIiOo+Bt1aiouL0adPH6xevbpW+8fHx+PBBx/E2LFjER4ejoMHDyIrKwvTpk0zc0mJiIiIiIjIHkiEEMLWhaiLJBIJ/vrrL0ydOlWzTCaT4f3338fvv/+OvLw89OzZE59//jlGjx4NANixYwdmzZoFmUwGqbTiecaePXvw4IMPQiaTwcnJyQZXQkRERERERLbClu4aeOmllxASEoI//vgDly5dwiOPPIKJEyciOjoaADBgwABIpVJs3LgRSqUS+fn52Lx5M8aPH8+Am4iIiIiIqAFiS3cl9Fu6ExMTERgYiMTERLRo0UKz3fjx4zF48GB89tlnAIDg4GDMmDED2dnZUCqVGDZsGPbt2wcfHx8bXAURERERERHZElu6TXT58mUolUp07twZnp6emn/BwcGIjY0FAKSlpeHZZ5/Fk08+iXPnziE4OBjOzs54+OGHwWcbREREREREDY+jrQtgL4qKiuDg4IDQ0FA4ODjorPP09AQArF69Gt7e3vjiiy8063799Ve0bt0aZ86cwdChQ61aZiIiIiIiIrItBt0m6tevH5RKJTIyMjBy5Eij25SUlGgSqKmpA3SVSmXxMhIREREREVHdwu7lWoqKihAeHo7w8HAAFVOAhYeHIzExEZ07d8Zjjz2G2bNnY+fOnYiPj8fZs2exdOlS/PPPPwCAyZMn49y5c/joo48QHR2NCxcuYM6cOWjbti369etnwysjIiIiIiIiW2AiNS3Hjh3DmDFjDJY/+eST2LRpExQKBT755BP88ssvSElJQbNmzTB06FAsWbIEvXr1AgD88ccf+OKLL3D9+nW4u7tj2LBh+Pzzz9G1a1drXw4RERERERHZGINuIiIiIiIiIgth93IiIiIiIiIiC2HQTURERERERGQh9SJ7eUpKCt555x3s378fJSUl6NixIzZu3IiBAweatL9KpUJqaiq8vLwgkUgsXFoiIiIiIiKyd0IIFBYWokWLFgazWGmz+6A7NzcXI0aMwJgxY7B//374+voiOjoajRs3NvkYqampaN26tQVLSURERERERPVRUlISWrVqVel6u0+k9u677+LkyZP477//an2M/Px8+Pj4ICkpCY0aNTJj6YiIiIiIiKg+KigoQOvWrZGXlwdvb+9Kt7P7oLt79+649957kZycjODgYLRs2RIvvvginn32WZOPUVBQAG9vb+Tn5zPoJiIiIiIiomqZGkfafSK1uLg4fP/99+jUqRMOHjyIefPm4ZVXXsHPP/9c6T4ymQwFBQU6/4iIiIiIiIjMze5bup2dnTFw4ECcOnVKs+yVV17BuXPnEBISYnSfDz/8EEuWLDFYzpZuIiIiIiIiMkWDaekOCAhA9+7ddZZ169YNiYmJle6zcOFC5Ofna/4lJSVZuphERERERETUANl99vIRI0YgKipKZ9n169fRtm3bSvdxcXGBi4uLpYtGRLWUUyxHekEZugWw5wkRERER2Te7b+lesGABTp8+jc8++wwxMTH47bff8OOPP2L+/Pm2LhoR1dKATw7hvu/+Q0RKvq2LQkRERER0R+w+6B40aBD++usv/P777+jZsyc+/vhjfPvtt3jsscdsXTQiqiV1pomQ2GzbFoSIiIiI6A7ZffdyALj//vtx//3327oYRERERERERDrsvqWbiIiIiIiIqK5i0E1ERERERERkIQy6iYiIiIiIiCyEQTcRERERERGRhTDoJqI6S0DYughERERERHeEQTcRERERERGRhTDoJqI6SwKJrYtARERERHRHGHQTERERERERWQiDbiIiIiIiIiILYdBNREREREREZCEMuomozmL2ciIiIiKydwy6iYiIiIiIiCyEQTcRERERERGRhTDoJiIiIiIiIrIQBt1EVGdxnm4iIiIisncMuomIiIiIiIgshEE3EdVZzF5ORERERPaOQTcRERERERGRhTDoJiIiIiIiIrIQBt1EREREREREFsKgm4iIiIiIiMhCGHQTERERERERWQiDbiIiIiIiIiILYdBNREREREREZCEMuomIiIiIiIgshEE3ERERERERkYUw6CYiIiIiIiKyEAbdRERERERERBbCoJuIiIiIiIjIQhh0ExEREREREVkIg24iIiIiIiIiC2HQTURERERERGQhDLqJiIiIiIiILIRBNxEREREREZGFMOgmIiIiIiIishAG3URUZwlh6xIQEREREd0ZBt1EREREREREFsKgm4iIiIiIiMhCGHQTUZ0lkdi6BEREREREd4ZBNxEREREREZGFMOgmIiIiIiIishAG3URUZzF7ORERERHZOwbdRERERERERBZS74LuZcuWQSKR4LXXXrN1UYiIiIiIiKiBq1dB97lz5/DDDz+gd+/eti4KERERERERUf0JuouKivDYY4/hp59+QuPGjW1dHCIiIiIiIqL6E3TPnz8fkydPxvjx421dFCIyE87TTURERET2ztHWBTCHP/74AxcuXMC5c+dM2l4mk0Emk2l+LygosFTRiIiIiIiIqAGz+5bupKQkvPrqq9iyZQtcXV1N2mfp0qXw9vbW/GvdurWFS0lEtcEpw4iIiIjI3tl90B0aGoqMjAz0798fjo6OcHR0RHBwMFasWAFHR0colUqDfRYuXIj8/HzNv6SkJBuUnIiIiIiIiOo7u+9ePm7cOFy+fFln2Zw5c9C1a1e88847cHBwMNjHxcUFLi4u1ioiERERERERNVB2H3R7eXmhZ8+eOss8PDzQtGlTg+VERERERERE1mT33cuJiIiIiIiI6iqbtnT/999/+OGHHxAbG4sdO3agZcuW2Lx5M9q3b4+77rqr1sc9duyY+QpJREREREREVEs2a+n+888/ce+998LNzQ1hYWGaKbzy8/Px2Wef2apYRERERERERGZjs6D7k08+wdq1a/HTTz/ByclJs3zEiBG4cOGCrYpFREREREREZDY2C7qjoqIwatQog+Xe3t7Iy8uzfoGIiIiIiIiIzMxmQbe/vz9iYmIMlp84cQKBgYE2KBERERERERGRedks6H722Wfx6quv4syZM5BIJEhNTcWWLVvw5ptvYt68ebYqFhEREREREZHZ2Cx7+bvvvguVSoVx48ahpKQEo0aNgouLC9588028/PLLtioWERERERERkdnYLOiWSCR4//338dZbbyEmJgZFRUXo3r07PD09bVUkIqpjhK0LQERERER0h2w6TzcAODs7o3v37rYuBhEREREREZHZ2SzoLisrw8qVKxEUFISMjAyoVCqd9Zw2jIgkti4AEREREdEdslnQPXfuXPz77794+OGHMXjwYEgkvL0mIiIiIiKi+sVmQffevXuxb98+jBgxwlZFICIiIiIiIrIom00Z1rJlS3h5ednq9EREREREREQWZ7Og++uvv8Y777yDGzdu2KoIRERERERERBZls+7lAwcORFlZGQIDA+Hu7g4nJyed9Tk5OTYqGRHVFZwyjIiIiIjsnc2C7lmzZiElJQWfffYZmjdvzkRqREREREREVO/YLOg+deoUQkJC0KdPH1sVgYiIiIiIiMiibDamu2vXrigtLbXV6YnIDrD/CxERERHZO5sF3cuWLcMbb7yBY8eOITs7GwUFBTr/iIiIiIiIiOydzbqXT5w4EQAwbtw4neVCCEgkEiiVSlsUi4iIiIiIiMhsbBZ0BwUF2erURGQnmL2ciIiIiOydzYLuu+++21anJiKCrFyJ1LwytG/mYeuiEBEREVE9ZrOg+/jx41WuHzVqlJVKQkQN0SNrQ3ApOR8/Pz0Yd3f2tXVxiIiIiKieslnQPXr0aINl2nN1c0w3EVnSpeR8AMCO0GQG3URERERkMTbLXp6bm6vzLyMjAwcOHMCgQYPw77//2qpYRNTACMGR40RERERkOTZr6fb29jZYds8998DZ2Rmvv/46QkNDbVAqIqpLOE83EREREdk7m7V0V6Z58+aIioqydTGIqA5gGzQRERER2TubtXRfunRJ53chBG7evIlly5ahb9++tikUERERERERkRnZLOju27cvJBKJwXjKoUOHYsOGDTYqFREREREREZH52Czojo+P1/ldKpXC19cXrq6uNioRERERERERkXnZLOhu27atrU5NRKTBceNEREREZElWDbpXrFhh8ravvPKKBUtCREREREREZHlWDbqXL19u0nYSiYRBNxEREREREdk9qwbd+uO4iYiIiIiIiOqzOjFPtxDCIIs5ERERERERkb2zadD9yy+/oFevXnBzc4Obmxt69+6NzZs327JIRERERERERGZjs+zl33zzDf7v//4PL730EkaMGAEAOHHiBF544QVkZWVhwYIFtioaETUk7GRDRERERBZks6B75cqV+P777zF79mzNsgceeAA9evTAhx9+yKCbiIiIiIiI7J7NupffvHkTw4cPN1g+fPhw3Lx50wYlIiIiIiIiIjIvmwXdHTt2xLZt2wyWb926FZ06dbJBiaihikjJx5SVJ3AiOsvWRSE9zK9IRERERPbO6t3LIyIi0LNnT3z00UeYMWMGjh8/rhnTffLkSRw5csRoMN7QJeeWQF6uQoC3G9ycHWxdnHpl7s/nkF4gw+PrzyBh2WRbF4esTHBQNxERERFZkNVbunv37o0hQ4YgKysLR48eRbNmzbBr1y7s2rULzZo1w9mzZ/HQQw9Zu1h13lMbz2Hs18EIS8q1dVHqndwSha2LQJWQSGxdAiIiIiKiO2P1lu7g4GBs3LgRb775JlQqFaZPn47ly5dj1KhR1i6KXZGqgw82ypkd4zoiIiIiIrIUq7d0jxw5Ehs2bMDNmzexcuVKJCQkYMyYMejcuTM+//xzpKWlWbtIdkF6q8lPxaDb7NiaSkRERERElmKzRGoeHh6YM2cOgoODERUVhUceeQSrV69GmzZt8MADD5h8nKVLl2LQoEHw8vKCn58fpk6diqioKAuW3DYkmqCbUbe5SdjWTUREREREFmKzoFtbx44d8d5772HRokXw8vLCP//8Y/K+wcHBmD9/Pk6fPo1Dhw5BoVBgwoQJKC4utmCJrY+9yy1Hypi7zuIzJiIiIiKyd1Yf063v+PHj2LBhA/78809IpVLMmDEDc+fONXn/AwcO6Py+adMm+Pn5ITQ0tF6NE5feejzClm7zk7B/eYPGtxQRERERWZJNgu7U1FRs2rQJmzZtQkxMDIYPH44VK1ZgxowZ8PDwuKNj5+fnAwCaNGlS6TYymQwymUzze0FBwR2d0xrUY7oFIwQiIiIiIiK7YfWg+7777sPhw4fRrFkzzJ49G08//TS6dOlilmOrVCq89tprGDFiBHr27FnpdkuXLsWSJUvMck5rUbfFqlQ2LUa9xIZuIiIiIiKyFKsH3U5OTtixYwfuv/9+ODg4mPXY8+fPR0REBE6cOFHldgsXLsTrr7+u+b2goACtW7c2a1nMTd0Fmu3c5seYu+7iAxEiIiIisndWD7r//vtvixz3pZdewt69e3H8+HG0atWqym1dXFzg4uJikXJYijrZF8d0mx/HdBMRERERkaXYPJHanRJC4OWXX8Zff/2FY8eOoX379rYukkVIOKbbYhhzExERERGRpdh90D1//nz89ttv2L17N7y8vJCWlgYA8Pb2hpubm41LZz7qlm7G3ObHmLvussbrne8pIiIiIrKkOjFP9534/vvvkZ+fj9GjRyMgIEDzb+vWrbYumlmpW7pVDBDMjt3LiYiIiIjIUuy+pbuhdLfmmG7LYchNRERERESWYvct3Q2FBMxebils6CYiIiIiIkth0G0npLdqqqG07FsXo24iIiIiIrIMBt12QqoZ082g29zY0l13sW6IiIiIyN4x6LYzjLnNj3Fd3WWV7OUctEFEREREFsSg205Imb3cYtiaWjPnEnJw8EqarYtBRERERGQXGHTbCWYvtxwpo+4aeWRtCJ7fHIob2cW2LgoREYCKfCdf/xuFAxF8IEhERHUPg247oZlLmjG32THkrp2UvFJbF4GICAAQFJWBlUdj8MKvobYuChERkQEG3XaCLd1U1/ClSEQAICtX4vj1TJQplDYrQ3qBzGbnJiIiqg6Dbjsh4Zhui5Gwe3mt8AEQEQHAh39fwewNZ/HGtos2KwM/xYmIqC5j0G0n1DcUzLRsfoy5a8caD4Cs8XrnswOiO/P72SQAwD+Xb9q4JERERHUTg247wezlVNdYo6Xb3KcQQiCjsMy8ByWqh0rk5RB8IkVERGQWDLrthPRWTfEmyPyYvbx2rPFaVJr5KdMb2y5i8KdHcPhqulmPS1SfJGaXoPsHB/HMz+dtXRST8WOciIjqMgbddkIzpptN3WbHm7XaUaqscQ7zvt53hqUAAFYGxZj1uET1ydbziQCAI5EZAICcYjle/SMMJ2OybFmsKkk4qpuIiOowBt124vaYbjI33qrVjjW6l1vqHOwxQlQ5B70nkZ/tu4bd4al4bN0ZG5XIBPwgJyKiOoxBt53gmG7LYfby2rFG4FpuoRe8uVvQieoTqVT3MzElt9RGJSEiIqofGHTbCfU9EFvozI8hd+1YI2611HAK7aCb7yiqK07GZGHRrssokZfbtBz6Ld1ERER0ZxxtXQAyjbo1ljG3BfD+slas0b3cUi3S1ngffXEgEmn5Zfh6Rh/2piCTqLtv+7g54817u9isHA4Ouq9Xe3j52kERiYioAWNLt51Q3/RYI9BpaHizVjvWaOlWWuj1bqnjaltzLBY7w1JwJbXA4uei+iUpt8Sm59dv6baHoJuIiKguY9BtJzim23LYClk71hjqYKnu5dZ8eCUrV1rtXETm4KA3ptseMoPzc5yIiOoyBt124nb2ckbd5lbdrVpeiRxbzyXiwdUnkVFYZpUy2QOrdC+30DmsOfWeNaZWIzInKQNYIiIis2LQbSekHNNdK/Jy3YhHCIHzCTnIL1VollV1fxkSm42+Hx3CO39exsWkPHx5IMpSRbU7KhODyQ0n4jF7w1mUKWre4mupMd3W7DHCTOlkbwxauhmDExER3REG3XZCequmrNlCZ+8+2XsV3T44gJiMQs2yfZfT8PDaEEz89rhmWVVdJ1ccidb5vaBMUcmWDU+xiRmWP9p7FcevZ2LruaQan8NyQbdW9nILv6U44wDVlK1jXP2g2x7YX4mJiKghYdBtJzTZy21cDnuy7kQ8lCqB5YdvB877Im4CAG7m3+4mXlUrjn53fnYVvu2D3Vc0P+eXKFAqr7olu0hW82mQLPX3tubDKz4nI3tj2NJ9+/cLibl8+EtERFRDDLrthPqWh9nLK3cjuxgLtoYjMk03W7RMcTtyMxZfV5WAR//Pzb+/ocIyBfp89C/6f3zI7MeuTZd0U1gje7kaXzNkb7Szl+sH2NPWnMJvZxOtXaRqsQs8ERHVZQy67UR9yF6elFOCN7ZdxPX0wuo3roW5P5/HX2EpeHDVSZ3lCq3mUmMBdk3u1Tg+11BkWkV9llYTINemm/U/l2/WqkzVseqY7lpct1Il2JrYgNm65qVaLd1KIaDf23zb+ZoPFbE0Bt1ERFSXMei2E5qbHjtuNXv2l/P480KyQVBsLjEZRQAAmV7yNO1kajW9L9P/a7PV0pD2n0SpEsgoMJ7h3Zp/uqwiWZXrdQNayxaspsGzUiUwYXkwHlpz0mbjwZNySlBci+EAVHepVAJzN53Dx3uv4kR0Fs7G51S6raN20K0SBp+bjG+JiIhqhkG3nZDUg5buqHTTWkTNTa7T0m24XlrVu0Dv712u1F2gUKowdfVJLNx5yaSy3MwvxZivjmH9iXiTtrcH2oHhkxvOYvBnR3A+wfCG3lov3U0n4zHwk8NYdTS60m10H56YP4TQ/pvU9D2bmFOC2MxiXEzOR7kN3vCxmUUY+UUQRnx+1OrnJssJT87DkcgMrD8Rj8fXn8GMH0JQXknSBO0x3eUqYdhDqA42K9vDXOL60gvKmGiRiKiBYNBtJ9T3OHW9pTWzUIboSrqPO9ooI65O93Ij66u6WTNIpKb39z8Vm43wpDz8fta07paf749EfFYxPt571aTt7YH2X+RETBYAYMsZwzGf1nrpfrin4m/71b/XK91Ge5iAueMHhVKFM1qtiDV9z2q/T/Qf8ljD8euZAIC8Embqr0+MvQwre6ajHXQrLfwaTMopwYoj0cgrkVv0PHXNn6HJGPLZESz++0r1GxMRkd1j0G0npHaQvTwqrRCDPj2Me5YfR1JOicF6S09Do3147QRc+nN166sye7l+IjW9u9SaBlT6Xd8t6UZ2Mb46GIXsarpa14SxVpmb+aUGy4y10Oo/wLAlSz4AWLovEv/78bTm95p2L9d+nyhMnQzdjGz1cMwaknJKEHzroUJdZs4aCIrMwFMbzyK/1DCorew9KZVot3SrLNK9PPRGDq6nF2Lq6pP45tB1vP9XhEn7FZYp8OSGswbjyutg43uVlh2IBAD8EnLDxiUhIiJrYNBtJ+whe/m9WnNfX07JN1jvVGU/7srJypVYGxxrkJVcn3YXyLXBsZqfI9MK8fHeqyhTKGucSE3/r60dTF5IzMWe8NQqy2RwPK0DTltz0qLdzKeuPolVQTF4a4dpXd9r650dlw2WKY0Ei+Z66cZmFt1xVnPt95G579U3nNSt05omUtMOehXlKmQUlOGjPVcRm1lklvJVe36H+vu1MPKLIDy54SxO3uqRYQ/utPvxnE3ncCwqE5/tizRy7ErPqvlJqRIGAe2dPpdJLyjD9O9DMGH5cWQXVzwMOFPFGHNtPwTHIfh6Jt628OcaAPwXnYnQG6aVi4hqRwiB3eEpiM8qtnVRiCyq/t5d1TPqbLJ1OObWYaycDg61u1Nb9188lu2PxMRv/6tyO+2jX07WDfrXn4jHhpPxxgOsKqcMq7xle9qaU9gZllJlmQyOB+2gPe+Ou5lX1Yqae6t7cFUJk2rKWL3KjYwLNZbl3Rwv3ePXMzHu62BM//7UHR3HulOG1X7fcpXA69suYsPJeDyw8oT5ClWF+tzSrWbO94QlyctVmPjtf5j/24U7PlZusZGWbhNemxUPGiuft9sUGYVlWH7ouqZXTEqeYe8YU193+aW1H/aQkleKcqUKRbJyPPz9Kaz7L67SbbOKZHhi/VlM/z6k1uerSl19l1XXM4zI3PZcuolX/wjHmK+O2booRBbFoNtOqO9x7CXpijq4zCyU4dytpFqm3lRdTs7HppPxkJUrNb+bQlrNjeAXB6KMBsk1uflRB93GulSbtn+tdjPq7R0XEfjePiRml6BMocRTG8/i51MJ5jvBHQhPykOJXC/7tRleu1/9GwUAuJJada+H6mg3xJu7W6qzo+7Hak3fs9pbK5QqXEzOAwAUy62TgNCxlg/HbOHglTRMXvEfYjIsMw2hrZ1LyEFUeiH+uXTnU+cZC5S1HyIu3h2Bh78/BYVSZTAjwZ2av+UCvjsSjSc3nAVg/LvA1OFHtX2/nozJwohlR/HkxrPYHHID52/k4pN/rqFcqUK6kRkXtGdAsJfvXVMFX8/EqVjD3h7hSXnovGg/vjlkPB/Gb2cS7eaBFZlXQZnCYu+DCzdyLXJcorqGQbedsLd5utXlHLHsKB5ZG4L/ojNNvqmasuoEPtxzFV0WHcCN7OKqs4tr0b4Zy6zBOGbt/fZeSkVusVxzE2/QvVwpsHDnJQxbWrPMzmn5ZbicnG+2ngoZhWXYdj4ZADDqyyBsPZeEY1GZFk/Ko1/8jXpdqdXSC2Q645qN7VsTh6+mY8YPIbik9QAmp1hebfKlym4SatPSHZ9VjA92RxhtpdPmrNc9u6ZBi3bRypUCTlbu7u1YzRtO/2FKUk4Jluy5guRcwzwOlvb85lBcSS3Aq3+EV7ttWGLtbux+CUnAd4crz4RvSTV9mQoh8GdoMq7dNHwoZSxY1Q66f74ViB6/nokvDkZplpcb6V5e1Sf5w9+f0jwwLVMocSAiDecSKv7219MrhkgYe405mfiwp7aPhLacqRg7fTImWye55hPrz2JIJTMuqNnL964p8ksrxsQ/+tMZg1Ztdc+rFUcMX+9n4rLx3l+XMeOHEAghqp2WkeqPmIxC9P7wXzz7y/ka7bftXBKW7rtWbbBeXYMJUX3haOsCkGm0x3QXy8qxcOdlTOoVgIk9/Q22Vd8UuzvbrnrVH7LqrsfBUZkGN1pCVExFk5ZfBoVShdZN3A2OszY4zuQPZO3NLpnYOg7o3sS99FsYnB2kkCtVOPz63QY3vZFphYhMq3mr2tClRwAAbZsaXqO+/FIF3JwcDFpM1VYHxeBLrZtiADYbo7pkT+Xd4/XroDYPHNR1+oyRL/v+Hx8CAMR8el+l45APX8vAPd2bGyzXHdNt2uvrkbUhyCqS4WJyPnbPHwGgoivm0cgMDA1sAi9XJ0glt1q6te5H1UH3X2HJuJFdgtfGd67yPNpDEBRKldW7e+vP0az9sGzbuSS8/eclLJ3WC7MGtwEAPLruNJJySnEqJhsHF4yyalnVCsqq73K86miM5ueavBQ/2F3xIGtqvxZo29SjpkW7IzVNPhgUlYE3tl8EACQsm6yzztjr6GJSPu7q1ExnmaxcpTO2UqlSGbREqd+X19ML4emi+z1z/kYu9l9Ow+guvuj70SGj5Yw20jPB1IeyQVHGE+FV1+Vd+2GY9oOskLhsABWtuAPbNdEs1/68qvg+M+/70FZxRqHWe0WuVOl8z1RVBUm5tx82vrHtInaGpWDjnEEY08XPIuWkuuPX0xWzkRy+llGj/d7+syLvwpiufhga2LTS7WryFae+bySyR2zpthOa7OUC+P5YLP6+mIoXfg012K5cqUKPxQfR/YODWB0UY7DeGCFErcdx5RTLdb7Eq6LdbXXupnOY+cNpqFQCQ5cewcgvgoweR6FUVRl0bzoZrxmXV9t5WvU/wNUPCk7FZtXolteULNU3sitvDUzIKkbPxQfRZ8m/6Lxov8F6ebkKB6+kGQTcAJB3B+McTZWSV4qCGp4nR2scaW2yl/u4OVW7zebTlWf/NXZzD+jeUGcWybA6KAbJuSVVPpFXt+xcTMrTLPvuyHW88GsoHv3pDO5ZHoxp358yaLFTH3LB1ov49nA0wrX2r65sCgu1dJcplPj3SprR95z2AwyF3nh99U3Uwp23k+cl5VTcjEdVMlWgPdN+PRTLrNO9/07oPxD86fjtMcvGHuI9vv6MwTL9Oi9XCU2yM203sosxYflxDF9m2OunsEyBd/80TLAIVAwXMtYzITaz2KTuq4laM2OcjMmCEAIZhWU4cyt4Boz3cNF+Hxl9kFXF18fbOy7ZvIt5mUKJcwk5d9zdX/vht/487VUFM9qfa+phWquPmnaPQfbN1F4olamuV4SpD9yupxei/8eHqszFQFSXMei2E+rPJPUNRmXySxWam3ZjwZkxL/0eht5LDta4u1iRrBz9Pz6EPkv+NVhn7P5E+0bnSGQGzibk6GRkvplveF0VQbfx85fKlfhwz1V88s815BTLa5xRVz3WvLLdYjOKatQ8qz+9U3aRDK9vDTd5DNysn06jSFZe6fofgmPx/GbDBy1Azael0rcjNBnzfg2tNCv45tM3MGLZUfT72HjLVWUeWXs74ZlKAF//G4VDV9MNtlMoVTgTl23w8Ce3RIGr1Yzfrqq1XT3HcJlCiT9Dk41uE3ojF18ejMJdnwfhmZ/P441tF/HFAd1Mzw+sup3ETDt42XHrmFdvFiAusxhhiXlw0Ltx1Z9xINdIl/jQGzl4+fcw7AhN1nk0Ua5SmTTGuqYBwaf/XMNzm0Mx71fDBF3a71PtJHm17Z5dFxSWKZCca3xYgBDCIPi4va525xNCYMHWcHz9r2mfwfqMBT9CCLy4JRRTV5+stLzarbln4rLx6b5rmt8rm/Ndf3pH/WkN/7pgLA+GBD+fqvxhV+iNXBy4kmZ03ZRVlScE/Pvi7dkgypUqnc+19Sfi0fl93YeRj607g93hqRi+9Ci2nEnULBcCCInNxiNrT2lmvdB+32r/XdSqeri7MywF127qPtBIzi25489dbVdTC7DhRHylQfULv4bikbUhOjNz3Cn9z9uqvkONDQmo7w2Otn7QUlfc6YPf6qdtNe2FtGTPFeSWKPDJP4bvXyJ7wKDbXlhwTPc/l26iTKHCzguGQcmJ6KxKx7DG3QqYVcJw3Kp+q6aA8S9t7Zt6YzcbCqVKk7ldX7lWkFsiL69xl6PgqEzEZBRVOk3FzyE3anRMhd5N7Sf/XMPOsBTM+KHq7Lfqm15jDx3KlSpcTs6HUiXwz+XKkymdryIRiX7LlZq8XIX1J+Lx/ObzeHP7ReyPSMOvlbQa/98u0+bQ1Rebeftve+RaOlYejTE6LuzjvVcx88fTWLTLsHVs0oqqs9ZXpVwlkJhdgq7/d0DT7bYqRyIz8OeFZKw5dvvGNr9UodNV3lmnJdjwNZuqV4/648ff2n5R5yYkJqMQ078PwZ6LqXhz+0WdGz1TupfnFMtx1+dB+MxIIHExKQ9zN50zSDT2+9mKAOWEkWEJ2kG+dqAWcYfJ6yypunvjwZ8e0W2JF7cfxrRfuA8d39+PYiMPvHSGIdTg4+VScj7+CkvBylq2BBq72VeqBPZdTkN4Up7RKRnPJeTo3IzO1MupUNnn+KPrdLfTD7p/OG6kVUkCdAvwqrT8u2o4laKaugVcpRK4Z/lxBL63D7+evgGlSuDjvVeNzpSw52KqzlSOAHCzoAyzfjqNcwm5mPjtf8gpllcbOKirV6FUYcrKE3hT7/Miv1QBlUpg1o+nEbjwH9z1eRBe2xpu9FixmUVYtj9Sp6cPUDHN5DM/n0N8VrFBz6xJK/7DR3uv4o9ziTDm2K1u9ZvuMFmm9vemfl1X9uBBpRJGx3nXtndZXZKWX4aDV9IMHqDsuZiKfh8fMppwrqGxdNBtaoMJx36TvWPQbSfUH0oqIVBJDAXAMHA9EZ2FXh8eNClo0v9AOxWThcfXn8EII90H9bcv1Wsh1Z+mef2JeKPdT7Vv6o1lTN13Oa3SD9qErNstNLXpclciV2L8N8FGu07WRoneTXtCtmlzTo78IsjociEEXvotDFNWncDLv18weSx5ZqFMZ2iB/o0VAByNTEfX/9uPj/dexcErt1ueI9MK8frWcFxIzMXbOy7iQMSdZ01WUydRMuaXkIpgX50crqb+b1cEfj+baBCslKtUGPWl8b9vddTH0n/5FcnK8fvZRJQrVSYNy9B/aWYVyTU31kIInIzJ1lmvfQlPbzpvEFDo++3MDaTkleJHI8HRtO9P4UhkBp77RbeHhLG31I7QZDy54SyKym6/jhW3rnFXWAqyCnV7wsjKlUYDVbUiWTk2nYxHmpGHScasDorB5wcM55KujRvZxYjW+rzR/3xS+1srODwVm22wvrI/fZlCWeV1GQsOq3Ijuxh/nL0dbGUXyxGRko8FWkGd9vvY2MMedWbwmlIPD1CLNJKETd/Z+Bwcj7ZMMHIuIQfF8nLNw9BFuyLQ4b19lW5/JNJwnKn+d9ajP52uNlhVf8+89NsFXE7JN5ghoUyhRHx2MULisjWvC+2WeW0PrT6JtcGxBnOJT1tzCoevZRg8eNTuYWRstg7tXib6n3GLdl3GpO/+q7SXkj7t72b9B7KVfdf+ezXd+PAR9X2JStS41T+jsAx/hSVrku6ZokyhxGf7ruF0nOF7tbbGfX0Mz28OxZ96jQ4v/x6GvBIFntp4zmznqszJmCys+y+uzrasV5ZfxlTVfR5qv+7ySyofwqafpJTI3vAVbCfUH0rhSXkGXw7aH9T6H26Prz+DwrJybD59Q/NlvjkkAQu2hhsEqp/8c01n6hTtL7YSeTmEEPhs3zV8c6vLpPb+Sr2bQFO/OrTLe+hqutGESDuMdAuWl6t0uikqbs29WhOmTPtVrv/0oArqhEtqNemubyzzs1IlNF0091023lXTmEGfHjYYWrDmWIzOTdmzv4QaDSh2hCZjZ1gKpq05hW3nk/GCke7H5vD2jov4+VTCraDf9GurzObTN7Bw52V0++CAzvLUPNMCPmPUwW6CkZ4QC3dexjt/XjbpNVdmZKqvvZduIrdYjsV/XzHIOK9935VfqtDJA/DUxrMGmam1WyHUZS1TKHEju1jzHo3XewBk7N7uze0XEXw9E99rdV+Vl6uwOigGr20Nx3d6LV1jvwpGzw8PGrtkAMBHe67gwz1X8fDa6udUv55eiC8PRuH7Y7G1mo5PP+nV3V8ewz3Lj1c7p7P2503w9QyDIER/aIDahOXHMXTpEcRkVP4gqTIZhWV4Z8clneBqzFfH8K7WOPn/orPwwKoTyCq6/UBQ+7Wm38tnbXAsSu5gSjntzx/tbtpV2VNJwHmnHlkbUmlX+Noy5YGlOv+D9kNIbaUKJVyMBB95JXJk3PreLJGXY3VQDApuPbiqLDjUf93M08rPov+aK1Mo8dCa2+8h9erMQhkUShV+PZ2IqzcLcMRIkiv1vUF+iQKHrqZDVq7UeQipf7+g/br6YPftB/WVDWmTSirO8fDaU3hw9ckaBd4z1oZgwdaLOgkOq7PpVAJ+PB5nMDPGnVBPxXhU6+GNzj1VLfPdVDVsRd9j687gk3+u4dh13SSB5UoVXvsjDJtDEmpVBmOSc0uw7r+4Kh+Y6tMe023swcC/V9Kq7BFQk5buT/dVPlxMO/gvlSs1ZSmVK/HaH2HYV0VvQEvJKpKhz5J/cSDizu9jrKFIVl7jB2SXkvNw/8r/2OvDDJi93E6oP5P0n74v2XMFG08m4JWxHfH6hC74t5IbBqCiBeH3c4n47dZN1T3dm+M+veznn++PxDcz+wLQDZy7f6B7cz20Q1Odp476NwqmPrF9ZK1u12v94L0y+tMWZRbWvLV6vwkfkhEppnepPXAlDfsu38T6E/FY8kAPgxakqoz+8pjBsupaOGviiwNR+OJAFPq29sHITs3gIJFAeUeTeN2Zihbtiocp60/oTjtWXaBUlTKF3nhUI/Oym6piui7gq3+Nz1mr//CrMp/uu4ZnRwXqLDsbn4N7lgfrBFVqyw5UPl7tWFQmjkVlIn7pJM3QB3dnB836FUejsdPIGFw3p9vbCCF0Xluf7buGjr6emt8ztVq0FUqVpiu6vuqmTlNnma5sLLW2CcuPa342pdfKldR8+DdyNVielFOic/OcWVgGbyPJ+BQqgbT8Mrhp/e1+PZ0IpQpYOq2XZlllH2PqZF6Hrqajo5+nwXrt9kKVSugMkXlnxyUERWVi6/kkTYZxY5esv0wn6Nb6nNT/bK6NqFrMyGBJP9ogUdKFxLwq17+45QL2vnyXwXJ1hvbwD+7BmmOxOj1Oqgo20rQecGtnZNd/zWnnPQEqvmvPJeTgkbUh6Op/u4u//gPi1UExWH8iHjvnDcerf4ThYnI+JvbQ/b6Xl1fk0gj09YSvl4tOeX8JuYFZg9vAzckBv59NMnoNEkhQLFdq/nYpeaVGZyExJuHWw8R9l2/ijQldKt3uVEwWvj0SjU+n9sQNvYeHCqUKf4WlYFhgU5PPWxntniSm5sOpyuPrzyAxpwSHFtwNV63P36r8GZqskw3+36vp2BWeil3hqXhiWDuD7dPyy9C8kUuNhsE9/H0I0grKEJ1ehM8f7q2z7kxcNvJKFbhX73Wi/WBXrlTBxfH29aQXlOG5W7lmtL+XtBnrbadN+/MxLlO3jm/ml2L5oeto38wT0VoPq3osPoCZg1rjwwd6YOOpeM3fSX/WBksb+MlhABU5F65/ch9OxmZhYNvGUKmAZ385jwf7tcBjQ9patUyVSS8ow5DPjmBoYBP88dywKreVlSuhUAp4ujjiqY3nkFMsx6M/nUHsZ5MqTXxXJCtHfGYxerZsVOsM8/klCsRnF6Nva59a7V/XsaXbTlQ2rnnjyQQAwIqjMdgdnoKP9lb+lPCjvVc1ATdQcRNx33e642XzShUolpXj7R0XqxyPqD/H55I9uq11CdnFtUoyU90e7d79B+3e/ceg5WLWT+Z78n0nXtxyAaE3cnH/ysqTBRljLMD+zcQWp5oIT8rDyqMxNer+OruW3VZrK8WEIM0a1D0DzNHlb8Zaw3H9xgJuwLReDe0X7kOPW6362jdExgJuoGIoRVBkBoQQ+E+vW/CPx+M0WckB3UBh9oazyCg0vcfG85vPI+RWN21jY9H1u5IKITSthGrqXj0nY7Lwi5EWniup+Zi84gQG3LrZUfv7YipGfhGk03OgsKzc6DG+PxaLoUuPoLBM9+Gd+gHDqqPR+GzfNYOHifqfaWfjb7dkKlUCCVnFt6a0ub2N/ph+7c8uU5MsAtDp9v/9sdgatVRVZ+l+83TrN5fvj5kvWVhN7A6v+iFdVZ/rfT86ZDDEQ65U4cKtruGmtnpuD03G9VtduSPTCrA2WPeYuSUKzcNq7deS/sfUlwejkFMsx7L9kbh4q1eFfnK7B1adxMwfT2PQp4dRUKYwePDw/OZQjP7qmNF534GK6dbitYKkmnQVVzP2kE2pEriSWpHL5NF1Z3A2PgfPbw7VyQvzz6Wb6PT+fqQSRSQAACiVSURBVLy941KlQ7RqQrvsa4y8/uIyi7Bw52UkZpdgc0gC3vvrcqX3OGUKJU7GZCMpp7RGU4zuvXRTZ/rPqnpSbQ5JwNClRyrtKRB8PROf7L1q0HtH/bDnSKRhA83MH0/j+c2hBg83tGe0KJPrJzu9/T0mK1fhoz1X8e6fl2rUW0A7+ej5G7nI1uoluHDnZWw7n4zPD0Tq9BBRCeD3s0nou+QQwrRet/9FG59S0Jib+aU6sx7UlP69wUd7r2DOxnN45ufzWHs8FmcTcvD+X7XLh2NMYnZFLwX9RidT/XOpoifA6TjD752MwjKd11uXRQfQc/FB5JcqdHJT9PrwoOY4+qatOYkpq05oegsVycpr3Ihy77fHMXX1SQRfN70e7Qlbuu2EKQ+NjE3DUh39L4SjkRl476/L2G1CIhztsXT6iXNWB8UatDqaor+J2bHN2b2srqrqAYo1Hbfyh983h+68lcEc+n18CC+N6WjymOSqnE0wPbgyVbFciVK5Ensr+QLUN2eTaWMTtVslTGml1nbwSjoOXknHB/d310kMeCU1H6/9EY7ojCJseWYIRnRsho0n441mnl9zLAZT+7bEY+sqprPq6t+oIuFZMw+89HuYzpRt2l75PcxgmXa3XGOWGQk2hRCa3g2xWjd5l5LzDB5SBkVl4rczifjfoNZYtOsyfj+bhGXTeqFT89stkEqVgBAqKFUCbs4OOn+XGT+EoEeLRlWWUW3uz7frLyQuGy/9dqHSOatrqjbd5Ouj2nyHVmfamlPo0twLeaWm98bS7vlhqsqmZCyUmXbT2/tDw1lIEnMqn+JSTXuY19s7LqFPax+09HHDMyMrevckZBWjVWM3ncBNm7EHzkv3XcO6E/GYN7qDZllSbonO9JDzf9Md+nQ6LhuHr6Zj3ugOaOrponu8/deQkFWMNY8NqLSVTqZQQQihacjQ9/Smc0jILsGZuGzE3RrGM76bH8Z2bW6wbapWL6BSuRIKpQpODlKk5JVCAqCFjxtyiuVYGxyLhwe00tl367kkjOjYDAAQYSRhotr/3RrO9vWh63h5XCeD9eocD409nDGqky96tfLWWZ9VJEdidgn2XErFE8PawsvldjgQn1WMtk09NL9rB8WFMgW83W/3HtJ+uFpQqsCGkxU91164+3bdfXckGs/fHQh3Z0ck55bA2UEKv1s9lfJLFPhaL5/PvC0XsO35ipZY/ZZvfaUKpc6MKM/9EoqeLRth5az+8PfW7Q1VKlfC2VGqeQ0MW1qR++HPecMwoG2TKs+jbc/FVPh6uRhMV6qez/xMfA46Nb/dA+piUh763Gq5vZ5eiPMJufjfoNY6jWkl8nK4OjroLMsvUWBVUDQe6tcK3Vs0wkNrTiK7WI6knBIsebCnyeVV83QxHvLlFMsx+NMjAICEZZN1erG9sU03oWSJXIn5v11AVHonvDSmo06Xf3Xenj0XUzGumx96Lq7ohXVlyb3wqOTc+tQPhQ5E3MTdnX3x0Z6rkEqARfd3N/Eq6zYG3XaiugzG5mRKwA1UPT8yYNhtmMgUh42MTbSVVSbOdW8r+mPYzaGmuRGM0X9gNHnF7Rvzx9adwfonB1Y61duvpxM1Ny8Aqs3+D1Tf1b0m5v58O8mV9oPFdyqZd/q9vy7jvb9ur3t352X88MQAze9TV5+ssrVLf8hQZfR7Rpgr4K6JdbMH4hkjsw9Q1awxh/2CrRexYOtFdPTzxBNDb3dn1U/UaEkXEvM0reUnYrIwqF0TfHkwCo8MaIUvH+mDvy+mYtu5JMwY1Fqzj36PjfwSBdbdunfQ7vFgLHmgNvWD+OTcUqy99f7LKZajiYczfrjVW+BMXDZ6tvLGsv2ReKBPC3i53r4FPn8jF+0XGk/YJ4TQdIeP08rxoZ8zpFypwrL9kTpDfmb9dBqBvh747ZmhmgR/UZ9MxNs7LuLwtQyD3hGZhTK0e/cfODlIdK5ZqRJwkEpwPb3QYNq4uMwiCABNPZxx6Gq6JmgHKno8fHkwChufGoRhHZrq7Dd97SlkFsrw5cEoTOh+++FBRoHsVo+divtO7d46d30ehGdHtsfYrs0xrENTncS+2skV9afG/PX0DfRv0xgP3+ql8dTwdpjY099oboiz8TlYfug6FtzTWaeOTFGqUOJcQi6GLj2C6E/vg5ODFAVlCpQrBUZ9EYRB7Rpj+cy+OsOOgqMy0bm5F9ILZJrhQjEZRTh+PRNPDGsLJwcptp1PgoujFH1a+eBlIw949blqdcF/cPVJLJvWC2uOxWoeZDk5SPDIwIr3QVxmEcZ9E4xxXf3wzcy+KJaVI8DbDZ/uu4pt55Px03/xGN+tuSbp7+FrGRjbLRM7LyTjowd7aq5FfyiTPu3AV1auRFFZOYKvZ+q8B6etOakz68zha8aHrK44Eg1nBwmeGRmIrCIZWjW+PbxDKpXg8VsPzQFg5o8haObpghkDW2NSrwDkFMvx4pZQTO/fSvM30FeuFCgs03qIM7oDmuk9TLNHElFX0yXW0OrVq/Hll18iLS0Nffr0wcqVKzF48GCT9i0oKIC3tzfy8/PRqJFprQ7WtvVcYqU3fbby5LC2+Dmk6sCbiIis58XRHYx2j9XXwtsVjg5Sk1oz1dTjJUvlSrg6SZGQXYIxXx3TrF86rRdyiuWaMbHOjtJKu5Z+cH93PH1XewBAjw8OaBJaaVvyQA+UKpRYExQDXy8Xzc3g2scH4AWt5GNk384vGo9J3/1Xo6EsVXltfCdkF8mx+fQNuDhKNb13PJwdcHcX3xolJgWgcwxtrRq7Yc1j/ZGaV4a/L6Ygs1CGcwmVT9+pdk/35jqts9bS2N0JuVVkBzfmjXs643R8do0f3nz2UC+dh5GVaebpUqOkszXh4ijF86MCscJIF/xJvfwrfR2seaw/XtxS0ZPirXu74O7OvpqhJW9O6FxpnpeaemRAKzzUvyUe/el2gCqVVHSd3/7CMCzZc8VoXiEPZwfN5+UTQ9vi46k9cSo2C8/9EorFU7rj8LV0XErOx/onB8HRQYIZP4Tg8+m94ewg1fR42/PSXXhozck7yh00tqsfUvMqhlCsf3KgzgPrysy9qz0up+RrhlYlLJuMfZdvooOvJ7r4e6Hdu/8AAHq2bIQyhUrTC+vIG3ejg69h/pS6wtQ4sl4E3Vu3bsXs2bOxdu1aDBkyBN9++y22b9+OqKgo+Pn5Vbu/PQTdR66lm/SCJrKVBeM7Y/lh83wZ2buxXf10EnpVp30zj0rni79T/o1cdZI2qam/3GuqbVN3nYzqVDc8c1d7TOzpjwFtG+N/P57GmVs3NWsfH4CJPf2RmleKlLxSDGzbGBcS8xDYzAO/nU2sUdIoY0mKnvn5HA5fy0DIwrEI8HYDUNFS9ek/V/HhAz3gIJVAKpFg/Yl4TWJD/eOob7S0qVup9Klbcw5dTcfV1AK8Mq4jdoenIjwpD+/e1xWuTg5IyinBJ/9cRa+W3lCqoPlcGtXZ1+hwGQeppNIEfutmD8ToLr5wdJDi/3ZFVNvDqz7r6u9VozHKRA1Bz5aNapR0l4x7ekR7Tcu2vq3PDcWQwKZG19UFDSroHjJkCAYNGoRVq1YBAFQqFVq3bo2XX34Z7777brX720PQfTW1AJNW/Ff9hqSjfTMPDG7XBE8Ob1fl32/NY/1xNj6n2rlc71QHXw+drjs11bqJmyYr+shOzbB0Wi/kFiuwaNdlTbIcfc/fHajpXmdui6d0x5I9V+EolSD60/vw3l8RCEvMrfbGbHKvAPxTzfQeG54aCAepFBEp+Xh4QCsM+eyIZl1HP088c1d7KFTCYA76Z0e2Ryc/L9zbwx/Dlx3RPBFu5ulcafIyU7g7O2imZdK++XxqeDsciEhDF38vzBnRDkMDm8JRKkFqXhkcHSQYrjdncEsfN6TklaKTnyfu6tQMi6f0AABcSMzF7rAU/HomsVbzzqvNGtwGhWUKHIvKRJsm7vhl7mA8uOqkThfsnS8OR7/WPlCqBP6LycIcI3PRTu/fCpN6+WNEx2YQoiIwuZySj/5tfJCaX2YwF7I16Xe9NEXY/90DH3cnHIvKxCt/hOkkUesW0AiFZYoaj2G3hN6tvCEvV+Hpu9qjX2sfrDwaU+l80EBFl9IfZw8wGJNYpqgY89/Yw7nSffNLFBj06WHIlSp8PLUnnhjaFql5pQi+nokh7ZsgJC4bY7v6YcWRGDw9op3OWPWaEkJgR2gy+rb2MTjOkxvOGiTOMXcW4lK5Em7ODlj3Xxw+PxCp8/qJ/HgiQuKyseivCKTkleLLh3tjbFc/eLs5GR2LLISAUiVwJDIDESn58HRxxNL9kZW2iGpbOasfXv49DFP6tMBbE7pouvhWZ3C7Jri3pz8+3nsVh1+/G008nDHmq2M1TlQ0oXtz/Dh7IFQqgU2nEvDZvmsGrV0JyyYjs1CGF7eEIiG7BPd0b47PHuqF3GI5+pmYd4UIqF3ruj2RSoDoTyfh+PVMk/OmUM0NbtcE216oOuO6LTWYoFsul8Pd3R07duzA1KlTNcuffPJJ5OXlYffu3Qb7yGQyyGS3v+QKCgrQunXrOh10CyEqHW9EtzlKJVj35EB4uzmhX5vGmuVKlUCH9yr+fvpdHh/q1xLL1dOkCYERy44iVSvZ0dy72mP9iXi4OknxyIDW8HBxxCMDW+H1bRcx7+5AqATg7+2KaXpJm54e0R6vjuuEPh9VJKgJ9PXA0TdGAzBs2RnTxRcb5wxGmUKJBVvD4ewoRfeARnigbwsAFVlmnaQSbJwzGM6OUqiEMNrVpkRejkd/OgNvNye8PbELHKVSdPH3Ql6JXDO1jbYXR3fA1H4t8fJvYUbHHb5+T2d8cyvJyaB2jfHIwNZ4e8clPDG0LRbd3w0ujg4oUyghlUh0Emqor8/JQYL3JnXDjIGtkVeqgLebEzycHSCRSHA+IQffHYlGYk4J1j4+AG2buqNcJbDueBweHtAabZoaTgGjPc5MbeK3x5FVJMPJd8fqTGViTJGsHBkFZVh/Ih6TewWgY3NPPLHuLKLSC/HIgFb44uHekEgk+CE4Fmfic/DT7IGQSiqSiymUKjz7y3lM7t0CTwxtW+34KbWz8Tm4lJyHe7o3R5sm7pBIJJoAoDIPrjph8BAlYdlkJGaXYNSXQVj/5ECM7eoHhVLAyUGC1PwytPB21fnblCmUcHKQ6iQOUqoE8krkBomGyhRK/HPpJt7YfhG9Wnrj75dG1GjKjzKFEgVlChy+mqHpUrh57mD0bumjef0P79AUp2J1uyj2bNkIe18eiRvZxfB2c0JsZhF6tfSBRFKRab9/m8Y4Hp2Jn08l4P1J3dCysRtcHR1QqlDCw8URSpVAXGYR7ll+HM08XdC3tQ8m9vTHwwNaaV6DIzs1w4anBhltNc0plqOxu5PmWlUqgS1nbmiSFO2aPwIZBWV4/tdQCFFxg7Vr/gj0buUDhVIFqUSCz/ZdqzR/xaEFo+Dm7ICP9lyFl6uTZpq5c++PR0peKcISczG5VwAG33qgFPXJxEpfw3GZRSiRK/HFwSjMH90BuSUKNPN0xsB2picAMsbYe8rasopk+ONsIsIS83AkMgMv3N0B797X1aLnfPaX8zh0Nb3GSZQqox5zC1S8HxykEkxZeQLebk6aXgdfP9IH0/USZwEV88W7OTlAiIqHb5tDbmDp9F5o5OqExOwS/HkhGU+PaK+TwEpbTEYhnvslFPf0aK55wPr59F7wdnPGe39dxpgufnhjQmcE6H1G1Mbu8BQsP3Qd47o117zuE5ZN1vQCOPv+OARHZeK7I9EGD7G+ndkXl5LzNa1ZzRu5IL3A+AMH7S602va/OhLdAhppPssWbLuI+KwiPDG0LT7bZ74s/H5eLjj21ug7mpKvTRN3k4ZvPDakDT59qBcyC2WY+WMIMgtkKKwmt0afVt7o16ZxlQ0FMwe2xtbzhtO9tfRxw4tjOuDnUwmYe1d7bDiRYFLegQXjO+NkbBaSckp0EkJWJWHZZBTJynEuPqfKoHTptF4Y3L4JArxdcSY+B1tOJ2rGE3fw9cDkXgF4ZVwnODpIUaZQolhWDlm5Cl8ejML4bs1RplBiWv+WkEgkOH4902qzruyaP0IzvZX6Ydy/V9NxNj4H3QK8EJdVjLDEvBrNVGELLX3c8Pzdgfjg1nefutW5q78XVj3aHx18KxLrzfrptNHs55am7kZfVzWYoDs1NRUtW7bEqVOnMGzY7acgb7/9NoKDg3HmzBmDfT788EMsWbLEYHldDrqBioQjeaUKeDg74Jmfz+P8jVwMbt8Efzw7FAu2hWN3eCpWP9ofPu5OCIrMwNhufjh+PQsv3B2I6+lFuJRckUFx08kEXErJQ0aBDG2bumPZ9N6Ql6swuF0TqITAxeQ8uDo5oKt/I6z7Lw5L90fiu//1RSNXJ+wOT8Gr4ztj5ZFo7NSbA7mphzPmjGiHe7r7Y+u5JKiEwN1dfDG6sy+AiulTnKRS5JUqsO/yTQQ280BUeiFmD2uHC4m5iMkowtS+LZFdLMO5hBx4uzmhk58XFmwNxycP9YQQFdNJjezcDJmFMrTwdsONnBL4eblU3LAAlWYmBYDLyfnIKpLhrk7NMGXlCUSmFWJ4h6bYOGeQzo1ukawca4JiMKlXAHq29EaJvBy/nUnEhO7+RgNBNSEE/r6Yih4tvHXm7s0ukuG/6CxM7Omvma9TCIHk3FK0auxmtRveIlk5tpy+gft6BsDDxQFuzg5wd76dWKNUrrz1BeaHoYFNNQGlQqnC9fRCdPNvBKlUYlKwGXw9E9vOJ+HjB3uiSRUtbeagUKqgEqLagLuq/QtKFQaBqC2lF5RpEg7tuZiKwe2boHcrH4uft0hWXmmGU1Pllcjh4eKoE+SqAzshBNIKyiAvV2HjyQQ8OyoQLX3c7rTYRh2/nonojCI8PaKdxd9j4Ul58PVygRACpXJljVuEc4vlEIDF3yt1XalcifM3cjA0sKnRhyTmJC9XIa9ErsmibAkq1e3p41Si6u8ne6NUCRyPzkSfVj4Wfd2qM07HZhaho6+nSQ861YQQUCgF5EoVPF0coVCqkJRTgpaN3Sr9vriYlIdzCTl4ekT7Gp3L2LmNfe6UKZTYczEVBWXlcHaQ4OrNAnz2UC+LfEYJIXDwSjq6BzSCVArczC/DwLaNLXKuMoUSp+OyMTSwqcnzkqvLaKuHftrnLlMokVeiQGMPJ1y7WQgJKnodlasEbmQX43p6ETaH3MB3s/rCz6t2nxnlShWupBagdytvSCQSJOWUwNfLBWUKJXzcnfHT8TisPhaDmQNb43h0FtY81h9NPJyxOigGd3VsBk9XRzT1cIaLowOyi2Wa4Tz/RWdi3X/x6OTnidFd/TDiVsK85Yev49fTiVg3eyAcHCRIyS1FSx833N3Z99Z861IcjcxAbGYRnrkrsEav9zKFEiFx2RjRoRmCojKQWyzHtP6tNI0v5bceSueXKrBsfyQe6t8SxbJyqARwV8dmcHN2QGJ2CTxdHTV/l0PX0vHK2E6IyyyCk6MUg9o1QZlCCRdHqc0fDFeHQXcVQbc9tnQTERERERFR3WFq0G33U4Y1a9YMDg4OSE/XzQSZnp4Of39/o/u4uLjAxaXutGoRERERERFR/WTZ/ltW4OzsjAEDBuDIkdtJllQqFY4cOaLT8k1ERERERERkbXbf0g0Ar7/+Op588kkMHDgQgwcPxrfffovi4mLMmTPH1kUjIiIiIiKiBqxeBN0zZ85EZmYmPvjgA6SlpaFv3744cOAAmjdvbuuiERERERERUQNm94nUzMEe5ukmIiIiIiKiuqPBJFIzB/Vzh4KCAhuXhIiIiIiIiOyBOn6srh2bQTeAwsJCAEDr1q1tXBIiIiIiIiKyJ4WFhfD29q50PbuXoyLbeWpqKry8vKwyAbt6XvCkpCR2Z6/jWFf2hfVlP1hX9oN1ZT9YV/aF9WU/WFf2w9p1JYRAYWEhWrRoAam08onB2NINQCqVolWrVlY/b6NGjfjGtROsK/vC+rIfrCv7wbqyH6wr+8L6sh+sK/thzbqqqoVbze7n6SYiIiIiIiKqqxh0ExEREREREVkIg24bcHFxweLFi+Hi4mLrolA1WFf2hfVlP1hX9oN1ZT9YV/aF9WU/WFf2o67WFROpEREREREREVkIW7qJiIiIiIiILIRBNxEREREREZGFMOgmIiIiIiIishAG3bV0/PhxTJkyBS1atIBEIsGuXbt01qenp+Opp55CixYt4O7ujokTJyI6Olpnm9jYWDz00EPw9fVFo0aNMGPGDKSnp2vWJyQkYO7cuWjfvj3c3NzQoUMHLF68GHK53BqXWG9Yo67U/vnnHwwZMgRubm5o3Lgxpk6dasErq3+WLl2KQYMGwcvLC35+fpg6dSqioqJ0tikrK8P8+fPRtGlTeHp6Yvr06QZ1kZiYiMmTJ8Pd3R1+fn546623UF5errPNsWPH0L9/f7i4uKBjx47YtGmTpS+vXrFmXamdPHkSjo6O6Nu3r6Uuq96yZn1t2bIFffr0gbu7OwICAvD0008jOzvb4tdYX5irrl555RUMGDAALi4uRt8zx44dw4MPPoiAgAB4eHigb9++2LJliyUvrd6xVl0BgBACX331FTp37gwXFxe0bNkSn376qaUurd4xR11dvHgRs2bNQuvWreHm5oZu3brhu+++MzgX7y/unDXrS83S9xgMumupuLgYffr0werVqw3WCSEwdepUxMXFYffu3QgLC0Pbtm0xfvx4FBcXa/afMGECJBIJjh49ipMnT0Iul2PKlClQqVQAgMjISKhUKvzwww+4cuUKli9fjrVr1+K9996z6rXaO2vUFQD8+eefeOKJJzBnzhxcvHgRJ0+exKOPPmq166wPgoODMX/+fJw+fRqHDh2CQqHAhAkTNHUBAAsWLMCePXuwfft2BAcHIzU1FdOmTdOsVyqVmDx5MuRyOU6dOoWff/4ZmzZtwgcffKDZJj4+HpMnT8aYMWMQHh6O1157Dc888wwOHjxo1eu1Z9aqK7W8vDzMnj0b48aNs8r11TfWqq+TJ09i9uzZmDt3Lq5cuYLt27fj7NmzePbZZ616vfbMHHWl9vTTT2PmzJlGz3Pq1Cn07t0bf/75Jy5duoQ5c+Zg9uzZ2Lt3r8Wurb6xVl0BwKuvvop169bhq6++QmRkJP7++28MHjzYItdVH5mjrkJDQ+Hn54dff/0VV65cwfvvv4+FCxdi1apVmm14f2Ee1qovNavcYwi6YwDEX3/9pfk9KipKABARERGaZUqlUvj6+oqffvpJCCHEwYMHhVQqFfn5+Zpt8vLyhEQiEYcOHar0XF988YVo3769+S+igbBUXSkUCtGyZUuxbt0661xIA5GRkSEAiODgYCFExd/dyclJbN++XbPNtWvXBAAREhIihBBi3759QiqVirS0NM0233//vWjUqJGQyWRCCCHefvtt0aNHD51zzZw5U9x7772WvqR6y1J1pTZz5kyxaNEisXjxYtGnTx/LX1A9Z6n6+vLLL0VgYKDOuVasWCFatmxp6Uuqt2pTV9pq8p6ZNGmSmDNnjlnK3RBZqq6uXr0qHB0dRWRkpMXK3tDcaV2pvfjii2LMmDGa33l/YRmWqi81a9xjsKXbAmQyGQDA1dVVs0wqlcLFxQUnTpzQbCORSHTmkHN1dYVUKtVsY0x+fj6aNGlioZI3POaqqwsXLiAlJQVSqRT9+vVDQEAA7rvvPkRERFjxauqf/Px8ANC85kNDQ6FQKDB+/HjNNl27dkWbNm0QEhICAAgJCUGvXr3QvHlzzTb33nsvCgoKcOXKFc022sdQb6M+BtWcpeoKADZu3Ii4uDgsXrzYGpfSIFiqvoYNG4akpCTs27cPQgikp6djx44dmDRpkrUurd6pTV3dybl4j1F7lqqrPXv2IDAwEHv37kX79u3Rrl07PPPMM8jJyTHvBTQg5qor/fcM7y8sw1L1BVjvHoNBtwWoK33hwoXIzc2FXC7H559/juTkZNy8eRMAMHToUHh4eOCdd95BSUkJiouL8eabb0KpVGq20RcTE4OVK1fi+eeft+bl1Gvmqqu4uDgAwIcffohFixZh7969aNy4MUaPHs0vxVpSqVR47bXXMGLECPTs2RMAkJaWBmdnZ/j4+Ohs27x5c6SlpWm20Q4K1OvV66rapqCgAKWlpZa4nHrNknUVHR2Nd999F7/++iscHR0tfCUNgyXra8SIEdiyZQtmzpwJZ2dn+Pv7w9vb2+jwHqpebeuqNrZt24Zz585hzpw5d1LkBsuSdRUXF4cbN25g+/bt+OWXX7Bp0yaEhobi4YcfNuclNBjmqqtTp05h69ateO655zTLeH9hfpasL2veYzDotgAnJyfs3LkT169fR5MmTeDu7o6goCDcd999kEor/uS+vr7Yvn079uzZA09PT3h7eyMvLw/9+/fXbKMtJSUFEydOxCOPPMKxcWZkrrpSj+1+//33MX36dAwYMAAbN26ERCLB9u3bbXZ99mz+/PmIiIjAH3/8YeuiUDUsVVdKpRKPPvoolixZgs6dO5v12A2ZJd9bV69exauvvooPPvgAoaGhOHDgABISEvDCCy+Y/VwNgbU+B4OCgjBnzhz89NNP6NGjh0XPVV9Zsq5UKhVkMhl++eUXjBw5EqNHj8b69esRFBRkkFyKqmeOuoqIiMCDDz6IxYsXY8KECWYsHemzVH1Z+x6DzQYWMmDAAISHhyM/Px9yuRy+vr4YMmQIBg4cqNlmwoQJiI2NRVZWFhwdHeHj4wN/f38EBgbqHCs1NRVjxozB8OHD8eOPP1r7Uuo9c9RVQEAAAKB79+6afVxcXBAYGIjExETrXlA98NJLL2Hv3r04fvw4WrVqpVnu7+8PuVyOvLw8naeb6enp8Pf312xz9uxZneOps1lqb6OfPTY9PR2NGjWCm5ubJS6p3rJkXRUWFuL8+fMICwvDSy+9BKDi5lMIAUdHR/z7778YO3asha+wfrH0e2vp0qUYMWIE3nrrLQBA79694eHhgZEjR+KTTz7RfFZS9e6krmoiODgYU6ZMwfLlyzF79mxzFL3BsXRdBQQEwNHRUScw6NatG4CKGQW6dOly5xfRQJijrq5evYpx48bhueeew6JFi3TW8f7CvCxZX1a/x7DISPEGBnrJuYy5fv26kEql4uDBg5Vuc+TIESGRSHQSZSQnJ4tOnTqJ//3vf6K8vNxcRW6wLFVX+fn5wsXFRSeRmlwuF35+fuKHH34wS9kbApVKJebPny9atGghrl+/brBenThjx44dmmWRkZFGkz2lp6drtvnhhx9Eo0aNRFlZmRCiItFJz549dY49a9YsJjqpAWvUlVKpFJcvX9b5N2/ePNGlSxdx+fJlUVRUZPkLrSes9d6aNm2amDFjhs6xT506JQCIlJQUS1xavWOOutJWVWKgoKAg4eHhIVatWmW28jck1qqrgwcPCgAiJiZGsyw8PFwAEFFRUea5mHrOXHUVEREh/Pz8xFtvvWX0PLy/MA9r1Je17zEYdNdSYWGhCAsLE2FhYQKA+Oabb0RYWJi4ceOGEEKIbdu2iaCgIBEbGyt27dol2rZtK6ZNm6ZzjA0bNoiQkBARExMjNm/eLJo0aSJef/11zfrk5GTRsWNHMW7cOJGcnCxu3ryp+Uems0ZdCSHEq6++Klq2bCkOHjwoIiMjxdy5c4Wfn5/Iycmx2rXau3nz5glvb29x7Ngxndd7SUmJZpsXXnhBtGnTRhw9elScP39eDBs2TAwbNkyzvry8XPTs2VNMmDBBhIeHiwMHDghfX1+xcOFCzTZxcXHC3d1dvPXWW+LatWti9erVwsHBQRw4cMCq12vPrFVX+pi9vHasVV8bN24Ujo6OYs2aNSI2NlacOHFCDBw4UAwePNiq12vPzFFXQggRHR0twsLCxPPPPy86d+6s+R5UZ5o/evSocHd3FwsXLtQ5T3Z2tlWv155Zq66USqXo37+/GDVqlLhw4YI4f/68GDJkiLjnnnuser32zBx1dfnyZeHr6ysef/xxnWNkZGRotuH9hXlYq770WfIeg0F3LQUFBQkABv+efPJJIYQQ3333nWjVqpVwcnISbdq0EYv+v727CYlyi+M4/hvnajha9D6ZVCZkENVkiwJBpDCYaIIKokAYJ9yoBBUjBRHUpoaCkZg2rhpbBBERudJFviyKFuFGe4MWDaJMbwuVoZp05txFF2nQ6HavZ8aa72f5nDPnOX/OLJ7f88yc58KFWa/AOXfunHG73aawsNBs2rTJhMNhk06nZ9qj0eic5+AHCr8mG2tlzLcn28Fg0KxevdosXrzY1NfXZ7yKDD/3o+97NBqd6fP582fT2tpqli1bZlwulzl8+PCsG1GxWMzs37/fFBcXm5UrV5pgMGimpqYy+vT395sdO3aYoqIiU1lZmXEO/Fw21+p7hO7/JpvrFYlEzJYtW0xxcbEpKyszDQ0NZnR0NBtl/hHma63q6urmHOfNmzfGGGMaGxvnbK+rq8tesb+5bK2VMcaMjY2ZI0eOmNLSUuN2u00gEOAGyS+Yj7W6ePHinGNs2LAh41xcX/x/2Vyv79m8xnD8UxgAAAAAAJhn7F4OAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAADyQCAQkMPhkMPhUGFhodxut/bt26ebN28qnU7/63E6Ozu1dOlSexMFAOAPQ+gGACBPeL1exeNxxWIxdXd3a8+ePTp16pR8Pp+mp6dzPT0AAP5IhG4AAPLEokWLtGbNGpWXl2vnzp06f/68urq61N3drc7OTklSe3u7tm3bppKSEq1bt06tra1KJBKSpIGBAZ04cUITExMzT80vXbokSUomk2pra1N5eblKSkq0e/duDQwM5KZQAAAWEEI3AAB5bO/evfJ4PLp//74kqaCgQJFIRM+fP9etW7fU19ens2fPSpJqamp0/fp1LVmyRPF4XPF4XG1tbZKkkydP6smTJ7pz546GhoZ09OhReb1evX79Ome1AQCwEDiMMSbXkwAAAHYFAgGNj4/rwYMHs9qOHz+uoaEhvXjxYlbbvXv31NzcrI8fP0r69p/u06dPa3x8fKbPyMiIKisrNTIyorVr184cr6+v165du3TlypV5rwcAgN/FX7meAAAAyC1jjBwOhyTp4cOHCoVCevXqlSYnJzU9Pa0vX77o06dPcrlcc35+eHhYqVRKVVVVGceTyaRWrFhhff4AACxkhG4AAPLcy5cvtXHjRsViMfl8PrW0tOjy5ctavny5Hj16pKamJn39+vWHoTuRSMjpdGpwcFBOpzOjrbS0NBslAACwYBG6AQDIY319fRoeHtaZM2c0ODiodDqtcDisgoJv277cvXs3o39RUZFSqVTGserqaqVSKb1//161tbVZmzsAAL8DQjcAAHkimUzq7du3SqVSevfunXp6ehQKheTz+eT3+/Xs2TNNTU3pxo0bOnjwoB4/fqyOjo6MMSoqKpRIJNTb2yuPxyOXy6Wqqio1NDTI7/crHA6rurpaHz58UG9vr7Zv364DBw7kqGIAAHKP3csBAMgTPT09KisrU0VFhbxer/r7+xWJRNTV1SWn0ymPx6P29nZdvXpVW7du1e3btxUKhTLGqKmpUXNzs44dO6ZVq1bp2rVrkqRoNCq/369gMKjNmzfr0KFDevr0qdavX5+LUgEAWDDYvRwAAAAAAEt40g0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAAS/4GZM+JbVI0lbQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.lineplot(data['Close'])\n",
        "plt.title('Starbucks Closed Finance Time Series')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "nA82T62G2Yrp",
        "outputId": "e3655f59-872c-4390-f9c5-4a2095d07b7a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Starbucks Closed Finance Time Series')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "split_time = '2020-01-01'\n",
        "train_data = data.loc[:split_time]\n",
        "test_data = data.loc[split_time:]"
      ],
      "metadata": {
        "id": "Pi3GIOt42Yn8"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_close_data = train_data['Close']\n",
        "valid_close_data = test_data['Close']\n",
        "\n",
        "print(f'Train Closed Data \\n{train_close_data.head()}\\n')\n",
        "print(f'Validation Closed Data \\n{valid_close_data.head()}\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hb4hE1yg2Yk7",
        "outputId": "70d7d7d2-fe89-4891-a67a-56283651abe3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Closed Data \n",
            "Date\n",
            "1992-06-26    0.335938\n",
            "1992-06-29    0.359375\n",
            "1992-06-30    0.347656\n",
            "1992-07-01    0.355469\n",
            "1992-07-02    0.355469\n",
            "Name: Close, dtype: float64\n",
            "\n",
            "Validation Closed Data \n",
            "Date\n",
            "2020-01-02    89.349998\n",
            "2020-01-03    88.830002\n",
            "2020-01-06    88.129997\n",
            "2020-01-07    87.860001\n",
            "2020-01-08    88.879997\n",
            "Name: Close, dtype: float64\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "$Forecasting \\ with \\ Machine \\ Learning$"
      ],
      "metadata": {
        "id": "z3BeUOSU5q8M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def window_dataset(series, window_size, batch_size=32,\n",
        "                   shuffle_buffer=1000):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
        "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
        "  dataset = dataset.shuffle(shuffle_buffer)\n",
        "  dataset = dataset.map(lambda window: (window[:-1], window[-1]))\n",
        "  dataset = dataset.batch(batch_size).prefetch(1)\n",
        "  return dataset\n"
      ],
      "metadata": {
        "id": "Ex18kuJ62YiN"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "keras.backend.clear_session()\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "\n",
        "window_size=30\n",
        "train_set = window_dataset(train_close_data, window_size)\n",
        "valid_set = window_dataset(valid_close_data, window_size)\n",
        "\n",
        "#Model building\n",
        "model = keras.models.Sequential([\n",
        "    keras.layers.Dense(1, input_shape=[window_size])\n",
        "])\n",
        "optimizer= keras.optimizers.SGD(learning_rate=1e-5, momentum=0.9)\n",
        "\n",
        "#Model Compiling\n",
        "model.compile(loss=keras.losses.Huber(),\n",
        "              optimizer=optimizer,\n",
        "              metrics=[\"mae\"])\n",
        "\n",
        "#Model Callbacks\n",
        "early_stopping = keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True)"
      ],
      "metadata": {
        "id": "TasYSr852Yfd"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_schedule = keras.callbacks.LearningRateScheduler(\n",
        "    lambda epoch: 1e-6 * 10**(epoch /30)\n",
        ")\n",
        "\n",
        "history = model.fit(train_set, epochs=200,\n",
        "                    validation_data=valid_set,\n",
        "                    callbacks=[early_stopping,lr_schedule])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mH9z1Erl2YdH",
        "outputId": "10f4da27-dcb6-40b6-d49d-c7db35e08bbf"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "216/216 [==============================] - 5s 21ms/step - loss: 25.1751 - mae: 25.6749 - val_loss: 67.2309 - val_mae: 67.7309 - lr: 1.0000e-06\n",
            "Epoch 2/200\n",
            "216/216 [==============================] - 3s 13ms/step - loss: 2.9050 - mae: 3.3700 - val_loss: 4.6155 - val_mae: 5.0949 - lr: 1.0798e-06\n",
            "Epoch 3/200\n",
            "216/216 [==============================] - 3s 12ms/step - loss: 0.5696 - mae: 0.8762 - val_loss: 4.5369 - val_mae: 5.0155 - lr: 1.1659e-06\n",
            "Epoch 4/200\n",
            "216/216 [==============================] - 3s 14ms/step - loss: 0.5620 - mae: 0.8689 - val_loss: 4.5364 - val_mae: 5.0151 - lr: 1.2589e-06\n",
            "Epoch 5/200\n",
            "216/216 [==============================] - 3s 12ms/step - loss: 0.5589 - mae: 0.8631 - val_loss: 4.4445 - val_mae: 4.9249 - lr: 1.3594e-06\n",
            "Epoch 6/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.5508 - mae: 0.8541 - val_loss: 4.4039 - val_mae: 4.8836 - lr: 1.4678e-06\n",
            "Epoch 7/200\n",
            "216/216 [==============================] - 2s 10ms/step - loss: 0.5520 - mae: 0.8538 - val_loss: 4.4361 - val_mae: 4.9157 - lr: 1.5849e-06\n",
            "Epoch 8/200\n",
            "216/216 [==============================] - 2s 7ms/step - loss: 0.5354 - mae: 0.8362 - val_loss: 4.3354 - val_mae: 4.8113 - lr: 1.7113e-06\n",
            "Epoch 9/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.5255 - mae: 0.8269 - val_loss: 4.2944 - val_mae: 4.7702 - lr: 1.8478e-06\n",
            "Epoch 10/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.5157 - mae: 0.8145 - val_loss: 4.3188 - val_mae: 4.7982 - lr: 1.9953e-06\n",
            "Epoch 11/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.5063 - mae: 0.8048 - val_loss: 4.3318 - val_mae: 4.8103 - lr: 2.1544e-06\n",
            "Epoch 12/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.4961 - mae: 0.7919 - val_loss: 4.0640 - val_mae: 4.5418 - lr: 2.3263e-06\n",
            "Epoch 13/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.4866 - mae: 0.7795 - val_loss: 4.1287 - val_mae: 4.6060 - lr: 2.5119e-06\n",
            "Epoch 14/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.4759 - mae: 0.7693 - val_loss: 4.0413 - val_mae: 4.5171 - lr: 2.7123e-06\n",
            "Epoch 15/200\n",
            "216/216 [==============================] - 2s 8ms/step - loss: 0.4645 - mae: 0.7552 - val_loss: 3.9227 - val_mae: 4.3970 - lr: 2.9286e-06\n",
            "Epoch 16/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.4517 - mae: 0.7406 - val_loss: 3.7819 - val_mae: 4.2555 - lr: 3.1623e-06\n",
            "Epoch 17/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.4372 - mae: 0.7246 - val_loss: 3.7493 - val_mae: 4.2221 - lr: 3.4145e-06\n",
            "Epoch 18/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.4266 - mae: 0.7114 - val_loss: 3.8143 - val_mae: 4.2886 - lr: 3.6869e-06\n",
            "Epoch 19/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.4092 - mae: 0.6903 - val_loss: 3.4923 - val_mae: 3.9689 - lr: 3.9811e-06\n",
            "Epoch 20/200\n",
            "216/216 [==============================] - 2s 7ms/step - loss: 0.3950 - mae: 0.6766 - val_loss: 3.3967 - val_mae: 3.8718 - lr: 4.2987e-06\n",
            "Epoch 21/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.3824 - mae: 0.6600 - val_loss: 3.3785 - val_mae: 3.8507 - lr: 4.6416e-06\n",
            "Epoch 22/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.3679 - mae: 0.6416 - val_loss: 3.2153 - val_mae: 3.6866 - lr: 5.0119e-06\n",
            "Epoch 23/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.3529 - mae: 0.6234 - val_loss: 3.1053 - val_mae: 3.5739 - lr: 5.4117e-06\n",
            "Epoch 24/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.3413 - mae: 0.6114 - val_loss: 3.1752 - val_mae: 3.6430 - lr: 5.8434e-06\n",
            "Epoch 25/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.3357 - mae: 0.6010 - val_loss: 2.9158 - val_mae: 3.3834 - lr: 6.3096e-06\n",
            "Epoch 26/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.3089 - mae: 0.5708 - val_loss: 2.8231 - val_mae: 3.2911 - lr: 6.8129e-06\n",
            "Epoch 27/200\n",
            "216/216 [==============================] - 2s 10ms/step - loss: 0.2991 - mae: 0.5563 - val_loss: 2.8263 - val_mae: 3.2918 - lr: 7.3564e-06\n",
            "Epoch 28/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.2871 - mae: 0.5415 - val_loss: 2.7271 - val_mae: 3.1921 - lr: 7.9433e-06\n",
            "Epoch 29/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.2812 - mae: 0.5322 - val_loss: 2.5941 - val_mae: 3.0566 - lr: 8.5770e-06\n",
            "Epoch 30/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.2612 - mae: 0.5107 - val_loss: 2.5455 - val_mae: 3.0102 - lr: 9.2612e-06\n",
            "Epoch 31/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.2565 - mae: 0.5054 - val_loss: 2.5754 - val_mae: 3.0374 - lr: 1.0000e-05\n",
            "Epoch 32/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.2404 - mae: 0.4808 - val_loss: 2.4167 - val_mae: 2.8783 - lr: 1.0798e-05\n",
            "Epoch 33/200\n",
            "216/216 [==============================] - 2s 10ms/step - loss: 0.2330 - mae: 0.4704 - val_loss: 2.3477 - val_mae: 2.8079 - lr: 1.1659e-05\n",
            "Epoch 34/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.2304 - mae: 0.4684 - val_loss: 2.3577 - val_mae: 2.8222 - lr: 1.2589e-05\n",
            "Epoch 35/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.2322 - mae: 0.4736 - val_loss: 2.2250 - val_mae: 2.6845 - lr: 1.3594e-05\n",
            "Epoch 36/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.2197 - mae: 0.4520 - val_loss: 2.1905 - val_mae: 2.6504 - lr: 1.4678e-05\n",
            "Epoch 37/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.2115 - mae: 0.4420 - val_loss: 2.1724 - val_mae: 2.6319 - lr: 1.5849e-05\n",
            "Epoch 38/200\n",
            "216/216 [==============================] - 4s 16ms/step - loss: 0.2212 - mae: 0.4546 - val_loss: 2.2137 - val_mae: 2.6688 - lr: 1.7113e-05\n",
            "Epoch 39/200\n",
            "216/216 [==============================] - 2s 8ms/step - loss: 0.2128 - mae: 0.4407 - val_loss: 2.1616 - val_mae: 2.6155 - lr: 1.8478e-05\n",
            "Epoch 40/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.2002 - mae: 0.4309 - val_loss: 2.1862 - val_mae: 2.6409 - lr: 1.9953e-05\n",
            "Epoch 41/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.1988 - mae: 0.4275 - val_loss: 2.0306 - val_mae: 2.4890 - lr: 2.1544e-05\n",
            "Epoch 42/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.2103 - mae: 0.4405 - val_loss: 2.0932 - val_mae: 2.5515 - lr: 2.3263e-05\n",
            "Epoch 43/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.2008 - mae: 0.4382 - val_loss: 2.1053 - val_mae: 2.5578 - lr: 2.5119e-05\n",
            "Epoch 44/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.2271 - mae: 0.4512 - val_loss: 2.0593 - val_mae: 2.5110 - lr: 2.7123e-05\n",
            "Epoch 45/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.2039 - mae: 0.4420 - val_loss: 2.0660 - val_mae: 2.5254 - lr: 2.9286e-05\n",
            "Epoch 46/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.2083 - mae: 0.4301 - val_loss: 1.8950 - val_mae: 2.3456 - lr: 3.1623e-05\n",
            "Epoch 47/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.2003 - mae: 0.4283 - val_loss: 1.9500 - val_mae: 2.4069 - lr: 3.4145e-05\n",
            "Epoch 48/200\n",
            "216/216 [==============================] - 2s 10ms/step - loss: 0.2103 - mae: 0.4473 - val_loss: 3.2992 - val_mae: 3.7728 - lr: 3.6869e-05\n",
            "Epoch 49/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.2334 - mae: 0.4980 - val_loss: 1.7959 - val_mae: 2.2430 - lr: 3.9811e-05\n",
            "Epoch 50/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.2051 - mae: 0.4234 - val_loss: 4.1871 - val_mae: 4.6748 - lr: 4.2987e-05\n",
            "Epoch 51/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.3518 - mae: 0.6301 - val_loss: 3.0582 - val_mae: 3.5335 - lr: 4.6416e-05\n",
            "Epoch 52/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.3332 - mae: 0.6222 - val_loss: 4.1165 - val_mae: 4.6073 - lr: 5.0119e-05\n",
            "Epoch 53/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.5658 - mae: 0.8628 - val_loss: 1.8879 - val_mae: 2.3398 - lr: 5.4117e-05\n",
            "Epoch 54/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.3239 - mae: 0.5630 - val_loss: 4.2775 - val_mae: 4.7678 - lr: 5.8434e-05\n",
            "Epoch 55/200\n",
            "216/216 [==============================] - 2s 10ms/step - loss: 0.4088 - mae: 0.6948 - val_loss: 2.8549 - val_mae: 3.3260 - lr: 6.3096e-05\n",
            "Epoch 56/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.4278 - mae: 0.7287 - val_loss: 1.9083 - val_mae: 2.3700 - lr: 6.8129e-05\n",
            "Epoch 57/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.2987 - mae: 0.5340 - val_loss: 7.2910 - val_mae: 7.7901 - lr: 7.3564e-05\n",
            "Epoch 58/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.4786 - mae: 0.7616 - val_loss: 1.5683 - val_mae: 2.0120 - lr: 7.9433e-05\n",
            "Epoch 59/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 0.8260 - mae: 1.1225 - val_loss: 18.0280 - val_mae: 18.5280 - lr: 8.5770e-05\n",
            "Epoch 60/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 0.8810 - mae: 1.1542 - val_loss: 1.6379 - val_mae: 2.0822 - lr: 9.2612e-05\n",
            "Epoch 61/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 1.0990 - mae: 1.3562 - val_loss: 17.0485 - val_mae: 17.5485 - lr: 1.0000e-04\n",
            "Epoch 62/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 1.7591 - mae: 2.0570 - val_loss: 18.7879 - val_mae: 19.2879 - lr: 1.0798e-04\n",
            "Epoch 63/200\n",
            "216/216 [==============================] - 2s 10ms/step - loss: 0.8210 - mae: 1.1170 - val_loss: 16.1789 - val_mae: 16.6789 - lr: 1.1659e-04\n",
            "Epoch 64/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 1.7863 - mae: 2.1414 - val_loss: 5.3442 - val_mae: 5.8413 - lr: 1.2589e-04\n",
            "Epoch 65/200\n",
            "216/216 [==============================] - 1s 6ms/step - loss: 1.2187 - mae: 1.4983 - val_loss: 17.4906 - val_mae: 17.9906 - lr: 1.3594e-04\n",
            "Epoch 66/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 1.9096 - mae: 2.1944 - val_loss: 5.7555 - val_mae: 6.2508 - lr: 1.4678e-04\n",
            "Epoch 67/200\n",
            "216/216 [==============================] - 1s 5ms/step - loss: 2.3770 - mae: 2.6213 - val_loss: 30.1468 - val_mae: 30.6468 - lr: 1.5849e-04\n",
            "Epoch 68/200\n",
            "216/216 [==============================] - 2s 7ms/step - loss: 2.1518 - mae: 2.4002 - val_loss: 13.0567 - val_mae: 13.5567 - lr: 1.7113e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_history(history):\n",
        "  sns.set()\n",
        "  fig, axs = plt.subplots(1,2, figsize=(15,5))\n",
        "  axs[0].plot(history.history['mae'])\n",
        "  axs[0].plot(history.history['val_mae'])\n",
        "  axs[0].set_title('Model MAE')\n",
        "  axs[0].set_ylabel('MAE')\n",
        "  axs[0].set_xlabel('Epoch')\n",
        "  axs[0].legend(['Train','Validation'], loc='upper left')\n",
        "\n",
        "  best_epoch = np.argmin(history.history['val_mae'])\n",
        "  axs[0].axvline(x=best_epoch, color='r', linestyle='--')\n",
        "\n",
        "  axs[1].plot(history.history['loss'])\n",
        "  axs[1].plot(history.history['val_loss'])\n",
        "  axs[1].set_title('Model Loss')\n",
        "  axs[1].set_ylabel('Loss')\n",
        "  axs[1].set_xlabel('Epoch')\n",
        "  axs[1].legend(['Train','Validation'], loc='upper left')\n",
        "\n",
        "  best_epoch = np.argmin(history.history['val_loss'])\n",
        "  print(best_epoch)\n",
        "  axs[1].axvline(x=best_epoch, color='r', linestyle='--')\n",
        "\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "plot_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "eW4c6GlB2Yap",
        "outputId": "2bbe70cd-6c81-4e71-fa42-73c139f78a57"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "57\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The best epoch is 48 it is Auto Restored by Early Stopping Callback"
      ],
      "metadata": {
        "id": "BKpei1BGAgw7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def model_forecast(model, series, window_size):\n",
        "  ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "  ds = ds.window(window_size, shift=1, drop_remainder=True)\n",
        "  ds = ds.flat_map(lambda w: w.batch(window_size))\n",
        "  ds = ds.batch(32).prefetch(1)\n",
        "  forecast = model.predict(ds)\n",
        "  return forecast"
      ],
      "metadata": {
        "id": "aslKZdF3BELK"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Forecasting\n",
        "forecast = model_forecast(model, valid_close_data, window_size)[:, 0]\n",
        "forecast"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fOvieVZwBgzW",
        "outputId": "5cf303ae-e8b5-488c-fccd-2abcb4cd4685"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "27/27 [==============================] - 0s 9ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 90.079094,  89.21432 ,  90.36965 ,  90.00034 ,  89.01612 ,\n",
              "        88.937065,  85.615974,  82.255135,  81.442154,  79.59165 ,\n",
              "        78.34855 ,  82.52318 ,  80.25312 ,  76.261604,  76.017426,\n",
              "        76.24796 ,  71.71159 ,  78.78416 ,  71.65435 ,  66.697716,\n",
              "        69.44358 ,  63.436535,  60.116272,  60.088596,  62.588226,\n",
              "        54.136402,  58.196243,  55.6006  ,  61.652752,  70.77816 ,\n",
              "        63.60753 ,  67.82    ,  68.24851 ,  66.88894 ,  66.05191 ,\n",
              "        68.278465,  66.25991 ,  70.507545,  66.98413 ,  65.79414 ,\n",
              "        71.151794,  68.85944 ,  71.83944 ,  73.655716,  78.5767  ,\n",
              "        71.656166,  78.737175,  78.55649 ,  77.93636 ,  79.70861 ,\n",
              "        75.432884,  78.533714,  68.551414,  78.69634 ,  74.05517 ,\n",
              "        70.381966,  77.77034 ,  74.41123 ,  76.41081 ,  75.79168 ,\n",
              "        73.817696,  71.56671 ,  75.4875  ,  76.22973 ,  74.426674,\n",
              "        77.763   ,  71.1603  ,  76.03394 ,  78.48716 ,  76.829124,\n",
              "        79.33835 ,  80.68325 ,  74.818245,  78.156204,  76.65276 ,\n",
              "        74.468864,  81.38479 ,  81.586174,  81.1262  ,  82.162056,\n",
              "        80.967636,  75.364586,  75.68163 ,  78.67189 ,  80.42267 ,\n",
              "        81.44468 ,  73.43193 ,  74.583984,  73.94827 ,  76.40525 ,\n",
              "        76.49447 ,  74.53164 ,  74.21814 ,  71.72415 ,  71.90368 ,\n",
              "        72.91244 ,  75.69323 ,  74.427536,  78.14979 ,  73.2275  ,\n",
              "        71.669975,  70.878876,  72.58087 ,  74.87415 ,  75.607994,\n",
              "        76.27123 ,  72.47844 ,  73.00555 ,  72.11288 ,  77.47938 ,\n",
              "        78.40419 ,  73.75111 ,  74.11225 ,  74.58049 ,  77.54403 ,\n",
              "        76.770164,  78.3469  ,  76.34855 ,  75.07636 ,  75.9854  ,\n",
              "        75.218185,  75.981476,  75.91511 ,  75.112076,  78.53386 ,\n",
              "        78.68086 ,  76.567635,  78.56287 ,  79.41238 ,  80.2225  ,\n",
              "        78.95324 ,  77.618004,  77.13479 ,  77.78236 ,  81.9191  ,\n",
              "        82.198296,  80.567406,  81.62913 ,  82.50248 ,  85.29867 ,\n",
              "        87.332794,  87.65577 ,  86.42088 ,  86.83219 ,  87.21001 ,\n",
              "        85.443474,  87.438614,  84.92826 ,  86.15296 ,  86.96644 ,\n",
              "        83.416504,  83.268425,  84.86965 ,  86.093925,  86.56799 ,\n",
              "        84.30939 ,  82.83474 ,  85.10014 ,  85.071594,  85.127205,\n",
              "        87.07805 ,  85.47558 ,  85.29745 ,  86.7371  ,  86.8332  ,\n",
              "        88.38029 ,  92.43978 ,  89.70854 ,  90.892975,  89.90012 ,\n",
              "        87.69648 ,  88.795616,  86.79249 ,  88.58666 ,  88.847046,\n",
              "        88.13046 ,  89.20539 ,  89.62293 ,  88.239815,  88.93229 ,\n",
              "        89.18344 ,  87.575806,  88.67132 ,  87.49776 ,  89.49539 ,\n",
              "        89.652145,  90.00363 ,  92.70867 ,  92.62744 ,  92.9272  ,\n",
              "        92.11294 ,  95.86329 ,  96.4923  , 100.21264 ,  96.50724 ,\n",
              "        97.62441 ,  98.64181 ,  96.86616 ,  98.06367 ,  97.450005,\n",
              "        96.78191 ,  96.41836 ,  98.305725,  97.12548 , 101.222466,\n",
              "       101.79039 , 101.22013 , 102.5938  ,  97.930336, 104.77489 ,\n",
              "       103.59968 , 104.196625, 102.65755 , 105.7813  , 101.202484,\n",
              "       101.74505 , 102.94336 , 102.11269 , 104.64944 , 100.80192 ,\n",
              "       101.63639 , 104.69926 , 104.50695 , 105.17833 , 105.238464,\n",
              "       102.79127 , 104.61005 , 105.75417 , 104.23436 , 107.25402 ,\n",
              "       101.706955, 101.82575 , 102.884995, 101.70345 , 102.36314 ,\n",
              "       106.407265, 101.23535 , 100.51623 , 103.59731 , 104.388435,\n",
              "       102.902306, 101.53093 ,  98.28843 ,  99.9382  ,  98.509346,\n",
              "       100.224144, 100.3115  , 104.67868 , 105.2096  , 102.87128 ,\n",
              "       104.45245 , 105.48327 , 107.25083 , 108.815636, 106.00436 ,\n",
              "       106.59369 , 104.76298 , 102.9058  , 100.49562 , 104.02221 ,\n",
              "        99.17646 , 104.540794, 104.37629 , 105.093864, 106.019325,\n",
              "       109.41822 , 105.05825 , 108.21276 , 109.92487 , 105.30143 ,\n",
              "       107.99386 , 105.40245 , 106.532814, 112.419914, 108.07996 ,\n",
              "       105.81492 , 105.02503 , 107.36992 , 108.15443 , 108.542   ,\n",
              "       107.46246 , 110.69353 , 107.89493 , 107.11002 , 106.13027 ,\n",
              "       109.48177 , 110.75029 , 113.59631 , 112.39251 , 111.24137 ,\n",
              "       113.16041 , 113.54424 , 117.48225 , 116.708786, 114.54855 ,\n",
              "       116.151405, 114.67842 , 114.22093 , 116.83618 , 117.343636,\n",
              "       118.4329  , 117.176125, 115.099   , 112.913506, 114.925606,\n",
              "       113.939735, 117.46137 , 115.49079 , 111.38997 , 112.0496  ,\n",
              "       114.69153 , 113.838646, 114.72706 , 112.61009 , 111.021126,\n",
              "       111.86161 , 111.716324, 109.3361  , 110.52968 , 109.93979 ,\n",
              "       110.54783 , 111.98079 , 109.84582 , 110.664986, 114.45107 ,\n",
              "       113.53689 , 114.6014  , 112.351074, 112.33605 , 113.22491 ,\n",
              "       113.04446 , 112.60947 , 109.10633 , 110.392296, 110.95573 ,\n",
              "       113.05018 , 111.87922 , 110.03568 , 111.463394, 110.616005,\n",
              "       111.35721 , 114.21501 , 111.48429 , 111.22339 , 112.29447 ,\n",
              "       112.29166 , 111.67074 , 111.474304, 112.21273 , 113.87762 ,\n",
              "       115.4549  , 115.1697  , 115.12026 , 117.487175, 117.680695,\n",
              "       120.287735, 118.903885, 117.74755 , 118.25586 , 116.149025,\n",
              "       118.64007 , 119.850525, 122.25721 , 122.41029 , 123.18377 ,\n",
              "       121.22666 , 121.79395 , 124.55766 , 124.09547 , 124.495865,\n",
              "       121.12092 , 118.31295 , 118.07092 , 119.441795, 118.79063 ,\n",
              "       117.38721 , 115.026344, 114.244095, 113.77347 , 114.76881 ,\n",
              "       117.86267 , 117.06602 , 118.38975 , 116.432274, 116.108765,\n",
              "       114.75532 , 113.356026, 114.92327 , 113.62033 , 114.7412  ,\n",
              "       115.63497 , 116.24963 , 116.47251 , 116.52594 , 116.62498 ,\n",
              "       116.69939 , 119.51637 , 119.91517 , 119.612335, 119.09878 ,\n",
              "       114.174065, 114.39093 , 115.66053 , 114.594604, 113.10343 ,\n",
              "       113.79926 , 110.969734, 111.89755 , 113.41192 , 111.445915,\n",
              "       114.57135 , 111.65261 , 111.822784, 111.52142 , 111.334564,\n",
              "       111.735725, 114.76103 , 111.82769 , 109.59499 , 111.020195,\n",
              "       107.96036 , 111.25647 , 111.396126, 110.924385, 113.34223 ,\n",
              "       114.9896  , 112.89918 , 115.01025 , 115.00532 , 113.25504 ,\n",
              "       115.07526 , 113.439156, 109.764435, 110.01651 , 112.06175 ,\n",
              "       113.11893 , 110.00675 , 113.206474, 112.36639 , 113.25403 ,\n",
              "       112.4886  , 113.84745 , 113.867424, 115.32645 , 111.488335,\n",
              "       112.23913 , 113.70037 , 110.767265, 111.54796 , 113.596695,\n",
              "       111.20219 , 108.15246 , 108.08317 , 110.184166, 111.822876,\n",
              "       114.835396, 111.20904 , 113.79271 , 112.20282 , 114.12182 ,\n",
              "       113.767075, 115.09405 , 115.58938 , 116.50703 , 117.01913 ,\n",
              "       112.315315, 110.32315 , 110.61113 , 112.04458 , 113.432846,\n",
              "       111.90712 , 108.60764 , 110.264305, 113.122345, 114.82849 ,\n",
              "       117.94625 , 118.804276, 116.37704 , 113.25614 , 114.573586,\n",
              "       110.29464 , 109.74162 , 106.255844, 104.01639 , 101.03634 ,\n",
              "        98.61087 ,  93.630104,  96.481834,  98.777115,  97.59926 ,\n",
              "        98.293236,  98.326256,  94.53476 ,  96.321785, 100.57516 ,\n",
              "        99.373886,  98.20422 ,  96.04159 ,  94.13389 ,  95.44776 ,\n",
              "        95.31472 ,  96.29921 ,  97.28986 ,  95.515656,  91.50962 ,\n",
              "        92.79743 ,  94.20051 ,  94.43952 ,  95.42832 ,  94.27505 ,\n",
              "        93.18243 ,  90.675896,  90.23449 ,  93.87854 ,  95.267296,\n",
              "        90.992134,  92.48176 ,  89.35715 ,  88.144035,  86.700005,\n",
              "        86.8823  ,  89.39133 ,  90.200325,  80.705124,  77.80706 ,\n",
              "        83.28428 ,  88.59379 ,  88.68554 ,  85.72447 ,  82.90612 ,\n",
              "        85.49817 ,  87.366516,  89.7194  ,  89.634964,  90.73934 ,\n",
              "        89.95344 ,  91.251686,  87.428955,  88.64328 ,  89.60982 ,\n",
              "        86.7739  ,  84.90526 ,  85.61058 ,  80.664604,  79.15281 ,\n",
              "        81.434135,  82.94908 ,  83.92341 ,  78.91661 ,  75.50344 ,\n",
              "        79.476814,  79.1787  ,  80.492935,  78.199585,  77.822014,\n",
              "        78.11177 ,  78.14324 ,  76.24097 ,  74.16209 ,  72.89556 ,\n",
              "        76.6858  ,  77.11411 ,  75.96501 ,  70.91394 ,  75.03484 ,\n",
              "        71.86704 ,  73.16242 ,  76.13289 ,  74.39579 ,  72.55069 ,\n",
              "        69.021355,  71.33896 ,  73.078835,  75.10755 ,  70.46572 ,\n",
              "        72.25768 ,  73.75146 ,  74.443306,  79.505806,  78.11884 ,\n",
              "        80.12671 ,  77.7893  ,  79.10398 ,  74.23536 ,  79.232796,\n",
              "        78.232   ,  80.241844,  76.451546,  69.16624 ,  76.924774,\n",
              "        71.42101 ,  71.72677 ,  71.685455,  73.8056  ,  71.26572 ,\n",
              "        75.55111 ,  76.87242 ,  76.202034,  79.155464,  75.551414,\n",
              "        80.20073 ,  81.35172 ,  79.35677 ,  78.77025 ,  79.64652 ,\n",
              "        75.05398 ,  76.78254 ,  78.43946 ,  75.82043 ,  80.06312 ,\n",
              "        78.14734 ,  79.31969 ,  82.516914,  83.58236 ,  83.97549 ,\n",
              "        85.320145,  79.85468 ,  81.35468 ,  86.89835 ,  84.319145,\n",
              "        84.33566 ,  83.94506 ,  84.43836 ,  85.5104  ,  85.84238 ,\n",
              "        83.42733 ,  86.095665,  85.808945,  86.19558 ,  89.13553 ,\n",
              "        88.29988 ,  90.0755  ,  88.28239 ,  87.33292 ,  86.949646,\n",
              "        85.38201 ,  85.20251 ,  88.519966,  87.49824 ,  83.43642 ,\n",
              "        83.04563 ,  82.40902 ,  85.82401 ,  85.96609 ,  83.23413 ,\n",
              "        80.85359 ,  86.25452 ,  89.26627 ,  88.12307 ,  89.61497 ,\n",
              "        87.10627 ,  92.27506 ,  93.3223  ,  90.22502 ,  89.097176,\n",
              "        92.22413 ,  88.973145,  87.64207 ,  86.962364,  83.90609 ,\n",
              "        86.16071 ,  84.53397 ,  83.71833 ,  83.99282 ,  84.15921 ,\n",
              "        87.591415,  90.91364 ,  90.0293  ,  85.68032 ,  86.79437 ,\n",
              "        87.78259 ,  91.000404,  91.5407  ,  88.23149 ,  85.01485 ,\n",
              "        86.182785,  85.57256 ,  84.08013 ,  89.6372  ,  84.45094 ,\n",
              "        86.164185,  87.23086 ,  84.21191 ,  84.78123 ,  89.380974,\n",
              "        88.134026,  85.39546 ,  85.783195,  86.648865,  89.60601 ,\n",
              "        91.28991 ,  89.71169 ,  96.20226 ,  94.64396 ,  97.51926 ,\n",
              "        97.05479 ,  97.80154 ,  96.75901 ,  99.184135,  96.00679 ,\n",
              "        98.180214, 100.27399 ,  98.964134,  97.081345,  97.95352 ,\n",
              "        99.41083 , 100.85771 , 106.11297 , 102.97748 , 101.48674 ,\n",
              "       102.75041 , 104.52035 , 105.335   , 102.4744  , 104.61831 ,\n",
              "        99.94344 ,  97.371574,  99.70647 ,  98.625374, 100.653984,\n",
              "        97.49221 ,  96.07515 ,  96.335915,  97.631454,  99.322685,\n",
              "       101.29066 , 100.072426, 100.3472  , 101.60093 , 102.8987  ,\n",
              "       103.38169 , 106.064224, 106.23836 , 105.613205, 107.093124,\n",
              "       105.67578 , 105.88819 , 106.132866, 102.816414, 105.33925 ,\n",
              "       107.0433  , 105.735535, 106.10609 , 105.63288 , 107.65162 ,\n",
              "       108.08045 , 108.387924, 111.474556, 111.222855, 106.84372 ,\n",
              "       105.53567 , 106.54279 , 108.502785, 105.38557 , 107.05818 ,\n",
              "       105.548256, 105.700325, 106.51392 , 105.252014, 108.67668 ,\n",
              "       106.705185, 106.49435 , 105.14829 , 103.73107 , 102.97029 ,\n",
              "       104.06284 , 102.242294, 100.667336, 101.858536, 101.53149 ,\n",
              "       102.14806 , 102.246254, 101.558784, 102.15812 , 101.96622 ,\n",
              "       100.36733 , 100.605934, 102.18735 ,  96.79941 ,  97.69316 ,\n",
              "        99.06232 ,  99.6805  ,  96.74403 ,  98.063995,  98.32937 ,\n",
              "        98.86373 , 101.55529 , 101.60046 , 104.59682 , 103.70367 ,\n",
              "       101.93583 , 102.834145, 103.79464 , 104.937164, 106.59596 ,\n",
              "       106.69756 , 104.85264 , 107.44951 , 107.89468 , 106.81515 ,\n",
              "       108.096436, 106.74415 , 106.58099 , 109.101425, 108.05629 ,\n",
              "       109.534904, 113.523415, 112.83184 , 113.95753 , 114.45521 ,\n",
              "       106.422554, 106.19454 , 110.114685, 110.608444, 105.614235,\n",
              "       106.08565 , 102.03384 , 105.04626 , 108.00449 , 102.93246 ,\n",
              "       105.20498 , 107.68529 , 104.204445, 103.81794 , 103.07926 ,\n",
              "       102.48731 , 103.894066, 102.532906,  95.74558 ,  95.10827 ,\n",
              "        92.12508 ,  97.37048 , 100.64353 ,  99.72235 ,  95.85382 ,\n",
              "        99.34693 ,  96.522545,  98.73407 , 104.24838 , 100.89595 ,\n",
              "       100.43512 , 102.21248 ,  99.12879 ,  99.60915 , 101.007744,\n",
              "        98.45536 ,  98.46949 , 100.938354,  98.43467 ,  98.20061 ],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast_df = pd.DataFrame(forecast[:-1],\n",
        "                           index=valid_close_data.index[window_size:window_size+len(forecast)],\n",
        "                           columns=['Forecast'])"
      ],
      "metadata": {
        "id": "dRmRnCLPBxlG"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "forecast_series = forecast_df['Forecast'].squeeze()\n",
        "forecast_series"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JmrXw73PCKA_",
        "outputId": "33acdff9-0e84-4222-b87f-478874abd6a8"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2020-02-14     90.079094\n",
              "2020-02-18     89.214317\n",
              "2020-02-19     90.369652\n",
              "2020-02-20     90.000343\n",
              "2020-02-21     89.016121\n",
              "                 ...    \n",
              "2023-06-23    101.007744\n",
              "2023-06-26     98.455360\n",
              "2023-06-27     98.469490\n",
              "2023-06-28    100.938354\n",
              "2023-06-29     98.434669\n",
              "Name: Forecast, Length: 849, dtype: float32"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_predictions(y_true, y_pred):\n",
        "  sns.set()\n",
        "  plt.figure(figsize=(15,5))\n",
        "  plt.plot(y_true, label='True Values')\n",
        "  plt.plot(y_pred, label='Forecasted Values')\n",
        "  plt.title('Forecasting of Starbucks Stock Prices using Simple NN')\n",
        "  plt.ylabel('Value')\n",
        "  plt.xlabel('Index')\n",
        "  plt.legend(loc ='upper left')\n",
        "  plt.show()\n",
        "\n",
        "plot_predictions(valid_close_data, forecast_series)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "OtXr0Y30CYQG",
        "outputId": "28714058-33f8-4a83-e3fc-22eb1be1c296"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(valid_close_data.head(5), '\\n')\n",
        "print(forecast_series.head(5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CDPThzrbDLSd",
        "outputId": "0068624a-a19d-401d-efec-c67aba02f94d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date\n",
            "2020-01-02    89.349998\n",
            "2020-01-03    88.830002\n",
            "2020-01-06    88.129997\n",
            "2020-01-07    87.860001\n",
            "2020-01-08    88.879997\n",
            "Name: Close, dtype: float64 \n",
            "\n",
            "Date\n",
            "2020-02-14    90.079094\n",
            "2020-02-18    89.214317\n",
            "2020-02-19    90.369652\n",
            "2020-02-20    90.000343\n",
            "2020-02-21    89.016121\n",
            "Name: Forecast, dtype: float32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Valid Data:', len(valid_close_data))\n",
        "print('Forecasted Data:', len(forecast_series))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBIIjbzODV9I",
        "outputId": "6c47cede-c6e4-4d1b-f50f-11500a5dc526"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valid Data: 879\n",
            "Forecasted Data: 849\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_close_data = valid_close_data[window_size: ]\n",
        "print('Valid Data:', len(valid_close_data))\n",
        "print('Forecasted Data:', len(forecast_series))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-48CacxDi2R",
        "outputId": "0f4efe34-ca59-4693-8dab-718646575b28"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Valid Data: 849\n",
            "Forecasted Data: 849\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(valid_close_data.head(5), '\\n')\n",
        "print(forecast_series.head(5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9MToqUGDsEE",
        "outputId": "11cf6476-3df5-4b9f-e513-3d111fe13ea6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date\n",
            "2020-02-14    89.279999\n",
            "2020-02-18    89.230003\n",
            "2020-02-19    90.139999\n",
            "2020-02-20    88.599998\n",
            "2020-02-21    87.360001\n",
            "Name: Close, dtype: float64 \n",
            "\n",
            "Date\n",
            "2020-02-14    90.079094\n",
            "2020-02-18    89.214317\n",
            "2020-02-19    90.369652\n",
            "2020-02-20    90.000343\n",
            "2020-02-21    89.016121\n",
            "Name: Forecast, dtype: float32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Mean Squared Error: {math.sqrt(mean_squared_error(valid_close_data, forecast_series).numpy())}')\n",
        "print(f'Mean Squared Error: {mean_squared_error(valid_close_data, forecast_series).numpy()}')\n",
        "print(f'Mean Absolute Error: {mean_absolute_error(valid_close_data, forecast_series).numpy()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w5T0oLWxDxZ6",
        "outputId": "cd192d3a-af90-4536-b18c-62031e11b6ed"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 2.632750377431503\n",
            "Mean Squared Error: 6.931374549865723\n",
            "Mean Absolute Error: 2.0119900703430176\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## GRU"
      ],
      "metadata": {
        "id": "HQfMWWNtEZ9Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.layers import LSTM, GRU\n",
        "\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score, r2_score\n",
        "from sklearn.metrics import mean_poisson_deviance, mean_gamma_deviance, accuracy_score\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "gNiwY-CbEZTm"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "_DBptILdEZQx",
        "outputId": "05506572-3b20-4fab-eeee-466dbf88e25a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close     Volume\n",
              "Date                                                                    \n",
              "1992-06-26  0.328125  0.347656  0.320313  0.335938   0.267131  224358400\n",
              "1992-06-29  0.339844  0.367188  0.332031  0.359375   0.285767   58732800\n",
              "1992-06-30  0.367188  0.371094  0.343750  0.347656   0.276449   34777600\n",
              "1992-07-01  0.351563  0.359375  0.339844  0.355469   0.282661   18316800\n",
              "1992-07-02  0.359375  0.359375  0.347656  0.355469   0.282661   13996800"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c9750407-fcf1-4e45-b209-1be90f2cfa1b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1992-06-26</th>\n",
              "      <td>0.328125</td>\n",
              "      <td>0.347656</td>\n",
              "      <td>0.320313</td>\n",
              "      <td>0.335938</td>\n",
              "      <td>0.267131</td>\n",
              "      <td>224358400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-06-29</th>\n",
              "      <td>0.339844</td>\n",
              "      <td>0.367188</td>\n",
              "      <td>0.332031</td>\n",
              "      <td>0.359375</td>\n",
              "      <td>0.285767</td>\n",
              "      <td>58732800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-06-30</th>\n",
              "      <td>0.367188</td>\n",
              "      <td>0.371094</td>\n",
              "      <td>0.343750</td>\n",
              "      <td>0.347656</td>\n",
              "      <td>0.276449</td>\n",
              "      <td>34777600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-07-01</th>\n",
              "      <td>0.351563</td>\n",
              "      <td>0.359375</td>\n",
              "      <td>0.339844</td>\n",
              "      <td>0.355469</td>\n",
              "      <td>0.282661</td>\n",
              "      <td>18316800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992-07-02</th>\n",
              "      <td>0.359375</td>\n",
              "      <td>0.359375</td>\n",
              "      <td>0.347656</td>\n",
              "      <td>0.355469</td>\n",
              "      <td>0.282661</td>\n",
              "      <td>13996800</td>\n",
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              "  </tbody>\n",
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          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.reset_index()"
      ],
      "metadata": {
        "id": "rn10lr1dYVNk"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "closed_data = data[['Date','Close']]\n",
        "closed_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEMUmYj8EZOC",
        "outputId": "c14aa67a-c794-49e3-e8c6-04d641e5b2c9"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7809, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 24
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    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "fig = px.line(closed_data, x=closed_data.Date, y=closed_data.Close,labels={'date':'Date','close':'Close Stock'})\n",
        "fig.update_traces(marker_line_width=2, opacity=0.8)\n",
        "fig.update_layout(title_text='Stock close price chart', plot_bgcolor='white', font_size=15, font_color='black')\n",
        "fig.update_xaxes(showgrid=False)\n",
        "fig.update_yaxes(showgrid=False)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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        "id": "rW6ozqdfEZIv",
        "outputId": "e8ee1d08-966c-4da4-96be-a111eb5ae389"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
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              "                            \n",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "closedf = closed_data[closed_data['Date'] > '2020-01-01']\n",
        "close_stock = closedf.copy()\n",
        "print(\"Total data for prediction: \",closedf.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rO2cSkCpIwGi",
        "outputId": "bbf58c21-bb7c-4ef4-acac-e4e1d16c66c1"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total data for prediction:  879\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.line(closedf, x=closedf.Date, y=closedf.Close,labels={'date':'Date','close':'Close Stock'})\n",
        "fig.update_traces(marker_line_width=2, opacity=0.8, marker_line_color='orange')\n",
        "fig.update_layout(title_text='Considered period to predict Stock close price', plot_bgcolor='white', font_size=15, font_color='black')\n",
        "fig.update_xaxes(showgrid=False)\n",
        "fig.update_yaxes(showgrid=False)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "DqXSZxxwIwD2",
        "outputId": "4fa3eba9-c4cd-40c1-d899-30fefcfeb6be"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del closedf['Date']\n",
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "closedf=scaler.fit_transform(np.array(closedf).reshape(-1,1))\n",
        "print(closedf.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-jJuoyZIv-P",
        "outputId": "4b1b67fe-f5be-45e7-eaed-043ba4758f87"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(879, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_size=int(len(closedf)*0.60)\n",
        "test_size=len(closedf)-training_size\n",
        "train_data,test_data=closedf[0:training_size,:],closedf[training_size:len(closedf),:1]\n",
        "print(\"train_data: \", train_data.shape)\n",
        "print(\"test_data: \", test_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zVhCsdiOJQul",
        "outputId": "0837cdb0-d0e7-4415-f0ab-a77795c925e7"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_data:  (527, 1)\n",
            "test_data:  (352, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert an array of values into a dataset matrix\n",
        "def create_dataset(dataset, time_step=1):\n",
        "    dataX, dataY = [], []\n",
        "    for i in range(len(dataset)-time_step-1):\n",
        "        a = dataset[i:(i+time_step), 0]\n",
        "        dataX.append(a)\n",
        "        dataY.append(dataset[i + time_step, 0])\n",
        "    return np.array(dataX), np.array(dataY)"
      ],
      "metadata": {
        "id": "3qwQWEyjJQrp"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "time_step = 30\n",
        "X_train, y_train = create_dataset(train_data, time_step)\n",
        "X_test, y_test = create_dataset(test_data, time_step)\n",
        "\n",
        "print(\"X_train: \", X_train.shape)\n",
        "print(\"y_train: \", y_train.shape)\n",
        "print(\"X_test: \", X_test.shape)\n",
        "print(\"y_test\", y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Dy3W1uDJQo3",
        "outputId": "66a6513c-1d94-40d6-d2fb-6b059a96f1bb"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train:  (496, 30)\n",
            "y_train:  (496,)\n",
            "X_test:  (321, 30)\n",
            "y_test (321,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape input to be [samples, time steps, features] which is required for LSTM\n",
        "X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
        "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)\n",
        "\n",
        "print(\"X_train: \", X_train.shape)\n",
        "print(\"X_test: \", X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rcfX-S8LJQmd",
        "outputId": "5ba1c9d9-f921-498b-fec0-d26453562535"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train:  (496, 30, 1)\n",
            "X_test:  (321, 30, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.backend.clear_session()\n",
        "model=Sequential()\n",
        "model.add(GRU(64,return_sequences=True,input_shape=(time_step,1)))\n",
        "model.add(Dropout(0.20))\n",
        "model.add(GRU(64,return_sequences=True))\n",
        "model.add(Dropout(0.20))\n",
        "model.add(GRU(64))\n",
        "model.add(Dropout(0.20))\n",
        "model.add(Dense(1))\n",
        "model.compile(loss='mean_squared_error',optimizer='adam',metrics=['mae'])"
      ],
      "metadata": {
        "id": "jJRqKJulJQhv"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "27EQPr-9JQfX",
        "outputId": "41c62df5-e985-4edd-8d81-15ce649dbda5"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " gru (GRU)                   (None, 30, 64)            12864     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 30, 64)            0         \n",
            "                                                                 \n",
            " gru_1 (GRU)                 (None, 30, 64)            24960     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 30, 64)            0         \n",
            "                                                                 \n",
            " gru_2 (GRU)                 (None, 64)                24960     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 62,849\n",
            "Trainable params: 62,849\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=200,batch_size=32,verbose=1,callbacks=[early_stopping, lr_schedule])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XVgfUcWXJoSA",
        "outputId": "47f4895e-6417-4c6e-dea6-99dfd67ca9a9"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "16/16 [==============================] - 9s 181ms/step - loss: 0.4308 - mae: 0.6080 - val_loss: 0.3052 - val_mae: 0.5268 - lr: 1.0000e-06\n",
            "Epoch 2/200\n",
            "16/16 [==============================] - 1s 85ms/step - loss: 0.4306 - mae: 0.6073 - val_loss: 0.3033 - val_mae: 0.5251 - lr: 1.0798e-06\n",
            "Epoch 3/200\n",
            "16/16 [==============================] - 1s 61ms/step - loss: 0.4231 - mae: 0.6022 - val_loss: 0.3013 - val_mae: 0.5233 - lr: 1.1659e-06\n",
            "Epoch 4/200\n",
            "16/16 [==============================] - 1s 59ms/step - loss: 0.4235 - mae: 0.6028 - val_loss: 0.2991 - val_mae: 0.5214 - lr: 1.2589e-06\n",
            "Epoch 5/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.4226 - mae: 0.6019 - val_loss: 0.2968 - val_mae: 0.5192 - lr: 1.3594e-06\n",
            "Epoch 6/200\n",
            "16/16 [==============================] - 1s 61ms/step - loss: 0.4173 - mae: 0.5976 - val_loss: 0.2943 - val_mae: 0.5170 - lr: 1.4678e-06\n",
            "Epoch 7/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.4128 - mae: 0.5950 - val_loss: 0.2915 - val_mae: 0.5145 - lr: 1.5849e-06\n",
            "Epoch 8/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.4102 - mae: 0.5919 - val_loss: 0.2887 - val_mae: 0.5119 - lr: 1.7113e-06\n",
            "Epoch 9/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.4063 - mae: 0.5902 - val_loss: 0.2856 - val_mae: 0.5090 - lr: 1.8478e-06\n",
            "Epoch 10/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.4021 - mae: 0.5863 - val_loss: 0.2822 - val_mae: 0.5059 - lr: 1.9953e-06\n",
            "Epoch 11/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.3969 - mae: 0.5822 - val_loss: 0.2787 - val_mae: 0.5026 - lr: 2.1544e-06\n",
            "Epoch 12/200\n",
            "16/16 [==============================] - 1s 78ms/step - loss: 0.3898 - mae: 0.5767 - val_loss: 0.2749 - val_mae: 0.4990 - lr: 2.3263e-06\n",
            "Epoch 13/200\n",
            "16/16 [==============================] - 2s 106ms/step - loss: 0.3863 - mae: 0.5747 - val_loss: 0.2708 - val_mae: 0.4952 - lr: 2.5119e-06\n",
            "Epoch 14/200\n",
            "16/16 [==============================] - 1s 71ms/step - loss: 0.3842 - mae: 0.5726 - val_loss: 0.2664 - val_mae: 0.4910 - lr: 2.7123e-06\n",
            "Epoch 15/200\n",
            "16/16 [==============================] - 1s 60ms/step - loss: 0.3803 - mae: 0.5688 - val_loss: 0.2618 - val_mae: 0.4865 - lr: 2.9286e-06\n",
            "Epoch 16/200\n",
            "16/16 [==============================] - 1s 59ms/step - loss: 0.3696 - mae: 0.5612 - val_loss: 0.2568 - val_mae: 0.4817 - lr: 3.1623e-06\n",
            "Epoch 17/200\n",
            "16/16 [==============================] - 1s 61ms/step - loss: 0.3649 - mae: 0.5568 - val_loss: 0.2515 - val_mae: 0.4765 - lr: 3.4145e-06\n",
            "Epoch 18/200\n",
            "16/16 [==============================] - 1s 61ms/step - loss: 0.3489 - mae: 0.5451 - val_loss: 0.2459 - val_mae: 0.4709 - lr: 3.6869e-06\n",
            "Epoch 19/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.3501 - mae: 0.5447 - val_loss: 0.2398 - val_mae: 0.4649 - lr: 3.9811e-06\n",
            "Epoch 20/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.3395 - mae: 0.5360 - val_loss: 0.2334 - val_mae: 0.4583 - lr: 4.2987e-06\n",
            "Epoch 21/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.3304 - mae: 0.5288 - val_loss: 0.2266 - val_mae: 0.4513 - lr: 4.6416e-06\n",
            "Epoch 22/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.3201 - mae: 0.5198 - val_loss: 0.2193 - val_mae: 0.4436 - lr: 5.0119e-06\n",
            "Epoch 23/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.3124 - mae: 0.5133 - val_loss: 0.2114 - val_mae: 0.4352 - lr: 5.4117e-06\n",
            "Epoch 24/200\n",
            "16/16 [==============================] - 1s 79ms/step - loss: 0.3021 - mae: 0.5044 - val_loss: 0.2030 - val_mae: 0.4261 - lr: 5.8434e-06\n",
            "Epoch 25/200\n",
            "16/16 [==============================] - 2s 106ms/step - loss: 0.2900 - mae: 0.4933 - val_loss: 0.1943 - val_mae: 0.4163 - lr: 6.3096e-06\n",
            "Epoch 26/200\n",
            "16/16 [==============================] - 1s 66ms/step - loss: 0.2736 - mae: 0.4789 - val_loss: 0.1848 - val_mae: 0.4056 - lr: 6.8129e-06\n",
            "Epoch 27/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.2628 - mae: 0.4683 - val_loss: 0.1749 - val_mae: 0.3939 - lr: 7.3564e-06\n",
            "Epoch 28/200\n",
            "16/16 [==============================] - 1s 60ms/step - loss: 0.2490 - mae: 0.4545 - val_loss: 0.1643 - val_mae: 0.3812 - lr: 7.9433e-06\n",
            "Epoch 29/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.2379 - mae: 0.4429 - val_loss: 0.1531 - val_mae: 0.3672 - lr: 8.5770e-06\n",
            "Epoch 30/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.2220 - mae: 0.4277 - val_loss: 0.1411 - val_mae: 0.3516 - lr: 9.2612e-06\n",
            "Epoch 31/200\n",
            "16/16 [==============================] - 1s 60ms/step - loss: 0.2030 - mae: 0.4079 - val_loss: 0.1285 - val_mae: 0.3345 - lr: 1.0000e-05\n",
            "Epoch 32/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.1902 - mae: 0.3923 - val_loss: 0.1154 - val_mae: 0.3158 - lr: 1.0798e-05\n",
            "Epoch 33/200\n",
            "16/16 [==============================] - 1s 61ms/step - loss: 0.1661 - mae: 0.3664 - val_loss: 0.1016 - val_mae: 0.2948 - lr: 1.1659e-05\n",
            "Epoch 34/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.1477 - mae: 0.3421 - val_loss: 0.0875 - val_mae: 0.2717 - lr: 1.2589e-05\n",
            "Epoch 35/200\n",
            "16/16 [==============================] - 1s 60ms/step - loss: 0.1314 - mae: 0.3206 - val_loss: 0.0732 - val_mae: 0.2462 - lr: 1.3594e-05\n",
            "Epoch 36/200\n",
            "16/16 [==============================] - 1s 96ms/step - loss: 0.1101 - mae: 0.2910 - val_loss: 0.0590 - val_mae: 0.2183 - lr: 1.4678e-05\n",
            "Epoch 37/200\n",
            "16/16 [==============================] - 2s 98ms/step - loss: 0.0914 - mae: 0.2617 - val_loss: 0.0453 - val_mae: 0.1883 - lr: 1.5849e-05\n",
            "Epoch 38/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0696 - mae: 0.2220 - val_loss: 0.0329 - val_mae: 0.1574 - lr: 1.7113e-05\n",
            "Epoch 39/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0560 - mae: 0.1965 - val_loss: 0.0222 - val_mae: 0.1265 - lr: 1.8478e-05\n",
            "Epoch 40/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 0.0397 - mae: 0.1632 - val_loss: 0.0139 - val_mae: 0.0987 - lr: 1.9953e-05\n",
            "Epoch 41/200\n",
            "16/16 [==============================] - 1s 59ms/step - loss: 0.0277 - mae: 0.1365 - val_loss: 0.0084 - val_mae: 0.0767 - lr: 2.1544e-05\n",
            "Epoch 42/200\n",
            "16/16 [==============================] - 1s 60ms/step - loss: 0.0212 - mae: 0.1169 - val_loss: 0.0055 - val_mae: 0.0620 - lr: 2.3263e-05\n",
            "Epoch 43/200\n",
            "16/16 [==============================] - 1s 60ms/step - loss: 0.0168 - mae: 0.1042 - val_loss: 0.0046 - val_mae: 0.0552 - lr: 2.5119e-05\n",
            "Epoch 44/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0140 - mae: 0.0949 - val_loss: 0.0046 - val_mae: 0.0548 - lr: 2.7123e-05\n",
            "Epoch 45/200\n",
            "16/16 [==============================] - 1s 61ms/step - loss: 0.0136 - mae: 0.0931 - val_loss: 0.0048 - val_mae: 0.0554 - lr: 2.9286e-05\n",
            "Epoch 46/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0147 - mae: 0.0968 - val_loss: 0.0048 - val_mae: 0.0552 - lr: 3.1623e-05\n",
            "Epoch 47/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0135 - mae: 0.0933 - val_loss: 0.0048 - val_mae: 0.0553 - lr: 3.4145e-05\n",
            "Epoch 48/200\n",
            "16/16 [==============================] - 2s 99ms/step - loss: 0.0136 - mae: 0.0947 - val_loss: 0.0049 - val_mae: 0.0558 - lr: 3.6869e-05\n",
            "Epoch 49/200\n",
            "16/16 [==============================] - 2s 95ms/step - loss: 0.0123 - mae: 0.0891 - val_loss: 0.0047 - val_mae: 0.0548 - lr: 3.9811e-05\n",
            "Epoch 50/200\n",
            "16/16 [==============================] - 1s 61ms/step - loss: 0.0123 - mae: 0.0895 - val_loss: 0.0047 - val_mae: 0.0548 - lr: 4.2987e-05\n",
            "Epoch 51/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0121 - mae: 0.0901 - val_loss: 0.0047 - val_mae: 0.0544 - lr: 4.6416e-05\n",
            "Epoch 52/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0121 - mae: 0.0879 - val_loss: 0.0045 - val_mae: 0.0535 - lr: 5.0119e-05\n",
            "Epoch 53/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0114 - mae: 0.0869 - val_loss: 0.0041 - val_mae: 0.0508 - lr: 5.4117e-05\n",
            "Epoch 54/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0112 - mae: 0.0835 - val_loss: 0.0039 - val_mae: 0.0500 - lr: 5.8434e-05\n",
            "Epoch 55/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0119 - mae: 0.0878 - val_loss: 0.0039 - val_mae: 0.0498 - lr: 6.3096e-05\n",
            "Epoch 56/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0116 - mae: 0.0869 - val_loss: 0.0042 - val_mae: 0.0515 - lr: 6.8129e-05\n",
            "Epoch 57/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0102 - mae: 0.0802 - val_loss: 0.0038 - val_mae: 0.0493 - lr: 7.3564e-05\n",
            "Epoch 58/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 0.0106 - mae: 0.0819 - val_loss: 0.0035 - val_mae: 0.0471 - lr: 7.9433e-05\n",
            "Epoch 59/200\n",
            "16/16 [==============================] - 1s 75ms/step - loss: 0.0101 - mae: 0.0785 - val_loss: 0.0034 - val_mae: 0.0466 - lr: 8.5770e-05\n",
            "Epoch 60/200\n",
            "16/16 [==============================] - 2s 106ms/step - loss: 0.0098 - mae: 0.0799 - val_loss: 0.0033 - val_mae: 0.0454 - lr: 9.2612e-05\n",
            "Epoch 61/200\n",
            "16/16 [==============================] - 1s 78ms/step - loss: 0.0090 - mae: 0.0736 - val_loss: 0.0033 - val_mae: 0.0455 - lr: 1.0000e-04\n",
            "Epoch 62/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0101 - mae: 0.0793 - val_loss: 0.0031 - val_mae: 0.0441 - lr: 1.0798e-04\n",
            "Epoch 63/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0093 - mae: 0.0761 - val_loss: 0.0029 - val_mae: 0.0428 - lr: 1.1659e-04\n",
            "Epoch 64/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0086 - mae: 0.0733 - val_loss: 0.0027 - val_mae: 0.0415 - lr: 1.2589e-04\n",
            "Epoch 65/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0094 - mae: 0.0766 - val_loss: 0.0029 - val_mae: 0.0430 - lr: 1.3594e-04\n",
            "Epoch 66/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0090 - mae: 0.0747 - val_loss: 0.0024 - val_mae: 0.0392 - lr: 1.4678e-04\n",
            "Epoch 67/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0083 - mae: 0.0719 - val_loss: 0.0023 - val_mae: 0.0386 - lr: 1.5849e-04\n",
            "Epoch 68/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0085 - mae: 0.0700 - val_loss: 0.0023 - val_mae: 0.0384 - lr: 1.7113e-04\n",
            "Epoch 69/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0073 - mae: 0.0657 - val_loss: 0.0022 - val_mae: 0.0375 - lr: 1.8478e-04\n",
            "Epoch 70/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0077 - mae: 0.0684 - val_loss: 0.0021 - val_mae: 0.0373 - lr: 1.9953e-04\n",
            "Epoch 71/200\n",
            "16/16 [==============================] - 1s 89ms/step - loss: 0.0072 - mae: 0.0649 - val_loss: 0.0021 - val_mae: 0.0369 - lr: 2.1544e-04\n",
            "Epoch 72/200\n",
            "16/16 [==============================] - 2s 105ms/step - loss: 0.0072 - mae: 0.0669 - val_loss: 0.0020 - val_mae: 0.0364 - lr: 2.3263e-04\n",
            "Epoch 73/200\n",
            "16/16 [==============================] - 1s 61ms/step - loss: 0.0071 - mae: 0.0650 - val_loss: 0.0020 - val_mae: 0.0364 - lr: 2.5119e-04\n",
            "Epoch 74/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0071 - mae: 0.0652 - val_loss: 0.0021 - val_mae: 0.0369 - lr: 2.7123e-04\n",
            "Epoch 75/200\n",
            "16/16 [==============================] - 1s 61ms/step - loss: 0.0073 - mae: 0.0640 - val_loss: 0.0022 - val_mae: 0.0381 - lr: 2.9286e-04\n",
            "Epoch 76/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0061 - mae: 0.0604 - val_loss: 0.0019 - val_mae: 0.0357 - lr: 3.1623e-04\n",
            "Epoch 77/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0063 - mae: 0.0609 - val_loss: 0.0019 - val_mae: 0.0357 - lr: 3.4145e-04\n",
            "Epoch 78/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0064 - mae: 0.0631 - val_loss: 0.0020 - val_mae: 0.0363 - lr: 3.6869e-04\n",
            "Epoch 79/200\n",
            "16/16 [==============================] - 1s 61ms/step - loss: 0.0057 - mae: 0.0601 - val_loss: 0.0019 - val_mae: 0.0358 - lr: 3.9811e-04\n",
            "Epoch 80/200\n",
            "16/16 [==============================] - 1s 61ms/step - loss: 0.0063 - mae: 0.0615 - val_loss: 0.0021 - val_mae: 0.0372 - lr: 4.2987e-04\n",
            "Epoch 81/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0064 - mae: 0.0620 - val_loss: 0.0020 - val_mae: 0.0364 - lr: 4.6416e-04\n",
            "Epoch 82/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0066 - mae: 0.0625 - val_loss: 0.0019 - val_mae: 0.0349 - lr: 5.0119e-04\n",
            "Epoch 83/200\n",
            "16/16 [==============================] - 2s 100ms/step - loss: 0.0058 - mae: 0.0594 - val_loss: 0.0021 - val_mae: 0.0368 - lr: 5.4117e-04\n",
            "Epoch 84/200\n",
            "16/16 [==============================] - 1s 92ms/step - loss: 0.0061 - mae: 0.0603 - val_loss: 0.0018 - val_mae: 0.0349 - lr: 5.8434e-04\n",
            "Epoch 85/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0067 - mae: 0.0640 - val_loss: 0.0018 - val_mae: 0.0344 - lr: 6.3096e-04\n",
            "Epoch 86/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0066 - mae: 0.0618 - val_loss: 0.0018 - val_mae: 0.0341 - lr: 6.8129e-04\n",
            "Epoch 87/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0056 - mae: 0.0582 - val_loss: 0.0018 - val_mae: 0.0347 - lr: 7.3564e-04\n",
            "Epoch 88/200\n",
            "16/16 [==============================] - 1s 60ms/step - loss: 0.0060 - mae: 0.0603 - val_loss: 0.0024 - val_mae: 0.0401 - lr: 7.9433e-04\n",
            "Epoch 89/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0058 - mae: 0.0584 - val_loss: 0.0019 - val_mae: 0.0354 - lr: 8.5770e-04\n",
            "Epoch 90/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0062 - mae: 0.0598 - val_loss: 0.0017 - val_mae: 0.0339 - lr: 9.2612e-04\n",
            "Epoch 91/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0051 - mae: 0.0558 - val_loss: 0.0019 - val_mae: 0.0342 - lr: 0.0010\n",
            "Epoch 92/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 0.0054 - mae: 0.0566 - val_loss: 0.0017 - val_mae: 0.0328 - lr: 0.0011\n",
            "Epoch 93/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0049 - mae: 0.0541 - val_loss: 0.0020 - val_mae: 0.0364 - lr: 0.0012\n",
            "Epoch 94/200\n",
            "16/16 [==============================] - 1s 74ms/step - loss: 0.0056 - mae: 0.0589 - val_loss: 0.0016 - val_mae: 0.0319 - lr: 0.0013\n",
            "Epoch 95/200\n",
            "16/16 [==============================] - 2s 109ms/step - loss: 0.0050 - mae: 0.0548 - val_loss: 0.0016 - val_mae: 0.0316 - lr: 0.0014\n",
            "Epoch 96/200\n",
            "16/16 [==============================] - 1s 77ms/step - loss: 0.0049 - mae: 0.0532 - val_loss: 0.0016 - val_mae: 0.0319 - lr: 0.0015\n",
            "Epoch 97/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0054 - mae: 0.0558 - val_loss: 0.0015 - val_mae: 0.0310 - lr: 0.0016\n",
            "Epoch 98/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0048 - mae: 0.0543 - val_loss: 0.0017 - val_mae: 0.0339 - lr: 0.0017\n",
            "Epoch 99/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0054 - mae: 0.0568 - val_loss: 0.0019 - val_mae: 0.0362 - lr: 0.0018\n",
            "Epoch 100/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0043 - mae: 0.0527 - val_loss: 0.0019 - val_mae: 0.0361 - lr: 0.0020\n",
            "Epoch 101/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0052 - mae: 0.0553 - val_loss: 0.0021 - val_mae: 0.0379 - lr: 0.0022\n",
            "Epoch 102/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0047 - mae: 0.0531 - val_loss: 0.0015 - val_mae: 0.0304 - lr: 0.0023\n",
            "Epoch 103/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0047 - mae: 0.0530 - val_loss: 0.0027 - val_mae: 0.0436 - lr: 0.0025\n",
            "Epoch 104/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0058 - mae: 0.0586 - val_loss: 0.0015 - val_mae: 0.0319 - lr: 0.0027\n",
            "Epoch 105/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0054 - mae: 0.0571 - val_loss: 0.0021 - val_mae: 0.0363 - lr: 0.0029\n",
            "Epoch 106/200\n",
            "16/16 [==============================] - 1s 82ms/step - loss: 0.0061 - mae: 0.0602 - val_loss: 0.0014 - val_mae: 0.0292 - lr: 0.0032\n",
            "Epoch 107/200\n",
            "16/16 [==============================] - 2s 99ms/step - loss: 0.0043 - mae: 0.0513 - val_loss: 0.0017 - val_mae: 0.0316 - lr: 0.0034\n",
            "Epoch 108/200\n",
            "16/16 [==============================] - 1s 71ms/step - loss: 0.0048 - mae: 0.0534 - val_loss: 0.0015 - val_mae: 0.0308 - lr: 0.0037\n",
            "Epoch 109/200\n",
            "16/16 [==============================] - 1s 65ms/step - loss: 0.0041 - mae: 0.0488 - val_loss: 0.0014 - val_mae: 0.0294 - lr: 0.0040\n",
            "Epoch 110/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 0.0051 - mae: 0.0562 - val_loss: 0.0013 - val_mae: 0.0281 - lr: 0.0043\n",
            "Epoch 111/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0042 - mae: 0.0506 - val_loss: 0.0015 - val_mae: 0.0303 - lr: 0.0046\n",
            "Epoch 112/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0057 - mae: 0.0581 - val_loss: 0.0019 - val_mae: 0.0355 - lr: 0.0050\n",
            "Epoch 113/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 0.0044 - mae: 0.0524 - val_loss: 0.0016 - val_mae: 0.0315 - lr: 0.0054\n",
            "Epoch 114/200\n",
            "16/16 [==============================] - 1s 65ms/step - loss: 0.0051 - mae: 0.0556 - val_loss: 0.0046 - val_mae: 0.0598 - lr: 0.0058\n",
            "Epoch 115/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0043 - mae: 0.0509 - val_loss: 0.0011 - val_mae: 0.0261 - lr: 0.0063\n",
            "Epoch 116/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0042 - mae: 0.0503 - val_loss: 0.0018 - val_mae: 0.0340 - lr: 0.0068\n",
            "Epoch 117/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0037 - mae: 0.0473 - val_loss: 0.0016 - val_mae: 0.0325 - lr: 0.0074\n",
            "Epoch 118/200\n",
            "16/16 [==============================] - 2s 98ms/step - loss: 0.0040 - mae: 0.0493 - val_loss: 0.0014 - val_mae: 0.0288 - lr: 0.0079\n",
            "Epoch 119/200\n",
            "16/16 [==============================] - 2s 96ms/step - loss: 0.0039 - mae: 0.0489 - val_loss: 0.0030 - val_mae: 0.0473 - lr: 0.0086\n",
            "Epoch 120/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0043 - mae: 0.0515 - val_loss: 0.0022 - val_mae: 0.0393 - lr: 0.0093\n",
            "Epoch 121/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0041 - mae: 0.0498 - val_loss: 0.0014 - val_mae: 0.0304 - lr: 0.0100\n",
            "Epoch 122/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 0.0039 - mae: 0.0482 - val_loss: 9.8929e-04 - val_mae: 0.0241 - lr: 0.0108\n",
            "Epoch 123/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 0.0033 - mae: 0.0444 - val_loss: 9.5484e-04 - val_mae: 0.0240 - lr: 0.0117\n",
            "Epoch 124/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 0.0036 - mae: 0.0478 - val_loss: 0.0026 - val_mae: 0.0430 - lr: 0.0126\n",
            "Epoch 125/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 0.0042 - mae: 0.0511 - val_loss: 0.0014 - val_mae: 0.0290 - lr: 0.0136\n",
            "Epoch 126/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0033 - mae: 0.0442 - val_loss: 0.0030 - val_mae: 0.0481 - lr: 0.0147\n",
            "Epoch 127/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0039 - mae: 0.0491 - val_loss: 0.0010 - val_mae: 0.0256 - lr: 0.0158\n",
            "Epoch 128/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0034 - mae: 0.0458 - val_loss: 9.2591e-04 - val_mae: 0.0232 - lr: 0.0171\n",
            "Epoch 129/200\n",
            "16/16 [==============================] - 1s 72ms/step - loss: 0.0030 - mae: 0.0420 - val_loss: 9.1622e-04 - val_mae: 0.0228 - lr: 0.0185\n",
            "Epoch 130/200\n",
            "16/16 [==============================] - 2s 105ms/step - loss: 0.0038 - mae: 0.0486 - val_loss: 0.0012 - val_mae: 0.0276 - lr: 0.0200\n",
            "Epoch 131/200\n",
            "16/16 [==============================] - 1s 79ms/step - loss: 0.0033 - mae: 0.0443 - val_loss: 7.6093e-04 - val_mae: 0.0209 - lr: 0.0215\n",
            "Epoch 132/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0032 - mae: 0.0430 - val_loss: 0.0013 - val_mae: 0.0289 - lr: 0.0233\n",
            "Epoch 133/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 0.0032 - mae: 0.0444 - val_loss: 9.1831e-04 - val_mae: 0.0234 - lr: 0.0251\n",
            "Epoch 134/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 0.0027 - mae: 0.0397 - val_loss: 0.0029 - val_mae: 0.0467 - lr: 0.0271\n",
            "Epoch 135/200\n",
            "16/16 [==============================] - 1s 61ms/step - loss: 0.0040 - mae: 0.0499 - val_loss: 7.2221e-04 - val_mae: 0.0203 - lr: 0.0293\n",
            "Epoch 136/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0036 - mae: 0.0459 - val_loss: 0.0017 - val_mae: 0.0343 - lr: 0.0316\n",
            "Epoch 137/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 0.0044 - mae: 0.0515 - val_loss: 8.6902e-04 - val_mae: 0.0230 - lr: 0.0341\n",
            "Epoch 138/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0032 - mae: 0.0434 - val_loss: 9.0524e-04 - val_mae: 0.0228 - lr: 0.0369\n",
            "Epoch 139/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0028 - mae: 0.0411 - val_loss: 0.0039 - val_mae: 0.0561 - lr: 0.0398\n",
            "Epoch 140/200\n",
            "16/16 [==============================] - 1s 63ms/step - loss: 0.0035 - mae: 0.0464 - val_loss: 0.0029 - val_mae: 0.0455 - lr: 0.0430\n",
            "Epoch 141/200\n",
            "16/16 [==============================] - 1s 92ms/step - loss: 0.0038 - mae: 0.0486 - val_loss: 0.0019 - val_mae: 0.0340 - lr: 0.0464\n",
            "Epoch 142/200\n",
            "16/16 [==============================] - 2s 106ms/step - loss: 0.0035 - mae: 0.0471 - val_loss: 0.0021 - val_mae: 0.0373 - lr: 0.0501\n",
            "Epoch 143/200\n",
            "16/16 [==============================] - 1s 62ms/step - loss: 0.0104 - mae: 0.0787 - val_loss: 0.0016 - val_mae: 0.0329 - lr: 0.0541\n",
            "Epoch 144/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 5.1252 - mae: 1.0111 - val_loss: 0.1639 - val_mae: 0.3729 - lr: 0.0584\n",
            "Epoch 145/200\n",
            "16/16 [==============================] - 1s 64ms/step - loss: 5.2590 - mae: 1.8508 - val_loss: 0.0310 - val_mae: 0.1509 - lr: 0.0631\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_history(history):\n",
        "  sns.set()\n",
        "  fig, axs