Nvidia_Call_Option_16_June_2024.ipynb

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        "<a href=\"https://colab.research.google.com/github/AnalyticalHarry/AB-Testing/blob/main/Nvidia_Call_Option_16_June_2024.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import yfinance as yf\n",
        "from scipy.stats import norm\n",
        "import matplotlib.pyplot as plt"
      ],
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      "execution_count": 3,
      "outputs": []
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      "source": [
        "call_option = pd.read_excel('nvidia_calls.xlsx')\n",
        "call_option"
      ],
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              "    Exp. Date    Last  Change     Bid     Ask  Volume  Open Int.  Strike\n",
              "0  2024-06-14  124.40   6.81   125.25  126.40      71        288  1095.0\n",
              "1  2024-06-14  121.45   2.43   120.50  122.00     520       3303  1100.0\n",
              "2  2024-06-14  110.60   1.35   116.25  117.40      59        401  1105.0\n",
              "3  2024-06-14  112.00   1.70   111.80  112.95     139       2384  1110.0\n",
              "4  2024-06-14   97.34  -9.03   107.50  108.60    1043        812  1115.0\n",
              "5  2024-06-14  100.95  -2.25   103.10  104.35     267       1741  1120.0\n",
              "6  2024-06-14   91.15  -7.00    99.00  100.00     985        674  1125.0\n",
              "7  2024-06-14   95.00   1.80    94.85   95.75     145       9780  1130.0\n",
              "8  2024-06-14   87.55  -1.85    89.60   90.65      58        842  1135.0\n",
              "9  2024-06-14   85.10  -1.04    85.65   86.65     243       7039  1140.0\n",
              "10 2024-06-14   83.35  -4.30    83.70   84.70      75        764  1142.5\n",
              "11 2024-06-14   80.98  -0.97    81.70   82.85     115       1526  1145.0\n",
              "12 2024-06-14   79.30  -4.60    80.00   80.85      84        537  1147.5\n",
              "13 2024-06-14   78.46   0.26    78.15   78.90    1561      14074  1150.0\n",
              "14 2024-06-14   75.90  -1.55    76.25   77.05      99        500  1152.5\n",
              "15 2024-06-14   73.90  -1.61    74.25   75.20     380        769  1155.0\n",
              "16 2024-06-14   71.90  -2.07    72.55   73.35     218        379  1157.5\n",
              "17 2024-06-14   70.26  -1.26    70.75   71.60     340       1536  1160.0\n",
              "18 2024-06-14   68.30  -1.76    68.80   69.75     109        305  1162.5\n",
              "19 2024-06-14   67.50  -1.75    67.20   68.00     163        802  1165.0\n",
              "20 2024-06-14   61.96  -3.04    63.90   64.50    1928       2447  1170.0\n",
              "21 2024-06-14   58.65  -3.35    60.60   61.20     509        969  1175.0\n",
              "22 2024-06-14   57.00  -1.42    57.40   57.95    1458       1289  1180.0\n",
              "23 2024-06-14   54.00  -2.00    54.30   54.85    1067        780  1185.0\n",
              "24 2024-06-14   51.50  -1.80    51.30   51.80    6429       1909  1190.0\n",
              "25 2024-06-14   47.45  -2.90    48.40   48.90    3472       1601  1195.0\n",
              "26 2024-06-14   46.15  -0.55    45.65   46.00   11390       8108  1200.0"
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              "      <th>Exp. Date</th>\n",
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              "      <th>0</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>124.40</td>\n",
              "      <td>6.81</td>\n",
              "      <td>125.25</td>\n",
              "      <td>126.40</td>\n",
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              "      <th>1</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>121.45</td>\n",
              "      <td>2.43</td>\n",
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              "      <th>2</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>110.60</td>\n",
              "      <td>1.35</td>\n",
              "      <td>116.25</td>\n",
              "      <td>117.40</td>\n",
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              "      <td>2024-06-14</td>\n",
              "      <td>112.00</td>\n",
              "      <td>1.70</td>\n",
              "      <td>111.80</td>\n",
              "      <td>112.95</td>\n",
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              "      <th>4</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>97.34</td>\n",
              "      <td>-9.03</td>\n",
              "      <td>107.50</td>\n",
              "      <td>108.60</td>\n",
              "      <td>1043</td>\n",
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              "      <th>5</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>100.95</td>\n",
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              "      <td>103.10</td>\n",
              "      <td>104.35</td>\n",
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              "      <td>1120.0</td>\n",
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              "      <th>6</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>91.15</td>\n",
              "      <td>-7.00</td>\n",
              "      <td>99.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>985</td>\n",
              "      <td>674</td>\n",
              "      <td>1125.0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>95.00</td>\n",
              "      <td>1.80</td>\n",
              "      <td>94.85</td>\n",
              "      <td>95.75</td>\n",
              "      <td>145</td>\n",
              "      <td>9780</td>\n",
              "      <td>1130.0</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>87.55</td>\n",
              "      <td>-1.85</td>\n",
              "      <td>89.60</td>\n",
              "      <td>90.65</td>\n",
              "      <td>58</td>\n",
              "      <td>842</td>\n",
              "      <td>1135.0</td>\n",
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              "      <th>9</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>85.10</td>\n",
              "      <td>-1.04</td>\n",
              "      <td>85.65</td>\n",
              "      <td>86.65</td>\n",
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              "      <th>10</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>83.35</td>\n",
              "      <td>-4.30</td>\n",
              "      <td>83.70</td>\n",
              "      <td>84.70</td>\n",
              "      <td>75</td>\n",
              "      <td>764</td>\n",
              "      <td>1142.5</td>\n",
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              "      <th>11</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>80.98</td>\n",
              "      <td>-0.97</td>\n",
              "      <td>81.70</td>\n",
              "      <td>82.85</td>\n",
              "      <td>115</td>\n",
              "      <td>1526</td>\n",
              "      <td>1145.0</td>\n",
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              "      <th>12</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>79.30</td>\n",
              "      <td>-4.60</td>\n",
              "      <td>80.00</td>\n",
              "      <td>80.85</td>\n",
              "      <td>84</td>\n",
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              "      <th>13</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>78.46</td>\n",
              "      <td>0.26</td>\n",
              "      <td>78.15</td>\n",
              "      <td>78.90</td>\n",
              "      <td>1561</td>\n",
              "      <td>14074</td>\n",
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              "      <th>14</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>75.90</td>\n",
              "      <td>-1.55</td>\n",
              "      <td>76.25</td>\n",
              "      <td>77.05</td>\n",
              "      <td>99</td>\n",
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              "      <td>1152.5</td>\n",
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              "      <th>15</th>\n",
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              "      <td>73.90</td>\n",
              "      <td>-1.61</td>\n",
              "      <td>74.25</td>\n",
              "      <td>75.20</td>\n",
              "      <td>380</td>\n",
              "      <td>769</td>\n",
              "      <td>1155.0</td>\n",
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              "      <td>71.90</td>\n",
              "      <td>-2.07</td>\n",
              "      <td>72.55</td>\n",
              "      <td>73.35</td>\n",
              "      <td>218</td>\n",
              "      <td>379</td>\n",
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              "      <th>17</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>70.26</td>\n",
              "      <td>-1.26</td>\n",
              "      <td>70.75</td>\n",
              "      <td>71.60</td>\n",
              "      <td>340</td>\n",
              "      <td>1536</td>\n",
              "      <td>1160.0</td>\n",
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              "      <th>18</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>68.30</td>\n",
              "      <td>-1.76</td>\n",
              "      <td>68.80</td>\n",
              "      <td>69.75</td>\n",
              "      <td>109</td>\n",
              "      <td>305</td>\n",
              "      <td>1162.5</td>\n",
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              "      <th>19</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>67.50</td>\n",
              "      <td>-1.75</td>\n",
              "      <td>67.20</td>\n",
              "      <td>68.00</td>\n",
              "      <td>163</td>\n",
              "      <td>802</td>\n",
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              "      <th>20</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>61.96</td>\n",
              "      <td>-3.04</td>\n",
              "      <td>63.90</td>\n",
              "      <td>64.50</td>\n",
              "      <td>1928</td>\n",
              "      <td>2447</td>\n",
              "      <td>1170.0</td>\n",
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              "      <th>21</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>58.65</td>\n",
              "      <td>-3.35</td>\n",
              "      <td>60.60</td>\n",
              "      <td>61.20</td>\n",
              "      <td>509</td>\n",
              "      <td>969</td>\n",
              "      <td>1175.0</td>\n",
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              "      <th>22</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>57.00</td>\n",
              "      <td>-1.42</td>\n",
              "      <td>57.40</td>\n",
              "      <td>57.95</td>\n",
              "      <td>1458</td>\n",
              "      <td>1289</td>\n",
              "      <td>1180.0</td>\n",
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              "      <th>23</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>54.00</td>\n",
              "      <td>-2.00</td>\n",
              "      <td>54.30</td>\n",
              "      <td>54.85</td>\n",
              "      <td>1067</td>\n",
              "      <td>780</td>\n",
              "      <td>1185.0</td>\n",
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              "      <th>24</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>51.50</td>\n",
              "      <td>-1.80</td>\n",
              "      <td>51.30</td>\n",
              "      <td>51.80</td>\n",
              "      <td>6429</td>\n",
              "      <td>1909</td>\n",
              "      <td>1190.0</td>\n",
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              "      <th>25</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>47.45</td>\n",
              "      <td>-2.90</td>\n",
              "      <td>48.40</td>\n",
              "      <td>48.90</td>\n",
              "      <td>3472</td>\n",
              "      <td>1601</td>\n",
              "      <td>1195.0</td>\n",
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              "      <th>26</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>46.15</td>\n",
              "      <td>-0.55</td>\n",
              "      <td>45.65</td>\n",
              "      <td>46.00</td>\n",
              "      <td>11390</td>\n",
              "      <td>8108</td>\n",
              "      <td>1200.0</td>\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4bd27ff6-e087-45c7-b3b8-ce998ba7ee95')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4bd27ff6-e087-45c7-b3b8-ce998ba7ee95 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "call_option",
              "summary": "{\n  \"name\": \"call_option\",\n  \"rows\": 27,\n  \"fields\": [\n    {\n      \"column\": \"Exp. Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-06-14 00:00:00\",\n        \"max\": \"2024-06-14 00:00:00\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2024-06-14 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Last\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.790916701971277,\n        \"min\": 46.15,\n        \"max\": 124.4,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          87.55\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Change\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"-1.85\\u00a0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.604605051087923,\n        \"min\": 45.65,\n        \"max\": 125.25,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          89.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ask\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.85042500867157,\n        \"min\": 46.0,\n        \"max\": 126.4,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          90.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2445,\n        \"min\": 58,\n        \"max\": 11390,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open Int.\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3363,\n        \"min\": 288,\n        \"max\": 14074,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          842\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Strike\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.410458534763976,\n        \"min\": 1095.0,\n        \"max\": 1200.0,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          1135.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ticker = yf.Ticker(\"NVDA\")\n",
        "current_price = ticker.history(period=\"1d\")['Close'].iloc[-1]\n",
        "current_price"
      ],
      "metadata": {
        "id": "TbzmTL2JvuXr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8520d567-5cf3-4893-ae46-daef07b960f5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1208.8800048828125"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Why Is the U.S. 3-Month T-Bill Used as the Risk-Free Rate?**\n",
        "\n",
        "There can never be a truly risk-free rate because even the safest investments carry a very small amount of risk. However, the interest rate on a three-month U.S. Treasury bill is often used as the risk-free rate for U.S.-based investors. This is a useful proxy because the market considers there to be virtually no chance of the U.S. government defaulting on its obligations. The large size and deep liquidity of the market contribute to the perception of safety."
      ],
      "metadata": {
        "id": "ybnZOsYgCsWN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def historical_data(ticker_symbol, period):\n",
        "    ticker = yf.Ticker(ticker_symbol)\n",
        "    if period.endswith('d'):\n",
        "        period = '1mo'\n",
        "    elif period.endswith('mo'):\n",
        "        period = period[:-2] + 'mo'\n",
        "\n",
        "    historical_data = ticker.history(period=period)\n",
        "    historical_data['Daily_Return'] = historical_data['Close'].pct_change()\n",
        "    historical_data.dropna(inplace=True)\n",
        "    return historical_data\n",
        "\n",
        "def volatility(data, trading_days_per_year):\n",
        "    vol = data['Daily_Return'].std() * np.sqrt(trading_days_per_year)\n",
        "    return vol\n",
        "\n",
        "def main():\n",
        "    ticker_symbol = 'NVDA'\n",
        "    periods = ['5d', '10d', '20d', '30d', '1mo', '3mo', '6mo', '1y']\n",
        "    for period in periods:\n",
        "        data = historical_data(ticker_symbol, period)\n",
        "\n",
        "        if period.endswith('d'):\n",
        "            trading_days_per_year = 252\n",
        "        elif period.endswith('mo'):\n",
        "            months = int(period[:-2])\n",
        "            trading_days_per_year = 21 * months\n",
        "        else:\n",
        "            trading_days_per_year = 252\n",
        "\n",
        "        vol = volatility(data, trading_days_per_year)\n",
        "        print(f\"Historical Volatility for {ticker_symbol} ({period}):\", vol)"
      ],
      "metadata": {
        "id": "n3HE-JhiIpZP"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "main()"
      ],
      "metadata": {
        "id": "gL39qCaoO7Vj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f271e2ad-42f5-4339-ac5c-973fa8a6bb1e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Historical Volatility for NVDA (5d): 0.499323231183347\n",
            "Historical Volatility for NVDA (10d): 0.499323231183347\n",
            "Historical Volatility for NVDA (20d): 0.499323231183347\n",
            "Historical Volatility for NVDA (30d): 0.499323231183347\n",
            "Historical Volatility for NVDA (1mo): 0.14414220096816954\n",
            "Historical Volatility for NVDA (3mo): 0.2561136758482956\n",
            "Historical Volatility for NVDA (6mo): 0.35175388364964827\n",
            "Historical Volatility for NVDA (1y): 0.4426483900295145\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "volatility = 0.141\n",
        "risk_free_rate = 0.0525\n",
        "epsilon = 1e-9"
      ],
      "metadata": {
        "id": "3sClNL428EOp"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def d1_d2(stock_price, strike_price, time_to_expiry, volatility, real_risk_free_rate):\n",
        "    time_to_expiry = max(time_to_expiry, epsilon)\n",
        "    d1 = (np.log(stock_price / strike_price) + (real_risk_free_rate + 0.5 * volatility**2) * time_to_expiry) / (volatility * np.sqrt(time_to_expiry))\n",
        "    d2 = d1 - volatility * np.sqrt(time_to_expiry)\n",
        "    return d1, d2"
      ],
      "metadata": {
        "id": "FlORRHYg86mB"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def delta(stock_price, strike_price, time_to_expiry, volatility, real_risk_free_rate):\n",
        "    d1, _ = d1_d2(stock_price, strike_price, time_to_expiry, volatility, real_risk_free_rate)\n",
        "    return norm.cdf(d1)"
      ],
      "metadata": {
        "id": "xevQoX5hvua7"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def gamma(stock_price, strike_price, time_to_expiry, volatility, real_risk_free_rate):\n",
        "    d1, _ = d1_d2(stock_price, strike_price, time_to_expiry, volatility, real_risk_free_rate)\n",
        "    return norm.pdf(d1) / (stock_price * volatility * np.sqrt(time_to_expiry))"
      ],
      "metadata": {
        "id": "UjjoEhJ_8_OJ"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def theta(stock_price, strike_price, time_to_expiry, volatility, real_risk_free_rate):\n",
        "    d1, d2 = d1_d2(stock_price, strike_price, time_to_expiry, volatility, real_risk_free_rate)\n",
        "    term1 = -stock_price * norm.pdf(d1) * volatility / (2 * np.sqrt(time_to_expiry))\n",
        "    term2 = real_risk_free_rate * strike_price * np.exp(-real_risk_free_rate * time_to_expiry) * norm.cdf(d2)\n",
        "    return term1 - term2\n"
      ],
      "metadata": {
        "id": "PkLPOAgw9G3a"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def vega(stock_price, strike_price, time_to_expiry, volatility, real_risk_free_rate):\n",
        "    d1, _ = d1_d2(stock_price, strike_price, time_to_expiry, volatility, real_risk_free_rate)\n",
        "    return stock_price * norm.pdf(d1) * np.sqrt(time_to_expiry)"
      ],
      "metadata": {
        "id": "_VrmZuQZ9Iwo"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def rho(stock_price, strike_price, time_to_expiry, volatility, real_risk_free_rate):\n",
        "    _, d2 = d1_d2(stock_price, strike_price, time_to_expiry, volatility, real_risk_free_rate)\n",
        "    return strike_price * time_to_expiry * np.exp(-real_risk_free_rate * time_to_expiry) * norm.cdf(d2)"
      ],
      "metadata": {
        "id": "k4eSaRC49K1p"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "current_date = pd.Timestamp.today()\n",
        "call_option['Exp. Date'] = pd.to_datetime(call_option['Exp. Date'])\n",
        "call_option['T'] = (call_option['Exp. Date'] - current_date).dt.days / 365.0"
      ],
      "metadata": {
        "id": "yw_HQrBV0FKO"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "call_option['Delta'] = call_option.apply(lambda row: delta(current_price, row['Strike'], row['T'], volatility, risk_free_rate), axis=1)\n",
        "call_option['Gamma'] = call_option.apply(lambda row: gamma(current_price, row['Strike'], row['T'], volatility, risk_free_rate), axis=1)\n",
        "call_option['Theta'] = call_option.apply(lambda row: theta(current_price, row['Strike'], row['T'], volatility, risk_free_rate), axis=1)\n",
        "call_option['Vega']  = call_option.apply(lambda row: vega(current_price, row['Strike'], row['T'], volatility, risk_free_rate), axis=1)\n",
        "call_option['Rho']   = call_option.apply(lambda row: rho(current_price, row['Strike'], row['T'], volatility, risk_free_rate), axis=1)"
      ],
      "metadata": {
        "id": "k8pvdFm88MGF"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "call_option[['Exp. Date', 'Strike', 'Delta', 'Gamma', 'Theta', 'Vega', 'Rho']]"
      ],
      "metadata": {
        "id": "n4Erhg8e8O9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 895
        },
        "outputId": "871e43ce-3df3-401b-fb1a-c158e5ec07f5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Exp. Date  Strike     Delta         Gamma       Theta       Vega  \\\n",
              "0  2024-06-14  1095.0  1.000000  4.182319e-09  -57.437969   0.000014   \n",
              "1  2024-06-14  1100.0  1.000000  1.632446e-08  -57.700415   0.000055   \n",
              "2  2024-06-14  1105.0  1.000000  5.945438e-08  -57.963304   0.000201   \n",
              "3  2024-06-14  1110.0  0.999999  2.022309e-07  -58.227611   0.000685   \n",
              "4  2024-06-14  1115.0  0.999997  6.430106e-07  -58.496155   0.002178   \n",
              "5  2024-06-14  1120.0  0.999991  1.912837e-06  -58.776472   0.006479   \n",
              "6  2024-06-14  1125.0  0.999973  5.328479e-06  -59.087214   0.018049   \n",
              "7  2024-06-14  1130.0  0.999924  1.391117e-05  -59.471097   0.047120   \n",
              "8  2024-06-14  1135.0  0.999804  3.406620e-05  -60.018465   0.115390   \n",
              "9  2024-06-14  1140.0  0.999520  7.831433e-05  -60.905426   0.265268   \n",
              "10 2024-06-14  1142.5  0.999266  1.159897e-04  -61.567647   0.392883   \n",
              "11 2024-06-14  1145.0  0.998893  1.691489e-04  -62.447239   0.572945   \n",
              "12 2024-06-14  1147.5  0.998355  2.429030e-04  -63.615465   0.822767   \n",
              "13 2024-06-14  1150.0  0.997590  3.435216e-04  -65.159474   1.163585   \n",
              "14 2024-06-14  1152.5  0.996520  4.784923e-04  -67.182981   1.620761   \n",
              "15 2024-06-14  1155.0  0.995044  6.565047e-04  -69.805839   2.223728   \n",
              "16 2024-06-14  1157.5  0.993039  8.873286e-04  -73.162086   3.005581   \n",
              "17 2024-06-14  1160.0  0.990356  1.181562e-03  -77.396128   4.002214   \n",
              "18 2024-06-14  1162.5  0.986819  1.550226e-03  -82.656771   5.250961   \n",
              "19 2024-06-14  1165.0  0.982223  2.004199e-03  -89.088982   6.788669   \n",
              "20 2024-06-14  1170.0  0.968916  3.206391e-03 -105.964347  10.860764   \n",
              "21 2024-06-14  1175.0  0.948365  4.841771e-03 -128.668352  16.400163   \n",
              "22 2024-06-14  1180.0  0.918389  6.905893e-03 -156.995690  23.391805   \n",
              "23 2024-06-14  1185.0  0.877061  9.310521e-03 -189.539562  31.536817   \n",
              "24 2024-06-14  1190.0  0.823169  1.187333e-02 -223.571245  40.217638   \n",
              "25 2024-06-14  1195.0  0.756653  1.433235e-02 -255.277760  48.546867   \n",
              "26 2024-06-14  1200.0  0.678895  1.638720e-02 -280.380323  55.507100   \n",
              "\n",
              "          Rho  \n",
              "0   17.984472  \n",
              "1   18.066592  \n",
              "2   18.148709  \n",
              "3   18.230817  \n",
              "4   18.312897  \n",
              "5   18.394891  \n",
              "6   18.476652  \n",
              "7   18.557811  \n",
              "8   18.637522  \n",
              "9   18.713975  \n",
              "10  18.749955  \n",
              "11  18.783568  \n",
              "12  18.813878  \n",
              "13  18.839656  \n",
              "14  18.859317  \n",
              "15  18.870865  \n",
              "16  18.871829  \n",
              "17  18.859219  \n",
              "18  18.829499  \n",
              "19  18.778576  \n",
              "20  18.594207  \n",
              "21  18.264470  \n",
              "22  17.745311  \n",
              "23  16.997586  \n",
              "24  15.996195  \n",
              "25  14.738907  \n",
              "26  13.252211  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c256078-89d5-4875-a22a-6df70e68d34c\" 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>Exp. Date</th>\n",
              "      <th>Strike</th>\n",
              "      <th>Delta</th>\n",
              "      <th>Gamma</th>\n",
              "      <th>Theta</th>\n",
              "      <th>Vega</th>\n",
              "      <th>Rho</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1095.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.182319e-09</td>\n",
              "      <td>-57.437969</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>17.984472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.632446e-08</td>\n",
              "      <td>-57.700415</td>\n",
              "      <td>0.000055</td>\n",
              "      <td>18.066592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1105.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.945438e-08</td>\n",
              "      <td>-57.963304</td>\n",
              "      <td>0.000201</td>\n",
              "      <td>18.148709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1110.0</td>\n",
              "      <td>0.999999</td>\n",
              "      <td>2.022309e-07</td>\n",
              "      <td>-58.227611</td>\n",
              "      <td>0.000685</td>\n",
              "      <td>18.230817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1115.0</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>6.430106e-07</td>\n",
              "      <td>-58.496155</td>\n",
              "      <td>0.002178</td>\n",
              "      <td>18.312897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1120.0</td>\n",
              "      <td>0.999991</td>\n",
              "      <td>1.912837e-06</td>\n",
              "      <td>-58.776472</td>\n",
              "      <td>0.006479</td>\n",
              "      <td>18.394891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1125.0</td>\n",
              "      <td>0.999973</td>\n",
              "      <td>5.328479e-06</td>\n",
              "      <td>-59.087214</td>\n",
              "      <td>0.018049</td>\n",
              "      <td>18.476652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1130.0</td>\n",
              "      <td>0.999924</td>\n",
              "      <td>1.391117e-05</td>\n",
              "      <td>-59.471097</td>\n",
              "      <td>0.047120</td>\n",
              "      <td>18.557811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1135.0</td>\n",
              "      <td>0.999804</td>\n",
              "      <td>3.406620e-05</td>\n",
              "      <td>-60.018465</td>\n",
              "      <td>0.115390</td>\n",
              "      <td>18.637522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1140.0</td>\n",
              "      <td>0.999520</td>\n",
              "      <td>7.831433e-05</td>\n",
              "      <td>-60.905426</td>\n",
              "      <td>0.265268</td>\n",
              "      <td>18.713975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1142.5</td>\n",
              "      <td>0.999266</td>\n",
              "      <td>1.159897e-04</td>\n",
              "      <td>-61.567647</td>\n",
              "      <td>0.392883</td>\n",
              "      <td>18.749955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1145.0</td>\n",
              "      <td>0.998893</td>\n",
              "      <td>1.691489e-04</td>\n",
              "      <td>-62.447239</td>\n",
              "      <td>0.572945</td>\n",
              "      <td>18.783568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1147.5</td>\n",
              "      <td>0.998355</td>\n",
              "      <td>2.429030e-04</td>\n",
              "      <td>-63.615465</td>\n",
              "      <td>0.822767</td>\n",
              "      <td>18.813878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1150.0</td>\n",
              "      <td>0.997590</td>\n",
              "      <td>3.435216e-04</td>\n",
              "      <td>-65.159474</td>\n",
              "      <td>1.163585</td>\n",
              "      <td>18.839656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1152.5</td>\n",
              "      <td>0.996520</td>\n",
              "      <td>4.784923e-04</td>\n",
              "      <td>-67.182981</td>\n",
              "      <td>1.620761</td>\n",
              "      <td>18.859317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1155.0</td>\n",
              "      <td>0.995044</td>\n",
              "      <td>6.565047e-04</td>\n",
              "      <td>-69.805839</td>\n",
              "      <td>2.223728</td>\n",
              "      <td>18.870865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1157.5</td>\n",
              "      <td>0.993039</td>\n",
              "      <td>8.873286e-04</td>\n",
              "      <td>-73.162086</td>\n",
              "      <td>3.005581</td>\n",
              "      <td>18.871829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1160.0</td>\n",
              "      <td>0.990356</td>\n",
              "      <td>1.181562e-03</td>\n",
              "      <td>-77.396128</td>\n",
              "      <td>4.002214</td>\n",
              "      <td>18.859219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1162.5</td>\n",
              "      <td>0.986819</td>\n",
              "      <td>1.550226e-03</td>\n",
              "      <td>-82.656771</td>\n",
              "      <td>5.250961</td>\n",
              "      <td>18.829499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1165.0</td>\n",
              "      <td>0.982223</td>\n",
              "      <td>2.004199e-03</td>\n",
              "      <td>-89.088982</td>\n",
              "      <td>6.788669</td>\n",
              "      <td>18.778576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1170.0</td>\n",
              "      <td>0.968916</td>\n",
              "      <td>3.206391e-03</td>\n",
              "      <td>-105.964347</td>\n",
              "      <td>10.860764</td>\n",
              "      <td>18.594207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1175.0</td>\n",
              "      <td>0.948365</td>\n",
              "      <td>4.841771e-03</td>\n",
              "      <td>-128.668352</td>\n",
              "      <td>16.400163</td>\n",
              "      <td>18.264470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1180.0</td>\n",
              "      <td>0.918389</td>\n",
              "      <td>6.905893e-03</td>\n",
              "      <td>-156.995690</td>\n",
              "      <td>23.391805</td>\n",
              "      <td>17.745311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1185.0</td>\n",
              "      <td>0.877061</td>\n",
              "      <td>9.310521e-03</td>\n",
              "      <td>-189.539562</td>\n",
              "      <td>31.536817</td>\n",
              "      <td>16.997586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1190.0</td>\n",
              "      <td>0.823169</td>\n",
              "      <td>1.187333e-02</td>\n",
              "      <td>-223.571245</td>\n",
              "      <td>40.217638</td>\n",
              "      <td>15.996195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1195.0</td>\n",
              "      <td>0.756653</td>\n",
              "      <td>1.433235e-02</td>\n",
              "      <td>-255.277760</td>\n",
              "      <td>48.546867</td>\n",
              "      <td>14.738907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2024-06-14</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>0.678895</td>\n",
              "      <td>1.638720e-02</td>\n",
              "      <td>-280.380323</td>\n",
              "      <td>55.507100</td>\n",
              "      <td>13.252211</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "      buttonEl.style.display =\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2084bd7b-4418-462d-9f75-7d8b1b46877e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"call_option[['Exp\",\n  \"rows\": 27,\n  \"fields\": [\n    {\n      \"column\": \"Exp. Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-06-14 00:00:00\",\n        \"max\": \"2024-06-14 00:00:00\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2024-06-14 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Strike\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.410458534763976,\n        \"min\": 1095.0,\n        \"max\": 1200.0,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          1135.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Delta\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08217642368169181,\n        \"min\": 0.6788945779794069,\n        \"max\": 0.9999999839657703,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          0.9998037708856538\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gamma\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004748626645291912,\n        \"min\": 4.182318602273998e-09,\n        \"max\": 0.016387196104352172,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          3.406619802516094e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Theta\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.03055292186919,\n        \"min\": -280.38032306909054,\n        \"max\": -57.43796863755502,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          -60.01846532896149\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vega\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.08466100563367,\n        \"min\": 1.4166448946208435e-05,\n        \"max\": 55.507100022840866,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          0.11538983540194017\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rho\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3478820005227519,\n        \"min\": 13.252210675892535,\n        \"max\": 18.871828637093877,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          18.63752240231121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_greeks(call_option):\n",
        "    fig, axs = plt.subplots(3, 2, figsize=(14, 14), facecolor='black')\n",
        "\n",
        "    for ax in axs.flat:\n",
        "        ax.set_facecolor('black')\n",
        "        ax.grid(True, color='gray', linestyle='--', linewidth=0.5)\n",
        "\n",
        "    axs[0, 0].plot(call_option['Strike'], call_option['Delta'], color='lime')\n",
        "    axs[0, 0].set_title('Delta vs Strike Price', color='white')\n",
        "    axs[0, 0].set_xlabel('Strike Price', color='white')\n",
        "    axs[0, 0].set_ylabel('Delta', color='white')\n",
        "    axs[0, 0].tick_params(axis='x', colors='white')\n",
        "    axs[0, 0].tick_params(axis='y', colors='white')\n",
        "\n",
        "    axs[0, 1].plot(call_option['Strike'], call_option['Gamma'], color='cyan')\n",
        "    axs[0, 1].set_title('Gamma vs Strike Price', color='white')\n",
        "    axs[0, 1].set_xlabel('Strike Price', color='white')\n",
        "    axs[0, 1].set_ylabel('Gamma', color='white')\n",
        "    axs[0, 1].tick_params(axis='x', colors='white')\n",
        "    axs[0, 1].tick_params(axis='y', colors='white')\n",
        "\n",
        "    axs[1, 0].plot(call_option['Strike'], call_option['Theta'], color='yellow')\n",
        "    axs[1, 0].set_title('Theta vs Strike Price', color='white')\n",
        "    axs[1, 0].set_xlabel('Strike Price', color='white')\n",
        "    axs[1, 0].set_ylabel('Theta', color='white')\n",
        "    axs[1, 0].tick_params(axis='x', colors='white')\n",
        "    axs[1, 0].tick_params(axis='y', colors='white')\n",
        "\n",
        "    axs[1, 1].plot(call_option['Strike'], call_option['Vega'], color='magenta')\n",
        "    axs[1, 1].set_title('Vega vs Strike Price', color='white')\n",
        "    axs[1, 1].set_xlabel('Strike Price', color='white')\n",
        "    axs[1, 1].set_ylabel('Vega', color='white')\n",
        "    axs[1, 1].tick_params(axis='x', colors='white')\n",
        "    axs[1, 1].tick_params(axis='y', colors='white')\n",
        "\n",
        "    axs[2, 0].plot(call_option['Strike'], call_option['Rho'], color='orange')\n",
        "    axs[2, 0].set_title('Rho vs Strike Price', color='white')\n",
        "    axs[2, 0].set_xlabel('Strike Price', color='white')\n",
        "    axs[2, 0].set_ylabel('Rho', color='white')\n",
        "    axs[2, 0].tick_params(axis='x', colors='white')\n",
        "    axs[2, 0].tick_params(axis='y', colors='white')\n",
        "\n",
        "    fig.delaxes(axs[2, 1])\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "a06G9Any-PZ5"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_greeks(call_option)"
      ],
      "metadata": {
        "id": "ZHuE-0Ec_mZQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c71be3e6-2ae2-4ef9-9158-0e0c00e77ee9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1400 with 5 Axes>"
            ],
            "image/png": 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8S5cuZfbs2cycORNjzHn3n5OTw8SJE5k4cSLh4eF06dKF++67j5EjR5Kbm8v//d//XdBzPLOG74WaN28eycnJ9OrVi/T09AK3NW7cmMjISPcyDL93Zt3bovq57bbbMMa415b97XIOF9N39erVCQkJ4ZdffjnvdsXRt4iIiJQ/aWlpgOu96e/97W9/Izg4mBo1avD5558XuC00NJTx48czatQoatSoUeC2wv7xfc+ePQV+T0lJITMzs8BH5c9cX7Vq1Qu6P+A+keC31/v4+BASEuL+joQOHTrw5JNPEh0dTVBQ0Fm9nmv5AoAZM2bw6quvMnLkSJ577jnANTn41Vdfuf92u3bt4pVXXuGBBx7ghhtu4LvvvmPu3Ll89tln5933GdOmTWPatGkEBwdzzTXXMGbMGG644QbmzZvHlVdeSXZ2dpH72L9/v/tkjQvx+uuvU6FCBdq0acOmTZsK3Na4cWOqVKlS6PtXuPD3lUOGDCE1NZXc3Fz27dtX6H9/XGjfDRs2ZOvWredcUq04+xaRP0aTtiLi0WrVqkWVKlXYsWMH4FqjCeCll14iNja20Puc2fZi3HDDDUyZMoUvv/ySl156iUOHDpGfn8+4cePca0qdz7x589xr665atYoRI0aQn5/PjBkz3NtkZWXRuXNnunbtSr9+/ejduzejRo1i6dKl9OzZk1OnTl1Qr0lJSUyfPp1Zs2axceNGRowYwZgxY877xuyM3/5r/YWYNWuW+83wBx98UOA2b29vvv76a1588cVC77tt27Yi95+ens7SpUuL3O5i+z6f4uhbREREyp/U1FQOHDjAlVdeedZtZ9a4PfMFsL/1xRdf0KFDB1566SXWrVvHyZMn8fb2JjY21v3e9rcKe093rvd5hX1661zbFrWPBg0asHTpUrZs2cLYsWPZu3cvOTk59O3bl7Fjxxba628dPHiQ7777jhEjRvDcc8/Rvn176taty8MPP1xguwcffJDJkyczaNAgevbsyZtvvsm4ceNo3749+/fvP+9jnJGWlsaSJUtYsmQJubm5jBkzhmuuuYbly5cXed+LfV85Z84cRo0axb/+9S9uvPHGAidbeHt7k5yczA033FDofc81Kfp7y5cvP2tS/veK+/1wcfQtIn+MJm1FxKOdOYP0zATtmX91zs3NvaDJvt871xmtw4YNIzExkSFDhhS4/sknn7yg/WZkZDB//nyGDx/O2LFjGTlyJN999x0HDx486/G/+eYbvvnmGx544AHGjRvHxIkT6dq160U/n7y8PDZs2ECTJk2oVq0aycnJRZ6xe7H++c9/kpeXxzvvvENaWhpTp05135aYmEilSpUuKYeScPjwYVJSUgr9D6nfsq1vERER8RwLFizgtttu4+qrr+bHH38scvsqVarQvXt3nnjiiQJf+NWoUaOSbPOSDBgwgAoVKjBw4MACZ+VezEflp0+fzrvvvkuTJk0YOXIk6enpzJs376ztfvnlF3755ReeffZZoqOj+f7777njjjt4/PHHL7rvn376iTFjxhAREQGc+/3+pYqJieHrr79m8uTJpKWlcdddd7lvS0xMpHv37qxcubLA0hJOSkxM5JprrsHX1/ecXyZmY98i5ZHWtBURj9W1a1cef/xxfv31V/fHzA4fPkxcXBx/+9vfCA8PP+s+1apVO+8+09PTC/0Y2pkzD357tkK7du2Ijo6+4H6nT59OrVq1uPXWW2ndujXTp08vcHtoaOhZ91m3bh3gWt/sXBo1akTt2rXPuj4kJITo6GiOHTvm/tfwM0sYVKlS5YL7Ph9jDLfffjszZ85kypQpDBgwwH3bmbNGevbsWWhvv10frTQYY4iJiWHAgAFERUWdczvb+hYRERHP8eKLL5Kens7HH39c6EfIf3/ma2HvMQHuv//+EuvxUhXWa+XKlbn55psveB+zZs0iLy+P0aNHM3z4cObPn09GRob79uDg4LPeayUkJJCfn3/e98OBgYG0b9++0Nv69OkDwNatW4Hifz8M8Omnn/L3v/+dO++8k+eff959/RdffIGvr2+hk81nlp4obbNmzaJ69ercc88959zGxr5FyiOdaSsiHqFPnz40a9YMX19fatSowZ///Gd69OjB7t27GThwYIH1qe6++25WrFhBQkICH374Ib/++is1atQgOjqayy+/nNatW5/zcdasWcOoUaN45ZVX+PHHHzl58iTz589n/vz5DB06lC+//JIFCxZQv3597rjjDjZt2lToumWFWbhwIampqbz88svk5eUxa9asArc/8cQTdO7cmQULFrB7927CwsK466672Lt3LytWrDjnfiMjI/nvf//LV199xXfffcexY8eoVasWN910E7Vq1eK+++5zL61w5gu4nn32WaZNm0Zubi7z5s0r8Gb5Yhlj+Mtf/kJMTAxffPEFffv2JS4ujpdeeomBAwcyf/58Jk+ezJo1awgKCqJly5YMGzaMevXqFfkxr+L2yCOP0LNnT5YtW8YHH3zA5s2biYiIYPjw4XTq1ImUlBQr+xYRERHPsGPHDq6//nqmTp3K1q1b+fzzz1m/fj1eXl7Ur1+f66+/nvz8fPbt2we4Psa/bNkyHnroIfz8/Ni/fz89e/akfv36Dj+Ts3399ddkZ2czb9483n//fSpVqsRtt93GoUOHLviLfs+cYDF27FgqV6581kkMf/7zn3nrrbeYMWMG27Ztw9fXl//7v/8jPz//rPfOv1WxYkVWrVrFqlWrWLRoEXv37qVKlSoMHjyYzp078+WXX7pPhkhMTOT48ePccccdpKWlkZ6ezg8//MCuXbsu9U8DwNtvv03lypWZOHEiKSkpPPfccyxfvpz33nuPRx55hNatW/P111+Tm5tL48aNGT58OPfdd995n1dJ+M9//sONN97Ia6+9Rrt27fjuu+8ICgqie/fuvPPOO8ydO9fKvkXKK6NSqVS21k033WR+Kysryxw4cMDExsaae++911SqVKnQ+9WvX99MnjzZHDhwwGRnZ5u9e/eauXPnmiFDhri36dKlizHGmC5durivq1ixovnss8/MsWPHjDHG7Ny5033bv/71L7Nz506TmZlp1qxZY/r27Ws++eSTAtsUVZ9++qkxxpivv/76rNu6du1qvvzyS7Nv3z6TlZVl9u3bZz7//HPTqFGj8+6zevXq5qGHHjJxcXFm//79Jicnxxw9etQsWbKkwPM9U48++qjZu3evycvLM8YYU7duXQMYY4yZNGlSoY9hjDHjx493/z5+/HhjjDFVq1Z1X1ehQgUTFxdnUlNTTbt27QxggoKCzLPPPmu2bdtmsrKyzKFDh8yKFSvM2LFjja+v73mfV1xcnElISCjyb3oxfQOmdu3aZvLkySY5OdlkZmaaHTt2mEmTJhk/Pz/3Nn+kb5VKpVKpVKoGDRqYt99+22zbts1kZGSY9PR0s2nTJvPOO++YVq1aFdi2Zs2aZtasWebYsWPm+PHjZvr06SY8PPyC3n8B5pNPPjFpaWln9fD791Jn3vsOHTq0wHZn3m9HRUUVuL6wx+vfv79Zt26dycjIML/++qv55z//acaMGVPgPWVR9de//tUYY0xKSooJCAgocFu9evXMv//9b7N9+3aTkZFhjhw5YpYuXWr+/Oc/n3efPj4+5q9//auZPXu2+/36yZMnzZo1a8wDDzxQ4H0eYAYMGGB++eUXk5OTY4wx5qabbir0b/b7v2dcXFyRf8/nn3/eGGPMXXfd5b7u1ltvNT/++KNJT083KSkpZv369eb555834eHh531e58q8qKzP1ze43rc//fTTJjEx0WRnZ5sDBw6YL774wtSvX7/Adpfat0qlKp7yOn1BRERERERERERERCygNW1FRERERERERERELKJJWxERERERERERERGLaNJWRERERERERERExCKatBURERERERERERGxiCZtRURERERERERERCyiSVsRERERERERERERi2jSVkRERERERERERMQimrQVt2HDhjndghRBGdlPGdlN+dhPGdlPGYkn0ri1m/KxnzKynzKynzKym435aNJW3NatW+d0C1IEZWQ/ZWQ35WM/ZWQ/ZSSeSOPWbsrHfsrIfsrIfsrIbjbmo0lbcQsICHC6BSmCMrKfMrKb8rGfMrKfMhJPpHFrN+VjP2VkP2VkP2VkNxvz0aStuLVq1crpFqQIysh+yshuysd+ysh+ykg8kcat3ZSP/ZSR/ZSR/ZSR3WzMR5O2IiIiIiIiIiIiIhbxAozTTYgdAgICyM7OdroNOQ9lZD9lZDflYz9lZD9lJJ5I49Zuysd+ysh+ysh+yshuNuajM23FrWvXrk63IEVQRvZTRnZTPvZTRvZTRuKJNG7tpnzsp4zsp4zsp4zsZmM+mrQVt9DQUKdbkCIoI/spI7spH/spI/spI/FEGrd2Uz72U0b2U0b2U0Z2szEfTdqK2+HDh51uQYqgjOynjOymfOynjOynjMQTadzaTfnYTxnZTxnZTxnZzcZ8tKatuAUFBZGenu50G3Ieysh+yshuysd+ysh+ykg8kcat3ZSP/ZSR/ZSR/ZSR3WzMR2faitvAgQOdbkGKoIzsp4zspnzsp4zsp4zEE2nc2k352E8Z2U8Z2U8Z2c3GfDRpKyIiIiIiIiIiImIRTdqK29q1a51uQYqgjOynjOymfOynjOynjMQTadzaTfnYTxnZTxnZTxnZzcZ8rJ20/dOf/sTcuXPZv38/xhgGDRpU5H26dOnCmjVryMrKYvv27dx0001nbXPXXXexc+dOMjMziY+P5+qrry6J9kVEREREREREREQuibWTtkFBQaxfv5677777gravV68eCxYsIC4ujtatW/P666/z73//m549e7q3GTFiBK+++ipPPvkkV111FevXryc2Npbq1auX1NPwKG3atHG6BSmCMrKfMrKb8rGfMrKfMhJPpHFrN+VjP2VkP2VkP2VkNxvz8XW6gXNZtGgRixYtuuDt77jjDnbu3MmDDz4IwJYtW+jUqRP/+Mc/+PrrrwEYO3YsH374IZMnT3bfp1+/ftxyyy288MILxf4c/rAIoE/pPVziNYkQWHqPV6aY0nmYX6/5FQIu8LELu/5ir7uUnxe77W/r1B/4vajbTgH5l1giIiIiIiIiUjaFhbF29GhYsABSU53uxs2LUptuunTGGAYPHsycOXPOuc2yZcv4+eef+cc//uG+bsyYMbz++utUqVIFPz8/MjIyGDZsWIH9TJ48mSpVqjB48OBC9+vv709AwP9myYwxnDx58o8/qQvRFfimdB5KRM7jIiZ8vfK8MNkGcoEcXD9/e7mwn9lA1m/q978XdlsGcBJIP13ZJfkHKDuCgoJIT093ug05D2VkP2Uknkjj1m7Kx37KyH7KyH7KyGJTpsCNN8L8+TBggNPduFl7pu3FCg8PJzk5ucB1ycnJhISEUKFCBUJDQ/H19S10m2bNmp1zv+PGjWPChAnu31NTUwkJCWHYsGH4+fkxa9YsunXrRpUqVTh06BCrV6+mf//+AKxZswYfHx9at24NwJw5c+jYsSPVqlXj2LFjLF++3D1ZvH79enJzc2nbti0ACxYsoGmDphxYc4DcvFwOHz5MzYiarh7SUsnPyyc0NBSAQ4cOEVw5mMAKgeTl55GclEytWrUASDuZRm5OLpdddhkAhw8fJqhSEBUDK5J/Kp+kg0nUrFkTLy8vvLy8OHzkMNWqVgPgyNEjBFYIJCgoiFPmFAcPHCQiIgJvb28yMjJIz0inejXX0hJHjx0lICCASkGVANi/fz/h4eH4+PiQmZlJWloaYWFhABw7fgw/Xz+Cg4MBOHDgANXDquPn60dWVhYpqSnUCKsBwPETx/Hx9qFy5coAHDx4kKrVquLv5092TjbHjx0nPDwcgJSUFABCQkIASEpKIvSyUAL8A8jJzeHokaNERES4c8w/lU9oFdffMPlQMiGVXWMlNy+Xw4cOU7Om6++dlpZGbl4ul4Ve9r+/d3AwgYGB5Ofnk5ScRM1arm3TT6aTnZPt/nsfOXKEoIpBBFYM5NSpUyQdTCKiZgReXl6kZ6STlZlF1apV3X/DChUqEFQxCIPh4IGDhIeH4+3tTWZmJukZ6UTUjCA3J5djx4/h7+dPUHCQ6+9y4CBhYWH4+PqQlZVF2sk0dzYnTpzA19fXvW1yUjJVq1XF19eX7JxsUlNT3UuEpKak4uXtRXBwMMbLcPjQYapc5vpHj9zcXE6knKB6teoYL8PJtJMYL0NwpWDwgqNHjxJcORg/fz/y8vM4cfwE1aq7xlJ6RjqnTp2iUrBrfBxPOU5QxSD8AvzIP5XPiZQTXFb1MvCCzOxM8k7lERQUBF6Qlp5GQIUAfP19McaQlp5GcGXXY+bk5pB3Ko8KFSsAkJmTia+vL75+vhgMmdmZVAisgPE25Jt88snHx9/H9Tv5ePl6YbxN0QvFeJ8uvyK2A4xT/w6WB345fvhm+5Kfmg8nobJvZXyzfTm6+yih/qGEeIXgddyLbT9uo12Tdvin+5O0NYmTe09ydeOr8Uv3Y/m85TRp2IS6deuSnZ3N7NmzGTlyJN7e3uzYsYP9+/fTpUsXAL799ltq165Nw4YNycvLY8aMGQwdOhR/f3927dpFYmIi3bp1A2DFihWEhYXRpEkTAKZOncqgQYOoWLEie/fuZdOmTfTq1QuAVatWERISQosWLQCYMWMGvXr1onLlyhw8eJC1a9fSt29fAH788UcCAgJo1aoVALNnz6Zr166EhoZy+PBhVq1axcCBAwHXwvKtWrXCx8cHgLlz5xIdHU316tU5fvw4cXFxDBkyBIANGzaQnZ3tXvd84cKFtGnThoiICFJTU4mNjWX48OEAbNq0iZSUFKKjowGIjY2lRYsW1K5dm4yMDObMmcPo0aMB2LZtG4cOHaJTp04ALF26lIYNG1KvXj1ycnKYNWsWw4cPx9fXl8TERPbu3cu1114LuP5xslatWjRq1IhTp04xffp0hgwZQkBAALt372b79u10794dgJUrV1K1alX3/79NmzaNAQMGEBQUxP79+0lISKB3794AxMfHExwczBVXXAHAzJkz6dGjByEhISQlJbFmzRr69esHwE8//YSfnx+RkZEAxMTE0LlzZy677DKOHDnCypUr3WvQr1u3jvz8fKKiogCYP38+7dq1IywsjBMnTrB06VKGDh0KQEJCApmZmbRr145q1arx6aefEhkZSc2aNUlLS2PhwoWMHDkSgM2bN3P8+HE6dOgAwOLFi2natCl16tQhMzOTmJgYRo0ahZeXF9u3b+fgwYN07twZgLi4OOrVq0f9+vXJzc1l5syZ7v8/37lzJ7t27aJr164ALF++nIiICBo3bowxhmnTpjF48GACAwPZs2cPW7dupUePHgB8//33hIaG0rx5cwCmT59O3759CQ4O5sCBA6xfv54+fVwfnVm9ejWBgYG0bNkSoETfR0RFRREeHk5KSgqLFy9m2LBhAGzcuJG0tDTat28PuD7d1LJlS2rVqkV6ejrz5s1j1KhRgOuTS0ePHqVjx44ALFmyhL59+5KTk1PirxF9+/Zl4cKFiBSH6OholixZ4nQbcg7Kx37KyH7KyH7KyFJ/+pNrwvbUKXjqKae7KaDMnGm7detWPvnkE55//nn3dX369GHhwoUEBgYSGhrKgQMHiI6OJj4+3r3NCy+8QJcuXdz/4fJ7jp5pW8pGjx7N1KlTnW5DzkMZlSCf4qnufbuz5Nsl4I9rkteP/10+13UBp6vCeeq3twcCFYGg078Xt1TgxOk6CiSfrkPn+JlZAj2UEB1D9lNG9lNG4ok0bu2mfOynjOynjOynjCzk6ws//wwtW9Jw6VIST58EY4syc6ZtUlISNWrUKHBdjRo1SElJISsriyNHjpCXl1foNklJSefcb05ODjk5OSXSs22OHz/udAtSBGVUgopp/Vrfy3zh2z++nwvmg2vyNgiodI7LZ36vDIQCVc5RlU7vs/LpqnOBPZyk4MTuAWD37yoJK/6JUMeQ/ZSR/ZSReCKNW7spH/spI/spI/spIwvdcw+0bAlHjlD3/fdJdLqf3ykzZ9o+//zz9O3b1/0RWYDPP/+cyy67zP2RxPj4eFavXs3f//53ALy8vNizZw9vvfWWnV9EVsoCAgLIztbCmDZTRvbz6Ix8KTiJGwpUA8KAGr/5+dvLF3qmbzawh7Mnc8/UPiCvOJ7E+Xl0PuWEMrKfMhJPpHFrN+VjP2VkP2VkP2VkmYgI2LIFKleGW28l4LPPrMynsO9wd7yCgoJMZGSkiYyMNMYYc//995vIyEhTu3ZtA5iJEyeaKVOmuLevV6+eOXnypHnhhRdM06ZNzZ133mlyc3NNz5493duMGDHCZGZmmhtvvNE0a9bMvPfee+bYsWMmLCzM8edrQ40ePdrxHlTKyNOr3GUUjKERhg4YrsNwB4anMfwHwzIMuzHkYYr8XzaGBAzTMYzHMBxDCwx+yqe8lTKyv5SRyhNL49buUj72lzKyv5SR/aWMLKvPPzcYY4iPN3h5WZmPtcsjtG3blm+//db9+2uvvQbA5MmTufnmm4mIiKBOnf99dnfXrl3069eP1157jfvuu499+/Zx66238vXXX7u3+eKLL6hevTpPPfUU4eHhrFu3jt69e3Po0KFSe14iImVK2unacZ5tfIFaQN1zVB1cZ+xeebp+Kw/YDmwENv2mtgLlY+UaERERERERKU7XXgvXX+/68rG77gJjnO7onByfOVbZUVdccYXjPaiUkaeXMrqE8sJQG0MvDGMx/BvD9xhSOPeZuXkYNmH4GMPtGFpi8FY+ZaGUkf2ljFSeWBq3dpfysb+Ukf2ljOwvZWRJ+foaNm50nWX71ltW52PtmbZS+mxcu0MKUkb2U0aXwAB7T1fs726rBbQArjj988zlKkDz03Xz6W3TgNXAKiD+dB0tuDvlYz9lZD9lJJ5I49Zuysd+ysh+ysh+ysgS998PLVrA4cPw2GPuq23Mx9vpBsQeV199tdMtSBGUkf2UUTHbDywGXgduBzrh+oK0mkA/4BlgCa4J22CgG/AYMB84AmwDpgB3Aq0h6pqoUm1fLp6OIfspI/FEGrd2Uz72U0b2U0b2U0YWqFULxo93XX7oIThxwn2TjfnoTFsREfE8B0/XwtO/e+M6CzcaaH/6Z3Og8em60bXZrMxZMPT0/RYCB0qzaREREREREXHMq69CpUqwciVMmeJ0N0XywvXBVBFCQkJISUlxug05D2VkP2VkkSrANfxvEvea09f91lpck7cLgB+AU6XXnhROx5D9lJF4Io1buykf+ykj+ykj+ykjh3XvDosXQ34+REXB+vUFbrYxHy2PIG5t2rRxugUpgjKynzKyyAlca+Q+CfQGLoOo26PgcVzr3Z4C2gCPAt8Dh4DPgNGubcUZOobsp4zEE2nc2k352E8Z2U8Z2U8ZOcjfH956y3X57bfPmrAFO/PRpK24RUREON2CFEEZ2U8ZWcxAk5NNXOvgRgM1gP8DpgLHgarADcB/cU3grgDGAa2cabe80jFkP2Uknkjj1m7Kx37KyH7KyH7KyEFjx0LTppCUBE88UegmNuajSVtxS01NdboFKYIysp8ysluBfI7gOrP2eqA68CfgOWAD4AN0BCYC64EEYCwQVqrtlks6huynjMQTadzaTfnYTxnZTxnZTxk5pHZtePxx1+V//hPOsQSCjfloTVtx8/X1JS8vz+k25DyUkf2Ukd0uOJ/aQB+gH9ADCDx9fR6u9W8/wbUWbm6JtFmu6RiynzIST6RxazflYz9lZD9lZD9l5JCZM2HoUFi+HLp0OedmNuajM23Fbfjw4U63IEVQRvZTRna74Hz2Ah8Ag4AI4G+41sH1PX1dDLAPeBVoWfx9lmc6huynjMQTadzaTfnYTxnZTxnZTxk5oFcv14RtXh7cffd5N7UxH03aioiInE8KrgncaKAF8CJwENdSCf/AtZzCT8DdQKhDPYqIiIiIiMj/BAT878vH3nwTfvnF2X4ugSZtxW3Tpk1OtyBFUEb2U0Z2+8P5bAYexrV8Qj9gJpADRAFv4ZrMnQ70xrUAkVw0HUP2U0biiTRu7aZ87KeM7KeM7KeMStk//wmNGsGBAzBhQpGb25iPJm3FLeUcizGLPZSR/ZSR3Yotn3xca9oOB2oCfwfWAgHACOArYA3QrXgerjzRMWQ/ZSSeSOPWbsrHfsrIfsrIfsqoFNWrB4884rr8wAOQllbkXWzMR5O24hYdHe10C1IEZWQ/ZWS3EsnnKDAJuApoDbyBa0mFNsASXBO4Wvf2gukYsp8yEk+kcWs35WM/ZWQ/ZWQ/ZVSKXn8dAgPhm29g2rQLuouN+WjSVkREpLisB+4HGgKv41o6oTewDvgYqOVQXyIiIiIiIuVBv34waBDk5sI99zjdzR/iBRinmxA7XHbZZRw7dszpNuQ8lJH9lJHdSj2fBsBEYOTp3zOB14AXgNTSa8OT6BiynzIST6RxazflYz9lZD9lZD9lVAoqVICNG6FBA3jxRXj44Qu+q4356ExbcWvRooXTLUgRlJH9lJHdSj2fX4FRwDXAciAQeARIBO4F/Eq3HU+gY8h+ykg8kcat3ZSP/ZSR/ZSR/ZRRKXj4YdeE7b598NRTF3VXG/PRpK241a5d2+kWpAjKyH7KyG6O5bMa6AIMBDYD1YA3T18e7kxLttIxZD9lJJ5I49Zuysd+ysh+ysh+yqiENWgA//qX6/I//gHp6Rd1dxvz0aStuGVkZDjdghRBGdlPGdnN8Xzm4fpSsr8BSbjWvv0CiAc6OdiXRRzPSIqkjMQTadzaTfnYTxnZTxnZTxmVsEmTXMsjLF4MM2de9N1tzceoVCqVSqUq5QrC8ASGNIz7fzEYmlrQm0qlOm/dddddZufOnSYzM9PEx8ebq6+++rzbDxs2zGzevNlkZmaaDRs2mD59+hS4/brrrjOxsbHmyJEjxhhjIiMjz9pHXFyc+b133323wDa1a9c28+fPN+np6SY5Odm8+OKLxsfHx/G/l0qlUqlUKlWJ1qBBBmMM2dmGJk2c76eYSmfaitvo0aOdbkGKoIzsp4zsZlU+6cBTQCPgPSAPGAT8ArwL1HCuNSdZlZEUqrxnNGLECF599VWefPJJrrrqKtavX09sbCzVq1cvdPvo6GimTp3KRx99RJs2bYiJiSEmJoYrrrjCvU1QUBArVqzg4SK+LOODDz4gPDzcXQ899JD7Nm9vbxYsWIC/vz8dOnTgpptuYsyYMTx1keu5lVXlfdzaTvnYTxnZTxnZTxmVkMBAeOMN1+WXX4Zt2y5pNzbmo0lbERERJyUDd+JaNiEG8AXuAHYATwBBjnUmIoUYO3YsH374IZMnT2bz5s3ccccdZGRkcMsttxS6/X333ceiRYt4+eWX2bJlC0888QQ///wz99xzj3ubzz77jKeffpolS5ac97EzMjJITk52V1pamvu2nj170qJFC/7yl7+wfv16Fi1axOOPP87dd9+Nn5++9VBERETKqEcegbp1YfduePZZp7spVpq0Fbdtl/ivEVJ6lJH9lJHdrM5nC3Ad0Bn4AagEPIlr8vZ2wMe51kqT1RkJUL4z8vPzIyoqqsDkqjGGJUuWEB0dXeh9oqOjz5qMjY2NPef253PDDTdw+PBhEhISmDhxIoGBgQUeJyEhgUOHDhV4nJCQkAJn9f6Wv78/wcHB7qpUqdJF9+QpyvO49QTKx37KyH7KyH7KqAQ0bgz//Kfr8v33wx9Yl9bGfHydbkDs8ds3+WInZWQ/ZWQ3j8jnO6A9MAx4DtfyCe8D9wMP4/oyszLMIzIq58pzRtWqVcPX15fk5OQC1ycnJ9OsWbNC7xMeHl7o9uHh4Rf12P/973/ZvXs3Bw4coFWrVrzwwgs0bdqUoUOHnvdxztxWmHHjxjFhwgT376mpqYSEhDBs2DD8/PyYNWsW3bp1o0qVKhw6dIjVq1fTv39/ANasWYOPjw+tW7cGYM6cOXTs2JFq1apx7Ngxli9fzuDBgwFYv349ubm5tG3bFoAFCxYQFRVFeHg4KSkpLF68mGHDhgGwceNG0tLSaN++PQCLFi2iZcuW1KpVi/T0dObNm8eoUaMA2LJlC0ePHqVjx44ALFmyhMaNG1O3bl2ys7OZPXs2I0eOxNvbm6NHj1KzZk26dOkCwLfffkvt2rVp2LAheXl5zJgxg6FDh+Lv78+uXbtITEykW7duAKxYsYKwsDCaNGkCwNSpUxk0aBAVK1Zk7969bNq0iV69egGwatUqQkJCaNGiBQAzZsygV69eVK5cmYMHD7J27Vr69u0LwI8//khAQACtWrUCYPbs2XTt2pXQ0FAOHz7MqlWrGDhwIABr164FoE2bNgDMnTuX6OhoqlevzvHjx4mLi2PIkCEAbNiwgezsbK6++moAFi5cSJs2bYiIiCA1NZXY2FiGDx8OwKZNm0hJSXH/I0JsbCwtWrSgdu3aZGRkMGfOHPfHRbdt28ahQ4fo1Mn1zZlLly6lYcOG1KtXj5ycHGbNmsXw4cPx9fUlMTGRvXv3cu211wKwbNkyatWqRaNGjTh16hTTp09nyJAhBAQEsHv3btLS0tyPs3LlSqpWreo+pqZNm8aAAQMICgpi//79JCQk0Lt3bwDi4+MJDg52/8PEzJkz6dGjByEhISQlJbFmzRr69esHwE8//YSfnx+RkZEAxMTE0LlzZy677DKOHDnCypUrGTRoEADr1q0jPz+fqKgoAObPn0+7du0ICwvjxIkTLF261H3sJSQkkJmZSbt27QD46quviIyMpGbNmqSlpbFw4UJGjhwJwObNmzl+/DgdOnQAYPHixTRt2pQ6deqQmZlJTEwMo0aNwsvLi+3bt3Pw4EE6d+4MQFxcHPXq1aN+/frk5uYyc+ZM97G6c+dOdu3aRdeuXQFYvnw5ERERNG7cGGMM06ZNY/DgwQQGBrJnzx62bt1Kjx49APj+++8JDQ2lefPmAEyfPp2+ffsSHBzMgQMHWL9+PX369KFChQqcOHGCwMBAWrZsCVCmXiN27NjB/v37Pfo1IjAw0H0clbXXiO3bt9O9e3ePf42oUKECiYmJZfI1AmD16tWl+hpRIzycJQ88wJGAACLWraNLYCCbWrW65NeIX375hQ4dOpTKa0Tfvn1ZuHAhF8LxhXVVdtTo0aMd70GljDy9lJHd5XH5+GG4F8Nh/vdlZcswtLOgN2VUbqs8ZxQREWGMMaZ9+/YFrn/hhRdMfHx8offJzs42o0aNKnDdnXfeaZKSks7atm7duuf8IrLfV9euXY0xxjRo0MAA5v333zeLFi0qsE1gYKAxxpjevXsXug9/f38THBzsrkqVKjn+Ny6pKs/j1hNK+dhfysj+Ukb2lzIq5hoyxPXlY1lZhkaNymQ+Wh5BRETEVrnAJKAhrrNuM/nf8gnTgQbOtSZSHh05coS8vDxq1Cj4TYE1atQgKSmp0PskJSVd1PYX6ocffgCgUaNG532cM7cVJicnh7S0NHedPHnyD/UkIiIiUiqCguD1112XX3gBduxwtJ2S5PjMscqOCgsLc7wHlTLy9FJGdpfH53M5ho8x5OM66zYbw2MW9KWMylWV94zi4+PNm2++6f7dy8vL7N271zz88MOFbj9t2jQzd+7cAtetXLnSvPvuu2dtezFn2nbo0MEYY0zLli0NYHr37m3y8vJM9erV3dvcdttt5sSJE8bf39/xv5vTVd7Hre2lfOwvZWR/KSP7SxkVYz33nOss219/NQQGluV8HG9AZUlFR0c73oNKGXl6KSO7q8zk0xLDQv63ZMJ/MQRY0JcyKhdV3jMaMWKEyczMNDfeeKNp1qyZee+998yxY8fcb/SnTJliJk6cWODvlZOTY8aOHWuaNm1qxo8fb7Kzs80VV1zh3iY0NNRERkaaPn36GGOMGTFihImMjDQ1atQwgGnQoIF57LHHzFVXXWXq1q1rBgwYYHbs2GG+/fZb9z68vb3Nhg0bzKJFi0yrVq1Mz549TXJysnn22Wcd/5vZUOV93Npeysf+Ukb2lzKyv5RRMVWzZoacHNekbf/+ZTofLY8gbvXq1XO6BSmCMrKfMrJbmcknAegL/BXXEgqjgTggzMmmikeZyagMK+8ZffHFFzz44IM89dRTrFu3jtatW9O7d2/3F7TVqVOHiIgI9/arVq3i+uuv5/bbb2f9+vUMGzaMwYMHs3HjRvc2AwcOZN26de4vpJg+fTrr1q3jjjvuAFzLGHTv3p2vv/6aLVu28MorrzBr1iwGDBjg3sepU6fo378/+fn5rFq1is8++4z//Oc/PPHEE6XxZ7FeeR+3tlM+9lNG9lNG9lNGxeStt8DPD+bNg/nzi223Nubj63QDYo+cnBynW5AiKCP7KSO7lbl8PgYSgdlANK61bvsDG893J7uVuYzKIGUEb7/9Nm+//Xaht535ZubfmjlzJjNnzjzn/qZMmcKUKVPOefu+ffvc37B9Pnv27HF/C7YUpHFrN+VjP2VkP2VkP2VUDEaMgG7dIDMT7ruvWHdtYz5euE65FREREU/VGJgPNAFSgZHAIkc7EhERERERKT6VKsGWLVCrFjzxBDz9tNMdlTgtjyBuw4cPd7oFKYIysp8ysluZzWc70B74FqiMawL3HicbunRlNqMyRBmJJ9K4tZvysZ8ysp8ysp8y+oPGj3dN2CYmwosvFvvubcxHk7bi5uur1TJsp4zsp4zsVqbzOQ70xLVkgg8w6XT5ONnUxSvTGZURykg8kcat3ZSP/ZSR/ZSR/ZTRH3DFFXD//a7L994L2dnF/hA25qNJW3FLTEx0ugUpgjKynzKyW5nPJxfXl5M9BJzCdbbtPFxn33qIMp9RGaCMxBNp3NpN+dhPGdlPGdlPGf0Bb78Nvr7w5Zfw1Vcl8hA25qNJW3Hbu3ev0y1IEZSR/ZSR3cpNPi8BQ4EMoA+wEqjnZEMXrtxk5MGUkXgijVu7KR/7KSP7KSP7KaNLdP310KULZGT872zbEmBjPpq0FbcL+VZicZYysp8yslu5yicG+BOwH7gS+AHXureWK1cZeShlJJ5I49Zuysd+ysh+ysh+yugSVK4ML7/suvzMM7BnT4k9lI35aNJWRESkrPoZaHf6ZxgQB4x2tCMREREREZELM348RETAtm3wyitOd1PqvADjdBNih5o1a3LgwAGn25DzUEb2U0Z2K7f5BAGfAYNP/z4BeNKpZs6v3GbkQZSReCKNW7spH/spI/spI/spo4tUsyYkJkKFCtCnDyxaVMIPZ18+OtNW3GrVquV0C1IEZWQ/ZWS3cptPOjAEePH07xOAz4EApxo6t3KbkQdRRuKJNG7tpnzsp4zsp4zsp4wu0rhxrgnb774r8QlbsDMfTdqKW6NGjZxuQYqgjOynjOxWrvMxwMPAX4Fc4HpcyyWEOdnU2cp1Rh5CGYkn0ri1m/KxnzKynzKynzK6CJdfDrfd5ro8fnypPKSN+WjSVtxOnTrldAtSBGVkP2VkN+UDfAz0BI4B0bi+oOwKRzsqQBnZTxmJJ9K4tZvysZ8ysp8ysp8yugjjxkFAACxbBnFxpfKQNuajNW1FRETKo8bAgtM/U4ERQKyjHYmIiIiISHlXuzbs2AH+/nDtta6J23JKZ9qK25AhQ5xuQYqgjOynjOymfH5jO9Ae+BaojGsC924nG3JRRvZTRuKJNG7tpnzsp4zsp4zsp4wu0COPuCZsv/mmVCdsbcxHk7biFhBg4TfSSAHKyH7KyG7K53eO4Voq4WPAB3gLmHT6skOUkf2UkXgijVu7KR/7KSP7KSP7KaMLUKcO3HKL63IprWV7ho35WD9pe9ddd7Fz504yMzOJj4/n6quvPue2vr6+PP744+zYsYPMzEzWrVtHr169Cmwzfvx4jDEFavPmzSX9NDzC7t27nW5BiqCM7KeM7KZ8CpGL68vJHj79+z3APFxn3zpAGdlPGYkn0ri1m/KxnzKynzKynzK6AI8+6jrLdskSWLGiVB/a1nyMrTVixAiTlZVlxowZY5o3b27ef/99c+zYMVO9evVCt3/++efNvn37TJ8+fUz9+vXNHXfcYTIyMkzr1q3d24wfP94kJCSYGjVquKtq1aqOP1cb6lx/V5U9pYzsL2VkdymfIuo6DOkYDIYEDHWVkUoZqcpGadzaXcrH/lJG9pcysr+UURFVr54hJ8d1dmWHDsoHjNVn2o4dO5YPP/yQyZMns3nzZu644w4yMjK45cyp0r/zf//3f0ycOJGvvvqKnTt38t5777Fw4UIeeOCBAtvl5eWRnJzsrqNHj5bG07Fe9+7dnW5BiqCM7KeM7KZ8ivAl8CfgAHAlsBrXurelSBnZTxmJJ9K4tZvysZ8ysp8ysp8yKsKjj4KfH3z9NXz/fak/vI35WDtp6+fnR1RUFEuWLHFfZ4xhyZIlREdHF3qfgIAAsrKyClyXmZlJp06dClzXuHFj9u/fT2JiIp999hm1a9c+Zx/+/v4EBwe7q1KlSn/gWYmIiFjuZ6AdsBYIA+KAUY52JCIiIiIiZVn9+jBmjOtyKa9lazNfpxs4l2rVquHr60tycnKB65OTk2nWrFmh94mNjWXs2LEsX76cxMREunXrxpAhQ/Dx+d83qvzwww+MGTOGrVu3EhERwfjx4/nuu++48sorOXny5Fn7HDduHBMmTHD/npqaSkhICMOGDcPPz49Zs2bRrVs3qlSpwqFDh1i9ejX9+/cHYM2aNfj4+NC6dWsA5syZQ8eOHalWrRrHjh1j+fLlDB48GID169eTm5tL27ZtAViwYAFRUVGEh4eTkpLC4sWLGTZsGAAbN24kLS2N9u1dpz8tWrSIli1bUqtWLdLT05k3bx6jRrn+C3vLli0cPXqUjh07ArBkyRIaN25M3bp1yc7OZvbs2YwcORJvb2+OHTtGzZo16dKlCwDffvsttWvXpmHDhuTl5TFjxgyGDh2Kv78/u3btcv+NAVasWEFYWBhNmjQBYOrUqQwaNIiKFSuyd+9eNm3a5F5feNWqVYSEhNCiRQsAZsyYQa9evahcuTIHDx5k7dq19O3bF4Aff/yRgIAAWrVqBcDs2bPp2rUroaGhHD58mFWrVjFw4EAA1q5dC0CbNm0AmDt3LtHR0VSvXp3jx48TFxfn/jbADRs2kJ2d7V4jeeHChbRp04aIiAhSU1OJjY1l+PDhAGzatImUlBT3PxbExsbSokULateuTUZGBnPmzGH06NEAbNu2jUOHDrn/oWDp0qU0bNiQevXqkZOTw6xZsxg+fDi+vr4kJiayd+9err32WgCWLVtGrVq1aNSoEadOnWL69OkMGTKEgIAAdu/ezfbt2wkMDGT06NGsXLmSqlWruo+FadOmMWDAAIKCgti/fz8JCQn07t0bgPj4eIKDg7niiisAmDlzJj169CAkJISkpCTWrFlDv379APjpp5/w8/MjMjISgJiYGDp37sxll13GkSNHWLlyJYMGDQJg3bp15OfnExUVBcD8+fNp164dYWFhnDhxgqVLlzJ06FAAEhISyMzMpF27dgB89dVXREZGUrNmTdLS0li4cCEjR44EYPPmzRw/fpwOHToAsHjxYpo2bUqdOnXIzMwkJiaGUaNG4eXlxfbt2zl48CCdO3cGIC4ujnr16lG/fn1yc3OZOXOm+1jduXMnu3btomvXrgAsX76ciIgIGjdujDGGadOmMXjwYAIDA9mzZw9bt26lR48eAHz//feEhobSvHlzAKZPn07fvn0JDg7mwIEDrF+/nj59+gCwc+dOrrzySlq2bAlQpl4jduzYwf79+z36NWLv3r3u47Usvkac+dfhP/waEdmDim9W5Ntbv+VIxyMwFVoNakXmE5kl/hoRGBhIlSpVyuxrxOrVqwkMDPTo14iUlBRGjx5d4q8Rffv2ZeHChYgUh5UrVzrdgpyH8rGfMrKfMrKfMjqPxx4DX19YtAji4x1pwdZ8HF+jobCKiIgwxhjTvn37Ate/8MILJj4+vtD7VKtWzXz55ZcmLy/P5Obmmi1btpi33nrLZGRknPNxQkJCzIkTJ8wtt9xS6O3+/v4mODjYXZUqVXL8b1NS1aZNG8d7UCkjTy9lZHcpn4ssbwwvYtz/u14ZqZSRyjNL49buUj72lzKyv5SR/aWMzlENGxpyc11r2bZrp3x+U9Yuj3DkyBHy8vKoUaNGgetr1KhBUlLSOe9z3XXXERQURN26dWnWrBknT57k119/PefjpKSksG3bNho1alTo7Tk5OaSlpbmrsLNxy4pzncEs9lBG9lNGdlM+F+kU8BAw8fTv/wauKtmHVEb2U0biiTRu7aZ87KeM7KeM7KeMzuHMWbYLF8Lq1Y61YWM+1k7a5ubmsmbNGvfH5gC8vLzo1q0bq1atOu99s7OzOXDgAL6+vgwdOpQ5c+acc9ugoCAaNmzIwYMHi613ERGRMuVxYD4QCMTgWutWRERERETkj2jUCP7v/1yXf7M0qfyP46f7nqtGjBhhMjMzzY033miaNWtm3nvvPXPs2DETFhZmADNlyhQzceJE9/bt2rUz1113nalfv77p1KmTWbJkiUlMTDQhISHubV566SXTuXNnU7duXRMdHW2+/vprc+jQIVOtWjXHn6/T5eXl5XgPKmXk6aWM7C7l8weqMobNuJZJWI7BTxmV11JGKk8sjVu7S/nYX8rI/lJG9pcyKqSmTHEtizBvnuO92JiPtWfaAnzxxRc8+OCDPPXUU6xbt47WrVvTu3dvDh06BECdOnWIiIhwb1+hQgWeeeYZNm3axJdffsn+/fvp1KkTKSkp7m0uv/xypk6dytatW/niiy84evQo7du358iRI6X+/GwzYMAAp1uQIigj+ykjuymfPyAVGASkAH8C3iyZh1FG9lNG4ok0bu2mfOynjOynjOynjH6nSRO44QbXZQvOsrUxH1+nGyjK22+/zdtvv13obWe+4fmM5cuXu7/9+lzOfIO3nC0oKMjpFqQIysh+yshuyucP2gZcD8wD7gDWAh8U70MoI/spI/FEGrd2Uz72U0b2U0b2U0a/8/jj4OMDc+fCmjVOd2NlPlafaSula//+/U63IEVQRvZTRnZTPsVgIfDo6ctvAZ2Kd/fKyH7KSDyRxq3dlI/9lJH9lJH9lNFvNG0KZ06qtOAsW7AzHy9c6ySIEBoayvHjx51uQ85DGdlPGdlN+RSjacBIIBloC+wrnt0qI/spI/FEGrd2Uz72U0b2U0b2U0a/8fnncP31EBMD113ndDeAnfnoTFtx6927t9MtSBGUkf2Ukd2UTzG6BVgH1ABigArFs1tlZD9lJJ5I49Zuysd+ysh+ysh+yui05s1h1CjXZUvOsgU789GkrYiIiFy8DGAwcBiIAj50tBsREREREfEETzwB3t4wezasX+90N1bTpK24xcfHO92CFEEZ2U8Z2U35FLPdwHAgD/gL8MAf36Uysp8yEk+kcWs35WM/ZWQ/ZWQ/ZQS0aAEjRrguW3SWLdiZjyZtxS04ONjpFqQIysh+yshuyqcELAPuP335BaDnH9udMrKfMhJPpHFrN+VjP2VkP2VkP2UEjB/vOst25kxISHC6mwJszEeTtuJ2xRVXON2CFEEZ2U8Z2U35lJC3gY8AH1xfUNbw0neljOynjMQTadzaTfnYTxnZTxnZr9xndOWV/zvL9sknne2lEDbmo0lbERER+ePuAlYBocAcoJKz7YiIiIiIiEXGj3f9/OIL+OUXZ3vxEF6AcboJsYOfnx+5ublOtyHnoYzsp4zspnxKWDjwE1ALiAGGcNHvMpSR/ZSReCKNW7spH/spI/spI/uV64xatXJ96dipU9CyJWza5HRHZ7ExH51pK249evRwugUpgjKynzKym/IpYUm4JmqzgcHAExe/C2VkP2Uknkjj1m7Kx37KyH7KyH7lOqPfnmVr4YQt2JmPJm3FLSQkxOkWpAjKyH7KyG7KpxSsBm4/fXkCcN3F3V0Z2U8ZiSfSuLWb8rGfMrKfMrJfuc0oMhKGDHGdZfvUU053c0425qNJW3FLSkpyugUpgjKynzKym/IpJf8BXv/N5Ssv/K7KyH7KSDyRxq3dlI/9lJH9lJH9ym1GEya4fk6bBps3O9rK+diYj9a0FbfKlSuTmprqdBtyHsrIfsrIbsqnFPkAsUA3IBG4Gjhe9N2Ukf2UkXgijVu7KR/7KSP7KSP7lcuM2rSBn3+G/Hy44grYutXpjs7Jxnx0pq249evXz+kWpAjKyH7KyG7KpxTlAyOAX4GGwHRcE7lFUEb2U0biiTRu7aZ87KeM7KeM7FcuMzpzlu3UqVZP2IKd+WjSVkRERErGMVxfSJYO9ABecLQbEREREREpLVFRMHCg6yxbi9eytZkmbcXtp59+croFKYIysp8yspvycUACcOPpyw8A/3f+zZWR/ZSReCKNW7spH/spI/spI/uVu4zOnGX7+eewfbujrVwIG/PRpK24+fn5Od2CFEEZ2U8Z2U35OGQ28PTpyx8Abc+9qTKynzIST6RxazflYz9lZD9lZL9yldHVV0P//pCXB08/XfT2FrAxH03ailtkZKTTLUgRlJH9lJHdlI+DxgNzgQrAl0CNwjdTRvZTRnDXXXexc+dOMjMziY+P5+qrrz7v9sOGDWPz5s1kZmayYcMG+vTpU+D26667jtjYWI4cOYIx5qy/cWhoKG+++SZbtmwhIyOD3bt388Ybb1C5cuUC2xljzqqRI0cWz5P2cBq3dlM+9lNG9lNG9itXGZ05y/azz2DHDkdbuVA25qNJWxERESl5BvgLsAm4HJgF+DvakcglGTFiBK+++ipPPvkkV111FevXryc2Npbq1asXun10dDRTp07lo48+ok2bNsTExBATE8MVV1zh3iYoKIgVK1bw8MMPF7qPmjVrUrNmTR588EGuvPJKxowZQ+/evfnoo4/O2nbMmDGEh4e7KyYmpliet4iIiMgFueYa6NvXo86ytZlRqQATGBjoeA8qZeTppYzsLuVjQTXGcByDwfC+MvLEKu8ZxcfHm0mTJrl/9/LyMvv27TMPP/xwodtPmzbNzJs3r8B1q1atMu++++5Z29atW9cYY0xkZGSRfQwbNsxkZWUZHx8f93XGGDNo0CDH/0Y2Vnkft7aX8rG/lJH9pYzsr3KT0VdfuT7u89FHzvfi4fnoTFtx69y5s9MtSBGUkf2Ukd2UjwW2A6OAfOB24I6CNysj+5XnjPz8/IiKimLJkiXu64wxLFmyhOjo6ELvEx0dXWB7gNjY2HNuf6FCQkJITU0lPz+/wPVvv/02hw8f5ocffuDmm28+7z78/f0JDg52V6VKlf5QTzYrz+PWEygf+ykj+ykj+5WLjNq3h969ITcXnnnG6W4uio35+DrdgNjjsssuc7oFKYIysp8yspvysUQsMA54EXgT2Ah857pJGdmvPGdUrVo1fH19SU5OLnB9cnIyzZo1K/Q+4eHhhW4fHh5+yX1UrVqVxx9/nA8++KDA9Y8//jjffPMNGRkZ9OzZk3feeYdKlSoxadKkQvczbtw4JpxZcw5ITU0lJCSEYcOG4efnx6xZs+jWrRtVqlTh0KFDrF69mv79+wOwZs0afHx8aN26NQBz5syhY8eOVKtWjWPHjrF8+XIGDx4MwPr168nNzaVtW9e3EC5YsICoqCjCw8NJSUlh8eLFDBs2DICNGzeSlpZG+/btAVi0aBEtW7akVq1apKenM2/ePEaNGgXAli1bOHr0KB07dgRgyZIlNG7cmLp165Kdnc3s2bMZOXIk3t7eVKlShZo1a9KlSxcAvv32W2rXrk3Dhg3Jy8tjxowZDB06FH9/f3bt2kViYiLdunUDYMWKFYSFhdGkSRMApk6dyqBBg6hYsSJ79+5l06ZN9OrVC4BVq1YREhJCixYtAJgxYwa9evWicuXKHDx4kLVr19K3b18AfvzxRwICAmjVqhUAs2fPpmvXroSGhnL48GFWrVrFwIEDAVi7di0Abdq0AWDu3LlER0dTvXp1jh8/TlxcHEOGDAFgw4YNZGdnu9daXrhwIW3atCEiIoLU1FRiY2MZPnw4AJs2bSIlJcX9jwixsbG0aNGC2rVrk5GRwZw5cxg9ejQA27Zt49ChQ3Tq1AmApUuX0rBhQ+rVq0dOTg6zZs1i+PDh+Pr6kpiYyN69e7n22msBWLZsGbVq1aJRo0acOnWK6dOnM2TIEAICAti9ezfh4eHux1m5ciVVq1Z1H1PTpk1jwIABBAUFsX//fhISEujduzcA8fHxBAcHu5cbmTlzJj169CAkJISkpCTWrFlDv379ANe3gvv5+bnXLIyJiaFz585cdtllHDlyhJUrVzJo0CAA1q1bR35+PlFRUQDMnz+fdu3aERYWxokTJ1i6dClDhw4FICEhgczMTNq1awfAV199RWRkJDVr1iQtLY2FCxe615bevHkzx48fp0OHDgAsXryYpk2bUqdOHTIzM4mJiWHUqFF4eXmxfft2Dh486J5EiIuLo169etSvX5/c3FxmzpzpPlZ37tzJrl276Nq1KwDLly8nIiKCxo0bY4xh2rRpDB48mMDAQPbs2cPWrVvp0aMHAN9//z2hoaE0b94cgOnTp9O3b1+Cg4M5cOAA69evp0+fPtSqVYsdO3YQGBhIy5YtAcrUa8SOHTvYv3+/R79G1KlTx30clbXXiO3bt9O9e3ePf42oVasWP/zwQ5l8jQBYvXo1Sa+8QjrQYMUK9u3fT7c+fTzmNcLf358OHTqUymtE3759WbhwIRfC8dN9VXZUjx49HO9BpYw8vZSR3aV8LKvPcS2TkIyhjjLylCrPGUVERBhjjGnfvn2B61944QUTHx9f6H2ys7PNqFGjClx35513mqSkpLO2vZDlEYKDg018fLxZuHCh8fX1PW+/Tz75pNmzZ885b/f39zfBwcHuqlSpkuN/45Kq8jxuPaGUj/2ljOwvZWR/lfmMOnRwLYuQk2OoW9f5fspAPloeQdxWrlzpdAtSBGVkP2VkN+VjmVuBn4Ew4EsgUBl5gvKc0ZEjR8jLy6NGjRoFrq9RowZJSUmF3icpKemitj+fSpUqsWjRItLS0rjuuuvIy8s77/Y//PADtWvXxt+/8G/9y8nJIS0tzV0nT5686J48RXket55A+dhPGdlPGdmvzGf05JOun598Art3O9vLJbAxH03aituZU/3FXsrIfsrIbsrHMpnAYOAQcBXwEQwcNNDRlqRo5fk4ys3NZc2aNe6PuwF4eXnRrVs3Vq1aVeh9Vq1aVWB7gB49epxz+3MJDg7m66+/Jicnh4EDB5KdnV3kfVq3bs2xY8fIycm5qMcqi8rzuPUEysd+ysh+ysh+ZTqjTp2ge3fIyYFnn3W6m0tiYz5a01ZEREScsxcYBiwFRsNmsxmmOtyTyHm8+uqrTJkyhZ9++onVq1dz//33ExQUxCeffALAlClT2L9/P4888ggAb7zxBsuWLWPs2LEsWLCAUaNG0bZtW26//Xb3PkNDQ6lTpw41a9YEoGnTpoDrLN3k5GT3hG3FihX5y1/+QuXKlalcuTIAhw8f5tSpU/Tv358aNWoQHx9PVlYWPXr04JFHHuHll18uzT+PiIiIlEdnzrL9+GPYs8fZXsoYx9doUNlRzZs3d7wHlTLy9FJGdpfysbjuwLW+bR6G1hb0ozpn6TjC3H333WbXrl0mKyvLxMfHm3bt2rlvi4uLM5988kmB7YcNG2a2bNlisrKyTEJCgunTp0+B22+66SZTmPHjxxvAdOnSpdDbjTGm7uk143r16mV+/vlnk5qaatLS0szatWvN7bffbry8vBz/e9lQGrd2l/Kxv5SR/aWM7K8ym1Hnzq61bLOzDbVrO99P2crH8QZUllSTJk0c70GljDy9lJHdpXwsr6m4Jm6/s6AX1TlLx5HKE0vj1u5SPvaXMrK/lJH9VWYziotzTdq+/bbzvZSxfLSmrbhFRUU53YIUQRnZTxnZTflY7gHwzfKFTsD1Tjcj56LjSDyRxq3dlI/9lJH9lJH9ymRG117rquxsmDjR6W7+EBvz0aStiIiI2OEAtIhp4br8ElDJ0W5EREREROR8zqxl++GHsH+/s72UQV64TrkVITg4mLS0NKfbkPNQRvZTRnZTPvarVLUSJ+NPQiPgeWCc0x3J7+k4Ek+kcWs35WM/ZWQ/ZWS/MpfRn/8MS5dCVhY0bAgHDjjd0R9iYz4601bc2rVr53QLUgRlZD9lZDflY79rWl8D95/+ZSyuyVuxio4j8UQat3ZTPvZTRvZTRvYrcxmdOcv2gw88fsIW7MxHk7biFhYW5nQLUgRlZD9lZDflY7+wsDBYACwE/IHXne1HzqbjSDyRxq3dlI/9lJH9lJH9ylRG3btDp06QmQnPP+90N8XCxnw0aStuJ06ccLoFKYIysp8yspvysZ87o/uBHKAf0NexdqQQOo7EE2nc2k352E8Z2U8Z2a9MZXTmLNv334eDB53tpZjYmI/WtBU3f39/cnJynG5DzkMZ2U8Z2U352K9ARs8DDwPbgStxTeKK43QciSfSuLWb8rGfMrKfMrJfmcmoa1f45hvXWbYNGkBSktMdFQsb89GZtuI2dOhQp1uQIigj+ykjuykf+xXI6BngANAY+IdDDclZdByJJ9K4tZvysZ8ysp8ysl+Zyeixx1w/P/ywzEzYgp35aNJWRERE7HQSeOj05ceAmg72IiIiIiJS3nXoAH/+M+TkwEsvOd1NmadJW3FLSEhwugUpgjKynzKym/Kx31kZfQ6sBCoBLzrQkJxFx5F4Io1buykf+ykj+ykj+5WJjB591PVzyhTYt8/ZXoqZjflo0lbcMjMznW5BiqCM7KeM7KZ87FdoRvcCp4AbgI6l3JCcRceReCKNW7spH/spI/spI/t5fEZt2kDfvpCfD88/73Q3xc7GfDRpK27t2rVzugUpgjKynzKym/KxX6EZrQU+PH15Enr34jAdR+KJNG7tpnzsp4zsp4zs5/EZnTnL9r//hV9/dbaXEmBjPvrPHhEREbHfo8BxoA1wm8O9iIiIiIiUJy1awNChcOoUPPec092UG16AcboJsUOVKlU4ceKE023IeSgj+ykjuykf+503o7uBt4CjQGNck7hS6nQciSfSuLWb8rGfMrKfMrKfR2f02Wdwww0wcyYMH+50NyXCxnx0pq24RUZGOt2CFEEZ2U8Z2U352O+8Gb0HbACqAk+XUkNyFh1H4ok0bu2mfOynjOynjOznsRk1bAijRrkuP/uss72UIBvzsX7S9q677mLnzp1kZmYSHx/P1Vdffc5tfX19efzxx9mxYweZmZmsW7eOXr16/aF9lic1a9Z0ugUpgjKynzKym/Kx33kzygf+fvryHUCrUmhIzqLjSDyRxq3dlI/9lJH9lJH9PDajf/0LfHxg/nxYt87pbkqMjflYPWk7YsQIXn31VZ588kmuuuoq1q9fT2xsLNWrVy90+2eeeYa//e1v3HvvvbRo0YL33nuPL7/8ktatW1/yPsuTtLQ0p1uQIigj+ykjuykf+xWZ0TJgOuCD60vJpNTpOBJPpHFrN+VjP2VkP2VkP4/MqHZtuOkm1+UyfJYt2JuPsbXi4+PNpEmT3L97eXmZffv2mYcffrjQ7ffv32/uuuuuAtfNnDnTfPrpp5e8z/JU3t7ejvegUkaeXsrI7lI+9tcFZXQ5hnQMBsNI53sub6XjSOWJpXFrdykf+0sZ2V/KyP7yyIwmTTIYY1i82PleymE+1p5p6+fnR1RUFEuWLHFfZ4xhyZIlREdHF3qfgIAAsrKyClyXmZlJp06dLnmf/v7+BAcHu6tSpUp/9KlZa+TIkU63IEVQRvZTRnZTPva7oIz2ARNPX34ZqFiCDclZdByJJ9K4tZvysZ8ysp8ysp/HZRQeDrfe6rpcxs+yBTvz8XW6gXOpVq0avr6+JCcnF7g+OTmZZs2aFXqf2NhYxo4dy/Lly0lMTKRbt24MGTIEHx+fS97nuHHjmDBhgvv31NRUQkJCGDZsGH5+fsyaNYtu3bpRpUoVDh06xOrVq+nfvz8Aa9aswcfHx708w5w5c+jYsSPVqlXj2LFjLF++nMGDBwOwfv16cnNzadu2LQALFiwgKiqK8PBwUlJSWLx4McOGDQNg48aNpKWl0b59ewAWLVpEy5YtqVWrFunp6cybN49RpxeJ3rJlC0ePHqVjx44ALFmyhMaNG1O3bl2ys7OZPXs2I0eOxNvbmypVqlCzZk26dOkCwLfffkvt2rVp2LAheXl5zJgxg6FDh+Lv78+uXbvcf2OAFStWEBYWRpMmTQCYOnUqgwYNomLFiuzdu5dNmza51xdetWoVISEhtGjRAoAZM2bQq1cvKleuzMGDB1m7di19+/YF4McffyQgIIBWrVwLF86ePZuuXbsSGhrK4cOHWbVqFQMHDgRg7dq1ALRp0waAuXPnEh0dTfXq1Tl+/DhxcXEMGTIEgA0bNpCdne1ez3jhwoW0adOGiIgIUlNTiY2NZfjpb0TctGkTKSkp7on92NhYWrRoQe3atcnIyGDOnDmMHj0agG3btnHo0CH3PxQsXbqUhg0bUq9ePXJycpg1axbDhw/H19eXxMRE9u7dy7XXXgvAsmXLqFWrFo0aNeLUqVNMnz6dIUOGEBAQwO7du9m+fTu1atVi9OjRrFy5kqpVq7rH7bRp0xgwYABBQUHs37+fhIQEevfuDUB8fDzBwcFcccUVAMycOZMePXoQEhJCUlISa9asoV+/fgD89NNP+Pn5uRfgjomJoXPnzlx22WUcOXKElStXMmjQIADWrVtHfn4+UVFRAMyfP5927doRFhbGiRMnWLp0KUOHDgUgISGBzMxM2rVrB8BXX31FZGQkNWvWJC0tjYULF7pfIDdv3szx48fp0KEDAIsXL6Zp06bUqVOHzMxMYmJiGDVqFF5eXmzfvp2DBw/SuXNnAOLi4qhXrx7169cnNzeXmTNnuo/VnTt3smvXLrp27QrA8uXLiYiIoHHjxhhjmDZtGoMHDyYwMJA9e/awdetWevToAcD3339PaGgozZs3B2D69On07duX4OBgDhw4wPr16+nTpw8AFStW5Morr6Rly5YAZeo1YseOHezfv9+jXyMqVarkPl7L4mtE9+7dATz6NaJWrVpUqVKlyNeIa/Zew4JDC0i/PJ3L372cPy36k0e8RqxevZrAwECPfo0IDQ1l9OjRJf4a0bdvXxYuXIiIiIiIOGDsWKhQAVauhG+/dbqbcsvx030Lq4iICGOMMe3bty9w/QsvvGDi4+MLvU+1atXMl19+afLy8kxubq7ZsmWLeeutt0xGRsYl79Pf398EBwe7q1KlSo7/bUqqWrdu7XgPKmXk6aWM7C7lY39dVEYDcS2RkIWhgfO9l5fScaTyxNK4tbuUj/2ljOwvZWR/eVRGVasa0tJcSyP07u18P+U0H2uXRzhy5Ah5eXnUqFGjwPU1atQgKSnpnPe57rrrCAoKom7dujRr1oyTJ0/y66+/XvI+c3JySEtLc9fJkyeL4dnZ6fjx4063IEVQRvZTRnZTPva7qIzmArFAAPBaCTUkZ9FxJJ5I49Zuysd+ysh+ysh+HpXRffdBpUqwZg0sWuR0N6XCxnysnbTNzc1lzZo17o/NAXh5edGtWzdWrVp13vtmZ2dz4MABfH19GTp0KHPmzPnD+ywPznwcXeyljOynjOymfOx30RndB+QCA4HeJdCQnEXHkXgijVu7KR/7KSP7KSP7eUxGISHw97+7LpeDtWzPsDEfaydtAV599VVuu+02brzxRpo1a8a7775LUFAQn3zyCQBTpkxh4sSJ7u3btWvHddddR/369enUqROLFi3C29ubF1988YL3KSIiIh5kK/DG6cuvA37OtSIiIiIi4vHuvts1cbtxI8TEON1Nuef4Gg3nq7vvvtvs2rXLZGVlmfj4eNOuXTv3bXFxceaTTz5x/965c2ezceNGk5mZaQ4fPmymTJliIiIiLmqf5bmqVavmeA8qZeTppYzsLuVjf11SRsEYDuJa3/ZB559DWS8dRypPLI1bu0v52F/KyP5SRvaXR2RUsaLh8GHXWrajRzvfj/JxvAGVJdWxY0fHe1ApI08vZWR3KR/765IzuhHXpG0qhnDnn0dZLh1HKk8sjVu7S/nYX8rI/lJG9pdHZPSPf7gmbLdtM/j4ON9POc/H6uURpHTVqVPH6RakCMrIfsrIbsrHfpec0afAKiAYeKEYG5Kz6DgST6RxazflYz9lZD9lZD/rMwoIgH/+03X5uecgP9/ZfkqZjflo0lbcMjMznW5BiqCM7KeM7KZ87HfJGRng78Ap4EYguvh6koJ0HIkn0ri1m/KxnzKynzKyn/UZ3XILRETAnj3w2WdOd1PqbMzHC9d/5oiIiIh4vg+BW4E1QDtck7giIiIiInJuvr6wYwfUrev6IrJ33nG6I0Fn2spvjBo1yukWpAjKyH7KyG7Kx35/OKNHgBNAFHDLH+9HzqbjSDyRxq3dlI/9lJH9lJH9rM7oL39xTdgePAgff+x0N46wMR9N2oqbl5eX0y1IEZSR/ZSR3ZSP/f5wRoeB8acvTwSq/LHdydl0HIkn0ri1m/KxnzKynzKyn7UZeXvDI4+4Lr/8MmRlOduPQ2zMR5O24rZ9+3anW5AiKCP7KSO7KR/7FUtG7wAbgerAk398d1KQjiPxRBq3dlM+9lNG9lNG9rM2oxEjoHFjOHoU3n/f6W4cY2M+mrQVt4MHDzrdghRBGdlPGdlN+divWDLKw/WlZAB3AVf+8V3K/+g4Ek+kcWs35WM/ZWQ/ZWQ/KzPy8vrfWbavvQbp6c724yAb89Gkrbh17tzZ6RakCMrIfsrIbsrHfsWW0TfATMAXeLN4dikuOo7EE2nc2k352E8Z2U8Z2c/KjAYOhJYtISUF3nrL6W4cZWM+mrQVERGRsulBIBPoCgxzuBcREREREds89pjr56RJrolbsYoXYJxuQuwQHh5OUlKS023IeSgj+ykjuykf+xV7Rk/gWtd2D9AcyCi+XZdXOo7EE2nc2k352E8Z2U8Z2c+6jHr1gkWLXEsi1K3rWtO2HLMuH3SmrfxGvXr1nG5BiqCM7KeM7KZ87FfsGb0I7ALqAA8X767LKx1H4ok0bu2mfOynjOynjOxnXUZnzrJ9771yP2ELFuaDJm3lN+rXr+90C1IEZWQ/ZWQ35WO/Ys8oCxh7+vJDQL3i3X15pONIPJHGrd2Uj/2Ukf2Ukf2syqhzZ+jUCbKy4JVXnO7GClblc5ombcUtNzfX6RakCMrIfsrIbsrHfiWS0ZfAEqAC8Grx77680XEknkjj1m7Kx37KyH7KyH5WZXTmLNuPPoKDB53txRJW5XOa1rQVERGRsq8FsB7wBXoCi51tR0RERETEEddcA/HxkJsLjRrBnj1OdyTnoDNtxW3YMH21tu2Ukf2Ukd2Uj/1KLKNNwKTTl98A/ErmYcoDHUfiiTRu7aZ87KeM7KeM7GdNRo8+6vr56aeasP0Na/L5DU3aipufn/4L1nbKyH7KyG7Kx34lmtGTwCGgOXBPyT1MWafjSDyRxq3dlI/9lJH9lJH9rMgoMhIGDID8fHj+eae7sYoV+fyOJm3FbefOnU63IEVQRvZTRnZTPvYr0YxSgH+dvjwBCCu5hyrLdBzBXXfdxc6dO8nMzCQ+Pp6rr776vNsPGzaMzZs3k5mZyYYNG+jTp0+B26+77jpiY2M5cuQIxhgiIyPP2kdAQABvvfUWR44cIS0tjZkzZxIWVnAQ165dm/nz55Oenk5ycjIvvvgiPj4+f/wJlwEat3ZTPvZTRvZTRvazIqNHHnH9nD4dtm93thfLWJHP72jSVtx27drldAtSBGVkP2VkN+VjvxLPaDLwI1AZuK9kH6qsKu/H0YgRI3j11Vd58sknueqqq1i/fj2xsbFUr1690O2jo6OZOnUqH330EW3atCEmJoaYmBiuuOIK9zZBQUGsWLGChx9++JyP+9prrzFgwACGDx9Oly5dqFmzJrNnz3bf7u3tzYIFC/D396dDhw7cdNNNjBkzhqeeeqr4nrwHK+/j1nbKx37KyH7KyH6OZ9SsGZxZAmDiRGd7sZDj+ZyDUakAM3r0aMd7UCkjTy9lZHcpH/urVDIahMFgOIYhyPnn7GlV3o+j+Ph4M2nSJPfvXl5eZt++febhhx8udPtp06aZefPmFbhu1apV5t133z1r27p16xpjjImMjCxwfeXKlU12drYZOnSo+7qmTZsaY4y55pprDGB69+5t8vLyTFhYmHubv/3tb+bEiRPGz8/P8b+b01Xex63tpXzsL2Vkfykj+8vxjKZMMRhjmD3b8b+FjeV4PoWUzrQVERGR8mUesA0IBW52uBfxKH5+fkRFRbFkyRL3dcYYlixZQnR0dKH3iY6OLrA9QGxs7Dm3L0xUVBT+/v4F9rN161Z2797t3k90dDQJCQkcOnSowOOEhIQUOKv3t/z9/QkODnZXpUqVLrgnERER8SD168P117suP/uss73IBfN1ugGxx/Lly51uQYqgjOynjOymfOxXKhmdAl4D3gX+Abxz+jq5IOX5OKpWrRq+vr4kJycXuD45OZlmzZoVep/w8PBCtw8PD7/gxw0PDyc7O5uUlJRz7udcj3PmtsKMGzeOCRMmuH9PTU0lJCSEYcOG4efnx6xZs+jWrRtVqlTh0KFDrF69mv79+wOwZs0afHx8aN26NQBz5syhY8eOVKtWjWPHjrF8+XIGDx4MwPr168nNzaVt27YALFiwgKioKMLDw0lJSWHx4sXub2zeuHEjaWlptG/fHoBFixbRsmVLatWqRXp6OvPmzWPUqFEAbNmyhaNHj9KxY0cAlixZQuPGjalbty7Z2dnMnj2bkSNH4u3tzZEjR6hZsyZdunQB4Ntvv6V27do0bNiQvLw8ZsyYwdChQ/H392fXrl0kJibSrVs3AFasWEFYWBhNmjQBYOrUqQwaNIiKFSuyd+9eNm3aRK9evQBYtWoVISEhtGjRAoAZM2bQq1cvKleuzMGDB1m7di19+/YF4McffyQgIIBWrVoBMHv2bLp27UpoaCiHDx9m1apVDBw4EIC1a9cC0KZNGwDmzp1LdHQ01atX5/jx48TFxTFkyBAANmzYQHZ2tnut5YULF9KmTRsiIiJITU0lNjaW4cOHA7Bp0yZSUlLck/+xsbG0aNGC2rVrk5GRwZw5cxg9ejQA27Zt49ChQ3Tq1AmApUuX0rBhQ+rVq0dOTg6zZs1i+PDh+Pr6kpiYyN69e7n22msBWLZsGbVq1aJRo0acOnWK6dOnM2TIEAICAti9ezcbNmxwP87KlSupWrWq+5iaNm0aAwYMICgoiP3795OQkEDv3r0BiI+PJzg42P0PEzNnzqRHjx6EhISQlJTEmjVr6NevHwA//fQTfn5+7jWjY2Ji6Ny5M5dddhlHjhxh5cqVDBo0CIB169aRn59PVFQUAPPnz6ddu3aEhYVx4sQJli5dytChQwFISEggMzOTdu3aAfDVV18RGRlJzZo1SUtLY+HChYwcORKAzZs3c/z4cTp06ADA4sWLadq0KXXq1CEzM5OYmBhGjRqFl5cX27dv5+DBg3Tu3BmAuLg46tWrR/369cnNzWXmzJnuY3Xnzp3s2rWLrl27Aq7X6YiICBo3bowxhmnTpjF48GACAwPZs2cPW7dupUePHgB8//33hIaG0rx5cwCmT59O3759CQ4O5sCBA6xfv54+ffpQoUIFGjZsSGBgIC1btgQoU68RO3bsYP/+/R79GrF79273cVTWXiO2b99O9+7dPf41okKFClSpUsWR14j1d91Fpq8vEevW4Z2URETbtsX6GgGwevVqj36N2LBhAx06dCiV14i+ffuycOFCLoTjp/uq7Ki2bds63oNKGXl6KSO7S/nYX6WWUSCGw7iWSRjq/PP2pCrPx1FERIQxxpj27dsXuP6FF14w8fHxhd4nOzvbjBo1qsB1d955p0lKSjpr23MtjzB69GiTlZV11vY//PCDef755w1g3n//fbNo0aICtwcGBhpjjOndu3ehvfn7+5vg4GB3VapUyfG/cUlVeR63nlDKx/5SRvaXMrK/HMuoVi1DdrZraYQOHRz/O9haNh5DWh5B3Bo3bux0C1IEZWQ/ZWQ35WO/UssoE9cZtgAPlM5DlhXl+Tg6cuQIeXl51KhRo8D1NWrUICkpqdD7JCUlXdT259pHQEAAISEh59zPuR7nzG2FycnJIS0tzV0nT5684J48TXket55A+dhPGdlPGdnPsYz++U/w94e4OPj+e2d68AA2HkOatBU3Y4zTLUgRlJH9lJHdlI/9SjWjt4EsIPp0yQUpz8dRbm4ua9ascX/cDcDLy4tu3bqxatWqQu+zatWqAtsD9OjR45zbF2bNmjXk5OQU2E+TJk2oW7euez+rVq2iZcuWVK9evcDjpKSksGnTpgt+rLKqPI9bT6B87KeM7KeM7OdIRmFhcPvtrstay/a8bD2GHD/dV6VSqVQqlcqR+hDXEgmzLOhF5RE1YsQIk5mZaW688UbTrFkz895775ljx46ZsLAwA5gpU6aYiRMnurePjo42OTk5ZuzYsaZp06Zm/PjxJjs721xxxRXubUJDQ01kZKTp06ePMcaYESNGmMjISFOjRg33Nu+8847ZtWuXufbaa81VV11lVq5caVauXOm+3dvb22zYsMEsWrTItGrVyvTs2dMkJyebZ5991vG/mUqlUqlUKofquedcyyKsWuV8L6pLKccbUFlSgwcPdrwHlTLy9FJGdpfysb9KPaPmuCZt8zE0dP75e0LpOMLcfffdZteuXSYrK8vEx8ebdu3auW+Li4szn3zySYHthw0bZrZs2WKysrJMQkKC6dOnT4Hbb7rpJlOY8ePHu7cJCAgwb731ljl69Kg5efKkmTVrVoFJXcDUqVPHLFiwwKSnp5tDhw6Zl156yfj4+Dj+97KhNG7tLuVjfykj+0sZ2V+lnlFoqCE11TVp26+f48/f9rLxGPJF5LTAwECnW5AiKCP7KSO7KR/7lXpGm4EFQD/gfuDe0n14T6TjCN5++23efvvtQm878+3tvzVz5kxmzpx5zv1NmTKFKVOmnPcxs7Ozueeee7jnnnvOuc2ePXvc34ItBWnc2k352E8Z2U8Z2a/UM/r73yE4GNatgwULSvexPZCNx5DWtBW3PXv2ON2CFEEZ2U8Z2U352M+RjF45/fMW4LLSf3hPo+NIPJHGrd2Uj/2Ukf2Ukf1KNaPgYLjvPtdlrWV7QWw8hjRpK25bt251ugUpgjKynzKym/KxnyMZxQE/AxWBO0r/4T2NjiPxRBq3dlM+9lNG9lNG9ivVjO68E0JDYfNmmD279B7Xg9l4DGnSVtx69OjhdAtSBGVkP2VkN+VjP8cyOnO27b2AvzMteAodR+KJNG7tpnzsp4zsp4zsV2oZBQbCAw+4Lk+cCKdOlc7jejgbjyFN2oqIiIh8AewFwoEbHO5FRERERORS3XYbhIXBr7/C1KlOdyN/gCZtxe377793ugUpgjKynzKym/Kxn2MZ5QFvnL481pkWPIWOI/FEGrd2Uz72U0b2U0b2K5WM/P3hoYdcl59/HvLzS/4xywgbjyFN2opbaGio0y1IEZSR/ZSR3ZSP/RzN6EMgFbgS6OVcG7bTcSSeSOPWbsrHfsrIfsrIfqWS0ZgxUKsW7NsHU6aU/OOVITYeQ5q0FbfmzZs73YIUQRnZTxnZTfnYz9GMUoF/n778oHNt2E7HkXgijVu7KR/7KSP7KSP7lXhGvr7w8MOuyy++CDk5Jft4ZYyNx5Cv0w2IiIiIWOMN4O9AdyASWO9sO1J8oqKiGDFiBHXq1MHfv+C3zQ0dOtShrkRERESKyU03QYMGkJwM//530duL9bwA43QTYgdvb29O6VsFraaM7KeM7KZ87GdFRv8FRgP/AW5ythUbWZHRRRo5ciT/+c9/iI2NpWfPnnz99dc0adKEGjVq8OWXX3LLLbc43aKUME8ct+WJ8rGfMrKfMrJfiWbk7w/bt0OdOnD//fDGG0XeRQqy8RjS8gji1rdvX6dbkCIoI/spI7spH/tZkdErp3+OBmo52YidrMjoIj3yyCP84x//YODAgeTk5HDffffRrFkzvvjiC/bs2eN0e1IKPHHclifKx37KyH7KyH4lmtFtt7kmbPftg/feK7nHKcNsPIY0aStuwcHBTrcgRVBG9lNGdlM+9rMiozXAt4AfcK+zrdjIiowuUsOGDVmwYAEAOTk5BAUFAfDaa69x++23O9malBJPHLflifKxnzKynzKyX4llFBgIjz7quvzMM5CdXTKPU8bZeAxp0lbcDhw44HQLUgRlZD9lZDflYz9rMjpztu3fgEpONmIfazK6CMePH3e/Ed+/fz9XXnklAFWqVKFixYpOtialxBPHbXmifOynjOynjOxXYhnddRdERMDOnfDxxyXzGOWArceQUakAU6VKFcd7UCkjTy9lZHcpH/vLmoy8MGzGYDDcZ0E/FpU1GV1Eff755+Yf//iHAcxjjz1mkpOTzQcffGB27txpZs2a5Xh/qpIvTxy35amUj/2ljOwvZWR/lUhGlSoZDh82GGO46SbHn6Mnl43HkM60Fbc+ffo43YIUQRnZTxnZTfnYz5qMDPDq6cv3Az7OtWIbazK6CPfccw/Tpk0D4Nlnn+XVV1+lRo0azJo1i7/+9a8OdyelwRPHbXmifOynjOynjOxXIhn9/e9QrRps3QqffVb8+y9HbDyGfJ1uQERERMRKnwLPAPWAIcAMR7uRP+D48ePuy8YYXnjhBQe7ERERESkGVarAP//pujxhAuTnO9mNlABN2orb6tWrnW5BiqCM7KeM7KZ87GdVRlnAO8AE4EE0aXuaVRldpOrVqxMWFoa3d8EPmyUkJDjUkZQWTx635YHysZ8ysp8ysl+xZzR2rGviNiEBpk8v3n2XQzYeQ9Yvj3DXXXexc+dOMjMziY+P5+qrrz7v9vfddx9btmwhIyODPXv28OqrrxIQEOC+ffz48RhjCtTmzZtL+ml4hMDAQKdbkCIoI/spI7spH/tZl9E7QCbQDujkcC+WsC6jC3DVVVeRkJDAwYMH2bBhA+vWrXPX2rVrnW5PSoEnjtvyRPnYTxnZTxnZr1gzqlYN7r/fdfmJJ8CY4tt3OWXjMVQik7be3t488MAD/PDDDxw8eJCjR48WqAs1YsQIXn31VZ588kmuuuoq1q9fT2xsLNWrVy90+9GjR/P888/z5JNP0rx5c/76178ycuRIJk6cWGC7X375hfDwcHd16qT/CgNo2bKl0y1IEZSR/ZSR3ZSP/azL6DDwn9OXH3CyEXtYl9EF+Pjjj9m2bRsdOnSgQYMG1K9f310NGjRwuj0pBZ44bssT5WM/ZWQ/ZWS/Ys3ooYcgOBjWrIGYmOLbbzlm4zFUIssjjB8/nltvvZVXXnmFZ555hmeffZZ69eoxePBgnnrqqQvez9ixY/nwww+ZPHkyAHfccQf9+vXjlltuKXQtsg4dOrBy5UqmTp0KwO7du5k6dSrXXHNNge3y8vJITk6+oB78/f0LnKlrjOHkyZMX/BxERETEw70G/A0YCDQGtjvbjly8Bg0aMHToUBITE51uRUREROSPCQ+He+5xXX7sMWd7kRLlhev7kYvVjh07+Pvf/87ChQtJTU2ldevW/Prrr9x77720b9+eG264och9+Pn5kZGRwbBhw5gzZ477+smTJ1OlShUGDx581n1Gjx7NO++8Q8+ePfnxxx+pX78+CxYs4NNPP+W5554DXBPK//znP0lJSSErK4tVq1Yxbtw49u7dW2gf48ePZ8KECe7fU1NTCQkJYdiwYfj5+TFr1iy6detGlSpVOHToEKtXr6Z///4ArFmzBh8fH1q3bg3AnDlz6NixI9WqVePYsWMsX77c/TzWr19Pbm4ubdu2BWDBggVERUURHh5OSkoKixcvZtiwYQBs3LiRtLQ02rdvD8CiRYto2bIltWrVIj09nXnz5jFq1CgAtmzZwtGjR+nYsSMAS5YsoXHjxtStW5fs7Gxmz57NyJEj8fb2ZteuXezevZsuXboA8O2331K7dm0aNmxIXl4eM2bMYOjQofj7+7Nr1y4SExPp1q0bACtWrCAsLIwmTZoAMHXqVAYNGkTFihXZu3cvmzZtolevXgCsWrWKkJAQWrRoAcCMGTPo1asXlStX5uDBg6xdu5a+ffsC8OOPPxIQEECrVq0AmD17Nl27diU0NJTDhw+zatUqBg4cCOD+eGObNm0AmDt3LtHR0VSvXp3jx48TFxfHkCFDANiwYQPZ2dnu5TYWLlxImzZtiIiIIDU1ldjYWIYPHw7Apk2bSElJITo6GoDY2FhatGhB7dq1ycjIYM6cOYwePRqAbdu2cejQIffZ20uXLqVhw4bUq1ePnJwcZs2axfDhw/H19SUxMZG9e/dy7bXXArBs2TJq1apFo0aNOHXqFNOnT2fIkCEEBASwe/dutm/fTo8ePTDGsHLlSqpWrUqzZs0AmDZtGgMGDCAoKIj9+/eTkJBA7969AYiPjyc4OJgrrrgCgJkzZ9KjRw9CQkJISkpizZo19OvXD4CffvoJPz8/IiMjAYiJiaFz585cdtllHDlyhJUrVzJo0CAA1q1bR35+PlFRUQDMnz+fdu3aERYWxokTJ1i6dClDhw4FXGsFZmZm0q5dOwC++uorIiMjqVmzJmlpaSxcuJCRI0cCsHnzZo4fP06HDh0AWLx4MU2bNqVOnTpkZmYSExPDqFGj8PLyYvv27Rw8eJDOnTsDEBcXR7169ahfvz65ubnMnDnTfazu3LmTXbt20bVrVwCWL19OREQEjRs3xhjDtGnTGDx4MIGBgezZs4etW7fSo0cPAL7//ntCQ0Np3rw5ANOnT6dv374EBwdz4MAB1q9f7/6myZ9//hk/Pz/3v9KVpdeIHTt2sH//fo9+jfD19XVnUxZfI7p37w7g0a8RXl5eLFy40L7XiNHTYCA0WtKIGhNqXPJrxOrVqwkMDPTo14hmzZpx+eWXl/hrRN++fVm4cCHF4csvv+TTTz9l9uzZxbI/8Tz+/v7k5OQ43Yacg/KxnzKynzKyX7Fl9OabcO+9sHIl6JPjxcbWY8gUd508edLUrl3bAObAgQOmTZs2BjD169c3J06cuKB9REREGGOMad++fYHrX3jhBRMfH3/O+917770mOzvb5OTkGGOMeeeddwrc3rt3bzNs2DDTsmVL07NnT7Ny5Uqza9cuU6lSpUL35+/vb4KDg911ru3KQvXp08fxHlTKyNNLGdldysf+sjajzhgMhgwM1SzoRxldVFWtWtXMnz/fPPHEE2bIkCFmwIABBcrp/lQlX544bstTKR/7SxnZX8rI/iqWjOrUMWRnu76g6dprHX9OZalsPIZKZHmEffv2ERERwd69e0lMTKRnz56sXbuWq6++muzs7JJ4SAC6dOnCI488wl133cUPP/xAo0aNeOONN3jsscd45plnANfZJGckJCTwww8/sHv3bkaMGMHHH3981j5zcnKsnGkvCVWqVHG6BSmCMrKfMrKb8rGftRktB34ErgbuBJ52th0nWZvReURHR9OxY0f3Gc+/ZYzB17dE3hKLRTxx3JYnysd+ysh+ysh+xZLRY4+Bvz8sXQrffvvH9yduNh5DJfJFZF9++aX7426TJk3i6aefZtu2bfznP/8pdGK0MEeOHCEvL48aNWoUuL5GjRokJSUVep+nn36aTz/9lI8++ohffvmFmJgYHnnkEcaNG4eXl1eh90lJSWHbtm00atToIp5h2XTo0CGnW5AiKCP7KSO7KR/7WZ3RK6d/3g0EnG/Dss3qjM5h0qRJfPbZZ0RERODj41OgNGFbPnjiuC1PlI/9lJH9lJH9/nBGDRvCzTe7Lj/++B9vSAqw9Rgq8dN5r7nmGvOPf/zD9O/f/6LuFx8fb9588033715eXmbv3r3m4YcfLnT7n376yTz//PMFrhs1apRJT0833t7ehd4nKCjIHD161Nx7772On/bsdAUHBzveg0oZeXopI7tL+dhfVmfkg2EXrmUS/mpBP8rogis1NdU0aNDA8T5UzpUnjtvyVMrH/lJG9pcysr/+cEb/+Y9rWYT58x1/LmWxLD2Gin+nf/rTn4yPj89Z1/v4+Jg//elPF7yfESNGmMzMTHPjjTeaZs2amffee88cO3bMhIWFGcBMmTLFTJw40b39+PHjTUpKihk5cqSpV6+e6d69u9m+fbuZNm2ae5uXXnrJdO7c2dStW9dER0ebr7/+2hw6dMhUq1bN6SAcr9GjRzveg0oZeXopI7tL+dhf1md0P65J200YvCzoRxldUE2ePNn89a9/dbwPlXPlieO2PJXysb+Ukf2ljOyvP5RR8+aG/HzXpO1VVzn+XMpi2XgMlcjnweLi4oiIiODw4cMFrg8JCSEuLu6CP4b2xRdfUL16dZ566inCw8NZt24dvXv3dp+yXKdOHU6dOuXe/plnnsEYwzPPPEOtWrU4fPgw8+bN49FHH3Vvc/nllzN16lSqVq3K4cOHWbFiBe3bt+fIkSPF8MxFRESkTPsImAA0B/oACx3tRi7Qtm3beO655+jUqRMJCQnk5uYWuH3SpEkOdSYiIiJyAZ58Ery9YdYs+Plnp7uRUuKFa/a2WOXn51OjRo2zJkIbN27MTz/9REhISHE/pBSDJk2asG3bNqfbkPNQRvZTRnZTPvbziIxeBP4JfAN0c7gXB3hERr/z66+/nvM2YwwNGzYsxW7ECZ44bssT5WM/ZWQ/ZWS/S86odWtYuxZOnYJWrWDjxmLvTew8hor1TNtZs2YBrje/kydPJjs7232bj48PrVq14vvvvy/Oh5Ri5OPj43QLUgRlZD9lZDflYz+PyOhN4H7gz0AbYK2j3ZQ6j8jodxo0aOB0C+IwTxy35YnysZ8ysp8yst8lZ/TUU66f06ZpwrYE2XgMeRfnzlJSUkhJScHLy4u0tDT37ykpKSQlJfHBBx/wl7/8pTgfUopR69atnW5BiqCM7KeM7KZ87OcRGe0Dpp++/ICTjTjDIzIS+R2NW7spH/spI/spI/tdUkbXXAMDBkB+PkyYUNwtyW/YeAwV65m2t9xyCwC7du3i5ZdfJiMjozh3LyIiImKHV4C/ACOBf+GayBWrDRs2jK5duxIWFoa3d8HzFoYOHepQVyIiIiLnceYs2ylTYPt2Z3uRUlcia9qKZ6pYsaIm2i2njOynjOymfOznURktwbWm7cu41rgtJzwqo9Nef/11/va3vxEXF0dycjLGFHz7e+bEAym7PHHclifKx37KyH7KyH4XnVHnzrBsGeTkQJMmsHt3yTUn1h5Dpjjq559/NmvWrLmgKq7HVBVv9ejRw/EeVMrI00sZ2V3Kx/7yqIz6YDAYTmAItqAfZXTOOnr0qOnTp4/jfaicK08ct+WplI/9pYzsL2Vkf110RsuWGYwxvP22472Xh7LxGCq25RFiYmKKa1fikGrVqjndghRBGdlPGdlN+djPozJaBGwCWgC3Aq85205p8aiMTktJSeHXX391ug1xkCeO2/JE+dhPGdlPGdnvojLq0cN1pm1WFjz7bMk1JW42HkPFNmn71Jl1NsRjHTt2zOkWpAjKyH7KyG7Kx34elZHBtbbtR8D9wCQgz8mGSodHZXTahAkTGD9+PLfccgtZWVlOtyMO8MRxW54oH/spI/spI/tdVEbPPOP6+c47cOBAyTQkBdh4DJXYmrYhISEMGzaMhg0b8tJLL3H8+HHatGlDcnIyBzTgrBQYGEhmZqbTbch5KCP7KSO7KR/7eVxGAcBuoAYwGpjmbDulweMyAipUqMCXX35Jx44d2bVrF7m5uQVuj4qKcqgzKS2eOG7LE+VjP2VkP2VkvwvOaMAAmDsX0tOhfn04fLjkmxMrjyHvoje5eC1btmTbtm08/PDDPPjgg1SpUgWAIUOG8Nxzz5XEQ0oxGDx4sNMtSBGUkf2Ukd2Uj/08LqNs4K3Tlx90spHS43EZAVOmTCEqKorPPvuMWbNmMWfOnAIlZZ8njtvyRPnYTxnZTxnZ74Iy8vKCp592XX7jDU3YliIbj6FiWx7ht1599VUmT57Mww8/TGpqqvv6hQsX8t///rckHlJERETEGe8C44AooAuwzNl25Gz9+vWjV69erFy50ulWRERERM5t2DCIjISUFHj5Zae7EYeVyJm2V199Ne+///5Z1+/fv5/w8PCSeEgpBuvXr3e6BSmCMrKfMrKb8rGfR2Z0FJh8+vIDDvZRSjwxo7179xY4kUDKH08ct+WJ8rGfMrKfMrJfkRn5+MCZ74t69VU4frzkmxI3G4+hEpm0zc7OpnLlymdd36RJEw7r1G5r/X59N7GPMrKfMrKb8rGfx2b0GnAKGAA0dbiXEuaJGT3wwAO8+OKL1K1b1+lWxCGeOG7LE+VjP2VkP2VkvyIzuv56aNYMjh6F118vlZ7kf2w8hkpk0nbu3Lk88cQT+Pq6Vl8wxlC7dm1eeOEFZs2aVRIPKcWgbdu2TrcgRVBG9lNGdlM+9vPYjHYAZ5ZGHetkIyXPEzP67LPP6Nq1K4mJiaSmpnL06NECJWWfJ47b8kT52E8Z2U8Z2e+8Gfn6woQJrssvvgj6hFCps/EYKpE1bR944AFmzpzJ4cOHCQwMZNmyZYSHh7Nq1SoeffTRknhIEREREWe9AlwH3Ag8BujDRda4//77nW5BRERE5NxuvhkaNIDkZHjrraK3l3LBCzAltfMOHToQGRlJpUqV+Pnnn1m6dGlJPZQUg8qVK2u9N8spI/spI7spH/t5fEbxwDXAk8AEZ1spKR6fkZRLGrd2Uz72U0b2U0b2O2dGAQGwfTvUrg333Qdvvln6zYmVx1CxL4/g5eXFzTffzLx583j//fe588476dSpEzVr1izuh5JiFhUV5XQLUgRlZD9lZDflYz+Pz+jMl/zeBVRwspGS4+kZBQQEEBwcXKAu1l133cXOnTvJzMwkPj6eq6+++rzbDxs2jM2bN5OZmcmGDRvo06fPWds8+eSTHDhwgIyMDBYvXkyjRo3ct3Xp0gVjTKF15qN8devWLfT2a6655qKfX1nk6eO2rFM+9lNG9lNG9jtnRrff7pqw3bcP3n+/dJsSNxuPoWKftJ07dy7/z959x0dR538cfyUkREoIAQmE/BCQDobQJaAUQ6Qo5eioB6jHHep5KirKnQriHdhOPcvZpahHkGIQBSIgRTEIRJp0YihCQg1JCCH1+/tjYSUSCGDIfDd5P+/xeezuzOzsZ/c9uzd+mcx88MEHhISEsHnzZrZs2ULt2rWZOnUqn3/+eVG/nBShGjVqON2CFEIZ2U8Z2U352M/jM/ocSACq4TpNQgnkiRmVL1+eN954g0OHDpGenk5ycnK+uhyDBw/mlVde4dlnn6VVq1Zs3LiRmJgYqlWrVuDy4eHhzJgxgw8//JCWLVsSHR1NdHQ0zZo1cy8zduxY/va3vzF69GhuvPFG0tPTiYmJwc/PD4Dvv/+eGjVq5Kv333+fn3/+mXXr1uV7vYiIiHzLxcXFXeanVTJ54nZbmigf+ykj+ykj+xWYUfny8Pe/u+4/9xxkZhZvU+Jm63fIFFWNHDnSpKSkmC5dupw3r2vXriYlJcX88Y9/LLLXUxVt9erVy/EeVMrI00sZ2V3Kx/4qERn9DYPBsB2DlwX9KCPz5ptvmi1btpj+/fub9PR0M3LkSPOPf/zD7Nu3z9xxxx2Xta7Vq1ebN954w/3Yy8vL/PLLL+aJJ54ocPmoqCgzf/78fNNiY2PN22+/7X588OBB8+ijj7ofV6pUyWRkZJghQ4YUuE4fHx9z6NAh89RTT7mn1a5d2xhjTFhYmOOft43lidttaSrlY38pI/tLGdlfBWb0+OOuP42Jjzf4+jreY2kuS79DRbeymJiYC+6wAmbcuHFm0aJFTr9h1QXKVz8Q1pcysr+Ukd2lfOyvEpFRRQzHcQ3c9ragH2Vk9u7dazp37mwAk5KSYurVq2cAc9ddd5mvvvrqst57dna26du3b77pU6dONdHR0Rd87YceeijftAkTJpgNGzYYwNStW7fAwdbly5eb1157rcB19u/f3+Tk5JiQkBD3tLODtnv37jWHDh0y3377rendu/dF30/ZsmWNv7+/uypWrOh4VlerPHG7LU2lfOwvZWR/KSP767yM/P0NR464Bm2HD3e8v9JeNn6HfChCzZs3Z+zYsRecv3DhQv72t78V5UtKERo4cCAzZsxwug25CGVkP2VkN+VjvxKR0UngXeBJ4FFgvrPtFDVPzKhKlSr8/PPPAKSmplKlShXi4+P57rvvePvtty95Pddeey0+Pj4cOnQo3/RDhw7RuHHjAp9To0aNApc/+yd4Z28vtsxv3XvvvcTExHDgwAH3tJMnTzJmzBhWrVpFXl4eAwYMIDo6mn79+jF/fsEb4bhx45gwYYL7cWpqKgEBAQwcOBBfX1/mzJlDREQElStX5vDhw6xZs4bbb78dgLi4OMqUKUOLFi0AmDdvHh07duTaa6/l+PHjrFy5kn79+gGwceNGsrOz3eff/eqrr2jdujU1atQgJSWFxYsXM3DgQAC2bNlCWloa7du3B2DRokWEhoYSEhJCeno68+fPZ+jQoQBs376dY8eO0bFjRwCWLFlCgwYNqF27NpmZmcydO5chQ4bg7e1N5cqVmTdvHp07dwZg+fLl1KpVi3r16pGTk8OsWbMYMGAAZcuWZc+ePcTHxxMREQHAd999R1BQEA0bNgRgxowZ9O3bl/Lly7N//362bt1K9+7dAYiNjSUgIICmTZsCMGvWLLp3706lSpVITExk/fr19OrVC4C1a9fi5+dH8+bNAZg7dy5du3YlMDCQI0eOEBsbS58+fQBYv349AC1btgRcp6QLDw+nWrVqJCcns2zZMvr37w/Apk2byMzMdJ9recGCBbRs2ZLg4GBSU1OJiYlh0KBBAGzdupWUlBTCw8MBiImJoWnTptSqVYtTp04xb948hg0bBsDOnTs5fPgwN910EwBLly6lXr161KlTh6ysLObMmcOgQYPw8fEhPj6e/fv306VLFwBWrFhBSEgI9evXJy8vj5kzZ9K/f3/8/PzYu3cvDRo0ICsrC4BVq1ZRtWpV93cqKiqK3r17U6FCBQ4cOMDmzZvp0aMHAKtXr8bf3999upHZs2cTGRlJQEAASUlJxMXFcdtttwGwbt06fH19CQsLAyA6OppOnTpRpUoVjh49yqpVq+jbty8AGzZsIDc3131+wy+//JJ27doRFBTEiRMnWLp0KQMGDABg8+bNZGRk0K5dO8D137xhYWHUrFmTtLQ0FixYwJAhQwDYtm0bycnJdOjQAYDFixfTqFEjrrvuOjIyMoiOjmbo0KF4eXmxa9cuEhMT6dSpEwDLli2jTp061K1bl+zsbGbPnu3+riYkJLBnzx66du0KwMqVKwkODqZBgwYYY4iKiqJfv36UK1eOffv2sWPHDiIjIwHX6VcCAwNp0qQJADNnzqRXr174+/tz8OBBNm7cSM+ePQkJCeHzzz+nXLlyhIaGApSo34jdu3dz4MABj/6NuPnmmzl58mSJ/I3YtWsX3bp18/jfiJCQED744AP3b0Rcr17svPZa/A8epFduLjtatPDY3wiANWvWePRvRNmyZdm1a1ex/Eb06tWLBQsWcCmKbAQ4MzPT1KhR44Lzg4ODzenTpx0fqVYVXMOGDXO8B5Uy8vRSRnaX8rG/SkxGNTFk4Trato0F/ZTyjDZu3Gg6depkALN48WLz0ksvGcA8+OCDZv/+/Ze8nuDgYGOMMe3bt883/YUXXjCrV68u8DmZmZlm6NCh+abdd999JikpyQAmPDzcGGPO24eeOXOmiYqKOm99ISEhJicnx/Tv37/QfqdNm2ZWrlx5wfml6UhbT9xuS1MpH/tLGdlfysj+ypdRYKDhxAnXUbaDBzvem8rO71CRXoisTJky5OTkXHB+bm4uPj5FenCvFKEtW7Y43YIUQhnZTxnZTfnYr8RkdBA4ezDqfU42UvQ8MaMpU6a4j5x5/vnneeCBB8jIyODVV1/lpZdeuuT1HD16lJycHKpXr55vevXq1UlKSirwOUlJSRdd/uztpa7z7rvv5tixY3zxxReF9vvDDz9Qv379C87PysoiLS3NXWeP0CqJPHG7LU2Uj/2Ukf2Ukf3yZfTooxAQAJs2waxZzjUlbjZ+h4p0BNXLy4upU6eSeYGr3Z29Aq7YKS0tzekWpBDKyH7KyG7Kx34lKqP3geHAQOCvQIaz7RQVT8zotddec99funQpjRs3pnXr1uzevZvNmzdf8nqys7OJi4sjIiKCefPmAa7934iICN58880CnxMbG0tERAT/+c9/3NMiIyOJjY0FICEhgcTERCIiIti4cSMA/v7+3HjjjQWeuuHuu+9m+vTpFz1Q4qwWLVqQmJh4ye+vJPPE7bY0UT72U0b2U0b2c2dUrRo89JDr/jPPgDHONSVuNn6HinTQdtq0aYUuM3369KJ8SSlC7du3JyEhwek25CKUkf2Ukd2Uj/1KVEargJ+B64G+QJSz7RQVT8rommuuISIigq+++gqASZMm5TuIoH379jzzzDMXPOCgIK+88grTpk1j3bp1rFmzhocffpgKFSowZcoUwLU/fODAAf7+978D8J///IcVK1YwZswYvvrqK4YOHUqbNm3485//7F7na6+9xlNPPcWuXbtISEjgueee4+DBg0RHR+d77VtuuYXrr7+eDz744Ly+hg8fTlZWlvvcp/379+eee+7hT3/60yW/t5LMk7bb0kj52E8Z2U8Z2c+d0RNPQMWKsG4dnPlHYHGejd+hIh20veeee4pydSIiIiKeywCfAM8Af6TEDNp6khEjRnDbbbe5B23/+te/smXLFjIyXIc9N27cmMTExHxH4hbms88+o1q1akycOJEaNWqwYcMGevToweHDhwG47rrryMvLcy8fGxvLHXfcwT//+U8mTZrErl276NevX74/wXvxxRepUKEC7733HpUrV+a7776jR48e5w0m33vvvaxatYodO3YU2NvTTz9N7dq1ycnJYfv27QwZMoQ5c+Zc8nsTERGRq6xmTbj/ftf9p55ythfxCI6fWFdlRwUGBjreg0oZeXopI7tL+dhfJS6j+rguRpaNoboF/ZSyjFauXGluv/129+PU1FRTt25d9+M777zTfP/99473qbr65UnbbWks5WN/KSP7SxnZX4GBgYY333RdfOzbbx3vR1VAPhb0cW4V6YXIxLOFhoY63YIUQhnZTxnZTfnYr8RltBuIxfW3TcMc7qWIeFJG9evXz3fO2tOnT+c7CnbNmjU0bdrUidakmHnSdlsaKR/7KSP7KSP7NYiMhFGjXA90lK11bPwOadBW3EJCQpxuQQqhjOynjOymfOxXIjP6+MztHx3tosh4UkaVK1fOdw7boKAg9u7d637s7e2tC+WWEp603ZZGysd+ysh+ysh+R0ePhrJlYckSWLHC6XbkN2z8DmnQVtzS09OdbkEKoYzsp4zspnzsVyIzmglkAa2AZg73UgQ8KaNffvmFG2644YLzmzdvzi+//FKMHYlTPGm7LY2Uj/2Ukf2UkeUaNCChUyfX/aefdrYXKZCN3yEvXOdJEMHLywtjtDnYTBnZTxnZTfnYr8RmNBf4A/AC8KTDvfxOnpTRa6+9Rrdu3WjduvV5F/W65pprWLduHUuWLOHhhx92pkEpNp603ZZGysd+ysh+yshyn3wCd94JX34JvXs73Y0UwMbvkI60FbehQ4c63YIUQhnZTxnZTfnYr8RmNP3M7V14/N6XJ2U0adIkqlSpwo4dO3jsscfo06cPffr04fHHH2fHjh0EBgYyadIkp9uUYuBJ221ppHzsp4zsp4wsdsMNMOzMxQ2eecbZXuSCbPwO+TjdgIiIiEiJtwA4DoQAXYGlzrZTWhw+fJgOHTrw9ttv8/zzz+Pl5QWAMYbFixdz//33c/jwYYe7FBERkRLtzTfB25taP/zA/vXrne5GPIgGbcVt+/btTrcghVBG9lNGdlM+9iuxGWUBUcD9wHA8etDW0zLas2cPPXv2JDAwkPr16wOwe/dukpOTHe5MipOnbbeljfKxnzKynzKy1B13QOfOcOoUlSZOdLobuQgbv0Me/gd6UpSOHTvmdAtSCGVkP2VkN+VjvxKd0cdnbvsDFZxs5Pfx1IySk5NZu3Yta9eu1YBtKeSp221poXzsp4zsp4ws5O8PL7/suv+vf5H200/O9iMXZeN3SIO24taxY0enW5BCKCP7KSO7KR/7leiMVgO7gIq4LkrmoUp0RlJiabu1m/KxnzKynzKy0IQJEBwMu3bByy8rI8vZmI8GbUVERESKy9mjbf/oaBciIiIicjXdcAP87W+u+w8+CFlZzvYjHskLME43IXaoVq0aR44ccboNuQhlZD9lZDflY78Sn1Fd4GcgF7gOOOhsO1eixGckJZK2W7spH/spI/spI8ssX+46l+3cuTBgAKCMbGdjPjrSVtwaNGjgdAtSCGVkP2VkN+VjvxKfUQLwLVAGuMPhXq5Qic9ISiRtt3ZTPvZTRvZTRhY55+JjPPKIe7IyspuN+WjQVtxq167tdAtSCGVkP2VkN+Vjv1KR0dlTJAx3tIsrVioykhJH263dlI/9lJH9lJElfnPxMfbtc89SRnazMR8N2opbZmam0y1IIZSR/ZSR3ZSP/UpFRrOA00AoEOZwL1egVGQkJY62W7spH/spI/spI0v85uJj51JGdrMxH53TVkRERKS4fQYMAv4NPOZwLyIiIiLy+91wA6xfDz4+0KMHxMQ43ZF4OB1pK25DhgxxugUphDKynzKym/KxX6nJaPqZ2ztxnd/Wg5SajKRE0XZrN+VjP2VkP2Vkgbfecg3YzplT4ICtMrKbjflo0FbcvL21OdhOGdlPGdlN+div1GS0CDgC1AC6OdzLZSo1GUmJou3WbsrHfsrIfsrIYXfcAZ06nXfxsXMpI7vZmI99Hf3G/fffT0JCAhkZGaxevZq2bdtedPmHHnqI7du3c+rUKfbt28crr7yCn5/f71pnabF7926nW5BCKCP7KSO7KR/7lZqMcoCoM/c97IJkpSYjKVG03dpN+dhPGdlPGTmoUqVfz1/7z3/C/v0FLqaM7GZrPsbWGjx4sDl9+rQZOXKkadKkiXn33XfN8ePHTbVq1QpcftiwYSYjI8MMGzbM1K5d20RGRpoDBw6Yf//731e8ztJUNWvWdLwHlTLy9FJGdpfysb9KVUZtMBgM6Rj8LehHGalKcGm7tbuUj/2ljOwvZeRgvfKKwRjDjh2GsmWVkYeWjflYfaTtmDFjeP/995k6dSrbtm1j9OjRnDp1invuuafA5Tt06MCqVauYMWMGe/fuZfHixcyYMYN27dpd8TrLli2Lv7+/uypWrHhV3qsNOnfu7HQLUghlZD9lZDflY79SldE6YDtQHhjgcC+XoVRlJCWGtlu7KR/7KSP7KSOH3HADPPig6/6DD0JW1gUXVUZ2szEfH6cbuBBfX19at27N5MmT3dOMMSxZsoTw8PACn/P9999z11130bZtW9auXUvdunXp1asXH3/88RWvc9y4cUyYMMH9ODU1lYCAAAYOHIivry9z5swhIiKCypUrc/jwYdasWcPtt98OQFxcHGXKlKFFixYAzJs3j44dO3Lttddy/PhxVq5cSb9+/QDYuHEj2dnZtGnTBoCvvvqK1q1bU6NGDVJSUli8eDEDBw4EYMuWLaSlpdG+fXsAFi1aRGhoKCEhIaSnpzN//nyGDh0KwPbt2zl27BgdO3YEYMmSJTRo0IDatWuTmZnJ3LlzGTJkCN7e3lSuXJmaNWu6N9Tly5dTq1Yt6tWrR05ODrNmzWLAgAGULVuWPXv2EB8fT0REBADfffcdQUFBNGzYEIAZM2bQt29fypcvz/79+9m6dSvdu3cHIDY2loCAAJo2bQrArFmz6N69O5UqVSIxMZH169fTq1cvANauXYufnx/NmzcHYO7cuXTt2pXAwECOHDlCbGwsffr0AWD9+vUAtGzZEoAvvviC8PBwqlWrRnJyMsuWLaN///4AbNq0iczMTPepMRYsWEDLli0JDg4mNTWVmJgYBg0aBMDWrVtJSUlxbyMxMTE0bdqUWrVqcerUKebNm8ewYcMA2LlzJ4cPH+amm24CYOnSpdSrV486deqQlZXFnDlzGDRoED4+PsTHx7N//366dOkCwIoVKwgJCaF+/frk5eUxc+ZM+vfvj5+fH3v37mXXrl2EhIQwbNgwVq1aRdWqVWncuDEAUVFR9O7dmwoVKnDgwAE2b95Mjx49AFi9ejX+/v40a9YMgNmzZxMZGUlAQABJSUnExcVx2223AbBu3Tp8fX0JCwsDIDo6mk6dOlGlShWOHj3KqlWr6Nu3LwAbNmwgNzeX1q1bA/Dll1/Srl07goKCOHHiBEuXLmXAANcoxObNm8nIyHD/A8rChQsJCwujZs2apKWlsWDBAvdJv7dt20ZycjIdOnQAYPHixTRq1IjrrruOjIwMoqOjGTp0KF5eXuzatYvExEQ6deoEwLJly6hTpw5169YlOzub2bNnu7+rCQkJ7Nmzh65duwKwcuVKgoODadCgAcYYoqKi6NevH+XKlWPfvn3s2LGDyMhIwPXbEhgYSJMmTQCYOXMmvXr1wt/fn4MHD7Jx40Z69uwJQPny5bnhhhsIDQ0FKFG/Ebt37+bAgQMe/RtRsWJF9/e1JP5GdOvmOjmqJ/9GhISEULly5RL7G7FmzRrKlSvn/o347NPPyH0ul6DHggjdH+oRvxGBgYEMGzbsqv9G9OrViwULFiAiIiJirXMvPvb11053IyWQ44f7FlTBwcHGGGPat2+fb/oLL7xgVq9efcHnPfjggyYzM9NkZWUZY4z573//+7vWWbZsWePv7++uihUrOv7ZXM3P3OkeVMrI00sZ2V3Kx/4qdRldh3H/r5YF/SgjVQktbbd2l/Kxv5SR/aWMHKg773SdFiE93VCrljLy8LIxH6tPj3C5OnfuzN///nfuv/9+WrVqxR/+8Aduu+02nnrqqSteZ1ZWFmlpae46efJkEXZsl1q1ajndghRCGdlPGdlN+div1GW0D1h25v6dTjZy6UpdRlIiaLu1m/KxnzKynzIqZpUqwUsvue5f5OJj51JGdrMxH2sHbY8ePUpOTg7Vq1fPN7169eokJSUV+JznnnuOjz/+mA8//JCffvqJ6Oho/v73vzNu3Di8vLyuaJ2lSb169ZxuQQqhjOynjOymfOxXKjP6+MztHx3t4pKVyozE42m7tZvysZ8ysp8yKmYTJkBwMOzcCf/+9yU9RRnZzcZ8rB20zc7OJi4uzn2uMwAvLy8iIiKIjY0t8Dnly5cnLy8v37Tc3Fz3c69knaVJTk6O0y1IIZSR/ZSR3ZSP/UplRrOBDKAp0NrhXi5BqcxIPJ62W7spH/spI/spo2J0GRcfO5cysput+Th+joYL1eDBg01GRoYZPny4ady4sXnnnXfM8ePHTVBQkAHMtGnTzKRJk9zLjx8/3qSkpJghQ4aYOnXqmG7dupldu3aZqKioS16nSqVSqVQqVbHX/3Cd1/Y1C3pRqVQqlUqlUl24Vqxwnct29mzne1GV9HK8gYvWAw88YPbs2WNOnz5tVq9ebdq1a+eet2zZMjNlyhT34zJlyphnnnnG7Nq1y5w6dcrs3bvXvPnmmyYgIOCS11maa8CAAY73oFJGnl7KyO5SPvZXqc2oJ65B20MYfCzoRxmpSlhpu7W7lI/9pYzsL2VUTHWZFx9TRp5TNubjg+Xeeust3nrrrQLnde3aNd/j3NxcJk6cyMSJE694naVZ2bJlnW5BCqGM7KeM7KZ87FdqM/oaOARUB7oDXznbzsWU2ozEo2m7tZvysZ8ysp8yKgaVKsHLL7vuP/fcJV187FzKyG425mPtOW2l+O3Zs8fpFqQQysh+yshuysd+pTajXOB/Z+4Pd7KRwpXajMSjabu1m/KxnzKynzIqBs8+CzVqwI4d8Morl/10ZWQ3G/PRoK24xcfHO92CFEIZ2U8Z2U352K9UZzT9zG0fIMDJRi6uVGckHkvbrd2Uj/2Ukf2U0VUWGgp//avr/mVcfOxcyshuNuajQVtxi4iIcLoFKYQysp8yspvysV+pzmgD8BNwDTDI2VYuplRnJB5L263dlI/9lJH9lNFV9tZb4OMDs2fD4sVXtAplZDcb89GgrYiIiIgtzh5t+0dHuxARERGRs+66C26+GdLTYcwYp7uRUsbxq6Gp7Khal3nlQ5UyUikjTyvlY3+V+oxCMORiMBjqWNCPMlKVkNJ2a3cpH/tLGdlfyugqVaVKhsREgzGGJ59URiW4bMxHR9qKW1BQkNMtSCGUkf2Ukd2Uj/1KfUYHgKVn7t/lZCMXVuozEo+k7dZuysd+ysh+yugq+Z0XHzuXMrKbjflo0FbcGjZs6HQLUghlZD9lZDflYz9lBHx85na4o11ckDIST6Tt1m7Kx37KyH7K6CoIDXVddAyu+OJj51JGdrMxHw3aioiIiNhkLpAONABudLgXERERkdLqrbegTJnfdfExkd/DC9d5EkRERETEFtNxXYzsLeCvDvciIiIiUtrcdRd8/LHr4mNNmsD+/U53JKWQjrQVt759+zrdghRCGdlPGdlN+dhPGZ1x9hQJQwFfJxs5nzIST6Tt1m7Kx37KyH7KqAhVqgQvveS6/9xzRTZgq4zsZmM+GrQVt/LlyzvdghRCGdlPGdlN+dhPGZ2xFDgIVAV6OdzLbygj8UTabu2mfOynjOynjIrQ2YuPbd/+uy8+di5lZDcb89Ggrbjt1+H+1lNG9lNGdlM+9lNGZ+QBn565b9kFyZSReCJtt3ZTPvZTRvZTRkWkefP8Fx/Lzi6yVSsju9mYj85pK25VqlTh+PHjTrchF6GM7KeM7KZ87KeMznEDsBnIAmoAyc62c5YyEk+k7dZuysd+ysh+yqiIrFwJN98Ms2bB4MFFumplZDcb89GRtuLWvXt3p1uQQigj+ykjuykf+ymjc/wEbADKAkOcbeVcykg8kbZbuykf+ykj+ymjInDXXa4B2/R0GDOmyFevjOxmYz4atBURERGx1fQzt390tAv5jfvvv5+EhAQyMjJYvXo1bdu2vejyAwcOZNu2bWRkZLBp0yZ69ux53jLPPvssBw8e5NSpUyxevJj69evnm5+QkIAxJl898cQT+ZYJDQ1l5cqVZGRksG/fPh5//PHf/2ZFRERKg4AAePll1/3nnoNffnG2H5EzjEoFmDp16jjeg0oZeXopI7tL+dhfyug3VQNDDgaDob4F/SgjM3jwYHP69GkzcuRI06RJE/Puu++a48ePm2rVqhW4fHh4uMnOzjaPPfaYady4sZk4caLJzMw0zZo1cy8zduxYk5ycbPr06WNCQ0NNdHS0iY+PN35+fu5lEhISzFNPPWWqV6/urvLly7vn+/v7m8TERPPxxx+bpk2bmiFDhpj09HQzatQoxz8zG6q0b7e2l/Kxv5SR/aWMfme99prrX0S3bTP4+iqjUliW5uN4AypLKiwszPEeVMrI00sZ2V3Kx/5SRgXUAlyDthMs6EUZmdWrV5s33njD/djLy8v88ssv5oknnihw+aioKDN//vx802JjY83bb7/tfnzw4EHz6KOPuh9XqlTJZGRkmCFDhrinJSQkmIceeuiCfY0ePdocO3bM+J7zH5qTJ08227Ztu+BzypYta/z9/d1VsWJFxz/fq1Wlfbu1vZSP/aWM7C9l9DuqeXNDTo5r0LZbN2VUSsvGfHwQOaNp06Zs3LjR6TbkIpSR/ZSR3ZSP/ZRRAT4GeuI6RcIEZ1uB0p2Rr68vrVu3ZvLkye5pxhiWLFlCeHh4gc8JDw/nlVdeyTctJiaGfv36AVC3bl2Cg4NZsmSJe35qaio//PAD4eHhzJw50z39ySef5Omnn2bfvn3873//49VXXyU3N9f9OitXriT7nKtcx8TE8OSTT1K5cmVOnDhxXm/jxo1jwoQJ+V43ICCAgQMH4uvry5w5c4iIiKBy5cocPnyYNWvWcPvttwMQFxdHmTJlaNGiBQDz5s2jY8eOXHvttRw/fpyVK1e63+PGjRvJzs6mTZs2AHz11Ve0bt2aGjVqkJKSwuLFixk4cCAAW7ZsIS0tjfbt2wOwaNEiQkNDCQkJIT09nfnz5zN06FAAtm/fzrFjx+jYsSMAS5YsoUGDBtSuXZvMzEzmzp3LkCFD8Pb2pnLlyhw5coTOnTsDsHz5cmrVqkW9evXIyclh1qxZDBgwgLJly7Jnzx7i4+OJiIgA4LvvviMoKIiGDRsCMGPGDPr27Uv58uXZv38/W7dudZ8HLzY2loCAAJo2bQrArFmz6N69O5UqVSIxMZH169fTq1cvANauXYufnx/NmzcHYO7cuXTt2pXAwECOHDlCbGwsffr0AWD9+vUAtGzZEoAvvviC8PBwqlWrRnJyMsuWLaN///4AbNq0iczMTPdpOxYsWEDLli0JDg4mNTWVmJgYBg0aBMDWrVtJSUlxb78xMTE0bdqUWrVqcerUKebNm8ewYcMA2LlzJ4cPH+amm24CYOnSpdSrV486deqQlZXFnDlzGDRoED4+PsTHx7N//366dOkCwIoVKwgJCaF+/frk5eUxc+ZM+vfvj5+fH3v37qVBgwbuz2zVqlVUrVqVxo0bAxAVFUXv3r2pUKECBw4cYPPmzfTo0QOA1atX4+/vT7NmzQCYPXs2kZGRBAQEkJSURFxcHLfddhsA69atw9fXl7CwMACio6Pp1KkTVapU4ejRo6xatYq+ffsCsGHDBnJzc2ndujUAX375Je3atSMoKIgTJ06wdOlSBgwYAMDmzZvJyMigXbt2ACxcuJCwsDBq1qxJWloaCxYsYMgQ14nJt23bRnJyMh06dABg8eLFNGrUiOuuu46MjAyio6MZOnQoXl5e7Nq1i8TERDp16gTAsmXLqFOnDnXr1iU7O5vZs2e7v6sJCQns2bOHrl27ArBy5UqCg4Np0KABxhiioqLo168f5cqVY9++fezYsYPIyEgAvv/+ewIDA2nSpAkAM2fOpFevXvj7+3Pw4EE2btxIz549CQkJ4eTJk5QrV47Q0FCAEvUbsXv3bg4cOODRvxHt2rVzr7ek/Ubs2rWLbt26XZXfiF633caS8eM5WqYM/jEx3F6tGgwbdlV+I0JCQti7d2+J/I0AWLNmjUf/RpQtW5YKFSoUy29Er169WLBgAZfC8ZFjlR01bNgwx3tQKSNPL2Vkdykf+0sZFVDlMKTiOtq2o/P9lOaMgoODjTHGtG/fPt/0F154waxevbrA52RmZpqhQ4fmm3bfffeZpKQkA67TJxhjTI0aNfItM3PmTBMVFeV+/Mgjj5jOnTub0NBQ85e//MUcP37c/Pvf/3bPj4mJMe+8806+dTRp0sQYY0zjxo0L7K00HWlbmrdbTyjlY38pI/tLGV1h/fGPriNsT540/N//KaNSXDbmoyNtxW3WrFlOtyCFUEb2U0Z2Uz72U0YFyABmA3cDw4FVzrajjJzx6quvuu9v3ryZrKws3n33XcaNG0dWVtYVrTMrK+uKn+tptN3aTfnYTxnZTxldgYAAeOkl1/2JE6/6xceUkd1szMfb6QbEHmf/ZEPspYzsp4zspnzsp4wu4OMzt4MBPycbKd0ZHT16lJycHKpXr55vevXq1UlKSirwOUlJSRdd/uzt5awT4IcffsDX15c6depc9HXOfY3SrDRvt55A+dhPGdlPGV2B//wHqleH7dvhnH8cvVqUkd1szEeDtuJWqVIlp1uQQigj+ykjuykf+ymjC1gO7AMqA7c72kmpzig7O5u4uDj3OcoAvLy8iIiIIDY2tsDnxMbG5lseIDIy0r18QkICiYmJ+Zbx9/fnxhtvvOA6AVq0aEFubi6HDx92v06nTp3w8fn1D+kiIyPZvn17geezLW1K83brCZSP/ZSR/ZTRZbr7bhgxAnJz4c9/hnPOCX+1KCO72ZqP4+doUNlRXbp0cbwHlTLy9FJGdpfysb+U0UVqEq7z2s5TRk7W4MGDTUZGhhk+fLhp3Lixeeedd8zx48dNUFCQAcy0adPMpEmT3MuHh4ebrKwsM2bMGNOoUSMzfvx4k5mZaZo1a+ZeZuzYseb48eOmd+/e5oYbbjCff/65iY+PN35+fgYw7du3Nw899JBp3ry5qVu3rrnjjjvMoUOHzNSpU93rqFSpkklMTDTTpk0zTZs2NYMHDzYnT540o0aNcvwzs6FK+3Zreykf+0sZ2V/K6DIqNNRw6pTrXLZPPqmMVDbn43gDKksqICDA8R5UysjTSxnZXcrH/lJGF6kmuAZtszBcq4ycrAceeMDs2bPHnD592qxevdq0a9fOPW/ZsmVmypQp+ZYfOHCg2b59uzl9+rTZvHmz6dmz53nrfPbZZ01iYqLJyMgwixcvNg0aNHDPa9mypYmNjTXJycnm1KlTZsuWLebJJ580ZcuWzbeO0NBQs3LlSpORkWH2799vxo4d6/hnZUtpu7W7lI/9pYzsL2V0ieXvb9ixwzVg+9VXBi8vZaSyOR/HG1BZUjZeKU+ljDytlJHdpXzsL2VUSK3FNXD7V2WkUl1Oabu1u5SP/aWM7C9ldIk1Y4ZrwHbvXkOVKspIZXU+OqetiIiIiKeYfub2j452ISIiIuJ57rsPhg51nb92yBA4ftzpjkQuSoO24rZ27VqnW5BCKCP7KSO7KR/7KaNCRAE5QDugkTMtKCPxRNpu7aZ87KeM7KeMCtGqFbz6quv+2LGwenWxt6CM7GZjPhq0FTc/Pz+nW5BCKCP7KSO7KR/7KaNCHAEWnbnv0NG2ykg8kbZbuykf+ykj+ymjiwgIgFmzwM8PPv8cXnvNkTaUkd1szEeDtuLWvHlzp1uQQigj+ykjuykf+ymjS3D2FAl3AV7F//LKSDyRtlu7KR/7KSP7KaOLmDIFrr8efv4Z7r7bsTaUkd1szEeDtiIiIiKeZD6QAtQGOjnci4iIiIjNHn4Y/vAHyMyEQYMgJcXpjkQumReuK5KJ4OfnR2ZmptNtyEUoI/spI7spH/spo0v0HjAK+Ai4t3hfWhmJJ9J2azflYz9lZD9lVID27WHlSvD1hfvvh7ffdrQdZWQ3G/PRkbbi1rVrV6dbkEIoI/spI7spH/spo0v08ZnbgUC54n1pZSSeSNut3ZSP/ZSR/ZTRb1StCp995hqwjYpyfMAWlJHtbMxHg7biFhgY6HQLUghlZD9lZDflYz9ldIm+AxKASkDf4n1pZSSeSNut3ZSP/ZSR/ZTROby8YPp0qFULduyAUaOc7ghQRrazMR8N2orbkSNHnG5BCqGM7KeM7KZ87KeMLpEBPjlz/4/F+9LKSDyRtlu7KR/7KSP7KaNzPPEE9OoFGRmu89iePOl0R4Aysp2N+eictuJWoUIF0tPTnW5DLkIZ2U8Z2U352E8ZXYYGwE4gB/g/4FDxvKwyEk+k7dZuysd+ysh+yuiMTp3gm2+gTBm45x6YMsXpjtyUkd1szEdH2opbnz59nG5BCqGM7KeM7KZ87KeMLsMuYDXgAwwrvpdVRuKJtN3aTfnYTxnZTxkBQUGu89eWKQNTp1o1YAvKyHY25qNBWxERERFPdfaCZMV8igQRERERq3h7w//+B8HB8NNP8MADTnck8rtp0Fbc1q9f73QLUghlZD9lZDflYz9ldJlmAllAK6BZ8bykMhJPpO3WbsrHfsrIfqU+o2eegYgI1/lrBw2CU6ec7ug8pT4jy9mYjwZtRURERDzVMWDBmfvDnWxERERExCGRkfD00677f/4zbN/ubD8iRUSDtuLWsmVLp1uQQigj+ykjuykf+ymjK/DJmduBxfNyykg8kbZbuykf+ykj+5XajGrWhE8/dZ0e4Z13YMYMpzu6oFKbkYewMR8N2oqIiIh4soVABnA9EOpwLyIiIiLFxcfHdeGxatVg/Xp4+GGnOxIpUl6AcboJsUOFChVIT093ug25CGVkP2VkN+VjP2V0heYBfYBngOeu7kspI/FE2m7tpnzsp4zsVyozev55eOIJSEmB1q0hPt7pji6qVGbkQWzMR0failt4eLjTLUghlJH9lJHdlI/9lNEVij5z2/fqv5QyEk+k7dZuysd+ysh+pS6j2293DdgC3HOP9QO2UAoz8jA25mP9oO39999PQkICGRkZrF69mrZt215w2WXLlmGMOa++/PJL9zJTpkw5b/7ChQuL461Yr1q1ak63IIVQRvZTRnZTPvZTRldoPpALtAZqXd2XUkbiibTd2k352E8Z2a9UZVS7Nkyb5rr/2mswd66j7VyqUpWRB7IxH6sHbQcPHswrr7zCs88+S6tWrdi4cSMxMTEX/CD79+9PjRo13NWsWTNycnKYNWtWvuUWLlyYb7lhw4YVx9uxXnJystMtSCGUkf2Ukd2Uj/2U0RU6Cqw6c/8qH22rjMQTabu1m/KxnzKyX6nJyNcXPvsMqlSBH36AsWOd7uiSlZqMPJSt+Rhba/Xq1eaNN95wP/by8jK//PKLeeKJJy7p+Q899JBJSUkx5cuXd0+bMmWK+fzzzy+5h7Jlyxp/f393VaxY0fHP5WqVn5+f4z2olJGnlzKyu5SP/aWMfkc9gsFgWKKMVKrflrZbu0v52F/KyP4qNRm99prrT6aPHTNcd53z/SijElM25uODpXx9fWndujWTJ092TzPGsGTJkks+z8S9995LVFQUp06dyje9S5cuHDp0iOTkZL755hueeuopjh8/XuA6xo0bx4QJE9yPU1NTCQgIYODAgfj6+jJnzhwiIiKoXLkyhw8fZs2aNdx+++0AxMXFUaZMGVq0aAHAvHnz6NixI9deey3Hjx9n5cqV9OvXD4CNGzeSnZ1NmzZtAPjqq69o3bo1NWrUICUlhcWLFzNw4EAAtmzZQlpaGu3btwdg0aJFhIaGEhISQnp6OvPnz2fo0KEAbN++nWPHjtGxY0cAlixZQoMGDahduzaZmZnMnTuXIUOG4O3tTeXKlZk3bx6dO3cGYPny5dSqVYt69eq5j1geMGAAZcuWZc+ePcTHxxMREQHAd999R1BQEA0bNgRgxowZ9O3bl/Lly7N//362bt1K9+7dAYiNjSUgIICmTZsCMGvWLLp3706lSpVITExk/fr19OrVC4C1a9fi5+dH8+bNAZg7dy5du3YlMDCQI0eOEBsbS58+fQBYv349AC1btgTgiy++IDw8nGrVqpGcnMyyZcvo378/AJs2bSIzM9N9uo0FCxbQsmVLgoODSU1NJSYmhkGDBgGwdetWUlJS3NtdTEwMTZs2pVatWpw6dYp58+a5j9beuXMnhw8f5qabbgJg6dKl1KtXjzp16pCVlcWcOXMYNGgQPj4+xMfHs3//frp06QLAihUrCAkJoX79+uTl5TFz5kz69++Pn58fe/fuZdeuXYwYMYIDBw6watUqqlatSuPGjQGIioqid+/eVKhQgQMHDrB582Z69OgBwOrVq/H396dZs2YAzJ49m8jISAICAkhKSiIuLo7bbrsNgHXr1uHr60tYWBgA0dHRdOrUiSpVqnD06FFWrVpF3759AdiwYQO5ubm0bt0agC+//JJ27doRFBTEiRMnWLp0KQMGDABg8+bNZGRk0K5dO8B1tHtYWBg1a9YkLS2NBQsWMGTIEAC2bdtGcnIyHTp0AGDx4sU0atSI6667joyMDKKjoxk6dCheXl7s2rWLxMREOnXqBLhOkVKnTh3q1q1LdnY2s2fPdn9XExIS2LNnD127dgVg5cqVBAcH06BBA4wxREVF0a9fP8qVK8e+ffvYsWMHkZGRAHz//fcEBgbSpEkTAGbOnEmvXr3w9/fn4MGDbNy4kZ49ewJQvnx5fvjhB0JDXZdwL0m/Ebt37+bAgQMe/Rtx8803c/LkyRL7G9GtWzcAj/6NCAkJ4YMPPiixvxFr1qyhXLlyV+U3In1jOnvZi1cXLyrWrkib69tcld+IQYMGkZycfNV/I3r16sWCBQsQKQr9+/dnxowZTrchF6B87KeM7FcqMhowAB56yHV/+HDYt8/Zfi5TqcjIg9maj+MjxwVVcHCwMcaY9u3b55v+wgsvmNWrVxf6/LZt2xpjjGnbtm2+6UOGDDG9e/c2N9xwg+nbt6/ZsmWL+eGHH4y3t3eB6ylNR9oOGzbM8R5UysjTSxnZXcrH/lJGv7M24jra9i5lpFKdW9pu7S7lY38pI/urxGdUr54hJcV1lO3kyc73o4xKXNmYj7VH2v5e9957L5s2bWLt2rX5ps+cOdN9/6effmLTpk38/PPPdOnShW+++ea89WRlZZGVlXXV+7XBpk2bnG5BCqGM7KeM7KZ87KeMfqdooDnQD/jk6ryEMhJPpO3WbsrHfsrIfiU6o2uugVmzoFIlWLkSnnrK6Y6uSInOqASwMR9rL0R29OhRcnJyqF69er7p1atXJykp6aLPLV++PEOHDuXDDz8s9HUSEhI4cuQI9evX/139lgSZmZlOtyCFUEb2U0Z2Uz72U0a/U/SZ2x7ANVfnJZSReCJtt3ZTPvZTRvYr0Rm99hq0bAmHD8OwYZCb63RHV6REZ1QC2JiPtYO22dnZxMXFuc91BuDl5UVERASxsbEXfe6gQYPw8/Pjk08KP8QkJCSEqlWrkpiY+Lt79nRnz90o9lJG9lNGdlM+9lNGv9N6YB9QAeh2dV5CGYkn0nZrN+VjP2VkvxKb0R13wF/+Anl5cOedcPCg0x1dsRKbUQlhYz7WDtoCvPLKK4waNYrhw4fTuHFj3n77bSpUqMCUKVMAmDZtGpMmTTrveffeey/R0dHnXVysQoUKvPjii9x4443Url2bW265hXnz5rF7925iYmKK5T2JiIiIXFXRZ277OdiDiIiIyO/VuDG8+67r/sSJsGSJs/2IOMDxE+terB544AGzZ88ec/r0abN69WrTrl0797xly5aZKVOm5Fu+YcOGxhhjunXrdt66rrnmGrNo0SJz6NAhk5mZaRISEsy7775rgoKCHH+fNlRAQIDjPaiUkaeXMrK7lI/9pYyKoLriuhjZYQzeykilAm23tpfysb+Ukf1V4jIqX96webPrwmOLFxsucPF4T6oSl1EJK0vzcbwBlSXVpUsXx3tQKSNPL2Vkdykf+0sZFUH5YDiOa+D2JmWkUoG2W9tL+dhfysj+KnEZTZ3qGrA9eNBQQg60K3EZlbCyMR+rT48gxSs4ONjpFqQQysh+yshuysd+yqgI5ABfnrnfr+hXr4zEE2m7tZvysZ8ysl+Jyugvf4ERI1wXHBs61HUBshKgRGVUAtmYjwZtxS01NdXpFqQQysh+yshuysd+yqiIRJ+57Vf0q1ZG4om03dpN+dhPGdmvxGQ0ahS8847r/lNPwcqVzvZThEpMRiWUjfl44TrkVgQfHx9ycnKcbkMuQhnZTxnZTfnYTxkVkQrAUeAaIBT4qehWrYzEE2m7tZvysZ8ysl+JyOivf4U33nDdf/11eOghZ/spYiUioxLMxnx0pK24DRo0yOkWpBDKyH7KyG7Kx37KqIikA4vP3O9XtKtWRuKJtN3aTfnYTxnZz+MzevTRXwdsX3qpxA3YQgnIqISzMR8N2oqIiIiURNFnbvs62YSIiIhIIf7xD3j5Zdf9556DsWOd7UfEEhq0FbetW7c63YIUQhnZTxnZTfnYTxkVoflAHtAG+L+iW60yEk+k7dZuysd+ysh+HpvRc8/BP//puv/UU/DMM872cxV5bEalhI35aNBW3FJSUpxuQQqhjOynjOymfOynjIrQEWDVmftFeLStMhJPpO3WbsrHfsrIfh6Z0YsvugZqAR57DP71L2f7uco8MqNSxMZ8NGgrbuHh4U63IIVQRvZTRnZTPvZTRkUs+sxtv6JbpTIST6Tt1m7Kx37KyH4elZGXl+tCY48/7nr84IPw738721Mx8KiMSiEb89GgrYiIiEhJNe/MbRegsnNtiIiIiACuAdt33nEN1OblwZ//DG++6XRXIlbyAozTTYgdqlSpwvHjx51uQy5CGdlPGdlN+dhPGV0Fm4BQ4C7g09+/OmUknkjbrd2Uj/2Ukf08IiNvb/jwQxg5EnJz4Z57YPp0p7sqNh6RUSlmYz460lbcmjZt6nQLUghlZD9lZDflYz9ldBVEn7ntVzSrU0biibTd2k352E8Z2c/6jHx84JNPXAO2OTlw112lasAWPCCjUs7GfDRoK261atVyugUphDKynzKym/KxnzK6CqLP3PYArvn9q1NG4om03dpN+dhPGdnP6ox8fSEqCoYNg6wsGDzY9biUsTojsTIfDdqK26lTp5xuQQqhjOynjOymfOynjK6CH4H9QEUg4vevThmJJ9J2azflYz9lZD9rM/Lzg7lzYcAAyMyE/v3h88+d7soR1mYkgL35GJVKpVKpVCpVCa7XMRgM71vQSwmo+++/3yQkJJiMjAyzevVq07Zt24suP3DgQLNt2zaTkZFhNm3aZHr27HneMs8++6w5ePCgOXXqlFm8eLGpX7++e17t2rXNBx98YH7++Wdz6tQps3v3bjNhwgTj6+ubb5mC3HjjjY5/XiqVSqUqpVWunGHRIoMxhlOnDJGRzvekUnlWOd6AypIaNmyY4z2olJGnlzKyu5SP/aWMrlLdgmvQ9hAGb2X0e2rw4MHm9OnTZuTIkaZJkybm3XffNcePHzfVqlUrcPnw8HCTnZ1tHnvsMdO4cWMzceJEk5mZaZo1a+ZeZuzYsSY5Odn06dPHhIaGmujoaBMfH2/8/PwMYLp3724++ugjExkZaerWrWt69+5tkpKSzEsvveRex9lB21tuucVUr17dXT4+Po5/ZjZUad9ubS/lY38pI/vLuowqVDB8841rwDYtzdC5s/M9KSOVh+Wj0yOIiIiIlHQrgWQgCAh3uBcPN2bMGN5//32mTp3Ktm3bGD16NKdOneKee+4pcPmHHnqIRYsW8fLLL7N9+3aeeeYZfvzxR/7617+6l3n44Yf55z//yRdffMHmzZsZPnw4NWvWpF+/fgDExMRwzz33sHjxYhISEpg/fz4vv/wy/fv3P+/1jh07xqFDh9yVk5NzwfdStmxZ/P393VWxYsXf9+GIiIgAVKoEMTHQtSukpkL37rBihdNdiXgcH6cbEHvs3LnT6RakEMrIfsrIbsrHfsroKskBvgT+CPQDVl35qkpzRr6+vrRu3ZrJkye7pxljWLJkCeHhBY+Gh4eH88orr+SbFhMT4x6QrVu3LsHBwSxZssQ9PzU1lR9++IHw8HBmzpxZ4HoDAgI4fvz4edO/+OILrrnmGnbu3MmLL77I/PnzL/h+xo0bx4QJE/K9bkBAAAMHDsTX15c5c+YQERFB5cqVOXz4MGvWrOH2228HIC4ujjJlytCiRQsA5s2bR8eOHbn22ms5fvw4K1eudL/HjRs3kp2dTZs2bQD46quvaN26NTVq1CAlJYXFixczcOBAALZs2UJaWhrt27cHYNGiRYSGhhISEkJ6ejrz589n6NChAGzfvp1jx47RsWNHAJYsWUKDBg2oXbs2mZmZzJ07lyFDhuDt7U1WVhY1a9akc+fOACxfvpxatWpRr149cnJymDVrFgMGDKBs2bLs2bOH+Ph4IiJcJ4H+7rvvCAoKomHDhgDMmDGDvn37Ur58efbv38/WrVvp3r07ALGxsQQEBLivQD1r1iy6d+9OpUqVSExMZP369fTq1QuAtWvX4ufnR/PmzQGYO3cuXbt2JTAwkCNHjhAbG0ufPn0AWL9+PQAtW7Z05xweHk61atVITk5m2bJl7kH8TZs2kZmZSdu2bQFYsGABLVu2JDg4mNTUVGJiYhg0aBAAW7duJSUlxb39xsTE0LRpU2rVqsWpU6eYN28ew4YNA1zf/cOHD3PTTTcBsHTpUurVq0edOnXIyspizpw5DBo0CB8fH+Lj49m/fz9dunQBYMWKFYSEhFC/fn3y8vKYOXMm/fv3x8/Pj71793Lw4EH366xatYqqVavSuHFjAKKioujduzcVKlTgwIEDbN68mR49egCwevVq/P39adasGQCzZ88mMjKSgIAAkpKSiIuL47bbbgNg3bp1+Pr6EhYWBkB0dDSdOnWiSpUqHD16lFWrVtG3b18ANmzYQG5uLq1btwbgyy+/pF27dgQFBXHixAmWLl3KgAEDANi8eTMZGRm0a9cOgIULFxIWFkbNmjVJS0tjwYIFDBkyBIBt27aRnJxMhw4dAFi8eDGNGjXiuuuuIyMjg+joaIYOHYqXlxe7du0iMTGRTp06AbBs2TLq1KlD3bp1yc7OZvbs2e7vakJCAnv27KFr164ArFy5kuDgYBo0aIAxhqioKPr160e5cuXYt28fO3bsIDIyEoDvv/+ewMBAmjRpAsDMmTPp1asX/v7+HDx4kI0bN9KzZ08CAgKoV68e5cqVIzQ0FKBE/Ubs3r2bAwcOePRvRGpqqvt75ORvRLvu3Vn25JMcr1ePMqmpRLz0Ev7BwcyBK/6N2LVrF926dfP434iAgAAqV65cIn8jANasWePRvxEHDx6kQ4cOxfIb0atXLxYsWMClcPxwX5UdVatWLcd7UCkjTy9lZHcpH/tLGV3F6o/rFAm7lNGVVnBwsDHGmPbt2+eb/sILL5jVq1cX+JzMzEwzdOjQfNPuu+8+k5SUZMB1+gRjjKlRo0a+ZWbOnGmioqIKXGe9evXMiRMnzJ/+9Cf3tKpVq5pHHnnEtGvXzrRp08ZMnjzZ5Obmmt69e1/w/ZQtW9b4+/u7q2LFio5/xlerSvN26wmlfOwvZWR/WZFR1aqGH390nRLhyBFDy5bO92RRWZGRyqPy0ekRxO3sv5qLvZSR/ZSR3ZSP/ZTRVRQDnAbqA82ufDXKyFk1a9Zk0aJFzJo1iw8++MA9/dixY7z66qusWbOGdevWMW7cOD755BMef/zxC64rKyuLtLQ0d508ebI43oIjtN3aTfnYTxnZz/GMgoJg2TJo2RIOHXKdGuHMXwmIi+MZyUXZmI8GbUVERERKg3Tg7F/g93WyEc919OhRcnJyqF69er7p1atXJykpqcDnJCUlXXT5s7eXss7g4GCWLVvG999/z5///OdC+/3hhx+oX79+ocuJiIj8LjVrus5ZGxoKBw9C587w009OdyVSIjh+uK/KjgoKCnK8B5Uy8vRSRnaX8rG/lNFVrntxnSJhjTK60lq9erV5/fXX3Y+9vLzM/v37zRNPPFHg8lFRUeaLL77IN23VqlXm7bffdj8+ePCgGTNmjPuxv7+/ycjIMEOGDHFPq1mzptmxY4f53//+Z7y9vS+p1/fee8/ExcU5/pnZUKV9u7W9lI/9pYzsL8cyqlXLsGuX65QIe/ca6tVz/LOwtfQ9srsszcfxBlSWVHh4uOM9qJSRp5cysruUj/2ljK5yBWHIxTVw+3/K6Epq8ODBJiMjwwwfPtw0btzYvPPOO+b48ePuHf1p06aZSZMm5fu8srKyzJgxY0yjRo3M+PHjTWZmpmnWrJl7mbFjx5rjx4+b3r17mxtuuMF8/vnnJj4+3vj5+RlwDdju3LnTLF682NSsWdNUr17dXWfXMXz4cDN06FDTqFEj06hRIzNu3DiTk5NjRo4c6fhnZkOV9u3W9lI+9pcysr8cyahuXUNCgmvANj7eULu245+DzaXvkd1lYz46PYK41alTx+kWpBDKyH7KyG7Kx37K6Co7DHx/5n6fK1tFac/os88+47HHHmPixIls2LCBFi1a0KNHDw4fPgzAddddR3BwsHv52NhY7rjjDv785z+zceNGBg4cSL9+/diyZYt7mRdffJE33niD9957j7Vr11KxYkV69OhBZmYmAJGRkTRo0IBu3bpx4MABkpKS3HWup59+mri4OH744Qf69u3LkCFDmDp16tX/UDxAad9ubad87KeM7FfsGTVoACtXQp06sHMndOoEe/cWbw8eRt8ju9mYj4/TDYg9srKynG5BCqGM7KeM7KZ87KeMikE0cBPQD/jv5T9dGcFbb73FW2+9VeC8rl27njdt9uzZzJ49+6LrHD9+POPHjy9w3rRp05g2bdpFnz99+nSmT59+0WVKM223dlM+9lNG9ivWjJo2haVLoUYN2LoVIiLgAud2l1/pe2Q3G/PxwnXIrYiIiIiUBvWBXUA2EASccLQbERER8STNm8OSJVCtGmzcCJGRcOSI012JlEg6PYK4DRo0yOkWpBDKyH7KyG7Kx37KqBjsBn4CfIFel/90ZSSeSNut3ZSP/ZSR/Yolo9atYdky14DtunVwyy0asL0M+h7ZzcZ8NGgrbj4+OluG7ZSR/ZSR3ZSP/ZRRMYk+c9vv8p+qjMQTabu1m/KxnzKy31XPaNQo+PZbqFIFYmOhWzc4fvzqvmYJo++R3WzMR4O24hYfH+90C1IIZWQ/ZWQ35WM/ZVRMos/c9gT8Lu+pykg8kbZbuykf+ykj+121jAICYOZMeO89KFcOFi6EW2+FlJSr83olmL5HdrMxHw3aitv+/fudbkEKoYzsp4zspnzsp4yKSRzwC1ARiLi8pyoj8UTabu2mfOynjOx3VTK68UZYvx4GD4bsbHj8cbjtNjh5suhfqxTQ98huNuajQVtx69Kli9MtSCGUkf2Ukd2Uj/2UUTGKPnPb7/KepozEE2m7tZvysZ8ysl+RZuTlBWPHwnffQd268PPP0LEjvPwyGF3L/krpe2Q3G/PRoK2IiIhIaRR95rYP2iMUERERl+rVYdEieOEF8PGBqCho2RLWrnW6M5FSyahUgKlZs6bjPaiUkaeXMrK7lI/9pYyKsXwwJGMwGDooI1XJLm23dpfysb+Ukf1VJBlFRhqSkgzGGNLTDffc4/j7Kkml75HdZWM+Oq5C3EJCQpxuQQqhjOynjOymfOynjIpRDvDlmfv9Lv1pykg8kbZbuykf+ykj+/2ujHx84Pnn4euvXUfabtoEbdrARx8VXYOi75HlbMxHg7biVr9+fadbkEIoI/spI7spH/spo2IWfeb2D5f+FGUknkjbrd2Uj/2Ukf2uOKM6deDbb+GJJ1yP33rLdQGybduKrDdx0ffIbjbmo0FbccvLy3O6BSmEMrKfMrKb8rGfMipmMcBpoD7Q9NKeoozEE2m7tZvysZ8yst8VZTRoEGzYAO3bQ3Iy9O8Pf/0rnD5d5P2Jvke2szEfL1znSRARERGR0mg+cDvwd2Cyw72IiIjI1VelCrz0Etxzj+vxqlVwxx2wb5+zfYlIPjrSVtz69+/vdAtSCGVkP2VkN+VjP2XkgOgzt/0ubXFlJJ5I263dlI/9lJH9Likjb2/4y19g507XgG1eHvzzn9C5swZsi4G+R3azMR8fpxsQe/j5+TndghRCGdlPGdlN+dhPGTlgPpAHtANCgAMXX1wZiSfSdms35WM/ZWS/QjNq3x7efBNat3Y93rgR7r8fvv/+6jcngL5HtrMxHx1pK2579+51ugUphDKynzKym/KxnzJywGEg9sz9PoUvrozEE2m7tZvysZ8yst8FMwoKgo8+gthY14BtcjI88IDrvgZsi5W+R3azMR8N2orbrl27nG5BCqGM7KeM7KZ87KeMHBJ95rZf4YsqI/FE2m7tpnzsp4zsd15GZcrA3/7mOhXC3Xe7pn3wATRqBP/9L+TmFn+TpZy+R3azMR8N2opbt27dnG5BCqGM7KeM7KZ87KeMHBJ95rYrEHDxRZWReCJtt3ZTPvZTRvbLl1GnTrB+PfznPxAQAGvXQrt2MGoUHDniXJOlnL5HdrMxHw3aioiIiJR2u4EtgC/Qy+FeRERE5MoEB8Onn8KKFRAaCkePugZq27d3DdyKiEfRoK24rVq1yukWpBDKyH7KyG7Kx37KyEHRZ277XXwxZSSeSNut3ZSP/ZSR5Xx9WXjDDbBjB9xxB+TluU6B0LCh65QIeXlOdyjoe2Q7G/OxftD2/vvvJyEhgYyMDFavXk3btm0vuOyyZcswxpxXX375Zb7lnn32WQ4ePMipU6dYvHgx9evXv9pvwyNUrVrV6RakEMrIfsrIbsrHfsrIQdFnbnsCF7l4rjIST6Tt1m7Kx37KyFK+vq7z1f70Eyf+/nfw93ddXKxNG9fFxpKTne5QzqHvkd1szMfqQdvBgwfzyiuv8Oyzz9KqVSs2btxITEwM1apVK3D5/v37U6NGDXc1a9aMnJwcZs2a5V5m7Nix/O1vf2P06NHceOONpKenExMTg5/fRf7rpJRo3Lix0y1IIZSR/ZSR3ZSP/ZSRg+KAXwB/4JYLL6aMxBNpu7Wb8rGfMrJMuXLw4IMQHw8ffQQNG+KXkgIjRsBNN7nOZyvW0ffIbjbmY/Wg7ZgxY3j//feZOnUq27ZtY/To0Zw6dYp77rmnwOWTk5M5dOiQuyIjIzl16lS+QduHH36Yf/7zn3zxxRds3ryZ4cOHU7NmTfr161fgOsuWLYu/v7+7KlaseDXeqoiIiIizDDDvzP1+DvYhIiIiBQsIgL//Hfbuhddfh1q14OBBePRRej/yCEyfDsY43aWIFBEvXLvo1vH19eXUqVMMHDiQefPmuadPnTqVypUrX3CQ9VybNm0iNjaWv/zlLwDUrVuXn3/+mRYtWrBx40b3csuXL2fDhg08/PDD561j/PjxTJgwwf04NTWVgIAABg4ciK+vL3PmzCEiIoLKlStz+PBh1qxZw+233w5AXFwcZcqUoUWLFgDMmzePjh07cu2113L8+HFWrlzpfh8bN24kOzubNm3aAPDVV1/RunVratSoQUpKCosXL2bgwIEAbNmyhbS0NNq3bw/AokWLCA0NJSQkhPT0dObPn8/QoUMB2L59O8eOHaNjx44ALFmyhAYNGlC7dm0yMzOZO3cuQ4YMwdvbm/j4eH755Rc6d+7s/lxq1apFvXr13EcsDxgwgLJly7Jnzx7i4+OJiIgA4LvvviMoKIiGDRsCMGPGDPr27Uv58uXZv38/W7dupXv37gDExsYSEBBA06ZNAZg1axbdu3enUqVKJCYmsn79enr1cl0FZe3atfj5+dG8eXMA5s6dS9euXQkMDOTIkSPExsbSp08fANaf+dfEli1bAvDFF18QHh5OtWrVSE5OZtmyZfTv39+9bWRmZrpPt7FgwQJatmxJcHAwqampxMTEMGjQIAC2bt1KSkoK4eHhAMTExNC0aVNq1arFqVOnmDdvHsOGDQNg586dHD58mJtuugmApUuXUq9ePerUqUNWVhZz5sxh0KBB+Pj4EB8fz/79++nSpQsAK1asICQkhPr165OXl8fMmTPp378/fn5+7N27l127drmvZrhq1SqqVq3q/pegqKgoevfuTYUKFThw4ACbN2+mR48eAKxevRp/f3+aNWsGwOzZs4mMjCQgIICkpCTi4uK47bbbAFi3bh2+vr6EhYUBEB0dTadOnahSpQpHjx5l1apV9O3bF4ANGzaQm5tL69atAfjyyy9p164dQUFBnDhxgqVLlzJgwAAANm/eTEZGBu3atQNg4cKFhIWFUbNmTdLS0liwYAFDhgwBYNu2bSQnJ9OhQwcAFi9eTKNGjbjuuuvIyMggOjqaoUOH4uXlxa5du0hMTKRTp06A6xQpderUoW7dumRnZzN79mz3dzUhIYE9e/bQtWtXAFauXElwcDANGjTAGENUVBT9+vWjXLly7Nu3jx07dhAZGQnA999/T2BgIE2aNAFg5syZ9OrVC39/fw4ePMjGjRvp2bMnAGvWrKFcuXKEhoYClKjfiN27d3PgwAH9Rug3Qr8RJfg3IqF+AqsnrsYvxY9+9/dj57ad5/1GNGzYkOuuu+6q/0b06tWLBQsWIFIUvLy8MBrIsJbysZ8yclj16vDww3D//VCpkmtafDy88IJroDYzUxl5AGVkN1vzMTZWcHCwMcaY9u3b55v+wgsvmNWrVxf6/LZt2xpjjGnbtq17Wnh4uDHGmBo1auRbdubMmSYqKqrA9ZQtW9b4+/u7q2LFio5/Nler+vTp43gPKmXk6aWM7C7lY38pI4fLF0MyBoMhXBmpSk5pu7W7lI/9pYwcqtq1DW++acjI+PWiPZs2GYYONZQpo4w8rJSR3WVjPj6UUPfeey+bNm1i7dq1v2s9WVlZZGVlFVFXdqtQoYLTLUghlJH9lJHdlI/9lJHDsoGvgDtxnSIh9vxFlJF4Im23dlM+9lNGxaxJE3jySbjjDvA5M2zz/fcweTJ89RUFnQJBGdlPGdnNxnysPaft0aNHycnJoXr16vmmV69enaSkpIs+t3z58gwdOpQPP/ww3/Szz7uSdZYGBw4ccLoFKYQysp8yspvysZ8yskD0mdt+Bc9WRuKJtN3aTfnYTxkVAx8f6N0boqNh61YYPtw1LSYGOneGjh3hyy8LHLAFZeQJlJHdbM3H8cN9L1SrV682r7/+uvuxl5eX2b9/v3niiScu+rwRI0aYjIwMU6VKlfPmHTx40IwZM8b92N/f32RkZJghQ4Y4/n6drsDAQMd7UCkjTy9lZHcpH/tLGVlQFTGcxnWKhMbKSFUyStut3aV87C9ldBUrLMzwyiuGQ4d+PQVCbq5h1ixDq1bKqASVMrK7bMzH2iNtAV555RVGjRrF8OHDady4MW+//TYVKlRgypQpAEybNo1Jkyad97x7772X6Ohojh8/ft681157jaeeeorevXtzww03MH36dA4ePEh0dPTVfjvWO3tRGrGXMrKfMrKb8rGfMrLASWDJmfv9zp+tjMQTabu1m/KxnzIqYkFBrguLbdjgqkcecU1LSoKXX4amTWHQIPjxx0tepTKynzKym435WH1O288++4xq1aoxceJEatSowYYNG+jRoweHDx8G4LrrriMvLy/fcxo2bMjNN9/svqLzb7344otUqFCB9957j8qVK/Pdd9/Ro0cPMjMzr/r7EREREfEI0cBtuAZtn3e0ExERkZKhbFm4/XYYORJ69vz1XLWZmTBvHkyb5joVQm6uo22KiF0cP9xXZUfVrVvX8R5UysjTSxnZXcrH/lJGllR1DLm4TpFQUxmpPL+03dpdysf+Uka/o9q0Mbz5puHo0V9Pf2CMITbWMHq0oYj+JFsZ2V/KyO6yMR+rj7SV4uXv7+90C1IIZWQ/ZWQ35WM/ZWSJQ0As0BHoA7zz6yxlJJ5I263dlI/9lNFl8PV1XTSsZ0/XhcWaNPl13i+/wMcfu46q3bGjSF9WGdlPGdnNxnysPqetFK9mzZo53YIUQhnZTxnZTfnYTxlZJPrMbb/8k5WReCJtt3ZTPvZTRoWoWRPuvRfmzIGjR2HZMhg71jVgm5EBn34Kt94KtWvD3/9e5AO2oIw8gTKym4356EhbERERETlfNPAScAsQAKQ42o2IiIg9fHwgPBx69XIdURsWln/+4cOwcOGvlZrqTJ8i4tG8cJ0nQQRfX1+ys7OdbkMuQhnZTxnZTfnYTxlZZgvQFBgGRLkmKSPxRNpu7aZ87KeMgBo1oEcP10DtrbdCQMCv8/Ly4IcfXAO0CxbAjz+CKd6hFmVkP2VkNxvz0ekRxC0yMtLpFqQQysh+yshuysd+ysgy0Wdu+/06SRmJJ9J2azflY79Sl1HZstCuHTz4oOvUBvHxkJgIU6bAoEGuAdujR+GTT+DOOyEoCDp0gOeeg7i4Yh+whVKYkQdSRnazMR+dHkHcAs79l0KxkjKynzKym/KxnzKyTDTwd6AXUBbIUkbimbTd2k352K/EZ1S7NrRvDzfe6Lpt1Qr8/PIvk5fnGpBdsMBV69a5plmixGdUAigju9mYjwZtxS0pKcnpFqQQysh+yshuysd+ysgy64ADQAiuc9suUkbimbTd2k352K9EZXTttRAa6jqStn17V9Wocf5yR4/C6tWu+uEHWLsWUuw9wXuJyqiEUkZ2szEfndNW3CpVqkSqTpBuNWVkP2VkN+VjP2VkobeA+4F3gdHKSDyTtlu7KR/7eWRGISHQpAk0bfrrbdOmrkHb38rOhg0bfh2gXb3adUoED+KRGZUyyshuNuajc9qK22233eZ0C1IIZWQ/ZWQ35WM/ZWSh6DO3fQEvZSSeSdut3ZSP/azNyMcH6tZ1XRzsscfgo48gNtZ1ROwvv8DixfCf/8Do0dCp068DtgkJ8NlnMGYMdOwIlSq5jrz9299+PYeth7E2I3FTRnazMR+dHkFERERELmw5kALUAG50thURESllKlZ0nW/2uusKvq1ZE8qUKfi5OTmwezds3Qrbtv16u2MHnDpVvO9DROQKaNBW3NatW+d0C1IIZWQ/ZWQ35WM/ZWShbOAr4A6gH6z7UBmJ59Fvi92Uj/2KPKOKFSEo6PwKDnYNyJ4dlA0MLHxdp0+7BmLPHZjduhV27XKd9qCU0PfIfsrIbjbmo0FbcfP19XW6BSmEMrKfMrKb8rGfMrJUNK5B277gO10ZiefRb4vdlI/9LpiRry8EBLiqcuX8t1Wq/DoYW61a/sHZcuUu/cWPHYN9+2Dv3vy3Z+8fPgxGl+rR98h+yshuNuajQVtxCwsLY+vWrU63IRehjOynjOymfOynjCy1CMgEGkPdnnVLfUb3338/jz/+ODVq1GDjxo08+OCDrF279oLLDxw4kOeee446deqwa9cunnjiCRYuXJhvmWeffZZRo0ZRuXJlVq1axX333cfu3bvd8wMDA3njjTfo3bs3eXl5zJkzh4ceeoj09HT3MqGhobz11lu0bduWI0eO8MYbb/DSSy8V/QfggfTbYjflcxV5ecE117gGSX9b5ctf8vSTzZrBiRPnD8xWqHDlvZ086RpwPbcOHco/ILt/v2s5KZS+R/ZTRnazMR8N2oqIiIjIxaUBS4Fe8EvrX5zuxlGDBw/mlVdeYfTo0fzwww88/PDDxMTE0KhRI44cOXLe8uHh4cyYMYNx48bx5ZdfcscddxAdHU2rVq3YsmULAGPHjuVvf/sbI0aMICEhgeeee46YmBiaNm1KZmYmAJ9++inBwcFERkbi6+vLlClTeO+997jzzjsB8Pf35+uvv2bJkiWMHj2a0NBQPvroI06cOMH7779ffB+QyFleXgXfL+BxXpkyriM2z847O7+w+5e63G+f4+39a12txz4+BZevb9FO9/MrfBC2COwrbIG0NNegbkrKr7fJyecPyp6tI0d0XlkRkUJ4Afo7AgGgXLlyZGRkON2GXIQysp8yspvysZ8ystgo4D3wWuOFubH07j6uXr2atWvX8uCDDwLg5eXF/v37eeONN3jhhRfOWz4qKooKFSrQu3dv97TY2Fg2bNjAfffdB8DBgwf597//zb///W8AKlWqxKFDhxg5ciQzZ86kcePGbNu2jTZt2hAXFwdA9+7dWbBgAf/3f/9HYmIio0eP5l//+hc1atQg+8w5HCdPnky/fv1o0qRJge+lbNmy+Pn5uR8bYzhZXEe0ffghnBlwvup+O2Do6S7l/VyNZby9C19e7Jad7Roozcg4vy40/cw836wsso8ezT8we/Z+airk5jr85kT7UPZTRnazMR8daStunTp1IiYmxuk25CKUkf2Ukd2Uj/2UkcW+AN6BMk3LkOOf4zr6tpTx9fWldevWTJ482T3NGMOSJUsIDw8v8Dnh4eG88sor+abFxMTQr18/AOrWrUtwcDBLlixxz09NTeWHH34gPDycmTNnEh4eTnJysnvAFmDJkiXk5eVx4403Eh0dTXh4OCtXrnQP2J59nSeffJLKlStz4sSJ83obN24cEyZMyPe6AQEBDBw4EF9fX+bMmUNERASVK1fm8OHDrFmzhttvvx2AuLg4ypQpQ4sWLQCYN28eHTt25Nprr+X48eOsXLnS/R43btxIdnY2bdq0AeCrr76i3P/9H4fOGTAWKS5eeXmQl4fJzcXbywsvYyAvj9zsbHx9fMAY8rKzIS8PnzJl8DKGzIwMyvr4UMbbm7zcXE6fOkXFcuXAGLJOn8bk5lLuzPZ8MjWVcn5+lPX2xmRlceLoUapVqYJ3bi6nUlPJOX2aKpUq4Z2by6GDBwkoX56K11xDXlYWe+PjaVC3Lt65uZw4doz0lBRqh4TglZvLnl27qOLvT9XKlcnLymLjunW0CgvDNy+P4wcOcGTfPlo2bkyZrCw2xsYS5O9PvZo18c7MJPp//6PPrbdSwc+Pffv2sWPHDiIjIwH4/vvvCQwMdP/jzsyZM+nVqxf+/v4cPHiQjRs30rNnT6pVq8ZX331HuXLlCA0NheDgq/ob0bp1a2rUqEFKSgqLFy9m4MCBAGzZsoW0tDTat28PwKJFiwgNDSUkJIT09HTmz5/P0KFDAdi+fTvHjh2jY8eOgOt3s0GDBtSuXZvMzEzmzp3LkCFD8Pb2Zvfu3Rw4cIDOnTsDsHz5cmrVqkW9evXIyclh1qxZDBgwgLJly7Jnzx7i4+OJiIgA4LvvviMoKIiGDRsCMGPGDPr27Uv58uXZv38/W7dupXv37oDrH+0CAgJo2rQpALNmzaJ79+5UqlSJxMRE1q9fT69evQBYu3Ytfn5+NG/eHIC5c+fStWtXAgMDOXLkCLGxsfTp0weA9evX06JFC7zO/EPLF198QXh4ONWqVSM5OZlly5bRv39/ADZt2kRmZiZt27YFYMGCBbRs2ZLg4GBSU1OJiYlh0KBBAGzdupWUlBT3/8ed/SuQWrVqcerUKebNm8ewYcMA2LlzJ4cPH+amm24CYOnSpdSrV486deqQlZXFnDlzGDRoED4+PsTHx7N//366dOkCwIoVKwgJCaF+/frk5eUxc+ZM+vfvj5+fH3v37mXXrl1069YNgFWrVlG1alUaN24MuP5xtHfv3lSoUIEDBw6wefNmevToAbj+odXf359mzZoBMHv2bCIjIwkICCApKYm4uDhuu+02wHURKl9fX8LCwgCIjo6mU6dOVKlShaNHj7Jq1Sr69u0LwIYNG8jNzaV169YAfPnll7Rr146goCBOnDjB0qVLGTBgAACbN28mIyODdu3aUa1aNaZPn05YWBg1a9YkLS2NBQsWMGTIEAC2bdtGcnIyHTp0AGDx4sU0atSI6667joyMDKKjoxk6dCheXl7s2rWLxMREOnXqBMCyZcuoU6cOdevWJTs7m9mzZ7v//zwhIYE9e/bQtWtXAFauXElwcDANGjTAGENUVBT9+vWjXLlyV/wbAbBmzZpffyPA434jMjIyOHz4cLH8RvTq1YsFCxZQGB1pK27Dhg1jxowZTrchF6GM7KeM7KZ87KeMLNceBtcfzGeffOZ0J44IDg7m4MGDhIeHs3r1avf0F154gc6dO7v/A+FcmZmZjBgxgqioKPe0++67j/Hjx1OjRg3Cw8P5/vvvCQ4OJikpyb3MzJkzMcYwdOhQxo0bx4gRI9z/gXrWoUOHGD9+PO+88w4xMTEkJCQwevRo9/wmTZqwdetWmjRpwvbt28/rzdEjbQMDXX/KXUz69uvHvOjoq/9CxXUxpCt9nSt53rnP+e3zL+fxReb1HzCAubNn/zrNmF/r3McXm3exZeV30/8/208Z2U8Z2c3GfHSkrbgdPXrU6RakEMrIfsrIbsrHfsrIcqsh2T/Z6S6kiGRlZZGVleXMiycnu6qYnNq5Ew4cKLbXk8uTtnev60/txVr6/2f7KSP7KSO72ZiPTkwkbqtWrXK6BSmEMrKfMrKb8rGfMrJfac7o6NGj5OTkUL169XzTq1evnu8o2XMlJSVddPmzt4UtExQUlG9+mTJlqFKlSr5lClrHua9RmpXm7dYTKB/7KSP7KSP7KSO72ZiPBm3F7ez5WcReysh+yshuysd+ysh+pTmj7Oxs4uLi3OcoA9eFyCIiIoiNjS3wObGxsfmWB4iMjHQvn5CQQGJiYr5l/P39ufHGG93LxMbGEhgYSKtWrdzL3HLLLXh7e/PDDz+4l+nUqRM+Pj75Xmf79u0Fns+2tCnN260nUD72U0b2U0b2U0Z2szEfDdqKiIiIiFyiV155hVGjRjF8+HAaN27M22+/TYUKFZgyZQoA06ZNY9KkSe7l//Of/9CjRw/GjBlDo0aNGD9+PG3atOHNN990L/Paa6/x1FNP0bt3b2644QamT5/OwYMHiT5zDtbt27ezcOFC3n//fdq2bUuHDh148803iYqKIjExEYD//e9/ZGVl8eGHH9K0aVMGDx7MQw89dN5F0ERERETEcxiVCjBNmjRxvAeVMvL0UkZ2l/Kxv5SR/aWMMA888IDZs2ePOX36tFm9erVp166de96yZcvMlClT8i0/cOBAs337dnP69GmzefNm07Nnz/PW+eyzz5rExESTkZFhFi9ebBo0aJBvfmBgoPn0009NamqqOXHihPnwww9NhQoV8i0TGhpqVq5caTIyMsz+/fvN2LFjHf+sbCltt3aX8rG/lJH9pYzsL2Vkd9mYj9eZOyI0bNiQnTt3Ot2GXIQysp8yspvysZ8ysp8yEk+k7dZuysd+ysh+ysh+yshuNuaj0yOIW+vWrZ1uQQqhjOynjOymfOynjOynjMQTabu1m/KxnzKynzKynzKym435aNBWRERERERERERExCI6PYK4+fv7k5aW5nQbchHKyH7KyG7Kx37KyH7KSDyRtlu7KR/7KSP7KSP7KSO72ZiPjrQVt3bt2jndghRCGdlPGdlN+dhPGdlPGYkn0nZrN+VjP2VkP2VkP2VkNxvz0aCtuAUFBTndghRCGdlPGdlN+dhPGdlPGYkn0nZrN+VjP2VkP2VkP2VkNxvz0aCtuJ04ccLpFqQQysh+yshuysd+ysh+ykg8kbZbuykf+ykj+ykj+ykju9mYj85pK25ly5YlKyvL6TbkIpSR/ZSR3ZSP/ZSR/ZSReCJtt3ZTPvZTRvZTRvZTRnazMR8daStuAwYMcLoFKYQysp8yspvysZ8ysp8yEk+k7dZuysd+ysh+ysh+yshuNuajQVtxy87OdroFKYQysp8yspvysZ8ysp8yEk+k7dZuysd+ysh+ysh+yshuNuaj0yOIiIiIiIiIiIiIWERH2goAFStWJCUlhYoVKzrdilyAMrKfMrKb8rGfMrKfMhJPpO3WbsrHfsrIfsrIfsrIbrbmo0FbAcDLy4tKlSrh5eXldCtyAcrIfsrIbsrHfsrIfspIPJG2W7spH/spI/spI/spI7vZmo8GbUVEREREREREREQsokFbEREREREREREREYto0FYAyMzMZMKECWRmZjrdilyAMrKfMrKb8rGfMrKfMhJPpO3WbsrHfsrIfsrIfsrIbrbm4wUYp5sQERERERERERERERcdaSsiIiIiIiIiIiJiEQ3aioiIiIiIiIiIiFhEg7YiIiIiIiIiIiIiFtGgrYiIiIiIiIiIiIhFNGgrIiIiIiIiIiIiYhEN2pYgN998M1988QUHDhzAGEPfvn3zzf/DH/5ATEwMR48exRhDWFjYeevw8/PjzTff5OjRo6SlpTF79myCgoLyLVOrVi2+/PJL0tPTOXToEC+++CJlypS5qu+tpPi9GQUGBvL666+zfft2Tp06xd69e/nPf/5DpUqV8i2njK5MUXyHzrVgwYIC16N8rlxRZdS+fXuWLl3KyZMnSUlJYcWKFVxzzTXu+YGBgXzyySekpKSQnJzMBx98QIUKFa7qeyspiiKj6tWrM336dBITEzl58iRxcXH0798/3zLK6MpdLCMfHx+ef/55Nm3axMmTJzlw4ADTpk0jODg43zou5fMPDQ1l5cqVZGRksG/fPh5//PFieX9SMmk/127ax7Wf9nPtp/1c+2k/124lcR9Xg7YlSIUKFdi4cSMPPPDABed/9913PPHEExdcx6uvvkrv3r0ZNGgQnTt3pmbNmsydO9c939vbm6+++oqyZcvSoUMHRowYwciRI5k4cWKRv5+S6PdmVLNmTWrWrMljjz3GDTfcwMiRI+nRowcffvihexlldOWK4jt01sMPP4wx5rzpyuf3KYqM2rdvz6JFi/j6669p164dbdu25c033yQvL8+9zKeffkqzZs2IjIzk9ttvp1OnTrz33ntF/n5KoqLIaPr06TRq1Ig+ffoQGhrK3Llz+eyzz2jRooV7GWV05S6WUfny5WnVqhXPPfccrVq1on///jRq1Igvvvgi33KFff7+/v58/fXX7N27l9atW/P4448zYcIERo0addXfn5RM2s+1m/Zx7af9XPtpP9d+2s+1W0ndxzWqklfGGNO3b98C59WuXdsYY0xYWFi+6ZUqVTKZmZlmwIAB7mmNGjUyxhhz4403GsD06NHD5OTkmKCgIPcyf/nLX8yJEyeMr6+v4+/bk+pKMiqoBg4caE6fPm3KlCmjjCzJJywszOzfv99Ur179vPUoH+czio2NNRMnTrzgehs3bmyMMaZ169buad27dze5ubkmODjY8fftSXWlGaWlpZm77ror37SjR4+ae++9VxkVY0Znq02bNsYYY2rVqnXJn//o0aPNsWPH8v2uTZ482Wzbts3x96zy/NJ+rt2lfVz7S/u59pf2c+0v7efaXSVlH1dH2opb69atKVu2LEuWLHFP27FjB3v37iU8PByA8PBwNm/ezOHDh93LxMTEEBAQQLNmzYq9Z4GAgABSU1PJzc0FlJHTypUrx//+9z8eeOABDh06dN585eOsatWq0b59ew4fPsyqVatISkpi+fLldOzY0b1MeHg4ycnJxMXFuactWbKEvLw8brzxRifaLnW+//57hgwZQmBgIF5eXgwZMoRrrrmG5cuXA8qouAUEBJCXl8eJEyeAS/v8w8PDWblyJdnZ2e5lYmJiaNy4MZUrVy7O9kUA7ed6Iu3j2kf7uXbTfq5n0H6uPTxhH1eDtuJWo0YNMjMzSUlJyTf90KFD1KhRw73Mb/8P+uzjs8tI8alatSpPP/10vsP1lZGzXn31Vb7//vvz/sziLOXjrOuvvx6ACRMm8P7779OjRw9+/PFHli5dSv369QFXDuf+xwZAbm4ux48fV0bFZPDgwfj6+nL8+HEyMzN59913+cMf/kB8fDygjIqTn58fL7zwAjNmzCAtLQ24tM9fv3ViG+3nehbt49pJ+7l2036uZ9B+rh08ZR/Xp8jXKCLFwt/fn6+++oqtW7cyYcIEp9sRoHfv3txyyy20bNnS6VbkAry9Xf9W+e677zJ16lQANmzYQEREBPfccw9///vfHexOznruueeoXLkyERERHD16lH79+vHZZ59x880389NPPzndXqnh4+PDZ599hpeXF/fdd5/T7YhIKaF9XDtpP9d+2s/1DNrPdZ4n7ePqSFtxS0pKws/Pj4CAgHzTq1evTlJSknuZ6tWrnzf/7DwpHhUrVmTRokWkpaXxhz/8gZycHPc8ZeScW265hXr16nHixAmys7PdfzIxZ84cli1bBigfpyUmJgKwdevWfNO3bdvGddddB7hy+O3VxMuUKUOVKlWUUTG4/vrrefDBB7nnnnv45ptv2LRpExMnTmTdunXuiwooo6vv7M5s7dq1iYyMdB+BAJf2+eu3Tmyj/VzPoH1ce2k/137az7Wf9nOd52n7uBq0Fbe4uDiysrKIiIhwT2vYsCG1a9cmNjYWgNjYWEJDQ6lWrZp7mcjISFJSUs77Pwe5Os5erTArK4s+ffqQmZmZb74ycs7zzz9P8+bNadGihbsAHnnkEe6++25A+Thtz549HDhwgEaNGuWb3rBhQ/bu3Qu4MgoMDKRVq1bu+bfccgve3t788MMPxdpvaVS+fHmAfFc5BtefJp09gkQZXV1nd2YbNGhAt27dOH78eL75l/L5x8bG0qlTJ3x8fv2jrsjISLZv3+4+b5hIcdJ+rv20j2s37efaT/u59tN+rrM8dR/X8au6qYqmKlSoYMLCwkxYWJgxxpiHH37YhIWFua+EFxgYaMLCwkzPnj2NMcYMHjzYhIWFmerVq7vX8d///tfs2bPHdOnSxbRq1cqsWrXKrFq16tcr13l7m02bNplFixaZ5s2bm1tvvdUcOnTI/Otf/3L8/XtC/d6M/P39TWxsrNm4caO5/vrrTfXq1d3l7e2tjBzOp6D67VUrlY/zGT300EPmxIkTZsCAAaZevXpm4sSJ5tSpU+b66693L7NgwQITFxdn2rZtazp06GB27NhhPv30U8ffvyfU783Ix8fH7Ny506xYscK0bdvWXH/99WbMmDEmNzfX9OzZUxld5Yx8fHxMdHS02bdvn2nevHm+/5859yq5hX3+lSpVMomJiWbatGmmadOmZvDgwebkyZNm1KhRjr9/lWeW9nPtLu3j2l/az7W/tJ9rf2k/1+4qofu4zn+wqqKpzp07m4JMmTLFAGbEiBEFzh8/frx7HX5+fubNN980x44dMydPnjRz5sw57/+or7vuOvPVV1+Z9PR0c/jwYfPSSy+ZMmXKOP7+PaF+b0YXer4xxtSuXVsZOZxPQWVM/p1Z5WNHRk888YTZt2+fOXnypFm1apXp2LFjvvmBgYHm008/NampqebEiRPmww8/NBUqVHD8/XtCFUVG9evXN7NnzzZJSUnm5MmTZsOGDeauu+5SRsWQUe3atQucZ4wxnTt3vqzPPzQ01KxcudJkZGSY/fv3m7Fjxzr+3lWeW9rPtbu0j2t/aT/X/tJ+rv2l/Vy7qyTu43qduSMiIiIiIiIiIiIiFtA5bUVEREREREREREQsokFbEREREREREREREYto0FZERERERERERETEIhq0FREREREREREREbGIBm1FRERERERERERELKJBWxERERERERERERGLaNBWRERERERERERExCIatBURERERERERERGxiAZtRUQst2zZMl599VX344SEBB566CEHO3IZP34869evd7oNEREREfFA2scVEbk4DdqKiBSxa6+9lv/+97/s3buX06dPk5iYyKJFi+jQoYN7GWMMffv2vaT19e/fn6effvpqtUvnzp0xxrgrKSmJ2bNnU7du3Ys+7+WXXyYiIuKq9SUiIiIi9tA+rohI8fJxugERkZJmzpw5lC1blhEjRvDzzz9TvXp1IiIiqFq16mWtx9fXl+zsbJKTk69Sp/k1bNiQtLQ0GjRowHvvvcf8+fNp3rw5eXl55y1bpkwZ0tPTSU9PL5beRERERMRZ2scVESl+RqVSqVRFUwEBAcYYYzp16nTBZRISEsy5EhISDGDGjx9v1q9fb+69917z888/m9zcXAOYZcuWmVdffTXf8x966CH343vvvdckJyebW265xQCmWbNmZsGCBSYtLc0kJSWZ6dOnm6pVq16wn86dOxtjjAkICHBPGzZsmDHGmIYNG7rn9+jRw6xbt85kZmaazp07u/s9d1133323+emnn8zp06fNwYMHzRtvvJHvs3n//ffN4cOHTUpKilm6dKlp3ry545mpVCqVSqVSqS5e2sfVPq5KpSr+0ukRRESK0MmTJ0lLS6Nfv36ULVu2wGXatm0LwMiRI6lRo4b7MUD9+vUZMGAA/fv3p0WLFoW+3uOPP87zzz/PrbfeyjfffENAQADffPMN69evp02bNvTo0YPq1avz2WefXdb7yMjIAMj3Hp5//nmefPJJmjRpwqZNm857zujRo3nrrbd47733CA0NpU+fPuzevds9f9asWQQFBdGzZ09at27Njz/+yNKlSwkMDLys3kRERESkeGkfV/u4IuIMx0eOVSqVqiRV//79zbFjx8ypU6fMd999Z/71r3+Z0NDQfMsYY0zfvn3zTRs/frzJzMw01157bb7pFzoK4fnnnzcHDhwwTZs2dc/7xz/+YRYtWpTv+SEhIcYYYxo0aFBgv789CqFGjRrmu+++M/v37ze+vr7u+X369Dmv33OPQvjll1/Mc889V+BrdOzY0Zw4ccKULVs23/Rdu3aZUaNGOZ6ZSqVSqVQqleripX3c819D+7gqlepqlo60FREpYnPnzqVmzZr06dOHRYsW0aVLF3788UdGjBhR6HP37t3L0aNHC13u0UcfZdSoUdx0001s3brVPT0sLIyuXbuSlpbmru3btwNQr169i67zl19+4eTJkyQmJlKhQgUGDBhAdna2e/66desu+Nxq1aoREhLC0qVLC5wfFhZGxYoVOXbsWL7e6tatW2hfIiIiIuI87eOeT/u4InI16UJkIiJXQWZmJkuWLGHJkiX885//5P333+fZZ59l2rRpF33epV704Ntvv+W2225j8ODBvPDCC+7pFStWZP78+TzxxBPnPScxMfGi67z55ptJTU3l8OHDnDx58rJ6O/unZhdSsWJFEhMT6dKly3nzTpw4cdHnioiIiIgdtI+bn/ZxReRq0qCtiEgx2Lp1K/369XM/zsrKokyZMle8vjVr1vDmm2+yaNEicnJy+Pe//w3Ajz/+yIABA9izZw+5ubmXtc6EhARSUlKuqJ+TJ0+SkJBAREQEy5cvP2/+jz/+SI0aNcjJyWHv3r1X9BoiIiIiYhft42ofV0SuHp0eQUSkCFWpUoWlS5dy5513EhoaSp06dRg4cCBjx45l3rx57uX27NlDREQE1atXp3Llylf0WrGxsfTq1Yvx48fz0EMPAfDWW29RpUoVZsyYQZs2bbj++uu59dZb+eijj/D2vro/+RMmTODRRx/lwQcfpH79+rRs2ZK//vWvACxZsoTY2Fiio6OJjIykdu3ahIeH889//pPWrVtf1b5ERERE5PfRPq72cUWk+OlIWxGRInTy5El++OEHHnnkEerVq4evry/79+/n/fffZ9KkSe7lHn30UV555RVGjRrFgQMHqFu37hW93qpVq7jttttYsGABubm5vPnmm3Ts2JEXXniBr7/+Gj8/P/bu3cuiRYvIy8srqrdZoOnTp3PNNdfwyCOP8PLLL3P06FFmz57tnt+rVy/+9a9/MWXKFKpVq0ZSUhIrV67k0KFDV7UvEREREfl9tI+rfVwRKX5euK5IJiIiIiIiIiIiIiIW0OkRRERERERERERERCyiQVsRERERERERERERi2jQVkRERERERERERMQiGrQVERERERERERERsYgGbUVEREREREREREQsokFbEREREREREREREYto0FZERERERERERETEIhq0FREREREREREREbGIBm1FRERERERERERELKJBWxERERERERERERGLaNBWRERERERERERExCIatBURERERERERERGxiAZtRURERERERERERCyiQVsRERERERERERERi2jQVkRERERERERERMQiGrQVERERERERERERsYgGbUVEREREREREREQsokFbEfF4nTt3xhjDgAEDnG6lxFu2bBnLli1zP/aUz/63fYuIiIhI6TVixAiMMdSuXds9LSEhgfnz5zvYVeEK6ltESi4N2oqIlYwxl1SdO3e+Kq8fHh7O+PHjCQgIuCrrv5puv/12li9fzqFDh0hPTyc+Pp6ZM2fSvXt39zLBwcGMHz+esLAwBzu9NOPHj8+XeXp6Olu2bOG5557D39/f6fZERERErsi8efNIT0+nYsWKF1zmk08+ITMzkypVqhRjZ3aoXbs2H330Ebt37yYjI4PExERWrFjBhAkT8i133333MWLECGeavAy1a9fOt0+bk5PD3r17mTt3rkfsk4tI8fNxugERkYLcdddd+R4PHz6cW2+99bzp27Zto0mTJkX++h06dGDChAlMnTqVlJSUIl//1fLoo4/y8ssvs3z5ciZPnsypU6eoX78+3bp1Y+jQocTExABQs2ZNJkyYwJ49e9i4ceMlr//WW2+9Wq0XavTo0Zw8eZKKFSty66238tRTT3HLLbfQsWPHQp/rZN8iIiIiBfn000/p06cPf/jDH/j444/Pm1+uXDn69u3LokWLOH78uAMdOqdevXqsXbuWjIwMPvroI/bs2UNwcDCtWrXiiSeeyDdwe//993P06FGmTZt2yev/+OOPiYqKIjMz8yp0f3H/+9//WLBgAWXKlKFJkybcd9999OzZk/bt2xe6X+5k3yJS/DRoKyJW+vTTT/M9bt++Pbfeeut504GrMmjricqUKcPTTz/N119/ne+o2rOqVat2xesuV64cGRkZZGdn/54Wf5fZs2dz7NgxAN59911mz57NgAEDaN++PatXry7wOTb0LSIiIlKQL774gtTUVO64444CB2379u1LxYoVC9z/LekeeeQRKlasSIsWLdi3b1++eb9nn7Z8+fKcOnWKvLw8xwY+f/zxx3yZrlq1ivnz53PfffcxevToAp9jQ98iUvx0egQRKTG8vb35+9//zv79+8nIyGDJkiXUq1fvvOXatWvHwoULOXHiBOnp6SxfvpwOHTq4548fP56XX34ZgD179rj/hOnsuaNGjhzJ0qVLOXToEKdPn2bLli0X3ME616OPPooxhuuuu+68eZMmTSIzM5PKlSsDUL9+fWbPnk1iYiIZGRns37+fGTNmUKlSpQuu/9prryUgIIBVq1YVOP/IkSOA6zy069atA2Dq1Knu93f2z8qWLVvG5s2badWqFStWrCA9PZ1Jkya55xV2btiyZcsyf/58Tpw4QXh4OABeXl489NBD/PTTT2RkZJCUlMQ777zjfr9X4ptvvgGgbt26V9S3n58f48ePZ8eOHWRkZHDw4EHmzJnD9ddf717mavQtIiIiAnD69Gnmzp1LREREgQORd9xxB6mpqXzxxRcABAQE8Oqrr7Jv3z5Onz7Nrl27GDt2LF5eXvmeV6VKFaZPn05KSgrJyclMnTqV5s2b59vfAwgNDWXKlCnEx8e7Tz/w4YcfFnoqhqCgILKzs3nmmWfOm9ewYUOMMTzwwAMA+Pj48Mwzz7Bz504yMjI4evQo3377Ld26dbvoFJyPrwABAABJREFUa9SrV49ffvnlvAFb+HWfFlznob3hhhvo0qWLe5/27D7f2fO/durUibfeeotDhw7xyy+/5JtX2Llhhw8fTnZ2Ni+++KJ7WmH/LXG5frtPeyV99+jRg+XLl5OamkpKSgpr1qxh2LBh+ZYp6r5F5OrTkbYiUmI8+eST5OXl8fLLLxMQEMDYsWP59NNPad++vXuZrl27snDhQuLi4nj22WfJy8vj7rvv5ptvvuHmm29m7dq1zJ07l4YNG3LHHXfw8MMPc/ToUeDXHcT77ruPLVu28MUXX5CTk0Pv3r15++238fb25r///e8F+/vss8948cUXGTx4sHtQ+KzBgwfz9ddfc+LECXx9fYmJicHPz4833niDpKQkQkJCuP3226lcuTKpqakFrv/w4cOcOnWK3r1788Ybb5CcnFzgctu2bePpp5/mueee49133+Xbb78F4Pvvv3cvU7VqVRYuXEhUVBSffPIJhw4duoQE4JprrmHevHm0adOGbt26uQeH3333XUaOHMmUKVN4/fXXqVu3Ln/9619p2bIlHTt2JCcn55LWf66zA/Jnj769nL69vb358ssv6datGzNmzOA///kP/v7+REZGcsMNN/Dzzz9ftb5FREREzvr0008ZOXIkgwcP5q233nJPDwwMpHv37syYMYPTp09Trlw5VqxYQUhICO+++y779u2jQ4cOTJ48meDgYB555BHA9Q/O8+fPp127drz99tts376dvn37FnjqgMjISK6//nqmTJlCUlISzZo1489//jPNmjXLt//8W4cPH2bFihUMHjyYiRMn5ps3ZMgQcnJymDVrFgATJkxg3LhxfPDBB6xZs4ZKlSrRpk0bWrVqxZIlSy74Gnv37qVbt2507dr1ogcMPPzww7zxxhucPHmSf/3rXwDn7f/997//5ciRI0ycOJEKFSpccF2/NWrUKN555x0mTZrE008/DVzaf0tcroL2aS+n7xEjRvDRRx+xZcsWJk+ezIkTJ2jZsiU9evRgxowZV61vESkeRqVSqWyvN954wxhjCpzXuXNnY4wxW7ZsMb6+vu7pDz74oDHGmGbNmrmn7dixwyxcuDDf86+55hoTHx9vYmJi3NMeffRRY4wxtWvXPu/1rrnmmvOmLVy40OzevbvQ97Fq1Sqzdu3afNPatGljjDHmrrvuMoAJCwszxhgzYMCAy/6cJkyYYIwxJi0tzXz11Vdm3LhxpmXLluct17p1a2OMMSNGjDhv3rJly4wxxvz5z38ucN6yZcvO++wHDBhgKlSoYJYtW2YOHz5swsLC3Mt07NjRGGPMsGHD8q3r1ltvLXD6b2v8+PHGGGMaNGhgqlatamrXrm1GjRplMjIyTGJioilXrtxl9z1y5EhjjDEPP/zwBV/39/atUqlUKpVKVVh5e3ubAwcOmFWrVuWb/uc//9kYY0xkZKQBzD/+8Q+TlpZm6tevn2+5SZMmmezsbPN///d/BjB/+MMfjDHG/O1vf3Mv4+XlZZYsWXLevl9B+7RDhgwxxhhz0003XbTvUaNGnbefDZiffvrJLFmyxP14/fr1Zv78+Zf9uTRt2tSkp6cbY4z58ccfzauvvmr69Onj3u87tzZv3pxvP+9sjRgxwhhjzMqVK423t3eB887d109ISHD3+uCDD5rc3Fzzj3/8I9/zLvW/JQqq2rVrG2OMefrpp03VqlVNUFCQ6dSpk4mLizPGGPOHP/zhsvuuVKmSSUlJMbGxscbPz++Cr/17+lapVM6VTo8gIiXGlClT8p279OwRpGf/3L1FixY0bNiQ//3vf1StWtVdFSpUYOnSpXTq1Om8Py8ryOnTp933K1WqRNWqVVmxYgX16tW76OkLAGbOnEmbNm3y/Qn+kCFDOH36NPPmzQNwX/ise/fulCtX7hLfvcuECRMYNmwY69evp3v37kyaNIkff/yRuLg4GjdufMnrOX36NFOmTLnk5QMCAvj6669p3LgxXbp0yXcRhUGDBnHixAkWL16c73OPi4sjLS2Nrl27XtJr7Ny5k6NHj7Jnzx7ee+89du/ezW233UZGRsZl9z1gwACOHDnCG2+8ccFliqpvERERkQvJy8sjKiqKDh065PuT9zvuuIOkpCSWLl0KuPZLvv32W5KTk/PtlyxZsgQfHx86deoEuP5MPisri/fff9+9LmNMvqN4zzp3n9bPz4+qVau6rxPQqlWri/Y9d+5csrOzGTJkiHtas2bNaNasGTNnznRPO3HiBM2aNaN+/fqX87GwdetWWrRowccff0ydOnV4+OGHmTdvHocOHeJPf/rTZa3r/fffJy8v75KXf/zxx3n99dd54okn3EfvQtH9t8TEiRM5evQohw4dcv83xNixY/n8888vu+/IyEgqVarE888/f8Fz3RZV3yJS/HR6BBEpMX57zquzpwcIDAwEoEGDBgBMnz79gusICAjgxIkTF32dDh068OyzzxIeHn7enyoFBARc8PQFALNmzeKVV15hyJAhTJ48GXDthC9cuJC0tDTAdR7df//73zz66KPceeedfPvtt3zxxRd88sknF133WVFRUURFReHv78+NN97IyJEjufPOO5k/fz433HDDJV284MCBA5d18a7XXnuNa665hpYtW7J169Z88xo0aEDlypXznX/sXEFBQZf0Gv379yc1NZXs7Gx++eUX9ykMrqTvevXqsWPHDnJzcy+4TFH1LSIiInIxn376KWPGjOGOO+5g8uTJhISEcPPNN/P666+7B+0aNGhAWFiY+7Rdv3V2v6R27druayKca/fu3ec9JzAwkPHjxzN06FCqV6+eb15AQMBFez527BhLly5l8ODB7nPbDhkyhOzsbObOnete7plnnmHevHns2rWLzZs3s2jRIj7++GM2b95cyKcCu3btYvjw4Xh7e9O0aVNuv/12xo4dy/vvv09CQoJ7QLswCQkJl7QcuK79cPvtt/P888+fdzqzovpviXfffZdZs2aRl5fHiRMn2LJlC1lZWVfU99lTK/z0008XXKao+haR4qdBWxEpMS40AHf2X469vV1/XPDYY4+xYcOGApc9efLkRV/j+uuvZ+nSpWzfvp0xY8awf/9+srKy6NWrF2PGjHG/xoUkJiby7bffMnjwYCZPnkz79u2pXbs2TzzxRL7lHnvsMaZOnUrfvn259dZbef311xk3bhzt27fnwIEDF32Ns9LS0liyZAlLliwhOzubkSNHcuONN7Jy5cpCn/vbHf3CzJs3j6FDh/Lkk08yfPhwXGeycPH29ubQoUPceeedBT73QoOiv7Vy5crzzvX1W5fb98UUVd8iIiIiF/Pjjz+ybds2hg0bxuTJkxk2bBje3t58+umn7mW8vb35+uuv810Q61w7d+687Nf97LPP6NChAy+99BIbNmzg5MmTeHt7ExMTU+g+LbgOFJg6dSphYWFs3LiRwYMHs3Tp0nz7a99++y316tVz79P+6U9/4pFHHmH06NF8+OGHl9RnXl4eP/30Ez/99BOxsbEsX76cO++885IHbS9n/3DLli1UrlyZP/7xj7z77rvs2bPHPa8o/lsCXIPRl9J7Ue3XFlXfIlL8NGgrIqVGfHw8AKmpqYXuKJ076Hiu3r17c80119CnTx/279/vnn45fyo/c+ZM3n77bRo2bMiQIUNIT09n/vz55y13duf0X//6F+Hh4Xz//feMHj3afSGEy7Fu3TpGjhxJcHAwcOH3d6Wio6P5+uuvmTp1Kmlpadx///3uefHx8XTr1o1Vq1bl+zM8J8XHx3PjjTfi4+NzwYuJ2di3iIiIlEyffvop//znPwkNDeWOO+5g586d7gu6gmu/pGLFioXuw+7du5euXbtSrly5fIN+vz09QeXKlenWrRvPPPMMzz333AWXu5jo6GgyMzPdp0ho1KiR+y/JzpWcnMzUqVOZOnUqFSpUYOXKlUyYMOGSB23PdfYzObtPC0W7X3v06FEGDhzId999x9KlS7nppptITEwELu+/JYrL2Z5uuOEG9/0LLWNT3yJyaXROWxEpNeLi4ti9ezePPfZYgVdgvfbaa93309PTAdcO7bnOHs177nmfKlWqxN13333JfcyZM4ecnByGDRvGoEGD+PLLLzl16pR7vr+/P2XKlMn3nM2bN5Obm4ufn98F11uuXLkLXum3Z8+eAOzYsQO48Pv7PT7++GP+9re/cd999/H888+7p3/22Wf4+PgUONhcpkyZQv/87mqYM2cO1apV469//esFl7GxbxERESmZzh5VO3HiRFq2bJnvKFv49ajYW2+99bznBgQEuPcdY2JiKFu2LKNGjXLP9/Ly4oEHHsj3nIL2aQEefvjhS+45JSWFmJgYBg8ezNChQ8nMzCQ6OjrfMlWqVMn3OD09nd27d190nxbgpptuwsfn/GPMevXqBfy6T3t2nUW5T3vgwAG6detGuXLlWLx4sfs9XM5/SxSXr7/+mtTUVMaNG3fBz9TGvkXk0uhIWxEpNYwx/OlPf2LhwoVs2bKFKVOmcODAAUJCQujatSupqan06dMHcO3cAPzrX/8iKiqK7Oxs5s+fz9dff01mZibz58/n3XffpWLFiowaNYrDhw9Ts2bNS+rjyJEjLFu2jDFjxlCpUqV8F2sAuOWWW3jzzTeZNWsWO3fuxMfHhz/+8Y/k5uYyZ86cC663fPnyxMbGEhsby6JFi9i/fz+VK1emX79+dOrUic8//9z9J1Hx8fEkJyczevRo0tLSSE9P54cffsj3J2BX4q233qJSpUpMmjSJlJQUJk+ezMqVK3nnnf9n777jq6rvP46/QhYrBGQIRCAIYQoBA2hAGbKUIZQhYFvlZ7XV1oUbF4gDFQFnpXVbLVCGoIAiUEYZMiJL9gojhB2SEEIS4Pz+uOUKZdwEkpzPvXk/f4/vw0u4ufnE1zn54beHc8fw7LPP0qRJE3788UdycnKIiYmhb9++PPLII5f8vgrCl19+yV133cXo0aNp0aIF//nPfyhVqhQdOnTgr3/9K99++63JuUVERCQwJSYmsmjRInr27Alw3qbtiBEjuP3225k2bRqff/45CQkJlCpVikaNGtGnTx+io6M5fPgwU6ZMYenSpYwcOZLatWuzceNGbr/9du/G45mrUtPT05k/fz5PPfUUoaGhJCUl0alTJ2rWrJmnucePH8/XX3/Nn//8Z2bOnOl9Q90z1q9fz7x580hISODIkSM0a9aMPn368P7771/ydZ9++mni4uKYPHkya9asATxvjnbXXXdx+PBh3n77be9zExISeOCBB3juuefYunUrBw4cYO7cuXn6Pv7Xtm3b6NSpE/PmzWPmzJnccsstpKen5/q/JQpLeno6gwYN4pNPPmH58uX885//JCUlhdjYWEqWLMnAgQPz9N9AImKPo6WlpWV9vffee47j+VPmeatNmzaO4zhO7969z/l4jRo1HMdxnLvvvvucj8fGxjoTJ050Dh486GRmZjo7duxwxo0b57Rr1+6c5z333HPO7t27nZMnTzqO4zg1atRwAKdbt27OqlWrnOPHjzvbt293nnzySWfgwIHnPMfX+sMf/uA4juOkpqY64eHh5/xedHS08/HHHztbtmxxjh8/7hw6dMiZM2eOc8stt1zyNYODg50//OEPzuTJk50dO3Y4mZmZzrFjx5yEhATn8ccfd0JDQ895fvfu3Z1ffvnFyc7OPuff09y5c521a9de8GvMnTvXmTt3rs9/96+//rrjOI7z5z//2fuxe++911m+fLmTkZHhpKamOqtXr3Zef/11p3Llypf8voYMGeI4juOUL1/+ks/Ly9yAU7x4cefll192tm3b5mRlZTl79+51/vWvfzk1a9Y853mXO7eWlpaWlpaWVl7WAw884DiO4/z0008X/P1SpUo5r776qrN582bnxIkTzoEDB5yFCxc6jz32mBMSEuJ9Xvny5Z2vvvrKSU1NdVJSUpxPP/3UiY+PdxzHce644w7v86pWrepMmjTJOXLkiJOSkuKMHz/eqVy5suM4jjNkyJBczVy6dGknIyPDcRzHufPOO8/7/Weffdb56aefnCNHjjgZGRnO+vXrncGDB58z74VWfHy889577zlr1qxxUlJSnKysLCcxMdH59NNPz/uzWqVKlZzvvvvOSU1NdRzH8f6Z7+6773Ycx3Hi4uLOe/0zv3f2n9137NjhfPfdd+c8r3nz5k5qaqozb948p3jx4g7k/r8l/ned+W+Txx9//JLPy+vc4Pnvk4ULFzoZGRnO0aNHnZ9++snp16/fOc+53Lm1tLTcW0H/fSAiIiIiIiIiAahHjx5MmTKFVq1asXjxYrfHERGRXNCmrYiIiIiIiEiAKF68+DlvolqsWDF+/PFHmjVrRuXKlfUGqyIifkL3tBUREREREREJEO+99x4lSpRgyZIlhIeH06tXL1q1asXgwYO1YSsi4kd0pa2IiIiIiIhIgBgwYACPP/44tWvXpnjx4mzdupUPP/yQDz74wO3RREQkD7RpKyIiIiIiIiIiImJIMbcHEBEREREREREREZFfadNWRERERERERERExBBt2oqIiIiIiIiIiIgYok1b8erTp4/bI4gPamSfGtmmPvapkX1qJP5Ix61t6mOfGtmnRvapkW0W+wTUpu2OHTtwHOec9fTTT5/znEaNGrFgwQIyMzPZtWsXTz75pEvT2rNq1Sq3RxAf1Mg+NbJNfexTI/vUSPyRjlvb1Mc+NbJPjexTI9ss9glxe4D89sILL/DRRx95f52enu59HBERwY8//sjs2bO5//77adSoEZ9++ilHjx4953OKqvDwcLdHEB/UyD41sk197FMj+9RI/JGOW9vUxz41sk+N7FMj2yz2CagrbcGzSbt//37vOn78uPf3fvvb3xIWFsY999zD+vXrGT9+PO+++y6PPfaYixPb0bhxY7dHEB/UyD41sk197FMj+9RI/JGOW9vUxz41sk+N7FMj2yz2CbhN22eeeYZDhw7x888/88QTTxAcHOz9vfj4eBYsWEBOTo73YzNnzqRevXqULVv2gq8XFhZGRESEd5UuXbqgvwUREREREREREREpwoIAx+0h8sugQYP4+eefOXLkCC1btmT48OF89tlnPP7444Bng3bHjh3cf//93s+pX78+69evp379+mzcuPG81xwyZAhDhw71/jotLY3IyEj69OlDaGgokyZNon379pQtW5YDBw6wbNkyunXrBkBCQgLBwcE0adIEgKlTp9KqVSsqVKjAkSNHWLBgAT179gRg9erV5OTk0KxZMwCmT59OXFwclStXJjU1lVmzZnlvirxu3TrS09O58cYbAfjhhx9o1KgRUVFRZGRk8N1339G/f38ANm7cyOHDh2nVqhUAs2fPJiYmhho1apCVlcXkyZPp168fxYoVIzExkZ07d9KmTRsA5s2bR7Vq1ahVqxYnT55kwoQJ9O7dm7CwMBITE9m2bRvt27cHYOHChVSqVIk6deoAMHbsWHr06EHJkiXZvXs369evp3PnzgAsWbKEyMhIGjRoAMCECRPo3LkzZcqUITk5mZUrV9KlSxcAli9fTnh4uPd/8Zg8eTLt2rWjXLlyHDx4kCVLlnD77bcDsHLlSgCaNm0KwLfffkt8fDwVK1YkJSWFuXPn0qtXLwDWrFlDVlYWzZs3B2DGjBk0bdqUKlWqkJaWxsyZM+nbty8A69evJzU1lfj4eO9x1KBBA6pVq8bx48eZOnUqAwYMAGDz5s0cOHCAm266CYA5c+ZQq1YtoqOjyc7OZtKkSfTt25eQkBC2bdvG7t27adu2LQDz588nKiqK2rVrc/r0acaPH0+vXr0IDw9n586dbNmyhU6dOnH69GkWLVpE+fLlqVevHgDjxo2je/fulCpViqSkJNauXcutt94KwE8//URERAQNGzYEYOLEiXTs2JHIyEj27dtHQkICXbt2BWDFihWEhoYSGxsLwJQpU2jdujVXXXUVhw4dYtGiRfTo0QPw3O/l1KlTxMXFATBt2jRatGhBpUqVOHr0KHPmzKF3794ArF27lszMTFq0aAHA999/T2xsLFWrViU9PZ0ZM2bQr18/ADZs2EBKSgotW7YEYNasWdStW5fq1auTmZnJlClT6N+/P0FBQWzZsoXk5GRat24NwNy5c4mOjqZmzZrk5OQwceJE77m6Y8cOEhMTadeuHQALFiygSpUqxMTE4DgO48aNo2fPnpQoUYJdu3axadMmOnbsCMDixYspV64c9evXB2D8+PF06dKFiIgI9u7dy+rVq7ntttu8x2FISAiNGjUCCKifEVu3biUpKcmvf0ac3SYQf0Z06NABwK9/RhQrVozp06cH7M+IZcuWUaJECb/+GVG/fn2ioqIK/GdEly5dmDFjBiL5ITw8nKysLLfHkItQH/vUyD41sk+NbLPax7G8hg8f7vhSt27dC37u//3f/znZ2dlOWFiYAzgzZ850xowZc85z6tev7ziO49SrV++CrxEWFuZERER4V+nSpV3/d1JQ69Zbb3V9Bi018velRraX+thfamR/qZGWPy4dt7aX+thfamR/qZH9pUa2l8U+5t+IbOTIkXz++eeXfM727dsv+PGlS5cSGhpKdHQ0mzdvZt++fVx99dXnPOfMr/ft23fB18jOziY7Ozvvg/uhcuXKuT2C+KBG9qmRbepjnxrZp0bij3Tc2qY+9qmRfWpknxrZZrGP+U3bQ4cOcejQocv63CZNmnDq1CkOHDgAeP7K7auvvkpISAgnT54EoGPHjmzcuJGjR4/m18h+6+DBg26PID6okX1qZJv62KdG9qmR+CMdt7apj31qZJ8a2adGtlnsEzD3tL3xxhu54YYbmDt3Lunp6cTHxzN69Gi+//57Bg4cCECZMmXYtGkTP/74I2+88QbXXXcdn376KYMGDeKjjz5y9xswoFSpUmRkZLg9hlyCGtmnRrapj31qZJ8aiT/ScWub+tinRvapkX1qZJvFPsXcHiC/ZGVl0b9/f+bPn8+6det47rnnGD16NH/84x+9z0lLS6NTp07UrFmThIQERo4cybBhw7Rh+19n3qxH7FIj+9TINvWxT43sUyPxRzpubVMf+9TIPjWyT41ss9jH/O0RcmvlypXed+6+lLVr13rfRVpERERERERERETEmoC50lau3MqVK90eQXxQI/vUyDb1sU+N7FMj8Uc6bm1TH/vUyD41sk+NbLPYR5u2IiIiIiIiIiIiIoZo01a8mjZt6vYI4oMa2adGtqmPfWpknxqJP9Jxa5v62KdG9qmRfWpkW1xsnNsjnCdg7mkbiGJjYcQIOHky7ysnJ++fc/31e0hNvbyvd6F1+rTb/wZFREREREREREQurhWteGv6W6SQwgxmuD2OVxDguD2EXFj79jB7tttTXJnL2TwuqE3pgn7Nsz//1KmC+fdZqlQpMjIyCubFJV+okW3qY58a2adG4o903NqmPvapkX1qZJ8a2RRMMD/zM41pzN/5O3/iT26P5KUrbQ1btw5+9zsICfGs4OBfH+f3Cg2Fq66KJCMj9bI+92JCQy/9+4Hs7E3c/90QvtSvL/V7FSqUYffuDHJy8K4zz/nfx75+7eu52dnnrjMf0xXUlxYfH89sf/9fWwKY+tinRvapkfgjHbe2qY99amSfGtmnRjY9yIM0pjFpoWk8m/Os2+OcQ5u2hu3bB19/XXhfb8CALowdO/ayPrdYsbxt8hbkBvSlvk5oaN4+PzfPCw6+8L+TgtmwTs7vF8yzU6fO38i90Oaur4+dWSdOQGam55+X8zgrCxxDf1+gYsWKbo8gl6A+9qmRfWok/kjHrW3qY58a2adG9qmRPZWpzDCGATC+yXgOLz/s8kTn0qateKWkpFz2554+/esmXFETFOR7w9fXr3P7OdddV5dt2zZ5N4TPfDw3v87Lc0NDISzMs0L+56dEcDCUKOFZVpzZwL3Y5m5mJqSnQ1rapf/5vx/Lycn7LFdyHknBUx/71Mg+NRJ/pOPWNvWxT43sUyP71MieEYygDGVYylK+ueobt8c5j+5pK17h4eFkZWW5PYZcghuNgoLO3cQNCzv/15f7seLFITzcswFcvPiv6+xfX+hxiRIXv8I5P2Vl5X2jNy0tlL17czh0CA4f9ryG2KGfc/apkX1qJP5Ix61t6mOfGtmnRvapkS1taMM85nGa07SgBb+E/2Kuj660Fa9evXpd9u0RpHC40chxbF5FHRKS+03ekiUhIsKzypTx/c+SJT1fIzwcKlb0rNw79/Lc9HQ4dOjcdfDgub9OToakJM8tUS7n6l7JPf2cs0+N7FMj8Uc6bm1TH/vUyD41sk+N7AghhA/4AIAxjCGBBAb0GmCujzZtRcQvnTwJx455Vn4LDs7bJu+Zf0ZGQs2akYSGplKhgmdj+czr1KyZu6+9f79nAzcpCfbuvfDjI0fy/3sWERERERERKQoe5mEa0pCDHOR5nnd7nIvSpq14rVmzxu0RxAc1KhynTsHRo56VVw0bXsO6dakEBXk2citU8FypW6HChVfFilC1qmeFhcHVV3vW9ddf/GtkZsLOnbB1K2zbdu4/ExN1te6l6ByyT43sUyPxRzpubVMf+9TIPjWyT41sqEpVhjIUgKd5mhQ89xq22EebtuJl7d4dcj41su9MI8eB1FTP2rbN9+cFBUH58hAV5VlVq174ccWKnts+1KvnWf/r1CnYvfvCG7pbtnjemK0o0zlknxrZp0bij3Tc2qY+9qmRfWpknxrZMJKRRBDBYhbzOZ97P26xjzZtxat58+Zs3brV7THkEtTIvstt5Di/3uN29eqLPy8szLOJe+21UKsW1K597j9Ll4boaM/q0OHczz11CjZt8rz+2Ss5Oc/j+i2dQ/apkX1qJP5Ix61t6mOfGtmnRvapkftu4Rb6059TnOLP/BkHx/t7Fvto01ZExI9kZ3tugZCYCP/+9/m/f/XV52/mnllXXQUNGnjWgAG/fs7Bg7BmzbkbuevX6zYLIiIiIiIiEhhCCfW++dgHfMBqLnG1lBFBcNa2shRpkZGRpKamuj2GXIIa2We5UZUqEBt77qpb1/PGa/8rJwc2bIAVK2DBAvjPf2D79sKfOb9Z7iMeamSfGok/0nFrm/rYp0b2qZF9auSup3iKN3iD/eynLnVJ5dwWFvsUc3sAsaNp06ZujyA+qJF9lhslJ8MPP8Abb8Cdd0LDhp7bKTRrBn/4A7z7LsyfDykpEBoKjRvDPffA55977ou7Zw+MHQt//jNcd53nPrz+xnIf8VAj+9RI/JGOW9vUxz41sk+N7FMj91SjGi/yIgBP8uR5G7Zgs49ujyBeVapUcXsE8UGN7PO3RidOQEKCZ52tWjVo0gRatoSbb4bmzT1vhNa/v2cBHDkCCxd6rsJdsAB+/hlOniz0byFP/K1PUaRG9qmR+CMdt7apj31qZJ8a2adG7hnFKEpRiv/wH/7BPy74HIt9tGkrXmlpaW6PID6okX2B0mj3bs/67jvPr4sXhxtu8Gzgtm4N8fGee+TefrtnAWRkwE8/ee61O3UqrFvn3vwXEyh9Apka2adG4o903NqmPvapkX1qZJ8auaMTnehDH05ykr/wl4s+z2If3dNWvEJCQjhp/TK5Ik6N7CsqjUJCoGnTXzdxb7oJypc/9zlbt8KUKZ4N3MWL4fRpV0Y9R1Hp48/UyD41En+k49Y29bFPjexTI/vUqPCFEcYv/EIMMYxmNI/x2EWfa7GP7mkrXn379nV7BPFBjewrKo1OnoTly2HUKOjZEypW9Nwj989/hmnTPLddqF0bnnjCc/uE5GT4+GPo3t1z1a5bikoff6ZG9qmR+CMdt7apj31qZJ8a2adGhe8JniCGGJJJZghDLvlci320aSsiIlfMcWD9evjwQ8/GbIUK0Ls3/OMfnnvfVqrkebOzb7+FQ4dg0iS46y7PLRZERERERERE8lMNavAczwHwOI+TTrrLE+WdNm3Fa/369W6PID6okX1q5JGRAZMnezZmr74abrkF3n0Xdu6EUqWgVy/44gvYv99zD9wHH4Ry5Qp+LvWxT43sUyPxRzpubVMf+9TIPjWyT40K19u8TUlKMpe5jGWsz+db7KNNW/FKTU11ewTxQY3sU6PznTwJc+fCI49AdLTnXrhDh8KqVZ5747ZrB++9B3v3wpdfeu6PW1DUxz41sk+NxB/puLVNfexTI/vUyD41Kjxd6EJPepJDDg/yYK4+x2IfbdqKV3x8vNsjiA9qZJ8a+bZqFbz0kmfztmZNePRRz8eKF4ff/95zD9z16z0fz+/bJ6iPfWpknxqJP9Jxa5v62KdG9qmRfWpUOIpTnHd5F/Bcbbue3F1Ba7GPNm1FRMQ1iYnwzjueDdzmzeGjj+DYMahfH0aP9lx9+9VX0Lq125OKiIiIiIiIdU/xFLWoxR72MIxhbo9zRYIAx+0hxIarrrqKI0eOuD2GXIIa2adGVy4iAgYMgD/9Ca6//tePb9rk2dT94gvPm5ldDvWxT43sUyPxRzpubVMf+9TIPjWyT40K3rVcyzrWUZzi3MEdTGBCrj/XYh9daSteDRo0cHsE8UGN7FOjK5eeDn//O8TFedbf/ub5WN268NZbkJQEY8dC27Z5f231sU+N7FMj8Uc6bm1TH/vUyD41sk+NCt67vEtxijOLWXnasAWbfbRpK17VqlVzewTxQY3sU6P89fPPcP/9ULUq3HcfLF8OYWHQv7/nzc0WLoQOHXL/eupjnxrZp0bij3Tc2qY+9qmRfWpknxoVrO50pytdySabh3goz59vsY82bcXr+PHjbo8gPqiRfWpUMI4dg48/hhYtPPe//fBDyMyEVq1g1izPm5e1b+/7ddTHPjWyT43EH+m4tU197FMj+9TIPjUqOCUo4X3zsZGMZBOb8vwaFvvonrYiIuKXKleGp5/2XIlbvLjnY//5DwwZ4rkKV0RERERERALfMIbxAi+wi13Upz7HsbcBezl0pa14DRgwwO0RxAc1sk+NCs++fTBoEFx7LbzzDpw4ATffDP/+N8ybd+F73qqPfWpknxqJP9Jxa5v62KdG9qmRfWpUMGpTm6d4CoBHefSyN2wt9tGmrYiI+LXkZHj0UahVC957D7KyoE0bz9W2c+d6HouIiIiIiEjgeZ/3CSecH/iBb/jG7XHylTZtxWvz5s1ujyA+qJF9auSevXvh4Yc9m7fvv+/ZvG3b1nPV7b//7bkKV33sUyP71Ej8kY5b29THPjWyT43sU6P89xt+Q2c6k0XWZb352Nks9tGmrXgdOHDA7RHEBzWyT43cl5QEDz0EtWvDBx94Nm/btYMFC+Cdd37huuvcnlAuReeQfWok/kjHrW3qY58a2adG9qlR/ipJSd7mbQDe5E22svWKXs9iH23aitdNN93k9gjigxrZp0Z27NkDDz7o2bz98EPIzoZWrbL4+WcYPhxKlHB7QrkQnUP2qZE7hgwZguM456wNGzZ4fz88PJz333+fQ4cOkZ6ezsSJE6lUqZKLE9ui49Y29bFPjexTI/vUKH89z/NUpzqJJDKc4Vf8ehb7aNNWREQC2p498Oc/Q0wMLFt2DaGh8MwzsG4d3Hqr29OJiOTeL7/8QuXKlb3r7P+4GD16NN27d6dv3760adOGqlWrMnnyZBenFRERESkYdanL4zwOwMM8TCaZLk9UcBwtLcCpVKmS6zNoqZG/LzWyvSpVquR0746zc+evF6uNG4dTubL7s2n92sjtGbTUyOIaMmSIs3Llygv+XpkyZZysrCynd+/e3o/VrVvXcRzHueGGG1yf3cLScWt7qY/9pUb2lxrZX2qUf2sWsxwHx/mO7wK6j660Fa9atWq5PYL4oEb2qZFttWrV4rvvoEEDGDkSTp2Cfv1g40a4/34ICnJ7QtE5ZJ8auScmJoakpCS2bdvGV199RbVq1QCIi4sjLCyM2bNne5+7adMmdu7cSXx8/EVfLywsjIiICO8qXbp0gX8PbtFxa5v62KdG9qmRfWqUP/rSlw504AQneIRH8u11LfYJcXsAsSM6OpolS5a4PYZcghrZp0a2nemTkQFPPAFffQV/+xu0aOG57+3dd8Of/gRr1rg9adGlc8g+NXLH0qVLGThwIJs2baJKlSoMGTKE//znP1x33XVUrlyZrKwsUlNTz/mc/fv3U7ly5Yu+5uDBgxk6dKj312lpaURGRtKnTx9CQ0OZNGkS7du3p2zZshw4cIBly5bRrVs3ABISEggODqZJkyYATJ06lVatWlGhQgWOHDnCggUL6NmzJwCrV68mJyeHZs2aATB9+nTi4uKoXLkyqampzJo1iz59+gCwbt060tPTufHGGwH44YcfaNSoEVFRUWRkZPDdd9/Rv39/ADZu3Mjhw4dp1aoVALNnzyYmJoYaNWqQlZXF5MmT6devH8WKFaNs2bLs3LmTNm3aADBv3jyqVatGrVq1OHnyJBMmTKB3796EhYWRmJjItm3baN++PQALFy6kUqVK1KlTB4CxY8fSo0cPSpYsye7du1m/fj2dO3cGYMmSJURGRtKgQQMAJkyYQOfOnSlTpgzJycmsXLmSLl26ALB8+XLCw8Np3LgxAJMnT6Zdu3aUK1eOgwcPsmTJEm6//XYAVq5cCUDTpk0B+Pbbb4mPj6dixYqkpKQwd+5cevXqBcCaNWvIysqiefPmAMyYMYOmTZtSpUoV0tLSmDlzJn379gVg/fr1pKamejf3Z86cSYMGDahWrRrHjx9n6tSpDBgwAPC8q/aBAwe8t+WYM2cOtWrVIjo6muzsbCZNmkTfvn0JCQlh27Zt7N69m7Zt2wIwf/58oqKiqF27NqdPn2b8+PH06tWL8PBwdu7cSUxMDNHR0QAsWrSI8uXLU69ePQDGjRtH9+7dKVWqFElJSaxdu5Zb/3t/o59++omIiAgaNmwIwMSJE+nYsSORkZHs27ePhIQEunbtCsCKFSsIDQ0lNjYWgClTptC6dWuuuuoqDh06xKJFi+jRowcAq1at4tSpU8TFxQEwbdo0WrRoQaVKlTh69Chz5syhd+/eAKxdu5bMzExatGgBwPfff09sbCxVq1YlPT2dGTNm0K9fPwA2bNhASkoKLVu2BGDWrFnUrVuX6tWrk5mZyZQpU+jfvz9BQUFs2bKF5ORkWrduDcDcuXOJjo6mZs2a5OTkMHHiRO+5umPHDhITE2nXrh0ACxYsoEqVKsTExOA4DuPGjaNnz56UKFGCXbt2sWnTJjp27AjA4sWLKVeuHPXr1wdg/PjxdOnShYiICPbu3cvq1au57bbbiIqK4sCBA5QoUYJGjRoBBNTPiK1bt5KUlOTXPyOuu+4673kUaD8jtmzZQocOHfz+Z0RUVBQbNmwIyJ8RAMuWLSvwnxHFc4rzyqRXwIFp102jXvV67J61O19+RoSFhREUFFQoPyO6dOnCjBkzyA3XL/fVsrHO/it1WjaXGtlfamR7XahPsWI4f/kLTmqq53YJOTk4b7yBU7Kk+/MWxaVzyP5SIxsrMjLSOXr0qHPPPfc4AwYMcE6cOHHec5YuXeq8/vrrF32NsLAwJyIiwrtKly7t+vdVUEvHre2lPvaXGtlfamR/qdGVrzd503FwnK1sdYpTPOD7BP33gYiISJFWtSq8/Tb898ICEhPhL3+BXP4PoCIihW7ZsmXMnj2bWbNm8e9//5uyZcuec7VtYmIib7/9Nm+//bZ7Q4qIiIjkgwY0YBWrCCWUrnRlBoH/H2q6p614nfkrEGKXGtmnRrZdqs/evXDHHdCtG+zcCdHRMH06/OtfUKFC4c1Y1Okcsk+NbChVqhS1atUiOTmZhIQEsrOzvX8ND6BOnTrUqFFDt7L4Lx23tqmPfWpknxrZp0ZX5gM+IJRQpjClQDZsLfbRpq14hYToFsfWqZF9amRbbvpMn+55o7IRI+DkSc+VtytXwn9vLSUFTOeQfWrkjhEjRtC6dWtq1KhBfHw833zzDadOnWLs2LGkpaXxySefMGrUKNq2bcv111/PZ599xuLFi1m6dKnbo5ug49Y29bFPjexTI/vU6PINYABtactxjvMojxbI17DYR5u24rVt2za3RxAf1Mg+NbItt32OH4ennoJmzWDDBrjmGpg/Hx57rIAHFJ1DfkCN3HHNNdcwduxYNm3axL/+9S8OHz7MjTfeyKFDhwAYNGgQ06ZNY9KkSSxYsIB9+/Z533RGdNxapz72qZF9amSfGl2eMpRhJCMBeJVX2cnOAvk6Vvu4fmNdLRurSpUqrs+gpUb+vtTI9rqcPqVK4Xz9tedNyhwHZ9IknMhI97+XQF06h+wvNdLyx6Xj1vZSH/tLjewvNbK/1Ojy1ihGOQ6Os5nNThhhRaqPrrQVr7Zt27o9gvigRvapkW2X0ycjA377W3jgAcjKgl69ICEBmjTJ9/EEnUP+QI3EH+m4tU197FMj+9TIPjXKu0Y04iEeAuBBHiSb7AL7Whb7aNNWREQkF8aMgVatYMcOqFULliyB++5zeyoREREREZHANJrRhBDCRCbyIz+6PY4rXL/cV8vGqlq1quszaKmRvy81sr3yo0/Zsjjffvvr7RK++AKnZEn3v7dAWTqH7C810vLHpePW9lIf+0uN7C81sr/UKG+rNa0dB8fJIsupQY0i2UdX2opXVFSU2yOID2pknxrZlh99jh6FHj3g6afh5Em46y5YuhTq1r3y+UTnkD9QI/FHOm5tUx/71Mg+NbJPjfLmJV4C4BM+KbA3HzubxT7atBWv2rVruz2C+KBG9qmRbfnVx3HgzTfhllsgORmuuw5WrIB+/fLl5Ys0nUP2qZH4Ix23tqmPfWpknxrZp0a51/a//5dFFq/xWqF8TYt9tGkrXqdPn3Z7BPFBjexTI9vyu89//gNNm8K//w2lS8O4cfD++xAWlq9fpkjROWSfGok/0nFrm/rYp0b2qZF9apR7Z66y/ZiP2cOeQvmaFvsE4blPgoiIiFymYsXgpZfg+ec9v16+HHr1gj2F8+cLERERERGRgHALtzCHOWSRRS1qkUSS2yO5RlfailevXr3cHkF8UCP71Mi2gupz+jS88AJ06QKHD0Pz5jBvHlSrViBfLqDpHLJPjcQf6bi1TX3sUyP71Mg+NcqdM1fZ/p2/F+qGrcU+2rQVr/DwcLdHEB/UyD41sq2g+3z/PVx/PWzdCrVqwdy52rjNK51D9qmR+CMdt7apj31qZJ8a2adGvnWgAzdxEyc4wXCGF+rXtthHm7bitXNnwb8bn1wZNbJPjWwrjD67dkG7dtq4vVw6h+xTI/FHOm5tUx/71Mg+NbJPjXw7c5Xt3/gbySQX6te22EebtuK1ZcsWt0cQH9TIPjWyrbD67Nlz/sbtNdcUypf2ezqH7FMj8Uc6bm1TH/vUyD41sk+NLq0TnWhJSzLJ5HVeL/Svb7GPNm3Fq0OHDm6PID6okX1qZFth9jmzcbttm2fjdt48bdzmhs4h+9RI/JGOW9vUxz41sk+N7FOjSztzle0YxrCPfYX+9S320aatiIhIAdmzB9q21catiIiIiIjIxdzKrdzIjRznOG/whtvjmOJoaQFO9erVXZ9BS438famR7eVWn2uuwdm6FcdxPP+85hr3/11YXTqH7C810vLHpePW9lIf+0uN7C81sr/U6OJrKUsdB8d5i7fU56ylK23Fq3z58m6PID6okX1qZJtbfXTFbe7pHLJPjcQf6bi1TX3sUyP71Mg+NbqwLnShBS04znHe5E3X5rDYR5u24lWvXj23RxAf1Mg+NbLNzT7/e49bvTnZhekcsk+NxB/puLVNfexTI/vUyD41urChDAXgAz7gAAdcm8NiH23aioiIFJLdu3/duK1dWxu3IiIiIiJSdHWjG81pTgYZjGCE2+OYE4TnPgkiBAUF4Tg6HCxTI/vUyDYrfapV82zY1qoFW7d6NnL37HF7KhusNJKLUyPxRzpubVMf+9TIPjWyT43Ot4IVxBHHG7zBMzzj6iwW++hKW/Hq3r272yOID2pknxrZZqXPmStut2//9YrbqCi3p7LBSiO5ODUSf6Tj1jb1sU+N7FMj+9ToXLdzO3HEcYxjJq6ytdhHm7biVapUKbdHEB/UyD41ss1Sn927PW9Odmbjdt48bdyCrUZyYWok/kjHrW3qY58a2adG9qnRuc7cy/Y93uMwh90dBpt9tGkrXklJSW6PID6okX1qZJu1Ptq4PZ+1RnI+NRJ/pOPWNvWxT43sUyP71OhXPelJU5qSTjpv8Zbb4wB2+zj+sJ599lln0aJFTkZGhpOSknLB51SrVs2ZNm2ak5GR4ezfv9958803neDg4HOe06ZNGychIcE5ceKEs2XLFufuu+92/XuzssqVK+f6DFpq5O9LjWwvq32qVcPZtg3HcXC2bMGJinJ/JjXSUiOtQFo6bm0v9bG/1Mj+UiP7S408K4ggZxWrHAfHeYVXXJ/Hch+/udI2LCyMCRMm8OGHH17w94sVK8b06dMJCwujZcuW3H333QwcOJBhw4Z5nxMdHc306dOZO3cuTZo04e233+bjjz+mU6dOhfVtmHbrrbe6PYL4oEb2qZFtVvvoHre/stpIfqVG4o903NqmPvapkX1qZJ8aefyG3xBLLGmkMZKRbo/jZbFPiNsD5NbQoUMBuPvuuy/4+506daJBgwZ06NCBAwcOsHr1al544QXeeOMNhg4dSk5ODvfffz87duzgiSeeAGDjxo3cdNNNDBo0iB9//PGCrxsWFkZ4eLj3147jcOzYsfz95kREpMjbtcuzcTt3LsTEeP7Zrh0Y/Vs6IiIiIiIieRJEkPdetu/wDimkuDuQcX6zaetLfHw8a9eu5cCBA96PzZw5kzFjxtCwYUNWrVpFfHw8s2fPPufzZs6cydtvv33R1x08eLB3wxggLS2NyMhI+vTpQ2hoKJMmTaJ9+/aULVuWAwcOsGzZMrp16wZAQkICwcHBNGnSBICpU6fSqlUrKlSowJEjR1iwYAE9e/YEYPXq1eTk5NCsWTMApk+fTlxcHJUrVyY1NZVZs2bRp08fANatW0d6ejo33ngjAD/88AONGjUiKiqKjIwMvvvuO/r37w94NqYPHz5Mq1atAJg9ezYxMTHUqFGDrKwsJk+eTL9+/ShWrBgpKSlUrVqVNm3aADBv3jyqVatGrVq1OHnyJBMmTKB3796EhYWRmJjItm3baN++PQALFy6kUqVK1KlTB4CxY8fSo0cPSpYsye7du1m/fj2dO3cGYMmSJURGRtKgQQMAJkyYQOfOnSlTpgzJycmsXLmSLl26ALB8+XLCw8Np3LgxAJMnT6Zdu3aUK1eOgwcPsmTJEm6//XYAVq5cCUDTpk0B+Pbbb4mPj6dixYqkpKQwd+5cevXqBcCaNWvIysqiefPmAMyYMYOmTZtSpUoV0tLSmDlzJn379gVg/fr1pKamEh8f7z1mGjRoQLVq1Th+/DhTp05lwIABAGzevJkDBw5w0003ATBnzhxq1apFdHQ02dnZTJo0ib59+xISEsK2bdvYvXs3bdu2BWD+/PlERUVRu3ZtTp8+zfjx4+nVqxfh4eHs3LmTLVu2ULJkSQYMGMCiRYsoX7489erVA2DcuHF0796dUqVKkZSUxNq1a73/K9FPP/1EREQEDRs2BGDixIl07NiRyMhI9u3bR0JCAl27dgVgxYoVhIaGEhsbC8CUKVNo3bo1V111FYcOHWLRokX06NEDgFWrVnHq1Cni4uIAmDZtGi1atKBSpUocPXqUOXPm0Lt3bwDWrl1LZmYmLVq0AOD7778nNjaWqlWrkp6ezowZM+jXrx8AGzZsICUlhZYtWwIwa9Ys6tatS/Xq1cnMzGTKlCn079+foKAgtmzZQnJyMq1btwZg7ty5REdHU7NmTXJycpg4caL3XN2xYweJiYm0a9cOgAULFlClShViYmJwHIdx48bRs2dPSpQowa5du9i0aRMdO3YEYPHixZQrV4769esDMH78eLp06UJERAR79+5l9erV3HbbbQDs2rWL6667jkaNGgEE1M+IrVu3kpSU5Nc/I5KSkrznq9WfEW+91YWhQ+cTE5PBggXBjBrVjSNHSubqZ0SHDh0A/PpnRMmSJSlbtmzA/oxYtmwZJUqU8OufEenp6QwYMKDAf0Z06dKFGTNmIJIffvrpJ7dHkEtQH/vUyD41sk+NoDe9aUQjUkllFKPcHuccVvu4fo+GvKy77777gve0/dvf/ub88MMP53ysRIkSjuM4zq233uoAzqZNm5xnnnnmnOfcdtttjuM4TvHixS/49cLCwpyIiAjvKl26tOv/DgpqNW7c2PUZtNTI35ca2V7+0qd6dZzt2z33uN28GadqVfdnUiMtNdLy56Xj1vZSH/tLjewvNbK/inqjIIKctax1HBxnKENdn8cf+rh6T9vhw4d7/ov0Eqtu3bpujkh2djbp6eneFci3RjhzlZXYpUb2qZFt/tJn1y5o2xZ27PDcKmHePKha1e2pCoe/NCrK1Ej8kY5b29THPjWyT43sK+qN+tCH67iOoxxlNKPdHuc8Fvu4enuEkSNH8vnnn1/yOdu3b8/Va+3bt8/7V6/PuPrqq72/d+afZz529nNSU1M5ceJELqcWEREpeGc2bufN+3Xjtm1b2LvX3blERERERETyohjFGMIQAEYzmlRSXZ7If7h+uW9e1sVuj3Drrbc6J0+edCpWrOj92H333eccPXrUCQsLcwDn9ddfd9asWXPO53399dfO999/7/r3ZWGFhoa6PoOWGvn7UiPbyx/71KhRtG6V4I+NitpSIy1/XDpubS/1sb/UyP5SI/urKDfqRz/HwXGOcMQpQxnX5/GXPq7eHiEvqlWrRmxsLNWrVyc4OJjY2FhiY2MpVaoUAD/++CPr16/nH//4B40bN6ZTp0688sorfPDBB2RnZwMwZswYrr32Wt544w3q1q3LAw88wB133MHo0aPd/NbMOPOGKmKXGtmnRrb5Y5+dO6Fdu6JzqwR/bFTUqJH4Ix23tqmPfWpknxrZV1QbnX2V7ShGkUaayxNdmMU+frNpO2zYMFatWsWwYcOIiIhg1apVrFq1yvsuyadPn6Zbt26cOnWKJUuW8NVXX/Hll1/y4osvel8jMTGRrl270rFjR1avXs3jjz/Ovffey48//ujWt2VKZGSk2yOID2pknxrZ5q99/nfjdu7cwN249ddGRYkaiT/ScWub+tinRvapkX1FtVE/+lGf+hzhCO/wjtvjXJTFPq7e0zYv/u///o//+7//u+Rzdu3aRdeuXS/5nPnz53P99dfn52gB48y9f8UuNbJPjWzz5z5nNm7nzYM6dTwbt+3aBd49bv25UVGhRuKPdNzapj72qZF9amRfUWxUjGK8iOdiypGMJJ10lye6OIt9gvDcJ0GEMmXKkJZm8zJ18VAj+9TItkDoU6OGZ+M2Oho2bw68jdtAaBTo1Ej8kY5b29THPjWyT43sK4qNfstv+YqvOMxhalLT9KatxT5+c3sEKXi+rlIW96mRfWpkWyD02bkT2raFxETPFbfTpkF4uNtT5Z9AaBTo1Ej8kY5b29THPjWyT43sK2qNggn2XmX7Fm+Z3rAFm320aSsiIuJnzmzcHjwITZvCiBFuTyQiIiIiIvKrO7mTOtThEId4n/fdHscvadNWvFasWOH2COKDGtmnRrYFUp+dO+Huuz2PH3oIfvMbd+fJL4HUKFCpkfgjHbe2qY99amSfGtlXlBoFE8wLvADACEZwjGMuT+SbxT7atBWv0NBQt0cQH9TIPjWyLdD6fP89vPmm5/Gnn3ruc+vvAq1RIFIj8Uc6bm1TH/vUyD41sq8oNfodvyOGGA5ykA/4wO1xcsViH23aildsbKzbI4gPamSfGtkWiH2eew6WLIGyZWHcODD4Z408CcRGgUaNxB/puLVNfexTI/vUyL6i0iiEEO9Vtm/yJhlkuDxR7ljso01bERERP3byJPTvDykpcMMN8Nprbk8kIiIiIiJF1e/5PbWoxX7281f+6vY4fi0IcNweQmwoUaIEmZmZbo8hl6BG9qmRbYHcp0cPmDLF87hbN5g+3dVxLlsgNwoUaiT+SMetbepjnxrZp0b2FYVGIYSwiU1cy7U8zuOMYpTbI+WaxT660la8Wrdu7fYI4oMa2adGtgVyn6lT4Z13PI+/+AKiotyd53IFcqNAoUbij3Tc2qY+9qmRfWpkX1FodDd3cy3Xso99fMiHbo+TJxb7aNNWvK666iq3RxAf1Mg+NbIt0Ps89RQkJED58jB2LAQHuz1R3gV6o0CgRuKPdNzapj72qZF9amRfoDcKJZTneR6AN3iDTGxdteqLxT7atBWvQ4cOuT2C+KBG9qmRbYHeJzsb+vWDtDS4+WYYOtTtifIu0BsFAjUSf6Tj1jb1sU+N7FMj+wK90UAGEk00ySQzhjFuj5NnFvvonrbiVbJkSY4fP+72GHIJamSfGtlWVPr06wfjxsHp09C5M8ye7fZEuVdUGvkzNRJ/pOPWNvWxT43sUyP7ArlRKKFsYQs1qMEjPMK7vOv2SHlmsY+utBWvHj16uD2C+KBG9qmRbUWlz/jx8Le/QbFi8NVXcPXVbk+Ue0WlkT9TI/FHOm5tUx/71Mg+NbIvkBvdwz3UoAZ72cvf+bvb41wWi320aSsiIhKAHn0U1q71bNh+/bVnA1dERERERCQ/hRHGczwHwHCGc4ITLk8UOPSfcOK1atUqt0cQH9TIPjWyrSj1OXEC7rgDMjKgfXt49lm3J8qdotTIX6mR+CMdt7apj31qZJ8a2Reojf7AH6hGNfawh4/4yO1xLpvFPtq0Fa9Tp065PYL4oEb2qZFtRa3Pxo3wwAOex0OHQuvWro6TK0WtkT9SI/FHOm5tUx/71Mg+NbIvEBuFE86zeK4OGc5wsshyeaLLZ7GPNm3FKy4uzu0RxAc1sk+NbCuKff7xD/j8cwgOhn/+EypUcHuiSyuKjfyNGok/0nFrm/rYp0b2qZF9gdjoXu7lGq5hN7v5mI/dHueKWOyjTVsREZEA9+CDsGEDREXBF19AUJDbE4mIiIiIiD8LJ5zBDAbgNV4jm2yXJwo8QYDj9hBiQ0REBOnp6W6PIZegRvapkW1FuU+jRrB0KZQoAU8+CW+95fZEF1aUG/kLNRJ/pOPWNvWxT43sUyP7Aq3RQzzEu7zLLnYRQ4zfb9pa7KMrbcWrRYsWbo8gPqiRfWpkW1Hus3YtPPKI5/Frr8GNN7o7z8UU5Ub+Qo3EH+m4tU197FMj+9TIvkBqVJzi3qtsX+VVv9+wBZt9tGkrXpUqVXJ7BPFBjexTI9uKep+PPoJx4yA01PPPsmXdnuh8Rb2RP1Aj9z399NM4jsPo0aO9HwsPD+f999/n0KFDpKenM3HiRLU6i/5d2KY+9qmRfWpkXyA1+hN/ogpVSCSRz/jM7XHyhcU+2rQVr6NHj7o9gvigRvapkW3qA3/8I2zdCjVqwKefuj3N+dTIPjVyV7NmzfjTn/7E6tWrz/n46NGj6d69O3379qVNmzZUrVqVyZMnuzSlPTpubVMf+9TIPjWyL1AaFac4T/M04LnKNocclyfKH1b7OFpagBMWFub6DFpq5O9LjWwv9fGs66/HycrCcRycBx90fx418q+lRu6tUqVKOZs2bXLat2/vzJ071xk9erQDOGXKlHGysrKc3r17e59bt25dx3Ec54Ybbrhky4iICO8qXbq0699jQS0dt7aX+thfamR/qZH9FSiN/syfHQfHSSTRCSXU9XkCuU8IIv/Vu3dvxo4d6/YYcglqZJ8a2aY+Hj//DE88Ae++63lDssWLPR+zQI3sUyP3fPDBB0yfPp05c+bw/PPPez8eFxdHWFgYs2fP9n5s06ZN7Ny5k/j4eJYuXXrB1xs8eDBDhw71/jotLY3IyEj69OlDaGgokyZNon379pQtW5YDBw6wbNkyunXrBkBCQgLBwcE0adIEgKlTp9KqVSsqVKjAkSNHWLBgAT179gRg9erV5OTk0KxZMwCmT59OXFwclStXJjU1lVmzZtGnTx8A1q1bR3p6Ojf+98bbP/zwA40aNSIqKoqMjAy+++47+vfvD8DGjRs5fPgwrVq1AmD27NnExMRQo0YNsrKymDx5Mv369aNYsWKULVuWqVOn0qZNGwDmzZtHtWrVqFWrFidPnmTChAn07t2bsLAwEhMT2bZtG+3btwdg4cKFVKpUiTp16gAwduxYevToQcmSJdm9ezfr16+nc+fOACxZsoTIyEgaNGgAwIQJE+jcuTNlypQhOTmZlStX0qVLFwCWL19OeHg4jRs3BmDy5Mm0a9eOcuXKcfDgQZYsWcLtt98OwMqVKwFo2rQpAN9++y3x8fFUrFiRlJQU5s6dS69evQBYs2YNWVlZNG/eHIAZM2bQtGlTqlSpQlpaGjNnzqRv374ArF+/ntTUVOLj4wGYOXMmDRo0oFq1ahw/fpypU6cyYMAAADZv3syBAwe46aabAJgzZw61atUiOjqa7OxsJk2aRN++fQkJCWHbtm3s3r2btm3bAjB//nyioqKoXbs2p0+fZvz48fTq1Yvw8HB27txJTEwM2dme+yEuWrSI8uXLU69ePQDGjRtH9+7dKVWqFElJSaxdu5Zbb70VgJ9++omIiAgaNmwIwMSJE+nYsSORkZHs27ePhIQEunbtCsCKFSsIDQ0lNjYWgClTptC6dWuuuuoqDh06xKJFi+jRowcAq1at4tSpU8TFxQEwbdo0WrRoQaVKlTh69Chz5syhd+/eAKxdu5bMzEzvvRC///57YmNjqVq1Kunp6cyYMYN+/foBsGHDBlJSUmjZsiUAs2bNom7dulSvXp3MzEymTJlC//79CQoKYsuWLSQnJ9O6dWsA5s6dS3R0NDVr1iQnJ4eJEyd6z9UdO3aQmJhIu3btAFiwYAFVqlQhJiYGx3EYN24cPXv2pESJEuzatYtNmzbRsWNHABYvXky5cuWoX78+AOPHj6dLly5ERESwd+9eVq9ezW233UZUVBTffPMNJUqUoFGjRgAB9TNi69atJCUl+fXPiJtvvpljx44F5M+ILVu20KFDB7//GREVFcXHH3/s1z8jJo+bzNDwoXACJsVMotTBUtx2220ALFu2zK9/RoSFhbFly5ZC+RnRpUsXZsyYQW64vnOsZWMNGDDA9Rm01MjflxrZXupz7vrmG8/Vtlu24EREuD+PGvnHUiN3Vr9+/Zw1a9Y44eHhDnDOlbYDBgxwTpw4cd7nLF261Hn99dcv+ppF6UpbHbe2l/rYX2pkf6mR/RUIje7hHsfBcZJIcsIJd32eQO+je9qK19q1a90eQXxQI/vUyDb1Odc998DOnVC7Nvz9725P46FG9qlR4bvmmmt45513+O1vf0tWVla+vW52djbp6enedeYKrUCk49Y29bFPjexTI/v8vVEwwQxmMABv8RZZ5N+fSSyw2EebtuKVmZnp9gjigxrZp0a2qc+5UlKgXz/IyYH+/eG++9yeSI38gRoVvri4OK6++mp+/vlncnJyyMnJoW3btjz88MPk5OSwf/9+wsPDiYyMPOfzrr76avbt2+fS1LbouLVNfexTI/vUyD5/b9SPftSmNoc4xN/4m9vj5DuLfbRpK15n7sMkdqmRfWpkm/qcb+lSePZZz+N33oH/3oLKNWpknxoVvjlz5nDdddfRpEkT71q+fDlff/01TZo0YcWKFWRnZ3vvnQZQp04datSowZIlS1yc3A4dt7apj31qZJ8a2efPjYII4lk8/9EwilEc57jLE+U/i330RmQiIiJF3MiR0K4ddOkC48dD8+aQkeH2VCJyxrFjx1i3bt05H8vIyODw4cPej3/yySeMGjWKI0eOkJaWxnvvvcfixYsv+iZkIiIiIrnVk540pCFHOcoHfOD2OEWK6zfW1bKxypYt6/oMWmrk70uNbC/1ufiqUAFnzx7PG5N99pkaaamR9XX2G5EBTnh4uPP+++87hw8fdo4dO+ZMmjTJufrqq12f08rScWt7qY/9pUb2lxrZX/7cKIEEx8FxXuIl12cpSn10ewTxio2NdXsE8UGN7FMj29Tn4g4dgjvvhFOnYOBAuOsud+ZQI/vUyIZ27doxaNAg76+zsrJ48MEHKV++PKVLl6Z3797s37/fxQlt0XFrm/rYp0b2qZF9/troNm7jeq7nGMd4h3fcHqfAWOyjTVvxqlq1qtsjiA9qZJ8a2aY+l7ZgAQwd6nn8179C3bqFP4Ma2adG4o903NqmPvapkX1qZJ+/Nnqe5wH4kA85whGXpyk4Fvto01a80tPT3R5BfFAj+9TINvXx7bXXYM4cKFUK/vUvKF68cL++GtmnRuKPdNzapj72qZF9amSfPzZqS1ta0pITnGAkI90ep0BZ7BOE5z4JIhQrVozTp0+7PYZcghrZp0a2qU/uVK4Mq1bB1VfDmDHwwAOF97XVyD41En+k49Y29bFPjexTI/v8sdFsZtOe9rzHezzMw26PU6As9tGVtuLVr18/t0cQH9TIPjWyTX1yZ98++N3v4PRpuP9+uOOOwvvaamSfGok/0nFrm/rYp0b2qZF9/tYonnja054cchjBCLfHKXAW+2jTVkRERM4zezYMH+55/NFHcO217s4jIiIiIiKF5zmeA+ALvmA3u12epmjSpq14bdiwwe0RxAc1sk+NbFOfvBkyBP7zHyhTBsaOhWKF8KcGNbJPjcQf6bi1TX3sUyP71Mg+f2rUhCZ0pSunOMXrvO72OIXCYh9t2opXSkqK2yOID2pknxrZpj55c+oUDBgAR49CixZw330F/zXVyD41En+k49Y29bFPjexTI/v8qdGZq2zHMpZtbHN5msJhsY82bcWrZcuWbo8gPqiRfWpkm/rkXVISvPCC5/Grr8JVVxXs11Mj+9RI/JGOW9vUxz41sk+N7POXRg1oQB/6APAar7k8TeGx2EebtiIiInJJH34Ia9ZA+fKejVsREREREQlMgxkMwCQmsQF7twwoSoIAx+0hxIYKFSpw6NAht8eQS1Aj+9TINvW5fDffDAsWwOnTnlslJCQUzNdRI/vUSPyRjlvb1Mc+NbJPjezzh0bXci2b2UwwwVzP9axkpdsjFRqLfXSlrXjVrVvX7RHEBzWyT41sU5/L95//wFdfed6M7P33ISioYL6OGtmnRuKPdNzapj72qZF9amSfPzR6hmcIJpjpTC9SG7Zgs482bcWrevXqbo8gPqiRfWpkm/pcmaeegvR0uPFGGDiwYL6GGtmnRuKPdNzapj72qZF9amSf9UbVqMbd3A3AK7zi8jSFz2IfbdqKV2ZmptsjiA9qZJ8a2aY+VyY5GYYO9Tx+/XUoWzb/v4Ya2adG4o903NqmPvapkX1qZJ/1Rk/yJGGE8W/+zU/85PY4hc5iH93TVkRERHItJARWr4YGDeDdd+GRR9yeSERERERErsTVXM0OdlCCEtzCLcxlrtsjCbrSVs7Sv39/t0cQH9TIPjWyTX2u3MmT8NBDnsd/+Qs0bpy/r69G9qmR+CMdt7apj31qZJ8a2We50WM8RglKsJjFRXbD1mIfbdqKV1BBvauM5Bs1sk+NbFOf/PHvf8O//gXBwZ43JctPamSfGok/0nFrm/rYp0b2qZF9VhtdxVX8mT8DRfNetmdY7KNNW/HasmWL2yOID2pknxrZpj7554knICMDbr4Z7rwz/15XjexTI/FHOm5tUx/71Mg+NbLPaqNHeITSlOZnfuZ7vnd7HNdY7KNNW/FKTk52ewTxQY3sUyPb1Cf/7N4Nr77qefzWWxARkT+vq0b2qZH4Ix23tqmPfWpknxrZZ7FRGcrwMA8D8CqvujyNuyz20aateLVu3drtEcQHNbJPjWxTn/w1ciRs2QJVqsCLL+bPa6qRfWok/kjHrW3qY58a2adG9lls9Gf+TFnKsp71fMM3bo/jKot9tGkrIiIilyU7Gx72/A/zPPII1K/v7jwiIiIiIpI7JSnJYzwGeK6ydXBcnkguxNHSApzKlSu7PoOWGvn7UiPbS30KZk2ZguM4OLNnq1FRWGqk5Y9Lx63tpT72lxrZX2pkf1lr9CiPOg6Os4UtTjDBrs/j9rLWB3B0pa14RUdHuz2C+KBG9qmRbepTMB59FE6cgPbtoU+fK3stNbJPjcQf6bi1TX3sUyP71Mg+S43CCedJngTgdV7nFKdcnsh9lvqcoU1b8apZs6bbI4gPamSfGtmmPgUjMRFef93zeNQoKFny8l9LjexTI/FHOm5tUx/71Mg+NbLPUqOBDKQqVdnNbr7kS7fHMcFSnzO0aSteOTk5bo8gPqiRfWpkm/oUnDfegB07oFo1eO65y38dNbJPjcQf6bi1TX3sUyP71Mg+K41CCOEZngHgDd4gBxtzuc1Kn7MF4blPgoiIiMgVuf12mDrV8wZl110HW7a4PZGIiIiIiJztbu7mcz5nH/uoSU1OcMLtkeQidKWtePW50hsRSoFTI/vUyDb1KVjffgvffw9hYfDOO5f3GmpknxqJP9Jxa5v62KdG9qmRfRYaFaMYz/IsACMZqQ3bs1jo87+0aSteoaGhbo8gPqiRfWpkm/oUvEce8Vxpe9ttnitv80qN7FMj8Uc6bm1TH/vUyD41ss9Co770pQ51OMxhxjDG7XFMsdDnf2nTVrx27Njh9gjigxrZp0a2qU/B27IF3nrL8/jtt6F48bx9vhrZp0bij3Tc2qY+9qmRfWpkn9uNggjiOTxvPvE2b3OMY67OY43bfS5Em7bilZiY6PYI4oMa2adGtqlP4Xj1Vdi9G2rWhKefztvnqpF9aiT+SMetbepjnxrZp0b2ud2oO91pRCNSSeU93nN1Fovc7nMh2rQVr3bt2rk9gvigRvapkW3qUziOH4fHH/c8fuYZz+ZtbqmRfWok/kjHrW3qY58a2adG9rnd6HmeB+B93ieVVFdnscjtPheiTVsRERHJdxMmwJw5ntsjjBrl9jQiIiIiIkVXJzrRnOZkkMHbvO32OJIHjpYW4ERFRbk+g5Ya+ftSI9tLfQp31a+Pk52N4zg4t96qRoGy1EjLH5eOW9tLfewvNbK/1Mj+crPRfOY7Do4zkpGu/3uwuiyeQ7rSVryqVKni9gjigxrZp0a2qU/h2rAB3n3X8/jddyEszPfnqJF9aiT+SMetbepjnxrZp0b2udXoZm6mNa3JIou3eMuVGfyBxXNIm7biFRMT4/YI4oMa2adGtqlP4XvpJUhOhpgYeOwx389XI/vUSPyRjlvb1Mc+NbJPjexzq9GZe9l+wickk+zKDP7A4jmkTVvxchzH7RHEBzWyT41sU5/Cl54OTz7pefz881Ct2qWfr0b2qZH4Ix23tqmPfWpknxrZ50aj5jSnE504yUne5M1C//r+xOI5FITnPgkiIiIiBWbBArj5ZvjXv6BfP7enEREREREJfFOYQg968BmfcQ/3uD2O5JHfXGn77LPPsmjRIjIyMkhJSbngcxzHOW/1+5//MmzTpg0JCQmcOHGCLVu2cPfddxfG+H6hZ8+ebo8gPqiRfWpkm/q458EH4dQpuOMOuOWWiz9PjexTI/FHOm5tUx/71Mg+NbKvsBs1pjE96MFpTjOc4YX6tf2RxXPIbzZtw8LCmDBhAh9++OElnzdw4EAqV67sXVOmTPH+XnR0NNOnT2fu3Lk0adKEt99+m48//phOnToV8PT+oUSJEm6PID6okX1qZJv6uGfNGvjrXz2P33sPQkMv/Dw1sk+NxB/puLVNfexTI/vUyL7CbvQszwIwnvFsYUuhfm1/ZPEcCnF7gNwaOnQogM8rY48ePcr+/fsv+Hv3338/O3bs4IknngBg48aN3HTTTQwaNIgff/zxgp8TFhZGeHi499eO43Ds2LHL+A7s27Vrl9sjiA9qZJ8a2aY+7nrhBc+tERo0gIceglGjzn+OGtmnRuKPdNzapj72qZF9amRfYTaqS1360heA13it0L6uP7N4DvnNpm1uffDBB3z88cds376dMWPG8Nlnn3l/Lz4+ntmzZ5/z/JkzZ/L2229f9PUGDx7s3TAGSEtLIzIykj59+hAaGsqkSZNo3749ZcuW5cCBAyxbtoxu3boBkJCQQHBwME2aNAFg6tSptGrVigoVKnDkyBEWLFjgvfx69erV5OTk0KxZMwCmT59OXFwclStXJjU1lVmzZtGnTx8A1q1bR3p6OjfeeCMAP/zwA40aNSIqKoqMjAy+++47+vfvD3g2pg8fPkyrVq0AmD17NjExMdSoUYOsrCwmT55Mv379KFasGPv27aNq1aq0adMGgHnz5lGtWjVq1arFyZMnmTBhAr179yYsLIzExES2bdtG+/btAVi4cCGVKlWiTp06AIwdO5YePXpQsmRJdu/ezfr16+ncuTMAS5YsITIykgYNGgAwYcIEOnfuTJkyZUhOTmblypV06dIFgOXLlxMeHk7jxo0BmDx5Mu3ataNcuXIcPHiQJUuWcPvttwOwcuVKAJo2bQrAt99+S3x8PBUrViQlJYW5c+fSq1cvANasWUNWVhbNmzcHYMaMGTRt2pQqVaqQlpbGzJkz6dvX8wNu/fr1pKamEh8f7z1mGjRoQLVq1Th+/DhTp05lwIABAGzevJkDBw5w0003ATBnzhxq1apFdHQ02dnZTJo0ib59+xISEsK2bdvYvXs3bdu2BWD+/PlERUVRu3ZtTp8+zfjx4+nVqxfh4eHs3LmTLVu2ULt2bapXr86iRYsoX7489erVA2DcuHF0796dUqVKkZSUxNq1a7n11lsB+Omnn4iIiKBhw4YATJw4kY4dOxIZGcm+fftISEiga9euAKxYsYLQ0FBiY2MBmDJlCq1bt+aqq67i0KFDLFq0iB49egCwatUqTp06RVxcHADTpk2jRYsWVKpUiaNHjzJnzhx69+4NwNq1a8nMzKRFixYAfP/998TGxlK1alXS09OZMWOG91YmGzZsICUlhZYtWwIwa9Ys6tatS/Xq1cnMzGTKlCn079+foKAgtmzZQnJyMq1btwZg7ty5REdHU7NmTXJycpg4caL3XN2xYweJiYm0a9cOgAULFlClShViYmJwHIdx48bRs2dPSpQowa5du9i0aRMdO3YEYPHixZQrV4769esDMH78eLp06UJERAR79+5l9erV3Hbbbd5z7rrrrqNRo0YAAfUzYuvWrSQlJfn1z4js7Gzv+RqIPyM6dOgAYPZnRLNmLfjmm3T+9KdlDB0KmZk9OXq0xDk/I8LCwli3bl3A/oxYtmwZJUqU8OufESVLlmTAgAEF/jOiS5cuzJgxA5H8sGnTJrdHkEtQH/vUyD41sq8wGw1mMMUoxhSm8Au/FNrX9WdWzyHHn9bdd9/tpKSkXPD3nn/+eadly5ZOkyZNnKeeesrJzMx0HnroIe/vb9q0yXnmmWfO+ZzbbrvNcRzHKV68+AVfMywszImIiPCu0qVLu/7voKDWgAEDXJ9BS438famR7aU+7q+gIJwlSzy3nv/HP9TIH5caafnj0nFre6mP/aVG9pca2V+F1SiaaCeHHMfBcZrRzPXv21+WxXPI1XvaDh8+/IJvHnb2qlu3bq5f75VXXmHx4sWsWrWKN998kzfffJMnn3zyimbMzs4mPT3duwL11ggiIiKFwXE8b0p2+jT87ndw881uTyQiIiIiEjie5mlCCOEHfmAFK9weR66Aq7dHGDlyJJ9//vkln7N9+/bLfv2lS5fy4osvEhYWRnZ2Nvv27ePqq68+5zlXX301qampnDhx4rK/TqBYvHix2yOID2pknxrZpj42JCTARx/Bn/7keVOyuDg4dcrze2pknxqJP9Jxa5v62KdG9qmRfYXRKIoo/o//A+AVXinwrxdILJ5Drl5pe+jQITZt2nTJlZOTc9mv36RJE44cOUJ2djbguU/imXunndGxY0eWLFlyRd9HoChXrpzbI4gPamSfGtmmPnY89xwcPgyxsfDAA79+XI3sUyPxRzpubVMf+9TIPjWyrzAaPcEThBPOfOaziEUF/vUCicVzyNVN27yoVq0asbGxVK9eneDgYGJjY4mNjaVUqVIAdOvWjT/84Q80bNiQWrVqcf/99/Pss8/y3nvveV9jzJgxXHvttbzxxhvUrVuXBx54gDvuuIPRo0e79W2ZcuYNVMQuNbJPjWxTHzsOH/Zs3AK8/DJUrOh5rEb2qZH4Ix23tqmPfWpknxrZV9CNKlGJP/JHQFfZXg6L55Crt0fIi2HDhjFw4EDvr1etWgVA27ZtmT9/Pjk5OfzlL39h9OjRBAUFsXXrVh577DE++ugj7+ckJibStWtXRo8ezSOPPMKePXu49957+fHHHwv5uxEREZGPPoI//hGuvx6GD4d773V7IhERERER/zSYwZSkJD/xE7OZ7fY4kg+C8LwjmQjFihXj9OnTbo8hl6BG9qmRbepjz403wpm7FN14IyxfrkbW6TwSf6Tj1jb1sU+N7FMj+wqy0TVcw1a2Ek44HejAHOYUyNcJZBbPIb+5PYIUvC5durg9gvigRvapkW3qY89PP8Fnn3kev/8+dOt2m7sDiU86j8Qf6bi1TX3sUyP71Mi+gmz0HM8RTjjzmKcN28tk8RzSpq14RUREuD2C+KBG9qmRbepj0zPPQGoqNGsG3bsfcHsc8UHnkfgjHbe2qY99amSfGtlXUI2iieYP/AGAF3ihQL5GUWDxHNKmrXjt3bvX7RHEBzWyT41sUx+bDhyAF1/0PO7bdyVly7o6jvig80j8kY5b29THPjWyT43sK6hGL/IioYTyAz+wkIUF8jWKAovnkO5pK15ly5bl6NGjbo8hl6BG9qmRbepjV3AwrF4NDRvCK6/AC7pIwCydR+KPdNzapj72qZF9amRfQTSqQx3Ws55ggmlBC5azPF9fvyixeA5d1pW2UVFRPPDAAwwfPpyRI0ees8R/3Xab7iNonRrZp0a2qY9dp07B8897Hj/6KFSo4Oo4cgk6j9xx//33s3r1alJTU0lNTWXx4sXceuut3t8PDw/n/fff59ChQ6SnpzNx4kQqVark4sS26Li1TX3sUyP71Mi+gmg0lKEEE8xUpmrD9gpZPIdC8voJt9xyC99++y3bt2+nXr16/PLLL0RHRxMUFMTPP/9cEDOKiIhIETBlCmzffhXXXnuEZ56BJ55weyIRO/bs2cMzzzzDli1bCAoK4u6772bq1Kk0bdqU9evXM3r0aLp27Urfvn1JTU3l/fffZ/Lkydx0001ujy4iIiIFoBGNGMAAwHOLBAlMTl7W0qVLnaFDhzqAk5aW5tSsWdMpVaqUM2XKFOf+++/P02tp2Vq1atVyfQYtNfL3pUa2l/rYX//3f1Ucx8HJzMSpWtX9ebTOXzqP7KzDhw8799xzj1OmTBknKyvL6d27t/f36tat6ziO49xwww2uz2lh6bi1vdTH/lIj+0uN7K/8bjSZyY6D44xjnOvfWyAsi+dQnm+PUL9+fb788ksATp48SYkSJcjIyODFF1/k6aefzuvLiSElSpRwewTxQY3sUyPb1Me+5cuv4j//geLF4bnn3J5GLkTnkfuKFStGv379KFWqFEuWLCEuLo6wsDBmz57tfc6mTZvYuXMn8fHxF32dsLAwIiIivKt06dKFMb4rdNzapj72qZF9amRffjaKI47f8BtOcYqhDM231y3KLJ5Deb49QkZGBmFhYQAkJydTq1Yt1q9fD0AF3YDOrzVq1IhffvnF7THkEtTIPjWyTX3sa9SoMc8/v4758+G++2DECEhMdHsqOZvOo8sTHh7u/TP0Genp6Xl6jeuuu44lS5ZQvHhxjh07xm9+8xs2bNhAkyZNyMrKIjU19Zzn79+/n8qVK1/09QYPHszQoUO9v05LSyMyMpI+ffoQGhrKpEmTaN++PWXLluXAgQMsW7aMbt26AZCQkEBwcDBNmjQBYOrUqbRq1YoKFSpw5MgRFixYQM+ePQFYvXo1OTk5NGvWDIDp06cTFxdH5cqVSU1NZdasWfTp0weAdevWkZ6ezo033gjADz/8QKNGjYiKiiIjI4PvvvuO/v37A7Bx40YOHz5Mq1atAJg9ezYxMTHUqFGDrKwsJk+eTL9+/ShWrBhly5blyJEjtGnTBoB58+ZRrVo1atWqxcmTJ5kwYQK9e/cmLCyMxMREtm3bRvv27QFYuHAhlSpVok6dOgCMHTuWHj16ULJkSXbv3s369evp3LkzAEuWLCEyMpIGDRoAMGHCBDp37kyZMmVITk5m5cqVdOnSBYDly5cTHh5O48aNAZg8eTLt2rWjXLlyHDx4kCVLlnD77bcDsHLlSgCaNm0KwLfffkt8fDwVK1YkJSWFuXPn0qtXLwDWrFlDVlYWzZs3B2DGjBk0bdqUKlWqkJaWxsyZM+nbty8A69evJzU11bu5P3PmTBo0aEC1atU4fvw4U6dOZcAAz1+93bx5MwcOHPDecmPOnDnUqlWL6OhosrOzmTRpEn379iUkJIRt27axe/du2rZtC8D8+fOJioqidu3anD59mvHjx9OrVy/Cw8PZuXMnMTExNGrUCIBFixZRvnx56tWrB8C4cePo3r07pUqVIikpibVr13rv5/zTTz8RERFBw4YNAZg4cSIdO3YkMjKSffv2kZCQQNeuXQFYsWIFoaGhxMbGAjBlyhRat27NVVddxaFDh1i0aBE9evQAYNWqVZw6dYq4uDgApk2bRosWLahUqRJHjx5lzpw59O7dG4C1a9eSmZlJixYtAPj++++JjY2latWqpKenM2PGDPr16wfAhg0bSElJoWXLlgDMmjWLunXrUr16dTIzM5kyZQr9+/cnKCiILVu2kJycTOvWrQGYO3cu0dHR1KxZk5ycHCZOnOg9V3fs2EFiYiLt2rUDYMGCBVSpUoWYmBgcx2HcuHH07NmTEiVKsGvXLjZt2kTHjh0BWLx4MeXKlaN+/foAjB8/ni5duhAREcHevXtZvXo1t912G1FRUWRmZlKiRAlvq0D6GbF161aSkpL8+mdEfHy8t02g/YzYsmULHTp08PufEVFRUezZsydffkY8OP1BSINF0YtoGt+Uo3OPuvozAmDZsmV+/TMiLCyMMmXKFMrPiC5dujBjxgxyI0+X5n7zzTfOvffe6wDOiBEjnM2bNzvPPvuss2LFCmfWrFmuXzqsdflrwIABrs+gpUb+vtTI9lIf++tMo5kzcRwH59NP3Z9J68KNtHyvEiVKOO+9956zf/9+5+TJk+etvL5eaGioU6tWLef66693XnvtNefAgQNO/fr1nQEDBjgnTpw47/lLly51Xn/99Yu+XlhYmBMREeFdpUuXdv3fWUEtHbe2l/rYX2pkf6mR/ZVfjVrRynFwnGyynWu51vXvK1CW0XMob59Qs2ZNp1GjRg7glCxZ0vnwww+d1atXOxMnTnSqV6/u9jejdQUrLCzM9Rm01MjflxrZXupjf51p1Ly5Z9P25EmcunXdn0vr/EZavtf777/vrFu3zunVq5eTkZHhDBw40HnuueecXbt2OXfeeecVv/6sWbOcMWPGOO3atXMcx3EiIyPP+f3ExETn0Ucfdf3fg4Wl49b2Uh/7S43sLzWyv/Kr0VzmOg6OM4Yxrn9PgbSMnkOuD6BlZN12222uz6ClRv6+1Mj2Uh/76+xGU6Z4Nm7HjXN/Lq0LN9K69Nq5c6fTpk0bB3BSU1O9b3Dxu9/9zpk+ffoVv/6cOXOczz77zPtGZL169fL+Xp06dfRGZGctHbe2l/rYX2pkf6mR/ZUfjW7hFsfBcU5wwrmGa1z/ngJpWTyH8vxGZNu2beOqq6467+ORkZFs27Ytry8nhpQtW9btEcQHNbJPjWxTH/vObvTCC55/9usH/72dmxig8yj3rrrqKrZv3w547hd75s/QCxcu9N6nMrdee+01br75ZmrUqMF1113Ha6+9Rtu2bfn6669JS0vjk08+YdSoUbRt25brr7+ezz77jMWLF7N06dJ8/778kY5b29THPjWyT43sy49Gr/AKAGMYwx72XPHrya8snkN53rSNjo4mODj4vI+Hh4cTFRWVL0OJOw4cOOD2COKDGtmnRrapj31nN1q7FsaN8zx++WWXBpLz6DzKve3bt1OzZk3A82YXd9xxBwDdu3fn6NGjeXqtSpUq8eWXX7Jp0ybmzJlD8+bN6dy5M7NnzwZg0KBBTJs2jUmTJrFgwQL27dvnfdMZ0XFrnfrYp0b2qZF9V9qoC12IJ57jHGc4w/NpKjnD4jkUhOeSW5+6d+8OeN5B7+677z7n3WmDg4Np3749HTt29L6Dn/ifiIiIPL+LsRQuNbJPjWxTH/v+t1GdOrB+PQQHw403gi4adJ/Oo9x79NFHOXXqFO+99x7t27fnu+++IygoiNDQUB577DHeffddt0csMnTc2qY+9qmRfWpk35U0CiKIFazgeq7nTd7kaZ7O5+nE6jmUq/sonDp1yjl16pRz8uRJ7+Mz68SJE87GjRudrl27un6/B63LX0bfKU9LjfxqqZHtpT7214Uaffqp5962P/7o/nxaOo+uZFWvXt35zW9+431TX63CWzpubS/1sb/UyP5SI/vrShr1opfj4DhppDnlKe/69xKIy+I5FEIunbklwvbt22nevDmHDx/O7aeKiIiIXLaXXoLf/hY6doQ2bWD+fLcnErk8u3btYteuXW6PISIiIn6kGMUYxjAARjOaw2g/rqjI9abtGddee633cXh4OFlZWfk6kLgnISHB7RHEBzWyT41sUx/7LtRo50746CP4y1/glVfg5ptdGEy8dB7l3kMPPXTBjzuOw4kTJ9i6dSsLFizg9OnThTxZ0aPj1jb1sU+N7FMj+y63UT/60ZCGpJDCKEbl81RyhsVzKM+btkFBQTz33HPcf//9XH311dSpU4cdO3YwbNgwEhMT+fTTTwtiTikEF3qDObFFjexTI9vUx76LNXr1VbjnHrjpJrj1Vvjhh0IeTLx0HuXeoEGDqFixIiVLliQlJQWAcuXKcfz4cY4dO0alSpXYvn077dq1Y88evQN0QdJxa5v62KdG9qmRfZfTKJhgXuIlAEYwglRSfXyGXC6L51CxvH7C888/z8CBA3nqqafIzs72fvyXX37h3nvvzdfhpHA1adLE7RHEBzWyT41sUx/7LtYoORk++MDz+JVXCm8eOZ/Oo9x79tlnWb58OTExMVSoUIEKFSpQp04dli5dyiOPPEL16tXZt28fo0ePdnvUgKfj1jb1sU+N7FMj+y6n0V3cRQwxHOQg76I3MC1IFs+hPG/a3nXXXfzxj3/kn//8J6dOnfJ+fPXq1dSrVy9fhxMRERE54403ID0d4uLgN79xexoR31555RUGDRrE9u3bvR/btm0bTzzxBMOHDycpKYmnnnqKVq1auTiliIiIWBRGGEMYAsBwhpNBhssTiRvy9M5lx48fd6pXr+4ATlpamlOzZk0HcOrXr++kp6e7/s5qWpe/SpYs6foMWmrk70uNbC/1sb98NRo2DMdxcH75BadYMffnLYpL51HuV0ZGhhMXF3fex5s1a+ZkZGQ4gFOjRg39GboQlo5b20t97C81sr/UyP7Ka6MHeMBxcJwkkpziFHd9/kBfFs+hPF9pu379em6+wDuA9OnTh5UrV+b15cQQXeVhnxrZp0a2qY99vhqNHAkpKdCwIfTvX0hDyTl0HuXe3Llz+dvf/nbOX7dr0qQJH374If/+978BaNSoETt27HBpwqJDx61t6mOfGtmnRvblpVFxivM8zwPwCq9wghMFNZb8l8VzKM9vRDZs2DC++OILoqKiKFasGL169aJu3brcdddddOvWrSBmlEJSoUIFt0cQH9TIPjWyTX3s89UoNRVGjIDXXoOXXoJ//QtOniyk4QTQeZQXf/jDH/jHP/5BQkICOTk5AISEhDBnzhz+8Ic/AHDs2DEef/xxN8csEnTc2qY+9qmRfWpkX14aPcADVKUqiSTyCZ8U4FRyhsVzKM+btt9++y3du3fnxRdfJCMjg2HDhvHzzz/TvXt3Zs+eXRAzSiE5cuSI2yOID2pknxrZpj725abRu+/CI49A7dowcCB8/HHBzyW/0nmUe/v376dTp07UrVuXOnXqALBp0yY2b97sfc68efNcmq5o0XFrm/rYp0b2qZF9uW1UilIMZjAAwxhGNtkFOZb8l8VzKAjPfRJEKFGiBJmZmW6PIZegRvapkW3qY19uGz3yCLz9NuzeDTExkJVV8LOJh86jvAsNDaVmzZps27btnDfylcKj49Y29bFPjexTI/ty22gwg3mN19jCFupTn1Pozw6FweI5lOd72p4RGhpKVFQU1apVO2eJ/+rZs6fbI4gPamSfGtmmPvblttGYMbBnD1SrBn/8Y8HOJOfSeZR7JUqU4OOPP+b48eOsW7eO6tWrA/Duu+/y9NNPuzxd0aLj1jb1sU+N7FMj+3LTKJJInuRJAIYyVBu2hcjiOZTnTdvatWuzYMECMjMz2blzJzt27GDHjh0kJibqTRRERESkUGRlwcsvex4/+yyULOnuPCIXMnz4cGJjY2nbti0nTvz6BiKzZ8+mX79+Lk4mIiIiFj3GY5SjHOtYxzjGuT2OuCzP97T9/PPPOXnyJN26dSM5ORnH0d0VAsXq1avdHkF8UCP71Mg29bEvL40++wyefhquvRYefBDefLMABxMvnUe517NnT/r168fSpUvP+TPzunXrqFWrlouTFT06bm1TH/vUyD41ss9Xo/KUZxCDAHiRFznN6cIYS/7L4jmU503bJk2aEBcXx6ZNmwpiHnHRmXc1FrvUyD41sk197MtLo5wcGDoUvvzSs3k7ZgykpRXcbOKh8yj3KlasyIEDB877eKlSpXThQyHTcWub+tinRvapkX2+Gj3FU0QQwc/8zDd8U0hTyRkWz6E83x5h/fr1VKhQoSBmEZc1a9bM7RHEBzWyT41sUx/78tro669hwwa46ioYNKiAhpJz6DzKvRUrVtC1a1fvr89s1N57770sWbLErbGKJB23tqmPfWpknxrZd6lGlanMgzwIwAu8gIP+x93CZvEcytWVthEREd7HTz/9NG+++SbPPvssa9euPW8nOj09PX8nFBEREbmI06fhxRdhwgR47DF47z04csTtqaSoa9iwIevWrWPw4MH88MMPNGjQgNDQUB555BEaNGhAy5YtadOmjdtjioiIiBGDGUxJSrKEJcxghtvjiBFB4Hv7/tSpU+f8Fa6goKDz/krXmY+FhOT5jgtiRJkyZUjT3ys1TY3sUyPb1Me+y2kUFAQJCdC0KbzxBjzzTAENJ4DOo9w4deoUy5cv5+OPP2bp0qU89NBDxMbGUrp0aX7++WfeeOMNfvnlF7fHLFJ03NqmPvapkX1qZN/FGlWjGlvYQjjhtKc9/+bfLkwnFs+hXN0eoV27dtxyyy3e9b+/Pvtj4r/i4uLcHkF8UCP71Mg29bHvcho5DrzwgufxQw9B5cr5PJScQ+eRb23atGHdunWMHDmSxYsXExYWxhNPPEHDhg35/e9/rw1bF+i4tU197FMj+9TIvos1eo7nCCecuczVhq2LLJ5DubosdsGCBbzwwgu89dZbZGZmFvRM4pLK+q9c89TIPjWyTX3su9xG06fDkiUQHw/PPgsPP5zPg4mXziPfFi5cyMKFC3nooYe44447GDhwIPPmzWPr1q188sknfPHFF+zfv9/tMYsUHbe2qY99amSfGtl3oUbXci33cA/guZetuMfiOZTrNyIbMmQIpUuXLshZxGWpqalujyA+qJF9amSb+th3JY2ee87zzz/9CapXz6eB5Dw6j3Lv+PHjfP7557Rt25Y6deowYcIE/vKXv7Br1y6mTp3q9nhFio5b29THPjWyT43su1CjF3mRUEL5nu9ZxCIXppIzLJ5DubqnLXjuzVW5cmUOHjxYwCOJW0JDQ897YzmxRY3sUyPb1Me+K200Zw7ccgt8/DHcd18+DiZeOo8uX8mSJfntb3/L8OHDKVu2rN4LohDpuLVNfexTI/vUyL7/bVSPevzCLwQTTDOakUCCi9OJxXMo11faAue9+ZgElj59+rg9gvigRvapkW3qY9+VNnr+ec8/Bw6E2rWvfB45n86jvLv55pv57LPP2LdvHyNGjGDy5Mm0atXK7bGKFB23tqmPfWpknxrZ97+NhjKUYIKZwhRt2Bpg8RzK0/+8v3nzZp8bt+XLl7+igUREREQu15Ilnvvbdu0KQ4fC737n9kRSVFWpUoWBAwcycOBAateuzeLFi3n44Yf517/+xfHjx90eT0RERFzUmMb0ox/guUWCyIXkadN2yJAhJu/xIPlj3bp1bo8gPqiRfWpkm/rYlx+Nnn/es2k7YAAMHw7Knr90Hvk2Y8YMOnTowKFDh/jyyy/59NNP2bx5s9tjFWk6bm1TH/vUyD41su/sRsMYBsA4xrGWtW6NJGexeA7ladN23LhxuqdtAEtPT3d7BPFBjexTI9vUx778aLRqFUyYAH37wrBh0Lv3lc8lv9J55FtOTg59+vRh2rRpnD592u1xBB231qmPfWpknxrZd6ZRc5rTgx6c4hRDGeruUOJl8RzK9T1tdT/bwHfjjTe6PYL4oEb2qZFt6mNffjUaMgROn4ZevSAuLl9eUv5L55FvPXr04Ntvv9WGrSE6bm1TH/vUyD41su9Mo5d5GYB/8A82scnNkeQsFs+hXG/aBgUFFeQcIiIiIvlmwwb46ivP45dfdncWERERERGAm7mZznQmhxzvLRJELiYI0CW0AkC5cuVISUlxewy5BDWyT41sUx/78rPRtdfCxo0QGgo33QSLFuXLyxZ5Oo/EH+m4tU197FMj+9TIvnJlyzHl6BRa05oxjOEBHnB7JDmLxXMo11faSuBr1KiR2yOID2pknxrZpj725Wej7dvh0089j199Nd9etsjTeST+SMetbepjnxrZp0b2/V+1/6M1rTnBCV7hFbfHkf9h8RzSpq14RUVFuT2C+KBG9qmRbepjX343evllOHEC2rSBDh3y9aWLLJ1H4o903NqmPvapkX1qZN8Dez1X1o5hDEkkuTyN/C+L55A2bcUrIyPD7RHEBzWyT41sUx/78rtRUhKMGeN5rKtt84fOI/FHOm5tUx/71Mg+NbKtG92ofbg2GWTwOq+7PY5cgMVzSPe0Fa+goCAcR4eDZWpknxrZpj72FUSjSpU8t0ooVQpuvx2++y5fX77I0Xkk/kjHrW3qY58a2adGdgURxM/8TBOa8DqvM5jBbo8kF2DxHNKVtuLVv39/t0cQH9TIPjWyTX3sK4hGBw7AO+94Hr/8MgQF5fuXKFJ0Hok/0nFrm/rYp0b2qZFdAxhAE5pwPOQ4Ixjh9jhyERbPIW3aioiISMB76y1ITYXYWLjjDrenEREREZGiIIII3uItAL5r8B1HOOLyROJPtGkrXhs3bnR7BPFBjexTI9vUx76CapSS4tm4BXjpJQgOLpAvUyToPBJ/pOPWNvWxT43sUyObhjKUKlRhC1sYFTTK7XHkEiyeQ9q0Fa/Dhw+7PYL4oEb2qZFt6mNfQTZ65x04dAjq1oXf/77AvkzA03kk/kjHrW3qY58a2adG9jSkIQ/zMAAP8RD7Uva5PJFcisVzSJu24tWqVSu3RxAf1Mg+NbJNfewryEbp6fD6f9+sd8gQCA0tsC8V0HQeiT/ScWub+tinRvapkT0f8AEhhDCZycxkphoZZ7GPNm1FRESkyPjrX2HvXoiOhnvucXsaEREREQlEd3InbWjDcY4ziEFujyN+Kghw3B5CbKhYsSIHDx50ewy5BDWyT41sUx/7CqPRgw/Ce+/Bzp0QEwM5OQX65QKOziPxRzpubVMf+9TIPjWyI4IINrGJKlThOZ7jNV4D1Mg6i310pa14xcTEuD2C+KBG9qmRbepjX2E0+vhjSE6GGjVg4MAC/3IBR+eR+CMdt7apj31qZJ8a2XH2m4+9xVvej6uRbRb7aNNWvGrUqOH2COKDGtmnRrapj32F0ejECXjjDc/jZ5+FkJAC/5IBReeR+CMdt7apj31qZJ8a2XAd153z5mPZZHt/T41ss9hHm7bilZWV5fYI4oMa2adGtqmPfYXV6G9/g337PPe2veuuQvmSAUPnkfgjHbe2qY99amSfGtnwPu+f8+ZjZ1Mj2yz20T1tRUREpEgaNAhGjYLt26FuXTh50u2JRERERMRf3cmdfM3XHOc49anPLna5PZL4OV1pK179+vVzewTxQY3sUyPb1Me+wmw0Zgzs3w/XXgu/+12hfVm/p/NI/JGOW9vUxz41sk+N3BVBhPf+ta/wygU3bNXINot9tGkrXsWK6XCwTo3sUyPb1Me+wmyUmQkjRngeP/ccBAcX2pf2azqPxB/puLVNfexTI/vUyF1n3nxsM5sZycgLPkeNbLPYx95E4pqtW7e6PYL4oEb2qZFt6mNfYTf68EM4eBBq14Y77yzUL+23dB6JP9Jxa5v62KdG9qmRe85+87GHeficNx87mxrZZrGPNm3FKykpye0RxAc1sk+NbFMf+wq70fHj8Jbnb7Lx/PO62jY3dB6JP9Jxa5v62KdG9qmRez7gA0IIYRKTznvzsbOpkW0W+2jTVrzatGnj9gjigxrZp0a2qY99bjT64AM4dAjq1IH+/Qv9y/sdnUfij3Tc2qY+9qmRfWrkjju5k9a05jjHGcSgSz5XjWyz2EebtiIiIlKkZWTAyP/eeuz558Hg7axERERExJgylDnnzcd2s9vliSQQOVpagFOlShXXZ9BSI39famR7qY/95Vaj0qVxDh3CcRycAQPc//dgeek80vLHpePW9lIf+0uN7C81Kvw1ilGOg+NsYpMTRpga+fmy2McvriWpUaMGH3/8Mdu3b+f48eNs3bqVoUOHEhoaes7zGjVqxIIFC8jMzGTXrl08+eST571Wnz592LBhA5mZmaxZs4bbbrutsL4N86pVq+b2COKDGtmnRrapj31uNTp2DEaP9jzW1baXpvPIHc888wzLli0jLS2N/fv3880331CnTp1znhMeHs7777/PoUOHSE9PZ+LEiVSqVMmliW3RcWub+tinRvapUeG6jut4iIcAeIiHLvrmY2dTI9ss9vGL/ySpV68exYoV409/+hMNGzZk0KBB3H///bz22mve50RERPDjjz+yc+dO4uLiePLJJxk6dCj33Xef9znx8fGMHTuWTz75hKZNmzJlyhSmTJlCw4YN3fi2zKlVq5bbI4gPamSfGtmmPva52ei99yAlBRo0gD59XBvDPJ1H7mjTpg0ffPABN954Ix07diQ0NJQff/yRkiVLep8zevRounfvTt++fWnTpg1Vq1Zl8uTJLk5th45b29THPjWyT40K19lvPvYjP+bqc9TINqt9XL/c93LWE0884Wzbts376/vvv985fPiwExoa6v3Y8OHDnQ0bNnh/PW7cOOe7774753WWLFnifPjhh65/PxZW3759XZ9BS438famR7aU+9pfbjV54wXOLhLVrcYKC3P/3YXG53UjLsypUqOA4juPcfPPNDuCUKVPGycrKcnr37u19Tt26dR3HcZwbbrjB9XndXjpubS/1sb/UyP5So8Jbv+W3joPjZJDhVKOaGgXIMtrH9QEua7388svO8uXLvb/+4osvnG+++eac57Rt29ZxHMcpW7asAzg7d+50HnnkkXOeM3ToUGfVqlUX/TphYWFORESEd5UuXdr1711LS0tLS0urYFZkJE5Kimfjtk8f9+fR0rrYqlWrluM4jtOwYUMHcNq1a+c4juNERkae87zExETn0UcfveBr6M+5WlpaWlpaeVtlKOMkk+w4OM4zPOP6PFqBvULwQ7Vq1eKhhx7iiSee8H6scuXK7Nix45zn7d+/3/t7R48epXLlyt6Pnf2cypUrX/RrDR48mKFDh3p/nZaWRmRkJH369CE0NJRJkybRvn17ypYty4EDB1i2bBndunUDICEhgeDgYJo0aQLA1KlTadWqFRUqVODIkSMsWLCAnj17ArB69WpycnJo1qwZANOnTycuLo7KlSuTmprKrFmz6PPfv6u5bt060tPTufHGGwH44YcfaNSoEVFRUWRkZPDdd9/Rv39/ADZu3Mjhw4dp1aoVALNnzyYmJoYaNWqQlZXF5MmT6devH8WKFaN06dJMnz6dNm3aADBv3jyqVatGrVq1OHnyJBMmTKB3796EhYWRmJjItm3baN++PQALFy6kUqVK3nurjR07lh49elCyZEl2797N+vXr6dy5MwBLliwhMjKSBg0aADBhwgQ6d+5MmTJlSE5OZuXKlXTp0gWA5cuXEx4eTuPGjQGYPHky7dq1o1y5chw8eJAlS5Zw++23A7By5UoAmjZtCsC3335LfHw8FStWJCUlhblz59KrVy8A1qxZQ1ZWFs2bNwdgxowZNG3alCpVqpCWlsbMmTPp27cvAOvXryc1NZX4+HgAZs6cSYMGDahWrRrHjx9n6tSpDBgwAIDNmzdz4MABbrrpJgDmzJlDrVq1iI6OJjs7m0mTJtG3b19CQkLYtm0bu3fvpm3btgDMnz+fqKgoateuzenTpxk/fjy9evUiPDycnTt3smXLFn7/+9+TnJzMokWLKF++PPXq1QNg3LhxdO/enVKlSpGUlMTatWu59dZbAfjpp5+IiIjw3gZk4sSJdOzYkcjISPbt20dCQgJdu3YFYMWKFYSGhhIbGwvAlClTaN26NVdddRWHDh1i0aJF9OjRA4BVq1Zx6tQp4uLiAJg2bRotWrSgUqVKHD16lDlz5tC7d28A1q5dS2ZmJi1atADg+++/JzY2lqpVq5Kens6MGTPo168fABs2bCAlJYWWLVsCMGvWLOrWrUv16tXJzMxkypQp9O/fn6CgILZs2UJycjKtW7cGYO7cuURHR1OzZk1ycnKYOHGi91zdsWMHiYmJtGvXDoAFCxZQpUoVYmJicByHcePG0bNnT0qUKMGuXbvYtGkTHTt2BGDx4sWUK1eO+vXrAzB+/Hi6dOlCREQEe/fuZfXq1d77Y4eFhZGQkECjRo0AAupnxNatW0lKSvLrnxHx8fGcOHEiYH9GdOjQAcCvf0ZUqVKFTz/91NWfEXPmrKV3718YPrwEYWE9OH2afPsZsWzZMkqUKOHXPyN69epFWlpagf+M6NKlCzNmzEDOFxQUxNtvv83ChQtZt24d4PnzblZWFqmpqec891J/1tWfc/3r/4dB4P45Nzo6GsdxAP/+/2EQuH/OrVKlClOnTvX7/x+mP+f6588IS3/O/d3Pv6PypsrsCN1B0m+SGBA8wK/+nFtQPyPA//+cGxQURGJiYqH8jMjLn3Nd2zEePny440vdunXP+ZyqVas6W7ZscT766KNzPj5z5kxnzJgx53ysfv36juM4Tr169RzAycrKcvr373/Ocx544AFn3759F52xKF2BMGDAANdn0FIjf19qZHupj/1loVHZsjhHj3qutv3Nb9z/d2JtWWhU1Ndf//pXZ8eOHU5UVNQ5XU6cOHHec5cuXeq8/vrrF3wd/TlXy8pSH/tLjewvNSr41YhGTg45joPjdKKTGgXYstjH1SttR44cyeeff37J52zfvt37uEqVKsydO5fFixfzxz/+8Zzn7du3j6uvvvqcj5359b59+y75nDO/fyHZ2dlkZ/t+F8BAkJiY6PYI4oMa2adGtqmPfRYaHT0K774LL7wAL74IU6bAfy8AE2w0Ksree+89unXrRuvWrUlKSvJ+fN++fYSHhxMZGXnO1baX+rOu/pwrVqiPfWpknxoVvDNvPjaRibl+87GzqZFtVvu4vnOcm1W1alVn06ZNzj//+U+nWLFi5/3+mTciCwkJ8X7s1VdfPe+NyL799ttzPm/RokV6I7L/rkqVKrk+g5Ya+ftSI9tLfewvK43KlcNJTfVcbdujh/vzWFpWGhXF9d577zl79uxxateufd7vnXkjsl69enk/VqdOHb0R2X+XjlvbS33sLzWyv9SoYNeZNx87xrE8vfmYGvnPMtrH9QF8rqpVqzqbN292Zs2a5VStWtW5+uqrvevMc8qUKeMkJyc7X3zxhdOgQQPnjjvucI4dO+bcd9993ufEx8c72dnZzmOPPebUrVvXGTJkiJOVleV9A4eivixeCq6lRv621Mj2Uh/7y1KjV17xbNomJLg/i6VlqVFRWh988IGTkpLitG7d+pw/CxcvXtz7nL/+9a9OYmKi07ZtW+f66693Fi1a5CxatMj12S0sHbe2l/rYX2pkf6lRwa38evMxNbK9LPYphh/o2LEjMTExdOjQgaSkJPbt2+ddZ6SlpdGpUydq1qxJQkICI0eOZNiwYXz00Ufe5yxZsoQ777yTP/7xj6xevZo+ffrQs2dP7xs4iIiIiJwxahSkp8P110P37m5PI0Xdn//8Z8qWLcv8+fPP+bPwmTcvARg0aBDTpk1j0qRJLFiwgH379nnfeEZEREQuz0u8RGUqs4lNjGKU2+NIEeP6zrGWjVWt2uVd4q+lRlpq5C9Lfewva41ee81zte2KFe7PYmVZa6SllZul49b2Uh/7S43sLzUqmHX2m491pKMaBfCy2McvrrSVwlGpUiW3RxAf1Mg+NbJNfeyz1mjkSDh2DOLioEsXt6exwVojkdzQcWub+tinRvapUcE4+83HZjHril5LjWyz2EebtuJVp04dt0cQH9TIPjWyTX3ss9bo8GH46189j4cMcXcWK6w1EskNHbe2qY99amSfGuW/3/E7buZmMsjgMR674tdTI9ss9tGmrYiIiMglvPUWZGRAixZw661uTyMiIiIiBa0MZRjBCABe5mV2s9vliaQoCsJznwQRERERuYgRI+CJJ+CnnyA+3u1pRERERKQgjWY0j/IoG9lIYxqTQ47bI0kRpCttxatHjx5ujyA+qJF9amSb+thntdGIEXD8ONx4I3Tq5PY07rLaSORSdNzapj72qZF9apR/GtGIh3gIgId4KN82bNXINot9tGkrXiVLlnR7BPFBjexTI9vUxz6rjQ4cgDFjPI+L+r1trTYSuRQdt7apj31qZJ8a5Z8P+IBggpnABGYzO99eV41ss9hHm7bitXu37tFinRrZp0a2qY99lhuNGAGZmdCyJbRv7/Y07rHcSORidNzapj72qZF9apQ/8vvNx86mRrZZ7KNNW/Fav3692yOID2pknxrZpj72WW60bx/8/e+ex0X5alvLjUQuRsetbepjnxrZp0ZX7n/ffGwPe/L19dXINot9tGkrXp07d3Z7BPFBjexTI9vUxz7rjd54A06cgJtvhnbt3J7GHdYbiVyIjlvb1Mc+NbJPja7cS7xEZSqzkY2MYlS+v74a2WaxjzZtRURERHIpORk++sjzuChfbSsiIiISSBrTuEDefEzkSjlaWoATHR3t+gxaauTvS41sL/Wxv/yhUVQUzokTOI6D07q1+/OokZaW76Xj1vZSH/tLjewvNbqytYAFjoPjjGe8GhXRZbGPrrQVr8jISLdHEB/UyD41sk197POHRklJ8PHHnsdF8Wpbf2gk8r903NqmPvapkX1qdPnOfvOxx3m8wL6OGtlmsY82bcWrQYMGbo8gPqiRfWpkm/rY5y+NXn8dsrPhllvgppvcnqZw+UsjkbPpuLVNfexTI/vU6PJEEslbvAXAMIbl+5uPnU2NbLPYR5u2IiIiInm0Zw98+qnncVG82lZEREQkELzES1zN1WxkI6MZ7fY4IucIwnOfBBFCQkI4efKk22PIJaiRfWpkm/rY50+NqleHLVsgLAxatYLFi92eqHD4UyORM3Tc2qY+9qmRfWqUd41pzM/8TDDBdKADc5hToF9PjWyz2EdX2opX586d3R5BfFAj+9TINvWxz58a7doFn3/uefzii66OUqj8qZHIGTpubVMf+9TIPjXKm5KUZCxjCSaYCUwo8A1bUCPrLPbRpq14lSlTxu0RxAc1sk+NbFMf+/yt0WuvQU4OdO4MN9zg9jSFw98aiYCOW+vUxz41sk+N8uZDPqQBDdjLXv7CXwrla6qRbRb7aNNWvJKTk90eQXxQI/vUyDb1sc/fGu3cCV984XlcVO5t62+NREDHrXXqY58a2adGufcH/sBd3MVJTtKf/hzkYKF8XTWyzWIf3dNWvCIjI0lNTXV7DLkENbJPjWxTH/v8sVHNmrB5M4SEQIsWsHy52xMVLH9sJKLj1jb1sU+N7FOj3Ikllp/4ieIU52me5k3eLLSvrUa2WeyjK23Fq0uXLm6PID6okX1qZJv62OePjXbsgH/8w/O4KFxt64+NRHTc2qY+9qmRfWrkWwQRTGACxSnONKYxghGF+vXVyDaLfbRpKyIiInKFXn0VTp6Erl2hWTO3pxERERGR//UxHxNDDDvZyd3cjaO/eC7GadNWvJYH+t/nDABqZJ8a2aY+9vlro23b4OuvPY9feMHdWQqavzaSok3HrW3qY58a2adGl/YX/sId3EE22dzBHRzhSKHPoEa2WeyjTVvxCg8Pd3sE8UGN7FMj29THPn9u9MorcOoU3H47NG3q9jQFx58bSdGl49Y29bFPjexTo4trRjNGMQqAJ3mSZSxzZQ41ss1iH23ailfjxo3dHkF8UCP71Mg29bHPnxtt3Qr//Kfn8YsvujtLQfLnRlJ06bi1TX3sUyP71OjCylKWCUwgjDAmMYl3ede1WdTINot9tGkrIiIikk9efRVOn4aePSE21u1pRERERIq2L/iCaKLZxjbu4R63xxHJkyDQnZfFIzw8nKysLLfHkEtQI/vUyDb1sS8QGn39Ndx5J0yeDL17uz1N/guERlL06Li1TX3sUyP71Oh8j/M4b/EWJzhBS1qykpWuzqNGtlnsoyttxatdu3ZujyA+qJF9amSb+tgXCI1eftlztW2vXtCokdvT5L9AaCRFj45b29THPjWyT43O1ZKWvM7rADzCI65v2IIaWWexjzZtxatcuXJujyA+qJF9amSb+tgXCI02boR//cvz+IUX3J2lIARCIyl6dNzapj72qZF9avSrClRgPOMJIYSv+Zq/83e3RwLUyDqLfbRpK14HDx50ewTxQY3sUyPb1Me+QGl05mrbvn2hYUO3p8lfgdJIihYdt7apj31qZJ8aeQQRxFd8xTVcwwY28Cf+5PZIXmpkm8U+uqeteJUqVYqMjAy3x5BLUCP71Mg29bEvkBqNHw933AGTJkGfPm5Pk38CqZEUHTpubVMf+9TIPjXyeI7neIVXOM5xWtCCdaxzeyQvNbLNYh9daStet99+u9sjiA9qZJ8a2aY+9gVSo6FD4dQpz5uRNW/u9jT5J5AaSdGh49Y29bFPjexTI2hLW17iJQAe4AFTG7agRtZZ7KNNWxEREZECsGEDfPGF5/Hrr7s7i4iIiEggu5qrGctYggnmEz7hS750eySRK6ZNW/FaudL9d1OUS1Mj+9TINvWxL9AaDR0KWVlwyy3QsaPb0+SPQGskRYOOW9vUxz41sq8oNypGMcYylspUZg1reIiH3B7pgopyI39gsY82bUVEREQKyO7d8MEHnsfDh0NQkLvziIiIiASaoQylHe1IJ52+9CWTTLdHEskX2rQVr6ZNm7o9gvigRvapkW3qY18gNnrtNUhLg7i4wHhDskBsJIFPx61t6mOfGtlXVBt1pjMv8AIA93Ivm9ns8kQXV1Qb+QuLfbRpKyIiIlKADh+GESM8j195BUJC3J1HREREJBBcwzV8xVcAfMAH/It/uTyRSP4KAhy3hxAbSpUqRUZGhttjyCWokX1qZJv62BeojUqVgu3boVIl+OMf4aOP3J7o8gVqIwlsOm5tUx/71Mi+otYohBDmMY9WtGIFK2hFK7LJdnusSypqjfyNxT660la84uPj3R5BfFAj+9TINvWxL1AbZWTAyy97Hg8ZAiVKuDvPlQjURhLYdNzapj72qZF9Ra3Ra7xGK1pxlKPcwR3mN2yh6DXyNxb7aNNWvCpWrOj2COKDGtmnRrapj32B3Ojvf4cdOyAqCh580O1pLl8gN5LApePWNvWxT43sK0qNbud2nuRJAAYykB3scHmi3ClKjfyRxT7atBWvlJQUt0cQH9TIPjWyTX3sC+RG2dnw4ouex4MHQ9myro5z2QK5kQQuHbe2qY99amRfUWkUTTSf8zkAIxnJVKa6O1AeFJVG/spiH93TVrzCw8PJyspyewy5BDWyT41sUx/7Ar1RsWKwahU0agTDh8Ozz7o9Ud4FeiMJTDpubVMf+9TIvqLQKIwwFrKQ5jRnMYtpQxtOctLtsXKtKDTyZxb76Epb8erVq5fbI4gPamSfGtmmPvYFeqPTp3/dqH3kEahSxd15LkegN5LApOPWNvWxT43sKwqN3uItmtOcQxyiH/38asMWikYjf2axjzZtRURERArRtGmwcCGULAkvvOD2NCIiIiL29aUvD/EQAL/n9+xhj8sTiRQ8bdqK15o1a9weQXxQI/vUyDb1sa+oNHrmGc8/77sPatd2d5a8KiqNJLDouLVNfexTI/sCuVEMMXzMxwC8yqv8wA8uT3R5ArlRILDYR5u24mXt3h1yPjWyT41sUx/7ikqjRYs8V9yGhMCwYW5PkzdFpZEEFh23tqmPfWpkX6A2Kk5xJjCBMpRhHvMYwhC3R7psgdooUFjso01b8WrevLnbI4gPamSfGtmmPvYVpUbPPuu5x+2AAdCkidvT5F5RaiSBQ8etbepjnxrZF6iN3uVdYollP/sZwABOccrtkS5boDYKFBb7aNNWRERExAVr18I//+l5/Npr7s4iIiIiYs3v+T33cR+nOc0ABrCPfW6PJFKoggDH7SHEhsjISFJTU90eQy5BjexTI9vUx76i1qhmTdi0CUJDoW1bmD/f7Yl8K2qNJDDouLVNfexTI/sCrVEDGrCMZZSiFC/wAq/witsjXbFAaxRoLPbRlbbi1bRpU7dHEB/UyD41sk197CtqjXbsgL/9zfN4+HB3Z8mtotZIAoOOW9vUxz41si+QGpWiFBOYQClKMZOZvMqrbo+ULwKpUSCy2EebtuJVpUoVt0cQH9TIPjWyTX3sK4qNXnkFMjIgPh569HB7Gt+KYiPxfzpubVMf+9TIvkBpFEooX/EVDWjAHvbwO36HEyB/QTxQGgUqi320aSteaWlpbo8gPqiRfWpkm/rYVxQb7d8Po0d7Hr/2GhQz/qezothI/J+OW9vUxz41si8QGoUTzmQm05OeZJFFf/pziENuj5VvAqFRILPYR/e0Fa+QkBBOnjzp9hhyCWpknxrZpj72FdVGZcrA9u1QvjwMHAhffOH2RBdXVBuJf9Nxa5v62KdG9vl7oxKUYApT6EQnjnOcnvRkFrPcHitf+XujQGexj/FrOaQw9e3b1+0RxAc1sk+NbFMf+4pqo7Q0z1W2AC+9BOHh7s5zKUW1kdtuvvlmvv32W5KSknAchx4XuJfGSy+9xN69ezl+/DizZs2idu3aLkxqk45b29THPjWyz58blaIUM5hBJzpxjGN0oUvAbdiCfzcqCiz20aatiIiIiAF//Svs3g01asD997s9jVhTqlQpVq9ezV/+8pcL/v5TTz3Fww8/zP33388NN9xARkYGM2fOJNzy/wIgIiJFXhnKMJOZtKUtqaTSiU78P3t3Hldlnf5//AWyiIiICwqEQO6aorkkai4pqVjKgKbONFk2TU37OtV3psmWmWqWbJ/JpnV+jZpLLmk6ai655RIuuWuAhiii7CDr/fvj5DFywYXD/Tnwfvq4HhzOuc/hOrzPfbq7vP2cVayyuy0RY1gqFWBFR0fb3oNKGbl7KSOzS/mYX3U9o0mTsCwLKyMDKyDA/n6UkZllWZY1evToStcdOXLEeuyxx5zfN2rUyCoqKrLGjRt33sfx8fGxAgICnNWwYUPbn5urSq9bs0v5mF/KyPxyx4yCCLI2stGysKyTnLR60tP2npRR3S0T8/FC5Ec5OTl2tyBVUEbmU0ZmUz7mq+sZffwxPPEEdOgAjz7qWCrBNHU9IxNFRUUREhLCsmXLnNfl5ubyzTffEBMTw4wZM855v6effprJkydXuk9gYCBjxozB29ub2bNnM2TIEBo3bkxGRgYbN27kpptuAmDLli3Uq1ePbt26ATBv3jz69etHs2bNOHnyJKtXryY+Ph6Abdu2UVpaSs+ePQFYuHAhPXr0oGXLluTk5LB06VLGjBkDwM6dO8nLy6NPnz4ALF68mC5duhAWFkZBQQELFixg/PjxAOzZs4cTJ07Qr18/AJYtW0bbtm2JiIiguLiYOXPmMG7cODw9PcnKyuL48eMMHDgQgJUrVxIeHk7r1q0pKytj5syZJCYm4uPjQ0pKCgcPHmTIkCEArFmzhuDgYNq1awfAtGnTGD16NA0aNODw4cPs2rWLYcOGAbB+/XoCAwPp1KkTADNnzmTYsGE0atSI9PR0kpKSiIuLA2DTpk34+vrStWtXAObMmcPgwYMJCgri+PHjrF+/nlGjRgGQlJQEQPfu3QGYP38+MTExNG/enKysLFasWEFCQgIA27dvp7i4mF69egGwaNEiunfvTkhICLm5uSxZssT5T0B37dpFTk4OMTExACxZsoROnToRHh5OYWEh8+bNY8KECQDs27ePjIwM+vfvD8Dy5ctp3bo1kZGRlJSUMHv2bMaOHYuXlxcHDx7k8OHDDBo0CIBVq1YRFhZGmzZtqKioYMaMGSQkJODr60tqaioFBQXOn7N27VqaNm1Khw4dAJg+fTo333wz/v7+pKWlsWPHDoYPHw7Ahg0bCAgIoHPnzgDMmjWL2NhYAgMDOXr0KFu2bGHkyJEAbN68GW9vb6KjowGYO3cuAwYMoEmTJmRmZrJ27VrnsiNbt26lvLycHj16APDFF1/Qu3dvgoODyc7OZvny5SQmJgKwY8cOioqK6N27NwBffvkl0dHRhIaGkpeXx6JFixg3bhwAu3fvJisri759+wKwdOlS2rdvT6tWrSgqKmLu3LmMHz8eDw8P9u/fT3p6OgMGDABgxYoVREZGEhUVRWlpKbNmzXLuq8nJyaSkpDB48GAAVq9eTUhICG3btsWyLKZPn058fDx+fn4cOnSIvXv3EhsbC8C6desICgqiY8eOAMyYMYO4uDgCAgI4cuQI27ZtY8SIEfj5+ZGfn4+fnx9dunQBqFXvEQcOHCAtLc2t3yMCAwOd+5E7vEcUpBbwRfEXRGZHctLzJA92eJC20W1pXdH6rPeI/fv3M3ToULd/j/Dz8yM1NbVWvkcAbNy40a3fI3bt2kXfvn1r5D0iLi6ORYsWcTFsnxyrzKgJEybY3oNKGbl7KSOzS/mYX8oIKyHBcbZtbi5W8+b296OMzKufn2kbExNjWZZltWzZstJ2M2bMsKZPn37ex6lLZ9rqdWt2KR/zSxmZX+6UUTDB1g52WBaWdZSjVmc6296TMlKZmI/WtBURERExyJw5sGkTBATAH/5gdzdSm5WUlJCXl+es/Px8u1sSEZFaLpRQVrGKa7iGNNIYyEB2stPutkSMZfvkWGVGNWnSxPYeVMrI3UsZmV3Kx/xSRo664QbH2bbFxVgREfb3o4zMqp+faRsVFWVZlnXWWmwrV660XnvtNdv7NaH0ujW7lI/5pYzML3fIqBWtrAMcsCwsK4UU62qutr0nZaQyOR+daStOp9fVEXMpI/MpI7MpH/MpI4evvoKlS8HHx7x1bZWReZKTk0lPT3eunQYQEBDAddddx/r1623szBx63ZpN+ZhPGZnP9Iyu5mpWs5rWtOYgBxnAAL7ne7vbqlGmZ1TXmZiPhrbiFB4ebncLUgVlZD5lZDblYz5ldMbTTzu+/vrX8OPnZxhBGdnD39+f6Oho5wekREVFER0d7czjtdde449//CM333wz11xzDZ988glHjhxh7ty5NnZtDr1uzaZ8zKeMzGdyRu1oxypWEUEEe9nLQAZyiEN2t1XjTM5IzMxHQ1txKiwstLsFqYIyMp8yMpvyMZ8yOmPLFvjsM/D0hD//2e5uzlBG9ujZsydbt25l69atAEyZMoWtW7fy/PPPA/DXv/6VN998k6lTp7Jp0yYaNmzI8OHDKS4utrFrc+h1azblYz5lZD5TM+pEJ1axiqu4ip3sZCADSSPN7rZsYWpG4mBiPh441kkQEREREcO0awc7d4KXF/TtC/qX7iIiIuIuoolmKUtpTnO2spVYYskk0+62RNyGzrQVpwkTJtjdglRBGZlPGZlN+ZhPGVW2bx988IHj8ssv29vLacpI3JFet2ZTPuZTRuYzLaMe9OArvqI5zdnEJm7ghjo/sDUtI6nMxHw0tBUREREx2PPPQ1ERDBgAw4fb3Y2IiIjIhfWhD8tZThOasI51DGUoWWTZ3ZaI29HQVpz27dtndwtSBWVkPmVkNuVjPmV0trQ0ePNNx+WXXgIPD3v7UUbijvS6NZvyMZ8yMp8pGQ1gAEtZSiCBrGQlwxhGLrl2t2UEUzKSczMxHw1txSkjI8PuFqQKysh8yshsysd8yujcXn4ZsrOhWzcYP97eXpSRuCO9bs2mfMynjMxnQkZDGMKXfElDGrKUpcQRRz75drdlDBMykvMzMR+3GNpGRETw73//m++//57CwkIOHDjA5MmT8fb2rrSNZVln1XXXXVfpscaMGcPu3bspKipi+/btjBgxoqafjrH69+9vdwtSBWVkPmVkNuVjPmV0bllZ8Ne/Oi6/8AL85BCoxikjcUd63ZpN+ZhPGZnP7oxGMIIv+IIGNGAhC7mZmymiyNaeTGN3RnJhJubjFkPbDh064Onpyd13303nzp155JFHuOeee/jLX/5y1rZDhgyhZcuWztqyZYvztpiYGKZNm8b7779P9+7dmTt3LnPnzqVz5841+XRERERELtnrr0N6OrRuDb/5jd3diIiIiDiMZjRzmUt96vM5n5NAAsUU292WSK1guWM9/vjj1sGDB53fR0REWJZlWdHR0ee9z/Tp060FCxZUum79+vXWP//5T9ufjwkVHBxsew8qZeTupYzMLuVjfimjC9c99zj+MVF6OlaDBspIpbrY0uvW7FI+5pcyMr/syugWbrFKKbUsLGs60y0vvGz/XZha2o/MLhPzcYszbc8lMDCQkydPnnX9/PnzOXbsGF9//TU333xzpdtiYmJYtmxZpeuWLFlCTEzMeX+Oj48PAQEBzmrYsGH1PAEDtW7d2u4WpArKyHzKyGzKx3zK6ML+/W84cABatoSHHrKnB2Uk7kivW7MpH/MpI/PZkdGt3Mp/+S9eePEJn/ArfkUZZTXeh7vQfmQ2E/PxsruBy9G6dWseeOABHn/8ced1+fn5PProo6xdu5aKigoSExOZO3cu8fHxLFiwAICWLVty7NixSo917NgxWrZsed6f9fTTTzN58mTn97m5uQQGBjJmzBi8vb2ZPXs2Q4YMoXHjxmRkZLBx40ZuuukmALZs2UK9evXo1q0bAPPmzaNfv340a9aMkydPsnr1auLj4wHYtm0bpaWl9OzZE4CFCxfSo0cPWrZsSU5ODkuXLmXMmDEA7Ny5k7y8PPr06QPA4sWL6dKlC2FhYRQUFLBgwQLG//gpJXv27OHEiRP069cPgGXLltG2bVsiIiIoLi5mzpw5jBs3Dk9PTxo3bkxqaioDBw4EYOXKlYSHh9O6dWvKysqYOXMmiYmJ+Pj4kJKSwsGDBxkyZAgAa9asITg4mHbt2gEwbdo0Ro8eTYMGDTh8+DC7du1i2LBhAKxfv57AwEA6deoEwMyZMxk2bBiNGjUiPT2dpKQk4uLiANi0aRO+vr507doVgDlz5jB48GCCgoI4fvw469evZ9SoUQAkJSUB0L17d8AxwI+JiaF58+ZkZWWxYsUKEhISANi+fTvFxcX06tULgEWLFtG9e3dCQkLIzc1lyZIljB07FoBdu3aRk5PjHO4vWbKETp06ER4eTmFhIfPmzWPChAmA49MGMzIynGuhLF++nNatWxMZGUlJSQmzZ89m7NixeHl5cfDgQQ4fPsygQYMAWLVqFWFhYbRp04aKigpmzJhBQkICvr6+pKamsn//fvr160dkZCRr166ladOmdOjQAYDp06dz88034+/vT1paGjt27GD48OEAbNiwgYCAAOcyILNmzSI2NpbAwECOHj3Kli1bGDlyJACbN2/G29ub6OhoAObOncuAAQNo0qQJmZmZrF27ltGjRwOwdetWysvL6dGjBwBffPEFvXv3Jjg4mOzsbJYvX05iYiIAO3bsoKioiN69ewPw5ZdfEh0dTWhoKHl5eSxatIhx48YBsHv3brKysujbty8AS5cupX379rRq1YqioiLmzp3L+PHj8fDwYP/+/aSnpzNgwAAAVqxYQWRkJFFRUZSWljJr1iznvpqcnExKSgqDBw8GYPXq1YSEhNC2bVssy2L69OnEx8fj5+fHoUOH2Lt3L7GxsQCsW7eOoKAgOnbsCMCMGTOIi4sjICCAI0eOsG3bNuf62A0aNCAvL48uXboA1Kr3iAMHDpCWlubW7xHXXHMNkZGRtfY9YujQoQBu/R4RFhbG7t27a+17xMaNG/Hz87ui94gvv8zngQfW8+STkJmZQH6+b42+R3Tv3p3IyEiXv0fExcWxaNEiRKpDZGQk69evt7sNOQ/lYz5lZL6azmgSk3iP9/DEk/d4j3u4hwoqauznuyPtR2YzNR/bTvN96aWXrKq0b9++0n1CQ0Ot/fv3W++9916Vj//xxx9bq1evdn5fXFxsjR8/vtI2v/vd76yjR4+e9zF8fHysgIAAZzVs2ND206NdVYmJibb3oFJG7l7KyOxSPuaXMqq6PDywkpIcyyS88ooyUqkupvS6NbuUj/mljMyvmszod/zO+fHvb/Km5YGH7c/fHUr7kdllYj4eP16wRbNmzWjatOkFt/n+++8pLS0FICQkhJUrV7JhwwZuv/12LOvCrd9777388Y9/JDQ0FIDU1FReffVVXn/9dec2kydPJj4+3nkWi4iIiIjphg+HL7+EoiJo2xbS0uzuSEREROqCh3mYKUwB4B/8g8d5vIp7iMiVsH1yfDEVGhpq7d271/rvf/9reXp6XtR9pk6dam3ZssX5/fTp06358+dX2mbt2rX6ILIfa+zYsbb3oFJG7l7KyOxSPuaXMrr4WrnScYrL1KnKSKWqqvS6NbuUj/mljMwvV2fkgYf1J/7kPMP2RV60/Tm7W2k/MrtMzMct1rQNDQ1l5cqVpKam8vjjj9O8eXPnbafXqL3tttsoKSlxrmuakJDApEmT+M1vfuPc9vXXX2fVqlU8+uijLFy4kPHjx9OzZ09++9vf1uwTMpSXl1u8HOo0ZWQ+ZWQ25WM+ZXTxnn4a1q2DSZPgH/+AvXtr5ucqI3FHet2aTfmYTxmZz5UZBRPMf/gPN3IjAM/wDC/yost+Xm2l/chspuZj++S4qpo4ceJ517w9vc1tt91m7dy508rPz7eys7OtDRs2nHM9ijFjxlh79uyxTp06Ze3YscMaMWKE7c/PlOrdu7ftPaiUkbuXMjK7lI/5pYwurebOdZzu8tlnykilulDpdWt2KR/zSxmZX67KKJZYK510y8KyCiiwJjHJ9ufqrqX9yOwyNB/bG1AZUiEhIbb3oFJG7l7KyOxSPuaXMrq06twZq7zcMbjt2VMZqVTnK71uzS7lY34pI/OrujPywst6iZecyyFsY5vVkY62P093Lu1HZpeJ+Xgi8qNBgwbZ3YJUQRmZTxmZTfmYTxldmp074ZNPHJdfew08a+DIThmJO9Lr1mzKx3zKyHzVmVEkkXzN1zzFUwC8wztcx3XsZne1/Yy6SPuR2UzMR0NbERERETf2zDOQmwv9+sFDD9ndjYiIiLizsYxlK1vpQx+yyCKRRO7jPk5xyu7WROok20/3VZlRoaGhtvegUkbuXsrI7FI+5pcyury6807Hv10sLMRq314ZqVQ/L71uzS7lY34pI/PrSjPyw896l3edyyGsYY3Vila2P6/aVNqPzC4T89GZtuIUFhZmdwtSBWVkPmVkNuVjPmV0ed5/HxYvBj8/+Ogj1y6ToIzEHel1azblYz5lZL4ryagzndnEJn7Lb6mgghd5kYEM5BCHqrFD0X5kNhPz0dBWnNq0aWN3C1IFZWQ+ZWQ25WM+ZXT5fvMbyM6GPn3gscdc93OUkbgjvW7NpnzMp4zMd7kZ/ZbfsolNdKYz6aQTSyzP8AzllFdzh6L9yGwm5qOhrThVVFTY3YJUQRmZTxmZTfmYTxldvrQ0ePhhx+UXXoCOHV3zc5SRuCO9bs2mfMynjMx3qRk1pjEzmcm7vIsffixiEdFE8xVfuahD0X5kNhPz8cCxToKIiIiI1AILFsBNN8GmTRATA+U6UUZERER+YixjeZVXuYqrKKWUp3iKKUzB0nhIxCg601acEhIS7G5BqqCMzKeMzKZ8zKeMrtxvfwtZWdCrFzzxRPU/vjISd6TXrdmUj/mUkfkuJqNOdGI5y/mMz7iKqzjAAfrSl1d5VQPbGqD9yGwm5qOhrTj5+vra3YJUQRmZTxmZTfmYTxldufR0eOABx+XnnoNrrqnex1dG4o70ujWb8jGfMjLfhTIKIIB/8A+2sY0buIEiivgTf6ILXdjM5hrssm7TfmQ2E/PR0FacUlNT7W5BqqCMzKeMzKZ8zKeMqsenn8LcueDjAx99BF5e1ffYykjckV63ZlM+5lNG5jtfRrdyK/vYx6M8ihdefM7ndKQjL/ACpzhVw13WbdqPzGZiPhraitP+/fvtbkGqoIzMp4zMpnzMp4yqzz33wIkT0KMHPPVU9T2uMhJ3pNet2ZSP+ZSR+X6eUTTRfM3X/If/0JKW7GMfwxlOAgmkYt5wqi7QfmQ2E/PR0Fachg4dancLUgVlZD5lZDblYz5lVH2OHYP773dc/tOfIDq6eh5XGYk70uvWbMrHfMrIfKczakxj3uRNtrCF/vSngAKe4im60IUlLLG5y7pN+5HZTMxHQ1sRERGRWmr6dJg9G7y9HcskeHvb3ZGIiIi4goflwZ3cyT72cT/3U496TGc67WnPK7xCCSV2tygil8FSqQCrVatWtvegUkbuXsrI7FI+5pcyqv5q3hwrIwPLsrAmT1ZGqrpZet2aXcrH/FJGZlcvellJPkmW5fjPvfUd31mDGWx7X6rKpf3I7DIxH51pK05Nmza1uwWpgjIynzIym/IxnzKqfsePw733Oi7/4Q/QvfuVPZ4yEnek163ZlI/5lJGZrud6vuRLNrKRbiXdyCWXR3iEbnRjBSvsbk9+RvuR2UzMR0NbcerQoYPdLUgVlJH5lJHZlI/5lJFrzJoFM2aAlxd8/DH4+Fz+YykjcUd63ZpN+ZhPGZkljji+5mtWs5rhDKecclZFraI97XmN1yijzO4W5Ry0H5nNxHw0tBURERGpA+67z/HhZF26OD6YTERERNyHJ56MYxxJJLGQhfSnP8UU8y/+RTva8W6fdznKUbvbFJFq5IFjnQQRPDw8sCy9HEymjMynjMymfMynjFwrPh4+/xzKy6FPH9i8+dIfQxmJO9Lr1mzKx3zKyD4++HAbt/EkT9KGNgDkkce/+BdTmEI66YAycgfKyGwm5qMzbcXp5ptvtrsFqYIyMp8yMpvyMZ8ycq25c+HTT6FePccyCb6+l/4YykjckV63ZlM+5lNGNc8ffx7hEb7ne97jPdrQhhOc4E/8iQgi+D2/dw5sQRm5A2VkNhPz0dBWnPz9/e1uQaqgjMynjMymfMynjFzvwQchPR06dYLnnrv0+ysjcUd63ZpN+ZhPGdWcIIL4E38ilVRe5VXCCOMHfuARHiGCCF7gBbLIOut+ysh8yshsJuajoa04paWl2d2CVEEZmU8ZmU35mE8Zud7Jk3D33Y7Ljz8O1113afdXRuKO9Lo1m/IxnzJyLU88GcpQ/sN/+IEfeI7naEpT9rOf3/AbWtOa13iNAgrO+xjKyHzKyGwm5qM1bcUpKCiIrKyz/8ZOzKGMzKeMzKZ8zKeMas7HH8Ntt8GePdC9O5w6dXH3U0bijvS6NZvyMZ8yco12tGMiE/k1vyaccOf1W9nKS7zELGZRQcVFPZYyMp8yMpuJ+ehMW3EaPny43S1IFZSR+ZSR2ZSP+ZRRzXnoIThyBDp0gBdeuPj7KSNxR3rdmk35mE8ZVZ9AAvktv2Ud69jLXv6P/yOccLLI4h3e4Tquozvd+YzPLnpgC8rIHSgjs5mYj5fdDYiIiIhIzcvOhrvugoUL4dFH4fPPYd06u7sSERGpfepRj1himchE4omnPvUBKKOMJSzhIz5iAQsoptjmTkXENJZKBVhRUVG296BSRu5eysjsUj7mlzKq+Xr/fSzLwtq3D8vPTxmpamfpdWt2KR/zSxldXl3DNdYrvGId4YhlOf5za1lY1na2W4/yqNWCFsqoDpUyMrtMzEdn2opTQECA3S1IFZSR+ZSR2ZSP+ZRRzXv0UYiNhbZt4S9/gUceufD2ykjckV63ZlM+5lNGF6cBDRjMYOKIYwQjiCLKeVsmmfyX//IRH5FEUrX/bGVkPmVkNhPz0Zq24tS5c2e7W5AqKCPzKSOzKR/zKaOal5MDv/mN4/LDD8OAARfeXhmJO9Lr1mzKx3zK6Pza0paHeIjFLOYEJ/iCL7iXe4kiimKKmctc4oknlFAe4iGXDGxBGbkDZWQ2E/PRmbYiIiIiddz//gdTp8JvfwsffADR0VBQYHdXIiIi5qlPfQYxiLgf/7SmdaXbk0nmS75kEYtYwQoKKbSpUxFxdx441kkQwdvbm9LSUrvbkAtQRuZTRmZTPuZTRvYJCIAdOyAiAt56Cx544NzbKSNxR3rdmk35mK8uZ+SFF13pSj/6MZzhDGYwfvg5by+hhNWsZhGL+JIv2cMeW/qsyxm5C2VkNhPz0fII4hQbG2t3C1IFZWQ+ZWQ25WM+ZWSfvDy4807H5fvvh8GDz72dMhJ3pNet2ZSP+epSRmGEkUgif+NvfM3X5JLLFrbwBm8QRxx++HGIQ/yLfzGa0TSlKbHEMoUptg1soW5l5K6UkdlMzEfLI4hTYGCg3S1IFZSR+ZSR2ZSP+ZSRvZYvh3fegXvvdSyT0KUL5OdX3kYZiTvS69Zsysd8tTUjP/zoQQ/6/ORPGGFnbXeSk2xkI8tZziIWsYtdNnR7YbU1o9pEGZnNxHw0tBWno0eP2t2CVEEZmU8ZmU35mE8Z2e/3v4cRIyAqCv72N/jd7yrfrozEHel1azblY77akFEwwXSiEx3pSBe6cB3X0ZWueP1sLFJGGdvZzoaf/NnPfpu6vni1IaPaThmZzcR8tKatODVq1Ijc3Fy725ALUEbmU0ZmUz7mU0ZmGDQIVqxwXL7xRli69MxtykjckV63ZlM+5nOnjK7iKjr9+KcjHZ1fm9L0nNunkVZpQLuFLRRRVMNdXzl3yqiuUkZmMzEfrWkrTiNHjrS7BamCMjKfMjKb8jGfMjLDypXw5puOy//+NzRqdOY2ZSTuSK9bsykf85mUkQcetKQl13EdYxnL7/k9H/Ih3/ANueRymMMsYQlTmMJv+S396U9TmlJBBQc4wHzm8wqvMIYxXPXjnzGM4e/8nTWsccuBLZiVkZybMjKbifloeQQREREROctTT0FcHLRuDf/4B9x1l90diYhIXVCf+oQTTgQRtPrxz+nLEUQQTjg++Jz3/qWUsp/97Prxz252s4td7GMfpzhVg89EROTKaGgrTps3b7a7BamCMjKfMjKb8jGfMjJHYSHcfjusWgW/+Q3MmgVLligjcU963ZpN+ZjvSjNqQAOCL+JPCCEEE1zl45VTThpppP74Zw97nAPaAxygjLIr6tcdaT8ynzIym4n5aGgrTt7e3na3IFVQRuZTRmZTPuZTRmZZswZefx0eecSxTMI11ygj091777088cQTtGzZkm3btvHAAw+wadMmu9uynV63ZlM+5vP28sYffxr/+CeQwEpfz3VdE5o4h7ENaHBJPy+ffFJJ5RCHzvn1CEcop9xFz9Y9aT8ynzIym4n5aGgrTtHR0ezatcvuNuQClJH5lJHZlI/5lJF5/vAHGDkS2rWDKVNg6VJlZKpbbrmFV199lXvuuYdvvvmGhx9+mCVLltC+fXuOHz9ud3u20nuL2ZTPpfPAAy+88MUXv5/8aUCDSt9f7G1V3d5wT0PqUe+Kei6iiGMcI+Mcf45znAwyOMYxDnGILLKq6TdVd2g/Mp8yMpuJ+WhoKyIiIiLnVVTkWCZhzRq44w7IzExj2jS7u5JzefTRR3nvvff46KOPALjnnnsYOXIkkyZN4pVXXrG3uZ/pTnfCCa+xn9fjhx4UUODSn+GBh0sf/0p/zuXc76f3+fn9L+X7qrbtm9LXeZ3Hj38u5vLl3sfzJ39+/n11Xed1nj/eeFfL9Vc6QL1kluNLKaVkk00OOWT/+Of05Z9fl0VWpcGsq/dBEZHaxgPn26/UdX5+fhQVuecnZdYVysh8yshsysd8yshcf/0rPPEEpKdD586QpZOgjOLt7U1hYSFjxoxh3rx5zus/+ugjGjduTHx8/Fn38fHxwdfX1/m9ZVnk5+fXRLt8zMfcxm018rNE6oKic/wppPCc11/ONhX1K0g/lU4R+m+0qXQMZT5lZDYT89GZtuI0YMAAlixZYncbcgHKyHzKyGzKx3zKyFx/+hPExcH+/a0oLj5kdzvyM82aNcPLy4tjx45Vuv7YsWN06NDhnPd5+umnmTx5svP73NxcAgMDGTNmDN7e3syePZshQ4bQuHFjMjIy2LhxIzfddBMAW7ZsoV69enTr1g2AefPm0a9fP5o1a8bJkydZvXq1c1C8bds2SktL6dmzJwALFy6kvEk5+/P2U1ZeRk5ODk2bNAWgsKiQ8vJyAhoGAJCdnU2DBg3w8fGhvKKcrKwsmjVtBkBRURFlZWUEBDi2zcnJoX79+vj6+lJhVXDy5EmaNm3qONPSE3Kyc2gU2MjxXHNy8fH1oX79+liWxYkTJ2japCkenh4Unyrm1KlTBDYOdP5evL298fPzAyAzM5MmQU3wrOdJcXExRUVFNG7cGIC8vDy86nnh18Cx7YnMEzRu3Jh6XvUoKSmhoKCAoKAgAPLz8/H08KSBv2OtzxMnThAYGIiXlxelpaXk5eXRpEkTAAoKHGco+vv7O7Y9eYJGAY3w8vairKyM3JxcmjR1bFtYUEiFVUHDhg0BOJl1kob+DfHx8aGsvIzsrGyaNXP8DguKCigvKyegkeN3mJWVRYMGDRy/w/IKTpw8QbPmZ37fJaUlBDZy/F6ysrOo71ff8TussDieeZxmzZvh4eHBqaJTnCo+5fy9ZGdn4+Pr4/i9WHAs4xjNmzfH09OTolNF+Pj44OnpieVhkZ2djbe3t/P3cuToEZoHN8ernhdFxUXk5uXSvHlzx3PLPkk9r3rO10DakTTHtt5enCo+RXZWNi1CWjh/Dx6eHgQ2DqTCo4JDPxwiODgYb19vikqKOHrsKK0iWmF5WBw/cZxyq5xmwc2wsPg+5XuCWwbj18CPopIiUlJTaNu+LRYWxzKPUVJWQkhYCAB7D+ylRcsW+Dfyp7C0kB17dhB9bTTlHuWkHUsjpzCHq9tdTblHOUk7kmgR1oImLZpQWFrI6nWrGXjDQCo8K0hJS+Fo5lG69exGuUc56zauo8VVLQiLCKO4vJjPv/ickaNGgi/sP7Sf5NRkBg8eDMDq1asJCQmhbdu2WJbF9OnTiY+Px8/Pj0OHDrF3715iY2MBWLduHUFBQXTs2BGAuTPmEhcXR0hACEeOHGHvtr2MGDGC5s2bs3DhQvz8/OjSpQuAS98jevToQcuWLcnJyWHp0qWMGTMGgJ07d5KXl0efPn0AWLx4MV26dCEsLIyCggIWLFjA+PHjAdizZw8nTpygX79+ACxbtoy2bdsSERFBcXExc+bMYdy4cXh6enLgwAHS0tIYOHAgACtXriQ8PJzWrVtTVlbGzJkzSUxMxMfHh5SUFA4ePMiQIUMAWLNmDcHBwbRr1w6AadOmMXr0aBo0aMDhw4fZtWsXw4YNA2D9+vUEBgbSqVMnAGbOnMmwYcNo1KgR6enpJCUlERcXB8CmTZvw9fWla9euAMyZM4fBgwcTFBTE8ePHWb9+PaNGjQIgKSmJbt264eHhOLN8/vz5xMTE0Lx5c7KyslixYgUJCQkAbN++neLiYnr16gXAokWL6N69OyEhIeTm5rJkyRLGjh0LwK5du8jJySEmJgaAJUuW0KlTJ8LDwyksLGTevHlMmDABgH379pGRkUH//v0BWL58Oa1btyYyMpKSkhJmz57N2LFj8fLy4uDBgxw+fJhBgwYBsGrVKsLCwmjTpg0VFRXMmDGDhIQEfH19SU1NZf/+/QwdOhSAtWvX0rRpU+d/26ZPn87NN9+Mv78/aWlp7Nixg+HDhwOwYcMGAgIC6Ny5MwCzZs0iNjaWwMBAjh49ypYtWxg5ciTg+BAqb29voqOjHfvC3LkMGDCAJk2akJmZydq1axk9ejQAW7dupby8nB49egDwxRdf0Lt3b4KDg8nOzmb58uUkJiYCsGPHDoqKiujduzfNmzfnk08+ITo6mtDQUPLy8li0aBHjxo0DYPfu3WRlZdG3b18Ali5dSvv27WnVqhVFRUXMnTuX8ePH4+Hhwf79+0lPT2fAgAEArFixgsjISKKioigtLWXWrFnO/54nJyeTkpJSLe8RM2bMIC4ujoCAAI4cOcK2bdsYMWIEABs3bnTr94iioiIyMjJq5D0iLi6ORYsWcTEslQqwJkyYYHsPKmXk7qWMzC7lY34pI7Orfn1lZGqFhIRYlmVZffr0qXT9K6+8Ym3YsOGc9/Hx8bECAgKc1bBhQ9ufh6tKr1uzS/mYX8rI/FJG5pcyMrtMzEdn2opTZmam3S1IFZSR+ZSR2ZSP+ZSR2U6dUkamyszMpKysjBYtWlS6vkWLFhw9evSc9ykpKaGkpKQm2rOdXrdmUz7mU0bmU0bmU0ZmMzEfrWkrTg0aNKCwsNDuNuQClJH5lJHZlI/5lJH5lJG5NmzYwMaNG3nwwQcB8PDw4NChQ7z11lvGfRBZTdPr1mzKx3zKyHzKyHzKyGwm5uNpdwNijtPrs4i5lJH5lJHZlI/5lJH5lJG5Xn31Ve666y5uu+02OnTowD//+U/8/f358MMP7W7Ndnrdmk35mE8ZmU8ZmU8Zmc3EfLQ8goiIiIhILfDZZ5/RvHlznn/+eVq2bMnWrVsZPnw4GRkZdrcmIiIiIpdIQ1tx2rp1q90tSBWUkfmUkdmUj/mUkfmUkdnefvtt3n77bbvbMI5et2ZTPuZTRuZTRuZTRmYzMR8tjyBO5eXldrcgVVBG5lNGZlM+5lNG5lNG4o70ujWb8jGfMjKfMjKfMjKbifloaCtOPXr0sLsFqYIyMp8yMpvyMZ8yMp8yEnek163ZlI/5lJH5lJH5lJHZTMxHQ1sRERERERERERERg3gAlt1NiBkCAgLIy8uzuw25AGVkPmVkNuVjPmVkPmUk7kivW7MpH/MpI/MpI/MpI7OZmI/OtBWn3r17292CVEEZmU8ZmU35mE8ZmU8ZiTvS69Zsysd8ysh8ysh8yshsJuajoa04BQcH292CVEEZmU8ZmU35mE8ZmU8ZiTvS69Zsysd8ysh8ysh8yshsJuajoa04ZWdn292CVEEZmU8ZmU35mE8ZmU8ZiTvS69Zsysd8ysh8ysh8yshsJuajNW3FycfHh5KSErvbkAtQRuZTRmZTPuZTRuZTRuKO9Lo1m/IxnzIynzIynzIym4n56ExbcUpMTLS7BamCMjKfMjKb8jGfMjKfMhJ3pNet2ZSP+ZSR+ZSR+ZSR2UzMR0NbcSotLbW7BamCMjKfMjKb8jGfMjKfMhJ3pNet2ZSP+ZSR+ZSR+ZSR2UzMR8sjiIiIiIiIiIiIiBhEZ9oKAA0bNiQnJ4eGDRva3YqchzIynzIym/IxnzIynzISd6TXrdmUj/mUkfmUkfmUkdlMzUdDWwHAw8ODRo0a4eHhYXcrch7KyHzKyGzKx3zKyHzKSNyRXrdmUz7mU0bmU0bmU0ZmMzUfDW1FREREREREREREDKKhrYiIiIiIiIiIiIhBNLQVAIqLi5k8eTLFxcV2tyLnoYzMp4zMpnzMp4zMp4zEHel1azblYz5lZD5lZD5lZDZT8/EALLubEBEREREREREREREHnWkrIiIiIiIiIiIiYhANbUVEREREREREREQMoqGtiIiIiIiIiIiIiEE0tBURERERERERERExiIa2IiIiIiIiIiIiIgbR0LYWuf7665k/fz5paWlYlsXo0aMr3f6LX/yCJUuWkJmZiWVZREdHn/UYvr6+vPXWW2RmZpKXl8esWbMIDg6utE14eDhffPEFBQUFHDt2jL/+9a/Uq1fPpc+ttrjSjIKCgnjjjTfYs2cPhYWFpKam8vrrr9OoUaNK2ymjy1Md+9BPLVq06JyPo3wuX3Vl1KdPH5YvX05+fj45OTmsWrWK+vXrO28PCgri//2//0dOTg5ZWVn8+9//xt/f36XPrbaojoxatGjBJ598Qnp6Ovn5+WzZsoWEhIRK2yijy3ehjLy8vHj55ZfZvn07+fn5pKWl8fHHHxMSElLpMS7m99+lSxdWr15NUVERhw4d4oknnqiR5ye1k45zzaZjXPPpONd8Os41n45zzVYbj3E1tK1F/P392bZtG/fdd995b1+zZg1PPvnkeR9jypQp3HzzzYwdO5aBAwcSGhrKnDlznLd7enqycOFCfHx86Nu3LxMnTuT222/n+eefr/bnUxtdaUahoaGEhoby+OOPc80113D77bczfPhw3n//fec2yujyVcc+dNrDDz+MZVlnXa98rkx1ZNSnTx8WL17M//73P3r37k2vXr146623qKiocG7z6aef0rlzZ2JjY7npppsYMGAAU6dOrfbnUxtVR0affPIJ7du3Z9SoUXTp0oU5c+bw2Wef0a1bN+c2yujyXSijBg0acO211/LCCy9w7bXXkpCQQPv27Zk/f36l7ar6/QcEBPC///2P1NRUevTowRNPPMHkyZO56667XP78pHbSca7ZdIxrPh3nmk/HuebTca7ZausxrqWqfWVZljV69Ohz3hYREWFZlmVFR0dXur5Ro0ZWcXGxlZiY6Lyuffv2lmVZ1nXXXWcB1vDhw62ysjIrODjYuc3dd99tZWdnW97e3rY/b3eqy8noXDVmzBjr1KlTVr169ZSRIflER0dbhw8ftlq0aHHW4ygf+zNav3699fzzz5/3cTt06GBZlmX16NHDed2wYcOs8vJyKyQkxPbn7U51uRnl5eVZt956a6XrMjMzrTvvvFMZ1WBGp6tnz56WZVlWeHj4Rf/+77nnHuvEiROV3tdeeukla/fu3bY/Z5X7l45zzS4d45pfOs41v3Sca37pONfsqi3HuDrTVpx69OiBj48Py5Ytc163d+9eUlNTiYmJASAmJoYdO3aQkZHh3GbJkiUEBgbSuXPnGu9ZIDAwkNzcXMrLywFlZDc/Pz/++9//ct9993Hs2LGzblc+9mrevDl9+vQhIyODtWvXcvToUVauXEm/fv2c28TExJCVlcWWLVuc1y1btoyKigquu+46O9quc9atW8e4ceMICgrCw8ODcePGUb9+fVauXAkoo5oWGBhIRUUF2dnZwMX9/mNiYli9ejWlpaXObZYsWUKHDh1o3LhxTbYvAug41x3pGNc8Os41m45z3YOOc83hDse4GtqKU8uWLSkuLiYnJ6fS9ceOHaNly5bObX7+H+jT35/eRmpO06ZNeeaZZyqdrq+M7DVlyhTWrVt31j+zOE352Ovqq68GYPLkybz33nsMHz6cb7/9luXLl9OmTRvAkcNP/2cDoLy8nJMnTyqjGnLLLbfg7e3NyZMnKS4u5t133+UXv/gFBw8eBJRRTfL19eWVV15h2rRp5OXlARf3+9d7nZhGx7nuRce4ZtJxrtl0nOsedJxrBnc5xvWq9kcUkRoREBDAwoUL2bVrF5MnT7a7HQFuvvlmbrjhBrp37253K3Ienp6Ov6t89913+eijjwDYunUrQ4YMYdKkSfzf//2fjd3JaS+88AKNGzdmyJAhZGZmEh8fz2effcb111/Pd999Z3d7dYaXlxefffYZHh4e/O53v7O7HRGpI3SMayYd55pPx7nuQce59nOnY1ydaStOR48exdfXl8DAwErXt2jRgqNHjzq3adGixVm3n75NakbDhg1ZvHgxeXl5/OIXv6CsrMx5mzKyzw033EDr1q3Jzs6mtLTU+U8mZs+ezYoVKwDlY7f09HQAdu3aVen63bt306pVK8CRw88/TbxevXo0adJEGdWAq6++mgceeIBJkybx1VdfsX37dp5//nk2b97s/FABZeR6pw9mIyIiiI2NdZ6BABf3+9d7nZhGx7nuQce45tJxrvl0nGs+Hefaz92OcTW0FactW7ZQUlLCkCFDnNe1a9eOiIgI1q9fD8D69evp0qULzZs3d24TGxtLTk7OWf9xENc4/WmFJSUljBo1iuLi4kq3KyP7vPzyy3Tt2pVu3bo5C+CRRx7hjjvuAJSP3VJSUkhLS6N9+/aVrm/Xrh2pqamAI6OgoCCuvfZa5+033HADnp6efPPNNzXab13UoEEDgEqfcgyOf5p0+gwSZeRapw9m27Zty9ChQzl58mSl2y/m979+/XoGDBiAl9eZf9QVGxvLnj17nOuGidQkHeeaT8e4ZtNxrvl0nGs+Hefay12PcW3/VDdV9ZS/v78VHR1tRUdHW5ZlWQ8//LAVHR3t/CS8oKAgKzo62hoxYoRlWZZ1yy23WNHR0VaLFi2cj/HOO+9YKSkp1qBBg6xrr73WWrt2rbV27dozn1zn6Wlt377dWrx4sdW1a1frxhtvtI4dO2b9+c9/tv35u0NdaUYBAQHW+vXrrW3btllXX3211aJFC2d5enoqI5vzOVf9/FMrlY/9GT300ENWdna2lZiYaLVu3dp6/vnnrcLCQuvqq692brNo0SJry5YtVq9evay+fftae/futT799FPbn7871JVm5OXlZe3bt89atWqV1atXL+vqq6+2Hn30Uau8vNwaMWKEMnJxRl5eXtbcuXOtQ4cOWV27dq3035mffkpuVb//Ro0aWenp6dbHH39sderUybrlllus/Px866677rL9+avcs3Sca3bpGNf80nGu+aXjXPNLx7lmVy09xrX/F6uqnho4cKB1Lh9++KEFWBMnTjzn7c8++6zzMXx9fa233nrLOnHihJWfn2/Nnj37rP9Qt2rVylq4cKFVUFBgZWRkWH/729+sevXq2f783aGuNKPz3d+yLCsiIkIZ2ZzPucqyKh/MKh8zMnryySetQ4cOWfn5+dbatWutfv36Vbo9KCjI+vTTT63c3FwrOzvbev/99y1/f3/bn787VHVk1KZNG2vWrFnW0aNHrfz8fGvr1q3WrbfeqoxqIKOIiIhz3mZZljVw4MBL+v136dLFWr16tVVUVGQdPnzY+v3vf2/7c1e5b+k41+zSMa75peNc80vHueaXjnPNrtp4jOvx4wURERERERERERERMYDWtBURERERERERERExiIa2IiIiIiIiIiIiIgbR0FZERERERERERETEIBraioiIiIiIiIiIiBhEQ1sRERERERERERERg2hoKyIiIiIiIiIiImIQDW1FREREREREREREDKKhrYiI4VasWMGUKVOc3ycnJ/PQQw/Z2JHDs88+S1JSkt1tiIiIiIgb0jGuiMiFaWgrIlLNmjVrxjvvvENqaiqnTp0iPT2dxYsX07dvX+c2lmUxevToi3q8hIQEnnnmGVe1y8CBA7Esy1lHjx5l1qxZREVFXfB+f//73xkyZIjL+hIRERERc+gYV0SkZnnZ3YCISG0ze/ZsfHx8mDhxIt9//z0tWrRgyJAhNG3a9JIex9vbm9LSUrKyslzUaWXt2rUjLy+Ptm3bMnXqVBYsWEDXrl2pqKg4a9t69epRUFBAQUFBjfQmIiIiIvbSMa6ISM2zVCqVSlU9FRgYaFmWZQ0YMOC82yQnJ1s/lZycbAHWs88+ayUlJVl33nmn9f3331vl5eUWYK1YscKaMmVKpfs/9NBDzu/vvPNOKysry7rhhhsswOrcubO1aNEiKy8vzzp69Kj1ySefWE2bNj1vPwMHDrQsy7ICAwOd102YMMGyLMtq166d8/bhw4dbmzdvtoqLi62BAwc6+/3pY91xxx3Wd999Z506dco6cuSI9eabb1b63bz33ntWRkaGlZOTYy1fvtzq2rWr7ZmpVCqVSqVSqS5cOsbVMa5Kpar50vIIIiLVKD8/n7y8POLj4/Hx8TnnNr169QLg9ttvp2XLls7vAdq0aUNiYiIJCQl069atyp/3xBNP8PLLL3PjjTfy1VdfERgYyFdffUVSUhI9e/Zk+PDhtGjRgs8+++ySnkdRURFApefw8ssv89RTT9GxY0e2b99+1n3uuece3n77baZOnUqXLl0YNWoUBw4ccN4+c+ZMgoODGTFiBD169ODbb79l+fLlBAUFXVJvIiIiIlKzdIyrY1wRsYftk2OVSqWqTZWQkGCdOHHCKiwstNasWWP9+c9/trp06VJpG8uyrNGjR1e67tlnn7WKi4utZs2aVbr+fGchvPzyy1ZaWprVqVMn521/+MMfrMWLF1e6f1hYmGVZltW2bdtz9vvzsxBatmxprVmzxjp8+LDl7e3tvH3UqFFn9fvTsxB++OEH64UXXjjnz+jXr5+VnZ1t+fj4VLp+//791l133WV7ZiqVSqVSqVSqC5eOcc/+GTrGValUriydaSsiUs3mzJlDaGgoo0aNYvHixQwaNIhvv/2WiRMnVnnf1NRUMjMzq9zuscce46677qJ///7s2rXLeX10dDSDBw8mLy/PWXv27AGgdevWF3zMH374gfz8fNLT0/H39ycxMZHS0lLn7Zs3bz7vfZs3b05YWBjLly8/5+3R0dE0bNiQEydOVOotKiqqyr5ERERExH46xj2bjnFFxJX0QWQiIi5QXFzMsmXLWLZsGS+++CLvvfcezz33HB9//PEF73exH3rw9ddfM3LkSG655RZeeeUV5/UNGzZkwYIFPPnkk2fdJz09/YKPef3115Obm0tGRgb5+fmX1Nvpf2p2Pg0bNiQ9PZ1BgwaddVt2dvYF7ysiIiIiZtAxbmU6xhURV9LQVkSkBuzatYv4+Hjn9yUlJdSrV++yH2/jxo289dZbLF68mLKyMv7xj38A8O2335KYmEhKSgrl5eWX9JjJycnk5ORcVj/5+fkkJyczZMgQVq5cedbt3377LS1btqSsrIzU1NTL+hkiIiIiYhYd4+oYV0RcR8sjiIhUoyZNmrB8+XJ+9atf0aVLFyIjIxkzZgy///3vmTdvnnO7lJQUhgwZQosWLWjcuPFl/az169cTFxfHs88+y0MPPQTA22+/TZMmTZg2bRo9e/bk6quv5sYbb+SDDz7A09O1b/mTJ0/mscce44EHHqBNmzZ0796d+++/H4Bly5axfv165s6dS2xsLBEREcTExPDiiy/So0cPl/YlIiIiIldGx7g6xhWRmqczbUVEqlF+fj7ffPMNjzzyCK1bt8bb25vDhw/z3nvv8Ze//MW53WOPPcarr77KXXfdRVpaGlFRUZf189auXcvIkSNZtGgR5eXlvPXWW/Tr149XXnmF//3vf/j6+pKamsrixYupqKiorqd5Tp988gn169fnkUce4e9//zuZmZnMmjXLeXtcXBx//vOf+fDDD2nevDlHjx5l9erVHDt2zKV9iYiIiMiV0TGujnFFpOZ54PhEMhERERERERERERExgJZHEBERERERERERETGIhrYiIiIiIiIiIiIiBtHQVkRERERERERERMQgGtqKiIiIiIiIiIiIGERDWxERERERERERERGDaGgrIiIiIiIiIiIiYhANbUVEREREREREREQMoqGtiIiIiIiIiIiIiEE0tBURERERERERERExiIa2IiIiIiIiIiIiIgbR0FZERERERERERETEIBraioiIiIiIiIiIiBhEQ1sRERERERERERERg2hoKyIiIiIiIiIiImIQDW1FREREREREREREDKKhrYiIiIiIiIiIiIhBNLQVERERERERERERMYiGtiJSJyQnJ7NgwQK72zBaREQElmUxceJE53UffvgheXl5NnZVtXP1LSIiIiIiIuLONLQVEbc0ceJELMtyVmlpKT/88AMffvghoaGhdrfnMv7+/kyePJkdO3aQn59PZmYmSUlJvPbaa4SEhDi3GzFiBM8++6yNnV685OTkSlkeO3aM1atXEx8fb3drIiIiIiIiIrbwsrsBEZEr8cwzz5CcnEz9+vXp06cPt99+O/379+eaa66huLjY7vaqlZeXF6tXr6ZDhw58/PHHvPnmmzRs2JDOnTvzy1/+ks8//5z09HQA4uLiuP/++3nuuecu+vFTU1OpX78+paWlrnoK55WUlMQ//vEPAEJDQ7n77rv5/PPPueeee3j33XcveF87+xYRERERERFxBQ1tRcStffnll2zZsgWA999/n8zMTJ566ilGjRrFzJkzbe6uesXHx3Pttdfyy1/+kmnTplW6zdfXFx8fn8t63Hr16uHp6Ulpaaltg+60tDQ+/fRT5/effPIJBw4c4JFHHjnv0NaEvkVERERERERcQcsjiEit8vXXXwPQunXrc97er18/vvnmG4qKijh48CC//vWvz9omKiqKzz77jBMnTlBQUMD69euJi4ur8mfv2LGDr7766qzrPTw8+OGHHyoNkceNG8fmzZvJzc0lJyeH7du38+CDD17w8U8/p7Vr1551W3FxsXPt2Q8//JD7778foNKyA3Bm/dfHHnuMhx56iAMHDlBcXEynTp0uem3Y6OhoMjIyWLFiBf7+/oDj7Nj333+fo0ePcurUKb777jvuuOOOCz7OhRw7dozdu3cTFRV12X23b9+eGTNmkJGRQWFhIXv27OHFF1+stE119y0iIiIiIiJSHXSmrYjUKpGRkQBkZWWddVubNm2YNWsW77//Ph9//DGTJk3io48+YsuWLezatQuA4OBg1q1bR4MGDXjjjTc4ceIEEydOZP78+YwZM4a5c+ee92fPmDGDyZMn06JFC44dO+a8vn///oSFhTF9+nQAhg4dyvTp01m2bBlPPvkkAB07dqRfv3688cYb53381NRUAG677bazho8/9e677xIaGsqNN97Irbfees5t7rjjDurXr8/UqVMpLi7m5MmTeHpW/fd4PXv2ZMmSJWzevJnRo0dz6tQpgoOD2bBhA5Zl8dZbb3H8+HFGjBjBBx98QKNGjXj99derfNyf8/LyIjw8nBMnTlxW3126dOHrr7+mtLSUqVOnkpKSQuvWrbn55pv54x//COCSvkVERERERESqi6VSqVTuVhMnTrQsy7JuuOEGq2nTplZYWJiVkJBgHTt2zCoqKrLCwsIqbZ+cnGxZlmX179/feV2zZs2soqIi629/+5vzuldffdWyLMvq16+f8zp/f3/r4MGD1vfff295eHict6e2bdtalmVZ9913X6Xr33rrLSs3N9eqX7++BVhTpkyxsrOzLU9Pz0t6zvXr17d2795tWZZlJScnWx988IF1xx13WM2bNz9r2zfffNOyHKfXVqqIiAjLsiwrOzvbatas2TlvmzhxovO6Dz/80MrLy7MAq2/fvlZ2dra1YMECy8fHx7nNe++9Z6WlpVlNmjSp9Hj//e9/raysLOfzPl8lJydbixcvtpo2bWo1bdrU6tKli/Xf//7XsizLev311y+r75UrV1o5OTlWeHj4eX/ulfatUqlUKpVKpVKpVCqVq0rLI4iIW1u+fDmZmZn88MMPzJ49m4KCAkaNGkVaWtpZ2+7cuZM1a9Y4v8/MzGTv3r1cffXVzuvi4uL45ptvKi1BUFBQwNSpU4mKiqJTp07n7WX//v0kJSUxbtw453Wenp6MGTOGBQsWcOrUKQCys7Px9/cnNjb2kp7rqVOnuO666/jrX/8KOM46/eCDD0hPT+eNN964pDVtZ8+eTWZm5kVvP2jQIJYsWcLy5ctJSEigpKTEeVtiYiILFizAw8ODpk2bOmvJkiU0btyYa6+9tsrHHzZsGJmZmWRmZrJ9+3bGjh3LJ5984jwT+VL6btasGQMHDuSDDz7g8OHD592uOvoWERERERERcQUNbUXErd17770MHTqUxMREFi5cSLNmzc77oVSHDh0667qsrCyCgoKc30dERLB3796zttu9e7fz9guZMWMG/fr1IzQ0FHAMO1u0aMGMGTOc27zzzjvs27ePxYsXc/jwYd5//32GDRtW9ZMFcnNzefLJJ4mKiiIiIoJJkyaxd+9eHnjgAZ555pmLegyA5OTki962fv36LFy4kKSkJG655RZKS0udtzVv3pygoCDuvvtu59D1dH300UeAYxmCqmzYsIGhQ4cyZMgQYmJiaNasGRMnTnQOui+l79ND+O++++6821RX3yIiIiIiIiKuoDVtRcStbdy4kS1btgAwd+5c1qxZw3//+1/at29PQUFBpW3Ly8vP+RgeHh7V1s+MGTN4+eWXGTt2LK+//jq33HIL2dnZLF682LnN8ePH6datG8OGDWPEiBGMGDGCSZMm8fHHH3P77bdf9M86dOgQH374IZ9//jnff/89v/rVry56cFtUVHTRP6e4uJhFixYxevRohg8fzsKFC523nV5P9j//+Q8ff/zxOe+/ffv2Kn9GZmYmy5cvr3K7S+n7QqqrbxERERERERFX0NBWRGqNiooKnn76aVauXMn999/PK6+8csmPkZqaSvv27c+6vkOHDs7bLyQlJYVvvvmGcePG8dZbb5GQkMDcuXMrLScAUFpayhdffMEXX3yBh4cH77zzDvfccw8vvPACBw8evKSes7OzOXjwINdcc43zOsdyttXDsix+9atfMW/ePGbOnMmIESNYtWoV4BhA5+bmUq9evYsautaE77//HqDS7+PnTOxbRERERERE5DQtjyAitcqqVav45ptvePjhh/H19b3k+y9atIjrrruOPn36OK9r0KABv/3tb0lOTmbXrl1VPsaMGTOIiYlh0qRJNG/evNLSCABNmjSp9L1lWc6zOi/Uc9euXWnatOlZ17dq1YpOnTpVWtbh9FnGgYGBVfZ7MUpLS0lISGDTpk0sWLCAXr16AY5B+ezZs0lMTKRz585n3a9Zs2bV8vMvRWZmJqtWrWLSpEmEh4efcxsT+xYRERERERE5TWfaikit87e//Y1Zs2Zx++238+67717SfV9++WUmTJjAl19+yRtvvMHJkyeZOHEiUVFRJCYmXtQZrJ999hl///vf+fvf/86JEydYtmxZpdv//e9/06RJE7766it++OEHIiIieOCBB0hKSnKunXsusbGxPPfcc8yfP58NGzaQn5/P1VdfzaRJk/D19WXy5MnObU8vGfHGG2+wZMkSysvLzxoeX6pTp05x00038dVXX/Hll18ycOBAdu7cyVNPPcXgwYP55ptveO+999i1axdNmjTh2muvZejQoeccNLvagw8+yJo1a/j222+ZOnUqycnJREZGMnLkSLp37w5gZN8iIiIiIiIip1kqlUrlbjVx4kTLsiyrR48eZ93m4eFh7d+/39q/f7/l6elpAVZycrK1YMGCs7ZdsWKFtWLFikrXRUVFWZ999pl18uRJq7Cw0NqwYYMVFxd3Sf19/fXXlmVZ1tSpU8+6LSEhwVq8eLF19OhR69SpU1ZKSor1z3/+02rRosUFHzMyMtKaPHmytW7dOuvo0aNWSUmJdezYMWvBggXWoEGDKm3r6elpvf7669axY8es8vJyy3JMm62IiAjLsizrscceO+vxT982ceJE53UffvihlZeXV2m7Jk2aWN9995115MgRq3Xr1hZgNW/e3HrzzTet1NRUq7i42Dpy5Ii1dOlS6ze/+U2Vv6vzZXOu3i62b8Dq1KmTNXv2bGeOu3fvtp577rlK21xJ3yqVSqVSqVQqlUqlUrmqPH68ICIiIiIiIiIiIiIG0Jq2IiIiIiIiIiIiIgbR0FZERERERERERETEIBraioiIiIiIiIiIiBhEQ1sRERERERERERERg2hoKyIiIiIiIiIiImIQDW1FREREREREREREDKKhrYiIiIiIiIiIiIhBNLQVERERERERERERMYiGtuI0ZswYu1uQKigj8ykjsykf8ykj8ykjERERERHX09BWnLZu3Wp3C1IFZWQ+ZWQ25WM+ZWQ+ZSQiIiIi4noa2oqTr6+v3S1IFZSR+ZSR2ZSP+ZSR+ZSRiIiIiIjraWgrTl27drW7BamCMjKfMjKb8jGfMjKfMhIRERERcT0NbUVEREREREREREQM4gFYdjdRXa6//nqeeOIJevToQWhoKPHx8cybN895e3BwMK+88go33ngjjRs3ZvXq1TzwwAMcOHDAxq7N4evrS3Fxsd1tyAUoI/MpI7MpH/MpI/MpIxERERER16tVZ9r6+/uzbds27rvvvnPePnfuXK6++mpGjx5N9+7dSU1NZdmyZTRo0KCGOzXT4MGD7W5BqqCMzKeMzKZ8zKeMzKeMRERERERcz8vuBqrT4sWLWbx48Tlva9u2LTExMXTu3Jldu3YB8Lvf/Y6jR48yYcIE3n///XPez8fHp9IHbliWRX5+fvU3b4CgoCC7W5AqKCPzKSOzKR/zKSPzKSMREREREderVUPbCzk9eD116pTzOsuyKC4upn///ucd2j799NNMnjzZ+X1ubi6BgYGMGTMGb29vZs+ezZAhQ2jcuDEZGRls3LiRm266CYAtW7ZQr149unXrBsC8efPo168fzZo14+TJk6xevZr4+HgAtm3bRmlpKT179gRg4cKF9OjRg5YtW5KTk8PSpUsZM2YMADt37iQvL48+ffoAjmF1ly5dCAsLo6CggAULFjB+/HgA9uzZw4kTJ+jXrx8Ay5Yto23btkRERFBcXMycOXMYN24cnp6eeHl5ERoaysCBAwFYuXIl4eHhtG7dmrKyMmbOnEliYiI+Pj6kpKRw8OBBhgwZAsCaNWsIDg6mXbt2AEybNo3Ro0fToEEDDh8+zK5duxg2bBgA69evJzAwkE6dOgEwc+ZMhg0bRqNGjUhPTycpKYm4uDgANm3ahK+vr/NDT+bMmcPgwYMJCgri+PHjrF+/nlGjRgGQlJQEQPfu3QGYP38+MTExNG/enKysLFasWEFCQgIA27dvp7i4mF69egGwaNEiunfvTkhICLm5uSxZsoSxY8cCsGvXLnJycoiJiQFgyZIldOrUifDwcAoLC5k3bx4TJkwAYN++fWRkZNC/f38Ali9fTuvWrYmMjKSkpITZs2czduxYvLy8OHjwIIcPH2bQoEEArFq1irCwMNq0aUNFRQUzZswgISEBX19fUlNT2b9/P82aNWPChAmsXbuWpk2b0qFDBwCmT5/OzTffjL+/P2lpaezYsYPhw4cDsGHDBgICAujcuTMAs2bNIjY2lsDAQI4ePcqWLVsYOXIkAJs3b8bb25vo6GjAcXb6gAEDaNKkCZmZmaxdu5bRo0cDsHXrVsrLy+nRowcAX3zxBb179yY4OJjs7GyWL19OYmIiADt27KCoqIjevXsD8OWXXxIdHU1oaCh5eXksWrSIcePGAbB7926ysrLo27cvAEuXLqV9+/a0atWKoqIi5s6dy/jx4/Hw8GD//v2kp6czYMAAAFasWEFkZCRRUVGUlpYya9Ys576anJxMSkqK8wyx1atXExISQtu2bbEsi+nTpxMfH4+fnx+HDh1i7969xMbGArBu3TqCgoLo2LEjADNmzCAuLo6AgACOHDnCtm3bGDFiBAClpaVcc801dOnSBaBWvUccOHCAtLQ0t36PKC8vd+6vtfE9YujQoQBu/R7RrFkzGjduXGvfIzZu3Iifn59bv0f4+PgwYcIEl79HxMXFsWjRIkRERERE6qJatabtT1mWVWlNWy8vLw4cOMA333zD3XffTUFBAY888givvPIKS5Yscf7P68/VpTNt/f39KSgosLsNuQBlZD5lZDblYz5lZD5lJCIiIiLierVqTdsLKSsrIyEhgXbt2pGVlUVhYSGDBw9m0aJFVFRUnPd+JSUl5OXlOau2DmwB59loYi5lZD5lZDblYz5lZD5lJCIiIiLienVmeQSAb7/9lu7du9OoUSN8fHzIzMxkw4YNbN682e7WRERERERERERERIA6dKbtT+Xm5pKZmUmbNm3o2bOncwmFuu70mrBiLmVkPmVkNuVjPmVkPmUkIiIiIuJ6tepMW39/f9q0aeP8PioqiujoaE6ePMnhw4cZM2YMx48f59ChQ3Tp0oXXX3+duXPnsnTpUhu7FhERERERERERETmjVp1p27NnT7Zu3crWrVsBmDJlClu3buX5558HICQkhP/85z/s2bOHN954g//85z/OT/QW6N69u90tSBWUkfmU0eXz8ABPD6jnCV71wLse+HiBrzf4+UADX/D3hYb1IcDPcd2lUj7mU0bmU0YiIiIiIq5Xq860XbVqFR4eHue9/c033+TNN9+swY5EROqGRn7QIhBaNoYm/o7vL6YCfvx6OQNYgLJyyCmE7ELH15yiH7/+eDm7oPJ1nZoeJaIZHD4BFVa1/gpEREREREREqo0HoP9tFcCxvERBQYHdbcgFKCPz1aaM6ntDSGPHILZFILQMPMflRo6vlzt0tUtJGXyfAQeOwYGjjq8HMxyXUzIdw2CxR23ah2orZSQiIiIi4nq16kxbuTIxMTEsW7bM7jbkApSR+dwpo8AGENHs/NUi8NIeL7cIjmbDiXzHWa25RY7KO3Xm8rkqrwiKShxnvlqW428SLesC3/94nZen4zkENoBAP8fXxg0ufF1kywa08C/E1xs6hDrq58rKITXzx4HuMdh/FDYcgC3JGubWBHfah+oqZSQiIiIi4noa2opT8+bN7W5BqqCMzGdSRr7ecHUwtGnhqKjmlYeyjf2rfoyiEscg9miO4+ux3J99n+O4fCzHsW1NKgYKiuFI1sXfZ8KE0cyYPo2rmkLr07+blmd+R21aONbObd3CUcN+ct+CU47h7eq98PUex+Wafs51gUn7kJybMhIRERERcT0NbcUpK+sSJh9iC2VkvprO6PRgtu2Pg8effg1vAp5VfNxkZp7jrNLz1cn8mnkeNSUrK4sKCw5lOmrFrrO3adm48hD3mnDo3w6aBsCQaxwFUFoGm5Ph6x+HuGv2OtbWlSuj9znzKSMREREREdfTmrbi5OvrS3Fxsd1tyAUoI/O5IiMPD7iqCXQMPfNP+tu1dJwhWtVgNrfI8c/7DxxzrOH604HsoUzHmap1yeXm4+Hh+P1f3wEGdIDr20N408rbVFTAdz84hrirdsPCrVBYx36/1UHvc+ZTRiIiIiIirqehrThNmDCBadOm2d2GXIAyMt+VZOTj5Tizs2OYYzDrHNKGgH/9898vp/DMYPbnX4/nXuYTqaWqcx+KaPbjALcDDGgP7X+2Pu7JfPj3Snjrf3D4RLX8yDpB73PmU0YiIiIiIq6n5RFERGqYn49jINv5KkedHs5eHQxe9c59n5IyxyB2zxFH7U0/M5zNzKvZ/sUhNRP+s8ZRAMGNoH97x1m4N1/rWBP39zfBY3EwZxO8vhjW7rO3ZxEREREREXEPOtNWnDp37szOnTvtbkMuQBmZ76cZ+XpD+5Afh7NhjrVRO4c5hrPnW9IgpxB2/ziY3Z0Ge9IdX5OPQ1l5DT6RWqqm9iFPD4jrBg8Nh6HXnLl+S7JjeDtjg2MQL2fT+5z5lJGIiIiIiOvpTFtx0vp05lNGZqrn6VjWoGsruL7rEUKHOga1bVs6bjuX47mO9U93/gC70n4c0h6Bo9k12nqdU1P7UIUFXyQ5qvNV8OAw+HV/6BEFn/wO/joB/rXcUcdyaqQlt6H3OfMpIxERERER19PQVpx69erFgQMH7G5DLkAZ2a9pQ8dwtmsr6Bru+Nr5KseSBw6VP1U9q8AxmD09oD39VWvN2sOOfWjnD3D3+/B/M+CuG+C+WMcHy01OhP8bDdPXO86+/TalRtsylt7nzKeMRERERERcT0NbEZFz8K7nWGf25wPa0KBzb19wyjGQLfS5mvmrvmdnGnx3GNKza7RtMdiJfHh5Pvx9IST0goeGQd92cNv1jlqz1zG8/XwzlFfY3a2IiIiIiIjYSWvailNgYCA5Ofp3uiZTRq7RsvGZoezpAW3HMPA5z19rHTwG2w/B9sOwLdXx9fsMsCxlZDrT8ul5tWN4e0ufM6+3Q5nw9lJ4b4XjTO26xrSM5GzKSERERETE9TS0FadBgwaxcuVKu9uQC1BGV6a+N3QKO/vs2eaNzr19TuGZ4ez2Q4767gfIP3X+n6GMzGZqPi0bw++GwD1DIDjQcV1hMfxnDbyxxLHucV1hakZyhjISEREREXE9LY8gTiEhIXa3IFVQRhevZWPoFnGmuoZDu5BzfzBYeQXsS688nN1+2HHG46VSRmYzNZ+j2fDsbPjLfBgf4zj7tnsk3D3EUUt3wOtLYNFWxxndtZmpGckZykhERERExPU0tBWn3Fx9MpLplNHZPD0cw9ifDmi7RUCLwHNvn5nnGMpuO3RmQLsrDU6VVk8/yshspudTXAofr3bU9R3g4eEwugfEdnHU/qOOM28/Wn3hM77dmekZiTISEREREakJWh5BnLy8vCgrK7O7DbmAup6Rvy90Ca88nO0SDg18z962vAL2psPWVMe6s6eHtK7+YLC6npHp3DGfiGZw/43wm0HQ2N9xXU4hfLAK3lwCycdtba/auWNGdY0yEhERERFxPQ1txWnChAlMmzbN7jbkAupSRj9f3qBbK2jbEjzPsbxBwSnHUHZr6pn67gcoKqnxtutURu7InfPx94XbrocHh0GHUMd1FRXw7lfwyP9znKVbG7hzRnWFMhIRERERcT0tjyAitrrU5Q2OZFUezm5NhYPHoEJ//SS1XEEx/HMZ/Gs53NgFHhoOI6Lhd0Oh19Uw5nVIvYx1mEVERERERMQ8GtqK065du+xuQarg7hn5+TiWM+geCd0vYXmD07UtFTIMX0rR3TOq7WpDPpYFS7Y7KrYL/Pc+6Hk1bHkRfvWO43p3Vhsyqu2UkYiIiIiI62loK045OTl2tyBVcKeMgvwrD2e7Rzr+SXc9w5c3uFLulFFdVNvyWboDevwRZj4IvVvDoifguc/hhc8dw113VNsyqo2UkYiIiIiI62loK04xMTGkpKTY3YZcgKkZXdXkxwFtpGNI2z3S8eFJ53IsB5JSIOnH4WxSSu1a3sDUjMShNuZzKBOufx5e+7VjqYTnEqFPG/jV25BVYHd3l642ZlTbKCMREREREdfT0FZELtrp9Wd/fgZts4Bzb3/wmGM4m5TiqK2pkJ5dY+2K1BklZXDvh7B+P7x7p2Ot22//DImvwbcpdncnIiIiIiIil8oDqCXnt8mVatKkCSdPnrS7DbmAmszI1xuuuary2bNdw8G//tnblpXDrrQzZ9CeHtDmFtVIq0bRfmS2upBP11Yw+yFo0xJOlcD9H8P7K+3u6uLVhYzcnTISEREREXE9DW3FqX///qxZs8buNuQCXJVRI78zZ812j4Bro6BjKHjVO3vb0+vP/nQ4+90PUFxa7W25Je1HZqsr+QQ2gE/ugVE9HN+/vxLu/whOucF+WlcycmfKSERERETE9bQ8gjiFh4fb3YJUoToyahFY+ezZayOhdYtzb5uZV/ns2aQU2H+09qw/6wraj8xWV/LJKYT4KfDkzfDiWLhzkGOfH/M6JB+3u7sLqysZuTNlJCIiIiLiehrailNhYaHdLUgVLjWjyOZnzpw9PaQNDTr3tqmZZw9of9C/fr1k2o/MVpfysSx4eT5sPAjT7nO8D2z5M/z6n7Awye7uzq8uZeSulJGIiIiIiOtpeQSRWqCeJ7Q//QFhkY6zZ7tFQJD/2dtWVMDe9MrD2aRUOJlfoy2LSA0KawIzH4SYto7vX/gcJs/WWfMiIiIiIiKm0tBWnCZMmMC0adPsbkMuYMKECcyZNY0u4ZWXN+jaCvx8zt6+pAx2HK58Bu32Q1BQXLN91yXaj8xWl/Pxrgf/+BU8MMzx/f92wC/fghOG/YVNXc7IXSgjERERERHX0/IIIgYL+PEDwq6NdAxpB3dbxCcjzv0BYfmnHB8KlpQC36Y4hrS7foDS8hpuWkSMVFoOD34CGw7A1Dvhxi7w7Z8d69xu+t7u7kREREREROSnNLQVp3379tndQp3WvNHZ68+2bfnzrXIAOJ5beXmDb1PgwDHHGpZiL+1HZlM+8N91sO0QzHkY2oXAmmcdw9x3l9vdmYMyMp8yEhERERFxPQ1txSkjI8PuFuqMVs1+HNBGnlmH9qom5972UOaZAe3h/Gb8b3OmPiDMYNqPzKZ8HHb+AL2egQ/vhoRe8K9JENMGfvchFJXY25syMp8yEhERERFxPQ1txal///5ao66aeXo4zpY9vfZs90jHsLZpwNnbVlTA/qNnljY4fRbtT9ebnDAhlh9OKiOTaT8ym/I5I7cIEl+Dx0fCy+Nh4gDHciyJr8PBY/b1pYzMp4xERERERFxPQ1uRauJdDzpfVXlAG90KGtY/e9vSMtiZBt8mnxnQbjvkWJdWRKQm/X2hY03bGQ9AdARseRFu+xfM32J3ZyIiIiIiInWXB6BVMAWA4OBg/ZPHi9TA1zGQdQ5oI+CacPA5x1+DFBY7BrLODwhLge9+gJKyS/+5ysh8yshsyuf8QhrDZw9C//aO71+aD8/MhPKKmu1DGZlPGYmIiIiIuJ7OtBWn1q1b63/CzqFxg8pnz14bCe1DwNPz7G2zCioPZ5NSYe8RqKimvxpRRuZTRmZTPueXng2D/wx/nQCPjICnR0Hvq2HC244PP6wpysh8ykhERERExPU0tBWnyMhI1q9fb3cbtmrZuPJwtnsERAWfe9sjWWcGs6eXOUg57tr+lJH5lJHZlM+FlZXDo/8PNhyA9++CIdfAt3+Gsa87rqsJysh8ykhERERExPU0tBWnkhKbPzK8hkU1h2ujHIPZ019bNj73tgePnVl79vRZtMdyaq7X0+paRu5IGZlN+VyczzbAjsMw+yHoGAarn4H7PoL3Vrj+Zysj8ykjERERERHX05q2UuvV83QsZ/DTJQ66R0Bj/7O3La+APUfODGa/TYGtqZBTWJMdi4iYoWF9+OC3MPY6x/fxr8I8fUCZiIiIiIiIy2loK05jx45l5syZdrdxRXy84JqrKp9B2zXc8cFhP1dc6jiT7Kfrz24/BEUGn0BUGzKq7ZSR2ZTP5XnrdrgvFvKKoO9z8N1h1/0sZWQ+ZSQiIiIi4npaHkGcvLzc6+XQsD5Et/rJgDYSOoWB9zmeRl6R44zZpNQzQ9pdaY71G92Ju2VUFykjsymfy/Pwf6BjKNzQGeY9Cr2fgRP5rvlZysh8ykhERERExPV01C1OBw8etLuF82ra8OzlDdq2BE/Ps7fNzKu8vEFSChw4BlYtOKfc5IzEQRmZTflcnrJyGPsGbHoBrg6Gzx6EYa+45i++lJH5lJGIiIiIiOtpaCtOhw+78N+7XoKwJmeGs9dGOga0rZqde9vDJ34ynE2Fb5Phh5M11mqNMyUjOT9lZDblc/lO5sOof8D6yY4zbqfcCg98XP0/RxmZTxmJiIiIiLiehrbiNGjQIKZNm1ZjP8/DA1oHV15/tnsENG907u33pTsGsz89gzYzr8baNUJNZySXThmZTflcmZ0/wK3/dCyRcP+NjnXA31tRvT9DGZlPGYmIiIiIuJ6GtlIjvOo51kP86RIH3SKgkd/Z25aVO9ab/ekSB9sOOdalFRERe83fAn+cCS+Ohbdvh91HYM1eu7sSERERERGpXTyAWrDSp1SH0NBQjhw5csWPU98buraqvLxBl3Co73P2tqdKHAPZ00sbJKU6PpX8VOkVt1ErVVdG4jrKyGzKp/rMeABu6QMZOdDrT3Aos3oeVxmZTxmJiIiIiLiezrQVp7CwsEv+n7BGfo4zZn+6Bm2HUMeZtT+XUwhbf7K8wbcpsOcIlFdccet1xuVkJDVLGZlN+VSfO6Y6PhCye6RjuYR+z0Fh8ZU/rjIynzISEREREXE9DW3FqU2bNmzatOm8twc3cvzP+U/PoG3T8tzbZuRU/nCwpFT4PgMsndd9RarKSOynjMymfKpPYTGMfhU2v+D4y7uP7oZb3rjyx1VG5lNGIiIiIiKup6GtOFVUnDnltVWzM4PZ00PasCbnvl9qZuUPB/s2BY5kubrbuumnGYmZlJHZlE/1OnwCEl6Dr/4AY6+DP8bDi3Ov7DGVkfmUkYiIiIiI62lN2zrO0+PMP289vcRB9whoGnD2thUVsO9o5QFtUiqczK/JjkVExDR3DoJ/3+W4/IspMHezre2IiIiIiIi4PQ1t6xDvetDpqjNn0F4bBdGtoGH9s7ctLYOdaWeWNvg2BbalQkE1rFcoly8hIYE5c+bY3YZcgDIym/JxnTdugweGQf4piJns+FDJy6GMzKeMRERERERcr1Ytj3D99dfzxBNP0KNHD0JDQ4mPj2fevHnO2/39/Xn55ZeJj4+nadOmJCcn88Ybb/Duu+/a2LVrNPCFruGVz6C95irw9T5728Ji2HYI8r3bMGPpAZJS4LsfoKSsZnuWqvn6+trdglRBGZlN+bjOo59CpzAYcg3MfxR6PQMnLuNfYigj8ykjERERERHXq1VDW39/f7Zt28YHH3zA559/ftbtr776KjfccAO33norKSkp3HjjjbzzzjscOXKEBQsW2NBx9WjcALpFVj6Dtn0I1PM8e9vsgrM/IGxfOpRXQN++waxbd6CGu5dLkZqaancLUgVlZDbl4zpl5XDLm7DxeWjdAmY+BDe+7Lj+Uigj8ykjERERERHXq7XLI1iWddaZtjt27GDGjBm8+OKLzus2b97Ml19+yTPPPGNHm5esReCZM2dPf706+NzbpmedWdrg9Dq0KcfP/9jNmzfn+PELbCC2U0bmU0ZmUz6u1ykMNjwHAX7w9lK4/6NLu78yMp8yEhERERFxvXOci1l7rVu3jlGjRhEaGgrAoEGDaNeuHf/73//Oex8fHx8CAgKc1bBhw5pql6YN4Rc94YWx8MXjcOQtOPoOLPo9/PkWSOx9ZmD7fQbM3gh/+Azi/goh90Ho/TDyb/DMTJiz6cIDW4ChQ4e6/knJFVFG5lNGZlM+rrcrDX71juPDK++Lhd/ecGn3V0bmU0YiIiIiIq5Xq5ZHqMoDDzzA1KlTSUtLo7S0lIqKCu666y6+/vrr897n6aefZvLkyc7vc3NzCQwMZMyYMXh7ezN79myGDBlC48aNycjIYOPGjdx0000AbNmyhXr16tGtWzcA5s2bR79+/WjWrBknT55k9erVxMfHA7Bt2zZKS0vp2bMnAAsXLuTehFY8H/tdpX4qLA+ST/iw4wcv8n3akpLThPfnbKFVm2jCwsIosApYvH0B48ePB2DPnj2cOHGCfv36AbBs2TLatm1LREQExcXFzJkzh3HjxuHp6Unjxo0JDQ1l4MCBAKxcuZLw8HBat25NWVkZM2fOJDExER8fH1JSUjh48CBDhgwBYM2aNQQHB9OuXTsApk2bxujRo2nQoAGHDx9m165dDBs2DID169cTGBhIp06dAJg5cybDhg2jUaNGpKenk5SURFxcHACbNm3C19eXrl27AjBnzhwGDx5MUFAQx48fZ/369YwaNQqApKQkALp37w7A/PnziYmJoXnz5mRlZbFixQoSEhIA2L59O8XFxfTq1QuARYsW0b17d0JCQsjNzWXJkiWMHTsWgF27dpGTk0NMTAwAS5YsoVOnToSHh1NYWMi8efOYMGECAPv27SMjI4P+/fsDsHz5clq3bk1kZCQlJSXMnj2bsWPH4uXlxcGDBzl8+DCDBg0CYNWqVYSFhdGmTRsqKiqYMWMGCQkJ+Pr6kpqayv79+wkLC2PChAmsXbuWpk2b0qFDBwCmT5/OzTffjL+/P2lpaezYsYPhw4cDsGHDBgICAujcuTMAs2bNIjY2lsDAQI4ePcqWLVsYOXIk4Djz3Nvbm+joaADmzp3LgAEDaNKkCZmZmaxdu5bRo0cDsHXrVsrLy+nRowcAX3zxBb179yY4OJjs7GyWL19OYmIi4DjLvaioiN69ewPw5ZdfEh0dTWhoKHl5eSxatIhx48YBsHv3brKysujbty8AS5cupX379rRq1YqioiLmzp3L+PHj8fDwYP/+/aSnpzNgwAAAVqxYQWRkJFFRUZSWljJr1iznvpqcnExKSgqDBw8GYPXq1YSEhNC2bVssy2L69OnEx8fj5+fHoUOH2Lt3L7GxsYDjL3yCgoLo2LEjADNmzCAuLo6AgACOHDnCtm3bGDFiBAANGjTgmmuuoUuXLgAufY/o0aMHLVu2JCcnh6VLlzJmzBgAdu7cSV5eHn369AFg8eLFdOnSxfEeUVDAggWX9x5x4MAB0tLS3Po9omHDhs79tTa+R5weppnwHvHmqmU8NPg4b9/hQVjHG/j7Jxsv6j0iLCyMxo0b19r3iI0bN+Ln5+fW7xFBQUFMmDDB5e8RcXFxLFq0CBERERGRuqhOLY/w2GOPcdddd/H444+TmprKgAEDeOmll/jFL37B8uXLz/k4Pj4+lT5ww7Is8vMv45NVLkOrZjDn4TPLGySlwPbDjg8Oc8nPa9WKQ4cOuebBpVooI/MpI7Mpn5o17X4YHwPHc6HnM3Aos+r7KCPzKSMREREREderM2fa1q9fn7/85S/84he/cJ61sWPHDrp168bjjz9+3qFtSUkJJSUlNdmq06FM6PnHmvt5TZs21f+EGU4ZmU8ZmU351KxJU6FdS8cHZM5/FPo+V/VfPCoj8ykjERERERHXqzNr2np7e+Pj40NFRUWl68vLy/H0rDO/hgs6/c9oxVzKyHzKyGzKp2YVlUD8FDiWA9ER8PE94OFx4fsoI/MpIxERERER16tV00p/f3+io6Oda+1FRUURHR1NeHg4eXl5rFy5kr/97W8MHDiQyMhIJk6cyG233cbnn39uc+ciIiK10+ETkPAalJTBmN7wx3i7OxIRERERETFfrVrTduDAgaxcufKs6z/66CPuuOMOWrRowUsvvcSNN95IkyZNSE1NZerUqUyZMqXmmzWQh4cHllVrXg61kjIynzIym/Kxz6SB8P5vHZd/MQXmbj73dsrIfMpIRERERKRmWCoVYI0aNcr2HlTKyN1LGZldysfeeu3XWNanWHnvY10TrozctZSRSqVSqVQqlUrl+qpVyyPIlfH397e7BamCMjKfMjKb8rHXY5/Csu+gYX3HB5M1bXj2NsrIfMpIRERERMT1NLQVp7S0NLtbkCooI/MpI7MpH3uVV8C4N+HAUYgKhlkPgVe9ytsoI/MpIxERERER16tVa9rKlQkKCiIrK8vuNuQClJH5lJHZlI8ZOoXBhucgwA/eWQr3fXTmNmVkPmUkIiIiIuJ6OtNWnIYPH253C1IFZWQ+ZWQ25WOGXWnwy7ehogLujYW7h5y5TRmZTxmJiIiIiLiehrYiIiJS475Igj/OdFx+8zYY0MHefkREREREREyioa04bdiwwe4WpArKyHzKyGzKxywvzYfp68Hby7G+bUQzZeQOlJGIiIiIiOtpaCtOAQEBdrcgVVBG5lNGZlM+5pk0FbYkQ/NGMO9RCG7SwGRuH+cAAFrPSURBVO6WpAraj0REREREXE9DW3Hq3Lmz3S1IFZSR+ZSR2ZSPeYpKIP5VOJYD0RHw+oRcu1uSKmg/EhERERFxPQ1tRURExFY/nIRfTIGSMrgu5DCJve3uSERERERExF4egGV3E2IGb29vSktL7W5DLkAZmU8ZmU35mO3ZBJicCN9nQKffQ7GiMpL2IxERERER19OZtuIUGxtrdwtSBWVkPmVkNuVjtr8thMxCX64OhoeG2d2NnI/2IxERERER19PQVpwCAwPtbkGqoIzMp4zMpnzMVlgMn+3tDsAf4iG4kb39yLlpPxIRERERcT0NbcXp6NGjdrcgVVBG5lNGZlM+5pu9pT6bDkIjP3hhrN3dyLloPxIRERERcT2taStOjRo1IjdXn9ptMmVkPmVkNuVjvkaNGnFNy1zWPgvlFXDtH2D7Ibu7kp/SfiQiIiIi4no601acRo4caXcLUgVlZD5lZDblY76RI0eybh/M2AD1POHVX9ndkfyc9iMREREREdfT0FZERESM8+Q0OFUCQ66Bm6+1uxsREREREZGapaGtOG3evNnuFqQKysh8yshsysd8pzNKzYRXv3Rc949fgXc9G5uSSrQfiYiIiIi4noa24uTt7W13C1IFZWQ+ZWQ25WO+n2b00nw4mg1tW8J9sfb1JJVpPxIRERERcT0NbcUpOjra7hakCsrIfMrIbMrHfD/NKP8U/OEzx+VnE6BpQ5uakkq0H4mIiIiIuJ6GtiIiImKsj1ZDUgo09ofJiXZ3IyIiIiIiUjM8AMvuJsQMfn5+FBUV2d2GXIAyMp8yMpvyMd+5MhrYEVb+EcrKoevTsDvNpuYE0H4kIiIiIlITdKatOA0YMMDuFqQKysh8yshsysd858po1W6Yswm86jk+lEzspf1IRERERMT1NLQVpyZNmtjdglRBGZlPGZlN+ZjvfBn9fhqUlMGIaBiuJVVtpf1IRERERMT1NLQVp8zMTLtbkCooI/MpI7MpH/OdL6ODx+CNJY7L//iV46xbsYf2IxERERER19OatuLUoEEDCgsL7W5DLkAZmU8ZmU35mO9CGQU2gP3/gOaN4P6P4O2lNdubOGg/EhERERFxPZ1pK06jR4+2uwWpgjIynzIym/Ix34UyyimEP81yXH4uERo3qKGmpBLtRyIiIiIirqehrYiIiLiN91bAd4ehaQD8KcHubkRERERERFxDQ1tx2rp1q90tSBWUkfmUkdmUj/mqyqi8Ah791HH5/lho29L1PUll2o9ERERERFxPQ1txKi8vt7sFqYIyMp8yMpvyMd/FZLR0B3yRBN5e8Pdf1kBTUon2IxERERER19PQVpx69OhhdwtSBWVkPmVkNuVjvovN6PFPobQMRvWAIZ1d3JRUov1IRERERMT1NLQVERERt7M3Hd5Z5rj86q3g6WFvPyIiIiIiItXJA7DsbkLMEBAQQF5ent1tyAUoI/MpI7MpH/NdSkZB/nDgVWjSEH77b8eHlInraT8SEREREXE9nWkrTr1797a7BamCMjKfMjKb8jHfpWSUVQCT5zguvzgWGvm5qCmpRPuRiIiIiIjraWgrTsHBwXa3IFVQRuZTRmZTPua71Iz+uQz2HIHgQPi/0S5qSirRfiQiIiIi4noa2opTdna23S1IFZSR+ZSR2ZSP+S41o7JyeOxTx+WHh0NU8+rvSSrTfiQiIiIi4npa01acfHx8KCkpsbsNuQBlZD5lZDblY77LzWjJU3BjF5i1Eca+7oLGxEn7kYiIiIiI6+lMW3FKTEy0uwWpgjIynzIym/Ix3+Vm9Oj/g/IKGNMbBnSo5qakEu1HIiIiIiKup6GtiIiIuL2dP8DUrxyXp9wKnh729iMiIiIiInIlNLQVpx07dtjdglRBGZlPGZlN+ZjvSjL60yzIKYRro+C266uxKalE+5GIiIiIiOtpaCtORUVFdrcgVVBG5lNGZlM+5ruSjDLz4IXPHZf/cgv4+1ZTU1KJ9iMREREREdfT0FacevfubXcLUgVlZD5lZDblY74rzejN/8GBoxASBE+NqqampBLtRyIiIiIirqehrYiIiNQaJWXwxDTH5cfioFUze/sRERERERG5HB6AZXcTYobGjRuTnZ1tdxtyAcrIfMrIbMrHfNWV0Vd/gMGdYNo6+OXbV96XnKH9SERERETE9XSmrThFR0fb3YJUQRmZTxmZTfmYr7oyeuT/QUUFTOgLMW2r5SHlR9qPRERERERcT0NbcQoNDbW7BamCMjKfMjKb8jFfdWW0LRU+WOW4POVW8PColocVtB+JiIiIiNQEDW3FKS8vz+4WpArKyHzKyGzKx3zVmdEfZ0JeEVzXBibEVNvD1nnaj0REREREXE9r2oqTp6cnFRUVdrchF6CMzKeMzKZ8zFfdGT01Cl4aB4dPQPvHoaik2h66ztJ+JCIiIiLiejrTVpzGjRtndwtSBWVkPmVkNuVjvurOaMqXkHIcwpvC4yOr9aHrLO1HIiIiIiKup6GtiIiI1FrFpfD7aY7LT94EoUH29iMiIiIiInIxNLQVp927d9vdglRBGZlPGZlN+ZjPFRnN/AbW7AX/+vCXW6r94esc7UciIiIiIq6noa04ZWVl2d2CVEEZmU8ZmU35mM9VGT3y/xxfJw6AHlEu+RF1hvYjERERERHXq1VD2+uvv5758+eTlpaGZVmMHj260u2WZZ2zHn/8cZs6Nkvfvn3tbkGqoIzMp4zMpnzM56qMNn8Pn3ztuPzar13yI+oM7UciIiIiIq5Xq4a2/v7+bNu2jfvuu++ct7ds2bJS3XHHHVRUVDB79uwa7lRERERq2v99BgWnoH97GNPb7m5ERERERETOzwOw7G7CFSzLIj4+nnnz5p13m88//5yAgACGDh163m18fHzw9fWt9Lj5+fnV2qspmjVrRmZmpt1tyAUoI/MpI7MpH/O5OqM/JcBziZCcAR1/7/igMrk02o9ERERERFzPy+4G7BIcHMzIkSOZOHHiBbd7+umnmTx5svP73NxcAgMDGTNmDN7e3syePZshQ4bQuHFjMjIy2LhxIzfddBMAW7ZsoV69enTr1g2AefPm0a9fP5o1a8bJkydZvXo18fHxAGzbto3S0lJ69uwJwMKFC+nRowctW7YkJyeHpUuXMmbMGAB27txJXl4effr0AWDx4sV06dKFsLAwCgoKWLBgAePHjwdgz549nDhxgn79+gGwbNky2rZtS0REBMXFxcyZM4dx48bh6elJRUUFX3/9NQMHDgRg5cqVhIeH07p1a8rKypg5cyaJiYn4+PiQkpLCwYMHGTJkCABr1qwhODiYdu3aATBt2jRGjx5NgwYNOHz4MLt27WLYsGEArF+/nsDAQDp16gTAzJkzGTZsGI0aNSI9PZ2kpCTi4uIA2LRpE76+vnTt2hWAOXPmMHjwYIKCgjh+/Djr169n1KhRACQlJQHQvXt3AObPn09MTAzNmzcnKyuLFStWkJCQAMD27dspLi6mV69eACxatIju3bsTEhJCbm4uS5YsYezYsQDs2rWLnJwcYmJiAFiyZAmdOnUiPDycwsJC5s2bx4QJEwDYt28fGRkZ9O/fH4Dly5fTunVrIiMjKSkpYfbs2YwdOxYvLy8OHjzI4cOHGTRoEACrVq0iLCyMNm3aUFFRwYwZM0hISMDX15fU1FT279/PLbfcwsmTJ1m7di1NmzalQ4cOAEyfPp2bb74Zf39/0tLS2LFjB8OHDwdgw4YNBAQE0LlzZwBmzZpFbGwsgYGBHD16lC1btjBy5EgANm/ejLe3N9HR0QDMnTuXAQMG0KRJEzIzM1m7dq1z2ZGtW7dSXl5Ojx49APjiiy/o3bs3wcHBZGdns3z5chITEwHYsWMHRUVF9O7tOLXtyy+/JDo6mtDQUPLy8li0aBHjxo0DHB9wk5WV5fznt0uXLqV9+/a0atWKoqIi5s6dy/jx4/Hw8GD//v2kp6czYMAAAFasWEFkZCRRUVGUlpYya9Ys576anJxMSkoKgwcPBmD16tWEhITQtm1bLMti+vTpxMfH4+fnx6FDh9i7dy+xsbEArFu3jqCgIDp27AjAjBkziIuLIyAggCNHjrBt2zZGjBgBQH5+PsnJyXTp0gWgVr1HHDhwgLS0NLd+j+jQoQN+fn619j3i9F9CuvN7RJMmTfj0009d9h6xKiOcE0U7iQou5I4BkBlUs+8RGzduxM/Pz63fI2JjY7Esy+XvEXFxcSxatAgRERERkbrKqo1lWZY1evTo897+xBNPWCdOnLB8fX0v+Dg+Pj5WQECAsxo2bGj7c3NVTZgwwfYeVMrI3UsZmV3Kx/yqiYweHIZlfYq142X7n687lvYjlUqlUqlUKpXK9VWr1rS9FJMmTeLTTz+luLj4gtuVlJSQl5fnrNq6NAJAUVGR3S1IFZSR+ZSR2ZSP+Woio49WQ/4puCYcBndy+Y+rdbQfiYiIiIi4Xp1c07Z///58/fXXREdHs337dhu6ExERETu9fTvcGwtzNkHia3Z3IyIiIiIiUlmdPNP2zjvvZPPmzRrY/szp9evEXMrIfMrIbMrHfDWV0VtLHV9H94BWzWrkR9Ya2o9ERERERFyvVg1t/f39iY6Odn5ASlRUFNHR0YSHhzu3CQgIYOzYsfz73/+2q01jeXh42N2CVEEZmU8ZmU35mK+mMtqdBsu+g3qe8LshNfIjaw3tRyIiIiIirlerhrY9e/Zk69atbN26FYApU6awdetWnn/+eec2pz9Retq0aTZ1aa79+/fb3YJUQRmZTxmZTfmYryYzeut/jq+/GQz1vWvsx7o97UciIiIiIq5Xa9e0lUsXFhZGWlqa3W3IBSgj8ykjsykf89VkRp4ecHAKRDaHO951fECZVE37kYiIiIiI69WqM23lygwYMMDuFqQKysh8yshsysd8NZlRhQXvLHNcfuDGGvuxbk/7kYiIiIiI62loKyIiInXW+yuhqASujYKYtnZ3IyIiIiIi4qChrTitWLHC7hakCsrIfMrIbMrHfDWd0cl8+O86x2WdbXtxtB+JiIiIiLiehrbiFBkZaXcLUgVlZD5lZDblYz47Mjr9gWRjekPLxjX+492O9iMREREREdfT0FacoqKi7G5BqqCMzKeMzKZ8zGdHRltTYc1e8PaCu2+o8R/vdrQfiYiIiIi4noa24lRaWmp3C1IFZWQ+ZWQ25WM+uzJ688ezbe++Abzr2dKC29B+JCIiIiLieh6AZXcTIiIiInbyqgepr0NoEEx4C6avt7sjERERERGpy3SmrTiNGTPG7hakCsrIfMrIbMrHfHZlVFYO/1ruuKwPJLsw7UciIiIiIq6noa04eXt7292CVEEZmU8ZmU35mM/OjKZ+BSVl0LcdXBtpWxvG034kIiIiIuJ6GtqKU3Jyst0tSBWUkfmUkdmUj/nszOhYDsz8xnH5fp1te17aj0REREREXE9DW3FKSUmxuwWpgjIynzIym/Ixn90Znf5Asgkx0CzA1laMZXdGIiIiIiJ1gYa24jR48GC7W5AqKCPzKSOzKR/z2Z3RNwdg00Go7wN3DrK1FWPZnZGIiIiISF2goa2IiIjIT7y11PH13qFQT0dKIiIiIiJiA/2viDitXr3a7hakCsrIfMrIbMrHfCZkNGMDHM+FVs1g1LV2d2MeEzISEREREantNLQVp5CQELtbkCooI/MpI7MpH/OZkFFxKUz9ynFZH0h2NhMyEhERERGp7TS0Fae2bdva3YJUQRmZTxmZTfmYz5SM/rUcysrhhs7Q+Sq7uzGLKRmJiIiIiNRmGtqKk2VZdrcgVVBG5lNGZlM+5jMlox9Owtwtjss627YyUzISEREREanNPAAdeYuIiIj8zMCOsPKPUHAKwh6AnEK7OxIRERERkbpCZ9qKU3x8vN0tSBWUkfmUkdmUj/lMymjVbthxGPzrwx0D7O7GHCZlJCIiIiJSW2loK05+fn52tyBVUEbmU0ZmUz7mMy2jN5c4vt4XCx4e9vZiCtMyEhERERGpjTS0FadDhw7Z3YJUQRmZTxmZTfmYz7SMPl0HWQXQpiUM72p3N2YwLSMRERERkdpIQ1tx2rt3r90tSBWUkfmUkdmUj/lMy6iwGD5Y5bj8wDB7ezGFaRmJiIiIiNRGGtqKU2xsrN0tSBWUkfmUkdmUj/lMzOidpVBRASOioW1Lu7uxn4kZiYiIiIjUNhraioiIiFzA9xmwaJvj8r1D7e1FRERERETqBg1txWndunV2tyBVUEbmU0ZmUz7mMzWj0x9IdsdA8Pe1txe7mZqRiIiIiEhtoqGtOAUFBdndglRBGZlPGZlN+ZjP1IyWfgd7j0BgA/h1f7u7sZepGYmIiIiI1CYa2opTx44d7W5BqqCMzKeMzKZ8zGdqRpYFby9zXL7/Rnt7sZupGYmIiIiI1CYa2oqIiIhchI9WQ14RdL4KbuhsdzciIiIiIlKbeQCW3U2IGTw9PamoqLC7DbkAZWQ+ZWQ25WM+0zN663a4LxY+3wQJr9ndjT1Mz0hEREREpDbQmbbiFBcXZ3cLUgVlZD5lZDblYz7TM3rrf46vo3pAq2b29mIX0zMSEREREakNNLQVp4CAALtbkCooI/MpI7MpH/OZntGeI7B0B9TzhHuH2t2NPUzPSERERESkNtDQVpyOHDlidwtSBWVkPmVkNuVjPnfI6K2ljq+/GQT1vW1txRbukJGIiIiIiLvTmrbi1LhxY7Kzs+1uQy5AGZlPGZlN+ZjPHTLy9IADr0JUMEyaCh+usrujmuUOGYmIiIiIuDudaStOI0aMsLsFqYIyMp8yMpvyMZ87ZFRhwTvLHJcfuNHeXuzgDhmJiIiIiLg7DW1FRERELtEHq6CoBLpHQt92dncjIiIiIiK1jYa24rRx40a7W5AqKCPzKSOzKR/zuUtGJ/Ph07WOy3XtbFt3yUhERERExJ1paCtOfn5+drcgVVBG5lNGZlM+5nOnjE5/IFliLwhpbGsrNcqdMhIRERERcVca2opTly5d7G5BqqCMzKeMzKZ8zOdOGW1Lha/3gLcX3D3E7m5qjjtlJCIiIiLirjS0FREREblMb/7P8fXuG8C7nr29iIiIiIhI7eEBWHY3IWbw8fGhpKTE7jbkApSR+ZSR2ZSP+dwtI696kPIahDWBX74N09bZ3ZHruVtGIiIiIiLuSGfaitOQIXXo33a6KWVkPmVkNuVjPnfLqKwc/rXccbmufCCZu2UkIiIiIuKONLQVp8aNG9vdglRBGZlPGZlN+ZjPHTOa+hWUlEFMW+gRZXc3rueOGYmIiIiIuBujhra//vWv2b59O0VFRRQVFbFt2zZuvfVWu9uqMzIyMuxuQaqgjMynjMymfMznjhll5MJnGxyX768DZ9u6Y0YiIiIiIu7GmDVtH3nkEV544QXeeust1q5dC0D//v257777+OMf/8hrr71mb4N1QEBAAHl5eXa3IRegjMynjMymfMznrhn1bg3fPA+nSiD8Qch0v6dw0dw1IxERERERd2LMmbYPPPAAv/vd73jqqadYsGABCxYs4Mknn+Tee+/lwQcftLu9OuGmm26yuwWpgjIynzIym/Ixn7tmtPGgo+r7wF2D7e7Gtdw1IxERERERd2LM0DYkJIR1687+yOV169YREhJiQ0ciIiIiF++t/zm+/m4o1DPmCEtERERERNyRMf9LceDAAW655Zazrh83bhz79++3oaO6Z8uWLXa3IFVQRuZTRmZTPuZz54xmbICMHAhvCqN72N2N67hzRiIiIiIi7sLL7gZOe/bZZ5kxYwYDBgxwrmnbr18/hgwZcs5hrlS/evXq2d2CVEEZmU8ZmU35mM+dMyopg6kr4I/xjg8km7PJ7o5cw50zEhERERFxF8acaTtnzhyuu+46MjMziY+PJz4+nszMTHr37s3cuXPtbq9O6Natm90tSBWUkfmUkdmUj/ncPaN/LYeychjcCa4Jt7sb13D3jERERERE3IExZ9oCfPvtt/z617+2uw0RERGRy5J2Ej7fDGOvg/tj4Z4P7O5IRERERETckQdg2d3EaR4eHrRp04bg4GA8PSufBPz111/b1FXd0aBBAwoLC+1uQy5AGZlPGZlN+ZivNmQ0oAOsegYKTsFVD0C2ez+ds9SGjERERERETGfM8gjXXXcdBw4cYPfu3axevZqVK1c6a8WKFRf1GNdffz3z588nLS0Ny7IY/f/bu/PwqMt7//+vQBYghBCWsKRAMGyCIewSdowpEITQBAxc7XWptX6P/dIetT3V2nP8ycFqrW0P7an1tLVVa49H9i9BCUGILIcYBCIJSEAWk4AxYctCAhiy3L8/Ip82ZRmETD53Js/Hdd1XJjOTmffwlKne/eQziYlX3WfIkCFKTU1VeXm5qqqqtHv3bvXp46O/v/gVTZw40e0R4AGN7Ecju9HHfr7QaMdhaf8JKbid9NBUt6dper7QCAAAALCdNZu2v//977V3717ddddd6tKli8LCwpzVpUuXm3qM4OBg5ebmavHixde8/Y477tDOnTt1+PBhTZs2TcOHD9dzzz2nL774oilfSovVrVs3t0eABzSyH43sRh/7+Uqj377X8HVxvNTGz91ZmpqvNAIAAABsZs05bQcOHKj58+fr+PHjt/wY6enpSk9Pv+7tzz//vNLS0vTUU08513366ac3fMzAwEAFBQU53xtjVFVVdcsz2qy0tNTtEeABjexHI7vRx36+0uh/PpBeWiRF9ZBmjZA27HN7oqbjK40AAAAAm1mzafvhhx9qwIABt7VpeyN+fn6aPXu2XnrpJaWnp2vkyJHKz8/Xz372M6Wmpl73555++mktWbLE+f78+fMKDQ3V/PnzFRAQoDVr1iguLk6dO3fW6dOntXv3bt13332SpOzsbLVt29b5lOXU1FRNnDhR3bp1U2lpqXbs2KF58+ZJknJzc1VTU6MxY8ZIkjZs2KDRo0erZ8+eqqio0ObNmzV//nxJ0sGDB1VZWanx48dLatisjo6OVkREhC5cuKB33nlHCxculCQdPnxY586dc36VccuWLRo4cKD69eun6upqrV27VikpKWrTpo0KCwvVu3dvTZ3a8Luc27ZtU58+fRQVFaXa2lqtWrVKycnJCgwMVEFBgY4fP664uDhJ0s6dOxUeHq5BgwZJkt5++20lJiaqQ4cOOnnypPLy8jRjxgxJUlZWlkJDQzV06FBJ0qpVqzRjxgx16tRJxcXF2rdvnxISEiRJe/bsUVBQkIYPHy5JWrt2raZPn66wsDCdOXNGWVlZmjt3riRp376G/yIeOXKkJGn9+vWKjY1V9+7dVVZWpq1btyopKUmStH//flVXV2vs2LGSpLS0NI0cOVK9evXS+fPntWnTJi1YsECSlJeXp4qKCsXGxkqSNm3apKFDh6pPnz66ePGiUlNTtWjRIknSkSNHdPr0aU2aNEmSlJGRoaioKEVGRury5ctas2aNFixYIH9/fx0/flwnT57UtGnTJEnbt29XRESEBgwYoPr6eq1YsUJJSUkKCgpSYWGhjh49qm7dumnRokXKzMxU165dNWTIEEnS8uXLNWfOHAUHB6uoqEgHDhzQzJkzJUm7du1SSEiIhg0bJklavXq14uPjFRoaqpKSEmVnZ2v27NmSpL179yogIEAxMTGSpHXr1mnKlCnq0qWLzp49q8zMTOe0Izk5Oaqrq9Po0aMlSe+++67GjRun8PBwlZeXKyMjQ8nJyZKkAwcO6NKlSxo3bpwkaePGjYqJiVHv3r1VWVmptLQ0paSkSJIOHTqksrIyTZgwQZK0efNmDR48WH379tWlS5e0bt06LVy4UH5+fjp69KiKi4s1ZcoUSdLWrVsVGRmp/v37q6amRqtXr3b+rubn56ugoEDTp0+XJO3YsUO9evXSwIEDZYzR8uXLNW/ePLVv314nTpzQJ598ovj4eEnSBx98oLCwMN15552SpBUrVighIUEhISH6/PPPlZubq1mzZjl/LnfddZeio6MlyafeI44dO6aioqIW/R5RXFzs/H31xfeIe++9V5Ja9HtEmzZt1Llz5xb/HnFPfIKyTh1Vwh2f6OF7AtRpSMPfz927d6t9+/Yt+j3iiy++0KJFi7z+HpGQkKC0tDQBAAAArZVxa0VHRztr3rx55uOPPzYPPPCAGTVqVKPboqOjv/JjG2NMYmKi832PHj2MMcZUVVWZxx9/3MTExJinnnrK1NXVmSlTplz3cQIDA01ISIizOnbs6Nqfl7fXokWLXJ+BRaOWvmhk96KP/cuXGo2KlDFvyVx4TSY4yP15aMRisVgsFovFYrWc5eqRtjk5OTLGyM/vbyd7e+211666nzFG/v63N2qbNg2n701NTdWvf/1rSQ1HpUyYMEGPPvqoduzYcc2fu3z5si5fvnxbzw0AAFqfjwqkYyXSgJ7S7JHSyl1uTwQAAACgpXB107Z///43db/g4ODbfq6zZ8+qpqZGeXl5ja4/dOiQ82uqrV1ubq7bI8ADGtmPRnajj/18rdHKD6WfJEop431n09bXGgEAAAA2cnXT9sSJEze8PTAwUIsXL9aTTz6pXr163dZz1dTUaM+ePRo8eHCj6wcNGqTCwsLbemxfUVNT4/YI8IBG9qOR3ehjP19rtGJXw6ZtQozUsZ1U9YXbE90+X2sEAAAA2KiN2wMEBgbqhRde0J49exp9gMmDDz6o/Px8PfHEE1q2bNlNPVZwcLBiYmKcD0jp37+/YmJi1KdPH0nSL37xC6WkpOg73/mOoqKitHjxYs2ZM0evvPKKd15cC3Plw0tgLxrZj0Z2o4/9fK3R/hPSJ59L7QKluaPcnqZp+FojAAAAwEaub9ouXbpU3/3ud1VQUKDIyEitWrVKf/jDH/TEE0/oBz/4gSIjI/XSSy/d1GONGTNGOTk5ysnJkSQtW7ZMOTk5Wrp0qaSGT7l+9NFH9eSTT+rAgQP6zne+o+TkZGVmZnrr5QEAgFZuxZenRbh/vLtzAAAAAGg5/NTwiWSuOX78uB5//HG98847GjZsmPbv36833nhDDz/8sJtjtUqdOnXS+fPn3R4DN0Aj+9HIbvSxny82GvY16eOfS9U1Uo//K1VcdHui2+OLjQAAAADbuH6k7de+9jVlZ2dLkg4ePKjq6uqbPh0Cmtbo0aPdHgEe0Mh+NLIbfezni40OftawggJ84xQJvtgIAAAAsI3rm7Zt27bV5cuXne9ra2tVVVXl4kStV8+ePd0eAR7QyH40sht97OerjVZ+2PA1xQdOkeCrjQAAAACb+Ls9gJ+fn9544w1VV1dLktq1a6ff//73unDhQqP7JScnuzFeq1JRUeH2CPCARvajkd3oYz9fbbRyl/TvydLXo6XOHaTyFnyKBF9tBAAAANjE9XPavvbaazd1v29/+9tengQBAQGqqalxewzcAI3sRyO70cd+vtwo92fS8L7St/8ovb7d7WlunS83AgAAAGxiWCxJZtGiRa7PwKJRS180snvRx/7ly41+kihj3pLZ+KT7s9CIxWKxWCwWi8Wye7l+TlsAAIDWYOWuhq/33iV17ejuLAAAAADsxqYtHAcPHnR7BHhAI/vRyG70sZ8vNzp2SvooX/JvK31jrNvT3DpfbgQAAADYgk1bOCorK90eAR7QyH40sht97OfrjVZ+2PA1Zby7c9wOX28EAAAA2IBNWzjGj2/B/wXZStDIfjSyG33s5+uNrpwiYfpQKbyTu7PcKl9vBAAAANiATVsAAIBmkn9G2nNcattGSmrBp0gAAAAA4F1+avhEMkBhYWEqKytzewzcAI3sRyO70cd+raHRDxOkX35T2pYnTX/e7Wm+utbQCAAAAHAbR9rCER0d7fYI8IBG9qOR3ehjv9bQaNXuhq9Thkg9O7s6yi1pDY0AAAAAt7FpC0dERITbI8ADGtmPRnajj/1aQ6MTZ6Wso1KbNtL8cW5P89W1hkYAAACA29i0hePChQtujwAPaGQ/GtmNPvZrLY1WfPmBZPff7e4ct6K1NAIAAADcxDlt4fDz85Mx/ONgMxrZj0Z2o4/9WkujiC7SZ79tuPy170tFpe7O81W0lkYAAACAmzjSFo6FCxe6PQI8oJH9aGQ3+tivtTQqKpX+93DD5ZZ2ioTW0ggAAABwE5u2AAAALlj5YcPXlPHuzgEAAADAPmzawnH48GG3R4AHNLIfjexGH/u1pkard0v19VLsQKlvN7enuXmtqREAAADgFjZt4Th37pzbI8ADGtmPRnajj/1aU6OScmnHl/ufC1rQKRJaUyMAAADALWzawjFx4kS3R4AHNLIfjexGH/u1tkYrdjV8vb8FnSKhtTUCAAAA3MCmLQAAgEvW7pHq6qVxUVL/7m5PAwAAAMAWfpKM20PADt27d9eZM2fcHgM3QCP70chu9LFfa2y0+Wnp3rukp96WXnrX7Wk8a42NAAAAgObGkbZwDBw40O0R4AGN7Ecju9HHfq2x0covT5GQ0kJOkdAaGwEAAADNjU1bOPr16+f2CPCARvajkd3oY7/W2GjtHqm2ThrVXxrQw+1pPGuNjQAAAIDmxqYtHNXV1W6PAA9oZD8a2Y0+9muNjc5VSRkHGy63hA8ka42NAAAAgObGOW0BAABc9tBU6bX/I+UWSiN+4vY0AAAAANzGkbZwpKSkuD0CPKCR/WhkN/rYr7U2WrdXqqmVYvpJg3u5Pc2NtdZGAAAAQHNi0xaONm34x8F2NLIfjexGH/u11kZlF6T3DjRctv0UCa21EQAAANCc+LduOI4dO+b2CPCARvajkd3oY7/W3Gjlhw1fUyzftG3NjQAAAIDmwqYtHEVFRW6PAA9oZD8a2Y0+9mvNjVKzpeoaadjXGpatWnMjAAAAoLmwaQvH1KlT3R4BHtDIfjSyG33s15obVVyUNu1vuHz/3e7OciOtuREAAADQXNi0BQAAsMSKXQ1fbT9FAgAAAADv8pNk3B4CdujVq5eKi4vdHgM3QCP70chu9LFfa28U0l46/YrULlCKeVraf8Ltia7W2hsBAAAAzYEjbeHo06eP2yPAAxrZj0Z2o4/9WnujyktSWm7DZVuPtm3tjQAAAIDmwKYtHFFRUW6PAA9oZD8a2Y0+9qORtPLLUyTYel5bGgEAAADex6YtHLW1tW6PAA9oZD8a2Y0+9qOR9O4+6WK1NKCnNCrS7WmuRiMAAADA+zinLQAAgGVW/rO04G7p5+9IP17u9jQAAAAAmhtH2sKRnJzs9gjwgEb2o5Hd6GM/GjVYYfEpEmgEAAAAeB+btnAEBga6PQI8oJH9aGQ3+tiPRg3ScqSqL6T+4dLYO9yepjEaAQAAAN7Hpi0cBQUFbo8AD2hkPxrZjT72o1GDS5eldz5quJwS6+4s/4hGAAAAgPexaQvH8ePH3R4BHtDIfjSyG33sR6O/Wflhw9cF4yQ/P3dn+Xs0AgAAALyPTVs44uLi3B4BHtDIfjSyG33sR6O/2ZgrVV6S+naTxg9we5q/oREAAADgfWzaAgAAWKi6RkrNbrhs4weSAQAAAPAeNm3h2Llzp9sjwAMa2Y9GdqOP/WjU2IpdDV8X3G3PKRJoBAAAAHgfm7ZwhIeHuz0CPKCR/WhkN/rYj0aNvXdAKr8gRXSRJg5ye5oGNAIAAAC8j01bOAYNsuS/BnFdNLIfjexGH/vRqLHLtdK6L0+RkDLe3VmuoBEAAADgfWzaAgAAWGzll6dImD9OamPJKRIAAAAAeJefJOP2EAAAALi2gLZSyStSl47S9OelbXluTwQAAADA2zjSFo7ExES3R4AHNLIfjexGH/vR6Go1ddL/29tw+f673Z1FohEAAADQHNi0haNDhw5ujwAPaGQ/GtmNPvaj0bWt+PIUCcljpbYu/9sbjQAAAADv86lN28mTJ2v9+vUqKiqSMeaqI0Fef/11GWMarY0bN7o0rX1Onjzp9gjwgEb2o5Hd6GM/Gl3b+wels5VSeKg0fai7s9AIAAAA8D6f2rQNDg5Wbm6uFi9efN37bNy4UT179nTWokWLmnFCu+XlcZI829HIfjSyG33sR6Nrq6uX1uxuuHz/eHdnoREAAADgfT61aZuenq5nnnlG69atu+59qqurderUKWeVl5c323y2mzFjhtsjwAMa2Y9GdqOP/Wh0fVdOkZA0RvJv694cNAIAAAC8z6c2bW/GtGnTdOrUKR0+fFivvPKKunTpcsP7BwYGKiQkxFkdO3ZspkkBAAD+Zsdh6VSF1DVEihvm9jQAAAAAvMnf7QGaU3p6utauXav8/HxFRUXphRde0MaNGxUbG6v6+vpr/szTTz+tJUuWON+fP39eoaGhmj9/vgICArRmzRrFxcWpc+fOOn36tHbv3q377rtPkpSdna22bdtqxIgRkqTU1FRNnDhR3bp1U2lpqXbs2KF58+ZJknJzc1VTU6MxY8ZIkjZs2KDRo0erZ8+eqqio0ObNmzV//nxJ0sGDB1VZWanx48c7rys6OloRERG6cOGC3nnnHS1cuFCSdPjwYZ07d04TJ06UJG3ZskUDBw5Uv379VF1drbVr1yolJUVt2rRRWVmZevfuralTp0qStm3bpj59+igqKkq1tbVatWqVkpOTFRgYqIKCAh0/flxxcXGSpJ07dyo8PFyDBg2SJL399ttKTExUhw4ddPLkSeXl5TlH5mRlZSk0NFRDhzaclG/VqlWaMWOGOnXqpOLiYu3bt08JCQmSpD179igoKEjDhw+XJK1du1bTp09XWFiYzpw5o6ysLM2dO1eStG/fPknSyJEjJUnr169XbGysunfvrrKyMm3dulVJSUmSpP3796u6ulpjx46VJKWlpWnkyJHq1auXzp8/r02bNmnBggWSGn4NtKKiQrGxsZKkTZs2aejQoerTp48uXryo1NRU5zQbR44c0enTpzVp0iRJUkZGhqKiohQZGanLly9rzZo1WrBggfz9/XX8+HGdPHlS06ZNkyRt375dERERGjBggOrr67VixQolJSUpKChIhYWFOnr0qNq3b69FixYpMzNTXbt21ZAhQyRJy5cv15w5cxQcHKyioiIdOHBAM2fOlCTt2rVLISEhGjas4b/wV69erfj4eIWGhqqkpETZ2dmaPXu2JGnv3r0KCAhQTEyMJGndunWaMmWKunTporNnzyozM9M5V3ROTo7q6uo0evRoSdK7776rcePGKTw8XOXl5crIyFBycrIk6cCBA7p06ZLGjRsnqeE0JTExMerdu7cqKyuVlpamlJQUSdKhQ4dUVlamCRMmSJI2b96swYMHq2/fvrp06ZLWrVunhQsXys/PT0ePHlVxcbGmTJkiSdq6dasiIyPVv39/1dTUaPXq1c7f1fz8fBUUFGj69OmSpB07dqhXr14aOHCgjDFavny55s2bp/bt2+vEiRP65JNPFB8fL0n64IMPFBYWpjvvvFOStGLFCiUkJCgkJESff/65cnNzNWvWLElSYWGh7rrrLkVHR0uST71HHDt2TEVFRS36PaKoqMj5++qL7xH33nuvJLXo94j27durc+fOPvsesXv3brVv3/6W3yM+/LyL5oZ+ph8k99aOo2WuvEecP39eixYt8vp7REJCgtLS0gQAAAC0VsYXlzHGJCYm3vA+/fv3N8YYc88991z3PoGBgSYkJMRZHTt2dP21eWvFxMS4PgOLRi190cjuRR/7F41uvKYMkTFvyZT9USagLY1YLBaLxWKxWCxfXa3u9Ah/Lz8/X2fOnNGAAQOue5/Lly+rsrLSWVVVVc04YfO6clQb7EUj+9HIbvSxH41ubOcn0udlUudg6evD3ZmBRgAAAID3tepN24iICHXt2lXFxcVujwIAAOBRvZFW7264fP/d7s4CAAAAwHv81HDIrU8IDg52jprNycnRE088oa1bt6q0tFSlpaV69tlntWbNGpWUlCgqKkovvfSSQkJCFB0drcuXL7s8vfv8/f1VW1vr9hi4ARrZj0Z2o4/9aOTZhEFS5rPS+UtS+Hel6prmfX4aAQAAAN7nU0fajhkzRjk5OcrJyZEkLVu2TDk5OVq6dKnq6uo0fPhwrV+/XkeOHNGf//xnZWdna/LkyWzYfunKhwDBXjSyH43sRh/70cizrKPSZ6VSp/bSDBdOkUAjAAAAwPv83R6gKW3fvl1+fn7Xvf3KJ2Xj2jp16uT2CPCARvajkd3oYz8aeWaMtHKX9IMEKWW8tD67eZ+fRgAAAID3+dSRtrg9nNvXfjSyH43sRh/70ejmrPyw4evcUVK7gOZ9bhoBAAAA3udT57TF7QkNDVVFRYXbY+AGaGQ/GtmNPvaj0c0r+I3Ur5uU/Gtp7Z7me14aAQAAAN7HkbZwJCQkuD0CPKCR/WhkN/rYj0Y3b+Wuhq/3j2/e56URAAAA4H1s2gIAALRAK77ctL1vhNQhyNVRAAAAADQxNm3h2LOnGX+3EreERvajkd3oYz8a3bzsfOn4KSm4nTR7RPM9L40AAAAA72PTFo6gIA7TsR2N7Ecju9HHfjT6aq58IFlKM54igUYAAACA97FpC8fw4cPdHgEe0Mh+NLIbfexHo6/mynltE0ZIHds1z3PSCAAAAPA+Nm0BAABaqJxC6Uix1D5QmjPK7WkAAAAANBU/ScbtIWCHoKAgVVdXuz0GboBG9qOR3ehjPxp9dc8tkP5tnvT/9khJv/b+89EIAAAA8D6OtIVj+vTpbo8AD2hkPxrZjT72o9FX9/++/Fywe++SAtp6//loBAAAAHgfm7ZwhIWFuT0CPKCR/WhkN/rYj0Zf3b5C6VSFFNJemjTY+89HIwAAAMD72LSF48yZM26PAA9oZD8a2Y0+9qPRV2eMlJ7bcHlWjPefj0YAAACA93FOWziCg4N14cIFt8fADdDIfjSyG33sR6NbkzJeWv596eBn0l1Pefe5aAQAAAB4H0fawjF37ly3R4AHNLIfjexGH/vR6Na8d0Cqq5eGfU3q09W7z0UjAAAAwPvYtAUAAGjhyi5Iu441XG6OUyQAAAAA8C42beHYt2+f2yPAAxrZj0Z2o4/9aHTrNjbTeW1pBAAAAHgfm7YAAAA+YGNOw9e4YVJAW1dHAQAAAHCb2LSFY+TIkW6PAA9oZD8a2Y0+9qPRrdtXKJ2qkELaS5MGe+95aAQAAAB4H5u2AAAAPsAYKf3LUyQkjHB1FAAAAAC3yU+ScXsI2CE4OFgXLlxwewzcAI3sRyO70cd+NLo994+XVnxfOviZdNdT3nkOGgEAAADex5G2cMTGxro9Ajygkf1oZDf62I9Gt2fzAamuXhr2NalPV+88B40AAAAA72PTFo7u3bu7PQI8oJH9aGQ3+tiPRren7IK061jD5Vkx3nkOGgEAAADex6YtHGVlZW6PAA9oZD8a2Y0+9qPR7dv45XltvbVpSyMAAADA+zinLRxBQUGqrq52ewzcAI3sRyO70cd+NLp9oyKl7OelyktSt0ely7VN+/g0AgAAALyPI23hSEpKcnsEeEAj+9HIbvSxH41u375CqaRcCmkvTRrc9I9PIwAAAMD72LQFAADwIcZI6fsbLnvrFAkAAAAAvItNWzj279/v9gjwgEb2o5Hd6GM/GjUNb57XlkYAAACA97FpCwfnp7MfjexHI7vRx340ahqbD0h19dKwr0l9ujbtY9MIAAAA8D42beEYO3as2yPAAxrZj0Z2o4/9aNQ0yi5Iu441XG7qo21pBAAAAHgfm7YAAAA+6MopEhJGuDoGAAAAgFvgJ8m4PQTsEBoaqoqKCrfHwA3QyH40sht97EejpjMyUvroeanqC6nrP0mXa5vmcWkEAAAAeB9H2sIxcuRIt0eABzSyH43sRh/70ajp5BRKJeVSx3bSpMFN97g0AgAAALyPTVs4evXq5fYI8IBG9qOR3ehjPxo1HWOk9P0Nl5vyvLY0AgAAALyPTVs4zp8/7/YI8IBG9qOR3ehjPxo1rSvntW3KTVsaAQAAAN7HOW3h8Pf3V21tE53wDl5BI/vRyG70sR+NmlZYsHTm91LbNlK/x6QTZ2//MWkEAAAAeB9H2sKxYMECt0eABzSyH43sRh/70ahplV2Qdh1ruNxUR9vSCAAAAPA+Nm0BAAB8WFpOw9emPEUCAAAAAO9i0xaOvLw8t0eABzSyH43sRh/70ajpXTmvbdwwKdD/9h+PRgAAAID3sWkLR0VFhdsjwAMa2Y9GdqOP/WjU9HIKpZJyqWM7adLg2388GgEAAADex6YtHLGxsW6PAA9oZD8a2Y0+9qNR0zNGSt/fcLkpTpFAIwAAAMD72LQFAADwcVdOkcB5bQEAAICWwU+ScXsI2KFLly4qLS11ewzcAI3sRyO70cd+NPKOzh2ks3+Q2raR+j0mnTh7649FIwAAAMD7ONIWjqFDh7o9Ajygkf1oZDf62I9G3lF+Uco62nD5do+2pREAAADgfWzawtGnTx+3R4AHNLIfjexGH/vRyHua6hQJNAIAAAC8j01bOC5evOj2CPCARvajkd3oYz8aec+VTdu4YVKg/60/Do0AAAAA7+OctgAAAK2An5/0+ctSz85S3AvS+wfdnggAAADA9XCkLRyLFi1yewR4QCP70chu9LEfjbzHGCl9f8Pl2zlFAo0AAAAA72PTFgAAoJVIy2n4mjDCzSkAAAAAeMKmLRxHjhxxewR4QCP70chu9LEfjbxr8wGprl4aGiH17XZrj0EjAAAAwPvYtIXj9OnTbo8AD2hkPxrZjT72o5F3lV+Uso42XL7VUyTQCAAAAPA+Nm3hmDRpktsjwAMa2Y9GdqOP/WjkfRtzG77e6qYtjQAAAADv86lN28mTJ2v9+vUqKiqSMUaJiYnXve9//dd/yRijxx57rBknBAAAcNeVTdu4YVKgv7uzAAAAALg2n9q0DQ4OVm5urhYvXnzD+82bN0/jx49XUVFRM03WMmRkZLg9Ajygkf1oZDf62I9G3pdTKBWXSR3bSZMGf/WfpxEAAADgfT61aZuenq5nnnlG69atu+59evfurd/+9rf65je/qZqaGo+PGRgYqJCQEGd17NixCSe2S1RUlNsjwAMa2Y9GdqOP/WjkfcZI6fsbLieM+Oo/TyMAAADA+1rVL8X5+fnpr3/9q37xi18oLy/vpn7m6aef1pIlS5zvz58/r9DQUM2fP18BAQFas2aN4uLi1LlzZ50+fVq7d+/WfffdJ0nKzs5W27ZtNWLECElSamqqJk6cqG7duqm0tFQ7duzQvHnzJEm5ubmqqanRmDFjJEkbNmzQ6NGj1bNnT1VUVGjz5s2aP3++JOngwYOqrKzU+PHjJTVsVkdHRysiIkIXLlzQO++8o4ULF0qSDh8+rHPnzmnixImSpC1btmjgwIHq16+fqqurtXbtWqWkpKhNmzbq3LmzCgsLNXXqVEnStm3b1KdPH0VFRam2tlarVq1ScnKyAgMDVVBQoOPHjysuLk6StHPnToWHh2vQoEGSpLfffluJiYnq0KGDTp48qby8PM2YMUOSlJWVpdDQUA0dOlSStGrVKs2YMUOdOnVScXGx9u3bp4SEBEnSnj17FBQUpOHDh0uS1q5dq+nTpyssLExnzpxRVlaW5s6dK0nat2+fJGnkyJGSpPXr1ys2Nlbdu3dXWVmZtm7dqqSkJEnS/v37VV1drbFjx0qS0tLSNHLkSPXq1Uvnz5/Xpk2btGDBAklSXl6eKioqFBsbK0natGmThg4dqj59+ujixYtKTU3VokWLJDV8ovbp06ed8/1lZGQoKipKkZGRunz5stasWaMFCxbI399fx48f18mTJzVt2jRJ0vbt2xUREaEBAwaovr5eK1asUFJSkoKCglRYWKijR49q4sSJioyMVGZmprp27aohQ4ZIkpYvX645c+YoODhYRUVFOnDggGbOnClJ2rVrl0JCQjRs2DBJ0urVqxUfH6/Q0FCVlJQoOztbs2fPliTt3btXAQEBiolpONHhunXrNGXKFHXp0kVnz55VZmamc9qRnJwc1dXVafTo0ZKkd999V+PGjVN4eLjKy8uVkZGh5ORkSdKBAwd06dIljRs3TpK0ceNGxcTEqHfv3qqsrFRaWppSUlIkSYcOHVJZWZkmTJggSdq8ebMGDx6svn376tKlS1q3bp0WLlwoPz8/HT16VMXFxZoyZYokaevWrYqMjFT//v1VU1Oj1atXO39X8/PzVVBQoOnTp0uSduzYoV69emngwIEyxmj58uWaN2+e2rdvrxMnTuiTTz5RfHy8JOmDDz5QWFiY7rzzTknSihUrlJCQoJCQEH3++efKzc3VrFmzJEkdOnRQZWWloqOjJcmn3iOOHTumoqKiFv0ecddddykyMtJn3yPuvfdeSWrR7xERERE6dOiQz75H7N69W+3bt3f9PSK7uFgP6YxSJnfS82ltv9J7xMiRIxUZGen194iEhASlpaUJAAAAaK2MLy5jjElMTGx03Y9//GOzadMm5/v8/Hzz2GOP3fBxAgMDTUhIiLM6duzo+mvz1kpOTnZ9BhaNWvqikd2LPvYvGjXP6txBpvavMuYtmb7daMRisVgsFovFYtm2/L684HOMMZo3b55SU1MlSaNGjdKGDRs0atQoFRcXS5Ly8/P161//Wr/5zW/cHBUAAKDZ/e//13BO20dfk/7AaWoBAAAAq/jUOW1vZPLkyQoPD9eJEydUU1OjmpoaRUZG6le/+pXy8/PdHs8KV37VF/aikf1oZDf62I9GzWdjbsPXWTFf7edoBAAAAHhfqzmn7V//+ldt2bKl0XWbNm3SX//6V73++usuTWUXf/9W849Di0Uj+9HIbvSxH42aT1qO9Pz9UtwwKdBfulx7cz9HIwAAAMD7fOrfuoODgzVgwADn+/79+ysmJkalpaU6efKkSktLG92/pqZGJSUlOnLkSHOPaqXjx4+7PQI8oJH9aGQ3+tiPRs0np1AqLpN6hUmTB0sZB2/u52gEAAAAeJ9PnR5hzJgxysnJUU5OjiRp2bJlysnJ0dKlS90drIU4efKk2yPAAxrZj0Z2o4/9aNS80vc3fJ014uZ/hkYAAACA9/nUpu327dvl5+d31XrooYeuef/+/fvzIWR/Z9q0aW6PAA9oZD8a2Y0+9qNR87qV89rSCAAAAPA+n9q0BQAAwM3bfECqq5eGRkh9u7k9DQAAAIAr2LSFY/v27W6PAA9oZD8a2Y0+9qNR8yq/KH3w5an9b/ZoWxoBAAAA3semLRwRERFujwAPaGQ/GtmNPvajUfO7coqEhJvctKURAAAA4H1s2sIxYMAAt0eABzSyH43sRh/70aj5Xdm0vWeYFOjv+f40AgAAALyPTVs46uvr3R4BHtDIfjSyG33sR6Pml1MoFZdJHdtJkwd7vj+NAAAAAO/zk2TcHgIAAADuee3/SA9NlX6VJv3LW25PAwAAAIAjbeFISkpyewR4QCP70chu9LEfjdxx5RQJN/NhZDQCAAAAvI9NWziCgoLcHgEe0Mh+NLIbfexHI3dsPiDV1klDI6R+3W58XxoBAAAA3semLRyFhYVujwAPaGQ/GtmNPvajkTvKL0pZRxsuezralkYAAACA97FpC8fRo0fdHgEe0Mh+NLIbfexHI/fc7CkSaAQAAAB4H5u2cNx7771ujwAPaGQ/GtmNPvajkXuubNreM0wK9L/+/WgEAAAAeB+btgAAAFBOoVRcJnVsJ00e7PY0AAAAQOvGpi0cmZmZbo8AD2hkPxrZjT72o5G7nFMkjLj+fWgEAAAAeB+btnB07drV7RHgAY3sRyO70cd+NHLXzZzXlkYAAACA97FpC8eQIUPcHgEe0Mh+NLIbfexHI3dt/liqrZOGRkj9ul37PjQCAAAAvI9NWwAAAEiSKi5KWUcbLt/oaFsAAAAA3uUnybg9BOzg5+cnY/jHwWY0sh+N7EYf+9HIfU/PlV5IkdZnS4n/cfXtNAIAAAC8jyNt4ZgzZ47bI8ADGtmPRnajj/1o5L4r57W9Z5gU6H/17TQCAAAAvI9NWziCg4PdHgEe0Mh+NLIbfexHI/flFEqfl0kd20mTB199O40AAAAA72PTFo6ioiK3R4AHNLIfjexGH/vRyA7pXx5tO2vE1bfRCAAAAPA+Nm3hOHDggNsjwAMa2Y9GdqOP/WhkhyunSEi4xoeR0QgAAADwPjZt4Zg5c6bbI8ADGtmPRnajj/1oZIfNH0u1ddKdEVK/bo1voxEAAADgfWzaAgAAoJGKi1LW0YbLs65xtC0AAAAA72LTFo5du3a5PQI8oJH9aGQ3+tiPRva4coqEf9y0pREAAADgfWzawhESEuL2CPCARvajkd3oYz8a2SPty03buGFSoP/frqcRAAAA4H1s2sIxbNgwt0eABzSyH43sRh/70cgeuYXS52VScDtpypC/XU8jAAAAwPvYtAUAAMA1pV/nFAkAAAAAvMtPknF7CNghICBANTU1bo+BG6CR/WhkN/rYj0Z2mT9OWvWYdKhIGvpkw3U0AgAAALyPI23hiI+Pd3sEeEAj+9HIbvSxH43ssvljqbZOujNC6tet4ToaAQAAAN7Hpi0coaGhbo8AD2hkPxrZjT72o5FdKi5KHxxtuHzlFAk0AgAAALyPTVs4SkpK3B4BHtDIfjSyG33sRyP7bPyH89rSCAAAAPA+zmkLR6dOnXT+/Hm3x8AN0Mh+NLIbfexHI/vE9JNyXpAufCF1fVQKak8jAAAAwNs40haO2bNnuz0CPKCR/WhkN/rYj0b2yS2UPi+TgttJkwfTCAAAAGgObNoCAADghtL/4RQJAAAAALyLTVs49u7d6/YI8IBG9qOR3ehjPxrZ6e/Pa0sjAAAAwPvYtIUjICDA7RHgAY3sRyO70cd+NLLT5o+l2jrpzgipXzc+DgEAAADwNjZt4YiJ4XcebUcj+9HIbvSxH43sVHFR+uBow+VFUzu7OgsAAADQGrBpCwAAAI+unCJhRPjn7g4CAAAAtAJ+kvgdN0iS2rdvr0uXLrk9Bm6ARvajkd3oYz8a2Sumn5TzgnShWur6T1J1jdsTAQAAAL6LI23hmDJlitsjwAMa2Y9GdqOP/Whkr9xC6RfvSr/IHKn6erenAQAAAHwbm7ZwdOnSxe0R4AGN7Ecju9HHfjSy25NvS0cuDlFNnduTAAAAAL6NTVs4zp496/YI8IBG9qOR3ehjPxrZj0YAAACA93FOWzg6dOigixcvuj0GboBG9qOR3ehjPxrZj0YAAACA93GkLRyJiYlujwAPaGQ/GtmNPvajkf1oBAAAAHgfm7YAAAAAAAAAYBE2beHIyclxewR4QCP70chu9LEfjexHIwAAAMD72LSFo66Oj4K2HY3sRyO70cd+NLIfjQAAAADvY9MWjtGjR7s9Ajygkf1oZDf62I9G9qMRAAAA4H1s2gIAAAAAAACARfwkGbeHgB1CQkJUWVnp9hi4ARrZj0Z2o4/9aGQ/GgEAAADe51NH2k6ePFnr169XUVGRjDFKTExsdPuzzz6rQ4cOqaqqSqWlpdq8ebPGjRvn0rT24c/CfjSyH43sRh/70ch+NAIAAAC8z6c2bYODg5Wbm6vFixdf8/YjR47oe9/7nqKjozVp0iQVFBTovffeU7du3Zp5UjuFh4e7PQI8oJH9aGQ3+tiPRvajEQAAAOB9/m4P0JTS09OVnp5+3dvffvvtRt//4Ac/0He+8x0NHz5c77//vrfHs155ebnbI8ADGtmPRnajj/1oZD8aAQAAAN7ns+e0NcZo3rx5Sk1NvebtAQEB+ud//mf927/9mwYMGKBz585d836BgYEKCgpq9LhVVVVemdltgYGBunz5sttj4AZoZD8a2Y0+9qOR/WgEAAAAeJ9PHWl7M2bPnq3ly5erQ4cOKi4uVnx8/HU3bCXp6aef1pIlS5zvz58/r9DQUM2fP18BAQFas2aN4uLi1LlzZ50+fVq7d+/WfffdJ0nKzs5W27ZtNWLECElSamqqJk6cqG7duqm0tFQ7duzQvHnzJEm5ubmqqanRmDFjJEkbNmzQ6NGj1bNnT1VUVGjz5s2aP3++JOngwYOqrKzU+PHjJTUcYRwdHa2IiAhduHBB77zzjhYuXChJOnz4sM6dO6eJEydKkrZs2aKBAweqX79+qq6u1tq1a5WSkqI2bdqoc+fOSk1N1dSpUyVJ27ZtU58+fRQVFaXa2lqtWrVKycnJCgwMVEFBgY4fP664uDhJ0s6dOxUeHq5BgwZJajiqOTExUR06dNDJkyeVl5enGTNmSJKysrIUGhqqoUOHSpJWrVqlGTNmqFOnTiouLta+ffuUkJAgSdqzZ4+CgoI0fPhwSdLatWs1ffp0hYWF6cyZM8rKytLcuXMlSfv27ZMkjRw5UpK0fv16xcbGqnv37iorK9PWrVuVlJQkSdq/f7+qq6s1duxYSVJaWppGjhypXr166fz589q0aZMWLFggScrLy1NFRYViY2MlSZs2bdLQoUPVp08fXbx4UampqVq0aJGkhlNwnD59WpMmTZIkZWRkKCoqSpGRkbp8+bLWrFmjBQsWyN/fX8ePH9fJkyc1bdo0SdL27dsVERGhAQMGqL6+XitWrFBSUpKCgoJUWFioo0eP6oEHHlBRUZEyMzPVtWtXDRkyRJK0fPlyzZkzR8HBwSoqKtKBAwc0c+ZMSdKuXbsUEhKiYcOGSZJWr16t+Ph4hYaGqqSkRNnZ2Zo9e7Ykae/evQoICFBMTIwkad26dZoyZYq6dOmis2fPKjMz0zlXdE5Ojurq6jR69GhJ0rvvvqtx48YpPDxc5eXlysjIUHJysiTpwIEDunTpknMexI0bNyomJka9e/dWZWWl0tLSlJKSIkk6dOiQysrKNGHCBEnS5s2bNXjwYPXt21eXLl3SunXrtHDhQvn5+eno0aMqLi7WlClTJElbt25VZGSk+vfvr5qaGq1evdr5u5qfn6+CggJNnz5dkrRjxw716tVLAwcOlDFGy5cv17x589S+fXudOHFCn3zyieLj4yVJH3zwgcLCwnTnnXdKklasWKGEhASFhITo888/V25urmbNmiVJ6tChgz788ENFR0dLkk+9Rxw7dkxFRUUt+j1i8uTJzv/x5ovvEffee68ktej3iIiICP3pT3/y2feI3bt3q3379i36PWLBggUqKyvz+ntEQkKC0tLSBAAAALRWxheXMcYkJiZedX2HDh1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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nmpiqdh1MIPg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}