Multi_Strategy_Evolutionary_Game.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Fatehbs/AI-applications/blob/main/Multi_Strategy_Evolutionary_Game.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",
      "execution_count": null,
      "metadata": {
        "id": "6gUharUuF1tV"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Traits\n",
        "man_trait = [['Rich', 'Poor'], ['Strong', 'Weak'], ['Intelligent', 'Dumb'], ['Loyal', 'Cheat']]\n",
        "woman_trait = [['Beautiful', 'Ugly'], ['Caring', 'Toxic'], ['Creative', 'Dull'], ['Loyal', 'Cheat']]\n",
        "sigma = 2"
      ],
      "metadata": {
        "id": "kOfPdCIwG1UL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "man_payoff = pd.DataFrame(np.zeros((16, 16)), index = pd.MultiIndex.from_product(man_trait), columns = pd.MultiIndex.from_product(woman_trait))\n",
        "\n",
        "man_payoff['Beautiful'] += 1\n",
        "man_payoff.loc[:, pd.IndexSlice[:, 'Caring']] += 1\n",
        "man_payoff.loc[:, pd.IndexSlice[:, :, 'Creative']] += 1\n",
        "man_payoff.loc[:, pd.IndexSlice[:, :, :, 'Loyal']] += 1"
      ],
      "metadata": {
        "id": "SGWvjF0AIBkE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "woman_payoff = pd.DataFrame(np.zeros((16, 16)), index = pd.MultiIndex.from_product(woman_trait), columns = pd.MultiIndex.from_product(man_trait))\n",
        "\n",
        "woman_payoff['Rich'] += 1\n",
        "woman_payoff.loc[:, pd.IndexSlice[:, 'Strong']] += 1\n",
        "woman_payoff.loc[:, pd.IndexSlice[:, :, 'Intelligent']] += 1\n",
        "woman_payoff.loc[:, pd.IndexSlice[:, :, :, 'Loyal']] += 1"
      ],
      "metadata": {
        "id": "BT59VALaJVza"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "man_payoff"
      ],
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        "colab": {
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          "height": 645
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        "id": "GCUB-sNrv-1H",
        "outputId": "7e17d547-3862-419b-d5de-6e18aa1f7636"
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      "execution_count": null,
      "outputs": [
        {
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              "                              Beautiful                                   \\\n",
              "                                 Caring                      Toxic         \n",
              "                               Creative        Dull       Creative         \n",
              "                                  Loyal Cheat Loyal Cheat    Loyal Cheat   \n",
              "Rich Strong Intelligent Loyal       4.0   3.0   3.0   2.0      3.0   2.0   \n",
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              "            Dumb        Loyal       4.0   3.0   3.0   2.0      3.0   2.0   \n",
              "                        Cheat       4.0   3.0   3.0   2.0      3.0   2.0   \n",
              "     Weak   Intelligent Loyal       4.0   3.0   3.0   2.0      3.0   2.0   \n",
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              "                        Cheat       4.0   3.0   3.0   2.0      3.0   2.0   \n",
              "Poor Strong Intelligent Loyal       4.0   3.0   3.0   2.0      3.0   2.0   \n",
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              "                        Cheat       4.0   3.0   3.0   2.0      3.0   2.0   \n",
              "\n",
              "                                              Ugly                             \\\n",
              "                                            Caring                      Toxic   \n",
              "                               Dull       Creative        Dull       Creative   \n",
              "                              Loyal Cheat    Loyal Cheat Loyal Cheat    Loyal   \n",
              "Rich Strong Intelligent Loyal   2.0   1.0      3.0   2.0   2.0   1.0      2.0   \n",
              "                        Cheat   2.0   1.0      3.0   2.0   2.0   1.0      2.0   \n",
              "            Dumb        Loyal   2.0   1.0      3.0   2.0   2.0   1.0      2.0   \n",
              "                        Cheat   2.0   1.0      3.0   2.0   2.0   1.0      2.0   \n",
              "     Weak   Intelligent Loyal   2.0   1.0      3.0   2.0   2.0   1.0      2.0   \n",
              "                        Cheat   2.0   1.0      3.0   2.0   2.0   1.0      2.0   \n",
              "            Dumb        Loyal   2.0   1.0      3.0   2.0   2.0   1.0      2.0   \n",
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              "Poor Strong Intelligent Loyal   2.0   1.0      3.0   2.0   2.0   1.0      2.0   \n",
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              "     Weak   Intelligent Loyal   2.0   1.0      3.0   2.0   2.0   1.0      2.0   \n",
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              "            Dumb        Loyal   2.0   1.0      3.0   2.0   2.0   1.0      2.0   \n",
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              "\n",
              "                                                 \n",
              "                                                 \n",
              "                                     Dull        \n",
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              "            Dumb        Loyal   1.0   1.0   0.0  \n",
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              "            Dumb        Loyal   1.0   1.0   0.0  \n",
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              "     Weak   Intelligent Loyal   1.0   1.0   0.0  \n",
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              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Dumb</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('man_payoff')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#id_9b6f8f10-52c3-457b-b4eb-eaf86cf098ec button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('man_payoff');\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "man_payoff",
              "summary": "{\n  \"name\": \"man_payoff\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": [\n        \"Beautiful\",\n        \"Caring\",\n        \"Creative\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 4.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Beautiful\",\n        \"Caring\",\n        \"Creative\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 3.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Beautiful\",\n        \"Caring\",\n        \"Dull\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 3.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Beautiful\",\n        \"Caring\",\n        \"Dull\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 2.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Beautiful\",\n        \"Toxic\",\n        \"Creative\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 3.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Beautiful\",\n        \"Toxic\",\n        \"Creative\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 2.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Beautiful\",\n        \"Toxic\",\n        \"Dull\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 2.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Beautiful\",\n        \"Toxic\",\n        \"Dull\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Ugly\",\n        \"Caring\",\n        \"Creative\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 3.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Ugly\",\n        \"Caring\",\n        \"Creative\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 2.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Ugly\",\n        \"Caring\",\n        \"Dull\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 2.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Ugly\",\n        \"Caring\",\n        \"Dull\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        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     \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Ugly\",\n        \"Toxic\",\n        \"Dull\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "woman_payoff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "DQmPqwzBwBgw",
        "outputId": "7520fc42-a861-4eb6-cc2b-0060dafe35cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       Rich                                \\\n",
              "                                     Strong                          Weak   \n",
              "                                Intelligent        Dumb       Intelligent   \n",
              "                                      Loyal Cheat Loyal Cheat       Loyal   \n",
              "Beautiful Caring Creative Loyal         4.0   3.0   3.0   2.0         3.0   \n",
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              "\n",
              "                                                         Poor              \\\n",
              "                                                       Strong               \n",
              "                                       Dumb       Intelligent        Dumb   \n",
              "                                Cheat Loyal Cheat       Loyal Cheat Loyal   \n",
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              "          Toxic  Creative Loyal   2.0   2.0   1.0         3.0   2.0   2.0   \n",
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              "                 Dull     Loyal   2.0   2.0   1.0         3.0   2.0   2.0   \n",
              "                          Cheat   2.0   2.0   1.0         3.0   2.0   2.0   \n",
              "Ugly      Caring Creative Loyal   2.0   2.0   1.0         3.0   2.0   2.0   \n",
              "                          Cheat   2.0   2.0   1.0         3.0   2.0   2.0   \n",
              "                 Dull     Loyal   2.0   2.0   1.0         3.0   2.0   2.0   \n",
              "                          Cheat   2.0   2.0   1.0         3.0   2.0   2.0   \n",
              "          Toxic  Creative Loyal   2.0   2.0   1.0         3.0   2.0   2.0   \n",
              "                          Cheat   2.0   2.0   1.0         3.0   2.0   2.0   \n",
              "                 Dull     Loyal   2.0   2.0   1.0         3.0   2.0   2.0   \n",
              "                          Cheat   2.0   2.0   1.0         3.0   2.0   2.0   \n",
              "\n",
              "                                                                     \n",
              "                                             Weak                    \n",
              "                                      Intelligent        Dumb        \n",
              "                                Cheat       Loyal Cheat Loyal Cheat  \n",
              "Beautiful Caring Creative Loyal   1.0         2.0   1.0   1.0   0.0  \n",
              "                          Cheat   1.0         2.0   1.0   1.0   0.0  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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    \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Poor\",\n        \"Weak\",\n        \"Dumb\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "man_satisfaction = man_payoff - woman_payoff.T\n",
        "woman_satisfaction = woman_payoff - man_payoff.T"
      ],
      "metadata": {
        "id": "J7jmklgUK9ek"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "man_satisfaction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "A1E4Rm-ivLeQ",
        "outputId": "ff685329-65c4-4bac-95b7-64b62c414972"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Beautiful                                   \\\n",
              "                                 Caring                      Toxic         \n",
              "                               Creative        Dull       Creative         \n",
              "                                  Loyal Cheat Loyal Cheat    Loyal Cheat   \n",
              "Rich Strong Intelligent Loyal       0.0  -1.0  -1.0  -2.0     -1.0  -2.0   \n",
              "                        Cheat       1.0   0.0   0.0  -1.0      0.0  -1.0   \n",
              "            Dumb        Loyal       1.0   0.0   0.0  -1.0      0.0  -1.0   \n",
              "                        Cheat       2.0   1.0   1.0   0.0      1.0   0.0   \n",
              "     Weak   Intelligent Loyal       1.0   0.0   0.0  -1.0      0.0  -1.0   \n",
              "                        Cheat       2.0   1.0   1.0   0.0      1.0   0.0   \n",
              "            Dumb        Loyal       2.0   1.0   1.0   0.0      1.0   0.0   \n",
              "                        Cheat       3.0   2.0   2.0   1.0      2.0   1.0   \n",
              "Poor Strong Intelligent Loyal       1.0   0.0   0.0  -1.0      0.0  -1.0   \n",
              "                        Cheat       2.0   1.0   1.0   0.0      1.0   0.0   \n",
              "            Dumb        Loyal       2.0   1.0   1.0   0.0      1.0   0.0   \n",
              "                        Cheat       3.0   2.0   2.0   1.0      2.0   1.0   \n",
              "     Weak   Intelligent Loyal       2.0   1.0   1.0   0.0      1.0   0.0   \n",
              "                        Cheat       3.0   2.0   2.0   1.0      2.0   1.0   \n",
              "            Dumb        Loyal       3.0   2.0   2.0   1.0      2.0   1.0   \n",
              "                        Cheat       4.0   3.0   3.0   2.0      3.0   2.0   \n",
              "\n",
              "                                              Ugly                             \\\n",
              "                                            Caring                      Toxic   \n",
              "                               Dull       Creative        Dull       Creative   \n",
              "                              Loyal Cheat    Loyal Cheat Loyal Cheat    Loyal   \n",
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              "                        Cheat   0.0  -1.0      1.0   0.0   0.0  -1.0      0.0   \n",
              "     Weak   Intelligent Loyal  -1.0  -2.0      0.0  -1.0  -1.0  -2.0     -1.0   \n",
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              "                        Cheat   1.0   0.0      2.0   1.0   1.0   0.0      1.0   \n",
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              "        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",
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              "summary": "{\n  \"name\": \"man_satisfaction\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": [\n        \"Beautiful\",\n        \"Caring\",\n        \"Creative\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": 0.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0,\n          4.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Beautiful\",\n        \"Caring\",\n        \"Creative\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": -1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0,\n          3.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Ugly\",\n        \"Toxic\",\n        \"Dull\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": -4.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -3.0,\n          0.0,\n          -2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "woman_satisfaction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "0gz0eF_rv8fw",
        "outputId": "d937ef07-fe43-45df-dd04-72500e9dcafc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                       Rich                                \\\n",
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\"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Poor\",\n        \"Weak\",\n        \"Dumb\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": -4.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -3.0,\n          0.0,\n          -2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "man_reward = man_payoff + man_satisfaction\n",
        "woman_reward = woman_payoff + woman_satisfaction"
      ],
      "metadata": {
        "id": "GeS800jIMtM1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "man_reward"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "qC5gud1LNENM",
        "outputId": "a9557014-ee18-4fed-aee8-f285b358b690"
      },
      "execution_count": null,
      "outputs": [
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              "      <th>Loyal</th>\n",
              "      <th>Cheat</th>\n",
              "      <th>Loyal</th>\n",
              "      <th>Cheat</th>\n",
              "      <th>Loyal</th>\n",
              "      <th>Cheat</th>\n",
              "      <th>Loyal</th>\n",
              "      <th>Cheat</th>\n",
              "      <th>Loyal</th>\n",
              "      <th>Cheat</th>\n",
              "      <th>Loyal</th>\n",
              "      <th>Cheat</th>\n",
              "      <th>Loyal</th>\n",
              "      <th>Cheat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"8\" valign=\"top\">Rich</th>\n",
              "      <th rowspan=\"4\" valign=\"top\">Strong</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Intelligent</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>-4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Dumb</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Weak</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Intelligent</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Dumb</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"8\" valign=\"top\">Poor</th>\n",
              "      <th rowspan=\"4\" valign=\"top\">Strong</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Intelligent</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Dumb</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Weak</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Intelligent</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Dumb</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-802ad265-5f13-4af3-9f34-aa4a051e7e47')\"\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        const element = document.querySelector('#df-802ad265-5f13-4af3-9f34-aa4a051e7e47');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      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",
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          },
          "metadata": {},
          "execution_count": 11
        }
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    },
    {
      "cell_type": "code",
      "source": [
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          "height": 645
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      "execution_count": null,
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              "      <th rowspan=\"2\" valign=\"top\">Dull</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Toxic</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Creative</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Dull</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"8\" valign=\"top\">Ugly</th>\n",
              "      <th rowspan=\"4\" valign=\"top\">Caring</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Creative</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Dull</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Toxic</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">Creative</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Dull</th>\n",
              "      <th>Loyal</th>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cheat</th>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83bdc9e8-a192-4109-91bd-828719095bf0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-83bdc9e8-a192-4109-91bd-828719095bf0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-83bdc9e8-a192-4109-91bd-828719095bf0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bc3882e6-39b0-4c67-a02d-e22fae4a438e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bc3882e6-39b0-4c67-a02d-e22fae4a438e')\"\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-bc3882e6-39b0-4c67-a02d-e22fae4a438e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8fd060ea-7344-41af-93a3-8231f3802a91\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    </button>\n",
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              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8fd060ea-7344-41af-93a3-8231f3802a91 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('woman_reward');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "woman_reward",
              "summary": "{\n  \"name\": \"woman_reward\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": [\n        \"Rich\",\n        \"Strong\",\n        \"Intelligent\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": 4.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5.0,\n          8.0,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Rich\",\n        \"Strong\",\n        \"Intelligent\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": 2.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.0,\n          6.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": 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\"Weak\",\n        \"Intelligent\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": 2.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.0,\n          6.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Rich\",\n        \"Weak\",\n        \"Intelligent\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": 0.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0,\n          4.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Rich\",\n        \"Weak\",\n        \"Dumb\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": 0.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0,\n          4.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Rich\",\n        \"Weak\",\n        \"Dumb\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": -2.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -1.0,\n          2.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Poor\",\n        \"Strong\",\n        \"Intelligent\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": 2.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.0,\n          6.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Poor\",\n        \"Strong\",\n        \"Intelligent\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": 0.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0,\n          4.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Poor\",\n        \"Strong\",\n        \"Dumb\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": 0.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0,\n          4.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Poor\",\n        \"Strong\",\n        \"Dumb\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": -2.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -1.0,\n          2.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Poor\",\n        \"Weak\",\n        \"Intelligent\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": 0.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0,\n          4.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Poor\",\n        \"Weak\",\n        \"Intelligent\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": -2.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -1.0,\n          2.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Poor\",\n        \"Weak\",\n        \"Dumb\",\n        \"Loyal\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": -2.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -1.0,\n          2.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"Poor\",\n        \"Weak\",\n        \"Dumb\",\n        \"Cheat\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0327955589886444,\n        \"min\": -4.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -3.0,\n          0.0,\n          -2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Payoff Matrix of Player A\n",
        "A = man_reward.values\n",
        "\n",
        "#Payoff Matrix of Player B\n",
        "B = woman_reward.values"
      ],
      "metadata": {
        "id": "UhLyteu3OD_l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pA = np.ones((16,)) / 16\n",
        "pB = np.ones((16,)) / 16\n",
        "\n",
        "pA_next = pA.copy()\n",
        "pB_next = pB.copy()"
      ],
      "metadata": {
        "id": "wvqxJH_2eoxp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def step_A():\n",
        "    global pA_next\n",
        "    fA = np.matmul(A, pB)\n",
        "    fA_av = np.dot(pA, fA)\n",
        "\n",
        "    mu_A = fA - fA_av\n",
        "    pA_next = pA * np.exp(mu_A)\n",
        "\n",
        "    pA_next = pA_next / np.sum(pA_next)"
      ],
      "metadata": {
        "id": "_jRtu4JvfB0o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def step_B():\n",
        "    global pB_next\n",
        "    fB = np.matmul(B, pA)\n",
        "    fB_av = np.dot(pB, fB)\n",
        "\n",
        "    mu_B = fB - fB_av\n",
        "    pB_next = pB * np.exp(mu_B)\n",
        "\n",
        "    pB_next = pB_next / np.sum(pB_next)"
      ],
      "metadata": {
        "id": "hurd1ERffEJ_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def step():\n",
        "    global pA\n",
        "    global pB\n",
        "\n",
        "    step_A()\n",
        "    step_B()\n",
        "\n",
        "    pA = pA_next\n",
        "    pB = pB_next"
      ],
      "metadata": {
        "id": "RwKJy7jCfbj4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for _ in range(10):\n",
        "    step()"
      ],
      "metadata": {
        "id": "5aob_W0GfoPX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prob_df = pd.DataFrame(np.reshape(pA, (16, 1)) * np.reshape(pB, (1, 16)),\n",
        "    index = man_reward.index,\n",
        "    columns = man_reward.columns\n",
        ")\n",
        "\n",
        "prob_df.index.name = 'Man'\n",
        "prob_df.columns.name = 'Woman'"
      ],
      "metadata": {
        "id": "0-fO_hcIf1lY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "g = sns.heatmap(prob_df)\n",
        "\n",
        "g.set_xlabel(\"Man\")\n",
        "g.set_ylabel(\"Woman\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "NR3wqRqThRvx",
        "outputId": "26551e25-1384-4f2f-8dae-3c3f8b7a6292"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(50.7222222222222, 0.5, 'Woman')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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UKlQqFbVq1cLe3p5JkyaRk5MDgK2tLVlZWbRo0aJa40hNTeUf//gH9erVw8DAADs7OwYNGsTNmzcBSExMRKVScfv27WqN46+q7AcPKJ0Qa8u3337LvHnztN5veSqTwBcXF7Nq1So6dOiAsbExZmZmtG3blmXLlnH//v3qDfQx/ysfPIQQQrzgioq0t9UAz7y0o2fPnkRHR5Ofn8+xY8cICQlBpVLx4Ycfolarsba2rtbxf/31V7p160afPn3YuXMnZmZmZGZmsn37du7du1epvvLy8tDT06umSGsWCwuLZx1CuYYOHcq3337LjBkziIqKwsrKitTUVJYtW4adnZ3MRAshhBCVJaUdfy99fX2sra2xtbUlKCiIgIAAdu3aBZQ9w3rmzBn69OmDqakpJiYmdOnShYyMDI0+IyMjsbGxwdLSkjFjxpCfn//E8ZOSkvjzzz9Zs2YNrVu3xt7eHj8/P5YuXYq9vT2ZmZn4+fkBYG5ujkqlIjQ0FHg4ixgREcH48eOpW7cuPXr0AGDv3r20b98efX19bGxsmDJlCgUFBcqYvr6+jB07lkmTJmFhYYG1tTWzZ8/WiOvcuXN07twZAwMDmjdvzu7du1GpVGzdurXC97ZkJj0+Pp62bdtiZGREp06dSE9PByAmJoY5c+aQmpqqfDMQExMDwO3btxkxYgRWVlaYmpri7+9Pampqhcd+fIY1KyuLl19+GUNDQ+zt7fniiy+ws7Nj2bJlSpvyxiyZPV+/fj12dnbUqVOHwYMHc/fuXeDhNxx79+5l+fLlyvVkZmaWGd/mzZvZsGEDX375JdOmTaNdu3bY2dkRGBjInj17lL95iae9p3Jzc5k4cSINGzakdu3adOjQgcTEROX4b7/9xquvvkrDhg0xMjKiZcuWfPnll8rxysQthBBCiOfHM0+kH3X69GkOHjz4xFnda9eu0bVrV/T19dmzZw/Hjh1j+PDhGklqQkICGRkZJCQkEBsbS0xMjJIclsXa2pqCggK2bNlCcXFxqeO2trZ88803AKSnp5OVlcXy5cuV47Gxsejp6ZGUlMTKlSu5du0avXv3pl27dqSmpvLpp5+ydu1a3n//fY1+Y2NjqV27NsnJySxatIi5c+cqHyAKCwsJCgrCyMiI5ORkVq1axfTp0yt8Hx83ffp0Fi9ezNGjR9HV1WX48OEADBo0iHfeeQc3NzeysrLIyspi0KBBAAwYMICbN2/y/fffc+zYMdq0aUO3bt34/fffqxRDcHAwv/zyC4mJiXzzzTesWrVKKZ0pUZExMzIy2Lp1K3FxccTFxbF3714++OADAJYvX07Hjh0ZOXKkcj22trZlxrNhwwacnZ0JDAwsdUylUlGnTh3ldXnvqYiICA4dOsTGjRs5efIkAwYMoGfPnpw/fx6AnJwcPD09+e677zh9+jSjRo1i6NChHD58uNJxCyGEEM+1okLtbTXAMy/tiIuLw9jYmIKCAnJzc9HR0SEqKqrMtitWrKBOnTps3LiRWrVqAeDk5KTRxtzcnKioKNRqNS4uLrz88svEx8czcuTIMvv08vJi2rRpDBkyhPDwcNq3b4+/vz/BwcHUr18ftVqtlCnUq1cPMzMzjfMdHR1ZtGiR8nr69OnY2toSFRWFSqXCxcWFX375hcmTJzNz5kx0dB5+dnF3d2fWrFlKH1FRUcTHx9O9e3d27dpFRkYGiYmJSmnL/Pnz6d69eyXvLsq5Pj4+AEyZMoWXX36ZnJwcDA0NMTY2RldXV6OE5sCBAxw+fJibN2+ir68PPJyR3bp1K19//TWjRo2q1Pjnzp1j9+7dHDlyhLZt2wKwZs0aHB0dKz1mUVERMTExmJiYAA/LM+Lj45k/fz516tRBT08PIyOjckuCzp8/j7Ozc4Xif9p76sqVK0RHR3PlyhUaNGgAwMSJE/nvf/9LdHQ0CxYsoGHDhkycOFHp76233mLnzp1s3ryZ9u3bVyru3NxccnNzNfYVFxejUqkqdC1CCCFEtZLSjr+Xn58fKSkpJCcnExISwrBhw3jllVfKbJuSkkKXLl2UJLosbm5uqNVq5bWNjY0y87lgwQKMjY2V7cqVK8DDRPP69eusXLkSNzc3Vq5ciYuLC6dOnSo3fk9PT43XaWlpdOzYUSOx8fb2Jjs7m59//lnZ5+7urnHeo3Gmp6dja2urkVS1b99eo32vXr2U63Bzc3tqjI+OZWNjA1BqNvhRqampZGdnY2lpqXG/Ll26VKqMpiLS09PR1dWlTZs2yr5mzZphbm5e6THt7OyUJLrkep52LfDwPVHSX69evQDK/Pbhaec/6T116tQpCgsLcXJy0oh77969StyFhYXMmzePli1bYmFhgbGxMTt37lTef5WxcOFC6tSpo7EVF92tdD9CCCFEtZCHDf9etWvXplmzZgB8/vnneHh4sHbtWsLCwkq1NTQ0LLe/x5NslUpF0f/9McLDwxk4cKByrGQGEcDS0pIBAwYwYMAAFixYQOvWrYmMjCQ2Nrbc+KviaXFWxJo1a3jw4EGZfT1trJIE/2ljZWdnY2Njo1HnW+LxGXltqeiYVblvO3bsUGqaS95DTk5OnDt3rkKxPW3M7Oxs1Go1x44d00i2AYyNjQH46KOPWL58OcuWLaNly5bUrl2b8ePHk5eXV6HxHzV16lQmTJigsc/c0qXS/QghhBDir3vmifSjdHR0mDZtGhMmTGDIkCGljru7uxMbG0t+fn65yWNZLCwsKrSahJ6eHg4ODsqqHSU124WF5dfruLq68s0332h83Z6UlISJiQmNGjWqUJzOzs5cvXqVGzduUL9+fQCOHDmi0aZhw4YV6qs8enp6pa6rTZs2XL9+HV1dXezs7P7yGM7OzhQUFHDixAllBv/ChQv88ccfWh+zrOtp0qRJqXZDhgxh8ODBbNu2rVSddHFxMXfu3NGok36S1q1bU1hYyM2bN+nSpUuZbZKSkggMDOT1118HHn6I+emnn2jevPlT4y6Lvr6+UvpSQso6hBBCPDektOPZGjBgAGq1mhUrVpQ6FhERwZ07dxg8eDBHjx7l/PnzrF+/XlmFoiri4uJ4/fXXiYuL46effiI9PZ3IyEh27NihJFhNmjRBpVIRFxfHr7/+SnZ29hP7e/PNN7l69SpvvfUW586dY9u2bcyaNYsJEyYo9dHl6d69Ow4ODoSEhHDy5EmSkpKYMWMGoP2kyc7OjkuXLpGSksKtW7fIzc0lICCAjh07EhQUxA8//EBmZiYHDx5k+vTpHD16tNJjuLi4EBAQwKhRozh8+DAnTpxg1KhRGBoaKtejrTHt7OxITk4mMzOTW7duPXG2euDAgQwaNIhXX32VBQsWcPToUS5fvkxcXBwBAQEkJCRUaDwnJydee+01goOD+fbbb7l06RKHDx9m4cKFfPfdd8DDGvhdu3Zx8OBB0tLSeOONN7hx40aV4hZCCCGeay9Yacdzl0jr6uoSERHBokWLSq3jbGlpyZ49e8jOzsbHxwdPT09Wr15dpdnpEs2bN8fIyIh33nmHVq1a4eXlxebNm1mzZg1Dhw4FHs7+zpkzhylTplC/fn0iIiKe2F/Dhg3ZsWMHhw8fxsPDg/DwcMLCwpREuCLUajVbt24lOzubdu3aMWLECGXVDgMDgypfa1leeeUVevbsiZ+fH1ZWVnz55ZeoVCp27NhB165dGTZsGE5OTgwePJjLly8rM+SVtW7dOurXr0/Xrl3p168fI0eOxMTERLkebY05ceJE1Go1zZs3x8rK6ol1yCqVii+++IIlS5awdetWfHx8cHd3Z/bs2QQGBipLGVZEdHQ0wcHBvPPOOzg7OxMUFMSRI0do3LgxADNmzKBNmzb06NEDX19frK2tS61RXdG4hRBCCPH8UBVX5qkr8cwkJSXRuXNnLly4gIODw7MO5y/7+eefsbW1Zffu3XTr1u1Zh1Oj6eppp8xHCCHE/76CvGvV2n/O/vVa68ugy1Ct9VVdnqsaafH/bdmyBWNjYxwdHblw4QLjxo3D29u7xibRJd8ktGzZkqysLCZNmoSdnR1du3Z91qEJIYQQQkuKi2vG+s/aIon0c+ru3btMnjyZK1euULduXQICAli8ePGzDqvK8vPzmTZtGhcvXsTExIROnTqxYcOGv1SWI4QQQgjxLElphxA1nJR2CCGEqKjqLu14kPi51voy9B2utb6qi8xICyGEEEII7ZDl74QQQgghhBDlkRlpIYQQQgihHTVk/WdtkURaCCGEEEJoxwtW2iGJtBBCCCGE0I4XbEZaaqSFEEIIIYSoApmRFkIIIYQQ2iGlHUIIIYQQQlSBlHaIEpmZmahUKlJSUirUPjQ0lKCgoGqNSTxdYmIiKpWK27dvP+tQKk3eP0IIIUTN8kIn0qGhoahUKlQqFbVq1cLe3p5JkyaRk5MDgK2tLVlZWbRo0aJa40hNTeUf//gH9erVw8DAADs7OwYNGsTNmzeBmpMclnzwKNlMTExwc3NjzJgxnD9//lmH91QxMTGYmZk96zCEEEKImq2oSHtbDfBCJ9IAPXv2JCsri4sXL7J06VI+++wzZs2aBYBarcba2hpd3eqrgPn111/p1q0bFhYW7Ny5k7S0NKKjo2nQoAH37t2rVF95eXnVFGXl7N69m6ysLFJTU1mwYAFpaWl4eHgQHx//rEMTQgghRHUqLtLeVgO88Im0vr4+1tbW2NraEhQUREBAALt27QLKLu04c+YMffr0wdTUFBMTE7p06UJGRoZGn5GRkdjY2GBpacmYMWPIz89/4vhJSUn8+eefrFmzhtatW2Nvb4+fnx9Lly7F3t6ezMxM/Pz8ADA3N0elUhEaGgqAr68vERERjB8/nrp169KjRw8A9u7dS/v27dHX18fGxoYpU6ZQUFCgjOnr68vYsWOZNGkSFhYWWFtbM3v2bI24zp07R+fOnTEwMKB58+bs3r0blUrF1q1by72nlpaWWFtb07RpUwIDA9m9ezcdOnQgLCyMwsJCoOwyhvHjx+Pr66sR51tvvcX48eMxNzenfv36rF69mnv37jFs2DBMTExo1qwZ33//fZn31d3dHQMDA7y8vDh9+nS5cT/NlStXCAwMxNjYGFNTUwYOHMiNGzeAh+8THR0djh49qnHOsmXLaNKkCUVFRRQWFhIWFoa9vT2GhoY4OzuzfPnyvxSTEEIIIZ6tFz6RftTp06c5ePAgenp6ZR6/du0aXbt2RV9fnz179nDs2DGGDx+ukaQmJCSQkZFBQkICsbGxxMTEEBMT88Qxra2tKSgoYMuWLRQXF5c6bmtryzfffANAeno6WVlZGglYbGwsenp6JCUlsXLlSq5du0bv3r1p164dqampfPrpp6xdu5b3339fo9/Y2Fhq165NcnIyixYtYu7cucoHiMLCQoKCgjAyMiI5OZlVq1Yxffr0Ct/Hx+no6DBu3DguX77MsWPHKnVubGwsdevW5fDhw7z11luMHj2aAQMG0KlTJ44fP85LL73E0KFDuX//vsZ57777LosXL+bIkSNYWVnRt2/fp36geZqioiICAwP5/fff2bt3L7t27eLixYsMGjQIADs7OwICAoiOjtY4Lzo6mtDQUHR0dCgqKqJRo0Z89dVXnD17lpkzZzJt2jQ2b95cpZiEEEKI59ILVtrxwq/aERcXh7GxMQUFBeTm5qKjo0NUVFSZbVesWEGdOnXYuHEjtWrVAsDJyUmjjbm5OVFRUajValxcXHj55ZeJj49n5MiRZfbp5eXFtGnTGDJkCOHh4bRv3x5/f3+Cg4OpX78+arUaCwsLAOrVq1eqjtfR0ZFFixYpr6dPn46trS1RUVGoVCpcXFz45ZdfmDx5MjNnzkRH5+FnJ3d3d6WExdHRkaioKOLj4+nevTu7du0iIyODxMRErK2tAZg/fz7du3ev5N39/1xcXICHs7ft27ev8HkeHh7MmDEDgKlTp/LBBx9Qt25d5X7OnDmTTz/9lJMnT+Ll5aWcN2vWLCXe2NhYGjVqxJYtWxg4cGClY4+Pj+fUqVNcunQJW1tbANatW4ebmxtHjhyhXbt2jBgxgvDwcJYsWYK+vj7Hjx/n1KlTbNu2DYBatWoxZ84cpU97e3sOHTrE5s2bKxVTbm4uubm5GvuKi4tRqVSVvi4hhBBC62pISYa2vPAz0n5+fqSkpJCcnExISAjDhg3jlVdeKbNtSkoKXbp0UZLosri5uaFWq5XXNjY2ykODCxYswNjYWNmuXLkCPExSr1+/zsqVK3Fzc2PlypW4uLhw6tSpcuP39PTUeJ2WlkbHjh01Eitvb2+ys7P5+eeflX3u7u4a5z0aZ3p6Ora2tkoSDZRKfnv16qVch5ubW7lxlsy2VzbhezROtVqNpaUlLVu2VPbVr18fQIm9RMeOHZV/W1hY4OzsTFpaGoDG3yA8PLzcGNLS0rC1tVWSaIDmzZtjZmam9BkUFIRarWbLli3Aw4cX/fz8sLOzU85ZsWIFnp6eWFlZYWxszKpVq5T3QEUtXLiQOnXqaGzFRXcr1YcQQghRbWRG+sVSu3ZtmjVrBsDnn3+Oh4cHa9euJSwsrFRbQ0PDcvt7PMlWqVQU/d+bITw8XGP2sUGDBsq/LS0tGTBgAAMGDGDBggW0bt2ayMhIYmNjy42/Kp4WZ0WsWbOGBw8elNlXWUoSTnt7e+BhucfjpSxllV6UFeej+0oS88rE/mjNu6mpaYXPexo9PT2Cg4OJjo6mf//+fPHFFxolOBs3bmTixIksXryYjh07YmJiwkcffURycnKlxpk6dSoTJkzQ2Gdu6aKVaxBCCCFE5bzwifSjdHR0mDZtGhMmTGDIkCGljru7uxMbG0t+fn6FksfHWVhYKGUaT6Onp4eDg4OyakdJzXbJg3pP4+rqyjfffKPxdX9SUhImJiY0atSoQnE6Oztz9epVbty4ocz4HjlyRKNNw4YNK9QXPExyP/74Y+zt7WndujUAVlZWpR4ATElJqdJ9LcuPP/5I48aNAfjjjz/46aefcHV1BVA+OFWUq6srV69e5erVq8qs9NmzZ7l9+zbNmzdX2o0YMYIWLVrwySefUFBQQP/+/ZVjSUlJdOrUiTfffFPZ9/hDqhWhr6+Pvr6+xj4p6xBCCPHckNKOF9uAAQNQq9WsWLGi1LGIiAju3LnD4MGDOXr0KOfPn2f9+vWkp6dXeby4uDhef/114uLi+Omnn0hPTycyMpIdO3YQGBgIQJMmTVCpVMTFxfHrr7+SnZ39xP7efPNNrl69yltvvcW5c+fYtm0bs2bNYsKECUp9dHm6d++Og4MDISEhnDx5kqSkJKVOuSJJ22+//cb169e5ePEi27dvJyAggMOHD7N27Vql7MXf35+jR4+ybt06zp8/z6xZs/7yyhqPmjt3LvHx8Zw+fZrQ0FDq1q1b7o+dFBYWkpKSorGlpaUREBBAy5Ytee211zh+/DiHDx8mODgYHx8f2rZtq5zv6uqKl5cXkydP5tVXX9X4BsPR0ZGjR4+yc+dOfvrpJ957771SH06EEEKIGu8FK+2QRPoxurq6REREsGjRolLrOFtaWrJnzx6ys7Px8fHB09OT1atX/6VZ1ObNm2NkZMQ777xDq1at8PLyYvPmzaxZs4ahQ4cCD2d/58yZw5QpU6hfvz4RERFP7K9hw4bs2LGDw4cP4+HhQXh4OGFhYUoiXBFqtZqtW7eSnZ2tPEhXsmqHgYFBuecHBARgY2NDy5YtmTJlCq6urpw8eVJZxg+gR48evPfee0yaNIl27dpx9+5dgoODKxxjeT744APGjRuHp6cn169f5z//+c8TV2MpkZ2dTevWrTW2vn37olKp2LZtG+bm5nTt2pWAgACaNm3Kpk2bSvURFhZGXl4ew4cP19j/xhtv0L9/fwYNGkSHDh347bffNGanhRBCCFHzqIrLWnNNiMckJSXRuXNnLly4gIODw7MO57k1b948vvrqK06ePPm3jamrV/EyGyGEEC+2grxr1dr/g81ztdaX4cCZWuurukiNtCjTli1bMDY2xtHRkQsXLjBu3Di8vb0liX6C7OxsMjMziYqKKrVmtxBCCPHCeMHmZ6W0Q5Tp7t27jBkzBhcXF0JDQ2nXrp2yJrIoLSIiAk9PT3x9fUuVdQghhBDif5OUdghRw0lphxBCiIqq9tKOL2dprS/DV+eU3+gZk9IOIYQQQgihHTVktQ1tkdIOIYQQQgghqkBmpIUQQgghhHbID7IIIYQQQghRBc/wB1lWrFiBnZ0dBgYGdOjQgcOHDz+1/bJly3B2dsbQ0BBbW1vefvttcnJyKjWmJNJCCCGEEEI7iou1t1XCpk2bmDBhArNmzeL48eN4eHjQo0cPbt68WWb7L774gilTpjBr1izS0tJYu3YtmzZtYtq0aZUaVxJpIYQQQghRoy1ZsoSRI0cybNgwmjdvzsqVKzEyMuLzzz8vs/3Bgwfx9vZmyJAh2NnZ8dJLL/Hqq6+WO4v9OEmkhRBCCCGEdjyD0o68vDyOHTtGQECAsk9HR4eAgAAOHTpU5jmdOnXi2LFjSuJ88eJFduzYQe/evSt1ufKwoRBCCCGE0A4tLn+Xm5tLbm6uxj59fX309fU19t26dYvCwkLq16+vsb9+/fqcO3euzL6HDBnCrVu36Ny5M8XFxRQUFBAeHi6lHX9FZmYmKpWKlJSUCrUPDQ0lKCioWmMST5eYmIhKpeL27dvV0r+vry/jx4+vlr6FEEII8WQLFy6kTp06GtvChQu10ndiYiILFizgk08+4fjx43z77bd89913zJs3r1L9vFCJdGhoKCqVCpVKRa1atbC3t2fSpEnKE5q2trZkZWXRokWLao0jNTWVf/zjH9SrVw8DAwPs7OwYNGiQUhBf3cmhtpR88CjZTExMcHNzY8yYMZw/f/5Zh1euvLw8Fi1ahIeHB0ZGRtStWxdvb2+io6PJz8//W2Oxs7Nj2bJlf+uYQgghhNYVF2ltmzp1Kn/++afGNnXq1FJD1q1bF7VazY0bNzT237hxA2tr6zLDfO+99xg6dCgjRoygZcuW9OvXjwULFrBw4UKKKjGr/kIl0gA9e/YkKyuLixcvsnTpUj777DNmzXr4c5ZqtRpra2t0dauv4uXXX3+lW7duWFhYsHPnTtLS0oiOjqZBgwbcu3evUn3l5eVVU5SVs3v3brKyskhNTWXBggWkpaXh4eFBfHz8sw7tifLy8ujRowcffPABo0aN4uDBgxw+fJgxY8bwr3/9izNnzjzrEIUQQogap7ioWGubvr4+pqamGtvjZR0Aenp6eHp6auQdRUVFxMfH07FjxzLjvH//Pjo6mmmwWq1+eA2VWDHkhUuk9fX1sba2xtbWlqCgIAICAti1axdQdmnHmTNn6NOnD6amppiYmNClSxcyMjI0+oyMjMTGxgZLS0vGjBnz1NnMpKQk/vzzT9asWUPr1q2xt7fHz8+PpUuXYm9vT2ZmJn5+fgCYm5ujUqkIDQ0FHpYZREREMH78eOrWrUuPHj0A2Lt3L+3bt0dfXx8bGxumTJlCQUGBMqavry9jx45l0qRJWFhYYG1tzezZszXiOnfuHJ07d8bAwIDmzZuze/duVCoVW7duLfeeWlpaYm1tTdOmTQkMDGT37t106NCBsLAwCgsLgbLLYMaPH4+vr69GnG+99Rbjx4/H3Nyc+vXrs3r1au7du8ewYcMwMTGhWbNmfP/992XeV3d3dwwMDPDy8uL06dNPjXnZsmXs27eP+Ph4xowZQ6tWrWjatClDhgwhOTkZR0dHpW1RUdFT793t27cZMWIEVlZWmJqa4u/vT2pqqnI8IyODwMBA6tevj7GxMe3atWP37t0a13358mXefvttZXZfCCGEEBU3YcIEVq9eTWxsLGlpaYwePVrJHwCCg4M1ZrP79u3Lp59+ysaNG7l06RK7du3ivffeo2/fvkpCXREvXCL9qNOnT3Pw4EH09PTKPH7t2jW6du2Kvr4+e/bs4dixYwwfPlwjSU1ISCAjI4OEhARiY2OJiYkhJibmiWNaW1tTUFDAli1byvzEY2tryzfffANAeno6WVlZLF++XDkeGxuLnp4eSUlJrFy5kmvXrtG7d2/atWtHamoqn376KWvXruX999/X6Dc2NpbatWuTnJzMokWLmDt3rvIBorCwkKCgIIyMjEhOTmbVqlVMnz69wvfxcTo6OowbN47Lly9z7NixSp0bGxtL3bp1OXz4MG+99RajR49mwIABdOrUiePHj/PSSy8xdOhQ7t+/r3Heu+++y+LFizly5AhWVlb07dv3qR9oNmzYQEBAAK1bty51rFatWtSuXVsjpifdO4ABAwZw8+ZNvv/+e44dO0abNm3o1q0bv//+OwDZ2dn07t2b+Ph4Tpw4Qc+ePenbty9XrlwB4Ntvv6VRo0bMnTuXrKwssrKyKnXPhBBCiOfGM/pBlkGDBhEZGcnMmTNp1aoVKSkp/Pe//1UeQLxy5YrGf19nzJjBO++8w4wZM2jevDlhYWH06NGDzz77rFLjvnCrdsTFxWFsbExBQQG5ubno6OgQFRVVZtsVK1ZQp04dNm7cSK1atQBwcnLSaGNubk5UVBRqtRoXFxdefvll4uPjGTlyZJl9enl5MW3aNIYMGUJ4eDjt27fH39+f4OBg6tevj1qtxsLCAoB69ephZmamcb6joyOLFi1SXk+fPh1bW1uioqJQqVS4uLjwyy+/MHnyZGbOnKl8beHu7q6UsDg6OhIVFUV8fDzdu3dn165dZGRkkJiYqNQSzZ8/n+7du1fy7v5/Li4uwMNZ/vbt21f4PA8PD2bMmAHA1KlT+eCDD6hbt65yP2fOnMmnn37KyZMn8fLyUs6bNWuWEm9sbCyNGjViy5YtDBw4sMxxzp8/rzEb/jRPu3cHDhzg8OHD3Lx5U/m6KTIykq1bt/L1118zatQoPDw88PDwUPqbN28eW7ZsYfv27URERGBhYYFarcbExOSJtVxCCCFEjfAMfyI8IiKCiIiIMo8lJiZqvNbV1WXWrFnKf9+r6oWbkfbz8yMlJYXk5GRCQkIYNmwYr7zySpltU1JS6NKli5JEl8XNzU3jKwAbGxvlocEFCxZgbGysbCUzkPPnz+f69eusXLkSNzc3Vq5ciYuLC6dOnSo3fk9PT43XaWlpdOzYUaMcwNvbm+zsbH7++Wdln7u7u8Z5j8aZnp6Ora2tRhL3ePLbq1cv5Trc3NzKjbNktr2yZQqPxqlWq7G0tKRly5bKvpJPlo//UtGjNVAWFhY4OzuTlpYGoPE3CA8P14ivsjGB5r1LTU0lOzsbS0tLjXEuXbqklABlZ2czceJEXF1dMTMzw9jYmLS0NOX9UBm5ubncuXNHY6vMtQghhBBCe164GenatWvTrFkzAD7//HM8PDxYu3YtYWFhpdoaGhqW29/jSbZKpVKe9gwPD9eYEW3QoIHyb0tLSwYMGMCAAQNYsGABrVu3JjIyktjY2HLjr4qnxVkRa9as4cGDB2X2VZaSJNbe3h54WO7xeMJXVulFWXE+uq8kMa9M7I/WvJuamgIPv1l40tqSFYmpZPzs7GxsbGxKfdIFlG8TJk6cyK5du4iMjKRZs2YYGhryz3/+s0oPiy5cuJA5c+ZoxqNjjEptWum+hBBCCK0rerEmd164RPpROjo6TJs2jQkTJjBkyJBSx93d3YmNjSU/P79CyePjLCwslDKNp9HT08PBwUFZtaOkZrvkQb2ncXV15ZtvvqG4uFhJMpOSkjAxMaFRo0YVitPZ2ZmrV69y48YNZcb3yJEjGm0aNmxYob7gYZL78ccfY29vr9QgW1lZlXoAMCUlpUr3tSw//vgjjRs3BuCPP/7gp59+wtXVFUD54PSoIUOGMG3aNE6cOFGqTjo/P5+8vLwKfWhp06YN169fR1dXFzs7uzLbJCUlERoaSr9+/YCHyXdmZqZGGz09vQr9vadOncqECRM09plbupR7nhBCCPG30OIPstQEL1xpx+MGDBiAWq1mxYoVpY5FRERw584dBg8ezNGjRzl//jzr168nPT29yuPFxcXx+uuvExcXx08//UR6ejqRkZHs2LGDwMBAAJo0aYJKpSIuLo5ff/2V7OzsJ/b35ptvcvXqVd566y3OnTvHtm3bmDVrFhMmTCi1rMuTdO/eHQcHB0JCQjh58iRJSUlKnXJFSjN+++03rl+/zsWLF9m+fTsBAQEcPnyYtWvXKmUv/v7+HD16lHXr1nH+/HlmzZpV7soalTF37lzi4+M5ffo0oaGh1K1b96k/ljN+/Hi8vb3p1q0bK1asIDU1lYsXL7J582a8vLwqvA52QEAAHTt2JCgoiB9++IHMzEwOHjzI9OnTOXr0KPCwrvrbb78lJSWF1NRUhgwZUmpG3c7Ojn379nHt2jVu3br1xPHKWgpIVvkQQgjx3HhGDxs+Ky98Iq2rq0tERASLFi0qtY6zpaUle/bsITs7Gx8fHzw9PVm9evVfmkVt3rw5RkZGvPPOO7Rq1QovLy82b97MmjVrGDp0KPBw9nfOnDlMmTKF+vXrP7FwvqTtjh07OHz4MB4eHoSHhxMWFqYkwhWhVqvZunUr2dnZtGvXjhEjRiirdhgYGJR7fkBAADY2NrRs2ZIpU6bg6urKyZMnlWX8AHr06MF7773HpEmTaNeuHXfv3iU4OLjCMZbngw8+YNy4cXh6enL9+nX+85//PHE1FniYkO7atYtJkybx2Wef4eXlRbt27fj4448ZO3ZshX+UR6VSsWPHDrp27cqwYcNwcnJi8ODBXL58WZndX7JkCebm5nTq1Im+ffvSo0cP2rRpo9HP3LlzyczMxMHBASsrq6rfCCGEEEL8bVTF8qSSKENSUhKdO3fmwoULODg4POtwxFPo6lW87EYIIcSLrSDvWrX2f3/ZG1rry2h85ZaiexZe6Bpp8f9t2bIFY2NjHB0duXDhAuPGjcPb21uSaCGEEEJUXA0pydAWSaQFAHfv3mXy5MlcuXKFunXrEhAQwOLFi591WEIIIYQQzy0p7RCihpPSDiGEEBVV7aUdkSO01pfRxDVa66u6yIy0EEIIIYTQjmf4y4bPwgu/aocQQgghhBBVITPSQgghhBBCO+SXDYUQQgghhKi84hds1Q4p7RBCCCGEEKIKZEZaCCGEEEJoh5R2CCGEEEIIUQUv2KodkkgLIYQQQgjteMFmpP+naqQzMzNRqVSkpKRUqH1oaChBQUHVGpO2xMTEYGZm9qzDeKLH76Wvry/jx49XXtvZ2bFs2bK/Pa6apCa9H4UQQghRwxLp0NBQVCoVKpWKWrVqYW9vz6RJk8jJyQHA1taWrKwsWrRoUS3jnzt3DpVKxY8//qix38vLCwMDAyUOgJycHAwMDFi7dm21xFIRiYmJqFQqbt++XeFzqiuZO3LkCKNGjdJ6v+WpaAL/vH9QEUIIIWqEoiLtbTVAjUqkAXr27ElWVhYXL15k6dKlfPbZZ8yaNQsAtVqNtbU1urrVU7Hi4uKCtbU1iYmJyr67d+9y/PhxrKysNBLsQ4cOkZubi7+/f7XEUtNYWVlhZGT0rMMQQgghRHUqKtbeVgPUuERaX18fa2trbG1tCQoKIiAggF27dgFll3acOXOGPn36YGpqiomJCV26dCEjI0Ojz8jISGxsbLC0tGTMmDHk5+c/cXw/Pz+NRPrAgQM4OTnRt29fjf2JiYk0adIEe3t7ALZt20abNm0wMDCgadOmzJkzh4KCAqX9kiVLaNmyJbVr18bW1pY333yT7OzsJ8bx66+/0rZtW/r160dubm5Fbp0y67pz505cXV0xNjZWPpgAzJ49m9jYWLZt26bM/Jdc09WrVxk4cCBmZmZYWFgQGBhIZmZmhcaF0jPD586do3PnzhgYGNC8eXN2796NSqVi69atSpvyxiyZPX/S38/X15fLly/z9ttvK9dTVVeuXCEwMBBjY2NMTU0ZOHAgN27cAB6+73R0dDh69KjGOcuWLaNJkyYUFRVRWFhIWFgY9vb2GBoa4uzszPLly6scjxBCCCGevRqXSD/q9OnTHDx4ED09vTKPX7t2ja5du6Kvr8+ePXs4duwYw4cP10hgExISyMjIICEhgdjYWGJiYoiJiXnimH5+fhw4cEDpIyEhAV9fX3x8fEhISNDo18/PD4D9+/cTHBzMuHHjOHv2LJ999hkxMTHMnz9faa+jo8PHH3/MmTNniI2NZc+ePUyaNKnMGK5evUqXLl1o0aIFX3/9Nfr6+hW+Z/fv3ycyMpL169ezb98+rly5wsSJEwGYOHEiAwcOVJLrrKwsOnXqRH5+Pj169MDExIT9+/eTlJSkJOF5eXkVHrtEYWEhQUFBGBkZkZyczKpVq5g+fbpGm4qO+bS/37fffkujRo2YO3eucj1VUVRURGBgIL///jt79+5l165dXLx4kUGDBgEPPyQEBAQQHR2tcV50dDShoaHo6OhQVFREo0aN+Oqrrzh79iwzZ85k2rRpbN68uUoxCSGEEM+l4iLtbTVAjVu1Iy4uDmNjYwoKCsjNzUVHR4eoqKgy265YsYI6deqwceNGatWqBYCTk5NGG3Nzc6KiolCr1bi4uPDyyy8THx/PyJEjy+zTz8+Pe/fuceTIETp27EhiYiLvvvsunTt3JiQkhJycHIqLizl8+DAjRowAYM6cOUyZMoWQkBAAmjZtyrx585g0aZJSlvL4g3nvv/8+4eHhfPLJJxrjp6en0717d/r168eyZcsqPcuan5/PypUrcXBwACAiIoK5c+cCYGxsjKGhIbm5uVhbWyvn/Pvf/6aoqIg1a9Yo40VHR2NmZkZiYiIvvfRSpWLYtWsXGRkZJCYmKuPMnz+f7t27K202bdpUoTGf9vezsLBArVZjYmKicT2VFR8fz6lTp7h06RK2trYArFu3Djc3N44cOUK7du0YMWIE4eHhLFmyBH19fY4fP86pU6fYtm0bALVq1WLOnDlKn/b29hw6dIjNmzczcODAKscmhBBCPFdqSEmGttS4GWk/Pz9SUlJITk4mJCSEYcOG8corr5TZNiUlhS5duihJdFnc3NxQq9XKaxsbG27evAnAggULMDY2VrYrV67QrFkzGjVqRGJiInfu3OHEiRP4+PhgY2ND48aNOXTokFIfXTIjnZqayty5czX6GjlyJFlZWdy/fx+A3bt3061bNxo2bIiJiQlDhw7lt99+U44DPHjwgC5dutC/f3+WL1+ukUS7ubkpfffq1euJ12tkZKQk0Y9f75OkpqZy4cIFTExMlDEsLCzIyckpVSZTEenp6dja2mokt+3bt6/SmE/7+z3Jo3+H8PDwcuNNS0vD1tZWSaIBmjdvjpmZGWlpaQAEBQWhVqvZsmUL8LCMxs/PDzs7O+WcFStW4OnpiZWVFcbGxqxatYorV66UO/6jcnNzuXPnjsZWXPxi/T8tIYQQ4nlR42aka9euTbNmzQD4/PPP8fDwYO3atYSFhZVqa2hoWG5/jyfZKpWKov97UjQ8PFxjtrBBgwbAw9rbhIQE3N3dcXR0pF69egBKeUdxcTHNmjVTEq/s7GzmzJlD//79S41vYGBAZmYmffr0YfTo0cyfPx8LCwsOHDhAWFgYeXl5ykN6+vr6BAQEEBcXx7vvvkvDhg2Vfnbs2KHUBj/tusu63vISsezsbDw9PdmwYUOpY1ZWVk89t6oqOubT/n5P8mgNvamp6V8L9P/o6ekRHBxMdHQ0/fv354svvtCogd64cSMTJ05k8eLFdOzYERMTEz766COSk5MrNc7ChQs1ZrYBVDrGqNTauQ4hhBDiryiuIattaEuNS6QfpaOjw7Rp05gwYQJDhgwpddzd3Z3Y2Fjy8/OfOiv9JBYWFlhYWJTa7+fnx9ixY2nevDm+vr7K/q5du7J69WqKi4uV2WiANm3akJ6ernwAeNyxY8coKipi8eLF6Og8/JKgrNpZHR0d1q9fz5AhQ5SHHkuS+yZNmlT6+sqip6dHYWGhxr42bdqwadMm6tWrp5XE09nZmatXr3Ljxg3q168PPFwerzrGLOt6nvR3eBJXV1euXr3K1atXlQ9HZ8+e5fbt2zRv3lxpN2LECFq0aMEnn3xCQUGBxgenpKQkOnXqxJtvvqnsq8ps/tSpU5kwYYLGPnNLl0r3I4QQQlQLKe2oWQYMGIBarWbFihWljkVERHDnzh0GDx7M0aNHOX/+POvXryc9Pf0vjVlSJ/3555/j4+Oj7Pfx8SE5OZnDhw9rJNIzZ85k3bp1zJkzhzNnzpCWlsbGjRuZMWMG8DCxy8/P51//+hcXL15k/fr1rFy5ssyx1Wo1GzZswMPDA39/f65fv/6XruVxdnZ2nDx5kvT0dG7dukV+fj6vvfYadevWJTAwkP3793Pp0iUSExMZO3YsP//8c6XH6N69Ow4ODoSEhHDy5EmSkpKUe1FSrqKtMe3s7Ni3bx/Xrl3j1q1bT21bWFhISkqKxpaWlkZAQAAtW7bktdde4/jx4xw+fJjg4GB8fHxo27atcr6rqyteXl5MnjyZV199VeObAUdHR44ePcrOnTv56aefeO+990p9eKgIfX19TE1NNba/shqJEEIIIaquxifSurq6REREsGjRIu7du6dxzNLSkj179pCdnY2Pjw+enp6sXr26SrPTj7K3t6dJkybcvXtXI5Fu3LgxDRo0IC8vT2OmukePHsTFxfHDDz/Qrl07vLy8WLp0qTKL7OHhwZIlS/jwww9p0aIFGzZsYOHChU+95i+//BI3Nzf8/f3LrQmujJEjR+Ls7Ezbtm2xsrIiKSkJIyMj9u3bR+PGjenfvz+urq6EhYWRk5NTpdlitVrN1q1byc7OVh7UK1m1w8DAAEBrY86dO5fMzEwcHBzKLUPJzs6mdevWGlvfvn1RqVRs27YNc3NzunbtSkBAAE2bNmXTpk2l+igpxxk+fLjG/jfeeIP+/fszaNAgOnTowG+//aYxOy2EEEL8T3jB1pFWFcuTSuI5kJSUROfOnblw4YLGw5A1zbx58/jqq684efLk3zamrl7D8hsJIYQQQEHetWrtP3tioNb6Mo7cprW+qkuNrpEWNdeWLVswNjbG0dGRCxcuMG7cOLy9vWtsEp2dnU1mZiZRUVG8//77zzocIYQQ4tmoITPJ2lLjSztEzXT37l3GjBmDi4sLoaGhtGvXTllzuSaKiIjA09MTX1/fUmUdQgghhPjfJKUdQtRwUtohhBCioqq7tOPu+L5a68tk2X+01ld1kdIOIYQQQgihHVLaIYQQQgghhCiPzEgLIYQQQgjtkF82FEIIIYQQogqktEMIIYQQQghRHpmRFkIIIYQQ2vGCzUhLIi2EEEIIIbTiRVtVWUo7hBBCCCGEqIIanUhnZmaiUqlISUmpUPvQ0FCCgoKqNSZtiYmJwczM7FmH8USP30tfX1/Gjx+vvLazs2PZsmV/e1za9vh1CSGEEOIpioq1t9UAz3UiHRoaikqlQqVSUatWLezt7Zk0aRI5OTkA2NrakpWVRYsWLapl/HPnzqFSqfjxxx819nt5eWFgYKDEAZCTk4OBgQFr166tllgqIjExEZVKxe3btyt8TnV9uDhy5AijRo3Ser/lqUwCn5eXx6JFi/Dw8MDIyIi6devi7e1NdHQ0+fn51RvoY/5XPngIIYR4wb1gifRzXyPds2dPJbE5duwYISEhqFQqPvzwQ9RqNdbW1tU2touLC9bW1iQmJuLl5QXA3bt3OX78OPXr1+fHH3/E19cXgEOHDpGbm4u/v3+1xVOTWFlZPesQniovL48ePXqQmprKvHnz8Pb2xtTUlB9//JHIyEhat25Nq1atnnWYQgghRI1SXEMSYG15rmekAfT19bG2tsbW1pagoCACAgLYtWsXUHZpx5kzZ+jTpw+mpqaYmJjQpUsXMjIyNPqMjIzExsYGS0tLxowZ89TZRz8/PxITE5XXBw4cwMnJib59+2rsT0xMpEmTJtjb2wOwbds22rRpg4GBAU2bNmXOnDkUFBQo7ZcsWULLli2pXbs2tra2vPnmm2RnZz8xjl9//ZW2bdvSr18/cnNzK3LrlPKQnTt34urqirGxMT179iQrKwuA2bNnExsby7Zt25SZ/5Jrunr1KgMHDsTMzAwLCwsCAwPJzMys0LhQeob13LlzdO7cGQMDA5o3b87u3btRqVRs3bpVaVPemCWz50/6+/n6+nL58mXefvtt5XqeZNmyZezbt4/4+HjGjBlDq1ataNq0KUOGDCE5ORlHR0elbVFREZMmTcLCwgJra2tmz56t0dft27cZMWIEVlZWmJqa4u/vT2pqqnI8IyODwMBA6tevj7GxMe3atWP37t3K8crELYQQQojnx3OfSD/q9OnTHDx4ED09vTKPX7t2ja5du6Kvr8+ePXs4duwYw4cP10hgExISyMjIICEhgdjYWGJiYoiJiXnimH5+fhw4cEDpIyEhAV9fX3x8fEhISNDo18/PD4D9+/cTHBzMuHHjOHv2LJ999hkxMTHMnz9faa+jo8PHH3/MmTNniI2NZc+ePUyaNKnMGK5evUqXLl1o0aIFX3/9Nfr6+hW+Z/fv3ycyMpL169ezb98+rly5wsSJEwGYOHEiAwcOVJLrrKwsOnXqRH5+Pj169MDExIT9+/eTlJSkJOF5eXkVHrtEYWEhQUFBGBkZkZyczKpVq5g+fbpGm4qO+bS/37fffkujRo2YO3eucj1PsmHDBgICAmjdunWpY7Vq1aJ27drK69jYWGrXrk1ycjKLFi1i7ty5yoc5gAEDBnDz5k2+//57jh07Rps2bejWrRu///47ANnZ2fTu3Zv4+HhOnDhBz5496du3L1euXKl03EIIIcRzTUo7ni9xcXEYGxtTUFBAbm4uOjo6REVFldl2xYoV1KlTh40bN1KrVi0AnJycNNqYm5sTFRWFWq3GxcWFl19+mfj4eEaOHFlmn35+fty7d48jR47QsWNHEhMTeffdd+ncuTMhISHk5ORQXFzM4cOHGTFiBABz5sxhypQphISEANC0aVPmzZvHpEmTmDVrFkCpB/Pef/99wsPD+eSTTzTGT09Pp3v37vTr149ly5ZVerYyPz+flStX4uDgAEBERARz584FwNjYGENDQ3JzczVKZP79739TVFTEmjVrlPGio6MxMzMjMTGRl156qVIx7Nq1i4yMDBITE5Vx5s+fT/fu3ZU2mzZtqtCYT/v7WVhYoFarMTExKbfk5/z580pZTnnc3d2Vv5ujoyNRUVHEx8fTvXt3Dhw4wOHDh7l586byAScyMpKtW7fy9ddfM2rUKDw8PPDw8FD6mzdvHlu2bGH79u1ERERUKm4hhBDiufZi/UL4859I+/n58emnn3Lv3j2WLl2Krq4ur7zySpltU1JS6NKli5JEl8XNzQ21Wq28trGx4dSpUwAsWLCABQsWKMfOnj1Ls2bNaNSoEYmJibi5uXHixAl8fHyoV68ejRs35tChQxQXF5Obm6vMSKemppKUlKQxA11YWEhOTg7379/HyMiI3bt3s3DhQs6dO8edO3coKCjQOA7w4MEDunTpwpAhQ0o9iObm5sbly5cB6NKlC99//32Z12tkZKQk0SXXe/PmzSfen5L4L1y4gImJicb+nJycUmUyFZGeno6tra1Gkti+ffsqjfm0v9+TGBsbK/9+/fXXWblyZaXWuXR3d9d4/eg9TE1NJTs7G0tLS402Dx48UOLOzs5m9uzZfPfdd2RlZVFQUMCDBw+UGenKyM3NLVXaU1xcLOUgQgghxDPw3CfStWvXplmzZgB8/vnneHh4sHbtWsLCwkq1NTQ0LLe/x5NslUpFUdHDj0/h4eEMHDhQOdagQQPgYQ1rQkIC7u7uODo6Uq9ePQClvKO4uJhmzZpha2sLPEyc5syZQ//+/UuNb2BgQGZmJn369GH06NHMnz8fCwsLDhw4QFhYGHl5eUoira+vT0BAAHFxcbz77rs0bNhQ6WfHjh1KbfDTrrus6y0viczOzsbT05MNGzaUOlZdDxFWdMyn/f2e5NEaelNTU+DhNxXnzp2rUGxPGzM7OxsbGxuNevkSJcsXTpw4kV27dhEZGUmzZs0wNDTkn//8Z5XKZBYuXMicOXM049ExRqU2rXRfQgghhLa9aA8bPveJ9KN0dHSYNm0aEyZMYMiQIaWOu7u7ExsbS35+/lNnpZ/EwsICCwuLUvv9/PwYO3YszZs31ygH6Nq1K6tXr6a4uFiZjQZo06YN6enpygeAxx07doyioiIWL16Mjs7DMvXNmzeXaqejo8P69esZMmSI8tBjSXLfpEmTSl9fWfT09CgsLNTY16ZNGzZt2kS9evWUxPOvcHZ25urVq9y4cYP69esDD5fHq44xy7qesv4OQ4YMYdq0aZw4caJUnXR+fj55eXkaddJP0qZNG65fv46uri52dnZltklKSiI0NJR+/foBD5Pvxx/cLCvuskydOpUJEyZo7DO3dCn3PCGEEOJv8YIl0jXqYUN4+GCXWq1mxYoVpY5FRERw584dBg8ezNGjRzl//jzr168nPT39L41ZUif9+eef4+Pjo+z38fEhOTmZw4cPayTSM2fOZN26dcyZM4czZ86QlpbGxo0bmTFjBvAwscvPz+df//oXFy9eZP369axcubLMsdVqNRs2bMDDwwN/f3+uX7/+l67lcXZ2dpw8eZL09HRu3bpFfn4+r732GnXr1iUwMJD9+/dz6dIlEhMTGTt2LD///HOlx+jevTsODg6EhIRw8uRJkpKSlHtRUpKgrTHt7OzYt28f165d49atW09sN378eLy9venWrRsrVqwgNTWVixcvsnnzZry8vDh//nyFxgsICKBjx44EBQXxww8/kJmZycGDB5k+fTpHjx4FHtZVf/vtt6SkpJCamsqQIUNKzaJXNG59fX1MTU01NinrEEIIIZ6NGpdI6+rqEhERwaJFi7h3757GMUtLS/bs2UN2djY+Pj54enqyevXqKs1OP8re3p4mTZpw9+5djUS6cePGNGjQgLy8PI2Z6h49ehAXF8cPP/xAu3bt8PLyYunSpcossoeHB0uWLOHDDz+kRYsWbNiwgYULFz71mr/88kvc3Nzw9/cvt8a5MkaOHImzszNt27bFysqKpKQkjIyM2LdvH40bN6Z///64uroSFhZGTk5OlWaL1Wo1W7duJTs7m3bt2jFixAhl1Q4DAwMArY05d+5cMjMzcXBweGoZir6+Prt27WLSpEl89tlneHl50a5dOz7++GPGjh1b4R/5UalU7Nixg65duzJs2DCcnJwYPHgwly9fVmbflyxZgrm5OZ06daJv37706NGDNm3aVCluIYQQ4rlWpMWtBlAVV+apKyG0JCkpic6dO3PhwgWNhyFF5enqNSy/kRBCCAEU5F2r1v7/GOCrtb7Mv0rUWl/VpUbVSIuaa8uWLRgbG+Po6MiFCxcYN24c3t7ekkQLIYQQosaSRFr8Le7evcvkyZO5cuUKdevWJSAggMWLFz/rsIQQQgihTTWkJENbpLRDiBpOSjuEEEJUVHWXdvzez6f8RhVksWWv1vqqLjIjLYQQQgghtOMFm5Gucat2CCGEEEII8TyQGWkhhBBCCKEVxS/YjLQk0kIIIYQQQjtesERaSjuEEEIIIYSoApmRFkIIIYQQWiGlHUIIIYQQQlTFC5ZIS2mHEEIIIYQQVSCJ9F+UmZmJSqUiJSWlQu1DQ0MJCgqq1pi0JSYmBjMzs2cdRqXV1LgBfH19GT9+/LMOQwghhKiS4iLtbZW1YsUK7OzsMDAwoEOHDhw+fPip7W/fvs2YMWOwsbFBX18fJycnduzYUakxJZEuR2hoKCqVCpVKRa1atbC3t2fSpEnk5OQAYGtrS1ZWFi1atKiW8c+dO4dKpeLHH3/U2O/l5YWBgYESB0BOTg4GBgasXbu2WmKpiMTEROV+6ejoUKdOHVq3bs2kSZPIysp6ZnFVxOzZs2nVqtWzDkMIIYSosZ5VIr1p0yYmTJjArFmzOH78OB4eHvTo0YObN2+W2T4vL4/u3buTmZnJ119/TXp6OqtXr6Zhw8r9WrAk0hXQs2dPsrKyuHjxIkuXLuWzzz5j1qxZAKjVaqytrdHVrZ5ycxcXF6ytrUlMTFT23b17l+PHj2NlZaWRYB86dIjc3Fz8/f2rJZbKSE9P55dffuHIkSNMnjyZ3bt306JFC06dOvWsQxNCCCHE/5glS5YwcuRIhg0bRvPmzVm5ciVGRkZ8/vnnZbb//PPP+f3339m6dSve3t7Y2dnh4+ODh4dHpcaVRLoC9PX1sba2xtbWlqCgIAICAti1axdQdmnHmTNn6NOnD6amppiYmNClSxcyMjI0+oyMjMTGxgZLS0vGjBlDfn7+E8f38/PTSKQPHDiAk5MTffv21difmJhIkyZNsLe3B2Dbtm20adMGAwMDmjZtypw5cygoKFDaL1myhJYtW1K7dm1sbW158803yc7OfmIcv/76K23btqVfv37k5uY+9Z7Vq1cPa2trnJycGDx4MElJSVhZWTF69GilTVllDEFBQYSGhiqv7ezseP/99wkODsbY2JgmTZqwfft2fv31VwIDAzE2Nsbd3Z2jR4+WimHr1q04OjpiYGBAjx49uHr16lNjLs+pU6fw9/fH0NAQS0tLRo0apdyvffv2UatWLa5fv65xzvjx4+nSpQsAv/32G6+++ioNGzbEyMiIli1b8uWXX/6lmIQQQojnybOYkc7Ly+PYsWMEBAQo+3R0dAgICODQoUNlnrN9+3Y6duzImDFjqF+/Pi1atGDBggUUFhZW6nolka6k06dPc/DgQfT09Mo8fu3aNbp27Yq+vj579uzh2LFjDB8+XCOBTUhIICMjg4SEBGJjY4mJiSEmJuaJY/r5+XHgwAGlj4SEBHx9ffHx8SEhIUGjXz8/PwD2799PcHAw48aN4+zZs3z22WfExMQwf/58pb2Ojg4ff/wxZ86cITY2lj179jBp0qQyY7h69SpdunShRYsWfP311+jr61f4ngEYGhoSHh5OUlLSE79meZKlS5fi7e3NiRMnePnllxk6dCjBwcG8/vrrHD9+HAcHB4KDgykuLlbOuX//PvPnz2fdunUkJSVx+/ZtBg8eXKlxH3Xv3j169OiBubk5R44c4auvvmL37t1EREQA0LVrV5o2bcr69euVc/Lz89mwYQPDhw8HHpbeeHp68t1333H69GlGjRrF0KFDy63hEkIIIWqMYpXWttzcXO7cuaOxlTWRd+vWLQoLC6lfv77G/vr165ea4Cpx8eJFvv76awoLC9mxYwfvvfceixcv5v3336/U5UoiXQFxcXEYGxtjYGBAy5YtuXnzJu+++26ZbVesWEGdOnXYuHEjbdu2xcnJiWHDhuHs7Ky0MTc3JyoqChcXF/r06cPLL79MfHz8E8f38/Pj3r17HDlyBHg48+zj40PXrl1JTk4mJyeHBw8ecPjwYSWRnjNnDlOmTCEkJISmTZvSvXt35s2bx2effab0O378ePz8/LCzs8Pf35/333+fzZs3lxo/PT0db29vevToQXR0NGq1ukr30cXFBXg4i18ZvXv35o033sDR0ZGZM2dy584d2rVrx4ABA3BycmLy5MmkpaVx48YN5Zz8/HyioqLo2LEjnp6exMbGcvDgwSonrV988QU5OTmsW7eOFi1a4O/vT1RUFOvXr1fGDQsLIzo6WjnnP//5Dzk5OQwcOBCAhg0bMnHiRFq1akXTpk1566236NmzZ5n3XAghhKiJtDkjvXDhQurUqaOxLVy4UCtxFhUVUa9ePVatWoWnpyeDBg1i+vTprFy5slL9SCJdAX5+fqSkpJCcnExISAjDhg3jlVdeKbNtSkoKXbp0oVatWk/sz83NTSMZtbGxUWZpFyxYgLGxsbJduXKFZs2a0ahRIxITE7lz5w4nTpzAx8cHGxsbGjduzKFDh5T66JJEOjU1lblz52r0NXLkSLKysrh//z4Au3fvplu3bjRs2BATExOGDh3Kb7/9phwHePDgAV26dKF///4sX74clUqlcR0lfffq1avc+1gyY/xoHxXh7u6u/Lvk02bLli1L7Xt0pltXV5d27dopr11cXDAzMyMtLY0rV65o3JcFCxaUG0NaWhoeHh7Url1b2eft7U1RURHp6enAwwdTL1y4oNStx8TEMHDgQOWcwsJC5s2bR8uWLbGwsMDY2JidO3dy5cqVCt+Lsj6dPzoTL4QQQvyvmDp1Kn/++afGNnXq1FLt6tati1qt1phQA7hx4wbW1tZl9m1jY4OTk5NGPubq6sr169fJy8urcIzygywVULt2bZo1awY8LE738PBg7dq1hIWFlWpraGhYbn+PJ9kqlYqioofFQOHh4coMJkCDBg2Ah/XECQkJuLu74+joSL169QCU8o7i4mKaNWuGra0tANnZ2cyZM4f+/fuXGt/AwIDMzEz69OnD6NGjmT9/PhYWFhw4cICwsDDy8vIwMjICHtaHBwQEEBcXx7vvvqvxNOuOHTuU2u6KXHdaWhrwsO4ZHpaWPJ4EllUr/uj9KknCy9pXcg/L06BBA42adgsLiwqdV5569erRt29foqOjsbe35/vvv9eoYf/oo49Yvnw5y5YtU2rTx48fX6n/wS5cuJA5c+Zo7FPpGKNSm2rlGoQQQoi/oriocpNlT6Ovr1+hUlI9PT08PT2Jj49XlhguKioiPj5eKcF8nLe3N1988QVFRUXo6DycV/7pp5+wsbF5YvluWWRGupJ0dHSYNm0aM2bM4MGDB6WOu7u7s3///qc+PPg0FhYWNGvWTNlKVgPx8/Pj4MGD7Nq1C19fX6V9165dSUxMJDExUZmNBmjTpg3p6ekafZVsOjo6HDt2jKKiIhYvXoyXlxdOTk788ssvZV7v+vXr8fT0xM/PT6NNkyZNlD7LWy7mwYMHrFq1iq5du2JlZQWAlZWVxpJ4hYWFnD59ukr37XEFBQUaDyCmp6dz+/ZtXF1d0dXV1bgfFUmkXV1dSU1N5d69e8q+pKQkdHR0NMp2RowYwaZNm1i1ahUODg54e3trtA8MDOT111/Hw8ODpk2b8tNPP1Xqusr6dK7SMalUH0IIIUR1eVbL302YMIHVq1cTGxtLWloao0eP5t69ewwbNgyA4OBgjdns0aNH8/vvvzNu3Dh++uknvvvuOxYsWMCYMWMqNa4k0lUwYMAA1Go1K1asKHUsIiKCO3fuMHjwYI4ePcr58+dZv3698vV/VZXUSX/++ef4+Pgo+318fEhOTtaojwaYOXMm69atY86cOZw5c4a0tDQ2btzIjBkzAGjWrBn5+fn861//4uLFi6xfv/6JdUFqtZoNGzbg4eGBv7//Ewv3H3Xz5k2uX7/O+fPn2bhxI97e3ty6dYtPP/1UaePv7893333Hd999x7lz5xg9ejS3b9+u4h3SVKtWLd566y2Sk5M5duwYoaGheHl50b59+6ee9+DBA1JSUjS2jIwMXnvtNQwMDAgJCeH06dMkJCTw1ltvMXToUI2HG3r06IGpqSnvv/++8j/eEo6OjuzatYuDBw+SlpbGG2+8UeprqPLo6+tjamqqsVW2VEYIIYT4XzNo0CAiIyOZOXMmrVq1IiUlhf/+97/Kf6OvXLmiMXlna2vLzp07OXLkCO7u7owdO5Zx48YxZcqUSo0riXQV6OrqEhERwaJFizRmKAEsLS3Zs2cP2dnZ+Pj44OnpyerVq59aM10R9vb2NGnShLt372ok0o0bN6ZBgwbk5eVpzFT36NGDuLg4fvjhB9q1a4eXlxdLly6lSZMmAHh4eLBkyRI+/PBDWrRowYYNG55awK+rq8uXX36Jm5sb/v7+5a684ezsTIMGDfD09OSDDz4gICCA06dP07x5c6XN8OHDCQkJITg4GB8fH5o2barxYeCvMDIyYvLkyQwZMgRvb2+MjY3ZtGlTuef99NNPtG7dWmN74403MDIyYufOnfz++++0a9eOf/7zn3Tr1o2oqCiN83V0dAgNDaWwsJDg4GCNYzNmzKBNmzb06NEDX19frK2ta8yvXAohhBAVUVys0tpWWREREVy+fJnc3FySk5Pp0KGDciwxMbHUCmkdO3bkxx9/JCcnh4yMDKZNm1bpBRVUxfKkkhBaFRYWxq+//sr27dv/lvF09Sr3K0xCCCFeXAV516q1/587aO9H4Rol79FaX9VFHjYUQkv+/PNPTp06xRdffPG3JdFCCCGEeHYkkRZCSwIDAzl8+DDh4eF07979WYcjhBBC/O20uWpHTSCJtBBa8uhSd0IIIcSL6EUrGJaHDYUQQgghhKgCmZEWQgghhBBaIaUdQgghhBBCVIEk0hVQVFTEhQsXuHnzZqmfZe7atatWAhNCCCGEEDXLi1YjXelE+scff2TIkCFcvnyZx5egVqlUFBYWai04IYQQQgghnleVTqTDw8Np27Yt3333HTY2NvLzxEIIIYQQApDSjnKdP3+er7/+mmbNmlVHPEIIIYQQooaqyk9712SVXv6uQ4cOXLhwoTpiEUIIIYQQosaodCL91ltv8c477xATE8OxY8c4efKkxva/LDMzE5VKRUpKSoXah4aGEhQUVK0xaUtMTAxmZmbPOoxKq+647ezsWLZsWbX1L4QQQvwvKS7S3lYTVDqRfuWVV0hLS2P48OG0a9eOVq1a0bp1a+X/1mShoaGoVCpUKhW1atXC3t6eSZMmkZOTA4CtrS1ZWVm0aNGiWsY/d+4cKpWKH3/8UWO/l5cXBgYGShwAOTk5GBgYsHbt2mqJpSISExOV+6Wjo0OdOnVo3bo1kyZNIisr65nFVVF37txh+vTpuLi4YGBggLW1NQEBAXz77belHqStbiqViq1bt/6tYwohhBDaVlSs0tpWE1S6RvrSpUvVEcdzo2fPnkRHR5Ofn8+xY8cICQlBpVLx4Ycfolarsba2rraxXVxcsLa2JjExES8vLwDu3r3L8ePHqV+/Pj/++CO+vr4AHDp0iNzcXPz9/astnopKT0/H1NSUO3fucPz4cRYtWsTatWtJTEykZcuWzzq8Mt2+fZvOnTvz559/8v7779OuXTt0dXXZu3cvkyZNwt/fv0bO0AshhBDi71PpGekmTZo8davp9PX1sba2xtbWlqCgIAICAti1axdQdmnHmTNn6NOnD6amppiYmNClSxcyMjI0+oyMjMTGxgZLS0vGjBlDfn7+E8f38/MjMTFReX3gwAGcnJzo27evxv7ExESaNGmCvb09ANu2baNNmzYYGBjQtGlT5syZQ0FBgdJ+yZIltGzZktq1a2Nra8ubb75Jdnb2E+P49ddfadu2Lf369SM3N/ep96xevXpYW1vj5OTE4MGDSUpKwsrKitGjRyttfH19GT9+vMZ5QUFBhIaGKq/t7Ox4//33CQ4OxtjYmCZNmrB9+3Z+/fVXAgMDMTY2xt3dnaNHj5aKYevWrTg6OmJgYECPHj24evXqU2OeNm0amZmZJCcnExISQvPmzXFycmLkyJGkpKRgbGystL1//z7Dhw/HxMSExo0bs2rVKo2+rl69ysCBAzEzM8PCwoLAwEAyMzOV40eOHKF79+7UrVuXOnXq4OPjw/HjxzWuG6Bfv36oVCrltRBCCFHTFBertLbVBJVOpEucPXuW//73v2zfvl1j+19y+vRpDh48iJ6eXpnHr127RteuXdHX12fPnj0cO3aM4cOHaySwCQkJZGRkkJCQQGxsLDExMcTExDxxTD8/Pw4cOKD0kZCQgK+vLz4+PiQkJGj06+fnB8D+/fsJDg5m3LhxnD17ls8++4yYmBjmz5+vtNfR0eHjjz/mzJkzxMbGsmfPHiZNmlRmDFevXqVLly60aNGCr7/+Gn19/QrfMwBDQ0PCw8NJSkri5s2blTp36dKleHt7c+LECV5++WWGDh1KcHAwr7/+OsePH8fBwYHg4GCN0ov79+8zf/581q1bR1JSErdv32bw4MFPHKOoqIiNGzfy2muv0aBBg1LHjY2N0dX9/1/WLF68mLZt23LixAnefPNNRo8eTXp6OgD5+fn06NEDExMT9u/fT1JSEsbGxvTs2ZO8vDzg4bcKISEhHDhwgB9//BFHR0d69+7N3bt3gYeJNkB0dDRZWVnKayGEEKKmKS5SaW2rCSpd2nHx4kX69evHqVOnUKlUSkJTsp50Tf9Blri4OIyNjSkoKCA3NxcdHR2ioqLKbLtixQrq1KnDxo0bqVWrFgBOTk4abczNzYmKikKtVuPi4sLLL79MfHw8I0eOLLNPPz8/7t27x5EjR+jYsSOJiYm8++67dO7cmZCQEHJyciguLubw4cOMGDECgDlz5jBlyhRCQkIAaNq0KfPmzWPSpEnMmjULQGM2uGTmNzw8nE8++URj/PT0dLp3706/fv1YtmxZldcJd3FxAR7O4terV6/C5/Xu3Zs33ngDgJkzZ/Lpp5/Srl07BgwYAMDkyZPp2LEjN27cUMps8vPziYqKokOHDgDExsbi6urK4cOHad++fakxbt26xR9//KHEWJGY3nzzTWX8pUuXkpCQgLOzM5s2baKoqIg1a9Yo9yo6OhozMzMSExN56aWXSpXfrFq1CjMzM/bu3UufPn2wsrICwMzMrFpLh4QQQgihXZWekR43bhz29vbcvHkTIyMjzpw5w759+2jbtq1G6UFN5efnR0pKivKV/7Bhw3jllVfKbJuSkkKXLl2UJLosbm5uqNVq5bWNjY0yS7tgwQKMjY2V7cqVKzRr1oxGjRqRmJjInTt3OHHiBD4+PtjY2NC4cWMOHTqk1EeXzEinpqYyd+5cjb5GjhxJVlYW9+/fB2D37t1069aNhg0bYmJiwtChQ/ntt9+U4wAPHjygS5cu9O/fn+XLl2sk0W5ubkrfvXr1Kvc+Pv4Bq6Lc3d2Vf9evXx9Ao866ZN+jM926urq0a9dOee3i4oKZmRlpaWlcuXJF474sWLCg0g8SPhqTSqXC2tpaGT81NZULFy5gYmKijGFhYUFOTo5S4nPjxg1GjhyJo6MjderUwdTUlOzsbK5cuVKpOAByc3O5c+eOxvZ3PxgphBBCPElxsfa2mqDSM9KHDh1iz5491K1bFx0dHXR0dOjcuTMLFy5k7NixnDhxojri/NvUrl1b+bGZzz//HA8PD9auXUtYWFiptoaGhuX293iSrVKpKCp6uKZLeHg4AwcOVI6VlBn4+vqSkJCAu7s7jo6OyoxuSXlHcXExzZo1w9bWFoDs7GzmzJlD//79S41vYGBAZmYmffr0YfTo0cyfPx8LCwsOHDhAWFgYeXl5GBkZAQ/rwwMCAoiLi+Pdd9+lYcOGSj87duxQarsrct1paWnA/6//1dHRKZXwlVUr/uj9KknCy9pXcg/L06BBA42adgsLC8zMzDAzM+PcuXMV6uNpf8Ps7Gw8PT3ZsGFDqfNKZppDQkL47bffWL58OU2aNEFfX5+OHTsqpR+VsXDhQubMmaMZj44xKrVppfsSQgghtK2mlGRoS6UT6cLCQkxMTACoW7cuv/zyC87OzjRp0kSpG/1foaOjw7Rp05gwYQJDhgwpddzd3Z3Y2Fjy8/OfOiv9JBYWFlhYWJTa7+fnx9ixY2nevLmySgdA165dWb16NcXFxcpsNECbNm1IT09/4q9NHjt2jKKiIhYvXoyOzsMvITZv3lyqnY6ODuvXr2fIkCHKQ48lyX1lHiR98OABq1atomvXrkoyaWVlpbEkXmFhIadPn9a4jqoqKCjg6NGjShlHeno6t2/fxtXVFV1d3TLvy+DBg1m/fj2zZs0qVSednZ2NgYGBRp30k7Rp04ZNmzZRr149TE3LTmaTkpL45JNP6N27N/CwBv3WrVsabWrVqlWhsqipU6cyYcIEjX3mlhUrURFCCCGqW01Ztk5bKl3a0aJFC1JTU4GHv3K4aNEikpKSmDt3Lk2bNtV6gM/agAEDUKvVrFixotSxiIgI7ty5w+DBgzl69Cjnz59n/fr1f/kDRUmd9Oeff46Pj4+y38fHh+TkZA4fPqyRgM6cOZN169YxZ84czpw5Q1paGhs3bmTGjBkANGvWjPz8fP71r39x8eJF1q9fz8qVK8scW61Ws2HDBjw8PPD39+f69evlxnvz5k2uX7/O+fPn2bhxI97e3ty6dYtPP/1UaePv7893333Hd999x7lz5xg9ejS3b9+u4h3SVKtWLd566y2Sk5M5duwYoaGheHl5lVkfXWL+/PnY2trSoUMH1q1bx9mzZzl//jyff/45rVu3fuqKJo967bXXqFu3LoGBgezfv59Lly6RmJjI2LFj+fnnnwFwdHRk/fr1pKWlkZyczGuvvVZqVt/Ozo74+HiuX7/OH3/88cTx9PX1MTU11diqWscuhBBCiL+m0on0jBkzlK+1586dy6VLl+jSpQs7duzg448/1nqAz5quri4REREsWrSIe/fuaRyztLRkz549ZGdn4+Pjg6enJ6tXr67S7PSj7O3tadKkCXfv3tVIpBs3bkyDBg3Iy8vTmKnu0aMHcXFx/PDDD7Rr1w4vLy+WLl2qzCJ7eHiwZMkSPvzwQ1q0aMGGDRtYuHDhU6/5yy+/xM3NDX9//3JX3nB2dqZBgwZ4enrywQcfEBAQwOnTp2nevLnSZvjw4YSEhBAcHIyPjw9NmzbVymw0gJGREZMnT2bIkCF4e3tjbGzMpk2bnnqOhYUFP/74I6+//jrvv/8+rVu3pkuXLnz55Zd89NFH1KlTp8Jj79u3j8aNG9O/f39cXV0JCwsjJydHmaFeu3Ytf/zxB23atGHo0KGMHTu21AOYixcvZteuXdja2tb4HzYSQgjx4nrRlr9TFWvhSaXff/8dc3NzmRkT4hnQ1WtYfiMhhBACKMi7Vq39n7Trq7W+3DP/o7W+qkula6TLUladrxBCCCGEEP/LKp1I5+Tk8K9//YuEhARu3rxZavWER3+xTQghhBBCvDhetIcNK51Ih4WF8cMPP/DPf/6T9u3bSzmHEEIIIYQAqDG1zdpS6UQ6Li6OHTt24O3tXR3xCCGEEEIIUSNUOpEu+WU8IYQQQgghHlVTfpFQWyq9/N3ixYuZPHkyly9fro54hBBCCCFEDVVUrNLaVhNUeka6bdu25OTk0LRpU4yMjEqtmfz7779rLTghhBBCCCGeV5VOpF999VWuXbvGggULqF+/vjxsKIQQQgghAHnYsFwHDx7k0KFDeHh4VEc8QgghhBCihqopJRnaUulE2sXFhQcPHlRHLEIIIYQQogZ7wZ41rPzDhh988AHvvPMOiYmJ/Pbbb9y5c0djE0IIIYQQ4kVQ6US6Z8+eHDp0iG7dulGvXj3Mzc0xNzfHzMwMc3Pz6ohR1AChoaEEBQUpr319fRk/frzy2s7OjmXLlv3tcdUkj99DIYQQoqZ50VbtqHQinZCQQEJCAnv27NHYSvb9FaGhoahUKlQqFXp6ejRr1oy5c+dSUFDwl/qtrL179+Lv74+FhQVGRkY4OjoSEhJCXl4eADExMZiZmf2tMVVFYmIiKpWK27dvV/ic6krmjhw5wqhRo7Teb3kqmsDXlL+pEEII8TwrLlZpbasJKl0j7ePjUx1xKHr27El0dDS5ubns2LGDMWPGUKtWLaZOnar1sfLy8tDT09PYd/bsWXr27Mlbb73Fxx9/jKGhIefPn+ebb76hsLDwL/f/orKysnrWIQghhBBCaFWlZ6QBbt++zeLFixkxYgQjRoxg6dKl/Pnnn1oJSF9fH2tra5o0acLo0aMJCAhg+/bt/PHHHwQHB2Nubo6RkRG9evXi/PnzGud+8803uLm5oa+vj52dHYsXL9Y4bmdnx7x58wgODsbU1LTMGdIffvgBa2trFi1aRIsWLXBwcKBnz56sXr0aQ0NDEhMTGTZsGH/++acyez579uyn9l+RuBYsWMDw4cMxMTGhcePGrFq1SqPNwYMHadWqFQYGBrRt25atW7eiUqlISUmp8L0tmXXduXMnrq6uGBsb07NnT7KysgCYPXs2sbGxbNu2Tbm2xMREAK5evcrAgQMxMzPDwsKCwMBAMjMzKzz24zPD586do3PnzhgYGNC8eXN2796NSqVi69atSpvyxiyZPY+MjMTGxgZLS0vGjBlDfn4+8LC85PLly7z99tvK9VTVlStXCAwMxNjYGFNTUwYOHMiNGzcAyMzMREdHh6NHj2qcs2zZMpo0aUJRURGFhYWEhYVhb2+PoaEhzs7OLF++vMrxCCGEEM+jIi1uNUGlE+mjR4/i4ODA0qVL+f333/n9999ZsmQJDg4OHD9+XOsBGhoakpeXR2hoKEePHmX79u0cOnSI4uJievfurSRNx44dY+DAgQwePJhTp04xe/Zs3nvvPWJiYjT6i4yMxMPDgxMnTvDee++VGs/a2pqsrCz27dtXZjydOnVi2bJlmJqakpWVRVZWFhMnTnxi/xWNa/HixbRt25YTJ07w5ptvMnr0aNLT0wG4c+cOffv2pWXLlhw/fpx58+YxefLkKt3P+/fvExkZyfr169m3bx9XrlxR4p84cSIDBw5UkuusrCw6depEfn4+PXr0wMTEhP3795OUlKQk4SXlLpVRWFhIUFAQRkZGJCcns2rVKqZPn67RpqJjJiQkkJGRQUJCArGxscTExCj39ttvv6VRo0bMnTtXuZ6qKCoqIjAwkN9//529e/eya9cuLl68yKBBg4CHHxICAgKIjo7WOC86OprQ0FB0dHQoKiqiUaNGfPXVV5w9e5aZM2cybdo0Nm/eXKWYhBBCiOdRMSqtbTVBpUs73n77bf7xj3+wevVqdHUfnl5QUMCIESMYP378ExPQyiouLiY+Pp6dO3fSq1cvtm7dSlJSEp06dQJgw4YN2NrasnXrVgYMGMCSJUvo1q2bkhw7OTlx9uxZPvroI0JDQ5V+/f39eeedd5447oABA9i5cyc+Pj5YW1vj5eVFt27dlFlmPT096tSpg0qlwtrautT5j/f/2muvVSiu3r178+abbwIwefJkli5dSkJCAs7OznzxxReoVCpWr16tzOBeu3aNkSNHVvq+5ufns3LlShwcHACIiIhg7ty5ABgbG2NoaEhubq7Gtf373/+mqKiINWvWKLO60dHRmJmZkZiYyEsvvVSpGHbt2kVGRgaJiYnKOPPnz6d79+5Km02bNlVoTHNzc6KiolCr1bi4uPDyyy8THx/PyJEjsbCwQK1WY2JiUubfqqLi4+M5deoUly5dwtbWFoB169bh5ubGkSNHaNeuHSNGjCA8PJwlS5agr6/P8ePHOXXqFNu2bQOgVq1azJkzR+nT3t6eQ4cOsXnzZgYOHFjl2IQQQgjx7FRpRnry5MlKEg2gq6vLpEmTSn21XRVxcXEYGxtjYGBAr169GDRoEKGhoejq6tKhQwelnaWlJc7OzqSlpQGQlpaGt7e3Rl/e3t6cP39eo7a5bdu2yr/Dw8MxNjZWNgC1Wk10dDQ///wzixYtomHDhixYsAA3N7cKzWg+2n9l4nJ3d1f+XZKk37x5E4D09HTc3d0xMDBQ2rRv316jTzc3N+U6evXq9cT4jIyMlCQawMbGRhnnSVJTU7lw4QImJibKGBYWFuTk5JCRkfHUc8uSnp6Ora2tRnL7+PVUdEw3NzfUanWlrufRv3l4eHi58aalpWFra6sk0QDNmzfHzMxMef8FBQWhVqvZsmUL8LCMxs/PDzs7O+WcFStW4OnpiZWVFcbGxqxatYorV66UO/6jcnNzSy05WVz8oq3aKYQQ4nlVVKy9rSao9Iy0qakpV65cwcXFRWP/1atXMTEx+csB+fn58emnn6Knp0eDBg3Q1dVl+/btf7nfErVr11b+PXfuXI2yjEc1bNiQoUOHMnToUObNm4eTkxMrV67UmFUsr//KqFWrlsZrlUpFUVHFK4R27NihlLkYGhpWapzyErHs7Gw8PT3ZsGFDqWPV9RBhRcesyn17tK7c1NT0rwX6f/T09AgODiY6Opr+/fvzxRdfaNRAb9y4kYkTJ7J48WI6duyIiYkJH330EcnJyZUaZ+HChaXegyodY1Rq7VyHEEII8VcU1ZCSDG2pdCI9aNAgwsLCiIyMVMoskpKSePfdd3n11Vf/ckC1a9emWbNmGvtcXV0pKCggOTlZGfO3334jPT2d5s2bK22SkpI0zktKSsLJyUljxvJR9erVo169euXGZG5ujo2NDffu3QMeJk0VXcGjKnE9ztnZmX//+9/k5uair68PPFxO7lFNmjSpUF/lKeva2rRpw6ZNm6hXr55WEk9nZ2euXr3KjRs3qF+/PlD6erQ1ZlnX8/j7qzyurq5cvXqVq1evKrPSZ8+e5fbt28r7D2DEiBG0aNGCTz75hIKCAvr3768cKylLKinfAao0mz916lQmTJigsc/c0uUJrYUQQghRnSpc2nHp0iXg4cN0/fv3Jzg4GDs7O5o0aUJoaCj//Oc/+fDDD6slSEdHRwIDAxk5ciQHDhwgNTWV119/nYYNGxIYGAjAO++8Q3x8PPPmzeOnn34iNjaWqKioJ844P8lnn33G6NGj+eGHH8jIyODMmTNMnjyZM2fO0LdvX+Dhw2XZ2dnEx8dz69Yt7t+//8T+tBHXkCFDKCoqYtSoUaSlpbFz504iIyMB/tJKFGWxs7Pj5MmTpKenc+vWLfLz83nttdeoW7cugYGB7N+/n0uXLpGYmMjYsWP5+eefKz1G9+7dcXBwICQkhJMnT5KUlMSMGTM0rkdbY9rZ2bFv3z6uXbvGrVu3ntq2sLCQlJQUjS0tLY2AgABatmzJa6+9xvHjxzl8+DDBwcH4+PholPK4urri5eXF5MmTefXVVzW+GXB0dOTo0aPs3LmTn376iffee6/Uh4eK0NfXx9TUVGPT9ntACCGEqKoX7WHDCifSDg4O2NvbEx4eTtu2bblw4QIpKSmkpqby+++/s3TpUmW2tDpER0fj6elJnz596NixI8XFxezYsUP5ar9NmzZs3ryZjRs30qJFC2bOnMncuXM1HuiriPbt25OdnU14eDhubm74+Pjw448/snXrVmUN7U6dOhEeHs6gQYOwsrJi0aJFT+xPG3GZmpryn//8h5SUFFq1asX06dOZOXMmgEbdtDaMHDkSZ2dn2rZti5WVFUlJSRgZGbFv3z4aN25M//79cXV1JSwsjJycnCrNFqvVarZu3Up2drbyoF7Jqh0l16OtMefOnUtmZiYODg7llqFkZ2fTunVrja1v376oVCq2bduGubk5Xbt2JSAggKZNm7Jp06ZSfYSFhZGXl8fw4cM19r/xxhv079+fQYMG0aFDB3777TeN2WkhhBDif8GLtvydqriCTyolJiYqW3JyMnl5eTRt2hR/f3/8/f3x9fVVvqYX1W/Dhg3KetZPq4muKZKSkujcuTMXLlzQeBiyppk3bx5fffUVJ0+e/NvG1NVr+LeNJYQQomYryLtWrf3/UH+w1vp66cZGrfVVXSpcI+3r64uvry8AOTk5HDx4UEmsY2Njyc/Px8XFhTNnzlRXrC+0devW0bRpUxo2bEhqaiqTJ09m4MCBNTaJ3rJlC8bGxjg6OnLhwgXGjRuHt7d3jU2is7OzyczMJCoqivfff/9ZhyOEEEKIv0GlHzaEh1+/+/v707lzZ/z8/Pj+++/57LPPOHfunLbjE//n+vXrzJw5k+vXr2NjY8OAAQOYP3/+sw6ryu7evcvkyZO5cuUKdevWJSAgoNQvPtYkERERfPnllwQFBZUq6xBCCCFeFDWlJENbKlzaAZCXl8ePP/5IQkKCUuJha2tL165d6dq1Kz4+PjRu3Lg64xVCPEZKO4QQQlRUdZd27NBiaUfv/6XSDn9/f5KTk7G3t8fHx4c33niDL774Ahsbm+qMTwghhBBCiOdShRPp/fv3Y2NjozxY6OPjg6WlZXXGJoQQQgghapCasmydtlR4+bvbt2+zatUqjIyM+PDDD2nQoAEtW7YkIiKCr7/+ml9//bU64xRCCCGEEM+5IpX2tpqgUjXSj7p79y4HDhxQ6qVTU1NxdHTk9OnT2o5RCPEUUiMthBCioqq7Rvo/1n/9V65L9L3+pdb6qi5VWrUDHv6Ut4WFBRYWFpibm6Orq0taWpo2YxNCCCGEEDVI0QtW2lHhRLqoqIijR4+SmJhIQkICSUlJ3Lt3j4YNG+Ln58eKFSvw8/OrzliFEEIIIcRzrEplDjVYhRNpMzMz7t27h7W1NX5+fixduhRfX98a+wMaQgghhBBC/BUVTqQ/+ugj/Pz8cHJyqs54hBBCCCFEDfWi/SBLhVfteOONNySJForQ0FCCgoKU176+vowfP155bWdnx7Jly/72uLTt8esSQgghxJMVqVRa22qCCifS1SE0NBSVSoVKpUJPT49mzZoxd+5cCgoK/tY49u7di7+/PxYWFhgZGeHo6EhISAh5eXkAxMTEYGZm9rfGVBWJiYmoVCpu375d4XMeT4i15ciRI4waNUrr/ZanMgl8Xl4eixYtwsPDAyMjI+rWrYu3tzfR0dHk5+dXb6CP+V/54CGEEOLFVqzFrSao8qod2tKzZ0+io6PJzc1lx44djBkzhlq1ajF16lStj5WXl4eenp7GvrNnz9KzZ0/eeustPv74YwwNDTl//jzffPMNhYWFf7n/F5WVldWzDuGp8vLy6NGjB6mpqcybNw9vb29MTU358ccfiYyMpHXr1rRq1epZhymEEEKI59gznZEG0NfXx9ramiZNmjB69GgCAgLYvn07f/zxB8HBwZibm2NkZESvXr04f/68xrnffPMNbm5u6OvrY2dnx+LFizWO29nZMW/ePIKDgzE1NS1zhvSHH37A2tqaRYsW0aJFCxwcHOjZsyerV6/G0NCQxMREhg0bxp9//qnMns+ePfup/VckrgULFjB8+HBMTExo3Lgxq1at0mhz8OBBWrVqhYGBAW3btmXr1q2oVCpSUlIqfG9LZtJ37tyJq6srxsbG9OzZk6ysLABmz55NbGws27ZtU64tMTERgKtXrzJw4EDMzMywsLAgMDCQzMzMCo/9+AzruXPn6Ny5MwYGBjRv3pzdu3ejUqnYunWr0qa8MUtmzyMjI7GxscHS0pIxY8Yos8e+vr5cvnyZt99+W7meJ1m2bBn79u0jPj6eMWPG0KpVK5o2bcqQIUNITk7G0dFRaVtUVMSkSZOwsLDA2tpa+fuXuH37NiNGjMDKygpTU1P8/f1JTU1VjmdkZBAYGEj9+vUxNjamXbt27N69WzlembiFEEKI51mRFrea4Jkn0o8zNDQkLy+P0NBQjh49yvbt2zl06BDFxcX07t1bSZqOHTvGwIEDGTx4MKdOnWL27Nm89957xMTEaPQXGRmJh4cHJ06c4L333is1nrW1NVlZWezbt6/MeDp16sSyZcswNTUlKyuLrKwsJk6c+MT+KxrX4sWLadu2LSdOnODNN99k9OjRpKenA3Dnzh369u1Ly5YtOX78OPPmzWPy5MlVup/3798nMjKS9evXs2/fPq5cuaLEP3HiRAYOHKgk11lZWXTq1In8/Hx69OiBiYkJ+/fvJykpSUnCS8pdKqOwsJCgoCCMjIxITk5m1apVTJ8+XaNNRcdMSEggIyODhIQEYmNjiYmJUe7tt99+S6NGjZg7d65yPU+yYcMGAgICaN26daljtWrVonbt2srr2NhYateuTXJyMosWLWLu3Lns2rVLOT5gwABu3rzJ999/z7Fjx2jTpg3dunXj999/ByA7O5vevXsTHx/PiRMn6NmzJ3379uXKlSuVjlsIIYR4nr1ov2z43CTSxcXF7N69m507d9K4cWO2b9/OmjVr6NKlCx4eHmzYsIFr164pM5hLliyhW7duvPfeezg5OREaGkpERAQfffSRRr/+/v688847ODg4lLlU34ABA3j11Vfx8fHBxsaGfv36ERUVxZ07dwDQ09OjTp06qFQqrK2tsba2xtjY+In9VzSu3r178+abb9KsWTMmT55M3bp1SUhIAOCLL75ApVKxevVqmjdvTq9evXj33XerdF/z8/NZuXIlbdu2pU2bNkRERBAfHw+AsbExhoaGyrcC1tbW6OnpsWnTJoqKilizZg0tW7bE1dWV6Ohorly5osxYV8auXbvIyMhg3bp1eHh40LlzZ+bPn6/RpqJjmpubExUVhYuLC3369OHll19WrsfCwgK1Wo2JiYlyPU9y/vx5XFxcKhS/u7s7s2bNwtHRkeDgYNq2bauMeeDAAQ4fPsxXX31F27ZtcXR0JDIyEjMzM77++msAPDw8eOONN2jRogWOjo7MmzcPBwcHtm/fXum4hRBCCFG2FStWYGdnh4GBAR06dODw4cMVOm/jxo2oVKoqPTP2zBPpuLg4jI2NMTAwoFevXgwaNIjQ0FB0dXXp0KGD0s7S0hJnZ2fl1xPT0tLw9vbW6Mvb25vz589r1Da3bdtW+Xd4eDjGxsbKBqBWq4mOjubnn39m0aJFNGzYkAULFuDm5lahmcFH+69MXO7u7sq/S5L0mzdvApCeno67uzsGBgZKm/bt22v06ebmplxHr169nhifkZGRxgcIGxsbZZwnSU1N5cKFC5iYmChjWFhYkJOTQ0ZGxlPPLUt6ejq2trYaCeLj11PRMd3c3FCr1ZW6nkf/5uHh4cDDD24V9ejf6vExU1NTyc7OxtLSUmOcS5cuKXFnZ2czceJEXF1dMTMzw9jYmLS0NGVGujJyc3O5c+eOxlaZaxFCCCGqUxEqrW2VsWnTJiZMmMCsWbM4fvw4Hh4e9OjRo9wcITMzk4kTJ9KlS5cqXe8zf9jQz8+PTz/9FD09PRo0aICurq4yU6cNj35FP3fuXI2yjEc1bNiQoUOHMnToUObNm4eTkxMrV65kzpw5Fe6/MmrVqqXxWqVSUVRU8YqgHTt2KGUuhoaGlRqnvMQrOzsbT09PNmzYUOpYdT1EWNExq3LfHq0rNzU1BcDJyYlz585VKLanjZmdnY2NjU2ZM/UlK71MnDiRXbt2ERkZSbNmzTA0NOSf//xnlcpkFi5cWOo9qdIxRqU2rXRfQgghhLY9q6mdJUuWMHLkSIYNGwbAypUr+e677/j888+ZMmVKmecUFhby2muvMWfOHPbv31+pVc9KPPNEunbt2jRr1kxjn6urKwUFBSQnJ9OpUycAfvvtN9LT02nevLnSJikpSeO8pKQknJycNGYsH1WvXj3q1atXbkzm5ubY2Nhw79494GF5R0VX8KhKXI9zdnbm3//+N7m5uejr6wMPl5N7VJMmTSrUV3nKurY2bdqwadMm6tWrpySef4WzszNXr17lxo0b1K9fHyh9Pdoas6zrefz9BTBkyBCmTZvGiRMnStVJ5+fnk5eXV6EPSW3atOH69evo6upiZ2dXZpukpCRCQ0Pp168f8DD5fvzBzYq+x6ZOncqECRM09plbVqxERQghhKhJcnNzyc3N1dinr6+v5EYl8vLyOHbsmMaKbzo6OgQEBHDo0KEn9j937lzq1atHWFgY+/fvr1KMz7y0oyyOjo4EBgYycuRIDhw4QGpqKq+//joNGzYkMDAQgHfeeYf4+HjmzZvHTz/9RGxsLFFRUU+ccX6Szz77jNGjR/PDDz+QkZHBmTNnmDx5MmfOnKFv377AwxUosrOziY+P59atW9y/f/+J/WkjriFDhlBUVMSoUaNIS0tj586dREZGAmh9RQc7OztOnjxJeno6t27dIj8/n9dee426desSGBjI/v37uXTpEomJiYwdO5aff/650mN0794dBwcHQkJCOHnyJElJScyYMUPjerQ1pp2dHfv27ePatWvcunXrie3Gjx+Pt7c33bp1Y8WKFaSmpnLx4kU2b96Ml5dXqRViniQgIICOHTsSFBTEDz/8QGZmJgcPHmT69OkcPXoUePh+/vbbb0lJSSE1NVX5+1Ylbn19fUxNTTU2WeVDCCHE80KbDxsuXLiQOnXqaGwLFy4sNeat/8fencfVmP7/A3+d9n1BaZESpSQqWSqkLCmSMSNLWuwxGCOjMRSyDrJNM4aMki1mjD5ZRmgqlH1JpiaJJktZI2VJdf3+8Ov+Om1OnfuU9H56nMejcy/v67rPfafrXPf7uu4nT1BWVsZ11lVo3bo18vPzq63nmTNn8NtvvyE8PFys4/0kG9IAEBERgW7dumHo0KGws7MDYwxHjx7lbrPb2Nhg//79iI6ORufOnREcHIyQkBD4+fnVqZwePXqgqKgI/v7+sLCwgKOjI86dO4eYmBg4OjoCeD9zh7+/P0aNGgUtLS2sXr26xnh81EtNTQ2HDh3CtWvXYGVlhQULFiA4OBgAhPKm+TB58mR07NgRtra20NLSQnJyMpSUlHDq1Cm0bdsWI0aMgLm5OSZOnIg3b97Uq7dYWloaMTExKCoqQvfu3TFp0iRu1o6K4+GrzJCQEOTk5KB9+/a1pqHIy8vjxIkTmDdvHrZs2YJevXqhe/fu2LRpE2bNmoXOnTuLVJ5AIMDRo0fRt29fjB8/Hqamphg9ejT+++8/7hd63bp10NTUhL29Pdzd3eHi4gIbG5t61ZsQQgj5lPE5/d38+fPx4sULoRcfzxl5+fIlvL29ER4ejlatWokVS8BopFKTsHv3bm4+69pyopuK5ORk9O7dG7du3ap2NhUiOhk5/cauAiGEkCaitOS+RONH6o/jLZbf/V0ibVdSUgIlJSX88ccfQjNv+Pr64vnz5/jf//4ntP21a9dgbW0tlHJbcadYSkoKmZmZIrdNGj1HmlQvKioKxsbG0NfXR2pqKgIDA+Hp6dlkG9EHDx6EiooKTExMcOvWLXzzzTdwcHCgRjQhhBDyGWmM3lk5OTl069YN8fHxXEO6vLwc8fHxmDFjRpXtzczMkJaWJrRs4cKFePnyJTZu3AgDAwORy6aG9CcqPz8fwcHByM/Ph66uLkaOHFll7uWm5OXLlwgMDERubi5atWqFAQMGVHniIyGEEEKatsZ6kMqcOXPg6+sLW1tb9OjRAxs2bEBxcTE3i4ePjw/09fWxcuVKKCgoVEnhrJhpS9TUzgrUkP5EzZs3D/PmzWvsavDGx8cHPj4+jV0NQgghhEhQYz3ae9SoUXj8+DHXCWllZYVjx45x45Vyc3MhJcX/0EDKkSakiaMcaUIIIaKSdI50eBv+cqQn3xMtR7oxUY80IYQQQgjhRWP1SDcWakgTQgghhBBesGb2aINPdh5pQgghhBBCPmXUI00IIYQQQnhBqR2EEEIIIYTUQ3NrSFNqByGEEEIIIfVAPdKEEEIIIYQXzW1OZWpIE0IIIYQQXjTWkw0bC6V2kM9OZGQk96jPpqZfv36YPXt2Y1eDEEIIISJo9g1pPz8/CAQCCAQCyMnJoUOHDggJCUFpaWmD1iMpKQnOzs5o0aIFlJSUYGJiAl9fX5SUlABoOo3DxMRE7vOUkpKCuro6rK2tMW/ePOTl5TV29Wq1ePFiWFlZNXY1CCGEkCarnMdXU9DsG9IAMHjwYOTl5SErKwsBAQFYvHgx1qxZI5GyKhrGH0pPT8fgwYNha2uLU6dOIS0tDT/99BPk5ORQVlYmdvzGkJmZiQcPHuDixYsIDAzEyZMn0blzZ6SlpTV21QghhBAiIdSQbobk5eWho6MDQ0NDTJs2DQMGDEBsbCwKCgrg4+MDTU1NKCkpwdXVFVlZWUL7HjhwABYWFpCXl4eRkRFCQ0OF1hsZGWHp0qXw8fGBmpoapkyZUqX848ePQ0dHB6tXr0bnzp3Rvn17DB48GOHh4VBUVERiYiLGjx+PFy9ecL29ixcvrjW+KPVasWIFJkyYAFVVVbRt2xZbt24V2iYlJQVWVlZQUFCAra0tYmJiIBAIcO3atY9+ptra2tDR0YGpqSlGjx6N5ORkaGlpYdq0adw21aUxDB8+HH5+fkL1XLZsGXx8fKCiogJDQ0PExsbi8ePH8PDwgIqKCrp06YJLly5VqUNMTAxMTEygoKAAFxcX3L1796P1rk1aWhqcnZ2hqKiIli1bYsqUKSgqKgIAnDp1CrKyssjPzxfaZ/bs2ejTpw8A4OnTpxgzZgz09fWhpKQES0tL7N27V6w6EUIIIZ8SxuOrKaCGdDUUFRVRUlICPz8/XLp0CbGxsTh79iwYY3Bzc8O7d+8AAJcvX4anpydGjx6NtLQ0LF68GEFBQYiMjBSKt3btWnTt2hVXr15FUFBQlfJ0dHSQl5eHU6dOVVsfe3t7bNiwAWpqasjLy0NeXh7mzp1bY3xR6xUaGgpbW1tcvXoV06dPx7Rp05CZmQkAKCwshLu7OywtLXHlyhUsXboUgYGBYn2m/v7+SE5OxqNHj+q07/r16+Hg4ICrV69iyJAh8Pb2ho+PD8aNG4crV66gffv28PHxAWP/92v36tUrLF++HFFRUUhOTsbz588xevToete/uLgYLi4u0NTUxMWLF/H777/j5MmTmDFjBgCgb9++MDY2xs6dO7l93r17h927d2PChAkAgDdv3qBbt244cuQIbty4gSlTpsDb2xsXLlyod70IIYQQ0nho1o4PMMYQHx+PuLg4uLq6IiYmBsnJybC3twcA7N69GwYGBoiJicHIkSOxbt069O/fn2scm5qaIj09HWvWrBHqVXV2dkZAQECN5Y4cORJxcXFwdHSEjo4OevXqhf79+3O9zHJyclBXV4dAIICOjk6V/SvH9/LyEqlebm5umD59OgAgMDAQ69evR0JCAjp27Ig9e/ZAIBAgPDwcCgoK6NSpE+7fv4/JkyfX+/M1MzMDAOTk5EBbW1vk/dzc3DB16lQAQHBwMDZv3ozu3btj5MiRXN3t7Ozw8OFD7vN59+4dwsLC0LNnTwDAjh07YG5ujgsXLqBHjx51rvuePXvw5s0bREVFQVlZGQAQFhYGd3d3/Pjjj2jdujUmTpyIiIgIfPfddwCAQ4cO4c2bN/D09AQA6OvrC30BmjlzJuLi4rB//36R6/T27Vu8fftWaBljDAJBMxsmTQgh5JNEs3Y0Q4cPH4aKigoUFBTg6uqKUaNGwc/PDzIyMlxDDABatmyJjh07IiMjAwCQkZEBBwcHoVgODg7IysoSym22tbXlfvb394eKigr3AgBpaWlERETg3r17WL16NfT19bFixQpYWFiINEDvw/h1qVeXLl24nysa6RW9xZmZmejSpQsUFBS4bSo39iwsLLjjcHV1/Wg9K3qM69ro+7CerVu3BgBYWlpWWfZhT7eMjAy6d+/OvTczM4OGhgYyMjKQm5srdA5WrFjx0TpkZGSga9euXCMaeP+ZlpeXc734fn5+uHXrFs6dOwfg/QBRT09Pbp+ysjIsXboUlpaWaNGiBVRUVBAXF4fc3FyRP4uVK1dCXV1d6MXKX4q8PyGEECJJzS1HmnqkATg5OWHz5s2Qk5ODnp4eZGRkEBsby1v8DxtfISEhQr2SH9LX14e3tze8vb2xdOlSmJqa4tdff8WSJUtEjl8XsrKyQu8FAgHKy0W/dI8ePcqluSgqKn50+4ovIEZGRgAAKSkpoXQMAFy8mupZ0QivbpmoddfT0xPK827RooVI+32MtrY23N3dERERgXbt2uGvv/5CYmIit37NmjXYuHEjNmzYAEtLSygrK2P27Nl1GiA6f/58zJkzR2iZZkszXupPCCGEkLqhhjTeN0Q7dOggtMzc3BylpaU4f/48l9rx9OlTZGZmolOnTtw2ycnJQvslJyfD1NQU0tLS1Zalra0tUlqDpqYmdHV1UVxcDAB1msGjPvWqrGPHjti1axfevn0LeXl5AMDFixeFtjE0NBQpFgC8fv0aW7duRd++faGlpQUA0NLSEupxLysrw40bN+Dk5CRy3JqUlpbi0qVLXC96ZmYmnj9/DnNzc8jIyFQ53x9jbm6OyMhIFBcXc19ckpOTISUlhY4dO3LbTZo0CWPGjEGbNm3Qvn17oTsDycnJ8PDwwLhx4wC8b/jfvHmTu55EIS8vz52PCpTWQQgh5FPRVAYJ8oVSO2pgYmICDw8PTJ48GWfOnEFqairGjRsHfX19eHh4AAACAgIQHx+PpUuX4ubNm9ixYwfCwsJq7HGuyZYtWzBt2jQcP34c2dnZ+OeffxAYGIh//vkH7u7uAN734hYVFSE+Ph5PnjzBq1evaozHR73Gjh2L8vJyTJkyBRkZGYiLi8PatWsBiNZwe/ToEfLz85GVlYXo6Gg4ODjgyZMn2Lx5M7eNs7Mzjhw5giNHjuDff//FtGnT8Pz5c5HrWBtZWVnMnDkT58+fx+XLl+Hn54devXp9NBf59evXuHbtmtArOzsbXl5eUFBQgK+vL27cuIGEhATMnDkT3t7eXGoJALi4uEBNTQ3Lli3D+PHjhWKbmJjgxIkTSElJQUZGBqZOnYqHDx/ycryEEELIp6AcjLdXU0AN6VpERESgW7duGDp0KOzs7MAYw9GjR7m0AhsbG+zfvx/R0dHo3LkzgoODERISIjSgTxQ9evRAUVER/P39YWFhAUdHR5w7dw4xMTFwdHQE8H7mDn9/f4waNQpaWlpYvXp1jfH4qJeamhoOHTqEa9euwcrKCgsWLEBwcDAACOVN16Rjx47Q09NDt27dsGrVKgwYMAA3btwQ6n2dMGECfH194ePjA0dHRxgbG/PSGw0ASkpKCAwMxNixY+Hg4AAVFRXs27fvo/vdvHkT1tbWQq+pU6dCSUkJcXFxePbsGbp3746vvvoK/fv3R1hYmND+UlJS8PPzQ1lZGXx8fITWLVy4EDY2NnBxcUG/fv2go6OD4cOH83K8hBBCCGl4AlY5SZWQGuzevZubz1qUnOjmauLEiXj8+DGvefa1kZHTb5ByCCGENH2lJfclGn+poRdvsYL+281bLEmhHGlSo6ioKBgbG0NfXx+pqakIDAyEp6cnNaJr8OLFC6SlpWHPnj0N1ogmhBBCPiXNrXeWGtKkRvn5+QgODkZ+fj50dXUxcuRILF++vLGr9cny8PDAhQsX4O/vj4EDBzZ2dQghhBAiYZTaQUgTR6kdhBBCRCXp1I7FPKZ2LKbUDkIIIYQQ0lw0tycbUkOaEEIIIYTwoqlMW8cXmv6OEEIIIYSQeqAeaUIIIYQQwovm1R9NDWlCCCGEEMKT8sauQAOj1A5CCCGEEELqgXqkCSGEEEIIL5rbYENqSBNCCCGEEF40r2Y0pXaQJi4yMhIaGhoSi29kZIQNGzZILD4hhBBCmq5m1ZD28/ODQCCAQCCAnJwcOnTogJCQEJSWljZoPZKSkuDs7IwWLVpASUkJJiYm8PX1RUlJCQDJNw75kpiYyH2eUlJSUFdXh7W1NebNm4e8vLzGrt5HFRYWYsGCBTAzM4OCggJ0dHQwYMAA/Pnnn2joB34KBALExMQ0aJmEEEII38p5fDUFzaohDQCDBw9GXl4esrKyEBAQgMWLF2PNmjUSKauiYfyh9PR0DB48GLa2tjh16hTS0tLw008/QU5ODmVlZWLHbwyZmZl48OABLl68iMDAQJw8eRKdO3dGWlpaY1etRs+fP4e9vT2ioqIwf/58XLlyBadOncKoUaMwb948vHjxorGrSAghhDQ55WC8vZqCZteQlpeXh46ODgwNDTFt2jQMGDAAsbGxKCgogI+PDzQ1NaGkpARXV1dkZWUJ7XvgwAFYWFhAXl4eRkZGCA0NFVpvZGSEpUuXwsfHB2pqapgyZUqV8o8fPw4dHR2sXr0anTt3Rvv27TF48GCEh4dDUVERiYmJGD9+PF68eMH19i5evLjW+KLUa8WKFZgwYQJUVVXRtm1bbN26VWiblJQUWFlZQUFBAba2toiJiYFAIMC1a9c++plqa2tDR0cHpqamGD16NJKTk6GlpYVp06Zx2/Tr1w+zZ88W2m/48OHw8/MTqueyZcvg4+MDFRUVGBoaIjY2Fo8fP4aHhwdUVFTQpUsXXLp0qUodYmJiYGJiAgUFBbi4uODu3bu11vmHH35ATk4Ozp8/D19fX3Tq1AmmpqaYPHkyrl27BhUVFW7bV69e1frZ3b17F56entDQ0ECLFi3g4eGBnJwcbv3FixcxcOBAtGrVCurq6nB0dMSVK1eEjhsAvvjiCwgEAu49IYQQQj5tza4hXZmioiJKSkrg5+eHS5cuITY2FmfPngVjDG5ubnj37h0A4PLly/D09MTo0aORlpaGxYsXIygoCJGRkULx1q5di65du+Lq1asICgqqUp6Ojg7y8vJw6tSpautjb2+PDRs2QE1NDXl5ecjLy8PcuXNrjC9qvUJDQ2Fra4urV69i+vTpmDZtGjIzMwG8T3Fwd3eHpaUlrly5gqVLlyIwMFCsz9Tf3x/Jycl49OhRnfZdv349HBwccPXqVQwZMgTe3t7w8fHBuHHjcOXKFbRv3x4+Pj5CqRevXr3C8uXLERUVheTkZDx//hyjR4+usYzy8nJER0fDy8sLenp6VdarqKhARub/xuHW9tm9e/cOLi4uUFVVxenTp5GcnAwVFRUMHjyYu2Pw8uVL+Pr64syZMzh37hxMTEzg5uaGly9fAnjf0AaAiIgI5OXlce8JIYSQpobx+GoKmu2sHYwxxMfHIy4uDq6uroiJiUFycjLs7e0BALt374aBgQFiYmIwcuRIrFu3Dv379+cax6ampkhPT8eaNWuEelWdnZ0REBBQY7kjR45EXFwcHB0doaOjg169eqF///5cL7OcnBzU1dUhEAigo6NTZf/K8b28vESql5ubG6ZPnw4ACAwMxPr165GQkICOHTtiz549EAgECA8Ph4KCAjp16oT79+9j8uTJ9f58zczMAAA5OTnQ1tYWeT83NzdMnToVABAcHIzNmzeje/fuGDlyJFd3Ozs7PHz4kPt83r17h7CwMPTs2RMAsGPHDpibm+PChQvo0aNHlTKePHmCgoICro6i1Kmmz27fvn0oLy/Htm3bIBAIALxvEGtoaCAxMRGDBg2Cs7OzULytW7dCQ0MDSUlJGDp0KLS0tAAAGhoa1Z7zD719+xZv374VWsYY48omhBBCGlNTyW3mS7PrkT58+DBUVFSgoKAAV1dXjBo1Cn5+fpCRkeEaYgDQsmVLdOzYERkZGQCAjIwMODg4CMVycHBAVlaWUG6zra0t97O/vz9UVFS4FwBIS0sjIiIC9+7dw+rVq6Gvr48VK1bAwsJCpAF6H8avS726dOnC/VzRSK/oLc7MzESXLl2goKDAbVO5AWphYcEdh6ur60frWdFjXNcG3of1bN26NQDA0tKyyrIPe7plZGTQvXt37r2ZmRk0NDSQkZGB3NxcoXOwYsWKOg8krO2zS01Nxa1bt6CqqsqV0aJFC7x58wbZ2dkAgIcPH2Ly5MkwMTGBuro61NTUUFRUhNzc3DrVAwBWrlwJdXV1oRcrf1nnOIQQQogkMB7/NQXNrkfayckJmzdvhpycHPT09CAjI4PY2Fje4isrK3M/h4SECKVlfEhfXx/e3t7w9vbG0qVLYWpqil9//RVLliwROX5dyMrKCr0XCAQoLxf9e+PRo0e5NBdFRcWPbl/xBaQi31dKSqpKA7YiXk31rGiEV7dM1Lrr6ekJ5Xm3aNECGhoa0NDQwL///itSjNo+u6KiInTr1g27d++usl9FT7Ovry+ePn2KjRs3wtDQEPLy8rCzs6vXYNH58+djzpw5Qss0W4rWs04IIYQQfjW7hrSysjI6dOggtMzc3BylpaU4f/48l9rx9OlTZGZmolOnTtw2ycnJQvslJyfD1NQU0tLS1Zalra0tUlqDpqYmdHV1UVxcDAB1msGjPvWqrGPHjti1axfevn0LeXl5AKiSp2toaChSLAB4/fo1tm7dir59+3KNSS0tLaEe97KyMty4cQNOTk4ix61JaWkpLl26xPWiZ2Zm4vnz5zA3N4eMjEyV8w0Ao0ePxs6dO7Fo0aIqedJFRUVQUFAQypOuiY2NDfbt2wdtbW2oqalVu01ycjJ++eUXuLm5AXg/OPHJkydC28jKyop0zuXl5blzVIHSOgghhHwqKLWjGTIxMYGHhwcmT56MM2fOIDU1FePGjYO+vj48PDwAAAEBAYiPj8fSpUtx8+ZN7NixA2FhYTX2ONdky5YtmDZtGo4fP47s7Gz8888/CAwMxD///AN3d3cA73txi4qKEB8fjydPnuDVq1c1xuOjXmPHjkV5eTmmTJmCjIwMxMXFYe3atQBEa6Q9evQI+fn5yMrKQnR0NBwcHPDkyRNs3ryZ28bZ2RlHjhzBkSNH8O+//2LatGl4/vy5yHWsjaysLGbOnInz58/j8uXL8PPzQ69evarNj66wfPlyGBgYoGfPnoiKikJ6ejqysrKwfft2WFtbo6ioSKSyvby80KpVK3h4eOD06dO4c+cOEhMTMWvWLNy7dw/A++tr586dyMjIwPnz5+Hl5VWlV9/IyAjx8fHIz89HQUFB/T8MQgghpBHR9HfNVEREBLp164ahQ4fCzs4OjDEcPXqUu61vY2OD/fv3Izo6Gp07d0ZwcDBCQkKEBvSJokePHigqKoK/vz8sLCzg6OiIc+fOISYmBo6OjgDez9zh7++PUaNGQUtLC6tXr64xHh/1UlNTw6FDh3Dt2jVYWVlhwYIFCA4OBgChvOmadOzYEXp6eujWrRtWrVqFAQMG4MaNG1xvPgBMmDABvr6+8PHxgaOjI4yNjXnpjQYAJSUlBAYGYuzYsXBwcICKigr27dtX6z4tWrTAuXPnMG7cOCxbtgzW1tbo06cP9u7dizVr1kBdXV3ksk+dOoW2bdtixIgRMDc3x8SJE/HmzRuuh/q3335DQUEBbGxs4O3tjVmzZlW5UxEaGooTJ07AwMAA1tbW9fsgCCGEENKgBKyhH+FGmoTdu3dz81mLkhNNGo+MnH5jV4EQQkgTUVpyX6Lxpxl58hZrc85+3mJJSrPLkSbVi4qKgrGxMfT19ZGamorAwEB4enpSI5oQQgghImsqKRl8oYY0AQDk5+cjODgY+fn50NXVxciRI7F8+fLGrhYhhBBCyCeLUjsIaeIotYMQQoioJJ3aMdloJG+xwnN+5y2WpFCPNCGEEEII4UVTeZAKX2jWDkIIIYQQQuqBeqQJIYQQQggvmtsDWaghTQghhBBCeNHcUjuoIU0IIYQQQnjR3HqkKUeaEEIIIYSQeqAeaUIIIYQQwovyZjarMjWkCSGEEEIIL5pXM5pSO5qUxYsXw8rKqrGrUaN+/fph9uzZ3HsjIyNs2LCBey8QCBATE9Pg9WpKKn+GhBBCCPl0NbmGtJ+fHwQCAQQCAeTk5NChQweEhISgtLS0Qco/duwYBAIB8vPzhZbr6urCyMhIaFlOTg4EAgHi4+MbpG7ViYyMhIaGRp32kVRjLi8vD66urrzH/RhRG/Cf+hcVQggh5FNXDsbbqylocg1pABg8eDDy8vKQlZWFgIAALF68GGvWrJFIWSUlJULve/fuDRkZGSQmJnLLMjIy8Pr1axQUFCAnJ4dbnpCQAHl5eTg4OEikbk2Njo4O5OXlG7sahBBCCJEQxuO/pqBJNqTl5eWho6MDQ0NDTJs2DQMGDEBsbCwKCgrg4+MDTU1NKCkpwdXVFVlZWUL7HjhwABYWFpCXl4eRkRFCQ0OF1hsZGWHp0qXw8fGBmpoapkyZIrReRUUF3bt3F2pIJyYmonfv3nBwcKiyvFevXlBQUAAAbNu2Debm5lBQUICZmRl++eUXodiBgYEwNTWFkpISjI2NERQUhHfv3tX4OWRnZ8PY2BgzZswAEzG5v6LXdefOnTAyMoK6ujpGjx6Nly9fAnjf45+UlISNGzdyPf8VXw5u3LgBV1dXqKiooHXr1vD29saTJ09EKheo2jOckpICKysrKCgowNbWFjExMRAIBLh27Rq3zcfK7NevH2bNmoV58+ahRYsW0NHRweLFi7n1FXcJvvjiCwgEgip3DeoiLS0Nzs7OUFRURMuWLTFlyhQUFRUBAE6dOgVZWdkqdypmz56NPn36AACePn2KMWPGQF9fH0pKSrC0tMTevXvrXR9CCCGENK4m2ZCuTFFRESUlJfDz88OlS5cQGxuLs2fPgjEGNzc3rjF6+fJleHp6YvTo0UhLS8PixYsRFBSEyMhIoXhr165F165dcfXqVQQFBVUpz8nJCQkJCdz7hIQE9OvXD46OjkLLExMT4eTkBADYvXs3goODsXz5cmRkZGDFihUICgrCjh07uO1VVVURGRmJ9PR0bNy4EeHh4Vi/fn21x3z9+nX07t0bY8eORVhYGAQCgcifV3Z2NmJiYnD48GEcPnwYSUlJWLVqFQBg48aNsLOzw+TJk5GXl4e8vDwYGBjg+fPncHZ2hrW1NS5duoRjx47h4cOH8PT0FLncDxUWFsLd3R2Wlpa4cuUKli5disDAQKFtRC1zx44dUFZWxvnz57F69WqEhITgxIkTAICLFy8CACIiIpCXl8e9r6vi4mK4uLhAU1MTFy9exO+//46TJ09ixowZAIC+ffvC2NgYO3fu5PZ59+4ddu/ejQkTJgAA3rx5g27duuHIkSO4ceMGpkyZAm9vb1y4cKFedSKEEEI+NeU8vpqCJj1rB2MM8fHxiIuLg6urK2JiYpCcnAx7e3sA7xuvBgYGiImJwciRI7Fu3Tr079+faxybmpoiPT0da9asgZ+fHxfX2dkZAQEBNZbr5OSEFStWIC8vD7q6ukhKSsJ3332H0tJSbN68GQBw+/Zt5Obmcg3pRYsWITQ0FCNGjAAAtGvXDunp6diyZQt8fX0BAAsXLuTKMDIywty5cxEdHY158+YJlZ+SkoKhQ4diwYIFtdazJuXl5YiMjISqqioAwNvbG/Hx8Vi+fDnU1dUhJycHJSUl6OjocPuEhYXB2toaK1as4JZt374dBgYGuHnzJkxNTetUhz179kAgECA8PBwKCgro1KkT7t+/j8mTJ9e5zC5dumDRokUAABMTE4SFhSE+Ph4DBw6ElpYWAEBDQ0PoeOpqz549ePPmDaKioqCsrMzVz93dHT/++CNat26NiRMnIiIiAt999x0A4NChQ3jz5g3X8NfX18fcuXO5mDNnzkRcXBz279+PHj16iFSPt2/f4u3bt0LLGGN1+iJFCCGESEpTyW3mS5PskT58+DBUVFSgoKAAV1dXjBo1Cn5+fpCRkUHPnj257Vq2bImOHTsiIyMDwPtc5sr5yg4ODsjKykJZWRm3zNbWlvvZ398fKioq3AsA7O3tIScnh8TERKSnp+P169ewsbGBra0tHj9+jDt37iAxMRGKioro1asXiouLkZ2djYkTJwrFWrZsGbKzs7my9u3bBwcHB+jo6EBFRQULFy5Ebm6uUH1zc3MxcOBABAcHV2lEfxjb39+/xs/PyMiIa0QD7wdKPnr0qNbPPDU1FQkJCUJlmJmZAYDQMYgqMzMTXbp04dJeAFRpTIpaZpcuXYT2+9jx5ObmCsX8sKFek4yMDHTt2pVrRAPvr53y8nJkZmYCeJ8Wc+vWLZw7dw7A+4Genp6e3D5lZWVYunQpLC0t0aJFC6ioqCAuLq7KOa7NypUroa6uLvRi5S9F3p8QQgiRpOaWI90ke6SdnJywefNmyMnJQU9PDzIyMoiNjeUt/oeNpZCQEKFeRABQUlJCjx49kJCQgGfPnqF3796QlpaGtLQ07O3tkZCQgISEBDg4OEBOTg4FBQUAgPDwcKGGPgBIS0sDAM6ePQsvLy8sWbIELi4uUFdXR3R0dJUcbi0tLejp6WHv3r2YMGEC1NTUuHUf5hZ/uLwyWVlZofcCgQDl5bXfRCkqKuJ6XyvT1dWtdd/6ErXMuh6Pnp6e0GfVokUL8SsLQFtbG+7u7oiIiEC7du3w119/CeXMr1mzBhs3bsSGDRtgaWkJZWVlzJ49u8qA1trMnz8fc+bMEVqm2dKMl/oTQgghpG6aZENaWVkZHTp0EFpmbm6O0tJSnD9/nkvtePr0KTIzM9GpUydum+TkZKH9kpOTYWpqyjVoK9PW1oa2tnaV5U5OToiOjkZBQQH69evHLe/bty8SExORlJTE9Qq3bt0aenp6uH37Nry8vKotJyUlBYaGhliwYAG37L///quynaKiIg4fPgw3Nze4uLjg+PHjXO9y5c+kvuTk5IR66AHAxsYGBw4cgJGREWRkxL9sOnbsiF27duHt27fcTB6V85f5KlNWVlboeGRkZOr8WZmbmyMyMhLFxcXcF63k5GRISUmhY8eO3HaTJk3CmDFj0KZNG7Rv317oDkhycjI8PDwwbtw4AO9TbG7evMldn6KQl5evMvMJpXUQQgj5VDSV3Ga+NMnUjuqYmJjAw8MDkydPxpkzZ5Camopx48ZBX18fHh4eAICAgADEx8dj6dKluHnzJnbs2IGwsLAqPc6icHJyQlZWFuLi4uDo6Mgtd3R0RExMDO7evcvlRwPAkiVLsHLlSmzatAk3b95EWloaIiIisG7dOq7+ubm5iI6ORnZ2NjZt2oSDBw9WW7aysjKOHDkCGRkZuLq6cjNH8MXIyAjnz59HTk4Onjx5gvLycnz99dd49uwZxowZg4sXLyI7OxtxcXEYP358lUa3KMaOHYvy8nJMmTIFGRkZiIuLw9q1awH8X8OQrzKNjIwQHx+P/Px87u5ATV6/fo1r164JvbKzs+Hl5QUFBQX4+vrixo0bSEhIwMyZM+Ht7Y3WrVtz+7u4uEBNTQ3Lli3D+PHjhWKbmJjgxIkTSElJQUZGBqZOnYqHDx+KfByEEELIp44xxturrn7++WcYGRlBQUEBPXv2rHUwf3h4OPr06QNNTU1oampiwIAB9Rr8/9k0pIH3MzN069YNQ4cOhZ2dHRhjOHr0KHfr38bGBvv370d0dDQ6d+6M4OBghISECA00FJWdnR3k5eXBGEO3bt245T179sS7d++4afIqTJo0Cdu2bUNERAQsLS3h6OiIyMhItGvXDgAwbNgwfPvtt5gxYwasrKyQkpJS7YwhFVRUVPDXX3+BMYYhQ4aguLi4zsdQk7lz50JaWhqdOnWClpYWcnNzoaenh+TkZJSVlWHQoEGwtLTE7NmzoaGhASmpul9GampqOHToEK5duwYrKyssWLAAwcHBAMDlTfNVZmhoKE6cOAEDAwNYW1vXuu3NmzdhbW0t9Jo6dSqUlJQQFxeHZ8+eoXv37vjqq6/Qv39/hIWFCe0vJSUFPz8/lJWVwcfHR2jdwoULYWNjAxcXF/Tr1w86OjoYPny4yMdBCCGEkOrt27cPc+bMwaJFi3DlyhV07doVLi4uNY6ZSkxMxJgxY5CQkICzZ8/CwMAAgwYNwv379+tUroDVp8lPiATs3r0b48ePx4sXL6CoqNjY1am3iRMn4vHjx7zm7ddGRk6/QcohhBDS9JWW1K2hWFcebYfyFut/uYdF3rZnz57o3r0718FVXl4OAwMDzJw5E99///1H9y8rK4OmpibCwsKqdITVpknmSJPPQ1RUFIyNjaGvr4/U1FQEBgbC09OzyTaiX7x4gbS0NOzZs6fBGtGEEELIp4TPHOnqpnytbqxQSUkJLl++jPnz53PLpKSkMGDAAJw9e1aksl69eoV3797VeQKCzyq1gzQt+fn5GDduHMzNzfHtt99i5MiR2Lp1a2NXq948PDwwaNAg+Pv7Y+DAgY1dHUIIIaRJq27K15UrV1bZ7smTJygrKxMaswS8n+yh8hOHaxIYGAg9PT0MGDCgTnWkHmnSaObNm1flYTNN2YdT3RFCCCHNEZ/zP1c35Wvl3mg+rFq1CtHR0UhMTBR6voUoqCFNCCGEEEJ4weeTDatL46hOq1atIC0tXWUmrIcPH370qcZr167FqlWrcPLkySoPeBMFpXYQQgghhJAmS05ODt26dUN8fDy3rLy8HPHx8bCzs6txv9WrV2Pp0qU4duyY0FOt64J6pAkhhBBCCC8aazK4OXPmwNfXF7a2tujRowc2bNiA4uJi7pkOPj4+0NfX53Ksf/zxRwQHB2PPnj0wMjLicqlVVFSgoqIicrnUkCaEEEIIIbxorCcbjho1Co8fP0ZwcDDy8/NhZWWFY8eOcQMQc3NzhZ5BsXnzZpSUlOCrr74SirNo0SIsXrxY5HJpHmlCmjiaR5oQQoioJD2P9CCDwbzFOn73GG+xJIVypAkhhBBCCKkHSu0ghBBCCCG84HPWjqaAGtKEEEIIIYQXzS1jmFI7PlGLFy+GlZVVY1ejRv369cPs2bO590ZGRtiwYQP3XiAQICYmpsHrxbfKx0UIIYQQUuGTbkj7+flBIBBAIBBATk4OHTp0QEhICEpLSxuk/GPHjkEgEFR5vKSuri6MjIyEluXk5EAgEAjNYdjQIiMjoaGhUad9KjeI+ZKXlwdXV1fe435MXRrwhYWFWLBgAczMzKCgoAAdHR0MGDAAf/75Z4N/o/5cvngQQghp3srBeHs1BZ90QxoABg8ejLy8PGRlZSEgIACLFy/GmjVrJFJWSUmJ0PvevXtDRkZG6NHPGRkZeP36NQoKCpCTk8MtT0hIgLy8PBwcHCRSt6ZGR0dHIo/x5Mvz589hb2+PqKgozJ8/H1euXMGpU6cwatQozJs3Dy9evGjsKhJCCCFNDuPxX1PwyTek5eXloaOjA0NDQ0ybNg0DBgxAbGwsCgoK4OPjA01NTSgpKcHV1RVZWVlC+x44cAAWFhaQl5eHkZERQkNDhdYbGRlh6dKl8PHxgZqaGqZMmSK0XkVFBd27dxdqSCcmJqJ3795wcHCosrxXr17cM9q3bdsGc3NzKCgowMzMDL/88otQ7MDAQJiamkJJSQnGxsYICgrCu3fvavwcsrOzYWxsjBkzZojcW1qRHrJz504YGRlBXV0do0ePxsuXLwG87/FPSkrCxo0buZ7/ii8HN27cgKurK1RUVNC6dWt4e3vjyZMnIpULVO1hTUlJgZWVFRQUFGBra4uYmBgIBAJcu3aN2+ZjZfbr1w+zZs3CvHnz0KJFC+jo6AjN9Vhxl+CLL76AQCCoctfgQz/88ANycnJw/vx5+Pr6olOnTjA1NcXkyZNx7do1ocnYX716hQkTJkBVVRVt27bF1q1bhWLdvXsXnp6e0NDQQIsWLeDh4SH0JevixYsYOHAgWrVqBXV1dTg6OuLKlSv1qjchhBBCPh2ffEO6MkVFRZSUlMDPzw+XLl1CbGwszp49C8YY3NzcuMbo5cuX4enpidGjRyMtLQ2LFy9GUFAQIiMjheKtXbsWXbt2xdWrVxEUFFSlPCcnJyQkJHDvExIS0K9fPzg6OgotT0xMhJOTEwBg9+7dCA4OxvLly5GRkYEVK1YgKCgIO3bs4LZXVVVFZGQk0tPTsXHjRoSHh2P9+vXVHvP169fRu3dvjB07FmFhYRAIBCJ/XtnZ2YiJicHhw4dx+PBhJCUlYdWqVQCAjRs3ws7ODpMnT0ZeXh7y8vJgYGCA58+fw9nZGdbW1rh06RKOHTuGhw8fwtPTU+RyP1RYWAh3d3dYWlriypUrWLp0KQIDA4W2EbXMHTt2QFlZGefPn8fq1asREhKCEydOAHjfYAWAiIgI5OXlce8rKy8vR3R0NLy8vKCnp1dlvYqKCmRk/m8cbmhoKGxtbXH16lVMnz4d06ZNQ2ZmJgDg3bt3cHFxgaqqKk6fPo3k5GSoqKhg8ODB3B2Oly9fwtfXF2fOnMG5c+dgYmICNzc37guNqPUmhBBCPnXljPH2agqazKwdjDHEx8cjLi4Orq6uiImJQXJyMuzt7QG8b7waGBggJiYGI0eOxLp169C/f3+ucWxqaor09HSsWbMGfn5+XFxnZ2cEBATUWK6TkxNWrFiBvLw86OrqIikpCd999x1KS0uxefNmAMDt27eRm5vLNaQXLVqE0NBQjBgxAgDQrl07pKenY8uWLfD19QUALFy4kCvDyMgIc+fORXR0NObNmydUfkpKCoYOHYoFCxbUWs+alJeXIzIyEqqqqgAAb29vxMfHY/ny5VBXV4ecnByUlJSgo6PD7RMWFgZra2usWLGCW7Z9+3YYGBjg5s2bMDU1rVMd9uzZA4FAgPDwcCgoKKBTp064f/8+Jk+eXOcyu3TpgkWLFgEATExMEBYWhvj4eAwcOBBaWloAAA0NDaHjqezJkycoKCiAmZmZSPV3c3PD9OnTAby/k7B+/XokJCSgY8eO2LdvH8rLy7Ft2zbuC05ERAQ0NDSQmJiIQYMGwdnZWSje1q1boaGhgaSkJAwdOlTkehNCCCGfuqbR/OXPJ98jffjwYaioqEBBQQGurq4YNWoU/Pz8ICMjg549e3LbtWzZEh07dkRGRgaA97nMlfOVHRwckJWVhbKyMm6Zra0t97O/vz/3jPWKW/v29vaQk5NDYmIi0tPT8fr1a9jY2MDW1haPHz/GnTt3kJiYCEVFRfTq1QvFxcXIzs7GxIkThWItW7YM2dnZXFn79u2Dg4MDdHR0oKKigoULFyI3N1eovrm5uRg4cCCCg4OrNKI/jO3v71/j52dkZMQ1ooH3AyUfPXpU62eempqKhIQEoTIqGp0fHoOoMjMz0aVLFy7tBQB69OhRrzK7dOkitN/Hjic3N1co5ooVK+o8kPDDMgUCAXR0dLgyU1NTcevWLaiqqnJltGjRAm/evOHq/fDhQ0yePBkmJiZQV1eHmpoaioqKqpxvUbx9+xaFhYVCr+Y21RAhhBDyqfjke6SdnJywefNmyMnJQU9PDzIyMoiNjeUtvrKyMvdzSEgI5s6dK7ReSUkJPXr0QEJCAp49e4bevXtDWloa0tLSsLe3R0JCAhISEuDg4AA5OTkUFBQAAMLDw4Ua+gAgLS0NADh79iy8vLywZMkSuLi4QF1dHdHR0VVyuLW0tKCnp4e9e/diwoQJUFNT49Z9mFv84fLKZGVlhd4LBAKUl5fX+pkUFRXB3d0dP/74Y5V1urq6te5bX6KWWdfj0dPTE/qsWrRoAQ0NDWhoaODff/8VqW61lVlUVIRu3bph9+7dVfar6Gn29fXF06dPsXHjRhgaGkJeXh52dnZVBreKYuXKlViyZIlwfaRUIJCu+RoghBBCGkpTmW2DL598Q1pZWRkdOnQQWmZubo7S0lKcP3+eS+14+vQpMjMz0alTJ26b5ORkof2Sk5NhamrKNWgr09bWhra2dpXlTk5OiI6ORkFBAfr168ct79u3LxITE5GUlMT1Crdu3Rp6enq4ffs2vLy8qi0nJSUFhoaGWLBgAbfsv//+q7KdoqIiDh8+DDc3N7i4uOD48eNc73Llz6S+5OTkhHroAcDGxgYHDhyAkZGRUK5wfXXs2BG7du3C27dvuZk8KucB81WmrKys0PHIyMhU+1mNHj0aO3fuxKJFi6rkSRcVFUFBQUGketjY2GDfvn3Q1tau8QtNcnIyfvnlF7i5uQF4Pzix8sDNyvWuyfz58zFnzhyhZZotRUtRIYQQQiStuTWkP/nUjuqYmJjAw8MDkydPxpkzZ5Camopx48ZBX18fHh4eAICAgADEx8dj6dKluHnzJnbs2IGwsLAqPc6icHJyQlZWFuLi4uDo6Mgtd3R0RExMDO7evcvlRwPAkiVLsHLlSmzatAk3b95EWloaIiIisG7dOq7+ubm5iI6ORnZ2NjZt2oSDBw9WW7aysjKOHDkCGRkZuLq6oqioqM71r42RkRHOnz+PnJwcPHnyBOXl5fj666/x7NkzjBkzBhcvXkR2djbi4uIwfvx4kRp7lY0dOxbl5eWYMmUKMjIyEBcXh7Vr1wIAl1fMV5lGRkaIj49Hfn4+d3egOsuXL4eBgQF69uyJqKgopKenIysrC9u3b4e1tbXIn7OXlxdatWoFDw8PnD59mkv1mTVrFu7duwfg/fneuXMnMjIycP78eXh5eUFRUbFe9ZaXl4eamprQqy6DTwkhhBBJYozx9moKmmRDGng/oKtbt24YOnQo7OzswBjD0aNHudvwNjY22L9/P6Kjo9G5c2cEBwcjJCREaKChqOzs7CAvLw/GGLp168Yt79mzJ969e8dNk1dh0qRJ2LZtGyIiImBpaQlHR0dERkaiXbt2AIBhw4bh22+/xYwZM2BlZYWUlJRqZwypoKKigr/++guMMQwZMgTFxcV1PoaazJ07F9LS0ujUqRO0tLSQm5sLPT09JCcno6ysDIMGDYKlpSVmz54NDQ0NSEnV/ZJRU1PDoUOHcO3aNVhZWWHBggUIDg4GAC5vmq8yQ0NDceLECRgYGMDa2rrG7Vq0aIFz585h3LhxWLZsGaytrdGnTx/s3bsXa9asgbq6ukjlKSkp4dSpU2jbti1GjBgBc3NzTJw4EW/evOF6qH/77TcUFBTAxsYG3t7emDVrVpU7H6LWmxBCCCGfDgFrKk1+8lnZvXs3xo8fjxcvXlTpnSV1IyOn39hVIIQQ0kSUltyXaPweeo4f30hEFx4k8RZLUj75HGnyeYiKioKxsTH09fWRmpqKwMBAeHp6UiOaEEII+Yw0lScS8oUa0qRB5OfnIzg4GPn5+dDV1cXIkSOxfPnyxq4WIYQQQki9UWoHIU0cpXYQQggRlaRTO2x1+/AW61Lead5iSQr1SBNCCCGEEF7Q9HeEEEIIIYSQj6IeaUIIIYQQwovmljFMDWlCCCGEEMILSu0ghBBCCCGEfBT1SBNCCCGEEF7QPNKEEEIIIYTUQznlSBNCCCGEEFJ3za1HmnKkSY0WL14MKyurxq5GnTXVegOAkZERNmzY0NjVIIQQQogIqCH9EX5+fhAIBBAIBJCTk0OHDh0QEhKC0tLSBin/2LFjEAgEyM/PF1quq6sLIyMjoWU5OTkQCASIj49vkLpVJzIykvu8pKWloampiZ49eyIkJAQvXrxotHqJws/PD8OHD2/sahBCCCFNVjljvL2aAmpIi2Dw4MHIy8tDVlYWAgICsHjxYqxZs0YiZZWUlAi97927N2RkZJCYmMgty8jIwOvXr1FQUICcnBxueUJCAuTl5eHg4CCRuolKTU0NeXl5uHfvHlJSUjBlyhRERUXBysoKDx48aNS6EUIIIURyGI//mgJqSItAXl4eOjo6MDQ0xLRp0zBgwADExsaioKAAPj4+0NTUhJKSElxdXZGVlSW074EDB2BhYQF5eXkYGRkhNDRUaL2RkRGWLl0KHx8fqKmpYcqUKULrVVRU0L17d6GGdGJiInr37g0HB4cqy3v16gUFBQUAwLZt22Bubg4FBQWYmZnhl19+EYodGBgIU1NTKCkpwdjYGEFBQXj37l2Nn0N2djaMjY0xY8aMWidcFwgE0NHRga6uLszNzTFx4kSkpKSgqKgI8+bNEzr2ymkMVlZWWLx4sVCsLVu2YOjQoVBSUoK5uTnOnj2LW7duoV+/flBWVoa9vT2ys7Or1GPLli0wMDCAkpISPD09xe4RT0pKQo8ePSAvLw9dXV18//333J2JqKgotGzZEm/fvhXaZ/jw4fD29gbw/vPz8PBA69atufN68uRJsepECCGEkMZDDel6UFRURElJCfz8/HDp0iXExsbi7NmzYIzBzc2Na4xevnwZnp6eGD16NNLS0rB48WIEBQUhMjJSKN7atWvRtWtXXL16FUFBQVXKc3JyQkJCAvc+ISEB/fr1g6Ojo9DyxMREODk5AQB2796N4OBgLF++HBkZGVixYgWCgoKwY8cObntVVVVERkYiPT0dGzduRHh4ONavX1/tMV+/fh29e/fG2LFjERYWBoFAUKfPTFtbG15eXoiNjUVZWVmd9q34onHt2jWYmZlh7NixmDp1KubPn49Lly6BMYYZM2YI7XPr1i3s378fhw4dwrFjx3D16lVMnz69TuV+6P79+3Bzc0P37t2RmpqKzZs347fffsOyZcsAACNHjkRZWRliY2O5fR49eoQjR45gwoQJAICioiK4ubkhPj4eV69exeDBg+Hu7o7c3Nx614sQQgj5lFBqB6kRYwwnT55EXFwc2rZti9jYWGzbtg19+vRB165dsXv3bty/fx8xMTEAgHXr1qF///4ICgqCqakp/Pz8MGPGjCppIc7OzggICED79u3Rvn37KuU6OTnh5s2byMvLA/C+Z9TR0RF9+/ZFUlISAOD27dvIzc3lGtKLFi1CaGgoRowYgXbt2mHEiBH49ttvsWXLFi7uwoULYW9vDyMjI7i7u2Pu3LnYv39/lfJTUlLQr18/zJ07l2s41oeZmRlevnyJp0+f1mm/8ePHw9PTE6ampggMDEROTg68vLzg4uICc3NzfPPNN0I98wDw5s0bLp2kb9+++OmnnxAdHV0l11xUv/zyCwwMDBAWFgYzMzMMHz4cS5YsQWhoKMrLy6GoqIixY8ciIiKC22fXrl1o27Yt+vXrBwDo2rUrpk6dis6dO8PExARLly5F+/bthRrfhBBCSFNGqR2kisOHD0NFRQUKCgpwdXXFqFGj4OfnBxkZGfTs2ZPbrmXLlujYsSMyMjIAvM9lrpyv7ODggKysLKFeWVtbW+5nf39/qKiocC8AsLe3h5ycHBITE5Geno7Xr1/DxsYGtra2ePz4Me7cuYPExEQoKiqiV69eKC4uRnZ2NiZOnCgUa9myZUIpEPv27YODgwN0dHSgoqKChQsXVukdzc3NxcCBAxEcHIyAgAChdR/G9vf3/+jnWJEOUtfe7C5dunA/t27dGgBgaWkptOzNmzcoLCzklrVt2xb6+vrcezs7O5SXlyMzMxOnT58Wqvvu3bs/WoeMjAzY2dkJ1d3BwQFFRUW4d+8eAGDy5Mk4fvw47t+/D+D9wMuKwarA+x7puXPnwtzcHBoaGlBRUUFGRkadeqTfvn2LwsJCoVdtaTaEEEIIkRyaR1oETk5O2Lx5M+Tk5KCnpwcZGRleexGVlZW5n0NCQjB37lyh9UpKSujRowcSEhLw7Nkz9O7dG9LS0pCWloa9vT0SEhKQkJAABwcHyMnJoaCgAAAQHh4u1NAHAGlpaQDA2bNn4eXlhSVLlsDFxQXq6uqIjo6uksOtpaUFPT097N27FxMmTICamhq37tq1a9zPHy6vSUZGBtTU1NCyZUsAgJSUVJVGYHU52rKystzPFY3S6paVl5d/tA7A+y8uH9a9onEuLmtra3Tt2hVRUVEYNGgQ/vnnHxw5coRbP3fuXJw4cQJr165Fhw4doKioiK+++qrKANParFy5EkuWLBFaJpBSgUD6458/IYQQImlNJSWDL9SQFoGysjI6dOggtMzc3BylpaU4f/487O3tAQBPnz5FZmYmOnXqxG2TnJwstF9ycjJMTU25Bm1l2tra0NbWrrLcyckJ0dHRKCgo4FIFAKBv375ITExEUlIS1yvcunVr6Onp4fbt2/Dy8qq2nJSUFBgaGmLBggXcsv/++6/KdoqKijh8+DDc3Nzg4uKC48ePQ1VVFQCqfCa1efToEfbs2YPhw4dDSur9jRAtLS0uXQUACgsLcefOHZFj1iY3NxcPHjyAnp4eAODcuXOQkpJCx44doaioWKe6A+/P5YEDB8AY4xruycnJUFVVRZs2bbjtJk2ahA0bNuD+/fsYMGAADAwMuHXJycnw8/PDF198AeB9D/WHs66IYv78+ZgzZ47QMs2WZnWKQQghhEhKU0nJ4AuldtSTiYkJPDw8MHnyZJw5cwapqakYN24c9PX14eHhAQAICAhAfHw8li5dips3b2LHjh0ICwur0uMsCicnJ2RlZSEuLg6Ojo7cckdHR8TExODu3btcfjQALFmyBCtXrsSmTZtw8+ZNpKWlISIiAuvWrePqn5ubi+joaGRnZ2PTpk04ePBgtWUrKyvjyJEjkJGRgaurK4qKimqtK2MM+fn5yMvLQ0ZGBrZv3w57e3uoq6tj1apV3HbOzs7YuXMnTp8+jbS0NPj6+tb4BaOuFBQU4Ovri9TUVJw+fRqzZs2Cp6cndHR0at3vxYsXuHbtmtDr7t27mD59Ou7evYuZM2fi33//xf/+9z8sWrQIc+bM4b4YAMDYsWNx7949hIeHc4MMK5iYmODPP//EtWvXkJqairFjx4rci15BXl4eampqQq+6psoQQgghhB/UkBZDREQEunXrhqFDh8LOzg6MMRw9epRLO7CxscH+/fsRHR2Nzp07Izg4GCEhIfDz86tzWXZ2dpCXlwdjDN26deOW9+zZE+/eveOmU6swadIkbNu2DREREbC0tISjoyMiIyPRrl07AMCwYcPw7bffYsaMGbCyskJKSkq1M4ZUUFFRwV9//QXGGIYMGYLi4uIaty0sLISuri709fVhZ2eHLVu2wNfXF1evXoWuri633fz58+Ho6IihQ4diyJAhGD58eLWDLeujQ4cOGDFiBNzc3DBo0CB06dKlyvR/1UlMTIS1tbXQa8mSJdDX18fRo0dx4cIFdO3aFf7+/pg4cSIWLlwotL+6ujq+/PJLqKioVHm4y7p166CpqQl7e3u4u7vDxcUFNjY2vBwvIYQQ8ilgrJy3V1MgYDRSiRBe9e/fHxYWFti0aVODlCcjp//xjQghhBAApSX3JRrfsGWXj28kov+eXuctlqRQjjQhPCkoKEBiYiISExNF6v0mhBBCPjfNrX+WGtKE8MTa2hoFBQX48ccf0bFjx8auDiGEEEIkjBrShPCkrjNwEEIIIZ+b8mY2awc1pAkhhBBCCC+aW2oHzdpBCCGEEEJIPVCPNCGEEEII4QU92ZAQQgghhDS41w9ON3YVxEZPNiSEEEIIIYR8FPVIE0IIIYQQXjS3wYbUkCaEEEIIIbxobtPfUWoHIYQQQggh9UANaQIAWLx4MaysrBq7GnUm6XoLBALExMRILD4hhBDyOWGM8fZqCqgh/QE/Pz8IBAIIBALIycmhQ4cOCAkJQWlpaYOUf+zYMQgEAuTn5wst19XVhZGRkdCynJwcCAQCxMfHN0jdqhMZGcl9XtLS0tDU1ETPnj0REhKCFy9eNFq9RJWfn4+ZM2fC2NgY8vLyMDAwgLu7e4N/phXn8tq1aw1aLiGEEMK3csZ4ezUF1JCuZPDgwcjLy0NWVhYCAgKwePFirFmzRiJllZSUCL3v3bs3ZGRkkJiYyC3LyMjA69evUVBQIPQI6oSEBMjLy8PBwUEidROVmpoa8vLycO/ePaSkpGDKlCmIioqClZUVHjx40Kh1q01OTg66deuGv//+G2vWrEFaWhqOHTsGJycnfP31141dPUIIIaRJoh7pZk5eXh46OjowNDTEtGnTMGDAAMTGxqKgoAA+Pj7Q1NSEkpISXF1dkZWVJbTvgQMHYGFhAXl5eRgZGSE0NFRovZGREZYuXQofHx+oqalhypQpQutVVFTQvXt3oYZ0YmIievfuDQcHhyrLe/XqBQUFBQDAtm3bYG5uDgUFBZiZmeGXX34Rih0YGAhTU1MoKSnB2NgYQUFBePfuXY2fQ3Z2NoyNjTFjxoxaL2aBQAAdHR3o6urC3NwcEydOREpKCoqKijBv3jyhY9+wYYPQvlZWVli8eLFQrC1btmDo0KFQUlKCubk5zp49i1u3bqFfv35QVlaGvb09srOzq9Rjy5YtMDAwgJKSEjw9PT/aIz59+nQIBAJcuHABX375JUxNTWFhYYE5c+bg3LlzQts+efIEX3zxBZSUlGBiYoLY2Fih9Tdu3ICrqytUVFTQunVreHt748mTJ9z6Y8eOoXfv3tDQ0EDLli0xdOhQoWNo164dAMDa2hoCgQD9+vWrte6EEEII+TRQQ/ojFBUVUVJSAj8/P1y6dAmxsbE4e/YsGGNwc3PjGqOXL1+Gp6cnRo8ejbS0NCxevBhBQUGIjIwUird27Vp07doVV69eRVBQUJXynJyckJCQwL1PSEhAv3794OjoKLQ8MTERTk5OAIDdu3cjODgYy5cvR0ZGBlasWIGgoCDs2LGD215VVRWRkZFIT0/Hxo0bER4ejvXr11d7zNevX0fv3r0xduxYhIWFQSAQ1Okz09bWhpeXF2JjY1FWVlanfSu+aFy7dg1mZmYYO3Yspk6divnz5+PSpUtgjGHGjBlC+9y6dQv79+/HoUOHcOzYMVy9ehXTp0+vsYxnz57h2LFj+Prrr6GsrFxlvYaGhtD7JUuWwNPTE9evX4ebmxu8vLzw7NkzAMDz58/h7OwMa2trXLp0CceOHcPDhw/h6enJ7V9cXIw5c+bg0qVLiI+Ph5SUFL744guUl5cDAC5cuAAAOHnyJPLy8vDnn3/W6TMjhBBCPhXlYLy9mgKa/q4GjDHEx8cjLi4Orq6uiImJQXJyMuzt7QG8b7waGBggJiYGI0eOxLp169C/f3+ucWxqaor09HSsWbMGfn5+XFxnZ2cEBATUWK6TkxNWrFiBvLw86OrqIikpCd999x1KS0uxefNmAMDt27eRm5vLNaQXLVqE0NBQjBgxAsD7Hs709HRs2bIFvr6+AICFCxdyZRgZGWHu3LmIjo4W6jUGgJSUFAwdOhQLFiyotZ4fY2ZmhpcvX+Lp06fQ1tYWeb/x48dzjdDAwEDY2dkhKCgILi4uAIBvvvkG48ePF9rnzZs3iIqKgr6+PgDgp59+wpAhQxAaGgodHZ0qZdy6dQuMMZiZmYlUJz8/P4wZMwYAsGLFCmzatAkXLlzA4MGDERYWBmtra6xYsYLbfvv27TAwMMDNmzdhamqKL7/8Uije9u3boaWlhfT0dHTu3BlaWloAgJYtW1ZbX0IIIaSpaCopGXyhHulKDh8+DBUVFSgoKMDV1RWjRo2Cn58fZGRk0LNnT267li1bomPHjsjIyADwPpe5cr6yg4MDsrKyhHplbW1tuZ/9/f2hoqLCvQDA3t4ecnJySExMRHp6Ol6/fg0bGxvY2tri8ePHuHPnDhITE6GoqIhevXqhuLgY2dnZmDhxolCsZcuWCaUP7Nu3Dw4ODtDR0YGKigoWLlyI3Nxcofrm5uZi4MCBCA4OrtKI/jC2v7//Rz/Hil+kuvZmd+nShfu5devWAABLS0uhZW/evEFhYSG3rG3btlwjGgDs7OxQXl6OzMxMnD59Wqjuu3fvrvMv+Yd1UlZWhpqaGh49egQASE1NRUJCglAZFQ30is8/KysLY8aMgbGxMdTU1LiBo5U/f1G8ffsWhYWFQq/m9p8WIYQQ8qmgHulKnJycsHnzZsjJyUFPTw8yMjJVcmLF8WEqQUhICObOnSu0XklJCT169EBCQgKePXuG3r17Q1paGtLS0rC3t0dCQgISEhLg4OAAOTk5FBQUAADCw8OFGvoAIC0tDQA4e/YsvLy8sGTJEri4uEBdXR3R0dFVcri1tLSgp6eHvXv3YsKECVBTU+PWfTijxIfLa5KRkQE1NTW0bNkSACAlJVWlwVddjrasrCz3c0UjvLplFWkRH2NraytU99atW+Pdu3cQCAT4999/RYrxYfkVdagov6ioCO7u7vjxxx+r7KerqwsAcHd3h6GhIcLDw6Gnp4fy8nJ07ty5ymBTUaxcuRJLliwRro+UCgTSHz8nhBBCiKQ1ldk2+EIN6UqUlZXRoUMHoWXm5uYoLS3F+fPnudSOp0+fIjMzE506deK2SU5OFtovOTkZpqamXIO2Mm1t7WrTHpycnBAdHY2CggKhgWd9+/ZFYmIikpKSuF7h1q1bQ09PD7dv34aXl1e15aSkpMDQ0BALFizglv33339VtlNUVMThw4fh5uYGFxcXHD9+HKqqqgBQ5TOpzaNHj7Bnzx4MHz4cUlLvb3poaWkhLy+P26awsBB37twROWZtcnNz8eDBA+jp6QEAzp07BykpKXTs2BGKiorV1t3FxQU///wzZs2aVSVP+vnz51XypGtiY2ODAwcOwMjICDIyVX+dKq6T8PBw9OnTBwBw5swZoW3k5OQAQKR88vnz52POnDlCyzRbipaiQgghhEgaayK5zXyh1A4RmJiYwMPDA5MnT8aZM2eQmpqKcePGQV9fHx4eHgCAgIAAxMfHY+nSpbh58yZ27NiBsLCwKj3OonByckJWVhbi4uLg6OjILXd0dERMTAzu3r3L5UcD7wfDrVy5Eps2bcLNmzeRlpaGiIgIrFu3jqt/bm4uoqOjkZ2djU2bNuHgwYPVlq2srIwjR45ARkYGrq6uKCoqqrWujDHk5+cjLy8PGRkZ2L59O+zt7aGuro5Vq1Zx2zk7O2Pnzp04ffo00tLS4OvrW+MXjLpSUFCAr68vUlNTcfr0acyaNQuenp615hv//PPPKCsrQ48ePXDgwAFkZWUhIyMDmzZtgp2dnchlf/3113j27BnGjBmDixcvIjs7G3FxcRg/fjzKysqgqamJli1bYuvWrbh16xb+/vvvKg1hbW1tKCoqcgMVa5txRF5eHmpqakKvuqbPEEIIIYQf1JAWUUREBLp164ahQ4fCzs4OjDEcPXqUu+1vY2OD/fv3Izo6Gp07d0ZwcDBCQkKEBhqKys7ODvLy8mCMoVu3btzynj174t27d9w0eRUmTZqEbdu2ISIiApaWlnB0dERkZCQ3rdqwYcPw7bffYsaMGbCyskJKSkq1M4ZUUFFRwV9//QXGGIYMGYLi4uIaty0sLISuri709fVhZ2fHDXC8evUql9oAvO9JdXR0xNChQzFkyBAMHz4c7du3r/NnU50OHTpgxIgRcHNzw6BBg9ClS5cq0/9VZmxsjCtXrsDJyQkBAQHo3LkzBg4ciPj4eG5Qpyj09PSQnJyMsrIyDBo0CJaWlpg9ezY0NDQgJSUFKSkpREdH4/Lly+jcuTO+/fbbKvOSy8jIYNOmTdiyZQv09PS4L2eEEEJIU9PcHsgiYDRSiZAmTUZO/+MbEUII+eS9fnBa4mXItjKWaHwFhba8xXrzpu6D8hsa9UgTQgghhBBSDzTYkBBCCCGE8IIGGxJCCCGEEFIPjDHeXnX1888/w8jICAoKCujZsyf35OCa/P777zAzM4OCggIsLS1x9OjROpdJDWlCCCGEEMKLxmpI79u3D3PmzMGiRYtw5coVdO3aFS4uLtwD1CpLSUnBmDFjMHHiRFy9ehXDhw/H8OHDcePGjTqVS4MNCWniaLAhIYR8Hj6HwYayPP5NeldyX+Rte/bsie7duyMsLAzA+we3GRgYYObMmfj++++rbD9q1CgUFxfj8OHD3LJevXrBysoKv/76q8jlUo80IYQQQgjhBePx9fbtWxQWFgq93r59W6XMkpISXL58GQMGDOCWSUlJYcCAATh79my19Tx79qzQ9sD7h7XVtH3NB0wIaTbevHnDFi1axN68eUNlNHIZn8MxUBmfTnwq49Mq43M4hk/BokWLqrSvFy1aVGW7+/fvMwAsJSVFaPl3333HevToUW1sWVlZtmfPHqFlP//8M9PW1q5THSm1g5BmpLCwEOrq6njx4gXU1NSojEYs43M4Birj04lPZXxaZXwOx/ApePv2bZUeaHl5ecjLywste/DgAfT19ZGSkiL0dOJ58+YhKSkJ58+frxJbTk4OO3bswJgxY7hlv/zyC5YsWYKHDx+KXEea/o4QQgghhHxyqms0V6dVq1aQlpau0gB++PAhdHR0qt1HR0enTtvXhHKkCSGEEEJIkyUnJ4du3bohPj6eW1ZeXo74+HihHuoP2dnZCW0PACdOnKhx+5pQjzQhhBBCCGnS5syZA19fX9ja2qJHjx7YsGEDiouLMX78eACAj48P9PX1sXLlSgDAN998A0dHR4SGhmLIkCGIjo7GpUuXsHXr1jqVSw1pQpoReXl5LFq0SKRbZVSGZMv4HI6Byvh04lMZn1YZn8MxNDWjRo3C48ePERwcjPz8fFhZWeHYsWNo3bo1ACA3NxdSUv+XiGFvb489e/Zg4cKF+OGHH2BiYoKYmBh07ty5TuXSYENCCCGEEELqgXKkCSGEEEIIqQdqSBNCCCGEEFIP1JAmhBBCCCGkHqghTQghhBBCSD1QQ5oQQgghhJB6oIY0IYQQQggh9UANaUII+f+cnZ3x/PnzKssLCwvh7Ozc8BWqp9zcXFQ3syljDLm5uY1Qo+YrKioKb9++rbK8pKQEUVFRjVCjuvtcrqeQkBC8evWqyvLXr18jJCSkEWpUd6dOnUJpaWmV5aWlpTh16lQj1IjQPNKEfKZGjBgh8rZ//vmnWGVJS0sjLy8P2traQsufPn0KbW1tlJWV1Svupk2bRN521qxZ9SrjQ1JSUsjPz69yHI8ePYK+vj7evXtXr7jW1tYQCAQibXvlypV6lfEhSZyPOXPmiLztunXr6hy/MmNjY1y8eBEtW7YUWv78+XPY2Njg9u3b9YpbWFgo8rZqamr1KuNDkjgXn8P1BADXr18XedsuXbrUq4wPSeo4GtLncAyfG3qyISGfKXV19QYrq6bv42/fvoWcnFy9465fv16k7QQCgVgN6Q//oKenpyM/P597X1ZWhmPHjkFfX7/e8YcPH17vfeuDMVZtQ6uoqAgKCgr1inn16lWRthO1gfcxOTk51TYK3r59i/v379c7roaGxkfrWPH58dEoqelc3Lt3r96/o5/D9QQAVlZWEAgENf7/UbFO0uciNTUVLVq0qHdcTU1Nka/7Z8+e1bscoOZjePr0KZSVlcWKTeqHGtKEfKYiIiIkXkZFj7FAIMC2bdugoqLCrSsrK8OpU6dgZmZW7/h37twRu46iqPiDLhAIqk3hUFRUxE8//VTv+IsWLRKneiKr6DUWCAQICgqCkpISt66srAznz5+HlZVVvWInJCTwUcWPio2N5X6Oi4sTamyWlZUhPj4eRkZG9Y7fUMdR0WssEAjQv39/yMj835/bsrIy3LlzB4MHD65X7M/hegIa7ve7oqErEAhgamoq1BAtKytDUVER/P396x1/w4YNPNSydhV3GAUCAfz8/IQeC15WVobr16/D3t5e4vUgVVFDmhBSbxU9xowx/Prrr5CWlubWycnJwcjICL/++mtjVU9kd+7cAWMMxsbGuHDhArS0tLh1cnJy0NbWFjq2T1VFrzFjDGlpaUJ3A+Tk5NC1a1fMnTu3saonkoreVoFAAF9fX6F1srKyMDIyQmhoaL3jOzo6ilM9kVUcx7Vr1+Di4iL0JbPid+PLL79skLrUl6SvJ0NDQ7HrKIoNGzaAMYYJEyZgyZIlQl/OKs6FnZ1dveNXvk4loaLOjDGoqqpCUVGRWycnJ4devXph8uTJEq8HqYpypAlpJv744w/s378fubm5KCkpEVonbh6lk5MT/vzzT2hqaooV52Pu3buH2NjYao+Bj7zchlBWVob169fXeC7EvfULAOPHj8fGjRt5yfGtyaVLl2o8BnFz7gGgXbt2uHjxIlq1aiV2rI959epVtcfBR17ujh07MGrUKLFSIGrzuVxPFdLT06s9jmHDhokdOykpCfb29pCVlRU7lijevHlT5TjE/QyXLFmCuXPnUhrHp4QRQj57GzduZCoqKmzGjBlMTk6OTZ06lQ0YMICpq6uzH374obGrJ5KTJ08yJSUl1rlzZyYjI8OsrKyYhoYGU1dXZ05OTryW9c8//7C//vqL/e9//xN68SEoKIjp6uqytWvXMgUFBbZ06VI2ceJE1rJlS7Zx40ZeypC0vXv3MllZWTZ06FAmJyfHhg4dykxNTZm6ujrz8/Nr7OqJ7NGjR2zIkCFMSkqq2ldT8DlcT4wxlp2dzbp06cIEAgGTkpJiAoGA+1kS5+L169fsxYsXQi8+FBUVsa+//pppaWk12WuK1A31SBPSDJiZmWHRokUYM2YMVFVVkZqaCmNjYwQHB+PZs2cICwsTuwxJ9xb36NEDrq6uWLJkCXcM2tra8PLywuDBgzFt2jSxy7h9+za++OILpKWlCQ2Cqsip5GPAU/v27bFp0yYMGTIEqqqquHbtGrfs3Llz2LNnj9hlAJLtMe7SpQumTp2Kr7/+mjsX7dq1w9SpU6Grq4slS5aIFb9CcXExkpKSqj0GPmZp8fLywn///YcNGzagX79+OHjwIB4+fIhly5YhNDQUQ4YMEbsMSfcYfw7XEwC4u7tDWloa27ZtQ7t27XDhwgU8ffoUAQEBWLt2Lfr06SN2Ga9evcK8efOwf/9+PH36tMp6Pn6/v/76ayQkJGDp0qXw9vbGzz//jPv372PLli1YtWoVvLy8xC5DkncXST00ckOeENIAFBUVWU5ODmOMMS0tLXbt2jXGGGM3b95kLVq0EDt+Q/QWq6iosFu3bjHGGNPQ0GA3btxgjDF27do1ZmhoyEsZQ4cOZR4eHuzx48dMRUWFpaens9OnT7MePXqwU6dO8VKGkpIS+++//xhjjOno6LDLly8zxt73yKmpqfFShqR7jJWUlNidO3cYY4y1aNGCXb9+nTHGWHp6OtPR0RE7PmOMXblyheno6DA1NTUmLS3NtLS0mEAgYMrKyqxdu3a8lKGjo8POnz/PGGNMVVWVZWZmMsYY+9///sccHBx4KUPSPcafw/XEGGMtW7ZkqampjDHG1NTU2L///ssYYyw+Pp5ZWVnxUsb06dOZubk5++OPP5iioiLbvn07W7p0KWvTpg3btWsXL2UYGBiwhIQExtj7ayorK4sxxlhUVBRzdXUVO/7ncHfxc0MPZCGkGdDR0eF6vtq2bYtz584B+L9BduKaP38+5s6di7S0NCgoKODAgQO4e/cuHB0dMXLkSLHjA4CysjLX+6Krq4vs7Gxu3ZMnT3gp4+zZswgJCUGrVq0gJSUFKSkp9O7dGytXruSlBxQA2rRpg7y8PADvexOPHz8OALh48aLQSHxxrFixAuvXr8ehQ4cgJyeHjRs34t9//4Wnpyfatm0rdnxNTU28fPkSAKCvr48bN24AeD/Hc3UPvKiPb7/9Fu7u7igoKICioiLOnTuH//77D926dcPatWt5KaO4uJibj1dTUxOPHz8GAFhaWvLWs7d7926Eh4cjICAAMjIyGDNmDLZt24bg4GDu91Acn8P1BLzvDVZVVQUAtGrVCg8ePADwfkBiZmYmL2UcOnQIv/zyC7788kvIyMigT58+WLhwIVasWIHdu3fzUsazZ89gbGwM4H0+dMX/u7179+blgSm//PILtm7dip9++glycnKYN28eTpw4gVmzZuHFixdixyf10NgteUKI5E2cOJEtXryYMcZYWFgYU1RUZAMGDGAaGhpswoQJYsdviN5iDw8PtnXrVsYYYwEBAaxDhw5s2bJlzMbGhvXv35+XMjQ0NNjt27cZY4wZGxuzv//+mzHG2K1bt5iioiIvZQQGBrLly5czxhiLjo5mMjIyrEOHDkxOTo4FBgbyUoake4zHjBnDQkNDGWOMhYSEMC0tLTZp0iRmaGjIvvjiC7HjM8aYuro61yuprq7O0tPTGWOMnTt3jnXs2JGXMmxtbdmxY8cYY4y5u7szb29vdu/ePTZv3jxmbGzMSxmS7jH+HK4nxhjr3bs3O3jwIGPs/fU1ePBgdubMGebj48MsLCx4KUNZWZk7F/r6+tzdiNu3bzNlZWVeyrC0tGSJiYmMMcb69+/PAgICGGPve5L19fXFji/pu4uk7mj6O0Kaga1bt6K8vBzA+xy+li1bIiUlBcOGDcPUqVPFjl9db7GFhQUA/nqL161bh6KiIgDvR64XFRVh3759MDEx4W3Gjs6dO3P5vj179sTq1ashJyeHrVu3cr1M4lq1ahX386hRo2BoaIiUlBSYmJjA3d2dlzKq6zG2tLTkrcc4LCwMb968AQAsWLAAsrKySElJwZdffomFCxeKHR94P9WdlNT7m6ba2trIzc2Fubk51NXVcffuXV7K+Oabb7je3EWLFmHw4MHYvXs35OTkEBkZyUsZFT3Gbdu25XqMbWxseOsx/hyuJwBYuHAhiouLAbx/lPfQoUPRp08ftGzZEvv27eOlDGNjY9y5cwdt27aFmZkZ9u/fjx49euDQoUPQ0NDgpYzx48cjNTUVjo6O+P777+Hu7o6wsDC8e/eOl/+nKu4uGhoacncXu3btytvdRVIPjd2SJ4Q0fQ3RW9wQjh07xg4cOMAYYywrK4t17NiRCQQC1qpVKxYfH89LGUVFRbzEqU1D9BhL2sCBA9nu3bsZY4xNmjSJ9ejRg+3atYu5uLiwHj16SKTM4uJidvnyZfb48WPeYkq6x/hzvp6ePn3KysvLeYu3bt06Li/9xIkTTEFBgcnLyzMpKSm2YcMG3sr5UE5ODjtw4ACX/y0uSd9dJHVHDWlCmgFDQ0O2ZMkS7rYm37Kzs7k/FEVFRWzq1KnM0tKSjRgxgrsNKS4fHx+WlJTES6y64PuPubKyMhs/fjw7ffo0bzEre/r0Kbt//z5jjLGysjK2cuVK5u7uzubMmcOePXsmdvy+ffuyHTt2sFevXokdqyYXL17kUmsePnzIXFxcmKqqKrOxseFuZ4urIn5DSklJYaGhoSw2NpaXeJ/D9cQYY9u3b5fo9VQdvhu5jL3/v1CSysrK2Lt377j3e/fuZTNnzmSbNm1ib9++lWjZpHrUkCakGVi/fj3r2rUrk5aWZgMGDGB79+5lb968aexq1YmHhweTlZVlHTp0YMuXL2f37t2TWFlZWVns2LFj3B92PhvSBw8e5I7FxMSErVy5kmukNBXffPMN09LSYmpqamzSpEns7NmzjV2lepGTk2PGxsZs6dKlLDc3t7GrUy+fw/XEGGPa2tpMVVWVTZgwgSUnJ0u8vNevX0skrkAgYP369WM7d+6UWBnk00INaUKakcuXL7OZM2eyVq1aMU1NTfb1119zg5/EVVBQwMLDw9n333/Pnj59ypXHZ4P30aNHLDQ0lHXp0oXJyMiwwYMHs99//52VlJTwEv/JkyfM2dmZexBERe/S+PHj2Zw5c3gpo0LFsVhaWjIZGRk2ZMgQduDAAaHeJnHcunWLLViwgI0ePZo9fPiQMcbY0aNHuYGg4nr37h07cOAAGzZsGJOVlWXm5uZszZo1LD8/n5f4FWWcOHGC/frrr6ywsJAxxtj9+/fZy5cveYn/+PFjtm7dOta1a1cmIyPDBg0axPbt28d7z15UVBSzt7dnurq63B2a9evXs5iYGN7K+Byupz///JO7njp27MhWrVrF8vLyeInPGGOlpaUsJCSE6enpMWlpae73e+HChWzbtm28lHH16lU2a9YspqWlxdTV1dmUKVPYuXPneIld4dSpU8zLy4v16tWL+/81KipKonclSM2oIU1IM1RSUsI2bNjA5Qd27dqV/fbbb/XueU1NTWVaWlqsQ4cOTEZGhvsDtWDBAubt7c1n1TmXL19mM2bMYAoKCqxVq1Zs9uzZ7ObNm2LF9Pb2Zi4uLuzu3btMRUWFO45jx46xTp068VHtam3atInJy8szgUDAtLS0WFBQECsuLq53vMTERC53Uk5OjjuOlStXsi+//JKvanMePnzIli5dyhQUFJisrCzz8PAQO6c8JyeHmZmZMSUlJaFGz6xZs9jUqVP5qLaQiuupZcuWrGXLlmzmzJm8pJD88ssvrFWrVmzZsmVMUVGRO46IiAjWr18/seNXp6lfT/n5+Wzt2rXM0tKSycrKMnd3dxYTE8PKysrEirtkyRJmbGzMdu3aJXQuoqOjWa9evfioOqfii6a7uzuTlZVlFhYWLDQ0lD169EisuBVzYE+aNInJy8tzx/DTTz/xMk81qTtqSBPSjJSUlLB9+/axwYMHM2lpaebg4MC2b9/OQkJCWOvWrdmYMWPqFbd///7su+++Y4wxoQZocnIyb9PffejBgwds1apVrGPHjkxZWZn5+Piw/v37MxkZGbZu3bp6x23dujXXePrwOLKzs3mbHqtCfn4++/HHH5m5uTlTUlJiXl5e7O+//2ZRUVHMwsKCDRw4sN6xe/XqxQ0O+/A4zp8/z8sUXB86f/488/f3ZxoaGqxt27YsODiYTZw4kSkqKnJTf9WHh4cHGzduHHv79q3QMSQkJLAOHTrwVX0h9+/fZ4sWLWLy8vJMWVmZSUtLs969e4vV62pubs5N6/bhcaSlpbGWLVvyUW3G2OdzPVU4d+4cmzJlCpOXl2dGRkZMXV2dGRkZcQ87qY/27duzkydPMsaEjyMjI4NpaGjwUe0q3rx5w9atW8d9sZGXl2fe3t7swYMH9YpnZWXFduzYwRgTPoYrV66w1q1b81ZvIjpqSBPSDHzY26alpcUCAgJYRkaG0DZpaWlMQUGhXvHV1NS4eaQ//M89JyeHycvLi1f5/6+kpIT98ccfbMiQIUxWVpZ169aNbd68mb148YLb5s8//xTrD6KKigrXq/3hcVy8eJG3OVoPHDjAhg4dymRlZVnXrl3ZTz/9xAoKCoS2uXXrFpOVla13GcrKytx82B8ex507d3g5Hw8fPmRr165lFhYWTE5Ojn355Zfsr7/+Erqjcfr0abG+fLRo0YKbR7ryMfA1pzdj76+r33//nbm6ujIZGRnWq1cvFh4ezoqKitidO3eYl5cXMzc3r3d8BQUFLp3jw+O4efNmvX/fPvQ5XE8V8vPz2Zo1a1inTp2YgoICGz16NDtx4gRj7P0g5nnz5rG2bdvWO35N5+Kff/7h/YvyxYsX2bRp05impiZr06YNW7BgAbt9+zY7deoU69+/P+vevXu94ioqKnJzelf+ss/nuSCio3mkCWkGunfvjoEDB2Lz5s0YPnw4ZGVlq2zTrl07jB49ul7x5eXlUVhYWGX5zZs3oaWlVa+Ylenq6qK8vBxjxozBhQsXYGVlVWUbJycnseaD7dOnD6KiorB06VIAgEAgQHl5OVavXg0nJ6d6x/3Q+PHjMXr0aCQnJ6N79+7VbqOnp4cFCxbUuwwNDQ3k5eWhXbt2QsuvXr0KfX39eset0KZNG7Rv3x4TJkyAn59ftee4S5cuNR6fKMrLy1FWVlZl+b1797gn4Ilr5syZ2Lt3Lxhj8Pb2xurVq9G5c2duvbKyMtauXQs9Pb16l9GuXTtcu3YNhoaGQsuPHTsGc3Pzeset8DlcTwDg7u6OuLg4mJqaYvLkyfDx8UGLFi249crKyggICMCaNWvqXUanTp1w+vTpKufijz/+gLW1db3jfmjdunWIiIhAZmYm3NzcEBUVBTc3N25O9Hbt2iEyMhJGRkb1iq+jo4Nbt25V2f/MmTO8zXVP6qixW/KEEMnjawq6mkycOJENHz6clZSUMBUVFXb79m3233//MWtra/bNN9/wUkZUVJTER8GnpaUxbW1tNnjwYCYnJ8e++uorZm5uzlq3bs31uItLnFxVUQUEBLDevXuzvLw8pqqqyrKystiZM2eYsbExNwetOE6dOsVDLWvn6enJJk+ezBhj3DX18uVL5uzszPz8/Hgpw9nZme3Zs6fWGWzevXvHPamuPsLDw5m+vj6Ljo5mysrKbO/evWzZsmXcz+L6HK4nxhibMGECS0lJqXWb8vJysf4vi4mJYerq6mzVqlVMSUmJrVmzhk2aNInJycmx48eP1zvuhzp06MBWrFhRa+rG27dvWWRkZL3ir1ixgnXq1ImdO3eOqaqqstOnT7Ndu3YxLS0ttmnTpvpWm4hBwBg9CoeQ5uLy5cvIyMgA8L53xsbGhpe4L168wFdffYVLly7h5cuX0NPTQ35+Puzs7HD06FEoKyvzUk6Fe/fuAXjfM8q3Fy9eICwsDKmpqSgqKoKNjQ2+/vpr6Orq8lZGWVkZYmJihM6Fh4cHpKWleYlfUlKCr7/+GpGRkSgrK4OMjAzKysowduxYREZG8lbO48ePkZmZCQDo2LEjb3cfgPfn2MXFBYwxZGVlwdbWFllZWWjVqhVOnToFbW1t3sqStN27d2Px4sXIzs4G8L6HeMmSJZg4cSIv8T+X66khnD59GiEhIUK/38HBwRg0aFBjV00kjDGsWLECK1eu5J4qKS8vj7lz53J30kjDooY0Ic3Ao0ePMGrUKCQlJXGpD8+fP4eTkxOio6N5awCdOXMG169f5/5ADRgwgJe4wPtb/cuWLUNoaCj3qHBVVVUEBARgwYIF3K3TT92tW7fg5uaG+/fvo2PHjgCAzMxMGBgY4MiRI2jfvj1vZeXm5uLGjRsoKiqCtbU1TExMeIn76tUrzJgxAzt37uTSL6SlpeHj44OffvoJSkpKvJRTWlqK6OhooWvKy8sLioqKvMQHgOzsbGzYsEGoEfrNN9/weh4qvHr1CkVFRbx+CfgcrqcKSUlJWLt2rdC5+O6779CnTx9ey5G058+f47fffuOOw8LCAhMmTIC6ujpvZZSUlODWrVsoKipCp06doKKiwltsUjfUkCakGRg1ahRu376NqKgoLi8zPT0dvr6+6NChA/bu3dvINfy4+fPn47fffsOSJUvg4OAA4H3DffHixZg8eTKWL1/OSznPnz/HhQsX8OjRI5SXlwut8/HxETu+m5sbGGPYvXs3lwP69OlTjBs3DlJSUjhy5IjYZUja1KlTcfLkSYSFhQmdi1mzZnG5+E1BXFwchg0bBisrK+44kpOTkZqaikOHDmHgwIGNXMOP+xyuJwDYtWsXxo8fjxEjRgidi4MHDyIyMhJjx47lraySkpJqf7/btm0rduxLly7BxcUFioqK6NGjBwDg4sWLeP36NY4fP87bXUDy6aCGNCHNgLq6Ok6ePFllMNKFCxcwaNAgPH/+XOwy4uPjER8fX+0fqO3bt4sdX09PD7/++iuGDRsmtPx///sfpk+fjvv374tdxqFDh+Dl5YWioiKoqalBIBBw6wQCAZ49eyZ2GcrKyjh37hwsLS2FlqempsLBwYHrbRdHWVkZIiMjazwff//9t1jxW7VqhT/++AP9+vUTWp6QkABPT088fvxYrPgVsrKykJCQUO0xBAcHix3f2toaLi4uWLVqldDy77//HsePH8eVK1fELuPhw4eYO3cudy4q/8mtbkBlXXwO1xMAmJubY8qUKfj222+Flq9btw7h4eFc7644srKyMGHCBKSkpAgtZ4xBIBCIfS6A9wOWO3TogPDwcMjIvJ/PobS0FJMmTcLt27dx6tQpseIXFxdj1apVNZ6L27dvixWf1B3N2kFIM1BeXl7tTB2ysrJV/iOujyVLliAkJAS2trbQ1dUVaoDy5dmzZzAzM6uy3MzMjJcGLgAEBARgwoQJWLFiBW/pCZXJy8vj5cuXVZYXFRVBTk6OlzK++eYbREZGYsiQIejcuTPv5+PVq1do3bp1leXa2tpc3qa4wsPDMW3aNLRq1Qo6OjpVvtTw0ZDOyMjA/v37qyyfMGECNmzYIHZ8APDz80Nubi6CgoIk8rvxOVxPwPsGoLu7e5Xlw4YNww8//MBLGX5+fpCRkcHhw4cl9v/UpUuXhBrRACAjI4N58+bB1tZW7PiTJk1CUlISvL29JXYMpI4aY4QjIaRhDRs2jPXt25fdv3+fW3bv3j3m6OjIhg8fLnZ8HR0dFhUVJXac2vTo0YPNnDmzyvIZM2awnj178lKGkpISNy+rpHh7ezMLCwt27tw5Vl5ezsrLy9nZs2dZ586dma+vLy9ltGzZkh05coSXWNVxdnZmI0eOFJpF5dWrV2zkyJGsf//+vJTRtm1btmrVKl5i1aRNmzZs//79VZbv27ePGRgY8FKGiooKu3r1Ki+xqvM5XE+MvX9Yyq+//lpl+ebNm3l7AI+SklKV+fP5pq2tzeLi4qosP3bsGNPW1hY7vrq6Ojtz5ozYcQh/qEeakGYgLCwMw4YNg5GREQwMDAAAd+/eRefOnbFz506x45eUlMDe3l7sOLVZvXo1hgwZgpMnT8LOzg4AcPbsWdy9exdHjx7lpQwXFxdcunRJovOxbtq0Cb6+vrCzs+PuEpSWlmLYsGG89YLKycmhQ4cOvMSqzsaNG+Hi4oI2bdqga9euAN6nEsjLy+P48eO8lFFQUICRI0fyEqsmkydPxpQpU3D79m3u+k1OTsaPP/6IOXPm8FKGgYFBlXQOPn0O1xPw/m7QrFmzcO3aNaFzERkZiY0bN/JSRqdOnfDkyRNeYtVk1KhRmDhxItauXSt0HN999x3GjBkjdnxNTU2h+bVJ46McaUKaCcYYTp48iX///RfA+5xEvmbVCAwMhIqKCoKCgniJV5MHDx7g559/FjqG6dOni/XAjNjYWO7nx48fIyQkBOPHj4elpWWVdJjK+dniuHXrFpf3aW5uzmtDJTQ0FLdv30ZYWJjEbv2+evUKu3fvFjoXfM6oMXHiRHTv3h3+/v68xKsOYwwbNmxAaGgoHjx4AOB9Lv53332Hb775hpcyjh8/jtDQUGzZsqXeD+EQRVO/ngDg4MGDCA0NFTqO7777Dh4eHvWO+eGDoi5duoSFCxdixYoV1f5+q6mp1bucCiUlJfjuu+/w66+/orS0FMD7FLpp06Zh1apVkJeXFyv+rl278L///Q87duyQWPoZqRtqSBPSjP37778YNmwYbt68Wed9P+yxKy8vx44dO9ClSxd06dKlyh+odevWiV3Xmty7dw8hISHYunVrvfYXddo8vgYj1eT69euwtbVFSUlJvfYfMWKE0Pu///4bLVq0gIWFRZXz8eeff9a7nrW5ffs2/P39690rvWnTJu7n4uJirFu3DkOGDKm20TNr1iyx6lpZRZ6xqqoqXr16JdQzWleamppCDc7i4mKUlpZCSUmpynHwld9f2edwPQHvZ9E5evRovWftkJKSEjoX7P8PLPwQ43GwYYVXr15x84a3b98ecnJyePToUb2+9FtbWwvV+datW2CMwcjIqMq54GOALKkbSu0gpBl7+/Yt9599XV29elXofcUju2/cuCG0XNKDYZ4+fYrffvut3g1pPgZb8oExJtYf8spz1H7xxRfiVqnOXr58ifj4+Hrvv379eqH3KioqSEpKQlJSktBygUDAe0P6w8eOZ2VloU+fPvU+H3ylVIjjc7ieAOC///6Dt7d3vRvSCQkJPNdINEpKSkIzqaSmpsLGxqZe52T48OE81ozwjRrShJB6aaw/UKR6ERERjV0Fsd25c6exq8ALX1/fxq6C2D6H6wkAHB0dG7sKYlu0aFFjV4HUomk8CowQ8kkqKyvD9evX8fr16yrrXr9+jevXr38yPb61+fvvv9GpUyehfMoKL168gIWFhdjzvzaEN2/eIDY2ttrp0AoLCxEbG4u3b982Qs3qprCwsNrrpry8vNpz9Cl68OAB5s6dW+M19d133+Hhw4eNUDPRfS7XU1ZWFsaMGVPjuRg7duwnP/9yQUEBfvrppxqPoaZ1RPKoIU0IqbedO3diwoQJ1c5XKysriwkTJmDPnj2NULO62bBhAyZPnlztYCN1dXVMnTq1StpBXRUWFtb6qq6xUldbtmzBxo0bhdIUKqipqWHTpk0IDw8XuxxJOnjwIGxtbfHmzZsq616/fo3u3bvj0KFDjVCzulm3bh0KCwtrvKZevnwp1tgBup5Et2bNGhgYGNR4LgwMDLBmzZpGqJnowsLCcOrUqRqP4fTp0/jpp58aoWaEUjsI+YxVHvRUWcWo8vr67bffMHfuXEhLS1dZV/EQgrCwMIwbN67eZVQe9FQZH09lTE1NxY8//ljj+kGDBmHt2rVilaGhoVHruahuEFRd7d69u9aZU2bPno2QkBDMmDGjXvErD3qqjI+HsWzevBnz5s2rdkYCZWVlBAYGIiwsrNqHd4jqw5laqsNHismxY8fw66+/1rjex8cHkydPrvW6q83ncD0BwgNMq8PHE0uTkpKwa9euGtd7enqK/Qjy69ev17o+MzNTrPgHDhxAaGhojeunTp2KuXPnYsGCBWKVQ+qOGtKEfMYkPegpMzMTvXr1qnF99+7dxX60b+VBT9Wt9/HxEauMhw8fVvvkxwoyMjJiP/a6IXLKs7KyuHmdq9OlSxdkZWXVO35DDHq6ceMGfvnllxrX9+3bFwsXLhSrDFGOQ9xG6J07d9C2bdsa17dp0wY5OTn1jv85XE9A1QGm1antcxRFbm4utLW1a1zfqlUr3L17V6wyrKysIBAIqp0zvGK5ONdUdnY2TExMalxvYmJS74HjRDzUkCbkMybpQU/FxcW15uW9fPlS7F7Khhj0pK+vjxs3btQ49+7169ehq6srVhkNMeiptLQUjx8/rrHh8fjxY7HuQjTEoKeCgoJa6/ju3TsUFBSIVUZD5O0rKioiJyenxnORk5Mj1pzbn8P1BDTMAFN1dXVkZ2fD0NCw2vW3bt0Sew5pSR+HtLQ0Hjx4UOO5ePDggchTeRJ+0adOSDMzffp03p7uZWJigpSUlBrXnzlzptZelPrau3cviouLeYvn5uaGoKCgGvNyFy1ahKFDh/JWXoUhQ4YgLy+Pt3gWFhY4efJkjeuPHz8OCwsL3soDgFWrVvGSXlPByMgIly5dqnH9pUuXamwQfUp69uxZ61NDo6Ki0KNHD17L/ByuJ+D93PB8ftnp27dvrfnDmzZtQp8+fcQqw9DQUKRXfVlbWyMmJqbG9QcPHoS1tXW94xMxNPxTyQkhjUlVVZVlZ2fzEuvHH39kLVu2ZKmpqVXWXbt2jbVs2ZL9+OOPvJT1IT6PgTHG8vPzmZ6eHjMwMGA//vgji4mJYTExMWzVqlXMwMCA6enpsfz8fN7Kq6CiosLrcWzZsoUpKyuzQ4cOVVkXGxvLlJWV2ZYtW3grjzH+z8UPP/zA2rZtW+3nnZeXx9q2bct++OEH3sqrwPdx/P3330xaWpoFBAQIHUt+fj6bM2cOk5aWZvHx8byVx9jncT0xxv+5uHLlCpOXl2dffvklO3/+PHv+/Dl7/vw5O3fuHBsxYgSTl5dnly9f5q28Cp07d2a5ubm8xPrjjz+YjIwM++mnn1hpaSm3vLS0lG3atInJysqy33//nZeySN1QQ5qQZobPP7YlJSWsX79+TEZGhg0ePJjNnj2bzZ49mw0ePJjJyMgwR0dHVlJSwktZH+K7wcAYYzk5OczV1ZVJSUkxgUDABAIBk5KSYq6uruz27du8llVBEsfh5eXFBAIBMzc3Z8OHD2fDhw9nZmZmTEpKio0ePZrXshjj/xgKCwuZhYUFU1VVZdOmTWMbNmxgGzZsYP7+/kxVVZV16tSJFRYW8lZeBUmci19//ZXJy8szKSkppqGhwTQ1NZmUlBSTl5dnv/zyC69lMfZ5XE+MSeY4Dh06xLS0tJiUlJTQS0tLi/3vf//jtawKfB/HDz/8wAQCAVNTU2NWVlbMysqKqampMSkpKRYYGMhbOaRu6BHhhDQzqqqqSE1NhbGxMS/x3r17h/Xr12PPnj3IysoCYwympqYYO3YsZs+eXe3UeOLi+xg+VFBQwD2C18TEBJqamryXUaFz587466+/YGBgwGvc/fv3V3s+PD09eS0HkMy5ePHiBebPn499+/Zx+dAaGhoYPXo0li9fLpFzIqlr6v79+9i/fz93TZmamuKrr75CmzZteC0H+DyuJ0By5+L169c4duyY0LkYNGhQtTPE8EESx3HhwgXs3r1b6BjGjh3Le5oQER01pAkhTc6ZM2dga2sLBQUFiZWxd+9eDBs2DMrKyhIr43Nw9+5d6OnpVTsForgYY3jy5AkYY9DS0pLo4+anTZuGpUuXolWrVhIrg4hm5cqVmDZtGjQ0NCRWxr1796CnpyfRAXpubm747bffxB6oTD5t1JAmpJnIzs5GREQEbt++jQ0bNkBbWxt//fUX2rZty+uAoenTpyMkJKTJN0jU1NRw7do1ifR6nz59Glu2bMHt27fx+++/Q19fHzt37kS7du3Qu3dvXssaMmQItm3b1qT/mK9atQr+/v4SbVi9efNGol/MAP6uqY/NWfyhLl26iFVWZZK+nkpKSnDnzh20b98eMjKSm1hMkr/fDcXS0hJHjx7l/Q4EqRua/o6QZiApKQmurq5wcHDAqVOnsGzZMmhrayM1NRW//fYb/vjjD97K2rVrF+bOnctLQ/pjDwD50JUrV8Qu70OS6mM4cOAAvL294eXlhStXrnCPWH7x4gVWrFiBo0eP8lreqVOnqn2Ee1197OE+H3r27JnY5X1oxYoV8PT05L0hXV5ejuXLl+PXX3/Fw4cPcfPmTRgbGyMoKAhGRkaYOHEir+XxdU3VNmcxIDxvcVlZGS9lVuDreqrs1atXmDlzJnbs2AEA3LmYOXMm9PX18f333/NaHp+/3x97wM+Hhg0bxlu5OTk5ePfuHW/xSP1QQ5qQZuD777/HsmXLMGfOHKHH/To7OyMsLIzXsvj8A9UQDwBpaMuWLcOvv/4KHx8fREdHc8sdHBywbNmyRqxZ7ST9cJ/aSOpLzbJly7Bjxw6sXr0akydP5pZ37twZGzZs4L0hzZeGmHu5oc2fPx+pqalITEzE4MGDueUDBgzA4sWLeW9I80nU/6ck8cWGND5qSBPSDKSlpWHPnj1Vlmtra/M2p7QkNMQDQGry119/QU9Pj/e4mZmZ6Nu3b5Xl6urqvM7HXMHQ0LDWpzaKStIP92kMUVFR2Lp1K/r37w9/f39uedeuXfHvv//yXt64cePEfvAHgEadR5uv66mymJgY7Nu3D7169RK682FhYSGRJ/b98MMPaNGiBS+xGuIBP9Xp06ePWA/1IfyghjQhzYCGhgby8vLQrl07oeVXr16Fvr4+r2W9fPmS13iNhe9c5Qo6Ojq4desWjIyMhJafOXNGIvmaN27c4CVObU+wrIyPxuKH0tPTJfKl5v79+9U+zbK8vFwit8zXr1/PSx52Y6USAPxdT5U9fvy42sd4FxcX8z7ItKSkBCNGjICKigqvcRsa32lgpH6oIU1IMzB69GgEBgbi999/h0AgQHl5OZKTkzF37lz4+PjwUoYkBjM2RF5uQ+dhT548Gd988w22b98OgUCABw8e4OzZs5g7dy6CgoLEjl+B7wGNGhoaH/2cJJWXK6nBVJ06dcLp06er9PD+8ccfvD0lThJ52A2RStDQAxptbW1x5MgRzJw5EwC4a23btm2ws7MTOz4guTzsTZs2ibztrFmz6hy/Mb84kY+jhjQhzcCKFSvw9ddfw8DAAGVlZejUqRPKysowduxYLFy4UOz4khrM2BB5uQ2dh/3999+jvLwc/fv3x6tXr9C3b1/Iy8tj7ty5XCNCXJIY0JiQkMBL3WrT0AMa79bKhwAAcz9JREFUg4OD4evri/v376O8vBx//vknMjMzERUVhcOHD4sdH5BMHnZDpBI09IDGFStWwNXVFenp6SgtLcXGjRuRnp6OlJQUJCUliR0fkFwe9vr160XaTiAQ1KshTTnYnzaa/o6QZiQ3Nxc3btxAUVERrK2tYWJiwktcOzs7jBw5khvMWPEQggsXLmDEiBG4d+8eL+V8TkpKSnDr1i0UFRWhU6dOvN5mtra2xrfffgsfHx+h83H16lW4uroiPz+ft7L4VNFTKAq+crZPnz6NkJAQpKamoqioCDY2NggODsagQYN4id+hQwds2bIF/fv3FzoX//77L+zs7LgHznxq/vvvP5G35StnOzs7G6tWrRI6F4GBgbC0tOQlvqGhIZeH/eG5uHXrFmxsbOqUvkRIBeqRJqQZOHPmDHr37o22bduibdu2vMdviMGMubm5ta6XxHFJwq5duzBixAgoKSmhU6dOEilD0gMaT506Vev66soWRWMMaOzTpw9OnDghsfiSzsMOCQmpdX1wcHC94jbGgMb27dsjPDxcYvEbMg+bNB/UkCakGXB2doa+vj7GjBmDcePG8d6Aa4jBjEZGRrX+savvLc2GTif49ttv4e/vj2HDhmHcuHFwcXHh/amAkh7Q2K9fvyrLPvwM63suGnpA46RJkzBu3Lhqj4cvks7DPnjwoND7d+/e4c6dO5CRkUH79u3r3ZBu6LzcAQMGYNy4cRgxYgTvg1UrNEQe9oQJE2pdv3379jrHlHQONhEPNaQJaQYePHiA6Oho7N27F6tWrUKXLl3g5eWFMWPGoE2bNmLHb4jBjFevXhV6/+7dO1y9ehXr1q3D8uXL6x23oedHzsvLw7Fjx7B37154enpCSUkJI0eOhJeXF+zt7XkpQ9IDGiunI1Sci6CgILHORUMPaHz8+DEGDx4MLS0tjB49Gl5eXrCyshI77ocknYdd+fcCeP+FxM/PD1988UW94zZ0Xq6FhQXmz5+P6dOnY8iQIRg3bhzc3Nx4nWqvIfKwq/vduHHjBp4/fw5nZ+d6xZR0DjYREyOENCu3b99my5YtYxYWFkxaWpo5OTmJHfPt27ds0qRJTEZGhgkEAiYrK8ukpKTYuHHjWGlpKQ+1rtnhw4eZo6OjRMuQlOLiYrZr1y7m5ubG5OTkmLGxMS9xy8vL2bJly5iysjITCARMIBAwBQUFtnDhQl7i1yQxMZHZ2NiItb+oL748e/aMbdmyhTk6OjIpKSnWqVMntnz5cnbnzh3eyjh16hQbMGAA09LSYoqKiszBwYHFxcXxFr86169fZ4aGhhItg29lZWUsLi6O+fr6MjU1NaapqckmT57M6/m+desWmzRpEuvevTszNzdnXl5e7Pr167zFr05ZWRmbMmUK+/HHHyVaDmkcNNiQkGaorKwMf/31F4KCgnD9+nXeRnpLajBjbW7duoWuXbuiuLhY7FiNkYf95MkTREdH49dff0VGRgavo+4lOaCxOv/++y9sbW1RVFQk0XIk5d69e9i7dy+2b9+OrKwslJaWNnaV6u3MmTNwd3f/ZAczfsybN29w6NAhLF++HGlpaU1+NorMzEz069cPeXl5jV0VwjNK7SCkGUlOTsbu3bvxxx9/4M2bN/Dw8MDKlSvFjivpwYxA1fxZxhjy8vKwePFi3hrsksrDruzVq1c4ePAgdu/ejfj4eBgYGGDMmDH1niawMkkPaKw8x3DFuVi1ahVvqRGSGtBYk3fv3uHSpUs4f/48cnJy0Lp1a17iSjoPu3L+bMW52LlzJ1xdXXkpQ1IDGmuSn5+P6Oho7Nq1C9evX0ePHj14idsQedg1yc7O5uWLmSRysIl4qEeakGZg/vz5iI6OxoMHDzBw4EB4eXnBw8MDSkpKvMSXk5OT6GBGAJCSkqrSyGWMwcDAANHR0bwMFkpNTRV6XzkPe8SIEWKXMXr0aBw+fBhKSkrw9PSEl5cXbwOdKmhpaeH169cSG9BYcS4q//no1asXtm/fDjMzM17KqIyPAY2VJSQkYM+ePThw4ADKy8sxYsQIeHl5wdnZmZeZHDw8PBAXFyexPOzKA3ylpKSgpaUFZ2dnzJ8/H6qqqmKXUXlQZOUBjXw8qKiwsBAHDhzAnj17kJiYCGNjY3h5ecHLywvt27cXOz4AfPPNN9i/fz9evHghsTzsOXPmCL2v+GJz5MgR+Pr6IiwsTKz4lfPeK+dg//nnn2LFJ3VHDWlCmgEHBwd4eXnB09MTrVq14j1+RXrC3r17cfbsWd4HMwKoMhioosHQoUMHyMhI9ubakSNHsGbNGiQmJoodq6JxIInZOiqUlpZyAxr/97//8T6gsfIcwxXngo/HX1d48eKF0PvKAxr79+8vdhn6+vp49uwZBg8eDC8vL7i7u0NeXl7suJUVFBTg999/x549e3D69GmYmZnBy8sLY8eOrTKzSlPx4YBGb29vseMpKipCU1MTo0aNgpeXF2xtbXmoZVXl5eU4efIk9uzZg4MHD0JaWhpfffUVvLy84OjoKHZ8JycnofcffrGZMGGCRP6vKi8vx7Rp09C+fXvMmzeP9/ikdtSQJoTw6s6dO9izZw/27t2Lf//9F3379sXff//d2NUSC5952A2tIo1kz549OHnyJNq0aYPs7OzGrla9JSUlYc6cObh8+bLYscLDwzFy5EhoaGiIXzERSSIPmzGGp0+fQiAQoGXLljzUUjRpaWlwd3dHTk6O2LFOnDiB/v37V3snQlI+pzxsysFuPJQjTchnKjY2Fq6urpCVlf3onLB8zANboV27dvj+++/RtWtXBAUF8Tat1N9//40///wTOTk5EAgEaNeuHb766itec2UllYe9adMmTJkyBQoKCh+dE5bv6auUlJTg4uKCgoIC/Pfff8jIyBArXnl5OSIjI6s9F97e3hJ/sEXr1q2RmZnJS6wPH9ndEPjOw87Pz8e8efMQGxuLly9fAng/v/YXX3yBlStX8pbnXZMXL15UuXNQXwMHDuQljqgklYcNvL9DV/G7YWRk1CBfbvjKwSZ1Rz3ShHympKSkkJ+fD21t7Vp7efiaBxaofjCjl5cXBg8eLFZcf39/bN26FZqamjA1NQVjDFlZWXj+/DmmT5+On376iZf6SyoPu127drh06RJatmxZJaf1QwKBALdv365XGZXVNKDRy8ur3jnMjDG4u7vj6NGj6Nq1K8zMzMAYQ0ZGBtLS0jBs2DDExMTwUv/aBjSWlpbizJkz9Yo7YsQIREZGQk1N7aM573zlm0oiD7uwsBBWVlYoKiriziljDOnp6di7dy80NTVx5coVXmZqqW1Ao6OjY7VPNRWFjY0N4uPjoampCWtr61o/i6aQh/3PP/9g2rRpSE5OFlru6OiIzZs3o2PHjmKXIekcbFJ31CNNyGeqvLy82p8lofJgxo0bN/I2mPHgwYOIiIjA9u3b4evry/2xregZnTZtGgYOHMhLr3pCQoLQe77ysO/cuVPtz5JSeUBjUFAQLwMaIyMjcerUKcTHx1fJBf37778xfPhwREVF8fIQHisrq1oHNNaXuro6dw2pqalJvAf9wzzsrVu38paHvXHjRkhLS+Off/6BlpaW0LqFCxfCwcEBmzZtwg8//CB2WZUfCFLxe+Hr64v58+fXO66Hhwf3WXh4eDTI3YyKPOyVK1fymoedn58PR0dHaGlpYd26dUJfbMLDw9GnTx/cuHGj2keU10XlB/BUnIvQ0NCPzuhBJKRhpqsmhDSmHTt2sDdv3lRZ/vbtW7Zjxw6x49vb27Off/6ZPX78WOxYlbm7u7Pvv/++xvXz5s1jw4YN471cSVmyZAkrLi6usvzVq1dsyZIlvJQxduxYduTIEd4fhjNw4EC2cuXKGtcvX76cDRo0iJeycnJyhF65ubns9evXvMRuSFu3bmUFBQW8x+3Zsyfbvn17jet/++031qtXL97LbcqOHz/OysrKJBJ73rx5zMbGptpr9NWrV8zGxqbW/8dI00WpHYQ0A9LS0sjLy6vSG/L06VNoa2t/0oNs2rRpgz///LPGHMbz58/jyy+/xL1798QuqyHysJvyudDR0cGxY8dqnL7t6tWrcHV1RX5+fsNWrJ4qpgurPNiwsLAQw4cP/6QHybZo0QJnz56tMV3g33//hb29PZ49e8ZLeUzCAxqNjY1x8eLFKrGfP38OGxsb3lKeJMXGxgbff/89PD09q10fHR2N1atX85Ki0hg52KRmlNpBSDPAGKv2tum9e/egrq5er5gNNZjxyZMntU6h16ZNGzx9+rTe8StUl4edkpKCn3/+mdc87JrORWpqKlq0aFHvuA0xoPHZs2e1DmBr3bo1L0/Sa6gBjYmJiSgpKamy/M2bNzh9+nS94zZEHnZhYWGts41oaGhUGTxbHw01oDEnJ6faL5Fv374V60tyQ+Vh3759GzY2NjWut7W1FfvLQEPkYJO6o4Y0IZ+xij8cAoEA/fv3F8rzLSsrw507d+o9EHD48OHcYMbhw4fXuN3/a+++w6K6tr+Bf2dAmsCACFZEjTTFGjXGChjBruiNiYjUEPWqYInBq1GvNWLX4I0K9gZeO+aqiQXsBaUZg6AiaIwoNhQQGdnvH74zv4GZQZhTiLA+zzNP4JzJXgcPw+xzZq21uRYzvn37ttwFE/T19TVOhipDjDxsS0tL5blwcHBQW1zk9evXGDt2rM7jr1y5EqNGjYKRkZFaTqsqiUSi80T63bt35eaK6+npce4cwBjD4MGDlQWNrVu3VhY0+vv7Y//+/ZwLGlULGW/evFnqDvq7d+9w7NgxNGrUSOfxxcjDZox9sIiY6wfOeXl56Nq1K16/fo2AgAC1gsZz585xLmhUvQg/fvx4qQv7d+/e4eTJk+UW6H6IWHnYr169Kne1RDMzM7x+/Vrn8cXKwSaVR6kdhFRjc+fOVf536tSppd7wDAwM0LRpUwwfPhwGBgZVdYgfJJVK8e2332otXCwoKEBkZCSnyfrgwYPRqlUrrculh4WFIS0tDYcOHdI5xtatW8EYQ2BgIFatWlVqwqA4F3yvcMg3qVSKfv36aS2WKyoqwrFjxzidi82bNyM0NBSHDh3SWtAYERHBqaBRtTuLprdAY2Nj/PTTT3/r4i2pVFpqwl4WYwx5eXmczsX8+fOxbds2XLhwQa2g8fHjx+jWrRsCAgI4FTQqLgY0Tfxr1aqFpk2bYvny5Rg4cKDOMcSgp6eH9PR0tX8nhZycHDg5Oel8PsLCwnDixAmcP39ebeGjwsJCdO/eHR4eHlr/hhHh0ESakBpg69at+Oqrr3hdeU7Vtm3b8NVXX6lNsN6+fYvo6GhOkx5XV9cK3UUq23GjMsTMw46Pj0fXrl15XZa4rHnz5uG7775Tu/goLCzE0qVLMXv2bJ3GDQgIqNDzNm/erNP4AODh4QF3d3dMnz5d4/5FixYhPj4ex48f1zlGVlYWGGNo3rw5rly5UmryY2BgABsbG95WnRQqD3vr1q0Vep6fn59O4wPvO6SMGTNG63nftGkTIiMjcfHiRZ1jKDRr1gxXr14VZOVVBSHzsDW1zlSlSOnSdSItZg42qRyaSBNCOPuYC+gAwMjICHfv3kXDhg017v/zzz/RokULFBYW8hr3zZs3amkp5X08XFEf8/mobgWNqv3cVT1+/BiNGjVCcXFxFR3Zh4ld0Cg0beciJycHtra2nFLEKrrwlK7LkFtYWCAhIQEtWrTQuP/27dvo2LEjXrx4odP4RHeUI01IDfDu3TusXLkSe/bsQXZ2ttobBtc3QiGKGctz/vx5dOzYkZd+vIA4edgKBQUF+P7777Fnzx6NRZJ8THKFKmjUZPfu3Rg8eDBq167Ny3hiFTSqunnzpsbXBZeceKHzsDX55z//iXnz5vF2V1esgkaF/Px8xMfHazwXXFb8FDoPG9B9glxRQudgE93RRJqQGmDu3LmIiorC1KlT8cMPP2DmzJm4d+8eDh48qPPH/ICwxYzl6devH5KSktC8eXPexpw1a1a5edh8mTZtGk6fPo2ff/4Zo0ePxtq1a/Hnn39i/fr1WLx4MaexhS5o1GTMmDH47LPPeDsXYhQ0Kty9exdeXl5ITU0tlaOr+HfjclGjWFBGIpHA3d1dbb8iD5tPO3bswHfffcfbRFqMgkaFxMRE9O/fHwUFBcjPz0edOnWQm5sLExMT2NjYcJpIK4qhJRKJWqqLah423wYMGICoqCg0aNCAl/FevXqlNT0vLy+Pt3NBKocm0oTUADt37kRkZCQGDBiAf//73xg5ciQ++eQTtGnTBpcuXdL5TUrxBpWUlARPT0+txYx84/sNo2fPnrh169YHn8OH2NhYbNu2Da6urggICECPHj3QokUL2NnZYefOnRg1apTOY69atUpZ0Dh37lxRChr5PheMMfj7+5db0MiX0NBQNGvWTHlH8sqVK3j69CmmTp2KZcuWcRo7MzNTtDxsBSHORdkLMqHiTZ48GYMGDcK6desgk8lw6dIl1KpVCz4+PggNDeU0tmJlVzHysFWdOXOGt3Qwxbkob7/QK0MSzWgiTUgN8OjRI7Ru3RoAYGpqipcvXwIABg4ciFmzZuk87pw5cwAATZs2FbSYUWhxcXGixXr27Jny7q25ubkyraZ79+4YN24cp7EVd9uaNWsmeEGjUCpSHMfHEuQAcPHiRZw6dQp169aFVCqFVCpF9+7d8eOPPyIkJERtOebKsLOzA/B/k7iPEZei0cpKSkrC+vXrIZVKoaenh6KiIjRv3hxLliyBn5/fB/txV0RmZiYPR1o1uBRTE2HRRJqQGqBx48b466+/0KRJE3zyySf49ddf0aFDB1y9epWXPGMunQF0sX79et4WgtCG7zxshebNmyMzMxNNmjSBk5MT9uzZg86dOyM2NrbcfNTKUM3XFKqgUeHo0aO85vqKOXl79+4dzMzMAAB169bFw4cP4ejoCDs7uw9+QlFZQuRhl6VYMIUvYr6ua9WqpUwjsbGxQXZ2NpydnSGTyXD//n3e4giVh62JnZ0dbxezQudgEw6EW32cEPJ3ERYWxhYuXMgYYyw6Oprp6+uzFi1aMAMDAxYWFsZ5fLlczpYuXco6derE6tWrxywtLUs9PkZmZmbszp07vI+7YsUKtnr1asYYY7/99hszMjJihoaGTCqVslWrVvESIz8/n40fP55ZW1szqVSq9uDq7t27LD09XW17eno6y8zM5Dx+Wbt27WKvX7/mfdzu3buzAwcOMMYYGzlyJOvbty87d+4c8/X1Za1ateIlxp07d1ibNm2YRCJhUqmUSSQS5dd8nIuXL19qfOTl5bGioiIefoLSxo0bx548ecL7uH369GE7d+5kjDH2zTffsM6dO7MdO3YwT09P1rlzZ15iXL9+ndWvX5+Zm5szPT09Zm1tzSQSCatduzZr1qwZLzHE1L9/f/bw4cOqPowajybShNRAFy5cYMuXL2eHDx/mZbxZs2axBg0asGXLljEjIyM2f/58FhQUxKysrJSTRq6GDh3KvLy81B7Dhg1j3t7ebPbs2SwtLY2XWIwxZmpqKshEuqx79+6xffv2seTkZN7G/Oc//8mcnZ3Z3r17mbGxMdu0aRObP38+a9y4MduxYwfn8Xv27Mm2bNmitn379u2sV69enMcvS6iLmmPHjrF9+/YxxhjLyMhgjo6OTCKRsLp167KTJ0/yEmPgwIFsyJAh7MmTJ8zU1JTdvHmTnT17lnXu3JmdOXOG8/iqk3JNjyZNmrDZs2ezd+/e8fDTCHcurl69yk6dOsUYYywnJ4d5enoyMzMz1qFDB5aUlMRLjF69erHg4GD27t075es7Ozub9ezZU/l7wNWmTZvYnj171Lbv2bNH42uGC7H+RpHy0USaEMJZ8+bN2ZEjRxhj7/+43759mzHG2OrVq9nIkSN5ieHn58dkMhmzs7Njw4YNY8OGDWNNmzZlFhYWbMSIEczR0ZEZGhqyc+fO8RJPjDepwsJCQca1tbVlp0+fZoy9n/hkZGQwxhjbtm0b69evH+fxVcdUlZGRwWQyGefxyxJzwvD06VNWUlLC23hWVlbKiyRzc3Plxd7JkydZu3btOI+/detW1rhxY/bDDz+ww4cPs8OHD7MffviB2drasvXr17MFCxYwCwsL5SdSXH3MkzeZTKb895fJZOzmzZuMMcYuXbrEHB0deYlhb2+vvCBQFRcXxxwcHHiJofAxn4vqRHtfG0JItbJ9+3Z069YNDRs2RFZWFoD3XR64LHutUF4x4y+//MJ5fOD9Qh3e3t64e/cu9u3bh3379uHOnTvw8fHBJ598gj/++AN+fn4ICwvjJZ5Qedjv3r3D/Pnz0ahRI5iamipXU5s1axY2btzIS4zyChrPnDnDeXyJRKIxH/fly5d/68VetLl9+zaOHz+OwsJC3vtsa8rDBsBbHvbWrVuxfPlyzJ8/H4MGDcKgQYMwf/58LFu2DDExMZg5cybWrFmDbdu2cY4lNLlcjhMnTmD9+vXK36+HDx/y1h9ZUx42AF7zsLOzszX2pLazs1PG4wufOdhEdzSRJqQG+PnnnzFlyhT0798fL168UE52LCwssGrVKs7jK4oZASiLGQHwVswIABs3bsSkSZNK9bWVSqWYOHEiNmzYAIlEggkTJuDGjRu8xPP29uZtkRFVCxcuxJYtW7BkyRIYGBgot7u4uCAqKoqXGIqCRgDKgkYAvBU09uzZEz/++GOpSfO7d+/w448/onv37pzHL4vvgkaFp0+fonfv3nBwcED//v2Vv8NBQUGYOnUqLzFcXFyQnJwMAPjss8+wZMkSnD9/HvPmzeOl9/aFCxfQvn17te3t27dXLt3dvXt33iZxr1694rV/u0JWVhZat26NIUOGYPz48Xjy5AkAIDw8HN999x0vMdq3b4+rV68CeF+8N3v2bOzcuROTJk2Ci4sLLzFsbGxKLcajkJycrLY0OVc3btyAra0tr2MSHVT1LXFCiPCcnZ2VRVWqHwempqYyKysrzuMLXczIGGMWFhbs0KFDatsPHTrELCwsGGPvi90UX+tCjDzsTz75hJ04cYIxVvpc/PHHH5yOXZXQBY2///47s7KyYp988gnz9/dn/v7+7JNPPmHW1tYsNTWV8/iMiVPQOHr0aObp6cnu379f6lwcO3aMtWzZkpcYQudh29vba3yNhYWFKVMJrl69yho2bKhzDDEKGocMGcJ8fHxYUVFRqXNx+vRp1qJFC15iiJGH/f333zM7Ozt26tQpJpfLmVwuZydPnmR2dnZs6tSpnMcXMwebVAxNpAmpAYyMjNi9e/cYY6Unb+np6czIyIj3eHwXMzLG2MSJE1ndunXZihUr2NmzZ9nZs2fZihUrWN26dVlISAhjjLHIyEjWrVs3nWOIkYet7Vz8/vvvrHbt2jqPWx4hChr//PNP9q9//Yv179+fDR8+nM2dO5c9ffqUt/HFKGisV6+ecgKlei7u3Lkj2LlgjN887EOHDjEDAwPWpk0bFhQUxIKCgljbtm2ZoaEhi42NZYwx9p///IdNnjxZ5xhiFDTWqVNHeZGqei4yMzOZsbGxzuOKraioiI0YMYJJJBJWq1YtVqtWLaanp8cCAgJ4uegQMwebVAz1kSakBmjWrBmSkpKUi0QoHDt2DM7OzrzH+/zzz3lfQW/lypWoV68elixZgpycHABAvXr1MHnyZGVetIeHB6clyRV52BEREcoUkpKSEoSGhsLMzAzR0dEYO3YswsLCcO7cOZ1itGzZEmfPnlU7F3v37tX4ET1Xb968gZ2dnVo8rho2bIhFixbxOqaqxMREdOvWTW17ly5dMGHCBF5i5Ofna1wW/tmzZ7z3D799+zbu3LmDnj17ok6dOrytCjh48GCkpaVh/fr1SE9PBwD069cPBw8eRNOmTQGA80I/W7ZswcyZM+Hv74/OnTsDAK5cuYKtW7fihx9+wJMnT7Bs2TIYGhpixowZOsUoKSnRmF//4MEDZY45H+RyOeLi4nDnzh14e3vDzMwMDx8+hLm5eamVWXVlYGCAmJgYzJ8/H8nJyTA2Nkbr1q15e/2JmYNNKqiqZ/KEEOFFRkayRo0asejoaFa7dm22e/dutmDBAuXXfNi2bRvr2rUra9CggfKO68qVK9nBgwd5GV+V4qNlvtWtW5fdunVLbfutW7eUKTApKSmcOlMcPHiQyWQytnjxYmZiYsKWLl3KvvnmG2ZgYMB+/fVXncdVJZfL2bx581jDhg2Znp6e8u7eDz/8wKKionQaMzk5WXnHMTk5udwHH8zNzdn169fVtickJDBTU1NeYvTr14/98MMPjLH3d0Hv3r3L3r17x7788ks2fPhwXmLk5uYyd3d35V1dxbkICAhgU6ZM4SWG0Nzd3VlMTIza9piYGObu7s4Ye//659L5YsSIESw4OJgx9n/n4tWrV8zd3Z35+/vrPK6qe/fuMScnJ2ZiYlLqdRESEsLGjBnDSwyh2draakxxO3jwIGvUqFEVHBGhiTQhNcSOHTtYixYtlAtCNGrUSOdJVVn/+c9/WN26ddmCBQuYsbGx8g1q8+bNzNXVlZcYYhAjD5sxxs6cOcO++OILZm1tzYyNjVm3bt3Y8ePHOY2pau7cuax58+Zsx44dpc5HdHQ069Kli05jSiQSlpOTo/xadXER1Qcfi4ww9r7/8pdffsnkcrlym1wuZ8OHD2d9+/blJUZqaiqzsbFhffv2ZQYGBuwf//gHc3Z2ZvXq1VO2cORKjDzs58+fs+PHj7Pt27ezrVu3lnrwwcjISGu+uiLt4u7du5xSMLKzs1nLli2Zs7Mz09fXZ126dGFWVlbM0dFR+XvHlVB52JMnT1YuGDR58uRyH1wJnYNNKo9SOwip5uRyOXbt2gVPT0+MGjUKBQUFeP36NWxsbHiL8dNPPyEyMhJDhw7F4sWLlds7duzIW8V9Tk4OvvvuO5w8eRKPHz9W+2icj7Zro0ePRlBQEGbMmIFOnToBeN95ZNGiRfD19QUAxMfHo1WrVjqNL5fLsWjRIgQGBuK3337jfLzabNu2DRs2bEDv3r0xduxY5fa2bdsiLS1NpzEzMzNhbW2t/Fpo4eHh6NmzJxwdHdGjRw8AwNmzZ5GXl4dTp07xEsPFxQXp6emIiIiAmZkZXr9+jWHDhmH8+PFo0KABLzF+/fVXHD9+HI0bNy613d7eXtmGkovY2FiMGjUKr1+/hrm5OSQSiXKfRCJR/t5yYWtri40bN5Z6bQPvO+koukY8ffoUlpaWnGIkJycjJiYGycnJeP36NYKCgjBq1CgYGxtzOn6Fs2fP4sKFC6W65QBA06ZN8eeff+o8bmJiIoqLi5Vfa6N6bnQ1f/583Lt3D71794a+/vspXElJCXx9fQVNtSLa0USakGpOX18fY8eOxR9//AEAMDEx0ZgXykVmZqbG/F5DQ0Pk5+fzEsPf3x/Z2dmYNWsWGjRowMubUllC52Hr6+tjyZIlvExuyvPnn3+iRYsWattLSkqUb/iVpZrjWV6+Z9kLHF21bNkSKSkpiIiIUOaa+vr6YsKECbz0ei4uLkbfvn2xbt06zJw5k4cj1kzoPOypU6ciMDAQixYt4v11rbBs2TJ8+eWXOHr0qPICMyEhAWlpadi7dy+A9xecX331lU7jFxcXw8nJCUeOHMGoUaMwatQo3o5dlVB52KdPn9b4tRCEzsEmlUcTaUJqgM6dOyMxMVGwP7ZiFDOeO3cOZ8+eRbt27XgZTxM9PT3MnDkTM2fORF5eHoD3C5qoatKkCacYvXv3Rnx8vLIQTAhCFzT6+/tj7dq1an227927h9GjR+Ps2bOcYwDCFjTWqlVLY79fvvXo0QPbtm3D/PnzAby/K1lSUoIlS5bAzc2N8/h//vknQkJCBJtEA8IXNNaqVQtv3rzh41DL5eHhgVWrVmHDhg0A3p+L169fY86cOejfvz8vMZ48eaL85Kas1NRU5cJVXDk4OMDBwYGXsQg3NJEmpAb45z//ialTp+LBgwf49NNP1SZAbdq04TT+lClTMH78eLx58waMMVy5cgW7d+/Gjz/+yNsiI7a2trzd7ayIshNovvTr1w/Tp09HamqqxnMxePBgzjFmz54NPz8//PnnnygpKcH+/ftx69YtbNu2DUeOHOE8fnJyMtq0aYMdO3You7Ns3boVISEhcHd313nclJQUuLi4QCqVfnCSy/V3FgB8fHw0pizwacmSJejduzcSEhLw9u1bfP/99/j999/x7NkznD9/nvP4np6eSEhIEGSRFFXNmjUT9N9p/PjxCA8PR1RUlDJlgW/Lli1D37590bJlS7x58wbe3t7IyMhA3bp1sXv3bl5itG7dGhs3bsSAAQPUYs+aNQuFhYWVHnPKlCmYP38+ateujSlTppT73BUrVlR6fMKNhIn5zkQIqRKqqwEqSCQSMMYgkUh4yS/euXMn/v3vf+POnTsA3t9NnDt3LoKCgjiPDbzPNV2+fDnWr18v2N1cMfKwNZ0LBb7OBfA+H3TevHnKfNMOHTpg9uzZ8PDw4Dx2cXExZsyYgTVr1mDq1Km4ffs2jh49ihUrViA4OFjncaVSKR49egQbGxtIpVLl72hZfP07TZw4Edu2bYO9vb3Gixq+JiUvX75UpqgozgVfedgbN27EvHnzEBAQgNatW6stGc3HhRkAvHjxAleuXMHjx49RUlJSah8fqUpeXl44efIkTE1N0bp1a7VzsX//fs4xgPd1Cqp52B06dOA1D3vJkiWYPXs2AgICsGLFCjx79gy+vr5ITU3F+vXr4eXlVekx3dzccODAAVhYWJT7KYZEIuGtfoBUHE2kCakBPlTUxCXlQ7WYsV69eoIUMwKApaUlCgoKIJfLYWJiojZhePbsGecY/fr1Q3Z2NiZMmKAxD3vIkCGcYwhNtaCxbIEb3+bMmYP58+dDX18f8fHxnHuHZ2VloUmTJpBIJIL+zioIPSlRzcO2t7fnNJY2YlyYfaigkY/XXkBAQLn7N2/ezGl81TxsIXrnq0pMTMTo0aNRVFSEZ8+e4bPPPsOmTZtQv359QeOSqkETaUIIZyYmJvjjjz8ELXjZunVrufv9/Pw4xzAzMxM8D1sMpqamuHHjhmB37ouLizF9+nSsXbsWU6dOxblz55Ceno6NGzfylmtaHsUnKR8Da2trXLhwQbCJtBgcHBzQv39/QQsaxdCoUSOcOHFC8In0q1evEBwcjH379gEAoqKiePn7BIiXg00qoSp67hFCxJGQkMBcXV01Ll7y4sUL5urqqlwimYtevXqxAwcOcB6nqjk7O2tcBIQPJ0+eZM7OzlrPRcuWLVl8fDwvsQYPHqxxeW2+tGnThrVo0YJdvHiRMcZYSUkJW7x4MTM0NGTjxo3jJYafn5+yN6+qzMxM1r17d05jy+VylpyczAoKCtT2FRQUlFp8hqtJkyaxsLAwXsaqKiYmJsqey3wrKChghw4dYnl5eWr7Xr58yQ4dOsTevHnDS6yFCxcyPz8/VlxczMt4mpw7d441bdqUdejQgd28eZNFRkYyMzMzNmLECPbs2TPO49erV48dOXJEbfvSpUuZkZER5/FJ5VGxISHV2PLly+Hu7q6xcE4mk6FPnz5YunQpduzYwSmOUMWMeXl5ymNXdNHQho/iwFWrVmH69OmC5GGvWrUKwcHBWs/FmDFjsHLlSvTs2ZNzLKELGjt27Ig1a9Yox5VIJAgLC4OHhwdGjx7NaWwFoQoaAWD79u2IiIjA5cuX1fbVqlULgYGBmDRpEnx8fDjFAd6n2mzatAknTpzgLQ97zZo1+Pbbb2FkZIQ1a9aU+9yQkJBKj1+WkAWNGzZswOHDhzX+Tpqbm2PNmjXKdCuurl69ipMnT+LXX38VLA/b3d0dkydPxvz581GrVi04OzvDzc0NPj4+aN26NR48eMBp/ClTpmD48OEac7B37drF+fhJ5VFqByHV2CeffIIDBw5oncimpqZiyJAhuHv3Lqc4QhUz6unp4a+//ipVfFYW1xiqhMzDtrOzK7cdYFpaGjw8PJCdna1zDAWxCho1KSoq4qU/slAFjcD7lnTjx4/H119/rXH/nj17EBERgTNnznCKAwiTh92sWTMkJCTAysoKzZo1K3d8rq9tQNiCxs6dO2PWrFkYNGiQxv1HjhzBvHnzcOXKFZ1jKAidhw28X7CpV69eattLSkqwcOFCzJo1i3MMysH+e6GJNCHVmJGREf744w+tb7aZmZlo2bKlTi2ZVAlVGBYfH49u3bopi9nKo+nNq7KEzMM2MjLCjRs3NC6UAgC3b99G69atOZ8LscTHx2PZsmXKhX5atmyJadOmKVch5AvfBY0AYGNjgytXrmj91CEzMxOdO3fGkydPOMeqDoS8MLO0tERycrLW/uzZ2dlo27Ytnj9/rnOMqvDkyRPcunULAODo6Kg1r1kXQuZgEx1UZV4JIURYjRs3ZkePHtW6/3//+x9r3LixiEekm+LiYjZ37lx2//79qj4UnTVv3rzcPPJ9+/axZs2aiXdAHGzfvp3p6+uzESNGsNWrV7PVq1ezESNGsFq1arGdO3fyEuPt27dsypQpzNDQkM2YMYP17NmT1a9fn/3yyy+cxzYxMWHJycla9ycnJzMTExNOMcTIw3779i1r3rw5u3nzJqdxqpKpqSlLSEjQuj8hIYGZmppyiiFmHnZ+fj4LCAhg+vr6TCKRMIlEwvT19VlgYCDLz8/nPL7QOdik8mgiTUg15u/vr7Uwq6SkhHXr1o35+/vrPL5YxYyMvX/DzczM5GUsVarH/vLly3IfXEyYMIG5uLiwwsJCtX0FBQXMxcWFTZw4kVMMsQoanZyc2IoVK9S2L1++nDk5OXEenzFhCxrbtm3Lfv75Z637165dy9q2bcspxubNm9mnn37K5HK52r7i4mL26aefsu3bt3OKwRhjDRs2/Kgn0p999hlbvHix1v2LFi1in332GacYq1atYu7u7lr39+7dm/3000+cYih8++23rHnz5ux///uf8u/GL7/8wj755BM2duxYzuMbGBiwsLAw9vbtW+W227dvsy5durBGjRpxHp9UHk2kCanGbt++zWQyGevcuTOLiYlhSUlJLCkpiUVHR7NOnToxmUzGMjIydB5/5MiRbN68eVr3L1y4kI0aNUrn8VUJ1YlCKpWynJwcxhhjEomESaVStYdiOxePHj1iDRs2ZLa2tiw8PJwdPHiQHTx4kC1evJjZ2tqyhg0bskePHnGKMWjQII0TXIXVq1ezoUOHcorB2Ps3c02/NxkZGczQ0JDz+IwxFhgYqLFrx/Xr11mrVq04jR0eHs6srKw03pVOSkpiVlZWLDw8nFOM7t27s927d2vdHxMTw3r06MEpBmPCdaJYvXq18qJP8amDtgcX69evZ7Vr12axsbFq+w4fPsxq167N1q9fzylGp06d2OHDh7Xuj42NZZ06deIUQ8HKyoqdPn1abfupU6dY3bp1OY8fFxencfu7d+/K/VtMhEM50oRUcwkJCfD398fNmzeVxXqMMbRs2RKbN29Gp06ddB5brGJGAFi3bh3mzp2LUaNG8dqJQsw87KysLIwbNw7Hjx9XrtgnkUjg6emJtWvXlls4VhFiFTS2aNEC06ZNw5gxY0ptX7duHZYvX46MjAxO438I14LG4uJieHh44Ny5c/jiiy/g5OQE4P2/z4kTJ9CtWzf89ttvakV1lSFWHrZQKwKKWdDo4+ODXbt2wcnJCY6OjgDen4v09HSMGDGC8/LdYuZhm5iY4Nq1a2qvwd9//x2dO3dGfn4+5xiAsDnYpHJoIk1IDZGUlISMjAwwxuDg4MDLoiNiFTMCwneiEHNFwOfPn+P27dtgjMHe3h6Wlpa8jCt0QWNgYCBWr16NHTt2YNKkSQgMDETXrl0BAOfPn8eWLVuwevVqtQm2roQsaCwuLsbKlSuxa9euUq8Lb29vTJo0CQYGBpzGr127Ni5evKj1IjMlJQWff/4554mVGJ0oxLBnzx6N52LEiBGcxzYzM0NcXBw+/fRTjfuvXbsGV1dXvHr1inOs3r17w8rKCtu2bYORkREAoLCwEH5+fnj27BlOnDjBafyCggJMmDAB27dvV/7N09PTg6+vL3766aePesGcj1ZV3QonhFSNc+fO8VZYU12KGRWEysPWZteuXRrTF3QldEGjahrM/v37Wbdu3VidOnVYnTp1WLdu3djBgwd1HrssMQoahSRGHrYYqkNBoxh52IrXRkpKCmvYsCGzsrJi7u7uzN3dnVlZWbFGjRqxGzducIrBmPA52KTyaCJNSA1jZmbG2yplQhczik3oFQHL4vNcMCZ8QaNEIlFOpIUmRkGjqnHjxrEnT57wNp4YedhiEbugsX///uzhw4e8jSdGHrbqayM/P59t2LCBTZkyhU2ZMoVFRkZq7N6iC6FzsEnlUWoHITWMmZkZkpOTeVml7M6dO/j000/h6OiIqVOnlspvXL58OdLT05GQkKA11aCy8vPzER8fj+zsbLx9+7bUPj5WcBMqD1sbPs8FAOTk5KBDhw7Q09PDhAkTSp2PtWvX4t27d7h+/Trq1aun0/hSqRQZGRkfzMfkY5VJQ0ND/P7772q/O7dv34aLiwvevHnDOYYqc3NzJCUl8XYuxMjDVti7dy/27Nmj8XVx/fp1zuMvWrQI6enpiIqKgr6+8Asi8/26AITPw5ZKpXj06BFsbGz4OFytxMrBJhVHE2lCahi+36SELGZUlZiYiP79+6OgoAD5+fmoU6cOcnNzYWJiAhsbG14KGsVeEVCICYOQBY3aVpdUYDyuMil2QaMQ50LoPGzg/XLhM2fOhL+/PzZs2ICAgADcuXMHV69exfjx47Fw4ULOMYQqaNRGiHMBCJuHLZVKsWDBApiampb7PK4X/ELnYJPKo4k0ITXMrl27MGTIELU3Q66EKGZU5erqCgcHB6xbtw4ymQzJycmoVasWfHx8EBoaimHDhvEaTwznzp1Dp06deFlSuywhChqlUin27duHOnXqlPs8Lt1NxC5oVBBq8iY0JycnzJkzByNHjiz1M8yePRvPnj1DREQE5xhiFzS6uLjg6NGjsLW15XVcIUmlUjRu3Bh6enpan8Olw4menh7++usv5OTkoG/fvigqKkLbtm0BAMnJyTAyMsLx48fRqlUrncYnuqOJNCGEV+fPn0fHjh15nxxaWFjg8uXLcHR0hIWFBS5evAhnZ2dcvnwZfn5+SEtL4zWeUDIzMyGXy2Fvb19qe0ZGBmrVqqW1XZqudu/ejcGDB/Ny4STGx9eKCYONjQ0OHDiA5cuXK7t2ODs7Y9q0aRgyZIhg8YX0z3/+E/PmzUPdunV5G9PExAR//PEH7OzsYGNjg99++w1t27ZFRkYGunTpgqdPn/IWqzoZMGAAoqKi0KBBA17GE/q1oTp+QUEBdu7cqfyb5+zsjFGjRsHY2FiQ2KR8wic7EUKqnJeXl8aP5CUSCYyMjNCiRQt4e3srcwe56NevH6+5pgq1atVSpl7Y2NggOzsbzs7OkMlkuH//Pm9xhM7D9vf3R2BgoNpE+vLly4iKikJcXBznGKrGjBmDzz777KO506p6b8fLywteXl6CxcrLy9O4XSKRwNDQkJfUC1U7duzAd999x+tEun79+nj27Bns7OzQpEkTXLp0CW3btkVmZiY+pvtkmzdvhqmpKb788stS2//73/+ioKAAfn5+vMY7c+YML205FcpLeeKbiYkJgoODRYtHykcTaUJqAJlMhoMHD8LCwkLZS/X69et48eIFPDw8EBMTg/DwcJw8eRLdunXjFEuoN+/27dvj6tWrsLe3R69evTB79mzk5uZi+/btcHFx4SXGh/Kw+ZhIJyYmavw37tKlCyZMmMB5/LL4PB92dnZqH13zfWcPAF69eqXM/9SGj4JGCwuLcidAjRs3hr+/P+bMmVNu/nxFCfHacHd3x+HDh9G+fXsEBARg8uTJ2Lt3LxISEnhNdxK6oPHHH3/E+vXr1bbb2Njg22+/5X0izTdN5/bBgwdo2LAhL787ABAVFSV4DjapPJpIE1ID1K9fH97e3oiIiFD+US8pKUFoaCjMzMwQHR2NsWPHIiwsDOfOnavio9Vs0aJFygUTFi5cCF9fX4wbNw729vbYtGkTLzEmT56MQYMGKfOwL126VCoPmw8SiUTjwg8vX77kvZiRb5mZmWrb+L6zBwAODg5a9/FZ0LhlyxZloV7nzp0BAFeuXMHWrVvxww8/4MmTJ1i2bBkMDQ0xY8YMzvGEsGHDBpSUlAAAxo8fDysrK1y4cAGDBw/mLY9ctaDx0KFDagWNfMjOztZYCGtnZ8d5JU5N7OzseOmYojBnzhy1SW7Lli15/XRu3bp1H8zBpol0FRCz1x4hpGrUrVuX3bp1S237rVu3mJWVFWOMsZSUFCaTyTjH2rlzJ6+LjIhJJpOxtLQ05deK3rmXLl1ijo6OvMQYOHAg+/LLL5lcLlduk8vlbPjw4axv3768xFB19uxZ3hbg0cTU1JTXXtgSiYTt37+fxcXFlfvgg7u7O4uJiVHbHhMTw9zd3RljjG3bto23c/+xcnR0ZLt27WKMlT7fs2bNYuPHj+clhq2tLTt06JDa9oMHD7JGjRrxEkNsfL42xOzhTiqH7kgTUgPI5XKkpaWp3elLS0tT3tkzMjLiJc/P29ub8xiqCgsL8dtvv8HNzQ1mZmal9uXl5SEuLg6enp68FDeKkYcdHh6Onj17wtHRUbnU9dmzZ5GXl4dTp07xEkO1oLF79+7K7UIUNPJ9Zw8AunXrJng/XgC4cOEC1q1bp7a9ffv2uHjxIgCge/funO6ICpWHnZGRgdmzZ2P9+vVqaS4vX77EuHHjsGDBAl7uhmZnZys7pxgbGys/URk9ejS6dOnCS2eQkSNHIiQkBGZmZujZsyeA90vEh4aG4uuvv+Y8PiB+HjafxMzBJpVDE2lCaoDRo0cjKCgIM2bMUPZ1vnr1KhYtWgRfX18A79+0dG2dJGQx44YNG3D48GGNi6GYm5tjzZo1uH//Pi8fMYuRh92yZUukpKQgIiICycnJMDY2hq+vLyZMmPDBtnIVJWZB440bN3gbS2y2trbYuHEjFi9eXGr7xo0bla3Xnj59yql1oFB52EuXLoWtra3GXHGZTAZbW1ssXboUP//8s07HrUqMgsb58+fj3r176N27t3LRl5KSEvj6+mLRokW8xBA7D3vGjBm8vaY1/TvznYNNdFTVt8QJIcKTy+VswYIFrH79+kwikTCJRMLq16/PFi5cqEwxyMrKYvfv39dpfD8/PyaTyZidnR0bNmwYGzZsGGvatCmzsLBgI0aMYI6OjszQ0JCdO3eu0mN36tSJHT58WOv+2NhY1qlTJ52Ou6yrV6+yU6dOMcYYy8nJYZ6enszMzIx16NCBJSUl8RJDDGZmZiwjI0Nte0ZGhs7pO8nJyRV+cNG0aVOWm5tbahvfS0YrHDp0iBkYGLA2bdqwoKAgFhQUxNq2bcsMDQ2Vy0n/5z//YZMnT9Y5xtatW1njxo3ZDz/8wA4fPswOHz7MfvjhB2Zra8vWr1/PFixYwCwsLNjChQsrNa6DgwO7cuWK1v0JCQnMwcFB5+NWFRQUxP79738zxhiLiIhgxsbG7IsvvmAWFhYsMDCQlxgKt27dYnv27GGxsbHs3r17vI5taGjIMjMz1bZnZmYyIyMjXmLMnj2b9+NmjLF///vfLD8/v9Q2MzMzXtOqiG6ojzQhNYzio2Y+uh4oTJ8+HXl5eVqLGRcuXIixY8fi999/r3Qxo6WlJZKTk9GkSRON+7Ozs9G2bVs8f/6c888hlJSUFLi4uEAqlSIlJaXc57Zp04ZzPJlMhri4OLRv377U9mvXrsHV1VVjseOHKFY11PaWodj3sawAqZCZmYn169cjPT0dAODo6IgxY8bwlv7Su3dvjBkzRm31vD179mD9+vU4efIktm/fjoULF1aqF7qxsTHS0tJgZ2encX9WVhacnZ1RUFDA6fiB96/lkpIS5Z3i6OhoXLhwAfb29hgzZgzvbQKF0qRJE0RERKh9unXo0CGMHz8eDx484ByjXbt2uHHjBnr16oWgoCAMHz5ckAWXgI93EaHqhibShBDOrK2tcf78ebUc7PT0dHTt2hW5ublITU1Fjx498OLFi0qNbWZmhri4OGXbvrK4TA4VhM7DVl1MobwJKV+T0EGDBsHY2Bi7d+9WVvm/e/cOX331FfLz83H06NFKj5mVlVXh52qb3OnqY54wGBsbIyUlReMCPG3btkVBQQEyMzPRqlWrSk1669evj127dsHd3V3j/pMnT2LUqFF49OgRp+MX0pQpUzB//nzUrl0bU6ZMKfe5K1as4BwvLCwMMTEx2Lx5c6k87MDAQPzjH//AsmXLOMcA3re43Lx5M3bv3g25XI6vv/4agYGByrQ6vnzMr4vqhHKkCakBcnJy8N133+HkyZN4/Pix2iSO6+RNyGLGVq1a4cSJE1on0r/++ivnZXGFzsPOzMyEtbW18muhCVHQyPfkuLKx+S5oVHjx4gWuXLmCx48fK9vIKSjqB7gQKg+7Z8+e+Omnn7ROpNesWaM897oSuqAxMTERxcXFyq+14avQTow8bOB9rUX79u2xfPlyxMbGYvPmzejWrRucnJwQFBQEf39/yGQyznH4zMEmuqM70oTUAP369UN2djYmTJiABg0aqL0xcV1yOSQkBLt379ZYzOjt7Y3Vq1cjKioKW7ZsqXRqx4YNGzBlyhRER0dj4MCBpfbFxsZi5MiRWLFiBb799ludj79z586YNWsWBg0apHH/kSNHMG/ePFy5ckXnGBWhSI3gw8OHD0sVNLZp04a3gsZt27aVu5+PCagYYmNjMWrUKLx+/Rrm5ual/u0lEgmePXvGOcbhw4fx5ZdfwsnJSfnaSEhIQFpaGvbu3YuBAwfi559/RkZGRqXuuiYmJuLzzz/HwIED8f333ysLedPS0rBkyRL88ssvuHDhAjp06KDzsX/77bewsLDAkiVLNO4PCwtDXl4eLwWNYkpPT1e+Llq3bi3YReLbt29x4MABbNq0CadOnULXrl3x8OFD5OTkIDIyEl999VWlx5wzZw4CAwOr9MKWlFFFudmEEBGZmpqyxMREwcYXuphx1KhRTCKRMGdnZzZ06FA2dOhQ5uTkxKRSKfv66685H7+FhQXLysrSuj8rK4tZWFhwjsPY+8JMTX22MzMzWffu3XmJITQLC4tSj9q1azOJRMIMDQ2ZpaWlzuOKVdCoYG9vz0JDQ9WKuPh29+5dFhYWxry8vJiXlxebPn26xqK3yoqNjWXW1tZMKpWWelhbW2vsyVxZYhY0Pn78WOu+lJQUXmKIJSEhgY0fP57VqVOHNWjQgIWFhZUq/l2zZg2zsbHRaey2bdsyPT095u7uznbu3Cloj3hSMXRHmpAaoGXLlti5c6da8ZkQhChmBN4XZ+3atQsZGRlgjMHBwQHe3t5qRVy6ECMPW6F9+/bIy8vDjh078PnnnwMAtm7dipCQELi7u+PAgQM6jSt2QWNZGRkZGDduHKZNmwZPT0+dxhC7oLF27dpITU39qHNMCwsLcezYMdy+fVv5uvDw8ICJiQnnscUsaKxfvz42btyIAQMGlNq+bNkyzJo1S+fVM8XOw27dujXS0tLg4eGB4OBgDBo0SG01wtzcXNjY2KilElWUWDnYpGIoR5qQGmDVqlWYPn061q9fz+tiHJrwPYFWGDFiBC+TZk3EyMNWuHLlCmbMmAFXV1dMnToVt2/fxtGjR7FixQoEBwfrPG67du2UBY3t2rUTvKCxLHt7eyxevBg+Pj6V6j6hSoz8cVWenp5ISEgQfCItZB62sbExvLy8APDfV1gmk+HOnTtaJ9K3b9/m7fU+ZcoUDB8+HAEBAVixYgWePXsGX19fpKamYteuXTqPK3Ye9ogRIxAYGIhGjRppfU7dunV1nkQD4uVgk4qhO9KE1ACWlpYoKCiAXC6HiYmJWuEW11xQoYsZyxowYACioqLQoEEDXsYTIw+7rDlz5mD+/PnQ19dHfHy88u60rrKystCkSRNIJJIPdtgQKr8yKSkJPXv21Lqa39/Nxo0bMW/ePAQEBKB169ZqrwtNxaeVJUYetoK5uTmSkpJ4uzAYMWIEiouLtX5KMmTIEBgYGOC///0vL/ESExMxevRoFBUV4dmzZ/jss8+wadMm1K9fn5fxqxshcrBJ5dFEmpAaYOvWreXu57qil9DFjGUJ0fbJx8cHu3btgpOTU6nCrfT0dIwYMQK7d+/mJU5xcTGmT5+OtWvXYurUqTh37hzS09OxceNG9O/fn5cY5WE8FDQePnxYbcy//voLERERsLW11am9XlliFDSWd+eWrzv3Dg4O6N+/PxYtWsRLukV5+H5diFHQqOrVq1cIDg7Gvn37AABRUVG8rjb45MkTZfecslJTU9G6dWudxv1QyogqPtJHrl27pkztMDQ0hK+vL7755hu0aNECAPDTTz9hwYIFyMnJ4RyLVEBVJGYTQqoXoYsZNcUTYkWvmJgYNmTIENayZUvm7OzMhgwZwmJiYniN0aZNG9aiRQt28eJFxhhjJSUlbPHixczQ0JCNGzeOlxhCFzQqCkoVD6lUyurVq8dGjhzJ2wqEQhU0is3ExES01eeEeF0IXdCocO7cOda0aVPWoUMHdvPmTRYZGcnMzMzYiBEj2LNnz3iJUa9ePXbkyBG17UuXLuW0sqGrq2uFHm5ublwOnzHGmIuLC9PX12f9+/dnBw4cUBZzq3ry5AmTSCScY5GKoYk0IdXUy5cvS31d3oMrZ2dndv36dc7jVFSrVq1Ydna2aPH4FBgYqHGSe/36ddaqVSteYrRr1441b96cXbhwQblty5YtzNzcnA0dOlSnMfn4PeEqPT2d9e7dmx07dqyqD6XCvLy8eL8Y02bRokXs+fPnvI9bUFDA9u/fz5YsWcLCw8PZgQMHeO90YmBgwMLCwtjbt2+V227fvs26dOnCGjVqxEuM8PBwZmhoyMaOHcsKCgrYgwcPmLu7O7O2tmb79+/nJYbQ5s2bxx48eFDVh0FUUGoHIdWUnp4e/vrrr1Kr6ZXFeOqA8Ouvv2L58uWiFDOKhe887IooKiriZTnh4uJizJgxA2vWrOGtoFH198nd3R379++HhYUF52OtrISEBE4FjWvWrMG3334LIyMjrFmzptznhoSE6BRDldB52GL3Fea7oFEhPj4evXr1UtteUlKChQsXYtasWbzEoTxswjeaSBNSTcXHx6Nbt27KYrbyaHoDqwyhihk/1MZNFd8t3YRcfjc+Ph7Lli3DH3/8AeB9e8Jp06ZxXomuLD4LGmUyGS5dugRnZ2dIpVLk5ORozTcVEteCxmbNmiEhIQFWVlZo1qyZ1udJJBLcvXtX18NUEjoPu127drhx4wZ69eqFoKAgDB8+nJeLMW34Lmgs68mTJ7h16xYAwNHRkfffMaHzsL28vDTetJBIJDAyMkKLFi3g7e2tzDevCLFzsEnlUPs7QqopxeRYLpcjPj4egYGBaNy4sSCxVq1aJci45bVxA/jvKyyGHTt2ICAgAMOGDVPe8Tx//jx69+6NLVu2wNvbm3MM1YLGf/3rXzh37hyGDRvGqaDxiy++gJubG5ydnQG8nzAYGBhofK6uy5CrKq+gsVu3bjqPq9piT4x2e1zanFVEUlKSsq9waGgoxo8fL2hfYaHuvRUUFGDChAnYvn278rWsp6cHX19f/PTTT7wUap4/fx4+Pj6oU6cOUlJScP78eUycOBH/+9//sG7dukov066JTCbDwYMHYWFhoWynef36dbx48QIeHh6IiYlBeHg4Tp48WeHf4/La9qniq4UfqRy6I01IDWBmZobU1NSPLu3iQ23cVPH90baLiwuOHj0KW1tbXsd1dnbGt99+i8mTJ5favmLFCkRGRirvUnPRtm1bFBQUYPv27ejSpQsYY1iyZIkyDeA///lPpccsLCzE1q1bcefOHSxfvhzBwcFaJzcrV67k+iOo3cmVSCSwtraGu7s7li9fzjnlpri4GE5OTjhy5Ijy4uBjV1xcrOwrfPz4cUH6Cgv1Sc2YMWNw4sSJUhdK586dQ0hICPr06cPLMuSGhoaYPHky5s+fr/zU7M6dO/Dx8cH9+/fx4MEDzjGmT5+OvLw8REREKH+HS0pKEBoaCjMzMyxcuBBjx47F77//jnPnznGOR6oeTaQJqQGGDBmCYcOG8foRZl5ennIxhg99zC7UIi0fI0NDQ/z+++/KVlUKt2/fhouLC968ecM5RlBQENasWYPatWuX2q7ID71x4wan8d3c3HDgwAHec6RVf6fE0KhRI5w4cYL3ibTYedgKYvQV/vHHHzFu3Djez33dunWxd+9euLq6ltp++vRpjBgxAk+ePOEcQ4w8bGtra5w/fx4ODg6ltqenp6Nr167Izc1FamoqevTogRcvXnCOR6oeTaQJqQHWrVuHuXPnYtSoUfj000/VJli6FDyJWcwICNdXWOw87BYtWmDatGkYM2ZMqe3r1q3D8uXLkZGRwTlGefgqaBSC2AWNixYtQnp6OqKioqCvz1+mo9h52EL3FRajoNHExATXrl1Tu6j5/fff0blzZ+Tn5/MWS8g8bEtLS2zdulXtb+rhw4fh5+eH58+fIyMjA507d8bz588rPb4QOdiEG5pIE1IDCFHwJGYxIwC1/MXi4mIUFBTAwMAAJiYmOhc0Ki4ChM7DDgwMxOrVq7Fjxw5MmjQJgYGB6Nq1K4D3uZtbtmzB6tWr1SbYuuK7oHHKlCmYP38+ateu/cHiJ10LnsQuaPTy8sLJkydhamqK1q1bq11g7t+/X7DYfGndujXS0tLg4eGB4OBgDBo0CHp6eqWek5ubCxsbG53ztcUoaOzduzesrKywbds2GBkZAXifTuTn54dnz57hxIkTnGOIkYcdEhKC3bt3Y8aMGcoc9atXr2LRokXw9vbG6tWrERUVhS1btuiU2uHv719uDnZycjLu3btXqRxswg1NpAkhnMjlcixatEjQYkZtMjIyMG7cOEybNg2enp46jSFWHrbq3dYDBw5g+fLlykmus7Mzpk2bxtsKkKoFjYo30/Pnz+PAgQM6FzSqpnO4urqWW9h0+vRpnY57+PDhOH/+PJydnREfH4+uXbsKWtAYEBBQ7v7NmzdzGl+MPOz58+cjMDAQjRo1EmR8BUVB4+7duyGXy3kraFS8LnJyctC3b18UFRWhbdu2AIDk5GQYGRnh+PHjaNWqFeefQYw87Hfv3mHx4sWIiIhQfgJQr149TJw4EWFhYdDT00N2djakUqlOfy8pB/vvhybShBDOqrKYkWtfYbFIpVI8evQINjY2gscSo6BRCGIXNIpBqDzsqsJ3QaPq66KgoAA7d+5UvpadnZ0xatQoGBsb83LsYuRhq1LUjvCZ90852H8/1P6OkBoiPz8f8fHxyM7Oxtu3b0vt41rw5O7ujvj4+CqZSOvr6+Phw4e8jCVUHrbCq1evlB9ba8PHm+7du3cxaNAgte2DBw/GjBkzOI+vSFMxMzMrtT0/Px8TJ07Epk2bdBrX2NgYY8eOBfD+Aik8PLxKFn3h0/jx4xEeHs5rHnZV9hVmjKG4uBhv374FYwyWlpaIiIjArFmzOBc0mpiY6LxgUEUUFBSgXr16atsVk3guLC0tNX5KI5PJ4ODggO+++w59+vThFAN4/wlgWlqa2kQ6LS1Nma5iZGRErfBERHekCakBEhMT0b9/fxQUFCA/Px916tRBbm4uTExMYGNjw7ngSYhixrLK6ytsa2uLo0ePco4hVB42AK0FmQp8FmYKXdComqaiKjc3F/Xr14dcLuc0vpj27t2LPXv2aLzAvH79OufxhcjDdnNzq9DzJBIJLykwgHAFjVKpFAsWLICpqWm5z+Oju4mQedhbt27VuP3Fixe4du0aYmJisHfvXo0XuJUhdA42qTyaSBNSA7i6usLBwQHr1q2DTCZDcnIyatWqBR8fH4SGhmLYsGGcxhd69TZNMfjuK6wNH3nYwPvj37dvH+rUqVPu87gUZgpd0JiXl6e8C5mRkVGqEPDdu3eIjY3F9OnTdf6EQIyCRlVr1qzBzJkz4e/vjw0bNiAgIAB37tzB1atXMX78eCxcuJBzDKHzsMUgZEGjIle47HiquHY3ETMPW5sVK1Zg7969uHDhAqdxhM7BJpVHE2lCagALCwtcvnwZjo6OsLCwwMWLF+Hs7IzLly/Dz8/vb5tfLHZfYW34yMMWI0da6ILGD91Vl0gkmDt3LmbOnKnT+GIUNKpycnLCnDlzMHLkyFILjcyePRvPnj1DREQE5xjVgZAFjWK8LsTMw9YmPT0dXbp04fSpVllC5GCTyqMcaUJqgFq1ainv6NrY2CA7OxvOzs6QyWS4f/9+FR+ddpaWlqL2FdaGzzxsIaneF/Hy8oKXlxev458+fRqMMbi7u6vdXTcwMICdnR0aNmzIaXyFuLg4LodaIdnZ2co79sbGxnj16hUAYPTo0ejSpctHMZEWo68wHwuVaCN2Lq/QedjaFBUVae1AUxFi5WCTyqOJNCE1QPv27XH16lXY29ujV69emD17NnJzc7F9+3a4uLjwEkOIYkZTU1M8ffoUNjY2iIuLQ3FxMR+HqlV5edhce7La2dmpfXw9YMAAREVF8ZqWImRBoyLtJDMzE02aNBF0EiRUQaOq+vXr49mzZ7Czs0OTJk1w6dIltG3bFpmZmVr7iutCyDxsmUxWbl/hmJgYhIeHV7qvsFgFjZr+nR88eICGDRuWmzJWWVFRUaLkYWuzceNGtGvXTuf/f9WqVRq3K3KwBw4cyEsONqk8Su0gpAZISEjAq1ev4ObmhsePH8PX1xcXLlyAvb09Nm3apMwX1JVQxYxi9xUWOw9bNZ2AD2IVNJ45c6bc/T179uQ0PiBOQeM333wDW1tbzJkzB2vXrsW0adPQrVs3JCQkYNiwYdi4cSPnGELnYQvVV1isgsa5c+di2rRppdocmpubIykpidfXhdB52NouPF6+fInr168jPT0dZ86cUV7s8I2vHGxSeTSRJoRwJlQxoxh9hasyD1uIibTQBY2KOGWpTuC5TNSFLmhUVVJSgpKSEmVbuujoaOUF5pgxYzh9FK8gdB52dewrLMTrQug8bG0XHubm5nB0dMS4cePKXS6eKyFysEkFMUJItVVQUMAOHTrE8vLy1Pa9fPmSHTp0iL1584ZzHJlMxtLS0pRf37x5kzHG2KVLl5ijoyPn8RljzNXVlT1//pyXsVRJpVKWk5PDGGPMzc1NkBjatGrVimVnZ/M2nkQiUf4sQnrx4kWpx5MnT9ivv/7KPvvsM3bixAlOY0skEiaVSrU+9PT02IIFC3j6SYRnbGzM7t27xxhjzNramiUlJTHGGEtPT2d16tThPL6FhQU7dOiQ2vZDhw4xCwsLZSzF1x8DU1NTdufOHd7GU32NV1cpKSmsXr16VX0YNRLlSBNSjW3YsAGHDx/W2MfZ3Nwca9aswf379zF+/HhOccQoZuSjS4MmYudhq7px44ZosfikaQW7Pn36wMDAAFOmTMG1a9d0HlvogkbgfUvD2bNnY/369WqfRrx8+RLjxo3DggULeLkjKnQe9ujRoxEUFKSxr7BiAaH4+HhOrd3EKGhUNWPGjA9+qlIZmv6dhcjDrkpcc7CJ7mgiTUg1tnPnznIr7idNmoR58+ZxnkgLVcwoRl/hL774Am5ubsolnL28vHjPw05JSanwc9u0aaNTDEC8gkZt6tWrh1u3bnEaQ4yCxqVLl8LW1lZjSo9MJoOtrS2WLl2Kn3/+mXMsd3d3HD58GO3bt0dAQAAmT56MvXv3KvOwuVq5ciXq1auHJUuWlOorPHnyZISFhQEAPDw80LdvX51jCFXQqGrOnDkIDAyEnZ0d/vWvf+l8rNrGLlto2LJlS17zsIVW0RxsIj7KkSakGrO0tERycjKaNGmicX92djbatm2L58+fc4ojVDGjGH2FxcjDVhQBavtzq9jH1+I1qvjONwXULwzY/+9usnjxYsjlcl5WVBOyoNHR0RE7duxQ3sEt69q1a/D29uZ8UQCIk4etIFRfYaEKGlW1a9cON27cQK9evRAUFIThw4fD0NCQzx+jFCFeF0Kq6hxsoh1NpAmpxszMzBAXF6e1UvzatWtwdXVV9s+t6VQn7nzKysqq8HPt7Ox4jS3EhEHbhUGXLl2wadMmODk58RKjLL4KGo2NjZGWlqb13zorKwvOzs4oKCjQOYbQxOwrLFZBY2JionIZcrlcjq+//hqBgYFaL3i4+Ngm0uTvi1I7CKnGWrVqhRMnTmidSP/666+ccicLCwvx22+/wc3NTa3fb15eHuLi4uDp6cnLnSUx+goLlYfN9+S4srFr1arF65iZmZmlvpdKpbC2tv5g/+rKKPspSXFxMRITEzFr1izOLeNkMhnu3Lmj9bzcvn2b811dofOwxewrLJfLkZaWpjaRTktLU17QGBkZcU7Dad++Pdq3b4/ly5cjNjYWmzdvRrdu3eDk5ISgoCD4+/trzM/XBd952KTmojvShFRjGzZswJQpUxAdHY2BAweW2hcbG4uRI0dixYoV+Pbbb3Uaf/Xq1Th8+DBOnjypcf8XX3wBLy8vzjnYgHB9hcXIw1a1bdu2cvcrCsSIZvHx8ZwLGkeMGIHi4mIcOHBA4/4hQ4bAwMAA//3vf3WO8e2338LCwgJLlizRuD8sLAx5eXm85GFrwmdf4ZCQEOzevVtjQaO3tzdWr16NqKgobNmyhZe0nrdv3+LAgQPYtGkTTp06ha5du+Lhw4fIyclBZGQkvvrqK53GVc3DJoQvNJEmpJrz8fHBrl274OTkpKyqT0tLQ3p6OkaMGIHdu3frPHbnzp0xa9YsrXe9jhw5gnnz5uHKlSs6xxC6r7AYediqLC0tS31fXFyMgoICGBgYwMTEROc+sGIVNCoIsZJlRaSlpaFjx454/fq1zmMkJibi888/x8CBA/H999+Xel0sWbIEv/zyCy5cuIAOHTroHEPMPGxN+Owr/O7dOyxevBgRERGlChonTpyIsLAw6OnpITs7W7nwia6uXbumTO0wNDSEr68vvvnmG7Ro0QIA8NNPP2HBggXKY6gssfOwSQ0hfsc9QojYYmJi2JAhQ1jLli2Zs7MzGzJkCIuJieE8roWFBcvKytK6Pysri3P/2urWV1iT9PR01rt3b3bs2DGdx1D8O0kkEo0PxT6pVMr5eK9fv87q16/PzM3NmZ6eHrO2tmYSiYTVrl2bNWvWjPP4jDGWnJxc6pGUlMSOHj3KevXqxbp168Z5/NjYWGZtba32+2Rtba2xL3NlGRkZKftHa3Lv3j1mbGzMOY42QvUVfvnyJXv58iXv47q4uDB9fX3Wv39/duDAASaXy9We8+TJEyaRSDjFuX79Ops4cSKrW7cus7CwYGPHjmVXrlzhNCap2eiONCFEZ2IUM8bHxwveV1hBjDxsbRISEuDj44O0tDSd/n8xCxqFWslSlRgFjYWFhTh27Bhu374NxhgcHBzg4eGhtWtLZdSvXx+7du2Cu7u7xv0nT57EqFGj8OjRI86xNJk0aRLS0tJw7NgxnccQs6Bx/vz5CAwMRKNGjXgZ70OKi4uVedjHjx8XJA+b1Aw0kSakhuGzr3CXLl3g5eWl7Fdb1o8//ohDhw7h0qVLnGNlZWUJ1ldYQag87IpISkpCz549lS3M/s4sLCxw+fJlODo6wsLCAhcvXoSzszMuX74MPz8/nS8GVJW9MBCioFEV3wt0CJ2HXdG+wtouciti69atGrcrChpjYmJ4K2gUm1B52KTmoa4dhNQwZ86cQWFhIS9jBQYGYsqUKWjVqpXGYsaFCxfyUqAHvJ9YlXfXlUtfYUUeNmMMr169KjVZe/fuHf73v/+pTa51dfjw4VLfs//fgzkiIkLnxSzKErqgUYyVLMUuCON7gY5//etf+Pzzz/GPf/xDYx728ePHORUCJiYmatxubm6OPn36YP/+/Zz7Cvv5+ZW7v127dvjxxx91nkh/qLhXFV9/RzTlYa9du7ZUHnZISAhNpEmF0R1pQmoYvvunClnMqErIvsKKNAJtJBIJ5s6di5kzZ+ocQzVW2bGtra3h7u6O5cuX8/JJgVAFjQoeHh7w9/eHt7c3goODkZKSgpCQEGzfvh3Pnz/H5cuXOY2vIGZBoxB9hY8cOYLAwEA8ffq01HYrKytERUVh8ODBvMWqClwLGrUtMlKWRCLReVVRVa1bt0ZaWho8PDwQHByMQYMGqa0EmpubCxsbG5SUlHCOR2oGmkgTUsO4uLjg6NGjsLW15W3MPXv2YNeuXcjIyFDmmnp7e2PEiBG8xXj58mWp78v2Fe7du7fOYwudh52Xl8f7anOVlZGRgXHjxmHatGnw9PTkNJZQK1mqSkxMRP/+/VFQUID8/HzUqVMHubm5MDExgY2NDe7evcs5hiqhFugQMg+7qqWmpqJPnz6C5XnzTew8bFIz0ESaEPJR46OvsIJQediqudfu7u7Yv38/76snVgTXgkYxiVHQqOrHH3/EuHHjBD0vfOdhVzU+ChoJ+djRRJqQakrsvsIKfBYzVgQffYUVzpw5U+5+XfOwZTIZLl26BGdnZ0ilUuTk5JTqhy0WPgsa5XI54uLicOfOHXh7e8PMzAwPHz6Eubk5TE1NOY8vRkGj2At0mJub85qHLTQxChoVvLy8NF7ASiQSGBkZoUWLFvD29lamj1VUVeRhk5qFig0JqabatWunsX2YgmKfRCLhlF9cFp/FjKrKXhgoivQWL16Mdu3a8RLD1dVVbRsfedhffPEF3Nzc4OzsDOD9pMHAwEDjc/nIBRW6oDErKwt9+/ZFdnY2ioqK0KdPH5iZmSE8PBxFRUVYt24d5xhiFDQeOnQICxcuFG2Bjo/tvpUYBY0KMpkMBw8ehIWFhXJifv36dbx48QIeHh6IiYlBeHg4Tp48WanfYW0/Q1lCdgMi1RtNpAmppjIzM6v6EHil7cJA0VeYD8+fPy/1fdk8bF3t2LEDW7duxZ07dxAfH49WrVoJmiM7dOjQUt+XLWjkKjQ0FB07dkRycjKsrKyU2728vBAcHMx5fABo3749rl69Cnt7e/Tq1QuzZ89Gbm4utm/fDhcXF15iJCUlITExEZs3b0ZoaCjGjx+Pr7/+GoGBgVpXJKxJ+FjJs6Lq168Pb29vREREKC+gSkpKEBoaCjMzM0RHR2Ps2LEICwur1DLkYv4MpIYSaeEXQkgN0apVK5adnc37uPfu3Sv1yM7OZoWFhbzH0SQuLo516NCBl7FcXV3Z8+fPeRlLlRCrzWlTp04dlpaWxhhjzNTUlN25c4cxxlhmZiZvq/VdvXqVnTp1ijHGWE5ODvP09GRmZmasQ4cOLCkpiZcYqt6+fcv27dvHBg4cyGrVqsVat27NVq1axV68eMFbjEWLFgly7quDunXrslu3bqltv3XrFrOysmKMvV+tUSaTiXxkhJSP7kgTUgMI3VdY1Y0bN3gbS5XYfYVV1atXD7du3eJlLKHukFlaWopW0FhSUqIxzeXBgwdqq0LqqmPHjsqvbWxsBC9oY4yhuLgYb9++BWMMlpaWiIiIwKxZszgt0KGah/2vf/2L56OuPuRyOdLS0uDg4FBqe1pamvJ3zcjIiFMKhlB52KRmo2JDQmoAIfoKV0Uxo9B9hcvLw5bL5ZX6SFnVlClTMH/+fNSuXfuDxU+6FjyJWdD41VdfQSaTYcOGDTAzM0NKSgqsra0xZMgQNGnSBJs3b+YljtAFjYDmBTq++eabUgt0LFiwADk5OTqN365dO9y4cUO0POyPVUhICHbv3o0ZM2Yo02quXr2KRYsWwdvbG6tXr0ZUVBS2bNmi8+vQ39+/3Dzs5ORk3Lt3r9J52KRmo4k0ITUU177CikVMtP0J4buYUYy+wtp+JkUetpOTk07jurm54cCBA7CwsICrq2u5d9V0vWM9fPhwnD9/Hs7OzoiPj0fXrl0FK2h88OABPD09wRhDRkYGOnbsiIyMDFhZWeHs2bO8rAJZtqAxPT0dzZs3R2hoKG8FjWIt0KHIw969ezfkcjnlYWvw7t07LF68GBEREcqLlnr16mHixIkICwuDnp4esrOzIZVK0bhxY51iTJ8+HXl5eVrzsBcuXIixY8fi999/13myTmoemkgTUoNx6Stc3nLdZfGRliFGX+GyP5NUKoW1tXWpJcP/rgoLC5UFjcuXL0dwcLDWgsaVK1dyjieXyxEdHY2UlBS8fv0aHTp0wKhRo2BsbMx5bOB9waSZmRk2btwIKysr5WIpcXFxCA4ORkZGBucYYi/QUVxcjNjYWGzevBnHjx+Hk5MTgoKC4O/vD5lMJsoxfAwU7Rn5XsTI2toa58+fV0sfSU9PR9euXZGbm4vU1FT06NEDL1684DU2qcaqJDObEPK3kJiYyMzMzKr6MCpEJpMpC9xkMhm7efMmY4yxS5cuMUdHx6o8tEoJCAhgeXl5attfv37NAgICeIkhVEGjQm5urvLr7OxsNmvWLPbdd9+xM2fO8BZDjIJGsRUVFbHo6Gjm4eHB9PX1Wc+ePVmLFi2YmZkZi46OrurDqxIWFhbM0tJS7dG0aVPm4eHBfv31V15jHTp0SG37oUOHmIWFBWOMsfT0dOXXhFQEFRsSUgMI3VdYjGJGMfoKA8LnYW/duhWLFy9WK8orLCzEtm3beGnlJ1RBY2pqKgYNGoT79+/D3t4e0dHR6Nu3L/Lz8yGVSrFy5Urs3btXrf2eLoQqaKyKBTo05WGvXbu2VB52SEiIzgWNH7NVq1Zp3P7ixQtcu3YNAwcOxN69ezFo0CDOsUaPHo2goCCNediKv1GK9pSEVBSldhBSA5RdkrhsX2GuqxAKUcxYloeHB/z9/eHt7Y3g4GCkpKQgJCQE27dvx/Pnz3H58mXOMYTMw87Ly1N2g8jIyChVCPju3TvExsZi+vTpePjwoU7ji1HQ2K9fP+jr62P69OnYvn07jhw5Ak9PT0RGRgIAJk6ciGvXruHSpUs6ja9KqIJGNze3Cj1PIpHwsjiOWHnY1dWKFSuwd+9eXLhwgfNYYuRhk5qHJtKEVFN5eXm85xhWBtdixrISEhLw6tUruLm54fHjx/D19cWFCxdgb2+PTZs2oW3btpxjCJmHrShk1EYikWDu3LmYOXOmTuOLUdBYt25dnDp1Cm3atMHr169hbm6Oq1evKjsgpKWloUuXLrzkl4pR0CgGsfOwq5v09HR06dKFl4txVULlYZOahybShFRTenp6ovUV1oZLMWNVsLCwwOXLl+Ho6AgLCwtcvHgRzs7OuHz5Mvz8/Dj9HPHx8WCMwd3dHfv27UOdOnWU+wwMDGBnZ4eGDRvy8WMIRiqV4tGjR8pJrJmZmbIIEABycnLQsGFD3pacF7qgkfz9paamok+fPnj06JHOY1haWmq8sJTJZHBwcMB3332HPn36cDlMUoNRjjQh1ZSpqSmePn0KGxsbxMXFobi4WPRj0NfX1zlVQROh+woLmYfdq1cvAO+Xbm/SpAmnhSU+JDAwEKtXr1bLJc7Pz8fEiRM55WGXPW6hfo6nT5/CysoKPj4+uH//PiIjI3Hr1i0kJCSgR48evMQQaoGOqsjDrq42btyIdu3acRpDzDxsUvPQHWlCqikx+wqXV8xoa2uLo0ePchofEKevsBh52GfOnCl3f8+ePTnHUP00QlVubi7q168PuVyu07hSqRT9+vVTLigSGxsLd3d31K5dGwBQVFSEY8eOcboj/aGCxvz8fN4KGoVaoEPsPOyPmbaLjpcvX+L69etIT0/HmTNnlOdHCHzmYZOahybShFRTYvYVFrqYERCnr7AYedhl/62A0nd1uUxChS5oDAgIqNDzuKxsKGZBIy3QUfW0XXSYm5vD0dER48aNQ7NmzQQ9BqHysEnNQBNpQmoA1UI0vohdzGhlZYULFy7A0dGxVG7uvXv30LJlSxQUFIh2LFy8fPmy1PfFxcVITEzErFmzsHDhQvTu3VvnsYUuaBSDmAWNtEAHAfjJwyY1F+VIE1IDCNFX2NLSUtRiRqH6CpcldB62phXs+vTpAwMDA0yZMgXXrl3TeezTp09/9AWNz549Q/369QG8z/OvXbt2qfaKlpaWePXqFS+x5HI50tLS1CbSaWlpyt81IyMjTnngQuVhE/7wkYdNai6aSBNSTQndV1jsYkYPDw+sWrUKGzZsAPB+IvL69WvMmTMH/fv35yVG2TzsPn36wMzMDOHh4bzlYWtTr1493Lp1i9MYYhY0CkmsgkYxFuiQyWTl5mHHxMQgPDy80nnYpOIqmodNiC5oIk1INZWYmKic3F6/fp33ycgXX3wBNzc3ODs7A3h/502oYkYAWL58OTw9PdGyZUu8efMG3t7eyr7Cu3fv5jw+AISGhqJjx45ITk6GlZWVcruXlxeCg4N5iZGSklLqe0Vh5uLFi3m7K5aVlYWsrCyt+/koaBSSv7+/sqDxzZs3GDt2bKmCRr6sXLkS9erVw5IlS0ot0DF58mSEhYUBeH8B17dvX51j1K9fH97e3lrzsKOjozF27FiEhYVRHrZAEhMTNW43NzdHnz59sH//fsHzsEn1RTnShBCdiFnMqCB0X2Ex8rAVecxl//R26dIFmzZtgpOTEy8xyuKroFFoYhQ0aiLUAh2Uh01I9UZ3pAmpAYToK2xsbIyxY8cCeN/tIjw8XNAcaTH6CouRh52ZmVnqe6lUCmtraxgZGfEyPgA8f/681PdlCxr/zvieIGsi5gIdYuRhE0KqDt2RJqQGEKqvsBjE7Cv81VdfQSaTYcOGDTAzM0NKSgqsra0xZMgQNGnSRJRJnpDi4+M5FzRWB1u3btW4XbFAR0xMDG8LdISEhGD37t0a87C9vb2xevVqREVFYcuWLZTaQchHiCbShFRjQvYVFrqYUUHMvsIPHjyAp6cnGGPIyMhAx44dlXnYZ8+eVbsQ0VV+fj7i4+ORnZ2Nt2/fltoXEhLCSwxN0tLS0LFjR7x+/VqwGNUBnwt0vHv3DosXL0ZERESpPOyJEyciLCwMenp6yM7OhlQqRePGjTnHI4SIiybShFRjQvYVVu1N7erqWm4cLu33xOwrDAifh52YmIj+/fujoKAA+fn5qFOnDnJzc2FiYgIbGxvcvXuXc4zyChrlcjnd+fwAoRboECoPmxBSdWgiTUg1Fh8f/9H3FZZKpXj06JHybrBqESAA5OTkoGHDhrwU0CnysAEo87ALCwsxePBg3vKwXV1d4eDggHXr1kEmkyE5ORm1atWCj48PQkNDMWzYMM4xxChorM74WKBDzDxsQkjVoWJDQqoxsfoKC1HMqErovsIfysNeuXIlb3nYSUlJWL9+PaRSKfT09FBUVITmzZtjyZIl8PPz42UiLUZBY3XGxwIdq1at0rhdkYc9cOBA3vKwCSFVh+5IE1IDfGixAa59hYUsZpRKpejXr5+yr3BsbCzc3d1L9RU+duwYpzvSYuZhW1tb48KFC7C3t4eDgwN++ukneHp6Ii0tDZ9++iny8/M5xyDlq+gCHYr0ISHwmYdNCKk6NJEmpAYQqq+wkMWMCmL0FRYzD9vDwwP+/v7w9vZGcHAwUlJSEBISgu3bt+P58+e4fPky5xhA1RU0fgzc3Nw0bjc3N4ejoyPGjRsn+AIdQuVhE0LERRNpQmqAly9flvq+bF/h3r176zSukMWMYhIzDzshIQGvXr2Cm5sbHj9+DF9fX+Ud6k2bNqFt27acY4hR0Ei44SMPmxBS9ShHmpAaQCaTqW3r06cPDAwMOPUVPn369EdfzKggdB62QseOHZVf29jY4NixY7zHmDx5MgYNGqQsaLx06VKpgkZS9fjIwyaEVD2aSBNSg9WrVw+3bt3S+f8Xq5hRDP7+/so87Ddv3mDs2LGl8rD5JJfLERcXhzt37sDb2xtmZmZ4+PAhzM3NYWpqynl8MQoaSfkqmodNCPm40USakBqgvL7CfNwVy8rKQlZWltb9XIsZhebn51fqex8fH7Xn+Pr68hIrKysLffv2RXZ2NoqKitCnTx+YmZkhPDwcRUVFWLduHecYtWrVUubF29jYIDs7G87OzpDJZLh//z7n8cmHJSYmatxubm6OPn36YP/+/YLnYRNChEcTaUJqgHbt2pXbV5grV1dXtW18FDOKRcylv0NDQ9GxY0ckJycre1YDgJeXF4KDg3mJ0b59e1y9ehX29vbo1asXZs+ejdzcXGzfvh0uLi68xCDl47IIESHk40ETaUJqAKH7Cj9//rzU92WLGcn/OXv2LC5cuAADA4NS25s2bYo///yTlxiLFi3Cq1evAAALFy6Er68vxo0bpyxoJIQQwg+aSBNSA9jZ2Qk6vlDFjNVRSUmJxjv0Dx48UFvQRldiFDQSQgihiTQhNUZV9BXmWsxYHXl4eGDVqlXYsGEDgPcpMK9fv8acOXPQv39/3uIIXdBICCGE+kgTUiMI3Ve4vGJGuVyOc+fOcRq/Onnw4AE8PT3BGENGRgY6duyIjIwMWFlZ4ezZs2qrQ+qibEFjeno6mjdvjtDQUN4KGgkhhNBEmpAawdXVFQ4ODsq+wsnJyaX6CnNth6ZYmEVbMaOTkxOn8asbuVyO6OhopKSk4PXr1+jQoQNGjRoFY2NjXsYfOnQozMzMsHHjRlhZWSkXl4mLi0NwcDAyMjJ4iUMIITUdTaQJqQEsLCxw+fJlODo6wsLCAhcvXoSzszMuX74MPz8/pKWlcRq/bOs7vosZq5OnT58qu3Xcv38fkZGRKCwsxODBg9GjRw9eYlhZWeHChQtwdHQstUrjvXv30LJlSxQUFPAShxBCajrKkSakBhC6r7DQxYzVQWpqKgYNGoT79+/D3t4e0dHR6Nu3L/Lz8yGVSrFy5Urs3bsXQ4cO5RxLjIJGQgghNJEmpEYQo69wVRQzfky+//57tG7dGjt37sT27dsxcOBADBgwAJGRkQCAiRMnYvHixbxMpMUqaCSEkJqOUjsIqQESEhLw6tUruLm54fHjx/D19cWFCxeUfYXbtm3LaXyhixmrg7p16+LUqVNo06YNXr9+DXNzc1y9ehWffvopACAtLQ1dunTBixcvOMcSo6CREEIITaQJITwQupixOpBKpXj06JFyEquauwwAOTk5aNiwIW+rQApd0EgIIQSQVvUBEELEIZfLceLECaxfv1656t3Dhw/x+vVrzmMnJSVh6tSpkEql0NPTQ1FREWxtbbFkyRLMmDGD8/jVheqy6Zq+58vTp0+hr68PHx8fTJw4EXXr1sWtW7eQkJAgSDxCCKmpKEeakBqgbF/hPn36wMzMDOHh4bz0FRa6mLG68Pf3h6GhIQDgzZs3GDt2LGrXrg0AKCoq4jy+mAWNhBBC6I40ITVCaGgoOnbsiOfPn5f6aN/LywsnT57kPL6imBGAsphx586dmDRpEm/FjB87Pz8/2NjYQCaTQSaTwcfHBw0bNlR+b2NjA19fX04xFAWNZ86cgaurq7Kg8eXLl3j+/DnGjBmDxYsX8/QTEUIIoRxpQmoAofsKC13MSCpGzIJGQgghlNpBSI0gdF/hjh07Kr+2sbHBsWPHOI9JKu/Zs2eoX78+AMDU1BS1a9eGpaWlcr+lpaUyP54QQgh3lNpBSA2g6CusIERfYSGLGUnFiVXQSAghhFI7CKkRhO4rXLaYMT09Hc2bN0doaCgvxYykYqRSKfr166csaIyNjYW7u3upgsZjx47x1mKPEEJqOppIE1JDCNlXeOjQoTAzM8PGjRthZWWlzMGOi4tDcHAwMjIyePgJyIcEBARU6HmbN28W+EgIIaRmoIk0ITXA06dPYWVlBQC4f/8+IiMjUVhYiMGDB6NHjx6cxxe6mJEQQgj5O6IcaUKqsdTUVDRt2hQ2NjZwcnJCUlISOnXqhJUrV2LDhg1wc3PDwYMHOccRupiREEII+TuiiTQh1ZhYfYXFKGYkhBBC/m4otYOQakysvsJCFzMSQgghf0c0kSakGpNKpXj06JFyIquavwwAOTk5aNiwIS9dHIQsZiSEEEL+jii1g5BqToy+wk+fPoW+vj58fHwwceJE1K1bF7du3UJCQgLvsQghhJC/C7ojTUg1JnRf4dTUVAwaNAj379+Hvb09oqOj0bdvX+Tn50MqlSI/Px979+7F0KFD+fqRCCGEkL8NmkgTUo0J3Ve4X79+0NfXx/Tp07F9+3YcOXIEnp6eiIyMBABMnDgR165dw6VLl3QanxBCCPk7o4k0IURnYhUzEkIIIX9HlCNNCNHZs2fPUL9+fQCAqakpateuDUtLS+V+S0tLvHr1qqoOjxBCCBEUTaQJIZyIUcxICCGE/B3pV/UBEEI+bv7+/spixjdv3mDs2LGlihkJIYSQ6opypAkhOhO6mJEQQgj5O6OJNCGEEEIIITqgHGlCCCGEEEJ0QBNpQgghhBBCdEATaUIIIYQQQnRAE2lCCCGEEEJ0QBNpQgghf2v+/v6QSCQYO3as2r7x48dDIpHA399f/AMjhNR4NJEmhBDyt2dra4vo6GgUFhYqt7158wa7du1CkyZNqvDICCE1GU2kCSGE/O116NABtra22L9/v3Lb/v370aRJE7Rv31657dixY+jevTssLCxgZWWFgQMH4s6dO8r99+7dg0Qiwf79++Hm5gYTExO0bdsWFy9eFPXnIYRUDzSRJoQQ8lEIDAwstbjPpk2b1BYFys/Px5QpU5CQkICTJ09CKpXCy8sLJSUlpZ43c+ZMfPfdd0hKSoKDgwNGjhwJuVwuys9BCKk+aEEWQgghf2v+/v548eIFIiMjYWtri1u3bgEAnJyccP/+fXzzzTewsLDAli1b1P7f3NxcWFtbIzU1FS4uLrh37x6aNWuGqKgoBAUFAQBu3ryJVq1a4Y8//oCTk5OYPxoh5CNHd6QJIYR8FKytrTFgwABs2bIFmzdvxoABA1C3bt1Sz8nIyMDIkSPRvHlzmJubo2nTpgCA7OzsUs9r06aN8usGDRoAAB4/fizsD0AIqXb0q/oACCGEkIoKDAzEhAkTAABr165V2z9o0CDY2dkhMjISDRs2RElJCVxcXPD27dtSz6tVq5bya4lEAgBq6R+EEPIhNJEmhBDy0ejbty/evn0LiUQCT0/PUvuePn2KW7duITIyEj169AAAnDt3rioOkxBSQ9BEmhBCyEdDT08Pf/zxh/JrVZaWlrCyssKGDRvQoEEDZGdnY/r06VVxmISQGoJypAkhhHxUzM3NYW5urrZdKpUiOjoa165dg4uLCyZPnoylS5dWwRESQmoK6tpBCCGEEEKIDuiONCGEEEIIITqgiTQhhBBCCCE6oIk0IYQQQgghOqCJNCGEEEIIITqgiTQhhBBCCCE6oIk0IYQQQgghOqCJNCGEEEIIITqgiTQhhBBCCCE6oIk0IYQQQgghOqCJNCGEEEIIITqgiTQhhBBCCCE6oIk0IYQQQgghOvh/lI6YhcVJPP0AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Itjicnd2hVzA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}