Variaveis_Dummy_409.ipynb

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        "**Exercicio Pratico**\n",
        "\n",
        "O banco que solicitou o modelo de limite de credito incluiu novas variaveis no dataset como escolaridade, gênero e Estado do cliente.\n",
        "Faça as transformacoes necessarias nessas variaveis Dummies e tire o\n",
        "modelo OLS até que o modelo fique ajustado\n"
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      "source": [
        "import pandas as pd\n",
        "import numpy as ny\n",
        "import statsmodels.formula.api as smf\n",
        "import seaborn as sns"
      ]
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      "source": [
        "df = pd.read_excel('409 - Limite_Credito_Dummy.xlsx')\n",
        "df.head()"
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              "   Idade  RendimentoTotal  Salario  LimitedeCreditoImediato  \\\n",
              "0     70            10736     5214                      400   \n",
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              "   Idade  RendimentoTotal  Salario  LimitedeCreditoImediato  \\\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 48,\n  \"fields\": [\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 30,\n        \"max\": 94,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          82,\n          74,\n          62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RendimentoTotal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12640,\n        \"min\": 4300,\n        \"max\": 81000,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          21950,\n          30000,\n          4400\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salario\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9332,\n        \"min\": 4027,\n        \"max\": 54500,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          15000,\n          40000,\n          4400\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LimitedeCreditoImediato\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 938,\n        \"min\": 340,\n        \"max\": 3500,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          400,\n          380,\n          1000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Escolaridade\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Superior_Pos\",\n          \"Fundamental_Medio\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Regiao\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"SP\",\n          \"RJ\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Genero\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LimitedoChequeEspecial\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6131,\n        \"min\": 500,\n        \"max\": 35000,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2350,\n          18185\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# transformando variáveis categóricas em numéricas\n",
        "df = pd.get_dummies(df, columns=['Escolaridade', 'Regiao','Genero'], prefix = ['Escolaridade', 'Regiao', 'Genero'])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "QuwkZi52EvxY",
        "outputId": "134872b0-a596-476a-d0c4-ecb446e1a6be"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Idade  RendimentoTotal  Salario  LimitedeCreditoImediato  \\\n",
              "0     70            10736     5214                      400   \n",
              "1     68             5000     5000                      380   \n",
              "2     44             5800     5800                      500   \n",
              "3     72             4300     4300                     2000   \n",
              "4     75             4400     4400                     3000   \n",
              "\n",
              "   LimitedoChequeEspecial  Escolaridade_Fundamental_Medio  \\\n",
              "0                     500                            True   \n",
              "1                     600                            True   \n",
              "2                     800                            True   \n",
              "3                    1000                            True   \n",
              "4                    1000                            True   \n",
              "\n",
              "   Escolaridade_Superior_Pos  Regiao_RJ  Regiao_SP  Genero_F  Genero_M  \n",
              "0                      False       True      False      True     False  \n",
              "1                      False       True      False      True     False  \n",
              "2                      False       True      False      True     False  \n",
              "3                      False       True      False      True     False  \n",
              "4                      False       True      False      True     False  "
            ],
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4ea5dd6a-d3dc-43f6-aa3c-5a13f2c87647')\"\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-4ea5dd6a-d3dc-43f6-aa3c-5a13f2c87647 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 48,\n  \"fields\": [\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 30,\n        \"max\": 94,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          82,\n          74,\n          62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RendimentoTotal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12640,\n        \"min\": 4300,\n        \"max\": 81000,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          21950,\n          30000,\n          4400\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salario\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9332,\n        \"min\": 4027,\n        \"max\": 54500,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          15000,\n          40000,\n          4400\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LimitedeCreditoImediato\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 938,\n        \"min\": 340,\n        \"max\": 3500,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          400,\n          380,\n          1000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LimitedoChequeEspecial\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6131,\n        \"min\": 500,\n        \"max\": 35000,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2350,\n          18185,\n          2000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Escolaridade_Fundamental_Medio\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Escolaridade_Superior_Pos\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Regiao_RJ\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Regiao_SP\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Genero_F\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Genero_M\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mudando o tipo das variaveis das colunas novas de boolean para int\n",
        "df['Escolaridade_Fundamental_Medio'] = df['Escolaridade_Fundamental_Medio'].astype(int)\n",
        "df['Escolaridade_Superior_Pos'] = df['Escolaridade_Superior_Pos'].astype(int)\n",
        "df['Regiao_RJ'] = df['Regiao_RJ'].astype(int)\n",
        "df['Regiao_SP'] = df['Regiao_SP'].astype(int)\n",
        "df['Genero_F'] = df['Genero_F'].astype(int)\n",
        "df['Genero_M'] = df['Genero_M'].astype(int)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "iQIsaLQwFx1l",
        "outputId": "cc06cfba-1dea-4112-a0ca-52162e1aa84c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Idade  RendimentoTotal  Salario  LimitedeCreditoImediato  \\\n",
              "0     70            10736     5214                      400   \n",
              "1     68             5000     5000                      380   \n",
              "2     44             5800     5800                      500   \n",
              "3     72             4300     4300                     2000   \n",
              "4     75             4400     4400                     3000   \n",
              "\n",
              "   LimitedoChequeEspecial  Escolaridade_Fundamental_Medio  \\\n",
              "0                     500                               1   \n",
              "1                     600                               1   \n",
              "2                     800                               1   \n",
              "3                    1000                               1   \n",
              "4                    1000                               1   \n",
              "\n",
              "   Escolaridade_Superior_Pos  Regiao_RJ  Regiao_SP  Genero_F  Genero_M  \n",
              "0                          0          1          0         1         0  \n",
              "1                          0          1          0         1         0  \n",
              "2                          0          1          0         1         0  \n",
              "3                          0          1          0         1         0  \n",
              "4                          0          1          0         1         0  "
            ],
            "text/html": [
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert: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",
              "    .colab-df-buttons div {\n",
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              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-bd5f4a5b-9c64-41b6-9a6d-73b5f27a3f27 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-bd5f4a5b-9c64-41b6-9a6d-73b5f27a3f27');\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",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
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              "      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-779e89c6-6c13-448c-8052-3ea1be55e06a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 48,\n  \"fields\": [\n    {\n      \"column\": \"Idade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 30,\n        \"max\": 94,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          82,\n          74,\n          62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RendimentoTotal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12640,\n        \"min\": 4300,\n        \"max\": 81000,\n        \"num_unique_values\": 38,\n        \"samples\": [\n          21950,\n          30000,\n          4400\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salario\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9332,\n        \"min\": 4027,\n        \"max\": 54500,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          15000,\n          40000,\n          4400\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LimitedeCreditoImediato\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 938,\n        \"min\": 340,\n        \"max\": 3500,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          400,\n          380,\n          1000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LimitedoChequeEspecial\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6131,\n        \"min\": 500,\n        \"max\": 35000,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          2350,\n          18185,\n          2000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Escolaridade_Fundamental_Medio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Escolaridade_Superior_Pos\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Regiao_RJ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Regiao_SP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Genero_F\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Genero_M\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#verificando nulos\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YHM6nglgGalw",
        "outputId": "feea862e-dbc7-4836-b52d-2eaaf00ace56"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Idade                             0\n",
              "RendimentoTotal                   0\n",
              "Salario                           0\n",
              "LimitedeCreditoImediato           0\n",
              "LimitedoChequeEspecial            0\n",
              "Escolaridade_Fundamental_Medio    0\n",
              "Escolaridade_Superior_Pos         0\n",
              "Regiao_RJ                         0\n",
              "Regiao_SP                         0\n",
              "Genero_F                          0\n",
              "Genero_M                          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#verificando duplicados\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5d2k_YqFGeoc",
        "outputId": "b9064cf0-459b-48a7-b01e-08c7746db350"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# verificando correlação\n",
        "sns.heatmap(df.corr(), annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "lMzYX8AtJGJv",
        "outputId": "1189081a-cc2f-41a2-90d7-92c0412178a1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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IHewB0Cf+tT7LC0ajId+2/yIip14gyAeSk5PZuHEj3377Lc2bNwdg69atFC1q/jGcAQMGmP4uWbIkK1eupHbt2iQnJ+Pg4MCaNWsoVaqUKcpfrlw5bt68ycKFC0315s+fT8+ePU0TcMuUKcPKlStp3Lgxa9aswdb2/8crU6mTc4ZDHmeeX2qIi8OmaLFc6bAbMATD8xi0VzOdAlXnHmDQo/75xzezx9m6PXLf3NljP3gIhtiYXEXlLbYvl6GLiTcr18XEoyzpa7GO3MMVXWx2ebmnq+l38u+XSTz8B9pHESiKF8JrbF9Um2YT2mmcxWiXS5dWJJ++gi4iFkpnse/l+Yq3cL5y2T92AzP6x+zBALCpWw+nyTNAaYvheSyJk8dhTEzIlU5bLxcANNHm8uroBGw9XXKlwxoRJ67z5NeLpIRH4+DnxTuTu9JwxwR+azcTDObfdZS6ZJw//XPz/tE/j0PuZ/n85Yb02/d4PnsRuoePkXm44TS4D17rlxPRbSDG1LQ31qd60V/qaPMHK3VMIiqvXKY8SSTUnt2LqAuBxAf+9bzsjGtOhuH5c7NyQ1wcimK5G1MOn3yCPuavXXMAyhfH/Or40UQnoHzRV/mBVGlDhWndebLnD3TJls+bzPXFGIp9ZQzFxmFT4q+PITMkEtwnfIr66i20wWH5o1PwtyGceoEgHwgJCSE9PZ26deuaytzc3ChXrpyZ3OXLl5k1axbXr18nLi4OwwunKTw8nIoVK3L37l0zHQD16tUz+339+nVu3LjBjh07TGVGoxGDwUBoaCgVKlTIZp9Go0Gj0ZiVSTUalMq85bv+k1F17oGycTMSJowEbcanYWWly6IK6Ej88MF/uz123Xpg27QZcWMz7fknkLj/d9PfmqCHqO+FUebkRuzfrUzKH9fNZOU+7jg0qsHj4ZYnzuYFVZceKJs0I2F89v7RXrtK3NBBSJ2csW3TDseps4gfMQRjQnw2Pb4f1afmooGm32d6L853W1/y+Oc/TX8n3ntEwp1w2pxfjlf9ikSdyb8vY+aE+o8Lpr+1wQ/Q3LpL4V++w65FE1L2HXxt/RIf1ufdhZnBht/6LMmzTXXn9cWlXFEOfTgnz7rygl2PHtg2a0bcqFGQnrtrrshHDaiyeJDp94Ve1ic95xcSuYya34xEIpFwc6LlHPy/C/cpw7Ep5cezfqMLxoD/aNpMfiGceoHgbyIlJQV/f3/8/f3ZsWMHnp6ehIeH4+/vT3ou/+FAxluBTz75hBEjRmTbV8xK9Gr+/PnMnj3brGza+BHMmJC3CXVvE0NiAka9Dqmrq1m51NUVQ9xzK7UyUHXsiqpLDxKnjEUf9sBUbvNOFSQurrhu220qk8jk2A8aiqpDJ+L6dbNuT0IO9jx/jT2du2LXvQfx48eif/AgR9kc29fpkXu4mJXLPVzQRVteLUMXE4fcPffyANpHEehiE7ApXghecepdOrVEH59E0vHz2e17eb5c/sL56tQVVdceJE4aiz7UQv9o1BiePsHw9AnJ9+7gumkHtq3bkrZrRzbRZ4evcPRKZmqOTJHxb07p6Yw6Kt5UbuvpTPzthzna9aakhEejiU3EvoQ3vOLUG+Izzp/Mzbx/ZG6uGGJz7p83wZicgi78MXLfwrmSf3TkCjFXM/tL+qK/bD2dSMvaXx5OxN0Of62+OnP7ULRFdQ5/NJfUZ3k7roxrTo/Uzc2sXOrqiv4115xd167Y9+hB3Nix6N7gmos4fJm4K5mrT72cDKv0dEaTpT+Uns4k3ArLtV5rvHToVUU9ONdprtUoPYA+7sUYcn9lDLm7oo/JecWc3OA+eRh279Xl2YCx6KNi8qzvL/EfTZvJL0ROvUCQD5QqVQobGxvOn890duLi4ggKylxq8N69e8TGxrJgwQIaNWpE+fLls02SrVChAhcuXDAr+/PPP81+16hRgzt37lC6dOlsm0KhsGjf5MmTSUhIMNsmjhyS18N+u+h06O4HmU9ylUiwqVYD3V3rUVBVp+6ouvchcfoEdPfNl3nTHD9C/NABxH82yLTpY6JJ+3EniVPHv96eoCCzSa5IJCiq10B7x7o9dl27Y9+rD/GTJqALsrzsXK7Q6VDfCsa+fjWz9u3rVSP1quXl+lKv3sO+flWzMvuG1a3KQ0Y0XubqiC4qu5Pg0rElCXt+A52FNaBfnq/qFs5XDv2j6twdVY8+JE7Nfr6sIpEgsbG88oguRU1KWKRpSwx6QlpkHF4NM/P65Q4q3KqXIvbS/dy1l0tUhdxQuDqgjoy3YJiO9HtBKGtXNzsOZe3qaG7eyTcbJCpbZEUKo4/JnUOtS1GTFBZp2hKCnpAaGU+hLP1l46DCs3opoi/n3F915vahWOtaHOkyj+RHuV9VyrpxOnSBgeaTXCUSFDVror1jvc/sunXDvndv4idMQBf4ZtecPkVNalikaUsOfIw6Mg6PRu+YZOQOKlyqlyIuj+PnpUNvX9KHP7t8gTYuOecKOh2au0HY1jUfQ6q61dHcyNsYcp88DLtmDXg2eAK6J9aX6hT8sxGReoEgH3BwcGDgwIGMHz8ed3d3vLy8mDp1KlJp5nNzsWLFUCgUrFq1iiFDhnDr1i3mzDF/PT1kyBC+/PJLxo8fz6BBg7h8+TJbtmwxk5k4cSLvvvsuw4YNY9CgQdjb23Pnzh2OHj3K6tWWl9NTKpXZUm206XmLxKSmphH++Knp95OnkdwLCsHZyZFCPn99ol5W0vbsxnHsZHT376ELvIdth05IlCrURzPSChzGTsEQG03qlvVAhoNo13sASQvnoI+MQOKaEeEzpqWBOg1jUiL6Vydh6nUY4p6jf/Lo9cf8w26cJk5GF3QP7b172HXshMRWRdrhDHscJ07BEBNNysYMe+y6dce+7wAS583BEBGBNIs9RnVGRE7i6IjMyxupe8aKFzLfjNxYw/Pn2SLcsZv2UHjxGNJu3iftehDu/QOQ2tmaJq0WXjIGXUQsUUu2AvB8yz78vluA28APST5xEed276F6pzTPpq7KaNvOFs8RPUg6dBZddFxGTv3EAaQ/fEbKafMcZPv6VVEU8yFu12Hr5+un3TiOy+gfXeA9bD/M6B/1kRfna3xG/6RufnG+uuR8vlDaYtejN+nnzmJ4HovEyRnVBx8i9fBAc/rka8/XS4LXH6LCqA4kh0aQEh5NpYmdSIuMN1t3/r3dk3ly8BIhmzP6UmanxKGET+bxF/PEuVJx0uOTSXsSi8xOScWxH/HkwEXUUfE4+HlTeXp3kkMjiTx5w6IdSd/9gPvMiaTfDSL99j0cu3dEqrIl5ZeMPnWbNRF9dAwJX23MqCCXY1MyY/lRiY0cmacHNmVLYUxNQ/fi2nMe+Qnq0+fQPYtE5umO88f9wGAg9fBvue6fV7m74RCVR3Qg8UEkyY+iqDa+E6mR8YQfzuyvlrsmE37wEoFbMvqr7rx+lOhQjxMDlqFNVmPrmZGLrk1KRa+2vnLP60j5/nucJ09GGxiI9u5d7Dp1QmJri/pgxphymjwZQ0wMyetfXHPdu+PQvz8Jc+eij4gwRfmNaWkZ4+ov8GD9QcqM6kDKgwhSw6MoP7Ez6sg4s3Xn3/1+KhEHLxK26QiQMX7ss4wfu2KeOFUqjvbF+JHIZdTaMArnyiW40HsREqkU5Ys+S49Pxqi1/PGkxO0/4jFnAum3g9DcCsSp14dIVLYk7c0YQx5zJ6CPiiFu5Ys0HrkcRamXY8gGmZcHinKlMKSmoXuUMYbcpwzHvk0zokbNxJiSanoTYEhOwaj5m1MF/6MfjcovhFMvEOQTixcvJjk5mfbt2+Po6MjYsWNJSMicXOXp6cmWLVuYMmUKK1eupEaNGixZsoQPPvjAJFOsWDF+/PFHRo8ezapVq6hTpw7z5s0zm2BbpUoVTp06xdSpU2nUqBFGo5FSpUrRtWvXv/V4b927z4DhE02/F636BoCANi34YtrYfGkj/fcTpDi7YNdrwIuPPQWTOH08xheTMWVeXmava23bBiCxUeA0zfxhKfXbzaTu2JJnezQnT5Ds7IJ9vwEZH8MKCSZ+0niMcZbtUbUPQKJQ4DzL3J6UrZtJ2ZZhj7J+A5wmTDbtc54+K5vMSxIPnEbm5oznqF7IPVzR3H1AeP8Z6F9MhrUp5Gk2QTPtyl0ej16M15jeeI3tS/rDJzz6dC6aoBdpJ3oDtuX8cPmoOTJHe7RRz0k5c5Wopdsxppsv9enSuRWpl++Q/sD6xMf0Uy/OV58X/fMgmMSpWc6Xp5dZzqxt24z+cZr+yvnavpnUb7eAwYCsaDEcp/sjdXLGkJSILugeCWNHoH8YZtWOVwn8aj8yOyU1Fw/ExsmOmAtBnOmxEIMm09m09/NG6Za56o9b1ZI0/mma6XfV2b0BCNv1O5dGrcNoMOBcsRjFuzRC4WRPWmQckaducnvh9xjSLS+Tmnb0JPEuzjh/0g+ZuyvpQSFEj5iE4cXkWZmPFxgzz5/M0x2fHd+Yfjv17opT766oL18jekjGNSb38sR97lSkzk7o4xJIv36LyP7DMMTnbiKxJW5/vR+5nZJ6iwagcLIj6mIQx3otMusvx+Je2Gbpr3J9Mz5g5v/jNDNdZ0evI2T36b9si+bECZJcXHDo39/08am4CRNME9Zl3t5mfWYXkDGmXD7/3ExP8pYtpLwSIMktIat/QW6npMqSQdg42fH8QiDnuy/INn4UWfrDpVpJ6v80w/S70ud9AHi06xTXRq7FtlDmkpiNf8tcCAHgj48+J/YPyx9XSzl8CqmrC65D+yLzcEUTGELk0CkYnscDIPfxMrsHyL3cKbJ7baZd/brg0q8LaRevEzEoY018p64Z/4MKbTJfhjl6+mKS9x3JXSflFyL9Jk9IjEaj8fViAoHg/xvamL+W2/02Seidt69g5jeGf858VgBiwqwsl1hAeJV+TbrA38zJG0VfL/Q3UrfoPyuN4cTTQgVtghn+ZQrgi6U5cOHeP6t/3vEuoLx2K5S4nv3bFPmN5u6JfNOlrNA033T9WxCReoFAIBAIBAJBwSNWv8kTwqkXCAQCgUAgEBQ8Iv0mTwinXiAQCAQCgUBQ8IhIfZ4QS1oKBAKBQCAQCAT/ckSkXiAQCAQCgUBQ4BiNYknLvCCceoFAIBAIBAJBwSNy6vOESL8RCAQCgUAgEAj+5YhIvUAgEAgEAoGg4BETZfOEcOoFAoFAIBAIBAWPSL/JEyL9RiAQCAQCgUAg+JcjIvUCgUAgEAgEgoLHIFa/yQvCqRcIBAKBQCAQFDwi/SZPCKdeIPiPktC7f0GbkA3n7ZsL2gQzjOqUgjbBjKcNpha0CWY4LJpY0CaYcTFge0GbYIZ/A7uCNsEMm93GgjbBDH26pKBNMOO0bUFbYE7T5qqCNkHwL0M49QKBQCAQCASCgkesfpMnhFMvEAgEAoFAICh4RPpNnhBOvUAgEAgEAoGg4BGR+jwhlrQUCAQCgUAgEAj+5YhIvUAgEAgEAoGg4BGR+jwhnHqBQCAQCAQCQYFjNIp16vOCSL8RCAQCgUAgEAj+5QinXiAQCAQCgUBQ8BgM+be9IV999RV+fn7Y2tpSt25dLly4YFW2SZMmSCSSbFvbtm1NMv369cu2v3Xr1n+pW3KLSL8RCAQCgUAgEBQ8BbSk5a5duxgzZgxr166lbt26LF++HH9/fwIDA/Hy8som/9NPP5Genm76HRsbS9WqVencubOZXOvWrdm8OfOjikql8u0dBCJSL/gHMGvWLKpVq2b63a9fPzp06FBg9vwbOXnyJBKJhPj4+II2RSAQCASCfxVLly5l8ODB9O/fn4oVK7J27Vrs7OzYtGmTRXk3Nzd8fHxM29GjR7Gzs8vm1CuVSjM5V1fXt3ocIlIvMNGvXz+2bt0KgFwup2jRonTu3JnPP/8cW9u/7/vZK1aswGj8Z3zOXCKRsGfPnlw/ZJw8eZKmTZvmKHPixAmaNGlicd+WLVsYNWpUgTrntu06oOrUDamrG7oHIaSsWYEu6J5FWWXrdtg290dWvAQAuuBAUrestypvP2wMqrYBJK9bhXrvD/lq96VrN9n83Q/cuRdMdOxzVsyfTvP36udrGwD/23uQLbv3EfM8nnKlijN5+EAqly9jUVar07Hhuz3sO3KSqJjn+PkWZvTgXjSsU91MLjI6lmXrv+XMhauoNen4FvFh7vihVCpXOptOjz7v4/Xxh8g9XUm7G8qTmd+Qev2+VXud329AobE9URT1QhP2lKcLtpJ04rJpv8+o7ri0b4RNYQ+MWh1pN4N5tvhbUq8FmWS8h3XGqVktVBVLYkzXcrNKD6vt7Tx0hq2//EZMfBJlixdm0oCPqFy6uFX5bw+cYveRs0TExOPiZE/LulUY0aMdSoVNZv88j2f5t/s5e+0uao0WXx8PPh/ajUqlilnV+zpaju5Ene7NUDnZE3YpkD3TNhEbFpGruk0+/YA2E7tzZtNBfvl821+24SU2Dd9H0ewjJI6uGJ6Gov5xHYZw6+f0JfLqjVD1nYD25p+oN36RJxsqj+9IqR5NsXGyJ+ZSEBcnbSI5NNKqvGfd8lQY2hbXyiWw83Hl9wFLeXLospmM3E5J1andKOpfC4WrAymPognaeJjg7cdztMXuow449OyKzM0NbXAICUtXor1r+Z4iL+GH46D+2JQvi7yQDwnLV5Oy+0dzIakUx4F9Ufm3RObuhj4mhtQDh0nesj13nQO0sjBeYt5gvLw/sTunXxkvdbs3o1pAA4pU8sPW0Y4ZVQaiTkx9rT6bBu9j0/RD03jR7Pkmd+OlWiNs+4xHd/NP1JvnmcpllethU781sqKlkNg7kbpkJIanobk6tnwnH1e/0Wg0aDQaszKlUpktWp6ens7ly5eZPHmyqUwqldKiRQvOnTuXq7Y2btxIt27dsLe3Nys/efIkXl5euLq60qxZM+bOnYu7u/tfPKLXIyL1AjNat27Ns2fPePDgAcuWLWPdunXMnDnzb7XB2dkZFxeXv7XN/KJ+/fo8e/bMtHXp0sXUpy+3+vXz39HMLxTvNcX+489I3bGV+OGD0YeG4DR3CRJnF4vyNlWqoTl5nIRJo0gYMxRDdDROXyxB6u6RXXf9RtiUr4g+Jvqt2J6WpqZc6ZJMHTv0regHOHTiLIvXbmVIn87sXruIsqX8+GTiXGLjEizKr9r0P37Yf5TJwweyd9NyurRvxaiZi7l7/4FJJiEpmT4jpyGXy1mzYCp7Ny1j/JA+ODk6ZNPn0q4hhacNJGLFTgLbjSbtbhglt89G7u5ssX27muXxWzWO2N1HCWw7ioQj5ynxzRRsy2Y6w+rQJzyesY7AVsMJ7jiR9MdRlNo+G5mbk0lGYiMn/sBZYr49mHP//HGVJdv28kknf3YuHEu54oX59It1xCYkWZT/9cxlVny3nyGd/dmzbBKzhnTl8LlrrPzfAZNMYnIq/aavRC6X8dWUj/lp2UTG9vkAJ3u7HG3JicZD2tOgf2v2TN3I6g7TSU/TMHDbJORKm9fWLVqlJHV7NOfp3Yd/uf2syKs3RNlhEJpD/yN1ySj0T0KxG/I5EgfL5/QlEjcvlAED0IXcyrMNFT5rR9kB/lyctJmj7WagS9XQ9LtJSHPoD7mdkrjb4VyessWqTPVZvSjUpArnhn/Nr43HE7j+IDW/6EuRVjWs1rFt3hTnEZ+StGkr0f0/RhscgvuyRUhdXSzKS2yV6J8+JXHNN+hjYi3KOPTqjt2HASQsXUlU974kfv0NDj27Yd/5I6t2ZKXJi/Hy09SNrPoL4+VdK+PFRqUk8NR1fvv651zZASCv1hBFwEDSD+8kdeloDE/DUH08+/XjxdULxQf90Yfczr5PoUQfegfN/q25tuOtYTTk2zZ//nycnZ3Ntvnz52drMiYmBr1ej7e3t1m5t7c3ERGvf3C7cOECt27dYtCgQWblrVu3Ztu2bRw/fpyFCxdy6tQp2rRpg17/9lb4EU69wIyXr4p8fX3p0KEDLVq04OjRowAYDBkXSYkSJVCpVFStWpUffsiMtr5MATl+/Di1atXCzs6O+vXrExgYaNbGggUL8Pb2xtHRkYEDB6JWq832v5p+06RJE4YPH86oUaNwdXXF29ub9evXk5KSQv/+/XF0dKR06dIcPGjucNy6dYs2bdrg4OCAt7c3vXv3JiYmxkzviBEjmDBhgulV2qxZs0z7/fz8APjwww+RSCSm3wBr1qyhVKlSKBQKypUrx/btGREfhUJh9qpNpVKZvX5TKpUMGjQIV1dX7OzsaNOmDffv3zf1X//+/UlISDBNqnlpz/bt26lVqxaOjo74+PjQo0cPoqKicn9ic4nqwy6oD+5Hc/Qg+vCHJK/6EqNGjW2r9y3KJy+ai/rAXvQPgtE/Did5xSKQSrGpVtNMTurugf2nI0haNBf0uny3G6BRvdqM+LgvLRo3eCv6Abb98Asd32/Bh62bUcrPlxmjPkalVLLn0G8W5fcf+51BPT7kvbo18C3sTdcP/GlUtzpbv//FJLNp5158PN2ZO+EzKpcvQ9FC3tSvVQ3fwj7Z9HkOCiB25xGef38czf1HPJ7yNYY0DW5dWlhs37N/exJPXSF63R40wY+J+HIHabce4NE3czJX/M+/k3z2OumPIlHff8STORuROdmjquBnkolY9j+iN+5DfS8sx/7Zvv8kHzWvR4emdSlV1Idpgztjq1Cw98R5i/LXAsOoVq4E7zesSREvN+pXLU/rBjW4FRye2T8/H8fb3YU5Q7tTuXRxinq5U79qeXx9sj845paGA9rw26o93Dl6mYh74ewe8zVO3q5UalUrx3oKOyXdlg/jx0nrSUtI+cvtm+ls0gHtucPoLhzHEPkIzfdfY0zXYFO3pfVKEimqXmNJP/gdxljr0fTcUm5Qa26v2MuTw5eJv/uIP0esQeXtQtHWNa3WeXbiOjcXfc/jQ5esynjUKkPo96eJOneXlMcxhOw4QfydcNyqlbJax6FbZ1L3HSDtwCF0YQ9JWLQUo0aNXbs2FuW1dwNJ/God6mMnMGq1FmUUlSuhPn0WzR9/oo+IRH3idzQXLmFTsbxVO7LScEAbjmcZL7veYLx0Xz6MH6yMlzObDnJyzT7Cr74+yv4Sm8YBaP88gu7icYyRj9D88DVGrQZ5Hcv3AAAkUmx7jSX98P8wxGZ3UnWXT6I9sgt90PVc2/FvYPLkySQkJJhtWaPx+cXGjRupXLkyderUMSvv1q0bH3zwAZUrV6ZDhw7s37+fixcvcvLkyXy34SXCqRdY5datW/zxxx8oFAoA5s+fz7Zt21i7di23b99m9OjR9OrVi1OnTpnVmzp1Kl9++SWXLl1CLpczYMAA077du3cza9Ys5s2bx6VLlyhUqBBff/31a23ZunUrHh4eXLhwgeHDh/Ppp5/SuXNn6tevz5UrV2jVqhW9e/cmNTXj1WV8fDzNmjWjevXqXLp0iUOHDhEZGUmXLl2y6bW3t+f8+fMsWrSIzz//3PQQc/HiRQA2b97Ms2fPTL/37NnDyJEjGTt2LLdu3eKTTz6hf//+nDhx4rXH0a9fPy5dusS+ffs4d+4cRqOR999/H61WS/369Vm+fDlOTk6mqP64ceMA0Gq1zJkzh+vXr7N3717CwsLo16/fa9t7I+Ry5GXKor2W5RW60Yj22mXkFSrlSoVEqUQik2NISsxSKMFh3FTSftiJPjwsf23+G9FqtdwJesC7NaqYyqRSKe/WqMz1O4EW66Sna1G+uH5eolQouHorM5Xg5B+XqFiuFGNmL6FxxwF0/mQcPxw4akGbBLvKpUk+cy2zyGgk+cx17GtYdk7sa5Qn+Yz5P+qk369YlZfYyHHv4Y8+IZm0O2/2+l2r03H3wWPerVzWVCaVSnm3chluBFmOalcr58fdB4+4GZyx/3FkDGeu3qFR9QommVOXblOppC/jlm6hyaDpdJmwhB+P5e6VuCXcfL1w8nLl/tnMCLc6KY1H10IoVsNyGtVLOswZwL0TVwk+m/foOAAyOdKipc2dKaMRfdA1pH7lrFZT+HfDkJyA9rylcfJm2BfzROXtSsTpzAiuNimN2KsheNTMuT9eR8yl+xRpVQOVT0YesVf9ijiW9CHi1E3LFeRybMqVRXPJ/B6kuXgFm3dydw+yRPrN2yhr1UDmWzSjmdKlUFR9B80566ubvCSn8VK8wMbLtcwyoxF90HVkftYfUBStumJMjkeXD+PlrZOPq98olUqcnJzMNksTVT08PJDJZERGmj8gR0ZG4uOTPbiSlZSUFHbu3MnAgQNfe2glS5bEw8OD4ODgN+uTN0Dk1AvM2L9/Pw4ODuh0OjQaDVKplNWrV6PRaJg3bx7Hjh2jXr16QMYAPXPmDOvWraNx48YmHV988YXp96RJk2jbti1qtRpbW1uWL1/OwIEDTRfA3LlzOXbsWLZo/atUrVqVadOmARlP3wsWLMDDw4PBgwcDMGPGDNasWcONGzd49913Wb16NdWrV2fevMy8wU2bNuHr60tQUBBly2Y4HlWqVDGlF5UpU4bVq1dz/PhxWrZsiaenJwAuLi5mF/aSJUvo168fQ4dmpHmMGTOGP//8kyVLluSYT3///n327dvH2bNnTSk4O3bswNfXl71799K5c2ecnZ2RSCTZbiRZH4xKlizJypUrqV27NsnJyTg4ZE/TeBVLuYUagwGlNPO5XurknOGQx8WZyRni4rApmrvcZbsBQzA8j0F7NfOfsqpzDzDoUf/8Yw41//nEJSShNxhwdzV/ze3u6kLooycW69SvXY1tP/xCzSoV8S3szZ9XbnL8zHn0WfJGHz+LZPe+I/Tp1I7BPT7iVmAIC1ZvxkZuQ4B/k0xlUhkSuQxtTLxZG9qYeJSlilhsX+7pYlFe7mk+WcupWS2Krx6PVKVEGxVHcK8Z6OMsp8xYIy4xJaN/XBzNyt1dHAl9avmt0vsNaxKXmEK/6asAIzq9gc4t6zPoo8wo9eOoWHYf/YPebZsw8MMW3A4JZ+HmPdjIZXzQpI5FvTnh6Jlx/pKjzVOmkqMTcPR0sVqvavt6FK7kx+qAaW/cpjUk9k5IZDIMSebXnDEpHpl3UYt1ZCUqYvNuS1IXj8wXG1ReLgCoX+kPdXQCti/2/VUuT9tKnUUD6XBlNQatDqPByIXxG4g+bzk/XurijEQuQ//8lXvQ8zgUxf/6/Ink7d8hsbfD639bMxw+qZSkdRtJO3LstXWtjZekXIyXIpX8WPUWxosxKd6s3JgUj9TL8j1AWqIC8rotSf0yf8bLW6cAVr9RKBTUrFmT48ePm7IEDAYDx48fZ9iwYTnW/f7779FoNPTq1eu17Tx+/JjY2FgKFSqUH2ZbRDj1AjOaNm3KmjVrSElJYdmyZcjlcjp27Mjt27dJTU2lZUvzV8Lp6elUr24+6a9KlcxI5svBGxUVRbFixbh79y5Dhgwxk69Xr95ro9xZdcpkMtzd3alcubKp7GUu3MuUlOvXr3PixAmLDm9ISIiZU5+VQoUKvTat5e7du3z88cdmZQ0aNGDFihWvrSeXy6lbt66pzN3dnXLlynH37t0c616+fJlZs2Zx/fp14uLiMLxwCsPDw6lYsWKOdSHjLcvs2bPNysaXKsaEMn6vrZtbVJ17oGzcjIQJI0GbsdSXrHRZVAEdiR8+ON/a+Tcx6bP+zPpyLR/0H4kE8C3sQ4B/U/YeyhzvBqORSmVLMnJQTwAqlClJcFg4u385Yu7Uv0WSz90ksM0o5G5OuHdvhd/XE7kfMA5drOW5AvnFxdvBbNxzjKmDOlG5TDHCI2JYtHkP6344wiedWgFgMBipVMqXET0yUoYqlChKcHgE3x/9I1dOfbWABnw0LzPXdfOARW9sp3MhN9rP6MuG3vPQaSynePwtKFXY9hqDetdqjCmJr5e3QPEP61N7UWZU8VTvxfllXTbKDmiFe83SnOq7hNTHMXi+W55a8/qRFhlH5Onsud1vC9vmTbBr1YK4WXPRPQjDpmxpnEd+hj4mlrSDh81kq+fTePlgRl/W/xPGS48xaHavhpQ3e0gvMPJxouybMGbMGPr27UutWrWoU6cOy5cvN6X4AvTp04ciRYpky8nfuHEjHTp0yDb5NTk5mdmzZ9OxY0d8fHwICQlhwoQJlC5dGn9//7d2HMKpF5hhb29P6dIZK25s2rSJqlWrsnHjRt555x0ADhw4QJEi5hGBV19n2dhkTh6SSCQAJif0r5JV50u9ObWTnJxM+/btWbhwYTZdWZ+SLenNq635TUpKCv7+/vj7+7Njxw48PT0JDw/H39/fbJ3cnJg8eTJjxowxK0vq3NbstyExAaNeh/SVJbekrq4Y4p7nqF/VsSuqLj1InDIWfVjmJFCbd6ogcXHFddtuU5lEJsd+0FBUHToR169bruz/J+Dq7IhMKs02KTY2Lh53NxeLddxcnFk5ZyKa9HTiE5Lw8nBj2fpvKVooc91jTzcXShX3NatXslhRjv3+Sh66QY9Rp8fGw7wtGw8XdNHxFtvXRcdbkX8lEpqmIf3hM9IfPiP1aiAVTq7FrWtLor7O/QpFrk72Gf0Tb+48xMYn4eHiZLHOV7t+pd17tfio+bsAlClWmDR1OnO+2c3gj1oglUrxdHWiZFHzCWwli3pz7PyNXNl159hlHl3LfN0tf7GqjoOnM0lZ+s3B05mnd8Is6ihSuSSOns6M2J9lxRC5jBJ1ylOvTyumlu2N0fDmK3YZUxIx6vVIHV3JeteROLpgSIzLJi/18EHq7o1q0PQswhn3Pocv95IybwhGCznTWXly5AqxV0MydSoy3ABbT2fUUfGmcltPZ+Ju//XJwDJbG6pM6sqZgct4evwaAPF3H+FaqTgVhrS16NQb4hMw6vTI3FzJ6gpL3VzRP8/5HpQTzp8NIWn7/1Afy3iY1j0IRebjjUOfHtmc+jvHLhOei/HimMN4KfpivIy0MF7q92nFlDyOF4mji1m5xNElW/QeQOqeMV5sB2YfL/aL95C64NPXjpf/Cl27diU6OpoZM2YQERFBtWrVOHTokClgGB4ejlRqnrEeGBjImTNnOHLkSDZ9MpmMGzdusHXrVuLj4ylcuDCtWrVizpw5b3WteuHUC6wilUqZMmUKY8aMISgoCKVSSXh4uFmqzZtSoUIFzp8/T58+fUxlf/75Z36Ya0aNGjX48ccf8fPzQy7/68PcxsYm20z1ChUqcPbsWfr27WsqO3v27Gsj5hUqVECn03H+/HlT+k1sbCyBgYGmugqFIlt79+7dIzY2lgULFuDrm+H8XbpkfXKaJSwu4/XKDQqdDt39IGyq1ST93JmMMokEm2o1UO/bY1W3qlN3VN16kThtPLr75rnlmuNHzFJxAJzmLkbz2xE0R3JeSeWfho2NDRXLluT81Zs0b5gRITYYDPx59SbdO1iexPcSpUKBt6c7Wp2OY6fP49+4nmlftXfKE/ZK+k7Y46cU8n51IqiR1JvBODSoSsKRFw6/RIJDgyrEbD2AJVKu3MOhQRWiN+0zlTk2qkbKFcvpDyakEqSK16/skRUbuZwKJYty/lYQzepkvEUzGAycv3Wfbq0bWqyj1mhND+Qvkb0Yly9dnmrlShD2SvrOw6dRFH4lhcga6SlqYlPM0/sSo+IoXf8dnt3JcFqVDip8q5Xiz28t5xwHn73F0lbjzco6Lx5CdMhTTq7d95ccNAD0OgyPg5GVqYLu5ov7oESCrGxVtKezn1ND5GNSFnxmVqZo2xuJUoXmp28wxsdkq/MquhQ1ya/0R1pkHD4NKxH/womXO6hwr16K+9ten55iDYlcjkwhz9Y3Rn1G+otl43RoA4NQ1KyB+vezLxRJUNaqQcqP1u9Br7XFVpk9rUNvyDb2ADQpajQWxksZC+PlXA7j5ctXxkuXxUOIyrfxUhX9rcx7gKxMFbRnLIyXqMekLjJPH1G06QVKFel71+dqvPztFGBQbdiwYVbTbSxNbi1XrpzV5bdVKhWHDx+2uO9tIpx6QY507tyZ8ePHs27dOsaNG8fo0aMxGAw0bNiQhIQEzp49i5OTk5mDmxMjR46kX79+1KpViwYNGrBjxw5u375NyZIl89Xuzz77jPXr19O9e3fT6jbBwcHs3LmTDRs2IJPJcqXHz8+P48eP06BBA5RKJa6urowfP54uXbpQvXp1WrRowS+//MJPP/3EsWM5/wMsU6YMAQEBDB48mHXr1uHo6MikSZMoUqQIAQEBpvaSk5M5fvw4VatWxc7OjmLFiqFQKFi1ahVDhgzh1q1bzJkzJ899ZIm0PbtxHDsZ3f176ALvYduhExKlCvXRDAfcYewUDLHRpG5ZD4Cqc3fseg8gaeEc9JERSFzdADCmpYE6DWNSIvqkV9IE9DoMcc/RP3mUr7anpqYR/vip6feTp5HcCwrB2cmRQj7Zvwj4V+jTqT1TF66mUtlSVC5fmu0/HiBNraGDf8ZciikLVuLl4c6oF6k0N+4GERXznHKlShAVE8uabbsxGA3079YhU2fHdvQeMZX1O37Ev0l9bt4L5scDx5gx+pNs7Udv+JliX44i9UYwqdeD8BzwAVI7W55/n7Hud7Glo9BGPOfZooy1sKM3/0KZXfPwHNyBxN8u4tr+PVSVS/No0lcASFVKvId1IeHYBbRRz5G7OuHRty023u7EHzhjatemsAdyF0dsCnuCTIqqYsZ3CZDKwJD5ENq7XROmf/UdlUr68k7p4nz76ynSNOl0aJKRcjZ19Q683JwZ2aMdAI1rVmL7gZOUL1GEymWK8ygihq92HeS9mpVMzn2vto3pO30FG346Sqv61bgVHM4Px/9kxsfmk97fhDObDtJseAdiwiKIexRFq7GdSYyM4/aRzIflwTumcuvwRc5tO0J6iprIoMdmOtLTNKTGJ2crf1PST+7Ftsdo9I+CMYQHYdM4AInCFu35jPuJbc/RGBJiSd+/DXRaDBHh5grSUjBC9vI3IHDDISqN7EBSaATJ4dFUmdCJtMh4HmdZd77prsk8PnSJ+5szHFm5nRKHEplzfxx8PXGpVJz0+GRSn8SiS04j8o87VJveHb06nZTHMXjVq4Bfp0Zcnf2tVVuSd36P67RJaO8Fob1zF/uunZDY2pK6/xAALtMno4+OJmnthowKcjnyEhnfQZDI5cg8PZCXKYUxNQ39k4z7gfrMORz79kIfGYXuQSg2Zctg360zqQdyF1jIOl6e5zBebh++yB/bjqDJ5Xhx8HTG0dMFj+IZ/ehTzhdNipr4JzFWV1fSnvoZZfdRGB4Fow8PQtH4AyQKW3QXMu4Byu6jMCY+J/2A5fFiTEtBwivjxc4BqYsnEueM+/fL/HxjUpzFNwBvlQL6ouz/F4RTL8gRuVzOsGHDWLRoEaGhoXh6ejJ//nwePHiAi4sLNWrUYMqUKbnW17VrV1NumVqtpmPHjnz66af5/kRbuHBhzp49y8SJE2nVqhUajYbixYvTunXrbK/QcuLLL79kzJgxrF+/niJFihAWFkaHDh1YsWIFS5YsYeTIkZQoUYLNmzdb/aBUVjZv3szIkSNp164d6enpvPfee/z666+mNKD69eszZMgQunbtSmxsLDNnzmTWrFls2bKFKVOmsHLlSmrUqMGSJUv44IMP/mr3WCX99xOkOLtg12sAUjc3dCHBJE4fjzE+IxVA5uVldtO1bRuAxEaB0zTzh4zUbzeTumNLvtuXE7fu3WfA8Imm34tWfQNAQJsWfDFtbL600bppA54nJPLVlp3ExMVTvpQfaxdMxeNF+s2zqBgkkszxpUnXsmrTTh4/i8ROZUujutWZN2kETg6ZHyh5p3xpls8ez/KN37F2+w8UKeTFhKH9aNfivWztx+8/g9zdmUJjemR8fOrOAx70mYXuxWRYRWFPyBIFTL18j7ARX1JoXE8Kje+NJuwpoR/PQx2U8Q/daDCgLF0Uv07NkLs6oY9PJPV6MPc7T0J9P/Ohq9CYnrh1bm76Xe5gxvyR9OA/MaRkpkW0rl+duMRkvt59iJj4RMr5FeHrKZ+YJs9GxMQhzRIdHdyxJRIJfLXzIFHPE3B1sqdxzUoM656ZGvZO6WIsHTeAld8dYN2PRyji5caEvh1o28j6couv49TaX1ColHScPwhbJzvCLgayqe8Cs/xnt+Le2Ls55qAlf9BdPYPG3hllm55InFwxPHlA6rqZGJPjAZC4eiJ9yx/ju/vVfuR2SmovGojCyY7oi0Gc7LkQQ5b+cPDzRpmlP9yqlqT5j5mTQGvM7g3Ag12/c370OgD++HQ1Vad0pd7qoShcHEh9EsONhbsJ3mb941Pq4ydIcHHGcXC/jI9P3Q8hdsxE0wR+mbeXWTRX5uGO19YNmXb27IZDz25orlwjdthoABKWrcRx8ACcx41E5uqa8fGpn38haVPuPhx20sJ42fjKeHH/C+OlXs8WtBzVyfR76PezANg1bg2Xf/jdYh3dtTNIHJxRtO5hGi9p38wyjRepqyeGNxwv8kp1sO0+yvTbts8EANIP/4/0w/97I12CgkVi/Kd8ulMgEPytxLT562lUbwvn7ZsL2gQzjOr8WYs8v7jTYGpBm2BG+X1DXi/0NzIzIPdfCP07mNYhuaBNMGP/bstzGwqKxn5PXy/0N7L8ac7LF/7dzPjon3X/cVi67/VCeSRt35J806X6YFy+6fq3ICL1AoFAIBAIBIKCR6Tf5Anx8SmBQCAQCAQCgeBfjojUCwQCgUAgEAgKnn/YktL/NoRTLxAIBAKBQCAoeET6TZ4Q6TcCgUAgEAgEAsG/HBGpFwgEAoFAIBAUPCL9Jk8Ip14gEAgEAoFAUPAIpz5PCKdeIBAIBAKBQFDwiE8n5QmRUy8QCAQCgUAgEPzLEZF6gUAgEAgEAkHBI9Jv8oRw6gUCgUAgEAgEBY9w6vOEcOoFgv8ohvSCtiA7RnVKQZtghsTWvqBNMEOl0ha0CWZIFKqCNsGMeHQFbYIZUjengjbBjASZpKBNMEPp+s9y4LRP/2H53DbCRRO8GWLECAQCgUAgEAgKHvHxqTwhnHqBQCAQCAQCQcEj0m/yhFj9RiAQCAQCgUAg+JcjIvUCgUAgEAgEgoJHrFOfJ4RTLxAIBAKBQCAoeET6TZ4Q6TcCgUAgEAgEAsG/HBGpFwgEAoFAIBAUPCJSnyeEUy8QCAQCgUAgKHjEkpZ5Qjj1AoFAIBAIBIICx2gQE2XzgsipFwgEAoFAIBAI/uUIp14geIts2bIFFxeXPOsJCwtDIpFw7dq1POsSCAQCgeAficGQf9t/EJF+IxDkQHR0NDNmzODAgQNERkbi6upK1apVmTFjBg0aNPjb7PD19eXZs2d4eHi89bZUAR2w69INqZsbupAQklatQBd4z6Ks7fvtsG3lj9yvBAC6oECSN643k1c2bISqfQDysmWROjnz/OOB6EKCc23P//YeZMvufcQ8j6dcqeJMHj6QyuXLWJTV6nRs+G4P+46cJCrmOX6+hRk9uBcN61Q3k4uMjmXZ+m85c+Eqak06vkV8mDt+KJXKlc61Xa/j0rWbbP7uB+7cCyY69jkr5k+n+Xv186zXpUc73AZ2QubhiubeA6LmrkF9M8iqvIN/QzxG9sGmiDfah0+IXrKZlN8vmvZL7GzxHNsfh+b1kbk4on0cSdz2n0nY9SsAUmcHPIb3xr5BDeSFPNE/TyD5+DliVmyz2N7OX0+yZc8RYuITKetXlMmDu1K5bAmr9m3fd5zdh34nIuY5Lo4OtKxfnZG9P0SpsAEgJU3N6h37+O38NZ4nJFG+hC8TB3XhnTJ+b9Rv7Ud3pVH35qic7Am5dI/vpq0nKiwiV3X9P+3ARxN7cnzTAXZ/vsVU7uTpQsfJvanQqAq29rZEPnjKr6t/4uqh81Z1yWu1xKZ+WyQOzhgiw0k/uBXD0wevtUFW6V1sOw5Hd+8Smt3LMnfYKFE074asfC0kKgeM8dFoLxxGd/l4ro7tJbXGdqRC96Yone2IuBjE6SmbSQiLtCpf/bP2lGhTG5dShdCr04m4fJ8/5+0i4cEzi/LvbxtPsaZVOTRoGWGHL+doi7JtB1QduyF1dUMXGkLq2hXogizfg5T+7VA280f28h4UHEja1vVW5e0/G4Pt+wGkfLMK9c8/5GhHVlqP7ky97s2wdbIn7FIg30/bSEwux0/zTz+g3cQenNr0K3s/z7hu7JztaT26M+UaVcGliAcpsYncPHKRg0t3o05Ky1GfTb3W2LzXAYmjC4ZnYWh+3oDh8evvqfKqDbDtMRbd7fOoty3MKJTKUPj3QFauBlJ3b4zqVPT3b5B+cDvGpLhcHV++InLq84SI1AsEOdCxY0euXr3K1q1bCQoKYt++fTRp0oTY2Ni/zYb09HRkMhk+Pj7I5W/3OVzZpCkOQz4jZdtWng8ZjC4kBJeFS5BYedugqFoNzW/HiR87irjhQ9FHR+OyaAnSLA8fElsV6bdukrx+3Rvbc+jEWRav3cqQPp3ZvXYRZUv58cnEucTGJViUX7Xpf/yw/yiThw9k76bldGnfilEzF3P3fqbTlJCUTJ+R05DL5axZMJW9m5YxfkgfnBwd3ti+nEhLU1OudEmmjh2abzod27yH56SPiflqBw8/Go4mMJSiG+Yic3O2KG9bvQKFv5xEwg+HefjhMJKOnaPI6ukoyhQ3yXhN+hj7hrV4NmERoW0/Jm7bXrynD8W+aV0A5F7uyL3ciFq0gbD2nxIxeSn2jWri88XobO0dOnOJxZt+YEi3duxaOoVyfkUZMnsVsfGJFu07cOoCK7bvYUjXtuxdNZPZw3pz+MxlVn671yQza/V2/rx+ly9G9efHFdOpV60CH89cTmRs7h0O/yEBNOvfhh1Tv2FBh8lo0jSM2DYNudLmtXWLVynFez1a8uhuWLZ9/b8chnfJwnw9aCGf+4/l6qHzfPzVGHwr+VnUJav4LopWPdGe+om0b6ZhiAjHtucksHPK0QaJsweKlj3RP8zuqCpa9UJWugqaPV+T9vV4tOcPomjTF1nZGq89tpdU+7Qdlfu34vSUTfzUfibaNA1tv52ILIf+KfRuBW5vPcqegFns77EQqVxOux0TkauU2WSrDGqd648KKRo1xX7wZ6R9t5WEEYPRh4bgOGcJEmcXi/I2lauh+f04iZNHkTB2KIboaBznLEHqnj0AoqjXCHn5ihhionNly0uaDfmA9/q35vupG1jeYRqaNA1Dtk3O1fjxrVKSej1a8OTuQ7NyJ29XnLxd2TfvWxa1Gs9349ZQvnE1ui0ckqM+eZUGKNr1J/34blJXjsPwLAzVwBlI7C3fA14icfVE0bYf+ge3zXcolEiLlET72/ekrhiHevsipJ6Fse03+bXHJvjnIZx6gcAK8fHxnD59moULF9K0aVOKFy9OnTp1mDx5Mh988AEAS5cupXLlytjb2+Pr68vQoUNJTk62qjMkJISAgAC8vb1xcHCgdu3aHDt2zEzGz8+POXPm0KdPH5ycnPj4448tpt+cOnWKOnXqoFQqKVSoEJMmTUKn0+XpmO06dSHt1/2oDx9E//AhScu/xKhRo2r9vkX5xPlzSdu3F11IMPpH4SR9uQgkUhTVa5pk1MeOkLp9K+mXc47OWWLbD7/Q8f0WfNi6GaX8fJkx6mNUSiV7Dv1mUX7/sd8Z1OND3qtbA9/C3nT9wJ9Gdauz9ftfTDKbdu7Fx9OduRM+o3L5MhQt5E39WtXwLezzxvblRKN6tRnxcV9aNM6/Nzqu/T4k4fuDJP50lPSQcCJnrsKg1uDcsZVl+d4BpJy5RNymH0l/8IjYldtR3wnBtWd7k4yqWgUS9x4j7cJNdE+iSNh9EE3gA1RVygGQfv8hT0d8QcqJ82gfPSP1/HWil2194fRLzNrb9vMxOrZqQIfm9SnlW5jpn/ZApbRh7/E/LNp3PTCEauVL0bZxHYp4e1C/ekXaNKrNrfthAKg16Rw7d5XRfT+iVqUyFCvkxdDu7fH18WL3od9z3W/NB7Tl11U/cv3oJZ7cC2fzmNW4eLtSrVXtHOsp7WwZuHwE2yetJTUhJdv+kjXLcWLrQcKuBxPzKIpfV/9EamIKxd4paVGfTb026K6cQHf9d4wxT0g/sAmjVoNN9cbWjZBIUH70GdqTP2CIi8q2W+ZbBt310xge3sWYEIPuygkMEeFIi5TKuVOyUHlga66s+pmwI1d4fu8RJ0atxc7bBT//mlbr/Np7EYHfnyYu6Amxd8M5MWYdjkU98KziZybnXrEYVT5+nxPj1ufKFtsPu6A5tB/NsYPoHz0kZfWXoFajbGX5HpS8ZC6aA3vRPwjG8DiclJWLQCpFXtXcdqm7B3ZDRpC8eC5G/ZvdJxsPaMORVXu4dfQyz+6F892Yr3DydqVyq1o51lPYKem1fDi7J31D2ivjJyLoMVs+Xcbt41eIDY8k+Nxtfl2yk0rNayCVWXfNbBq1R3vhKLpLv2GMeoxmzzqMWg3y2s2sGyKRYtttNOlHd2J4/srbF3Uq6g2z0d34A2PMUwzhQWh+3oCsaGkkLm//zXA2DMb82/6DCKdeILCCg4MDDg4O7N27F41GY1FGKpWycuVKbt++zdatW/ntt9+YMGGCVZ3Jycm8//77HD9+nKtXr9K6dWvat29PeHi4mdySJUuoWrUqV69eZfr06dn0PHnyhPfff5/atWtz/fp11qxZw8aNG5k7d+5fP2C5HHnZsqRfyeJ8G42kX7mMTcVKuVIhUSqRyOUYkixHZt8ErVbLnaAHvFujiqlMKpXybo3KXL8TaLFOeroWpUJhVqZUKLh6KzPCefKPS1QsV4oxs5fQuOMAOn8yjh8OHM2zvX8HtpXKkPrHtcwCo5HUc9ewrVbBoryqWgVzeSDl7GUz+bRrd7Fv9i5yL/eMOnWroPArQsrZK1btkDnaY0hOBTL/cWq1Ou6GhPNulUzdUqmUulUrcD3QcnpJ1XKluBsSzs2gUAAeR0Rz+sotGtZ4BwC9wYDeYEBhYx4RtVXacPVO7lK4PHy9cPZy5e7Zm6YydVIqodeCKVmjXI51u88ZyM0TV7iXpW5WHlwOpFa7+tg5OyCRSKjVvj42ShuC/ryTXVgqQ1qoBPrQW1kKjehDbyEtajmdDMDmvY8wpiSgu3bK4n79o/vIytZA4uia0YxfRaTuPuhDLNv8Ko7FPLH3duHx6Uy70pPSiLoWgk8N63a9isLJDgB1fKbzKrdV0HzVZ5yZtoW0aMtv18yQy5GXLkv6tVfuQdcuY1M+d/cglEokMjnGrPcgiQSHsVNR/7gTfXhY7vS8wN3XCycvV4LMxk8aD68F41ejbI51O80ZwN0TVwk6eytHuZfYOtqhTk7DoLeSgiKTIy1SCv39G5llRiP64BvIilkfy4oWnTEmJ6C7mLuULImtHUaDAWNa9gfZt47Iqc8TIqdeILCCXC5ny5YtDB48mLVr11KjRg0aN25Mt27dqFIlw9EcNWqUSd7Pz4+5c+cyZMgQvv76a4s6q1atStWqVU2/58yZw549e9i3bx/Dhg0zlTdr1oyxY8eafoeFhZnp+frrr/H19WX16tVIJBLKly/P06dPmThxIjNmzEAqNX9e12g02R5MNAYDyixyUmdnJDI5hjjztAZDXBxy32I59FQm9oOHYIiN+UtR+VeJS0hCbzDg7mr+Wtnd1YXQR08s1qlfuxrbfviFmlUq4lvYmz+v3OT4mfPos9zgHz+LZPe+I/Tp1I7BPT7iVmAIC1ZvxkZuQ4B/kzzb/daQypDIZeheSTvRx8ShKFHUYhW5h6tFebmHq+l31Jw1eM8ZQanfv8Wo1WE0GomcvoK0S5YdEZmLE+6fdidh90EcGnubyuOSkjPOl4t5Kom7syOhjy3nHrdtXIf4pGT6TlkCRiM6vYHOrd9jcOc2ANirbKlariTf7D5ASV8f3J2dOHj6ItcDH+Dr42Wlo8xx8nQBIDE63qw8MToe5xf7LFGrfX2KVSrJvIBJVmW+GbaUwatHs+z6ZvRaHelp6az5ZDHRD7Mfr8TOEYlUhjHF3Lk1piQi9ShsUb/Utyzy6k1IW2c9FSL90FYU7QZiN3p1RgTaaCR9/wYM4ZZzyl/F7kUfpMWYP4inRSei8so5pcOEREKDmb14diGQuMDHpuL6M3sRefk+YUesPyCaqXHKuAcZ483HrDE+Dklu70H9h2B4HoM2y4OBbaceGPV61Pt+zJWOrDi+6J/kVx5KkqMTTPssUb19PYpUKsGygKm5s9vVkVbDP+Lc/6w73hI7RyQyGcbkeLNyY1I8Us8iFutI/cojr92C1OVjcmUHchsUbXqju34GNDnn9gv+eYhIvUCQAx07duTp06fs27eP1q1bc/LkSWrUqMGWLVsAOHbsGM2bN6dIkSI4OjrSu3dvYmNjSU1NtagvOTmZcePGUaFCBVxcXHBwcODu3bvZIvW1auX8Wvfu3bvUq1cPiSQz/aFBgwYkJyfz+PHjbPLz58/H2dnZbFsZFp5NLi/YdeuBbdNmxM+cBtr0fNWdWyZ91p9iRQrxQf+R1PDvxvxVGwnwb4pUknmrMxiNVChTgpGDelKhTEk6t2tJx7bN2f3LkQKxuaBx6f0BqqrlefzpLB52HE70wvV4zxiKXb1q2WSl9nYUWTcbTUg4Mau/zXPbF28GsuGHQ0z9pDs7v5zKskmfcPrSTdbtOmCSmTeqP0agxYBJ1Oo8jO8O/EabRrWRSiUWddYJaMiK29tNm8zmzWNXroXc6TqjPxtHrUCn0VqVCxjTDTsne5b1mM28DyZxbOMvfPzVGAqXy50DmiMKW5QdPiV9/wZIs57SJ6/TClmR0qh3LiFt/TTSj+5A0aYf0hKWI9tlOtRn4L0Npk1qI8uzqY2+6ItbuaIc++wrU1nxljUo0qAiZ2dtz7P+3GLbuQeK95qRNDfzHiQrXRZVQEeSl83PlY4aAQ1YcHuLaZP9hf5xKeTOhzP68u2o1TmOn5coHVQM3jyRyOAnHFqe+8m7r0Vhi23XkWh+/BpSk14vL5Vh23McSCRo9rz5HKh8QUTq84SI1AsEr8HW1paWLVvSsmVLpk+fzqBBg5g5cyZNmjShXbt2fPrpp3zxxRe4ublx5swZBg4cSHp6OnZ2dtl0jRs3jqNHj7JkyRJKly6NSqWiU6dOpKebO8H29vb5egyTJ09mzBjzSE1CQFuz34aEBIx6HVJXV7NyqasrhufPc9Sv6twVu+49iB8/Fv2D16/kkRtcnR2RSaXZJsXGxsXj7uZisY6bizMr50xEk55OfEISXh5uLFv/LUULZUZ1Pd1cKFXc16xeyWJFOfa79RVL/hEY9Bh1euTu5udH5uGKLsbypFFdTFyO8hKlAs9RfXkyfA4ppzJWxNEEhaEsXxK3AR1JPXfNVE9ir6LohjkYUtJ4OmwO6PRmel0dHTLO1yuTYmMTkvBwtTwRdPV3v9CuSV06tmwIQFm/IqSp0/n8628Z3LkNUqkU30KebP5iLKlqDSmpajzdnBm/eD1FvS3n+14/donQa5mpOXJFxr85J08Xs2i9k6cLj+6EWdRRrHJJnDxdmLp/UWa/yWWUqVOBJn1a81nZHrgX9aRpvzbMajmaZ/czHqQf331I6doVaNLHn++mmueQG1OTMBr02SY0SuydMCZnT02RunojdfVC2W1sFuGMBxm7adtI+2ocxqQ4FM26otm9DP39awDooh4h9S6OTb22aEJvZ9MbdvQKkddCMo/rRf+oPJxIjcrsH5WnE7G3X//g33BOH4o3r87PneaSEpF5nyhSvyJOxb0YcPsbM/lW60YScSGQfV2+yKbLmJhxD5K4mI9ZiYsrxric70G2H3VF1akHiVPHog/LvAfZVKqCxNkV1y27M/XJ5NgNHIptQCfiB3Qz03P72GWWmI2fjNQvB09ns/Hj4OnM0zvmk19fUrRyCRw9XRi7P/NBQiaXUbJOeRr28Wd82V6mjywp7W35ZOtkNMlpbPrkSwyvXFdZMaYmYdTrkTi4mJVLHF0wJsVnk5e6+yB188a275QswhljyH7e96QuGYbxZY79C4de4uJJ2voZBRelz+WEaoFlhFMvELwhFStWZO/evVy+fBmDwcCXX35pSnfZvXt3jnXPnj1Lv379+PDDD4GMyP2rqTW5oUKFCvz4448YjUZTtP7s2bM4OjpStGj2VAylUolSab4qhfqVFB10OnRBQSiq1yT97JmMMokERfUapO3dY9UWu67dsevRi/hJ49EFWc51/yvY2NhQsWxJzl+9SfOGdQAwGAz8efUm3Tu0ybGuUqHA29MdrU7HsdPn8W9cz7Sv2jvlCXslfSfs8VMKWXES/0mob9/Hrl41ko+fyyiQSLB7txrxO/ZZlE+7dhe7etWI27bXVGZfvzrqa3czqsvlSBQ22SeVGQyQNTXL3o6iG+diTNfyZOhsjOnZo482NnIqlCrG+Rv3aPZutRdqDJy/cY/u7zexfDyadKQS84j7y2vp1f/tdrZK7GyVJCan8MfVO4zu+5FFnZoUNdEp5ukvCVFxlK//Do9fOPG2DipKVCvNqW8PW9Rx7+xNZrcyfwjuu3goESFPObx2L0aDAcWLVV5e/QKmwWAwezOUuUOP4VkoshKV0Ae+TA2RICvxDrqL2d8SGWKekrpmolmZomlnJEpbNIe2Y0yIBbkCiUyevbOMBiSWbAC0KWq0KWqzspTIeIo0rETsnQwn3sZBhVe1UtzennMOdsM5fSjRuhb7On9B0iPzFWWufv0Ld3eeNCvremwBf8z+lofHrlpWqNOhCw7CplpNtH9m3oNsqtVAvd/6Pci2Y3dUXXuRNH08+mDze5DmtyNmqTgATp8vRnPiCOqjB7Pp0qSo0bzSP4lRcZSt/47JiVc6qCherTR/fGt5Ls79s7dY2GqcWVn3xZ8SFfKU42t/znToHVQM2TYZXbqODYMWvz6qr9dheBKCrHQV9HcuZJRJJMhKV0H7x6/ZxA3RT0hdOsqsTOHfHZQq0vdtyhhDkOnQexQi7ZsZkGr9zZDgn41w6gUCK8TGxtK5c2cGDBhAlSpVcHR05NKlSyxatIiAgABKly6NVqtl1apVtG/fnrNnz7J27docdZYpU4affvqJ9u3bI5FImD59Ooa/8Jpw6NChLF++nOHDhzNs2DACAwOZOXMmY8aMyZZP/yak/rAbp4mT0QXdQ3vvHnYdOyGxVZF2OOOfn+PEKRhioknZmBGFtOvWHfu+A0icNwdDRARSVzcAjGlpGNUZkR6JoyMyL2+k7hkTMWW+GVFyw/PnGF4TfevTqT1TF66mUtlSVC5fmu0/HiBNraGDf1MApixYiZeHO6MG9QTgxt0gomKeU65UCaJiYlmzbTcGo4H+3Tpk6uzYjt4jprJ+x4/4N6nPzXvB/HjgGDNGf/KX+80SqalphD9+avr95Gkk94JCcHZypFAu88FfJW7LHnwWjEV96z7qG4G49u2AVKUk4acM58JnwVh0UbHELN2SIb/9Z4ptW4Rr/49IOXkBx7aNsa1UhogZKwEwpKSSeuEGnuMHYtBo0D2JQlWnMk4BzYlekHGOMxz6L5CqlDwZvxipgx04ZH8LBdAnoAXTVmyhYuniVC7jx7e//EaaOp0OzTPW55+yfDPe7i6M7J3xUNu4dmW27ztO+ZK+VC5bgkfPovjqu300rl0F2YsVQM5evY3RCH5FvHn0LIqlW37Cr6gPAc1zv+b/8U0HeH94R6LCIoh5FEXA2K7ER8Zx7Ujmev2jd8zg6uELnNx2CE2KmqdBj8x0aNI0pMQnmcojQp4QGfqMXvM+5od520mOS6Jaq9pUaFiFrwYssGiH9txBlB0+wfA0FP3TEGzqtkZio0T7YhKsImAIxqQ4tL/tAr0WY7R5Kp1RnZHWZypPT0MfdgdFi+5otOkYE2KQFa+AvEoj0o/kPj3q5sZD1BzegYTQSJIeRVF7XCdSI+PN1pNv97/JhB66xO2tGWOt0Rf9KB1Qj0ODlpGeokblmfEGIj0pFb1aS1p0gsXJsclPY7M9AGRFvWc3DmMmo79/D13QPWwDMu5BmhcOuMOYKRhio0ndmjE+bTt1x67XAJIXzUEfFYEkyz0IdRrGpET0r0zcN+p1GOKeY3hifo6tcWrTQVoO/5DosAieP4qizdguJEbGcfPIJZPMpzumcfPwRc5sO4wmRU1EkPm5S38xfl6WKx1UDNk+BYWtgm9HfYWtowpbR1VGH8UmZntYfIn29C8ouwzH8DgY/eP7KBq2R2KjRHcpY0UwZZcRGBNjST+0A3RaDJHmb1uMaSlIILNcKsO213ikRUqi3jIv42HwxZsAY1oyvOFKQXnmP5o2k18Ip14gsIKDgwN169Zl2bJlhISEoNVq8fX1ZfDgwUyZMgWVSsXSpUtZuHAhkydP5r333mP+/Pn06dPHqs6lS5cyYMAA6tevj4eHBxMnTiQx8c1XiilSpAi//vor48ePp2rVqri5uTFw4ECmTZuWl0NGc/IEyc4u2PcbkPHhl5Bg4ieNx/hi8qzMy8vs4yCq9gFIFAqcZ80x05OydTMp27YAoKzfAKcJmRP9nKfPyiZjjdZNG/A8IZGvtuwkJi6e8qX8WLtgKh4v0m+eRcWYRSQ16VpWbdrJ42eR2KlsaVS3OvMmjcDJITOd6Z3ypVk+ezzLN37H2u0/UKSQFxOG9qNdi/fetLty5Na9+wwYnhlpXbQqIw0hoE0Lvpg21lq1HEk6+DsyN2c8hvdC5umG5m4IjwdPRx8bD4BNYS+zqK366l2ejluI56i+eIzuhzbsCU+GzSH9fmbawNMxC/Ac049Ciycgc3ZE+zSKmOVbid+ZkdeurFQKVbXyAJQ8usnMHk3wOdBmRjVbN6xFXEISX//vF2LiEilXoihrZg43TZ6NiH5uFpn/uMv7SCQSVu/YR9TzeFydHGhcuwrDewaYZJJT0lixfS+RsfE4O9rRol51hvfsgI0897nOh9f+jEJlS6/5n2DnZEfwxXus7PuFWWTUo7g3Dm6OudZp0OlZ3X8eH07syWcbJqK0tyXqYQRbxn7FrZOWI9H6O3+Sbu+ITZNOKBycMUQ+RP3dQkjJuAdInd0xvGH6gebH1dg074ryw6EZH59KiCH9xO43+vjUtTX7kdspabxgAAqnjI9PHei9CH2W/nEu7oUqS/9U6tMCgIDvze85J8asI/D70290DFlJP32CVGcXVL1e3IMeBJM0Y7xp8qzU0wtjlnuQ7fsBSGwUOE41vwel7thM2ndb/rIdWflt7T4UKiVd5g9G5WRH6MVA1vVdkG382L/B+Cn6Tgn8qmesLjTt9xVm+z5vOJy4x5YffHQ3ziKxd0LRqnvGx6eehpK2aY4phUvq4oHhDT7gJHF2Q14p402o3ailZvvS1k3Pvq7926YAl6L86quvWLx4MREREVStWpVVq1ZRp04di7Jbtmyhf//+ZmVKpRK1OvN+aDQamTlzJuvXryc+Pp4GDRqwZs0aypTJ/apSb4rEaBQJTALBf5Go5jmsjV1AuGxdXdAmmCGxzd+5DXnlQcPPCtoEM/z2WF++tSAY0cbyqlMFxdIBitcL/Y1sX295cnFB0blK7iLlfxdzbxcqaBPMmNO1YBYcsIbDwp/eehupiwfkmy678ZteL/SCXbt20adPH9auXUvdunVZvnw533//PYGBgXh5ZX+zumXLFkaOHElgYGa6l0Qiwds7c0WwhQsXMn/+fLZu3UqJEiWYPn06N2/e5M6dO9ja2ubt4KwgVr8RCAQCgUAgEPxnWbp0KYMHD6Z///5UrFiRtWvXYmdnx6ZN1h8MJBIJPj4+pi2rQ280Glm+fDnTpk0jICCAKlWqsG3bNp4+fcrevXvf2nEIp14gEAgEAoFAUPDk4xdlNRoNiYmJZpulD0mmp6dz+fJlWrRoYSqTSqW0aNGCc+fOWTU1OTmZ4sWL4+vrS0BAALdvZ6YqhYaGEhERYabT2dmZunXr5qgzrwinXiAQCAQCgUBQ4BgNhnzbLH2fZf787N8riImJQa/Xm0XaAby9vYmIsPzhvHLlyrFp0yZ+/vlnvv32WwwGA/Xr1zd9J+ZlvTfRmR+IibICgUAgEAgEgv9XWPo+y6tLO/9V6tWrR716mUsl169fnwoVKrBu3TrmzJmTQ823i3DqBQKBQCAQCAQFTz6ufmPp+yyW8PDwQCaTERkZaVYeGRmJj49PrtqysbGhevXqBAdnfLjsZb3IyEgKFcqcgB0ZGUm1atVyeQRvjki/EQgEAoFAIBAUPEZD/m25RKFQULNmTY4fz1wG1mAwcPz4cbNofE7o9Xpu3rxpcuBLlCiBj4+Pmc7ExETOnz+fa51/BRGpFwgEAoFAIBD8ZxkzZgx9+/alVq1a1KlTh+XLl5OSkmJai75Pnz4UKVLElJP/+eef8+6771K6dGni4+NZvHgxDx8+ZNCgQUDGyjijRo1i7ty5lClTxrSkZeHChenQocNbOw7h1AsEAoFAIBAICp4C+vhU165diY6OZsaMGURERFCtWjUOHTpkmugaHh5u9rX2uLg4Bg8eTEREBK6urtSsWZM//viDihUrmmQmTJhASkoKH3/8MfHx8TRs2JBDhw69tTXqQTj1AoFAIBAIBIJ/Aobcp83kN8OGDWPYsGEW9508edLs97Jly1i2bFmO+iQSCZ9//jmff/55fpn4WkROvUAgEAgEAoFA8C9HROoFAoFAIBAIBAVPAaXf/H9BOPUCwX+UmDCHgjYhG08bTC1oE8xQqbQFbYIZJc98VdAmmJHQs39Bm2BGZb1vQZtghubyo4I2wYyaBreCNsGM0OuuBW2CGalyfUGbYIbEXlXQJvz9vMGqNYLsCKdeIBAIBAKBQFDwiEh9nhA59QKBQCAQCAQCwb8cEakXCAQCgUAgEBQ4xgJc/eb/A8KpFwgEAoFAIBAUPCL9Jk+I9BuBQCAQCAQCgeBfjojUCwQCgUAgEAgKHhGpzxPCqRcIBAKBQCAQFDxiScs8IdJvBAKBQCAQCASCfzkiUi8QCAQCgUAgKHhE+k2eEJH6fEIikbB379486ejXrx8dOnTIF3tepUmTJowaNeqt6M4vXj3+f4PNr8PPz4/ly5ebfufHOBEIBAKB4P8jRoMx37b/IiJS/wb069eP+Ph4i07Zs2fPcHXN2yevV6xYgdGYORCbNGlCtWrVzJzCvxOj0cj69evZuHEjt2/fRi6XU7p0aXr16sXHH3+MnZ3dW23/p59+wsbGxvTbz8+PUaNGvbGjX9D9mJU3HSezZs1i7969XLt27a3Z5NqrLe6DOyL3dEVzN5Rns9eivhFkVd6xTUO8RvfCpqg36WFPiVq0meSTl0z7Cy8ajUvHFmZ1kn+/THj/GQDY1a2M33cLLOoObD8G+2pl8fr4Q+SerqTdDeXJzG9IvX7fqj3O7zeg0NieKIp6oQl7ytMFW0k6cdm032dUd1zaN8KmsAdGrY60m8E8W/wtqdcyj9F7WGecmtVCVbEkxnQtN6v0MO1z6dEOt4GdkHm4orn3gKi5a1DftN4/Dv4N8RjZB5si3mgfPiF6yWZSfr9o2i+xs8VzbH8cmtdH5uKI9nEkcdt/JmHXrwBInR3wGN4b+wY1kBfyRP88geTj54hZsc1qm7nh0rWbbP7uB+7cCyY69jkr5k+n+Xv186TTErbtO6Dq1A2pmxu6ByGkfL0CXeA9i7LKNu2wbeGPrHgJAHTBgaRuXm8mb9erH4omzZB5emHU6l7IbEAXePeN7KoztiOVujdF6WzHs4tBnJyymYSwSKvyNT9rT8k2tXEtVQidOp2Iy/f5Y94u4h88M5PzqVGadyd0xrt6KYx6I9F3HrKv10L0aq3lY36/A7YduiF1dUMfFkLKNyvQ37fSPy3boWia2T/6kEBSt6/PJi8tWhy7vp8gr1QViUyG/tFDkhdMxxATBYCqxwCULdtRU+VA0qV7PJy8Dk3os2ztZcWrbxt8Pu2AjacLqXfCCJ++gZRrmdehRGmD74z+uAc0RKKQk3DyGg+nrEMXk2CSKfb5QBxqV0BVrhjq4MfcbjXGTG/avYdoY+JRFPVCVboo8ccuETJovpkdrm3rU3h8D5RFvVCHPePJvG0k/HbZTKbwuO54dG+J3Nme5Iv3eDhlrdnxFRreCefmtVBVKoExXce1Sj2zHa/v54OYUrsihcv6EhHyhLnvjzfb3350Vxp1b47KyZ6QS/f4btp6osIicuzDl/h/2oGPJvbk+KYD7P58CwDuRT2Zd+Zri/Lrhn7JlV//tKpPXqslNvXbInFwxhAZTvrBrRiePnitHbJK72LbcTi6e5fQ7F5mKrefscOifPrR79CeO/BavYJ/DiJSn0/4+PigVCrzpMPZ2RkXF5f8MSgf6N27N6NGjSIgIIATJ05w7do1pk+fzs8//8yRI0cs1klPT8+39t3c3HB0dMw3ff8E8mOc5CdObRvhPWUw0Su/48EHI1DfC6X4ljnI3J0tyqtqVKDo8gnEf3+EB+1HkHT0HL5rpqEsW9xMLvnUJQLr9jJtj0cuMu1LvXLXbF9g3V7E7TpEengEymI+FJ42kIgVOwlsN5q0u2GU3D4buRV77GqWx2/VOGJ3HyWw7SgSjpynxDdTsC1bzCSjDn3C4xnrCGw1nOCOE0l/HEWp7bORuTmZZCQ2cuIPnCXm24Nm+h3bvIfnpI+J+WoHDz8ajiYwlKIb5iJzs2yPbfUKFP5yEgk/HObhh8NIOnaOIqunoyiT2T9ekz7GvmEtnk1YRGjbj4nbthfv6UOxb1oXALmXO3IvN6IWbSCs/adETF6KfaOa+Hwx2mKbuSUtTU250iWZOnZonvTkhKJxU+w//ozUHVuJ/2ww+gchOH2xBImzi0V5myrV0Jw4TsKEUSSMHoohOhqneUuQunuYZPRPHpPy1QriPulPwthhGCIicJq/BImz5XNgiRqftqNq/1acnLKJ79vPRJum4YNvJyJT2litU/jdCtzcepQfAmbxc4+FSOVyPtgxEbkq8/r1qVGa9tsnEP77Lb5vP5Pd7WZwc8tRq1FCRcOm2A34jLRdW0kYMxhdaAiOs6z3j7xyNdJPHydp2igSJwzFEBOdIe+W2T9Sn8I4zV+F/nE4SVNHkTByAGm7t2LUZtyLbT/qjrLtR6Ss+ZI77SdiSNVQdscMJDkcu9sHDfCd2Z+nS3dxu/VYUu+EUXbHDLPrsNisAbi0rEXwJ4u513EaCh83Sm+YmE1XzM7jPP/ljEW9afce4tSoGjG7jpN45nq2uvY1y1Hyq7HE7DzGndZjiD90nlIbJmFbLvP69hn6IV792xE+eS13209An6qm7LczzY5PopATt/8s0dsOWT1mgD92/8al/X9kK/cfEkCz/m3YMfUbFnSYjCZNw4ht05Dn0IcvKV6lFO/1aMmju2Fm5c+fxjK+9mCzbd/SXaiT07h98ppVfbKK76Jo1RPtqZ9I+2YahohwbHtOAjsnq3UAJM4eKFr2RP8w+wNk6pdDzTbNz+swGg3o7l547fHlOwZj/m3/QYRTn09kTasICwtDIpGwe/duGjVqhEqlonbt2gQFBXHx4kVq1aqFg4MDbdq0ITo62qQja/pJv379OHXqFCtWrEAikSCRSAgLCwPg1q1btGnTBgcHB7y9venduzcxMTEmPSkpKfTp0wcHBwcKFSrEl19+mc1ejUbDuHHjKFKkCPb29tStW5eTJ0+a9u/evZsdO3bwv//9jylTplC7dm38/PwICAjgt99+o2nTpmY2f/HFFxQuXJhy5coB8OjRI7p06YKLiwtubm4EBASY7AfQ6/WMGTMGFxcX3N3dmTBhgtlbCjBPv2nSpAkPHz5k9OjRpv54yY8//kilSpVQKpX4+flZPN6s+Pn5MXfuXFMfFS9enH379hEdHU1AQAAODg5UqVKFS5cumdU7c+aM6Xz6+voyYsQIUlJSTPujoqJo3749KpWKEiVKsGNH9ujHq+k3EydOpGzZstjZ2VGyZEmmT5+OVpsR4duyZQuzZ8/m+vXrpmPesmULAOHh4SZbnZyc6NKlC5GR1qOO1nAf8CHxuw6R8OMx0oMf8Wzaagxpalw6tbIo79bvA5J/v0zs+p9ID3lE9LJvSbsdgmvvdmZyhnQt+pg402ZITM7cqdWZ7dPHJ+LY4l3ifzyK56AAYnce4fn3x9Hcf8TjKV9jSNPg1qUFlvDs357EU1eIXrcHTfBjIr7cQdqtB3j0bWuSif/5d5LPXif9USTq+494MmcjMid7VBX8TDIRy/5H9MZ9qO+Fmel37fchCd8fJPGno6SHhBM5cxUGtQbnjpb7x7V3AClnLhG36UfSHzwiduV21HdCcO3Z3iSjqlaBxL3HSLtwE92TKBJ2H0QT+ABVlYxrJ/3+Q56O+IKUE+fRPnpG6vnrRC/banL6/yqN6tVmxMd9adG4QZ705ITqoy6oD+1Hc+Qg+vCHJK/8EqNGja3/+xblkxfORb1/L/oHwegfhZO8bBFIpNhUr2mS0Zw4hvbqZQwRz9A/DCPlm6+Q2jsgL1Eq13ZVHdiaS6t+JvTIFWLvPeLYqLXYe7tQ0r+m1Tq/9F7Eve9P8zzoCbF3wzk2Zh1ORT3wquJnkmk4sxc3Nh/hyte/8DzoCfEPnhG8/zyGdJ1FnbYBXdAc2U/68YMYHj0kdc2XoFGjbGG5f1KWzkVzcC/60GAMT8JJWb0IiVSKTdVMu1W9BqG9fJ60rWvRh97HEPEU7YU/MCbEZ7TZvjPq77ejvXCWtLsPCR25AoW3G67+1seT9+APiP7uKDG7f0N9/zEPJ63FkKbBo1tzAGSOdnh0a86j2ZtJOnuT1JsPCB29CsfaFbCvUdakJ3zGRqK2HkTzMNKi3rDxX6GLS0SqkKONis9ux8D2JJy8QuTavaiDH/N0yXek3nqAV7/M/vIa2J5nK3cTf+QCaXcfEjZqBTbebrhkOb6nX+4kcsMvpN17aPWYH83YwMnth4l5lP0+2nxAW35d9SPXj17iyb1wNo9ZjYu3K9Va1baqD0BpZ8vA5SPYPmktqQkpZvuMBgOJ0fFmWzX/Olw6cA5NqtqqTpt6bdBdOYHu+u8YY56QfmATRq0Gm+qNrRsikaD86DO0J3/AEBeVbbcxJcFsk5WriSHsDsb4aAvK3jIGQ/5t/0GEU/8WmTlzJtOmTePKlSvI5XJ69OjBhAkTWLFiBadPnyY4OJgZM2ZYrLtixQrq1avH4MGDefbsGc+ePcPX15f4+HiaNWtG9erVuXTpEocOHSIyMpIuXbqY6o4fP55Tp06ZIuonT57kypUrZvqHDRvGuXPn2LlzJzdu3KBz5860bt2a+/czXq/u2LGDcuXKERAQkM02iUSCc5Yo2fHjxwkMDOTo0aPs378frVaLv78/jo6OnD59mrNnz+Lg4EDr1q1Nkfwvv/ySLVu2sGnTJs6cOcPz58/Zs2eP1b786aefKFq0KJ9//rmpPwAuX75Mly5d6NatGzdv3mTWrFlMnz7d5PxaY9myZTRo0ICrV6/Stm1bevfuTZ8+fejVqxdXrlyhVKlS9OnTx/SgERISQuvWrenYsSM3btxg165dnDlzhmHDhpl09uvXj0ePHnHixAl++OEHvv76a6Kist9As+Lo6MiWLVu4c+cOK1asYP369SxblvFatGvXrowdO5ZKlSqZjrlr164YDAYCAgJ4/vw5p06d4ujRozx48ICuXbvm2FY2bOTYvlOalD+uZZYZjaT8cQ276uUtVrGrXp6Us9fMylJOX8kmb1+3MmUv7KDU0XX4fD4UmYv1Ny6Ozesic3Ekfu9v2FUuTfIZc3uSz1zHvoZle+xrlCf5lQhf0u9XrMpLbOS49/BHn5BM2p1Qqza9lLWtVIbUV/on9dw1bKtVsFhHVa2CuTyQcvaymXzatbvYN3sXuZd7Rp26VVD4FSHlrPk1mhWZoz2G5NQc7S1w5HLkZcqivZIlNcJoRHv1MvKKlXKlQqJUIpHLMSQlWm3D9v32GJKT0D0IyZVOp2Ke2Hu78Oj0LVNZelIakddC8KlRJlc6AJROGemG6vgM50zl7oRPjdKkxSTQcc8MBlz5ig+/n0qh2mUtK5DLkZUqi/b6K/1z/TLycrnrH5RKkMkxvuwfiQRFrXronz7CcdZiXLbuxWnxGmzqNgRA6l0IqZu7WZv6pFSSr97HoWY5i01IbOTYVylF4uks15XRSOKZG6Y6dlVKIVXYmMmoQ56geRxlVS8SLOs9fR37Gpbr2NcsR+LpG2ZliaeumtpQFPNG4e1mJqNPSiXlWpB1O94QD18vnL1cuXv2pqlMnZRK6LVgSlqx+yXd5wzk5okr3MtS1xrF3ilJsUolOLvruHUhqQxpoRLoQ29lKTSiD72FtKj1sWzz3kcYUxLQXTv1Wjuwd0JWphraq7mQfRuISH2eEDn1b5Fx48bh7+8PwMiRI+nevTvHjx+nQYOMSNnAgQOtOp/Ozs4oFArs7Ozw8fExla9evZrq1aszb948U9mmTZvw9fUlKCiIwoULs3HjRr799luaN8+IqmzdupWiRYua5MPDw9m8eTPh4eEULlzYZOuhQ4fYvHkz8+bN4/79+6ao++uwt7dnw4YNKBQKAL799lsMBgMbNmwwRdQ3b96Mi4sLJ0+epFWrVixfvpzJkyfz0UcfAbB27VoOHz5stQ03NzdkMhmOjo5m/bF06VKaN2/O9OnTAShbtix37txh8eLF9OvXz6q+999/n08++QSAGTNmsGbNGmrXrk3nzp2BjAh6vXr1iIyMxMfHh/nz59OzZ0/Tm4MyZcqwcuVKGjduzJo1awgPD+fgwYNcuHCB2rUzojcbN26kQgXLzt9Lpk2bZvrbz8+PcePGsXPnTiZMmIBKpcLBwQG5XG52zEePHuXmzZuEhobi6+sLwLZt26hUqRIXL140tf865K5OSOQydDHxZuW6mHiUJX0t1/FwRRebXV7umTlPIPn3yyQe/gPtowgUxQvhNbYvqk2zCe00zmL0xKVLK5JPX4F0HRK5DO0r9mhj4lGWKmLZHk8Xi/JZ7QFwalaL4qvHI1Up0UbFEdxrBvq4JIs6XyJ72T+xcWbl+pg4FCWKWqyT0T/Z5eUemfZEzVmD95wRlPr9W4xaHUajkcjpK0i7dOtVdRl2uDjh/ml3EnYfxKlTnRxtLkikTs5IZHIM8ebHb4iLw8a3mJVa5tgNHIIhNsb8wQCwqVsPp8kzQGmL4XksiZPHYUxMsKLlFZ2eLgCkxpg/KKRGJ2LnlcsUHomERjN78fRCIM8DHwMZDwsAdcZ8xNm5/yP69kPKd2pIh/9N5rsWk7Ll60te9I/x1f6Jj8OmaC77p88QDM9jTE66xNkVicoOVccepO7YiG7rOmxq1MFh0hySpo0Cve5FG8/N9Ohi4rHxcrHYhtzN8cV1aN6/2uh4bF9chzaeLhg0WvSJqa/IJGDjaVmvRCazqFcXk4Bt6aLoX4lkv2zn1ftTRhuupv0vj8eaTF5xetFGYrR5G4nR8ThbOVaAWu3rU6xSSeYFTMpVOw26NuPp/cc8uGJ9vo7EzhGJVIYxxbwPjSmJSD0KW6wj9S2LvHoT0tZNzpUdNlXfg3Q1+rsXXy8s+MchnPq3SJUqVUx/e3t7A1C5cmWzstdFcl/l+vXrnDhxAgcHh2z7QkJCSEtLIz09nbp1M189urm5mTnoN2/eRK/XU7aseURJo9Hg7p4RPXw1FSYnKleubHLoX9oYHBycLR9erVYTEhJCQkICz549M7NRLpdTq1atN2oX4O7du9neJjRo0IDly5ej1+uRyWQW6+Xm3EBGSo2Pjw/Xr1/nxo0bZik1RqMRg8FAaGgoQUFByOVyatbMfC1evnz5186R2LVrFytXriQkJITk5GR0Oh1OTjnnRt69exdfX1+TQw9QsWJFXFxcuHv3rkWnXqPRoNFozMrSjfoc2/mrJO7/PbPdoIeo74VR5uRG7N+tTMof5lF1uY87Do1q8Hi45Ymz+UXyuZsEthmF3M0J9+6t8Pt6IvcDxqGLzZ1jmJ+49P4AVdXyPP50FronkahqV8Z7xlB0UbGknrtmJiu1t6PIutloQsKJWf3tP9qpzyuqLj1QNmlGwviRoDWfm6O9dpW4oYOQOjlj26YdjlNnET9iiCnFJCtlO9SnyYIBpt/7+y3Js22Nv+iLW7mi/PjRHFOZRJrxovvWjhPc3Z0x5s/cfkjRBpWo2LUx5xbuznO7WbHt2ANFo2YkTc3SP9KMoEn6+bNo9n2PonELVJ17g9GI45ylJE0fk682/BcoUq4YRcoXY8Xt7QCsHjD/NTWy41rIna4z+rO89xx0GssTprNio1RQJ6AhB1b+8MZt5YjCFmWHT0nfvwHSkl8vD8irNUZ38yzoX2/3W+E/GmHPL4RT/xbJunLLy4j1q2WGN8z7Sk5Opn379ixcuDDbvkKFChEcHJwrHTKZjMuXL2dzel8+LJQtW5Z79yyvyPAq9vb22fTXrFnTYk65p6dnrnS+bXJzbgDT+UlOTuaTTz5hxIgR2XQVK1aMoCDr0RVrnDt3jp49ezJ79mz8/f1xdnZm586dr50T8FeYP38+s2fPNisb7lmBFTo9cg8Xs3K5hwu6aPNo4kt0MXHI3XMvD6B9FIEuNgGb4oXgFafepVNL9PFJJB0/DxIJRp0em1fssfFwQfdKlMxkT3S8FflXoqFpGtIfPiP94TNSrwZS4eRa3Lq2JOpr6/9E9XGJGHV65O7mET+Zhyu6mJz6x7q8RKnAc1RfngyfQ8qpjEiYJigMZfmSuA3oaObUS+xVFN0wB0NKGk+HzQHd23kIyy8MiQkY9TqkLubHL3V1xRD33EqtDFSduqLq2oPESWPRh1pYxUOjxvD0CYanT0i+dwfXTTuwbd2WtF3Z7zGhR68QeS0zNUemyPg3Z+fhRGqWvG07Tydiboe/9rjem9MHv+bV+anTXFIiMo8j5YWu50FPzOTjgp/iUMQ9mx7ji/6RvNo/Lq/vH9sOXbH9qAdJM8eif5jZP8bEBIw6HfpHYQCkXziLLvAuth92Q1a6nOlthtTFDX2WNuQeLqTdtpx+pnue9OI6NH+LYePpgvbFdaiNjkeqtEHmZGcWrbfxdDbJZDt+vd6iXrmHM9ooy9eTNjo+2/0po4040/6Xx5NVh42nM6lWju91RDx4SmTIU74alPE/Vv5i/Dh5uphF6508XXh0J8yijmKVS+Lk6cLU/ZkLBMjkMsrUqUCTPq35rGwPjFn+99d4/10Utkr+/Ol3S+pMGFOTMBr0SOzN+1Bi74QxOXuAQurqjdTVC2W3sVmEM/632U3bRtpX4zBmybGXFiuH1KMwmh9X5WjH2+RNA3sCc0RO/T8YhUKBXm/+j7xGjRrcvn0bPz8/SpcubbbZ29tTqlQpbGxsOH/+vKlOXFycmdNZvXp19Ho9UVFR2XS8TPPo0aMHQUFB/Pzzz9nsMhqNJCRYj3DWqFGD+/fv4+XllU2/s7Mzzs7OFCpUyMxGnU7H5cuXreq01h8VKlTg7NmzZmVnz56lbNmyVqP0f4UaNWpw586dbMdTunRpFAoF5cuXz3YMgYGBxMfHW9X5xx9/ULx4caZOnUqtWrUoU6YMDx+aT+SydsyPHj3i0aNHprI7d+4QHx9PxYoVLbY1efJkEhISzLYBDsVQ3wrGvn61TEGJBPt61Ui9avmBLvXqPezrVzUrs29Y3ao8ZETjZa6O6Cz843bp2JKEPb9lOKxaHak3g3FokEW/RIJDgyqkXLGsP+XKPRwaVDErc2xUzaq8CakEqSLnlSuMWh3q2/exq1fNzB67d6uhvmZ5OcW0a3fN5QH7+tVN8hK5HInCJns0ymAAaebtWGpvh+/GLzBqdTwZOhtjegFFzd4EnQ7d/SCzSa5IJNhUq4Huzm2r1VSdu6Pq0YfEqRPQ3Q/MXVsSCRIby+dPm6ImISzStD0PekJKZDxFG2bmrds4qPCuVoqIK9aXSoUMh75k61rs7TqPpEfmkwaTHkWTHPEc11KFzMpdSviQ9CQ2uzKdDn1IEDZVXumfKjXQBVrvH9sPu2PbpQ9JsyegD36lf3Q6dMH3kBV5kb6TloYh4knGg8KTR+gfhWF4HotNlRqmKlIHFQ7Vy5B82XJfG7U6Um6E4NQwy3UlkeDUsLKpTuqNEAzpWjMZ21KFURb1sqoXI1b0ViHliuU6KZcDzeUBp0bVTG2kh0eSHvncTEbqoMK+WlnrdrwGvVaHTqsj+mEE0Q8jeHb/MQlRcZSv/07msTqoKFGtNA+s2H3v7E1mtxrD3PfHm7aw68Fc2HuGue+PN3PoISP15vqxSyQ/tzKX5CUGPYZnochKZJ2DIUFW4h0Mj7OPZUPMU1LXTCRt3RTTpg+8giHsDmnrpmBMMB+n8mpN0D99gCHy9Q+7gn8mwql/QxISErh27ZrZltW5yk/8/Pw4f/48YWFhxMTEYDAY+Oyzz3j+/Dndu3fn4sWLhISEcPjwYfr3749er8fBwYGBAwcyfvx4fvvtN27dukW/fv2QZnEYypYtS8+ePenTpw8//fQToaGhXLhwgfnz53PgQMaatF26dKFr1650796defPmcenSJR4+fMj+/ftp0aIFJ06csGp3z5498fDwICAggNOnTxMaGsrJkycZMWIEjx9n5KOOHDmSBQsWsHfvXu7du8fQoUNzdIBf9sfvv//OkydPTKv9jB07luPHjzNnzhyCgoLYunUrq1evZty4cXnsfXMmTpzIH3/8wbBhw7h27Rr379/n559/Nk2ULVeuHK1bt+aTTz7h/PnzXL58mUGDBqFSqazqLFOmDOHh4ezcuZOQkBBWrlyZbbKwn58foaGhXLt2jZiYGDQaDS1atKBy5cr07NmTK1eucOHCBfr06UPjxo2pVauWxbaUSiVOTk5mm0IiI3bTHly6+uP8UXMUpXwpNOczpHa2xP9wFIDCS8bgNa6vSc/zLftweK8mbgM/RFGyKJ4jeqB6pzRx2/cDGWuwe00agKpaOWyKeGFfvyq+62aQ/vAZKafNH9rs61dFUcyHuF2ZcymiN/yMe7dWuHZshrJ0UYp+8SlSO1uef58xeazY0lEUmtAnU37zLzg1roHn4A4oSxXBZ1R3VJVLE7M1YxxLVUoKje+NXfVy2BTxRPVOKXwXj8DG2534A2dMemwKe6CqWAKbwp4gk6KqWAJVxRLE/W8/zp1b49ShBYqSvnjPGoZUpSThp4z+8VkwFo8x/Ux64rb/jH3Dmrj2/whFiaK4D+uJbaUyxO34BQBDSiqpF27gOX4gqjqVsSnijdOHLXAKaE7y0Yyl9KT2dhTd+AVSlS0RU5cjdbBD5uGKzCNvOcKpqWncCwrhXlBGFPvJ00juBYXwLOLNUgBzIu2n3di2aYuyhT8y3+LYDx+DxFaF+kjGUqEO46dg13+wSV7VpTt2fQaQvHQh+sgIJK5uSFzdwPbFdaO0xa7/YOTlKyL18kZWuiwOYyYi9fBAc/pkru26vvEQtYZ3wK9lDdzLF6Xl8k9IiYznweHMMRnwv8lU7tvS9LvxF/0o92EDjgz/Gm2KGjtPZ+w8nZHZZj5MXF17gCr9W1Hq/do4+3lTd1wnXEsX5s5Oy7apf96NslVbFE39M9aWHzIGbFVojmX0j/2oKah6Z/aP7UfdUfUcQMqqhRiiIpC4uCFxydI/gHrPThQNm6Js2Q6pTxGU73+ITe16aA7uzdj/y/fYdumDTZ36qMoXo+SKkaRHPifucGZQpdyu2Xj1a2P6Hbl+H549WuLeuSm2pYtSfMEnSFW2xLyYxKlPSiVm53F8Z/bHsf472FUuSYmlw0m+dI+ULDnhSj8fVJX8sPFyRWKr4PnBc3j2aoVHt+YZeucPQeZgR/LV+8hdHLCvUZbiS4ahqpixLn/kxl9walId748DsC1VhMJjumFXpRRRW341tRG18RcKjeiMc8vaqMoXp8TyUWgjnxOf5fgUL65vRREPJFmub6mdrZmtRSv64eTpgo1SQdGKfhSt6IfMRs7xTQd4f3hHqrSoReFyxei/dBjxkXFcO5KZdz56xwya9GkNgCZFzdOgR2abJk1DSnwST4PM/QXP4j6UqVMh5wmyWdCeO4i8RlPkVRoh8SiMom1/JDZKtC8mwSoChmDT7MWiCXotxujH5ps6FaNGjTH6MRiyBIwUKuQV66C7ejJXdrw1xETZPCHSb96QkydPUr16dbOygQMHvpW2xo0bR9++falYsSJpaWmEhobi5+fH2bNnmThxIq1atUKj0VC8eHFat25tctwXL15sStNxdHRk7Nix2SLrmzdvZu7cuYwdO5YnT57g8X/s3XdYFMf/wPH3HeU4ei8qiBU7ir13wRZ7jB01amKLYu/G3pLYEjXG3o1G7D3iV42xd6ooYgEVadIOuLvfH+jhSS8G88u8nmcfvb3Z2c/tzR6zszOz1tbUq1ePDh3SpiaUSCTs3LmTX3/9lY0bNzJ//nx0dXUpV64c/fv31wwAzoyhoSH/+9//mDRpEl27duXt27cUL16cli1bavqLjxs3jrCwMAYMGIBUKmXQoEF06dIl2zsAc+bMYdiwYZQpUwaFQoFarcbNzY29e/cyc+ZM5s6di4ODA3PmzMl2kGx+VKtWjfPnzzNt2jQaN26MWq2mTJkyWjPObNq0ia+//pqmTZtiZ2fHvHnzNAN4M/PFF18wduxYRo4ciUKhoH379syYMYPZs2dr0nTr1o0//viD5s2bEx0dzaZNm/D09OTgwYOMGjWKJk2aIJVK8fDwYNWqvN8yjT16AR1LM2zG9EXX2gKF3yNCB85E+W4wrJ6DjdaPY+JNP56NXYqtVz9sxw0g+clznn47D0XguzsMShUGLs6Yd22JjokRKa8iib94i1c/bkP90TR/5j3akHDDl+RHzzTroo9cRNfKDAev3mkPn/J9xKP+szUD4fSLaceTcMOfkNE/4DC+Dw4T+qEIecHjoQtICkxraVKrVMjKlsC5ewt0LUxRRseScOchQT0mkxSU/sfVwasPlj1aal67HF8BQGj/ibxe8hvWo/qiY2OJwi+YZ0NmpB+fYrbwwe3ipFt+vBi/GJsxA7Ae60lKyHOej5xLclD6HZgXXouw8fLEYelEdMxMSHnxiojlW4jenXYhIqtcBnn1tNl7Sp/eqHXMUiJDQZX5dIk5ue8fxKBR6fOIL1n1KwCd2rZi/vRxWW2WJ8nnzxFvZo5h/0FILSxJffSQ2GkTNINDdWxstQZLG7TvhERfH9MZc7XySdi2iYTtm0GlQqeEEyYz3JGamqF6G0tqoD8x40ajfBKS67hurjmCrqGM5osGITNNe/jU4X5LUH7Q39mspC1yy/RxQFX7p02j2vX36Vp5nfFah//vFwC4s+EkOjJ9Gs3qi4G5ERG+oRzsvYjYJ5lfKCVfPIfE1Bx577Tjo3z8kLffT0Adk3Z8pNbax0fm0QmJnj4mk7WPT+KuTSTu3gxAyt8XiF/zI/LufTAcMhrl81DiFs0k1S9txpWkP3YhMZBjNHw8lcaZ8PaaH4F956L+4LPLStqj+8FzGyIPXULX0pTi479Cz8aChAePCew7R+vBUqGzN+KoUlP214lIZHrE+twmZOo6rTidl47A9IMWbqdpaQ0Excf3QtfSlATfxygTkqiwN/3z2XzVCpuvWnG9RGfibwTweOSPFJ/Yh+KT+qJ4/ILgrxeRFJDekhz+ywGkhgY4Lx6OjqkRcdf8COw7R+vzFRvfG+svW2heVz6VNsNYQI/pvL18/12sI5lRPz3WGceWAjC10XBOrj2IvtyAvguHYWhqyMNr/qwcMF+rv7x1STuMLbXHkeVGwy+bEx0Wie//Ms7Tnxml798kG5mg16w7+sZmqF4+IWnnYohPa+WXmlmhykcXFt0q9UAiIfV+xnn6/1H/0cp4YZGoRQcmQfhP8i3TPudE/7Dk1MLrMlUY5PLPq9tL6Ys/F3UIWmL6DCzqELTs9s181qai0tvt09zFza/g65ZFHYIWieTzqn6s1835YVL/pB8H6eec6B+U1ZNnC1Ps4NY5J8ol0w2nCy2vfwvRUi8IgiAIgiAUuayexizkjqjUC4IgCIIgCEVPVOoLRAyUFQRBEARBEIR/OdFSLwiCIAiCIBS9vD26R/iIqNQLgiAIgiAIRU70qS8YUakXBEEQBEEQip6o1BeI6FMvCIIgCIIgCP9yoqVeEARBEARBKHqiT32BiEq9IAiCIAiCUOREn/qCEd1vBEEQBEEQBOFfTrTUC4IgCIIgCEVPdL8pEFGpF4T/KNuycUUdQgbGSyYVdQhaJPryog5BS0yfgUUdghazHZuKOgQtJ91GF3UIWgZ/UbuoQ9By9nZ4UYeg5dsmYUUdgpbw85/X+S4t5ljUIfzjRPebghHdbwRBEARBEIT/tJ9//hlnZ2cMDAyoW7cuV69ezTLt+vXrady4MRYWFlhYWNCqVasM6T09PZFIJFqLh4fHJ/0MolIvCIIgCIIgFD1VIS55sGfPHry8vJg1axY3b97E1dUVd3d3Xr16lWl6Hx8fevXqxblz57h8+TKOjo60adOG58+fa6Xz8PAgLCxMs+zatStvgeWRqNQLgiAIgiAIRU6tKrwlL3788UeGDBnCwIEDqVSpEmvXrsXQ0JCNGzdmmn7Hjh0MHz6c6tWrU6FCBX777TdUKhVnz57VSieTybC3t9csFhYW+T00uSIq9YIgCIIgCML/KwqFgtjYWK1FoVBkSJecnMyNGzdo1aqVZp1UKqVVq1Zcvnw5V/tKSEggJSUFS0tLrfU+Pj7Y2tri4uLCt99+y5s3bwr2oXIgKvWCIAiCIAhC0SvE7jcLFy7EzMxMa1m4cGGGXUZERKBUKrGzs9Nab2dnR3h47gaXT5o0iWLFimldGHh4eLB161bOnj3L4sWLOX/+PG3btkWpVObliOSJmP1GEARBEARBKHJ57TaTnSlTpuDl5aW1TiaTFd4O3lm0aBG7d+/Gx8cHAwMDzfqvvvpK8/+qVatSrVo1ypQpg4+PDy1btiz0OEBU6gVBEARBEITPQSFW6mUyWa4q8dbW1ujo6PDy5Uut9S9fvsTe3j7bbZctW8aiRYs4c+YM1apVyzZt6dKlsba25uHDh5+sUi+63wiCIAiCIAj/Sfr6+tSsWVNrkOv7Qa/169fPcrslS5Ywd+5cTpw4Qa1atXLcz7Nnz3jz5g0ODg6FEndmREu9IAiCIAiCUOQKs/tNXnh5eTFgwABq1apFnTp1WL58OfHx8QwcmPbAv/79+1O8eHFNn/zFixczc+ZMdu7cibOzs6bvvbGxMcbGxsTFxfH999/TrVs37O3tCQ4OZuLEiZQtWxZ3d/dP9jlEpV4QBEEQBEEockVVqe/ZsyevX79m5syZhIeHU716dU6cOKEZPBsaGopUmt65Zc2aNSQnJ9O9e3etfGbNmsXs2bPR0dHh7t27bNmyhejoaIoVK0abNm2YO3fuJ+nX/56o1BciiUTCgQMH6Ny5c77z8PT0JDo6Gm9v70KL671mzZpRvXp1li9fXqj5fsqY/4s2b97MmDFjiI6OzvU2zs7OjBkzhjFjxnyyuARBEATh/6uRI0cycuTITN/z8fHReh0SEpJtXnK5nJMnTxZSZLknKvV5lF0FNiwsrMAPFlixYgVqtVrz+lNVxHNLrVazfv16NmzYwIMHD9DV1aVs2bL07duXoUOHYmhoWCRx5VdISAilSpXK9L3Lly9Tr169fziijHr27Em7du2KbP8GHTsj7/4VUktLUh8FE//LClID/DNNK2vbAYNW7uiUTDumqQ8DSNi0Xiu9YV9P9Ju1QMfGFnVK6rs0v5Ea4JereHafuMiWw38SEf2W8iWLMXlQV6qWLZll+u1Hz7P31CXCI6IxNzWidd1qjO7dAZm+nibNy8holm8/wqXbfiQpUnC0t2bO8K+oXMYp53iO+bD5wCkiomMp71yCKUN6UrV85mUKYNuhs+w98T/CIyIxNzGmdYMafNeviyae+MQkVu84xJ9XbhMZ85YKpRyZ9PWXVCnnnKvj87l9X7l1/fY9Nu3ch6//Q16/iWTFwhm0bNKgUPeRnT5efWjT2x0jUyP8rvvxy9RfCAt5ke02lnZWeE7xpGbzmsjkMsJCwlgxfjkP7z4sUCy7bz5my9Vg3sQrKG9ryqRWVajqkPXfktikFFZf8OfPwDBiklJwMJUzoUVlGpexy3KbnDT26oZrr+bITA15fj2Qk9M2ERXyMsv0Nfq2pEbflpiVsAEgIugZl1Yc4JHPXU0a117NqdypAXZVnJGZyPmp6lAUsQk5xqLfqhOydl8iMbNE+TSYpK2rUD4KyDStbq1GyDr2RseuOOjqoAp/juL476RcOqOVRr9FR3ScyyM1MeXttKGoQoNze2gA6O3Vh9bvyov/dT/W5FBevhrbm15je2ute/bwKSNafKt5bV/SnoHTBlOxdiX09PW4ef4Gv85cR0xEdLax7L4ZwpZrj9LLS8vKVHUwzzTtwftPmXX8rtY6fR0pV73aal6/iVew/Lw/f4e85q0iBbcSVkxqVZmSFkbZxvEpFFVL/f8XolJfiHIaJZ0bZmZmhRBJ4enXrx9//PEH06dPZ/Xq1djY2HDnzh2WL1+Os7Nzge5KFKUzZ85QuXJlrXVWVlZFFI02uVyOXC4vkn3rN22O0dARxK36kVR/X+RdemA6fxlRg/uijonOkF6vWnUU586S4nsfUpKRf9kb0wXLiB7qiepNBADK58+I/3kFyrAXSGSytDwXLiNqYG/UMTHZxnPir1ss2+rN9CE9qFquJDuOnufb+es4uHwKVmYmGdIfu3iDFTuP8P23X+FavhRPwl4x85ddIJEwYUBnAGLjEvCcsZJalcvx89ShWJgaExr2GlOjnC9QT1y8ztKN+5jxbW+qlndm+6E/+eb7VRz6eTZW5qYZ0h89f5UV2w7w/cj+VK9QmicvXjFj5RYkEgkTBvUAYPbqbTwMfcH8MQOxtTTjiM8Vhs5azoFVs7Czyr6R4HP7vvIiMTEJl7Kl6dK+DWOmziu0fHOj27fd6DCwI8u9fuLl05f0Gd+XOdvnMLzlt6QoUjLdxsjMiCV/LOHe5bvM7j+b2MgYijkXIy4mrkCxnPR7zg/nfJnWpipVHSzYcf0Rw/de4eDXzbE0ynibPkWp4pu9l7E0lLG0Uy1sTQwIi0nAxEAvk9xzp+43Hajp2Yaj49YR/fQ1TcZ1p+e2SaxvNQllFsfjbVgkPov3EPU4HCQSqnZvTLf1XmxqN42IoOcA6Mn1eXT+Lo/O36XZ5J65ikWvbjMMen9D4qblKIP9kXl0xWjiYt5O9EQdG50hvTruLYpDO1CFPYXUFHSr10c+ZCLq2GhS710HQCIzQBl4n5Qr5zH8elyej0/Xb7vRfmBHVnxQXmZvn8PIbMoLwJOAJ8zsPU3zWpmaXmOVyWXM3j6XEN/HzPhqKgC9x/dl+saZTOw0Tqtx70Mn/V/wg48f01pXoaqDOTtuPGb471c4OLhZpuUFwFhfF+/BTTWvJRKJ5v9qtZqxB66jqyPlpy61MJbpsu3aY77Ze4U/BjZBrv8PVxPVkpzTCFkSs98UIolEomnBDwkJQSKRsHfvXho3boxcLqd27doEBgZy7do1atWqhbGxMW3btuX169eaPDw9PTUVZU9PT86fP8+KFSuQSCRIJBLNLZ/79+/Ttm1bjI2NsbOzo1+/fkRERGjyiY+Pp3///hgbG+Pg4MAPP/yQId6oqCj69++PhYUFhoaGtG3blqCgIM37e/fuZceOHezatYupU6dSu3ZtnJ2d6dSpE3/++SfNmzfXym/ZsmU4ODhgZWXFiBEjSElJ/7FTKBSMHz+e4sWLY2RkRN26dTPcztq8eTNOTk4YGhrSpUsXfvjhB8zNzTM9Nu+NGTOGZs2aaV6rVCoWLlxIqVKlkMvluLq6sm/fvgyf3crKSuvRzfb29ujppf1RvHPnDs2bN8fExARTU1Nq1qzJ9evXNTGam5vj7e1NuXLlMDAwwN3dnadPn2rlf/DgQdzc3DAwMKB06dJ8//33pKamat6Pjo5m2LBh2NnZYWBgQJUqVThy5IjWPt4LDg6mU6dO2NnZYWxsTO3atTlz5gyfgrzrlySdOILi1HGUoU+IW/kDakUSBu6Z3zmIWzyPpCPeKB89RPk0lLifloBEil6Nmpo0inNnSLl1A1V4GMonIcT/+jNSI2N0S5XJMZ5tR3zo2rI+nZvXpUwJe6YP6YGBvj7e565kmv52QAjVXUrRrlFNitta0sC1Ah4N3bj/MFSTZuPBs9hZmTN3eC+qli1JCVsrGrhWwNHeOsd4th48Q7c2DencsgFlHIsx49veyGV6eJ/9K9P0dwKCqV6hDO2b1qG4nTUNalSibePa3A8KASBJkcyZy7cYO6ArtSqXw8nBluG9OuJob8veE//LMZ7P7fvKi8b1azN66ABaNW1YqPnmxheDO7F31R6unL5CiH8IP439EUtbS+q1yXqmi+7fdiciLIIV41cQdCeQl09fcuvCLcKf5O7hNFnZdv0RXas50bmqE2WsTZjuXg0DPR2874Vmmt77biixSSn81KU2NUpYUtzMkFpO1rjY5r9BqPZgD/5afZCg0zd57f+UI15rMbY1p3ybmllu8/DsLR6du0NUyEuiHofzv6W/k5yQRDG3spo01zee5O81h3lxK/d3MvTbdifZ5xgpF06ievGExE3LUSsU6DfxyDS90v8OqTcuoXoRiupVGMmn/kD19BE65ato0qRcOoPCexupD27kOo4PdRzcid9X7eHq6Ss88Q9heS7KC4AyVUn062jN8jYqVvNexVqVsC1hy4pxP/Ek4AlPAp6wwusnylYrS7WGWU+NuO36Y7pWc6RzVce08tKmalp5uf80y22QgLWxgWax+qDyHxoVz92waKa2rkIVB3OcLY2Z1qYKSalKjvtnf+dK+PyISv0nNmvWLKZPn87NmzfR1dWld+/eTJw4kRUrVnDhwgUePnzIzJkzM912xYoV1K9fnyFDhhAWFkZYWBiOjo5ER0fTokULatSowfXr1zlx4gQvX77kyy+/1Gw7YcIEzp8/z8GDBzl16hQ+Pj7cvHlTK39PT0+uX7/OoUOHuHz5Mmq1mnbt2mkq4zt27MDFxYVOnTpliE0ikWjdVTh37hzBwcGcO3eOLVu2sHnzZjZv3qx5f+TIkVy+fJndu3dz9+5devTogYeHh+Yi4sqVKwwePJiRI0dy+/Ztmjdvzrx5eW+9W7hwIVu3bmXt2rU8ePCAsWPH0rdvX86fP5/rPPr06UOJEiW4du0aN27cYPLkyZoKP6Q9Dnr+/Pls3bqVS5cuER0drfWQiQsXLtC/f3++++47fH19WbduHZs3b2b+/PlA2oVH27ZtuXTpEtu3b8fX15dFixaho6OTaTxxcXG0a9eOs2fPcuvWLTw8POjYsSOhoZn/0c83XV10y5Un5eYHf/jUalJu3UC3UuWst/uARCZDoquL6m1s5gl0dTFo1xFV3FtSH2V/+zslNRW/R8+oV7W8Zp1UKqVe1XLcDXyS6TbVXZzxe/SUew/T3n/2MoKLt3xpXKOiJs356w+oXNqR8T9uptnXM/hy4jL2n8n5UeApKan4BYdSr1p6XlKplLquFbkT8CjTbVxdyuAXHMq9wMdp8YS/5sLN+zRyS6twKFUqlCoV+nrarawGMj1u+eZQEfrMvq9/CzsnOyxtLbl98bZmXcLbBAJvB1ChZoUst6vTui4P7wYxac1ktt3czvJjK2jTq2CzWKQoVfiFx1DXOf2CUiqRULekNXdfRGW6jU/wS6oVs2Dh6Xu0WH2Sbht9+O1yEEpV5i27OTFztMHY1pyQi/c16xRvE3lxO5jibuVylYdEKqFix3royWU8vxmU8wZZ0dFFx7k8qQ8++FulVpP64CY6ZSvlLotKNZA6lEAZcC//cXzgfXm5k0l5ccmmvAAUK1WMTde2sO7ib3itGI91MRvNe3oyPVBDSnJ641eyIhm1Sk3F2pmfv5ryUjKz8hKdZRyJyUrarvsT97VnGXPgOg8j3qbvU5l290Cmk14dlEok6OtIufUs8zL4KalVhbf8F4nuN5/Y+PHjNdMXfffdd/Tq1YuzZ8/SsGFa69TgwYO1Kr8fMjMzQ19fH0NDQ62uPatXr6ZGjRosWLBAs27jxo04OjoSGBhIsWLF2LBhA9u3b9c84GDLli2UKFFCkz4oKIhDhw5x6dIlGjRI68e6Y8cOHB0d8fb2pkePHgQFBeHi4pKrz2lhYcHq1avR0dGhQoUKtG/fnrNnzzJkyBBCQ0PZtGkToaGhFCtWTHNcTpw4waZNm1iwYAErVqzAw8ODiRMnAlC+fHn++usvTpw4kav9Q9rdgAULFnDmzBnN3LKlS5fm4sWLrFu3jqZN028/NmjQQGskO6RVniFtlPuECROoUCHtB7tcOe0/bCkpKaxevZq6desCace2YsWKXL16lTp16vD9998zefJkBgwYoIlh7ty5TJw4kVmzZnHmzBmuXr2Kn58f5cuX16TJiqurK66urprXc+fO5cCBAxw6dCjLQT2ZHRuFQqG9TqVC9sExkJqaIdHRRRWt/UOuiopCzzHnvuYAhoO/QfUmQruiCejVrY/plJkgM0AV+YbYKeNRx2bflSMqNh6lSoWVuXY3GytzEx6/eJXpNu0a1SQqNh7PGasANalKFT1aN+Drrq01aZ69esPe03/Rr30zBndpxYPgUBZvOoCerg5fNKuTdTxv497Fo93NxsrMhMfPMm+tbd+0DtFv4xgwdVla5USpoodHE4b0SOvPaiQ3wNWlNL/uPUppR3uszEw5fuEadwIe4Whvm+3x+dy+r38LC5u0Lk3RH/Vbjo6IxsLGPMvt7B3tadu3Hd6/efP76r2Ucy3H0O+HkpqSwp/7/sxXLFEJySjVaqwMtbtNWBnJCInMvFvP8+h4rsUk0q5ScVZ3r8vTqHgWnL5HqkrFNw1z93v9IWNbcwDiI7Qv7OIjYjGyyb7138alBP0OzEZXpkdyfBJ/DFvOm6D8t+5KTMyQ6OigjtEu0+rYKKTFHLPeUG6E6co9oKsHKhWJW1aQej9/rfIfy295CbwVwIpxP/E8+DmWtpZ8NaYXC/ctZnTrESTGJxJw05+khCQGTBnItsVbkUig/2RPdHR1sLDNvNtdVGIW5cVQRkhkfKbbOFsYM9ujGuVsTIhLTmXrtUd47viL/YOaYGcix9nSGAdTOSsvBDCjTVXkejpsv/6Yl2+TiIhPyv2BKiRqleh+UxCiUv+JffiEsfdTI1WtWlVr3atXmVdQsnLnzh3OnTuHsbFxhveCg4NJTEwkOTlZU+kEsLS01Kqg+/n5oaurq5XGysoKFxcX/PzSBsRl1acvM5UrV9ZqaXZwcODevbSWknv37qFUKjUV2PcUCoWmH7ufnx9dunTRer9+/fp5qtQ/fPiQhIQEWrdurbU+OTmZGjVqaK3bs2cPFStWJDNeXl58/fXXbNu2jVatWtGjRw/KlEnveqCrq0vt2rU1rytUqIC5uTl+fn7UqVOHO3fucOnSJU3LPIBSqSQpKYmEhARu375NiRIlMhyPrMTFxTF79myOHj1KWFgYqampJCYm5qmlfuHChXz//fda6yaUdmJiWedc55ET+Ze9kTVrQcyE7yAlWeu9lNu3iBr+NVJTMwzadsBk2myiR3+Tab/vgrj24CEbDpxh2tfdqVrOidDwCJZsOsC6facY1r0NACqVmsplHBnduz0AFUuV4GFoOL+f/ivbSn2+4rkXwG/7TjBtWC+qlivF0/BXLP5tL+v2HGVYz7T9LxgzkJmrt9Jq0GR0pFIqlnGkbePa+AYX8p2Yj3wO39c/oWnnZoxYOELzeo7n99mkzppEKuHh3YdsW7IVgEcPHlHSpSRt+7TLd6U+P1RqsDTUZ4a7KzpSCZXszXkVl8SWq8G5qtRX6twAjwWDNK9/H7gs37G8eRTGxrbTkJnIqdCuDh1+GMaOnvMKVLHPl6QE4qYNBQM5upXdkPf+FtWrMJT+d/KcVdPOzfj2g/IyN5/l5aZP+kXFE/8QAm8HsP6vjTTs0Igze04TGxnLkm8X8c2C4XQY2BG1Ss3/Dp3n4b2HqPN51yUzrsUtcC2efpHgWsyCrhvPs+9OKCMauaCnI+WHTjWZfeIuTVadQuddy3/DUjbZ5Cp8rkSl/hP7sNvG+8EpH69TqfJ2nyguLo6OHTuyePHiDO85ODjw8GHBZmJ4r3z58vj7Zz6Lxsf0Puo+8OHniouLQ0dHhxs3bmToYpLZhUlWpFJphguND/vtv29pP3r0KMWLF9dK9/G8sI6OjpQtW5bMzJ49m969e3P06FGOHz/OrFmz2L17d4aLjqy8f+hE165dM7xnYGCQ50Gw48eP5/Tp0yxbtoyyZcsil8vp3r07ycnJOW/8zpQpU/Dy8tJa97Zbe63XqtgY1MpUpObarURSCwtUUZHZ5i/v3hN5z97ETh6H8nEmXVEUSahePEf14jlx/r5YbNyBgUd7EvfsyDJPC1MjdKRS3kS/1Vr/Jvot1pkMSgX4ec8xOjSpRdeWabMYlXMqRmJSMnN/3cuQrq2QSqXYWJhSuoT2LCGlS9hx5srdzLJMj8fE+F082i2ab2LeYm2ReTyrdx6mQ7O6dGvdCIDyzsVJTEpmzi/bGdKjLVKpFEcHGzbNH0dCkoL4hCRsLM2YsHQ9Jeyy7+P/uX1fn6urp68QeCt95hQ9Wdpvlbm1OVGv0luEza3NeeT7OMt8ol5F8TRI+0LradBTGrTN/5gAC0N9dCQS3iRo30V7E6/AOotBjzZGMnR1pOhI01s0S1kZExGvIEWpQk8n+161D0/fZOOt9K5Uuu8GQhpZmxL/Klqz3sjalFe+2V9YqlKURD9JmyHn5f0QHFxLU2ugByenbsx2u6yo38agViqRmGmXaYmpBerobMq0Wo3qVdqFRHJoMDrFnJB17EVCPir1V09fISCX5eVxNuXlY/Gx8bx4/BwH52Kadbcv3OKbxkMwsTBFpVQSHxvP5uvbuBia+Z0/C3kW5SUh6/LyMT0dKS62pjyNSp+FqJK9GXs9G/NWkUKKUoWloYy+2y9Rye6fn7jjv9ptprCIPvWfOX19fZRKpdY6Nzc3Hjx4gLOzM2XLltVajIyMKFOmDHp6ely5kj6YMCoqisDAQM3rihUrkpqaqpXmzZs3BAQEUKlSWt/F3r17ExgYyMGDBzPEpVaricnlTBg1atRAqVTy6tWrDPG+71ZUsWJFrVgA/v77b63XNjY2hIWFaa27ffu25v+VKlVCJpMRGhqaYT+Ojtncus1E+fLlGTt2LKdOnaJr165s2rRJ815qaqpm4CxAQEAA0dHRmpZ/Nzc3AgICMsRQtmxZpFIp1apV49mzZ1rfR3YuXbqEp6cnXbp0oWrVqtjb2+c4R+7HZDIZpqamWovso+5HpKaSGhSoNWgSiQS96m6k+j7IMm95j17Ie/cndtpEUoMyn3YuA4kEiV72s3Xo6epSsXQJrtxPP04qlYor94OoVj7zKS2TFClaMzsA6Lz7nO8vB6u7lCLko+47T168ophN9jPN6OnpUrGME1fupl/oqlQqrtz1x9Ul8+5TSYpkpB/F877b18c3wgwNZNhYmhEbF89ft3xpXseVbH1m39fnKjE+kbAnYZolNDCUyFeRuDasrkkjN5ZTvroL/jeybsTwu+5L8TIltNYVL12cV8/ydqf1Q3o6Uiram3H1SfokByq1mqtPIqhWLPPy6FrCktCoeFQfFKAnkfHYGMlyrNADJMcnEf3kpWaJCHpO3KtonBum9+PWN5ZTrHqZPPePl0glmouEfFGmogwJRLfSB3dWJRJ0K9dA+dA3D4Hkv7wmxicS/iRMszx9V16qZVJeArIpLx8zMDTAvqQDUa8yXpy8jYolPjaeqg2qYWZtxtXTmU8EkHV5eUO1Yua5ikOpUvMw4m2mFwEmMj0sDWU8iYrHNzyaZmXzP0VqfqnVkkJb/otES30+xMTEaFUm4dNNh+js7MyVK1cICQnB2NgYS0tLRowYwfr16+nVqxcTJ07E0tKShw8fsnv3bn777TeMjY0ZPHgwEyZMwMrKCltbW6ZNm6bVh7xcuXJ06tSJIUOGsG7dOkxMTJg8eTLFixfXDIz98ssvOXDgAL169WL69Om0adMGGxsb7t27x08//cSoUaNyNaVl+fLl6dOnD/379+eHH36gRo0avH79mrNnz1KtWjXat2/P6NGjadiwIcuWLaNTp06cPHkyQ9ebFi1asHTpUrZu3Ur9+vXZvn079+/f13StMTExYfz48YwdOxaVSkWjRo2IiYnh0qVLmJqaavq4Q9oFzPvHOr9nbm6OWq1mwoQJdO/enVKlSvHs2TOuXbtGt27dNOn09PQYNWoUK1euRFdXl5EjR1KvXj3q1EnrujFz5kw6dOiAk5MT3bt3RyqVcufOHe7fv8+8efNo2rQpTZo0oVu3bvz444+ULVsWf39/JBIJHh4ZZ3goV64cf/zxBx07dkQikTBjxow8393JrcQ/9mIyfgqpgf6kBvhj0KU7EgM5SaeOA2A8YSqqiNckbFoPgPzLXhj2G8TbxXNRvgxHYmEJgDoxEZISQWaAYe9+JF++hCryDRJTM+RfdEFqbY3igk+O8fTr0IwZP++kcmlHqpQtyfZj50lUJNO5WVq3sWmrd2BracZ3vTsA0LRmZbYd9aFCqeJULVeSp+ER/LznOE1qVtZU7vu2b8qAGSv47Y/TtGlQnfsPQ9l39m9mDv0yyzje69+pFdNXbKZS2ZJULefM9sN/kpiUTOeWaeNSpi7fhJ2VOd/1S7ur07R2VbYdOkuF0o5ULV+Kp2Gv+HnnIZrWrobOuwrYpVsPUKvBubgdT8Ne8ePmP3AuYU+nljnP2f65fV95kZCQSOiz9G4az1+8xD8wGDNTExxyGE9QUIc2HKTn6J68CHnOy9CX9B3fl8hXkfx9Kn3A9Lxd87l84jJHt6TNSnXwt4MsObCUHiN6cPHIRcpXL497bw9WT15doFj61SrNjGO3qWRvThUHc3Zcf0RiipJOVdPGRUw/egtbYwNGN01rNPiyujN7boaw5Ox9ermV4klUPBv+DqJXzayflZCTaxtO0GBUZyIfvyTm6Ssaj+tO3KtoAk+ldyH5aucUAk9e5+aW0wA0nfglj3zuEPviDfpGBlTq1ACnehXZ02+JZhsjGzOMbMwwd06rHNq4OJIcn0js8zckxWTeBzz5+D7kQyehfByI8pE/+u7dkMgMSP5f2oN85MMmoYqKQLF3AwCyjr3S0r58gURPD13Xuug1bE3i5hWaPCVGJkisbJFapP2d1nFIa+RRx0Rm6L+fmcMbDvLl6J6EvSsvvTMpL3N2zefvE5c59q68eE4bxLUzV3n9/BWWdpb08uqDSqnifwfTJ21o2aMVTx8+JTYyBhe3Cnw9eyiHfjvI80fPs4ylX61SzDh25115MWPH9RASU1LpVCXtM00/ehtbEwNGN0kbE7buryCqOpjjZGHEW0UKW64+Iiw2kS7V0hu6TgWEYSHXx8FUTtDrWJb86UvzsvY0KIIuOKKlvmBEpT4ffHx8MvTRHjx48CfZ1/jx4xkwYACVKlUiMTGRx48f4+zszKVLl5g0aRJt2rRBoVBQsmRJPDw8NBX3pUuXarrpmJiYMG7cuAwt65s2beK7776jQ4cOJCcn06RJE44dO6bpSiORSNi5cye//vorGzduZP78+ejq6lKuXDn69++vGQCcG5s2bWLevHmMGzeO58+fY21tTb169ejQIa0yVq9ePdavX8+sWbOYOXMmrVq1Yvr06cydO1eTh7u7OzNmzGDixIkkJSUxaNAg+vfvr+m7D2mDSG1sbFi4cCGPHj3C3NwcNzc3pk6dqhVPq1atMsS4a9cuunbtyps3b+jfvz8vX77E2tqarl27avVHNzQ0ZNKkSfTu3Zvnz5/TuHFjNmzYoBXnkSNHmDNnDosXL0ZPT48KFSrw9ddfa9Ls37+f8ePH06tXL+Lj4ylbtiyLFi3K9Nj9+OOPDBo0iAYNGmBtbc2kSZOIjc1itpICSj5/jngzcwz7D0JqYUnqo4fETpuA+t1gTB0bW/jggsKgfSck+vqYzpirlU/Ctk0kbN8MKhU6JZwwmeGO1NQM1dtYUgP9iRk3GuWTkBzj8WhQg6jYOH7Ze4KI6FhcnIvzy9RhmsGz4RFRWi3hQ7q1RiKBn3cf51VkDBamRjStWZmRvdK7GlUp68SP4wexcudR1u0/RXFbSyYO6Ez7xllP36eJp1EtomLe8suuw0RExeJSqgRrZo3SDJ4Nfx2pFc/QL9shkUhYveMQryKjsTA1pmntaozqkz6jVFx8Iiu2efPyTTRmJoa0ql+DUX06o6eb+WxIH/rcvq+8uO8fxKBRkzSvl6z6FYBObVsxf3re5xLPi/1r9mMgN2DkwlEYmRrhe92XWf1mas05bu9kj6llereqoLtBLBg6n/6TBvDVd714+fQl679fz3lvnwLF4l6xOFGJyay5GEBEvAIXW1N+6VFXM+1gWGwiH97ssTeV80uPuiz78wE9Np3H1sSA3jVLM7Bu5t0Jc+PK2iPoG8rwWDgIA1NDnl0PZE//JVpz1Fs42WJokT5o3dDalA4/foORrTmKtwm89n/Knn5LtGbRqdGnJY3GpndD7LtvBgBHx63j3r4LmcaScsUHiYkZBt08kZhZoAwNJn7pZNSxaWVaamWrfZtLZoDBgNFILW1QJytQhT0lce1CUq74aJLoujXAcOjE9NhHpsWR9McWFAe25nh8/nhXXoa/Ky9+1335PofyYu1gzfjVEzAxNyUmMga/a75M7DyO2Mj03+7iZYrTb9IAjM2NefXsFb+v2suh37yzjcW9QjGiEpJZcykwvbx0r5NeXt4mat2tjE1KYe6pe0TEKzCV6VHR3pQtvRtQxjr9u4yIS+KHc768iVdgY2xAh8rFGVo/dzMfCZ8XiTovoyEF4R+0efNmxowZQ3R0dFGHovE5xpRfEe5Nc070DzNeMinnRP8giX7RPAQsK2/HzC7qELSY7diUc6J/UFe30UUdgpa9s7Kf8vCftmJOwebUL2zfNgnLOdE/qP/5z+t83z0zd5Mp/FPkX//4yffxtHbLQsvL8drZQsvr30K01AuCIAiCIAhFTjQzF4wYKCsIgiAIgiAI/3KiUi98tjw9PT+7bi6fY0yCIAiC8P+BWiUptOW/SHS/EQRBEARBEIrcf7UyXlhES70gCIIgCIIg/MuJlnpBEARBEAShyImBsgUjKvWCIAiCIAhCkRPdbwpGdL8RBEEQBEEQhH850VIvCIIgCIIgFDm1WrTUF4So1AuCIAiCIAhFTq0q6gj+3USlXhAEQRAEQShyKtFSXyCiUi8I/1E+d0sUdQgZXOu0rahD0BJNalGHoKWq0rGoQ9By0m10UYeg5Y+bK4s6BC3yYo2LOgQt4S3LFnUIWnaf+7zK8+65xYo6BC09Zj0o6hC0HPm6qCMQciIq9YIgCIIgCEKRE33qC0ZU6gVBEARBEIQiJ6a0LBgxpaUgCIIgCIIg/MuJlnpBEARBEAShyIknyhaMqNQLgiAIgiAIRU50vykY0f1GEARBEARBEP7lREu9IAiCIAiCUOTEPPUFIyr1giAIgiAIQpETU1oWjOh+IwiCIAiCIAj/cv+aSn2zZs0YM2ZMgfOZPXs21atXzzaNp6cnnTt3LtB+QkJCkEgk3L59u0D5FDWJRIK3t3dRh/FJFcb3XZh8fHyQSCRER0cDsHnzZszNzYs0JkEQBEH41NTqwlv+i/LU/cbT05MtW7ZkWO/u7s6JEycKLahPafz48YwaNaqow8izrI59UFAQZct+Xo/+LirNmjWjevXqLF++/JPt4/33MGzYMNauXav13ogRI/jll18YMGAAmzdvLrR99uzZk3bt2hVafrlRaUI3SvVpjr6pERHXArk1eSNxj19mmd66XgXKf9sei2qlkNtb8NfAH3lx4oZWmlrLh+Hcs4nWuvBzd7jYe0m+Ymw9tjt1erVAbmpEyPUADkzfyJuQ8Fxt2+zbL2g7qRcXNx7n8Jyted53x7E9adyrJXJTI4Kv+7Nz+npe5XLf7t92puukPpzdeJS9czZr1pvamNNtSj8qNq6GgZEBLx+94NjqP7h14kqu8q0zrhuVezVHZmZI2LVAfKZuIiYk6++s5oiOlG5bG4syDqQmJRN+I4i/Fuwh+lGYVjp7t7LUm9gDuxplUCvVvPZ9wqG+i1EmpeQqrvf6ePWhTW93jEyN8Lvuxy9TfyEs5EW221jaWeE5xZOazWsik8sICwljxfjlPLz7ME/7zo3rt++xaec+fP0f8vpNJCsWzqBlkwaFvp+86ty5LcOG9MPNrRpWVhbUrN2GO3ceFOo+DDp0Rt79K6QWlqQ+CiZ+zQpSA/0zTSvz6IBBS3d0SpYCIPVhAAmb12eZ3mikF/L2nYhbt4ok7325jqnW+G5UeFeew68FcmHqJmKz+Q2qPqIjpdrWxrysA8qkZMKvB3FlwR5iPirP77XdNgGn5q6cHPwTISdvZJomK7tvPGLLlYe8iVdQ3taUSa2rUbWYRZbpY5NSWP0/X/4MCCMmKQUHUzkTWlWlcRm7PO03O328+uKudX79zIsczi8rOys8pwzUOr+Wj//pk5xfuSX61BdMnlvqPTw8CAsL01p27dr1KWIrVGq1mtTUVIyNjbGysirqcPIls2NfqlSpog7rP8fR0ZHdu3eTmJioWZeUlMTOnTtxcnIq9P3J5XJsbW0LPd+suIzoQNnB7tyctIk/289EmaCg0a7JSGV6WW6jaygjxjeUW1M3Z5t3+J93OFxtuGa58u3qfMXY9JuONBzowYFpG1jdeQbJiQoGb52MbjYxvleiWmnq9m7JC78n+dq3+zedaDGwLTum/cqizlNQJCoYvXV6rvZdsloZmvRuzVO/kAzvDfxhJHali/HL14uZ4z6OWyeuMPRnLxwrO+eYr9u3HXAd2AafqRv5veMsUhIVfLF9EjrZxFSsXkXubTnNvk6zOdh7MVJdXb7YMQlduUyTxt6tLB23TST0f/f5veMs9naYyb3Np1Gr8tYM1u3bbnQY2JFfpvzM+C/GkZSQxJztc9DLJj4jMyOW/LEEZWoqs/vPZkTL4Wycu4G4mLg87Tu3EhOTcClbmmnjhn+S/PPLyMiQS39dZcrU+Z8kf/0mzTEaOoKEHVuIHjUE5eNgTOctQ2Jmnml6vWrVUficJWbyGGK8hqN6/RrT+cuQWllnzLtBY/QqVEIZ8TpPMbkO70CVgW24MGUjBzrOIjVBQfucynP9ijzYchrvL2ZzpNdipHq6tN+pXZ7fq/q1R76bck/6PeeHPx8wrJELuwY2pbytGcP3XCYyXpFp+hSlim92/8WLmESWdqmN95CWzGxbHVtjg3ztPzPdvu1Ox4Ed+XnKz4z7wuvd+TU3h/PLmCV/LCU1NZXZ/WcxvOW3bJj72yc7v3JLrZYU2vJflOdKvUwmw97eXmuxsLBArVYze/ZsnJyckMlkFCtWjNGjR2u2UygUTJo0CUdHR2QyGWXLlmXDhg2a98+fP0+dOnWQyWQ4ODgwefJkUlNTs4xj27Zt1KpVCxMTE+zt7enduzevXr3SvP++C8Px48epWbMmMpmMixcvZuh+o1Qq8fLywtzcHCsrKyZOnIj6o5P9xIkTNGrUSJOmQ4cOBAcHa6W5evUqNWrUwMDAgFq1anHr1q0MMd+/f5+2bdtibGyMnZ0d/fr1IyIiokDHXkdHJ9PuI2PGjKFZs2aa182aNWP06NFMnDgRS0tL7O3tmT17ttY2QUFBNGnSBAMDAypVqsTp06czxDBp0iTKly+PoaEhpUuXZsaMGaSkpLfYvT++GzduxMnJCWNjY4YPH45SqWTJkiXY29tja2vL/Pnaf6Cio6P5+uuvsbGxwdTUlBYtWnDnzp0M+W7btg1nZ2fMzMz46quvePv2LZDWgn7+/HlWrFiBRCJBIpEQEhKCUqlk8ODBlCpVCrlcjouLCytWrMj1Mc+Mm5sbjo6O/PHHH5p1f/zxB05OTtSoUUMrrUqlYuHChZr9u7q6sm+fdmvVsWPHKF++PHK5nObNmxMSEqL1fmbdb9asWUOZMmXQ19fHxcWFbdu2FegzfajsEA/8l3sTdvIGMX5PuTp6DXI7c4p51Mxym/A/7/Bg8e+8OH4927yVySkoXsdolpSYhHzF2GhQW/5cdQDf0zcI9w9lr9cvmNpZULlNrWy30zeU8dXykeyfvJ7EmPh87bvloPYcW7WfO6ev89w/lE1eqzG3s6B6m9rZbiczNGDw8tFsm7yWhEz2XbqmC+e2HCfkzkMinr7i2Oo/SIiNx6lK6Rxjch3swfVVB3l86iZv/J9yZsxajOzMKe2e9Xd2uN8S/H+/QGTgc974hXLGax2mJayxreasSdNoVl/ubjrFzV8OExn4nOhHYTw8cgVVcta/zZn5YnAn9q7aw5XTVwjxD+GnsT9iaWtJvTb1s9ym+7fdiQiLYMX4FQTdCeTl05fcunCL8Ce5uyOSV43r12b00AG0atrwk+SfXzt27Gfe/OWc/fPCJ8lf3uVLko4fQXH6OMrQJ8St+gG1IgmDNpnfHYxbMo+ko94oHz1E+SyUuBVLQCpFr7p2WZNaWWP07WjeLpkHyryVl6qDPbi58iBPTt0k0u8p58asxdDOHOdsyvOxvksI/P0CUYHPifQLxWfsOkxKWGPzQXkGsKrkRLVh7fAZtz5PMb237epDurqWpHO1kpSxNmW6hysGejp43828kcD77hNik5L5qWsdapSwori5IbWcrHGxM8vX/jPTaXAn9qzaw5XTfxPiH8KPY3/A0taS+jmeX69ZMX45gf/A+fVv8PPPP+Ps7IyBgQF169bl6tWr2ab//fffqVChAgYGBlStWpVjx45pva9Wq5k5cyYODg7I5XJatWpFUFDQp/wIhdenfv/+/fz000+sW7eOoKAgvL29qVq1qub9/v37s2vXLlauXImfnx/r1q3D2NgYgOfPn9OuXTtq167NnTt3WLNmDRs2bGDevHlZ7i8lJYW5c+dy584dvL29CQkJwdPTM0O6yZMns2jRIvz8/KhWrVqG93/44Qc2b97Mxo0buXjxIpGRkRw4cEArTXx8PF5eXly/fp2zZ88ilUrp0qULKpUKgLi4ODp06EClSpW4ceMGs2fPZvz48Vp5REdH06JFC2rUqMH169c5ceIEL1++5Msvv8z1MS6oLVu2YGRkxJUrV1iyZAlz5szRVNxVKhVdu3ZFX1+fK1eusHbtWiZNmpQhDxMTEzZv3oyvry8rVqxg/fr1/PTTT1ppgoODOX78OCdOnGDXrl1s2LCB9u3b8+zZM86fP8/ixYuZPn06V66kdyvo0aMHr1694vjx49y4cQM3NzdatmxJZGSkVr7e3t4cOXKEI0eOcP78eRYtWgTAihUrqF+/PkOGDNHcxXB0dESlUlGiRAl+//13fH19mTlzJlOnTmXv3r0FOpaDBg1i06ZNmtcbN25k4MCBGdItXLiQrVu3snbtWh48eMDYsWPp27cv58+fB+Dp06d07dqVjh07cvv2bb7++msmT56c7b4PHDjAd999x7hx47h//z7Dhg1j4MCBnDt3rkCfCcDIyQa5nQUvL6Tf3k99m0jkrWCsapUrcP429SvS4d4vuF9YSo1FA9G3MM5zHpaOtpjaWhB06b5mXdLbRJ7eDsbJLfsYO88dhP+5Wzz8YNu8sHa0xczWAr9L9z7YdwKPbz+ktJtLttv2mjuYe+du4v/Bth96dCOAWh0aYGhmjEQioVbHBujJ9Aj82zfbfE2dbDCyM+fphfTPlPw2kZe3g7HP4Xh8SGZqmPZ5otMuOORWpti7lSUxIoZuB2Yy6ObPdPl9Gg61y+c6TwA7JzssbS25ffG2Zl3C2wQCbwdQoWaFLLer07ouD+8GMWnNZLbd3M7yYyto08s9T/sWcqCri2658qTc/qD7iVpNyu0b6FasnKssJDIZEh1dVG9jP1gpwXj8NBL37UYZGpKnkEzelefnH5XnV7eDsauZ+/Ks/1F5BtA10Kfl6hFcnLaZxNcxeYoL0lrd/cJjqOtso1knlUio62zD3edRmW7jExROteKWLDx1lxYrT9Dttz/57a9AlHm825UVOyf7TM+vgBzOr7qt6xJ09yGT10xh+80drDi2EvfP4Pwqqj71e/bswcvLi1mzZnHz5k1cXV1xd3fXaiz+0F9//UWvXr0YPHgwt27donPnznTu3Jn799PL7ZIlS1i5ciVr167lypUrGBkZ4e7uTlJSUkEOUbbyPKXlkSNHNJXx96ZOnYqBgQH29va0atUKPT09nJycqFOnDgCBgYHs3buX06dP06pVKwBKl05vffrll19wdHRk9erVSCQSKlSowIsXL5g0aRIzZ85EKs147TFo0CDN/0uXLs3KlSupXbs2cXFxWvHNmTOH1q1bZ/l5li9fzpQpU+jatSsAa9eu5eTJk1ppunXrpvV648aN2NjY4OvrS5UqVdi5cycqlYoNGzZgYGBA5cqVefbsGd9++61mm9WrV1OjRg0WLFiglY+joyOBgYGUL5/zH8qPj33btm35/fffc9zuvWrVqjFr1iwAypUrx+rVqzl79iytW7fmzJkz+Pv7c/LkSYoVKwbAggULaNu2rVYe06dP1/zf2dmZ8ePHs3v3biZOnKhZr1Kp2LhxIyYmJlSqVInmzZsTEBDAsWPHkEqluLi4sHjxYs6dO0fdunW5ePEiV69e5dWrV8hkabdKly1bhre3N/v27WPo0KGafDdv3oyJiQkA/fr14+zZs8yfPx8zMzP09fUxNDTE3t5eE4uOjg7ff/+95nWpUqW4fPkye/fuLdAFVd++fZkyZQpPnqS1zly6dIndu3fj4+OjSaNQKFiwYAFnzpyhfv20FpPSpUtz8eJF1q1bR9OmTTUt7j/88AMALi4u3Lt3j8WLF2e572XLluHp6cnw4WndBLy8vPj7779ZtmwZzZs3z/dnAjCwNU+L/aM/eEmvYzCwMS9Q3uHn7vD82DXiQ19j7GxLlSk9abRjIn92mAV5+ANnYpPWwhX3UYxxr2MwySZG1471KVbZmdWdpmeZJiem7/KPfR2ttT72dTRm2ey7VscGOFUuzYJOWV+w/TryR4asHstPdzahTEklOTGZNcOW8jqHljPDd/tNiIjVWp/wOhZD21y2BkokNJ7VlxdXA4gMeAakXSwA1PHqyqV5u3j94AkVujei864p7Gw1Odv++h+ysEnraxwdEa21PjoiGotsjpm9oz1t+7bD+zdvfl+9l3Ku5Rj6/VBSU1L4c9+fuftcQrakpmZpFfIo7QqpKioKvRK560poOOgbVJERpNxKvzCQ9+gNKiVJB/fnOab35Tnxo/Kc+DoWQ5vcl+cGs/sSdjWAqHflGaD+7L6E3wjiyambeY4LICpBgVKtxspIu0uPlZGMkDdvM93meXQC155E0K5yCVZ/WY+nUfEsOHmHVJWKbxplXenOrfTzS/s7jI6Ixtwm637+9o72tOvbDu/fDrB39R7KuZZn6PfDSElJ5c99ZwscV34VVZ/6H3/8kSFDhmga59auXcvRo0fZuHFjpg1tK1aswMPDgwkTJgAwd+5cTp8+zerVq1m7di1qtZrly5czffp0OnXqBMDWrVuxs7PD29ubr7766pN8jjxX6ps3b86aNWu01llaWhIfH8/y5cspXbo0Hh4etGvXjo4dO6Krq8vt27fR0dGhadOmmebp5+dH/fr1kUjSv8yGDRsSFxfHs2fPMu2n/L5F/M6dO0RFRWlazUNDQ6lUqZImXa1aWd+Oj4mJISwsjLp162rW6erqUqtWLa0uOEFBQcycOZMrV64QERGhta8qVapo7gIYGKT3kXtfiXvvzp07nDt3LsMFEaS1QOemUv/xsTcyMspxmw99fKfCwcFBcxXq5+eHo6OjpkKf2WeAtKvZlStXEhwcTFxcHKmpqZiammqlcXZ21lS8Aezs7NDR0dG6OLOzs9Ps+86dO8TFxWUY65CYmKjVzenjfD+MPzs///wzGzduJDQ0lMTERJKTk3OcASknNjY2tG/fns2bN6NWq2nfvj3W1tp9Sh8+fEhCQkKGi8rk5GRNNx0/Pz+t8geZH/cP+fn5aS503mvYsGG23YoUCgUKhXafzxS1ktLdGlNzyWDNuov9lma774J4dvBvzf9j/Z8S4xtK2yvLsW1QiVcXsx74V71TQ7ou+FrzetOgvA+sNXOwpOPMAfzWbwGpitwP8KzTqRF9FgzTvF49aGGe923hYEXPmQNZ3m9utvvu5PUVhqZG/NT7e+Ki3lK9TW2G/uzF0h4zeREQqklXvnMDmi1Kb9Q44rkszzF9rOn8AVi6lGB/17madZJ35+v9Hefw2/s/AC4+eEKJhpWp1LMplxdnfreraedmjFg4QvN6juf3mabLiUQq4eHdh2xbkjaQ+dGDR5R0KUnbPu3+31bqe/Xqwpqf0y/oO3Tsy8VL2XcBKEryHr2RNW1BzMTvICUZAJ2y5ZF36kb0qCG5yqNslwY0+aA8Hx9Q8PLc6F15PvhBeS7Z2o3iDSuxz31agfPPC5VajaWRjBke1dGRSqhkb86rt4lsufIwX5X6Zp2bMWLhSM3r7z1n5yuu9+fX1o/Or3Z92hZppb4wZfZ3TyaTaRoP30tOTubGjRtMmTJFs04qldKqVSsuX76cad6XL1/Gy8tLa527u7tmtsDHjx8THh6uacgGMDMzo27duly+fPnzqdQbGRllOtuKpaUlAQEBnDlzhtOnTzN8+HCWLl3K+fPnkcvlhRLse/Hx8bi7u+Pu7s6OHTuwsbEhNDQUd3d3kpOTM8RbUB07dqRkyZKsX7+eYsWKoVKpqFKlSoZ9ZScuLo6OHTtm2gLr4OCQqzyyOvZSqTTDOIAP+7m/p6enPWhGIpFoLlBy4/Lly/Tp04fvv/8ed3d3zMzM2L17t6aVObv9ZLfvuLg4HBwctFq53/uwL3l+4t+9ezfjx4/nhx9+oH79+piYmLB06VKtrj/5NWjQIEaOTPtx/fnnnzO8HxeXNuDo6NGjFC9eXOu9j39UPrWFCxdq3bEA6GFUhd4njTh9M/3CSUc/7SdBZmNG0qtozXoDGzOiH+RvYGlW4kNfo3gTi1EpO8imUu975gZPb6fPxqCrn1YOjG3MePtBi7mxjRkvfEMyzaN41dKY2Jgx+kj6nTIdXR1K1alA/f5tmFa+X6aDP++cuc5jrX2nHR9TG3Ot1npTG3OeZrFvp6qlMbUxZ9qR9IsRHV0dytWpSLP+Howo3xurEjY092zL7NZjCQtKa1l85veEsrUr0qy/Ozunpff/fXz6Ji9vZ/zODK1NSfjgOzO0MSXiQfrFQFaazO2Pc8sa/NF9HvHh6d3d4t/lFRn4XCt91MMXGBfPerKBq6evEHgrQPP6/WA9c2tzol6ltyaaW5vzyPdxlvlEvYriaZB2/E+DntKg7efV570wHT58iqtX08djPX/+afs3q2JjUCtTkVpot+hKLSxQRUVmsVUaebeeyL/sTezUcShDHmnW61WphsTcAout6Rd9Eh1djL4ejrxzd6I8tSszT07dZN+tjOVZ/lF5ltuY8iYX5bnhvP6UbFWDQ93mER+W/hmKN6yEaUlbBvr+qpW+9a/fEX41gMM9ch6IbGEoQ0ci4c1Hg2LfxCuwNsp84KuNsQG6Ugk60vRGy1JWJkTEK0hRqtDTyVsv6CunrxCQ6fllkeH8euz7KMP270W9iiI0k/OrYduinfGpMAe4ZvZ3b9asWRnGE0ZERKBUKrGz056NyM7ODn//zGd1Cg8PzzR9eHi45v3367JK8ykU6hNl5XI5HTt2pGPHjowYMYIKFSpw7949qlatikql4vz581pXLe9VrFiR/fv3o1arNa31ly5dwsTEhBIlSmRI7+/vz5s3b1i0aBGOjo4AXL+e/QC9zJiZmeHg4MCVK1do0iRtqr3U1FRNn26AN2/eEBAQwPr162ncuDEAFy9ezBD/tm3bSEpK0rTW//3331pp3Nzc2L9/P87OzujqFu6DfG1sbLT6cQHcvn07QyU4OxUrVuTp06eEhYVpLjI+/gx//fUXJUuWZNq09JaO991PCsLNzY3w8HB0dXVxdnbOdz76+voolUqtdZcuXaJBgwaaripAhkHO+eXh4UFycjISiQR394x9EStVqoRMJiM0NDTLu1QVK1bk0KFDWus+Pu6ZbXPp0iUGDBigWXfp0iWtO1QfmzJlSoZWhaPlh5Ian0RqvHb/vsSXUdg2qkzMu0q8rrEcyxplCN5yJtu48kruYIm+hTFJL6OzTZccn8Sbj2KMfRVF2QZVCPNNi1FmLMexehn+3p5xcDfAw0v3+bHNBK11PZZ+w+vgF/isPZTlbC6K+CRex2v/AMe8iqJCgyo8e1eJNzCWU6p6Wc5vP5lJDuB/6R7ft9E+9gOWDic8+AUn13qjVqnQfzdDx8dxqFQqpBLtP/op8UnEfHQ84l9GU6JRZSJ80/5I6xnLsatehvvbsm9xazK3P6U9anGgx3zePtWeoeTt09fEhUdiUUa70cG8lD1PfO5mmWdifCKJ8Yla6yJfReLasDqP31Xi5cZyyld34di241nm43fdl+JltH//i5cuzqtnOd+d+7eKi4snLi5/A7jzJTWV1KBA9KrXJPnyu79rEgl61d1IOnQgy83k3Xsh/6ovsdMnkBoUoPWe4uwpra44AKbzlqL48xSKUxm/75T4JFIyKc/FG1XmzQfl2bZ6GXy3Zl+eG87rTymPWhzKpDzf+vkwfrt8tNZ9eXYRl7/fzpPTGSe2yIyejpSK9mZcDXlNi/Jp54VKrebqk9d85Zb5THSuJSw5/uAZKrUa6bv6zZPIOGyMZXmu0EPW51f1hq6aSrzcWI5LdReObzuWWRYA+F73pUQZ7camtPMrbzMVFbbC7H6T2d+9f7pB7Z+W5xKlUCgIDw/XWiIiIti8eTMbNmzg/v37PHr0iO3btyOXyylZsiTOzs4MGDCAQYMG4e3tzePHj/Hx8dEMVhw+fDhPnz5l1KhR+Pv7c/DgQWbNmoWXl1em/emdnJzQ19dn1apVPHr0iEOHDjF37twM6XLju+++Y9GiRXh7e+Pv78/w4cM1D/0BsLCwwMrKil9//ZWHDx/y559/ZigkvXv3RiKRMGTIEHx9fTl27BjLlmnfQhwxYgSRkZH06tWLa9euERwczMmTJxk4cGCGimhetWjRguvXr7N161aCgoKYNWtWhkp+Tlq1akX58uUZMGAAd+7c4cKFC1qVd0jrhx8aGsru3bsJDg5m5cqVGQYV50erVq2oX78+nTt35tSpU4SEhPDXX38xbdq0PF2sOTs7c+XKFUJCQjTdpMqVK8f169c5efIkgYGBzJgxg2vXrhU4Zkjrr+/n54evry86OjoZ3jcxMWH8+PGMHTuWLVu2EBwczM2bN1m1apXmmQPffPMNQUFBTJgwgYCAAHbu3JnjHPcTJkxg8+bNrFmzhqCgIH788Uf++OOPDIOzPySTyTA1NdVa9CQZYwZ4uP4EFcd0xqGNG6YVHKm96hsSX0ZrzTvfZO8UygxM71akYyjDrHJJzCqXBNIG3JpVLon8XYuujqGMqjN6YelWFsMS1tg2qkyDzV7EPX7Jy2wqiFm5uPE4LUZ1pmKrmti7ONLzx2+JfRnFg1Pp5WXIjmnU798GSLsweBn4TGtJTlSQEB3Hy8BnWe0mU2c3HqXdqG5Ua1WLYi5ODPxxJNEvo7h9Kr1cjd0xk2b9PYC0C4MXgU+1FkWigvjot7wIfApAePBzXj4Oo++CoTi7lsXayY5WX3egYqNq3D6Vc/eLOxtOUGtUZ5xbu2FVoQStlw8j/mU0jz6Yf7vTrilUHZD+nTWd74lLl4acGvULKfFJGNqYYWhjho5BemPArbVHqTawDWXa1cbM2Y6647tjUbYYvrt98nTMDm04SM/RPanTug4lXUri9ZMXka8i+ftU+q3tebvm035AB83rg78dxKWGCz1G9MChpANNOzXFvbcHR7cezdO+cyshIRH/wGD8A9Mu+p+/eIl/YDBh4UV7EWFhYY6ra2UqVUzrolm+fBlcXStjZ2eTw5a5k3hgLwYe7ZG1ckfHsSRGI72QyOQknU6rgBuPm4qhZ3pXGnmPXhj2H0TcT4tRvgxHYmGJxMISDNLuyKvfxqJ88lhrQZmKKioS5fOnuYrp3oYTuI3uTMnWblhWKEHz5cNIeBmtNZ98h91TqOyZXp4bzfekXJeGnB35CylxSchtzJB/UJ4TX8cQFfBMawGIe/4mwwVAdvrVKcsfd55w6F4ojyLeMv/kHRKTlXSqltZNePrhG6z0SR/c/mWNUsQmpbDk9D2eRMbxv4fhbLgcxJdZXATkx8ENB+k5+ivqtK777vwaR+SrSC5/cH7N3zWfDlrnlzcuNSrQY8SXmvPLo7cHR7ceKbS4ilpmf/cyq9RbW1ujo6PDy5fa44RevnypNUbvQ/b29tmmf/9vXvIsDHluMj5x4kSG7iIuLi4sWrSIRYsW4eXlhVKppGrVqhw+fFjTT3rNmjVMnTqV4cOH8+bNG5ycnJg6dSoAxYsX59ixY0yYMAFXV1csLS0ZPHiw1qDMD9nY2LB582amTp3KypUrcXNzY9myZXzxxRd5/TiMGzeOsLAwBgwYgFQqZdCgQXTp0oWYmLRBeFKplN27dzN69GiqVKmCi4sLK1eu1Jou0tjYmMOHD/PNN99Qo0YNKlWqxOLFi7UG2BYrVoxLly4xadIk2rRpg0KhoGTJknh4eGR64ZIX7u7uzJgxg4kTJ5KUlMSgQYPo378/9+5lPstGZqRSKQcOHGDw4MHUqVMHZ2dnVq5ciYeHhybNF198wdixYxk5ciQKhYL27dszY8aMDLey8koikXDs2DGmTZvGwIEDef36Nfb29jRp0iTDravsjB8/ngEDBlCpUiUSExN5/Pgxw4YN49atW/Ts2ROJREKvXr0YPnw4x49n3UKYFx+PJ/jY3LlzsbGxYeHChTx69Ahzc3Pc3Nw0Zd/JyYn9+/czduxYVq1aRZ06dViwYIHWQPCPde7cmRUrVrBs2TK+++47SpUqxaZNm7TKZEEE/HwEHUMZNZcORs/UkIirgVzsvRjVB/3BjZztkFmmj2+wdC1N0z/Sz1fX7/sBELLnf1wfsw61SoVZJSdKftkYfVMjEl9G8fL8PR4s/j3P0yMCnF97GH25jG4Lv8bA1JCQawFsHLBIq8+6ZUk7jD6IsbCcXHsQfbkBfRcOw9DUkIfX/Fk5YL7Wvq1L2mGch32rUpWsHriALpP6MOK3SciMDHj1JJzN437mvk/OrYg31xxB11BG80WDkJmmPXzqcL8lKD+IyaykLfIPYqraP+2uadfftX9nz3itw//3tOkT72w4iY5Mn0az+mJgbkSEbygHey8i9kneKrr71+zHQG7AyIWjMDI1wve6L7P6zSTlg/jsnewxtUw/n4LuBrFg6Hz6TxrAV9/14uXTl6z/fj3nvX3ytO/cuu8fxKBR6TN+LVmV1k2jU9tWzJ8+7pPsMzc6dmjDxg3pM4zt2pE2rmrO3B+YM/fHAuef/L9zxJuZY9h3EFJLS1KDHxI7YwLq6LSuHDq2tqBO7+Zo0L4TEj19TKdrN6QlbN9Ewo7NBY4H4M4vR9AzlNFk8SD0TdMePnWsr3Z5Ni1pi8EH5bnygLTy/MU+7fJ8buw6An8vvOlA3SsWJypBwZoL/kTEK3CxNeWXnvWwetf9Jiw2UWt8oL2pnF961mfZ2fv02HAOWxMDetcqzcB6BZ9N7L39a/ZhIDdg1Afn18x+Mz46vxwynF/zh85jwCRPemnOr1/x+UTnV24VxYNg9fX1qVmzJmfPntVMD65SqTh79qymi+3H6tevz9mzZxkzZoxm3enTpzVj4kqVKoW9vT1nz57VjOGLjY3lypUrWpOoFDaJ+uPO2IIg/Cfsc+hT1CFkcE2W+zEe/4Ro8n7B8SlVVX5et45PSqKLOgQtf9xcWdQhaJEXa1zUIWgJb/l5PX18/z3Hog5BS/+5xXJO9A/qMatwnxxcUEdCP81dsg/95dAt50S51CAs97Mv7dmzhwEDBrBu3Trq1KnD8uXL2bt3L/7+/tjZ2dG/f3+KFy/OwoVpkyX89ddfNG3alEWLFtG+fXt2797NggULuHnzJlWqVAFg8eLFLFq0iC1btlCqVClmzJjB3bt38fX11ZpYpTAVbuduQRAEQRAEQfgX6dmzJ69fv2bmzJmEh4dTvXp1Tpw4oektEBoaqtWrokGDBuzcuZPp06czdepUypUrh7e3t6ZCDzBx4kTi4+MZOnQo0dHRNGrUiBMnTnyyCj2IlvrPwsfTcH7M19c302k9hcL1X/seREt9zkRLffZES332REt99kRLffb+iy31l+y7F1peDcP35Zzo/xnRUv8ZKFasGLdv3872feHTE9+DIAiCIBSdz6tZ599HVOo/A7q6upnOPy/8s8T3IAiCIAhFR03RPFH2/4uCTbsiCIIgCIIgCEKREy31giAIgiAIQpHL4jmAQi6JSr0gCIIgCIJQ5FSi+02BiO43giAIgiAIgvAvJ1rqBUEQBEEQhCInBsoWjKjUC4IgCIIgCEVOTGlZMKL7jSAIgiAIgiD8y4mWekEQBEEQBKHIie43BSMq9YLwH1W3RHhRh5CBe0PDog5Bi9TStKhD0KK48bSoQ9Ay+IvaRR2CFnmxxkUdgpbEFxeKOgQtG6vPLOoQtHSr+nmV5x6zooo6BC2/f1+5qEP4x4nuNwUjKvWCIAiCIAhCkROV+oIRfeoFQRAEQRAE4V9OtNQLgiAIgiAIRU70qS8YUakXBEEQBEEQipxK1OkLRHS/EQRBEARBEIR/OdFSLwiCIAiCIBQ5leh+UyCiUi8IgiAIgiAUOXVRB/AvJ7rfCIIgCIIgCMK/nGipFwRBEARBEIqcmKe+YERLfRFo1qwZY8aMKXA+s2fPpnr16tmm8fT0pHPnzgXaT0hICBKJhNu3bxcon3+L/9rnFQRBEITPgUoiKbTlv+g/21Lv6enJli1bMqx3d3fnxIkTRRBR3o0fP55Ro0YVdRh59vr1a2bOnMnRo0d5+fIlFhYWuLq6MnPmTBo2bFjU4eHo6EhYWBjW1tafdD/Ozs48efIEAENDQ1xcXJgyZQo9evT4pPvNiXGPTpj0/RIdK0uSg4KJXrqKZN+ATNPqli6J2TBP9CuUR7eYPVE//kzcrj+00pgO6Y/Z0AFa61JCQgnvMTDfMeo1aod+i65ITCxQvXhM0v51qEKDctxOt0Zj5AMmknLvb5I2zM/XvnVrtUavQXskxmaoXoaSfHwLqhePctxOp3I9DLqNItX/Ooq9P33wYWTot/wKnQq1kMiNUUe/JuXqSVJvnM1VPLJ2nTHo/BVSC0uUIcHE/7oCZZB/5mlbd0C/uTs6JUsBoAwOIGHb+gzppSVKYjhgGLqVXZHo6KB8+oS4RTNQRbzKVUwf233zMVuuBvMmXkF5W1MmtapCVQeLLNPHJqWw+oI/fwaGEZOUgoOpnAktKtO4jF2+9p8XnTu3ZdiQfri5VcPKyoKatdtw586DT77fj12/fY9NO/fh6/+Q128iWbFwBi2bNPhk+6s1vhsVejVHZmZI+LVALkzdROzjl1mmrz6iI6Xa1sa8rAPKpGTCrwdxZcEeYh6FZZq+7bYJODV35eTgnwg5eSPbWAw6dEbePa1Mpz4KJn7NClIDsyjTHh0waJleplMfBpCweX2W6Y1GeiFv34m4datI8t6XbRzZ6ePVF/fe7hiZGuF33Y9fpv7Mi5AX2W5jZWeF55SB1GxeE5lcRlhIGMvH/8TDuw/zHQfA7huP2HLlYfr51boaVYvlcH79z5c/Az44v1pV/UfOL+HT+k+31Ht4eBAWFqa17Nq1q6jDypFarSY1NRVjY2OsrKyKOpw869atG7du3WLLli0EBgZy6NAhmjVrxps3b4o6NJKTk9HR0cHe3h5d3fxf8yYnJ+cq3Zw5cwgLC+PWrVvUrl2bnj178tdff+V7vwUlb90M8zHfEPvbVsL7fUNKUDA2qxYjtTDPNL3UwIDU52FEr/4NZUTW319K8GOee3TXLK++/i7fMerWaISs89coTuwiYdkYlM8fY/jNHCTGZtluJ7G0RdZpEKnB9/O9b51K9dBv04eU83+Q+Ot0VOGhGPSZDIam2e/bzBr91n1QPslY0dBv0xedstVQHPiFxF8mkHLlOPptB6BT3i3HePQbNcdw0AgS92whxmsIqY+DMZm9DImZeabpdatWJ/nCWd5OH0PsxOGoIl6npbdMv4CV2hfDdOEqlM9CeTttDDHfDSJx7xbUKbkr0x876fecH875MqxheXYNaEJ5G1OG771CZLwi0/QpShXf7L3Mi5gElnaqhffXzZnpXg1bE4N87T+vjIwMufTXVaZMzd9FX2FJTEzCpWxppo0b/sn35Tq8A1UGtuHClI0c6DiL1AQF7bdPQkeml+U2xepX5MGW03h/MZsjvRYj1dOl/c5J6MplGdJW/doD1LkbAqnfpDlGQ0eQsGML0aOGoHwcjOm8rMu0XrXqKHzOEjN5DDFew1G9fo3p/GVIrTI2yug3aIxehUooI17nKpasdPu2Ox0HduTnKT8z7gsvkhKSmLN9LnrZHC8jM2OW/LGU1NRUZvefxfCW37Jh7m/ExcQVKJaTfs/54c8HDGvkwq6BTSlva8bwPZezP792/8WLmESWdqmN95CWzGxbHVvjf+b8yom6EJf/ov90pV4mk2Fvb6+1WFhYoFarmT17Nk5OTshkMooVK8bo0aM12ykUCiZNmoSjoyMymYyyZcuyYcMGzfvnz5+nTp06yGQyHBwcmDx5MqmpqVnGsW3bNmrVqoWJiQn29vb07t2bV6/SW8R8fHyQSCQcP36cmjVrIpPJuHjxYobuN0qlEi8vL8zNzbGysmLixImoP/ohPXHiBI0aNdKk6dChA8HBwVpprl69So0aNTAwMKBWrVrcunUrQ8z379+nbdu2GBsbY2dnR79+/YiIiMjxmEdHR3PhwgUWL15M8+bNKVmyJHXq1GHKlCl88cUXQObdX6Kjo5FIJPj4+Ggdk6NHj1KtWjUMDAyoV68e9+9rV9guXrxI48aNkcvlODo6Mnr0aOLj4zXvOzs7M3fuXPr374+pqSlDhw7NdP85fafNmjVj5MiRjBkzBmtra9zd3XM8FoDmOy9fvjw///wzcrmcw4cPA3Dv3j1atGiBXC7HysqKoUOHEheX/gfAx8eHOnXqYGRkhLm5OQ0bNtS0/OeXSe/uxHkfI/7wSVIfPyFq4XJUSQqMvvDINH2ybwAxK38l8fQ51MkpWearVipRvYlKX2Ji8x2jfrPOpFw+SerVs6hePkXx+y+okxXo1W2d9UYSKfK+40g+vhP1m6xbH3OiV78tqTfPkXrnf6gjnpN8dCPqFAV6NZpms28Jsq4jSPHZhyoqY0u3jmM5Uu9cQPXED3VMBKk3z6EKD0VavEyO8Rh0+hLFqSMknz2O6ukTEtb8AIokZK3aZZo+/sd5KI57o3z8ENXzUOJXL0EilaLnWlOTRt73a1JuXCFxy1qUj4NQhb8g5epfqGOic4wnM9uuP6JrNSc6V3WijLUJ092rYaCng/e90EzTe98NJTYphZ+61KZGCUuKmxlSy8kaF9vsL9oKy44d+5k3fzln/7zwj+wvK43r12b00AG0avrp715WHezBzZUHeXLqJpF+Tzk3Zi2GduY4u9fMcptjfZcQ+PsFogKfE+kXis/YdZiUsMammrNWOqtKTlQb1g6fcetzFYu8y5ckHT+C4vRxlKFPiFv1A2pFEgZtMi/TcUvmkXTUG+WjhyifhRK3YglIpehV145damWN0bejebtkHiiz/nucG50Gd2LPqj1cOf03If4h/Dj2ByxtLanfpn6W23T/tjsRYa9ZMX45gXcCefn0Jbcu3CL8SXiBYtl29SFdXUvSuVpJylibMt3DNe38upv53wLvu0+ITUrmp651qFHCiuLm784vu3/m/MqJqhCX/6L/dKU+K/v37+enn35i3bp1BAUF4e3tTdWqVTXv9+/fn127drFy5Ur8/PxYt24dxsbGADx//px27dpRu3Zt7ty5w5o1a9iwYQPz5s3Lcn8pKSnMnTuXO3fu4O3tTUhICJ6enhnSTZ48mUWLFuHn50e1atUyvP/DDz+wefNmNm7cyMWLF4mMjOTAgQNaaeLj4/Hy8uL69eucPXsWqVRKly5dUKnSToG4uDg6dOhApUqVuHHjBrNnz2b8+PFaeURHR9OiRQtq1KjB9evXOXHiBC9fvuTLL7/M8dgaGxtjbGyMt7c3CkXmLQl5MWHCBH744QeuXbuGjY0NHTt2JCUlrXIZHByMh4cH3bp14+7du+zZs4eLFy8ycuRIrTyWLVuGq6srt27dYsaMGRn2kdvvdMuWLejr63Pp0iXWrl2b58+iq6uLnp4eycnJxMfH4+7ujoWFBdeuXeP333/nzJkzmthTU1Pp3LkzTZs25e7du1y+fJmhQ4ciKUg/Ql1d9CuUR3H1Zvo6tRrF1ZvIqlbKf76ArmNxih3bg4P3NiznTkHHzjZ/GenoIi1RFmXgHa0YlYG3kTq7ZLmZvvtXqOJiSLlyOn/7BZDqIHUohfLxhxeOapSP7yMtUS7LzfSadEUdH0Pq7fOZvq98GoROeTckJmm3y6XOlZBa2aMMvpd9PLq66JQpT8qdD7oyqNWk3LmBrkvl3H0mmQx0dFG/fXeRJZGgX6s+yhdPMZm9FPMt3pguXYNe3Ua5y+8jKUoVfuEx1HX+4E6ARELdktbcfRGV6TY+wS+pVsyChafv0WL1Sbpt9OG3y0EoVf/VtrdPy8TJBiM7c55fSC/XyW8TeXU7GLuaWZfrj+mbGgKQFJ3eaKJroE/L1SO4OG0zia9jcs5EVxfdcuVJuf1Rmb59A92KuSvTEpkMiY4uqrcfNBxIJBiPn0bivt0oQ0NylU9W7JzssbS15PbF25p1CW8TCLgdQIWaFbLcrm7rugTdfcjkNVPYfnMHK46txL1X7hp/spJ+ftlo1kklEuo623D3eRbnV1A41YpbsvDUXVqsPEG33/7kt78CP5vzSyUpvOW/6D/bpx7gyJEjmsr4e1OnTsXAwAB7e3tatWqFnp4eTk5O1KlTB4DAwED27t3L6dOnadWqFQClS5fWbP/LL7/g6OjI6tWrkUgkVKhQgRcvXjBp0iRmzpyJVJrxOmrQoEGa/5cuXZqVK1dSu3Zt4uLitOKbM2cOrVtn3Rq5fPlypkyZQteuXQFYu3YtJ0+e1ErTrVs3rdcbN27ExsYGX19fqlSpws6dO1GpVGzYsAEDAwMqV67Ms2fP+PbbbzXbrF69mho1arBgwQKtfBwdHQkMDKR8+fJZxqirq8vmzZsZMmQIa9euxc3NjaZNm/LVV19leqGSk1mzZmmOyZYtWyhRogQHDhzgyy+/ZOHChfTp00czKLlcuXKsXLmSpk2bsmbNGgwM0m43tmjRgnHjxmnyDAkJ0dpHbr/TcuXKsWTJkjx/BkjrrvPDDz8QExNDixYt2LlzJ0lJSWzduhUjIyMg7bh37NiRxYsXo6enR0xMDB06dKBMmbQW3YoVK2aZv0KhyHARpVCpkH1QHqXmZkh0dVBGav8xUEZGoevsmK/PBZD8wJ/I75eQ+uQZOtaWmA7pj+365YR/NRh1QmKe8pIYmSLR0UH1VjtG9dtodOxKZLqNTqlK6NVrTcLS/Hf5AZAYmiCR6qCO166cqONjkVoXy3QbqWN5dGs0I3HdlCzzTT6xBf0OgzEcuxq1MhXUapKP/IYqNPM+wZp4TM2Q6OiijtY+FqroKPRKOOXqMxn2/wZVZITmwkBiZoFEboi8W28Sdmwgdcs69NzqYDx5Lm+njyH1wZ0cctQWlZCMUq3GylC7S4aVkYyQyMy7HTyPjudaTCLtKhVndfe6PI2KZ8Hpe6SqVHzTMOsLNyF/DG3MAUiM0L57lvg6FkObXLbeSiQ0mN2XsKsBRAU806yuP7sv4TeCeHLqZjYbp5O+K9OqqI/KdFQeyvSgd2X6VvqFgbxHb1ApSTq4P1d5ZMfCJu3iOzpCO8boiGjMbbLux27vaE+7vu3w/u0Ae1fvoZxreYZ+P4yUlFT+3Je78TMfi0pQpJ1fRpmcX2/eZrrN8+gErj2JoF3lEqz+sl7a+XXyTtr51SjrixLh3+E/3VLfvHlzbt++rbV888039OjRg8TEREqXLs2QIUM4cOCApqvF7du30dHRoWnTzG+3+/n5Ub9+fa0W04YNGxIXF8ezZ88y3ebGjRt07NgRJycnTExMNHmHhmrfnq5Vq1aWnyUmJoawsDDq1q2rWaerq5thm6CgIHr16kXp0qUxNTXF2dlZa1/v7wK8r/AC1K+vfUvxzp07nDt3TtPqbmxsTIUKaT8GH3flyUy3bt148eIFhw4dwsPDAx8fH9zc3Ni8eXOO237sw9gsLS1xcXHBz89PE+fmzZu14nR3d0elUvH48WPNdtkdV8j9d1qzZta3qrMyadIkjI2NMTQ0ZPHixSxatIj27dvj5+eHq6urpkL/fp8qlYqAgAAsLS3x9PTE3d2djh07smLFCsLCMh+gBrBw4ULMzMy0lp/DQvIcb34k/XWVxLP/I+XhI5L+vs7r76YgNTHCsFWzT79zmRyDvl4k7VmNOj7/XX7yRd8AWedvST7yGyRm3W9Wt04bdIqXJWn3MhLXTyf59A7023oiLZXL1vZ8MujWG/3GLYhbOB3e95eXppXx5CuXUBz6HeXjhyTt30nK9cvIPDp90njeU6nB0lCfGe6uVLI3x71icb6uX459twvWtSwzvXp1IToyULM0alin0PfxuSnbpQGDAn7TLFI9nQLn2Wj+ACxdSnB2xM+adSVbu1G8YSX+mrWtwPnnlrxHb2RNWxA7J71M65Qtj7xTN+J+WJivPJt1bsbvfvs0i65u/o6XRCoh+H4wW5ds5dGDR5zceYKTu07Srk/bfOWXXyq1GksjGTM8qqefXw3Ks+9WyD8aR1ZUSApt+S/6T7fUGxkZUbZs2QzrLS0tCQgI4MyZM5w+fZrhw4ezdOlSzp8/j1wuL9QY3nezcHd3Z8eOHdjY2BAaGoq7u3uGwZYfVvDyq2PHjpQsWZL169dTrFgxVCoVVapUyfXATkjrovO+xfhjDg4OucrDwMCA1q1b07p1a2bMmMHXX3/NrFmz8PT01LR8fzge4H2XmryIi4tj2LBhWuMh3nNySm/1KYzjmt98JkyYgKenp2ZsQl66z2zatInRo0dz4sQJ9uzZw/Tp0zl9+jT16tXLkHbKlCl4eXlprXvVXLuSpoqOQZ2qRMdSu7VJx9IC1ZvIPHyq7Knj4kkNfYauY+at29luGx+LWqlEamKh1WdSYmKOKjbj7WaptT1SKzvkX3/QrerdMTb+wZv4Bd+gfpO7Pq3qhLeoVUokRtqtlxIjU9RxGbsWSC3skFrYIvtq3AeJ0/ZtOH0riT+PR/02Cv0WPVHs/Qll0G0AUl89RWpXEr367VE8znrWFXVsDGplKhJz7e9Lam6BKir778ugc08Muvbm7axxKJ+kz9yjjo1BnZqK8mmIVnrl0yfoVqpKXlkY6qMjkfAmQfsu0Zt4BdZGGQdUAtgYydDVkaIjTT8XSlkZExGvIEWpQk+n8NqiDh8+xdWr6WOGnj8vWP/mf4Mnp26y71Z644uOflo1QG5tSsKraM16uY0pbx5kPu7hQw3n9adkqxoc6jaP+LD0cle8YSVMS9oy0PdXrfStf/2O8KsBHO6RcSCy6l2Zllp8VKYtci7T8m49kX/Zm9ip41CGpJdpvSrVkJhbYLF1r2adREcXo6+HI+/cnSjPr7LN98rpKwTcSp/96/1gWHNrC6Jepf/mmFub89g361mwol5FERqkfTyfBj2lYdv8z2hkYShLO7/iMzu/Mh/4amNsgK5U8tH5ZfJJzq/8+Dw6Af17/acr9dmRy+V07NiRjh07MmLECCpUqMC9e/eoWrUqKpWK8+fPa7rffKhixYrs378ftVqtqaBdunQJExMTSpTI2D3A39+fN2/esGjRIhwd07o4XL9+Pc/xmpmZ4eDgwJUrV2jSpAmQ1u/6xo0buLmlzaLx5s0bAgICWL9+PY0bNwbSBpJ+HP+2bdtISkrStNb//fffWmnc3NzYv38/zs7OBZoh5kOVKlXC29sbABubtP6BYWFh1KhRAyDLOeP//vtvTQU9KiqKwMBATTcUNzc3fH19M71wy4u8fqd5YW1tnWl8FStWZPPmzcTHx2suFi5duoRUKsXFJb0LQo0aNahRowZTpkyhfv367Ny5M9NKvUwmQybTrkTFfNwVLDWVZP9AZLVrkHj+Uto6iQRZ7RrE/e5doM/5IYncAJ3ixVBGnMn7xspUVM8eolOuGqn3/tbEqFPelZQLRzMkV718RvyiEVrr9Nv3QyKTo/jjV9TROQ/uTs9MiSrsMTqlKqMMeH9rX4JOqSqkXjuVMXnECxLWTNLed/MeSGQGKE5sQx3zBnT1kejoZpwZRK1CIsnhj2tqKsrgQPSq1STlyrvzWCJBr5obSccOZLmZQZdeGPToy9vZE1A+/Giq0tRUUh/6o1Ncu6uDTnFHVK/yPsBYT0dKRXszrj6JoEW5tAt+lVrN1ScRfOXmnOk2riUsOe77HJVajfTd+fYkMh4bI1mhVzji4uKJi4vPOeH/IynxSaTEJ2mti38ZTfFGlXnjm1bp1DOWY1u9DL5bs+8W0nBef0p51OJQj/m8fao9o8ytnw/jt8tHa92XZxdx+fvtPDmdcfIFIK38BQWiV70myZc/KNPV3Ug6lHWZlnfvhfyrvsROn0BqkHaZVpw9pdUVB8B03lIUf55Ccep4tp8PIDE+kcR47W6Cka8iqd7QVVOJlxvLcanuwvFtx7LMx/e6LyXKFNdaV7x0cV49y/9MPJrzK+Q1Lcp/eH695iu3Uplu41rCkuMPnn10fsVhY1z455fwz/tPf4MKhYLw8HCtJSIigs2bN7Nhwwbu37/Po0eP2L59O3K5nJIlS+Ls7MyAAQMYNGgQ3t7ePH78GB8fH/buTWsFGD58OE+fPmXUqFH4+/tz8OBBZs2ahZeXV6b96Z2cnNDX12fVqlU8evSIQ4cOMXfu3Hx9nu+++45Fixbh7e2Nv78/w4cPJzo6WvO+hYUFVlZW/Prrrzx8+JA///wzQ+tt7969kUgkDBkyBF9fX44dO8ayZcu00owYMYLIyEh69erFtWvXCA4O5uTJkwwcOBClUpltjG/evKFFixZs376du3fv8vjxY37//XeWLFlCp05pLcdyuZx69eppBgWfP3+e6dOnZ5rfnDlzOHv2LPfv38fT0xNra2vNw7YmTZrEX3/9xciRI7l9+zZBQUEcPHgww0DZnOT1Oy0Mffr0wcDAgAEDBnD//n3OnTvHqFGj6NevH3Z2djx+/JgpU6Zw+fJlnjx5wqlTpwgKCsq2X31uvN25D+PO7TFs3wZdZycsJo9BKjcg/nDa2AzL2ZMwGzE4fQNdXfTKl0GvfBkkerro2FijV74MuiXSW+HNvhuGzK0aOg526FerhPXSOaBSkXDyz3zFmOzjjV59d3Rrt0BqVwJZj+FI9A1IuZJ2kWDQZyz6HfqnJU5NQRUeqrWQGI9akZj2/zzOgpFy+Ti6bs3RrdYYiXUx9NsPRKInI+XdIFj9Tt+g16JnWmJlCurXz7SXpATUiiTUr5+BSgnJiShDfNFv1QtpyYpIzG3QdW2CbrXGpPpfyzGepIN7kbVpj35z97S55b/xAgM5ijNplRWjMVOR9xuiSW/QtRfyPoOIX7UY1atwJOaWSMwtwSD9DmTSgd3oN2qOrHUHpPbFkbXrgl7t+iiOe+fpWL3Xr1Zp/rgTyqH7T3n05i3zT90lMUVJp6ppFw7Tj95i5Xk/TfovqzsTm5TCkrP3eRIZx/+CX7Lh7yC+zOIioLBZWJjj6lqZShXTxgaVL18GV9fK2NnZ5LBl4UpISMQ/MBj/wLRW9ecvXuIfGExYeP6eFZCdextO4Da6MyVbu2FZoQTNlw8j4WW01nzyHXZPobJn+piuRvM9KdelIWdH/kJKXBJyGzPkNmboGKS1ZCe+jiEq4JnWAhD3/E2GC4APJR7Yi4FHe2St3NFxLInRSC8kMjlJp9PKtPG4qRh6ppdpeY9eGPYfRNxPi1G+DEdiYYnEIr1Mq9/GonzyWGtBmYoqKhLl86f5Ol4HNxyk5+ivqNO6LiVdSuL10zgiX0Vy+dRlTZr5u+bTYUCH9G1+88alRgV6jPgSh5IONO3UFI/eHhzdeiRfMbzXr05Z/rjzhEP3QnkU8Zb5J++QmKykU7V359fhG6z08dWk/7JGqbTz6/S9tPPrYTgbLgfxZRYXAf80MVC2YP7TLfUnTpzI0F3ExcWFRYsWsWjRIry8vFAqlVStWpXDhw9r5oRfs2YNU6dOZfjw4bx58wYnJyemTp0KQPHixTl27BgTJkzA1dUVS0tLBg8enGWl1MbGhs2bNzN16lRWrlyJm5sby5Yt00zvmBfjxo0jLCyMAQMGIJVKGTRoEF26dCEmJq1rgFQqZffu3YwePZoqVarg4uLCypUradasmSYPY2NjDh8+zDfffEONGjWoVKkSixcv1hpgW6xYMS5dusSkSZNo06YNCoWCkiVL4uHhkWMl19jYmLp16/LTTz8RHBxMSkoKjo6ODBkyRHMMIW3g7eDBg6lZsyYuLi4sWbKENm3aZMhv0aJFfPfddwQFBVG9enUOHz6Mvr4+ANWqVeP8+fNMmzaNxo0bo1arKVOmDD179szTcc3rd1oYDA0NOXnyJN999x21a9fG0NCQbt268eOPP2re9/f3Z8uWLbx58wYHBwdGjBjBsGHDCrTfxNM+RJubYTbMEx0rC5IDg3k9ejKqd4NndexttVqVdWyssN+RfnvdtF9PTPv1JOnGbV5/k9btRNfWBqt505CamaKMiiH5zn1eDhyJKjoXs2FkIvXWRRRGZsja9kFiaoHq+SMS1s1CHRcNgMTCBmku58TOK6Xv3yQbmaDXrDv6xmaoXj4haedieNdfX2pmhSqP+1bsX41ey57IugxPe/hUTATJ5/bm6uFTyRfPITE1R957UNrDpx4/5O33E1DHpH1fUmtbUKV3VJJ5dEKip4/JZO2Gg8Rdm0jcvRmAlL8vEL/mR+Td+2A4ZDTK56HELZpJql8Os/Fkwb1icaISk1lzMYCIeAUutqb80qOuZnBfWGwiH/Y6szeV80uPuiz78wE9Np3H1sSA3jVLM7Buwe645VbHDm3YuCH94WC7dqwBYM7cH5gz98d/JAaA+/5BDBqVfqdnyaq086xT21bMnz4uq83y5c4vR9AzlNFk8SD0TdMePnWs7xKUivRuj6YlbTGwNNG8rjwg7U71F/u0fwfPjV1H4O/5nw40+X/niDczx7DvIKSWlqQGPyR2xgTNgHAdW1tQp5dpg/ZpZdp0unaZTti+iYQdm/MdR3b2r9mHgdyAUQtHYWRqhO91X2b2m0HKB8fL3skBU8v051cE3Q1i/tB5DJjkSa/vevHy6UvWf/8rPt4+BYrFvWJxohIUrLngn35+9ayH1bvuN2nnV/oJZm8q55ee9Vl29j49NpxLO79qlWZgvdzPdPQp/VenoiwsEvXHE5kLwr+Aj48PzZs3JyoqCnNz86IO51/pae2WRR1CBuYNDYs6BC3SD/4ofw4UNzIfbF9U5F/ULuoQtJh8+3k9PDDxRdHOdf+xjdVnFnUIWrpVzV9L+afi+cA450T/oN+//7SD5fNKPjB/s7vlxebifQstL8/n2wstr3+L/3RLvSAIgiAIgvB5EK3MBfOf7lMvFL7Q0FCtKSQ/Xj6epvP/qx07dmR5DCpX/rxaXwRBEAThcyD61BeMaKkXClWxYsWynKnm/fuFoVmzZnzOPce++OILrWcGfEhPT+8fjkYQBEEQhP/vRKVeKFS6uroFnkLy/wMTExNMTExyTigIgiAIAvDvGCgbGRnJqFGjOHz4MFKplG7durFixQqMjTMfkxEZGcmsWbM4deoUoaGh2NjY0LlzZ+bOnYuZWfpzTzJ7Ts2uXbv46qvsn6XwIVGpFwRBEARBEIrcv6FS36dPH8LCwjh9+jQpKSkMHDiQoUOHsnPnzkzTv3jxghcvXrBs2TIqVarEkydP+Oabb3jx4gX79u3TSrtp0yY8PDw0r/M6EYio1AuCIAiCIAhFTv2Z94X38/PjxIkTXLt2jVq1agGwatUq2rVrx7JlyzLtYlylShX279+veV2mTBnmz59P3759SU1N1XqIp7m5Ofb29vmOTwyUFQRBEARBEP5fUSgUxMbGai0KhaJAeV6+fBlzc3NNhR6gVatWSKVSrly5kut8YmJiMDU11arQQ9rDPa2tralTpw4bN27M89hBUakXBEEQBEEQipyqEJeFCxdiZmamtSxcuLBA8YWHh2Nra6u1TldXF0tLS8LDw3OVR0REBHPnzmXo0KFa6+fMmcPevXs5ffo03bp1Y/jw4axatSpP8YnuN4IgCIIgCEKRK8w+9VOmTMHLy0trnUwmyzTt5MmTWbx4cbb5+fn5FTim2NhY2rdvT6VKlZg9e7bWezNmzND8v0aNGsTHx7N06VJGjx6d6/xFpV4QBEEQBEH4f0Umk2VZif/YuHHj8PT0zDZN6dKlsbe359WrV1rrU1NTiYyMzLEv/Nu3b/Hw8MDExIQDBw7kOL113bp1mTt3LgqFItefQ1TqBUEQBEEQhCJXVE+fsbGxwcbGJsd09evXJzo6mhs3blCzZk0A/vzzT1QqVZbPpoG0Fnp3d3dkMhmHDh3CwMAgx33dvn0bCwuLXFfoQVTqBeE/69wLh6IOIQO9vZ/XA8VidD6vqRhqqiyLOgQtZ2/nrg/pPyW85ef1jIyN1WcWdQhaBt2eU9QhaJEXa1zUIWjZYdWsqEPQEvnr9aIOQUvxgZ9+H5/7k2ArVqyIh4cHQ4YMYe3ataSkpDBy5Ei++uorzcw3z58/p2XLlmzdupU6deoQGxtLmzZtSEhIYPv27ZpBu5B2MaGjo8Phw4d5+fIl9erVw8DAgNOnT7NgwQLGjx+fp/hEpV4QBEEQBEEQcmHHjh2MHDmSli1bah4+tXLlSs37KSkpBAQEkJCQAMDNmzc1M+N8/HDOx48f4+zsjJ6eHj///DNjx45FrVZTtmxZfvzxR4YMGZKn2ESlXhAEQRAEQShy/4aHT1laWmb5oCkAZ2dnrakomzVrluPUlB4eHloPncovUakXBEEQBEEQity/oVL/ORPz1AuCIAiCIAjCv5xoqRcEQRAEQRCK3Oc1VcK/j6jUC4IgCIIgCEXuc5/95nMnKvWCIAiCIAhCkRN96gtG9KkXBEEQBEEQhH850VIvCIIgCIIgFDnRp75gRKVeEARBEARBKHIqUa0vEFGpF4Q82Lx5M2PGjCE6OrqoQ/nHuY7vRrnezdE3NeT19UD+nrKJt49fZpm+ysiOOLWtjVlZB1KTknl9PYibC/YQGxyW531XndCNMr2bo2dqRMT1QK5N3khcNvu2qVuBisPbY1G1FIb2Fvxv0I88P3FDK42uoQzXaV9Rwr0W+hbGxD99TeCGkzzcdjZXMdUa142KvZojMzMk/FogF6ZuIiYk65hqjOhIqba1MS/jgDIpmfAbQfy9YA8xjzI/Hu22TsCpuSsnvv4J6yrOVOzVHLmpnLfX/XkyZR2Kx9kfR9sBbbH/tjN6NuYk+IYQOuM34m8Had6XyPRwnDkQq06NkOjrEuNzmydT15EaEaNJ4zRnMMa1KyJ3cSLp4TMetPHS2oeOTA+P+QOxq1oK67LFeHj2Fn8MXQ5AY69uuPZqjszUkOfXAzk5bRNR2R2fvi2p0bclZiVsAIgIesalFQd45HNXk8a1V3Mqd2qAXRVnZCZyfqo6FEVsQrbHwaBDZ+Tdv0JqYUnqo2Di16wgNdA/07Qyjw4YtHRHp2QpAFIfBpCweX2W6Y1GeiFv34m4datI8t6XbRwfqzW+GxU+Kj+x2ZTp6u/LT9l35ed6EFeyKT9tt6WVn5ODfyLk5I1M0+TF9dv32LRzH77+D3n9JpIVC2fQskmDAudbUJ07t2XYkH64uVXDysqCmrXbcOfOg0LfT5UJ3Sjd591v0LVAbuT0G1SvAi7ftseyWink9hZcHJjxN6hn2I5Mt709ZycBa45mmbdRt04Y9+mJjqUlKQ+Dif5xFSm+mZdR3VLOmA7xRK9CeXQd7Ile/jPxe/ZrpbH7Yye6DvYZto3b703MspUZ1gufL9GnXvh/w9PTE4lEgkQiQU9Pj1KlSjFx4kSSkpIKbR89e/YkMDCw0PLLiY+Pj+YzSSQSbGxsaNeuHffu3dNK5+npSefOnT9ZHJWHd6DioDZcmbyRYx1nkZqgoNWOSUhlelluY1evIgFbTnOs42zO9FqMVE+XVjsnoSuX5WnfFUd0oPwgd65N3sTpDjNJTVDQfOfkbPetaygj6kEoN6ZuzjJNjdl9cWhWjcujfuFY0wkErD9OzfkDKN7GLceYqn/bgaoD23Bh6kb+6DiLlEQF7bdPQiebmBzqVeTBltMc6DSbI70XI9XVpcOOzI9Hta894N0TCEt51NLsy7fjJFQJCsrvmIkkm31ZftEQx1kDefHjHh54jCPBN4TyO2aia2WmSeM0exDmrWvxcNhS/LtNR9/ekrK/TcqQV8Tus0QevpjpeWMXwQAAz8BJREFUfqRSKSlJydzYdJKQi+kVqbrfdKCmZxtOTt3I1k6zSElQ0HNb9sfnbVgkPov3sLnDdDZ3nMGTv3zptt4L63LFNWn05Po8On+Xyz8fyjKfD+k3aY7R0BEk7NhC9KghKB8HYzpvGRIz80zT61WrjsLnLDGTxxDjNRzV69eYzl+G1Mo6Y94NGqNXoRLKiNe5iuVDrsM7UGVgGy5M2ciBd+dTTuWnWP208uP9xWyOvDuf2mdxPlX9oPwUlsTEJFzKlmbauOGFmm9BGRkZcumvq0yZOv+T7aPCiA6UG+zO9UmbONN+JsoEBU13Zf8bpGMoI9o3+9+gg9WGay1Xx6xDrVLx7OjVLLeRt2yG2ehvebthK688h5ESFIz1T4uRWphnml5iICP1RRixv6xHGfEm0zSvB31LWPtumiVi9HgAEs+ezzKOT0VViMt/kajUC/+veHh4EBYWxqNHj/jpp59Yt24ds2bNKrT85XI5tra2hZZfbgUEBBAWFsbJkydRKBS0b9+e5OTkf2z/Fb/24O6Kgzw9dZNov6dc/G4thnbmOLnXzHKbs32XELz3AjGBz4nyDeXSmHUYl7DGsppznvbt8rUHD1Z48/zkDaL9nvL36DXI7cwp4ZH1vsPO3eHekt95duJ6lmmsa5Xj8e8XeHXZj/hnEQTvOEe0byiW1cvkGFPVwR7cXHWQkFM3ifR/yrkxacfDOZvjcazfEgJ+v0BU4HPe+IVyzmsdJiWssfnoeFhVcqLa0HacG78egJKt3DT7SvR7wuPvVqBvZ4mFe90s92U35Ate7zxNxN4/SQp6xpPJa1ElKrD+qiUAOiaGWH/Vkqffb+LtpXsk3HvE47GrMKldESO38pp8Qmdu4NWW4yieZN4imZKo4NT0zdzZ7UP862jN+tqDPfhr9UGCTt/ktf9TjnitxdjWnPJtsj4+D8/e4tG5O0SFvCTqcTj/W/o7yQlJFHMrq0lzfeNJ/l5zmBe3HmaZz4fkXb4k6fgRFKePowx9QtyqH1ArkjBo0y7T9HFL5pF01Bvlo4con4USt2IJSKXoVdeOW2pljdG3o3m7ZB4oU3MVy4eqDvbg5sqDPDl1k0i/XJafvksIfFd+Iv1C8RmbTfkZ1g6fcevzHFd2GtevzeihA2jVtGGh5ltQO3bsZ9785Zz988In20f5IR74LvfmxckbxPg95cq736Di2fwGhf95h/uLf+f58ax/g5Jex2gtxTxq8uqSL/GhWV8oGvfqQfyhYyQcPUFqyBOil/yEWqHAsEPbTNOn+AUQu3odiWfOoU5JyTSNKjoGVWSUZjFoWJ/UZ89JvnUnyzg+FXUhLv9FolIv/L8ik8mwt7fH0dGRzp0706pVK06fPg2ASqVi4cKFlCpVCrlcjqurK/v2ad8yP3ToEOXKlcPAwIDmzZuzZcsWJBKJprvN5s2bMTc316QPDg6mU6dO2NnZYWxsTO3atTlz5oxWnlFRUfTv3x8LCwsMDQ1p27YtQUFB5IWtrS329va4ubkxZswYnj59ir9/5rdbC5uxkw2GduaEXbyvWZfyNpHXt4KxqVku1/nomxoCkBwdn+ttjJxskNtZEH4hvRU45W0ib24FY52HfWcm4noQxdu4Ibe3AMC2QSVMStsTfv5ettuZONlgZGfOswvpxyP5bSKvbgdj75b345H0wfHQNdCn5aoRXJy+mcTXad1gDMyNtPalfJtA3K0gjGu6ZJqvRE8Xo2pliL3wwR9ktZrYi3c12xhWK4NUX08rTVLwcxTPXmWZb26ZOdpgbGtOyAflRfE2kRe3gymey+MjkUqo2LEeenIZz2/m7VzR0NVFt1x5Um5/0OVBrSbl9g10K1bOXRwyGRIdXVRvYz9YKcF4/DQS9+1GGRqS57Del5/nmZQfu3ycTxnKz+oRXJyWXn6Egnn/G/Qys9+gWgX7DfqQzNqUYi2r82hXNq3jurrouZRHcU27TCuu3UC/SqXCCURXF7l7K+KPHC+c/IR/lOhTL/y/df/+ff766y9KliwJwMKFC9m+fTtr166lXLly/O9//6Nv377Y2NjQtGlTHj9+TPfu3fnuu+/4+uuvuXXrFuPHj892H3FxcbRr14758+cjk8nYunUrHTt2JCAgACcnJyCta0xQUBCHDh3C1NSUSZMm0a5dO3x9fdHTy/r2bWZiYmLYvXs3APr6+vk4KnkntzUHIOl1rNb6pIhY5LZmmWyRCYmE2t/35dXVAKIDnuVj39oVlKTXMRi8ey+/bkzfQp0lg+l8czWqlFTUKjVXJ/zG6yvZXywZ2qTtNzFC+3gkvs7b8Wg4qy9hVwOI+uB4NJjVl5c3ggg5dVM774/2lRoRjV4Wn1/X0gSJrg4pEdrHLOV1NAZl0rqy6NmYo1KkoPyoP3rK6xj0bDLPN7eM38UV/1HM8RGxGNlkf3xsXErQ78BsdGV6JMcn8cew5bwJepGvOKSmZmkV8qgorfWqqCj0SjjlKg/DQd+giowg5VZ6JUreozeolCQd3J/NltnkmU35Mczh+GhIJDSYnbH81J/dl/AbQTz5qPwI+WeQ3W9QAc+VD5X6sgkpcUk8O3YtyzRSczMkujqoIrXLtDIyClnJ3JXpnMibNkRqbEzC0ZOFkl9e/Ve7zRQWUakX/l85cuQIxsbGpKamolAokEqlrF69GoVCwYIFCzhz5gz169cHoHTp0ly8eJF169bRtGlT1q1bh4uLC0uXLgXAxcWF+/fvM39+1n01XV1dcXV11byeO3cuBw4c4NChQ4wcOVJTmb906RINGqQNKtuxYweOjo54e3vTo0ePXH2uEiVKABAfn9Yq98UXX1ChQoVcHxeFQoFCodBal6JWoifRyZC2VJcG1Fs8SPP6z/7Lcr2frNRdMABzlxKc6DI323QluzSg9pLBmtfn+y0t8L6zUn5QG6xqluX8gGUkPIvApl4Fai3wJPFllFarXLnODWiyKP14HPMs+PFoPH8Ali4l8O6afjxKtnbDuY0bekYGlP2iXoH38U+p1LkBHgsGoSPTQwJc23Ai33m9eRTGxrbTkJnIqdCuDh1+GMaOnvPyXbEvCHmP3siatiBm4nf8H3v3HdfU1QZw/JcQCGFvwYk4cO+968StdSvuatVWq0XraGvdWrXaV23V1l1Hrda9R6tV3AOcgCxxsJS9R/L+gQYjKwgGbM+3n3xqTs695+FyE07Ofc65pGakuulVrIyiZx+iJo7Rej8Ve2ueP8eHF/z8afHq/Dn41vlTqnk19nb6usD7L64GDerN2p++Vz/v1t2Vi+4555+/i3IfN6P+G59BF97jZ9Cbyg9qTdA+d5TJ2afI6IpRty4kXbmGMof8+/dN3FG2YESnXvhX+eijj1i7di3x8fGsXLkSmUxGnz59uH//PgkJCXTo0EGjfkpKCnXr1gUy8tYbNmyo8XqjRo1ybS8uLo45c+Zw9OhRgoODSUtLIzExkaCgIAAePnyITCajcePM/Gdra2ucnZ15+PCh1j/XhQsXMDIy4sqVKyxatIh169ZpvS1kXKWYO3euRlkvk5r0NquVpe6TU7d4cdtP/VxqkPExYWhrRmJYlLrc0MaMyPtBebbdaMEwSrevy8mPF5AQHJFr3WenbvEy27bNSXqzbVtzIu8/zrPtnOgZ6lNrxgAujl7J87MeAEQ9fIJl9XJUHddVo1MfePoWoR6ZMem9iklhY0bCGzEpbM14qcXxaDF/GOXa1eVg3wXEh2Qej1LNqqGwMUOlzJoN6rLpS/Z1m61+LrOxIPF+QLb7T4uIRZWWjr6N5qivvq0Fqa/y3lPDo5DK9dEzM9IYrde3NVfX0Zbv6Vtsuu3HRzMGYGCqIDEiFgBjGzPi3zg+xjZmhD3I/fgoU9OJepW/H3ovEIfaTjQY6cLJWZvyFROAMiYaVXoaUktLjXKppSXKyNzPQ0WfASj6DyZmlhvpgf7qcv0atZBYWGK57Q91mURPhvEnE1D06kvkiIFZ9vX41C323i6886f5gmGUa1+XQ30WEP/G+6lU82qYlbNj5INfNOp3+OULQq55c7jf+5tIqiuHD5/i2rXb6ufPnoUUehvPTt7i5S3tPoOiCvAZ9Cabxs6YVSzJ5U9X51pPGRWNKi0dqZXmOa1nZUn6y9zPaW3o2ZdA3rAeETMLbx6aoFuiUy/8qxgbG1OxYsbEuk2bNlG7dm02btxIjRo1ADh69CilSpXS2EYuz99qLG+aOnUqp0+fZvny5VSsWBGFQkHfvn0LfRJr+fLlsbCwwNnZmbCwMAYMGMA///yj9fYzZ87kyy81lyPcU+XTbOumxScRG6+5YlBCaBQOLaqrO/H6Jgps61bAZ1vuyz82WjCMsi4NONlvIXFP8l4lJC0+ibi32k4MjcS+RXX1H1CZiQLruhV4tO1MdrvQikQmQ89AlqUDrUpXglRzqlFqfBKpb8UUHxpFqRbVefkg83jY1anA/TyWw2wxfxjlXRpwqN9CYt86Hrd/PszD389plA04s4SU2ASe/JOZ5y81UWBStxLh27IfEVelphF/xw+zFrWIOvlqFFMiwaxFTUI3Z+TJJtzxQ5mSilmLWkQeuwKAYYWSyEvbEXfTO9ef4W0p8Unqh0RPyotHz4gLi8KxeXV1J97AREHJOhW4vV275UJfk0glyAze8c9UWhppj3zQr1OflMuvVu+RSNCvU4+kQ/tz3EzRdxCKga7EfDONtEeaxyL57CmNVBwAswXLSP7rFMmnss9Bzs/58yCP91PzBbmcPz8d5uGucxpl/c8u4fLc7Tw+fZt/g7i4eOLitJ+T8y5y+gwqkc1nkO/Wd/8MepPToDZEePoTlceXXtLSSPX2Qd6gHkn/uGeUSSTIG9Qjbu+BAsdh1NUFZWQUSZeuFHhf70qsU18wolMv/GtJpVJmzZrFl19+iY+PD3K5nKCgIFq3bp1tfWdnZ44dO6ZRdv16zvmNAO7u7owYMYLevXsDGSP3gYGB6terVq1KWloaV69eVaffvHz5Em9vb6pVe7eJTZ999hmLFy9m//796nbzIpfLs3x5yS71JicPN5yg5qRexPiHEvckjDrT+pIQGkXQG+tfd9g9k6DjN/DekjExufGiEZTv1ZS/R60kNS4Jw1f5wqmxCaQnaX+J2XvDCap/0YvYgBDigsKp9VVfEkOjePrGms8f7Z7J0xM3eLQ5o22ZkRyT8pnrLpuUscWiejlSouJIePaStLhEQi89oM63g0hPSiH+6QvsmlbFsW9Lbs/dnmdMdzeeoP7EXkQHhBL7JIyGUzOOx5vrgXfbNZOAEze4vzUjppYLR1CxZ1NOfLKSlPgkFK+OR8qr45EYHp3t5MaAkzepMbQ9Ybf9UDx+TKlpg0kJjSDy5FV1Hefdc4k8foWwLRkdy9BfD1F+5STi7/gRf/sRJcZ0Q6ow5MXujE5jemwCL34/S5nvRpIWFUd6bALlFowh7oYX8bcyl2yVO9ojNTZE384SiaEBiuqOACT5PEWVmrHqi3WlkujpyzC0MMHAxBC7amV5eOQKzSb2IiIglOgnYbR060tcWBQ+pzKPz8CdM/E5eYNbr45P66/643/Ok5jnLzEwNqRaz2aUbVKV3UOXqrcxtjXH2NYcC8cSANg6lyElPpGYZy9Jis7a2Uvc/wembjNJe+RFmrcXhr36IpErSDqdcZxM3GahfBlOwpaMlWIU/QZhNHQUsd/PJz00BImlFQCqxERISkQVG0N6rGYuPOlpKCMjSH/2JJszJXt3N56g3qTM86dBdufP76/On1fvpxYLR1CxV1NOjs54P2l7/sQ9e5nlC8C7SEhIJOhpZhrUs+ehePn4YW5mioO97lcEe83S0oKyZUtR0iHjnKhcOWP1qpCQMEJDC/5zA/j8eoJqkzM+g+KDwqkxPeMz6M1159v8MZOnx2/gm8NnkHFZzc+g12QmCsp0b4TH3J1axRK3aw+W384g1cublPtemAzsg8TQkIQjGV/yLWfPID38BTFrN7xqQIZ++Yx5ZRKZDD1bG/QrVUCZmEj6G79PJBKMurqQcOwUpBddZrvo0heM6NQL/2r9+vVj2rRprF+/nqlTpzJlyhSUSiUtWrQgOjoad3d3zMzMGD58OJ9++ikrVqxg+vTpjB49Gg8PD7Zs2QKARJJ9ol+lSpXYt28f3bt3RyKR8O2336JUKjVe79mzJ2PGjGH9+vWYmpoyY8YMSpUqRc+ePd/pZzIyMmLMmDF899139OrVK8fYCtP9n48gM5LTdOkoDMyMCLvuwxnXpRr5n6bl7DC0MlU/dx7eHoBOf36jsS/3Kevx+0P75ece/pTRdsOlozNufHXdh3NDvtdo28SxBPI32raq7US7N9qtN3coAP67/+HqlPUAXBq/htqzBtB0zQQMLExIePaCO9//gW8eo6UAHmszYmq9JON4hFz34ejQpaS/EZN5OTsUb8RUfVjG8ei5R/N4/P3lerz35Hw8Ak7cIPbpC1ovGYWhmRGx1x/i4zof1RttycvZI7MyUz+POOSOzMqMUlMHom9rScL9AHxc52ncWCpozibKKFVU/OUrJHJ9Ys55EDhrvUbbjss+w6xZDfXzGqdWAuDZeCwpTzM6TP03T8O8jK26zqjjiwBw/99+XBZnxPz0hg+7h2keH8uydhhZZh4fIxszuq0Yh7GdBcmxCYR7PWH30KUaq+jUHdKOFlM+Vj933fstAEfd1nN3b9ZjmPLP38SbW2DkOgqplRVpfr7EfDsNVVTGREM9OztQZb5fDbv2RKJvgNk3mnM/ErZvJmHHliz7f1eePx9B30hOq+8zz59jrprHx+yt91P1V++nHnvfOn+mrMcnl/OnsNzzesSoiZn3MVi6OiPNp2fn9iz8xu29t5+T7t06smnjSvXzXTvWAjBv/g/Mm7+iUNrwevUZ1GDZq8+gaz6cH5z7Z5BlbSfa7sv8XdV99RkUsPsfrk3OfJ+V7dUEJBKC9l/SKpbEs+eQWlpg+slI9KwtSX3kx4sp09UTwvVK2KF642+Qno01dtsylzc1HTIA0yEDSL7lwYvPMq/eyhvWR+ZQgoQiXvVGTJQtGIlKVch3qBCEIjJixAiioqI4cOCARvmSJUtYsWIFAQEBbNiwgbVr1+Lv74+FhQX16tVj1qxZtGrVCshY0tLNzY0nT57QtGlTBgwYwPjx40lMTMTQ0DDLHWUDAwMZNWoUV65cwcbGhunTp7Nnzx7q1KnDjz/+CGQsafnFF19w6NAhUlJSaNWqFatXr6ZSpbyXQzt37hwfffQRkZGRGktpPnnyBCcnJ3bs2EH//v0ZNmwYCQkJWZbozM22Uq5a19UV/WL2cRStV7xmbdVXvt/Ug/w6KzMu6hA0fFJN+9FyXfjzbpmiDkHDKI95RR2CBkXJlkUdgoYd1m2KOgQNLRzzf/ft96nU5b/eexszHQcX2r4WB2p39ePfRHTqBSEXCxcuZN26dTx5Urw6C29zcXGhYsWKrFmzRuttRKc+b6JTnzvRqc+d6NTnTnTqc/df7NRPdxxUaPv6PnBXoe3rQyHSbwThDT///DMNGzbE2toad3d3li1bxueff17UYeUoMjISd3d3zp07x7hx44o6HEEQBEF4Z8VrWOfDIzr1gvCGR48esWDBAiIiIihbtixubm7MnDnzvbXXuXNnLlzIPh921qxZzJo1K9ftR40axfXr13Fzc3vnHH1BEARBED58olMvCG9YuXIlK1euzLtiIdmwYQOJiYnZvmZlZZXn9vv357w0nyAIgiB8SMRE2YIRnXpBKEJvr5kvCIIgCP9VYp36gpHmXUUQBEEQBEEQhOJMjNQLgiAIgiAIRU6M0xeM6NQLgiAIgiAIRU7k1BeMSL8RBEEQBEEQhA+cGKkXBEEQBEEQipxKJOAUiOjUC4IgCIIgCEVOpN8UjOjUC4IgCIIgCEVOLGlZMKJTLwj/UZ0qPS3qELJIT5EUdQga5JbFa9wowNOyqEPQML5VcFGHoOH3v8sUdQga+tR8UtQhaFCUbFnUIWhIfJ793bSLSlj3T4o6BA0XAx2KOgQNA4o6ACFPolMvCIIgCIIgFDkxTl8wolMvCIIgCIIgFDmRflMwYklLQRAEQRAEQfjAiU69IAiCIAiCUOSUhfh4XyIiIhgyZAhmZmZYWFgwevRo4uLict2mTZs2SCQSjce4ceM06gQFBdG1a1eMjIyws7Nj2rRppKWl5Ss2kX4jCIIgCIIgFLkPYZ36IUOGEBwczOnTp0lNTWXkyJGMHTuWnTt35rrdmDFjmDdvnvq5kZGR+t/p6el07doVe3t7Ll26RHBwMMOGDUNfX59FixZpHZvo1AuCIAiCIAhCHh4+fMiJEye4fv06DRo0AGD16tV06dKF5cuXU7JkyRy3NTIywt7ePtvXTp06xYMHDzhz5gwlSpSgTp06zJ8/n+nTpzNnzhwMDAy0ik+k3wiCIAiCIAhFrjDTb5KTk4mJidF4JCcnFyi+y5cvY2Fhoe7QA7Rv3x6pVMrVq1dz3XbHjh3Y2NhQo0YNZs6cSUJCgsZ+a9asSYkSJdRlnTp1IiYmhvv372sdn+jUC4IgCIIgCEVOVYj/LV68GHNzc43H4sWLCxRfSEgIdnZ2GmUymQwrKytCQkJy3G7w4MFs376dv//+m5kzZ/Lbb7/h6uqqsd83O/SA+nlu+32bSL8RBEEQBEEQ/lVmzpzJl19+qVEml8uzrTtjxgy+//77XPf38OHDd45l7Nix6n/XrFkTBwcH2rVrh5+fHxUqVHjn/b5NdOoFIQ9btmxh8uTJREVFFXUogiAIgvCvVZir1sjl8hw78W9zc3NjxIgRudZxcnLC3t6esLAwjfK0tDQiIiJyzJfPTuPGjQHw9fWlQoUK2Nvbc+3aNY06oaGhAPnar+jUCx+0ESNGsHXrViDjEljp0qXp168f8+bNw9DQsFDaGDBgAF26dCmUfWnr119/Zc2aNfj5+SGTyShfvjz9+/dn5syZAMyZM4e5c+cCoKenR+nSpenduzfz58/HxMSkUGNR9OqF8cCBSK2sSPP1JWbVKtK8vLKv27Urhp06IStfHoBUHx/ifv01x/raMPq4FyZDBqBnZUWqrx/RK1aR+jD7/cnKO2L6yUj0q1RG5mBP9I9riP/jT81KUimmo4ej6NQBPWsr0l+8IOHoSeK2/KZVPPKuvVD0GYjU0oq0AD8S1v2PNJ/s45F36oa8bSf0HDOOR5qvN4lbf82xvvFnX2LYpSfxv6wm6eDeLG0Zu20m3uNRjrFZdm1GyWmDkZe2IykwmGeLthH9102NOiWnDsJmUAdk5sbEXffi8ax1JAcEq193mNgX83YNUFQvjyolDY/qQ7K0U2beJ5g0qIpR1TIonwcR982n6tcM2vdE3qU/EnMr0p/4kbRtNen+3tnGK2vQAnn3weiVKAUyPZQhz0g+vodU9zMadQzadkfPsTJSUzNivx6LMsgvx2OQkwZT+1Bl0EfIzY0Iue7DhVmbiQkIzbF+nc+6U75zQywqOpCelELIjUdcXbSbaP/gbOt3/m0aZT+qzcnRKwk8eTPbOq8ZduuFou+r36u/H/FrczmHXLph2K4TeuUyz6GELbmcQ59/iaJrT+LWrybpwN5c43gXvXp15tMxQ6lXrxbW1pbUb9gRT0/tc34Lyw2Pu2zeuZcHXr6Ev4zgf4u/pV2rZu+lLeM+PTU+g6JWrCb1Qc6fQWZjRqg/g6J+/In43ZqfQSX27UTmkLWjFvfnAaKXr9IqphrT+uA05CP0zYx5cd2HmzM2EZfL+WzbpArO47tiVas8CntLLo5cwbMTmufpgOAd2W7rMW8n3muPahVXQSlVRbP6ja2tLba2tnnWa9q0KVFRUdy8eZP69esD8Ndff6FUKtUddW14eHgA4ODgoN7vwoULCQsLU6f3nD59GjMzM6pVq6b1fkVOvfDBc3FxITg4GH9/f1auXMn69ev57rvvCm3/CoUiSw7d+7Rp0yYmT57MpEmT8PDwwN3dna+++irLOrjVq1cnODiYwMBAvv/+e3755Rfc3NwKNRb5Rx9hOmECcVu28HLMGFL9/LBctgyJhUW29fXr1CHp7Fkip0wh4rPPUIaFYbl8OVIbm3dq37DdR5hPGk/spq2EjxxLqq8f1iuXIrXMvn2JoZz058+JWfsL6S9eZlvHxHUQRr17Er1iFWGDhhPz8y+YDBmIcb+P84zHoOVHGI/5jMSdW4meNIb0AD9M5y9HYp59PPo165D8z1liZk4m2m0CyvBwTOcvR2qd9XgYNG2JrEo1lC/Cc2yr0vbvkFmbZ9uWcX1nnH5y48XvZ3jg8iVRJ65SYcMMDJ3LquvYT+iN3chuBM1cx8PuX5GekETl7d8hketnHkMDGZFH3AnfdiLXY/Fi9xlSr57T/Hkbt8Fw8DiS9m8j7ttxKIP8MP7qeyRm2R8fVVwsyYd2EDdvInGzxpDyz0kUY75CVjNzEppEbki6zz2Sdv+aazy5qT2hGzVGduTCzE3s7/4daQnJdN0+Hb03fu63lWxalftbT3OgxxyODPoeqb6MrjunI1NkHfmr+YkLaNkZMWj1EcZjPyNhx1aiJmb8Xs0W5HIO1apD8rmzRM+YTPSXGeeQ2cIczqFmLdGvUo30V+fQ+2BsbIT7pWvMnLXwvbWhjcTEJJwrOvG124T32o6iXZuMz6CN2wgb8Smpj/ywWfl9rp9Bac+Difn51xw/g8JHjSe4ax/148WkqQAknj2vVUxVPutGpdGduDF9M2e6ziY9IZnWu2YgzeV81jOSE/UgiJuztuRY52CtCRqPa5PXo1IqeXr0Wo7bFDZVIT7eh6pVq+Li4sKYMWO4du0a7u7ufP755wwcOFC98s2zZ8+oUqWKeuTdz8+P+fPnc/PmTQIDAzl06BDDhg2jVatW1KpVC4COHTtSrVo1hg4diqenJydPnuSbb77hs88+0/pqA4hOvfAvIJfLsbe3p0yZMvTq1Yv27dtz+vRpAJRKJYsXL6Z8+fIoFApq167N3r2ao1eHDh2iUqVKGBoa8tFHH7F161YkEok63WbLli1YvNGJ9fPzo2fPnpQoUQITExMaNmzImTNnNPYZGRnJsGHDsLS0xMjIiM6dO/PoUc4jrG/H079/f0aPHk3FihWpXr06gwYNYuFCzT+iMpkMe3t7SpcuzYABAxgyZAiHDh3K59HLnXG/fiQePUrSiROkP35M7IoVqJKSUORw5SJm4UISDx4kzdeX9KAgYpYtA4kEg3r13ql9k4H9SDh0lMSjJ0gLfEz00hWokpMw6tY52/qpD72J+Wk9SWf+RpWamm0dg5rVSbrgTvKlK6SHhJL09z8kX7uBfrUqecZj2Ls/ySeOkHzmOOlPHhO/5gdISkLeMfvjEbd8AclHD5Du74vyaRDxq5aCVIqsdn2NelJrG4zGTSJu2QJU6Wk5tqVMSsZmYLts2yoxujvR524Ruu4ASb5Peb58Jwn3/LEbkRmb3ejuBK/6g6hT10h8+JjAyf9Dv4QVFp0yR5ie//A7oRsOk+j1OMfj8GT2BsK3HkcZpjlqbdC5LynnjpF64STK549J3PwjquRkDFq5ZLufdC9P0m66o3wehDIsmJRT+1A+8Uevcg11nVT3MyQf+I20+7mPfuem5mgXbq06yONTt4h4+IS/J6/DqIQFjp3q57jNMdel+Oy5QKTPMyIeBnFuynpMS9tgW8tRo551tbLU+rQL59y0+9Kh6N2fpONHSD59nPSgx8St/gFVchKGOZ1DSxeQ9OocSn8aRNz/Ms4h/TpZzyHj8ZOIXboA0vN3w5r82LHjTxYs/JGzf114b21oo2XThkwaO5z2rZu/13ZMBvUj/tAxEl59BkUtXYkqOTn3z6A160nM5TNIGRWNMiJS/TBs3pS0p89Iue2pVUyVx7jw4McDPD95k+iHT7g6aS2KEhaUcsn5fA75y5N73+/h2fEbOdZJCo/WeJR0qU+Y+wPig97fl8QP0Y4dO6hSpQrt2rWjS5cutGjRgl9++UX9empqKt7e3urVbQwMDDhz5gwdO3akSpUquLm50adPHw4fPqzeRk9PjyNHjqCnp0fTpk1xdXVl2LBhGuvaa0N06oV/lXv37nHp0iX1mq6LFy9m27ZtrFu3jvv37zNlyhRcXV05fz5jRCQgIIC+ffvSq1cvPD09+fTTT/n6669zbSMuLo4uXbpw9uxZbt++jYuLC927dycoKEhdZ8SIEdy4cYNDhw5x+fJlVCoVXbp0ITWHD/k32dvbc+XKFR4/zrlTlR2FQkFKSkq+tsmVTIbM2ZmUm290plQqUm7eRF/Ly4ESuRyJTIYyNvad2td3rkzyDc32k6/fQr9G9fzv75WUu/eRN6iHXpnSGc1UrIBB7RokX85jNEomQ1axMikebx0Pj5voV9EyHrkciZ4MVWxMZplEgonb1yT9+TvpQYEZZVJptm3FXPDEuJ5ztrs2ru9MzIU7GmUx529jUj+jvkHZEhiUsNKokx6bQLyHj7pOgejJ0HOsTNr9Wxoxp92/hV5F7c4XvWp1kTqUJt37bsHjecW0rC3GJSx4duGeuiwlNpEwDz9K1K+k9X4MzDJuFJMUFa8ukxka0G7NZ1z8eguJ4dF570QmQ1apMqlv/V5TPW4iq6rdOSR5dQ4p3z6Hpn5N4t43ziGh4F5/Bl1/+zPoJgY1tE+JyKsNRaf2xB85rlV147K2KEpYEnohM+UpNTaRl7f9sGmg/fmcF7mNGSXb1cF/l3ZXDwqLElWhPd4XKysrdu7cSWxsLNHR0WzatEkj7dXR0RGVSkWbNm0AKFOmDOfPn+fly5ckJSXx6NEjli5dipmZmcZ+y5Urx7Fjx0hISCA8PJzly5cjk+UvS17k1AsfvCNHjmBiYkJaWhrJyclIpVLWrFlDcnIyixYt4syZMzRt2hTImOhy8eJF1q9fT+vWrVm/fj3Ozs4sW7YMAGdnZ+7du5dlVPxNtWvXpnbt2urn8+fPZ//+/Rw6dIjPP/+cR48ecejQIdzd3WnWLCPHc8eOHZQpU4YDBw7Qr1+/XH+e7777jo8//hhHR0cqV65M06ZN6dKlC3379kUqzf57+M2bN9m5cydt27bN17HLjdTcHImeHsqICI1yZWQkBmXL5rCVJpNPPyX9xQvNLwbatm9hjkSmR3pEpGb7EZEYlNOu/ezE/bYTibERdru2glIJUimx6zeSeOpMrttJzMwzOuRRmvGooiKRlNEuHuOR41BGvNDo1Bn2HYwqPZ2kQ5l5txJDRbZtpb2IxrBi6Wz3rW9rQdqLKI2y1PBo9G0t1a9n7CPnOgUhMc04X1TRbx2fmEikJcvkvKHCGLNVu0GmD0oliVv/R9q9dx+Vf5vRq5878UWMRnlieAxGttmnMmUhkdBsjivB17yJ9H6qLm46x5WQm494fOpWLhtnkr46h5SRb53TkZHol9buHDIa9eocup15jBT9BoMynaSDf+aypZBfrz+DlG99BqVHRCIvwGfQmxStmyM1MSHh6Emt6hvaWQAZo+pvSgqPxvDVuV4YyvdvRWpcEk+PXS+0fWrjQ7ijbHEmOvXCB++jjz5i7dq1xMfHs3LlSmQyGX369OH+/fskJCTQoUMHjfopKSnUrVsXAG9vbxo2bKjxeqNGjXJtLy4ujjlz5nD06FGCg4NJS0sjMTFRPVL/8OFDZDKZxqQZa2trnJ2dtVoSy8HBgcuXL3Pv3j3++ecfLl26xPDhw9mwYQMnTpxQd+zv3r2LiYkJ6enppKSk0LVrV9asWZPtPpOTk7PcdCNZqUSew5eEwmA0eDCGbdsSOXkyFOYVhAIybNcGo47tiZyzgDT/QPQrV8T8i89If/GSxOPa/WF9p3b7DcagVVtiZnwBqRnHQ69iZRQ9+xA1acx7a7fYS0og7uuxYKhAVr0eisHjUYYFk+6lXSrC2yr2bkarJaPUz48PX17gEFssHI6Vc2kOfjxfXVauQz1KNa/G3k65X9krTIp+g5G3bkv0V9mcQxML/xwaNKg3a3/KXOavW3dXLrrrLr/6v8CoWxeSrlxDmUP+fbmPm1F/6Wj18wtDl+kkrvKDWhO0zx1lct5Xl4XiQ3TqhQ+esbExFStWBDImmdauXZuNGzdSo0ZGXu7Ro0cpVaqUxjb5mXjytqlTp3L69GmWL19OxYoVUSgU9O3bt3BTX4AaNWpQo0YNJkyYwLhx42jZsiXnz5/no48+AjKuKhw6dAiZTEbJkiVzvY304sWL1avlvOZWrhzTHB1z3EYZHY0qPR2plZVGudTSkvS3Ru/fZjRgAMaDBxPp5kaav38eP2kO7UdFo0pLR8/Kkjf/rEit8m4/N+afjSP2t10knfkbgDT/APTsS2AybHCunXpVTDSq9DQkFpqj2hILS1SRucdj+PEAFH0HE/O1G+mBmcdDv3otJOaWWG75I3N/ejIUg4ajUqmytCWzMSc1THPU8LXU8ChkNhYaZfq25qSGR6pfz9iHhcY+9G3NSbgfkGv82lDFZpwvEvO3jo+ZJaqoXI6PSoUy7DkAKUF+6JUsi7z7IBLesVP/+NQt9t7OXB1HzyDjz5zCxoyEsCh1ucLWjJf3g97ePIvmC4ZRrn1dDvVZQHxw5s9Rqnk1zMrZMfLBLxr1O/zyBSHXvDncL+vVPuWrc0hqqXmMpJaWKPM4hxR9BqDoP5iYWW+dQzVqIbGwxHKb5jlk/MkEFL36EjliYJ4/Y04OHz7FtWu31c+fPdP+Jjj/Bq8/g6RWmr8vPStL0l+++2eQej/2JZA3rEfEzJwXdnh28hYvb2Wez9JX57OhrTlJb5zPhrbmRN3PX8pmTmwaO2NWsSSXP11dKPvLj8Jc0vK/SHTqhX8VqVTKrFmz+PLLL/Hx8UEulxMUFETr1q2zre/s7MyxY8c0yq5fz/1yo7u7OyNGjKB3795Axsh9YGCg+vWqVauSlpbG1atX1ek3L1++xNvbO19LU73p9Xbx8Zn5vAYGBuovM3nJ7iYcUd265b5RWhpp3t4Y1KtH8sWLGWUSCQb165Owf3+OmxkNHIixqytRX31Fmnf2SxlqJS2NVG8fDOrXI+kfd3X78gb1iP8z5/bzIjGUg+qtPx3pSiQSSZ7xpPn6oF+nPqlXMo+Hfp16JB3JOR7DPoNQDHAl9ttppPtqHo/kv05p5lcDZvOWkfz3KfQbNM7SllmLWoRt0TxfX4u/6Z3x+sbMyVdmLesQdzOjzZSgUFJCIzBrUYvEBxmdeKmJAuM6lQnLY6UbraSnkR7og6xaXdJuZv6+ZNXrknL6gPb7kUiQ6Oe8ikdeUuOTSI1P0iiLD42iVIvqvHyQ0YnXN1FgV6cCD7adzXVfzRcMo7xLAw71W0jsE83Jgrd/OszDXec0yvqfXcLludt5fPo22UpLI+1RxjmUcvmtc+hQzueQou8gFANdiflmGmmP3jqHzp7SSMUBMFuwjOS/TpF8Srs87ZzExcUTFxefd8V/q1efQfIGWT+D4vYeKPDujbq6oIyMIunSlZxDiE8i7q3zOTE0khItqqs78TITBdZ1K+C7NfcUQm05DWpDhKc/UQ/y/tJb2N5nLvx/gejUC/86/fr1Y9q0aaxfv56pU6cyZcoUlEolLVq0IDo6Gnd3d8zMzBg+fDiffvopK1asYPr06YwePRoPDw+2bNkCkGMnr1KlSuzbt4/u3bsjkUj49ttvUSqVGq/37NmTMWPGsH79ekxNTZkxYwalSpWiZ8+eecY/fvx4SpYsSdu2bSldujTBwcEsWLAAW1tb9dyA/MruJhyJWqTexO/Zg/nMmaR6e5P68CFGffsiMTQk6XhGZ8Fs5kyUL14Q92vGyh9GgwZhMnIk0QsWkB4Soh7lVyUmokpMzHfccb/vwfKbGaR6+ZD64CHGAzLaTziS0Qm1+HYm6eHhxK7bkLGBTIasfDkAJDIZerY2yCpVQJWQSPqzjNHgpIuXMR3uSnpoGGn+AehXroTxwH4kHM27A5S0/w9MvpxJ+iMv0ny8MOzZF4mhguTTGduafDkL5ctwErZmHA/DvoMwch1F3NL5pIeFILHMPB4kJaKKjSE9VjPXW5WehjIygsTd27O0JVUY8mJ3RkfU8ccvSA15ybMl2wEI3XgY570LKTG2J9Fnb2DVsyVGtSoQOP1n9b7DNh7GYVI/kgKek/IkjJJTB5MaGkHUyavqOgYlbdCzMMWglA0SPSmKahnroycHBqNMyOhcyB3tkRopkJhbgYEcadmMOyKmnNyP4pOppAf4kO7vhUGnPkjkhqT8k3EFRPHpdJSRL0j+Y2PGfroPyqgb+hyJvj6y2o3Rb96BxC3/U8cjMTZFYm2H1NIaAD2HjPx8VXRElvz9nNzdeIJ6k3oRHRBK7JMwGkztS0JolMZ68t1+n0nAiRvc35KxclaLhSOo2KspJ0evJDUuCcWr/PuU2ATSk1JJDI/OdnJs3LOXWb4AvClx/x+Yus0k7ZEXad5eGPbqi0SuIOn1OeT26hzaknEOKfoNwmjoKGK/n096qHbnEK/OofRnT7Q6PvlhaWlB2bKlKOmQcQv7ypUzfvchIWGEhupulZSEhESCnj5XP3/2PBQvHz/MzUxxsC+8JYjjdu3B8tsZpHp5k3LfC5OBfTQ+gyxnzyA9/AUxazM/g/Tf+gzSr1QBZWIi6W/Ei0SCUVcXEo6dgvT8jU/7/HqCapN7ERsQQnxQODWm9yUxNEpj3fk2f8zk6fEb+G7OOJ9lRnJMymeujW9c1haL6uVIiYoj4Vlm6o/MREGZ7o3wmLszXzEJxYPo1Av/OjKZjM8//5ylS5cSEBCAra0tixcvxt/fHwsLC+rVq8esWbMAKF++PHv37sXNzY3//e9/NG3alK+//prx48fnmKKzYsUKRo0aRbNmzbCxsWH69OnExGj+Ud28eTNffPEF3bp1IyUlhVatWnHs2DH0tRiBbN++PZs2bWLt2rW8fPkSGxsbmjZtytmzZ7G2ti74AcqH5L//JtbCApORI9U3n4r86iv1RD+9EiU01uc26tkTiYEBFm8twxW3ZQvxr74s5UfS2b+JtjDHdMyIjBu/PPLj5ZfT32jfLmOy6yt6NtbYbd2gfm4yZCAmQwaSfMuDl59PASB65SpMx4zCfOoX6FlaZtx86uBhYjdtyzOelAt/k2BugcJ11KsbB/kSO3uaekKr1NYO1RtXAQy79ESib4Dp1/M19pOwYzOJO3M/Htm19WjoXNJeZHQk5aVsQZl57ONvehPw+QpKfTWEUtNdSQ54jt8nS0jyzhxtC/l5P1IjQxy/n4CemTFx1x/i4zoP1Rt5syWnDsamf+aE6+qnVgLg3e8bYi9nrCDjuOxzTJtmLjtpujAjBSVmymCSdq3DsM8IJOaWpAf5Eb9sBqqYV8fH2k5zPXe5IYbDJyG1skWVkowy+AmJ6xZrrH8vq9cMo7FfqZ8bff4tAEn7tpK8P+/fGYDnz0fQN5LT6vtRGJhl3HzqmOtS0t/4uc3K2WFoZZr5cw9vD0CPvd9o7OvvKevx2fPuyzmm/PM38eYWGLmOynhP+fkS823mOaRnZ6dxJcmwa8Y5ZPbNW+fQ9s0k7NjyznG8q+7dOrJp40r181071gIwb/4PzJu/Qmdx3PN6xKiJ09XPl67OOAd7dm7Pwm8K734diWfPIbW0wPSTkehZW5L6yI8XUzQ/g1RvfwZty1ze1HTIAEyHDCD5lgcvPsu8WipvWB+ZQwkStFz15k1ePx1BZiSnwbLRGJgZEX7Nh/ODv9fIfzdxLIH8jfPZsrYTbfdlnst15w4FIGD3P1ybvF5dXrZXE5BICNp/Kd9xFQYxUbZgJCpVEd2+SxCKqYULF7Ju3TqePCn8Ua7iJPTVclvFSXpKHikwOia3LF4ZngGeBV+lpjBV+ugdlip9j37/26GoQ9DQp2bx+gyxP+tb1CFoSHxetGvdvy2s+ydFHYKGi4HF63zO6Y6zhenjcj0KbV/7HhfufVs+BGKkXvjP+/nnn2nYsCHW1ta4u7uzbNkyPv/886IOSxAEQRAEQWvi5lPCf96jR4/o2bMn1apVY/78+bi5uTFnzpz31l7nzp0xMTHJ9rFo0aL31q4gCIIgFGcqlarQHv9FYqRe+M9buXIlK1euzLtiIdmwYQOJOUwatXpr+UhBEARB+K8Qq98UjOjUC4KOvb1mviAIgiAIYp36ghLpN4IgCIIgCILwgRMj9YIgCIIgCEKRE0taFozo1AuCIAiCIAhFTuTUF4xIvxEEQRAEQRCED5wYqRcEQRAEQRCK3H91KcrCIjr1giAIgiAIQpETq98UjEi/EQRBEARBEIQPnBipF4T/qGteDkUdQhYXDIs6Ak2pz4vXpeAEWXpRh6Ah5LyiqEPQ8Pv8kkUdgoZ+30UWdQgadli3KeoQNIR1/6SoQ9Bgd3hDUYegYUjJlkUdgoYBOmhDrH5TMKJTLwiCIAiCIBQ5sfpNwYj0G0EQBEEQBEH4wImRekEQBEEQBKHIidVvCkZ06gVBEARBEIQiJ9JvCkZ06gVBEARBEIQiJybKFozIqRcEQRAEQRCED5wYqRcEQRAEQRCKnFLk1BeI6NQLgiAIgiAIRU506QtGpN8IgiAIgiAIwgdOjNQLgiAIgiAIRU6sflMwolMvCIIgCIIgFDnRqS8Y0akX/rVCQkJYvHgxR48e5enTp5ibm1OxYkVcXV0ZPnw4RkZGRR3iOwsMDKR8+fJZyocMGcL27dsLvH/nr/pSdkhb9M2Mibjuzd3pm4gPCMmxvlWTKlSY0A2LWk4Y2ltyfcQPhJy4oX5dItOjyoz+2LWrg1E5O9JiEgm/cJeHC34nOTQyz3g6TulLo0FtUZgZE3jDm/3fbOJFYM7xvKnN+B50mT6IC5uOc3jeNnV540FtqdOzOaWqO2JoasTsWqNJiknQap8ALlP60XRQWwxfxbTnm41ax9RufA+6TR/M+U3HOPAqJiNzY1ym9MO5ZS0sStkQ/zKGu6euc3zFHyTFJua5z+5TBtByUDsUZsb43fBi5ze/EqZlPJ3G9+Lj6UM4u+kof8zbAoB1aVsWXfw52/rrJ/zArWNXct3n4C+H0GFwJ4zNjPG68ZC1s34mOPB5jvUHThnMoCmDNcqe+j7hs7bj1c/ty9kz8uvRVG1YDX0DfW6dv8kvs9cT/SJKq5/ztd9v+rP1qi8v45OpbGfG9A61qFnSMsf6MUmprPnnAX95BxOdlIqDmYJp7WvSskKJfLWbmyFfutLp1fF6eOMhP8/6iee5HC8A6xLWjJg5kvof1UeukBMcGMyPU1fie8c33+3XmNYHpyEfoW9mzIvrPtycsYm4gNAc69s2qYLz+K5Y1SqPwt6SiyNX8OzETY06A4J3ZLutx7ydeK89muO+jfv0xGTIAPSsrEj19SNqxWpSH3hlW1dW3hGzMSPQr1IZmYM9UT/+RPzuPzXqlNi3E5mDfZZt4/48QPTyVTnGkR83PO6yeedeHnj5Ev4ygv8t/pZ2rZoVyr4Lolevznw6Zij16tXC2tqS+g074ul5v6jDEt4T0akX/pX8/f1p3rw5FhYWLFq0iJo1ayKXy7l79y6//PILpUqVokePHkUSm0qlIj09HZms4G+/M2fOUL16dfVzhUJR4H1W+Lw75Ue7cHvSWhKCwqkyvR+Nf5/BuVbTUCanZruNzEhOzP0gnuw6R8PNblle11MYYF6zPD4r9xNz/zH65sbUWDCcRtumcqHT17nG02Zcd5qPdGG321oinoTTya0fo7fN4IcO00jLIZ7XStdyosngdjx/+DjLa/oKOd7nPfE+70mX6YNy3c/b2o7rQauRLux0+5mXT8Lp7NafcdtmsqTD1DxjKlPLiaaD2/PsrZjMSlhiVsKSQ4u2E/LoGZalbOi38BPMS1ixZcLKXPfZaVxP2o7szBa3Nbx4EkYPt4FM2vYNczpMyTOecrUq0GpwB548DNQoj3j+kmkNx2iUtRzUno5je3D/nEeu+/x4fB+6juzO/75cSeiTUIZMdWXO9nl83m48qbnE89j7MbMHZ54P6WlK9b/lCjlzts8n8EEA3w6cBcDgqa58s2k2X/V00/pOlCcfPuOHv+7zdaeMjvyO6/5M2H2Zg2PbYWUsz1I/NV3JuN8vYWUsZ1nvhtiZKAiOScBUrq9Ve9roM74v3Ud2Z+WXKwl9EoLr1KHM2z6f8e3G5Xi8jM1NWLpvGXcu32HOsO+IjoimpGNJ4qLj8t1+lc+6UWl0J65+sZ74oDBqftWP1rtmcLz1Vzm+5/WM5EQ9CCLg9/O02DQl2zoHa03QeO7QtjYNV4zh6dFrOcaiaNcG80njiVr6Iyn3H2IyoA82K78ndOBwlJFRWepLDOWkPQ8m8a/zmH8xIesOgfBR40GaOYVQv0J5bFYtJ/Hs+RzjyK/ExCScKzrRu2tHJs9aUGj7LShjYyPcL11jz97D/LJ+eVGHkydxR9mCERNlhX+lCRMmIJPJuHHjBv3796dq1ao4OTnRs2dPjh49Svfu3QGIiorik08+wdbWFjMzM9q2bYunp6d6P3PmzKFOnTr89ttvODo6Ym5uzsCBA4mNjVXXUSqVLF68mPLly6NQKKhduzZ79+5Vv37u3DkkEgnHjx+nfv36yOVyLl68SHJyMpMmTcLOzg5DQ0NatGjB9evX8/VzWltbY29vr36Ym5sX8MiB05jO+Py4n9CTN4l9GMTtiT9jWMISe5cGOW4T9pcn3t//QcjxG9m+nhabyJUBiwg+dIV4v2Cibvlyd9ZmLGo7oShlnWs8LUZ15uzq/Tw4fZMQryB2f/kzZiUsqd4x53gADIzkDPrxc/bO+JXE6Pgsr1/cdJxzaw8RdPtRrvvJTutRnTm1ej/3Tt8k2CuInV/+hFkJS2pqEZPrjxP5Y8YvWWIK8XnKlvEruX/2Fi+DQvG9fJ9jy3+nert6SPVy/6huN6orx1b/iefpGzzzCmLzl2uwKGFJnY4Nc91ObmTI6B8n8duMdSS8FY9KqSQmPErjUadTI24cvUxyQlKu++0+uid7Vu/m2umrPPYK5McpK7Cys6JJx6a5bpeelk5UeJT6ERsZo36taoNq2JW2439uK3ns/ZjH3o/535crqVirIrWa18p1v2/67ZovH9cuR69a5ahgY8Y3LrUx1NfjwJ2sX/wADtx5TExSCis/bkTd0taUsjCiQVkbnEsU/L32Ws/RPdm9ejdXT18h0CuQFVN+wMrOiqa5HK++4/vyIjic/039ER9PH0KfhHL7wm1CHmt3deZNlce48ODHAzw/eZPoh0+4OmktihIWlHKpn+M2IX95cu/7PTzL4T0PkBQerfEo6VKfMPcHxAeF57iNyaB+xB86RsLRE6QFPiZq6UpUyckYdeucbf3Uh97ErFlP4pm/UaVm/wVEGRWNMiJS/TBs3pS0p89Iue2Zbf130bJpQyaNHU771s0LbZ+FYceOP1mw8EfO/nWhqEPRihJVoT3+i0SnXvjXefnyJadOneKzzz7D2Ng42zoSiQSAfv36ERYWxvHjx7l58yb16tWjXbt2REREqOv6+flx4MABjhw5wpEjRzh//jxLlixRv7548WK2bdvGunXruH//PlOmTMHV1ZXz5zVHgWbMmMGSJUt4+PAhtWrV4quvvuLPP/9k69at3Lp1i4oVK9KpUyeNtnXNqKwdhiUsefHPPXVZWmwiUbf9sGxQqVDb0jc1QqVUkhqdc8qLVRk7zOwseeSeGU9SbCJPPPwoVy/3eHrNH4XX37fxfWPbwmD9KiYf97saMT328MWxXuVct+07fxQP/76Nj5YxGZoakRSXiDJdmWMdmzJ2mNtZ8lAjngQCPHxxquec6/4HzR/N3b9v4fXGtjkpW8OJstXL4777bK71SpQtgZWdFZ4XPdRlCbEJ+Hh441y/Sq7blixfks3Xt7L+4ga+/N9UbEraql/Tl+uDClJTMjtuKckpqJQqqjasnt3uskhNV/IwJJrGjpn7lUokNHa05c6z7NPAzj0KoVYpKxafukPbVSfos+EvNlzyIV1ZOJ2GEmXtsbKzwuOt4+Xt4U2VXI5X4w6NeXTHlxlrZ7L91g7+d2wVnQZ1ynf7xmVtUZSwJPRCZkpGamwiL2/7YVOI73m5jRkl29XBf1cuo+MyGfrOlUm+/kYaj0pF8vWbGNSoVjiByGQoOrUn/sjxwtmfIBQjolMv/Ov4+vqiUqlwdtbs0NjY2GBiYoKJiQnTp0/n4sWLXLt2jT179tCgQQMqVarE8uXLsbCw0BhpVyqVbNmyhRo1atCyZUuGDh3K2bMZHZvk5GQWLVrEpk2b6NSpE05OTowYMQJXV1fWr1+v0f68efPo0KEDFSpUQC6Xs3btWpYtW0bnzp2pVq0av/76KwqFgo0bN2r9szZr1kz9M5mYmHD79u1s6yUnJxMTE6PxSFWlZ6knt8sYfUwOj9bcPjwauZ2F1nHlRSrXp+o3g3i2/xJpcTnni5vaZsQT91Y8seHRmNrmHE/t7k0pVd2R40t/L5R4NWOyyDamuDxiqtu9KaWql+fI0l1atWNsaUrHiR9zeVfunWizV23GhEdplMeER2GeSzwNujejbHUn9i/dqVU8zQe05fmjp/jf8sm1nqVtRm561Ft57lEvorDMJR6f2978z20lc4Z+x7pZP1OiTAkW7/0ehXFGSpn3LS+SEpIYPnMkBoZy5Ao5I78ejZ5MD0u7nPPh3xSZkEy6SoX1W2k21sZyXsRnf/XhWVQCZ7yeo1SpWNO/CWObO/PbNV9+veStVZt5yTxeml8qol5EYWGb889lX8aeLq5deB7wjNlDv+XY9mOMnfspbfu2y1f7hq/e10lvnc9J4dEY5vL7yq/y/VuRGpfE02M5X42UWpgjkemhjNA8FukRkehZWxVKHIrWzZGamJBw9GSh7E8oXKpC/O+/SHTqhf+Ma9eu4eHhQfXq1UlOTsbT05O4uDisra01OsYBAQH4+fmpt3N0dMTU1FT93MHBgbCwMCDjC0RCQgIdOnTQ2Me2bds09gHQoEFmaoafnx+pqak0b555qVZfX59GjRrx8OFDrX+m3bt34+HhoX5Uq5b9aNbixYsxNzfXeOyJf0Cpj5vT2W+z+iHVf//TbCQyPer/8gUSiYS70zdpvFa3Z3Pm39+sfui9QzzmDlb0mD2cXZN/yjOfXBv1ejZnyf0t6oeevl6+92HhYE3v2cPZPnmNVjHJTRSM2TydUN9nnPhxr8ZrjXq24H/3f1M/3uUYWTpYM2D2SDZO/p9W8ejLDWjUs0W2o/Ste7Xh94d71A+9d5wrcuvcTS4ddeexVyC3/7nFvBFzMDYzpnm3FgDERMSwdPwSGrZvxG6vPey6/wfG5sb43vVFVUij5tlRqlRYGcv51qUO1ewt6FS1FJ80q8ze24HvtL82vdqw5+Fe9UMmy//5BCCRSvC758e2pdvwv+/PyZ0nOLnrJF2GZJ+m8lq5j5vxse9G9UP6Dufzuyg/qDVB+9xzzNHXFaNuXUi6cg3li5dFGsf7MGhQb6IifNSPFs0bFXVI+aZSqQrt8b5EREQwZMgQzMzMsLCwYPTo0cTF5TyXJTAwEIlEku1jz5496nrZvf777/kbmBITZYV/nYoVKyKRSPD21hxJc3JyAjInk8bFxeHg4MC5c+ey7MPCwkL9b319zQlxEokEpVKp3gfA0aNHKVWqlEY9uVxzNDCnVKCCKFOmDBUrVsyz3syZM/nyyy81ys5U+oSQkzeJvJW5Uob01eQ/ua05yWFR6nK5rTnR9wILHO/rDr2itA2X+y7IMkr/4MxNgjwy45EZZMRjYmtO7Bsj0aa25jx/kH08pWs6YWprzhdHFqnL9GR6lG9UhWbDOjKr8tB8dQLvn7nJ8hxienN03MTWnOcPss/LLl2zPKa2FrgdWawRk1OjKrQY1olplV3VMcmNDfl060yS4xLZ9OkPKNM0r6h4nrlBgEY8GR/jZrYWGvGY2VrwJIdjVLamE2a2Fnx9ZKlGPJUaVaXNMBc+qzwYlTIz5adelyYYGMq5su+fLPu6dvoq3rcz32v6r84hCxsLIsMyR1wtbCwIeBCQbTzZiY+J53nAMxwcS6rLPC7cZlzLMZhamqFMTyc+Jp4tN37jYpB2eeSWRnL0JBJexidrlL+MT8bG2DDbbWxNDJFJJehJJeqy8tamvIhPJjVdiX4e8x3edjXH42WZzfHyz3E/kWGRBD0K0ih78ugJzTvnvuLKs5O3eHkrc8BB+ur8MbQ1J+mN97yhrTlR97M/n/PLprEzZhVLcvnT1bnWU0ZFo0pLR2qleYVCz8qS9JcFT0vUsy+BvGE9ImZ+V+B9FUeHD5/i2rXMq7XPnuV/fkVR+xBy4YcMGUJwcDCnT58mNTWVkSNHMnbsWHbuzP6qZ5kyZQgODtYo++WXX9RX6t+0efNmXFxc1M/f7ItoQ3TqhX8da2trOnTowJo1a5g4cWKOnel69eoREhKCTCbD0dHxndqqVq0acrmcoKAgWrdurfV2FSpUwMDAAHd3d8qVKwdAamoq169fZ/Lkye8US27kcnmWLxn6Ej3S45NIeCvtICk0EpuWNYh59QddZqLAom4FArecLlAMrzv0xk72XO4zn9TIrCMbyfFJJL8VT0xYJJWa1SD4VYdZbqKgTJ0KXN6efTy+7vf4oeM0jbL+y8YR5vecc+sO5XtUN6eYKjeroe7Ey00UlKtTkUs5xPTI/R7fd5yqUTZo2XjC/J5zdt3BzA69iYJx22aSlpLGhk+WZTuKnhyfRHi85h/r6LBIqjSrwdNXnXhDEwXl61Tk/PbsUwy83O8yt6Pml7zhyyYQ4veck+sOaHToISP1xvPMDeIiYnhbYnwiifGaX84iwiKo1byOuhOvMFFQuY4zJ37TPo/Z0MgQ+3IOnNv3d5bXXk+grdmsFuY25lw7fVWrferrSalqb861wHDaVnYAMkbirz0OZ2C9rEvEAtQubcXx+09RqlRIX83FeRwRh62JPN8desj5eNVpXlvdiVeYKHCu48zx347luJ8HNx5QuoLmQEIpp1KEPc15EipAWnwScW+dz4mhkZRoUV3diZeZKLCuWwHfrWe0/rly4zSoDRGe/kQ9CMq9Yloaqd4+yBvUI+kf94wyiQR5g3rE7T1Q4DiMurqgjIwi6VLuy7F+qOLi4omLy7oogFB4Hj58yIkTJ7h+/br66vvq1avp0qULy5cvp2TJklm20dPTw95ec0nV/fv3079/f0xMTDTKLSwsstTND9GpF/6Vfv75Z5o3b06DBg2YM2cOtWrVQiqVcv36dby8vKhfvz7t27enadOm9OrVi6VLl1K5cmWeP3/O0aNH6d27t0a6TE5MTU2ZOnUqU6ZMQalU0qJFC6Kjo3F3d8fMzIzhw4dnu52xsTHjx49n2rRpWFlZUbZsWZYuXUpCQgKjR48u7MORL/6/HqfS5F7E+4eQEBRGlen9SAqN1Fh3vsmerwk5fp3ATaeAjOXtjMtnfhAZlbXFrHo5UqPiSHz2EolMjwYbJmNeszzXhi5FIpUif5UvnxIVhyo1a37/axc3HaftxF68CAwh4kkYHd36ERMayf1TmfGM2fE1909e59K2UyTHJxHq81RjHymJySRExWmUm9iaY2prgU25jLjtncuQHJ9E1LMX2a6W86bzm47TYWJvwl/F1NmtPzGhkdx9I6bxO77h7snrXNx2kuT4JEKyiSk+KlZdLjdRMO63WRgYGrB98k8YmiowNH11VellTK5fRs5uOkqXiX0ICwzhxZMweroNICo0Eo9TmfnLU3bM5vbJa5zbdoLk+CSe+zzR2Efyq3jeLrctZ0+lRlVZM3Ix2jq88SD9Jw0gOPAZoUGhDJ7qSkRYBFdOXVbXmbdrIVdOXObY1iMAjPh6FNfPXCP8WRhWJawY9OUQlOlK/jmYObGyXb/2PPF9QkxENM71qvDJnLEc2nCQZ/7PtI5taKOKfHvkFtUcLKjhYMmOG34kpqTTs1ZZAL45fBM7UwWT2mSksvWvW57dNwNYevougxo48Tgijo2XHzGoQfZfAt7FwY0HGTBpIM8CnxMalLGkZURYBJffOF4Ldy3k8onLHHl1vA5uOMCy/cvp91l/Lh65QOU6lXEZ7MKaGbmPhmfH59cTVJvci9iAEOKDwqkxvS+JoVEa6863+WMmT4/fwHdzxhdXmZEckzfe88ZlbbGoXo6UqDgSnmWmtshMFJTp3giPudrN3YjbtQfLb2eQ6uVNyn0vTAb2QWJoSMKREwBYzp5BevgLYtZueNWADP3yGQMjEpkMPVsb9CtVQJmYSPrTN9b5l0gw6upCwrFTkMvE83eVkJBI0BvtPXseipePH+ZmpjjY2xV6e9qytLSgbNlSlHTIuKdC5coVAAgJCSM0NPcvgEWhMNNmkpOTSU7WvCqX3QBXfly+fBkLCwuN/kH79u2RSqVcvXqV3r1757mPmzdv4uHhwU8//ZTltc8++4xPPvkEJycnxo0bx8iRI9ULe2hDdOqFf6UKFSpw+/ZtFi1axMyZM3n69ClyuZxq1aoxdepUJkyYgEQi4dixY3z99deMHDmS8PBw7O3tadWqFSVKaH9Tmfnz52Nra8vixYvx9/fHwsKCevXqMWvWrFy3W7JkCUqlkqFDhxIbG0uDBg04efIklpbaTfp7X/zWHEZmJKfW8k/QNzMi4po3Vwct0ciFNXYsgYFV5jwDizpONNs3W/28+rxhADzZfR6PL9Zh6JC5JGbrv77XaO/Sx/N4eSnneQTn1h3GQCGnz+JPMDQzIvC6NxuHL9EYxbYuVwLjN+LRRtMh7ekwua/6+YQ9cwDYPXUtN/dmTTN501/rDmGgkNN/8RgUZkYEXPdm/Vsx2eQzptI1yuNYN2O1kW/++Z/Ga/NaTCQylxHYk+sOYqAwxHXxpxiZGeF73YtVwxdmicckn8cIoHn/j4gKjuDBP9ov/7dv7Z8YKgyZsHjiq5spPWDu0Nkaa67bl7XHzMosMz4HG6aumYaphRnREdE8vP6Ar3q5EfPG1YFSFUoxdPpwTCxMCHsaxp7Vf3Bow4F8/TydqpYiMiGZtRe8eBGfjLOdGT8PaIL1q/Sb4JhEjT+i9mYKfh7QlOVn79Fv49/YmRoyuIETI5sU3sowf67di6HCkImvjteDGw+YPfTbt46Xg8bxenTnEQvHLmD49BEM+mIQoU9C+XXuL5w7cC7f7Xv9dASZkZwGy0ZjYGZE+DUfzg/+XuM9b+JYAvkb549lbSfa7vtG/bzu3KEABOz+h2uTMxcJKNurCUgkBO2/pFUsiWfPIbW0wPSTkehZW5L6yI8XU6ajjMxITdIrYadxJUnPxhq7bb+qn5sOGYDpkAEk3/LgxWeZV6PkDesjcyhBwnta9eae1yNGTZyufr509S8A9OzcnoXfZL13h65079aRTRsz73Oxa8daAObN/4F581cUVVg5Ksz0m8WLFzN37lyNsu+++445c+a88z5DQkKws9P8kiaTybCysiIkRLt0p40bN1K1alWaNdNMlZs3bx5t27bFyMiIU6dOMWHCBOLi4pg0aZLW8UlUYqV/QfhPOmyfvxsu6cKF7NOai0xqMcvvTCDnKxpFIUSZ951uden3uYW07GEh6fdd8bpz59BUi6IOQUMLx+C8K+mQ3eENRR2CBkXJlkUdgoa0FO2viL2r2vaFdxfea4//1nqkfsaMGXz//fdZyt/08OFD9u3bx9atW7PM2bOzs2Pu3LmMHz8+h60zJCYm4uDgwLfffoubW+5f9mbPns3mzZt58uRJrvXeJEbqBUEQBEEQhCJXmEtR5ifVxs3NjREjRuRax8nJCXt7e/Xqd6+lpaURERGhVS783r17SUhIYNiwYXnWbdy4MfPnzyc5OVnrn0N06gWhGBo3bhzbt2/P9jVXV1fWrVun44gEQRAE4f1SFlHyiK2tLba2tnnWa9q0KVFRUdy8eZP69TPuuPzXX3+hVCpp3Lhxnttv3LiRHj16aNWWh4cHlpaW+ZoDIDr1glAMzZs3j6lTp2b7mpmZWbblgiAIgiC8P1WrVsXFxYUxY8awbt06UlNT+fzzzxk4cKB65Ztnz57Rrl07tm3bRqNGmfcK8PX15Z9//uHYsayrWh0+fJjQ0FCaNGmCoaEhp0+fZtGiRTn2A3IiOvWCUAzZ2dllmYwjCIIgCP9mH8KdYHfs2MHnn39Ou3btkEql9OnTh1WrVqlfT01Nxdvbm4SEBI3tNm3aROnSpenYsWOWferr6/PTTz8xZcoUVCoVFStWZMWKFYwZMyZfsYlOvSAIgiAIglDkiir9Jj+srKxyvNEUZNyFPrs1aBYtWsSiRYuy2QJcXFw0bjr1rvJ/5wxBEARBEARBEIoVMVIvCIIgCIIgFLkPIf2mOBOdekEQBEEQBKHIfQjpN8WZ6NQLgiAIgiAIRU6M1BeMyKkXBEEQBEEQhA+cGKkXBEEQBEEQipxIvykY0akXhP+oGiVeFHUIWXzUTlHUIWjSL14fkRLj4nV8pCXLFHUIGvp9d7+oQ9CwZ271og5BQ8QvN4o6BA0XAx2KOgQNQ0q2LOoQNCQ+v1DUIeicSL8pGJF+IwiCIAiCIAgfuOI1DCUIgiAIgiD8J6lUyqIO4YMmOvWCIAiCIAhCkVOK9JsCEek3giAIgiAIgvCBEyP1giAIgiAIQpFTidVvCkR06gVBEARBEIQiJ9JvCkak3wiCIAiCIAjCB06M1AuCIAiCIAhFTqTfFIzo1AuCIAiCIAhFTtxRtmBEp14QBEEQBEEocuKOsgUjOvXCv0ZISAiLFy/m6NGjPH36FHNzcypWrIirqyvDhw/HyMioqEN8Z4GBgZQvXx6pVEpQUBClSpVSvxYcHEyZMmVIT08nICAAR0fHArVlOqAH5sP7oWdjRYqPHy+X/ETKPe9s6+pXKIflhOEYVK2Efil7Xi79mZgd+zXqmI8aiHG7FuiXL4MqOZkkjwdE/riB1MdPtYpHv3kX9D/qjcTUEuXzAJL3/4Iy6FGe28nqtMRw2DTS7l4hafMidblezaboN3NBr3QFJMZmJCz/AuXzAK1iAdBv6oJ+q15ITC1QBgeSfHADyqe+ecdTuzmGg91Iu3+VpG3fZxRK9TDoNBg953pIrUugSkog/dEdUo7/hio2Uqt4ZA06oN+sKxITc5ShQaQc34ryuX+e2+lVb4Jhn4mked0g+Y+V6nLj2TuyrZ9yeiepl4/mud/fbwWy9bo/L+OTqWxnxvR21anpYJFt3YP3nvDd8TsaZQZ6Uq592Vn9/GV8Mj+e9+JKYDixyanUK23N9PbVKWdpnGcsuRnypSudBnfC2MyYhzce8vOsn3ge+DzXbaxLWDNi5kjqf1QfuUJOcGAwP05die+dvH//ufn9pj9br/pmHrMOtahZ0jLH+jFJqaz55wF/eQcTnZSKg5mCae1r0rJCiXdq37hPT0yGDEDPyopUXz+iVqwm9YFXtnVl5R0xGzMC/SqVkTnYE/XjT8Tv/lOjTol9O5E52GfZNu7PA0QvX6VVTDWm9cFpyEfomxnz4roPN2dsIi4gNMf6tk2q4Dy+K1a1yqOwt+TiyBU8O3FTo86A4OzPbY95O/Fem/e5rY1evTrz6Zih1KtXC2trS+o37Iin5/1C2Xd+3PC4y+ade3ng5Uv4ywj+t/hb2rVqpvM4BN0SE2WFfwV/f3/q1q3LqVOnWLRoEbdv3+by5ct89dVXHDlyhDNnzhRZbCqVirS0tELZV6lSpdi2bZtG2datWzU6+QVh3Kk11lM/JWr9dp4PHE+Ktz/2axcjtbLItr7EUE7q02AiV20kLfxltnUMG9QiZvchng+dRMinM5DIZNivW4JEYZhnPLI6LTDoOZqUk7+TsGIKyueBKMbORWJinut2Eks7DHqMJN0v6x9TiYGc9IAHJB/Zmmf7WeKp1RyDbiNJOfsHCaumogwORDF6NhLjvOKxxaDrCNL934rHQI60lBOpf+0h4X9TSfptKVLbkhiOmKlVPHrVmmDQcQip5/eR+Ms3KEOCMBwyA4zMco/H3AaDDkNIf5y145bwwwSNR/LB9ahUStIeXssznpNez/nh3EM+bVaJXcNaUNnWlAl7rhIRn5zjNiYGMs6Mb6d+HP+0rfo1lUrFlP03eBadwMreDfh9eEsczBSM++MqiSnv/p7qM74v3Ud256eZP+HW40uSEpKYt30++nL9HLcxNjdh6b5lpKWlMWfYd0xoN56N8zcQFx33znEAnHz4jB/+us+nLZzZNbI1le3MmbD7co7HLDVdybjfL/E8OpFlvRtyYEw7Zneug51J3u+n7CjatcF80nhiN24jbMSnpD7yw2bl90gtLbKtLzGUk/Y8mJiffyX9Rfbv+fBR4wnu2kf9eDFpKgCJZ89rFVOVz7pRaXQnbkzfzJmus0lPSKb1rhlIc/n96BnJiXoQxM1ZW3Ksc7DWBI3HtcnrUSmVPD2a97mtLWNjI9wvXWPmrIWFts93kZiYhHNFJ752m1CkceSXSqUqtMd/kejUC/8KEyZMQCaTcePGDfr370/VqlVxcnKiZ8+eHD16lO7duwMQFRXFJ598gq2tLWZmZrRt2xZPT0/1fubMmUOdOnX47bffcHR0xNzcnIEDBxIbG6uuo1QqWbx4MeXLl0ehUFC7dm327t2rfv3cuXNIJBKOHz9O/fr1kcvlXLx4keTkZCZNmoSdnR2Ghoa0aNGC69ev5+vnHD58OJs3b9Yo27x5M8OHD3+Xw5aF2dA+xO47TtzBk6T6B/Fywf9QJSVj2qtTtvVT7vsQufJX4k+cQ5WSmm2d0AmziDt0ilS/x6T4+BM+exmykiWQV62UZzz6rXuSeuUUadfPogp9QvLen1GlJiNr1D7njSRSDF3dSDm5C+XLkCwvp908R+qp3aT7eGazcR7xtOxO6rXTpN34C1XYU5L3r8+Ip2HbnDeSSDEcOIWU07+jjHhrpDEpgaQNc0m7cwnVi+cog3xIPrgBvdIVkVjY5B1P086k3fqbNM9/UL14RsrRTahSk9Gv2zqXeCTIP/6M1HN7UUaGZXlZFR+t8dBzro8y8AGqqPA84/ntRgAf1ypDr5plqGBjyjcda2Kor8eBe09yiQdsTAzVD2tjufqloMh47gRHMatDDWo4WOBoZcLXHWuQlJbOca/cR9Vz03N0T3av3s3V01cI9ApkxZQfsLKzomnHpjlu03d8X14Eh/O/qT/i4+lD6JNQbl+4TcjjrOdYfvx2zZePa5ejV61yVLAx4xuX2hnH7M7jbOsfuPOYmKQUVn7ciLqlrSllYUSDsjY4l8j9i2VOTAb1I/7QMRKOniAt8DFRS1eiSk7GqFvnbOunPvQmZs16Es/8jSo1+/e8MioaZUSk+mHYvClpT5+Rclu791zlMS48+PEAz0/eJPrhE65OWouihAWlXOrnuE3IX57c+34Pz47fyLFOUni0xqOkS33C3B8QH5T3ua2tHTv+ZMHCHzn714VC2+e7aNm0IZPGDqd96+ZFGkd+KVEV2uO/SHTqhQ/ey5cvOXXqFJ999hnGxtlfkpdIJAD069ePsLAwjh8/zs2bN6lXrx7t2rUjIiJCXdfPz48DBw5w5MgRjhw5wvnz51myZIn69cWLF7Nt2zbWrVvH/fv3mTJlCq6urpw/rzkKNWPGDJYsWcLDhw+pVasWX331FX/++Sdbt27l1q1bVKxYkU6dOmm0nZcePXoQGRnJxYsXAbh48SKRkZHqLy0FIpMhr1qZxCu3MstUKhKv3EJeq1rB9/+K1CTjd5QeE5t7RT0Z0tIVSffx0Ign3ccTPccqOW5m0HEAqrgo0q6eLoRo34qnVAXSH72RLqJSke57B72yzjnH074fqrho0q6f1aoZiaERKqUSVWJ87hWlekgdypMecO+NQhXpAfeQls75C5N+q49RxUeT5qHFqKmxGXqV6pB6O++6qelKHoZE07hc5pcRqURC43I23HkeleN2iSnpdF7/F53WnWXy/hv4vsg8L1LSlQDI9TL/VEklEgz0pNx+ql160ttKlLXHys4Kj4se6rKE2AS8PbypUj/n86pxh8Y8uuPLjLUz2X5rB/87topOg7L/sqst9TFztFWXSSUSGjvacudZ9j/fuUch1CplxeJTd2i76gR9NvzFhks+pCvfoRMjk6HvXJnk62+kqahUJF+/iUGNQnrPy2QoOrUn/shxraobl7VFUcKS0AuZV7VSYxN5edsPmwZ5DwRoS25jRsl2dfDfpd3VA0H4EIhOvfDB8/X1RaVS4eys2bGysbHBxMQEExMTpk+fzsWLF7l27Rp79uyhQYMGVKpUieXLl2NhYaEx0q5UKtmyZQs1atSgZcuWDB06lLNnMzpkycnJLFq0iE2bNtGpUyecnJwYMWIErq6urF+/XqP9efPm0aFDBypUqIBcLmft2rUsW7aMzp07U61aNX799VcUCgUbN27U+mfV19fH1dWVTZs2AbBp0yZcXV3R18/5srS29CzNkcj0SH+p2ZlIfxmJnk3O+b35IpFg/dV4km7fI9U3MPeqxmZI9PRQxUZplKtio5CYWmS7jbR8VWSNO5D0x5rCiffNeIxMM+KJy0c8jlWQNWxP0p8/a9eITB+DzkNJ87wIyYl5xyPVQxUfrRlPfEyO6UnSMpWR1W1D8uENWoWjX7sVpCSR/jDvK0qRiSmkq1RYG8k1yq2N5LzIIZXE0dKEOS61WNmrPgu71kGpUjFixyVCYzN+dkcrExzMFKy64E1MUiqp6Uo2X/UjNDaJF/FJWv0Mb7O0zTiXo15onudRL6KwsM35PLcvY08X1y48D3jG7KHfcmz7McbO/ZS2fdu9UxwAkQnJGcfM+K1jZizP8ed7FpXAGa/nKFUq1vRvwtjmzvx2zZdfL2U/7yU3UouM97wy4q33fEQketZW+d5fdhStmyM1MSHh6Emt6hvaWQAZo+pvSgqPxtDWolBiAijfvxWpcUk8PZa/q6XC+yXSbwpGTJQV/rWuXbuGUqlkyJAhJCcn4+npSVxcHNbW1hr1EhMT8fPzUz93dHTE1NRU/dzBwYGwsIw0BV9fXxISEujQoYPGPlJSUqhbt65GWYMGDdT/9vPzIzU1lebNMy+F6uvr06hRIx4+fJivn2vUqFE0a9aMRYsWsWfPHi5fvpxnzn5ycjLJyZodq2SlErlUt9/rrWdNRL+CI8EjphT+zuUKDAd/SfIfayA+j6sAumBgiOGAL0j+82dI0CIeqR6GQ6aCRELy/vV513+HeOS9xpNyZAMkapcHLqvTmrS77pCefZpFQdUuZUntUpkd6dolLfl403n2egbxWQtn9PWk/NCzPnNO3KHV6lPovRr5b17eNpe9amrTqw2fLf5c/XzuiDnvFKtEKsH3ji/blmbMafG/708553J0GdKZv/ZqdxWmMChVKqyM5XzrUgc9qYRq9haExSay9aov41rkfKWhqBh160LSlWsoc8i/L/dxM+ovHa1+fmHoMp3EVX5Qa4L2uaNMfvdze9Cg3qz96Xv1827dXbnoXnj5+f9FYknLghGdeuGDV7FiRSQSCd7emiNVTk5OACgUCgDi4uJwcHDg3LlzWfZhYWGh/vfbo94SiQSlUqneB8DRo0ezTE6VyzVH23JKBSqomjVrUqVKFQYNGkTVqlWpUaMGHh4euW6zePFi5s6dq1E2ya48k+0rqJ+nR0ajSktHz1pztFLP2pL0F++W6vAm65mfY9SqMcGj3EgPe5FnfVV8DKr09Cyj4BJTiyyj9wBSa3uk1iUwHP3tG5Uz0q6Ml+0nYcl4VNnk2GtLlRCbEY9JPuKxKoHh8FlZ41m0h4Tln6N6nWP/qkMvsbAl8dfZeY7Sq+NRpmeZpCsxNkMVF52lvtSyBFJLO+QD3bLEY/TNNhJ/morqjRx7aVlnpDYlSf5zdZ6xAFgqDNCTSHiZoPnl8WVCMjZvjUTnRF9PirOdGU8iE9Rl1ezN+WNES2KTM0bqrYzkuG53p5qWOeRXT1/F+3bmZ8PrybAWNpZEhmWe1xY2FgQ8yHnVoMiwSIIeBWmUPXn0hOad331FEUsjecYxe+tKxsv4ZGyMs5/4amtiiEwqQU8qUZeVtzblRXwyqelK9PW0/6KujMp4z0ut3nrPW1mS/lL7tMCc6NmXQN6wHhEzv8uxzrOTt3h5K3NQRWqQ0S0xtDUnKSxKXW5oa07U/eznGeSXTWNnzCqW5PKn2p3bOTl8+BTXrt1WP3/2rGDzKwShoESnXvjgWVtb06FDB9asWcPEiRNz7EzXq1ePkJAQZDLZOy/7WK1aNeRyOUFBQbRunctkxLdUqFABAwMD3N3dKVeuHACpqalcv36dyZMn5zuOUaNGMWHCBNauXatV/ZkzZ/Lll19qlD1v3luzUloayQ99MGxcl4S/L2WUSSQoGtcl5veD+Y7xTdYzP8eobXOCR08lTds/fOlpKJ/6olepNun3rqrj0atUi9SLWZefU4Y9JWHp5xplBp1dQa4g5cCvqKLy/iKRZzzP/NCrWIv0B9cy46lYi9RLx7LGE/6MhBWTNePpNCgjnkObUEW/Grl83aG3cSDxl9mQoOVqKsp0lMEB6JWvTrr365xoCXrla5B2/VTW6i+ek7B2umY8H/VDIjck+cRvmfG8IqvThvTn/ihDNTuyOdHXk1LV3pxrj1/QtlLGcoZKlYprj18ysF45rfaRrlTh+yKWFuXtsrxm+qoz/jgyngchUUxoXlmrfSbGJ5IYr/klKSIsgjrNa6s78QoTBc51nDn+W9bf42sPbjygdAXNL/KlnEoR9vTdJ1mqj1lgOG0rOwCvj1k4A+uVz3ab2qWtOH7/KUqVCumrL2WPI+KwNZHnq0MPQFoaqd4+yBvUI+kf94wyiQR5g3rE7T3wrj+WmlFXF5SRUSRdupJzCPFJxL2VapQYGkmJFtXVnXiZiQLruhXw3Vo4q5g5DWpDhKc/UQ+0O7dzEhcXT1xcHnNfhHz5r6bNFBbRqRf+FX7++WeaN29OgwYNmDNnDrVq1UIqlXL9+nW8vLyoX78+7du3p2nTpvTq1YulS5dSuXJlnj9/ztGjR+ndu7dGukxOTE1NmTp1KlOmTEGpVNKiRQuio6Nxd3fHzMwsx1VojI2NGT9+PNOmTcPKyoqyZcuydOlSEhISGD16dLbb5GbMmDH069dP4wpDbuRyeZYrCS+zSb2J+e1PbOZ/Rcp9H5LveWPm2huJwpDYAxn5sDYLviI97AWRqzJy+pHJMKiQ0WGT6OujZ2eDgXMFlAmJpD3JWJ3EetZEjDu3JWzyd6jiE9RXApRx8aiSU3KNO/X8QeSDJqN84kt6kA8GrXsgMTAk7VpGuoN80GRUMRGkHN0GaakoQzT/SKsS45GAZrmRCVILWyTmGTnDUruMjpoqNjLbEXeNeC4cRt5/IsqnvqQ/fYRBi+5I9OWk3fgrI57+k1DFvCTlxI6MeEJziOd1uVQPQ9dpSEs5kbRlERKJFF5dCVAlxkF67mlVqZePI+/1KcrnAaQ/90O/sQsSfTmprybBGvQchyo2ktS/dkN6KqpwzXsDqJIyRsTfLsdAgaxaI1JO78y1/bcNbVCeb495Us3eghoO5uy4EUhiaho9a5QB4JujHtiZGjKpVUaayPpLj6jpYEFZS2Nik1PZes2f4JhEetcqo97nKe9gLBUGOJgpeBQew9K/HvBRRXua5SMF520HNx5kwKSBPAt8TmhQCK5ThxIRFsHlU5fVdRbuWsjlE5c5svVIxjYbDrBs/3L6fdafi0cuULlOZVwGu7BmRsFGe4c2qsi3R25RzcGCGg6W7LjhR2JKOj1rlQXgm8M3sTNVMKlNxsTV/nXLs/tmAEtP32VQAyceR8Sx8fIjBjXI/ktAXuJ27cHy2xmkenmTct8Lk4F9kBgaknDkBACWs2eQHv6CmLWv5mHIZOiXf/Wel8nQs7VBv1IFlImJpD99Y0UiiQSjri4kHDsFryY8a8vn1xNUm9yL2IAQ4oPCqTG9L4mhURrrzrf5YyZPj9/Ad3PGhHiZkRyT8plr4xuXtcWiejlSouJIeJb5hVVmoqBM90Z4zM3fua0tS0sLypYtRUmHjHsGVK6ccTU0JCSM0NDCW2UnLwkJiQS98ft49jwULx8/zM1McbDP+qW5uPivrlpTWESnXvhXqFChArdv32bRokXMnDmTp0+fIpfLqVatGlOnTmXChAlIJBKOHTvG119/zciRIwkPD8fe3p5WrVpRooT2N22ZP38+tra2LF68GH9/fywsLKhXrx6zZs3KdbslS5agVCoZOnQosbGxNGjQgJMnT2Jpmf9JqDKZDBubvJc8zK/4k+eRWlpgOWE4ejaWJHv7ETphFsqIqIx27e3gjVU2ZHbWlPpjnfq5xYj+WIzoT+J1T0I+yVib2mxADwAcNv2g0Vb4t8uIO5R1RPlNaR4XkZiYY+AyGImZJcpn/iT+Mkc9WVVqaZvvHExZ9UYYDpqsfm447CsAUk7uIuXkrtzjueOOxNgMg46DMm4+9TyAxE3z1ekuUgsblCrtOzAScytk1RsBYDR5hcZrieu/zbqu/VvSH1whxdgU/TZ9MTAxRxn6mKSd30N8TEY85tbvlKMqq9EEJBLS7l3K13adqpQkMiGFte4+vIhPxtnOjJ/7NlJPBA2OTVSvRAUZN1Gaf+ouL+KTMZPrU9XejK2Dm1HBJnNOy4u4JH74+wEv45OxNTGkW/VSjG1asFVQ/ly7F0OFIRMXT8TYzJgHNx4we+i3pL6RX21f1gEzq8z1/h/decTCsQsYPn0Eg74YROiTUH6d+wvnDpwrUCydqpYiMiGZtRe8Mo/ZgCZYv0q/CY7RPGb2Zgp+HtCU5Wfv0W/j39iZGjK4gRMjm7zbMUk8ew6ppQWmn4xEz9qS1Ed+vJgyHWVkRmqSXgk7VMrMc1rPxhq7bb+qn5sOGYDpkAEk3/LgxWeZVwPlDesjcyhBgpar3rzJ66cjyIzkNFg2GgMzI8Kv+XB+8Pca+e8mjiWQW2WeJ5a1nWi77xv187pzhwIQsPsfrk3OnKNStlfGuR20P3/ntra6d+vIpo2ZN3PbtSPjauq8+T8wb/6KnDYrdPe8HjFqYuaVuaWrfwGgZ+f2LPzGLafNhA+cRCWudQjCf1JA7Q55V9Ix23aKog5Bk37xGveQGBev4yMt+W53MH1f+s3L/wow79OeudWLOgQNEb/kvIZ7UbgY6FDUIWgY8vJcUYegIfF50a51/zZ9G6f33oaZceG1EROf9521/22K118sQRAEQRAE4T9JrH5TMGKdekEoBsaNG6deU//tx7hx44o6PEEQBEF471SF+N9/kRipF4RiYN68eUydOjXb18zMzLItFwRBEARBeE106gWhGLCzs8POrviuSCAIgiAI75tIvykY0akXBEEQBEEQipxYu6VgRE69IAiCIAiCIHzgxEi9IAiCIAiCUOT+qxNcC4vo1AuCIAiCIAhFTqTfFIxIvxEEQRAEQRAELSxcuJBmzZphZGSEhYWFVtuoVCpmz56Ng4MDCoWC9u3b8+jRI406ERERDBkyBDMzMywsLBg9ejRxcXH5ik106gVBEARBEIQip1KpCu3xvqSkpNCvXz/Gjx+v9TZLly5l1apVrFu3jqtXr2JsbEynTp1ISkpS1xkyZAj379/n9OnTHDlyhH/++YexY8fmKzaRfiMIgiAIgiAUuQ8h+Wbu3LkAbNmyRav6KpWKH3/8kW+++YaePXsCsG3bNkqUKMGBAwcYOHAgDx8+5MSJE1y/fp0GDRoAsHr1arp06cLy5cspWbKkVm2JkXpBEARBEAThXyU5OZmYmBiNR3Jyss7jCAgIICQkhPbt26vLzM3Nady4MZcvXwbg8uXLWFhYqDv0AO3bt0cqlXL16lXtG1MJgiAUQFJSkuq7775TJSUlFXUoKpVKxJMXEU/uRDy5E/HkTsRTfHz33XcqMgb/1Y/vvvuu0Pa/efNmlbm5eZ713N3dVYDq+fPnGuX9+vVT9e/fX6VSqVQLFy5UVa5cOcu2tra2qp9//lnrmMRIvSAIBZKcnMzcuXOLZAQkOyKe3Il4cifiyZ2IJ3cinuJj5syZREdHazxmzpyZbd0ZM2YgkUhyfXh5een4J8g/kVMvCIIgCIIg/KvI5XLkcrlWdd3c3BgxYkSudZycnN4pDnt7ewBCQ0NxcHBQl4eGhlKnTh11nbCwMI3t0tLSiIiIUG+vDdGpFwRBEARBEP6zbG1tsbW1fS/7Ll++PPb29pw9e1bdiY+JieHq1avqFXSaNm1KVFQUN2/epH79+gD89ddfKJVKGjdurHVbIv1GEARBEARBELQQFBSEh4cHQUFBpKen4+HhgYeHh8aa8lWqVGH//v0ASCQSJk+ezIIFCzh06BB3795l2LBhlCxZkl69egFQtWpVXFxcGDNmDNeuXcPd3Z3PP/+cgQMHar3yDYiRekEQCkgul/Pdd99pfZnzfRPx5E7EkzsRT+5EPLkT8fz7zZ49m61bt6qf161bF4C///6bNm3aAODt7U10dLS6zldffUV8fDxjx44lKiqKFi1acOLECQwNDdV1duzYweeff067du2QSqX06dOHVatW5Ss2iUol7skrCIIgCIIgCB8ykX4jCIIgCIIgCB840akXBEEQBEEQhA+c6NQLgiAIgiAIwgdOdOoFQRAEQRAE4QMnOvWCIAiCIAiC8IETS1oKgiC8R0+fPgWgdOnSRRyJIAiFadiwYfz000+YmpoC4OnpSbVq1dDX1y+SeIKCgrSqV7Zs2fcciVBUxJKWgiC8s5SUFAICAqhQoQIymW7HCGJiYrSua2Zm9h4jyUqpVLJgwQJ++OEH9Q1JTE1NcXNz4+uvv0YqLZqLpDdv3uThw4cAVKtWjXr16hVJHELunjx5gkQiUX8RvHbtGjt37qRatWqMHTu2iKMrWvHx8UydOpVDhw6RkpJCu3btWL169Xu7G2hu9PT0CA4Oxs7ODsj4nPHw8MDJyUnnsbyO57XXXTuJRKJRJpFISE9P13lsgm6IkXpBEPItISGBiRMnqm/A4ePjg5OTExMnTqRUqVLMmDHjvcdgYWGh8QcrO0X1R+zrr79m48aNLFmyhObNmwNw8eJF5syZQ1JSEgsXLtRpPGFhYQwcOJBz585hYWEBQFRUFB999BG///67TjpE+bmJyqRJk95jJNk7f/48y5cv1/jSM23aNFq2bKnzWAYPHszYsWMZOnQoISEhdOjQgerVq7Njxw5CQkKYPXu2TuL4+OOP86wjk8mwt7enQ4cOdO/e/b3H9O233/Lbb78xZMgQFAoFO3fuZOzYseq7d+rS22OiRT1G+vqL4IgRI+jevbvOB1qEoidG6gVByLcvvvgCd3d3fvzxR1xcXLhz5w5OTk4cPHiQOXPmcPv27fcew/nz57Wu27p16/cYSVYlS5Zk3bp19OjRQ6P84MGDTJgwgWfPnuk0ngEDBuDv78+2bduoWrUqAA8ePGD48OFUrFiRXbt2vfcYypcvr1U9iUSCv7//e45G0/bt2xk5ciQff/yx+kuYu7s7+/fvZ8uWLQwePFin8VhaWnLlyhWcnZ1ZtWoVu3fvxt3dnVOnTjFu3DidHZ+RI0fmWUepVBIWFsb58+eZOnUq8+bNe68xlS9fnqVLl9KvXz8g4+pTkyZNSExM1HknViqVEhISoh6pNzU1xdPTs8hG6kNCQti6dSubN28mKioKV1dXRo8erX7PC/9+olMvCEK+lStXjt27d9OkSRONP2S+vr7Uq1cvX6kx/0aGhobcuXOHypUra5R7e3tTp04dEhMTdRqPubk5Z86coWHDhhrl165do2PHjkRFRek0nuKmatWqjB07lilTpmiUr1ixgl9//VU9eq8rJiYm3Lt3D0dHR3r06EHz5s2ZPn06QUFBODs76/z80caRI0eYMGGC1nnd70pfX5/Hjx9TsmRJdZmRkRFeXl46zxWXSqX89ddfWFlZAdCsWTP++OOPLPNnatWqpdO4IOPK4ObNm9mzZw/VqlVj9OjRjB49ushS/wTdEL9dQRDyLTw8XD069ab4+Pg8U2Lep4SEBLy8vLhz547GQ9dq167NmjVrspSvWbOG2rVr6zwepVKZ7eQ9fX19lEqlzuMpbvz9/bNNHenRowcBAQE6j6d69eqsW7eOCxcucPr0aVxcXAB4/vw51tbWOo8nN3v37gWgRYsWNGjQ4L23l925LJPJiixPvF27dtSpU4c6deqQkJBAt27dqFOnDnXr1lX/vyi0aNGCjRs38ujRI4yMjBg3btx//sv7f4FIuBIEId8aNGjA0aNHmThxIpA5GWvDhg00bdpU5/GEh4czcuRIjh8/nu3ruv6Dv3TpUrp27cqZM2fUx+Py5cs8efKEY8eO6TQWgLZt2/LFF1+wa9cu9Qjns2fPmDJlCu3atdN5PJCxKtChQ4cICgoiJSVF47UVK1boNJYyZcpw9uxZKlasqFF+5swZypQpo9NYAL7//nt69+7NsmXLGD58uPqL4KFDh2jUqJFOY0lLS8PLywsDAwONK08HDx5k9uzZeHl50bdvXywsLNi3b997j0elUtGuXTuNVJuEhAS6d++OgYGBuuzWrVvvPZai+MKnrUuXLrFp0yb27NmDs7MzP/30k3o+jfDvJTr1giDk26JFi+jcuTMPHjwgLS2N//3vfzx48IBLly7lK9e9sEyePJmoqCiuXr1KmzZt2L9/P6GhoeoVaHStdevW+Pj48NNPP+Hl5QVkTDqcMGGCRtqArqxZs4YePXrg6Oio7qQ+efKEGjVqsH37dp3Hc/bsWXr06IGTkxNeXl7UqFGDwMBAVCpVkazI4+bmxqRJk/Dw8KBZs2ZARk79li1b+N///qfzeNq0acOLFy+IiYnB0tJSXT527FiMjIx0Fse9e/fo1q0bT548AaBnz56sXbuW/v37c+/ePcaMGcPRo0d1Fg/Ad999l6WsZ8+eOo3htXLlyuWr/oQJE5g3bx42NjbvJZ7g4GC2bdvG5s2biYyMZMiQIbi7u1OjRo330p5Q/IicekEQ3omfnx9LlizB09OTuLg46tWrx/Tp06lZs6bOY3FwcODgwYM0atQIMzMzbty4QeXKlTl06BBLly7l4sWLOo+puFGpVJw5c0b9JaNq1aq0b9++SGJp1KgRnTt3Zu7cueo5GXZ2dgwZMgQXFxfGjx+v85j279/PDz/8oM6fr1q1KtOmTSuyDiNkXIHy9vYGwNnZWefLNnbt2pXk5GQmT57Mrl272LVrF87OzowePZrPPvsMhUKh03g+dO97yUt9fX1KlSrF8OHD6dGjR47r5RdFjr+gG6JTLwjCB8/MzIw7d+7g6OhIuXLl2LlzJ82bNycgIIDq1auTkJDw3mO4c+cONWrUQCqV5pnHr+s/qtu2bWPAgAHI5XKN8pSUFH7//XeGDRum03hMTU3x8PCgQoUKWFpacvHiRapXr46npyc9e/YkMDBQp/EUN/Hx8UycOJFt27ap5zzo6ekxbNgwVq9erbPRejs7O06dOkWdOnWIjo7G0tKSrVu3MnToUJ20nx/nz58nPj6epk2balzdKE7e9+o4b06CfZ0S+XYXT6xT/+8m0m8EQdBKcb7Zk7OzM97e3jg6OlK7dm3Wr1+Po6Mj69atw8HBQScx1KlTR728XZ06dZBIJNmuW10Uf1RHjhyJi4tLlsnNsbGxjBw5UuedemNjY3UevYODA35+flSvXh2AFy9e6DSW4ujLL7/k/PnzHD58WOM+B5MmTcLNzY21a9fqJI4XL16o08XMzc0xNjamSZMmOmk7J99//z1xcXHMnz8fyOi0du7cmVOnTgEZX0TOnj2rPp/+S4pzjr+gG6JTLwiCVrS52dNruu60fvHFFwQHBwMZObcuLi7s2LEDAwMDtmzZopMYAgIC1OkRxe2P6+ubcL3t6dOnmJub6zyeJk2acPHiRapWrUqXLl1wc3Pj7t277Nu3T2edRisrK3x8fLCxscHS0jLXczsiIkInMb32559/snfvXtq0aaMu69KlCwqFgv79++usUy+RSIiNjcXQ0FB9DiUmJmb5gq/LL/G7d+9m+vTp6ud79+7ln3/+4cKFC1StWpVhw4Yxd+5c/vjjD53FVFwUtxx/QfdEp14QBK38/fff6n8HBgYyY8YMRowYobG6y9atW1m8eLHOY3N1dVX/u379+jx+/Fi9brWu/mC9/oOamprK3Llz+fbbb7W+4dL7UrduXSQSCRKJJMuKIenp6QQEBKiXS9SlFStWEBcXB8DcuXOJi4tj9+7dVKpUSWcr36xcuRJTU1MAfvzxR520qa2EhARKlCiRpdzOzk4nqWSvqVQqjRVvVCqVxhKNRXHH5oCAAI30tWPHjtG3b1/1FY1vvvlGfWMqIXfbt29n6tSpolP/LyJy6gVByLd27drxySefMGjQII3ynTt38ssvv3Du3DmdxjNv3jymTp2aJdc4MTGRZcuWMXv2bJ3GY25ujoeHR5F36ufOnav+v5ubGyYmJurXDAwMcHR0pE+fPhpLAQpFr127dlhbW7Nt2zYMDQ2BjHN5+PDhREREcObMGZ3Eoe1KVrq8Y/PbeelVqlRh8uTJjBs3DqBY36CrqO84+7biFo9QcKJTLwhCvhkZGeHp6UmlSpU0yn18fNQ3YdElPT09goODs+SMv3z5Ejs7O52nAw0fPpw6depkuUNpUdm6dSsDBgxQdxCF4j1H5O7du3Tq1ImUlBT1GvWenp4YGhpy8uTJYpsvvmTJEsaNG/de10OvU6cOkydPZsSIEQQFBeHo6Mi9e/eoVq0akLE+e//+/Xn69Ol7i+FdjR8/nvnz5xebkXHRqf/3Eek3giDkW5kyZfj1119ZunSpRvmGDRuK5GY9OeWMe3p6qm/hrkuVKlVi3rx5uLu7U79+fYyNjTVenzRpkk7jGT58uE7by05xy2EvznNEatasia+vLzt37lQvsTlo0CCGDBlSrJeRXLRoEf3793+vnfrPPvuMzz//nAsXLnDlyhWaNm2q7tAD/PXXX0VyF9eoqCg2btyo/n1Vr16dUaNGacxZ0dVcCOG/S3TqBUHIt5UrV9KnTx+OHz9O48aNAbh27RqPHj3izz//1FkcrzuHEomEypUra3TS0tPTiYuLU1+W16WNGzdiYWHBzZs3uXnzpsZrEolE55369PR0Vq5cyR9//JHtHVx10Yl+M4d95cqVWneo35fiOkfkypUrHD58mJSUFNq2bcsnn3yi0/YLQhcX/seMGYOenh6HDx+mVatWWW5G9fz5c0aNGvXe43jTjRs36NSpEwqFQn3H3xUrVrBw4UJOnTpVJDdUE/6bRPqNIAjv5OnTp6xdu1bjZj3jxo3T6Uj91q1bUalUjBo1ih9//FFjVOx1zvjrTtp/2ezZs9mwYQNubm588803fP311wQGBnLgwAFmz56t8y8ZxU1xmSOyd+9eBgwYgEKhQF9fn5iYGL7//numTp2qk/YLqjimc+giJahly5ZUrFiRX3/9VT0ZPS0tjU8++QR/f3/++eef99Z2QRTH35dQMKJTLwjCB+/8+fM0a9Ysxzso/tdVqFCBVatW0bVrV40bP61atYorV66wc+dOncZz7Ngx9PT06NSpk0b5qVOnSE9Pp3PnzjqNp7jMEalfvz4NGzbkp59+Qk9Pj8WLF7Ns2TKdL6n5ropjJ/F938UVQKFQcPv2bapUqaJR/uDBAxo0aKDzOUbaKm45/kLBifQbQRDeWUJCQrbpHLq+Y2rr1q1JT0/nzz//1Mhp7dGjB3p6ejqN5bWnT59y6NChbI+PrpZtfC0kJISaNWsCYGJiQnR0NADdunXj22+/1WksADNmzGDJkiVZypVKJTNmzNB5p764zBHx9vZm9+7d6nPWzc2N2bNnExYWlmUSuKAdXYxbmpmZERQUlKVT/+TJE3XKma6JHP//JtGpFwQh38LDwxk5ciTHjx/P9nVdTyz09fWlS5cuPHv2DGdnZwAWL15MmTJlOHr0KBUqVNBpPGfPnqVHjx44OTnh5eVFjRo1CAwMRKVSFUl+benSpQkODqZs2bJUqFBBned7/fp15HK5zuN59OiRxuTG16pUqYKvr6/O4ykuc0QSEhI0VtoxMDDA0NCQuLg40akvxgYMGMDo0aNZvnw5zZo1A8Dd3Z1p06ZlSenSBZHj/98lOvWCIOTb5MmTiYqK4urVq7Rp04b9+/cTGhrKggUL+OGHH3Qez6RJk6hQoQJXrlxRr3bz8uVLXF1dmTRpEkePHtVpPDNnzmTq1KnMnTsXU1NT/vzzT+zs7BgyZEiR3Oypd+/enD17lsaNGzNx4kRcXV3ZuHEjQUFBRbLsprm5Of7+/jg6OmqU+/r6ZlkpSBe6dOmCj48Pa9euxcvLC4Du3bvrfI4IZFwdePN+AmlpaWzZskUjRaK4zoFo2bJlsV6d531Zvnw5EomEYcOGkZaWBoC+vj7jx4/P9orU+zZlyhR69OiRbY7/5MmTi22Ov1BwIqdeEIR8c3Bw4ODBgzRq1AgzMzNu3LhB5cqVOXToEEuXLuXixYs6jcfY2JgrV66oU0xe8/T0pHnz5uq7l+rKm3nrlpaWXLx4kerVq+Pp6UnPnj0JDAzUaTxvu3z5MpcvX6ZSpUp0795d5+1/+umnXL58mf3796uvovj6+tKnTx8aNmzIhg0bdB5TceDo6JjnqkASiQR/f38dRZQpPT2dAwcOFJv0Nm297zz/9PR03N3dqVmzJnK5HD8/PyBjHsvbN8PTlQ81x18oODFSLwhCvsXHx6vTASwtLQkPD6dy5crUrFmTW7du6TweuVxObGxslvK4uLgiuVuqsbGxOo/ewcEBPz8/9Q2DXrx4ofN43ta0adMiXRVo6dKluLi4UKVKFUqXLg1kzEFo2bIly5cvL5KYLly4wPr16/H392fPnj2UKlWK3377jfLly9OiRQudxFDUX/Zy4uvrS9euXXn69GmxSG8rTvT09OjYsSMPHz6kfPnyWQYWikJxzPEXdEN06gVByDdnZ2e8vb1xdHSkdu3arF+/HkdHR9atW4eDg4PO4vjnn39o2rQp3bp1Y+zYsWzcuFGdQ3r16lXGjRtHjx49dBbPa02aNOHixYtUrVqVLl264Obmxt27d9m3bx9NmjTRSQyHDh2ic+fO6Ovrc+jQoVzr6voYmZubc+nSJU6fPo2npycKhYJatWrRqlUrncbx2p9//snQoUMZMmQIt27dIjk5GYDo6GgWLVrEsWPHiiSuvNSsWZNjx4699xShSZMm4eTkxOXLl4tFelt+6CIlqEaNGvj7+1O+fPn32o62iluOv6BDKkEQhHz67bffVJs3b1apVCrVjRs3VDY2NiqpVKoyNDRU/f777zqLQyqVqkJDQ1WRkZGqHj16qCQSicrAwEBlYGCgkkqlql69eqmioqJ0Fs9rfn5+Kk9PT5VKpVLFxcWpPv30U1XNmjVVH3/8sSowMFAnMUgkElVoaKj63zk9pFKpTuLJSWJiokqpVBZpDHXq1FFt3bpVpVKpVCYmJio/Pz+VSqVS3bp1S1WiRImiDC1Xb8b6PhkZGanu3LmTpdzDw0NlbGz83tvPSVpammrv3r2q+fPnq+bPn6/at2+fKi0tTedxHD9+XFWnTh3V4cOHVc+fP1dFR0drPHQtOTlZNWnSJPXnoFQqVcnlctXkyZNVSUlJOo9H0B2RUy8IQoElJCTg5eVF2bJldbrmsVQqJSQkRJ0K9OjRI/VEx6pVq1KxYkWdxSJoT6lUsnDhQtatW0doaCg+Pj44OTnx7bff4ujoyOjRo3Uaj5GREQ8ePMDR0VEjB9vf359q1aqRlJSk03i0pat14a2srDhy5Ih61Pc1d3d3unfvXiTr6GeXEuTt7V0kKUFSqVT97zfnRKhUKiQSiU5XAyuOOf6C7oj0G0EQCszIyKjIlkl7849opUqVstxASCh+FixYwNatW1m6dCljxoxRl9eoUYMff/xR5516e3t7fH19s6zGc/HixWJ1I6WiUtzS26B4pQT9/fffOmsrL8Uxx1/QHdGpFwRBK19++aXWdXV5c6URI0bkudb6vn373nsclpaWea5c8pouRjZXrVqldV1dL5G4bds2fvnlF9q1a8e4cePU5bVr11ZfadGlMWPG8MUXX7Bp0yYkEgnPnz/n8uXLTJ06tUhuzlXcrFq1iuHDh9O0aVP1XZvT0tLo0aMH//vf/4okpvPnz2ssYQtgbW3NkiVLaN68uU5jad26tU7by0txy/EXdEd06gVB0Mrt27c1nt+6dYu0tDT1pW8fHx/09PSoX7++TuMyNTUtFmtj//jjj0UdgoaVK1dqPA8PDychIQELCwsg446TRkZG2NnZ6bxT/+zZs2xTo5RKJampqTqNBTLucKtUKmnXrh0JCQm0atUKuVzO1KlTmThxos7jKW4sLCw4ePAgvr6+6iUtizq9rbiteFUcVk96bcGCBUydOpX58+dTv379LPd+ePMGZ8K/TBHn9AuC8AH64YcfVN27d1dFRESoyyIiIlQ9e/ZULV++XGdxvDkZVMjZjh07VM2bN1d5eXmpy7y8vFQtW7ZUbd++Xefx1KtXT/Xbb7+pVCrNyZ5z585VtWjRQufxvJacnKy6f/++6urVq6rY2Ngii0NbupooWxwNHTpUVb16ddWVK1dUSqVSpVQqVZcvX1bVqFFDNXz4cJ3GsnfvXpVCoVB98sknKrlcrv6drF69WtW5c2edxqJSqbJMhH/9KA4T44X3S0yUFQQh30qVKsWpU6fUa6+/du/ePTp27Mjz5891Eoeenh7BwcHqibLFUVJSknrN+td0PVJWoUIF9u7dS926dTXKb968Sd++fQkICNBpPAcPHmT48OHMnDmTefPmMXfuXLy9vdm2bRtHjhyhQ4cOOo2nOElNTcXFxYV169blOT9k586d9OzZUyd34X369CmHDh0iKCgoy/msy3S716Kiohg+fDiHDx/OkhK0ZcsWzM3NdRZL3bp1mTJlCsOGDdOYvHz79m06d+5MSEiIzmKBjNSk3BS3dCGh8Ij0G0EQ8i0mJobw8PAs5eHh4dleEn9fiuuYRHx8PNOnT+ePP/7g5cuXWV7X5WoYAMHBwerb178dR2hoqE5jAejZsyeHDx9m3rx5GBsbM3v2bOrVq8fhw4d12qEfNWqUVvU2bdr0niPJpK+vz507d7SqO3jw4PccTYazZ8/So0cPnJyc8PLyokaNGgQGBqJSqYpsgnxxSgny9vbO9h4L5ubmREVF6Twe0Wn/75LmXUUQBEFT7969GTlyJPv27ePp06c8ffqUP//8k9GjR/Pxxx/rLI6///5bY6JccfHVV1/x119/sXbtWuRyORs2bGDu3LmULFmSbdu26Tyedu3a8emnn2rc7ffmzZuMHz+e9u3b6zweyLgp0OnTpwkLCyMhIYGLFy/SsWNHncawZcsW/v77b6KiooiMjMzxoWuurq5s3LhR5+3mZObMmUydOpW7d+9iaGjIn3/+yZMnT2jdujX9+vUr0tgqVqxI9+7d6d69e5Hl+L9ePeltRbl60oULF3B1daVZs2Y8e/YMgN9++42LFy8WSTyCboiRekEQ8m3dunVMnTqVwYMHk5qaikqlQl9fn9GjR7Ns2TKdxfHmiNT58+dZvny5etSuWrVqTJs2jZYtW+osntcOHz7Mtm3baNOmDSNHjqRly5ZUrFiRcuXKsWPHDoYMGaLTeDZt2sTw4cNp0KCBRqpCp06d2LBhg05jedONGzc0fl+6nmQ9fvx4du3aRUBAACNHjsTV1bVYfElMS0tj06ZNnDlzJtuJjrpOd3n48CG7du0CQCaTkZiYiImJCfPmzaNnz56MHz9ep/G8VlxSgorb6kkf6h2ShYITOfWCILyz+Ph4jZub6CK3Nzvbt29n5MiRfPzxx+rl7Nzd3dm/fz9btmzRWZrCayYmJjx48ICyZctSunRp9u3bR6NGjQgICKBmzZrExcXpNJ7XfHx81EtGVqlShcqVKxdJHE+fPmXQoEG4u7trrMbTrFkzfv/9d0qXLq2zWJKTk9m3bx+bNm3i0qVLdO3aldGjR9OxY0etlygtbB999FGOr0kkEv766y8dRpMxEv33339TtWpVqlWrxpIlS+jRoweenp40b968SM7nvFKCdHmMVCoVixYtYvHixSQkJACoV0+aP3++zuJ4rbjl+Au6Izr1giBoTdvUGl2sC/+mqlWrMnbsWKZMmaJRvmLFCn799Vf1aLCu1KpVi9WrV9O6dWvat29PnTp1WL58OatWrWLp0qU8ffpUp/G8lpKSQkBAABUqVEAmK7oLtS4uLkRFRbF161aNu4GOHDkSMzMzTpw4USRxPX78mC1btrBt2zbS0tK4f/8+JiYmRRJLcdKrVy+6du3KmDFjmDp1KgcPHmTEiBHs27cPS0tLzpw5o/OYGjVqROfOnZk7d66642pnZ8eQIUNwcXEpkqsHKSkp+Pr6EhcXR7Vq1Yrs3PlQ75AsFJxIvxEEQWu6XFEiP/z9/enevXuW8h49ejBr1iydxzNy5Eg8PT1p3bo1M2bMoHv37qxZs4bU1NQiWSkkISGBiRMnsnXrViBjxN7JyYmJEydSqlQpZsyYodN4zp8/z6VLl9QdegBnZ2dWr15dJOlSr0mlUiQSCSqVSueTmXPy+gugLq9evG3FihXq0fi5c+cSFxfH7t27qVSpUpGcz1A8U4IMDAyoVq2aztt9m7hD8n+X6NQLgqC1zZs3F3UI2SpTpgxnz57NMlHuzJkzlClTRufxvHnFoH379nh5eXHz5k0qVqxIrVq1dB7PzJkz8fT05Ny5c7i4uGjENmfOHJ136suUKZPtTabS09MpWbKkTmN5M/3m4sWLdOvWjTVr1uDi4oJUWjRrSSiVShYsWMAPP/yg7kybmpri5ubG119/rfO43uwIGhsbs27dOp22nx1jY2N1Hr2DgwN+fn7qJXZfvHih01ji4+NZsmQJZ8+eJSwsDKVSqfG6v7+/TuMpbjn+gu6ITr0gCB88Nzc3Jk2ahIeHB82aNQMycuq3bNmi09vYX758mZcvX9KtWzd12bZt2/juu++Ij4+nV69erF69GrlcrrOYAA4cOMDu3btp0qSJRp549erV1XMidGnZsmVMnDiRn376iQYNGgAZk2a/+OILli9frrM4JkyYwO+//06ZMmUYNWoUu3btwsbGRmft5+Trr79m48aNLFmyRD1H5OLFi8yZM4ekpCQWLlxYxBEWvSZNmnDx4kWqVq1Kly5dcHNz4+7du+zbt48mTZroNJZPPvmE8+fPM3ToUBwcHIpsLsZr4g7J/10ip14QhH+F/fv388MPP2isWT1t2jR69uypsxg6d+5MmzZtmD59OgB3796lXr16jBgxgmrVqrF06VI+/fRT5syZo7OYICPH9t69ezg5OWnk2Hp6etKqVSuio6N1Go+lpSUJCQmkpaWpc/tf//vtydYRERHvLQ6pVErZsmWpW7durh0xXc8RKVmyJOvWraNHjx4a5QcPHmTChAnqJQrfJysrK3x8fLCxscHS0jLX4/M+f0c58ff3Jy4ujlq1ahEfH4+bmxuXLl1SpwSVK1dOZ7FYWFhw9OhR9Rew4qK45PgLuiNG6gVB+Ffo3bs3vXv3LtIYPDw8NFa7+P3332ncuDG//vorkJEX/d133+m8U9+gQQOOHj2qHqV73UHbsGEDTZs21WksAD/++KPO28zOsGHDinxUNTsRERFUqVIlS3mVKlV01oFeuXIlpqamQPH5fb2pOKUEWVpaFoulUN9WXHL8Bd0RI/WCIHzwnJycuH79OtbW1hrlUVFR1KtXT2c5rYaGhjx69Eidx9+iRQs6d+7M119/DUBgYCA1a9bU6V13ISN1o3Pnzri6urJlyxY+/fRTHjx4wKVLlzh//rzO14f/UD19+pSSJUu+95z2xo0b07hxY1atWqVRPnHiRK5fv86VK1fea/tC/mzfvp2DBw+ydetWjIyMijqcYpfjL+iOGKkXBOGDFxgYmO1qJcnJyf9v776jojrXtoFfA0pRilhAsOBgEFGwoDExtqhobDGKscREFDXGYxILiiWIohgLEY96fPOpURETzYkQY0ksGEDsGqWJSldRAxaIBYigw/7+4GVeRtDgOeHZe+D6reVazLNnnX0ty8k9e+7nfoS0KpSysbHBtWvX0KxZMxQVFSEmJgZLlizRXn/8+LH28CeRunfvjri4OKxcuRKurq4IDw+Hm5sbzpw5A1dXV+F5YmJiULt2be299+3bh+DgYLRp0wb+/v4wMjISnqky2rRpg7i4uCqfIBIYGIjBgwfj119/1X6TcubMGdy8eVOWg4MePXpU4bpKpYKxsbGwPy+ltgQFBQUhPT0dNjY2aNGiRbl/42VPchZBaT3+JA6LeiLSW/v379f+fOTIEZ2RmxqNBhEREeXGulWlQYMGYf78+Vi1ahX27t2LOnXq6IxoTEhIQMuWLYXlKatly5baNiC5ffLJJ5g/fz5cXV2RkZGB0aNHw8PDA6GhoSgoKFBkuwdQcsiQCL169UJKSgr+53/+R3tYmIeHB6ZNmyZ8OhBQ0jP+ssKwadOmmDBhAhYvXlyl32IotSVo2LBhckfQcejQIUX2+FPVY/sNEemt0gKidLZ4WbVr10aLFi0QFBSkM42mKt2/fx8eHh44efIkzMzMEBISotPn37dvX7z55puyTS+5e/duhV/Hix6zaWlpiZiYGLRs2RKrVq1CZGQkjhw5glOnTmHMmDG4efOm0DyVVXaTcU2yY8cO+Pr6YsKECejSpQsA4Pz58wgJCcHChQtx7949rF69Gj4+PrKcC0G61Go1Dh48CGdnZ7mjkGB8Uk9Eequ0OFWr1fjtt99kH0fYsGFDHD9+HA8fPoSZmRkMDQ11roeGhsoygeLixYsYP348rl69Wu7Dj0qlEn7QkiRJ2j+7X3/9Vfuhq1mzZsJnjCtFQkICXFxcYGBggISEhJe+V/SHsJCQEAQFBWHUqFHatXfffReurq7YtGkTIiIi0Lx5c3z55ZfCinqltASVevDgAcLCwpCeng4fHx/Ur18fMTExsLGxQZMmTYRmCQgIwKJFixTT40/i8Ek9EVE11759e7Rs2RLz5s2DjY1NuVYKkeP/AKBPnz5o1qwZ3N3dMWnSJFy5cgWvvfYaoqOjMX78eFy/fl1onsqqyif1BgYGyM7OhrW1tc7Jts+T40OYqakpEhIS4OjoqLOempqK9u3bo6CgANeuXUPbtm1RUFAgJFPp79GLiGoJAko+kLm7u8PS0hLXr19HcnIyHBwcsHDhQmRmZmLHjh1Vev/ndezYEenp6ZAkSRE9/iQOn9QTUbUQERHxwmkP27ZtkymVMmRkZODHH38sd+KuXNauXYsPP/wQe/fuha+vrzZXWFiY9vAwJarKDYfXrl1Do0aNtD8rSbNmzbSHYZW1detW7aSnnJwcWFlZCcu0ffv2SrUEGRsbV/m3B97e3pgwYQICAwO1Pf9AyR6bsWPHVum9K6K0Hn8Sh0/qiUjvLVmyBEuXLkXnzp0rnPbw008/yZRMGYYNG4Zx48ZhxIgRckd5qSdPnsDQ0FCWCUGVIaKn/unTp/jkk0/g5+cHtVpdZfd5Ffv378fIkSPRunVrvP766wBKTgBOSkpCWFgYhgwZgv/3//4fUlNTsWbNGiGZ+vbti08++USnJQgAdu/erW0J+vbbb/Hll19qNxtXlbJ7RMr+Hblx4wacnJzw5MmTKr0/USkW9USk92xtbREYGIhx48bJHUWR7t+/j/Hjx6NLly5wcXEpVzQ/f3JpVTl//jw6depUbq9BqcLCQuzbt69coaYUN2/ehJ2d3Qvz/10sLS0RFxenmKIeKPn2YNOmTUhJSQEAODk54ZNPPhE6XaosJbUEWVtb48iRI+jYsaNOUX/06FFMnDhRlo3fSurxJ3FY1BOR3mvQoAHOnz8v27hIpTtw4ADGjRtX4eZCkT3ahoaGyMrKgrW1NQDAwsJCZ+77nTt3YGdnJySPh4dHpd+7Z8+eKkxS3vjx49GhQwfMmjVL6H31SatWreDh4VGuJWj+/Pn46aefkJycjAsXLuC9996r8rMqJk+ejJycHOzevRv169dHQkICDA0NMWzYMPTs2VP4+E2l9fiTOOypJyK9N3nyZOzatQt+fn5yR1Gkzz//HB999BH8/PxgY2MjW47nnyFV9ExJ1HOmsmcaKI2joyOWLl2KU6dOoVOnTqhbt67O9enTpwvPdOLECWzatAkZGRkIDQ1FkyZN8O2330KtVqN79+7C86xevRojR47EoUOHKmwJAoDffvsNo0ePrvIsQUFBeP/992FtbY0///wTvXr1QnZ2tmzja5XW40/i8Ek9Eem9GTNmYMeOHWjXrh3atWtXrr1EVJ+vUpmbmyMuLk72bzLKTngpzVW2R13kk3ole1nbjUqlQkZGhsA0wI8//ohx48bhww8/xLfffosrV67AwcEBGzZswMGDB2U55RZQXkvQqVOnEB8fj7y8PLi5ucHd3V2WHOzxr7n4pJ6I9F5CQgI6dOgAAEhMTNS5xiPSS1pNoqKiZC/qqXKUNv1m2bJl2LhxIzw9PfHvf/9bu96tWzcsW7ZMtlxqtbpc+41If/75JyIiIrTnLPz8888oLCwEABw8eBDh4eFYunQpTExMhOYyNjausNUuJSVFO2GJqicW9USk96KiouSOoGitWrXCggULcPLkSbi6upb7JkNkO8eVK1eQnZ0NoKTVJikpCXl5eQAg68FTYWFh2L17NzIzM1FUVKRzTa653kVFRbh27RpatmyJWrXk+891cnIyevbsWW7d0tISDx48EB/of8ndEhQSEoJffvlFW9Rv2LABbdu2hampKQAgKSkJtra2wvdGDB06FEuXLsXu3bsBlDzYyMzMxLx58xQ/AYv+SxIRUTWRmpoqHT58WCooKJAkSZKKi4tlTqQMLVq0eOEvtVotLIdKpZIMDAwklUpV7lfpuoGBgbA8pdatWyeZmZlJn332mWRkZCR98sknkru7u2RpaSl98cUXwvPk5+dLEydOlAwNDSVDQ0MpPT1dkiRJ+uyzz6QVK1YIz6NWq6WjR49KkiRJZmZm2jwhISGSs7Oz8DySJElhYWGSqampNHnyZMnY2Fib6V//+pc0cOBAIRm6d+8u7d+/X/u67O+NJEnSt99+K7355ptCspT14MEDyd3dXapXr55kaGgoNWvWTKpdu7bUo0cPKS8vT3geEodP6olI7+Xk5GDUqFGIioqCSqVCamoqHBwcMGnSJFhZWSEoKEjuiLJSSjuHUnI87+uvv8bmzZvxwQcfYPv27Zg7dy4cHBywaNEi5ObmCs+zYMECxMfH49ixYxgwYIB23d3dHf7+/pg/f77QPB9//DFmzJiBbdu2QaVS4ffff8eZM2cwe/ZsLFq0SGiWUkpoCUpLS4Orq6v2tYmJic7ptV26dMGnn34qJEtZlpaWOHr0qGJ6/EkcFvVEpPdmzZqF2rVrIzMzE87Oztr10aNHw9vbu8YX9Uphb28vd4QKZWZmak+yNTU1xePHjwEA48aNw5tvvokNGzYIzbN371788MMPePPNN3X2hLRt2xbp6elCswAlYyKLi4vRt29fFBQUoGfPnjA2NoaPjw8mT54sPA+gjJagBw8eaHvoAeDevXs614uLi3WuVzWl9viTOCzqiUjvhYeH48iRI2jatKnOuqOjI27cuCFTKvl5e3tX6n1yTAcq7YdOT09HWFiYrCMSGzdujNzcXNjb26N58+Y4e/Ys2rdvj2vXrgkbsVnWvXv3tBOCysrPz5dl47dKpYKvry98fHyQlpaGvLw8tGnTBps2bYJardbukRCpcePGSEtLKzfp5uTJk1V64m9ZTZs2RWJiIpycnCq8npCQUO7/k6qSUnv8SRyDv34LEZGy5efno06dOuXWc3NzYWxsLEMiZYiNjf3LX3FxccJz/fjjj3jnnXdgamqK2NhY7dPEhw8fYvny5cLz9OnTB/v37wcAeHl5YdasWejXrx9Gjx6N4cOHC8/TuXNn/PLLL9rXpYX8li1b0LVrV2E5CgsLsWDBAnTu3BndunXDwYMH0aZNG1y+fBlOTk5Yt26dbAViaUvQuXPntC1BO3fuxOzZs/GPf/xDSIZBgwZh0aJFFY6I/PPPP7FkyRIMHjxYSBYA2LlzJ6ZMmaKztmvXLkRFRSEqKgpfffWVdvMsVVNyN/UTEf23Bg4cKC1cuFCSpJLNahkZGZJGo5FGjhwpjRgxQuZ09LwOHTpIISEhkiTpbi6MiYmRbGxshOfRaDTS06dPta+///576fPPP5fWr18vFRYWCs9z4sQJyczMTJo6dapkYmIizZgxQ+rXr59Ut25d6cKFC8JyzJ07V7K0tJRGjBgh2draSrVq1ZI+/vhjydXVVfr++++lZ8+eCcvyvOLiYmnZsmVS3bp1tZutTUxMJD8/P+1G+aqWnZ0tNW7cWGrevLkUGBgo7d27V9q7d6+0atUqqVmzZpKtra2UnZ0tJIskSVLjxo2la9euaV83bNhQ53VycrJkYWEhLA+Jx6KeiPTepUuXJGtra2nAgAGSkZGR9P7770vOzs6SjY2NlJaWJnc8RSkuLpZ9KpCpqam22Chb1Kenp0vGxsbC89y4caPC35Pi4mLpxo0bwvNIkiSlpaVJkydPll5//XXJ2dlZ+vDDD6WEhAShGdRqtbRv3z5Jkkr+jalUKsnLy0v2vz9lFRYWSpcvX5bOnTsnPX78WFq9erXQD4YZGRnSO++8ozPVycDAQHrnnXd0JuGIYGJiIiUlJb3w+tWrV2X590XisKeeiPSei4sLUlJSsGHDBpibmyMvLw8eHh749NNPYWtrK3c8RdixYwe++uorpKamAiiZXe/j44Nx48YJz6KEfuiy1Go1srKyyvWx5+bmQq1Wy3LCbcuWLfHNN98Iv29Zt27dQqdOnQCU/BszNjbGrFmzZD3QrbCwEP7+/jh69Kh2s+6wYcMQHByM4cOHw9DQUGhLkFqtxuHDh5Gbm4u0tDQAwGuvvYb69esLy1BKaT3+JB6LeiKqFiwtLeHr6yt3DEVas2YN/Pz88Nlnn6Fbt24ASgroqVOn4v79+8L7ol80InHOnDnw8/MTmgUoOQSrokI1Ly9PtkkhGo0GP/30E65evQoAaNOmDd577z2hh1BpNBoYGRlpX9eqVQtmZmbC7l+RRYsWYdOmTXB3d8fp06cxcuRIeHl54ezZswgKCsLIkSNhaGgoPFf9+vXRpUsX4fctq7THf/DgweX+3srR40/iqSRJhq39RER/sydPniAhIQF3795FcXGxzrWhQ4fKlEoZ1Go1lixZAk9PT531kJAQ+Pv7C58fL0kSli9fjhUrVqCgoABAydH2c+bMQUBAgLAcpdOB1q1bh48//lhns7VGo8G5c+dgaGiIU6dOCcsEAJcvX8bQoUORnZ2tfeqakpKCRo0a4cCBA3BxcRGSw8DAAAMHDtRuNj9w4AD69OmDunXr6rxvz549QvIAgIODA9auXYuhQ4ciMTER7dq1w4QJE7B161ZZv0FQgjt37qBDhw4wMjLCZ599hlatWgEoGf+5YcMGPHv2DLGxsbCxsZE5KVUVFvVEpPcOHz4MT09P3L9/v9w1lUolS/uEkpiYmCAxMRGvvfaaznpqaipcXV0rnN4hQlFRkc6IRNFPgXv37g0AiI6ORteuXXWeShsZGaFFixaYM2cOHB0dhebq2rUrGjVqhJCQEFhZWQEA/vjjD0yYMAH37t3D6dOnheTw8vKq1PuCg4OrOMn/MTIywrVr19CkSRMAJecKnD9/XucQqJrs2rVr+Mc//oGjR49qx7GqVCr069cPX3/9tSztbSQOi3oi0nuOjo7o378/Fi1axKdQFXBxccHYsWPxxRdf6KwvW7YMP/zwAy5duiRTMmXw8vLCunXrYGFhIXcUACWF6oULF9C2bVud9cTERLz++uv4888/ZUomP0NDQ2RnZ6NRo0YAAHNzcyQkJECtVsucTFmU0ONP4rGnnoj03p07d+Dt7c2C/gWWLFmC0aNH4/jx49qe+lOnTiEiIkKWudW9e/d+aatEZGSkwDRinzRXRqtWrXDnzp1yRf3du3fLfdtS00iShAkTJmhbgp48eYKpU6fK2hKkREro8SfxWNQTkd57//33cezYMbRs2VLuKIo0YsQInDt3Dv/85z+xd+9eAICzszPOnz+Pjh07Cs/ToUMHnddPnz5FXFwcEhMTMX78eOF58vPzsXLlSkRERFS4JyMjI0NonhUrVmD69Onw9/fHm2++CQA4e/Ysli5dilWrVuHRo0fa9yrl2wVRnv/78dFHH8mUhEh52H5DRHqvoKAAI0eORKNGjeDq6oratWvrXJ8+fbpMyehV+Pv7Iy8vD6tXrxZ63w8++ADR0dEYN24cbG1ty32LMGPGDKF5DAz+77D30ixl+6NLX3O/CBGVxaKeiPTe1q1bMXXqVJiYmKBBgwY6RZlKpRL+pFVpDA0NK5zDnpOTA2tra8UUhmlpaejSpQtyc3OF3rdevXr45ZdftK1JcouOjq70e3v16lWFSYhIn7D9hoj0nq+vL5YsWYL58+frPOWkEi96dlNYWKgz8UVuZ86ckWUuvJWVlaI2ErJQJ6L/BIt6ItJ7RUVFGD16NAv656xfvx5AybcVW7Zs0RkZqdFocPz4cbRu3Vp4Lg8PD53XkiQhKysLFy5ckOXwqYCAACxatAghISE6s+rlcvz48Zde79mzp6AkRKRP2H5DRHpv1qxZaNSoUbmRjTVd6Zi/GzduoGnTpjonbZbOYV+6dCneeOMNobmen39uYGCARo0aoU+fPujfv7/QLADQsWNHpKenQ5IktGjRotyejJiYGKF5KvpwWralTCntUkSkLHxST0R6T6PRIDAwEEeOHEG7du3KFWVr1qyRKZm8Sk+K7d27N/bs2aM9yEhuShshOWzYMLkj6Pjjjz90Xj99+hSxsbHw8/PDl19+KVMqIlI6PqknIr1XejJoRVQqlfC550RVITo6Gt7e3rh48aLcUYhIgVjUExFVQ97e3ggICEDdunXh7e390veK/ibDysrqpYdPlSVqEs6DBw8QFhaG9PR0+Pj4oH79+oiJiYGNjQ2aNGkiJMNfSUpKQufOnZGXlyd3FCJSILbfEBFVQ7GxsXj69Kn25xepbHH9d/Lz88OyZcvwzjvvoGvXrgBKJt8cOXIEfn5+wifRJCQkwN3dHZaWlrh+/To+/vhj1K9fH3v27EFmZiZ27NghPE9ZpRuJV65cWe7gLiKiUnxST0R6ycPDA9u3b4eFhUW5aSrPq+lHxivNiBEj0Lt3b3z22Wc66xs2bMCvv/6qPfVWFHd3d7i5uSEwMBDm5uaIj4+Hg4MDTp8+jbFjx+L69etC8xgYGEClUpUbRfrmm29i27ZtskwsIiLl45N6ItJLlpaW2qfMlpaWMqehV3HkyBGsWrWq3PqAAQMwf/584Xl+++03bNq0qdx6kyZNkJ2dLTxP6QbnUqXTgeSY4U9E+oNFPRHppbITVJQ2TUUJ/urbi7JEf5PRoEED7Nu3D7Nnz9ZZ37dvHxo0aCA0CwAYGxvj0aNH5dZTUlLQqFEj4Xns7e2F35OI9B+LeiKiaqjstxeSJOGnn36CpaUlOnfuDAC4ePEiHjx48ErF/99lyZIlmDx5Mo4dO6adkX/u3DkcPnwY33zzjfA8Q4cOxdKlS7F7924AJfsMMjMzMW/ePIwYMUJYjjNnziAnJwdDhgzRru3YsQOLFy9Gfn4+hg0bhn/9618wNjYWlomI9Ad76olIL3Xs2LHSmzxFHx6kNPPmzUNubi42btyoPYBKo9Fg2rRpsLCwwFdffSU807lz57B+/XpcvXoVAODs7Izp06cLPwgLAB4+fIj3338fFy5cwOPHj2FnZ4fs7Gx07doVBw8eRN26dYXkGDhwIN5++23MmzcPAHDp0iW4ublhwoQJcHZ2xldffYVPPvkE/v7+QvIQkX5hUU9EemnJkiXan588eYKvv/4abdq00U5TOXv2LC5fvoxp06ZhxYoVcsVUhEaNGuHkyZNwcnLSWU9OTsZbb72FnJwcmZIpy8mTJ5GQkIC8vDy4ubnB3d1d6P1tbW1x4MAB7bcpvr6+iI6OxsmTJwEAoaGhWLx4Ma5cuSI0FxHpB7bfEJFeWrx4sfbnyZMnY/r06QgICCj3nps3b4qOpjjPnj1DUlJSuaI+KSkJxcXFsmRKT09HcHAwMjIysHbtWlhbW+PQoUNo3rw52rZtK0um7t27o3v37rLcGyg5SdbGxkb7Ojo6GgMHDtS+fv311/n3mYheiEU9Eem90NBQXLhwodz6Rx99hM6dO2Pbtm0ypFIOLy8vTJo0Cenp6ejSpQuAkvaXlStXwsvLS3ie0mK1W7duOH78OJYtWwZra2vEx8dj69atCAsLE57pt99+Q1RUFO7evVvug46ow7lsbGxw7do1NGvWDEVFRYiJidH5Rurx48eoXbu2kCxEpH9Y1BOR3jM1NcWpU6fg6Oios37q1CmOAQSwevVqNG7cGEFBQcjKygJQ0urh4+NTbgKNCPPnz8eyZcvg7e0Nc3Nz7XqfPn2wYcMG4XmWL1+OhQsXwsnJCTY2Njp7NUQezjVo0CDMnz8fq1atwt69e1GnTh306NFDez0hIQEtW7YUloeI9AuLeiLSezNnzsQ//vEPxMTE6DyJ3rZtG/z8/GROJz8DAwPMnTsXc+fO1Y5utLCwkC3PpUuXsGvXrnLr1tbWuH//vvA869atw7Zt2zBhwgTh9y4rICAAHh4e6NWrF8zMzBASEgIjIyPt9W3btqF///4yJiQiJWNRT0R6b/78+XBwcMC6devw3XffASiZphIcHIxRo0bJnE4Znj17hmPHjiE9PR1jx44FAPz++++wsLCAmZmZ0Cz16tVDVlYW1Gq1znpsbCyaNGkiNAtQ8qGnW7duwu/7vIYNG+L48eN4+PAhzMzMtJOKSoWGhur8Wd26dQt2dnYwMDAQHZWIFIjTb4iIqrkbN25gwIAByMzMRGFhIVJSUuDg4IAZM2agsLAQGzduFJpnzpw5OHfuHEJDQ9GqVSvExMTgzp078PT0hKenp84maBECAwPx+++/Y+3atULv+9+ysLBAXFwcHBwc5I5CRArAop6Iqo2ioqIKNzo2b95cpkTKMGzYMJibm2Pr1q1o0KAB4uPj4eDggGPHjuHjjz9Gamqq0DxFRUX49NNPsX37dmg0GtSqVQsajQZjx47F9u3byz2hrmrFxcUYPHgwUlJS0KZNm3KbUUWfuFtZ5ubm2j9LIiK23xCR3ktNTcXEiRNx+vRpnXVJkqBSqaDRaGRKpgwnTpzA6dOndfqzAaBFixa4ffu28DxGRkb45ptv4Ofnh8TEROTl5aFjx47lNjqLMn36dERFRaF3795o0KCB0M2xRER/Fxb1RKT3JkyYgFq1auHnn3+Gra0ti7LnFBcXV/jB5tatWzrTZ0Rr3ry5Ir5FCQkJwY8//ojBgwfLHYWI6D/Gop6I9F5cXBwuXryI1q1byx1Fkfr374+1a9di8+bNAErGNObl5WHx4sUYNGiQ8DwajQbbt29HREREhe1SkZGRQvPUr1+foyKJSO+xqCcivdemTRtZRiHqi9WrV2PAgAFo06YNnjx5grFjxyI1NRUNGzbE999/LzzPjBkzsH37dgwePBguLi6yf7Pi7++PxYsXIzg4GHXq1JE1y6uQ+/eNiJSFG2WJSO9FRkZi4cKFWL58OVxdXcttdJRzJrtSPHv2DD/88APi4+ORl5cHNzc3fPjhhzA1NRWepWHDhtixY4cs3xJUpGPHjkhPT4ckSWjRokW5vz8xMTEyJXs5bpQlorJY1BOR3iud0/38k0tulAWePn2K1q1b4+eff4azs7PccQAAdnZ2OHbsGFq1aiV3FADAkiVLXnpd9IjNUmlpaUhPT0fPnj1hamqq/ftc6ubNm7CzsxM+LYiIlIlFPRHpvejo6Jde79Wrl6AkytSkSRP8+uuviinqg4KCkJGRgQ0bNrCFpAI5OTkYPXo0IiMjoVKpkJqaCgcHB0ycOBFWVlYICgqSOyIRKRCLeiKiam758uVISUnBli1bUKuWPFupPDw8dF5HRkaifv36aNu2rd7MhRfF09MTd+/exZYtW+Ds7KxtsTly5Ai8vb1x+fJluSMSkQJxoywRVQsnTpzApk2bkJGRgdDQUDRp0gTffvst1Go1unfvLnc8Wf3222+IiIhAeHg4XF1dUbduXZ3rIopoS0tLndfDhw+v8ntWlkajwT//+U/s3r0bmZmZKCoq0rmem5srNE94eDiOHDmCpk2b6qw7Ojrixo0bQrMQkf5gUU9Eeu/HH3/EuHHj8OGHHyImJgaFhYUAgIcPH2L58uU4ePCgzAnlVa9ePYwYMULWDMHBwbLe/2WWLFmCLVu2YPbs2Vi4cCF8fX1x/fp17N27F4sWLRKeJz8/v8IpPLm5uTA2Nhaeh4j0A9tviEjvdezYEbNmzYKnp6fORJDY2FgMHDgQ2dnZckckAE+ePEF4eDh69+5d7tCrR48e4dixY3jnnXeEF64tW7bE+vXrMXjwYJibmyMuLk67dvbsWezatUtonkGDBqFTp04ICAiAubk5EhISYG9vjzFjxqC4uBhhYWFC8xCRfjCQOwAR0X8rOTkZPXv2LLduaWmJBw8eiA+kEH/++Sf279+Px48fl7v26NEj7N+/X/uthgibNm3CunXrKjzF1sLCAuvXr8c333wjLE+p7OxsuLq6AgDMzMzw8OFDAMCQIUPwyy+/CM8TGBiIzZs3Y+DAgSgqKsLcuXPh4uKC48ePY9WqVcLzEJF+YFFPRHqvcePGSEtLK7d+8uTJGj3De/PmzYoqonfu3ImZM2e+8PrMmTOxY8cOYXlKNW3aFFlZWQBKntqHh4cDKNmLIEe7i4uLC1JSUtC9e3e89957yM/Ph4eHB2JjY3nyLRG9EHvqiUjvffzxx5gxYwa2bdsGlUqF33//HWfOnMHs2bNl6YlWip07d8LPz++F12fOnImlS5fis88+E5InNTUV7du3f+H1du3aITU1VUiWsoYPH46IiAi88cYb+Pzzz/HRRx9h69atyMzMxKxZs4TnAUq+ZfL19ZXl3kSkn1jUE5Hemz9/PoqLi9G3b18UFBSgZ8+eMDY2ho+PDyZPnix3PNkorYh+9uwZ7t27h+bNm1d4/d69e3j27JmwPKVWrlyp/Xn06NFo3rw5zpw5A0dHR7z77rtCMiQkJFT6ve3atavCJESkr1jUE5HeU6lU8PX1hY+PD9LS0pCXl4c2bdpg06ZNUKvVNXajrNKK6LZt2+LXX39Fp06dKrweHh6Otm3bCsvzIl27dkXXrl2F3rNDhw5QqVTlTo0tnWVRdq0mn5BMRC/Gop6I9FZhYSH8/f1x9OhR7ZP5YcOGITg4GMOHD4ehoaFs7RNKoLQieuLEifD29kbbtm0xZMgQnWsHDhzAl19+iTVr1gjJsn///kq/d+jQoVWYpMS1a9e0P8fGxmLOnDnw8fHRfrg4c+YMgoKCEBgYWOVZiEg/caQlEemtefPmYdOmTXB3d8fp06dx7949eHl54ezZs/jiiy8wcuRIGBoayh1TNps3b4a3tzf+/e9/V1hEf/DBB1izZg2mTJkiLNNHH32EXbt2oXXr1nBycgIAJCUlISUlBaNGjcL3338vJIeBge6ciNKn5M+vAeKfjHfp0gX+/v4YNGiQzvrBgwfh5+eHixcvCs1DRPqB02+ISG+FhoZix44dCAsLQ3h4ODQaDZ49e4b4+HiMGTOmRhf0ADBlyhQMGzYMQ4cORZs2bTB8+HAMHz4czs7OGDZsGN59912hBT0AfPfdd/j3v/+NVq1aISUlBcnJyXBycsL3338vrKAHgOLiYu2v8PBwdOjQAYcOHcKDBw/w4MEDHDp0CG5ubjh8+LCwTKUuXboEtVpdbl2tVuPKlSvC8xCRfuCTeiLSW0ZGRrh27RqaNGkCADA1NcX58+e1M8epxO7du7Fr1y6kpqZCkiS0atUKY8eOxahRo+SOpgguLi7YuHEjunfvrrN+4sQJTJkyBVevXhWax83NDS4uLtiyZQuMjIwAAEVFRZg8eTISExMRExMjNA8R6Qf21BOR3tJoNNqiBwBq1aoFMzMzGRMp06hRoxRXwGs0Guzdu1dbMLdt2xZDhw6V5duV9PR01KtXr9y6paUlrl+/LjzPxo0b8e6776Jp06baSTcJCQlQqVQ4cOCA8DxEpB/4pJ6I9JaBgQEGDhyoPSDowIED6NOnD+rWravzvj179sgRT1HS09MRHByMjIwMrF27FtbW1jh06BCaN28ufOJMWloaBg0ahNu3b2v76pOTk9GsWTP88ssvwg9Y6tmzJ0xMTPDtt9/CxsYGAHDnzh14enriyZMniI6OFpoHAPLz87Fz504kJSUBAJydnTF27Nhyf7eJiEqxqCciveXl5VWp9wUHB1dxEmWLjo7GwIED0a1bNxw/fhxXr16Fg4MDVq5ciQsXLiAsLExonkGDBkGSJOzcuRP169cHAOTk5OCjjz6CgYEBfvnlF6F50tLSMHz4cKSkpKBZs2YAgJs3b8LR0RF79+7Fa6+9JjQPEdF/gkU9EVE117VrV4wcORLe3t4wNzdHfHw8HBwccP78eXh4eODWrVtC89StWxdnz54tt/chPj4e3bp1Q15entA8QMk8+KNHj+o8GXd3d9eZDy/alStXkJmZiaKiIp11ESM2iUj/sKeeiKiau3TpEnbt2lVu3draGvfv3xeex9jYGI8fPy63npeXp7NHQiSVSoX+/fujf//+sty/rIyMDAwfPhyXLl3SGbUp14hNItIPLOqJiKq5evXqISsrq9yYxNjYWO3kIJGGDBmCKVOmYOvWrejSpQsA4Ny5c5g6dapsT6EjIiIQERGBu3fvori4WOfatm3bhGaZMWMG1Go1IiIioFarcf78eeTk5GD27NlYvXq10CxEpD84p56IqJobM2YM5s2bh+zsbKhUKhQXF+PUqVOYM2cOPD09hedZv349WrZsia5du8LExAQmJibo1q0bXnvtNaxbt054niVLlqB///6IiIjA/fv38ccff+j8Eu3MmTNYunQpGjZsCAMDAxgYGKB79+5YsWIFpk+fLjwPEekH9tQTEVVzRUVF+PTTT7F9+3ZoNBrUqlULGo0GY8eOxfbt22U7pCs1NVWnh12uDam2trYIDAzEuHHjZLn/86ysrBATEwO1Wo2WLVtiy5Yt6N27N9LT0+Hq6oqCggK5IxKRArH9hoiomjMyMsI333wDPz8/JCYmIi8vDx07doSjo6OsuRwdHWXPAJR86HnrrbfkjqHl4uKC+Ph4qNVqvPHGGwgMDISRkRE2b94MBwcHueMRkULxST0REVU5b2/vSr93zZo1VZikvHnz5sHMzAx+fn5C7/siR44cQX5+Pjw8PJCWloYhQ4YgJSUFDRo0wA8//IA+ffrIHZGIFIhP6omIqiGlFdGxsbE6r2NiYvDs2TPt4VMpKSkwNDREp06dqjzL8548eYLNmzfj119/Rbt27VC7dm2d66I/ZLzzzjvan1977TUkJSUhNzcXVlZWso7YJCJlY1FPRFQNKa2IjoqK0v68Zs0amJubIyQkBFZWVgCAP/74A15eXujRo4eQPGUlJCSgQ4cOAIDExESda0opoksP6SIiehG23xARVXNr1qzBsWPHXlhEz549W2ieJk2aIDw8HG3bttVZT0xMRP/+/fH7778LzaMEHh4elX7vnj17qjAJEekrjrQkIqrmgoKCsGLFCm1BD5RMWFm2bBmCgoKE53n06BHu3btXbv3evXsVHkpVE1haWmp/WVhYICIiAhcuXNBev3jxIiIiImBpaSljSiJSMrbfEBFVc0oroocPHw4vLy8EBQXpHD7l4+PzSk+s/y69e/d+aZtNZGRklWcIDg7W/jxv3jyMGjUKGzdu1I4b1Wg0mDZtGiwsLKo8CxHpJ7bfEBFVc56enjhx4kSFRXSPHj0QEhIiNE9BQQHmzJmDbdu24enTpwCAWrVqYdKkSfjqq69Qt25doXlmzZql8/rp06eIi4tDYmIixo8fL/xArEaNGuHkyZPa/Q+lkpOT8dZbbyEnJ0doHiLSDyzqiYiqOaUV0aXy8/ORnp4OAGjZsqVsOV7E398feXl5WL16tdD7WllZYfv27Xjvvfd01vft24cJEybIcsotESkfi3oiohpCaUV0Wloa0tPT0bNnT5iamkKSJMVMmwFK8nXp0gW5ublC7+vt7Y0dO3bgiy++0PlmZeXKlRg3bpzwEZtEpB/YU09EVENkZWUhKytL9iI6JycHo0aNQlRUFFQqFVJTU+Hg4IBJkybByspKls27FTlz5gxMTEyE33f16tVo3LgxgoKCkJWVBQCwtbWFj4+P8ElFRKQ/+KSeiKiae1ERPXHiRFmKaE9PT9y9exdbtmyBs7Mz4uPj4eDggCNHjsDb2xuXL18Wmuf5zbmSJCErKwsXLlyAn58fFi9eLDRPWY8ePQIAbpAlor/EkZZERNXcrFmzULt2bWRmZqJOnTra9dGjR+Pw4cPC84SHh2PVqlVo2rSpzrqjoyNu3LghPE/ZcZKWlpaoX78+3n77bRw8eFDWgh4oKeZZ0BNRZbD9hoiomgsPD8eRI0cUU0Tn5+frfLgolZubC2NjY2E5MjIyoFardcZJysXNzQ0RERGwsrJCx44dX9oWFRMTIzAZEekLFvVERNWcUoroUj169MCOHTsQEBAAAFCpVCguLkZgYCB69+4tLIejoyOysrJgbW0NoOSbi/Xr18PGxkZYhlLvvfee9s9i2LBhwu9PRPqPPfVERNXcoEGD0KlTJwQEBMDc3BwJCQmwt7fHmDFjUFxcjLCwMKF5EhMT0bdvX7i5uSEyMhJDhw7F5cuXkZubi1OnTqFly5ZCchgYGCA7O1tb1Jubm2v7++Wi0Whw6tQptGvXDvXq1ZMtBxHpHz6pJyKq5gIDA9G3b19cuHABRUVFmDt3rk4RLZqLiwtSUlKwYcMGmJubIy8vDx4eHvj0009ha2srPI+SGBoaon///rh69SqLeiJ6JSzqiYiqOaUV0ZmZmWjWrBl8fX0rvNa8eXMhOVQqVbnedSXMyXdxcdH2+xMRVRbbb4iIqrnSIrqiglVkEV3K0NBQp5e9VE5ODqytraHRaITkMDAwwMCBA7W97AcOHECfPn3KHcq1Z88eIXlKHT58GAsWLEBAQAA6depULg+n4RBRRVjUExFVc0opoksZGBjgzp07aNSokc76jRs30KZNG+Tn5wvJ4eXlVan3iZ6OY2Dwf9Omy34QKz0sTPSfFxHpB7bfEBFVcy86OTYvL0/oiane3t4ASgpVPz8/nYk8Go0G586dQ4cOHYTledVi/datW7Czs9MpuqtCVFRUlf7vE1H1xKKeiKiaUloRHRsbC6DkQ8alS5dgZGSkvWZkZIT27dtjzpw5wvK8qjZt2iAuLq7Kp+P06tWrSv/3iah6YlFPRFRNKa2ILn0C7eXlhXXr1uldb7jobtWCggJkZmaiqKhIZ71du3ZCcxCRfmBPPRFRNaevRbTSiJpjf+/ePXh5eeHQoUMVXmdPPRFVhE/qiYiqOdEbPSvi4eGB7du3w8LCAh4eHi99r+hpM0ozc+ZMPHjwAOfOncPbb7+Nn376CXfu3MGyZcsQFBQkdzwiUigW9URE1ZDSimhLS0vtZl1LS8sqv58+i4yMxL59+9C5c2cYGBjA3t4e/fr1g4WFBVasWIHBgwfLHZGIFIhFPRFRNaS0IrrstwVK+ObgPyHqYKr8/Hzt+FErKyvcu3cPrVq1gqurK2JiYoRkICL9w6KeiKgaqg5FtNKI2oLm5OSE5ORktGjRAu3bt8emTZvQokULbNy4UZYTgIlIP3CjLBERCZWTk4NFixYhKioKd+/eRXFxsc713NxcWXKlpaUhPT0dPXv2hKmpabn5/jdv3oSdnR0MDQ2rNMd3332HZ8+eYcKECbh48SIGDBiA3NxcGBkZYfv27Rg9enSV3p+I9BOLeiKiak5pRfSgQYOQlpaGSZMmwcbGplxby/jx44XmycnJwejRoxEZGQmVSoXU1FQ4ODhg4sSJsLKykn1zakFBAZKSktC8eXM0bNhQ1ixEpFxsvyEiqubGjRv30iJatBMnTuDkyZNo3769rDlKzZo1C7Vq1UJmZiacnZ2166NHj4a3t7fsRX2dOnXg5uYmawYiUj4W9URE1ZzSiujWrVvjzz//lDuGVnh4OI4cOYKmTZvqrDs6OuLGjRtCMpSe/lsZa9asqcIkRKSvWNQTEVVzSiuiv/76a8yfPx+LFi2Ci4sLateurXNd9CFZ+fn5qFOnTrn13NxcGBsbC8lQevrvX5H7WxYiUi4W9URE1ZzSiuh69erh0aNH6NOnj8566cZU0Sem9ujRAzt27EBAQACAksK5uLgYgYGB6N27t5AMUVFRQu5DRNUXi3oiompOaUX0hx9+iNq1a2PXrl2K6PEPDAxE3759ceHCBRQVFWHu3Lm4fPkycnNzcerUKVmz3bp1CwDKtQYRET2P02+IiKq5Ll26oFatWpgxY0aFRXSvXr2E5qlTpw5iY2Ph5OQk9L4v8/DhQ2zYsAHx8fHIy8uDm5sbPv30U1nmwhcXF2PZsmUICgpCXl4eAMDc3ByzZ8+Gr68vDAwMhGciIuXjk3oiomouMTFRUUV0586dcfPmTcXkAUpO3fX19ZU7BgDA19cXW7duxcqVK9GtWzcAwMmTJ+Hv748nT57gyy+/lDkhESkRn9QTEVVzPXv2xKJFi+Du7i53FABAaGgo/P394ePjA1dX13I9/u3atavyDAkJCZV+r4g8ZdnZ2WHjxo0YOnSozvq+ffswbdo03L59W2geItIPLOqJiKo5JRTRZVXUPqJSqYT2+BsYGOjcs1TpfxLLronec2BiYoKEhAS0atVKZz05ORkdOnRQ1CQjIlIOFvVERNWcEorosv5q9ru9vb3QDLGxsZgzZw58fHzQtWtXAMCZM2cQFBSEwMBADBs2rMrzlPXGG2/gjTfewPr163XWP//8c/z22284e/as0DxEpB9Y1BMRVXNKKKKVrEuXLvD398egQYN01g8ePAg/Pz9cvHhRaJ7o6GgMHjwYzZs31/mQcfPmTRw8eBA9evQQmoeI9AOLeiIiqnL79+/HwIEDUbt2bezfv/+l732+l7yqmZqaIiYmBs7OzjrrV69ehZubmyztLrdv38bXX3+NpKQkAICzszOmTZsGOzs74VmISD+wqCciqoaUVkQbGBggOzsb1tbWLx3JKEc7kJubG1xcXLBlyxYYGRkBAIqKijB58mQkJiYiJiZGaB4iov8Ei3oiompIyUX0i9y8eRNLly7FN998I/S+58+fx7vvvgtJkrSbhhMSEqBSqXDgwAF06dJFaJ7g4GCYmZlh5MiROuuhoaEoKCjA+PHjheYhIv3Aop6IqIaSq4h+kfj4eLi5ucnyISM/Px87d+7UaXcZO3Ys6tatKzxLq1atsGnTJvTu3VtnPTo6GlOmTEFycrLwTESkfCzqiYhqKDmL6IooLY9cTExMkJSUhBYtWuisX79+Hc7OzhxpSUQV4omyREREAK5cuYLMzEwUFRXprIveuGttbY2EhIRyRX18fDwaNGggNAsR6Q8W9UREVKNlZGRg+PDhuHTpknZ+P/B/B1CJ/ubggw8+wPTp02Fubo6ePXsCKGm9mTFjBsaMGSM0CxHpDxb1REQkhIeHx0uvP3jwQEyQ58yYMQNqtRoRERFQq9U4f/48cnJyMHv2bKxevVp4noCAAFy/fh19+/ZFrVol/5kuLi6Gp6cnli9fLjwPEekH9tQTEVVTlSmio6OjhT2J9vLyqtT7goODqziJroYNGyIyMhLt2rWDpaUlzp8/DycnJ0RGRmL27NmIjY0VmqdUamoq4uLiYGpqCldX1xp/SBgRvRyf1BMRVVOWlpZ/ed3T01NQGvHFemVpNBqYm5sDKCnwf//9dzg5OcHe3l7WSTOOjo5wdHSERqPBpUuXYGFhASsrK9nyEJGysagnIqqmlFpEK42Liwvi4+OhVqvxxhtvIDAwEEZGRti8eTMcHByE55k5cyZcXV0xadIkaDQa9OrVC6dPn0adOnXw888/4+233xaeiYiU78UnkhAREdUACxcuRHFxMQBg6dKluHbtGnr06IGDBw9i/fr1wvOEhYWhffv2AIADBw4gIyMDSUlJmDVrFnx9fYXnISL9wJ56IiKi5+Tm5sLKyko7AUckExMTpKWloWnTppgyZQrq1KmDtWvX4tq1a2jfvj0ePXokPBMRKR+f1BMRET2nfv36shT0AGBjY4MrV65Ao9Hg8OHD6NevHwCgoKAAhoaGsmQiIuVjTz0REdU4fzUZqKw9e/ZUYZLyvLy8MGrUKNja2kKlUsHd3R0AcO7cObRu3VpoFiLSHyzqiYioxik7GUiSJPz000+wtLRE586dAQAXL17EgwcPXqn4/7v4+/vDxcUFN2/exMiRI2FsbAwAMDQ0xIIFC4TnISL9wJ56IiKq0ebNm4fc3Fxs3LhR296i0Wgwbdo0WFhY4KuvvhKSY9CgQfj++++1HzhWrlyJqVOnol69egCAnJwc9OjRA1euXBGSh4j0C4t6IiKq0Ro1aoSTJ0/CyclJZz05ORlvvfUWcnJyhOQwNDREVlYWrK2tAQAWFhaIi4vTjtW8c+cO7OzshB0WRkT6hRtliYioRnv27BmSkpLKrSclJWlHXYrw/DM2PnMjolfBnnoiIqrRvLy8MGnSJKSnp6NLly4ASjalrly5El5eXjKnIyKqHBb1RERUo61evRqNGzdGUFAQsrKyAAC2trbw8fHB7NmzheVQqVTlxmjKNVaTiPQPe+qJiIj+V+nBThYWFsLvbWBggIEDB2qn3Rw4cAB9+vRB3bp1AQCFhYU4fPgwe+qJqEIs6omIiBSgsq0+wcHBVZyEiPQRi3oiIqpx3NzcEBERASsrK3Ts2PGlbS4xMTECkxER/WfYU09ERDXOe++9p21zGTZsmLxhiIj+BnxST0RENZZGo8GpU6fQrl077SFPRET6iEU9ERHVaCYmJrh69SrUarXcUYiI/mM8fIqIiGo0FxcXZGRkyB2DiOi/wif1RERUox0+fBgLFixAQEAAOnXqpB0hWUqO8ZZERK+KRT0REdVoBgb/96V12Sk4kiRBpVJxLjwR6QVOvyEiohotKipK7ghERP81PqknIiIiItJzfFJPREQEoKCgAJmZmSgqKtJZb9eunUyJiIgqj0U9ERHVaPfu3YOXlxcOHTpU4XX21BORPuBISyIiqtFmzpyJBw8e4Ny5czA1NcXhw4cREhICR0dH7N+/X+54RESVwif1RERUo0VGRmLfvn3o3LkzDAwMYG9vj379+sHCwgIrVqzA4MGD5Y5IRPSX+KSeiIhqtPz8fFhbWwMArKyscO/ePQCAq6srYmJi5IxGRFRpLOqJiKhGc3JyQnJyMgCgffv22LRpE27fvo2NGzfC1tZW5nRERJXDkZZERFSjfffdd3j27BkmTJiAixcvYsCAAcjNzYWRkRG2b9+O0aNHyx2RiOgvsagnIiIqo6CgAElJSWjevDkaNmwodxwiokphUU9EREREpOc4/YaIiGocb2/vSr93zZo1VZiEiOjvwaKeiIhqnNjY2Eq9T6VSVXESIqK/B9tviIiIiIj0HEdaEhER/a9bt27h1q1bcscgInplLOqJiKhGKy4uxtKlS2FpaQl7e3vY29ujXr16CAgIQHFxsdzxiIgqhT31RERUo/n6+mLr1q1YuXIlunXrBgA4efIk/P398eTJE3z55ZcyJyQi+mvsqSciohrNzs4OGzduxNChQ3XW9+3bh2nTpuH27dsyJSMiqjy23xARUY2Wm5uL1q1bl1tv3bo1cnNzZUhERPTqWNQTEVGN1r59e2zYsKHc+oYNG9C+fXsZEhERvTq23xARUY0WHR2NwYMHo3nz5ujatSsA4MyZM7h58yYOHjyIHj16yJyQiOivsagnIqIa7/bt2/j666+RlJQEAHB2dsa0adNgZ2cnczIiosphUU9EREREpOfYU09ERDVacHAwQkNDy62HhoYiJCREhkRERK+ORT0REdVoK1asQMOGDcutW1tbY/ny5TIkIiJ6dSzqiYioRsvMzIRarS63bm9vj8zMTBkSERG9Ohb1RERUo1lbWyMhIaHcenx8PBo0aCBDIiKiV8einoiIarQPPvgA06dPR1RUFDQaDTQaDSIjIzFjxgyMGTNG7nhERJXC6TdERFSjFRUVYdy4cQgNDUWtWrUAAMXFxfD09MTGjRthZGQkc0Iior/Gop6IiAhAamoq4uLiYGpqCldXV9jb28sdiYio0ljUExERlaHRaHDp0iXY29vDyspK7jhERJXCnnoiIqrRZs6cia1btwIoKeh79eoFNzc3NGvWDMeOHZM3HBFRJbGoJyKiGi0sLAzt27cHABw4cAAZGRlISkrCrFmz4OvrK3M6IqLKYVFPREQ12v3799G4cWMAwMGDBzFq1Ci0atUKEydOxKVLl2ROR0RUOSzqiYioRrOxscGVK1eg0Whw+PBh9OvXDwBQUFAAQ0NDmdMREVVOLbkDEBERycnLywujRo2Cra0tVCoV3N3dAQDnzp1D69atZU5HRFQ5LOqJiKhG8/f3h4uLC27evImRI0fC2NgYAGBoaIgFCxbInI6IqHLYfkNERDXSoEGD8PDhQwDA+++/j8LCQpiZmWmvDxkyBPPnz5crHhHRK+GceiIiqpEMDQ2RlZUFa2trAICFhQXi4uLg4OAAALhz5w7s7Oyg0WjkjElEVCl8Uk9ERDXS88+0+IyLiPQZi3oiIiIiIj3Hop6IiGoklUoFlUpVbo2ISB9x+g0REdVIkiRhwoQJ2mk3T548wdSpU1G3bl0AQGFhoZzxiIheCTfKEhFRjeTl5VWp9wUHB1dxEiKi/x6LeiIiIiIiPceeeiIiIiIiPceinoiIiIhIz7GoJyIiIiLScyzqiYiIiIj0HIt6IiIiIiI9x6KeiIiIiEjPsagnIiIiItJz/x88TW1fOYuwCwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando modelo MRLS\n",
        "function = 'LimitedoChequeEspecial~Idade+RendimentoTotal+Salario+LimitedeCreditoImediato+Escolaridade_Fundamental_Medio+Escolaridade_Superior_Pos+Regiao_RJ+Regiao_SP+Genero_F+Genero_M'\n",
        "model = smf.ols(formula = function, data = df).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGhYsRNrGikt",
        "outputId": "75638c13-f31d-4015-8fe2-495929738f00"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                              \n",
            "==================================================================================\n",
            "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.886\n",
            "Model:                                OLS   Adj. R-squared:                  0.865\n",
            "Method:                     Least Squares   F-statistic:                     44.20\n",
            "Date:                    Wed, 29 May 2024   Prob (F-statistic):           7.44e-17\n",
            "Time:                            02:09:37   Log-Likelihood:                -434.21\n",
            "No. Observations:                      48   AIC:                             884.4\n",
            "Df Residuals:                          40   BIC:                             899.4\n",
            "Df Model:                               7                                         \n",
            "Covariance Type:                nonrobust                                         \n",
            "==================================================================================================\n",
            "                                     coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------------------\n",
            "Intercept                        398.6632    705.403      0.565      0.575   -1027.009    1824.335\n",
            "Idade                             -4.9864     22.665     -0.220      0.827     -50.793      40.820\n",
            "RendimentoTotal                    0.1354      0.101      1.336      0.189      -0.069       0.340\n",
            "Salario                            0.2799      0.142      1.977      0.055      -0.006       0.566\n",
            "LimitedeCreditoImediato            0.2439      0.414      0.589      0.559      -0.593       1.081\n",
            "Escolaridade_Fundamental_Medio  -223.4354    572.451     -0.390      0.698   -1380.402     933.531\n",
            "Escolaridade_Superior_Pos        622.0986    629.942      0.988      0.329    -651.062    1895.259\n",
            "Regiao_RJ                       -967.2953    445.962     -2.169      0.036   -1868.619     -65.972\n",
            "Regiao_SP                       1365.9585    598.088      2.284      0.028     157.177    2574.740\n",
            "Genero_F                       -1456.4227    533.381     -2.731      0.009   -2534.425    -378.420\n",
            "Genero_M                        1855.0859    718.221      2.583      0.014     403.507    3306.665\n",
            "==============================================================================\n",
            "Omnibus:                       15.742   Durbin-Watson:                   1.724\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               41.550\n",
            "Skew:                          -0.679   Prob(JB):                     9.50e-10\n",
            "Kurtosis:                       7.351   Cond. No.                     3.64e+20\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 1.82e-31. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Idade tem p-value = 0.827 (maior que 0.1, eg. 10%, então vai ser excluída)\n",
        "function = 'LimitedoChequeEspecial~RendimentoTotal+Salario+LimitedeCreditoImediato+Escolaridade_Fundamental_Medio+Escolaridade_Superior_Pos+Regiao_RJ+Regiao_SP+Genero_F+Genero_M'\n",
        "model = smf.ols(formula = function, data = df).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v7tgPouaHMg8",
        "outputId": "5a88c183-ce43-4f27-ae72-42c84a2ec220"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                              \n",
            "==================================================================================\n",
            "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.885\n",
            "Model:                                OLS   Adj. R-squared:                  0.869\n",
            "Method:                     Least Squares   F-statistic:                     52.78\n",
            "Date:                    Wed, 29 May 2024   Prob (F-statistic):           9.97e-18\n",
            "Time:                            02:10:52   Log-Likelihood:                -434.24\n",
            "No. Observations:                      48   AIC:                             882.5\n",
            "Df Residuals:                          41   BIC:                             895.6\n",
            "Df Model:                               6                                         \n",
            "Covariance Type:                nonrobust                                         \n",
            "==================================================================================================\n",
            "                                     coef    std err          t      P>|t|      [0.025      0.975]\n",
            "--------------------------------------------------------------------------------------------------\n",
            "Intercept                        279.5417    446.865      0.626      0.535    -622.921    1182.004\n",
            "RendimentoTotal                    0.1315      0.099      1.334      0.190      -0.068       0.331\n",
            "Salario                            0.2840      0.139      2.048      0.047       0.004       0.564\n",
            "LimitedeCreditoImediato            0.2410      0.409      0.589      0.559      -0.585       1.067\n",
            "Escolaridade_Fundamental_Medio  -280.4080    504.571     -0.556      0.581   -1299.409     738.593\n",
            "Escolaridade_Superior_Pos        559.9498    556.481      1.006      0.320    -563.887    1683.786\n",
            "Regiao_RJ                      -1011.0898    394.413     -2.564      0.014   -1807.623    -214.557\n",
            "Regiao_SP                       1290.6315    484.666      2.663      0.011     311.828    2269.435\n",
            "Genero_F                       -1510.5634    467.696     -3.230      0.002   -2455.094    -566.033\n",
            "Genero_M                        1790.1052    647.038      2.767      0.008     483.386    3096.825\n",
            "==============================================================================\n",
            "Omnibus:                       15.771   Durbin-Watson:                   1.746\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               41.413\n",
            "Skew:                          -0.684   Prob(JB):                     1.02e-09\n",
            "Kurtosis:                       7.340   Cond. No.                     4.15e+20\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 1.4e-31. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Escolaridade_Fundamental_Medio tem p-value = 0.581\n",
        "function = 'LimitedoChequeEspecial~RendimentoTotal+Salario+LimitedeCreditoImediato+Escolaridade_Superior_Pos+Regiao_RJ+Regiao_SP+Genero_F+Genero_M'\n",
        "model = smf.ols(formula = function, data = df).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJdThVH1HYJW",
        "outputId": "f84dc279-91bd-4102-89bc-841eb0811e5c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                              \n",
            "==================================================================================\n",
            "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.885\n",
            "Model:                                OLS   Adj. R-squared:                  0.869\n",
            "Method:                     Least Squares   F-statistic:                     52.78\n",
            "Date:                    Wed, 29 May 2024   Prob (F-statistic):           9.97e-18\n",
            "Time:                            02:11:43   Log-Likelihood:                -434.24\n",
            "No. Observations:                      48   AIC:                             882.5\n",
            "Df Residuals:                          41   BIC:                             895.6\n",
            "Df Model:                               6                                         \n",
            "Covariance Type:                nonrobust                                         \n",
            "=============================================================================================\n",
            "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------------------\n",
            "Intercept                   139.3377    579.345      0.241      0.811   -1030.673    1309.349\n",
            "RendimentoTotal               0.1315      0.099      1.334      0.190      -0.068       0.331\n",
            "Salario                       0.2840      0.139      2.048      0.047       0.004       0.564\n",
            "LimitedeCreditoImediato       0.2410      0.409      0.589      0.559      -0.585       1.067\n",
            "Escolaridade_Superior_Pos   840.3578    963.762      0.872      0.388   -1105.999    2786.715\n",
            "Regiao_RJ                 -1081.1918    386.454     -2.798      0.008   -1861.651    -300.732\n",
            "Regiao_SP                  1220.5295    555.961      2.195      0.034      97.744    2343.315\n",
            "Genero_F                  -1580.6654    398.071     -3.971      0.000   -2384.585    -776.745\n",
            "Genero_M                   1720.0031    739.536      2.326      0.025     226.480    3213.526\n",
            "==============================================================================\n",
            "Omnibus:                       15.771   Durbin-Watson:                   1.746\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               41.413\n",
            "Skew:                          -0.684   Prob(JB):                     1.02e-09\n",
            "Kurtosis:                       7.340   Cond. No.                     3.22e+20\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 2.31e-31. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Intercept tem p-value= 0.811 (coloca -1 na funcao)\n",
        "function = 'LimitedoChequeEspecial~RendimentoTotal+Salario+LimitedeCreditoImediato+Escolaridade_Superior_Pos+Regiao_RJ+Regiao_SP+Genero_F+Genero_M-1'\n",
        "model = smf.ols(formula = function, data = df).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3Hjq3d0HlFk",
        "outputId": "7662bcce-6d64-47fe-e943-ad131cca4353"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                              \n",
            "==================================================================================\n",
            "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.885\n",
            "Model:                                OLS   Adj. R-squared:                  0.869\n",
            "Method:                     Least Squares   F-statistic:                     52.78\n",
            "Date:                    Wed, 29 May 2024   Prob (F-statistic):           9.97e-18\n",
            "Time:                            02:12:32   Log-Likelihood:                -434.24\n",
            "No. Observations:                      48   AIC:                             882.5\n",
            "Df Residuals:                          41   BIC:                             895.6\n",
            "Df Model:                               6                                         \n",
            "Covariance Type:                nonrobust                                         \n",
            "=============================================================================================\n",
            "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------------------\n",
            "RendimentoTotal               0.1315      0.099      1.334      0.190      -0.068       0.331\n",
            "Salario                       0.2840      0.139      2.048      0.047       0.004       0.564\n",
            "LimitedeCreditoImediato       0.2410      0.409      0.589      0.559      -0.585       1.067\n",
            "Escolaridade_Superior_Pos   840.3578    963.762      0.872      0.388   -1105.999    2786.715\n",
            "Regiao_RJ                 -1011.5229    566.749     -1.785      0.082   -2156.096     133.050\n",
            "Regiao_SP                  1290.1984    800.435      1.612      0.115    -326.313    2906.709\n",
            "Genero_F                  -1510.9966    464.720     -3.251      0.002   -2449.517    -572.476\n",
            "Genero_M                   1789.6720    996.429      1.796      0.080    -222.657    3802.001\n",
            "==============================================================================\n",
            "Omnibus:                       15.771   Durbin-Watson:                   1.746\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               41.413\n",
            "Skew:                          -0.684   Prob(JB):                     1.02e-09\n",
            "Kurtosis:                       7.340   Cond. No.                     1.99e+20\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 6.06e-31. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LimitedeCreditoImediato tem p-value= 0.559\n",
        "function = 'LimitedoChequeEspecial~RendimentoTotal+Salario+Escolaridade_Superior_Pos+Regiao_RJ+Regiao_SP+Genero_F+Genero_M-1'\n",
        "model = smf.ols(formula = function, data = df).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_FVAHdAzHq_s",
        "outputId": "e47500ed-253c-4219-c7fa-6b5a3918554f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                              \n",
            "==================================================================================\n",
            "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.884\n",
            "Model:                                OLS   Adj. R-squared:                  0.871\n",
            "Method:                     Least Squares   F-statistic:                     64.26\n",
            "Date:                    Wed, 29 May 2024   Prob (F-statistic):           1.39e-18\n",
            "Time:                            02:13:24   Log-Likelihood:                -434.44\n",
            "No. Observations:                      48   AIC:                             880.9\n",
            "Df Residuals:                          42   BIC:                             892.1\n",
            "Df Model:                               5                                         \n",
            "Covariance Type:                nonrobust                                         \n",
            "=============================================================================================\n",
            "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------------------\n",
            "RendimentoTotal               0.1210      0.096      1.257      0.216      -0.073       0.315\n",
            "Salario                       0.2937      0.137      2.149      0.037       0.018       0.569\n",
            "Escolaridade_Superior_Pos   788.0449    952.173      0.828      0.413   -1133.519    2709.608\n",
            "Regiao_RJ                  -756.1926    362.315     -2.087      0.043   -1487.374     -25.011\n",
            "Regiao_SP                  1609.6887    584.102      2.756      0.009     430.923    2788.455\n",
            "Genero_F                  -1365.9501    391.057     -3.493      0.001   -2155.135    -576.765\n",
            "Genero_M                   2219.4462    673.400      3.296      0.002     860.469    3578.423\n",
            "==============================================================================\n",
            "Omnibus:                       18.013   Durbin-Watson:                   1.737\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               53.309\n",
            "Skew:                          -0.774   Prob(JB):                     2.66e-12\n",
            "Kurtosis:                       7.925   Cond. No.                     2.24e+20\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 4.74e-31. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Escolaridade_Superior_Pos tem p-value= 0.413\n",
        "function = 'LimitedoChequeEspecial~RendimentoTotal+Salario+Regiao_RJ+Regiao_SP+Genero_F+Genero_M-1'\n",
        "model = smf.ols(formula = function, data = df).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yyTxDSsRH-Se",
        "outputId": "7e4e24f3-f8d3-4d48-ffae-7349a096a508"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                              \n",
            "==================================================================================\n",
            "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.883\n",
            "Model:                                OLS   Adj. R-squared:                  0.872\n",
            "Method:                     Least Squares   F-statistic:                     80.75\n",
            "Date:                    Wed, 29 May 2024   Prob (F-statistic):           2.02e-19\n",
            "Time:                            02:14:05   Log-Likelihood:                -434.83\n",
            "No. Observations:                      48   AIC:                             879.7\n",
            "Df Residuals:                          43   BIC:                             889.0\n",
            "Df Model:                               4                                         \n",
            "Covariance Type:                nonrobust                                         \n",
            "===================================================================================\n",
            "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------\n",
            "RendimentoTotal     0.1031      0.093      1.103      0.276      -0.085       0.291\n",
            "Salario             0.3239      0.131      2.469      0.018       0.059       0.588\n",
            "Regiao_RJ        -753.0020    360.965     -2.086      0.043   -1480.957     -25.047\n",
            "Regiao_SP        1811.9221    528.588      3.428      0.001     745.924    2877.921\n",
            "Genero_F        -1481.9930    363.717     -4.075      0.000   -2215.498    -748.488\n",
            "Genero_M         2540.9131    548.070      4.636      0.000    1435.624    3646.202\n",
            "==============================================================================\n",
            "Omnibus:                       21.294   Durbin-Watson:                   1.615\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               65.346\n",
            "Skew:                          -0.973   Prob(JB):                     6.46e-15\n",
            "Kurtosis:                       8.374   Cond. No.                     2.79e+20\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 3.07e-31. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# RendimentoTotal tem p-value= 0.276\n",
        "function = 'LimitedoChequeEspecial~Salario+Regiao_RJ+Regiao_SP+Genero_F+Genero_M-1'\n",
        "model = smf.ols(formula = function, data = df).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NkgGkrXfIKT8",
        "outputId": "b2ebfed1-4566-404a-e304-a4aeae165ac9"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                              OLS Regression Results                              \n",
            "==================================================================================\n",
            "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.879\n",
            "Model:                                OLS   Adj. R-squared:                  0.871\n",
            "Method:                     Least Squares   F-statistic:                     106.7\n",
            "Date:                    Wed, 29 May 2024   Prob (F-statistic):           3.24e-20\n",
            "Time:                            02:14:50   Log-Likelihood:                -435.50\n",
            "No. Observations:                      48   AIC:                             879.0\n",
            "Df Residuals:                          44   BIC:                             886.5\n",
            "Df Model:                               3                                         \n",
            "Covariance Type:                nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Salario        0.4616      0.041     11.367      0.000       0.380       0.543\n",
            "Regiao_RJ   -762.7325    361.748     -2.108      0.041   -1491.788     -33.677\n",
            "Regiao_SP   1633.0786    504.366      3.238      0.002     616.596    2649.561\n",
            "Genero_F   -1546.3828    359.891     -4.297      0.000   -2271.696    -821.070\n",
            "Genero_M    2416.7289    537.714      4.494      0.000    1333.037    3500.421\n",
            "==============================================================================\n",
            "Omnibus:                       11.932   Durbin-Watson:                   1.646\n",
            "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               29.897\n",
            "Skew:                          -0.402   Prob(JB):                     3.22e-07\n",
            "Kurtosis:                       6.782   Cond. No.                     1.76e+20\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 3.02e-31. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nosso modelo ajustado contem as colunas Salario, Regiao_RJ, Regiao_SP, Genero_F, Genero_M."
      ],
      "metadata": {
        "id": "i4ue4DkUITpa"
      }
    }
  ]
}