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    {
      "cell_type": "markdown",
      "source": [
        "1. Подготовка данных.\n",
        "1.1. Загрузите данные, и проделайте чистку/подготовку данных согласно коду ниже:"
      ],
      "metadata": {
        "id": "hRMy1SwIL_Hu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "G0Y956CeMBU-"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Загрузка CSV-файла"
      ],
      "metadata": {
        "id": "FsjBPyUwMJsG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"Mall_Customers.csv\")"
      ],
      "metadata": {
        "id": "k8EzxZXYMLJG"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Удалим ID клиента и закодируем пол"
      ],
      "metadata": {
        "id": "W0MQ2-R-MOsN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(\"ID клиента\", axis=1)\n",
        "df[\"Пол\"] = df[\"Пол\"].map({\"Male\": 0, \"Female\": 1})\n",
        "df.head()"
      ],
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        "id": "zQix7NF7MRA-",
        "outputId": "525ef645-57b4-4aa9-9cb1-7c65744d8806"
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      "execution_count": 28,
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              "   Пол  Возраст  Годовой доход (в тыс. $)  Оценка расходов (1–100)\n",
              "0    0       19                        15                       39\n",
              "1    0       21                        15                       81\n",
              "2    1       20                        16                        6\n",
              "3    1       23                        16                       77\n",
              "4    1       31                        17                       40"
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    {
      "cell_type": "markdown",
      "source": [
        "# 2. Контролируемое обучение (Supervised Learning)\n",
        "**Задача: Предсказать категорию клиента (например, высокий или низкий доход)**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "2.1. Создайте новый признак согласно коду ниже:\n",
        "\n"
      ],
      "metadata": {
        "id": "cgNxRPe7Mn71"
      }
    },
    {
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      "source": [
        "# Создадим бинарную метку\n",
        "# Категоризация по доходу: больше 70 — высокий доход (1), иначе низкий (0)\n",
        "df['Высокий доход'] = (df['Годовой доход (в тыс. $)'] > 70).astype(int)"
      ],
      "metadata": {
        "id": "QnWqHR04MtSn"
      },
      "execution_count": 29,
      "outputs": []
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      "source": [
        "2.2. Определите входные данные (X), и прогнозируемую переменную согласно коду ниже:"
      ],
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        "id": "Z_NzB6GvNJL1"
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        "# Используем все признаки кроме дохода и метки\n",
        "X = df.drop(['Годовой доход (в тыс. $)', 'Высокий доход'], axis=1)\n",
        "y = df['Высокий доход']\n",
        "X.head(5)\n"
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      },
      "execution_count": 30,
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              "   Пол  Возраст  Оценка расходов (1–100)\n",
              "0    0       19                       39\n",
              "1    0       21                       81\n",
              "2    1       20                        6\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1beaabaa-794f-4b5f-97ae-a0f73abc1a75 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1beaabaa-794f-4b5f-97ae-a0f73abc1a75');\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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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-11e921bd-1636-4e98-9679-ce9a86888c9f')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
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              "    </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",
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              "    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-11e921bd-1636-4e98-9679-ce9a86888c9f button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"\\u041f\\u043e\\u043b\",\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\": \"\\u0412\\u043e\\u0437\\u0440\\u0430\\u0441\\u0442\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 70,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          55,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\u041e\\u0446\\u0435\\u043d\\u043a\\u0430 \\u0440\\u0430\\u0441\\u0445\\u043e\\u0434\\u043e\\u0432 (1\\u2013100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          83,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "eJm44NMm1HHF",
        "outputId": "a2127b33-eabc-4a6f-911f-d4f3a636b4c1"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: Высокий доход, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Высокий доход</th>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.3. Разделите данные на тренировочную и тестовую выборки."
      ],
      "metadata": {
        "id": "v-fHvzEFaWz5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "** Предобработка данных**\n",
        "• Проверим пропущенные значения и основные статистики:"
      ],
      "metadata": {
        "id": "_8HJYIWygZ0f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum()) # Проверка пропущенных значений\n",
        "print(df.describe()) # Основные статистики"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iTar64kKgc3Q",
        "outputId": "03573079-135a-461d-e6f9-512b977a6cce"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Пол                         0\n",
            "Возраст                     0\n",
            "Годовой доход (в тыс. $)    0\n",
            "Оценка расходов (1–100)     0\n",
            "Высокий доход               0\n",
            "dtype: int64\n",
            "              Пол     Возраст  Годовой доход (в тыс. $)  \\\n",
            "count  200.000000  200.000000                200.000000   \n",
            "mean     0.560000   38.850000                 60.560000   \n",
            "std      0.497633   13.969007                 26.264721   \n",
            "min      0.000000   18.000000                 15.000000   \n",
            "25%      0.000000   28.750000                 41.500000   \n",
            "50%      1.000000   36.000000                 61.500000   \n",
            "75%      1.000000   49.000000                 78.000000   \n",
            "max      1.000000   70.000000                137.000000   \n",
            "\n",
            "       Оценка расходов (1–100)  Высокий доход  \n",
            "count               200.000000     200.000000  \n",
            "mean                 50.200000       0.370000  \n",
            "std                  25.823522       0.484016  \n",
            "min                   1.000000       0.000000  \n",
            "25%                  34.750000       0.000000  \n",
            "50%                  50.000000       0.000000  \n",
            "75%                  73.000000       1.000000  \n",
            "max                  99.000000       1.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.4. Обучите модель логистической регрессии (LogisticRegression) или дерево решений\n",
        "**Логистическая регрессия (Logistic Regression)**"
      ],
      "metadata": {
        "id": "D-9G68wEh3Y_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "import pandas as pd # Ensure pandas is imported\n",
        "\n",
        "# Ensure X is preprocessed correctly for the model\n",
        "# Based on earlier steps and the error, 'Пол' is likely still strings and 'ID клиента' might be present\n",
        "# Re-apply transformations to X if they weren't applied correctly upstream\n",
        "if 'Пол' in X.columns and X['Пол'].dtype == 'object':\n",
        "    X['Пол'] = X['Пол'].map({'Male': 0, 'Female': 1})\n",
        "\n",
        "if 'ID клиента' in X.columns:\n",
        "    X = X.drop('ID клиента', axis=1)\n",
        "\n",
        "# Разделение данных на обучающую и тестовую выборки\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
        "\n",
        "# Обучение модели\n",
        "model = LogisticRegression(max_iter=1000)\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "8Vacws_Ph393",
        "outputId": "512c2120-cf5c-4e8a-9b94-d957e6a7ca04"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.5. Оцените точность модели на тестовой выборке."
      ],
      "metadata": {
        "id": "t6hqEOkc3IH-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Оценка точности\n",
        "accuracy = model.score(X_test, y_test)\n",
        "print(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xInTsdXf3JIM",
        "outputId": "7d259c46-d540-4647-fc98-8140203253e3"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6166666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.6. Постройте confusion matrix (Матрица ошибок)\n",
        "**Пример кода для бинарной классификации**\n"
      ],
      "metadata": {
        "id": "18iEfhUV3R-N"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PB1xGFdQ4slK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Сделаем предсказания на тестовой выборке\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# 5. Визуализация матрицы ошибок (Confusion Matrix)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "xticklabels=['Отрицательный', 'Положительный'],\n",
        "yticklabels=['Отрицательный', 'Положительный'])\n",
        "plt.xlabel('Прогноз')\n",
        "plt.ylabel('Факт')\n",
        "plt.title('Матрица ошибок')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "eQYQZAjw4aNF",
        "outputId": "ebaee0bf-475b-4ff2-c0c1-d75f39ba836f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "PWbVAGfz5Uf_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Неконтролируемое обучение (Unsupervised Learning)\n",
        "**Задача: Кластеризация клиентов по доходу и уровню расходов**\n"
      ],
      "metadata": {
        "id": "uHi5mhgp5V5k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3.1. Выберите 2 признака для кластеризации согласно коду ниже:**"
      ],
      "metadata": {
        "id": "eXQH8Sil5gIV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['Годовой доход (в тыс. $)', 'Оценка расходов (1–100)']]"
      ],
      "metadata": {
        "id": "CSV9AwWb5l8k"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3.2. Проведите масштабирование признаков перед кластеризацией, согласно коду ниже:**\n",
        "\n"
      ],
      "metadata": {
        "id": "pQCKLX3r5qtk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "HsC4NoCe89aa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn.datasets import make_blobs\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_scaled = StandardScaler().fit_transform(X)"
      ],
      "metadata": {
        "id": "egVw7_5w5rW5"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3.3. Примените алгоритм K-Means для кластеризации данных.**"
      ],
      "metadata": {
        "id": "p1xSR0Se8O9S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Обучение K-means (4 кластера)\n",
        "kmeans = KMeans(n_clusters=4, random_state=0, n_init=10)\n",
        "kmeans.fit(X_scaled)\n",
        "y_kmeans = kmeans.predict(X_scaled)\n",
        "centroids = kmeans.cluster_centers_"
      ],
      "metadata": {
        "id": "J__dDIiR8RBq"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Реализация K-means в scikit-learn**\n"
      ],
      "metadata": {
        "id": "hwVKIlgc9UFS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Визуализация\n",
        "plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=y_kmeans, s=50, cmap='viridis')\n",
        "plt.scatter(centroids[:, 0], centroids[:, 1], c='red', s=200, alpha=0.75,\n",
        "label='Центроиды')\n",
        "plt.title('Кластеризация K-means')\n",
        "plt.xlabel('Признак 1 (масштабированный)')\n",
        "plt.ylabel('Признак 2 (масштабированный)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "c2jmtD2F9WYq",
        "outputId": "09296f43-dbe6-4dc7-caf9-b79f45c2d068"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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UqKgoo/u92E5RFLVt27Zq6dKl1cjIyGTtLF0+P3Xq1GTnDhw4oDo4OKh+fn5qQEBAsvNJMab2+r7885f0c/LkyROj42vWrFFfe+011cnJSXVyclLLlSunDhkyRL127ZqhjTWvcUolCl7+GV29erXaunVrtWDBgqqtra1atGhRdeDAgWpgYKDJ10sQMoKkqlb2jQuCIFigePHiVKpUic2bN2d3KGnStGlTmjZtKvb2EoQ8TswREgRBEAThlSUSIUEQhBTUqVNHrFYShFeAmCwtCIKQgh9++CG7QxAEIQuIOUKCIAiCILyyxNCYIAiCIAivLJEICYIgCILwyhJzhMxQFIWHDx/i4uJi9U7dgiAIgiBkD1VViYiIoFChQshy6v0+IhEy4+HDh8l2bRYEQRAEIXe4f/8+RYoUSfW8SITMcHFxAfQvpKurazZHIwiCIAiCJcLDw/H19TW8j6dGJEJmJA2Hubq6ikRIEARBEHIZc9Nacs1k6SlTplC7dm1cXFwoWLAgnTt35tq1a2Yft2rVKsqVK4e9vT2VK1fm33//zYJoBUEQBEHIDXJNIrRv3z6GDBnC0aNH2bFjBwkJCbRu3ZqoqKhUH3P48GHefPNN3n//fc6cOUPnzp3p3LkzFy9ezMLIBUEQBEHIqXJtQcUnT55QsGBB9u3bR+PGjVNs06tXL6Kioow2faxXrx7VqlVj0aJFFt0nPDycfPnyERYWJobGBEEQBCGXsPT9O9fOEQoLCwPAw8Mj1TZHjhxhxIgRRsfatGnD+vXrMzM0QRAEIRMpikJ8fHx2hyFkMxsbGzQaTbqvkysTIUVR+OSTT2jYsCGVKlVKtd2jR4/w8vIyOubl5cWjR49SfUxcXBxxcXGG78PDw9MfsCAIgpAh4uPj8ff3R1GU7A5FyAHc3Nzw9vZOV52/XJkIDRkyhIsXL3Lw4MEMv/aUKVOYOHFihl9XEARBSB9VVQkMDESj0eDr62uySJ6Qt6mqSnR0NI8fPwbAx8cnzdfKdYnQxx9/zObNm9m/f7/JAkkA3t7eBAUFGR0LCgrC29s71ceMGTPGaDgtqQ6BIAiCkL0SExOJjo6mUKFCODo6pv+Cqgpix4Bcy8HBAYDHjx9TsGDBNA+T5ZpESFVVhg4dyrp169i7dy9+fn5mH1O/fn127drFJ598Yji2Y8cO6tevn+pj7OzssLOzy4iQBUEQhAyk0+kAsLW1TdsFbtyALVvg9Gm4ehXi48HWFsqVgxo1oF07KF06AyMWMltSQpyQkJD3E6EhQ4bw559/smHDBlxcXAzzfPLly2fICvv06UPhwoWZMmUKAMOHD6dJkyZMnz6d9u3bs2LFCk6ePMnixYuz7XmkR1R4NDuX7efIxhNER8TiVbwAbfo1o0bLyqKLWBCEV4bV80ECAuC772DPHoiMBFkGBwfQaCAuDvbvh717YfFiaNYMvvgCChfOlNiFjJURe4DmmkRo4cKFADRt2tTo+JIlS+jXrx8A9+7dM0oIGjRowJ9//sn//vc/vvzyS0qXLs369etNTrDOqU5sO8vXPaYTExWLhL5H99rJm+xdcYgytUoyefMY3Avmy+4wBUEQcpZdu2DcOH0y5OEBxYunPBymqhAWBhs3wqlT8PXX0KJFlocrZL1cW0coq+SEOkJXj9/g00bj0OkUVCX5P5eslSlewZe5x6Zga2eTDREKgiBkvtjYWPz9/fHz88Pe3t78A3btghEj9L1AhQvre4DM0en0SZOzM8yYIZKhHM7Uz4Sl799iPCUX+G383yiKmmISBKAkKtw+f5f9q46YvM6zwBAOrT/O/tVHuHc1IDNCFQRByBkePND3BEVGgq+vZUkQ6Nv5+uofl9STlE79+vWjc+fOyY7v3bsXSZIIDQ1N9z2EtMs1Q2Ovqsf3n3Jyxzkw028nyxKbFm6j5TvJq2wH3X3Cj5/9zqH1x1F0/9XeqNiwLB98/y4VG5TN6LAFQRCy1/ff65OYYsWsXxkmSfoepLt39XOL5s7NnBiFHEH0COVwATcCzSZBAIqicj+FXp5A/yCG1PmCwxuMkyCAK0euM7LpBE5uP5dR4QqCIGS/69f1E6M9PCzvCXqZRqN//J49+tVmWSA0NJQBAwZQoEABXF1dad68OefO/ff3+auvvqJatWpGj3mxVynp/1P7Ali6dClubm6sX7+e0qVLY29vT5s2bbh//77RdRcuXEjJkiWxtbWlbNmyLFu2zOi8JElGuzT88ssvSJJktEo76X4vx/HiczDVKxYaGookSezdu9fi1zAtRCKUw9nYWt5pp02h7dR+84kIjkSXmLwKq6KoKIrC5F4ziI2OS3ZeEAQhV9q6VT+0lS+dC0jy5dNfZ8uWjInLjB49evD48WO2bNnCqVOnqFGjBi1atCA4ONiixzdo0IDAwEACAwNZs2YNgOH7wMBAQ7vo6Gi++eYbfv/9dw4dOkRoaCi9e/c2nF+3bh3Dhw9n5MiRXLx4kYEDB9K/f3/27NmT4n2joqIYN24czs7OKZ53dXU1xDBy5EhLX44sI4bGcriS1f2wd7IjNsp0oqLRytRoVcXo2J1L97lw4IrJx6mKSlRYNHv/Pkzb/s3SHa8g5DbRsfH8c+wK6w5dJDA4HAdbG5pVK0n3xlXx8059L0PhP4qicuTKXVbtO8fFO/o33IrFvOnZpCr1KxRHlrO4aOHp0/ol8uldWi1J+uucPZshYZly8OBBjh8/zuPHjw217KZNm8b69etZvXo1H374odlr2NraGgoGJ+3DmVIB4YSEBObNm0fdunUB+O233yhfvjzHjx+nTp06TJs2jX79+jF48GAARowYwdGjR5k2bRrNmiV/n/jhhx+oUKECiYmJyc7FxcUZxZVaspSdRI9QDufgZE/b/s2RNab/qXSJCp2GtDM6dnLbWSQL/gBJssSJrWfSFacg5Eb3HofQbdLvTFmxm2v3HxMWFcujkAhW7jtH90m/sXq/GDY2Jz4hkc8Wb2LovHUcuuRPcEQMwRExHL58h6Hz1/PZ4k3EJyR/g8xUV6/q6wRlBAcHuHw5Y65lwrlz54iMjMTT0xNnZ2fDl7+/P7du3TK0u3DhgtH5du3ambhqyrRaLbVr1zZ8X65cOdzc3LhyRf/B+cqVKzRs2NDoMQ0bNjScf9HDhw+ZMWMG06dPT/Fez549s2jFdZEiRXBxccHPz48PPvjAsLF6VhA9QrlAn4k9ObXjHAE3HyWb55Okx8gOlK9rXBE1PjYBWZbRKTqT11cVlTgxNCa8YqJj4xk4aw1PwyIB46l4uucrNL/9azcF3JxpUqVkNkSYO3z39x72nb8N/Pe6vfj/+87fZsqK3Ux4t3XWBKSq+orRGbArOaC/Tnx8pm/HERkZiY+PT4rzYdzc3Az/X7ZsWTZu3Gj4/tixY7zzzjuZFpc5Y8eOpUePHlStWjXF87dv37ZoJ4gDBw7g4uLCnTt3GDBgAGPHjmXy5MkZHW6KRI9QLuDi7sysg5Np0qO+oWcoaeKbq6cLg2b044Mf3k32OO/iBdAlmk6CQD+s5lPCK2ODFoQc7t8TVwkKiTB6836ZLEn8uPloFkaVuwSFRLDh8EVMlaNTVZWNRy7xKDgia4KSJP22GTrzf/ssotPpr5fJe5LVqFGDR48eodVqKVWqlNFX/vz5De1sbW2NzhVOQwXsxMRETp48afj+2rVrhIaGUr58eQDKly/PoUOHjB5z6NAhKlSoYHTs7NmzrF692mTCsn//fho1amQ2Jj8/P0qVKkXLli3p0aMHZ7NgODKJ6BHKJVw9Xfjyz0/4aEZfTmw9S2xUHAV8Pandtho2tikXUWzQuQ6Org5Eh8eYvLYuUaHd+6JomPBqWXfwApKk/6CfGkVVuXr/MbcDn1HCxzPrgssl/jl2BQkJ1czSVgmJf45d4f12dbImsHLl9NtmZISYGHg+lyY9wsLCkr2537x5E9APd9WrV4/69evTuXNnfvjhB8qUKcPDhw/5559/6NKlC7Vq1Up3DElsbGwYOnQoc+bMQavV8vHHH1OvXj3q1NH/+4waNYqePXtSvXp1WrZsyaZNm1i7di07d+40us60adMYOXIkhQoVSnaPmJgYfv75Z27dukW7du0M22JFRkaSmJhIcHCwYR4T6OcSxcbGcufOHbZs2cJrr72WYc/XHJEI5TIe3u606WfZpGZ7Rzve+V93Fn++LNU2skamfodalKhSLKNCFIRcITA4wmQS9HJbkQglFxQSgSxLKDrTL6QsSwSFZFGPEOg3UN27N/3DWaoKigIvLVlPi71791K9evUUzzVu3Jg9e/bw77//MnbsWPr378+TJ0/w9vamcePGeHllbI+9o6Mjo0eP5q233iIgIIBGjRrxyy+/GM537tyZ2bNnM23aNIYPH46fnx9LlixJtsWVi4sLn3/+eYr3+Pvvvxk2bBiAYVL2i7p27Wo0DJg0mTp//vy0bt3asGdoVhCJUB7XfWQHwp9FsOL79Wi0smEZfdL/12hZhdHLhmZzlIJgOUVRiYlPwN5WiyYdmw072GoJtaJtVlBVldiERGRJws4m5/95tre1sSiZVFWwz6LXENDvIr94sX7vsBfm11gtLEy/1UYaJiS/aOnSpSxdujTV88WLFwf0icWcOXOYM2dOiu2++uorvvrqK6NjTZs2TXFoMrXjSbp27UrXrl1TPT9o0CAGDRqU6vmUrv3y/Ka+ffum+LzPnj1rqDdkLs6s2AUs5/+mCekiSRLvT3mbZm++xqaF2zi79xK6BB0lqhaj46A2VGteSexcL+QK1+4/5o/dp9l24hoJOgWtRqZVjTK83aI6FYolXyJsTrNqpfh771mTc4QAXB3tqFTc+utbIzo2nrWHLvL33rMEPNWvlinh40HvptXoWL8itjk0KWpcuQTLdp4y206nKDSuUiILInqudGn9LvIbN4KLS9omTut0EBwMHTvqr5eJKlSokCOXlaeHg4MD+VKp42RjY2M0LJbdxKarZuSETVcF4VX37/ErjF+6DUkyXpmkkSUUVWXcO63o3KCSVde8GxRC14lLTfZoyJJE/za1GdKpYeqN0ikkIpoPZq7C/1EwqP+tXkuav1TFz4f5w7riZG+baTGklaqqdJ/0O/ceh6SaUGpkCd8CbqyZ0NewyCOtrNp0NSAAevSAoCD93mHW3FtV4f598PKCVav0223kEUuXLuWTTz7JM/ubiU1XBUHI8y7fDWL80m0oqprszVanqKgqfL18B+duPbTqusW83Bn7Vksg5fdIWZKoVrIQA15P/0RZU0Yt3szdoBBU1XgJf1KCdvHOIyYt25GpMaSVJElM/fANHO1t0aRQs0wjSzja2zL1wzfSnQRZrXBh+Ppr/dDW/fuWryLT6fTtnZ31j89DSRDoN4DNK0lQRhGJUA53/1oAS/73F1Pfm8/84b9ydo/ppaqCkNf8seu02Q/zsiSxbJf5IZqXdX2tMrMHd6J8UePJqK6OdrzXtg7zh3XN1Lk6l+8+4vTNAJPDc4qqsvP0dR4+y7oCc9Yo4ePJ8i/eomWN0kbJkEaWaFG9NMu/eIuShfKbuEImatECZszQ9+zcvQuhoakvE1RV/fm7d/XtZ8zQP17I83LmwLNAdEQMP/Sbx6F1x5E18vM3Aon1c7dQtHxhvlo7Ct+yeeuTiiC8LCFRx45T183O49EpKnvP3iImLgEHu5TLSaSmUeUSNKpcAv9HwTwKjsDBTkuFol5ZMi9n64lraGTJ7POTJIntJ6/Tr01tk+2yi28BN6a8357Pe0ZzI+ApAKUK5cfD1TFT7mfVh8EWLfTL6b/7Tr+B6p07+m0zHBz0c4d0Ov0SeUXR9wJ17AhffJHneoLyqozoGBCJUA6UmJDI/zpM4dKhawDJqkk/uB7IJ6+NY+Gp7ylYtEB2hCgIWSIqNp5EJeVq6i9TVJXw6FirE6Ekft4eWb63WHBEtJkKPHqyLBESaboeWE7g7uJInXJFM+36mueTnuPj43GwZguNwoVh7lz9LvJbtsCZM3Dlir5itKOjvk5QtWr61WGZPDFayFjR0dGAfgJ2WolEKAfav/ooF/anvlmqolOICoti+eQ1jFj8URZGJghZy9HOBlnST4g2RwJcHOxSPa8oKvefhBITn4CXmzPuLhnXW/HwWTjh0bG4OTng7eFi8ePyOdkjIYGZdEhRVVwcU39urwqtVoujoyNPnjzBxsbG+hWvvr7w4ualKdUZio1Nf6BCplNVlejoaB4/foybm5shSU4LkQjlQBvmbdEXKTPRXa5LVNi5bD8Dp76LUz6nLIxOELKOrY2WxlVKcODCbZPDRxpZol75YjimsLIqUaewct9Z/tx9hofPwgH9e99rlfx4v21dqpTwSXN8205e4/ftJ7ly/7HhWGU/b/q1rk2zaqXMPr5ljTL8tees2XaKotKyuuipkCQJHx8f/P39uXv3bnaHI+QAbm5uhmKMaSUSoRzo1tk7JpOgJAlxCTy4HkjZ2ub/4ApCbvVOixrsPXfLZBudovJOixrJjifqFEYt3sT+55uCJlFVOHzpDocv3eG7Ae1pkYYkY+76gyzZdgL5pR6FS3eCGPnjJoZ0bGh2S4lqJQtR1rcANwOemlx+XrdcUYpn8bBdTmVra0vp0qWJj4/P7lCEbGZjY5OunqAkIhHKiaxZZprVS1IFIYvVKF2EUT2aMnXV3mQTi5N6Tod1eY265ZNvE7N0+wn2X7id4sCTTlGRgDG//MvGSe9ZNaS179wtlmw7AZBs2C7p+/kbD1HZz9vknBlJkpg+sCPvT1/Jk7DIZB+AZEmiaEF3vu6fvsrGeY0sy+brCAmChUQilAOVrV2SiwevJpsk/TI7RzuKlku+2V1uEXT3CZt/3MHlw9dQFIWSVYvzxketKV7R16rrJCYkcmTjSXb+sZ+QR2G4ejjTpGcDmvSsj20OLEInWO/N5tUpWciT33ec5Mjlu4bEpnYZX95tVZMGFYone0yCTsdfu8+YLJiooh92WnvwAoM7NrA4nuW7Tpudu6SRJf7cfcbs5OFCnq78OeYtlu86zZoD5wmPjgPA2cEOL3dn7G20/G/JFppVLUm7OuVzZGFFQcjNRGVpM7KjsvSBNUeZ1GO6yTayVqb9B60YNn9AlsSUkVRV5c9v1/Lb+L+RZMmQ8MlaGSVRof2HLRk6bwAarfkuz4CbgYxp+w2Bt4OQNTKKTkGSJVRFxd0rH9/88yWla2RhaX8h00VExxIWFYuroz2uTqn3Cpy5GcD701dadE3fAm5smNTf4vs3GbnQoraSBEfnDMPGgp9l0Cdv1x88YcJv27gdGGzoAUuqMu1kb8u0gR2om4krswQhrxCVpXOxBp1rU7tddaQUKrWCfsNU94L5eGdctyyOLGOsm/MvS8etQFVVo14v5fmGsP/+tJOFI5aavU7Y03BGNp1A0L0n+sc/v5b6fHgh7GkEo1pMJNA/KIOfgZCdXBztKVLAzWQSBBAZE2fxNaNiLW8bGWv53BRVhZi4BIvb63Qq45du425QiP775z/LSR9Xo+PiGTZ/PVfuiZ9pQcgoIhHKgTQaDV+t+YzWfZsiyRKyLKG10aDR6v+5StcsyZzD3+Dh7Z7NkVovNjqOpeNXmGyjqrBx/jaC7j4x2W7Twu2EPAo1JFAvU3QKMVGxrJq2Kc3xClnvaVgUe8/dYufp69wOfJbm6+S3cDWlBOR3tXzlpZuTA3IqH1JeZqvVpLiSLTVbT1zF/1FwqhOnVRUUReHHzUctvqaQNqqqcvluEDtOXWf/hdtERItl9XmVmCOUQ9na2/LZL4PpN6kXu/88yNOAYBxdHWjYuU6uHuo5sOYoMRHm/6BIssTWX3fTd2KvFM+rqsqmRdvMrq5TEhW2L93DwGnvYmeixoyQ/R4FRzBz7X52nb5hNPemip8Pw7q8Ro3SRay6XjnfghQt6Mb9x6FmixZ2smLDVgc7G1pUL83uMzfMLul/vW55tBrLP2+u2n/OMAyWGp2icuDibZ6ERVIgX97asTyn2HP2Jgs2HubWC4m4rVbDG/XKM7xLI1wcxUTtvET0COVw+Qt70nNUJwbP6k+/Sb1zdRIE8ODaQzQ2FsyXUFUe3AhM9XR8bDzBgaEW3TMuxvK2QvZ4+CyMd7/7k91nbiSbgHzxziMGzlrNoYv+Vl1TkiQ+eL2eySRIliXyOTvwRr3yVl27T8uaJpMV6fn9325e3arrJm2+ao6qwoMnOXPvsdxu7cELjPxxE7cfGfdGxifq2HD4Ev2m/i16h/IYkQgJWUprqzX9cTeJLGFjm3qHpSUTqZPdV8ixJi7bQWhUTIo9LIqqoqgqo3/5x6r5NgDt65ZnYPt6AMl2R5clCRcHOxYO62r1J/yKxb35pn87NLKU7LoaWUKrkZn6wRtWbzZqTe+RpROwBcs9fBbGt3/uAlL+M6VTVO49DmH2ugNZHJmQmcS7g5ClqjWrxO9fmV/JoyQqVGlSMdXzWhstFeqX4erxm6bLDEjgXbwgnoVy33yqV4X/o2BOXLtvso2qQnRsAltOXKXra5Wtuv7AN+pTt3xR/t57ln3nbxOXkEhBN2e6vVaFrq9VTrYxaMDTMFYfOM/OU9eJjI0nfz4nOjWoRMd6FYwmaLepXZbSRfKzct85thy/SmRMHC6O9rxRrzw9m1SlaEHrf+bqli9mdsgNwNnellJp2NE9IVHHztM3WH3gPP6PgrHRyNQtX5SeTapRqXj6qvPmBWsOXMDcjic6RWXz0StiiCwPEYmQkKUqvVaOouUL8+B6YKoJjCSBo6sjzXqbruvSeejrfPvWLJNtJCQ6f9zO+j2JhCxz9PJds/NiQP9zcfjSHasTIYBqJQtTraR+N3FVVZFSKUS67cQ1xi3dioJqmH8WHhXLzDX7+HXLMRYO70ZZ34KG9iV8PPmid3O+6N3c5HUt1bNJVXacum6yjSxLdHmtMvZW9nKGREQzeM5arj14YlQDacvxq2w+eoV+rWsxtPNr6X4Oudn+87ctquofn6jj9M0AmlQpmQVRCZlNvDsIWUqSJEb/PhQbWy1yCsMAkiyBJPH5bx+bndzcpGd9Gnevl+ofbkmWqNSoHB0Gt8mQ2IXMEZeQmGybipSoKsTGWzc0lpLUfl7O3Axg7JItJCqK0Zuh+vze4TFxfDR7DcHh0VZd1xo1ShWmd9NqqZ7XyBJ+Xh4MeL2uVddVFJVh89dz8+FT/fcvZJ1JvU9Lt5/krz1nrA86D4lLSMyUtkLOJhIhIcuVqVmSWQcnU7b2C5+mnr+H+JYtxJQtY2nQsbbZ68iyzJd/fkLvLzpj76zvok56M7Kxs6HDR62ZsmUstnY2Gf4chIxTKL+r2aEg0CcBhfO7ZVocv2w5ZnLHGkVRiYiOY+2hC5kWgyRJjOrZlOFdGuH6fLf5pJA0skSbWmX5ZWRPXKxcAXn82j0u3Q0y+zr/vOU4CYm6tISeJ/gWcLO4NEKR/PkyORohq4ihMSFblKrux5zD33L7/F2uHL2OoqiUqFKMCvXLWPXJWqPV8N43b/Hml105/s9pQh7rt9io83oNnN0srw0jZJ8mVUri4mBHhJkCiDpFpXPD1OeNpcfTsCijrTtSo6j67TgGtLOuR8YakiTRt3Ut3mxWjcOX7xIUEoGjnQ0NKhbH04p6Ry/acPhSsn3aUhIaGcPhy3de2SGfrq9V5sgV07vaSxL4eXtQvqhXFkUlZLZclQjt37+fqVOncurUKQIDA1m3bh2dO3dOtf3evXtp1qxZsuOBgYF4e4uJgTlBiSrFKFEl+WaZ1nJwsqdJT8v3ihJyDjsbLR+0r8eM1ftSbSNLEq9VKp5pbz6PQyPNJkGGtiERmRLDy2xttDStmjEJycNnYRb1uklAUEhkhtwzN2pctQSlC+fnduAzk0Uth3Rs+ErPpcprctXQWFRUFFWrVmX+/PlWPe7atWsEBgYavgoWLGj+QYIgZJm3m1fn/bZ1AONl7kn/X7usL9++97rZ66iqSkx8AvFWzt9wsGL4VKvJfcvWnexsseRtWwWrJ2En6hSiY+PJC9tW2mg0LBjWlRI+ngBGw2SyLCFLEmPfakGzaqWyK0QhE+SqHqF27drRrl07qx9XsGBB3NzcMj4gQRAyhCRJDOnUkDa1yrJq/zlOXLtPok6hVOH89GhclbrlipqcuxERE8eaA+f5e+9ZQ49G6cL5ebNZddrXK4+NmeTFx93FopVrAC6Oua9CeeOqJTl29Z7ZdrIkUb+CZT20hy768+eeMxx9PqTobG9L54aV6N2sOoU8s2aD6szg6erE8jFvsf/cbdYcPM/9J6HY29jQqLIf3RpVobCYG5Tn5KpEKK2qVatGXFwclSpV4quvvqJhw4bZHZIgCCkoVTg/Y95sYdVjHodGMmDGSgKehhv1Stx6+JRJy3ew5cRVZg/ubLKn4/7TMIuSIIDwaMs3aM0p2tctz9z1B4mNT0j1eWpkiRbVS5vdtkNVVWatPcCynafQyJJhSDEyNp6/9pxh7cELzB/alaolC2Xsk8hCNhoNLWqUpkWN0tkdipAFctXQmLV8fHxYtGgRa9asYc2aNfj6+tK0aVNOnz6d6mPi4uIIDw83+hIEIWdSVZVPFmwg8Fl4sqGZpCkep64/4Pu/d5u8TqLO8pVSqgVzbXIaFwc7fhjQHlmSU+xZ08gSRfK78UXv5mavtf7QRZbtPAWQbB6NTlGJjU9k6Px1hETGZEzwgpDJ8nSPUNmyZSlbtqzh+wYNGnDr1i1mzpzJsmXLUnzMlClTmDhxYlaFmOvpdDpObjvH8X9PExcdj1fxArTu2xSvYgWyOzThFXD6RgBX7z822UZRVTYfvczHnRoaVl1Fxcaz5fgVrtzTP9bPx8Pie+bGoTGAhpX8+GVkTxZuOmw0TGZvq6Vj/YoM6tCAfE6mKyWrqsqv206YLL6sqCpRsQlsOHyRfq3Nl8EQhOyWpxOhlNSpU4eDBw+men7MmDGMGDHC8H14eDi+vr5ZEVquc/3ULSZ1n07Q3SfP9/5SUVX4feJKWvdtyvCFH4oaPkKm2nLiKhpZRqeY2GYF/ZvzrjM36dmkKqsPnGfGqn3EJiQaJmNbsqIqSW4upFelhA8Lh3fj4bMw7j0OxUajoXzRgjja21r0+Cv3HhPw1Pxmr6qqsunIZZEICbnCK5cInT17Fh8fn1TP29nZYWeXOz/xZaU7l+4zsukE4mP1lX51LxVh2/H7PqJCo5mw5jOxzFTINCER0WaTIACNLBMcEc3agxcMm2qCdQlQkpgMqG6d3Qp55qOQp/WTfkMiUq6qnWJbMTQm5BK5KhGKjIzk5s2bhu/9/f05e/YsHh4eFC1alDFjxhAQEMDvv/8OwKxZs/Dz86NixYrExsby888/s3v3brZv355dTyHP+GXMH8THJqS6X5iqqBxaf5yzey5Svbn1e0MJgiVcnewtKhSoUxTsbbVMX5V6rSJLuThkz0abqqpy/0ko0XEJFMjnlObiiunhambozKhtLh1CFF49uSoROnnypFGBxKQhrL59+7J06VICAwO5d++/se/4+HhGjhxJQEAAjo6OVKlShZ07d6ZYZFGw3OP7Tzn2z2mzdUM0WpkN87eKREjINC1rlGHD4UuWNVbT35ujkSXa1SmXrmtYS1FU1h48z/Jdp7n3OBTQFz6sV6EY77WtQ83SRbIkjmNX7/HLlmMWtZUliXa1s/Z1EoS0ylWJUNOmTU2++S5dutTo+88//5zPP/88k6N69fifv2tR8TRdosL1E7eyICLhVVW/fDGKFnQj4GnqlZNlWaJ5tVIEBkeg1cgkptKLaameTaqm6/HWUBSVsUu2sO3kNaOCiCpw/Oo9jl65y6S+bWlft3ymxrH24AUm/7HTos1xJQlstBq6viY+AAm5Q55ePi9kEivm/Ij5QUJmkmWJOUM64+bsYFSROokkSZQulJ9x77Sy5sc2WRVmzfOqwl/3a4uft+UrzNLr771n2XbyGpB8lZZO0S9OmPD7Nu4GhWRaDNcfPOGbP3cCxrvWp0QjS2hlmWkfvkEBN9P1iAQhp8hVPUJCzlCquh+SLJmtp6LRylRoWCaLohJeVUULuvPnl2+zfOdp1h28QGRsPAAF3Zzp1bQqvZtWx8HOhorFvFi575xF1yxSwI37T0IB/TBPk6ol6duqFpX9Ul9okdEURTXU6zFFAlbtP8dnPZpmShx/7z2LLEnoLOgFblOrLH1a1aJMEcvLZ6iqyvnbgaw7dIE7QSHY2WipX74YnRpUxN3FMT2hC4JFJDUvbBCTicLDw8mXLx9hYWG4uubesvEZbWL3aRzecCLVydJJZuybROVGmdttLwhJEhJ1PAmLQiNL5M/nhEb+r9M7Nj6R1qN/NCRKKZEAZwc7tn//IdFxCUTFxOHm7ICzQ9ZP/L12/zFvfvuHRW3zuzqy/fuBmRJH4xHziYxJ/TV70fqJ/Sha0N3ia0fHxvP5z/9w+NIdo0nvkgRaWWb8u60zfdhPyLssff8WQ2NCmgz47m0cXOyRNan8CEnQ8p3GVHpNTJgUso6NVkMhT1e83F2MkiDQFw40tX1HUpHAL99sgZ2NFndnB4oUcMuWJAgwmbC9LCou85b0R1tx7cgYy7cfUVWVzxZv4ujlu4BxKQNVhQSdwrilW9l3XswzFDKXSISENClcyofZh76haPnCAGi0GrQ2GiRZQqPV0HV4ez77dbCYIyTkKO3qlOPb99oZlnZrNTLa58m8q5M9U95/nTa1y5q6RJbJb8XyeM9MHELycLb82tYs6T914wFHr9wzOe9IkmDOuoN5Ymd7IecSc4SENCtWvgiLz03n0uFr/22xUawAzd9+DbcCYodmIWdqW7sczauVYs/ZW1y+FwRAhaJeNK9eChut6V3qs1IxL3fK+xbk2oMnZpIFiU4NKmVaHB0bVGTp9hMoJuYEyrJE9ZKF8XJ3sfi6aw5cMFsDSlXB/1EwF/wfUaVE1s3PEl4tIhES0kWSJCo1LEelhmIITMg9bG20tKldNsf0/qTm/XZ1+Gzx5lTPy5KEo70NXRpmXiLUo3EV/tp9hriExFQTMkVR6d/Wuu00bgc+s7iy992gYJEICZlGJEKCIAg5xY0bsGULnD4NV6/SPD6ebTEJHFEcuF6wCAeKlOO+m35FlixJONrZMH9oVzxcM29ozMvdhTkfd2bYvPXJkiGNLKEoKqN7N6dBheJWXdfWit63nNRTJ+Q9IhESBEFIhaqqnL4RwN/7znLmZgCKolKmSAF6NKlK48olDPOL0i0gAL77DvbsgchIkGVwcACNhgKyQpuoIBpduUvXy0c4XqgUGxq9QeN2jejeqEqW1OupWboIayb0ZdX+c2w4fImQiGgc7GxoVaMMvZpVo5xvQauvWbd8Ma7ce2y2NpEsSVQvVTitoQs5VFBIBGsPXmDLiatERMfh4eLIG/XK07lBpSwvmyCWz5shls8LwqtJpyhM/mMnGw5fMprLIj/vBalZugizBnfCycKd21O1axeMG6dPhjw8IF++lIuWqiqEhaEGByMVLgxffw0tUl8Fl5lUVU33QojA4HDe+N8vmHoH0sgSjSqXYMZHHdN1LyFnOXDhNqMWbyZRpxglwpIEjna2zPu4C1VLFkr3fcTyeUEQhHRYuOmIYR+zF+eyJE0aPnMrgLG//pu+m+zaBSNGQFAQFCsGbm6pV26XJHBzQypWTN9+xAj947NBRqwG9fFw5bPuTVM9r5El3Jwd+Lyn2BsyL7l2/zEjf9xEQqIuWW+gqkJMXAJD5q7l4bPwLIvJ6qExf39/Dhw4wN27d4mOjqZAgQJUr16d+vXrY2+fPbsyC1krIiSSg2uPERIUhlM+R+p3rEVB3/zZHZYgZJiImDiWm6nqrCgq+y/4c/3BE6sqKRs8eKDvCYqMBF9fy7eu0Wj07e/f1z++XDkonDuHjt5sXh0XJzvmrT/E49BIQy0nCahXvhhfvtUCbw/LV6IJOd/S7SdRVTXZljFJFFUlLiGRv/ee5dNujbMkJouHxv744w9mz57NyZMn8fLyolChQjg4OBAcHMytW7ewt7fn7bffZvTo0RQrViyz484yYmjsP4kJifw0ejmbFm4jIT4RjUZjqCzdqFtdPvlxIC7uYn8hIfdb93yTUXN/HDWyRK+m1dK2vcXQobBxo74nSJOGycA6Hdy9Cx07wty51j8+B9EpCsev3uf+k1BstRpql/WlcH5RgiOviYqNp+nIBRatFnR2sGPf9EHp6n209P3boh6h6tWrY2trS79+/VizZg2+vr5G5+Pi4jhy5AgrVqygVq1aLFiwgB49eqQ5eCHnURSFyb1ncnj9CUNxM12iznD+4Lrj3LsawOxD3+Do4pBdYQpChngUEoHGgp3qFVUlKCTC+htcv66fGO3hkbYkCPSP8/DQX+fGDShdOm3XyQE0skz9CsWoT975EC0kFxwRbXHJhMiYOGITEnGwtcnkqCycI/Tdd99x7NgxBg8enCwJArCzs6Np06YsWrSIq1evUqJEiQwPNK9RFIXoiBh0Op35xjnAwbXHOLTueKoVXhWdwr0rAayattHqa8fHxhMbHSeqxwo5hoOdjckCgklkSUrbH+qtW/VDYvnS2euRL5/+Olu2pO86gpAFrPldkSTrSiykh0WJUJs2bSy+oKenJzVr1kxzQHnd7fN3mfb+At5weodO+frQ3vFtvnlrJleO3cju0ExaP29L6vuKPafoFDYt3EZiQqLZ6yXEJ/DvTzv5oMoI2ju+TQfnd3inxBBWTdtIVHh0RoUtCGnSqJKf2WXdoJ9E3ahyGj74nT6tXyKf3knHkqS/ztmz6buOIGQBT1dHShfOj2zmx14jSzSoUDzZfoGZxeq7hIeHm/wSUrdv5WEG1/qcncv2kfB8I0Ndgo4Dq48yrMGXbFq0PZsjTJmqqlw+fM3sTvMAYU8jeHgryGSbmKhYRrf6mpkf/cjdSw8Mxx/ffcJPXyxnaN0xBD8KSXfcgpBWJQvlp2bpwmhM/MWWJQlPV0eaVitp/Q2uXtXXCcoIDg5w+XLGXEsQMpEkSbzdogbmOlt1isqbzatnTVCkIRFyd3dP8cvNzQ13d/fMiDFPuH3+LlPemY0uUUGXaJxQ6BIVUGHOkJ84t+9SNkWYOlVVLRomSPLi3KGUzBn0E5cOXQWVZMNhqqLy8NYjJnSZKobKhGw1qV9bPFwcU0yGNLKEjVbD9IEdsbF2jo+qQnx82ucGJQtGo7+e+H0RcoEO9SrQvm75FM8l/ab1b1Pb6krl6WFRIlS3bl1WrVoFgJ+fH05OTnz99dfs3r3b8LVnzx52796dqcHmZmtn/2O2jSzLaZpjk9lkWaZwaR+LZu/b2NvgVSz1pcRPA56x688DJhMrXaLC1WM3uHL0epriFYSM4OPhyvIv3qJ93fLYvDAsLEnwWiU/fh/9Ztr2v5IksLXVr/rKCDqd/noZUNtHEDKbJElM7NOGkd2bUPClqui+Bd2Y8G5rPu7UMEtjsmjV2E8//US9evXo0KEDV65cYe7cuXzzzTecOXOGH374AT8/v8yOM1dTFIXdfx5M1hOUrJ1O4di/p4kMjcLZzSmLorNMp8FtWfDJEpNtNFqZVu80NrlqbP+qoxbdT6PVsOuPA1Son7M3xRTytgJuznzVpw0jujXR7wKvKJTw8Uz/thblysH+/RkTZEwM1K2bMdcShCwgy/ohst7NqnH13mPComPxcHGkbJECGVKs0+p4LGlUvnx5EhMTiYyMxMbGhhEjRnDjxg0KFy5MlSpVGDlyJKGhoZkcau4VGxVnmBNklgoRwZGZG1AatOnflCJlC6HRpvwjI2tkHJwd6D2mi8nrhDwOMzvpGvTJY9hTMedMyBlcneypXdaXuuWLZczeXjVqgKKkfzhLVfXXqVYt/TEJQhbTyDIVi3vToEJxyvkWzJYkCCxMhDp16kS7du3In/+/6sEeHh7MmjWLM2fOcOfOHUqVKsWsWbMyK85czc7RFo2N5fMBnNws33AuLiYO/wt38b9wl5io2LSEZxEHZwem7Z5AyWr63r+khCgpqfHwcWfanq/w8fMyeR1XD2dUCyZdy7KMcxZsJikI2aJdO3B2hrCw9F0nLEx/nXbtMiYuIVs8CYvk6v3H3H8SKuZGZgOLKktfvHiRSpUqAfriii9nbaqqcvPmTaKjo3NNXRxLZVRl6W/emsmB1UdNDo/JGpkqjSswddcEs9cLeRzGiinr2PLrLmIi9AmQnaMdbfs3480vu+LpkzkT11VV5fz+y+xctp/gwBCc3Z14rWs9GnSshcaCmg+Bt4PoU/pjzJbsBX7YOZ7qzStnQNSCkAOJytKvvONX7/HrthMcv3rPcMzP24N3WtSgc8NK2dZDkldY+v5t9e7zEydONHl+wgTzb+K5SUYlQleO3WBYgy/NJgCTN4+h7us1TLZ58uAZwxuO5dnDkGRL2mWtTL78rsw+OBmfEqZ7Z7LL+M4/cOyfU6kux9doZYqULcxP56eLPwRC3hUQAD166DdQtWavMdAPid2/D15esGpVrt1r7FW2/tBFJi3fgSxLRotHkvZb61i/AhPebS3+BqZDpiVCr5qM3Gts08JtzPn4Z2RZNkoCZI3++35f9+btsd3MXmd4w7FcO3Ez1d4ljVamWEVfFp2emiN/icKDI/is2VfcuXQf9aXVY7JGxt0rHzP3f51jEzlByDBJu89HRuqTGUt6hnQ6fRLl7AwzZkCLFpkfp5ChbgY8pdc3y8xOERv7Vgu6NaqSNUHlQZa+f2dN2UYBgA6D2jBt11fUblvtv4IJQJXGFZi8eYxFSdCN07e5fOS6ySE2XaLC7XN3uXT4WgZEnfFcPVyYfWgyfSf2wuOFITynfI50H9GBhad+EEmQkG1i4hJYd/ACH89bR98f/uLzxZvZd/4WOsX83DartWihT2a8vPTDXKGhqU+gVlX9+bt39e1FEpRr/b3vLLKZD6kSsHzXaTFnKAtY3SMky7LJXgYxR8gykaFRRARH4uTmiKuHi8WPWzpuBSu+X2d2Kb5Gq6Hzx235aEa/dEaauXQ6HcGBoSg6BQ8fN2yyYIM9QUjNmZsBfLJwAxHRcUiSPvfQyBI6RaWkjydzP+6CtxW/rxYLCIDvvtNvoBoZqd82w8FB30Ok0+mXyCuKvheoWTP44gsxHJaLNR25gPDoOIvarp3Ql+LeHpkcUd6UobvPv2jdunXpCkzQc3ZzSlOtoKiwaIuHu6LCcv6eXRqNhgJFPLM7DEHg1sOnDJ6zloTnldGTPiIm7ZZ9JyiYD2et4q8v38HJ3jZjb164sH7C840b+g1Uz5yBK1f0FaMdHfV1gqpV068Oy8W7zAt60bEWllMBomLjMzESAdKQCHXq1Mno+7Vr13L27FkqVapEz549MyywV0lESCSXDl0jLiaeQiW9KFXdL9Vkx8PH3cLtLlSjYSdByK2uP3jCvcch2Gg0VClZCHfnDNqj6yU/bzlGok6X6marOkUl4EkYm45epnfTapkSA6VLGyc6qioqRudB7i4OPAmLsqitp2vOKq6bF1mdCL3o+++/Z/z48VStWpWZM2dy5cqVPLdqLDOFB0fw8+jl7Fy+n4S4/3ZsL17Jl/e+eYv6HWole0zzt17j1//9afbaukSFlu82ztB4BSErHb1yl7nrDnLl/mPDMa1GpnXNsnzarVGGvkGERcWy8/QNQ++PKav2ncu8ROhlIgnKkzo1qMSvW4+nmnSDflPfqiULZc5QrGAkXZOlf/vtN37++WeOHz/Ohg0bWLLE9BYMwn/CgyMY3mAs25buNUqCAO5eesD4Tt+zdcmeZI/zKlaA2m3N78pbuVF5fMuKOQRC7rTj1HWGzF3L1QePjY4n6hS2nbzKu9//xVMLP1Fb4uGzMIuSIBW4/zg0w+4rvJq6N66Cva3W5IRpRVV5r03tLIzq1ZWuROjhw4fUq1cPgHr16hEQEJAhQb0KFo9axsNbQSnW0kmavz7zw0U8vv802bmAm4Fmr//w9qM8N3FdeDWERsYwbulWUFNeQKVTVJ6ERjJlxa4Mu6c1O8hrLNgiRhBMKejmzJwhnfXJkGycDGlkCQkY3asZDSuJfTyzQrp+o3U6HVqtfnRNo9GgZMby0jwo/FkEu/44kGpBwSSKTuGLNpM5u+eiITk6v+8yD288MnuPZwEhnNx6NiPCFYQstfHIJRJ0OpO1R3WKyr5ztwkKiciQexbzdsfNgrlHGlmiTlnfDLmn8GqrUboIayb0pV/r2ni6OiJJ4GBnQ/u65Vk+5i16ZdXwq2D9HKEXt9iIiYmhQ4cO2NrailoHVji//zKJ8YnmGwL3rwYwqsVEWvVpwshfBnFi6xk0Wg26RNO9PRobDSe2nqVu+5oZEbIgZJmDF/0t2otUUVWOXb1Hx/oV031PG42GHo2r8POWYybvrVNUejWrlu77CQKAl7sLH3dqyMedGqKqao4sgPsqsDoR6ty5s+H/X15B9uK5zLB//36mTp3KqVOnCAwMZN26dWbvuXfvXkaMGMGlS5fw9fXlf//7H/369cvUOM2Jj7F+OeTOZftwK5iPhLgEy+ZPqhCXhvsIQnaLtfBDAkCcFW3NqV6qsNkEzEYjU7ZwgQy7pyAkEUlQ9rE6EcrOVWFRUVFUrVqV9957j65du5pt7+/vT/v27fnoo4/4448/2LVrFwMGDMDHx4c2bdpkQcQp8/YraPVjVBXWzfmXdyf0QGfB7u2qqqbpPrlFYkIix/45zf2rAWi0Gqo0qUDZ2qWyOywhA/gWcOPKvSCLJi9nZG385TtPIUtg6raJOoV1hy8yoF3dDLnn7cBnHL58l/iERAp5utK0ainsbfV/lqNi49l95gaPQ6NwtLOhUWU/ihRwy5D7CoLwn3Qtn89q7dq1o127dha3X7RoEX5+fkyfPh2A8uXLc/DgQWbOnJmtiVD5emUoUsaHBzcCLdqFPYkuQYdGq0GWZXSK6aExVVVp3bdp+gLNobb/tpfFny8j7Ek4Gq2MqoCiKJSq7sdnvw6mZNXi2R2ikA6dG1Ziy4mrFrWd8udulu84xZLPeuPh6pjmez4Ni+LI5btmfx1VYO3BC+lOhB4+C2PCb9s4dSMAWZKQJAmdouBkb8v77eoQFRvP8p2niUtIRCPLKKrCtFV7aVjJjwnvthK1ZQQhA1n9ecrd3R0PD49Uv3KSI0eO0LJlS6Njbdq04ciRI6k+Ji4ujvDwcKOvjCZJEv0nv2lVEgT6zVQjQ6LoMvx1k8NjkiTxxsBWebJi88YF25jafz5hT/T/LrpExTBJ//b5u3zy2v+4ff5udoYopFOtMkWoUaowGtmyoYL7T8LoOP5XwqNj03zPx6GRFv86PgmNTPN9AAKDw+nz/V+cvfUQ0M91StrHLCo2njnrDvLLluPEJeiH/XSKgqrq/1wcuXyHflP/JjQyJl0xCILwH6sToVmzZjFz5kxmzJhBTEwMY8aMYebMmYavnOTRo0d4eRlv3unl5UV4eDgxMSn/IZkyZQr58uUzfPn6Zs4Kkcbd6zN03gAkWTLagNUURVGxd7JjwHdv0+a95oA+OUqi0eqXADft3ZDBs/pneMzZLSQolAWf/JrqeUWnEB+bwMwPF2VhVEJGkySJmYM6UsnPx+LHRMclMGnZjjTf08HO8j3u7GzS15E+a+0BwqJiLRv6e4lOUXkUHM5P/x5NVwzCq0FVVWLiEgzbxggps/o3um/fvob/Hzp0KN26daNEiRIZGlR2GjNmDCNGjDB8Hx4enmnJUMfBbajdthqLRv7G4Q0nzLZXdAr13qiJRqNh5E+DeH1ASzYt3MblI9dAhbJ1StFhUBsqNiibJyfe/fvzLrPbiyg6havHb3Lr3B0xRJaLuTja8/OIHhy8cJsRP26yaBXZvvO3URQFWbbu893DZ+GsPXAeCfOdtBpZomnVtM9FexoWxa7TN0xWFDZHp6isP3SJjzu9ZlUCJ7w6noVHsXLfOVbtP2/oPaxashBvNqtGy+plktUuetXlqjlC1vL29iYoKMjoWFBQEK6urjg4pFwzxM7ODjs7u6wIDwCfEl6MXzWSd/wGE/woNNXaQrJWplztUkZv7uXrlqZ83VdnA8aLB6+iWvApWpIkLh68KhKhXE4jy1Qo5m1REgT6IaS7QSH4+Vg+JHzu1kOGzF1LXEKiRUNjOkWlV9OqFl//ZZfuPEpXEpQkJj6BGwFPqVLC8l4z4dVwO/AZH8xYRVh0rNEHxwu3Azl36yFta9/i635t0Vj5gSEvS/crkZN7HurXr8+uXcbVZ3fs2EH9+vWzKaKUabQavlo7Clt7G+QUqtZqtDL5PF0Y88fwbIgu50hMsHCptITZOktC7hCfYN2/Y5wV/+4hkTEMnb+O2PhEs8NUSX/mhnZuSGUrhuxelmjBik9L6UQBW+ElcQmJDJ6zlvCXkiDAkIBvPXGNX7Ycz47wciyre4ReXLYeGxvLRx99hJPTfysY1q5dmzGRpSAyMpKbN28avvf39+fs2bN4eHhQtGhRxowZQ0BAAL///jsAH330EfPmzePzzz/nvffeY/fu3axcuZJ//vkn02JMq7K1SzH36BSWjlvB4Y0nDD0fWlstzXo3pP/kNzN08vOzwBB2/LaXgBuBaG21ePsVJCQojIiQSFw9XGjauyFla5W0+rpR4dHs/uMAN077I0n659XszYY4WFC198mDZ2z/bS+Bt4KwtbehVptq1G1fwzD3ya9SUS7sv4wu0fQbgKqoFK8oqv/mBV7uLkhSylttpOTYlXus3HsOWZaoWsKHVjXLGpajv2zDoYtExcZbdO0i+fMxuGND2tQqa0X0yZUolDG/w7IkUayge4ZcS8g7dp6+wWMLJvMv33Wavq1rpXu+W14hqVaWhO7f3/Qk3MzceHXv3r00a9Ys2fG+ffuydOlS+vXrx507d9i7d6/RYz799FMuX75MkSJFGDdunFUFFcPDw8mXLx9hYWG4urpmwLMw71lgCHcvP0CWJUpULYZrBu4+rNPp+Hn0ctbO/lf/7iJJRsNxskZGkvSrsSo2LMf4VSPw8LbsD+6mhdv48bPfiYuNR/N87yadToe9ox1DZr9H2+cTvF+WmJDIgk+XsnnRdiRJev7pW0KXqMOzkDv/+3sElRqW4+7l+wyoNCLFaxhIULBoAZbdmmf1XBEhZxowYyWnb5jfx/D5jw2ypP931ykKzg52jH+nJS1rlEnWvtvE3/B/FGxxHE2qlODrfm1xdkjf0Hn/aX9z4XZgmofIkuYpTf3wjXTFIeQ9Q+au5diVexb9bM0c1JEmVaz/sJubWPr+bXUi9KrJjkQoM80Z8hObFm23aOm+Rivj7efF/ONTcMpnum7JpoXbmDPkZ5NtPvt1MG36GSeyqqryQ7957Fp+IMVtWmRZQmOrZeb+rylbqyTTP1jItl/3pL6liwQTVn/Ga10ypuCdkP3uBoXQbeJvaUockgbup3/UkaZVjf/oNx+1yKpl6LIkUdnPm8Wf9sBGa/kmrS87d+shA2asQlEVi3u6XozB1kbDstFvUrJQ/jTHIORNvSYv40bAU/MNgfHvtKJzw0qZHFH2svT9W3xkzmVUVeXBjUBunvEnJCjUqsf6X7jLpoWWJUGg7xUKvPWIjQu2m2wXHRHDj5/9bvZ6Cz9dSlxMnNGxaydusnPZ/lQTG0VR0SXoWDxKf/3hCz6g3QB9z1LSfCrpeUE6W3sbRv82VCRBeUwxL3d+/LQ7NmnY9V19/jX5j50k6oznD7k6Wtezo6gq524Hsv3UdavjeFHVkoWYM6QzTva2wH/zj5LqJjWoUIyqJQoZHZOfN8rnZM+i4d1FEiSkyM3ZwbItmAAXK3/+8zKre4Re3HQ1JadPn053UDlJTukR0ul0bF60g7WzNvPw1vOVcBLUbludt77sSqWG5cxeY+7HP/PP4h1m59i8zLOQO3/eW5TqUNPmH3cwe/BiixKsz5d+TKs+TQzf/9B/Hrv/OGjR5OZfr8zCt2xhAB7cCGTLTzu5dzUAjY2GKo0r0LpvU5zdRMXdvCoxUWHpjhNsPHyJyNh4nOxtKeTpyqnrDyzqLfJ2d+HDN+rRqX5FJEli0eYj/PzvMat6mmRJonzRgiz74q30PBVAv/Jr28lrHL50h9j4RIrkz0enBhUp61sQVVW54P+ITUcvERQSiaOdDc2qlqJ59VLp6o0S8ra1By8w+Y+dZts52Nqw84eBeb78gqXv32nedFVVVaZMmcJHH32U4ypK5zU6nY5v35zF/jVHkV6svqjCqe3nOLntLF/+MZymvRqavM61EzetToIAnj0MISI4knz5U/5Bunn6NhqtBp2ZFT5aGw03z/gbJULXTtyyeIXXrbN3DIlQkdI+fPDDuxY+AyEv0GplBrSra7S9xec/bUa1sIvzUUgEk5bt4MLtQP73dku6vVaZpdtOkKDTWTxEpagq1x88SUv4yTjY2tC5QSU6N0g+PCFJElVK+Ijl8YJV2tUux9z1B4mIjks1wZck6N64Sp5PgqyRrk1Xp0+fzvDhw/NUQcWcaO3Mf9i/5iioJPujnzTR+bt351K2Til8/LxSuoReOkodmCyTYOF11RSa5uTyC0LOl5afnnWHLlK5hA+dG1Ri+sAOjFi0EZ2imNxs1fim4mdWyJkc7GyYM6Qzg2avIS7BuCxE0k9tvfLF+LiT6Q/NrxoxRyiH0+l0rJm12eywk6qqbF5keouBivXLplinyJyCxQrg4uGc6vlydUqZ7Q0C/aaxZesYF4CsUL+M0TYhppRJw3J+Ie8KiYgmOi7B6gnHkqTfaV5VVRpW8mPZF2/RqoZly+JlSaJiMRMfNgQhm1X28+Gvse/QpWFl7Gz+G0b1LejG572aMWtwJzG8+hKRCOVwN0/78+xhiNl2ik5h79+HTLZ546NWqVauTo0kS3Qe0tZkz03T3g1xcLE3ey0XD2de61rH6FjHwW3MDtfJGpkaLStTqKS3ZUELed4/x67QdsxPHLp0x+rHqircDgzm/pMwAMoUKcCUAa/TvVEVw6Tk1CiqSq+m1dIQsSBkHd8Cbnz5Vgv2TBvM5snvsW3KB6z7qh+9mlbDRiOSoJdZPTT24j5c8fHxfPPNN+TLl89wbMaMGRkTmQBAVFi0xW2jw00vBfYtW5ieozqxcuoGi64na2SKV/TljUGtTbazd7Rj6LwB/NB3nsl2wxd+iI2t8bh0qWp+dBzcho0LtqUag629DR/N6GdRzELet+/8LcYt3Zru60TFGq9g/OiN+hy4cJun4VEpVpqWJYnaZX1pUf3V2dZGyN3sbbUU8sxnvuErzupE6MyZM4b/b9CgAbdv3zZ8L+Z7ZDwPH8urx7p7u5ltM+C7t3FwtufPKWtJjEtE1khGPTIarQZVVVF0CrXbVmP070NxcDLf29Pq3SbIssy8ob8QGRqF9nmXbGKCDldPF4Yv/IDG3VPe2mTInPdwyufIqumb0CXo0GhlVFW/TUahkl6MXfEpfpWKWvYiCHmaqqrMXnfAqmrTqfF0NV5h6OHqyJJRvfnil384fzsQjawvy6AoKioqr9cpx5dvtUSbhuFlQRByLlFQ0YzsXj6vqioDq33GnYv3Uy8iiH4I64Pv3qHHZx0tum5kaBR7/jpo2GKjSNlCPH3wjPBnkbh6utC0VwOKlClkdbzxcQkcXHOUG6f9Af38oQadayfrCUpJeHAEu/88aLTFRpUmFUSCLRicvx1Iv6kr0nUNWZKoUbowiz/tkWqba/cfs/vsTSJj4ing5kTbWuXwzsAK74IgZD5RWTqDZHciBLBv5WEm956Z6nlZI+Pk6sjS63Nw9RR/rIW8a9ORS0z43XSBT0ssGNaVeuWLZUBEgiDkVJlWRwjg5MmTrFy5knv37hEfH290LjM3XX1VNenZgMDbQfzy5Z9otLLRUJaskXF0ceC77f8TSZCQ56VntYtG1g9zjXmzuUiCBEEwsDoRWrFiBX369KFNmzZs376d1q1bc/36dYKCgujSpUtmxCgAvb/oQpWmFdkwbwuH1p8gPjYeDx932g9oSfuBLS3eGFUQcrPqpQpbND9IkvTLiB88CSMkMhoHWxta1SxDr6bVKOdbMGuCzUD3Hoeyev85dp25QXRcAl5uznRuWIn29Srgks5NYLNbXEIi209eY83BC9wNCsHWRkPDin70bFI1V/5bCbmP1UNjVapUYeDAgQwZMgQXFxfOnTuHn58fAwcOxMfHh4kTJ2ZWrNkiJwyNpURVVTF3RnglfbpwAwcv+qe4siuJJMHmye/j4+Ga639XNh65xKTlO5DA8JyTno2HqyM/ftKdEj6e2RZfejwJi2TQ7DXcDgxGliRDNWSNLKFTVD56oz4ftq+XzVEKuVWmbbp669Yt2rdvD4CtrS1RUVFIksSnn37K4sWL0x6xYJXc/IddENJjdK/muDk7GDYkTcln3Zvi46H/w5ebf1eOXbnLxN+36zcffiHxS9pMNjQyhoGzVhMRHZttMaZVok5hyNx13A3S10l7cUuIpOe6aPMR1h28kC3xCa8OqxMhd3d3IiIiAChcuDAXL14EIDQ0lOhoy2veCIIgpIW3hwu/j36TeuWLGXpGkv5b0M2ZSf3a8Gbz6tkVXob68Z+jJhM5naISHBHNhiOXszCqtAkMDmfF3rP8suU46w9fZMepa9wMeGqyZw/0r4FOsX6PREGwlNVzhBo3bsyOHTuoXLkyPXr0YPjw4ezevZsdO3bQokWLzIhREATBiI+HK3M/7kLA0zBOXLtPfKIO3wJu1Cnni0bOG3V+Ap6GcfbWQ/MNVVh38ALvtKiR+UGlQVhULF8v38GeczcBkCUZnaIgoU9gzc3NeBwaycnrD6hbTtQSEzKH1YnQvHnziI3Vd8OOHTsWGxsbDh8+TLdu3fjf//6X4QEKgiCkpnD+fBTOnzcr5z4KibConWpF26wWGRPH+9P/5k5QiGGCu07V9+5YMzk1KIc+PyFvsDoR8vDwMPy/LMt88cUXGRqQIAiCAA4WFCFNYm+TpkoomW7JthPceRRiNP8nLax5LQTBWmnqQ7527RoPHjwA4Pjx44wfP56//vorQwMTBEF4lZUpUgBPF0ez7TSyRPPqpbIgIuskJOpYc+B8upMgG41MHTEsJmQiqxOhGTNmUL58eUqUKMGiRYto0aIFW7ZsYcCAAXlu6bwgCEJ20WpkejerjrlFb4qi0qNx1awJygp3g0IIj44z39AEWZZ4vU558lmw36EgpJXVidDcuXOZMWMGf/31F8OHD2fBggWcOHGCP/74gyVLlmRGjIIgCK+kPq1rUq9csRSToaRjn/VoSpkiBbI2MAukd6WXLEuU8PZgRI8mGRSRIKTM6oHlBw8e0LNnTwoVKoQsy9Srpy92VbduXQICAjI8QMFyj+48Zsdv+wi6+wQ7R1vqtKtOrbbV0GjSvi2BIAjZx0ajYdbgTizZdoIVe88SGhljOFemcAE+aF+P5tVy3rAYQKH8+bDRyCTozCdE9rZaYuMTDd872NnQpWElPnqjPs65vHK2kPNZnQjpdDpsbPQT17RaLVqt/hKyLKOIWg/ZIj4ugTmDf2Lb0j3Isvz8k6LExgXb8CpWgPGrR1KmZsnsDlMQhDSw0Wr4sH09+retzeU7QUTHJVDQzYmShfJnd2gmuTjY0a5OOf45dsVsraAR3RpTr3wxHjwNw0aroUJRLxzsxARpIWukaalBixYt0Gq1xMTE0KFDB2xtbUlMTDT/QCHDqarKt2/N4vCGE6CC8tKnrycPnjGy6QTmHp1C8Yq+2RSlIAjpZaPRULVkoewOwyrvt6vL7jM3iY5PQEkhGdLIEsW83GlfrwIOtjYUKeCW9UEKrzyr9xozNyF6woQJ6Qoop8mpe40lObP7Ap+3nGSyjayRqdOuOl9vfHVKHeh0OgJuPCI+Np6Cvvlx9XTJ7pAE4ZV09f5jhs9fz5OwKGRZQlFUw15ilYp7M3NQRzxdnbI7TCEPsvT92+pE6FWT0xOhid2ncWTjCXSJpoclJUliuf98ChbNeZMqM1JCfAJrZ/3L+nlbePrgGaBPBBt0qs3bY7tRqrpfNkcoCK+eBJ2OfedusfvMTSJj4ymQz4n2dctTvVThXL0XnJCzWfr+neYqXKdOneLKlSsAVKxYkerV88bePrnNtRO3zCZBoB9C879wL08nQvFxCYxt/y3n9l5CfaEbXtEpHNl4gqObTzFpw2hqt6mWfUEKwivIRqOhZY0ytKxRJrtDEYRkrE6EHj9+TO/evdm7dy9ubm6AfsPVZs2asWLFCgoUyLtvtDmR+DD1n9/G/50sCUqiS1SQJJWJ3abyx52F5Muf83r3BEEQhKxndR2hoUOHEhERwaVLlwgODiY4OJiLFy8SHh7OsGHDMiNGwYQKDcqg0Zr/Z5RkiZJ5eFgoNjqOTYu2pZgEJVFVlfjYBLYt2ZOFkQmCIAg5mdWJ0NatW1mwYAHly5c3HKtQoQLz589ny5YtGRqcYF6nwW3NDo3JGpmGnWqTv5CHyXa52bm9l4iJiDXbTlVU9q48nAURCYIgCLmB1YmQoiiGOkIvsrGxEXWEskHFhuVo+W5jSGWITNbIOLjYM+D7d7I2sCwWFRZtedtQy9sKgiAIeZvViVDz5s0ZPnw4Dx8+NBwLCAjg008/pUWLFhkanGCeJEl89stgun3yBhqtBkmW0Npo0Gj11aSLli/M7EPfULiUTzZHmrk8fdwtaifJEp6FLWsrCNlJUVTO3XrI9lPXOHTRn5i4hOwOSRDyJKuXz9+/f5+OHTty6dIlfH19DccqVarExo0bKVKkSKYEml1y+vL5F4U+CWP3HwcJuvsEWwdb6ravQcUGZV+J5ak6nY63iw3i2cMQs20/+3Uwbfo1y4KoBCFtNh25xOJ/jxHwNMxwzMHOhm6vVWZwx4bY26Z5wa8gvDIytY6Qqqrs3LmTq1evAlC+fHlatmyZ9mitMH/+fKZOncqjR4+oWrUqc+fOpU6dOim2Xbp0Kf379zc6ZmdnR2ys+bkkSXJTIvSq27hgG3M//jnV87JGxsPHnaXXZmMn9i8Scqif/z3Ggk0pz2OTJYnKft4sHN5dJEOCYEam1hGSJIlWrVrRqlWrNAeYFn///TcjRoxg0aJF1K1bl1mzZtGmTRuuXbtGwYIFU3yMq6sr165dM3z/KvSOvKo6DGpN4O0gVs/YhEYrG00il2QJt4L5+H77OJEECTnW5buPUk2CABRV5YL/I5ZsO86gDg2yMDJByLusniMEsGvXLt544w1KlixJyZIleeONN9i5c2dGx5bMjBkz+OCDD+jfvz8VKlRg0aJFODo68uuvv6b6GEmS8Pb2Nnx5eXllepxC9pAkiYHT+jB11wTqvVELW3tbZFnCu3hB3v/2bX66MJ2i5Qpnd5iCkKq/955DI5v+sKaoKqv2nychUZdFUQlC3mZ1j9CCBQsYPnw43bt3Z/jw4QAcPXqU119/nZkzZzJkyJAMDxIgPj6eU6dOMWbMGMMxWZZp2bIlR44cSfVxkZGRFCtWDEVRqFGjBt9++y0VK1ZMtX1cXBxxcXGG78PDwzPmCQhZplqzSlRrVgnQD+OKXkAhtzhw4bbZndoBQiNjuBHwhArFvLMgKkHI26xOhL799ltmzpzJxx9/bDg2bNgwGjZsyLfffptpidDTp0/R6XTJenS8vLwMc5VeVrZsWX799VeqVKlCWFgY06ZNo0GDBly6dCnVSd1Tpkwxu7GskHuIJEjITeKt6OWJjU/MxEgE4dVhdSIUGhpK27Ztkx1v3bo1o0ePzpCgMkr9+vWpX7++4fsGDRpQvnx5fvzxR77++usUHzNmzBhGjBhh+D48PNywOk7QiwiJ5ODaY4QEheGUz5H6HWtR0Dd/dodlxP/iPU7vOE98bALefgVp2Lk2tva22R2WIJjk4+nK7YfPsGQFi4+nWLwhCBnB6kSoY8eOrFu3jlGjRhkd37BhA2+88UaGBfay/Pnzo9FoCAoKMjoeFBSEt7dl3cM2NjZUr16dmzdvptrGzs4OOzsxmTYliQmJ/DR6OZsWbiMhPhGNRoOiU5g/7FcadavLJz8OxMXdOVtjDLwdxA/953PxwBUkWUKWJXSJCk75HHl3fA+6ftJe9BIJOVa3RlWY+rfpLWBkSaJWWV98PEQiJAgZwepEqEKFCnzzzTfs3bvX0Nty9OhRDh06xMiRI5kzZ46hbUbuPWZra0vNmjXZtWsXnTt3BvRVrnft2mU0TGeKTqfjwoULvP766xkW16tCURQm957J4fUnSKq4oHuhG//guuPcuxrA7EPf4OjikC0xPrrzmKH1xhAREgXot9NImm8RFRbNopG/EREcSb+ve2dLfIJgTod6Ffh9x0mehEamOFcoKYUf2L5e1gYmCHmY1XWE/Pws27hTkiRu376dpqBS8/fff9O3b19+/PFH6tSpw6xZs1i5ciVXr17Fy8uLPn36ULhwYaZMmQLApEmTqFevHqVKlSI0NJSpU6eyfv16Tp06RYUKFSy6p6gjpLd/9RG+7jnDZBtZI/PWl13pO7FXFkVl7KtuUzmy8SSKzvRWLz9fnEGxCsbDnbpEHXEx8Tg42yNJEvGx8aiqmieX2ut0OuKi47F3skOW07RwVMhE95+EMmj2Gh4+C0eWJJTnf6JlSUIjS3z73uu0qFE6m6MUhJwv0+oI+fv7pyuw9OjVqxdPnjxh/PjxPHr0iGrVqrF161bDBOp79+4Z/WEPCQnhgw8+4NGjR7i7u1OzZk0OHz5scRIk/Gf9vC3IGtlkkqHoFDYt3Mbb/+uG1iZri709DXjG4Q0nTO4+DyBrZTYu2MbQeQMAOLn9HOvm/MOJrWdRFRUbOy12jnZEPu9V8inpReeP29FuQAscnOwz/XlkpqvHb7B21j/sX3MUXYIOGzstzd98jS7D21OyavHsDk94zreAG2sm9GXH6etsOHSRwOAInOxtaVmjNJ0bVqJAvuwdfhaEvCZNlaVfJaJHSL8EvZ1db7O73Cf55fKsLK/Xc2DtMSZ1n2ZR22IVivDzxZn8OvZP/pqyznSCJ4GERPFKvkzdNYF8+XPnz8A/i3cwa9BiNBrjQpMarYyqwhfLhtGsd8NsjFAQBCFjZVqP0IsrqlIyY4bp4RMh91FVFcWC2iZJdNlQ6E2XYPlS4sREHTuW7eOvKesATA+lqaCicvfyAyb3msnUXRPSG2qWu3DgCrMGLQaVZMls0vffvTsH33KFKFXNsqFvQRCEvMLqRGjWrFm4uLhQs2ZNXu5MEqtx8iZZlilc2oeA64HJ/s1fZmNvg1exAlkU2X+KVbSsxIFGK1OiclH+mrIOSQJL+0MVncLZPRe5de5OqsNIwY9C2P7bPgKuP0Rrq0Vjo+HyketEhUXjlM+Rzh+3pXXfrN/sdeW0Dciy6WFNSYJ1s/9l1JKU64DpdDqO/XOak9vOER8Tj08JL1r1bZJi2QRFUTi7+yJHNp4kOjKGAoU9admnCUVK+2TYcxIEQcgoVg+N/fLLL4wfP56KFSsyffp0KleunFmx5QhiaExv/dwtLPhkiclESKOVadOvGZ8u/igLI/vP0HpjuHbyltl5Qp8uHsjMD3+0+voarUy3TzvwwffvGB3X6XT88sUfrJn1j+H1SS0GBxd7ZuyblGU9L1Hh0XTx6Gf2NQHQ2mrZHLUcjUZjdPzy0et83XMGTx88Q6PVACqqqn+Or3/Yko/nvGeYE3bn0n2+6jqVgBuBRm0VnUKTng347NfB2DvmvQnogiDkPJa+f1u9ZOT999/nxo0b1K9fn4YNG/LBBx8kq+0j5D1t+jelSNlCaLQp/8jIGhkHZwd6j+mStYG94MOpfZBlKdWeSVkjU+f16nj7pX2/udAnYcmOLfxkKatmbELRKaiKajLpiImI5eO6Ywi6+zjNMVgjMiTKoiQIIDE+kZiIWKNjN8/6M6rFRIIDQwD9sKcuUdE/V1Xl38U7+aHffFRVJfB2EJ82Gkfg7aBkbQEOrDnK+E7fo9OJPbIEQcg50rR21tHRkYkTJ3Lt2jV0Oh1lypRh0qRJxMTEZHR8Qg7h4OzAtN0TKPm8JyMpIZI1+v96+Lgzbc9X+KQjyUivyo3KM2nDFzi46Fd3Sc83r0yKtUGnWoxbORJXj7SuupGSFYy8c+k+G+ZvxaJSwM/pEnRMH7AojTFYxymfo8VtNVoZB2fjlXGLRy0jMT4x1WE1VVXZ89dBLh+5zm8T/iY6IibVtopO4cyuCxzddMpkHAnxCdy5dJ9b5+4QFR5tcfyCIAhpYfUcoY0bNxp937lzZ4oVK8bUqVNZvHgxDx48yLDghJzFw9udecemcH7/ZXYu209wYAjO7k681rUeDTrWej4Ukr3qtKvOioDF7PnrECe3nyU+Oh6fkl60e78FJaoUA6BE1WJ4FS9A0N0n1iUwiToa96hvdGzzou1otLL5FXWqqp+I89zZPRdJTExEq828MgO3zt3hz2/XWNRWo5V5rWtdo3/Dh7cecWbXBYseu3bWZg6tP2G2hpOskVk/bwsNO9dJdi4qPJqVP2xg8487CH8WAeiH61q89RpvftmVwqXEHCNBEDKe1X+Fk6o6pyQqKio9sQi5gCRJVG1SkapNKmZ3KKlycLLn9QEteH1AixTPy7JMz886Mffjny2+pkYrU7KaH+XrGheyu3byVopJUGE1gto8ohSh+BKBDQoJqsx9XLiJGyd03jy4FkhxCyd5W+v0zvP8r8MUs4lJEp1Ooevw9kbHbp29Y9ljExUuH71h0WpBRadw43TyQqsRIZGMaDKee1cCjGJOjE9k5/L9HFh7jGm7v6J0jRIWxSQIgmApq4fGFEUx+SUIuUGHQa1p+35z4L8htNTIGpn8hT35au2oZPOPXv7eU41hiHqGrzhCN25Qmac4kIiMggOJVOYp3bjBVxzBY8p4CAjI2CeGPqmY0OUHEuN1ZnuqkoY2P57zPhXqlzU+ac0qUGuapnDdWR8tTpYEJdElKsRGxfG/Dt+REJ9g+Y0EQRAskLXlfwUhh5AkiRGLP6Ja00qsmbWZG6f0vRSSLKG10ZIQp3/DdfFwpsNHren6SfsUiylWqFeaayduougUqqtB9OUS+YkhHFtCcSTlDEHFmQRc9u+CHtfg66+hRcq9Vy9TFP08m61L9hB05wmOLvbU61CLVu82ximfEwDbl+4lLjrebKkDgOotKtN7dGeqNauU7FyZmiX04Zu5jEYrU+W1chxYe4yEONP1nCRZonBpH77vM5eHtx5h72RHhfplObDmqMlJ3YpOITgwhMPrT9CkZwOzz0sQBMFSZpfPq6rKjh07aN26teHYP//8w/fff8/ly5eRJIkKFSowevToPLmZqVg+/2oIeRxGbFQsbgVcsbW35dnDYEA/CdzUdiEPrj+kf7nhVFeDGMh5HEjkCQ6oZrpIXD2cKVGxiL5HyNkZZswwmwyFPgljXIfvuHr8JrJWRklUDHmWvaMd41eNpHbb6gxrMJYrR69b9LwnrvucBp1qp3p+7BvfcnLbObNDbPOOTWHTou3s+H2fRcNxFs2reomskWnUrS7/W2G6qKsgCAJk8PL5Dh06cOfOHQB+/vlnunTpQtmyZZk+fTrTpk2jdOnSdOnShSVLlmRI8IKQ1dwL5sPHzwsHZwc0Wg0FixagYNECZvdMK1KmEO8NakRfLuFAIo8tSIIkWcK3XGHQaMDXFyIjYdw4k8NkCfEJfNFmMtefz69RkpIIVf8VFx3PuI7fc/nodSJDIi1+3uZWZX04tQ92jraGIbTkTwbaf9iSsrVL0XdiL1w9XVItsZDUHpJXuLaEolOIChOryARByFhmEyFJknBzczPM//n++++ZOXMmP/30E3379qVv3778/PPPzJgxg++++y7TAxaEnCT4UQhN7hykiKPK0+dDYaam1mhtNJSrXQobWy2x0XGEPo0gzN4V3b37YOL358Dqo9w6e+e/BOglqqqiqiq/jV9B/iKeyGbmPSXx8HYzeb5Y+SLMOjCZws+rQsuyrE+KJH2vTq9RnRg6X7+BbYEinsw5/A0lnlfe1mhltDYafW2npHjSsbOhRivj4eOe9gsIgiCkwKI5Qt7e3gQEBFCiRAkePHhAmzZtkrVp06YNI0eOzPAABSEnenzvCT9+9jv+a3cxXneIKCQUwNbeBkdXB2ztbdFoNUQER6JL1D3vZfLE1cOFqPBo7l0NMOrdcCKefL+tQur6NgWa1Ut2v02LtiPLksk93xSdwumdF/hoRl+Llr27e+VLcW7Qywr4elK5UTkCbz0iMeG/lWGFSnpToX5Zo0rUPiW8WHDie66duMnhDSeIjYojfxFPjm85zfl9ly1exZYSXaJCq3ebpPnxgiAIKbEoEWrdujWzZ8+mUaNGlCpVil27dlGqVCmjNjt27KBIkSKZEqQg5CSB/kEMqz+W8OAIOuoeYk/i84nRkBCbQFhcIn6Vi+Lq4YxXUeO9uCJCIrl9/h4vd41EYYNrRASbO3xC23PrKVTS2+j8vSsBFm98W7i0D17FCvDkwTOTiUfvL7qYrf0UERLJJ6/9jwfXA5Nd68H1QCZ0+YFPfxzI6x+0NDpXtnYpytb+72/Ehnlb0pUEabQyJaoWp2rTnFu2QRCE3MmiOUKjR4/m+PHj9OnTh2bNmjF8+HAGDRrEsmXLWLZsGR999BGffvopn3zySSaHKwjZb/r7CwgPjkBJVChFKAoSSZNfVPTDVHcv3Uf30hu/oijcufTg+TDWy1eV0CFRODqIH/rNS3ZPra3lCzwdnO35bvs43L3yJSsNID+fv9N5aDu6DDO/uOHnL/5IMQkCDKvSZg9azKM7ZrYMsWZ5/QsxS89f2kKlfJi86QuxsbMgCBnOokQof/78nDp1CltbW1auXElCQgI//vgjffv2Zfjw4Zw/f55ly5bx8ccfZ3a8gmAVnU7H4Q0n+KLtZLp7vU9PnwFM7D6NM7svWLS8/GV3rzzg3N7Lhrk6vkQQR/JeFZ1OIfTxf/uSRUfEcPvcXZNFB+PQ4KuGc+nQNfwv3DU6V7NVFdOTkJ+zc7SjTM0SFCntw7zj39G4e31s7P5LovIX9mD4wg8YPKs/107c5Id+8+hd5EO6F3yPT5uMZ/efBwy1eiJDo9jx+17zPTmSxD+Ld5ps8vLWJKaUr1cara0WWSPjW74IQ+cOYP6J7/DwFvODBEHIeBZ/zCxQoAA//2x5JV5ByG4xUbFM6PwDZ3ZdQNbIhjf0IxtPcHDtMVq83YhRS4ZYtTXIqW3n/quto6rYoJBamhARHImHjztBdx7z6M4Ts9dWABsUZFni5LZz+FUuZjjXcXAbdvy+z+TjJVmibf9mODg78DTgGZ+3nMT9aw+NelieBQQze9BP7PrjABcPXjVaxh5x+BoXD1xh1fRNfLftf1w+ct1sXSDQz006uvkk73/7VqptIkMtrzr/9tju1GlXHVVVRQ+QIAiZLk2brgpCbvBDv3mc23MJwKhXI+mNf/efB/j5i+VWXTM+Nv6/N2dJIgE51V8iRacQ/DDEoiQI9L+MCchIskxcTLzRuaLlixg2k02NqqhUbFiWxIREvmgzmYe3HhmOJ0l67hcPXjX6PilegNvn7zKuw3fERcdZFDdArJm21swPSnruIgkSBCErpKmy9OrVq1m5ciX37t0jPt74D/bp06czJDBBSI+7Vx5wcM0xk21UFdbP28qbY7ri6uny/JjKub2XuHbiFqqqUqq6HzVaVkaW9emOu7e7UWJxHxcq8zTF68sa2fzcmRfYoeMaHugSdXj7FTQ6t2v5fmIiYk0+XpJg9YzNaG203L2c9s2PFZ3ClWM3CHu+8aklJCSTPTiFSnnzNCDYooTIp0RBs20EQRAyitU9QnPmzKF///54eXlx5swZ6tSpg6enJ7dv36Zdu3aZEaMgWG370r0WzanRJejY+/dhQL8jfL+ywxjVYiK/jv2TJf/7izFtJ/NuyY859q8+wVcV4zk+N3FDTqpq+JKE+EQS4s0PLempyKjcIh8OLva81rWu0dl/f95ldusvVYXrJ2+xbs6/qRdAtJCskTmy4YTF7QNvB/FehU+4cOBKiufbf9DSbBIkSRIlqhSj5PM6RIIgCFnB6r+WCxYsYPHixcydOxdbW1s+//xzduzYwbBhwwgLCzN/AUHIAk8ePE1hZVZyGq3Mk/tPOb3zPF+0+ZrA20GAvlck6Y37yb0njOvwHYfWHyckKNxozs0JvIlFixPJNwNVLNiNPYkTCcSi5QTevD22O/aOdkbng+4+sej5ADy68zhdS9VB//yD7lk2pJck4EYgo1pM5Ny+S8nONexSh+KVfE0mp6qq0u/r3mJITBCELGV1InTv3j0aNNBveujg4EBEhL77/N133+Wvv/7K2OgEIY3sHe0t2jxdUVRs7G34od88FEVNceNPfQKiMu39BdjYGU+sDpBcOEsBXIlHeqFXSAIkC3tlJFRciecsBWg46m16juqYrI0lvVtJHJxNzyWyKCYJ7J2su46qqKiKwg/95hsq0SexsbXhu23j9FuLgFGPlayRkWWJET99RP0OtdIduyAIgjWsToS8vb0JDtZvSFm0aFGOHj0KgL+/f5qWIwtCZqj3Rk2L9rNSdApObo48exhicvdzVYXIkChUlWTtVlCOpzhQgBiShshUwN3LzYLeDZUCxBDt7E61bcv54Id3U3yMk5uT2eeSpGHnOsnqB1lLBVq+3Qg7B1urHqcoKo/vPuHU9nPJznn6uLPw1A/87+8RVGtWEW+/ghSv6EvPUZ347eY82r1vetNZQRCEzGD1ZOnmzZuzceNGqlevTv/+/fn0009ZvXo1J0+epGvXrpkRoyBYrW77GuQv4klwYEiqw0SyVqZszZIEPwxFY6NBl2B6KEuj1RBw4xG12lTl9K4LhlpCzyQHflMrMpDzFCSGpzggabV4+rgTExFDSFBoisNa0vMkKFaywev3RTi1Tr61RpKIYMs3Ui1TsyQarYbEhMQ07e0lyRL2jna0G9CSR/5P2Lhwm1VDbRqthosHr1K7bfVk57Q2Wpr0qE+THvWtD0wQBCETWN0jtHjxYsaOHQvAkCFD+PXXXylfvjyTJk1i4cKFGR6gIKSFRqvhq7WjsLW3SXHisKyVcfVwYcyfw9ElJFpc+FiXqGPkL4PJX8jDUKUZ4IzkxY9UIRR7vKVoShZ3Q5b0q6XsHO1eGqZTcSIeb6IJleyJ+WoyTl06mLyvqlieiDi6OjBm+TAkSUpx81VJlgwFC18ma2Q0Gplxq0bi6OLAe9++Salqxa2afC1JmCwcKQiCkJNYnQg9ePDAaJPF3r17M2fOHIYOHYqtrXXd6IKQmcrWKsm8Y1Oo36GW0VCR1lZLi7caseDk93gVKwASRpuJpkZRFIpX9CV/IQ/mHZtC237NsLG3MZw/I3nxU8lO3C5ejZhnYcReuoYc8IDSvs4U9rDDXY6nINF4E40NKjcKV0Jes5qK44eYvbc1Q2NFyhaicff6TN05gYqvlTM65+BiT9fh7Vl6bQ5vDGyF3UuTsn1KFKRWm2ocWnec3X8eQGOjZdreiXT/9A2jCtWmJCboKFbR1+J4BUEQspOkWjmxR6PREBgYSMGCr0atj/DwcPLly0dYWBiurq7ZHY6QRs8CQ7h7+QGyLFGiajFcPVzwv3iPid2mEXAj0KJraG00/P3wJ0PNIYCosCiun7zN5h93cHDtMVRUZFmmkBpBzcQAyttHU8fPHmd7LTqtlrD8RYgsXhr7bp0p2MLy4aHeRT7k2cMQi9ouPPUDpar7Gb5/eOsRgbeDsLW3pXTNEkYr0qIjYrh28iZbf9nNvpWHURTVUDNJl6jDxcOZ0b99TN32NXl05zF9Sw81O0zm6OrIysDF2DnYmWwnCIKQmSx9/7Z6jpCYEC3kRp4+7nj6/LdX1cNbj/i08TizRQpf9Pb/uhslQQBO+Zw4tP44+9ccMczH0Sk67uPIfak0GxIk5Jsy03aNp1KjCngAHmmI/+VK06aEPDYuY1GopHey3eyTOLo4cHr7eXb/edBwTPdCraTIkCjGdfqeKVvGUrNVVao1q8TpnedN3r9x93oiCRIEIddIU9W1Bw8ecO/evRS/BCE3WDp+BbGRseaL/MkSkizxzrjuvP2/bsnO3zzrz4b5W1OdlKwqKjqdjtlD0rdPn62djflGz7l6WL7BaaB/ECt+WJ/qeVVVQYW5Q38hPDgixRpBLzu88YRh41ZBEIScLk1bbNSuXTvZsaTy+jqdmCQp5GxhT8PZv+qIRcvrvYoVYOb+SeQv7Gl0PCo8mr+/X8/qGZvN31CFOxfv836lT+n7VU8ad7d+xZS9k+U9LPmLWN7n9O9Pu5Bl2WRCqKoqAdcD+W3C34aVcqaEP43g8PoTNOnZwOI4BEEQskuaEqFjx45RoECBjI5FELLEnUv3LUqCAKJCo5IlQeHBEYxoMoH7VwOsWlZ+/0oAX/ecwTvjutN3Yi+rYrZm9/a7lwPw9LEsGbpx6pZFz0GSJa4eu4kkS6g608PjGhsNN8/eEYmQIAi5gtWJkCRJFC1a9JWZLC3kPVZt4ZBC21kDf7Q6CYL/5tct/3o15euVoU675HV2Un2sxQv8Uww59bZWFF4UO18IgpAXicnSwivHr3JRtLZaEs1siCprJAqX9uG7d+cQeDsIeyc7KtYvx8G1x9P1eyBrZNbM3GRVIuTq4USEhbvBF6tQxOLrlqtTmtM7L5hN6lRFpXKj8lw7ecvsNXUJOiKCI/ii7WSiw2PwKpaf1n2bUrN1VcOKNEEQhJzC6r9K/v7+2TosNn/+fIoXL469vT1169bl+PHjJtuvWrWKcuXKYW9vT+XKlfn333+zKFIhp3Jxd6b5m68ZFURMiaJTuXrsBnv/PsTlI9c5vfMCy75ele4PA4pO4fTOC8RExlj8mOhwy9s+umP5Zqmvf9Ay2b5gyUhQrKIvb4/rjp29BbXCJPhn8U5O7zjHlaPX2b/mKF++/i1Dan9B8CPLSgAIgiBkFasTobCwMC5cuMD58+dT/MpMf//9NyNGjGDChAmcPn2aqlWr0qZNGx4/fpxi+8OHD/Pmm2/y/vvvc+bMGTp37kznzp25ePFipsYp5Hx9J/XCxd3Z9Gamz4eCLJ1PZK1oK5buW1Lw0XBdK5KmAkU88atU1HQjFRp2ro1zPicqNChr/qLP88SkfDFpgvXtC3cZ3epr4mMtLwUgCIKQ2awuqCjLMpIkGX0qTvpelmUSE00PN6RH3bp1qV27NvPmzQP0lX59fX0ZOnQoX3zxRbL2vXr1Iioqis2b/1vZU69ePapVq8aiRYssuqcoqJh3Pbz1iMm9ZnDjtD8arf7nWtEpqCTfWDWjSZLEwtM/8PDmI2SNTNk6pchfKPUJzh9UHsGdS/ctuvaPZ6dRokoxi9rePn+XgdU+M9vOxcOZ2Ye/4f0Kn6T7tfns18G06dcsXdcQBEEwJ9MKKvr7+6d4PDY2lvLly1t7OYvFx8dz6tQpxowZYzgmyzItW7bkyJEjKT7myJEjjBgxwuhYmzZtWL9+far3iYuLIy4uzvB9eHh4+gIXcqxCJb2Zf+J7rp24yZGNJ4mNiiN/EU+O/XOKCweuWD0Z2hqqqvJR9VGG7yVZomHnOgyc1gfv4skXIrTp34zFo5aZHJaTJImiFYrgV9lMD88L/v1pJxqtbLbXKyI4kg+rjCTF3WOtIMkSmxZuE4mQIAg5htWJULFiKX/SjIuLs241jpWePn2KTqfDy8vL6LiXlxdXr15N8TGPHj1Ksf2jR49Svc+UKVOYOHFi+gMWcgVJkihXpzTl6pQ2HFs3599MTYJSoioqRzae4ML+y8w58m2yStCt+zXlz2/XEhUWnWpsqqry9thuVv0e3rsaYPHQn7nJ5ZZQFZX71x6m+zqCIAgZRSzheMmYMWMICwszfN2/b9lwhJB32Nha/vnAmuXn5ugSFSJDo/ih37xk51w9XPh++zicXB2S7SifNM+p39e9ada7oVX3tGZX+YyitUlT+TJBEIRMYfVfpGbNmqX4idPsypN0yp8/PxqNhqCgIKPjQUFBeHunvI+St7e3Ve0B7OzssLMT+yS9ymq2rkrQT4/N9pTIWpmK9cty5dgNFJ2Cb9lCRIVF8ywwJM3zaHSJCpcOXcP/wl38Khv3vpauUYJ5x79jwSdLOLn9HLoEHUjg4+fFuxN70rz3a1bfz5qK1RlBo5Wp0apylt5TEATBFKs/DlarVo2qVasm+6pcOXP/uNna2lKzZk127dplOKYoCrt27aJ+/ZS3LKhfv75Re4AdO3ak2l4QADoMam1REtTqncbM2DeJLbF/sTV+BT9fnEmfr3qmezKxrJE5ue1csuMPrj9kZLOvOPbvaZTE56vIVHjoH8R378zhn8U7rL5XbFSc+UYZSJeo0GlIuyy9pyAIgilW9wjNnDkzxeOxsbEsWLAg3QGZMmLECPr27UutWrWoU6cOs2bNIioqiv79+wPQp08fChcuzJQpUwAYPnw4TZo0Yfr06bRv354VK1Zw8uRJFi9enKlxCrmbX6Wi9PmqJ79/tTLF87JGpkART97/7h3DsaRe0gr1yyDLEko6kiFJkpLtNh8dEcOolpMIDgwB1XiP16Tl6bM+Wkz+wh7UbV/T4ntl9VwoO0c7ilf0zdJ7CoIgmJJhEwQyc6J0kl69ejFt2jTGjx9PtWrVOHv2LFu3bjVMiL537x6BgYGG9g0aNODPP/9k8eLFVK1aldWrV7N+/XoqVaqU6bEKOYdOp+P4ljOs+H49K6du4Pz+y2aLIr47vgfDFnyAu1c+4L/tJSRZon7HWsw98i3uBfMle9yGeVvTvReFLlGHt5/xyrFdy/fzNOCZycRFliV+n7jKqnsVKuFlupZSBouLjmP7b3uz7H6CIAjmWF1HaOPGjSkeT0hIoGfPnnlu93lRRyh3O7T+OPOG/crTB88ME4MVnUKRsoUY+dNHVHrNdMmHxIRETm0/x6M7T7BztKNmqyoUKOKZatvObn2T9eZYy8HFnpWBP2Pv+N/8nUE1P+fWWX+LVq//cnkWRcsVtuheV4/fYGi9L9MaqvUkKFa+CD9fTLlnWRAEIaNkWh2hzp07p3ouK3qFBMFSe/8+xDdvzTJ8/2JvSsCNQEa1mMj3O8ZTpXGFVK+htdFaPNQUGRqV7iQI4O2x3Y2SIICgu08sLuHz+N5TixOhsrVLUaxCEe5efmBtmGmjwpP7z7LmXoIgCBawuk9cUZRUv/Jab5CQe8VExTLjg+fVw1NIIFRFRadTmNp/vtkVj6qqEhMZQ2JCynV04uMSiImKxdbBgn24XvDi0vWk4amen3Wk56iOydpaM3xlbiWYTqcjOiIGRVEIfRLOg+uBJttnNDtH614nQRCEzCQKegh50p6/DhETaXovL1VReeT/mNM7L1CrddVk5x/ff8qGuVv45+edRIVGI0lQrXllugx7ndptq7HrjwOsn7uFm2f01dY9C7mjsdHol7WbobXRUK9DTW6fu4eslanapCIdBrWmZNXiKbZ3cnMi9LFlVc6LlPFJ8fjV4zdYO+sf9q85ii5Bh42dluIVfTO99MWLNFqZ17rUzbL7CYIgmGN1IjRs2DBKlSrFsGHDjI7PmzePmzdvMmvWrIyKTRDS7OKhK8ga2eyqKI1Ww8WDV5IlQtdO3GR066+JiYw1XENV4dzeS5zZdYECvp48uf/MqKDis4eW76yemKDj0x8/wtXTxaL2EcGRFl/77qUHuDU1nsj9z+IdzBq0GI3mv+00EuISuXE65S1zMotOp9BhcJssvacgCIIpVg+NrVmzhoYNk1evbdCgAatXr86QoAQhvRRLd4yXku8uHxkaxZh23xATGZMskUr6PmmeS3pqBukSLR9KVq3otXn5uhcOXGHWoMWgJn+uWSUpYRwy+z3zu90LgiBkIat7hJ49e0a+fMmXDbu6uvL06dMMCUoQ0qto+SIWbRCqS9AZ1bWJiYpl9qDFVvXApIWLhzOu+S3rDQL90FhEcJRFbYuULWT0/cppG5Bl871j5tjY2ZAQl5Cmx5asWpx3xnWnYec66YpBEAQho1ndI1SqVCm2bt2a7PiWLVsoUaJEhgQlCOnVpn8zi+r5OOVzpFE3/ZyVbUv30MvnA/b+fThTY5M1Mm8MbIVGo7H4MQmxlicg4U8jDP8fFR7NsX9OpzsJKlg0P+0GNE/z3mRf/jlcJEGCIORIVvcIjRgxgo8//pgnT57QvHlzAHbt2sX06dPF/CAhx/D0cafX5534a8o6k+0GfPcOtva27Fi2j2nvZW5ldNBPFs5XIB9dhre36nHxViRCIY/DDP8fGRKV7i0/AD6a3peD646l+fGhj8PxLWvZkn5BEISsZHUi9N577xEXF8c333zD119/DUDx4sVZuHAhffr0yfAABSGt+n3dG0WnsHLqRiRZ0veKSPp6V7Is8+HUd3ljYCviY+OZP/zXTIlBo9VPTk6auO1TwovJm8ekWJXaFBd3J4uH61w9nA3/75TP0ar7JMWZVBPMxk7LJ4sG0qhbPc7tvZTmWmEuL8QkCIKQk1hdWfpFT548wcHBAWfnvPtHTlSWzv0e33vCvz/v4va5u8gaiXJ1y9D2vWa4FdAnI7v+OMB3787J8Pu+M747QXeeEPYkHNf8LjTt1ZDabashy9YPLy0dv4K/pqwzO8RVwNeT5f4LjO7xeatJnNt7yeRj9bvCV8GvUjHuXXmAxkZDlcYVaN23Kc5uTgCc33+ZkU0nWBe4BEVK+/Drldmi4KogCFkq0ypLv6hAgQLpebggZImCRQvQb1LvVM/fPOOP1kZDogX1fywha2QKFs3PO+O6WzUPyJT2H7ZixffrwUyI3T59I1mi1WNkB87sumDycTqdwjv/606F+mVTbVO5UXlKVC3G3Uv3LV99pkKPzzqJJEgQhBwrTTMfV69eTc+ePalXrx41atQw+hKE3CYt79HO7k5GNYSSyBoZF3cnJm8ek2FJEOiXxFsy1ychNnn169ptq9N/8puG+F6U9P3Hc943mQSBfkhx4rrPcfd2NztpWn7+2nQY1Jp27zc3G7cgCEJ2sToRmjNnDv3798fLy4szZ85Qp04dPD09uX37Nu3atcuMGAUhU5WtXcri3iDPQh6M+Okj/ri7kAFT3qaA738bsDq42NNlaDsWnZlKsfJFMjTGf37cYVG7dXP/SbE+0VtfduWbf76kSpMX9lWToFabqkzdNYFOQ9padH3v4gVZcPJ72vRrio3tfx3KskY26vXJ75sfv8pFuXnGn2/fmsWJrWdQFAVVVTm39xLf95nL8IZjGd16EmtmbiYiJHPLFQiCIKTG6jlC5cqVY8KECbz55pu4uLhw7tw5SpQowfjx4wkODmbevHmZFWu2EHOE8r6E+AR6Fx5IeHBEivuSJbFzsGVl0M84OjsYjimKQnBgCIkJOjx83LG1s8mUGN8pMZigO08sajv78DdUqFcm1fPhwRFEhUbj4uFsmP9jKZ1Ox/xhv7Jp4XZkrWwoXJk0ybph5zqc3nmemKhYJPSlnJLa+VUuir2THVeO3jBMIgd9T5ONvQ1jlg8T228IgpBhLH3/trpH6N69ezRo0AAABwcHIiL0NUveffdd/vrrrzSGKwjZx8bWhk9+HKj/xsQw2dD5A4ySIABZlslf2BPv4gUzLQkCiA6PybC2rh4u+JTwsjoJAlg8ahmbF20HjKt3J03EPrT+ODFRsaD+V88yqZ3/hXtcOXoDMK5wraoqCbEJfN1zBmf3XLQ6JkEQhPSwerK0t7c3wcHBFCtWjKJFi3L06FGqVq2Kv78/6ViAJgiZ6uSOc5zefg5JI9OoWz3K1S5ldL5R17qMX/UZswb+SPizCLQ2+vk9iQk6nN2cGDy7P63ebZKuGMKfRXDx0FUS4xMpVMqbUtX8LH6sp4+7xcvnPbzd0hgh3Ll0n3tXHqC10VKmVgkCbjwi9HEYzu5OuOZ3Yd3sf80X7E7DnwFVVZFU+HXsn8w5/G2aYhcEQUgLqxOh5s2bs3HjRqpXr07//v359NNPWb16NSdPnqRr166ZEaMgpNn23/ex4JNfiQqNNhxb+cMG3L3y8fnvQ6nV6r/NVht1rUu9N2pweP0Jrh6/CUCp6n406lYXW3vbNMcQ+iSMnz5fzu4/DxjNRSpZrTjvf/sWtdtWN3uNNv2bsXjUMpMfNiRJomiFIvhVtn4vr3P7LvHLF39w5dgNqx+bURRF5crRG9y9fJ9iFXzNP0AQBCEDWD1HSFEUFEVBq9XnUCtWrODw4cOULl2agQMHYmub9jeMnEjMEcq9NszbwrxhJgolSjBpwxfUf6NmpsUQ8jiM4Q3GEnTvSbKNYCVZAhW+WDaU5m81Mnmd8OAI3ir6EXHR8SbbfbZkMG36NrMqxqObTzGhyw+oqpohVajTa8Kaz8RcIUEQ0i3T6gjJsmxUp6R379707p16jRZByA6x0bHMH77EdCMVJveazqaI5WkqcmiJBZ/8StDdJykWM0xKOqb2n0/1FpVx93JL9TrPAoLNJkGSJHHvcoBV8UWFR/PNmzNRFcWSPWqzxIur0QRBEDKb1X9x9u/fb/J848aN0xyMIGSUJf/726I5a/ExCWz/bS9t+zdHVVVO7zzPxgXbuHL0OqoKZWqVoOOgNtRuV93qZCn4UQj7Vx01Ww1ap1PY8stu3voy9aHljQu2Ga20Somqqmz+cQd9vuqBnYOd2fiuHr/B/GG/EhsVZ7ZtVtHaaChvYsWbIAhCRrM6EWratKmhXsjLbzSSJKHTZUx1XkFIj2P/nLK47c5l+2n5TmO+e3cu+1YeNko4Tm47x/F/z1Dn9eqMXzXSogQjydk9pre1SKIqKsf+OWUyETq6+ZRF1Zyjw6O5fvI2lRuVT/1+qsrPX/zByqkbzF4vK8lamWZvvoarp0t2hyIIwivE6vGAqlWrUqhQIcaNG8fNmzcJCQkxfAUHB2dGjIJgtcT45BWWUxMfG8/CEUvZv+oIYLy0OymRObH1LDM//NGqGOJjTA9lvSg22nSvTJwV1zLXdt3sf3NkEpS/kAcffP9OdociCMIrxupE6MyZM6xdu5aAgADq1q3L4MGDOXv2LPny5SNfPut21BaSU1Udatw+1MjFqFE/o8afEGUJ0sCzsLvlbQt5sPnHHSZfZ1VR2fXnAQJvB1l8XW+/gha1k7UyRUr7mGzj4GKfIfeNj0tg+eTVFl8rozi7OfHh1D4MX/ghbgWTT1qs/0Yt5h791uQ8qYykqArHn97k99v7+f32fo4/uyl+zwThFZWmGaK1a9fmp59+4vbt2zRo0IBOnTr9v737jo+qShs4/jszqUMaCYEAoRMISO9NRUVBVrEg9ldBxbJiA12wIDYW22JB165gQewu4NoWQQWRJqFIr6GGkEr6zNzz/jFkJCSZkkwymeT57icrM/fce58kk8yTU57Diy++6OPQGh5d9AM6/Rx01kR03gvoE8+jM69DH78QXeL5UI+AGx+/yuO2bbomerRaymQy8eP7P3t83R5nd6Vp6yZu2xk2gwtvGeGmkedv0q6G49Z8u97jekS+oJSiQ882fHLkLa6YfBEFuQWUFFodx0zKuc9bXnY+1mLPe/Gq4/fjO7n8l38xae27/Hvn9/x7x/dMWvMul//yL1Yd91/5ACGEf1R5qcyBAwd4/vnnefrpp+nTpw/Dhg3zZVwNji5cjM6+C4zSHgc7cPINzb4Pnfl/6JI1/gov4PQ5rwcJ7d33yHQ/swuFJ4owu9lEFBybsx5LPe5xDCaTybnZaaVtzCa6DO5EnxHdXbYr9GJCs6sY0w9kVLhZbE1QSqFMikmv3EJIaDBvT/uIt6Z+SMEJR+VrbWjnSrXNy7dy16AHOXbA869vVaxI3869a+dypDAbAENrjJMVIA8XZnHP2rn8lr69RmMQQtQtXidCX3/9NaNHj2bAgAEUFhby008/8dNPP9GvX7+aiK/e0tpAG3mOoTAjH537cOmRClobgIHOeVC6773w+h/PEdei8iGydt1b8+ySRwlrFOrh0nFFWCPPJ0sDjLj+LG7/142gyu78XvrvTn3b89SiaW5XpHlzX1dtwxqF1litoFMTLJNJYQ4288gnk+k2NJndG/a5nJdktxnkZpzgrakf1EhsAFbDxuMbP0Of/N/pSp9/fNPn2AxZ9FFVxXYrRXarv8MQwmNerxq7/PLLSUxMZOzYsdhsNl577bUyx2fPnu2z4Oojbd2Gzp8HRYuAEiAIgrqAdreXlAH2VCj5DUKH1kKkga9RlIX5qa/z9Zxv+Xz2IjKPZINy7KB+/fSxjLjesWXGwL/1Yf4/v3R7PbvNzsAqFF8ce99FDLyoL4tf+54136dgLbbRKrkFf7v1fAb+rQ9ms9ntNYZeOoCFr31frijj6SJjI+jcv0Olx/uN7IkyKZ8mQ0opBo/px4msPNJTM7BEhXPm2EFceMt5xDV3JKKLPFj+b7cZ/Pr572S9kF0jc4WWpW0h21rgso0Gskry+fnYFs5LcN1LJ/5SZLey8OBaPt2/ktQCR69eK0scV7YZzCWJ/Qgz169Cu6J+8ToROuuss1BK8eeff5Y7VrqsXlRMF/4XnTPl5KPSvzhtYPN0o0kzumQdShIhj5lMJi6/529cfs/fKm3TZVAnOvRsw97NByqdX2Mym2jWJp6+5/eoUhyJSc25ffZ4bq/S2XDxHSP5z6vfuWyjTIoxfx9JcEjlm782aRnHmZcPZPnXq90mVZ4yBZm465WbadIyrtI2KUs3e7T8324z2L5mN4NqoNr3hqz9BCkTNu06jiBlYkPWfkmEPJRrLeTONe+wI/dwmX62gwUZzN66mEUH1/HvAbcQFRxe6TWE8CevE6Fly5bVQBj1n7ZuO5kEVdTl7ulf56qS80V1KKV45NMp3DPkYfJz8su9YZvMJsIahfLYlw/UWAVqd1ont+TuVyfy0h1vYjIpjNN6dJRJ0f3MLlz78Fi317r73xPZvWE/R/akeVTnqDImk0Jr+MfcSS6TIKDMHmvu2G018xq3u0mAqtq2oXt0w6fsPHGk3G+x0se78o7y6IZPeLHf+FqOTAjP+Oe3egOk8+f54Co2VFCSD64jTpeY1JxX1zzN2VcOwRz011CVyWxi2GUDeHX107Tv0caPEcJFt53Pkwun0bFP+zLPR8ZGcO1DlzPru0cICa28N6hUdJMoXl45k4tuO7/M5+pOqKXs3KMugzvx9A/TOfca9wsl2vdsgznIs183bbomehyTN9pFNPUowbFrg3YRnpU+aOj25h3jt+PbMVxMsjO05rfjO9ibd6wWIxPCcx5tunr77bfzyCOPkJjo/hfUJ598gs1m47rrrvNJgP7mi01XtTbQaT1wzAmqBhWFaroCpbybsCu8k3M8lz0b96M1tOvWqtZq23gjddshjqUeJ6xRKJ37d3A5HObKjnW7ubP/NLftmrZuwtt/vsCuP/ZSXFhCQrumbmsfnWrNd+t5aPQ/XbYxmRRnDEtm9rInPL6uN05YC7lw6SxKDNfL9ENNQXx7zkNEBHteu6mhenPn/3hvzzK3CaZZmbix/dncnnR+LUUmhI83XY2Pj+eMM85g6NChXHzxxfTr148WLVoQFhZGVlYWW7ZsYfny5SxYsIAWLVrw5ptv+uwTqRd0AdVOggAV+Q9JgmpBdJMoep9bt+eHtE5uSevkltW+Tqe+HRg98Ty+ffsnlysSb//XjYQ3CnO5dYcrfS/oSZ8R3SvddkSZFCaziVtm1dwfUJHB4dzacQSv7HA91+q2pPMlCfJQVkk+nswMVSfbClEXedRX/eSTT7Jjxw6GDh3Kv//9bwYNGkTr1q1p2rQpnTt35oYbbmDPnj28+eab/P777/ToUbUJpfWWCse76Vil3xZ18iMIFfkIynKlz0MT4u5XJzL6VkdBx9Jl/UoplFKEhAXzj7mTOHPsoGrdw2Qy8diXDzBwdB8A5zBZ6ZJ7S2Q4Ty56kK6DO1frPu78X7szuS1pBCYUplPewksf3550Pte1lZponooMDvdohqMGooJksrSomzwaGjtdVlYWqampFBYW0qRJEzp06FDjK8YyMzO56667WLRoESaTibFjx/LSSy8RERFR6TnDhw/n55/LVgK+7bbbeP311z2+ry+GxgCM7Pug6DtcT3Y2Q3APCBkMtm2ACRXcGyxjUabYKt9bCE8c3HmEb99eQurWg5iDzfQ4qysX3DiciJhGPr3PrvV7+e7dnziy9xhhjULpP6o3w68aQpil9no704tyWXhwLVtzDwHQJaollyT2o0lY1X/GG6IduYe5/rdXPGr7/uA7SY6ufi+mEJ7y9P27SomQP1x44YUcOXKEN954A6vVyoQJE+jfvz/z58+v9Jzhw4fTqVMnnnjirzkHFovFq4TGV4mQLtmAzrwSdyvEVOM3UaHDq3wfIYSoTbeueoNN2QcqnSdkVia6RbfirUG31XJkoqHz9P07IFaNbd26le+++463336bgQMHMmzYMObMmcOCBQs4fPiwy3MtFgsJCQnOj+okM9WhQnqiombgGOo6faWO47GKuE+SICFEQPlnz2toHhZTZqixlAlFQlgM/+zleqsZIfzJ6zpC/rBy5UpiYmLKbOMxYsQITCYTq1at4rLLLqv03I8++ogPP/yQhIQELr74YqZPn47FYqm0fXFxMcXFf+3rlJub65tPAlCWa8HcAZ3/DpT8jLN3KKQ/qtFNkgT5iDayoPBLdPEvoIvA3AZlGQfB/aTopyjH0AZrM/aw6NA6DhVk0igolGFNk/lbiz4yadoDTcKimDvkThbsW8Hnqb87q3fHBFsY23ogV7cZSnRI5b9zhfC3gEiEjh49StOmZet6BAUFERsby9GjRys979prr6VNmza0aNGCjRs3MnXqVLZv386XX1a+ncKsWbN4/PHHfRb76VToQFToQLSRC0Y2mKJQppgau19D46je/Q/AijPRtG5EF30NIYMg5lWUKdKPEYq6JLskn8nr3mdzzgHMyuQc3lmdsYt/7/iBf/a6hqHxNTuBuz6ICg7n1qQR3NThHI4XnwCgSWgkQSbP61QJ4S9+HRqbNm2ac3VKZR/btm2r8vVvvfVWRo4cSffu3bnuuut4//33+eqrr9i9e3el5zz44IPk5OQ4Pw4cOFDl+7uiTFGooNaSBPmQLl6OzrmPMkkQ4JygXrIGnXU7WqoGCxybsN619j3nhOlT57hooMhewv1/fMDGrFQ/RRh4gkxmEsJjSAiPkSRIBAy/9ghNmTKF8ePHu2zTvn17EhISOHasbFVSm81GZmYmCQkJHt9v4MCBAOzatYsOHSremDI0NJTQ0MCs1aO1ButGMA4DYY4hN1Plq+q0dSvY9wPBENIHZap8p/a6TmsbOre0J6+yCel2sK6BkuUQelZthSZ8yGbYWZ+1j+ySfKJDLPRu3JZgU9V+jS1N+5PtuZXPMdQ4fqZe3/kDj/UYx5acQxjaoENEM9pExFfxMxBC1DVe/Qb573//y5dffklsbCw33XQTycnJzmNZWVmMHTuWn376yePrxcfHEx/v/hfK4MGDyc7OZt26dfTt69iM8aeffsIwDGdy44mUlBQAmjf3vCJuoNBF36FPvAj2Pac8G4q2jEVF3F8mIdLFv6JP/AtsW05pG4QOuwgVORVldr1vVF2itYaCeei810BneXCGGV2wACWJUEAxtMFH+5bz4d5fyxTmiwm2cE3bYdzQ/izMyrsO7i9SV2FCYbhYyWmgWZu5h4uXPYs+pV3vxm25q/OFdItp5f0nI4SoUzz+zTF//nzGjBnD0aNHWblyJb179+ajjz5yHi8pKSlXs8dXunTpwqhRo5g4cSKrV69mxYoVTJo0iauvvpoWLVoAcOjQIZKTk1m9ejUAu3fv5sknn2TdunXs27ePhQsXcsMNN3DWWWfVu4KPOv8DdPbdYN972pFiKFiAzrwGbeQ52hYuRmfdAratp7W1QdEidOYVaPvxWom7urTW6NzH0Cf+6WESBGAH266aDEv4mNaaJzd/yZzt35WrTpxtLeC1nT8wY8OnGF4Oee7LT3eZBJWJ4bR2G7L2c+uqN1mbUfkwuxAiMHicCD333HPMnj2bxYsX8+uvvzJv3jxuu+023nnnnZqMz+mjjz4iOTmZ8847j9GjRzNs2LAyW3lYrVa2b99OQYFjxUJISAj/+9//uOCCC0hOTmbKlCmMHTuWRYsW1Uq8tUXb9qJPPFX6qIIWBth2ovNeQNsz0DlTT7arqK0d7EfRuU/WWLw+VbwUCj/2/jwV4vtYRI3539FNfHPoD5dtfji6kW8Pp3h13SBV9TksBhq7NngwZb7bvcuEEHWbx0NjO3fu5OKLL3Y+vvLKK4mPj2fMmDFYrVaXS9h9ITY21mXxxLZt25bZK6lVq1Y11kNVl+iCj3Hks64qVhtQ8Dlaxbhph+N48fdo+zGUuW7vwK0L5uGoweTuczqVGUKG1FBEoiZ8sv83t0NYAM9s+Q8aOD+hO6Fm95vQDmzSkW8Pp3i0I31FNJocayE/Hd3MqBa9qnQNIYT/edwjFBUVRVpaWpnnzjnnHBYvXswDDzzAnDlzfB6c8EDxT3iWCBSe3OLDk1/6BpSsrF5cNUxrG5T8jndJEIDhqOckAkKRvYSN2akeDWEV2a08selzblj5KseL3Nf/uqL1oConQaVMSrHy+I5qXUMI4V8eJ0IDBgzg22+/Lff82WefzaJFi3jxxRd9GZfwlC5238bJi7a6yOtQatfpS+Q9oyLuRwW19Xk0omYU270fdkrNP87da99zm+R0jU5kfPuzqxoa4Ji/VGy3VusaQgj/8jgRuu+++wgLq7jK6vDhw1m0aBE33HCDzwITHjK3wuNvo7kN5bf3cHXduiwMVIznzU1NUFH/REVMrLGIhO9FBIdhMXtXzsKuDXblpbEifbvbtnckXcCULhcRE+yofFxad9zT+uMmZaJFuGyILEQgC5hNV/3FV5uu1hRduBCdc7+bVgqCuqAiH0BnTXB/UVNzVPxPqGpMJq0Nxol/Qf5buB7uUxA8GBX7NkoFRCF1cZrZW7/hs9SVXg1jmZRiWHwyz/f5P4/a2ww7K4/v4GhhNuHmEHrEtOH/Vr5Cob3E7bmfDruXthF1ez6dEA1Rvdp0VbgQNgrMHXDX06Mi74WQwRDcx6O2dT0JAlCW60FFUvnL2ASEoKIfkSQogF3Tdghh5uAKN/WsjKE1hwoyPW4fZDJzZtMujGszmIsS+9I6ogk3uhk2M6G4IKGHJEFCBDhJhAKcUiGo2PfA3O7kM6d+S02ACRU1CxU6HKVMqMavQ3D3k8dPTXbMgHIUVAx3vwJQawNt5KG1t5OVfUeZm6Fi54Fzm5JT3ygVqDBU47dRQR39EJ3wlebhjXm1/81eb4BqCapehfgJ7YdzbdthAGWKNZb+e2h8Zx7pPrZa9whEhjbItxVjM/z3sy+EL8nQmBt1fWislNYlUPQ/dOEnYD8AKhxCz0NZrkaZW5zW1g7FPzuW3tv3ACEQeibKcg0qqF3FNyg917YLnf8+FH4NFAFmx30a3YgK6V9Tn57rmIw8RzHIwq/AOA4qBhX+NwgfK3u51SN5tiK+2L+KV3d+77atQjGp80j+r131K4jvyD3M56mrWJ+1F7s26BzZgrGtB9I3tj1Ked5LFej256WzYP9vfHPoD4oMKyYUZzXtwtVth9Antr2/wxOiHE/fvyURciNQEqHaoIt+QmdPwrFa69S/Bh21fFTkVFSjm/0TnGgwntz0Bd8c+qPSJfUKCDYFsXj4VGJCGtVucPXUb+nbeWD9hxhal5mrZVYm7Nrgzk4j3Q4lClHb/DJHaMuWLe4biYCkbfvQ2XfhSIBO7xJ3PNYnnkEXL6vlyERDc1/y32gX0bTCOUMmFArFUz2vliTIRw4VZPKP9R9hM+zlJqyXPn51x/f8nCa//0Vg8joRGj9+PIZR9ofBMAxmzpxJ//7+GRoRNU8XfIhjdZbrDkSd8xjafqhWYhINU0RwGG8Nuo0r2wwh3Fx2u5Rejdvy2oBbGN6sq5+iq3++SF2FzbC7/Mk3oZi3p/5X8hf1k9dLadavX8+4ceNYsGABwcHB/Pnnn4wfP57s7OwKCy6KeqLwazyq4mwcRqefgw67HBX9BEr29RI1ICIojMld/sYdSeezNfcQVsNGoiWOlhap6eNr3xyufBiylIFmc84BjhRm0Ty8cS1FJoRveN0jtGzZMo4cOcLo0aN56qmn6NevH4MHD2bDhg2cdVb1JyaKukdrA7T7LQvKKPoKnX0vMgVN1KTwoBD6xLZjYJMkSYJqSE5Jgcdts0ryazASIWqG14lQ48aN+fHHH9FaM2PGDD7++GNefvllLBZLTcQn6gClTKC8nW+hofh/J/cDE0IEKm/KFkQFh9dgJELUDK8TodzcXOx2O/Pnz+fcc89lxowZ7N+/n9zcXHJzvew1EIEj7G94vD2Hk/nk3CIhRKAa2bxnmTpKFTGh6BiZQEvZbkQEIK/nCMXExDhrZ5QOe7Rv3x6tNUop7HYpslUfKcv/oQs/9/IsO1g31Ug8QojacUXrQXyRusplGwPN/7U7s0HVVRL1h9eJ0NKlS2siDlHHqeDOED0LnTMNR6UWT/d9kl+MQgSydhFNebzHlczY+Knjj91TltCbUBhorm0zlFHNe/kvSCGqwetE6OyzpWhWINL2o+iCT8C6BrQNgpJRlqtQwV08voYKvwzMbdB5r0LJrx6cYYaQPlUPWjRoWmv+zDnI1wfXsDfvGCGmIAbEdWRMYl/iQiP9HV6DMrJFT1paYvlg7y/8nLbFuYqsW0wrrmk7lHObdZPeIBGwqlxZuqCggNTUVEpKyu7O3KNHD58EVlfUh8rSOv9d9IlnTz4q/WvOUQ2asEtQ0TO9XuZuZN4OJctw1zOkYj/y29YbInAV2Ut4ZMMn/HJsq7N6MTi2zjArxdQzLuWSxH5+jrJhyrMVkVNSQKOgUClaKeo0T9+/ve4RSk9PZ8KECZXWDJI5QnWLLvgEfeLpCo6c/D4VLUQThIqZ5dV1VdSD6Iw1oAuouL6QgrAxECxvVsI7Wmse2fAJy49tAygzFKPR2LRm5uYviQgK5byE7pVdRtSQiKAwIoK82wBXiLrM61Vj9957L9nZ2axatYrw8HC+++475s2bR1JSEgsXLqyJGEUVaV2CPvEvd62g6Au0bY9X11ZBbVBxn0BQh5PPmHHk1SbHfy03oqJn+a27XBs56KKl6KJv0dbNUs8ogGzOOcAvx7a6LeI3Z/t3GNrTuWpCNDw7cg+z5Ogmfjm2lWwv6kE1NF73CP3000/85z//oV+/fphMJtq0acP5559PVFQUs2bN4m9/+1tNxCmqongp6GwPGprRBZ+hoqZ6dXkV1BHiFoH1D3Txz6CLHDvdh49BmfyzjFYbOegTz52shH3KsG1QJ4i4DxV2nl/iEp77+sCaMsNhlTlcmMUfmXvpF9fBZTshGpqV6Tt4dcf37DhxxPlckDIxsnkv7k4eReOQCD9GV/d4nQjl5+fTtGlTwFFcMT09nU6dOtG9e3f++OMPnwcoqsG2B+dcIJfsJ9t6TykFIX1RIX2rdL4vaSMHnXEl2FMp9znbdqKz74ComSjLOL/EJzyzN++Y2ySo1P7845IICXGK7w6nMGPjp5y+YtemDb47ksL6rL28N/gOSYZO4fXQWOfOndm+fTsAPXv25I033uDQoUO8/vrrNG/e3OcBimpQIbjbJPVkw5NtA5s+8WzFSZDjqOP/c6ej7YdrNS7hnWCT54U7vWkrRH2XUXyCJzZ9gcYxn+50dm2QVpTD81sW135wdZjXPUL33HMPR444uttmzJjBqFGj+OijjwgJCWHu3Lm+jk9UR8hAPK33o0IG1mwsNUwbOR5vDKsLPkFF3lfjMYmqGRDXkQ1Z+93OEQLoE9uuFiKqfXnWIr45/AcLD64lrSgHizmUcxO6MbbVQFo1ivN3eKKOWnhwndt5c3Zt8FPaZjKKT0gZipO8ToSuv/5657/79u3L/v372bZtG61bt6ZJkyY+DU5Ujwruhg46A2zbqDxBUEAohF9ae4HVhJI1gNWDhoZj7pQkQnXWJYn9eHv3T+BigrtZmegf24FES/1LCvbkpXHn6nfILMlzpoK51kI+2b+CBftW8Ei3y7ko0f9D0aLuWZ6+zaM/IOzaYG3GHka26FkLUdV9Xg+Nnc5isdCnTx9JguooFf0MqFAq3idMnWwzC2UK8PFiXeRF28Kai0NUW5OwKP7RdUylx83KRERQGFPPuKQWo6odudZC/r76HbKt+eXezuxaY6B5YvMXrDq+0y/xibqtyF7ivtFJxYYnfzg2DF73CE2ePNnl8dmzZ1c5GOF7KrgTxH6Gzp0B1rVlD5pboyIfQoWd45/gqkFrKxQvc0zyVkFoFe3hmWYwt6nJ0IQPXNZqABFBYczZ/h1Hi7JR/DXbrV9se6adcSktLYG/weeevDR+P76TEruN5uGNOVqUTdYpPUEVMaF4Z/dSBjZJqrU4RWBoZYljj4eLDVqEN66FiAKD14nQ+vXrnf9evnw5ffv2JTw8HEBKrNdRKjgJFTcfbdsFJesAOwQlQXC/gPye6cIvHROjjUwcPV0ax1yoEBzDY67eRuwoy5W1EKWorvOb9+C8hG6szdhDasFxgpSZvrHt68UcmcMFWTyx6XP+yNqLCeXcw0uh3A5sGGhSsvZxqCCzXiSDwncuSezPT2l/um2XEBZTb+fXVUW1Nl2NjIxk/vz5tG/f3qdBiZqhgjpCUEd/h1EtuuAjdO7jpzxz6twnd0mQ2VFPKPTcmglO+JxJmRjQpCMDCOzX7amOFmYz4fd/k2t1DNEaaOd8qIpW+lTmWFGOJEKijIFNOtI9phVbcg657BW6o9P5mFS1Z8bUG/KVEAFD24+jc59y1YK/amecOifq5Ms8qBOq8Tso5XX+L4TPvLz9W3KthR7XSqpMmDnwS14I3zIpE//qcyPJUS1OPv6rx9+sFAq4N3k0F7bo7acI6yZ5RxCBo/Az3NdFOnk87BKwbgSKwNwWZbkKQs9FqeAaDlKIyh0vPsFPRzd7tLLHlbiQCJIiE3wUlahPYkIsvD3odlakb+fL1FXszz9OsMnM4CadGNt6IK0bycKm03mdCJ26n5hhGCxZsoTNmzc7nxszpvLVHkJUhy5Zi2d1kRQquBsqpqLNZoXwny05B6udBCkUV7YZQpAUkxSVMCsTZzXtwllNu/g7lIDgdSJ06aWXlnl82223Of+tlJLd50UN8nS5p8KTwopC1DabUb3XpUIxIK4j/9fuTB9FJITweo6QYRiVftRkEjRz5kyGDBmCxWIhJibGo3O01jz66KM0b96c8PBwRowYwc6dUn8jYAV1puJ6SKczAn5SuKif2kc09aidCUVEUFiZ52KCLUzseC6z+/5fwPUGaa35I3MPs7cu5olNn/PvHd+zN++Yv8MSAgigOUIlJSWMGzeOwYMH884773h0zrPPPsvLL7/MvHnzaNeuHdOnT2fkyJFs2bKFsLAw9xcQdYqyXIUumOeuFZiaQ8jgWolJCG+0jWhKj5g2bM5OdTlEZqCZ1esamoRGkV6ci8UcQtfoxIBLgAAO5Gfwj/UfsjsvDfMpK5Xm7vmZYfHJPNHzynJJnxC1yeseoePHj3PLLbcwYcIEMjMzeeaZZ+jRowfjx48nNze3JmIE4PHHH+e+++6je/fuHrXXWvPiiy/yyCOPcMkll9CjRw/ef/99Dh8+zNdff11jcYqao4I6Qvg4Tt9VuVy7qIdQsjRU1FF3dx6FUgpVyevYhGJQXBL94zrQIbIZg5ok0aNxm4BMgo4V5TBx1Rvsy08HHFs7lH4A/Ja+nbvXvIfVsPkzTNHAef1u8fe//50NGzZw5MgRLr/8cj788ENuueUWVq9ezQMPPFATMVbJ3r17OXr0KCNGjHA+Fx0dzcCBA1m5cmWl5xUXF5Obm1vmQ9QdKuqxk8kQ/DVMpijdM01FP4sKu8AvsQnhiR6N2/BC3xtpFBQK4EyISntLhjVN5pne19WLOi9z9ywjx1pQaakAA83mnAP8cGQjOSUF7Mg9zF4PKyML4SteD4399NNP/PDDD3Ts2JHGjRvz448/cu6553LGGWcwfvz4Ggixao4ePQpAs2bNyjzfrFkz57GKzJo1i8cff7zS48K/lApGRT+FbnQLuvBTsO4GFYQKGQDhl6FMUf4OUQi3BjVJ4r/nTOOHIxv5LX0HxYaVluGxjEnsS6eTNWACXaGthEUH//AoqZm9dTFPbvrCOVzYJDSSK1sP5pq2Qwk1S8kLUbO8ToTy8/Np2rQpUVFRWCwW2rRx7NvUqVMnjh8/7tW1pk2bxjPPPOOyzdatW0lOTvY2zCp78MEHy+ynlpubS6tWrWrt/sIzKqgtKvIf/g5DiCoLM4cwJrEfYxL7+TuUGnGgIMPjjT1P2Mpumny8+ASv7fyRFenbmdP/JsIkGRI1yOtEqGXLluzfv5/ExES+/fZbEhMTAUhLS6NpU89WRJSaMmWK216kqm7fkZCQ4IyrefPmzufT0tLo1atXpeeFhoYSGhpapXvWW1pDAO5JJoTwn+r+ytBoNmWn8uqO75jS5WLfBCVEBbxOhGbNmkV0tGOn72HDhjmf3717NxMmTPDqWvHx8cTHx3sbgkfatWtHQkICS5YscSY+ubm5rFq1ijvuuKNG7llv7NwJ334Lf/wB27ZBSQmEhEByMvTpAxdeCEmy87WonwptJXx/ZANL0/7khLWQZmHRjG7ZmyHxncusehKutbI0wWIOocBeUuVrGGi+PrCW25MucM6pEsLXvE6Exo0bV+HzV111VbWDcSU1NZXMzExSU1Ox2+2kpKQA0LFjRyIiIgBITk5m1qxZXHbZZSiluPfee3nqqadISkpyLp9v0aJFuaKQ4qRDh+Dpp2HpUsjLA5MJwsPBbIbiYvjlF1i2DN58E845B6ZNg5Yt/R21ED6TkrWPKeve54St6ORO8JotOQdZkraZ9hFNeanveJqFx/g7zIAQZg5mTGJ/PktdWa3Jz8WGldXHd3JOQjcfRifEX7xOhNytooqKqpnJqo8++ijz5v1VQ6Z3b8emcUuXLmX48OEAbN++nZycHGebf/zjH+Tn53PrrbeSnZ3NsGHD+O6776SGUEWWLIHp0x3JUGwstG1bcd+21pCTAwsXwrp18OSTcN55tR6uEL62+0Qad52ylLt0J/jSCbz7849zx+q3+WDoXdI74aEJHc5mWdpm0otPVCsZyrMV+zAqIcpSWmuvNr4xmUyoCt4gtdb1couN3NxcoqOjycnJqbEkz++WLIHJkx29QC1bOnqA3LHbHUlTRATMni3JkAh4D6cs4Ke0zS7fsBUwpcvFXNlGCnZ6Kq0oh0dSFrAhez9mZUKhMLTh1Z5rL/Udz+D4TjUYpaiPPH3/9rpHqH379hw7doxp06YxdOjQagUp6oCDBx09QXl50KqV5zMczWZH+wMHHOcnJ8swmQhYOSUFbpMgAA18nvq7JEJeaBYWzVuDbmNH7mGWpW0hz1ZEXGgkFnMIz21d5Pb8xiGN6B/XoRYiFQ2V14nQ1q1bmTNnDjNnzmT9+vU8++yztGvXriZiE7XhmWccPTtt2ni/zEMpR/Kzf79jbtGcOTUToxA17EhhlsdDNwcKMmo4mvqpU1SLMjWSrIaNj/YtJ60ox+XX/oZ2ZwdkVW0ROLxeAhEcHMzkyZPZuXMnLVu2pEePHkyZMoXs7OwaCE/UqB07HBOjY2M9Gw6riNnsOH/pUsdqMyECULAXb7RByg9vyt7NYAgIwaYgXul/E3EhkeW2GyldnXdFq4Fc21ZGHkTNqvKmq7Gxsbz44otMmjSJqVOn0rFjRx555BHuvfdeH4YnatR33zmGxNq2rd51oqNh3z7HkntZVi8CUJtG8cQEW8i2FrhsZ1Ym+sVVrbaZK3Zt8Fv6dj5L/Z3tOYdpeSiDSzalM+RgAU32HkLV0xIWiZY4Ph52NwsPruXz1FUcKcwiyGRmQFxHrmozmAFxHSuckyqEL3k9Wbp3797lXphaa3bt2kVBQYFMlg4k48c7lsSfrA5eLfv3w/Dh8O671b+WEH7wxs4feXf3UrdTeH09cbfIXsI//viI3zN20iwjj+s/+Y1+G/ZjKbJiN0GwJZL4RjGY7AYUFoJhOBYp1MMSFqWLboTwhRqbLC01eOqRbdscdYJ8ITwctmzxzbWE8INejdu6TYKCTWY6RTZ308o7T276gtUZu+i/fh+3vf8z8Rl55ESFcSg6CpRj0KgkTNHKcrJyfz0uYSFJkPAHrxOhGTNm1EQcorZp7agYXdW5Qaczmx3Xk+04RID6aN9yTCiXy7rthsF/Dq3lpg7n+OSe+/OP8+PRTfRfv4973lqCpbCEw82i0aa/foY0cKwol4TwGIKV2fHzFRMDkZGOhQ6TJ0sJCyGqocr14teuXcsHH3zABx98wLp163wZk6gNSjnmHPhqKNNud1xPkiARgI4Xn2DV8Z1ua9s4tnxY47P7fnPoDxKO53Hb+z9jKSzhaHxkmSToL5rM4ryyT5WWsMjL+6sYqhDCa173CB08eJBrrrmGFStWEBMTA0B2djZDhgxhwYIFzk1YRQBITnbMEfKFwkIYONA31xKilqUX5Xpc3i+92HV1fW+kFeVw/ae/EZ+Rx+Fm0S7+kFBYjQr+aJESFkJUm9c9QrfccgtWq5WtW7eSmZlJZmYmW7duxTAMbrnllpqIUdSUPn0cEy+ruzRXa8d1Tm5uK0SgCTcHe9w21FTlxbbltDhwjH4b9pMTFVZJT9BfTJUlSVLCQgQwuzbItxVjVGMLlury+if6559/5rfffqNz587O5zp37sycOXM488wzfRqcqGEXXujYQDUnxzHnoKpychyrWC680GehCVGbWjdqQkJYDEeLsl22MysTZzfr6rP7jkg5TFiRlcxo1ytSNZroYEvlDaSEhQgwm7JTWbBvBUvT/sSmDUJMQYxq3pOr2w6lY2RCrcbidY9Qq1atsFqt5Z632+20aNGigjNEnZWU5FiCm5lZ9blCdrvj/HPOkV/AImCZlIlr2g7F3Qw3uzYY19p322u023kIZTa7XC2lUFjMoa43elUKTCZISfFZbELUlM9Tf+fm31/np5NJEECJYeObw+v5v99eYcnRTbUaj9eJ0HPPPcddd93F2rVrnc+tXbuWe+65h+eff96nwYlaUFqH5NAh74fItHac17Kl4zpCBLBxrQdxZtMuFSZDpZWP7+w0km4xrXx2T9O2bcRGN8F0cjPSiu4bpEy0j2zq/mJSwkIEgLUZu3l2y0KAclur2LWBoQ0e2fAJu04crbWYvC6o2LhxYwoKCrDZbAQFOUbWSv/dqFGjMm0zMzN9F6mf1OuCiqXqyO7z2n4UCr9C21OBUFToMAgdjlK+m5MhhCs2w84He3/lw72/csJW6Hy+lSWO25PO5/zmPXx3M62hb1+w2SiMieRwYRbZJfnOSdsKRWxII1pYYj2bl5SRAUFBjtpCAbZ6M70ol28O/cGBggxCTEEMiOvAmU27yB5j9dDda99j9fFdLldompWJi1r04eHul1frXjVWUPHFF1+sTlyiLjrvPEcyM326Y/VJbKxjzkFFv0xLi7llZjqSJh8Uc9Pais6dCYULTj6jAIUunA+mphDzEiqkb7XuIYQnDhdm8f2RFE7YCjGd0kNzoCCD749sYHB8JyKCwnxzs9ISFsXFhJtD6BDRjBLDTpG9BIDwoFCClRed9nY7WCwBlQTZDDsvbfuWz1JXAn8VVPziwCriQiKY2etq+sT6fksT4R+51kJWHd/pdoWmXRt8dySFh7pdVitFNr1OhG688caaiEP423nnOZbTP/20Y/XJvn2OOQfh4Y4eIru9bHn/MWN8Ut5fa43OeRiK/gMV/XgYx9GZN0Lcx6jg7tW6lxCupBXlMHHVG+RaHT1Bp//FuvzYNu5dO5fXBtxCsK9Wjp1WwiLEZCbEVMVq7wFYwuKZLf9h4cG1f32lTxmgyCrJ56417/HGwFt9Ohwp/CenpMDjMhXFho0iu5XwoJAajQm8SIRycz2rnVFvh48agpYtHXVIdu50rD5Zvx62bnVUjLZYHL9ke/Xy7YaP1o1Q9LWLBgZgQ+c+g4r70Df3FKIC7+1eSq61sNy8hVIGmo3Zqfzv6CYubNHbNzft0weWLat+RfYALGGxPfcw/zm4ttLjBhq0wQvbFvPOoDtqMbLad7wol4ySPBoFhdIyPLbebjUSGex5kh+kzISaa2dahMd3iYmJcfnNKd0sr75tutogJSWVTXRqcNsMXTAfMAOuXjcGWFejbXtQQdJNLnyvwFbMN4f+qDQJKmVC8cn+lb5LhBpwCYsvUldhViaXX3MDzabsA+w+cZQOtbykujasydjN3D3LWJOx2/lc20bxXNt2GJck9qt3CVFMiIXejduyIWu/2zlC5yV0w+TN0HA1eJVuff7558TGxtZULKKuqskfRusmXCdBp7bdCpIIiRpwsCCDYsPmtp2BZmfuEd/duLSExcKFjr3DqrL3X2kJizFjAqqExZacg24Tz1Lbc4/Uu0ToPwfXMnPzl+UKZe7PT+eff37Fxuz9TO82tt4lQ9e3O5P1WftctjG0wdVthtROQHiZCA0dOpSmTT1YximEp+rZD7kIVJ6/DrXHsxw8NG2aY6XXoUOOvcO8+ZkI4BIWFZULaCh2nzjKPzd/BYBx2sLt0keLD/1B95jWXNZqQC1HV7PObNqFWzuex5u7lpTb5Li0h3DaGZdyRi3OC5N1yQFM21LRhQugJMXxRHA3lOXqwBo+Cu4Ntj141Cskk6VFDWlliUVR4XT9cqJcVXiuitLVl5Mnw4EDFZawsGuDjOI8cqwF2LSdUFMwccEWotIyURERjvOruXDBW3nWIr45/AfLj20j31ZMS0ssF7fsS/+4Dh71YvRo3JpdeUc96hU6I7p+7WH5aervmJTC7qJ6jQLm713OpYn9612v0C0dzyM5qiUf7fuVdZl7AUdiPLhJJ65vN6zWVwpKIhSAtNbovBcg/3XKzK+xrkcXzEVbxqMip6FqaXy1OpTlWnThp25amSFkICqoda3EJBqe9OITHvfzFNiKfR+AixIWudZCduelYdeGI1nTGlNBLgW5B0lrGkuzp58i2gd1vLyxIn07D6V8TJG9xPl125p7iO+PbKBbdCtm972BmJBGLq9xeauBfJb6u8s2JhQ9G7ehTUS8jyKvG/53ZJPbBFAD+wuOk5p/vN59/gDDmiYzrGkyOSUF5NmKiA62EBHso9IUXvL4nVIpVe+y0oCV//rJJAjK9qSc/HfBXEeiFABUcFcIv85FCzOoEFTkQ7UWk2h48mxFHre16hpaEHLeefDZZ465PlYr7NtHyd49HD+wm4icfBpnF9DsWA4t0nIIstr5ZVAS9z94MbdG7qbIXn7bo5qyMSuV+//4oEwSBH9VCd6ae4i71r6H1c2cqw6RzbimzdBKj5tQBJuCmNLlYl+EXacU2D1PpvO9aBuIokMstLTE+i0JAi96hLTWjB8/ntBQF/vdAF9++WW1gxKVM2xHIW+O+4b57zh6hsxxPo9BaztY/wB7OpgiIWQASrl+XVR6LftRCBkERjoU/4QjmSsdFrCBuRUq5kVUcCdfhS9EOXGhkR63ddfTUS2nlbBY/7/PaLRzD0FWO0VhZv7s3JwdHZqxsl8HDrQ8uXAl7xg/HNnAmMR+Ht9m14mj7M9PJ9gURPeYVjQOifD43Nd3/uDola7kuF0bbM89zNK0P7mgeU+X17on+UIaBYUyb+/P2AwDs1Lok9doYYnlqZ5X0SmqucexBYrYkAjSiz0rSRPnxfdGVI3HiZAUUvQvbRQ4enkKPgLcr24Bw1Gfp9HNvotBayj8GJ33Ohin7AOjItGW61ERd6KUZ8WvtC0VfeJpKF5CmZkZpjYQ0g3MLVEhZ55MsqQnUtSsZmHRHi3rNaEY07IWqpwnJZHeqhl3d0pDc6bLEhYmFJ+nrvIoEVp9fBev7vierbmHnM+ZlYnzE7pzd/JomrhJCA8VZLI2c4/b+5hQfJG6ym0iZFImbk0awdVth/LDkQ0cyHdssdG/SQf6x3o21ygQXZzYl7m7l7l9rfVo3IZm4TG1F1gD5XEi9N5779VkHMIFrQvRmTeAbTOOAoOeMDnq7vgyjhPPQMG7FR2A/NfR1k3Q+A2UCnZ9HdsedMaVoPMpNz3VOABFR1Cx76FC+vsueCHcGN9+OPesm1vpcROKUHMwl7eunerNBwsy/lqh5iIhMNCk5qe7vd6So5t4OGVBubdeuzb48egm1mft471Bd9AkrPKiuKn5xz0JHQPNPg9iKhUVHM4VrQd53D7QXd5qAB/vW0Gx3VppMmSgGd/+7FqOrGGq+7NpBTrvVS+ToJPcJCRexVD8W8VJ0F8toGQFFLiv/qyz7z+ZBFU01+JkJensu9C6pIrRCuG9wfGd+EfXMSgcvSSnKk2CXuh7I83ComslHm+28XC3OWlOSQEzNn6GPvm/09m1wfHiEzy95T9uYvK8zlGwkg1TK9M0LJoX+95IqDm4zJ524HjtKeCBLhczJL6zfwJsYCQRquO0LoaCj/E6CcKGCvHdX1i64H3+mrtTaSt0wftoF6shtHXjyaTOTSVpIxOKfqxCpEJU3RWtB/H+kEmMbtEbiznk5A7wEdzQ/iw+HXYvfWLb1UocedYiUrL2lXuTrIhZmRgQ19Flm8WH/sBq2FyujLNrg1+PbSOtMLvSNl2iEwk1uf8Dy6xMDGjiOqaGrndsOz498z5uaH82cSERKBTh5hAubNGLeYPvZFybwf4OscGQ5fN1nfVPx9CTt1QshPpmSa3WGop/xaNaP/ZDYD8AQW0qPl68HPdbagCY0SW/osL/5l2wQlRT56gWTO8+lundxzq3DqpNe/LSuHP1O2SW5Hm0pN+uDca5GVb67fh2j66l0azO2M3FiRXPg2oUFMpFLfvw1cHV5QoBlo9J3sjdaRYWzd87XcDfO13gl9eacJAeobpOe76st4ygZLdzdbwIAvBiea6LmLUuwrMqvrrqn7sQPlLbb0y51kL+vvodsq35Htc1CjUF09HN9hOeLq9XQLHhuu3tSefTMjy23PDhqca3P5su0bVb4DHQSRLkP5II1XXmKlZUta5E2w76JASlTGDydJ8fE5ibVX4tcyKe7S2mqv65C+GlQlsJ3x5ez3u7l7Fg3wr2ezgp2NcWHVxHVkmey4rDpys2rCw6tM5lm0SL68SllAZahDd22SY6xMI7g25neLOuzqG70rfwmGALU7pcxB1JF3gSuhB1ggyN1XEqqDU6uJ+jbo9X84RMUPQVRNzlmzgsV6PzXnYTgxlCz0eZYipvEjYacp8C3PX22FHhV3gdpxDeMLTBe3uW8f6eXyi0l2BWJgytmb3tGwbGdWR697E0raXJ0QBfH1zj9U5mCvj6wBqubTus0jaXJPbj28Mpbq8VFxrpdr4ROGopzep1LelFuaw6vpNCewkJ4TEMbtLJ7cRtIeoa6REKACri7qqchbb7cJdsy9VgakzlE6YVYEJF3O46KlMEKmKim5uZIOwSVFBb7+MUwgv/2rqYN3b+j0K7Y4WiXRvOVVVrM/dw08rXOV5chTl6VXSsKMfrczSQ5ua83o3b0Te2Xbmdzk93W8cRXiUy8WFRXJTYl3FtBnNm0y6SBImAFDCJ0MyZMxkyZAgWi4WYmBiPzhk/frxza5DSj1GjRtVsoDVAhQ5CRT+P1x14ynebQypTLKrx+2CKLX3m1KNAKKrx644tM9xpdCdYbjj54NRfnCf/HXouKvqpascs6idDG+Tbij3arNOVjVn7Xe51ZdcGGSUneGNH7a1eDDNXbV6fu/OUUjzb+3q6Rzv26zt1NVrpkNnfky7g0lZSu0s0PAEzNFZSUsK4ceMYPHgw77zzjsfnjRo1qkwxSHdbhNRVKvwiCOmHPvESFH3hwRk2VOg5vo0hOAma/AhFi9GFX4JxDFQUKmw0WK5AOZMkN9dRJlTUI+jwy9AF86FkLWCHoDNQlmulmrSo0I7cw3y8bwU/HNmIVdsJUmZGJHTj6rZD6VqF3ck/S/0dszK5TKjs2uC/R9Zzd/KFRAaHVyd8jwxv2pWFh9Z5leSZlYnhzc5w2y4yOJzXB05kZfoOvjywmr15xwg2mRnYpCNjWw2kbUTT6oQuRMAKmETo8ccfB2Du3LlenRcaGkpCgqcTfes2ZU6A6H+ibZvBtovKJx2bHRONQ3y/fFWZLGC5EmW5svrXCj4DFT3TB1GJ+u67wyk8tvEzlFLOJMGm7fx4dBPfH9nIw90u82qvLYB1mXs8Sjishp1tuYfpH9ehSrF744o2g/j64BqvzjG0wRUeVrs2K5Nz128hhEPADI1V1bJly2jatCmdO3fmjjvuICMjw98hVYtSChXzEqhIKp6vYwZlQcW84ljtJUSA25pziMc2foaBLpe4lM7pmbn5SzZm7ffqut70ulR3GM5TSZHNuS/ZUTtLuSkzUXp8cvJFJEXWv41JhagtAdMjVBWjRo3i8ssvp127duzevZuHHnqICy+8kJUrV2I2Vzypr7i4mOLiYufj3FzPdgiuTSqoPcR9ic6bDUXf8dcmrCYIvQAVeZ9MNBb1xvx9yx1DpS6WlJuUiY/2LadH478Kee7JS+O7wykcLz5BRFAYw5udQe/GbZ3Drh0jE1iXscflxpel2jaKr/4n4qGr2w6lWXgMr+34weV+XUmRCdzc8VzO8WBYTAhROb8mQtOmTeOZZ55x2Wbr1q0kJ1etG/fqq692/rt79+706NGDDh06sGzZMs47r+Kqy7NmzXIOw9VlKigRFTMbbTwC1u2AhqBOKHMTf4cmhM9YDRtLjm5y2yNj1wY/p22h0FaCHYNHN3zK8vRtZWrnLNj/Gx0imvFs7+tp1SiOK1oNZE3GbpfXNSnFoLgkEmpxB/BCWwk/HNnAvvz0MpOaDTTNQqO5qeM59IhpQ4fIyut1CSE859exkylTprB161aXH+3bt/fZ/dq3b0+TJk3YtWtXpW0efPBBcnJynB8HDhzw2f1rgjLFokIHo0KHSBIk6p18Wwk2D4elDDQZJXncveY9fkvfDjgSpNIPgH356Uxc9QbHinI4s2kXesa0qXQ/LxOKIGXm9qTzffPJeMBm2JnyxwcsPfon4PicSj8Ajpfk8uqO76u8ukwIUZ5fe4Ti4+OJj6+9LueDBw+SkZFB8+aVj6eHhoYG7MoyIeobS1AIJpRHw1cKWH18J5tzKv/jxa4NcqwFzNvzMw90HcMLfW/kwZT5rMrY5VxBVnq/yOAwnul9Hcm1uFXEL8e2sjaz8l4qu9bk24p5a9cSHusxrtbiEqI+C5jZtKmpqaSkpJCamordbiclJYWUlBTy8vKcbZKTk/nqq68AyMvL44EHHuD3339n3759LFmyhEsuuYSOHTsycuRIf30aQggvhJiCOKtpF7fbQ5hQDG7Sia8PrnU7ydiuDRYeXEeBrZiI4DDm9L+JuYP/zqWJ/Rka35lzE7rxWPdxLB4+jT6xvuuR9sRnqSvd7jhv1wY/HNlATklBLUUlRP0WMJOlH330UebNm+d83Lt3bwCWLl3K8OHDAdi+fTs5OY4Kq2azmY0bNzJv3jyys7Np0aIFF1xwAU8++aT0+AgRQK5pO5Rlx7a4bGOgubbtMO5ZN9dZGdqVYsPKocJM52qrrtGJVapF5Gvbcg971Ptl0wb789PpEdLGbVshhGsBkwjNnTvXbQ0hfcqqkvDwcL7//vsajkoIUdOshieb9EKJYXPbG1TXBXr8QgSigEmEhBAN0/x9y91WgDahmL9vOV2iW/Bn9kG3vSoWcwitLHVvccEZ0YmsydyN4Wb3+RBTEO0jZNVYTdFasz5rHwsPruVAQQZh5mCGNOnERS37Eh3iu62LRN0giZAQos4qtltZeXyH28EiA83azD083O1yNmW7XulpVooxif3r5MqrK9sMZlVG5atawVEd+sLmvYgIDqulqBqWPGsRD6z/kHWZe8ok4Gsz9vDazh95vMc4zkvo7ucohS8FzGRpIUTDU2i3ejBj5i8DYjvQP65DpROOzcpEfGgUEzqc7ZsAfWxofGfOjE+udIjMrExEB1uYmFRxHTRRPXZtMPmP91mfudf5uJRGYzVsPJyygNXHXSerIrBIIiSEqLMigkIJVhVXgT+dWZk4YStiTMu+9GzcFsVftYBKV511i27FW4Nup3FIRA1GXXUmZWJW72sZk9gXE+pk/CZn/J0im/POoNtpGhbt50jrp9+P7yQla1+lQ6v65McrO2T+aX0iQ2NCiDoryGRmZIuefHs4xe0cocigMK7/bY7zOYVja4yuMYkkhMUwvFlXOkW1qIWoqyfEFMTD3S7n1o4j+P7IBo4V5WAJCuWspl3qxMq2+uzL1FWYUdhd9ENqNNtyD7HzxBHZ462ekERICFGnXdt2GN8dTnHZxkCTYy0s85wG9hccJ6Mkj3cH3UGrRnE1F2QNiA+L4vp2Z/o7jAZlT94xl0nQqfbnH5dEqJ6QoTEhRJ3WMTKBWb2uLTPEVap0LpCCCusH2bVBnq2IxzZ9WhuhigAXbPJsGBbweMhW1H2SCPmZtm7FyJmOcewsjLQBGBnj0AVfoHUR2shF58/DOH6x49ixczByn0HbUv0dthC16uxmXVkw7B7GtR5EVHA4CogMCqNfnKPys6u/4e3aYFP2AXaeOFIrsYrANahJktsq5uCYj9ajcetaiEjUBqW1m4IVDVxubi7R0dHk5OQQFRXl02vrvDfQef8CzEBp0TjH37aYWoLOB51T2vrkfx1/hajoZ1DhY3wajxCBQmuNUorpGz7hxyMb3dYNMqH4e6eR3ND+rFqKUASi/XnpjFv+gss2ZmXi/ITuPNHzqlqKSlSVp+/f0iPkJ7rwq5NJEPyVBIEz4TEOgc7mr3UKnNLWjs55AF28qhYiFaLuUcoxJFZsWD3aksKkFMWGtabDEgGuTUQ8d3aqfC9KszIRFxLJ3cmjazEqUdNksrQfaG2g816u5lVM6PzXUKEDfROTPQOKfwQjC0yNIXQEylz3Ku8KcaoW4bFuq06DY2+uUFMQC/atoNBuJSE8muFNzyA8KKTaMRwvPsHPaVvItRYSE2JheLOudXZ5vnDvxvZnEx1s4Y2dP5JRklfaR49CMTS+M1O7XkKT0Eh/hyl8SIbG3KiJoTFdsgadeZ1PrqXif0GZE6oeiy5E5z4JhV8BBn8N05kg7FJU9KMoFe6TWIXwtd0n0rhmxUtu25lQGGgUCpNS2LVBuDmEG9ufzYT2w509TN4osBXz3JaFfHs4BY3GpEwY2sCkTIxp2ZfJXS4itA5WrxaesRl2Vmfs4lBBJqHmYAbEdSQhPMbfYQkvePr+LT1C/mA/6ttrVTER0roEnXkLWNfhSIIAbKUXhqKv0Pb9EDsXpar/l7MQvtYhshldoxPZknPQZbvSFWUajf3k336F9hJe3/kjmcV53N/1Yq/uW2y3ctfad8vsa1baK2XXBv85uUfVy/0mEOTFSiRRdwSZzAyJ7+zvMEQtkDlC/uDLHpbqXKvgU7Cu5a8k6HSGI0kq/MzrS2tdhNbFHrYtdvRMSeek8FKetYhdue5Xg7l6ZX2aupLNbvYnO90XB1ax2cXmrqV7ny0+9IdX1xVC1D5JhPwhZCDggx4WUwIEdazSqVprdMEHnrXNf9+jJEUbBej89zHSR6DTeqDTumOkj0IXfITWRafdvwRd8AnG8YvQad3RaT3R6eeg899BG3lV+pxEw/Pfw+uxarv7hi6YlYnPUld63F5rzaf7V+I6vXLMKflk/2/Vik0IUfMkEfIDZYqE8Cuo3pdfoRrdiKpqUS99Aux7cffLHLSjnc513crIQmdehT4xE+yn/HVt34vOfQKdcR3aOHGybQE6czw6dzrYdv7V1jiMPvEsOmMs2p5etc9LNCgbsvZBJRuUesquDdZl7PG4fWZJHocLszz4ydHszkuj0FZSrfiEEDVLEiE/UZH3Q1BnKv4WmPjrl3tFv+QVhA4Hy43ViMDmvkkZrv/q1tmTwbaL8sv9Tz62bUHnTHU8kzsDrH+ccpyy7e2p6Oy/y1CZcMumDdwn8+65W3VW/p41c20hRO2TRMhPlCkCFTsfLONBNSp7MLgPqvE8VMzLEJR02omRENwTVDQ679WqV5lW0aBiPGwb42hfCW3dDiUrcJ0s2aH4fxjF66BoEZXPSzrZ1roBrCmexScarPYRTVHV7BEyoegY6fmCg7iQCCKCwjxqGxsSQaOg0KqGJoSoBZII+ZEyNcIUNQ3VdCUq9iNU4/dQTX7EFDcfFToIFTYKFbcIFbcYop5wzAnSJ8C62ZFM5L+OPj4CI3tyuTk4bu+tzGC5FvcvARNYrnU5BKeLFlFa8do1M+S/5mGEQeiihR62FQ3VJYn9q90fZKAZ23qQx+2DTGYubdXfuc9ZZUwoxrUeVKWl+UKI2iOJUB2gVBgqpD8qdCgqqM1pxxSoEDjxHBil82ZslFaYBqDov+isO9FeThpVjW4AUzyVJzFmMMU72rliZOLZPA0FRgaeveyMk9cVonIJ4TFc03ao23aVvTpNKHo1bsswL5dJX9t2GDEhjSrdl8qsTDQNi/IqwRJC+IckQgFAn3gRdAGVDz0ZUPIrFP/i1XWVKRYV+xGYSzcPNJf9r7m1o6fKFOvmQpUPm5WlwRSN62GxUiYvrisasrs6j+KaNkNRlN2NXqEIUmauaTOUyGBHmYnS46UJzIAmHZnd9wava/00CY3kjYG30jy8cZnrlf63taUJrw+4lZgQS3U/PSFEDZPK0m7U5KarntD2DHT6MNxNVgYzhAzGFPuu9/fQBpQsRxcudm6xocL/BiFnojzYiVmXbEBnjvPsZjFvQ/ZEPJngqhrPQ4UO9uy6osE7VJDJfw6uZfeJo5iUiZ6N23BRy77EhFgotltZcnQzy9O3UWgvoXl4DBe17EvX6MRq3dOuDVam7+DHo5vIKcmncUgEI1v0ZEBcB0we/OwIIWqOp+/fkgi54fdEqHgVOuv/PGusYjA1W12zAVVAa43OvNIxd6nShM0Mwf0xxb2PkXUHFC9z3dbcHtVkscyvEEIIUSWy+3x94VUi4J+kQSmFiplzcquPSsoBmFuhYmY72kfPOlkIsqK2ZjDFoRq/JkmQEEKIGid7jdV1QZ1wfJvc1f0xQ0jvWgioYsqcAHFfovPnQcF80NmOA6ZYCL8W1Wg8yuTIyJUpBmIXQMH76IIP/5oEriLBcjXKMgHZ+V74S2ZxHi9v/5bf0rdTbNiIDArjopZ9uKnjuYSY5FemEPWNDI254e+hMQAj+wEoWoy7eUKq8Tuo0DNrJygXtLb+ldyYmqJU5W8eWttPtrU7VqjJ5q7Cjz5L/Z3ntlRctiFYmXljwES6NW5d4XEhRN0iQ2P1iIq8D1QUlS9zVxA6EkKG1WZYlVIqGGVu4fhwkQQ52ppR5gSUuaUkQcKvlqVtqTQJArBqOxNXv0l6kevtZoQQgUUSoQCgzC1QcZ+e3JIDHAlREI5vnwnCr0HFzJY5NUJUw7+2LnLbxq4N/rV1cS1EI4SoLTLgHSBUUBuI+wqsG9HFP4EudMzLCbsYZY73d3hCBLQD+RmkFeV41PbXY1trOBohRG2SRCiAKKUgpCcqpKe/QxGiXvkz+4DHba1eVnAXQtRtMjQmhGjwwszB/g5BCOEnkggJIRq8QU2SPK7C1TikUY3GIoSoXZIICSEavLCgEHo2buO+IXBV6yE1HI0QojZJIiSEEMDTva4j1OR6iKyNpQnj259dSxEJIWpDQCRC+/bt4+abb6Zdu3aEh4fToUMHZsyYQUlJicvzioqKuPPOO4mLiyMiIoKxY8eSlpZWS1ELIQJJbGgEn581mVaW2AqP94/rwMfD7sZkCohfm0IIDwXEqrFt27ZhGAZvvPEGHTt2ZPPmzUycOJH8/Hyef/75Ss+77777+Oabb/jss8+Ijo5m0qRJXH755axYsaIWoxdCBIpmYdF8cdb97M9LZ/6+FeRaC2gR3pgb259NVIjF3+EJIWpAwG6x8dxzz/Haa6+xZ8+eCo/n5OQQHx/P/PnzueKKKwBHQtWlSxdWrlzJoEGDPLpPXdhiQwghhBDeqfdbbOTk5BAbW3EXNsC6deuwWq2MGDHC+VxycjKtW7dm5cqVlZ5XXFxMbm5umQ8hhBBC1E8BmQjt2rWLOXPmcNttt1Xa5ujRo4SEhBATE1Pm+WbNmnH06NFKz5s1axbR0dHOj1atWvkqbCGEEELUMX5NhKZNm4ZSyuXHtm3bypxz6NAhRo0axbhx45g4caLPY3rwwQfJyclxfhw44HnFWSGEEEIEFr9Olp4yZQrjx4932aZ9+/bOfx8+fJhzzjmHIUOG8Oabb7o8LyEhgZKSErKzs8v0CqWlpZGQkFDpeaGhoYSGhnoUvxBCCCECm18Tofj4eOLjPdsw9NChQ5xzzjn07duX9957z+0S1r59+xIcHMySJUsYO3YsANu3byc1NZXBgwdXO3YhhBBCBL6AmCN06NAhhg8fTuvWrXn++edJT0/n6NGjZeb6HDp0iOTkZFavXg1AdHQ0N998M5MnT2bp0qWsW7eOCRMmMHjwYI9XjAkhhBCifguIOkI//vgju3btYteuXSQmJpY5Vrr632q1sn37dgoKCpzHXnjhBUwmE2PHjqW4uJiRI0fy73//u1ZjF0IIIUTdFbB1hGqL1BESQgghAk+9ryMkhBBCCFFdATE05k+lHWZSWFEIIYQIHKXv2+4GviQRcuPEiRMAUlhRCCGECEAnTpwgOjq60uMyR8gNwzA4fPgwkZGRKKX8HU6DkZubS6tWrThw4IDMzfIT+R74n3wP/E++B/5Vna+/1poTJ07QokULlyV3pEfIDZPJVG6lmqg9UVFR8svHz+R74H/yPfA/+R74V1W//q56gkrJZGkhhBBCNFiSCAkhhBCiwZJESNRJoaGhzJgxQ/Z98yP5HviffA/8T74H/lUbX3+ZLC2EEEKIBkt6hIQQQgjRYEkiJIQQQogGSxIhIYQQQjRYkggJIYQQosGSREjUeTNnzmTIkCFYLBZiYmL8HU6D8Oqrr9K2bVvCwsIYOHAgq1ev9ndIDcovv/zCxRdfTIsWLVBK8fXXX/s7pAZl1qxZ9O/fn8jISJo2bcqll17K9u3b/R1Wg/Laa6/Ro0cPZyHFwYMH8+2339bIvSQREnVeSUkJ48aN44477vB3KA3CJ598wuTJk5kxYwZ//PEHPXv2ZOTIkRw7dszfoTUY+fn59OzZk1dffdXfoTRIP//8M3feeSe///47P/74I1arlQsuuID8/Hx/h9ZgJCYm8vTTT7Nu3TrWrl3LueeeyyWXXMKff/7p83vJ8nkRMObOncu9995Ldna2v0Op1wYOHEj//v155ZVXAMd+e61ateKuu+5i2rRpfo6u4VFK8dVXX3HppZf6O5QGKz09naZNm/Lzzz9z1lln+TucBis2NpbnnnuOm2++2afXlR4hIYRTSUkJ69atY8SIEc7nTCYTI0aMYOXKlX6MTAj/ycnJARxvxKL22e12FixYQH5+PoMHD/b59WXTVSGE0/Hjx7Hb7TRr1qzM882aNWPbtm1+ikoI/zEMg3vvvZehQ4fSrVs3f4fToGzatInBgwdTVFREREQEX331FV27dvX5faRHSPjFtGnTUEq5/JA3XiGEv915551s3ryZBQsW+DuUBqdz586kpKSwatUq7rjjDm688Ua2bNni8/tIj5DwiylTpjB+/HiXbdq3b187wQinJk2aYDabSUtLK/N8WloaCQkJfopKCP+YNGkSixcv5pdffiExMdHf4TQ4ISEhdOzYEYC+ffuyZs0aXnrpJd544w2f3kcSIeEX8fHxxMfH+zsMcZqQkBD69u3LkiVLnJNzDcNgyZIlTJo0yb/BCVFLtNbcddddfPXVVyxbtox27dr5OySB43dRcXGxz68riZCo81JTU8nMzCQ1NRW73U5KSgoAHTt2JCIiwr/B1UOTJ0/mxhtvpF+/fgwYMIAXX3yR/Px8JkyY4O/QGoy8vDx27drlfLx3715SUlKIjY2ldevWfoysYbjzzjuZP38+//nPf4iMjOTo0aMAREdHEx4e7ufoGoYHH3yQCy+8kNatW3PixAnmz5/PsmXL+P77731/My1EHXfjjTdqoNzH0qVL/R1avTVnzhzdunVrHRISogcMGKB///13f4fUoCxdurTC1/yNN97o79AahIq+9oB+7733/B1ag3HTTTfpNm3a6JCQEB0fH6/PO+88/cMPP9TIvaSOkBBCCCEaLFk1JoQQQogGSxIhIYQQQjRYkggJIYQQosGSREgIIYQQDZYkQkIIIYRosCQREkIIIUSDJYmQEELUMq01NpvN32EIIZBESAhRy9555x0uuOACf4dRq/Lz83nsscfo168fCQkJhIaG8vbbb/s7rICSm5tLr169yMvL4+DBg849qEpt2bKFxMRE8vPz/RShCFSSCAnhhfHjx6OUqvQjOzvb3yHWiqKiIsaPH0/37t0JCgpy7kvmyXnTp09nxowZNRtgHVJUVMTQoUNZunQpTz31FL/++itbt27l5ptv9ndoASUqKophw4YRExND27ZtueOOO8oc79q1K4MGDWL27Nl+ilAEKkmEhPDSqFGjOHLkSJmPL774wt9h1Sq73U54eDh33303I0aM8Pi8zz//nKioKIYOHVqD0dUtzz33HI0bN2bJkiWMGjWKpKQkOnToQHBwsL9DCzivvPIKx44dIzMzkylTppQ7PmHCBF577TUZdhRekURICC+FhoaSkJBQ5iM2NrZMm7lz5xITE8PXX39NUlISYWFhjBw5kgMHDjjbPPbYY/Tq1cv5uKSkhI4dO5bpWfr000/p0KEDYWFhxMXFccUVV5Cenu48RynF119/Xebew4cP595773U+/uCDD+jXrx+RkZEkJCRw7bXXcuzYMefxZcuWlblnVlYWPXr04IYbbqCyHXgaNWrEa6+9xsSJE0lISPD4a7dgwQIuvvjiMs+V9rKd/pf8ZZddhlKKuXPnOp+bOnUqnTp1wmKx0L59e6ZPn47Vai1z3qJFi+jfvz9hYWE0adKEyy67zHmsbdu2vPjii87HL774Im3bti1z/qlfU1e9f8uWLfMopsWLF9O2bVsGDx6MxWKhVatWzJw5s8zX9vS4lixZglKqTE/b8OHDmTRpEpMmTSI6OpomTZowffr0MtfJysrihhtuoHHjxlgsFi688EJ27tzpPD537lxn/GazmRYtWjB16lQMwwAcCe7NN99Mu3btCA8Pp3Pnzrz00kvlvl+n9wCWvt5Lnf7ahvKvs9PPOVVKSgpKKfbt21eubWxsLFFRUZx11lkopZybMAOcf/75ZGZm8vPPP1d4XSEqIomQEDWkoKCAmTNn8v7777NixQqys7O5+uqrK23/yiuvkJaWVua55ORk5s6dy/bt2/n+++/Zt28fU6dO9SoOq9XKk08+yYYNG/j666/Zt28f48ePr7BtXl4eo0ePpn379rz77rsopby6lzvLly+nX79+5Z5v2bIlb731lvPx4cOHWbFiBRaLpUy7yMhI5s6dy5YtW3jppZd46623eOGFF5zHv/nmGy677DJGjx7N+vXrWbJkCQMGDKhyvKf2+gF88cUXzsdDhgzxKKb09HTmzp3LxRdfTEpKCs888wzPPPMMr7zySoX3NAyDKVOmEBERUe7YvHnzCAoKYvXq1bz00kvMnj27zFyj8ePHs3btWhYuXMjKlSvRWjN69OgyiVlUVBRHjhwhNTWVF154gWeffda5o7dhGCQmJvLZZ5+xZcsWHn30UR566CE+/fTTKn8Na8KXX37J+vXryz0fEhJCr169+PXXX/0QlQhUQf4OQIj6ymq18sorrzBw4EDA8SbWpUsXVq9eXe7NOTMzk6eeeoqpU6cyffp05/M9evRw/rtx48bExcVht9u9iuOmm25y/rt9+/a8/PLL9O/fn7y8vDJvtsXFxVxxxRVYLBY++eQTgoJ8++shOzubnJwcWrRoUe5Yv3792Lt3L7/++itnnnkm7777LldffTXvv/9+mXaPPPKI899t27bl/vvvZ8GCBfzjH/8AYObMmVx99dU8/vjjznY9e/ascsyn93bFxsaWe85dTIZhcP755/Poo48C0KlTJ/bs2cMzzzzDXXfdVe6e8+bNo7i4mEsuuYS8vLwyx1q1asULL7yAUorOnTuzadMmXnjhBSZOnMjOnTtZuHAhK1ascCZpH330Ea1ateLrr79m3LhxgKOXq/RzaNeuHSaTiejoaACCg4PLfO3atWvHypUr+fTTT7nyyiu9/wLWAKvVytSpU8v9rJRq0aIF+/fv90NkIlBJj5AQNSQoKIj+/fs7HycnJxMTE8PWrVvLtX3iiSc455xzGDZsWLljv/76KxEREcTExFBYWMi//vWvMsevueYaIiIinB+n/zW8bt06Lr74Ylq3bk1kZCRnn302AKmpqWXaXXfddSxZsoSzzz6b0NDQKn/elSksLAQgLCyswuMTJ07kzTffxDAM3nnnHSZOnFiuzSeffMLQoUNJSEggIiKCRx55pMznkZKSwnnnnefz2F1xFxNQbk7UsGHDOHToELm5uWWeLygo4JFHHuHZZ5+tMBEdNGhQmV66wYMHs3PnTux2O1u3biUoKMiZeAPExcXRuXPnMq+5nJwcIiIiCA8PZ9CgQUydOtWZOAG8+uqr9O3bl/j4eCIiInjzzTfLfT6LFy8u85q7/fbby8W6adOmMm0uvPDCcm1KY4mKiiIpKYn777+/3FDn6V599VWio6O57rrrKjweHh5OQUGBy2sIcSpJhITws507d/L222/zzDPPVHi8X79+rF+/nh9++IGMjIwyQ0gAL7zwAikpKc6PU4ee8vPzGTlyJFFRUXz00UesWbOGr776CnDMSTrV0aNH+eKLL/jnP//Jpk2bfPxZOt6UlVJkZWVVePz666/nv//9LwsWLCAhIYHu3buXOb5y5Uquu+46Ro8ezeLFi1m/fj0PP/xwmc8jPDzc53G74klMjRs3rvT804cen3vuOTp37lxuHpUvRUZGkpKSwsaNG/nyyy95/fXXnZP9FyxYwP3338/NN9/MDz/8QEpKChMmTCj3WjnnnHPKvOaeeOKJcvfp3LlzmTYVlQsojWXdunU8//zzvP322+XmJJ0qKyuLJ598ktmzZ1c6bJuZmUl8fLw3XxLRwMnQmBA1xGazsXbtWucw2Pbt28nOzqZLly5l2k2dOpVbbrmFjh07cvDgwXLXCQ8PJykpiaSkJG699VbeeustHnzwQefxhISEMjVVTk0Gtm3bRkZGBk8//TStWrUCYO3atRXGu3DhQtq3b8/EiROZMGECv//+u0+Hx0JCQujatStbtmypsI5QTEwMY8aM4fbbby8zcbjUb7/9Rps2bXj44Yedz50+BNKjRw+WLFnChAkTfBa3K57ElJyczIoVK8o8t3z5chITE4mMjHQ+d+TIEV577TWXE31XrVpV5vHvv/9OUlISZrOZLl26YLPZWLVqlbOHJyMjg+3bt9O1a1fnOSaTyfl6SUpK4sMPP+Srr75i7NixzmG1v//97872u3fvLhdHo0aNyrzmmjZtWq5NSEhImTYVvbZPj+X8888nJSWl0pWITz75JGeeeSZnnXWWcyL16TZv3swVV1xR4TEhKiI9QkLUkODgYO666y5WrVrFunXrGD9+PIMGDSozP2jXrl0sW7bMOX/kdAsWLGDNmjWkpqayZMkSXn/9dXr37u1xDK1btyYkJIQ5c+awZ88eFi5cyJNPPllh29KVb08//TRZWVk8/fTTLq+9ZcsWUlJSyMzMJCcnx/mXvysjR45k+fLllR6fNm0aDz30EFdddVW5Y0lJSaSmprJgwQJ2797Nyy+/7OzdKjVjxgw+/vhjZsyYwdatW9m0aVO5njabzUZRURFFRUXYbDa01s7HRUVFLuOvSkz33nsv//vf/3jiiSfYsWMHH3/8Mc8++6xzDlGpV199lcsuu8zl9zc1NZXJkyezfft2Pv74Y+bMmcM999zjjOWSSy5h4sSJLF++nA0bNnD99dfTsmVLLrnkEuc1tNYcPXqUI0eOsHTpUn7++WeSk5Od11i7di3ff/89O3bsYPr06axZs8arr4m3ioqKKCwsZN26dSxfvpxu3bpV2K6goIA333yTZ599ttJr7du3j0OHDnlV0kEISYSEqCEWi4WpU6dy7bXXMnToUCIiIvjkk0/KtMnPz+fhhx8ut/y+1NatWxk7dixJSUnccMMNDB06lDlz5ngcQ3x8PHPnzuWzzz6ja9euPP300zz//PMuz2nUqBHvvvsuM2fOZPPmzZW2Gz16NL1792bRokUsW7aM3r17u03Sbr75Zv773/+Sk5NT4fHOnTszbdo0GjVqVO7YmDFjuO+++5g0aRK9evXit99+KzdZdvjw4Xz22WcsXLiQXr16ce6557J69eoybR544AHCw8MJDw/ngQceIDU11fnY26E1T2IaOHAg8+fPZ8GCBXTr1o2pU6cybdo0Jk2aVKadYRjMnDnT5f1uuOEGCgsLGTBgAHfeeSf33HMPt956q/P4e++9R9++fbnooosYPHgwWmv++9//lqlZlJubS/PmzWnZsiXXXnst48aN44EHHgDgtttu4/LLL+eqq65i4MCBZGRklOkd8rWcnBzCw8Np1KgRF110EZdddhmTJ0+usK3VamXChAl06tSp0ut9/PHHXHDBBbRp06amQhb1kNKVFQoRQlTZ3LlzuffeextMpWlvjBs3jj59+pQZ3hPuDR8+nF69elU4bCgcc96SkpKYP39+gyrYKapPeoSEELXqueeeq7BGjhDVkZqaykMPPSRJkPCaTJYWQtSqtm3bVlg/R4jq6NixY7mNWIXwhAyNCSGEEKLBkqExIYQQQjRYkggJIYQQosGSREgIIYQQDZYkQkIIIYRosCQREkIIIUSDJYmQEEIIIRosSYSEEEII0WBJIiSEEEKIBksSISGEEEI0WP8PhLCLv5ncLXEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**# 3.4. Попробуйте поэкспериментировать с разнымми значениями K и выбрать самое оптимальное значение с помощью Метода Локтя (Elbow Method). Нарисуйте график по Методу Локтя (Elbow Method).**"
      ],
      "metadata": {
        "id": "r2KnqySl9oNQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Выбираем два признака для кластеризации\n",
        "X = df[[\"Годовой доход (в тыс. $)\", \"Оценка расходов (1–100)\"]]\n",
        "\n",
        "# Рассчитаем значение инерции для K от 1 до 10\n",
        "inertias = []\n",
        "K_range = range(1, 11)\n",
        "\n",
        "for k in K_range:\n",
        "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
        "    kmeans.fit(X)\n",
        "    inertias.append(kmeans.inertia_)\n",
        "\n",
        "# Построим график метода локтя\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(K_range, inertias, marker='o')\n",
        "plt.title(\"Метод локтя для определения оптимального K\")\n",
        "plt.xlabel(\"Количество кластеров (K)\")\n",
        "plt.ylabel(\"Сумма квадратов ошибок (Inertia)\")\n",
        "plt.grid(True)\n",
        "plt.xticks(K_range)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "2rQsvOF4tdiF",
        "outputId": "899c99d6-2a38-4fa8-915a-953980ed605d"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Описание. На графике по методу локтя видно, что \"излом\" (elbow) происходит примерно на значении K = 5. Это означает, что оптимальное количество кластеров для сегментации клиентов по доходу и оценке расходов — 5."
      ],
      "metadata": {
        "id": "pIe6AHPRvCI9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3.5. Сохраните спрогнозированные алгоритмом значения в переменной clusters.**"
      ],
      "metadata": {
        "id": "kojQCi63y919"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Сохраняем метки кластеров в переменной clusters\n",
        "clusters = kmeans.labels_"
      ],
      "metadata": {
        "id": "RXtWEQqAwASt"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3.6. Используйте следующий код для добавления кластеров в основной датасет в качестве новой колонки:**"
      ],
      "metadata": {
        "id": "Yp1fkDO_zjW2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Cluster'] = clusters"
      ],
      "metadata": {
        "id": "BCAq74d6vD32"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3.7. Визуализируйте кластера с помощью следующего кода:**"
      ],
      "metadata": {
        "id": "m5SGYBEZzwxU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "# Визуализация\n",
        "pca = PCA(n_components=2)\n",
        "X_pca = pca.fit_transform(X_scaled)\n",
        "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=clusters, cmap='Set1')\n",
        "plt.title('Кластеризация клиентов')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "kAosNGH6zykL",
        "outputId": "b52abf69-3f06-41d0-a1b2-9df4b57d7690"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Вопросы\n",
        "4.1. В чём ключевое отличие между контролируемым и неконтролируемым обучением?\n",
        "\n",
        "4.2. Что влияет на выбор количества кластеров в K-Means?\n",
        "\n",
        "4.3. Для чего при визуализации кластеров использовался метод PCA?"
      ],
      "metadata": {
        "id": "HsW4hoqh1KgU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.1 Ответ.\n",
        "Неконтролируемое обучение – это метод, при котором модель\n",
        "обучается на неразмеченных данных, пытаясь выявить скрытые\n",
        "структуры и зависимости.\n",
        "\n",
        "Контролируемое обучение – это метод, при котором модель обучается\n",
        "на размеченных данных. Каждому входному примеру соответствует\n",
        "целевая переменная (метка), которую алгоритм пытается предсказать."
      ],
      "metadata": {
        "id": "0C5NuFTU1pil"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.2 Ответ.\n",
        "Методы оценки качества кластеризации.\n",
        "**Метод локтя (Elbow method): ** Строится график зависимости внутрикластерной суммы квадратов ошибок (inertia) от числа кластеров. Ищется \"излом\" — точка, после которой уменьшение ошибки замедляется.\n",
        "\n",
        "Распределение данных. Если данные визуализируемы, можно попытаться \"увидеть\" количество групп."
      ],
      "metadata": {
        "id": "N7fEymug29_T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.3\n",
        "**Метод главных компонент** (Principal Component Analysis или же PCA) — алгоритм обучения без учителя, используемый для понижения размерности и выявления наиболее информативных признаков в данных.\n",
        "\n",
        "Его суть заключается в предположении о линейности отношений данных и их\n",
        "проекции на подпространство ортогональных векторов, в которых дисперсия будет максимальной."
      ],
      "metadata": {
        "id": "nWGFPd5i4W5K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "1y3VYfZ95DPS"
      }
    }
  ]
}