{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Лабораторная работа №2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Диабет у индейцев Пима**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
      "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "df = pd.read_csv(\"..//static//csv//diabetes.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проблемная область: медицина и эпидемиология\n",
    "\n",
    "Объект наблюдения: женщины индейского племени Пима, проживающие вблизи Финикса, штат Аризона, США\n",
    "\n",
    "Атрибуты: беременность, глюкоза, артериальное давление, толщина кожи, инсулин, индекс массы тела, родословная диабета, возраст, исход\n",
    "\n",
    "Пример бизнес-цели:\n",
    "1. Повышение эффективности скрининга диабета. Цель технического проекта: Разработать и обучить модель машинного обучения с точностью предсказания не менее 85% для автоматизированного скрининга диабета на основе данных датасета \"Диабет у индейцев Пима\".\n",
    "\n",
    "2. Снижение медицинских расходов. Цель технического проекта: Оптимизировать модель прогнозирования таким образом, чтобы минимизировать количество ложноотрицательных результатов (пациенты с диабетом, которые не были выявлены), что позволит снизить затраты на лечение осложнений.\n",
    "\n",
    "3. Повышение качества жизни пациентов. Цель технического проекта: Разработать интерфейс для модели, который будет предоставлять пациентам персонализированные рекомендации по профилактике и лечению диабета на основе их индивидуальных рисков, определенных моделью."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем на наличие выбросов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество выбросов в столбце 'Glucose': 5\n",
      "Количество выбросов в столбце 'SkinThickness': 1\n",
      "Количество выбросов в столбце 'Insulin': 34\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Загрузка датасета\n",
    "df = pd.read_csv(\"..//static//csv//diabetes.csv\")\n",
    "\n",
    "# Выбираем столбцы для анализа\n",
    "columns_to_check = ['Glucose', 'SkinThickness', 'Insulin']\n",
    "\n",
    "# Функция для подсчета выбросов\n",
    "def count_outliers(df, columns):\n",
    "    outliers_count = {}\n",
    "    for col in columns:\n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Считаем количество выбросов\n",
    "        outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
    "        outliers_count[col] = len(outliers)\n",
    "    \n",
    "    return outliers_count\n",
    "\n",
    "# Подсчитываем выбросы\n",
    "outliers_count = count_outliers(df, columns_to_check)\n",
    "\n",
    "# Выводим количество выбросов для каждого столбца\n",
    "for col, count in outliers_count.items():\n",
    "    print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
    "    \n",
    "# Создаем гистограммы\n",
    "plt.figure(figsize=(15, 10))\n",
    "for i, col in enumerate(columns_to_check, 1):\n",
    "    plt.subplot(3, 3, i)\n",
    "    sns.histplot(df[col], kde=True)\n",
    "    plt.title(f'Histogram of {col}')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно увидеть, что количество выбросов маленькое. Можем сделать очистку для столбца 'Insulin'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество удаленных строк: 34\n",
      "Количество выбросов в столбце 'Insulin': 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Загрузка датасета\n",
    "df = pd.read_csv(\"..//static//csv//diabetes.csv\")\n",
    "\n",
    "# Выбираем столбцы для очистки\n",
    "columns_to_clean = ['Insulin']\n",
    "\n",
    "# Функция для удаления выбросов\n",
    "def remove_outliers(df, columns):\n",
    "    for col in columns:\n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Удаляем строки, содержащие выбросы\n",
    "        df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]\n",
    "    \n",
    "    return df\n",
    "\n",
    "# Удаляем выбросы\n",
    "df_cleaned = remove_outliers(df, columns_to_clean)\n",
    "\n",
    "# Выводим количество удаленных строк\n",
    "print(f\"Количество удаленных строк: {len(df) - len(df_cleaned)}\")\n",
    "\n",
    "df = df_cleaned\n",
    "\n",
    "# Функция для подсчета выбросов\n",
    "def count_outliers(df, columns):\n",
    "    outliers_count = {}\n",
    "    for col in columns:\n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Считаем количество выбросов\n",
    "        outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
    "        outliers_count[col] = len(outliers)\n",
    "    \n",
    "    return outliers_count\n",
    "\n",
    "# Подсчитываем выбросы\n",
    "outliers_count = count_outliers(df, columns_to_clean)\n",
    "\n",
    "# Выводим количество выбросов для каждого столбца\n",
    "for col, count in outliers_count.items():\n",
    "    print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
    "\n",
    "# Создаем гистограммы для очищенных данных\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "# Гистограмма для Insulin\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.histplot(df_cleaned['Insulin'], kde=True)\n",
    "plt.title('Histogram of Insulin (Cleaned)')\n",
    "plt.xlabel('Insulin')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверка на пропущенные значения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество пропущенных значений в каждом столбце:\n",
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Проверка на пропущенные значения\n",
    "missing_values = df.isnull().sum()\n",
    "\n",
    "# Вывод результатов\n",
    "print(\"Количество пропущенных значений в каждом столбце:\")\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пропущенных значений нет. Делаем разбиение на выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 440\n",
      "Размер контрольной выборки: 147\n",
      "Размер тестовой выборки: 147\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение на признаки (X) и целевую переменную (y)\n",
    "X = df.drop('Outcome', axis=1)  # Признаки\n",
    "y = df['Outcome']  # Целевая переменная\n",
    "\n",
    "# Разбиение на обучающую и оставшуюся часть (контрольная + тестовая)\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "\n",
    "# Разбиение оставшейся части на контрольную и тестовую выборки\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "# Вывод размеров выборок\n",
    "print(\"Размер обучающей выборки:\", X_train.shape[0])\n",
    "print(\"Размер контрольной выборки:\", X_val.shape[0])\n",
    "print(\"Размер тестовой выборки:\", X_test.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разбивка на выборки сделана, проведем проверку на сбалансированность выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Сбалансированность обучающей выборки:\n",
      "Outcome\n",
      "0    0.661364\n",
      "1    0.338636\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Сбалансированность контрольной выборки:\n",
      "Outcome\n",
      "0    0.659864\n",
      "1    0.340136\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Сбалансированность тестовой выборки:\n",
      "Outcome\n",
      "0    0.659864\n",
      "1    0.340136\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение на признаки (X) и целевую переменную (y)\n",
    "X = df.drop('Outcome', axis=1)  # Признаки\n",
    "y = df['Outcome']  # Целевая переменная\n",
    "\n",
    "# Разбиение на обучающую и оставшуюся часть (контрольная + тестовая)\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42, stratify=y)\n",
    "\n",
    "# Разбиение оставшейся части на контрольную и тестовую выборки\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp)\n",
    "\n",
    "# Функция для проверки сбалансированности выборок\n",
    "def check_balance(y_train, y_val, y_test):\n",
    "    print(\"Сбалансированность обучающей выборки:\")\n",
    "    print(y_train.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nСбалансированность контрольной выборки:\")\n",
    "    print(y_val.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nСбалансированность тестовой выборки:\")\n",
    "    print(y_test.value_counts(normalize=True))\n",
    "\n",
    "# Проверка сбалансированности\n",
    "check_balance(y_train, y_val, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выборки относительно сбалансированы, но не идеально. Сделаем приращение данных методом выборки с избытком (oversampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Сбалансированность обучающей выборки после SMOTE:\n",
      "Outcome\n",
      "0    0.5\n",
      "1    0.5\n",
      "Name: proportion, dtype: float64\n",
      "Сбалансированность обучающей выборки:\n",
      "Outcome\n",
      "0    0.5\n",
      "1    0.5\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Сбалансированность контрольной выборки:\n",
      "Outcome\n",
      "0    0.659864\n",
      "1    0.340136\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Сбалансированность тестовой выборки:\n",
      "Outcome\n",
      "0    0.659864\n",
      "1    0.340136\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Разделение на признаки (X) и целевую переменную (y)\n",
    "X = df.drop('Outcome', axis=1)  # Признаки\n",
    "y = df['Outcome']  # Целевая переменная\n",
    "\n",
    "# Разбиение на обучающую и оставшуюся часть (контрольная + тестовая)\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42, stratify=y)\n",
    "\n",
    "# Разбиение оставшейся части на контрольную и тестовую выборки\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp)\n",
    "\n",
    "# Применение SMOTE для балансировки обучающей выборки\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "# Функция для проверки сбалансированности выборок\n",
    "def check_balance(y_train, y_val, y_test):\n",
    "    print(\"Сбалансированность обучающей выборки:\")\n",
    "    print(y_train.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nСбалансированность контрольной выборки:\")\n",
    "    print(y_val.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nСбалансированность тестовой выборки:\")\n",
    "    print(y_test.value_counts(normalize=True))\n",
    "\n",
    "# Проверка сбалансированности после SMOTE\n",
    "print(\"Сбалансированность обучающей выборки после SMOTE:\")\n",
    "print(y_train_resampled.value_counts(normalize=True))\n",
    "\n",
    "# Проверка сбалансированности контрольной и тестовой выборок\n",
    "check_balance(y_train_resampled, y_val, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Объекты вокруг земли**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проблемная область: космические объекты и их угроза для Земли\n",
    "\n",
    "Объект наблюдения: астероиды и другие малые тела Солнечной системы\n",
    "\n",
    "Атрибуты: имя объекта, минимальный и максимальный оценочные диаметры, относительная скорость, расстояние промаха, орбитальное тело, объекты программы \"Сентри\", абсолютная звездная величина, опасность\n",
    "\n",
    "Пример бизнес-цели:\n",
    "\n",
    "1. Разработка и продажа страховых продуктов для космических рисков. Цель технического проекта: разработка системы оценки рисков и ценообразования для страховых продуктов, защищающих от космических угроз.\n",
    "\n",
    "2. Разработка и продажа технологий для мониторинга и предотвращения космических угроз. Цель технического проекта: создание системы мониторинга и прогнозирования траекторий небесных тел для предотвращения космических угроз.\n",
    "\n",
    "3. Образовательные программы и сервисы. Цель технического проекта: разработка интерактивных образовательных материалов и сервисов, основанных на данных о небесных телах."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'name', 'est_diameter_min', 'est_diameter_max',\n",
      "       'relative_velocity', 'miss_distance', 'orbiting_body', 'sentry_object',\n",
      "       'absolute_magnitude', 'hazardous'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# вывод всех столбцов\n",
    "df = pd.read_csv(\"..//static//csv//neo.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверяем на выбросы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество выбросов в столбце 'relative_velocity': 1574\n",
      "Количество выбросов в столбце 'miss_distance': 0\n",
      "Количество выбросов в столбце 'absolute_magnitude': 101\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Выбираем столбцы для анализа\n",
    "columns_to_check = ['relative_velocity', 'miss_distance', 'absolute_magnitude']\n",
    "\n",
    "# Функция для подсчета выбросов\n",
    "def count_outliers(df, columns):\n",
    "    outliers_count = {}\n",
    "    for col in columns:\n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Считаем количество выбросов\n",
    "        outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
    "        outliers_count[col] = len(outliers)\n",
    "    \n",
    "    return outliers_count\n",
    "\n",
    "# Подсчитываем выбросы\n",
    "outliers_count = count_outliers(df, columns_to_check)\n",
    "\n",
    "# Выводим количество выбросов для каждого столбца\n",
    "for col, count in outliers_count.items():\n",
    "    print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
    "    \n",
    "# Создаем гистограммы\n",
    "plt.figure(figsize=(15, 10))\n",
    "for i, col in enumerate(columns_to_check, 1):\n",
    "    plt.subplot(2, 3, i)\n",
    "    sns.histplot(df[col], kde=True)\n",
    "    plt.title(f'Histogram of {col}')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В столбцах 'relative_velocity'и 'absolute_magnitude' присутствуют выбросы. Теперь можно очистить их от выбросов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество выбросов в столбце 'relative_velocity': 1574\n",
      "Количество выбросов в столбце 'absolute_magnitude': 101\n",
      "Количество удаленных строк: 1678\n"
     ]
    },
    {
     "data": {
      "image/png": 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6a6VtTJw40bGspKTEaNGihWGxWIwXX3zRsTw9Pd1wd3ev8JxUZcuWLQZgTJgwocLyhx9+2ACMX375xbHsjjvuMDw9PU+5vZPHJicnG8nJycb+/fuNV1991bBYLEaXLl0Mu91uGIZhZGdnG35+fsbdd99d4f4JCQmGr69vheXl+3+iqp67YcOGGa1bt66wrHPnzsall15aaezy5csNwFi+fLlhGIZht9uN5s2bGyNHjqww7osvvqjwepxJ7ur07t3baNGihVFaWupYtnjxYgMw/v3vfxuGYRgff/yxYbVajd9++63CfWfNmmUAxurVqx3LTv4dmDp1qgFUuG92drYRFRVltGrVyvG4H374oQEYr7/+eqWM5a+TYRiV3tOvvPKKARgHDx6scJ+MjAzDzc3NeOyxxyosf+CBBwxPT08jJyfnlM9LixYtKj3/mZmZBmBcd911p7zviU5+Pp599lnD09PT2Lt3b4Vxf/vb3wybzWYcPnzYsezk91VRUZHRpUsX4/LLL6+wHDBcXFwc/yYYhmFs3brVAIy3337bseyuu+4ywsLCjJSUlAr3HzVqlOHr6+t4vDfffNMAjC+++MIxJjc312jbtm2F9+mJ2rVrZ1x11VWneTZERETOjI7HG//xuGEYxquvvmq4u7sbWVlZhmEYxt69ew3A+OabbyqMK3+9o6OjjaKiIsfyl19+2QCM7777zjAMw/jmm28MwNiwYUO1j1nT184wKr9nqjrePzHficed1R3fn8kxX1UuvfRSx3u7V69exl133WUYRtnr6eLiYnz00UeOzxDz58933K+q99DatWsNwPjvf//rWHb//fcbFovF2Lx5s2NZamqqERAQUGkfIyMjK70Hk5KSDFdXV+Ohhx5yLDv5M41hGMbw4cONyMjISpmqei6r2kZRUZEREhJi9OjRwygsLHSMe++99wygwnN/Jp9ZqnLJJZcY0dHRlZb7+/sb3bt3P+V9T3TppZeeda6afq6s6WtS0/dh+e/Lyy+/7BhTUlJiDBgwwACM2bNnV8o1dOhQo2PHjqd4JkRqh9q2iDQC77zzDkuXLq10q8lFFP38/NixYwf79u0748ddtGgRNpuNBx54oMLyhx56CMMw+PHHHwEcrRnuu+++CuPuv//+arc9adKkSsvc3d0d/19QUEBKSgp9+/YFqLKtx4QJExz/b7PZ6NWrF4ZhcNdddzmW+/n50b59+9P2Qlu0aBEADz74YIXlDz30EECVLT5qKjc3l+DgYIKDg2nbti0PP/ww/fv357vvvnPM3lm6dCkZGRncdtttpKSkOG42m40+ffqwfPnyUz7Gic9d+cyoSy+9lAMHDpCZmXnGmS0WCzfffDOLFi2q0Ef6888/p3nz5o5Te881N8CYMWM4evRohb6T8+bNw8XFxTHrff78+XTs2JEOHTpUeJzy2S+nepxFixZx0UUXOTJD2YyTiRMnEhcXx86dO4GyPphBQUFVvm/P5mJSvr6+XHfddXz66aeO2VylpaV8/vnnjnYkp5KamlrhzAQou9AVgLe39xnnKTd//nwGDBiAv79/hedyyJAhlJaWVngdTnxfpaenk5mZyYABA6r8fRwyZEiF2fndunXDx8fH8btnGAZfffUV1157LYZhVHjsYcOGkZmZ6djuokWLCAsLqzC7xsPDg4kTJ1a7X+X7IyIiUhd0PN64j8fnzp3L8OHDHcdQF1xwAdHR0dW2bpk4cWKFs//uvfdenJycHBnLLxK/YMGCas8+qOlrV1fO5JjvdEaPHs3XX39NUVERX375JTabrdqz/U58DxUXF5Oamkrbtm3x8/Or8B5avHgx/fr1o0ePHo5lAQEB1V7vqFOnTgwYMMDxc3BwcI3eV+dq48aNJCUlMWnSpApnKZS3FznRuXxmgaqP/6HsM8C5Hv/XNNeZfK6syWtS0/fhokWLcHJy4t5773Xc12aznfLfMB3/S31R2xaRRuCiiy6iV69elZbX5I/F3//+d6677jratWtHly5duPLKKxk7dmyNDvQPHTpEeHh4pT/UHTt2dKwv/6/VaiUqKqrCuJOvEH6ik8cCpKWlMWPGDD777DOSkpIqrKuqANyyZcsKP/v6+uLm5uZonXHi8pP7NJ6sfB9OzhwaGoqfn59jX8+Gm5sbP/zwA1DWx/nll192XOSoXPmHqfIDmJP5+Pic8jFWr17NM888w9q1ayv1Kc/MzKx0YFcTt956K2+++Sbff/89o0ePJicnh0WLFnHPPfc4isnnmhtg1KhRPPjgg8ybN49BgwY5LkB01VVXOQ4e9+3bx65duwgODq5yGye/X0506NAh+vTpU2n5ie/jLl26EBsbS/v27Wv1YqDjxo3j888/57fffmPgwIEsW7aMxMRExo4dW6P7lxfdy5U/n9nZ2Wedad++fWzbtq1Gz+WCBQt47rnn2LJlS6Veoyc7+fcRyv6NKu8PmpycTEZGBu+99x7vvffeKR/70KFDtG3bttLjtG/fvtr9MgzjrL7kEBERqQkdjzfe4/Fdu3axefNmxo0bx/79+x3LBw0axDvvvENWVlalY9YLLrigws9eXl6EhYU5Wi9eeumljBw5khkzZvDGG28waNAgrr/+ekaPHu1oO1LT166unMkx3+mMGjWKhx9+mB9//JG5c+dyzTXXVFvMzc/P54UXXmD27NkcO3aswvHsie+hQ4cO0a9fv0r3r+49e7pjzbpS/jqd/J5wdnamdevWFZady2eWcicf/0PZZ4BzPf6vaa4z+VxZk9ekpu/DQ4cOERYWVqmtjo7/pSFQ8VzkPDdw4EBiY2P57rvv+Omnn3j//fd54403mDVrVoWZIvXtxMJxuVtuuYU1a9bwyCOP0KNHD7y8vLDb7Vx55ZVVXtSmvOf26ZZB1QchVamLP742m63CRV+GDRtGhw4duOeee/j+++8BHPv38ccfExoaWmkbpyroxsbGMnjwYDp06MDrr79OREQELi4uLFq0iDfeeOOMLgh0or59+9KqVSu++OILRo8ezQ8//EB+fj633nqrY8y55C4XEhLCFVdcwVdffcU777zDDz/8QHZ2doVZJ3a7na5du/L6669XuY0Tew02JMOGDaNZs2Z88sknDBw4kE8++YTQ0NBKFwGqSmBgYKUPAz4+PoSHh7N9+/azzmS327niiit49NFHq1zfrl07AH777TdGjBjBwIEDmTlzJmFhYTg7OzN79mzmzZtX6X6n+90rf6+MGTOm2t6FNSkiVCc9Pb3ShxoREZGGQMfjZcw6Hv/kk08AmDZtWpW9kb/66ivuvPPOM9qmxWLhyy+/ZN26dfzwww8sWbKEv/zlL7z22musW7furPpqn7z9qpSWltZ4GzU95quJsLAwBg0axGuvvcbq1av56quvqh17//33M3v2bKZOnUq/fv3w9fXFYrEwatSos/5cAuf+vjpZbTzHJzvXzyxVHf8DdOjQgS1btlBUVFRlj/7aynWmnytr8prU5vvwZOnp6ZW+qBOpCyqeizQBAQEB3Hnnndx5553k5OQwcOBApk+f7jhYr+7AITIykmXLlpGdnV1hZsHu3bsd68v/a7fbOXjwYIXi1YkzO04nPT2dn3/+mRkzZvD00087lp/N6a1no3wf9u3b55gNAmUXOMzIyKh00ZZzERYWxrRp05gxYwbr1q2jb9++jnYXISEhNSqsnuiHH36gsLCQ77//vsK3/1WdFnimH0ZuueUW3nrrLbKysvj8889p1aqV49Rd4Jxyn+j2229n8eLF/Pjjj8ybNw8fHx+uvfbaCo+zdetWBg8efMb7EBkZyZ49eyotP/l93KZNG37//XeKi4srXaTzVE6Vx2azMXr0aObMmcNLL73Et99+y913313tgeaJOnTowMGDBystv+aaa3jvvfdYu3ZtlbN1TqdNmzbk5OSc9vX66quvcHNzY8mSJRUu3DR79uwzfkwoO43T29ub0tLS0z52ZGQk27dvrzSbpKrXEaCkpIQjR44wYsSIs8omIiJS13Q8fnp1cTxuGAbz5s3jsssuq9TSBuDZZ59l7ty5lYrn+/bt47LLLnP8nJOTw/Hjx7n66qsrjOvbty99+/blH//4B/PmzeP222/ns88+Y8KECTV+7apSfvZlRkaGo0UMVD1bvbr3Tk2P+Wpq9OjRTJgwAT8/v0rPw4m+/PJL7rjjDl577TXHsoKCAjIyMiqMi4yMrPL9eSbv2Zqo7vk58Tk+0cnPcfnrtG/fvgpn2xYXF3Pw4EG6d+/uWHYun1mg7Pi/qi8mrr32WtauXctXX33FbbfddsbbrWmuM/lceSaPXZP3YWRkJD///DM5OTkVvnyq7vgfqPT8i9QV9TwXOc+dfHqkl5cXbdu2rdCCobz38skHDldffTWlpaX861//qrD8jTfewGKxcNVVVwFls2sBZs6cWWHc22+/XeOc5cXEk2cO1PQq8Oeq/ADw5Mcr/3Z++PDhtfp4999/Px4eHrz44otA2XPo4+PD888/X2XfxOTk5Gq3VdVzl5mZWWWR09PTs9LrfCq33norhYWFfPTRRyxevJhbbrmlwvpzyX2i66+/Hg8PD2bOnMmPP/7IjTfeiJubm2P9LbfcwrFjx/jPf/5T6b75+fnk5uZWu+2rr76a9evXs3btWsey3Nxc3nvvPVq1akWnTp0AGDlyJCkpKZXe73DqGS3V/f6UGzt2LOnp6dxzzz3k5OQwZsyYard1on79+rF9+/YKv6sAjz76KJ6enkyYMIHExMRK94uNjeWtt96qdru33HILa9euZcmSJZXWZWRkUFJSApS9rywWS4XZN3FxcXz77bc1yn8ym83GyJEj+eqrr6qcOX/ie+Xqq68mPj6eL7/80rEsLy+v2nYvO3fupKCggIsvvvissomIiNQlHY/XTF0cj69evZq4uDjuvPNObrrppkq3W2+9leXLlxMfH1/hfu+9916FY9t3332XkpISx/Odnp5e6Xkq791d/rrW9LWrSvkElRP7kufm5vLRRx9VGlvd8X1Nj/lq6qabbuKZZ55h5syZp5z9bLPZKj03b7/9dqUZ3cOGDWPt2rVs2bLFsSwtLa3aPvRny9PTs8qWQ1U9x6WlpZWON3v16kVwcDCzZs2iqKjIsXzOnDmVnvdz+cwCZcf/6enplfq4T5o0ibCwMB566CH27t1b6X5JSUk899xz1W63prnO5HNlTdX0fXj11VdTUlLCu+++61hfWlpa7b9hmZmZxMbG6vhf6oVmnouc5zp16sSgQYOIjo4mICCAjRs38uWXXzJlyhTHmOjoaAAeeOABhg0bhs1mY9SoUVx77bVcdtllPPnkk8TFxdG9e3d++uknvvvuO6ZOneo44IiOjmbkyJG8+eabpKam0rdvX1auXOn4w16Tb919fHwYOHAgL7/8MsXFxTRv3pyffvqpypm3daF79+7ccccdvPfee2RkZHDppZeyfv16PvroI66//voKM09qQ2BgIHfeeSczZ85k165ddOzYkXfffZexY8fSs2dPRo0aRXBwMIcPH2bhwoX079+/yqIuwNChQ3FxceHaa691FGj/85//EBISwvHjxyuMjY6O5t133+W5556jbdu2hISEVNuvHKBnz560bduWJ598ksLCwgotW6DsdTvb3Cfy8vLi+uuvd7QDOflCQWPHjuWLL75g0qRJLF++nP79+1NaWsru3bv54osvWLJkSZV9SAH+9re/8emnn3LVVVfxwAMPEBAQwEcffcTBgwf56quvsFrLvkceN24c//3vf3nwwQdZv349AwYMIDc3l2XLlnHfffdx3XXXVbn98t+fJ598klGjRuHs7My1117r+BB84YUX0qVLF8eFenr27Hna5wPguuuu49lnn2XlypUMHTrUsbxNmzbMmzePW2+9lY4dOzJu3Di6dOlCUVERa9asYf78+YwfP77a7T7yyCN8//33XHPNNYwfP57o6Ghyc3P5448/+PLLL4mLiyMoKIjhw4fz+uuvc+WVVzJ69GiSkpJ45513aNu2Ldu2bavRPpzsxRdfZPny5fTp04e7776bTp06kZaWxqZNm1i2bBlpaWkA3H333fzrX/9i3LhxxMTEEBYWxscff4yHh0eV2126dCkeHh5cccUVZ5VLRESkLul4vGbq4nh87ty52Gy2agvvI0aM4Mknn+Szzz6rcKHSoqIiBg8ezC233MKePXuYOXMml1xyieMst48++oiZM2dyww030KZNG7Kzs/nPf/6Dj4+P40uAmr52VRk6dCgtW7bkrrvu4pFHHsFms/Hhhx86jrNPVN3xfU2P+WrK19eX6dOnn3bcNddcw8cff4yvry+dOnVi7dq1LFu2jMDAwArjHn30UT755BOuuOIK7r//fjw9PXn//fdp2bIlaWlptda+Jzo6ms8//5wHH3yQ3r174+XlxbXXXkvnzp3p27cvjz/+OGlpaQQEBPDZZ59V+lLB2dmZ5557jnvuuYfLL7+cW2+9lYMHDzJ79uxKPc/P5TMLlH1B5OTkxLJly5g4caJjub+/P9988w1XX301PXr0YMyYMY5/MzZt2sSnn356yjNSa5rrTD5X1lRN34fXXnst/fv3529/+xtxcXF06tSJr7/+usovPgCWLVuGYRjVfkYTqVWGiDRYs2fPNgBjw4YNVa6/9NJLjc6dO1dYFhkZadxxxx2On5977jnjoosuMvz8/Ax3d3ejQ4cOxj/+8Q+jqKjIMaakpMS4//77jeDgYMNisRgn/tOQnZ1tTJs2zQgPDzecnZ2NCy64wHjllVcMu91e4XFzc3ONyZMnGwEBAYaXl5dx/fXXG3v27DEA48UXX3SMe+aZZwzASE5OrrQ/R48eNW644QbDz8/P8PX1NW6++WYjPj7eAIxnnnnmtNu44447DE9Pzxo9T1UpLi42ZsyYYURFRRnOzs5GRESE8fjjjxsFBQU1epyqnGpsbGysYbPZKrxey5cvN4YNG2b4+voabm5uRps2bYzx48cbGzdudIwp3/8Tff/990a3bt0MNzc3o1WrVsZLL71kfPjhhwZgHDx40DEuISHBGD58uOHt7W0AxqWXXup4XMBYvnx5pZxPPvmkARht27atdj9rkvt0Fi5caABGWFiYUVpaWml9UVGR8dJLLxmdO3c2XF1dDX9/fyM6OtqYMWOGkZmZ6Rh38u+AYZQ91zfddJPh5+dnuLm5GRdddJGxYMGCSo+Rl5dnPPnkk473QGhoqHHTTTcZsbGxjjEnvx8NwzCeffZZo3nz5obVaq30nBuGYbz88ssGYDz//PM1fj4MwzC6detm3HXXXVWu27t3r3H33XcbrVq1MlxcXAxvb2+jf//+xttvv13hPVvV85GdnW08/vjjRtu2bQ0XFxcjKCjIuPjii41XX321wr8NH3zwgXHBBRcYrq6uRocOHYzZs2dX+f4DjMmTJ1fKWNVjJyYmGpMnTzYiIiIcz/HgwYON9957r8K4Q4cOGSNGjDA8PDyMoKAg469//auxePHiKt+nffr0McaMGVPd0ygiInLWdDzeeI/Hi4qKjMDAQGPAgAGnHBcVFWVceOGFhmH87/VeuXKlMXHiRMPf39/w8vIybr/9diM1NdVxn02bNhm33Xab0bJlS8PV1dUICQkxrrnmmkrHvjV97ao6ZoqJiTH69OljuLi4GC1btjRef/11R76aHN+XP35NjvmqUpPXrPwzxPz58x3L0tPTjTvvvNMICgoyvLy8jGHDhhm7d++uch83b95sDBgwwHB1dTVatGhhvPDCC8Y///lPAzASEhIqPD/Dhw+vMuOJ+1vVZ5qcnBxj9OjRhp+fnwEYkZGRjnWxsbHGkCFDDFdXV6NZs2bGE088YSxdurTK482ZM2caUVFRhqurq9GrVy/j119/rfT4hlHzzyzVGTFihDF48OAq18XHxxvTpk0z2rVrZ7i5uRkeHh5GdHS08Y9//KPCts8lV00/V9b0NTGMmr8PU1NTjbFjxxo+Pj6Gr6+vMXbsWGPz5s0GYMyePbvCNm+99VbjkksuOc2zKVI7LIZxlldXEBE5jS1btnDhhRfyySefVJpJLNKUvPXWW0ybNo24uLgqr0pfnY8//pjJkydz+PDhCv0u5X+2bNlCz5492bRpk+N0aRERESmj43FpbKZOncq///1vcnJyanSdoPPNb7/9xqBBg9i9e3eF6xfI/yQkJBAVFcVnn32mmedSL9TzXERqRX5+fqVlb775JlarlYEDB5qQSKRhMAyDDz74gEsvvfSMCudQ1r6mZcuWvPPOO3WUrvF78cUXuemmm1Q4FxGRJk/H49LYnPyeTU1N5eOPP+aSSy5pkoVzgAEDBjB06FBefvlls6M0WG+++SZdu3ZV4VzqjWaei0itmDFjBjExMVx22WU4OTnx448/8uOPPzJx4kT+/e9/mx1PpN7l5uby/fffs3z5cv7zn//w3XffOfpkioiIiNQ2HY9LY9OjRw8GDRpEx44dSUxM5IMPPiA+Pp6ff/5ZX/iISIOh4rmI1IqlS5cyY8YMdu7cSU5ODi1btmTs2LE8+eSTODnp2sTS9MTFxREVFYWfnx/33Xcf//jHP8yOJCIiIucxHY9LY/PEE0/w5ZdfcvToUSwWCz179uSZZ55hyJAhZkcTEXFQ8VxERERERERERERE5CTqeS4iIiIiIiIiIiIichIVz0VERERERERERERETqLGZzVgt9uJj4/H29sbi8VidhwRERERaQAMwyA7O5vw8HCsVs1Jaah0LC8iIiIiJzqT43gVz2sgPj6eiIgIs2OIiIiISAN05MgRWrRoYXYMqYaO5UVERESkKjU5jlfxvAa8vb2BsifUx8fH5DQiIiIi0hBkZWURERHhOFaUhknH8iIiIiJyojM5jlfxvAbKT+/08fHRAbeIiIiIVKBWIA2bjuVFREREpCo1OY5Xc0YRERERERERERERkZOoeC4iIiIiIiIiIiIichIVz0VERERERERERERETqLiuYiIiIiIiIiIiIjISVQ8FxERERERERERERE5iYrnIiIiIiIiIiIiIiInUfFcREREREREREREROQkKp6LiIiIiIiIiIiIiJxExXMRERERERERERERkZOoeC4iIiIiIiIiIiIichIVz0VERERERERERERETqLiuYiIiIiIiIiIiIjISUwtnr/wwgv07t0bb29vQkJCuP7669mzZ0+FMYMGDcJisVS4TZo0qcKYw4cPM3z4cDw8PAgJCeGRRx6hpKSkwpgVK1bQs2dPXF1dadu2LXPmzKnr3RMRERERERERERGRRsrU4vnKlSuZPHky69atY+nSpRQXFzN06FByc3MrjLv77rs5fvy44/byyy871pWWljJ8+HCKiopYs2YNH330EXPmzOHpp592jDl48CDDhw/nsssuY8uWLUydOpUJEyawZMmSettXEREREREREREREWk8LIZhGGaHKJecnExISAgrV65k4MCBQNnM8x49evDmm29WeZ8ff/yRa665hvj4eJo1awbArFmzeOyxx0hOTsbFxYXHHnuMhQsXsn37dsf9Ro0aRUZGBosXLz5trqysLHx9fcnMzMTHx+fcd1REREREGj0dIzYOep1ERERE5ERncnzYoHqeZ2ZmAhAQEFBh+dy5cwkKCqJLly48/vjj5OXlOdatXbuWrl27OgrnAMOGDSMrK4sdO3Y4xgwZMqTCNocNG8batWvraldEREREREREREREpBFzMjtAObvdztSpU+nfvz9dunRxLB89ejSRkZGEh4ezbds2HnvsMfbs2cPXX38NQEJCQoXCOeD4OSEh4ZRjsrKyyM/Px93dvcK6wsJCCgsLHT9nZWXV3o6KiIiIiIiIiIiISIPXYIrnkydPZvv27axatarC8okTJzr+v2vXroSFhTF48GBiY2Np06ZNnWR54YUXmDFjRp1sW0REREREREREREQavgbRtmXKlCksWLCA5cuX06JFi1OO7dOnDwD79+8HIDQ0lMTExApjyn8ODQ095RgfH59Ks84BHn/8cTIzMx23I0eOnN2OiYiIiIiIiIiIiEijZGrx3DAMpkyZwjfffMMvv/xCVFTUae+zZcsWAMLCwgDo168ff/zxB0lJSY4xS5cuxcfHh06dOjnG/PzzzxW2s3TpUvr161flY7i6uuLj41PhJiIiIiIiIiIiIiJNh6nF88mTJ/PJJ58wb948vL29SUhIICEhgfz8fABiY2N59tlniYmJIS4uju+//55x48YxcOBAunXrBsDQoUPp1KkTY8eOZevWrSxZsoSnnnqKyZMn4+rqCsCkSZM4cOAAjz76KLt372bmzJl88cUXTJs2zbR9FxEREREREREREZGGy2IYhmHag1ssVS6fPXs248eP58iRI4wZM4bt27eTm5tLREQEN9xwA0899VSF2eCHDh3i3nvvZcWKFXh6enLHHXfw4osv4uT0v5buK1asYNq0aezcuZMWLVrwf//3f4wfP75GObOysvD19SUzM1Oz0KVBGj9hEsnpmVWuC/b3Zc77s+o5kYiIyPlPx4iNg14nEZH6MfXRJ0jJyK5yXZCfN2++/Hw9JxIRqdqZHB+aesHQ09XtIyIiWLly5Wm3ExkZyaJFi045ZtCgQWzevPmM8ok0FsnpmQyZVPVFbpfNeqae04iIiIiIiEhTk5KRTfTN91e5Lmb+2/WcRkSkdjSIC4aKiIiIiIiIiIiIiDQkKp6LiIiIiIiIiIiIiJxExXMRERERERERERERkZOoeC4iIiIiIiIiIiIichIVz0VERERERERERERETqLiuYiIiIiInJEXXniB3r174+3tTUhICNdffz179uypMGbQoEFYLJYKt0mTJlUYc/jwYYYPH46HhwchISE88sgjlJSUVBizYsUKevbsiaurK23btmXOnDl1vXsiIiIiIgA4mR1ARE5v/IRJJKdnVrv+j+07GFKPeURERKRpW7lyJZMnT6Z3796UlJTwxBNPMHToUHbu3Imnp6dj3N13383f//53x88eHh6O/y8tLWX48OGEhoayZs0ajh8/zrhx43B2dub5558H4ODBgwwfPpxJkyYxd+5cfv75ZyZMmEBYWBjDhg2rvx0WERERkSZJxXORRiA5PZMhk2ZUuz5m0vX1F0ZERESavMWLF1f4ec6cOYSEhBATE8PAgQMdyz08PAgNDa1yGz/99BM7d+5k2bJlNGvWjB49evDss8/y2GOPMX36dFxcXJg1axZRUVG89tprAHTs2JFVq1bxxhtvqHguIiIiInVObVtEREREROScZGaWnSEXEBBQYfncuXMJCgqiS5cuPP744+Tl5TnWrV27lq5du9KsWTPHsmHDhpGVlcWOHTscY4YMqXh+3bBhw1i7dm1d7YqIiIiIiINmnouIiIiIyFmz2+1MnTqV/v3706VLF8fy0aNHExkZSXh4ONu2beOxxx5jz549fP311wAkJCRUKJwDjp8TEhJOOSYrK4v8/Hzc3d0r5SksLKSwsNDxc1ZWVu3sqIiIiIg0OSqei4iIiIjIWZs8eTLbt29n1apVFZZPnDjR8f9du3YlLCyMwYMHExsbS5s2beoszwsvvMCMGdW3uxMRERERqSm1bRERERERkbMyZcoUFixYwPLly2nRosUpx/bp0weA/fv3AxAaGkpiYmKFMeU/l/dJr26Mj49PlbPOAR5//HEyMzMdtyNHjpz5jomIiIiIoOK5iIiIiIicIcMwmDJlCt988w2//PILUVFRp73Pli1bAAgLCwOgX79+/PHHHyQlJTnGLF26FB8fHzp16uQY8/PPP1fYztKlS+nXr1+1j+Pq6oqPj0+Fm4iIiIjI2VDxXEREREREzsjkyZP55JNPmDdvHt7e3iQkJJCQkEB+fj4AsbGxPPvss8TExBAXF8f333/PuHHjGDhwIN26dQNg6NChdOrUibFjx7J161aWLFnCU089xeTJk3F1dQVg0qRJHDhwgEcffZTdu3czc+ZMvvjiC6ZNm2bavouIiIhI06Ge5yLnEbvdIDY5h4SsAuxG2aywZP8uHE7No2Wgh9nxRERE5Dzx7rvvAjBo0KAKy2fPns348eNxcXFh2bJlvPnmm+Tm5hIREcHIkSN56qmnHGNtNhsLFizg3nvvpV+/fnh6enLHHXfw97//3TEmKiqKhQsXMm3aNN566y1atGjB+++/z7Bhw+plP0VERESkaVPxXOR8YHNh8+F0Nh/JILugpOK6oB4MfGU5fVsHMLZvK67uGorFYnGsHj9hEsnpmdVuOtjflznvz6qr5CIiItIIGYZxyvURERGsXLnytNuJjIxk0aJFpxwzaNAgNm/efEb5RERERERqg4rnIo1cVkExLkP+yq/7UgBwd7ZxQTMvXGxWLBbYuWMHeV7hrDuQxroDaVzVJZQXb+yGr4czAMnpmQyZNKPa7S+b9Uy97IeIiIiIiIiIiEhDouK5SCOWnlvE15uPYfUJwcvViYtaBdAxzBsn2/8uZ5C39Bfe++BDPv39MP/+NZYftyew9UgGb466kIuiAkxMLyIiIiIiIiIi0nDpgqEijVRSVgHzY46SU1iCPSuRW3q1oGsL3wqF83LN/dx5eFh7vrr3YloFehCfWcDo/6zjpx0JJiQXERERERERERFp+FQ8F2mE8otL+X5rPPnFpYR4u1K0/F283ZxPe79uLfxY8MAAruoSSondYPK8TWR7Nq+HxCIiIiIiIiIiIo2L2raINDKGYfDzrkRyi0rx93Dmxp7NefPD3GrHb9u2leEjb6u4DSz4hPYny7sVh5oN4GBKLlFBnnUdXUREREREREREpNFQ8Vykkdl1PJvY5FysFriycyiuTrZTji+2U+UFQUvtBou3J7A/OYeFfxzn5ugWNPNxq6vYIiIiIiIiIiIijYratog0Ipn5xazYmwRA39aBhJxDsdtmtXBll1BKj++i1G6w8I/j5BWV1FZUERERERERERGRRk3Fc5FGorxdS3GpQbivG9GR/ue8TZvVQvHv8/Bzdya7oIQftydgtxu1kFZERERERERERKRxU/FcpJE4kp7PkfR8bBYLQzuHYrVYamfDxQVc0y0MZ5uFo+n5/LY/pXa2KyIiIiIiIiIi0oipeC7SCBjAmtiyonbX5r74ujvX6vYDvVwZ2ikUgC1HMjiYUv0FSEVERERERERERJoCFc9FGoFszwgSswpxtlno1erc27VUpW2IFxdG+AGwbFei+p+LiIiIiIiIiEiTpuK5SANXajdICuwOQI8IPzxdnerssS5uE0igpwt5RaX8sjsJw1D/cxERERERERERaZpUPBdp4L7bcoxCVz9cnaxEt6ybWeflnGxWhnUOxWqB2ORcdh3PrtPHExERERERERERaajqbgqriJwzwzCYuSIWgOhIf1ydbXX+mMHervRrHcjq2FRW7k2mlZNHnT+miIiIiIiINE1TH32ClIzKE7eC/Lx58+XnTUgkIvI/Kp6LNGCr96eyPykHq72Ybi186+1xe0b6cyAll+OZBRwP7o1hGFgslnp7fBEREREREWkaUjKyib75/krLY+a/bUIaEZGK1LZFpAGbsyYOAL+sWFyd6n7WeTmrxcLgDiFYLZDtFcGP2xPq7bFFREREREREREQaAhXPRRqoI2l5/Lw7EYCAjL31/viBXq70igwA4Jnvd5CZV1zvGURERERERERERMyiti0iDcD4CZNITs+ssCwh8EKMgM545sazd/NahpuQq3crf7bsPUhyti8v/LiLF0d2MyGFiIiIiIiIiIhI/VPxXKQBSE7PZMikGY6fi0vtfLDqIJTYubxfNPMXlZiSy8lmJX/NXGyD7uOzDUf4/ev38ShIdqwP9vdlzvuzTMkmIiIiIiIiIiJSl1Q8F2mA9iRkU1hix8fNiVZBnqZmKU4+SNcwH3YezyK747Vc07slVmvZxUOXzXrG1GwiIiIiIiIiIiJ1RT3PRRqg7fFlLVy6tfDDarGYnAYuaRuEm5OVlJwith7NMDuOiIiIiIiIiIhInVPxXKSBSc8tIjGrEIsFOoZ5mx0HAHcXG/3bBgGw7kAaOYXmtJERERERERERERGpLyqeizQwuxKyAIgM8MDDpeF0Vuoc7kOojxtFpXZ+25d8+juIiIiIiIiIiIg0YiqeizQghmGwOyEbgI5hPianqchisXBZh2AswN7EHA6n5ZkdSUREREREREREpM6oeC7SgMRnFJBdUIKLzUprky8UWpUQbze6tfAFYPmeJOwW/RMiIiIiIiIiIiLnJ1W+RBqQ8pYtbUO8cLI1zF/Pfm0C8XCxkZFXTKpfJ7PjiIiIiIiIiIiI1ImGWZ0TaYJKSu3sS8oBGs6FQqvi6mRjwAVlFw9NDujCEbVvERERERERERGR85CK5yINxMGUXIpK7Hi7OdHcz93sOKfUvpk3LfzdMaxOzPhhh9lxREREREREREREap2K5yINxJ7EsguFtm/mjcViMTnNqVksFi5rH4LFKGXZriSW7kw0O5KIiIiIiIiIiEitUvFcpAEotTgRl1rW/qRds4bbsuVEAZ4uBKbvAmD69zvILyo1OZGIiIiIiIiIiEjtcTI7gIhAjmc4pXYDX3dngrxczI5TY8Fpf+DaujfHMvL51/J9PDKsg9mRRERERERE5Dw39dEnSMnIrrR85/Y/6NSla5X3CfLz5s2Xn6/raCJynlHxXKQByPKKBKBtiFeDb9lyIqtRyjPXdmLixzG89+sBbriwBW1DvMyOJSIiIiIiIuexlIxsom++v9LytRvGV7kcIGb+23UdS0TOQ2rbImKyguJScjzDARpl4fmKTs0Y3CGE4lKDp779A8MwzI4kIiIiIiIiIiJyzlQ8FzHZyr3J2K3OeLs50czb1ew4Z8xisTB9RGfcnK2sO5DGZxuOmB1JRERERERERETknKl4LmKyxdsTAGgT3LhatpwoIsCDh4e2B+D5hbtIyCwwOZGIiIiIiIiIiMi5UfFcxERFJXaW7UoE4IJG2LLlRHf2j6JHhB/ZhSVq3yIiIiIiIiIiIo2eLhgqYqLVsSlkF5TgVJJHmK+b2XHOic1q4eWbujH8n7+xbFcS32+N57oezc2OJSIiIiIiImdo6qNPkJKRXeW6ID9v3nz5+Rpvq8RuJ961Jfd/uhlnmwVXJyvN/dwZ3SeSAE+X2oosIlInVDwXMdGSP1u2eOccwWLpbnKac9eumTf3X34Bry/dy9Pf7aBPVCChjfxLARERERERkaYmJSOb6Jvvr3JdzPy3a7QNwzDYk5jN2thUsjw7cWBrfIX1766I5c7+URRbnM85r4hIXVHxXKSejJ8wieT0TMfPBrA3aiQ4uZO6fRWMvMa8cLXo3kFtWLozkT+OZfLQ/C18/Jc+WK2Ns5e7iIiIiIiInLlSu8GCbfHEpeYB4GIvYMqwbrg4WSkstvPTzgR2xGfxr+X7cfIdQPO0PCICPExOLSJSmYrnIvUkOT2TIZNmOH5OyCpg54YjONssZCfsMzFZ7XK2WXlzVA+u+ecqVu9P5cPVB5kwoLXZsURERERERKQeGIbB0l2JxKXm4WS10DsqADZ8xgODRzrGPDC4LT/tTOS1n/awNzGHb7ccY1C7ELq28DUxuYhIZSqei5jkYHIuAJEBnmw3Sk1Oc3a2bdvK8JG3VbnOz6ct+c368vLiPVzcJohO4T71nE5ERERERETqk2EY/LYvhT0J2VgtMLxbGK0CPYnZUPEzr8ViYVjnUC5tF8yARz8k2TWcX/YkkZpbyMB2wVgt9Xv2cnU93s+0v7uInH9UPBcxycHUsuJ5VLAn203OcraK7VSYTX8iwzCY/f1ysr0imDJvE99O6Y+Pm3rZiYiIiIiInK+2HMlg85EMAK7o2IxWgZ6nHO/mbKNd7jYu6NSVNbGpbD2aSX5xKUM7hdZD2v+prsd7Tfu7i8j5y2p2AJGmKLugmOTsQgBaBZ6ffd0sFgvhiesI93XjQEouD36+FbvdMDuWiIiIiIiI1IE8qyer96cCMKBtEB3Canb2sQXo3SqAq7uEYrXA3sQcFv5xnFKVrESkAdC/RCImOJhSNus8zNcND5fz9wQQJ3shs8ZG4+JkZdmuRP61fL/ZkURERERERKSW2e0G+z07U2oYRAZ6cGFLvzPexgXNvLm2Wzg2q4WDKbns9I4mp7Ck9sOKiJwBFc9FTFBePI8KOvUpbOeDbi38eO76LgC8sWwvv+xONDmRiIiIiIiI1KbPNx4hyzkAJ6uFy9uHYDnLnuWtgjy5oUdzXGxWMp0Duf3938nIK6rltCIiNafiuUg9Ky61cyQ9H2gaxXOAW3pFcHuflhgGTJm3ma1/9sATERERERGRxi0pq4DnF+0CoF+bQHzcz+1aV8393bmxZ3Oc7EVsPZLBrf9eR1JWQW1EFRE5Yyqei9SzI2l5lNoNfNycCPR0MTtOvXnm2s4MuCCIvKJS7pyzgQPJOWZHEhERERERkXP0j0W7yC4owaskkx4RfrWyzWY+bnTL+p0Qb1f2JGYzctYath/LrJVti4icCRXPRerZiS1bzvZUtsbIxcnKu2Oi6drcl7TcIsZ+sJ5EzR4QERERERFptHbEZ/LdlngA2ubuwFqLn3E97Ll8OeliWgZ4cCQtnxtnruHjtXEYhlFrjyEicjoqnovUI8MwiEvNA8p6uTU1Xq5OzL6zN60CPTiWkc9t763jeGa+2bFERERERETkLLy6ZA8A13YPx6s0q9a33zLQg++n9GdIx2YUldr5v+92cM/HMRRY3Wv9sUREquJkdgCRpiQtt4icwhJsVgst/JrmH/sgL1c+vqsPo95bx4GUXG6etZa5E/oQGdj0vkwQERERERFprDbEpbF8TzI2q4UHr2jH/y2vm8fx83DhP+Oi+WDVQV78cTc/7UzE4jsAY18KvVv54+psq9F2NsXEMGbi/VWv27KN6JtrM7WInC9UPBepR4f+nHXews8dJ1vTPfEjIsCDLyb14/b/rCMuNY+bZ63lkwl9aNfM2+xoIiIiIiIichqGYfDy4t0A3NIrgqg6PrPaYrEwYUBrLm4TxD8W7WT1/lRiDqfzR3wmXZv70qOFH15upy5xFdoh+uaqi+drN4yvg9Qicj5Q8VykHsWllvU7jwz0MDmJ+Zr7uRO291uOe11EEn5c+eoyWiSuxjv3GADB/r7MeX+WySlFRERERETkRJtiYhg+5e/s9O6F1Shl/5KPGLO4sNZnb59qprjrkRw8Lx5NWm4RMYfS2Xw4nfbNvDG8QmovgIgIKp6L1JtSixPxGWUXyGylFiUAZKUlcccNPVm47TjHMuBw+GX0axNI70h/fv73dLPjiYiIiIiIyEkK7JAS3h+yC+kRGcjFQyYCtT97+1Qzxdc9Op4xfVpyMCWXTYczOJaRz66EbOh/N99sPsZFrQJo7t80W6WKSO1S8VyknuR5NKPUMPBxc8LPw9nsOA2Gu7ONGy5szq97k9l2LJO1sakkZBbgYnMzO5qIiIiIiIicLKg1SdmFOFktREf6mxbDYrHQOtiL1sFeJGQWsOlwOvsSsziclsfhtDy6t/Clf9sgnJtwy1QROXf6F0SknmR7hAMQGeiJxWIxOU3DYrNauKxDCJd3CMFmsXAwJZf9La9h6c5Es6OJiIiIiIjInwzDwN76EgC6tvDFw6VhzMkM9XXj6q5hWH+bSedwHwC2Hs1k7u+Hic/INzmdiDRmKp6L1APDMMjxLCuet1K/82p1be7LqIsiCPJyodTJjbv/u5G/fraZpKwCs6OJiIiIiIg0eUfT88E/ApvVQnRL82adV8eSn8mQjs24vkc4Xq5OZOYX8/XmYxxJyzM7mog0Ug3jK0KR89zBlFyKnb2xWSy08G86xfNt27YyfORt1a7/Y/sOhpy0LMjLlVt7R/DZV9+TFtiZ77bE8/OuJKYOuYA7Lm6lU+5ERERERERMsj4uDYDO4T54ujbcklJkoCdj+rZk8fYE4lLz+H5rPIZ/S7NjiUgj1HD/pRM5j6zYkwxAuJ8bLk5Np/hbbIchk2ZUuz5m0vVVLneyWklaPocLel/G8ZCLyCGI5xbu4uVvfickdSs+OYcI8fdlzvuz6ii5iIiIiIiInCg+I79s5rm91NRe5zXl6mRjeLcwFmw7zqHUPOh5K8cy8mnupwuJikjNNZ0qnoiJfttXVjyPDPQ0OUnjUWyHa+/8K3df3ZfBHUJwd7ZR5OLD0bABJF94F7FFPtjthtkxRUREREREmoTyWeeW+D/wcXM2OU3NOFmtXNM1jJYBHuDkwoJt8eQWlpgdS0QaERXPRepYYUkp6w6UHWS0DGg6LVtqi8VioUtzX8Zf3Iq+rQNwsVlJzinkSPilDH3zV76MOUphSanZMUVERERERM5bCVkFHErNw2IBy4HVZsc5I042K9d2C4OsBAqK7fy0MxHD0EQsEakZFc9F6timQxnkF5fiVJJPkJeL2XEaLRcnK32iAhnfvxW9W/ljLS1if1IOD8/fSv8Xf+Hlxbs5mq6LwIiIiIiIiNS29QfLJoR1aOaNJT/D3DBnwclmxbrtW2xWC4fT8thyJMPsSCLSSKh4LlLHylu2eOYlYLFYTE7T+Lk727i4TRDFC/5OSMomnErySMkpYuaKWC558Re6TPk3A8c+xB0TJpkdVUREREREpNFLyi7gYEouFqB3qwCz45w1S24qAy4IAmD1/lSSswtNTiQijYGK5yJ1bNX+FAC88uJNTnJ+KS4q5LZbb2XSFd0Y3jWMCH93sFjI8WrB4eaXs8b3Ml77aQ/7k7LNjioiIiIiItJolc86b9fMG3/Pxn02dbfmvrQK9KDUMFi6KxG72reIyGk4mR1A5HyWnlvEH8cygbKZ51L7bFYLbUO8aBviRXpuEduOZbLreBaFePH2L/t5+5f9dAzz4boe4VzbPVxXVhcREREREamhlJxCYpNzAejdyt/kNOfOYrEwpGMz/rvuEMnZhew6nkXncN+z2tbUR58gJaPqyVpBft68+fLz5xJVRBoIFc9F6tDq2BQMA9o388ZpX77Zcc57/p4uXNoumIvbBPLdZx/RYdCNrNybzK7jWew6nsWLP+6mV6Q/V3RqxuCOIbQJ9qqylc74CZNITs+s9nGC/X2Z8/6sutwVERERERGRGqurQm75rPMLQrwI9HI963wNiaerE32iAvhtXwprYlO5IMT7rLaTkpFN9M33V7kuZv7b5xJRRBoQFc9F6tCqfWUtWy65IIh1q0wO04Q426z4ZcfxwfjepOcW8eP2BL7feozfD6ax8VA6Gw+l88KPu4kM9ODyDiEM6diM3q0CcHEq62SVnJ7JkEkzqt3+slnP1NeuiIiIiIiInFZdFHJTcwrZl5QDwEVRjbfXeVW6t/Bj29FMMvOL2RCXZnYcEWnAVDwXqSOGYfDbn8XzARcEsc7kPE2Vv6cLo/u0ZHSfliRkFvDTzgTemL+cDKdADqXmMXt1HLNXx2EtLcIzPxGvvHiOHYhniNnBRURERERETLQhLh2ANsGeBJ0ns87L2awWBl4QxA/bjrP5SAaG+9m1bhGR858uGCpSRw6m5HIsIx8Xm5U+UYFmxxEg1NeNcf1aEX7oJyZd1o7hXcPoFOaDu7MNu82FbK8Ijof0wTrsUeasiWP5niQOJOdQVGI3O7qIiIiIiEi9ybN6sjexrA3M+TbrvFxUkCcR/u6U2g2MCy4zO46INFCaeS5SR8pnnfdq5Y+7i83kNHIyFyer40KjhmGQlF3IodQ8DqXlciwtl8x82HY0k21HM7FaIMzXnchADyIDPND12EVERERE5Hx21L01BmUF5hBvN7Pj1AmLxcKAC4KZt/4wRlhnUnIKz7sZ9iJy7jTzXKSOrN5fVjzv3zbI5CRyOhaLhWY+blwUFcDN0REUfvcM13QLo2tzX3zdnbEbcCwjnzWxqXy64Qh7om5i6meb+SrmKEnZBWbHFxERERERqTWHUnNJcgkDzt9Z5+WCvV25IMQLgHUHUk1OIyINkakzz1944QW+/vprdu/ejbu7OxdffDEvvfQS7du3d4wpKCjgoYce4rPPPqOwsJBhw4Yxc+ZMmjVr5hhz+PBh7r33XpYvX46Xlxd33HEHL7zwAk5O/9u9FStW8OCDD7Jjxw4iIiJ46qmnGD9+fH3urjQhpXbD8YdXxXNzbNu2leEjb6ty3R/bd5y6p3lJIW2CvWgTXHYQlZFX9Oes9DyOpudRjBvfbonn2y3xALgVpOKbHYdvdhzhPi7MeX9WLe+NiIiIiIhI/fjnz/vBYiUy0INQn/Nz1vmJ+kQFsC8xm9jkXJKyC87bmfYicnZMLZ6vXLmSyZMn07t3b0pKSnjiiScYOnQoO3fuxNPTE4Bp06axcOFC5s+fj6+vL1OmTOHGG29k9erVAJSWljJ8+HBCQ0NZs2YNx48fZ9y4cTg7O/P8888DcPDgQYYPH86kSZOYO3cuP//8MxMmTCAsLIxhw4aZtv9y/toZn0VWQQnerk50CfcxO06TVGyHIZNmVLkuZtL1Z7QtPw8X/Dxc6B7hR6nd4LWnH6L/+Cc4nJZHUnYhBW6BFLgFkhgczbHceJbuTOTyDiHYrJZa2BMREREREZH6sSchm683HwWgbxO5dleglyuWhB0YYV34/UAa13YPNzuSiDQgphbPFy9eXOHnOXPmEBISQkxMDAMHDiQzM5MPPviAefPmcfnllwMwe/ZsOnbsyLp16+jbty8//fQTO3fuZNmyZTRr1owePXrw7LPP8thjjzF9+nRcXFyYNWsWUVFRvPbaawB07NiRVatW8cYbb6h4LnViTWxZy5Y+rQNwsqk70vnEZrVgpBygf9sg+gO5hSXsT85hT0I2xzMLyPUM5+7/bsS5OJvAjD34Z+7DapQ67h/s76uZ6SIiIiIi0iC9smQPhgGBRQmE+l5gdpx6Y4ldBWFdOJCSS2JWAc2awIx7EamZBlXVy8zMBCAgoKynVkxMDMXFxQwZ8r8GCx06dKBly5asXbsWgLVr19K1a9cKbVyGDRtGVlYWO3bscIw5cRvlY8q3IVLb1sSWtWzp10YtW853nq5OdG/hxy29Ihh/cStKdq/A1clKsbM3CcG9ONx5DCHXTOOyidMZMmkGyemZZkcWERERERGpJOZQGst2JWK1QGTePrPj1CtLbirtQ70B9T4XkYpMnXl+IrvdztSpU+nfvz9dunQBICEhARcXF/z8/CqMbdasGQkJCY4xJxbOy9eXrzvVmKysLPLz83F3d6+wrrCwkMLCQsfPWVlZ576D0mQUldhZfzANgIvbNI3T3KSMr7szJX8s5K5Jd7H7eDYbDqWRXVDC8j3JbD6cwWUdQk7Zix00M11EREREROqfYRi89OMeAG7pFcHRJbkmJ6p/F0UFsCcxm7jUPBIyCwj11exzEWlAxfPJkyezfft2Vq1aZXYUXnjhBWbMqLpXssjpbD2aQX5xKYGeLrRv5m12HDGBs81K1xa+dAz3ZsexLNbHpZGRX8w3m49B9CguHnsTHi5V//O7bNYz9ZxWRERERETOZ5tiYhgz8f5Ky4P8vHnz5bJrxa3Yk8z6uDRcnaz8dcgFPLKkvlOaz9/DhY6hPuw8nsW6A6lcf2FzsyOJSAPQIIrnU6ZMYcGCBfz666+0aNHCsTw0NJSioiIyMjIqzD5PTEwkNDTUMWb9+vUVtpeYmOhYV/7f8mUnjvHx8ak06xzg8ccf58EHH3T8nJWVRURExLntpDQZa/aXneLVt00gVl0wsklzslrpHuFHhzBv1samsvVoJrZW0Xyy7jDDOjcjMtDT7IgiIiIiInKeK7RD9M2Vi+cx898GoLjUzj8W7QJg/MWtCPOtXCdpKi6KCmB3QhaH0vKIz8g3O46INACm9jw3DIMpU6bwzTff8MsvvxAVFVVhfXR0NM7Ozvz888+OZXv27OHw4cP069cPgH79+vHHH3+QlJTkGLN06VJ8fHzo1KmTY8yJ2ygfU76Nk7m6uuLj41PhJlJT5RcLVcsWKefqZGNQ+xBu7RWBPfM4+cWlfLslnjWxKdjthtnxRERERESkCZv3+2H2J+UQ4OnCfZe1NTuOqXzdnekYVlYDUu9zEQGTi+eTJ0/mk08+Yd68eXh7e5OQkEBCQgL5+WXf7vn6+nLXXXfx4IMPsnz5cmJiYrjzzjvp168fffv2BWDo0KF06tSJsWPHsnXrVpYsWcJTTz3F5MmTcXV1BWDSpEkcOHCARx99lN27dzNz5ky++OILpk2bZtq+y/kpv6iUzYczALhYFwuVk4T6ulG07J90CS87GNsQl843W45RUFxqcjIREREREWmKMvKKeGPZXgAevKIdvu7OJicy30WtArBa4Eh6PplO/mbHERGTmVo8f/fdd8nMzGTQoEGEhYU5bp9//rljzBtvvME111zDyJEjGThwIKGhoXz99deO9TabjQULFmCz2ejXrx9jxoxh3Lhx/P3vf3eMiYqKYuHChSxdupTu3bvz2muv8f777zNs2LB63V85/8UcSqeo1E6YrxutAj3MjiMNkb2EwR2bcWXnUJxtFo6m5zN/41Gy8ovNTiYiIiIiIk3Mm8v2kZFXTIdQb0b1VrtaAB93ZzqH+wJw2L1pz8QXEZN7nhvG6dsVuLm58c477/DOO+9UOyYyMpJFixadcjuDBg1i8+bNZ5xR5EysdrRsCcJiUb9zqV77UG8CPF34fms8aXlFfL7xCCO6h5sdS0REREREmog8qycfrzsEwP9d0wknm6nzKxuU3q382RmfRaZzIGtiU3RmuUgTpn8ZRWrRmtiynmjqdy41Eeztyq29IgjyciGvqJSvNx0jz00HZSIiIiIiUrcMw+CAZ0dK7QZDOobQv60+h5zI282ZLs3L2m2+sXRvjSZ/isj5ScVzkVqSVVDMH0czAOin4rnUkJebEzdFt6CFnztFpXYONR9MzKE0s2OJiIiIiMh5bH9SDhnOQbg4Wfm/azqZHadB6tUqAItRyoa4dFbtTzE7joiYRMVzkVoy+sHnsBvgUpTF3Xf9heEjb6tw+2P7DrMjSgPl6mRjRI9wWvi7Y7c6M+6D9WyMUwFdREQarhdeeIHevXvj7e1NSEgI119/PXv27KkwpqCggMmTJxMYGIiXlxcjR44kMTGxwpjDhw8zfPhwPDw8CAkJ4ZFHHqGkpKTCmBUrVtCzZ09cXV1p27Ytc+bMqevdExE5rxWV2Pl1X1kx+N5L2xAZ6GlyoobJy9WJsMIjwJnPPt8UE8OYifdXuk199Im6iisidcTUnuci55P4Um8A2kW1YPBVMyqtj5l0fT0nksbE2WZlRPdwPlq0ilzCuHPOBuZP6keHUB+zo4mIiFSycuVKJk+eTO/evSkpKeGJJ55g6NCh7Ny5E0/PsiLMtGnTWLhwIfPnz8fX15cpU6Zw4403snr1agBKS0sZPnw4oaGhrFmzhuPHjzNu3DicnZ15/vnnATh48CDDhw9n0qRJzJ07l59//pkJEyYQFhbGsGHDTNt/EZHGbP3BNHIKS3ArzePeQW3MjtOgtcg/QJp3azYdzmDl3mQGtQ+p0f0K7RB98/2VlsfMf7u2I4pIHdPMc5FakusRCkCEv4fJSaSxcrZZaRm/gotaBZBdUMIdH67nWEa+2bFEREQqWbx4MePHj6dz5850796dOXPmcPjwYWJiYgDIzMzkgw8+4PXXX+fyyy8nOjqa2bNns2bNGtatWwfATz/9xM6dO/nkk0/o0aMHV111Fc8++yzvvPMORUVFAMyaNYuoqChee+01OnbsyJQpU7jpppt44403TNt3EZHGLDWnkM1H0gFonbcLN2ebyYkaNhejiLF9IwF4efEe7Hb1PhdpajTzXKQWpOQUUujqD0ALf3eT00hjZjVK+c+4Xtz87zXsTczhjg/X8+Wkfvh5uDB+wiSS0zOrvW+wvy9z3p9Vj2lFRETKZGaW/X0KCAgAICYmhuLiYoYMGeIY06FDB1q2bMnatWvp27cva9eupWvXrjRr1swxZtiwYdx7773s2LGDCy+8kLVr11bYRvmYqVOn1v1OiYicZwzD4Jc9SdgNaB3kSUBastmRGoV7B7Xls/VH2Hk8i2+3HOPGni3MjiQi9UjFc5FasO5AKgBBXi54uOjXSs6Nr4czc+68iJHvrmF/Ug4T/xvDJxP6kJyeyZBJlVsClVs265l6TCkiIlLGbrczdepU+vfvT5cuXQBISEjAxcUFPz+/CmObNWtGQkKCY8yJhfPy9eXrTjUmKyuL/Px83N0rT1ooLCyksLDQ8XNWVta57aCIyHliR3wW8RkFONssXNo+mH17zU7UOAR4unDfZW15afFuXlmyh6u7hmnGvkgToiqfSC1YE1tWPG+hli1SS8L93PnoLxcxcuYa1selMf0HXXBWREQapsmTJ7N9+3ZWrVpldhSg7GKmM2ZU/2WziEhTlFtYwqr9ZRcJ7dc6EB83Z8dFLauyacs2om+uz4QN2539W/HJukMcy8jng1UHmXxZW7MjiUg9UfFcpBas/bN4HqGWLXKOtm3byvCRtzl+9vcIJzv8Mub9fhh7rhdDTnFfERGR+jZlyhQWLFjAr7/+SosW/zuNPTQ0lKKiIjIyMirMPk9MTCQ0NNQxZv369RW2l5iY6FhX/t/yZSeO8fHxqXLWOcDjjz/Ogw8+6Pg5KyuLiIiIs99JEZHzwK97kykssRPi7Ur3CD+g+otaAqzdML7+wjUCbs42Hh7Wjmmfb+XdFbGM6q2/KyJNhYrnIucoPiOfgym5YNhpruK5nKNiO5Vas2yMS2N1bCqWbtdyND1PZziIiIjpDMPg/vvv55tvvmHFihVERUVVWB8dHY2zszM///wzI0eOBGDPnj0cPnyYfv36AdCvXz/+8Y9/kJSUREhICABLly7Fx8eHTp06OcYsWrSowraXLl3q2EZVXF1dcXV1rbV9FRFp7IygNuxNysFigcEdQ7BaLGZHapSu696cD1YdZPuxLN5Ypp43Ik2F1ewAIo1d+axz94JUXJ3U90xqX3SkP+2aeWGx2vhxewK5hSVmRxIRkSZu8uTJfPLJJ8ybNw9vb28SEhJISEggPz8fAF9fX+666y4efPBBli9fTkxMDHfeeSf9+vWjb9++AAwdOpROnToxduxYtm7dypIlS3jqqaeYPHmyo/g9adIkDhw4wKOPPsru3buZOXMmX3zxBdOmTTNt30VEGpPiUjv2TlcCcGGEHyHebiYnarysVgtPXl325e683w+Ta/MyOZGI1AcVz0XOUfnFQj3zE08zUuTsWCwWhnRshj3jOHlFpSzekYDdMMyOJSIiTdi7775LZmYmgwYNIiwszHH7/PPPHWPeeOMNrrnmGkaOHMnAgQMJDQ3l66+/dqy32WwsWLAAm81Gv379GDNmDOPGjePvf/+7Y0xUVBQLFy5k6dKldO/enddee43333+fYcOG1ev+iog0VusOpIK7H95uTvRtHWh2nEavX5tAruwcit2Agx4dMPS5TOS8p7YtIjU0fsIkktMzKy3fF3kduHiTtHsDcEP9B5MmwdlmpXjdx3gNf4yj6fn8fiCNfm108CsiIuaoSbHAzc2Nd955h3feeafaMZGRkZXaspxs0KBBbN68+Ywziog0dUlZBWw+nAHAZe1DcLZp/mRteOLqjvyyO4kMgjiYkkvrYM1AFzmfqXguUkPJ6ZmVelHnFJSwY/VBLEBRYqw5waTJMLKTubxDCEt2JLI+Lo1wPzciAz3NjiUiIiIiIg2M3W7w8+4kDMByfAdRgy8wO9J5o2WgB3+5JIpZK2P5bV8KkYGe2KzqIy9yvtLXjiLn4FhGWV/PYG9XKCk0OY00BR1CfegS7gPATzsTyS8qNTmRiIiIiIg0NFuOZpCUXYirkxXL7qVmxznvTL6sDc72QjLyi9l6NMPsOCJSh1Q8FzkHRzPyAGju725yEmlKLm0XTICnC3lFpfy8O1F99kRERERExCEjr4i1sWXX5rqkbRCWolyTE51/vN2ciczbC8DvB9PIKyoxOZGI1BUVz0XOQXx6AQDN/VQ8l/rjZLMyrHMzrBaITc5lV0K22ZFERERERKQBMIyydi0ldoMW/u50/vOsVal9zYqOEeztSlGJnbUHUs2OIyJ1RMVzkbOUV1RCWl4RoOK51L8Qbzf6ti67YOjKPclk5RebnEhERERERMy2Iz6Lo+n5OFktDO4QgsWiXtx1xQJcekEwADuOZZGcrVauIucjFc9FzlJ5v/NALxfcnG0mp5GmKDrSnzBfN4pK7SzZmYCBDoxFRERERJqqnIISftuXAkC/NoH4ebiYnOj819zfnbYhXhjAr/uS1VJT5Dyk4rnIWTqWXlY8b6FZ52ISq8XCsM6hONssxGcUkOrX0exIIiIiIiJiAgNYvieJolI7zXxc6RHhZ3akJmNA2yBsVgtH0/M5kKL+8iLnGxXPRc5S+cxztWwRM/m6O3Npu7JTBZOCurMzPsvkRCIiIiIiUt9SXEI5kJKL1QJDOjbDqnYt9cbH3ZkL//yyYs3+VOyafS5yXlHxXOQsFBSXkpJT1u88XMVzMVmnMB9aB3liWGxM+3wLBcWlZkcSEREREZF6kp5bxAGPsrNQe7cKIMjL1eRETU+vVv64OllJyytiT0K22XFEpBapeC5yFuL/nHXu7+GMp6uTyWmkqbNYLAzuGIKtJJ89idm89tMesyOJiIiIiEg9eXbBToqtrgR6utC7VYDZcZokVycbvVr5A7DuQCqGReU2kfOFqn4iZ0EtW6Sh8XBxonnSOg6HX8b7qw5yZZdQoiN14CwiIiIicrKpjz5BSkbVs4OD/Lx58+Xn6znR2ftldyJfbz4GhsGQjs2wWdWuxSzdW/ix+XAGWQUlWFpcaHYcEaklKp6LnIWjf14stLm/iufScHjnHmNkzxZ8tekoj8zfxqK/DsDN2WZ2LBERERGRBiUlI5vom++vcl3M/LfrOU1lNS3up+cW8dhXfwDQvCCOUN929ZZRKnO2WbkoKoAVe5Ix2lxCcakdZ5tmoIs0diqei5yhohI7ydmFgGaeS8Pz9LWdWLU/mQMpuby6ZA9PXdPJ7EgiIiIiIuekIcwUr88MNSnuG4bBU99uJzm7kAtCvAjevQ8YWmsZ5Ox0Cfdl06F0svBi29FMoiP9zY4kIudIxXORMxSfmY8B+Lo74+3mbHYckQp83Z158cZu3DlnAx+sLmvf0kt9D0VERESkEWsIM8VPleE/fxvPmIlVr6ur4v73W+NZ+MdxnKwWXr+lBy/9/ctafww5czarhYuiAli2K4lNh9Pp3sIXJ80+F2nUVDwXOUPH/mzZEu7nZnISkapd1iGEm6Jb8GXMUR75chuLHhiAu4vat4iIiIiI1IVCO/Va3D+emc//fbsdgPsvv4CuLXxr/THk7HUI9WHZpn3k4cuu49k1fn2qO7uhsfXhFznfqHgucobKLxbaws/D5CQi1fu/azqxal8KB1NyefWnPfyf2reIiIiIiDR6BhYe+HQzWQUldGvhy32XtTE7kpzEZrVgifsdo+NQYg6n0zncB2sNLuRa3dkNDaEPv0hTpnNHRM5AcamdxKwCQBcLlYbN192ZF0Z2BeDD1QfZEJdmciIRERERETlXh93bsiEuHS9XJ/456kJdkLKBshzbgpuzlcz8YvYl5ZgdR0TOgWaei5yBhMwC7AZ4uTrh46ZfH2lYtm3byvCRt1VY5hfSlwzfttz+zyX0yVrFx++/Y1I6ERERERE5F4dSczni1hqAF27sSqsgT5MTSXUspcX0aOHHuoNpbDyURrtmXlgsp599LiINj6p/ImegvGVLcz93/eGTBqfYDkMmzaiwrLC4lE9+P0wOPuywRZmUTEREREREzkVOQQlLdiSCxcLoPi25tnu42ZHkNLpH+BFzOJ2UnCIOpebpyw6RRkrn94icgfKLhaplizQWrs42BncIASDNrwPrD6p9i4iIiIhIY1JcaueHbfHkF5fiUZLF07qeUaPg5myjS3jZxUI3H8kwN4yInDUVz0VqyG6xcry837mfiufSeLQK8qRTmA9YLDzy5VbyikrMjiQiIiIiIjVgGAZLdyaSlF2Iu7ONTjmbcXO2mR1Laqh7hB8Ah9PySMstMjeMiJwVtW0RqaF810BK7Qbuzjb8PZzNjiNyRga2C2Lv4eMcSoWXF+9h+ojOZkcSEREREakzUx99gpSM7CrXbdqyjeib6znQWfr9YBr7knKwWmB41zASluWbHUnOgK+7M62DPDmQksvWoxn4mB1IRM6YiuciNZTnXtb6Qv3OpTFydbLRPGkdh5oPZs6aOK7qEkqf1oFmxxIRERERqRMpGdlE33x/levWbhhfv2HO0vZjmfz+Z9vFyzuE0NzfnQSTM8mZ6x7hx4GUXHYdzyLaojKcSGOjti0iNZTnHgxAuJ+byUlEzo5X3nFu7RUBwCNfblP7FhERERGRBmr38Sx+3p0EQHSkP53/7J0tjU+EvzsBni4UlxokujQ3O46InCF95SVSA3a7QZ5befFc/c6l8Xrymo78ti+Zw2l5at8iIiIiItIApTg3Y/XORAC6tfClfxudMWqWTTExjJlY9RkMNW3/Y7FY6N7Cl+V7kjnu1hK73cBq1dnsIo2FiuciNbA/OQe7zRVnm4VgL1ez44icNR83Z14c2Y1xH65nzpo4ruwSSl+1bxERERERaRDmbzzCHq/uGEDncB8GtQtW21ATFdqplfY/HcN8WB2bSgGerNybzGUdQmopoYjUNRXPRWpgQ1xZn7lQHzd9QyyN3sB2wdx2UQSfrj/Co19uY/HUAXi46M+BiIiIiIhZDMPgrZ/38eayfWCx0jHUm8s7hFQqnFc3E7oxXQS1KXK2Wekc5sPmIxnM/f2wiucijYiqJSI1EBOXDkCYWrZII7Zt21aGj7wNgFKrM84th3M4Dfo/8DbdS/Yw5/1ZJicUERERETkz50MxOb+olKe+3c5Xm44C0CI/lis6Datyxnl1M6Eby0VQm7IuzX3ZfCSDX3YnkpBZQKivrqcm0hioeC5SAxsOlc08D9cfN2nEiu0wZNIMx8+H0/L4ZvMx0v3acSD+mInJRERERETOTmMvJm8/lslfP9tMbHIuNquFZ6/rwqIPFmOxXGl2NKllAZ4u+BSnkeUcwPyNR7h/8AVmRxKRGrCaHUCkoUvMKuBIWj4YdsJ8NfNczh8tAzy4sKUfAMdC+pGcXWhuIBERERGRJuL+R5/k0gfe4Np//kpsci4u9gI6Zqxn0QevsGnLNrPjSR0JLTwCwGcbjlBqN0xOIyI1oeK5yGls/LNli1thBi5O+pWR88vFrQMJ9HKh1MmNR7/cimHoAE5EREREpK6UlNr5YsMRfjIu5JBHOwyLlTbBnowf1InBN4wm+ub7KSwuNjum1JHAokR83Jw4lpHPb/uSzY4jIjWgSqDIaZRfLNSjIMnkJCK1z8lm5crOoVjspSzfk8wn6w6ZHUlERERE5LxzIDmH15fu5fLXVvLoV9sotLnj5erE0E7NGN41DHdnm9kRpR7YsHNjzxYAfLb+iMlpRKQm1PNc5DRiDpXNPPfI17fCcn4K8nKlWeomEoJ789zCXfRrE0jbEG+zY4mIiIiINBqldoPCklIKS+wUFtspKCklI6+YvZ5dGfrGSvYm5jjG+nk445fyB8MHDcfJpjmNTc1tF7Vkzpo4lu1KJCm7gBBvXVtNpCFT8VzkFHILS9h5PAsAjwIVz+X8dezX+fhc2Zxcz3Cuef5boo4sxordsT7Y35c5788yMaGIiIiISP0rKrFzOC2PVOdgNh9OJ7uwhJyCEvKKSikoKaWw2E7p4Ef41/L9VW/AtTlJiTnYrBYGXBDE9T2ac0WnZtxz/w8qnDdR7UO96dnSj02HM/gq5hj3DmpjdiQROQUVz0VOYcuRDErtBs393HEuyTM7jkidKbYbjBrWn7m/H6KAADyGTOaStkGO9ctmPWNiOhERERE5X0x99AlSMrKrXBfk582bLz9fz4nKZo3nFJaQkVdEvGtLpn+/g7jUXA6m5HI0Pb/swo7e0ezal1L1BpxcHP/rYrPi6mzFzcmGl5sTJUe28sw9o+gZ6U+Ap0vV95cm55ZeEWXF801HmXRpaywWi9mRRKQaKp6LnEJ5v/PerfzZt8HkMCJ1zMvVicEdmrHwj+PEHEonMsCDiAAPs2OJiIiIyHkkJSOb6Jvvr3JdzPy3a/3xSu0G6XlFZOUXk1VQQnZBMfauI/h601HyikrJLSqhoPh/Z1zi2YkDa+IqbMPDxYYlL43w8OZ4uTnh7eqEh4sTbs5WXJ1tzH/9cSY8+SquNitWa8UiaMzerxjSqVmt75c0bld3C+OZ73ewPymHbUcz6R7hZ3YkEamGiucip1De7zy6VQD7TM4iUh/ahnjROdyHHfFZLN6RwOiLWuLpqj8VIiIiItJ4ZOYXY4+6mG82H+N4Zj7FpUbFAeFdOZKeX2GRzWLB190ZMo5w05B+tAr0JCqo7NbMx5Wx9zxA9LCqi/6WvPRqL/i5KSaGMRMr32/Tlm1E33x2+yeNn4+bM1d2CeW7LfF8GXNUxXORBkwVEZFqlJTa2fRn8bx3K38+MzmPSH25tF0wCZkFpOYWsXhHAjdc2NzsSCIiIiIipxWfkc/mwxnEJudgtLuMw2llrTddbFb8PJzxcXPG282JLcu+ZOgNt+Ph6oSHiw3PP2eRWywWYuZ/zxNX/6XWMhXaqXKm/doN42vtMaRxGtmzBd9tief7rfE8dU1Hs+OISDVUPBepxu6EbHKLSvF2c6JdiLfZcUTqjbPNytVdw/hsw2GOpuez/mCa2ZFERERERKqVX1zKL7uT2J+U87+FKQe49OI+NPdzJ8jLpUJP6W3v/06HsHtNSCryP/3bBhHq40ZCVgG/7EoyO46IVEOXdhapxsY/+51HR/pX6lsncr4L8HTh8vYhAPx+MI0c91CTE4mIiIiIVGYEtmbuukPsT8rBaoHO4T7c3qcltphP6RHhR7C3qy7GKA2SzWrhhp5lZ/l+GXPU5DQiUh0Vz0WqseHPli29Iv1NTiJijg5hPnQO9wHgaGh/krIKTE4kIiIiIvI/6+PSsPe6jdyiUvw9nLmlVwRDOjYjyMvV7GgiNTKyZwsAVuxNpsjiYnIaEamKiuciVTAMwzHzvFerAJPTiJhnULtggrxcKHVy5/5PN1NSajc7koiIiIg0cYZhsCY2hbWxqQB0a+HLbRe1pJmPm8nJRM5M2xAvukf4UWo3SHYJMzuOiFRBxXORKhxNzycxqxBnm4XuLfzMjiNiGqc/+59b7cX8fjCNN5ftMzuSiIiIiDRhhmHw674UNsSVnSls2b2My9qH4GxTeUMapxt6hAOQ7KriuUhDpL8uIlWI+bNlS+dwX9xdbCanETGXv4cL4Ym/A/Cv5ftZvlsXsxERERERc6yOTWXLkQwABrUPxnrod3MDiZyj4d3CsVogx8mPjLwis+OIyElUPBepwoY/W7b0bqV+5yIAvjlx3N6nJQAPfLaZuJRckxOJiIiISFOz/VimY6LT4A4hOktYzgvB3q70bxsEwN7EHJPTiMjJVDwXqUL5AVl0pPqdi5R7+tpO9GzpR3ZBCRM/3khOYYnZkURERESkiTiUmssve8rOgOwTFUCX5r4mJxKpPdd2L2vdsicxG8MwTE4jIidS8VzkJJl5xexJzAagl2aeizi4Otl4d0w0wd6u7E3M4ZH5W3VgJyIiIiJ1LtfmxaI/EjAM6BDqTZ8oTXKS88uwzqFYDDtpuUWk5Kh1i0hDouK5yEk2HUnHMCAqyJMgL1ez44g0KM183Jg1pifONgs/bk9g5opYsyOJiIiIyHksM6+YXV4XUlRqp7mfO4M7hmCxWMyOJVKrfN2dCSguO7Ni75+T+USkYVDxXOQkmxwtWzTrXKQq0ZEBzBjRBYBXf9rDij26gKiIiIiI1L5Su8FfP99Mgc0TbzcnhncNw8mqMoacn4ILjwNlxXOd4SvScOivjshJyvud92yp4rlIdUb3acltF0VgGPDAp7qAqIiIiIjUvteX7mHFnmSsRinXdgvH3cVmdiSROuNfnIyzzUJWQQkJWQVmxxGRPzmZHUCkIRk34V7WBQwDqzMfvPk8c4syHev+2L6DISZmE2lopo/ozO6EbDYfzuCej2P4+r6L8XTVnxUREREROXc//nGcd5aXtQhsm7udYO8OJicSqVs27LQO9mJPQjb7knII83U3O5KIoJnnIhUczbVgtzrjYrMy/M5pDJk0w3ErKikxO55Ig+LqZGPWnxcQ3ZOYzUNfbMVu1+mFIiIiInJu9iZm89D8rQBMuCSKkKLjJicSqR9tg70A2J+Uo9YtIg2EpgiKnCDfPQiAUF83XYRGpAbKLiAazU0zf2PxjgT63PMCIWl/VBgT7O/LnPdnmZRQRERERBqTzLxiJv53I3lFpVzcJpC/XdWB8d+bnUqkfkQGeuBktZBdUEJSdiHNfNzMjiTS5Kl4LnKCPLdgAMJ89QdKpKaiI/0JS1pPfLN+JAd2p89lV9LmzxkTAMtmPWNiOhERERFpLAzgr59vJi41j+Z+7vxrdE+cbDphXpoOZ5uVVkGe7E/KYX9SjornIg2A/gqJnEDFc5Gz458VS48WfgAs2ZFASk6huYFEREREpNGJc2/Pij3JuDlb+ffYaAI8XcyOJFLvHK1bktW6RaQh0MxzkT8lZxdS7OINlLVtEZH/2bZtK8NH3lbt+j+27+CBiUGk5BZyND2fH7bGM+qilrg72+oxpYiIiIg0VluPZnDMPQqAl0Z2o0tzX5MTiZijVZAHNouFjLxi0nKL2BQTw5iJ91c5NsjPmzdffr6eE4o0LSqei/xp0+F0AAI9XXB1UsFP5ETFdhgyaUa162MmXY/NauHqrmF8tv4wWQUl/PjHca7v0bweU4qIiIhIY3QwJZeVe5IBeHhoO67TMaQ0Ya5ONloGenAwJZf9STkU2iH65qqL5zHz367ndCJNj9q2iPypvHiuli0iZ8/d2ca13cNxtlk4kp7Pb/tTzI4kIiIiIg1YYlYBP24/jgE0KzzK5Mvamh1JxHQntm4REXOpeC7yp02H/iye+7mbnESkcQvycmVop1AAthzJIN2ntcmJRERERKQhSskp5NvNxyguNYjwd6dN7g4sFovZsURM1zrYE6sFUnKKMDz8zY4j0qSpeC4CFJXY2XY0E9DMc5Ha0DbEiz5RAQAcD+7jOLNDRERERAQgPa+Irzcdo6DETqiPG9d0C8eKLo4oAuDmbKO5f9nEPiO4nclpRJo2Fc9FgJ3HsygssWMrLcDP3dnsOCLnhT5RAbQJ9sSw2rjn4xgSMgvMjiQiIiIiDUBWfjFfbzpGfnEpwV6uXNcjHBcnlSdETtQ6qKx1ixGiVkYiZtJfJxEg5s+WLe75KTpNUKSWWCwWhnYKxbUwneTsQu75eCMFxaVmxxIRERERExVaXPl68zFyCksI8HDh+gvDcXO2mR1LpMGJCvIs+x+/lvocJWIiFc9F+N/FQj0Kkk1OInJ+cXGy0jJ+JX4ezmw9msmMH3aaHUlERERETJKSU8h2n95k5hfj6+7MDT2b4+HiZHYskQbJ192ZAE8XsFo5lJpndhyRJkvFcxH+d7FQFc9Fap9LSQ5vjboQiwU+XX+Y+RuPmB1JREREROpZZn4xYz9YT77NCy9XJ268sDleriqci5xK+ezzgym5JicRabr0l0qavPiMfI5nFmCzWnAvSDU7jsh5Z9u2rbz8+AMEBXQlObA7j36xiXdfewH3orIvrYL9fZnz/iyTU4qIiIhIXSkoLmXCRxvYdTwLZ3shN/aMxEfXmhI5rdZBnsQcSicuNRe73cBqVZtZkfqm4rk0eeUtWzqF+WDfoz5iIrWt2A5DJs3AMAy+2xrPodQ8Ujtcz229I3B1trFs1jNmRxQRERGRWjT10SdIycgGwMDCLq8epLk0w2YvxrZxHv5XPG9yQpHGIdTXDYryKMSD45kFNPd3NzuSSJOjti3S5JVfLLRnSz9zg4ic5ywWC1d2DsXbzYnM/GKW7EzEMAyzY4mIiIhILUvJyCb65vvpedMUMrrcXFY4t1q4oVcUxenxZscTaTSsFguW5P0AHEjJMTmNSNOk4rk0eZsOZwDQM9Lf3CAiTYCbs43hXcOwWS0cTMll459fXomIiIjI+WfzkQx2xGdhAa7qEqpZsyJn48/iufqei5hDxXNp0gqKS9lxLBOAaBXPRepFMx83BrULBmBtbCo57qEmJxIRERGR2nYsPZ9V+1MAGNgumDbBXiYnEmmcLKkHsFogPa+YjLwis+OINDkqnkuT9sexTErsBiHerjT30ywIkfrSOdyHTmE+GMDR0EtIzCowO5KIiIiI1JIiiyuLth/HMKB9M2+6t/A1O5JIo2UpKST8z3rFodQ8k9OIND26YKg0aeX9zqMj/bFYdNVqkfpisVi4rH0wSdkFpOTA5U99QuSxX7BQuQd6sL8vc96fZUJKERERETmVEy8MWs7AwkZrJ+xFpQR6ujC4Y4g+a4mco8gAD46m53MoLY/uEX5mxxFpUlQ8lybtfxcLVcsWkfrmZLNydZcwPlq1j1yPMPyufIDerQIqjVs26xkT0omIiIjI6ZRfGPREMYfSse9PwcVmZXi3MJxtOuFdpNymmBjGTLy/6nVbthF9c9X3iwz0ZHVsKkfT8yi1G9is+kJKpL6oeC5NlmEYbD78Z/Fc/c5FTOHv6ULJ5m9w7n0raw+k0sLfnTBftVASERERaYwy84tZdyAVgIHtgvD3cDE5kUjDUmin0hdO5dZuGF/t/YK8XPBwsZFXVEp8Rj4RAR51lFBETmbqV8C//vor1157LeHh4VgsFr799tsK68ePH4/FYqlwu/LKKyuMSUtL4/bbb8fHxwc/Pz/uuusucnJyKozZtm0bAwYMwM3NjYiICF5++eW63jVpBA6n5ZGSU4SLzUqX5j5mxxFpskrjNtKumReGAT9uT6CwuNTsSCIiIiJyhgzDYPmeJErsBqTF0SlMn7FEaovFYqHlnwXzw2nqey5Sn0wtnufm5tK9e3feeeedasdceeWVHD9+3HH79NNPK6y//fbb2bFjB0uXLmXBggX8+uuvTJw40bE+KyuLoUOHEhkZSUxMDK+88grTp0/nvffeq7P9ksZh8+EMADo398HVyWZuGJEm7vIOIfi4OZFdUMLPu5MwjMq9z0VERESk4dqbmMOh1DxsFgvWHT+qz7lILYv8s3h+SMVzkXplatuWq666iquuuuqUY1xdXQkNDa1y3a5du1i8eDEbNmygV69eALz99ttcffXVvPrqq4SHhzN37lyKior48MMPcXFxoXPnzmzZsoXXX3+9QpFdmh5Hyxb1OxcxnauTjau6hDE/5gj7knJoGZ9Fl+a+ZscSERERafKquihoufIezYUlpazcmwxA7yh/NixOq8+IIk1CeauW5OxCcgtL8HRVJ2aR+tDgf9NWrFhBSEgI/v7+XH755Tz33HMEBgYCsHbtWvz8/ByFc4AhQ4ZgtVr5/fffueGGG1i7di0DBw7ExeV/vdaGDRvGSy+9RHp6Ov7+Kpw2VZuPZADQQ1eqFmkQQn3d6NcmkNX7U1m5N5lwP3cCPNUnU0RERMRMVV0UtFx5j+bNhzPILy7F38OZ6Eh/NpzlY1V3McVTXUhRpKnwdHUi2MuV5JxCjqTn0SFUrZFE6kODLp5feeWV3HjjjURFRREbG8sTTzzBVVddxdq1a7HZbCQkJBASElLhPk5OTgQEBJCQkABAQkICUVFRFcY0a9bMsa6q4nlhYSGFhYWOn7Oysmp718RkBcWl7Iwve10vbOlnbhgRcYhu6c+RtHwOp+Xx4/bj3No7wuxIIiIiInIKBcWljpaY/VoH4mQ9++6w1V1M8VQXUhRpSloGepCcU8ihVBXPReqLqT3PT2fUqFGMGDGCrl27cv3117NgwQI2bNjAihUr6vRxX3jhBXx9fR23iAgVb843O+IzKbEbBHu70tzP3ew4IvIni8XC0E7NcHe2kZJTxLpYnfIrIiIi0pBtPpxBUamdQC8X2oZ4mR1H5LwWecJFQ3WdKJH60aBnnp+sdevWBAUFsX//fgYPHkxoaChJSUkVxpSUlJCWlubokx4aGkpiYmKFMeU/V9dL/fHHH+fBBx90/JyVlaUC+nli/IRJJKdnkuLXEYKjKTi+j2tu+sCx/o/tOxhiYj4RKTsdcUjHEH7YdpyYw+m0cgs5/Z1EREREpN4Zzu5s+bMdZt+oQF0kVKSOhfm54WS1kFdUSkpOkdlxRJqERlU8P3r0KKmpqYSFhQHQr18/MjIyiImJITo6GoBffvkFu91Onz59HGOefPJJiouLcXZ2BmDp0qW0b9++2n7nrq6uuLq61sMeSX1LTs9kyKQZLPrjOIlJOXTrfiG9rhvsWB8z6XrzwomIQ+tgLzqH+7AjPotjoReTXVCMt5uz2bFERERE5ARGqz4UldoJ9nKlTbCn2XFEzntOVist/N2JS83jUFou+rpKpO6Z2rYlJyeHLVu2sGXLFgAOHjzIli1bOHz4MDk5OTzyyCOsW7eOuLg4fv75Z6677jratm3LsGHDAOjYsSNXXnkld999N+vXr2f16tVMmTKFUaNGER4eDsDo0aNxcXHhrrvuYseOHXz++ee89dZbFWaWS9NzPLMAKLtAoYg0TAMvCMbHzYliZy/+/sNOs+OIiIiIyAkKiksxWvYGoG/rAM06F6knkYFlX1QdSs0zOYlI02Bq8Xzjxo1ceOGFXHjhhQA8+OCDXHjhhTz99NPYbDa2bdvGiBEjaNeuHXfddRfR0dH89ttvFWaFz507lw4dOjB48GCuvvpqLrnkEt577z3Hel9fX3766ScOHjxIdHQ0Dz30EE8//TQTJ06s9/2VhiGnoIScwhIsQIi3iuciDZWLk5WhnUPBMJgfc5QlOxLMjiQiIiIif9oZnwVOLgR5uRAVpFnnIvWlvO/58YwCSrGZnEbk/HdWxfMDBw7UyoMPGjQIwzAq3ebMmYO7uztLliwhKSmJoqIi4uLieO+992jWrFmFbQQEBDBv3jyys7PJzMzkww8/xMur4kVKunXrxm+//UZBQQFHjx7lscceq5X80jglZJXNOg/0csHFqUFfM1ekyWvu505Q+g4AHv/6D5KzC01OJCLS+NXGsfyvv/7KtddeS3h4OBaLhW+//bbC+vHjx2OxWCrcrrzyygpj0tLSuP322/Hx8cHPz4+77rqLnJycCmO2bdvGgAEDcHNzIyIigpdffvmcs4vIuTMMg23HMgHo3sJPs85F6pGfhzPebk6UGgaZzlW3IxaR2nNWlcO2bdty2WWX8cknn1BQUFDbmUTqVHnxXC1bRBqH4LRtdAj1Ji23iMe/3qaryouInKPaOJbPzc2le/fuvPPOO9WOufLKKzl+/Ljj9umnn1ZYf/vtt7Njxw6WLl3KggUL+PXXXyucHZqVlcXQoUOJjIwkJiaGV155henTp1c4y1REzHEoLY/M/GIoLqB9qLfZcUSaFIvF4ph9nu4cZHIakfPfWRXPN23aRLdu3XjwwQcJDQ3lnnvuYf369bWdTaROJPzZ7zzMx93kJCJSE1bDzpujeuBis7JsVxJfbDxidiQRkUatNo7lr7rqKp577jluuOGGase4uroSGhrquPn7/2923K5du1i8eDHvv/8+ffr04ZJLLuHtt9/ms88+Iz4+Hihrz1hUVMSHH35I586dGTVqFA888ACvv/762e24iNSabUfLZp1bjm3F2aazeUXqW8vAsuJ5hornInXurP7K9ejRg7feeov4+Hg+/PBDjh8/ziWXXEKXLl14/fXXSU5Oru2cIrXCwEKiZp6LNDodQn14eFg7AP7+w06OpuviOCIiZ6u+juVXrFhBSEgI7du359577yU1NdWxbu3atfj5+dGrVy/HsiFDhmC1Wvn9998dYwYOHIiLi4tjzLBhw9izZw/p6enVPm5hYSFZWVkVbiJSe7LyizmYkguA5cgmk9OINE0t/T2wWCDf5qXPRiJ17Jy+InZycuLGG29k/vz5vPTSS+zfv5+HH36YiIgIxo0bx/Hjx2srp0itKHD1o8Ru4Opkxd/D2ew4InIG7rqkNb1b+ZNbVMrjX/+h9i0iIueoLo/lr7zySv773//y888/89JLL7Fy5UquuuoqSktLAUhISCAkJKRSnoCAABISEhxjTr7eUfnP5WOq8sILL+Dr6+u4RUREnPV+iEhlf/zZ67xlgAeWvDST04g0Ta7ONkJ9yiYE/rYvxeQ0Iue3cyqeb9y4kfvuu4+wsDBef/11Hn74YWJjY1m6dCnx8fFcd911tZVTpFbku5Wd0tTMx00XtRFpJLZt28rwkbcx4ubRpK+Yg8Vewm/7Uuj/l6cZP2GS2fFERBqtujyWHzVqFCNGjKBr165cf/31LFiwgA0bNrBixYra24FqPP7442RmZjpuR46o3ZdIbSmx29kRX3Y2R7cWvianEWnaWv7Z9/zXver+IFKXnM7mTq+//jqzZ89mz549XH311fz3v//l6quvxmotq8VHRUUxZ84cWrVqVZtZRc5Z3p/Fc7VsEWk8iu0wZNIMx8+bDqfz274UUsIvxnP/lyYmExFpnP6fvfuOr6q+/zj+OnflZu9JQth7B0FUrIOKiJvSKlJHqZRWrda29meHq622tFq31LqtVkur1olbQTaBEPYmg+y9k5t7z++PQDQyhJDk3CTv5+NxHzzuOd978869Ifnmk+/5fK2Yyw8YMICYmBh2797NueeeS0JCAkVFRW3GNDc3U1ZWRkJCAgAJCQkUFha2GXPo/qExRxIQEEBAQECHZReRL2WV1lHv8RIcYKd/dLDVcUR6tdToIFbvK+OL3SU0e304tP+ASKdo1/+sJ554gjlz5pCVlcUbb7zBhRde2DrZPiQuLo6nn366Q0KKdJRDK88Tw1Q8F+muxqVEkBjupsnrIz/uVLVvERE5QVbM5XNzcyktLSUxMRGAKVOmUFFRQXp6euuYTz75BJ/Px+TJk1vHLF26FI/H0zrmww8/ZOjQoW02HxWRrrOjoBqAofGh2Gy6klfESvFhbhy+JqobmtmYW2F1HJEeq10rz3ft2vWNY1wuF9dcc017nl6kU1TUNdHkarm0MF4rz0W6LZthMG14PC+vyaYmOInF6bl8d6L62YqIHK+OmMvX1NSwe/fu1vv79u0jIyODqKgooqKiuPvuu5k1axYJCQns2bOH2267jUGDBjF9+nQAhg8fzvnnn8/111/PokWL8Hg83HjjjVxxxRUkJSUBMGfOHO6++27mzZvHr371KzZv3sxDDz3E3/72t5N8BUSkPRqbvew9uFHo0IRQi9OIiM0wCG8uo9SVwLJdJaSlRlkdSaRHatfK82effZbFixcfdnzx4sU8//zzJx1KpDNk5FQAEBHkJNBptzaMiJyUqGAXUwZEA/D7t7dSUNlgcSIRke6jI+by69atY/z48YwfPx6AW2+9lfHjx3PHHXdgt9vJzMzk4osvZsiQIcybN4+0tDSWLVvWpp3KSy+9xLBhwzj33HO54IILOOOMM3jyySdbz4eHh/PBBx+wb98+0tLS+PnPf84dd9zB/PnzT/IVEJH22FNci9dnEhnkJDZErZFE/EGkp2WzUPU9F+k87Vp5ft999/H3v//9sONxcXHMnz9fK87FL23IrgDUskWkpxjfN4L0zdupJoZfv76Jp6+ZqI2ARUSOQ0fM5c8666xjts16//33v/E5oqKiePnll485ZsyYMSxbtuwbn0tEOl9ry5aEUM25RPxExMHieUZOBZX1HsIDnRYnEul52rXyPDs7m/79+x92PDU1lezs7JMOJdIZNhxcea6WLSI9g80wSCpcictu45PtRby2/oDVkUREugXN5UXkRNU2NpNTVge09DsXEf/g9jUwIDYYnwkr95RYHUekR2pX8TwuLo7MzMzDjm/cuJHo6OiTDiXS0Xw+k4zsckArz0V6EndTJTdPGwzA3W9tobi60eJEIiL+T3N5ETlRu4pqMIH4sAAiglxWxxGRrzhzcCwAS3epeC7SGdrVtuXKK6/kpz/9KaGhoZx55pkAfP7559x8881cccUVHRpQpCPsK62lqqEZw9dMtPrzifQYmZkbMR+9A3fKDKqI4tz/e4qUgi9az8dGhvPcU4ssTCgi4n80lxeRE9XaskWrzkX8zplDYnhuxX6W7izGNE21VRLpYO0qnv/+979n//79nHvuuTgcLU/h8/m4+uqruffeezs0oEhHONTvPLCxFLtNP0hEegqPD7694C4Kqxp4dW0OVaH9GHjGafSPCQbgo0V3WpxQRMT/aC4vIieiwRZIQVUDBjBExXMRvzO5fzROu0FueT37S+tafxcSkY7RruK5y+Xi1Vdf5fe//z0bN24kMDCQ0aNHk5qa2tH5RDrEhoMtWwLrdRmTSE8UH+ZmfN8I1mdX8Mn2Ir5/aiouR7s6k4mI9Hiay4vIiSh1xgGQFBFIcEC7Sggi0omCAxykpUayam8Zy3YVq3gu0sFO6iffkCFDGDJkSEdlEek0h1aeBzWoeC7SU506IJrdRTVUNTSzcm8p3xoSa3UkERG/prm8iByPMldL8XxgrApyIv5q6uBYVu0tY+nOEq6e0s/qOCI9SruK516vl+eee46PP/6YoqIifD5fm/OffPJJh4QT6Qh1Tc1sL6gCIFDFc5Eey2m3cc6wON7IyCMjp0I9OUVEjkJzeRE5XuW1TVQ6ogAYEBticRoROZpvDYnlL+/vYOWeEjxeH067rsIV6SjtKp7ffPPNPPfcc8ycOZNRo0ZpMwLxa5m5lfhMSAx34/TWWx1HRDpRanQwwxJC2V5QzUfbC4lHP59ERL5Oc3kROV4fby8CwyAmxEV4oNPqOCJyFCMSw4gKdlFW28T6rHImD4i2OpJIj9Gu4vkrr7zCv//9by644IKOziNyUq794QKKyyvbHCuJHAExE6jN3cGBzVuYZlE2EekaUwfHsL+0ltKaJuyRI6yOIyLidzSXF5Hj9cGWAgAGatW5iF+z2QzOGBTDmxvzWLarRMVzkQ7U7g1DBw0a1NFZRE5acXkl0xbc3ebY25l5FBbXMnZ8Gh8uabYomYh0lSCXgzMHx/LB1kKKo8awr6RWm+aIiHyF5vIicjzqm7ws3VUMqHgu0h1MHXyoeF7ML6YPtTqOSI/RruL5z3/+cx566CEeffRRXeYpfs00TfIrGwBICHdbnEZEusqh1i3ZZXX85vVNvPTDyfp5JSJykObyInI8lu4qpsHjI8BbT0yIy+o4IvINzhwSC0DmgUrKa5u4++67KKmoPmxcTEQoDy68t6vjiXRb7Sqef/HFF3z66ae89957jBw5Eqezbe+z1157rUPCiZys6sZm6pq82AyICw2wOo6IdBHDMDhnWBzPf7GbFXtK+V9GHpeO72N1LBERv6C5vIh83S23/fqwItvO4NEQ0Adv3hYMY4xFyUTkeMWHuRkaH8qOwmq+2F1CSUU1abNvOmxc+uJHLEgn0n21q3geERHBZZdd1tFZRDpcwcFV5zEhATi027RIrxIe6CS2bBNFMeP5wztbOXtoHOFB2uhKRERzeRH5uq8X2Xw+k3XL9kKzD1/+NguTiciJmDo4hh2F1Sw72HJJRE5eu4rnzz77bEfnEOkUBWrZItKr5S99laCZ/SkhgrNufZSk4jVtzsdGhvPcU4ssSiciYg3N5UXkmxRUNdDQ7CPAYcNTkWN1HBE5TmcOieWpL/axbFcJA6wOI9JDtKt4DtDc3Mxnn33Gnj17mDNnDqGhoeTl5REWFkZIiDYTEf9QUNVSPE8MU/FcpDfyeL1cOGUk/11/gPKIIZw37dw2f0z7aNGdFqYTEbGO5vIiciz7S2sBSI0OYo9pWpxGRI7XpP5RuBw28isbSLQFWx1HpEdoVx+LrKwsRo8ezSWXXMINN9xAcXHL5SB//vOf+cUvftGhAUXay+szKapuBLTyXKQ3S44MYnhCKACfbC/C59MvgCLSu2kuLyLfZH9JHQD9olV8E+lO3E47k/tHAVDujLE4jUjP0K7i+c0338zEiRMpLy8nMDCw9fhll13Gxx9/3GHhRE5GcU0jXp+J22kjPFB9jkV6szMGxxDgsFFc08jG3Aqr44iIWEpzeRE5lprGZoprWhYhpUYHWZxGRE7U1MEtRfMKFc9FOkS7iufLli3jt7/9LS6Xq83xfv36ceDAgQ4JJnKyWvudh7kxDMPiNCJipSCXgzMGtUweV+4tpaah2eJEIiLW0VxeRI4l62DLlviwAIJc7e70KiIWOXNILACVziiavT6L04h0f+0qnvt8Prxe72HHc3NzCQ0NPelQIh1Bm4WKyFeNTAojMdyNx2vyuXafF5FeTHN5ETkWtWwR6d6GxocSFxqAz7BzoKLe6jgi3V67iufnnXceDz74YOt9wzCoqanhzjvv5IILLuiobCIn5dBmoQnaLFREaPlZdfbQOAwDdhfVsL+k1upIIiKW0FxeRI7G6zPJLjtYPI9R8VykOzIMg3OGxQFf/jFMRNqvXcXz+++/n+XLlzNixAgaGhqYM2dO62Wef/7znzs6o8gJq2tqprLeA2jluYh8KTY0gHEpEQB8uqMIn2G3NpCIiAU0lxeRo8mvrKfJ6yPQaSc+NMDqOCLSTmcfLJ7vK63FNE2L04h0b+1qYJacnMzGjRt55ZVXyMzMpKamhnnz5nHVVVe12XRIxCqFVS0b3EQGOQlwqDgmIl86tX80uwprqGpoxhU1yuo4IiJdTnN5ETmaQ6tUU6ODtG+USDd2xqAYDNNHZb2H8joPUcGub36QiBxRu3f/cDgczJ07tyOziHSYwir1OxeRI3M5bHxrSCzvbMqnNHIEu4uqGRSnHr8i0rtoLi8iR7L/4Gah6ncu0r0FBzgIby6jwhnD/pJaFc9FTkK7iucvvPDCMc9fffXV7Qoj0lEO9TuPD1XxXEQONzA2mH7RQewvreO3b2zmX9efqtVVItJraC4vIkdS1eChtLYJg5aV5yLSvUU1FVPhjGFfSS0TUiOtjiPSbbWreH7zzTe3ue/xeKirq8PlchEUFKQJt1jKNM3WlefxWnkuIkdgGAZnDY3j+S92s2pvGa9vOMDlE5KtjiUi0iU0lxeRI8k62LIlIdyN26nWlyLdXaSnCBhOXmU9jR4vAfp/LdIu7dowtLy8vM2tpqaGHTt2cMYZZ/Cvf/2rozOKnJCqhmYaPD7shkFMiC5NEpEjCw90Elu2CYB7393WusmwiEhPp7m8iBxJa8uWGLVsEekJAn31RAY58ZmQVVZndRyRbqtdxfMjGTx4MH/6058OW8ki0tUOrTqPCXXhsHXYl7iI9EDR5dsYEBtMSU0Tf/twp9VxREQso7m8SO/mwyD7YHGtv/qdi/QY/Q/+MWx/Sa3FSUS6rw6tLDocDvLy8jryKUVOWGu/8zC1bBGRY7Ph456LRwHwwsr9bMmrtDiRiIh1NJcX6b0qHVE0+0yCXXZdvSvSg7QWz0vr8JmmxWlEuqd29Tx/880329w3TZP8/HweffRRTj/99A4JJtJehZUtxfMEFc9F5DicMTiGmWMSeScznzv/t4V//2gKNps2DxWRnktzeRH5unJXLACp0cHaRF2kB0kMD8TlsFHv8VJY1UBieKDVkUS6nXYVzy+99NI29w3DIDY2lnPOOYf777+/I3KJtIuJQVF1I6CV5yJy/H47czifbi9iXVY5r204wHfStHmoiPRcmsuLyNeVO2MA6BcTZHESEelIdptBalQQu4pq2F9Sp+K5SDu0q3ju8/k6OodIh2h0hdPsM3HZbUQGOa2OIyLdRGJ4IDefO5j73tvOfe9u49sj4gkP1PcQEemZNJcXka/KKq2l3h6CzYC+USqei/Q0/WOC2VVUw76SWqYMjLY6jki3067iuYi/qne3rJiICwvQ5YYickKuO70/i9Nz2V1UwwMf7ODuS0ZZHUlERESk0322oxiApPBAAhx2i9NIT7U+PZ2582868rmMTNJmd3GgXiQ1uuWPYsU1jdQ0NFucRqT7aVfx/NZbbz3usQ888EB7PoRIu9S7W/6Kqn7nInKiXA4b91w8kjlPrebFVVnMnpjCqD7hVscSEelwmsuLyFd9uqMIgH4HNxYU6QyNPkibfeTi+cq113ZtmF4myOUgIcxNQVUD+0trrY4j0u20q3i+YcMGNmzYgMfjYejQoQDs3LkTu93OhAkTWsdp5a90tUPFc/U7F5HjkZm5kZmzrmxzLCzhDKpC+zHngTfJ+MtcbR4qIj2O5vIickh9k5eVe0oB6Betli0iPVX/mGAKqhrYV1JLktVhRLqZdhXPL7roIkJDQ3n++eeJjIwEoLy8nOuuu46pU6fy85//vENDihyP+iYvDa4IQCvPReT4eHwwbcHdbY7VNDTzwqr9VBHFf9bn8t2JKRalExHpHJrLi8ghq/aW0tjsI8BbT1Swy+o4ItJJ+scEs3JvKdlldSRgszqOSLfSrv8x999/P/fdd1/rZBsgMjKSP/zhD9x///0dFk7kRGzJqwTDRrDLTohb7fxFpH1C3A4m92+5imXhkh1UN3gsTiQi0rE0lxeRQz472LIl0lOsq01EerCYEBchAQ6afSaVziir44h0K+0qnldVVVFcXHzY8eLiYqqrq086lEh7ZORUAGrZIiInb1xKBK6mKkpqGnns0z1WxxER6VCay4sIgGmafHpws9BIz+HfE0Sk5zAMo7U1U5kz1uI0It1Lu4rnl112Gddddx2vvfYaubm55Obm8t///pd58+Zx+eWXd3RGkeOSmVsJQHy4iucicnLsNoOEknQAnvliH1naWEdEehDN5UUEYG9JLdlldbjsNiI8ZVbHEZFO1v/gpsBlzlhM07Q4jUj30a7i+aJFi5gxYwZz5swhNTWV1NRU5syZw/nnn8/jjz/e0RlFjsvG3ApA/c5FpGOE1B5g6uAYmrw+/vjONqvjiIh0GM3lRQTg0+0tLVsmD4jCjtfiNCLS2VKigrDbDBrtQewqqrE6jki30a7ieVBQEI8//jilpaVs2LCBDRs2UFZWxuOPP05wcHBHZxT5RuW1TWSV1gEQHxpgcRoR6QkM4I4LR2C3GXywtZAVu0usjiQi0iE0lxcRgM8Otmw5a2icxUlEpCs47TaSIwMB+GhbocVpRLqPk9piNz8/n/z8fAYPHkxwcLAu+xDLHFp17mqqIsBptzaMiPQImZkbueUn8wkva1l1ft1jS7hg1hxmzrqSmbOu5NofLrA4oYjIydFcXqT3qm1sZs2+llYtZw9V/2OR3mLAwdYtH21V8VzkeDna86DS0lK++93v8umnn2IYBrt27WLAgAHMmzePyMhI7r///o7OKXJMh/qdBzZoZaiIdAyPD6YtuJsGj5fnV+yngUjiL7yFMckRAHy06E5rA4qItJPm8iK91y23/ZqSimpKnbE0habh9tbxu9/czoaMTNJmW51ORDpb/5hgPt1RzIacCkpqGokJ0ZX7It+kXSvPf/azn+F0OsnOziYoKKj1+Pe+9z2WLFnSYeFEjtfGnAoAAhtKrQ0iIj2O22nn1AHRAKzcW0qDRz1BRaR701xepPcqqagmbfZN2IedDcDQ1EQmzr6JRo/H4mQi0hVC3U6CmysxzS/3PRCRY2tX8fyDDz7gz3/+M8nJyW2ODx48mKysrA4JJnK8TNNsbdsS2KjiuYh0vNF9wokKdtHg8bH64CXOIiLdlebyIr2baZrsL2nZL6pftPY5EOltojwt+x2o77nI8WlX8by2trbNKpVDysrKCAjQJR/StQ5U1FNS04TDZuBuVFFLRDqezWZw5uAYADJzKyirbbI4kYhI+2kuL9K7Fdc0UtPYjMNmtG4eKCK9R3RTy4rzZbtKdFWtyHFoV/F86tSpvPDCC633DcPA5/OxcOFCzj777A4LJ3I8DvU7H54Yhs30WZxGRHqq1Ohg+scE4zNh+W7tryAi3Zfm8iK9297iWgBSo4Nw2NtVEhCRbizYW0V8WAB1TV5W7dXV+yLfpF0bhi5cuJBzzz2XdevW0dTUxG233caWLVsoKytj+fLlHZ1R5JgO9TsfmxLOBmujiEgPN3VQDPtLa9lbUkt/d6zVcURE2kVzeZHebW9JS/F8QEyIxUlExAoGcO7weF5enc1H2wo5a2ic1ZFE/Fq7iuejRo1i586dPProo4SGhlJTU8Pll1/ODTfcQGJiYkdnFDmmjIPF8zHJESqei0inigx2MTIxjM15VewNHMIFs67EOMrY2MhwnntqUZfmExE5HprLi/RejTY3xdWNAPSLObx9k4j0fOvT0ylzvg6haSxevoN97z7Z+jtNTEQoDy6819J8Iv7mhIvnHo+H888/n0WLFvGb3/ymMzKJHDevz2TTgZa2LeNSInje4jwi0vNN7h/N9oJqmmP6M/Cc0xgQe+RVWx8turOLk4mIfDPN5UV6tzJny5VzieFuglztWksnIt1cow/Ovui77Fy6l0YCST1/PrGhLXue/OP/rmXu/JuO+DgV1qW3OuGflk6nk8zMzM7IInLC9hTXUNfkJdhlZ+BRClgiIh0pxO1gXEoE67LKWbGnlH4xwdiMo60/FxHxL5rLi/Rupa54AP3uJNLLOew2+kYFsbeklr3FNa3F80YfpM0+cvE8ffEjXRlRxG+0a3eQuXPn8vTTT3d0FpETdqhly+jkcOw2Fa9EpGtMTI3EbKqjtLaJ7fnVVscRETkhmsuL9E7VDR4qHVEADIgJtjiNiFitf2zL94FD+yCIyJG16zqt5uZmnnnmGT766CPS0tIIDm77g/eBBx7okHAi36R1s9DkCEtziEjvEuC007ztE5xjL2Tl3lKGxIfgsLfr79EiIl1Oc3mR3unzncWYho3IICeRwS6r44iIxfpHt/z8L6pupKaxmZAAtXISOZIT+p+xd+9e+vXrx+bNm5kwYQIAO3fubDPG0KXr0oU25lYAMDYlwtIcItL7eHcvJ3LSpdQ0NpOZW8mE1EirI4mIHJPm8iK920dbCwEYEKOWLSICwQEOEsLcFFQ1sK+kltF9wq2OJOKXTqh4PnjwYPLz8/n0008B+N73vsfDDz9MfHx8p4QTOZYGj7e1XYKK5yLS5XzNnDogio+2FbFmfxkjk8IIcNqtTiUiclSay4v0Xk3NPj7ZXgR82apBRKR/TLCK5yLf4ISK56Zptrn/3nvvUVur3kjSta794QKKyyupc8fQnHI+juZ6rp/3Awxg0+YtTLM6oIj0GsMTw9iQXUFpbRPrsso5fVCM1ZFERI5Kc3mR3mv57hKqGppx+hpIDHdbHUdE/ET/mGBW7i0lu6wOj9dndRwRv3RSDVq/PgEX6QrF5ZVMW3A3fc/9PgApCTF8e8HdTFtwN03NzRanE5HexGYYnDYwGmjZwLimQd+DRKT70FxepPd4Z1M+ADFNhdjUnklEDooJcRHqduD1meSU1VkdR8QvnVDx3DCMw/ogqi+iWKWgqgGA+LAAi5OISG/WPyaYxHA3zT6T1ftLrY4jInJUmsuL9E5NzT4+2FIAQExTgcVpRMSfGIbBgJiWVk57S3Q1msiRnHDblmuvvZaAgJZiZUNDAwsWLCA4uG3PtNdee63jEoocReHB4nlCmC47FBHrGIbB6QNj+M/6XLbmVTExNYrwQKfVsUREDqO5vEjvtHxPS8uWmJAAwsrKrY4jIn6mf0wwG3Mr2VdSi65JEzncCRXPr7nmmjb3586d26FhRI5Xg8dLRZ0HgHgVz0XEYn0iA+kbFUR2WR2r95Vy3ogEqyOJiBxGc3mR3undzJaWLTNGJbAv2+IwIuJ3+kQG4rQb1DV5sYXq9xiRrzuh4vmzzz7bWTlETsihVefhgU7cTrvFaUREYMqAaLLL6tieX80pqVFWxxEROYzm8iK9j8fr44OthQBcMDqRx961OJCI+B2HzUZKZBB7S2oxYwdaHUfE75zUhqEiVimsagTU71xE/EdCuJv+McGYwKp96n0uIiIi1lu+u4TKeg8xIQFM6q8/7ovIkaVGBwFgxqh4LvJ1Kp5Lt6R+5yLij6YMiAZgZ2ENDa4Ia8OIiIhIr/fuppaWLeePisdu0wbBInJk/aIP7n8S0YcGj9faMCJ+RsVz6XZMoOBg8Vz9zkXEn8SGBjA4LgSAougxFqcRERGR3qzB42XJ5gKgpWWLiMjRhAU6iQpygWEju6zO6jgifkXFc+l2mh1B1DV5MYyWQpWIiD85dUA0BlAd0pdNuZVWxxEREZFe6qNthVQ1NJMU7mZy/2ir44iIn0uNaWndklWq4rnIV6l4Lt1OfUDLxC8mOACnXV/CIuJfooJdDE0IBeD+D3dYnEZERER6q/+k5wJw+YRktWwRkW90qHVLVmktpmlanEbEfzisDiByourdLcVzbRYqIv5qcv8otudX8tmOYtKzykhL1QZdIiIicuJuue3XlFRUH/FcTEQoDy6894jnCqsaWLqzGIDvpCV3Wj4R6TmSItzQ3EQtLkpqmnSlv8hBKp5Lt9NaPA9Xv3MR8U8RQS4iqvZQET6Y+z/YycvXn2p1JBEREemGSiqqSZt90xHPpS9+5KiPe33DAXwmnNIvkn4xwZ0VT0R6EIfNBmX7IW4I+0trVTwXOUg9L6Rb8fnM1rYt8aEqnouI/4ot24TLbmPFnlJW7C6xOo6IiIj0EqZptrZs0apzETkRRskeAPaX1lqcRMR/qHgu3crekhp8dhcOm0F0sMvqOCIiR+VqruPKSSkA3P/hTvUNFBERkS6xMbeS3UU1uJ02LhidaHUcEelGjOKW4nl+ZQONHq/FaUT8g4rn0q1szKkEIC4sAJs2vRERP3fD2YMIcNhIzyrns4N9R0VEREQ60+J1OQDMGJVIqNtpcRoR6U6Mhkoig5yYJmSX1VkdR8QvqHgu3crG3AoAEsLUskVE/F9cmJurp6QC8MAHWn0uIiIinavB4+XNjXmAWraISPv0i27ZJ2F/qYrnIqDiuXQzG3MqAIhX8VxEuokF3xpIkMvOpgOVfLC10Oo4IiIi0oN9uLWQ6oZmksLdTBkQbXUcEemGUqODAMgqq9XiHxFUPJdupLHZy9b8KkDFcxHpPqJDAvjB6f2BltXnPp8moCIiItI5Dm0UOistWW0uRaRd+kQG4rAZ1DZ6KalpsjqOiOVUPJduY3t+NR6vib25gTC3w+o4IiLH7fqpAwh1O9hRWM3bm/KtjiMiIiI9UEFlA8t2teyxMmuCWraISPs4bDZSolpWn+8vrbU4jYj1VDyXbuNQv/PAxlIMQ6soRKT7CA9yMn/qAAAe/HAnzV6fxYlERESkp3l9wwF8JkzqF0W/mGCr44hIN9baukV9z0XQ8l3pNjIO9jsPbCi1NoiIyHHIzNzIzFlXtt73Gg7s/S9lbwnM/Om9vP/Yby1MJyIiIj2JaZr8Jz0H0EahInLyWjYNLSavsp7GZi8BDrvVkUQsY+nK86VLl3LRRReRlJSEYRi88cYbbc6bpskdd9xBYmIigYGBTJs2jV27drUZU1ZWxlVXXUVYWBgRERHMmzePmpqaNmMyMzOZOnUqbreblJQUFi5c2NmfmnSCja3F8xJrg4iIHAePD6YtuLv1Nv1Hv2PK0JZfZve6B9PUrNXnIiIi0jEycirYU1xLoNPOBWMSrY4jIt1ceKCTyCAnpgnZZVp9Lr2bpcXz2tpaxo4dy2OPPXbE8wsXLuThhx9m0aJFrF69muDgYKZPn05DQ0PrmKuuuootW7bw4Ycf8vbbb7N06VLmz5/fer6qqorzzjuP1NRU0tPT+ctf/sJdd93Fk08+2emfn3ScqgYPe0taem0FNmrluYh0T2OSwwly2fE4Q1h8cHWYiIiIyMk6tFHojFEJhAToAnMROXmp0S3tn9S6RXo7S3+qzpgxgxkzZhzxnGmaPPjgg/z2t7/lkksuAeCFF14gPj6eN954gyuuuIJt27axZMkS1q5dy8SJEwF45JFHuOCCC/jrX/9KUlISL730Ek1NTTzzzDO4XC5GjhxJRkYGDzzwQJsiu/i3TbmVmCakRAXi2NVodRwRkXZx2m2c0i+Kz3cW88jHu5k1IRm3U5dAioiISPs1eLy8uTEPUMsWEek4/aKDyMipIKu0DtM0rY4jYhm/3TB03759FBQUMG3atNZj4eHhTJ48mZUrVwKwcuVKIiIiWgvnANOmTcNms7F69erWMWeeeSYul6t1zPTp09mxYwfl5eVd9NnIyTrU73xcSqS1QURETtKoPmE4PbUUVDXw4sosq+OIiIhIN/fB1kKqG5rpExHIqQOirY4jIj1En4hA7DaDmsZmymqbrI4jYhm/LZ4XFBQAEB8f3+Z4fHx867mCggLi4uLanHc4HERFRbUZc6Tn+OrH+LrGxkaqqqra3MRah4rnY5PDrQ0iInKSHDYbsWWZADzyyS4q6jQRFRERkfY71LJl1oQ+2GyGxWlEpKdw2G0kRwQCkKW+59KL+W3x3Er33Xcf4eHhrbeUlBSrI/Vqpml+ZeV5hKVZREQ6QkTVXoYlhFLV0Mwjn+y2Oo6IiIh0UwWVDXyxqxiAWWrZIiIdrG90EADZ6nsuvZjf7iSSkJAAQGFhIYmJX+4WXlhYyLhx41rHFBUVtXlcc3MzZWVlrY9PSEigsLCwzZhD9w+N+brbb7+dW2+9tfV+VVWVCugWyq9soLi6EbvNYFQfrTwXke7PwOT2C4ZzzTNreGHlfq6Z0q91YioiIiJyvF7bkIvPhEn9o1o397vltl9TUlF9xPHrMzJJm92VCUWkO0uNCmIZkFtRTx+tv5Veym+/8vv3709CQgIff/xx67GqqipWr17NlClTAJgyZQoVFRWkp6e3jvnkk0/w+XxMnjy5dczSpUvxeDytYz788EOGDh1KZOSR+2cHBAQQFhbW5ibW2Xhw1fmwhFBtrCciPca3hsQydXAMHq/Jwve3Wx1HROSELF26lIsuuoikpCQMw+CNN95oc940Te644w4SExMJDAxk2rRp7Nq1q82YsrIyrrrqKsLCwoiIiGDevHnU1NS0GZOZmcnUqVNxu92kpKSwcOHCzv7URLoNky9btnx1o9CSimrSZt90xFvjV34vFhH5JlHBLkICHHh9JpUO7UEnvZOlxfOamhoyMjLIyMgAWjYJzcjIIDs7G8MwuOWWW/jDH/7Am2++yaZNm7j66qtJSkri0ksvBWD48OGcf/75XH/99axZs4bly5dz4403csUVV5CUlATAnDlzcLlczJs3jy1btvDqq6/y0EMPtVlZLv4tI7cCgLFq2SIiPcztM4ZjGPB2Zj7rs7WJtYh0H7W1tYwdO5bHHnvsiOcXLlzIww8/zKJFi1i9ejXBwcFMnz6dhoaG1jFXXXUVW7Zs4cMPP+Ttt99m6dKlzJ8/v/V8VVUV5513HqmpqaSnp/OXv/yFu+66iyeffLLTPz+R7qDaHs7e4loCnXYuGJ34zQ8QETlBhmHQN6rlCtkKZ4zFaUSsYWnblnXr1nH22We33j9U0L7mmmt47rnnuO2226itrWX+/PlUVFRwxhlnsGTJEtxud+tjXnrpJW688UbOPfdcbDYbs2bN4uGHH249Hx4ezgcffMANN9xAWloaMTEx3HHHHW0m5uLfMrIrAPU7F5GeZ0RSGN+ZkMzi9FzuenMLb/zkdG30JSLdwowZM5gxY8YRz5mmyYMPPshvf/tbLrnkEgBeeOEF4uPjeeONN7jiiivYtm0bS5YsYe3atUycOBGARx55hAsuuIC//vWvJCUl8dJLL9HU1MQzzzyDy+Vi5MiRZGRk8MADD2guLwIUBfQBYMboBEIC/LYjq4h0c6nRQWzNr6JcxXPppSz9CXvWWWdhmuZRzxuGwT333MM999xz1DFRUVG8/PLLx/w4Y8aMYdmyZe3OKdbx+kw2HagEVDwXkZ7ptvOHsWRzAZm5lfx7XQ5XTOprdSQRkZOyb98+CgoKmDZtWuux8PBwJk+ezMqVK7niiitYuXIlERERrYVzgGnTpmGz2Vi9ejWXXXYZK1eu5Mwzz8TlcrWOmT59On/+858pLy8/agvGxsZGGhsbW+9XVVV1wmcpYq1mr49iV8tq8+9oo1AR6UQpB1ee1zlCKaxqID7M/Q2PEOlZ/LbnuQjA7qIa6pq8BLvsDIwNsTqOiEiHiw0N4OZpgwFY+P4OKuvUi1REureCggIA4uPj2xyPj49vPVdQUEBcXFyb8w6Hg6ioqDZjjvQcX/0YR3LfffcRHh7eektJSTm5T0jED+0prsVrc9InIpBT+0dbHUdEerBAp534sAAAlu0qsTiNSNdT8Vz8WkZOSw/gMckR2NXKQER6qGtO68eguBDKapv420c7rY4jItKt3X777VRWVrbecnJyrI4k0uG25rdcUTErLVkt30Sk0x3qe750Z7HFSUS6nhqjiV/LyGlp2aLNQkWkJ3Pabdx10UjmPr2aF1dl8d2JKYxICrM6lohIuyQkJABQWFhIYuKXmxgWFhYybty41jFFRUVtHtfc3ExZWVnr4xMSEigsLGwz5tD9Q2OOJCAggICAgJP+PET8VXWDh+yyOgBmTehjcZqeZ316OnPn33T48YxM0mZbEEjED6RGBbN2fzlf7C7B5zP1RzvpVbTyXPxaRk4FoH7nItLznTE4hhmjEvD6TG5/LROv7+h7goiI+LP+/fuTkJDAxx9/3HqsqqqK1atXM2XKFACmTJlCRUUF6enprWM++eQTfD4fkydPbh2zdOlSPJ4v21l9+OGHDB069Kj9zkV6g+0F1QCEecpIjQ62OE3P0+iDtNk3HXZr9Ki1nvReCeFu7GYzZbVNbMnTXiLSu6h4Ln6rrqmZnYUtE0MVz0WkN7jr4pGEBjjYmFvJ8yv2Wx1HROSoampqyMjIICMjA2jZJDQjI4Ps7GwMw+CWW27hD3/4A2+++SabNm3i6quvJikpiUsvvRSA4cOHc/7553P99dezZs0ali9fzo033sgVV1xBUlISAHPmzMHlcjFv3jy2bNnCq6++ykMPPcStt95q0WctYj3TNFtbtsQ3HrA4jYj0FnabQbinFIClu9S6RXoXFc/Fb23Jq8LrM4kPCyAhXLs5i0jPFx/m5lczhgHw1w92cKCi3uJEIiJHtm7dOsaPH8/48eMBuPXWWxk/fjx33HEHALfddhs33XQT8+fP55RTTqGmpoYlS5bgdn85p3vppZcYNmwY5557LhdccAFnnHEGTz75ZOv58PBwPvjgA/bt20daWho///nPueOOO5g/f37XfrIifqSgqoGKOg9Ou0FM09E3zhUR6WiRnpbNQtX3XHob9TwXv5WRXQFo1bmI9C5zJvXlfxkHWLu/nN+9sZmnr5mIYainoIj4l7POOgvTPHp7KcMwuOeee7jnnnuOOiYqKoqXX375mB9nzJgxLFu2rN05RXqarQfbJQyKC8Fe7LU4jYj0JhEHi+fpWeXUNDYTEqCSovQO+koXv5WRWwFos1AR6XkyMzcyc9aVRz2/I7cE41s38cn2Is647jdEVO9rcz42MpznnlrU2TFFRETEjzT7fOwqqgFgeEIYRVssDiQivUqgr57U6CCySutYtaeUaSPirY4k0iVUPBe/tfHQZqHJEZbmEBHpaB4fTFtw91HPpy+4lLMHx7FyTynFfaZy3qlXEeZ2tp7/aNGdXRFTRERE/Eh2aR2NzT6CA+z0iQykyOpAItLrTB0cQ1ZpNkt3Fat4Lr2Gep6LXyqpaSS3vB7DgNHJ4VbHERHpchP7RpIQ5qbJ6+PDrYXHbI8gIiIiPd+OgmoAhsSFYlNLNxGxwJmDYwFYtqvE4iQiXUfFc/FLh1adD4oNIfQrqy1FRHoLm81g+sh4HDaD3PJ6Nhz8vigiIiK9T1Ozj70ltQAMTQi1OI2I9FZTBkbjsBnsK6klp6zO6jgiXULFc/FLGQeLROp3LiK9WUSQq3V1x4o9pRRXN1qcSERERKywt6SGZp9JRKCTuNAAq+OISC8V6nYyoW8kAEt3FVucRqRrqOe5+J1rf7iAtYFpEJzE8vf+y8xX/tTm/KbNW5hmUTYRka42qk8Ye0tq2F9ax7ub87nylL5WRxIREZEudqhly9CEUAy1bBERC00dHMOa/WUs3VnMVZNTrY4j0ulUPBe/U1ReSXNCMjT7OGvmd4gLc7c5n77gUmuCiYhYwDAMzhuRwMtrsqmo8/Dx9kL98BYREelF6pu8ZB9sjzA0Xi1bRMRaZw6J5f4Pd7J8dykerw+nXU0tpGfTV7j4nSZnKI3NPuw2g+gQXZIoIhLosjNjVAKGATsLaygPG2x1JBEREekiu4qq8ZkQFxpAZLDL6jgi0suN7hNOdLCLmsZm1u0vtzqOSKdT8Vz8Tr07BmiZHNptuiRRRAQgKSKQ0we2fH8siJ3YurGyiIiI9Gw7Cg+2bNGqcxGx0Pr0dObOv4mrF/wUe9k+AH7x8MvcctuvLU4m0rlUPBe/U++OBiD+a+1aRER6uwl9I+gfE4xps/OjF9Mpqm6wOpKIiIh0ogabm7yKlp/3g+NDLE4jIr1Zow/SZt/UcktLA6AhZhglFdUWJxPpXCqei985tPI8QcVzEZE2DMNg+sh4XE2VFFQ18ON/rqex2Wt1LBEREekkJa4EAPpEBBLqdlqcRkSkRd+oIAygtLaJBptqN9KzqXgufqXB46UhIBKAhHB9AxYR+boAh52+eZ8R6naQnlXOXW9uwTRNq2OJiIhIJyh2JQEwNEEtW0TEf7id9taaTbkz1uI0Ip1LxXPxK1vyKjENO4FOO2Fuh9VxRET8UoCnmoevGI9hwL/W5PD0F/usjiQiIiIdbFdhNbWOMGwGDIpTyxYR8S/9YoIBKHfGWJxEpHOpeC5+JT2rZafmpAg3hqHNQkVEjubsYXH8esZwAP747jY+2FJgcSIRERHpSG9uzAMgNTqYQKfd4jQiIm31j24pnlc4o9VKUno0Fc/Fr6zPqgDUskVE5Hj8cGp/5kzui2nCza9kkJlbYXUkERER6QCmabYWz4fGq2WLiPifmBAXwS47PsPBmn1lVscR6TQqnovfME2T9OyWleeJ4YEWpxER8X+GYXDPxSM5c0gs9R4v855fx4GKeqtjiYiIyEnamFtJVmkdNrOZAbHBVscRETmMYRitrVs+3V5scRqRzqOm0uI3csvrKa5uBNNHfGiA1XFERLoFh93GY3PGM3vRSrYXVHPeH94gJes97D7PEcfHRobz3FOLujiliIiInIj/ZRwAILqpCKd9uMVpRESOrF90MFvyqvhkeyG/u3C42u9Kj6TiufiN9QdXnQc2luGw66IIEZHjFep28vS1p3DpY8sprg6jftJ1XDwmCZvt8MnrR4vutCChiIiIHC+vz+TtzHwAYpvyLU4jInJ0faOCMEwf+0vr2F1Uw2C1mZIeSBVK8RvrD24WGlivy31ERE5Un4hAnr5mIoavmazSOj7bWYxpmlbHEhERkRO0am8pxdWNRAQ5ifCUWB1HROSoXA4bEZ5SAD7YWmhxGpHOoZXn4jfWZ1cAENSgCaKIyLFkZm5k5qwrj3jOW2piO/X7bDpQSUSQkwl9I7s4nYiIiJyMQy1bLhidSHau/hAuIv6tZvdaGHEBi95Zzcp/PdTmXExEKA8uvNeiZCIdQ8Vz8Qt1Tc1sza8CILBBK89FRI7F44NpC+4+4rn0BZfy7e/HsGxXCct2lRAe6GRgbEgXJxQREZH2aPB4eW9zAQCXjE3ikfcsDiQi8g28RbtgBFQ7Ihh28Y8JDviy1Ji++BELk4l0DLVtEb+QmVuJ12eSEObG2VxndRwRkW5tfEoEo/uEA7BkcwGFVQ0WJxIREZHj8dmOYqobmkkMd3NKvyir44iIfCOjsYaEMDcAe4trLU4j0vFUPBe/cGiz0AmpEWhvZhGRk2MYBmcNiSU1Oohmn8mbG/OoqvdYHUtERES+wVsb8wC4aOyRN/4WEfFHA2KDAdhTUmNxEpGOp+K5+IX1WRUA6s0rItJBbDaDGaMSiA5xUdfk5c2NeTQ2e62OJSIiIkdR3eDho20tG+5dPDbJ4jQiIsdvQExL8Ty3rJ6mZp/FaUQ6lornYjnTNL+y8lzFcxGRjhLgsHPJ2CSCXXZKa5t4d1MBpq7vERER8UsfbCmksdnHwNhgRiaFWR1HROS4RQW7CA904jVNskrVukV6FhXPxXJZpXWU1Tbhsts0SRQR6WChbicXjU3CYTPILqsjP/YUqyOJiIjIEfzvYMuWS8b1wTD0x24R6T4Mw2Bga+sWFc+lZ1HxXCx3aNX56ORwAhx2i9OIiPQ88WFuzh+VAEB5xBBeXZttcSIRERH5qpKaRpbvLgHUskVEuqeBsSEA7Cuupdmr1i3Sc6h4LpZLzzrYsqVvhLVBRER6sIGxIUwZGA3A7/63hU25lRYnEhERkUPe3ZSP12cyNjmcfgd7B4uIdCeJ4W5CAhw0eX1kldVZHUekwzisDiCyPrsC0GahIiKd7ZTUSFYsW0pT0kgu/9sSBmS/i8PX1GZMbGQ4zz21yKKEIiIivdP/Mlpatlw8ro/FSUSkO1mfns7c+Tcd+VxGJmmzuy6LYRgMjgthQ04FOwurW1eii3R3Kp6LpWoam9lRUAVos1ARkc5mGAaNa14h/so/UUkIDZOu5eKxSdi+0lf1o0V3WphQRESk98kpqyM9qxzDgIvGJFodR0S6kUYfpM0+cvF85dpruzYMMCQ+lA05FewrqcWj1i3SQ6hti1hqY04FPhP6RAQSH+a2Oo6ISM/naWDm6EQcNoOs0jrW7CuzOpGIiEiv9lZmy6rzKQOiidPvRCLSjcWHBRDmduDxmuzXxqHSQ2jluVhq/cF+52ladS4i0mViQwM4Z1gcH2wtZPW+MhLC3OqvKiIiYpFF760HI5iijZ8xd/5Lbc51ddsFEZGTYRgGg+NDSc8qZ2dRDQlWBxLpACqei6XSs7VZqIiIFYYnhpFf2cCmA5Us2VLAlZP6Eh7otDqWiIhIr7KjoJoqIxi7YXDuBZfgdtrbnLei7YKIyMkYEhdCelY5+0tqicX+zQ8Q8XNq2yKW8flMNhzaLFQrz0VEutyZQ2KIDwugsdnHu5vyafapL6GIiEhX+l/GAQD6xQQdVjgXEemOYkMDCA900uwzKXPFWR1H5KSpeC6W2VtSS2W9B7fTxvDEMKvjiIj0Og6bjZmjE3E7bRRVN7J8V6nVkURERHoNr8/k9Q0txfOh8aEWpxER6RiGYTAkPgSAYpc2QZbuT8VzscyhfudjkiNw2vWlKCJihVC3k/NGtHQjzMitoCo42eJEIiIivcPKPaXkVzbg8DXRP1Z7j4hIz3HoD4LlzhhKaxotTiNyclSxFMusz9ZmoSIi/qB/TDDjD+49kRc/hQMV9dYGEhER6QX+k54DQExTAQ6bfjUXkZ4jOiSAuNAATMPG/zLyrI4jclL0E1osk551aLNQFc9FRKx2+sCW/udeewA3/2sDzV71PxcREeks1Q0elmwpACC+8YDFaUREOt6h9rz/XZ9rcRKRk6PiuVjiqutvYldRDQB/vfNXzJx1Zett0+YtFqcTEel97DaDGaMSsXmbWJdVzt8+2ml1JBERkR7rvU0FNHh8DIgNJsRbaXUcEZEONzQ+FMP0sSWviu0FVVbHEWk3h9UBpHfKbnQDEBXk4vz5v25zLn3BpRYkEhGR8EAnzWtfxXbq93nsk928/eLfCakvaD0fGxnOc08tsjChiIhIz/CfgysxZ01IZuUOi8OIiHSCQJedKE8Rpa4E/puey29mjrA6kki7aOW5WKIuMA6ApAi3xUlEROSrmnIyGZUUBoZB8YDpTLnud0xbcDfTFtxNcblWxomIiJys7NI61uwrwzDg8gl9rI4jItJp4hpb+p2/viFPbSGl21LxXCxR544FoE9EoMVJRETk6741JJboYBd1TV7e31qAaZpWRxIREekxDq06P2NQDInh+n1IRHquSE8xUcEuSmoaWbarxOo4Iu2i4rl0ubqmZurd0QAkqXguIuJ3HHYbM0Yl4LAZ5JTVs/bgBs8iIiJycpq9Pv69NgeA76QlW5xGRKRz2TC5eGwSAIvTcyxOI9I+Kp5Ll8vIrgDDRkiAg7BAp9VxRETkCKJDAjhraMtVQqv2lpJXUW9xIhERke7v0x3FFFQ1EBXs4vxRCVbHERHpdN+dmALAh1sLKalptDiNyInThqHS5dbsLwPU71xExN+NSAwjp7yeHQXVvLe5gD42l9WRREREurWXV2cBLavOAxz2k3qu9enpzJ1/0+HHMzJJm31STy0i0mFGJIUxNiWCjTkV/Dc9lx99a6DVkUROiIrn0uXWHiyeq9+5iIh/MwyDc4bGUVjZQEW9h7z40/D5TGw2w+poIiIi3U5ueR2f7SwG4MpJfU/6+Rp9kDb78OL5yrXXnvRzi4h0pDmTUtiYU8G/1mQz/8wB/OxXv6GkovqIY2MiQnlw4b1dnFDk6FQ8ly7l8fpYn1UBqN+5iEh34HLYmDE6gX+vy6U6JJknPt/DDWcPsjqWiIhIt/Pq2hxME04bGE3/mGCr44iIdJkLxyRxz1tb2V9ax8q9pZRUVB/xj38A6Ysf6eJ0IsemnufSpbbkVVHv8WL3NhIdrMv/RUS6g7hQd2v/879+sIOlB1fNiYiIyPHxeH28enCj0DmTT37VuYhIdxIc4OCS8X0A+NcabRwq3YuK59Kl1u5radkSVF+MYeiyfxGR7mJUUjgRlbswTbj5lQ3kltdZHUlERKTb+HhbEUXVjcSEuDhvhDYKFZHeZ87BdlXvby7AYzgtTiNy/NS2RbrUoc1Cg+qLLE4iIiInKrF4LX1HTiQzt5IfvZjO4gVTCHJpKiEiInLILbf9+oh9fDeFngLOaGZPTMHl0Bo2Eel9RvUJZ3SfcDYdqKQooI/VcUSOm37jlS7j85mtm4UGNah4LiLS3dhMH0/MTePiR75gS14Vt7ySwaK5adpAVERE5KAj9fEtrm7kizXZ4POx7vWnmPtaY5vz6zMySZvdlSlFRKxx5aS+bHp9EwUByZimqY4E0i2oeC5dZkdhNRV1HoJcdgIbSq2OIyIi7dAnIpC/fz+NOf9YzQdbC/nLBzv41fnDrI4lIiLitzbklANgFG7jtLnzDzu/cu21XZxIRMQaF49L4g/vbKWuKYS8igb6RAZaHUnkG+l6Mekyq/a2FMwn9ovCwLQ4jYiItNfEflH8+TujAXjisz0sXqdNf0RERI6ktrGZnQU1ABhZayxOIyJirZAABxePTQJgU16lxWlEjo+K59JlDhXPTx0QZXESERE5WZeNT+aGswcC8H+vbeKT7YUWJxIREfE/mw5U4jVNEsLcGJV5VscREbHclQc3Dt1dVEODx2txGpFvprYt0iV8PpPV+1r6nZ86IJp3LM4jIiIn7+ffHkpeRQOvbzjAT15azz/nTWZiP/2BVEREBKDZ5yMzt2Vl5fi+EXxgcR4Rka62Pj2dufPb7gNhAoZzPN7QeLblVzG+b6Q14USOk4rn0iV2Fn3Z73x0n3Cr44iISAew2QwWfmcMFXVNfLqjmB88t5Z/L5jCsIQwq6OJiIhYbkdBNfUeLyEBDgbGhlgdR0SkyzX6OGwTZYAVjzwMI2awOa+KcSkR2jhU/JratkiXWLXny37nTru+7EREegqn3cbjV6WRlhpJVUMzV/1jNTsKqq2OJSIiYinTNEnPatkodFxKBHabCkMiIocY+Vtw2AzKapvIr2ywOo7IMWnluXSJVXsPtWzR5fwiIj1NoMvOM9ecwul3/JvS2ggu+OsH9DvwEe6mijbjYiPDee6pRdaEFBER6UJ7imspr/MQ4LAxqo+uyBIR+SqjuZEh8aFsza9ic14lSRGBVkcSOSoVz6XTtfQ7P7RZaLTFaUREpDOEBznpk/UB5eO+T1E15A26hMvG9yE2NKB1zEeL7rQwoYiISNcwTZO1+1sWD41JDifAYbc4kYiI/xnVJ4yt+VXsLKzhzMFe3E59rxT/pP4Z0ul2FlVTrn7nIiI9nsPXxGXj+xAXGkC9x8t/1udyoLze6lgiIiJdKqe8nqLqRhw2g3EpEVbHERHxSwlhbqJDXHh9pto+il/TynPpdOp3LiLSe7iddi4f34c3N+aRV9nA6xkHmDEq4Rs3Srv2hwsoLq886nm1fBERke5i3cFV5yOTwghy6VduEZEjMQyD0UnhfLazmE15lYxJDtfGoeKX9JNcOp36nYuI9C4BTjuXje/De5sL2FtSyzuZ+Zw1NPaYjykur2TagruPel4tX0REpDuotoeTU16PzYAJqZFWxxER8WtDE0JZtruE0pomCqsaSQh3Wx1J5DBaBiydyuszWblX/c5FRHobh93GzNGJjEwKwwQ+3VFMXuwpeLw+q6OJiIh0muzAgUBLQSjM7bQ4jYiIf3M77QyJa7lCddOBo1+FKmIlrTyXTrU1r4rKeg+hAQ7GqN+5iEi3lpm5kZmzrjzq+U2btzDtK/dtNoNzh8URHuhkxZ5SyiOGcs0za3j8qglEBLk6P7CIiEgXysytoNwVh2HApH666lZE5HiM6hPOtoJqdhZWc+aQGKvjiBxGxXPpVF/sLgFg8oBoHOp3LiLSrXl8HLO1SvqCSw87ZhgGp/SLIirYxbsZ2azYU8oljy3n6WsmMigutBPTioiIdK0HP9oFwLCEUP2RWETkOCWGu4kKdlFW26SNQ8UvqZopnWrFnpbi+emD1LJFRKQ3GxgbQv+c90mODCSrtI7LHlvBpzuKrI4lIiLSITbmVPDJ9iIwfVp1LiJyAgzDYFRSGACbD1RhWpxH5OtUPJdO0+DxsvbgTvOnD9KlNyIivZ27qYL/3XA6k/pFUd3YzLzn1vL3z/dgmpoii4hI9/bQxy2rzuOa8rXqXETkBA1PDMNuMyiuaaTGHmZ1HJE21LZFOsW1P1zA/gY3DcnfxtFczy0/mY/xlfNf74srIiK9Q3RIAP/84WTufHMz/1qTw33vbWdrfhU+w251NBERkXY5tOrcbjNIqd8DnGV1JBGRbsXttDMoLoQdBdVsrg5k7vybjjguJiKUBxfe28XppLdT8Vw6RXF5JbHfvpr9+8sZmBzHt6e37ZF7pL64IiLSO7gcNu69bDTDE8O4562t/C8jD3fydKoaPIS5nVbHExEROSGHVp1fOq4PhR/VWZxGRKR7Gp0Uzo6CarwJIxl97ndwOQ5vlpG++BELkklvp7Yt0mlyyuoB6BsZZHESERHxN4ZhcPWUfvzzh5OJCnbR4I7ilTU5HCivtzqaiIjIcfvqqvObzhlkdRwRkW4rKcJNZJATHC52FGrjUPEfKp5Lp/DanBRWNQCQEhVocRoREfFXpw6I5s0bT8fdUEa9x8trG3LZmFOhPugiItItPPjRTqBl1Xm/mGCL04iIdF8tG4eGA7D5QKXFaUS+pOK5dIrawHhMICLQSaguwRcRkWNIjgyif+77DIkPwWfCZzuL+XRHMT6fCugiIuK/MnIq+HRHsVadi4h0kOGJYeBrpqi6kaKDCzJFrKbiuXSK2qAEAFKi1LJFRES+mc30cv7IBM4YFAPApgOVvJWZR1Ozz+JkIiIiR/aQVp2LiHSoQJcdo3AHAJvzqixOI9JCxXPpFDVBiYBatoiIyPEzDIO01Ehmjk7EbjPYX1rHf9fnUtvYbHU0ERGRNrTqXESkcxi5GQDsLKym2auFNGI9Fc+lw+WU1dHkCscwtFmoiIicuEFxIcya0IdAp52i6kZeXZdDgyvc6lgiIiKtHvl4F6BV5yIiHa5sP6FuB43NPvYU11qdRgSH1QGk51m6qxiAhDA3AU67xWlERMRfZGZuZOasK494btPmLUz7yv3E8EC+OzGZ/2XkUVHvoTZ5Oiv2lHDawJiuCSsiInIU1972Rz6zjQPTZP8nLzP3o6daz63PyCRttnXZRES6OwMYnhDGmv1lbM2vYmhCqNWRpJdT8Vw63NKdLcXz1GitOhcRkS95fDBtwd1HPJe+4NLDjkUEufjuxBTeyswjvxKueWYND3x3HBeNTerkpCIiInDLbb+mpKL6sOMrvQMhFoYlhnHGtHltz629tovSiYj0XCOSWorn2WV1VDd4CHU7rY4kvZiK59KhPF4fK3aXApAarcsXRUTk5AS67Fw+vg8vvvUJVaGp/PSVDdQ2NnPFpL5WRxMRkR6upKKatNk3tTlWXN3IF2uyATilX5QVsUREerzwQCd9IgI5UFHPtvxqJvXX91uxjnqeS4fakF1BdWMzdm8DcaEBVscREZEewGG3kVzwBXMm98U04f9e28RTy/ZaHUtERHqhNfvKABgSH0JUsMviNCIiPdeIpDAAtuZXYZqmxWmkN/Pr4vldd92FYRhtbsOGDWs939DQwA033EB0dDQhISHMmjWLwsLCNs+RnZ3NzJkzCQoKIi4ujl/+8pc0Nzd39afSaxxq2RJSl4/NMCxOIyIiPYWByR8vHcWPzhwAwB/e2cbfPtypibSIiHSZkppGdhfXADBJq85FRDrV4LgQnHaDynoPeRUNVseRXszv27aMHDmSjz76qPW+w/Fl5J/97Ge88847LF68mPDwcG688UYuv/xyli9fDoDX62XmzJkkJCSwYsUK8vPzufrqq3E6ndx7771d/rn0Bp8fKp7X5lucREREepLMzI1c+J05mEBc5CiKYsbx0Me7ePE/bzLau5vnn1pkdUQREenh1h5cdU7BNqJDBlsbRkSkh3PabQyOC2VrfhVb8ivpExlodSTppfy+eO5wOEhISDjseGVlJU8//TQvv/wy55xzDgDPPvssw4cPZ9WqVZx66ql88MEHbN26lY8++oj4+HjGjRvH73//e371q19x11134XLpMruOVFLTyKYDlQAE16l4LiIiHefrm41m5FTw+c5iyiKHs7HSiddnYrfpiicREekcZbVN7CxqWXVu2/sFcLG1gUREeoERSWFsza9id1ENZw3xWR1Heim/btsCsGvXLpKSkhgwYABXXXUV2dktm7Okp6fj8XiYNm1a69hhw4bRt29fVq5cCcDKlSsZPXo08fHxrWOmT59OVVUVW7Zs6dpPpBf4YlcJAMMTw3B66y1OIyIiPdm4lAi+PTweA6gIH8TNr2zA49WEWsSfqAWj9CRr9resOh8YG4xRXWRxGhGR3iEp3E1EoBOP12RXUbXVcaSX8uvi+eTJk3nuuedYsmQJTzzxBPv27WPq1KlUV1dTUFCAy+UiIiKizWPi4+MpKCgAoKCgoE3h/ND5Q+eOprGxkaqqqjY3+WaHWrZ8a0isxUlERKQ3GJEUxoxRCWD6eDsznxteWk9js9fqWCLyFSNHjiQ/P7/19sUXX7Se+9nPfsZbb73F4sWL+fzzz8nLy+Pyyy9vPX+oBWNTUxMrVqzg+eef57nnnuOOO+6w4lORXqy8romdBS1FG/U6FxHpOoZhMPwrG4eKWMGvi+czZsxg9uzZjBkzhunTp/Puu+9SUVHBv//97079uPfddx/h4eGtt5SUlE79eD2B12fy2Y6WFRgqnouISFcZHB9K3/zPcdltfLC1kB+9mE6DRwV0EX9xqAXjoVtMTAzwZQvGBx54gHPOOYe0tDSeffZZVqxYwapVqwBaWzD+85//ZNy4ccyYMYPf//73PPbYYzQ1NVn5aUkvs3ZfGSbQPyaYuDC31XFERHqV4QmhGEBeRQP1tiCr40gv5NfF86+LiIhgyJAh7N69m4SEBJqamqioqGgzprCwsLVHekJCwmGXfh66f6Q+6ofcfvvtVFZWtt5ycnI69hPpgTJyyimv8xDmdjCxX6TVcUREpBcJrT3A09dOxO208dmOYq57di21jWrrIOIP1IJRuruKuia2Fx5cdd5fq85FRLpaqNtJ36iWonlRQB+L00hv1K2K5zU1NezZs4fExETS0tJwOp18/PHHred37NhBdnY2U6ZMAWDKlCls2rSJoqIve9J9+OGHhIWFMWLEiKN+nICAAMLCwtrc5Ng+3nZw1fnQOJz2bvVlJSIiPcDUwbE8f90kgl12Vu4t5Zpn1lDV4LE6lkivphaM0hOkZ5VjmpAaFUSCVp2LiFhixMHWLYWuJLw+0+I00tv4dZXzF7/4BZ9//jn79+9nxYoVXHbZZdjtdq688krCw8OZN28et956K59++inp6elcd911TJkyhVNPPRWA8847jxEjRvD973+fjRs38v777/Pb3/6WG264gYCAAIs/u57lk+0txfNzh8VZnERERHqryQOi+ecPJxPmdrAuq5y5T62mok6tHUSsohaM0t01GQFsy29ZdX6Kep2LiFhmQEwwAQ4bTfZAlu8usTqO9DJ+XTzPzc3lyiuvZOjQoXz3u98lOjqaVatWERvb0lP7b3/7GxdeeCGzZs3izDPPJCEhgddee6318Xa7nbfffhu73c6UKVOYO3cuV199Nffcc49Vn1KPdKCinu0F1dgM9TsXERFrje8bycvXn0pkkJPM3EqueHIVJTWNVscSEdSCUbqfA+5UvKZJYribpAitOhcRsYrDbmNoQigAr67Vz3XpWn5dPH/llVfIy8ujsbGR3NxcXnnlFQYOHNh63u1289hjj1FWVkZtbS2vvfbaYRPp1NRU3n33Xerq6iguLuavf/0rDoejqz+VHu3QqvO01Egig10WpxERkd5uVJ9wXv3RFGJCAtheUM0VT66isKrB6lgivZ5aMEp3UlnnocDdF4CJ/SIxDMPiRCIivduopHAA3t9SQHG1FsdI11EVWU7aJ9taVgCdMyz+G0aKiIh0vMzMjcycdeVhxyOcoVT0mcbuIvju31fyz3mTSTm42ZCIdL5f/OIXXHTRRaSmppKXl8edd955xBaMUVFRhIWFcdNNNx21BePChQspKChQC0bpMi+s3I/XcBAd7KJ/dLDVcUREer3Y0ABCmyuodkTw3/W5LPjWwG9+kEgHUPFcTkpdUzPL95QCcO5w9TsXEZGu5/HBtAV3H/FcVb2Hf36+iaxSmPXECl6cN7n1kk8R6VyHWjCWlpYSGxvLGWeccVgLRpvNxqxZs2hsbGT69Ok8/vjjrY8/1ILxxz/+MVOmTCE4OJhrrrlGLRil09U3eXl2xX5Aq85FRPxJfEMO1SERvLImm/lTB2Cz6fuzdD4Vz+WkrNhdSlOzj+TIQAbHhVgdR0REpI2wQCcNHzxI0LSbKCKCGfd/SGrepwQ1fLnRUGxkOM89tcjClCI90yuvvHLM84daMD722GNHHXOoBaNIV3p1bTZltU0EeOsYEqc/uIqI+IvYpgIKAsazv7SOlXtLOX1QjNWRpBfw657n4v8+3t7SsuXcYXFakSEiIn7JU1fFtd+eQGK4G589gJzUGQz8zm1MW3A30xbcTXF5pdURRUTET3i8Pv6xbB8AyQ37tKpRRMSP2PFy6fgkAF5ek21xGuktVDyXdvP6TD7c2rKB07nD1e9cRET8l9tp57LxfUiNDqLZZ/J2Zh7bC6qsjiUiIn7mfxl5HKioJybERVzjAavjiIjI11w5qWUz5w+2FFBSo41DpfOpeC7ttiG7nJKaRkLdDk4dEG11HBERkWNy2m1cNCaJoQmh+Ex4f0shGTkVVscSERE/4fOZLPp8DwA/OKM/dnwWJxIRka8bmRTO2ORwPF6Tf6/LsTqO9AIqnku7vb+lAGhp2eJy6EtJRET8n91mMH1EPOOSIwD4fGcxBTET8PlMa4OJiIjlPtxWyO6iGkIDHMw9NdXqOCIichSHvke/uDKLZq/+0CmdSxuGSrtc88MFrAg7C1yhpL/3L2YuXtjm/KbNW5hmTTQREZFjMgyDM4fEEBhgZ+WeUkojR/CTl9bzt++NI9BltzqeiIhYwDRNHv+sZdX53CmphLmdFicSEZGjuWhsEn96bzv5lQ28v6WQmWMSrY4kPZiK59IuubUGnphQ7DaDi678AU5725Xn6QsutSaYiIjIcTAMg0n9ogh3O3l/0wGWbCmg4B+reOqaicSEBFgdT0REutjKPaVszKkgwGHjB6f3tzqOiIgcg9tp56rJfXn4k908u3yfiufSqdRrQ9qlKiQFgNSooMMK5yIiIt3F0IRQUg98RESQk4ycCi57fDm7i6qtjiUiIl3s4U92AfC9U1KIDdUfUUVE/N3cU1Nx2g3WZZWzKbfS6jjSg6nqKe1SfbB4PjA2xOIkIiIiJye4oZjXfnwaqdFB5JTVc/njK1i5p9TqWCIi0kXW7i9j1d4ynHaDH31roNVxRETkOMSFuZk5umXF+bPL91mcRnoyFc/lhOWU1dEQEIVhQP/YYKvjiIiInLQBsSG89uPTSEuNpKqhmaufWc1Lq7OsjiUiIl3g0U92AzBrQjJ9IgItTiMiIsfruoNttt7KzKOousHiNNJTqXguJ+z9LQUA9IkIJNCpjdVERKRniA4J4KUfTuaisUl4vCa/eX0zt7+2iaZmn9XRRESkk1xz2718vrMYTB+7P/wnc+ff1Hpbn5FpdTwRETmGsSkRTOgbgcdr8vyK/VbHkR5KG4bKCXt3Uz6gli0iItIzZGZuZOasK1vvm0Bc5AiKosfzrzXZ7Cys5om5E4gLdVsXUkREOsWm5gRwwbDEcE6f9sM251auvdaaUCIictx+9K2B/OjFdF5YkcX8MwcSHui0OpL0MCqeywk5UFHP+uwKME0Gxal4LiIi3Z/HB9MW3H3Y8f0ltby1fj/pWeVc/Mhy/v79NMamRHR9QBER6RTb8qsoc8UDcEq/KIvTiIhIe3x7eDxD4kPYWVjDiyv3c+M5g62OJD2M2rbICXk3s2XVeVB9ESEB+tuLiIj0XP1ighmQ8x6D4kIoqGpg9t9X8p/0XKtjiYhIB3ns05Ze54PjQogKdlmcRkRE2sNmM7jh7EEAPP3FPuqami1OJD2NiudyQt4+2LIlvEabqImISM8X4Knm9Z+cxrTh8TQ1+/jF4o3c/lomDR6v1dFEROQk7Cmu4Z2Dv9to1bmISPc2c3QiqdFBlNd5eHl1ttVxpIdR8VyOW05ZHRtzKrAZEFqjb0YiItLzZWZu5IqrribvzfuJLd0Ipsm/1uQw7pcv8+0rrufaHy6wOqKIiLTD45/uwTQhqqmQ2NAAq+OIiMhJcNht/PhbAwF4culeLXSRDqW+G3LcDm0UOrl/NFU7GyxOIyIi0vm+3g89q7SW97cUUk8UWQMvpyHnUwvTiYhIe2SX1vFGxgEAUur3AGdYG0hERE7a5ROSeejjXeRXNvDKmmyuPb2/1ZGkh9DKczlubx/sdz5zTKLFSURERKyRGh3MnEl9SQx30+T1kZP0Le55aytNzT6ro4mIyHF64vM9eH0mZw6JJdRbZXUcERHpAC6HjZ8c7H3+6Ke7qW1U73PpGFp5Lsclq7SWTQcqsRkwY1QC/7I6kIiIiEVC3A5mTUhmxZ4S1mdX8MzyfazPLueRK8eTEhVkdTwRETmGAxX1/Cc9B4CfnjOIB1dZHEhERI7b+vR05s6/6bDjMRGhPLjwXq44JYWnl+1lf2kdTy3bx83TBluQUnoarTyX43Jo1flpA2OIDlFPQBER6d3sNoOpg2NJyfuMMLeDjJwKLnhoGf872AZARET804Mf7sTjNTltYDQTtVGoiEi30uiDtNk3HXYrqagGwGm38fPzhgLw5NI9lNY0WhlXeggVz+UbmabZWgy4UC1bREREWu1f+Q7x2xYTVF9EdWMzN7+SwdifPMz53/k+M2ddqQ1FRUT8yO6iav67PheAX04fanEaERHpDDNHJzIyKYzaJi+PfbrH6jjSA6hti3yjbfnV7CyswWW3MWO0iuciIiKHeHxwwQ9vw+czWbO/jDX7yqgMGwjxwzh/VAKbX77P6ogiInLQX9/fic+E80bEM75vpNVxRESkgxzWzsURDWGn8OwXe8hZtph/LLzTunDS7al4Lt/o0E705w6PIzzQaXEaERER/2OzGZw6IJqUqCDe31JAZb2HxetyiI0cgc9nYrMZVkcUEenVNuZUsGRLATYDfqFV5yIiPcqhdi6HmKZJ1YYD5JTXs96TbGEy6QlUPJdj8vq+bNly6fg+FqcRERHxb30iApkzqS+fbC9iV1ENhTET+P4zq3ngu+OID3NbHU9EpNf6y/s7ALhsfDJD4kMtTiMiIp3JMFr2J/rXmmxKXQlc+JM7iWguO2zcoY1GRY5FxXM5plV7SymsaiQ80MlZQ2OtjiMiIuL33E47M0Yl0De/ik+25LF8dykzHlrGn2eN4dsj4q2OJyLS63y6o4gvdpfgtBvcMm2w1XFERKQLxIYGMKpPOJsOVJKfdAZnn9L3sKtB0xc/YlE66U60Yagc0+sbWladzxyTSIDDbnEaERGR7sEwDEYlhTMw+11GJIZRVtvE9S+s45eLN1Ld4LE6nohIr+Hx+vjD21sBuO70/qREBVmcSEREusqUgdHgqae0polNeZVWx5FuSivP5ajqm7y8tykfgMvVskVEROSE7UhfxqjmGqKjxlIaOYLF6bm8sWo7fQpX0s/dwHNPLbI6oohIj/biyiz2FNcSHezixnMGWR1HRES6UKDTjrF7Kebw6azaU8qQ+FACnVoYKidGK8/lqD7cVkhtk5fkyEDSUrUbvYiIyIny+OC8H93J3NmX8p20FMIDnXicIexP/jaZ9sHUN3mtjigi0mOV1Tbx4Ec7Afj5eUMJczstTiQiIl3NyEknOthFQ7OP5btLrI4j3ZBWnstR/Sc9F4BLx/XBMIxvGC0iIiLH0ieyZTPRZbuL2XygirLIYcx8ZBl/nT2WCX31R2oRkY72tw93UtXQzPDEMFb/dxHvPl192Jj1GZmkzbYgnIiIdAnDNDl7WBz/Sc9lS14VwxPC6BMZaHUs6UZUPJcjyquoZ+mOIjAM3n/2r3z6ZE2b85s2b2GaRdlERES6K5fDxrnD4hkYE8I76bvZWwyznljBdaf15xfThxDk0tRMRKQjbD5QyUurswC448IRPPbnf5M2+6bDxq1ce20XJxMRka7WJyKQUUlhbM6r4pPtRcyZ3Be7TYtE5fjoNzQ5ov+m54JhkBwRyAXzfnnY+fQFl3Z9KBERkR6iX0wwje/+iaizrqUybADPLN/Hi59tIqlwFSH1BQDERoarJ7qIyAm45bZfU1JRjQlsDJuCzxFOTGM+j/35Lq0wFxHp5U4fFMOe4lrK6ppIzypnUv8oqyNJN6Ge53IYn89k8cGWLSOSwixOIyIi0jN5Guu59rLpXDIuiVC3A48zhKzkafjO/DFnzLuD4vJKqyOKiHQrJRXVpM2+CfvkudQ4wglw2Lj03NNIm30TjR6P1fFERMRCbqedM4fEALBmfxkVdU0WJ5LuQsVzOcyqfaVkl9Vh8zYxKC7E6jgiIiI9Wr/oYOZOTmVscjgA2/KreXFVFpUh/TBN0+J0IiLdS1WDh5V7S4GWVYbBAbrYWkREWgyNDyUlKhCvz+TTHcVopi3HQ8VzOczidS2rzsNr9uO060tERESks7kcNs4aGsfstGSiglzUNXnJTTyDK55cxY6Cwze4ExGRw5nAZzuK8XhNEsPdjNJVtCIi8hWGYXDO0DjsNoPssjqKXYlWR5JuQJVRaaOqwcO7m/IBiKjcY3EaERGR3iUpIpArJ6dw6oAoDF8zq/eVccHDy7j7rS1U1qvlgIjIsRS5kthXUovNgHOHxWEY2gxORETaighyMalfS7/zfUHD1L5FvpGK59LGmxl5NDb7GBofSmBjqdVxREREeh2Hzcbk/tEMynqLGaMS8PpMnl2+n3Pv/4zF63Lw+XSBqYjI1+WU1bE3eAQApw6IJjokwOJEIiLir9JSI4kKcuGxBfDnJdutjiN+TsVzaWWaJv9clQXAd09JQes0RERErONqruWJuWm8OG8SA2ODKalp4pf/yeTCR77g0+1F6ocuInKQ12fy839vxGs4SAx3k5YaaXUkERHxY3abwTnD4gD415ocVu3V4lE5OhXPpdX67HK2F1Tjdtr4zoRkq+OIiIgIMHVwLO/dfCa/vmAYIQEOtuZXcd1za/nOopWa6IuIAP9Ytpc1+8uwm81MH5mATe1aRETkG/SJDCShIQeA2/6TSV1Ts8WJxF9p63Fp9c9V2QBcPDaJ8CCnxWlERER6t8zMjcycdWWbY31sLkoiR1IWMZT0rHKueHIVUwfH8IvzhjI2JcKaoCIiFlqfXc79H+wAoH/tNsIDh1ucSEREuot+dduxJwwhu6yOhUt2cNfFI62OJH5IxXMBoLSmkXcyWzYKnXtqqsVpRERExOODaQvuPuK5msZmXnt7CTXRw1i2q4Rlu0o4c0gsPzpzAKcNjNYmeSLSK5TWNHLDS+vxeE0uGJ1A5edLrI4kIiLdiAMvf5o1mu8/vYbnVuzn/FEJnDog2upY4mfUtkUAWJyeS5PXx5jkcMYkR1gdR0RERI4hJMBBUvFaPvn5WcyakIzNgKU7i7nqqdXMfPgL/r02h/omr9UxRUQ6jddncsurGeRXNjAgJpg/zxqjPZtEROSErE9P5+9/uYf4g+1brn3iE66cfwu33PZri5OJP9HKc8HnM3lpdctGoVp1LiIi0j1kZm5kwfXzABjoCKE0chjlYYPYml/Fbf/N5PfvbGXWhGQuG9+HMcnhWo0uIj3Kwx/vYtmuEgKddp6Ym0aoW20nRUTkxDT6IG32TYxq9vLPVdnUNAZRP2Y2JZmvWB1N/IiK58Lnu4rJKasnzO3gojFJVscRERGR43Ckti71Hi9bDlSyZtt+qgnluRX7eW7FflKjg7hoTBIXj0tiSHyoRYlFRDrGu5vyeejjXQDce/kohibo+5qIiLRfgMPOtOFxvJGRR0ZuBaMdkVZHEj+iti3CM1/sA2D2xBQCXXaL04iIiEh7BTrtTOwXxeCs//H8DyYxc0wibqeNrNI6Hv10N+f9bSnnP7iURz/Zxc7CakzTtDqyiMgJ2ZhTwc9ezQDgutP7cdn4ZGsDiYhIj5AaHczIpDAAdgWPoq6p2eJE4i+08rwXu/aHC8iphT2pF4HpY+mLDzDz2drW85s2b2GahflERESkfTZlbmTh7T8FoJ/hoCY4mcrQVGqCk9heUM32gmr++sFO+kYFMW14PNOGx3FK/yicdq2rEBH/lVdRzw9fWEdjs49zhsXx25kjrI4kIiI9yNTBMWSV1lHTGMzCJTu46+KRVkcSP6DieS9WXF6J+1s/gbwqBsWHccG029qcT19wqTXBRERE5KQcqaULQIPHy57iGlat20BTeArZZXU8s3wfzyzfR6jbwVlD45g2PI6zhsQRHqT+wSLiPyrrPfzgubUUVzcyLCGUh68cj92mvRxERKTjfLV9y3Mr9jNteDxnDI6xOpZYTMXzXqzZ7mZ3QTUA41MirA0jIiIinc7ttDMyKZz8vE/5919f5IvdJXy0tZBPthdRWtvEWxvzeGtjHnabwaR+UZw7PI5vj4gnNTrY6ugi0ovVN3n54fNr2V5QTUxIAE9dM5GQAP0qKyIiHS81OpiEhmwK3H352b8zWHLzVKJDAqyOJRbSjKMXKwsfgtdnkhDmJjHcbXUcERER6SKZmRv57pzvt96PxyDMHU11cDLVwck0BkSwcm8pK/eW8od3thHQWEFobS6htQfoG9jE808tsjC9iPQmHq+PG15ez9r95YS6HYyuWsX//ep/Rxy7PiOTtNldHFBERHqc/nXbCe07gl1FNfzyP5k8fc1EDENXO/VWKp73Ug0eL2XhQwAY3zdC3wRERER6kaO1dTlk4c9/wLdvvp+9JbXkVdTTGBBBY0AEJVGj2NtYw7ifPExoTS7BdfnYzbabKcVGhvOciusicpJuue3XFFdUszN4DMUBSdhML/2KVrF63VKu/+M/jviYlWuv7dqQIiLSI9nx8fCV47nkseV8sr2I51fs59rT+1sdSyyi4nkv9dr6A3gdbkLdDgbFhlgdR0RERPyIWVvG+L6RjO8bSaPHy/7SOvaW1JBVWkcjIVQEhFARNhCHzWBwfAijksJJDHdjGAYfLbrT6vgi0gMUV1RTOmI2xQXVGAZcODaF/jHD2LryY6ujiYhILzA8MYxfzxjGXW9t5d53tzMhNZIxyRFWxxILqHjeCzV7fTzx+W6gpde5TRvtiIiIyFEEOO0MTQhlaEIoXp/JA3f+gsnf/z/2ltRSWe9hW3412/KriQp2MTIpjGaby+rIItLNeX0mu4JHU3SwcD5jVAL9Y7T3goiIdK1rTuvH8j2lfLi1kAUvpvPWTWfw+3vupqSi+rCxMRGhPLjwXgtSSmdT8bwXenNjHjll9dibGxjVJ9zqOCIiItJN2G0GvuI9nDkklqmDY8ivbGBzXiW7Cmsoq21i2a4SjP6zuPmVDcw7o79W54jICWv2+rjtv5kUBfRpKZyPTGBwXKjVsUREpBcyDIP7vzuWSx5dzr6SWn76ygaoqGHi7JsOG5u++BELEkpXsFkdQLqW12fy2Kctq86jK7bhtOtLQERERE6cYRgkRQRy3ogEfji1P2cPjSU2NADTZud/GXlc/Ohyvvv3lXy8rRDTNK2OKyLdQGOzlxteXs9r6w+A6WspnMercC4iItYJcztZNDeNIJed5btL2R842OpI0sVUOe1llmwuYE9xLWFuB1GVO62OIyIiIj1AgMPOmOQI5kzqy4Dsd7lsfB8cNoM1+8qY9/w6LnzkC97fUoDPpyK6iBxZbWMz855bx/tbCnHZbQyvyVDhXERE/MLQhFAWfmcMAAcCB7Alr9LiRNKVVDzvRUzT5NGDq86vO70/dp/H4kQiIiLS0wQ2lvG3741j2a/O5kdnDiDIZWdLXhU/ejGdCx/5gqU7i62OKCJ+pri6kTn/WMUXu0sIctl59rpTiPYUWR1LRESk1YVjkrjh7IEAfLK9iKzSWosTSVdR8bwX+WhbEdvyqwh22bnu9H5WxxEREZEeLDE8kNsvGM4XvzqHG88eREiAg635VVz9zBq+//RqtuZVWR1RRPzA7qJqLnt8ORtzK4kIcvLSDydz+qAYq2OJiIgc5hfnDSW2MQ+fCe9uKqC4utHqSNIFtGFoL+H1mfzl/e0AXH1aPyKCXBYnEhERkZ4oM3MjM2ddedjxPjYXJVGjKQ0fzLJdJSzb+TlRFTuIK8tsvRouNjKc555a1NWRRcQiK3aXsOCf6VQ1NNMvOohnr5tE/5hgq2OJiIgckWEYDK7dRED8QHIr6vlfxgFmpSUTqRpbj6bieS/x+oYD7CysIcztYMGZA62OIyIiIj2UxwfTFtx91PMLf/4DRl37R3YV1VAWOZzGhFGcNSSOQXEhPPCTS49YeD9ExXWRnsE0Tf6+dC9/encbGAZhnnLid3/M7379WuuY9RmZpM22MKSIiMgR2DC5cEwii9fnUlrTxGvrDzBrQh+rY0knUvG8F2jwePnbhy2bg/7k7EGEBzktTiQiIiK9lVlbxgWjE8kqreXTHcVU1nt4Z1M+wxJC8djcxyy8f7Tozi5MKiKdoarBwy8Xb+T9LYVgGAxPDOWcoQNx2Ce1Gbdy7bXWBBQREfkGAU47l4/vw3/XH6Cston/rj/AUFug1bGkk6jneS/w0upsDlTUEx8WwLWn9bM6joiIiAip0cHMndyXU/pFYgDbC6oJOO9WDpTXWx1NRDrJ8t0lzHhwGe9vKcRltzGodjPfHh6Pw65fS0VEpHsJcjm4fHwfooJd1DQ2sylsMtvytadPT6RZSg9X3eDhsU93A3DLtCG4nXaLE4mIiIi0cNhtnDYwhu+kJRMe6MQIjuS/G3JZn12OaZpWxxORDlLb2Mzv3tjMVU+t5kBFPSlRgSxeMIWExlwMw7A6noiISLsEB7QU0KODXTTZ3MxetJKlO4utjiUdTMXzHu7RT3ZTVtvEgJhgZqclWx1HRERE5DBJEYHMmdQXb1Y6pgnLdpWwZEsBHq/P6mgichKavT7+tSabs/76GS+uygLg+6emsuTmMxmbEmFtOBERkQ4QHOBgdloy4Z5Sahqbue65tby8OlsLQXoQFc97sN1F1Tz9xT4AfjNzuC6HFBEREb/lctjwrHmFs4bEYjNgZ2EN/16XQ01js9XRROQENXt9vJ2ZxwUPL+P21zZRXN1IanQQL/1wMr+/dBTBAdp6S0REeo4Ap52R1eu4fHwfvD6TX7++iZ+9mqF5bA+hWUsPZZomd725lWafybnD4jh3eLzVkURERES+0diUCGJCA3gnM5+Smib+vS6HS8YmER0SYHU0EfkGpTWN/Cc9l+dX7CevsgEAp+khuW43iWXZPLHwNZ74yvj1GZmkzbYmq4iISEeyYXL/d8cyMC6EBz7cyRsZeWTkVPDQFeN1tVU3p+J5D/Xe5gK+2F2Cy2HjzotGWh1HRERE5Lj1iQjke6ek8L+MA5TXeVicnstFY5LIzNzIzFlXHvVxsZHhPPfUoi5MKiIVdU18tK2INzfmsXx3CV5fy2Xq0cEurjo1lTX/eYLJ3/nxER+7cu21XZhURESkcxmGwQ1nD+LUAVH89F8Z7C+t47LHlzP31FR+ft5QwgOdVkeUdlDxvAeqa2rmD29vBSC8MIMfz3/hiOM2bd7CtK4MJiIiInKcwgOdzJ6Ywlsb88ivbOCNjAN4YwYxbcHdR33MR4vu7MKEIj3XLbf9mpKK6sOOb928ieGjRlNnD6HMGUu5M5YqRyR8ZdPP0X3C+f6pqVw8Lgm3087cxbpkXUREer716enMnX9T6/0Uw0FT0AiKA5J4YWUW72Tm84vpQ5k1IRmXQ22VuxMVz3ughUt2kFfZgNNTw+xZl+G0zzriuPQFl3ZtMBEREZETEOi0c/n4PryzKZ/9pXU4z/gB2WV19I0KsjqaSI9WUlFN2uwvCwBen8mBinrKq19nU9JpVDW0LYhHB7sILt7C83dcT/+Y4K6OKyIiYrlGH21+dgJMBnLK6nhv3Q5Ka0O4/bVNPPrJbn581kBmT0wmwGG3JqycEBXPe5gVe0p4bsV+AJKKVuO0j7c2kIiIiMhJcNhtzByTyLubCthXUsubG/O4aEwiqdEq0Il0Jq/PJKu0lh0F1ewvraPJ64PUSVQ1NGO3GSRHBjIgJph+0cGEBTr5x//dye9+veew51FfcxER6c1SooIYX7mcc6/+GYs+38OBinp++8ZmHvxoJ3Mm9WXO5FQSwt1Wx5RjUPG8B6lu8PDLxZkAzJncl43/zLc4kYiIiMjJc9hszBydyEP/fAP6jOKtzHwuHJNIPxXQRTpcVmkt+wKHkv7FPuo93tbjgU47DfvSmXnBDPpGBeG0t73k/Egr7kB9zUVERGyYzDujP1dN7ssra7L5+9K95Fc28PAnu3n8sz1MH5XANVP6cUq/SIyvtEIT/6DieQ/yx3e2caCinuTIQH59wXC+90+rE4mIiIh0DLvNwLPynwz58SPsKa7l7Y35zByTqBYRIh0kPauMfyzdx/tbCzAD+4PHS6DTzrDEUIbEhRIfFsBjb9/DwGu0jFxERKQ93E47157en6tOTeWDLYU8v3I/a/aV8U5mPu9k5jM8MYxrpqRyybg+3P7b3x1x/xGAmIhQHlx4bxen771UPO8hlmwu4JW1OQD8dfZYQgL01oqIiEgPY3qZMSqRJZsL2F1cw9uZecwcnciA2BCrk4l0W9sLqrjv3e18vrO49VhkUzFnTBxDanQwdptWwImISO/y9c0/25xrRzuyoz2fCzg7Ip6EUy/m9Q0H2JZfxf+9ton73ttOSGMi377wKsIDnYc9Ln3xIycWQE6KKqw9wJ7iGn6xeCMA10/tz6kDoi1OJCIiItI57DaD80cl8P6WAnYV1fDupoKWFi5agS5yQgqrGnjgg50sTs/BZ4LDZjBrQjLzpvbnnt/9mgGxp1kdUURExBJHa0UG7WtHdqznS1/8CPddPoZfnT+Mf6/L4YWVWeSW11MZOIDnVuxnQEwwY5LD6RsVpJYuFlHxvJurbWxmwYvp1DQ2M6l/FLedP8zqSCIiIiKdym4zOH9kAiYF7C6q4e1N+Vw0JtHqWCLdQk1jM09+vofHPt6B17ADEN1UQL+6nRz4oI57PtAmnyIiIl3l66vSU4FQZyw7mqLwRfdnb0kte0tqiQsN4PRBMfSNCrIubC+l4nk3Zpomt/03k11FNcSHBfDonPGHbdwjIiIi0hPZDhbQ3/Xls7eklrcy80kJjLc6lojfavb6eHVdDn/7cBclNY1g2EkMdzN1cAyJ4YOBqa1jtcmniIhI1zjaqvSdt13L9+98kszcCrbmV1FU3cjrGw6QGhVEpD3UgqS9lyqt3dhf3t/BO5n5OO0Gj181gbhQt9WRRERERLqM3WYwY3QC/aKD8PpMchPOoL7Ja3UsEb9imiYfbS3k/IeW8ZvXN1NS00i/6CCGVW9gdloyieGBVkcUERGRI4gKdnHW0DiuPa0fY5PDsRmQVVZHRthp3PpqBrnldVZH7BW08ryb+sfSvTz+2R4A/nDpKNJSoyxOJCIiItL1HDYbM0cn8sHWQprTlxDo+o7VkUT8RmZuBX98Zxur95UBEBnk5OZzBzNncio/+Mlr6p0qIiLSDQS5HJw1NI5xKRGs3FPKzqIaXttwgNfX55DYkEVKw16cpgeAmIhQHlx4r8WJexYVz7uhxety+OO72wAYULuVF/70T144wrhNm7cwrWujiYiIiHQ5h93GBaMT+Wh5idVRRPzCvpJaHvxoJ//LyAPAZnpJasgiuXwvHz3/Fh89r77mIiIi3U1EkIsZoxPZ/YfbSbrgBnLL68kL7E9JyEAm9otkXEoEma89ZnXMHkfF827m1bXZ3P7aJgDmnzmAL57+J9MW3H3EsekLLu3CZCIiIiIiYqWs0loe/ng3b2QcwOszwTQZlhjGlIHRhLmHtRmrvuYiIiLdk1GVz+Xj+5BdVscXu0soqWlixZ5SNuZUkBiQTLPXh0N7InYYvZLdhGmaPPrJLn713034TLhyUgq3zxiGLrQUEREREem9TNNkxZ4SfvTiOs7+62f8d30uXp/JucPiGFe1gukjEwhzO62OKSIiIh3IMAxSo4OZM6kv00fGE+Z2UNvkZXfwKM57cCmvb8jF4/VZHbNH0MrzbsDj9XHPW1t5cVUWADecPZBfnDdUPQpFRERERHqp/SW1vJ2Zx/8y8thVVNN6/KyhsdwybQjjUiKYu+JIzR1FRESkpzAMg2EJYQyKC2HzgSqWbz/A3mL42asb+cuSHcybOoDZE5P1h/SToOK5n8spq+Onr2xgQ3YFhgF3XjiCa0/vb3UsERERERHpQg0eL+v2l/PF7hKW7SpmS15V67kgl53LJ/Thmin9GBwfamFKERERsYLDZmNcSgSNq15m6pU38uzy/eRVNvD7t7fy1/d3cOGYRK6Y1JcJfSO0GPcEqXjux97JzOf//ptJdWMzYW4Hf5k9lukjE6yOJSIiIiIiJ+GW235NSUX1YcdjIkJ5cOG9NHi8bM2vYvOBSjYfqGTTgSp2FVbT7DNbx9ptBqcNjOaisUl8/u8n2f9uBXe/2/b5tCmoiIhI7+LAyw1nD2LeGf15fcMBnvliH7uKalicnsvi9Fz6RgUxc0wiM0cnMjIprEsL6Ueb/8CXcyB/pOK5n1qyuYAbXl4PQFpqJA9dMY7kyCCLU4mIiIiIyMkqqagmbfZNAHh9JkXVDRRWNfLWZx/xwS9eoc4eDMbh21MlhLk5fVAMZwyOZurgWGJCAgB488mK1uf7Km0KKiIi0ju5nXaunNSXK05JIT2rnJ898T8OEE12WR1PfLaHJz7bg8vXQKSnhBRnLY/+5ickhLs7NdNX5z9fl774kU792CdDxXM/de7wONJSI5kyIJpbpg3WLrkiIiIiIj1AeW0Tpc5YvthdQn5lPYVVjXgPrShPGkvdwXFBLjtxoQHEhbqJCwugcOm/+Pd9C3WptYiIiBw3wzCY2C+K1PL1XHT5DewrqWVXYQ37S2tpwk1hQDKFwKn3fUxSuJvxfSMZEh/K4PgQBsaGkBDmJizQ0avnH72qeP7YY4/xl7/8hYKCAsaOHcsjjzzCpEmTrI51RE67jVfmn8r1P/oJlzxZedRxmzZvYVoX5hIRERER6WrdaR7/VaZpsreklvT95aRnlbMuq4w9xbUQmgZZ5a3j3E4bCWFusla/z4WXf5e4UDfBAfY2v6hW+Bp69S+uIiIi8s3Wp6czd/7hq7tbWrnZGBIfypD4UJq9Pg5U1JNdVseOvdnUO8PIq2wgb1M+72zKb/PYAIeNmJAAAl12glx2nHYbzV4fHq9Js6/lX4/Xh9dn0uwz8X7l1uzzYTMMglx2asLPZOeabIJcdkICHIS4HUQGuYgIdNKMvateohPWa4rnr776KrfeeiuLFi1i8uTJPPjgg0yfPp0dO3YQFxdndbwjctptFJdXMm3B3Ucdk77g0q4LJCIiIiLSxbrbPN7j9fH0F/tYt7+c9dnllNU2HTYm0FtD/+QkkiLcJIUHEhHkxDAMHn15KQNif3DE5z3aL8Og3uYiIiLSotHHcbVyc9htpEYHkxodTFDGK/z9kb+xMaeCzAOV7C6qYVdRDftLaqms99DY3FJoPxl1TV6wB9FQ3XjkAVHfprCqgfiwzm0d0x69pnj+wAMPcP3113PdddcBsGjRIt555x2eeeYZ/u///s/idCIiIiIiciTdbR7vsBk888U+ig7+cuhy2BibHE5aahQTUyOZkBrJT3/2c9LOO3Ih/GiO9sswqLe5iIiItN/69HR+dNPP2hwLBc6OCOVPf/w9xdWNlNQ00uDx0eDx0tjs47kXXqS6tg7D9GFgYjv4r4GPbdt38N2b78EwwGYYmLQsLvjXw/cwc/7t1DV6qWlspqrBQ0Vdy62xqZG40ABLPv9v0iuK501NTaSnp3P77be3HrPZbEybNo2VK1damExERERERI6mO87jDcPgh1P787+338NeXUCItwpbkcnm9bAZeA6tFBcRERH/cbQ/0P/j/66lpOKWIz4mMyOT6//4jyOfu+1aIoNdhx03Kg4wICbkiI9Zs/hxDOOS4w/dhXpF8bykpASv10t8fHyb4/Hx8Wzfvv2w8Y2NjTQ2fnkZQWVlS8/xqqqqzg16BB6Ph4bamqOe9/m8Rz1/rHMne95fn9tfc+m59dx6bj23nlvf+/Xcnf/cHo+nS+drhz6WaZpd9jF7mxOdx4N/zOWvGBfLx89sYtylPzri+eWrFhzxa9nnbT76/492nPOH5/OHDB39fP6QoaOfzx8ydPTz+UMGf38+f8jQ0c/nDxk6+vn8IUNHP58/ZOjo5/OHDB39fPWeZkZecN0RH3O0uUy7MzTV++083jB7wWw/Ly+PPn36sGLFCqZMmdJ6/LbbbuPzzz9n9erVbcbfdddd3H330fuMi4iIiIgcsmfPHgYMGGB1jB7pROfxoLm8iIiIiByfnJwckpOTjznG0UVZLBUTE4PdbqewsLDN8cLCQhISEg4bf/vtt3Prrbe23vf5fJSVlREdHd1lO9xXVVWRkpJCTk4OYWFhXfIx5fjovfFven/8l94b/6X3xr/p/fFflZWV9O3bl6ioKKuj9FgnOo8H/5jLg/7vdha9rp1Dr2vn0OvaOfS6dh69tp1Dr2vnOJnX1TRNqqurSUpK+saxvaJ47nK5SEtL4+OPP+bSSy8FWibRH3/8MTfeeONh4wMCAggIaNukPiIioguSHi4sLEz/sfyU3hv/pvfHf+m98V96b/yb3h//ZbPZrI7QY53oPB78ay4P+r/bWfS6dg69rp1Dr2vn0OvaefTadg69rp2jva9reHj4cY3rFcVzgFtvvZVrrrmGiRMnMmnSJB588EFqa2u57roj9+4RERERERHraR4vIiIiIlbpNcXz733vexQXF3PHHXdQUFDAuHHjWLJkyWGbD4mIiIiIiP/QPF5ERERErNJriucAN95441Ev7/Q3AQEB3HnnnYddcirW03vj3/T++C+9N/5L741/0/vjv/TedJ3uNI8/RF8fnUOva+fQ69o59Lp2Dr2unUevbefQ69o5uup1NUzTNDv1I4iIiIiIiIiIiIiIdDPa3UhERERERERERERE5GtUPBcRERERERERERER+RoVz0VEREREREREREREvkbFcz/02GOP0a9fP9xuN5MnT2bNmjVWR+rW7rvvPk455RRCQ0OJi4vj0ksvZceOHW3GNDQ0cMMNNxAdHU1ISAizZs2isLCwzZjs7GxmzpxJUFAQcXFx/PKXv6S5ubnNmM8++4wJEyYQEBDAoEGDeO655w7Lo/f36P70pz9hGAa33HJL6zG9N9Y6cOAAc+fOJTo6msDAQEaPHs26detaz5umyR133EFiYiKBgYFMmzaNXbt2tXmOsrIyrrrqKsLCwoiIiGDevHnU1NS0GZOZmcnUqVNxu92kpKSwcOHCw7IsXryYYcOG4Xa7GT16NO+++27nfNLdgNfr5Xe/+x39+/cnMDCQgQMH8vvf/56vbmOi96brLF26lIsuuoikpCQMw+CNN95oc96f3ovjydKTHOu98Xg8/OpXv2L06NEEBweTlJTE1VdfTV5eXpvn0Hsj8M3/z6+99loMw2hzO//887/xeXv73OObXtevv6aHbn/5y1+O+px33XXXYeOHDRvWyZ+Jf+mo33++rrd/n/qm17WsrIybbrqJoUOHEhgYSN++ffnpT39KZWXlMZ+3vd8/eorj+Xo966yzDnuNFixYcMzn1dfrsV/X/fv3H/V77OLFi4/6vL396/WJJ55gzJgxhIWFERYWxpQpU3jvvfdaz+t7a/sd67W1/PurKX7llVdeMV0ul/nMM8+YW7ZsMa+//nozIiLCLCwstDpatzV9+nTz2WefNTdv3mxmZGSYF1xwgdm3b1+zpqamdcyCBQvMlJQU8+OPPzbXrVtnnnrqqeZpp53Wer65udkcNWqUOW3aNHPDhg3mu+++a8bExJi3335765i9e/eaQUFB5q233mpu3brVfOSRR0y73W4uWbKkdYze36Nbs2aN2a9fP3PMmDHmzTff3Hpc7411ysrKzNTUVPPaa681V69ebe7du9d8//33zd27d7eO+dOf/mSGh4ebb7zxhrlx40bz4osvNvv372/W19e3jjn//PPNsWPHmqtWrTKXLVtmDho0yLzyyitbz1dWVprx8fHmVVddZW7evNn817/+ZQYGBpp///vfW8csX77ctNvt5sKFC82tW7eav/3tb02n02lu2rSpa14MP/PHP/7RjI6ONt9++21z37595uLFi82QkBDzoYceah2j96brvPvuu+ZvfvMb87XXXjMB8/XXX29z3p/ei+PJ0pMc672pqKgwp02bZr766qvm9u3bzZUrV5qTJk0y09LS2jyH3hsxzW/+f37NNdeY/9/enYdVVa1/AP8ehsMgICgIiILggAM4p4GlFeaQGg5XkUgR53LMWbs5ZEY9aTcrIzMULUjNME0SUwRUxFkUlBgUtbwoSYqiggjv7w9/7Os5nANoKojfz/PwPJy91nn3Ou/aLtZe7rN3r169JDs7W/n5+++/y43JuUfFeb0/n9nZ2bJ69WpRqVRy5swZvTEXLFggrVq10njfX3/99Zg/SfXyKM5/dHnWx6mK8pqcnCwDBw6UrVu3SmZmpsTExEjTpk1l0KBB5cZ9mPGjJqnM8dqtWzcZM2aMRo7y8vLKjcvjtfy83r17t8wYu2jRIrGwsJAbN27ojfusH69bt26VqKgoSU9Pl7S0NJk3b54YGxtLSkqKiHBs/SfKy21Vj69cPK9mOnXqJBMmTFBeFxcXS/369SU4OLgKW1Wz5OTkCACJj48XkXsnz8bGxvLjjz8qdVJTUwWAJCYmisi9ib2BgYFcunRJqRMSEiJWVlZSWFgoIiKzZs2SVq1aaezLz89Pevbsqbxm/+p248YNadq0qezcuVO6deumLJ6zb6rW7Nmz5YUXXtBbXlJSIg4ODvLJJ58o265duyYmJibyww8/iIjI6dOnBYAcPnxYqbN9+3ZRqVRy8eJFERH56quvxMbGRumv0n27u7srr4cMGSJ9+vTR2H/nzp1l3Lhx/+xDPqX69OkjI0eO1Ng2cOBACQgIEBH2TVXSXvypTn1RmbbUZLoW5rQdOnRIAMj58+dFhH1DuulbPPf19X2gOJx7aKrMv1FfX1955ZVXyq2zYMECadOmzaNrWA3wMOc/2jhOlaWdV102btwoarVaioqK9NZ5mPGjJtOV1/vPESuDx2tZlTle27ZtW+YcQxuP17JsbGzk22+/5dj6GJTmVpcnOb7yti3VyJ07d3D06FF0795d2WZgYIDu3bsjMTGxCltWs5R+raNOnToAgKNHj6KoqEgj782bN4ezs7OS98TERHh6esLe3l6p07NnT1y/fh2nTp1S6twfo7ROaQz2r34TJkxAnz59yuSPfVO1tm7dio4dO2Lw4MGoV68e2rVrh1WrVinlWVlZuHTpkkbeateujc6dO2v0j7W1NTp27KjU6d69OwwMDHDw4EGlTteuXaFWq5U6PXv2RFpaGq5evarUKa8PnzXe3t6IiYlBeno6AODEiRPYt28fevfuDYB9U51Up76oTFuedXl5eVCpVLC2tgbAvqEHExcXh3r16sHd3R1vvfUWcnNz9dbl3OPBXb58GVFRURg1alSFdTMyMlC/fn24ubkhICAAFy5ceAItrL4e5vxHG8epsrTzqq+OlZUVjIyMyo31IONHTacvr+Hh4bC1tYWHhwfmzp2LW7du6Y3B47Wsio7Xo0ePIikpqVJjLI/Xe4qLi7F+/XrcvHkTXl5eHFsfIe3c6vIkx9fy90BP1JUrV1BcXKyxCAgA9vb2+P3336uoVTVLSUkJpk6dii5dusDDXGIFnAAAHNNJREFUwwMAcOnSJajVauVEuZS9vT0uXbqk1NHVL6Vl5dW5fv06bt++jatXr7J/dVi/fj2OHTuGw4cPlylj31Sts2fPIiQkBNOmTcO8efNw+PBhTJ48GWq1GoGBgUp+deXt/tzXq1dPo9zIyAh16tTRqOPq6lomRmmZjY2N3j4sjfGsmTNnDq5fv47mzZvD0NAQxcXFWLJkCQICAgCAfVONVKe+qExbnmUFBQWYPXs2/P39YWVlBYB9Q5XXq1cvDBw4EK6urjhz5gzmzZuH3r17IzExEYaGhmXqc97/4NauXQtLS0sMHDiw3HqdO3dGWFgY3N3dkZ2djUWLFuHFF19ESkoKLC0tn1Brq4+HPf/RxnFKk668arty5QoWL16MsWPHlhvrQcePmkxfXt944w24uLigfv36OHnyJGbPno20tDRERkbqjMPjVVNljtfQ0FC0aNEC3t7e5cbi8QokJyfDy8sLBQUFsLCwwObNm9GyZUskJSVxbP2H9OVW25MeX7l4Ts+UCRMmICUlBfv27avqphCAP/74A1OmTMHOnTthampa1c0hLSUlJejYsSM+/PBDAEC7du2QkpKCr7/+GoGBgVXcumfbxo0bER4ejoiICLRq1QpJSUmYOnUq6tevz74heghFRUUYMmQIRAQhISFV3Rx6Cg0dOlT53dPTE61bt0bjxo0RFxcHHx+fKmxZzbF69WoEBARUOGcs/RYWALRu3RqdO3eGi4sLNm7cWKkrKmsanv88HhXl9fr16+jTpw9atmyJhQsXlhuL48f/6Mvr/Qtknp6ecHR0hI+PD86cOYPGjRs/6WY+dSo6Xm/fvo2IiAi89957Fcbi8Qq4u7sjKSkJeXl52LRpEwIDAxEfH1/VzaoR9OX2/gX0qhhfeduWasTW1haGhoZlnsR7+fJlODg4VFGrao6JEydi27ZtiI2NRYMGDZTtDg4OuHPnDq5du6ZR//68Ozg46OyX0rLy6lhZWcHMzIz9q8PRo0eRk5OD9u3bw8jICEZGRoiPj8fnn38OIyMj2Nvbs2+qkKOjY5n/5W3RooXy1efS3JSXNwcHB+Tk5GiU3717F3///fcj6cNntX9mzpyJOXPmYOjQofD09MSwYcPwzjvvIDg4GAD7pjqpTn1RmbY8i0oXzs+fP4+dO3cqV50D7Bt6eG5ubrC1tUVmZqbOcs49HszevXuRlpaG0aNHP/B7ra2t0axZM719UZP9k/MfbRyn/kdfXkvduHEDvXr1gqWlJTZv3gxjY+MHil/R+FFTVZTX+3Xu3BkA9OaIx+v/VCavmzZtwq1btzB8+PAHjv8sHq9qtRpNmjRBhw4dEBwcjDZt2mD58uUcWx8BfbktVVXjKxfPqxG1Wo0OHTogJiZG2VZSUoKYmBi99/ihiokIJk6ciM2bN2P37t1lvlrdoUMHGBsba+Q9LS0NFy5cUPLu5eWF5ORkjRPo0hPs0sVFLy8vjRildUpjsH/L8vHxQXJyMpKSkpSfjh07IiAgQPmdfVN1unTpgrS0NI1t6enpcHFxAQC4urrCwcFBI2/Xr1/HwYMHNfrn2rVrOHr0qFJn9+7dKCkpUSa9Xl5e2LNnD4qKipQ6O3fuhLu7O2xsbJQ65fXhs+bWrVswMND8E25oaIiSkhIA7JvqpDr1RWXa8qwpXTjPyMjArl27ULduXY1y9g09rD///BO5ublwdHTUWc65x4MJDQ1Fhw4d0KZNmwd+b35+Ps6cOaO3L2qiR3H+o43jVMV5Be7lpEePHlCr1di6detDfbu2ovGjpqlMXrUlJSUBgN4c8Xh9sLyGhobi9ddfh52d3QPv51k7XnUpKSlBYWEhx9bHoDS3QBWPr//4kaP0SK1fv15MTEwkLCxMTp8+LWPHjhVra2u5dOlSVTftqfXWW29J7dq1JS4uTrKzs5WfW7duKXXGjx8vzs7Osnv3bjly5Ih4eXmJl5eXUn737l3x8PCQHj16SFJSkkRHR4udnZ3MnTtXqXP27FkxNzeXmTNnSmpqqqxYsUIMDQ0lOjpaqcP+rZj2k9TZN1Xn0KFDYmRkJEuWLJGMjAwJDw8Xc3Nz+f7775U6H330kVhbW8uWLVvk5MmT4uvrK66urnL79m2lTq9evaRdu3Zy8OBB2bdvnzRt2lT8/f2V8mvXrom9vb0MGzZMUlJSZP369WJubi4rV65U6iQkJIiRkZEsXbpUUlNTZcGCBWJsbCzJyclPJhnVTGBgoDg5Ocm2bdskKytLIiMjxdbWVmbNmqXUYd88OTdu3JDjx4/L8ePHBYB8+umncvz4cTl//ryIVK++qExbapLy+ubOnTvy+uuvS4MGDSQpKUljjlBYWKjEYN+QSPnH0o0bN2TGjBmSmJgoWVlZsmvXLmnfvr00bdpUCgoKlBivvPKKfPHFF8przj0qHj9FRPLy8sTc3FxCQkJ0xtDO6/Tp0yUuLk6ysrIkISFBunfvLra2tpKTk/PYP0918SjOf0RE3N3dJTIyUnn9rI9TFeU1Ly9POnfuLJ6enpKZmalR5+7du0qc+/Na2fGjJqsor5mZmfL+++/LkSNHJCsrS7Zs2SJubm7StWtXjTg8XjVVZhwQEcnIyBCVSiXbt2/XGYfHq6Y5c+ZIfHy8ZGVlycmTJ2XOnDmiUqnkt99+ExGOrf9Eebmt6vGVi+fV0BdffCHOzs6iVqulU6dOcuDAgapu0lMNgM6fNWvWKHVu374tb7/9ttjY2Ii5ubkMGDBAsrOzNeKcO3dOevfuLWZmZmJrayvTp0+XoqIijTqxsbHStm1bUavV4ubmprGPUuzf8mkvnrNvqtYvv/wiHh4eYmJiIs2bN5dvvvlGo7ykpETee+89sbe3FxMTE/Hx8ZG0tDSNOrm5ueLv7y8WFhZiZWUlQUFBcuPGDY06J06ckBdeeEFMTEzEyclJPvroozJt2bhxozRr1kzUarW0atVKoqKiHv0Hfkpcv35dpkyZIs7OzmJqaipubm7y7rvvaiz4sW+enNjYWJ1/ZwIDA0WkevVFZdpSk5TXN1lZWXrnCLGxsUoM9g2JlH8s3bp1S3r06CF2dnZibGwsLi4uMmbMmDKL4C4uLrJgwQKNbc/63KOi8VNEZOXKlWJmZibXrl3TGUM7r35+fuLo6ChqtVqcnJzEz89PMjMzH/MnqV4e1fmP9nue9XGqorzqO54BSFZWlkac0vdUdvyoySrK64ULF6Rr165Sp04dMTExkSZNmsjMmTMlLy+vTBwer/9TmXFARGTu3LnSsGFDKS4u1huHx+v/jBw5UlxcXEStVoudnZ34+PgoC+ciHFv/ifJyW9Xjq+r/gxMRERERERERERER0f/jPc+JiIiIiIiIiIiIiLRw8ZyIiIiIiIiIiIiISAsXz4mIiIiIiIiIiIiItHDxnIiIiIiIiIiIiIhICxfPiYiIiIiIiIiIiIi0cPGciIiIiIiIiIiIiEgLF8+JiIiIiIiIiIiIiLRw8ZyIiIiIiIiIiIiISAsXz4mIqqG4uDioVCpcu3atWsR53MLCwmBtbV1t4xERERFRzfUk5swvvfQSpk6d+tjiP40WLlyItm3bPpbYPB8gokeFi+dERI/YiBEjoFKpoFKpYGxsDFdXV8yaNQsFBQWPdb+6JuTe3t7Izs5G7dq1H8s+jx49CpVKhQMHDugs9/HxwcCBAx/Lvsvj5+eH9PR05fXjnJgTERERUfWXmJgIQ0ND9OnTp6qbUinnzp2DSqVCUlLSP45Ven4yfvz4MmUTJkyASqXCiBEj/vF+HtSMGTMQExOjvB4xYgT69+//xNtBRFQeLp4TET0GvXr1QnZ2Ns6ePYv//Oc/WLlyJRYsWPDE26FWq+Hg4ACVSvVY4nfo0AFt2rTB6tWry5SdO3cOsbGxGDVq1GPZd3nMzMxQr169J75fIiIiIqqeQkNDMWnSJOzZswf//e9/q7o5T1zDhg2xfv163L59W9lWUFCAiIgIODs7V0mbLCwsULdu3SrZNxFRZXHxnIjoMTAxMYGDgwMaNmyI/v37o3v37ti5c6dSXlJSguDgYLi6usLMzAxt2rTBpk2b9MbLzc2Fv78/nJycYG5uDk9PT/zwww9K+YgRIxAfH4/ly5crV72fO3dO4yuo169fh5mZGbZv364Re/PmzbC0tMStW7cAAH/88QeGDBkCa2tr1KlTB76+vjh37pzeto0aNQobNmxQ3l8qLCwMjo6O6NWrFwoLCzFjxgw4OTmhVq1a6Ny5M+Li4srNYUhICBo3bgy1Wg13d3d89913GuXXrl3DuHHjYG9vD1NTU3h4eGDbtm3Kvku/phkWFoZFixbhxIkTSm7CwsIwcuRI9O3bVyNmUVER6tWrh9DQ0HLbRkRERERPj/z8fGzYsAFvvfUW+vTpg7CwMJ31EhIS0Lp1a5iamuL5559HSkqKUnb+/Hn069cPNjY2qFWrFlq1aoVff/1VKY+Pj0enTp1gYmICR0dHzJkzB3fv3tXbJpVKhZ9//lljm7W1tdI2V1dXAEC7du2gUqnw0ksvKfW+/fZbtGjRAqampmjevDm++uqrCnPQvn17NGzYEJGRkcq2yMhIODs7o127dhp1o6Oj8cILL8Da2hp169ZF3759cebMGY06+/fvR9u2bWFqaoqOHTvi559/1rhSvvQ8JCYmBh07doS5uTm8vb2RlpamxLj/26ELFy7E2rVrsWXLFmXOHhcXp/OWOklJScr5TqmwsDA4OzvD3NwcAwYMQG5ubpkcbNmyBe3bt4epqSnc3NywaNGicvuIiAjg4jkR0WOXkpKC/fv3Q61WK9uCg4Oxbt06fP311zh16hTeeecdvPnmm4iPj9cZo6CgAB06dEBUVBRSUlIwduxYDBs2DIcOHQIALF++HF5eXhgzZgyys7ORnZ2Nhg0basSwsrJC3759ERERobE9PDwc/fv3h7m5OYqKitCzZ09YWlpi7969SEhIgIWFBXr16oU7d+7obFtAQAAKCws1Fv9FBGvXrsWIESNgaGiIiRMnIjExEevXr8fJkycxePBg9OrVCxkZGTpjbt68GVOmTMH06dORkpKCcePGISgoCLGxsQDu/edD7969kZCQgO+//x6nT5/GRx99BENDwzKx/Pz8MH36dLRq1UrJjZ+fH0aPHo3o6GhkZ2crdbdt24Zbt27Bz89PZ7uIiIiI6OmzceNGNG/eHO7u7njzzTexevVqiEiZejNnzsSyZctw+PBh2NnZoV+/figqKgJw7/YmhYWF2LNnD5KTk/Hxxx/DwsICAHDx4kW89tpreO6553DixAmEhIQgNDQUH3zwwUO3uXSev2vXLmRnZyuL3uHh4Zg/fz6WLFmC1NRUfPjhh3jvvfewdu3aCmOOHDkSa9asUV6vXr0aQUFBZerdvHkT06ZNw5EjRxATEwMDAwMMGDAAJSUlAIDr16+jX79+8PT0xLFjx7B48WLMnj1b5z7fffddLFu2DEeOHIGRkRFGjhyps96MGTMwZMgQ5Ru82dnZ8Pb2rvAzAcDBgwcxatQoTJw4EUlJSXj55ZfL5H7v3r0YPnw4pkyZgtOnT2PlypUICwvDkiVLKrUPInqGCRERPVKBgYFiaGgotWrVEhMTEwEgBgYGsmnTJhERKSgoEHNzc9m/f7/G+0aNGiX+/v4iIhIbGysA5OrVq3r306dPH5k+fbryulu3bjJlyhSNOtpxNm/eLBYWFnLz5k0REcnLyxNTU1PZvn27iIh899134u7uLiUlJUqMwsJCMTMzkx07duhty9ChQ6Vbt27K65iYGAEgGRkZcv78eTE0NJSLFy9qvMfHx0fmzp0rIiJr1qyR2rVrK2Xe3t4yZswYjfqDBw+W1157TUREduzYIQYGBpKWlqazPdrxFixYIG3atClTr2XLlvLxxx8rr/v16ycjRozQ+zmJiIiI6Onj7e0tn332mYiIFBUVia2trcTGxirlpXPm9evXK9tyc3PFzMxMNmzYICIinp6esnDhQp3x582bV2YOvWLFCrGwsJDi4mIRKTtXByCbN2/WiFO7dm1Zs2aNiIhkZWUJADl+/LhGncaNG0tERITGtsWLF4uXl5fezx8YGCi+vr6Sk5MjJiYmcu7cOTl37pyYmprKX3/9Jb6+vhIYGKj3/X/99ZcAkOTkZBERCQkJkbp168rt27eVOqtWrdJob2lOd+3apdSJiooSAMr7tOfope28n67zouPHjwsAycrKEhERf39/5TyhlJ+fn8b5gI+Pj3z44Ycadb777jtxdHTU+7mJiEREeOU5EdFj8PLLLyMpKQkHDx5EYGAggoKCMGjQIABAZmYmbt26hVdffRUWFhbKz7p168p8HbJUcXExFi9eDE9PT9SpUwcWFhbYsWMHLly48EDteu2112BsbIytW7cCAH766SdYWVmhe/fuAIATJ04gMzMTlpaWSrvq1KmDgoICvW0D7l3FsmfPHqXO6tWr0a1bNzRp0gTJyckoLi5Gs2bNND5vfHy83pipqano0qWLxrYuXbogNTUVwL2vajZo0ADNmjV7oM+vbfTo0crVN5cvX8b27dv1Xg1DRERERE+ftLQ0HDp0CP7+/gAAIyMj+Pn56bxNn5eXl/J7nTp14O7ursw/J0+ejA8++ABdunTBggULcPLkSaVuamoqvLy8NJ4z1KVLF+Tn5+PPP/98ZJ/l5s2bOHPmDEaNGqUxr/7ggw/KnauXsrOzU25bs2bNGvTp0we2trZl6mVkZMDf3x9ubm6wsrJCo0aNAEA590hLS1Nub1OqU6dOOvfZunVr5XdHR0cAQE5OTqU/c2Wkpqaic+fOGtvu70vg3nnO+++/r5G30m/tat9+kojofkZV3QAiopqoVq1aaNKkCYB7C8lt2rRBaGgoRo0ahfz8fABAVFQUnJycNN5nYmKiM94nn3yC5cuX47PPPoOnpydq1aqFqVOn6r2Vij5qtRr/+te/EBERgaFDhyIiIgJ+fn4wMrr35yA/Px8dOnRAeHh4mffa2dnpjevj4wNnZ2eEhYVh5syZiIyMxMqVK5WYhoaGOHr0aJnbqpR+1fVBmZmZPdT7tA0fPhxz5sxBYmIi9u/fD1dXV7z44ouPJDYRERERVb3Q0FDcvXsX9evXV7aJCExMTPDll1+idu3alYozevRo9OzZE1FRUfjtt98QHByMZcuWYdKkSQ/VLpVKVebWMaW3iNGn9Dxi1apVZRaLdd2+UJeRI0di4sSJAIAVK1borNOvXz+4uLhg1apVqF+/PkpKSuDh4fHA5x4AYGxsrPxe+p8Lpbd/qQwDg3vXfN6fq4rypEt+fj4WLVqEgQMHlim7/z8BiIi0cfGciOgxMzAwwLx58zBt2jS88cYbaNmyJUxMTHDhwgV069atUjESEhLg6+uLN998E8C9CWd6ejpatmyp1FGr1SguLq4wVkBAAF599VWcOnUKu3fv1rgfYPv27bFhwwbUq1cPVlZWD/QZg4KCEBoaCicnJ2WRHrj3kKPi4mLk5ORUemG6RYsWSEhIQGBgoLItISFB+bytW7fGn3/+ifT09Epdfa4vN3Xr1kX//v2xZs0aJCYm6rznIxERERE9ne7evYt169Zh2bJl6NGjh0ZZ//798cMPP2D8+PHKtgMHDsDZ2RkAcPXqVaSnp6NFixZKecOGDTF+/HiMHz8ec+fOxapVqzBp0iS0aNECP/30E0REWSBOSEiApaUlGjRooLNtdnZ2Gs/eycjI0LgCuvR5SffPYe3t7VG/fn2cPXsWAQEBD5WT0mcZqVQq9OzZs0x5bm4u0tLSsGrVKmXuvm/fPo067u7u+P7771FYWKhc/HP48OGHas/9dM3ZSy/gyc7Oho2NDQAoDyUt1aJFCxw8eFBj24EDBzRet2/fHmlpacoFTkRElcXbthARPQGDBw+GoaEhVqxYAUtLS8yYMQPvvPMO1q5dizNnzuDYsWP44osv9D7op2nTpti5cyf279+P1NRUjBs3DpcvX9ao06hRIxw8eBDnzp3DlStX9F7R0bVrVzg4OCAgIACurq4aV60EBATA1tYWvr6+2Lt3L7KyshAXF4fJkydX+JXToKAgXLx4EfPmzYO/v79ydXizZs0QEBCA4cOHIzIyEllZWTh06BCCg4MRFRWlM9bMmTMRFhaGkJAQZGRk4NNPP0VkZCRmzJgBAOjWrRu6du2KQYMGYefOncjKysL27dsRHR2tM16jRo2QlZWFpKQkXLlyBYWFhUrZ6NGjsXbtWqSmpmos1hMRERHR023btm24evUqRo0aBQ8PD42fQYMGlbl1y/vvv4+YmBikpKRgxIgRsLW1Rf/+/QEAU6dOxY4dO5CVlYVjx44hNjZWWVh/++238ccff2DSpEn4/fffsWXLFixYsADTpk1TrpzW9sorr+DLL7/E8ePHceTIEYwfP17jKu169erBzMwM0dHRuHz5MvLy8gAAixYtQnBwMD7//HOkp6cjOTkZa9aswaefflqpnBgaGiI1NRWnT5/WebW6jY0N6tati2+++QaZmZnYvXs3pk2bplHnjTfeQElJCcaOHYvU1FTs2LEDS5cuBQCNW9c8qEaNGuHkyZNIS0vDlStXUFRUhCZNmqBhw4ZYuHAhMjIyEBUVhWXLlmm8b/LkyYiOjsbSpUuRkZGBL7/8ssx5wfz587Fu3TosWrQIp06dQmpqKtavX49///vfD91eInpGVO0t14mIah5dD7oREQkODhY7OzvJz8+XkpIS+eyzz8Td3V2MjY3Fzs5OevbsKfHx8SJS9sE4ubm54uvrKxYWFlKvXj3597//LcOHD9fYT1pamjz//PNiZmamPEBH34NHZ82aJQBk/vz5ZdqZnZ0tw4cPF1tbWzExMRE3NzcZM2aM5OXlVfjZe/ToIQDk0KFDGtvv3Lkj8+fPl0aNGomxsbE4OjrKgAED5OTJkyJS9gGfIiJfffWVuLm5ibGxsTRr1kzWrVunUZ6bmytBQUFSt25dMTU1FQ8PD9m2bZvOeAUFBTJo0CCxtrYWAMqDmERESkpKxMXFpcxDhoiIiIjo6da3b1+9c7yDBw8KADlx4oQyZ/7ll1+kVatWolarpVOnTnLixAml/sSJE6Vx48ZiYmIidnZ2MmzYMLly5YpSHhcXJ88995yo1WpxcHCQ2bNnS1FRkVKu/cDQixcvSo8ePaRWrVrStGlT+fXXXzUeGCpy7yGcDRs2FAMDA+nWrZuyPTw8XNq2bStqtVpsbGyka9euEhkZqTcP+s5PSmk/MHTnzp3SokULMTExkdatW0tcXFyZB5wmJCRI69atRa1WS4cOHSQiIkIAyO+//y4ilXvQp/YDQ3NycuTVV18VCwsLAaA81HXfvn3i6ekppqam8uKLL8qPP/6oEUdEJDQ0VBo0aCBmZmbSr18/Wbp0aZnzi+joaPH29hYzMzOxsrKSTp06yTfffKM3L0REIiIqEa2bbBERET1D8vPz4eTkhDVr1ui8ByIREREREZUvPDwcQUFByMvLe2TPJyIiqg54z3MiInomlZSU4MqVK1i2bBmsra3x+uuvV3WTiIiIiIieCuvWrYObmxucnJxw4sQJzJ49G0OGDOHCORHVOFw8JyKiZ9KFCxfg6uqKBg0aICwsDEZG/JNIRERERFQZly5dwvz583Hp0iU4Ojpi8ODBWLJkSVU3i4jokeNtW4iIiIiIiIiIiIiItOh+7DMRERERERERERER0TOMi+dERERERERERERERFq4eE5EREREREREREREpIWL50REREREREREREREWrh4TkRERERERERERESkhYvnRERERERERERERERauHhORERERERERERERKSFi+dERERERERERERERFq4eE5EREREREREREREpOX/AGsythTocF3hAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Выбираем столбцы для анализа\n",
    "columns_to_check = ['relative_velocity', 'absolute_magnitude']\n",
    "\n",
    "# Функция для подсчета выбросов\n",
    "def count_outliers(df, columns):\n",
    "    outliers_count = {}\n",
    "    for col in columns:\n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Считаем количество выбросов\n",
    "        outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
    "        outliers_count[col] = len(outliers)\n",
    "    \n",
    "    return outliers_count\n",
    "\n",
    "# Подсчитываем выбросы\n",
    "outliers_count = count_outliers(df, columns_to_check)\n",
    "\n",
    "# Выводим количество выбросов для каждого столбца\n",
    "for col, count in outliers_count.items():\n",
    "    print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
    "    \n",
    "# Выбираем столбцы для очистки\n",
    "columns_to_clean = ['relative_velocity', 'absolute_magnitude']\n",
    "\n",
    "# Функция для удаления выбросов\n",
    "def remove_outliers(df, columns):\n",
    "    for col in columns:\n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Удаляем строки, содержащие выбросы\n",
    "        df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]\n",
    "    \n",
    "    return df\n",
    "\n",
    "# Удаляем выбросы\n",
    "df_cleaned = remove_outliers(df, columns_to_clean)\n",
    "\n",
    "# Выводим количество удаленных строк\n",
    "print(f\"Количество удаленных строк: {len(df) - len(df_cleaned)}\")\n",
    "\n",
    "# Создаем гистограммы для очищенных данных\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "# Гистограмма для relative_velocity\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.histplot(df_cleaned['relative_velocity'], kde=True)\n",
    "plt.title('Histogram of Relative Velocity (Cleaned)')\n",
    "plt.xlabel('Relative Velocity')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Гистограмма для absolute_magnitude\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.histplot(df_cleaned['absolute_magnitude'], kde=True)\n",
    "plt.title('Histogram of Absolute Magnitude (Cleaned)')\n",
    "plt.xlabel('Absolute Magnitude')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Сохраняем очищенный датасет\n",
    "df_cleaned.to_csv(\"..//static//csv//neo.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно заметить, что выбросов стало меньше"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество пропущенных значений в каждом столбце:\n",
      "id                    0\n",
      "name                  0\n",
      "est_diameter_min      0\n",
      "est_diameter_max      0\n",
      "relative_velocity     0\n",
      "miss_distance         0\n",
      "orbiting_body         0\n",
      "sentry_object         0\n",
      "absolute_magnitude    0\n",
      "hazardous             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Проверка на пропущенные значения\n",
    "missing_values = df.isnull().sum()\n",
    "\n",
    "# Вывод результатов\n",
    "print(\"Количество пропущенных значений в каждом столбце:\")\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пропущенных значений в датасете нет. Можно перейти к созданию выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: (54501, 9)\n",
      "Размер контрольной выборки: (18167, 9)\n",
      "Размер тестовой выборки: (18168, 9)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение на признаки (X) и целевую переменную (y)\n",
    "# Предположим, что 'hazardous' - это целевая переменная\n",
    "X = df.drop('hazardous', axis=1)\n",
    "y = df['hazardous']\n",
    "\n",
    "# Разбиение на обучающую и остальную выборку (контрольную + тестовую)\n",
    "X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.6, random_state=42)\n",
    "\n",
    "# Разбиение остатка на контрольную и тестовую выборки\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_rem, y_rem, test_size=0.5, random_state=42)\n",
    "\n",
    "# Вывод размеров выборок\n",
    "print(\"Размер обучающей выборки:\", X_train.shape)\n",
    "print(\"Размер контрольной выборки:\", X_val.shape)\n",
    "print(\"Размер тестовой выборки:\", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов в обучающей выборке:\n",
      "hazardous\n",
      "False    0.902681\n",
      "True     0.097319\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Распределение классов в контрольной выборке:\n",
      "hazardous\n",
      "False    0.902681\n",
      "True     0.097319\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Распределение классов в тестовой выборке:\n",
      "hazardous\n",
      "False    0.902686\n",
      "True     0.097314\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение на признаки (X) и целевую переменную (y)\n",
    "X = df.drop('hazardous', axis=1)\n",
    "y = df['hazardous']\n",
    "\n",
    "# Разбиение на обучающую и остальную выборку (контрольную + тестовую)\n",
    "X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.6, random_state=42, stratify=y)\n",
    "\n",
    "# Разбиение остатка на контрольную и тестовую выборки\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_rem, y_rem, test_size=0.5, random_state=42, stratify=y_rem)\n",
    "\n",
    "# Функция для анализа сбалансированности\n",
    "def analyze_balance(y_train, y_val, y_test):\n",
    "    print(\"Распределение классов в обучающей выборке:\")\n",
    "    print(y_train.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nРаспределение классов в контрольной выборке:\")\n",
    "    print(y_val.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nРаспределение классов в тестовой выборке:\")\n",
    "    print(y_test.value_counts(normalize=True))\n",
    "\n",
    "# Анализ сбалансированности\n",
    "analyze_balance(y_train, y_val, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выборки хорошо сбалансированы и не нуждаются в корректировках"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Онлайн обучение**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проблемная область: Анализ гибкости и адаптации студентов к онлайн-обучению, включая их предпочтения, проблемы и успеваемость.\n",
    "\n",
    "Объект наблюдения: Студенты, участвующие в онлайн-обучении, и их взаимодействие с образовательными платформами и ресурсами.\n",
    "\n",
    "Атрибуты: возраст, пол, уровень образования, предпочтения в обучении, проблемы в обучении, успеваемость\n",
    "Возраст: Возраст студента.\n",
    "\n",
    "Пример бизнес-цели: Разработка и внедрение платформы для онлайн-обучения, адаптированной к предпочтениям и потребностям студентов, с целью повышения их вовлеченности и успеваемости."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Education Level', 'Institution Type', 'Gender', 'Age', 'Device',\n",
      "       'IT Student', 'Location', 'Financial Condition', 'Internet Type',\n",
      "       'Network Type', 'Flexibility Level'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"..//static//csv//students_adaptability_level_online_education.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При помощи ящика с усами и колонки возраста проверим набор на баланс."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box plot для столбца 'Age'\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=df['Age'])\n",
    "plt.title('Box Plot для Age')\n",
    "plt.xlabel('Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим на шум"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot для столбцов 'Age' и 'Financial Condition'\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='Age', y='Financial Condition', data=df)\n",
    "plt.title('Scatter Plot для Age и Financial Condition')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Financial Condition')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разбиение набора данных на обучающую, контрольную и тестовую выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 723\n",
      "Размер контрольной выборки: 241\n",
      "Размер тестовой выборки: 241\n"
     ]
    }
   ],
   "source": [
    "#Удаление строк с пустыми значениями\n",
    "df_cleaned = df.dropna()\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки\n",
    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применение методов приращения данных (аугментации)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение Gender в обучающей выборке после oversampling:\n",
      "Gender\n",
      "Male      397\n",
      "Female    397\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение Gender в контрольной выборке после oversampling:\n",
      "Gender\n",
      "Male      140\n",
      "Female    140\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение Gender в тестовой выборке после oversampling:\n",
      "Gender\n",
      "Female    126\n",
      "Male      126\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение Gender в обучающей выборке после undersampling:\n",
      "Gender\n",
      "Female    326\n",
      "Male      326\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение Gender в контрольной выборке после undersampling:\n",
      "Gender\n",
      "Female    101\n",
      "Male      101\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение Gender в тестовой выборке после undersampling:\n",
      "Gender\n",
      "Female    115\n",
      "Male      115\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Разделение на обучающую и тестовую выборки\n",
    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
    "\n",
    "def check_balance(df, name):\n",
    "    counts = df['Gender'].value_counts()\n",
    "    print(f\"Распределение Gender в {name}:\")\n",
    "    print(counts)\n",
    "    print()\n",
    "\n",
    "def oversample(df):\n",
    "    X = df.drop('Gender', axis=1)\n",
    "    y = df['Gender']\n",
    "    \n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
    "    \n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "train_df_oversampled = oversample(train_df)\n",
    "val_df_oversampled = oversample(val_df)\n",
    "test_df_oversampled = oversample(test_df)\n",
    "\n",
    "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")\n",
    "\n",
    "def undersample(df):\n",
    "    X = df.drop('Gender', axis=1)\n",
    "    y = df['Gender']\n",
    "    \n",
    "    undersampler = RandomUnderSampler(random_state=42)\n",
    "    X_resampled, y_resampled = undersampler.fit_resample(X, y)\n",
    "    \n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "train_df_undersampled = undersample(train_df)\n",
    "val_df_undersampled = undersample(val_df)\n",
    "test_df_undersampled = undersample(test_df)\n",
    "\n",
    "check_balance(train_df_undersampled, \"обучающей выборке после undersampling\")\n",
    "check_balance(val_df_undersampled, \"контрольной выборке после undersampling\")\n",
    "check_balance(test_df_undersampled, \"тестовой выборке после undersampling\")"
   ]
  }
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