code_refactored_xgboost_onehot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 기본적으로 모든 조각들에 대한 컴포넌트화가 목적"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from pprint import pprint\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    classification_report,\n",
    "    confusion_matrix,\n",
    "    f1_score,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    ")\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold\n",
    "from tqdm import tqdm\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from collections import Counter\n",
    "\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 동일하거나 모두다른값 삭제 + OK -> NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_and_save_train_data(train_data, save_filename=\"cleaned_train_data.csv\"):\n",
    "    root_dir=\"data\"\n",
    "    # 칼럼 리스트\n",
    "    columns_to_clean = [\n",
    "        'HEAD NORMAL COORDINATE X AXIS(Stage1) Collect Result_Dam',\n",
    "        'HEAD NORMAL COORDINATE X AXIS(Stage1) Collect Result_Fill1',\n",
    "        'HEAD NORMAL COORDINATE X AXIS(Stage1) Collect Result_Fill2'\n",
    "    ]\n",
    "    #------------------------\n",
    "    #동일하거나 모두다른값 삭제\n",
    "    #------------------------\n",
    "    train_cleaned = train_data.dropna(axis=1, how='all')\n",
    "    constant_columns = [col for col in train_cleaned.columns\n",
    "                        if train_cleaned[col].nunique(dropna=False) == 1]\n",
    "\n",
    "    all_unique_columns = [col for col in train_cleaned.columns\n",
    "                          if train_cleaned[col].nunique() == len(train_cleaned)]\n",
    "    train_cleaned = train_cleaned.drop(columns=constant_columns)\n",
    "    train_cleaned = train_cleaned.drop(columns=all_unique_columns)\n",
    "    #------------------------\n",
    "    # OK->NaN 변환\n",
    "    #------------------------\n",
    "    def replace_ok_with_nan(column):\n",
    "        return column.replace('OK', np.nan)\n",
    "\n",
    "    for col in columns_to_clean:\n",
    "        if col in train_cleaned.columns:\n",
    "            train_cleaned[col] = replace_ok_with_nan(train_cleaned[col])\n",
    "    #------------------------\n",
    "    # 저장\n",
    "    #------------------------\n",
    "    # 5. Save the cleaned DataFrame\n",
    "    #save_path = os.path.join(root_dir, save_filename)\n",
    "    #train_cleaned.to_csv(save_path, index=False)\n",
    "\n",
    "    return train_cleaned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OK 결측치 이후 뒤로 밀린값 앞으로 당기기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "def adjust_and_save_dataframe(train_data, root_dir, save_filename=\"cleaned.csv\"):\n",
    "    root_dir = \"data\"\n",
    "    rows = train_data.shape[0]\n",
    "\n",
    "    # tqdm을 사용하여 진행 상황 표시\n",
    "    for i in tqdm(range(0, rows), desc=\"Adjusting DataFrame\"):\n",
    "        if pd.isna(train_data.iloc[i, 16]):\n",
    "            train_data.iloc[i, 16] = train_data.iloc[i, 18]\n",
    "            temp_values = train_data.iloc[i, 18:69].values\n",
    "            train_data.iloc[i, 18:68] = temp_values[1:]\n",
    "            train_data.iloc[i, 69] = None\n",
    "        if pd.isna(train_data.iloc[i, 93]):\n",
    "            train_data.iloc[i, 93] = train_data.iloc[i, 95]\n",
    "            temp_values = train_data.iloc[i, 95:116].values\n",
    "            train_data.iloc[i, 95:115] = temp_values[1:]\n",
    "            train_data.iloc[i, 116] = None\n",
    "        if pd.isna(train_data.iloc[i, 126]):\n",
    "            train_data.iloc[i, 126] = train_data.iloc[i, 128]\n",
    "            temp_values = train_data.iloc[i, 128:149].values\n",
    "            train_data.iloc[i, 128:148] = temp_values[1:]\n",
    "            train_data.iloc[i, 149] = None\n",
    "    \n",
    "    # 밀린 맨끝값들 없애기\n",
    "    train_data = train_data.drop(train_data.columns[[149, 116, 69]], axis=1)\n",
    "\n",
    "    # 저장 (현재는 주석 처리됨)\n",
    "    # save_path = os.path.join(root_dir, save_filename)\n",
    "    # train_data.to_csv(save_path, index=False)\n",
    "\n",
    "    return train_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 통계량 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_statistics(dataframe):\n",
    "    statistics = dataframe.describe()\n",
    "    return statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 이상치, 결측치, Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "def analyze_and_handle_missing_data(df):\n",
    "    # nan확인\n",
    "    nan_counts = df.isna().sum()\n",
    "    nan_counts_with_nan = nan_counts[nan_counts > 0]\n",
    "\n",
    "    # 결측치출력\n",
    "    for column, count in nan_counts_with_nan.items():\n",
    "        print(f\"{column}: {count}개의 결측치\")\n",
    "        print(f\"{column}: {df[column].dtype}\")\n",
    "        print(df[column].value_counts())  # Print non-NaN value counts\n",
    "\n",
    "    # 결측치 평균으로 대체\n",
    "    #for column in nan_counts_with_nan.index:\n",
    "    #    if df[column].dtype in [np.float64, np.int64]:  # Only fill for numeric columns\n",
    "    #        df[column].fillna(df[column].mean(), inplace=True)\n",
    "\n",
    "    # outlier\n",
    "    def detect_outliers_zscore(data):\n",
    "        z_scores = np.abs(stats.zscore(data))\n",
    "        return z_scores > 3\n",
    "\n",
    "    for column in df.select_dtypes(include=[np.number]).columns:\n",
    "        # Detect outliers using Z-score\n",
    "        outliers_zscore = detect_outliers_zscore(df[column])\n",
    "        print(f\"Column {column} has {outliers_zscore.sum()} outliers detected by Z-score\")\n",
    "        # Histogram visualization\n",
    "        #plt.figure(figsize=(10, 6))\n",
    "        \n",
    "        # Overall data histogram visualization\n",
    "        #sns.histplot(df[column], kde=True, color='lightblue', label='Data', bins=30)\n",
    "        \n",
    "        # Outliers data histogram visualization\n",
    "        #sns.histplot(df.loc[outliers_zscore, column], kde=True, color='red', label='Z-score Outliers', bins=30)\n",
    "        \n",
    "        #plt.title(f'Z-score Outliers Detection in {column}')\n",
    "        #plt.legend()\n",
    "        #plt.show()\n",
    "\n",
    "    return df\n",
    "\n",
    "# Example usage:\n",
    "# cleaned_df = analyze_and_handle_missing_data(your_dataframe)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 인코딩"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  num_columns,columns_to_convert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 숫자 데이터(int, float)로 이루어진 열 중 원핫인코딩 대상\n",
    "num_columns = [\n",
    "    'CURE END POSITION X Collect Result_Dam',\n",
    "    'CURE END POSITION Z Collect Result_Dam',\n",
    "    'CURE END POSITION Θ Collect Result_Dam',\n",
    "    'CURE START POSITION X Collect Result_Dam',\n",
    "    'CURE START POSITION Θ Collect Result_Dam',\n",
    "    'HEAD NORMAL COORDINATE X AXIS(Stage1) Collect Result_Dam', \n",
    "    'HEAD NORMAL COORDINATE X AXIS(Stage2) Collect Result_Dam',\n",
    "    'HEAD NORMAL COORDINATE X AXIS(Stage3) Collect Result_Dam',\n",
    "    'HEAD NORMAL COORDINATE Y AXIS(Stage1) Collect Result_Dam',\n",
    "    'HEAD NORMAL COORDINATE Y AXIS(Stage2) Collect Result_Dam',\n",
    "    'HEAD NORMAL COORDINATE Y AXIS(Stage3) Collect Result_Dam',\n",
    "    'HEAD NORMAL COORDINATE Z AXIS(Stage1) Collect Result_Dam',\n",
    "    'HEAD NORMAL COORDINATE Z AXIS(Stage2) Collect Result_Dam',\n",
    "    'HEAD NORMAL COORDINATE Z AXIS(Stage3) Collect Result_Dam',\n",
    "    'HEAD Standby Position X Collect Result_Dam',\n",
    "    'HEAD Standby Position Y Collect Result_Dam',\n",
    "    'HEAD Standby Position Z Collect Result_Dam',\n",
    "    'Head Clean Position X Collect Result_Dam',\n",
    "    'Head Clean Position Y Collect Result_Dam',\n",
    "    'Head Clean Position Z Collect Result_Dam',\n",
    "    'Head Purge Position X Collect Result_Dam',\n",
    "    'Head Purge Position Y Collect Result_Dam',\n",
    "    'Head Purge Position Z Collect Result_Dam',\n",
    "    'Head Zero Position X Collect Result_Dam',\n",
    "    'Head Zero Position Y Collect Result_Dam',\n",
    "    'Head Zero Position Z Collect Result_Dam',\n",
    "    'PalletID Collect Result_Dam',\n",
    "    'Receip No Collect Result_Dam',\n",
    "    'HEAD NORMAL COORDINATE X AXIS(Stage1) Collect Result_Fill1', \n",
    "    'HEAD NORMAL COORDINATE X AXIS(Stage2) Collect Result_Fill1',\n",
    "    'HEAD NORMAL COORDINATE X AXIS(Stage3) Collect Result_Fill1',\n",
    "    'HEAD NORMAL COORDINATE Y AXIS(Stage1) Collect Result_Fill1',\n",
    "    'HEAD NORMAL COORDINATE Y AXIS(Stage2) Collect Result_Fill1',\n",
    "    'HEAD NORMAL COORDINATE Y AXIS(Stage3) Collect Result_Fill1',\n",
    "    'HEAD NORMAL COORDINATE Z AXIS(Stage1) Collect Result_Fill1',\n",
    "    'HEAD NORMAL COORDINATE Z AXIS(Stage2) Collect Result_Fill1',\n",
    "    'HEAD NORMAL COORDINATE Z AXIS(Stage3) Collect Result_Fill1',\n",
    "    'HEAD Standby Position X Collect Result_Fill1',\n",
    "    'HEAD Standby Position Y Collect Result_Fill1',\n",
    "    'HEAD Standby Position Z Collect Result_Fill1',\n",
    "    'Head Clean Position X Collect Result_Fill1',\n",
    "    'Head Clean Position Y Collect Result_Fill1',\n",
    "    'Head Clean Position Z Collect Result_Fill1',\n",
    "    'Head Purge Position X Collect Result_Fill1',\n",
    "    'Head Purge Position Y Collect Result_Fill1',\n",
    "    'Head Purge Position Z Collect Result_Fill1',\n",
    "    'PalletID Collect Result_Fill1',\n",
    "    'Receip No Collect Result_Fill1',\n",
    "    'CURE END POSITION X Collect Result_Fill2',\n",
    "    'CURE END POSITION Z Collect Result_Fill2',\n",
    "    # 'CURE END POSITION Θ Collect Result_Fill2', 값이 모든 열 제거에서 제거됨이 확인\n",
    "    'CURE STANDBY POSITION Z Collect Result_Fill2',\n",
    "    'CURE START POSITION X Collect Result_Fill2',\n",
    "    'CURE START POSITION Z Collect Result_Fill2',\n",
    "    'HEAD NORMAL COORDINATE X AXIS(Stage1) Collect Result_Fill2', \n",
    "    'HEAD NORMAL COORDINATE X AXIS(Stage2) Collect Result_Fill2',\n",
    "    'HEAD NORMAL COORDINATE X AXIS(Stage3) Collect Result_Fill2',\n",
    "    'HEAD NORMAL COORDINATE Y AXIS(Stage1) Collect Result_Fill2',\n",
    "    'HEAD NORMAL COORDINATE Y AXIS(Stage2) Collect Result_Fill2',\n",
    "    'HEAD NORMAL COORDINATE Y AXIS(Stage3) Collect Result_Fill2',\n",
    "    'HEAD NORMAL COORDINATE Z AXIS(Stage1) Collect Result_Fill2',\n",
    "    'HEAD NORMAL COORDINATE Z AXIS(Stage2) Collect Result_Fill2',\n",
    "    'HEAD NORMAL COORDINATE Z AXIS(Stage3) Collect Result_Fill2',\n",
    "    'HEAD Standby Position X Collect Result_Fill2',\n",
    "    'HEAD Standby Position Y Collect Result_Fill2',\n",
    "    'HEAD Standby Position Z Collect Result_Fill2',\n",
    "    'Head Clean Position X Collect Result_Fill2',\n",
    "    'Head Clean Position Y Collect Result_Fill2',\n",
    "    'Head Clean Position Z Collect Result_Fill2',\n",
    "    'Head Purge Position X Collect Result_Fill2',\n",
    "    'Head Purge Position Y Collect Result_Fill2',\n",
    "    'Head Purge Position Z Collect Result_Fill2',\n",
    "    'PalletID Collect Result_Fill2',\n",
    "    'Receip No Collect Result_Fill2',\n",
    "]\n",
    "columns_to_convert = [\n",
    "        'HEAD NORMAL COORDINATE X AXIS(Stage1) Collect Result_Dam',\n",
    "        'HEAD NORMAL COORDINATE X AXIS(Stage1) Collect Result_Fill1',\n",
    "        'HEAD NORMAL COORDINATE X AXIS(Stage1) Collect Result_Fill2'\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 이게 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "\n",
    "# OneHotEncoder 정의\n",
    "encoder = OneHotEncoder(drop='first', sparse_output=False, dtype=int, handle_unknown='ignore')\n",
    "\n",
    "\n",
    "def encode_and_transformation_train(df, num_columns=num_columns, columns_to_convert=columns_to_convert):\n",
    "    # 1. 지정된 열들을 숫자형으로 변환\n",
    "    for col in columns_to_convert:\n",
    "        df[col] = pd.to_numeric(df[col], errors='coerce')\n",
    "\n",
    "    # 2. Object 열들에 대한 인코딩 수행\n",
    "    object_columns = df.select_dtypes(include=['object']).columns.tolist()\n",
    "    # 라벨인코딩\n",
    "    # label_encoders = {}\n",
    "    # for col in object_columns:\n",
    "    #     if col==\"target\":\n",
    "    #         print(\"not Target Column.\")\n",
    "    #         continue\n",
    "    #     le = LabelEncoder()\n",
    "    #     df[col] = le.fit_transform(df[col].astype(str))\n",
    "    #     label_encoders[col] = le\n",
    "    \n",
    "    # 원핫인코딩 - 위의 라벨 인코딩이랑 둘중 하나는 주석 걸어주셔야 합니다~\n",
    "    columns_to_encode = [col for col in object_columns if col != 'target']\n",
    "\n",
    "    # 원핫 인코딩 수행 (훈련 데이터에 맞추어 인코딩 수행)\n",
    "    train_encoded = encoder.fit_transform(df[columns_to_encode])\n",
    "\n",
    "    # 인코딩된 열의 이름을 가져옴 (OneHotEncoder는 자동으로 새로운 열 이름을 생성하지 않으므로 수동으로 관리해야 함)\n",
    "    encoded_columns = encoder.get_feature_names_out(columns_to_encode)\n",
    "\n",
    "    # DataFrame으로 변환\n",
    "    train_cleaned_encoded = pd.DataFrame(train_encoded, columns=encoded_columns)\n",
    "\n",
    "    # train_cleaned_encoded에 원래의 다른 열 추가\n",
    "    train_cleaned_encoded = pd.concat([df.drop(columns=columns_to_encode).reset_index(drop=True), train_cleaned_encoded], axis=1)\n",
    "\n",
    "    print(\"One-hot encoding completed.\")\n",
    "\n",
    "    # 3. 숫자형 열 정규화\n",
    "    if num_columns:\n",
    "        scaler = StandardScaler()\n",
    "        train_cleaned_encoded[num_columns] = scaler.fit_transform(train_cleaned_encoded[num_columns])\n",
    "    return train_cleaned_encoded, columns_to_encode, encoded_columns\n",
    "\n",
    "def encode_and_transformation_test(df, columns_to_encode, encoded_columns, num_columns=num_columns, columns_to_convert=columns_to_convert):\n",
    "    # 1. 지정된 열들을 숫자형으로 변환\n",
    "    for col in columns_to_convert:\n",
    "        df[col] = pd.to_numeric(df[col], errors='coerce')\n",
    "    \n",
    "    # test 데이터 원핫 인코딩\n",
    "    test_encoded = encoder.transform(df[columns_to_encode])\n",
    "\n",
    "    # DataFrame으로 변환\n",
    "    test_data_encoded = pd.DataFrame(test_encoded, columns=encoded_columns)\n",
    "\n",
    "    # test_data_encoded에 원래의 다른 열 추가\n",
    "    test_data_encoded = pd.concat([df.drop(columns=columns_to_encode).reset_index(drop=True), test_data_encoded], axis=1)\n",
    "\n",
    "    print(\"One-hot encoding completed.\")\n",
    "\n",
    "    # 3. 숫자형 열 정규화\n",
    "    if num_columns:\n",
    "        scaler = StandardScaler()\n",
    "        test_data_encoded[num_columns] = scaler.fit_transform(test_data_encoded[num_columns])\n",
    "    return test_data_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 언더샘플링 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def undersample_data(df, normal_ratio=1.0, random_state=None):\n",
    "    # 정상 및 비정상 클래스를 분리\n",
    "    df_normal = df[df[\"target\"] == \"Normal\"]\n",
    "    df_abnormal = df[df[\"target\"] == \"AbNormal\"]\n",
    "\n",
    "    # 각 클래스의 샘플 수를 확인\n",
    "    num_normal = len(df_normal)\n",
    "    num_abnormal = len(df_abnormal)\n",
    "    \n",
    "    print(f\"Normal samples: {df_normal.shape}, AbNormal samples: {df_abnormal.shape}\")\n",
    "    print(f\"Total: Normal: {num_normal}, AbNormal: {num_abnormal}\")\n",
    "\n",
    "    if num_abnormal == 0 or num_normal == 0:\n",
    "        raise ValueError(\"One of the classes has no samples, check the data for issues.\")\n",
    "    \n",
    "    # 정상 클래스를 비정상 클래스와 동일한 비율로 언더샘플링\n",
    "    df_normal = df_normal.sample(n=int(num_abnormal * normal_ratio), replace=False, random_state=random_state)\n",
    "\n",
    "    # 언더샘플링된 정상 클래스와 비정상 클래스를 결합\n",
    "    df_balanced = pd.concat([df_normal, df_abnormal], axis=0).reset_index(drop=True)\n",
    "\n",
    "    # 최종 통계 정보 출력\n",
    "    num_normal_balanced = len(df_balanced[df_balanced[\"target\"] == \"Normal\"])\n",
    "    num_abnormal_balanced = len(df_balanced[df_balanced[\"target\"] == \"AbNormal\"])\n",
    "    print(f\"Balanced data: Normal: {num_normal_balanced}, AbNormal: {num_abnormal_balanced}\" + f\" ratio: {num_abnormal_balanced/num_normal_balanced:.2f}\")\n",
    "\n",
    "    return df_balanced\n",
    "\n",
    "# 사용 예시:\n",
    "# df_balanced = undersample_data(train_cleaned_encoded, random_state=110)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 오버샘플링 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "import pandas as pd\n",
    "\n",
    "def oversample_data(df, normal_ratio=1.0, random_state=None):\n",
    "    # 정상 및 비정상 클래스를 분리\n",
    "    df_normal = df[df[\"target\"] == \"Normal\"]\n",
    "    df_abnormal = df[df[\"target\"] == \"AbNormal\"]\n",
    "    print(df[\"target\"])\n",
    "    \n",
    "    # 각 클래스의 샘플 수를 확인\n",
    "    num_normal = len(df_normal)\n",
    "    num_abnormal = len(df_abnormal)\n",
    "    \n",
    "    print(f\"Normal samples: {df_normal.shape}, AbNormal samples: {df_abnormal.shape}\")\n",
    "    print(f\"Total: Normal: {num_normal}, AbNormal: {num_abnormal}\")\n",
    "\n",
    "    if num_abnormal == 0 or num_normal == 0:\n",
    "        raise ValueError(\"One of the classes has no samples, check the data for issues.\")\n",
    "    \n",
    "    # 오버샘플링: SMOTE를 사용하여 소수 클래스를 증강\n",
    "    smote = SMOTE(sampling_strategy=normal_ratio, random_state=random_state)\n",
    "    X = df.drop(columns=[\"target\"])\n",
    "    y = df[\"target\"]\n",
    "    \n",
    "    X_resampled, y_resampled = smote.fit_resample(X, y)\n",
    "    \n",
    "    # 오버샘플링된 데이터를 다시 결합\n",
    "    df_resampled = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    \n",
    "    # 통계 정보 출력 함수\n",
    "    def print_stats(df: pd.DataFrame):\n",
    "        num_normal = len(df[df[\"target\"] == \"Normal\"])\n",
    "        num_abnormal = len(df[df[\"target\"] == \"AbNormal\"])\n",
    "        print(f\"Total: Normal: {num_normal}, AbNormal: {num_abnormal}\" + f\" ratio: {num_abnormal/num_normal:.2f}\")\n",
    "\n",
    "    print(\"Resampled data stats:\")\n",
    "    print_stats(df_resampled)\n",
    "\n",
    "    return df_resampled\n",
    "\n",
    "# 사용 예시:\n",
    "# df_resampled = oversample_data(train_cleaned_encoded, random_state=110)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 전처리, 학습, 테스트데이터 예측함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 전처리 함수\n",
    "def preprocess_data(train_data, test_data, ROOT_DIR):\n",
    "    train_cleaned = clean_and_save_train_data(train_data, ROOT_DIR)\n",
    "    test_cleaned = clean_and_save_train_data(test_data, ROOT_DIR)  \n",
    "    print(\"Data cleaning completed\")\n",
    "\n",
    "    train_cleaned = adjust_and_save_dataframe(train_cleaned, ROOT_DIR)\n",
    "    test_cleaned = adjust_and_save_dataframe(test_cleaned, ROOT_DIR)  \n",
    "    print(\"Data adjustment completed\")\n",
    "\n",
    "    stats = calculate_statistics(train_cleaned)\n",
    "    print(\"Statistics calculation completed\")\n",
    "    print(stats)\n",
    "\n",
    "    train_encoded, columns_to_encode, encoded_columns = encode_and_transformation_train(train_cleaned)\n",
    "    test_encoded = encode_and_transformation_test(test_cleaned, columns_to_encode, encoded_columns)  \n",
    "    print(\"Data encoding and transformation completed\")\n",
    "\n",
    "    return train_encoded, test_encoded\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 학습 함수\n",
    "def train_model(df_train, RANDOM_STATE):\n",
    "    model = XGBClassifier(random_state=RANDOM_STATE, eval_metric='mlogloss')\n",
    "    features = []\n",
    "\n",
    "    # 피처 선택 및 타입 변환\n",
    "    for col in df_train.columns:\n",
    "        if col != 'target':  # 'target' 열 제외\n",
    "            try:\n",
    "                df_train[col] = df_train[col].astype(int)  # 타입 변환 시도\n",
    "                features.append(col)  # 성공적으로 변환된 피처 추가\n",
    "            except:\n",
    "                continue  # 변환 실패 시 무시\n",
    "\n",
    "    train_x = df_train[features]\n",
    "    train_y = df_train[\"target\"]\n",
    "\n",
    "    skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_STATE)\n",
    "    accuracy_scores = cross_val_score(model, train_x, train_y, cv=skf, scoring='accuracy')\n",
    "    f1_scores = cross_val_score(model, train_x, train_y, cv=skf, scoring='f1_macro')\n",
    "    precision_scores = cross_val_score(model, train_x, train_y, cv=skf, scoring='precision_macro')\n",
    "\n",
    "    print(f\"정확도: {np.mean(accuracy_scores):.4f}\")\n",
    "    print(f\"정밀도: {np.mean(precision_scores):.4f}\")\n",
    "    print(f\"F1스코어: {np.mean(f1_scores):.4f}\")\n",
    "    print(\"Model training completed\")\n",
    "\n",
    "    # 모델 학습\n",
    "    model.fit(train_x, train_y)\n",
    "    return model, features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 테스트 데이터 예측 및 결과 저장 함수\n",
    "def predict_and_save_results(model, test_encoded, features):\n",
    "    # 테스트 데이터 준비\n",
    "    df_test_x = test_encoded[features]\n",
    "\n",
    "    # 타입 변환\n",
    "    for col in df_test_x.columns:\n",
    "        try:\n",
    "            df_test_x[col] = df_test_x[col].astype(int)\n",
    "        except:\n",
    "            continue\n",
    "\n",
    "    # 테스트 데이터 예측\n",
    "    test_pred = model.predict(df_test_x)\n",
    "    print(\"Test data prediction completed\")\n",
    "\n",
    "    # Feature importance plot\n",
    "    ftr_importances_values = model.feature_importances_\n",
    "    ftr_importances = pd.Series(ftr_importances_values, index=features)\n",
    "    ftr_top30 = ftr_importances.sort_values(ascending=False)[:30]\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.title('Top 30 Feature Importances')\n",
    "    sns.barplot(x=ftr_top30, y=ftr_top30.index)\n",
    "    plt.show()\n",
    "    print(\"Feature importance plotting completed\")\n",
    "\n",
    "    # Count predictions\n",
    "    count = Counter(test_pred)\n",
    "    for element, frequency in count.items():\n",
    "        print(f'Element: {element}, Count: {frequency}')\n",
    "\n",
    "    # Predict probabilities\n",
    "    proba = model.predict_proba(df_test_x)\n",
    "    print(proba)\n",
    "\n",
    "    # 결과 저장\n",
    "    df_sub = pd.read_csv(\"submission.csv\")\n",
    "    df_sub[\"target\"] = test_pred\n",
    "    df_sub.to_csv(\"submission.csv\", index=False)\n",
    "    print(\"Submission file saved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loading completed\n",
      "Train data shape: (40506, 464)\n",
      "Test data shape: (17361, 465)\n",
      "Data cleaning completed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Adjusting DataFrame:   0%|          | 0/40506 [00:00<?, ?it/s]C:\\Users\\1233d\\AppData\\Local\\Temp\\ipykernel_32684\\3787398044.py:23: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '114.612' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  train_data.iloc[i, 128:148] = temp_values[1:]\n",
      "C:\\Users\\1233d\\AppData\\Local\\Temp\\ipykernel_32684\\3787398044.py:13: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '0.012' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  train_data.iloc[i, 18:68] = temp_values[1:]\n",
      "Adjusting DataFrame: 100%|██████████| 40506/40506 [09:20<00:00, 72.32it/s] \n",
      "Adjusting DataFrame:   0%|          | 0/17361 [00:00<?, ?it/s]C:\\Users\\1233d\\AppData\\Local\\Temp\\ipykernel_32684\\3787398044.py:13: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '-0.054' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  train_data.iloc[i, 18:68] = temp_values[1:]\n",
      "Adjusting DataFrame:   0%|          | 5/17361 [00:00<06:17, 45.95it/s]C:\\Users\\1233d\\AppData\\Local\\Temp\\ipykernel_32684\\3787398044.py:23: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '114.612' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  train_data.iloc[i, 128:148] = temp_values[1:]\n",
      "Adjusting DataFrame: 100%|██████████| 17361/17361 [05:50<00:00, 49.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data adjustment completed\n",
      "Statistics calculation completed\n",
      "       CURE END POSITION X Collect Result_Dam  \\\n",
      "count                            40506.000000   \n",
      "mean                               530.370809   \n",
      "std                                369.283055   \n",
      "min                                240.000000   \n",
      "25%                                240.000000   \n",
      "50%                                240.000000   \n",
      "75%                               1000.000000   \n",
      "max                               1000.000000   \n",
      "\n",
      "       CURE END POSITION Z Collect Result_Dam  \\\n",
      "count                            40506.000000   \n",
      "mean                                 6.320669   \n",
      "std                                  4.858988   \n",
      "min                                  2.500000   \n",
      "25%                                  2.500000   \n",
      "50%                                  2.500000   \n",
      "75%                                 12.500000   \n",
      "max                                 12.500000   \n",
      "\n",
      "       CURE END POSITION Θ Collect Result_Dam  CURE SPEED Collect Result_Dam  \\\n",
      "count                            40506.000000                   40506.000000   \n",
      "mean                               -21.227966                      73.864119   \n",
      "std                                 87.461776                       9.761997   \n",
      "min                                -90.000000                      70.000000   \n",
      "25%                                -90.000000                      70.000000   \n",
      "50%                                -90.000000                      70.000000   \n",
      "75%                                 90.000000                      70.000000   \n",
      "max                                 90.000000                     105.000000   \n",
      "\n",
      "       CURE START POSITION X Collect Result_Dam  \\\n",
      "count                              40506.000000   \n",
      "mean                                 743.449859   \n",
      "std                                  364.424068   \n",
      "min                                  280.000000   \n",
      "25%                                  280.000000   \n",
      "50%                                 1030.000000   \n",
      "75%                                 1030.000000   \n",
      "max                                 1030.000000   \n",
      "\n",
      "       CURE START POSITION Θ Collect Result_Dam  \\\n",
      "count                              40506.000000   \n",
      "mean                                 -21.227966   \n",
      "std                                   87.461776   \n",
      "min                                  -90.000000   \n",
      "25%                                  -90.000000   \n",
      "50%                                  -90.000000   \n",
      "75%                                   90.000000   \n",
      "max                                   90.000000   \n",
      "\n",
      "       DISCHARGED SPEED OF RESIN Collect Result_Dam  \\\n",
      "count                                  40506.000000   \n",
      "mean                                      12.882437   \n",
      "std                                        2.997650   \n",
      "min                                       10.000000   \n",
      "25%                                       10.000000   \n",
      "50%                                       10.000000   \n",
      "75%                                       16.000000   \n",
      "max                                       16.000000   \n",
      "\n",
      "       DISCHARGED TIME OF RESIN(Stage1) Collect Result_Dam  \\\n",
      "count                                       40506.000000     \n",
      "mean                                           14.272249     \n",
      "std                                             3.743857     \n",
      "min                                             9.600000     \n",
      "25%                                            13.100000     \n",
      "50%                                            13.200000     \n",
      "75%                                            17.000000     \n",
      "max                                            21.300000     \n",
      "\n",
      "       DISCHARGED TIME OF RESIN(Stage2) Collect Result_Dam  \\\n",
      "count                                       40506.000000     \n",
      "mean                                            6.861304     \n",
      "std                                             1.994393     \n",
      "min                                             3.800000     \n",
      "25%                                             4.900000     \n",
      "50%                                             6.700000     \n",
      "75%                                             8.400000     \n",
      "max                                            10.600000     \n",
      "\n",
      "       DISCHARGED TIME OF RESIN(Stage3) Collect Result_Dam  ...  \\\n",
      "count                                       40506.000000    ...   \n",
      "mean                                           14.262638    ...   \n",
      "std                                             3.733232    ...   \n",
      "min                                             9.600000    ...   \n",
      "25%                                            13.100000    ...   \n",
      "50%                                            13.200000    ...   \n",
      "75%                                            17.000000    ...   \n",
      "max                                            21.400000    ...   \n",
      "\n",
      "       Head Clean Position X Collect Result_Fill2  \\\n",
      "count                                     40506.0   \n",
      "mean                                        119.0   \n",
      "std                                           0.0   \n",
      "min                                         119.0   \n",
      "25%                                         119.0   \n",
      "50%                                         119.0   \n",
      "75%                                         119.0   \n",
      "max                                         119.0   \n",
      "\n",
      "       Head Clean Position Y Collect Result_Fill2  \\\n",
      "count                                     40506.0   \n",
      "mean                                         50.0   \n",
      "std                                           0.0   \n",
      "min                                          50.0   \n",
      "25%                                          50.0   \n",
      "50%                                          50.0   \n",
      "75%                                          50.0   \n",
      "max                                          50.0   \n",
      "\n",
      "       Head Clean Position Z Collect Result_Fill2  \\\n",
      "count                                4.050600e+04   \n",
      "mean                                 9.180000e+01   \n",
      "std                                  2.842206e-14   \n",
      "min                                  9.180000e+01   \n",
      "25%                                  9.180000e+01   \n",
      "50%                                  9.180000e+01   \n",
      "75%                                  9.180000e+01   \n",
      "max                                  9.180000e+01   \n",
      "\n",
      "       Head Purge Position X Collect Result_Fill2  \\\n",
      "count                                     40506.0   \n",
      "mean                                        270.0   \n",
      "std                                           0.0   \n",
      "min                                         270.0   \n",
      "25%                                         270.0   \n",
      "50%                                         270.0   \n",
      "75%                                         270.0   \n",
      "max                                         270.0   \n",
      "\n",
      "       Head Purge Position Y Collect Result_Fill2  \\\n",
      "count                                     40506.0   \n",
      "mean                                         50.0   \n",
      "std                                           0.0   \n",
      "min                                          50.0   \n",
      "25%                                          50.0   \n",
      "50%                                          50.0   \n",
      "75%                                          50.0   \n",
      "max                                          50.0   \n",
      "\n",
      "       Head Purge Position Z Collect Result_Fill2  \\\n",
      "count                                40506.000000   \n",
      "mean                                    88.674268   \n",
      "std                                      9.762404   \n",
      "min                                     85.000000   \n",
      "25%                                     85.000000   \n",
      "50%                                     85.000000   \n",
      "75%                                     85.000000   \n",
      "max                                    114.612000   \n",
      "\n",
      "       Machine Tact time Collect Result_Fill2  PalletID Collect Result_Fill2  \\\n",
      "count                            40506.000000                   40506.000000   \n",
      "mean                                19.769209                       7.531082   \n",
      "std                                  1.481388                       4.498450   \n",
      "min                                 14.700000                       1.000000   \n",
      "25%                                 19.400000                       4.000000   \n",
      "50%                                 19.700000                       7.000000   \n",
      "75%                                 20.000000                      11.000000   \n",
      "max                                284.100000                      16.000000   \n",
      "\n",
      "       Production Qty Collect Result_Fill2  Receip No Collect Result_Fill2  \n",
      "count                         40506.000000                    40506.000000  \n",
      "mean                            176.798622                      108.691453  \n",
      "std                             118.608114                      130.758018  \n",
      "min                               0.000000                        0.000000  \n",
      "25%                              78.000000                        1.000000  \n",
      "50%                             163.000000                       49.000000  \n",
      "75%                             259.000000                      196.000000  \n",
      "max                             608.000000                      608.000000  \n",
      "\n",
      "[8 rows x 127 columns]\n",
      "One-hot encoding completed.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\1233d\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:242: UserWarning: Found unknown categories in columns [2, 5, 11, 15] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One-hot encoding completed.\n",
      "Data encoding and transformation completed\n",
      "Encoded train data shape: (40506, 2812)\n",
      "Encoded test data shape: (17361, 2811)\n"
     ]
    }
   ],
   "source": [
    "ROOT_DIR = \"data\"\n",
    "RANDOM_STATE = 110\n",
    "\n",
    "#데이터 전처리\n",
    "train_data = pd.read_csv(os.path.join(ROOT_DIR, \"train.csv\"))\n",
    "test_data = pd.read_csv(os.path.join(ROOT_DIR, \"test.csv\"))\n",
    "print(\"Data loading completed\")\n",
    "print(f\"Train data shape: {train_data.shape}\")\n",
    "print(f\"Test data shape: {test_data.shape}\")\n",
    "\n",
    "\n",
    "train_encoded, test_encoded = preprocess_data(train_data, test_data, ROOT_DIR)\n",
    "\n",
    "print(f\"Encoded train data shape: {train_encoded.shape}\")\n",
    "print(f\"Encoded test data shape: {test_encoded.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0          Normal\n",
      "1          Normal\n",
      "2          Normal\n",
      "3          Normal\n",
      "4          Normal\n",
      "           ...   \n",
      "40501      Normal\n",
      "40502      Normal\n",
      "40503      Normal\n",
      "40504      Normal\n",
      "40505    AbNormal\n",
      "Name: target, Length: 40506, dtype: object\n",
      "Normal samples: (38156, 2812), AbNormal samples: (2350, 2812)\n",
      "Total: Normal: 38156, AbNormal: 2350\n",
      "Resampled data stats:\n",
      "Total: Normal: 38156, AbNormal: 38156 ratio: 1.00\n",
      "Train set shape: (76312, 2812)\n",
      "정확도: 0.8528\n",
      "정밀도: 0.8541\n",
      "F1스코어: 0.8526\n",
      "Model training completed\n",
      "Test data prediction completed\n"
     ]
    },
    {
     "data": {
      "image/png": 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oGN4lcipUqEDHjh2Jjo4mV65cLFiwgLdv32Z4ncxo164da9asoXv37uzfv5/KlSvz9u1brly5wpo1a9i1axdlypShTp06GBkZ0aBBA7p168bz589ZvHgxuXPn1hr6Vbp0aRYuXMj48eNxdnYmd+7c1KhRgzp16uDg4IC/vz+DBg1CX1+fJUuWYGNjw+3btzMV76BBg9i8eTP169fHz8+P0qVLk5SUxIULF/jtt9+Ii4sjV65cn9wuWWVnZ8eUKVOIi4vD1dWV1atXExkZyc8//6zME9O1a1eCgoLw8/PjzJkzODo68ttvvxEREcHs2bO15mbSpVChQlhbW7No0SIsLS0xNzenfPnyuLu7U6hQIQYOHMjdu3fJli0b69at05orJyu8vLxo164dgYGBxMbG4uPjQ0pKCocPH8bLy4vevXtTqFAhxo8fz7Bhw4iLi6NRo0ZYWlpy8+ZNNmzYQNeuXRk4cCD79u2jd+/eNG/eHFdXV968ecPy5cvR19enadOmHx2jEP9JX2StLCGEEEIIIb4CHy4/rlar1c+ePVP369dPbWdnpzY0NFS7uLiop02bpiy3nApQ9+rVS/3rr7+qXVxc1MbGxmpPT0/1/v37M7zuqVOn1M2bN1c7ODiojY2N1ebm5upSpUqpZ86cqU5OTtYq/+TJE7W/v786Z86cajMzM3W1atXUp06dytQ96lpu+0OvX79WT5kyRV2kSBG1sbGxOnv27OrSpUurx4wZo05ISFDKbd68WV28eHG1iYmJ2tHRUT1lyhT1kiVLtJbjvn//vvq7775TW1paqgGNpcjPnDmjLl++vNrIyEjt4OCgnjlzZprLj3/33Xc643327Jl62LBhamdnZ7WRkZE6V65c6kqVKqmnT5+uLPWdlfZI/S7fl7oE+LRp0zT279+/X2sZ7dQ6T58+ra5YsaLaxMREXaBAAfW8efO0rv/HH3+oO3bsqM6VK5fayMhIXaxYMa2lxNO6dqpNmzapCxcurDYwMNBYijw6Olpdq1YttYWFhTpXrlzqLl26qKOiorSWK+/QoYPa3Nxcq15dy8O/efNGPW3aNLW7u7vayMhIbWNjo65bt676zJkzGuXWrVun/t///qc2NzdXm5ubq93d3dW9evVSx8TEqNVqtfrGjRvqTp06qQsVKqQ2MTFR58iRQ+3l5aUODw/XeY9CiLSp1Op/YJYuIYQQQgghvjEqlYpevXppDcMS/z3Vq1fn0aNHXLx48UuHIoT4D5A5coQQQgghhBBCCCH+JSSRI4QQQgghhBBCCPEvIYkcIYQQQgghhBBCiH8JmSNHCCGEEEIIIYQQ4l9CeuQIIYQQQgghhBBC/EtIIkcIIYQQQgghhBDiX8LgSwcghBBCiL9XSkoK9+7dw9LSEpVK9aXDEUIIIYQQH1Cr1Tx79gw7Ozv09NLvcyOJHCGEEOIbd+/ePezt7b90GEIIIYQQIgN37twhf/786ZaRRI4QQgjxjbO0tASgaLdZ6BuZfuFohBBCCCG+fofGt/pHr5eYmIi9vb3y97b0SCJHCCGE+MalDqfSNzJF31gSOUIIIYQQGcmWLdsXuW5mhsHLZMdCiG/C69evcXZ25ujRo186lM/m0aNH5M6dm99///0fuV5cXBwqlYrIyEgADhw4gEql4unTp//I9b82KpWKjRs3/i11+/n50ahRI+Vz9erV6du3r/LZ0dGR2bNn/y3XFkIIIYQQ/27ffCLnw78sp/rwBSX1s67t/v37Guf+/vvvGBkZUbRoUZ3XfP9cc3NzXFxc8PPz48yZMxnG6+joiEql4vjx4xr7+/btS/Xq1TX2PXnyhL59+1KgQAGMjIyws7OjU6dO3L59W6sNUuMxNDSkYMGCDB48mL/++ktn3B9e+9WrV+TMmROVSsWBAwe0Yu7WrRv6+vqsXbtW69jo0aMpWbJkhvf9vtevXzN16lRKlCiBmZkZuXLlonLlyoSEhJCcnKyUu3PnDp06dcLOzg4jIyMKFChAnz59ePz4sVadly5dwtfXFxsbG4yNjXF1dWXkyJG8ePFCo1xq+6tUKszMzChWrBi//PKLRpn3nxU9PT2srKzw9PRk8ODBxMfHp3v/o0ePRqVS0b17d41ykZGRqFQq4uLitGL39vZGX1+fU6dOAf/3sp3eFhoamqVnOtX27dsxMjLi7NmzGvtnzJhBrly50jzvfe7u7hgbG2uVTUpKolChQvTv319jf1xcHNmyZWPx4sWA7uTB4sWLKVGiBBYWFlhbW+Pp6cmkSZM06lm0aBEFCxakUqVKyr6DBw9So0YNcuTIgZmZGS4uLnTo0IHXr18DEBoairW1dYb39HcZPXo07u7umJubkz17dmrVqsWJEyeU47ly5aJ9+/aMGjUqw7rUajU///wz5cuXV9qpTJkyzJ49W+s5/1Iymxj68Nm1sbGhXr16XLhw4Z8JVIcPk1yZkfp7/3ALDw9nzpw5hIaGZqqeJ0+e8MMPP+Dm5oapqSkODg4EBASQkJDwcTcjhBBCCCH+1b75RE5WxcTEEB8fr7Hlzp1bo0xoaCi+vr4kJiZqvHS9LyQkhPj4eC5dusT8+fN5/vw55cuXZ9myZRnGYGJiwpAhQ9It8+TJEypUqEB4eDiLFi3i2rVrhIWFce3aNcqWLcuNGzc0yvv4+BAfH8+NGzeYNWsWQUFBOl8O7e3tCQkJ0di3YcMGLCwsdMbx4sULwsLCGDx4MEuWLMnw3jLy+vVrvL29mTx5Ml27duXo0aOcPHmSXr16MXfuXC5dugTAjRs3KFOmDLGxsaxatYpr166xaNEi9u7dS8WKFXny5IlS5/HjxylfvjyvX79m27ZtXL16lQkTJhAaGkrt2rWVl/pUY8eOJT4+nosXL9K2bVu6dOnCjh07tGKNiYnh3r17nDp1iiFDhhAeHk7RokUzfNk0MTEhODiY2NjYDNvj9u3bHD16lN69eyvta29vr/F8DhgwgCJFimjsa9GihUacGT3TqerVq0f79u1p3749r169AiA6OpqffvqJ+fPnY2trm268R44c4eXLlzRr1oylS5dqHDM3NyckJIS5c+dy+PBh4F3yoWPHjlSuXJkuXbrorHPJkiX07duXgIAAIiMjiYiIYPDgwTx//lwpo1armTdvHv7+/sq+6OhofHx8KFOmDIcOHeLChQvMnTsXIyMj3r59m+59/FNcXV2ZN28eFy5c4MiRIzg6OlKnTh0ePnyolOnYsSMrVqzQeKZ1adeuHX379qVhw4bs37+fyMhIRowYwaZNm9i9e/fffSt/i9Rnd9euXbx69YrvvvtO6/f6tfvwtxkfH0/VqlWxsrLKdBLx3r173Lt3j+nTp3Px4kVCQ0PZuXOnxvMuhBBCCCH+OySR84HcuXNja2ursb2/9JdarSYkJIR27drRunVrgoODddZjbW2Nra2t8mL222+/0aZNG3r37s2ff/6Zbgxdu3bl+PHjbN++Pc0yP/74I/fu3SM8PJy6devi4OBA1apV2bVrF4aGhvTq1UujvLGxMba2ttjb29OoUSNq1arFnj17tOrt0KEDYWFhvHz5Utm3ZMkSOnTooDOOtWvXUrhwYYYOHcqhQ4e4c+dOuveWkdmzZ3Po0CH27t1Lr169KFmyJE5OTrRu3ZoTJ07g4uICQK9evTAyMmL37t1Uq1YNBwcH6tatS3h4OHfv3uXHH38E3n1f/v7+eHh4sH79esqVK0eBAgVo3rw5W7Zs4dixY8yaNUsjBktLS2xtbXFycmLIkCHkyJFDZ1ulPiuurq60bNmSiIgIbGxs6NGjR7r36ObmhpeXlxJjekJCQqhfvz49evRg1apVvHz5En19fY3n08LCAgMDA419pqb/NwdGRs/0h2bNmsXz588ZNWoUb968oUOHDjRo0EAjOZSW4OBgWrduTbt27XQm9qpWrcoPP/xAx44dSUpKYs6cOURGRmr1enrf5s2b8fX1xd/fH2dnZ4oUKUKrVq2YMGGCUubMmTNcv36d7777Ttm3e/dubG1tmTp1KkWLFqVQoUL4+PiwePFiTE1NOXDgAB07diQhIUHpKTF69GgAli9fTpkyZZRnoXXr1jx48EArLhcXF0xMTPDy8mLp0qVavU2OHDlClSpVMDU1xd7enoCAAJKSkpTjrVu3platWjg5OVGkSBFmzpxJYmIi58+fV8oUKVIEOzs7NmzYkGYbrVmzhhUrVrBq1SqGDx9O2bJlcXR0pGHDhuzbtw8vLy/g3RLUY8eOJX/+/BgbG1OyZEl27tyZZr26ZHRPr169YsiQIdjb22NsbIyzszPBwcHExcUpcWTPnh2VSoWfn1+610p9dkuVKkXfvn25c+cOV65cyXQsCxYsUL6jPHny0KxZM+WYrqFLJUuWVJ6BDxUsWBAAT09PVCqVVg/JtHz427S1tcXIyCjN3qK6FC1alHXr1tGgQQMKFSpEjRo1mDBhAlu2bOHNmzc6z3n16hWJiYkamxBCCCGE+DZIIieL9u/fz4sXL6hVqxZt27YlLCxM48UhPf369ePZs2c6kwLvK1iwIN27d2fYsGGkpKRoHU9JSSEsLIw2bdpo9ZAwNTWlZ8+e7Nq1K81/wb948SJHjx7FyMhI61jp0qVxdHRk3bp1wLseIYcOHaJdu3Y66woODqZt27ZYWVlRt27dTA8VSMuKFSuoVasWnp6eWscMDQ0xNzfnyZMn7Nq1i549e2okLABsbW1p06YNq1evRq1WExkZSXR0NP3799dKXpQoUYJatWqxatUqnbGkpKSwbt06/vzzT51t9SFTU1O6d+9ORESE1kv/hyZPnsy6des4ffp0mmVSk4Zt27bF3d0dZ2dnfvvttwzj+FSWlpYsWbKEGTNm0KZNG+7cucPChQszPO/Zs2esXbuWtm3bUrt2bRISEpSeN++bMGECBgYGtG3bluHDhzN37lzy5cuXZr22trYcP36cW7dupVnm8OHDuLq6aszwbmtrS3x8PIcOHdJ5TqVKlZg9ezbZsmVTekoMHDgQgOTkZMaNG0dUVBQbN24kLi5OI+lw8+ZNmjVrRqNGjYiKiqJbt25aibnr16/j4+ND06ZNOX/+PKtXr+bIkSP07t1bZzyvX7/m559/xsrKihIlSmgcK1eunM62TLVixQrc3Nxo2LCh1jGVSoWVlRUAc+bMYcaMGUyfPp3z58/j7e3N999/n6neYZm9p/bt27Nq1SoCAwO5fPkyQUFBWFhYYG9vr/y5ktrTZs6cOZm6bkJCAmFhYQDKbzGjWE6fPk1AQABjx44lJiaGnTt3UrVq1UxdT5eTJ08CEB4eTnx8POvXr//ouj6HhIQEsmXLhoGB7jULJk2ahJWVlbLJ0uNCCCGEEN+O/0QiZ+vWrVhYWGhsdevW1Vk2f/78GuWKFCmicTw4OJiWLVuir69P0aJFcXJy0jk3jC7u7u4AOudB+dBPP/3EzZs3WbFihdaxhw8f8vTpUzw8PHSe6+HhgVqt5tq1a8q+1DYwMTGhWLFiPHjwgEGDBuk8v1OnTkpvitDQUOrVq4eNjY1WudjYWI4fP6701Gjbti0hISGo1eoM7y8tsbGxSjulV0atVqd7/3/++ScPHz7k6tWryr60yqaWSTVkyBAsLCwwNjamWbNmZM+enc6dO2cq/sx+x6VKlcLX1zfdIXTh4eG8ePECb29v4F37ptUDLD0ZPdO61KhRg2bNmrFmzRoCAwPJmTNnhueEhYXh4uJCkSJF0NfXp2XLljrjNTU1Zc6cOWzcuJHq1avTtm3bdOsdNWoU1tbWODo64ubmhp+fH2vWrNFIct66dQs7OzuN85o3b06rVq2oVq0aefPmpXHjxsybN0/pmWBkZISVlRUqlUqjdxO8+w3UrVsXJycnKlSoQGBgIDt27FCGcwUFBeHm5sa0adNwc3OjZcuWWr1LJk2aRJs2bejbty8uLi5UqlSJwMBAli1bpjE/1fu/zVmzZrFnzx5y5cqlUZednV26iazY2Fjc3NzSbUeA6dOnM2TIEFq2bImbmxtTpkyhZMmSmZ5UN6N7unr1KmvWrGHJkiU0btwYJycnatasSYsWLdDX1ydHjhzA//W0SU0wpSX12bW2tmblypV8//33ym8so1hu376Nubk59evXp0CBAnh6ehIQEJCp+9Ql9c/AnDlzYmtrq9xLRi5cuKDx+ytXrtxHx5Dq0aNHjBs3jq5du6ZZZtiwYSQkJCjbp/aWFEIIIYQQX4//RCLHy8uLyMhIjS2toRyHDx/WKPf+8KanT5+yfv16jRfPrLxcpyY4MrOcmI2NDQMHDmTkyJFpzgmRlYRJahucOHGCDh060LFjR5o2baqzbNu2bTl27Bg3btwgNDSUTp066Sy3ZMkSvL29lZfOevXqkZCQwL59+zId14eyck9/V9lBgwYRGRnJvn37KF++PLNmzcLZ2TlL18nMdzx+/HgOHz6c5vwlS5YsoUWLFsq/uLdq1YqIiAiuX7+eyTt5J71nOi13795l586dmJmZpdsT5MN4P/xtrF27lmfPnmmVDQ4OxszMjAsXLmQ4YWvevHk5duwYFy5coE+fPspwLx8fHyWZ8/LlS0xMTDTO09fXJyQkhN9//52pU6eSL18+Jk6cqMxZkp4zZ87QoEEDHBwcsLS0pFq1agDKROIxMTGULVtW45wPX9CjoqIIDQ3VeIn39vYmJSWFmzdvKuVSf5tHjx7Fx8cHX19frR5dpqam6U5YnJnnOzExkXv37lG5cmWN/ZUrV+by5csZnp+Ze4qMjERfX19pr091+PBhzpw5Q2hoKK6urixatCjTsdSuXZsCBQrg5OREu3btWLFixReZ9NnNzU3j95faK+ljJSYm8t1331G4cOE0h4HBu+G02bJl09iEEEIIIcS34T+RyDE3N8fZ2VljS2soR8GCBTXKFShQQDm2cuVK/vrrL8qXL4+BgQEGBgYMGTKEI0eOaPXq0CX1ZSl1roWM9O/fn5cvX7JgwQKN/TY2NlhbW6f58nX58mVUKpVG8iG1DUqUKMGSJUs4ceJEmgmonDlzUr9+ffz9/fnrr7909l56+/YtS5cuZdu2bUpbmJmZ8eTJk0+a9NjV1VVjDgxdnJ2dUalU6d5/9uzZsbGxwdXVVdmXVtnUMqly5cqFs7MzVapUYe3atQQEBBAdHZ2p+FOv4+jomGHZQoUK0aVLF4YOHar1Iv7kyRM2bNjAggULlPbNly8fb968yXL7pvdMp6VLly6ULl2arVu3snDhQg4ePJhu+ejoaI4fP87gwYOVeCtUqKBMhv2+1atXs3XrVo4ePYqlpSX9+vXL1H0ULVqUnj178uuvv7Jnzx727NmjxJUrV640557Kly8f7dq1Y968eVy6dIm//vpLIyHwoaSkJLy9vcmWLRsrVqzg1KlTyvw0WZlo9/nz53Tr1k3jJT4qKorY2FgKFSqklEv9bVaoUIHg4GAMDAy0fptPnjzR2SsuVWZ+N59DRvf04VDHT1WwYEHc3Nzo0KEDnTt31pinKaNYLC0tOXv2LKtWrSJv3ryMHDmSEiVKKHMY6enpaf3u3l8V73MxMjLS+P19yhCnZ8+e4ePjg6WlJRs2bMDQ0PAzRiqEEEIIIf4t/hOJnM8lODiYAQMGaL04VKlSJVMv16nzcdSqVStT17OwsGDEiBFMmDBBo1eDnp4evr6+rFy5UmuJ59TEj7e3d5pd//X09Bg+fDg//fSTxqTG7+vUqRMHDhygffv26Ovrax3fvn07z54949y5cxrtsWrVKtavX5/h8sJpad26NeHh4Zw7d07rWHJyMklJSeTMmZPatWuzYMECrfjv37/PihUraNGiBSqVipIlS+Lu7s6sWbO05huKiooiPDycVq1apRmPvb09LVq0YNiwYRnG/vLlS37++WeqVq2a7kv3+0aOHMnVq1e1kh0rVqwgf/78REVFabTvjBkzCA0N/VtXXfrll184cuQIwcHBeHl50aNHDzp16pTuXFDBwcFUrVpVK97+/ftrJCX++OMPevXqxfjx4ylRogShoaEsW7ZM56pg6SlcuDCAEpOnpydXrlzJsGdK9uzZyZs3r3KerhWsrly5wuPHj5k8eTJVqlTB3d1dq4eMm5ub1vxGqcvDpypVqhTR0dFaSWRnZ+d051xKSUlRVgxLdfHiRZ3zRqVq3bo1V69eZdOmTVrH1Gq1Mp+KnZ0dERERGscjIiKU9sxIRvdUrFgxUlJS0kz8pd73xzy/vXr14uLFi0pSLTPta2BgQK1atZg6dSrnz58nLi5O6TFoY2Oj0TMrMTFRo6fU54z9c0hMTKROnToYGRmxefNmrR5oQgghhBDiv0MSOR948OAB9+/f19iSk5OJjIzk7NmzdO7cmaJFi2psrVq1YunSpRqrhzx9+pT79+9z69Yt9uzZQ7NmzVi5ciULFy7M9JKz8G4FKysrK1auXKmxf+LEidja2lK7dm127NjBnTt3OHToEN7e3iQnJzN//vx0623evDn6+vpplvPx8eHhw4eMHTtW5/Hg4GC+++47SpQoodEWvr6+WFtba8zt8/LlS62hbWkND+rbty+VK1emZs2azJ8/n6ioKG7cuMGaNWuoUKGCMinrvHnzePXqFd7e3spqWTt37qR27drky5dPWdFIpVIRHBxMdHQ0TZs25eTJk9y+fZu1a9fSoEEDKlasSN++fdNtqz59+rBlyxatF/fUZyU2NpawsDAqV67Mo0ePMjUxcKo8efLQv39/AgMDtdq3WbNmWs+av78/jx49ytJKQ2k907rcunWL/v37M336dKXnzpQpU1CpVAwdOlTnOcnJySxfvpxWrVppxdu5c2dOnDihLBvftWtXPDw8lDYvV64cgwYNomvXrmkOserRowfjxo0jIiKCW7ducfz4cdq3b4+NjQ0VK1YE3g1Pev78uXIdeDePTY8ePdi9ezfXr1/n0qVLDBkyhEuXLtGgQQPgXc+p58+fs3fvXh49esSLFy9wcHDAyMiIuXPncuPGDTZv3sy4ceM0YurWrRtXrlxhyJAhyrwwqRN9pw6rGzJkiLJ0fGRkJLGxsWzatEmZjDcpKYnhw4crEzmfOXOGTp06cffuXZo3b65c68WLF5w5c4Y6dero/oIBX19fWrRoQatWrZg4cSKnT5/m1q1bbN26lVq1arF//37g3bDBKVOmsHr1amJiYhg6dCiRkZH06dMnzbrfl9E9OTo60qFDBzp16sTGjRu5efMmBw4cYM2aNQAUKFAAlUrF1q1befjwocYS8hkxMzOjS5cujBo1CrVanWEsW7duJTAwkMjISG7dusWyZctISUlR5hKqUaMGy5cv5/Dhw1y4cIEOHTroTFqnyp07N6ampuzcuZM//vgjwyGBn1NqEicpKYng4GASExOV3/KXSiwJIYQQQogvRxI5H3BzcyNv3rwa25kzZwgODqZw4cI6J+Jt3LgxDx480Jh7pGPHjuTNmxd3d3d69OiBhYUFJ0+epHXr1lmKx9DQkHHjxmlMjgrvhj8dP34cLy8vunXrRqFChfD19aVQoUKcOnUKJyendOs1MDCgd+/eTJ06VWdPC5VKRa5cuXT2HPjjjz/Ytm2bzjl29PT0aNy4sUYvjKtXr+Lp6amxdevWTWdcxsbG7Nmzh8GDBxMUFESFChUoW7YsgYGBBAQEULRoUQBcXFw4ffo0Tk5Oyn137doVLy8vjh07ptEbqVKlShw/fhx9fX3q1q2Ls7Mzw4YNo0OHDuzZswdjY+N026pw4cLUqVOHkSNHaux3c3PDzs6O0qVLM3nyZGrVqsXFixcz3bsh1cCBA5VJduHd/CxRUVE629fKyoqaNWtmadLjtJ7pD6Uu1V6xYkWNSVTNzMwIDQ1Nc4jV5s2befz4MY0bN9Y65uHhgYeHB8HBwSxbtozw8HBCQkI0VhAbM2YM1tbWaQ6xqlWrFsePH6d58+a4urrStGlTTExM2Lt3rzIJc86cOWncuLFGArFcuXI8f/6c7t27U6RIEapVq8bx48fZuHGjModLpUqV6N69Oy1atMDGxoapU6diY2NDaGgoa9eupXDhwkyePJnp06drxFSwYEF+++031q9fT/HixVm4cKGyalXq81S8eHEOHjzI1atXqVKlCp6enowcOVKZlFlfX58rV67QtGlTXF1dadCgAY8fP+bw4cMaE1Jv2rQJBwcHqlSporN94N3vdeXKlcycOVO5v+LFizN69GgaNmyoTJgdEBBA//79GTBgAMWKFWPnzp3KMuqZkdE9ASxcuJBmzZrRs2dP3N3d6dKli/JnTL58+RgzZgxDhw4lT548aa7glZbevXtz+fJl1q5dm2Es1tbWrF+/nho1auDh4cGiRYtYtWqV0rbDhg2jWrVq1K9fn++++45GjRppDHn7kIGBAYGBgQQFBWFnZ6dzhbC/y9mzZzlx4gQXLlzA2dlZ47cskxgLIYQQQvz3qNSfssSQEEJ8Jc6fP0/t2rW5fv26RmLsnzJhwgQWLVr02V+sK1SoQEBAQJaTwEK8LzExESsrK2WYnRBCCCGE+Lpk5e9r0iNHCPFNKF68OFOmTEl3npPPacGCBZw6dYobN26wfPlypk2bRocOHT7rNR49ekSTJk3SncdJCCGEEEII8d8iPXKEEOIj9OvXj9WrV/PkyRMcHBxo164dw4YNU5aLF9++9Hp+7dixI93hcP806ZEjhBBCCPF1y8rf1ySRI4QQQnyEa9eupXksX758n3059k+R+heDEj8sQt/464lLCCHEv9OZae2/dAhCfHNkaJUQ4pv2+PFjcufOTVxc3JcO5bOJjo4mf/786S7z/jkdOHAAlUrF06dPAQgNDc3Sinrfkri4OFQqFZGRkVk6T9fS56nb15TEEUIIIYQQ35ZvLpHj5+dHo0aNtPZ/+NKS+lnXdv/+fY1zf//9d4yMjJQVkz70/rnm5ua4uLjg5+enc2WgDzk6OqJSqTh+/LjG/r59+1K9enWNfU+ePKFv374UKFAAIyMj7Ozs6NSpE7dv39Zqg9R4DA0NKViwIIMHD9Za+Sq1zIfXfvXqFTlz5kSlUnHgwAGtmLt164a+vj5r167VOjZ69GhKliyZ4X2/7/Xr10ydOpUSJUpgZmZGrly5qFy5MiEhIRrLZN+5c4dOnTphZ2eHkZERBQoUoE+fPjx+/FirzkuXLuHr64uNjQ3Gxsa4uroycuRIXrx4oVEutf1VKhVmZmYUK1aMX375RaPM+8+Knp4eVlZWeHp6MnjwYOLj49O9/9GjR6NSqejevbtGucjISFQqlc5EhLe3N/r6+pw6dQr4v5fM9LbQ0NAsPdMABw8exNDQkCNHjmjsT0pKwsnJiYEDB2qd876XL1+SI0cOcuXKxatXrzSO3bt3j+zZs2stq37ixAkMDQ3ZvXs3oJ08ePv2LZMnT8bd3R1TU1Ny5MhB+fLltb6TCRMm0LBhQxwdHZV9GzZsoEKFClhZWWFpaUmRIkU0lpb/mGfzc3ny5Ak//PADbm5umJqa4uDgQEBAgMYS1oULF6ZChQrMnDkzw/oy+5v5kjKbGAoNDdX4feXNm5cWLVpo/bn2T/rw/xeZ8eGfu3ny5KF27dosWbKElJSUvy9YIYQQQgjxn/PNJXKyKiYmhvj4eI0td+7cGmVCQ0Px9fUlMTGREydO6KwnJCSE+Ph4Ll26xPz583n+/Dnly5dn2bJlGcZgYmLCkCFD0i3z5MkTKlSoQHh4OIsWLeLatWuEhYVx7do1ypYty40bNzTK+/j4EB8fz40bN5g1axZBQUGMGjVKq157e3tCQkI09m3YsCHNuR9evHhBWFgYgwcPZsmSJRneW0Zev36Nt7c3kydPpmvXrhw9epSTJ0/Sq1cv5s6dy6VLlwC4ceMGZcqUITY2llWrVnHt2jUWLVrE3r17qVixIk+ePFHqPH78OOXLl+f169ds27aNq1evMmHCBEJDQ6lduzavX7/WiGHs2LHEx8dz8eJF2rZtS5cuXdixY4dWrDExMdy7d49Tp04xZMgQwsPDKVq0KBcuXEj3Hk1MTAgODiY2NjbD9rh9+zZHjx6ld+/eSvva29trPJ8DBgygSJEiGvtatGihEWdGzzRAtWrV+OGHH/Dz89PoBTJ48GBMTU0ZP358urGuW7eOIkWK4O7uzsaNGzWO2dnZMXfuXIYNG6bc98uXL+nQoQOdO3emTp06OuscM2YMs2bNYty4cURHR7N//366du2q8UL94sULgoOD8ff3V/bt3buXFi1a0LRpU06ePMmZM2eYMGHCV5PUuHfvHvfu3WP69OlcvHiR0NBQdu7cqXEPAB07dmThwoW8efMmzboy+5v5N8mWLRvx8fHcvXuXdevWERMTQ/Pmzb90WFmW+uduXFwcO3bswMvLiz59+lC/fv10v1MhhBBCCCGy4j+fyMmdOze2trYam57e/zWLWq0mJCSEdu3a0bp1a4KDg3XWY21tja2tLY6OjtSpU4fffvuNNm3a0Lt3b/788890Y+jatSvHjx9n+/btaZb58ccfuXfvHuHh4dStWxcHBweqVq3Krl27MDQ0pFevXhrljY2NsbW1xd7enkaNGlGrVi327NmjVW+HDh0ICwvj5cuXyr4lS5akufrO2rVrKVy4MEOHDuXQoUOfvNTy7NmzOXToEHv37qVXr16ULFkSJycnWrduzYkTJ3BxcQGgV69eGBkZsXv3bqpVq4aDgwN169YlPDycu3fv8uOPPwLvvi9/f388PDxYv3495cqVo0CBAjRv3pwtW7Zw7NgxZs2apRGDpaUltra2ODk5MWTIEHLkyKGzrVKfFVdXV1q2bElERAQ2Njb06NEj3Xt0c3PDy8tLiTE9ISEh1K9fnx49erBq1SpevnyJvr6+xvNpYWGBgYGBxr73h3Fk9Ey/b+LEiRgZGSmJxP379/PLL7+wbNkyTExM0o01ODiYtm3b0rZtW52/i7Zt2+Lt7Y2fnx8pKSkMGzaM5ORkpk2blmadmzdvpmfPnjRv3pyCBQtSokQJ/P39NXoHbd++HWNjYypUqKDs27JlC5UrV2bQoEG4ubnh6upKo0aNmD9/PvAuGTtmzBiioqI0ejEBzJw5k2LFimFubo69vT09e/bk+fPnGnEtXrwYe3t7zMzMaNy4MTNnztTqbbJp0yZKlSqFiYkJTk5OjBkzRnl5L1q0KOvWraNBgwYUKlSIGjVqMGHCBLZs2aLxgl+7dm2ePHnCwYMH02yjzP5mXr16RUBAALlz58bExIT//e9/Si+vzErvngCePn1Kt27dyJMnDyYmJhQtWpStW7dy4MABOnbsSEJCgtLeo0ePTvM6KpUKW1tb8ubNS6VKlfD39+fkyZMkJiZmKha1Ws3o0aNxcHDA2NgYOzs7AgICNOr/MNlobW2tPAPvi4uLw8vLC4Ds2bOjUqnw8/PLVHul/rmbL18+SpUqxfDhw9m0aRM7duzQuFZGz1xqb6atW7fi5uaGmZkZzZo148WLFyxduhRHR0eyZ89OQEAAb9++zVRsQgghhBDi2/GfT+RkZP/+/bx48YJatWrRtm1bwsLCMj2HRb9+/Xj27JnOpMD7ChYsSPfu3Rk2bJjOLvgpKSmEhYXRpk0bbG1tNY6ZmprSs2dPdu3apdEr5X0XL17k6NGjGBkZaR0rXbo0jo6OrFu3DnjXI+TQoUO0a9dOZ12pL+9WVlbUrVtX54tQVqxYsYJatWrh6empdczQ0BBzc3OePHnCrl276Nmzp9a8E7a2trRp04bVq1ejVquJjIwkOjqa/v37ayUvSpQoQa1atVi1apXOWFJSUli3bh1//vmnzrb6kKmpKd27dyciIoIHDx6kW3by5MmsW7eO06dPp1kmNWnYtm1b3N3dcXZ25rfffsswjk9hYmLCsmXL+Pnnn9m0aROdOnVi+PDhlC5dOt3zrl+/zrFjx/D19cXX15fDhw9z69YtrXKLFi0iNjaWNm3aMG/ePEJCQtJd6cfW1pZ9+/bx8OHDNMscPnxYKz5bW1suXbrExYsXdZ7TokULrZ5Mqb2Y9PT0CAwM5NKlSyxdupR9+/YxePBg5dyIiAi6d+9Onz59iIyMpHbt2kyYMEErpvbt29OnTx+io6MJCgoiNDRUq9z7Uicxe3+VKyMjI0qWLMnhw4fTPC8zvxl417Nq3bp1LF26lLNnz+Ls7Iy3t3eaf058KKN7SklJoW7dukRERPDrr78SHR3N5MmT0dfXp1KlSsyePVvpaRMfH5/hUL1UDx48YMOGDejr66Ovr5+pWNatW6f0PIyNjWXjxo0UK1YsU9f7kL29vfLnYWrvtjlz5nxUXQA1atSgRIkSrF+/XtmX0TMH73qeBQYGEhYWxs6dOzlw4ACNGzdm+/btbN++neXLlxMUFJTmnxGvXr0iMTFRYxNCCCGEEN+GbzKRs3XrViwsLDS2unXr6iybP39+jXJFihTROB4cHEzLli3R19enaNGiODk56ZwbRhd3d3eATE3I+tNPP3Hz5k1WrFihdezhw4c8ffoUDw8Pned6eHigVqs1VlBJbQMTExOKFSvGgwcPGDRokM7zO3XqpAzjCQ0NpV69etjY2GiVi42N5fjx48oLcNu2bQkJCeFTFj6LjY1V2im9Mmq1Ot37//PPP3n48CFXr15V9qVVNrVMqiFDhmBhYYGxsTHNmjUje/bsdO7cOVPxZ/Y7LlWqFL6+vukOoQsPD+fFixd4e3sDpNnTJSMZPdMfKlOmDMOGDaNJkybkzJkzUz2HlixZQt26dcmePTs5cuTA29tba4gevOsdNG7cOMLCwujatStVq1ZNt96ZM2fy8OFDbG1tKV68ON27d9ca5nbr1i3s7Ow09v3www+ULVuWYsWK4ejoSMuWLVmyZIkyd4+pqalWT6bUpGDfvn3x8vLC0dGRGjVqMH78eNasWaPUPXfuXOrWrcvAgQNxdXWlZ8+eWn+ejBkzhqFDh9KhQwecnJyoXbs248aNIygoSOd9Pnr0iHHjxtG1a1etY3Z2djqTYqky85tJSkpi4cKFTJs2jbp161K4cGEWL16Mqalppp+pjO4pPDyckydPsn79emrXro2TkxP169enbt26GBkZYWVlpfS0Se1JlpaEhAQsLCwwNzcnT5487N+/n169eilJqYxiuX37Nra2ttSqVQsHBwfKlStHly5dMnWfH9LX1ydHjhzA//Vus7Ky+qi6Urm7u2v8GZHRMweQnJzMwoUL8fT0pGrVqjRr1owjR44QHBxM4cKFqV+/Pl5eXuzfv1/nNSdNmoSVlZWy2dvbf9I9CCGEEEKIr8c3mcjx8vIiMjJSY/twstRUhw8f1ij3/vCmp0+fsn79etq2bavsy8rLdWqCQ6VSZVjWxsaGgQMHMnLkSK05XD6sLzNS2+DEiRN06NCBjh070rRpU51l27Zty7Fjx7hx4wahoaF06tRJZ7klS5bg7e1Nrly5AKhXrx4JCQns27cv03F9KCv39HeVHTRoEJGRkezbt4/y5csza9YsnJ2ds3SdzHzH48eP5/Dhw8pEvx9asmQJLVq0UHpotGrVioiICK5fv57JO3knvWc6LSNGjCAlJYWhQ4dq9BDR5e3btyxdulTrdxEaGqrVo+zt27eEhoZiZmbG8ePHM5wnpHDhwly8eJHjx4/TqVMnHjx4QIMGDTQSay9fvtQa9mVubs62bdu4du0aP/30ExYWFgwYMIBy5cppTXD9ofDwcGrWrEm+fPmwtLSkXbt2PH78WDkvJiaGcuXKaZzz4eeoqCjGjh2rkUDr0qUL8fHxWtdPTEzku+++o3DhwjqHG5mamqYbc2ae7evXr5OcnEzlypWVfYaGhpQrV47Lly9neH5m7ikyMpL8+fPj6uqaqfrSY2lpSWRkJKdPn2bGjBmUKlVKozdTRrE0b96cly9f4uTkRJcuXdiwYcNXNSeNWq3W+DMio2cOwMzMjEKFCimf8+TJg6Ojo0ZCLE+ePGn2Bhw2bBgJCQnK9qnDYIUQQgghxNfjm0zkmJubay0Fmy9fPp1lCxYsqFGuQIECyrGVK1fy119/Ub58eQwMDDAwMGDIkCEcOXJEq1eHLqkvTAULFsxU3P379+fly5csWLBAY7+NjQ3W1tZpvoBdvnwZlUqlkXxIbYMSJUqwZMkSTpw4kWYCKmfOnNSvXx9/f3/++usvnb2XUl/et23bprSFmZkZT548+aRJj11dXbly5Uq6ZZydnVGpVOnef/bs2bGxsVFeKtMr++GLZ65cuXB2dqZKlSqsXbuWgIAAoqOjMxV/6nXeXz0pLYUKFaJLly4MHTpU62X8yZMnbNiwgQULFijtmy9fPt68eZPl9k3vmU5LavImoyQOwK5du7h7966SdDIwMKBly5bcunWLvXv3apSdPn06N27c4PTp0/z+++9MnDgxw/r19PQoW7Ysffv2Zf369YSGhhIcHMzNmzeBd99XWvNOFSpUiM6dO/PLL79w9uxZoqOjWb16dZrXiouLo379+hQvXpx169Zx5swZZV6dtBKqujx//pwxY8ZoJNAuXLhAbGysRtLp2bNn+Pj4YGlpyYYNGzA0NNSq68mTJzp7xKXKzG/mc8jonj7n8tp6eno4Ozvj4eFB//79qVChgsbcUxnFYm9vT0xMDAsWLFCGm1atWlWZ7FqlUmn95v7JibAvX76s/H8gs8/ch89G6mpYH+5La0UsY2NjsmXLprEJIYQQQohvwzeZyPlcgoODGTBggMbLQ1RUFFWqVMnUy3XqHBG1atXK1PUsLCwYMWIEEyZM4NmzZ8p+PT09fH19WblypdYy0qmJH29vb2U4wIf09PQYPnw4P/30k8akxu/r1KkTBw4coH379sq8FO/bvn07z54949y5cxrtsWrVKtavX5+lZXrf17p1a8LDwzl37pzWseTkZJKSksiZMye1a9dmwYIFWvHfv3+fFStW0KJFC1QqFSVLlsTd3Z1Zs2ZpveBERUURHh5Oq1at0ozH3t6eFi1aMGzYsAxjf/nyJT///DNVq1ZN98X7fSNHjuTq1auEhYVp7F+xYgX58+cnKipKo31nzJhBaGjoVzWhaepwww97vbVs2VIjWXjp0iVGjRrFwoUL8fDwYOHChYwfP57z589n6XqFCxcGUOam8vT0zFSizdHRETMzM+U8IyMjrXY8c+YMKSkpzJgxgwoVKuDq6sq9e/c0yri5uWlNEvzh51KlShETE6OVQHZ2dlbmakpMTKROnToYGRmxefPmNCeTvnjxos75b1Jl5jdTqFAhjIyMiIiI0Dh26tQppT0zktE9FS9enN9//z3NpLau9s6soUOHsnr1as6ePZupWOBdT6YGDRoQGBjIgQMHOHbsmLKinI2NDfHx8Ur9sbGx6fZ6Sp0j63P87vbt28eFCxeUHpGZeeaEEEIIIYRIz38+kfPgwQPu37+vsSUnJxMZGcnZs2fp3LkzRYsW1dhatWrF0qVLtVZvuX//Prdu3WLPnj00a9aMlStXsnDhQq3VbdLTtWtXrKysWLlypcb+iRMnYmtrS+3atdmxYwd37tzh0KFDeHt7k5ycrPyLblqaN2+Ovr5+muV8fHx4+PAhY8eO1Xk8ODiY7777jhIlSmi0ha+vL9bW1hpz+7x8+VLrJT+t4UF9+/alcuXK1KxZk/nz5xMVFcWNGzdYs2YNFSpUUJaunjdvHq9evcLb21tZLWvnzp3Url2bfPnyKcMwVCoVwcHBREdHK0tR3759m7Vr19KgQQMqVqxI3759022rPn36sGXLFq2JiVOfldjYWMLCwqhcuTKPHj1i4cKF6db3vjx58tC/f38CAwO12rdZs2Zaz5q/vz+PHj1i586dmb5GWs/05/Dw4UO2bNlChw4dtGJt3749Gzdu5MmTJ7x584YOHTrQpEkTmjRpAkDTpk1p2rQpfn5+aQ57adasGbNmzeLEiRPcunWLAwcO0KtXL1xdXZV5Yby9vbl06ZJGr5zRo0czePBgDhw4wM2bNzl37hydOnUiOTmZ2rVrA+8SOzdv3iQyMpJHjx7x6tUrnJ2dSU5OZu7cudy4cYPly5ezaNEijZh++OEHtm/fzsyZM4mNjSUoKIgdO3ZoDJUZOXIky5YtY8yYMVy6dInLly8TFhbGTz/9BPxfEicpKYng4GASExOV7+b9ZEFcXBx3795NN/mbmd+Mubk5PXr0YNCgQezcuZPo6Gi6dOnCixcvtJY8T0tG91StWjWqVq1K06ZN2bNnDzdv3mTHjh3Ks+ro6Mjz58/Zu3cvjx49ynCI2/vs7e1p3LgxI0eOzFQsqb22Ll68yI0bN/j1118xNTVVeqPVqFGDefPmce7cOU6fPk337t119oZKVaBAAVQqFVu3buXhw4daq5il5dWrV9y/f5+7d+9y9uxZJk6cSMOGDalfvz7t27cHyNQzJ4QQQgghRHr+84kcNzc38ubNq7GdOXNGmVBS16SijRs35sGDBxpzj3Ts2JG8efPi7u5Ojx49sLCw4OTJk7Ru3TpL8RgaGjJu3Dj++usvjf05c+bk+PHjeHl50a1bNwoVKoSvry+FChXi1KlTODk5pVuvgYEBvXv3ZurUqTpX3VKpVOTKlUvnak1//PEH27Zt0znHjp6eHo0bN9boiXH16lU8PT01tm7duumMy9jYmD179jB48GCCgoKoUKECZcuWJTAwkICAAIoWLQqAi4sLp0+fxsnJSbnvrl274uXlxbFjxzR6I1WqVInjx4+jr69P3bp1cXZ2ZtiwYXTo0IE9e/ZgbGycblsVLlyYOnXqKC+Rqdzc3LCzs6N06dJMnjyZWrVqcfHixUz3cEg1cOBAjXkuzpw5Q1RUlM72tbKyombNmlma9DitZ/pzWLZsGebm5tSsWVPrWM2aNTE1NeXXX39l4sSJ3L17l3nz5mmUmT9/PvHx8WkOsfL29mbLli00aNAAV1dXOnTogLu7O7t371aGfRUrVoxSpUppTA5brVo1bty4Qfv27XF3d6du3brcv3+f3bt34+bmBrxLJPn4+ODl5YWNjQ2rVq2iRIkSzJw5kylTplC0aFFWrFjBpEmTNGKqXLkyixYtYubMmZQoUYKdO3fSr18/jR413t7ebN26ld27d1O2bFkqVKjArFmzlETC2bNnOXHiBBcuXMDZ2Vnju3l/7pJVq1ZRp06ddIfDZfY3M3nyZJo2bUq7du0oVaoU165dY9euXWTPnj3Nuj/8LtK7J3i3WlTZsmVp1aoVhQsXZvDgwUpiqlKlSnTv3p0WLVpgY2PD1KlTM3XdVP369WPbtm2cPHkyw1isra1ZvHgxlStXpnjx4oSHh7NlyxZy5swJwIwZM7C3t6dKlSq0bt2agQMHYmZmlua18+XLp0ywnCdPHnr37p2pmHfu3EnevHlxdHTEx8eH/fv3ExgYyKZNm5Sejpl55oQQQgghhEiPSv0pSw4JIcQXsG3bNgYNGsTFixe1lpn/J3Tp0oUrV66ku0x4Vr1+/RoXFxdWrlypMUmxEJ9DYmIiVlZWyrL3QgghhBDi65KVv69lPLOpEEJ8Zb777jtiY2O5e/fuP7Ks8vTp06lduzbm5ubs2LGDpUuXak1K/qlu377N8OHDJYkjhBBCCCGESJf0yBFCiAz4+vpy4MABnj17hpOTEz/88APdu3f/0mGJf8jt27fTHUIZHR2Ng4PDPxhR1kmPHCGEEEKIr1tW/r4miRwhhBAiHW/evCEuLi7N446OjsocTl+r1L8YlPhhEfrGn2/peCGEEF+fM9Paf+kQhBAfISuJnP/8ZMdCfCqVSsXGjRv/8evGxcWhUqmIjIz8x6/9X/NvauvRo0dTsmTJLx3GVyc0NDRLKwi+z8DAQOfS56nb157EEUIIIYQQ3xZJ5AiRjvv37/PDDz/g5OSEsbEx9vb2NGjQgL17937p0P5Rjo6OqFSqNDc/P78vHWKaqlevnuGS81+DGTNmkD17dq0V6wBevHhBtmzZtJat/9q4u7tjbGzM/fv3s3zupybL9u/fT7169ciZMydmZmYULlyYAQMGcPfu3Y+q75+ydetWqlWrhqWlJWZmZpQtW5bQ0FCNMrra5tmzZ3h5eVG4cGF+//33fzZoIYQQQgjxRUkiR4g0xMXFUbp0afbt28e0adO4cOECO3fuxMvLi169en3p8P42r1+/1tp36tQp4uPjiY+PZ926dQDExMQo++bMmfNPh/nNadeuHUlJSaxfv17r2G+//cbr169p27btF4gsc44cOcLLly9p1qwZS5cu/UevHRQURK1atbC1tWXdunVER0ezaNEiEhISmDFjxj8aS1bMnTuXhg0bUrlyZU6cOMH58+dp2bIl3bt3Z+DAgWme9/DhQ7y8vEhKSuLw4cPkz5//H4xaCCGEEEJ8aZLIESINPXv2RKVScfLkSZo2bYqrqytFihShf//+HD9+XKPso0ePaNy4MWZmZri4uLB582bl2Nu3b/H396dgwYKYmpri5uamlfjw8/OjUaNGTJw4kTx58mBtbc3YsWN58+YNgwYNIkeOHOTPn5+QkBCtOK9cuUKlSpUwMTGhaNGiHDx4UOP4xYsXqVu3LhYWFuTJk4d27drx6NEj5Xj16tXp3bs3ffv2JVeuXHh7e2tdw8bGBltbW2xtbcmRIwcAuXPnVvYdOHCAUqVKYWJigpOTE2PGjOHNmzfK+SqViqCgIOrXr4+ZmRkeHh4cO3aMa9euUb16dczNzalUqRLXr19XzkkdIhQUFIS9vT1mZmb4+vqSkJCQma8vTbqGwllbW2v0gjh58iSenp6YmJhQpkwZzp07p1XP5s2bcXFxwcTEBC8vL5YuXYpKpeLp06dKmSNHjlClShVMTU2xt7cnICCApKQknXHlzp2bBg0asGTJEq1jS5YsoVGjRuTIkYMhQ4bg6uqKmZkZTk5OjBgxguTk5DTvV1ePpEaNGmn0onr16hUDBw4kX758mJubU758eQ4cOJBmnboEBwfTunVr2rVrp/MeMmr3ggULAuDp6YlKpaJ69eoApKSkMHbsWPLnz4+xsTElS5Zk586dSh2///47AQEBBAQEsGTJEqpXr46joyNVq1bll19+YeTIkTrjvX79Og0bNiRPnjxYWFhQtmxZwsPDlePDhw+nfPnyWueVKFGCsWPHKp9/+eUXPDw8MDExwd3dPdOrmd25c4cBAwbQt29fJk6cSOHChXF2dmbAgAFMmzaNGTNmcOLECZ3nValSBSsrK/bt20fOnDkzdT0hhBBCCPHtkESOEDo8efKEnTt30qtXL8zNzbWOfzjXxpgxY/D19eX8+fPUq1ePNm3a8OTJE+Ddi2j+/PlZu3Yt0dHRjBw5kuHDh7NmzRqNOvbt28e9e/c4dOgQM2fOZNSoUdSvX5/s2bNz4sQJunfvTrdu3bSGUQwaNIgBAwZw7tw5KlasSIMGDXj8+DEAT58+pUaNGnh6enL69Gl27tzJH3/8ga+vr0YdS5cuxcjIiIiICBYtWpSltjp8+DDt27enT58+REdHExQURGhoKBMmTNAoN27cONq3b09kZCTu7u60bt2abt26MWzYME6fPo1araZ3794a51y7do01a9awZcsWdu7cyblz5+jZs2eW4suq58+fU79+fQoXLsyZM2cYPXq0Vu+Imzdv0qxZMxo1akRUVBTdunXjxx9/1Chz/fp1fHx8aNq0KefPn2f16tUcOXJE6x7f5+/vz759+7h165ay78aNGxw6dAh/f38ALC0tCQ0NJTo6mjlz5rB48WJmzZr1Sffcu3dvjh07RlhYGOfPn6d58+b4+PgQGxubqfOfPXvG2rVradu2LbVr1yYhIYHDhw9nKYaTJ08CEB4eTnx8vNIzac6cOcyYMYPp06dz/vx5vL29+f7775XY1q5dy+vXrxk8eLDOetOaF+f58+fUq1ePvXv3cu7cOXx8fGjQoAG3b98GoE2bNpw8eVIjuXjp0iXOnz9P69atAVixYgUjR45kwoQJXL58mYkTJzJixIhM9Uj67bffSE5O1tnzplu3blhYWLBq1SqN/TExMVSuXJnChQuzfft2LCws0qz/1atXJCYmamxCCCGEEOLbIIkcIXS4du0aarUad3f3TJX38/OjVatWODs7M3HiRJ4/f668mBoaGjJmzBjKlClDwYIFadOmDR07dtRK5OTIkYPAwEDc3Nzo1KkTbm5uvHjxguHDh+Pi4sKwYcMwMjLiyJEjGuf17t2bpk2b4uHhwcKFC7GysiI4OBiAefPm4enpycSJE3F3d8fT05MlS5awf/9+rl69qtTh4uLC1KlTcXNzw83NLUttNWbMGIYOHUqHDh1wcnKidu3ajBs3jqCgII1yHTt2xNfXF1dXV4YMGUJcXBxt2rTB29sbDw8P+vTpo9UL5K+//mLZsmWULFmSqlWrMnfuXMLCwj5qDpbMWrlyJSkpKQQHB1OkSBHq16/PoEGDNMoEBQXh5ubGtGnTcHNzo2XLllrzBE2aNIk2bdrQt29fXFxcqFSpEoGBgSxbtkznPDgA3t7e2NnZafS8Cg0Nxd7enpo1awLw008/UalSJRwdHWnQoAEDBw7Uepay4vbt24SEhLB27VqqVKlCoUKFGDhwIP/73/909gDTJSwsDBcXF4oUKYK+vj4tW7ZUnsHMsrGxASBnzpwaPb+mT5/OkCFDaNmyJW5ubkyZMoWSJUsye/ZsAGJjY8mWLRt58+bN0vVKlChBt27dKFq0KC4uLowbN45ChQopvemKFClCiRIlWLlypXLOihUrKF++PM7OzgCMGjWKGTNm0KRJEwoWLEiTJk3o16+f1rOvy9WrV7GystIZt5GREU5OThq/UYD27dvj7OzM2rVrMTY2Trf+SZMmYWVlpWz29vYZxiSEEEIIIf4dJJEjhA5qtTpL5YsXL678t7m5OdmyZePBgwfKvvnz51O6dGlsbGywsLDg559/Vv7lP1WRIkXQ0/u/n2SePHkoVqyY8llfX5+cOXNq1AtQsWJF5b8NDAwoU6YMly9fBiAqKor9+/djYWGhbKnJqfd7GpQuXTpL9/u+qKgoxo4dq3GNLl26EB8fz4sXL5Ry77dRnjx5ADTuL0+ePPz1118aPQccHBzIly+fxr2mpKQQExPz0fFm5PLlyxQvXhwTExON674vJiaGsmXLauwrV66cxueoqChCQ0M12sXb25uUlBRu3ryp89r6+vp06NCB0NBQ1Go1KSkpLF26lI4dOyrPxurVq6lcuTK2trZYWFjw008/aT1LWXHhwgXevn2Lq6urRqwHDx7UeEbSs2TJEo35e9q2bcvatWt59uzZR8cF75ZgvHfvHpUrV9bYX7lyZeUZV6vVqFSqLNf9/PlzBg4ciIeHB9bW1lhYWHD58mWNtmzTpo2SyFGr1axatYo2bdoAkJSUxPXr1/H399dot/Hjx2e63bLq+++/5/DhwzrnUfrQsGHDSEhIULY7d+78LTEJIYQQQoh/nqyZKoQOLi4uqFQqrly5kqnyhoaGGp9VKhUpKSnAu94KAwcOZMaMGVSsWBFLS0umTZumNf+FrjrSqzcznj9/ToMGDZgyZYrWsfd7AugaPpaVa4wZM4YmTZpoHXs/GfL+vaS+eOval5X7+xgqlUorUZfeHDMf6/nz53Tr1o2AgACtYw4ODmme16lTJyZNmsS+fftISUnhzp07dOzYEYBjx47Rpk0bxowZg7e3N1ZWVoSFhaU7oa+enl669/v8+XP09fU5c+YM+vr6GuXSG7qTKjo6muPHj3Py5EmGDBmi7H/79i1hYWF06dIF+Pva3dXVlYSEBOLj47PUK2fgwIHs2bOH6dOn4+zsjKmpKc2aNdOY7LtVq1YMGTKEs2fP8vLlS+7cuUOLFi2Ad+0GsHjxYq25dD5sx/TivnfvHnZ2dhrHXr9+zfXr1/Hy8tLY/+OPP1K8eHFat26NWq3WGiL5PmNj4wx77QghhBBCiH8nSeQIoUOOHDnw9vZm/vz5BAQEaCU6nj59mubcGx+KiIigUqVKGnO7fM5/sT9+/DhVq1YF4M2bN5w5c0aZh6VUqVKsW7cOR0dHDAz+np97qVKliImJUYabfE63b9/WeNE9fvw4enp6WR7+9T4bGxvi4+OVz7GxsRo9hzw8PFi+fDl//fWXkoj6cHJrNzc3tm/frrHv1KlTGp9LlSpFdHR0ltulUKFCVKtWjSVLlqBWq6lVqxYFChQA4OjRoxQoUEBjPp7359PR5cP7ffv2LRcvXlSSBJ6enrx9+5YHDx5QpUqVLMUK7yY5rlq1KvPnz9fYHxISQnBwsJLIyajdjYyMlPhSZcuWDTs7OyIiIqhWrZqyPyIiQukB1axZM4YOHcrUqVN1zhWU1m81IiICPz8/GjduDLxLzMTFxWmUyZ8/P9WqVWPFihW8fPmS2rVrkzt3buBdDzI7Oztu3Lih9NLJiqZNmzJkyBBmzJihlYhbtGgRSUlJtGrVSuu8ESNGoKenR5s2bVCr1UpiSQghhBBC/HfI0Coh0jB//nzevn1LuXLlWLduHbGxsVy+fJnAwECtoTbpcXFx4fTp0+zatYurV68yYsQIrZf+T41zw4YNXLlyhV69evHnn3/SqVMnAHr16sWTJ09o1aoVp06d4vr16+zatYuOHTtqvDB/ipEjR7Js2TLGjBnDpUuXuHz5MmFhYfz000+fXLeJiQkdOnQgKiqKw4cPExAQgK+vL7a2tgBs2LAh0/MYpapRowbz5s3j3LlznD59mu7du2v0DGrdujUqlYouXboQHR3N9u3bmT59ukYd3bp148qVKwwZMoSrV6+yZs0aZfWl1J5FQ4YM4ejRo/Tu3ZvIyEhiY2PZtGlTupMdp/L392f9+vVs2LBBmeQY3j1Lt2/fJiwsjOvXrxMYGMiGDRsyvN9t27axbds2rly5Qo8ePTRW1nJ1daVNmza0b9+e9evXc/PmTU6ePMmkSZPYtm1bunUnJyezfPlyWrVqRdGiRTW2zp07c+LECS5dupSpds+dOzempqbKhNypq5MNGjSIKVOmsHr1amJiYhg6dCiRkZH06dMHAHt7e2bNmsWcOXPw9/fn4MGD3Lp1i4iICLp168a4ceN0xu7i4sL69euJjIwkKiqK1q1b6+wN1qZNG8LCwli7dq1WwmbMmDFMmjSJwMBArl69yoULFwgJCWHmzJnpthu865U1depUZs+ezY8//siVK1e4fv06M2fOZPDgwQwYMEDnqlnwrmfOuHHjaNOmjdaEyEIIIYQQ4tsniRwh0uDk5MTZs2fx8vJiwIABFC1alNq1a7N3714WLlyY6Xq6detGkyZNaNGiBeXLl+fx48efdeWlyZMnM3nyZEqUKMGRI0fYvHkzuXLlAlB6M7x9+5Y6depQrFgx+vbti7W1tcZ8PJ/C29ubrVu3snv3bsqWLUuFChWYNWuW0ovkUzg7O9OkSRPq1atHnTp1KF68uMbyzgkJCRnOl5OSkqLRG2nGjBnY29tTpUoVWrduzcCBAzEzM1OOW1hYsGXLFi5cuICnpyc//vij1tC0ggUL8ttvv7F+/XqKFy/OwoULlV4yqcNZihcvzsGDB7l69SpVqlTB09OTkSNHag2j0aVp06YYGxtjZmZGo0aNlP3ff/89/fr1o3fv3pQsWZKjR48yYsSIdOvq1KkTHTp0oH379lSrVg0nJyetITshISG0b9+eAQMG4ObmRqNGjTh16lS6Q8Dg3RLsjx8/Vnq1vM/DwwMPDw9l0uOM2t3AwIDAwECCgoKws7OjYcOGAAQEBNC/f38GDBhAsWLF2Llzp7L0e6qePXuye/du7t69S+PGjXF3d6dz585ky5ZN56pQADNnziR79uxUqlSJBg0a4O3tTalSpbTKNWvWjMePH/PixQuN7wKgc+fO/PLLL4SEhFCsWDGqVatGaGiospR6Rvr27cuGDRs4fPgwZcqUoWjRoqxcuZKFCxdqJQ8/NHToUCZOnEi7du00JmQWQgghhBDfPpU6q7O6CiHEP2D06NFs3LiRyMjIT6on9aU+rRf6z2XChAksWrRIJpUVX6XExESsrKxISEggW7ZsXzocIYQQQgjxgaz8fU3myBFCfJMePHjAjh07iImJUZbu/pwWLFhA2bJlyZkzJxEREUybNi1Tw6aEEEIIIYQQ4lPI0CohxDfJx8eH0aNHExgYiKen52evPzY2loYNG1K4cGHGjRvHgAEDGD169Ge/zpdWt25djeW1398mTpz4pcP7ak2cODHNdqtbt+6XDk8IIYQQQvyLydAqIYQQabp79y4vX77UeSxHjhzkyJHjH47o3+HJkyc8efJE5zFTU1Py5cv3j8aT2lW3xA+L0Dc2/UevLYT4up2Z1v5LhyCEEAIZWiWEEOIz+acTDt8KSXIJIYQQQoi/iwytEuIbd+DAAVQqlcaS0/9lcXFxqFSqDCdRrl69On379v1HYvoSPnwuQkNDsba2/qIxfSmZfSaEEEIIIYT4GkgiR4i/mZ+fn9ayxfD1JFhGjx6NSqVKcxszZswXjcnAwABHR0f69evH8+fPP7lue3t74uPjKVq0KJD297B+/XrGjRv3yddLy8GDBzE0NOTIkSMa+5OSknByckp3la3Xr18zdepUSpQogZmZGbly5aJy5cqEhISQnJz8t8WcFZlNDIWGhirftZ6eHnnz5qVFixbcvn377w8yDR/z2/Tz81Puw9DQkDx58lC7dm2WLFlCSkrK3xesEEIIIYT4z5FEjhD/cQMHDiQ+Pl5r8/Pzw9ramtatW3903Z+SVChSpAjx8fHExcUxZcoUfv75ZwYMGPDR9aXS19fH1tYWA4P0R5bmyJEDS0vLT75eWqpVq8YPP/yAn58fSUlJyv7BgwdjamrK+PHjdZ73+vVrvL29mTx5Ml27duXo0aOcPHmSXr16MXfuXC5duvS3xfx3yZYtG/Hx8dy9e5d169YRExND8+bNv3RYWebj46M8szt27MDLy4s+ffpQv3593rx586XDE0IIIYQQ3whJ5AjxFTly5AhVqlTB1NQUe3t7AgICNF7yly9fTpkyZbC0tMTW1pbWrVvz4MEDjTq2b9+Oq6srpqameHl5ERcXl+41LSwssLW11dj27t3L8uXLCQsLw8XFRSm7adMmSpUqhYmJCU5OTowZM0bjBVWlUrFw4UK+//57zM3NmTBhAgALFy6kUKFCGBkZ4ebmxvLlyzNsCwMDA2xtbcmfPz8tWrSgTZs2bN68GYBXr14REBBA7ty5MTEx4X//+x+nTp1Szv3zzz9p06YNNjY2mJqa4uLiQkhICKA5jCYuLg4vLy8AsmfPjkqlws/PD9AeWvXnn3/Svn17smfPjpmZGXXr1iU2NlY5ntoDZdeuXXh4eGBhYaG82Kdl4sSJGBkZMWTIEAD279/PL7/8wrJlyzAxMdF5zuzZszl06BB79+6lV69elCxZEicnJ1q3bs2JEyeU7yujNsqMjL7vp0+f0q1bN/LkyYOJiQlFixZl69atHDhwgI4dO5KQkKD0UklvRS+VSoWtrS158+alUqVK+Pv7c/LkSRITEzMVi1qtZvTo0Tg4OGBsbIydnR0BAQEa9W/cuFHjmtbW1oSGhmrFkt4zkRFjY2NsbW3Jly8fpUqVYvjw4WzatIkdO3ZoXGvmzJkUK1YMc3Nz7O3t6dmzp0Zvs9RnaevWrbi5uWFmZkazZs148eIFS5cuxdHRkezZsxMQEMDbt28zFZsQQgghhPh2SCJHiK/E9evX8fHxoWnTppw/f57Vq1dz5MgRevfurZRJTk5m3LhxREVFsXHjRuLi4jReMu/cuUOTJk1o0KABkZGRdO7cmaFDh2YpjjNnztClSxcmT56Mt7e3sv/w4cO0b9+ePn36EB0dTVBQEKGhoUqyJtXo0aNp3LgxFy5coFOnTmzYsIE+ffowYMAALl68SLdu3ejYsSP79+/PUlympqa8fv0aeNdrZd26dSxdupSzZ8/i7OyMt7e3skrQiBEjiI6OZseOHVy+fJmFCxeSK1curTrt7e1Zt24dADExMcTHxzNnzhyd1/fz8+P06dNs3ryZY8eOoVarqVevnkavoxcvXjB9+nSWL1/OoUOHuH37drpDpExMTFi2bBk///wzmzZtolOnTgwfPpzSpUunec6KFSuoVauWziXVDQ0NMTc3z1QbZSSj7zslJYW6desSERHBr7/+SnR0NJMnT0ZfX59KlSoxe/ZspadNfHx8uu3wvgcPHrBhwwb09fXR19fPVCzr1q1j1qxZBAUFERsby8aNGylWrFimrvehrDwTmVGjRg1KlCjB+vXrlX16enoEBgZy6dIlli5dyr59+xg8eLDGeS9evCAwMJCwsDB27tzJgQMHaNy4Mdu3b2f79u0sX76coKAgfvvtN53XffXqFYmJiRqbEEIIIYT4NsiqVUL8A7Zu3YqFhYXGvg//JX3SpEm0adNG6QXi4uJCYGAg1apVY+HChZiYmNCpUyelvJOTE4GBgZQtW5bnz59jYWGh9HyZMWMGAG5ubly4cIEpU6ZkKs4HDx7QuHFjmjZtqvXiPWbMGIYOHUqHDh2U648bN47BgwczatQopVzr1q3p2LGj8rlVq1b4+fnRs2dPAPr378/x48eZPn260vMhI2fOnGHlypXUqFGDpKQkFi5cSGhoKHXr1gVg8eLF7Nmzh+DgYAYNGsTt27fx9PSkTJkyADg6OuqsV19fX1lZKHfu3GnO6RIbG8vmzZuJiIigUqVKwLuEir29PRs3blSGASUnJ7No0SIKFSoEQO/evRk7dmy691amTBmGDRtGkyZN8PT05Mcff0y3fGxsLNWrV0+3TGbaKCMZfd/h4eGcPHmSy5cv4+rqqpRJZWVlpfS0yUhCQgIWFhao1WpevHgBQEBAgJKUyiiW27dvY2trS61atTA0NMTBwYFy5cpleF1dMvtMZIW7uzvnz59XPr/f08vR0ZHx48fTvXt3FixYoOxPTk5Wfs8AzZo1Y/ny5fzxxx9YWFhQuHBhvLy82L9/Py1atNC65qRJk77I/FZCCCGEEOLvJz1yhPgHeHl5ERkZqbH98ssvGmWioqIIDQ3FwsJC2by9vUlJSeHmzZvAu4RGgwYNcHBwwNLSkmrVqgEoE8NevnyZ8uXLa9RbsWLFTMWYnJxMs2bNyJMnD4sXL9Y6HhUVxdixYzXi69KlC/Hx8crLN6AkT1JdvnyZypUra+yrXLkyly9fTjeeCxcuYGFhgampKeXKlaNixYrMmzeP69evk5ycrFGnoaEh5cqVU+rs0aMHYWFhlCxZksGDB3P06NFMtUFaLl++jIGBgUbb5syZEzc3N437MDMzU168AfLmzas19E2XESNGkJKSwtChQzOcu0etVmdYX2baKCMZfd+RkZHkz59fSeJ8CktLSyIjIzl9+jQzZsygVKlSGj29MoqlefPmvHz5EicnJ7p06cKGDRu+qjlp1Go1KpVK+RweHk7NmjXJly8flpaWtGvXjsePH2v8jj58lvLkyYOjo6NGQjhPnjxpPl/Dhg0jISFB2e7cufM33JkQQgghhPgSpEeOEP8Ac3NznJ2dNfb9/vvvGp+fP39Ot27dNOb2SOXg4EBSUhLe3t54e3uzYsUKbGxsuH37Nt7e3sqQo08REBBAbGwsp06d0jk/y/PnzxkzZgxNmjTROvZ++dReFJ/Kzc2NzZs3Y2BggJ2dHUZGRgD88ccfGZ5bt25dbt26xfbt29mzZw81a9akV69eTJ8+/bPElhZDQ0ONzyqVKlOJl9TkTUZJHABXV1euXLnycQFmQUbft6mp6We7lp6envL78PDw4Pr16/To0UOZSymjWOzt7YmJiSE8PJw9e/bQs2dPpk2bpqwMput7+CdX97p8+TIFCxYE3s3BU79+fXr06MGECRPIkSMHR44cwd/fn9evX2NmZgbofpZ07UtrRSxjY2OMjY3/hrsRQgghhBBfmvTIEeIrUapUKaKjo3F2dtbajIyMuHLlCo8fP2by5MlUqVIFd3d3rX+N9/Dw4OTJkxr7jh8/nuG1f/75Z5YsWcK6devInz9/mvHFxMTojE9PL+0/Sjw8PIiIiNDYFxERQeHChdONycjICGdnZxwdHZUkDqBMmvx+ncnJyZw6dUqjThsbGzp06MCvv/7K7Nmz+fnnn9O8DmgPdfvwHt68ecOJEyeUfY8fPyYmJibD+/jcWrduTXh4OOfOndM6lpycTFJSUqbbKD0Zfd/Fixfn999/5+rVqzrPNzIy+uiJeIcOHcrq1as5e/ZspmKBd3MoNWjQgMDAQA4cOMCxY8e4cOEC8O5ZeH/S6djYWI3eL7pih/Sficzat28fFy5coGnTpsC7XnUpKSnMmDGDChUq4Orqyr179z75OkIIIYQQ4r9DeuQI8ZUYMmQIFSpUoHfv3nTu3Blzc3Oio6PZs2cP8+bNw8HBASMjI+bOnUv37t25ePEi48aN06ije/fuzJgxg0GDBtG5c2fOnDmjc2We90VERPDDDz8wcuRInJycuH//vsZxU1NTrKysGDlyJPXr18fBwYFmzZqhp6dHVFQUFy9eTHOpbIBBgwbh6+uLp6cntWrVYsuWLaxfv57w8PCPaidzc3N69OjBoEGDyJEjBw4ODkydOpUXL17g7+8PwMiRIyldujRFihTh1atXbN26FQ8PD531FShQAJVKxdatW6lXrx6mpqZa8xm5uLjQsGFDunTpQlBQEJaWlgwdOpR8+fLRsGHDj7qPj9W3b1+2bdtGzZo1GTduHP/73/+wtLTk9OnTTJkyheDgYEqWLJlhG2Uko++7WrVqVK1alaZNmzJz5kycnZ25cuUKKpUKHx8fHB0def78OXv37qVEiRKYmZkpvU0yYm9vT+PGjRk5ciRbt27NMJbQ0FDevn1L+fLlMTMz49dff8XU1JQCBQoA7yYcnjdvHhUrVuTt27cMGTJEq3fL+zLzTOjy6tUr7t+/z9u3b/njjz/YuXMnkyZNon79+rRv3x4AZ2dnkpOTmTt3Lg0aNCAiIoJFixZlql2EEEIIIYQA6ZEjxFejePHiHDx4kKtXr1KlShU8PT0ZOXIkdnZ2wLteBaGhoaxdu5bChQszefJkraFCDg4OrFu3jo0bN1KiRAkWLVrExIkT073uL7/8wuvXr/npp5/Imzev1tanTx8AvL292bp1K7t376Zs2bJUqFCBWbNmKS/LaWnUqBFz5sxh+vTpFClShKCgIEJCQjKcsDc9kydPpmnTprRr145SpUpx7do1du3aRfbs2YF3PSqGDRtG8eLFqVq1Kvr6+oSFhemsK1++fMpkunny5NFYJex9ISEhlC5dmvr161OxYkXUajXbt29PNyHwdzA2NmbPnj0MHjyYoKAgKlSoQNmyZQkMDCQgIICiRYsCGbdRRjLzfa9bt46yZcvSqlUrChcuzODBg5VeLJUqVaJ79+60aNECGxsbpk6dmqX77NevH9u2bePkyZMZxmJtbc3ixYupXLkyxYsXJzw8nC1btpAzZ04AZsyYgb29PVWqVKF169YMHDgw3aRSZp+JD+3cuZO8efPi6OiIj48P+/fvJzAwkE2bNikrcJUoUYKZM2cyZcoUihYtyooVK5g0aVKW2kYIIYQQQvy3qdSZmcBBCCGEEP9aiYmJWFlZkZCQQLZs2b50OEIIIYQQ4gNZ+fua9MgRQgghhBBCCCGE+JeQRI4QQgiRjtu3b2ssff7hdvv27S8dohBCCCGE+A+RyY6FEEKIdNjZ2REZGZnu8X+Lqj+tQt/48y0dL4T4/M5Ma/+lQxBCCPGVkx45QgiRjtDQUKytrZXPo0ePpmTJkl8snr/TgQMHUKlUPH36FNC+9/+SuLg4VCoVkZGRGBgY6Fz6PHUzMMj6v4lUr16dvn37Kp8dHR2ZPXu28lmlUrFx48ZPvxEhhBBCCPHNkUSOEOKb5ufnh0qlQqVSYWRkhLOzM2PHjuXNmzf/yPXfTwi878OE0OjRo5U4DQwMyJUrF1WrVmX27Nm8evUqw+u8fv2aqVOnKkt958qVi8qVKxMSEkJycvJnvquPk9nEUGhoqNIWenp65M2blxYtWnzRIUwfJrky4/1n7/3t2rVrrF+/nnHjxmWqnri4OPz9/SlYsCCmpqYUKlSIUaNG8fr164+8GyGEEEII8W8mQ6uEEN88Hx8fQkJCePXqFdu3b6dXr14YGhoybNiwLx2ahiJFihAeHk5KSgqPHz/mwIEDjB8/nuXLl3PgwAEsLS11nvf69Wu8vb2Jiopi3LhxVK5cmWzZsnH8+HGmT5+Op6fnv64XUbZs2YiJiUGtVnPz5k169uxJ8+bNOXHixJcOLUtSn7332djYKMuRZ8aVK1dISUkhKCgIZ2dnLl68SJcuXUhKSmL69OmfO2QhhBBCCPGVkx45QohvnrGxMba2thQoUIAePXpQq1YtNm/eDMDMmTMpVqwY5ubm2Nvb07NnT54/f56l+n/55Rc8PDwwMTHB3d2dBQsWKMcKFiwIgKenJyqViurVq6dZj4GBAba2ttjZ2VGsWDF++OEHDh48yMWLF5kyZUqa582ePZtDhw6xd+9eevXqRcmSJXFycqJ169acOHECFxcXAF69ekVAQAC5c+fGxMSE//3vf5w6dSpL97pp0yZKlSqFiYkJTk5OjBkzRqN309OnT+nWrRt58uTBxMSEokWLsnXrVg4cOEDHjh1JSEhQeqaMHj06zeuoVCpsbW3JmzcvlSpVwt/fn5MnT5KYmJipWNRqNaNHj8bBwQFjY2Ps7OwICAjQqP/DoUvW1taEhoZqxRIXF4eXlxcA2bNnR6VS4efnl6n2Sn323t/09fW1hlalJzUZVKdOHZycnPj+++8ZOHAg69evz9T5QgghhBDi2yI9coQQ/zmmpqY8fvwYAD09PQIDAylYsCA3btygZ8+eDB48WCMZk54VK1YwcuRI5s2bh6enJ+fOnaNLly6Ym5vToUMHTp48Sbly5QgPD6dIkSIYGRllKVZ3d3fq1q3L+vXrGT9+fJox1KpVC09PT61jhoaGGBoaAjB48GDWrVvH0qVLKVCgAFOnTsXb25tr166RI0eODGM5fPgw7du3JzAwkCpVqnD9+nW6du0KwKhRo0hJSaFu3bo8e/aMX3/9lUKFChEdHY2+vj6VKlVi9uzZjBw5kpiYGAAsLCwy1QYPHjxgw4YN6OvrKz1ZMopl3bp1zJo1i7CwMIoUKcL9+/eJiorK1PU+ZG9vz7p162jatCkxMTFky5YNU9MvO2FwQkJCut/Zq1evNIbkvZ8AE0IIIYQQ/26SyBFC/Geo1Wr27t3Lrl27+OGHHwC0JpwdP3483bt3z3QiZ9SoUcyYMYMmTZoA73rgREdHExQURIcOHbCxsQEgZ86c2NraflTc7u7u7N69O83jsbGx6fb0AUhKSmLhwoWEhoZSt25dABYvXsyePXsIDg5m0KBBGcYxZswYhg4dSocOHQBwcnJi3LhxDB48mFGjRhEeHs7Jkye5fPkyrq6uSplUVlZWSk+bjCQkJGBhYYFarebFixcABAQEYG5unqlYbt++ja2tLbVq1cLQ0BAHBwfKlSuX4XV10dfXV5ImuXPnztIE0Fu3btVIWNWtW5e1a9d+VByprl27xty5c9MdVjVp0iTGjBnzSdcRQgghhBBfJ0nkCCG+eakv08nJyaSkpNC6dWtlWE94eDiTJk3iypUrJCYm8ubNG/766y9evHiBmZlZuvUmJSVx/fp1/P396dKli7L/zZs3WFlZfbb41Wo1KpUq3eMZuX79OsnJyVSuXFnZZ2hoSLly5bh8+XKm4oiKiiIiIoIJEyYo+96+fau0V2RkJPnz51eSOJ/C0tKSs2fPkpyczI4dO1ixYoXGdTOKpXnz5syePRsnJyd8fHyoV68eDRo0+KgVpj6Fl5cXCxcuVD6nJqI+1t27d/Hx8aF58+Yaz9yHhg0bRv/+/ZXPiYmJ2Nvbf9K1hRBCCCHE10ESOUKIb17qy7SRkRF2dnbKy3xcXBz169enR48eTJgwgRw5cnDkyBH8/f15/fp1homc1Ll0Fi9eTPny5TWOZWUy24xcvnxZmWtHF1dXV65cufLZrpeW58+fM2bMGKX30ftMTEw+63AjPT09nJ2dAfDw8OD69ev06NGD5cuXZyoWe3t7YmJiCA8PZ8+ePfTs2ZNp06Zx8OBBDA0NUalUWgmwv2N1L3Nzc+U+PtW9e/fw8vKiUqVK/Pzzz+mWNTY2xtjY+LNcVwghhBBCfF0kkSOE+Oal9TJ95swZUlJSmDFjBnp67+Z+X7NmTabrzZMnD3Z2dty4cYM2bdroLJM6J87bt28/IvJ3Kxbt3Lkz3RW2WrduzfDhwzl37pzWPDnJycm8fv2aQoUKYWRkREREBAUKFFCOnTp1KtOT7pYqVYqYmJg0ExPFixfn999/5+rVqzp75RgZGX10OwwdOpRChQrRr18/SpUqlWEs8G4upAYNGtCgQQN69eqFu7s7Fy5coFSpUtjY2BAfH6+UjY2NVYZw6fKp3+Onunv3Ll5eXpQuXZqQkBDleRVCCCGEEP89ksgRQvxnOTs7k5yczNy5c2nQoAEREREsWrQoS3WMGTOGgIAArKys8PHx4dWrV5w+fZo///yT/v37kzt3bkxNTdm5cyf58+fHxMQkzWFXb9684f79+1rLj5csWTLdOWz69u3Ltm3bqFmzJuPGjeN///sflpaWnD59milTphAcHEzJkiXp0aMHgwYNIkeOHDg4ODB16lRevHiBv79/pu515MiR1K9fHwcHB5o1a4aenh5RUVFcvHiR8ePHU61aNapWrUrTpk2ZOXMmzs7OXLlyBZVKhY+PD46Ojjx//py9e/dSokQJzMzMMuz1lMre3p7GjRszcuRItm7dmmEsoaGhvH37lvLly2NmZsavv/6KqampksSqUaMG8+bNo2LFirx9+5YhQ4Yok0LrUqBAAVQqFVu3bqVevXqYmppmerLmT3X37l2qV69OgQIFmD59Og8fPlSOfey8S0IIIYQQ4t9L/klPCPGfVaJECWbOnMmUKVMoWrQoK1asYNKkSVmqo3Pnzvzyyy+EhIRQrFgxqlWrRmhoqDIUysDAgMDAQIKCgrCzs6Nhw4Zp1nXp0iXy5s2Lg4MD1atXZ82aNQwbNozDhw+nmzQwNjZmz549DB48mKCgICpUqEDZsmUJDAwkICCAokWLAjB58mSaNm1Ku3btKFWqFNeuXWPXrl1kz549U/fq7e3N1q1b2b17N2XLlqVChQrMmjVLSY4ArFu3jrJly9KqVSsKFy7M4MGDlV4slSpVonv37rRo0QIbGxumTp2aqeum6tevH9u2bePkyZMZxmJtbc3ixYupXLkyxYsXJzw8nC1btpAzZ04AZsyYgb29PVWqVKF169YMHDgw3aRSvnz5lAmW8+TJQ+/evbMU+6fYs2cP165dY+/eveTPn5+8efMqmxBCCCGE+O9RqTMzS6YQQggh/rUSExOxsrIiISGBbNmyfelwhBBCCCHEB7Ly9zXpkSOEEEIIIYQQQgjxLyGJHCGEECKLbt++jYWFRZrb7du3v3SIQgghhBDiGyWTHQshhBBZZGdnR2RkZLrHv0ZVf1qFvvHnWyZeCPFpzkxr/6VDEEII8S8kiRwhhBAiiwwMDNJd+lwIIYQQQoi/iwytEkKI/y8uLg6VSpVuT4tvxejRoylZsqTy2c/Pj0aNGn2xeL6k0NBQrK2tv3QYQgghhBBCZIokcoQQXwU/Pz9UKhUqlQpDQ0MKFizI4MGD+euvv/6xGOzt7YmPj1eW6/5Yjo6OqFQqjh8/rrG/b9++VK9e/ZPqBrh//z4//PADTk5OGBsbY29vT4MGDdi7d+8n1/25ZDYx9DV87x/6MMmVGanfuUqlwtTUFEdHR3x9fdm3b9/fE6QQQgghhPjPkkSOEOKr4ePjQ3x8PDdu3GDWrFkEBQUxatSof+z6+vr62NraYmDw6aNOTUxMGDJkyGeISlNcXBylS5dm3759TJs2jQsXLrBz5068vLzo1avXZ7/eP+FLf++fy9ixY4mPjycmJoZly5ZhbW1NrVq1mDBhwpcOTQghhBBCfEMkkSOE+GoYGxtja2uLvb09jRo1olatWuzZs0c5npKSwqRJkyhYsCCmpqaUKFGC3377TaOOS5cuUb9+fbJly4alpSVVqlTh+vXryvFffvkFDw8PTExMcHd3Z8GCBcqxD4dWHThwAJVKxbZt2yhevDgmJiZUqFCBixcvZngvXbt25fjx42zfvj3NMikpKYwdO5b8+fNjbGxMyZIl2blzZ7r19uzZE5VKxcmTJ2natCmurq4UKVKE/v37a/QAun37Ng0bNsTCwoJs2bLh6+vLH3/8kWHc78f2sW09evRoli5dyqZNm5ReKgcOHEjzWp/6vf/555+0adMGGxsbTE1NcXFxISQkBPi/7/Dp06dK+cjISFQqFXFxcVqxhIaGMmbMGKKiopTYQ0NDM9VmlpaW2Nra4uDgQNWqVfn5558ZMWIEI0eOJCYmBoC3b9/i7++v3Iubmxtz5szRqCe1N9PEiRPJkycP1tbWjB07ljdv3jBo0CBy5MhB/vz5lXvU5dWrVyQmJmpsQgghhBDi2yCJHCHEV+nixYscPXoUIyMjZd+kSZNYtmwZixYt4tKlS/Tr14+2bdty8OBBAO7evUvVqlUxNjZm3759nDlzhk6dOvHmzRsAVqxYwciRI5kwYQKXL19m4sSJjBgxgqVLl6Yby6BBg5gxYwanTp3CxsaGBg0akJycnO45BQsWpHv37gwbNoyUlBSdZebMmcOMGTOYPn0658+fx9vbm++//57Y2Fid5Z88ecLOnTvp1asX5ubmWsdT53lJSUmhYcOGPHnyhIMHD7Jnzx5u3LhBixYt0o35fZ/S1gMHDsTX11fpaRMfH0+lSpUydd2P+d5HjBhBdHQ0O3bs4PLlyyxcuJBcuXJl+l7f16JFCwYMGECRIkWU2LPSbh/q06cParWaTZs2Ae++m/z587N27Vqio6MZOXIkw4cPZ82aNRrn7du3j3v37nHo0CFmzpzJqFGjqF+/PtmzZ+fEiRN0796dbt268fvvv+u87qRJk7CyslI2e3v7j74HIYQQQgjxdZFVq4QQX42tW7diYWHBmzdvePXqFXp6esybNw9418Ng4sSJhIeHU7FiRQCcnJw4cuQIQUFBVKtWjfnz52NlZUVYWBiGhoYAuLq6KvWPGjWKGTNm0KRJE+BdsiU6OpqgoCA6dOiQZlyjRo2idu3aACxdupT8+fOzYcMGfH19072fn376iZCQEFasWEG7du20jk+fPp0hQ4bQsmVLAKZMmcL+/fuZPXs28+fP1yp/7do11Go17u7u6V537969XLhwgZs3byov8MuWLaNIkSKcOnWKsmXLpnv+52hrU1NTXr16ha2tbbrXgk//3m/fvo2npydlypQB3s1X87FMTU2xsLDAwMAgU7FnJEeOHOTOnVvp/WNoaMiYMWOU4wULFuTYsWOsWbNG43nKkSMHgYGB6Onp4ebmxtSpU3nx4gXDhw8HYNiwYUyePJkjR44oz8/7hg0bRv/+/ZXPiYmJkswRQgghhPhGSCJHCPHV8PLyYuHChSQlJTFr1iwMDAxo2rQp8C6J8eLFCyWhkur169d4enoC74bMVKlSRUksvC8pKYnr16/j7+9Ply5dlP1v3rzBysoq3bhSEwjw7gXbzc2Ny5cvZ3g/NjY2DBw4kJEjR2r16khMTOTevXtUrlxZY3/lypWJiorSWZ9arc7wmgCXL1/G3t5e48W9cOHCWFtbc/ny5QwTOZ/a1ln1qd97jx49aNq0KWfPnqVOnTo0atQo0z2A/glqtRqVSqV8nj9/PkuWLOH27du8fPmS169fa02uXKRIEfT0/q/TbJ48eTQm4dbX1ydnzpw8ePBA5zWNjY0xNjb+vDcihBBCCCG+CpLIEUJ8NczNzXF2dgZgyZIllChRguDgYPz9/Xn+/DkA27ZtI1++fBrnpb6wmpqapll36vmLFy+mfPnyGsf09fU/2z18qH///ixYsEBjLp6P5eLigkql4sqVK58hsrR9altn1ad+73Xr1uXWrVts376dPXv2ULNmTXr16sX06dOVZMj7SbCMhsV9To8fP+bhw4cULFgQgLCwMAYOHMiMGTOoWLEilpaWTJs2jRMnTmic92GCLHVVrw/3pTVsTwghhBBCfLtkjhwhxFdJT0+P4cOH89NPP/Hy5UsKFy6MsbExt2/fxtnZWWNL7XlSvHhxDh8+rPNFPU+ePNjZ2XHjxg2t81NfstPy/iTCf/75J1evXsXDwyNT92FhYcGIESOYMGECz549U/Zny5YNOzs7IiIiNMpHRERQuHBhnXXlyJEDb29v5s+fT1JSktbx1Al9PTw8uHPnDnfu3FGORUdH8/Tp0zTrft+ntjWAkZERb9++zfBaH/qY7x3e9X7q0KEDv/76K7Nnz+bnn39W9gPEx8crZVMns07Lx8auy5w5c9DT01OWYo+IiKBSpUr07NkTT09PnJ2dNSbjFkIIIYQQIiOSyBFCfLWaN2+Ovr4+8+fPx9LSkoEDB9KvXz+WLl3K9evXOXv2LHPnzlUmK+7duzeJiYm0bNmS06dPExsby/Lly5UVg8aMGcOkSZMIDAzk6tWrXLhwgZCQEGbOnJluHGPHjmXv3r1cvHgRPz8/cuXKpbyYZ0bXrl2xsrJi5cqVGvsHDRrElClTWL16NTExMQwdOpTIyEj69OmTZl3z58/n7du3lCtXjnXr1hEbG8vly5cJDAxUhoDVqlWLYsWK0aZNG86ePcvJkydp37491apVU+aRSc/naGtHR0fOnz9PTEwMjx49ylIvmKx+7yNHjmTTpk1cu3aNS5cusXXrViXRlprwGT16NLGxsWzbto0ZM2ake31HR0du3rxJZGQkjx494tWrV5mK+9mzZ9y/f587d+5w6NAhunbtyvjx45kwYYLS48jFxYXTp0+za9curl69yogRIzh16lSm20YIIYQQQghJ5AghvloGBgb07t2bqVOnkpSUxLhx4xgxYgSTJk3Cw8MDHx8ftm3bpvSoyZkzJ/v27eP58+dUq1aN0qVLs3jxYmVISufOnfnll18ICQmhWLFiVKtWjdDQ0Ax75EyePJk+ffpQunRp7t+/z5YtWzRWVcqIoaEh48aN46+//tLYHxAQQP/+/RkwYADFihVj586dbN68GRcXlzTrcnJy4uzZs3h5eTFgwACKFi1K7dq12bt3LwsXLgTeDbnZtGkT2bNnp2rVqtSqVQsnJydWr16d6Zg/ta27dOmCm5sbZcqUwcbGRqvnUXqy+r0bGRkxbNgwihcvTtWqVdHX1ycsLExp+1WrVnHlyhWKFy/OlClTGD9+fLrXb9q0KT4+Pnh5eWFjY8OqVasyFffIkSPJmzcvzs7OtGvXjoSEBPbu3cuQIUOUMt26daNJkya0aNGC8uXL8/jxY3r27JnpthFCCCGEEEKlzuzsmUII8R9z4MABvLy8+PPPP5WlvYX4N0pMTMTKyoqEhASyZcv2pcMRQgghhBAfyMrf16RHjhBCCCGEEEIIIcS/hCRyhBBCiHSsWLECCwsLnVuRIkW+dHhCCCGEEOI/RoZWCSGEEOl49uwZf/zxh85jhoaGFChQ4B+OKOtSu+qW+GER+safb+l4IcT/OTOt/ZcOQQghxL+YDK0SQvyjQkNDZQ4ZkaEDBw6gUqmUZdL/Lc+NpaWl1tLnqdu/IYkjhBBCCCG+LZLIEeIb5ufnh0ql0tp8fHw+63VatGjB1atXP2udfydHR0dmz56d6fKpCQiVSoWenh5WVlZ4enoyePBg4uPj/75AMyk0NFSJT19fn+zZs1O+fHnGjh1LQkLClw4vTf+25+bvVKRIEXbv3g1AnTp1WLZsmcbxSZMmUbZsWSwtLcmdOzeNGjVSlnoXQgghhBD/LZLIEeIb5+PjQ3x8vMaW2eWUM8vU1JTcuXN/1jq/RjExMdy7d49Tp04xZMgQwsPDKVq0KBcuXPjSoZEtWzbi4+P5/fffOXr0KF27dmXZsmWULFmSe/fufenwdPovPDevX7/OsMzTp0+5evUqFSpU4O3btxw7dozKlStrlDl48CC9evXi+PHj7Nmzh+TkZOrUqUNSUtLfFboQQgghhPhKSSJHiG+csbExtra2Glv27NmV47GxsVStWhUTExMKFy7Mnj17UKlUbNy4EdAeDgMQGRmJSqUiLi4O0B4iM3r0aEqWLElQUBD29vaYmZnh6+ur0TvEz8+PRo0aMXHiRPLkyYO1tTVjx47lzZs3DBo0iBw5cpA/f35CQkI07ufOnTv4+vpibW1Njhw5aNiwoRLH+/VOnz6dvHnzkjNnTnr16kVycjIA1atX59atW/Tr10/pxZJZuXPnxtbWFldXV1q2bElERAQ2Njb06NFDKXPq1Clq165Nrly5sLKyolq1apw9e1ajHpVKRVBQEPXr18fMzAwPDw+OHTvGtWvXqF69Oubm5lSqVInr169nOjaVSoWtrS158+bFw8MDf39/jh49yvPnzxk8eLBSbufOnfzvf//D2tqanDlzUr9+fY3rxMXFoVKpWLNmDVWqVMHU1JSyZcty9epVTp06RZkyZbCwsKBu3bo8fPgw3Zi2b9+Oq6srpqameHl5aXxPoP3cREVF4eXlhaWlJdmyZaN06dKcPn1ao+zGjRtxcXHBxMQEb29v7ty5o1Hnpk2b+H/s3XlcTun/+PHXXbRvIgpRtIeUNcaSKZXR2LNERbaRyb6OJWsYa4xlBhWDDLIMMUSRiCxlC4lkTPiMJVKy1O+Pfvf5urXaxjDX8/E4j8fc51znOu/r3Oc2ztu1ODg4oKamRq1atZg2bRovX75UuE+rV6+mU6dOaGhoYG5uzq5du6TjDx8+xMvLCwMDA9TV1TE3N1d4Bsv6/M2aNYuqVatiaWlZ4j0CiI+Px9bWFh0dHRITE9HU1KR27doKZfbt24evry+2trbY2dkRGhpKeno6p0+fLrV+QRAEQRAE4csiEjmC8B+Wl5dH586dUVFR4cSJE6xcuZJx48Z9kLqvXbvGb7/9xu+//86+ffs4e/YsQ4YMUShz6NAh/vrrL44cOcLChQuZOnUq7du3p0KFCpw4cYLBgwczaNAg/vzzTwBevHiBq6sr2traxMbGEhcXh5aWFm5ubgo9H6Kjo0lNTSU6OpqwsDBCQ0MJDQ0FICIigurVqzN9+nSph9K7UldXZ/DgwcTFxXHv3j2gYGJcHx8fjh49Snx8PObm5rRr144nT54onDtjxgy8vb1JTEzEysqKXr16MWjQICZMmMCpU6fIz89n6NCh7xwbFCSevLy82LVrF69evQLg6dOnjBw5klOnTnHw4EGUlJTo1KkTeXl5CudOnTqVSZMmcebMGcqVK0evXr0YO3YsS5YsITY2lmvXrjFlypRir33r1i06d+6Mh4cHiYmJ9O/fn/Hjx5cYr5eXF9WrVychIYHTp08zfvx4ypcvLx3Pzs5m1qxZrFu3jri4OB49ekSPHj2k47GxsXh7ezNs2DAuXbrEqlWrCA0NZdasWQrXmTZtGp6enpw7d4527drh5eXFgwcPAJg8eTKXLl1i7969JCcns2LFCipVqgSU/fk7ePAgV65c4cCBA+zevbvY9tarVw89PT06d+7MxYsX0dPTo2XLlvz999/o6elRr169Ys+VJ0X19fWLPJ6bm8vjx48VNkEQBEEQBOHLUO5TByAIwse1e/dutLS0FPZNnDiRiRMnEhUVxeXLl/njjz+oWrUqALNnz8bd3f29r/vs2TPWrVtHtWrVAFi6dCnffPMNCxYswNDQECh4CQ0ODkZJSQlLS0vmzZtHdnY2EydOBGDChAnMmTOHo0eP0qNHDzZv3kxeXh6rV6+WetKEhISgp6dHTEwMbdu2BaBChQosW7YMZWVlrKys+Oabbzh48CADBgxAX18fZWVltLW1pTjeh5WVFVDQk6Vy5cq0adNG4fjPP/+Mnp4ehw8fpn379tL+vn374unpCcC4ceNwdHRk8uTJuLq6AjBs2DD69u37QeJ78uQJ9+/fp3LlynTp0kXh+Nq1azEwMODSpUvUqVNH2j969GiFWHr27MnBgwelIT9+fn5ScqwoK1asoHbt2ixYsAAAS0tLzp8/z9y5c4s9Jz09nTFjxkj31NzcXOH4ixcvWLZsGU2aNAEgLCwMa2trTp48SePGjZk2bRrjx4/Hx8cHgFq1ajFjxgzGjh3L1KlTpXp8fX3p2bMnUPC8BwcHc/LkSdzc3EhPT8fe3p6GDRsCBfMpyZX1+dPU1GT16tWoqKgU21Yo6LH08uVL2rdvz/Dhw3F2dqZfv364ubnh6elJuXJF/y86Ly+P4cOH07x5c4Xv7HVBQUFMmzatxOsLgiAIgiAInyfRI0cQvnBOTk4kJiYqbIMHDwYgOTkZY2NjKYkD4Ojo+EGuW6NGDSmJI683Ly9PYYJWW1tblJT+74+hKlWqULduXemzsrIyFStWlHq7JCUlce3aNbS1tdHS0kJLSwt9fX2ePXumMDzI1tYWZWVl6bORkZFUx4eWn58PIL3Y3717lwEDBmBubo6uri46OjpkZWWRnp6ucN7rvS2qVKkCoND2KlWq8OzZs/fuSfFmfCkpKfTs2ZNatWqho6MjJSreJb6S7mlycrKUcJEr7dkaOXIk/fv3x9nZmTlz5hQaWlauXDkaNWokfbayskJPT4/k5GSg4PmYPn269GxoaWkxYMAAMjIyyM7OLrJtmpqa6OjoSG357rvvCA8Pp379+owdO5Zjx45JZcv6/NWtW7fUJA5A9erVUVNTIzU1lR49emBkZERCQgI9e/bExMSE6tWrF3mev78/Fy5cIDw8vNi6J0yYQGZmprS9OQRNEARBEARB+HyJHjmC8IXT1NTEzMzsnc+XJ1rkCQFAmm/mfb0+bAYKkg1F7ZMP+8nKyqJBgwZs2LChUF0GBgYl1vvm0KEPRZ5EkCdEfHx8uH//PkuWLKFmzZqoqqri6OhYaNLb12OUJ1mK2ve+cScnJ6Ojo0PFihUB8PDwoGbNmvzyyy9UrVqVvLw86tSp807xfeh7GhgYSK9evdizZw979+5l6tSphIeH06lTpzKdn5WVxbRp0+jcuXOhY2pqatJ/l/R8uLu7c/PmTSIjIzlw4ABff/01/v7+zJ8/v8zPn6amZqmxDh48mF9//ZW8vDxyc3MxNDQkPz+f7OxsrK2tAbh06RI1atRQOG/o0KHs3r2bI0eOFJvogYK5sVRVVUuNQxAEQRAEQfj8iESOIPyHWVtbc+vWLTIyMjAyMgIKJl59nfwFNSMjQ5okOTExsdS609PT+euvv6TePvHx8dIQqnfl4ODA5s2bqVy5Mjo6Ou9cj4qKijRnzPvIycnh559/pmXLltJ9iouLY/ny5bRr1w4omCvm77//fu9rvYt79+6xceNGOnbsiJKSEvfv3+fKlSv88ssvtGjRAoCjR49+lGtbW1srTCIMhZ+tolhYWGBhYcGIESPo2bMnISEhUiLn5cuXnDp1isaNGwMFq4g9evRISnw4ODhw5cqV90pcQsEz7+Pjg4+PDy1atGDMmDHMnz//gz1/ANOnT2f06NFMnDiR6tWrM2TIEBYtWsTjx4+lYWCv95TLz8/n+++/Z/v27cTExGBqavpe1xcEQRAEQRA+X2JolSB84XJzc7lz547CJk8sODs7Y2FhgY+PD0lJScTGxvLDDz8onG9mZoaxsTGBgYGkpKSwZ88ead6TkqipqSnUGxAQgKen53vNS+Pl5UWlSpXo0KEDsbGx3Lhxg5iYGAICAqQJkcvCxMSEI0eOcPv27bdKsty7d487d+6QkpJCeHg4zZs35++//2bFihVSGXNzc9avX09ycjInTpzAy8sLdXX1t2rnu8jPz+fOnTtkZGSQnJzM2rVradasGbq6usyZMwcomDuoYsWK/Pzzz1y7do1Dhw4xcuTIjxLP4MGDSUlJYcyYMVy5coWNGzeWOKdOTk4OQ4cOJSYmhps3bxIXF0dCQoKUpIGCnjTff/89J06c4PTp0/j6+tK0aVMpsTNlyhTWrVvHtGnTuHjxIsnJyYSHhzNp0qQyxz1lyhR27tzJtWvXuHjxIrt375Zi+FDPHxRMRG1mZsa5c+fw8PDAzMyM5ORk2rVrh5mZGWZmZgpz5Pj7+/Prr7+yceNGtLW1pd9yTk7OW11XEARBEARB+PyJRI4gfOH27duHkZGRwvbVV18BBcOmtm/fTk5ODo0bN6Z///6FVvgpX748mzZt4vLly9SrV4+5c+cyc+bMUq9rZmZG586dadeuHW3btqVevXosX778vdqioaHBkSNHqFGjBp07d5aW2X727Nlb9ZCYPn06aWlp1K5dW2FITGksLS2pWrUqDRo0YM6cOTg7O3PhwgVsbGykMmvWrOHhw4c4ODjQp08fAgICqFy58lu18108fvwYIyMjqlWrhqOjI6tWrcLHx4ezZ89Kva2UlJQIDw/n9OnT1KlThxEjRvDjjz9+lHhq1KjBtm3b2LFjB3Z2dqxcuZLZs2cXW15ZWZn79+/j7e2NhYUFnp6euLu7K0zYq6Ghwbhx4+jVqxfNmzdHS0uLzZs3S8ddXV3ZvXs3+/fvp1GjRjRt2pRFixZRs2bNMsetoqLChAkTqFevHi1btkRZWVmai+ZDPX9yd+7c4caNGzRt2pTnz58THx9Py5Ytiyy7YsUKMjMzad26tcJv+fX2C4IgCIIgCP8NsvzXJ74QBEGgYM6Q7du307Fjx3c6PzAwkB07dpRpCJYglEVoaCjDhw/n0aNHnzqUz9Ljx4/R1dUlMzPzvYeFCYIgCIIgCB/e2/x9TfTIEQRBEARBEARBEARB+EyIRI4gCP957u7uCktWv76VNBzon2Jra1tsfEWtoCQIgiAIgiAIwpdLDK0SBOE/7/bt28VOGquvr4++vv4/HJGimzdvFrvke5UqVdDW1v6HIxI+N/Kuunbfr0RZ9eNPvi0IX7LTP3p/6hAEQRCEL9DbDK0Sy48LgvDRPX/+HBsbG9atW0ezZs0+dTiFVKtW7a3Pef78ORYWFmzdupWGDRt+hKj+T82aNT+reYdMTEwYPnw4w4cP/9ShCIIgCIIgCMIX55MOrfL19S1yMtWYmBhkMpk0qaX8c1HbnTt3FM79888/UVFRoU6dOkVe8/VzNTU1MTc3x9fXl9OnT5car4mJCTKZjPj4eIX9w4cPp3Xr1gr7Hjx4wPDhw6lZsyYqKipUrVqVfv36kZ6eXugeyOMpX748pqamjB07lmfPnhUZ95vXzs3NpWLFishkMmJiYgrFPGjQIJSVldmyZUuhY4GBgdSvX7/Udr/u+fPnzJs3Dzs7OzQ0NKhUqRLNmzcnJCREocfArVu36NevH1WrVkVFRYWaNWsybNgw7t+/X6jOixcv4unpiYGBAaqqqlhYWDBlyhSys7MVysnvv0wmQ0NDg7p167J69WqFMq8/K0pKSujq6mJvb8/YsWPJyMgosf2BgYHIZDIGDx6sUC4xMRGZTEZaWlqh2F1dXVFWViYhIQGAtLS0Yp9V+RYaGvpWz7RcZGQkKioqnDlzRmH/ggULqFSpUrHnvc7KygpVVdVCZZ8+fUrt2rULLUWdlpaGjo4Ov/zyC1D4twnwyy+/YGdnh5aWFnp6etjb2xMUFKRQz8qVKzE1NVVI4hw+fJg2bdqgr6+PhoYG5ubm+Pj48Pz5c6Bgcls9Pb1S2/SxRERE0LZtW+n39WYCRUVFhdGjRzNu3LgS65E/E59DAqZu3bqFnn+59evXo6qq+lbLtf9T3vxztEqVKri4uLB27Vry8vI+dXiCIAiCIAiC8EF9VnPkXLlyhYyMDIXtzWV9Q0ND8fT05PHjx5w4caLIekJCQsjIyODixYv89NNPZGVl0aRJE9atW1dqDGpqaqW+uD148ICmTZsSFRXFypUruXbtGuHh4Vy7do1GjRpx/fp1hfJubm5kZGRw/fp1Fi1axKpVq5g6dWqheo2NjQkJCVHYt337drS0tIqMIzs7m/DwcMaOHcvatWtLbVtpnj9/jqurK3PmzGHgwIEcO3aMkydP4u/vz9KlS7l48SIA169fp2HDhqSkpLBp0yauXbvGypUrOXjwII6Ojjx48ECqMz4+niZNmvD8+XP27NnD1atXmTVrFqGhobi4uEgv9XLTp08nIyODCxcu0Lt3bwYMGMDevXsLxXrlyhX++usvEhISGDduHFFRUdSpU4fz58+X2EY1NTXWrFlDSkpKqfcjPT2dY8eOMXToUOn+GhsbKzyfo0aNwtbWVmFf9+7dFeIs7ZmWa9euHd7e3nh7e5ObmwvApUuXmDRpEj/99BOGhoYlxnv06FFycnLo2rUrYWFhCsc0NTUJCQlh6dKlxMbGApCfn0/fvn1p3rw5AwYMKLLOtWvXMnz4cAICAkhMTCQuLo6xY8eSlZUllcnPz2fZsmX4+flJ+y5duoSbmxsNGzbkyJEjnD9/nqVLl6KiosKrV69KbMc/5enTp3z11VfMnTu32DJeXl4cPXpUevY/d35+foSHhxc5zCwkJIRvv/2WSpUqfYLISif/czQtLY29e/fi5OTEsGHDaN++PS9fvvzU4QmCIAiCIAjCB/NZJXIqV66MoaGhwqak9H9NyM/PJyQkhD59+tCrVy/WrFlTZD16enoYGhpiYmJC27Zt2bp1K15eXgwdOpSHDx+WGMPAgQOJj48nMjKy2DI//PADf/31F1FRUbi7u1OjRg1atmzJH3/8Qfny5fH391cor6qqiqGhIcbGxnTs2BFnZ2cOHDhQqF4fH59CL1lr167Fx8enyDi2bNmCjY0N48eP58iRI9y6davEtpVm8eLFHDlyhIMHD+Lv70/9+vWpVasWvXr14sSJE5ibmwPg7++PiooK+/fvp1WrVtSoUQN3d3eioqK4ffs2P/zwA1Dwffn5+WFtbU1ERASNGzemZs2adOvWjd9//53jx4+zaNEihRi0tbUxNDSkVq1ajBs3Dn19/SLvlfxZsbCwoEePHsTFxWFgYMB3331XYhstLS1xcnKSYixJSEgI7du357vvvmPTpk3k5OSgrKys8HxqaWlRrlw5hX3q6v83P0Vpz/SbFi1aRFZWFlOnTuXly5f4+Pjg4eGhkBwqzpo1a+jVqxd9+vQpMrHXsmVLvv/+e/r27cvTp09ZsmQJiYmJhXo9vW7Xrl14enri5+eHmZkZtra29OzZk1mzZkllTp8+TWpqKt988420b//+/RgaGjJv3jzq1KlD7dq1cXNz45dffkFdXZ2YmBj69u1LZmam1NMiMDAQKOgZ0rBhQ+lZ6NWrF/fu3SsUl7m5OWpqajg5OREWFlaoJ9HRo0dp0aIF6urqGBsbExAQwNOnT6Xjffr0YcqUKTg7Oxfb/goVKtC8eXPCw8OLLfOmonoa7dixA5lMprBvzpw50vw3fn5+hXrpvXz5koCAAPT09KhYsSLjxo3Dx8dHoZdjXl4eQUFBmJqaoq6ujp2dHVu3bi02tt69e5OTk8O2bdsU9t+4cYOYmBj8/PxITU2lQ4cOVKlSBS0tLRo1akRUVFSxdRbVI+nRo0eFehFeuHBBmnS6SpUq9OnT5616/8j/HK1WrRoODg5MnDiRnTt3snfvXkJDQ6VyCxcupG7dumhqamJsbMyQIUMUEo/y72f37t1YWlqioaFB165dyc7OJiwsDBMTEypUqEBAQECZk44mJibMnj2bfv36oa2tTY0aNfj5558VyowbNw4LCws0NDSoVasWkydPVujlKO9BuH79ekxMTNDV1aVHjx48efKkzPdIEARBEARB+DJ8Vomc0kRHR5OdnY2zszO9e/cmPDxc4cWsJCNGjODJkydFJgVeZ2pqyuDBg5kwYUKRXfbz8vIIDw/Hy8urUA8JdXV1hgwZwh9//KHQK+V1Fy5c4NixY6ioqBQ61qBBA0xMTKSXrPT0dI4cOUKfPn2KrGvNmjX07t0bXV1d3N3dFV5m3sWGDRtwdnbG3t6+0LHy5cujqanJgwcP+OOPPxgyZIhCwgLA0NAQLy8vNm/eTH5+PomJiVy6dImRI0cWSl7Y2dnh7OzMpk2biowlLy+Pbdu28fDhwyLv1ZvU1dUZPHgwcXFxhV763zRnzhy2bdvGqVOnii0jTxr27t0bKysrzMzMSnxB/lC0tbVZu3YtCxYswMvLi1u3brFixYpSz3vy5Albtmyhd+/euLi4kJmZKfW8ed2sWbMoV64cvXv3ZuLEiSxdurTE+WMMDQ2Jj4/n5s2bxZaJjY3FwsJCYUJeQ0NDMjIyOHLkSJHnNGvWjMWLF6OjoyP1VBo9ejQAL168YMaMGSQlJbFjxw7S0tLw9fWVzr1x4wZdu3alY8eOJCUlMWjQoEKJudTUVNzc3OjSpQvnzp1j8+bNHD16lKFDhxbbjuI0bty4yHv5Pn777TcCAwOZPXs2p06dwsjIiOXLlyuUmTt3Lhs2bCAkJIS4uDgeP37Mjh07FMoEBQWxbt06Vq5cycWLFxkxYgS9e/fm8OHDRV63UqVKdOjQoVCiLzQ0lOrVq9O2bVuysrJo164dBw8e5OzZs7i5ueHh4VFo2OjbePToEW3atMHe3p5Tp06xb98+7t69i6en5zvXCdCmTRvs7OyIiIiQ9ikpKREcHMzFixcJCwvj0KFDjB07VuG87OxsgoODCQ8PZ9++fcTExNCpUyciIyOJjIxk/fr1rFq16q1+8wsWLKBhw4acPXuWIUOG8N1333HlyhXpuLa2NqGhoVy6dIklS5bwyy+/FEpkp6amsmPHDnbv3s3u3bs5fPgwc+bMKfJ6ubm5PH78WGETBEEQBEEQvgyfPJGze/fuQsvpuru7F1m2evXqCuVsbW0Vjq9Zs4YePXqgrKxMnTp1qFWrVpFzwxTFysoKoMh5UN40adIkbty4UeSyv//73/949OgR1tbWRZ5rbW1Nfn4+165dk/bJ74Gamhp169bl3r17jBkzpsjz+/XrJ71khYaG0q5dOwwMDAqVS0lJIT4+Xuqp0bt3b0JCQnifRcpSUlKk+1RSmfz8/BLb//DhQ/73v/9x9epVaV9xZeVl5MaNG4eWlhaqqqp07dqVChUq0L9//zLFX9bv2MHBAU9PzxKH0EVFRZGdnY2rqytQcH+L6wFWktKe6aK0adOGrl278ttvvxEcHEzFihVLPSc8PBxzc3NsbW1RVlamR48eRcarrq7OkiVL2LFjB61bt6Z3794l1jt16lT09PQwMTHB0tISX19ffvvtN4Uk582bN6latarCed26daNnz560atUKIyMjOnXqxLJly6SXTRUVFXR1dZHJZAq9m6DgN+Du7k6tWrVo2rQpwcHB7N27V+pVsWrVKiwtLfnxxx+xtLSkR48eCokeKEhweHl5MXz4cMzNzWnWrBnBwcGsW7euUM+X0lStWrXERNa7WLx4MX5+fvj5+WFpacnMmTOxsbFRKLN06VImTJhAp06dsLKyYtmyZQo9fXJzc5k9ezZr167F1dWVWrVq4evrS+/evVm1alWx1/bz8yMmJoYbN24ABUnLsLAwfHx8UFJSws7OjkGDBlGnTh3Mzc2ZMWMGtWvXZteuXe/c3mXLlmFvb8/s2bOxsrLC3t6etWvXEh0dXejPgLdlZWWl8JsfPnw4Tk5OmJiY0KZNG2bOnMlvv/2mcM6LFy9YsWIF9vb2tGzZkq5du3L06FHWrFmDjY0N7du3x8nJiejo6DLH0a5dO4YMGYKZmRnjxo2jUqVKCudPmjSJZs2aYWJigoeHB6NHjy4UV15eHqGhodSpU4cWLVrQp08fDh48WOT1goKC0NXVlTZjY+MyxyoIgiAIgiD8u33yRI6TkxOJiYkKW3FDOWJjYxXKvT686dGjR0RERCi8eL7Ny7U8wfHm8IaiGBgYMHr0aKZMmVJoDpc36ysL+T04ceIEPj4+9O3bly5duhRZtnfv3hw/fpzr168TGhpKv379iiwnf3mTz2fRrl07MjMzOXToUJnjetPbtOljlR0zZgyJiYkcOnSIJk2asGjRIszMzN7qOmX5jmfOnElsbCz79+8v8vjatWvp3r075coVLPzWs2dP4uLiSE1NLWNLCpT0TBfn9u3b7Nu3Dw0NjTL3BFm7dm2h38aWLVuKHJaxZs0aNDQ0OH/+PJmZmSXWa2RkxPHjxzl//jzDhg2Thnu5ublJyZycnBzU1NQUzlNWViYkJIQ///yTefPmUa1aNWbPni3NJ1SS06dP4+HhQY0aNdDW1qZVq1YAUo+QK1eu0KhRI4VzGjdurPA5KSmJ0NBQhSSaq6sreXl5UgKjrNTV1QtNzP2+kpOTadKkicI+R0dH6b8zMzO5e/euQruUlZVp0KCB9PnatWtkZ2fj4uKi0M5169aV+Jy6uLhQvXp1aT6ugwcPkp6eTt++fQHIyspi9OjRWFtbo6enh5aWFsnJye/VIycpKYno6GiFOOWJ17f9Tb0pPz9f4TcfFRXF119/TbVq1dDW1qZPnz7cv39f4TvU0NCgdu3a0ucqVapgYmKiMB9ZlSpVSu3d97p69epJ/y1PUL5+/ubNm2nevLmUtJw0aVKhe2piYqLQs83IyKjYGCZMmEBmZqa0ve/QWkEQBEEQBOHf45MncjQ1NTEzM1PYihvKYWpqqlCuZs2a0rGNGzfy7NkzmjRpQrly5ShXrhzjxo3j6NGjZfoX3eTkZOkaZTFy5EhycnIKDXcwMDBAT09Pqq+o68hkMoXkg/we2NnZsXbtWk6cOFFsAqpixYq0b99emjOjqN5Lr169IiwsjD179kj3QkNDgwcPHrzXpMcWFhZcvny5xDJmZmbIZLIS21+hQgUMDAywsLCQ9hVXVl5GrlKlSpiZmdGiRQu2bNlCQEAAly5dKlP88uuYmJiUWrZ27doMGDCA8ePHF0o0PXjwgO3bt7N8+XLp/larVo2XL1++9f0t6ZkuzoABA2jQoAG7d+9mxYoVxQ6Tkbt06RLx8fGMHTtWirdp06bSZNiv27x5M7t37+bYsWNoa2szYsSIMrWjTp06DBkyhF9//ZUDBw5w4MABKa5KlSoVO/dUtWrV6NOnD8uWLePixYs8e/aMlStXFnudp0+f4urqio6ODhs2bCAhIYHt27cDFJtULUpWVhaDBg1SSKIlJSWRkpKi8AJfFg8ePCiyV1xxlJSUCj1Tr8+F8qHIeyjt2bNHoZ2XLl0qcUiQkpISvr6+hIWFkZeXR0hICE5OTtSqVQuA0aNHs337dmbPni0lIuvWrVvs/ZcPm3y9zW+2NysrCw8Pj0JJ/ZSUFFq2bPle9yE5OVn6cz0tLY327dtTr149tm3bxunTp/npp58AxeenfPnyCnXIV8N6c9/brIhV0vnHjx/Hy8uLdu3asXv3bs6ePcsPP/xQ6J6+TQyqqqro6OgobIIgCIIgCMKX4ZMncj6UNWvWMGrUqEIvZi1atCjTy7V8Po6SJjZ9nZaWFpMnT2bWrFkKvRqUlJTw9PRk48aNhZZ4lid+XF1d0dfXL7JeJSUlJk6cyKRJk4pcOQYKhpbExMTg7e2NsrJyoeORkZE8efKEs2fPKtyPTZs2ERERoTDh69vo1asXUVFRnD17ttCxFy9e8PTpUypWrIiLiwvLly8vFP+dO3fYsGED3bt3RyaTUb9+faysrFi0aFGhl5GkpCSioqLo2bNnsfEYGxvTvXt3JkyYUGrsOTk5/Pzzz7Rs2bLML91Tpkzh6tWrhZIdGzZsoHr16iQlJSnc3wULFhAaGvpRV11avXq1NMTDycmJ7777jn79+pU4F9SaNWto2bJloXhHjhypkDC8e/cu/v7+zJw5Ezs7O0JDQ1m3bl2Rq4KVRD4ESB6Tvb09ly9fLrXnVYUKFTAyMpLOK2oFq8uXL3P//n3mzJlDixYtsLKyKtQjwdLSstD8RvLl4eUcHBy4dOlSoSSymZlZmeZcet2FCxeKnDeqOAYGBjx58kThO3tzaXJra+tCq+7Fx8dL/62rq0uVKlUU2vXq1SuFpeltbGxQVVUlPT29UBtLG2bTt29fbt26RUREBNu3b1dYcSwuLg5fX186depE3bp1MTQ0LHG4ovz39npPqzfb6+DgwMWLFzExMSkUq6amZomxluTQoUOcP39e6uF4+vRp8vLyWLBgAU2bNsXCwoK//vrrnev/UI4dO0bNmjX54YcfaNiwIebm5h98uJ4gCIIgCILw5fisEjn37t3jzp07CtuLFy9ITEzkzJkz9O/fnzp16ihsPXv2JCwsTGH52UePHnHnzh1u3rzJgQMH6Nq1Kxs3bmTFihWFVpMpycCBA9HV1WXjxo0K+2fPno2hoSEuLi7s3buXW7duceTIEVxdXXnx4oX0L8DF6datG8rKysWWc3Nz43//+x/Tp08v8viaNWv45ptvsLOzU7gXnp6e6OnpKcztk5OTU+hfwYsbyjB8+HCaN2/O119/zU8//URSUhLXr1/nt99+o2nTptKS3cuWLSM3NxdXV1dptax9+/bh4uJCtWrVpBWNZDIZa9as4dKlS3Tp0oWTJ0+Snp7Oli1b8PDwwNHRkeHDh5d4r4YNG8bvv/9e6MVd/qykpKQQHh5O8+bN+fvvv8s0MbBclSpVGDlyJMHBwYXub9euXQs9a35+fvz999/s27evzNco7pkuys2bNxk5ciTz58+Xeu7MnTsXmUzG+PHjizznxYsXrF+/np49exaKt3///pw4cUJaOnvgwIFYW1tL97xx48aMGTOGgQMHFjvE6rvvvmPGjBnExcVx8+ZN4uPj8fb2xsDAQBoK5OTkRFZWlsIS3atWreK7775j//79pKamcvHiRcaNG8fFixfx8PAACnpOZWVlcfDgQf7++2+ys7OpUaMGKioqLF26lOvXr7Nr1y5mzJihENOgQYO4fPky48aN4+rVq/z222/SRN/yITbjxo2Tlo6X9/zYuXOnwmTHDx48kHqwQMGQrcTExEIJ2tjYWNq2bVvk/SlKkyZN0NDQYOLEiaSmprJx48ZCE5EPGzaMtWvXEhISwtWrV5k6dWqhJc6///57goKC2LlzJ1euXGHYsGE8fPhQaqO2tjajR49mxIgRhIWFkZqaypkzZ1i6dGmh5effZGpqSps2bRg4cCCqqqp07txZOmZubk5ERISULO/Vq1eJPVPU1dVp2rQpc+bMITk5mcOHDzNp0iSFMv7+/jx48ICePXuSkJBAamoqf/zxB3379i1zYjQ3N5c7d+5w+/Ztzpw5w+zZs+nQoQPt27fH29sbKOgx+OLFC+n5Wb9+fYk9wP4p5ubmpKenEx4eTmpqKsHBwVJPM0EQBEEQBEF402eVyLG0tMTIyEhhO336tDQBZVET8Xbq1Il79+4pzD3St29fjIyMsLKy4rvvvkNLS4uTJ0/Sq1evt4qnfPnyzJgxo9DkqBUrViQ+Ph4nJycGDRpE7dq18fT0pHbt2iQkJEhDFIpTrlw5hg4dyrx584rsaSGTyahUqVKRPQfu3r3Lnj17ipxjR0lJiU6dOin0wrh69Sr29vYK26BBg4qMS1VVlQMHDjB27FhWrVpF06ZNadSoEcHBwQQEBFCnTh2g4KXk1KlT1KpVS2r3wIEDcXJy4vjx4wq9kZo1a0Z8fDzKysq4u7tjZmbGhAkT8PHx4cCBA6iqqpZ4r2xsbGjbti1TpkxR2G9paUnVqlVp0KABc+bMwdnZmQsXLhSaMLY0o0ePVpgX4/Tp0yQlJRV5f3V1dfn666/fatLj4p7pN8mXand0dGTgwIHSfg0NDUJDQ4sdYrVr1y7u379Pp06dCh2ztrbG2tqaNWvWsG7dOqKioggJCVFYQWzatGno6ekVO8TK2dmZ+Ph4unXrhoWFBV26dEFNTY2DBw9KkzBXrFiRTp06KSQQGzduTFZWFoMHD8bW1pZWrVoRHx/Pjh07pDlvmjVrxuDBg+nevTsGBgbMmzcPAwMDQkND2bJlCzY2NsyZM4f58+crxGRqasrWrVuJiIigXr16rFixQlq1Sv481atXj8OHD3P16lVatGiBvb09U6ZMUZiUedeuXdjb20vLpvfo0QN7e3uFF//jx4+TmZlJ165di7w/gJTkkM+npK+vz6+//kpkZCR169Zl06ZN0tLqct27d2fy5MmMHTuWBg0acPPmTb777juFMuPGjaNnz554e3vj6OgozfPz+nxEM2bMYPLkyQQFBWFtbY2bmxt79uwp0xBSPz8/Hj58SK9evRTqXLhwIRUqVKBZs2Z4eHjg6uqKg4NDiXWtXbuWly9f0qBBA4YPH87MmTMVjletWpW4uDhevXpF27ZtqVu3LsOHD0dPT6/QinbF2bdvH0ZGRpiYmODm5kZ0dDTBwcHs3LlT6rloZ2fHwoULmTt3LnXq1GHDhg0EBQWVqf6P6dtvv2XEiBEMHTqU+vXrc+zYMSZPnvypwxIEQRAEQRD+pWT577OMkSAIQhmcO3cOFxcXUlNTFRJj/5RZs2axcuXKDz7ha/fu3bGzs2PixInFlomPj8fR0ZH//e9/0uTjH0NeXh7W1tZ4enoW6qUkCI8fP0ZXV5fMzEwxX44gCIIgCMK/0Nv8fa3cPxSTIAj/YfXq1WPu3LncuHGDunXrfvTrLV++nEaNGlGxYkXi4uL48ccfFYZNfQjPnz+nbt26xfZWevnyJWlpafz444/Y2dl98CTOzZs32b9/P61atSI3N5dly5Zx48aNt+5ZKAiCIAiCIAjC50X0yBEE4YszYsQINm/ezIMHD6hRowZ9+vRhwoQJ0vCmf0JiYiLNmjWjfv36rFy5UmH56Q/h1q1b9OjRgwsXLpCfn0+dOnWYM2fOe6/y9G+Tnp5e4pDIS5cuUaNGjX8wIkWxsbFFrh4oJ1897FMTPXIEQRAEQRD+3d7m72sikSMIgiD8a8l7NhXHxMTkH03QvSknJ4fbt28Xe9zMzOwfjKZ48r8Y2H2/EmVV9U8djiB8Uqd/9P7UIQiCIAhCIWJolSAIgvBFKFeu3L8mGVIUdXX1f3V8giAIgiAIwpfns1q1ShCEz19oaCh6enqfOoxP4s22BwYGUr9+femzr68vHTt2/EdiMTExYfHixdJnmUzGjh07AEhLS0Mmk5GYmPjBr/tPXUcQBEEQBEEQvlQikSMIAlCQRJDJZIU2Nze3D3qd7t27c/Xq1Q9a58f0ZsKjNDExMUXex0mTJr11248cOYKHhwdVq1ZVSICUVevWrYuM5eXLlyQkJCgsZV+agIAAGjRogKqqqkLy6X0YGxuTkZFBnTp1Pkh9nzN/f39p9bPZs2fTr18/heMRERG4uLhgYGCAjo4Ojo6O/PHHH58iVEEQBEEQBOETE4kcQRAkbm5uZGRkKGybNm36oNdQV1encuXKH7TOf6MrV64o3Mfx48e/ddufPn2KnZ0dP/300zvHMWDAgELfably5TAwMEBDQ+Ot6urXrx/du3d/51jepKysjKGh4Sed4+Zje/XqFXl5eaWWO378OM2bNwcKJlCW/7fckSNHcHFxITIyktOnT+Pk5ISHhwdnz579KHELgiAIgiAI/14ikSMIgkRVVRVDQ0OFrUKFCtLxlJQUWrZsiZqaGjY2Nhw4cEChp4i8N8qjR4+kcxITE5HJZNKEtcUNL1q1ahXGxsZoaGjg6elJZmamVEY+5Gj27NlUqVIFPT09pk+fzsuXLxkzZgz6+vpUr16dkJAQhfbcunULT09P9PT00NfXp0OHDgoT58rrnT9/PkZGRlSsWBF/f39evHgBFPRouXnzJiNGjJB6s5RV5cqVFe6jlpbWWw8rc3d3Z+bMmXTq1KnM57xJQ0Oj0HcKb9/TKDg4GH9/f2rVqlXmc4p6Xl735tCqhw8f4uXlhYGBAerq6pibm0vfqbxseHg4zZo1Q01NjTp16nD48GGFOi9cuIC7uztaWlpUqVKFPn368Pfff0vHW7duTUBAAGPHjkVfXx9DQ0MCAwOl4/n5+QQGBlKjRg1UVVWpWrUqAQEB0vHc3FxGjx5NtWrV0NTUpEmTJsTExEjH5d/xrl27sLGxQVVVlfT09BLv09OnT7lw4QLNmjUjLy9PIakjt3jxYsaOHUujRo0wNzdn9uzZmJub8/vvv5f6PQiCIAiCIAhfFpHIEQShTPLy8ujcuTMqKiqcOHGClStXMm7cuA9S97Vr1/jtt9/4/fff2bdvH2fPnmXIkCEKZQ4dOsRff/3FkSNHWLhwIVOnTqV9+/ZUqFCBEydOMHjwYAYNGsSff/4JwIsXL3B1dUVbW5vY2Fji4uLQ0tLCzc2N58+fS/VGR0eTmppKdHQ0YWFhhIaGEhoaChQMZ6levTrTp0+XerMIZfMuz8vkyZO5dOkSe/fuJTk5mRUrVlCpUiWFMmPGjGHUqFGcPXsWR0dHPDw8uH//PgCPHj2iTZs22Nvbc+rUKfbt28fdu3fx9PRUqCMsLAxNTU1OnDjBvHnzmD59upRk2rZtG4sWLWLVqlWkpKSwY8cO6tatK507dOhQjh8/Tnh4OOfOnaNbt264ubmRkpIilcnOzmbu3LmsXr2aixcvFtsLa8iQIejp6WFkZMSLFy8wNTWlQoUKZGZm0rRpU/T09IpNAuXl5fHkyRP09fWLPJ6bm8vjx48VNkEQBEEQBOHLIBI5giBIdu/ejZaWlsI2e/ZsAKKiorh8+TLr1q3Dzs6Oli1bSsfe17Nnz1i3bh3169enZcuWLF26lPDwcO7cuSOV0dfXJzg4GEtLS/r164elpSXZ2dlMnDgRc3NzJkyYgIqKCkePHgVg8+bN5OXlsXr1aurWrYu1tTUhISGkp6cr9KCoUKECy5Ytw8rKivbt2/PNN99w8OBB6ZrKyspoa2sr9GYpi+rVqyvcR3my4Z+2fPlyhThGjRr1j1z3XZ6X9PR07O3tadiwISYmJjg7O+Ph4aFQZujQoXTp0gVra2tWrFiBrq4ua9asAWDZsmXY29sze/ZsrKyssLe3Z+3atURHRyvMTVSvXj2mTp2Kubk53t7eNGzYUPrO09PTMTQ0xNnZmRo1atC4cWMGDBggHQsJCWHLli20aNGC2rVrM3r0aL766iuF3mAvXrxg+fLlNGvWDEtLy2KHsE2fPp3ExEQ8PT3x8/MjMTERf39/OnXqRGJiIomJiVStWrXIc+fPn09WVlahJJVcUFAQurq60mZsbFzivRcEQRAEQRA+H1/uxASCILw1JycnVqxYobBP/i/+ycnJGBsbK7xYOjo6fpDr1qhRg2rVqinUm5eXx5UrV6Tkia2tLUpK/5d7rlKlisIkucrKylSsWJF79+4BkJSUxLVr19DW1la41rNnz0hNTZU+29raoqysLH02MjLi/Pnz792m2NhYhWu/PkTtn+Tl5cUPP/wgff6nVgx7l+flu+++o0uXLpw5c4a2bdvSsWNHmjVrplDm9TrKlStHw4YNSU5OBgq+8+joaLS0tArVnZqaioWFBVCQyHmdkZGR9Nx069aNxYsXU6tWLdzc3GjXrh0eHh6UK1eO8+fP8+rVK6keudzcXCpWrCh9VlFRKXSNolSqVIlKlSpx7NgxlixZgomJCQkJCfj4+GBiYlLseRs3bmTatGns3Lmz2N4+EyZMYOTIkdLnx48fi2SOIAiCIAjCF0IkcgRBkGhqamJmZvbO58sTLfn5+dI++Xwz76t8+fIKn2UyWZH75BPLZmVl0aBBAzZs2FCoLgMDgxLrLcvktKUxNTX9Vyyzrqur+17f6T/J3d2dmzdvEhkZyYEDB/j666/x9/dn/vz5ZTo/KysLDw8P5s6dW+iYkZGR9N8lfefGxsZcuXKFqKgoDhw4wJAhQ/jxxx85fPgwWVlZKCsrc/r0aYXkH6CQPFJXVy91PqUNGzYwaNAgoGCOnI4dOyKTycjOziYuLo7BgwezatUqvLy8FM4LDw+nf//+bNmyBWdn52LrV1VVRVVVtcQYBEEQBEEQhM+TSOQIglAm1tbW3Lp1i4yMDOmlOD4+XqGMPEGSkZEh9UCRT2RbkvT0dP766y+p90Z8fDxKSkpYWlq+c7wODg5s3ryZypUro6Oj8871qKio8OrVq3c+/7+qLM9LUQwMDPDx8cHHx4cWLVowZswYhUROfHw8LVu2BODly5ecPn2aoUOHAgXf+bZt2zAxMXmvlbDU1dXx8PDAw8MDf39/rKysOH/+PPb29rx69Yp79+7RokWLd64f4Ntvv6VJkybs3LmTiIgIwsLCOHbsGDNnziQyMhIo6HX2uk2bNtGvXz/Cw8P55ptv3uv6giAIgiAIwudLzJEjCIIkNzeXO3fuKGzyFX+cnZ2xsLDAx8eHpKQkYmNjFYbsAJiZmWFsbExgYCApKSns2bOHBQsWlHpdNTU1hXoDAgLw9PR8qzlp3uTl5UWlSpXo0KEDsbGx3Lhxg5iYGAICAqQJkcvCxMSEI0eOcPv2bYXVj/4JWVlZ0lwpADdu3CAxMbHUVZA+hmvXrpGYmMidO3fIycmR4np94ujXleV5edOUKVPYuXMn165d4+LFi+zevRtra2uFMj/99BPbt2/n8uXL+Pv78/DhQ/r16weAv78/Dx48oGfPniQkJJCamsoff/xB3759y5yMCw0NZc2aNVy4cIHr16/z66+/oq6uTs2aNbGwsMDLywtvb28iIiK4ceMGJ0+eJCgoiD179pSpfjltbW3MzMxISUnB2dkZMzMz0tLScHJywszMDDMzM4WheRs3bsTb25sFCxbQpEkT6ff5+upugiAIgiAIwn+DSOQIgiDZt28fRkZGCttXX30FFAyb2r59Ozk5OTRu3Jj+/fsza9YshfPLly/Ppk2buHz5MvXq1WPu3LnMnDmz1OuamZnRuXNn2rVrR9u2balXrx7Lly9/r7ZoaGhw5MgRatSoQefOnbG2tsbPz49nz569VQ+d6dOnk5aWRu3atRWGZP0TTp06hb29Pfb29gCMHDkSe3t7pkyZ8o/GAdC/f3/s7e1ZtWoVV69eleL666+/iixfluflTSoqKkyYMIF69erRsmVLlJWVCQ8PVygzZ84c5syZg52dHUePHmXXrl3SylZVq1YlLi6OV69e0bZtW+rWrcvw4cPR09NTmF+pJHp6evzyyy80b96cevXqERUVxe+//y7NgRMSEoK3tzejRo3C0tKSjh07kpCQQI0aNcpU/5tiYmKkHkaHDx+W/vtNP//8My9fvsTf31/h9zls2LB3uq4gCIIgCILw+ZLlvz6ZhSAIwluSyWRs376djh07vtP5gYGB7Nixo0xDsIT/rrS0NExNTTl79iz169f/1OF8dh4/foyuri6ZmZnvNdRQEARBEARB+Dje5u9rokeOIAiCIAiCIAiCIAjCZ0IkcgRBEN6Cu7s7WlpaRW6zZ8/+x+KIjY0tNo6ilt8WBEEQBEEQBOHLIIZWCYIgvIXbt2+Tk5NT5DF9fX309fX/kThycnK4fft2scc/lyXHhX+GvKuu3fcrUVZV/9ThCMIHd/pH708dgiAIgiC8l7cZWiWWHxf+0953fpf/in/bferTpw/W1tZMnDjxH792tWrVPkq9PXr0oFGjRowaNapM5dXV1d8rWdO6dWvq16/P4sWLgYLVuYYPH87w4cPfuc7Pla+vL48ePWLHjh2fOhRBEARBEARBKJUYWiV8cXx9fZHJZMhkMsqXL0+VKlVwcXFh7dq15OXlKZTNyMjA3d39E0X68dWtW5fBgwcXeWz9+vWoqqr+40tqv6+kpCQiIyMJCAiQ9t24cYNevXpRtWpV1NTUqF69Oh06dODy5ctAwUS5Mpnsk02ofPHiRbp06YKJiQkymUxKnrxu0qRJzJo1q0zLSZ89e5Zu3bpRpUoV1NTUMDc3Z8CAAVy9evUjRP9uTExMimxnUeXkv1cNDQ3q1q3L6tWrP36AJWjduvVbJbTkz5d809bWxtbWFn9/f1JSUj5eoIIgCIIgCMJ/kkjkCF8kNzc3MjIySEtLY+/evTg5OTFs2DDat2/Py5cvpXKGhoaoqqp+wkg/Lj8/P8LDw4scChQSEsK3334rLd38uVi6dCndunWT5oF58eIFLi4uZGZmEhERwZUrV9i8eTN169bl0aNHnzbY/y87O5tatWoxZ84cDA0NiyxTp04dateuza+//lpiXbt376Zp06bk5uayYcMGkpOT+fXXX9HV1WXy5MkfI/yPbvr06WRkZHDhwgV69+7NgAED2Lt376cO661FRUWRkZFBUlISs2fPJjk5GTs7Ow4ePPipQxMEQRAEQRC+ICKRI3yRVFVVMTQ0pFq1ajg4ODBx4kR27tzJ3r17CQ0NlcrJZDJpOMXz588ZOnQoRkZGqKmpUbNmTYKCghTKrlixAnd3d9TV1alVqxZbt25VuO6tW7fw9PRET08PfX19OnToQFpamnTc19eXjh07Mn/+fIyMjKhYsSL+/v68ePFCKrN8+XLMzc1RU1OjSpUqdO3aVTqWl5dHUFAQpqamqKurY2dnVyiG1/Xu3ZucnBy2bdumsP/GjRvExMTg5+cHwIoVK6hduzYqKipYWlqyfv36YuuMiYlBJpMpJEkSExORyWRSW0NDQ9HT02P37t1YWlqioaFB165dyc7OJiwsDBMTEypUqEBAQACvXr2S6snNzWX06NFUq1YNTU1NmjRpQkxMjHT81atXbN26FQ8PD2nfxYsXSU1NZfny5TRt2pSaNWvSvHlzZs6cSdOmTQEwNTUFwN7eHplMRuvWrQFISEjAxcWFSpUqoaurS6tWrThz5oxCey9fvsxXX32FmpoaNjY2REVFKTw3UPr33qhRI3788Ud69OhRYuLQw8OD8PDwYo9nZ2fTt29f2rVrx65du3B2dsbU1JQmTZowf/58Vq1aJZU9fPgwjRs3RlVVFSMjI8aPH6+QxCzNo0eP6N+/PwYGBujo6NCmTRuSkpIUyvz+++80atQINTU1KlWqRKdOnYCCHi03b95kxIgRUi+Vkmhra2NoaEitWrUYN24c+vr6HDhwoMyxJCUl4eTkhLa2Njo6OjRo0IBTp04BBcvbv7lc+eLFizExMSkyFl9fXw4fPsySJUuk2F//LktSsWJFqR0dOnQgKiqKJk2a4OfnJz3nqampdOjQgSpVqqClpUWjRo2IiopSqMfExISZM2fi7e2NlpYWNWvWZNeuXfzvf/+jQ4cOaGlpUa9ePamNgiAIgiAIwn+LSOQI/xlt2rTBzs6OiIiIIo8HBweza9cufvvtN65cucKGDRsKvexNnjyZLl26kJSUhJeXFz169CA5ORko6Bni6uqKtrY2sbGxxMXFoaWlhZubG8+fP5fqiI6OJjU1lejoaMLCwggNDZWSS6dOnSIgIIDp06dz5coV9u3bR8uWLaVzg4KCWLduHStXruTixYuMGDGC3r17c/jw4SLbVKlSJTp06MDatWsV9oeGhlK9enXatm3L9u3bGTZsGKNGjeLChQsMGjSIvn37Eh0d/ba3WEF2djbBwcGEh4ezb98+YmJi6NSpE5GRkURGRrJ+/XpWrVqlkIgaOnQox48fJzw8nHPnztGtWzfc3Nyk4Snnzp0jMzOThg0bSucYGBigpKTE1q1bFZJCrzt58iTwfz0m5M/AkydP8PHx4ejRo8THx2Nubk67du148uQJUJA46tixIxoaGpw4cYKff/6ZH374QaHusn7vZdG4cWNOnjxJbm5ukcf/+OMP/v77b8aOHVvkcT09PaBgQuZ27drRqFEjkpKSWLFiBWvWrGHmzJlljqVbt27cu3ePvXv3cvr0aRwcHPj666958OABAHv27KFTp060a9eOs2fPcvDgQRo3bgxAREQE1atXl3raZGRklOmaeXl5bNu2jYcPH6KiolLmWLy8vKhevToJCQmcPn2a8ePHU758+TK39XVLlizB0dGRAQMGSLEbGxu/U11KSkoMGzaMmzdvcvr0aQCysrJo164dBw8e5OzZs7i5ueHh4UF6errCuYsWLaJ58+acPXuWb775hj59+uDt7U3v3r05c+YMtWvXxtvbm+LWK8jNzeXx48cKmyAIgiAIgvBlEJMdC/8pVlZWnDt3rshj6enpmJub89VXXyGTyahZs2ahMt26daN///4AzJgxgwMHDrB06VKWL1/O5s2bycvLY/Xq1VIPhJCQEPT09IiJiaFt27YAVKhQgWXLlqGsrIyVlRXffPMNBw8eZMCAAaSnp6OpqUn79u3R1tamZs2a2NvbAwUvZrNnzyYqKgpHR0cAatWqxdGjR1m1ahWtWrUqsl1+fn64u7tz48YNTE1Nyc/PJywsDB8fH5SUlJg/fz6+vr4MGTIEgJEjRxIfH8/8+fNxcnJ653v94sULqacPQNeuXVm/fj13795FS0sLGxsbnJyciI6Opnv37qSnpxMSEkJ6ejpVq1YFYPTo0ezbt4+QkBBmz57NzZs3UVZWpnLlytJ1qlWrRnBwMGPHjmXatGk0bNgQJycnvLy8qFWrFlCQ7IH/6zEh16ZNG4WYf/75Z/T09Dh8+DDt27fnwIEDpKamEhMTI503a9YsXFxcpHPK+r2XRdWqVXn+/Dl37twp8vmTJ7SsrKxKrGf58uUYGxuzbNkyZDIZVlZW/PXXX4wbN44pU6agpFRyDv/o0aOcPHmSe/fuST2I5s+fz44dO9i6dSsDBw5k1qxZ9OjRg2nTpknn2dnZAQWrdykrK0s9bUozbtw4Jk2aRG5uLi9fvkRfX1/6nZUllvT0dMaMGSPdF3Nz81KvWRxdXV1UVFTQ0NAoU+ylkceUlpZG48aNsbOzk+4TFPw5sn37dnbt2sXQoUOl/e3atWPQoEEATJkyhRUrVtCoUSO6desGFNwzR0dH7t69W2ScQUFBCt+NIAiCIAiC8OUQPXKE/5T8/Pxih3n4+vqSmJiIpaUlAQEB7N+/v1AZeQLl9c/yHjlJSUlcu3YNbW1ttLS00NLSQl9fn2fPnpGamiqdY2tri7KysvTZyMiIe/fuAeDi4kLNmjWpVasWffr0YcOGDWRnZwNw7do1srOzcXFxkerX0tJi3bp1CvW/ycXFherVqxMSEgLAwYMHSU9Pp2/fvgAkJyfTvHlzhXOaN28utetdaWhoSEkcgCpVqmBiYiLNbSPfJ2/7+fPnefXqFRYWFgrtO3z4sNS+nJwcVFVVC32H/v7+3Llzhw0bNuDo6MiWLVuwtbVVGJ5TlLt37zJgwADMzc3R1dVFR0eHrKwsqXfElStXMDY2VnhRlvc6kSvr914W6uoFy0LLv/M3Fdf74k3Jyck4Ojoq3KfmzZuTlZXFn3/+Wer5SUlJZGVlUbFiRYXv4saNG1KbEhMT+frrr8sUT2nGjBlDYmIihw4dokmTJixatEhakasssYwcOZL+/fvj7OzMnDlz3vq+f0zy70z+XWRlZTF69Gisra3R09NDS0uL5OTkQj1y6tWrJ/13lSpVgILJy9/cJ//9vGnChAlkZmZK261btz5cowRBEARBEIRPSvTIEf5TkpOTpflS3uTg4MCNGzfYu3cvUVFReHp64uzsXOIcNK/LysqiQYMGbNiwodAxeY8QoNCQD5lMJq2mpa2tzZkzZ4iJiWH//v1MmTKFwMBAEhISyMrKAgqGtLy5BHZJ864oKSnh6+tLWFgYgYGBhISE4OTkJPVWeVvy3hyvJxVen+OnpHaW1PasrCyUlZU5ffq0QqILkJI/lSpVIjs7m+fPnysMvYGCe+fh4YGHhwczZ87E1dWVmTNnKvSeeZOPjw/3799nyZIl1KxZE1VVVRwdHd9qSFRZv/eykA8VKu48CwsLoGDenjeTih9SVlYWRkZGCvMTycmHb8mTTh9CpUqVMDMzw8zMjC1btlC3bl0aNmyIjY1NmWIJDAykV69e7Nmzh7179zJ16lTCw8Pp1KkTSkpKhRJgRT2vH4s8ISr/c2f06NEcOHCA+fPnY2Zmhrq6Ol27di30zL3+W5EngYra9+ZKfHKqqqpf9ETugiAIgiAI/2WiR47wn3Ho0CHOnz9Ply5dii2jo6ND9+7d+eWXX9i8eTPbtm2TXq4B4uPjFcrHx8djbW0NFCSCUlJSqFy5svRSKt90dXXLHGe5cuVwdnZm3rx5nDt3jrS0NA4dOoSNjQ2qqqqkp6cXqr+0OTz69u3LrVu3iIiIYPv27dIkxwDW1tbExcUplI+Li8PGxqbIuuRJhtfnPfkQy3rb29vz6tUr7t27V6h98h4x8klrL126VGJd8uFET58+BZCSPm/OoRMXF0dAQADt2rXD1ta20HLslpaW3Lp1i7t370r7EhISFOr4UN87wIULF6hevXqxK4m1bduWSpUqMW/evCKPyyegtra25vjx4woJjLi4OLS1talevXqpcTg4OHDnzh3KlStXqE3y2OrVq1fiakwqKirFzllUEmNjY7p3786ECRPKHAsUJLlGjBjB/v376dy5s9QDzcDAgDt37ijci9Ke13eN/U15eXkEBwdjamoqDZGMi4vD19eXTp06UbduXQwNDcs8mbIgCIIgCIIggEjkCF+o3Nxc7ty5w+3btzlz5gyzZ8+mQ4cOtG/fHm9v7yLPWbhwIZs2beLy5ctcvXqVLVu2YGhoKP2rP8CWLVtYu3YtV69eZerUqZw8eVKa18LLy0uaXDg2NlZaGSogIKBMw1mgYGnp4OBgEhMTuXnzJuvWrSMvLw9LS0u0tbUZPXo0I0aMICwsjNTUVM6cOcPSpUsJCwsrsV5TU1PatGnDwIEDUVVVpXPnztKxMWPGEBoayooVK0hJSWHhwoVEREQwevToIuuSJ44CAwNJSUlhz549LFiwoEztK4mFhQVeXl54e3sTERHBjRs3OHnyJEFBQezZswcoeCl3cHDg6NGj0nmJiYl06NCBrVu3cunSJa5du8aaNWtYu3YtHTp0AKBy5cqoq6uzb98+7t69S2ZmJlAwl8r69etJTk7mxIkTeHl5KfQ0cXFxoXbt2vj4+HDu3Dni4uKYNGkS8H89IsryvT9//pzExEQSExN5/vw5t2/fJjExkWvXrincg9jY2BLn1NHU1GT16tXs2bOHb7/9lqioKNLS0jh16hRjx45l8ODBAAwZMoRbt27x/fffc/nyZXbu3MnUqVMZOXJkqfPjADg7O+Po6EjHjh3Zv38/aWlpHDt2jB9++EFaKWnq1Kls2rSJqVOnkpyczPnz55k7d65Uh4mJCUeOHOH27dsKybGyGDZsGL///junTp0qNZacnByGDh1KTEwMN2/eJC4ujoSEBCnB2rp1a/73v/8xb948UlNT+emnn0pd2tzExIQTJ06QlpbG33//XWyvlzfdv3+fO3fucP36dWlVsZMnT7JmzRqpl5m5uTkREREkJiaSlJREr169yly/IAiCIAiCIIBI5AhfqH379mFkZISJiQlubm5ER0cTHBzMzp07Cw3bkdPW1mbevHk0bNiQRo0akZaWRmRkpMKL77Rp0wgPD6devXqsW7eOTZs2ST1XNDQ0OHLkCDVq1KBz585YW1vj5+fHs2fP0NHRKVPcenp6RERE0KZNG6ytrVm5ciWbNm3C1tYWKJgYdfLkyQQFBWFtbY2bmxt79uwpdrjY6/z8/Hj48CG9evVCTU1N2t+xY0eWLFnC/PnzsbW1ZdWqVYSEhEhLdL+pfPnyUsKrXr16zJ07961WQypJSEgI3t7ejBo1CktLSzp27EhCQgI1atSQyvTv319hGFP16tUxMTFh2rRpNGnSBAcHB5YsWcK0adOkFabKlStHcHAwq1atomrVqlKCZ82aNTx8+BAHBwf69OlDQECAwkTKysrK7Nixg6ysLBo1akT//v2lOuX3sCzf+19//YW9vT329vZkZGQwf/587O3tpQl9AZ49e8aOHTsYMGBAifeoQ4cOHDt2jPLly9OrVy+srKzo2bMnmZmZ0vdQrVo1IiMjOXnyJHZ2dgwePBg/Pz8pCVUamUxGZGQkLVu2pG/fvlhYWNCjRw9u3rwpzc3SunVrtmzZwq5du6hfvz5t2rSRVgcDmD59OmlpadSuXfuth5jZ2NjQtm1bpkyZUmosysrK3L9/H29vbywsLPD09MTd3V2a6Nfa2prly5fz008/YWdnx8mTJ4tNUsqNHj0aZWVlbGxsMDAwKDR/TXGcnZ0xMjKibt26jB8/Hmtra86dO6cwafjChQupUKECzZo1w8PDA1dXVxwcHN7q/giCIAiCIAj/bbL8ss6eKQj/cTKZjO3bt9OxY8dPHcp/Wk5ODpaWlmzevPmjzhNTnLi4OL766iuuXbumMJnz+1qxYgXbt28vcpJtQXhfjx8/RldXl8zMzDInlgVBEARBEIR/ztv8fU1MdiwIwmdFXV2ddevWvfVwnXe1fft2tLS0MDc359q1awwbNozmzZt/0CQOFPR0Wrp06QetUxAEQRAEQRCEL49I5AiC8NkpbtjXx/DkyRPGjRtHeno6lSpVwtnZ+YPMCfSm14dZCf8ugwcP5tdffy3yWO/evVm5cuU/HJEgCIIgCILwXyaGVgmCIAhCCe7du8fjx4+LPKajo6Mwr9K/lbyrrt33K1FW/XBLxwvC+zr9Y9ELEAiCIAjCf40YWiUIgiAIH0jlypU/i2SNIAiCIAiC8N8gVq0ShC9MTEwMMpmMR48efepQPguhoaEKS8wXRyaTsWPHjo8ez6f05r0IDAykfv36nyyeT+lj/45MTExYvHix9Pn15ystLQ2ZTEZiYuJHubYgCIIgCILweROJHEH4wHx9fYtc2erflGAxMTFBJpMhk8nQ1NTEwcGBLVu2fOqwitW6dWspXjU1NWxsbFi+fPkHqbt79+5cvXpV+lxc8iIjIwN3d/cPcs3iyNtY3FaSx48f88MPP2BlZYWamhqGhoY4OzsTERHBv2UEbVkTQ4GBgVKblZWVMTY2ZuDAgTx48ODjB1mMsib8Xvf6c/v69vLlSxISEhg4cGCZ6woICKBBgwaoqqr+Z5NrgiAIgiAIQgGRyBGE/6jp06eTkZHB2bNnadSoEd27d+fYsWPvXN+LFy8+YHSFDRgwgIyMDC5duoSnpyf+/v5s2rTpvetVV1cv07AZQ0NDVFVV3/t6JcnIyCi0HT9+HC0tLfz9/Ys979GjRzRr1ox169YxYcIEzpw5w5EjR+jevTtjx44lMzPzo8b9Mdja2pKRkUF6ejohISHs27eP77777lOH9dbkz+3rW7ly5TAwMEBDQ+Ot6urXrx/du3f/SJEKgiAIgiAInwuRyBGET+jo0aO0aNECdXV1jI2NCQgI4OnTp9Lx9evX07BhQ7S1tTE0NKRXr17cu3dPoY7IyEgsLCxQV1fHycmJtLS0Ml1bXqeFhQU//fQT6urq/P7770DRw4j09PQIDQ0F/m/ox+bNm2nVqhVqamps2LCBly9fEhAQgJ6eHhUrVmTcuHH4+Pgo9FDKy8sjKCgIU1NT1NXVsbOzY+vWraXGq6GhgaGhIbVq1SIwMBBzc3N27doFQHp6Oh06dEBLSwsdHR08PT25e/eudG5SUhJOTk5oa2ujo6NDgwYNOHXqFKDY0yI0NJRp06aRlJQk9Z6Qt/nNe3L+/HnatGmDuro6FStWZODAgWRlZUnH5T2z5s+fj5GRERUrVsTf37/EhJehoaHCpqOjw+DBg2nYsKHCMJw3TZw4kbS0NE6cOIGPjw82NjZYWFgwYMAAEhMT0dLSAuDhw4d4e3tToUIFNDQ0cHd3JyUlpdR7/7rVq1djbW2NmpoaVlZWhXpG/fnnn/Ts2RN9fX00NTVp2LAhJ06cKPHeFqVcuXIYGhpSrVo1nJ2d6datGwcOHChzLM+fP2fo0KEYGRmhpqZGzZo1CQoKAooeuvTo0SNkMhkxMTGFYomJiaFv375kZmZKsQcGBpbpfsmf29c3KDy0qjTBwcH4+/tTq1atMpXPzc3l8ePHCpsgCIIgCILwZRCJHEH4RFJTU3Fzc6NLly6cO3eOzZs3c/ToUYYOHSqVefHiBTNmzCApKYkdO3aQlpaGr6+vdPzWrVt07twZDw8PEhMT6d+/P+PHj3/rWMqVK0f58uV5/vz5W503fvx4hg0bRnJyMq6ursydO5cNGzYQEhJCXFwcjx8/LpQQCgoKYt26daxcuZKLFy8yYsQIevfuzeHDh9/q2urq6jx//py8vDw6dOjAgwcPOHz4MAcOHOD69esKPRe8vLyoXr06CQkJnD59mvHjx1O+fPlCdXbv3p1Ro0ZJvUEyMjKK7AHx9OlTXF1dqVChAgkJCWzZsoWoqCiF7w4gOjqa1NRUoqOjCQsLIzQ0tMTkxZvkyYMtW7ZQrlzRc9Pn5eURHh6Ol5cXVatWLXRcS0tLOtfX15dTp06xa9cujh8/Tn5+Pu3atStzb6oNGzYwZcoUZs2aRXJyMrNnz2by5MmEhYUBkJWVRatWrbh9+za7du0iKSmJsWPHkpeXV+Z7W5S0tDT++OMPVFRUyhxLcHAwu3bt4rfffuPKlSts2LABExOTMl3vTc2aNWPx4sXo6OhIsY8ePfqd6vqnBAUFoaurK23GxsafOiRBEARBEAThAxGrVgnCR7B7926pF4Tcq1evFD4HBQXh5eXF8OHDATA3Nyc4OJhWrVqxYsUK1NTU6Nevn1S+Vq1aBAcH06hRI7KystDS0mLFihXUrl2bBQsWAGBpacn58+eZO3dumWN9/vw5CxYsIDMzkzZt2rxVO4cPH07nzp2lz0uXLmXChAl06tQJgGXLlhEZGSkdz83NZfbs2URFReHo6Ci16+jRo6xatYpWrVqVes1Xr16xadMmzp07x8CBAzl48CDnz5/nxo0b0svqunXrsLW1JSEhgUaNGpGens6YMWOwsrICCu51UdTV1aXEh7znRFE2btzIs2fPWLduHZqamlJbPTw8mDt3LlWqVAGgQoUKLFu2DGVlZaysrPjmm284ePAgAwYMKLWdQUFB7Nmzh7i4OCpVqlRsub///puHDx9KbStOSkoKu3btIi4ujmbNmgEFyRBjY2N27NhBt27dSo1p6tSpLFiwQPrOTU1NuXTpEqtWrcLHx4eNGzfyv//9j4SEBPT19QEwMzOTzi/LvZU7f/48WlpavHr1imfPngGwcOHCMseSnp6Oubk5X331FTKZjJo1a5Z6zeKoqKigq6uLTCYrU+yvW758OatXr5Y+Dxo0SPq9fkwTJkxg5MiR0ufHjx+LZI4gCIIgCMIXQiRyBOEjcHJyYsWKFQr7Tpw4Qe/evaXPSUlJnDt3jg0bNkj78vPzycvL48aNG1hbW3P69GkCAwNJSkri4cOH5OXlAQVDiWxsbEhOTqZJkyYK15EnSEozbtw4Jk2axLNnz9DS0mLOnDl88803b9XOhg0bSv+dmZnJ3bt3ady4sbRPWVmZBg0aSHFfu3aN7OxsXFxcFOp5/vw59vb2JV5L/kL8/PlzlJWVGTFiBN999x3Lli3D2NhY4SXVxsYGPT09kpOTadSoESNHjqR///6sX79eGqZTu3btt2rr65KTk7Gzs5OSOADNmzcnLy+PK1euSIkcW1tblJWVpTJGRkacP3++1PojIyOZPHkymzZtws7OrsSyZZ3IODk5mXLlyik8LxUrVsTS0pLk5ORSz3/69Cmpqan4+fkpJKJevnyJrq4uAImJidjb20tJnPdhaWnJrl27ePbsGb/++iuJiYl8//33ZY7F19cXFxcXLC0tcXNzo3379rRt2/a943pbXl5e/PDDD9Lnt50w+V2pqqp+9DmdBEEQBEEQhE9DJHIE4SPQ1NRU6IkABXOHvC4rK4tBgwYREBBQ6PwaNWpIw3dcXV3ZsGEDBgYGpKen4+rq+tZDoIoyZswYfH190dLSokqVKgqrIslkskIJgqKG37yeyCgL+Rwye/bsoVq1agrHSnvplL8Qq6urY2RkhJJS2UeGBgYG0qtXL/bs2cPevXuZOnUq4eHhUs+hj+XN4VsymUxKahXn6tWr9OrVi/Hjx5epl4yBgQF6enpcvnz5vWItjfy7++WXXwolD+XJKnV19Q92PRUVFek3JE8yTps2jRkzZpQpFgcHB27cuMHevXuJiorC09MTZ2dntm7dKj07rz/jH2uybl1d3UJ/FgiCIAiCIAjC+xBz5AjCJ+Lg4MClS5cwMzMrtKmoqHD58mXu37/PnDlzaNGiBVZWVoUmOra2tubkyZMK++Lj48t0/UqVKmFmZoahoWGhpa0NDAzIyMiQPqekpJCdnV1ifbq6ulSpUoWEhARp36tXrzhz5oz02cbGBlVVVdLT0wu1ubRhH/IX4mrVqikkcaytrbl16xa3bt2S9l26dIlHjx5hY2Mj7bOwsGDEiBHs37+fzp07ExISUuR1VFRUCg2De5O1tTVJSUkKE1PHxcWhpKSEpaVlieeW5PHjx3To0IGWLVsyY8aMMp2jpKREjx492LBhA3/99Veh41lZWbx8+RJra2tevnzJiRMnpGP379/nypUrCvepOFWqVKFq1apcv3690HdnamoKQL169UhMTCx2mfCy3NviTJo0ifnz5/PXX3+VKRYAHR0dunfvzi+//MLmzZvZtm0bDx48wMDAAEDhGX994uMPHbsgCIIgCIIgfEgikSMIn8i4ceM4duwYQ4cOJTExkZSUFHbu3ClNmFujRg1UVFRYunQp169fZ9euXYVe7gcPHkxKSgpjxozhypUrbNy48a0m0y1OmzZtWLZsGWfPnuXUqVMMHjy4yMmB3/T9998TFBTEzp07uXLlCsOGDePhw4dSokhbW5vRo0czYsQIwsLCSE1N5cyZMyxdulSapPZtOTs7U7duXby8vDhz5gwnT57E29ubVq1a0bBhQ3Jychg6dCgxMTHcvHmTuLg4EhISsLa2LrI+ExMTbty4QWJiIn///Te5ubmFynh5eaGmpoaPjw8XLlwgOjqa77//nj59+kjDqt5Wfn4+Xl5eZGdns2DBAu7evcudO3cUtuISCbNmzcLY2JgmTZqwbt06Ll26REpKCmvXrsXe3p6srCzMzc3p0KEDAwYM4OjRoyQlJdG7d2+qVatGhw4dyhTjtGnTCAoKIjg4mKtXr3L+/HlCQkKkuWt69uyJoaEhHTt2JC4ujuvXr7Nt2zaOHz9e5ntbHEdHR+rVq8fs2bPLFMvChQvZtGkTly9f5urVq2zZsgVDQ0P09PRQV1enadOmzJkzh+TkZA4fPsykSZNKvL6JiQlZWVkcPHiQv//+u9TE5od27do1EhMTuXPnDjk5OSQmJpKYmPhBeucJgiAIgiAInxeRyBGET6RevXocPnyYq1ev0qJFC+zt7ZkyZYq08pCBgQGhoaFs2bIFGxsb5syZw/z58xXqqFGjBtu2bWPHjh3Y2dmxcuVK6UX3fSxYsABjY2NatGhBr169GD16NBoaGqWeN27cOHr27Im3tzeOjo5oaWnh6uqKmpqaVGbGjBlMnjyZoKAgrK2tcXNzY8+ePQo9Kd6GTCZj586dVKhQgZYtW+Ls7EytWrXYvHkzUDDU5v79+3h7e2NhYYGnpyfu7u5MmzatyPq6dOmCm5sbTk5OGBgYsGnTpkJlNDQ0+OOPP3jw4AGNGjWia9eufP311yxbtuyd2gAF8x7t3r2b9PR0LCwsMDIyKrS93uvodfr6+sTHx9O7d29mzpyJvb09LVq0YNOmTfz444/SvDEhISE0aNCA9u3b4+joSH5+PpGRkWVK0gH079+f1atXExISQt26dWnVqhWhoaHSd6eiosL+/fupXLky7dq1o27dusyZM0ca7lSWe1uSESNGsHr1am7dulVqLNra2sybN4+GDRvSqFEj0tLSiIyMlHpzrV27lpcvX9KgQQOGDx/OzJkzS7x2s2bNGDx4MN27d8fAwIB58+a9Vezvq3///tjb27Nq1SquXr2Kvb099vb2RfbCEgRBEARBEL5ssvyyzpQpCILwlvLy8rC2tsbT07PMQ4UEQfjwHj9+jK6uLpmZmejo6HzqcARBEARBEIQ3vM3f18Rkx4IgfDA3b95k//79tGrVitzcXJYtW8aNGzfo1avXpw5NEARBEARBEAThiyCGVgmC8MEoKSkRGhpKo0aNaN68OefPnycqKqrY+WgE4XMVGxuLlpZWsZsgCIIgCIIgfCxiaJUgCIIgvKWcnBxu375d7PF/25Lj8q66dt+vRFn1wy0TL/w3nf7R+1OHIAiCIAhfnLcZWiV65AhCEWQyGTt27PjUYfzr/dvuU58+fT7IZM//Jk2bNmXbtm3/2PVMTExYvHix9Pnf9h3/k1q3bs3w4cOLPKaurl5o6fPXt9KEhoaip6cnfQ4MDKR+/frSZ19fXzp27Ph+DRAEQRAEQRC+SCKRI/xn+Pr6IpPJkMlklC9fnipVquDi4sLatWvJy8tTKJuRkYG7u/snivTjq1u3LoMHDy7y2Pr161FVVeXvv//+h6N6P0lJSURGRhIQECDtk8/PU7VqVdTU1KhevTodOnTg8uXLAKSlpSGTyUhMTPwkMUdERNCwYUP09PTQ1NSkfv36rF+/XqHMpEmTGD9+fKFntCjR0dG0a9eOihUroqGhgY2NDaNGjSqx58g/rayJIflvVSaToaOjQ6NGjdi5c+fHD7AEbya5ShMTE6PQDvk2adIkunfvztWrV8tc15EjR/Dw8KBq1ar/6eSaIAiCIAiCIBI5wn+Mm5sbGRkZpKWlsXfvXpycnBg2bBjt27fn5cuXUjlDQ0NUVVU/YaQfl5+fH+Hh4eTk5BQ6FhISwrfffkulSpU+QWTvbunSpXTr1k2an+TFixe4uLiQmZlJREQEV65cYfPmzdStW5dHjx592mD/P319fX744QeOHz/OuXPn6Nu3L3379uWPP/6Qyri7u/PkyRP27t1bYl2rVq3C2dkZQ0NDtm3bxqVLl1i5ciWZmZksWLDgYzflowgJCSEjI4NTp07RvHlzunbtyvnz5z91WG/typUrZGRkSNv48eNRV1encuXKZa7j6dOn2NnZ8dNPP33ESAVBEARBEITPgUjkCP8pqqqqGBoaUq1aNRwcHJg4cSI7d+5k7969hIaGSuVe/xfv58+fM3ToUIyMjFBTU6NmzZoEBQUplF2xYgXu7u6oq6tTq1Yttm7dqnDdW7du4enpiZ6eHvr6+nTo0IG0tDTpuHwYxfz58zEyMqJixYr4+/vz4sULqczy5csxNzdHTU2NKlWq0LVrV+lYXl4eQUFBmJqaoq6ujp2dXaEYXte7d29ycnIKDdm5ceMGMTEx+Pn5AbBixQpq166NiooKlpaWhXqLvE7e++D1JEliYiIymUxqq3w4ye7du7G0tERDQ4OuXbuSnZ1NWFgYJiYmVKhQgYCAAF69eiXVk5uby+jRo6lWrRqampo0adKEmJgY6firV6/YunUrHh4e0r6LFy+SmprK8uXLadq0KTVr1qR58+bMnDmTpk2bAmBqagqAvb09MpmM1q1bA5CQkICLiwuVKlVCV1eXVq1acebMGYX2Xr58ma+++go1NTVsbGyIiooq1FOitO+9devWdOrUCWtra2rXrs2wYcOoV68eR48elcooKyvTrl07wsPDi733f/75JwEBAQQEBLB27Vpat26NiYkJLVu2ZPXq1UyZMkUqu23bNmxtbVFVVcXExOStkzyltQlg7dq10jWMjIwYOnQoUNCjBaBTp07IZDLpc3H09PQwNDTEwsKCGTNm8PLlS6Kjo8scS0xMDI0bN0ZTUxM9PT2aN2/OzZs3gaKHLg0fPlx6Bt7UunVrbt68yYgRI6SeNWVVuXJlDA0NpU1LS6vQ0KrSuLu7M3PmTDp16lTmcwRBEARBEIQvk0jkCP95bdq0wc7OjoiIiCKPBwcHs2vXLn777TeuXLnChg0bCr2ATp48mS5dupCUlISXlxc9evQgOTkZKOgZ4urqira2NrGxscTFxaGlpYWbmxvPnz+X6oiOjiY1NZXo6GjCwsIIDQ2VkkunTp0iICCA6dOnc+XKFfbt20fLli2lc4OCgli3bh0rV67k4sWLjBgxgt69e3P48OEi21SpUiU6dOjA2rVrFfaHhoZSvXp12rZty/bt2xk2bBijRo3iwoULDBo0iL59+yq8SL+L7OxsgoODCQ8PZ9++fcTExNCpUyciIyOJjIxk/fr1rFq1SiERNXToUI4fP054eDjnzp2jW7duuLm5kZKSAsC5c+fIzMykYcOG0jkGBgYoKSmxdetWhaTQ606ePAlAVFQUGRkZ0jPw5MkTfHx8OHr0KPHx8Zibm9OuXTuePHkCFCSOOnbsiIaGBidOnODnn3/mhx9+UKi7rN+7XH5+PgcPHuTKlSsK3y1A48aNiY2NLfaebtmyhefPnzN27Ngij8sTBqdPn8bT05MePXpw/vx5AgMDmTx5skISsyRladOKFSvw9/dn4MCBnD9/nl27dklzxiQkJAD/19NG/rk0L1++ZM2aNQCoqKiUKZaXL1/SsWNHWrVqxblz5zh+/DgDBw58qwTM6yIiIqhevTrTp0+Xetb8m+Xm5vL48WOFTRAEQRAEQfgylPvUAQjCv4GVlRXnzp0r8lh6ejrm5uZ89dVXyGQyatasWahMt27d6N+/PwAzZszgwIEDLF26lOXLl7N582by8vJYvXq19BIZEhKCnp4eMTExtG3bFoAKFSqwbNkylJWVsbKy4ptvvuHgwYMMGDCA9PR0NDU1ad++Pdra2tSsWRN7e3ug4IVt9uzZREVF4ejoCECtWrU4evQoq1atolWrVkW2y8/PD3d3d27cuIGpqSn5+fmEhYXh4+ODkpIS8+fPx9fXlyFDhgAwcuRI4uPjmT9/Pk5OTu98r1+8eCH19AHo2rUr69ev5+7du2hpaWFjY4OTkxPR0dF0796d9PR0QkJCSE9Pp2rVqgCMHj2affv2ERISwuzZs7l58ybKysoKQ1WqVatGcHAwY8eOZdq0aTRs2BAnJye8vLyoVasWUJDsAahYsSKGhobSuW3atFGI+eeff0ZPT4/Dhw/Tvn17Dhw4QGpqKjExMdJ5s2bNwsXFRTqnrN97ZmYm1apVIzc3F2VlZZYvX65QD0DVqlW5desWeXl5KCkVzr+npKSgo6ODkZFRifd+4cKFfP3110yePBkACwsLLl26xI8//oivr2+J55a1TTNnzmTUqFEMGzZMOq9Ro0bA/91veU+b0vTs2RNlZWVycnLIy8vDxMQET0/PMsXSsGFDMjMzad++vfSsWVtbl3rN4ujr66OsrIy2tnaZYn9d9erVFT7LewV9TEFBQUybNu2jX0cQBEEQBEH454keOYJAQW+I4v6l3tfXl8TERCwtLQkICGD//v2FysgTKK9/lvfISUpK4tq1a2hra6OlpYWWlhb6+vo8e/aM1NRU6RxbW1uUlZWlz0ZGRty7dw8AFxcXatasSa1atejTpw8bNmwgOzsbgGvXrpGdnY2Li4tUv5aWFuvWrVOo/00uLi5Ur16dkJAQAA4ePEh6ejp9+/YFIDk5mebNmyuc07x5c6ld70pDQ0N6sQaoUqUKJiYm0tw28n3ytp8/f55Xr15hYWGh0L7Dhw9L7cvJyUFVVbXQd+jv78+dO3fYsGEDjo6ObNmyBVtbWw4cOFBijHfv3mXAgAGYm5ujq6uLjo4OWVlZpKenAwVznhgbGyu80Ddu3FihjrJ+79ra2iQmJpKQkMCsWbMYOXKkwrAxKFghKS8vj9zc3CLjLen5fV1x32lKSkqxvZbepk337t3jr7/+4uuvvy61rrJYtGgRiYmJ7N27FxsbG1avXo2+vn6ZYtHX18fX1xdXV1c8PDxYsmTJJ+tFExsbS2JiorRVqFDho19zwoQJZGZmStutW7c++jUFQRAEQRCEf4bokSMIFLzgyudLeZODgwM3btxg7969REVF4enpibOzc4lz0LwuKyuLBg0asGHDhkLH5D0UAMqXL69wTCaTSSsVaWtrc+bMGWJiYti/fz9TpkwhMDCQhIQEsrKyANizZw/VqlVTqKOkCZuVlJTw9fUlLCyMwMBAQkJCcHJyknqrvC15T5H8/Hxp3+tz/JTUzpLanpWVhbKyMqdPn1ZIdAFS8qdSpUpkZ2fz/PlzaeiNnLa2Nh4eHnh4eDBz5kxcXV2ZOXNmoV4vr/Px8eH+/fssWbKEmjVroqqqiqOjY5FDoopT1u9dSUlJGnpUv359kpOTCQoKUpir5cGDB2hqaqKurl7ktSwsLMjMzCQjI6PUXjnvo7Q2FdVb6H0YGhpKy3mHhITQrl07Ll26ROXKlct0f0NCQggICGDfvn1s3ryZSZMmceDAAZo2bYqSkpLCswpFP68fgqmp6VvNh/MhqKqqftETtguCIAiCIPyXiR45wn/eoUOHOH/+PF26dCm2jI6ODt27d+eXX35h8+bNbNu2jQcPHkjH4+PjFcrHx8dLwzgcHBxISUmhcuXK0kupfNPV1S1znOXKlcPZ2Zl58+Zx7tw50tLSOHToEDY2NqiqqpKenl6ofmNj4xLr7Nu3L7du3SIiIoLt27dLkxxDwTCUuLg4hfJxcXHY2NgUWZf85fn1Xg8fYllve3t7Xr16xb179wq1T94jpn79+gBcunSpxLpkMhlWVlY8ffoU+L/5Vt7sjRIXF0dAQADt2rWTJu19fTl2S0tLbt26xd27d6V9b8738q7fe1E9by5cuCANpStK165dUVFRYd68eUUel09AXdx3amFhUShJVpTS2qStrY2JiQkHDx4sto7y5cuXqffPmxo3bkyDBg2YNWtWmWKRs7e3Z8KECRw7dow6deqwceNGoOB5fbOHTmnPq4qKyjvFLgiCIAiCIAgfkkjkCP8pubm53Llzh9u3b3PmzBlmz55Nhw4daN++Pd7e3kWes3DhQjZt2sTly5e5evUqW7ZswdDQUOFf2Lds2cLatWu5evUqU6dO5eTJk9JKPV5eXtLkwrGxsdLKUAEBAfz5559linv37t0EBweTmJjIzZs3WbduHXl5eVhaWqKtrc3o0aMZMWIEYWFhpKamcubMGZYuXUpYWFiJ9ZqamtKmTRsGDhyIqqoqnTt3lo6NGTOG0NBQVqxYQUpKCgsXLiQiIoLRo0cXWZc8cRQYGEhKSgp79uz5IMteW1hY4OXlhbe3NxEREdy4cYOTJ08SFBTEnj17gIKXcgcHB4XVnhITE+nQoQNbt27l0qVLXLt2jTVr1rB27Vo6dOgAFKwmpK6uzr59+7h79y6ZmZkAmJubs379epKTkzlx4gReXl4KvWFcXFyoXbs2Pj4+nDt3jri4OCZNmgQgDXEqy/ceFBTEgQMHuH79OsnJySxYsID169fTu3dvhXsQGxsrzalTFGNjYxYtWsSSJUvw8/Pj8OHD3Lx5k7i4OAYNGsSMGTMAGDVqFAcPHmTGjBlcvXqVsLAwli1bVux3+qaytCkwMJAFCxYQHBxMSkqK9CzKyRM9d+7c4eHDh2W6rtzw4cNZtWoVt2/fLjWWGzduMGHCBI4fP87NmzfZv38/KSkpUoK1TZs2nDp1inXr1pGSksLUqVO5cOFCidc3MTHhyJEj3L59WyGx90/IysqShmZBwQpziYmJ0nA/QRAEQRAE4b9DJHKE/5R9+/ZhZGSEiYkJbm5uREdHExwczM6dO4vtkaCtrc28efNo2LAhjRo1Ii0tjcjISIVhJNOmTSM8PJx69eqxbt06Nm3aJPVc0dDQ4MiRI9SoUYPOnTtjbW2Nn58fz549Q0dHp0xx6+npERERQZs2bbC2tmblypVs2rQJW1tboGCC5cmTJxMUFIS1tTVubm7s2bOn2OFir/Pz8+Phw4f06tULNTU1aX/Hjh1ZsmQJ8+fPx9bWllWrVhESElLs8szly5eXEl716tVj7ty5zJw5s0ztK01ISAje3t6MGjUKS0tLOnbsSEJCAjVq1JDK9O/fX2GYTfXq1TExMWHatGk0adIEBwcHlixZwrRp06QVpsqVK0dwcDCrVq2iatWqUoJnzZo1PHz4EAcHB/r06UNAQIDCRMrKysrs2LGDrKwsGjVqRP/+/aU65fewLN/706dPGTJkCLa2tjRv3pxt27bx66+/ShNnA9y+fZtjx45JcxcVZ8iQIezfv5/bt2/TqVMnrKys6N+/Pzo6OlKixsHBgd9++43w8HDq1KnDlClTmD59epkmOi5rm3x8fFi8eDHLly/H1taW9u3bS6uLASxYsIADBw5gbGxcYi+jori5uWFqasqsWbNKjUVDQ4PLly/TpUsXLCwsGDhwIP7+/gwaNAgAV1dXJk+ezNixY2nUqBFPnjwpNpkrN336dNLS0qhdu7bC8Lh/wqlTp7C3t5fu2ciRI7G3t1dYWl4QBEEQBEH4b5DlvzlJgCAIb0Umk7F9+3Y6duz4qUP5T8vJycHS0pLNmzcXmnz6nxAXF8dXX33FtWvXFCZzfl/jxo3j4cOH/Pzzzx+sTuG/5/Hjx+jq6pKZmVnmBLIgCIIgCILwz3mbv6+JyY4FQfgiqKurs27dun9syMv27dvR0tLC3Nyca9euMWzYMJo3b/5BkzhQMPxr5MiRH7ROQRAEQRAEQRA+XyKRIwjCF6O4YV8fw5MnTxg3bhzp6elUqlQJZ2fnDzIn0JtGjRr1wesUPgx3d3diY2OLPDZx4kQmTpz4D0ckCIIgCIIg/BeIoVWCIAiC8A5u375NTk5Okcf09fXR19f/hyMqnryrrt33K1FWLXoZe0EozekfS55HShAEQRCEdyeGVgmCILyFwMBAduzY8UGWSy+NiYkJw4cPZ/jw4R/9WsLHVa1atU8dgiAIgiAIgvAfJFatEgThX2nlypVoa2vz8uVLaV9WVhbly5cvNIQqJiYGmUxGamrqPxzlv5Ovry8ymQyZTEb58uWpUqUKLi4urF27lry8vE8dnoLBgwcjk8lYvHixwn55/DKZjHLlylGjRg1GjhxJbm6uVCYjI4NevXphYWGBkpJSkcmx7t2707hxY169eiXte/HiBQ0aNMDLy6vI672+hYeHS2XOnTtHixYtUFNTw9jYmHnz5hW63pYtW7CyskJNTY26desSGRmpcPzu3bv4+vpStWpVNDQ0cHNzU1jVqzizZs2iWbNmaGhooKenV2p5QRAEQRAE4cslEjmCIPwrOTk5kZWVxalTp6R9sbGxGBoacuLECZ49eybtj46OpkaNGm890XB+fr5CouhDefXq1UdLmDx//rxM5dzc3MjIyCAtLY29e/fi5OTEsGHDaN++/Udp87vYvn078fHxVK1atcjjISEhZGRkcOPGDZYvX8769esVlrTPzc3FwMCASZMmYWdnV2Qdy5cvJz09nTlz5kj7ZsyYQUZGBsuWLSvyeq9v8tXoHj9+TNu2balZsyanT5/mxx9/JDAwUGE1sWPHjtGzZ0/8/Pw4e/YsHTt2pGPHjly4cAEoeN46duzI9evX2blzJ2fPnqVmzZo4Ozvz9OnTEu/V8+fP6datG999912J5QRBEARBEIQvn0jkCILwr2RpaYmRkRExMTHSvpiYGDp06ICpqSnx8fEK+52cnMjNzSUgIIDKlSujpqbGV199RUJCgkI5mUzG3r17adCgAaqqqhw9erTQtVNTU6lVqxZDhw4lPz+fhw8f4u3tTYUKFdDQ0MDd3V2hF0VoaCh6enrs2rULGxsbVFVVSU9P5969e3h4eKCuro6pqSkbNmwodK1Hjx7Rv39/DAwM0NHRoU2bNiQlJUnHAwMDqV+/PqtXr8bU1BQ1NbUy3T9VVVUMDQ2pVq0aDg4OTJw4kZ07d7J3715CQ0OlcgsXLqRu3bpoampibGzMkCFDyMrKKtS23bt3Y2lpiYaGBl27diU7O5uwsDBMTEyoUKECAQEBCr1eSnP79m2+//57NmzYQPny5Ysso6enh6GhIcbGxrRv354OHTpw5swZ6biJiQlLlizB29sbXV3dIuuoWLEiP//8M9OnT+fcuXOcOnWKoKAgVq9eTYUKFYq83uub/H5v2LCB58+fs3btWmxtbenRowcBAQEsXLhQOn/JkiW4ubkxZswYrK2tmTFjBg4ODlLCKCUlhfj4eFasWEGjRo2wtLRkxYoV5OTksGnTphLv17Rp0xgxYgR169Yt/eYKgiAIgiAIXzSRyBEE4V/LycmJ6Oho6XN0dDStW7emVatW0v6cnBxOnDiBk5MTY8eOZdu2bYSFhXHmzBnMzMxwdXXlwYMHCvWOHz+eOXPmkJycTL169RSOnTt3jq+++opevXqxbNkyZDIZvr6+nDp1il27dnH8+HHy8/Np164dL168kM7Lzs5m7ty5rF69mosXL1K5cmV8fX25desW0dHRbN26leXLl3Pv3j2F63Xr1o179+6xd+9eTp8+jYODA19//bVCzNeuXWPbtm1ERES81zw+bdq0wc7OjoiICGmfkpISwcHBXLx4kbCwMA4dOsTYsWMVzsvOziY4OJjw8HD27dtHTEwMnTp1IjIyksjISNavX8+qVavYunVrmeLIy8ujT58+jBkzBltb2zKdc/XqVQ4dOkSTJk3K3uD/79tvv6VHjx54e3vj4+ODj48P7dq1e6s6jh8/TsuWLVFRUZH2ubq6cuXKFR4+fCiVcXZ2VjjP1dWV48ePA0jDwl5PxikpKRWbUHwfubm5PH78WGETBEEQBEEQvgxismNBEP61nJycGD58OC9fviQnJ4ezZ8/SqlUrXrx4wcqVK4GCl+fc3Fxat27NgAEDCA0Nxd3dHYBffvmFAwcOsGbNGsaMGSPVO336dFxcXApd79ixY7Rv354ffvhBWvY7JSWFXbt2ERcXR7NmzYCC3hnGxsbs2LGDbt26AQXzrixfvlwa4nP16lX27t3LyZMnadSoEQBr1qzB2tpaut7Ro0c5efIk9+7dQ1VVFYD58+ezY8cOtm7dysCBA4GCYTXr1q3DwMDgve+plZUV586dkz6/Pq+MiYkJM2fOZPDgwSxfvlza/+LFC1asWCENXevatSvr16/n7t27aGlpYWNjIyXdunfvXmoMc+fOpVy5cgQEBJRYrmfPnigrK/Py5Utyc3Np3749EyZMeMsWF1i8eDHVqlVDR0dHoRdNUdd73aVLl6hRowZ37tzB1NRU4ViVKlUAuHPnDhUqVODOnTvSvtfL3LlzByi49zVq1GDChAmsWrUKTU1NFi1axJ9//klGRsY7tas4QUFBTJs27YPWKQiCIAiCIPw7iB45giD8a7Vu3ZqnT5+SkJBAbGwsFhYWGBgY0KpVK2menJiYGGrVqkVmZiYvXrygefPm0vnly5encePGJCcnK9TbsGHDQtdKT0/HxcWFKVOmSEkcgOTkZMqVK6fQE6RixYpYWloq1KuioqLQu0d+XoMGDaR9VlZWChPVJiUlkZWVRcWKFdHS0pK2GzduKEzcXLNmzQ+SxIGCeVpkMpn0OSoqiq+//ppq1aqhra1Nnz59uH//PtnZ2VIZDQ0NhfmHqlSpgomJCVpaWgr73uxtVJTTp0+zZMkSQkNDFeIoyqJFi0hMTCQpKYndu3dz9epV+vTp8zbNlWzatAmZTMbff//N5cuXS7ze61tx8/e8i/LlyxMREcHVq1fR19dHQ0OD6Oho3N3dUVIq+N/x4MGDFZ6FdzVhwgQyMzOl7datWx+qGYIgCIIgCMInJnrkCILwr2VmZkb16tWJjo7m4cOHtGrVCoCqVatibGzMsWPHiI6Opk2bNm9Vr6amZqF9BgYGVK1alU2bNtGvXz90dHTeqk51dfVSExNvysrKKjQPkNzrCZ+i4n1XycnJUs+StLQ02rdvz3fffcesWbPQ19fn6NGj+Pn58fz5czQ0NAAKzWEjXw3rzX1lmeA5NjaWe/fuUaNGDWnfq1evGDVqFIsXLyYtLU3ab2hoiJmZGVAwZ9KTJ0/o2bMnM2fOlPaXxfXr1xk7diwrVqwgOjoaX19fzp49K/WCKup6bzI0NOTu3bsK++SfDQ0NSywjPw7QoEEDEhMTyczM5Pnz5xgYGNCkSRMpuTh9+nRGjx5d5rYVR1VVtVD7BEEQBEEQhC+D6JEjCMK/mpOTEzExMcTExCgsO96yZUtp6JKTkxO1a9dGRUWFuLg4qcyLFy9ISEjAxsam1Ouoq6uze/du1NTUcHV15cmTJwBYW1vz8uVLTpw4IZW9f/8+V65cKbFeKysrXr58yenTp6V9V65c4dGjR9JnBwcH7ty5Q7ly5TAzM1PYKlWqVJbb81YOHTrE+fPn6dKlC1DQOyYvL48FCxbQtGlTLCws+Ouvvz74dV/Xp08fzp07V6jXy5gxY/jjjz9KPFc+7CknJ6fM18vLy8PX15evv/4ab29vFi9ezJMnT5gyZcpbxe3o6MiRI0cU5kU6cOAAlpaW0qTJjo6OHDx4UOG8AwcO4OjoWKg+XV1dDAwMSElJ4dSpU3To0AGAypUrKzwHgiAIgiAIgvAm0SNHEIR/NScnJ/z9/Xnx4oXUIwegVatWDB06lOfPn+Pk5ISmpibfffcdY8aMQV9fnxo1ajBv3jyys7Px8/Mr07U0NTXZs2cP7u7uuLu7s2/fPszNzenQoQMDBgxg1apVaGtrM378eKpVqya9fBfF0tISNzc3Bg0axIoVKyhXrhzDhw9HXV1dKuPs7IyjoyMdO3Zk3rx5UiJlz549dOrUqcghYGWVm5vLnTt3ePXqFXfv3mXfvn0EBQXRvn17vL29gYIeTy9evGDp0qV4eHgQFxcnzT30sVSsWJGKFSsq7CtfvjyGhoZYWloq7H/06BF37twhLy+PlJQUpk+fjoWFhcI8Q/LJn7Oysvjf//5HYmIiKioqUpJtyZIlXLx4kYsXLwIFCZTVq1fTvn17unTpQuPGjQtd73Xa2tpoamrSq1cvpk2bNez10gABAABJREFUhp+fH+PGjePChQssWbKERYsWSWWHDRtGq1atWLBgAd988w3h4eGcOnVKYYnyLVu2YGBgQI0aNTh//jzDhg2jY8eOtG3btsT7lp6ezoMHD0hPT+fVq1dSu83MzN5rCJYgCIIgCILw+RE9cgRB+FdzcnIiJycHMzMzhYlkW7VqxZMnT6RlygHmzJlDly5d6NOnDw4ODly7do0//vij0DLTJdHS0mLv3r3k5+fzzTff8PTpU0JCQmjQoAHt27fH0dGR/Px8IiMji102Wy4kJISqVavSqlUrOnfuzMCBA6lcubJ0XCaTERkZScuWLenbty8WFhb06NGDmzdvFpo0923t27cPIyMjTExMcHNzIzo6muDgYHbu3Cn1bLGzs2PhwoXMnTuXOnXqsGHDBoKCgt7ruh9S3759MTIyonr16vTs2RNbW1v27t1LuXL/928Q9vb22Nvbc/r0aTZu3Ii9vb20ItXVq1f54YcfWLp0qcLwJldXV/r27Yuvr6+0ktTr13t9W7p0KVCQANq/fz83btygQYMGjBo1iilTpkgTUgM0a9aMjRs38vPPP2NnZ8fWrVvZsWMHderUkcpkZGTQp08frKysCAgIoE+fPqUuPQ4wZcoU7O3tmTp1KllZWVK7T5069e43WBAEQRAEQfgsyfLz8/M/dRCCIAiCIHw8jx8/RldXl8zMzLee/0kQBEEQBEH4+N7m72uiR44gCIIgCIIgCIIgCMJnQiRyBEEQPiPp6ekKy1O/uaWnp3/S+GJjY0uMTxAEQRAEQRCE9yMmOxYEQfiMVK1aVZrotrjjn1LDhg1LjE/4tFpO2oSyqnrpBQUBOP2j96cOQRAEQRCEIohEjiAIwmdEvlT5v5W6uvq/Oj5BEARBEARB+NyJoVWCIAiCxNfXl44dO0qfW7duzfDhwz9ZPJ9SYGAg9evX/yh1p6WlIZPJpN5LMTExyGQyHj16BEBoaCh6enof5dqCIAiCIAjC500kcgThA7lz5w7ff/89tWrVQlVVFWNjYzw8PDh48KBURiaTsWPHjkLnFvXyLJPJkMlkqKmpYWFhQVBQEK8vMid/ESxqi4+PLzbO4s4JDw8H/u+F0tbWllevXimcq6enR2hoqPTZxMREOl9dXR0TExM8PT05dOhQqffrzTba2NiwfPlyhTI5OTlMnToVCwsLVFVVqVSpEt26dePixYsK5bKzs5kwYQK1a9dGTU0NAwMDWrVqxc6dOxWuN3z48BLvm3wLDQ0t9GIN8OrVKxYtWkTdunVRU1OjQoUKuLu7ExcXpxBPaGgoMpkMNzc3hf2PHj1CJpMRExNT5D2RX7O4zcnJqcR7eu3aNfr27Uv16tVRVVXF1NSUnj17/quWqC5rYqgsv4F/2pu/07Io6nv86quvMDY2JiMjQ2Fp8pJERETg4uKCgYEBOjo6ODo68scff7xDKwRBEARBEITPnUjkCMIHkJaWRoMGDTh06BA//vgj58+fZ9++fTg5OeHv7/9OdQ4YMICMjAyuXLnChAkTmDJlCitXrixULioqioyMDIWtQYMGJdYdEhJS6Jw3X1CvX7/OunXrSo1z+vTpUpzr1q1DT08PZ2dnZs2aVeY2Xrp0CU9PT/z9/dm0aRMAubm5ODs7s3btWmbOnMnVq1eJjIzk5cuXNGnSRCFZNXjwYCIiIli6dCmXL19m3759dO3alfv37xe6pvwFWr6NGjUKW1tbhX3du3cvdF5+fj49evRg+vTpDBs2jOTkZGJiYjA2NqZ169aFEnTlypUjKiqK6OjoUu+DXLNmzQp9LxkZGaxatQqZTMaQIUOKPffUqVM0aNCAq1evsmrVKi5dusT27duxsrJi1KhRZY7h36Ssv4F/uzd/b7t27UJZWRlDQ0PKlSvbCOcjR47g4uJCZGQkp0+fxsnJCQ8PD86ePfuRoxcEQRAEQRD+bUQiRxA+gCFDhiCTyTh58iRdunTBwsICW1tbRo4cWWLvmJJoaGhgaGhIzZo16du3L/Xq1ePAgQOFylWsWBFDQ0OFrXz58iXWraenV+gcNTU1hTLff/89U6dOJTc3t8S6tLW1MTQ0pEaNGrRs2ZKff/6ZyZMnM2XKFK5cuVKmNtaqVYvAwEDMzc3ZtWsXAIsXL+b48ePs3r0bT09PatasSePGjdm2bRvW1tb4+flJvTN27drFxIkTadeuHSYmJjRo0IDvv/+efv36Fbqm/AVavmlpaVGuXDmFferqhSeD/e2339i6dSvr1q2jf//+mJqaYmdnx88//8y3335L//79efr0qVReU1OTfv36MX78+BLvwetUVFQKfS8PHz5k9OjRTJw4kW7duhV5Xn5+Pr6+vpibmxMbG8s333xD7dq1qV+/PlOnTlXomXT+/HnatGmDuro6FStWZODAgWRlZZU5xtzcXEaPHk21atXQ1NSkSZMmhXoYxcXF0bp1azQ0NKhQoQKurq48fPgQX19fDh8+zJIlS6TeKWlpacVeq7TfQGmx3Lx5Ew8PDypUqICmpia2trZERkYCRQ9d2rFjBzKZrMhYAgMDCQsLY+fOnVLsxfWsetObvzd9ff1CQ6tKs3jxYsaOHUujRo0wNzdn9uzZmJub8/vvv5fpfEEQBEEQBOHLIRI5gvCeHjx4wL59+/D390dTU7PQ8fed5yI/P5/Y2FguX76MiorKe9X1NoYPH87Lly9ZunTpW587bNgw8vPzFRIIZaGurs7z588B2LhxIy4uLtjZ2SmUUVJSYsSIEVy6dImkpCQADA0NiYyM5MmTJ28da1lt3LgRCwsLPDw8Ch0bNWoU9+/fL5RoCwwM5Pz582zduvWdrvno0SM6dOhA69atmTFjRrHlEhMTuXjxIqNGjUJJqfAf6/Jn8OnTp7i6ulKhQgUSEhLYsmULUVFRDB06tMwxDR06lOPHjxMeHs65c+fo1q0bbm5upKSkSLF8/fXX2NjYcPz4cY4ePYqHhwevXr1iyZIlODo6Sj1tMjIyMDY2LvWaxf0GSovF39+f3Nxcjhw5wvnz55k7d+47L4E+evRoPD09cXNzk2Jv1qzZO9X1IeTl5fHkyRP09fWLPJ6bm8vjx48VNkEQBEEQBOHLIBI5gvCerl27Rn5+PlZWVh+03uXLl6OlpYWqqiotW7YkLy+PgICAQuWaNWuGlpaWwlaanj17FjonPT1doYyGhgZTp079f+zdeVxN+f/A8dettCmlREVU2iOyx5CSKdvQWLIWsu97trHvyyD70oKJMPYtZJc9CkmyJF+yjCWyhOr3R4/Oz3Vvm2WYmc/z8TiPx9xzPufzeZ/TuY3z7rMwffp0UlNTCxW7gYEBJUuWzLO3xccyMjL4448/uHTpEu7u7gBcv34de3t7peVz9l+/fh2AFStWcPLkSQwNDalevTqDBw9WmLfmSxUmnhympqYMHDiQMWPG8OHDh0K1l5mZSfv27VFTUyMsLCzXniKAlLjI7xlct24db9++Zc2aNVSoUAF3d3cWLVrE2rVrefjwYb4xJScnExISwqZNm6hbty7ly5dn2LBh/PTTT4SEhAAwa9YsqlWrxpIlS6hUqRKOjo7069ePEiVKoKenh7q6utTTxtjYGFVV1Vzby+s7UJBYkpOTqVOnDhUrVsTS0pKmTZtSr169fK9TGR0dHbS0tNDQ0JBiL2hi9dPvm7J5sgprzpw5pKWl0aZNG6XHp0+fjp6enrQVJGEmCIIgCIIg/DOIRI4gfKFvNflqhw4diImJISoqikaNGjFmzBilPQA2bNhATEyM3JafefPmKZxjamqqUM7f3x9DQ0NmzpxZ6PizsrLyTD7A/7+oa2lp0b17dwYPHkzv3r3l6iiIevXqcevWLQ4ePEirVq2Ii4ujbt26efZi+Ryf87MOCAjg8ePHBAcHF+q80aNHc+rUKbZv346uru5XiSs+Pp5KlSrJ9RyrU6cOmZmZ+Q6Dg+xhWRkZGdjY2MglJo4ePcrNmzeB/++R8zXk9R0oSCwDBgxgypQp1KlTh/Hjx3Pp0qWvEldhffp9a9iw4RfVt27dOiZOnMjGjRspWbKk0jKjRo0iNTVV2u7evftFbQqCIAiCIAg/joLNsigIQq6sra2RyWRcu3Yt37K6urpKe7c8f/4cPT09uX16enpYWVkB2fOzWFlZUatWLTw8POTKmZmZSeUKytjYuEDnqKmpMXXqVDp37lyo4TdPnjzh8ePHWFhY5FmuQ4cOjBkzBi0tLUxMTOSGBdnY2BAfH6/0vJz9NjY20r4iRYpQt25d6tatS0BAAFOmTGHSpEkEBAR8lSFphY0nh76+PqNGjWLixIk0bdq0QG2Fh4czZ84cdu/ejbW1dYFiA7h27RrOzs4FauNzpKWloaqqSnR0tEJPmpyeYMrmF/pceX0HChJLt27d8PT0ZPfu3ezfv5/p06czd+5c+vfvj4qKikIC7P37918t9o8p+749fvz4s+oKDw+nW7dubNq0SeF3wcc0NDTQ0ND4rDYEQRAEQRCEH5vokSMIX8jAwABPT08WL14sN9ltjo+Xr7a1tSU6OlrueEZGBrGxsUqTADl0dHQYOHAgw4YN+9uXX27dujWOjo5MnDixwOcsWLAAFRWVfJdqznlRL126tMLcLm3btiUyMlKaBydHZmYm8+bNw8HBQWH+nI85ODjw4cMH3r59W+C489K2bVsSExOVTi47d+5cDA0Nc+1pkZM4WLBgQb7txMTE4O/vz4wZM/D09CxQbJUrV8bBwYG5c+eSmZmpcDznGbS3tyc2NlbuOY2KikJFRQVbW9t823F2diYjI4NHjx5hZWUltxkbGwPg5OTEwYMHc61DXV1dYVn7gvj0O1CQWCA70ZmzqtnQoUNZuXIlAEZGRrx8+VLuXuTXm+1zY/9a1q9fT5cuXVi/fj1NmjT5bnEIgiAIgiAI35dI5AjCV7B48WIyMjKkVZUSExOJj48nMDAQFxcXqdyQIUNYtWoVS5YsITExkZiYGHr06MGzZ8/o1q1bnm307NmT69evs3nzZrn9T5484cGDB3JbfsmL58+fK5yjLAmVY8aMGQQHByst8/LlSx48eMDdu3c5duwYPXr0YMqUKUydOrXQPYU+NnjwYGrUqEGzZs3YtGkTycnJnDt3jpYtWxIfH09QUJA0dKt+/fosX76c6OhokpKS2LNnD6NHj8bNzY1ixYp9dgwfa9u2Ld7e3vj5+REUFERSUhKXLl2iZ8+e7Nixg1WrVimd7BpAU1OTiRMnEhgYmGcbf/31Fy1atKB+/fp07NhR4WeUWy8OmUxGSEgI169fp27duuzZs4dbt25x6dIlpk6dSvPmzYHsHlCampr4+flx5coVDh8+TP/+/enUqROlSpXK9x7Y2NjQoUMHfH192bJlC7dv3+bs2bNMnz6d3bt3A9lDes6dO0efPn24dOkS165dY+nSpfz1118AmJubc+bMGZKSkvjrr7+UJp5y8/F3oCCxDBo0iH379nH79m0uXLjA4cOHpfmMatasiba2NqNHj+bmzZusW7eO0NDQPNs3Nzfn0qVLJCQk8Ndff32zHjzKrFu3Dl9fX+bOnUvNmjWlZ6Kw81cJgiAIgiAI/3wikSMIX4GlpSUXLlzAzc2NoUOHUqFCBRo2bMjBgwdZunSpVK5du3asWrWK4OBgqlatipeXFw8ePODYsWP5vkgbGBjg6+vLhAkT5F5+PTw8MDExkdvym0y1S5cuCufktTqVu7s77u7uSifsHTduHCYmJlhZWdGpUydSU1M5ePAgAQEBecaQH01NTQ4dOoSvry+jR4/GysoKLy8vVFVVOX36NLVq1ZLKenp6snr1an7++Wfs7e3p378/np6ebNy48Yti+JhMJmPjxo2MHj2aefPmYWtrS926dblz5w5HjhzJt/eRn58flpaWeZbZvXs3d+7cYc+ePQo/HxMTE6pXr57ruTVq1OD8+fNYWVnRvXt37O3t+eWXX4iLi2P+/PlA9gTW+/bt4+nTp1SvXp1WrVrRoEEDFi1aVOD7EBISgq+vL0OHDsXW1pYWLVpw7tw5ypYtC2Qne/bv309sbCw1atTAxcWF7du3o6aWPZJ32LBhqKqq4uDggJGRkcIk23n59DuQXywZGRn07dsXe3t7vLy8sLGxYcmSJVJdf/zxB3v27KFixYqsX7+eCRMm5Nl+9+7dsbW1pVq1ahgZGX31CbXzsmLFCj58+EDfvn3lnomBAwf+bTEIgiAIgiAIPwZZ1t89TkMQBEEQhL/Vixcv0NPTIzU19av1UhMEQRAEQRC+nsL8e030yBEEQRAEQRAEQRAEQfiH+OxEzocPH4iMjGT58uW8fPkSgPv375OWlvbVghMEQRCEH9W0adPklj7/eGvUqNH3Dk8QBEEQBEH4l/qsoVV37tzBy8uL5ORk0tPTuX79OpaWlgwcOJD09HSWLVv2LWIVBEEQhB/G06dPefr0qdJjWlpalC5d+m+OKHc5XXUr9V+GqsbXWyJe+PeKnu37vUMQBEEQhP+Ubz60auDAgVSrVo1nz56hpfX//yD09vbOc9lZQRAE4cd15MgRZDKZtFx5aGgo+vr63zWm7yUpKQmZTJbnkuQGBgYKS5/nbPklcerXr8+gQYOkz+bm5tKk1JA9uXZ+k5YLgiAIgiAI/02flcg5fvw4Y8eORV1dXW6/ubk59+7d+yqBCf8unTt3RiaTIZPJKFKkCKVKlaJhw4YEBwcrLD/86QtNbGwsv/zyCyVLlkRTUxNzc3N8fHx49OiR3HmbN2+mfv366OnpoaOjg5OTE5MmTZL+Yp7XS2luL009e/ZEVVWVTZs2KRybMGGCdE2qqqqYmZnRo0cPpX+hv3jxIj4+PpiYmKChoUG5cuVo2rQpO3fuJKdTXM6Lo7Lt9OnT0jV83Gbx4sWpWbMmkyZNKtAyxCtXrqRSpUro6Oigr6+Ps7Mz06dPV3pNampqmJubM3jwYGnIZGFj/HjT1NSU2inM86DM06dPGTRoEOXKlUNdXR1TU1O6du2qsALSx+18vN24cUNpvTmJjJzNyMiIxo0bc/ny5QLV6+XlJZXJ77n9NFGQ87lkyZLScNUclStXzndFpXfv3jFr1iwqVaqEtrY2JUqUoE6dOoSEhPyty2TnpaCJoY+fIRUVFUxMTPDx8SnUCldf26dJroLI6/nbsmULkydPLnBdU6dOpXbt2mhra/9nk2uCIAiCIAhCts9K5GRmZpKRkaGw/3//+x+6urpfHJTw7+Tl5UVKSgpJSUns3bsXNzc3Bg4cSNOmTZUuaw3w+PFjGjRogIGBAfv27SM+Pp6QkBBMTU159eqVVG7MmDH4+PhQvXp19u7dy5UrV5g7dy6xsbGsXbv2s+J9/fo14eHhjBgxguDgYKVlHB0dSUlJITk5mZCQECIiIujdu7dcme3bt1OrVi3S0tJYvXo18fHxRERE4O3tzdixYxUSMJGRkaSkpMhtVatWlY4XK1aMlJQU/ve//3Hy5El69OjBmjVrqFy5Mvfv38/1eoKDgxk0aBADBgwgJiaGqKgoRowYoTCvVc41JSUlMXPmTFasWMHQoUM/K8aPtzt37sjV8TnPA2QncWrVqkVkZCTLli3jxo0bhIeHc+PGDapXr86tW7eUtvPxZmFhkWv9AAkJCaSkpLBv3z7S09Np0qQJ7969y7fe9evXAwV/bpV5+fIlc+bMybPMp969e4enpyczZsygR48enDx5krNnz9K3b18WLlxIXFxcoer7EeQ8Q/fu3WPz5s0kJCTQunXr7x1WoeX2/BkYGBTq/5fv3r2jdevWCr9fBEEQBEEQhP8etc856eeff2b+/PmsWLECyO7NkJaWxvjx42ncuPFXDVD499DQ0MDY2BiA0qVLU6VKFWrVqkWDBg0IDQ2lW7duCudERUWRmprKqlWrUFPLflwtLCxwc3OTypw9e5Zp06Yxf/58Bg4cKO03NzenYcOGhfoL+sc2bdqEg4MDI0eOxNTUlLt372JmZiZXRk1NTe6aWrduTUhIiHT81atX+Pv706RJE7Zs2SJ3rr29Pf7+/nw6TZWhoaFUpzIymUw6bmJigr29Pc2aNcPR0ZERI0bwxx9/KD1vx44dtGnTBn9/f2mfo6OjQrmPr8nHx4eDBw+yY8cOli9f/lkx5uZzngfITtrdv3+fGzduSOeXLVuWffv2YW1tTd++fdm7d6/SdgqqZMmS6OvrY2xszKBBg/jll1+4du0aTk5OBaq3IM9tbvr378/vv/9O3759KVmyZIHinT9/PseOHeP8+fM4OztL+y0tLWndurWUhEpPT2f48OGEh4fz4sULqlWrxrx586hevXqB2oHsxOTEiRO5evUqpqam+Pn5MWbMGOk6nz9/TkBAANu2bSM1NRUrKytmzJiBjo4OXbp0AbKfD4Dx48fn2tPo0+fc39+fAQMG8OLFC2nMcF6xZGVlMXHiRIKDg3n48CGGhoa0atWKwMBAqf6tW7fSokULqU19fX3mz59P586d5WJJSkqSfnbFixcHwM/Pj9DQ0HzvV27PSf369alcubJc78O8TJw4EaBAbQqCIAiCIAj/bp/VI2fu3LlERUXh4ODA27dvad++vTSsaubMmV87RuFfzN3dnUqVKikkOXIYGxvz4cMHtm7dqpDwyBEWFoaOjg59+vRRevxzhyEEBQXRsWNH9PT0aNSoUb4vUElJSezbt09uyOH+/ft58uQJI0aMyPW8nJfaL1GyZEk6dOjAjh07lPaWg+x7efr0aYWeMfnR0tJS6I3yreT3PGRmZhIeHk6HDh0UXo61tLTo06cP+/bty3UC2sJKTU0lPDwcQGEoaV4K8tzmpl27dlhZWTFp0qQCnxMWFoaHh4dcEidHkSJFKFq0KAAjRoxg8+bNrF69mgsXLmBlZYWnp2eB79fx48fx9fVl4MCBXL16leXLlxMaGsrUqVOB7J9Po0aNiIqK4o8//uDq1avMmDEDVVVVateuzfz58+V6aw0bNqxA7T569IitW7eiqqqKqqpqgWLZvHkz8+bNY/ny5SQmJrJt2zYqVqxYoPY+ZWZmxubNm4H/7621YMGCz6rr75Kens6LFy/kNkEQBEEQBOHf4bMSOWXKlCE2NpbRo0czePBgnJ2dmTFjBhcvXizwX5AFIYednR1JSUlKj9WqVYvRo0fTvn17SpQoQaNGjZg9ezYPHz6UyiQmJmJpaUmRIkXybSs1NVXpUsGfSkxM5PTp0/j4+ADQsWNHQkJCFF7KL1++jI6ODlpaWlhYWBAXF0dAQIB0/Pr16wDY2tpK+86dOyfX9q5du+TqrF27dr7xKWNnZ8fLly958uSJ0uPjx49HX18fc3NzbG1t6dy5Mxs3bsxzTpro6GjWrVuHu7t7oWJUdp8LuhxzXs/D48ePef78Ofb29kqP29vbk5WVJTcHzq5du+TiKMjwnDJlykjzCK1bt45ffvkFOzs7uTKf1qujo8O0adOAgj23uZHJZMyYMYMVK1Zw8+bNfMtD9vP6aXyfevXqFUuXLmX27Nk0atQIBwcHVq5ciZaWFkFBQQVqZ+LEiYwcORI/Pz8sLS1p2LAhkydPlnprRUZGcvbsWbZs2ULDhg2xtLSkadOmNGrUCHV1dfT09KSeNsbGxnk+2znPUNGiRSlVqhSHDx+mb9++UlIqv1iSk5MxNjbGw8ODsmXLUqNGDbp3716g6/yUqqoqBgYGQHbS1NjYGD09vQKd+znP39cwffp09PT0pO3T3oSCIAiCIAjCP9dnDa2C7OEXHTt2/JqxCP9RWVlZefZKmTp1KkOGDOHQoUOcOXOGZcuWMW3aNI4dO0bFihUL1eNBV1eXCxcuKOy3traW+xwcHIynpyclSpQAoHHjxvj7+3Po0CEaNGgglbO1tWXHjh28ffuWP/74g5iYGPr3759nDE5OTtIEt9bW1grzwWzYsCHXREVecu5DbvfSxMSEU6dOceXKFY4dO8bJkyfx8/Nj1apVREREoKKSndfNSU5lZGTw7t07mjRpwqJFiwoVo7L7/PEKd/ldR369lArzM3dzc2Pp0qXS55xEQF6OHz+OtrY2p0+fZtq0aSxbtizfegHpZR/yf27z4unpyU8//cRvv/3GunXr8o23IPfj5s2bvH//njp16kj7ihQpQo0aNYiPj8/3fMiewDkqKkrq9QKQkZHB27dvef36NTExMZQpUwYbG5sC1ZeXnGfo/fv37N27l7CwMLl284uldevWzJ8/H0tLS7y8vGjcuDHNmjWThoD9XT7n+fsaRo0axZAhQ6TPL168EMkcQRAEQRCEf4nP/hdtYmIihw8f5tGjRwp/0R83btwXByb8d8THx+c7+ayhoSGtW7emdevWTJs2DWdnZ+bMmcPq1auxsbHhxIkTvH//Pt9eOSoqKlhZWeVZJiMjg9WrV/PgwQO5l76MjAyCg4PlEjnq6upSfTNmzKBJkyZMnDhRWo0mJ0GUkJBArVq1gOw5M/KKwczMLN8YlYmPj6dYsWIYGhrmWa5ChQpUqFCBPn360KtXL+rWrcvRo0elOUByklNqamqYmpoqHVKUX4wFuc95XUduz4ORkRH6+vq5Jh7i4+ORyWRybRctWrTQsVhYWKCvr4+trS2PHj3Cx8eHY8eOyZUpSL15Pbf5mTFjBi4uLgwfPjzfsjY2Nly7di3fcl8qLS2NiRMn8uuvvyoc09TULHCyriA+fobs7e25efMmvXv3liYvzy8WMzMzEhISiIyM5MCBA/Tp04fZs2dz9OhRihQpgkwmU0iAfYvVvT7n+fsaNDQ00NDQ+NvbFQRBEARBEL69zxpatXLlSuzt7Rk3bhx//vknW7dulTZlSzgLQm4OHTrE5cuXadmyZYHPUVdXp3z58tLqP+3btyctLY0lS5YoLV/YyY737NnDy5cvuXjxIjExMdK2fv16tmzZkmd9Y8eOZc6cOdLqUT///DMGBgbffO6oR48esW7dOlq0aCH1rCkIBwcHALmVlHKSU+bm5oWaF+ZryO95UFFRoU2bNqxbt44HDx7IHXvz5g1LlizB09NTrmfMl+rbty9Xrlxh69atX1TPp89tfmrUqMGvv/7KyJEj8y3bvn17IiMjuXjxosKx9+/f8+rVK8qXL4+6ujpRUVFyx86dOyc9B/mpUqUKCQkJWFlZKWwqKio4OTnxv//9TxpS+Cl1dfVc53DKz8iRI9mwYYPU0yu/WCC7F1izZs0IDAzkyJEjnDp1SlpK3sjIiJSUFKn+xMREXr9+nWv7Od+Fz41fEARBEARBEL6Wz+qRM2XKFKZOnSo3F4gg5Cc9PZ0HDx6QkZHBw4cPiYiIYPr06TRt2hRfX1+l5+zatYvw8HDatm2LjY0NWVlZ7Ny5kz179kirQ9WsWZMRI0YwdOhQ7t27h7e3N6ampty4cYNly5bx008/ya1mlZ+goCCaNGlCpUqV5PY7ODgwePBgwsLC6Nu3r9JzXVxccHJyYtq0aSxatAgdHR1WrVqFj48PTZo0YcCAAVhbW5OWlkZERASANHlrjidPnigkKfT19dHU1ASyh9E8ePCArKwsnj9/zqlTp5g2bRp6enrMmDEj1+vq3bs3pqamuLu7U6ZMGVJSUpgyZQpGRka4uLgU+P4UJsZPlSxZUnrJ/pznAWDatGkcPHiQhg0bMmvWLCpUqMDt27cZO3Ys79+/Z/HixYW6lvxoa2vTvXt3xo8fT4sWLaRhXznxf0xNTY0SJUoU6LktiKlTp+Lo6JjvcKBBgwaxe/duGjRowOTJk/npp5/Q1dXl/PnzzJw5k6CgICpXrkzv3r0ZPnw4BgYGlC1bllmzZvH69Wu5lczyMm7cOJo2bUrZsmVp1aoVKioqxMbGcuXKFaZMmYKrqyv16tWjZcuW/P7771hZWXHt2jVkMhleXl6Ym5uTlpbGwYMHqVSpEtra2mhraxeobTMzM7y9vRk3bhy7du3KN5bQ0FAyMjKoWbMm2tra/PHHH2hpaVGuXDkge2LtRYsW4eLiQkZGBgEBAXn26CtXrhwymYxdu3bRuHFjtLS0Cjx/1deQnJzM06dPSU5OJiMjQxqeaWVl9bfGIQiCIAiCIHx/n9Uj59mzZ3/bhI3Cv0dERAQmJiaYm5vj5eXF4cOHCQwMZPv27QrJjBwODg5oa2szdOhQKleuTK1atdi4cSOrVq2iU6dOUrmZM2eybt06zpw5g6enJ46OjgwZMgQnJyf8/PwKHOPDhw/ZvXu30h4hKioqeHt75zsx7ODBg1m1ahV3794FwNvbm5MnT6KtrY2vry+2tra4u7tz6NAhwsPDadq0qdz5Hh4emJiYyG0f93R78eIFJiYmlC5dGhcXF5YvX46fnx8XL17ExMQk17g8PDw4ffo0rVu3xsbGhpYtW6KpqcnBgwfzHY6lrK6CxPjp9ujRI6nM5zwPkD1c6fTp07i5udGzZ0/Kly9PmzZtKF++POfOncPS0rJQ11IQ/fr1Iz4+nk2bNinE//H2008/AQV/bvNjY2ND165defv2bZ7lNDQ0OHDgACNGjGD58uXUqlWL6tWrExgYyIABA6hQoQKQPVyrZcuWdOrUiSpVqnDjxg327dsnLamdH09PT3bt2sX+/fupXr06tWrVYt68eVJyBLJXi6pevTrt2rXDwcGBESNGSL1YateuTa9evfDx8cHIyIhZs2YV+F5A9ndr9+7dnD17Nt9Y9PX1WblyJXXq1MHJyYnIyEh27twpPetz587FzMyMunXr0r59e4YNG5ZnUql06dLSBMulSpWiX79+hYr9S40bNw5nZ2fGjx9PWloazs7OODs7c/78+b81DkEQBEEQBOH7k2UVdm1cwN/fn+rVq9OrV69vEZMgCIIgCF/Rixcv0NPTIzU1lWLFin3vcARBEARBEIRPFObfa581tMrKyorffvuN06dPU7FiRYXu6AMGDPicagVBEARBEARBEARBEIQ8fFaPnLxWGJLJZNy6deuLghIEQRCEH1lycnKek0RfvXqVsmXL/o0R5U30yBEEQRAEQfixffMeObdv3/6swARBEATh38DU1FSacDi34z+iemPXo6rx9ZaJF/5ZomfnPpG8IAiCIAj/HJ+VyPlYToeenJVcBEEQBOHfTk1NDSsrq+8dhiAIgiAIgvAf9FmrVgGsWbOGihUroqWlhZaWFk5OTqxdu/ZrxiYIwr/IhAkTqFy58vcO4z/B3Nyc+fPnf/V6ZTKZ3OpkgnJJSUnIZLI8e+wIgiAIgiAIwuf6rETO77//Tu/evWncuDEbN25k48aNeHl50atXL+bNm/e1YxQE4TtZunQpTk5OFCtWjGLFiuHi4sLevXu/SVs5L785m6GhIT///DMXL178Ju39aOrXr8+gQYMU9oeGhqKvr1+ous6dO0ePHj2kz/klYEJDQ+XuvbItKSmJlJQUGjVqVKhYvpWePXuiqqoqtyR8YXxJsuvGjRt06dKFMmXKoKGhgYWFBe3atRNLgQuCIAiCIAh/i89K5CxcuJClS5cyc+ZMfvnlF3755RdmzZrFkiVLCAwM/NoxCoLwnZQpU4YZM2YQHR3N+fPncXd3p3nz5sTFxeV6zvv377+ozcjISFJSUti3bx9paWk0atSI58+ff5O2voUfISYjIyO0tbULXN7Hx4eUlBRpc3FxoXv37nL7zMzMMDY2RkND4xtGXjCvX78mPDycESNGEBwc/Le2ff78eapWrcr169dZvnw5V69eZevWrdjZ2TF06NC/NRZBEARBEAThv+mzEjkpKSnUrl1bYX/t2rVJSUn54qAEQfgxNGvWjMaNG2NtbY2NjQ1Tp05FR0eH06dPS2VkMhlLly7ll19+oWjRokydOhWAGTNmUKpUKXR1dfH39+ft27cFatPQ0BBjY2OqVavGnDlzePjwIWfOnJF67GzYsAFXV1c0NTUJCwsDYNWqVdjb26OpqYmdnR1LliyR6nv37h39+vXDxMQETU1NypUrx/Tp04HsOb4mTJhA2bJl0dDQwNTUlAEDBshd26c9WfT19QkNDQX47Ji+ROfOnWnRogVz5szBxMQEQ0ND+vbtK5dA+ri3ibm5OQDe3t7IZDLp88e0tLQwNjaWNnV1dbS1teX2qaqqyt2PnGvfuHEjdevWRUtLi+rVq3P9+nXOnTtHtWrV0NHRoVGjRjx+/FiuvS+9N5s2bcLBwYGRI0dy7Ngx7t69K3dcWe+mFi1a0LlzZ+n4nTt3GDx4sNTjKMfmzZtxdHREQ0MDc3Nz5s6dKx3Lysqic+fOWFtbc/z4cZo0aUL58uWpXLky48ePZ/v27UrjzcjIwN/fHwsLC7S0tLC1tWXBggXS8f3796OpqamQsBw4cCDu7u7S5xMnTkj32szMjAEDBvDq1Sulbaanp/PixQu5TRAEQRAEQfh3+KxEjpWVFRs3blTYv2HDBqytrb84KEEQfjwZGRmEh4fz6tUrXFxc5I5NmDABb29vLl++TNeuXdm4cSMTJkxg2rRpnD9/HhMTk89KZGhpZa+u8+7dO2nfyJEjGThwIPHx8Xh6ehIWFsa4ceOYOnUq8fHxTJs2jd9++43Vq1cDEBgYyI4dO9i4cSMJCQmEhYVJyYzNmzczb948li9fTmJiItu2baNixYqFjrOwMX2pw4cPc/PmTQ4fPszq1asJDQ2VkkufOnfuHAAhISGkpKRIn7+W8ePHM3bsWC5cuICamhrt27dnxIgRLFiwgOPHj3Pjxg3GjRsnlf8a9yYoKIiOHTuip6dHo0aNcr323GzZsoUyZcowadIkqccRQHR0NG3atKFt27ZcvnyZCRMm8Ntvv0n1x8TEEBcXx9ChQ1FRUfzfZ25D4DIzMylTpgybNm3i6tWrjBs3jtGjR0v/H23QoAH6+vps3rxZOicjI4MNGzbQoUMHAG7evImXlxctW7bk0qVLbNiwgRMnTtCvXz+lbU6fPh09PT1pMzMzK9Q9EgRBEARBEH5cn7Vq1cSJE/Hx8eHYsWPUqVMHgKioKA4ePKg0wSMIwj/X5cuXcXFx4e3bt+jo6LB161YcHBzkyrRv354uXbpIn9u2bYu/vz/+/v4ATJkyhcjIyAL3ygF4/vw5kydPRkdHhxo1avDmzRsABg0axK+//iqVGz9+PHPnzpX2WVhYcPXqVZYvX46fnx/JyclYW1vz008/IZPJKFeunHRucnIyxsbGeHh4UKRIEcqWLUuNGjUKfY8KG9OXKl68OIsWLUJVVRU7OzuaNGnCwYMH6d69u0JZIyMjIDvJYGxs/MVtf2rYsGF4enoC2T1I2rVrx8GDB6X/N/j7+8slWr703iQmJnL69Gm2bNkCQMeOHRkyZAhjx44t8OqJBgYGqKqqoqurK3dPfv/9dxo0aMBvv/0GgI2NDVevXmX27Nl07tyZxMREAOzs7ArUTo4iRYowceJE6bOFhQWnTp1i48aNtGnTBlVVVdq2bcu6deuk78zBgwd5/vw5LVu2BLITMx06dJB6GllbWxMYGIirqytLly5FU1NTrs1Ro0YxZMgQ6fOLFy9EMkcQBEEQBOFf4rN65LRs2ZIzZ85gaGjItm3b2LZtGyVKlODs2bN4e3t/7RgFQfiObG1tiYmJ4cyZM/Tu3Rs/Pz+uXr0qV6ZatWpyn+Pj46lZs6bcvk978eSmdu3a6OjoULx4cWJjY9mwYQOlSpVS2tarV6+4efMm/v7+6OjoSNuUKVO4efMmkD0UKSYmBltbWwYMGMD+/ful81u3bs2bN2+wtLSke/fubN26lQ8fPhTsxuRy/QWJ6Us5OjqiqqoqfTYxMeHRo0dfpe7CcnJykv475+f0ca+mUqVKSbF9jXsTHByMp6cnJUqUAKBx48akpqZy6NChL76W+Ph4KQGVo06dOiQmJpKRkUFWVtZn17148WKqVq2KkZEROjo6rFixguTkZOl4hw4dOHLkCPfv3weyey41adJE6uUTGxtLaGio3H3z9PQkMzOT27dvK7SnoaEhTVKeswmCIAiCIAj/Dp/VIwegatWq0lwQgiD8e6mrq2NlZQVkf+/PnTvHggULWL58uVSmaNGiX629DRs24ODggKGhodKhKh+3lZaWBsDKlSsVEkc5iY4qVapw+/Zt9u7dS2RkJG3atMHDw4M///wTMzMzEhISiIyM5MCBA/Tp04fZs2dz9OhRihQpgkwmU3h5VzaZcWFjUqZYsWKkpqYq7H/+/Dl6enpy+4oUKSL3WSaTkZmZmWvd39LHseT0iPl0X05sn3tvcmRkZLB69WoePHiAmpqa3P7g4GAaNGgAgIqKSoF+boVlY2MDwLVr13B2di7weeHh4QwbNoy5c+fi4uKCrq4us2fP5syZM1KZ6tWrU758ecLDw+nduzdbt26V68mUlpZGz5495eZwylG2bNnPvyhBEARBEAThH6dQiRwVFZV8u67LZLLP+ou2IAj/DJmZmaSnp+dZxt7enjNnzuDr6yvt+3iC5LyYmZlRvnz5ApUtVaoUpqam3Lp1S5pLRJlixYrh4+ODj48PrVq1wsvLi6dPn2JgYICWlhbNmjWjWbNm9O3bFzs7Oy5fvkyVKlUwMjKSm8A9MTGR169ff5WYPmVrayvXWyjHhQsXpATC5ypSpAgZGRlfVMfX8Ln3JseePXt4+fIlFy9elEv8XLlyhS5duvD8+XP09fUVfm4ZGRlcuXIFNzc3aZ+6urrCPbG3tycqKkpuX1RUFDY2NqiqqlK5cmUcHByYO3cuPj4+CvPk5LT/qaioKGrXrk2fPn2kfcp6IHXo0IGwsDDKlCmDiooKTZo0kY5VqVKFq1evSklVQRAEQRAE4b+rUImcrVu35nrs1KlTBAYGfre/CguC8PWNGjWKRo0aUbZsWV6+fMm6des4cuQI+/bty/O8gQMH0rlzZ6pVq0adOnUICwsjLi4OS0vLrx7jxIkTGTBgAHp6enh5eZGens758+d59uwZQ4YM4ffff8fExARnZ2dUVFTYtGkTxsbG0upTGRkZ1KxZE21tbf744w+0tLSkeXTc3d1ZtGgRLi4uZGRkEBAQoNAb5nNiUqZ3794sWrSIAQMG0K1bNzQ0NNi9ezfr169n586dX3SPzM3NpXlrNDQ0KF68+BfV9yU+597kCAoKokmTJlSqVEluv4ODA4MHDyYsLIy+ffvi7u7OkCFD2L17N+XLl+f3339XWBHK3NycY8eO0bZtWzQ0NChRogRDhw6levXqTJ48GR8fH06dOsWiRYukibplMhkhISF4eHhQt25dxowZg52dHWlpaezcuZP9+/dz9OhRhbitra1Zs2YN+/btw8LCgrVr13Lu3DksLCzkynXo0IEJEyYwdepUWrVqJbfUe0BAALVq1aJfv35069aNokWLcvXqVQ4cOMCiRYsK8yMQBEEQBEEQ/uEKNUdO8+bNFTY7OztCQ0OZM2cOrVu3JiEh4VvFKgjC3+zRo0f4+vpia2tLgwYNOHfuHPv27aNhw4Z5nufj48Nvv/3GiBEjqFq1Knfu3KF3797fJMZu3bqxatUqQkJCqFixIq6uroSGhkovybq6usyaNYtq1apRvXp1kpKS2LNnDyoqKujr67Ny5Urq1KmDk5MTkZGR7Ny5E0NDQwDmzp2LmZkZdevWpX379gwbNgxtbe0vjkkZS0tLjh07xrVr1/Dw8KBmzZps3LiRTZs24eXl9UX3aO7cuRw4cAAzM7NCDQn6Fj7n3gA8fPiQ3bt3S5P/fkxFRQVvb2+CgoIA6Nq1K35+fvj6+uLq6oqlpaVcbxyASZMmkZSURPny5aUJoatUqcLGjRsJDw+nQoUKjBs3jkmTJknLlgPUqFGD8+fPY2VlRffu3bG3t+eXX34hLi5OWvL9Uz179uTXX3/Fx8eHmjVr8uTJE7neOTmsrKyoUaMGly5dUuix5OTkxNGjR7l+/Tp169bF2dmZcePGYWpqmud9EwRBEARBEP59ZFmfOXvj/fv3GT9+PKtXr8bT05Pp06dToUKFrx2fIAiCIAhf6MWLF+jp6ZGamiomPhYEQRAEQfgBFebfa4VetSo1NZWAgACsrKyIi4vj4MGD7Ny5UyRxBEEQBEEQBEEQBEEQvrFCJXJmzZqFpaUlu3btYv369Zw8eZK6det+q9gEQRCE/5Bp06bJLa/98daoUaPvHZ4gCIIgCIIg/BAKNbRKRUUFLS0tPDw88lwqdsuWLV8lOEEQBOG/4+nTpzx9+lTpMS0tLUqXLv03R/TvkdNVt1L/ZahqaH3vcITvJHq2b/6FBEEQBEH4LgoztKpQq1b5+vrmu/y4IAjCP8GECRPYtm0bMTEx37wtc3NzBg0axKBBg755W/9kBgYGGBgYfO8wBEEQBEEQBOGHVqihVaGhoYSEhOS7CYIgfI5ly5ahq6vLhw8fpH1paWkUKVKE+vXry5U9cuQIMpmMmzdv/s1R/pg6d+6MTCZDJpNRpEgRSpUqRcOGDQkODiYzM/N7h8eECROws7OjaNGiFC9eHA8PD86cOSNXJif+06dPy+1PT0/H0NAQmUzGkSNHpP1Tp06ldu3aaGtro6+vr9Dmnj17UFdX58KFC3L7586dS4kSJXjw4AEADx48oH///lhaWqKhoYGZmRnNmjXj4MGD0jn169eX4svZevXqJVfvuXPnaNCgAfr6+hQvXhxPT09iY2Plyly6dIm6deuiqamJmZkZs2bNyvfeHTt2jGbNmmFqaopMJmPbtm35niMIgiAIgiD8exV6smNBEIRvxc3NjbS0NM6fPy/tO378OMbGxpw5c4a3b99K+w8fPkzZsmUpX758odrIysqSSxR9LRkZGd8sYfLu3bsClfPy8iIlJYWkpCT27t2Lm5sbAwcOpGnTpt/kmgvDxsaGRYsWcfnyZU6cOIG5uTk///wzjx8/litnZmam8AeBrVu3oqOjo1Dnu3fvaN26da5L2zdu3BhfX198fX1JT08H4OrVq4wdO5bFixdjbGxMUlISVatW5dChQ8yePZvLly8TERGBm5sbffv2lauve/fupKSkSNvHSZi0tDS8vLwoW7YsZ86c4cSJE+jq6uLp6cn79++B7O6yP//8M+XKlSM6OprZs2czYcIEVqxYkee9e/XqFZUqVWLx4sV5lhMEQRAEQRD+G0QiRxCEH4atrS0mJiZyvS6OHDlC8+bNsbCwkOupceTIEdzc3EhPT2fAgAGULFkSTU1NfvrpJ86dOydXTiaTsXfvXqpWrYqGhgYnTpxQaPvmzZtYWlrSr18/srKyePbsGb6+vhQvXhxtbW0aNWpEYmKiVD40NBR9fX127NiBg4MDGhoaJCcn8+jRI5o1a4aWlhYWFhaEhYUptPX8+XO6deuGkZERxYoVw93dXa7nxoQJE6hcuTKrVq3CwsICTU3NAt0/DQ0NjI2NKV26NFWqVGH06NFs376dvXv3EhoaKpX7/fffqVixIkWLFsXMzIw+ffqQlpamcG27du3C1tYWbW1tWrVqxevXr1m9ejXm5uYUL16cAQMGkJGRUaDY2rdvj4eHB5aWljg6OvL777/z4sULLl26JFfOz8+P8PBw3rx5I+0LDg7Gz89Poc6JEycyePBgKlasmGu78+bNIy0tjfHjx/Phwwf8/Pxo1qwZPj4+APTp0weZTMbZs2dp2bIlNjY2ODo6MmTIEIWeQdra2hgbG0vbx2OXr127xtOnT5k0aRK2trY4Ojoyfvx4Hj58yJ07dwAICwvj3bt3BAcH4+joSNu2bRkwYAC///57nveuUaNGTJkyBW9v7zzLCYIgCIIgCP8NIpEjCMIPxc3NjcOHD0ufDx8+TP369XF1dZX2v3nzhjNnzuDm5saIESPYvHkzq1ev5sKFC1hZWeHp6akwae7IkSOZMWMG8fHxODk5yR27dOkSP/30E+3bt2fRokXIZDI6d+7M+fPn2bFjB6dOnSIrK4vGjRtLvSsAXr9+zcyZM1m1ahVxcXGULFmSzp07c/fuXQ4fPsyff/7JkiVLePTokVx7rVu35tGjR+zdu5fo6GiqVKlCgwYN5GK+ceMGmzdvZsuWLV80j4+7uzuVKlWSm4ReRUWFwMBA4uLiWL16NYcOHWLEiBFy571+/ZrAwEDCw8OJiIjgyJEjeHt7s2fPHvbs2cPatWtZvnw5f/75Z6FjevfuHStWrMiefLdSJbljVatWxdzcnM2bNwOQnJzMsWPH6NSp02dcPejq6hIcHMzcuXPp0KEDd+/eZenSpUD25MoRERH07duXokWLKpz76XCtsLAwSpQoQYUKFRg1ahSvX7+Wjtna2mJoaEhQUBDv3r3jzZs3BAUFYW9vj7m5OQCnTp2iXr16qKurS+d5enqSkJDAs2fPPuv6cpOens6LFy/kNkEQBEEQBOHfoVCTHQuCIHxrbm5uDBo0iA8fPvDmzRsuXryIq6sr79+/Z9myZUD2C3F6ejr169ene/fuhIaGSstTr1y5kgMHDhAUFMTw4cOleidNmkTDhg0V2jt58iRNmzZlzJgxDB06FIDExER27NhBVFQUtWvXBrJf4s3MzNi2bRutW7cG4P379yxZskRKRly/fp29e/dy9uxZqlevDiC9zOc4ceIEZ8+e5dGjR2hoaAAwZ84ctm3bxp9//kmPHj2A7GTHmjVrMDIy+uJ7amdnJ9fz5eNJl83NzZkyZQq9evViyZIl0v7379+zdOlSaehaq1atWLt2LQ8fPkRHRwcHBwcp6ZbTuyU/u3btom3btrx+/RoTExMOHDhAiRIlFMp17dqV4OBgOnbsSGhoKI0bN/6i++Du7k6rVq0IDw9nw4YNGBoaAtnJsqysLOzs7PKto3379pQrVw5TU1MuXbpEQEAACQkJUoJMV1eXI0eO0KJFCyZPngyAtbU1+/btQ00t+3+1Dx48wMLCQq7eUqVKSceKFy/+2df4qenTpzNx4sSvVp8gCIIgCILw4xA9cgRB+KHUr1+fV69ece7cOY4fP46NjQ1GRka4urpK8+QcOXIES0tLUlNTef/+PXXq1JHOL1KkCDVq1CA+Pl6u3mrVqim0lZycTMOGDRk3bpyUxAGIj49HTU2NmjVrSvsMDQ2xtbWVq1ddXV2ud0/OeVWrVpX22dnZyfXsiI2NJS0tDUNDQ3R0dKTt9u3bchM3lytX7qskcSB7XqCPVxyMjIykQYMGlC5dGl1dXTp16sSTJ0/kephoa2vLzT9UqlQpzM3N5eaqKVWqlEJvo7y4ubkRExPDyZMn8fLyok2bNkrP79ixI6dOneLWrVuEhobStWvXwl6ynHv37hEREYG2tjbHjx+X9mdlZRW4jh49euDp6UnFihXp0KEDa9asYevWrdLP7M2bN/j7+1OnTh1Onz5NVFQUFSpUoEmTJnLDxPJy/PhxuWdC2bC8gho1ahSpqanSdvfu3c+uSxAEQRAEQfixiB45giD8UKysrChTpgyHDx/m2bNnuLq6AmBqaoqZmRknT57k8OHDuLu7F6peZUNnjIyMMDU1Zf369XTt2lVuzpOC0NLSkkuQFERaWprCPEA5Pk74KIv3c8XHx0s9QZKSkmjatCm9e/dm6tSpGBgYcOLECfz9/Xn37h3a2tpAdkLsYzmrYX26rzATPBctWhQrKyusrKyoVasW1tbWBAUFMWrUKLlyhoaGNG3aFH9/f96+fUujRo14+fLl51w6kD1JcdWqVRkzZgwNGzakVatWuLq6Ym1tjUwm49q1a4WuMyfJd+PGDcqXL8+6detISkri1KlTqKhk/41k3bp1FC9enO3bt9O2bVuMjY15+PChXD05n42NjTE3N5cbRpfTW+dzaGhoSD2+BEEQBEEQhH8X0SNHEIQfjpubG0eOHOHIkSNyy47Xq1dPGrrk5uZG+fLlUVdXJyoqSirz/v17zp07h4ODQ77taGlpsWvXLjQ1NfH09JSSBfb29nz48EFueewnT56QkJCQZ712dnZ8+PCB6OhoaV9CQgLPnz+XPlepUoUHDx6gpqYmJTVyNmXDjL7UoUOHuHz5Mi1btgQgOjqazMxM5s6dS61atbCxseH+/ftfvd2CyMzMlFaT+lTXrl05cuQIvr6+qKqqfnYbq1at4sSJEwQFBeHm5kbv3r3p2rUrr169wsDAAE9PTxYvXsyrV68Uzv345/apnISLiYkJkD2nkIqKilxiL+dzTrLLxcWFY8eOyc2zdODAAWxtbSlevDhaWlpyz4Ouru5nX7cgCIIgCILw7yUSOYIg/HDc3Nw4ceIEMTExUo8cAFdXV5YvX867d+9wc3OjaNGi9O7dm+HDhxMREcHVq1fp3r07r1+/xt/fv0BtFS1alN27d6OmpkajRo1IS0vD2tqa5s2b0717d06cOEFsbCwdO3akdOnSNG/ePNe6bG1t8fLyomfPnpw5c4bo6Gi6deuGlpaWVMbDwwMXFxdatGjB/v37SUpK4uTJk4wZM0Zu2fXPkZ6ezoMHD7h37x4XLlxg2rRpNG/enKZNm+Lr6wtk93h6//49Cxcu5NatW6xdu1aae+hbefXqFaNHj+b06dPcuXOH6Ohounbtyr1796T5hj7l5eXF48ePmTRpUq71JicnExMTQ3JyMhkZGcTExBATEyOtwHXnzh2GDBnCnDlzKFeuHAAzZ85EJpMxcuRIABYvXkxGRgY1atRg8+bNJCYmEh8fT2BgIC4uLkD2imaTJ08mOjqapKQkduzYga+vL/Xq1ZOG1jVs2JBnz57Rt29f4uPjiYuLo0uXLqipqeHm5gZkz7Ojrq6Ov78/cXFxbNiwgQULFjBkyJA8719aWpp0bQC3b9+WrlsQBEEQBEH47xGJHEEQfjhubm68efMGKysrueElrq6uvHz5UlqmHGDGjBm0bNmSTp06UaVKFW7cuMG+ffsKNXGsjo4Oe/fuJSsriyZNmvDq1StCQkKoWrUqTZs2xcXFhaysLPbs2aMwvOhTISEhmJqa4urqyq+//kqPHj0oWbKkdFwmk7Fnzx7q1atHly5dsLGxoW3btty5c+eLhtIAREREYGJigrm5OV5eXhw+fJjAwEC2b98u9WqpVKkSv//+OzNnzqRChQqEhYUxffr0L2o3P6qqqly7dk1a3rtZs2Y8efKE48eP4+joqPQcmUxGiRIl5FZ4+tS4ceNwdnZm/PjxpKWl4ezsjLOzM+fPnycrKwt/f39cXFykCaQhe+6f0NBQli5dytGjR7G0tOTChQu4ubkxdOhQKlSoQMOGDTl48KC0upW6ujqRkZH8/PPP2NnZMXToUFq2bMnOnTuleu3s7Ni5cyeXLl3CxcWFunXrcv/+felnAqCnp8f+/fu5ffs2VatWZejQoYwbN04uPmXOnz8vXRvAkCFDcHZ2Zty4cQX7AQiCIAiCIAj/KrKswsz2KAiCIAjCP86LFy/Q09MjNTW10HNBCYIgCIIgCN9eYf69JnrkCIIgCIIgCIIgCIIg/EOIRI4gCMIPLjk5WW5Z6k+37z1XyqfLZn+6CYIgCIIgCILw9YjlxwVBEH5wpqamcstSKzv+PVWrVi3P+IQfR72x61HV0Mq/oPCvEj3b93uHIAiCIAjCVyQSOYIg/OO8e/cOBwcH1qxZQ+3atb93OF/FX3/9hYODAxcuXKBMmTJyx3KWKv+akpKSsLCw4OLFi1SuXJkjR47g5ubGs2fP0NfXL1RdOctm/5PJZDK2bt1KixYtvncogiAIgiAIgpCnf9XQqs6dOyv9R/iRI0eQyWQ8f/5c7rOy7cGDB3Ln/u9//0NdXZ0KFSoobfPjc4sWLYq1tTWdO3cmOjo633jNzc2RyWScPn1abv+gQYOoX7++3L6nT58yaNAgypUrh7q6OqampnTt2lVhSEXnzp2leIoUKYKFhQUjRozg7du3SuP+tO309HQMDQ2RyWQcOXJEIeaePXuiqqrKpk2bFI5NmDCBypUr53vdH3v37h2zZs2iUqVKaGtrU6JECerUqUNISAjv37+Xyt29e5euXbtiamqKuro65cqVY+DAgTx58kShzri4ONq0aYORkREaGhrY2Ngwbtw4Xr9+LVcu5/7LZDK0tbWpWLEiq1atkivz8bOioqKCnp4ezs7OjBgxgpSUlDyvf8KECchkMnr16iVXLiYmBplMRlJSkkLsnp6eqKqqcu7cOSD7ZTu3ZzVnCw0NLdQznWPPnj2oq6tz4cIFuf1z586lRIkSuZ73MTs7OzQ0NBTKvnr1ivLlyyssq5yUlESxYsVYuXIloPjdBFi5ciWVKlVCR0cHfX19nJ2dFVZVWrZsGRYWFnJJnKNHj+Lu7o6BgQHa2tpYW1vj5+fHu3fvAAgNDS10guJref/+PQEBAVSsWJGiRYtiamqKr68v9+/fl8qUKFECX19fxo8fn299WVlZrFixgpo1a0r3qVq1asyfP1/hOf9elP1s8yqXsxkZGdG4cWMuX7789wSqRM73rjC9jHK+7zKZDDU1NUqUKEG9evWYP38+6enp3y5YQRAEQRAE4T/nX5XIKayEhARSUlLkto+XCYbsl782bdrw4sULzpw5o7SekJAQUlJSiIuLY/HixaSlpVGzZk3WrFmTbwyampoEBATkWebp06fUqlWLyMhIli1bxo0bNwgPD+fGjRtUr16dW7duyZX38vIiJSWFW7duMW/ePJYvX6705dDMzIyQkBC5fVu3bs11TovXr18THh7OiBEjCA4Ozvfa8vPu3Ts8PT2ZMWMGPXr04OTJk5w9e5a+ffuycOFC4uLiALh16xbVqlUjMTGR9evXc+PGDZYtW8bBgwdxcXHh6dOnUp2nT5+mZs2avHv3jt27d3P9+nWmTp1KaGgoDRs2lF7qc0yaNImUlBSuXLlCx44d6d69O3v37lWINSEhgfv373Pu3DkCAgKIjIykQoUK+b5sampqEhQURGJiYr73Izk5mZMnT9KvXz/p/pqZmck9n0OHDsXR0VFun4+Pj1yc+T3TORo3boyvry++vr7Si+bVq1cZO3YsixcvxtjYOM94T5w4wZs3b2jVqhWrV6+WO1a0aFFCQkJYuHAhx48fB7KTD126dKFOnTp0795daZ3BwcEMGjSIAQMGEBMTQ1RUFCNGjCAtLU0qk5WVxaJFi/D395f2Xb16FS8vL6pVq8axY8e4fPkyCxcuRF1dnYyMjDyv4+/w+vVrLly4wG+//caFCxfYsmULCQkJ/PLLL3LlunTpQlhYmNwzrUynTp0YNGgQzZs35/Dhw8TExPDbb7+xfft29u/f/y0v5ZvJeXb37dtHeno6TZo0Ufi+/uhyvpvJyckcPnyY1q1bM336dGrXrs3Lly+/d3iCIAiCIAjCv8R/OpFTsmRJjI2N5TYVlf+/JVlZWYSEhNCpUyfat29PUFCQ0nr09fUxNjbG3Nycn3/+mT///JMOHTrQr18/nj17lmcMPXr04PTp0+zZsyfXMmPGjOH+/ftERkbSqFEjypYtS7169di3bx9FihShb9++cuU1NDQwNjbGzMyMFi1a4OHhwYEDBxTq9fPzIzw8nDdv3kj7goOD8fPzUxrHpk2bcHBwYOTIkRw7doy7d+/meW35mT9/PseOHePgwYP07duXypUrY2lpSfv27Tlz5gzW1tYA9O3bF3V1dfbv34+rqytly5alUaNGREZGcu/ePcaMGQNk/7z8/f2xt7dny5Yt1KhRg3LlytG6dWt27tzJqVOnmDdvnlwMurq6GBsbY2lpSUBAAAYGBkrvVc6zYmNjQ9u2bYmKisLIyIjevXvneY22tra4ublJMeYlJCSEpk2b0rt3b9avX8+bN29QVVWVez51dHRQU1OT26el9f/zXeT3TH9q3rx5pKWlMX78eD58+ICfnx/NmjWTSw7lJigoiPbt29OpUyelib169erRv39/unTpwqtXr1iwYAExMTEKvZ4+tmPHDtq0aYO/vz9WVlY4OjrSrl07pk6dKpWJjo7m5s2bNGnSRNq3f/9+jI2NmTVrFhUqVKB8+fJ4eXmxcuVKtLS0OHLkCF26dCE1NVXqNTFhwgQA1q5dS7Vq1aRnoX379jx69EghLmtrazQ1NXFzc2P16tUKvU1OnDhB3bp10dLSwszMjAEDBvDq1SsA9PT0OHDgAG3atMHW1pZatWqxaNEioqOj5XrVOTo6YmpqytatW3O9Rxs3biQsLIz169czevRoqlevjrm5Oc2bN+fQoUO4ubkBkJmZyaRJkyhTpgwaGhpUrlyZiIiIXOtVJq9rguwefAEBAZiZmaGhoYGVlRVBQUEkJSVJcRQvXhyZTEbnzp3zbCvn2a1SpQqDBg3i7t27XLt2rcCxLFmyRPoZlSpVilatWknHzM3NmT9/vlx7lStXlp6BT1lYWADg7OyMTCZT6CGZm5zvpqmpKRUrVqR///4cPXqUK1euMHPmTKlcfs9cTi+lffv24ezsjJaWFu7u7jx69Ii9e/dib29PsWLFaN++/Q/TA0sQBEEQBEH4+/ynEzn5OXz4MK9fv8bDw4OOHTsSHh4u9+KQl8GDB/Py5UulSYGPWVhY0KtXL0aNGkVmZqbC8czMTMLDw+nQoYNCDwktLS369OnDvn37cv0L/pUrVzh58iTq6uoKx6pWrYq5uTmbN28GsnuEHDt2jE6dOimtKygoiI4dO6Knp0ejRo0IDQ3N89ryExYWhoeHB87OzgrHihQpQtGiRXn69Cn79u2jT58+cgkLAGNjYzp06MCGDRvIysoiJiaGq1evMmTIEIXkRaVKlfDw8GD9+vVKY8nMzGTz5s08e/ZM6b36lJaWFr169SIqKkrhpf9TM2bMYPPmzZw/fz7XMjlJw44dO2JnZ4eVlRV//vlnvnF8KV1dXYKDg5k7dy4dOnTg7t27LF26NN/zXr58yaZNm+jYsSMNGzYkNTVV6nnzsalTp6KmpkbHjh0ZPXo0CxcupHTp0rnWa2xszOnTp7lz506uZY4fP46NjQ26urpy56WkpHDs2DGl59SuXZv58+dTrFgxqafSsGHDgOxhT5MnTyY2NpZt27aRlJQkl3S4ffs2rVq1okWLFsTGxtKzZ0+FxNzNmzfx8vKiZcuWXLp0iQ0bNnDixAn69euX63XkJJU+He5Vo0YNpfcyR1hYGLa2tjRv3lzhmEwmQ09PD4AFCxYwd+5c5syZw6VLl/D09OSXX34pUO+wgl6Tr68v69evJzAwkPj4eJYvX46Ojg5mZmbS75WcnjYLFiwoULupqamEh4cDSN/F/GI5f/48AwYMYNKkSSQkJBAREUG9evUK1J4yZ8+eBSAyMpKUlBS2bNny2XXZ2dnRqFEjuTrye+ZyTJgwgUWLFnHy5Enu3r1LmzZtmD9/PuvWrWP37t3s37+fhQsXKm03PT2dFy9eyG2CIAiCIAjCv8O/LpGza9cuhaVvGzVqpLRsmTJl5Mo5OjrKHQ8KCqJt27aoqqpSoUIFLC0tlc4No4ydnR2A0nlQPjV27Fhu375NWFiYwrHHjx/z/Plz7O3tlZ5rb29PVlYWN27ckPbl3ANNTU0qVqzIo0ePGD58uNLzu3btKvWmCA0NpXHjxhgZGSmUS0xM5PTp01JPjY4dOxISEkJWVla+15ebxMRE6T7lVSYrKyvP63/27BmPHz/m+vXr0r7cyuaUyREQEICOjg4aGhq0atWK4sWL061btwLFX9CfcZUqVWjTpk2eQ+giIyN5/fo1np6eQPb9za0HWF7ye6aVcXd3p1WrVmzcuJHAwEAMDQ3zPSc8PBxra2scHR1RVVWlbdu2SuPV0tJiwYIFbNu2jfr169OxY8c86x0/fjz6+vqYm5tja2tL586d2bhxo1yS886dOwqrNLVu3Zp27drh6uqKiYkJ3t7eLFq0SHp5VVdXR09PD5lMJte7CbK/A40aNcLS0pJatWoRGBjI3r17peFcy5cvx9bWltmzZ2Nra0vbtm0VXrqnT59Ohw4dGDRoENbW1tSuXZvAwEDWrFmjMD8VwNu3bwkICKBdu3YUK1ZM7pipqWmeiazExERsbW3zvI8Ac+bMISAggLZt22Jra8vMmTOpXLmyQs+U3OR3TdevX2fjxo0EBwfj7e2NpaUlDRo0wMfHB1VVVQwMDID/72mTk2DKTc6zq6+vz7p16/jll1+k71h+sSQnJ1O0aFGaNm1KuXLlcHZ2ZsCAAQW6TmVyfgcaGhpibGwsXcvnsrOzk/s9kd8zl2PKlCnUqVMHZ2dn/P39OXr0KEuXLsXZ2Zm6devSqlUrDh8+rLTN6dOno6enJ21mZmZfdA2CIAiCIAjCj+Nfl8hxc3MjJiZGbsttKMfx48flyn08vOn58+ds2bJF7sWzMC/XOQkOmUyWb1kjIyOGDRvGuHHjcp0TojAJk5x7cObMGfz8/OjSpQstW7ZUWrZjx46cOnWKW7duERoaSteuXZWWCw4OxtPTkxIlSgDZ86ukpqZy6NChAsf1qcJc07cqO3z4cGJiYjh06BA1a9Zk3rx5BV59pzA/4ylTpnD8+PFc5y8JDg7Gx8cHNbXsheTatWtHVFQUN2/eLOCVZMvrmc7NvXv3iIiIQFtbO8+eIJ/G++l3Y9OmTUrnAQkKCkJbW5vLly+TmpqaZ70mJiacOnWKy5cvM3DgQGm4l5eXl5TMefPmDZqamnLnqaqqEhISwv/+9z9mzZpF6dKlmTZtmjRnSV6io6Np1qwZZcuWRVdXF1dXVwBpyFNCQgLVq1eXO6dGjRpyn2NjYwkNDZVLonl6epKZmcnt27flyr5//542bdqQlZWltPeTlpZWnsNlCvJ8v3jxgvv371OnTh25/XXq1CE+Pj7f8wtyTTExMaiqqkr360sdP36c6OhoQkNDsbGxYdmyZQWOpWHDhpQrVw5LS0s6depEWFjYDzXkKCsrS+73RH7PXA4nJyfpv0uVKoW2tjaWlpZy+3LrEThq1ChSU1Ol7UuHwgqCIAiCIAg/jn9dIqdo0aJYWVnJbbkN5bCwsJArV65cOenYunXrePv2LTVr1kRNTQ01NTUCAgI4ceKEQq8OZXJelnLmWsjPkCFDePPmDUuWLJHbb2RkhL6+fq4vX/Hx8chkMrnkQ849qFSpEsHBwZw5cybXBJShoSFNmzbF39+ft2/fKu29lJGRwerVq9m9e7d0L7S1tXn69OkXTXpsY2MjNweGMlZWVshksjyvv3jx4hgZGWFjYyPty61sTpkcJUqUwMrKirp167Jp0yYGDBjA1atXCxR/Tjvm5ub5li1fvjzdu3dn5MiRCi/iT58+ZevWrSxZskS6v6VLl+bDhw+Fvr95PdO56d69O1WrVmXXrl0sXbqUo0eP5ln+6tWrnD59mhEjRkjx1qpVS5oM+2MbNmxg165dnDx5El1dXQYPHlyg66hQoQJ9+vThjz/+4MCBAxw4cECKq0SJErnOPVW6dGk6derEokWLiIuL4+3bt3IJgU+9evUKT09PihUrRlhYGOfOnZPmpynMRLtpaWn07NlTLokWGxtLYmIi5cuXl8rlJHHu3LnDgQMHFHrjQPbzoKxXXI6CfG++hvyu6dOhjl/KwsICW1tb/Pz86Natm9w8TfnFoqury4ULF1i/fj0mJiaMGzeOSpUqSXMYqaioKHzvPl4V71uLj4+X/l9QmGeuSJEi0n/nrET4MZlMpnRILmTPlVasWDG5TRAEQRAEQfh3+Nclcr6WoKAghg4dqvDiULdu3QK9XOfMx+Hh4VGg9nR0dPjtt9+YOnWqXK8GFRUV2rRpw7p16xSWeM5J/Hh6euba9V9FRYXRo0czduxYuUmNP9a1a1eOHDmCr68vqqqqCsf37NnDy5cvuXjxotz9WL9+PVu2bMl3eeHctG/fnsjISC5evKhw7P3797x69QpDQ0MaNmzIkiVLFOJ/8OABYWFh+Pj4IJPJqFy5MnZ2dsybN0/h5SY2NpbIyEjatWuXazxmZmb4+PgwatSofGN/8+YNK1asoF69enm+dH9s3LhxXL9+XSHZERYWRpkyZYiNjZW7v3PnziU0NPSbrrq0atUqTpw4QVBQEG5ubvTu3ZuuXbvmORdUUFAQ9erVU4h3yJAhcgnDhw8f0rdvX6ZMmUKlSpUIDQ1lzZo1SlcFy4uDgwOAFJOzszPXrl3Lt2dK8eLFMTExkc5TtoLVtWvXePLkCTNmzKBu3brY2dkp9HCwtbVVmN8oZ3n4HFWqVOHq1asKSWQrKytpnpecJE5iYiKRkZG5DmG7cuWK0nmjcrRv357r16+zfft2hWNZWVmkpqZSrFgxTE1NiYqKkjseFRUl3c/85HdNFStWJDMzM9fEX851f87z27dvX65cuSIlOApyf9XU1PDw8GDWrFlcunSJpKQkqcegkZGRXM+sFy9eKPSU+lqxf+ratWtERERIvSIL8swJgiAIgiAIQl7+04mcR48e8eDBA7nt/fv3xMTEcOHCBbp160aFChXktnbt2rF69Wo+fPgg1fP8+XMePHgg/ZW9VatWrFu3jqVLlypMZJqXHj16oKenx7p16+T2T5s2DWNjYxo2bMjevXu5e/cux44dw9PTk/fv37N48eI8623dujWqqqq5lvPy8uLx48dMmjRJ6fGgoCCaNGlCpUqV5O5FmzZt0NfXl5vb582bNwpD23IbHjRo0CDq1KlDgwYNWLx4MbGxsdy6dYuNGzdSq1YtaVLWRYsWkZ6ejqenp7RaVkREBA0bNqR06dLSikYymYygoCCuXr1Ky5YtOXv2LMnJyWzatIlmzZrh4uLCoEGD8rxXAwcOZOfOnQov7jnPSmJiIuHh4dSpU4e//vqrQBMD5yhVqhRDhgwhMDBQ4f62atVK4Vnz9/fnr7/+KtRKQ7k908rcuXOHIUOGMGfOHKnnzsyZM5HJZIwcOVLpOe/fv2ft2rW0a9dOId5u3bpx5swZadn4Hj16YG9vL93zGjVqMHz4cHr06JHrEKvevXszefJkoqKiuHPnDqdPn8bX1xcjIyNcXFyA7KGDaWlpUjuQPY9N79692b9/Pzdv3iQuLo6AgADi4uJo1qwZkN1zKi0tjYMHD/LXX3/x+vVrypYti7q6OgsXLuTWrVvs2LGDyZMny8XUs2dPrl27RkBAgDQvTM5E3znDZQICAqSl42NiYkhMTGT79u3SZLzv37+nVatWnD9/nrCwMDIyMqSfz8e9MF6/fk10dDQ///yz8h8w0KZNG3x8fGjXrh3Tpk3j/Pnz3Llzh127duHh4SHNmTJ8+HBmzpzJhg0bSEhIYOTIkcTExDBw4MBc6/5Yftdkbm6On58fXbt2Zdu2bdy+fZsjR46wceNGAMqVK4dMJmPXrl08fvxYYf6XvGhra9O9e3fGjx9PVlZWvrHs2rWLwMBAYmJiuHPnDmvWrCEzM1OaS8jd3Z21a9dy/PhxLl++jJ+fn9KkdY6SJUuipaVFREQEDx8+zHdIYI4PHz7w4MED7t+/z+XLl1m4cCGurq5UrlxZmqesIM+cIAiCIAiCIOTlP53IsbW1xcTERG6Ljo4mKCgIBwcHpRPxent78+jRI7m5R7p06YKJiQl2dnb07t0bHR0dzp49S/v27QsVT5EiRZg8ebLC5KiGhoacPn0aNzc3evbsSfny5WnTpg3ly5fn3LlzcnMmKKOmpka/fv2YNWuW0p4WMpmMEiVKKF2t6eHDh+zevVvpHDsqKip4e3vL9cK4fv06zs7OclvPnj2VxqWhocGBAwcYMWIEy5cvp1atWlSvXp3AwEAGDBhAhQoVALC2tub8+fNYWlpK192jRw/c3Nw4deqUXG+k2rVrc/r0aVRVVWnUqBFWVlaMGjUKPz8/Dhw4gIaGRp73ysHBgZ9//plx48bJ7be1tcXU1JSqVasyY8YMPDw8uHLlSoF7N+QYNmyYNMkuZM+VERsbq/T+6unp0aBBg0JNepzbM/2pnKXaXVxc6NGjh7RfW1ub0NDQXIdY7dixgydPnuDt7a1wzN7eHnt7e4KCglizZg2RkZGEhITIrSA2ceJE9PX1cx1i5eHhwenTp2ndujU2Nja0bNkSTU1NDh48KPVgMTQ0xNvbWy6BWKNGDdLS0ujVqxeOjo64urpy+vRptm3bJs0/Urt2bXr16oWPjw9GRkbMmjULIyMjQkND2bRpEw4ODsyYMYM5c+bIxWRhYcGff/7Jli1bcHJyYunSpdKqVTnPk5OTE0ePHuX69evUrVsXZ2dnxo0bJ03KfO/ePXbs2MH//vc/KleuLPfzOXnypNTW9u3bKVu2LHXr1lV6fyD7+7pu3Tp+//136fqcnJyYMGECzZs3lybMHjBgAEOGDGHo0KFUrFiRiIgIaRn1gsjvmgCWLl1Kq1at6NOnD3Z2dnTv3l36HVO6dGkmTpzIyJEjKVWqVJ4reCnTr18/4uPj2bRpU76x6Ovrs2XLFtzd3bG3t2fZsmWsX79emux71KhRuLq60rRpU5o0aUKLFi3khrx9Sk1NjcDAQJYvX46pqanSFcKUiYuLw8TEhLJly1K/fn02btzIqFGjOH78uPS9L8gzJwiCIAiCIAh5kWV9ybJDgiAI38GlS5do2LAhN2/elEuM/V2mTp3KsmXLvvoEsrVq1WLAgAGFTgILQn5evHiBnp6eNPROEARBEARB+LEU5t9r/+keOYIg/DM5OTkxc+bMPOc5+ZqWLFnCuXPnuHXrFmvXrmX27Nn4+fl91Tb++usvfv311zzncRIEQRAEQRAEQRA9cgRBEPIxePBgNmzYwNOnTylbtiydOnVi1KhR0nLxwr9fXj2/9u7dm+dwuB+B6JEjCIIgCILwYyvMv9dEIkcQBEEQ8nHjxo1cj5UuXfqrL8f+teX8w6BS/2WoavzYsQpfV/Rs3+8dgiAIgiAIBVCYRI74c7IgCIIg5MPKyup7hyAIgiAIgiAIgJgjRxAE4T8pKSkJmUxGTEwMAEeOHEEmk/H8+fPvGtf3IpPJ2LZt2/cOQxAEQRAEQRDyJRI5giB8sQcPHtC/f38sLS3R0NDAzMyMZs2acfDgQalMbi/KnTt3pkWLFtLn+vXrI5PJkMlkaGpqYmNjw/Tp0/l4FGhOEkLZdvr06VzjPHr0KO7u7hgYGKCtrY21tTV+fn68e/cO+P9kRs5WqlQpWrZsya1bt6Q6zM3NlbY7Y8aMAscWGhoq7VNVVaV48eLUrFmTSZMmkZqamu/9zsrKYsWKFdSsWRMdHR309fWpVq0a8+fP5/Xr1/me/3coaGLo03tuZGRE48aNuXz58t8TqBKfJrkKYsKECdI1qKmpUaJECerVq8f8+fNJT0//dsEKgiAIgiAI/zliaJUgCF8kKSmJOnXqoK+vz+zZs6lYsSLv379n37599O3bl2vXrhW6zu7duzNp0iTS09M5dOgQPXr0QF9fn969e8uVi4yMxNHRUW6foaGh0jqvXr2Kl5cX/fv3JzAwEC0tLRITE9m8eTMZGRlyZRMSEtDV1SUxMZEePXrQrFkzLl26hKqqKgCTJk2ie/fucufo6uoWKrZixYqRkJBAVlYWz58/5+TJk0yfPp2QkBCioqIwNTXN9f506tSJLVu2MHbsWBYtWoSRkRGxsbHMnz8fc3NzucTYP0VCQgLFihXj/v37DB8+nCZNmnDjxg3U1dW/d2gF5ujoSGRkJJmZmTx58oQjR44wZcoU1q5dy5EjRxSeEUEQBEEQBEH4HKJHjiAIX6RPnz7IZDLOnj1Ly5YtsbGxwdHRkSFDhuTZOyYv2traGBsbU65cObp06YKTkxMHDhxQKGdoaIixsbHcVqRIEaV17t+/H2NjY2bNmkWFChUoX748Xl5erFy5UmGi2pIlS2JiYkK9evUYN24cV69elZvsVldXV6HdokWLFio2mUyGsbExJiYm2Nvb4+/vz8mTJ0lLS2PEiBG53puNGzcSFhbG+vXrGT16NNWrV8fc3JzmzZtz6NAh3NzcAMjMzGTSpEmUKVMGDQ0NKleuTERERP43/yMnTpygbt26aGlpYWZmxoABA3j16pV0PD09nYCAAMzMzNDQ0MDKyoqgoCCSkpKkOIoXL45MJqNz5855tlWyZEmMjY2pUqUKgwYN4u7du3JJwPxiWbJkCdbW1mhqalKqVClatWolHTM3N2f+/Ply7VWuXJkJEyYojcXCwgIAZ2dnZDIZ9evXL8DdAjU1NYyNjTE1NaVixYr079+fo0ePcuXKFWbOnCmVW7t2LdWqVZOeo/bt2/Po0SPpeE4vpX379uHs7IyWlhbu7u48evSIvXv3Ym9vT7FixWjfvv0P0wNLEARBEARB+PuIRI4gCJ/t6dOnRERE0LdvX4VEBoC+vv4X1Z+VlcXx48e5du3aF/fMMDY2JiUlhWPHjhXqvJwkT87wq2+pZMmSdOjQgR07dij0EsoRFhaGra0tzZs3Vzgmk8nQ09MDYMGCBcydO5c5c+Zw6dIlPD09+eWXX0hMTCxQLDdv3sTLy4uWLVty6dIlNmzYwIkTJ+jXr59UxtfXl/Xr1xMYGEh8fDzLly9HR0cHMzMzNm/eDGT3tElJSWHBggUFajc1NZXw8HAA6WeeXyznz59nwIABTJo0iYSEBCIiIqhXr16B2lPm7NmzQHavqpSUFLZs2fLZddnZ2dGoUSO5Ot6/f8/kyZOJjY1l27ZtJCUlKU10TZgwgUWLFnHy5Enu3r1LmzZtmD9/PuvWrWP37t3s37+fhQsXKm03PT2dFy9eyG2CIAiCIAjCv4MYWiUIwme7ceMGWVlZ2NnZfdV6lyxZwqpVq3j37h3v379HU1OTAQMGKJSrXbs2Kiry+ei0tDSldbZu3Zp9+/bh6uqKsbExtWrVokGDBvj6+ua6vF9KSgpz5syhdOnS2NraSvsDAgIYO3asXNm9e/dSt27dz4rtY3Z2drx8+ZInT55QsmRJheOJiYlyseRmzpw5BAQE0LZtWwBmzpzJ4cOHmT9/PosXL873/OnTp9OhQwcGDRoEgLW1NYGBgbi6urJ06VKSk5PZuHEjBw4cwMPDAwBLS0vpfAMDAyA7OVWQhF6ZMmUApF42v/zyi/RcFSSWokWL0rRpU3R1dSlXrhzOzs75tpkbIyMj4P97VX0pOzs79u/fL33u2rWr9N+WlpYEBgZSvXp10tLS0NHRkY5NmTKFOnXqAODv78+oUaO4efOmdJ9btWrF4cOHCQgIUGhz+vTpTJw48YtjFwRBEARBEH48IpEjCMJn+3gC4q+pQ4cOjBkzhmfPnjF+/Hhq165N7dq1Fcpt2LABe3v7AtWpqqpKSEgIU6ZM4dChQ5w5c4Zp06Yxc+ZMzp49i4mJiVS2TJkyZGVl8fr1aypVqsTmzZvlegQNHz5coQdF6dKlPzu2j+XcU5lMlufxvLx48YL79+9LSYAcderUITY2tkBxxMbGcunSJcLCwuTazszM5Pbt21y+fBlVVVVcXV0LVF9+jh8/jra2NqdPn2batGksW7aswLE0bNiQcuXKYWlpiZeXF15eXnh7e6Otrf1VYvtSWVlZcj/P6OhoJkyYQGxsLM+ePSMzMxOA5ORkHBwcpHJOTk7Sf5cqVQptbW25ZFmpUqWk3kOfGjVqFEOGDJE+v3jxAjMzs692TYIgCIIgCML3IxI5giB8Nmtra2QyWYEmNNbV1VW6ItPz58+l4UA59PT0sLKyArLnhLGysqJWrVpSz48cZmZmUrmCKl26NJ06daJTp05MnjwZGxsbli1bJtd74fjx4xQrVoySJUsqnaC2RIkS+bb7ObEBxMfHU6xYsVwnbbaxsfmsCaQLKy0tjZ49eyrtCVW2bFm5OYO+BgsLC/T19bG1teXRo0f4+PhIw+Dyi0VdXZ0LFy5w5MgR9u/fz7hx45gwYQLnzp1DX18fFRUVhQTY+/fvv2r8eYmPj5fm3Xn16hWenp54enoSFhaGkZERycnJeHp6Kgzf+3ROpU/nf5LJZFIS6FMaGhpoaGh85SsRBEEQBEEQfgRijhxBED6bgYEBnp6eLF68WG7i2RwfLz1ta2tLdHS03PGMjAxiY2OxsbHJtQ0dHR0GDhzIsGHDvnoPoOLFi2NiYqIQu4WFBeXLl//bVxl69OgR69ato0WLFgrDsnK0b9+e69evs337doVjWVlZpKamUqxYMUxNTYmKipI7HhUVJdfjIy9VqlTh6tWrWFlZKWzq6upUrFiRzMxMjh49qvT8nB5Muc31k5e+ffty5coVtm7dWqBYIHuiYQ8PD2bNmsWlS5dISkri0KFDQPZQqZSUFKn+Fy9ecPv27Vzb/5LYP3Xt2jUiIiJo2bKl9PnJkyfMmDGDunXrYmdnJzfRsSAIgiAIgiDkRyRyBEH4IosXLyYjI4MaNWqwefNmEhMTiY+PJzAwEBcXF6nckCFDWLVqFUuWLCExMZGYmBh69OjBs2fP6NatW55t9OzZk+vXr0sT6OZ48uQJDx48kNvevn2rtI7ly5fTu3dv9u/fz82bN4mLiyMgIIC4uDiaNWtWqGt++fKlQrufTiabX2xZWVk8ePCAlJQU4uPjCQ4Opnbt2ujp6TFjxoxc227Tpg0+Pj60a9eOadOmcf78ee7cucOuXbvw8PDg8OHDQPbwr5kzZ7JhwwYSEhIYOXIkMTExDBw4sEDXGBAQwMmTJ+nXrx8xMTEkJiayfft2aYJhc3Nz/Pz86Nq1K9u2beP27dscOXKEjRs3AlCuXDlkMhm7du3i8ePHBZofKIe2tjbdu3dn/PjxZGVl5RvLrl27CAwMJCYmhjt37rBmzRoyMzOluYTc3d1Zu3Ytx48f5/Lly/j5+UlLyStTsmRJtLS0iIiI4OHDh0p7kinz4cMHHjx4wP3797l8+TILFy7E1dWVypUrM3z4cOD/exAtXLiQW7dusWPHDiZPnlzgeyMIgiAIgiAIIpEjCMIXsbS05MKFC7i5uTF06FAqVKhAw4YNOXjwIEuXLpXKtWvXjlWrVhEcHEzVqlXx8vLiwYMHHDt2jFKlSuXZhoGBAb6+vkyYMEFuKImHhwcmJiZy27Zt25TWUaNGDdLS0ujVqxeOjo64urpy+vRptm3bVuh5XsaNG6fQ7qdLhucX24sXLzAxMaF06dK4uLiwfPly/Pz8uHjxotx8PZ+SyWSsW7eO33//XYrdycmJCRMm0Lx5czw9PQEYMGAAQ4YMYejQoVSsWJGIiAh27NiBtbV1ga7RycmJo0ePcv36derWrYuzszPjxo3D1NRUKrN06VJatWpFnz59sLOzo3v37lLvptKlSzNx4kRGjhxJqVKl5Fa7Koh+/foRHx/Ppk2b8o1FX1+fLVu24O7ujr29PcuWLWP9+vU4OjoC2fPFuLq60rRpU5o0aUKLFi0oX758rm2rqakRGBjI8uXLMTU1VbpCmDJxcXGYmJhQtmxZ6tevz8aNGxk1ahTHjx+XJjE2MjIiNDSUTZs24eDgwIwZM5gzZ06h7o0gCIIgCILw3ybL+lazlQqCIAiC8EN48eIFenp60tA7QRAEQRAE4cdSmH+viR45giAIgiAIgiAIgiAI/xAikSMIgiAI+dDR0cl1O378+PcOTxAEQRAEQfgPEcuPC4IgCEI+YmJicj1WunTpvy+QL1Rv7HpUNbS+dxjC3yB6tu/3DkEQBEEQhG9E9MgRBOEf5+DBg9jb23+V5aF/FMuWLSv06llfYsKECVSuXFn63LlzZ1q0aPG3tf8jCQ0NRV9fP88yypY+z9m0tERiRBAEQRAEQfj7iETOD6hz587IZDJkMhlFihShVKlSNGzYkODgYLkVeyB7CeD58+dLn2NjY/nll18oWbIkmpqamJub4+Pjw6NHj+TO27x5M/Xr10dPTw8dHR2cnJyYNGkST58+BfJ+sZHJZEpXBurZsyeqqqps2rRJ4diECROka1JVVcXMzIwePXpI7X3s4sWL+Pj4YGJigoaGBuXKlaNp06bs3LmTnLm5k5KSpPo+3U6fPi1dw8dtFi9enJo1azJp0qQ8lxP++P4r28zNzQGoX78+gwYNks6rX78+MplM6dLRTZo0QSaTMWHCBIXyn269evXKNTaAu3fv0rVrV0xNTVFXV6dcuXIMHDiQJ0+eyJXLrf4PHz4orffj+6WiooKJiQk+Pj4kJycXqN6P4z569Cju7u4YGBigra2NtbU1fn5+vHv3DoAjR44gk8l4/vy53GdHR0eF5Iy+vj6hoaFy+0aMGMHYsWOlJaQzMjKYMWMGdnZ2aGlpYWBgQM2aNVm1apVc3B//vP5OsbGxtGvXDjMzM7S0tLC3t2fBggVyZbp27cqFCxcKNEznwYMH9O/fH0tLSzQ0NDAzM6NZs2YcPHjwW11CoRU0MfTp7zsLCwtGjBiR6zLyf4dPk1wFYW5uLl2HlpYW5ubmtGnThkOHDn2bIAVBEARBEIT/LJHI+UF5eXmRkpJCUlISe/fuxc3NjYEDB9K0adNcX8QfP35MgwYNMDAwYN++fcTHxxMSEoKpqam0JDDAmDFj8PHxoXr16uzdu5crV64wd+5cYmNjWbt27WfF+/r1a8LDwxkxYgTBwcFKyzg6OpKSkkJycjIhISFERETQu3dvuTLbt2+nVq1apKWlsXr1auLj44mIiMDb25uxY8cqJGAiIyNJSUmR26pWrSodL1asGCkpKfzvf//j5MmT9OjRgzVr1lC5cmXu37+vNM4FCxbI1QcQEhIifT537lyu98HMzEwh6XDv3j0OHjyodEnp7t27K8Q/a9asXOu/desW1apVIzExkfXr13Pjxg2WLVvGwYMHcXFxUUiMKatfTS33EZU59+vevXts3ryZhIQEWrduXai4r169ipeXF9WqVePYsWNcvnyZhQsXoq6unm8Pmlu3brFmzZo8y5w4cYKbN2/SsmVLad/EiROZN28ekydP5urVqxw+fJgePXpIiaLvLTo6mpIlS/LHH38QFxfHmDFjGDVqFIsWLZLKqKur0759ewIDA/OsKykpiapVq3Lo0CFmz57N5cuXiYiIwM3Njb59+37rS/kmcn7f3bp1i3nz5rF8+XLGjx//vcMqtEmTJpGSkkJCQgJr1qxBX18fDw8Ppk6d+r1DEwRBEARBEP5FRCLnB6WhoYGxsTGlS5emSpUqjB49mu3bt7N3716FREGOqKgoUlNTWbVqFc7OzlhYWODm5sa8efOwsLAA4OzZs0ybNo25c+cye/Zsateujbm5OQ0bNmTz5s34+fl9VrybNm3CwcGBkSNHcuzYMe7evatQRk1NTbomDw8PWrduzYEDB6Tjr169wt/fnyZNmrB7925+/vlnLC0tsbe3x9/fn9jYWPT09OTqNDQ0xNjYWG4rUqSIdFwmk2FsbIyJiYlUz8mTJ0lLS2PEiBFKr0VPT0+uPsjuFZLz2cjIKNf70LRpU/766y+ioqKkfatXr+bnn3+mZMmSCuW1tbUV4s9rqbm+ffuirq7O/v37cXV1pWzZsjRq1IjIyEju3bvHmDFj8q0/Lx/fr9q1a+Pv78/Zs2d58eJFgePev38/xsbGzJo1iwoVKlC+fHm8vLxYuXJlvkNQ+vfvz/jx40lPT8+1THh4OA0bNkRTU1Pat2PHDvr06UPr1q2xsLCgUqVK+Pv7M2zYMCC718fRo0dZsGCB1GsiKSmJjIwM/P39sbCwQEtLC1tbW4WeMh8+fGDAgAHo6+tjaGhIQEAAfn5+cr1NMjMzmT59ulRPpUqV+PPPP6XjXbt2ZcGCBbi6umJpaUnHjh3p0qULW7ZskWurWbNm7Nixgzdv3uR6/X369EEmk3H27FlatmyJjY0Njo6ODBkyROqNBpCcnEzz5s3R0dGhWLFitGnThocPH+Z5/z+W3zUBxMXF0bRpU4oVK4auri5169bl5s2bTJgwgdWrV7N9+3bpfh85ciTXtnJ+35mZmdGiRQs8PDzkfjfkF8uzZ8/o0KEDRkZGaGlpYW1tTUhICKDY+wuy57vJeQY+FRoaysSJE4mNjZViz+137qd0dXUxNjambNmy1KtXjxUrVvDbb78xbtw4EhISAAr0zOX0Zpo2bRqlSpVCX1+fSZMm8eHDB4YPH46BgQFlypSRrlEQBEEQBEH4bxGJnH8Qd3d3KlWqpPDyl8PY2JgPHz6wdetWaQjSp8LCwtDR0aFPnz5Kj+c3T0RugoKC6NixI3p6ejRq1CjfF5+kpCT27duHurq6tG///v08efIk1wQLZCcavlTJkiXp0KEDO3bs+OpzrKirq9OhQwe5F6zQ0FC6du36xXU/ffqUffv20adPH4WEiLGxMR06dGDDhg25/uwL69GjR2zduhVVVVVpCFNBGBsbk5KSwrFjxwrd5qBBg/jw4QMLFy7Mtczx48epVq2aQpuHDh3i8ePHSs9ZsGABLi4ucj2JzMzMyMzMpEyZMmzatImrV68ybtw4Ro8ezcaNG6VzZ86cSVhYGCEhIURFRfHixQuFoYXTp09nzZo1LFu2jLi4OAYPHkzHjh05evRorteRmpqKgYGB3L5q1arx4cMHzpw5o/Scp0+fEhERQd++fSlatKjC8Zzvb2ZmJs2bN+fp06ccPXqUAwcOcOvWLXx8fHKN51P5XdO9e/eoV68eGhoaHDp0iOjoaLp27cqHDx8YNmwYbdq0kXrapKSkULt27QK1e+XKFU6ePCn3uyG/WH777TeuXr3K3r17iY+PZ+nSpZQoUaLA1/oxHx8fhg4dKvUgTElJKdR9+9TAgQPJyspi+/btAAV65gAOHTrE/fv3OXbsGL///jvjx4+nadOmFC9enDNnztCrVy969uzJ//73P6Xtpqen8+LFC7lNEARBEARB+HcQiZx/GDs7O6V/RQaoVasWo0ePpn379pQoUYJGjRoxe/Zsub/CJyYmYmlpKddrJTepqalKl9r9VGJiIqdPn5Zedjp27EhISIhCQuHy5cvo6OigpaWFhYUFcXFxBAQESMevX78OgK2trbTv3Llzcm3v2rVLrs7atWvnG58ydnZ2vHz5UmFema+ha9eubNy4kVevXnHs2DFSU1Np2rSp0rJLlixRiD8sLExp2cTERLKysrC3t1d63N7enmfPnsklMz6tf+jQoXnGnvMzL1q0KKVKleLw4cNKkwZ5xd26dWvatWuHq6srJiYmeHt7s2jRogK9SGprazN+/HimT5+e6zxGd+7cwdTUVG7f77//zuPHjzE2NsbJyYlevXqxd+9e6bienh7q6upyPYlUVVUpUqQIEydOpFq1alhYWNChQwe6dOki91K9cOFCRo0ahbe3N3Z2dixatEgu4Zmens60adMIDg7G09MTS0tLOnfuTMeOHVm+fLnSazh58iQbNmygR48eCtevp6fHnTt3lJ5348YNsrKysLOzy/M+Hjx4kMuXL7Nu3TqqVq1KzZo1WbNmDUePHs1zaGBhrmnx4sXo6ekRHh5OtWrVsLGxoUuXLtja2krf85yeNsbGxnKJmU/t2rULHR0dNDU1qVixIo8ePWL48OEFjiU5ORlnZ2eqVauGubk5Hh4enz1xtJaWFjo6OlIPQmNj4y+azNjAwICSJUtKv7cL8szlnBcYGIitrS1du3bF1taW169fM3r0aKytrRk1ahTq6uqcOHFCabvTp09HT09P2szMzD77GgRBEARBEIQfi1h+/B8mKysrz14pU6dOZciQIRw6dIgzZ86wbNkypk2bxrFjx6hYsWKhemvo6upy4cIFhf3W1tZyn3NesHL+At64cWP8/f05dOgQDRo0kMrZ2tqyY8cO3r59yx9//EFMTAz9+/fPMwYnJydp2V9ra2uF+YE2bNiQa2IjLzn34Wv08PlUpUqVsLa25s8//+Tw4cN06tQp13lpOnTooDAcqlSpUnnWX5if4af159fjKudn/v79e/bu3UtYWJjS+T3yiltVVZWQkBCmTJkiPYfTpk1j5syZnD17VulcQR/z9/dn7ty5zJw5k2nTpikcf/PmjdywKgAHBweuXLlCdHQ0UVFRHDt2jGbNmtG5c2e5CY+VWbx4McHBwSQnJ/PmzRvevXsnTXSbmprKw4cPqVGjhlReVVWVqlWrShOP37hxg9evX9OwYUO5et+9e4ezs7NCe1euXKF58+aMHz+en3/+WeG4lpYWr1+/VhprQX/28fHxmJmZyb28Ozg4oK+vT3x8PNWrV8/z/IJcU0xMDHXr1i1QUjg/bm5uLF26lFevXjFv3jzU1NSkOZAKEkvv3r1p2bIlFy5c4Oeff6ZFixYF7gH0d/j093Zez1wOR0dHVFT+/28tpUqVokKFCtJnVVVVDA0NFSayzzFq1CiGDBkifX7x4oVI5giCIAiCIPxLiETOP0x8fLw0301uDA0Nad26Na1bt2batGk4OzszZ84cVq9ejY2NDSdOnOD9+/f5voCpqKhgZWWVZ5mMjAxWr17NgwcP5JIVGRkZBAcHyyVy1NXVpfpmzJhBkyZNmDhxIpMnTwb+P0GUkJBArVq1gOy5M/KKwczMLN8YlYmPj6dYsWIYGhoW+tyC6Nq1K4sXL+bq1aucPXs213J6enoFjt/KygqZTEZ8fDze3t4Kx+Pj4ylevLjcHD6FqR/kf+b29vbcvHmT3r17K0yCXZB6S5cuTadOnejUqROTJ0/GxsaGZcuWMXHixDzPU1NTY+rUqXTu3Jl+/fopHC9RogTPnj1TGnv16tWpXr06gwYN4o8//qBTp06MGTMm1+9MeHg4w4YNY+7cubi4uKCrq8vs2bNzHdqkTFpaGgC7d++mdOnScsc0NDTkPl+9epUGDRrQo0cPxo4dq7S+p0+f5joPk7W1NTKZjGvXrhU4vs9RkGv6mktuFy1aVHqegoODqVSpEkFBQfj7+xcolkaNGnHnzh327NnDgQMHaNCgAX379mXOnDlSMuTjJNj79++/Wuz5efLkCY8fP5aewYI+c5/+fs5Z1evTfZ+uZJhDQ0ND4fkTBEEQBEEQ/h3E0Kp/kEOHDnH58mW51Xryo66uTvny5aVVq9q3b09aWhpLlixRWr6wq/zs2bOHly9fcvHiRWJiYqRt/fr1bNmyJc/6xo4dy5w5c6TVo37++WcMDAyYOXNmoWIorEePHrFu3TpatGgh9xfvr6l9+/ZcvnyZChUq4ODg8FXqNDQ0pGHDhixZskRhMtwHDx4QFhaGj4/PV+1lNHLkSDZs2KC0Z1ZhFC9eHBMTE7nV0/LSunVrHB0dlSZ9nJ2duXr1ar515Nz3nDaVrZoVFRVF7dq16dOnD87OzlhZWXHz5k3puJ6eHqVKlZIbjpSRkSF3PxwcHNDQ0CA5ORkrKyu57eMeEHFxcbi5ueHn55frKkY3b97k7du3SnvyQPZwG09PTxYvXqz0XuZ83+zt7bl7967cpONXr17l+fPnBXoeC3JNTk5OHD9+PNekSEFWKVNGRUWF0aNHM3bsWN68eVPg+2tkZISfnx9//PEH8+fPZ8WKFdJ+QFqBDpB6+eXmc2NXZsGCBaioqEiTY+f3zAmCIAiCIAhCfkSPnB9Ueno6Dx48ICMjg4cPHxIREcH06dNp2rQpvr6+Ss/ZtWsX4eHhtG3bFhsbG7Kysti5cyd79uyRJt+tWbMmI0aMYOjQody7dw9vb29MTU2lZax/+uknBg4cWOA4g4KCaNKkCZUqVZLb7+DgwODBgwkLC8t1SWQXFxecnJyYNm0aixYtQkdHh1WrVuHj40OTJk0YMGAA1tbWpKWlERERAaAw6e6TJ0948OCB3D59fX1p6E1WVhYPHjwgKyuL58+fc+rUKaZNm4aenh4zZswo8HUWVvHixUlJScm319Pr168V4tfQ0KB48eJKyy9atIjatWvj6enJlClTpLmGhg8fTunSpb/6MsdmZmZ4e3szbtw4ufmJ8op7+fLlxMTE4O3tTfny5Xn79i1r1qwhLi4uz0mMPzVjxgw8PT0V9nt6erJ69Wq5fa1ataJOnTrUrl0bY2Njbt++zahRo7CxsZHmkzE3N+fMmTMkJSWho6ODgYEB1tbWrFmzhn379mFhYcHatWs5d+6cXA+e/v37M336dKysrLCzs2PhwoU8e/ZMSpjp6uoybNgwBg8eTGZmJj/99BOpqalERUVRrFgx/Pz8uHLlCu7u7nh6ejJkyBDp3qmqqsr1vjl+/DiWlpaUL18+1/uyePFi6tSpQ40aNZg0aRJOTk58+PCBAwcOsHTpUuLj4/Hw8KBixYp06NCB+fPn8+HDB/r06YOrq6vCRNHKFOSa+vXrx8KFC2nbti2jRo1CT0+P06dPU6NGDWxtbTE3N2ffvn0kJCRgaGiInp5egYdhtW7dmuHDh7N48WKGDRuWbyzjxo2jatWqODo6kp6ezq5du6QhlzkJnwkTJjB16lSuX7/O3Llz82zf3Nyc27dvExMTQ5kyZdDV1S1Q75aXL1/y4MED3r9/z+3bt/njjz9YtWqV9PwABXrmBEEQBEEQBCEvokfODyoiIgITExPMzc3x8vLi8OHDBAYGsn379lxXEHJwcEBbW5uhQ4dSuXJlatWqxcaNG1m1ahWdOnWSys2cOZN169Zx5swZPD09paWLnZycCrX8+MOHD9m9e7fSHkIqKip4e3sTFBSUZx2DBw9m1apVUs8Bb29vTp48iba2Nr6+vtja2uLu7s6hQ4cIDw9XmDTYw8MDExMTue3jFYVevHiBiYkJpUuXxsXFheXLl+Pn58fFixfznavlS+nr6ytdWehjK1euVIi/Xbt2uZa3trbm/PnzWFpa0qZNG8qXL0+PHj1wc3Pj1KlTCqsgfQ2DBw9m9+7dckPE8oq7Ro0apKWl0atXLxwdHXF1deX06dNs27YNV1fXArfr7u6Ou7u7wrxIHTp0IC4uTlrOGbKTOzt37qRZs2bY2Njg5+eHnZ0d+/fvl4b8DRs2DFVVVRwcHDAyMiI5OZmePXvy66+/4uPjQ82aNXny5InCim4BAQG0a9cOX19fXFxc0NHRwdPTU26ensmTJ/Pbb78xffp07O3t8fLyYvfu3dLL+Z9//snjx4/5448/5O7Zp3PVrF+/nu7du+d5XywtLblw4QJubm4MHTqUChUq0LBhQw4ePMjSpUuB7CE327dvp3jx4tSrVw8PDw8sLS3ZsGFDge9/ftdkaGjIoUOHSEtLw9XVlapVq7Jy5UopWdO9e3dsbW2pVq0aRkZGREVFFbhtNTU1+vXrx6xZs3j16lW+sairqzNq1CicnJyoV68eqqqqhIeHA9lDlNavX8+1a9dwcnJi5syZTJkyJc/2W7ZsiZeXF25ubhgZGbF+/foCxT1u3DhMTEywsrKiU6dOpKamcvDgQblJ3QvyzAmCIAiCIAhCXmRZX2utYkEQhL/J8OHDefHiRa6rQn1LmZmZ2Nvb06ZNG2l+p68hLi4Od3d3rl+/jp6e3lerVxAgO6mtp6dHamoqxYoV+97hCIIgCIIgCJ8ozL/XRI8cQRD+ccaMGUO5cuVynej1a7pz5w4rV67k+vXrXL58md69e3P79m3at2//VdtJSUlhzZo1IokjCIIgCIIgCEKeRI8cQRCEPNy9e5e2bdty5coVsrKyqFChAjNmzKBevXrfOzThbxIWFkbPnj2VHitXrhxxcXF/c0SFJ3rkCIIgCIIg/NgK8+81kcgRBEEQhDy8fPmShw8fKj1WpEgRypUr9zdHVHg5/zCo1H8Zqhpfb+l44ccUPVv5ogiCIAiCIPy4CpPIEatWCYIgCEIedHV10dXV/d5hCIIgCIIgCAIg5sgRhC/WuXNnWrRo8d3r+JqOHDmCTCbj+fPn3zuU/5S/877Xr1+fQYMGSZ/Nzc2ZP3/+N2/3R/Sjff8EQRAEQRAEIS8ikSN8FY8fP6Z3796ULVsWDQ0NjI2N8fT0lFtyWCaTyS0N/nfasmULDRs2xMjIiGLFiuHi4sK+ffvyPS8rK4sVK1ZQs2ZNdHR00NfXp1q1asyfP5/Xr18DsGDBAkJDQ7/xFSgaMGAAVatWRUNDg8qVKxfoHHNzc2QyGTKZDC0tLczNzWnTpg2HDh2SK1e7dm1SUlIKNPHuPy3pkzNRsampKZqampQpU4bmzZtz7dq17x1agV28eJHWrVtTqlQpNDU1sba2pnv37ly/fv17hyYpaGLo42dSW1ubihUrsmrVqm8fYB4+TXLlJykpSboGmUyGrq4ujo6O9O3bl8TExG8XqCAIgiAIgvCfJBI5wlfRsmVLLl68yOrVq7l+/To7duygfv36PHny5HuHBsCxY8do2LAhe/bsITo6Gjc3N5o1a8bFixfzPK9Tp04MGjSI5s2bc/jwYWJiYvjtt9/Yvn07+/fvB0BPTw99ff1c63j37t3XvBQ5Xbt2xcfHp1DnTJo0iZSUFBISElizZg36+vp4eHgwdepUqYy6ujrGxsbIZLKvHfJ39f79exo2bEhqaipbtmwhISGBDRs2ULFixX9MImrXrl3UqlWL9PR0wsLCiI+P548//kBPT4/ffvvte4f3WXKeyStXrtCxY0e6d+/O3r17v3dYhRYZGUlKSgqxsbFMmzaN+Ph4KlWqxMGDB793aIIgCIIgCMK/iEjkCF/s+fPnHD9+nJkzZ+Lm5ka5cuWoUaMGo0aN4pdffgGy/+oO4O3tjUwmkz7fvHmT5s2bU6pUKXR0dKhevTqRkZFy9aekpNCkSRO0tLSwsLBg3bp1Cn/tf/78Od26dZN63Li7uxMbGysdnz9/PiNGjKB69epYW1szbdo0rK2t2blzZ67XtXHjRsLCwli/fj2jR4+mevXqmJub07x5cw4dOoSbmxugOCyjfv369OvXj0GDBlGiRAk8PT0BiIuLo2nTphQrVgxdXV3q1q3LzZs3lbadmZnJ9OnTsbCwQEtLi0qVKvHnn3/KlQkMDKRv375YWlrm/sNRQldXF2NjY8qWLUu9evVYsWIFv/32G+PGjSMhIQFQ7GVz584dmjVrRvHixSlatCiOjo7s2bOHpKQk6T4UL14cmUxG586dAYiIiOCnn35CX18fQ0NDmjZtKne9Ob0YtmzZgpubG9ra2lSqVIlTp07JxRsVFUX9+vXR1tamePHieHp68uzZswLfp4/FxcVx8+ZNlixZQq1atShXrhx16tRhypQp1KpVSy6u8PBwateujaamJhUqVODo0aNydV25coVGjRqho6NDqVKl6NSpE3/99Zd0vCCx7dmzBxsbG7S0tHBzcyMpKSnPn93r16/p0qULjRs3ZseOHXh4eGBhYUHNmjWZM2cOy5cvl8oePXqUGjVqoKGhgYmJCSNHjuTDhw951v+x/L5TADt37qR69epoampSokQJvL29gezvwJ07dxg8eLDUSyUvOc+kpaUlAQEBGBgYcODAgQLHEhsbi5ubG7q6uhQrVoyqVaty/vx5ACZMmKDQY23+/PnS76BPde7cmaNHj7JgwQIp9vx+LjkMDQ2l62jevDmRkZHUrFkTf39/MjIygIL9zjM3N2fKlCn4+vqio6NDuXLl2LFjB48fP6Z58+bo6Ojg5OQkXaMy6enpvHjxQm4TBEEQBEEQ/h1EIkf4Yjo6Oujo6LBt2zbS09OVljl37hwAISEhpKSkSJ/T0tJo3LgxBw8e5OLFi3h5edGsWTOSk5Olc319fbl//z5Hjhxh8+bNrFixgkePHsnV37p1ax49esTevXuJjo6mSpUqNGjQgKdPnyqNJzMzk5cvX2JgYJDrdYWFhWFra0vz5s0VjslksjyHHa1evRp1dXWioqJYtmwZ9+7do169emhoaHDo0CGio6Pp2rVrri/W06dPZ82aNSxbtoy4uDgGDx5Mx44dFZIJX8vAgQPJyspi+/btSo/37duX9PR0jh07xuXLl5k5cyY6OjqYmZmxefNmABISEkhJSWHBggUAvHr1iiFDhnD+/HkOHjyIiooK3t7eZGZmytU9ZswYhg0bRkxMDDY2NrRr1066LzExMTRo0AAHBwdOnTrFiRMnaNasmfRSXNj7ZGRkhIqKCn/++adUR26GDx/O0KFDuXjxIi4uLjRr1kzqYfb8+XPc3d1xdnbm/PnzRERE8PDhQ9q0aSOdn19sd+/e5ddff6VZs2bExMTQrVs3Ro4cmWdM+/bt46+//mLEiBFKj+f0DLt37x6NGzemevXqxMbGsnTpUoKCgpgyZUqe9X8sv+/U7t278fb2pnHjxly8eJGDBw9So0YNIHsoY5kyZaSeNikpKQVqMzMzk82bN/Ps2TPU1dULHEuHDh0oU6YM586dIzo6mpEjR1KkSJECX+vHFixYgIuLC927d5diNzMz+6y6VFRUGDhwIHfu3CE6Ohoo2O88gHnz5lGnTh0uXrxIkyZN6NSpE76+vnTs2JELFy5Qvnx5fH19yW3hyenTp6Onpydtn3sNgiAIgiAIwo9HrFolfDE1NTVCQ0Pp3r07y5Yto0qVKri6utK2bVucnJyA7BdoyH7RNDY2ls6tVKkSlSpVkj5PnjyZrVu3smPHDvr168e1a9eIjIzk3LlzVKtWDYBVq1ZhbW0tnXPixAnOnj3Lo0eP0NDQAGDOnDls27aNP//8kx49eijEPGfOHNLS0uRevD+VmJiIra3tZ90Ta2trZs2aJX0ePXo0enp6hIeHSy+YNjY2Ss9NT09n2rRpREZG4uLiAoClpSUnTpxg+fLluLq6flZMeTEwMKBkyZK59jxITk6mZcuWVKxYUYrn43MBSpYsKTfErGXLlnJ1BAcHY2RkxNWrV6lQoYK0f9iwYTRp0gSAiRMn4ujoyI0bN7Czs2PWrFlUq1aNJUuWSOUdHR2Bz7tPpUuXJjAwkBEjRjBx4kSqVauGm5sbHTp0UOjZ1K9fP+kali5dSkREBEFBQYwYMYJFixbh7OzMtGnT5K7PzMyM69evU65cuXxjW7p0KeXLl2fu3LkA2NraSkmy3OTMt2JnZ5drGYAlS5ZgZmbGokWLkMlk2NnZcf/+fQICAhg3bhwqKnnn8AvynZo6dSpt27Zl4sSJ0nk532UDAwNUVVWlnjb5CQgIYOzYsaSnp/Phw/+xd99RVRxvA8e/F5DeFFFAUUCqCIi9/ERRFDASO4q99yBiQY2919i7CGJUjCXG2EUlKootgg0VC2oSiB1FEEF4/+DcfbleykVNTMx8ztlzcndnZ58d9hJ3mHkmmzJlytCvXz+VY3nw4AGjR4+W2iX/74eSMjIyQlNTE11dXZViL448pqSkJOrUqVPs7zy5li1bMnDgQAAmTZrEqlWrqF27Nh07dgTy2qx+/fr8+eefBcY5btw4goODpc8vX74UnTmCIAiCIAhfCDEiR/gk2rdvzx9//MGePXvw8fEhOjqaGjVqFJsEOC0tjVGjRuHk5ISxsTH6+vokJCRIf52+efMmGhoa1KhRQzrH1taW0qVLS5/j4+NJS0vDxMREGh2kr6/PvXv3Cpy6tGXLFqZOncoPP/xAuXLlCo2tsL90q6JmzZoKn+Pi4mjUqJFKowRu375Neno6zZs3V7ifiIiIQqdifQq5ubmFToEJDAxkxowZNGzYkMmTJ3P58uVi60tMTCQgIAAbGxsMDQ2lqSzvjzyQd/YBmJubA0gjruQjcgryoe00dOhQUlJS2Lx5M/Xr12f79u04OzsrTOUBpA4YyOusrFWrFgkJCUDeM3f8+HGF68pf2O/cuaNSbAkJCdStW7fQaxZE1WcyISGB+vXrK/w8GzZsSFpaGr/99lux56vynSrqZ1NSo0ePJi4ujmPHjlG3bl0WLVqEra2tyrEEBwfTr18/vLy8mDNnzl/6PSkp+c9M/rMo7neeXP7vRfny5QGkjtT8+94fnSinpaWFoaGhwiYIgiAIgiB8GcSIHOGT0dbWpnnz5jRv3pyJEyfSr18/Jk+eLOVMKcioUaM4cuQICxYswNbWFh0dHTp06FCiBMFpaWmYm5sTHR2tdOz9JMSRkZH069eP7du34+XlVWS99vb2H7ySkZ6ensJnHR0dlc9NS0sD8qauVKhQQeGYfETCp/b06VMeP36MtbV1gcf79euHt7c3+/bt4/Dhw8yePZuFCxfyzTffFFqnn58flStXZt26dVhYWJCTk0O1atWUfrb5O7fkL7vy6VdFtdvHtJOBgQF+fn74+fkxY8YMvL29mTFjBs2bNy/yvPzX9vPzK3D0jLm5OVevXv3g2IoiH8V148aNYjt9PoYq36mSPNPFKVu2LLa2ttja2rJ9+3ZcXFyoVasWVatWVSmWKVOm0KVLF/bt28eBAweYPHkykZGRtG3bFjU1NaUOsKysrE8We3HknX/y75aqv/MK+l4U9V0RBEEQBEEQ/jvEiBzhL1O1alVev34tfS5VqpRSXpKYmBh69epF27ZtcXFxwczMTGF6j4ODA9nZ2QqrS92+fVtKdgtQo0YNUlJS0NDQkF4G5VvZsmWlclu3bqV3795s3bpVmspTlC5dunDr1q0C88bk5uaSmpqqUjtA3l/XT548qdILZNWqVdHS0uLBgwdK9/NXTY1YsmQJampqCkmb32dpacmgQYPYtWsXI0eOZN26dQBSLpP8P9unT59y8+ZNJkyYQLNmzXByclL4manK1dW10BV/PlU7yace5X9WAWJjY6X/zs7O5uLFizg5OQF5z9y1a9ewsrJSuraenp5KsTk5OXHu3LlCr1mQFi1aULZsWYVpe/nJk1M7OTlx5swZhQ6MmJgYDAwMqFixYrFtosp3qqifDeQ9F8XlISqIpaUlnTp1Yty4cSrHAnmdXCNGjODw4cO0a9eOsLAwIG9aZ0pKikJbxMXFFRnDh8b+vpycHJYuXYq1tTXu7u5A8b/zBEEQBEEQBKE4oiNH+GhPnz6ladOmfP/991y+fJl79+6xfft25s2bp5Ao2MrKiqNHj5KSkiK91NvZ2bFr1y7i4uKIj4+nS5cuCn9hdnR0xMvLiwEDBnDu3DkuXbrEgAED0NHRkf4i7eXlRf369WnTpg2HDx8mKSmJ06dP8+2330qrumzZsoUePXqwcOFC6tatS0pKCikpKUV2xvj7+9OpUycCAgKYNWsWFy5c4P79++zduxcvLy+OHz+uchsNGzaMly9f0rlzZy5cuEBiYiKbNm2SVonKz8DAgFGjRjFixAg2btzInTt3+PXXX1m2bBkbN26Uyt2+fZu4uDhSUlLIyMggLi6OuLi4YkczvXr1ipSUFB4+fMiJEycYMGAAM2bMYObMmdJ0lvcFBQVx6NAh7t27x6+//srx48elTo3KlSsjk8nYu3cvjx8/Ji0tjdKlS2NiYsLatWu5ffs2x44dU8jXoapx48Zx/vx5hgwZwuXLl7lx4warVq3iyZMnKrdTfnFxcbRu3ZodO3Zw/fp1bt++TWhoKBs2bFBKar1ixQp+/PFHbty4wdChQ3n+/Dl9+vQB8qZnPXv2jICAAM6fP8+dO3c4dOgQvXv35t27dyrFNmjQIBITExk9ejQ3b95ky5YtxU5F1NPTY/369ezbt4+vv/6aqKgokpKSuHDhAmPGjGHQoEEADBkyhIcPH/LNN99w48YNfvrpJyZPnkxwcHCx+XFAte/U5MmT2bp1K5MnTyYhIUEpv4+VlRUnTpzg999/V1jNSxXDhw/n559/5sKFC8XGkpGRwbBhw4iOjub+/fvExMRw/vx56fls0qQJjx8/Zt68edy5c4cVK1YUu7S5lZUVZ8+eJSkpiSdPnqg86uXp06ekpKRw9+5daVWxc+fOERoairq6OlD87zxBEARBEARBKI7oyBE+mr6+vpTXwsPDg2rVqjFx4kT69+/P8uXLpXILFy7kyJEjWFpaSn+d/u677yhdujQNGjTAz88Pb29vhXw4ABEREZQvXx4PDw/atm1L//79MTAwQFtbG8gbUbF//348PDzo3bs39vb2dO7cmfv370t5JNauXUt2djZDhw7F3Nxc2oYPH17ofclkMrZs2cJ3333H7t27ady4Ma6urkyZMoXWrVtLy4qrwsTEhGPHjpGWlkbjxo2pWbMm69atKzRnzvTp05k4cSKzZ8/GyckJHx8f9u3bpzD1qV+/fri7u7NmzRpu3bqFu7s77u7u/PHHH0XGMmnSJMzNzbG1taV79+6kpqZy9OhRQkJCCj3n3bt3DB06VIrF3t5eSkBcoUIFpk6dytixYylfvjzDhg1DTU2NyMhILl68SLVq1RgxYgTz589Xub3k7O3tOXz4MPHx8dSpU4f69evz008/oaGhoXI75VexYkWsrKyYOnUqdevWpUaNGixZsoSpU6fy7bffKpSdM2cOc+bMwc3NjVOnTrFnzx5pBIiFhQUxMTG8e/eOFi1a4OLiQlBQEMbGxlJHSXGxVapUiZ07d7J7927c3NxYvXq1QvLkwrRu3ZrTp09TqlQpunTpgqOjIwEBAaSmpkqrUlWoUIH9+/dz7tw53NzcGDRoEH379mXChAkqtbsq36kmTZqwfft29uzZQ/Xq1WnatKnCCKNp06aRlJRElSpVpGTnqqpatSotWrRg0qRJxcairq7O06dP6dGjB/b29vj7++Pr6yslYXZycmLlypWsWLECNzc3zp07x6hRo4q8/qhRo1BXV6dq1aqYmpoq5a8pjJeXF+bm5ri4uDB27FicnJy4fPkynp6eUhlVfucJgiAIgiAIQlFkuR+T0VUQPoPffvsNS0tLoqKiPlmyVUGQS0pKwtramkuXLlG9evXPHY4gfBIvX77EyMiI1NRUkfhYEARBEAThH6gk/14TyY6Ffzz5SBYXFxeSk5MZM2YMVlZWeHh4fO7QBEEQBEEQBEEQBOFvJaZWCf94WVlZjB8/HmdnZ9q2bYupqSnR0dEqLeUtCILwsQYNGqSw9Hn+TZ6XSBAEQRAEQRD+LmJqlSAIgiAU4dGjR7x8+bLAY4aGhpQrV+5vjqjk5EN13b5ZjbrWp1s6XvjnuDi/x+cOQRAEQRCEj1CSqVViRI4gCMIXJCkpCZlMVuwS2yUVHR2NTCaTljj/LylXrpzS0ufyraBOnPDwcIyNjf/+QAVBEARBEIT/BNGRI/yrnThxAj8/PywsLJDJZOzevbtE5zdp0oSgoCCVyslkMmQyGdra2lStWlVatelL9+bNG3r16oWLiwsaGhq0adOmROer+lIbHh4utbGamhoVK1akd+/ePHr06MMC/5cp7Pnt1atXidrc0tKS5ORkqlWrBqjWAdOrVy+p7QvarKysaNCgAcnJyRgZGZXwzv4ajo6OaGlpkZKSUuJzP7az6/jx47Rs2RITExN0dXWpWrUqI0eO5Pfff/+g+gRBEARBEAShJERHjvCv9vr1a9zc3FixYsVffq3+/fuTnJzM9evX8ff3Z+jQoWzdurXAsm/fvv3L4ympD43p3bt36OjoEBgYiJeX1yeOSpGhoSHJycn89ttvrFu3jgMHDtC9e/dC48rJyflL4ympf0JM6urqmJmZSUu0q2LJkiUkJydLG0BYWJj0+fz582hqamJmZoZMJvurQlfZqVOnyMjIoEOHDmzcuPFvvfaaNWvw8vLCzMyMnTt3cv36dVavXk1qaioLFy78W2MRBEEQBEEQ/ptER47wr+br68uMGTNo27ZtoWVWrlyJnZ0d2tralC9fng4dOgB5oxB++eUXlixZIo08SEpKKrQeXV1dzMzMsLGxYcqUKdjZ2bFnzx4gb8TOsGHDCAoKomzZsnh7ewNw9epVfH190dfXp3z58nTv3p0nT55Ide7YsQMXFxd0dHQwMTHBy8uL169fA3kjKerUqYOenh7GxsY0bNiQ+/fvS7G/P0ojKCiIJk2aSJ8/NKb36enpsWrVKvr374+ZmVmBZeLj4/H09MTAwABDQ0Nq1qzJhQsXiI6Opnfv3qSmpkptPGXKlEKvJZPJMDMzw8LCAl9fXwIDA4mKiiIjI0Ma2bNnzx6qVq2KlpYWDx48IDMzk1GjRlGhQgX09PSoW7cu0dHRUp3379/Hz8+P0qVLo6enh7OzM/v37wfg+fPndO3aFVNTU3R0dLCzsyMsLExq//dHssTFxSk8Jx8a08ewsrJi1qxZ9OnTBwMDAypVqsTatWul4/lHmyQlJeHp6QlA6dKlkclk9OrVS6lOIyMjzMzMpA3A2NhY+ixPMJ6/PeT3vnfvXhwcHNDV1aVDhw6kp6ezceNGrKysKF26NIGBgbx790661qdom9DQULp06UL37t3ZsGGD0vGCRjcZGxsTHh4OgLW1NQDu7u7IZDLpe5OTk8O0adOoWLEiWlpaVK9enYMHD0p1/PbbbwQGBhIYGMiGDRto0qSJtILe+vXrmTRpUoHx3rlzh9atW1O+fHn09fWpXbs2UVFR0vHx48dTt25dpfPc3NyYNm2a9Hn9+vU4OTmhra2No6Pjf2ZUoCAIgiAIgqBIdOQIX7QLFy4QGBjItGnTuHnzJgcPHpSWLV+yZAn169eXRtokJydjaWmpct06OjoKo1w2btyIpqYmMTExrF69mhcvXtC0aVPc3d25cOECBw8e5M8//8Tf3x+A5ORkAgIC6NOnDwkJCURHR9OuXTtyc3PJzs6mTZs2NG7cmMuXL3PmzBkGDBhQ4tEQJY3pQ3Xt2pWKFSty/vx5Ll68yNixYylVqhQNGjRg8eLF0kib5ORkRo0apXK9Ojo65OTkkJ2dDUB6ejpz585l/fr1XLt2jXLlyjFs2DDOnDlDZGQkly9fpmPHjvj4+JCYmAjA0KFDyczM5MSJE1y5coW5c+eir68PwMSJE7l+/ToHDhwgISGBVatWUbZs2RLd+4fE9LEWLlxIrVq1uHTpEkOGDGHw4MHcvHlTqZylpSU7d+4E4ObNmyQnJ7NkyZJPEgPk3fvSpUuJjIzk4MGDREdH07ZtW/bv38/+/fvZtGkTa9asYceOHdI5H9s2r169Yvv27XTr1o3mzZuTmprKyZMnSxT3uXPnAIiKiiI5OZldu3YBeb8TFi5cyIIFC7h8+TLe3t58/fXXUmzbt2/n7du3jBkzpsB6C5tCmJaWRsuWLTl69CiXLl3Cx8cHPz8/Hjx4AOR9f86dO8edO3ekc65du8bly5fp0qULAJs3b2bSpEnMnDmThIQEZs2axcSJEwsdkZSZmcnLly8VNkEQBEEQBOHLoPrYe0H4F3rw4AF6enq0atUKAwMDKleujLu7O5A3CkFTU1MaaaOqd+/esXXrVi5fvsyAAQOk/XZ2dsybN0/6PGPGDNzd3Zk1a5a0b8OGDVhaWnLr1i3S0tLIzs6mXbt2VK5cGQAXFxcAnj17RmpqKq1ataJKlSoAODk5lfj+SxqTvb19ia8Bee08evRoHB0dpevKGRkZSSNtSiIxMZHVq1dTq1YtDAwMgLyl6FeuXImbm5t03bCwMB48eICFhQUAo0aN4uDBg4SFhTFr1iwePHhA+/btpba1sbFRiNvd3Z1atWoBeaNdSupDYvpYLVu2ZMiQIQCEhISwaNEijh8/joODg0I5dXV1ypQpA+Ql7P3UCXizsrJYtWqV9Ix26NCBTZs28eeff6Kvr0/VqlXx9PTk+PHjdOrU6ZO0TWRkJHZ2djg7OwPQuXNnQkNDadSokcpxm5qaAmBiYqLwXC5YsICQkBA6d+4MwNy5czl+/DiLFy9mxYoVJCYmYmhoiLm5ucrXgryRNfLnA2D69On8+OOP7Nmzh2HDhuHs7Iybmxtbtmxh4sSJQF7HTd26dbG1tQVg8uTJLFy4kHbt2gF5o4quX7/OmjVr6Nmzp9I1Z8+ezdSpU0sUpyAIgiAIgvDvIEbkCF+05s2bU7lyZWxsbOjevTubN28mPT39g+pauXIl+vr66Ojo0L9/f0aMGMHgwYOl4zVr1lQoHx8fz/Hjx9HX15c2eUfHnTt3cHNzo1mzZri4uNCxY0fWrVvH8+fPAShTpgy9evXC29sbPz8/KYdJSZU0pg8VHBxMv3798PLyYs6cOR9cV2pqKvr6+ujq6uLg4ED58uXZvHmzdFxTUxNXV1fp85UrV3j37h329vYK9/TLL79IMQQGBjJjxgwaNmzI5MmTuXz5snT+4MGDiYyMpHr16owZM4bTp0+XOOYPielj5b+evJPscySF1tXVlTpxAMqXL4+VlZU04km+Tx7bp2ibDRs20K1bN+lzt27d2L59O69evfqoe3n58iV//PEHDRs2VNjfsGFDEhISAMjNzf2gHEFpaWmMGjUKJycnjI2N0dfXJyEhQRqRA3mjcrZs2SJdZ+vWrXTt2hXIywV2584d+vbtq9BuM2bMKLTdxo0bR2pqqrQ9fPiwxHELgiAIgiAI/0xiRI7wRTMwMODXX38lOjqaw4cPM2nSJKZMmcL58+dLPDqha9eufPvtt+jo6GBubo6ammI/qJ6ensLntLQ0/Pz8mDt3rlJd5ubmqKurc+TIEU6fPs3hw4dZtmwZ3377LWfPnsXa2pqwsDACAwM5ePAg27ZtY8KECRw5coR69eqhpqZGbm6uQp1ZWVlK1ylpTB9qypQpdOnShX379nHgwAEmT55MZGRkkbmLCiL/eampqWFubo6Ojo7CcR0dHYUX6bS0NNTV1bl48SLq6uoKZeWdCf369cPb25t9+/Zx+PBhZs+ezcKFC/nmm2/w9fXl/v377N+/nyNHjtCsWTOGDh3KggULpJ9v/nYuqI0/JKbC7j01NVVp/4sXL5RWiipVqpTCZ5lM9lmSLBcUR1GxfWjbyF2/fp3Y2FjOnTtHSEiItP/du3dERkbSv39/6ZqqfD9Kyt7entTUVJKTk0v0fRk1ahRHjhxhwYIF2NraoqOjQ4cOHRSmZgYEBBASEsKvv/5KRkYGDx8+pFOnTkBeuwGsW7dOKZfO++0op6WlhZaWVklvURAEQRAEQfgXECNyhC+ehoYGXl5ezJs3j8uXL5OUlMSxY8eAvNEU+ROxFsXIyAhbW1sqVKig1IlTkBo1anDt2jWsrKywtbVV2OQdLDKZjIYNGzJ16lQuXbqEpqYmP/74o1SHu7s748aN4/Tp01SrVk36i72pqanSCB1VllJWJaYPZW9vz4gRIzh8+DDt2rWTkgaXpI3V1NSwtbXFxsZGqROnIO7u7rx7945Hjx4p3U/+KTOWlpYMGjSIXbt2MXLkSNatWycdMzU1pWfPnnz//fcsXrxYShwsn36Tv51VaWNVY3qfg4MDFy9eVNj37t074uPjP3jKG+S1v7yuz+1D20YuNDQUDw8P4uPjiYuLk7bg4GBCQ0Olcu9/PxITExVG4hXUJoaGhlhYWBATE6NwzZiYGKpWrQrkTR3T1NRUmK6YX2FLvMfExNCrVy/atm2Li4sLZmZmSonVK1asSOPGjdm8eTObN2+mefPmlCtXDsgb1WRhYcHdu3eV2k2euFkQBEEQBEH47xAjcoR/tbS0NG7fvi19vnfvHnFxcZQpU4ZKlSqxd+9e7t69i4eHB6VLl2b//v3k5ORIuUSsrKw4e/YsSUlJ6OvrU6ZMGZU6aVQxdOhQ1q1bR0BAAGPGjKFMmTLcvn2byMhI1q9fz4ULFzh69CgtWrSgXLlynD17lsePH+Pk5MS9e/dYu3YtX3/9NRYWFty8eZPExER69OgBQNOmTZk/fz4RERHUr1+f77//nqtXr0r5fz40psL+un/9+nXevn3Ls2fPePXqldShUb16dTIyMhg9ejQdOnTA2tqa3377jfPnz9O+fXupjdPS0jh69Chubm7o6uqiq6v7SdrY3t6erl270qNHDxYuXIi7uzuPHz/m6NGjuLq68tVXXxEUFISvry/29vY8f/6c48ePS/mGJk2aRM2aNXF2diYzM5O9e/dKx2xtbbG0tGTKlCnMnDmTW7duqbS8tCoxFSQ4OJi+ffvi6OhI8+bNef36NcuWLeP58+f069fvg9uocuXKyGQy9u7dS8uWLdHR0VFp9Mtf4UPbBvJG1GzatIlp06ZRrVo1hWP9+vXju+++49q1azg7O9O0aVOWL19O/fr1effuHSEhIQojhcqVK4eOjg4HDx6kYsWKaGtrY2RkxOjRo5k8eTJVqlShevXqhIWFERcXJ03vs7S0ZNGiRQwbNoyXL1/So0cPrKys+O2334iIiEBfX7/AZ8TOzo5du3bh5+eHTCZj4sSJBY6g6tq1K5MnT+bt27csWrRI4djUqVMJDAzEyMgIHx8fMjMzuXDhAs+fPyc4OLhEPwdBEARBEATh302MyBH+1S5cuIC7u7vUgREcHIy7u7u0DLCxsTG7du2iadOmODk5sXr1arZu3SolSh01ahTq6upUrVoVU1NThZwVH0v+1/13797RokULXFxcCAoKwtjYGDU1NQwNDTlx4gQtW7bE3t6eCRMmsHDhQnx9fdHV1eXGjRu0b98ee3t7BgwYwNChQxk4cCAA3t7eTJw4kTFjxlC7dm1evXoldfJ8TEyFadmyJe7u7vz8889ER0crtLm6ujpPnz6lR48e2Nvb4+/vj6+vr5RotUGDBgwaNIhOnTphampa6GiGDxUWFkaPHj0YOXIkDg4OtGnThvPnz1OpUiUgb9TF0KFDcXJywsfHB3t7e2nZZk1NTcaNG4erqyseHh6oq6sTGRkJ5E0b2rp1Kzdu3MDV1ZW5c+cyY8aMTxJTQQICAli/fj0bNmygZs2a+Pj4kJKSwokTJyhfvvwHt0+FChWYOnUqY8eOpXz58gwbNuyD6/oUPqRtAPbs2cPTp08LnK7n5OSEk5OTNCpn4cKFWFpa0qhRI7p06cKoUaMUOg81NDRYunQpa9aswcLCgtatWwN5+ZSCg4MZOXIkLi4uHDx4kD179igk7x4yZAiHDx/m999/p23btjg6OtKvXz8MDQ0LXZHtu+++o3Tp0jRo0AA/Pz+8vb2pUaOGUrkOHTrw9OlT0tPTadOmjcKxfv36sX79esLCwnBxcaFx48aEh4eLETmCIAiCIAj/QbLc9xMJCIIgCILwRXn58iVGRkakpqZiaGj4ucMRBEEQBEEQ3lOSf6+JETmCIAiCIAiCIAiCIAj/EqIjRxAEQfhH8PX1VVheO/82a9aszx2eIAiCIAiCIPwjiGTHgiAIwj/C+vXrycjIKPBYmTJl/uZovkweE7airlX8inDCP9PF+cXnQhMEQRAE4csnRuQIgvC3Cg8Px9jY+HOH8Vm8f+9TpkyhevXq0udevXopJbn9q8hkMnbv3g1AUlISMplMWo0sOjoamUxW6HLaH+r9et9vjwoVKigtry3fREeOIAiCIAiCIOQRHTmCIAB5nQgymUxp8/Hx+aTX6dSpE7du3fqkdf6VrKysWLx4scrl5Z0V728TJkwo8b3Pnj2b2rVrY2BgQLly5WjTpg03b94sUezvx1GxYkUAkpOT8fX1VfmeWrdujbm5OXp6elSvXl1akvtj/Nuehb+Ss7Mzhw8fBqBFixZEREQoHP/YZ0EQBEEQBEH4coipVYIgSHx8fAgLC1PYp6Wl9UmvoaOjg47Olz+14+bNmwrZ5vX19Ut877/88gtDhw6ldu3aZGdnM378eFq0aMH169fR09NTqY5p06bRv39/6bO6ujoAZmZmKsdx+vRpXF1dCQkJoXz58uzdu5cePXpgZGREq1atVK7nff+FZ+Ht27doamoWWebFixfcunWLevXq8e7dO86cOcOqVasUynyKZ0EQBEEQBEH4MogROYIgSLS0tDAzM1PYSpcuLR1PTEzEw8MDbW1tqlatypEjRxSm6BQ0JScuLg6ZTEZSUhJQ+PSiNWvWYGlpia6uLv7+/qSmpkpl5FOOZs2aRfny5TE2NmbatGlkZ2czevRoypQpQ8WKFZU6oR4+fIi/vz/GxsaUKVOG1q1bS3Hkr3fBggWYm5tjYmLC0KFDycrKAqBJkybcv3+fESNGSCNaVFWuXDmFdtTX1y/xtLKDBw/Sq1cvnJ2dcXNzIzw8nAcPHnDx4kWV6zAwMFCIw9TUFFCcWlWc8ePHM336dBo0aECVKlUYPnw4Pj4+7Nq1q8jz9u/fj729PTo6Onh6eiq0PSg/C/Hx8Xh6emJgYIChoSE1a9bkwoULCmV3796NnZ0d2traeHt78/DhQ4U6f/rpJ2rUqIG2tjY2NjZMnTqV7Oxs6bhMJmP9+vW0bdsWXV1d7Ozs2LNnj3T8+fPndO3aFVNTU3R0dLCzs1N4rlR9pmbOnImFhQUODg7Ftm9sbCzOzs4YGhoSFxeHnp4eVapUUSjzKZ4FQRAEQRAE4csgOnIEQVBJTk4O7dq1Q1NTk7Nnz7J69WpCQkI+Sd23b9/mhx9+4Oeff+bgwYNcunSJIUOGKJQ5duwYf/zxBydOnOC7775j8uTJtGrVitKlS3P27FkGDRrEwIED+e233wDIysrC29sbAwMDTp48SUxMDPr6+vj4+PD27Vup3uPHj3Pnzh2OHz/Oxo0bCQ8PJzw8HIBdu3ZRsWJFpk2bRnJyMsnJyZ/kfj+UvHPrn5AvJjU1tcg4Hj58SLt27fDz8yMuLo5+/foxduzYIuvs2rUrFStW5Pz581y8eJGxY8dSqlQp6Xh6ejozZ84kIiKCmJgYXrx4QefOnaXjJ0+epEePHgwfPpzr16+zZs0awsPDmTlzpsJ1pk6dir+/P5cvX6Zly5Z07dqVZ8+eATBx4kSuX7/OgQMHSEhIYNWqVZQtWxZQ/Zk6evQoN2/e5MiRI+zdu7fQ+3V1dcXY2Jh27dpx7do1jI2N8fDw4MmTJxgbG+Pq6lroucU9C5mZmbx8+VJhEwRBEARBEL4MoiNHEATJ3r17C132OSoqihs3bhAREYGbmxseHh6fbEnoN2/eEBERQfXq1fHw8GDZsmVERkaSkpIilSlTpgxLly7FwcGBPn364ODgQHp6OuPHj8fOzo5x48ahqanJqVOnANi2bRs5OTmsX78eFxcXnJycCAsL48GDB0RHR0v1li5dmuXLl+Po6EirVq346quvOHr0qHRNdXV1hVEtqqpYsaJCOz59+vSj2ignJ4egoCAaNmxItWrVVD4vJCREIY6lS5d+VBwAP/zwA+fPn6d3796Fllm1ahVVqlRh4cKFODg40LVrV3r16lVkvQ8ePMDLywtHR0fs7Ozo2LEjbm5u0vGsrCyWL19O/fr1qVmzJhs3buT06dOcO3cOyOugGTt2LD179sTGxobmzZszffp01qxZo3CdXr16ERAQgK2tLbNmzSItLU2q48GDB7i7u1OrVi2srKzw8vLCz88PUP2Z0tPTY/369Tg7O+Ps7Fzo/e7fv5+4uDhsbGxYtWoVcXFx1K1bl1mzZhEXF8f+/fsLPE+VZ2H27NkYGRlJm6WlZZFtLwiCIAiCIPx7iBw5giBIPD09lXJzyP/in5CQgKWlJRYWFtKx+vXrf5LrVqpUiQoVKijUm5OTw82bN6XOE2dnZ9TU/r/vuXz58govserq6piYmPDo0SMgb5rO7du3MTAwULjWmzdvuHPnjvTZ2dlZyhsDYG5uzpUrVz76nk6ePKlw7fxT1D7E0KFDuXr1qtRRparRo0crdKDIR5d8qOPHj9O7d2/WrVtXZCdFQkICdevWVdhX3PMSHBxMv3792LRpE15eXnTs2FFhipGGhga1a9eWPjs6OmJsbExCQgJ16tQhPj6emJgYhRE47969482bN6Snp6OrqwugMNJFT08PQ0ND6bkZPHgw7du359dff6VFixa0adOGBg0aAKo/Uy4uLsXmxYG8zr6UlBTu3LlD586dKVWqFOfPn2fjxo1Fdryo8iyMGzeO4OBg6fPLly9FZ44gCIIgCMIXQnTkCIIg0dPTw9bW9oPPl3e05ObmSvvk+WY+Vv4pNpCX66SgfTk5OQCkpaVRs2bNAldXkueJKaxeeR0fw9ra+pMtsz5s2DD27t3LiRMnpFWnVFW2bNmP+pnm98svv+Dn58eiRYvo0aPHJ6kzvylTptClSxf27dvHgQMHmDx5MpGRkbRt21al89PS0pg6dSrt2rVTOqatrS39d1E/c19fX+7fv8/+/fs5cuQIzZo1Y+jQoSxYsEDlZ0qV5MODBg3i+++/Jycnh8zMTMzMzMjNzSU9PR0nJycArl+/TqVKlRTOU/VZ0NLS+uSJygVBEARBEIR/BjG1ShAElTg5OfHw4UOFPDGxsbEKZeQvs/nLxMXFFVv3gwcP+OOPPxTqVVNTUylRbGFq1KhBYmIi5cqVw9bWVmEzMjJSuR5NTU3evXv3wXF8jNzcXIYNG8aPP/7IsWPHsLa2/ixxQF4i66+++oq5c+cyYMCAYss7OTlJ05Xk3n9eCmJvb8+IESM4fPgw7dq1U0g0nJ2dLSU/hryVwV68eCF1fNSoUYObN28q/bxtbW0VRnMVx9TUlJ49e/L999+zePFi1q5dK9X/KZ4pyFtNLC4ujlatWhEUFERcXBy9evWiW7duxMXFERcXpzD67Z/0LAiCIAiCIAifl+jIEQRBkpmZSUpKisL25MkTALy8vLC3t6dnz57Ex8dz8uRJvv32W4XzbW1tsbS0ZMqUKSQmJrJv3z4WLlxY7HW1tbUV6g0MDMTf379EOWne17VrV8qWLUvr1q05efIk9+7dIzo6msDAQCkhsiqsrKw4ceIEv//+u9QWf5ehQ4fy/fffs2XLFgwMDKSfSUZGxt8ax/Hjx/nqq68IDAykffv2UhzyBMEFGTRoEImJiYwePZqbN2+yZcsWKYl0QTIyMhg2bBjR0dHcv3+fmJgYzp8/L3XSQN5Imm+++YazZ89y8eJFevXqRb169ahTpw4AkyZNIiIigqlTp3Lt2jUSEhKIjIxkwoQJKt/rpEmT+Omnn7h9+zbXrl1j7969Ugyf6pkCpM6gy5cv4+fnh62tLQkJCbRs2VLqHNLQ+P9Bs/+UZ0EQBEEQBEH4/ERHjiAIkoMHD2Jubq6w/e9//wPypk39+OOPZGRkUKdOHfr166e0GlCpUqXYunUrN27cwNXVlblz5zJjxoxir2tra0u7du1o2bIlLVq0wNXVlZUrV37Uvejq6nLixAkqVapEu3btcHJyom/fvrx58wZDQ0OV65k2bRpJSUlUqVJFYfrM32HVqlWkpqbSpEkThZ/Jtm3b/tY4Nm7cSHp6OrNnz1aIo6ApTHKVKlVi586d7N69Gzc3N1avXl1kcmx1dXWePn1Kjx49sLe3x9/fH19fX6ZOnSqV0dXVJSQkhC5dutCwYUP09fUV2sLb25u9e/dy+PBhateuTb169Vi0aBGVK1dW+V41NTUZN24crq6ueHh4oK6uTmRkpHT9T/FMyaWkpHDv3j3q1avH27dviY2NxcPDo8Cy/5RnQRAEQRAEQfj8ZLn5k1kIgiCUkEwm48cff6RNmzYfdP6UKVPYvXu3SlOwhP+u8PBwgoKCePHixecO5V/p5cuXGBkZkZqa+kGdToIgCIIgCMJfqyT/XhMjcgRBEARBEARBEARBEP4lREeOIAhCCfj6+qKvr1/gVtTUoU9t8+bNhcZR1LLggiAIgiAIgiD8u4mpVYIgCCXw+++/F5pgtkyZMpQpU+ZviePVq1f8+eefBR4rVapUifLCCF8++VBdt29Wo66l87nDEUrg4vwenzsEQRAEQRD+BiWZWqVR5FFBEARBQYUKFT53CAAYGBhgYGDwucMQBEEQBEEQBOFvJqZWCYLwRXj79i22tracPn36c4fyyTx58oRy5cqVeGnrD5WUlIRMJpMST0dHRyOTyf6zCYZlMhm7d+/+S+ru1auXQoLwJk2aEBQUJH22srJi8eLFf8m1BUEQBEEQhH+3L74j5/1/LMu9/4Ii/1zQlpKSonDub7/9hqamJtWqVSvwmvnP1dPTw87Ojl69enHx4sVi47WyskImkxEbG6uwPygoiCZNmijse/bsGUFBQVSuXBlNTU0sLCzo06cPDx48UGoDeTylSpXC2tqaMWPG8ObNmwLjfv/amZmZmJiYIJPJiI6OVop54MCBqKurs337dqVjU6ZMoXr16sXed35v375l3rx5uLm5oaurS9myZWnYsCFhYWFkZWVJ5R4+fEifPn2wsLBAU1OTypUrM3z4cJ4+fapU57Vr1/D398fU1BQtLS3s7e2ZNGkS6enpCuXk7S+TydDV1cXFxYX169crlMn/rKipqWFkZIS7uztjxowhOTm5yPufMmUKMpmMQYMGKZSLi4tDJpORlJSkFLu3tzfq6uqcP38e+P+X7aK28PDwEj3Tck2aNCmy3l9++aXA8/JzdHRES0tL6RqvX7+mSpUqBAcHK+xPSkrC0NCQdevWKbRv/s6DdevW4ebmhr6+PsbGxri7uzN79myFelavXo21tTUNGjSQ9v3yyy80bdqUMmXKoKuri52dHT179uTt27dA3kpIxsbGxd7TXyErK4uQkBBcXFzQ09PDwsKCHj168Mcff0hlypYtS48ePZg8eXKx9eXm5rJ27Vrq1q0rtVOtWrVYvHix0nP+uajaMfT+s2tqakrLli25cuXK3xNoAd7v5FKF/Pv+/hYVFcWSJUsIDw9Xua61a9fSpEkTDA0N/9Oda4IgCIIgCMJ/oCOnpG7evElycrLCVq5cOYUy4eHh+Pv78/LlS86ePVtgPWFhYSQnJ3Pt2jVWrFhBWloadevWJSIiotgYtLW1CQkJKbLMs2fPqFevHlFRUaxevZrbt28TGRnJ7du3qV27Nnfv3lUo7+PjQ3JyMnfv3mXRokWsWbOmwJdDS0tLwsLCFPb9+OOP6OvrFxhHeno6kZGRjBkzhg0bNhR7b8V5+/Yt3t7ezJkzhwEDBnD69GnOnTvH0KFDWbZsGdeuXQPg7t271KpVi8TERLZu3crt27dZvXo1R48epX79+jx79kyqMzY2lrp16/L27Vv27dvHrVu3mDlzJuHh4TRv3lx6qZebNm0aycnJXL16lW7dutG/f38OHDigFOvNmzf5448/OH/+PCEhIURFRVGtWrViXza1tbUJDQ0lMTGx2PZ48OABp0+fZtiwYVL7WlpaKjyfI0eOxNnZWWFfp06dFOIs7pmW27Vrl1LZ+/fvU61aNWrVqkXdunWLjPfUqVNkZGTQoUMHNm7cqHBMT0+PsLAwli1bxsmTJ4G8zofevXvTsGFD+vfvX2CdGzZsICgoiMDAQOLi4oiJiWHMmDGkpaVJZXJzc1m+fDl9+/aV9l2/fh0fHx9q1arFiRMnuHLlCsuWLUNTU5N3794VeR9/h/T0dH799VcmTpzIr7/+yq5du7h58yZff/21QrnevXuzefNmhWe6IN27dycoKIjWrVtz/Phx4uLimDhxIj/99BOHDx/+K2/lLyN/dg8dOkRmZiZfffWV0vf1n+7972ZycjIeHh4YGRmVqBMxPT0dHx8fxo8f/9cFKwiCIAiCIPwriI6c95QrVw4zMzOFTU3t/5spNzeXsLAwunfvTpcuXQgNDS2wHmNjY8zMzLCysqJFixbs2LGDrl27MmzYMJ4/f15kDAMGDCA2Npb9+/cXWubbb7/ljz/+ICoqCl9fXypVqoSHhweHDh2iVKlSDB06VKG8lpYWZmZmWFpa0qZNG7y8vDhy5IhSvT179iQyMlIhmeuGDRvo2bNngXFs376dqlWrMnbsWE6cOMHDhw+LvLfiLF68mBMnTnD06FGGDh1K9erVsbGxoUuXLpw9exY7OzsAhg4diqamJocPH6Zx48ZUqlQJX19foqKi+P333/n222+BvJ9X3759cXJyYteuXdSpU4fKlSvTsWNHfv75Z86cOcOiRYsUYjAwMMDMzAwbGxtCQkIoU6ZMgW0lf1bs7e3p3LkzMTExmJqaMnjw4CLv0cHBAU9PTynGooSFhdGqVSsGDx7M1q1bycjIQF1dXeH51NfXR0NDQ2Gfjs7/JzMt7pnOr0yZMkplp0+fzpMnT/jxxx/R1tYuMt7Q0FC6dOlC9+7dC+zY8/Dw4JtvvqF37968fv2aJUuWEBcXpzTqKb89e/bg7+9P3759sbW1xdnZmYCAAGbOnCmVuXjxInfu3OGrr76S9h0+fBgzMzPmzZtHtWrVqFKlCj4+Pqxbtw4dHR2io6Pp3bs3qamp0kiJKVOmALBp0yZq1aolPQtdunTh0aNHSnHZ2dmhra2Np6cnGzduVBopcerUKRo1aoSOjg6WlpYEBgby+vVrAIyMjDhy5Aj+/v44ODhQr149li9fzsWLFxVG1Tk7O2NhYcGPP/5YaBv98MMPbN68ma1btzJ+/Hhq166NlZUVrVu35tixY3h6egKQk5PDtGnTqFixIlpaWlSvXp2DBw8WWm9BironyBvBFxISgqWlJVpaWtja2hIaGkpSUpIUR+nSpZHJZPTq1avIa8mf3Ro1ahAUFMTDhw+5ceOGyrGsXLlS+hmVL1+eDh06SMcKmrpUvXp16Rl4n7W1NQDu7u7IZDKlEZKFef+7aWZmhqamZqGjRQsTFBTE2LFjqVevnsrnCIIgCIIgCF8m0ZFTQsePHyc9PR0vLy+6detGZGSkwotDUUaMGMGrV68K7BTIz9ramkGDBjFu3DhycnKUjufk5BAZGUnXrl0xMzNTOKajo8OQIUM4dOhQoX/Bv3r1KqdPn0ZTU1PpWM2aNbGysmLnzp1A3oiQEydO0L179wLrCg0NpVu3bhgZGeHr61uiqQIF2bx5M15eXri7uysdK1WqFHp6ejx79oxDhw4xZMgQhQ4LADMzM7p27cq2bdvIzc0lLi6O69evExwcrNR54ebmhpeXF1u3bi0wlpycHHbu3Mnz588LbKv36ejoMGjQIGJiYpRe+t83Z84cdu7cyYULFwotI+807NatG46Ojtja2rJjx45i4/iUVq5cSUREBDt37qRixYpFln316hXbt2+nW7duNG/enNTUVGnkTX4zZ85EQ0ODbt26MX78eJYtW1ZkAmEzMzNiY2O5f/9+oWVOnjyJvb29QvJfMzMzkpOTOXHiRIHnNGjQgMWLF2NoaCiNlBg1ahSQN+1p+vTpxMfHs3v3bpKSkhQ6He7du0eHDh1o06YN8fHxDBw4UKlj7s6dO/j4+NC+fXsuX77Mtm3bOHXqFMOGDSv0PuSdSu+P1KhTp06BbSm3efNmHBwcaN26tdIxmUyGkZERAEuWLGHhwoUsWLCAy5cv4+3tzddff63S6DBV76lHjx5s3bqVpUuXkpCQwJo1a9DX18fS0lL6vSIfabNkyRKVrpuamkpkZCSA9F0sLpYLFy4QGBjItGnTuHnzJgcPHsTDw0Ol6xXk3LlzAERFRZGcnMyuXbs+uK6/Q2ZmJi9fvlTYBEEQBEEQhC/Df6IjZ+/evejr6ytsvr6+BZatWLGiQjlnZ2eF46GhoXTu3Bl1dXWqVauGjY1NgblhCuLo6AhQYB6U902YMIF79+6xefNmpWOPHz/mxYsXODk5FXiuk5MTubm53L59W9onbwNtbW1cXFx49OgRo0ePLvD8Pn36SKMpwsPDadmyJaampkrlEhMTiY2NlabxdOvWjbCwMD5mRfvExESpnYoqk5ubW+T9P3/+nMePH3Pr1i1pX2Fl5WXkQkJC0NfXR0tLiw4dOlC6dGn69eunUvyq/oxr1KiBv79/kVPooqKiSE9Px9vbG8hr38JGgBWluGe6MCdOnCAoKIgVK1Yo5J0pTGRkJHZ2djg7O6Ourk7nzp0LjFdHR4clS5awe/dumjRpQrdu3Yqsd/LkyRgbG2NlZYWDgwO9evXihx9+UOjkvH//PhYWFgrndezYkYCAABo3boy5uTlt27Zl+fLl0gutpqYmRkZGyGQyhdFNkPcd8PX1xcbGhnr16rF06VIOHDggTedas2YNDg4OzJ8/HwcHBzp37qw0umT27Nl07dqVoKAg7OzsaNCgAUuXLiUiIkIpPxXAmzdvCAkJISAgQGm5QQsLiyI7shITE3FwcCiyHQEWLFhASEgInTt3xsHBgblz51K9enWVk+oWd0+3bt3ihx9+YMOGDbRt2xYbGxuaNWtGp06dUFdXl5Zml4+0kXcwFUb+7BobG7Nlyxa+/vpr6TtWXCwPHjxAT0+PVq1aUblyZdzd3QkMDFTpPgsi/x1oYmKCmZmZysvMX7lyReH7V6dOnQ+OoSRmz56NkZGRtFlaWv4t1xUEQRAEQRD+ev+JjhxPT0/i4uIUtsKmcpw8eVKhXP7pTS9evGDXrl0KL54lebmWd3DIZLJiy5qamjJq1CgmTZpUaE6IknSYyNvg7Nmz9OzZk969e9O+ffsCy3br1o0zZ85w9+5dwsPD6dOnT4HlNmzYgLe3N2XLlgWgZcuWpKamcuzYMZXjel9J7umvKjt69Gji4uI4duwYdevWZdGiRdja2pboOqr8jGfMmMHJkycLzV+yYcMGOnXqhIaGBgABAQHExMRw584dFe8kT1HPdGEePHhAhw4dGDBggMqdWBs2bFD6bmzfvp1Xr14plQ0NDUVXV5crV66QmppaZL3m5uacOXOGK1euMHz4cLKzs+nZsyc+Pj5SZ05GRobStC91dXXCwsL47bffmDdvHhUqVGDWrFlSzpKiXLx4ET8/PypVqoSBgQGNGzeW2gXyRpTUrl1b4Zz3X9Dj4+MJDw9XeIn39vYmJyeHe/fuKZTNysrC39+f3NxcVq1apRSPjo5OkQmLVXm+X758yR9//EHDhg0V9jds2JCEhIRiz1flnuLi4lBXV5fa62OdPHmSixcvEh4ejr29PatXr1Y5lubNm1O5cmVsbGzo3r07mzdv/ixJnx0cHBS+f/JRSX+1cePGkZqaKm0fO+1VEARBEARB+Of4T3Tk6OnpYWtrq7AVNpXD2tpaoVzlypWlY1u2bOHNmzfUrVsXDQ0NNDQ0CAkJ4dSpU0qjOgoif1mS51ooTnBwMBkZGaxcuVJhv6mpKcbGxoW+fCUkJCCTyRQ6H+Rt4ObmxoYNGzh79myhHVAmJia0atWKvn378ubNmwJHL717946NGzeyb98+qS10dXV59uzZRyU9tre3V8iBURBbW1tkMlmR91+6dGlMTU2xt7eX9hVWVl5GrmzZstja2tKoUSO2b99OYGAg169fVyl++XWsrKyKLVulShX69+/P2LFjlV7Enz17xo8//sjKlSul9q1QoQLZ2dklbt+inumCZGRk0LZtW5ydnVUeqXH9+nViY2MZM2aMFG+9evWkZNj5bdu2jb1793L69GkMDAwYMWKESteoVq0aQ4YM4fvvv+fIkSMcOXJEWkWrbNmyheaeqlChAt27d2f58uVcu3aNN2/eKHQIvO/169d4e3tjaGjI5s2bOX/+vJSfpiSJdtPS0hg4cKDCS3x8fDyJiYlUqVJFKifvxLl//z5HjhxRGo0Dec9DQaPi5FT53nwKxd3T+1MdP5a1tTUODg707NmTfv36KSTxLi4WAwMDfv31V7Zu3Yq5uTmTJk3Czc1NymGkpqam9L3Lvyrep6Kpqanw/fu7RsZoaWlhaGiosAmCIAiCIAhfhv9ER86nEhoaysiRI5VeHBo1aqTSy7U8H4eXl5dK19PX12fixInMnDlTYVSDmpoa/v7+bNmyRWmJZ3nHj7e3d6FD/9XU1Bg/fjwTJkxQSGqcX58+fYiOjqZHjx6oq6srHd+/fz+vXr3i0qVLCu2xdetWdu3a9cFL43bp0oWoqCguXbqkdCwrK4vXr19jYmJC8+bNWblypVL8KSkpbN68mU6dOiGTyahevTqOjo4sWrRIKd9QfHw8UVFRBAQEFBqPpaUlnTp1Yty4ccXGnpGRwdq1a/Hw8CjypTu/SZMmcevWLaXOjs2bN1OxYkXi4+MV2nfhwoWEh4f/pasu9evXj2fPnrF9+3ZpNFBxQkND8fDwUIo3ODhYocPwzz//ZOjQocyYMQM3NzfCw8OJiIgocFWwolStWhVAyk/l7u7OjRs3ih2ZUrp0aczNzaXzClrB6saNGzx9+pQ5c+bQqFEjHB0dlXIeOTg4KOU3ki8PL1ejRg2uX7+u1Ilsa2sr5XmRd+IkJiYSFRWFiYlJgXFfvXq1wLxRcl26dOHWrVv89NNPSsdyc3NJTU3F0NAQCwsLYmJiFI7HxMRI7Vmc4u7JxcWFnJycQpepl9/3hzy/Q4cO5erVq1Knmirtq6GhgZeXF/PmzePy5cskJSVJIwZNTU0VRma9fPlSaaTUp4pdEARBEARBED4l0ZHznkePHpGSkqKwZWVlERcXx6+//kq/fv2oVq2awhYQEMDGjRvJzs6W6nnx4gUpKSnSX9k7dOjAli1bWLVqVYmWnB0wYABGRkZs2bJFYf+sWbMwMzOjefPmHDhwgIcPH3LixAm8vb3JyspixYoVRdbbsWNH1NXVCy3n4+PD48ePmTZtWoHHQ0ND+eqrr3Bzc1NoC39/f4yNjRVy+2RkZChNbStselBQUBANGzakWbNmrFixgvj4eO7evcsPP/xAvXr1pKSsy5cvJzMzE29vb2m1rIMHD9K8eXMqVKggrWgkk8kIDQ3l+vXrtG/fnnPnzvHgwQO2b9+On58f9evXJygoqMi2Gj58OD///LPSi7v8WUlMTCQyMpKGDRvy5MmTAqfGFKZ8+fIEBwezdOlSpfbt0KGD0rPWt29fnjx5UqKVhgp7pgsyf/58tm/fzurVq8nOzlY6r6COv6ysLDZt2kRAQIBSvP369ePs2bPSsvEDBgzAyclJavM6deowevRoBgwYUOgUq8GDBzN9+nRiYmK4f/8+sbGx9OjRA1NTU+rXrw/kTR1MS0uTrgN5eWwGDx7M4cOHuXPnDteuXSMkJIRr167h5+cH5I2cSktL4+jRozx58oT09HQqVaqEpqYmy5Yt4+7du+zZs4fp06crxDRw4EBu3LhBSEiIlBdGnuhbPq0uJCREWjo+Li6OxMREfvrpJykZb1ZWFh06dODChQts3ryZd+/eSe2cf+RPeno6Fy9epEWLFoX+jP39/enUqRMBAQHMmjWLCxcucP/+ffbu3YuXlxfHjx8H8qYNzp07l23btnHz5k3Gjh1LXFwcw4cPL7Tu/Iq7JysrK3r27EmfPn3YvXs39+7dIzo6mh9++AGAypUrI5PJ2Lt3L48fP1ZYQr44urq69O/fn8mTJ5Obm1tsLHv37mXp0qXExcVx//59IiIiyMnJkXIJNW3alE2bNnHy5EmuXLlCz549C+y0litXrhw6OjocPHiQP//8s9gpgZ9aSkoKcXFxUu6zK1euEBcXV+yy9IIgCIIgCMKXR3TkvMfBwQFzc3OF7eLFi4SGhlK1atUCE/G2bduWR48eKeQe6d27N+bm5jg6OjJ48GD09fU5d+4cXbp0KVE8pUqVYvr06UrJUU1MTIiNjcXT05OBAwdSpUoV/P39qVKlCufPn8fGxqbIejU0NBg2bBjz5s0rcNUtmUxG2bJlC1yt6c8//2Tfvn0F5thRU1Ojbdu2CqMwbt26hbu7u8I2cODAAuPS0tLiyJEjjBkzhjVr1lCvXj1q167N0qVLCQwMpFq1agDY2dlx4cIFbGxspPseMGAAnp6enDlzRmE0UoMGDYiNjUVdXR1fX19sbW0ZN24cPXv25MiRI2hpaRXZVlWrVqVFixZMmjRJYb+DgwMWFhbUrFmTOXPm4OXlxdWrV1Ue3SA3atQoKcku5OVniY+PL7B9jYyMaNasWYmSHhf2TBdk5cqVZGVl4ePjo3SOubk527ZtUzpnz549PH36lLZt2yodc3JywsnJidDQUCIiIoiKiiIsLExhBbGpU6dibGxc6BQrLy8vYmNj6dixI/b29rRv3x5tbW2OHj0qjWAxMTGhbdu2Ch2IderUIS0tjUGDBuHs7Ezjxo2JjY1l9+7dUg6XBg0aMGjQIDp16oSpqSnz5s3D1NSU8PBwtm/fTtWqVZkzZw4LFixQiMna2podO3awa9cuXF1dWbVqlbRqlfx5cnV15ZdffuHWrVs0atQId3d3Jk2aJCVl/v3339mzZw+//fYb1atXV2jn06dPS9f66aefqFSpEo0aNSqwfSDv+7plyxa+++476f5cXV2ZMmUKrVu3lhJmBwYGEhwczMiRI3FxceHgwYPSMuqqKO6eAFatWkWHDh0YMmQIjo6O9O/fX/odU6FCBaZOncrYsWMpX758kSt4FWTYsGEkJCSwffv2YmMxNjZm165dNG3aFCcnJ1avXs3WrVulZN/jxo2jcePGtGrViq+++oo2bdooTHl7n4aGBkuXLmXNmjVYWFgUuELYX2n16tW4u7vTv39/ADw8PHB3d2fPnj1/axyCIAiCIAjC5yfL/ZglhgRBEP4hLl++TPPmzblz545Cx9jfZebMmaxevfqTJ5WtV68egYGBJe4EFoT8Xr58iZGRkTTNThAEQRAEQfhnKcm/18SIHEEQvgiurq7MnTu3yDwnn9LKlSs5f/48d+/eZdOmTcyfP5+ePXt+0ms8efKEdu3aFZnHSRAEQRAEQRCE/xYxIkcQBOEDjBgxgm3btvHs2TMqVapE9+7dGTdunMoJooV/v6JGfh04cKDI6XB/NzEiRxAEQRAE4Z+tJP9eEx05giAIgvAB5ImHC1KhQoVPvhz7x5D/w8Dtm9Woa/1z4hIKd3F+j88dgiAIgiAIfyMxtUoQBEEosSlTplC9enXpc69evWjTps1ni+dzCg8PL3aFwYKWPpdvxXXiyGQydu/eDUBSUhIymYy4uDgAoqOjkclkvHjx4uNvRBAEQRAEQfjiiI4c4V8tJSWFb775BhsbG7S0tLC0tMTPz4+jR49KZfK/MOX3/ktqkyZNkMlkyGQytLW1sbe3Z/bs2eQftCZ/4Spoi42NLTTOX375haZNm1KmTBl0dXWxs7OjZ8+evH37ll69ehVap0wmw8rKSqpn9uzZqKurM3/+fKVrhIeHI5PJ8PHxUdj/4sULZDIZ0dHRCm0i3/T09LCzs6NXr15Kq1nJXyjlm46ODs7OzqxduxaA6dOnY25urrQEcnx8PFpaWuzdu7fA9sh/z5qamtja2jJt2jSys7OlMu/evWPRokW4uLigra1N6dKl8fX1JSYmRqGud+/eMWfOHBwdHdHR0aFMmTLUrVuX9evXK1xP/rMuqq1lMhlTpkxRerGW27hxI7Vr10ZXVxcDAwMaN26sdI/yNnN2dubdu3cKx4yNjaVlyt9X1LMlk8mwtrYu8Dw5Vb4Ln5uqHUP5n49SpUphbW3NmDFjlFbv+zu938mlCisrK6WfY8WKFQFITk7G19dXpXqio6Np3bo15ubm6OnpUb16dYUV2gRBEARBEIT/FtGRI/xrJSUlUbNmTY4dO8b8+fO5cuUKBw8exNPTk6FDh35Qnf379yc5OZmbN28ybtw4Jk2axOrVq5XKRUVFkZycrLDVrFmzwDqvX7+Oj48PtWrV4sSJE1y5coVly5ahqanJu3fvWLJkiUI9AGFhYdLn8+fPS3Vt2LCBMWPGsGHDhgKvpaGhQVRUFMePHy/2XuXXuHbtGitWrCAtLY26desSERGhVPbmzZskJydz/fp1Bg4cyODBgzl69Cjjxo3D0tJSob2zsrLo2bMn3bp1o1WrVoVe38fHh+TkZBITExk5ciRTpkyROqhyc3Pp3Lkz06ZNY/jw4SQkJBAdHY2lpSVNmjRR6JibOnUqixYtYvr06Vy/fp3jx48zYMCAQkcz5G/rxYsXY2hoqLBv1KhRBZ43atQoBg4cSKdOnbh8+TLnzp3jf//7H61bt2b58uVK5e/evVtgWxbG0tJS6ZlKTk7m559/Rl1dvchn+q/4Lnxu8ufj7t27LFq0iDVr1jB58uTPHVaJTZs2TeHneenSJQDMzMykpeqLc/r0aVxdXdm5cyeXL1+md+/e9OjRo9COUkEQBEEQBOHLJjpyhH+tIUOGIJPJOHfuHO3bt8fe3h5nZ2eCg4OLHB1TFF1dXczMzKhcuTK9e/fG1dWVI0eOKJUzMTHBzMxMYStVqlSBdR4+fBgzMzPmzZtHtWrVqFKlCj4+Pqxbtw4dHR2MjIwU6oG8kRvyz6ampkDeqJ6MjAymTZvGy5cvOX36tNK19PT06NOnD2PHji32XuXXsLKyokWLFuzYsYOuXbsybNgwnj9/rlC2XLlymJmZYW1tTWBgINbW1vz6669oaGgQERHB7t272bFjB5C3DPeLFy9YtGhRkdfX0tKS2nrw4MF4eXmxZ88eAH744Qd27NhBREQE/fr1w9raGjc3N9auXcvXX39Nv379eP36NQB79uxhyJAhdOzYUSrXt2/fQjtk8re1kZERMplMYV9BCWxjY2NZuHAh8+fPZ9SoUdja2uLk5MTMmTMJCgoiODhYadnxb775hsmTJ5OZmVnszwJAXV1d6ZmSyWQMHjyYgICAQu8HVP8uPHjwgNatW6Ovr4+hoSH+/v78+eefKsUHkJOTw+zZs7G2tkZHRwc3Nzfp5y537do1WrVqhaGhIQYGBjRq1Ig7d+4wZcoUNm7cyE8//SSNTsk/Sux98ufD0tKSNm3a4OXlpfBdLC6W58+f07VrV0xNTdHR0cHOzo6wsDCg4KlLcXFxyGQykpKSlGIJDw9n6tSpxMfHS7EXNrLqfQYGBgo/U/n3ubCRggUZP34806dPp0GDBlSpUoXhw4fj4+PDrl27VDpfEARBEARB+LKIjhzhX+nZs2ccPHiQoUOHoqenp3S8uNwWxcnNzeXkyZPcuHEDTU3Nj6rLzMyM5ORkTpw48VH1hIaGEhAQQKlSpQgICCA0NLTAclOmTOHKlStKL9iqGDFiBK9evSqw8wry2uXgwYM8ePCAunXrAuDo6Mjs2bMZPHgwhw4dYvbs2YSFhZV4ZRwdHR3evn0LwJYtW7C3t8fPz0+p3MiRI3n69KkUo5mZGceOHePx48clul5JbN26FX19fQYOHFhgPFlZWezcuVNhf1BQENnZ2SxbtuyDrpmVlUX79u0xMzNj3bp1hZZT9buQk5ND69atefbsGb/88gtHjhzh7t27dOrUSeWYZs+eTUREBKtXr+batWuMGDGCbt268csvvwDw+++/4+HhgZaWFseOHePixYv06dOH7OxsRo0ahb+/vzTSJjk5mQYNGqh03atXr3L69GmF72JxsUycOJHr169z4MABEhISWLVqFWXLllX5XvPr1KkTI0eOxNnZWYq9JO32V0hNTaVMmTKFHs/MzOTly5cKmyAIgiAIgvBlEOvkCv9Kt2/fJjc3F0dHx09a78qVK1m/fj1v374lKysLbW1tAgMDlco1aNAANTXFftC0tLQC6+zYsSOHDh2icePGmJmZUa9ePZo1a0aPHj1U7ux4+fIlO3bs4MyZMwB069aNRo0asWTJEqURJBYWFgwfPpxvv/22xIlq5e35/qgEeV6PzMxMcnJymDZtGh4eHtLx4cOH89NPP9GyZUu++eYbPD09Vb5mbm4uR48e5dChQ3zzzTcA3Lp1CycnpwLLy/ffunULgO+++44OHTpgZmaGs7MzDRo0oHXr1irnH1HFrVu3qFKlSoGdehYWFhgaGkrxyOnq6jJ58mTGjx9P//79MTIyKtE1hw0bxp07dzh//jza2tqFllP1u3D06FGuXLnCvXv3sLS0BCAiIgJnZ2fOnz9P7dq1izw/MzOTWbNmERUVRf369QGwsbHh1KlTrFmzhsaNG7NixQqMjIyIjIyURqjZ29tLdejo6JCZmSmNPCvK3r170dfXJzs7m8zMTNTU1KQpbKrE8uDBA9zd3alVqxaAQq6pktLR0UFfXx8NDQ2VYs8vJCSECRMmSJ9nzZpV4O+Ukvjhhx84f/48a9asKbTM7NmzmTp16kddRxAEQRAEQfhnEiNyhH+l/AmIP6WuXbsSFxdHTEwMvr6+fPvttwWOGti2bRtxcXEKW2HU1dUJCwvjt99+Y968eVSoUIFZs2ZJf91XxdatW6lSpQpubm4AVK9encqVK7Nt27YCy4eEhPD48eNCc+kURt6uMplMYf/Jkyel+1y/fj2zZs1i1apV0nGZTMa3335LTk6OwktrUeQv6tra2vj6+tKpUyemTJmiFEtxqlatytWrV4mNjaVPnz48evQIPz8/+vXrp9L5qvqQZ65v376YmJgwd+7cEp23evVqwsPD2blzp9SJ9rFxJSQkYGlpKXXiQF7bGRsbk5CQUOz5t2/fJj09nebNm6Ovry9tERER3LlzB8ibntSoUaNCpxmWhKenJ3FxcZw9e5aePXvSu3dv2rdvr3IsgwcPJjIykurVqzNmzJgCpyL+HUaPHq3we6JHj49bUvr48eP07t2bdevW4ezsXGi5cePGkZqaKm3vT/0TBEEQBEEQ/r3EiBzhX8nOzg6ZTMaNGzeKLWtgYEBqaqrS/hcvXiiNkjAyMsLW1hbI+6u3ra0t9erVw8vLS6GcpaWlVE5VFSpUoHv37nTv3p3p06djb2/P6tWrVfqreWhoKNeuXUND4/+/sjk5OWzYsIG+ffsqlTc2NmbcuHFMnTq1yITD75O/0L+/QpK1tbU0RcfZ2ZmzZ88yc+ZMBg8eLJWRx5Y/xqJ4enqyatUqNDU1sbCwUDjP3t6+0M4F+f78Iz3U1NSoXbs2tWvXJigoiO+//57u3bvz7bffFrvakyrs7e05deoUb9++VRqV88cff/Dy5UuFeOQ0NDSYOXMmvXr1YtiwYSpd69SpUwQGBrJy5UqVph6V5LvwMeQjzvbt20eFChUUjsmT9ha35HZJ6OnpSd+xDRs24ObmRmhoKH379lUpFl9fX+7fv8/+/fs5cuQIzZo1Y+jQoSxYsEAaTZe/EywrK+uTxZ5f2bJlS/y7ojC//PILfn5+LFq0qNgOIS0tLZWTKQuCIAiCIAj/LmJEjvCvVKZMGby9vVmxYoWU9Da//ElMHRwclJbVfvfuHfHx8QW+fMvp6+szfPhwRo0a9clHAJUuXRpzc/MCY3/flStXuHDhAtHR0Qp/2Y+OjubMmTOFvsB/8803qKmpsWTJEpXjkq/i9H7H1fvU1dXJyMhQud6CyF/UK1WqpNT507lzZxITE/n555+Vzlu4cCEmJiY0b9680LqrVq0KoFL7qqJz586kpaUVOJVlwYIFlCpVShot8r6OHTvi7OysUofdw4cPad++PQMGDFB5RJGq3wUnJycePnyoMDLj+vXrvHjxQmqvolStWhUtLS0ePHiAra2twiYf5ePq6srJkycL7RSRr9RWUmpqaowfP54JEyaQkZGhUiwApqam9OzZk++//57Fixezdu1aaT+gMCKuqFF1HxP7pxIdHc1XX33F3LlzGTBgwGeLQxAEQRAEQfj8xIgc4V9rxYoVNGzYkDp16jBt2jRcXV3Jzs7myJEjrFq1Shq5ERwcTN++fXF0dKR58+a8fv2aZcuW8fz582JflgcOHMj06dPZuXMnHTp0kPY/ffqUlJQUhbLGxsYF5jJZs2YNcXFxtG3blipVqvDmzRsiIiK4du2aSolwQ0NDqVOnjkJOGrnatWsTGhoqLdudn7a2NlOnTi10+ekXL16QkpJCZmYmt27dYs2aNezevZuIiAilZNGPHj3izZs3ZGZmcu7cOTZt2qTQHp9a586d2b59Oz179mT+/Pk0a9aMly9fsmLFCvbs2cP27dulxL4dOnSgYcOGNGjQADMzM+7du8e4ceOwt7f/ZDmU6tevz/Dhwxk9ejRv376lTZs2ZGVl8f3337NkyRIWL16s0IHwvjlz5uDt7V3kNd68eUPbtm2pUKECY8eOVXq+gELzs6jyXfDy8sLFxYWuXbuyePFisrOzGTJkCI0bN5byyBTFwMCAUaNGMWLECHJycvjf//5HamoqMTExGBoa0rNnT4YNG8ayZcvo3Lkz48aNw8jIiNjYWOrUqYODgwNWVlYcOnSImzdvYmJigpGRkcrTsDp27Mjo0aNZsWIFo0aNKjaWSZMmUbNmTZydncnMzGTv3r1SfiV5h8+UKVOYOXMmt27dYuHChUVe38rKinv37hEXF0fFihUxMDD420a8HD9+nFatWjF8+HDat28vPRuamppFJjwWBEEQBEEQvkxiRI7wr2VjY8Ovv/6Kp6cnI0eOpFq1ajRv3pyjR48q5G8JCAhg/fr1bNiwgZo1a+Lj40NKSgonTpygfPnyRV6jTJky9OjRgylTppCTkyPt9/LywtzcXGErbCnhOnXqkJaWxqBBg3B2dqZx48bExsaye/duGjduXOT13759y/fff1/oaI/27dsTERFR6AiInj17YmNjU+Cx3r17Y25ujqOjI4MHD0ZfX59z587RpUsXpbIODg6Ym5tja2tLSEgIAwcO/ODVmFQhk8n44YcfGD9+PIsWLcLBwYFGjRpx//59oqOjFZI4e3t78/PPP+Pn54e9vT09e/bE0dGRw4cPqzzNSxWLFy9m5cqVbN26lWrVqlGrVi1OnDjB7t27pSTNhWnatClNmzYlOzu70DJnz57l4sWLXLp0CUtLS6Xny9zcvNBzVfkuyGQyfvrpJ0qXLo2HhwdeXl7Y2NgUmmepINOnT2fixInMnj0bJycnfHx82LdvnzR9zcTEhGPHjpGWlkbjxo2pWbMm69atkzpr+vfvj4ODA7Vq1cLU1JSYmBiVr62hocGwYcOYN28er1+/LjYWTU1Nxo0bh6urKx4eHqirqxMZGQlAqVKl2Lp1Kzdu3MDV1ZW5c+cyY8aMIq/fvn17fHx88PT0xNTUlK1bt6oc+8fauHEj6enpzJ49W+F5aNeu3d8WgyAIgiAIgvDPIcv9q7LGCoIgCILwj/Dy5UuMjIxITU1VebU8QRAEQRAE4e9Tkn+viRE5giAIgiAIgiAIgiAI/xKiI0cQBEEQSmjz5s0KS5/n34paFlwQBEEQBEEQPpZIdiwIgiAIJfT1119Tt27dAo+pmsD5c/CYsBV1rU+3TLzw17g4v+jl5QVBEARB+G8THTmCIAiCUEIGBgYYGBh87jAEQRAEQRCE/yAxtUoQhP+0KVOmUL169b/lWlZWVixevPhvuZYgCIIgCIIgCF8m0ZEjCMI/zurVqzEwMFBYrjstLY1SpUrRpEkThbLR0dHIZDLu3LnzN0f5z9SrVy9kMhkymYxSpUpRvnx5mjdvzoYNG8jJyfnc4TFlyhQcHR3R09OjdOnSeHl5cfbsWYUyMpmM3bt3K53bq1cvhaXnT5w4gZ+fHxYWFgWe8/r1a6pUqUJwcLDC/qSkJAwNDVm3bh3w/89QQVtKSgoAu3btolatWhgbG6Onp0f16tXZtGmTUnzvn+/j46NQxsrKSqnMnDlzimyz5ORkunTpgr29PWpqagQFBRVZXhAEQRAEQfiyiY4cQRD+cTw9PUlLS+PChQvSvpMnT2JmZsbZs2d58+aNtP/48eNUqlSJKlWqlOgaubm5Ch1Fn8q7d+/+sg6Tt2/fqlTOx8eH5ORkkpKSOHDgAJ6engwfPpxWrVr9JfdcEvb29ixfvpwrV65w6tQprKysaNGiBY8fPy5xXa9fv8bNzY0VK1YUeFxPT4+wsDCWLVvGyZMngbyfe+/evWnYsCH9+/dXKH/z5k2Sk5MVtnLlygFQpkwZvv32W86cOcPly5fp3bs3vXv35tChQwp1yNtevm3dulUprmnTpimU+eabb4q8z8zMTExNTZkwYQJubm4qt48gCIIgCILwZRIdOYIg/OM4ODhgbm5OdHS0tC86OprWrVtjbW1NbGyswn5PT08yMzMJDAykXLlyaGtr87///Y/z588rlJPJZBw4cICaNWuipaXFqVOnlK59584dbGxsGDZsGLm5uTx//pwePXpQunRpdHV18fX1JTExUSofHh6OsbExe/bsoWrVqmhpafHgwQMePXqEn58fOjo6WFtbs3nzZqVrvXjxgn79+mFqaoqhoSFNmzYlPj5eOi6f9rV+/Xqsra3R1tZWqf20tLQwMzOjQoUK1KhRg/Hjx/PTTz9x4MABwsPDpXLfffcdLi4u6OnpYWlpyZAhQ0hLS1O6t7179+Lg4ICuri4dOnQgPT2djRs3YmVlRenSpQkMDOTdu3cqxdalSxe8vLywsbHB2dmZ7777jpcvX3L58mWVzs/P19eXGTNm0LZt20LLeHh48M0339C7d29ev37NkiVLiIuLY/369Uply5Urh5mZmcKmppb3v8kmTZrQtm1bnJycqFKlCsOHD8fV1VXpGZK3vXwrXbq00nUMDAwUyujp6RV5n1ZWVixZsoQePXpgZGSkStOQmZnJy5cvFTZBEARBEAThyyA6cgRB+Efy9PTk+PHj0ufjx4/TpEkTGjduLO3PyMjg7NmzeHp6MmbMGHbu3MnGjRv59ddfsbW1xdvbm2fPninUO3bsWObMmUNCQgKurq4Kxy5fvsz//vc/unTpwvLly5HJZPTq1YsLFy6wZ88ezpw5Q25uLi1btiQrK0s6Lz09nblz57J+/XquXbtGuXLl6NWrFw8fPuT48ePs2LGDlStX8ujRI4XrdezYkUePHnHgwAEuXrxIjRo1aNasmULMt2/fZufOnezatYu4uLgPbs+mTZvi5ubGrl27pH1qamosXbqUa9eusXHjRo4dO8aYMWMUzktPT2fp0qVERkZy8OBBoqOjadu2Lfv372f//v1s2rSJNWvWsGPHjhLH9PbtW9auXYuRkdFfOtJk5syZaGho0K1bN8aPH8+yZcuoUKHCB9eXm5vL0aNHuXnzJh4eHgrHoqOjKVeuHA4ODgwePJinT58qnT9nzhxMTExwd3dn/vz5f8koqdmzZ2NkZCRtlpaWn/wagiAIgiAIwuchVq0SBOEfydPTk6CgILKzs8nIyODSpUs0btyYrKwsVq9eDcCZM2fIzMykSZMm9O/fn/DwcHx9fQFYt24dR44cITQ0lNGjR0v1Tps2jebNmytd7/Tp07Rq1Ypvv/2WkSNHApCYmMiePXuIiYmhQYMGAGzevBlLS0t2795Nx44dAcjKymLlypVSZ8StW7c4cOAA586do3bt2gCEhobi5OQkXe/UqVOcO3eOR48eoaWlBcCCBQvYvXs3O3bsYMCAAUBeZ0dERASmpqYf3aaOjo4KI1/y51qxsrJixowZDBo0iJUrV0r7s7KyWLVqlTR1rUOHDmzatIk///wTfX19qlatKnW6derUSaU49u7dS+fOnUlPT8fc3JwjR45QtmxZhTIBAQGoq6sr7MvMzOSrr74q6W2jo6PDkiVL8PHxwdfXl27duhVYrmLFigqfK1euzLVr16TPqampVKhQgczMTNTV1Vm5cqXCs+Tj40O7du2wtrbmzp07jB8/Hl9fX86cOSPdS2BgIDVq1KBMmTKcPn2acePGkZyczHfffVfi+yrKuHHjFHIDvXz5UnTmCIIgCIIgfCFER44gCP9ITZo04fXr15w/f57nz59jb2+PqakpjRs3pnfv3rx584bo6GhsbGxITU0lKyuLhg0bSueXKlWKOnXqkJCQoFBvrVq1lK714MEDmjdvzsyZMxU6NxISEtDQ0KBu3brSPhMTExwcHBTq1dTUVBjdIz+vZs2a0j5HR0eMjY2lz/Hx8aSlpWFiYqIQS0ZGhkLi5sqVK3+SThzIG0kik8mkz1FRUcyePZsbN27w8uVLsrOzefPmDenp6ejq6gKgq6urkH+ofPnyWFlZoa+vr7Dv/dFGRfH09CQuLo4nT56wbt06/P39OXv2rJSPBmDRokV4eXkpnBcSEqLyFK73hYaGoqury5UrV0hNTS1witLJkycVlhQvVaqUwnEDAwPi4uJIS0vj6NGjBAcHY2NjIyXg7ty5s1TWxcUFV1dXqlSpQnR0NM2aNQNQ6FxxdXVFU1OTgQMHMnv2bLS0tBTatVu3blKnZUlpaWlJHYSCIAiCIAjCl0V05AiC8I9ka2tLxYoVOX78OM+fP6dx48YAWFhYYGlpyenTpzl+/DhNmzYtUb0F5SMxNTXFwsKCrVu30qdPHwwNDUtUp46OjkIHiSrS0tKU8gDJ5e/wKS5/SkkkJCRgbW0N5K3c1KpVKwYPHszMmTMpU6YMp06dom/fvrx9+1bqyHm/M0O+Gtb7+0qS4FlPTw9bW1tsbW2pV68ednZ2hIaGMm7cOKmMmZkZtra2CucZGBjw4sWLktwyANu2bWPv3r2cOXOGgIAARowYwYYNG5TKWVtbK7T9+9TU1KSYqlevTkJCArNnz1ZaSU3OxsaGsmXLcvv2bakj531169YlOzubpKQkHBwcFKbPlfQ5FARBEARBEP4bRI4cQRD+sTw9PYmOjiY6OlrhZdnDw0OauuTp6UmVKlXQ1NQkJiZGKpOVlcX58+epWrVqsdfR0dFh7969aGtr4+3tzatXrwBwcnIiOztbYXnsp0+fcvPmzSLrdXR0JDs7m4sXL0r7bt68qdAJUaNGDVJSUtDQ0JA6NeTb+9OMPoVjx45x5coV2rdvD8DFixfJyclh4cKF1KtXD3t7e/74449Pfl1V5OTkkJmZ+ZfU/eeffzJ06FBmzJiBm5sb4eHhREREcODAgY+uu7i4f/vtN54+fYq5uXmhZeLi4lBTU5NGI+V/DvKPUBIEQRAEQRAEOTEiRxCEfyxPT0+GDh1KVlaWNCIHoHHjxgwbNoy3b9/i6emJnp4egwcPZvTo0ZQpU4ZKlSoxb9480tPT6du3r0rX0tPTY9++ffj6+uLr68vBgwexs7OjdevW9O/fnzVr1mBgYMDYsWOpUKECrVu3LrQuBwcHfHx8GDhwIKtWrUJDQ4OgoCB0dHSkMl5eXtSvX582bdowb948qSNl3759tG3btsApYKrKzMwkJSWFd+/e8eeff3Lw4EFmz55Nq1at6NGjB5DXYZCVlcWyZcvw8/MjJibmg6fxqOr169fMnDmTr7/+GnNzc548ecKKFSv4/fffpXxDJZGWlsbt27elz/fu3SMuLk56BgAGDBiAk5OTNGWuTp06jB49mgEDBnD16lWFKVaPHj1SWNoe8qbSlSpVitmzZ1OrVi2qVKlCZmamlOh51apVUixTp06lffv2mJmZcefOHcaMGSMl3Ya8nE7y5NwGBgacOXOGESNG0K1btwJXt8pPPlInLS2Nx48fExcXh6ampkodlYIgCIIgCMKXRYzIEQThH8vT05OMjAxsbW0pX768tL9x48a8evVKWqYc8lYCat++Pd27d6dGjRrcvn2bQ4cOFfuCnJ++vj4HDhwgNzeXr776itevXxMWFkbNmjVp1aoV9evXJzc3l/379ytNL3pfWFgYFhYWNG7cmHbt2jFgwACFERYymYz9+/fj4eFB7969sbe3p3Pnzty/f1/hXj/EwYMHMTc3x8rKCh8fH44fP87SpUv56aefpKS7bm5ufPfdd8ydO5dq1aqxefNmZs+e/VHXLY66ujo3btygffv22Nvb4+fnx9OnTzl58iTOzs4lru/ChQu4u7vj7u4O5OWfcXd3Z9KkSQBEREQQFRVFWFiYtIw4wNSpUzE2NmbEiBEK9cmfp/ybfFTV69evGTJkCM7OzjRs2JCdO3fy/fff069fP+neLl++zNdff429vT19+/alZs2anDx5UspVo6WlRWRkJI0bN8bZ2ZmZM2cyYsQI1q5dW+y9yu/z4sWLbNmyBXd3d1q2bFniNhMEQRAEQRD+/WS5ubm5nzsIQRAEQRD+Oi9fvsTIyIjU1FSRe0cQBEEQBOEfqCT/XhMjcgRBEARBEARBEARBEP4lREeOIAjCv8SDBw/Q19cvdHvw4MFnje/kyZNFxicIgiAIgiAIwscTyY4FQRD+JSwsLBSWpy7o+OdUq1atIuMTPj+PCVtR19IpvqDw2Vyc3+NzhyAIgiAIwj+cGJEjCMK/ytu3b7G1teX06dOfO5RP5u3bt1hZWXHhwoUiyxW0VHn+TUND9b55mUzG7t27AUhKSkImk310J4yOjk6R8f1TWVlZsXjx4s8dhiAIgiAIgiCo5IvpyOnVqxdt2rRR2h8dHY1MJuPFixcKnwvaUlJSFM797bff0NTUpFq1agVeM/+5enp62NnZ0atXL2mVk6JYWVkhk8mIjY1V2B8UFESTJk0U9j179oygoCAqV66MpqYmFhYW9OnTR2kaRa9evaR4SpUqhbW1NWPGjFFaTlde5v1rZ2ZmYmJigkwmIzo6WinmgQMHoq6uzvbt25WOTZkyherVqxd73/m9ffuWefPm4ebmhq6uLmXLlqVhw4aEhYWRlZUllXv48CF9+vTBwsICTU1NKleuzPDhw3n69KlSndeuXcPf3x9TU1O0tLSwt7dn0qRJpKenK5STt79MJkNXVxcXFxfWr1+vUCb/s6KmpoaRkRHu7u6MGTOG5OTkIu9/ypQpyGQyBg0apFAuLi4OmUxGUlKSUuze3t6oq6tz/vx54P9frovawsPDS/RMyzVp0qTIen/55ZcCz8vP0dERLS0tpWu8fv2aKlWqEBwcrLA/KSkJQ0ND1q1bp9C+8u8mwLp163Bzc0NfXx9jY2Pc3d2VVlJavXo11tbWNGjQQNr3yy+/0LRpU8qUKYOuri52dnb07NmTt2/fAhAeHo6xsXGx9/RX2bVrFy1atJC+X+93mGhqajJq1ChCQkJUqm/nzp00adIEIyMj9PX1cXV1Zdq0aTx79uwviL7kVO0Yev8ZL1OmDI0bN+bkyZN/T6CFyN/JpYrw8HDpHtTV1SldujR169Zl2rRppKam/nWBCoIgCIIgCP9JX0xHTkndvHmT5ORkhS3/0sCQ949zf39/Xr58ydmzZwusJywsjOTkZK5du8aKFStIS0ujbt26REREFBuDtrZ2sS9uz549o169ekRFRbF69Wpu375NZGQkt2/fpnbt2ty9e1ehvI+PD8nJydy9e5dFixaxZs0aJk+erFSvpaUlYWFhCvt+/PHHQvNYpKenExkZyZgxY9iwYUOx91act2/f4u3tzZw5cxgwYACnT5/m3LlzDB06lGXLlnHt2jUA7t69S61atUhMTGTr1q3cvn2b1atXc/ToUerXr6/w4hobG0vdunV5+/Yt+/bt49atW8ycOZPw8HCaN28uvdTLTZs2jeTkZK5evUq3bt3o378/Bw4cUIr15s2b/PHHH5w/f56QkBCioqKoVq0aV65cKfIetbW1CQ0NJTExsdj2ePDgAadPn2bYsGFS+1paWio8nyNHjsTZ2VlhX6dOnRTiLO6Zltu1a5dS2fv371OtWjVq1apF3bp1i4z31KlTZGRk0KFDBzZu3KhwTE9Pj7CwMJYtWya9kOfm5tK7d28aNmxI//79C6xzw4YNBAUFERgYSFxcHDExMYwZM4a0tDSpTG5uLsuXL6dv377SvuvXr+Pj40OtWrU4ceIEV65cYdmyZWhqavLu3bsi7+Pv8vr1a/73v/8xd+7cQst07dqVU6dOSc9+Yb799ls6depE7dq1OXDgAFevXmXhwoXEx8ezadOmTx363yIqKork5GROnDiBhYUFrVq14s8///zcYZWIoaEhycnJ/Pbbb5w+fZoBAwYQERFB9erV+eOPPz53eIIgCIIgCMIX5D/bkVOuXDnMzMwUNjW1/2+O3NxcwsLC6N69O126dCE0NLTAeoyNjTEzM8PKyooWLVqwY8cOunbtyrBhw3j+/HmRMQwYMIDY2Fj2799faJlvv/2WP/74g6ioKHx9falUqRIeHh4cOnSIUqVKMXToUIXyWlpamJmZYWlpSZs2bfDy8uLIkSNK9fbs2ZPIyEgyMjKkfRs2bKBnz54FxrF9+3aqVq3K2LFjOXHiBA8fPizy3oqzePFiTpw4wdGjRxk6dCjVq1fHxsaGLl26cPbsWezs7AAYOnQompqaHD58mMaNG1OpUiV8fX2Jiori999/59tvvwXyfl59+/bFycmJXbt2UadOHSpXrkzHjh35+eefOXPmDIsWLVKIwcDAADMzM2xsbAgJCaFMmTIFtpX8WbG3t6dz587ExMRgamrK4MGDi7xHBwcHPD09pRiLEhYWRqtWrRg8eDBbt24lIyMDdXV1hedTX18fDQ0NhX06Ov+f66K4Zzq/MmXKKJWdPn06T5484ccff0RbW7vIeENDQ+nSpQvdu3cvsGPPw8ODb775ht69e/P69WuWLFlCXFyc0qin/Pbs2YO/vz99+/bF1tYWZ2dnAgICmDlzplTm4sWL3Llzh6+++krad/jwYczMzJg3bx7VqlWjSpUq+Pj4sG7dOnR0dIiOjqZ3796kpqZKoyamTJkCwKZNm6hVq5b0LHTp0oVHjx4pxWVnZ4e2tjaenp5s3LhRaSTRqVOnaNSoETo6OlhaWhIYGMjr16+l4927d2fSpEl4eXkVev+lS5emYcOGREZGFlrm3LlzzJo1i4ULFzJ//nwaNGiAlZUVzZs3Z+fOnQrf31WrVlGlShU0NTVxcHAocSfP1atX8fX1RV9fn/Lly9O9e3eePHkiHc/JyWHevHnY2tqipaVFpUqVpJ+VtbU1AO7u7shkMqVRhu8zMTHBzMyMatWqMX78eKXO8+Ji2bFjBy4uLujo6GBiYoKXl5fU/k2aNCEoKEjhem3atKFXr14FxmJlZQVA27Ztkclk0ufiyGQyzMzMMDc3x8nJib59+3L69GnS0tIYM2aMVO7gwYP873//w9jYGBMTE1q1asWdO3ek4/JRSj/88IP0TNWuXZtbt25x/vx5atWqhb6+Pr6+vjx+/Fil2ARBEARBEIQvy3+2I6c4x48fJz09HS8vL7p160ZkZKTCi1lRRowYwatXrwrsFMjP2tqaQYMGMW7cOHJycpSO5+TkEBkZSdeuXTEzM1M4pqOjw5AhQzh06FCh0ymuXr3K6dOn0dTUVDpWs2ZNrKys2LlzJ5A3IuTEiRN07969wLpCQ0Pp1q0bRkZG+Pr6Eh4eXuS9FWfz5s14eXnh7u6udKxUqVLo6enx7NkzDh06xJAhQxQ6LADMzMzo2rUr27ZtIzc3l7i4OK5fv05wcLBS54WbmxteXl5s3bq1wFhycnLYuXMnz58/L7Ct3qejo8OgQYOIiYlReul/35w5c9i5c2eRuU/knYbdunXD0dERW1tbduzYUWwcn9LKlSuJiIhg586dVKxYsciyr169Yvv27XTr1o3mzZuTmppa4FSYmTNnoqGhQbdu3Rg/fjzLli2jQoUKhdZrZmZGbGws9+/fL7TMyZMnsbe3x8DAQOE8+WiOgjRo0IDFixdLIyaSk5MZNWoUAFlZWUyfPp34+Hh2795NUlKSwgv+vXv36NChA23atCE+Pp6BAwcqdczduXMHHx8f2rdvz+XLl9m2bRunTp1i2LBhhd5HYerUqVPktKLNmzejr6/PkCFDCjwunz72448/Mnz4cEaOHMnVq1cZOHAgvXv35vjx4yrF8eLFC5o2bYq7uzsXLlzg4MGD/Pnnn/j7+0tlxo0bx5w5c5g4cSLXr19ny5YtlC9fHsjrcIL/H2mza9cula6bkZEhjWaUfxeLiyU5OZmAgAD69OlDQkIC0dHRtGvXjtzcXJWu+T751Eb5aEv55w9Rrlw5unbtyp49e6TRYa9fvyY4OJgLFy5w9OhR1NTUaNu2rdL/AyZPnsyECRP49ddf0dDQoEuXLowZM4YlS5Zw8uRJbt++zaRJkwq9dmZmJi9fvlTYBEEQBEEQhC/DF7Vq1d69e5WmBhU2teL9l9XKlSsrTGkIDQ2lc+fOqKurU61aNWxsbNi+fXuhf8XNz9HREaDAPCjvmzBhAmFhYWzevFmpE+Xx48e8ePECJyenAs91cnIiNzeX27dvU6dOHeD/2yA7O5vMzEzU1NRYvnx5gef36dOHDRs20K1bN8LDw2nZsiWmpqZK5RITE4mNjZVexrp160ZwcDATJkxAJpMVe48FSUxMLPav9ImJieTm5hZ5/8+fP+fx48fcunVL2ldY2VOnTinsCwkJYcKECWRmZpKdnU2ZMmXo16+fSvHn/xkXNn0JoEaNGvj7+xMSEsLRo0cLLBMVFUV6ejre3t5AXvuGhoYW2qlWmOKe6cKcOHGCoKAgVq5cqXyFpAUAAE2LSURBVJB3pjCRkZHY2dnh7OwMQOfOnQkNDaVRo0YK5XR0dFiyZAk+Pj74+vrSrVu3IuudPHky7dq1w8rKCnt7e+rXr0/Lli3p0KGD1Dl3//59pZWZOnbsyKFDh2jcuDFmZmbUq1ePZs2a0aNHDwwNDdHU1MTIyEgaMZFfnz59pP+2sbFh6dKl1K5dm7S0NPT19VmzZg0ODg7Mnz8fyBtldfXqVYVRQrNnz6Zr167SqA87OzuWLl1K48aNWbVqVbGjm/KzsLAosiMrMTERGxsbSpUqVWQ9CxYsoFevXlKHT3BwMLGxsSxYsABPT89i41i+fDnu7u7MmjVL2rdhwwYsLS25desW5ubmLFmyhOXLl0ujgKpUqcL//vc/AOn3iHykTXEaNGiAmpoa6enp5ObmUrNmTZo1a6ZSLGlpaWRnZ9OuXTsqV64MgIuLS7HXLIw8dvloy4/l6OjIq1evePr0KeXKlaN9+/YKxzds2ICpqSnXr19XyMc2atQo6XfC8OHDCQgI4OjRozRs2BCAvn37FtmhPnv2bKZOnfrR8QuCIAiCIAj/PF/UiBxPT0/i4uIUtsKmcpw8eVKhXP7pTS9evGDXrl0KL57yl2tVyP8SrEonh6mpKaNGjWLSpElKOVzer08V8jY4e/YsPXv2pHfv3kovDnLdunXjzJkz3L17l/DwcIWX2vw2bNiAt7c3ZcuWBaBly5akpqZy7NgxleN6X0nu6a8qO3r0aOLi4jh27Bh169Zl0aJFKq+sU5Kf8YwZMzh58iSHDx8u8PiGDRvo1KmTtOJQQEAAMTExCtMtVFHUM12YBw8e0KFDBwYMGKByJ5a880+uW7dubN++nVevXimVDQ0NRVdXlytXrhSb9NXc3JwzZ85w5coVhg8fTnZ2Nj179sTHx0carZCRkaHUMaKurk5YWBi//fYb8+bNo0KFCsyaNUvKJ1SUixcv4ufnR6VKlTAwMKBx48ZSu0Be3qHatWsrnCPvNJWLj48nPDwcfX19afP29iYnJ4d79+4Vef336ejoKCXmzk/V5zshIUF64Zdr2LAhCQkJKp0fHx/P8ePHFe5J3nl5584dEhISyMzMlDpbPta2bdu4dOkSO3fuxNbWlvDwcKmzqrhY3NzcaNasGS4uLnTs2JF169YVO6317/T+74rExEQCAgKwsbHB0NBQmrr1fvJ6V1dX6b/lI53yd1CVL1++yBGB48aNIzU1Vdo+djqsIAiCIAiC8M/xRXXk6OnpKS13W9hUDmtra4Vy8r/kAmzZsoU3b95Qt25dNDQ00NDQICQkhFOnTkkjP4oif1mS54koTnBwMBkZGaxcuVJhv6mpKcbGxoW+fCUkJCCTyRQ6H+Rt4ObmxoYNGzh79myhHVDy/Ax9+/blzZs3+Pr6KpV59+4dGzduZN++fVJb6Orq8uzZs49Kemxvb8+NGzeKLGNra4tMJivy/kuXLo2pqSn29vbSvsLKysvIlS1bFltbWxo1asT27dsJDAzk+vXrKsUvv44q+TOqVKlC//79GTt2rNKL+LNnz/jxxx9ZuXKl1L4VKlQgOzu7xO1b1DNdkIyMDNq2bYuzs7PKSy9fv36d2NhYxowZI8Vbr149KRl2ftu2bWPv3r2cPn0aAwMDRowYodI1qlWrxpAhQ/j+++85cuQIR44ckVbRKlu2bKEv6RUqVKB79+4sX76ca9eu8ebNG1avXl3odV6/fo23tzeGhoZs3ryZ8+fP8+OPPwIU2qlakLS0NAYOHKjQiRYfH09iYiJVqlRRuR7Iex4KGhUnZ29vz927dxVWdfsrpKWl4efnp9QxnpiYiIeHh9JUx49laWmJnZ0dbdu2ZdasWbRt25bMzEyVYlFXV+fIkSMcOHCAqlWrsmzZMhwcHKRONDU1NaXv3V/dfvklJCRgaGiIiYkJAH5+fjx79ox169Zx9uxZKRfQ+89c/lFX8k6g9/cVNCVXTktLC0NDQ4VNEARBEARB+DJ8UR05n0poaCgjR45UejFr1KiRSi/X8nwcRSU2zU9fX5+JEycyc+ZMhVENampq+Pv7s2XLFqUlnuUdP97e3pQpU6bAetXU1Bg/fjwTJkxQSGqcX58+fYiOjqZHjx6oq6srHd+/fz+vXr3i0qVLCu2xdetWdu3apZDwtSS6dOlCVFQUly5dUjqWlZXF69evMTExoXnz5qxcuVIp/pSUFDZv3kynTp2QyWRUr14dR0dHFi1apPRyEx8fT1RUFAEBAYXGY2lpSadOnRg3blyxsWdkZLB27Vo8PDyKfOnOb9KkSdy6dUups2Pz5s1UrFiR+Ph4hfZduHAh4eHhf+mqS/369ePZs2ds375dGg1UnNDQUDw8PJTiDQ4OVugw/PPPPxk6dCgzZszAzc2N8PBwIiIiClwVrChVq1YFkPJTubu7c+PGjWJHppQuXRpzc3PpvIJWsLpx4wZPnz5lzpw5NGrUCEdHR6URDg4ODkr5jd7PmVKjRg2uX7+u1Ilsa2urUs6l/K5evVpg3ii5Ll26kJaWptTpKyf/Pjo5ORETE6NwLCYmRmrP4tSoUYNr165hZWWldE96enrY2dmho6NT6HRB+X1/yPPboUMHNDQ0pHssLhbI69Ro2LAhU6dO5dKlS2hqakqdcqampgojs969e8fVq1eLjKFUqVKf5Lv36NEjtmzZQps2bVBTU+Pp06fcvHmTCRMm0KxZM2l6qCAIgiAIgiCUxH+2I+fRo0ekpKQobFlZWcTFxfHrr7/Sr18/qlWrprAFBASwceNGsrOzpXpevHhBSkoK9+/f58iRI3To0IEtW7awatUqKfGoKgYMGICRkRFbtmxR2D9r1izMzMxo3rw5Bw4c4OHDh5w4cQJvb2+ysrJYsWJFkfV27NgRdXX1Qsv5+Pjw+PFjpk2bVuDx0NBQvvrqK9zc3BTawt/fH2NjYzZv3iyVzcjIUPqreWHTg4KCgmjYsCHNmjVjxYoVxMfHc/fuXX744Qfq1asnLdm9fPlyMjMz8fb2llbLOnjwIM2bN6dChQpSrhKZTEZoaCjXr1+nffv2nDt3jgcPHrB9+3b8/PyoX7++0so17xs+fDg///yz0ou7/FlJTEwkMjKShg0b8uTJE1atWlVkffmVL1+e4OBgli5dqtS+HTp0UHrW+vbty5MnTzh48KDK1yjsmS7I/Pnz2b59O6tXryY7O1vpvII6/rKysti0aRMBAQFK8fbr14+zZ89KOXkGDBiAk5OT1OZ16tRh9OjRDBgwoNApVoMHD2b69OnExMRw//59YmNj6dGjB6amptSvXx/ImzqYlpamkPtnzZo1DB48mMOHD3Pnzh2uXbtGSEgI165dw8/PD8gbOZWWlsbRo0d58uQJ6enpVKpUCU1NTZYtW8bdu3fZs2cP06dPV4hp4MCB3Lhxg5CQEG7dusUPP/wg5SWRj5IICQmRlo6XjxT56aefFJIdP3v2TErIDXlTtuLi4pQ6aE+ePEmLFi0KbB+AunXrMmbMGEaOHMmYMWM4c+YM9+/f5+jRo3Ts2FFaCn706NGEh4ezatUqEhMT+e6779i1a5eU5Lk4Q4cO5dmzZwQEBHD+/Hnu3LnDoUOH6N27N+/evUNbW5uQkBDGjBlDREQEd+7cITY2VurMK1euHDo6OlJi4uKm1eUnk8kIDAxkzpw5pKenFxvL2bNnmTVrFhcuXODBgwfs2rWLx48fS/mymjZtyr59+9i3bx83btxg8ODBxXZAW1lZcfToUVJSUlTuaMnNzSUlJYXk5GQSEhLYsGEDDRo0wMjIiDlz5gB5HYwmJiasXbuW27dvc+zYMYKDg1VuG0EQBEEQBEGA/3BHjoODA+bm5grbxYsXCQ0NpWrVqlIOhvzatm3Lo0ePFHKP9O7dG3NzcxwdHRk8eDD6+vqcO3eOLl26lCieUqVKMX36dN68eaOw38TEhNjYWDw9PRk4cCBVqlTB39+fKlWqcP78eWxsbIqsV0NDg2HDhjFv3rwCV92SyWSULVu2wJEDf/75J/v27Sswx458pZX8ozBu3bqFu7u7wjZw4MAC49LS0uLIkSOMGTOGNWvWUK9ePWrXrs3SpUsJDAyUkn7a2dlx4cIFbGxspPseMGAAnp6enDlzRmE0UoMGDYiNjUVdXR1fX19sbW0ZN24cPXv25MiRI2hpaRXZVlWrVqVFixZKK8E4ODhgYWFBzZo1mTNnDl5eXly9elXl0Q1yo0aNUkjGffHiReLj4wtsXyMjI5o1a6ZyXiZ5nAU90wVZuXIlWVlZ+Pj4KJ1jbm7Otm3blM7Zs2cPT58+pW3btkrHnJyccHJyIjQ0lIiICKKioggLC1NYQWzq1KkYGxsXOsXKy8uL2NhYOnbsiL29Pe3bt0dbW5ujR49K01JMTExo27atQgdinTp1SEtLY9CgQTg7O9O4cWNiY2PZvXu3lPOmQYMGDBo0iE6dOmFqasq8efMwNTUlPDyc7du3U7VqVebMmcOCBQsUYrK2tmbHjh3s2rULV1dXVq1aJa1aJX+eXF1d+eWXX7h16xaNGjXC3d2dSZMmKSRl3rNnD+7u7tKy6Z07d8bd3V1h6teZM2dITU2lQ4cOBbaP3Ny5c9myZQtnz57F29sbZ2dngoODcXV1lRIPt2nThiVLlrBgwQKcnZ1Zs2YNYWFhxSYYl7OwsCAmJoZ3797RokULXFxcCAoKwtjYWPqZTpw4kZEjRzJp0iScnJzo1KmTNKJJQ0ODpUuXsmbNGiwsLGjdurVK15Xr2bMnWVlZLF++vNhYDA0NOXHiBC1btsTe3p4JEyawcOFCaaponz596NmzJz169KBx48bY2NgUm/B54cKFHDlyBEtLyyJHSOX38uVLzM3NqVChAvXr12fNmjX07NmTS5cuYW5uDuT93oyMjOTixYtUq1aNESNGSIm0BUEQBEEQBEFVstwPXaNVEAThM7h8+TLNmzfnzp07SqvU/R1mzpzJ6tWrP3ny2E6dOuHm5sb48eM/ab2CAHkdTUZGRqSmpop8OYIgCIIgCP9AJfn32n92RI4gCP9Orq6uzJ07t8QrQn2olStXcv78ee7evcumTZuYP3++NPLlU3n79i0uLi4qJ4QWBEEQBEEQBOG/S4zIEQRBKMKIESPYtm0bz549o1KlSnTv3p1x48apnCBa+Pdzdnbm/v37BR5bs2YNXbt2/ZsjKjkxIkcQBEEQBOGfrST/XhMdOYIgCIJQhPv37xeaOLx8+fIYGBj8zRGVnPwfBm7frEZd69MuHy98vIvze3zuEARBEARB+MzE1CpB+Ifp1asXbdq0+dxhlMi/MeYvwd/V7klJSchkMuLi4gCIjo5GJpMVu6LTl0omk7F79+4Cj1WuXLnA5eVtbW3/FZ04giAIgiAIwpdFdOQIf7vHjx8zePBgKlWqhJaWFmZmZnh7exMTEyOVKeql6q926tQpGjZsiImJCTo6Ojg6OrJo0aIizynuJXjJkiXSstX/hDhlMhlqamoYGRnh7u7OmDFjSE5O/uCY/22dPj/++CP16tXDyMgIAwMDnJ2di12e/p8kNzeXtWvXUrduXfT19TE2NqZWrVosXryY9PT0zx0eoHrHUP5nUiaTYWpqSsuWLbly5crfE2gB3u/kUsWUKVOke9DQ0KBs2bJ4eHiwePFiMjMz/7pgBUEQBEEQhP8ckeRB+Nu1b9+et2/fsnHjRmxsbPjzzz85evQoT58+/dyhAaCnp8ewYcNwdXVFT0+PU6dOMXDgQPT09BgwYMAH1WlkZPSJo/y4OG/evImhoSEvX77k119/Zd68eYSGhhIdHY2Li8tfFvM/wdGjR+nUqRMzZ87k66+/RiaTcf36dY4cOfK5Q1NZ9+7d2bVrFxMmTGD58uWYmpoSHx/P4sWLsbKy+ld1qsnJn8k//viD0aNH89VXX3H79m00NTU/d2gqc3Z2JioqipycHJ4+fUp0dDQzZsxg06ZNREdHi9E7giAIgiAIwichRuQIf6sXL15w8uRJ5s6di6enJ5UrV6ZOnTqMGzeOr7/+GgArKysA2rZti0wmkz7fuXOH1q1bU758efT19alduzZRUVEK9ScnJ/PVV1+ho6ODtbU1W7ZswcrKisWLFyvE0K9fP0xNTTE0NKRp06bEx8dLx93d3QkICMDZ2RkrKyu6deuGt7c3J0+e/OD7fn/ESpMmTQgMDGTMmDGUKVMGMzMzpkyZotRWf1Wc5cqVw8zMDHt7ezp37kxMTAympqYMHjy40Jh37NiBi4sLOjo6mJiY4OXlxevXr5kyZQobN27kp59+kkYkREdHAxASEoK9vT26urrY2NgwceJEhVwjU6ZMoXr16mzatAkrKyuMjIzo3Lkzr169ksrk5OQwb948bG1t0dLSolKlSsycOVM6/vDhQ/z9/TE2NqZMmTK0bt2apKSkQu/9559/pmHDhowePRoHBwfs7e1p06YNK1asUIprzZo1WFpaoquri7+/P6mpqQp1rV+/HicnJ7S1tXF0dGTlypUKx4uL7d27dwQHB2NsbIyJiQljxoyhuLRlP/zwA5s3b2br1q2MHz+e2rVrY2VlRevWrTl27Bienp5Su02bNo2KFSuipaVF9erVOXjwYJF1v+/UqVM0atQIHR0dLC0tCQwM5PXr19LxzMxMQkJCsLS0REtLC1tbW0JDQ0lKSpLiKF26NDKZjF69ehV5LfkzWaNGDYKCgnj48CE3btxQOZaVK1diZ2eHtrY25cuXp0OHDtKx938HAFSvXl3pOydnbW0N5H3HZDIZTZo0UaG1QENDAzMzMywsLHBxceGbb77hl19+4erVq8ydO1cqt2nTJmrVqoWBgQFmZmZ06dKFR48eScflo5QOHTqEu7s7Ojo6NG3alEePHnHgwAGcnJwwNDSkS5cu/5gRWIIgCIIgCMLfR3TkCH8rfX199PX12b17d6HTDc6fPw9AWFgYycnJ0ue0tDRatmzJ0aNHuXTpEj4+Pvxfe3cel2PW/wH8c9O+J6VFupHKkqU8jDFTIgphLGMsI1kytmlmbMkaM8g6xIw1iqeZjOWJBxlbPVNJaLQMSZrSDC1jq25adf3+6NX1cysUrXzer9f9mu5znetc53y7u6a+zjnXkCFDkJ6eLp7r6uqKe/fuISwsDEeOHMGuXbvk/kACgE8//VT8gygmJgY2Njbo168fHj58WGl/rl27hosXL8Le3r4mQiAKCAiAuro6oqOjsW7dOqxcuVJuVkhd9lNVVRXTp09HZGRkhXgBZQmysWPHYvLkyUhMTERYWBhGjBgBQRAwb948jB49Gs7OzsjIyEBGRgY+/PBDAICmpib8/f1x48YNbNmyBbt3766w/CslJQXBwcE4ceIETpw4gf/973/w8fERj3t5ecHHxwdLly7FjRs38NNPP6FFixYAgOLiYjg5OUFTUxPh4eGIjIyEhoYGnJ2dUVRUVOlYDQ0Ncf36dfzxxx+vjMnt27fxyy+/4L///S9Onz6Na9euYebMmeLxwMBALFu2DKtWrUJiYiJWr16NpUuXIiAgoMp927hxI/z9/bF3715ERETg4cOH+M9//vPKfgUGBsLS0hLDhg2rcEwikYgzqbZs2YKNGzdiw4YNiI+Ph5OTE4YOHYrk5ORXtl8uJSUFzs7OGDlyJOLj43Hw4EFERERg9uzZYh1XV1f8/PPP8PX1RWJiInbu3AkNDQ2YmpriyJEjAMpm2mRkZGDLli1Vum5OTg6CgoIAQJyN87q+XL16FR4eHli5ciWSkpJw+vRp2NnZVel6lbl8+TIA4Ny5c8jIyMDRo0ffuC0rKysMHDhQro3i4mJ8++23iIuLQ3BwMNLS0ipNdHl7e2Pbtm24ePGimBTcvHkzfvrpJ5w8eRJnzpzB1q1bK71uYWEhcnNz5V5ERERE9G7g0iqqUwoKCvD394e7uzt27NgBGxsb2NvbY8yYMejcuTMAQF9fHwCgo6MDQ0ND8dwuXbqgS5cu4vtvv/0W//nPf3D8+HHMnj0bN2/exLlz53DlyhV0794dQNmMiXbt2onnRERE4PLly8jOzoaysjIAYMOGDQgODsbhw4flliS1bNkS//zzD0pKSuDt7Y2pU6fWaCw6d+6M5cuXAwDatWuHbdu24fz58+jfv3+99NPKygpA2f4gBgYGcscyMjJQUlKCESNGwMzMDADEJVhAWSKosLBQ7vsFAEuWLBG/lkqlmDdvHoKCgrBgwQKxvLS0FP7+/uKykwkTJuD8+fNYtWoV8vLysGXLFmzbtg0TJ04EALRt2xYfffQRAODgwYMoLS3Fnj17IJFIAJQlAHV0dBAWFoYBAwZUGOeXX36J8PBwWFtbw8zMDB988AEGDBiA8ePHi7EGgIKCAuzfvx8mJiYAgK1bt2Lw4MHYuHEjDA0NsXz5cmzcuBEjRowAUDaL48aNG9i5cycmTpxYpb5t3rwZXl5eYhs7duzAr7/++srvU3JyMiwtLV9ZByj7vHh6emLMmDEAgLVr1yI0NBSbN2+Wm330MmvWrMH48ePFvYPatWsHX19f2NvbY/v27UhPT8cvv/yCs2fPwtHREQDQpk0b8fxmzZoBKJtpo6Oj89rrtWzZEgDEWTZDhw4VP5NV6Yu6ujpcXFygqakJMzMzdOvW7bXXfJnye5Cenl6Fz/SbsLKywpkzZ8T3kydPFr9u06YNfH198a9//QsymQwaGhrise+++w69e/cGAEyZMgVeXl5ISUkR4zxq1CiEhobC09OzwjXXrFmDFStWvHXfiYiIiKjhYSKH6tzIkSMxePBghIeH49KlSwgJCcG6deuwZ8+eVy6/kMlk8Pb2xsmTJ8XEQn5+vjgjJykpCQoKCrCxsRHPMTc3h66urvg+Li4OMpkMenp6cm3n5+cjJSVFriw8PBwymQyXLl3CwoULYW5ujrFjx9ZABMqUJ67KGRkZibNh6qOf5Ut6ypMOz+vSpQv69esHa2trODk5YcCAARg1apRcbCtz8OBB+Pr6IiUlBTKZDCUlJRUepSeVSuX2Dnk+DomJiSgsLES/fv0qbT8uLg63b9+usPdIQUFBhTiVU1dXx8mTJ5GSkoLQ0FBcunQJc+fOxZYtWxAVFQU1NTUAQKtWrcQkDgD06tULpaWlSEpKgqamJlJSUjBlyhS4u7uLdUpKSsQZMa/rW05ODjIyMtCzZ0/xmIKCArp37/7K5VWvW3oFlD268N69e2ISoFzv3r3llue9SlxcHOLj4xEYGCh37dLSUqSmpiIhIQFNmzatsZlq4eHhUFNTw6VLl7B69Wrs2LGjyn3p378/zMzM0KZNGzg7O8PZ2RnDhw8Xv5f1TRAEuZ+rmJgYeHt7Iy4uDo8ePUJpaSkAID09HR06dBDrPX+PaNGihbhE8fmy8tlDL/Ly8sKcOXPE97m5uTA1Na2xMRERERFR/WEih+qFiooK+vfvj/79+2Pp0qWYOnUqli9f/spEzrx583D27Fls2LAB5ubmUFVVxahRo166hKYyMpkMRkZG4h4uz3tx1kD5PhnW1tbIysqCt7d3jSZyFBUV5d5LJBLxD7r66GdiYiKA/9+j6HlNmzbF2bNncfHiRXE5x+LFixEdHS1e/0VRUVEYP348VqxYAScnJ2hrayMoKAgbN26Uq/eqOKiqqr6yzzKZDLa2tnJ/4Jcrn1XxMm3btkXbtm0xdepULF68GBYWFjh48CAmTZr0yvPKrwsAu3fvlkvEAGWxetu+vYqFhYXc3jG1RSaT4YsvvoCHh0eFY61atcLt27dr9HqtW7eGjo4OLC0tkZ2djc8++wy//fZblfqipKSE33//HWFhYThz5gyWLVsGb29vXLlyBTo6OmjSpEmFBNjzezXVtsTERPHn5MmTJ3BycoKTkxMCAwOhr6+P9PR0ODk5VbiXPf+zIZFIXvmz8iJlZWW5GWZERERE9O7gHjnUIHTo0EFu41JFRUU8e/ZMrk5kZCTc3NwwfPhwWFtbw9DQUG7jWEtLS5SUlODatWti2e3bt/Ho0SPxvY2NDTIzM6GgoABzc3O5V/PmzV/av9LS0jp9hHBd9zM/Px+7du2CnZ3dS5MMEokEvXv3xooVK3Dt2jUoKSmJ+7koKSlV+H5dvHgRZmZmWLx4Mbp374527drhzp071epXu3btoKqqivPnz1d63MbGBsnJyTAwMKgQp+o8dUsqlUJNTU3uM5ieno579+6J7y9duoQmTZrA0tISLVq0gLGxMf78888K1y3/g/11fdPW1oaRkRGio6PFa5SUlCAmJuaVfR03bhxu3bqFY8eOVTgmCAJycnKgpaUFY2NjREZGyh2PjIyUm/HxKjY2Nrhx40aFvpubm0NJSQnW1tYoLS3F//73v0rPL9/f5sXPRVXMmjULf/zxh/j5el1fgLLZTI6Ojli3bh3i4+ORlpaGCxcuAChLnGVkZIjt5+bmIjU19aXXf5u+v+jmzZs4ffo0Ro4cKb5/8OABfHx88PHHH8PKyqrSfamIiIiIiF6GiRyqUw8ePEDfvn3x73//G/Hx8UhNTcWhQ4ewbt06uc1bpVIpzp8/j8zMTDER065dOxw9ehSxsbGIi4vDuHHj5P412srKCo6Ojpg2bRouX76Ma9euYdq0aVBVVRWXNTg6OqJXr1745JNPcObMGaSlpeHixYtYvHgxrl69CgD44Ycf8N///hfJyclITk6Gn58fNmzYgM8///y140tISEBsbKz4quoylhfVdj+zs7ORmZmJ5ORkBAUFoXfv3rh//z62b99eaf3o6GisXr0aV69eRXp6Oo4ePYp//vkH7du3B1D2/YqPj0dSUhLu37+P4uJitGvXDunp6QgKCkJKSgp8fX1fu5Hvi1RUVODp6YkFCxZg//79SElJwaVLl+Dn5wcAGD9+PJo3b45hw4YhPDwcqampCAsLg4eHB/7+++9K2/T29saCBQsQFhaG1NRUXLt2DZMnT0ZxcTH69+8vd+2JEyciLi4O4eHh8PDwwOjRo8U9U1asWIE1a9bA19cXt27dQkJCAvbt24dNmzZVuW9fffUVfHx8EBwcjJs3b2LmzJl4/PjxK2MyevRofPbZZxg7dqz4Pblz5w5OnDgBR0dHhIaGAgDmz5+PtWvX4uDBg0hKSsLChQsRGxuLr776qkqx9/T0xMWLFzF79mzExsYiOTkZx44dEzcYlkqlmDhxIiZPnozg4GBxfL/88gsAwMzMDBKJBCdOnMA///wjzmKqCjU1Nbi7u2P58uUQBOG1fTlx4gR8fX0RGxuLO3fuYP/+/SgtLRX3Eurbty8OHDiA8PBwJCQkYOLEieLMqcoYGBhAVVUVp0+fRlZWVoWnlb1MSUkJMjMzce/ePSQkJGDr1q2wt7dH165dMX/+fAD/P4No69at+PPPP3H8+HF8++23VY4NERERERGXVlGd0tDQQM+ePfH9998jJSUFxcXFMDU1hbu7OxYtWiTW27hxI+bMmYPdu3fDxMQEaWlp2LRpEyZPnowPP/wQzZs3h6enZ4Unsezfvx9TpkyBnZ0dDA0NsWbNGly/fh0qKioAymaVnDp1CosXL8akSZPwzz//wNDQEHZ2duKTkEpLS+Hl5YXU1FQoKCigbdu2WLt2Lb744ovXju/FJ+U0bdoUJSUl1Y5TbffT0tISEokEGhoaaNOmDQYMGIA5c+a8dGNXLS0t/Pbbb9i8eTNyc3NhZmaGjRs3YuDAgQAAd3d3hIWFoXv37pDJZAgNDcXQoUPxzTffYPbs2SgsLMTgwYOxdOnSlz7y+WWWLl0KBQUFLFu2DPfu3YORkRGmT58OoOwP/t9++w2enp4YMWIE8vLyYGJign79+lXYi6ecvb09fvjhB7i6uiIrKwu6urro1q0bzpw5I7eJsLm5OUaMGIFBgwbh4cOHcHFxkXu8+NSpU6Gmpob169dj/vz5UFdXh7W1tbghb1X6NnfuXGRkZGDixIlo0qQJJk+ejOHDh78ycSCRSPDTTz9h165d2Lt3L1atWgUFBQW0a9cOrq6ucHJyAgB4eHggJycHc+fORXZ2Njp06IDjx4/Lbf79Kp07d8b//vc/LF68GB9//DEEQUDbtm3x2WefiXW2b9+ORYsWYebMmXjw4AFatWol/hybmJhgxYoVWLhwISZNmgRXV1f4+/tX6doAMHv2bGzatAmHDh3C6NGjX9kXHR0dHD16FN7e3igoKEC7du3w888/o2PHjgAg/py4uLhAW1sb33777Stn5CgoKMDX1xcrV67EsmXL8PHHH1e6zPFF169fh5GREZo2bQptbW106NABXl5emDFjhrjMSV9fH/7+/li0aBF8fX1hY2ODDRs2YOjQoVWODRERERG93yRCVXbOJGqk/v77b5iamuLcuXMv3TCX6EXe3t4IDg5GbGxsfXeFqEbk5uZCW1tbXHpHRERERA1LdX5f44wceqdcuHABMpkM1tbWyMjIwIIFCyCVSivMlCEiIiIiIiJqjJjIoXdKcXExFi1ahD///BOampr48MMPERgYWOFpL0RE1aGhofHSYyEhIfj444/rsDdERERE9D7j0ioiIqLXeNXj1k1MTKCqqlqHvam+8qm6Xb7cgabKDbuv74uY9a713QUiIiJqQLi0ioiIqAaZm5vXdxeIiIiIiADw8eNERPD29kbXrl3r5FpSqRSbN2+uk2sREREREdG7h4kcImqQduzYAU1NTbnHt8tkMigqKqJPnz5ydcPCwiCRSJCSklLHvWyY3NzcIJFIIJFIoKioiBYtWqB///7Yu3cvSktL67t78Pb2hpWVFdTV1aGrqwtHR0dER0fL1ZFIJAgODq5wrpubGz755BPx/W+//YYhQ4bA2Ni40nOePHmCtm3bYs6cOXLlaWlp0NLSwu7duwH8/2foxdeSJUvEc+Lj4/Hxxx9DRUUFpqamWLdu3UvHGBQUBIlEItfXqo69Mh4eHrC1tYWysnKdJR2JiIiIqGFiIoeIGiQHBwfIZDJcvXpVLAsPD4ehoSGio6NRUFAgloeGhqJVq1Zo27Ztta4hCIJcoqimPHv2rNYSJkVFRVWq5+zsjIyMDKSlpSEkJAQODg746quv4OLiUitjrg4LCwts27YNCQkJiIiIgFQqxYABA/DPP/9Uu60nT56gS5cu+OGHHyo9rq6ujn379mHr1q0IDw8HUPZ9nzRpEnr37g13d3e5+klJScjIyBBfCxcuBFC2ZnnAgAEwMzNDTEwM1q9fD29vb+zatavCNdPS0jBv3rxKN0B+m7FPnjwZn3322WvrEREREdG7jYkcImqQLC0tYWRkhLCwMLEsLCwMw4YNQ+vWrXHp0iW5cgcHBxQWFsLDwwMGBgZQUVHBRx99hCtXrsjVk0gkCAkJEWc3REREVLh2SkoK2rRpg9mzZ0MQBDx69Aiurq7Q1dWFmpoaBg4ciOTkZLG+v78/dHR0cPz4cXTo0AHKyspIT09HdnY2hgwZAlVVVbRu3RqBgYEVrvX48WNMnToV+vr60NLSQt++fREXFyceL1/2tWfPHrRu3RoqKipVip+ysjIMDQ1hYmICGxsbLFq0CMeOHUNISAj8/f3Feps2bYK1tTXU1dVhamqKmTNnQiaTVRjbiRMnYGlpCTU1NYwaNQpPnz5FQEAApFIpdHV14eHhgWfPnlWpb+PGjYOjoyPatGmDjh07YtOmTcjNzUV8fHyVzn/ewIED8d1332H48OEvrWNnZ4cvv/wSkyZNwpMnT7BlyxbExsZiz549FeoaGBjA0NBQfJU/rSowMBBFRUXYu3cvOnbsiDFjxsDDwwObNm2SO//Zs2cYP348VqxYgTZt2tTY2H19fTFr1qxK2yQiIiKi9wsTOUTUYDk4OCA0NFR8Hxoaij59+sDe3l4sz8/PR3R0NBwcHLBgwQIcOXIEAQEB+P3332Fubg4nJyc8fPhQrt2FCxfCx8cHiYmJ6Ny5s9yx+Ph4fPTRRxg3bhy2bdsGiUQCNzc3XL16FcePH0dUVBQEQcCgQYNQXFwsnvf06VOsXbsWe/bswfXr12FgYAA3Nzf89ddfCA0NxeHDh/Hjjz8iOztb7nqffvopsrOzERISgpiYGNjY2KBfv35yfb59+zaOHDmCo0ePIjY29o3j2bdvX3Tp0gVHjx4Vy5o0aQJfX19cv34dAQEBuHDhAhYsWCB33tOnT+Hr64ugoCCcPn0aYWFhGD58OE6dOoVTp07hwIED2LlzJw4fPlztPhUVFWHXrl1lT1Tq0uWNx/Y6q1atgoKCAj7//HMsWrQIW7duhYmJSZXPj4qKgp2dHZSUlMQyJycnJCUl4dGjR2LZypUrYWBggClTpry2zdoce2FhIXJzc+VeRERERPRu4FOriKjBcnBwwNdff42SkhLk5+fj2rVrsLe3R3FxMXbs2AGg7A/swsJC9OnTB+7u7vD398fAgQMBALt378bZs2fh5+eH+fPni+2uXLkS/fv3r3C9ixcvwsXFBYsXL8bcuXMBAMnJyTh+/DgiIyPx4YcfAiibnWFqaorg4GB8+umnAIDi4mL8+OOP4h/kt27dQkhICC5fvox//etfAAA/Pz+0b99evF5ERAQuX76M7OxsKCsrAwA2bNiA4OBgHD58GNOmTQNQ9gf//v37oa+v/9YxtbKykpv98fXXX4tfS6VSfPfdd5g+fTp+/PFHsby4uBjbt28Xl66NGjUKBw4cQFZWFjQ0NNChQwcx6VbVpT8nTpzAmDFj8PTpUxgZGeHs2bNo3ry5XJ2xY8eiadOmcmWFhYUYPHhwdYcNVVVVbNmyBc7Ozhg4cCA+//zzSuu1bNlS7v2dO3egp6eHzMxMtG7dWu5YixYtAACZmZnQ1dVFREQE/Pz8Xptsq8rY39aaNWuwYsWKGm2TiIiIiBoGJnKIqMHq06cPnjx5gitXruDRo0ewsLCAvr4+7O3tMWnSJBQUFCAsLAxt2rRBTk4OiouL0bt3b/F8RUVF9OjRA4mJiXLtdu/evcK10tPT0b9/f6xatUouuZGYmAgFBQX07NlTLNPT04OlpaVcu0pKSnKze8rPs7W1FcusrKygo6Mjvo+Li4NMJoOenp5cX/Lz8+U2bjYzM6uRJA5Qtj+MRCIR3587dw5r1qzBzZs3kZubi5KSEhQUFODp06dQU1MDAKipqcntP9SiRQtIpVJx2VF52YuzjV7FwcEBsbGxuH//Pnbv3o3Ro0cjOjoaBgYGYp3vv/8ejo6Ocud5enpWeQnXi/z8/KCmpoaEhATk5ORAW1u7Qp3w8HBoamqK73V1davUdl5eHiZMmIDdu3e/NinzqrEPHDhQ3MvHzMwM169fr8YI/5+Xl5fcBs+5ubkwNTV9o7aIiIiIqGFhIoeIGixzc3O0bNkSoaGhePToEezt7QEAxsbGMDU1xcWLFxEaGoq+fftWq111dfUKZfr6+jA2NsbPP/+MyZMnQ0tLq1ptqqqqyiVIqkImk1XYB6jc8wmfyvr7phITE8WZJWlpaXBxccGMGTOwatUqNGvWDBEREZgyZQqKiorERI6ioqJcG+VPw3qxrDobPKurq8Pc3Bzm5ub44IMP0K5dO/j5+cHLy0usY2hoCHNzc7nzNDU18fjx4+oMGQBw8OBBnDhxAlFRURg7diy++eYb7N27t0K91q1by8X++b5kZWXJlZW/NzQ0REpKCtLS0jBkyBDxeHk8FBQUkJSUJCbDXjX2PXv2ID8/H0DFuFeHsrKyOMuLiIiIiN4t3COHiBo0BwcHhIWFISwsTO6x43Z2duLSJQcHB7Rt2xZKSkqIjIwU6xQXF+PKlSvo0KHDa6+jqqqKEydOQEVFBU5OTsjLywMAtG/fHiUlJXKPiH7w4AGSkpJe2a6VlRVKSkoQExMjliUlJcklIWxsbJCZmQkFBQXxD/vyV00vtQGACxcuICEhASNHjgQAxMTEoLS0FBs3bsQHH3wACwsL3Lt3r8avWxWlpaUoLCyslbazsrIwa9YsfPfdd+jSpQv8/f2xf/9+hISEVLmNXr164bfffpPbF+ns2bOwtLSErq4urKyskJCQgNjYWPE1dOhQcfbNq2bDPD92ExMT8TNgZmb25oMmIiIioncWEzlE1KA5ODggIiICsbGx4owcALC3t8fOnTtRVFQEBwcHqKurY8aMGZg/fz5Onz6NGzduwN3dHU+fPq3SxrNA2UyJkydPQkFBAQMHDoRMJkO7du0wbNgwuLu7IyIiAnFxcfj8889hYmKCYcOGvbQtS0tLODs744svvkB0dDRiYmIwdepUqKqqinUcHR3Rq1cvfPLJJzhz5gzS0tJw8eJFLF68WO6x62+isLAQmZmZuHv3Ln7//XesXr0aw4YNg4uLC1xdXQGUzXgqLi7G1q1b8eeff+LAgQPi3kO15cmTJ1i0aBEuXbqEO3fuICYmBpMnT8bdu3fF/YaqQyaTiYkTAEhNTUVsbCzS09PFOtOmTUP79u3FJXM9evTA/PnzMW3aNOTk5FTpOuPGjYOSkhKmTJmC69ev4+DBg9iyZYu4fElFRQWdOnWSe+no6EBTUxOdOnWCkpLSW4399u3biI2NRWZmJvLz88UxV/Vx9ERERET07mAih4gaNAcHB+Tn58Pc3FzcXBYoS+Tk5eWJjykHAB8fH4wcORITJkyAjY0Nbt++jV9//bXK+5wAgIaGBkJCQiAIAgYPHownT55g3759sLW1hYuLC3r16gVBEHDq1KnXLn3Zt28fjI2NYW9vjxEjRmDatGlye8BIJBKcOnUKdnZ2mDRpEiwsLDBmzBjcuXNHbqxv4vTp0zAyMoJUKoWzszNCQ0Ph6+uLY8eOiRsId+nSBZs2bcLatWvRqVMnBAYGYs2aNW913ddp2rQpbt68iZEjR8LCwgJDhgzBgwcPEB4ejo4dO1a7vatXr6Jbt27o1q0bAGDOnDno1q0bli1bBgDYv38/zp07h3379qFJk///X96KFSugo6ODb775pkrX0dbWxpkzZ5CamgpbW1vMnTsXy5YtEzekroq3GfvUqVPRrVs37Ny5E7du3RLHXF8zqIiIiIio/kgEQRDquxNERERUe3Jzc6GtrY2cnJxq7/9ERERERLWvOr+vcUYOEREREREREVEjwUQOEVEjkp6eDg0NjZe+nt8bpj6Eh4e/sn9ERERERPR2+PhxIqJGxNjYWNzY92XH61P37t1f2T+qX3ZLfkZTZdXXV6QaEbPetb67QERERO8gJnKI6L3l7e2N4ODgOkk8SKVSfP311+KTk95U+aPKGypVVdUG3T8iIiIiosaOS6uIqEHZsWMHNDU1UVJSIpbJZDIoKiqiT58+cnXDwsIgkUiQkpJSx71smNzc3CCRSCCRSKCoqIgWLVqgf//+2Lt3L0pLS+u7e3KmT58OiUSCzZs3VzgWGhqKQYMGQU9PD2pqaujQoQPmzp2Lu3fvAgAKCgrg5uYGa2trKCgo4JNPPqnQhqenJ6RSKfLy8uTKhwwZAjs7OzEeUqm00j54e3uja9eulfY9KCgIEomkwnXLY//ia/369RXaKCwsRNeuXSGRSF6bSMzIyMC4ceNgYWGBJk2avHUykIiIiIgaNyZyiKhBcXBwgEwmw9WrV8Wy8PBwGBoaIjo6GgUFBWJ5aGgoWrVqhbZt21brGoIgyCWKasqzZ89qLWFSVFRUpXrOzs7IyMhAWloaQkJC4ODggK+++gouLi61MuY38Z///AeXLl2qdBnYzp074ejoCENDQxw5cgQ3btzAjh07kJOTg40bNwIoi7Oqqio8PDzg6OhY6TVWrlwJDQ0NzJkzRyzbu3cvQkNDKzyKvDrS0tIwb948fPzxxxWOZWRkyL327t0LiUSCkSNHVqi7YMGCKi+DKywshL6+PpYsWYIuXbq8Ub+JiIiI6N3BRA4RNSiWlpYwMjJCWFiYWBYWFoZhw4ahdevWuHTpkly5g4MDCgsL4eHhAQMDA6ioqOCjjz7ClStX5OpJJBKEhITA1tYWysrKiIiIqHDtlJQUtGnTBrNnz4YgCHj06BFcXV2hq6sLNTU1DBw4EMnJyWJ9f39/6Ojo4Pjx4+jQoQOUlZWRnp6O7OxsDBkyBKqqqmjdujUCAwMrXOvx48eYOnUq9PX1oaWlhb59+yIuLk48Xj4jZM+ePWjdujVUVFSqFD9lZWUYGhrCxMQENjY2WLRoEY4dO4aQkBD4+/uL9TZt2gRra2uoq6vD1NQUM2fOhEwmqzC2EydOwNLSEmpqahg1ahSePn2KgIAASKVS6OrqwsPDA8+ePatS3wDg7t27+PLLLxEYGAhFRUW5Y3///Tc8PDzg4eGBvXv3ok+fPpBKpbCzs8OePXuwbNkyAIC6ujq2b98Od3d3GBoavjQOAQEBCAgIwOnTp5Geno5vvvkG69atq3bir9yzZ88wfvx4rFixAm3atKlw3NDQUO517NgxODg4VKgbEhKCM2fOYMOGDVW6rlQqxZYtW+Dq6gptbe036jsRERERvTuYyCGiBsfBwQGhoaHi+9DQUPTp0wf29vZieX5+PqKjo+Hg4IAFCxbgyJEjCAgIwO+//w5zc3M4OTnh4cOHcu0uXLgQPj4+SExMROfOneWOxcfH46OPPsK4ceOwbds2SCQSuLm54erVqzh+/DiioqIgCAIGDRqE4uJi8bynT59i7dq12LNnD65fvw4DAwO4ubnhr7/+QmhoKA4fPowff/wR2dnZctf79NNPkZ2djZCQEMTExMDGxgb9+vWT6/Pt27dx5MgRHD169K328enbty+6dOmCo0ePimVNmjSBr68vrl+/joCAAFy4cAELFiyQO+/p06fw9fVFUFAQTp8+jbCwMAwfPhynTp3CqVOncODAAezcuROHDx+uUj9KS0sxYcIEzJ8/Hx07dqxw/NChQygqKqrQj3I6OjpVHzQAW1tbeHl5YerUqZgwYQJ69OiBGTNmVKuN561cuRIGBgaYMmXKa+tmZWXh5MmTFepmZWXB3d0dBw4cgJqa2hv35XUKCwuRm5sr9yIiIiKidwM3OyaiBsfBwQFff/01SkpKkJ+fj2vXrsHe3h7FxcXYsWMHACAqKgqFhYXo06cP3N3d4e/vj4EDBwIAdu/ejbNnz8LPzw/z588X2125ciX69+9f4XoXL16Ei4sLFi9ejLlz5wIAkpOTcfz4cURGRuLDDz8EAAQGBsLU1BTBwcH49NNPAQDFxcX48ccfxSUvt27dQkhICC5fvox//etfAAA/Pz+0b99evF5ERAQuX76M7OxsKCsrAwA2bNiA4OBgHD58GNOmTQNQtpxq//790NfXf+uYWllZIT4+Xnz//D4rUqkU3333HaZPn44ff/xRLC8uLsb27dvFGSyjRo3CgQMHkJWVBQ0NDXTo0EFMun322Wev7cPatWuhoKAADw+PSo8nJydDS0sLRkZGbzjKipYsWYJ9+/YhOjoat27dgkQiqVDH09MTS5YskSsrKipChw4dxPcRERHw8/OrckItICAAmpqaGDFihFgmCALc3Nwwffp0dO/eHWlpaW80pqpYs2YNVqxYUWvtExEREVH94YwcImpw+vTpgydPnuDKlSsIDw+HhYUF9PX1YW9vL+6TExYWhjZt2iAnJwfFxcXo3bu3eL6ioiJ69OiBxMREuXa7d+9e4Vrp6eno378/li1bJiZxACAxMREKCgro2bOnWKanpwdLS0u5dpWUlORm95SfZ2trK5ZZWVnJzSaJi4uDTCaDnp4eNDQ0xFdqaqrcxs1mZmY1ksQBypIIzycxzp07h379+sHExASampqYMGECHjx4gKdPn4p11NTU5JYhtWjRAlKpFBoaGnJlL842qkxMTAy2bNkCf3//SpMplfWxJpw9exaZmZkoLS2VW273vPnz5yM2NlbuNX36dPF4Xl4eJkyYgN27d6N58+ZVuu7evXsxfvx4uSVxW7duRV5eHry8vF563vOfh+f7UF1eXl7IyckRX3/99dcbt0VEREREDQtn5BBRg2Nubo6WLVsiNDQUjx49gr29PQDA2NgYpqamuHjxIkJDQ9G3b99qtauurl6hTF9fH8bGxvj5558xefJkaGlpVatNVVXVaicfZDJZhX2Ayj2f8Kmsv28qMTERrVu3BlC2Ya+LiwtmzJiBVatWoVmzZoiIiMCUKVNQVFQkLvl5cQ+b8qdhvVhWlQ2ew8PDkZ2djVatWollz549w9y5c7F582akpaXBwsICOTk5yMjIqJFZOY8ePYK7uzuWLFkCQRAwc+ZM2NvbV0jGNG/evMIj05s1ayZ+nZKSgrS0NAwZMkQsKx+zgoICkpKS5BJe4eHhSEpKwsGDB+XavHDhAqKiosRZWOW6d++O8ePHIyAgQG7GT3U/i89TVlaucB0iIiIiejdwRg4RNUgODg4ICwtDWFiY3GPH7ezsxKVLDg4OaNu2LZSUlBAZGSnWKS4uxpUrV+SWxryMqqoqTpw4ARUVFTg5OYmPq27fvj1KSkoQHR0t1n3w4AGSkpJe2a6VlRVKSkoQExMjliUlJeHx48fiexsbG2RmZkJBQQHm5uZyr6rO+KiOCxcuICEhQXx6UkxMDEpLS7Fx40Z88MEHsLCwwL1792r8us+bMGEC4uPj5Wa9GBsbY/78+fj1118BlC3dUlJSwrp16ypt4/kYVsWXX34JQ0NDLFq0CIsXL4aJiQlmzZpV7b5bWVkhISFBru9Dhw6Fg4MDYmNjYWpqKlffz88Ptra2FZ4w5evri7i4OLGNU6dOAQAOHjyIVatWAYDcZ8HAwKDafSUiIiKidx9n5BBRg+Tg4IBZs2ahuLhYnJEDAPb29pg9ezaKiorg4OAAdXV1zJgxA/Pnz0ezZs3QqlUrrFu3Dk+fPq3SprRA2cyXkydPYuDAgRg4cCBOnz6Ndu3aYdiwYXB3d8fOnTuhqamJhQsXwsTEBMOGDXtpW5aWlnB2dsYXX3yB7du3Q0FBAV9//TVUVVXFOo6OjujVqxc++eQTrFu3TkyknDx5EsOHD690CVhVFRYWIjMzE8+ePUNWVhZOnz6NNWvWwMXFBa6urgDKkgXFxcXYunUrhgwZgsjISHHvodqip6cHPT09uTJFRUUYGhrC0tISAGBqaorvv/8es2fPRm5uLlxdXSGVSvH3339j//790NDQEB9BfuPGDRQVFeHhw4fIy8sTZ7J07doVQNkjzg8dOoSYmBgoKJT9ry4gIADdu3fHkSNHKn0k+MuoqKigU6dOcmXlM6deLM/NzcWhQ4fEfj7v+dlIAMQlam3btkXLli1f2Yfy8clkMvzzzz+IjY2FkpJSlZKVRERERPRu4YwcImqQHBwckJ+fD3Nzc7Ro0UIst7e3R15enviYcgDw8fHByJEjMWHCBNjY2OD27dv49ddfoaurW+XraWhoICQkBIIgYPDgwXjy5An27dsHW1tbuLi4oFevXhAEAadOnaqwvOhF+/btg7GxMezt7TFixAhMmzZNbnaFRCLBqVOnYGdnh0mTJsHCwgJjxozBnTt35Mb6Jk6fPg0jIyNIpVI4OzsjNDQUvr6+OHbsGJo2bQoA6NKlCzZt2oS1a9eiU6dOCAwMxJo1a97qujVl5syZOHPmDO7evYvhw4fDysoKU6dOhZaWFubNmyfWGzRoELp164b//ve/CAsLQ7du3dCtWzcAwP379zF9+nQsX75cLtFibW2N5cuXY+bMmbh//36t9D8oKAiCIGDs2LE12m75+GJiYvDTTz+hW7duGDRoUI1eg4iIiIgaB4kgCEJ9d4KIiIhqT25uLrS1tZGTk/NWe+8QERERUe2ozu9rXFpFRET0jiv/N5vc3Nx67gkRERERVab897SqzLVhIoeIqBFIT09/5X4oN27cqLAHS10KDw/HwIEDX3pcJpPVYW/oRQ8ePACAChszExEREVHDkpeXB21t7VfW4dIqIqJGoKSkBGlpaS89LpVKxU1960N+fj7u3r370uMvPt6b6tbjx4+hq6uL9PT01/5iQDUjNzcXpqam+Ouvv7icrQ4w3nWPMa9bjHfdYrzrHmNeNhMnLy8PxsbGaNLk1dsZc0YOEVEjUP6o8oZKVVW1QffvfVf+y4C2tvZ7+8tRfdHS0mLM6xDjXfcY87rFeNctxrvuve8xr+o/uPGpVUREREREREREjQQTOUREREREREREjQQTOURERO84ZWVlLF++HMrKyvXdlfcGY163GO+6x5jXLca7bjHedY8xrx5udkxERERERERE1EhwRg4RERERERERUSPBRA4RERERERERUSPBRA4RERERERERUSPBRA4RERERERERUSPBRA4REVED98MPP0AqlUJFRQU9e/bE5cuXX1n/0KFDsLKygoqKCqytrXHq1Cm544IgYNmyZTAyMoKqqiocHR2RnJwsV+fhw4cYP348tLS0oKOjgylTpkAmk9X42Bqq+oi5VCqFRCKRe/n4+NT42Bqimo730aNHMWDAAOjp6UEikSA2NrZCGwUFBZg1axb09PSgoaGBkSNHIisrqyaH1aDVR8z79OlT4TM+ffr0mhxWg1WT8S4uLoanpyesra2hrq4OY2NjuLq64t69e3Jt8D5e9zHnfbzm7ine3t6wsrKCuro6dHV14ejoiOjoaLk67/VnXCAiIqIGKygoSFBSUhL27t0rXL9+XXB3dxd0dHSErKysSutHRkYKTZs2FdatWyfcuHFDWLJkiaCoqCgkJCSIdXx8fARtbW0hODhYiIuLE4YOHSq0bt1ayM/PF+s4OzsLXbp0ES5duiSEh4cL5ubmwtixY2t9vA1BfcXczMxMWLlypZCRkSG+ZDJZrY+3vtVGvPfv3y+sWLFC2L17twBAuHbtWoV2pk+fLpiamgrnz58Xrl69KnzwwQfChx9+WFvDbFDqK+b29vaCu7u73Gc8JyentobZYNR0vB8/fiw4OjoKBw8eFG7evClERUUJPXr0EGxtbeXa4X287mPO+3jN3VMCAwOFs2fPCikpKcIff/whTJkyRdDS0hKys7PFOu/zZ5yJHCIiogasR48ewqxZs8T3z549E4yNjYU1a9ZUWn/06NHC4MGD5cp69uwpfPHFF4IgCEJpaalgaGgorF+/Xjz++PFjQVlZWfj5558FQRCEGzduCACEK1euiHVCQkIEiUQi3L17t8bG1lDVR8wFoewPgO+//74GR9I41HS8n5eamlppUuHx48eCoqKicOjQIbEsMTFRACBERUW9xWgah/qIuSCUJXK++uqrt+p7Y1Sb8S53+fJlAYBw584dQRB4H6+PmAsC7+PlaiPeOTk5AgDh3LlzgiDwM86lVURERA1UUVERYmJi4OjoKJY1adIEjo6OiIqKqvScqKgoufoA4OTkJNZPTU1FZmamXB1tbW307NlTrBMVFQUdHR10795drOPo6IgmTZpUmNb8rqmvmJfz8fGBnp4eunXrhvXr16OkpKSmhtYg1Ua8qyImJgbFxcVy7VhZWaFVq1bVaqcxqq+YlwsMDETz5s3RqVMneHl54enTp9VuozGpq3jn5ORAIpFAR0dHbIP38bqNeTnex2s+3kVFRdi1axe0tbXRpUsXsY339TMOAAr13QEiIiKq3P379/Hs2TO0aNFCrrxFixa4efNmpedkZmZWWj8zM1M8Xl72qjoGBgZyxxUUFNCsWTOxzruqvmIOAB4eHrCxsUGzZs1w8eJFeHl5ISMjA5s2bXrrcTVUtRHvqsjMzISSklKFP8Cq205jVF8xB4Bx48bBzMwMxsbGiI+Ph6enJ5KSknD06NHqDaIRqYt4FxQUwNPTE2PHjoWWlpbYBu/jdRtzgPfx59VEvE+cOIExY8bg6dOnMDIywtmzZ9G8eXOxjff1Mw4wkUNERETUIMyZM0f8unPnzlBSUsIXX3yBNWvWQFlZuR57RlQzpk2bJn5tbW0NIyMj9OvXDykpKWjbtm099qzxKi4uxujRoyEIArZv317f3XkvvCrmvI/XLAcHB8TGxuL+/fvYvXs3Ro8ejejo6AoJnPcRl1YRERE1UM2bN0fTpk0rPEknKysLhoaGlZ5jaGj4yvrl/31dnezsbLnjJSUlePjw4Uuv+66or5hXpmfPnigpKUFaWlp1h9Fo1Ea8q8LQ0BBFRUV4/PjxW7XTGNVXzCvTs2dPAMDt27ffqp2GrDbjXZ5QuHPnDs6ePSs3M4T38bqPeWV4H3+7eKurq8Pc3BwffPAB/Pz8oKCgAD8/P7GN9/UzDjCRQ0RE1GApKSnB1tYW58+fF8tKS0tx/vx59OrVq9JzevXqJVcfAM6ePSvWb926NQwNDeXq5ObmIjo6WqzTq1cvPH78GDExMWKdCxcuoLS0VPzD611VXzGvTGxsLJo0afJO/8tjbcS7KmxtbaGoqCjXTlJSEtLT06vVTmNUXzGvTPkjyo2MjN6qnYastuJdnlBITk7GuXPnoKenV6EN3sfrNuaV4X28Zu8ppaWlKCwsFNt4Xz/jAPj4cSIiooYsKChIUFZWFvz9/YUbN24I06ZNE3R0dITMzExBEARhwoQJwsKFC8X6kZGRgoKCgrBhwwYhMTFRWL58eaWPwtbR0RGOHTsmxMfHC8OGDav08ePdunUToqOjhYiICKFdu3bvzSM96yPmFy9eFL7//nshNjZWSElJEf79738L+vr6gqura90Ovh7URrwfPHggXLt2TTh58qQAQAgKChKuXbsmZGRkiHWmT58utGrVSrhw4YJw9epVoVevXkKvXr3qbuD1qD5ifvv2bWHlypXC1atXhdTUVOHYsWNCmzZtBDs7u7odfD2o6XgXFRUJQ4cOFVq2bCnExsbKPeq6sLBQbIf38bqNOe/jNRdvmUwmeHl5CVFRUUJaWppw9epVYdKkSYKysrLwxx9/iO28z59xJnKIiIgauK1btwqtWrUSlJSUhB49egiXLl0Sj9nb2wsTJ06Uq//LL78IFhYWgpKSktCxY0fh5MmTcsdLS0uFpUuXCi1atBCUlZWFfv36CUlJSXJ1Hjx4IIwdO1bQ0NAQtLS0hEmTJgl5eXm1NsaGpq5jHhMTI/Ts2VPQ1tYWVFRUhPbt2wurV68WCgoKanWcDUVNx3vfvn0CgAqv5cuXi3Xy8/OFmTNnCrq6uoKampowfPhwuUTPu66uY56eni7Y2dkJzZo1E5SVlQVzc3Nh/vz5Qk5OTm0PtUGoyXiXP+K9sldoaKhYj/fxuo057+M1F+/8/Hxh+PDhgrGxsaCkpCQYGRkJQ4cOFS5fvizXxvv8GZcIgiDU3fwfIiIiIiIiIiJ6U9wjh4iIiIiIiIiokWAih4iIiIiIiIiokWAih4iIiIiIiIiokWAih4iIiIiIiIiokWAih4iIiIiIiIiokWAih4iIiIiIiIiokWAih4iIiIiIiIiokWAih4iIiIiIiIiokWAih4iIiIiIiIiokWAih4iIiIiI6pybmxskEgl8fHzkyoODgyGRSOqpV0REDR8TOUREREREVC9UVFSwdu1aPHr0qL67QkTUaDCRQ0RERERE9cLR0RGGhoZYs2bNS+scOXIEHTt2hLKyMqRSKTZu3Ch3XCqVYvXq1Zg8eTI0NTXRqlUr7Nq1S67OX3/9hdGjR0NHRwfNmjXDsGHDkJaWVhtDIiKqdUzkEBERERFRvWjatClWr16NrVu34u+//65wPCYmBqNHj8aYMWOQkJAAb29vLF26FP7+/nL1Nm7ciO7du+PatWuYOXMmZsyYgaSkJABAcXExnJycoKmpifDwcERGRkJDQwPOzs4oKiqqi2ESEdUoJnKIiIiIiKjeDB8+HF27dsXy5csrHNu0aRP69euHpUuXwsLCAm5ubpg9ezbWr18vV2/QoEGYOXMmzM3N4enpiebNmyM0NBQAcPDgQZSWlmLPnj2wtrZG+/btsW/fPqSnpyMsLKwuhkhEVKOYyCEiIiIionq1du1aBAQEIDExUa48MTERvXv3livr3bs3kpOT8ezZM7Gsc+fO4tcSiQSGhobIzs4GAMTFxeH27dvQ1NSEhoYGNDQ00KxZMxQUFCAlJaUWR0VEVDsU6rsDRERERET0frOzs4OTkxO8vLzg5uZW7fMVFRXl3kskEpSWlgIAZDIZbG1tERgYWOE8fX39N+ovEVF9YiKHiIiIiIjqnY+PD7p27QpLS0uxrH379oiMjJSrFxkZCQsLCzRt2rRK7drY2ODgwYMwMDCAlpZWjfaZiKg+cGkVERERERHVO2tra4wfPx6+vr5i2dy5c3H+/Hl8++23uHXrFgICArBt2zbMmzevyu2OHz8ezZs3x7BhwxAeHo7U1FSEhYXBw8Oj0g2WiYgaOiZyiIiIiIioQVi5cqW4JAoom03zyy+/ICgoCJ06dcKyZcuwcuXKai2/UlNTw2+//YZWrVphxIgRaN++PaZMmYKCggLO0CGiRkkiCIJQ350gIiIiIiIiIqLX44wcIiIiIiIiIqJGgokcIiIiIiIiIqJGgokcIiIiIiIiIqJGgokcIiIiIiIiIqJGgokcIiIiIiIiIqJGgokcIiIiIiIiIqJGgokcIiIiIiIiIqJGgokcIiIiIiIiIqJGgokcIiIiIiIiIqJGgokcIiIiIiIiIqJGgokcIiIiIiIiIqJG4v8AeypIJRNOyFUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importance plotting completed\n",
      "Element: 1, Count: 14850\n",
      "Element: 0, Count: 2511\n",
      "[[0.44433862 0.5556614 ]\n",
      " [0.16771895 0.83228105]\n",
      " [0.2529121  0.7470879 ]\n",
      " ...\n",
      " [0.37371618 0.6262838 ]\n",
      " [0.18198287 0.8180171 ]\n",
      " [0.45203036 0.54796964]]\n",
      "Submission file saved\n"
     ]
    }
   ],
   "source": [
    "# 모델 학습\n",
    "df_train = oversample_data(train_encoded, random_state=RANDOM_STATE)\n",
    "\n",
    "#XGBoost는 target 칼럼도 인코딩 필요\n",
    "# 레이블 인코더 생성\n",
    "label_encoder = LabelEncoder()    \n",
    "# 레이블 변환\n",
    "df_train['target'] = label_encoder.fit_transform(df_train['target'])\n",
    "\n",
    "print(f\"Train set shape: {df_train.shape}\")\n",
    "\n",
    "model, features = train_model(df_train, RANDOM_STATE)\n",
    "\n",
    "# 테스트 데이터 예측 및 결과 저장\n",
    "predict_and_save_results(model, test_encoded, features)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "submission.csv에서 target이 1이면 Normal, 0이면 AbNormal로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "변환 완료\n"
     ]
    }
   ],
   "source": [
    "# 파일 경로 설정\n",
    "file_path = 'submission.csv'\n",
    "\n",
    "# submission.csv 파일 읽기\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# 'target' 열의 값을 1은 'Normal', 0은 'AbNormal'로 변환\n",
    "df['target'] = df['target'].map({1: 'Normal', 0: 'AbNormal'})\n",
    "\n",
    "# 변환된 DataFrame을 원본 CSV 파일에 덮어쓰기\n",
    "df.to_csv(file_path, index=False)\n",
    "\n",
    "print(\"변환 완료\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "yunha",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}