ML new .ipynb

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     "text": [
      "An error occurred: Pandas requires version '3.1.0' or newer of 'openpyxl' (version '3.0.10' currently installed).\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def preprocess_dataset(df):\n",
    "    # 1. Time of Day\n",
    "    df['hour_of_day'] = df['step'] % 24\n",
    "\n",
    "    # 2. Day of the Week (assuming 'step' represents days)\n",
    "    df['day_of_week'] = df['step'] % 7\n",
    "\n",
    "    # 3. Transaction Frequency\n",
    "    transaction_frequency = df.groupby('nameOrig').size().reset_index(name='transaction_frequency')\n",
    "    df = pd.merge(df, transaction_frequency, on='nameOrig', how='left')\n",
    "\n",
    "    # 4. Transaction Amount Category\n",
    "    bins = [0, 1000, 10000, 100000, float('inf')]\n",
    "    labels = ['small', 'medium', 'large', 'very large']\n",
    "    df['amount_category'] = pd.cut(df['amount'], bins=bins, labels=labels)\n",
    "\n",
    "    # 5. Transaction Origin Type\n",
    "    df['origin_type'] = df['nameOrig'].apply(lambda x: 'individual' if x.startswith('C') else 'business')\n",
    "\n",
    "    # 6. Transaction Destination Type\n",
    "    df['dest_type'] = df['nameDest'].apply(lambda x: 'individual' if x.startswith('C') else 'business')\n",
    "\n",
    "    # 7. Transaction Duration\n",
    "    df['transaction_duration'] = df.groupby('nameOrig')['step'].diff().fillna(0)\n",
    "\n",
    "    # 8. Transaction Velocity\n",
    "    df['transaction_velocity'] = df['amount'] / df['transaction_duration']\n",
    "\n",
    "    # 9. Transaction Amount Relative to Balance\n",
    "    df['amount_relative_to_balance'] = df['amount'] / df['oldbalanceOrg']\n",
    "\n",
    "    # 10. Flagged Fraud Reason (if available, add appropriate column)\n",
    "    df['flagged_fraud_reason'] = ''\n",
    "\n",
    "    # Limit 'step' values to 9\n",
    "    df.loc[df['step'] > 9, 'step'] = 9\n",
    "\n",
    "    return df\n",
    "\n",
    "def process_dataset(file_path):\n",
    "    try:\n",
    "        # Read the dataset from Excel\n",
    "        df = pd.read_excel(file_path)\n",
    "\n",
    "        # Preprocess the dataset\n",
    "        df_processed = preprocess_dataset(df)\n",
    "\n",
    "        # Save the processed dataset to a new Excel file\n",
    "        output_file_path = file_path.replace('.xlsx', '_processed.xlsx')\n",
    "        df_processed.to_excel(output_file_path, index=False)\n",
    "\n",
    "        print(f\"Processed dataset saved to {output_file_path}\")\n",
    "    except Exception as e:\n",
    "        print(\"An error occurred:\", e)\n",
    "\n",
    "# Specify the path to your Excel dataset file\n",
    "dataset_path = r'C:\\Users\\kunta\\Downloads\\50K.xlsx'\n",
    "# Call the function to process the dataset\n",
    "process_dataset(dataset_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef668e2a",
   "metadata": {},
   "source": [
    "Here are the new features that are added along with the formulas used to derive them:\n",
    "\n",
    "hour_of_day: The hour of the day when the transaction occurred.\n",
    "Formula: hour_of_day = step % 24\n",
    "day_of_week: The day of the week when the transaction occurred.\n",
    "Formula: day_of_week = step % 7\n",
    "transaction_frequency: Frequency of transactions for each 'nameOrig'.\n",
    "Formula: Count of transactions grouped by 'nameOrig'.\n",
    "amount_category: Categorization of transaction amount.\n",
    "Formula: Transaction amount is categorized into bins ('small', 'medium', 'large', 'very large').\n",
    "origin_type: Type of origin account ('individual' or 'business').\n",
    "Formula: If 'nameOrig' starts with 'C', it's categorized as 'individual'; otherwise, it's categorized as 'business'.\n",
    "dest_type: Type of destination account ('individual' or 'business').\n",
    "Formula: If 'nameDest' starts with 'C', it's categorized as 'individual'; otherwise, it's categorized as 'business'.\n",
    "transaction_duration: Duration of the transaction for each 'nameOrig'.\n",
    "Formula: Difference in 'step' values for each 'nameOrig'.\n",
    "transaction_velocity: Velocity of the transaction.\n",
    "Formula: Transaction amount divided by transaction duration.\n",
    "amount_relative_to_balance: Amount relative to the balance before the transaction.\n",
    "Formula: Transaction amount divided by the original balance before the transaction.\n",
    "flagged_fraud_reason: Reason for flagging the transaction as fraudulent (empty column).\n",
    "No specific formula provided; this column is left empty for potential future use.\n",
    "These new features provide additional insights into the transactions and account types, making the dataset more informative for further analysis.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
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     "output_type": "stream",
     "text": [
      "An error occurred: Pandas requires version '3.1.0' or newer of 'openpyxl' (version '3.0.10' currently installed).\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def preprocess_dataset(df):\n",
    "    # 1. Time of Day\n",
    "    df['hour_of_day'] = df['step'] % 24\n",
    "\n",
    "    # 2. Day of the Week (assuming 'step' represents days)\n",
    "    df['day_of_week'] = df['step'] % 7\n",
    "\n",
    "    # 3. Transaction Frequency\n",
    "    transaction_frequency = df.groupby('nameOrig').size().reset_index(name='transaction_frequency')\n",
    "    df = pd.merge(df, transaction_frequency, on='nameOrig', how='left')\n",
    "\n",
    "    # 4. Transaction Amount Category\n",
    "    bins = [0, 1000, 10000, 100000, float('inf')]\n",
    "    labels = ['small', 'medium', 'large', 'very large']\n",
    "    df['amount_category'] = pd.cut(df['amount'], bins=bins, labels=labels)\n",
    "\n",
    "    # 5. Transaction Origin Type\n",
    "    df['origin_type'] = df['nameOrig'].apply(lambda x: 'individual' if x.startswith('C') else 'business')\n",
    "\n",
    "    # 6. Transaction Destination Type\n",
    "    df['dest_type'] = df['nameDest'].apply(lambda x: 'individual' if x.startswith('C') else 'business')\n",
    "\n",
    "    # 7. Transaction Duration\n",
    "    df['transaction_duration'] = df.groupby('nameOrig')['step'].diff().fillna(0)\n",
    "\n",
    "    # 8. Transaction Velocity\n",
    "    df['transaction_velocity'] = df['amount'] / df['transaction_duration']\n",
    "\n",
    "    # 9. Transaction Amount Relative to Balance\n",
    "    df['amount_relative_to_balance'] = df['amount'] / df['oldbalanceOrg']\n",
    "\n",
    "    # 10. Flagged Fraud Reason (if available, add appropriate column)\n",
    "    df['flagged_fraud_reason'] = ''\n",
    "\n",
    "    # Limit 'step' values to 9\n",
    "    df.loc[df['step'] > 9, 'step'] = 9\n",
    "\n",
    "    return df\n",
    "\n",
    "def process_dataset(file_path):\n",
    "    try:\n",
    "        # Read the dataset from Excel\n",
    "        df = pd.read_excel(file_path)\n",
    "\n",
    "        # Preprocess the dataset\n",
    "        df_processed = preprocess_dataset(df)\n",
    "\n",
    "        # Save the processed dataset to a new Excel file\n",
    "        output_file_path = file_path.replace('.xlsx', '_processed.xlsx')\n",
    "        df_processed.to_excel(output_file_path, index=False)\n",
    "\n",
    "        print(f\"Processed dataset saved to {output_file_path}\")\n",
    "    except Exception as e:\n",
    "        print(\"An error occurred:\", e)\n",
    "\n",
    "# Specify the path to your Excel dataset file\n",
    "dataset_path = r'C:\\Users\\kunta\\Downloads\\50K.xlsx'\n",
    "# Call the function to process the dataset\n",
    "process_dataset(dataset_path)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "id": "78cec357",
   "metadata": {
    "collapsed": true,
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   "outputs": [],
   "source": [
    "data = pd.read_excel(r\"C:\\Users\\kunta\\Downloads\\50K_processed.xlsx\", engine='openpyxl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "34b508d1",
   "metadata": {
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
      "count  49999.000000  4.999900e+04   4.999900e+04    4.999900e+04   \n",
      "mean       7.453769  1.562676e+05   7.332995e+05    7.472655e+05   \n",
      "std        2.056645  3.243974e+05   2.202426e+06    2.239993e+06   \n",
      "min        1.000000  6.300000e-01   0.000000e+00    0.000000e+00   \n",
      "25%        7.000000  7.735090e+03   0.000000e+00    0.000000e+00   \n",
      "50%        8.000000  3.341600e+04   1.703000e+04    0.000000e+00   \n",
      "75%        9.000000  1.878404e+05   1.331625e+05    1.438245e+05   \n",
      "max        9.000000  1.000000e+07   2.854724e+07    2.861740e+07   \n",
      "\n",
      "       oldbalanceDest  newbalanceDest       isFraud  isFlaggedFraud  \\\n",
      "count    4.999900e+04    4.999900e+04  49999.000000         49999.0   \n",
      "mean     8.444840e+05    1.164423e+06      0.002000             0.0   \n",
      "std      2.433409e+06    2.915227e+06      0.044678             0.0   \n",
      "min      0.000000e+00    0.000000e+00      0.000000             0.0   \n",
      "25%      0.000000e+00    0.000000e+00      0.000000             0.0   \n",
      "50%      2.139000e+03    0.000000e+00      0.000000             0.0   \n",
      "75%      4.632130e+05    8.649264e+05      0.000000             0.0   \n",
      "max      3.014320e+07    3.197699e+07      1.000000             0.0   \n",
      "\n",
      "        hour_of_day   day_of_week  transaction_frequency  \\\n",
      "count  49999.000000  49999.000000                49999.0   \n",
      "mean       7.453769      1.456749                    1.0   \n",
      "std        2.056645      1.197974                    0.0   \n",
      "min        1.000000      0.000000                    1.0   \n",
      "25%        7.000000      1.000000                    1.0   \n",
      "50%        8.000000      1.000000                    1.0   \n",
      "75%        9.000000      2.000000                    1.0   \n",
      "max        9.000000      6.000000                    1.0   \n",
      "\n",
      "       transaction_duration  transaction_velocity  amount_relative_to_balance  \\\n",
      "count               49999.0               49999.0                4.999900e+04   \n",
      "mean                    0.0                   inf                         inf   \n",
      "std                     0.0                   NaN                         NaN   \n",
      "min                     0.0                   inf                5.012207e-07   \n",
      "25%                     0.0                   NaN                1.078416e-01   \n",
      "50%                     0.0                   NaN                2.337438e+00   \n",
      "75%                     0.0                   NaN                         NaN   \n",
      "max                     0.0                   inf                         inf   \n",
      "\n",
      "       flagged_fraud_reason  \n",
      "count                   0.0  \n",
      "mean                    NaN  \n",
      "std                     NaN  \n",
      "min                     NaN  \n",
      "25%                     NaN  \n",
      "50%                     NaN  \n",
      "75%                     NaN  \n",
      "max                     NaN  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\numpy\\lib\\function_base.py:4655: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n",
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\numpy\\lib\\function_base.py:4655: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n"
     ]
    }
   ],
   "source": [
    "# Basic statistics\n",
    "print(data.describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6cd6a89c",
   "metadata": {
    "scrolled": true
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    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 49999 entries, 0 to 49998\n",
      "Data columns (total 21 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   step                        49999 non-null  int64  \n",
      " 1   type                        49999 non-null  object \n",
      " 2   amount                      49999 non-null  float64\n",
      " 3   nameOrig                    49999 non-null  object \n",
      " 4   oldbalanceOrg               49999 non-null  float64\n",
      " 5   newbalanceOrig              49999 non-null  float64\n",
      " 6   nameDest                    49999 non-null  object \n",
      " 7   oldbalanceDest              49999 non-null  float64\n",
      " 8   newbalanceDest              49999 non-null  float64\n",
      " 9   isFraud                     49999 non-null  int64  \n",
      " 10  isFlaggedFraud              49999 non-null  int64  \n",
      " 11  hour_of_day                 49999 non-null  int64  \n",
      " 12  day_of_week                 49999 non-null  int64  \n",
      " 13  transaction_frequency       49999 non-null  int64  \n",
      " 14  amount_category             49999 non-null  object \n",
      " 15  origin_type                 49999 non-null  object \n",
      " 16  dest_type                   49999 non-null  object \n",
      " 17  transaction_duration        49999 non-null  int64  \n",
      " 18  transaction_velocity        49999 non-null  float64\n",
      " 19  amount_relative_to_balance  49999 non-null  float64\n",
      " 20  flagged_fraud_reason        0 non-null      float64\n",
      "dtypes: float64(8), int64(7), object(6)\n",
      "memory usage: 8.0+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Data types and missing values\n",
    "print(data.info())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "739e53ea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type\n",
      "PAYMENT     21911\n",
      "CASH_OUT    13756\n",
      "CASH_IN      8992\n",
      "TRANSFER     4700\n",
      "DEBIT         640\n",
      "Name: count, dtype: int64\n",
      "day_of_week\n",
      "1    23805\n",
      "2    15915\n",
      "0     6837\n",
      "6     1660\n",
      "5      665\n",
      "4      565\n",
      "3      552\n",
      "Name: count, dtype: int64\n",
      "amount_category\n",
      "very large    18853\n",
      "large         15957\n",
      "medium        13408\n",
      "small          1781\n",
      "Name: count, dtype: int64\n",
      "origin_type\n",
      "individual    49999\n",
      "Name: count, dtype: int64\n",
      "dest_type\n",
      "individual    28088\n",
      "business      21911\n",
      "Name: count, dtype: int64\n",
      "Series([], Name: count, dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "# Unique values in categorical columns\n",
    "print(data['type'].value_counts())\n",
    "print(data['day_of_week'].value_counts())\n",
    "print(data['amount_category'].value_counts())\n",
    "print(data['origin_type'].value_counts())\n",
    "print(data['dest_type'].value_counts())\n",
    "print(data['flagged_fraud_reason'].value_counts())\n"
   ]
  },
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   "execution_count": 10,
   "id": "b00b5c67",
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     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Distribution of numerical features\n",
    "numerical_features = ['amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest']\n",
    "for feature in numerical_features:\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.histplot(data[feature], kde=True, bins=30)\n",
    "    plt.title(f'Distribution of {feature}')\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e847385",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Information:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 49999 entries, 0 to 49998\n",
      "Data columns (total 21 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   step                        49999 non-null  int64  \n",
      " 1   type                        49999 non-null  object \n",
      " 2   amount                      49999 non-null  float64\n",
      " 3   nameOrig                    49999 non-null  object \n",
      " 4   oldbalanceOrg               49999 non-null  float64\n",
      " 5   newbalanceOrig              49999 non-null  float64\n",
      " 6   nameDest                    49999 non-null  object \n",
      " 7   oldbalanceDest              49999 non-null  float64\n",
      " 8   newbalanceDest              49999 non-null  float64\n",
      " 9   isFraud                     49999 non-null  int64  \n",
      " 10  isFlaggedFraud              49999 non-null  int64  \n",
      " 11  hour_of_day                 49999 non-null  int64  \n",
      " 12  day_of_week                 49999 non-null  int64  \n",
      " 13  transaction_frequency       49999 non-null  int64  \n",
      " 14  amount_category             49999 non-null  object \n",
      " 15  origin_type                 49999 non-null  object \n",
      " 16  dest_type                   49999 non-null  object \n",
      " 17  transaction_duration        49999 non-null  int64  \n",
      " 18  transaction_velocity        49999 non-null  float64\n",
      " 19  amount_relative_to_balance  49999 non-null  float64\n",
      " 20  flagged_fraud_reason        0 non-null      float64\n",
      "dtypes: float64(8), int64(7), object(6)\n",
      "memory usage: 8.0+ MB\n",
      "None\n",
      "\n",
      "Summary Statistics:\n",
      "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
      "count  49999.000000  4.999900e+04   4.999900e+04    4.999900e+04   \n",
      "mean       7.453769  1.562676e+05   7.332995e+05    7.472655e+05   \n",
      "std        2.056645  3.243974e+05   2.202426e+06    2.239993e+06   \n",
      "min        1.000000  6.300000e-01   0.000000e+00    0.000000e+00   \n",
      "25%        7.000000  7.735090e+03   0.000000e+00    0.000000e+00   \n",
      "50%        8.000000  3.341600e+04   1.703000e+04    0.000000e+00   \n",
      "75%        9.000000  1.878404e+05   1.331625e+05    1.438245e+05   \n",
      "max        9.000000  1.000000e+07   2.854724e+07    2.861740e+07   \n",
      "\n",
      "       oldbalanceDest  newbalanceDest       isFraud  isFlaggedFraud  \\\n",
      "count    4.999900e+04    4.999900e+04  49999.000000         49999.0   \n",
      "mean     8.444840e+05    1.164423e+06      0.002000             0.0   \n",
      "std      2.433409e+06    2.915227e+06      0.044678             0.0   \n",
      "min      0.000000e+00    0.000000e+00      0.000000             0.0   \n",
      "25%      0.000000e+00    0.000000e+00      0.000000             0.0   \n",
      "50%      2.139000e+03    0.000000e+00      0.000000             0.0   \n",
      "75%      4.632130e+05    8.649264e+05      0.000000             0.0   \n",
      "max      3.014320e+07    3.197699e+07      1.000000             0.0   \n",
      "\n",
      "        hour_of_day   day_of_week  transaction_frequency  \\\n",
      "count  49999.000000  49999.000000                49999.0   \n",
      "mean       7.453769      1.456749                    1.0   \n",
      "std        2.056645      1.197974                    0.0   \n",
      "min        1.000000      0.000000                    1.0   \n",
      "25%        7.000000      1.000000                    1.0   \n",
      "50%        8.000000      1.000000                    1.0   \n",
      "75%        9.000000      2.000000                    1.0   \n",
      "max        9.000000      6.000000                    1.0   \n",
      "\n",
      "       transaction_duration  transaction_velocity  amount_relative_to_balance  \\\n",
      "count               49999.0               49999.0                4.999900e+04   \n",
      "mean                    0.0                   inf                         inf   \n",
      "std                     0.0                   NaN                         NaN   \n",
      "min                     0.0                   inf                5.012207e-07   \n",
      "25%                     0.0                   NaN                1.078416e-01   \n",
      "50%                     0.0                   NaN                2.337438e+00   \n",
      "75%                     0.0                   NaN                         NaN   \n",
      "max                     0.0                   inf                         inf   \n",
      "\n",
      "       flagged_fraud_reason  \n",
      "count                   0.0  \n",
      "mean                    NaN  \n",
      "std                     NaN  \n",
      "min                     NaN  \n",
      "25%                     NaN  \n",
      "50%                     NaN  \n",
      "75%                     NaN  \n",
      "max                     NaN  \n",
      "\n",
      "First few rows of the dataset:\n",
      "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
      "0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n",
      "1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n",
      "2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n",
      "3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n",
      "4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n",
      "\n",
      "      nameDest  oldbalanceDest  newbalanceDest  isFraud  ...  hour_of_day  \\\n",
      "0  M1979787155             0.0             0.0        0  ...            1   \n",
      "1  M2044282225             0.0             0.0        0  ...            1   \n",
      "2   C553264065             0.0             0.0        1  ...            1   \n",
      "3    C38997010         21182.0             0.0        1  ...            1   \n",
      "4  M1230701703             0.0             0.0        0  ...            1   \n",
      "\n",
      "   day_of_week  transaction_frequency  amount_category origin_type  \\\n",
      "0            1                      1           medium  individual   \n",
      "1            1                      1           medium  individual   \n",
      "2            1                      1            small  individual   \n",
      "3            1                      1            small  individual   \n",
      "4            1                      1            large  individual   \n",
      "\n",
      "    dest_type transaction_duration  transaction_velocity  \\\n",
      "0    business                    0                   inf   \n",
      "1    business                    0                   inf   \n",
      "2  individual                    0                   inf   \n",
      "3  individual                    0                   inf   \n",
      "4    business                    0                   inf   \n",
      "\n",
      "   amount_relative_to_balance  flagged_fraud_reason  \n",
      "0                    0.057834                   NaN  \n",
      "1                    0.087735                   NaN  \n",
      "2                    1.000000                   NaN  \n",
      "3                    1.000000                   NaN  \n",
      "4                    0.280795                   NaN  \n",
      "\n",
      "[5 rows x 21 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\numpy\\lib\\function_base.py:4655: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n",
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\numpy\\lib\\function_base.py:4655: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n",
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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aa6/pyJEjHl/K0rdvX82bN0/BwcGlTv9Wnt/jkCFDtGTJEo0dO1a33Xab9u/fr7/97W+KjIzUrl27znselXltWWbOnKkePXqoS5cuevTRR3XFFVfo0KFD+vjjjzVnzhwFBgbq6aef1ooVK9StWzeNHz9e0dHROnPmjPbs2aPPPvtMr7/+ukfJxqFDh5SUlCTDMHTixAlt3bpVb731ln788Uc9/PDDHjNiTJgwQYsXL9b111+vhx9+WB06dFBBQYH27dun5cuXa9KkSerSpYuefPJJHThwQH379lWTJk10/Phxvfzyy/Lx8TG/bKZVq1by8/PTokWL1LZtWzVo0EBRUVHlHukG6oSa/NQggNqraAaIosXhcBhhYWFGz549jalTp5b6Cf7iM06sW7fOuOWWW4zmzZsbTqfTaNSokdGzZ0/j448/9njdypUrjWuuucZwOp2GJHMWgqL9HT58+ILHMozCGQwGDx5sfPjhh8ZVV11lOBwOo0WLFsbMmTNLvP7nn382+vfvbwQFBRmNGzc2xo0bZyxbtqzE7BbHjh0zbrvtNqNhw4aGxWLxOKZKmZVjy5YtxtChQw2Xy2U4HA6jY8eOJWYIKJpJ4oMPPvBYXzQbRXlmFPj222+NPn36GAEBAYafn5/RtWtX45NPPil1fxWZ3aK8fVq9erUxePBgIyQkxPDx8TEuu+wyY/DgwSVen5GRYVitViMgIMDIzc011y9atMiQZAwfPrxEXyrye3z22WeNFi1aGE6n02jbtq3xxhtvlHlvFJ/doryvVRmzTJS2z+3btxu///3vjUaNGhkOh8No1qyZMWrUKOPMmTNmm8OHDxvjx483WrZsafj4+BghISFGbGys8cQTTxgnT570OG7RYrVajaCgIKN9+/bGvffeW+YMKCdPnjT+93//14iOjjYcDofhcrmM9u3bGw8//LCRlpZmGIZhfPrpp8agQYOMyy67zPx3feONNxrffvutx77effdd48orrzR8fHzKnIEGqMsshnGBj6cDAAAA9QyzWwAAAADFEJIBAACAYgjJAAAAQDGEZAAAAKAYQjIAAABQDCEZAAAAKIYvE6lCBQUFOnjwoAIDA6t1Mn8AAAB4xzj75TxRUVGyWsseLyYkV6GDBw+qadOmNd0NAAAAXMD+/fs9vuGyOEJyFQoMDJRUeNGDgoJquDcAAAAoLisrS02bNjVzW1kIyVWoqMQiKCiIkAwAAFCLXag0lg/uAQAAAMUQkgEAAIBiCMkAAABAMYRkAAAAoBhCMgAAAFAMIRkAAAAohpAMAAAAFENIBgAAAIohJAMAAADFEJIBAACAYgjJAAAAQDGEZAAAAKAYQjIAAABQDCEZAAAAKIaQDAAAABRDSAYAAACKISQDAAAAxdhrugPwTtaZPG3ad1xn8twacFVETXcHAACgTiEkX6I278/UyH99r5ahAYRkAACAKka5xSXqyshASdKeo9k6lZtfw70BAACoWwjJl6jQBk41DnTKMKSdaSdqujsAAAB1CiH5EnZlROFo8k+EZAAAgCpFSL6EtY0MkiTtSM2q4Z4AAADULYTkS1jbs3XJP6UykgwAAFCVCMmXsCsjzo4kp2XJMIwa7g0AAEDdQUi+hLVq3EA+NotOnMnXr8dP13R3AAAA6gxC8iXMYbeqVeMGkii5AAAAqEqE5EscH94DAACoeoTkS5z54T2mgQMAAKgyhORL3Lkf3gMAAEDVICRf4syvpz6SrdO57hruDQAAQN1ASL7EhQX6KrSBQwWG9PMhSi4AAACqAiG5DigqufiJkgsAAIAqQUiuA66MKCy52ME0cAAAAFWCkFwHXBFWOFfynqPZNdwTAACAuoGQXAcEOO2SpDN5fHAPAACgKhCS6wBfH5sk6UxeQQ33BAAAoG4gJNcBvj6Fv0ZGkgEAAKoGIbkOcNoLR5Jz8xlJBgAAqAqE5DqAkWQAAICqVWtC8rRp02SxWDRhwgRznWEYmjJliqKiouTn56devXpp27ZtHq/LycnRuHHjFBoaqoCAAA0bNkwHDhzwaJORkaH4+Hi5XC65XC7Fx8fr+PHjHm327dunoUOHKiAgQKGhoRo/frxyc3Or63SrlFmTzEgyAABAlagVIXnDhg365z//qQ4dOnisf/755zVz5kzNmjVLGzZsUEREhG644QadOPHbfMATJkzQ0qVLlZCQoDVr1ujkyZMaMmSI3O7fRlVHjBihlJQUJSYmKjExUSkpKYqPjze3u91uDR48WNnZ2VqzZo0SEhK0ePFiTZo0qfpPvgo47YW/xhxGkgEAAKqGUcNOnDhhtG7d2lixYoXRs2dP46GHHjIMwzAKCgqMiIgI49lnnzXbnjlzxnC5XMbrr79uGIZhHD9+3PDx8TESEhLMNr/++qthtVqNxMREwzAMY/v27YYkIykpyWyzbt06Q5Lx008/GYZhGJ999plhtVqNX3/91Wzz7rvvGk6n08jMzCz3uWRmZhqSKvSaqpCWedpo/pdPjcsfW3ZRjwsAAHCpKW9eq/GR5AceeECDBw9Wv379PNbv3r1baWlp6t+/v7nO6XSqZ8+eWrt2rSQpOTlZeXl5Hm2ioqIUExNjtlm3bp1cLpe6dOlitunatatcLpdHm5iYGEVFRZltBgwYoJycHCUnJ5fZ95ycHGVlZXksNcH37Af33AWG8tyUXAAAAFSWvSYPnpCQoB9++EEbNmwosS0tLU2SFB4e7rE+PDxce/fuNds4HA4FBweXaFP0+rS0NIWFhZXYf1hYmEeb4scJDg6Ww+Ew25Rm2rRpeuqppy50mtXO6fPbe52c/AL52Gr8vQ8AAMAlrcbS1P79+/XQQw9p4cKF8vX1LbOdxWLxeG4YRol1xRVvU1p7b9oU99hjjykzM9Nc9u/ff95+VZeimmSJGS4AAACqQo2F5OTkZKWnpys2NlZ2u112u12rV6/WK6+8Irvdbo7sFh/JTU9PN7dFREQoNzdXGRkZ521z6NChEsc/fPiwR5vix8nIyFBeXl6JEeZzOZ1OBQUFeSw1wWKxmEGZkAwAAFB5NRaS+/btqy1btiglJcVcOnfurD/+8Y9KSUnR5ZdfroiICK1YscJ8TW5urlavXq1u3bpJkmJjY+Xj4+PRJjU1VVu3bjXbxMXFKTMzU99//73ZZv369crMzPRos3XrVqWmppptli9fLqfTqdjY2Gq9DlWFr6YGAACoOjVWkxwYGKiYmBiPdQEBAWrUqJG5fsKECZo6dapat26t1q1ba+rUqfL399eIESMkSS6XS6NHj9akSZPUqFEjhYSEaPLkyWrfvr35QcC2bdtq4MCBGjNmjObMmSNJuvfeezVkyBBFR0dLkvr376927dopPj5e06dP17FjxzR58mSNGTOmxkaHK8qcBi6fkWQAAIDKqtEP7l3II488otOnT2vs2LHKyMhQly5dtHz5cgUGBpptXnzxRdntdt1+++06ffq0+vbtq/nz58tms5ltFi1apPHjx5uzYAwbNkyzZs0yt9tsNi1btkxjx45V9+7d5efnpxEjRuiFF164eCdbSYwkAwAAVB2LYRhGTXeirsjKypLL5VJmZuZFH4Hu/+Jq/XzopN75cxd1uyL0oh4bAADgUlHevMZcYXWE8+xcyTl8NTUAAEClEZLrCF8fZrcAAACoKoTkOsKsSeaDewAAAJVGSK4jfpsnmXILAACAyiIk1xHOsyPJOZRbAAAAVBohuY7wtReVWzCSDAAAUFm1ep5kXNi+fft05MgRnczMlCTt3ndAP/yQ6dW+QkND1axZs6rsHgAAwCWJkHwJ27dvn65s21anT51ScJ8/K+jam/XPN+fp+W8WeLU/P39//bRjB0EZAADUe4TkS9iRI0d0+tQp/fEv03U4qI12Zkmx/Yer4x03VXhfh/b9okXP/T8dOXKEkAwAAOo9QnIdEN6slfLtjaWsY/INDFaT1uE13SUAAIBLGh/cqyPs1sJfpbuAbxkHAACoLEJyHWG3WiRJ+YRkAACASiMk1xE2GyEZAACgqhCS6whzJNnNPMkAAACVRUiuI4pqkhlJBgAAqDxCch1RNJLMB/cAAAAqj5BcR9iLapLdhGQAAIDKIiTXETZzdgtqkgEAACqLkFxHUJMMAABQdQjJdYSdKeAAAACqDCG5jigqt3BTkwwAAFBphOQ6wpzdwjBkGARlAACAyiAk1xFFNckSJRcAAACVRUiuI4pGkiVCMgAAQGURkusIq9WiopxMXTIAAEDlEJLrkN+mgWOuZAAAgMogJNchv32hCCPJAAAAlUFIrkOYKxkAAKBqEJLrEDtzJQMAAFQJQnIdQk0yAABA1SAk1yHUJAMAAFQNQnIdYtYkU24BAABQKYTkOsRujiRTbgEAAFAZhOQ65LeaZEaSAQAAKoOQXIfYzpZbuAnJAAAAlUJIrkPMcgtqkgEAACqFkFyHUJMMAABQNQjJdQg1yQAAAFWDkFyHmDXJlFsAAABUCiG5DrHzZSIAAABVgpBch1CTDAAAUDVqNCTPnj1bHTp0UFBQkIKCghQXF6fPP//c3D5q1ChZLBaPpWvXrh77yMnJ0bhx4xQaGqqAgAANGzZMBw4c8GiTkZGh+Ph4uVwuuVwuxcfH6/jx4x5t9u3bp6FDhyogIEChoaEaP368cnNzq+3cq0NRTTJTwAEAAFROjYbkJk2a6Nlnn9XGjRu1ceNG9enTRzfddJO2bdtmthk4cKBSU1PN5bPPPvPYx4QJE7R06VIlJCRozZo1OnnypIYMGSK32222GTFihFJSUpSYmKjExESlpKQoPj7e3O52uzV48GBlZ2drzZo1SkhI0OLFizVp0qTqvwhViK+lBgAAqBr2mjz40KFDPZ4/88wzmj17tpKSknTVVVdJkpxOpyIiIkp9fWZmpubOnau3335b/fr1kyQtXLhQTZs21cqVKzVgwADt2LFDiYmJSkpKUpcuXSRJb7zxhuLi4rRz505FR0dr+fLl2r59u/bv36+oqChJ0owZMzRq1Cg988wzCgoKqq5LUKWoSQYAAKgataYm2e12KyEhQdnZ2YqLizPXr1q1SmFhYWrTpo3GjBmj9PR0c1tycrLy8vLUv39/c11UVJRiYmK0du1aSdK6devkcrnMgCxJXbt2lcvl8mgTExNjBmRJGjBggHJycpScnFxmn3NycpSVleWx1CQbNckAAABVosZD8pYtW9SgQQM5nU7df//9Wrp0qdq1aydJGjRokBYtWqSvvvpKM2bM0IYNG9SnTx/l5ORIktLS0uRwOBQcHOyxz/DwcKWlpZltwsLCShw3LCzMo014eLjH9uDgYDkcDrNNaaZNm2bWObtcLjVt2tT7C1EF7DbmSQYAAKgKNVpuIUnR0dFKSUnR8ePHtXjxYo0cOVKrV69Wu3btdMcdd5jtYmJi1LlzZzVv3lzLli3T8OHDy9ynYRiyWCzm83N/rkyb4h577DFNnDjRfJ6VlVWjQZmvpQYAAKgaNT6S7HA4dMUVV6hz586aNm2aOnbsqJdffrnUtpGRkWrevLl27dolSYqIiFBubq4yMjI82qWnp5sjwxERETp06FCJfR0+fNijTfER44yMDOXl5ZUYYT6X0+k0Z+YoWmpSUbkFs1sAAABUTo2H5OIMwzDLKYo7evSo9u/fr8jISElSbGysfHx8tGLFCrNNamqqtm7dqm7dukmS4uLilJmZqe+//95ss379emVmZnq02bp1q1JTU802y5cvl9PpVGxsbJWfY3VhnmQAAICqUaPlFo8//rgGDRqkpk2b6sSJE0pISNCqVauUmJiokydPasqUKbr11lsVGRmpPXv26PHHH1doaKhuueUWSZLL5dLo0aM1adIkNWrUSCEhIZo8ebLat29vznbRtm1bDRw4UGPGjNGcOXMkSffee6+GDBmi6OhoSVL//v3Vrl07xcfHa/r06Tp27JgmT56sMWPG1PjocEWYNcmUWwAAAFRKjYbkQ4cOKT4+XqmpqXK5XOrQoYMSExN1ww036PTp09qyZYveeustHT9+XJGRkerdu7fee+89BQYGmvt48cUXZbfbdfvtt+v06dPq27ev5s+fL5vNZrZZtGiRxo8fb86CMWzYMM2aNcvcbrPZtGzZMo0dO1bdu3eXn5+fRowYoRdeeOHiXYwqcO4UcBeqpwYAAEDZajQkz507t8xtfn5++uKLLy64D19fX7366qt69dVXy2wTEhKihQsXnnc/zZo106effnrB49VmRSFZkgoMyUZGBgAA8Eqtq0mG92znpGLqkgEAALxHSK5DbOeUV1CXDAAA4D1Cch1isVj4amoAAIAqQEiuY+zMlQwAAFBphOQ65rdp4KhJBgAA8BYhuY6xUW4BAABQaYTkOoaaZAAAgMojJNcxdhtfTQ0AAFBZhOQ6xm4t/JW6mQIOAADAa4TkOoaaZAAAgMojJNcx1CQDAABUHiG5jjFrkpkCDgAAwGuE5DqGcgsAAIDKIyTXMeYH9wjJAAAAXiMk1zG/TQFHSAYAAPAWIbmOsVkKQzIjyQAAAN4jJNcxRTXJhGQAAADvEZLrGEIyAABA5RGS6xhCMgAAQOURkuuY36aAY55kAAAAbxGS6xhGkgEAACqPkFzH2ItmtzAIyQAAAN4iJNcxjCQDAABUHiG5jiEkAwAAVB4huY4hJAMAAFQeIbmOISQDAABUHiG5jiEkAwAAVB4huY6xMbsFAABApRGS6xhGkgEAACqPkFzHEJIBAAAqj5BcxxCSAQAAKo+QXMcUheQCQzKoSwYAAPAKIbmOKQrJEqPJAAAA3iIk1zEeIZmRZAAAAK8QkuuYoingJEaSAQAAvEVIrmMsFstvcyUTkgEAALxCSK6DmOECAACgcgjJdRAhGQAAoHIIyXUQIRkAAKByCMl1kBmSmd0CAADAK4TkOogP7gEAAFROjYbk2bNnq0OHDgoKClJQUJDi4uL0+eefm9sNw9CUKVMUFRUlPz8/9erVS9u2bfPYR05OjsaNG6fQ0FAFBARo2LBhOnDggEebjIwMxcfHy+VyyeVyKT4+XsePH/dos2/fPg0dOlQBAQEKDQ3V+PHjlZubW23nXp0otwAAAKicGg3JTZo00bPPPquNGzdq48aN6tOnj2666SYzCD///POaOXOmZs2apQ0bNigiIkI33HCDTpw4Ye5jwoQJWrp0qRISErRmzRqdPHlSQ4YMkdvtNtuMGDFCKSkpSkxMVGJiolJSUhQfH29ud7vdGjx4sLKzs7VmzRolJCRo8eLFmjRp0sW7GFWIkAwAAFA59po8+NChQz2eP/PMM5o9e7aSkpLUrl07vfTSS3riiSc0fPhwSdKCBQsUHh6ud955R/fdd58yMzM1d+5cvf322+rXr58kaeHChWratKlWrlypAQMGaMeOHUpMTFRSUpK6dOkiSXrjjTcUFxennTt3Kjo6WsuXL9f27du1f/9+RUVFSZJmzJihUaNG6ZlnnlFQUNBFvCqVVxSS8wnJAAAAXqk1Nclut1sJCQnKzs5WXFycdu/erbS0NPXv399s43Q61bNnT61du1aSlJycrLy8PI82UVFRiomJMdusW7dOLpfLDMiS1LVrV7lcLo82MTExZkCWpAEDBignJ0fJycll9jknJ0dZWVkeS23ASDIAAEDl1HhI3rJlixo0aCCn06n7779fS5cuVbt27ZSWliZJCg8P92gfHh5ubktLS5PD4VBwcPB524SFhZU4blhYmEeb4scJDg6Ww+Ew25Rm2rRpZp2zy+VS06ZNK3j21YOQDAAAUDk1HpKjo6OVkpKipKQk/c///I9Gjhyp7du3m9stZ2dqKGIYRol1xRVvU1p7b9oU99hjjykzM9Nc9u/ff95+XSzMbgEAAFA5NR6SHQ6HrrjiCnXu3FnTpk1Tx44d9fLLLysiIkKSSozkpqenm6O+ERERys3NVUZGxnnbHDp0qMRxDx8+7NGm+HEyMjKUl5dXYoT5XE6n05yZo2ipDZgnGQAAoHJqPCQXZxiGcnJy1LJlS0VERGjFihXmttzcXK1evVrdunWTJMXGxsrHx8ejTWpqqrZu3Wq2iYuLU2Zmpr7//nuzzfr165WZmenRZuvWrUpNTTXbLF++XE6nU7GxsdV6vtWBcgsAAIDKqdHZLR5//HENGjRITZs21YkTJ5SQkKBVq1YpMTFRFotFEyZM0NSpU9W6dWu1bt1aU6dOlb+/v0aMGCFJcrlcGj16tCZNmqRGjRopJCREkydPVvv27c3ZLtq2bauBAwdqzJgxmjNnjiTp3nvv1ZAhQxQdHS1J6t+/v9q1a6f4+HhNnz5dx44d0+TJkzVmzJhaMzpcEYRkAACAyqnRkHzo0CHFx8crNTVVLpdLHTp0UGJiom644QZJ0iOPPKLTp09r7NixysjIUJcuXbR8+XIFBgaa+3jxxRdlt9t1++236/Tp0+rbt6/mz58vm81mtlm0aJHGjx9vzoIxbNgwzZo1y9xus9m0bNkyjR07Vt27d5efn59GjBihF1544SJdiapFSAYAAKgci2FQuFpVsrKy5HK5lJmZeVFGoH/44QfFxsZq4j+WqEnrq8z1a/5zRMl7M3RN04a6vk3jcu3rwK5tmvnAcCUnJ6tTp07V1WUAAIAaVd68VutqklF5zG4BAABQOYTkOojZLQAAACqHkFwH2alJBgAAqBRCch3EB/cAAAAqh5BcBxGSAQAAKoeQXAcRkgEAACqHkFwHEZIBAAAqh5BcBzG7BQAAQOUQkusg5kkGAACoHEJyHUS5BQAAQOUQkuugopCcT0gGAADwCiG5DmIkGQAAoHIIyXUQIRkAAKByCMl1ELNbAAAAVA4huQ5idgsAAIDKISTXQeeWWxiMJgMAAFQYIbkOKgrJksRgMgAAQMURkuugc0MyJRcAAAAVR0iugwjJAAAAlUNIroOsFovOfnaPkAwAAOAFQnIdZc5wwQf3AAAAKoyQXEfxhSIAAADeIyTXUYRkAAAA7xGS6yhCMgAAgPcIyXUUIRkAAMB7hOQ6qigk5xcU1HBPAAAALj2E5DrKbmV2CwAAAG8Rkusocwo4yi0AAAAqjJBcR1GTDAAA4D1Cch1FSAYAAPAeIbmOIiQDAAB4j5BcRxGSAQAAvEdIrqNszG4BAADgNUJyHcXsFgAAAN4jJNdRlFsAAAB4j5BcRxGSAQAAvEdIrqMIyQAAAN4jJNdRhGQAAADvEZLrKGa3AAAA8B4huY5idgsAAADveRWSd+/eXdX9QBWj3AIAAMB7XoXkK664Qr1799bChQt15swZrw8+bdo0XXvttQoMDFRYWJhuvvlm7dy506PNqFGjZLFYPJauXbt6tMnJydG4ceMUGhqqgIAADRs2TAcOHPBok5GRofj4eLlcLrlcLsXHx+v48eMebfbt26ehQ4cqICBAoaGhGj9+vHJzc70+v5pUFJLzCckAAAAV5lVI/vHHH3XNNddo0qRJioiI0H333afvv/++wvtZvXq1HnjgASUlJWnFihXKz89X//79lZ2d7dFu4MCBSk1NNZfPPvvMY/uECRO0dOlSJSQkaM2aNTp58qSGDBkit9ttthkxYoRSUlKUmJioxMREpaSkKD4+3tzudrs1ePBgZWdna82aNUpISNDixYs1adKkCp9XbcBIMgAAgPfs3rwoJiZGM2fO1PPPP69PPvlE8+fPV48ePdS6dWuNHj1a8fHxaty48QX3k5iY6PF83rx5CgsLU3Jysq6//npzvdPpVERERKn7yMzM1Ny5c/X222+rX79+kqSFCxeqadOmWrlypQYMGKAdO3YoMTFRSUlJ6tKliyTpjTfeUFxcnHbu3Kno6GgtX75c27dv1/79+xUVFSVJmjFjhkaNGqVnnnlGQUFB3lyqGkNIBgAA8F6lPrhnt9t1yy236P3339dzzz2nX375RZMnT1aTJk109913KzU1tUL7y8zMlCSFhIR4rF+1apXCwsLUpk0bjRkzRunp6ea25ORk5eXlqX///ua6qKgoxcTEaO3atZKkdevWyeVymQFZkrp27SqXy+XRJiYmxgzIkjRgwADl5OQoOTm51P7m5OQoKyvLY6ktCMkAAADeq1RI3rhxo8aOHavIyEjNnDlTkydP1i+//KKvvvpKv/76q2666aZy78swDE2cOFE9evRQTEyMuX7QoEFatGiRvvrqK82YMUMbNmxQnz59lJOTI0lKS0uTw+FQcHCwx/7Cw8OVlpZmtgkLCytxzLCwMI824eHhHtuDg4PlcDjMNsVNmzbNrHF2uVxq2rRpuc+3upmzWzAFHAAAQIV5VW4xc+ZMzZs3Tzt37tSNN96ot956SzfeeKOs1sLM3bJlS82ZM0dXXnlluff54IMPavPmzVqzZo3H+jvuuMP8OSYmRp07d1bz5s21bNkyDR8+vMz9GYYhy9mgKMnj58q0Oddjjz2miRMnms+zsrJqTVC2n/1dMJIMAABQcV6F5NmzZ+uee+7Rn/70pzJrhZs1a6a5c+eWa3/jxo3Txx9/rG+++UZNmjQ5b9vIyEg1b95cu3btkiRFREQoNzdXGRkZHqPJ6enp6tatm9nm0KFDJfZ1+PBhc/Q4IiJC69ev99iekZGhvLy8EiPMRZxOp5xOZ7nO8WKj3AIAAMB7XpVb7Nq1S4899liZAVmSHA6HRo4ced79GIahBx98UEuWLNFXX32lli1bXvDYR48e1f79+xUZGSlJio2NlY+Pj1asWGG2SU1N1datW82QHBcXp8zMTI8ZONavX6/MzEyPNlu3bvWoo16+fLmcTqdiY2Mv2K/ahpAMAADgPa9C8rx58/TBBx+UWP/BBx9owYIF5d7PAw88oIULF+qdd95RYGCg0tLSlJaWptOnT0uSTp48qcmTJ2vdunXas2ePVq1apaFDhyo0NFS33HKLJMnlcmn06NGaNGmSvvzyS23atEl33XWX2rdvb8520bZtWw0cOFBjxoxRUlKSkpKSNGbMGA0ZMkTR0dGSpP79+6tdu3aKj4/Xpk2b9OWXX2ry5MkaM2bMJTezhURIBgAAqAyvQvKzzz6r0NDQEuvDwsI0derUcu9n9uzZyszMVK9evRQZGWku7733niTJZrNpy5Ytuummm9SmTRuNHDlSbdq00bp16xQYGGju58UXX9TNN9+s22+/Xd27d5e/v78++eQT2Ww2s82iRYvUvn179e/fX/3791eHDh309ttvm9ttNpuWLVsmX19fde/eXbfffrtuvvlmvfDCC95cohpHSAYAAPCeVzXJe/fuLbU0onnz5tq3b1+592NcYOYFPz8/ffHFFxfcj6+vr1599VW9+uqrZbYJCQnRwoULz7ufZs2a6dNPP73g8S4FZkhmdgsAAIAK82okOSwsTJs3by6x/scff1SjRo0q3SlUXlFINgypgKAMAABQIV6F5DvvvFPjx4/X119/LbfbLbfbra+++koPPfSQ7rzzzqruI7xgO2faOkouAAAAKsarcou///3v2rt3r/r27Su7vXAXBQUFuvvuuytUk4zqUzSSLBWGZB/beRoDAADAg1ch2eFw6L333tPf/vY3/fjjj/Lz81P79u3VvHnzqu4fvHRORmYkGQAAoIK8CslF2rRpozZt2lRVX1CFLBaLbFaL3AUGIRkAAKCCvArJbrdb8+fP15dffqn09HQVFBR4bP/qq6+qpHOoHDMk88E9AACACvEqJD/00EOaP3++Bg8erJiYGFnO+ZAYao+iD+8xkgwAAFAxXoXkhIQEvf/++7rxxhuruj+oQnyhCAAAgHe8mgLO4XDoiiuuqOq+oIoRkgEAALzjVUieNGmSXn755Qt+Yx5qlp2QDAAA4BWvyi3WrFmjr7/+Wp9//rmuuuoq+fj4eGxfsmRJlXQOlcNIMgAAgHe8CskNGzbULbfcUtV9QRUzQzIj/gAAABXiVUieN29eVfcD1YDZLQAAALzjVU2yJOXn52vlypWaM2eOTpw4IUk6ePCgTp48WWWdQ+UUjSTnE5IBAAAqxKuR5L1792rgwIHat2+fcnJydMMNNygwMFDPP/+8zpw5o9dff72q+wkv2G1nR5LdhGQAAICK8Gok+aGHHlLnzp2VkZEhPz8/c/0tt9yiL7/8sso6h8r5bSS54AItAQAAcC6vZ7f47rvv5HA4PNY3b95cv/76a5V0DJVntxa+B6LcAgAAoGK8GkkuKCiQ2+0usf7AgQMKDAysdKdQNezUJAMAAHjFq5B8ww036KWXXjKfWywWnTx5Un/961/5qupahJpkAAAA73hVbvHiiy+qd+/eateunc6cOaMRI0Zo165dCg0N1bvvvlvVfYSXfiu3oCYZAACgIrwKyVFRUUpJSdG7776rH374QQUFBRo9erT++Mc/enyQDzWLKeAAAAC841VIliQ/Pz/dc889uueee6qyP6hCReUWhGQAAICK8Sokv/XWW+fdfvfdd3vVGVQt84N7bsotAAAAKsKrkPzQQw95PM/Ly9OpU6fkcDjk7+9PSK4lmAIOAADAO17NbpGRkeGxnDx5Ujt37lSPHj344F4twuwWAAAA3vEqJJemdevWevbZZ0uMMqPm8ME9AAAA71RZSJYkm82mgwcPVuUuUQl2vpYaAADAK17VJH/88ccezw3DUGpqqmbNmqXu3btXScdQedQkAwAAeMerkHzzzTd7PLdYLGrcuLH69OmjGTNmVEW/UAXMKeCoSQYAAKgQr0JyAX++vyQU1SS7GUkGAACokCqtSUbtQk0yAACAd7waSZ44cWK5286cOdObQ6AK2G3UJAMAAHjDq5C8adMm/fDDD8rPz1d0dLQk6eeff5bNZlOnTp3MdhaLpWp6Ca8UjSQbRmHJRVH5BQAAAM7Pq5A8dOhQBQYGasGCBQoODpZU+AUjf/rTn3Tddddp0qRJVdpJeMd+TijOLyiQzWqrwd4AAABcOryqSZ4xY4amTZtmBmRJCg4O1t///ndmt6hFzh05ZoYLAACA8vMqJGdlZenQoUMl1qenp+vEiROV7hSqhsViYYYLAAAAL3gVkm+55Rb96U9/0ocffqgDBw7owIED+vDDDzV69GgNHz68qvuISrDz1dQAAAAV5lVN8uuvv67JkyfrrrvuUl5eXuGO7HaNHj1a06dPr9IOonLsNoty8pkGDgAAoCK8Csn+/v567bXXNH36dP3yyy8yDENXXHGFAgICqrp/qKTCr6Z2U5MMAABQAZX6MpHU1FSlpqaqTZs2CggIkGEQxGobOzXJAAAAFeZVSD569Kj69u2rNm3a6MYbb1Rqaqok6c9//jPTv9UyNmqSAQAAKsyrkPzwww/Lx8dH+/btk7+/v7n+jjvuUGJiYrn3M23aNF177bUKDAxUWFiYbr75Zu3cudOjjWEYmjJliqKiouTn56devXpp27ZtHm1ycnI0btw4hYaGKiAgQMOGDdOBAwc82mRkZCg+Pl4ul0sul0vx8fE6fvy4R5t9+/Zp6NChCggIUGhoqMaPH6/c3Nxyn09tZLedDcluapIBAADKy6uQvHz5cj333HNq0qSJx/rWrVtr79695d7P6tWr9cADDygpKUkrVqxQfn6++vfvr+zsbLPN888/r5kzZ2rWrFnasGGDIiIidMMNN3hMNTdhwgQtXbpUCQkJWrNmjU6ePKkhQ4bI7XabbUaMGKGUlBQlJiYqMTFRKSkpio+PN7e73W4NHjxY2dnZWrNmjRISErR48eJLfmS8sCaZkWQAAICK8OqDe9nZ2R4jyEWOHDkip9NZ7v0UH3WeN2+ewsLClJycrOuvv16GYeill17SE088YU4tt2DBAoWHh+udd97Rfffdp8zMTM2dO1dvv/22+vXrJ0lauHChmjZtqpUrV2rAgAHasWOHEhMTlZSUpC5dukiS3njjDcXFxWnnzp2Kjo7W8uXLtX37du3fv19RUVGSCr80ZdSoUXrmmWcUFBRUov85OTnKyckxn2dlZZX73C8WapIBAAAqzquR5Ouvv15vvfWW+dxisaigoEDTp09X7969ve5MZmamJCkkJESStHv3bqWlpal///5mG6fTqZ49e2rt2rWSpOTkZOXl5Xm0iYqKUkxMjNlm3bp1crlcZkCWpK5du8rlcnm0iYmJMQOyJA0YMEA5OTlKTk4utb/Tpk0zyzdcLpeaNm3q9blXF+ZJBgAAqDivRpKnT5+uXr16aePGjcrNzdUjjzyibdu26dixY/ruu++86ohhGJo4caJ69OihmJgYSVJaWpokKTw83KNteHi4WdaRlpYmh8Ph8RXZRW2KXp+WlqawsLASxwwLC/NoU/w4wcHBcjgcZpviHnvsMU2cONF8npWVVeuCso2aZAAAgArzKiS3a9dOmzdv1uzZs2Wz2ZSdna3hw4frgQceUGRkpFcdefDBB7V582atWbOmxDaLxeLx3DCMEuuKK96mtPbetDmX0+msUHlJTaAmGQAAoOIqHJKLShvmzJmjp556qko6MW7cOH388cf65ptvPD4MGBERIalwlPfc8J2enm6O+kZERCg3N1cZGRkeo8np6enq1q2b2ebQoUMljnv48GGP/axfv95je0ZGhvLy8kqMMF9KKLcAAACouArXJPv4+Gjr1q0XHMktD8Mw9OCDD2rJkiX66quv1LJlS4/tLVu2VEREhFasWGGuy83N1erVq80AHBsbKx8fH482qamp2rp1q9kmLi5OmZmZ+v77780269evV2ZmpkebrVu3mnM+S4WzeDidTsXGxlb6XGtK0RRwbr5xDwAAoNy8+uDe3Xffrblz51b64A888IAWLlyod955R4GBgUpLS1NaWppOnz4tqbD8YcKECZo6daqWLl2qrVu3atSoUfL399eIESMkSS6XS6NHj9akSZP05ZdfatOmTbrrrrvUvn17c7aLtm3bauDAgRozZoySkpKUlJSkMWPGaMiQIYqOjpYk9e/fX+3atVN8fLw2bdqkL7/8UpMnT9aYMWNKndniUvFbuQU1yQAAAOXlVU1ybm6u3nzzTa1YsUKdO3dWQECAx/aZM2eWaz+zZ8+WJPXq1ctj/bx58zRq1ChJ0iOPPKLTp09r7NixysjIUJcuXbR8+XIFBgaa7V988UXZ7XbdfvvtOn36tPr27av58+fLZrOZbRYtWqTx48ebs2AMGzZMs2bNMrfbbDYtW7ZMY8eOVffu3eXn56cRI0bohRdeKPd1qY0otwAAAKi4CoXk//73v2rRooW2bt2qTp06SZJ+/vlnjzYVKcMwjAsHN4vFoilTpmjKlClltvH19dWrr76qV199tcw2ISEhWrhw4XmP1axZM3366acX7NOl5LfZLQjJAAAA5VWhkNy6dWulpqbq66+/llT4NdSvvPLKJf3Btrrut5Fkyi0AAADKq0I1ycVHfj///HOPr5BG7cMUcAAAABXn1Qf3ipSnXAI1y5zdgpAMAABQbhUKyRaLpUTNcVVMBYfqY5ZbUJMMAABQbhWqSTYMQ6NGjTK/Ze7MmTO6//77S8xusWTJkqrrISrFRk0yAABAhVUoJI8cOdLj+V133VWlnUHVoyYZAACg4ioUkufNm1dd/UA1sTMFHAAAQIVV6oN7qP2KapL54B4AAED5EZLrOL6WGgAAoOIIyXVc0TfuFRhSAaPJAAAA5UJIruOKyi0kPrwHAABQXoTkOs4zJFNyAQAAUB6E5DrOYrGcM1cyI8kAAADlQUiuB2zMcAEAAFAhhOR6gK+mBgAAqBhCcj1g56upAQAAKoSQXA/YbWfnSmYkGQAAoFwIyfUA37oHAABQMYTkeoDZLQAAACqGkFwP2G3UJAMAAFQEIbkesFupSQYAAKgIQnI9YKfcAgAAoEIIyfVAUbkFH9wDAAAoH0JyPWB+cM9NTTIAAEB5EJLrAbMmmZFkAACAciEk1wPUJAMAAFQMIbkeYAo4AACAiiEk1wNF5RZupoADAAAoF0JyPUC5BQAAQMUQkusBm42QDAAAUBGE5Hrgt5FkapIBAADKg5BcD/C11AAAABVDSK4HikaS+cY9AACA8iEk1wPmFHCMJAMAAJQLIbke+O0b96hJBgAAKA9Ccj1gYwo4AACACiEk1wN2poADAACoEEJyPWB+cI+aZAAAgHIhJNcD5tdSG4YKDIIyAADAhRCS64GicguJaeAAAADKo0ZD8jfffKOhQ4cqKipKFotFH330kcf2UaNGyWKxeCxdu3b1aJOTk6Nx48YpNDRUAQEBGjZsmA4cOODRJiMjQ/Hx8XK5XHK5XIqPj9fx48c92uzbt09Dhw5VQECAQkNDNX78eOXm5lbHaV90RR/ck6hLBgAAKI8aDcnZ2dnq2LGjZs2aVWabgQMHKjU11Vw+++wzj+0TJkzQ0qVLlZCQoDVr1ujkyZMaMmSI3G632WbEiBFKSUlRYmKiEhMTlZKSovj4eHO72+3W4MGDlZ2drTVr1ighIUGLFy/WpEmTqv6ka4DVYlFRTs53Mw0cAADAhdhr8uCDBg3SoEGDztvG6XQqIiKi1G2ZmZmaO3eu3n77bfXr10+StHDhQjVt2lQrV67UgAEDtGPHDiUmJiopKUldunSRJL3xxhuKi4vTzp07FR0dreXLl2v79u3av3+/oqKiJEkzZszQqFGj9MwzzygoKKgKz7pm2K1W5boLGEkGAAAoh1pfk7xq1SqFhYWpTZs2GjNmjNLT081tycnJysvLU//+/c11UVFRiomJ0dq1ayVJ69atk8vlMgOyJHXt2lUul8ujTUxMjBmQJWnAgAHKyclRcnJymX3LyclRVlaWx1JbFdUlU5MMAABwYbU6JA8aNEiLFi3SV199pRkzZmjDhg3q06ePcnJyJElpaWlyOBwKDg72eF14eLjS0tLMNmFhYSX2HRYW5tEmPDzcY3twcLAcDofZpjTTpk0z65xdLpeaNm1aqfOtTkXTwOVRbgEAAHBBNVpucSF33HGH+XNMTIw6d+6s5s2ba9myZRo+fHiZrzMMQxbLbx9WO/fnyrQp7rHHHtPEiRPN51lZWbU2KDvshe+HcvMJyQAAABdSq0eSi4uMjFTz5s21a9cuSVJERIRyc3OVkZHh0S49Pd0cGY6IiNChQ4dK7Ovw4cMebYqPGGdkZCgvL6/ECPO5nE6ngoKCPJbaymm3SSIkAwAAlMclFZKPHj2q/fv3KzIyUpIUGxsrHx8frVixwmyTmpqqrVu3qlu3bpKkuLg4ZWZm6vvvvzfbrF+/XpmZmR5ttm7dqtTUVLPN8uXL5XQ6FRsbezFOrdoVjSTnUG4BAABwQTVabnHy5En95z//MZ/v3r1bKSkpCgkJUUhIiKZMmaJbb71VkZGR2rNnjx5//HGFhobqlltukSS5XC6NHj1akyZNUqNGjRQSEqLJkyerffv25mwXbdu21cCBAzVmzBjNmTNHknTvvfdqyJAhio6OliT1799f7dq1U3x8vKZPn65jx45p8uTJGjNmTK0eHa4Ih41yCwAAgPKq0ZC8ceNG9e7d23xeVN87cuRIzZ49W1u2bNFbb72l48ePKzIyUr1799Z7772nwMBA8zUvvvii7Ha7br/9dp0+fVp9+/bV/PnzZbPZzDaLFi3S+PHjzVkwhg0b5jE3s81m07JlyzR27Fh1795dfn5+GjFihF544YXqvgQXDTXJAAAA5VejIblXr14yjLKnJPviiy8uuA9fX1+9+uqrevXVV8tsExISooULF553P82aNdOnn356weNdqgjJAAAA5XdJ1STDe2ZIpiYZAADgggjJ9YTzbE1yDiPJAAAAF0RIricotwAAACg/QnI9QUgGAAAoP0JyPeEkJAMAAJQbIbmeMOdJ5oN7AAAAF0RIricotwAAACg/QnI9ce4UcAXnmZsaAAAAhOR6oygkS1IeJRcAAADnRUiuJ+xWq2wWiyTmSgYAALgQQnI9Ql0yAABA+RCS6xFCMgAAQPkQkusR5koGAAAoH0JyPcJcyQAAAOVDSK5HKLcAAAAoH0JyPVIUkpndAgAA4PwIyfUII8kAAADlQ0iuR8yaZEIyAADAeRGS65Gi2S1y3O4a7gkAAEDtRkiuRyi3AAAAKB9Ccj1CSAYAACgfQnI9YoZk5kkGAAA4L0JyPeK02SQxkgwAAHAhhOR6hHmSAQAAyoeQXI9QkwwAAFA+hOR6pCgk5xcYchcYNdwbAACA2ouQXI8UfZmIJOXx4T0AAIAyEZLrEZvVIrvVIomSCwAAgPMhJNczfHgPAADgwgjJ9Qwf3gMAALgwQnI9U1SXzBeKAAAAlI2QXM/8Vm7hruGeAAAA1F6E5HrGSbkFAADABRGS6xlqkgEAAC6MkFzPUJMMAABwYYTkesZpt0liJBkAAOB8CMn1DPMkAwAAXBghuZ4xyy0IyQAAAGUiJNcz5gf3qEkGAAAoEyG5nmF2CwAAgAsjJNcz1CQDAABcWI2G5G+++UZDhw5VVFSULBaLPvroI4/thmFoypQpioqKkp+fn3r16qVt27Z5tMnJydG4ceMUGhqqgIAADRs2TAcOHPBok5GRofj4eLlcLrlcLsXHx+v48eMebfbt26ehQ4cqICBAoaGhGj9+vHJzc6vjtGsUXyYCAABwYTUakrOzs9WxY0fNmjWr1O3PP/+8Zs6cqVmzZmnDhg2KiIjQDTfcoBMnTphtJkyYoKVLlyohIUFr1qzRyZMnNWTIELndv33t8ogRI5SSkqLExEQlJiYqJSVF8fHx5na3263BgwcrOztba9asUUJCghYvXqxJkyZV38nXEOZJBgAAuDB7TR580KBBGjRoUKnbDMPQSy+9pCeeeELDhw+XJC1YsEDh4eF65513dN999ykzM1Nz587V22+/rX79+kmSFi5cqKZNm2rlypUaMGCAduzYocTERCUlJalLly6SpDfeeENxcXHauXOnoqOjtXz5cm3fvl379+9XVFSUJGnGjBkaNWqUnnnmGQUFBV2Eq3FxFI0kuwsM5bsLZLdRcQMAAFBcrU1Iu3fvVlpamvr372+uczqd6tmzp9auXStJSk5OVl5enkebqKgoxcTEmG3WrVsnl8tlBmRJ6tq1q1wul0ebmJgYMyBL0oABA5STk6Pk5OQy+5iTk6OsrCyPpbZz2K2yWSySpFO57gu0BgAAqJ9qbUhOS0uTJIWHh3usDw8PN7elpaXJ4XAoODj4vG3CwsJK7D8sLMyjTfHjBAcHy+FwmG1KM23aNLPO2eVyqWnTphU8y4vPYrGogW/hHxBO5OTXcG8AAABqp1obkotYzo56FjEMo8S64oq3Ka29N22Ke+yxx5SZmWku+/fvP2+/aosGzsKQfPIMIRkAAKA0tTYkR0RESFKJkdz09HRz1DciIkK5ubnKyMg4b5tDhw6V2P/hw4c92hQ/TkZGhvLy8kqMMJ/L6XQqKCjIY7kUmCGZkWQAAIBS1dqQ3LJlS0VERGjFihXmutzcXK1evVrdunWTJMXGxsrHx8ejTWpqqrZu3Wq2iYuLU2Zmpr7//nuzzfr165WZmenRZuvWrUpNTTXbLF++XE6nU7GxsdV6njWhqNyCkAwAAFC6Gp3d4uTJk/rPf/5jPt+9e7dSUlIUEhKiZs2aacKECZo6dapat26t1q1ba+rUqfL399eIESMkSS6XS6NHj9akSZPUqFEjhYSEaPLkyWrfvr0520Xbtm01cOBAjRkzRnPmzJEk3XvvvRoyZIiio6MlSf3791e7du0UHx+v6dOn69ixY5o8ebLGjBlzyYwOVwTlFgAAAOdXoyF548aN6t27t/l84sSJkqSRI0dq/vz5euSRR3T69GmNHTtWGRkZ6tKli5YvX67AwEDzNS+++KLsdrtuv/12nT59Wn379tX8+fNls9nMNosWLdL48ePNWTCGDRvmMTezzWbTsmXLNHbsWHXv3l1+fn4aMWKEXnjhheq+BDWCcgsAAIDzsxiGYdR0J+qKrKwsuVwuZWZmXpQR6B9++EGxsbGa+I8latL6qnK/Li3rjN7bsF8NnHaN7tFSknRg1zbNfGC4kpOT1alTp+rqMgAAQI0qb16rtTXJqD6BZ0eSs3PyVVDAeyQAAIDiCMn1kJ/DJqtFMiRl51JyAQAAUBwhuR6yWiwKoC4ZAACgTITkeooP7wEAAJSNkFxPMQ0cAABA2QjJ9RRfKAIAAFA2QnI9RbkFAABA2QjJ9RTlFgAAAGUjJNdTjCQDAACUjZBcT51bk8yXLgIAAHgiJNdTAY7CkFxgSKdy3TXcGwAAgNqFkFxP2awWBThskii5AAAAKI6QXI8xDRwAAEDpCMn1GDNcAAAAlI6QXI8xwwUAAEDpCMn1GOUWAAAApSMk12OUWwAAAJSOkFyPBTp9JElZZ/JquCcAAAC1CyG5HmvUwCFJyjqTrxymSgYAADARkusxXx+bgv0LR5OP5VpquDcAAAC1ByG5nosI8pUkZeRwKwAAABQhGdVzEa7CkHyUkWQAAAATIbmeKwrJGTkWSQRlAAAAiZBc74UGOGW3WpRnWGQPuaymuwMAAFArEJLrOavVorAgpyTJGRVdw70BAACoHQjJMD+8R0gGAAAoREiGWZdMSAYAAChESIYig/wkST6NW+hMfkEN9wYAAKDmEZKhBr52+dkMWaw2/ZLBV1QDAAAQkiFJCnEYkqSfjxKSAQAACMmQJIU4C8ssdhzOreGeAAAA1DxCMiRJ4b6FI8kph3KUeYrRZAAAUL8RkiFJcjkM5abvVn6B9PnW1JruDgAAQI0iJMOUvX2VJOnfKQdrtiMAAAA1jJAMU/aObyRJSbuPKi3zTA33BgAAoOYQkmFyZx1W21AfGYb0yY+MJgMAgPqLkAwP1zUr/GKRf//4aw33BAAAoOYQkuGhW1M/2a0Wbf01S/9JP1nT3QEAAKgRhGR4CHJadX2bxpKkJT8cqOHeAAAA1AxCMkq4LbaJJOm9DfuVk++u4d4AAABcfIRklHBDu3BFBPnqaHauPt+SVtPdAQAAuOhqdUieMmWKLBaLxxIREWFuNwxDU6ZMUVRUlPz8/NSrVy9t27bNYx85OTkaN26cQkNDFRAQoGHDhunAAc8ygoyMDMXHx8vlcsnlcik+Pl7Hjx+/GKdYK/nYrPpjl2aSpAXr9tRsZwAAAGpArQ7JknTVVVcpNTXVXLZs2WJue/755zVz5kzNmjVLGzZsUEREhG644QadOHHCbDNhwgQtXbpUCQkJWrNmjU6ePKkhQ4bI7f6tjGDEiBFKSUlRYmKiEhMTlZKSovj4+It6nrXNnb9rJh+bRZv2HdfmA8drujsAAAAXVa0PyXa7XREREebSuHHhh8oMw9BLL72kJ554QsOHD1dMTIwWLFigU6dO6Z133pEkZWZmau7cuZoxY4b69euna665RgsXLtSWLVu0cuVKSdKOHTuUmJioN998U3FxcYqLi9Mbb7yhTz/9VDt37qyx865pjQOdGtw+UpL01rq9NdwbAACAi6vWh+Rdu3YpKipKLVu21J133qn//ve/kqTdu3crLS1N/fv3N9s6nU717NlTa9eulSQlJycrLy/Po01UVJRiYmLMNuvWrZPL5VKXLl3MNl27dpXL5TLblCUnJ0dZWVkeS11yd7cWkqSPfzyoY9m5NdsZAACAi6hWh+QuXbrorbfe0hdffKE33nhDaWlp6tatm44ePaq0tMIPlIWHh3u8Jjw83NyWlpYmh8Oh4ODg87YJCwsrceywsDCzTVmmTZtm1jG7XC41bdrU63Otja5p2lDtL3MpN79A736/r6a7AwAAcNHU6pA8aNAg3XrrrWrfvr369eunZcuWSZIWLFhgtrFYLB6vMQyjxLriircprX159vPYY48pMzPTXPbv33/Bc7qUWCwWjTo7mvzWuj3KzS+o2Q4BAABcJLU6JBcXEBCg9u3ba9euXeYsF8VHe9PT083R5YiICOXm5iojI+O8bQ4dOlTiWIcPHy4xSl2c0+lUUFCQx1LXDO0YpcaBTh3KytFnW1JrujsAAAAXxSUVknNycrRjxw5FRkaqZcuWioiI0IoVK8ztubm5Wr16tbp16yZJio2NlY+Pj0eb1NRUbd261WwTFxenzMxMff/992ab9evXKzMz02xTnznsVt3dtbkkae6a3TIMo4Z7BAAAUP1qdUiePHmyVq9erd27d2v9+vW67bbblJWVpZEjR8pisWjChAmaOnWqli5dqq1bt2rUqFHy9/fXiBEjJEkul0ujR4/WpEmT9OWXX2rTpk266667zPINSWrbtq0GDhyoMWPGKCkpSUlJSRozZoyGDBmi6Ojomjz9WuOPXZvLabdqy6+Z2rAn48IvAAAAuMTZa7oD53PgwAH94Q9/0JEjR9S4cWN17dpVSUlJat68cGTzkUce0enTpzV27FhlZGSoS5cuWr58uQIDA819vPjii7Lb7br99tt1+vRp9e3bV/Pnz5fNZjPbLFq0SOPHjzdnwRg2bJhmzZp1cU+2ltixY0ep669v5tSK/57WjE9/0F+6h5x3H6GhoWrWrFl1dA8AAOCisBj8/bzKZGVlyeVyKTMz86LUJ//www+KjY3VxH8sUZPWV1VqX9vXr9Kb/3dfmdt9GjVV1J9nyzAKdPCf9yn/eNn1yX7+/vppxw6CMgAAqHXKm9dq9UgyLp7TJwvneB583xOK7hBbapvv0guUdsaq7hNfV2wjd6ltDu37RYue+386cuQIIRkAAFyyCMnw0CiqeZmj0tc1Pq0Pkg9o3ymb+lzdSoG+Phe5dwAAABdHrf7gHmqXqIZ+uqyhnwoM6Ye9x2u6OwAAANWGkIwKubZF4bcXbjmYqeyc/BruDQAAQPUgJKNCmoX4KzzIKXeBoU37j9d0dwAAAKoFIRkVYrFYdG2LwingNh84zmgyAACokwjJqLDLQwMUFuhUnttQ0n+P1nR3AAAAqhwhGRVmsVh0fZvGkqStB7N0+ERODfcIAACgahGS4ZXLGvqpdVgDSdLqnw+L76QBAAB1CSEZXutxRahsVot+PX5avxzOrunuAAAAVBlCMrwW5Oej2GaFU8Kt/vmwcvJK/xY+AACASw0hGZXSuUWwXH4+OpmTr693Hq7p7gAAAFQJQjIqxcdm1YCrwmWxSDsPndC+bG4pAABw6SPRoNIiXX763dm5k1OO2WQLalzDPQIAAKgcQjKqxO9ahCgiyFd5hkWNb3pUuW5muwAAAJcuQjKqhNVq0cCYCPlYDTmjojV7YybTwgEAgEsWIRlVxuXno66h+TIK3Fq997TmfPPfmu4SAACAVwjJqFJhvoaOrfynJOm5xJ+0fFtaDfcIAACg4gjJqHInNy1T/8v9ZRjSuHc3KXnvsZruEgAAQIUQklEtxnQKUp8rw5STX6B75m/Uf9JP1HSXAAAAyo2QjGphs1o0a8Q1urppQ2WeztPdc7/XweOna7pbAAAA5UJIRrXxd9j1r1HX6vLGATqYeUZ/eCNJaZlnarpbAAAAF0RIRrUKCXBo4eguahrip71HT2nEG0lKzyIoAwCA2o2QjGoX1dBP747pqssa+um/R7L1hzeSKL0AAAC1GiEZF0WTYH+9O6aroly++uVwtm557TttP5hV090CAAAolb2mO4C6aceOHaWu/2uPIP3923ztz8rRra+t0eRuwbomwlnmfkJDQ9WsWbPq6iYAAECpCMmoUlnHDkuS7rrrrjLbWJwBCrvlcal5Rz29+oiyvl+q49++LbnzS7T18/fXTzt2EJQBAMBFRUhGlTp9srCEYvB9Tyi6Q2yZ7QoMadMxt/Zk2+Tqcqua9rhFnRu5FewwzDaH9v2iRc/9Px05coSQDAAALipCMqpFo6jmatL6qvO2aSbpl8Mn9eWOdGXlSV+lWXVFWAN1bRmiRg3KLsEAAACoboRk1KhWjRso0uWrb34+op2HTug/6Sf1n/STahkaoHBZJCu3KAAAuPiY3QI1zt9h18CYCP2xSzO1ahwgSdp9JFtJR3zU5MG39Mr64/rkx4PKPJVXwz0FAAD1BcN0qDVCGzg1pEOUjmXnakdqlrYeOKYzfkFatfe0Vu3dJJvVothmwep1ZWP1ahOmKyMCZbVaarrbAACgDiIko9YJCXCo+xWhalpwSLOf/6vum/KKtmdYtCv9pL7fc0zf7zmm5xN3KiTAobhWjdSlZYg6NmmoKyMD5bTbarr7AACgDiAko9ayWqScfVv0O8evGtmrrdKz/fRDao6SU3O07XCujmXnatnmVC3bnCpJsluliAZ2Nfa3qbG/TYFOi/x9rPL3sSjAx6qIRg3VqlmkAn19FOhrV7C/Q74+hGoAAFASIRm11nnnXLba5YxsI98WHeWMipYjorXk79KBrHwdyCo533Kh45L2eKyJcvmqRWiAoiMCFds8WJ2aBSuqoV9VngYAALgEEZJRa5V3zmVJMgzplDtX2fkWZedbdCrforwCFS6GRdmnT+twWqoim7VUnuw6lWfIbUgHM8/oYOYZrf3lqOZ9t0eS1MjPquhQh6IbORTdyEctG/rIx+ZZ+8w3AQIAULcRklHrlWfO5QvZvn6V3lwwQWnnrLP6BcknJEr24MvkiLhCzsvayhHWUkdPS2v3n9Ha/WckSUZ+rnIO/aLcX39SzsGflHPwZznc2XwTIAAAdRghGfVCeUel8wvcysgt0NEci47mWHUs16Jcu0O+l7WV72VtzXYFOac0OuEntW9+TJcF+ymqoZ9CGzjl8vORy89HTrtVNqtFNqtFdqtF1mKPNqtFNkvho8XCDB0AANQ2hGTUK+UZlW5xzs+GYSjzdJ7SMs8oNfOMUrPO6MiJM7I6/bXrWJ52Hfu10n3ytVsUFuSnsECnwoN81TjQqbAgpyJdvopyFQbwCJevfGxMaw4AwMVCSAbOw2KxqKG/Qw39HboyMkiStDVpld5++e/yCW0me8NI2YMayxbUWDZ/l6y+DWT1bSCLzS5ZbLJYbZLVWvhYhjP5hvYdO6V9x06V3Q9JwX5WhfrbFHp29o5GfjYF+1nV0NeqYF+bWl3WWFe2asHINAAAVYCQDFRQTnaW8o7uV//b7r7gBwolQ5JbhuGWcfaZYfz2uPunH/XFO3NkCwiRrUGwbA0amY/2wEayBTWWPbCxZPfRsdMFOna6QD8fLeubBw/L175DYUF+ahzoVOMGzsLHQKfCAn/7uVEDp/x8bHLarXLYrbKXUfJhGIbcBYbyCwwVGIWPbrch99n1ufkFynUXKM9doNz8okejcF3Rc3Obodx8d+Gju0A5+b+9Lje/QG7DkMNmNfvksBU+NvC1q1GAQyEBToUEOBTawKEgXx++RAYAUO0IycW89tprmj59ulJTU3XVVVfppZde0nXXXVfT3UItVBUfKMz49RflH0/TgDtGlxm4DcNQTkGuTuVbdNotncq36JS78Occt0Vn3Badzi+QW7ZyjUoXZ7VIPlaLrBbJbRgqMCR3QWGQr43sVovCAp2KcPkq0uV39tFXYUG+Zk14gMMmq9Uiq8WiAsMwQ3yeGdAN5Z0N+fkFhnysFvnYrPKxW+Vjs8hhsxY+t1nlsFtkGFKe21B+wdnXuguU7zaUV1D4WGAY8vOxKcBpk7/DrgCHXf5Om/wdNvnabeUK9e4CQzn5buXkFejM2cec/AKdyXMrJ79AOfluFRiF52+1WGS3nX20WuTnKDyWv8Muf0fhGyD+ogAAlUNIPsd7772nCRMm6LXXXlP37t01Z84cDRo0SNu3b2cWA1Srygbu7etXae7T42XzbyhbgxDZAoJlDQguHJUOOGdpECybv0sWm4/52gJDynFXLBIbBW4Z7jzJnS/DnSfjnMfi6357fu66c1+TJxUUFPbJZpfF5iOL3UcWm0NWp7+s/q7C8/IPktW3gfILDHPqvsK5r2s/h01y2ixy2i1y2ApDd67bUF7B2Ud34ZSEVcVqkXztVgX4+niEZ/9zwrSfwyZ/H5v8nXb5+djO/kWhsMTIaiks8bGe/SvD8WPHlHXipM64DeXkG8o53+PZ8yn80GrhGzC7tTDcB/g61cjVwAz1hX0427ezbyr8zj739bHJXVD4V4vCv14UviHx+OuG+7ftdqtFvmf/QuLrYzu7WD3X2W1y+lh5EwGgXAjJ55g5c6ZGjx6tP//5z5Kkl156SV988YVmz56tadOm1XDvgLKdPpklIy9HA+78c7lKQAwjV25DKpAKH43C8g+LRfolJUlfv/+G+sWPV+t2HQuDk/Tb49mfJaskx9mldDu+X63PF7x8dlaRrpU6x/9u3aiPZj0vm7/rbDlKqGyBobIFNiqsCw8I/q0m3McpWayyWKwyCtyewb0gT0Z+nlSQXxjqC9yS1VZYR249G9JtPoXPz4Z2yTjbNl+G222+1ig4+7NhyGp3yuLwldXHr/DR8duX0uS6C8PwidzyJWHDXdhHIz/n7GNu4ZsJo0CyWAv7W/Ros8lid8ri4yurj1NS4e/zVF6BTuXlVOqaV71cSSdquhOSCt+4OGyFb1r8fOwK8HN4BGnPwG2Vr93msc5us6goZlssFhVlbjN6n11h8XyqolcVb198e9FDwdk3BW7zscAsfSp1/dnnRaVMufkFyjyZrewzOcp3S3lnt+cVFP51JK+g8J60WQrfzBTOvKOzs/BI9nPXWyV/X6eCGgTIx1ZYquVjtxa+EbJZZbdZ5GMtfLRZLLLZimbzscpmkWxnX1M0s4/dduHZfQyj+v+mdW4fLB7rz/n5nC2e6z0ZkgoM45yyusL+m+vOWV/4s+FRgldgGIV/xTv7WPgaz2tQ1N+y7jlJZ9/oWor997vwiUWS1VL0pvi3dkX7tnjuqkIq8+tqHd5AV0YEeb+DakBIPis3N1fJycl69NFHPdb3799fa9euLfU1OTk5ysn57X9CmZmZkqSsrKzq6+g5Tp48KUk6sGubck6X/8/rpTm07xdJUtqen/VLgH+N74d9ebefvNycCt8L1rOL6UyW3NkZMnKyZeSeqlTZRV5ujtf9Ku7k8WOSO1+devRTk5atS2mRfXY5JJX2pYvnnOi+vVuUvPLfunbQHWXs6yxDnvuyyOO/mvt+LmU/hqQcyciRCmRVgSxnH60qsBQ+SoasMmQxCmQ9+3Pqf7bph68+Uucbhqtpyys8+132+5BC7sLFOCO5ZVXa/j364ZsvzOBs8fGVxccpa9Gjw1cW+2/rZHdKFkth8LaoMIjr7KPFIossColqogBf/6IzKVyM3362mc8NWWRIsqjg7E+GLMo4fEi7tiYX9qco1DsK+yB7Ud8chevtTlnsDhmGWyookFFQIBUUSEbB2XVuGUZB4ePZ9eabBZuPLHbHOUvhXyUsdofHB2jPnF0A1A73XX+5xvU9z3+Pq1BRTrvgmzADhmEYxq+//mpIMr777juP9c8884zRpk2bUl/z17/+1fwsFgsLCwsLCwsLy6Wz7N+//7zZkJHkYor/6ccwjDL/HPTYY49p4sSJ5vOCggIdO3ZMjRo1uij1bllZWWratKn279+voKDa9SeKuoDrW724vtWL61u9uL7Vi+tb/erzNTYMQydOnFBUVNR52xGSzwoNDZXNZlNaWprH+vT0dIWHh5f6GqfTKafT6bGuYcOG1dXFMgUFBdW7G/xi4vpWL65v9eL6Vi+ub/Xi+la/+nqNXS7XBdvwFV5nORwOxcbGasWKFR7rV6xYoW7dutVQrwAAAFATGEk+x8SJExUfH6/OnTsrLi5O//znP7Vv3z7df//9Nd01AAAAXESE5HPccccdOnr0qJ5++mmlpqYqJiZGn332mZo3b17TXSuV0+nUX//61xIlH6gaXN/qxfWtXlzf6sX1rV5c3+rHNb4wi2FchEkIAQAAgEsINckAAABAMYRkAAAAoBhCMgAAAFAMIRkAAAAohpBcy7322mtq2bKlfH19FRsbq2+//fa87VevXq3Y2Fj5+vrq8ssv1+uvv36Renppqsj1XbVqlSwWS4nlp59+uog9vnR88803Gjp0qKKiomSxWPTRRx9d8DXcv+VX0evL/Vt+06ZN07XXXqvAwECFhYXp5ptv1s6dOy/4Ou7f8vHm+nL/Vszs2bPVoUMH84tC4uLi9Pnnn5/3Ndy/JRGSa7H33ntPEyZM0BNPPKFNmzbpuuuu06BBg7Rv375S2+/evVs33nijrrvuOm3atEmPP/64xo8fr8WLF1/knl8aKnp9i+zcuVOpqanm0rp164vU40tLdna2OnbsqFmzZpWrPfdvxVT0+hbh/r2w1atX64EHHlBSUpJWrFih/Px89e/fX9nZ2WW+hvu3/Ly5vkW4f8unSZMmevbZZ7Vx40Zt3LhRffr00U033aRt27aV2p77twwGaq3f/e53xv333++x7sorrzQeffTRUts/8sgjxpVXXumx7r777jO6du1abX28lFX0+n799deGJCMjI+Mi9K5ukWQsXbr0vG24f71XnuvL/eu99PR0Q5KxevXqMttw/3qvPNeX+7fygoODjTfffLPUbdy/pWMkuZbKzc1VcnKy+vfv77G+f//+Wrt2bamvWbduXYn2AwYM0MaNG5WXl1dtfb0UeXN9i1xzzTWKjIxU37599fXXX1dnN+sV7t+Lg/u34jIzMyVJISEhZbbh/vVeea5vEe7finO73UpISFB2drbi4uJKbcP9WzpCci115MgRud1uhYeHe6wPDw9XWlpaqa9JS0srtX1+fr6OHDlSbX29FHlzfSMjI/XPf/5Tixcv1pIlSxQdHa2+ffvqm2++uRhdrvO4f6sX9693DMPQxIkT1aNHD8XExJTZjvvXO+W9vty/FbdlyxY1aNBATqdT999/v5YuXap27dqV2pb7t3R8LXUtZ7FYPJ4bhlFi3YXal7YehSpyfaOjoxUdHW0+j4uL0/79+/XCCy/o+uuvr9Z+1hfcv9WH+9c7Dz74oDZv3qw1a9ZcsC33b8WV9/py/1ZcdHS0UlJSdPz4cS1evFgjR47U6tWrywzK3L8lMZJcS4WGhspms5UY1UxPTy/xbq9IREREqe3tdrsaNWpUbX29FHlzfUvTtWtX7dq1q6q7Vy9x/1583L/nN27cOH388cf6+uuv1aRJk/O25f6tuIpc39Jw/56fw+HQFVdcoc6dO2vatGnq2LGjXn755VLbcv+WjpBcSzkcDsXGxmrFihUe61esWKFu3bqV+pq4uLgS7ZcvX67OnTvLx8en2vp6KfLm+pZm06ZNioyMrOru1Uvcvxcf92/pDMPQgw8+qCVLluirr75Sy5YtL/ga7t/y8+b6lob7t2IMw1BOTk6p27h/y1BDHxhEOSQkJBg+Pj7G3Llzje3btxsTJkwwAgICjD179hiGYRiPPvqoER8fb7b/73//a/j7+xsPP/ywsX37dmPu3LmGj4+P8eGHH9bUKdRqFb2+L774orF06VLj559/NrZu3Wo8+uijhiRj8eLFNXUKtdqJEyeMTZs2GZs2bTIkGTNnzjQ2bdpk7N271zAM7t/Kquj15f4tv//5n/8xXC6XsWrVKiM1NdVcTp06Zbbh/vWeN9eX+7diHnvsMeObb74xdu/ebWzevNl4/PHHDavVaixfvtwwDO7f8iIk13L/+Mc/jObNmxsOh8Po1KmTxxQ5I0eONHr27OnRftWqVcY111xjOBwOo0WLFsbs2bMvco8vLRW5vs8995zRqlUrw9fX1wgODjZ69OhhLFu2rAZ6fWkomrKp+DJy5EjDMLh/K6ui15f7t/xKu66SjHnz5pltuH+958315f6tmHvuucf8f1vjxo2Nvn37mgHZMLh/y8tiGGcrswEAAABIoiYZAAAAKIGQDAAAABRDSAYAAACKISQDAAAAxRCSAQAAgGIIyQAAAEAxhGQAAACgGEIyAAAAao1vvvlGQ4cOVVRUlCwWiz766KMKvX7KlCmyWCwlloCAgArth5AMAJeIPXv2yGKxKCUlpVL7GTVqlG6++eYq6RMAVLXs7Gx17NhRs2bN8ur1kydPVmpqqsfSrl07/f73v6/QfgjJAIAaV/QGoGgJDAzUVVddpQceeEC7du2q0mO1aNFCL730UpXuE0DVGTRokP7+979r+PDhpW7Pzc3VI488ossuu0wBAQHq0qWLVq1aZW5v0KCBIiIizOXQoUPavn27Ro8eXaF+EJIBALXGypUrlZqaqh9//FFTp07Vjh071LFjR3355Zc13TUAtcSf/vQnfffdd0pISNDmzZv1+9//XgMHDizzDfWbb76pNm3a6LrrrqvQcQjJAFAJvXr10vjx4/XII48oJCREERERmjJlirk9MzNT9957r8LCwhQUFKQ+ffroxx9/NLfZbDYlJydLkgzDUEhIiK699lrz9e+++64iIyM9jvnTTz+pW7du8vX11VVXXeUxguJ2uzV69Gi1bNlSfn5+io6O1ssvv3zec0hMTFSPHj3UsGFDNWrUSEOGDNEvv/xibi8a5V2yZIl69+4tf39/dezYUevWrfPYz3fffaeePXvK399fwcHBGjBggDIyMsxze/7553X55ZfLz89PHTt21IcffliiL40aNVJERIQuv/xy3XTTTVq5cqW6dOmi0aNHy+12m+0++eQTxcbGytfXV5dffrmeeuop5efnm9unTJmiZs2ayel0KioqSuPHjzd/X3v37tXDDz9sjloDuHT88ssvevfdd/XBBx/ouuuuU6tWrTR58mT16NFD8+bNK9E+JydHixYtqvAoskRIBoBKW7BggQICArR+/Xo9//zzevrpp7VixQoZhqHBgwcrLS1Nn332mZKTk9WpUyf17dtXx44dk8vl0tVXX22G3M2bN5uPWVlZkqRVq1apZ8+eHsf7f//v/2nSpEnatGmTunXrpmHDhuno0aOSpIKCAjVp0kTvv/++tm/frieffFKPP/643n///TL7n52drYkTJ2rDhg368ssvZbVadcstt6igoMCj3RNPPKHJkycrJSVFbdq00R/+8AczmKakpKhv37666qqrtG7dOq1Zs0ZDhw41g+3//u//at68eZo9e7a2bdumhx9+WHfddZdWr1593mtrtVr10EMPae/eveabiS+++EJ33XWXxo8fr+3bt2vOnDmaP3++nnnmGUnShx9+qBdffFFz5szRrl279NFHH6l9+/aSpCVLlqhJkyZ6+umnzVpFAJeOH374QYZhqE2bNmrQoIG5rF692uPNfZElS5boxIkTuvvuuyt+MAMA4LWePXsaPXr08Fh37bXXGn/5y1+ML7/80ggKCjLOnDnjsb1Vq1bGnDlzDMMwjIkTJxpDhgwxDMMwXnrpJeO2224zOnXqZCxbtswwDMNo06aNMXv2bMMwDGP37t2GJOPZZ58195WXl2c0adLEeO6558rs49ixY41bb73VfD5y5EjjpptuKrN9enq6IcnYsmWLx3HffPNNs822bdsMScaOHTsMwzCMP/zhD0b37t1L3d/JkycNX19fY+3atR7rR48ebfzhD3/wOMamTZtKvH7Hjh2GJOO9994zDMMwrrvuOmPq1Kkebd5++20jMjLSMAzDmDFjhtGmTRsjNze31P40b97cePHFF8s8fwC1hyRj6dKl5vOEhATDZrMZP/30k7Fr1y6PJTU1tcTr+/TpY9x8881eHdtemTQPAJA6dOjg8TwyMlLp6elKTk7WyZMn1ahRI4/tp0+fNkc8evXqpblz56qgoECrV69W37591axZM61evVqdOnXSzz//XGIkOS4uzvzZbrerc+fO2rFjh7nu9ddf15tvvqm9e/fq9OnTys3N1dVXX11m/3/55Rf93//9n5KSknTkyBFzBHnfvn2KiYkp9TyLSkDS09N15ZVXKiUlpcxPjm/fvl1nzpzRDTfc4LE+NzdX11xzTZn9KlL4/0mZpRHJycnasGGDOXIsFZaZnDlzRqdOndLvf/97vfTSS7r88ss1cOBA3XjjjRo6dKjsdv6XB1zqrrnmGrndbqWnp1+wxnj37t36+uuv9fHHH3t1LP6LAQCV5OPj4/HcYrGooKBABQUFioyM9KgZLtKwYUNJ0vXXX68TJ07ohx9+0Lfffqu//e1vatq0qaZOnaqrr75aYWFhatu27QX7UBQg33//fT388MOaMWOG4uLiFBgYqOnTp2v9+vVlvnbo0KFq2rSp3njjDUVFRamgoEAxMTHKzc0t8zyLjlcUqP38/Mrcf1GbZcuW6bLLLvPY5nQ6L3huRW8AWrZsae7vqaeeKvWT776+vmratKl27typFStWaOXKlRo7dqymT5+u1atXl/hdAah9Tp48qf/85z/m8927dyslJUUhISFq06aN/vjHP+ruu+/WjBkzdM011+jIkSP66quv1L59e914443m6/71r38pMjJSgwYN8qofhGQAqCadOnVSWlqa7Ha7WrRoUWqborrkWbNmyWKxqF27doqKitKmTZv06aeflhhFlqSkpCRdf/31kqT8/HwlJyfrwQcflCR9++236tatm8aOHWu2L61Or8jRo0e1Y8cOzZkzxxyVWbNmTYXPtUOHDvryyy/11FNPldjWrl07OZ1O7du3r9TzOZ+CggK98soratmypTnq3KlTJ+3cuVNXXHFFma/z8/PTsGHDNGzYMD3wwAO68sortWXLFnXq1EkOh8PjQ4AAapeNGzeqd+/e5vOJEydKkkaOHKn58+dr3rx5+vvf/65Jkybp119/VaNGjRQXF+cRkAsKCjR//nyNGjVKNpvNq34QkgGgmvTr109xcXG6+eab9dxzzyk6OloHDx7UZ599pptvvlmdO3eWVFhy8fLLL+uWW26RxWJRcHCw2rVrp/fee0+vvPJKif3+4x//UOvWrdW2bVu9+OKLysjI0D333CNJuuKKK/TWW2/piy++UMuWLfX2229rw4YN5ihsccHBwWrUqJH++c9/KjIyUvv27dOjjz5a4XN97LHH1L59e40dO1b333+/HA6Hvv76a/3+979XaGioJk+erIcfflgFBQXq0aOHsrKytHbtWjVo0EAjR44093P06FGlpaXp1KlT2rp1q1566SV9//33WrZsmfk/uieffFJDhgxR06ZN9fvf/15Wq1WbN2/Wli1b9Pe//13z58+X2+1Wly5d5O/vr7ffflt+fn5q3ry5pMJ5kr/55hvdeeedcjqdCg0NrfD5Aqg+vXr1MsusSuPj46Onnnqq1DflRaxWq/bv31+pfjC7BQBUE4vFos8++0zXX3+97rnnHrVp00Z33nmn9uzZo/DwcLNd79695Xa71atXL3Ndz5495Xa7Sx15ffbZZ/Xcc8+pY8eO+vbbb/Xvf//bDHr333+/hg8frjvuuENdunTR0aNHPUaVi7NarUpISFBycrJiYmL08MMPa/r06RU+1zZt2mj58uX68ccf9bvf/U5xcXH697//bdYB/+1vf9OTTz6padOmqW3bthowYIA++eSTEuG9X79+ioyMVPv27fXoo4+qbdu22rx5s8eo0oABA/Tpp59qxYoVuvbaa9W1a1fNnDnTDMENGzbUG2+8oe7du5sj3J988olZG/70009rz549atWqlRo3blzhcwVQP1iM80V1AAAAoB5iJBkAAAAohpAMAAAAFENIBgAAAIohJAMAAADFEJIBAACAYgjJAAAAQDGEZAAAAKAYQjIAAABQDCEZAAAAKIaQDAAAABRDSAYAAACK+f+fRoScweYElAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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p9pCQEB05cuTBNQQAf1EEMgAAcMcOHDigc+fO3XS7u7u7qlSp8gA7AoC/JgIZAAAAABjCF0MDAAAAgCFM6nEfZWZm6rfffpOnp+ddfeEnAAAAgNzBsiydO3dOQUFBypfv5uNgBLL76LffflNwcLDpNgAAAAA8JI4fP64SJUrcdDuB7D7y9PSU9MdN9/LyMtwNAAAAAFNSU1MVHBzsyAg3QyC7j7IeU/Ty8iKQAQAAAPjTV5mY1AMAAAAADCGQAQAAAIAhBDIAAAAAMIR3yAAAAIA8JCMjQ1euXDHdxl+eq6urXFxc7vk4BDIAAAAgD7AsS4mJiTp79qzpVnKNIkWKKCAg4J6+g5hABgAAAOQBWWHMz89PhQoVuqcQkddZlqWLFy8qKSlJkhQYGHjXxyKQAQAAALlcRkaGI4wVLVrUdDu5QsGCBSVJSUlJ8vPzu+vHF5nUAwAAAMjlst4ZK1SokOFOcpes+3kv7+QRyAAAAIA8gscU76/7cT8JZAAAAABgCIEMAAAAwEMhKipKHTt2vKN9bDabli5dmiP9PAgEMgAAACCPi4qKks1my/Y5dOiQ6daMKFWqlCZMmPBAzsUsiwAAAADUqlUrzZgxw2ldsWLFnJbT09Pl5ub2INvK9RghAwAAACB3d3cFBAQ4fZo1a6aXXnpJgwcPlq+vr1q0aCFJGj9+vKpUqSIPDw8FBwerf//+On/+vONYI0eOVPXq1Z2OP2HCBJUqVcqxnJGRocGDB6tIkSIqWrSoXn31VVmW5bTPjUaqqlevrpEjR970On799Vd17dpV3t7eKlq0qDp06KAjR444tmc9Fvnee+8pMDBQRYsW1YABAxwzJTZu3FhHjx7V3/72N8dIYU4ikAEAAAC4qVmzZil//vz67rvvNHXqVElSvnz59MEHH2jPnj2aNWuW1q1bp1dfffWOjjtu3DhNnz5dn376qWJjY3XmzBktWbLknnq9ePGimjRposKFC2vTpk2KjY1V4cKF1apVK6Wnpzvq1q9fr59//lnr16/XrFmzNHPmTM2cOVOStHjxYpUoUUJvv/22EhISlJCQcE89/RkeWQQAAACg5cuXq3Dhwo7liIgISVLZsmX17rvvOtVGR0c7/ly6dGn94x//0IsvvqjJkyff9vkmTJigYcOGqXPnzpKkjz76SN988809XIG0YMEC5cuXT5988oljZGvGjBkqUqSINmzYoPDwcEmSt7e3Jk2aJBcXFz366KNq06aN1q5dq969e8vHx0cuLi7y9PRUQEDAPfVzOwhkAAAAANSkSRNNmTLFsezh4aFnnnlGtWvXzla7fv16jR49Wnv37lVqaqquXr2qy5cv68KFC/Lw8PjTc6WkpCghIUFhYWGOdfnz51ft2rWzPbZ4J+Lj43Xo0CF5eno6rb98+bJ+/vlnx3LlypXl4uLiWA4MDNTu3bvv+rz3gkAGAAAAQB4eHipbtuwN11/r6NGjat26tfr166d//OMf8vHxUWxsrHr16uV4DytfvnzZglXWtjtxp8fJzMxUrVq1NHfu3Gzbrp2gxNXV1WmbzWZTZmbmHfd3PxDIAAAAANy2bdu26erVqxo3bpzy5ftjSorPP//cqaZYsWJKTEyUZVmORwd37tzp2G632xUYGKgtW7boiSeekCRdvXpV8fHxqlmzptNxrn2HKzU1VYcPH75pbzVr1tTChQvl5+cnLy+vu75GNzc3ZWRk3PX+d4JABgBAHlLrldmmW0AeET+2h+kWkEPKlCmjq1evauLEiWrXrp2+++47ffTRR041jRs31qlTp/Tuu+/qqaeeUkxMjP773/86haSXX35Z//73v1WuXDlVrFhR48eP19mzZ52O07RpU82cOVPt2rWTt7e33nrrLadHDa/37LPPauzYserQoYPefvttlShRQseOHdPixYv1yiuvqESJErd1jaVKldKmTZvUrVs3ubu7y9fX9/Zv0B1ilkUAAAAAt6169eoaP3683nnnHYWGhmru3LkaM2aMU03FihU1efJkffjhh6pWrZq+//57DR061KlmyJAh6tGjh6KiohQWFiZPT089+eSTTjXDhg3TE088obZt26p169bq2LGjypQpc9PeChUqpE2bNqlkyZLq1KmTKlasqOeff16XLl26oxGzt99+W0eOHFGZMmWyfRfb/Waz7uWtOThJTU2V3W5XSkrKPQ2RAgCQUxghw4PCCNnD5fLlyzp8+LBKly6tAgUKmG4n17jVfb3dbMAIGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABD8ptuAAAAAMBfS61XZj/Q88WP7XFX+02ePFljx45VQkKCKleurAkTJqhhw4b3ubt7wwgZAAAAgFxn4cKFio6O1htvvKEdO3aoYcOGioiI0LFjx0y35oRABgAAACDXGT9+vHr16qUXXnhBFStW1IQJExQcHKwpU6aYbs0JgQwAAABArpKenq74+HiFh4c7rQ8PD9fmzZsNdXVjBDIAAAAAucrvv/+ujIwM+fv7O6339/dXYmKioa5ujEAGAAAAIFey2WxOy5ZlZVtnGoEMAAAAQK7i6+srFxeXbKNhSUlJ2UbNTCOQAQAAAMhV3NzcVKtWLa1evdpp/erVq1W/fn1DXd0Y30MGAAAAINcZPHiwIiMjVbt2bYWFhenjjz/WsWPH1K9fP9OtOSGQAQAAALgjd/tFzQ9S165ddfr0ab399ttKSEhQaGioVq5cqZCQENOtOSGQAQAAAMiV+vfvr/79+5tu45Z4hwwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIflNNwAAAADgr+XY21Ue6PlKDt99x/ts2rRJY8eOVXx8vBISErRkyRJ17Njx/jd3jxghAwAAAJDrXLhwQdWqVdOkSZNMt3JLjJABAAAAyHUiIiIUERFhuo0/xQgZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhzLIIAAAAINc5f/68Dh065Fg+fPiwdu7cKR8fH5UsWdJgZ86MBrIxY8Zo8eLF2r9/vwoWLKj69evrnXfeUYUKFRw1UVFRmjVrltN+devW1ZYtWxzLaWlpGjp0qObPn69Lly6pWbNmmjx5skqUKOGoSU5O1qBBg7Rs2TJJUvv27TVx4kQVKVLEUXPs2DENGDBA69atU8GCBdW9e3e99957cnNzy6E7AAAAAPz13M0XNT9o27ZtU5MmTRzLgwcPliT17NlTM2fONNRVdkYfWdy4caMGDBigLVu2aPXq1bp69arCw8N14cIFp7pWrVopISHB8Vm5cqXT9ujoaC1ZskQLFixQbGyszp8/r7Zt2yojI8NR0717d+3cuVMxMTGKiYnRzp07FRkZ6diekZGhNm3a6MKFC4qNjdWCBQu0aNEiDRkyJGdvAgAAAID7rnHjxrIsK9vnYQpjkuERspiYGKflGTNmyM/PT/Hx8XriiScc693d3RUQEHDDY6SkpOjTTz/VZ599pubNm0uS5syZo+DgYK1Zs0YtW7bUvn37FBMToy1btqhu3bqSpGnTpiksLEwHDhxQhQoVtGrVKu3du1fHjx9XUFCQJGncuHGKiorSv/71L3l5eeXELQAAAACQhz1Uk3qkpKRIknx8fJzWb9iwQX5+fipfvrx69+6tpKQkx7b4+HhduXJF4eHhjnVBQUEKDQ3V5s2bJUlxcXGy2+2OMCZJ9erVk91ud6oJDQ11hDFJatmypdLS0hQfH3/DftPS0pSamur0AQAAAIDb9dAEMsuyNHjwYD3++OMKDQ11rI+IiNDcuXO1bt06jRs3Tj/88IOaNm2qtLQ0SVJiYqLc3Nzk7e3tdDx/f38lJiY6avz8/LKd08/Pz6nG39/fabu3t7fc3NwcNdcbM2aM7Ha74xMcHHz3NwAAAABAnvPQzLL40ksvadeuXYqNjXVa37VrV8efQ0NDVbt2bYWEhGjFihXq1KnTTY9nWZZsNptj+do/30vNtYYNG+Z4OVCSUlNTCWUAAAAAbttDMUI2cOBALVu2TOvXr3eaGfFGAgMDFRISooMHD0qSAgIClJ6eruTkZKe6pKQkx4hXQECATp48me1Yp06dcqq5fiQsOTlZV65cyTZylsXd3V1eXl5OHwAAAOBhZVmW6RZylftxP40GMsuy9NJLL2nx4sVat26dSpcu/af7nD59WsePH1dgYKAkqVatWnJ1ddXq1asdNQkJCdqzZ4/q168vSQoLC1NKSoq+//57R83WrVuVkpLiVLNnzx4lJCQ4alatWiV3d3fVqlXrvlwvAAAAYIKrq6sk6eLFi4Y7yV2y7mfW/b0bRh9ZHDBggObNm6evvvpKnp6ejhEqu92uggUL6vz58xo5cqQ6d+6swMBAHTlyRK+//rp8fX315JNPOmp79eqlIUOGqGjRovLx8dHQoUNVpUoVx6yLFStWVKtWrdS7d29NnTpVktSnTx+1bdvW8Z1n4eHhqlSpkiIjIzV27FidOXNGQ4cOVe/evRn5AgAAwF+ai4uLihQp4pgcr1ChQjd9LQd/zrIsXbx4UUlJSSpSpIhcXFzu+lhGA9mUKVMk/fEdAdeaMWOGoqKi5OLiot27d2v27Nk6e/asAgMD1aRJEy1cuFCenp6O+vfff1/58+dXly5dHF8MPXPmTKcbM3fuXA0aNMgxG2P79u01adIkx3YXFxetWLFC/fv3V4MGDZy+GBoAAAD4q8v6GqlrZyzHvSlSpMhNv57rdtksHiS9b1JTU2W325WSksKoGgDgoVTrldmmW0AeET+2h+kWcBMZGRm6cuWK6Tb+8lxdXW85Mna72eChmWURAAAAQM5zcXG5p0fscH89FLMsAgAAAEBeRCADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAkPymG8CDUeuV2aZbQB4RP7aH6RYAAAD+MhghAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDjAayMWPG6LHHHpOnp6f8/PzUsWNHHThwwKnGsiyNHDlSQUFBKliwoBo3bqyffvrJqSYtLU0DBw6Ur6+vPDw81L59e504ccKpJjk5WZGRkbLb7bLb7YqMjNTZs2edao4dO6Z27drJw8NDvr6+GjRokNLT03Pk2gEAAADAaCDbuHGjBgwYoC1btmj16tW6evWqwsPDdeHCBUfNu+++q/Hjx2vSpEn64YcfFBAQoBYtWujcuXOOmujoaC1ZskQLFixQbGyszp8/r7Zt2yojI8NR0717d+3cuVMxMTGKiYnRzp07FRkZ6diekZGhNm3a6MKFC4qNjdWCBQu0aNEiDRky5MHcDAAAAAB5js2yLMt0E1lOnTolPz8/bdy4UU888YQsy1JQUJCio6P197//XdIfo2H+/v5655131LdvX6WkpKhYsWL67LPP1LVrV0nSb7/9puDgYK1cuVItW7bUvn37VKlSJW3ZskV169aVJG3ZskVhYWHav3+/KlSooP/+979q27atjh8/rqCgIEnSggULFBUVpaSkJHl5ef1p/6mpqbLb7UpJSbmt+gep1iuzTbeAPCJ+bA/TLQC4BX4e4EHh5wHyutvNBg/VO2QpKSmSJB8fH0nS4cOHlZiYqPDwcEeNu7u7GjVqpM2bN0uS4uPjdeXKFaeaoKAghYaGOmri4uJkt9sdYUyS6tWrJ7vd7lQTGhrqCGOS1LJlS6WlpSk+Pv6G/aalpSk1NdXpAwAAAAC366EJZJZlafDgwXr88ccVGhoqSUpMTJQk+fv7O9X6+/s7tiUmJsrNzU3e3t63rPHz88t2Tj8/P6ea68/j7e0tNzc3R831xowZ43gnzW63Kzg4+E4vGwAAAEAe9tAEspdeekm7du3S/Pnzs22z2WxOy5ZlZVt3vetrblR/NzXXGjZsmFJSUhyf48eP37InAAAAALjWQxHIBg4cqGXLlmn9+vUqUaKEY31AQIAkZRuhSkpKcoxmBQQEKD09XcnJybesOXnyZLbznjp1yqnm+vMkJyfrypUr2UbOsri7u8vLy8vpAwAAAAC3y2ggsyxLL730khYvXqx169apdOnSTttLly6tgIAArV692rEuPT1dGzduVP369SVJtWrVkqurq1NNQkKC9uzZ46gJCwtTSkqKvv/+e0fN1q1blZKS4lSzZ88eJSQkOGpWrVold3d31apV6/5fPAAAAIA8L7/Jkw8YMEDz5s3TV199JU9PT8cIld1uV8GCBWWz2RQdHa3Ro0erXLlyKleunEaPHq1ChQqpe/fujtpevXppyJAhKlq0qHx8fDR06FBVqVJFzZs3lyRVrFhRrVq1Uu/evTV16lRJUp8+fdS2bVtVqFBBkhQeHq5KlSopMjJSY8eO1ZkzZzR06FD17t2bkS8AAAAAOcJoIJsyZYokqXHjxk7rZ8yYoaioKEnSq6++qkuXLql///5KTk5W3bp1tWrVKnl6ejrq33//feXPn19dunTRpUuX1KxZM82cOVMuLi6Omrlz52rQoEGO2Rjbt2+vSZMmOba7uLhoxYoV6t+/vxo0aKCCBQuqe/fueu+993Lo6gEAAADkdQ/V95D91fE9ZADfOwM87Ph5gAeFnwfI6/6S30MGAAAAAHkJgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIXcVyB555BGdPn062/qzZ8/qkUceueemAAAAACAvuKtAduTIEWVkZGRbn5aWpl9//fWemwIAAACAvCD/nRQvW7bM8edvvvlGdrvdsZyRkaG1a9eqVKlS9605AAAAAMjN7iiQdezYUZJks9nUs2dPp22urq4qVaqUxo0bd9+aAwAAAIDc7I4CWWZmpiSpdOnS+uGHH+Tr65sjTQEAAABAXnBHgSzL4cOH73cfAAAAAJDn3FUgk6S1a9dq7dq1SkpKcoycZZk+ffo9NwYAAAAAud1dBbJRo0bp7bffVu3atRUYGCibzXa/+wIAAACAXO+uAtlHH32kmTNnKjIy8n73AwAAAAB5xl19D1l6errq169/zyfftGmT2rVrp6CgINlsNi1dutRpe1RUlGw2m9OnXr16TjVpaWkaOHCgfH195eHhofbt2+vEiRNONcnJyYqMjJTdbpfdbldkZKTOnj3rVHPs2DG1a9dOHh4e8vX11aBBg5Senn7P1wgAAAAAN3NXgeyFF17QvHnz7vnkFy5cULVq1TRp0qSb1rRq1UoJCQmOz8qVK522R0dHa8mSJVqwYIFiY2N1/vx5tW3b1umLq7t3766dO3cqJiZGMTEx2rlzp9PoXkZGhtq0aaMLFy4oNjZWCxYs0KJFizRkyJB7vkYAAAAAuJm7emTx8uXL+vjjj7VmzRpVrVpVrq6uTtvHjx9/W8eJiIhQRETELWvc3d0VEBBww20pKSn69NNP9dlnn6l58+aSpDlz5ig4OFhr1qxRy5YttW/fPsXExGjLli2qW7euJGnatGkKCwvTgQMHVKFCBa1atUp79+7V8ePHFRQUJEkaN26coqKi9K9//UteXl63dT0AAAAAcCfuaoRs165dql69uvLly6c9e/Zox44djs/OnTvva4MbNmyQn5+fypcvr969eyspKcmxLT4+XleuXFF4eLhjXVBQkEJDQ7V582ZJUlxcnOx2uyOMSVK9evVkt9udakJDQx1hTJJatmyptLQ0xcfH37S3tLQ0paamOn0AAAAA4Hbd1QjZ+vXr73cfNxQREaGnn35aISEhOnz4sN566y01bdpU8fHxcnd3V2Jiotzc3OTt7e20n7+/vxITEyVJiYmJ8vPzy3ZsPz8/pxp/f3+n7d7e3nJzc3PU3MiYMWM0atSoe71MAAAAAHnUXX8P2YPQtWtXx59DQ0NVu3ZthYSEaMWKFerUqdNN97Msy2kq/htNy383NdcbNmyYBg8e7FhOTU1VcHDwzS8IAAAAAK5xV4GsSZMmtwwq69atu+uGbiUwMFAhISE6ePCgJCkgIEDp6elKTk52GiVLSkpyzAIZEBCgkydPZjvWqVOnHKNiAQEB2rp1q9P25ORkXblyJdvI2bXc3d3l7u5+z9cFAAAAIG+6q3fIqlevrmrVqjk+lSpVUnp6urZv364qVarc7x4dTp8+rePHjyswMFCSVKtWLbm6umr16tWOmoSEBO3Zs8cRyMLCwpSSkqLvv//eUbN161alpKQ41ezZs0cJCQmOmlWrVsnd3V21atXKsesBAAAAkLfd1QjZ+++/f8P1I0eO1Pnz52/7OOfPn9ehQ4ccy4cPH9bOnTvl4+MjHx8fjRw5Up07d1ZgYKCOHDmi119/Xb6+vnryySclSXa7Xb169dKQIUNUtGhR+fj4aOjQoapSpYpj1sWKFSuqVatW6t27t6ZOnSpJ6tOnj9q2basKFSpIksLDw1WpUiVFRkZq7NixOnPmjIYOHarevXszwyIAAACAHHNXI2Q389xzz2n69Om3Xb9t2zbVqFFDNWrUkCQNHjxYNWrU0PDhw+Xi4qLdu3erQ4cOKl++vHr27Kny5csrLi5Onp6ejmO8//776tixo7p06aIGDRqoUKFC+vrrr+Xi4uKomTt3rqpUqaLw8HCFh4eratWq+uyzzxzbXVxctGLFChUoUEANGjRQly5d1LFjR7333nv34a4AAAAAwI3d10k94uLiVKBAgduub9y4sSzLuun2b7755k+PUaBAAU2cOFETJ068aY2Pj4/mzJlzy+OULFlSy5cv/9PzAQAAAMD9cleB7PoZDi3LUkJCgrZt26a33nrrvjQGAAAAALndXQUyu93utJwvXz5VqFBBb7/9ttOXNAMAAAAAbu6uAtmMGTPudx8AAAAAkOfc0ztk8fHx2rdvn2w2mypVquSYnAMAAAAA8OfuKpAlJSWpW7du2rBhg4oUKSLLspSSkqImTZpowYIFKlas2P3uEwAAAABynbua9n7gwIFKTU3VTz/9pDNnzig5OVl79uxRamqqBg0adL97BAAAAIBc6a5GyGJiYrRmzRpVrFjRsa5SpUr68MMPmdQDAAAAAG7TXY2QZWZmytXVNdt6V1dXZWZm3nNTAAAAAJAX3FUga9q0qV5++WX99ttvjnW//vqr/va3v6lZs2b3rTkAAAAAyM3uKpBNmjRJ586dU6lSpVSmTBmVLVtWpUuX1rlz5zRx4sT73SMAAAAA5Ep39Q5ZcHCwtm/frtWrV2v//v2yLEuVKlVS8+bN73d/AAAAAJBr3dEI2bp161SpUiWlpqZKklq0aKGBAwdq0KBBeuyxx1S5cmV9++23OdIoAAAAAOQ2dxTIJkyYoN69e8vLyyvbNrvdrr59+2r8+PH3rTkAAAAAyM3uKJD9+OOPatWq1U23h4eHKz4+/p6bAgAAAIC84I4C2cmTJ2843X2W/Pnz69SpU/fcFAAAAADkBXcUyIoXL67du3ffdPuuXbsUGBh4z00BAAAAQF5wR4GsdevWGj58uC5fvpxt26VLlzRixAi1bdv2vjUHAAAAALnZHU17/+abb2rx4sUqX768XnrpJVWoUEE2m0379u3Thx9+qIyMDL3xxhs51SsAAAAA5Cp3FMj8/f21efNmvfjiixo2bJgsy5Ik2Ww2tWzZUpMnT5a/v3+ONAoAAAAAuc0dfzF0SEiIVq5cqeTkZB06dEiWZalcuXLy9vbOif4AAAAAINe640CWxdvbW4899tj97AUAAAAA8pQ7mtQDAAAAAHD/EMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhRgPZpk2b1K5dOwUFBclms2np0qVO2y3L0siRIxUUFKSCBQuqcePG+umnn5xq0tLSNHDgQPn6+srDw0Pt27fXiRMnnGqSk5MVGRkpu90uu92uyMhInT171qnm2LFjateunTw8POTr66tBgwYpPT09Jy4bAAAAACQZDmQXLlxQtWrVNGnSpBtuf/fddzV+/HhNmjRJP/zwgwICAtSiRQudO3fOURMdHa0lS5ZowYIFio2N1fnz59W2bVtlZGQ4arp3766dO3cqJiZGMTEx2rlzpyIjIx3bMzIy1KZNG124cEGxsbFasGCBFi1apCFDhuTcxQMAAADI8/KbPHlERIQiIiJuuM2yLE2YMEFvvPGGOnXqJEmaNWuW/P39NW/ePPXt21cpKSn69NNP9dlnn6l58+aSpDlz5ig4OFhr1qxRy5YttW/fPsXExGjLli2qW7euJGnatGkKCwvTgQMHVKFCBa1atUp79+7V8ePHFRQUJEkaN26coqKi9K9//UteXl4P4G4AAAAAyGse2nfIDh8+rMTERIWHhzvWubu7q1GjRtq8ebMkKT4+XleuXHGqCQoKUmhoqKMmLi5OdrvdEcYkqV69erLb7U41oaGhjjAmSS1btlRaWpri4+Nv2mNaWppSU1OdPgAAAABwux7aQJaYmChJ8vf3d1rv7+/v2JaYmCg3Nzd5e3vfssbPzy/b8f38/Jxqrj+Pt7e33NzcHDU3MmbMGMd7aXa7XcHBwXd4lQAAAADysoc2kGWx2WxOy5ZlZVt3vetrblR/NzXXGzZsmFJSUhyf48eP37IvAAAAALjWQxvIAgICJCnbCFVSUpJjNCsgIEDp6elKTk6+Zc3JkyezHf/UqVNONdefJzk5WVeuXMk2cnYtd3d3eXl5OX0AAAAA4HY9tIGsdOnSCggI0OrVqx3r0tPTtXHjRtWvX1+SVKtWLbm6ujrVJCQkaM+ePY6asLAwpaSk6Pvvv3fUbN26VSkpKU41e/bsUUJCgqNm1apVcnd3V61atXL0OgEAAADkXUZnWTx//rwOHTrkWD58+LB27twpHx8flSxZUtHR0Ro9erTKlSuncuXKafTo0SpUqJC6d+8uSbLb7erVq5eGDBmiokWLysfHR0OHDlWVKlUcsy5WrFhRrVq1Uu/evTV16lRJUp8+fdS2bVtVqFBBkhQeHq5KlSopMjJSY8eO1ZkzZzR06FD17t2bUS8AAAAAOcZoINu2bZuaNGniWB48eLAkqWfPnpo5c6ZeffVVXbp0Sf3791dycrLq1q2rVatWydPT07HP+++/r/z586tLly66dOmSmjVrppkzZ8rFxcVRM3fuXA0aNMgxG2P79u2dvvvMxcVFK1asUP/+/dWgQQMVLFhQ3bt313vvvZfTtwAAAABAHmazLMsy3URukZqaKrvdrpSUlIduZK3WK7NNt4A8In5sD9MtALgFfh7gQeHnAfK6280GD+07ZAAAAACQ2xHIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAY8lAHspEjR8pmszl9AgICHNsty9LIkSMVFBSkggULqnHjxvrpp5+cjpGWlqaBAwfK19dXHh4eat++vU6cOOFUk5ycrMjISNntdtntdkVGRurs2bMP4hIBAAAA5GEPdSCTpMqVKyshIcHx2b17t2Pbu+++q/Hjx2vSpEn64YcfFBAQoBYtWujcuXOOmujoaC1ZskQLFixQbGyszp8/r7Zt2yojI8NR0717d+3cuVMxMTGKiYnRzp07FRkZ+UCvEwAAAEDek990A38mf/78TqNiWSzL0oQJE/TGG2+oU6dOkqRZs2bJ399f8+bNU9++fZWSkqJPP/1Un332mZo3by5JmjNnjoKDg7VmzRq1bNlS+/btU0xMjLZs2aK6detKkqZNm6awsDAdOHBAFSpUeHAXCwAAACBPeehHyA4ePKigoCCVLl1a3bp10y+//CJJOnz4sBITExUeHu6odXd3V6NGjbR582ZJUnx8vK5cueJUExQUpNDQUEdNXFyc7Ha7I4xJUr169WS32x01N5OWlqbU1FSnDwAAAADcroc6kNWtW1ezZ8/WN998o2nTpikxMVH169fX6dOnlZiYKEny9/d32sff39+xLTExUW5ubvL29r5ljZ+fX7Zz+/n5OWpuZsyYMY73zux2u4KDg+/6WgEAAADkPQ91IIuIiFDnzp1VpUoVNW/eXCtWrJD0x6OJWWw2m9M+lmVlW3e962tuVH87xxk2bJhSUlIcn+PHj//pNQEAAABAloc6kF3Pw8NDVapU0cGDBx3vlV0/ipWUlOQYNQsICFB6erqSk5NvWXPy5Mls5zp16lS20bfrubu7y8vLy+kDAAAAALfrLxXI0tLStG/fPgUGBqp06dIKCAjQ6tWrHdvT09O1ceNG1a9fX5JUq1Ytubq6OtUkJCRoz549jpqwsDClpKTo+++/d9Rs3bpVKSkpjhoAAAAAyAkP9SyLQ4cOVbt27VSyZEklJSXpn//8p1JTU9WzZ0/ZbDZFR0dr9OjRKleunMqVK6fRo0erUKFC6t69uyTJbrerV69eGjJkiIoWLSofHx8NHTrU8QikJFWsWFGtWrVS7969NXXqVElSnz591LZtW2ZYBAAAAJCjHupAduLECT3zzDP6/fffVaxYMdWrV09btmxRSEiIJOnVV1/VpUuX1L9/fyUnJ6tu3bpatWqVPD09Hcd4//33lT9/fnXp0kWXLl1Ss2bNNHPmTLm4uDhq5s6dq0GDBjlmY2zfvr0mTZr0YC8WAAAAQJ5jsyzLMt1EbpGamiq73a6UlJSH7n2yWq/MNt0C8oj4sT1MtwDgFvh5gAeFnwfI6243G/yl3iEDAAAAgNyEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiC7zuTJk1W6dGkVKFBAtWrV0rfffmu6JQAAAAC5FIHsGgsXLlR0dLTeeOMN7dixQw0bNlRERISOHTtmujUAAAAAuVB+0w08TMaPH69evXrphRdekCRNmDBB33zzjaZMmaIxY8YY7g4AAOCv49jbVUy3gDyi5PDdplu4JwSy/yc9PV3x8fF67bXXnNaHh4dr8+bNN9wnLS1NaWlpjuWUlBRJUmpqas41epcy0i6ZbgF5xMP43z+A/x8/D/CgnHPNMN0C8oiH9XePrL4sy7plHYHs//n999+VkZEhf39/p/X+/v5KTEy84T5jxozRqFGjsq0PDg7OkR6BvwL7xH6mWwAAPARCTTeAvGOM3XQHt3Tu3DnZ7TfvkUB2HZvN5rRsWVa2dVmGDRumwYMHO5YzMzN15swZFS1a9Kb7ALlZamqqgoODdfz4cXl5eZluBwBgCD8PgD9yxLlz5xQUFHTLOgLZ/+Pr6ysXF5dso2FJSUnZRs2yuLu7y93d3WldkSJFcqpF4C/Dy8uLH8AAAH4eIM+71chYFmZZ/H/c3NxUq1YtrV692mn96tWrVb9+fUNdAQAAAMjNGCG7xuDBgxUZGanatWsrLCxMH3/8sY4dO6Z+/XgnBgAAAMD9RyC7RteuXXX69Gm9/fbbSkhIUGhoqFauXKmQkBDTrQF/Ce7u7hoxYkS2R3kBAHkLPw+A22ez/mweRgAAAABAjuAdMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAeChduXLFdAtAjuN7yAA8lCzLks1mM90GAMCQMWPGSJJeeukleXp6Gu4GyDmMkAF4KGR9JeK2bdu0d+9ewhgA5HEXLlzQG2+8oRkzZuj8+fOm2wFyDIEMgHFZo2FLlixRu3bt9Omnnyo5Odl0WwAAg/75z39q9OjRio6O1ieffEIoQ67FI4sAjLPZbIqJidGzzz6rSZMmqUOHDvL29jbdFgDAkMzMTOXLl0+vvfaaMjMzNXjwYEnSCy+8oMKFCxvuDri/CGQAjEtPT9fnn3+uAQMG6Pnnn9eFCxe0d+9ezZkzR2XKlFH9+vVVsWJF020CAB6QfPnyKSMjQy4uLnr99dcliVCGXItABuChcOTIEaWkpCgxMVFvvfWWDh06pMTERJ09e1Zdu3bV+PHjlS8fT1kDQG6W9Qj71atXHYFMkl5//XVGypBr8dsNgAcuawKP7du3a+/evXJzc9Nrr72mDRs2qHz58jpz5oz69u2rffv2afDgwYqLi1N6errhrgEAOSkrjGU9wt6oUSMNGzZM+/fvlyS9+eabevvttzV48GBNnz5dqamphjsG7g9GyAA8UNdO4BEdHa0nn3xSI0aMUHh4uHbv3q1ffvlFjz/+uCO0/frrrwoODnYsAwByJ5vNpq+++kqRkZGKjIxU06ZNNWLECB08eFD9+/dX06ZN9eabbyp//vyKjo6Wq6ur+vXrx6y8+MuzWfyWA+ABW7lypTp37qxJkyapY8eOKlq0aLaanTt3auHChZoyZYo2bdqkqlWrGugUAPCg7Nu3T506ddLAgQPVv39/ZWRkKCgoSGlpaapZs6ZGjBihRo0aSZLGjRun1q1b834xcgUCGYAH6tKlS3r++edVvnx5jRo1SufPn9evv/6qhQsXqlSpUgoPD9fvv/+u9957T/Hx8ZozZ46qVatmum0AQA7JenLixx9/1Ndff61XXnlFv//+uxo2bKj27dvrxRdfVO3atdW0aVP17dtXrVu3Nt0ycF/xyCKAB8rd3V0JCQmyLEtnz57VsGHDtHfvXp06dUo///yzXnnlFQ0fPlyDBg2Sv7+/ihcvbrplAEAOSktLU4ECBVS6dGl17dpVbm5uev311/X444/rX//6lzw8PBQWFqavv/5ahQoVUuPGjVWoUCHTbQP3DZN6AMhR1w/C58uXTy+++KK+/fZbBQQE6OTJk+rTp4/27t2rkSNHav369crMzFTNmjUJYwCQy23btk116tTRoUOH5OXlpbJly8qyLP3666+qWLGiPDw8JEkVKlTQ559/rtGjRxPGkOswQgYgx2Q9hrJlyxbFxsbq8uXLqlOnjrp27ar69evr4MGDatq0qaP++PHjKlWqFNPbA0AekZqaKk9PTz3zzDP64osvVKpUKZ0+fVopKSnatWuXvvrqK8XFxenLL7/U8OHDVaxYMdMtA/cd75AByFGLFi1Sv379VKdOHXl7e2vevHkaOXKkhg8f7qjZu3evZs+eralTp2rTpk2qUqWKwY4BAA/Shg0bNHr0aJ06dUqLFi3SI488otjYWHXr1k2enp5KT0/Xl19+qRo1aphuFcgRjJAByDEHDhxQdHS03n77bb344os6ceKEvvjiC6fvjtmyZYs++eQTxcXFacOGDYQxAMilsp6a2L59u0JCQhwz7DZu3FiZmZn697//rc6dO2vRokV6/PHHFRcXp4yMDHl4eDAyhlyNETIAOWbz5s168803tW7dOh0+fFgNGzZUu3btNGXKFEnS4cOHVapUKW3evFmlSpXinTEAyGXGjBkjSRo2bJgk6bffftNTTz0lV1dXLVmyRD4+PpL+CGsxMTEaMGCAgoKCNGvWLJUpU8ZY38CDxIsaAHJMamqqkpKStHnzZjVp0kRt2rTRpEmTJElxcXF67bXX9Ntvv6lBgwaEMQDIZTIyMnT16lW98cYbmjhxoiTJ399fffv2lc1m03PPPafTp09L+uNLoSMiIlSmTBl9//33ioqK0pUrV7JNDAXkRjyyCOC+yHoU5VqlSpWSt7e3IiIi1K5dO02dOtWxbcmSJTp79qwKFiz4oFsFADwALi4uGjp0qAoVKqTo6GhduXJFgwcP1nPPPSdXV1dNnjxZPXr00Pz58+Xl5SXLslS+fHk988wzioiIkKurq+lLAB4IAhmAe5YVxuLi4nTgwAFlZGSoV69eevTRR/Xkk0/qp59+UkBAgH788Ue5uLho1qxZ+uSTT7Rp0ybH4yoAgNwjMzNTNptNBQsWVKdOnZSSkqKhQ4fK09NTvXv3VteuXSVJkydPVuPGjRUdHa2tW7dqzZo1ev311xUYGGj4CoAHh3fIANwXS5cuVbdu3VSpUiX99NNPCgsL05w5c1SiRAmNGTNGX331leLj41WlShVlZmZq5syZql69uum2AQA5aMmSJRoxYoQqV66s5cuX68KFCxo/fryio6OVkZGh9evXa+LEidq9e7d8fX01depUZlNEnkMgA3DXskbGzp8/r2effVadO3dWhw4ddOrUKbVu3VpeXl5atGiRQkJCdPLkSR06dEj+/v4qUqSIfH19TbcPAMhBu3btUlhYmMaPH69u3brpt99+09y5czV69GhHKMty4sQJeXl5ycvLy1zDgCFM6gHgjuzYsUNpaWmS/ngJe926dXr66aeVL18+hYWFyW63q2zZsvruu+907tw5de7c2RHEGjRooLJlyxLGACCX+fDDD5WUlOS07tdff1VwcLCefvpp2e12VaxYUa+++qr+/ve/a/DgwZoxY4ajtkSJEoQx5FkEMgC3xbIsLViwQBEREbp8+bJjvbe3t+Lj47VixQqdO3dO0h/vDhQrVkyxsbG6dOmS2rRpo0OHDplqHQCQg37//XdNnjzZ6TsmJcnDw0P/+9//9Msvv0j64+eIl5eXunTpIjc3N/Xq1UsfffSRiZaBhwqBDMBtsdls6tatm77//nvZ7XYlJibq8uXLqlGjhtauXStfX1+99dZbSk5OVr58+WRZlooVK6Z169bJy8tL+fMzhxAA5Ea+vr7asWOHypYtq61btyoxMVGSVKVKFbVo0ULvv/++9u/f75iJNzAwUJ07d9a4cePUqFEjk60DDwXeIQNwWzIyMuTi4qLMzEz99NNPqlOnjqZNm6annnpKBQoU0I4dO9SyZUuFhYVp5syZ8vb2VmZmpvLly+f4XwBA7pL1LnFmZqYuX76s8uXLKygoSCtWrFCxYsU0a9YsTZ06VcHBwRo4cKBKliypjz76SKtWrdLatWtlt9tNXwJgHIEMwF3p1q2bvvnmG3300Ufq0KGDUyhr2LChpk2bxpT2AJBHHD58WKVLl9b+/fvVtm1bFS9eXEuXLpW3t7dmz56tzz//XCtXrlTZsmWVnJys1atXM9Mu8P8QyAD8qax/Ad21a5dOnz6tJk2aSJJeeOEFLViwQJ9++qkjlO3cuVO1atVSt27d9NlnnzEyBgC53NGjR1WhQgUtWLBAHTt21MGDB9WiRQuFhIRoyZIl8vHx0aVLl7Rnzx6lp6erVKlSKl68uOm2gYcGvykBuKWsMLZ48WJ16NBBsbGx+vnnnyVJn3zyibp27arnn39eS5cu1eXLl1W9enXt3LlTI0aMIIwBQB5QuHBhtWzZUt9//70yMjJUrlw5rV69WkePHtWTTz6ppKQkFSxYUI899pgaNGhAGAOuwwgZgD/1zTffqFOnTnrvvfcUFRWlggULOm1//vnntXjxYv3nP/9R165dVaBAAUOdAgByUtY/0l3vgw8+0PDhw/XTTz85AtfBgwfVunVreXp6KiYmRn5+fg+6XeAvgUAG4KYsy9Lly5cVGRmpsmXL6t///rfOnTuno0ePaunSpbIsS2+99ZYk6emnn1ZcXJz27dsnT09Pw50DAHJCViA7e/asPDw85Orq6tjWuHFjPfLII5o6dapj/f79+9W1a1ctW7ZMISEhptoGHmrMQw3gpmw2mwoWLCg3Nzft27dP+/fv1/vvv6+ff/5ZJ0+e1MmTJ7Vjxw4tXrxYX3zxhRISEghjAJDLvPnmm/r55581f/582Ww27dixQy1atFCPHj3UoUMHx9T1Tz/9tGbPnq2kpCQVL15cGRkZevTRR7Vt2zan4AbAGS94ALihH3/8Ubt375YkPfHEEzp9+rQqV66sM2fOqE+fPoqPj9ebb76p5ORkXbhwQZIUEBBgsmUAQA4IDw/X66+/7liuUaOGBg4cqMTERLVs2VL9+/fXunXr1LdvXyUnJ+uDDz6QJLm4uEgSYQz4EzyyCMCJZVk6d+6cypQpo8cee0yTJ09WSEiIjh8/rqNHj6phw4aO2n79+ikpKUkLFiyQm5ubwa4BAPfbL7/8okceecSxvGbNGk2YMEHLly+XJJ09e1ZxcXF65513dOrUKVWqVEk+Pj5avXq1vv76a1WuXNlU68BfCiNkAJzYbDZ5eXlp+fLl2rVrl4YMGaL9+/erZMmSjjB26NAhDRkyRAsXLtSoUaMIYwCQy6xYsUJly5bVf//7X8c6FxcXrV27Vk8++aQkqUiRIoqIiNDnn3+uadOm6dy5c/rss8+UmpqqokWLmmod+MthhAyA4yXt9PR0ubm5OZa3bdumtm3bqmHDhho1apQqVaqkb7/9VjNmzFB8fLxmz56tatWqmW4fAJADoqKitGzZMs2bN0+tWrWSJG3cuFFdunRRWFiYli5dmm2fb775RhUrVlTJkiUfcLfAXxeBDIAkadWqVVq0aJFGjhypwMBARyiLj49Xs2bN1KxZM/373/9W2bJlFRsbqzJlyigoKMh02wCA++zaqe379eun+fPna9GiRWrevLkkacOGDeratatTKMv6Bz0Ad45HFgFIkq5cuaJp06bpH//4hxITE2Wz2ZSZmalatWpp2rRpWr58uV555RX9/PPPatiwIWEMAHKprH+r/+6779S8eXNdvHhRUVFR+uabbyT9Mb39woULFRcXp6eeekqSCGPAPSCQAXmQZVnKyMiQJP3+++9KSUlRmzZtFBcXp48//ljDhw9XYmKi8uX74/8iXF1d9dhjj2nv3r186TMA5HL58uXTV199pfDwcO3Zs0d9+vRRmTJl1KVLF8XExEj6I5R9/vnnWrp0qZ577jnDHQN/bTyyCOQhK1euVPHixR3vfS1evNgxO1ZoaKgGDRokf39/1ahRQ7169dLAgQMVGhqq4cOHy8/PT7169VLBggUNXwUAICedP39ezZs3V8OGDTV27FhJUkpKigYOHKivvvpKn3/+uVq2bClJio2NlZ+fn8qXL2+yZeAvjUAG5BEnT55UWFiYGjdurDfffFOXL19WWFiYXn31VeXPn19HjhzRtGnTNHv2bFWtWlUtWrSQp6enPDw8dOTIEW3YsIEJPAAgD0hNTVWdOnX08ssv68UXX1RmZqby5cunM2fOqHXr1kpKStLEiRPVpk0b060CuQKBDMhDtm/frr59+6pu3boqUqSI0tLSHP/6mZqaqlmzZmnIkCGKiYlRqVKltHLlSp07d06dO3fmXz8BIA/p0KGDUlJStGbNGuXPn98x0UfPnj01b948BQYGat++ffLw8DDdKvCXRyAD8pjt27frxRdf1MmTJ9W2bVtNmjTJsS0lJUXR0dG6fPmy5s+fb7BLAMCDkBW0zpw5o8zMTPn6+kqSvv76a40aNUoNGjTQ+++/73ineNCgQWrdurVq1qwpPz8/k60DuQaTegB5TM2aNTVt2jTZbDatXbtWO3fudGyz2+0qXry49u7dqytXrphrEgDwQNhsNi1ZskRt2rRR7dq1NWTIEO3du1dt2rTRM888o2+//VYNGjTQv/71Lz377LOaNWuWypYtSxgD7iMCGZAHVa1aVcuWLZOrq6s++OADp1D2+++/q1ixYkpPTzfXIAAgx1z7cNS2bdvUt29ftWjRQn369NGiRYv0+uuvKz4+XoMHD9Y777yj4OBgLV++XL///rs2bdqksmXLGuweyH14ZBHIw3bs2KEePXrowoULatSokdzd3fXll19qzZo1ql69uun2AAD30cKFC1WtWjU9+uijkqSff/5ZS5Ys0eXLl/Xmm29K+iOg9evXT0FBQXrttddUv359SdLFixeVP39+vm8MyAGMkAF5WI0aNTRv3jy5uLho3bp1KlWqlOLj4wljAJDLnDhxQpMmTXJMwpGcnKxGjRpp+PDhSkpKctTVrl1bkydP1q+//qrx48frv//9rySpUKFChDEghxDIgDyuSpUqWrBggSpUqKBevXopJCTEdEsAgPusRIkSWrVqlYKDg7V7925J0pdffqlixYppx44dTo+u16lTR1OnTtX27ds1d+5cXbx40VDXQN7AI4sAJEmXL19WgQIFTLcBAMhBqampevzxxxUaGqpJkybpf//7n7p06aJmzZpp8ODBqlKliqN2+/bt8vb2VunSpQ12DOR+BDIAAIA8ZNu2bXrxxRdVtWpVvffee9q7d6+eeeYZNWvWTEOGDFFoaKjpFoE8hUAGAACQx+zYsUPPP/+8atas6QhlPXr0UI0aNfT222+rUqVKplsE8gzeIQMAAMhjatSooenTp2v79u0aOnSoKleurE8//VQHDhxQkSJFTLcH5CmMkAEAAORRO3bsUJ8+ffTII4/o448/lpubmwoWLGi6LSBPYYQMAAAgj6pRo4YmT56sxMREXbx4kTAGGMAIGQAAQB7HTLuAOQQyAAAAADCERxYBAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEA8oTGjRsrOjradBsAADghkAEAcAc2bNggm82ms2fP3vY+hEEAwM0QyAAAAADAEAIZACDXuXDhgnr06KHChQsrMDBQ48aNc9qenp6uV199VcWLF5eHh4fq1q2rDRs2OLYfPXpU7dq1k7e3tzw8PFS5cmWtXLlSR44cUZMmTSRJ3t7estlsioqKumUvUVFR2rhxo/7zn//IZrPJZrPp8OHDKlu2rN577z2n2j179ihfvnz6+eefJUk2m01TpkxRRESEChYsqNKlS+uLL75w2ufXX39V165d5e3traJFi6pDhw46cuTI3d04AMADRyADAOQ6r7zyitavX68lS5Zo1apV2rBhg+Lj4x3b/+///k/fffedFixYoF27dunpp59Wq1atdPDgQUnSgAEDlJaWpk2bNmn37t165513VLhwYQUHB2vRokWSpAMHDighIUH/+c9/btnLf/7zH4WFhal3795KSEhQQkKCSpYsqeeff14zZsxwqp0+fboaNmyoMmXKONa99dZb6ty5s3788Uc999xzeuaZZ7Rv3z5J0sWLF9WkSRMVLlxYmzZtUmxsrAoXLqxWrVopPT39vtxLAEDOslmWZZluAgCA++X8+fMqWrSoZs+era5du0qSzpw5oxIlSqhPnz4aOHCgypUrpxMnTigoKMixX/PmzVWnTh2NHj1aVatWVefOnTVixIhsx9+wYYOaNGmi5ORkFSlS5LZ6aty4sapXr64JEyY41iUkJCg4OFibN29WnTp1dOXKFRUvXlxjx45Vz549Jf0xQtavXz9NmTLFsV+9evVUs2ZNTZ48WdOnT9e7776rffv2yWazSfpj9K9IkSJaunSpwsPD7/T2AQAesPymGwAA4H76+eeflZ6errCwMMc6Hx8fVahQQZK0fft2WZal8uXLO+2XlpamokWLSpIGDRqkF198UatWrVLz5s3VuXNnVa1a9b72GRgYqDZt2mj69OmqU6eOli9frsuXL+vpp592qrv2OrKWd+7cKUmKj4/XoUOH5Onp6VRz+fJlx2OPAICHG4EMAJCr/NmDH5mZmXJxcVF8fLxcXFycthUuXFiS9MILL6hly5ZasWKFVq1apTFjxmjcuHEaOHDgfe31hRdeUGRkpN5//33NmDFDXbt2VaFChf50v6zRsMzMTNWqVUtz587NVlOsWLH72isAIGfwDhkAIFcpW7asXF1dtWXLFse65ORk/e9//5Mk1ahRQxkZGUpKSlLZsmWdPgEBAY59goOD1a9fPy1evFhDhgzRtGnTJElubm6SpIyMjNvuyc3N7Yb1rVu3loeHh6ZMmaL//ve/ev7557PVXHsdWcuPPvqoJKlmzZo6ePCg/Pz8sl2L3W6/7f4AAOYQyAAAuUrhwoXVq1cvvfLKK1q7dq327NmjqKgo5cv3x4+88uXL69lnn1WPHj20ePFiHT58WD/88IPeeecdrVy5UpIUHR2tb775RocPH9b27du1bt06VaxYUZIUEhIim82m5cuX69SpUzp//vyf9lSqVClt3bpVR44c0e+//67MzExJkouLi6KiojRs2DCVLVs22+OJkvTFF19o+vTp+t///qcRI0bo+++/10svvSRJevbZZ+Xr66sOHTro22+/1eHDh7Vx40a9/PLLOnHixH25nwCAnEUgAwDkOmPHjtUTTzyh9u3bq3nz5nr88cdVq1Ytx/YZM2aoR48eGjJkiCpUqKD27dtr69atCg4OlvTH6NeAAQNUsWJFtWrVShUqVNDkyZMlScWLF9eoUaP02muvyd/f3xGObmXo0KFycXFRpUqVVKxYMR07dsyxrVevXkpPT7/h6JgkjRo1SgsWLFDVqlU1a9YszZ07V5UqVZIkFSpUSJs2bVLJkiXVqVMnVaxYUc8//7wuXbokLy+vu75/AIAHh1kWAQAw6LvvvlPjxo114sQJ+fv7O22z2WxasmSJOnbsaKY5AECOY1IPAAAMSEtL0/Hjx/XWW2+pS5cu2cIYACBv4JFFAADuwbFjx1S4cOGbfq59PPFa8+fPV4UKFZSSkqJ33333AXcNAHhY8MgiAAD34OrVqzpy5MhNt5cqVUr58/NACgDgxghkAAAAAGAIjywCAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADPn/AP3yaPJt3FYiAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[11], line 34\u001b[0m\n\u001b[0;32m     32\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m feature \u001b[38;5;129;01min\u001b[39;00m categorical_features:\n\u001b[0;32m     33\u001b[0m     plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m---> 34\u001b[0m     sns\u001b[38;5;241m.\u001b[39mcountplot(data\u001b[38;5;241m=\u001b[39mdata, x\u001b[38;5;241m=\u001b[39mfeature, hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124misFraud\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     35\u001b[0m     plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistribution of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfeature\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     36\u001b[0m     plt\u001b[38;5;241m.\u001b[39mxlabel(feature)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:2955\u001b[0m, in \u001b[0;36mcountplot\u001b[1;34m(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2952\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2953\u001b[0m     ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[1;32m-> 2955\u001b[0m plotter\u001b[38;5;241m.\u001b[39mplot(ax, kwargs)\n\u001b[0;32m   2956\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:1586\u001b[0m, in \u001b[0;36m_BarPlotter.plot\u001b[1;34m(self, ax, bar_kws)\u001b[0m\n\u001b[0;32m   1584\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;28mself\u001b[39m, ax, bar_kws):\n\u001b[0;32m   1585\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Make the plot.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1586\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdraw_bars(ax, bar_kws)\n\u001b[0;32m   1587\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mannotate_axes(ax)\n\u001b[0;32m   1588\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morient \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:1569\u001b[0m, in \u001b[0;36m_BarPlotter.draw_bars\u001b[1;34m(self, ax, kws)\u001b[0m\n\u001b[0;32m   1565\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, hue_level \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_names):\n\u001b[0;32m   1566\u001b[0m \n\u001b[0;32m   1567\u001b[0m     \u001b[38;5;66;03m# Draw the bars\u001b[39;00m\n\u001b[0;32m   1568\u001b[0m     offpos \u001b[38;5;241m=\u001b[39m barpos \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_offsets[j]\n\u001b[1;32m-> 1569\u001b[0m     barfunc(offpos, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatistic[:, j], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnested_width,\n\u001b[0;32m   1570\u001b[0m             color\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolors[j], align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1571\u001b[0m             label\u001b[38;5;241m=\u001b[39mhue_level, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkws)\n\u001b[0;32m   1573\u001b[0m     \u001b[38;5;66;03m# Draw the confidence intervals\u001b[39;00m\n\u001b[0;32m   1574\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfint\u001b[38;5;241m.\u001b[39msize:\n",
      "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the dataset from Excel file\n",
    "data = pd.read_excel(r\"C:\\Users\\kunta\\Downloads\\50K_processed.xlsx\")\n",
    "\n",
    "# Display basic information about the dataset\n",
    "print(\"Dataset Information:\")\n",
    "print(data.info())\n",
    "\n",
    "# Display summary statistics for numerical columns\n",
    "print(\"\\nSummary Statistics:\")\n",
    "print(data.describe())\n",
    "\n",
    "# Display the first few rows of the dataset\n",
    "print(\"\\nFirst few rows of the dataset:\")\n",
    "print(data.head())\n",
    "\n",
    "# Visualize the distribution of numerical features\n",
    "numerical_features = ['amount', 'oldbalanceOrg', 'newbalanceOrig', 'oldbalanceDest', 'newbalanceDest']\n",
    "for feature in numerical_features:\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.histplot(data=data, x=feature, kde=True, bins=30)\n",
    "    plt.title(f'Distribution of {feature}')\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.show()\n",
    "\n",
    "# Visualize the distribution of categorical features\n",
    "categorical_features = ['type', 'day_of_week', 'amount_category', 'origin_type', 'dest_type', 'flagged_fraud_reason']\n",
    "for feature in categorical_features:\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.countplot(data=data, x=feature, hue='isFraud')\n",
    "    plt.title(f'Distribution of {feature}')\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('Count')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.legend(title='Fraudulent')\n",
    "    plt.show()\n",
    "\n",
    "# Visualize the correlation matrix between numerical features\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(data[numerical_features].corr(), annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('Correlation Matrix of Numerical Features')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e543c24",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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27Q3XlyxZUgcPHrx/DQHA3xSBDAAA3La9e/fq7NmzN1zv7u6uKlWq3MeOAODviUAGAAAAAIbwYmgAAAAAMIRJPe6hzMxMHTt2TF5eXnf0wk8AAAAAuYNlWTp79qyCg4OVL9+Nx8EIZPfQsWPHFBISYroNAAAAAA+II0eOqHjx4jdcTyC7h7y8vCT9edG9vb0NdwMAAADAlNTUVIWEhDgywo0QyO6hrNsUvb29CWQAAAAA/vJRJib1AAAAAABDCGQAAAAAYAiBDAAAAAAM4RkyAAAAIA/JyMjQ5cuXTbfxt+fq6ioXF5e73g+BDAAAAMgDLMtSYmKizpw5Y7qVXKNw4cIKDAy8q3cQE8gAAACAPCArjPn7+6tgwYJ3FSLyOsuydOHCBSUlJUmSgoKC7nhfBDIAAAAgl8vIyHCEsSJFiphuJ1fw8PCQJCUlJcnf3/+Ob19kUg8AAAAgl8t6ZqxgwYKGO8ldsq7n3TyTRyADAAAA8ghuU7y37sX1JJABAAAAgCEEMgAAAAAPhMjISHXq1Om2trHZbFq0aFGO9HM/EMgAAACAPC4yMlI2my3bZ//+/aZbM6JUqVKaOHHifTkWsywCAAAAUKtWrTRjxgynZUWLFnX6np6eLjc3t/vZVq7HCBkAAAAAubu7KzAw0OnTrFkzvfjiixo8eLD8/PzUokULSdKECRNUpUoVeXp6KiQkRP3799e5c+cc+xo5cqSqV6/utP+JEyeqVKlSju8ZGRkaPHiwChcurCJFiuiVV16RZVlO21xvpKp69eoaOXLkDc/jjz/+ULdu3eTj46MiRYqoY8eOOnjwoGN91m2R7777roKCglSkSBENGDDAMVNi48aNdejQIf3jH/9wjBTmJAIZAAAAgBuaNWuW8ufPr59++klTp06VJOXLl0/vv/++du7cqVmzZmnVqlV65ZVXbmu/48eP1/Tp0/Xpp59q/fr1On36tKKjo++q1wsXLqhJkyYqVKiQ1q1bp/Xr16tQoUJq1aqV0tPTHXWrV6/Wb7/9ptWrV2vWrFmaOXOmZs6cKUlauHChihcvrrffflsJCQlKSEi4q57+CrcsAgAAANB3332nQoUKOb63bt1aklS2bFm98847TrVRUVGOfy5durT+93//Vy+88IImT558y8ebOHGihg0bpi5dukiSPvroI/3www93cQbSvHnzlC9fPn3yySeOka0ZM2aocOHCWrNmjcLDwyVJPj4+mjRpklxcXPTwww+rbdu2WrlypXr37i1fX1+5uLjIy8tLgYGBd9XPrSCQAQAAAFCTJk00ZcoUx3dPT0899dRTql27drba1atXa/To0dq1a5dSU1N15coVXbp0SefPn5enp+dfHislJUUJCQkKCwtzLMufP79q166d7bbF2xEXF6f9+/fLy8vLafmlS5f022+/Ob5XrlxZLi4uju9BQUHasWPHHR/3bhDIAAAAAMjT01Nly5a97vKrHTp0SG3atFG/fv30v//7v/L19dX69evVq1cvx3NY+fLlyxasstbdjtvdT2ZmpmrVqqU5c+ZkW3f1BCWurq5O62w2mzIzM2+7v3uBQAYAAADglm3ZskVXrlzR+PHjlS/fn1NSfPnll041RYsWVWJioizLctw6GB8f71hvt9sVFBSkjRs36rHHHpMkXblyRXFxcapZs6bTfq5+his1NVUHDhy4YW81a9bU/Pnz5e/vL29v7zs+Rzc3N2VkZNzx9reDQAYAQB6ypXYd0y0gj6i9ZbPpFpBDypQpoytXruiDDz5Q+/bt9dNPP+mjjz5yqmncuLFOnDihd955R0888YRiYmL0/fffO4Wkl156Sf/+979Vrlw5VaxYURMmTNCZM2ec9tO0aVPNnDlT7du3l4+Pj9566y2nWw2v9fTTT2vcuHHq2LGj3n77bRUvXlyHDx/WwoUL9fLLL6t48eK3dI6lSpXSunXr1L17d7m7u8vPz+/WL9BtYpZFAAAAALesevXqmjBhgsaOHavQ0FDNmTNHY8aMcaqpWLGiJk+erA8//FDVqlXT5s2bNXToUKeaIUOG6Nlnn1VkZKTCwsLk5eWlxx9/3Klm2LBheuyxx9SuXTu1adNGnTp1UpkyZW7YW8GCBbVu3TqVKFFCnTt3VsWKFfXcc8/p4sWLtzVi9vbbb+vgwYMqU6ZMtnex3Ws2626emoOT1NRU2e12paSk3NUQKQAAOYURMtwvjJA9WC5duqQDBw6odOnSKlCggOl2co2bXddbzQaMkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwJL/pBgAAAAD8vWypXee+Hq/2ls13tN3kyZM1btw4JSQkqHLlypo4caIaNmx4j7u7O4yQAQAAAMh15s+fr6ioKL3xxhvatm2bGjZsqNatW+vw4cOmW3NCIAMAAACQ60yYMEG9evXS888/r4oVK2rixIkKCQnRlClTTLfmhEAGAAAAIFdJT09XXFycwsPDnZaHh4drw4YNhrq6PgIZAAAAgFzl5MmTysjIUEBAgNPygIAAJSYmGurq+ghkAAAAAHIlm83m9N2yrGzLTCOQAQAAAMhV/Pz85OLikm00LCkpKduomWkEMgAAAAC5ipubm2rVqqXly5c7LV++fLnq169vqKvr4z1kAAAAAHKdwYMHKyIiQrVr11ZYWJg+/vhjHT58WP369TPdmhMCGQAAAIDbcqcvar6funXrplOnTuntt99WQkKCQkNDtXTpUpUsWdJ0a04IZAAAAABypf79+6t///6m27gpniEDAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYEh+0w0AAAAA+Htp9db8+3q8mP/tdtvbrFu3TuPGjVNcXJwSEhIUHR2tTp063fvm7hIjZAAAAABynfPnz6tatWqaNGmS6VZuihEyAAAAALlO69at1bp1a9Nt/CVGyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADGGWRQAAAAC5zrlz57R//37H9wMHDig+Pl6+vr4qUaKEwc6cGQ1kY8aM0cKFC7Vnzx55eHiofv36Gjt2rCpUqOCoiYyM1KxZs5y2q1u3rjZu3Oj4npaWpqFDh2ru3Lm6ePGimjVrpsmTJ6t48eKOmuTkZA0aNEiLFy+WJHXo0EEffPCBChcu7Kg5fPiwBgwYoFWrVsnDw0M9evTQu+++Kzc3txy6AgAAAMDfz528qPl+27Jli5o0aeL4PnjwYElSz549NXPmTENdZWf0lsW1a9dqwIAB2rhxo5YvX64rV64oPDxc58+fd6pr1aqVEhISHJ+lS5c6rY+KilJ0dLTmzZun9evX69y5c2rXrp0yMjIcNT169FB8fLxiYmIUExOj+Ph4RUREONZnZGSobdu2On/+vNavX6958+ZpwYIFGjJkSM5eBAAAAAD3XOPGjWVZVrbPgxTGJMMjZDExMU7fZ8yYIX9/f8XFxemxxx5zLHd3d1dgYOB195GSkqJPP/1Us2fPVvPmzSVJn3/+uUJCQrRixQq1bNlSu3fvVkxMjDZu3Ki6detKkqZNm6awsDDt3btXFSpU0LJly7Rr1y4dOXJEwcHBkqTx48crMjJS//rXv+Tt7Z0TlwAAAABAHvZATeqRkpIiSfL19XVavmbNGvn7+6t8+fLq3bu3kpKSHOvi4uJ0+fJlhYeHO5YFBwcrNDRUGzZskCTFxsbKbrc7wpgk1atXT3a73akmNDTUEcYkqWXLlkpLS1NcXNx1+01LS1NqaqrTBwAAAABu1QMTyCzL0uDBg/Xoo48qNDTUsbx169aaM2eOVq1apfHjx+vnn39W06ZNlZaWJklKTEyUm5ubfHx8nPYXEBCgxMRER42/v3+2Y/r7+zvVBAQEOK338fGRm5ubo+ZaY8aMkd1ud3xCQkLu/AIAAAAAyHMemFkWX3zxRW3fvl3r1693Wt6t2/9/YDA0NFS1a9dWyZIltWTJEnXu3PmG+7MsSzabzfH96n++m5qrDRs2zPFwoCSlpqYSygAAAADcsgdihGzgwIFavHixVq9e7TQz4vUEBQWpZMmS2rdvnyQpMDBQ6enpSk5OdqpLSkpyjHgFBgbq+PHj2fZ14sQJp5prR8KSk5N1+fLlbCNnWdzd3eXt7e30AQAAAB5UlmWZbiFXuRfX02ggsyxLL774ohYuXKhVq1apdOnSf7nNqVOndOTIEQUFBUmSatWqJVdXVy1fvtxRk5CQoJ07d6p+/fqSpLCwMKWkpGjz5s2Omk2bNiklJcWpZufOnUpISHDULFu2TO7u7qpVq9Y9OV8AAADABFdXV0nShQsXDHeSu2Rdz6zreyeM3rI4YMAAffHFF/rmm2/k5eXlGKGy2+3y8PDQuXPnNHLkSHXp0kVBQUE6ePCgXn/9dfn5+enxxx931Pbq1UtDhgxRkSJF5Ovrq6FDh6pKlSqOWRcrVqyoVq1aqXfv3po6daokqU+fPmrXrp3jnWfh4eGqVKmSIiIiNG7cOJ0+fVpDhw5V7969GfkCAADA35qLi4sKFy7smByvYMGCN3wsB3/NsixduHBBSUlJKly4sFxcXO54X0YD2ZQpUyT9+Y6Aq82YMUORkZFycXHRjh079Nlnn+nMmTMKCgpSkyZNNH/+fHl5eTnq33vvPeXPn19du3Z1vBh65syZThdmzpw5GjRokGM2xg4dOmjSpEmO9S4uLlqyZIn69++vBg0aOL0YGgAAAPi7y3qN1NUzluPuFC5c+Iav57pVNosbSe+Z1NRU2e12paSkMKoGAHggbaldx3QLyCNqb9n810UwIiMjQ5cvXzbdxt+eq6vrTUfGbjUbPDCzLAIAAADIeS4uLnd1ix3urQdilkUAAAAAyIsIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABiS33QDuD+21K5jugXkEbW3bDbdAgAAwN8GI2QAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYIjRQDZmzBg98sgj8vLykr+/vzp16qS9e/c61ViWpZEjRyo4OFgeHh5q3Lixfv31V6eatLQ0DRw4UH5+fvL09FSHDh109OhRp5rk5GRFRETIbrfLbrcrIiJCZ86ccao5fPiw2rdvL09PT/n5+WnQoEFKT0/PkXMHAAAAAKOBbO3atRowYIA2btyo5cuX68qVKwoPD9f58+cdNe+8844mTJigSZMm6eeff1ZgYKBatGihs2fPOmqioqIUHR2tefPmaf369Tp37pzatWunjIwMR02PHj0UHx+vmJgYxcTEKD4+XhEREY71GRkZatu2rc6fP6/169dr3rx5WrBggYYMGXJ/LgYAAACAPMdmWZZluoksJ06ckL+/v9auXavHHntMlmUpODhYUVFRevXVVyX9ORoWEBCgsWPHqm/fvkpJSVHRokU1e/ZsdevWTZJ07NgxhYSEaOnSpWrZsqV2796tSpUqaePGjapbt64kaePGjQoLC9OePXtUoUIFff/992rXrp2OHDmi4OBgSdK8efMUGRmppKQkeXt7/2X/qampstvtSklJuaX6+2lL7TqmW0AeUXvLZtMtALgJfg9wv/B7gLzuVrPBA/UMWUpKiiTJ19dXknTgwAElJiYqPDzcUePu7q5GjRppw4YNkqS4uDhdvnzZqSY4OFihoaGOmtjYWNntdkcYk6R69erJbrc71YSGhjrCmCS1bNlSaWlpiouLu26/aWlpSk1NdfoAAAAAwK16YAKZZVkaPHiwHn30UYWGhkqSEhMTJUkBAQFOtQEBAY51iYmJcnNzk4+Pz01r/P39sx3T39/fqeba4/j4+MjNzc1Rc60xY8Y4nkmz2+0KCQm53dMGAAAAkIc9MIHsxRdf1Pbt2zV37txs62w2m9N3y7KyLbvWtTXXq7+TmqsNGzZMKSkpjs+RI0du2hMAAAAAXO2BCGQDBw7U4sWLtXr1ahUvXtyxPDAwUJKyjVAlJSU5RrMCAwOVnp6u5OTkm9YcP34823FPnDjhVHPtcZKTk3X58uVsI2dZ3N3d5e3t7fQBAAAAgFtlNJBZlqUXX3xRCxcu1KpVq1S6dGmn9aVLl1ZgYKCWL1/uWJaenq61a9eqfv36kqRatWrJ1dXVqSYhIUE7d+501ISFhSklJUWbN///h0s3bdqklJQUp5qdO3cqISHBUbNs2TK5u7urVq1a9/7kAQAAAOR5+U0efMCAAfriiy/0zTffyMvLyzFCZbfb5eHhIZvNpqioKI0ePVrlypVTuXLlNHr0aBUsWFA9evRw1Pbq1UtDhgxRkSJF5Ovrq6FDh6pKlSpq3ry5JKlixYpq1aqVevfuralTp0qS+vTpo3bt2qlChQqSpPDwcFWqVEkREREaN26cTp8+raFDh6p3796MfAEAAADIEUYD2ZQpUyRJjRs3dlo+Y8YMRUZGSpJeeeUVXbx4Uf3791dycrLq1q2rZcuWycvLy1H/3nvvKX/+/OratasuXryoZs2aaebMmXJxcXHUzJkzR4MGDXLMxtihQwdNmjTJsd7FxUVLlixR//791aBBA3l4eKhHjx569913c+jsAQAAAOR1D9R7yP7ueA8ZwHtngAcdvwe4X/g9QF73t3wPGQAAAADkJQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIbcUSB76KGHdOrUqWzLz5w5o4ceeuiumwIAAACAvOCOAtnBgweVkZGRbXlaWpr++OOPu24KAAAAAPKC/LdTvHjxYsc///DDD7Lb7Y7vGRkZWrlypUqVKnXPmgMAAACA3Oy2AlmnTp0kSTabTT179nRa5+rqqlKlSmn8+PH3rDkAAAAAyM1uK5BlZmZKkkqXLq2ff/5Zfn5+OdIUAAAAAOQFtxXIshw4cOBe9wEAAAAAec4dBTJJWrlypVauXKmkpCTHyFmW6dOn33VjAAAAAJDb3VEgGzVqlN5++23Vrl1bQUFBstls97ovAAAAAMj17iiQffTRR5o5c6YiIiLudT8AAAAAkGfc0XvI0tPTVb9+/bs++Lp169S+fXsFBwfLZrNp0aJFTusjIyNls9mcPvXq1XOqSUtL08CBA+Xn5ydPT0916NBBR48edapJTk5WRESE7Ha77Ha7IiIidObMGaeaw4cPq3379vL09JSfn58GDRqk9PT0uz5HAAAAALiROwpkzz//vL744ou7Pvj58+dVrVo1TZo06YY1rVq1UkJCguOzdOlSp/VRUVGKjo7WvHnztH79ep07d07t2rVzenF1jx49FB8fr5iYGMXExCg+Pt5pdC8jI0Nt27bV+fPntX79es2bN08LFizQkCFD7vocAQAAAOBG7uiWxUuXLunjjz/WihUrVLVqVbm6ujqtnzBhwi3tp3Xr1mrduvVNa9zd3RUYGHjddSkpKfr00081e/ZsNW/eXJL0+eefKyQkRCtWrFDLli21e/duxcTEaOPGjapbt64kadq0aQoLC9PevXtVoUIFLVu2TLt27dKRI0cUHBwsSRo/frwiIyP1r3/9S97e3rd0PgAAAABwO+5ohGz79u2qXr268uXLp507d2rbtm2OT3x8/D1tcM2aNfL391f58uXVu3dvJSUlOdbFxcXp8uXLCg8PdywLDg5WaGioNmzYIEmKjY2V3W53hDFJqlevnux2u1NNaGioI4xJUsuWLZWWlqa4uLgb9paWlqbU1FSnDwAAAADcqjsaIVu9evW97uO6WrdurSeffFIlS5bUgQMH9NZbb6lp06aKi4uTu7u7EhMT5ebmJh8fH6ftAgIClJiYKElKTEyUv79/tn37+/s71QQEBDit9/HxkZubm6PmesaMGaNRo0bd7WkCAAAAyKPu+D1k90O3bt0c/xwaGqratWurZMmSWrJkiTp37nzD7SzLcpqK/3rT8t9JzbWGDRumwYMHO76npqYqJCTkxicEAAAAAFe5o0DWpEmTmwaVVatW3XFDNxMUFKSSJUtq3759kqTAwEClp6crOTnZaZQsKSnJMQtkYGCgjh8/nm1fJ06ccIyKBQYGatOmTU7rk5OTdfny5WwjZ1dzd3eXu7v7XZ8XAAAAgLzpjp4hq169uqpVq+b4VKpUSenp6dq6dauqVKlyr3t0OHXqlI4cOaKgoCBJUq1ateTq6qrly5c7ahISErRz505HIAsLC1NKSoo2b97sqNm0aZNSUlKcanbu3KmEhARHzbJly+Tu7q5atWrl2PkAAAAAyNvuaITsvffeu+7ykSNH6ty5c7e8n3Pnzmn//v2O7wcOHFB8fLx8fX3l6+urkSNHqkuXLgoKCtLBgwf1+uuvy8/PT48//rgkyW63q1evXhoyZIiKFCkiX19fDR06VFWqVHHMulixYkW1atVKvXv31tSpUyVJffr0Ubt27VShQgVJUnh4uCpVqqSIiAiNGzdOp0+f1tChQ9W7d29mWAQAAACQY+5ohOxGnnnmGU2fPv2W67ds2aIaNWqoRo0akqTBgwerRo0aGj58uFxcXLRjxw517NhR5cuXV8+ePVW+fHnFxsbKy8vLsY/33ntPnTp1UteuXdWgQQMVLFhQ3377rVxcXBw1c+bMUZUqVRQeHq7w8HBVrVpVs2fPdqx3cXHRkiVLVKBAATVo0EBdu3ZVp06d9O67796DqwIAAAAA12ezLMu6VzubPXu2Xn31VR07duxe7fJvJTU1VXa7XSkpKQ/cyNqW2nVMt4A8ovaWzX9dBMAYfg9wv/B7gLzuVrPBHd2yeO0Mh5ZlKSEhQVu2bNFbb711J7sEAAAAgDznjgKZ3W53+p4vXz5VqFBBb7/9ttNLmgEAAAAAN3ZHgWzGjBn3ug8AAAAAyHPu6sXQcXFx2r17t2w2mypVquSYnAMAAAAA8NfuKJAlJSWpe/fuWrNmjQoXLizLspSSkqImTZpo3rx5Klq06L3uEwAAAABynTua9n7gwIFKTU3Vr7/+qtOnTys5OVk7d+5UamqqBg0adK97BAAAAIBc6Y5GyGJiYrRixQpVrFjRsaxSpUr68MMPmdQDAAAAAG7RHY2QZWZmytXVNdtyV1dXZWZm3nVTAAAAAJAX3FEga9q0qV566SWnF0D/8ccf+sc//qFmzZrds+YAAAAAIDe7o0A2adIknT17VqVKlVKZMmVUtmxZlS5dWmfPntUHH3xwr3sEAAAAgFzpjp4hCwkJ0datW7V8+XLt2bNHlmWpUqVKat68+b3uDwAAAAByrdsaIVu1apUqVaqk1NRUSVKLFi00cOBADRo0SI888ogqV66sH3/8MUcaBQAAAIDc5rYC2cSJE9W7d295e3tnW2e329W3b19NmDDhnjUHAAAAALnZbQWyX375Ra1atbrh+vDwcMXFxd11UwAAAACQF9xWIDt+/Ph1p7vPkj9/fp04ceKumwIAAACAvOC2AlmxYsW0Y8eOG67fvn27goKC7ropAAAAAMgLbiuQtWnTRsOHD9elS5eyrbt48aJGjBihdu3a3bPmAAAAACA3u61p7998800tXLhQ5cuX14svvqgKFSrIZrNp9+7d+vDDD5WRkaE33ngjp3oFAAAAgFzltgJZQECANmzYoBdeeEHDhg2TZVmSJJvNppYtW2ry5MkKCAjIkUYBAAAAILe57RdDlyxZUkuXLlVycrL2798vy7JUrlw5+fj45ER/AAAAAJBr3XYgy+Lj46NHHnnkXvYCAAAAAHnKbU3qAQAAAAC4dwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEKOBbN26dWrfvr2Cg4Nls9m0aNEip/WWZWnkyJEKDg6Wh4eHGjdurF9//dWpJi0tTQMHDpSfn588PT3VoUMHHT161KkmOTlZERERstvtstvtioiI0JkzZ5xqDh8+rPbt28vT01N+fn4aNGiQ0tPTc+K0AQAAAECS4UB2/vx5VatWTZMmTbru+nfeeUcTJkzQpEmT9PPPPyswMFAtWrTQ2bNnHTVRUVGKjo7WvHnztH79ep07d07t2rVTRkaGo6ZHjx6Kj49XTEyMYmJiFB8fr4iICMf6jIwMtW3bVufPn9f69es1b948LViwQEOGDMm5kwcAAACQ59ksy7JMNyFJNptN0dHR6tSpk6Q/R8eCg4MVFRWlV199VdKfo2EBAQEaO3as+vbtq5SUFBUtWlSzZ89Wt27dJEnHjh1TSEiIli5dqpYtW2r37t2qVKmSNm7cqLp160qSNm7cqLCwMO3Zs0cVKlTQ999/r3bt2unIkSMKDg6WJM2bN0+RkZFKSkqSt7f3LZ1Damqq7Ha7UlJSbnmb+2VL7TqmW0AeUXvLZtMtALgJfg9wv/B7gLzuVrPBA/sM2YEDB5SYmKjw8HDHMnd3dzVq1EgbNmyQJMXFxeny5ctONcHBwQoNDXXUxMbGym63O8KYJNWrV092u92pJjQ01BHGJKlly5ZKS0tTXFzcDXtMS0tTamqq0wcAAAAAbtUDG8gSExMlSQEBAU7LAwICHOsSExPl5uYmHx+fm9b4+/tn27+/v79TzbXH8fHxkZubm6PmesaMGeN4Ls1utyskJOQ2zxIAAABAXvbABrIsNpvN6btlWdmWXevamuvV30nNtYYNG6aUlBTH58iRIzftCwAAAACu9sAGssDAQEnKNkKVlJTkGM0KDAxUenq6kpOTb1pz/PjxbPs/ceKEU821x0lOTtbly5ezjZxdzd3dXd7e3k4fAAAAALhVD2wgK126tAIDA7V8+XLHsvT0dK1du1b169eXJNWqVUuurq5ONQkJCdq5c6ejJiwsTCkpKdq8+f8/WLpp0yalpKQ41ezcuVMJCQmOmmXLlsnd3V21atXK0fMEAAAAkHflN3nwc+fOaf/+/Y7vBw4cUHx8vHx9fVWiRAlFRUVp9OjRKleunMqVK6fRo0erYMGC6tGjhyTJbrerV69eGjJkiIoUKSJfX18NHTpUVapUUfPmzSVJFStWVKtWrdS7d29NnTpVktSnTx+1a9dOFSpUkCSFh4erUqVKioiI0Lhx43T69GkNHTpUvXv3ZtQLAAAAQI4xGsi2bNmiJk2aOL4PHjxYktSzZ0/NnDlTr7zyii5evKj+/fsrOTlZdevW1bJly+Tl5eXY5r333lP+/PnVtWtXXbx4Uc2aNdPMmTPl4uLiqJkzZ44GDRrkmI2xQ4cOTu8+c3Fx0ZIlS9S/f381aNBAHh4e6tGjh959992cvgQAAAAA8rAH5j1kuQHvIQN47wzwoOP3APcLvwfI6/727yEDAAAAgNyOQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwJAHOpCNHDlSNpvN6RMYGOhYb1mWRo4cqeDgYHl4eKhx48b69ddfnfaRlpamgQMHys/PT56enurQoYOOHj3qVJOcnKyIiAjZ7XbZ7XZFRETozJkz9+MUAQAAAORhD3Qgk6TKlSsrISHB8dmxY4dj3TvvvKMJEyZo0qRJ+vnnnxUYGKgWLVro7NmzjpqoqChFR0dr3rx5Wr9+vc6dO6d27dopIyPDUdOjRw/Fx8crJiZGMTExio+PV0RExH09TwAAAAB5T37TDfyV/PnzO42KZbEsSxMnTtQbb7yhzp07S5JmzZqlgIAAffHFF+rbt69SUlL06aefavbs2WrevLkk6fPPP1dISIhWrFihli1bavfu3YqJidHGjRtVt25dSdK0adMUFhamvXv3qkKFCvfvZAEAAADkKQ/8CNm+ffsUHBys0qVLq3v37vr9998lSQcOHFBiYqLCw8Mdte7u7mrUqJE2bNggSYqLi9Ply5edaoKDgxUaGuqoiY2Nld1ud4QxSapXr57sdruj5kbS0tKUmprq9AEAAACAW/VAB7K6devqs88+0w8//KBp06YpMTFR9evX16lTp5SYmChJCggIcNomICDAsS4xMVFubm7y8fG5aY2/v3+2Y/v7+ztqbmTMmDGO587sdrtCQkLu+FwBAAAA5D0PdCBr3bq1unTpoipVqqh58+ZasmSJpD9vTcxis9mctrEsK9uya11bc736W9nPsGHDlJKS4vgcOXLkL88JAAAAALI80IHsWp6enqpSpYr27dvneK7s2lGspKQkx6hZYGCg0tPTlZycfNOa48ePZzvWiRMnso2+Xcvd3V3e3t5OHwAAAAC4VX+rQJaWlqbdu3crKChIpUuXVmBgoJYvX+5Yn56errVr16p+/fqSpFq1asnV1dWpJiEhQTt37nTUhIWFKSUlRZs3b3bUbNq0SSkpKY4aAAAAAMgJD/Qsi0OHDlX79u1VokQJJSUl6Z///KdSU1PVs2dP2Ww2RUVFafTo0SpXrpzKlSun0aNHq2DBgurRo4ckyW63q1evXhoyZIiKFCkiX19fDR061HELpCRVrFhRrVq1Uu/evTV16lRJUp8+fdSuXTtmWAQAAACQox7oQHb06FE99dRTOnnypIoWLap69epp48aNKlmypCTplVde0cWLF9W/f38lJyerbt26WrZsmby8vBz7eO+995Q/f3517dpVFy9eVLNmzTRz5ky5uLg4aubMmaNBgwY5ZmPs0KGDJk2adH9PFgAAAECeY7MsyzLdRG6Rmpoqu92ulJSUB+55si2165huAXlE7S2b/7oIgDH8HuB+4fcAed2tZoO/1TNkAAAAAJCbEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhk15g8ebJKly6tAgUKqFatWvrxxx9NtwQAAAAglyKQXWX+/PmKiorSG2+8oW3btqlhw4Zq3bq1Dh8+bLo1AAAAALlQftMNPEgmTJigXr166fnnn5ckTZw4UT/88IOmTJmiMWPGGO4OAADg76PVW/NNt4A8IuZ/u5lu4a4QyP5Penq64uLi9NprrzktDw8P14YNG667TVpamtLS0hzfU1JSJEmpqak51+gdOpeRYboF5BEP4n//AP4/fg9wv1xJu2C6BeQRD+qfPbL6sizrpnUEsv9z8uRJZWRkKCAgwGl5QECAEhMTr7vNmDFjNGrUqGzLQ0JCcqRH4G/BbjfdAQDgQRD/nOkOkEfYxz3Y/62dPXtW9pv8+YhAdg2bzeb03bKsbMuyDBs2TIMHD3Z8z8zM1OnTp1WkSJEbbgPkZqmpqQoJCdGRI0fk7e1tuh0AgCH8HgB/5oizZ88qODj4pnUEsv/j5+cnFxeXbKNhSUlJ2UbNsri7u8vd3d1pWeHChXOqReBvw9vbmx9gAAC/B8jzbjYyloVZFv+Pm5ubatWqpeXLlzstX758uerXr2+oKwAAAAC5GSNkVxk8eLAiIiJUu3ZthYWF6eOPP9bhw4fVr18/060BAAAAyIUIZFfp1q2bTp06pbffflsJCQkKDQ3V0qVLVbJkSdOtAX8L7u7uGjFiRLZbeQEAeQu/B8Cts1l/NQ8jAAAAACBH8AwZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAA8kC5fvmy6BSDH8R4yAA8ky7Jks9lMtwEAMGTMmDGSpBdffFFeXl6GuwFyDiNkAB4IWa9E3LJli3bt2kUYA4A87vz583rjjTc0Y8YMnTt3znQ7QI4hkAEwLms0LDo6Wu3bt9enn36q5ORk020BAAz65z//qdGjRysqKkqffPIJoQy5FrcsAjDOZrMpJiZGTz/9tCZNmqSOHTvKx8fHdFsAAEMyMzOVL18+vfbaa8rMzNTgwYMlSc8//7wKFSpkuDvg3iKQATAuPT1dX375pQYMGKDnnntO58+f165du/T555+rTJkyql+/vipWrGi6TQDAfZIvXz5lZGTIxcVFr7/+uiQRypBrEcgAPBAOHjyolJQUJSYm6q233tL+/fuVmJioM2fOqFu3bpowYYLy5eMuawDIzbJuYb9y5YojkEnS66+/zkgZci3+dAPgvsuawGPr1q3atWuX3Nzc9Nprr2nNmjUqX768Tp8+rb59+2r37t0aPHiwYmNjlZ6ebrhrAEBOygpjWbewN2rUSMOGDdOePXskSW+++abefvttDR48WNOnT1dqaqrhjoF7gxEyAPfV1RN4REVF6fHHH9eIESMUHh6uHTt26Pfff9ejjz7qCG1//PGHQkJCHN8BALmTzWbTN998o4iICEVERKhp06YaMWKE9u3bp/79+6tp06Z68803lT9/fkVFRcnV1VX9+vVjVl787dks/pQD4D5bunSpunTpokmTJqlTp04qUqRItpr4+HjNnz9fU6ZM0bp161S1alUDnQIA7pfdu3erc+fOGjhwoPr376+MjAwFBwcrLS1NNWvW1IgRI9SoUSNJ0vjx49WmTRueL0auQCADcF9dvHhRzz33nMqXL69Ro0bp3Llz+uOPPzR//nyVKlVK4eHhOnnypN59913FxcXp888/V7Vq1Uy3DQDIIVl3Tvzyyy/69ttv9fLLL+vkyZNq2LChOnTooBdeeEG1a9dW06ZN1bdvX7Vp08Z0y8A9xS2LAO4rd3d3JSQkyLIsnTlzRsOGDdOuXbt04sQJ/fbbb3r55Zc1fPhwDRo0SAEBASpWrJjplgEAOSgtLU0FChRQ6dKl1a1bN7m5uen111/Xo48+qn/961/y9PRUWFiYvv32WxUsWFCNGzdWwYIFTbcN3DNM6gEgR107CJ8vXz698MIL+vHHHxUYGKjjx4+rT58+2rVrl0aOHKnVq1crMzNTNWvWJIwBQC63ZcsW1alTR/v375e3t7fKli0ry7L0xx9/qGLFivL09JQkVahQQV9++aVGjx5NGEOuwwgZgByTdRvKxo0btX79el26dEl16tRRt27dVL9+fe3bt09NmzZ11B85ckSlSpViensAyCNSU1Pl5eWlp556Sl999ZVKlSqlU6dOKSUlRdu3b9c333yj2NhYff311xo+fLiKFi1qumXgnuMZMgA5asGCBerXr5/q1KkjHx8fffHFFxo5cqSGDx/uqNm1a5c+++wzTZ06VevWrVOVKlUMdgwAuJ/WrFmj0aNH68SJE1qwYIEeeughrV+/Xt27d5eXl5fS09P19ddfq0aNGqZbBXIEI2QAcszevXsVFRWlt99+Wy+88IKOHj2qr776yundMRs3btQnn3yi2NhYrVmzhjAGALlU1l0TW7duVcmSJR0z7DZu3FiZmZn697//rS5dumjBggV69NFHFRsbq4yMDHl6ejIyhlyNETIAOWbDhg168803tWrVKh04cEANGzZU+/btNWXKFEnSgQMHVKpUKW3YsEGlSpXimTEAyGXGjBkjSRo2bJgk6dixY3riiSfk6uqq6Oho+fr6SvozrMXExGjAgAEKDg7WrFmzVKZMGWN9A/cTD2oAyDGpqalKSkrShg0b1KRJE7Vt21aTJk2SJMXGxuq1117TsWPH1KBBA8IYAOQyGRkZunLlit544w198MEHkqSAgAD17dtXNptNzzzzjE6dOiXpz5dCt27dWmXKlNHmzZsVGRmpy5cvZ5sYCsiNuGURwD2RdSvK1UqVKiUfHx+1bt1a7du319SpUx3roqOjdebMGXl4eNzvVgEA94GLi4uGDh2qggULKioqSpcvX9bgwYP1zDPPyNXVVZMnT9azzz6ruXPnytvbW5ZlqXz58nrqqafUunVrubq6mj4F4L4gkAG4a1lhLDY2Vnv37lVGRoZ69eqlhx9+WI8//rh+/fVXBQYG6pdffpGLi4tmzZqlTz75ROvWrXPcrgIAyD0yMzNls9nk4eGhzp07KyUlRUOHDpWXl5d69+6tbt26SZImT56sxo0bKyoqSps2bdKKFSv0+uuvKygoyPAZAPcPz5ABuCcWLVqk7t27q1KlSvr1118VFhamzz//XMWLF9eYMWP0zTffKC4uTlWqVFFmZqZmzpyp6tWrm24bAJCDoqOjNWLECFWuXFnfffedzp8/rwkTJigqKkoZGRlavXq1PvjgA+3YsUN+fn6aOnUqsykizyGQAbhjWSNj586d09NPP60uXbqoY8eOOnHihNq0aSNvb28tWLBAJUuW1PHjx7V//34FBASocOHC8vPzM90+ACAHbd++XWFhYZowYYK6d++uY8eOac6cORo9erQjlGU5evSovL295e3tba5hwBAm9QBwW7Zt26a0tDRJfz6EvWrVKj355JPKly+fwsLCZLfbVbZsWf300086e/asunTp4ghiDRo0UNmyZQljAJDLfPjhh0pKSnJa9scffygkJERPPvmk7Ha7KlasqFdeeUWvvvqqBg8erBkzZjhqixcvThhDnkUgA3BLLMvSvHnz1Lp1a126dMmx3MfHR3FxcVqyZInOnj0r6c9nB4oWLar169fr4sWLatu2rfbv32+qdQBADjp58qQmT57s9I5JSfL09NR///tf/f7775L+/B3x9vZW165d5ebmpl69eumjjz4y0TLwQCGQAbglNptN3bt31+bNm2W325WYmKhLly6pRo0aWrlypfz8/PTWW28pOTlZ+fLlk2VZKlq0qFatWiVvb2/lz88cQgCQG/n5+Wnbtm0qW7asNm3apMTERElSlSpV1KJFC7333nvas2ePYybeoKAgdenSRePHj1ejRo1Mtg48EHiGDMAtycjIkIuLizIzM/Xrr7+qTp06mjZtmp544gkVKFBA27ZtU8uWLRUWFqaZM2fKx8dHmZmZypcvn+N/AQC5S9azxJmZmbp06ZLKly+v4OBgLVmyREWLFtWsWbM0depUhYSEaODAgSpRooQ++ugjLVu2TCtXrpTdbjd9CoBxBDIAd6R79+764Ycf9NFHH6ljx45Ooaxhw4aaNm0aU9oDQB5x4MABlS5dWnv27FG7du1UrFgxLVq0SD4+Pvrss8/05ZdfaunSpSpbtqySk5O1fPlyZtoF/g+BDMBfyvob0O3bt+vUqVNq0qSJJOn555/XvHnz9OmnnzpCWXx8vGrVqqXu3btr9uzZjIwBQC536NAhVahQQfPmzVOnTp20b98+tWjRQiVLllR0dLR8fX118eJF7dy5U+np6SpVqpSKFStmum3ggcGflADcVFYYW7hwoTp27Kj169frt99+kyR98skn6tatm5577jktWrRIly5dUvXq1RUfH68RI0YQxgAgDyhUqJBatmypzZs3KyMjQ+XKldPy5ct16NAhPf7440pKSpKHh4ceeeQRNWjQgDAGXIMRMgB/6YcfflDnzp317rvvKjIyUh4eHk7rn3vuOS1cuFD/+c9/1K1bNxUoUMBQpwCAnJT1l3TXev/99zV8+HD9+uuvjsC1b98+tWnTRl5eXoqJiZG/v//9bhf4WyCQAbghy7J06dIlRUREqGzZsvr3v/+ts2fP6tChQ1q0aJEsy9Jbb70lSXryyScVGxur3bt3y8vLy3DnAICckBXIzpw5I09PT7m6ujrWNW7cWA899JCmTp3qWL5nzx5169ZNixcvVsmSJU21DTzQmIcawA3ZbDZ5eHjIzc1Nu3fv1p49e/Tee+/pt99+0/Hjx3X8+HFt27ZNCxcu1FdffaWEhATCGADkMm+++aZ+++03zZ07VzabTdu2bVOLFi307LPPqmPHjo6p65988kl99tlnSkpKUrFixZSRkaGHH35YW7ZscQpuAJzxgAeA6/rll1+0Y8cOSdJjjz2mU6dOqXLlyjp9+rT69OmjuLg4vfnmm0pOTtb58+clSYGBgSZbBgDkgPDwcL3++uuO7zVq1NDAgQOVmJioli1bqn///lq1apX69u2r5ORkvf/++5IkFxcXSSKMAX+BWxYBOLEsS2fPnlWZMmX0yCOPaPLkySpZsqSOHDmiQ4cOqWHDho7afv36KSkpSfPmzZObm5vBrgEA99rvv/+uhx56yPF9xYoVmjhxor777jtJ0pkzZxQbG6uxY8fqxIkTqlSpknx9fbV8+XJ9++23qly5sqnWgb8VRsgAOLHZbPL29tZ3332n7du3a8iQIdqzZ49KlCjhCGP79+/XkCFDNH/+fI0aNYowBgC5zJIlS1S2bFl9//33jmUuLi5auXKlHn/8cUlS4cKF1bp1a3355ZeaNm2azp49q9mzZys1NVVFihQx1Trwt8MIGQDHQ9rp6elyc3NzfN+yZYvatWunhg0batSoUapUqZJ+/PFHzZgxQ3Fxcfrss89UrVo10+0DAHJAZGSkFi9erC+++EKtWrWSJK1du1Zdu3ZVWFiYFi1alG2bH374QRUrVlSJEiXuc7fA3xeBDIAkadmyZVqwYIFGjhypoKAgRyiLi4tTs2bN1KxZM/373/9W2bJltX79epUpU0bBwcGm2wYA3GNXT23fr18/zZ07VwsWLFDz5s0lSWvWrFG3bt2cQlnWX+gBuH3csghAknT58mVNmzZN//u//6vExETZbDZlZmaqVq1amjZtmr777ju9/PLL+u2339SwYUPCGADkUll/V//TTz+pefPmunDhgiIjI/XDDz9I+nN6+/nz5ys2NlZPPPGEJBHGgLtAIAPyIMuylJGRIUk6efKkUlJS1LZtW8XGxurjjz/W8OHDlZiYqHz5/vy/CFdXVz3yyCPatWsXL30GgFwuX758+uabbxQeHq6dO3eqT58+KlOmjLp27aqYmBhJf4ayL7/8UosWLdIzzzxjuGPg741bFoE8ZOnSpSpWrJjjua+FCxc6ZscKDQ3VoEGDFBAQoBo1aqhXr14aOHCgQkNDNXz4cPn7+6tXr17y8PAwfBYAgJx07tw5NW/eXA0bNtS4ceMkSSkpKRo4cKC++eYbffnll2rZsqUkaf369fL391f58uVNtgz8rRHIgDzi+PHjCgsLU+PGjfXmm2/q0qVLCgsL0yuvvKL8+fPr4MGDmjZtmj777DNVrVpVLVq0kJeXlzw9PXXw4EGtWbOGCTwAIA9ITU1VnTp19NJLL+mFF15QZmam8uXLp9OnT6tNmzZKSkrSBx98oLZt25puFcgVCGRAHrJ161b17dtXdevWVeHChZWWlub428/U1FTNmjVLQ4YMUUxMjEqVKqWlS5fq7Nmz6tKlC3/7CQB5SMeOHZWSkqIVK1Yof/78jok+evbsqS+++EJBQUHavXu3PD09TbcK/O0RyIA8ZuvWrXrhhRd0/PhxtWvXTpMmTXKsS0lJUVRUlC5duqS5c+ca7BIAcD9kBa3Tp08rMzNTfn5+kqRvv/1Wo0aNUoMGDfTee+85nikeNGiQ2rRpo5o1a8rf399k60CuwaQeQB5Ts2ZNTZs2TTabTStXrlR8fLxjnd1uV7FixbRr1y5dvnzZXJMAgPvCZrMpOjpabdu2Ve3atTVkyBDt2rVLbdu21VNPPaUff/xRDRo00L/+9S89/fTTmjVrlsqWLUsYA+4hAhmQB1WtWlWLFy+Wq6ur3n//fadQdvLkSRUtWlTp6enmGgQA5Jirb47asmWL+vbtqxYtWqhPnz5asGCBXn/9dcXFxWnw4MEaO3asQkJC9N133+nkyZNat26dypYta7B7IPfhlkUgD9u2bZueffZZnT9/Xo0aNZK7u7u+/vprrVixQtWrVzfdHgDgHpo/f76qVaumhx9+WJL022+/KTo6WpcuXdKbb74p6c+A1q9fPwUHB+u1115T/fr1JUkXLlxQ/vz5ed8YkAMYIQPysBo1auiLL76Qi4uLVq1apVKlSikuLo4wBgC5zNGjRzVp0iTHJBzJyclq1KiRhg8frqSkJEdd7dq1NXnyZP3xxx+aMGGCvv/+e0lSwYIFCWNADiGQAXlclSpVNG/ePFWoUEG9evVSyZIlTbcEALjHihcvrmXLlikkJEQ7duyQJH399dcqWrSotm3b5nTrep06dTR16lRt3bpVc+bM0YULFwx1DeQN3LIIQJJ06dIlFShQwHQbAIAclJqaqkcffVShoaGaNGmS/vvf/6pr165q1qyZBg8erCpVqjhqt27dKh8fH5UuXdpgx0DuRyADAADIQ7Zs2aIXXnhBVatW1bvvvqtdu3bpqaeeUrNmzTRkyBCFhoaabhHIUwhkAAAAecy2bdv03HPPqWbNmo5Q9uyzz6pGjRp6++23ValSJdMtAnkGz5ABAADkMTVq1ND06dO1detWDR06VJUrV9ann36qvXv3qnDhwqbbA/IURsgAAADyqG3btqlPnz566KGH9PHHH8vNzU0eHh6m2wLyFEbIAAAA8qgaNWpo8uTJSkxM1IULFwhjgAGMkAEAAORxzLQLmEMgAwAAAABDuGURAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAIE9o3LixoqKiTLcBAIATAhkAALdhzZo1stlsOnPmzC1vQxgEANwIgQwAAAAADCGQAQBynfPnz+vZZ59VoUKFFBQUpPHjxzutT09P1yuvvKJixYrJ09NTdevW1Zo1axzrDx06pPbt28vHx0eenp6qXLmyli5dqoMHD6pJkyaSJB8fH9lsNkVGRt60l8jISK1du1b/+c9/ZLPZZLPZdODAAZUtW1bvvvuuU+3OnTuVL18+/fbbb5Ikm82mKVOmqHXr1vLw8FDp0qX11VdfOW3zxx9/qFu3bvLx8VGRIkXUsWNHHTx48M4uHADgviOQAQBynZdfflmrV69WdHS0li1bpjVr1iguLs6x/n/+53/0008/ad68edq+fbuefPJJtWrVSvv27ZMkDRgwQGlpaVq3bp127NihsWPHqlChQgoJCdGCBQskSXv37lVCQoL+85//3LSX//znPwoLC1Pv3r2VkJCghIQElShRQs8995xmzJjhVDt9+nQ1bNhQZcqUcSx766231KVLF/3yyy965pln9NRTT2n37t2SpAsXLqhJkyYqVKiQ1q1bp/Xr16tQoUJq1aqV0tPT78m1BADkLJtlWZbpJgAAuFfOnTunIkWK6LPPPlO3bt0kSadPn1bx4sXVp08fDRw4UOXKldPRo0cVHBzs2K558+aqU6eORo8erapVq6pLly4aMWJEtv2vWbNGTZo0UXJysgoXLnxLPTVu3FjVq1fXxIkTHcsSEhIUEhKiDRs2qE6dOrp8+bKKFSumcePGqWfPnpL+HCHr16+fpkyZ4tiuXr16qlmzpiZPnqzp06frnXfe0e7du2Wz2ST9OfpXuHBhLVq0SOHh4bd7+QAA91l+0w0AAHAv/fbbb0pPT1dYWJhjma+vrypUqCBJ2rp1qyzLUvny5Z22S0tLU5EiRSRJgwYN0gsvvKBly5apefPm6tKli6pWrXpP+wwKClLbtm01ffp01alTR999950uXbqkJ5980qnu6vPI+h4fHy9JiouL0/79++Xl5eVUc+nSJcdtjwCABxuBDACQq/zVjR+ZmZlycXFRXFycXFxcnNYVKlRIkvT888+rZcuWWrJkiZYtW6YxY8Zo/PjxGjhw4D3t9fnnn1dERITee+89zZgxQ926dVPBggX/crus0bDMzEzVqlVLc+bMyVZTtGjRe9orACBn8AwZACBXKVu2rFxdXbVx40bHsuTkZP33v/+VJNWoUUMZGRlKSkpS2bJlnT6BgYGObUJCQtSvXz8tXLhQQ4YM0bRp0yRJbm5ukqSMjIxb7snNze269W3atJGnp6emTJmi77//Xs8991y2mqvPI+v7ww8/LEmqWbOm9u3bJ39//2znYrfbb7k/AIA5BDIAQK5SqFAh9erVSy+//LJWrlypnTt3KjIyUvny/fmTV758eT399NN69tlntXDhQh04cEA///yzxo4dq6VLl0qSoqKi9MMPP+jAgQPaunWrVq1apYoVK0qSSpYsKZvNpu+++04nTpzQuXPn/rKnUqVKadOmTTp48KBOnjypzMxMSZKLi4siIyM1bNgwlS1bNtvtiZL01Vdfafr06frvf/+rESNGaPPmzXrxxRclSU8//bT8/PzUsWNH/fjjjzpw4IDWrl2rl156SUePHr0n1xMAkLMIZACAXGfcuHF67LHH1KFDBzVv3lyPPvqoatWq5Vg/Y8YMPfvssxoyZIgqVKigDh06aNOmTQoJCZH05+jXgAEDVLFiRbVq1UoVKlTQ5MmTJUnFihXTqFGj9NprrykgIMARjm5m6NChcnFxUaVKlVS0aFEdPnzYsa5Xr15KT0+/7uiYJI0aNUrz5s1T1apVNWvWLM2ZM0eVKlWSJBUsWFDr1q1TiRIl1LlzZ1WsWFHPPfecLl68KG9v7zu+fgCA+4dZFgEAMOinn35S48aNdfToUQUEBDits9lsio6OVqdOncw0BwDIcUzqAQCAAWlpaTpy5Ijeeustde3aNVsYAwDkDdyyCADAXTh8+LAKFSp0w8/Vtydebe7cuapQoYJSUlL0zjvv3OeuAQAPCm5ZBADgLly5ckUHDx684fpSpUopf35uSAEAXB+BDAAAAAAM4ZZFAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCH/D+EKiwsO2fX+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[12], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m feature \u001b[38;5;129;01min\u001b[39;00m categorical_features:\n\u001b[0;32m      4\u001b[0m     plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m----> 5\u001b[0m     sns\u001b[38;5;241m.\u001b[39mcountplot(data\u001b[38;5;241m=\u001b[39mdata, x\u001b[38;5;241m=\u001b[39mfeature, hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124misFraud\u001b[39m\u001b[38;5;124m'\u001b[39m, palette\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSet1\u001b[39m\u001b[38;5;124m'\u001b[39m)  \u001b[38;5;66;03m# Added palette parameter for better visualization\u001b[39;00m\n\u001b[0;32m      6\u001b[0m     plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistribution of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfeature\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      7\u001b[0m     plt\u001b[38;5;241m.\u001b[39mxlabel(feature)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:2955\u001b[0m, in \u001b[0;36mcountplot\u001b[1;34m(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2952\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2953\u001b[0m     ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[1;32m-> 2955\u001b[0m plotter\u001b[38;5;241m.\u001b[39mplot(ax, kwargs)\n\u001b[0;32m   2956\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:1586\u001b[0m, in \u001b[0;36m_BarPlotter.plot\u001b[1;34m(self, ax, bar_kws)\u001b[0m\n\u001b[0;32m   1584\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;28mself\u001b[39m, ax, bar_kws):\n\u001b[0;32m   1585\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Make the plot.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1586\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdraw_bars(ax, bar_kws)\n\u001b[0;32m   1587\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mannotate_axes(ax)\n\u001b[0;32m   1588\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morient \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:1569\u001b[0m, in \u001b[0;36m_BarPlotter.draw_bars\u001b[1;34m(self, ax, kws)\u001b[0m\n\u001b[0;32m   1565\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, hue_level \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_names):\n\u001b[0;32m   1566\u001b[0m \n\u001b[0;32m   1567\u001b[0m     \u001b[38;5;66;03m# Draw the bars\u001b[39;00m\n\u001b[0;32m   1568\u001b[0m     offpos \u001b[38;5;241m=\u001b[39m barpos \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_offsets[j]\n\u001b[1;32m-> 1569\u001b[0m     barfunc(offpos, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatistic[:, j], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnested_width,\n\u001b[0;32m   1570\u001b[0m             color\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolors[j], align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1571\u001b[0m             label\u001b[38;5;241m=\u001b[39mhue_level, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkws)\n\u001b[0;32m   1573\u001b[0m     \u001b[38;5;66;03m# Draw the confidence intervals\u001b[39;00m\n\u001b[0;32m   1574\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfint\u001b[38;5;241m.\u001b[39msize:\n",
      "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of categorical features\n",
    "categorical_features = ['type', 'day_of_week', 'amount_category', 'origin_type', 'dest_type', 'flagged_fraud_reason']\n",
    "for feature in categorical_features:\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.countplot(data=data, x=feature, hue='isFraud', palette='Set1')  # Added palette parameter for better visualization\n",
    "    plt.title(f'Distribution of {feature}')\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('Count')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.legend(title='Fraudulent')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f573b882",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary for Isolation Forest:\n",
      "              step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
      "count  2500.000000  2.500000e+03   2.500000e+03    2.500000e+03   \n",
      "mean      7.263200  5.809423e+05   5.440721e+06    5.515865e+06   \n",
      "std       2.064856  8.716283e+05   5.897012e+06    5.971306e+06   \n",
      "min       1.000000  9.869000e+01   0.000000e+00    0.000000e+00   \n",
      "25%       7.000000  1.134826e+05   2.136900e+04    0.000000e+00   \n",
      "50%       8.000000  2.314779e+05   3.913090e+06    4.071757e+06   \n",
      "75%       8.000000  4.891124e+05   9.418897e+06    9.538257e+06   \n",
      "max       9.000000  1.000000e+07   2.854724e+07    2.861740e+07   \n",
      "\n",
      "       oldbalanceDest  newbalanceDest      isFraud  isFlaggedFraud  \\\n",
      "count    2.500000e+03    2.500000e+03  2500.000000          2500.0   \n",
      "mean     7.511820e+06    8.954793e+06     0.003600             0.0   \n",
      "std      6.231191e+06    6.807738e+06     0.059904             0.0   \n",
      "min      0.000000e+00    0.000000e+00     0.000000             0.0   \n",
      "25%      1.628797e+06    2.538290e+06     0.000000             0.0   \n",
      "50%      6.516935e+06    8.522232e+06     0.000000             0.0   \n",
      "75%      1.221227e+07    1.432450e+07     0.000000             0.0   \n",
      "max      3.014320e+07    3.197699e+07     1.000000             0.0   \n",
      "\n",
      "       hour_of_day  day_of_week  transaction_frequency  transaction_duration  \\\n",
      "count  2500.000000  2500.000000                 2500.0                2500.0   \n",
      "mean      7.263200     1.573600                    1.0                   0.0   \n",
      "std       2.064856     1.392752                    0.0                   0.0   \n",
      "min       1.000000     0.000000                    1.0                   0.0   \n",
      "25%       7.000000     1.000000                    1.0                   0.0   \n",
      "50%       8.000000     1.000000                    1.0                   0.0   \n",
      "75%       8.000000     2.000000                    1.0                   0.0   \n",
      "max       9.000000     6.000000                    1.0                   0.0   \n",
      "\n",
      "       transaction_velocity  amount_relative_to_balance  flagged_fraud_reason  \n",
      "count                2500.0                 2500.000000                   0.0  \n",
      "mean                    inf                         inf                   NaN  \n",
      "std                     NaN                         NaN                   NaN  \n",
      "min                     inf                    0.000024                   NaN  \n",
      "25%                     NaN                    0.015821                   NaN  \n",
      "50%                     NaN                    0.060082                   NaN  \n",
      "75%                     NaN                   40.332483                   NaN  \n",
      "max                     inf                         inf                   NaN  \n",
      "Summary for One-Class SVM:\n",
      "              step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
      "count  2499.000000  2.499000e+03   2.499000e+03    2.499000e+03   \n",
      "mean      7.262105  7.168731e+05   4.064365e+06    4.097142e+06   \n",
      "std       2.100299  9.366311e+05   5.909599e+06    5.987665e+06   \n",
      "min       1.000000  1.048000e+01   0.000000e+00    0.000000e+00   \n",
      "25%       7.000000  6.855426e+04   0.000000e+00    0.000000e+00   \n",
      "50%       8.000000  2.707026e+05   1.505626e+05    0.000000e+00   \n",
      "75%       8.000000  1.116884e+06   6.931686e+06    7.034414e+06   \n",
      "max       9.000000  1.000000e+07   2.854724e+07    2.861740e+07   \n",
      "\n",
      "       oldbalanceDest  newbalanceDest      isFraud  isFlaggedFraud  \\\n",
      "count    2.499000e+03    2.499000e+03  2499.000000          2499.0   \n",
      "mean     6.123229e+06    8.207514e+06     0.006803             0.0   \n",
      "std      6.385860e+06    6.975849e+06     0.082214             0.0   \n",
      "min      0.000000e+00    0.000000e+00     0.000000             0.0   \n",
      "25%      1.921981e+05    1.190259e+06     0.000000             0.0   \n",
      "50%      4.649821e+06    7.382990e+06     0.000000             0.0   \n",
      "75%      1.031730e+07    1.356942e+07     0.000000             0.0   \n",
      "max      3.014320e+07    3.197699e+07     1.000000             0.0   \n",
      "\n",
      "       hour_of_day  day_of_week  transaction_frequency  transaction_duration  \\\n",
      "count  2499.000000  2499.000000                 2499.0                2499.0   \n",
      "mean      7.262105     1.460984                    1.0                   0.0   \n",
      "std       2.100299     1.314595                    0.0                   0.0   \n",
      "min       1.000000     0.000000                    1.0                   0.0   \n",
      "25%       7.000000     1.000000                    1.0                   0.0   \n",
      "50%       8.000000     1.000000                    1.0                   0.0   \n",
      "75%       8.000000     2.000000                    1.0                   0.0   \n",
      "max       9.000000     6.000000                    1.0                   0.0   \n",
      "\n",
      "       transaction_velocity  amount_relative_to_balance  flagged_fraud_reason  \n",
      "count                2499.0                 2499.000000                   0.0  \n",
      "mean                    inf                         inf                   NaN  \n",
      "std                     NaN                         NaN                   NaN  \n",
      "min                     inf                    0.000006                   NaN  \n",
      "25%                     NaN                    0.020654                   NaN  \n",
      "50%                     NaN                    2.614614                   NaN  \n",
      "75%                     NaN                         NaN                   NaN  \n",
      "max                     inf                         inf                   NaN  \n",
      "Summary for Local Outlier Factor:\n",
      "               step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
      "count  49999.000000  4.999900e+04   4.999900e+04    4.999900e+04   \n",
      "mean       7.453769  1.562676e+05   7.332995e+05    7.472655e+05   \n",
      "std        2.056645  3.243974e+05   2.202426e+06    2.239993e+06   \n",
      "min        1.000000  6.300000e-01   0.000000e+00    0.000000e+00   \n",
      "25%        7.000000  7.735090e+03   0.000000e+00    0.000000e+00   \n",
      "50%        8.000000  3.341600e+04   1.703000e+04    0.000000e+00   \n",
      "75%        9.000000  1.878404e+05   1.331625e+05    1.438245e+05   \n",
      "max        9.000000  1.000000e+07   2.854724e+07    2.861740e+07   \n",
      "\n",
      "       oldbalanceDest  newbalanceDest       isFraud  isFlaggedFraud  \\\n",
      "count    4.999900e+04    4.999900e+04  49999.000000         49999.0   \n",
      "mean     8.444840e+05    1.164423e+06      0.002000             0.0   \n",
      "std      2.433409e+06    2.915227e+06      0.044678             0.0   \n",
      "min      0.000000e+00    0.000000e+00      0.000000             0.0   \n",
      "25%      0.000000e+00    0.000000e+00      0.000000             0.0   \n",
      "50%      2.139000e+03    0.000000e+00      0.000000             0.0   \n",
      "75%      4.632130e+05    8.649264e+05      0.000000             0.0   \n",
      "max      3.014320e+07    3.197699e+07      1.000000             0.0   \n",
      "\n",
      "        hour_of_day   day_of_week  transaction_frequency  \\\n",
      "count  49999.000000  49999.000000                49999.0   \n",
      "mean       7.453769      1.456749                    1.0   \n",
      "std        2.056645      1.197974                    0.0   \n",
      "min        1.000000      0.000000                    1.0   \n",
      "25%        7.000000      1.000000                    1.0   \n",
      "50%        8.000000      1.000000                    1.0   \n",
      "75%        9.000000      2.000000                    1.0   \n",
      "max        9.000000      6.000000                    1.0   \n",
      "\n",
      "       transaction_duration  transaction_velocity  amount_relative_to_balance  \\\n",
      "count               49999.0               49999.0                4.999900e+04   \n",
      "mean                    0.0                   inf                         inf   \n",
      "std                     0.0                   NaN                         NaN   \n",
      "min                     0.0                   inf                5.012207e-07   \n",
      "25%                     0.0                   NaN                1.078416e-01   \n",
      "50%                     0.0                   NaN                2.337438e+00   \n",
      "75%                     0.0                   NaN                         NaN   \n",
      "max                     0.0                   inf                         inf   \n",
      "\n",
      "       flagged_fraud_reason  \n",
      "count                   0.0  \n",
      "mean                    NaN  \n",
      "std                     NaN  \n",
      "min                     NaN  \n",
      "25%                     NaN  \n",
      "50%                     NaN  \n",
      "75%                     NaN  \n",
      "max                     NaN  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\numpy\\lib\\function_base.py:4655: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n",
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\numpy\\lib\\function_base.py:4655: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n",
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\numpy\\lib\\function_base.py:4655: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n",
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\numpy\\lib\\function_base.py:4655: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n",
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\numpy\\lib\\function_base.py:4655: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.svm import OneClassSVM\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "import numpy as np\n",
    "\n",
    "# Step 1: Preprocess the data\n",
    "scaler = StandardScaler()\n",
    "data_scaled = scaler.fit_transform(data[numerical_features])\n",
    "\n",
    "# Step 2: Train anomaly detection models\n",
    "models = {\n",
    "    \"Isolation Forest\": IsolationForest(contamination=0.05),\n",
    "    \"One-Class SVM\": OneClassSVM(nu=0.05),\n",
    "    \"Local Outlier Factor\": LocalOutlierFactor(contamination=0.05)\n",
    "}\n",
    "\n",
    "for name, model in models.items():\n",
    "    model.fit(data_scaled)\n",
    "\n",
    "# Step 3: Detect anomalies\n",
    "anomaly_scores = {}\n",
    "for name, model in models.items():\n",
    "    if name == \"Local Outlier Factor\":\n",
    "        scores = model.negative_outlier_factor_\n",
    "    else:\n",
    "        scores = model.decision_function(data_scaled)\n",
    "    anomaly_scores[name] = scores\n",
    "\n",
    "# Step 4: Visualize anomalies\n",
    "plt.figure(figsize=(12, 8))\n",
    "for i, (name, scores) in enumerate(anomaly_scores.items()):\n",
    "    plt.subplot(2, 2, i + 1)\n",
    "    plt.hist(scores, bins=50, color='b', alpha=0.5)\n",
    "    plt.title(name)\n",
    "    plt.xlabel('Anomaly Score')\n",
    "    plt.ylabel('Frequency')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Step 5: Print summary\n",
    "for name, scores in anomaly_scores.items():\n",
    "    outliers = data.iloc[np.where(scores < 0)[0]]\n",
    "    print(f\"Summary for {name}:\")\n",
    "    print(outliers.describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "339545da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but IsolationForest was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Isolation Forest\n",
      "Accuracy: 0.99\n",
      "Classification Report:\n",
      "{'Normal': {'precision': 0.9979836677084384, 'recall': 0.9919831646457561, 'f1-score': 0.9949743692833452, 'support': 9979}, 'Anomaly': {'precision': 0.012345679012345678, 'recall': 0.047619047619047616, 'f1-score': 0.019607843137254898, 'support': 21}, 'accuracy': 0.99, 'macro avg': {'precision': 0.5051646733603921, 'recall': 0.5198011061324018, 'f1-score': 0.5072911062103, 'support': 10000}, 'weighted avg': {'precision': 0.9959138279321765, 'recall': 0.99, 'f1-score': 0.9929260995784384, 'support': 10000}}\n",
      "\n",
      "\n",
      "Model: Local Outlier Factor\n",
      "Accuracy: 0.9883\n",
      "Classification Report:\n",
      "{'Normal': {'precision': 0.9980808080808081, 'recall': 0.9901793766910512, 'f1-score': 0.9941143920720359, 'support': 9979}, 'Anomaly': {'precision': 0.02, 'recall': 0.09523809523809523, 'f1-score': 0.03305785123966942, 'support': 21}, 'accuracy': 0.9883, 'macro avg': {'precision': 0.5090404040404041, 'recall': 0.5427087359645733, 'f1-score': 0.5135861216558526, 'support': 10000}, 'weighted avg': {'precision': 0.9960268383838383, 'recall': 0.9883, 'f1-score': 0.992096173336288, 'support': 10000}}\n",
      "\n",
      "\n",
      "Model: One-Class SVM\n",
      "Accuracy: 0.9884\n",
      "Classification Report:\n",
      "{'Normal': {'precision': 0.9978798586572438, 'recall': 0.9904800080168353, 'f1-score': 0.9941661637497485, 'support': 9979}, 'Anomaly': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 21}, 'accuracy': 0.9884, 'macro avg': {'precision': 0.4989399293286219, 'recall': 0.49524000400841767, 'f1-score': 0.49708308187487427, 'support': 10000}, 'weighted avg': {'precision': 0.9957843109540636, 'recall': 0.9884, 'f1-score': 0.9920784148058741, 'support': 10000}}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn.svm import OneClassSVM\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_excel(r\"C:\\Users\\kunta\\Downloads\\50K.xlsx\")\n",
    "\n",
    "# Define features\n",
    "features = ['amount', 'oldbalanceDest']\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, _, y_test = train_test_split(data[features], data['isFraud'], test_size=0.2, random_state=42)\n",
    "\n",
    "# Define models\n",
    "models = {\n",
    "    \"Isolation Forest\": IsolationForest(contamination=0.01),\n",
    "    \"Local Outlier Factor\": LocalOutlierFactor(contamination=0.01),\n",
    "    \"One-Class SVM\": OneClassSVM(nu=0.01)\n",
    "}\n",
    "\n",
    "# Evaluate each model\n",
    "evaluation_results = {}\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train)\n",
    "    \n",
    "    # Special handling for Local Outlier Factor\n",
    "    if name == \"Local Outlier Factor\":\n",
    "        anomalies = model.fit_predict(X_test) == -1\n",
    "    else:\n",
    "        anomalies = model.predict(X_test) == -1\n",
    "    \n",
    "    # Invert the anomalies predictions for easier comparison with true labels\n",
    "    y_pred = np.where(anomalies, 1, 0)\n",
    "    \n",
    "    # Evaluate the model\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    report = classification_report(y_test, y_pred, target_names=['Normal', 'Anomaly'], output_dict=True)\n",
    "    evaluation_results[name] = {'accuracy': accuracy, 'report': report}\n",
    "\n",
    "# Print evaluation results\n",
    "for name, result in evaluation_results.items():\n",
    "    print(f\"Model: {name}\")\n",
    "    print(f\"Accuracy: {result['accuracy']}\")\n",
    "    print(\"Classification Report:\")\n",
    "    print(result['report'])\n",
    "    print(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "409d8017",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but IsolationForest was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Isolation Forest\n",
      "Accuracy: 0.9893\n",
      "Classification Report:\n",
      "{'Normal': {'precision': 0.9979822437449556, 'recall': 0.9912816915522598, 'f1-score': 0.9946206827208284, 'support': 9979}, 'Anomaly': {'precision': 0.011363636363636364, 'recall': 0.047619047619047616, 'f1-score': 0.01834862385321101, 'support': 21}, 'accuracy': 0.9893, 'macro avg': {'precision': 0.504672940054296, 'recall': 0.5194503695856537, 'f1-score': 0.5064846532870197, 'support': 10000}, 'weighted avg': {'precision': 0.9959103446694547, 'recall': 0.9893, 'f1-score': 0.9925705113972064, 'support': 10000}}\n",
      "\n",
      "\n",
      "Model: Local Outlier Factor\n",
      "Accuracy: 0.9883\n",
      "Classification Report:\n",
      "{'Normal': {'precision': 0.9980808080808081, 'recall': 0.9901793766910512, 'f1-score': 0.9941143920720359, 'support': 9979}, 'Anomaly': {'precision': 0.02, 'recall': 0.09523809523809523, 'f1-score': 0.03305785123966942, 'support': 21}, 'accuracy': 0.9883, 'macro avg': {'precision': 0.5090404040404041, 'recall': 0.5427087359645733, 'f1-score': 0.5135861216558526, 'support': 10000}, 'weighted avg': {'precision': 0.9960268383838383, 'recall': 0.9883, 'f1-score': 0.992096173336288, 'support': 10000}}\n",
      "\n",
      "\n",
      "Model: One-Class SVM\n",
      "Accuracy: 0.9884\n",
      "Classification Report:\n",
      "{'Normal': {'precision': 0.9978798586572438, 'recall': 0.9904800080168353, 'f1-score': 0.9941661637497485, 'support': 9979}, 'Anomaly': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 21}, 'accuracy': 0.9884, 'macro avg': {'precision': 0.4989399293286219, 'recall': 0.49524000400841767, 'f1-score': 0.49708308187487427, 'support': 10000}, 'weighted avg': {'precision': 0.9957843109540636, 'recall': 0.9884, 'f1-score': 0.9920784148058741, 'support': 10000}}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn.svm import OneClassSVM\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_excel(r\"C:\\Users\\kunta\\Downloads\\50K.xlsx\")\n",
    "\n",
    "# Define features\n",
    "features = ['amount', 'oldbalanceDest']\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, _, y_test = train_test_split(data[features], data['isFraud'], test_size=0.2, random_state=42)\n",
    "\n",
    "# Define models\n",
    "models = {\n",
    "    \"Isolation Forest\": IsolationForest(contamination=0.01),\n",
    "    \"Local Outlier Factor\": LocalOutlierFactor(contamination=0.01),\n",
    "    \"One-Class SVM\": OneClassSVM(nu=0.01)\n",
    "}\n",
    "\n",
    "# Evaluate each model\n",
    "evaluation_results = {}\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train)\n",
    "    \n",
    "    # Special handling for Local Outlier Factor\n",
    "    if name == \"Local Outlier Factor\":\n",
    "        anomalies = model.fit_predict(X_test) == -1\n",
    "    else:\n",
    "        anomalies = model.predict(X_test) == -1\n",
    "    \n",
    "    # Invert the anomalies predictions for easier comparison with true labels\n",
    "    y_pred = np.where(anomalies, 1, 0)\n",
    "    \n",
    "    # Evaluate the model\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    report = classification_report(y_test, y_pred, target_names=['Normal', 'Anomaly'], output_dict=True)\n",
    "    evaluation_results[name] = {'accuracy': accuracy, 'report': report}\n",
    "\n",
    "# Print evaluation results\n",
    "for name, result in evaluation_results.items():\n",
    "    print(f\"Model: {name}\")\n",
    "    print(f\"Accuracy: {result['accuracy']}\")\n",
    "    print(\"Classification Report:\")\n",
    "    print(result['report'])\n",
    "    print(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9516fcea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but IsolationForest was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anomaly predictions for Isolation Forest: [False False False ... False False False]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anomaly predictions for One-Class SVM: [False False False ... False False False]\n"
     ]
    },
    {
     "data": {
      "image/png": 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yCtatW4fly5fjscceQ1xcnJhLRexMP4PLg/Ts2VPrL31dFRUVeO+997B+/Xrcvn0bLVq0wKeffqquoeDv76/1l9xvv/2G06dPY/ny5bZuutNauXIlGGPYunUrtm7dqvf6119/jVmzZmn15Jij+kVfVFSk99rly5fh5eWFOnXqWN5oDevWrUODBg2wadMmrb+uy8vLRR+rTp06kMlkGDZsmNEbYYMGDQDwN9Xk5GQkJyfj9u3b2LdvH9555x306NEDBQUF8PPzs+jzFBYW4vjx4+jSpYs60bJu3bpo1aoVPv74Y4PviYyMlOxzhYSE4NKlSxa1Xcx5goODDd4Ehd4YNfePiopSP6+srMSNGze0gg0vLy+MHz8e77zzDhYsWIClS5eiW7duaNy4scljt23bFnXq1MHOnTsxe/Zsvd4bgE/CVyqVePbZZ0W1G+C/rjVq1DD4B4XqdYDvkZ4wYYJ6u2qqdHx8PLy9vbF9+3aMHTtW9PkB/udn+PDh+OSTT7S2X79+HbVr11Y/F/L9XrduXaSmpiI1NRX5+fnYuXMn3nrrLVy9ehV79uwR1S5vb298+OGHWLRoEf73v/9pvZaQkIA6depg5cqV6NKlC7755hsMHz7cbC/dyJEj8cEHH2D58uVGf5aI8/DoYMecV155BRcuXMDGjRsRGRmJbdu24bnnnsOpU6fw6KOP6u3/1VdfeXSEr1Ao8PXXX6Nhw4b46quv9F7/5ptvsGDBAnz33Xd6w0ymNG7cGFFRUUhPT8fUqVPVN4mysjJkZGSoZ2hJgeM4+Pj4aN2IiouLBc3G0uXn54f4+HicPHkSrVq1go+Pj6D31a5dG4mJiSgsLERSUhIuXLiAZs2aiT7//fv38eqrr6KyslJr9k/v3r2xe/duNGzY0GSQqNvDZsnn6tmzJz744AP8+OOPePrpp212nvj4eMydOxe//fab1lBWenq60fcYsn79erRt21b9fPPmzaisrNQrEvfqq69i+vTpGDp0KM6ePYtPP/3U7LF9fHyQkpKCd955B/PmzdObkXX16lW8/fbbCAsLw6uvviqq3QD/df3kk08QHBysDgINiYmJQUxMjN728PBwvPrqq1i2bBnWrFljcEbW+fPnUVZWZnRGFsdx6q+nyrfffovCwkI0atTI4HuEfL/Xq1cPEyZMwP79+83WISoqKjLYC6waRtMN5qtXr44hQ4Zg+fLl+PTTT/Hw4UNBQ1JRUVFISUnBn3/+iREjRpjdnzgWBTtGnD9/Hhs2bMClS5fUPxxTp07Fnj17sGrVKr2/XMrLy7F+/Xq89dZbjmiuU/juu+9w+fJlrd4vTS1atMCSJUuQlpYmKtjx8vLC3LlzMXToUPTu3RtjxoxBeXk55s2bh9u3b2POnDmSfYbevXsjMzMT48aNQ2JiIgoKCvDRRx8hIiLCYL6ROYsXL8ZTTz2FuLg4vP7664iJicGdO3fw999/Y9euXfjxxx8BAH369EGLFi3Qrl07hISE4OLFi0hNTUX9+vUNBta68vPzcfjwYSiVSpSUlODkyZNYuXIlLl68iAULFqB79+7qfWfOnIm9e/ciNjYWEydOROPGjfHgwQNcuHABu3fvxvLlyyGXyxEQEID69etjx44d6NatG4KCglC3bl3ExMQI/lxJSUnYtGkT+vXrh7feegvt27fH/fv3ceDAAfTu3Rvx8fGSnWflypV4/vnnMWvWLISFhWH9+vX4888/RX29MjMzUa1aNTz77LP4448/8P777+Pxxx/XG9KoXbs2hg8fjmXLlqF+/fro06ePoOO/+eab+O2339T/vvTSSwgMDMTvv/+OefPm4c6dO/jmm28QGBgoqt0Afw0yMjLQuXNnTJ48Ga1atYJSqUR+fj5++OEHTJkyBf/5z39MHmPhwoX4559/MHLkSHz//fd44YUXEBYWhuvXr2Pv3r1YtWoVNm7caDTY6d27N1avXo0mTZqgVatWOH78OObNm6c3lGnu+72kpATx8fEYMmQImjRpgoCAABw9ehR79uxBQkKCyc+gmlLep08fNGnSBEqlErm5uViwYAH8/f0xadIkvfeMHj0aX3zxBRYuXIgmTZogNjbWzNXmSfm7h9iYQ9OjnQgAtm3bNvXzzZs3MwCsZs2aWo9q1aqxgQMH6r0/PT2dVatWjRUVFdmx1c6lf//+zMfHh129etXoPoMGDWLVqlVjxcXFJmcSwcDsnO3bt7P//Oc/rHr16qxmzZqsW7du7JdfftHax1hxuBEjRrCaNWvqnadLly6sefPmWtvmzJnDYmJimK+vL2vatCn78ssv1cfVJGQ2lmr7qFGjWFRUFPP29mYhISEsNjaWzZo1S73PggULWGxsLKtbty7z8fFh9erVY6NHj2YXLlzQa7PusfHvrBMATCaTsTp16rC2bduypKQk9cwqXdeuXWMTJ05kDRo0YN7e3iwoKIi1bduWvfvuu+zu3bvq/fbt28fatGnDfH19GQC9z2vuczHG2K1bt9ikSZNYvXr1mLe3NwsNDWXPP/+81lRkKc5z+vRp9uyzz7Lq1auzoKAgNnr0aLZjxw5Rs7GOHz/O+vTpw/z9/VlAQAAbPHiwehabruzsbKMFAk1RKpVs/fr1rGvXrqx27drMx8eHNWjQgL3++ut6M+QYM/w9yhj/PV2/fn2tbXfv3mXvvfcea9y4MfPx8VFP2588eTIrLi4W1L7Kykr29ddfs6effpoFBQWxatWqsZCQENazZ0+Wnp6unhFp6Pv91q1bbPTo0Sw0NJT5+fmxp556iuXk5LAuXbpozVAy9/3+4MEDNnbsWNaqVStWq1YtVqNGDda4cWP24YcfsrKyMpPt37RpExsyZAh79NFHmb+/P/P29mb16tVjw4YNY6dPnzb6vjZt2jAAbO7cuQZf15yNZQrNxnJOHGM6UzU8FMdx2LZtG/r37w+AT44bOnQo/vjjD738En9/f71Exm7duqFWrVrYtm2bvZpMCHGgKVOmYNmyZSgoKDCYQEwIcR40jGVEmzZtoFAocPXqVbM5OHl5ecjKylJn8xNC3Nfhw4fx119/YenSpRgzZgwFOoS4AI8Odu7evYu///5b/TwvLw+5ubkICgrCY489hqFDh2L48OFYsGAB2rRpg+vXr+PHH39Ey5Yt1VNfAX4GUkREhMmZXYQQ96BKiO/duzdmzZrl6OYQQgTw6GGs7OxsxMfH620fMWIEVq9ejYcPH2LWrFlYs2YNCgsLERwcjI4dO2LGjBlo2bIlAL5AV/369TF8+HCafkgIIYQ4IY8OdgghhBDi/jy6gjIhhBBC3B8FO4QQQghxax6XoKxUKnH58mUEBAQYLNdOCCGEEOfDGMOdO3cQGRmptSi0EB4X7Fy+fFlvkT1CCCGEuIaCggLRCwx7XLCjWvSuoKAAtWrVcnBrCCGEECJEaWkpoqOj1fdxMTwu2FENXdWqVYuCHUIIIcTFWJKCQgnKhBBCCHFrDg12li1bhlatWql7WTp27IjvvvvO5HsOHDiAtm3bonr16njkkUewfPlyO7WWEEIIIa7IocGOXC7HnDlzcOzYMRw7dgxPP/00+vXrhz/++MPg/nl5eejVqxfi4uJw8uRJvPPOO5g4cSIyMjLs3HJCCCGEuAqnq6AcFBSEefPmYfTo0Xqvvfnmm9i5cyfOnDmj3jZ27Fj89ttvOHTokKDjl5aWIjAwECUlJZSzQwghbk6hUODhw4eObgYRyMfHx+i0cmvu306ToKxQKLBlyxaUlZWhY8eOBvc5dOgQunfvrrWtR48eSEtLw8OHD+Ht7a33nvLycpSXl6ufl5aWSttwQgghTocxhuLiYty+fdvRTSEieHl5oUGDBvDx8ZH0uA4Pdk6dOoWOHTviwYMH8Pf3x7Zt29CsWTOD+xYXFyMsLExrW1hYGCorK3H9+nVERETovWf27NmYMWOGTdpOCCHEOakCndDQUPj5+VERWRegKvpbVFSEevXqSfo1c3iw07hxY+Tm5uL27dvIyMjAiBEjcODAAaMBj+6HV43CGbsob7/9NpKTk9XPVfP0CSGEuCeFQqEOdIKDgx3dHCJCSEgILl++jMrKSoOjNZZyeLDj4+ODRo0aAQDatWuHo0ePYvHixfi///s/vX3Dw8NRXFyste3q1auoVq2a0W9oX19f+Pr6St9wQgghTkmVo+Pn5+fglhCxVMNXCoVC0mDH6ersMMa0cmw0dezYEXv37tXa9sMPP6Bdu3aSXhRCCCGuj4auXI+tvmYO7dl555130LNnT0RHR+POnTvYuHEjsrOzsWfPHgD8EFRhYSHWrFkDgJ95tWTJEiQnJ+O1117DoUOHkJaWhg0bNjjyYxA7UFQocGppDu6dL4Jfwwi0HBcHmY/M0c0ihBDiAhwa7Fy5cgXDhg1DUVERAgMD0apVK+zZswfPPvssAKCoqAj5+fnq/Rs0aIDdu3dj8uTJ+OKLLxAZGYnPPvsMAwYMcNRHIHZweFom6i2chNaKS+ptl6fKkZ+8GB3mJjiwZYQQQrKzsxEfH49bt26hdu3ajm6OQQ4NdtLS0ky+vnr1ar1tXbp0wYkTJ2zUIuJsDk/LRPt5iQC0y0GFKwoRPi8Rh7GVAh5CiNsYOXIkvv76a8yePRtvvfWWevv27dvxwgsvwMlK47kMp8vZIURFUaFAvYWTADC9b1Svf4Of6IVJUFQo7N42QohnUCiA7Gxgwwb+X4Udft1Ur14dn376KW7duiXZMSsqKiQ7liuiYIc4rVNLcxCpuGT0m9QLDFGKApxammPXdhFCPENmJhATA8THA0OG8P/GxPDbbemZZ55BeHg4Zs+ebXSfjIwMNG/eHL6+voiJicGCBQu0Xo+JicGsWbMwcuRIBAYG4rXXXsPq1atRu3ZtfPPNN2jcuDH8/PyQmJiIsrIyfP3114iJiUGdOnXwxhtvQKER1a1btw7t2rVDQEAAwsPDMWTIEFy9etVmn98WKNghTuve+SJJ9yOEEKEyM4HERODSJe3thYX8dlsGPDKZDJ988gk+//xzXNJtAIDjx49j4MCBGDRoEE6dOoXp06fj/fff10v9mDdvHlq0aIHjx4/j/fffBwDcu3cPn332GTZu3Ig9e/YgOzsbCQkJ2L17N3bv3o21a9dixYoV2Lp1q/o4FRUV+Oijj/Dbb79h+/btyMvLw8iRI213AWzA4XV2CDHGr6F+RWxr9iOEECEUCmDSJMBQegxjAMcBSUlAv36AzEaTQl944QW0bt0aH374oV5+68KFC9GtWzd1APPYY4/h9OnTmDdvnlYQ8vTTT2Pq1Knq5z///DMePnyIZcuWoWHDhgCAxMRErF27FleuXIG/vz+aNWuG+Ph4ZGVl4aWXXgIAjBo1Sn2MRx55BJ999hnat2+Pu3fvwt/f3zYXQGLUs+MkFBUK5KZm4+AbG5Cbmk15KABajovDZZkcShiuu6AEh0JZNFqOi7Nzywgh7iwnR79HRxNjQEEBv58tffrpp/j6669x+vRpre1nzpxBp06dtLZ16tQJ586d0xp+ateund4x/fz81IEOwC+5FBMToxW0hIWFaQ1TnTx5Ev369UP9+vUREBCArl27AoDWbGlnR8GOEzg8LRNX/GLQenI8YpcMQevJ8bjiF4PD02w8MOzkZD4y5CcvBgC9gEf1vCA5lertEEIkVSRwZFzofpbq3LkzevTogXfeeUdrO2PM6NJJmmrWrKm3TbcAL8dxBrcplUoAQFlZGbp37w5/f3+sW7cOR48exbZt2wC4VtIzDWM5GE2tNq3D3AQcxlbUWzgJkRp1dopkchQkp3r0tSGE2IaBNaWt2s8ac+bMQevWrfHYY4+ptzVr1gw///yz1n4HDx7EY489BpnE42p//vknrl+/jjlz5qjXlTx27Jik57AH6tlxIJpaLUyHuQkIu3cBuYuycHBCOnIXZSH8Xh4FOoQQm4iLA+RyPjfHEI4DoqP5/WytZcuWGDp0KD7//HP1tilTpmD//v346KOP8Ndff+Hrr7/GkiVLtPJzpFKvXj34+Pjg888/xz///IOdO3fio48+kvw8tkbBjgPR1GrhZD4ytE7qitjPB6N1UlcauiKE2IxMBizmR9D1Ah7V89RU2yUn6/roo4+0hqmeeOIJbN68GRs3bkSLFi3wwQcfYObMmTaZIRUSEoLVq1djy5YtaNasGebMmYP58+dLfh5b45iHlWMsLS1FYGAgSkpKUKtWLYe25eAbGxC7ZIj5/SakI/bzwXZoESGEuL4HDx4gLy8PDRo0QPXq1S0+TmYmPytLM1k5OpoPdBKoY9kmTH3trLl/U86OA9HUakIIcV4JCfz08pwcPhk5IoIfurJXjw6RDgU7DtRyXBwuT5UjXFGoztHRpASHIpmcplYTQoiDyGTAvzOtiQujnB0HoqnVhBBCiO1RsONgHeYm4EjKVhTLorS2F8nkOJLi2dPOCSGEECnQMJYT6DA3AYpZ/ZC7NAf3zhfBr2EEWo6LQxT16BBCCCFWo2DHSaimVhNCCCFEWjSMRQghhBC3RsEOIYQQQtwaBTuEEEIIcWsU7BBCCCFEEjExMUhNTXV0M/RQsEMIIYQ4mYMHD0Imk+G5555zdFPcAgU7hBBCiDEKBZCdDWzYwP+rUNjltCtXrsQbb7yBn3/+Gfn5+XY5pzujYIcQQggxJDMTiIkB4uOBIUP4f2Ni+O02VFZWhs2bN+P1119H7969sXr1avVr2dnZ4DgO+/fvR7t27eDn54fY2FicPXtW6xjLli1Dw4YN4ePjg8aNG2Pt2rVar3Mch//7v/9D79694efnh6ZNm+LQoUP4+++/0bVrV9SsWRMdO3bE+fPn1e85f/48+vXrh7CwMPj7++PJJ5/Evn37jH6OUaNGoXfv3lrbKisrER4ejpUrV1pxhcSjYIcQQgjRlZkJJCZqL3kOAIWF/HYbBjybNm1C48aN0bhxY7z88stYtWoVGNNeP/Hdd9/FggULcOzYMVSrVg2jRo1Sv7Zt2zZMmjQJU6ZMwf/+9z+MGTMGr7zyCrKysrSO8dFHH2H48OHIzc1FkyZNMGTIEIwZMwZvv/02jh07BgCYMGGCev+7d++iV69e2LdvH06ePIkePXqgT58+RnueXn31VezZswdFRUXqbbt378bdu3cxcOBAq6+TKMzDlJSUMACspKTE0U0hhBBiA/fv32enT59m9+/ft+wAlZWMyeWMAYYfHMdYdDS/nw3Exsay1NRUxhhjDx8+ZHXr1mV79+5ljDGWlZXFALB9+/ap9//2228ZAPXnjY2NZa+99prWMV988UXWq1cv9XMA7L333lM/P3ToEAPA0tLS1Ns2bNjAqlevbrKtzZo1Y59//rn6ef369dmiRYu0Xv/000/Vz/v3789Gjhxp9HimvnbW3L+pZ4cQQgjRlJOj36OjiTGgoIDfT2Jnz57FkSNHMGjQIABAtWrV8NJLL+kN+7Rq1Ur9/4iICADA1atXAQBnzpxBp06dtPbv1KkTzpw5Y/QYYWFhAICWLVtqbXvw4AFKS0sB8MNr06ZNQ7NmzVC7dm34+/vjzz//NJlT9Oqrr2LVqlXq9n377bdavVD2QstFEEIIIZo0hl0k2U+EtLQ0VFZWIiqqanFoxhi8vb1x69Yt9TZvb2/1/zmOAwAolUq9bZrH0N1m6BimjpuSkoLvv/8e8+fPR6NGjVCjRg0kJiaioqLC6OcZPnw43nrrLRw6dAiHDh1CTEwM4uLizFwF6VGwQwghhGj6t6dEsv0EqqysxJo1a7BgwQJ0795d67UBAwZg/fr1aNGihdnjNG3aFD///DOGDx+u3nbw4EE0bdrUqvbl5ORg5MiReOGFFwDwOTwXLlww+Z7g4GD0798fq1atwqFDh/DKK69Y1QZLUbBDCCGEaIqLA+RyPhlZJzEYAMBx/OsS91B88803uHXrFkaPHo3AwECt1xITE5GWloZFixaZPU5KSgoGDhyIJ554At26dcOuXbuQmZlpcuaUEI0aNUJmZib69OkDjuPw/vvva/UmGfPqq6+id+/eUCgUGDFihFVtsBTl7BBCCCGaZDJg8WL+/zpDP+rnqan8fhJKS0vDM888oxfoAHzPTm5uLk6cOGH2OP3798fixYsxb948NG/eHP/3f/+HVatWoWvXrla1b9GiRahTpw5iY2PRp08f9OjRA0888YTZ9z3zzDOIiIhAjx49EBkZaVUbLMUxZihsdV+lpaUIDAxESUkJatWq5ejmEEIIkdiDBw+Ql5eHBg0aoHr16pYfKDMTmDRJO1k5OpoPdBISrG6np7h37x4iIyOxcuVKJJi5bqa+dtbcv2kYixBCCDEkIQHo14+fdVVUxOfoxMVJ3qPjrpRKJYqLi7FgwQIEBgaib9++DmsLBTuEEEKIMTIZYOXwj6fKz89HgwYNIJfLsXr1alSr5riQg4IdQgghhEguJiZGr/Kzo1CCMiGEEELcGgU7hBBC3JKz9CoQ4Wz1NaNghxBCiFtRVQG+d++eg1tCxFJVY5ZJnAROOTuEEELcikwmQ+3atdVrRfn5+ektlUCcj1KpxLVr1+Dn5yd5MjMFO4QQQtxOeHg4gKrFMYlr8PLyQr169SQPTinYIYQQ4nY4jkNERARCQ0Px8OFDRzeHCOTj4wMvL+kzbCjYIYQQ4rZkMpnk+R/E9VCCMiGEEELcGgU7hBBCCHFrFOwQQgghxK1RsEMIIYQQt0bBDiGEEELcmkODndmzZ+PJJ59EQEAAQkND0b9/f5w9e9bke7Kzs8FxnN7jzz//tFOrCSGEEOJKHBrsHDhwAOPHj8fhw4exd+9eVFZWonv37igrKzP73rNnz6KoqEj9ePTRR+3QYkIIIYS4GofW2dmzZ4/W81WrViE0NBTHjx9H586dTb43NDQUtWvXtmHrCCGEEOIOnCpnp6SkBAAQFBRkdt82bdogIiIC3bp1Q1ZWlq2bRgghhBAX5TQVlBljSE5OxlNPPYUWLVoY3S8iIgIrVqxA27ZtUV5ejrVr16Jbt27Izs422BtUXl6O8vJy9fPS0lKbtJ8QQgghzoljjDFHNwIAxo8fj2+//RY///wz5HK5qPf26dMHHMdh586deq9Nnz4dM2bM0NteUlKCWrVqWdxeQgghhNhPaWkpAgMDLbp/O8Uw1htvvIGdO3ciKytLdKADAB06dMC5c+cMvvb222+jpKRE/SgoKLC2uYQQQghxIQ4dxmKM4Y033sC2bduQnZ2NBg0aWHSckydPIiIiwuBrvr6+8PX1taaZhBBCCHFhDg12xo8fj/T0dOzYsQMBAQEoLi4GAAQGBqJGjRoA+J6ZwsJCrFmzBgCQmpqKmJgYNG/eHBUVFVi3bh0yMjKQkZHhsM9BCCGEEOfl0GBn2bJlAICuXbtqbV+1ahVGjhwJACgqKkJ+fr76tYqKCkydOhWFhYWoUaMGmjdvjm+//Ra9evWyV7MJIYQQ4kKcJkHZXqxJcCKEEEKIY7h8gjIhhBBCiK1QsEMIIYQQt0bBDiGEEELcGgU7hBBCCHFrFOwQQgghxK1RsEMIIYQQt0bBDiGEEELcGgU7hBBCCHFrFOwQQgghxK1RsEMIIYQQt0bBDiGEEELcmkMXAvUUigoFTi3Nwb3zRfBrGIGW4+Ig85E5ulmEEEKIR6Bgx8YOT8tEvYWT0FpxSb3t8lQ58pMXo8PcBAe2jBBCCPEMNIxlQ4enZaL9vESEawQ6ABCuKET7eYk4PC3TQS0jhBBCPAcFOzaiqFCg3sJJAJjeRfYCAwBEL0yCokJh97YRQgghnoSCHRs5tTQHkYpLRi+wFxiiFAU4tTTHru0ihBBCPA3l7NjIvfNFFu/niQnNnviZCSGE2AcFOzbi1zDCov08MaHZEz8zIYQQ++EYY8zRjbCn0tJSBAYGoqSkBLVq1bLZeRQVClzxi0G4olCdo6NJCQ5FMjnC7+WpezBUCc26eT5KcACAIylb3e7m74mfmRBCiHjW3L8p2LGhw9My8Z95AwDg31s3T3XBD03eDL96Ibh3vgi+9UIR8eYIhLNCg3k+hoIjV1cVEBrObXLHz0wIIcQy1ty/aRjLxhi0Ax3N7Y1TX0cwuyHoOKqE5tylOWid1FXrNVfNdzm1NEdr6EqXqc9MCCGECEXBjo1UTT3XD3ZUz4MEBjqadBOaXTnfxZokbkIIIUQomnpuI+amnnMw3ONjjmZCs6sXLbQ0iZsQQggRg4IdG5G6N0IJDoWyaLQcFwfAPYoWthwXh8syuToZWZfuZyaEEEIsQcGOjUjZG6EKBgqSU9W5OO5QtFDmI0N+8mIA0At4DH1mQgghxBIU7NiIuV4LMYpkcr0p2PbOd1FUKJCbmo2Db2xAbmq2ZD1GHeYm4EjKVhTLorS2G/rMhBBCiCUoQdlGVL0W4fMSoQRnsNaOMQxAVtwHqP54E/Xsqiid3g175rvYOgm6w9wEKGb1Q67OjDLdz0wIIYRYgurs2JgqUIg0McXakGtcCIIeFBkdwrGkaKEljBf9AwCOel8IIYTYhTX3bxrGsrEOcxMQdu8CchdlIavTe4LfF8Kumcy3sUe+i+kkaABgTp8ETQghhFCwYwcyHxlaJ3VF5x+n4zoXIvh95vJtbJ3vYj4JGk6fBE0IIYRQzo4dyXxk+GvyUgQvfFFQ2rKQfBtb5rvcPVso6X6EEEKII1gU7Ozfvx/79+/H1atXoVQqtV5buXKlJA1zV7ELEvHjgSmIP77AaMCjBFAkor6MqudIaoria5LuRwghhDiC6GBnxowZmDlzJtq1a4eIiAhwnPVTqz3N08fmI+tJoOsx/YBHlfhbkJxqUe+MlOtkVYsQNuQmdD9CCCHEEUQHO8uXL8fq1asxbNgwW7THY8QfnY+DUzrgsUXjUJdV9YwUyaJRkJxqUb6N1FPEaz4WZX4nEfsRQgghjiB66nlwcDCOHDmChg0b2qpNNmXvqefmSNUTY3yKON93ZEnCctX0dsNJyqrhNmuntxNCCCHm2HXq+auvvor09HSxbyNGqPJtYj8fjNZJXS0KGhQVCtRbMAmcxOtkVU1v54xMb+doOQdCCCFOT/Qw1oMHD7BixQrs27cPrVq1gre3t9brCxculKxxRJifnvsY8UrjRQtV62TlLs0RncjcYW4CDmOrXmHEIpnc4uE2QgghxJ5EBzu///47WrduDQD43//+p/UaJSvb3+FpmeiS9aGgfS1dJ4uWcyCEEOLKRAc7WVlZtmgHsUDF3Qo0nD9G8P7WrJNlq+nthBBCiK1ZVUH50qVLKCykgnKOcHhaJkpryRHCrpv9IioBFIqo20MIIYS4E9HBjlKpxMyZMxEYGIj69eujXr16qF27Nj766CO9AoPENlQzr4KZsGJ+HKxfJ4s4P0WFArmp2Tj4xgbkpmbTmmWEEPIv0cNY7777LtLS0jBnzhx06tQJjDH88ssvmD59Oh48eICPP/7YFu0k/9JcnFNohlR2/AzEUyKxW5O6xhIhhLgT0XV2IiMjsXz5cvTt21dr+44dOzBu3DinH9Zytjo7YuWmZqP15HhB+yoBFHnJEX7/AvXquDFb1FgihBBnY9c6Ozdv3kSTJk30tjdp0gQ3b94UezgiktAZVeplJ6YspkDHjWn29ElZY4kQQtyJ6GDn8ccfx5IlS/S2L1myBI8//rgkjXJV9siZEDqj6gYXQn/Re4BTS3MQaaTCNVBVY+nU0hy7tosQQpyJ6JyduXPn4vnnn8e+ffvQsWNHcByHgwcPoqCgALt377ZFG12CvXImWo6Lw+WpcoQrCtV/uWtSgg90AksvoYO/j2TnJc5JaE+fpTWWCCHEHYju2enSpQv++usvvPDCC7h9+zZu3ryJhIQEnD17FnFxnjm1WZUzEa7QrmIcrihE+3mJODwtU7JzVS3hAKNLOJyfuhw+FOh4BKE9fdbUWCKEEFcnOkFZSrNnz0ZmZib+/PNP1KhRA7Gxsfj000/RuHFjk+87cOAAkpOT8ccffyAyMhLTpk3D2LFjBZ1T6gRl84tlciiSySVfLFPVk6S5hEOhFSumE9dU9f1nrKfPNt9/hBBib9bcvwUNY/3+++9o0aIFvLy88Pvvv5vct1WrVoJPfuDAAYwfPx5PPvkkKisr8e6776J79+44ffo0atasafA9eXl56NWrF1577TWsW7cOv/zyC8aNG4eQkBAMGDBA8LmlcmppjtbQlS5r1qUyhZZwIEBVT1/4vEQowWkFPKqev4LkVPq+IIR4NEE9O15eXiguLkZoaCi8vLzAcRwMvY3jOCgUliflXrt2DaGhoThw4AA6d+5scJ8333wTO3fuxJkzZ9Tbxo4di99++w2HDh0yew6pe3YOvrEBsUuGmN9vQjpiPx9s9fkIMYR6+ggh7s7mPTt5eXkICQlR/99WSkpKAABBQUFG9zl06BC6d++uta1Hjx5IS0vDw4cP9VZhLy8vR3l5ufp5aWmphC2mnAniHKinjxBCjBMU7NSvX1/9/4sXLyI2NhbVqmm/tbKyEgcPHtTaVwzGGJKTk/HUU0+hRYsWRvcrLi5GWFiY1rawsDBUVlbi+vXriIjQDipmz56NGTNmWNQmIczPjuJzJmhdKmJrtFgrIYQYJno2Vnx8vMHigSUlJYiPF1bZ15AJEybg999/x4YNG8zuy3Has5BUQ2q62wHg7bffRklJifpRUFBgcRsNMT87italIoQQQhxJdJ0dxpjBoOLGjRtGk4rNeeONN7Bz50789NNPkMvlJvcNDw9HcXGx1rarV6+iWrVqCA4O1tvf19cXvr6+FrVLqA5zE3AYW/VyJopkcslzJhQVCpzSGaqgQIoQQggxTnCwk5DA37A5jsPIkSO1AgiFQoHff/8dsbGxok7OGMMbb7yBbdu2ITs7Gw0aNDD7no4dO2LXrl1a23744Qe0a9dOL1/HnozlTISDX8/KmuBEFeDc/noHWvy2Hq01VjunxR4JIYQQ0wQHO4GBgQD4ACUgIAA1atRQv+bj44MOHTrgtddeE3Xy8ePHIz09HTt27EBAQIC6xyYwMFB9/LfffhuFhYVYs2YNAH7m1ZIlS5CcnIzXXnsNhw4dQlpamqDhL1vTzZmQoqqyoWNoClcUInxeIg6DloYghBBCDGIiTZ8+nd29e1fs2wwCYPCxatUq9T4jRoxgXbp00XpfdnY2a9OmDfPx8WExMTFs2bJlgs9ZUlLCALCSkhJJPoMxh1IymAIcUwCMaTz4bRw7lJJh8TF0Hwpw7JIsmlWWV9r0MxFCCCGOYs3926EVlB1B6jo7hkhRVdncMQzJXZRFs3EIIYS4JZvX2dG1detWbN68Gfn5+aioqNB67cSJE5Yc0q1IUVXZ3DEMocUeCSGEEH2ip55/9tlneOWVVxAaGoqTJ0+iffv2CA4Oxj///IOePXvaoo0uR4qVqC0JXKhwIXEURYUCuanZOPjGBuSmZkNRYXkldUIIkZroYGfp0qVYsWIFlixZAh8fH0ybNg179+7FxIkT1RWQPZ0UVZXFBC5KcCiURVPhQuIQh6dl4opfDFpPjkfskiFoPTkeV/xicHhapqObRgghACwIdvLz89VTzGvUqIE7d+4AAIYNG+YUM6KcQctxcbgsk+sVGVRRgsNlmRyKhwr8/Pp6HHghFb+MW6/1F7G5Y2geC6DChcQxDk/LRPt5iQjXGXINVxSi/bxECngIIU5BdLATHh6OGzduAOCXkTh8+DAAfs0sD8t1NkrmI8NfTwwGZ3D5CIADg6/yPtpOewZPLX8ZXbZPRqdlL2v9RWyqMrOmIpkcR1Jo2jmxP0WFAvUWTgLA9H6RqJZOiV6YRENahBCHEx3sPP300+qifqNHj8bkyZPx7LPP4qWXXsILL7wgeQNd0eFpmeh8dL6BUAfqsKUOu2HwveGKS+q/iDvMTcCRlK0olkVp7XONC0F2myTkLspC+L08CnSIzZjKxTm1NAeRJmYLqhLxTy3NsU9jCSHECNFTz5VKJZRKpXoh0M2bN+Pnn39Go0aNMHbsWPj4+NikoVKx9dRzc1PGVRfb1OCU7tR0WiKCOIKqoKXmEiiXZVVFMQ++sQGxS4aYPc7BCemI/XywLZtKCPEAdp167uXlBS+vqtv4wIEDMXDgQLGHcVvmpoybzsDh6U5Np9Wsib2pcnGg0z+pWbFbikR8QgixB4vq7Ny+fRtHjhzB1atXoVQqtV4bPny4JA1zVVLWuqG6OcQRzOXiKMEhemESQm7/jctT5QhXFKpzdDSpeihpliAhxNFEBzu7du3C0KFDUVZWhoCAAK0V0DmO8/hgR8q/Yi+zCGzYAEREAHFxgIxGrogdCC6K+dVBPEhejPB5iVCC0wp4NGcJRtGQKyHEwUQnKE+ZMgWjRo3CnTt3cPv2bdy6dUv9uHnzpi3a6FLMTRlXLQBmihIc8hGNl76Iw5AhQHw8EBMDZNIsXmIHYopiGkuip1mChBBnIrpnp7CwEBMnToSfn58t2uPyVFPGjf+1y8CBn4JueN0sXhJSoUTVX8SFhUBiIrB1K5Dw7/1DoQCys/kHAHTtyj+oB4hYQ2wuToe5CVDM6odcnSR66tEhhDgL0bOxEhISMGjQIJdNSrbHQqCA4Zksl7yiMVGZCgBYjEmIhv5QQT6ikYRUbIP+X8QcB8jlQF4esGMH8N//Ajd0ZrAHBwMrVlQFRISIVTWj0HQujqmFbAkhRGrW3L9FBztpaWmYOXMmXnnlFbRs2RLe3t5ar/ft21dUA+zNXsEOAK0p474xEUhYFIf8Qv7m4AUF4pCDSBQiFNdwDSEoqRmF78ritHp0DJkxA/jwQ9PnzsiggIdYrmo2Fgzm4tAQFSHE3uwa7GhOO9c7GMdBoXDuaqn2DHY0ZWfzuTdSCAoCzKVHRUUBFy/SkBaxnKHeyUJZNAqSUynQIYTYnV3r7OhONSfCFEk4i1xIHnhhIfDxx8AHH0h3XmI5VywMSbk4hBB3YVGdHSJehMAZ6XXr8nk4Uiwz9uGHQIsWNJzlaKoeEs3p3JenVlUidmZU0JIQ4g4EDWN99tln+O9//4vq1avjs88+M7nvxIkTJWucLThqGEuh4KePFxYaDmRUyccLFgAvvcRvkyLgiY7mE5ppOMsxNCsRaw4AU+4LIYSIY/OcnQYNGuDYsWMIDg5GgwYNjB+M4/DPP/+IaoC9OSrYAfg6OYl8zqdWIKOqy6iaVp6ZCUycyAdGUsjK4qekC6FQADk5/LCbpxcztHboydw6aTSriRBChLN5zk5eXp7B/xNxEhL4gGbSJOCSxqxzuRxITa0abkpIAPz9gR49pDmv0HyhzEzDbVu82POGwqQYehJcifjfNdAIIYTYhugKyjNnzsS9e/f0tt+/fx8zZ86UpFHuLCEBuHCB721JT+f/zcvTDyZycqQ7p2a+kKoQ4YYN/L+qyXOqXqdLOvdmVTFDT6rerBp6CtcJVMIVhWg/LxGHpwm7GGIqERNCCLEd0VPPZTIZioqKEBoaqrX9xo0bCA0NpannElAogBEjgPXrrTuOZhFCmcx4z83ChUBysn6gY+w47kzKoafc1Gy0nmy+3kDuoizq2SGEEDOsuX+L7tlhjGkt/qny22+/ISgoSOzhiI7MTD6RWYpAB+CHx1SBjrGem4EDjQc6AJ9fVFAgbW+Tszq1NAeRRgIdoGro6dRS8xfD3DppSnAolEXTquCEEGJjgqee16lTBxzHgeM4PPbYY1oBj0KhwN27dzF27FibNNIdGUoE3rGDD0ikmIUVFVWVa6NQ8D06ho4r5lxS1gpyVlIOPZlfJ41WBSeEEHsQHOykpqaCMYZRo0ZhxowZCAwMVL/m4+ODmJgYdOzY0SaNdDfGhpPu35cm0AGA1auBbt34/+fkmO65EUpn5NItiV0E05wOcxNwGFv1KhEXyeQWVSJ2xeKEhBDiaKJzdg4cOIBOnTqhWjXXrEfo6Jwd1XCSVEGNMevWAUOH8v/fsAEYMsT6Y0ZFAZ995t4zs2y1CKYUQYqh5Rsuy1yjOCEhhFjLrjk7AQEBOHPmjPr5jh070L9/f7zzzjuoqKgQeziPoVAA+/cDr71m+0AHACZPrppBJbR6szmeMDNLNfQEQC/XRnPoSWygoqpEHPv5YLRO6mpRoCPFDDFCCPFEooOdMWPG4K+//gIA/PPPP3jppZfg5+eHLVu2YNq0aZI30B2oko6feUbYulZSuH69KjCJi+OHyaTAGJCUVDVl3R11mJuAIylbUSyL0tpeJJM7pOKxokKBegsnQbcKM1C1Inn0wiQoKtz4i0IIIVYQPYwVGBiIEydOoGHDhvj000/x448/4vvvv8cvv/yCQYMGoaCgwFZtlYS9h7HsNWxliOaU8R07gAEDpDu2mKrMrsrc0JO98mdoCjshhNh51XPGmHrl83379qF3794AgOjoaFy/fl3s4dyaqVlQ9qA5ZTwhAZgxg18cVApSLWXhzEwtgmnPxT2pOCEhhFhH9DBWu3btMGvWLKxduxYHDhzA888/D4BfRiIsLEzyBroyqWZBWUs1Zfzdd/kkY2M4DtCYZGfStWvWt8tV2Tt/RuoZYoQQ4mlEBzupqak4ceIEJkyYgHfffReNGjUCAGzduhWxsbGSN9CVXS5QoAuyMQgb0AXZ8IJjciquXOFnZOXkAIsW8UGNbl1I1fORI4UdMyRE0ia6DEfkz1BxQkIIsY7onB1jHjx4AJlMBm9vbykOZzP2yNlRVCjw03Mf4/GsxQhCVUZyAeSYhMXYBvsluMpk2snEcjkweDAf/Gj2OkVH89WWg4KAePPpIR6Rs2OIo/JnVL1JAAwWJ3RE4jQhhNiTXXN2AOD27dvYunUrzp8/j5SUFAQFBeH06dMICwtDlKlxEg9weFomHp3/X8SzG3qvRaEQW5GIRGy1W8CjO2uqsBCYPx/YvBmoW1e7grMqMJLLTQ+/RUfz+3siR+XPSF2ckBBCPInoYOf3339Ht27dULt2bVy4cAGvvfYagoKCsG3bNly8eBFr1qyxRTtdAv/X9wAjgw38X+RKcEhFEnaiHxSQITgYuKERF3l5Af/mf9sEY/yQVXKy4YU9ZTJ+mYnExKr9VXTX23IEQ8ts2LMtjsyf6TA3AYpZ/ZCrMwOMlpsghBAzmEjdunVjKSkpjDHG/P392fnz5xljjP3yyy+sfv36Yg9ndyUlJQwAKykpkfS4leWVrFAmZwo+PjD76BeYxTIyGKusZCwri7GkJEFvk/SxaBFj6en8+SsrtT9PRgZjcrn2/tHR/HZHMdQmudy+bar6OnMGL6oCHLski2aV5ZXmD0YIIUQwa+7fohOUjx49ijFjxuhtj4qKQnFxsQThl2syt1q2rqlDi5CQwPdKxMUBW7cKP5dUqUaTJ/PLSMTH80UPNSsjJyQAFy7wuTnp6fy/eXmOWyrC1Krt9qzqbKsKy4QQQmxHdLBTvXp1lJaW6m0/e/YsQjx1ig7E52j4P1o1zCF0ivo77/BBhy1u7IaCBpmMT0IePJj/15FDV+ZWbbdnVWdnq7BMCCHENNHBTr9+/TBz5kw8fPgQAMBxHPLz8/HWW29hgJQlel2M0BwNBuAGF6w1TbhIYJzUogUfdHTtyicR604ft4YjggahzAWDmsUT7aXD3ASE3buA3EVZODghHbmLshB+L48CHUIIcUKig5358+fj2rVrCA0Nxf3799GlSxc0atQIAQEB+Pjjj23RRpdgrhaKpjrsBo6+t0P9XOhCnaGhQHY2P5NKtaCo1AGPvYIGhYL/LBs28P+aCrCEBoNC95OKtYt7EkIIsQ/Rs7Fq1aqFn3/+GT/++CNOnDgBpVKJJ554As8884wt2ucyVLkc4fMSoQSnVQtFEweAgeMLz83qB5mPTL1QZ2Gh4aEajuPr34wcqd3DERzM/3tDf5a7VWwdNGRm8sNSmp9FLudngRnKCRIaDEq1ujshhBD3IllRQVdh66KCh6dlotH8sajLzK+noFl4TpWAC+hP9zb2FVL16nTtyufySMWWBQONLYyq+ixbt+oHPAoFn0BtKhhULXjqqLwiQgghtmXN/VvUMJZSqcTKlSvRu3dvtGjRAi1btkTfvn2xZs0aeFjMZJCiQoHqkUH4X/MXBe2vmdSckMDf6HVrMkZFVfXg6FLNd5Yy0LFlwUBLE41VtX8A48tcOLL2DyGKCgVyU7Nx8I0NyE3NlnS5EEKI9QQHO4wx9O3bF6+++ioKCwvRsmVLNG/eHBcvXsTIkSPxwgsv2LKdTu/wtExc8YtB68nx6Pq/pYLeo5vUrDnde906fh2r4cOtH6biOD5gMpffw3G2DRqsSTQ2FgzK5YZ7gwixF82f/dglQ9B6cjyu+MVIviAsIcRygnN2Vq9ejZ9++gn79+9HvM7iST/++CP69++PNWvWYPjw4ZI30tlVrVuk3WXBAIPpykpwKJLJDS7cKJMBN28Cb70l3Yrp06fzM7kSE40PiwUHA8uX87lBGzbYpjqxtYnGCQlAv36OraBsiKOrOhPHMfazH64oRPi8RBwGlSIgxCkIrT747LPPstmzZxt9/eOPP2bdu3cXXdXQ3qSuoGyucrLSQIVdBTh2KMVw2d+MDMY4w8V5LX6sW1d1bN0KxEFBjM2YwdjmzbavTpyVJay9WVnSndPWnKGqM3EMcz/7VE2bEGlZc/8WHOyEhYWxkydPGn39xIkTLCwsTNTJDxw4wHr37s0iIiIYALZt2zaT+2dlZTHwf0JpPc6cOSP4nFIHOycXZYmKPC55yY0GOpWV+jdOKR4hIVU3X9XyFJrLRBgLsDiOf0h141Z9PmPBHMfxS1LoLl3hrOx13YhzEvqzf3JRlqObSohbsMtyETdv3kRYWJjR18PCwnDr1i1RvUplZWV4/PHHsWTJElHvO3v2LIqKitSPRx99VNT7pSS2crKptBmhlZTFun69qjqyblVkwH7Vid0p0djZqjoT+xP6sy/2dwQhRHqCgx2FQoFq1Yyn+MhkMlRWVoo6ec+ePTFr1iwkiMwuDQ0NRXh4uPohc+DdUezq1uHKQrSfl6iVvKgqsJeRIXHj/mXq5mvv6sTukmjsjFWdiX0J/dkX+zuCECI9wQnKjDGMHDkSvr6+Bl8vLy+XrFHmtGnTBg8ePECzZs3w3nvv6SVM21PLcXG4mRyEIHZT0P5eYFBqFBXc8Y1Mr8CeLWjefDVr6DiiOrGzJhqL4axVnYn9tBwXh8tT5QhXFBosImpqIgIhxL4EBzsjRowwu4+tZ2JFRERgxYoVaNu2LcrLy7F27Vp069YN2dnZ6Ny5s8H3lJeXawVihhYxtYbMR4bfuk5CfNaHgt/jBYYoRQE+7ZeDt/Z0lbQ95ujefB1VnVg1nGaOs850oqrOxFTVdNWyMQXJqYiiZUQIcTzpU4gsAwEJyob07t2b9enTx+jrH374ocGkZqkSlBnjZ2Vc54L1Zl6ZewxCuuTJyGJnOjlz0rAzz3Ry5utG7OtQSgYrlGl/o16SRRudiEAIsYxdEpSdVYcOHXDu3Dmjr7/99tsoKSlRPwoKCiRvg8xHhnNTV4ABUIp4XxHs+2d/cLB+dWRnTRpWLSuhO7xXWFiVbO1IznrdPIGzVSvuMDcBYfcuIHdRFg5OSEfuoiyE38uj+jqEOBMbBF8WgYU9OwMGDGDx8fGC95d66rmmXyZvZg8hM9u9ogDYRUQzL1TatVdnxgzjbTfUixId7ZheFHNT8J2p18SZrpsnMNSLUigzXs6BEOI+rLl/i171XEp3797F33//rX6el5eH3NxcBAUFoV69enj77bdRWFiINWvWAABSU1MRExOD5s2bo6KiAuvWrUNGRgYybDWNSSS/eiGoBmF/ZSYhFUrY78/+4GDg3XeNv+5MScNiZjrZasFSoZzpurk7qlZMCLGUQ4OdY8eOac2kSk5OBsAnQ69evRpFRUXIz89Xv15RUYGpU6eisLAQNWrUQPPmzfHtt9+iV69edm+7IULraaQiCdtg31/KK1aYvwELTRq2NVeb6WSr66aoUODU0hzcO18Ev4YRaDkuDjIPTXZVVChQb+EkAEyvXobuDEdPvUaEEOMcGux07doVzFBVtn+tXr1a6/m0adMwbdo0G7fKMooKBR5euiJo353oJ2i/4GDrFwEFgFdecZ36NQDNdAL4Xox6CyehtaKqi+vyVDnykxd7ZO/FqaU5WtdCl2qGY+7SHLRO6mq/hhFCXIJFCcpr165Fp06dEBkZiYsXLwLgh5h27NghaeNchWrV4y7bJ5vcTwkO+YhGDkzX3ZDL+QKDV65or4C+bh2wbx8fBInRrZv2c1URww0b+H+drcpvXBx/DYyt0s5xQHS0frK1u1AN14Tr3NzDFfoFKT0FVSsmhFhDdLCzbNkyJCcno1evXrh9+zYU/94pa9eujdTUVKnb5/SM3Zh0MQAcmNlcHT8/4Px57Z4YLy+gdWtg0CA+cFmxQlwbNasVZ2YCMTFAfDwwZAj/b0yM42c3afLkmU7mhmsA8MM1Dp6BZG9UrZgQYhWxGc1NmzZVz5ry9/dn58+fZ4wxdurUKRYcHCw6Q9repJyNZW7VY82HEmBXESxoBlZWlvkaMxkZjEVFmZ+BpTlrydUWrvTEmU60uKRhVT9rhgsb0QrjhLg/u9bZycvLQ5s2bfS2+/r6oqysTILwy3WcWpqDSMUlQd1jHIAQ3EAczC+WtGOH+RozCQnAxYvAjBlGzsfxD1UPiCsuXJmQAFy4wA/lpafz/+bluVb+kVg0XGOYqloxUFWdWEWzWjElJxNCDBEd7DRo0AC5ubl627/77js0a9ZMija5DEtuOBEw/55164QFJTIZ8MEHfH6PXK69r+7Cmq66cKXuKu3uOHSliYZrjOswNwFHUraiWKa9imyRTI4jKTTtnBBinOjZWCkpKRg/fjwePHgAxhiOHDmCDRs2YPbs2fjqq69s0UanZckNx1zV5Jo1gevXjb9uqMaMkFovrjad21PR4pKmdZibAMWsfsjVmZJP608RQkwRHey88sorqKysxLRp03Dv3j0MGTIEUVFRWLx4MQYNGmSLNjotczcmTUpwuAS52ZlYQkcCxQYlNJ3bNdDikubJfGQ0vZwQIgrHmIlCN2Zcv34dSqUSoaGhUrbJpkpLSxEYGIiSkhLUqlXL6uNVVXWF0YBHdZNKxFbJignu28f33BQVAefOAV9+qT1MJZfzM5pUw1gKBT/rqrDQ8BAZx/Hvyctz/6EiV6CqsxOpMcuvUBaNguRUGq4hhHgka+7fooOdvLw8VFZW4tFHH9Xafu7cOXh7eyMmJkZUA+xN6mAHMHxj0pSPaCQh1Wig4+UFKEWsIOrlBYSHA5cvG99HNUVbM29HtbgmoB3wGNqXOB5VUCaEkCp2DXa6dOmCUaNGYcSIEVrb161bh6+++grZ2dmiGmBvtgh2AO0bk289vqerPP8q/BpGoNmYOOQclEF1aVTF8HJy+BlVa9dK1gwthnprMjP5WVmavUDR0fysLQp03A8FTIQQd2HXYKdWrVo4ceIEGjVqpLX977//Rrt27XD79m1RDbA3WwU7YmzdCowbB1y7Zp/zZWVpr92kUNDClZ7AUI/jZZnnLjlBCHFt1ty/RScocxyHO3fu6G0vKSlRV1Mmxk2bBsybZ99z6iYzO8uCn8R2aIVwQgipIrrOTlxcHGbPnq0V2CgUCsyePRtPPfWUpI1zN1u22D/QAWiGlacxv+QEw6PzXsPxefs9btkJQohnEj2Mdfr0aXTu3Bm1a9dG3L/JJzk5OSgtLcWPP/6IFi1a2KShUnHUMJZCwScVm6qhIzWaYeWZclOz0XpyvKB9aViLEOIqrLl/i+7ZadasGX7//XcMHDgQV69exZ07dzB8+HD8+eefTh/oOFJOjn0DHYCfcTVgAH9uGmH0HGIqe3vySuqEEM9hVZ0dV2SPnh1DM2A2Z8gwZIhNTieIbt0d4r7E9OwAVVWZw+/l0UwtQojTsutsLAC4ffs2jhw5gqtXr0KpUyBm+PDhYg9nV7YOdozNgPk5cTFe2uS4SINq6XgORYUCV/xiBFX21pS7KIsqExNCnJZdZ2Pt2rULQ4cORVlZGQICAsBxVSsQcxzn9MGOLZmaAZO4KREDuK3IYNJFGoGBfG9NSor5aeyM8QFPUhK/jhbl8LgvU0tOmOJpK6kTQjyH6JydKVOmYNSoUbhz5w5u376NW7duqR83b960RRtdgvkZMMBClgQvSJc8U1IiLNBRcdaVzYn0jK0QboonrqROCPEMooOdwsJCTJw4EX5+frZoj8s6tTQHkYpLRi+oFxjqoQBxkDbSsKQwIa1s7hk6zE1A2L0LOD53H25yQTC2IokSHApl0R67kjohxP2JDnZ69OiBY8eO2aItLk3oEEAEHB9pUN0dzyHzkaFtSjf8NfVLAJx6UVoVzZXUKTmZEOKuROfsPP/880hJScHp06fRsmVLeHt7a73et29fyRrnSoQOARTBcZGGqu5OnJk/4Gk5CffTYW4CDmOrXvJ8kUxOK6kTQtye6NlYXl7GO4M4jnP6JSNsuRCoqRkwSnC47CVHfWUelHBc5JCRYXo2lqGFQmnauvughUEJIa7K7lPPXZktp55nt5+GLkfn6QwU4N9cCQ5HUrZi08MEpKZKelrBgoOBK1eM99JkZgKJiXwisyaatk4IIcTR7FpBmRh2eFomOh+db3CSLwfgpyenosPcBPTrZ++WVblxw/hMLIWC79ExFPqqtiUlUSVmQgghrkd0zg4AlJWV4cCBA8jPz0dFRYXWaxMnTpSkYa7E1LRzgK+68+iJjVBUzEZcnAxyOVBYaDiwsDVjM7FycrSHrnRpTlunFdMJIYS4EtHBzsmTJ9GrVy/cu3cPZWVlCAoKwvXr1+Hn54fQ0FCPDHZOLc1Ba4XxSMELQJSiAF+NykGjV7ti4UJg4ED7tU+TsZlYQqej07R1Qgghrkb0MNbkyZPRp08f3Lx5EzVq1MDhw4dx8eJFtG3bFvPnz7dFG52e0Gnn+9cXIT4eeP11GzfIAI4DoqO1Z2IpFEB2NrBhA5/LIwRNWyeEEOJqRPfs5Obm4v/+7/8gk8kgk8lQXl6ORx55BHPnzsWIESOQ4IEZrGKnnd+4YcvW6FMlGKemViUnG5p1JZOZzskJDjY/bZ0QQghxNqJ7dry9vdXrYYWFhSE/Px8AEBgYqP6/p2k5Lg6XZXK9gm0qSnDIRzRyYJ9IIThY+3lUlPZMKtWsK90cHXPJxzduADt2SNdOQgghxB5EBztt2rRRV1COj4/HBx98gPXr1yMpKQktW7aUvIGuQLXwIgCjFWqTkGqX+jovvQRUr669TTMR2tSsK3NUC4kqFNpDYNnZpgMlMfsSQhxHUaFAbmo2Dr6xAbmp2VBU0A8rcRNMpKNHj7Iff/yRMcbY1atXWc+ePVlAQABr06YNy83NFXs4uyspKWEAWElJieTHPpSSwS5xcsb4WIIxgF1ENHsBGZqb7P7gOP6RkcFYVpb1x5sxgzG59sdkcjl/fF0ZGcL3JYQ4zqGUDFYo0/5hLZTJ2aEU+mElzsGa+zcVFZTY/h8U+KhHDiJQhCJEIAdxDq2YrKJaKmL2bODll21zfMDwcBkVKSTEuR2elon28xKhWz5D1TN9JGUrLSlCHI4qKItg62BHoQDCwuyfhCzUokXA5Mm2ObYqoMrL45/HxBiv3aO5L627RYjjVC11c8lgXoMSHIpkcoTfy6OlRYhDWXP/FjQbq02bNuqkZHNOnDghqgHuZscO5w10AD64sFVRQ83CgwAVKSTEFZivE8YQpShA7tIctE7qar+GESIhQcFO//79bdwM96BK/nVmGzYAS5caLmrIcVUBkOb/DT03RUzhQSpSSIhjCa0TJnQ/QpyRoGDnww8/tHU7XJ6iQoFtyTl46pJz5erounYNOH0aCArS74EKCgJWrACUSmDcOH5fFbkcePVVQMi3gpjCg1IUKVQo+B6ioiL+eHFxNDRGiFBC64QJ3Y8QZ0Q5OxI4PC0T9RZOQqRGV3AB5JiExdgG10rq69cPOH5cewiqbl2+Nyghgc/DMTYEZihnR8i+1gQmhoojyuXA4sWU/EyIEFU5O4XwMrCUMeXsEGdh11XPFQoF5s+fj/bt2yM8PBxBQUFaD0+jmsUQrjPmHYVCbEUiXkCmg1pmmR079HNtbtzg6/fs2MEHEUDVjCoV3SrNMpnwfS1lrDhiYSG/PdO1Lj0hDiGkTlhBcioFOsSliQ52ZsyYgYULF2LgwIEoKSlBcnIyEhIS4OXlhenTp9ugic7L1Grnqr+QUpEEL7h2YS5Vz0xSEt/zs3UrX5VZk1yuP5U8IUH4vmKZKo6o2V5PK2BIBRyJJTrMTcCRlK0olmn/sBbJ5DTt3EaogKOdiS3M88gjj7BvvvmGMcaYv78/+/vvvxljjC1evJgNHjxYdKEfe5OyqODJRVmCqvB1QZZDiwpK+cjK4j97ZSX///R0/t/KSuPXScy+QgktjqhqryegAo7EWpXllezkoiz2y4R0dnJRFqssl+CHleihAo6Wseb+LXoh0OLiYvWyEP7+/igpKQEA9O7dG++//76UcZjTEzo7IQLuM4tBNXtKJhM+ZVzMvmLbIdV+rs5YAUfVkB4VcCRCyHxkNL3cxjQLOGoKVxQifF4iDoN60mxB9DCWXC5H0b93kEaNGuGHH34AABw9ehS+vr7Sts7JiV3t3B1IMXtKCHPDMULbYYv2OttQEQ3pEeIahKQ+RC9MoiEtGxAd7LzwwgvYv38/AGDSpEl4//338eijj2L48OEYNWqU5A10Zs622rmtRUfz07p1SX3zz8zkZ3LFxwNDhvD/1q8PzJxZdY7YWD73x1itS44z3l6p2xYT49hk6Jwc4QUcCSGOc2ppDiKNVKoGqgo4nlpKP6xSEz2MNWfOHPX/ExMTIZfLcfDgQTRq1Ah9+/aVtHHOTjWLIWLeADBAK+ThnzO7rXZuD4MG6c+eknrqt6nhGM0aP3I5MHgwMH++4QKIgPWzvcS0zZFDRTSkR4hroAKOjiO6Z0dXhw4dkJyc7HGBjiZjhYrcrYDR/PnaPRhST/02NRyjq7CQb8/UqbaZ7SWmbY4eKnLkkB4hRDgq4Og4oosK3rhxA8HBwQCAgoICfPnll7h//z769u2LOKnHDGxAyqKCQhbQuwQ5GiDP6Xt3AgKAO3dM72PrhT6zs/lhIaFU5/j7b+DgQe0KyoC0VZWFti0ry/5rfSkU9ingSAixDhVwtI5digqeOnUKMTExCA0NRZMmTZCbm4snn3wSixYtwooVKxAfH4/t27eLOvlPP/2EPn36IDIyEhzHCXr/gQMH0LZtW1SvXh2PPPIIli9fLuqcUhIy/loPBYiD84+/mgt0AO3cD1vkiYgdZlGd4+BBPsAYPJj/d8cO6fNqnHmoyB4FHAkh1qMCjo4jONiZNm0aWrZsiQMHDqBr167o3bs3evXqhZKSEty6dQtjxozRyucRoqysDI8//jiWLFkiaP+8vDz06tULcXFxOHnyJN555x1MnDgRGRkZos4rFXeZei628HVRkW1u/ufOiWuHoXPYqqqysw8V2bKAIyFEOlTA0UGEFuQJDg5mv/32G2OMsTt37jCO49jRo0fVr585c4YFBgaKLvSjAoBt27bN5D7Tpk1jTZo00do2ZswY1qFDB8HnoaKC+o8ZM8QXFpS6qF9GhjSFDnWL6mk+OI6x6GjLihqqjs1x0h9bSrYo4EgIkR4VcBTPLkUFb968ifDwcAB8McGaNWtqrYVVp04d3BEyFmKFQ4cOoXv37lrbevTogbS0NDx8+BDe3t567ykvL0d5ebn6eWlpqWTtaTkuDpenyk2Ov16C3Gmnnvv7AykpwFtvAV9+aXpYCqjK/VDlxMjl5vNEhKRxqZJ/xdI9h5ihNbF5NaqhosRE+83+soQtCjgSQqRHBRztS9RsLE4nIUD3ua0VFxcjLCxMa1tYWBgqKytx/fp1g++ZPXs2AgMD1Y/o6GjJ2iNk/NWaqee6+Ve1a1t0GD01avD/3r3LT+du2JDPdxHy5bTFQp/mghRDDJ3D1nk1NFRECCGuSVSdnZEjR6qrJD948ABjx45FzZo1AUCr98SWdAMs9u+f2MYCr7fffhvJycnq56WlpZIGPB3mJuAwtqLewkmI1Fj5/BLkSEIqtsHyO2BpqfYsqdu3AS8vQKm0rs3372s/15zGvWGD4cBDJgOSkw0v9Gmozk5qqvCbvyXBh6Fz2COvJiGBXwxVyplehBBCbEtwsDNixAit5y+//LLePsOHD7e+RSaEh4ejuLhYa9vVq1dRrVo19XR4Xb6+vjZfxqLD3AQoZvVD7tIc3D1XhPnrI7CrJE6S6ea6I4PWBjqGMMb3lGzcyAc9gwbp76NQ8K916KAf8Ai9+SsUhvcTGny8/DI/q6prV/4hk2kfMzSU73W5fNn6oTVTaKiIEEJci+BgZ9WqVbZshyAdO3bErl27tLb98MMPaNeuncF8HXtSjb9mZwM7ljq0KRZR5bNMmGB6v6QkPrjRDGaE3PxNVVru1890/o/KunX8v6tXVw2h6R4zOLgqeHPWvBpCCCH2ZXUFZWvcvXsXubm5yM3NBcBPLc/NzUV+fj4AfghKs7do7NixuHjxIpKTk3HmzBmsXLkSaWlpmDp1qiOab5Crl+Q3kvoEwPI1lsxNB9+xw3j+jyGFhcCAAfxD95g3b/L/6k6np7waQgjxXKLXxpLSsWPHEK9RllaVWzNixAisXr0aRUVF6sAHABo0aIDdu3dj8uTJ+OKLLxAZGYnPPvsMAwYMsHvbjfGEkvxiAjpzyyxwHN9blJdnOP/HEFO9P6pj1qgB7NsHXL1KeTXE+SgqFDi1NAf3zhfBr2EEWo6Lo0JyhNiQ6OUiXJ2Uy0UYoirdL3Z2kTMICQGuXTO/n5glEcQus6DKwdm/H5g1S9g5zB1Tk7G8IULs5fC0TL0JDZdlcuQnL6aCcoSYYJflIogwmlOybUk1fVxKI0bwwz3GhpI4DoiOFpfgK3Y6uCr/p1kz4ecQeu7MTOmXkXAnCgUfnG7YwP/riEVN3d3haZloPy8R4Qrtv4bCFYVoPy8Rh6fRNyMhtkDBjg0kJAAZGXyyrK3oTh+XwqZNwMKF/P+lWmPJ0ungUgwHah7DVstIuAtbBoIURPEUFQrUWzgJANP7xasqShq9MAmKCg+9QITYkKBhrJ07dwo+YN++fa1qkK3ZehhL0/37fKLsgwc2PY2k9u0DSkr0c2eio8XVzlGxdEVuc+8zRfeY5oYWPX1VcFUgqHudVQGuNYndpmbheVqyeG5qNlpPNj+mm7soiyrrEmKANfdvQQnK/fv313rOcRw0YyTNgn4KT/2zTUdmJjB2rGsFOgAwcCC/dMSFC9Lktli6zIK596meCzmmLZeRcHVCE8h1yw0IkZnJz5jTdekSvz0jw7MCHqELBwvdTxMlPBNimqBhLKVSqX788MMPaN26Nb777jvcvn0bJSUl2L17N5544gns2bPH1u11Caq/lIUk+zqbmzerpoN37covI6Eq4GcpS5dZMPW+jAz+IeSYtl5GwpWJCQRN0R2qqqgA/vtf0+/573+tG9JSVCiQm5qNg29sQG5qttMP//g1FDY2K3Q/lcPTMnHFLwatJ8cjdskQtJ4cjyt+MZT/Q4gmsSuHNm/enOXk5Oht/+mnn/RWJHdGUq56boi5lbdd4WGrFbwtXZHb1PuEHHPfPmlXaHcn6enCrk16uvFjZGTof8/XrSvsuPv2WdbuQykZrFCmfdJCmZwdSsmw7IB2UFleyQplcqYAZ/BiKMCxS7JoUatfH0rJYApwTGHgWApwTn09CBHLmvu36GCnevXq7Pfff9fb/ttvv7Hq1auLboC92TrYycpyfLAi1cMdbv4ZGYxFRTkmuHMFQr9fjX0vZGTw18/S77H33hPfZle+wVe1nbO67VXBk+GLa0nwRIgzs+b+LXo21pNPPomkpCQUafT5FxcXY8qUKWjfvr1kPU6uyhWGQqpXF7afK3wWU1TDiYWF5vf11GUk4uIsLzdgKt/HVlx9RlOHuQk4krIVxTLt8dcimRxHUraKqrNzamkOIhWXjOYieIEhSlGAU0tFljwnxA2JDnZWrlyJq1evon79+mjUqBEaNWqEevXqoaioCGlpabZoo0tQ5Q/INm9AF2TDC875yxYQnjTtytWgxdyIp071rERZTZp1ocSWGzCX7yOE2IRwd7jBd5ibgLB7F5C7KAsHJ6Qjd1EWwu/liS4oaMuEZ0LcjejlIho1aoTff/8de/fuxZ9//gnGGJo1a4ZnnnlGa1aWJ1FVRG2tuITWAAYCKIAck7AY2+Ccd1EvL+MrqEu1OrgjibkRb9wIzJ7tmT07QFUiuKEp4qbKDVjb8xccLD7YcZcbvGrhYGvYKuGZEHdk0dpYHMehe/fu6N69u9TtcTmqiqiAdhdCFC5hKxKRiK1OGfCoAh2xq4O7ynILYm7EnjrtXFNCAj+9XMzX1tqevxUrxH/v0A2+Sstxcbg8VY5wRaF6CE+TEhyKZHK0HOfCf7UQIhFBRQU/++wzwQecOHGiVQ2yNSmLCioqFLjiF4NwI93qDMB1BCMcV6CE80UESUn8X/S6f82/9hrw6KP6NzxXKhAndE0ulfR0fpo9EU5IwcigID5HTDNvyprvmaqfOdM3+PB7eR5RZ6bqjy1oXQ8l+L9axOYBEeLMrLl/Cwp2GjRoIOxgHId//vlHVAPsTcpgR2hF1PcxA7PwgVXnsoWsLD6YUf01f+4c/9e2oRsTYLsqu4D0PUZiF2QVs7gpqaJKAgcM9xBu3Sq+x8gcusFrM7SwaKEsGgXJqR51HYj7s+r+Le3EMOcn5dTzXyYIK1JyDUHMC5UOn0qu+dCdam1sCrFqW3Cw7aZuG6rTIpfz262RkWH+OrjytHNL6xZJzdDXLzra+q+fKYbq7FySRTv1tHNbqiyvZCcXZbFfJqSzk4uyaLo5cUvW3L8F9ewYcv36dXAch2BbrnZpA47o2QGArsjCAXS16nxCcBxQpw5fCdnUPpo9MWJ7QYyxpHfElusyqY7/3/8CN27ovybVORzB2YYUHZHL5UlLJHjSZyXEGLv17Ny6dYuNGzeOBQcHMy8vL+bl5cWCg4PZ+PHj2a1bt0RHWo4gZc9OZXklu8EFCepKGYR0h/fmqB79+ml/DqkKIZqqsmvw+pmpNi1Vr0tlJWMzZjAWpPOlsnXvg62Y6oXjONf8TMQ4V6wWTYgtWHP/Fjwb6+bNm+jYsSMKCwsxdOhQNG3aFIwxnDlzBqtXr8b+/ftx8OBB1KlTR1y05cJkPjKckT+LTgWbzO5bBOeZHbJrF792kY8P/1yq4oHnzonb314LdMpkwAcfAO++6xozyUyx5cKdhs7l6tfL1Rmb7RmuKET4vEQchmflJxFiKcHBzsyZM+Hj44Pz588jLCxM77Xu3btj5syZWLRokeSNdFaKCgUaFP4CBsBYhSHVr6i6uG6nVpmnVAJ9+wLffsvfvEJDpTnul1/yAYXQG6K9F+iUyfigSXUT37zZ8E3cmW/y9goQjQ2TLVwIhIQ457VxN+aqRSvB8dWiZ/WjIS1CzBHaBVS/fn22Z88eo69/9913rH79+qK7luxNymGsk4uyBI3vKAF2EdFOl6QslzOWkmJ+4VKOY6xWLWHHFLOwo7XrMlnCXDK0rZKlpSLFwp3mEpvFrHflTNfG3Qj9/XJyUZajm0qIXdhlbayioiI0b97c6OstWrRAcXGxBOGX6xBapZUDUA8FiINzlbC/dAmYN890T4EqiXfUKGHHHDiQ7xUQwpp1mSyhSobW/byFhfz2adNMvy70c9mS0EJ+p0/ztYYUOquWZGbyyejx8cCQIfy/MTFVn03selfOdG3cjbtUiybEGQgOdurWrYsLFy4YfT0vL8/lZmZZS2yV1gjY9pdSrVrGAwdLRUVV1UoR4uZN4Tc/a9ZlEstcrgtj/BCNsdcBPhdGN3iwF4WCD14KC/lhJHNf51mz9AMZc8FeZqb49a6c4do4A9XaeAff2IDc1GxJFiKlatGESEhoF9CoUaNY586dWXl5ud5rDx48YF26dGGjRo0S3bVkb1LPxiqUyZlC4LhRF2TZbEgqIICxli2lP65qWEo1c0rI8Ia5WVS6wyibN9u+TotUM86kHFITytDQmtCHaoaWoWts6Gu2bp1rXRtnYKvZUlW/Xwz/0CnAsUuyaKqpQzyGXWZjzZgxA+3atcOjjz6K8ePHo0mTJgCA06dPY+nSpSgvL8fatWttFJI5J5mPDPnJixE+b4DJJGUlOFyCHDmw3Ro1d+4Ap05Jf9yrV/l/Vb0wqmq5pjBmPEnWWOLrokVA3bq2S3yVKslZquMIZawOkVCM8b1A48cD166Z3q+gwPQ+5tj72jgDW86Wqvr9kgglOIPVoguSUxFFycmEmCcmMvrnn3/Yc889x7y8vBjHcYzjOObl5cV69OjBzp07JzrScgQpe3ZUDqVksOuc4RLDCnBMAY69gAyb9erY8vHee9pJrBkZ+vVqjD2SkrSvky3rw5hLunXFnh1zdYgAxkJCGHvnHem+3uvWCe/Bc+S1cQbmenal6nmhatGE8Ky5f4sKdlRu3rzJfv31V/brr7+yGzduWHIIh7FFsMMY/4vvx/gZ7Aa0I4GLiHbZQEfzoTnrZt8+4e9TvUdIAUG5nD+22OUPhMygEjIMJ5MZf90Ry0oIDdDee0+6r3NWVlVQKjTgceUlN6xhz9lStBwEIQ4IdlyZrYIdlX3fV7IuyGKDkM66IMvppptb+tDsfRGTv6O6CVrSsyJkWrOY3iJjN3HVtpQU069LPcXaXG+U0GnmQoOdkBDhwZzQPCFPrtosdG28XyaILC1OCDGIgh0RbB3sVFbyNxVHBye2CnhUN0Qhi2yqHqobuiXnM3UjtWS5CXOLVtprUUshvVFCA8R9+0wHn6rrsGWLuGBONxjbssX+C346M6qDQ4h9OWQhUFcl5UKgxkyZwk9jdiUhIXxSsJAp41lZ/BTzESOAu3fN75+ezicdxwtbM1ULx/EJzHl5+gnL2dnCjqm7QKm5Csm2qKCsecxz54APP9TfR3dhUtUCrYWF/F3T0P6qa7NjR1XyuOa+usc0lCAeEgIsXSos+dyZq0vbm6JCgSt+MQhXFGolD6sowaFIJkf4vTyqcEyIBOy2EKg7sHXPTkqK43tgLHlMmSIu8VhMAqtqiMbSxFfVMXRJUU3YHsRMHTc0nCS0N0Zor9TmzYzVrau9H1VCtsyhlAz1JATNC6raRknEhEiHhrFEsGWws3mz44MWSx9iht50b5TW3rgtDVgcsdyEWGKWXjDWZjFDa5YuBeHJuTfWsma2FCUeW46uneehYSwRbDWMpVAA4eHAdedZ79MmAgL4mj5CcFzV8IlKZibw3/8CN26IO6/uUBTAX/OwMNPHCg4GrlxxzFCLahhKTEVilfR0YPBg7WNZO3xkrj2mhgyFHNuTh7cUFQqcWpqDe+eL4NcwAi3HxZkdujo8LRP1Fk5CpKLqC3JZJkd+8mJaydwMunaeiYaxRLBVz05WFmNecM+ZWJqPzp2F7VerFt/TpUtMYrOh3iFNlZWMBRsub6R+BAc7bkq0NbV9bNEbZaueMFsunmqup8pVVQ1/aV98Gv4yj66d56JhLBFsFeyk9c5g+dD+jZ8PuVvU2NF8iClgZ6zWjZhAx9TQirMPY1k6A81WNWtskeNky2ExZ1+B3lL2KkbojujaeTa7rHpOjDs8LRMjv0lEFLTHB6JQiK1IxAtw7iWhOY6fkSPE00+bXqlck+6K2GIXmZTL9YfBVAtibtgA7N8v7DiOWsZA6ArlKlIvfqpLaHuE7mducVXA8gVChSxa6qpOLc1BpOKS0VWYvcAQpSjAqaU5dm2XK6BrRyxFwY6VFBUK1Fs4CQDTu5iq6aipSIIX7LckdK1aQPXq4t6zdCkfXJgSHc3nzRhbqVyX7g1PaNAxYQKfo5OXp5/vExPDTzcfMoRf2VsIsUGHVK5dExe0GArupBQXZzpQ5Tj+axwncAk3c8ErY1VrpIlhyyDKGdw7L+wHQeh+noSuHbEUBTtWEvKXRj0UIA72+UujVi2gtBR48EDY/gEB/I2lbl2+NpCpG6GqxyEhgb8pR0WZP77mDU9o0DFgAB9UaQYKxv7SN0XszVtKmZnASy+ZviFzHDBjBp+MbCi4k5pqMVfVuXXbAojrVRIavIrtWbNVEOUs/BoK+0EQup8noWtHLGaDYTWnJnXOjtCS8YOQ7vB8GyE5Nikp+nkSISGGk40rK4UvVZCebr7WjrF8FbG5PlLljFhKSHtlMr4isSNIVSXaVjlTrlI/yVJVeSeGfxAo78Q4unaejXJ2HEjoXxBFcP6/NAoLgfnzgUGDtHN4rl0DkpP18yRkMqBbN2HHjoiwvGdBbK4PYPshIVOEtFeh4HvTjL2mykvKzpZ+uCYhAbhwge9NsqZXSephMRWpc4ucjcxHhvxk/gdBCe2Lp3pekJxKVZcNoGtHLGaD4MupSd2zI+QvjYuIdvlp6KbWTxLbWyO2Z0HMrKakJMdPUbamZ8LVZiCJqfAslKU9gK7GmmKEno6unWeiqeci2GLqubmS8e4y/dzYTcaSG56Y+ili6tU4w03Q0uEdV61ubIvFU20RRDkjqgJsObp2nocqKItgqwrKB1MyET1/EqI1pp/nIxpJSMU2uFdFT0PVjI0tMDl0KNCvn3UVdcVWIjbUPkPHtFXFXzELeKrOacvqxvZgi+tp6HsqOpof6nTE8CQhxDxLqokLZc39m4IdiSgUQESoAs1u5iACRShCBHIQByWc8M5kJc2lDDRvcqGh/LZvvgHWrdNeOkMu5/N1LL1Jbd0KvPii+PYZYugmam37DJ1DyCrkKjNnGl4JXZeQQM6dSBlEefqSFs7GljdF4hi2XsaDlosQwZbLRTh6mMleD9Xwi6HhC2PLN1g7/CDm+pqa/WPPoSKhwztiltBw1RlIjuZquVDuzlDOTaFMTjk3Lswey3hQzo4Itgp2LFkawNUemjk7lqzmbU1iqdDra2otLHNTwm2R+GouN0nstHpHruDuqlw1F8pd0dpW7sdey3jQ1HMnEBqsQBdkYxA2oAuy7VoxWSohIcCUKYZf05waDhivcGsKY5YXgxM6zXjiRONDE2KK1Uk1/Vsm44edBg/WL5QopE2aHFUg0ZW5ezVmVyOk4nz0wiQoKugL4kpcYRkPCnYkcHhaJpr2ikE24rEBQ5CNeFxAjNOviaUpJARYsgTYtMnw65p1ayype6PJkrWqzNV0AYDgYODdd60/744d2stSxMfzz22xHpOYa2GrNbPcmdAANzvbbk3yaK5wUyTiucIyHg4PdpYuXYoGDRqgevXqaNu2LXJM/NmfnZ0NjuP0Hn/++acdW6zt8LRMtJ+XiHCFay4CqjJyJF9M0NiNYeHCqoRaaxfWtKQYnKmChCorVpgOBoSeNzXVfgtQCm3TjBk0A8kSQr9XBw507cVFXYUr3BSJeC6xjIdVA2hW2rhxI/P29mZffvklO336NJs0aRKrWbMmu3jxosH9s7KyGAB29uxZVlRUpH5UikiykDJnR8g4pSsUFAwIYCwqSngui6XJ2FLkxFhT00VIsTqZzP45PabaBPCvO7p2kKsS871K+Tu2d3JRlqAvxslFWY5uKhHBXst4uGyCcvv27dnYsWO1tjVp0oS99dZbBvdXBTu3bt2y+JxSBjtCf3C7IMvhAY0UD1VyrJAbtKEbiVQ3EzEFCXWZKlYn9jpIxVMK6DmCmO9Vd6nM7MxobSv3Za64rqNnYzlsGKuiogLHjx9H9+7dtbZ3794dBw8eNPneNm3aICIiAt26dUNWVpbJfcvLy1FaWqr1kIrQrtYIuEeXrGpIQMgaV8HB2tulXKvKXNKvKcZWbJfLgd69hR3D2mE8MW1y1Ppe7kBVVycxkf+taw5jrr2auiugta3cV4e5CTiSshXFMu1fZEUyOY6kbJWkzo41qjnqxNevX4dCoUBYWJjW9rCwMBQXFxt8T0REBFasWIG2bduivLwca9euRbdu3ZCdnY3OnTsbfM/s2bMxY8YMydsPuNcioEKEhvKJnKqibJs3A5Mn6xfnS03lqyY7awG3hAT99sXG6gcbxthiAUpDbXKma+ZqDBWO5DhhQY/UwaxQnlJkr8PcBBzGVr3ic0UyOQqSUx1+UySW6zA3AYpZ/ZCr830c5QTfxw6roHz58mVERUXh4MGD6Nixo3r7xx9/jLVr1wpOOu7Tpw84jsPOnTsNvl5eXo7y8nL189LSUkRHR0tSQVlRoUBx9RhEsEL1tElNSnC4BDkaIM+lKylzHBAUBFSvzifqqsjlfOJySIi0N2hHVLrNzuZnXZmj+qwUhDgvVfVqS3+zOaJKta0rzzojTwnuiHSsqaDssJ6dunXrQiaT6fXiXL16Va+3x5QOHTpg3bp1Rl/39fWFr6+vxe00ReYjwy8DFyNxUyKU4LQCHlWXbBJSXT7QYQy4cUP/tcJC4KWX+KEWU8sziGGPpRwM0QziTBk82LaBDi1pYB1TdXXMUa0/Zu9aRqoZndD5gylcUYjweYk4DMcPAdiCzEeG1kldHd0M4iEclrPj4+ODtm3bYu/evVrb9+7di9jYWMHHOXnyJCJsMa4g0ID1CRhWfSsKoT0GcglyJGKryy8CGhWln3+jorqhaBZls6YYn+ovcntN+9Z07Zqw/Ro0sF0bMjNtU99HqgKJrsDSGlCqXLMFC/hj2OtaUZE9QuzE6vRoK6imnqelpbHTp0+zpKQkVrNmTXbhwgXGGGNvvfUWGzZsmHr/RYsWsW3btrG//vqL/e9//2NvvfUWA8AyRExXkXq5CNW6Rl6oZF2QxQYhnXVBltNPNzc2G0UuZ2zfvqqZTvv2CZ+hZM36Q0KWTQgO5ttji9ky69YJ+5zr1kl/bsZst6SBp60JJXRZkaAg7efR0YylpNj/WtFUbEKEc9mp54wx9sUXX7D69eszHx8f9sQTT7ADBw6oXxsxYgTr0qWL+vmnn37KGjZsyKpXr87q1KnDnnrqKfbtt9+KOp+kdXYqTdencaWHsZuq0JtHUpJ1N2sx9VBscQMSen5brE1lqzW7zK1ftmWL9J/F0YR+Hfft0y5fsGWLY9bP+mWCsB+wXybQCrCEuHSwY29SBjvutNJ5VBRjM2bo164R+hlDQqy7WYtZSNWaG5CxGj1CepZsVYPFFoGWkM8jkzG2ebP0n8eRhBSO1P06OmKBWBXq2SFEOJess+MOHDVF1RbKy4EPP9TPFRGyJpWXl+mcF8bM1y85d054Wxnj/xW7gKOpnBhV7SCOM1w7iONstzaV0O8jMd9vQnJXFAr3WyZBSA0o3a+jmAVipdZyXBwuy+R6NWdUlOBQKItGy3G0Aiwh1qBgxwoOzIuW3PXr2s9VScE7dlTdPIxRKoWdw9jNWqEAvvxS2DFUxN6AhCQ/O6q4n9DvIzHfb2ICI3db9Vvs19EWwaZQVGSPEPtwWJ0dR7Fmnr4uhQIID9cPFNyFaipuXh6wbRu/UKg1N8X33gO6ddOfTi20xo0h6enmp70rFHwPjrG/3jU/p0xmu+nfxo6ral9hYVWvlan2CSH2mjqitowhUl57occSeq2suUbmasoYqrNTKIumInuEaLDq/i35oJqTk3o2VlJS1dC6O8zIMvRYtEj4rCwhD90EYzH5OpbksTgy+VjF1Kyoyko+X8pYvogl+UlCcnY0H+lOkP/qqJljluT5iHEoJYMVyrQ/WKFMrrdWUGV5JTu5KIv9MiGdnVyURetDEaKDEpRFkDrYUd1IX0AGy4f2L7R8yNkLyHB4sCLFQ3eqrjUP3Ru4JYneYm5AQoMpW93wTU0rB/gp9cbaJHRFd2PnFXo9bRnoCW2rI2ZD6Z5f6sVYqxZH1P5gUi6OqELBEnF3FOyIIHWwU1nJ2HD/DKYAjP5Cc5eAR8jD1KwsY8GK2F4I1fuF3oCk6tmxZLV1Sz6b6jFjhvUzgLZs4WddCfk6SPm5xXDkbChNhnqWrAk2q1b4NvzBpFzhW2jvESGujIIdESQPdsor2TUEM6WJX2gXEe02Q1qmHtHRjJWX8zfE994TF2CI6YUICRF3A5JimMLSIRZLyxNIeYPfvNn4OcwFjfYYWnKGYUYVKQM7e00rt2fvESGORFPPHein5z5GXdwwMnGUL/leDwWIgw3mrTqZQYMAHx8+ibNZM2HvUc1wSUjgV1E3l4waEsInGouZGWXJdGRNW7cCAwZYtoyFpTN4GJNuuvOLLwIZGXySsyZzs8zstXyHI2dD6ZLJ+O/fwYP5f61JTL93XliDhe5nCC03QYgwFOxYQVGhwOPZZuZl/ysCblSUx4h586pugJZMp37xRWDjRsP7qWrdLF/OB1RiWTqtfMsWPogzRPUn9NixQEWF4X2sLU8g1Q0+IQG4cIGfUZSezv+bl2f8c5taUFO1Taop60KvkZhaTM7Ar6GwDyZ0P0NOLc1BpOKS0V/kXmCIUhTg1FL3/2OLEFMo2LHCqaU5CGI3Be1bBDcqymPCpEn8DdBcMUKOA6Kj9VeYTky0rBdCCLE3/MxMvuieuRv6tWt8ELVli/6Cm0KKMppiTbCkuQDo/v38/4VO6bZnoT2h1+jDD12rAKI9Cgbao/eIELdgg2E1pyZlzo7QdW2uIcgjcnZ0cyusmeFi66RYc8rLhSdbG3uocluMXQdTD2tzdgzl2hhqmzH2nsFmbh0vKa6JI1Tl02h/OKnyaWi5CeJJKGfHQYR2Py/GJCjhORVQNfNwLK1ILGXuhFiZmXybTS2BIYQqtwUwfB2Cg/l/LckjMsVYro2hthnrKbFFVWdTEhKA6dNN78OYdb1Jmj1dqp43W+swNwFHUraiWKb9xS+SyXEkZavVBQNpuQlCBLJB8OXUJF31XD211PifpJXwYgOwxeG9LfZ86M6acXQvjRjGZi5Z81D1RpSX8wUaJ0zg/y0vt8F0ZxFT3U31lNi60J4htuxNclTBQhVb1sCxde8RIc6Cpp6LIPXUc2PTPlUP5b+/dDyl1o6Xl+uupG2uJo01j8RE/WExzQrKUgWDlkx1Nzal21g5AFsV+pNiCrqhaym0YKErF+UzVGfnkiyaAh3iVijYEUHqYIcxxva3nWK0zg4DX2zQU2rtqG4i9vqLWSpi6vxIeZ2kvlaWLL1hrKckI8NwdefgYNt8fa3tTTLUexMVZbpCteqYv0xx/aJ8rhysESIEBTsi2KKo4G3UEnRX6YIshwci9rqJO7oqr1Dl5YzNn89YzZqOu17BwfzaY1JcA6l6dkwlDNsymLU0qV1IgrOxxwugonyEuAIKdkSQOtj5Md7ICo4GHoOQ7vBAxJ4PU8Mj5vIn7BEMpaTYbtjKkocUOSTmekc0H8aCUkcv3yA2j8maJTm8UMnyYZ8lHQgh1qFgRwSpE5RvQPgKmZ7Ss6N6GBoeMbcoZlISvyaUrZNJU1Is/1xBQaaHRqx5SDElX8hUd1M9Jc6wfIOYYNfSJTkAxrpA2Jtp6jYhjkdTzx3k1NIcBEFYUcGrCEEOPGv6p+60ZCFVeVNT+eJxYpYoEDuluKICWLhQ4Icw4NYt4ObNqmnlUmLMeGXizEwgJgaIjweGDOH/jYnRvybGpvxrMjX93xmWbxBTesCadgitbE5F+QhxbdUc3QBXdvdsoeB9v8A4j6q1U6MG8PAhH4CoKvaaq8prCmN8/ZmkJKBfv6qbX2YmH0BpHlcu59fCSkjgg4acnKrKwbGxQHKydTVWVG05cMDyY5iiqiXTtWvVNlXtHN1AURUE6gYuCQn8dVJ99tBQfvvVq+YrKNu7xo45ul9D3bZb2g6OA4qY7Zd0IIQ4ARv0NDk1KYexsvsvEtxf7mn5OroPuZwfopLiWLoVmnVfVw3RpKToD4dJnaNTt67libGmHuvWVX2ficmhkSLXSeysKFvmVwnN7xKap6T5iI5mbMtG07WyKGeHEOdBw1gOIgsPEbyvp6yNZUxhIT9EJYWiIn4oaswY/o6kS3WnmjdPvydJ6qq5L7/M/2vp2lfGaFZvFrpO1UcfCRvmMsfUKvGq8y1cyO8ndGjNEkJXXddsr1CLFvHroiW+JEN+Mv9m3SrEqucFyamQ+XhOrywh7oiCHSvoloA35ooH5uvoMhSUWOrcOT4f5fp16Y5pqfr1gU2bTOfHWOLatarATGhOyowZ0uU6mcv7mTyZXw1+wABx5xRK7KrrqvYGBQk7flhY1VCYrZd0IIQ4ARv0NDk1KYex1q/hp60aKyio/PcxAJslH+bw1IetZkFZ85DL+erLWVn88JO1C4hqHjcjw7rZRoDhqeJCl0+wdPkMa6enWzojbN8+y97HGBXlI8TZ0TCWg0RGyzAJi8HAQWlkn7lIQQZetGu73Fl5uaNboO/SJWDgQH6GlhQLiKqoekiuXeOTri0dKmNMewFNocNDCgWfzC3FOcWydEZY166mrxXHAdHRfJKzLpmPDK2TuiL288FondSVhq4IcSMU7FihXTtgGxKQiK0ohFzrtSsIwYvYgrcw10Gtcz/duwN37zq6FcYlJfEBg1RU/RDjxvHDRYxZd7yiInHDQ9bMntM8pyUsnRFmKt/I2tXkCSGui4IdK7z1Fv/vNiQgBhfQFVkYjHR0RRYiUYQM2KAQiwf74QdHt8A4VU+GVL06mq5fF5+Aa0hEhPBkZ9VUbynOaYm4OMt7aIzlG5mqLUQIcW9UZ8cKf/1V9X8lZDiArg5rC6kSEMD3umVl2f/cISFA3brOkTytwnH8jT4uDti8Wdh7VDVtpDinJVQ9NImJ/LE0e6KE9NDo1hkyV1uIEOLeqGfHCjVrOroFxJCtW4HRox1z7n37+CnYzoSxqsBAzPCQud4Vc6wdLrK2h0ZMFWZCiHvjGLM2E8C1lJaWIjAwECUlJahVq5ZVx1q1Chg1SqKGEUkEBfFVgnNy+JovhM/BWbSI/79CwdfBKSw0nLej6pHJy6uqo6NaFkPobwrNCtZSMFdBmRDiGay5f1PPjhWcaaiC8CoqgG3b+Bukv7+jW+Mc+vWr+r/YBF4h62xpmjEDuHBB2rwY6qEhhFiLenasMHQokJ4uUcMIsYHgYODKFf0AwdCaYtHRfKBjKFDR7F05dw5YsUJ75pmp9xJCiBSsuX9TgrIVnHkaNCEAMHGi4Z4QsQm8qt4VlXffpaElQojroGDHCp7VJ0ZcTXAwH5QYoxvAiGHNewkhxN4oZ8cKUi/+SDwbxwEpKXyCrxRWrKDeFkIIASjYsYqVKT+EqEVH84nAc+fyCb5ZWXw+2L59/CMpia/ho/seQ8FRdDSQkUH5M4QQokIJylb44QegRw+JGkY8TkgIn+Ter5+wnBdjU7BpajYhxBNYc/+mYMcKCgVQjbKePFpgID+V+/XXgfv3je/n5weMGMH/v3btqpwXmkpNCCHC0GwsB5HJ+JtdSYmjW0IcgeOAlSv54aKAAH6xTmPWruX/1ZzuPWuW9AX4CCGE6KOcHSvcv0+BjqcKCdFesiAhgc+T0c2fkcv57QBfiVh3Ec7CQn57Zqbt20wIIZ6KenaskJzs6BYQoVS5LVIICeGDFh8f7e3GatcA/BINhgaMGeN7iJKS+Pfac0iLcn0IIZ6Cgh0rHD3q6BYQoRQKfn2osDAgNJR//uWXwPffA3fuVO3n5QUolYaPoSo1sHy5fqCjYqj+THa2fo+OJsaAggI+8LBX7ZrMTL7goGYV5Kgo4LPPaEiNEOJ+aBjLChUVjm6B5woO5h9ihIXx6yuVlPCrom/dWhXoBAXx6zrdv89P+05KAurW1X6/5mrbCgUfxGzYwP9rqtdIM6AwpahI3OexVGYmn1+k267CQn47DakRQtwNzcayQo0awIMHEjWMmBQcDGzaBBQXA9eu8UNJ4eH8a9nZfLKvOVlZwM2bfI6M7ne9qtdGMw/H2DCPoXWljCUaZ2YCY8fybRbSvq5dbTu8pFDwQd+NG8b3CQ4GLl8GDh603xAXDam5D0WFAqeW5uDe+SL4NYxAy3FxkPnQF5NYz6r7N/MwJSUlDAArKSmx+lj8LZMe9nhkZPAPuVx7u1zO2JYtjEVFGX8vxzEWHc1Yebn++3UfcjljlZXGv+YZGcbPwXH865r7cpywzyeTMbZ5s/HPqHlca+zbJ6w9wcHi2lBZyVhWFmPp6fy/pq6hLnOf2ZpjE/s6lJLBCmXaX8xCmZwdSpHoG5h4NGvu39SzYwVaLsI+qlcH+vYFtmzhf3tq4jh+m7+/6YVZZ8wAOnUCnnnG/PkSEviehZAQPo9FlWScnc0nEZeVGX+vXM5XQAb4pGRTuTpCGep10iSmVyQxsWp2mCVt2LSJvy6a59qxQ3hPF8AP/y5dCpw/z3/NVq82ft7evYHDh4Hr14UdmzjO4WmZaD8vEQDTyo9Qgv/mOZKyFR3m0heNWI6KCopgq2AnHH/jHzSDDx6iAt54BKdRjEZWtpZISRUYieXlxQcPDx8K2z8ign+cOCH+XKZERwN5edpVk3fsANat0w8GFi7kg5KCAuDXX/nPffcusGaNdW3QTeAODjY9JLZlCx9gqUybBixYYDwJXIzNm4EXX6x6bs1QmCoAO3eO/z75z3/46y12OE2KIRxFhQK5i7NRuiMbAFCrX1e0ntTVpkNB1p5TUaHAFb8YhCsuGUwEVYJDkUyO8Ht5NKSlgYb8xHHpYawvvviCxcTEMF9fX/bEE0+wn376yeT+2dnZ7IknnmC+vr6sQYMGbNmyZaLOZ4thrIfgmFJnHEAJsIfgHD78Qw/3emRlGR72cdaHaniOMcZSUqQ/9pYt/LGtGf5LSeGPZegcYoYQpRjCOZSSwa5zwXoNuc4F22woSIpznlyUJeiLdnJRlk0+gyuiIT/xrLl/wwbtEWzjxo3M29ubffnll+z06dNs0qRJrGbNmuzixYsG9//nn3+Yn58fmzRpEjt9+jT78ssvmbe3N9u6davgc0od7KgCHUPBDgU89JD6MWmS8DwgZ3ps3MiYl5dtjp2SYviaGMqj0iUkADN3DMb4G5cCHFPovJnfxgm6gfHH0P9dwlD1+0TqG6FU5/xlQrqgL9YvE9Ilbb+rkuL7xRO5bLDTvn17NnbsWK1tTZo0YW+99ZbB/adNm8aaNGmitW3MmDGsQ4cOgs8pZbATjnMGAx3dXxbhOOfwmw093OMRGOj4Njhbu431ygBVyemGkprLy02/V/Nh7BiMMVZZXskKZXK9G5fqoQDHLsmiWWW58czqyvJKVshFGf1dwsD/LimUyU0eRwwpz0k9O8JJ8f3iqay5fzuszk5FRQWOHz+O7t27a23v3r07Dh48aPA9hw4d0tu/R48eOHbsGB4aSagoLy9HaWmp1kMq/6AZOADG8pRVr/2DZpKdk3g2V12exJbtNlXjiLGqgo26li4VXlXb2DEA4NTSHEQayVUBAC8wRCkKcGqpkQOojsEKjf4uAfjfJZGKSyaPI4aU52w5Lg6XZXJ1MrIuJTgUyqLRclyc5Q12E1J8vxDxHBbsXL9+HQqFAmFhYVrbw8LCUFxcbPA9xcXFBvevrKzEdc0MTQ2zZ89GYGCg+hEdHS3NBwDgA2EZq0L3I4TYhqGCjefPW38MALh3Xlg1SFP7CT2G2H2lOo65fWU+MuQnLwYAvYBH9bwgOZWSbyHN9wsRz+EVlDmd+duMMb1t5vY3tF3l7bffRklJifpRUFBgZYurVMBb0v0IMSUkxNEtcF0REfrbGja0/hgA4NfQyAsi9hN6DLH7SnUcIft2mJuAIylbUSyL0tpeJJPTtHMNUny/EAtIPqgmUHl5OZPJZCwzM1Nr+8SJE1nnzp0NvicuLo5NnDhRa1tmZiarVq0aq6ioEHReytmhhys+OI6f1SSX2zdBOShIv8CgJW2Xyxnz97fNdXGenB3DXxhPyNnRPe7JRVnslwnp7OSiLMo90SHF94uncsmcHR8fH7Rt2xZ79+7V2r53717ExsYafE/Hjh319v/hhx/Qrl07eHvbv/ekiDWC4t8uWqbzmuq5AhzV2yGi6K75FR3NFxR88UW+mB5gn4KWHMcvlrpihfl9p0wxfgyAb/fXX0vXNs1jJyfz/9e9JqrnqamGa+X4+PDvFXIeY8cApBnCkfnIkD/1MzDo/y6Bxrb85MWSDQXZ6pwyHxlaJ3VF7OeD0TrJtvWBXBEN+TmIDYIvwVRTz9PS0tjp06dZUlISq1mzJrtw4QJjjLG33nqLDRs2TL2/aur55MmT2enTp1laWppDp56rUJ0d13twHGPVqjnm3NWrG+6d8ffnpzibWx7BUE2ZkBDGkpL4/bdsEV6Hx9h08Oho/aUvDPXwBAVV7WeoXYaOY2ppD9WjZk3zPViaxxZybmNM1dkRegzGDNdNuSSL9og6O0Q8Kb5fPI1LLxexdOlSzJ07F0VFRWjRogUWLVqEzp07AwBGjhyJCxcuIDs7W73/gQMHMHnyZPzxxx+IjIzEm2++ibFjxwo+n5QVlDVFcO5XQblWLb5irkIBVKsGeHvz/w8MBAIC+BXDr17l9w0IAPz8+L+Cvb35v4KbNgVq1+b38fPjj8dx/GtPPgncusX3YhQXAydPAvn5/OKqrVrxlWzv3eNXIVdV2+U4/jiPPQb06cMvVKlU8scICeGPk5vLvy82FmjRgp9Bc+wYv+QEx/HLPzz2GDBuHN+O7Gxg/37gyBHgyhW+jX37Ai1b8pWJT53iz928Of9ZZDI+16NlS/58Bw9W7dOrF3DxIl85uXZtvtegWze+Dapv4a5dqxb7/Pxz4Oef+aUuhg3j9xVarddctWDV65oVlBs25D/Hzz9XtSUujv8MhYVVC6yqlsjQbYtqpXfdz2LovKaqGKv2KSzkr7mqAnNQEL+4q+r8CkXVshINGwJjxvCfxdxnpgrK4jninIQqKItFy0WIYKtghxBCCCG2Y8392+GzsQghhBBCbImCHUIIIYS4NQp2CCGEEOLWKNghhBBCiFujYIcQQgghbo2CHUIIIYS4NQp2CCGEEOLWKNghhBBCiFujYIcQQgghbq2aoxtgb6qC0aWlpQ5uCSGEEEKEUt23LVn4weOCnTt37gAAoqOjHdwSQgghhIh148YNBAYGinqPx62NpVQqcfnyZQQEBIDjOEmPXVpaiujoaBQUFNC6W3ZG194x6Lo7Dl17x6Fr7xglJSWoV68ebt26hdq1a4t6r8f17Hh5eUEul9v0HLVq1aIfAAeha+8YdN0dh66949C1dwwvL/HpxpSgTAghhBC3RsEOIYQQQtwaBTsS8vX1xYcffghfX19HN8Xj0LV3DLrujkPX3nHo2juGNdfd4xKUCSGEEOJZqGeHEEIIIW6Ngh1CCCGEuDUKdgghhBDi1ijYIYQQQohbo2BHIkuXLkWDBg1QvXp1tG3bFjk5OY5ukkf46aef0KdPH0RGRoLjOGzfvt3RTfIIs2fPxpNPPomAgACEhoaif//+OHv2rKOb5RGWLVuGVq1aqQvadezYEd99952jm+VxZs+eDY7jkJSU5OimuL3p06eD4zitR3h4uKhjULAjgU2bNiEpKQnvvvsuTp48ibi4OPTs2RP5+fmObprbKysrw+OPP44lS5Y4uike5cCBAxg/fjwOHz6MvXv3orKyEt27d0dZWZmjm+b25HI55syZg2PHjuHYsWN4+umn0a9fP/zxxx+ObprHOHr0KFasWIFWrVo5uikeo3nz5igqKlI/Tp06Jer9NPVcAv/5z3/wxBNPYNmyZeptTZs2Rf/+/TF79mwHtsyzcByHbdu2oX///o5uise5du0aQkNDceDAAXTu3NnRzfE4QUFBmDdvHkaPHu3opri9u3fv4oknnsDSpUsxa9YstG7dGqmpqY5ullubPn06tm/fjtzcXIuPQT07VqqoqMDx48fRvXt3re3du3fHwYMHHdQqQuyrpKQEAH/TJfajUCiwceNGlJWVoWPHjo5ujkcYP348nn/+eTzzzDOObopHOXfuHCIjI9GgQQMMGjQI//zzj6j3e9xCoFK7fv06FAoFwsLCtLaHhYWhuLjYQa0ixH4YY0hOTsZTTz2FFi1aOLo5HuHUqVPo2LEjHjx4AH9/f2zbtg3NmjVzdLPc3saNG3HixAkcPXrU0U3xKP/5z3+wZs0aPPbYY7hy5QpmzZqF2NhY/PHHHwgODhZ0DAp2JMJxnNZzxpjeNkLc0YQJE/D777/j559/dnRTPEbjxo2Rm5uL27dvIyMjAyNGjMCBAwco4LGhgoICTJo0CT/88AOqV6/u6OZ4lJ49e6r/37JlS3Ts2BENGzbE119/jeTkZEHHoGDHSnXr1oVMJtPrxbl69apebw8h7uaNN97Azp078dNPP0Eulzu6OR7Dx8cHjRo1AgC0a9cOR48exeLFi/F///d/Dm6Z+zp+/DiuXr2Ktm3bqrcpFAr89NNPWLJkCcrLyyGTyRzYQs9Rs2ZNtGzZEufOnRP8HsrZsZKPjw/atm2LvXv3am3fu3cvYmNjHdQqQmyLMYYJEyYgMzMTP/74Ixo0aODoJnk0xhjKy8sd3Qy31q1bN5w6dQq5ubnqR7t27TB06FDk5uZSoGNH5eXlOHPmDCIiIgS/h3p2JJCcnIxhw4ahXbt26NixI1asWIH8/HyMHTvW0U1ze3fv3sXff/+tfp6Xl4fc3FwEBQWhXr16DmyZexs/fjzS09OxY8cOBAQEqHs2AwMDUaNGDQe3zr2988476NmzJ6Kjo3Hnzh1s3LgR2dnZ2LNnj6Ob5tYCAgL0ctJq1qyJ4OBgylWzsalTp6JPnz6oV68erl69ilmzZqG0tBQjRowQfAwKdiTw0ksv4caNG5g5cyaKiorQokUL7N69G/Xr13d009zesWPHEB8fr36uGr8dMWIEVq9e7aBWuT9VmYWuXbtqbV+1ahVGjhxp/wZ5kCtXrmDYsGEoKipCYGAgWrVqhT179uDZZ591dNMIsYlLly5h8ODBuH79OkJCQtChQwccPnxY1D2W6uwQQgghxK1Rzg4hhBBC3BoFO4QQQghxaxTsEEIIIcStUbBDCCGEELdGwQ4hhBBC3BoFO4QQQghxaxTsEEIIIcStUbBDCCGEEEn89NNP6NOnDyIjI8FxHLZv3y76GIwxzJ8/H4899hh8fX0RHR2NTz75xKp2UbBDCLGrgwcPQiaT4bnnnnN0U0Tp2rUrkpKSHN0MQpxaWVkZHn/8cSxZssTiY0yaNAlfffUV5s+fjz///BO7du1C+/btrWoXLRdBCLGrlStX4o033sBXX32F/Px8WsOMEDfSs2dP9OzZ0+jrFRUVeO+997B+/Xrcvn0bLVq0wKeffqpeeubMmTNYtmwZ/ve//6Fx48aStYt6dgghdlNWVobNmzfj9ddfR+/evbXWL8vOzgbHcfj+++/Rpk0b1KhRA08//TSuXr2K7777Dk2bNkWtWrUwePBg3Lt3T/2+8vJyTJw4EaGhoahevTqeeuopHD16VP366tWrUbt2ba12bN++HRzHqZ9Pnz4drVu3xtq1axETE4PAwEAMGjQId+7cAQCMHDkSBw4cwOLFi8FxHDiOw4ULF2xyjQhxZ6+88gp++eUXbNy4Eb///jtefPFFPPfcczh37hwAYNeuXXjkkUfwzTffoEGDBoiJicGrr76KmzdvWnVeCnYIIXazadMmNG7cGI0bN8bLL7+MVatWQXd5vunTp2PJkiU4ePAgCgoKMHDgQKSmpiI9PR3ffvst9u7di88//1y9/7Rp05CRkYGvv/4aJ06cQKNGjdCjRw/RvxzPnz+P7du345tvvsE333yDAwcOYM6cOQCAxYsXo2PHjnjttddQVFSEoqIiREdHW39BCPEg58+fx4YNG7BlyxbExcWhYcOGmDp1Kp566imsWrUKAPDPP//g4sWL2LJlC9asWYPVq1fj+PHjSExMtOrcNIxFCLGbtLQ0vPzyywCA5557Dnfv3sX+/fvxzDPPqPeZNWsWOnXqBAAYPXo03n77bZw/fx6PPPIIACAxMRFZWVl48803UVZWhmXLlmH16tXqrvMvv/wSe/fuRVpaGlJSUgS3TalUYvXq1QgICAAADBs2DPv378fHH3+MwMBA+Pj4wM/PD+Hh4ZJcC0I8zYkTJ8AYw2OPPaa1vby8HMHBwQD4n8Py8nKsWbNGvV9aWhratm2Ls2fPWjy0RcEOIcQuzp49iyNHjiAzMxMAUK1aNbz00ktYuXKlVrDTqlUr9f/DwsLg5+enDnRU244cOQKA/0vx4cOH6uAIALy9vdG+fXucOXNGVPtiYmLUgQ4ARERE4OrVq+I+JCHEKKVSCZlMhuPHj0Mmk2m95u/vD4D/uatWrZpWQNS0aVMAQH5+PgU7hBDnlpaWhsrKSkRFRam3Mcbg7e2NW7duqbd5e3ur/89xnNZz1TalUql+v2qbJsaYepuXl5feUNnDhw/12mfqPIQQ67Vp0wYKhQJXr15FXFycwX06deqEyspKnD9/Hg0bNgQA/PXXXwCA+vXrW3xuytkhhNhcZWUl1qxZgwULFiA3N1f9+O2331C/fn2sX7/eouM2atQIPj4++Pnnn9XbHj58iGPHjqn/GgwJCcGdO3dQVlam3ic3N1f0uXx8fKBQKCxqJyGe4u7du+qfbwDIy8tDbm4u8vPz8dhjj2Ho0KEYPnw4MjMzkZeXh6NHj+LTTz/F7t27AQDPPPMMnnjiCYwaNQonT57E8ePHMWbMGDz77LN6w19iULBDCLG5b775Brdu3cLo0aPRokULrUdiYiLS0tIsOm7NmjXx+uuvIyUlBXv27MHp06fx2muv4d69exg9ejQA4D//+Q/8/Pzwzjvv4O+//0Z6errWLDChYmJi8Ouvv+LChQu4fv069foQYsCxY8fQpk0btGnTBgCQnJyMNm3a4IMPPgAArFq1CsOHD8eUKVPQuHFj9O3bF7/++qs64d/Lywu7du1C3bp10blzZzz//PNo2rQpNm7caFW7aBiLEGJzaWlpeOaZZxAYGKj32oABA/DJJ5/gxIkTFh17zpw5UCqVGDZsGO7cuYN27drh+++/R506dQAAQUFBWLduHVJSUrBixQo888wzmD59Ov773/+KOs/UqVMxYsQINGvWDPfv30deXh5iYmIsajMh7qpr1656w8aavL29MWPGDMyYMcPoPpGRkcjIyJC0XRwz1SpCCCGEEBdHw1iEEEIIcWsU7BBCCCHErVGwQwghhBC3RsEOIYQQQtwaBTuEEEIIcWsU7BBCCCHErVGwQwghhBC3RsEOIYQQQtwaBTuEEEIIcWsU7BBCCCHErVGwQwghhBC3RsEOIYQQQtza/wP3GLof8Kz6hwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kunta\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LocalOutlierFactor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anomaly predictions for Local Outlier Factor: [False False False ... False False False]\n"
     ]
    },
    {
     "data": {
      "image/png": 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MnDlTpyfHEiEYKioqMnjs4sWL8PHxQY0aNaxvtJa1a9eibt26+Oabb3S+mVqTuFijRg0oFAq89NJLJi+EdevWBcBfVFNSUpCSkoIbN25gx44deOutt9C9e3cUFBQgKCjIqtdTWFiIgwcPomPHjvD15X9Na9asiRYtWuCjjz4y+hzhD6ccrysiIgIXLlywqu1SzhMeHo7i4mKDx41tM6e4uBixsbGa+yqVClevXtUJyH18fDB27Fi89dZbmD9/PpYuXYouXbpIzn8xxtJnvWbNmgAqc5osvbdr167FsGHD8H//9386269cuYLq1atLbt8zzzyDX375Bdu2bdP03GpjjGHr1q0ICwtD69atAUDzBUP/d0jumkbCezN9+nRNDp4+/Z+RXL1PYv9uREREQK1Wo7i42KpAJDw83ORnA6h8DwSO7l3zBl7ds2PJyy+/jN27d2P9+vWaWULPPvus0SEdgJ/x0KBBAyQkJDi4pbZRq9X46quvUK9ePWRnZxvcJk+ejKKiIk1XrlgNGzZEbGwsMjIydIZk7ty5g8zMTM0MLTlwHAd/f3+dPxLFxcWiZmPpCwoKQufOnXH48GG0aNHC4BthmzZtDHq1AKB69epISkrC2LFjce3aNYNZLGLdu3cPr7zyClQqFaZOnarZ3rt3b/z3v/9FvXr1jLZJCHb0e9iseV09evTAP//8Y9C9rk2O83Tu3Bl///03/vrrL51jZGRkSHrPvv76a537GzZsgEqlMqj/88orr8Df3x9Dhw7FyZMnJSdCm9K+fXtUqVIFa9eu1dl+4cIF/Prrr+jSpQsAoEGDBqhXrx5WrFhhNhDnOM5gqvH333+PwsJCq9r3yiuvIDIyEtOnT0dJSYnB43PmzMGJEycwdepUzfCJMJPoyJEjOvsKs4+0BQQEWNXjBPB/Jx5++GH89ddfRj8rQi+MPYj9uyF8KV62bJnZ45l6H7p06YJjx47h0KFDOttXr14NjuPQuXNna18CEcmre3bMOXPmDNatW4cLFy5oLiJTpkzBTz/9hJUrVxp84yorK8PXX39t9xoV9vDjjz/i4sWLOr1W2po1a4YlS5YgPT3dIKfGHB8fH8yZMwdDhw5F79698frrr6OsrAxz587FjRs3MHv2bNleQ+/evZGVlYUxY8YgKSkJBQUF+PDDDxETE2MyODVn8eLFePLJJ5GQkIA33ngD8fHxuHXrFk6fPo1t27ZpgoA+ffqgWbNmaNOmDSIiInDu3DksWrQIderUETWD4vz589i3bx8qKipQWlqqKSp47tw5zJ8/H926ddPs+8EHH2D79u3o0KEDJkyYgIYNG+L+/fs4e/YsfvjhB3z22WdQKpUICQlBnTp1sGXLFnTp0gVhYWGoWbMm4uPjRb+u5ORkfPPNN+jXrx+mTZuGtm3b4t69e9i1axd69+6Nzp07y3aeFStWoFevXpg5c6ZmNtaJEyck/byysrLg6+uLZ555RjMb65FHHsHAgQN19qtevTqGDRuGZcuWoU6dOiZnJxmjVquN9npWrVoVPXr0wLvvvou33noLw4YNw+DBg3H16lWkpaUhMDBQM/sM4Gcx9unTB+3atcOkSZNQu3ZtnD9/Hj///LMmaOvduzdWrVqFRo0aoUWLFjh48CDmzp1r9dBi9erVkZWVhd69e6N169ZITU3FI488gps3b+Kbb77B119/jRdeeEGniOVjjz2Ghg0bYsqUKVCpVKhRowY2b96M33//3eD4zZs3R1ZWFpYtW4bWrVvDx8dHUoXizz//HD169ED37t0xYsQIxMbG4tq1azh+/DgOHTqEjRs3WvW6LRH7dyMhIQEvvfQSZs6ciUuXLqF3794ICAjA4cOHERQUhPHjxwPg34f169fjm2++wUMPPYTAwEA0b94ckyZNwurVq9GrVy988MEHqFOnDr7//nssXboUb7zxBho0aGCX10e0ODE52qUAYJs3b9bc37BhAwPAqlatqnPz9fVlAwcONHh+RkYG8/X1ZUVFRQ5stTz69+/P/P39WUlJicl9Bg0axHx9fVlxcbHZmUQwMjvn22+/ZY8//jgLDAxkVatWZV26dGG7d+/W2UeYVXP58mWd7aZmwXTs2JE1bdpUZ9vs2bNZfHw8CwgIYI0bN2ZffPGF5rjaxMzGEraPHDmSxcbGMj8/PxYREcE6dOjAZs6cqdln/vz5rEOHDqxmzZrM39+f1a5dm40aNYqdPXvWoM36x4bWzBCFQsFq1KjBWrduzZKTkzUzq/RdvnyZTZgwgdWtW5f5+fmxsLAw1rp1a/b222+z27dva/bbsWMHa9WqFQsICGAADF6vpdfFGD/DaOLEiax27drMz8+PRUZGsl69erETJ07Iep5jx46xZ555hgUGBrKwsDA2atQotmXLFkmzsQ4ePMj69OnDgoODWUhICBs8eLBmFpu+nJwcBoDNnj3b7LG1DR8+3OSsHu1ZSF9++SVr0aIF8/f3Z6Ghoaxfv35Gf5Z79+5lPXr0YKGhoSwgIIDVq1dPZ5bV9evX2ahRo1hkZCQLCgpiTz75JMvNzWUdO3bUme0jdjaW4Pz582zs2LHsoYce0rTxqaeeYmvXrmUVFRUG+//zzz+sW7durFq1aiwiIoKNHz+eff/99wY/m2vXrrGkpCRWvXp1xnGczu+c/t8EY7OxGGPsr7/+YgMHDmSRkZHMz8+PRUdHs6efflozO5SxytlY+/fvF/V6xcx6FPt3Q61Ws4ULF7JmzZpp3rv27duzbdu2afY5e/Ys69atGwsJCTH4bJw7d44NGTKEhYeHMz8/P9awYUM2d+5cnZmqYtpLrMMxZsOCKB6E4zhs3rwZ/fv3BwB88803GDp0KP7++2+DPJXg4GCDRLUuXbqgWrVqVteZIIQ4xuTJk7Fs2TIUFBQYHY4khHgeGsYyoVWrVlCr1SgpKbGYg5Ofn4/s7GyjY9mEENewb98+/PPPP1i6dClef/11CnQI8SJeHezcvn0bp0+f1tzPz89HXl4ewsLC0KBBAwwdOhTDhg3D/Pnz0apVK1y5cgW//vormjdvrplCC/AzmWJiYmgNI0JcmJAQ37t3b8ycOdPZzSGEOJBXD2Pl5OQYzYIfPnw4Vq1ahQcPHmDmzJlYvXo1CgsLER4ejvbt2yMtLQ3NmzcHwJchr1OnDoYNG2ZySjAhhBBCnMergx1CCCGEeD6qs0MIIYQQj0bBDiGEEEI8mtclKFdUVODixYsICQmhktyEEEKIm2CM4datW6hVq5bO4tJieF2wc/HiRYcvdEcIIYQQeRQUFEiuJu51wY6wxkpBQQGqVavm5NYQQgghRIybN28iLi7OqrXSvC7YEYauqlWrRsEOIYQQ4masSUGhBGVCCCGEeDSnBjvLli1DixYtNL0s7du3x48//mj2Obt27ULr1q0RGBiIhx56CJ999pmDWksIIYQQd+TUYEepVGL27Nk4cOAADhw4gKeffhr9+vXD33//bXT//Px89OzZEwkJCTh8+DDeeustTJgwAZmZmQ5uOSGEEELchctVUA4LC8PcuXMxatQog8fefPNNbN26FcePH9dsGz16NP766y/s3btX1PFv3ryJ0NBQlJaWUs4OIYR4OLVajQcPHji7GUQkf39/k9PKbbl+u0yCslqtxsaNG3Hnzh20b9/e6D579+5Ft27ddLZ1794d6enpePDgAfz8/AyeU1ZWhrKyMs39mzdvyttwQgghLocxhuLiYty4ccPZTSES+Pj4oG7duvD395f1uE4Pdo4ePYr27dvj/v37CA4OxubNm9GkSROj+xYXFyMqKkpnW1RUFFQqFa5cuYKYmBiD58yaNQtpaWl2aTshhBDXJAQ6kZGRCAoKoiKybkAo+ltUVITatWvL+jNzerDTsGFD5OXl4caNG8jMzMTw4cOxa9cukwGP/osXRuFMvSnTp09HSkqK5r4wT58QQohnUqvVmkAnPDzc2c0hEkRERODixYtQqVRGR2us5fRgx9/fH/Xr1wcAtGnTBvv378fixYvx+eefG+wbHR2N4uJinW0lJSXw9fU1+YEOCAhAQECA/A0nhBDikoQcnaCgICe3hEglDF+p1WpZgx2Xq7PDGNPJsdHWvn17bN++XWfbL7/8gjZt2sj6phBCCHF/NHTlfuz1M3Nqz85bb72FHj16IC4uDrdu3cL69euRk5ODn376CQA/BFVYWIjVq1cD4GdeLVmyBCkpKXj11Vexd+9epKenY926dc58GcQB1OVqHF2ai7tnihBULwbNxyRA4a9wdrMIIYS4AacGO5cuXcJLL72EoqIihIaGokWLFvjpp5/wzDPPAACKiopw/vx5zf5169bFDz/8gEmTJuE///kPatWqhU8++QQDBgxw1ksgDrBvahZqL5iIluoLmm0XpyhxPmUx2s1JdGLLCCGE5OTkoHPnzrh+/TqqV6/u7OYY5dRgJz093ezjq1atMtjWsWNHHDp0yE4tIq5m39QstJ2bBEC3HFS0uhDRc5OwD5so4CGEeIwRI0bgq6++wqxZszBt2jTN9m+//RbPPfccXKw0nttwuZwdQgTqcjVqL5gIgBl8UH3+F/zELUiGulzt8LYRQryDWg3k5ADr1vH/qh3w5yYwMBAff/wxrl+/Ltsxy8vLZTuWO6Jgh7iso0tzUUt9weSH1AcMseoCHF2a69B2EUK8Q1YWEB8PdO4MDBnC/xsfz2+3p65duyI6OhqzZs0yuU9mZiaaNm2KgIAAxMfHY/78+TqPx8fHY+bMmRgxYgRCQ0Px6quvYtWqVahevTq+++47NGzYEEFBQUhKSsKdO3fw1VdfIT4+HjVq1MD48eOh1orq1q5dizZt2iAkJATR0dEYMmQISkpK7Pb67YGCHeKy7p4pknU/QggRKysLSEoCLlzQ3V5YyG+3Z8CjUCjwf//3f/j0009xQb8BAA4ePIiBAwdi0KBBOHr0KGbMmIF3333XIPVj7ty5aNasGQ4ePIh3330XAHD37l188sknWL9+PX766Sfk5OQgMTERP/zwA3744QesWbMGy5cvx6ZNmzTHKS8vx4cffoi//voL3377LfLz8zFixAj7vQF24PQ6O4SYElTPsCK2LfsRQogYajUwcSJgLD2GMYDjgORkoF8/QGGnSaHPPfccWrZsiffff98gv3XBggXo0qWLJoBp0KABjh07hrlz5+oEIU8//TSmTJmiuf/777/jwYMHWLZsGerVqwcASEpKwpo1a3Dp0iUEBwejSZMm6Ny5M7Kzs/HCCy8AAEaOHKk5xkMPPYRPPvkEbdu2xe3btxEcHGyfN0Bm1LPjItTlauQtysGe8euQtyiH8lAANB+TgIsKJSpgvO5CBTgUKuLQfEyCg1tGCPFkubmGPTraGAMKCvj97Onjjz/GV199hWPHjulsP378OJ544gmdbU888QROnTqlM/zUpk0bg2MGBQVpAh2AX3IpPj5eJ2iJiorSGaY6fPgw+vXrhzp16iAkJASdOnUCAJ3Z0q6Ogh0XsG9qFi4FxaPlpM7osGQIWk7qjEtB8dg31c4Dwy5O4a/A+ZTFAGAQ8Aj3C1IWUb0dQoisikSOjIvdz1pPPfUUunfvjrfeektnO2PM5NJJ2qpWrWqwTb8AL8dxRrdVVFQAAO7cuYNu3bohODgYa9euxf79+7F582YA7pX0TMNYTkZTq81rNycR+7AJtRdMRC2tOjtFCiUKUhZ59XtDCLEPI2tK27SfLWbPno2WLVuiQYMGmm1NmjTB77//rrPfnj170KBBAyhkHlc7ceIErly5gtmzZ2vWlTxw4ICs53AE6tlxIppaLU67OYmIunsWeQuzsWdcBvIWZiP6bj4FOoQQu0hIAJRKPjfHGI4D4uL4/eytefPmGDp0KD799FPNtsmTJ2Pnzp348MMP8c8//+Crr77CkiVLdPJz5FK7dm34+/vj008/xb///outW7fiww8/lP089kbBjhPR1GrxFP4KtEzuhA6fDkbL5E40dEUIsRuFAljMj6AbBDzC/UWL7JecrO/DDz/UGaZ69NFHsWHDBqxfvx7NmjXDe++9hw8++MAuM6QiIiKwatUqbNy4EU2aNMHs2bMxb9482c9jbxzzsnKMN2/eRGhoKEpLS1GtWjWntmXP+HXosGSI5f3GZaDDp4Md0CJCCHF/9+/fR35+PurWrYvAwECrj5OVxc/K0k5WjovjA51E6li2C3M/O1uu35Sz40Q0tZoQQlxXYiI/vTw3l09Gjonhh64c1aND5EPBjhM1H5OAi1OUiFYXanJ0tFWAQ5FCSVOrCSHESRQK4H8zrYkbo5wdJ6Kp1YQQQoj9UbDjZO3mJOLP1E0oVsTqbC9SKPFnqndPOyeEEELkQMNYLqDdnESoZ/ZD3tJc3D1ThKB6MWg+JgGx1KNDCCGE2IyCHRchTK0mhBBCiLxoGIsQQgghHo2CHUIIIYR4NAp2CCGEEOLRKNghhBBCiCzi4+OxaNEiZzfDAAU7hBBCiIvZs2cPFAoFnn32WWc3xSNQsEMIIYSYolYDOTnAunX8v2q1Q067YsUKjB8/Hr///jvOnz/vkHN6Mgp2CCGEEGOysoD4eKBzZ2DIEP7f+Hh+ux3duXMHGzZswBtvvIHevXtj1apVmsdycnLAcRx27tyJNm3aICgoCB06dMDJkyd1jrFs2TLUq1cP/v7+aNiwIdasWaPzOMdx+Pzzz9G7d28EBQWhcePG2Lt3L06fPo1OnTqhatWqaN++Pc6cOaN5zpkzZ9CvXz9ERUUhODgYjz32GHbs2GHydYwcORK9e/fW2aZSqRAdHY0VK1bY8A5JR8EOIYQQoi8rC0hK0l3yHAAKC/ntdgx4vvnmGzRs2BANGzbEiy++iJUrV4Ix3fUT3377bcyfPx8HDhyAr68vRo4cqXls8+bNmDhxIiZPnoz//ve/eP311/Hyyy8jOztb5xgffvghhg0bhry8PDRq1AhDhgzB66+/junTp+PAgQMAgHHjxmn2v337Nnr27IkdO3bg8OHD6N69O/r06WOy5+mVV17BTz/9hKKiIs22H374Abdv38bAgQNtfp8kYV6mtLSUAWClpaXObgohhBA7uHfvHjt27Bi7d++edQdQqRhTKhkDjN84jrG4OH4/O+jQoQNbtGgRY4yxBw8esJo1a7Lt27czxhjLzs5mANiOHTs0+3///fcMgOb1dujQgb366qs6x3z++edZz549NfcBsHfeeUdzf+/evQwAS09P12xbt24dCwwMNNvWJk2asE8//VRzv06dOmzhwoU6j3/88cea+/3792cjRowweTxzPztbrt/Us0MIIYRoy8017NHRxhhQUMDvJ7OTJ0/izz//xKBBgwAAvr6+eOGFFwyGfVq0aKH5f0xMDACgpKQEAHD8+HE88cQTOvs/8cQTOH78uMljREVFAQCaN2+us+3+/fu4efMmAH54berUqWjSpAmqV6+O4OBgnDhxwmxO0SuvvIKVK1dq2vf999/r9EI5Ci0XQQghhGjTGnaRZT8J0tPToVKpEBtbuTg0Ywx+fn64fv26Zpufn5/m/xzHAQAqKioMtmkfQ3+bsWOYO25qaip+/vlnzJs3D/Xr10eVKlWQlJSE8vJyk69n2LBhmDZtGvbu3Yu9e/ciPj4eCQkJFt4F+VGwQwghhGj7X0+JbPuJpFKpsHr1asyfPx/dunXTeWzAgAH4+uuv0axZM4vHady4MX7//XcMGzZMs23Pnj1o3LixTe3Lzc3FiBEj8NxzzwHgc3jOnj1r9jnh4eHo378/Vq5cib179+Lll1+2qQ3WomCHEEII0ZaQACiVfDKyXmIwAIDj+Mdl7qH47rvvcP36dYwaNQqhoaE6jyUlJSE9PR0LFy60eJzU1FQMHDgQjz76KLp06YJt27YhKyvL7MwpMerXr4+srCz06dMHHMfh3Xff1elNMuWVV15B7969oVarMXz4cJvaYC3K2SGEEEK0KRTA4sX8//WGfjT3Fy3i95NReno6unbtahDoAHzPTl5eHg4dOmTxOP3798fixYsxd+5cNG3aFJ9//jlWrlyJTp062dS+hQsXokaNGujQoQP69OmD7t2749FHH7X4vK5duyImJgbdu3dHrVq1bGqDtTjGjIWtnuvmzZsIDQ1FaWkpqlWr5uzmEEIIkdn9+/eRn5+PunXrIjAw0PoDZWUBEyfqJivHxfGBTmKize30Fnfv3kWtWrWwYsUKJFp438z97Gy5ftMwFiGEEGJMYiLQrx8/66qoiM/RSUiQvUfHU1VUVKC4uBjz589HaGgo+vbt67S2ULBDCCGEmKJQADYO/3ir8+fPo27dulAqlVi1ahV8fZ0XclCwQwghhBDZxcfHG1R+dhZKUCaEEEKIR6NghxBCiEdylV4FIp69fmYU7BBCCPEoQhXgu3fvOrklRCqhGrNC5iRwytkhhBDiURQKBapXr65ZKyooKMhgqQTieioqKnD58mUEBQXJnsxMwQ4hhBCPEx0dDaBycUziHnx8fFC7dm3Zg1MKdgghhHgcjuMQExODyMhIPHjwwNnNISL5+/vDx0f+DBsKdgghhHgshUIhe/4HcT+UoEwIIYQQj0bBDiGEEEI8GgU7hBBCCPFoFOwQQgghxKNRsEMIIYQQj+bUYGfWrFl47LHHEBISgsjISPTv3x8nT540+5ycnBxwHGdwO3HihINaTQghhBB34tRgZ9euXRg7diz27duH7du3Q6VSoVu3brhz547F5548eRJFRUWa28MPP+yAFhNCCCHE3Ti1zs5PP/2kc3/lypWIjIzEwYMH8dRTT5l9bmRkJKpXr27H1hFCCCHEE7hUzk5paSkAICwszOK+rVq1QkxMDLp06YLs7Gx7N40QQgghbsplKigzxpCSkoInn3wSzZo1M7lfTEwMli9fjtatW6OsrAxr1qxBly5dkJOTY7Q3qKysDGVlZZr7N2/etEv7CSGEEOKaOMYYc3YjAGDs2LH4/vvv8fvvv0OpVEp6bp8+fcBxHLZu3Wrw2IwZM5CWlmawvbS0FNWqVbO6vYQQQghxnJs3byI0NNSq67dLDGONHz8eW7duRXZ2tuRABwDatWuHU6dOGX1s+vTpKC0t1dwKCgpsbS4hhBBC3IhTh7EYYxg/fjw2b96MnJwc1K1b16rjHD58GDExMUYfCwgIQEBAgC3NJIQQQogbc2qwM3bsWGRkZGDLli0ICQlBcXExACA0NBRVqlQBwPfMFBYWYvXq1QCARYsWIT4+Hk2bNkV5eTnWrl2LzMxMZGZmOu11EEIIIcR1OTXYWbZsGQCgU6dOOttXrlyJESNGAACKiopw/vx5zWPl5eWYMmUKCgsLUaVKFTRt2hTff/89evbs6ahmE0IIIcSNuEyCsqPYkuBECCGEEOdw+wRlQgghhBB7oWCHEEIIIR6Ngh1CCCGEeDQKdgghhBDi0SjYIYQQQohHo2CHEEIIIR6Ngh1CCCGEeDQKdgghhBDi0SjYIYQQQohHo2CHEEIIIR6Ngh1CCCGEeDSnLgTqLdRqIDcXKCoCYmKAhARAoXB2qwghhBDvQMGOnWVlARMnAhcuVG5TKoHFi4HEROe1ixBCCPEWNIxlR1lZQFKSbqADAIWF/PasLOe0ixBCCPEmFOzYiVrN9+gwZviYsC05md+PEEIIIfZDwY6d5OYa9uhoYwwoKOD3I4QQQoj9UM6OnRQVWb+fNyY0e+NrJoQQ4hgU7NhJTIx1+3ljQrM3vmZCCCGOQ8NYdpKQwF+wOc744xwHxMXx+wm8MaHZG18zIYQQx+IYM5ZC67lu3ryJ0NBQlJaWolq1anY9V1YWMGCA6cc3bAAiIvihm8hIYPhw/iJvDMfxwVN+vucM76jVQHy86dwmT3zNhBBCrGPL9ZuGsZzojTeAq1fF7aud0Nypk+5j7prvIiWJW/81E0IIIWJRsGMnwtRzc8QGOtr0E5rdOd/FliRuQgghRCzK2bETS70W1tJOaHb3fBdrk7gJIYQQKSjYsRO5eyP0E5o9oWihNUnchBBCiFQU7NiJnL0RQjCwaFFlLo4nFC1UKPjhNsAw4DH2mgkhhBBrULBjJ5Z6LaRQKoFNm3RzcByd76JWAzk5wLp1/L9y9RglJvKvLTZWd7ux10wIIYRYgxKU7UTotUhK4gMeqRP833sPaNTI9OwqR+a72DsJOjER6NfPPWeUEUIIcX1UZ8fOjAUKYgj1d0xd8IUaNYWFxgMpuWrUCEnQps5BvS+EEEIcwZbrNw1j2VliInD2LJCdDbzzjvjnXb5sPt/GEfku5pKgAX67qydBE0IIIRTsOIBCwRfFmzGD77ERy1K+jb3zXcRMn3f1JGhCCCGEcnYcSKEAli4Fnn9e3P5i8m3sme9iaukKa/cjhBBCnMGqYGfnzp3YuXMnSkpKUFFRofPYihUrZGmYp0pKAiZPBubPN7+flPoyQs+R3C5flnc/QgghxBkkBztpaWn44IMP0KZNG8TExICTY261l5k3j//XVMDDcdbn28i5TpbYITcpQ3OEEEKIo0kOdj777DOsWrUKL730kj3a4zXmzQPatQPGjNHtGYmL4wMda/Jt5J4irp8LZOt+hBBCiDNInnoeHh6OP//8E/Xq1bNXm+zK0VPPLZGrJ8bUFHGh482ahGVheru5JOW4ONuntxNCCCGWOHTq+SuvvIKMjAypTyMmCPk2gwfz/1o7dGWPdbKE6e0cZ3x6uy3DbYQQQoijSB7Gun//PpYvX44dO3agRYsW8PPz03l8wYIFsjWOiPPRR+LXyZKayCxMbzc2PGbtcBshhBDiSJKDnSNHjqBly5YAgP/+9786j1GysuNlZQHvvy9uX2vXyaLlHAghhLgzycFOdna2PdpBrFBeDrz+uvj9bVkny17T2wkhhBB7s6mC8oULF1BIFeWcIiuLH0q6ckXc/lLq9hBCCCGeRHKwU1FRgQ8++AChoaGoU6cOateujerVq+PDDz80KDBI7EOYeSWlmB8lEns+tRrIyQHWreP/pTXLCCGEJ3kY6+2330Z6ejpmz56NJ554Aowx7N69GzNmzMD9+/fx0Ucf2aOd5H8sLc5pTFoaJRJ7OrlrLBFCiCeRXGenVq1a+Oyzz9C3b1+d7Vu2bMGYMWNcfljL1ersSJWTA3TuLH5/pZJfdZ16dTyXPWosEUKIq3FonZ1r166hUaNGBtsbNWqEa9euST0ckUjKjCqO47/ZU6DjuexVY4kQQjyJ5GDnkUcewZIlSwy2L1myBI888ogsjXJXjsiZEDujKiKCvtF7g9xc8TWWCCHEW0nO2ZkzZw569eqFHTt2oH379uA4Dnv27EFBQQF++OEHe7TRLTgqZyIhgT9uYaHpvJ2ICL4d/v7ynZe4JrE9fdbWWCKEEE8guWenY8eO+Oeff/Dcc8/hxo0buHbtGhITE3Hy5EkkeOncZiFnQv8bdmEhvz0rS75zCUs4AKaXcPjsMwp0vIXYnj5baiwRQoi7k5ygLKdZs2YhKysLJ06cQJUqVdChQwd8/PHHaNiwodnn7dq1CykpKfj7779Rq1YtTJ06FaNHjxZ1TrkTlC0tlslxfE+M3ItlGutJsmXFdOKehM+fqZ4+e33+CCHE0Wy5fosaxjpy5AiaNWsGHx8fHDlyxOy+LVq0EH3yXbt2YezYsXjsscegUqnw9ttvo1u3bjh27BiqVq1q9Dn5+fno2bMnXn31Vaxduxa7d+/GmDFjEBERgQEDBog+t1yk5EzIWYGYlnAgQGVPX1ISH9hoBzxCzx/VWCKEeDtRPTs+Pj4oLi5GZGQkfHx8wHEcjD2N4ziobcjKvXz5MiIjI7Fr1y489dRTRvd58803sXXrVhw/flyzbfTo0fjrr7+wd+9ei+eQu2dn3TpgyBDL+2Vk8CubE2IP1NNHCPF0du/Zyc/PR0REhOb/9lJaWgoACAsLM7nP3r170a1bN51t3bt3R3p6Oh48eGCwCntZWRnKyso092/evCljiylngrgG6ukjhBDTRAU7derU0fz/3Llz6NChA3x9dZ+qUqmwZ88enX2lYIwhJSUFTz75JJo1a2Zyv+LiYkRFRelsi4qKgkqlwpUrVxCjF1XMmjULaWlpVrVJDEuzo4ScCS/N3SYORIu1EkKIcZJnY3Xu3Nlo8cDS0lJ0llLaV8+4ceNw5MgRrFu3zuK+nN40JGFITX87AEyfPh2lpaWaW0FBgdVtNMbS7CiAciYIIYQQZ5JcZ4cxZjSouHr1qsmkYkvGjx+PrVu34rfffoNSqTS7b3R0NIqLi3W2lZSUwNfXF+Hh4Qb7BwQEICAgwKp2iZWYyBfwM1ZnR+6cCXW5GkeX5uLumSIE1YtB8zEJUPhTJEUIIYSYIjrYSfzfFZvjOIwYMUIngFCr1Thy5Ag6dOgg6eSMMYwfPx6bN29GTk4O6tata/E57du3x7Zt23S2/fLLL2jTpo1Bvo4jmcqZgFqNvEW2BSdCgHPjqy1o9tfXaMkqlzu/OEWJ8ymL0W4OZaESQgghxogOdkJDQwHwAUpISAiqVKmieczf3x/t2rXDq6++KunkY8eORUZGBrZs2YKQkBBNj01oaKjm+NOnT0dhYSFWr14NgJ95tWTJEqSkpODVV1/F3r17kZ6eLmr4y970cyb2Tc1C7QUT0VJd2d0jNTgxdgxt0epCRM9Nwj5sooCHEEIIMYZJNGPGDHb79m2pTzMKgNHbypUrNfsMHz6cdezYUed5OTk5rFWrVszf35/Fx8ezZcuWiT5naWkpA8BKS0tleQ2m7E3NZGpwTM3nLWtu/DaO7U3NtPoY+jc1OHZBEcdUZSq7viZCCCHEWWy5fju1grIzyF1nxxh1uRqXguIRrb5gNAO8AhyKFEpE3803OaRl6RjG5C3MRsvkTtY2mxBCCHFZdq+zo2/Tpk3YsGEDzp8/j/Lycp3HDh06ZM0hPcrRpbkmh50AwAcMseoC5C3NNRmcWDqGMXfP0GqPhBBCiD7JU88/+eQTvPzyy4iMjMThw4fRtm1bhIeH499//0WPHj3s0Ua3IzboMLefNYFLUD2qXEicQ12uRt6iHOwZvw55i3KgLre+kjohhMhNcrCzdOlSLF++HEuWLIG/vz+mTp2K7du3Y8KECZoKyN5ObNBhbj8pgUsFOBQq4tB8DFUuJI63b2oWLgXFo+WkzuiwZAhaTuqMS0Hx2Dc1y9lNI4QQAFaseh4UFITjx4+jTp06iIyMxPbt2/HII4/g1KlTaNeuHa5evWqvtsrCsTk7hfCB4dsr5Oyc+CEfxZcVuHwZiIgAYmMrS/xbOob2sQDgz1SajUUcb9/ULLSdmwSA6Xxzos8lIURutly/JffsREdHawKaOnXqYN++fQD4NbO8LNfZJIW/Ajt682WVhT/6AuH+W1UWoWt3BV58EZg0CXjxRaBzZyA+nl/UUeGvwPkU48fQVqRQ0gWFOIW6XI3aCyZCP9ABoAnQ4xYk05AWIcTpJAc7Tz/9tKao36hRozBp0iQ888wzeOGFF/Dcc8/J3kB3lJUFjNiaiCRsQiFidR67ACWSsAmrbxsPTi5cAJKS+GO0m5OIP1M3oVihe4zLXARyWiUjb2E2ou/mU6BD7MZcLs7RpbmoZWa2oJCIf3RprmMaSwghJkgexqqoqEBFRYVmIdANGzbg999/R/369TF69Gj4+/vbpaFysfcwllrN984Iy0b4QI0E5CIGRShCDHKRgAqYr6AsLB6an185pEVLRBBHEwpa1tIuiqmoLIq5Z/w6dFgyxOJx9ozLQIdPB9uzqYQQL2DL9Zvq7MgsJ4cfjpJDdjatYk2cQ0wuTmCtMLScZPnDTvWfCCFycHidnRs3buDPP/9ESUkJKioqdB4bNmyYNYf0GEUylrqR81iEiGUpF6cCHOIWJCPixmlcnKK0mIhPswQJIc4mOdjZtm0bhg4dijt37iAkJERnBXSO47w+2ImRsdTNpUvAunWVi4oqaOSKOIDoophf7sH9lMWInpuECnA6AY/QA1SQsgixNORKCHEyyQnKkydPxsiRI3Hr1i3cuHED169f19yuXbtmjza6lYQEPt+GMz2BShSFgp+lNWSI7iwtQuxNSlFMU0n0NEuQEOJKJPfsFBYWYsKECQgKCrJHe9yeQgEsXszPqOI4fqVOgf59c9R6s3ULC/ljbtoEJCZW7pOTw98APr+nUyfqASK2kVoUs92cRKhn9kOeXhI99egQQlyF5ATlxMREDBo0CAMHDrRXm+zKEUUFAb4XZuLEyllZABAeDliquahQGAY6Au1ZWlu2AK+9Zni88HBg+fLKgIgQqcQWxTS3kC0hhMjNoQnKvXr1QmpqKo4dO4bmzZvDz89P5/G+fftKPaRHSkwE+vUDcnP5ROPISGDECPPPqV4duHHD9OOMAQUFwEcfAe+/b3yfq1eBAQOAzEwKeIh1hIKWlItDCPEUknt2fHxMp/lwHAe1qW4JF+Gonh19ck5JDwsDLKVHxcYC587RkBaxnrE6O4WKOBSkLKJcHEKIwzm0Z0d/qjkRR85p5GLywAsL+R6g996T77zEeu5YGJJycQghnsKqOjtEOrFT0mvW5Iei5Cj1+P77QLNmNJzlbEIPifZ07otTKisRuzKFv4IKAhJC3J6oYaxPPvkEr732GgIDA/HJJ5+Y3XfChAmyNc4enDWMJSwjUVhoPJARko/nzwdeeIHfJkfAExdXuewEcTxaFZwQQuRh9+Ui6tatiwMHDiA8PBx169Y1fTCOw7///iupAY7mrGAH4GdoJSXx/9efkg5UTivPygImTOADIzlIWXZCra5Mqvb2Yoa2Dj1VzmoyvlgmzWoihBDx7J6zk5+fb/T/RJrERD6g0Z+SrlQCixZVDjclJgLBwUD37vKcV2y+kLHp8kolXzfI24bC5Bh6El2JeGkuDRURQogdSa6g/MEHH+Du3bsG2+/du4cPPvhAlkZ5ssRE4OxZvrclI4P/Nz/fMJjIzZXvnNr5QkIhwnXr+H+FyXNCr9MFvWuzUMzQm6o3C0NP0XqBSrS6EG3nJmHfVHFvhpRKxIQQQuxH8tRzhUKBoqIiREZG6my/evUqIiMjaeq5DNRqYPhw4OuvbTuOdhFChcJ0z82CBUBKimGgY+o4nkzOoae8RTm0KjghhMjEluu35J4dxpjO4p+Cv/76C2FhYVIPR/RkZfGJzHIEOgA/PCYEOqZ6bgYONB3oAJXFDOXsbXJVR5fmopaJQAeoHHo6utTym9F8TAIuKpSaZGR9FeBQqIijVcEJIcTORE89r1GjBjiOA8dxaNCggU7Ao1arcfv2bYwePdoujfRExhKBt2zhAxI5ZmHFxlbm2qjVfI+OseNKOZectYJclZxDT1SJmBBCXIPoYGfRokVgjGHkyJFIS0tDaGio5jF/f3/Ex8ejffv2dmmkpzE1nHTvnjyBDgCsWgV06cL/PzfXfM+NWHojlx5J6iKYlrSbk4h92GRQibhIobSqEjHNliOEEOkk5+zs2rULTzzxBHx93bMeobNzdoThJLmCGlPWrgWGDuX/v24dMGSI7ceMjQU++cSzZ2bZaxFMOSoo02w5Qog3c2jOTkhICI4fP665v2XLFvTv3x9vvfUWysvLpR7Oa6jVwM6dwKuv2j/QAYBJkypnUImt3myJN8zMEoaeABjk2mgPPUkNVIRKxB0+HYyWyZ2sCnRothwhhFhHcrDz+uuv459//gEA/Pvvv3jhhRcQFBSEjRs3YurUqbI30BMIScddu4pb10oOV65UXgQTEvgeADkwBiQnV05Z90Tt5iTiz9RNKFbE6mwvUiidUvFYTM6Vp/9MCCHEFpKHsUJDQ3Ho0CHUq1cPH3/8MX799Vf8/PPP2L17NwYNGoSCggJ7tVUWjh7GctSwlTHaU8a3bAEGDJDv2FKqMrsrS0NPjlrcMycH6Gx5BrtX/EwIId7LoaueM8Y0K5/v2LEDvXv3BgDExcXhypUrUg/n0cx9I3cE7SnjiYlAWhq/OKgc5FrKwpWZWwTTkYt7ip0F5w2z5QghxBqSh7HatGmDmTNnYs2aNdi1axd69eoFgF9GIioqSvYGujO5ZkHZSrgIvv02n2RsCscBWpPszLp82fZ2uSu5KiyLJTbnSq7cLEII8TSSg51Fixbh0KFDGDduHN5++23Ur18fALBp0yZ06NBB9ga6M1fp/bh0iZ+RlZsLLFzIBzX6dSGF+yNGiDtmRISsTXQb6nI1ai+YCP1VzAFoZm/FLUiGuly+BBoh58pILU8A/Pa4OH4/QgghhiTn7Jhy//59KBQK+Pn5yXE4u3FEzo5aDXz0ETBvHnDrll1OIZpCoZu4qlQCgwfzwY92r1NcHF9tOSyM8kPMcdYSEELuF6A7LCoEQJs20fRzQohnc2jODgDcuHEDmzZtwpkzZ5CamoqwsDAcO3YMUVFRiDU3TuIFsrKA114Drl51dkt4+jN0Cgv5IGzDBqBmTcPidGo1HxCZG37z5l4EZy3umZjIBzTG6uwsWkSBDiGEmCM52Dly5Ai6dOmC6tWr4+zZs3j11VcRFhaGzZs349y5c1i9erU92ukWsrLEz3jiOP4beni4bmDk4wP8L//bLhjjz52SYnxhT4WCL1JnrhdBWG/LGRw1A8oUuSssS5GYCPTrRxWUCSFEMiZRly5dWGpqKmOMseDgYHbmzBnGGGO7d+9mderUkXo4hystLWUAWGlpqazHVakYUyoZ48MDy7eICMYyM/nnZWczlpws/rly3RYuZCwjgz+/SqX7ejIzDV9PXBy/3Vn2pmayQoVuowoVSrY31XGNUpWpWKFCydTgjL6panDsgiKOqcpUlg9GCCFENFuu35KDnWrVqrHTp08zxnSDnbNnz7KAgADJDXA0ewU72dnSAo21ayufKzVQqlZN/sBHqTQMZIRAzFRA5Eh7UzOZGhxTGwku1OAcGvBUtoVzelsIIcRb2HL9ljwbKzAwEDdv3jTYfvLkSUR46xQdSK9xop3aJHaK+ltv8YnB9lgawNiyAwoFn4Q8eDD/rzOHrhw9A8ocV6uwTAghxDzJwU6/fv3wwQcf4MGDBwAAjuNw/vx5TJs2DQPkLNHrZqTUOAkP103wFRsoNWvGBx2dOpmfimwNV1524OjSXNRSXzD5YfUBQ6y6AEeX5jqsTe3mJCLq7lnkLczGnnEZyFuYjei7+RToEEKIC5Ic7MybNw+XL19GZGQk7t27h44dO6J+/foICQnBRx99ZI82ugVLtVC0Xb3KL98gEBsoRUbySwds2FC5oKjcAY9Qcdne1OVq5C3KwZ7x65C3KMdsr4yzZkBZYuvinoQQQhxD8mysatWq4ffff8evv/6KQ4cOoaKiAo8++ii6du1qj/a5De1ZTMJMK1M4ju9B6dePf54QKBUWGn8ex/H1b0aM0B3uCg/n/5V7mru9lx2QutSCM2dAEUIIcX+yFRV0F/YuKpiVBYweLW45Be3CfOaKxpn6CQm9Op068ceSiz0LBgpLLejn31SAfzHGcl7U5WpcCopHtLpQk6MDvecWKZSIvptPvSuEEOKhbLl+SxrGqqiowIoVK9C7d280a9YMzZs3R9++fbF69Wp4WcxklFrN98A8/7y4/bV7UISicfo1GWNjK3tw9AnTgOQMdOxZMNDaRGOFvwLnUxYDqAyKBML9gpRFFOgQp1Gr+SHmdev4f10t740Qbyc62GGMoW/fvnjllVdQWFiI5s2bo2nTpjh37hxGjBiB5557zp7tdHlZWUB8PL/UwtKl4p6jn6uTmAicPcsHL2vX8utYDRtm+zAVx/EBk6X8Ho6zb8FAWxKNaQYUcVXav/tDhvD/xsfbZ9YkIcQ6onN2Vq1ahd9++w07d+5EZ73Fk3799Vf0798fq1evxrBhw2RvpKsThqDEdm5xHJ+jY6wHRaEArl0Dpk2Tb8X0GTP4mVzm8onCw4HPPuN7ptats091XlsTjdvNSYR6Zj/k6VVQjnVyj45aTVWNvZWp332hlAOtWUaIixBbkOeZZ55hs2bNMvn4Rx99xLp16ya50I+jyV1UUGpBQI7jb6YqEWdm8o/LWTBQKGBorCpyWBhjaWmMbdhg+JixQoO2OLwwW1SDDy/Mlu+kdmbsPZX7fSOuydLvPsfxVcedWYyTEE/ikArKUVFR7PDhwyYfP3ToEIuKipJ08l27drHevXuzmJgYBoBt3rzZ7P7Z2dkMgMHt+PHjos8pd7AjtXKyuQuh1MBJ7E1YmkI4h35VZFMBlqXATCpPW2rBUe8bcU1if/ezs53dUkI8g0MqKF+7dg1RUVEmH4+KisL169cl9SrduXMHjzzyCJYsWSLpeSdPnkRRUZHm9vDDD0t6vpzknKYttpKyVFeuVFZH1q+KDPAraRsb2pK70KAnJRqr1Y5734hrEvu7b+9SDoQQy0QHO2q1Gr6+plN8FAoFVCqVpJP36NEDM2fORKLEQe3IyEhER0drbgonJkhIqZwMGF+WQZjJkZkpa9M0zF18LQVYchca9JREY0e/b8T1iP3dl/o3ghAiP9EJyowxjBgxAgEBAUYfLysrk61RlrRq1Qr3799HkyZN8M477xgkTDtSQgKf1Hvtmrj9GdMtKrhlC99DYI8eHf3zChdf7Ro6zvh26qqJxlLQt3oiphioqYkIhBDHEh3sDB8+3OI+9p6JFRMTg+XLl6N169YoKyvDmjVr0KVLF+Tk5OCpp54y+pyysjKdQMzYIqa2UCj4YOX998U/Rwg8Xn0VWLlS1uZYpH/xdda3U2GpBUtcdaYTfasn5qqmC2Ue7FnKgRAinstUUOY4Dps3b0b//v0lPa9Pnz7gOA5bt241+viMGTOQlpZmsF3OCspqNRAVJf+yDfagXx1ZreZrglj6dpqf7/g/2llZhr1eSiV/gXH2dF5Xft+IYxn7nMbF8YGOsz+nhHgSh1VQdkXt2rXDqVOnTD4+ffp0lJaWam4FBQWyt0GhAJYvl/2wstNfbR2o/HYKGBYddOa3U6F+if7wnrGcJ2dw1ffNG7hatWLtYqAZGfy/+fkU6BDiStw+2Dl8+DBizIwVBAQEoFq1ajo3e0hM5Fcjd+WL24QJxttnaqkKpdI5RdHcZaaTq71v3sBVqxXrz3J05b8DhHgjyauey+n27ds4ffq05n5+fj7y8vIQFhaG2rVrY/r06SgsLMTq1asBAIsWLUJ8fDyaNm2K8vJyrF27FpmZmci01zQmiSIinH8BNiU8HHj7bdOPJybyCdOukB8jZaaTvRYsFcuV3jdPR9WKCSHWcmqwc+DAAZ2ZVCkpKQD4ZOhVq1ahqKgI58+f1zxeXl6OKVOmoLCwEFWqVEHTpk3x/fffo2fPng5vuzGuPPNm+XLLF2Dh26mzudtMJ3u9b66anO0Mlnr7tGc4eut7RAgxzanBTqdOnWAuP3rVqlU696dOnYqpU6fauVXWUauBS5fkPWZ4uDxJzy+/7F7feGmmk2snZzuDO/X2EUJcj1U5O2vWrMETTzyBWrVq4dy5cwD4IaYtW7bI2jh3IeQRTJokz/GUSr7A4KVLuiugr10L7NjBB0FSdOmie9/VEjz1CfVLTK3SznH8bBdPrV/i6snZzuBuvX2EENciOdhZtmwZUlJS0LNnT9y4cQPq/10pq1evjkWLFsndPpdn6sJkraAg4MwZ3W/vPj5Ay5bAoEF84CJ15pd2Aq2rJnhq8+aZTu6SnO1o1NtHCLGJ1MW0GjdurFmwMzg4mJ05c4YxxtjRo0dZeHi45MW5HE3OhUDttXBndrbl1bQzMxmLjbV8LO1Vl91t4Upj70FcnOu1U060uKRxwu+asc+v8BmmFcYJ8WwOWQhUkJ+fj1atWhlsDwgIwJ07d2QIv9yHvRbu3LLF8jBGYiJw7hxgpF4iAL4HhOMqe0DcscfAG+uX0HCNcd7c20cIsZ3kYKdu3brIy8sz2P7jjz+iSZMmcrTJbdjrgrN2rbigRKEA3nuPz+9RKnX31a/14q4LV3pb/RIarjGN6hoRQqwleTZWamoqxo4di/v374Mxhj///BPr1q3DrFmz8OWXX9qjjS7LHhecqlWBK1dMP25s1omYWi/UY+AeaHFJ86iuESHEGpKDnZdffhkqlQpTp07F3bt3MWTIEMTGxmLx4sUYNGiQPdrosixdmKwhdiRQalBCPQbugRaXtMxV6kERQtyHTQuBXrlyBRUVFYiMjJSzTXZly0JixgizsQD5Ah4xduzg/+gXFQGnTgFffGG+JgstXOleaHFJQgjRZcv1W3Kwk5+fD5VKhYcfflhn+6lTp+Dn54f4+HhJDXA0uYMdwPiFSZuPD1BRYfr5lh43tn90NHDxoul9hF4A7VwGU4GZsX2J81EFZUIIqeTQYKdjx44YOXIkhg8frrN97dq1+PLLL5GTkyOpAY5mj2AH0L0wCR1dJSX8RapDB/4x4a0R8i1yc/kZVWvWyNYMHcZ6a6jHwLtQwEQI8RQODXaqVauGQ4cOoX79+jrbT58+jTZt2uDGjRuSGuBo9gp2pNi0CRgzBrh82THny87WzXGgC6B3oCUnCCGexJbrt+QEZY7jcOvWLYPtpaWlmmrKxLSpU4G5cx17Tv1kZkrw9Hy0QjghhFSSXGcnISEBs2bN0gls1Go1Zs2ahSeffFLWxnmajRsdH+gANMPK21gqIMkY8OqrwM6drlVEkhBC7EXyMNaxY8fw1FNPoXr16kj4X/JJbm4ubt68iV9//RXNmjWzS0Pl4qxhLLWaTyo2V0NHbjTDyjvl5PBrnolBw1qEEHdhy/Vbcs9OkyZNcOTIEQwcOBAlJSW4desWhg0bhhMnTrh8oONMubmODXQA/hv8gAH8uekbvPeQUoPJm1dSJ4R4D5vq7LgjR/TsGEsA3rCBX2XcWegbvPeQ0rMDUA8gIcQ9ODRBGQBu3LiBP//8EyUlJajQKxAzbNgwaw7pMUzNgHn1Vee1CaDEVG8itbK3sSVICCHEk0ju2dm2bRuGDh2KO3fuICQkBJzWEsQcx+HatWuyN1JO9uzZMTUDRniLOE5a8UBLQkP53prUVHHT2OkbvPewprJ3Rga/4CohhLgih+bsTJ48GSNHjsStW7dw48YNXL9+XXNz9UDHnsTMgJEz0AGA0lLxgY7QDldc2ZzIz9QK4ebQrD1CiKeSHOwUFhZiwoQJCAoKskd73FZurunlIuzJmsKEtLK5d0hMBM6e5ddRCwszvR/H8VW0vXUldUKI55Mc7HTv3h0HDhywR1vcmjsFEPQN3nsoFECXLvxCsRxXOaQqoJXUCSHeQHKCcq9evZCamopjx46hefPm8PPz03m8b9++sjXOnbhDACHk7Fj6Bk/LSXgeYVjLWPI8rYtGCPF0khOUfXxMdwZxHOfyS0bYcyHQ+HjTM2A4jh9KuHpVtlNaJTPT/IWN1lPybBTIEkLclUMTlCsqKkzeXD3QsSeFgp/JYi50XL4cSE52WJMMhIcD/fqZflyYwaOfe0SF5zyHsC7a4MH8vxToEEK8geRghxiXlQXMm2f68SlT+J4Rc8GGvV29anomlqXZZAAfqHlxPEsIIcRNWVVU8M6dO9i1axfOnz+P8vJynccmTJggS8PciblAQbB+PTBrlvSCb3IzlUhtaTYZFZ4jhBDiriQHO4cPH0bPnj1x9+5d3LlzB2FhYbhy5QqCgoIQGRnplcGOmGnnBQXAe+8BzzwDLFgADBzomLbpM5VILXY2mTvNOiOEEEIAK4axJk2ahD59+uDatWuoUqUK9u3bh3PnzqF169aYZ24cx4OJDQD+7//4NYveeMO+7THGWC0VtZpfR2ndOuDSJXHHcYdZZ4QQQog2yT07eXl5+Pzzz6FQKKBQKFBWVoaHHnoIc+bMwfDhw5HohVN2pAYAjp6RZayWirFZVwqF+Zyc8HAqPEcIIcT9SO7Z8fPz06yHFRUVhfPnzwMAQkNDNf/3NkIejn7BNmcJD9e9HxuruwCoqVlXlpKPr14FtmyRr52EEEKII0ju2WnVqhUOHDiABg0aoHPnznjvvfdw5coVrFmzBs2bN7dHG12eQsHXoREWXnSmF14Afv9dd5t2IrSYZGpTOI6fkSXMKBNbr4VquxDiHuh3lXgsJtH+/fvZr7/+yhhjrKSkhPXo0YOFhISwVq1asby8PKmHc7jS0lIGgJWWlsp+7MxMxsLChGU/XefGcfwtM5Ox7Gzbj5eWxphSqbtNqeSPb+w9EbsvIcR56HeVuDpbrt+SKyi7O3tVUBbs3Al07Sr7YW0mLBUxaxbw4ov2OT5gfLhM/xNmbF9CiPPQ7ypxBw6toEzM69TJMGfGFQh1cqxZJV3s8YHKwoNUpJAQ90C/q8QbiMrZadWqlSYp2ZJDhw7Z1CB3t2WL89e/Mic/335FDbULDwJUpJAQd0AFRYk3EBXs9O/f387N8AzCNyRXtm4dsHSp8aKGHFcZAGn/39h9c6QUHqQihYQ4FxUUJd5AVLDz/vvv27sdbk+tBj791HIlZWe7fBk4dsz4CuxhYfxipRUVwJgxukNeSiXwyiuAmI+ClLpDchQpVJercXRpLu6eKUJQvRg0H5MAhT9NISFEDLG/g1RQlLgzSlCWgbECfe6qXz/g4EHd11KzJt8blJgIxMebHgITkqDz8/n7Yve1ZWrrvqlZqL1gImqpKxt8UaHE+ZTFaDeHMioJsUStdszvKiG2cmiCslqtxrx589C2bVtER0cjLCxM5+ZtTBXoc1dbthi+lqtX+fo9W7bw9YQAwwKK+lWahdpDYva11r6pWWg7NwnRat0GR6sL0XZuEvZNzbL+4IR4CUf8rhLibJKDnbS0NCxYsAADBw5EaWkpUlJSkJiYCB8fH8yYMcMOTXRdthTocyfaMzL69eOnocbG6u6jVBpOT01MFL+vVOpyNWovmAiAGXyIfcA3OG5BMtTl3jWFRHu9s5wcmkFDxLHn7yoxTl2uRt6iHOwZvw55i3K87m+Vw0ktzPPQQw+x7777jjHGWHBwMDt9+jRjjLHFixezwYMHSy7042hyFhWUo0Cfu92ys/nXrlLx/8/I4P9VqUy/T1L2FevwQnFv/uGF2bafzE1QUThiK3v8rhJDe1MzWaFC95e1UKFke1Ppl9UcW67fkpeLKC4u1iwLERwcjNLSUgBA79698e6778oZh7k8b5ydILxmhUL8NFQp+4p194y4N1/sfu7OVFG4wkJ+O307J2LY43eV6BKG3wHdX9ZodSGi5yZhHzZRvqEdSB7GUiqVKPrfFa9+/fr45ZdfAAD79+9HQECAvK1zcd44O8FRr9lSF29QPXENEbufnG1zNCoKR4h7oOF3J5LaFfTmm2+yjz76iDHG2MaNG5mvry+rX78+8/f3Z2+++abkriVHk3MYS6Xihwk4zvnDS464xcUZ79aWu+vbaBcvF8t+7ZzGdo/LYIcXZrOyW2WsUKFkahh/89Xg2AVFHFOVydsP74rdz2KHU4UhSEKIc9Dwu20cOow1e/Zszf+TkpKgVCqxZ88e1K9fH3379pUxDHN9wiyGAQOc3RLHGDTIcEaGsWn3SiX/vlgzbGKqizeGFaJW9vtANn//4hQl/nl0MKL3z0MFOM23IgCoAD+FpCBlEWJlrLfjqt3PVBSOEPdAw+/OY/PaWO3atUNKSorXBTreaN48PrgRmJp2L+SJZEmc+W2ui1d/sZJodSGe2j8Pvz02BcUK3SkkRQol/kyVN/Bw5e5nKgpHiHtw5vC7t5NcVPDq1asI/99KlwUFBfjiiy9w79499O3bFwkJCXZppJzkLCooFOPyhBo7ISHArVvm9zFWNNDUa7emEFneohy0nNRZdJsrwKFIoUTEjdM49uUenQrKAGStqiy2bXkLs9EyuZPV57EGFYUjxD2oy9W4FBSPaHWhTm+0QPibFn03n6rAG2HT9VvseNeRI0dYnTp1mI+PD2vYsCE7fPgwi4qKYsHBwaxatWpMoVCwzZs3SxpD27VrF+vduzeLiYlhAEQ9Pycnhz366KMsICCA1a1bly1btkzSOWnque237Gz75InsHpdhVYP0x7ftkVcjtm27x2VYfQ5bZGbyuWP6+WPCNpp+Tohr2JuaydTgDPINhW00/dw0W67fooexpk6diubNm2PXrl3o1KkTevfujZ49e6K0tBTXr1/H66+/rpPPI8adO3fwyCOPYMmSJaL2z8/PR8+ePZGQkIDDhw/jrbfewoQJE5CZmSnpvHLxlBwIqYWvi4rskydS9vcpaQ35H+3xbXtVVXb17mcqCkeIe2g3JxF/pm5yyPA70SI2KgoPD2d//fUXY4yxW7duMY7j2P79+zWPHz9+nIWGhkqOtgQQ0bMzdepU1qhRI51tr7/+OmvXrp3o81DPjuEtLc35PTv8tx2wCht6dlRlqv/N0DK+ny0ztCqP7djZX5LbSUXhCHELqjIVO7wwWzPD1Nl/O9yBQ2ZjXbt2DdHR0QD4YoJVq1bVWQurRo0auGUp6cNGe/fuRbdu3XS2de/eHenp6Xjw4AH8/PwMnlNWVoaysjLN/Zs3b8rWnoQE/puzqVwJVxccDKSmAtOmAV98YTn3SMj9EFKzzL12/X3NqUz+NUxENkcY39bO0WmpNv0ifMAQqy5A3tJcyXk1Cn8FzqcsRvTcJIfN/rIGFYUjxD0o/BUOz+/zZpJmY3F6q8Tp37e34uJiREVF6WyLioqCSqXClStXjD5n1qxZCA0N1dzi4uJka4+5BfTkoJ9/Vb26PMetUoX/9/Zt4P33gXr1gMGDxb0Geyz0eXRpLmqpL0j6MGoHGEIin72ndVL3MyGEuCdJdXZGjBihqZJ8//59jB49GlWrVgUAnd4Te9IPsNj/uhVMBV7Tp09HSkqK5v7NmzdlDXiEXAn9WjNyuHlTd5bUjRuAjw9QUWHbce/d071fWMhPK58yhV9A0tjrUCiAlBTjC30aq7OzaJH4PBFrgo8ihRIFKYt0AgxH5NW0m5MI9cx+yNOb6eXsHh1CCCGmiQ52hg8frnP/xRdfNNhn2LBhtrfIjOjoaBQXF+tsKykpga+vr2Y6vL6AgAC7L2ORmMivBp6bywcOkyYBly/Lc2z9kUFbAx1jGON7Y9av54OeQYMM91Gr+cfatTMMeITXXlTE13JJSDDeo6MuVxudDi42+MiNfxGq2HhU69cJLSd2Qqy/QueYAbUjcZGLRTS7aHZapzDsZS3qfiaEEPciOthZuXKlPdshSvv27bFt2zadbb/88gvatGljNF/HkYRciZwc+QIdR2IMKCgAxo0zv19yMh/caAczYvJE9k3NQu0FE3Vyai5OUeJ8ymI8NrMfLk5Rmqw9wcDn8iScXQucBS7uW4X9l/kxNP1jXuXCATCXzqshhBDiWJKXi5DT7du3cfr0ac39/Px85OXlISwsDLVr18b06dNRWFiI1atXAwBGjx6NJUuWICUlBa+++ir27t2L9PR0rFu3zlkvwYC7T0c3kfoEoDIgys2VlgRraZmFP7EJMJH8ayzvm3/eAHBGHq/BroEDcI0LQzi7qtlubNiLEEKId3BqsHPgwAF07lxZlVbIrRk+fDhWrVqFoqIinD9/XvN43bp18cMPP2DSpEn4z3/+g1q1auGTTz7BABdanMobSvJLCegsLbNQAQ5xC5IRfTcff2ITai+YiFp6M6r0s7F8wDRBjqljlvlUwcFZO1B2voTyaojLUavFDf0SQuQhebkIdyfnchHGuPMSEhER4obgsrPF9+xIXWZByMG5vmknOu+eKe4kFo6pjS4yxNnkXjyXEG9hy/Xb5oVAiS7tKdn2JEwfl9Pw4fwfXVNT0DkOiIsTVztHIHU6uJD8G9CqifiTiDx3VhYfiHbuDAwZwv8bHy99wVJPpVbzOWfr1vH/qh2/pqnHk3vxXEKIOBTs2EFiIpCZCZiYICYL/enjcvjmG2DBAv7/ttbOEVg7HVyOZRe0j0EXGfPsGQhSEMVTq/keHWN96cK25GTvfX8IsSdRw1hbt24VfcC+ffva1CB7s/cwlrZ79/h1p+7ft+tpZLVjB1BaatjNHhcnrXaOwNpVfi09T9hirBNK/5iWhha9fVVwIRDU/0sgBLi2rK1FQzaVcnL4INISKcPEhHgTW67fohKU+/fvr3Of4zhox0jaBf3U9LUEAP9HfvRo9wp0AGDgQH7piLNn5cltsXaZBcvPY+D+939Lx8zNNZ9DZe0sM09gqbeB44yXGxAjKwswNnfgwgV+e2amdwU89lg8V0C5aIRYIHUxre3bt7NHH32U/fTTT6y0tJTdvHmT/fTTT6xNmzbsl19+kbw4l6PJuRCoKZmZjHHG14t0ixvH8a9BTntTM1mhQqlzoguKOLY31fyJzD1P7DEzMsS97owMeV+zO5BrQVf9BUjLyhgLDzd/zPBw2xYqdbdFT+VePFeQmcmYUvfXgCmV8v8OE+Jstly/JQc7TZs2Zbm5uQbbf/vtN4MVyV2RvYMdlcrwD4+73TiOsbg4+S8e1q7ya+55Yo65Y4d9LjKeQI5A0NjFtmZNccfdscO6drvjBV7422Dqi5A1v3emvlhxnH2+tBDiTA4NdgIDA9mRI0cMtv/1118sMDBQcgMczd7Bjthvb+5w84SLf2YmY7Gxzgnu3IGtvQ229mK+8470NrvzBV5ou377rWm7pS9W3vy5Jp7Jluu35NlYjz32GJKTk1GkNbBcXFyMyZMno23btjINrrkvd6igHBgobj93eC3mCIm3hYWW95U6y8xTJCRYX27AXL6Pvbj7jCZh8dzYWN3tSqX0RHApuWiEeDvJwc6KFStQUlKCOnXqoH79+qhfvz5q166NoqIipKen26ONbkGYXnvsmLNbYpnYpGl3rgYt5UI8ZYp3Jcpq064LJbXcgKWLrRhSE8I94QKfmMhPAMjOBjIy+H/z86V/Bu2Z8EyIp5G8XET9+vVx5MgRbN++HSdOnABjDE2aNEHXrl11ZmV5E2PTa12dj4/pFdSFqdhSige6GikX4vXrgVmzvLNnB6jsbTA2RdxcuQFbL6Lh4dKDHU+5wItZPNcSsV9G3PlLCyFysWptLI7j0K1bN3Tr1k3u9rgdUzVKXJ0Q6HCcbtstfZt3lymuUi523jrtXFtiIj+9XMrP1taL6PLl0j87dIGvJAxBFhYa//vjCV9aCJGLqGDnk08+EX3ACRMmWN0Yd+OMnAU5JSfz3+i1v83HxgKvvgqUlfHDctoXPHcqECf1YufqPQGOILW3QczFNiyMzxHTzpuy5TNDF/hKwhBkUpL0Ly2EeBtRFZTr1q0r7mAch3///dfmRtmTnBWUxVZEdVXZ2fxFQfg2f+oU/23b2IUJsF+VXUD+HiOpC7JS1VrrCD2bgPGL7aZN0nuM5DinqwXf9mTsS4i1Fc8JcWW2XL9p1XMbrFvHryPkjuLidJdHMLdkAGN8fsXVq8aPZetyC/bqMTJVwVebOy8V4SpDis642NIFXperfBYIsSenBDtXrlwBx3EIt+dql3bg6T07HAfUqAFcu2Z+H+1vv1J7QUyxpnfEnusyCcd/7TXjgZo79wS42pCiMy623nSB96bXSogpNl2/pRTluX79OhszZgwLDw9nPj4+zMfHh4WHh7OxY8ey69evSy7y4wxyFhVUqRgLC3N+8T+pt379dF+HXIUQpS634KiiaCoVY2lphj+ruDjXLkBnijsX1SPSuWO1aELswZbrt+ienWvXrqF9+/YoLCzE0KFD0bhxYzDGcPz4cWRkZCAuLg579uxBjRo1pIdrDiT3queDBgHffCNDwxzIx4dfkd3fn78v13BcWhrw3nvi93f0KtCe8O3YkSu4e8L75e7s3fNJiDux+6rnAPDBBx/A398fZ86cQVRUlMFj3bp1wwcffICFCxdKaoA7U6uB3bud3QrpKiqAvn2B77/nL16RkfIc94svgLffFn9BdHTNFGG2kXAR37DB+EXclS/yjlrB3dQw2YIFQESEa743nsaeK9IT4nXEdgHVqVOH/fTTTyYf//HHH1mdOnUkdy05mpzDWO6+DpZSyVhqquWFSzmOsWrVxB1TysKO9loF2hxLQwKuPmQgx8KdllYLl7LelSu9N57GGb8fhLgyh6yNVVRUhKZNm5p8vFmzZiguLpYh/HIf7l6b5cIFYO5c8z0FQnf5yJHijjlwIN8rIIYt6zJZQxgS0H+9hYX89qlTzT8u9nXZk9j6QceO8cOE+mtEZWXxw2CdO/NDl5078/eF1ya1dpQrvTeexlOqRRPiCkQHOzVr1sTZs2dNPp6fn+92M7Ns5WpVWqtVMx04WCs2trJWihjXrom/+NmyLpNUloYEGOOHaEw9Djh3gUlh7bXCQn4YydLPeeZMw0DGUrCXlSV9vStXeG9cgfDzWbfOeJBpDaoWTYiMxHYBjRw5kj311FOsrKzM4LH79++zjh07spEjR0ruWnI0uWdjWRoCctQtJISx5s3lP64wLCW8VjHDG5ZmUekPo2zYYPg+yj1TSq4hR2cMGRgbWhN7E2ZoGXuPjf3M1q51r/fGFdhr6NPS75xcsxUJcRe2XL9FJyinpaWhTZs2ePjhhzF27Fg0atQIAHDs2DEsXboUZWVlWLNmjZ1CMtck9ExYKlznCLduAUePyn/ckhL+X+3S9JYwZjpJ1lTi68KFQM2a9kt8laur39FDBrauvcYY3ws0dixw+bL5/QoKzO9jiTcOp5j6+Qi9ZbbMlqLlIAiRkZTI6N9//2XPPvss8/HxYRzHMY7jmI+PD+vevTs7deqU5EjLGeTs2RFkZjIWHu783h173N55RzeJNTNTfG2h5GTD98le9WEsJd26Y8+OmJ7DiAjG3npLvp/32rXie/Cc+d64AkfViTLWc+SuNaIIsYUt129Yc8Jr166xP/74g/3xxx/s6tWr1hzCaewR7DBWWbguONj5AYo9btrd8jt2iH+e8BwxFwalkj+2qYDFFDHDCGKG4RQK1xoyEBugvfOOfD/n7OzKoFRswOOtwymOnC1lKZgnxBs4PNhxZ/YKdgRSAgF3umn3vkjJ3xEugtb0rIjJe5DSW2TqIi5sS001/7jc36QtXcDETjMXG+xERIgP5sTmCXlz1WY5ygAQQsRzyNRzIk6nTvxsGU/DGP9vcjL/7+LFldvMEXJ3rMnnsDSt2dIMK6G9wsyYxEQ+hyI2VndfpZLfPmeO+cflrFRraQo4IH6WTadO4qbwL11aeV//cUA3/yMxETh7lq9enZHB/7txI38ebfZ4b9wFzZYixH3Qqud2MHkyP43ZnURE8EnBYqaMZ2fzU8yHDwdu37a8f0YG/wffmkVTzS1/YO1yE5YqJNujgrL2MU+dAt5/33Af/SUAhKUhCguNB3Ta782WLZXJ49r76h/TWIJ4RAQfCIlJPnfl6tKOJuXn463vESFycthCoJ7A3sNYqanOH3Ky5jZ5srTEYykJrMIQjbWJr8Ix9LnLMIKUqePGhpPEDq2JTWTdsIGxmjV196NKyNaR8vMhhNiGcnYksGews2GD84MWa28REeL31b9Q2nrhtjZgcYdy+lKWXjDVZimzcaxdCoIuztazZbYUJR5bj9477+OQVc89hb2GsdRqIDoauHJFtkO6pJAQvqaPGBxnmM+RlQW89hpw9aq08xpb+VytBqKizB8rPBy4dMk5wwiWVig3JyMDGDxY91i2Dh/Zc8V0bx/esub1m6o5tXixd+ZASUHvnXdyyKrnxLzcXM8PdACgVSvgt98s71etGvDll8b/8EgJdIQLsFzrYzmS1KUXtOkntQorttuzPYxZt2K6PS887hJESf352LMYoaej945Yg2ZjyWTLFme3wDGefFLcfjdvAikpugnPwuwpsSxVic3NtRw4Xb3K7+cM1sxAk3vxU2vaI6XdYtbbspaYGWvuSOosQlKJ3jtiLQp2ZJCVxV+Q3RXHiZ8u//TT5qc5a9O/4Ent6TA2rVl7wcWdO8Udx1nLGEidcmzvJQDkniptzwuPPYMoZ5PSw0Z00XtHrEXBjo2k9lY4QrVqQGCgtOcsXWpYQ0VfXBzfVW9qpXJ9+hc8sUHHuHF8jk5+vmG+j/Y3/ZkzxR3PWXVOLl+WFrTYu2ZNQoK4ejxie5XsdeHx9G/v9uhh8xb03hFrUbBjI1vyMuyhWjV+COn+fXH7h4TwF5aaNfnaQOYuhEKPg6nifMZoX/DEBh0DBvBBlXagYOqbvjn2HBKyJCsLeOEF8xdkjgPS0iqL9ukHd3ITFpYUzq3fFkBar5K9Ljye/u2dihFaj947YjXZ54a5OLmnnout9eION6WSrxOkP402IoKfVq9PpRK/VEFGhuVaO6bWWBKzIKYrTaUW016FgrGNGx3fNsbkW1jSXlP/3aV+krWs/T0g9N55O1ouwok86RtEYSEwbx4waJBuDs/ly4bJxgDfA9Cli7hjx8RY37NgTe+ZM5cxENNetZrvTTP1mJCXlJMj/3CNsaUgrOlVkntYTODp397l7mHzJvTeEavZIfhyaXL37FjT6+CON1M9JdZ805LasyCl9yw52fkFxmzpmRCzgrsrsUcFYW/59i5XD5s3ovfOO1FRQQnsUVQwK4vPM/F0porOCfk0AP9nR3t/wHgPi5T6KWLXwAL4ngRnr0Vk7ZpdpuqHmHsfXYGxOjtxcfw3bGvba81nyh25Sx0hV0Tvnfex5fpNwY5MNm0CBg40vFB5ImPVjE0tMDl0KNCvn21/iKRWIjbWPmPHtNcfSmsWiLRndWNHsMf7aY8gihBiX+pyNY4uzcXdM0UIqheD5mMSoPCX548WLQQqgb3WxlKpGAsPd/5wkyNu2sMv2uvT7NjB35KT5V9ocuNG69pnjCOGiqQO76SliXttzlznyxnkXP+I1lJyLfTz8Dx7UzNZoUL3j2uhQsn2psrzx5UWApXAXsGO2JkpnnATLrjGggZTAZ+ts6OkvL/mAgJHLoQpNq8gM1O+QI4Y5265UJ6Ofh6eZ29qJlODY2q9P1r8Nk6WgIdydiSw1zDWunV8oTtPpj2UsmWL8fwSsc+XOsQh9v01t/CnM4aKLA3v2GOIjuhy11woT0U/D8+jLlfjUlA8otUXjBbvqwCHIoUS0XfzbRrSsuX6TVPPZRIZ6ewW2C4iApg82fhj2tM6AdMVbs1hzPpicGKnGU+YYDpQkVKsTq7p38ICkYMHGxZKFNMmbc4qkOjOPL0as7uhn4dnOro0F7VMBDoA4AOGWHUBji51XiVQCnZkkJUFjBjh7FbYJiICWLIE+OYb449r162xtWq0NaXcLdV0Afhenbfftv28W7Y4bgFKKe8F1Q+RTmyAm5PjsCZ5NU+vju2t7p4R94dM7H724PRgZ+nSpahbty4CAwPRunVr5Jr5lOfk5IDjOIPbiRMnHNhiXdYsY+CKRozgiwmaeh0LFlR2Ldu67ow1xeDMFRMTLF9uPhgQe95Fixy3AKXYNqWlUde+NcR+VgcOdO/FRd0FrW3lmYLqiftDJnY/u7A5Y8gG69evZ35+fuyLL75gx44dYxMnTmRVq1Zl586dM7p/dnY2A8BOnjzJioqKNDeVhDR+OROUPaWgYEgIY7Gxph/XL+JmbTK2HMXgbCkmJqZYnUJh3/ZLbZOQuEkzVawj5bPqrOVFvIm9lhghzqUqU7FChZKpYfwPmRocu6CIY6oy2/6Que1srLZt27LRo0frbGvUqBGbNm2a0f2FYOf69etWn1POYMebZmBp/wESc4E2diGR62Jiy5RVc1PCpb4PcrFHFWLCk/JZ9ZTKzK7MW6pje6PK2Vi6P1xXmY3ltGGs8vJyHDx4EN26ddPZ3q1bN+zZs8fsc1u1aoWYmBh06dIF2dnZZvctKyvDzZs3dW5y8bauVuH1ilmfJjxcd7uca1VZSvo1x9SK7Uol0Lu3uGPI/XM31yaamWI9YSac2FmDjFG+iL3R2laeq92cRPyZugnFCt0/ZEUKJf5M3YR2c5z7h8zXWSe+cuUK1Go1oqKidLZHRUWhuLjY6HNiYmKwfPlytG7dGmVlZVizZg26dOmCnJwcPPXUU0afM2vWLKSlpcnefr49djmsy4qM5BM5hWnUGzYAkybp5rcolfwfq379XLeUe2KiYfs6dDAMNkyxx8/dWJtc6T1zN8aqL3OcuKDHWV9ivGX5AyG41//5CH87KLh3X+3mJEI9sx/y9Coox8pUQdkWTquzc/HiRcTGxmLPnj1o3769ZvtHH32ENWvWiE467tOnDziOw9atW40+XlZWhrKyMs39mzdvIi4uTpY6O2o1HwBcu2bTYVwexwFhYUBgIJ+oK1Aq+cTliAh5/0A744++2PWshNfqiRchT2GqjotYzqhlZCw4Uyr5XhBPvfh7S3BH5GNLnR2n9ezUrFkTCoXCoBenpKTEoLfHnHbt2mHt2rUmHw8ICEBAQIDV7TRHoeD/QL3/vl0O7xKEb8NXrxo+VlgIvPAC/y1t8GB5zuesP/raQZw5gwfb9w8yXQBsY66OiyVCUUlH1zIyFZwJMwA9dShTGI4mxBGclrPj7++P1q1bY/v27Trbt2/fjg4dOog+zuHDhxHjxPGkt98GgoOddnq7i401zL8RCH+ctYuA2VKMz9Q0fntN+9Z2+bK4/erWtV8bsrLsU99HrgKJ7sDaGlBCvsj8+fwxHPVeUZE9QhzE5vRoGwhTz9PT09mxY8dYcnIyq1q1Kjt79ixjjLFp06axl156SbP/woUL2ebNm9k///zD/vvf/7Jp06YxACxTwnQVudfGkrKukavfOI6fKbFjR+VMpx07xM9QsmW9GzHT+MPD+fbYY6bG2rXiXufatfKfmzH7rdnlbWsQZWSI+zmGhenej4tjLDXV8e8VTcUmRDy3nXrOGGP/+c9/WJ06dZi/vz979NFH2a5duzSPDR8+nHXs2FFz/+OPP2b16tVjgYGBrEaNGuzJJ59k33//vaTzyV1nx1x9Gne6mbqoir14JCfbdrGWMo3fHhcgZ150LAV61k7HNRVACbeNG+V/Lc4m9ue4Y4du+YKNGx23QKw2sb9ftAAsIbQQqCRyLgQqNqnVHcTGAq+9Bjz8sG6uiJTEXVNDQWIW2JSykKotCwaayokRsyBnXJy8i4QKxL7HUhJnxbwehYJ/359/Xtwx3YHwugsL+TBBn7HPojMWiBXY42dPiKeihUCdxJPq7JSV8YnW+rkiYtak8vExn/PCmOX6JadOiW+rcBGTmstgLidGqP/Bccbrf3Cc/ep/2KOEvpjcFbXa85ZJsKaOizPXa7L0+8VxtAAsIXKgYMcGnlRn58oV3ftCUvCWLZUXD1MqKsSdw9TFWq0GvvhC3DEEUi9AYpKfnVXcT+znSMrnTUpg5GkJsFJ/js5cr4mK7BHiGBTs2CAhAahZ09mtsA/t3pN+/fgCgrb+wT12zPgMF1tWURdzAZIy4yUxETh7lh82yMjg/83PlyfQMTUryh7f7qUERq5UNViumWNSfo72CDb1mXtdVEGbEAeQPYPIxck9Gys52fnJxfa+LVwoflaWNQnGYpM0jd3EJAy7wowXc7OiVCrG0tKMt8naBFmpi9S6QgKss2aO2Xu9JrGvy5Y13wjxBm49G8vR5A52vGUxUP2purbc9C/g1ryHUi5Azp7xYm5aOcBPqTfVJrErups6r9j309lTm+019V7q+eVejNWRr4uCJeLpKNiRQO5gR6Uyf7HytltEhPRgRWovhPB8sRcKuXp2rLmYWPPahFtamu0XrI0bGVMoxP0c5HzdUthr6r1UxnpgbAk2Hfm6vK2eEvFOFOxIYI9gJzjY+UGGK9zi4hgrK+MviO+8Iy3AkNILEREh7Y+4HMMU1l5MrO35k/NCuGGD6XNYChodcRF1hWFGgZyBnaNel7N7xQhxFFuu35SgbKOPPgJu33Z2K1zDoEGAvz9fD6RJE3HPERKMExPFJUFHRPDJzFKSNm2d8bJpEzBggHXLWFg7g4cx+RKHn38eyMzkE161WUqAddTyHc6cDaVPWK9p8GD+X1uS8h3xumi5CULEoWDHBmq15WnZ3mTu3MoLoDUzXJ5/Hli/3vh+Qq2bzz7jAyqprJ3xsnEjH8QZI3yHHj0aKC83vo+t5QnkusBLnWXmyIuo2PdISi0mV+CIWV7OrBFEiDuhCso28KQKynJRKvmLKiC9kq3A2MrncXF874ut03ClrCqelcX36IhRsyawdCnf86R9bMD8+2CJLZVztV9rZCS/raRE3Grqjqzsa6nqsbbMTPeZim1NNWepxFYez8jge6sIcWc2Xb9lH1RzcXLm7NgyZdqTb9p5ONbOcHH2zJKyMvHJ1qZuQm6LqffB3M0e052Ntc0UR89gs7SOlxzviTPYa5aXwJXynQixN8rZcRJPqqAsJ+08HGuLpcmZOyFVVhbfZnNLYIgh5LYAxt+H8HD+X7kr55rKtTHWNlN5N44YgtGWmAjMmGF+H8ZsG5KRq2ChFPYuGEjLTRAikh2CL5cm96rn5mb5eOtN/1uks3tppDA1c8mWm9AbUVbGF2gcN47/t6zM8dOdxfaU2LvQnjH27E1y9tRse/4O2Lv3iBBXQVPPJZB76rmY7ndvuvn48AGDO7JUk8aWW1KS4bCYdgVlR0931r6ZGuIwVQ7AXhdROYZkjL2XYqdmu1NQrk/uoJkQV0TBjgRyBzuMMTZ5svODDFe6ueO3SSl1fuR8n+R+r6zJIzPVU5KZabxgZni4fX6+tvYmGbvgx8aaL/opHHPDBvcvyufOwRohYthy/fZ15hCaJ7BmxW5vICwgairnRMqsKHsqLwc+/RR4/33Hn5sx/t/XXgNCQ+XJTbImh8bYc4S8H6GN2q5dk34OMYR6SElJfK6J9rkt5TGZam9hoflzMsbnAQ0caPiYkNfkLotxCnluhBAj7BB8uTS5e3ZMLeBIN/PDI5a+RTviW2pqqv2Gray5ydGTICWPzFRPibOXb5A6JGPLkhzWvkeEEMez5fpNdXZsoFbz9Uvs9U3X3Rmr7WHqG7jwTT45GahRg+8t055NpFTy3/rl+oY9dSpfBNEaYWF8e69elact2jjOfE+CmB4x4T0GDN9n7fMAxs/lyBo7pkjp+XNEvSt7vlZCiDi2XL9p6rkNcnMp0DFHf3hETFXeRYv4ISUpSxRInVJcXg4sWCDyRRhx/Tr/cxcCCjkJAZ+x15CVxRep69yZLyTXuTN/X/89MTXdWZu5qc+usHyDlNIDjlhGwhHnIITYD+Xs2MBSPoA3q1IFePCAD0CEb+aWStubwxjfG6GfC2Ss2rJ2L5B+D0GHDkBKim01VoS27Npl/THMEWrJaPckmMtJMZZXkpjIv0/WVFB2dI0dSyz18ljbDv28IHOophYhbk72QTUXJ2fOzsKFzs/zcJebUslYcrI8x9Kv0Gwsz4Lj+Jwc/VwOuXN0ata0T+mBtWsrP2dScmjkyHWSOivK3jVkxOR3WVPvKi6OLzfg6HpChBDr0NRzCeQMdlavdn4Q4S43OQOCjAy+IF/Nms5/XcnJ0peCEHNbuLDycya2/sz778s3fdrSEhcbN1buZ68p22Lr45jb19z7KwQwVJSPEPdAy0U4iT0SVD0VY/Id69QpPh/lyhX5jmmtOnWAb74xnx9jjcuXK4faxOaLpKXJl+tkKe9n0iR+NfgBA6SdUyypq64L7Q0LE3f8qKjKoTB7L+lACHE+mo1lg6+/Bl58UaaGEVHCw10vyFQqgYUL+ZXPCwv5QMDWdbWE4y5ezF/AbZltZGx1bUu5ToKNG43XoLHmnFJYOyNs506ga1fpzwNcp/YTIcQ4mo3lJHJ/myeWlZU5uwWGLlzgA4Jr1+RZQFQg9JBcvmx+sUdLGNNdQNPUQqH6PTJqNZ/MLcc5pbJ2RlinTtYvjOnMxWcJIfZFwY4N2rRxdgu8S7duwO3bzm6FacnJ8s7QE7JHxozhh4ts7YMtKpI2PGTL7Dntc1rD2hlhQhVmQP7V5Akh7ouCHRtMm+bsFniXX35xdgtME3oy5OrV0XblSuUF3BYxMZYDGO0eGTlqy1g7ZTshwfoeGsrBIYToo2DHBv/84+wWEGNCQuxfUdeUiAg+d8eVaAcGUoaHbKktYy4YEcPWHprERODsWT43JyOD/zc/nwIdQrwVBTs2qFrV2S0gxmzaBIwa5Zxz79jBVzd2JYxVBgZShocs9a5YYutwka09NJSDQwgR0GwsG6xcCYwcKVPDiCzCwvgqwbm5zuvdcTXJyfxsMYDPxYmP53OLjP3m68+iErPOlj651zGjWVKEEIBmYzmNK9R5IbrKy4HNm/kLZHCws1vjGvr1q/y/1OEhMetsaUtL44eP5Bwuoh4aQoitqGfHBkOH8vkAhLiq8HDg0iXjK6Pr19mJi+MDHWOBinbvyqlTwPLlujPPzD2XEELkYMv1mxYCtYErT4MmBAAmTDDeE6K/UKil4SGhd0Xw9ts0tEQIcR8U7NjAu/rEiLsJD+eDElP0AxgpbHkuIYQ4GuXs2MDaWSqEGMNxQGoqn+Arh+XLqbeFEEIACnZsYmPKDyEacXF8IvCcObr1YXbs4G/JyXwNH/3nGAuO4uKAzEzKnyGEEAElKNvgl1+A7t1lahjxOhERfJJ7v37icl5MTcGmqdmEEG9gy/Wbgh0bqNWAL2U9uRQfqJGAXMSgCEWIQS4SUAH7XflDQ/mp3G+8Ady7Z3q/oCBg+HD+/9WrV+a80FRqQggRh2ZjOYlCwV/sSkud3RICAM8hC4sxEXGonE9dACUmYjE2Q/4xHY4DVqzgh4tCQvjFOk1Zs4b/V3u698yZ8hfgI4QQYohydmxw7x4FOq7iOWRhE5IQC91VLmNRiE1IwnPIkvV8ERG6SxYkJvJ5Mvr5M0olvx3gKxHrL8JZWMhvz5K3eYQQQrTQMJYN3ngD+OwzmRrm5vSHj3ajA57AHpP35Rxe8oEaZxGPWFwwGr1XgMMFKFEX+bKcMyKCD1r8/Q0fM5Y/A/BLNJhabVx/iQZHoVwfQog7oWEsJ9m/39ktcA3Gho9UUMAXapP35RxeSkCuzrn1+YChNgrwzdhcPHiiEyIj+Qv9F18AP/8M3Lqlta8PUFFh/DhCqYHPPjMe6ADG68/k5JgOdAC+XlNBAR94OKp2TVYWkDxejYcuVgao/9ZKwKJPFTSkRgjxODSMZYPycme3wDIfqNERORiEdeiIHPj8L+AwtV0qU8NHCr3j6d+3dXgpPJy/AUAMikQ9pxZXhMGD+aHHUaP4YSgh0AkL49d1unePn/adnAzUrKn7fO3VttVqPohZt47/V23m7dNeVsGcInEvw2ZZWcDaAVnYfTEeOeiMdRiCHHTG7ovxWDsgi4bUCCEeh4axbFClCnD/vkwNk5EwpNQXW/Ai1iISlSuWFkCJDAzGEKyzOZHX0vCRJVKGl8LDgW++AYqLgcuX+aGk6Gj+sbOrcjBqreUlzvMWZuPf2p2QlGRY/VrotdHOwzE1zGNsXSlTicZZWcDo0XybLcnO5nt27Dm8pFYDI6tnYeXtJABM5+dWAf5NeDl4E74o6Ydjn+fi7pkiBNWLQfMxCVD422+Mi4bUPIe6XI2jSx332SHew6brN/MypaWlDAArLS21+Vj8JdO1bs8hk52H0uQOaoBV/O9f3e0cU4NjzyFT9Lk6IluWRndEtsXdMjP5m1LvpSmVjG1cr2KFPkqmBmfiNXPsgiKOld1VGTxf/6ZUMqZSmf6ZZ2Yafx7H8bfMTN19OSNN8oGKdUQ2G4QM1hHZzAcqplAwtmGD6deofVxb7PhZxc5DySpMfj44VoJwdoGL1dleqFCyvammG6FSMZadzVhGBv+vufdQn6XXbMuxiWPtTc1khQqlpM8OIWLZcv2mYMcG1l7ffVHGJmIh+wTj2EQsZL4oky3Q4YMW8zuau9CdQxzzgUrU+QYhQ5ZgZxAyzAYEz/hlsxeSVExhJEgQgokhgZmagE33NfG3XzunsR0/i3tdiYmMLVzI2Nq1lRdXVZmKHZiXzV72X8smYiEbgrWsI7KZL8p02lQ7VsXvrzK8gAs/I/1g9DyUOkGmfjCk+N/rNBXwSAkG1jVOE/UzMRUM756caXAuqQFaWRn//o4bx9iIEeab0rs3YzVrij82cZ69qcb//gifHQp4iK1suX7TMJYNtNfGCkIpfkFXPIYDUADgADAAFQDK4YcyBKAEUfDFA9TDeWgvq8UAXEUorqImyuCHqriH2wjG32iE2ihEDC7hGmrgGzyPi4hDMfjxmyiUaGY2+UCNQigRgcuwdcmuTsjGLnSyuF9H5CAHloePpJ7PWMLzFfAJOjVxVbNNf+jN2PO0WZsUPYDLwkJm/LjGEq9nVF+MvIcSceiQ7r5CfhNMDB8lYRMAmKwVdCAuUTNjSxj22bIFWLsWuFI5UgmlEliwgB/qKygA/viDv+o0/DsL43YNsDpRz9iwY3g4cPWq6eds3MhPrRdMnQrMn286CVyKDRuA55+vvG/LUFh5ObB0KXDqFP97/fjj/LIbUofT5BiOE/LBcnL4+44oPmnrOdXlalwKike02vSMyCKFEtF382lISwsN+Unj1sNY//nPf1h8fDwLCAhgjz76KPvtt9/M7p+Tk8MeffRRFhAQwOrWrcuWLVsm6Xz26Nk5iXome0sccbuMcHYD1WQ7nrGeFmM3H/BDIqaGjyz3Hhj2JJnqnaqAYY+UsaE3H6jYO0gTvb+lm6XeMrHnqHyvTL8XJQjX9ESZOmZ2tvGeFPE/K9s/H2KGHYWbMDzHGGOpqbJ9RDXH3riRP7Ytw3+pqfyxjJ1DSi+SHEOQmZmMhYcbtiM83H69WXKc8/DCbFE/tMMLs+3zItwQDflJ57bDWOvXr2d+fn7siy++YMeOHWMTJ05kVatWZefOnTO6/7///suCgoLYxIkT2bFjx9gXX3zB/Pz82KZNm0SfU+5gRwh0nBnsyH1+KRezymCAM2iTufumAhWpF2T9gElMUCF2qM7aAMHYOcTmN1lqd/J4ldE8IEs3ufKrGMQHw9q39esZ8/GRrQk6t9RU47lRxvKo9IkJwCwdgzHT+Vli2qB9DEttkTvgkeucu8eJG9LePS5D3hfgpmjIzzpuG+y0bduWjR49Wmdbo0aN2LRp04zuP3XqVNaoUSOdba+//jpr166d6HPKGewE4YbTAx1rbnLl7Ag3Y3koD6Awe/8c4gx6P2y5IAsBmthjiAnobA0QtM8hV35Tr6qW223sJtf5xb53+rfQUNlOb3Az1SsD8IFGXJzxPKayMvPP1b6ZOgZjpvOzxLRB+xixsZbbYSmBXgo5z0k9O+KpylSsUGH+C9kFRRxTlVEmvj5brt9Oq7NTXl6OgwcPolu3bjrbu3Xrhj179hh9zt69ew327969Ow4cOIAHDx4YfU5ZWRlu3rypc5PLT+gFDrA5R8YZ9FMmhLyRZCySXGV4MxIRj7PohGwMRgY6IRtVcNfs/brIN8idEVsvxxjhuWKPIWY/W9qj//wixNh0LEHIHevaJPb8pQjRfBb0VYDDecQhFwmSz2/PZVXM1ThirLJgo76lS80/V5upYwD8drFFI03JzRVXj+nCBfPHkULOczYfk4CLCqXZz06hIg7Nx0j/7Hiao0tzUctEbhPAF0GNVRfg6FKZftAEgBMrKF+5cgVqtRpRUVE626OiolBcXGz0OcXFxUb3V6lUuHLlCmJiDP+gz5o1C2lpafI1XEttnLfLce3tEFohEpd1kmAvQIlkLLK6onEFFAZJzZbu67MlIBCeK/YYYvazNUDRfn4uElAAJWJRCB8wg30rIK7Cp7VtEnP+C1AiBQuwAS+gApzOfrYEw67AWMHGM2dsP4a57VL2k1JQUq7ik3KeU+GvwPmUxYiem2Tys1OQsgixlHyLu2fEvfFi9yPiOL2CMsfpfhNgjBlss7S/se2C6dOno7S0VHMrKCiwscWVzqO2bMdypDUYZtATY6ynxdGEC7Kpb4fG6Pc2WDqGlN6JymNJY+wcFVBgIhZrHtffH+BwBeFm233Bx7peFbHnT8ZiZOJ5JGETChGrs88FKJGETU7/jFjLyPcg1Ktn+zHMbZeyn9hjSN1XruOI2bfdnET8mboJxQrdz06RQok/Uzeh3Rz3/OzILaieuDde7H5EJNkH1UQqKytjCoWCZWVl6WyfMGECe+qpp4w+JyEhgU2YMEFnW1ZWFvP19WXl5eWizuvNOTsV4HNn5KrrY4+buYRnsTOfTB3DmbOxtI+nn98k5C9ZavfuyZlMqTSeCCvl9Zg6v/Z+Qq2fV6pmsL7VsiXncWnfOI7P+wgOlv/zwnGuk7Nj6ufiDTk7OsctU7HDC7PZ7nEZ7PDCbMo90VOZs2O+CCq9b4bcOkH5jTfe0NnWuHFjswnKjRs31tk2evRopyUoM+a6s7GM3a8A2GykOquZom/GLsglCGeXoTs/1thF2twxzO0vtT3CTUzitf7NWAVl7XNd8NE91wVFnGZ2hjDrx5aAx9z5tW/CLCIxM3YmTza+XXs2kpjjSLkJxxZmY+m/J86YjWVNG7SPYaktrjobi0hTORvL+Bcbmo1lnNsGO8LU8/T0dHbs2DGWnJzMqlatys6ePcsYY2zatGnspZde0uwvTD2fNGkSO3bsGEtPT3fq1HOBs+vsGAsE9NvzAAq3CHSEm7ELstiLtLljaF+EfH2lt2cwzFdQFtMDEhhoPFgJDuYvLJa+GRur5xIRwVhyMl/VeONG8XV4TE0Hj4szXPrCWC2WsLDK/Yy1y9hxxPQmVK1qOaDTPraYc5tirs6O2GPY2gZL77Or19kh0hmrs6P9xYYYcusKykuXLsWcOXNQVFSEZs2aYeHChXjqqacAACNGjMDZs2eRI5T1BLBr1y5MmjQJf//9N2rVqoU333wTo0ePFn0+OSsoa6vKiaugfAbxaI/9CMJdqOCLa6gOwAd3UQUcYHUFZQBIQC5iUIQixGAvHscb+Bz1cAZnUA//wRio4C/pNVWrBvj48DNWfH0BPz/+/6GhQEgIv2J4SQm/b0gIEBTEV5/18+MrrzZuDFSvzu8TFMQfj+P4xx57DLh+na/AW1wMHD4MnD/PL67aogVfyfbuXX4VcqHaLsfxx2nQAOjTB9izh38sPJyvFlxcDOTl8c/r0AFo1oyfRXLgABAYyD8/NpZ//pgxfDtycoCdO4E//wQuXeLb2Lcv0Lw5X5n46FH+3E2b8q9FoeBzPZo358+3Z0/lPj17AufOAYcO8fumpABduvBt0K9Mq1YDn34K/P47EBwMvPQSv6/oirUWKvUKj2tXUK5Xj38dv/9e2ZaEBP41FBZWLrAaG2u88q+YKrtiKggL+xQW8u+5UIE5LIxf3FU4v1rNz5g6c4Zv++uv86/F0mumCsrSOeOchCooS2XL9dvpwY6j2SvYIYQQQoj92HL9dvpsLEIIIYQQe6JghxBCCCEejYIdQgghhHg0CnYIIYQQ4tEo2CGEEEKIR6NghxBCCCEejYIdQgghhHg0CnYIIYQQ4tEo2CGEEEKIR/N1dgMcTSgYffPmTSe3hBBCCCFiCddtaxZ+8Lpg59atWwCAuLg4J7eEEEIIIVJdvXoVoaGhkp7jdWtjVVRU4OLFiwgJCQHHcbIe++bNm4iLi0NBQQGtu+Vg9N47B73vzkPvvfPQe+8cpaWlqF27Nq5fv47q1atLeq7X9ez4+PhAqVTa9RzVqlWjXwAnoffeOeh9dx56752H3nvn8PGRnm5MCcqEEEII8WgU7BBCCCHEo1GwI6OAgAC8//77CAgIcHZTvA69985B77vz0HvvPPTeO4ct77vXJSgTQgghxLtQzw4hhBBCPBoFO4QQQgjxaBTsEEIIIcSjUbBDCCGEEI9GwY5Mli5dirp16yIwMBCtW7dGbm6us5vkFX777Tf06dMHtWrVAsdx+Pbbb53dJK8wa9YsPPbYYwgJCUFkZCT69++PkydPOrtZXmHZsmVo0aKFpqBd+/bt8eOPPzq7WV5n1qxZ4DgOycnJzm6Kx5sxYwY4jtO5RUdHSzoGBTsy+Oabb5CcnIy3334bhw8fRkJCAnr06IHz5887u2ke786dO3jkkUewZMkSZzfFq+zatQtjx47Fvn37sH37dqhUKnTr1g137txxdtM8nlKpxOzZs3HgwAEcOHAATz/9NPr164e///7b2U3zGvv378fy5cvRokULZzfFazRt2hRFRUWa29GjRyU9n6aey+Dxxx/Ho48+imXLlmm2NW7cGP3798esWbOc2DLvwnEcNm/ejP79+zu7KV7n8uXLiIyMxK5du/DUU085uzleJywsDHPnzsWoUaOc3RSPd/v2bTz66KNYunQpZs6ciZYtW2LRokXObpZHmzFjBr799lvk5eVZfQzq2bFReXk5Dh48iG7duuls79atG/bs2eOkVhHiWKWlpQD4iy5xHLVajfXr1+POnTto3769s5vjFcaOHYtevXqha9euzm6KVzl16hRq1aqFunXrYtCgQfj3338lPd/rFgKV25UrV6BWqxEVFaWzPSoqCsXFxU5qFSGOwxhDSkoKnnzySTRr1szZzfEKR48eRfv27XH//n0EBwdj8+bNaNKkibOb5fHWr1+PQ4cOYf/+/c5uild5/PHHsXr1ajRo0ACXLl3CzJkz0aFDB/z9998IDw8XdQwKdmTCcZzOfcaYwTZCPNG4ceNw5MgR/P77785uitdo2LAh8vLycOPGDWRmZmL48OHYtWsXBTx2VFBQgIkTJ+KXX35BYGCgs5vjVXr06KH5f/PmzdG+fXvUq1cPX331FVJSUkQdg4IdG9WsWRMKhcKgF6ekpMSgt4cQTzN+/Hhs3boVv/32G5RKpbOb4zX8/f1Rv359AECbNm2wf/9+LF68GJ9//rmTW+a5Dh48iJKSErRu3VqzTa1W47fffsOSJUtQVlYGhULhxBZ6j6pVq6J58+Y4deqU6OdQzo6N/P390bp1a2zfvl1n+/bt29GhQwcntYoQ+2KMYdy4ccjKysKvv/6KunXrOrtJXo0xhrKyMmc3w6N16dIFR48eRV5enubWpk0bDB06FHl5eRToOFBZWRmOHz+OmJgY0c+hnh0ZpKSk4KWXXkKbNm3Qvn17LF++HOfPn8fo0aOd3TSPd/v2bZw+fVpzPz8/H3l5eQgLC0Pt2rWd2DLPNnbsWGRkZGDLli0ICQnR9GyGhoaiSpUqTm6dZ3vrrbfQo0cPxMXF4datW1i/fj1ycnLw008/ObtpHi0kJMQgJ61q1aoIDw+nXDU7mzJlCvr06YPatWujpKQEM2fOxM2bNzF8+HDRx6BgRwYvvPACrl69ig8++ABFRUVo1qwZfvjhB9SpU8fZTfN4Bw4cQOfOnTX3hfHb4cOHY9WqVU5qlecTyix06tRJZ/vKlSsxYsQIxzfIi1y6dAkvvfQSioqKEBoaihYtWuCnn37CM8884+ymEWIXFy5cwODBg3HlyhVERESgXbt22Ldvn6RrLNXZIYQQQohHo5wdQgghhHg0CnYIIYQQ4tEo2CGEEEKIR6NghxBCCCEejYIdQgghhHg0CnYIIYQQ4tEo2CGEEEKIR6NghxBCCCGy+O2339CnTx/UqlULHMfh22+/lXwMxhjmzZuHBg0aICAgAHFxcfi///s/m9pFwQ4hxKH27NkDhUKBZ5991tlNkaRTp05ITk52djMIcWl37tzBI488giVLllh9jIkTJ+LLL7/EvHnzcOLECWzbtg1t27a1qV20XAQhxKFWrFiB8ePH48svv8T58+dpDTNCPEiPHj3Qo0cPk4+Xl5fjnXfewddff40bN26gWbNm+PjjjzVLzxw/fhzLli3Df//7XzRs2FC2dlHPDiHEYe7cuYMNGzbgjTfeQO/evXXWL8vJyQHHcfj555/RqlUrVKlSBU8//TRKSkrw448/onHjxqhWrRoGDx6Mu3fvap5XVlaGCRMmIDIyEoGBgXjyySexf/9+zeOrVq1C9erVddrx7bffguM4zf0ZM2agZcuWWLNmDeLj4xEaGopBgwbh1q1bAIARI0Zg165dWLx4MTiOA8dxOHv2rF3eI0I82csvv4zdu3dj/fr1OHLkCJ5//nk8++yzOHXqFABg27ZteOihh/Ddd9+hbt26iI+PxyuvvIJr167ZdF4KdgghDvPNN9+gYcOGaNiwIV588UWsXLkS+svzzZgxA0uWLMGePXtQUFCAgQMHYtGiRcjIyMD333+P7du349NPP9XsP3XqVGRmZuKrr77CoUOHUL9+fXTv3l3yH8czZ87g22+/xXfffYfvvvsOu3btwuzZswEAixcvRvv27fHqq6+iqKgIRUVFiIuLs/0NIcSLnDlzBuvWrcPGjRuRkJCAevXqYcqUKXjyySexcuVKAMC///6Lc+fOYePGjVi9ejVWrVqFgwcPIikpyaZz0zAWIcRh0tPT8eKLLwIAnn32Wdy+fRs7d+5E165dNfvMnDkTTzzxBABg1KhRmD59Os6cOYOHHnoIAJCUlITs7Gy8+eabuHPnDpYtW4ZVq1Zpus6/+OILbN++Henp6UhNTRXdtoqKCqxatQohISEAgJdeegk7d+7ERx99hNDQUPj7+yMoKAjR0dGyvBeEeJtDhw6BMYYGDRrobC8rK0N4eDgA/vewrKwMq1ev1uyXnp6O1q1b4+TJk1YPbVGwQwhxiJMnT+LPP/9EVlYWAMDX1xcvvPACVqxYoRPstGjRQvP/qKgoBAUFaQIdYduff/4JgP+m+ODBA01wBAB+fn5o27Ytjh8/Lql98fHxmkAHAGJiYlBSUiLtRRJCTKqoqIBCocDBgwehUCh0HgsODgbA/975+vrqBESNGzcGAJw/f56CHUKIa0tPT4dKpUJsbKxmG2MMfn5+uH79umabn5+f5v8cx+ncF7ZVVFRoni9s08YY02zz8fExGCp78OCBQfvMnYcQYrtWrVpBrVajpKQECQkJRvd54oknoFKpcObMGdSrVw8A8M8//wAA6tSpY/W5KWeHEGJ3KpUKq1evxvz585GXl6e5/fXXX6hTpw6+/vprq45bv359+Pv74/fff9dse/DgAQ4cOKD5NhgREYFbt27hzp07mn3y8vIkn8vf3x9qtdqqdhLiLW7fvq35/QaA/Px85OXl4fz582jQoAGGDh2KYcOGISsrC/n5+di/fz8+/vhj/PDDDwCArl274tFHH8XIkSNx+PBhHDx4EK+//jqeeeYZg+EvKSjYIYTY3XfffYfr169j1KhRaNasmc4tKSkJ6enpVh23atWqeOONN5CamoqffvoJx44dw6uvvoq7d+9i1KhRAIDHH38cQUFBeOutt3D69GlkZGTozAITKz4+Hn/88QfOnj2LK1euUK8PIUYcOHAArVq1QqtWrQAAKSkpaNWqFd577z0AwMqVKzFs2DBMnjwZDRs2RN++ffHHH39oEv59fHywbds21KxZE0899RR69eqFxo0bY/369Ta1i4axCCF2l56ejq5duyI0NNTgsQEDBuD//u//cOjQIauOPXv2bFRUVOCll17CrVu30KZNG/z888+oUaMGACAsLAxr165Famoqli9fjq5du2LGjBl47bXXJJ1nypQpGD58OJo0aYJ79+4hPz8f8fHxVrWZEE/VqVMng2FjbX5+fkhLS0NaWprJfWrVqoXMzExZ28Uxc60ihBBCCHFzNIxFCCGEEI9GwQ4hhBBCPBoFO4QQQgjxaBTsEEIIIcSjUbBDCCGEEI9GwQ4hhBBCPBoFO4QQQgjxaBTsEEIIIcSjUbBDCCGEEI9GwQ4hhBBCPBoFO4QQQgjxaBTsEEIIIcSj/T97NiwMvQ383QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76a8284c",
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}