Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "09b65706",
   "metadata": {},
   "source": [
    "# Credit Card Approval Project - Mahender Kore"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f59916",
   "metadata": {},
   "source": [
    "## Project Goal:\n",
    "    \n",
    "1.Perform Data Cleaning and EDA to derive insights of factors effecting the credit card approval\n",
    "\n",
    "2.Build Machine learning models to predict the credit card approval status."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b8be21a",
   "metadata": {},
   "source": [
    "## Step1 : Defining Objective and Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5600fe43",
   "metadata": {},
   "source": [
    "### Research question:\n",
    "#### Why is your proposal important in today’s world? How predicting a good client is worthy for a bank?  \n",
    "Predicting a good client in credit card approval is crucial for banks as it reduces credit risk, minimizes defaults, and ensures a healthier credit card portfolio, leading to improved financial stability and profitability.\n",
    "\n",
    "#### How is it going to impact the banking sector? \n",
    "\n",
    "Implementing this credit card approval analysis can streamline the banking sector by facilitating faster and informed credit decisions, enhancing customer experience, reducing credit losses, and improving overall efficiency in credit operations.\n",
    "\n",
    "#### If any, what is the gap in the knowledge or how your proposed method can be helpful if required in future for any bank in India.\n",
    "\n",
    "The proposed method addresses the knowledge gap in credit risk assessment by leveraging advanced analytics and machine learning, enabling banks in India to optimize their credit card approval process and stay competitive in a data-driven financial landscape."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a84fe4b5",
   "metadata": {},
   "source": [
    "\n",
    "#### Problem Statement\n",
    "The problem statement is framed on the above reasearch question.\n",
    "\n",
    "This project focuses on credit card approvals, a crucial process that banks carry out to manage risk and maintain profitability.\n",
    "\n",
    "In particular, it deals with how banks factor in default risks during this process. \n",
    "\n",
    "When a person applies for a credit card, banks perform a comprehensive analysis, evaluating the likelihood of potential defaults based on the applicant's financial history, income stability, among other factors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90fd31ed",
   "metadata": {},
   "source": [
    "### Assumptions\n",
    "Assumptions for credit card approval analysis:\n",
    "\n",
    "1.The dataset used is assumed to be unbiased.\n",
    "\n",
    "2.The data is assumed to be accurate, complete, and reliable for meaningful analysis.\n",
    "\n",
    "3.Relationships between applicant attributes and creditworthiness are assumed to be stable over time.\n",
    "\n",
    "4.Decision criteria and risk assessment models are assumed to remain consistent for future applicants.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff437fa9",
   "metadata": {},
   "source": [
    "### Hypothesis : Annual Income on Credit risk\n",
    "\n",
    "#### Null Hypothesis (H0): \n",
    "The income by the applicant does not significantly affect their credit risk\n",
    "\n",
    "####  Alternative Hypothesis (Ha): \n",
    "High Annual income is associated with lower credit risk\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cc1a5af",
   "metadata": {},
   "source": [
    "## Step2 : Data collection and understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baeb9854",
   "metadata": {},
   "source": [
    "##### Loading libraries, Reading all the data files and creating Dataframes <a id='loading-libraries'></a>  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0036c12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the necessary libraries \n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "521263f9",
   "metadata": {},
   "source": [
    "##### Reading data from 'Credit_card.csv' and 'Credit_card_label.csv'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f8e0b97b",
   "metadata": {},
   "outputs": [],
   "source": [
    " # loading the data set,Read the file and save credit_raw ,credit_label_raw \n",
    "credit_raw = pd.read_csv(\"Credit_card.csv\")\n",
    "credit_label_raw = pd.read_csv(\"Credit_card_label.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d074937f",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ind_ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>Car_Owner</th>\n",
       "      <th>Propert_Owner</th>\n",
       "      <th>CHILDREN</th>\n",
       "      <th>Annual_income</th>\n",
       "      <th>Type_Income</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Housing_type</th>\n",
       "      <th>Birthday_count</th>\n",
       "      <th>Employed_days</th>\n",
       "      <th>Mobile_phone</th>\n",
       "      <th>Work_Phone</th>\n",
       "      <th>Phone</th>\n",
       "      <th>EMAIL_ID</th>\n",
       "      <th>Type_Occupation</th>\n",
       "      <th>Family_Members</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>M</td>\n",
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       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>Pensioner</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-18772.0</td>\n",
       "      <td>365243</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "      <th>1</th>\n",
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       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-13557.0</td>\n",
       "      <td>-586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>5009746</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5009749</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-13557.0</td>\n",
       "      <td>-586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5009752</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-13557.0</td>\n",
       "      <td>-586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    Ind_ID GENDER Car_Owner Propert_Owner  CHILDREN  Annual_income  \\\n",
       "0  5008827      M         Y             Y         0       180000.0   \n",
       "1  5009744      F         Y             N         0       315000.0   \n",
       "2  5009746      F         Y             N         0       315000.0   \n",
       "3  5009749      F         Y             N         0            NaN   \n",
       "4  5009752      F         Y             N         0       315000.0   \n",
       "\n",
       "            Type_Income         EDUCATION Marital_status       Housing_type  \\\n",
       "0             Pensioner  Higher education        Married  House / apartment   \n",
       "1  Commercial associate  Higher education        Married  House / apartment   \n",
       "2  Commercial associate  Higher education        Married  House / apartment   \n",
       "3  Commercial associate  Higher education        Married  House / apartment   \n",
       "4  Commercial associate  Higher education        Married  House / apartment   \n",
       "\n",
       "   Birthday_count  Employed_days  Mobile_phone  Work_Phone  Phone  EMAIL_ID  \\\n",
       "0        -18772.0         365243             1           0      0         0   \n",
       "1        -13557.0           -586             1           1      1         0   \n",
       "2             NaN           -586             1           1      1         0   \n",
       "3        -13557.0           -586             1           1      1         0   \n",
       "4        -13557.0           -586             1           1      1         0   \n",
       "\n",
       "  Type_Occupation  Family_Members  \n",
       "0             NaN               2  \n",
       "1             NaN               2  \n",
       "2             NaN               2  \n",
       "3             NaN               2  \n",
       "4             NaN               2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading top 5 records\n",
    "credit_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8deb7af7",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Ind_ID</th>\n",
       "      <th>label</th>\n",
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       "      <td>1</td>\n",
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       "      <td>5009744</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "    Ind_ID  label\n",
       "0  5008827      1\n",
       "1  5009744      1\n",
       "2  5009746      1\n",
       "3  5009749      1\n",
       "4  5009752      1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading top 5 records\n",
    "credit_label_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7868bc8",
   "metadata": {},
   "source": [
    "##### Merging the two table based on common column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c2cc653",
   "metadata": {},
   "outputs": [],
   "source": [
    "# joining the two table based on common column 'Ind_Id'\n",
    "credit = pd.merge(left= credit_raw, right= credit_label_raw, how = 'inner',on = 'Ind_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "90eae7d0",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Marital_status</th>\n",
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       "      <th>Birthday_count</th>\n",
       "      <th>Employed_days</th>\n",
       "      <th>Mobile_phone</th>\n",
       "      <th>Work_Phone</th>\n",
       "      <th>Phone</th>\n",
       "      <th>EMAIL_ID</th>\n",
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       "      <td>Pensioner</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-18772.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-13557.0</td>\n",
       "      <td>-586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5009746</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
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       "      <td>-586</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5009749</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-13557.0</td>\n",
       "      <td>-586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>5009752</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-13557.0</td>\n",
       "      <td>-586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "    Ind_ID GENDER Car_Owner Propert_Owner  CHILDREN  Annual_income  \\\n",
       "0  5008827      M         Y             Y         0       180000.0   \n",
       "1  5009744      F         Y             N         0       315000.0   \n",
       "2  5009746      F         Y             N         0       315000.0   \n",
       "3  5009749      F         Y             N         0            NaN   \n",
       "4  5009752      F         Y             N         0       315000.0   \n",
       "\n",
       "            Type_Income         EDUCATION Marital_status       Housing_type  \\\n",
       "0             Pensioner  Higher education        Married  House / apartment   \n",
       "1  Commercial associate  Higher education        Married  House / apartment   \n",
       "2  Commercial associate  Higher education        Married  House / apartment   \n",
       "3  Commercial associate  Higher education        Married  House / apartment   \n",
       "4  Commercial associate  Higher education        Married  House / apartment   \n",
       "\n",
       "   Birthday_count  Employed_days  Mobile_phone  Work_Phone  Phone  EMAIL_ID  \\\n",
       "0        -18772.0         365243             1           0      0         0   \n",
       "1        -13557.0           -586             1           1      1         0   \n",
       "2             NaN           -586             1           1      1         0   \n",
       "3        -13557.0           -586             1           1      1         0   \n",
       "4        -13557.0           -586             1           1      1         0   \n",
       "\n",
       "  Type_Occupation  Family_Members  label  \n",
       "0             NaN               2      1  \n",
       "1             NaN               2      1  \n",
       "2             NaN               2      1  \n",
       "3             NaN               2      1  \n",
       "4             NaN               2      1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading top 5 records\n",
    "credit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7016e000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1548, 19)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total no of records\n",
    "credit.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9683de32",
   "metadata": {},
   "source": [
    " There are 1548 records with 19 different columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3be729ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Ind_ID', 'GENDER', 'Car_Owner', 'Propert_Owner', 'CHILDREN',\n",
       "       'Annual_income', 'Type_Income', 'EDUCATION', 'Marital_status',\n",
       "       'Housing_type', 'Birthday_count', 'Employed_days', 'Mobile_phone',\n",
       "       'Work_Phone', 'Phone', 'EMAIL_ID', 'Type_Occupation', 'Family_Members',\n",
       "       'label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying the column variables\n",
    "credit.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66b6581c",
   "metadata": {},
   "source": [
    "##### Formatting the column name in same format for all, label into credit status 0 :approved 1: rejected\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "62812683",
   "metadata": {},
   "outputs": [],
   "source": [
    "#renaming the column names\n",
    "credit.columns = ['Id', 'Gender', 'Car_owner', 'Property_owner', 'Children',\n",
    "       'Annual_income', 'Type_income', 'Education', 'Marital_status',\n",
    "       'Housing_type', 'Birthday_count', 'Employed_days', 'Mobile_phone',\n",
    "       'Work_phone', 'Phone', 'Email', 'Type_occupation', 'Family_members',\n",
    "       'Credit_status']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a14d9e",
   "metadata": {},
   "source": [
    "##### converting the \"Birthday count\" and \"Employed_days \" into age and no of Employed years respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "176ea401",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting into age\n",
    "credit['Age'] = ((credit['Birthday_count']*(-1)) +1 )//365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c9fa1d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# coverting employee_days to  employee_years\n",
    "credit['Employed_years'] = ((credit['Employed_days']*(-1)) +1 )//365"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2017e98",
   "metadata": {},
   "source": [
    "##### Checking for error ,  Age would be always greater then 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7b228ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    1548\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the inconsitencies in Age column\n",
    "(credit[\"Age\"] < 0).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f1a500",
   "metadata": {},
   "source": [
    "There is no inconsitency Age column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc9887c0",
   "metadata": {},
   "source": [
    "##### As the Data set source , mentioned negative employed_years represents unemployed(0 years of employement)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "344847aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    1287\n",
       "True      261\n",
       "Name: Employed_years, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##checking the inconsitencies in Employed_years column\n",
    "(credit[\"Employed_years\"] < 0).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d230f44f",
   "metadata": {},
   "source": [
    " There are 261 unemployed peresonal, Assigning them 0 Employment_years "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ae6b97f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace unemployed_years as 0\n",
    "credit['Employed_years']= credit['Employed_years'].apply(lambda x : 0 if x < 0 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "35d2f7bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# droping the columns 'Birth_day 'count and 'Employee_days'\n",
    "credit.drop(columns=['Birthday_count','Employed_days'],axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45a26c77",
   "metadata": {},
   "source": [
    "#### Understanding the Data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "94055e19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1548 entries, 0 to 1547\n",
      "Data columns (total 19 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Id               1548 non-null   int64  \n",
      " 1   Gender           1541 non-null   object \n",
      " 2   Car_owner        1548 non-null   object \n",
      " 3   Property_owner   1548 non-null   object \n",
      " 4   Children         1548 non-null   int64  \n",
      " 5   Annual_income    1525 non-null   float64\n",
      " 6   Type_income      1548 non-null   object \n",
      " 7   Education        1548 non-null   object \n",
      " 8   Marital_status   1548 non-null   object \n",
      " 9   Housing_type     1548 non-null   object \n",
      " 10  Mobile_phone     1548 non-null   int64  \n",
      " 11  Work_phone       1548 non-null   int64  \n",
      " 12  Phone            1548 non-null   int64  \n",
      " 13  Email            1548 non-null   int64  \n",
      " 14  Type_occupation  1060 non-null   object \n",
      " 15  Family_members   1548 non-null   int64  \n",
      " 16  Credit_status    1548 non-null   int64  \n",
      " 17  Age              1526 non-null   float64\n",
      " 18  Employed_years   1548 non-null   int64  \n",
      "dtypes: float64(2), int64(9), object(8)\n",
      "memory usage: 241.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Data basic understanding\n",
    "credit.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8660917b",
   "metadata": {},
   "source": [
    " All were correcting data typed and there are missing values, we shall deal with them in step 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "782183c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Children</th>\n",
       "      <th>Annual_income</th>\n",
       "      <th>Mobile_phone</th>\n",
       "      <th>Work_phone</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Email</th>\n",
       "      <th>Family_members</th>\n",
       "      <th>Credit_status</th>\n",
       "      <th>Age</th>\n",
       "      <th>Employed_years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.548000e+03</td>\n",
       "      <td>1548.000000</td>\n",
       "      <td>1.525000e+03</td>\n",
       "      <td>1548.0</td>\n",
       "      <td>1548.000000</td>\n",
       "      <td>1548.000000</td>\n",
       "      <td>1548.000000</td>\n",
       "      <td>1548.000000</td>\n",
       "      <td>1548.000000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>1548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.078920e+06</td>\n",
       "      <td>0.412791</td>\n",
       "      <td>1.913993e+05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.208010</td>\n",
       "      <td>0.309432</td>\n",
       "      <td>0.092377</td>\n",
       "      <td>2.161499</td>\n",
       "      <td>0.113049</td>\n",
       "      <td>43.445609</td>\n",
       "      <td>5.667959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.171759e+04</td>\n",
       "      <td>0.776691</td>\n",
       "      <td>1.132530e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.406015</td>\n",
       "      <td>0.462409</td>\n",
       "      <td>0.289651</td>\n",
       "      <td>0.947772</td>\n",
       "      <td>0.316755</td>\n",
       "      <td>11.587528</td>\n",
       "      <td>6.517409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.008827e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.375000e+04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.045070e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.215000e+05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.078842e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.665000e+05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.115673e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.250000e+05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.150412e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.575000e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Id     Children  Annual_income  Mobile_phone   Work_phone  \\\n",
       "count  1.548000e+03  1548.000000   1.525000e+03        1548.0  1548.000000   \n",
       "mean   5.078920e+06     0.412791   1.913993e+05           1.0     0.208010   \n",
       "std    4.171759e+04     0.776691   1.132530e+05           0.0     0.406015   \n",
       "min    5.008827e+06     0.000000   3.375000e+04           1.0     0.000000   \n",
       "25%    5.045070e+06     0.000000   1.215000e+05           1.0     0.000000   \n",
       "50%    5.078842e+06     0.000000   1.665000e+05           1.0     0.000000   \n",
       "75%    5.115673e+06     1.000000   2.250000e+05           1.0     0.000000   \n",
       "max    5.150412e+06    14.000000   1.575000e+06           1.0     1.000000   \n",
       "\n",
       "             Phone        Email  Family_members  Credit_status          Age  \\\n",
       "count  1548.000000  1548.000000     1548.000000    1548.000000  1526.000000   \n",
       "mean      0.309432     0.092377        2.161499       0.113049    43.445609   \n",
       "std       0.462409     0.289651        0.947772       0.316755    11.587528   \n",
       "min       0.000000     0.000000        1.000000       0.000000    21.000000   \n",
       "25%       0.000000     0.000000        2.000000       0.000000    34.000000   \n",
       "50%       0.000000     0.000000        2.000000       0.000000    42.000000   \n",
       "75%       1.000000     0.000000        3.000000       0.000000    53.000000   \n",
       "max       1.000000     1.000000       15.000000       1.000000    68.000000   \n",
       "\n",
       "       Employed_years  \n",
       "count     1548.000000  \n",
       "mean         5.667959  \n",
       "std          6.517409  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          4.000000  \n",
       "75%          8.000000  \n",
       "max         40.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statitics of numerical columns\n",
    "credit.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "078b243b",
   "metadata": {},
   "source": [
    "From primary analyis there are ouliers in 'Children','Annual_income','Age','Employement_years' , we shall address this in step 3: Outlier Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "283f849c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Car_owner</th>\n",
       "      <th>Property_owner</th>\n",
       "      <th>Type_income</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Housing_type</th>\n",
       "      <th>Type_occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1541</td>\n",
       "      <td>1548</td>\n",
       "      <td>1548</td>\n",
       "      <td>1548</td>\n",
       "      <td>1548</td>\n",
       "      <td>1548</td>\n",
       "      <td>1548</td>\n",
       "      <td>1060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>Laborers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>973</td>\n",
       "      <td>924</td>\n",
       "      <td>1010</td>\n",
       "      <td>798</td>\n",
       "      <td>1031</td>\n",
       "      <td>1049</td>\n",
       "      <td>1380</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender Car_owner Property_owner Type_income  \\\n",
       "count    1541      1548           1548        1548   \n",
       "unique      2         2              2           4   \n",
       "top         F         N              Y     Working   \n",
       "freq      973       924           1010         798   \n",
       "\n",
       "                            Education Marital_status       Housing_type  \\\n",
       "count                            1548           1548               1548   \n",
       "unique                              5              5                  6   \n",
       "top     Secondary / secondary special        Married  House / apartment   \n",
       "freq                             1031           1049               1380   \n",
       "\n",
       "       Type_occupation  \n",
       "count             1060  \n",
       "unique              18  \n",
       "top           Laborers  \n",
       "freq               268  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statitics of categorical columns\n",
    "credit.describe(include = 'object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e2010214",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender\n",
      "nunique:  2\n",
      "unqiune values:  ['M' 'F' nan]\n",
      "----------------------------------------------------------------------\n",
      "Car_owner\n",
      "nunique:  2\n",
      "unqiune values:  ['Y' 'N']\n",
      "----------------------------------------------------------------------\n",
      "Property_owner\n",
      "nunique:  2\n",
      "unqiune values:  ['Y' 'N']\n",
      "----------------------------------------------------------------------\n",
      "Type_income\n",
      "nunique:  4\n",
      "unqiune values:  ['Pensioner' 'Commercial associate' 'Working' 'State servant']\n",
      "----------------------------------------------------------------------\n",
      "Education\n",
      "nunique:  5\n",
      "unqiune values:  ['Higher education' 'Secondary / secondary special' 'Lower secondary'\n",
      " 'Incomplete higher' 'Academic degree']\n",
      "----------------------------------------------------------------------\n",
      "Marital_status\n",
      "nunique:  5\n",
      "unqiune values:  ['Married' 'Single / not married' 'Civil marriage' 'Separated' 'Widow']\n",
      "----------------------------------------------------------------------\n",
      "Housing_type\n",
      "nunique:  6\n",
      "unqiune values:  ['House / apartment' 'With parents' 'Rented apartment'\n",
      " 'Municipal apartment' 'Co-op apartment' 'Office apartment']\n",
      "----------------------------------------------------------------------\n",
      "Type_occupation\n",
      "nunique:  18\n",
      "unqiune values:  [nan 'Core staff' 'Cooking staff' 'Laborers' 'Sales staff' 'Accountants'\n",
      " 'High skill tech staff' 'Managers' 'Cleaning staff' 'Drivers'\n",
      " 'Low-skill Laborers' 'IT staff' 'Waiters/barmen staff' 'Security staff'\n",
      " 'Medicine staff' 'Private service staff' 'HR staff' 'Secretaries'\n",
      " 'Realty agents']\n",
      "----------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Understanding no of unique values in each categorical variable|\n",
    "for col in credit.describe(include= 'object').columns:\n",
    "    print(col)\n",
    "    print(\"nunique: \",credit[col].nunique())\n",
    "    print(\"unqiune values: \",credit[col].unique())\n",
    "    print('-'*70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e8e39195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# understanding  duplicates in the dataset\n",
    "credit.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6d355d",
   "metadata": {},
   "source": [
    "There are no duplicates present in dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb906ba",
   "metadata": {},
   "source": [
    "## Step3 : Data Cleaning and Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "436267c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'Gender', 'Car_owner', 'Property_owner', 'Children',\n",
       "       'Annual_income', 'Type_income', 'Education', 'Marital_status',\n",
       "       'Housing_type', 'Mobile_phone', 'Work_phone', 'Phone', 'Email',\n",
       "       'Type_occupation', 'Family_members', 'Credit_status', 'Age',\n",
       "       'Employed_years'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying the columns\n",
    "credit.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f59197",
   "metadata": {},
   "source": [
    "##### Dealing with Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2bc35a87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type_occupation    488\n",
       "Annual_income       23\n",
       "Age                 22\n",
       "Gender               7\n",
       "Id                   0\n",
       "Work_phone           0\n",
       "Credit_status        0\n",
       "Family_members       0\n",
       "Email                0\n",
       "Phone                0\n",
       "Housing_type         0\n",
       "Mobile_phone         0\n",
       "Marital_status       0\n",
       "Education            0\n",
       "Type_income          0\n",
       "Children             0\n",
       "Property_owner       0\n",
       "Car_owner            0\n",
       "Employed_years       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating no of missing values\n",
    "credit.isnull().sum().sort_values(ascending= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f153c34",
   "metadata": {},
   "source": [
    "##### There are more no of missing values in 'Type_occupation'(30%), dropping the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "389cab50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the column Type_occupation\n",
    "credit.drop(columns = ['Type_occupation'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1fdcb7",
   "metadata": {},
   "source": [
    "Imputating the 'Gender' (categorical variable) with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "257b41ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imputating the gender by mode value\n",
    "from sklearn.impute import SimpleImputer\n",
    "sc = SimpleImputer(strategy='most_frequent')\n",
    "credit['Gender'] = sc.fit_transform(credit[['Gender']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ad78e4d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Understanding the 'Age' variable through distribution plot \n",
    "plt.figure(figsize = (5,3))\n",
    "sns.histplot(credit[\"Age\"],kde = True)\n",
    "plt.axvline(credit['Age'].mean(),color = 'red',label = 'Mean')\n",
    "plt.axvline(credit['Age'].median(),label = 'Median')\n",
    "plt.title(\"Distribution of Age\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a902cd87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17200634004172913"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Skewness of the 'Age' Variable\n",
    "credit[\"Age\"].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdf94967",
   "metadata": {},
   "source": [
    "##### Age is based on the 'Type_income' like Pensioner will be having higher Age, We shall follow the mean imputation based on Income group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "565ef983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type_income\n",
       "Commercial associate    39.726519\n",
       "Pensioner               58.928302\n",
       "State servant           40.974138\n",
       "Working                 40.291188\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean age based on income type\n",
    "credit.groupby(['Type_income'])['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9cbdcfb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the mean of 'Annual_income' for each 'Type_income' group\n",
    "mean_income_by_type = credit.groupby('Type_income')['Age'].transform('mean')\n",
    "\n",
    "# Use 'mean_income_by_type' to fill missing values in 'Annual_income'\n",
    "credit['Age'] = credit['Age'].fillna(mean_income_by_type)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d225107",
   "metadata": {},
   "source": [
    "##### Annual_income is based on the 'Type_income' like Pensioner will be having lower Income, We shall follow the mean imputation based on Income group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b94a965a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type_income\n",
       "Commercial associate    234600.000000\n",
       "Pensioner               155175.096226\n",
       "State servant           211422.413793\n",
       "Working                 181048.757306\n",
       "Name: Annual_income, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean age based on income type\n",
    "credit.groupby(['Type_income'])['Annual_income'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5923eedd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the mean of 'Annual_income' for each 'Type_income' group\n",
    "mean_income_by_type = credit.groupby('Type_income')['Annual_income'].transform('mean')\n",
    "\n",
    "# Use 'mean_income_by_type' to fill missing values in 'Annual_income'\n",
    "credit['Annual_income'] = credit['Annual_income'].fillna(mean_income_by_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "443d72dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                0\n",
       "Gender            0\n",
       "Age               0\n",
       "Credit_status     0\n",
       "Family_members    0\n",
       "Email             0\n",
       "Phone             0\n",
       "Work_phone        0\n",
       "Mobile_phone      0\n",
       "Housing_type      0\n",
       "Marital_status    0\n",
       "Education         0\n",
       "Type_income       0\n",
       "Annual_income     0\n",
       "Children          0\n",
       "Property_owner    0\n",
       "Car_owner         0\n",
       "Employed_years    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the dataset missing values\n",
    "credit.isnull().sum().sort_values(ascending= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0c3800",
   "metadata": {},
   "source": [
    "#### Now, data set is free from missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a98988f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#copying the dataset into new dataframe\n",
    "credit_copy = credit.copy(deep = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef01c84d",
   "metadata": {},
   "source": [
    "##### From primary analyis we found, there are  ouliers in 'Children', 'Annual_income', 'Age' 'Employement_years' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "12a35e11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating the boxplot to detect outliers\n",
    "#create a 2x2 grid for subplots\n",
    "fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10, 6))\n",
    "\n",
    "# plot 1\n",
    "sns.boxplot(x='Employed_years', y='Type_income', data=credit_copy, ax=axs[0, 0])\n",
    "axs[0, 0].set_title(\"Employed Years by Type of Income\")\n",
    "\n",
    "# plot 2\n",
    "sns.boxplot(y='Type_income', x='Annual_income', data=credit_copy, ax=axs[0, 1])\n",
    "axs[0, 1].set_title(\"Annual Income by Type of Income\")\n",
    "\n",
    "# plot 3\n",
    "sns.boxplot(y='Type_income', x='Age', data=credit_copy, ax=axs[1, 0])\n",
    "axs[1, 0].set_title(\"Age by Type of Income\")\n",
    "\n",
    "# plot 4\n",
    "sns.boxplot(x='Family_members', y='Type_income', data=credit_copy, ax=axs[1, 1])\n",
    "axs[1, 1].set_title(\"Family Members by Type of Income\")\n",
    "\n",
    "# adjust layout to avoid overlapping titles and labels\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(hspace = 0.8)\n",
    "\n",
    "#show the combined plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47e111dd",
   "metadata": {},
   "source": [
    "The above boxplots shows there are outliers and treating them before data analysis is very important"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36a12df2",
   "metadata": {},
   "source": [
    "##### Detecting the ouliers using the IQR method and capping the ouliers to lower and upper limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "48bb8db9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of columns for which you want to replace outliers\n",
    "outlier_cols = ['Annual_income', 'Age', 'Employed_years', 'Family_members']\n",
    "\n",
    "# Calculate the first quartile (Q1) and third quartile (Q3) for each 'Type_income' group for all the columns\n",
    "q1 = credit_copy.groupby('Type_income')[outlier_cols].transform('quantile', 0.25)\n",
    "q3 = credit_copy.groupby('Type_income')[outlier_cols].transform('quantile', 0.75)\n",
    "\n",
    "# Calculate the interquartile range (IQR) for each 'Type_income' group for all the columns\n",
    "iqr = q3 - q1\n",
    "\n",
    "# Define the lower and upper bounds for outliers detection for all the columns\n",
    "lower_bound = q1 - 1.5 * iqr\n",
    "upper_bound = q3 + 1.5 * iqr\n",
    "\n",
    "# Replace outliers with the lower and upper bounds of their respective groups for all the columns\n",
    "for col in outlier_cols:\n",
    "    is_lower_outlier = credit_copy[col] < lower_bound[col]\n",
    "    is_upper_outlier = credit_copy[col] > upper_bound[col]\n",
    "    credit_copy.loc[is_lower_outlier, col] = lower_bound[col][is_lower_outlier]\n",
    "    credit_copy.loc[is_upper_outlier, col] = upper_bound[col][is_upper_outlier]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "89c01c95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outliers detection after the capping \n",
    "# Create a 2x2 grid for subplots\n",
    "fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10, 6))\n",
    "\n",
    "# Plot 1\n",
    "sns.boxplot(x='Employed_years', y='Type_income', data=credit_copy, ax=axs[0, 0])\n",
    "axs[0, 0].set_title(\"Employed Years by Type of Income\")\n",
    "\n",
    "# Plot 2\n",
    "sns.boxplot(y='Type_income', x='Annual_income', data=credit_copy, ax=axs[0, 1])\n",
    "axs[0, 1].set_title(\"Annual Income by Type of Income\")\n",
    "\n",
    "# Plot 3\n",
    "sns.boxplot(y='Type_income', x='Age', data=credit_copy, ax=axs[1, 0])\n",
    "axs[1, 0].set_title(\"Age by Type of Income\")\n",
    "\n",
    "# Plot 4\n",
    "sns.boxplot(x='Family_members', y='Type_income', data=credit_copy, ax=axs[1, 1])\n",
    "axs[1, 1].set_title(\"Family Members by Type of Income\")\n",
    "\n",
    "# Adjust layout to avoid overlapping titles and labels\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(hspace = 0.8)\n",
    "\n",
    "# Show the combined plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a61cdf0b",
   "metadata": {},
   "source": [
    "The above boxplots depicts the data set has no outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab44e1d",
   "metadata": {},
   "source": [
    "##### Now, the data set is free from ouliers,missing, inconsitences and ready for Analyis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b86054a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This Cleaned dataset is used for sql insights generation\n",
    "credit_copy.to_csv(\"credit_cleaned.csv\",index = False,header = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c942b4a8",
   "metadata": {},
   "source": [
    "## Step 4 : EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47cff546",
   "metadata": {},
   "source": [
    "##### 1.Finding no of credit cards Accepted and Rejected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "476d4ba0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1373\n",
      "1     175\n",
      "Name: Credit_status, dtype: int64\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Calculating the credit_card acceptance rate\n",
    "print(credit_copy['Credit_status'].value_counts())\n",
    "print('-'*50)\n",
    "plt.pie(credit_copy['Credit_status'].value_counts(),labels=['accepted','rejected'],autopct='%1.1f%%')\n",
    "plt.title(\"Percentage distribution of Credit_status\",color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de1891c3",
   "metadata": {},
   "source": [
    "The pie chart shows 88.7% applicants got accepted and only 11.3% applicants were rejected for Credit_card,\n",
    "\n",
    "This Shows the data is Imbalanced and care to be taken while modeling using machine learning techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be1c644c",
   "metadata": {},
   "source": [
    "##### 2.No of Male and Female applicants and their approval rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6037c1f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ISAUMWCIiIhXwEDEREZEKOIIlIiJSAQOWiIhIBQxYIiIiFfCHJuqprKwMf/zxB9zc3KCp64+XEhFRsySEQH5+Pnx9fdGqVe1jVAZsPf3xxx/wq+8fWiUiomYtKysL7ev4iSsGbD25/d8PpmZlZcHd3d3G1RARkS3k5eXBz8/PnAm1suXPSKWmporw8HDh4+MjAIikpCTF9WVlZWLu3LnCx8dHtG7dWvTt21dkZGQo+ly7dk1MmTJFeHp6CmdnZzF8+HCRVen38y5duiTGjh0r3N3dhbu7uxg7dqzIzc1tUK0mk0kAECaTyZJdJSKiZqAhWWDTSU5XrlzBvffei+XLl1d7/eLFi7FkyRIsX74caWlpMBgMGDhwIPLz8819oqOjkZSUhMTEROzZswcFBQUIDw9H6Q1/xmL06NE4cuQItm/fju3bt+PIkSMYN26c6vtHREQt2C0I/HpBpRFsWVmZMBgM4s033zS3Xbt2Teh0OrHy/34h/PLly8LBwUEkJiaa+/z++++iVatWYvv27UIIIX7++WcBQBw4cMDcZ//+/QKAOHHiRL3r4wiWiIiazAi2NpmZmTAajRg0aJC5TavVom/fvti3bx8AID09HSUlJYo+vr6+CAoKMvfZv38/dDodQm/4G2D33XcfdDqduU91ioqKkJeXp1iIiIjqq9EGrNFoBADo9XpFu16vN19nNBrh6OiItm3b1trH29u7yva9vb3NfaqTkJAAnU5nXjiDmIiIGqLRBmy5yt85FULU+T3Uyn2q61/XduLi4mAymcxLVlZWAysnIqKWrNEGrMFgAIAqo8ycnBzzqNZgMKC4uBi5ubm19vnzzz+rbP/ChQtVRsc30mq1cHd3VyxERET11WgDNiAgAAaDASkpKea24uJipKamok+fPgCAkJAQODg4KPpkZ2cjIyPD3Kd3794wmUw4ePCguc/3338Pk8lk7kNERGRtNv2hiYKCApw+fdp8OTMzE0eOHIGHhwf8/f0RHR2N+Ph4BAYGIjAwEPHx8XB2dsbo0aMBADqdDpGRkYiJiYGnpyc8PDwwffp0BAcHY8CAAQCArl27YsiQIZg0aRI++OADAMBzzz2H8PBwdO7c+dbvNBERtQg2DdhDhw7h4YcfNl+eNm0aAGD8+PFYv349YmNjUVhYiKioKOTm5iI0NBTJycmKX9BYunQp7O3tERERgcLCQvTv3x/r16+HnZ2duc9HH32El156yTzbeMSIETV+95aIbp1zC4JtXQK1EP5zfrrlt8k/uF5PeXl50Ol0MJlMPB9LZCUMWLpVrBWwDcmCRnsOloiIqCljwBIREamAAUtERKQCBiwREZEKGLBEREQqYMASERGpgAFLRESkAgYsERGRChiwREREKmDAEhERqYABS0REpAIGLBERkQoYsERERCpgwBIREamAAUtERKQCBiwREZEKGLBEREQqYMASERGpgAFLRESkAgYsERGRChiwREREKmDAEhERqYABS0REpAIGLBERkQoYsERERCpgwBIREamAAUtERKQCBiwREZEKGLBEREQqYMASERGpgAFLRESkAgYsERGRChiwREREKmDAEhERqaBRB+z169fx+uuvIyAgAE5OTujYsSMWLFiAsrIycx8hBObNmwdfX184OTkhLCwMx44dU2ynqKgIU6dOhZeXF1xcXDBixAicP3/+Vu8OERG1II06YBctWoSVK1di+fLlOH78OBYvXoy33noL7777rrnP4sWLsWTJEixfvhxpaWkwGAwYOHAg8vPzzX2io6ORlJSExMRE7NmzBwUFBQgPD0dpaaktdouIiFoAe1sXUJv9+/fj0UcfxbBhwwAAHTp0wMcff4xDhw4BkKPXZcuWYdasWXj88ccBABs2bIBer8fmzZsxefJkmEwmrFmzBhs3bsSAAQMAAJs2bYKfnx927NiBwYMH22bniIioWWvUI9gHHngAO3fuxKlTpwAAP/74I/bs2YOhQ4cCADIzM2E0GjFo0CDzOlqtFn379sW+ffsAAOnp6SgpKVH08fX1RVBQkLlPdYqKipCXl6dYiIiI6qtRj2BnzpwJk8mELl26wM7ODqWlpXjjjTfw1FNPAQCMRiMAQK/XK9bT6/U4e/asuY+joyPatm1bpU/5+tVJSEjA/Pnzrbk7RETUgjTqEewnn3yCTZs2YfPmzfjhhx+wYcMGvP3229iwYYOin0ajUVwWQlRpq6yuPnFxcTCZTOYlKyvL8h0hIqIWp1GPYGfMmIFXX30Vo0aNAgAEBwfj7NmzSEhIwPjx42EwGADIUaqPj495vZycHPOo1mAwoLi4GLm5uYpRbE5ODvr06VPjbWu1Wmi1WjV2i4iIWoBGPYK9evUqWrVSlmhnZ2f+mk5AQAAMBgNSUlLM1xcXFyM1NdUcniEhIXBwcFD0yc7ORkZGRq0BS0REdDMa9Qh2+PDheOONN+Dv74+7774bhw8fxpIlSzBx4kQA8tBwdHQ04uPjERgYiMDAQMTHx8PZ2RmjR48GAOh0OkRGRiImJgaenp7w8PDA9OnTERwcbJ5VTEREZG2NOmDfffddzJ49G1FRUcjJyYGvry8mT56MOXPmmPvExsaisLAQUVFRyM3NRWhoKJKTk+Hm5mbus3TpUtjb2yMiIgKFhYXo378/1q9fDzs7O1vsFhERtQAaIYSwdRFNQV5eHnQ6HUwmE9zd3W1dDlGzcG5BsK1LoBbCf85PVtlOQ7KgUZ+DJSIiaqoYsERERCpgwBIREamAAUtERKQCBiwREZEKGLBEREQqYMASERGpgAFLRESkAgYsERGRChiwREREKmDAEhERqYABS0REpAIGLBERkQoYsERERCpgwBIREamAAUtERKQCBiwREZEKGLBEREQqYMASERGpgAFLRESkAgYsERGRChiwREREKmDAEhERqYABS0REpAIGLBERkQoYsERERCpgwBIREamAAUtERKQCBiwREZEKGLBEREQqYMASERGpgAFLRESkAgYsERGRChiwREREKmj0Afv7779j7Nix8PT0hLOzM7p164b09HTz9UIIzJs3D76+vnByckJYWBiOHTum2EZRURGmTp0KLy8vuLi4YMSIETh//vyt3hUiImpBGnXA5ubm4v7774eDgwO+/vpr/Pzzz3jnnXfQpk0bc5/FixdjyZIlWL58OdLS0mAwGDBw4EDk5+eb+0RHRyMpKQmJiYnYs2cPCgoKEB4ejtLSUhvsFRERtQQaIYSwdRE1efXVV7F3715899131V4vhICvry+io6Mxc+ZMAHK0qtfrsWjRIkyePBkmkwnt2rXDxo0b8eSTTwIA/vjjD/j5+WHbtm0YPHhwvWrJy8uDTqeDyWSCu7u7dXaQqIU7tyDY1iVQC+E/5yerbKchWdCoR7BffvklevbsiZEjR8Lb2xvdu3fH6tWrzddnZmbCaDRi0KBB5jatVou+ffti3759AID09HSUlJQo+vj6+iIoKMjcpzpFRUXIy8tTLERERPXVqAP2t99+w4oVKxAYGIhvvvkGzz//PF566SX861//AgAYjUYAgF6vV6yn1+vN1xmNRjg6OqJt27Y19qlOQkICdDqdefHz87PmrhERUTPXqAO2rKwMPXr0QHx8PLp3747Jkydj0qRJWLFihaKfRqNRXBZCVGmrrK4+cXFxMJlM5iUrK8vyHSEiohbHooDt168fLl++XKU9Ly8P/fr1u9mazHx8fHDXXXcp2rp27Ypz584BAAwGAwBUGYnm5OSYR7UGgwHFxcXIzc2tsU91tFot3N3dFQsREVF9WRSwu3fvRnFxcZX2a9eu1TghyRL3338/Tp48qWg7deoUbr/9dgBAQEAADAYDUlJSzNcXFxcjNTUVffr0AQCEhITAwcFB0Sc7OxsZGRnmPkRERNZm35DOR48eNf//559/VowcS0tLsX37dtx2221WK+6VV15Bnz59EB8fj4iICBw8eBCrVq3CqlWrAMhDw9HR0YiPj0dgYCACAwMRHx8PZ2dnjB49GgCg0+kQGRmJmJgYeHp6wsPDA9OnT0dwcDAGDBhgtVqJiIhu1KCA7datGzQaDTQaTbWHgp2cnPDuu+9arbhevXohKSkJcXFxWLBgAQICArBs2TKMGTPG3Cc2NhaFhYWIiopCbm4uQkNDkZycDDc3N3OfpUuXwt7eHhERESgsLET//v2xfv162NnZWa1WIiKiGzXoe7Bnz56FEAIdO3bEwYMH0a5dO/N1jo6O8Pb2brahxe/BElkfvwdLt4otvgfboBFs+bnPsrIyy6sjAEDIjH/ZugRqIdLfetrWJRC1SA0K2BudOnUKu3fvRk5OTpXAnTNnzk0XRkRE1JRZFLCrV6/GCy+8AC8vLxgMBsX3STUaDQOWiIhaPIsCduHChXjjjTfMv/9LREREShZ9DzY3NxcjR460di1ERETNhkUBO3LkSCQnJ1u7FiIiombDokPEnTp1wuzZs3HgwAEEBwfDwcFBcf1LL71kleKIiIiaKosCdtWqVXB1dUVqaipSU1MV12k0GgYsERG1eBYFbGZmprXrICIialYa9Z+rIyIiaqosGsFOnDix1uvXrl1rUTFERETNhUUBW/lvq5aUlCAjIwOXL1+26t+DJSIiaqosCtikpKQqbWVlZYiKikLHjh1vuigiIqKmzmrnYFu1aoVXXnkFS5cutdYmiYiImiyrTnL69ddfcf36dWtukoiIqEmy6BDxtGnTFJeFEMjOzsZXX32F8ePHW6UwIiKipsyigD18+LDicqtWrdCuXTu88847dc4wJiIiagksCthdu3ZZuw4iIqJmxeI/uA4AFy5cwMmTJ6HRaHDnnXeiXbt21qqLiIioSbNoktOVK1cwceJE+Pj44KGHHsKDDz4IX19fREZG4urVq9aukYiIqMmxKGCnTZuG1NRU/Pe//8Xly5dx+fJlfPHFF0hNTUVMTIy1ayQiImpyLDpE/Nlnn+E///kPwsLCzG1Dhw6Fk5MTIiIisGLFCmvVR0RE1CRZNIK9evUq9Hp9lXZvb28eIiYiIoKFAdu7d2/MnTsX165dM7cVFhZi/vz56N27t9WKIyIiaqosOkS8bNkyPPLII2jfvj3uvfdeaDQaHDlyBFqtFsnJydaukYiIqMmxKGCDg4Pxyy+/YNOmTThx4gSEEBg1ahTGjBkDJycna9dIRETU5FgUsAkJCdDr9Zg0aZKife3atbhw4QJmzpxpleKIiIiaKovOwX7wwQfo0qVLlfa7774bK1euvOmiiIiImjqLAtZoNMLHx6dKe7t27ZCdnX3TRRERETV1FgWsn58f9u7dW6V979698PX1vemiiIiImjqLzsE+++yziI6ORklJCfr16wcA2LlzJ2JjY/lLTkRERLAwYGNjY3Hp0iVERUWhuLgYANC6dWvMnDkTcXFxVi2QiIioKbIoYDUaDRYtWoTZs2fj+PHjcHJyQmBgILRarbXrIyIiapJu6s/Vubq6olevXtaqhYiIqNmwaJKTrSQkJECj0SA6OtrcJoTAvHnz4OvrCycnJ4SFheHYsWOK9YqKijB16lR4eXnBxcUFI0aMwPnz529x9URE1JI0mYBNS0vDqlWrcM899yjaFy9ejCVLlmD58uVIS0uDwWDAwIEDkZ+fb+4THR2NpKQkJCYmYs+ePSgoKEB4eDhKS0tv9W4QEVEL0SQCtqCgAGPGjMHq1avRtm1bc7sQAsuWLcOsWbPw+OOPIygoCBs2bMDVq1exefNmAIDJZMKaNWvwzjvvYMCAAejevTs2bdqEn376CTt27LDVLhERUTPXJAL2xRdfxLBhwzBgwABFe2ZmJoxGIwYNGmRu02q16Nu3L/bt2wcASE9PR0lJiaKPr68vgoKCzH2qU1RUhLy8PMVCRERUXzc1yelWSExMRHp6Og4dOlTlOqPRCABV/jatXq/H2bNnzX0cHR0VI9/yPuXrVychIQHz58+/2fKJiKiFatQj2KysLLz88sv46KOP0Lp16xr7aTQaxWUhRJW2yurqExcXB5PJZF6ysrIaVjwREbVojTpg09PTkZOTg5CQENjb28Pe3h6pqan43//9X9jb25tHrpVHojk5OebrDAYDiouLkZubW2Of6mi1Wri7uysWIiKi+mrUAdu/f3/89NNPOHLkiHnp2bMnxowZgyNHjqBjx44wGAxISUkxr1NcXIzU1FT06dMHABASEgIHBwdFn+zsbGRkZJj7EBERWVujPgfr5uaGoKAgRZuLiws8PT3N7dHR0YiPj0dgYCACAwMRHx8PZ2dnjB49GgCg0+kQGRmJmJgYeHp6wsPDA9OnT0dwcHCVSVNERETW0qgDtj5iY2NRWFiIqKgo5ObmIjQ0FMnJyXBzczP3Wbp0Kezt7REREYHCwkL0798f69evh52dnQ0rJyKi5kwjhBC2LqIpyMvLg06ng8lkssr52JAZ/7JCVUR1S3/raVuXUKNzC4JtXQK1EP5zfrLKdhqSBY36HCwREVFTxYAlIiJSAQOWiIhIBQxYIiIiFTBgiYiIVMCAJSIiUgEDloiISAUMWCIiIhUwYImIiFTAgCUiIlIBA5aIiEgFDFgiIiIVMGCJiIhUwIAlIiJSAQOWiIhIBQxYIiIiFTBgiYiIVMCAJSIiUgEDloiISAUMWCIiIhUwYImIiFTAgCUiIlIBA5aIiEgFDFgiIiIVMGCJiIhUwIAlIiJSAQOWiIhIBQxYIiIiFTBgiYiIVMCAJSIiUgEDloiISAUMWCIiIhUwYImIiFTAgCUiIlJBow7YhIQE9OrVC25ubvD29sZjjz2GkydPKvoIITBv3jz4+vrCyckJYWFhOHbsmKJPUVERpk6dCi8vL7i4uGDEiBE4f/78rdwVIiJqYRp1wKampuLFF1/EgQMHkJKSguvXr2PQoEG4cuWKuc/ixYuxZMkSLF++HGlpaTAYDBg4cCDy8/PNfaKjo5GUlITExETs2bMHBQUFCA8PR2lpqS12i4iIWgB7WxdQm+3btysur1u3Dt7e3khPT8dDDz0EIQSWLVuGWbNm4fHHHwcAbNiwAXq9Hps3b8bkyZNhMpmwZs0abNy4EQMGDAAAbNq0CX5+ftixYwcGDx58y/eLiIiav0Y9gq3MZDIBADw8PAAAmZmZMBqNGDRokLmPVqtF3759sW/fPgBAeno6SkpKFH18fX0RFBRk7lOdoqIi5OXlKRYiIqL6ajIBK4TAtGnT8MADDyAoKAgAYDQaAQB6vV7RV6/Xm68zGo1wdHRE27Zta+xTnYSEBOh0OvPi5+dnzd0hIqJmrskE7JQpU3D06FF8/PHHVa7TaDSKy0KIKm2V1dUnLi4OJpPJvGRlZVlWOBERtUhNImCnTp2KL7/8Ert27UL79u3N7QaDAQCqjERzcnLMo1qDwYDi4mLk5ubW2Kc6Wq0W7u7uioWIiKi+GnXACiEwZcoUbNmyBd9++y0CAgIU1wcEBMBgMCAlJcXcVlxcjNTUVPTp0wcAEBISAgcHB0Wf7OxsZGRkmPsQERFZW6OeRfziiy9i8+bN+OKLL+Dm5mYeqep0Ojg5OUGj0SA6Ohrx8fEIDAxEYGAg4uPj4ezsjNGjR5v7RkZGIiYmBp6envDw8MD06dMRHBxsnlVMRERkbY06YFesWAEACAsLU7SvW7cOEyZMAADExsaisLAQUVFRyM3NRWhoKJKTk+Hm5mbuv3TpUtjb2yMiIgKFhYXo378/1q9fDzs7u1u1K0RE1MJohBDC1kU0BXl5edDpdDCZTFY5Hxsy419WqIqobulvPW3rEmp0bkGwrUugFsJ/zk9W2U5DsqBRn4MlIiJqqhiwREREKmDAEhERqYABS0REpAIGLBERkQoYsERERCpgwBIREamAAUtERKQCBiwREZEKGLBEREQqYMASERGpgAFLRESkAgYsERGRChiwREREKmDAEhERqYABS0REpAIGLBERkQoYsERERCpgwBIREamAAUtERKQCBiwREZEKGLBEREQqYMASERGpgAFLRESkAgYsERGRChiwREREKmDAEhERqYABS0REpAIGLBERkQoYsERERCpgwBIREamAAUtERKQCBiwREZEKGLBEREQqaFEB+/777yMgIACtW7dGSEgIvvvuO1uXREREzVSLCdhPPvkE0dHRmDVrFg4fPowHH3wQjzzyCM6dO2fr0oiIqBlqMQG7ZMkSREZG4tlnn0XXrl2xbNky+Pn5YcWKFbYujYiImiF7WxdwKxQXFyM9PR2vvvqqon3QoEHYt29ftesUFRWhqKjIfNlkMgEA8vLyrFJTaVGhVbZDVBdrPWfVkH+t1NYlUAthrddB+XaEEHX2bREB+9dff6G0tBR6vV7RrtfrYTQaq10nISEB8+fPr9Lu5+enSo1EatG9+7ytSyCyvQSdVTeXn58Pna72bbaIgC2n0WgUl4UQVdrKxcXFYdq0aebLZWVluHTpEjw9PWtch9SVl5cHPz8/ZGVlwd3d3dblEN1yfA3YnhAC+fn58PX1rbNviwhYLy8v2NnZVRmt5uTkVBnVltNqtdBqtYq2Nm3aqFUiNYC7uzvfXKhF42vAtuoauZZrEZOcHB0dERISgpSUFEV7SkoK+vTpY6OqiIioOWsRI1gAmDZtGsaNG4eePXuid+/eWLVqFc6dO4fnn+f5KSIisr4WE7BPPvkkLl68iAULFiA7OxtBQUHYtm0bbr/9dluXRvWk1Woxd+7cKofuiVoKvgaaFo2oz1xjIiIiapAWcQ6WiIjoVmPAEhERqYABS0REpAIGLBERkQoYsNRoTZgwARqNptqvUkVFRUGj0WDChAm3vjAiGyh/PVReTp8+bevSqAYMWGrU/Pz8kJiYiMLCij+OcO3aNXz88cfw9/e3YWVEt96QIUOQnZ2tWAICAmxdFtWAAUuNWo8ePeDv748tW7aY27Zs2QI/Pz90797dhpUR3XparRYGg0Gx2NnZ2bosqgEDlhq9Z555BuvWrTNfXrt2LSZOnGjDioiI6saApUZv3Lhx2LNnD86cOYOzZ89i7969GDt2rK3LIrrltm7dCldXV/MycuRIW5dEtWgxP5VITZeXlxeGDRuGDRs2QAiBYcOGwcvLy9ZlEd1yDz/8MFasWGG+7OLiYsNqqC4MWGoSJk6ciClTpgAA3nvvPRtXQ2QbLi4u6NSpk63LoHpiwFKTMGTIEBQXFwMABg8ebONqiIjqxoClJsHOzg7Hjx83/5+IqLFjwFKT4e7ubusSiIjqjX+ujoiISAX8mg4REZEKGLBEREQqYMASERGpgAFLRESkAgYsERGRChiwREREKmDAEhERqYABS0REpAIGLBFZVVhYGKKjo21dBpHNMWCJmiGj0YiXX34ZnTp1QuvWraHX6/HAAw9g5cqVuHr1qq3LI2oR+FvERM3Mb7/9hvvvvx9t2rRBfHw8goODcf36dZw6dQpr166Fr68vRowYYesya1RaWgqNRoNWrfj5n5o2PoOJmpmoqCjY29vj0KFDiIiIQNeuXREcHIwnnngCX331FYYPHw4AMJlMeO655+Dt7Q13d3f069cPP/74o3k78+bNQ7du3bBx40Z06NABOp0Oo0aNQn5+vrnPlStX8PTTT8PV1RU+Pj545513qtRTXFyM2NhY3HbbbXBxcUFoaCh2795tvn79+vVo06YNtm7dirvuugtarRZnz55V7w4iukUYsETNyMWLF5GcnIwXX3wRLi4u1fbRaDQQQmDYsGEwGo3Ytm0b0tPT0aNHD/Tv3x+XLl0y9/3111/x+eefY+vWrdi6dStSU1Px5ptvmq+fMWMGdu3ahaSkJCQnJ2P37t1IT09X3N4zzzyDvXv3IjExEUePHsXIkSMxZMgQ/PLLL+Y+V69eRUJCAj788EMcO3YM3t7eVr5niGxAEFGzceDAAQFAbNmyRdHu6ekpXFxchIuLi4iNjRU7d+4U7u7u4tq1a4p+d9xxh/jggw+EEELMnTtXODs7i7y8PPP1M2bMEKGhoUIIIfLz84Wjo6NITEw0X3/x4kXh5OQkXn75ZSGEEKdPnxYajUb8/vvvitvp37+/iIuLE0IIsW7dOgFAHDlyxDp3AlEjwXOwRM2QRqNRXD548CDKysowZswYFBUVIT09HQUFBfD09FT0KywsxK+//mq+3KFDB7i5uZkv+/j4ICcnB4Ac3RYXF6N3797m6z08PNC5c2fz5R9++AFCCNx5552K2ykqKlLctqOjI+65556b2GOixocBS9SMdOrUCRqNBidOnFC0d+zYEQDg5OQEACgrK4OPj4/iXGi5Nm3amP/v4OCguE6j0aCsrAwAIOrxp6TLyspgZ2eH9PR02NnZKa5zdXU1/9/JyanKhwKipo4BS9SMeHp6YuDAgVi+fDmmTp1a43nYHj16wGg0wt7eHh06dLDotjp16gQHBwccOHAA/v7+AIDc3FycOnUKffv2BQB0794dpaWlyMnJwYMPPmjR7RA1VZzkRNTMvP/++7h+/Tp69uyJTz75BMePH8fJkyexadMmnDhxAnZ2dhgwYAB69+6Nxx57DN988w3OnDmDffv24fXXX8ehQ4fqdTuurq6IjIzEjBkzsHPnTmRkZGDChAmKr9fceeedGDNmDJ5++mls2bIFmZmZSEtLw6JFi7Bt2za17gKiRoEjWKJm5o477sDhw4cRHx+PuLg4nD9/HlqtFnfddRemT5+OqKgoaDQabNu2DbNmzcLEiRNx4cIFGAwGPPTQQ9Dr9fW+rbfeegsFBQUYMWIE3NzcEBMTA5PJpOizbt06LFy4EDExMfj999/h6emJ3r17Y+jQodbedaJGRSPqcyKFiIiIGoSHiImIiFTAgCUiIlIBA5aIiEgFDFgiIiIVMGCJiIhUwIAlIiJSAQOWiIhIBQxYIiIiFTBgiYiIVMCAJSIiUgEDloiISAX/H39Adp6/TBOtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F    980\n",
      "M    568\n",
      "Name: Gender, dtype: int64\n",
      "F    63.307494\n",
      "M    36.692506\n",
      "Name: Gender, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Calculating Male and Female applicants\n",
    "plt.figure(figsize=(5,3))\n",
    "sns.countplot(x= 'Gender',data= credit_copy)\n",
    "plt.title(\"Count of Male and Female applicants\",color= 'Blue')\n",
    "plt.show()\n",
    "print(credit_copy['Gender'].value_counts())\n",
    "print(credit_copy['Gender'].value_counts(normalize=True)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08fffcd7",
   "metadata": {},
   "source": [
    "There are more female applicants (63.3%) then male applicants (36.7%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c14da2ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender  Credit_status\n",
      "F       0                89.795918\n",
      "        1                10.204082\n",
      "M       0                86.795775\n",
      "        1                13.204225\n",
      "Name: Credit_status, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Calculating the credit_card acceptance rate on Gender\n",
    "plt.figure(figsize=(5,3))\n",
    "sns.countplot(x= 'Gender',data= credit_copy,hue= 'Credit_status')\n",
    "plt.title(\"Approval rate of Male and Female applicants\",color= 'Blue')\n",
    "plt.legend(['accepted','rejected'],bbox_to_anchor=(1,1))\n",
    "plt.show()\n",
    "print(credit_copy.groupby(['Gender'])['Credit_status'].value_counts(normalize = True)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6365bf6c",
   "metadata": {},
   "source": [
    "Female applicants(89.8%) have higher chance of getting the approval "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a91896",
   "metadata": {},
   "source": [
    "##### 3.Understand the Type_income varaiable in according to related columns variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d68a66ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a 2x2 grid of subplots\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 8))\n",
    "\n",
    "# Plot the first countplot on the first subplot\n",
    "sns.countplot(x='Type_income', data=credit_copy, ax=axes[0, 0])\n",
    "axes[0, 0].set_title('Type of Income Count')\n",
    "\n",
    "# Plot the first barplot on the second subplot\n",
    "sns.barplot(y='Type_income', x='Annual_income', data=credit_copy, ax=axes[0, 1])\n",
    "axes[0, 1].set_title('Annual Income by Type of Income')\n",
    "\n",
    "# Plot the second barplot on the third subplot\n",
    "sns.barplot(y='Type_income', x='Age', data=credit_copy, ax=axes[1, 0])\n",
    "axes[1, 0].set_title('Age by Type of Income')\n",
    "\n",
    "# Plot the third barplot on the fourth subplot\n",
    "sns.barplot(y='Type_income', x='Employed_years', data=credit_copy, ax=axes[1, 1])\n",
    "axes[1, 1].set_title('Employed Years by Type of Income')\n",
    "\n",
    "# Adjust layout to prevent overlap of titles\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "933216e8",
   "metadata": {},
   "source": [
    " Working in private company are more likely to apply for credit card, where as State_servants are less likely to apply, The reason would be the Annual_income of each category.\n",
    "\n",
    "The salary slab would be the deciding factor, state servants are likely to get the benefits of Government\n",
    "\n",
    " The commerical associate have higher income followed by state servant,working and it is definite that pensior are non-employee(0) group  will have lower income\n",
    " \n",
    "Pensioner are the one retired , they have aged when compared to other groups\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0fd682a",
   "metadata": {},
   "source": [
    "##### 4.Type_income on Credit status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "16f8b9d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type_income           Credit_status\n",
      "Commercial associate  0                86.301370\n",
      "                      1                13.698630\n",
      "Pensioner             0                84.386617\n",
      "                      1                15.613383\n",
      "State servant         0                94.827586\n",
      "                      1                 5.172414\n",
      "Working               0                90.350877\n",
      "                      1                 9.649123\n",
      "Name: Credit_status, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Calculating the credit_card acceptance rate on Type_income\n",
    "plt.figure(figsize=(7,4))\n",
    "sns.countplot(x= 'Type_income',data= credit_copy,hue= 'Credit_status')\n",
    "plt.title(\"Income type on credit status\",color= 'Blue')\n",
    "plt.legend(['accepted','rejected'],bbox_to_anchor=(1,1))\n",
    "plt.show()\n",
    "print(credit_copy.groupby(['Type_income'])['Credit_status'].value_counts(normalize = True)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c4d417f",
   "metadata": {},
   "source": [
    "This is evident , the \"Working\" class in private company due to combined factors of low income and non-benefits  provided for the State servant, they are most likely to apply for credit card and also important observation we can interptered is the Job stability also places an important factor in credit card approval(i.e for state servant job stability is high, there credit card approval is high) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e833b543",
   "metadata": {},
   "source": [
    "##### 5.Credit dependent on car and property"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a1756324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_owner  Property_owner  Credit_status\n",
       "N          N               0                88.509317\n",
       "                           1                11.490683\n",
       "           Y               0                88.205980\n",
       "                           1                11.794020\n",
       "Y          N               0                87.037037\n",
       "                           1                12.962963\n",
       "           Y               0                90.441176\n",
       "                           1                 9.558824\n",
       "Name: Credit_status, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#understanding the property and car ownership on the credit status\n",
    "credit_copy.groupby(['Car_owner','Property_owner'])['Credit_status'].value_counts(normalize = True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b34d8117",
   "metadata": {},
   "source": [
    "We can depict that owing a car and property doesn't matter, has the average acceptance is 88% and rejection 12% in all conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba0a09a4",
   "metadata": {},
   "source": [
    "##### 6.Marital_status on credit card approval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "384f15ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Marital_status        Credit_status\n",
      "Civil marriage        0                96.039604\n",
      "                      1                 3.960396\n",
      "Married               0                89.132507\n",
      "                      1                10.867493\n",
      "Separated             0                85.416667\n",
      "                      1                14.583333\n",
      "Single / not married  0                84.581498\n",
      "                      1                15.418502\n",
      "Widow                 0                89.333333\n",
      "                      1                10.666667\n",
      "Name: Credit_status, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#understanding the marital status on credit_card acceptance.\n",
    "plt.figure(figsize=(8,4))\n",
    "sns.countplot(x= 'Marital_status',data= credit_copy,hue= 'Credit_status')\n",
    "plt.title(\"Marital_statuse on credit status\",color= 'Blue')\n",
    "plt.legend(['accepted','rejected'],bbox_to_anchor=(1,1))\n",
    "plt.show()\n",
    "print(credit_copy.groupby(['Marital_status'])['Credit_status'].value_counts(normalize = True)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63f961fa",
   "metadata": {},
   "source": [
    "The married living with Family are the highest among the credit card applicants followed by Single/not married ,\n",
    "\n",
    "The civil marriage had highest acceptance rate, lowest acceptance for Single/ not marrried."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60fdfb99",
   "metadata": {},
   "source": [
    "##### 7.Education level  on Credit status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d8a4c7fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Education                      Credit_status\n",
      "Academic degree                0                100.000000\n",
      "Higher education               0                 87.089202\n",
      "                               1                 12.910798\n",
      "Incomplete higher              0                 92.647059\n",
      "                               1                  7.352941\n",
      "Lower secondary                0                 71.428571\n",
      "                               1                 28.571429\n",
      "Secondary / secondary special  0                 89.427740\n",
      "                               1                 10.572260\n",
      "Name: Credit_status, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#understanding the Education level  on credit_card acceptance.\n",
    "plt.figure(figsize=(14,8))\n",
    "sns.countplot(x= 'Education',data= credit_copy,hue= 'Credit_status')\n",
    "plt.title(\"Education on credit status\",color= 'Blue')\n",
    "plt.legend(['accepted','rejected'],bbox_to_anchor=(1,1))\n",
    "plt.show()\n",
    "print(credit_copy.groupby(['Education'])['Credit_status'].value_counts(normalize = True)*100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f29b64",
   "metadata": {},
   "source": [
    " Importance observation that the 'Academic degree' holders get 100 % acceptance, we can observe higher the Education level , most likely to get the acceptance of credit card but the less in number where as 'Seconday' class consists of middle class received more applicants"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e66d5f3",
   "metadata": {},
   "source": [
    "##### 8.No of Family members on credit status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6ffb1fd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#understanding the Family members count on credit_card acceptance.\n",
    "plt.figure(figsize=(14,8))\n",
    "sns.countplot(x= 'Family_members',data= credit_copy,hue= 'Credit_status')\n",
    "plt.title(\"Family_members total on credit status\",color= 'Blue')\n",
    "plt.legend(['accepted','rejected'],bbox_to_anchor=(1,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "466bd193",
   "metadata": {},
   "source": [
    " Most Family are small family , newly married are more likely to apply for credit card`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ad2e650",
   "metadata": {},
   "source": [
    "##### 9.Correlation testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4bb985e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wdLtGeyE1rJsAXxcBv8ZpUFwOdHCT9lVWKerddJLxuxi/FbvXLcCwKW/Cs0MkTh9YjXVfPYHH/vM7bBwal9eivDSsX/QkwvtOwpjpHyEj+QR2rn4bltYOCOkmldcLR3/Dvk2fYOS0+fDs0A0F2Vfxxw+vAwAG3z8XAFBVUYyfPp4Kn+BeuP/Zb2Fl7YCi3DSYW9q0XuL/oo7eAoZFCNh2QkR6rohugQImD5Bhybamy+wDA2Q4lSzitzgRXs4CRkYKqGhQZu+LlmHvORGX0kUEewm4r48MP+zSldnoUAHdAgVsPqJBXjHg7gCM6SlDda3U6FpfsCfg4SCgtKL9N+oAwIBwOfqGybF+nwp5JSIGRcjx2EhTfLq+BjW1htfxdhEwZZAJ/rzRoNPJV4apg03wzZZapOdK6S4uB7YfUyO/RPoeGSTDw0NN8OWmWu2N6JjeJujgLsMve2pRWCYiyFOGcX1MUFKhQkJq+7pZrC/5zFYc2boQ0WP/A1ffSFw8tgY7Vj6FCS9shtKucZktLUjHzpVPI7jH/YiZ9CGyr53A4c3vwkLhAL+w4QAAVW0VrO294R82Akd+X2hwvwc2/h8Ksy8j5v4PYGXjgiunNmPb8scw4YUtUNi6tmiam0tb5d3YWWshatTa74XZl7H9u5nwDxvZMgltIf3DZOjTSYYNB6SGl4FdZZg+3ASfbahFjcrwOt7OAibHmGDXSTUupGrQyUeGKQNN8O1WFdLzpLLo5ybDkYsaZOSJkAnA0Eg5pg83xf821aL2xnYXrqnR226wpwzj+8px/lr7Kqvt+drOTgk8MkSG08ki9p0VUV0LONkAKnXjeBG1BM65cxcrLy/HI488AqVSCXd3d3zyySdtHaVmk7t9Hy69+Rmub9rZ1lFpE2f2r0BIj4kI7TkJ9q4B6DNuLpS2brgQ97PB8Alxq6G0c0efcXNh7xqA0J6TENJ9As7sW64N4xHQC/5hw2DvGgAbRx906fcIHNxCcP3qCW0Y306D4RMaAztnf9g5+6PnyJdgamaFnNTTLZ7mO9ErVMCpZBGnkkXklwA7T4ooqZCevhgSGSigpFwKl18CnEoWcTpFRO9Q3amyZ4iAlOvAoQQR+aXSv1ezpeV1LmcCSVnS07OCUmDPWRE1KsDTSRfG01HA2asiUnOkm6STSSKyiwB3B3ZNvdvE7/4OXfpMRHjfSXB0D8DgSfNgbeeGU/sMl9fT+1fD2t4dgyfNg6N7AML7TkKX6Ak49qeuvGamnIJnQCQ69RgLW0cv+Hfqh47d78H1a+e0YY7s+BbW9m4Y9cgCuPuFw9bRC76h0bB39mnxNN+pniECTqeIOJ0sla8/T4ooqQQiA5ooswECSiqkcPmlwOkbZbZXiK7M9ggWkJINHL5RZg/fKLM9guuVRycBlzJEJGUBxRXAxXQg5Trg5qC/P6UlMDxShl/jNFAbR9sO+nSWY89pNc5f0yC7UMTavSqYmgARHZq+BOzbWY4rGSL2nlEjt1j6NylTRN/Ocm2Yi2kaXErXIL9ERH6JiJ3H1ahRSQ1DdXxcBJy4rEbKdRFFZcCxRA2uF4h658L26NzBlQiOmoCQHpNg5xKA3mPmQmHrhotHVhsMf/Hoaijs3NF7zFzYuQQgpMckBEVOwNkDujLr7NUFPUe9gg7hYyA3adxDQlVbhavnd6LHiH/Dzb8HbBx9ETnkX7C298LFo4bPFe1RW+QdAFgqHGBl7az9pCXugbWDD9z8e7RIOltKn05y7D2jxoVUETlFItbvV8PUBOh6k/Lap5MMSZki9p2VGqf3ndUgKUtEn066db7fqcLJKxrkFIm4XihiwwEV7JQCPB11ZbGsUv8T6iNDSpauMaO9aM/XdgO7CEjKErH7tHRNV1QOXMkCKqpbLDvuCjK50CafuxEbd+5ir7zyCmJjY7Fx40bs2LEDe/bswfHjx9s6WvQ3qVU1yMs4D6+gvnrLvYL7IvvaSYPrZKeegldww/D9kJt+Hhp140e3oigi48phFOemNDnUS6NR48qp31FbUwFX34g7S0wLkskAd3sg5br+HVjydRFeTdxYeDkKSG4YPkuEuwMgu7GKZxNhmtqmIACdfASYmgAZebr10vNEBHkIsLaUvvu6AA7WQFKWkdwx0m1Rq2pwPfU8/Dr201vu17EvMpINl9fMlFPw66hfXv069Uf2tXNQ3yivngFRyE49j6yrZwBIvX2Sz+1FQNhA7TpJZ3bDzTcMv377PL56NRor54/H6QO/NGPqmlddmW1YvlJuUmY9HQWDZfyWZbbBNtNzRfi5CnBQSt9d7ABvZzTqbj+ulwxHLop6QxnaM3trwMZKwOUM3ZN3tQZIua6Bj2vTl4A+LjJcydB/Wn85vel1BAEI7yCDmQmQlqPL66vXRXT0kcHGSvrewV2Ak41+fNobtaoG+Znn4RGoXwY9A/siJ9Vwmc1JOwXPhuGD+iIvw3Ada4ioUUPUqCGvN6wSAOSm5si+dqKJtdqXtso7Q/FIOrUZwVETIAjGcwNnrwSsrQRcydSVIbVGKkc+Lk2nw9tZhiuZ+mXqSoYGPi5Nl3GLG21kTTU6KCyAEC8Bxy+3r7La3q/tAj0EFJQCU2JkeHG8DNOHyRDs+VdTSXTnOCzrLlVWVoZly5bh+++/x7BhwwAAK1euhJeXVxvHjP6uqopCiBo1LJWOesstlY6oKDU8MUVlaS4slf0ahRc1KlSVF8LKxgUAUFNZih/nx0CtqoFMJkPf8W82ahQqyErEpkVToVZVw9TMCsMf+RL2roHNmMLmYWUGyGQCyqr0l5dXA0oLw+soLKTf6yurAuQyAVbm0t9KA2HKq6V163O2BaYPlcFEDtSogHUHNHo3hNtPiBjTA3j+XjnUGhGiCPx+TET6TeYWIeNTWSaVV4W1fnlV2DihvMTwBAHlJXlQ2Djph7d2hEajQmVZIZS2LujYfQwqSwvw0ycPAqIIjUaFiP5T0WvEk9p1ivLScGrfz+g+ZAZ6j3waWVfPYPfa9yA3MUNY7/HNnta/q67Mljcss1WNy1cdhSVQfr1xeLlMgKW59LfSArfc5uGLIsxNgadGy6ARpQv+PWdFXKg3Z0V0RwEasfEwrfbM2lK6MSmr1I9zWSVgp2z6ZlFpaWgdUdsYXcfVXsDTY02l81wt8OOf+nP5bIlT4b5+Jnh9qrn2PLfhgArXsttvHlZXFN2oY/XLoKXSERVlTdWxebAMalgnOzWqY2/G1FwBF58InIpdDDvnAFgoHZF85nfkpp+BjaPvnSeoFbVV3jV0LWEXaqpKERR5319ety0pmyyv4m2UV/1lZZXS8qaM6mGCq9maJufy6RYoDUu90M6GT7bnazuFBWBuKiC6I7D3jDSEq4O7gPv7yfDjbg1SbzIn0D+dIDOeRtj2jo07d6mkpCTU1NQgOjpau8zBwQEhISE3Xa+6uhrV1fpnt1pRA1OBnbzam4ZPo0Rp4c1WuOVyU3MFJr6wEbU1Fci8chhxWxbCxsELHgG9tGFsnf0x8YWNqKkqQcrZHdjzy+sY+9QP7bKBxxABN/KqKQ1+rMue+ovF27gvyS8Flm7XwMIUCPEWMLaXVLnXXQT0CJK6Q/+yT43icmn4Qt2cO1ezbz89ZCQalldRvMUT5YblW9RbnnrpCA5v/xrDprwJd79wFOamYvfa96HY+hX6jH5Wuw83nzAMuHc2AMDVuxPys67g1P6f22Xjzp1qWBxv50F9wzCdvAWE+Qn49bCI3BIRrnYChnYTUFYJnL0qws1eKrPLd7SvG52GugbIML6v7tLu+x03ej40cV67mUanOaHxuS+vWMQXG2tgaS6gs58MkwaY4Nutujl3ojvJ4e0s4PsdtSgqE+HnJuDeaBOUVtQiKbP9NvAAjfNIFEUI+Ct1rNjE8qYNuP8DHNgwD6s/iIEgk8PRvRMCwu9BfuaF295Ge9AWeVff5fj18Arqf0cNQ62pawcZxkXrhjr+8Kc0+Y2hc9pfLS03W+eeXnK4OQj4dmvTPaOiguQ4nawxmrli2sO1Xd3ReilDxNEbE+1nF0m9fyIDBaTmtu9zHt0d2LhzlxJv5wxlwIIFC/D222/rLZsqOOAhuVMTa1Brs7CyhyCTN+qlU1WWD6sGvXnqWFo7o7JB+MqyfAgyE1hY2WmXCTIZbJ2kJ4ROHh1RmJOMU7FL9Bp35CZm2jDOXl2Qm34OZw98jwET32mO5DWbihpAoxEbPcmxMm/8FL+OoR4CCnNArRFReaPNs+4JT8MwDbep0UA7Tj2rUISHg4AewQL+iBdhIgcGhQtYd0CDKzeGfeQUi3C1A3qHynA1u33fQNLts1RK5bW8RL/8VZTmw8ra8HnVUK+eitICyGQmsFTaAQAObP4fOvcch/C+kwAAzp4hqK2uwI6f3kD0yGcgyGRQ2jrD0T1AbzsObh1w6eT2Zkpd86ors43KoIGeN3XKKxuXRysDZbbhNhueBwZHCDicIOJCmlR35haLsFUAfTpKc2N5O0tv0frXWN2DDplMwJCu0tw9i7a0jzKbkKpBWo5uUlSTG3MKKK0ElNbrDaCwaNw7oL6ySl2vnzpKi8ZPy9Uaae4JlIrIyFPDy0mGPp3l2HRQBRM5MLy7HKt2qZCYJuXP9UIR7o4a9O9igqTMOxty09LMrewM17HlBY16zNaxtHa6rTr2VmwcfTD6iR9QW1OB2qoyWNm4IHb1S1DaG8e4jrbMuzplhRnITDqMwQ9+/pfXbW0JqRqk5erOHXXl1dpS0CufCgsB5bcorw176SgspPNjQ2N6ydHRR4alf9Q2+fYmXxcBzrYC1uxpH+e1+trztV1FjbTNvGL9dfJKpEmc/3oTHdFfx+4Yd6nAwECYmpoiLi5Ou6ywsBCXLl266Xpz5sxBcXGx3ucBmcNN16HWJTcxg5NnZ2RcPqS3PP3yIbj6djO4jqtPBNIbhT8IZ6/OkMlNm96ZKEKtrmn6d0gNiZpbhGkLGg2QVQj4u+nfoPi7Cdq3RzSUni8aDJ9VAGhurJJhIEyHm2xTSwDkNx7QyQRALhcaVfMa8Y4fVFI7JTcxg5tPZ1xLOKi3/NrFQ/DsYLi8evhH4NpF/fJ6NeEAXH3DIL9RXlU1VRAa9KiUyeSQ+vhIR5Znh0gUZKfohSnMuQobh/Z5o9hkmXVtunxl5Ivwd21cHhuVWQNh6m/TRN74qa1GhPZR7LmrIpZu12DZDt2ntEJEXKKI1Xvbzw1QTa1uss+CUun15CUVIgI9dMeKXAb4u8mQepNG5NQcDQI99fMs0PPm6wDS+Usu0+3HRC40ylexnZ/n5CZmcPTojMwr+mUw88ohuPgYLrMu3hEGwh+Ek+ct6tgmmJpZwcrGBdWVxci4fBA+HYf85W20hfaQd5dObISFwgHeITF/ed3WVqNqXF5LK0QEeOgKiFwG+LkJSM1p+hojLVeDAA/9+iDQQ4bUHP3yek8vOTr7yLB8W+1NJ0mOCpYhI0+D64XtrzGiPV/baTRAVoH0KvT6HK0FFBvJ2xXbiiCXtcnnbnR3poqgVCoxc+ZMvPLKK9i1axfOnTuH6dOnQya7+X+5ubk5bGxs9D7tcUiWXGEFm66hsOkaCgCw8veCTddQWHi7t3HMWkd4/+m4eGwdLh5bj8LsJBzavABlRVno2HsKAODoH58gds1r2vAde09BWWEmDm9egMLsJFw8th6Jx9YjfMBj2jAnY79B+qWDKMlPQ1FOMs7s+w6XTvyKoG7jtGGObvsUWSnxKC1IR0FWIo5u+y+yko8iMGJs6yX+LzhyUUREBwFd/QU42gBDuwmwtQJOXJEq2YHhAsb20lXmJ65IT+uHRkjhu/oLiOggIO6i7gLpaKKIDm7Sq5MdraV//dyk5XUGhgvwdpZev+lsK709wdcZOH9VClOjAq7liBjcVQYfFylcuL+ALn4CEtN5AXC36T54Bs4cWoezh9YhPysJu9fNR0lhFrr2l8rrvk2f4PcVr2rDd+0/BSUFmdi9bgHys5Jw9tA6nD20Hj2G6sprQJdBOLX/ZyTE/46ivDRcTTiIA1v+h4Aug2808gBRgx9FVsppxG37GoU513Dh2GacOfALusU82LoZ8BccTRQR4S8g3F8qX0MjBNhYASeSbpTZLg3KbJIIGwUwJEIKH+4vlfcjiboye+ySVGZ73yizvUMF+LlKy+tcyRTRp5OAAHfA1kp63XmvYAGXbpTHyhogt1j/oxalp7oFpa2UOXfo0Hk1BnaVo5OvDK72Au4fYIJaFXAqWZdH9w8wwfDucr11Aj1lGBAuh7OtgAHhcgR6Cjh4XjdGY3iUHH6uAuyU0tw7w6Lk8HcTcDpJClNdCyRnaTCqp7TcXim9Lr1boAwXrrbvsR5hfR/FpePrcSl+PYpyknDk9wUoK85CaM/JAID47Z9i71pdHRvacwrKijJxZOtCFOUk4VL8elw6vgFd+unKrDTZcALyMxOgVteivCQH+ZkJKMm/pg2TfvkA0i/tR2lBOjKuHMQfS6fDxskfwVHGM3dMW+UdAIgaDS6f2IDAyPGQyY1zcMKhC2rEhMvR0UeAi52ACf3kqFUBp+uV14n95BgWWa+8XtAg0ENA/zAZnGyl16kHeAg4dEG3ztjecnQNkOGXfSpUq6SePkpLqWG7PnNTIMxXhvh2NpFyfe312g4A4hI06OQtbd9eCXQPEhDkARw3ornayLgZ55mPbstHH32EsrIyjBs3DtbW1nj55ZdRXFx86xWNgG1UGKJ3/aD93unjuQCAtO834MzMOW0VrVYT0HU0qiqKcGLXV6goyYWDWxBGzfgG1je6bleU5qKsKFMb3sbBCyMf+waHNy/E+cM/QWHjgj7j5qFDlxHaMKqaShzY9A7Ki6/DxNQCds7+GDzlQwR0Ha0NU1maj9g1r6KiJBdmFtZwdA/BqMe+bTTpcnuRkCbCyhzoFyZAaSEgtxhYvU+j7YqstARsFbqussXlwJq9GgzrJkNUkDTfxo4TIhLTddvMyAc2HtIgJlyGmC4CCsuk75kFujAKC2BcbxmUFtINTk4RsHqvBin15tLZeEiDQeECxveWwcJMev3ynrOi9uKE7h6h3UejsrwQh7YuQnlJDpzcgzFx1hLYOkrltawkF6WFutcy2Tl5Y+KsJdi9fgFO7VsFpa0Lhkyah5BuuvIaPeoZQBBwYPNnKCvKhqXSAQFdBqH/uJe0Ydz9wjH+qS+x79dPcWjrV7B19MKg++eiU09dg217k5AmwtIc6NdZV2bX7NcvszZW+mX2l30aDO0mQ1TgjTJ7snGZ3XRYg5guMsSECSgsBzY1KLM7TogY0AUYGSXTTrB5MknE/gvGXx73nVHDVA6M62MCSzPpzWDfba9FTb1RUXZK/R42qTki1sSqMCxKjqGRchSUili9W4X0enNGKC0FTIoxhbUVUFUDXC8QsWJ7rd6bflbH1mJEdxM8MNAUVuZAUZmIHcfVOHKx/d44AkCH8NGorijCqdhFqCjNhb1rEIY/8rV2eFRFaS7Ki3Vl1trBC8Me/RpHf1+IhLifYGXjgt5j5sIvbLg2TEVpLn79aoL2+7kDy3HuwHK4+ffA6Me/BwDUVJXi+I7/orz4OswtbeHXeTiihr94Rz1Y2kpb5R0AZCYdRnlRFoKjdGGNzf5zGpiaCBjX2wQW5lJ5XbFDhRqVLoydUqg3DxuQlivil70qDI00wZBuchSUAmv2qPR6nfQKlVpxHh+lfyytPyC9Ir1OF38ZIABnkttvGW3P13aJGcAf8dLDguGR0puz1h/U8GUZt3C3vpa8LQjinU7OQv8Yv5vefBJmatrFtRfbOgpGq6qq/V5YtHfzpshvHYgMWrqrrWNgvHLy2ndvjPasrEx160BkkK2t8TR80N2jlGX2jlha8PrkTt3N13ZxvXq2yX57HznaJvttSey5Q0REREREREStjq9Cbz7tbzIVIiIiIiIiIiK6bWzcISIiIiIiIiIyYhyWRUREREREREStjhMqNx/23CEiIiIiIiIiMmLsuUNERERERERErU5gz51mw547RERERERERERGjI07RERERERERERGjMOyiIiIiIiIiKjVCTL2N2kuzEkiIiIiIiIiIiPGnjtERERERERE1OoEGSdUbi7suUNEREREREREZMTYc4eIiIiIiIiIWp2Mr0JvNuy5Q0RERERERERkxNi4Q0RERERERERkxDgsi4iIiIiIiIhaHSdUbj7suUNEREREREREZMTYc4eIiIiIiIiIWp0gY3+T5sKcJCIiIiIiIiIyYmzcISIiIiIiIiIyYhyWRUREREREREStjhMqNx/23CEiIiIiIiIiMmLsuUNERERERERErU4mZ8+d5sLGHbqli2svtnUUjFbopNC2joLRSt2c2NZRoH+g4lKxraNgtFQq5t2dcrA3besoGK0psVPaOgpGa0n3H9s6CkZr78YjbR0FozTjhf5tHQWiuxobd4iIiIiIiIio1XHOnebDOXeIiIiIiIiIiIwYG3eIiIiIiIiIiIwYh2URERERERERUasTZOxv0lyYk0RERERERERERow9d4iIiIiIiIio1XFC5ebDnjtEREREREREREaMjTtEREREREREREaMw7KIiIiIiIiIqNVxWFbzYc8dIiIiIiIiIiIjxp47RERERERERNTq2HOn+bDnDhERERERERGREWPPHSIiIiIiIiJqdYKM/U2aC3OSiIiIiIiIiMiIsXGHiIiIiIiIiMiIcVgWEREREREREbU6mZwTKjcX9twhIiIiIiIiIjJi7LlDRERERERERK2Or0JvPuy5Q0RERERERERkxNi4Q0RERERERERkxNi48zcJgoBNmzY1+fuePXsgCAKKiooAACtWrICdnd1Nt/nWW28hIiKi2eJIRERERERE1N4IMlmbfO5GnHPnFq5fv473338fv//+OzIyMuDi4oKIiAi8+OKLGDJkyC3X79OnD7KysmBra9sKsTU+5w//hDN7l6GiNBf2roGIHjsX7v7dmwyfmXwUcVsWojD7CqxsXNA15nF06j1F+3vKuR04ufsblOSnQqNWwdbJF10GzEBw5L3aMBcO/4wLcT+jtDADAGDvGojIIc/CJ3RAyyW0HXHo1x0dXp4J28gwWHi4IH7iLGT/tquto9WqTu9fheO7l6G8JBeObkGImTAXngFNH3fpV45i38aFyL9+GQpbF3Qf/DjC+03VC1NVUYJDv/8XV87sRHVFMWwcvTDg3tfh3zkGAKBRqxC37QtcjN+M8tI8KGyc0annfeg1fNZdW8GQTluc6+J3foETf36lt11LpROm/edA8yewmcV0kSEyUICFGZCRD/xxTI3c4puvE+otYFBXGeyVQGEZsPuUBonpol6Y7kECojvJYG0J5BQBO46rkZpreHtjesoQFSTD9ng1jiTqtmOvBIZFyuDtLMBEDlzJFLEtXoPyqr+Z6BZw4fBPOL1/OSpLc2HvEoje98y56XGXlXwUcb9/gMKcK7CydkF4zEx06qU77i4e/QWXTv6GwuuXAQBOnp3QY8RLcPEO19tOeXE2jm77BGmJ+6BSVcPWyQ8DJr4HZ8/OLZPQNqLoPxzWQ8ZBbmOH2qx0FG1YgZqki02Gt+zeD9ZDx8HE2R1iZQWqEk6heOMP0FSUtWKsW8egrnJEBctgaQak54nYckSN3CLxput08hEwuJsJHKyBglJg10kVElJ16/QPk6GTrwxOtgJqVUBarogdx1XIL5F+lwnAkG5yBHsJsFcKqKoFkrM02HlcjdLKlkxty5sxxQfjRrjBWmGCC5dK8ek3SbiaVtFk+LHD3DBikAs6+FoBABKTyrDkh6tIuKw71uQyYMZUXwyLcYGjnSnyC2vwx+4crPwlFeLN/6vapRN7V+HozmUoK86Fk3sQhkyaC++gps93qZeOYve6hcjLugylrQt6DX8c3Qboru3U6lrEbfsG5+I2obQoGw6u/hh437/RobPufuHk3p9wcv/PKM6X7imc3IPQZ/QsBITFtFxCiW6CjTs3cfXqVfTt2xd2dnb48MMPER4ejtraWmzfvh3PPvssLl5sugKvY2ZmBjc3t2aNV01NDczMzJp1m20h6fRWHN68AP3GvwFX30gkHFmDP5Y/iQdmb4HS3qNR+JKCdGxb/hRCe07CoMkfIfvaCRzY9A4sFPbo0GUEAMDc0hbdBj8NO+cOkJuY4lrCHuxdOxeWCgd4h/QHAChsXdFz1MuwcfQBAFw6vgk7vn8WE57fAAe3oNbLgDYiV1ih5Ewi0lduQNTaL9s6Oq0u8cRW7N24AIMnvQkP/0icObQam75+AtPm/A4bh8bHXXF+GjZ98yTCoidh5LSPkJlyArvXvg1LpQOCIqTjTq2qwcZFM2Bp7Yh7ZvwPSjs3lBZmwcxCqd1O/K5vcebgaox46AM4uAUiJ+0cdvw0B+YW1ug28NFWSz+1vrY61wGAvWsQxjyxXPtdEOQtn+C/qU8nAb07Cvj1sAb5JSL6h8nw8GA5vtqsRo3K8DpeTsD9/WSIPaPBxTQRod4C7u8vw4odamTkS2E6+QoYESXD1mMapOWKiAyS4cFBcizaokZJg3ukEC8Bno4CSir073BM5cBDg+XILhTxwy41AGBguAxTYuRYtl3d3FnxtySd2YrDvy9E33v/A1ffSFw8sgbbVjyFSS9thtKuieNuxdMI7XE/Bk7+ENnXTuDgr+/CUuEA/7DhAIDM5GMIDB8N17HdIDcxx+l9y/DH8sdx/4ubobB1BQBUVxbjt68fhEdAL4ycsQSWSkeU5KfC3MK6VdPf0iwjo2E3YToKf1mKmuREKPoOhdMzc5H9/ktQF+Y3Cm/WIQQO0/6F4g0rUXkuHnJbB9hPfgL2Dz6N/KUft0EKWk6/MBmiO8mw8aDU8BITLsOjw0zw+cbaJsuwt7OASTEm2H1SjYRUDTr6yPBAjAmW/aFCep5UDv3cZDhyUYOMfBEyARjaTY5Hh5nii19rUasCTE0AD0cBe05rcL1QA0szAaN6yvHgYBN883sTOzYCD07wwuR7PTH/f5eQllmJRx/wwX/fCcODs46jstLweSeiiy3+3J+Lc9+WoKZGgwcneOGTt7rgkeeOI6+gRtruRG/cO9Id8z9LREpaBUIDrTHn+SCUlauwbktmaybxb0uI34pdaxdg+JQ34RkQiVP7V2PtV0/g8TcMX9sV5aVh3VdPIrzvJNwz4yNkJJ3AjtVvw0rpgJBIqZ7d/9tnOH/kN4x8+D04unZAyoX92PjNv/DwK6vh6t0JAGBt74aY8f+GvbN0T3EubhM2fP0sps/dCGePu/+eorlwQuXmw8fFNzFr1iwIgoCjR4/i/vvvR3BwMDp37ozZs2cjLi5OGy4vLw/33XcfrKysEBQUhN9++037W8NhWYYsXLgQrq6usLa2xsyZM1FVpf/4b/r06Rg/fjwWLFgADw8PBAcHAwAyMjIwefJk2Nvbw9HREffeey+uXr3aaL2PP/4Y7u7ucHR0xLPPPova2trmyaC/6cz+FQjpMRGhPSfB3jUAfcbNhdLWDRfifjYYPiFuNZR27ugzbi7sXQMQ2nMSQrpPwJl9uhsXj4Be8A8bBnvXANg4+qBLv0fg4BaC61dPaMP4dhoMn9AY2Dn7w87ZHz1HvgRTMyvkpJ5u8TS3B7nb9+HSm5/h+qadbR2VNnFiz3fo3HsiwqInwcEtAAMnzIPS3g1nDho+7s4cXA1re3cMnDAPDm4BCIuehM69JuB4rO64Ox+3HlUVxRj7+Ffw6BAFGwdPeAZ0h7NnqDZMVsopBIQNgX/ngbB19EJQxEj4hvRDdtq5Fk8zta22OtcBgEwmh5W1s/ZjqXRo0bQ2h16hMuw/JzXS5BYDvx7WwNQECPNr+uKvV6gMyddFHDwvIr8EOHheRMp1Eb1CdZc50aEynEwScTJJRF4JsOO4BsUVQPdg/Usha0tgVA8ZNh5SQ6PR34+3swA7hRSnnCKp989vcRp4Ognwd2tfF6dn969ESPcJCO0xCfYuAYgeW3fcrTYYPuGIdNxFj50Le5cAhPaYhOAo/eNu8JSP0Cn6QTh6dISdSwf0n/AORFGDjKTD2jCn9y6Fws4dMffPh4t3OKztPeEZGK19oHK3sB50D8oP70bF4d1QZWegeMNKqAvzoOg33GB4M79gqPNzULb3D6jzc1GTnIiyg3/C1KdDK8e85UV3lGPfWTUSUkXkFInYcEANUxMgvEPTtx29O8qQnCli/zkN8kqA/ec0SM4S0buTbp0f/lThVJIGuUUisgtFbDyogp1SgIejVPaqa4GVO1U4f02D/BKpx9DvR9TwdJLBVtHiyW4xD4z1xPdr07AvLh8pqRV4/7NEmJvJMWyAc5PrvPtpIjb9kYUrKeVIzajEh19dhkwGRHW104YJC7HGgSP5OHy8ENdzqrHnUB6OnixCaKCyye22V8d2fYfwPhPRtd8kOLkHYOgD82Bt74aT+wzXs6f2r4a1gzuGPjAPTu4B6NpvEsL7TMDRP+td2x35FdEjn0ZAWAzsnL3RLeZB+HfqpxcmMHwwAsJi4ODqDwdXfwy49yWYmVshM+VUSyeZyCA27jShoKAA27Ztw7PPPguFonGNUH/enLfffhsPPPAAzpw5g9GjR+Ohhx5CQUHBbe3nl19+wZtvvon3338f8fHxcHd3x6JFixqF27VrFxISErBz505s2bIFFRUVGDRoEJRKJfbt24cDBw5AqVRi5MiRqKmp0a4XGxuLpKQkxMbGYuXKlVixYgVWrFjxl/OjualVNcjLOA+voL56y72C+yL72kmD62SnnoJXcMPw/ZCbfh4adeMGK1EUkXHlMIpzU5rshq7RqHHl1O+oramAq2/EnSWGjIZaVYOctPPwDemnt9w3pC+yUgwfd9evnoJviP5x5xvaHzmp56C+cdwln9sNd78IxK59B0vm9cEPC+7B0R1fQ6PRPVHz6BCF1MtxKMxJAQDkZlxEZvJx+HVi1927WVuf64rzruHH9/rj54VD8Oeq2SjJT/ubKWpZdkrA2lJAcpaux4xaA1zLFuHt3HTjiZeTgKQs/V42SVkivG6sI5MB7g5oFCY5S4S3k/52x/eR4dAFjcFhYCZyXZzqqNSARiPC5ybxa21qVQ3yMs/Ds8Fx5xnUF9mpho+7nNRTjcJ7BfdFbobh4w4AVLVV0KhVMLfUDT2/lhALZ8/O+HPVi/jhvb7Y8PkEXDz6y99MUTsjl8PUuwOqLuo/FKq6eAbm/iEGV6lJSYTczhEWnboBAGTWtrCK6I2q84b/P4yVvRKwthJwJVO/DF+9fvMy7O0sw5Us/dbUK5ka+Dg3faticaMTe2V10/GxMAM0ooiqmqbDtGfurhZwdDDDsZOF2mW1KhGnzhcjLNTmtrdjbi6HiVxAaamuB9OZhBJEhdvB28MSABDgp0B4JxscPl7Y1GbaJbWqBtdTz8O/k/61nX/HvshINly+MpJPwb+j/vnOv1N/XL+mu7ZTqWohN9UfKWFiaoH0K/oPUepoNGpcOCbdU3h26HanyflHEmRCm3zuRhyW1YQrV65AFEWEhobeMuz06dMxdao0RnP+/Pn44osvcPToUYwcOfKW63722Wd47LHH8PjjjwMA3nvvPfz555+Neu8oFAosXbpUOxxr+fLlkMlkWLp0KQRBOji/++472NnZYc+ePRg+XHpyZG9vjy+//BJyuRyhoaEYM2YMdu3ahSeeeOL2M6MFVFUUQtSoYal01FtuqXRERWmewXUqS3NhqezXKLyoUaGqvBBWNi4AgJrKUvw4PwZqVQ1kMhn6jn+z0Y1SQVYiNi2aCrWqGqZmVhj+yJewdw1sxhRSe1RZLh13Vjb6x52VtRMqSg1PvFFekgffUCf98DaO0GhUqCorhMLWBcX5aUi7HIfQqLG49+klKMq9hti170CjUaH3yH8BALoPfQLVVaVYOX8UZIIcGlGNPmNeQmjUPS2TWGoX2vJc5+LdFQMnL4Sdkx8qyvJxcvdi/LpoKibN3gwLhX0zp7R5KC2kf8sazF9TVgXY3eTJu9ICjea8Ka/Sbc/KHJDJBJRXiQ3CiFBY6i7w+nYWoBGBo4mGJ5xIzxNRowKGdJNh9ykNBEh/y2QClJbtZ5KKqooi6Vyn1D93WSodUdnEcVdRmgevBsepldKp0XFX37Ftn0Bh4wrPwD7aZaUFaUg4shpd+k1HxKAnkZt2Foc2z4fMxAzBkeP/fuLaAZnCBoJcDk2pfgugprQYMhs7g+vUpFxCwfefw2HGixBMTSHITVB55hiK1i43GN5YKW+Up/LKxmXNTtH0zZTSEihrMC9OWaW0vCkje5jgWrYGOU3M5WMiA4ZFynE2WYPq9tFp/S9ztDcFABQU6yegsKgGbi4Wt72dpx/xQ25BDeJP6xpuVq1Ph9LKBD9+FQWNRoRMJuDbH69i1/4mJiJrpyrKblzbWeufvxTWTigvbvraTmHd4NrOWrq2qywrhNLWBf4d++HYrhXwDuoBeycfXE08jMund0EU9YfC5WYk4oePpkBVWw0zcyvc99RXcHLnPQW1DTbuNEG8MZNYXcPJzYSH6yYSVCgUsLa2Rk5Ozm3tJyEhAU8//bTesujoaMTGxuot69Kli948O8ePH8eVK1dgba0/hr2qqgpJSUna7507d4Zcrptjwd3dHWfPnm0yPtXV1aiu1n8Eoqo1g4mp+W2l569qmL+itPBmK9xyuam5AhNf2IjamgpkXjmMuC0LYePgBY+AXtowts7+mPjCRtRUlSDl7A7s+eV1jH3qBzbw/GM0PI5EA8vqB2/wW91MgzeWi6IIK6Ujhkx5FzKZHK7eYSgvzkH87mXaxp1LJ7fiYvxvGPXIJ3B0C0RuRgL2blgApa0LOvW8r3mSRe1WW5zr6k8S7wDA1TcCqz8YjkvHNyF8wIw7TEnzCvMTcE9P3ZP5n/fcuGhucK8mCI0WNXLHE4DeWM/dAegVIsOSP5qeO6eiGli3X4PRPWXoFSKHKALnronIzBehaT9tOzch3vS4a3ScNjjX1Xd671Iknd6KMU+s1LtGEEURTp6d0WPESwAAJ49OKMy+goS41XdN446WwYOuiYYGN0/Y3T8DJdvWoTrhNGQ29rAd/zDspzyBwp++btl4tqBwfxnGRuuuM1ftknqG3H7ONE24yTpjesnhai9g2R+GW21kAjApxgSCAGw50r7mw7qZYTHO+PczurlaXnv3vPRHg2NNEOqVz1t48D4vDO3vjOfnnUFNrW6dIf2dMWygC975NBEpqeUI8lfiuZkdkFdQg22xt3cf0540rmdvfr5r/Ftd3kjLhz4wD9tW/R+WvjUKEATYO3mjS/QEnD28QW8tB1d/zJi7CVWVJbh0cgd+X/kaHpz9Ixt4qE2wcacJQUFBEAQBCQkJGD9+/E3Dmpqa6n0XBAGahgP1/6aGQ8M0Gg2ioqKwatWqRmGdnXVjcP9q3BYsWIC3335bb9mwyW9gxJS37iDWTbOwsocgkzd6cl1Vlg+rBk8O61haOzd64lhZlg9BZgILKzvtMkEmg62TLwDAyaMjCnOScSp2iV7jjtzETBvG2asLctPP4eyB7zFg4jvNkTxqpywVN467Ev3jqKI0H1YNnuDUUdg4oaIkt0H4AshkJrBQ2N0I4wyZ3AQyme4C1961AypKcqFW1UBuYob9v36IHkOfREjkGACAk0cISgoycWznN2zcuYu19bmuPlMzKzi4BaM4/9rfSFHzupQu4ps83Y1X3bAnpaV+7x2FeeOeOfWVVTV+wq+w0G2joloaOqWw0L9dVFgI2u36OAtQWAAvjteVY5lMwLBIGXqFAp//KsUz+bqIL39Tw9Ic0GikeT5mT5Dj/LX207pjYWUnHXdlDY+jgka9yOpIPRgbhC9vfNwBwJl9y3FqzxKMnrkcju76w5CsrJ1g7xKgt8zOpQNSzu+4w9S0P5ryEohqdaNeOjJrW2hKDL/WzXrYfahOTkTZrs3SgsxUFP1SBZeX3kXxltXQlBS1bKRbyMU0DdLzdNeVcrl0Y6y0FFBWqV/WyiqbLiOGeukoLIFyA2+5Gt1TjlBvGZZtq200GTogNew8MNAE9krgux0qo+q1c+BoAS4k6ob9mJpKjd8OdmbIL9QlxM7WDAVFt07YlPGeePh+b7z05lkkXdPPrGem+2PV+jRtT53kaxVwdTbHw/d7G1XjjpVSqmfLDVzbKWyavrYrb+LazlJpJ23X2gETnl4EVW01KsuLoLR1wd5NH8PWyUtvPbmJGexdpLrY3bcLsq6eRfzu7zHyId5T3C6+Nbb5MCeb4ODggBEjRuCrr75CeXl5o99vNkHyX9GxY0e9yZkBNPpuSGRkJC5fvgwXFxcEBgbqff7Oa9fnzJmD4uJivc+QiXPueHtNkZuYwcmzMzIuH9Jbnn75EFx9DY9TdfWJQHqj8Afh7NUZMrmpwXUAAKIItfrmg61FUYTmFmHI+MlNzODi3RmpiQf1lqcmHoK7v+Hjzs0vAqmJ+sfdtcQDcPEJg/zGcefhH4mivFSI9RpOi3KuQmHjDLmJ1ONOVVPV6CmRIJPf9pM3Mk7t6VynVtWgKCcJVtZNT8LZ2mpU0mvL6z65xUBppYgO7rqyIpMBvq4C0nKbLivpeSI6NJjQOMBdQPqNdTQaIKsAetsFpO9pN97EcyZFxNe/q/HNVt2npELE4QQRq3Y3fvJfWS017Pi5So1Cl9LbT1mWm5jByaPxcZdx5RBcfQwfdy4+Eci40iD85YNw9tQ/7k7vW4YTuxdj5IwlcPYKa7QdV99IFOVd1VtWnHfV4Bu6jJZajdq0ZFiE6r8C3iIkHNUpiQZXEczMG/f00dYZxjv3Q41Kem153Se3SERphYjAemVNLgP83G5ehtNyNQhw178tCXSXITVX/4HkmF5ydPKV4bvttSgy8Ab5uoYdR2tgxQ7VTefjaY8qK9XIuF6l/VxNq0B+QQ16ROiG0pqYCIjobItzF0tuuq2p93ni0Qd88O+3zyHxSuPMsjCTQWzwvFejkd5EZkzkJmZw8+mMqwn613ZXEw41OfeNZ4cIXE3QP9+lXDgAN1/dtV0dE1NzWNu5QqNRIfHkDgSFD7lFjESoVbynoLbBxp2bWLRoEdRqNXr27In169fj8uXLSEhIwOeff47o6Ohm2ccLL7yA5cuXY/ny5bh06RLefPNNnD9//pbrPfTQQ3BycsK9996L/fv3IyUlBXv37sULL7yA9PT0O46Pubk5bGxs9D4tNSQrvP90XDy2DhePrUdhdhIObV6AsqIsdOw9BQBw9I9PELvmNW34jr2noKwwE4c3L0BhdhIuHluPxGPrET7gMW2Yk7HfIP3SQZTkp6EoJxln9n2HSyd+RVC3cdowR7d9iqyUeJQWpKMgKxFHt/0XWclHERgxtkXS2d7IFVaw6RoKm67SfFJW/l6w6RoKC2/3No5Z64gcOAPn4tbhfNw6FFxPwt4N81FamIXwvtJxd2DzJ9j+46va8OF9p6CkMBN7Ny5AwfUknI9bh/Nx6xE1SHfchfebiqryQuzZ8D4Kc1KQcn4Pju38Bl37P6QN4x82CMd2fI2U83tQnJ+OK6d34mTsdwgIH9p6iac20VbnurgtHyAz+ShKCtKRk3oaO398HjXVZQiOGt9qab8TRy5q0K+zDCFeApxtgXujZahVAeeu6m4M742WYXCETG+dAHcBfToJcLSRXqfu7ybgyEXdncvhixpEBgiI6CDAyQYYHimDrRVw/LIUprJGalyq/9FogLJKEfmluvh17SDA01GaOLaLn/TK9biL+mHagy79H0Vi/Hokxq9HYU4SDm+5cdz1mgxAqgtjf6l33PW6cdxtWYjCnKQb627QO+5O712K+B3/Q8z978Pa3hMVpbmoKM1FbbXuIViXvo8iJ/U0TsZ+g+K8a7hyagsuHl2Lzr0fbL3Et4LS2C1QRA+BVe9BMHH1hO2ERyF3cEL5AelNlDZjp8J+2rPa8FXn4mHZtScU/YZB7ugCM/8Q2N0/AzVXL0NTYlwT2N7K4QQ1+ofL0dFHgIudgPv6ylGrAs4k68rjhH5yDI3U9ZKLS9AgwENAvzAZnGyk16l38BAQd0G3zj295AjvIMO6fSrU1EpzaiktdD3+ZAIweaAJPB0FrNuvhkzQhZEb8R3PL5sz8PD93ujf2xH+PlaY+3wwqmvU2LlP1/Nk3ovBeGqan/b7g/d54fGH/LDwi0u4nlMFBztTONiZwtJClxGHjhVg2iRvREfZw83FHP17O2LyvV7YF5ffmslrFj2GzMDpg+tw5tA65GUlYdfa+SgpzEJEf6me3bvpE2xZobu2i+g/BSUFmdi1bgHyspJw5tA6nDm0Hj2H6s53mSmnkXhyB4py05B2OR5rv3gcokaDXsMf14bZu+lTpF2OR3F+OnIzErHv1/8i9dJRdOr5z7inaC6cULn5cFjWTfj7++PEiRN4//338fLLLyMrKwvOzs6IiorC4sWLm2UfkydPRlJSEl577TVUVVVh4sSJeOaZZ7B9+/abrmdlZYV9+/bhtddew4QJE1BaWgpPT08MGTIENja3P3t+WwroOhpVFUU4sesrVJTkwsEtCKNmfANre08AQEVpLsqKMrXhbRy8MPKxb3B480KcP/wTFDYu6DNuHjp0GaENo6qpxIFN76C8+DpMTC1g5+yPwVM+REDX0dowlaX5iF3zKipKcmFmYQ1H9xCMeuzbRpMu361so8IQvesH7fdOH88FAKR9vwFnZjZ/L632JiRyNKrKCxG3fREqinPg6B6Me59aAhsH6bgrL8lFSWGWNrytozfGP7UEezcuwJn9q6CwdcHACfMQFKE77qzt3XHfM8uxb+MC/PjBOChtXRER8wi6D9VNXD5o4v/h0Nb/Yffat1FRlg+ljQu69J2MXiN0F/90d2qrc11ZcTZ2//QyqiqKYKGwh4tPV4x/do12v+3VoQsiTOUiRveUwdIMyMgDftytRo3uJS+wVQh6vd7S84D1BzQY1FWGQeFAQZn0PaPePcqFayKszDQY0EUGpaX0GvOf9qhR3Lhz7k052QgYEiHFragcOHBOg7iL7afXTp2A8NGoLi/CiV2LUFGaCwfXIIyc/rXecVdepDvX2Th4YeT0r3H494W4EPcTrGxcED12LvzDdK/2vhD3MzTqWvy56gW9fUUOeRZRQ6X5xZy9u2DYw5/j2Pb/4uTuRbC290L0Pa8jsNvddbNTeeIwihTWsBk5EXIbe9RmpSFv8QKoC6WhIXJbe5jY64aEVBzZC8HcEsoBI2F73yMQK8tRdek8in/9sa2S0GIOnNPAVC7gnl4msDAHMnJFfL9TddMynJYrYu0+FYZ0M8HgCDkKS4Ff9qqQnqcL0zNUasV5bKR+z4oNB6RXpNtYAR19pMaLZ8fph1m+rRZXs9tfOb0dP21Ih7mZDC8/FQil0gQJl0ox+81zqKzU9Sh0dTLX64UzfpQ7zExleO/1TnrbWv7zNXy3OhUA8N9vk/D4g76Y/XQg7G1NkVdQg1+3Z2HFmtRWSVdz6th9NCrLC3Hw90UoL8mBk3swJj27BLaO0vmurDgXJQW6852dkzfuf3YJdq9bgJN7V0Fp64KhD8xDSGS9era2Gvt/+wxFeWkwM7dCh7AYjJn+ISysdPdZ5aV52LLiVZSX5MDcwhrOniGY9NzSRm/iImotgsgxAXQLn2ziIXKnQifd+m1rZFjqZsNd2+nWnrn1i/qoCTzf3bnycuOZtLS9sbI04m4FbWxK7JS2joLRWtL97mtYai2xG460dRSM0owX+rd1FIzWY4PbOgYtJ23WxDbZr/ei9W2y35bEnjtERERERERE1Oo4oXLzYU4SERERERERERkx9twhIiIiIiIiotYn3J2TG7cF9twhIiIiIiIiIrqJRYsWwd/fHxYWFoiKisL+/ftvGn7VqlXo2rUrrKys4O7ujhkzZiA/v+XeSMfGHSIiIiIiIiJqdcbyKvQ1a9bgxRdfxLx583Dy5En0798fo0aNQmqq4TfMHThwAI888ghmzpyJ8+fPY+3atTh27Bgef/zxv5tlTWLjDhERERERERH9Y1RXV6OkpETvU11d3WT4Tz/9FDNnzsTjjz+Ojh074rPPPoO3tzcWL15sMHxcXBz8/Pzw/PPPw9/fH/369cNTTz2F+Pj4lkoSG3eIiIiIiIiI6J9jwYIFsLW11fssWLDAYNiamhocP34cw4cP11s+fPhwHDp0yOA6ffr0QXp6OrZu3QpRFJGdnY1169ZhzJgxzZ6WOpxQmYiIiIiIiIhaXVu9Cn3OnDmYPXu23jJzc3ODYfPy8qBWq+Hq6qq33NXVFdevXze4Tp8+fbBq1SpMnjwZVVVVUKlUGDduHL744ovmSYAB7LlDRERERERERP8Y5ubmsLGx0fs01bhTR2jwZi9RFBstq3PhwgU8//zzeOONN3D8+HFs27YNKSkpePrpp5stDQ2x5w4RERERERERtbo7mdy4tTk5OUEulzfqpZOTk9OoN0+dBQsWoG/fvnjllVcAAOHh4VAoFOjfvz/ee+89uLu7N3s82XOHiIiIiIiIiMgAMzMzREVFYefOnXrLd+7ciT59+hhcp6KiArIGQ87kcjkAqcdPS2DjDhERERERERFRE2bPno2lS5di+fLlSEhIwEsvvYTU1FTtMKs5c+bgkUce0YYfO3YsNmzYgMWLFyM5ORkHDx7E888/j549e8LDw6NF4shhWURERERERETU6tpqQuW/avLkycjPz8c777yDrKwshIWFYevWrfD19QUAZGVlITU1VRt++vTpKC0txZdffomXX34ZdnZ2GDx4MD744IMWiyMbd4iIiIiIiIiIbmLWrFmYNWuWwd9WrFjRaNlzzz2H5557roVjpcPGHSIiIiIiIiJqdcYwobKxMI4+UEREREREREREZBAbd4iIiIiIiIiIjBiHZRERERERERFRq+OwrObDnjtEREREREREREaMPXeIiIiIiIiIqPUZyavQjQFzkoiIiIiIiIjIiLHnDhERERERERG1OkHgnDvNhT13iIiIiIiIiIiMGHvu0C1VVWnaOgpGK3VzYltHwWj5jA1p6ygYr1oed3cqM7OiraNgtBwdLdo6CkbLz7OtY2C8No5c09ZRMFo21WJbR8FohfTs2NZRMEo1tW0dA6K7Gxt3iIiIiIiIiKjVCZxQudkwJ4mIiIiIiIiIjBh77hARERERERFRqxNknFC5ubDnDhERERERERGREWPjDhERERERERGREeOwLCIiIiIiIiJqfZxQudkwJ4mIiIiIiIiIjBh77hARERERERFRq+OEys2HPXeIiIiIiIiIiIwYe+4QERERERERUasTBPY3aS7MSSIiIiIiIiIiI8bGHSIiIiIiIiIiI8ZhWURERERERETU+jihcrNhzx0iIiIiIiIiIiPGnjtERERERERE1OoEGfubNBfmJBERERERERGREWPjDhERERERERGREeOwLCIiIiIiIiJqdQInVG427LlDRERERERERGTE2HOHiIiIiIiIiFqfwP4mzYU5ecOKFStgZ2d3W2HfeustREREtGh8iIiIiIiIiIhuR6v03Dl06BD69++PYcOGYdu2ba2xyxb173//G88991xbR8PoRAUK6B0qQGkJ5BYDO09qkJbbdHgfZ2BoNxmcbYHSSiAuQcSJJFEvTIgXENNFBnslUFgG7D2jQWKG7vfIQAGRgQLsFNL33GLgwHkNkrJ0YUxNgMHhAoK9BFiaAcXlwLHLIk5c0d9Xe3N6/yoc370M5SW5cHQLQsyEufAM6N5k+PQrR7Fv40LkX78Mha0Lug9+HOH9puqFqaoowaHf/4srZ3aiuqIYNo5eGHDv6/DvHAMA0KhViNv2BS7Gb0Z5aR4UNs7o1PM+9Bo+6x/xGkOHft3R4eWZsI0Mg4WHC+InzkL2b7vaOlpkRIb3MEXvTiawMhdwLVuDDfuqkV1483NNlw5yjOxpBidbAXnFIv44UoNzKWq9MDYKAfdEmyHURw5TOZBbrMEvsTVIz9Vo99st0AS2SgFqNZCeq8EfR2qQmqNpsbTeqchAAb1DdHXFnyc1SMtrOryPMzAkol5dcVHESUN1RZgMdkqgqAzYc1aDS/Xqiln3yGCnaDzm//hlDbafkLZlagIMChcQ7HmjrqgA4i81rpfau7g/f8KBrctRWpwLF89AjHloDvxCDNcdJUU5+OOnD5F59Tzys68hetjDGPPwXL0wJ/ZvxPpv5zZa962lp2BqZt4iaWgNZw/+hBOxy1BRkgsHt0D0Hz8XHh2armMzrhzFgd8WouD6FShsXBA5+HGE9Zmi/T3h6AbsWt04n57+4DRMTKV8OrLtCxzb8ZXe71bWTnjs7QPNlKrWceHwTzi9fzkqS3Nh7xKI3vfMgbt/03mXlXwUcb9/gMKcK7CydkF4zEx06qXLu4tHf8Glk7+h8PplAICTZyf0GPESXLzDtWF+/mAIyooyG227U++p6HvvG82YurYxrr8VBnQzh5WFDCmZKqzaVobMPHWT4T2c5Lg3xgq+biZwspNj9Y4y/HmsSi/MqD6WiAwxg7ujHDUqICm9Fut2VyC7oOnttmen969C/K5618UT58LrZtfFl49ib/3r4iGPo2u96+K1n09D+pWjjdbz7xSD8U8vabT86I5vcHDLp+gW8wgGTpzXPIn6h+CcO82nVRp3li9fjueeew5Lly5FamoqfHx8WmO3LUapVEKpVLZ1NIxKR28Bw7oJ2HZcRFqeiMgAAVMGyPDNHxqUVDQOb6sAJsfIcCpJxK9xIrydBIyMElBeLSIxXQrj6QhM6CPD3rMiEtNFhHgJuK+vDN//qUFmgRSmtEJE7GkRhWXS93A/AZP6ybB0uwZ5JdKyYd0E+LoI+DVOg+JyoIObtK+ySlHv4r89STyxFXs3LsDgSW/Cwz8SZw6txqavn8C0Ob/DxsGjUfji/DRs+uZJhEVPwshpHyEz5QR2r30blkoHBEWMAACoVTXYuGgGLK0dcc+M/0Fp54bSwiyYWeiO9fhd3+LMwdUY8dAHcHALRE7aOez4aQ7MLazRbeCjrZb+tiJXWKHkTCLSV25A1Nov2zo6ZGQGdTNFTFdTrN5djdwiDYZGmeGpcRb44KdKVNcaXsfXVYZpw82x7WgtziWrENbBBI8MN8eXG6u0DTOW5sBz91ngSoYa326pQlmlCCcbAZXVukaH3CINNuyvRn6JCFM5ENPVFE+OtcCCVRUorzK877bQ0VvAsAgB206ISM8V0S1QwOQBMizZ1nRd8cAAGU4li/gtToSXs4CRkQIqGtQV90XLsPeciEvpIoK9BNzXR4YfdunqihU7NRDqXVs62wIPDpQjIU2Xh0MjpLritxt1hf+NuqK0UsTlxveU7dKZuK3Yumohxj76H/gGReJY7Bqs/PgpvLBgM+ycGtcd6tpaKGwcMHDcUzi47fsmt2tuqcRLH2zVW2bMDTuXT27F/k0LEDPxDbj7R+L8oTXYvORJPPjaFljbN86nkvx0bF76FDr3moRhD32ErJQT2Lv+HVgo7BHYdYQ2nJmFEg+9/ofeunUNO3Uc3IJw79PLtd9lMnkzp65lJZ3ZisO/L0Tfe/8DV99IXDyyBttWPIVJL22G0s5A3hWkY9uKpxHa434MnPwhsq+dwMFf34WlwgH+YcMBAJnJxxAYPhquY7tBbmKO0/uW4Y/lj+P+FzdDYesKABj/7FqIoq5RojD7MrYumwn/LiNbJ+EtaGS0JYb1ssB3m8twvUCNe/paYfaDNpj3dRGqaww3LpuZCsgtVCM+oRqThxm+ZwnxMUXs8SpczVRBJgPuG6jA7Adt8J9vClHTRJ3UXiWe2Io9G25cF3eIxNmDq7Fp8RN4ZG7T18Ubv3kSXaInYeQjHyEzWboutqp3XTx25hdQq3UZUVlehB8/uBdB3RofU9evncHZQ2vg5BHScokkug0t/qi9vLwcv/zyC5555hncc889WLFihfa3PXv2QBAE7Nq1C927d4eVlRX69OmDxMREbZi6IVA//PAD/Pz8YGtriylTpqC0tFQbxs/PD5999pnefiMiIvDWW29pv3/66afo0qULFAoFvL29MWvWLJSVld1RmhoOy5o+fTrGjx+Pjz/+GO7u7nB0dMSzzz6L2lrdCaG6uhqvvvoqvL29YW5ujqCgICxbtkz7+969e9GzZ0+Ym5vD3d0dr7/+OlQqlfb3gQMH4rnnnsOLL74Ie3t7uLq6YsmSJSgvL8eMGTNgbW2NgIAA/PGH/kXDhQsXMHr0aCiVSri6umLatGnIy7vJI9AW0itUwKlkEaeSReSXADtPiiipkJ7QGhIZKKCkXAqXXwKcShZxOkVE71DdIdszREDKdeBQgoj8Uunfq9nS8jqXM4GkLKCgVPrsOSuiRgV4OunCeDoKOHtVRGqO1GvnZJKI7CLA3aH9tiKf2PMdOveeiLDoSXBwC8DACfOgtHfDmYM/Gwx/5uBqWNu7Y+CEeXBwC0BY9CR07jUBx2N1F5Dn49ajqqIYYx//Ch4domDj4AnPgO5w9gzVhslKOYWAsCHw7zwQto5eCIoYCd+QfshOO9fiaW4Pcrfvw6U3P8P1TTvbOipkhAaEm+DP47U4m6zG9QIRP++qhpmJgG5BTT9nGdDVFJfS1Nh9ohY5RSJ2n6jF5Qw1BnQ11YYZ3M0URWUi1sTWIC1Hg8JSEZczNMgv0V30n7ysxuV0DQpKRGQXivj1YA0szQV4OLavHnc9QwScThFxOlk6r/95UkRJJRAZ0ERdESCgpEIKl18KnL5RV/QK0aWrR7CAlGzg8I264vCNuqJHsG6bFdVAeZXuE+ghoKBURGq93qVeTjfqilyp186p5PZfVzR0cNtKRMVMQI+Bk+DiGYAxD8+FrYMbjuxebTC8vbMn7nl4Lrr1Gw8Lq6YfagmCAGs7Z72PMTu1dwU69ZqIzr0nwcE1AP3vmwulnRvONlHHnju0GtZ27uh/31w4uAagc+9J6NhzAk7uWd4gpACFjbPepyGZTK73u6XSoQVS2HLO7l+JkO4TENpjEuxdAhA9di6Utm64EGf4GEs4shpKO3dEj50Le5cAhPaYhOCoCTizT5d3g6d8hE7RD8LRoyPsXDqg/4R3IIoaZCQd1oaxVDrAytpZ+0lN2AMbBx+4+/do8TS3tKE9LfH7wUqcSKxBZq4ayzeXwsxUQK/OTTegXs1SYd3uChy7UAOVynAD0GerS3DoTDUy89RIz1Hjuy2lcLSVw9fN+KZkPRH7HcJ6T0SXPpPg6BaAgRPnwdreDWcONHFdfGA1bOzdMXDiPDi6BaBLn0no3HsCju/WHXcWCju9sph68SBMTS0QHKHfuFNTXY4/vn8FQ6e+Bwsr2xZNJ9GttPhV3Zo1axASEoKQkBA8/PDD+O677yCK+ieZefPm4ZNPPkF8fDxMTEzw2GOP6f2elJSETZs2YcuWLdiyZQv27t2LhQsX/qV4yGQyfP755zh37hxWrlyJ3bt349VXX/3b6asTGxuLpKQkxMbGYuXKlVixYoVeQ9YjjzyC1atX4/PPP0dCQgK+/vprbe+fjIwMjB49Gj169MDp06exePFiLFu2DO+9957ePlauXAknJyccPXoUzz33HJ555hlMmjQJffr0wYkTJzBixAhMmzYNFRXS482srCzExMQgIiIC8fHx2LZtG7Kzs/HAAw80W7pvh0wGuNsDKdf1/9+Tr4vwcjJ8UezlKCC5YfgsEe4OQF3PPc8mwjS1TUEAOvkIMDUBMvJ066XniQjyEGBtKX33dQEcrIGkrPbZ1V6tqkFO2nn4hvTTW+4b0hdZKScNrnP96in4hvTVDx/aHzmp57RPJZLP7Ya7XwRi176DJfP64IcF9+Dojq+h0eiehHl0iELq5TgU5qQAAHIzLiIz+Tj8OsU0ZxKJ7joONgJsFDJcStOVJ7UGSMpUw8+t6arY11V/HQBITFXDt946nfxMkJarwSPDzfHWdCvMnmSBXh2bvjiXy4DoziaorBaRmd9+hmXV1RUNz+spN6krPB0Fg3XLLeuKm2xTJgPCfAWcSdFfJy1XRJCnNFwM0NUVDbfdXqlUNci8eh6BYfp1QWCXvki9bLjuuF01VRX46KXB+OCFgfj+k6eRefXC39peW1KrapCTfh7ewfr55B3SF9evNlHHXjsF7wZ1rE9IP+Smndd78l9bU4GV7w7Gd2/HYPPSp5Cb3jifivKuYflb/bHyvSHY/v1sFOenNUOqWodaVYO8zPPwDNLPC8+gvshONZx3OamnGoX3Cu6L3Izz0KgNdx9R1VZBo1bB3NLwjbRaVYPLpzYjuPsECILxNL4a4mQng51ShvPJNdplKjWQmFqLQK/mbYSxMpfyqrzKOM5pddSqGmSnnYdvqP51sU9oX2Q2cV2cdfUUfEL1jzu/0P7Irndd3NC5uPUIjhoDU3MrveW7174D/84x8A3p8zdS8Q8nk7XN5y7U4k2zy5Ytw8MPPwwAGDlyJMrKyrBr1y4MHTpUG+b9999HTIx0c/j6669jzJgxqKqqgoWFBQBAo9FgxYoVsLa2BgBMmzYNu3btwvvvv3/b8XjxxRe1f/v7++Pdd9/FM888g0WLFv3dJAIA7O3t8eWXX0IulyM0NBRjxozBrl278MQTT+DSpUv45ZdfsHPnTm26O3TooF130aJF8Pb2xpdffglBEBAaGorMzEy89tpreOONNyC7cfB17doV//d//wcAmDNnDhYuXAgnJyc88cQTAIA33ngDixcvxpkzZ9C7d28sXrwYkZGRmD9/vnZfy5cvh7e3Ny5duoTg4OBmSfutWJkBMpmAsgZd/8urAaWF4XUUFtLv9ZVVAXKZACtz6W+lgTDl1dK69TnbAtOHymAiB2pUwLoDuiFZALD9hIgxPYDn75VDrREhisDvx0Skt34Hp9tSWV4IUaOGlY2j3nIraydUlBqexKi8JA++oU764W0codGoUFVWCIWtC4rz05B2OQ6hUWNx79NLUJR7DbFr34FGo0Lvkf8CAHQf+gSqq0qxcv4oyAQ5NKIafca8hNCoe1omsUR3CRsr6aK5tEL/orm0QoSDddM3H9ZW0rAfvXUqRe32AMDRRkCfzibYe7oWu07UwttFhvv6m0GlAY4n6nqAdvSVY9pwc5iaAKXlIr7ZXNWuhmTV1RUN41Re1fi8XkdhCZRfbxxeLhNgaS79rbTAX9pmiKcAC1M0atzZcVLE6O7A8+N0dcXWdlxXNFRRWgSNRg2lrX5doLRxRFnxnSfCyd0fE5+YD1fvYFRXluHQjh+w5L2H8K/3NsLJze9vxrr1aetY64Z1rCMqSg3nU3lpLnys+zUKr9GoUFVeCIWNC+xdOmDolAVwdA9GTXUZTu/7Huu/eBBT/r0Jds5+AAA3364YOnUh7Jz9UFmWj2M7F2P951Mx9dXNsFTYt0h6m1NVRZGUd0r9Y8xS6YjKJvKuojQPXsoGea10gngj76xsXBqtc2zbJ1DYuMIz0PDN9NULu1BTVYrgqPvuMCXth61CugcoKdc/H5WUi3C0ad6b0weGKnAptRaZucY1505TZVZh7YRrN7ku9rNucF18o8xWlhVCaat/3F2/dgb5WZcw/EH9e8/E478jJ+08Hvz3+mZICdHf16KNO4mJiTh69Cg2bNgg7czEBJMnT8by5cv1GnfCw3UTorm7uwMAcnJytHPz+Pn5aRt26sLk5OT8pbjExsZi/vz5uHDhAkpKSqBSqVBVVYXy8nIoFIo7TmOdzp07Qy7XjYt2d3fH2bNnAQCnTp2CXC7XNmA1lJCQgOjoaL2nC3379kVZWRnS09O1+VA/n+RyORwdHdGlSxftMldXadxxXd4cP34csbGxBucHSkpKMti4U11djepq/RYTVa1JozHhzUEAcNNnAw1+rMue+ovF23i4kF8KLN2ugYUpEOItYGwvGX7crWvg6REkwNNRwC/71CguB3xcdHPuXM2+/fS0voY3hKKBZfWDN/itLvNuLBdFEVZKRwyZ8i5kMjlcvcNQXpyD+N3LtI07l05uxcX43zDqkU/g6BaI3IwE7N2wAEpbF3TqafwXUUTNJTJIjvsH6s6bS3+XWhcanrIE4RbnQQMrCdA/9wlC3QTJ0tPGjDwN3Bxk6NPZRK9xJylDjU/WVEJhKaB3JxNMG26Oz9dXoqzyr6WtvTGUp7dyszBd/QUkZaHRAwltXbH/Rl3hLGBElICyqvZeV+hrXHOIt5dpTfAJjIBPYITue1AkvnpjIuJ2rsI904x4UtFGdaaBZfor3HS5m18E3PwitEvd/SKx5tMJOLP/RwyYID248+04QG9NN98I/DB/OC4e24RuA2f8tfi3Kzc/xhr2rhEbXJ/Ud3rvUiSd3ooxT6xs8to0MX49vIP7Q2GgYai969XZHNNG667bP19TfOMv/TNdc/dHenCEAl4uJvjg++JbB26vDB5Ht19mxRt5bKi317nD6+DoHgw3X929WGlhFvZseB8TZi1vkfukfxJj72HXnrRo486yZcugUqng6empXSaKIkxNTVFYWKhdZmqqmzug7j9Xo9EY/L0uTP3fZTJZo6Fe9ee7uXbtGkaPHo2nn34a7777LhwcHHDgwAHMnDlTL9zfcbM4Wlpa3nRdURSbrNjqLze0j5vlnUajwdixY/HBBx802mddI1pDCxYswNtvv623bNDE/2DI/W/eNA03U1EDaDRio146VuaNn6bWMfRUVWEOqDUiKm+0PdX13mkYpuE2NRpoJ1TOKhTh4SCgR7CAP+JFmMilt5+sO6DBlRtv0MopFuFqB/QOleFqdvsZslDHUmEPQSZHRYn+U7CK0nxYNXgKUUdh44SKktwG4Qsgk5nAQmF3I4wzZHITvckb7V07oKIkF2pVDeQmZtj/64foMfRJhESOAQA4eYSgpCATx3Z+w8YdonrOX1Xj2hpdq4nJjWJlYyXo9d5RWgqNevPUV1ohwtpKv35QWur35impEJFdoH+uyi7UILyD/kSsNSogv0REfomI1OwavP6gJXp2NMXuE+1j5sy6uqLRud9Az5s65ZWN6wErA3VFw202Vf/YWAF+rsD6g/r5aSIHBnYRsO6g7m2LucUiXO2BXiHts65oyMraDjKZHKUNeumUlxRA2aAn6N8hk8ng5R+GvOxrzbbN1tRkHVuWDyul4XxSWDs36tVTUZqvV8c2JMhkcPHugqK8pvPJ1NwKju7BKL5JmPbEwspOyrsy/byoLCuAZRN5J/U6bhC+PB+CzAQWVnZ6y8/sW45Te5Zg9MzlcHQ3PHFtaWEGMq8cxtCHP7/zhLShU5drkLJUd49kIpfO/zYKGYrLdD1qrBUCSsqb57wzdbgCEcFm+PD7YhSWtv9zWUM3LbM3uS4ub9Crp7LBdXGd2ppKJJ74HdGjn9dbnp12HhWl+Vj10QTtMlGjRnrSMZzavwrPf3rW6CZEJ+PXYoPNVCoVvv/+e3zyySc4deqU9nP69Gn4+vpi1apVzbYvZ2dnZGXp3m1dUlKClJQU7ff4+HioVCp88skn6N27N4KDg5GZ2XqvtujSpQs0Gg327t1r8PdOnTrh0KFDeg1Uhw4dgrW1tV7D2F8VGRmJ8+fPw8/PD4GBgXqfpnorzZkzB8XFxXqfmHtfv+M4AFLjSlah9GaR+vzdBKTnGb6pSc8XDYbPKgA0N1bJMBCmw022qSUAdZ2sZAIglwuNnvxq/t6DzBYlNzGDi3dnpCYe1FuemngI7v7dDK7j5heB1MRDesuuJR6Ai08Y5HKpgdDDPxJFeakQ6zWcFuVchcLGGXITMwCAqqaqUcYIMnmjxlWif7rqWl1DSv6NSYxLyjUI9tJd6MllQICHHFevN30xfS1bg2Bv/YvDYG85rtVb52qWBs52+tW5s50MhWU3L5eCoGt0ag+arCtcmz6vZ+SL8HdtXA80qisMhDG0za7+AiqqoW3sr6OtKxqsIrbjuqIhExMzePh1xpVz+nXBlXOH4BNkuO64E6IoIiv1IqxtjXNSZbmJGVy8OiPtkn4+pV06BDe/JupY3wgD4Q/C2buzto5tSBRF5GUmGJxUuY5aVYOC7CRYWRtHXspNzODk0RkZl/XzIuPKIbj6GM47F58IZFxpEP7yQTh7doasXt6d3rcMJ3YvxsgZS+DsFdZkHC4d3wgLpQN8QoxzLsDqGhE5hRrtJzNPjaIyDTr7m2nDyGXSm66upKtusqXb8+AIBSJDzfDxj8XIKza+hh1AOu5cvTvjWsPr4ouH4NHEdbG7XwRSLza4Lr54AK71rovrXDr5B9SqGnTsMU5vuU9wb0x7fTMefnWT9uPqE4bQqLF4+NVNbNihNtFijTtbtmxBYWEhZs6cibCwML3P/fffr/emqL9r8ODB+OGHH7B//36cO3cOjz76qN4QqYCAAKhUKnzxxRdITk7GDz/8gK+//rrZ9n8rfn5+ePTRR/HYY49h06ZNSElJwZ49e/DLL78AAGbNmoW0tDQ899xzuHjxIn799Ve8+eabmD17tna+nTvx7LPPoqCgAFOnTsXRo0eRnJyMHTt24LHHHoNabXg8rbm5OWxsbPQ+zdHV8MhFEREdBHT1F+BoAwztJsDWCjhxRbpSHhguYGwv3RXyiSsibBXSq2cdbaQL7ogOAuIu6iqeo4kiOrgB0aECHK2lf/3cpOV1BoYL8HaWXpfrbCs9efV1Bs5flcLUqIBrOSIGd5XBx0UKF+4voIufgMT09ttgETlwBs7FrcP5uHUouJ6EvRvmo7QwC+F9pwAADmz+BNt/1E0YHt53CkoKM7F34wIUXE/C+bh1OB+3HlGDdJOXh/ebiqryQuzZ8D4Kc1KQcn4Pju38Bl37P6QN4x82CMd2fI2U83tQnJ+OK6d34mTsdwgI1w2zvJvJFVaw6RoKm67SG8Ss/L1g0zUUFt6Ge8IR1bfvjApDokwR5i+Hm4OAKYPNUaMScfKy7gJ96hAzjO6tu7Dcf6YWwd5yDOpmChc7AYO6mSLYS459p2vrbbcWvq4yDIk0haONgG5BcvTuZIKDZ6UwZibAqF6m8HGVwV4pwNNJhgcGmsFWIeD0lb9/c9CcjiaKiPAXEO4vndeHRgiwsQJOJN2oK7o0qCuSRNgogCERUvhwf6meOZKoqyuOXZLqit436oreoQL8XKXlDYX7CzhzVWzUiFNXVwyJkMHnRp3SxU9AmK+AS+24rmio78hHcXzvesTvXY+cjCT8vmoBivOz0HPwZADA9l8+xdpvXtNbJ/NaAjKvJaC6qgLlpYXIvJaAnIwr2t93bfwKl88cQEFOGjKvJWDD0v9DVupF7TaNUUTMdFw4sg4XjqxHQXYS9m9agLLCLIT1kerYQ1s+wc6fdPkU1mcKSgszsf/XBSjITsKFI+tx4ch6dBuoq2OPbv8S1y7uR3F+GnIzErB7zTzkZVxEWPQUbZgDv32AjCtHUZKfjuvXTuOPFc+jpqoMoT3Gt1ra/64u/R9FYvx6JMavR2FOEg5vWYCyoix07CUdD0e3fYrYX3R517HXFJQVZuLwloUozEm6se4GhA/Q5d3pvUsRv+N/iLn/fVjbe6KiNBcVpbmorS7X27eo0eDS8Q0IjhwPmdz43vjUlD+PVmJ0X0t0CzGDh7Mcj41VoqZWxJHzumkUHhurxISBuol+5TLA21UOb1c5TOSAnbUM3q5yuNjr7iseGqlA7zBzfLupFFU1ImwUAmwU0otHjE3koBk4d3gdzh1eh/zrSdhTd13c78Z18W+fYNsP9a6L+924Lt6wAPnXk6R149YjavBjjbZ97vA6BIQPbTTvlZmFEk4ewXofUzMrWCrs4OTROvOa3jU4oXKzabHiu2zZMgwdOhS2to1nsp84cSLmz5+PEydONMu+5syZg+TkZNxzzz2wtbXFu+++q9dzJyIiAp9++ik++OADzJkzBwMGDMCCBQvwyCOPNMv+b8fixYsxd+5czJo1C/n5+fDx8cHcuXMBAJ6enti6dSteeeUVdO3aFQ4ODpg5c6Z28uQ75eHhgYMHD+K1117DiBEjUF1dDV9fX4wcOfJvNRrdiYQ0EVbmQL8wAUoLAbnFwOp9GpRIL/aC0hKwVehm4SkuB9bs1WBYNxmiggSUVQI7TohITNdtMyMf2HhIg5hwGWK6CCgsk75nFujCKCyAcb1lUFpIT9JzioDVezVIqTc/wsZDGgwKFzC+twwWZtIrbvecFbUNT+1RSORoVJUXIm77IlQU58DRPRj3PrUENg5ST6/yklyUFOoePds6emP8U0uwd+MCnNm/CgpbFwycMA9BESO0Yazt3XHfM8uxb+MC/PjBOChtXRER8wi6D31CG2bQxP/Doa3/w+61b6OiLB9KGxd06TsZvUY823qJb0O2UWGI3vWD9nunj6UynPb9BpyZOaetokVGIvZkLUxNgIkDzGBpLiA1W4Mlm6tQXW9UlJ1SBlGs1yvnugY/7qjGqF5mGNnTFPklIn7YWY3UHF2YtBwNvttWjTG9zTCsuykKSkX8eqAGJy5LjfgaEXCxl6FHiAkUlgLKq0Sk5Wjw1aYqZBe2r/NcQpoIS3OgX2ddXbFmv35dIU0mrasrftmnwdBuMkQF3qgrTjauKzYd1iCmiwwxYQIKy4FNDeoKAPB3leqhM8mGn15vOqzBwHAB996oK0oqgL1nRW3DkzEI7z0aFWVFiP11EUqLcuHqFYRHXv4a9k5S3VFalIvifP1uS1/9RzfkIPPqeZw+vAV2Th545dNdAICqihJs+u4NlBbnwcLSGu6+HfHE3O/hHRAOYxXUbTSqKopwbMdXKC/JhaN7EO554httHVtRmovSQl0PcBtHL4x9/Bsc+HUhzh74CQpbFwy4bx4Cu+rq2OrKUuxZ+ybKS3JhbmkNJ8+OuO9fP8C13hwe5UXZ2P7jy6gqL4Klwh6uvl0x6YU12v0ag4Dw0aguL8KJXYtQUZoLB9cgjJz+NaztdXlXXqQ7xmwcvDBy+tc4/PtCXIj7CVY2LogeOxf+YcO1YS7E/QyNuhZ/rnpBb1+RQ55F1NB/ab9nXDmMsqIsBEdNwN1k2+FKmJkIeGikEgoLAckZKnz6cwmqa3TnHkdbuV6jtJ21DG8+rmuMGBlthZHRVki8VouPfpTm1RkUJU0b8eo0O739Ld9cikNnGryxpJ2ruy4+sn0Rym9cF49/Wv+6uLTBdfF9N66LT9ddF0/Uvy4GgMKcFGQmH8eEWctBZAwEkeMp6BbeX21cs+a3Jw527JJ5p3zGGh5PT7c2pjaxraNgtF5eVH7rQGSQo2MTr5+iWwr2M5KxXe1QVh7z7k5VV/MW4E5dTCi4dSBqpHt3w3Pg0K09PeLWYYxV2Vev3jpQC1A++2Gb7Lcl3Z39kYiIiIiIiIiI/iHYuGNA586doVQqDX6acyJoIiIiIiIion8sQdY2n7uQEU6Z1fK2bt3a5CvSXV1dWzk2RERERERERERNY+OOAb6+vm0dBSIiIiIiIiKi28LGHSIiIiIiIiJqfTJOjN9c7s7BZkRERERERERE/xDsuUNERERERERErU64Syc3bgvMSSIiIiIiIiIiI8bGHSIiIiIiIiIiI8ZhWURERERERETU+jihcrNhzx0iIiIiIiIiIiPGnjtERERERERE1OoEGfubNBfmJBERERERERGREWPPHSIiIiIiIiJqfQLn3Gku7LlDRERERERERGTE2LhDRERERERERGTEOCyLiIiIiIiIiFofJ1RuNsxJIiIiIiIiIiIjxp47RERERERERNT6OKFys2HPHSIiIiIiIiIiI8bGHSIiIiIiIiIiI8ZhWURERERERETU6gROqNxsmJNEREREREREREZMEEVRbOtIEBEREREREdE/S+WP89tkv5YPz22T/bYk9twhIiIiIiIiIjJinHOHiIiIiIiIiFqfjK9Cby7suUNEREREREREZMTYuENEREREREREZMQ4LIuIiIiIiIiIWp0gsL9Jc2FOEhEREREREREZMfbcISIiIiIiIqLWxwmVmw177hARERERERERGTE27hARERERERERGTEOyyIiIiIiIiKi1scJlZsNc5KIiIiIiIiIyIix5w4RERERERERtT6BEyo3F/bcISIiIiIiIiIyYmzcISIiIiIiIiIyYhyWRUREREREREStT8b+Js2FOUlEREREREREZMTYc4eIiIiIiIiIWh9fhd5smJNEREREREREREaMjTtERERERERE1PpkQtt87sCiRYvg7+8PCwsLREVFYf/+/TcNX11djXnz5sHX1xfm5uYICAjA8uXL72jft4PDsoiIiIiIiIiImrBmzRq8+OKLWLRoEfr27YtvvvkGo0aNwoULF+Dj42NwnQceeADZ2dlYtmwZAgMDkZOTA5VK1WJxvOt67uzZsweCIKCoqKjJMCtWrICdnZ32+1tvvYWIiIgWj1ud24kjEREREREREbW9Tz/9FDNnzsTjjz+Ojh074rPPPoO3tzcWL15sMPy2bduwd+9ebN26FUOHDoWfnx969uyJPn36tFgc20XjzvTp0yEIAp5++ulGv82aNQuCIGD69OnNtr/Jkyfj0qVLzbY9IiIiIiIiIvqLBFmbfKqrq1FSUqL3qa6uNhjFmpoaHD9+HMOHD9dbPnz4cBw6dMjgOr/99hu6d++ODz/8EJ6enggODsa///1vVFZWNnsW1mkXjTsA4O3tjdWrV+sltqqqCj///HOT3ZzulKWlJVxcXJp1m0RERERERETU/i1YsAC2trZ6nwULFhgMm5eXB7VaDVdXV73lrq6uuH79usF1kpOTceDAAZw7dw4bN27EZ599hnXr1uHZZ59t9rTUaTeNO5GRkfDx8cGGDRu0yzZs2ABvb29069ZNu6y6uhrPP/88XFxcYGFhgX79+uHYsWONtnfw4EF07doVFhYW6NWrF86ePav9reGwLEO+++47dOzYERYWFggNDcWiRYtuKx1Xr16FIAhYvXo1+vTpAwsLC3Tu3Bl79uxpFPb48ePo3r07rKys0KdPHyQmJur9vnjxYgQEBMDMzAwhISH44Ycf9H4XBAFLly7FfffdBysrKwQFBeG3337TC3PhwgWMHj0aSqUSrq6umDZtGvLy8m4rLUREREREREQtRhDa5DNnzhwUFxfrfebMmXOLqOpPxCyKYqNldTQaDQRBwKpVq9CzZ0+MHj0an376KVasWNFivXfaTeMOAMyYMQPfffed9vvy5cvx2GOP6YV59dVXsX79eqxcuRInTpxAYGAgRowYgYKCAr1wr7zyCj7++GMcO3YMLi4uGDduHGpra28rHt9++y3mzZuH999/HwkJCZg/fz7+85//YOXKlbedlldeeQUvv/wyTp48iT59+mDcuHHIz8/XCzNv3jx88skniI+Ph4mJiV5aN27ciBdeeAEvv/wyzp07h6eeegozZsxAbGys3jbefvttPPDAAzhz5gxGjx6Nhx56SJsXWVlZiImJQUREBOLj47Ft2zZkZ2fjgQceuO10EBEREREREd1NzM3NYWNjo/cxNzc3GNbJyQlyubxRL52cnJxGvXnquLu7w9PTE7a2ttplHTt2hCiKSE9Pb76E1NOuGnemTZuGAwcO4OrVq7h27RoOHjyIhx9+WPt7eXk5Fi9ejI8++gijRo1Cp06d8O2338LS0hLLli3T29abb76JYcOGoUuXLli5ciWys7OxcePG24rHu+++i08++QQTJkyAv78/JkyYgJdeegnffPPNbaflX//6FyZOnIiOHTti8eLFsLW1bRTH999/HzExMejUqRNef/11HDp0CFVVVQCAjz/+GNOnT8esWbMQHByM2bNnY8KECfj444/1tjF9+nRMnToVgYGBmD9/PsrLy3H06FEAUs+fyMhIzJ8/H6GhoejWrRuWL1+O2NhYzjlEREREREREdAtmZmaIiorCzp079Zbv3LmzyQmS+/bti8zMTJSVlWmXXbp0CTKZDF5eXi0Sz3b1KnQnJyeMGTMGK1euhCiKGDNmDJycnLS/JyUloba2Fn379tUuMzU1Rc+ePZGQkKC3rejoaO3fDg4OCAkJaRTGkNzcXKSlpWHmzJl44okntMtVKpVeq9ut1N+/iYkJunfv3mj/4eHh2r/d3d0BSK1/Pj4+SEhIwJNPPqkXvm/fvvjf//7X5DYUCgWsra2Rk5MDQBr2FRsbC6VS2Sh+SUlJCA4ObrS8urq60URS5ubmTbZiEhEREREREd0RWbvqb9Kk2bNnY9q0aejevTuio6OxZMkSpKamal8KNWfOHGRkZOD7778HADz44IN49913MWPGDLz99tvIy8vDK6+8gsceewyWlpYtEsd21bgDAI899hj+9a9/AQC++uorvd9EUQTw18a61Xc7YTQaDQBpaFavXr30fpPL5bdc/6/s39TUtNFvdfs3FN5QOutvo26dum1oNBqMHTsWH3zwQaO41DUmNbRgwQK8/fbbesvefPNNvPXWWwbDExEREREREd3NJk+ejPz8fLzzzjvIyspCWFgYtm7dCl9fXwDSlCipqana8EqlEjt37sRzzz2H7t27w9HREQ888ADee++9Fotju2vcGTlyJGpqagAAI0aM0PstMDAQZmZmOHDgAB588EEAQG1tLeLj4/Hiiy/qhY2Li9O+ZauwsBCXLl1CaGjoLffv6uoKT09PJCcn46GHHrrjdMTFxWHAgAEApF4/x48f1zZa3Y6OHTviwIEDeOSRR7TLDh06hI4dO972NiIjI7F+/Xr4+fnBxOT2/qvnzJmD2bNn6y1jrx0iIiIiIiJqdrfRAaO9mDVrFmbNmmXwtxUrVjRaFhoa2mgoV0tqd407crlcO3ypYU8ZhUKBZ555Bq+88gocHBzg4+ODDz/8EBUVFZg5c6Ze2HfeeQeOjo5wdXXFvHnz4OTkhPHjx99WHN566y08//zzsLGxwahRo1BdXY34+HgUFhY2avhoyldffYWgoCB07NgR//3vf1FYWNhocuibeeWVV/DAAw8gMjISQ4YMwebNm7Fhwwb8+eeft72NZ599Ft9++y2mTp2KV155BU5OTrhy5QpWr16Nb7/91mBPJA7BIiIiIiIiIjIu7a5xBwBsbGya/G3hwoXQaDSYNm0aSktL0b17d2zfvh329vaNwr3wwgu4fPkyunbtit9++w1mZma3tf/HH38cVlZW+Oijj/Dqq69CoVCgS5cujXoH3czChQvxwQcf4OTJkwgICMCvv/6qN3/QrYwfPx7/+9//8NFHH+H555+Hv78/vvvuOwwcOPC2t+Hh4YGDBw/itddew4gRI1BdXQ1fX1+MHDkSMiMZ20hERERERER3KYH3pc1FEOsmsqFmcfXqVfj7++PkyZOIiIho6+gQERERERERtUtVW5e0yX4tRj9560BGhs1kRERERERERERGjI07f9H8+fOhVCoNfkaNGtXW0SMiIiIiIiIyDjJZ23zuQhyW9RcVFBSgoKDA4G+Wlpbw9PRs5RgRERERERERGZ+qbUvbZL8WIx9vk/22pHY5oXJ75uDgAAcHh7aOBhEREREREZFxM6JXobd3d2d/JCIiIiIiIiKifwg27hARERERERERGTEOyyIiIiIiIiKi1iewv0lzYU4SERERERERERkx9twhIiIiIiIiotbHCZWbDXvuEBEREREREREZMfbcISIiIiIiIqLWJ2N/k+bCnCQiIiIiIiIiMmJs3CEiIiIiIiIiMmIclkVERERERERErU7khMrNhj13iIiIiIiIiIiMGHvuEBEREREREVHrE9jfpLkwJ4mIiIiIiIiIjBgbd4iIiIiIiIiIjBiHZRERERERERFR6+OwrGbDnCQiIiIiIiIiMmLsuUNERERERERErY6vQm8+7LlDRERERERERGTE2HOHiIiIiIiIiFof59xpNmzcoVtauqutY2C8ikvFto6C0crMrGjrKBitT2Yp2joKRut305C2joLRCk38o62jYLRWHfZp6ygYLXdXXsreqaxsVVtHwWidjU9r6ygYpQn3+7V1FIzW1L4cukS3xmYyIiIiIiIiIiIjxscdRERERERERNT6OKFys2HPHSIiIiIiIiIiI8aeO0RERERERETU+mTsb9JcmJNEREREREREREaMjTtEREREREREREaMw7KIiIiIiIiIqNWJnFC52bDnDhERERERERGREWPPHSIiIiIiIiJqfQL7mzQX5iQRERERERERkRFjzx0iIiIiIiIianUie+40G+YkEREREREREZERY+MOEREREREREZER47AsIiIiIiIiImp9fBV6s2HPHSIiIiIiIiIiI8aeO0RERERERETU6jihcvNhThIRERERERERGTE27hARERERERERGTEOyyIiIiIiIiKi1scJlZsNe+4QERERERERERkxNu4YMH36dIwfP95ot09ERERERETU7gmytvnchYxmWNbXX3+NV155BYWFhTAxkaJdVlYGe3t79O7dG/v379eG3b9/PwYMGIDExEQEBwe3VZTpNpzcuwrH/lyGsuJcOLkHYfCkufAK7N5k+LRLRxG7fiHysi5DaeuCnsMeR8SAqXph4nevwKl9P6O0MAuWCnsER47AgHtfhompuTZMaVE29m78CCkX9kNVUwV7Vz+MfPh9uPmEtVham9v5wz/hzN5lqCjNhb1rIKLHzoW7f9N5l5l8FHFbFqIw+wqsbFzQNeZxdOo9Rft7yrkdOLn7G5Tkp0KjVsHWyRddBsxAcOS92jDxO7/AiT+/0tuupdIJ0/5zoPkT2MKG9zBF704msDIXcC1bgw37qpFdKN50nS4d5BjZ0wxOtgLyikX8caQG51LUemFsFALuiTZDqI8cpnIgt1iDX2JrkJ6r0e63W6AJbJUC1GogPVeDP47UIDVH02JpJePm0K87Orw8E7aRYbDwcEH8xFnI/m1XW0erTW3ZsgXr161DQUEBfH198eRTTyEszPD5+/y5c1j+3XdIT0tDdXU1XFxcMGr0aNx3333aMDt37sR/P/200bqbfv0VZmZmLZaOlhDTRYbIQAEWZkBGPvDHMTVyi2++Tqi3gEFdZbBXAoVlwO5TGiSm658PuwcJiO4kg7UlkFME7DiuRmqu7neFBTAkQoYAd2nf13JEbIvXoKBUf19eTsCgrjJ4OgnQaIDrhcBPsWqo9E+lbe7kPun6pPzG9cmg+29xfXL5KPbUuz7pMexxRPTXvz45vnsFTu2vd33SbQT617s+ObXvJ5za/zNKCjIAAI7uQYgeNQsdOse0XEJbSFsdh288ZPjWZucJNQ4n3LyOb88mjbDD0GhrKC1luJxajaXr85F+vbbJ8F5uppg80h4dvM3g4mCK7zbmY+u+Er0wMhnwwAh79I9SwM5ajsJSNfYcLcP6nUUQjTertI7u/gmHti1DaVEuXDwDMXLqXPgGGy7DpUU52L7mA2RdPY/8nGvoNWQaRj04Vy/MheM7sH/LNyjIka6THVx90WfEDHTtc6/BbRK1BaNp3Bk0aBDKysoQHx+P3r17A5Aacdzc3HDs2DFUVFTAysoKALBnzx54eHj85YYdtVoNgWP+Ws3F+K3YvW4Bhk15E54dInH6wGqs++oJPPaf32Hj4NEofFFeGtYvehLhfSdhzPSPkJF8AjtXvw1LaweEdBsBALhw9Dfs2/QJRk6bD88O3VCQfRV//PA6AGDw/dJJuqqiGD99PBU+wb1w/7PfwsraAUW5aTC3tGm9xP9NSae34vDmBeg3/g24+kYi4cga/LH8STwwewuU9o3zrqQgHduWP4XQnpMwaPJHyL52Agc2vQMLhT06dJHyztzSFt0GPw075w6Qm5jiWsIe7F07F5YKB3iH9Nduy941CGOeWK79Lgjylk9wMxvUzRQxXU2xenc1/p+9+46PoswfOP6Z3fTeC0lIQgJJ6L13pIsoCioqouLZz3Znwd+dp56iZznLWbBQLFhRuhSl95ZQQkJJgPTee7I7vz82ybLJhpq2+n2/XvvSzD7P7DPDzDPPPPN9nsku0HNdPxseuMGON5aVU9lEWynYV8NdE2xZv7+a44k1dO9kxZwJtvzvl4r6jhl7W3jsJjvOpOr4bE0FJeUqXi4K5ZXGVlJ2gZ6fd1SSW6RirYVRvaz5yzQ7FnxTRmlFa2y9sDRaRweKjp4kZenP9Pvxf21dnDa3bds2Pl24kIcfeYSuXbvy67p1/PMf/+CThQvx8fFplN7Ozo5p06YRGhqKnZ0dsbGxfPD++9jZ2jJ5ypT6dA4ODnz62WcmeS2tY2doV4XBUQor9+jJLVIZ0V3DnWO1fLhaR1WN+TyBXnDLcA1bjuqJT1aJDFK4ZYSGJRt1pOYa0nQNVpjYT8O6A3qSs1X6dtYwe4yWj9boKCozpLl1pBadCt9v01FZDYOjNNw5TsvHq3VU64y/NXuMll2xetYf1KPTg6+b0u5uJOMPrWPLTwu47tYXCQgztE+Wf3g/91xG+2TK3DdJTTjMb9+/hIOTB10ubJ+sfJtJd75Gh059yM8ytk/G1LZPnN39GDn9b7h5dwQgdt8KVix8hDnP/YJXh86ttPXXri2Pw7eXm/5AeAeFGwZriEtuZwfZFZg+1pXrR7vy4bJs0rOruXm8G/940I/HF6RQUWl+u2ytFbJyq9lzpJS5N3qYTXPjWFfGD3Xmw2+zSU6vJqyjDQ/f5k1Zhb5RR5ClOb5/Heu/XcDUu/5Jx/C+HNz6PV//9y888u81uHk2PodraqpwdPZgxPUPsnfTUrPrtHd0ZeT1D+Llb2gnnzqylRWL5uPo4kF49xFm84jLo8r9d7OxmHikiIgIOnTowNatW+uXbd26lenTpxMWFsbu3btNlo8ZM4b8/HzmzJmDu7s7Dg4OTJ48mdOnT9enW7JkCW5ubqxZs4auXbtia2vL+fPnG/32oUOH8PHx4dVXX71kOf/1r3/Ru3dvFi5cSFBQEA4ODsycOZOCgoJGad966y38/f3x9PTkkUceobraeFd5uWXfsGEDUVFRODk5MWnSJNLT001+Y/HixURFRWFnZ0dkZCQfffTRJbehtRzcvJgeQ2+m57CZePqHMXbmCzi7+RGz/Vuz6Y/s+A5nd3/GznwBT/8weg6bSY8hMzjwm7GjIe1sDAFhfek6YBqunoGEdh1OVP/ryTh/vD7Nvo2f4ezux+Q5C/AP6YmrZyDBkUNwr21MWYKjO5YQMeBmIgfOxN03jKE3zMfJ1Y8Te83vu7i93+Hk5s/QG+bj7htG5MCZRPSfwdHtxn3XIWwQod3H4+4bhotnR3oMn4OHXwQZ5w6brEuj0eLg7F3/sXcy32hoz0b2tOK3Q9UcS9SRkafy7e+V2Fgp9OncdH/3yF7WnErWsflwNVkFKpsPV3M6VcfIXtb1acb2saagROX7LVUkZ+nJL1Y5nWpo3NaJPq3jdIqevCKVzHyVlbuqsLdV6OBpMdWxaGXZG7Zz6sV3yVixqa2L0i788ssvTJgwgUmTJtGxY0ceePBBvL29Wbt2rdn0YeHhjB49muDgYHx9fRk7diz9+vXjeGysSTpFUfDw8DD5WJpBkRp2HDfcHGcXwso9eqytoHtI0w3nQZEaEjNUdsWq5BbBrliVsxkqgyKNddKQSA3RCSrRCSo5RbDxkJ7CMujfxZDGwxkCvRXW7deRlge5xbDugB6bBr89oZ+W/SdVdp0wlC+vGOKSVXTtLHDx4O+L6TGktn3iF8bYW17A2d2PmB1NtE92foeLuz9jb3kBT78L2ie/N2ifdOpLVG37JCRqOJH9ricjydg+Cesxlk7dR+HhG4qHbygjbngSG1sH0s/FtPQmN6u2Og4BSitMPxGBCucyVQpKWnSTW9TUUS78vKmA/cfKSM6o5n/LsrG1URje16nJPAnJVXy1Op/d0aVU15jvAOoSYsfB42UcPlFOdn4Ne4+UceRkOWFBtmbTW5I9G5bQd8TN9Bs5E+8OYUyePR9XDz8ObjF/Drt7BTJ59gv0HnYjtvbm92to5CCi+o3Hu0MYHj4dGTx+Dr6BESSdOmw2vRBtwaLuJkaPHs2WLVvq/96yZQujR49m1KhR9curqqrYs2cPY8aMYe7cuRw8eJBVq1axZ88eVFVlypQpJp0oZWVlLFiwgM8//5zY2NhGT/22bt3KuHHjeOmll3jhhRcuq5xnzpzhhx9+YPXq1axfv56YmBgeeeQRkzRbtmwhISGBLVu2sHTpUpYsWcKSJUvqv7/csr/11lt89dVXbN++naSkJP72t7/Vf//ZZ5/xwgsv8OqrrxIXF8drr73GP/7xD5YuNd8j3Zp0NVVkJMUSEjXcZHlI1DBSE6PN5kk7G0NI1DDT9F1HkHn+ODqdYb8EhPUjMymW9HNHAcPTtMTj2wjrPro+T8LRzfgFd2flZ3/lw2eGsPS1Gzmy84dm3LqWpaupIic1lsDOpvsisMswMs+b33eZSTEEdmmYfjjZKbHodY1DVVRVJfXMHgqzzzYa6lWYc56v/z2Cb18fx2/fPEVRbvI1blHr8nBRcHHUcCrZOAZAp4eENB0hfk1XicG+pnkATibpCL4gT9cQK5Kz9cyZYMu/5jrw1Ew7BkU13WGk1cCQblaUV6qk5bazuxsh2qHq6mrOnD5N3759TZb36duXuBMnLmsdCWfOEBcXR48ePUyWl5eXc/fdd3PXnXfy4osvknDmTLOVuzW4OYGzvUJiuvFGTqeH85kqQd5N31QHeikkpJve/CWkqwTW5tFowN+DRmkS01WCvAxprGoDOC8cWqWqht+v+20HW8NvlVao3DNBy1MztNx9nZYg76ve5Bahq6kiM9l8+yStifZJeqKZ9kmUafskMKwfmcmm7ZOzsdvodEH75EJ6vY74g2uprirDP7TPNW5V62nL47AhRzvoHKAQnWC5UTs+nla4u1hx5GR5/bIaHZw4U0FE6LV1wsSfraB7Fzv8vQ3tlOAONkR2suPwibJrWm9bq6mpIu18LGHdTM/JsG7DSD5j/hy+UqqqknhiD7kZZwmOaHq4phCtzWKGZYGhc+fJJ5+kpqaG8vJyoqOjGTlyJDqdjvfffx+AvXv3Ul5ezvDhw5k3bx67du1i6NChAHzzzTcEBQWxYsUKZs6cCRgaih999BG9evVq9HsrV67krrvuYuHChdx+++2Nvm9KRUUFS5cuJTAwEIAPPviAqVOn8vbbb+Pn5weAu7s7//vf/9BqtURGRjJ16lR+//137r//fk6fPs2qVasuq+yffPIJYWFhADz66KO8/PLL9eV45ZVXePvtt5kxYwYAoaGhnDhxgoULF3L33Xdf/o5vAeUl+ah6HY7OnibLHV28KC3KNpuntCgHRxcv0/TOnuj1NZSX5OPk6kNU/6mUF+ex7O3ZoKro9TX0HnE7gyb+pT5PQU4yMdu/pf+4exg86UHSzx1l84//RmtlQ/fBNzb7tja3ijLDvrN3Mt139k6elBXnmM1TXpyNvdPwRulVfQ0Vpfk4uBg6NavKi/n6tVHoaqrQaDQMu/FFk04hn6BejL71ddy8QigrySV688es/Oh2Zj61GjtH92be0pbh4mBoBBaXmTb2istUPJybbng6OygUlzfIU67Wrw/A00VhaDcrth2p5vfD1QT5aLhphA01ejh00hgqHhWs5a4JtlhbQXGpysLVFTIkS4jLUFRUhF6vx83dtL5xd3MjPz//onnvuvNOCgsL0ev1zL7jDiZNmlT/XVBgIE89/TQhISGUlZWxcsUK/va3v/G/Dz8kICCgRbaluTnZGf5b0qAuKakAN8eL52tY/5RWGNfnYAsajaFTxjSNiqO9of7LKYSCEpWxvTWs3a+nqgaGRCo42ys42xvyudc+DB/VU8Omw3oy81V6hmq4a5yWT9bqGs3N01bq2icOLqbXWAfni7RPinNwcDZtnzi4mLZPIvtPpawkj2/fMbZPeo24nUET/mKSLzv1JMveuo2amkpsbB2Yfv+HePmHN+9GtqC2PA4b6tVJoaoa4pIst3PHzdnQc1pYbPpwqbBEh5f7td3Grfi9EAc7De8+F4heBY0C367LZ1d06TWtt62VFdfeY7g2vMfwpKTQfDv5clWUFfP204Z2sqJomHrXi406kcRV+INObtwWLKpzZ8yYMZSWlnLgwAHy8/Pp0qULPj4+jBo1irvuuovS0lK2bt1Kx44dOXnyJFZWVgwaNKg+v6enJxEREcTFxdUvs7GxoWfPno1+a9++faxZs4Yff/zRZNLFy9GxY8f6jh2AIUOGoNfrOXnyZH3nTrdu3dBqjXOV+Pv7c+zYMQDi4uIuq+wODg71HTt168jKygIgOzub5ORk7rvvPu6///76NDU1Nbi6ujZZ9srKSiorK02WVVfZYm3TQiGaDcZYqqp6iXmPGqRHNVmedGofezZ8wvjbXsQ/pCf52Uls/vFVHNd9yNApj9T/hl/H7oyc/hQAvkFdyU0/Q8yOby2ic6dOw/2kGhZeLMMll1vbOnLz479QXVVG2pk97F3zOi4egXQIMxyLHSNH1qf1AHyDe/PdGxM4dWgFPUfec5Vb0rL6dtZyy2jj8fv5WkPrsWFTT1EaL2ukQQIFTOaKUJS6CZINT2pTc/T4eWgY2s3KpHMnIVXH29+X42ivMLirFXdNsOX95eWUlCOEuAyN6r9LXjvgzbfeory8nJPx8SxevJgOHTowevRoACKjooiMiqpP27VrV/762GOsXrWKBx96qNnL3xy6hyhcP9DYIP52a+3NX8N66jLqtque86Y2n16FH3fomDZIyzMzrdDrVRIzDMNSLywHwOHTKkcSDRkz8vWE+mnpHaZhc0z7il5UaHg8XfwYa/Rd7U5VLmif7F3/CdfdamifFGQnsfmnV9nz64cMmWyM7vbwDWXO8yuoLC/iVMxGfv3qWW594ut228HTno7Dhnp30nDsXPsb9ncxw/s68sAsY0fhgs8yAXObeDmNlosb2seREf2ceO/rbFIyqggJsGXujR7kF+nYdsCCx7HVanQOq1y8nXwZbOwcefBfv1BVWcbZE3vY8N3ruHsHEho56NKZhWgFFtW5Ex4eTmBgIFu2bCE/P59RowxvD/Dz8yM0NJRdu3axZcsWxo4di9rEFaJhA9De3t7sxTosLAxPT08WLVrE1KlTr2lSxbr1X/g71tbWjdLo9fr6Ml5O2c2toy5v3bo+++wzk04iwKRTqaEFCxbw0ksvmSybdteL3HD3v5rMczXsndxRNFpKi0x70MuKcxs9/apjLqqnrDgPjcYKeyc3AHaufo9uA2+g5zBDdJN3QATVlWVsXPZPhkx6CEWjwcnVG0//MJP1ePh14lT0hmbaupZl52DYdw2jdCpKcnFoEM1Tx97Zm/IG6ctLclE0Vtg5uNUvUzQaXL2CAfDqEEV+ViIxWz6t79xpyNrGAQ+/LhTmNp6rqr2IPafj/PfGXpO64QMuDopJ9I6TvdIomudCxWUqzg6mdYWTvWk0T1GZSmaeaSsyM19Pz06m51xVDeQWqeQWqSRlVvHcbHsGRlmz+XDTb74QQoCLiwsajYb8vDyT5QWFhbi5uV00b93DldDQUPILCvjm66/rO3ca0mg0dO7ShdS0tOYodos4laKyMMf4NL+ubnOyN42acLRtHBFxoZIKQ54LOdoZ11FWCXq9iqOdwoV3k452isl60/Pg01912FobhpyWVcJ9E7Wk5Rny1HVeZxea1rM5RSquDpe1ya3iqtonZqJ66tondrXtk11r3qNrw/ZJlaF9MniioX0CoLWywd3HcB32C+5BxvljHN7yJRNmv0x71N6OwzodvcHLVWH5znb2GrZLOBhbxpm3Uuv/trIytDvcnLUUFBm3xdVJQ0HJtW3bXdM8WPF7AbtrI3WS0qvxcrfipnGuFt254+BsOIcbRumUFufi5GK+nXy5NBoNnr6G89O/YxTZ6YnsXPupdO5cI7VRZ7q4WhYXAzVmzBi2bt3K1q1bTRplo0aNYsOGDezdu5cxY8bQtWtXampq2LdvX32a3NxcTp06RdQFT+ea4uXlxebNm0lISODWW281mevmUpKSkki7oEG4Z88eNBrNZb+961rLDuDr60tAQACJiYmEh4ebfEJDQ5vM9/zzz1NYWGjymXz785f1m1dCa2WDX8dunI/bZbL8fPxuAjqZH1veIbQ35+N3myw7F7cT3+DuaLWGjq6aqgqUBqF9Go0WQ4yPoTEQ0KkveZlnTdLkZ53DxcMyQu+1VjZ4BXQj9bTpvkg5vRvfYPP7zrdjb1Iapd+Fd2A3NFprs3kAUFV0uqomv9bVVFGQlYCDczubNOECldXGjpTc2kmMi0r1dAk0drhoNRDWQcu5jKYf753P1NMlyLSTpkuQlvMX5DmXrsfbzfT483bTkF9y8cdrimJsEAshmmZtbU14585ER5vOmxB9+DBRXbte9npUVb3odV1VVRITEvBoMPyrPamqMbwuuu6TXWgYKtrJ39hI1mgg2FchObvpOiglR6WTn2nDOsxfISW77mGRoePmwvWC4e/knMbrraw23Ih7OBvmSDlZ+5aiglJDB7ini+l6PJwVCtvRKBCtlQ2+Qd04F2/aPjkXv5sOTbRP/Dv15tzltE80ptcHpUH7xCxVRVfT9HW4rbXX47B3mIa0XJXMgmvYuDZQUamSkVNT/0nJqCa/qIaeEcaeLystdA234+TZyous6dJsbRq/qU6vv3QUZHtnZWVDh+BuJJwwPScTYncTFN7M81epKjXt+PwUfz4W2bmzc+dOYmJi6iN3wNC589lnn1FRUcGYMWPo3Lkz06dP5/7772fnzp0cOXKEO++8k4CAAKZPn35Zv+Xj48PmzZuJj4/n9ttvp6amifc3NmBnZ8fdd9/NkSNH2LFjB3/961+ZNWtW/VPDS2mOsoPhzV0LFizgvffe49SpUxw7dozFixfzzjvvNJnH1tYWFxcXk09LDcnqP/Yeju7+iWO7fyI3PYHNP71GUX46vUbcBsD2FW+zdskz9el7jbiNorw0Nv+0gNz0BI7t/olju5cz4Lp769OE9RhDzI5viTu4loKcZM7F7WLnmvcI6zG2tpMH+o29m/SzR9i7/hPys85z4sBqju78gT6jZrfIdraEniPmEn/gJ+IPLCc/M4HdqxdQUpBO1GDDvtv/69ts+f7Z+vRRg2+jJD+NPasXkJ+ZQPyB5Zw8sJyeI437LnrLQlJO7aIoN5mCrESObl/MqcMr6dznhvo0e9e8QVriforyUshKOsKmr/9KVWUJXfrd2Grb3hy2H61hXD9ruodq8fNQuG2sLVU1KtGnjef47eNsmDLY2PG142g1XYK0jOljjY+bwpg+1nQJ1LL9SPUF660m2FfDuL7WeLoo9OmsZXBXK3YdM6SxsYLJg6zp6KvB3UkhwEvDrNE2uDoqHDlzefWL+PPROjrg0isSl16RADiEBuLSKxK7IP82LlnbuOmmm9iwYQMbN2wgKSmJTxcuJDs7mym1rzVfvHgxb731Vn361atXs2/vXlJTU0lNTWXjxo38vHw5Y8aOrU/zzTffcOjQIdLT00lISODd//6XxMREpkyd2urbdy32xesZ3k1DRKCCtytMH6KhugaOnzPewU0fomFsb41JnjB/haFdFTxdDK+xDvVT2Bdv7LjeE6+nb5hC704KXi4woa8GVwc4dNqYJqqjQrCPgpsTdAlUuHOslpMphuFZ9es5oWdghEJUkIK7E4zuqcHLBaIT2te4mf7j7qltY/xEbkYCW356jeK8dHoNr22frHybdUsvaJ8MN7RPtixfQG5Gbftkz3IGjDNeYzv1GMORHd8Sf0H7ZNdq0/bJjpXvkHLmIIW5KWSnnmTHqv+SfHo/UQOmte4OuEZteRyC4VrbNVhpd8fV1Vq7rYgZ17kysIcDQX7WPHK7N5VVKjsPG6NrHp3txeypxs5oKy2EdLAhpIMNVloFT1ctIR1s8PMyDto4FFvGjPFu9O1qj7e7FQN7ODBttCv7j7Wj3tarNGTiXA5v/4nDO5aTnZbA+m8XUJiXTv/RhnP4t5/e5ufPnjXJk54UR3pSHFUVZZSV5JGeFEdWqnFi/R1rF5IQu4u8rGSy0xPZvWExR/aspOeQGxCivbCoYVlg6NwpLy8nMjISX1/f+uWjRo2iuLiYsLAwgoKCAEMD7/HHH+f666+nqqqKkSNHsm7dukbDmS7Gz8+PzZs3M3r0aO644w6WLVt20WFNYBg+NmPGDKZMmUJeXh5Tpky54leQN0fZ582bh4ODA2+++SbPPPMMjo6O9OjRgyeeeOKKytJSIvtPobw0n93rPqK0KAsv/y7c/PCnuHoaImhKirIpzje+2t3NK4ibH/6UzcsXELP9G5xcfRg38wUi+kysTzNk8kOgKOxc/S4lBZnYO3kQ1mMMI254sj6Nf0hPbnzgf2xf+Q67132Iq2cgY26ZT9eBllM5h/WaQkVZAYd//5Cyomw8/Doz+Z6FOLsb9l1ZcTYlBcboMRePQCbdu5A9q18nds8yHF18GHrDC3TqYdx3NVXl7FzxMqWFGVhZ2+HmHcrY2/5DWK8p9WlKCjPZvOxpKsoKsHN0x6djL2585Pv637UUW6KrsbaCm0faYG+rkJSp59PVFVRe8CDfzUmDql4QlZOh5+uNlUweZMOkgdbkFql8tamSpCxjmuQsPYvXVzJ1sA3j+1uTV6yycmcVh08bQqf1Kvi4axgQYYWjvWFiyOQsPR+uqCAz33InfBQty7Vfd4b8/lX9313fmg9A8pc/c/S+5o+sbO/qrvfLli0jLy+PkJAQXnr55fo2QX5eHtm1888BqHo9S5YsISMjA61Wi7+/P/fccw+TpxjrttKSEt5//33y8/JwdHQkLCyM/7z5JhEREa2+fddi9wkVa63KlIEa7G0gNQe+3qyj6oK+Y1dHxWT4d0oOLN+pZ0wvDWN6Ql6J4e/UXGOeE+dVHGz0jOyhwckesgpg2VadScSNs73hZtvJDoor4GiiyvbjpjfX+06qWGn1TOinwd4WMvMN5ctvZyNAIvsZ2id7fjW2T2Zc0D4pLcymyEz7ZEtt+8TR1YexM1+gy4Xtk0kPoVDbPik0tk+GTzO2T0qLc1i39BlKi7KwsXPGOyCCmx/5vNGbuNq7tjwOwTAPkIJpZ5IlW7m5EBtrhXm3eOJor+HM+Ur+/UkGFZXG7fNytzKJwnF3seLNvxvbZjeMdeOGsW7EninnXx9mAPDFz7ncNtmdeTd74eqkIa9Ix6bdxfy08eKT01uC7gOnUFZSwLZVH1JSmI1PQGfueGIhbl6GfVJcmE1hnumw24X/Ms6xmn4+lmN71+Dq2YEn39wMQFVlOWu/epmi/AysbOzw8gtlxv3/ofvAKYhro8qEys1GUZua4EVclX/961+sWLGCmJiYti5Ks/n897YugeUqLJbT62qlpVn2qzjb0tsPX+SVJOKi1lpb1s18exJ58te2LoLF+mZPx7YugsXy97W455TtRnqmRIxerWMHk9u6CBZpxi0hbV0Ei3X7MMseLncxBdGb2+R33fqMvXQiCyNXRCGEEEIIIYQQQrQ+idxpNtK5c4W6devG+fPm3wy0cOHCVi6NEEIIIYQQQggh/uykc+cKrVu3rsk3bPj6+uLs7My//vWv1i2UEEIIIYQQQggh/rSkc+cKBQcHt3URhBBCCCGEEEIIi6cqf9z5hFqbDHATQgghhBBCCCGEsGASuSOEEEIIIYQQQohWJ69Cbz6yJ4UQQgghhBBCCCEsmETuCCGEEEIIIYQQovXJnDvNRiJ3hBBCCCGEEEIIISyYdO4IIYQQQgghhBBCWDAZliWEEEIIIYQQQohWJxMqNx/Zk0IIIYQQQgghhBAWTCJ3hBBCCCGEEEII0epUZELl5iKRO0IIIYQQQgghhBAWTDp3hBBCCCGEEEIIISyYDMsSQgghhBBCCCFEq5MJlZuP7EkhhBBCCCGEEEIICyaRO0IIIYQQQgghhGh9ikyo3FwkckcIIYQQQgghhBDCgknkjhBCCCGEEEIIIVqdKvEmzUb2pBBCCCGEEEIIIYQFk84dIYQQQgghhBBCCAsmw7KEEEIIIYQQQgjR6lSZULnZSOeOuKSsHF1bF8Fi1dSobV0Ei+XpadfWRRB/QpEnf23rIlis+IjJbV0Ei2X7bVxbF8Fi5eRJG+VqKRK/f9UGjQhu6yJYpLTsti6BEH9s0rkjhBBCCCGEEEKIVqdKT3OzkT0phBBCCCGEEEIIYcGkc0cIIYQQQgghhBDiIj766CNCQ0Oxs7OjX79+7Nix47Ly7dq1CysrK3r37t2i5ZPOHSGEEEIIIYQQQrQ6FaVNPlfq+++/54knnuCFF14gOjqaESNGMHnyZJKSki6ar7CwkDlz5jBu3Lir3UWXTTp3hBBCCCGEEEIIIZrwzjvvcN999zFv3jyioqJ49913CQoK4uOPP75ovgceeIDZs2czZMiQFi+jdO4IIYQQQgghhBCi1amKpk0+lZWVFBUVmXwqKyvNlrGqqopDhw4xYcIEk+UTJkxg9+7dTW7b4sWLSUhI4MUXX2zWfdYU6dwRQgghhBBCCCHEn8aCBQtwdXU1+SxYsMBs2pycHHQ6Hb6+vibLfX19ycjIMJvn9OnTPPfcc3zzzTdYWbXOS8rlVehCCCGEEEIIIYRodapy5fPfNIfnn3+ep556ymSZra3tRfMoDcqqqmqjZQA6nY7Zs2fz0ksv0aVLl2sv7GWSzh0hhBBCCCGEEEL8adja2l6yM6eOl5cXWq22UZROVlZWo2gegOLiYg4ePEh0dDSPPvooAHq9HlVVsbKyYuPGjYwdO/baN6IBGZYlhBBCCCGEEEIIYYaNjQ39+vVj06ZNJss3bdrE0KFDG6V3cXHh2LFjxMTE1H8efPBBIiIiiImJYdCgQS1SToncEUIIIYQQQgghRKu7mteSt4WnnnqKu+66i/79+zNkyBA+/fRTkpKSePDBBwHDMK/U1FS+/PJLNBoN3bt3N8nv4+ODnZ1do+XNSTp3hBBCCCGEEEIIIZpw6623kpuby8svv0x6ejrdu3dn3bp1BAcHA5Cenk5SUlKbllFRVVVt0xKIdu+173VtXQSLVVMjp9fVsrKyjF789mj+rdq2LoLFSkhMbOsiWKz4iMltXQSLdfzbuLYugsXS6+U6e7VqdLLvrpa9rcxscTUUjbTtrtbTN/5x913y6RNt8rtBnbu2ye+2JKmZhBBCCCGEEEIIISyYdO4IIYQQQgghhBBCWDCZc0cIIYQQQgghhBCtzlImVLYEErkjhBBCCCGEEEIIYcEkckcIIYQQQgghhBCtTlUk3qS5yJ4UQgghhBBCCCGEsGASuSOEEEIIIYQQQohWJ3PuNB+J3GkDW7duRVEUCgoK2rooQgghhBBCCCGEsHASudNC5s6dy9KlSwGwsrIiKCiIGTNm8NJLL7VxydpO33CFwREKTvaQXQi/RetJzmk6fUdvGNdbg7crFJfD3niV6ATVJE1EIIzqrsHNCQpKYOsxPadSjd8rCozsptAtWMHRDkoq4NhZlZ0nVMyZ3F+hT5iGTdF6Dpwyn6YtjeqhoW+4gp0NpObCrwd0ZBdePE9kkMKYXhrcnSC/BDbH6DmZYrpt/TsrDOmqwdkesgpg4yEdSdnm1zd1oIZ+nTVsOKhj30njetydYHxfDUHeClZaOJOmsv6gntKKa9zoa9QWx93D12twc2z8FOLQaT0bDhvWZW0FY3oqdAlQsLeBwjI4eErlcEL7O+5Ey1izZg3Lf/qJvLw8goOD+csDD9C9e3ezaWOPH2fR4sWkJCdTWVmJj48Pk6dM4aabbqpPs2nTJv77zjuN8q5YuRIbG5sW2472ymN4fzo9fR+ufbtj18GHgzc/TOaq39u6WK0qbu8yju1cRHlxNm4+4Qya+jx+If2bTJ9+dj/7171BQdYZ7J196DniPiIH3Vb/fX7maQ7//gG5qbGUFKQxaMpzdBt2t8k69Loaojf/j4QjaygvzsHe2ZvOfW+k9+iHUDSW80yxb5jCoAuvHTF6Ui5y7QjyhnG9jNeOfSdNrx1eLjCiuwY/d3BzVPgtWs+B06b1/ZBIhYhABQ9nqNEZrvNbjurJK26prbw6I7tr6BtmbIusP6gju+jieSIDFUb3NLZFthxt3BbpF64wJMrQFskuhA2HdSQ3aItczm8HeMKYXhoCPBX0esjIh2+36ajRGdOEd1AY2U2DjxtU6yApS+XHnfqr3ynN5MSeZRzZYThn3X3CGXz98/iHXuScTdzP3rVvkJ91BgdnH3qOuo+uF5yz8ft/4FT0KvIzTgPgFdCVAROfxCeoZ32aqspSDm18j3MnfqO8JA/PDlEMvX4+3kE9Wm5DW0DsnmUc3fYFZcXZuPuGM2Ta/Ivuu7TE/exd8zr5mWdwcPGh16h5dB1s3Hdnj28kevNCinKT0OtqcPUKpsfIe+jSd3p9mugtCzl3fBMFWYlore3wDe7DoClP4+bdqUW3VYimSOdOC5o0aRKLFy+murqaHTt2MG/ePEpLS7n11lvbumitLipIYXxvhfWHVVKyVfqEK9w6UsOn6/UUlTVO7+oIs0ZqiElUWbVXJdBbYVJfhbJKlZMphjQBnnDTEA3bjqucSlHpEqhw01ANX/2uJy3PkGZIpEKfcIXV+/TkFIK/h6FzorKaRo2qLgHQwUOhuKx93lwP7aowOEph5R49uUUqI7pruHOslg9X66iqMZ8n0AtuGa5hy1E98ckqkUEKt4zQsGSjjtRcQ5quwQoT+2lYd0BPcrZK384aZo/R8tEaXaN/m4hAhQBPhaIG+8haC3eM1ZKZr/LV74bW0+ieGm4bpeWLDTraSlsdd0s26VEu6NvxdoXZo7XEJRv323W9FYJ9FFbt1VNYCqF+CpP6KRSXq5xOa8GdItqFbdu28enChTz8yCN07dqVX9et45//+AefLFyIj49Po/R2dnZMmzaN0NBQ7OzsiI2N5YP338fO1pbJU6bUp3NwcODTzz4zyftn7NgB0Do6UHT0JClLf6bfj/9r6+K0usSj69i37nWGTPsHvsF9iT/wPRuXPsCMx1fj5NahUfrivBQ2LX2QLgNuYdTM/5B5/jB7Vr+CnaMHId0nAFBTXYGzexCh3Seyb+3rZn/36PbPid//PSNvXoCbb2dyUo+zY/l8bOyc6TZ0Totuc3OJClK4rrfChsMqKTkqfcIUbh2h4bMNF7l2jNBwJFFl9T6VQC+FiX0VyipUTtZ2/FtroaBEJT4Zrutt/nc7eiscOqOSnqeiUQwPdG4bqeGz9Xqq2+5SamJolMLgSMO1K7dYZUQ3DXeM0fLR2qbbIgGecPMwDVuPGdsiNw/TsOQ3HWl1bZGOChP7alh3UE9KjkrfcA2zR2n5eJ2xLXI5vx3gabje7jqhZ8NBPTo9+LorqBc0WyIDFa4fqGHzUT3nMg0DQnzc2n5YSMLRdexZ+zrDptees/u+Z/2SB5j5pPlztigvhfVLHiRywC2MvtVwzu5a+Qr2jh6E1p6zaYkHCO85Bd9pfdBa2XJk+xf8umgetzyxGkdXXwB2LP8/8jJPM3rWGzg4+3AmZjVrv7iXmU+uqU/T3iUcWcee1QsYfuM/8Q3uS9y+7/l10V+Y9dQanNyb2HeLHiBy4EzG3PommecPs3PFy9g5utOpx0QAbO1d6TP2Qdy8O6G1suZ83Fa2/Tgfe0cPgiJGAJCeeICuQ2bjHdgDVa/jwIb/su7zecx8eg3WNg6tug8smUyo3HxkT7YgW1tb/Pz8CAoKYvbs2dxxxx2sWLGi/vtDhw7Rv39/HBwcGDp0KCdPnjTJ//HHHxMWFoaNjQ0RERF89dVXJt8risLnn3/OTTfdhIODA507d2bVqlUmaU6cOMGUKVNwcnLC19eXu+66i5ycizx6aiEDIxSOnFU5kqiSWwy/RasUlRuejJnTN0yhqMyQLrcYjiSqHDmrMijCeMgO6KJwNhP2xBnS7IlTOZdpWF4nwEvhVKpKQrohMiI+Bc5mgJ+H6e852cOEvhpW7tWja599OwyK1LDjuKFhlF0IK/fosbaC7iFNN0gGRWpIzFDZFauSWwS7YlXOZqgMijTuxyGRGqITDE8Yc4pg4yE9hWXQv4tp9eBsD5MHaPhltw59g4dbQd4Kbo6GMmUVGKJ/Vu3VE+ClEOrXdg2mtjruyiqhtML4Ce+gkFesmkRDBXopHDtnWFZYBjGJKpkF4O/R9g1M0fJ++eUXJkyYwKRJk+jYsSMPPPgg3t7erF271mz6sPBwRo8eTXBwML6+vowdO5Z+/fpxPDbWJJ2iKHh4eJh8/qyyN2zn1IvvkrFiU1sXpU0c37WULv1mEDFgJm4+YQyeOh9HVz/i931nNn38/u9wdPNn8NT5uPmEETFgJp37zuDYzkX1abwDezBw8t/p1HMqWivznYbZyTF0jBpLUORonN0DCO0+kYDOw8hJPd4i29kSBnapvXacrb12xBiuHX2auHb0qbt2xNReO86qHDlneu1Iz4ctR1XiklVqmggQ+X6HnmPnDNfirEJYc0CPq6OCn3tLbOXVGRihYWesnviU2rbI3tq2SPBF2iIRtW2RE4b9s+uEytlM0/0zOEJDdKJKTGJtW+SwoSOtf2djmsv57Ql9tRw4pbI7TiW7CPJKIC5ZRVe7zxUFJvbT8FuMnsNnVPKKIbcYk4cvbeXYjqVE9J9B5ICZuPuEMWTafJxc/Tix1/w5G7fvO5zc/BkybT7uPmFEDphJl34zOLrdeM6Ove1Nug6ZjWeHKNx8OjFixsuoqp7UhD2AocP2bOwmBk3+G/6hA3D1CqbfdY/i7BHIiX3ftsp2N4ejO5YQMeBmIgfOxN03jKE31O0789sQt9ew74beMB933zAiB84kor/pvusQNojQ7uNx9w3DxbMjPYbPwcMvgoxzh+vTTLnvcyL6z8DDrzOeHSIZNXMBJQVp5KTEmvtZIVqcdO60Int7e6qrq+v/fuGFF3j77bc5ePAgVlZW3HvvvfXf/fLLLzz++OM8/fTTHD9+nAceeIB77rmHLVu2mKzzpZdeYtasWRw9epQpU6Zwxx13kJdnCB9IT09n1KhR9O7dm4MHD7J+/XoyMzOZNWtW62xwLY0G/N0hMcP0wnk2w/B0y5wAT4WzDdInZqj4e4BGMaZpuM7EButMyVYJ8VXwcDL87eNmCJ1OSDf9vRsGadgXb2hQtEduTuBsr5CYbtxenR7OZ6oEeTfdoAr0UkhIN91HCemGiBSo/bfxoFGaxHSVoAb/NjcO1bD7hN7sMDArrbFMdWp0oNerdLxI+VpSWx53DcvRPVjh6FnTPMnZKp0DDCH/AME+4OHcuLzij6e6upozp0/Tt29fk+V9+vYl7sSJy1pHwpkzxMXF0aOHadh8eXk5d999N3fdeScvvvgiCWfONFu5heXQ1VSRmxZLh/BhJssDwoeRlRRtNk9WcgwBDdN3HkZOaix6XbXZPOb4BPcjPWEvhTlnAchNjyfz3GECu4y6wq1oGxoN+LnD2Uwz1w7Py792nM1Q8bvg2nE17KwN/y2vuvp1NCc3x9q2SEaDtkiWsV1hTqCXmetmuvG6WdcWaZgm4YJr6+X8toOt4bdKK1TmXqflyZu0zBmnJcjLuE5/d3BxMETy3D9JyxM3arl9lAZvl6vbJ81FV1NFTlosAZ0bn4OZTZ2zSTGN0gd2GUb2Rc7ZmuoK9LoabO1dAdDrdah6HVorW5N0Vla2ZF7QidGe6WqqyEmNJdDMvsg8b37fZSbFENilYfrhZKeY33eqqpJ6Zg+F2WcvOtSrqsIwhtLWwfVKN+NPTUVpk88fkQzLaiX79+9n2bJljBs3rn7Zq6++yqhRhsbOc889x9SpU6moqMDOzo633nqLuXPn8vDDDwPw1FNPsXfvXt566y3GjBlTv465c+dy++23A/Daa6/xwQcfsH//fiZNmsTHH39M3759ee211+rTL1q0iKCgIE6dOkWXLl1aY9NxsAGNRmk090ppBTjamc/jaA+lGY3TazUK9raG/3ey45Lr3BOvYmsND0zRoFcNjaytx1ROJBkbB0OiFPRq42Fa7YlT7TaVNNjekgpDg+di+czto7r1OdjW/duoDdKoONobK71h3Qz7aP9J8/soJUelqgbG9dGwOUaPguH/NRoFJ/u22a9tedxdKCJAwc6aRp07G6NVpvSHv96gRadXUVVYd0C96JwO4o+hqKgIvV6Pm7vp43h3Nzfy8/MvmveuO++ksLAQvV7P7DvuYNKkSfXfBQUG8tTTTxMSEkJZWRkrV6zgb3/7G//78EMCAgJaZFtE+1RZVoCq12Hv5GWy3N7Jk7IS85VMeXEO9p09G6T3QtXXUFGaj4NL4+GC5vQcOY/qimKWvzsVRdGiqjr6jX+CsF5Tr25jWlmT147Kput5JztIrGyQvsG142qM66UhObv9PHiqexjRsC1SWmEYmtZkvmtoizjZKZf92+61D/JG9tDwW7SezAKVHiGGIewL1+nIKwF3J6U+zabDegpKVYZEaphznZYP1+ioaKOOtIrac9bBzDlbXmz+nC0rziHQyfScdbjEOXtg/ds4uvgSED4UABtbR3w69iZ688e4+YRh7+RJwpG1ZKUcxdUzuJm2rmVVlOXX1ncN6y9PyprYd+XF2dg7DW+UvuG+qyov5uvXRqGrqUKj0TDsxhcbdQrVUVWVPWtexy+kHx5+rXOPJURD0rnTgtasWYOTkxM1NTVUV1czffp0PvjgA07UPpnt2dM4mZm/vz8AWVlZdOzYkbi4OP7yl7+YrG/YsGG89957JssuXIejoyPOzs5kZWUBhmFfW7ZswcnJqVHZEhISzHbuVFZWUllp2kKpqbbCytq2UdqW1rBLQLmMDtaGaboGKXQPUVi5RyW7SMXXTeG6Pgol5XDsnIqfOwzorLBoY9tPoneh7iGG8eB1vt1aO9i+wU5RlMb7qSH1avtWavP5exhCqj/9tekB/2WV8NMOPVMGahgUoUVV4fh5lbRcFX377TMzqzmOuwv1ClVISG/cIB3Q2TB/0Q87dBSWGuZamNhPoaTCMMxL/PEpDQ4cVVUbLWvozbfeory8nJPx8SxevJgOHTowevRoACKjooiMiqpP27VrV/762GOsXrWKBx96qNnLL9q/hoeTqqooF3ta2ej4U5tY3rSzx9aRcGQ1o2e9iZtPZ/LS49i3dgEOzj507nvjZa+nvbnkHmjma92EvgrebvD15rZrn3QPVpg64IK2yDbzbZEmFl3R99C4vaKYy3eR3647TA+fMQypA8jI1xPqp6V3mIbNR4zz4dUN7wJYtU/P49O1dA1S2uFLDdSLnn/mriO1XzRKe2Tb5yQcWcfU+5eatOvHzHqDbctfYNmCUSgaLV4duhLe63py0i4vkrS9aLQvDAsvluGSy61tHbn58V+orioj7cwe9q55HRePQDqEDWqUbdfKV8jLOMkNDy67itIL0Tykc6cFjRkzho8//hhra2s6dOiAtbUhvrauc6fubzBWSPoLJjO5nIb/heuoy1O3Dr1ez7Rp03jjjTcala2uM6mhBQsWNHqj19ib/8G4mS82vaGXUFZlGJ7T8ImXo5knOXVKy41PdOo42IJOr1Je2/dUYiZawqHBE7KxvRX2xKmcqB1LnV2o4upomJTv2DnDkCZHO3h0mrHxotEojOtlmEPlozVt06g6laKyMMfYmVI37MnJ3rSTwPESTwRLKoxPu+rz2BnXUVZZ929j2oRytDM+texYu4+euFFb/71GozC+r4ZBkfD+SkM5EzNU/rdKh70t6PVQWQ1PzdASe75tGkptedzVcXGAEF9Yvsv0OLLSwugeCj/t0tcPEcwuVPF1N3SknctsX52Nonm5uLig0WjIrx1CW6egsBA3N7eL5vXz8wMgNDSU/IICvvn66/rOnYY0Gg2du3QhNU1m6P6zsXVwQ9FoGz21rijNa/R0u469s1ejCIHyklwUjRV2Dm6X/dsH1r9Fj5Hz6NTTEKnj4deFkoI0jm771CI6d5q6djRVz4P564Kjnem140qM76PQuYPC11v0FJdfef7mcipVJTX3grZIbVOpUVvkItdVqG2LmNk/DdsiTvambRGHC9oiJeWX/u26NDlFpu2OnEIVl9q5bev2Z06h6fCughJwdTTbndQq7OrO2ZKG52DT56yDs1ejc7y81Pw5e3T7ImK2fsqU+xbh6R9h8p2LZ0em/eUrqqvKqK4owcHFh9+XPYmzu2VEfNo5uJuv70pycWiyvvO+rPpO0Whw9TJEMHl1iCI/K5GYLZ826tzZtfIVzp/YzLQHv8bJza8ZturPRb2CBwji4mTOnRbk6OhIeHg4wcHBjTphLiUqKoqdO3eaLNu9ezdRFzyVvZS+ffsSGxtLSEgI4eHhJh9HR/Pxs88//zyFhYUmn1E3PndFZW9IrzdMJNhwYt1QX4WUHPMX0dRclVBf0/Sd/BTS86iPBGkqzYXrtNI2fhKkV6l/BHf8nMrnG/R8sdH4KS5T2XtS5bttbXeDXVVjeFVo3Se7EIrLVTr5G7dXo4FgX4Xk7KYbIik5Kp0a7Pcwf4WU2jx6PaTnYbJeMPydXLsfj55V+WStjoXrjJ+iMpU9cSrfbG4czVNeaejYCfE1dAqdSmmbhlJbHnd1eoUqlFXCmQZzPGkU0GqVRsemevEHdOIPwtramvDOnYmONp0LIPrwYaK6dr3s9aiqajKPm7nvExMS8Ggw/Ev88WmtbPDs0I20M7tNlqed2Y1Pxz5m8/gE9TaTfhdeAd3QaC+/DVNTVY7S4M0nikaLqlpGp3Xdq7Mb1vOhvgopuZd/7Qj1Vci44NpxuSb0UYgIUFi21fAmxbbUqC1SZGiLXHhd1Wgg2MfYrjAnJUdtdC2+8LpZ3xa5SJqC0kv/dkEpFJWpeDqbrsfTRanfl+l5KjU6FU+XC9ajgKsTFJS2XdSO1soGrw7dSD1teg6mntmNb1PnbMfepDY4Z1NP78K7wTl7ZPsXHN78MZPu+RTvwO5NlsHaxgEHFx8qywtJOb2L4K7jmkzbnmitbPAKaLzvUk7vxjfY/L7z7diblEbpd+EdeIn6TlXR6aou+FNl54qXOXt8E9f/ZQkuHoFXvyFCNAPp3Gmn/v73v7NkyRI++eQTTp8+zTvvvMPPP//M3/72t8texyOPPEJeXh633347+/fvJzExkY0bN3Lvvfei05kfYmNra4uLi4vJpzmGZO0/qdI7VKFnqIKns+E10C4O1Ie/ju6hMG2Q8UJ7OEHFxRHG9Tak7xmq0CtUYd9JY+PwwCmVTn4wONKQZnCkQoivYXmdM2kqQ7sqhPmDq4PhdeeDuij1HQ7lVYaOkws/OtXwFCiv+Jo3u1nti9czvJuGiEAFb1eYPkRDdY2hg6rO9CEaxvbWmOQJ81cY2lXB08XwOvVQP4V98cb9uCdeT98whd6dFLxcDG8Nc3WAQ6cNacztI70eSsoNb72o06uTQoCnYcx7jxDDK9f3xpumaW1tddzV6RmqcPSc2qgTp6rGMAnkuN4aOnob5gvoEaLQPVhps84w0bpuuukmNmzYwMYNG0hKSuLThQvJzs5mSu1rzRcvXsxbb71Vn3716tXs27uX1NRUUlNT2bhxIz8vX86YsWPr03zzzTccOnSI9PR0EhISePe//yUxMZEpUy1jrpPmpnV0wKVXJC69IgFwCA3EpVckdkHmI1f/aLoPu5tTh5Zz6uByCrIS2Ld2ASWF6UQOvBWAgxveYduPz9anjxx4GyUFaexb9zoFWQmcOricU4d+psdw48seDBM1x5GbFodOV01pURa5aXEU5Z6vTxMUOYYjWxeSHL+V4vxUzsVuInbnEoK7Xtd6G3+N9p9S6XXBtWNc7bUjuvbaMaqHwvUDjdeO6LprR6+mrx0ajeGlDj5uoNUYIlB83IzzxABM7KvQLVhh5T49VTWGqBRHO2P0bnuw/6Se4V0vaIsMqm2LXBClO32whrG9jG2R/af0hPkpDI0y7J+hUbVtkQv2z96Tevp0UuhV2xYZ38e0LXK5v70nXs+ALgpRQQruTjC6hwZPZ4hJNKynqgYOnVEZ1UNDJz9DeabUDj2LS2rb62+PEXdz8uByTh5cTn5WAnvWLKCkIJ2oQYZzdv/6d9jyg/GcjRp0GyX5aexZ8zr5WQm1eX+m50jjOXtk2+cc3Pgeo255FWf3AMqKsykrzqa60thzmHxqJ8knd1CUl0LK6V2s+Wwurl6hRPS7qfU2/hr1HDGX+AM/EX9gOfmZCexeXbvvBt8GwP5f32bL9xfsu8G1+271AvIzE4g/sJyTB5ab7LvoLQtJObWLotxkCrISObp9MacOr6Rznxvq0+xa8TJnolcz9va3sLZ1rN+/NdVXOdHWn5SqKm3y+SOSYVnt1I033sh7773Hm2++yV//+ldCQ0NZvHhxk+H35nTo0IFdu3bx7LPPMnHiRCorKwkODmbSpEloNK3brxeXrGJvC8O7KTjZKWQXGl75WVRm+N7J3vD2grpw2MJS+GG7nuv6aOgXbpgjZ2O0yskU4zpTc2HFHj2jemgY1V0hvxRW7NaTdsFIh42HVUb2gEn9NDjYGkJ5oxNUdpywvBvo3SdUrLUqUwZqsLeB1Bz4erOOqhpjGldHxTjeGkjJgeU79YzppWFMT8MrQZfv1JOaa8xz4ryKg42ekT00ONkbXmO+bKvuip8YerkojOttKFtBKew8rmdvfNvu57Y67gBCfQ3/HkcTzT+tXrFHz+ieCtMHa7CzgaIy2HZMbYfj/UVLGDVqFMXFxSxbtoy8vDxCQkJ46eWX8fX1BSA/L4/s2vnTAFS9niVLlpCRkYFWq8Xf35977rmHybWdQQClJSW8//775Ofl4ejoSFhYGP95800iIiIa/f6fgWu/7gz5/av6v7u+NR+A5C9/5uh9z7dVsVpNp55TqCwrIGbLR5QVZ+Pu25kJcz7BqXaoRVlxNqWFxrBCZ49Axt/9CfvXvk7c3mU4uPgweOp8QrpPqE9TVpzNyg9n1P99fOciju9chF/oAKbM+xKAIdP+j0O/vcfu1S9TUZKHg4sPEQNn0XvMw6205dcuLlnF3gaGdTVeO3648NphZ+basUPPdb019K29dmyKUTmZalynsx3cN8HYSzM4UmFwpKGjf9lWw3Wib7ihbXbnGNPenDX7Da9Ibw92x6lYaVUm969ti+TCN1tN2yKGt1GZtkV+3q1ndE8No3sYooB+3qUn7cK2SJKKvY2ekd0MbZHsQsMcP4VlV/bb+0+qWGn0jO+jwd4WMvPhmy068kuMaX6L1qPXa5g+RIO11hB59fXvOiou/6VwLSKs5xQqSws4/LvhnPXw7cykuZ/UD48qK86mtMB4zrp4BDJp7ifsWfs6J2rP2SHT5hN6wTl7Yu+36HXV/PbN4ya/1XfcI/S77lHA8IanAxv+S2lhBrYOroR2m8CAiU9cUcReWwvrNYWKsgIO//4hZUXZePh1ZvI9C032XUmBcYiyi0cgk+5dyJ7VrxO7ZxmOLj4MveEFOvWYWJ+mpqqcnSteprQwAytrO9y8Qxl7238I62W87ta9an3Nwjkm5Rk18zUi+s9AiNamqOpVT7cq/iRe+77piXTFxdXUyOl1tays/pg96q1h/q3t6DGvhUlITGzrIlis+IjJbV0Ei3X827i2LoLF0lvarP3tSI1O9t3VsreVwQ9XQ9FI2+5qPX3jH3ffnU44f+lELaBzmGW8Ee5KSM0khBBCCCGEEEIIYcGkc0cIIYQQQgghhBDCgsmcO0IIIYQQQgghhGh1Kn/cIWetTSJ3hBBCCCGEEEIIISyYRO4IIYQQQgghhBCi1UnkTvORyB0hhBBCCCGEEEIICyadO0IIIYQQQgghhBAWTIZlCSGEEEIIIYQQotXJsKzmI5E7QgghhBBCCCGEEBZMIneEEEIIIYQQQgjR6iRyp/lI5I4QQgghhBBCCCGEBZPIHSGEEEIIIYQQQrQ6VZXIneYikTtCCCGEEEIIIYQQFkw6d4QQQgghhBBCCCEsmAzLEkIIIYQQQgghRKuTCZWbj0TuCCGEEEIIIYQQQlgwidwRQgghhBBCCCFEq5PIneYjkTtCCCGEEEIIIYQQFkw6d4QQQgghhBBCCCEsmAzLEkIIIYQQQgghRKuTYVnNRyJ3hBBCCCGEEEIIISyYRO4IIYQQQgghhBCi1amqRO40F4ncEUIIIYQQQgghhLBgErkjLqmkpKati2CxPNyt27oIFiskoK1LIP6MvtnTsa2LYLFsv41r6yJYrO63R7V1ESzW6eXxbV0Ei1VeoW/rIlisQ3tT2roIFim4s09bF8GC2bV1AYQFkM4dIYQQQgghhBBCtDq9TKjcbGRYlhBCCCGEEEIIIYQFk8gdIYQQQgghhBBCtDp5FXrzkcgdIYQQQgghhBBCCAsmkTtCCCGEEEIIIYRodfIq9OYjkTtCCCGEEEIIIYQQFkw6d4QQQgghhBBCCCEsmAzLEkIIIYQQQgghRKuTCZWbj0TuCCGEEEIIIYQQQlgwidwRQgghhBBCCCFEq5MJlZuPRO4IIYQQQgghhBBCWDDp3BFCCCGEEEIIIYSwYDIsSwghhBBCCCGEEK1OJlRuPhK5I4QQQgghhBBCCGHBJHJHCCGEEEIIIYQQrU4mVG4+ErkjhBBCCCGEEEIIYcEkckcIIYQQQgghhBCtTt/WBfgDkcgdIYQQQgghhBBCCAsmnTtCCCGEEEIIIYQQFkyGZQkhhBBCCCGEEKLVyYTKzUcid/7gQkJCePfdd+v/VhSFFStWtFl5hBBCCCGEEEII0bwkcqeVzJ07l6VLlzZaPnHiRNavX99iv3vgwAEcHR1bbP3NYVwfLQMitNjbQnK2yqrdNWQVqBfN0y1Ew/i+WjxcFPKKVDYe0nHivHE6rkGRGgZGaXF3MvQEZxWobI7WcSrFmMbGCiYOsKJrsAYHW8gvUdkTq2NfvGVM63VizzKO7FhEeXE27j7hDL7+efxD+zeZPj1xP3vXvkF+1hkcnH3oOeo+ug66rf77+P0/cCp6FfkZpwHwCujKgIlP4hPU02Q9pYWZ7F//Nsknt1NTU4mrVwgjb/433gHdWmZDW8He35axc90iiguz8QkIZ+odzxMSYX5fFhVk8euy/5B2LpbczPMMGX8nU++cb5Lm8I5fWP7Z/EZ5//V5DNY2ti2yDaL9GdVDQ99wBTsbSM2FXw/oyC68eJ7IIIUxvTS4O0F+CWyO0XMyxbQ+7N9ZYUhXDc72kFUAGw/pSMo2fu9oB+N6awjzN/z2+SyV9Qf15BWb/lagF4zppSHAS0Gvh4x8WLZFR42ueba/ucTtXcaxnYa6zs0nnEFTn8cv5CJ13dn97F/3BgVZZ7B39qHniPuIvKCuy888zeHfPyA3NZaSgjQGTXmObsPuNlmHXldD9Ob/kXBkDeXFOdg7e9O57430Hv0QiuaP/1zMY3h/Oj19H659u2PXwYeDNz9M5qrf27pYrer47mXEbP2CsuJs3H3DGXbDfDp0avq4S0vYz67Vr5OfeQYHFx/6jJ5HtyG3mU17OmYtv33zNCHdxjF57of1yw9vXkjisU0UZCeitbLDL6QPg6c8jbtPp2bfvpbUN0xhUISCkz1kF8JvMXpScppOH+QN43pp8HaF4nLYd1IlOsFY73m5wIjuGvzcwc1R4bdoPQdOm9aLfcIU+oYpuNY2eXMKYecJPYkZLbGFre/m8a6MG+SIo72GM0lVLF6RT0pmdZPpA32tuWWCK50CbPD2sOLLVfn8urO4UTp3Fy2zp7jRK8IOG2uF9JwaPv0xl7OpTa+7PbuurxUDI2vvKbJUVuyuJiv/4vcU3UM0jO9vhaeLQm6RysaDNcSeM38vMLqXlkkDrdl5rIY1e2tMfrdnmAY3RwWdHlJy9Gw8UENy9sV/WxioSOROc/njt1DakUmTJpGenm7y+fbbb1v0N729vXFwcGjR37gWI3tqGdZdy+o9NXy0qpqScpV7J1ljY910niAfhdvGWBF9Rs8Hv1QTfUbP7WOtCPQ2VgyFpbDhgI4PV1bz4cpqEtL03HmdFT5uxjRTB1vRJVDDD1ur+e/yKnYd13H9ECuiOrb/0yLh6Dr2rH2dPmMe4KbHfsYvpB/rlzxASUGa2fRFeSmsX/IgfiH9uOmxn+k95i/sWf0aZ49vrE+TlniA8J5TuP7+JUx/6Fuc3Drw66J5lBZm1qepLC9k1Sez0WitmHTPp8x8cg2DpzyDrZ1zi29zSzm6dx3rvnmdUTc8wCMv/0xIl34sfesBCnLM70tddTWOLh6MvuEB/IIim1yvrb0Tz72/3eQjHTt/HkO7KgyOUvj1oJ7P1+soKVe5c6wWm4s8Ugn0gluGazh6Vs/CdTqOntVzywgNAZ7GNF2DFSb207DzuJ5P1+lIylaZPUaLywXV/K0jtbg7K3y/Tcen63QUlsKd47RYa01/a/YYLYnpKl+s1/H5eh0HTupR21k7NPHoOvate51eox5g+iM/4xvSj41Lm67rivNS2LT0QXxD+jH9kZ/pNeov7F37GucuqOtqqitwdg+i/8SnsHfyMrueo9s/J37/9wy5/v+Y8cRaBkz6G8d2LOLE3q9bZDvbG62jA0VHTxL7+MttXZQ2cSZmHbtWLaDvuAeZ+cQv+If2Z+0Xf6E4v+lr7NovHsA/tD8zn/iFvmMfYOfKV0k4uqFR2uL8VPas+Y/ZhzFpCQfoPnQ2Mx79nml/WYSqr2HNZ/Ooripr9m1sKVFBCtf1Vtgdp7Joo56UHJVbR2hM6qgLuTrCrBEaUnIM6ffEqYzvrRARYExjrYWCEpWtR1VKys1XUsVlKluP6lmyyfA5l6VyyzANXi4tsJGtbNpoZ6aMcGbxinxeeD+TgmId8+/3xs626RtiG2uFrLwavv21gPwi8z32jvYKLz3sS41O5Y1F2fzt7XS+XpNPaRP7uL0b1UvL8B5aVu6u5n8rqiguV5k32eai9xQdfRRuH2dN9Gkd7y2vIvq0jtnjrAnybrxvA70UBkZpSc9t3PGTXahn1a4a3l1excerqygoVrlvig2Ods25hUJcWvu/i/0DsbW1xc/Pz+Tj7u4OGIZLLVy4kOuvvx4HBweioqLYs2cPZ86cYfTo0Tg6OjJkyBASEhLq15eQkMD06dPx9fXFycmJAQMG8Ntvv5n8ZsNhWe3N0G5ath7REXteT2a+yo/barC2gt6dmj40h3XTciZVZdtRHdmFhv8mpKkM62a8c4lP1nMqRU9ukUpukcqmQzqqagwdQ3U6+igcPq3jbIZKQQkcOKknI08lwKv99x4f27GUiP4ziBwwE3efMIZMm4+Tqx8n9n5nNn3cvu9wcvNnyLT5uPuEETlgJl36zeDo9kX1acbe9iZdh8zGs0MUbj6dGDHjZVRVT2rCnvo0R7Z9jqObP6NueQ2foJ44uwcQED4EF8+OLb7NLWXX+qX0GzWDAaNn4hMQxtQ75+Pq4ce+zeb3pbt3ANffOZ8+w2/EzsGpyfUqioKzm7fJR/x5DIrUsOO4nvhklexCWLlHj7UVdA9pun4ZFKkhMUNlV6xKbhHsilU5m6EyKNJYHw6J1BCdYHiqnVMEGw/pKSyD/l0MaTycIdBbYd1+HWl5kFsM6w7osWnw2xP6adl/UmXXCUP58oohLllF184CF4/vWkqXfjOIGDATN58wBk+dj6OrH/H7zJ+f8fu/w9HNn8FT5+PmE0bEgJl07juDYzuNdZ13YA8GTv47nXpORWtlY3Y92ckxdIwaS1DkaJzdAwjtPpGAzsPIST3eItvZ3mRv2M6pF98lY8Wmti5KmziyfQmRA26m66CZuPuGMXz6fJzc/IjdY/6BXOye73By92f49Pm4+4bRddBMIgfM4Mi2RSbp9Hodvy37OwMmPIaLR2Cj9Vx//+dEDpiBh19nvDpEMmbWAkoK0shOiW2R7WwJA7soHDmrcuSsSm4x/BajUlRuiKwxp0+YQlGZIV1uMYa851QGRRjrvfR82HJUJS5ZpaaJOupMOiRkQF6J4bP9uEpVDXTwbP9tukuZPNyFFZsLOXC8nJTMaj7+Phcbaw3DejcdmZ+YUsWytQXsOVJGTY35zpppo13ILaxh4Y95JCRXkZOvI/ZMJVl5NWbTt3fDuluxJcYQdZOZr/LD1mrDPUWY9qJ5zqTq2XrEcE+x9YiOM6l6hnU3fRJjYwW3jrXm5+01lFc2Xs+RBD1n0vTkFatk5aus2VuDnY2Cn4fcaovWJUdcO/LKK68wZ84cYmJiiIyMZPbs2TzwwAM8//zzHDx4EIBHH320Pn1JSQlTpkzht99+Izo6mokTJzJt2jSSkpLaahOuiLszuDgonE41Xql1ejiboaejb9OHZkcfDWdSTa/up1OazqMo0LOTBhsrQ4hmnXMZKlEdjU+TOvkreLmYlqc90tVUkZMWS0DnYSbLAzoPIzMp2myerKSYRukDuwwjOzUWvc586G1NdQV6XQ229q71y87HbcE7oBu/ffMEX/17GD+/P4P4/T9c4xa1nZqaKtLOxRLe3XTfhPcYRtJp8/vyclVVlPHmk2N54/HRfPn2g6SdO3FN6xOWw80JnO0VEtON9Y1OD+czVbNPA+sEeikkpJs2whPS1fqoRI0G/D1olCYxXSWotlPaqrYNe+HQKlU1/H7dbzvYGn6rtELlnglanpqh5e7rtAS1s/5HXU0VuWmxdAhvUNeFDyOrqbouOYaAhuk7DyPnInWdOT7B/UhP2EthzlkActPjyTx3mMAuo65wK4Sl0dVUkZ0aS1AX0+MoqMswMs6bP+4yz8eYST+c7JRYdBccdwc3fYi9owdRA2+5rLJUVRiG0dg6uF4iZfug0YCfO5zNNK2jzmaoBDbRyRLgqXA2o3F6Pw/QXGW/jKIYIoisrSA11zKjUOr4eGhxd9Fy7FRF/bIaHcQlVtAl2Hzn9OXq19WBxJQqHr/Ti0/+GcCCx/0YO7B9T+XQFA9nxXBPkdLgniJdT/BF7imCfTUmecBwT9Ewz/Rh1pxMMnTgXIpWAwMjtZRXqmajfERjqqq0yeePSObcaUVr1qzBycn0Sf+zzz7LP/7xDwDuueceZs2aVb98yJAh/OMf/2DixIkAPP7449xzzz31eXv16kWvXr3q//73v//NL7/8wqpVq0w6gdorZ3vDSdUwxLakHNycmj7hnOzN5VFxtjdN5+uu8OA0a6y0UFUNX/9mOpfPmr013DTciudut0WnV1FV+HlnDecz23dDoKKsAFWvw6HBcAJ7J0/Ki80Pai8rziHQydNkmYOTF6q+horSfBxcfBrlObD+bRxdfAkIH1q/rDgvmbh939Fj+Fx6j/kL2cnH2L36NTRWNnTpe+O1b1wrKysuQK/X4eRqui+dXDwpKbzIBAGX4OUfys33v4ZvUBcqy0vYvfErPv33HTz671/w8gu5xlKL9s6pNgy7pMJ0eUkFuF2k3exkB6UN8pRWGNfnYAsajaFTxjSNimNtfZpTaBi+MLa3hrX79VTVwJBIBWd7BWd7Qz732svQqJ4aNh02POHsGarhrnFaPlmrazQ3T1uprK3rGg6dsnfypKzE/PlZXpyDfWfPBukvXteZ03PkPKoriln+7lQURYuq6ug3/gnCek29uo0RFqOiNN9wjXVueBx5UtbkNTYbe6fhJsscnD3R1x53ji4+pJ89TPyB5cx8csVllUNVVXatfh2/0H54+nW5qm1pbQ42dXWU6fLSSpocnuJkB4kNIiFKK0CrUbC3bVwnXoy3K8wZqzG0+2rg5116couubBvaG1dnQ499YYlpJ0FhiR4vt6YjUi6Hj4cV1w12Zt2OIlZuLiQsyJa7p7tTXQM7Dpde07pbm1PtPUBxg/uD4nIVd+eL31OYy+N8wTDCnp0Mc9P9b8XFHxBEdtRw+1hrrK2guAy+WFdFmZkoHyFaknTutKIxY8bw8ccfmyzz8PCo//+ePY0T1/r6+gLQo0cPk2UVFRUUFRXh4uJCaWkpL730EmvWrCEtLY2amhrKy8uvKXKnsrKSykrTmqimGqysr32ukF5hGm4cZjzkvtxYW0k26EtRLqMjtVH3i0KjuSJyClU++KUKe1uFbiEaZo604rN11fUdPEO6agnyVvhyYzUFJSohfgrTh1hRXFZNQlr77uAxT73ozlMafKfW7TAzeY5s+5yEI+uYev9Sk397VVXxCujGgIlPAuDVoSv5mWeI2/udRXbu1Gm4B9RL7MtL6Rjem47hvY1/d+7Lh/+8mb2bvuH6u1646vWK9ql7iML1A41P+b7dWhs2Y6Zuu1TNctVz3tTm06vw4w4d0wZpeWamFXq9SmKGahKRWHdoHz6tciTRkDEjX0+on5beYRo2x7SvJ40NT0VVVVEuNvlio3O36bquKWePrSPhyGpGz3oTN5/O5KXHsW/tAhycfehswXWduBKNj5eLHXeNr7HGPFUVJfz+7d8Zdcsr2Du6X9av7/jlFfLST3Ljw8suv8jt1CXPvGZqcuUWw6JNemytISLQUC9/vdWyOniG9XFg3gzjvcF/Fhtmy1cbXByaI+ZAoxiGb32/3jDT/7m0agJ9rbluiFO779zpHabhphHGyXSWrK8y/I+56+4VHl8KxjyujjBtiDWLfq265MsGEtL0vP9zFQ52hsid2ddZ8+GKqivqoPyzkgmVm4907rQiR0dHwsPDm/ze2tpYSdU1Eswt0+sNDe+///3vbNiwgbfeeovw8HDs7e255ZZbqKqquuoyLliwgJdeeslk2fBpLzBi+j+uep114pL0JGcZy2alNWyPk4Ni0mvuaNc4MudCJeXGqJ86TnZKo6fkOr1hHgmKVVJzdAR6aRjaTcuKXTVYaWFCfy3f/F7DyWTD/szIV/H31DOihxUJae33LQF2Dm4oGm2jJ9flJXnYN4jOqePg7NXoiWN5aS6Kxgo7BzeT5Ue3LyJm66dMuW8Rnv4Rjdbj7hNmsszNpxNnYzdiiRyc3dBotBQ3iNIpLcrDycX8vrwaGo2GwNDu5GSeb7Z1ivbjVIrKwhxjq69uaJSTvWn0juMlnkKXVBifPtbnsTOuo6wS9HoVRzuFC1uwjnamT8rT8+DTX3XYWhvCw8sq4b6JWtLyDHlKyg3psgtN69mcIhXXdjT/vm1dXdeg7qoobbqus3f2ahTBWF5ivq67mAPr36LHyHl06mmI1PHw60JJQRpHt30qnTt/cHaO7maPu/KSXOydm7rGeptNr9FYYevoRn7GGYrzU/l18UP136uqoe3xybPduP3vv+LqZZy7bseKVzh3YjM3Pvw1Tm5+zbVpLa6sqq6OMl3ucJG6r6SicVSPox3o9KrZuU0uRq83vGUQatt0HgoDOiusP2Q5D+wOnSjnTJLxFV/WVob2rpuzloJiY8e7i5OGwuJre7VhfrGOlCzT9m5qVjUDe9g3kaP9OJGkJ/ln4z2Ftva669zgnsLJTqm/5plj9p7C3pgnwEuDs4PCozcZh8BpNQoh/gpDumn5v0WV9R1B1TXUzvUJyVk1/G2WDQMiDHOLCtFapHPHgu3YsYO5c+dy0003AYY5eM6dO3dN63z++ed56qmnTJb9u5keGlVVQ57JNUSlqEwlvIOG9FxDxafVQKifhg0Hmp7MLSlLT3iAwq4L5hcMD9CQlHnxp82KYlh/3e9YaZVGvfnqtQVstAqtlQ1eHbqReno3od3G1y9PPbOb4KixZvP4dOxNUvxWk2Wpp3fhHdANjdbYgXhk+xdEb/6Eyfd+hndg90br8Q3uS0HOOZNlhTnncHLrcPUb1IasrGzoENKNM8d3062/cV+eOb6bqL7m9+XVUFWV9KR4fAMtI7ReXJmqGqgqMV1WXK7SyV8ho/YVrBoNBPsaXuHblJQclU5+CvvijRVTmL9CSu2rVPV6Q8dNJ3/F5PXoDf+uU1lb33o4G+bq2XLEkKagFIrKVDxdTDuJPJyVdhW1qLWywbNDN9LO7Cbkgrou7cxuOjZV1wX1JrlBXZd2ZhdeDeq6S6mpKkdRTOdcUDTa+hty8celtbLBO6AbKad306mH8bhLObWbkG7mjzvf4N6cP7HFZFnyqV14B3ZDq7XGzacTs55eZfL9/vXvUV1ZyrDayZrBcK3YueIVzh7/jRse/NLspMvtmV4PGfkQ6qtwKtVYl4T6Kpxqom5JzVXp7G9aF4X6KmTkGSIRr4WCsd1nKSoqVSoqTdvA+UU6enS241ztg0etFqI62fHtuoJr+q1T5yrp4G16K+jvbUVOfvvvjKiqhtxq0wOkqEwlPEBD2oX3FP4aft3f9D3F+Uw94QEadh43bnPnQA3na+8pzqTp+e9Ppr2Mt4yyJrtAZduRmktGBdU9yBYXd63nujCysCrPslVWVpKRkWHyycm5+nk9wsPD+fnnn4mJieHIkSPMnj27Pqrnatna2uLi4mLyaY4hWU3ZHatjdC8tXYM1+Lor3DLSiuoaiEk0bsctI62Y0F9rkic8QMPInlq8XRVG9tTWdvYYK+YJ/bSE+Cq4ORnm3hnfT0uon8KRBEOaympITNczeaBhubsT9O2soU+4hhPn2v9FrceIuzl5cDknDy4nPyuBPWsWUFKQTtSgWwHYv/4dtvzwbH36qEG3UZKfxp41r5OflVCb92d6jry3Ps2RbZ9zcON7jLrlVZzdAygrzqasOJvqSmNobo9hd5OVdIToLQspzDnPmZg1xO//kW6DZ7fexjezYZPu5tC25Rzctpys1ATWfrOAwtx0Bo417MsNP7zDjwufNcmTdj6OtPNxVFaUUVqcT9r5OLJSz9R///svH3L66E7yspJJOx/Hz5//H+lJ8fXrFH98++L1DO+mISJQwdsVpg/RUF0Dx88ZWzDTh2gY21tjkifMX2FoVwVPF8Pr1EP9FPbFG+vDPfF6+oYp9O6k4OUCE/pqcHWAQ6eNaaI6KgT7GOq/LoEKd47VcjLFMDyrfj0n9AyMUIgKMtR/o3saXhkcndC+Oi+6D7ubU4eWc+rgcgqyEti3dgElhelEDjScSwc3vMO2H43nZ+TA2ygpSGPfutcpyErg1MHlnDr0Mz2GG+s6w0TNceSmxaHTVVNalEVuWhxFucbIuqDIMRzZupDk+K0U56dyLnYTsTuXENz1utbb+DakdXTApVckLr0iAXAIDcSlVyR2Qf5tXLLW0WvkXOL2/0Tc/uXkZyawa9UCigvS6TbkNgD2rnub3781HnfdhtxGcX4au1YtID8zgbj9y4k/sJxeowzHnZW1LZ5+XUw+tnbOWNs64unXpf6tbTt+eZlTh1dz3ey3sLF1pKwom7KibGqqLWdcx/5TKr1CFXqGKng6w7jeCi4OEJ1gqH9G9VC4fqDxhjc6QcXFEcb1MqTvGarQK1Rh30ljXaTRgI+b4aPVGCIcfdyM84fVrTfQC1wdDHPvjOyu0NEbYpMs/67x151FTB/rSv9u9gT6WvPQLE+qqvXsijG2zx661ZPbJhkn3tZqIdjfmmB/a6yswN1VS7C/Nb6exs6cdTuKCe9oy/QxLvh6WjG0twNjBzmxcU87mXjtCu06XsOY3lZ0CzHcU8wcZW24p0gwtutnjbZm4gArkzydAzWM6mW4pxjVS0t4gIZdxw0dQlXVkJmvmnyqq6GswvD/ANZWMLG/FUG1190Ongo3j7DC1VHh6Nn2f08h/lgkcqcVrV+/Hn9/04ZRREQE8fHxV7W+//73v9x7770MHToULy8vnn32WYqKLGhgMbD9qA5rLdww1Ap7G0jJVlm8oZqqCyJ83JxMI2ySslS+31LD+H5aruurJa9Y5bvNNfVPt8EQUjlzlDXODlBRBRl5Kks2VHPmgidH322pZmJ/K2aNtsbB1jAJ6cZDOpMbqfYqrOcUKksLOPz7R5QVZ+Ph25lJcz/B2T0AMEzuWFqQXp/exSOQSXM/Yc/a1zmxdxkOLj4MmTaf0O4T6tOc2Pstel01v33zuMlv9R33CP2uM0zQ7R3Ug/F3vs+BDf8levNHOLsHMuT65wjvM60Vtrpl9Bw8hbKSAras/Ijigmx8Azsz5+lPcPcy7MvigmwKc9NN8nz4jxn1/592LpYje9bg5tWBv7/zOwAVZUWsWPxPigtzsLN3xj84ivvnf0lQWE/En8PuEyrWWpUpAzXY20BqDny9WUfVBQ8QXR0Vk3kUUnJg+U49Y3ppGNPT8Drf5Tv1pOYa85w4r+Jgo2dkDw1O9pBVAMu26ii8YHoEZ3tDp4+THRRXwNFEle3HTeu1fSdVrLR6JvTTYG8LmfmG8uU3iEBqa516TqGyrICYLYa6zt23MxPmfILThXVdofH8dPYIZPzdn7B/7evE1dZ1g6fOJ+SCuq6sOJuVHxrP4eM7F3F85yL8QgcwZd6XAAyZ9n8c+u09dq9+mYqSPBxcfIgYOIveYx5upS1vW679ujPk96/q/+761nwAkr/8maP3Pd9WxWo14b2nUFFWwKHfPqS0KBsPv85MvW+h8RpblE1JQVp9ehePQKbet5Bdq1/n+O5lOLr4MHz6C4T1nHhFv1v3qvWVn8wxWT5m1mtEDphhLku7E5esYm8Dw7oqONkpZBfCDzv0FJUZvneyM7wptS5Sp7DU8P11vTX0DTcMh9kUo3Iy1bhOZzu4b4LxId/gSIXBkXA+S2XZVkPd5mgL0wYZ6r3KasgqhO936DmX2Vpb3nJWby3GxlrDvTd54GivISG5ktc+y6ai0nj98HLTmlxP3F20vP6k8Z5j2igXpo1y4URCBa8szAIM8+2882U2t01yY8Z1rmTn1fDVqnx2RZe13sY1o21HdFhrFaYPs8beBpKzVb74tcr0nsKx8T3Ft5urmdDfivH9rMgrUln2ezXJ2ZffKaiq4O2mcGcXGxztoKwCUrL1LFxdRVa+5XcuCsuiqA1n6BKigflfyFTvV8vD/fKHAQhTIQFtXQLLdcsgCcq8Wi9/03T4trg4W1s57q5W99uj2roIFuv08qt7QCagvKL9P8xqr44eTL10ItFIcOfLe2OhaOz1+5t45dwfwLbYtulQHNWtHU002EykJSaEEEIIIYQQQghhwWRYlhBCCCGEEEIIIVqdqsrE081FIneEEEIIIYQQQgghLJh07gghhBBCCCGEEEJYMBmWJYQQQgghhBBCiFYnr3dqPhK5I4QQQgghhBBCCGHBJHJHCCGEEEIIIYQQrU6PTKjcXCRyRwghhBBCCCGEEMKCSeSOEEIIIYQQQgghWp28Cr35SOSOEEIIIYQQQgghhAWTzh0hhBBCCCGEEEKIi/joo48IDQ3Fzs6Ofv36sWPHjibT/vzzz4wfPx5vb29cXFwYMmQIGzZsaNHySeeOEEIIIYQQQgghWp2qts3nSn3//fc88cQTvPDCC0RHRzNixAgmT55MUlKS2fTbt29n/PjxrFu3jkOHDjFmzBimTZtGdHT0Ne6xpknnjhBCCCGEEEIIIUQT3nnnHe677z7mzZtHVFQU7777LkFBQXz88cdm07/77rs888wzDBgwgM6dO/Paa6/RuXNnVq9e3WJllAmVhRBCCCGEEEII0erUNnoVemVlJZWVlSbLbG1tsbW1bZS2qqqKQ4cO8dxzz5ksnzBhArt3776s39Pr9RQXF+Ph4XH1hb4EidwRQgghhBBCCCHEn8aCBQtwdXU1+SxYsMBs2pycHHQ6Hb6+vibLfX19ycjIuKzfe/vttyktLWXWrFnXXPamSOSOEEIIIYQQQggh/jSef/55nnrqKZNl5qJ2LqQoplFGqqo2WmbOt99+y7/+9S9WrlyJj4/PlRf2MknnjhBCCCGEEEIIIVqd/iomN24OTQ3BMsfLywutVtsoSicrK6tRNE9D33//Pffddx8//vgj11133VWX93LIsCwhhBBCCCGEEEIIM2xsbOjXrx+bNm0yWb5p0yaGDh3aZL5vv/2WuXPnsmzZMqZOndrSxZTIHSGEEEIIIYQQQrQ+VW2bCZWv1FNPPcVdd91F//79GTJkCJ9++ilJSUk8+OCDgGGYV2pqKl9++SVg6NiZM2cO7733HoMHD66P+rG3t8fV1bVFyiidO0IIIYQQQgghhBBNuPXWW8nNzeXll18mPT2d7t27s27dOoKDgwFIT08nKSmpPv3ChQupqanhkUce4ZFHHqlffvfdd7NkyZIWKaN07gghhBBCCCGEEKLVqW00587VePjhh3n44YfNfteww2br1q0tX6AGZM4dIYQQQgghhBBCCAsmnTtCCCGEEEIIIYQQFkyGZYlLcnW1busiWKzbttzW1kWwWL9M+r6tiyD+hPx95bJ4tXLydG1dBIt1enl8WxfBYnW+ObKti2Cxdi882tZFsFjJcWfbuggWaeq0oLYugmiH9FjGhMqWQCJ3hBBCCCGEEEIIISyYPKIUQgghhBBCCCFEq7OkCZXbO4ncEUIIIYQQQgghhLBg0rkjhBBCCCGEEEIIYcFkWJYQQgghhBBCCCFanarKhMrNRSJ3hBBCCCGEEEIIISyYRO4IIYQQQgghhBCi1ellQuVmI5E7QgghhBBCCCGEEBZMIneEEEIIIYQQQgjR6uRV6M1HIneEEEIIIYQQQgghLJh07gghhBBCCCGEEEJYMBmWJYQQQgghhBBCiFanIq9Cby4SuSOEEEIIIYQQQghhwSRyRwghhBBCCCGEEK1OXoXefCRyRwghhBBCCCGEEMKCSeeOEEIIIYQQQgghhAWTYVlCCCGEEEIIIYRodaoMy2o2ErkjhBBCCCGEEEIIYcEkckcIIYQQQgghhBCtTiJ3mo9E7gghhBBCCCGEEEJYsHbduRMSEsK7775b/7eiKKxYsaLNytNSlixZgpubW1sXQwghhBBCCCGEEBboioZlzZ07l6VLlzZafvr0acLDw5utUHUOHDiAo6Njs69XtB9xe5dxbOciyouzcfMJZ9DU5/EL6d9k+vSz+9m/7g0Kss5g7+xDzxH3ETnotvrv8zNPc/j3D8hNjaWkII1BU56j27C7TdZRXVnKod/e4/yJ36goycOzQxSDps7HO7BHi21nW3EcMQHncTegdXGjOj2Fgp+XUJUQ32R6+/7Dcb7uBqy8/VHLy6iIi6Hwl6/Ql5W0Yqlb3rFdyzi85QvKirLx8AtnxI3z6dCp6eMu9cx+dq56nbyMMzi6+NB37Dy6DzUed3H7f+b37+Y3yvfgG0ewsrYFYN/6Dziw8UOT7x2cvbj3pZ3NtFWiPYve/g0HfvuC0sJsvPw7M+aW+QSGN33MJZ/ez9blr5OTfhonVx8GjJ9H7xG3m6Q5tHkJMTu+pTg/HXtHd7r0mciI6U/XH3Mx25cRs+NbivJSAfD078yQyQ/TqduoltvQFtA3TGFQhIKTPWQXwm8xelJymk4f5A3jemnwdoXicth3UiU6wRjz7eUCI7pr8HMHN0eF36L1HDhtGhM+JFIhIlDBwxlqdJCaC1uO6skrbqmtbBnHdy8jZusXlBVn4+4bzrAbLl7XpSXsZ9fq18nPPIODiw99Rs+j25DbzKY9HbOW3755mpBu45g811i3Hd68kMRjmyjITkRrZYdfSB8GT3kad59Ozb597ZHH8P50evo+XPt2x66DDwdvfpjMVb+3dbFa3dg+WgZEaLG3geRsldV7asgquPjYi27BGq7rq8XDRSGvSGXTYR0nzuvrvx8YqWFQpBY3JwWArAKVLTE6TqUY03QN1jAwQksHLwVHO4X/ragiPc/yx3zce3swN0z0x9nJihOninnnk9OcTSprMv20CX5MGutHp2AHAE6eKWHhl2eJO22sxOzttdx/Rwgjh3jh7mrNqcQS3vssgfjTFlbRNeHglmXs2fAFxQXZeHcIZ+Jt8+nYxXz9V1yQxaYf3iD9fCx5WecZOO4uJt5m2q47vP0Hju5ZSXbqaQD8g7sx5qYnCejUs8W35Y9OryptXYQ/jCuO3Jk0aRLp6ekmn9DQ0JYoG97e3jg4OLTIuv8Mqqur27oIF5V4dB371r1Or1EPMP2Rn/EN6cfGpQ9QUpBmNn1xXgqblj6Ib0g/pj/yM71G/YW9a1/j3PGN9Wlqqitwdg+i/8SnsHfyMruenb/8H2lndjPqlje46a8r6RA+jPWL7qW0MLNFtrOt2PcdgtuMuRRt+JnMN56lMiEOr4fmo3X3NJveplMEHnc9StmeLWS+9hS5i97BpmMY7rMfbOWSt6zT0evYsWIB/a97kFuf/oUOof1Z/elfKM43f9wV5aaw+vMH6BDan1uf/oV+1z3A9l9e5cyRDSbpbOycuOdfO0w+dTfZdTz8Opt8f/vfV7XYdor2I/7QOrb8tIDBEx9izvMrCAjvx/IP76coz/wxV5CTzPKP/kJAeD/mPL+CQRMfZPOPr3Iq2njMndi/iu0r32bolEe55x/rmHjnq8QfXseOlW/Xp3F292Pk9L9x5zPLufOZ5XTsMpgVCx8hJ+10i29zc4kKUriut8LuOJVFG/Wk5KjcOkKDSxNNA1dHmDVCQ0qOIf2eOJXxvRUiAoxprLVQUKKy9ahKSbn5m76O3gqHzqh8+bue77bp0Shw20gN1toW2MgWciZmHbtWLaDvuAeZ+cQv+If2Z+0XF6nr8lJY+8UD+If2Z+YTv9B37APsXPkqCUc3NEpbnJ/KnjX/wT+08Y1SWsIBug+dzYxHv2faXxah6mtY89k8qquavhH9I9E6OlB09CSxj7/c1kVpMyN6aBnWTcvqPTV8tKqaknKVeyZZY3ORR8pB3gq3jrEiOkHPByuqiU7Qc9sYKwK9jTd9RaWw4aCOj1ZV89GqahLT9dwxzgofN2MaGys4n6Vn48GaltzEVnXHzUHcemMg7yw8w7ynDpObX8V/X+6JvX3TFVKfHm78tj2Lx+Yf4YG/R5OZXcE7L/fEy8OmPs1zj3VhQB93XnknnjmPHeRAdD7vvmKaxlLF7l/Hhu8WMHzKg9z/z1/o2KU/y977C4W55us/XU0Vjs4eDJ/6IL6BkWbTnD+5n+4Dp3LX35Zyz/Pf4eLhzzf/vY+i/D/W/YOwbFfcuWNra4ufn5/J57333qNHjx44OjoSFBTEww8/TEmJ8Ul/3bCjNWvWEBERgYODA7fccgulpaUsXbqUkJAQ3N3deeyxx9DpdPX5Gg7LutDYsWN59NFHTZbl5uZia2vL5s2bL7kdISEh/Pvf/2bOnDk4OTkRHBzMypUryc7OZvr06Tg5OdGjRw8OHjxokm/37t2MHDkSe3t7goKC+Otf/0ppaek1rxdgxYoVdOnSBTs7O8aPH09ycrLJ96tXr6Zfv37Y2dnRqVMnXnrpJWpqjBcvRVH45JNPmD59Oo6Ojvz73/8mPz+fO+64A29vb+zt7encuTOLFy++5P5pDcd3LaVLvxlEDJiJm08Yg6fOx9HVj/h935lNH7//Oxzd/Bk8dT5uPmFEDJhJ574zOLZzUX0a78AeDJz8dzr1nIrWqvHFqaa6gnOxmxgw8W/4hQ7AxTOYvuMexdk9kPj937bYtrYF5zHXU7pnM2V7NlOTmUrhz0vR5efgOHyC2fQ2IV3Q5WZRsu1XdLnZVCWepGTXb1h3/GM9bY3ZtoSug26m2+CZePiGMeKm+Ti5+XFsl/l//+O7v8PZzZ8RN83HwzeMboNnEjVwBtFbFzVIqeDo4m3yaUij0Zp8b+/k0QJbKNqbg78vpseQm+k5bCaefmGMveUFnN39iNlh/pg7svM7XNz9GXvLC3j6hdFz2Ex6DJnBgd+Nx1za2RgCOvUlasA0XD0DCYkaTmS/68lIOl6fJqzHWDp1H4WHbygevqGMuOFJbGwdSD8X09Kb3GwGdlE4clblyFmV3GL4LUalqBz6hJl/ytcnTKGozJAutxhD3nMqgyKMzZ30fNhyVCUuWaVGb3Y1fL9Dz7FzKjlFkFUIaw7ocXVU8HNvia1sGUe2LyFywM10HTQTd98whk831HWxe8wfd7F7vsPJ3Z/h0+fj7htG10EziRwwgyPbTOs6vV7Hb8v+zoAJj+HiEdhoPdff/zmRA2bg4dcZrw6RjJm1gJKCNLJTYltkO9ub7A3bOfXiu2Ss2NTWRWkzw7pp2XrEEHWTVaDy0/YarLXQK6zp246h3bQkpKlsP6ojp9Dw34Q0laHdjB0Y8cl6TqXoyS1SyS1S2XRIR1WNoWOoTkyCni0xOs6kNXFyW6CZNwTw5Q9JbN+Tw9mkMl79bzy2tlomjPJpMs/Lb8fzy7o0zpwtJSmlnDf+dwqNBvr3MlRiNjYaRg315qPFiRyJLSQ1vYJF354nPbOCm6Z0aK1NazF7Ny2hz/Cb6TNyJt4dwph423xc3P04uNV8/efmFcjE21+g19AbsbN3Mpvmpvvfov+Y2fh1jMLLvxPX3/0KqqrnbNyeltyUPwVVbZvPH1GzzLmj0Wh4//33OX78OEuXLmXz5s0888wzJmnKysp4//33+e6771i/fj1bt25lxowZrFu3jnXr1vHVV1/x6aef8tNPP13Wb86bN49ly5ZRWVlZv+ybb76hQ4cOjBkz5rLW8d///pdhw4YRHR3N1KlTueuuu5gzZw533nknhw8fJjw8nDlz5qDW/usfO3aMiRMnMmPGDI4ePcr333/Pzp07G3UyXel66/bPq6++ytKlS9m1axdFRUXcdpsxFHrDhg3ceeed/PWvf+XEiRMsXLiQJUuW8Oqrr5r89osvvsj06dM5duwY9957L//4xz84ceIEv/76K3FxcXz88cd4eZmPaGlNupoqctNi6RA+zGR5QPgwspKizebJSo4hoGH6zsPISY1Fr7u8KCVVr0PV69A2iKjQWtuSef7wFWxBO6fVYh3UiYr4IyaLK+KPYhsaYTZL1dmTaN08sevaBwCNsysOvQdTEWv+38MS6WqqyEqJJaiL6XEUFDGMjHPmtzPjfAxBEabpO0YMJzs5Ft0Fx111VRlLXxnL4pdGsfrzB8hOOdFoXQU551n0rxEs/fc4Nnz5FIW5yY3SiD8WXU0VmcmxhEQNN1keEjWMtETzx1x6YgwhUcMapB9B5vnj9cdcYFg/MpNjST93FDBE+5yN3Uan7qPNrlOv1xF/cC3VVWX4h/a5xq1qHRoN+LnD2UzTFtjZDJVAT/OdOwGeCmczGqf38wDNNUR921kb/ltedfXraE26miqyU83UdV2GkXHe/HGXeT7GTPrhZKeY1nUHN32IvaMHUQNvuayyVFUYhnjYOrheySYIC+XuDM4OCmdSjZ0rOj2cy9DT0afp246OPhpOp5p2yJxObTqPokCPUA02VpCU/Qe9SwM6+Nrh5WHL/uj8+mXVNSoxxwvoHuly2euxtdVipVUoKjGcy1qtgpVWoarKdJ9XVunp2dWyz1VdTRXp52Pp1M20PgvrNoyUhOZr01ZXlaPX1WDvaNn7S/yxXPGr0NesWYOTk7FHc/Lkyfz444/1f4eGhvLKK6/w0EMP8dFHH9Uvr66u5uOPPyYsLAyAW265ha+++orMzEycnJzo2rUrY8aMYcuWLdx6662XLMfNN9/MY489xsqVK5k1axYAixcvZu7cuSjK5bXgpkyZwgMPPADAP//5Tz7++GMGDBjAzJkzAXj22WcZMmQImZmZ+Pn58eabbzJ79myeeOIJADp37sz777/PqFGj+Pjjj7Gzs7uq9dbtn//9738MGjQIgKVLlxIVFcX+/fsZOHAgr776Ks899xx3322YP6ZTp0688sorPPPMM7z44ov12zR79mzuvffe+r+TkpLo06cP/fsbQqdDQkIua9+0tMqyAlS9rtHQKXsnT8pKzE+mUF6cg31nzwbpvVD1NVSU5uPg0vQTjDrWto74dOxNzJaPcfMOw87Jk8Sja8lOOYqLZ/DVb1A7o3F0QdFq0RcXmizXFxeicXEzm6fq7Cnyvnwfj3ueQLG2RtFaUX70AAU/NoxQsVzlpfmoeh0OzqbHkYOzJ2XF5o+70uJsOjoPb5ReX3vcObr44O7TietuW4CnfxeqKks4sv1Lln8wm9v+tgI37xAA/IJ7cd3tr+PmHUJ5SS4HNn3M8vdv5/ZnVmPvaEHhAOKKlJfUHnMuDY85L0qLss3mKS3OwcHZtG50cDEcc+Ul+Ti5+hDZfyplJXl8+85sUFX0+hp6jbidQRP+YpIvO/Uky966jZqaSmxsHZh+/4d4+Tf/HHktwcEGNBqF0grT5aWV4GhnPo+THSRWNkhfAVqNgr0tjdZ1ucb10pCcbYjksQQVTdR19k5N13VlxdnYO128rks/e5j4A8uZ+eSKyyqHqqrsWv06fqH98PTrclXbIiyLs72hDd5wyGNJhWGOq6Y42ZvJU67ibG+aztdd4YHrrbHSQlU1fPN7DdmXmMvHknm4G6LQ8wpMe5bzC6rw9WmiIjTjobtDyc6t4mCMoZOovFzHsbhC5t4WzLmUMvILqrhupA9duziTklbefBvQBspqr7uODa67ji6elBReZMK2K7R5+Ts4u/nSqevQZlvnn9UfNYqmLVxx586YMWP4+OOP6/92dHRky5YtvPbaa5w4cYKioiJqamqoqKigtLS0fkJkBweH+o4dAF9fX0JCQkw6inx9fcnKyrqsctja2nLnnXeyaNEiZs2aRUxMDEeOHLmit2n17GmcAMvX1xeAHj16NFqWlZWFn58fhw4d4syZM3zzzTf1aVRVRa/Xc/bsWaKioq5qvQBWVlb1HTAAkZGRuLm5ERcXx8CBAzl06BAHDhwwidTR6XRUVFRQVlZWPzfRhesAeOihh7j55ps5fPgwEyZM4MYbb2To0KYrocrKSpNoKICaautGc4c0l4b9cKqqonCRzrlGHXdqE8ubNvKWN9j58wt898YoFI0WT/+uhPW8nty0xpEWFs9sbWm+BrXyC8DtlnsoWv8TlXFH0Li443rjnbjfdj/5yz5p2XK2tkYHnpllphkuutwvpDd+Ib3rl/qH9OX7d2ZwdMfXjJzxfwAER400yekX3JuvXptA/IEV9Bl9z5WVX1icxvWaetEHEY2+qz2X69aTdGofe9d/wnW3voh/SE8KspPY/NOr7Pn1Q4ZMfqQ+m4dvKHOeX0FleRGnYjby61fPcusTX1tMB485l6ztm7mROKGvgrcbfL3ZEod5NN5bF7vGNjzu6i4hCgpVFSX8/u3fGXXLK5fdIb3jl1fISz/JjQ8vu/wiC4vSq5OG6cOMtxNfbmo6kvpKT02Fxs2YnEKV/62owt5GoVuIhltGWPHZr9V/mA6e8aN8+Psjxo7QZ14+ZvifhpunKJe9Q2fPCOK6kT48Nv8IVdXGTK+8E8/zj0ewcukQanQqpxKK2bQtiy5h5oclWRpz9dnlBgBcyu5fP+f4vrXM+fuXLXaPJMTVuOLOHUdHR5M3Y50/f54pU6bw4IMP8sorr+Dh4cHOnTu57777TCb0tba2NlmPoihml+n1l994mjdvHr179yYlJYVFixYxbtw4goMvP/riwt+vO9nNLasrk16v54EHHuCvf/1ro3V17NjxqtfbcLm5ZXq9npdeeokZM2Y0SlMXMQQ0ervY5MmTOX/+PGvXruW3335j3LhxPPLII7z11luN1gOwYMECXnrpJZNl42b+k/GzXjSb/mrZOrihaLSNniBWlOZh72R+wl97Zy/KG6QvL8lF0Vhh5+B22b/t4tmRKfd/RXVVGdUVJTi4+LDluydxcg+4dGYLoS8tQtXpGkXpaJxd0RcVms3jPP4mKhNPUvL7asOCtCQKfqjA58lXKFzzHfqigpYtdCuwd3Q3HHdFpsdRWUkuDk0cd47O3o2O07LiXDQaK+wc3czmUTQafIJ6UJBzvsmyWNs64OnfhcKLpBGWz97JcMyVNjzminMbRefUcTQT1VNWnGc45pzcANi15j26DryBnsMMEaHeARFUV5Wxcdk/GTzxIRSNYSiD1soGdx/DddEvuAcZ549xeMuXTJjd/id7LasCvV5tFKXjcJEInJKKxlE9jnag06uUV5rPczHj+yh07qDw9RY9xRb0MNuurq4zc820dzZf1zmYqevKSwx1na2jG/kZZyjOT+XXxQ/Vf6+qhnbMJ8924/a//4qrl7EttGPFK5w7sZkbH/4aJze/5to00c7EJelJzjZGlVhpDe1WJ3uF4gsicZzsGkfmXKik3JDnQo72CiUNznWdntq31qmk5uoI8NYwtKuWlbv/GBMo79yfy4lTxnk5bawNdbmHuw25+cb97O5q3Siax5zbbwrkrpkdeeIfR0g4V2ryXVpGBY89fwQ7Ww2ODlbk5lfx0jNRpGdeZYhjO+FQe91tGKVTVpzbKJrnauzZ8AU71y3kzqcX4RtkfqoDIdrKNc+5c/DgQWpqanj77bcZPHgwXbp0IS3N/Ezkza1Hjx7079+fzz77jGXLlpkMR2oJffv2JTY2lvDw8EYfG5trm1m+pqbGZJLlkydPUlBQQGRkZP1vnzx50uxvazQX/2f09vZm7ty5fP3117z77rt8+umnTaZ9/vnnKSwsNPmMuem5a9o2c7RWNnh26Ebamd0my9PO7Mano/n5IHyCeptJvwuvgG5otNZm81yMtY0DDi4+VJYXknp6Fx2jxl3xOtotnY7q5ETsIk1fz2gX0ZPKsyfNZlFsbBs/IqvvgPxjvKJQa2WDT2A3kk+ZHkfJp3bjF2L+uPML7m0m/S68g7qhbeK4U1WVnLQ4s5Mq19HVVJGXmYCDc9NphOXTWtngG9SNc/G7TJafi99Nh07mjzn/Tr05F296zJ2L24lvcPf6Y66mqqK+A6eOotECKurFHueqKroay5g4Rq+HjHwI9TWtf0J9FVJyzW9jaq5qNn1GHuiv8MH+hD4KEQEKy7bqKSy9dPr2RGtlg3dAN1JOmx5HKad24xds/rjzDe5Nirm6LtBQ17n5dGLWLMMw9AAAudNJREFU06uY+eQv9Z+QrmMJCBvEzCd/qe/AUVWVHb+8zNljm7jhgSVmJ10WfxxVNYbOlrpPVoFKcZlKeICxftJqIMRPQ1JW0w9wk7L0hHcwPXc7B1w8DxhaJ1YW9Ba7Sykv15GaXlH/OZtURk5eJQN6G6PlrKwUend343j8xceJ3n5TIHffGszf/nWUk2dKmkxXUaknN78KZ0crBvbxYOe+3GbbnragtbLBP7gbiSdM67PEE7sJDLu2Oed2r/+CHWs+ZvYTn9EhpMelM4jLolfb5vNHdMWROw2FhYVRU1PDBx98wLRp09i1axeffNJ6QzjmzZvHo48+ioODAzfddFOL/tazzz7L4MGDeeSRR7j//vtxdHQkLi6OTZs28cEHH1zTuq2trXnsscd4//33sba25tFHH2Xw4MEMHDgQMMzdc/311xMUFMTMmTPRaDQcPXqUY8eO8e9//7vJ9f7zn/+kX79+dOvWjcrKStasWVM/fMwcW1tbbG1NwwutrFsmFL37sLvZ/tNzeAV0x6djb04e+IGSwnQiBxrmXDq44R1KizIZNfMNACIH3kbc3mXsW/c6Ef1nkpUUw6lDPzN6ljEKSVdTRUFWguH/ddWUFmWRmxaHta1D/Zw6Kad3gqri6hVKUd55Dvz6Fi5eoXTp17LHT2sr3rIGj7seoyopkaqzp3Acdh1aDy9Kdxre4OEy7Xa0bh7kf/UhABXHD+J++wM4Dh9PRdwRtC7uuN18N1XnTqMvyr/YT1mU3qPmsmnZs/gEdccvpDexe36gJD+d7kMNE5jvXvM2pUVZjJ9tOO66D72No7u+YcfKBXQbPIuMczGc2LecCXcaj7v9G/6Hb3Av3LxDqKoo4eiOr8hJjWfUjH/Wp9m56g1Cu47B2b0DZSW5HNz0MVUVJUQOuLFVt1+0vv7j7mHd0mfw69idDp36cHTn9xTnpdNruOGY277ybUoKMply938A6DX8NqK3fcOW5QvoOWwWaYnRHNuznOvvMb7mvFOPMRzavBjfwK741Q7L2rX6PcJ6jEWjMdzp7Fj5DqHdRuLs7kdVRSnxh9aRfHo/Nz/yeevvhKu0/5TKtIEK6fmQmqPSO0zBxQGiEwytslE9FJztYc1+w9/RCSr9whXG9VKISVQJ8FLoFaqwcq/xOqbRgFftPKRajWGuDx83qK6B/Nr7n4l9Fbp2VPhpl56qGmM0UGU11Bhf6tmu9Ro5l9+/exbvwO74BffmxL4fKC5Ip9sQw3G3d93blBZmMe52Q13XbchtHN/1DbtWLaDroFlknI8h/sByrpttqOusrG0bzZtja+cMYLJ8xy8vczp6DZPnfoiNrSNltVFoNvbOWFlf/hwhlkrr6IBjuDGCySE0EJdekVTlFVKRnN6GJWs9u2J1jOqpJbdIJadQZXQvLdU6OJJgPA9vGWlFUanKxkOGE2rPCR3zplgzooeWuCQ9UR01hHVQ+HStMSJnfD8tp1L0FJaq2For9OykIdRPYclGYxp7G3BzUnB2MHQUebka/ltcrlJiQdF3F/pxVSp3zexISloZyWnlzJnVkcpKHRu3Gaey+L8nI8jOrWLhl2cBw1CseXeG8NJbcaRnVuDhZngwUF6ho7zC8O8wsI87igJJqeUE+NvzyD2dSE4tY+1vGa2/kc1s8Pi5rPjiWTqEdCegU2+it/9AYV46/UYb6r/fl79NcUEWN973Rn2ejKQ4AKoqyygrziMjKQ6tlTXeHQwjVnb/+jlbV77HTfe/hZtXACWFtXWbrQM2do4I0R5cc+dO7969eeedd3jjjTd4/vnnGTlyJAsWLGDOnDnNUb5Luv3223niiSeYPXu2yfCkltCzZ0+2bdvGCy+8wIgRI1BVlbCwsMuaAPpSHBwcePbZZ5k9ezYpKSkMHz6cRYuME9lOnDiRNWvW8PLLL/Of//wHa2trIiMjmTdv3kXXa2Njw/PPP8+5c+ewt7dnxIgRfPed+VeNt7ZOPadQWVZAzJaPKCvOxt23MxPmfFI/PKqsOJvSQmNDyNkjkPF3f8L+ta8Tt3cZDi4+DJ46n5Duxld7lxVns/JD49C14zsXcXznIvxCBzBl3peA4c0dhzb+l9LCDGztXQnpNoF+E564quif9qz88B4KHJ1xmXQzWhd3qtOTyfl4Abp8Q5iq1tUdK3fjsJCyfdtQbO1xGjkJ15vmoJaXUnEqlsKVX7fVJrSIzn2mUFFWwIGNH1JalI2nf2euv38hLh7G46443xh96OIZyLR5C9m58nWO7VyGo6sPI296gfBeE+vTVJYXs/XHFyktysbW3hmvgChuevQrfIONkVOlBZls+PppKkoLsHd0xze4FzMf/77+d8UfV2S/KZSX5rPn148oLcrCy78LMx7+FFdPw799aWE2RfnGus7NK4ibH/6ULcsXELP9GxxdfRg78wW69DEec0MmPYSCws7V71JSmMn/s3ff4VEU/x/A33t3qZfeE1JII4UeaugdBEHpGEVRwP7FXgAVRBEUCz87Kk0FQQHpvZeETighBBIC6b33K/v74yDHkUuAEHI5fL+e5x5ye7N7s8PM7e7sZ2YtrBzg37ovegx/ozpNaXEOti5/F6VFWTA1t4ZzsyCMfuW3Gk/iaspik0VYmALdQwVYmQvILgT+PqRGUZnmcytzwMZSwM3JJwpLNZ8PaCdBWICAknJgV7SIuFTtNq3NgcmDtLf6uwYL6BoMXM8SsXK/5qInLEATdfBUX92QgM3HNY9INwYB7TS/dad2a37rHNwCMWzyIljfPMYWZaOk4JbfOgdPDJu8CEc2zceFyJWQ27igx2Mz4d9mcG1fodfNR61v+Fn3PLDvuM8Q3Knm0PKHjW2HVgjf80f1+9AvZwAAkn9fh3OTpxsqW43q0HkVTGTAiHAZzE2BlGwRS7crUHXLyClbuaATLJyUJWL1fiUGhkkxIEyKvGIRq/YpkXLLk7CsLASM7WUCa0ugogrIyBexbKcCCWnaNMHeEozppT2fm9BX8/eeM0rsPWMkPbO3WbE2GWamErz5UiCsrUxw8XIR3vjoHMrLtfvj6myuE4kwcqgHTE0kmDu9pc62lqy8hiV/aYaDW8lleOFpXzg7maGoWIEDkTn45Y9EqFTG8RtXl5adh6K8tAAHN/2AksJsOHsE4onXFsHuxnG3pDAbRbm6I01+naO9yZt+PQYXjm2GraMHpn2+FwBwcv9KqJQKrPnpNZ31eg1/Bb0f+98D3qOHmyg+HCMEmgJBFI17furk5GQ0b94cJ06cQFhYmKGz81D6fI0xTiLZNDx5YMKdE5Fe/w5ZbegsGK3/DeNBsr5+3W3oHBivnDzjvHBqCizM73uU/H9W4OhgQ2fBaEUuOmfoLBitA/8eNXQWjNKL7/a6cyLS66meD++53R8HDfO9Ex/C6njfkTuGolAokJ6ejvfffx9du3Zlxw4RERERERER/ScZbefOkSNH0LdvX7Ro0QJr1qzR+ezQoUN45JFHal23pKT2ScWIiIiIiIiI6MEz7nFETYvRdu706dMHtY0o69ixI6Kjoxs3Q0REREREREREBmC0nTt1sbCwQEBAgKGzQURERERERES1eFgfS24InMGPiIiIiIiIiMiIPZSRO0RERERERETUtHHOnYbDyB0iIiIiIiIiIiPGzh0iIiIiIiIiIiPGYVlERERERERE1Og4LKvhMHKHiIiIiIiIiMiIMXKHiIiIiIiIiBodH4XecBi5Q0RERERERERkxNi5Q0RERERERERkxDgsi4iIiIiIiIgaHSdUbjiM3CEiIiIiIiIiMmKM3CEiIiIiIiKiRqdWGzoHDw9G7hARERERERERGTFG7hARERERERFRo+OcOw2HkTtEREREREREREaMnTtEREREREREREaMw7KIiIiIiIiIqNFxWFbDYeQOEREREREREZERY+QOERERERERETU6NSN3Ggw7d4geoF86/mnoLBgtm0r+0tefYOgMGK30TKWhs2C0BMYC11t5hdrQWTBakYvOGToLRqvbC20MnQWj5bb+kqGzYJQOHMozdBaM1lM9HQ2dBTICPBUjIiIiIiIiIjJijNwhIiIiIiIiokYnGmxG5Ycv0p2RO0RERERERERERoyRO0RERERERETU6Pgo9IbDyB0iIiIiIiIiIiPGyB0iIiIiIiIianRqPjCywTByh4iIiIiIiIjIiLFzh4iIiIiIiIjIiHFYFhERERERERE1Ok6o3HAYuUNEREREREREZMQYuUNEREREREREjU7NyJ0Gw8gdIiIiIiIiIiIjxs4dIiIiIiIiIiIjxmFZRERERERERNToOKFyw2HkDhERERERERGREWPkDhERERERERE1OtFgMyoLBvreB4eRO0RERERERERERoyRO0RERERERETU6Pgo9IbDyB0iIiIiIiIiIiPGzh0iIiIiIiIiIiPGzp0bBEHA+vXrAQDXrl2DIAiIjo42aJ6IiIiIiIiIHlaiaJjXw6hJz7mTkZGBuXPnYsuWLUhNTYWLiwvatWuH119/Hf37939g3+vl5YX09HQ4OTkBAPbv34++ffsiPz8fdnZ29739SZMmoaCgoLoz6W7Nnj0b69evf6g6nWKPrsT5w0tQXpwNO5cAdBk2HW7NO9aaPj3xOI5v/RwFWfGwsHZBm56TEdxlQvXn+ZlXcHrPd8hNjUFJQRq6DH0fLbs/o7ONvxf0R0lBWo1tB3d5At1GfNRwO9cI+raVokMLCSxMgZQcEZuPqZBdUPevVai3gH7tZXCwBvKKgT1nlIhN0q7Ts5UEoT4SONkKUCiB5GwRO08pkVuk+VwiAP3bS9HCU4C9lYAKBXA1XY1dp1QoLn+Qe9twLkatxNlDmnpn7xKAro9Oh7tvHfXu6nEc3fI58rPiYWntgja9JyP0lnp36fjfuHxmI/IzrgAAnJqFotPgN+Di1aY6zV+f6693oV2fQPfHjKveUf31bi1BWIAAc1MgNRfYdkKF7MK61wn2EtC3rQT2VkB+CbA3Wo24FN123jFQQHioBNYWQFYBsPOUCknZ2s8/elL/4X7XaRWiYg1/htOrlQRh/tpy2X5SheyiutcJ9hTQp422XPadq1kuHQIEhIdoyiW7ENhxWoXkbN3t3M13N3ME+raVoJmjALUayMgH/jqgglKlTRPgIaBXSwlc7ACFCkjKEvHPYXX9C6UBhPkL6BIkwOrG/u+OViMlp/b0Xs5A/7YSONsCxeXAsTgRZxK0ZepkozlGuNkDdnIBu8+oceKKbpm39xcQ5i/AVq55n1MIHL6oxtWMB7GHD1a/9lJ0CpLCwlRzLNwUpUTWHY6xLX0kGBAmhYONgLwiEbtOq3DxurYedA6WoEuwFHZWmqe0ZBWI2BetwuUUbZpQHwk6B0nh4SRAbi7g+/VVSM8zfDt90Bx6dITfW5NhG9YK5h4uODn6ZWRu3GPobDWq80dW4vS+xSgryoaDWwB6Pj4DHn61n5+kxh/H4Y3zkZcRD7mNC8L6TUGrbtrzk9jj67Bn1Ywa6734+VnITMwAACd3L8LV87uQn3UVMhNzuDVvj26PvgV7F7+G30EDGN7TAr3amcPSXEBimhIrd5QiLUdVa3oPJylG9LKAj5sMTnZSrNpVij0nKnTSBHrJMLirJo2dtQQ/rClC9GXFg94Vojtqsp07165dQ/fu3WFnZ4cvvvgCbdq0gUKhwI4dO/DKK6/g0qVLNdZRKBQwMTG57++WSqVwc3O77+1Q3a6e24pjW+cjfPiHcPUJw6UTq7Fz+QsY9domWNl51EhfnJeCXctfRItOY9B77BfIvH4aUZs+gbncAc1bDQIAKBUVsLb3gm+rwTi2Zb7e7x3+8j8Q1dof9fzMK9ixdDJ8Ww15MDv6gPRoJUF4qAT/HtF0vPRuI8EzA2X49l8FqpT61/FyFjC2twx7z6gQm6RGiLcE43rLsHibEik5mhPH5m4SHLukRmquCIkADGgvxTMDTfDdBgUUSsBEBng4Cth/Vo2MfDUsTAU80lmKiH4yLNpSyxc3IQnntiJqy3x0f+xGvTu2GtuXvYCxb+ivd0V5Kdi+7EUEdxqDPuM19e7Ihk9gIXeA7416l3b1BALaDIXr8PaQysxw9uBibFsyBWNe3wS5rSsA4PFX/oEo6ta7rYsnw7e1cdU7qr9uoQK6hgjYEKVGbpGInq0keKqfFD9sUtXaZj2dgDE9JNh3To1LySKCvQSM6SnBsp0qpOZq0oT6CBjcQYKtJ9RIzhYRFihBRF8pftysQlGZJs1Xa3W/IMBDwIiuEsQmG/6CsVuIgK7BAjYeVSO3WETPlhI82VeKH7fUXi7NHIHR3SXYf15bLqO7S7BstwppN8vFW8DgMAm2nlQjJUdEWIAEEb2l+Gmrtlzu5rubOQIRfaQ4clGNHSfVUKkBV3tB565fsKeARztLsPecGtcyRQgAXOwM+4jVEC8BA9oJ2HFaREqOiPb+Asb3lODXHerq/b+VrRwY11OCs1dFbDomwtNJwOAwAWUVIuJSNWlMpEBBiYhLycCAdvq/t7hMxP5zIvJLNO9bNRcwprsES3apkXOHDrumpGdrKbq3lGLtISVyCkX0bSfFs0NM8M2aqjqPseP7yrD7RodOqI8EE/rK8MsWBVKyNRWmqBTYcVKF3CLN+7BACZ7sL8MPGxTVHUemMuB6lhoXrokY2eP+z2uNhVRuiaJzcUhZvg4d/vne0NlpdFfObMWh9fPQe/RHcPcNQ0zkamz65XlEvLcZ1vZ6zk9yU7DptxfQsstYDHxyAdITT+PA2jkwl9sjoO3g6nSm5lZ48v1tOuve7NgBgLSEE2jdPQIu3q0hqlSI2vYNNi6agoh3N8PEzPLB7XAjGNLVHAM7m2Pp5lJk5qkwrLsF3njCBh8sykdllf51TE2AnAI1Tl0qw7gBcr1pzEwEpGQpceRcJV4ebf0A9+C/Qc0ZlRtMkx2W9fLLL0MQBBw/fhxjxoxBixYt0LJlS7z55ps4evQoAM1Qqp9//hmPPfYY5HI5Pv30UwDApk2b0KFDB5ibm8PPzw8ff/wxlErtkfjKlSvo1asXzM3NERoail27dul8963Dsq5du4a+ffsCAOzt7SEIAiZNmnTH/K9ZswatW7eGhYUFHB0dMWDAAJSWlmL27NlYvnw5NmzYAEEQIAgC9u/fDwB477330KJFC1haWsLPzw8ffvghFApNL/CyZcvw8ccf4+zZs9XrLVu2TO8QsoKCAp3t5ufn48knn4SzszMsLCwQGBiIpUuX1ue/pUFdOLIcLTqMQlCnsbBz8UfXYTMgt3XDpWOr9Ka/dHwV5Hbu6DpsBuxc/BHUaSwCw0bh/OEl1WmcPVuj8yPvwK/NMEhlpnq3YyF3gKW1c/UrOW4/rB284ebb6YHs54MSHiLFwfMqxCaJyCoQse6wCiYyoI1f7c26a4gEV9NEHLqgOck+dEGNq+kiuoZq1/ljtxLRCWpkF4jIzBfx7xEl7KwEeDhqLlQqFcDyXUrEXFcjt0gTMbTlmArNnCTVd2qbsvOHliOo4ygEdxoLexd/hA+fAStbN1w8qr/exR5bBSs7d4QPnwF7F38EdxqLFh1G4dxBbb3rN2EBQsMj4OgRAjsXP/QcNQeiqEZqQlR1Ggsr3XqXFLsfNg7ecDeyekf11yVYgkMXNJ0R2YXAhig1TGSai9+61rmaIeJIjIjcIuBIjIjEDBFdgrVtNjxYgjMJmgiLnCJg5yk1CsuAji20aUordF9BngKuZYooKHmgu3xXOgdJcDhGjUspN8rl6I1y8amjXIJulMtFEbnFwJGLIhIzRXQJ0u5z1yAJzlwVEX31Rrmc1nRqdAzUprmb7x4UJsWJyyIiY0VkFwF5JUBssgjVjUALQQAGd5Bgd7Qap+NF5BUDucUweMdZ5xYCziaKOJuoKaPd0SKKyjWRNfq09xdQVKZJl1sMzbrXdMs0PR/Yd05EbLIIZS1BSfHpQEKGppzySoCDF0RUKVF9DDEW3VtKsf+sppMmq0DEmoNKmEiBtv61H2O7tZQiIU3EwXMq5BRq/k1IE9GtpbQ6zaVkNS6naDp4c4tE7Dql6Uj0ctaWT3SCGvuiVYhPM2zkV2PL3nEQl2ctRMb6XXdO/BCKPrAMoV1Go2XXsXBw9UfPkTNgZeeG80f+0pv+QuQqWNu5o+fIGXBw9UfLrmMR0nkUzuxfcltKAXIbZ53XrUa88BtCOo+Co1sgnJoFY8CEeSjOT0NWSswD2tPG07+zBbYeKceZuCqkZauwdFMJTE2ALi3Nal3nWroKa/aW4cTFKiiV+n/HL1xVYP0BzXaJmpImGbmTl5eH7du3Y+7cuZDLa14t3jo0atasWZg3bx6++eYbSKVS7NixA0899RS+/fZb9OzZEwkJCXj++eer06rVaowaNQpOTk44evQoioqK8Prrr9eaFy8vL6xduxajR49GXFwcbGxsYGFhUWf+09PT8cQTT+CLL77AyJEjUVxcjEOHDkEURbz99tuIjY1FUVFRdQeLg4MDAMDa2hrLli2Dh4cHzp8/j6lTp8La2hrvvvsuxo8fjwsXLmD79u3YvXs3AMDW1haZmZl3LM8PP/wQFy9exLZt2+Dk5IT4+HiUlxt2/IxKWYXctBi06TVFZ3mzgO7ISjqjd52s5Gg0C+iumz6wOy6fWgu1SgGJ9N7vbqmUVUiI3oRW3SdBEIznxNPeCrC2FBCfpj3oqNTAtQwRXs4CTl7Wv56XswRRsbqhqPFpaoSHSAHoD1E1v9FHVl5Ze37MTQG1KKKiiR/jVMoq5KTFoG2f2+pdYHdk1lbvkqLRLFC33nm26I64k7XXO6WiAmqVEmYWtrXm40r0JrTuYVz1jurPzgqwthBwNV23zV7P1LTZ0/H6TyA9nQQcvaR7gZeQru3ckUgAdwfgcIzu+lfTRXg56a9bcnMgsJkmgsjQ7OQ3yiXjtnLJEuHpLOB0Qu3lcixON/9X00V0DtItlyO3DTlLyNBEpNztd1uaab7rwjU1Jg2Qwt4ayC0C9p1VIfnG8CZ3e8DGUhPJM3WIFHJzIDNfxO4z6jsOLXtQJBLAzR6IuqS7/4kZIjwdBQA1y7WZo4DEjJrp2/oKkAj1e1StIGiimkxkQGqu8dyZtbe+cYxN1dYxzTFWDW8XCU7E6W873i4SHInRPZZeSVXrdO7cShCAVs0lMJUBSdnGUz7U8FTKKmSlxCCs31Sd5V5B3ZFxTf/5Scb1aHgF6Z6feAf1QOyxtVCpFJDeOD9RVJVh+Sf9oFar4NQsGF2HvAZnz9Ba81JZXgwAMLfUfw5jLJzsJLCzkiAmUTtcSqkCLicp4d9MhoNn6jixJTJSTbJzJz4+HqIoIjg4+I5pIyIi8Nxzz1W/nzhxIt5//30884xmnhU/Pz988sknePfddzFr1izs3r0bsbGxuHbtGjw9PQEAn332GR555BG925dKpdWdLy4uLnc15056ejqUSiVGjRoFHx8fAEDr1q2rP7ewsEBlZWWNoV8ffPBB9d/NmzfHW2+9hdWrV+Pdd9+FhYUFrKysIJPJ7nnIWFJSEtq3b4+OHTtWb9vQKssKIKpVsLBy0lluYeWIshL9EwKUF+fAItDxtvROENVKVJTmw9LG5Z7zcT12D6oqihEYNvKe1zUkKwvNxUlpue7JYGmFCDt57Z0FVhZAyW39eiXlmuW1GdJJhuuZ6lrnGZBJgIFhUpy/qkZlEx9uXHGj3lnqqXflxfrrXVlxDjytdOud5R3q3YntX0Fu44pmAd30bvPaRU29a9HBuOod1Z+VuebfEt1h+yip0HQy1LVe6W3rlFZot2dpBkgkAkorav4WyC30/xa09RNQpYDOXFuGcvO35/ZyKa1AnZGA91MuVubCXX+3vZXm316tJdh9Ro3MAhGtm2uG0y3aqkJeCWB/Y+6UXq0l2HVajYJSEeHBEjw9QIofNqsM0ultaXpz/3WXl1ZqOvf0sTIHrt52rVNaAUglAizMapZ3XZxtgaf7SSCTAlVKYN0RdfW8bcbA+kbbKbntGKtpr3c6xt62TrkI69uOsa72Al541ERTPgpgxR7lHefLo4dbeWm+5vzE+rbzDWtHlNVyflJanA1v6x410qtvnJ/IbVxg7+KHARPmwdG9BaoqS3D24O9Y+10EJry9HnbOzWtsUxRFHN44H+6+HeDo3qLB9s8QbOWazv6iUt3O2KJSNRxtm+zglf+kh3VyY0Nokp074o3/4bu5o32zw+KmU6dO4cSJE5g7d271MpVKhYqKCpSVlSE2Nhbe3t7VHTsAEB4e3kA512jbti369++P1q1bY/DgwRg0aBDGjBkDe3v7Otdbs2YNFi5ciPj4eJSUlECpVMLGxua+8/PSSy9h9OjROH36NAYNGoTHH38c3brpv+isrKxEZaXu2Z1SYaIzNrch3f5fLIoiBNTx/16jToi1LL87V06uhWdgz3p1DDWmNr4SDA/X3vlbsUczzFDfb+G9/j7qv4erMayLFK72AhZv099rIxGAsb1lEARg87HaJ6dr+sQ669Dtv0U3f6P0rXP2wG9IOLsVw6Yur7XdxJ1cC68WPSFv4vWO6q9Vc80cLDf9tf9G+7itsQnCndtsvU96almvnZ8E569phxU1plY+AoZ1uqVcDugvl1oW3dPnQM2y0/t7V8d332zip+M1w5sAICNfDV83Kdr5S7D3rLo6zc3hXQCw8Zgarz0mRahX7dFHhnDHI2UDZTW3GFiySw0zE80QwEc7S/Dn/qbbwdPWT4LHumtPiX/fVfudinodY29bKadQxPfrq2BhKqBlcwnG9JTh120KdvCQnhNjPct0V6hzuVvzdnBr3q56qXvzMKz+ehTOHfoTvUZ9UGOtg+s+QW5aHEb/b+U9Zbsp6NLSFE89YlX9/ru/b/zg6PuNZ1Ojevrxxx+xYMECpKeno2XLlli4cCF69uxZa/oDBw7gzTffRExMDDw8PPDuu+/ixRdffGD5a5KdO4GBgRAEAbGxsXj88cfrTHv7sC21Wo2PP/4Yo0aNqpHW3Nxce1F2i4YeFiGVSrFr1y5ERkZi586d+O677zBz5kwcO3YMvr6+etc5evQoJkyYgI8//hiDBw+Gra0tVq1aha+++qrO75JINCfJt+7XzXl6bnrkkUdw/fp1bNmyBbt370b//v3xyiuv4Msvv6yxvXnz5uHjjz/WWdZ/7EcYOG7WXe373TKztIMgkda4G1FRmgeL26IkbrKwdqoRXVFekgtBIoO5pd0956EkPxVpCVHoF/HtPa/b2C4lq5GSo70Sk0pv3nkWdO4Sys2FGncNb6UvSkduAZTqGaU3tLMUwV4SLN6u0Dv5pkQAxvWRwd4KWLpT2eSjdgDA/Ga9K7m9HtVe7yytnWrU0/JS/fXu3MEliN7/C4ZOXgJH9yC92yvOT0VafBQGPNX06x3V3+UUEYtueRqH7EbfrJWFbqSI/A4RESUVetqsuXYbZZWaiQjl5rrdFnLzmlEbAODtDDjZClh72DCdsZdTRaTm3lIuN/p5apSLnsicW5XcEqVz6zq3l4smylFbLpa3lMvNKMa6vvtmmpwi3d/VnEIRNjfmGb35lMCcQt3hXQUlgK28ru7zB6es6ma90F1uWUd9K6moGdUjNwdUarHOYbn6qNWonlA5I1+Eu4OAToECtp9qmldUsUlqJGdrQ6xktxxji285plqZ14zMuZXmGKt7Tim3EGpEh6nUmqdVApr20MxZgm6hUmyIbPoPJaAHw0Jurzk/KdI93ygryYVlLecncmvnGucnZcW5kEhkMJfb6V1HkEjg4tUaBTnXa3x2YN0nSIzZi1Gv/AkrO+N7sEz0lSpcTSuofm9yox3bWElQWKo97tjIJTWieciwjKWzbfXq1Xj99dfx448/onv37li0aBEeeeQRXLx4Ed7e3jXSJyYmYujQoZg6dSr+/PNPHDlyBC+//DKcnZ0xevToB5LHJhmT5uDggMGDB+OHH35AaWlpjc8LCgpqXTcsLAxxcXEICAio8ZJIJAgNDUVSUhLS0rSPJI6Kiqp1ewBgaqqZdESluvuTYUEQ0L17d3z88cc4c+YMTE1N8e+//1Zv7/ZtHTlyBD4+Ppg5cyY6duyIwMBAXL+u+8Orbz1nZ82kaOnp6dXL9D0q3dnZGZMmTcKff/6JhQsX4pdfftGb7+nTp6OwsFDn1Xfk+3e933dLKjOFo0dLpMVH6ixPi4+Ei3d7veu4eLXTk/4InJq1rNd8O5dP/wtzuQO8gnrf87qNrUqpORG8+couEFFcJiLAXXsSKZUAzd0EJNcxbj85Ww1/d91mH+AuQVK27kFuWBcpQn0kWLpDoXfC1ZsdO47WwLKdyns+8TcUqcwUTh4tkXpFtx6lxkfCtbZ6590OqbfVu9QrR+B8W707e3AxTu/9CUOe/QXOnq1qzcPlU//C3MoB3kZQ76j+qpSai9ubr+xCoLhchN8tbVYiAXxc626zKTki/Nx0Lxb93YXqJ++o1UB6HnS2C2jeJ+fU3G47fwnSckVkFtzHzt2HGuVSpCkXX7fbysVFu4/6pOTorgMAfm5C9VP/qsuljjQFpXf+7oJSoKhMhKO17nYcbQQU3jg9Sc8ToVSJcLS5ZTsCYGsFFJQa5oz15uPafV118+3rKiCllrlvUnNFvekz8uo3386tBGiOUU3V7cfYrJvH2GbaTGuOsRIkZdV+UZiUpUaAh24ZBjarex1AUz4y/dPy0H+EVGYKF8+WSL6se76RfDkSbs31n5+4+bTTk/4InL1aVs+3cztRFJGTFqszqbIoijiwdg6untuFx19aBhtHT73rNnWVVUB2vrr6lZajQkGJGqG+2rKQSoAW3jIkpLIjle7d119/jcmTJ2PKlCkICQnBwoUL4eXlhZ9++klv+p9//hne3t5YuHAhQkJCMGXKFDz33HN6AywaSpM91P74449QqVTo3Lkz1q5diytXriA2NhbffvttncOoPvroI/z++++YPXs2YmJiEBsbi9WrV1fPZzNgwAAEBQXh6aefxtmzZ3Ho0CHMnDmzzrz4+PhAEARs3rwZ2dnZKCmp+/Eix44dw2effYaTJ08iKSkJ69atQ3Z2NkJCQgBo5rw5d+4c4uLikJOTA4VCgYCAACQlJWHVqlVISEjAt99+W90ZdFPz5s2RmJiI6Oho5OTkoLKyEhYWFujatSvmz5+Pixcv4uDBgzpz99wskw0bNiA+Ph4xMTHYvHlzdV5uZ2ZmBhsbG53XgxqS1ar7M7h8ai0un1yLgqwEHNsyDyWF6QjuPB4AcHLH1zjwz3vV6YM7T0BJQRqObZ2PgqwEXD65FpdPrUPrHto5lzQTNcciNy0WKpUCpUVZyE2LRVGubkeZqFbjyul1CAh7HBJpkwxgu6OoWBV6tpEixFuAi52Akd2lUCiBc1e1J5GjekgxIEx7xng0Vg1/DwE9WkngZKN5nLqfh4CjF7XrPNpFijZ+Eqw5qESVQnOn0spce+IpEYDxfWRo5ihgzSEVJII2TVM+eb+pdc9nEHdyLeJOrkV+VgKiNs9DSUE6Qrpo6t3x7V9j39/aehfSZQJK8tMQtXk+8rMSbqy7Dm16aevd2QO/4eTO/0PvMXNhbd8MZcXZKCvOhqJSt3NaVKtx+dQ6tDDiekf1d+ySGj1aShDkKcDZFngsXAKFErhwTXvl/Fi4BP3aSXTW8XcX0C1UgKON5nHqvm4Cjt0yyXLUJTXC/AW08xPgZAMMCpPA1hI4dUX3gtJUpnls+pmEpnXH8nicGj1CbymXLjfK5fot5dJVgn5tteVy/LIa/m4CuoUIcLTWPNLc1013kuWjcWq09xPQ9ka5DGxfs1zu5rujLqnRqYWAEC8B9lZAn9YSOFoD0Td+a6uUwKl4Eb1bS+DnpsnP0BtDzww5r9Hxy5rJkNv4avLUv50AG0vgzI1hYr1bC3i0s7Yj4kyCCBs50L+tJn0bXwFtfXXLVCIBXOw0L6lEE/XkYqedm+jmdj2dAFtLzdw7vVoJ8HYGYprAHE/34kiMCr3baG50uNgJGN1TBoUKOHtL+xnTS4ZBHbTH2KiLKgQ0k6BnaymcbAX0bC2Fv4eAyFsmWR7YQQofVwF2Vpq5dwZ2kMLXTUB0gjaNhSng7iDAxU5Tj5xsBbg7CHXOj/cwkMotYdM2GDZtNXNuWvp6wqZtMMy93A2cs8bRrvckXDy2BhePrUVeZgIOrZ+Hkvx0tOo2AQAQufkr7FqpPT9p1W0CivPTcGjDPORlJuDisbW4eGwt2vfRnp8c3/E9rl86hMLcZGSnxmLv6pnISb2EVuETqtMcWDsHcac2YdBTX8LETI7SomyUFmVDWXUPE201UXuOl2NoNwu0b2EKD2cpnh1uhSoFcCxGe1fyueFWGNlH+8h3qQTwcpHCy0UKmVSAvbUEXi5SONtrj0FmJto0AOBkq/nbwcYIToSbILUoGuRVWVmJoqIindft05PcVFVVhVOnTmHQoEE6ywcNGoTIyEi960RFRdVIP3jwYJw8ebLGSJuG0mSvLnx9fXH69GnMnTsXb731FtLT0+Hs7IwOHTrU2jsGaAps8+bNmDNnDr744guYmJggODgYU6Zono4jkUjw77//YvLkyejcuTOaN2+Ob7/9FkOGDKl1m82aNcPHH3+M999/H88++yyefvppLFu2rNb0NjY2OHjwIBYuXIiioiL4+Pjgq6++qp60eerUqdi/fz86duyIkpIS7Nu3D4899hjeeOMNvPrqq6isrMSwYcPw4YcfYvbs2dXbHT16NNatW4e+ffuioKAAS5cuxaRJk7BkyRI899xz6NixI4KCgvDFF1/oVCRTU1NMnz4d165dg4WFBXr27IlVq/Q/9rkx+bUZisqyAkTv+xFlxdmwdw3EoKd/hpV9MwBAWXE2Sgu1EUnWDp4Y+MzPOL5lPmKProSljQu6DpuB5q20+1pWnI0NP2iH5F04vAQXDi+Bm28nDJ3ye/XytIQolBako0WHmsP3jMXhC2qYSAU82kUGczMgNVvE77uUqLrlZoStXNAZspecLeKfg0r0by9Dv3ZS5BcDfx9QVt/JBoDOwZoD1XNDdO/6rDuseUS6jSUQ4q05eL0yQjfNku0KXMts2ifw/m2GorK0AKf3aOqdg2sghkz6Gda31rsCbb2zcfDEkEk/I2rLfFy8Ue/Ch8+A7y317uLRv6BWKbB7xWs63xXW/xV0GPBq9fvU+CiUGHm9o/qLvCjCRCpiaGcJLEyB1Bzgz72qOttsSg6w9rAafdtK0LeN5tHSaw+rkZqrXefidRGWpmr0ai2BlQWQVQCs3K+qjiy5qVVzzYxmt3YmNQWRsSJkUhGPdLxRLrnAiv265aJ5GpVuuayLVKNPGwn6tNZEAa07okbareWSJMLCVI1eLTXlkl2omeOnsOzevvt4nAiZRI2B7SWwMAMy84EV+1TVw44AYPcZNdRqCR4Ll8BEqomC+XOPChUGHK4amyzCwhToHirAylxAdiHw9yF19TBbK3NNud4cNlZYqvl8QDsJwgIElJQDu6JFxKVqt2ltDkwepO3M6BosoGuw5gljK/drOj3kZsDwLhJYmQOVCiCrEFh9SI1rd364Z5Ny6LwKJjJgRLgM5qZASraIpdsVetqr9n1SlojV+5UYGKa5sZJXLGLVPqVOFJqVhYCxvUxgbQlUVGmGrS3bqUDCLU+/DPaWYEwv7fF1Ql/N33vOKLH3jDHPb1c32w6tEL7nj+r3oV/OAAAk/74O5yZPN1S2Gk1g+6GoKCvAiZ0/oLQoG47ugXh06iLYOGjPT4rztSMPbBw9MXzKIhzeMB/nD6+E3NYFvUbOREDbwdVpKsuLsf+fWSgtyoaZhTWcmoVg5Kt/wNWnTXWaC5GaR63/++PTOvnpP+EzhHQ27vOV7UcrYGIiIGKIHHJzAVfTlPhmVREqb5no3sFGonN8sbOW4KMpdtXvB3e1wOCuFoi7rsCXKzTz+Pi4y/DOU9qniY0fqJkmJPJcBZZurjnqhJomfdORzJo1S+f6+6acnByoVCq4urrqLHd1dUVGRobe7WdkZOhNr1QqkZOTA3f3hu+4FkR9k9AQ3eLzNU3rLq8xKS1l2Gd92Vg32b7nJu/tUbxzVF9zVrDN1pfAaldvN+d4oXtXUsI2W1/dXmhz50Sk19X1lwydBaN07myeobNgtH6doX/upYfBJ38Z5nf83VGqGpE6ZmZmMDOrOWolLS0NzZo1Q2RkpM4oorlz5+KPP/7ApUs1fxNatGiBZ599FtOnazunjxw5gh49eiA9Pf2en4B9N3j1RERERERERESNTjRQHEFtHTn6ODk5QSqV1ojSycrKqhGdc5Obm5ve9DKZDI6OD6azjvfZ6iEpKQlWVla1vpKSkgydRSIiIiIiIiK6T6ampujQoQN27dqls3zXrl3o1q2b3nXCw8NrpN+5cyc6duwIE5N7fxjQ3WDkTj14eHjofSLVrZ8TERERERERUe2MZZaYN998ExMnTkTHjh0RHh6OX375BUlJSXjxxRcBaJ46nZqait9/18zz+uKLL+L777/Hm2++ialTpyIqKgqLFy/GX3/99cDyyM6depDJZAgICDB0NoiIiIiIiIjoARs/fjxyc3MxZ84cpKeno1WrVti6dSt8fHwAAOnp6TojeHx9fbF161a88cYb+OGHH+Dh4YFvv/0Wo0ePfmB5ZOcOEREREREREVEdXn75Zbz88st6P9P3NO3evXvj9OnTDzhXWuzcISIiIiIiIqJGp+aDmRsMJ1QmIiIiIiIiIjJijNwhIiIiIiIiokZnLBMqGwNG7hARERERERERGTF27hARERERERERGTEOyyIiIiIiIiKiRqfmqKwGw8gdIiIiIiIiIiIjxsgdIiIiIiIiImp0IkN3Ggwjd4iIiIiIiIiIjBgjd4iIiIiIiIio0fFJ6A2HkTtEREREREREREaMnTtEREREREREREaMw7KIiIiIiIiIqNGpOaFyg2HkDhERERERERGREWPkDhERERERERE1OpEzKjcYRu4QERERERERERkxdu4QERERERERERkxDssiIiIiIiIiokYnqg2dg4cHO3fojopLlIbOgtE68O8xQ2fBaAV1DjF0FoyYk6EzYLTOn0w2dBaMVpeePobOgtE6dTTF0FkwWsmxiYbOgtFyW3/J0FkwWn6PBxs6C0Yp+btoQ2eB6KHGzh0iIiIiIiIianRqTqjcYDjnDhERERERERGREWPkDhERERERERE1Oj4KveEwcoeIiIiIiIiIyIixc4eIiIiIiIiIyIhxWBYRERERERERNTq1msOyGgojd4iIiIiIiIiIjBgjd4iIiIiIiIio0XE+5YbDyB0iIiIiIiIiIiPGzh0iIiIiIiIiIiPGYVlERERERERE1OhETqjcYBi5Q0RERERERERkxBi5Q0RERERERESNTs0ZlRsMI3eIiIiIiIiIiIwYI3eIiIiIiIiIqNFxzp2Gw8gdIiIiIiIiIiIjxs4dIiIiIiIiIiIjxmFZRERERERERNToOCyr4TByh4iIiIiIiIjIiDFyh4iIiIiIiIgaHQN3Gg4jd4iIiIiIiIiIjBgjd5q4yMhI9OzZEwMHDsT27dsNnZ0Hol87KTq2kMDCFEjJEbHpqApZBXV34Yb6CBjQXgYHayCvGNh1WonYJO06vVpLEOojgbOtAIUSSMoWsfOkEjlF2m18OslU77a3n1DicIy6QfatsT07wRsjBrvBWi7DxcvF+HpRAq4ll9WafvhANwzu6wI/H0sAQFxCCX754xpir5RUp5FKgGef8MHA3i5wtDNBbn4Vtu3NwvK/kyA+RD3tI3paold7M1iaS5CYpsSK7SVIy1HVmt7DSYrHelvCx00GJzspVu0swe4TFTppHulmgbAgU7g7SlGlBBJSFFiztwyZebVvl/5bxg62w4Bwa1hZSHAlqRK/rc1FSoai1vSebiYYP8Qefl6mcHEwwdJ/c7H1YJFOGokEGDfYHj07yGFnLUV+sQr7j5dg7a4Co2yzF6NW4uyhJSgvzoa9SwC6Pjod7r4da02ffvU4jm75HPlZ8bC0dkGb3pMR2mVC9eeXjv+Ny2c2Ij/jCgDAqVkoOg1+Ay5ebarTVFWW4tTO/8O1i7tRXpIHR48QdHt0Bpy9Wj+4HW1Eowfaon8XOeQWEsQnVWHp+nykZNZR71xNMGaQLfyamcLZQYbfN+Zj2+HiGunsbaSIGGqHtkHmMDURkJ6jxC//5CIxtfZtG5vnnvDBiMHusLa6cZz9+QoSk+o4zg5yw5B+btrjbHwJFv2eiNgr2vKzsJBi6pPN0SvcCfa2Jrh8tQT/92sCLl2pWcbG4PyRlTi9bzHKirLh4BaAno/PgIdf7W02Nf44Dm+cj7yMeMhtXBDWbwpaddO22djj67Bn1Ywa6734+VnITMwAACd3L8LV87uQn3UVMhNzuDVvj26PvgV7F7+G38EmyKFHR/i9NRm2Ya1g7uGCk6NfRubGPYbOVqMb2FGGLiEyWJgBSVlqrD+kQGZ+3Qe+Vr4SDO5kAkdbAbmFIrYfVyDmmvY6YGBHGQZ2NNFZp7hMxCe/V+ikaesvhZ2VAKUaSM1WY/txBZKzjPCgS0aNnTtN3JIlS/C///0Pv/32G5KSkuDt7W3oLDWonq0k6BYqwbrDmo6XPm0lmDRIhoXrFKhS6l/Hy1nA+N4y7DmjwsUkNUK9JZjQR4ZftyqRkqP5EW3uJsGxS2qk5oiQCMCAMCkmDTLB/61XQHFju/NXV+lst0UzCR7vLkXMdePs2IkY5YnxjzXDZ/93Gclp5XhmnDe+mdMKES+fQnm5/s6Edq1tsftQNi78WoSqKjUiRnniq9mt8fT/TiEnT1M+EaO98NgQd3y2MA6JyWUIDrDG9GmBKClVYs3mtMbcxQdmSLgFBnYxx9JNJcjIU+HR7pZ4M8IGM38uQGWV/gOzqYmA7HwVTsZWYvxAK71pgrxNsO9UBa6lKSGRACP7yPFmhA0+XJSPqofnWofq6bF+tni0jy1+WJmN9GwFRg+0w4cvuuG1eSmoqNRf78xMBGTlKhB1thSTHnfQm+bxfrYY2M0aP/yVjeR0Bfy9TfHyBGeUVahrdAQ1dQnntiJqy3x0f+xDuPqE4dKx1di+7AWMfWMTrOw8aqQvykvB9mUvIrjTGPQZ/wUyr5/GkQ2fwELuAN9WgwAAaVdPIKDNULgObw+pzAxnDy7GtiVTMOb1TZDbugIADq39AHmZV9Bn3OewtHZBfPQmbFn8HMa+sbk6jbEa3scaQ3ta4+e/c5GercTI/jaYMdUZby5Ir7XemZoIyMpT4ti5Mkwcbq83jdxCwMcvuyImoQKfL8lGYYkKro4ylJY/PBc3T472wvjHPTF3YRySU8vwzHgffDOnDZ546UStx9n2re2w+2AWzscWokqhxpOjvPD1nDaY+MqJ6uPs+/9rAT8fOT75+hJy8ioxuI8rFn7SBk+9rE1jLK6c2YpD6+eh9+iP4O4bhpjI1dj0y/OIeG8zrO31tNncFGz67QW07DIWA59cgPTE0ziwdg7M5fYIaDu4Op2puRWefH+bzro3O3YAIC3hBFp3j4CLd2uIKhWitn2DjYumIOLdzTAxs3xwO9xESOWWKDoXh5Tl69Dhn+8NnR2D6NNOhp5tZPh7XxWyC0T07yDD1EfNsGBVBSprOefydpXgyYGm2HlCiQuJKrTyleKpgab4cUOlTsdMRp4av2yqrH5/+42S7AIR6w8rkFckwkQG9Gwjw5RhZvjirwqU6t73Iz04oXLD4bCsJqy0tBR///03XnrpJTz66KNYtmyZzucbN25EYGAgLCws0LdvXyxfvhyCIKCgoKA6TWRkJHr16gULCwt4eXlh2rRpKC0tbdwdqUO3UCkOnFPhYpKIrAIRaw+pYCID2vrVXjW7hUqQkCbi4Hk1cgqBg+fVSEgX0S1Uu87vu5Q4E69GVoGIjHwR6w4rYWcloJmjUJ2mpFz3FewtQWK6iPwSfd/a9I0b3gy//5OMg0dzkZhUhrkL42BmKsXAXs61rvPJ13FYvy0d8YmlSEotxxc/XIFEAnRoa1edplWQNQ4fy0XUqXxkZFVif2QOjp8pQHCA/g4NYzSgswW2HCnH6bgqpGWrsGRTMUxNBHRpaVbrOtfSlViztwwnLlZBqdR/UFq4qgiR5yqRlqNCSpYKSzcXw9FWCh839qsTMKy3DdbtKsDx82VIzlDg+5XZMDMV0COs9raVkFyFPzblI/JMKRS11LsWzc1x8kIZTl8sR3a+EkfPluFsXDn8vWqvz03V+UPLEdRxFII7jYW9iz/Ch8+Ala0bLh5dpTd97LFVsLJzR/jwGbB38Udwp7Fo0WEUzh1cUp2m34QFCA2PgKNHCOxc/NBz1ByIohqpCVEAAKWiAokxu9Dlkbfh7tsJtk4+6DDgVVg7eOLisb8aZb8fpEd62GD93kKcuFCOlEwFflqdC1MTCbq3k9e6ztWUKqzcUoCos2W1/t4N72OD3EIlFv2Th4TkKuTkqxATX4msvFru1BihsSOa4fe/k3AwKkdznP3mEszMpBjU26XWdeZ8dQn/bk3THGdTyvH595chkQAd22o6yUxNJejdzRk/Lr2KszGFSE2vwJK/riM9swIjh9bsDGnqog8sQ2iX0WjZdSwcXP3Rc+QMWNm54fwR/W3nQuQqWNu5o+fIGXBw9UfLrmMR0nkUzuxfcltKAXIbZ53XrUa88BtCOo+Co1sgnJoFY8CEeSjOT0NWSswD2tOmJXvHQVyetRAZ63cZOisG06O1DHtPK3EhUY3MfBGr9ypgIgPaBUhrXadnaymupKix74wS2QUi9p1RIj5VjZ6tdc/T1Grda4bbO2yi41WIT1Ujr1hEZr6ITZEKWJgJcHfkpTY1Lta4Jmz16tUICgpCUFAQnnrqKSxduhTija7ia9euYcyYMXj88ccRHR2NF154ATNnztRZ//z58xg8eDBGjRqFc+fOYfXq1Th8+DBeffVVQ+xODfZWgLWlgPg07YmiSg1cyxDh7SLUup6XswTxabrRNfGpani71F6dzW+MwCqr1P+53BwI8hRw6opxRu24u5rD0cEUJ87kVy9TKEVExxSiVbDNXW/HzEwKmVRAcbH2ZPxcbBE6tLGDl4cFAMC/uRxtQm0QdSq/ts0YFSc7CeysJIi5qr07qlQBcUkKBHg2bCeMpZmmXpdW8A7Ff52Lowz2NjKcjSuvXqZUARfjKxDke3+dMJcSK9CqhTncnTX118fDFMF+5jh9sfahI02RSlmFnLQYNAvsrrO8WWB3ZCad0btOVlJ0jfSeLbojOzUGapX+W7dKRQXUKiXMLGwBAGq1CqJaBalM9/9BJjND5rXT9d2dJsHFQQp7GynOX9ZemShVQOzVCrTw0T9U+W51CLXE1ZQqvPaUE37+qBnmveaGfp1r7zAyNh6u5nByMMPx24+zFwrqdZwtKtHUR6lUgEwqoKpK9/yjskqNNqG2DZP5RqJSViErJQZeLXTboFdQd2Rc099mM65HwytIN713UA9kJ8dAdUubVVSVYfkn/bD0497Y9NsLyE65WGdeKss1Q9rMLY2rDKl+HKwF2MgFXE7WRtCp1MDVNDV83Gq/PvB2leBKim7U3eUUVY11nGwFfDDRHO9HmCFigAkcrGu/TpFKgC6hMpRXikjLNc7risYmiqJBXg8j3j5uwhYvXoynnnoKADBkyBCUlJRgz549GDBgAH7++WcEBQVhwYIFAICgoCBcuHABc+fOrV5/wYIFiIiIwOuvvw4ACAwMxLfffovevXvjp59+grm5eaPv062sLDQ/jCW3hWyXlIuws6r9R9PKQtNrrruOZnltHukkw7VMda1z+bQPkKBSAVxMMs4fYUd7zVjgvELdi5f8giq4udz9//OLTzdHdl4VTp7VnryuWJsCK0sZ/vyhA9RqERKJgF//vIY9h7IbJvMGZivXHMCLSnXrRlGpCEebhu3/HjdAjstJCqRlc86d/zo7a82dxMJi3bpQWKKCk/39HZrX7ymEpbkEC9/3hFoEJALw19Z8HDnTdKI270ZFWQFEtQqWVk46yy2sHFFenKN3nbLiHHhaOeoss7RygqhWoqI0H5Y2NSMsTmz/CnIbVzQL6AYAMDWTw8W7Hc7s/Ql2Lv6wsHJEwtktyEo5B1tHnwbaO8OwvVnvSnSPdYUlajjZ1X53+264OMgwoKs1th4qwoa9hfD3MsMzj9lDoQQOnTauuqePg72m8yuvQHeYVH5BFVzv4Tj70jO+yM6twslozXG2vFyF87GFmDTBB9dSypBfUIUBvVwQ2sIaKWnld9ha01Jemq9ps9a3tUFrR5TV0mZLi7Phbd2jRnr1jTYrt3GBvYsfBkyYB0f3FqiqLMHZg79j7XcRmPD2etg5N6+xTVEUcXjjfLj7doCje4sG2z9quqwt67imqKMjxtpSQPFt9z2Ky7TbA4CkTDVW7a1CTqEIKwsB/TvI8MpIM3y1ukLnpnGItwQRA01hItNs49fNlSjjkCxqZOzcaaLi4uJw/PhxrFu3DgAgk8kwfvx4LFmyBAMGDEBcXBw6deqks07nzp113p86dQrx8fFYsWJF9TJRFKFWq5GYmIiQkJAa31tZWYnKSt3wFqVC0BnXXF9t/SQYEa49efxjtyY65PbuFkGouexO6lrn0S5SuDkI+HVr7ZOcdAiU4uxVNZRGcs09sLcz3n4psPr9e5/cCDu+rRdaEHDXPdMRIz0xoKczps08hyqFdp3+PZ0xsI8L5nwdh8SkUgT6WuF/k/2Qk1eF7fuy7n9nGlmXlmaYOFQ77OXb1YU3/rqt7Br4eyMGy+HpIsPnvxfeOTE9dHqEyfHCOG0nxbxfMwHo+92qxw/gbbq1l6NnByv835/ZSMmoQvNmZpj0uAPyi1Q4cMJIx53qEDU/brUQbvus+jdQzzpnD/yGhLNbMWzqcp3jXN9xn+PA2plYOa83BIkUTh6hCGj7KHLS6o4WaGq6t7fElFHauZm+WKrplL/9uNAQv3cSQTN8a/V2zW/ctTQFPF1NMCDcyig7dwb2dsE7r2g7Bt6dc17zx32ctESM8sKAXi7434yzOsfZT76+hOmvBWHD8nAoVSIuJxRj14EstPA30uHPt7c1Uc8y3RXqXO7WvB3cmrerXurePAyrvx6Fc4f+RK9RH9RY6+C6T5CbFofR/1t5T9km49E+UIpRvbSTHC/dqul0rdEUBX0L66Y5d9a+j0u+tTNcxPXMKrwfYY4OQTIcOqeNdI9PU2PhP5WQmwOdQ2R4aqApvltXyTl37oKac+40GHbuNFGLFy+GUqlEs2bNqpeJoggTExPk5+dDFMXaT2BvUKvVeOGFFzBt2rQa269tYuZ58+bh448/1lnW87EP0OvxD+u7K9Vik9RIztb+QMqkmvxbWwg6Pe1yc6HOCRj1RenIzYFSPTe4hnWRIsRbgt+2KVBUy4gEHxcBzrYCVu83nqidw8fzcDFOOzzAxEQTYeJgZ4rcfG0nlp2tKfIK7jxz74THm+GpMV54Y9Z5JFzXLaiXJvlixdrk6kidq9fL4OpshqfGeBll5070lSok/qaNTLpZD23kEhSWaHv3rOUCikobpk48MUiOdi1M8cXvhcgvNp56Rg3nZEwZ4r9MrX4vk2nqnZ21FAVF2npnayVBQcn99TJPHO6A9XsKEHkjUicpXQEnexlG9rc1qs4dc0s7CBIpykp07/iXl+TB4rbonJssrZ1qRAiUl+ZCkMhgbmmns/zcwSWI3v8Lhk5eAkf3IJ3PbBy9Mfz5P6CoKoOiogSWNi7Ys/INWNs3gzE5dbEc8UkZ1e9Nbq13t/wW2VhJakSR3av8YhVSsnSPN6lZCnRuXUdYbRN2+HguLl4+Wf3e9OZx1t4Uufna6B17W5Ma0Tz6PDHSExPHeuP1D88i4ZpuZ1daRgX+N/0szM0kkFvKkJtfhY/fDUF6pnFdFVrI7TVttki3DZaV5MKyljYrt3au0WbLinMhkchgLrfTu44gkcDFqzUKcq7X+OzAuk+QGLMXo175E1Z2bvXbEWryLl5TISnz1msKzb/WFgKKy7TXEFbmAorruKYoLhNhfdt825oRArWvo1AC6XlqONkKNZbnFonILQKSshR49wkzdA6RYd+Zh2feMWr6OOdOE6RUKvH777/jq6++QnR0dPXr7Nmz8PHxwYoVKxAcHIwTJ07orHfy5Emd92FhYYiJiUFAQECNl6mp/rH106dPR2Fhoc6r27B3G2S/qpSax5bffGUViCguE+Hvof1xlEqA5m4Ckup4dGBythr+HrpVN8BDgqQs3YvmR7tI0dJbgiXbFXVOktyhhQSpOWpk3OFRiU1JebkKqRkV1a9ryWXIzatCp3bap5jIZALatbTFhUt1Px3niZHN8Mw4b7z98QXExdcsKHNTCcTb+iPUas1TyIxRZZWIrHx19SstR4WCEjVa+mrbhFSiedJVfMr9H5AjBssRFmyKL/8sRE4hO3b+qyoqRWTkKKtfKRkK5Bcp0SZIe+ErkwKhAeaIS6xlcrC7ZGYq1HiSh1pd84ZAUyeVmcLJoyVSr0TqLE+Nj4Srd3u967h4t0Nq/G3prxyBc7OWkEi1d3nPHlyM03t/wpBnf4GzZ6ta82BiaglLGxdUlhci5coR+IT2v489anwVlSIyc5XVr5RMBfKLVGgdqB1GJJUCIX7muHz9/p7KdPlaJTycde8ZujvLkJNvJCGxtykvVyE1vaL6lZhUhpy8yprH2VZ2d3Gc9cQz433w9uxzeo+zN1VUqpGbXwVruQyd2zvg8LHcBtufxiCVmcLFsyWSL+u2weTLkXBrrr/Nuvm005P+CJy9WkIqNdG7jiiKyEmL1ZlUWRRFHFg7B1fP7cLjLy2DjaPnfe4NNWWVipsdKZpXZr6IolIRgV7aEQJSCeDnIcH1jNrPvZIy1Qj01B2SGugprXMdqQRwsZPodCLVRnZ/o12J7hkjd5qgzZs3Iz8/H5MnT4atre5EcGPGjMHixYuxbt06fP3113jvvfcwefJkREdHVz9N6+YJ/HvvvYeuXbvilVdewdSpUyGXyxEbG4tdu3bhu+++0/vdZmZmMDO7bRJJkwf3GM7Iiyr0biOt7unu3UYChRI4e1X7ozq6hxRFZcCu06ob66gx5REZeraSIDZZjRAvCfw9BPy6VXsCObyrFG38JFixR4lKpTbSp6IKOkOvzEyAVj4SbDtpnCeft/p7UyqeGuOF5PRypKSVY+IYL1RWqbDroHZunJmvt0BObhUW/XENgGYo1uQnfTDnq0vIyKqAg53mRKq8QoXyCs3/QeSJPEwc64XM7AokJpch0M8K4x/zxJbdGTXyYKx2Hy/H0O4WyMxXITNPhWHdLFClEHEsRnuR/dxwKxQUq7FuvyaySSoBPJw1R22ZFLCzlsDLVVrdeQQATw6Ro0tLM3z/TxEqqkTYyDVts7xShII3cv7zthwowqgBtsjIViA9W4FRA+xQWSXi8Gntxd+rEU7IK1Rh5RZNtJlMCni6mt74W4CjrRTNPUxRUaVGRo6mUp2KKcOogXbIKVAiOV0BX09TDO9ji73Hiht/J+9T657PYP/f78PZsxVcvNvh0vG/UVKQjpAu4wEAx7d/jdKiTPQd9zkAIKTLBFyMWomozfMR3HksspKiEXdyHfpN+LJ6m2cP/IaTu75Fvwlfwtq+GcqKNb+RJqaWMDHTTACcfPkwIIqwdfZFUe51HNv2JWydfBHUYWQjl0DD23a4CI/1s0X6jY7Gx/vZoEqhxpFobTTJS+MdkV+oxKobQ6ykUsDTRXN8kMkAe1spfNxNUFGl6TwCgK2HivHxK654rK8Njp4rg7+XKfp1scJva/MafycfkH82pmLiWG+kpJUhOa0cT4/zRmWlCjsPaKNYP3gjCNm5VVj0eyIAzVCsKU81x8dfxiI9U/9xtnN7ewgCkJRajmbuFnjlWT8kp5YZ5XG2Xe9J2LXyPbh4tYJb83aIifobJfnpaNVtAgAgcvNXKC3KwsAITZtt1W0Czh1ZgUMb5qFl13HIuBaNi8fWYtBT2jZ7fMf3cPVpCzvn5qiqKMG5Q38gJ/USeo/6qDrNgbVzcPn0Zgx77geYmMlRWqRp12bm1pCZGnaOycYglVtCHqCNyrf09YRN22BU5RWiIjndgDlrPIfPK9GvvQw5BWrkFIroFyaDQql5ktVN4/uaoLBUxPbjyhvrqPDiY6bo006GmGsqtGwuRWAzCX7coD3/G9ZVhtjrauSXiLCyAPqHmcDcFDgZp9muiQzoHybDxWsqFJVpRhOEt5TBVi7gXILxX180hod1cmNDYOdOE7R48WIMGDCgRscOAIwePRqfffYZ8vPzsWbNGrz11lv4v//7P4SHh2PmzJl46aWXqjtn2rRpgwMHDmDmzJno2bMnRFGEv78/xo8f39i7VKtDF9QwkQkY0VUGczMgJVvEsp1KVN1y4WtnJUC8ZcBscraIvw8oMSBMhv7tpcgrBlbvVyIlR5umS7DmonvKI7p3fdYe1jwi/abWvhJAAM5dNf6IipXrUmBmKsFbLwTAykqG2MvFeHPWBZSXaw8srk5mOlE4jz/iDlMTCT59P1RnW0v+uo6lq5IAAN/8moApET5488UA2NuaICevCht2pGPZ6qRG2a/GsD2qHKYyAU8OsYLcXMDVVCW+/qsIlVXaOuVoK9WJhrCzlmDWFO0d3CHhlhgSbom46wos+FNzQdS3g6ZX8d2Jdjrft2RTMSLP3V90Bhm/DXsLYWoiYMoYR8gtJIi/XolPf85ARaW2ojnZy3Tqnb2NDAve0Q4NGtHPDiP62SEmvhyzf9BcCC5el4sJj9hjymgn2FpJkFekwq7IYqzZaXxPuPNvMxSVpQU4vedHlBVnw8E1EEMm/Vw9PKqsOBulBdoLFxsHTwyZ9DOitszHxaMrYWnjgvDhM+DbalB1motH/4JapcDuFa/pfFdY/1fQYYDmaZJVFcU4seMblBZmwMzSFr4tB6HT4Nd1on+M1ab9xTA1keC5kQ6QW0iQkFyJz37N1q13dlKdk217Gynmv+Fe/X54bxsM722DiwkV+GSRpmPjakoVvv49GxOG2GHUAFtk5ynxx8Z8HDljXE9pq8uKtckwM5XgzZcCYW1lgouXi/DGR+d0j7PO5rh1+oiRQz1gaiLB3Oktdba1ZOU1LPlLM6zISi7DC0/7wtnJDEXFChyIzMEvfyRCpTK+C57A9kNRUVaAEzt/QGlRNhzdA/Ho1EWwcdC22eL8tOr0No6eGD5lEQ5vmI/zh1dCbuuCXiNnIqDt4Oo0leXF2P/PLJQWZcPMwhpOzUIw8tU/4OrTpjrNhUjNo9b//fFpnfz0n/AZQjqPepC73CTYdmiF8D1/VL8P/XIGACD593U4N3m6obLVqPZHK2EiA0b2NIWFGZCcpcavmytRectoUTtrQWcKnuuZaqzcXYXBnUwwqJMMuUUiVuyuQvItIwhsrQREDDCFpbnmEehJmWp8/28lCko0aUQRcLaTYOJgGeTmQFmF5rt/2lCJTCMaFUAPB0FkV9lDY+7cufj555+RnJzcoNv9YNmDi9x52B3495ihs2C0gjrXnPCb7s5vM53unIj0GvtGoqGzYLS69DTuJ0kZ0qmjKYbOgtFKjmWbra/xL/YydBaMlt/jwYbOglE68F20obNgtL540TjnL7sbkz8xzBN4F3/ofOdERoaRO0bsxx9/RKdOneDo6IgjR45gwYIFePXVVw2dLSIiIiIiIiJqROzcMWJXrlzBp59+iry8PHh7e+Ott97C9On/jdBLIiIiIiIiItJg544R++abb/DNN98YOhtERERERERE90xUc5aYhsJHoRMRERERERERGTFG7hARERERERFRo1Pz+U4NhpE7RERERERERERGjJE7RERERERERNToOOdOw2HkDhERERERERGREWPnDhERERERERGREeOwLCIiIiIiIiJqdCInVG4wjNwhIiIiIiIiIjJijNwhIiIiIiIiokan5oTKDYaRO0RERERERERERoydO0RERERERERERozDsoiIiIiIiIio0YkcltVgGLlDRERERERERGTEGLlDRERERERERI2Oj0JvOIzcISIiIiIiIiIyYozcISIiIiIiIqJGJ6rVhs7CQ4ORO0RERERERERERoydO0RERERERERERozDsoiIiIiIiIio0an5KPQGw8gdIiIiIiIiIiIjxsgduiMLc6mhs2C0nn2tp6GzYLSqFIbOAf0XjRrT3NBZMFpp2YbOgfHyCXQxdBaM1rDhXobOgtE6cCjP0FkwWsnfRRs6C0ap9//aGToLxuvFOEPn4IHho9AbDiN3iIiIiIiIiIiMGDt3iIiIiIiIiIiMGIdlEREREREREVGjEzmhcoNh5A4RERERERERkRFj5A4RERERERERNTpG7jQcRu4QERERERERERkxdu4QERERERERERkxDssiIiIiIiIiokanFtWGzsJDg5E7RERERERERERGjJE7RERERERERNToOKFyw2HkDhERERERERFRA8jPz8fEiRNha2sLW1tbTJw4EQUFBbWmVygUeO+999C6dWvI5XJ4eHjg6aefRlpa2j19Lzt3iIiIiIiIiKjRiWrRIK8HKSIiAtHR0di+fTu2b9+O6OhoTJw4sdb0ZWVlOH36ND788EOcPn0a69atw+XLlzFixIh7+l4OyyIiIiIiIiKi/4zKykpUVlbqLDMzM4OZmdl9bTc2Nhbbt2/H0aNH0aVLFwDAr7/+ivDwcMTFxSEoKKjGOra2tti1a5fOsu+++w6dO3dGUlISvL297+q7GblDRERERERERP8Z8+bNqx42dfM1b968+95uVFQUbG1tqzt2AKBr166wtbVFZGTkXW+nsLAQgiDAzs7urtdh5A4RERERERERNTpRNMyEytOnT8ebb76ps+x+o3YAICMjAy4uLjWWu7i4ICMj4662UVFRgffffx8RERGwsbG56+9m5A4RERERERER/WeYmZnBxsZG51VX587s2bMhCEKdr5MnTwIABEGosb4oinqX306hUGDChAlQq9X48ccf72mfGLlDRERERERERI1OrVYbOgt35dVXX8WECRPqTNO8eXOcO3cOmZmZNT7Lzs6Gq6trnesrFAqMGzcOiYmJ2Lt37z1F7QDs3CEiIiIiIiIiqpWTkxOcnJzumC48PByFhYU4fvw4OnfuDAA4duwYCgsL0a1bt1rXu9mxc+XKFezbtw+Ojo73nEcOyyIiIiIiIiIiuk8hISEYMmQIpk6diqNHj+Lo0aOYOnUqHn30UZ0nZQUHB+Pff/8FACiVSowZMwYnT57EihUroFKpkJGRgYyMDFRVVd31dzNyh4iIiIiIiIganag2zITKD9KKFSswbdo0DBo0CAAwYsQIfP/99zpp4uLiUFhYCABISUnBxo0bAQDt2rXTSbdv3z706dPnrr7XKDt3Jk2ahIKCAqxfv96g+Zg9ezbWr1+P6Ohog+aDiIiIiIiIiAzPwcEBf/75Z51pbn1KWPPmzRvkqWH31bkzadIkLF++vMbywYMHY/v27fezaXoIdQgQ0DVYgJUFkF0I7DqjRnJ27em9nYEB7SVwtgWKy4GjsSJOJ+hW+iBPoHdrCeytgPwS4MA5NeJStZ+HBQgICxBgJ9e8zy4EDseokZCu+12ONkC/thJ4OwOCAOQUAusi1Sgqa6CdfwBOH1iB47sWo6QwG07ugeg/dga8AjvWmj7p8nHsXTMfOelXYGXrgi6DpqB9ryeqP1epFDi6fREuHF2P4oJMOLj6os/It+HXsld1mjMHVuLMob9QmKspZCf3QHQb+jL8W/V+cDv6AJw9tAIn9yxGaVE2HN0C0Xv0DHj61152KVeO48C/85GbcQVyWxd07D8FbXtoy+6fbyciJf54jfV8Q3vj8Rd/qbH8+M5FOLL5a7Tv/TT6jJ7ZMDtFRuf43pWI3L4YxQXZcGkWgCFPzIBPC/31sLggCztWf470azHIzbqOLv0n4pGIGTppLp7aiUObFyEvKwlqlRIOrj7oNvhZtO32WGPszgMTE7US5w4sRllxNuxdAxA+fAbcfWtvr2lXj+Po5vnIz4yHpY0L2vaegtCu2gkQEy/sxJm9i1CUqyknWycftO71LFqEacvpzL5FuHZhFwqyrkJqYg5Xn/boMvQt2Dn7PdB9fVAGhMnQOVgKCzMgOUvE+kgFsvLrPols1VyCgR1lcLQRkFskYudJJWKu6Z/0sk9bKYZ0NsHh80psPqrU+d42/hLYyQWo1EBKjho7TyiRnG2cd2lP7luJqB2aNuvsEYDBE2bAu442u+vvz5F+PQZ5WdfRuf9EDJ6g22ZPH/wb56I2IDv1CgDA3acl+o58A8382jzwfTGE4T0t0KudOSzNBSSmKbFyRynSclS1pvdwkmJELwv4uMngZCfFql2l2HOiQidNoJcMg7tq0thZS/DDmiJEX1Y86F15oAZ2lKFLiAwWZkBSlhrrDymQeaf26ivB4E4mcLQVkFsoYvtxhU57HdhRhoEdTXTWKS4T8cnvFTpp2vpLYWclQKkGUrPV2H5cgeQs42yvd8uhR0f4vTUZtmGtYO7hgpOjX0bmxj2GztZ/iigax4TKxuC+I3eGDBmCpUuX6ixriOfD0/0TRREqlQoymeEDtEK8BAxsL2D7KRHJOSLC/AVM6CXBom36O1Bs5cD43hJEJ4jYcFSEl5OAIR0ElFaKiEvRpGnmCIzqJsGB8yLiUkQEeQoY2V2C33erkZanSVNcJmLfWRH5JZr3bZoLGNtDgt92qJFTpFlmZwU83V+Cs1dFHDwvolIBONkAytrPNwwu9uRW7PlnHgZNmIVm/mGIPrQK//wwFVM+2gIbB48a6QtykrHmh+fRpvtYPPrsAqQmnMbOVR/D0soBQWGDAQCHNi5EzLGNGPLUp3B09UPixUP4d9GreOqdVXD1CgUAWNu7offjb8Pe2RsAcOHoeqz7+RVMmvEvnD0CG68A7kPc6a3Yv24e+o2dBQ+/MJw/sgrrf5qKp2foL7vC3GT8u+h5tA4fiyFPL0Da1dPY+4+m7ALbacpu+OTvoFJpTybLSwvw5+ePIbD9kBrby7h+DucjV8PJI6jGZ/TfceH4Vmz/ax6GTfwI3gFhOLl/Nf785nm88ulm2DnWrIdKZRXk1g7o+eiLOLqr5k0VALCQ26LXoy/Cyd0PUpkJLp/dj/VLZkBu44CAVj0f9C49EAlntyJq0zz0ePwjuPqEIfbYamxb8jzGvbkZVvY1y6koLwXbl7yA4M5j0Xf8AmReP43D6+fAXG4Pv9aa9mpmYYv2/V6EnbOmnK7H7seBf2bAQu4AryBNOaVfPYHQ8Ag4e7aGqFbhxI5vsPW3KRj71maYmFo2ahncr95tpejRWop/DiiQUyiiX3sZpjxiii//qURVLdfA3i4Cnuhvgl03OnRaNpcgor8Jft5YVaNjxtNJQOcQKdJza56cZxeqsfGIGnnFImQyoGcrKSYPNcWC1ZUoraiRvEmLOb4VO1bNw9AnP4JnQBhOH1yNlf/3PF6asxm2etqs6kab7THsRRyrpc1ejzuOVp2HwdO/PWQmZojc/htWfDMZL87ZDBv7up+oYmyGdDXHwM7mWLq5FJl5KgzrboE3nrDBB4vyUVnLdBKmJkBOgRqnLpVh3AC53jRmJgJSspQ4cq4SL4+2foB70Dj6tJOhZxsZ/t5XhewCEf07yDD1UTMsWFWBytraq6sETw40xc4TSlxIVKGVrxRPDTTFjxsqdTpmMvLU+GVTZfX724MEsgtErD+sQF6RCBMZ0LONDFOGmeGLvyqMrr3eC6ncEkXn4pCyfB06/PP9nVcgasLue0JlMzMzuLm56bzs7e0BaJ7vvmjRIjz66KOwtLRESEgIoqKiEB8fjz59+kAulyM8PBwJCQnV25s9ezbatWuHRYsWwcvLC5aWlhg7diwKCgpqzUNlZSWmTZsGFxcXmJubo0ePHjhx4gQATQdHQEAAvvzyS511Lly4AIlEUv3dhYWFeP755+Hi4gIbGxv069cPZ8+e1Vln/vz5cHV1hbW1NSZPnoyKirv7pTt48CBMTEyQkZGhs/ytt95Cr17aqIjIyEj06tULFhYW8PLywrRp01BaWlr9+Z9//omOHTvC2toabm5uiIiIQFZWVvXn+/fvhyAI2LFjBzp27AgzMzMcOnQIZ8+eRd++fWFtbQ0bGxt06NABJ0+evKu8N5QuwQKir4qIvioitwjYdUZEUZkmskafsAABRaWadLlFQPRVEWcTRXQN1lbZzkECEjOAyFgRucWaf69lapbfdCUNSEgH8oo1r/3nRVQpgWZO2jR9WgtISBex96yIzAKgoBSITwfKKtFkndizFG26jUbbHmPh5O6PAeNmwtreDWcO/qU3ffShVbB2cMeAcTPh5O6Ptj3Gok23UTi+e0l1mphjGxA+5EX4t+oNO2cvtO8dAd/QHjppAtr0g3+r3nBw9YWDqy96PfYGTM0skZYY/aB3ucGc3rcUrbqORutuY+Ho5o8+ozVld+6w/rI7d3gVbOzd0Wf0TDi6+aN1t7Fo2XUUTu3Vlou53A5yG+fqV9KlIzAxMUeLdrqdO1WVpdj2+zsY8MSnMLe0faD7SU1b1I5lCOs5Gh16jYWzhz8eiZgBWwc3nNynvx7aO3nikYiZaNf9cZhZWOlN4xvcBSEdBsLZwx8OLt7oOvBpuHoGIeny6Qe5Kw/UuUPLENRpNII7j4W9qz+6jZgBK1s3XDyqv5xij66ClZ07uo2YAXtXfwR3HougjqNw7qC2vXr4d4Fvq4Gwd/WHjaM3Wvd4Gg5uQci4pi2noZN/Q1DHUXBwC4SjRzB6j52HkoI05KTEPPB9bmjdW8mwL1rTSZOZL+Lv/QqYyIB2/tI614lPVWP/WRWyC0XsP6tCfKoa3Vvp3iwylQHj+5lg3UElyvUcM88mqBGfpuncycoXsfmoEuamAtwcjO95Hkd3LUP7HqPR/kabHTxhBmzs3XByv/66aOfkicFPzETbbo/DvJY2O3Lql+jYNwJu3iFwcvfDo898AlFUIzE26kHuikH072yBrUfKcSauCmnZKizdVAJTE6BLy9pvCF9LV2HN3jKcuFgFpVJ/9MiFqwqsP6DZ7sOgR2sZ9p5W4kKipr2u3nujvQbU3l57tpbiSooa+84okV0gYt8ZJeJT1ejZWre9qtVASbn2dXuHTXS8pp3nFYvIzBexKVIBCzMB7o7G117vRfaOg7g8ayEy1u8ydFb+s0S1aJDXw+iBt9ZPPvkETz/9NKKjoxEcHIyIiAi88MILmD59enUHw6uvvqqzTnx8PP7++29s2rQJ27dvR3R0NF555ZVav+Pdd9/F2rVrsXz5cpw+fRoBAQEYPHgw8vLyIAgCnnvuuRrRRUuWLEHPnj3h7+8PURQxbNgwZGRkYOvWrTh16hTCwsLQv39/5OVpQkD+/vtvzJo1C3PnzsXJkyfh7u6OH3/88a7KoFevXvDz88Mff/xRvUypVOLPP//Es88+CwA4f/48Bg8ejFGjRuHcuXNYvXo1Dh8+rFM2VVVV+OSTT3D27FmsX78eiYmJmDRpkt7ymDdvHmJjY9GmTRs8+eST8PT0xIkTJ3Dq1Cm8//77MDExqbHegyKRAO72QGKGbiO6miHC00l/546no4Crt6dPF+HuAEhurNKsljS1bVMQgFBvASYyIDVHu16Ah4C8YmBCbwlef1yCSQMlaNHsXvey8aiUVchIioFvaA+d5b4h3ZF69YzedVKvRsM3pLtu+tCeyLh+oTriRKlUQGpiqpNGZmKOlHj9F4ZqtQoXT2yBoqoMzfza13d3GpVKWYXM5Bj4BOuWnXdwd6Ql6i+79GvR8A7WLbvmwT2RmXRBJ1rnVheOrkWLDsNgYqZ7h3/vP3Pg27I3fIJqfwwiPfyUyiqkXY+Bf0vdeuXfsjuS4/XXw3sliiKuXoxCbkYifIJqH8LUlKmUVchJjYFnoG45ebbojszr+sspMykani1uT98D2SkxUOtpr6IoIjU+CoXZiXUO9aqqKAYAmBlZp6yDtQAbSwFXUrRRNSo1kJiuho9r7aeAPq4SnXUA4EpKzXUe626CuCRNB86dSCVA52ApyitFvVE+TZlKWYX06zHw09NmUxIaps0CgKKqHGqVEhZy46pnd+JkJ4GdlQQxido2qFQBl5OU8G9m+OjypsLBWoCNXMDlZG3ouEoNXE1Tw8et9vbq7SrBlRTdcPPLKaoa6zjZCvhgojnejzBDxAATOFjrP18GNO21S6gM5ZUi0oysvRL9l933L+rmzZthZaV7R+K9997Dhx9+CAB49tlnMW7cuOrl4eHh+PDDDzF4sCY8+rXXXqvu4LipoqICy5cvh6enJwDgu+++w7Bhw/DVV1/Bzc1NJ21paSl++uknLFu2DI888ggA4Ndff8WuXbuwePFivPPOO3j22Wfx0UcfVT9rXqFQ4M8//8SCBQsAaGagPn/+PLKysqqHlH355ZdYv3491qxZg+effx4LFy7Ec889hylTpgAAPv30U+zevfuuo3cmT56MpUuX4p133gEAbNmyBWVlZdVls2DBAkREROD1118HAAQGBuLbb79F79698dNPP8Hc3BzPPfdc9fb8/Pzw7bffonPnzigpKdH5P5gzZw4GDhxY/T4pKQnvvPMOgoODq7fdmCxNAYlEQMltRVVaCViZ619Hbq75/FYlFYBUIsDSTPO3lZ40pZWadW/lbAtMGiCBTApUKYE1h7VDsuTmmpDe8BDgwDnNEC4/dwFjekjw5141kuqYE8hQykryIapVsLR21Fkut3ZCaaH+DJcW5UBu7aSzzNLaEWq1EuUl+bCydYFvSA+c2LMMXoGdYO/kjWtxUbhydg9EUfeEITs1Dn8smAClohKmZpYY+cIPcHIPaNidfEDKS2svu+vFtZdd8zuU3a0yrp9DbvplDIqYq7M87tQWZCXHIOLttQ2wJ2TMyoo19VBue1s9tHFESWHOfW27oqwYX73VGyplFQRBgmETZ9XoRDIWFWWacrKw0i0nCytHlBXrL6fy4mxYWPWokV5UK1FRmg9LG017rSovxp+facpJIpGg++OzanQK3SSKIqI2z4db8w5wcGvRAHvWeKwsNP8Wl+veCCkuF2Ffx4WdlYX+daxv6a9u4ydBMycB36+ve36TYG8JnuhnAhMZUFwGLN5a1aQjY/W5edyV2zR8m73V3rVfw9rOFX6hD9cNAFu5ppOhqFS3k6CoVA1H24c7KuReWFtq2mTJbW2vpFyEXR3t1dpSQPFtUxwUl2m3BwBJmWqs2luFnEIRVhYC+neQ4ZWRZvhqdYVOewzxliBioGl1e/11cyXKHuIhWUQPm/vu3Onbty9++uknnWUODg7Vf7dpo50UztVVM364devWOssqKipQVFQEGxsbAIC3t3d1xw4AhIeHQ61WIy4urkbnTkJCAhQKBbp3156UmZiYoHPnzoiNjQUAuLu7Y9iwYViyZAk6d+6MzZs3o6KiAmPHjgUAnDp1CiUlJXB01D1ol5eXVw/bio2NxYsvvqjzeXh4OPbt23c3xYRJkybhgw8+wNGjR9G1a1csWbIE48aNg1wur85DfHw8VqxYUb2OKIpQq9VITExESEgIzpw5g9mzZyM6Ohp5eXlQqzUHyaSkJISGhlav17Gj7t3HN998E1OmTMEff/yBAQMGYOzYsfD399ebz8rKSlRW6p51KRUyyEwafh4lAUCdAXG3fSgINRffzaTiucXAbzvUMDcBgrwEDO+i6bjJKdLkAQAup4o4flmzscwCTfRPWICApCY86aMg6B7oRYjaQtK/wm0Lbu6bZvmAcTOxfcUH+G32I4AgwN7JC63DR+F81DqdtRxcffHsjPWoKC/C5TM7sWX5e4h480+j6eABUKMsNLPT11F20FPWqPl/AAAXotbA0b0F3Hy0v33F+enYv24uRr285IG0JTJOwu11TkTdbfgumJrL8eLsf1FVWYbEi1HYsWo+7J094Rvc5b62a0g1f+twj791NZebmMkx+rV/oagqQ1p8FI5ung8bB094+NcspyMbPkFeRhxGvLiyHrlvXO38JRjZUxuZu2z7jaEqeo6n9/pQDgHadWzlwPBwEyzZVnXH+ekS0tT4dl0VLM01kTsRA0zww/oqo5zDo0ZdFPUfB+ojcttvuHBsC55+53ejP050aWmKpx7R3nT87u8bd9T01LkGeDiM0WofKMWoXtr2unSrpr3WKJI7njDXdHsbj0u+tWNNxPXMKrwfYY4OQTIcOqedBD0+TY2F/1RCbg50DpHhqYGm+G6d8c2RRcblYR0iZQj33bkjl8sREFD7Rd2tw39uHgD1LbvZUaHPzTT6DqA3HxlW84Ar6iybMmUKJk6ciG+++QZLly7F+PHjYWlpWf3d7u7u2L9/f43t29nZ1Zqve+Hi4oLhw4dj6dKl8PPzw9atW3W+T61W44UXXsC0adNqrOvt7Y3S0lIMGjQIgwYNwp9//glnZ2ckJSVh8ODBqKrSHWd8s8PoptmzZyMiIgJbtmzBtm3bMGvWLKxatQojR46s8V3z5s3Dxx9/rLOs7+gP0X/MrHrve1kVoFaLNaJ0LM1qjve9qbSiZgSO3AxQqcXqcf03o3duT3P7NtVqVE+onJ4vwsNBQKcWAradFFFWpdlmTqHuOjlFmkmc7/lo2ggsrewhSKQoLdK9W1hWnAu5jZPedeQ2Tigtyr4tfR4kEhksrOw027V2wKgXf4RSUYny0gJY2brgwPovYevkqbOeVGYKexcfAIC7T2ukXzuPk3t/x5An5zTQHj44FnJN2ZXdXnYlubC0rqPsbovqKb9RduZyO53liqpyxJ3egvChuu04MzkGZcW5WLFgVPUyUa1CSsIJRB9agWlfn4dEUvt4enq4WFpr6uHtd/xLi3NhdVtkwL2SSCRwdL3RPr1DkJ1+FYe3/GKUnTvmljfa621ROhUlubC00l9OFtbOKL8tfXlJLgSJDOaWdtXLBIkEtk6acnLyCEF+1lVE7/ulRufOkQ2f4PrFvRj+4p+wstO9udQUXUxSI3md9pxAeuNnxdpS0InEsTIXUFJe+3ZKygFrC93zKisL7TrNnCSwthTw6kjtUF6pREBzdwHhLaX4YEll9YWlQgnkFmnmz0vOUuLtcaboFCTF/rNN+KkFt7l53L29zWqOu/fXZgEgasdiHN66CE+9tQSuXsY/2X70lSpcTSuofm8i1dQlGysJCku1/+82ckmNaJ7/kovXVEjK1O6/7GZ7tRBQXKbbXm+PpLtVcZluVB2gib67PQLoVgolkJ6nhpOtUGP5zfaalKXAu0+YoXOIDPvOKGvZEhE1JU1yoGtSUhLS0tLg4aF5+kBUVBQkEglatKgZDh0QEABTU1McPnwYERERAACFQoGTJ09WD3ECgKFDh0Iul+Onn37Ctm3bcPDgwerPwsLCkJGRAZlMhubNm+vNU0hICI4ePYqnn366etnRo0fvab+mTJmCCRMmwNPTE/7+/jrRRmFhYYiJiam1o+z8+fPIycnB/Pnz4eXlBQD3NClyixYt0KJFC7zxxht44oknsHTpUr2dO9OnT8ebb76ps+ybDfdXTdRqID0f8HUTEJeqPdD4ugm4nKr/wJOSKyLQQ7dzxddNQHoecLNzNzVXhK+bUB1xAwB+bgJScu7QISNoT3jVaiA9T/Mo9Fs5WgsoLGt6HTuApnPFzbslrsUeQYt22uF312IjEdi2v951mvm1Q/w53SizxIuH4ebTClKp7vxLMhMzWNu5QqVSIO7MTgSHPXKHHIlQKY1jIkOpzBSuXi1xPe4IAtreMnTxUiT8W+svO/fm7XD1gm7ZXb90GK7eNcvu8pltUCmrENJphM5y7xZdMfH9TTrLdq6cDnsXP3QaMJUdO/8xMpkpPHxaIuFiJEI6aOthQkwkgtv3a9gvE0UojaR93k4qM4VTs5ZIvRIJ31backq5EonmofrLydW7Ha7H6rbXlCtH4OzZEhJpHXPNiSJUqqpb3oo4suETXIvZjeEv/A4bB8/a121CqhRArkL32FVUJiKgmQRpuZqLaqkE8HWXYNvx2i/WrmeqEdBMgsMXtBfigZ4SXL9xIRqfpsY3a3SjfMf0NkF2gYgDZ5V3jMaQSRsm2qWxSGWmcPdpiasXIxEcpq2LVy9GokW7+2uzkdsX4/CWnxDx+m/waN76zisYgcoqILtKt9OmoESNUF8TJGdq62ELbxnW7tPzyNT/iEoFUHl7ey0VEeglRVqupn1KJYCfhwRbj9Y+/DEpU41ATykOnbu1vUpxPaP2jjOpBHCxk+Ba+p07bWQ8RaEHTM1HoTeY+x7oWllZiYyMDJ1XTs79jT82NzfHM888g7Nnz+LQoUOYNm0axo0bV2NIFqCJUnnppZfwzjvvYPv27bh48SKmTp2KsrIyTJ48uTqdVCrFpEmTMH36dAQEBCA8PLz6swEDBiA8PByPP/44duzYgWvXriEyMhIffPBBdQfKa6+9hiVLlmDJkiW4fPkyZs2ahZiYe3tqxuDBg2Fra4tPP/20xjxD7733HqKiovDKK68gOjoaV65cwcaNG/G///0PgCZ6x9TUFN999x2uXr2KjRs34pNPPrnjd5aXl+PVV1/F/v37cf36dRw5cgQnTpxASEiI3vRmZmawsbHReTVEePCxSyLa+Qlo6yvA0QYY0F6ArSVwOl5zUOvTRsDwLtqTvdPxImzlwIB2mvRtfQW08xNw9JK28R+PE+HnBoQHC3C01vzb3E2z/KY+bQR4OWtCyJ1tNU/G8nEGYq5p0xyNVSPUS7N9eyugY6CAQA/g1JWm2bkDAJ36P4uzR9bgXOQa5KQnYM8/n6EoPx3tek4AABxY/xU2L3u3On27nhNQlJeGPWvmISc9Aeci1+Bc5Fp0HqCdxykt8SzizuxEQXYykq+cxD/fTYGoVqPLoCnVaQ6s/xrJV06iMDcF2alxOLjhGyRdPo7QzsMbb+fvU1jfZ3Ehag0uRK1BbkYC9q/7DMX56WjTQ1N2hzd+he1/aMuuTY8JKMpPw4F185CbkaBZ9+hadOj3XI1tX4haA/82A2Aht9dZbmpuBSePFjovE1NLWMjt4ORhXHN4UMMIHzwJpw+uwelDa5GdloDtf81DYV46OvbR1MPda77Cul/f01knPSkW6UmxqKooQ1lJHtKTYpGVGl/9+aEti5AQcwR5WcnITr+KyB1LcTZqA9qE63Y2GpM2PSfh0ok1uHRiLfIzExC5aR5KCtIR0lVTTse3fYV9q7XlFNJ1Akry0xC1aR7yMxNw6cRaxJ1Yiza9tO31zL5FSLl8BEW5ySjIuopzB5fi8ukNCGyvLacj6+cg/swm9HviS5iYyVFWnI2y4mwoFcY3NuHIBSX6tpOhZXMJXO0FjO1tAoUSiE7QXgiO62OCwZ1kOusEekrQu60UzrYCereVIqCZBEcuaC4EqxRAZr6o81IogLIKzd8AYCIDBneUwctFgJ0V4OEoYHRPGWzlAs4lGk/Uzk1dB07CmUNrEH1Y02Z3rtK02Q432uyetV9h/WLdNpuRFIuMpFhUVZahrDgPGUmxyE7TttnIbb9h//qFGD5pLuycmqGkMBslhdmoqijFw2bP8XIM7WaB9i1M4eEsxbPDrVClAI7FaDsJnxtuhZF9tCEoUgng5SKFl4sUMqkAe2sJvFykcLbXXr6YmWjTAICTreZvBxvjnMvn8Hkl+rXXttdxfW+013htmxnf1wRDOstuWUeFQE8J+rSTwdlOQJ92MgQ2k+DQeW3HzbCuMvi5S2BvLcDLRcDEQaYwNwVOxmm2ayIDhnSWwdtFgJ2VgGZOAsb0NtG01wTja6/3Qiq3hE3bYNi01cxNaunrCZu2wTD3cjdwzoju3X1H7mzfvh3u7rqVPygoCJcuXar3NgMCAjBq1CgMHToUeXl5GDp0aJ1Pppo/fz7UajUmTpyI4uJidOzYETt27Kh+JPtNkydPxmeffaYzMTGgGdK1detWzJw5E8899xyys7Ph5uaGXr16Vc8TNH78eCQkJOC9995DRUUFRo8ejZdeegk7duy46/2SSCSYNGkSPvvsM50IIEAzN9GBAwcwc+ZM9OzZE6Iowt/fH+PHjwcAODs7Y9myZZgxYwa+/fZbhIWF4csvv8SIEXWftEulUuTm5uLpp59GZmYmnJycMGrUqBpDrx602GQRlmZAj1YCrMwFZBcCqw6qUXTjho2VBWAr10bqFJYCqw+oMbC9BB0CNaHgO0+LiEvRbjM1F/g3Uo3ebSTo3VpAfonmfVqeNo3cHBjRVQIrc80dkqwCYNUBNRIztWniUoFtJ0V0CxUwKEzz5Ky1R9RIabg5EhtcSMehKC/Nx5EtP6K0KAtO7i0w9pVfYOuoecxXSWE2ivLSq9PbOXlhzCu/YO+aeThzYAWsbF0wYNxMBIUNrk6jVFTi0MaFKMhJhqmZJfxa9cawSV/A3FIb1lRanIPNy95FaVEWzMyt4dwsCGP/91uNJ3E1ZUFhQ1FRmo9jO35EaWEWHN1b4PEXf4GNg6bsSouyUZyvLTtbRy+MfOEXHPh3Hs4eWgG5rQv6jJ6JwHaDdbabn5WItKunMOrlJSC6k1adh6KspAAHNv6AksJsuDQLxJOvL4Kdk6YeFhdmozAvTWedRbO10Zbp12Nw/uhm2Dp64I0FewEAVZXl2PLHHBTlZ0Bmag4nN1+MmvoFWnUe2ng71sD82w5FRVkBTu/5AWVF2XBwC8Qjzy6Ctb2mnMqKs1FSoC0nGwdPDHluEaI2zUdM1ErIbVzQbcRM+LW+5beuqhyH189BaWEGZCbmsHP2Rb8JX8C/rbacbj5qffMi3WN177GfIajjKBiTA2dVMJEKeKy7CSxMgeRsEYu3VaHqlkAAO7mgE22TlCXir70KDOoow8AOMuQViVi5R4Hke5iHThQBZzsBT7UwhdwcKKsAUrLVWLSpCln5TffmSW1adh6K8tICHNykabPOHoF44rVFsLv1uJur22Z/naPbZi8c07TZaZ9r2uzJ/SuhUiqw5qfXdNbrNfwV9H7sfw94jxrX9qMVMDEREDFEDrm5gKtpSnyzqgiVtwQWOthIqqdbAAA7awk+mmJX/X5wVwsM7mqBuOsKfLlCM4+Pj7sM7zylfbrY+IGaqQkiz1Vg6Wbj6yTbH62EiQwY2dMUFmZAcpYav26uROWt7dVa0Jk04HqmGit3V2FwJxMM6iRDbpGIFburkJylTWVrJSBigCkszTXTFyRlqvH9v5UoKNGk0bRXCSYOllW31+QsNX7aUFndYfuwsu3QCuF7tE80Dv1yBgAg+fd1ODd5uqGyRVQvgig2ranMZs+ejfXr1yM6OrrBt33kyBH06dMHKSkp1Z02jW3q1KnIzMzExo0bDfL99TF31cPdY/8gubswlrW+qup+AAvV4cXBd05D+v11pEkdEo1KWhN8uqCxyM42ssdHNSGtgo178mFDOnAo786JSC97B8s7J6Iaev+vnaGzYLSGKeIMnYUHZtDEMwb53p1/tDfI9z5ITXLOnYZWWVmJ5ORkfPjhhxg3bpxBOnYKCwtx4sQJrFixAhs2bGj07yciIiIiIiKih9N/onPnr7/+wuTJk9GuXTv88ccfd16hHqysrGr9bNu2bfjwww9x/PhxvPDCCxg4cGCtaYmIiIiIiIj+C8Q6nppN96bJde7Mnj0bs2fPbtBtTpo0CZMmTWrQbd6urmFkzZo10/uYdSIiIiIiIiKi+9XkOneMVW2PMCciIiIiIiKimkQ15ztsKMb5nEAiIiIiIiIiIgLAzh0iIiIiIiIiIqPGYVlERERERERE1OhEkRMqNxRG7hARERERERERGTFG7hARERERERFRo1NzQuUGw8gdIiIiIiIiIiIjxs4dIiIiIiIiIiIjxmFZRERERERERNToRDUnVG4ojNwhIiIiIiIiIjJijNwhIiIiIiIiokYnckLlBsPIHSIiIiIiIiIiI8bIHSIiIiIiIiJqdKLIOXcaCiN3iIiIiIiIiIiMGDt3iIiIiIiIiIiMGIdlEREREREREVGj44TKDYeRO0RERERERERERoyRO0RERERERETU6EQ1J1RuKIzcISIiIiIiIiIyYuzcISIiIiIiIiIyYoIoipzBiIxWZWUl5s2bh+nTp8PMzMzQ2TEqLLv6Y9nVH8uuflhu9ceyqz+WXf2x7OqPZVd/LLv6Y9nRw4CdO2TUioqKYGtri8LCQtjY2Bg6O0aFZVd/LLv6Y9nVD8ut/lh29ceyqz+WXf2x7OqPZVd/LDt6GHBYFhERERERERGREWPnDhERERERERGREWPnDhERERERERGREWPnDhk1MzMzzJo1ixOf1QPLrv5YdvXHsqsfllv9sezqj2VXfyy7+mPZ1R/Lrv5YdvQw4ITKRERERERERERGjJE7RERERERERERGjJ07RERERERERERGjJ07RERERERERERGjJ07RERERERERERGjJ07RERERERERERGjJ07RER3KT4+Hjt27EB5eTkAgA8bJCIiIkOrqqpCXFwclEqlobNCRAbEzh2i/wiVSoXFixcjIiICAwYMQL9+/XReVLvc3FwMGDAALVq0wNChQ5Geng4AmDJlCt566y0D5854sHPs3v3xxx/o3r07PDw8cP36dQDAwoULsWHDBgPnzHiw3hE1fcuXL8eWLVuq37/77ruws7NDt27dqn/7qKaysjJMnjwZlpaWaNmyJZKSkgAA06ZNw/z58w2cu6bv9OnTOH/+fPX7DRs24PHHH8eMGTNQVVVlwJwR1Y/M0BkguhtvvvnmXaf9+uuvH2BOjNdrr72GZcuWYdiwYWjVqhUEQTB0lozGG2+8AZlMhqSkJISEhFQvHz9+PN544w189dVXBsxd05ebm4vx48dj7969EAQBV65cgZ+fH6ZMmQI7OzuWXy1++uknfPTRR3j99dcxd+5cqFQqAICdnR0WLlyIxx57zMA5bNpY7+7Nxo0b7zrtiBEjHmBOjNvy5cvh5OSEYcOGAdB0Uvzyyy8IDQ3FX3/9BR8fHwPnsGn67LPP8NNPPwEAoqKi8P3332PhwoXYvHkz3njjDaxbt87AOWyapk+fjrNnz2L//v0YMmRI9fIBAwZg1qxZeP/99w2Yu6bvhRdewPvvv4/WrVvj6tWrmDBhAkaOHIl//vkHZWVlWLhwoaGzSHRvRCIj0KdPH52XtbW1aGlpKbZv315s3769KJfLRRsbG7Fv376GzmqT5ejoKG7ZssXQ2TBKrq6uYnR0tCiKomhlZSUmJCSIoiiKV69eFeVyuSGzZhQmTpwoDh48WExOTtYpvx07doihoaEGzl3TFRISIv7777+iKOrWu/Pnz4uOjo4GzJlxYL27N4Ig3NVLIpEYOqtNWosWLcQ9e/aIoiiKkZGRooWFhbho0SJx+PDh4siRIw2cu6bLwsJCvH79uiiKovjuu++KEydOFEVRFC9cuCA6OTkZMmtNmre3txgVFSWKou5x4sqVK6K1tbUhs2YUbGxsxPj4eFEURXH+/PnioEGDRFEUxcOHD4uenp6GzBpRvTByh4zCvn37qv/++uuvYW1tjeXLl8Pe3h4AkJ+fj2effRY9e/Y0VBabPFNTUwQEBBg6G0aptLQUlpaWNZbn5OTAzMzMADkyLjt37sSOHTvg6empszwwMJDh9nVITExE+/btayw3MzNDaWmpAXJkXFjv7o1arTZ0Fh4KycnJ1cfa9evXY8yYMXj++efRvXt39OnTx7CZa8KsrKyQm5sLb29v7Ny5E2+88QYAwNzcvHpIJdWUnZ0NFxeXGstLS0sZoX0XRFGs/u3bvXs3Hn30UQCAl5cXcnJyDJk1onrhnDtkdL766ivMmzevumMHAOzt7fHpp58yzL4Ob731Fv7v//6P803UQ69evfD7779XvxcEAWq1GgsWLEDfvn0NmDPjwM6x+vH19UV0dHSN5du2bUNoaGjjZ8jIsN6RIdzspAA0HYwDBgwAwE6KOxk4cCCmTJmCKVOm4PLly9XD2mJiYtC8eXPDZq4J69Spk85cRTc7dH799VeEh4cbKltGo2PHjvj000/xxx9/4MCBA9X1LjExEa6urgbOHdG9Y+QOGZ2ioiJkZmaiZcuWOsuzsrJQXFxsoFw1fYcPH8a+ffuwbds2tGzZEiYmJjqfczx77RYsWIA+ffrg5MmTqKqqwrvvvouYmBjk5eXhyJEjhs5ek3ezc+yTTz4BwM6xu/XOO+/glVdeQUVFBURRxPHjx/HXX39h3rx5+O233wydvSaP9e7efPvtt3j++edhbm6Ob7/9ts6006ZNa6RcGZ+bnRTt27dnJ8U9+OGHH/DBBx8gOTkZa9euhaOjIwDg1KlTeOKJJwycu6Zr3rx5GDJkCC5evAilUon/+7//Q0xMDKKionDgwAFDZ6/JW7hwIZ588kmsX78eM2fOrI66W7NmDbp162bg3BHdO0HkbXwyMk8//TQOHDiAr776Cl27dgUAHD16FO+88w569eqF5cuXGziHTdOzzz5b5+dLly5tpJwYp4yMDPz00084deoU1Go1wsLC8Morr8Dd3d3QWWvyLl68iD59+qBDhw7Yu3cvRowYodM55u/vb+gsNlm//vorPv30UyQnJwMAmjVrhtmzZ2Py5MkGzlnTx3p3b3x9fXHy5Ek4OjrC19e31nSCIODq1auNmDPjUlBQgA8//BBJSUl46aWXqie5nTVrFkxNTTFz5kwD55AeNufPn8eXX36pc37y3nvvoXXr1obOWpOmUqlw+PBhtG7dGg4ODjqfVVRUQCqV1rgRStTUsXOHjE5ZWRnefvttLFmyBAqFAqIowsTEBJMnT8aCBQsgl8sNnUUiug07x+5PTk4O1Gq13rkVqHasd9SYlEol5s6di+eeew5eXl6Gzo5ROXjwYJ2f9+rVq5FyQv8l5ubmiI2NrbNDm8iYsHOHjFZpaSkSEhIgiiICAgLYqXMXlEol9u/fj4SEBERERMDa2hppaWmwsbGBlZWVobPXpFVUVODcuXPIysqqMfEoHwtMRESAZs6dCxcucAjWPZJIak4DeuuEwCqVqjGzYzSKior0LhcEAWZmZjA1NW3kHBmXTp06Yf78+ejfv7+hs0LUIDjnDhmNUaNG3VU6zh2j3/Xr1zFkyBAkJSWhsrISAwcOhLW1Nb744gtUVFTg559/NnQWm6zt27fj6aef1vvkBEEQeNJ5FwoKCnD8+HG9nWNPP/20gXLVtGVmZuLtt9/Gnj17kJWVVWMydNa7O2O9q7+UlBRs3LgRSUlJqKqq0vns66+/NlCumr4BAwZg//79mDRpkqGzYlTy8/N13isUCpw5cwYffvgh5s6da6BcNX12dnZ1PhXL09MTkyZNwqxZs/R2oP3XzZ07F2+//TY++eQTdOjQocaNYhsbGwPljKh+2LlDRsPW1tbQWTBqr732Gjp27IizZ89WT1QIACNHjsSUKVMMmLOm79VXX8XYsWPx0Ucf8ekJ9bBp0yY8+eSTKC0thbW1tc6JqCAIvMiuxaRJk5CUlIQPP/wQ7u7ufKztPWK9q789e/ZgxIgR8PX1RVxcHFq1aoVr165BFEWEhYUZOntN2iOPPILp06fjwoULei8WGempn75zvIEDB8LMzAxvvPEGTp06ZYBcNX3Lli3DzJkzMWnSJHTu3BmiKOLEiRNYvnw5PvjgA2RnZ+PLL7+EmZkZZsyYYejsNjk358QaMWKEzjFCFEXevCOjxGFZRP8RTk5OOHLkCIKCgmBtbY2zZ8/Cz88P165dQ2hoKMrKygydxSbLxsYGZ86c4QSs9dSiRQsMHToUn332md5HU5N+1tbWOHToENq1a2forBgl1rv669y5M4YMGYI5c+ZUHy9cXFzw5JNPYsiQIXjppZcMncUmq67oCF4s3rvY2Fh06tQJJSUlhs5Kk9S/f3+88MILGDdunM7yv//+G4sWLcKePXvwxx9/YO7cubh06ZKBctl03emJYr17926knBA1DEbuEP1HqNVqvSeVKSkpsLa2NkCOjMeYMWOwf/9+du7UU2pqKqZNm8YL7Hvk5eVVYygW3T3Wu/qLjY3FX3/9BQCQyWQoLy+HlZUV5syZg8cee4ydO3W4ffgf3Z1z587pvBdFEenp6Zg/fz7atm1roFw1fVFRUXqH1bdv3x5RUVEAgB49eiApKamxs2YU2HlDDxt27hD9RwwcOBALFy7EL7/8AkBzB7GkpASzZs3C0KFDDZy7pu3777/H2LFjcejQIbRu3brGozGnTZtmoJwZh8GDB+PkyZPw8/MzdFaMysKFC/H+++9j0aJFnJy1Hljv6k8ul6OyshIA4OHhgYSEBLRs2RIA9M49RvpVVFTA3Nzc0NkwCu3atYMgCDU6tLt27YolS5YYKFdNn6enJxYvXoz58+frLF+8eHH1E9tyc3Nhb29viOwZjbKyMr3zi7Vp08ZAOSKqH3buEP1HfP311+jXrx9CQ0NRUVGBiIgIXLlyBU5OTtV3aEm/lStXYseOHbCwsMD+/ftrzN3Bzp26DRs2DO+88w4uXryot3OMc1DoN378eJSVlcHf3x+WlpY1yi0vL89AOTMOrHf117VrVxw5cgShoaEYNmwY3nrrLZw/fx7r1q1D165dDZ29Jk2lUuGzzz7Dzz//jMzMTFy+fBl+fn748MMP0bx5c0yePNnQWWySEhMTdd5LJBI4Ozuzc+wOvvzyS4wdOxbbtm1Dp06dIAgCTpw4gdjYWKxduxYAcOLECYwfP97AOW2asrOz8eyzz2Lbtm16P+cwSjI2nHOH6D+kvLwcq1atwqlTp6BWqxEWFoYnn3wSFhYWhs5ak+bm5oZp06bh/fff59Mm6oFzUNTP8uXL6/z8mWeeaaScGCfWu/q7evUqSkpK0KZNG5SVleHtt9/G4cOHERAQgG+++QY+Pj6GzmKTNWfOHCxfvhxz5szB1KlTceHCBfj5+eHvv//GN998Uz1UhnT9/vvvGD9+PMzMzHSWV1VVYdWqVZwAvQ7Xr1/HTz/9hMuXL0MURQQHB+OFF15AQUEB52y7gyeffBLXrl3DwoUL0bdvX/z777/IzMzEp59+iq+++grDhg0zdBaJ7gk7d4j+AxQKBYKCgrB582aEhoYaOjtGx8HBASdOnOCcO0REVKeAgAAsWrQI/fv313l4waVLlxAeHl7jkd+kIZVKkZ6eDhcXF53lubm5cHFxYWfsXSooKMCKFSuwZMkSREdHs9zuwN3dHRs2bEDnzp1hY2ODkydPokWLFti4cSO++OILHD582NBZJLonHJZF9B9gYmKCyspKPkq5np555hmsXr2ajxGlRqdSqbB+/XrExsZCEASEhoZixIgRkEqlhs4a/UeUlJTUmCTYxsbGQLlp+lJTUxEQEFBjuVqthkKhMECOjMPNR0/fLiUlRe9j0knX3r17sWTJEqxbtw4+Pj4YPXo0fvvtN0Nnq8krLS2t7lB0cHBAdnY2WrRogdatW+P06dMGzh3RvWPnDtF/xP/+9z98/vnn+O233yCTsenfC5VKhS+++AI7duxAmzZtaszd8fXXXxsoZ8bjwIED+PLLL6s7KUJCQvDOO++gZ8+ehs5akxUfH4+hQ4ciNTUVQUFBEEURly9fhpeXF7Zs2cJIsrvAelc/iYmJePXVV7F//35UVFRUL795Ac5ogNq1bNkShw4dqjF07Z9//kH79u0NlKumq3379hAEAYIgoH///jrnJyqVComJiRgyZIgBc9h0paSkYNmyZViyZAlKS0sxbtw4KBQKrF27llHadykoKAhxcXFo3rw52rVrV/0Ag59//hnu7u6Gzh7RPeMVHtF/xLFjx7Bnzx7s3LkTrVu3hlwu1/l83bp1BspZ03f+/Pnqk/ILFy7ofMZoqDv7888/8eyzz2LUqFGYNm0aRFFEZGQk+vfvj2XLliEiIsLQWWySpk2bBn9/fxw9ehQODg4ANEMUnnrqKUybNg1btmwxcA6bNta7+nvyyScBAEuWLIGrqyt/5/6/vTsPqznt/wD+PpWStA76VY92IcVoMMpS2YUKz4OZ0aRl7GrM89ieGes8YvyMIVu2JAzDTIydGS0kDdUUkUxEGPsWLbSc3x9+zjPHKS3oPuf0fl2X6zrn/n7/eF/n+uZ7zud735+7BmbPng0/Pz/cuHED5eXliImJQXZ2NqKjo7Fv3z7R8ZSOr68vACA9PR19+/ZF48aNZce0tbVhbW2NoUOHCkqnvLy8vJCYmIiBAwdi+fLl6NevHzQ1NSvcFp0q9/nnn+PmzZsAXvzt9u3bF1u3boW2tjaioqLEhiOqBfbcIaonAgICXnt848aNdZSE6pvWrVtj9OjRmDx5stz4kiVLsG7dOmRlZQlKptz09PSQnJwMZ2dnufGMjAx06dIFT58+FZRMNfC6q73GjRsjNTUVLVu2FB1FJR0+fBhhYWFymxfMmjULffr0ER1NaW3atAnDhw/n7ljVpKWlhZCQEIwbNw4tWrSQjTdo0AAZGRmcuVNLhYWFuHDhAiwtLdGkSRPRcYhqjMUdIqIauH79OiQSCSwsLERHURk6Ojo4d+6cQh+KnJwcODk5yS37oP8yMTHBvn374ObmJjd+4sQJDBo0iFuhV4HXXe15enriyy+/RK9evURHIaIKnDx5EpGRkdixYwdatWoFPz8/DB8+HObm5izu1MLz58+Rm5sLOzs7ti4glcY9fYmIqlBeXo558+bB0NAQVlZWsLS0hJGREb7++muFRqOkqHnz5jh69KjC+NGjR9G8eXMBiVTDwIEDMXr0aPz222+QSqWQSqVITk7G2LFj4e3tLTqe0uN1V3vr16/HN998g02bNiE1NRVnzpyR+0dVS0lJwebNm7FlyxakpqaKjqP0ysrKsHjxYnTq1An/8z//AxMTE7l/JM/V1RXr1q3DzZs3MWbMGGzfvh0WFhYoLy/HL7/8gidPnoiOqBIKCwsRFBSERo0aoU2bNsjLywPwYln0woULBacjqjmWJonU2MtGhdXBXQEq9+WXX2LDhg1YuHAhunTpAqlUihMnTmDOnDkoLi7G/PnzRUdUav/85z8REhKC9PR0uLm5QSKRIDExEVFRUVi2bJnoeEorPDwc/v7+cHV1lTXxLi0thbe3Nz+3auB1V3t3797FpUuX5JbzSiQSNlSuhuvXr+Ojjz7CiRMnYGRkBODF9tRubm7Ytm0bC4uVmDt3LtavX48vvvgCM2fOxJdffokrV65g9+7dmDVrluh4SqtRo0YIDAxEYGAgsrOzZd9Vpk+fjt69e2PPnj2iIyq1GTNmICMjA/Hx8XKNu3v16oXZs2dj+vTpAtMR1RyXZRGpsblz58peFxcXY9WqVXB0dISrqysAIDk5GefOncP48eOxYMECUTGVnrm5OSIiIhRmS/z8888YP348bty4ISiZ6ti1axe+/fZbWZ+Tl7sW+fj4CE6m/P744w9cuHABUqkUjo6OFW6zTBXjdVc7jo6OaN26NaZOnVphQ+VXd4Ki/+rTpw/y8/OxadMmWc+i7OxsBAYGQk9PD0eOHBGcUDnZ2dkhPDwcAwYMgL6+PtLT02VjycnJ+P7770VHVBllZWXYu3cvIiMjWdypgpWVFX744Qd07twZ+vr6yMjIgK2tLXJycuDi4oL8/HzREYlqhMUdonoiODgYZmZm+Prrr+XGZ8+ejWvXriEyMlJQMuXXsGFDnDlzBg4ODnLj2dnZeP/991FUVCQoGRHR26enp4eMjAwWEmtBV1cXSUlJCtuep6WloUuXLrxfVEJPTw9ZWVmwtLSEmZkZ9u/fDxcXF1y+fBnt27fH48ePRUckNdSoUSNkZmbC1tZWrriTkZGB7t2787ojlcNlWUT1xM6dO5GSkqIwPnLkSHTo0IHFnddo164dVqxYgfDwcLnxFStWoF27doJSqZ7nz5/jzp07Cn2KLC0tBSVSbmVlZYiKisLRo0cr/NxiY2MFJVMtvO5qrkePHizu1JKlpSVKSkoUxktLS9mI/zX+9re/4ebNm7C0tIS9vT2OHDkCFxcXnD59Gjo6OqLjkZrq2LEj9u/fj0mTJgGAbJbiunXrZLPciVQJiztE9YSuri4SExPltswEgMTERG49WoVFixZhwIAB+PXXX+Hq6gqJRIKkpCRcu3YNBw4cEB1P6f3xxx8IDAxEUlKS3Dj7d7xeaGgooqKiMGDAADg5OVW7fxa9wOuu9gYNGoTJkyfj7NmzcHZ2lvV8eokNvSu3aNEiTJo0CStXrsQHH3wAiUSClJQUhIaGYvHixaLjKa3Bgwfj6NGj+PDDDxEaGoqPPvoIGzZsQF5eHiZPniw6HqmpBQsWoF+/fjh//jxKS0uxbNkynDt3DidPnkRCQoLoeEQ1xmVZRPXEwoULMWfOHAQHB6Nz584AXvTciYyMxKxZs9g0rgo3btzAqlWr5HqfjB8/Hubm5qKjKb0uXbpAS0sL06dPh5mZmUKRgrOfKtakSRNER0fDy8tLdBSVxOuu9jQ0Kt9MlYUxRcbGxnLXV0FBAUpLS2VbKr98raenhwcPHoiKqVJ+++03nDhxAvb29iwm0jt19uxZLF68GKmpqSgvL4eLiwumTZsGZ2dn0dGIaozFHaJ6ZMeOHVi2bJlcc9HQ0FAMGzZMcDJSZ3p6ekhNTUWrVq1ER1Ep5ubmiI+PV+j1RNXD647qyqZNm6p9rr+//ztMorqOHTsGNzc3WUHspdLSUiQlJaF79+6CkhERqQ4Wd4iIqrBx40Y0btwY//jHP+TGd+7cicLCQn5Zr0LHjh3x3XffoWvXrqKjqJRvv/0Wly9fxooVK7gkqxZ43dWcl5cXtm3bBkNDQwDA/PnzMWHCBNmW3vfv30e3bt1w/vx5gSlJHWlqauLmzZto1qyZ3Pj9+/fRrFkzzhajd8LDwwOBgYH4xz/+AV1dXdFxiN4YiztERFVo2bIlIiIi4OnpKTeekJCA0aNHIzs7W1Ay5fXX7UNTUlLw1VdfISwsrML+HQYGBnUdT2kNGTJE7n1sbCxMTEzQpk0bhc8tJiamLqOpBF53b+bVH9gGBgZIT0+Hra0tAOD27dswNzfnD+1quHPnToWNvNu2bSsokXLT0NDA7du30bRpU7nxixcvokOHDtySmt6Jf/7zn9i6dSuKioowbNgwBAUFyVoXEKkiNlQmUmOv9gF4HfYBqNzVq1dhY2OjMG5lZYW8vDwBiZSfkZGR3LUnlUrRs2dPuXPY2FbRyxkTLw0ePFhQEtVU0XXXo0cPhTFedxV79Xkfn//VXGpqKvz9/ZGVlaXw+fG6U/SyoC2RSDBq1Ci5nbHKyspw5swZuLm5iYpHau7bb7/FokWLsG/fPmzcuBHdu3eHvb09AgMD4efnB1NTU9ERiWqExR0iNbZ06VLREdRCs2bNcObMGVhbW8uNZ2Rk4L333hMTSsnFxcWJjqCSNm7cKDqCSuN1R6IFBATAwcEBGzZsgKmpKZdUVuFlQVsqlUJfX19uaYy2tjY6d+6Mzz77TFQ8qgc0NTXh4+MDHx8f3L17F2vWrMHMmTPx73//G15eXggJCUGPHj1ExySqFhZ3iNQYe8G8HSNGjEBISAj09fVlTR0TEhIQGhqKESNGCE6nnNzd3UVHUBt37txBdnY2JBIJHBwcFHpS0H+5u7ujsLAQU6ZMwe7du1FSUoJevXohPDwcTZo0ER1P6UkkEoViBIsTNZObm4uYmBjY29uLjqISXha0ra2t8a9//Qt6enqCE1F9derUKWzcuBHbtm1Ds2bNMGrUKNy8eRODBg3CuHHjsHjxYtERiarEnjtEaiw/P1/WV6Kq9ersP1G558+fw8/PDzt37pTt5FFeXo5PP/0UERER0NbWFpxQ+T18+BAbNmxAVlYWJBIJWrdujYCAAJiYmIiOprTy8/MxYcIEbN++XbaUQ1NTE8OHD8fKlSsVlnDRC1OmTMGqVavwySefQFdXF99//z08PDywc+dO0dGUnoaGBvr37y9bGrN371706NFD9oP72bNnOHToEJcWvYavry/8/PwwdOhQ0VFUSlFREaRSKRo1agTgxXLoXbt2wdHREX369BGcjtTVnTt3sHnzZmzcuBF//PEHBg0ahODgYPTt21dW2P7111/h6+uLp0+fCk5LVDUWd4jU2F+bY2poaFT4BJb9J6rv4sWLyMjIgK6uLpydnWFlZSU6kkpISEiAt7c3DA0N0aFDBwAv+lI8evQIe/bs4SyfSgwbNgzp6elYvnw5XF1dIZFIkJSUhNDQULRt2xY7duwQHVEp2dnZYf78+bJZdadOnUKXLl1QXFwMTU1NwemUW0BAQLXO4/LByt27dw/+/v7o1KkTnJycFBp5e3t7C0qm3Pr06YMhQ4Zg7NixePToEVq2bAltbW3cu3cPS5Yswbhx40RHJDWkra0NOzs7BAYGYtSoUQoNvYEXD1p8fHy47JdUAos7RGosISEBXbp0gZaWFhISEl57Ln9g07vi5OQENzc3rF69WvbjuqysDOPHj8eJEyeQmZkpOKFy0tPTw+HDhxW28j5+/Dj69euHgoICQcmUm7a2NnJzc2FhYSEb09XVxcWLF9G8eXOByag+2LNnD/z8/PDkyROFY3yQUrkmTZogISEBbdq0wfr167F8+XL8/vvv+OmnnzBr1ixkZWWJjkhq6Pjx4+jWrVuV5504cQIdOnSQa/hNpIzYc4dIjf21YMPiTe2VlZUhKioKR48erXBr29jYWEHJVMOlS5fw008/yc2a0NTUxBdffIHo6GiByZTbe++9V+HSK0NDQxgbGwtIpBrKysoUlkpqaWmhtLRUUCKqT0JCQuDn54eZM2dyp50aKCwshL6+PgDgyJEjGDJkCDQ0NNC5c2dcvXpVcDpSV9Up7ABA//79kZ6eDltb23eciOjNsLhDVI88evQIp06dqrBA8emnnwpKpfxCQ0MRFRWFAQMGwMnJiQ1Ga8jFxQVZWVlo2bKl3HhWVhbef/99MaFUwFdffSUrgJmZmQEAbt26hSlTpmDmzJmC0ykvqVSqsKVycXExxo4dK9esNSYmRkQ8UnP379/H5MmTWdipIXt7e+zevRuDBw/G4cOHMXnyZAAveqKwJyCJxoUupCq4LIuonti7dy8++eQTFBQUQF9fX65AIZFI8ODBA4HplFuTJk0QHR0NLy8v0VFU0g8//ICpU6di0qRJ6Ny5MwAgOTkZK1euxMKFC9G6dWvZuW3bthUVU+m0b98eOTk5ePbsGSwtLQEAeXl50NHRQYsWLeTOTUtLExFRKbFvDInk7++Pbt26ITg4WHQUlfLjjz/i448/RllZGXr27IkjR44AABYsWIBjx47h4MGDghNSfaavr4+MjAzO3CGlx+IOUT3h4OAALy8vhIWFyXajoOoxNzdHfHw8HBwcREdRSRoaGq89LpFI2Ni7AnPnzq32ubNnz36HSYiouubPn4+lS5diwIABcHZ2VmioHBISIiiZ8rt16xZu3ryJdu3aye4bp06dgoGBAVq1agUAuH79OszNzau8rxC9TSzukKpgcYeontDT08PZs2d5Y6qFb7/9FpcvX8aKFSu4JKsWatIvgTuQ1dy2bdvg7e0tt+SIiMSwsbGp9JhEIsHly5frMI36MTAwYO8TqnMs7pCqYM8donqib9++SElJ4Y2pFhITExEXF4eDBw+iTZs2Ck9i2bvj9apbsBkwYADWr18v6y9D1TNmzBh8+OGH/NsmUgK5ubmiI6g1PpMmEfhgj1QFiztEamzPnj2y1wMGDMCUKVNw/vz5CqeKe3t713U8lWFkZITBgweLjqH2jh07hqKiItExVA5/7BApn+fPnyM3Nxd2dnbQ0uLXbSJVxvssqQouyyJSY9Vdk85eJ6QMOO25dvi5ESmPwsJCTJo0CZs2bQIAXLx4Eba2tggJCYG5uTmmT58uOKFq4/93RESV46MEIjX26nbnRERE9O7MmDEDGRkZiI+PR79+/WTjvXr1wuzZs1ncIRKsffv21V5mxZ0oSdWwuEOk5mJjYzFx4kQkJyfDwMBA7tjjx4/h5uaGiIgIdOvWTVBC5eTi4oKjR4/C2Ni4yi8CvPkTEREA7N69Gz/88AM6d+4sd99wdHTEpUuXBCZTD+x9Qm/K19dX9rq4uBirVq2Co6MjXF1dAQDJyck4d+4cxo8fLyghUe2xuEOk5pYuXYrPPvtMobADAIaGhhgzZgyWLFnC4s4rfHx8oKOjA0D+iwAREVFl7t69i2bNmimMFxQUsDDxFrCbBL2p2bNny14HBwcjJCQEX3/9tcI5165dq+toRG+MPXeI1JyVlRUOHTqE1q1bV3j8woUL6NOnD/Ly8uo4mfrhltRvhr0Uqk8qlcp+KDo5OeHgwYNo3ry54FRE5O7ujr///e+YNGkS9PX1cebMGdjY2GDixInIycnBoUOHREdUSoGBgVi2bBn09fXlxgsKCjBp0iRERkYCAK5duwZzc3NoamqKiElqxtDQECkpKWjRooXc+B9//IEOHTrg8ePHgpIR1U71uq0Skcq6ffu2ws5Yf6WlpYW7d+/WYSL1NWbMGNy+fVt0DJX173//GyYmJqJjKI0FCxZUOF5WVoaPP/5Y9j4zM5OFHSIlsWDBAnz55ZcYN24cSktLsWzZMvTu3RtRUVGYP3++6HhKa9OmTRXullhUVITo6GjZ++bNm7OwQ2+Nrq4uEhMTFcYTExPRsGFDAYmI3gyLO0RqzsLCAmfPnq30+JkzZ2BmZlaHidQXJ0JWbvPmzejSpQvMzc1x9epVAC+WDP7888+yc2bMmAEjIyNBCZXP0qVLsXbtWrmxsrIyjBgxAunp6WJCEdFrubm54cSJEygsLISdnR2OHDkCU1NTnDx5Eh988IHoeEonPz8fjx8/hlQqxZMnT5Cfny/79/DhQxw4cKDCZW5Eb8Pnn3+OcePGYeLEidiyZQu2bNmCiRMnYsKECZg8ebLoeEQ1xp47RGrOy8sLs2bNQv/+/RWeQhQVFWH27NkYOHCgoHRUH6xevRqzZs3C559/jvnz56OsrAwAYGRkhKVLl8LHx0dwQuV04MAB9OrVC0ZGRhg2bBhKSkowfPhwXLhwAXFxcaLjEVElnJ2dZVuh0+sZGRlBIpFAIpHAwcFB4bhEIsHcuXMFJKP6YPr06bC1tcWyZcvw/fffAwBat26NqKgoDBs2THA6oppjzx0iNXf79m24uLhAU1MTEydORMuWLSGRSJCVlYWVK1eirKwMaWlpMDU1FR1V5bFnTMUcHR0RFhYGX19fuc8oMzMTHh4euHfvnuiISis+Ph4+Pj6Ijo7Ghg0bcOnSJcTGxvLvlUjJ3blzB3fu3EF5ebnceNu2bQUlUk4JCQmQSqXo0aMHfvrpJ7mludra2rCysoK5ubnAhEREqoMzd4jUnKmpKZKSkjBu3DjMmDFDtnRIIpGgb9++WLVqFX8o0juVm5uL9u3bK4zr6OigoKBAQCLV4eHhgc2bN2Po0KFo3bo1EhIS0KRJE9GxiKgSqamp8Pf3R1ZWlsJSXYlEIpu5SC+4u7sDeHGfsLS05I5iVOcePXqEH3/8EZcvX8a//vUvmJiYyB56WlhYiI5HVCMs7hDVA1ZWVjhw4AAePnyInJwcSKVStGjRAsbGxqKjUT1gY2OD9PR0WFlZyY0fPHgQjo6OglIppyFDhlQ43rRpUxgZGWH06NGysZiYmLqKRUTVFBAQAAcHB2zYsAGmpqYsVrzGmTNn4OTkBA0NDTx+/Pi1/QE544nehTNnzqBXr14wNDTElStXEBwcDBMTE+zatQtXr16Va+ZNpApY3CGqR4yNjdGxY0fRMdSWlZXVa3cmq6+mTJmCCRMmoLi4GFKpFKdOncK2bduwYMECrF+/XnQ8pWJoaFjheN++fes4CRHVRm5uLmJiYmBvby86itJ7//33cevWLTRr1gzvv/8+JBJJhRsTcMYTvStffPEFRo0ahUWLFkFfX1823r9/f7ldKYlUBYs7RERvSWZmpugISikgIAClpaWYOnUqCgsL8fHHH8PCwgLLli3DiBEjRMdTKhs3bgTwYue1vLw8NG3aFI0aNRKcioiqq2fPnsjIyGBxpxpyc3PRtGlT2Wuiunb69GmsWbNGYdzCwgK3bt0SkIjozbChMhFRBYyNjas9nf7BgwfvOI36uHfvHsrLy7m1bRXKy8vRsGFDnDt3Di1atBAdh4iq6d69e/D390enTp3g5OSkMJvT29tbUDIiepWpqSkOHTqE9u3by234cOTIEQQFBeHatWuiIxLVCGfuEBFVYOnSpaIjqCU2A64eDQ0NtGjRAvfv32dxh0iFJCUlITExEQcPHlQ4xuVF8vbs2VPtc1kUo3fBx8cH8+bNw44dOwC8+BvNy8vD9OnTMXToUMHpiGqOM3eIiOita9++fbVnPqWlpb3jNKpp//79WLhwIVavXg0nJyfRcYioGqytrTFw4EDMnDmTO1FWQUNDQ+79qz13/noPYVGM3oX8/Hx4eXnh3LlzePLkCczNzXHr1i24urriwIED0NPTEx2RqEZY3CEiqoGioiKUlJTIjRkYGAhKo7zmzp1b7XNnz579DpOoLmNjYxQWFqK0tBTa2trQ1dWVO87lgETKR19fH+np6bCzsxMdRaX8+uuvmDZtGsLCwuDq6gqJRIKkpCR89dVXCAsLQ+/evUVHJDUWGxuLtLQ0lJeXw8XFBb169RIdiahWWNwhIqpCQUEBpk2bhh07duD+/fsKx/lEkd6FTZs2vfa4v79/HSUhoury9/dHt27dEBwcLDqKSnFyckJERAS6du0qN378+HGMHj0aWVlZgpKROsvNzYWNjY3oGERvDXvuEBFVYerUqYiLi8OqVavw6aefYuXKlbhx4wbWrFmDhQsXio5HaorFGyLV4+DggBkzZiAxMRHOzs4KDZVDQkIEJVNuly5dgqGhocK4oaEhrly5UveBqF6wt7dH9+7dERQUhL///e9o2LCh6EhEb4Qzd4iIqmBpaYno6Gh4eHjAwMAAaWlpsLe3x+bNm7Ft2zYcOHBAdESlY2JigosXL6JJkyZV7jzG5UWVKysrw+7du5GVlQWJRAJHR0d4e3tDU1NTdDQiqsDrZgFIJBJcvny5DtOoju7du6NBgwbYsmULzMzMAAC3bt2Cn58fnj9/joSEBMEJSR1lZmYiMjISW7duxbNnzzB8+HAEBgbiww8/FB2NqFZY3CEiqkLjxo1x7tw5WFlZ4W9/+xtiYmLQqVMn5ObmwtnZGU+fPhUdUels2rQJI0aMgI6ODpcX1VJOTg68vLxw48YNtGzZElKpFBcvXkTz5s2xf/9+9vQgIrWRk5ODwYMHIzs7G5aWlgCAvLw8ODg4YPfu3bC3txeckNRZaWkp9u7di6ioKBw8eBAtWrRAUFAQ/Pz80LRpU9HxiKqNxR0ioiq0bdsWy5cvh7u7O/r06YO2bdti8eLFCA8Px6JFi3D9+nXREUkNeXl5QSqVYuvWrTAxMQEA3L9/HyNHjoSGhgb2798vOCER1ZaBgQHS09Nha2srOorSkEql+OWXX3DhwgVIpVI4OjqiV69e1d55kehNPXv2DKtWrcKMGTPw/PlzNGjQAMOHD8c333wjm1FGpMxY3CEiqsJ3330HTU1NhISEIC4uDgMGDEBZWRlKS0uxZMkShIaGio6o9MrKyrBr1y7Z8qLWrVvDx8cHWlps/VYZPT09JCcnw9nZWW48IyMDXbp04YwxIhWmr6+PjIwMFncqUFxcDB0dHRZ1qM6kpKQgMjIS27dvh56eHvz9/REUFIQ///wTs2bNwpMnT3Dq1CnRMYmqxG/VRERVmDx5suy1p6cnLly4gJSUFNjZ2aFdu3YCk6mGzMxM+Pj44NatW2jZsiUA4OLFi2jatCn27NmjULygF3R0dPDkyROF8adPn0JbW1tAIiKid6O8vBzz589HREQEbt++jYsXL8LW1hYzZ86EtbU1goKCREckNbRkyRJs3LgR2dnZ8PLyQnR0NLy8vKChoQHgRQ+tNWvWoFWrVoKTElWPhugARESqxtLSEkOGDGFhp5qCg4PRpk0bXL9+HWlpaUhLS8O1a9fQtm1bjB49WnQ8pTVw4ECMHj0av/32G6RSKaRSKZKTkzF27Fh4e3uLjkdE9Nb85z//QVRUFBYtWiRXvHZ2dsb69esFJiN1tnr1anz88cfIy8vD7t27MXDgQFlh5yVLS0ts2LBBUEKimuGyLCKiKsybN++1x2fNmlVHSVSTrq4uUlJS0KZNG7nxzMxMdOzYEUVFRYKSKaecnBzY29vj0aNH8Pf3x969e2XbKZeUlMDHxwdRUVEVbhtMRKqBy7Lk2dvbY82aNejZs6fcZ3PhwgW4urri4cOHoiMSESk9LssiIqrCrl275N6XlJQgNzcXWlpasLOzY3GnCi1btsTt27cVijt37tzhDigVcHBwgIWFBTw9PeHr64v//d//RXZ2tqzBKD8zItXHfjLybty4UeH/beXl5SgpKRGQiOqLR48eYcOGDXI9AYOCgvgAhVQSiztERFX4/fffFcby8/MxatQoDB48WEAi5Zefny97HRYWhpCQEMyZMwedO3cGACQnJ2PevHn45ptvREVUWgkJCUhISEB8fDwmTpyI4uJiWFpaokePHsjPz4euri4sLCxExySiN8CJ8/LatGmD48ePw8rKSm58586daN++vaBUpO5SUlLQt29f6OrqolOnTpBKpfjuu+8QFhaGI0eOwMXFRXREohrhsiwiolrKzMzEwIEDceXKFdFRlI6Ghobck+mXt5qXY399X1ZWVvcBVURJSQlOnjyJ+Ph4xMfHIzk5Gc+ePYO9vT2ys7NFxyOiV8THx8PDw6PK8xITE9GxY0fo6Oi8+1AqYO/evfDz88OMGTMwb948zJ07F9nZ2YiOjsa+ffvQu3dv0RFJDXXr1g329vZYt26dbPfO0tJSBAcH4/Llyzh27JjghEQ1w+IOEVEtJSYmYtCgQewFUIGEhIRqn+vu7v4Ok6iHoqIiJCYm4vDhw1i3bh2ePn3KohiREmrYsCEsLCwQEBAAf39/NG/eXHQklXH48GGEhYUhNTUV5eXlcHFxwaxZs9CnTx/R0UhN6erq4vfff1fYDev8+fPo0KEDCgsLBSUjqh0uyyIiqkJ4eLjce6lUips3b2Lz5s3o16+foFTKjQWbN1NcXIykpCTExcUhPj4ep0+fho2NDdzd3bF69Wp+vkRK6s8//8SWLVsQFRWFOXPmoGfPnggKCoKvr6/cLlD0X6WlpZg/fz4CAwNr9GCA6E0ZGBggLy9Pobhz7do16OvrC0pFVHucuUNEVAUbGxu59xoaGmjatCl69OiBGTNm8AtANbzasNDR0RGBgYFsWFgBd3d3nD59GnZ2dujevTvc3d3h7u4OU1NT0dGIqAbS09MRGRmJbdu2oby8HJ988gmCgoLQrl070dGUTuPGjZGZmQlra2vRUageCQkJwa5du7B48WK4ublBIpEgMTERU6ZMwdChQ7F06VLREYlqhMUdIiJ6pypqWJiSkoKioiI2LKxAgwYNYGZmBl9fX3h4eKB79+5o0qSJ6FhEVAt//vkn1q5di4ULF0JLSwvFxcVwdXVFRESEwg6C9Zmvry98fX0xatQo0VGoHnn+/DmmTJmCiIgIlJaWAnhxDx43bhwWLlzInlikcljcISKid4oNC2umoKAAx48fR3x8POLi4pCeng4HBwe4u7vDw8MD7u7uaNq0qeiYRFSJkpIS/Pzzz4iMjMQvv/yCDh06ICgoCB999BEePHiAadOmIT09HefPnxcdVWmsWbMGc+bMwSeffIIPPvgAenp6cse9vb0FJaP6oLCwEJcuXYJUKoW9vT0aNWokOhJRrbC4Q0RUhYKCAixcuBBHjx7FnTt3UF5eLnf88uXLgpKpBjYsfDNPnjxBYmKirP9ORkYGWrRogczMTNHRiOgVkyZNwrZt2wAAI0eORHBwMJycnOTOycvLg7W1tcK9pD7T0NCo9Bh3VSQiqh42VCYiqkJwcDASEhLg5+cHMzMzuS2+qWpsWPhm9PT0YGJiAhMTExgbG0NLSwtZWVmiYxFRBc6fP4/ly5dj6NChlTZQNjc3R1xcXB0nU24sdFFdGTJkSLXPjYmJeYdJiN4+FneIiKpw8OBB7N+/H126dBEdRSUNHz4cQUFBFTYs/Oijj0THUzrl5eVISUmRLcs6ceIECgoKYGFhAU9PT6xcuRKenp6iYxJRBY4ePVrlOVpaWtzx7v/FxsZi4sSJSE5OhoGBgdyxx48fw83NDREREejWrZughKRuuJEDqTMuyyIiqoKNjQ0OHDiA1q1bi46ikl5tWCiVSqGtrc2GhZUwMDBAQUEBzMzM4OHhAQ8PD3h6esLOzk50NCKqwJ49e6p9LnvHyPP29oanpycmT55c4fHw8HDExcVh165ddZyMiEj1sLhDRFSFLVu24Oeff8amTZvYZO8NsGFh9axZswaenp5wcHAQHYWIquF1/WL+ir1jFFlZWeHQoUOVPjy5cOEC+vTpg7y8vDpORvXJnTt3kJ2dDYlEAgcHBzRr1kx0JKJaYXGHiKgK7du3lxUlrK2t0aBBA7njaWlpgpIpt8DAwGqdFxkZ+Y6TEBGRMmrYsCEyMzNhb29f4fGcnBw4OzujqKiojpNRfZCfn48JEyZg+/btssKrpqYmhg8fjpUrV3IJF6kc9twhIqqCr6+v6AgqKSoqClZWVmjfvj34HIGIiF5lYWGBs2fPVlrcOXPmDMzMzOo4FdUXwcHBSE9Px759++Dq6gqJRIKkpCSEhobis88+w44dO0RHJKoRztwhIqJ3Yvz48di+fTssLS0RGBiIkSNHwsTERHQsIqK3Kjw8HKNHj0bDhg0RHh7+2nNDQkLqKJVqmDRpEuLj43H69Gk0bNhQ7lhRURE6deoET0/PKj9XotrQ09PD4cOH0bVrV7nx48ePo1+/figoKBCUjKh2WNwhIqqm58+f486dOwpbtlpaWgpKpPyePXuGmJgYREZGIikpCQMGDEBQUBD69OnDLeWJSC3Y2NggJSUF7733HmxsbCo9TyKR4PLly3WYTPndvn0bLi4u0NTUxMSJE9GyZUtIJBJkZWVh5cqVKCsrQ1paGkxNTUVHJTVkaWmJ/fv3w9nZWW78zJkz8PLywvXr1wUlI6odFneIiKpw8eJFBAUFISkpSW5cKpWyQWYNXL16FVFRUYiOjkZJSQnOnz+Pxo0bi45FREQCXb16FePGjcPhw4dlS3glEgn69u2LVatWwdraWmxAUltr167Fzp07ER0dLVv+d+vWLfj7+2PIkCEYM2aM4IRENcOeO0REVQgICICWlhb27dsHMzMzzjipJYlEAolEAqlUqjD7iYiI6icrKyscOHAADx8+RE5ODqRSKVq0aAFjY2PR0UjNrV69Gjk5ObCyspLNws7Ly4OOjg7u3r2LNWvWyM7l5hmkCljcISKqQnp6OlJTU9GqVSvRUVTOX5dlJSYmYuDAgVixYgX69etX7e2DiYhUhVQqxY8//oi4uLgKl/HGxMQISqb8jI2N0bFjR9ExqB7hhhmkbljcISKqgqOjI+7duyc6hsr5a0PlgIAAbN++He+9957oWERE70xoaCjWrl0LT09PmJqacqYnkRKbPXu26AhEbxV77hARVSE2NhZfffUVwsLC4OzsjAYNGsgdNzAwEJRMuWloaMDS0hLt27d/7Q8cPskmInVhYmKCLVu2wMvLS3QUIqqBp0+fKsy04/c7UjWcuUNEVIVevXoBAHr27Ck3zobKr/fpp5/yqTUR1SuGhoawtbUVHYOIqiE3NxcTJ05EfHw8iouLZeP8fkeqijN3iIiqkJCQUOmx33//HZ9//nndhSEiIqW1adMmHDp0CJGRkdDV1RUdh4hew83NDcCL5ZQVLaN0d3cXEYuo1ljcISKqocePH2Pr1q1Yv349MjIy+GSHiIgAAIWFhRgyZAhOnDgBa2trhWW83HGHSHk0btwYqampaNmypegoRG8Fl2UREVVTbGwsIiMjERMTAysrKwwdOhQbNmwQHYuIiJTEqFGjkJqaipEjR7KhMpGS69ixI65du8biDqkNFneIiF7j+vXriIqKQmRkJAoKCjBs2DCUlJTgp59+gqOjo+h4RESkRPbv34/Dhw+ja9euoqMQURXWr1+PsWPH4saNG3ByclKYade2bVtByYhqh8UdIqJKeHl5ITExEQMHDsTy5cvRr18/aGpqIiIiQnQ0IiJSQs2bN+cOO0Qq4u7du7h06RICAgJkYxKJhA2VSWWx5w4RUSW0tLQQEhKCcePGoUWLFrLxBg0aICMjgzN3iIhIzv79+7F8+XJERETA2tpadBwieg1HR0e0bt0aU6dOrXAZpZWVlaBkRLXD4g4RUSVOnjyJyMhI7NixA61atYKfnx+GDx8Oc3NzFneIiEiBsbExCgsLUVpaikaNGiks83jw4IGgZET0Kj09PWRkZMDe3l50FKK3gsuyiIgq4erqCldXVyxbtgzbt29HZGQkvvjiC5SXl+OXX35B8+bNoa+vLzomEREpiaVLl4qOQETV1KNHDxZ3SK1w5g4RUQ1kZ2djw4YN2Lx5Mx49eoTevXtjz549omMRERERUQ2sXbsW//nPfxAYGAhnZ2eFmXbe3t6CkhHVDos7RES1UFZWhr179yIyMpLFHSIiUlBUVISSkhK5MTZbJlIeGhoalR5jQ2VSRSzuEBERERG9BQUFBZg2bRp27NiB+/fvKxznj0UiInpXKi9XEhERERFRtU2dOhWxsbFYtWoVdHR0sH79esydOxfm5uaIjo4WHY+IAHh5eeHx48ey9/Pnz8ejR49k7+/fv89NM0glceYOEREREdFbYGlpiejoaHh4eMDAwABpaWmwt7fH5s2bsW3bNhw4cEB0RKJ6T1NTEzdv3kSzZs0AvFgumZ6eDltbWwDA7du3YW5uzpl2pHI4c4eIiIiI6C148OABbGxsALz4wfhy6/OuXbvi2LFjIqMR0f97dW4D5zqQumBxh4iIiIjoLbC1tcWVK1cAAI6OjtixYwcAYO/evTAyMhIXjIiI1B6LO0REREREb+Dy5csoLy9HQEAAMjIyAAAzZsyQ9d6ZPHkypkyZIjglEQEvdsKSSCQKY0Sqjj13iIiIiIjewKs9PIYPH47w8HA8e/YMKSkpsLOzQ7t27QSnJCLgxRbo/fv3h46ODoAXM+t69OgBPT09AMCzZ89w6NAh9twhlcPiDhERERHRG9DQ0MCtW7dkxR19fX1kZGTIGrQSkfIICAio1nkbN258x0mI3i4t0QGIiIiIiIiI6gKLNqSu2HOHiIiIiOgNsIcHERGJxpk7RERERERvQCqVYtSoUbIeHsXFxRg7dqysh8dLMTExIuIREVE9wOIOEREREdEb8Pf3l3s/cuRIQUmIiKi+YkNlIiIiIiIiIiIVxp47REREREREREQqjMUdIiIiIiIiIiIVxuIOEREREREREZEKY3GHiIiIiIiIiEiFsbhDRERERERERKTCWNwhIiIiIiIiIlJhLO4QEREREREREamw/wPx7KsDSN8yUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#understanding the correlation between various variables\n",
    "plt.figure(figsize=(14,8))\n",
    "sns.heatmap(credit_copy.corr(),annot = True,cmap='coolwarm')\n",
    "plt.title(\"Correlation relation\",color= 'Blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaa605c9",
   "metadata": {},
   "source": [
    "##### The credit_status shows positive co-relation with 'Annual_income','Age' and negative co-relation with 'Family_members'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c855fa7a",
   "metadata": {},
   "source": [
    "## Step 5 : Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c39e4eb9",
   "metadata": {},
   "source": [
    "##### Testing the Hypothesis using the one way Annova and taking the siginificance level of 5% , i.e alpha = 0.05,\n",
    "If P-value <= 0.05, reject the Null Hypothesis(Ho),\n",
    "\n",
    "If P-value > 0.05, we accept the Null Hypothesis (Ho)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d1d3a6e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-Statistic: 1.2569492832246965\n",
      "P-Value: 0.045159224398941086\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import f_oneway\n",
    "\n",
    "# Perform one-way ANOVA\n",
    "groups = [credit_copy[credit_copy['Annual_income'] == group]['Credit_status'] for group in credit_copy['Annual_income'].unique()]\n",
    "f_stat, p_value = f_oneway(*groups)\n",
    "print(\"F-Statistic:\", f_stat)\n",
    "print(\"P-Value:\", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04c73edd",
   "metadata": {},
   "source": [
    "##### The obtained P-Value < 0.05, We reject the Null Hypothesis and We consider the Alternate Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15614391",
   "metadata": {},
   "source": [
    "##### So , Higher Annual income people are more likely to get the credit card approved."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69b56ce9",
   "metadata": {},
   "source": [
    "## Final Report Conculsion:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b6f67c",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "1. The Hypothesis Testing shows that high-income people are more likely to get credit card approval.\n",
    "\n",
    "\n",
    "2. The pie chart shows that 88.7% of applicants got accepted, while only 11.3% were rejected for Credit card.\n",
    "\n",
    "\n",
    "3. There are more female applicants (63.3%) than male applicants (36.7%).\n",
    "\n",
    "\n",
    "4. Working in private companies makes applicants more likely to apply for a credit card, whereas State servants are less likely to apply due to income disparities. Commercial associates have higher income, followed by State servants and working professionals, while pensioners, who are non-employees, have lower income. Pensioners are typically retired and older than other groups.\n",
    "\n",
    "\n",
    "5. The \"Working\" class in private companies, due to lower income and lack of benefits provided for State servants, is most likely to apply for a credit card. Job stability also plays an important role in credit card approval, with State servants having higher stability and approval rates.\n",
    "\n",
    "\n",
    "6. Ownership of a car or property doesn't significantly impact credit card approval, as the average acceptance rate is 88% for all conditions.\n",
    "\n",
    "\n",
    "7. Married individuals living with family are the highest among credit card applicants, followed by single individuals. Civil marriages have the highest acceptance rate, while single individuals have the lowest acceptance rate.\n",
    "\n",
    "8. Academic degree holders have a 100% acceptance rate, indicating that higher education levels are more likely to lead to credit card approval. However, the number of applicants with an academic degree is less compared to those with a secondary education.\n",
    "\n",
    "\n",
    "9. Most families applying for credit cards are small families, and newly married individuals are more likely to apply.\n",
    "\n",
    "\n",
    "10. Credit_status shows positive correlation with Annual_income and Age, while it has a negative correlation with Family_members.\n",
    "\n",
    "\n",
    "The bank's main focus have to be  on middle-aged working professionals to generate high revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43c4c3e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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