{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1e233234",
   "metadata": {},
   "source": [
    "## Student Performance Indicator\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e05101",
   "metadata": {},
   "source": [
    "#### Life cycle of Machine learning Project\n",
    "\n",
    "- Understanding the Problem Statement\n",
    "- Data Collection\n",
    "- Data Checks to perform\n",
    "- Exploratory data analysis\n",
    "- Data Pre-Processing\n",
    "- Model Training\n",
    "- Choose best model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfcea981",
   "metadata": {},
   "source": [
    "### 1) Problem statement\n",
    "- This project understands how the student's performance (test scores) is affected by other variables such as Gender, Ethnicity, Parental level of education, Lunch and Test preparation course.\n",
    "\n",
    "\n",
    "### 2) Data Collection\n",
    "- Dataset Source - https://www.kaggle.com/datasets/spscientist/students-performance-in-exams?datasetId=74977\n",
    "- The data consists of 8 column and 1000 rows."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15b1355f",
   "metadata": {},
   "source": [
    "### 2.1 Import Data and Required Packages\n",
    "####  Importing Pandas, Numpy, Matplotlib, Seaborn and Warings Library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7eaae1d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3caeb0bb",
   "metadata": {},
   "source": [
    "#### Import the CSV Data as Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "57907087",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/stud.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c8fd8a",
   "metadata": {},
   "source": [
    "#### Show Top 5 Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d1a2a0b",
   "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",
       "        vertical-align: top;\n",
       "    }\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>gender</th>\n",
       "      <th>race_ethnicity</th>\n",
       "      <th>parental_level_of_education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test_preparation_course</th>\n",
       "      <th>math_score</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>writing_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race_ethnicity parental_level_of_education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test_preparation_course  math_score  reading_score  writing_score  \n",
       "0                    none          72             72             74  \n",
       "1               completed          69             90             88  \n",
       "2                    none          90             95             93  \n",
       "3                    none          47             57             44  \n",
       "4                    none          76             78             75  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56a49220",
   "metadata": {},
   "source": [
    "#### Shape of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "417e5820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "041aa39d",
   "metadata": {},
   "source": [
    "### 2.2 Dataset information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e3cff9d",
   "metadata": {},
   "source": [
    "- gender : sex of students  -> (Male/female)\n",
    "- race/ethnicity : ethnicity of students -> (Group A, B,C, D,E)\n",
    "- parental level of education : parents' final education ->(bachelor's degree,some college,master's degree,associate's degree,high school)\n",
    "- lunch : having lunch before test (standard or free/reduced) \n",
    "- test preparation course : complete or not complete before test\n",
    "- math score\n",
    "- reading score\n",
    "- writing score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c4b61b",
   "metadata": {},
   "source": [
    "### 3. Data Checks to perform\n",
    "\n",
    "- Check Missing values\n",
    "- Check Duplicates\n",
    "- Check data type\n",
    "- Check the number of unique values of each column\n",
    "- Check statistics of data set\n",
    "- Check various categories present in the different categorical column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c31d4123",
   "metadata": {},
   "source": [
    "### 3.1 Check Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "707d6a7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                         0\n",
       "race_ethnicity                 0\n",
       "parental_level_of_education    0\n",
       "lunch                          0\n",
       "test_preparation_course        0\n",
       "math_score                     0\n",
       "reading_score                  0\n",
       "writing_score                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8f7b83",
   "metadata": {},
   "source": [
    "#### There are no missing values in the data set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5840ff7f",
   "metadata": {},
   "source": [
    "### 3.2 Check Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae16686e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5d7ae8e",
   "metadata": {},
   "source": [
    "#### There are no duplicates  values in the data set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30dfacc8",
   "metadata": {},
   "source": [
    "### 3.3 Check data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "25f95bc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 8 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   gender                       1000 non-null   object\n",
      " 1   race_ethnicity               1000 non-null   object\n",
      " 2   parental_level_of_education  1000 non-null   object\n",
      " 3   lunch                        1000 non-null   object\n",
      " 4   test_preparation_course      1000 non-null   object\n",
      " 5   math_score                   1000 non-null   int64 \n",
      " 6   reading_score                1000 non-null   int64 \n",
      " 7   writing_score                1000 non-null   int64 \n",
      "dtypes: int64(3), object(5)\n",
      "memory usage: 62.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check Null and Dtypes\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dbbf5b4",
   "metadata": {},
   "source": [
    "### 3.4 Checking the number of unique values of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c2b61b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                          2\n",
       "race_ethnicity                  5\n",
       "parental_level_of_education     6\n",
       "lunch                           2\n",
       "test_preparation_course         2\n",
       "math_score                     81\n",
       "reading_score                  72\n",
       "writing_score                  77\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f6b022",
   "metadata": {},
   "source": [
    "### 3.5 Check statistics of data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "76c608dc",
   "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",
       "        vertical-align: top;\n",
       "    }\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>math_score</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>writing_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.08900</td>\n",
       "      <td>69.169000</td>\n",
       "      <td>68.054000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.16308</td>\n",
       "      <td>14.600192</td>\n",
       "      <td>15.195657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>57.00000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>57.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.00000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.00000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       math_score  reading_score  writing_score\n",
       "count  1000.00000    1000.000000    1000.000000\n",
       "mean     66.08900      69.169000      68.054000\n",
       "std      15.16308      14.600192      15.195657\n",
       "min       0.00000      17.000000      10.000000\n",
       "25%      57.00000      59.000000      57.750000\n",
       "50%      66.00000      70.000000      69.000000\n",
       "75%      77.00000      79.000000      79.000000\n",
       "max     100.00000     100.000000     100.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc41207",
   "metadata": {},
   "source": [
    "#### Insight\n",
    "- From above description of numerical data, all means are very close to each other - between 66 and 68.05;\n",
    "- All standard deviations are also close - between 14.6 and 15.19;\n",
    "- While there is a minimum score  0 for math, for writing minimum is much higher = 10 and for reading myet higher = 17"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac52d9cb",
   "metadata": {},
   "source": [
    "### 3.7 Exploring Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1afd3c09",
   "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",
       "        vertical-align: top;\n",
       "    }\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>gender</th>\n",
       "      <th>race_ethnicity</th>\n",
       "      <th>parental_level_of_education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test_preparation_course</th>\n",
       "      <th>math_score</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>writing_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race_ethnicity parental_level_of_education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test_preparation_course  math_score  reading_score  writing_score  \n",
       "0                    none          72             72             74  \n",
       "1               completed          69             90             88  \n",
       "2                    none          90             95             93  \n",
       "3                    none          47             57             44  \n",
       "4                    none          76             78             75  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9081742",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categories in 'gender' variable:      ['female' 'male']\n",
      "Categories in 'race_ethnicity' variable:   ['group B' 'group C' 'group A' 'group D' 'group E']\n",
      "Categories in'parental level of education' variable: [\"bachelor's degree\" 'some college' \"master's degree\" \"associate's degree\"\n",
      " 'high school' 'some high school']\n",
      "Categories in 'lunch' variable:      ['standard' 'free/reduced']\n",
      "Categories in 'test preparation course' variable:      ['none' 'completed']\n"
     ]
    }
   ],
   "source": [
    "print(\"Categories in 'gender' variable:     \",end=\" \" )\n",
    "print(df['gender'].unique())\n",
    "\n",
    "print(\"Categories in 'race_ethnicity' variable:  \",end=\" \")\n",
    "print(df['race_ethnicity'].unique())\n",
    "\n",
    "print(\"Categories in'parental level of education' variable:\",end=\" \" )\n",
    "print(df['parental_level_of_education'].unique())\n",
    "\n",
    "print(\"Categories in 'lunch' variable:     \",end=\" \" )\n",
    "print(df['lunch'].unique())\n",
    "\n",
    "print(\"Categories in 'test preparation course' variable:     \",end=\" \" )\n",
    "print(df['test_preparation_course'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2dd97e26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 3 numerical features : ['math_score', 'reading_score', 'writing_score']\n",
      "\n",
      "We have 5 categorical features : ['gender', 'race_ethnicity', 'parental_level_of_education', 'lunch', 'test_preparation_course']\n"
     ]
    }
   ],
   "source": [
    "# define numerical & categorical columns\n",
    "numeric_features = [feature for feature in df.columns if df[feature].dtype != 'O']\n",
    "categorical_features = [feature for feature in df.columns if df[feature].dtype == 'O']\n",
    "\n",
    "# print columns\n",
    "print('We have {} numerical features : {}'.format(len(numeric_features), numeric_features))\n",
    "print('\\nWe have {} categorical features : {}'.format(len(categorical_features), categorical_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ae2822d1",
   "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",
       "        vertical-align: top;\n",
       "    }\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>gender</th>\n",
       "      <th>race_ethnicity</th>\n",
       "      <th>parental_level_of_education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test_preparation_course</th>\n",
       "      <th>math_score</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>writing_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race_ethnicity parental_level_of_education     lunch  \\\n",
       "0  female        group B           bachelor's degree  standard   \n",
       "1  female        group C                some college  standard   \n",
       "\n",
       "  test_preparation_course  math_score  reading_score  writing_score  \n",
       "0                    none          72             72             74  \n",
       "1               completed          69             90             88  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "204aa708",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bd42eda6",
   "metadata": {},
   "source": [
    "### 3.8 Adding columns for \"Total Score\" and \"Average\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ffbfdf7",
   "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",
       "        vertical-align: top;\n",
       "    }\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>gender</th>\n",
       "      <th>race_ethnicity</th>\n",
       "      <th>parental_level_of_education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test_preparation_course</th>\n",
       "      <th>math_score</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>writing_score</th>\n",
       "      <th>total score</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>218</td>\n",
       "      <td>72.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "      <td>247</td>\n",
       "      <td>82.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "      <td>278</td>\n",
       "      <td>92.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "      <td>148</td>\n",
       "      <td>49.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>229</td>\n",
       "      <td>76.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race_ethnicity parental_level_of_education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test_preparation_course  math_score  reading_score  writing_score  \\\n",
       "0                    none          72             72             74   \n",
       "1               completed          69             90             88   \n",
       "2                    none          90             95             93   \n",
       "3                    none          47             57             44   \n",
       "4                    none          76             78             75   \n",
       "\n",
       "   total score    average  \n",
       "0          218  72.666667  \n",
       "1          247  82.333333  \n",
       "2          278  92.666667  \n",
       "3          148  49.333333  \n",
       "4          229  76.333333  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total score'] = df['math_score'] + df['reading_score'] + df['writing_score']\n",
    "df['average'] = df['total score']/3\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "26dc3844",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of students with full marks in Maths: 7\n",
      "Number of students with full marks in Writing: 14\n",
      "Number of students with full marks in Reading: 17\n"
     ]
    }
   ],
   "source": [
    "reading_full = df[df['reading_score'] == 100]['average'].count()\n",
    "writing_full = df[df['writing_score'] == 100]['average'].count()\n",
    "math_full = df[df['math_score'] == 100]['average'].count()\n",
    "\n",
    "print(f'Number of students with full marks in Maths: {math_full}')\n",
    "print(f'Number of students with full marks in Writing: {writing_full}')\n",
    "print(f'Number of students with full marks in Reading: {reading_full}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "572c8a75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of students with less than 20 marks in Maths: 4\n",
      "Number of students with less than 20 marks in Writing: 3\n",
      "Number of students with less than 20 marks in Reading: 1\n"
     ]
    }
   ],
   "source": [
    "reading_less_20 = df[df['reading_score'] <= 20]['average'].count()\n",
    "writing_less_20 = df[df['writing_score'] <= 20]['average'].count()\n",
    "math_less_20 = df[df['math_score'] <= 20]['average'].count()\n",
    "\n",
    "print(f'Number of students with less than 20 marks in Maths: {math_less_20}')\n",
    "print(f'Number of students with less than 20 marks in Writing: {writing_less_20}')\n",
    "print(f'Number of students with less than 20 marks in Reading: {reading_less_20}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "190e078c",
   "metadata": {},
   "source": [
    "#####  Insights\n",
    " - From above values we get students have performed the worst in Maths \n",
    " - Best performance is in reading section"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e598bc93",
   "metadata": {},
   "source": [
    "### 4. Exploring Data ( Visualization )\n",
    "#### 4.1 Visualize average score distribution to make some conclusion. \n",
    "- Histogram\n",
    "- Kernel Distribution Function (KDE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4726058",
   "metadata": {},
   "source": [
    "#### 4.1.1 Histogram & KDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c2510266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15, 7))\n",
    "plt.subplot(121)\n",
    "sns.histplot(data=df,x='average',bins=30,kde=True,color='g')\n",
    "plt.subplot(122)\n",
    "sns.histplot(data=df,x='average',kde=True,hue='gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7967c7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15, 7))\n",
    "plt.subplot(121)\n",
    "sns.histplot(data=df,x='total score',bins=30,kde=True,color='g')\n",
    "plt.subplot(122)\n",
    "sns.histplot(data=df,x='total score',kde=True,hue='gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d200b76",
   "metadata": {},
   "source": [
    "#####  Insights\n",
    "- Female students tend to perform well then male students."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "15522737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2VFbrUmFJjaoaWvTe2hINz0nQj/oky2ZlFjwAAMFuV0WDpry+Wit3Vh5030+GZWr6BQNNSAUAAAAAAIBAQ/EdAI7CpTBlXPs/qnLbFW6z6Mz+qeqfHivrD9rLD8yQTu2VpK+L9muVs0qrd1WpsqFZFw3NNDE5AAA4UQsL92rK66tV29Qqe5hV4/qlqE9qjLbtq9PHG/fq3TV79NX2/brv9DizowIAAAAAAMBkFN8Pw+l0qry83C/HSk5OVm5url+OBeDYuFratEE5CkuIVKSlVZee3Fs9oiMO+diIMKtO75uirASH/r2+VDv3N+ijDaUqsPs5NIAj8ucYLzHOA8Fs6bZy3fzySrW0GRqRm6AnrjxJ2YlR3vvX7a7WHf9are/K6vTA5xWyRiWYFxYAABwTzgsAAADQFSi+H4LT6dTAgQPU0NDol+NFRTlUWLjpmN6AG4ahm266SW+88YYqKyv17bffavjw4V0XsotMnjxZVVVVevvtt03NkZeXpylTpmjKlCmm5kBgcRuG3ltXogZFqrWuQkOTW9Qj+uhtZXulxOjCoRl6d80efVdWJyPW5oe0ADrD32O8dOzjPGO8bzHG43htKq3RTX/3FN5/XJCux684SeE2a4fHFGTH65Ubx2jSM0u1u7JRKZOmyzBMCgwAADotGM4LAsn48eM1fPhwzZkzJ6j2DQAAYAaK74dQXl6uhoZGvXzPZRqYm9Klxyp07tM1M/+l8vLyY3rz/eGHH2ru3LlatGiRevXqpeTk5C5MKe3cuVP9+vXTvn37FBdHS010D6ucldpd2Sir3Cr71x/k+PU9nX5uz6RonTc4XR+sL9XWWpscvU/uwqQAOsufY7x0fOM8YzxgvsbmNv2/l1ep1tWq0Xk9NPuy4QcV3tulxkXqH78cowseXyxlDdT2ulbl+DkvAAA4NsFwXnAkgXKhKwAAAA5G8f0IBuamaES/LLNjHNK2bduUkZGhsWPHHvL+5uZmRUQcujX28XjnnXc0fvz4Q34o7+tjAYGgrLZJX23bL0nqpVIV7dtxzPvomxar4dVNWr2rSkk/nqL9DW0+TgngeDHGf48xHjjYox9tVlF5vdLi7Hr2upGKDD9yF5v85GhdXRCr51bVaH2VTSNdrYqxc6oFAECgC+TzglBiGIba2toUFsb7IwAAEPoOPX0DAW3y5Mn6zW9+I6fTKYvFory8PI0fP16//vWvdccddyg5OVnnnnuuJGnjxo368Y9/rJiYGKWlpenaa6/tsJ6VYRh65JFH1KtXLzkcDg0bNkxvvPHGQcd855139JOf/MR7/J/+9KeaNWuWMjMz1a9fP0lScXGxLr/8ciUmJiopKUkXX3yxduzY4d1HW1ub7rjjDiUkJCgpKUl33XWXjP/oy5mXl3dQm6nhw4drxowZ3p+rqqr0X//1X0pLS1NkZKSGDBmi9957z3v/0qVLdcYZZ8jhcCgnJ0e33Xab6uvrvfeXlZXpoosuksPhUH5+vl555ZVj+w+AkOc2DC3YuFduQ+qdEq00VR/3vk7rk6SEcLdsUfF67tvj3w+A7oExnjEe5vumqEIvLS2SJD10yVAlRHXuApQJvaLk2rNZrYZFX3y3rysjAgCAbuKNN95QQUGBHA6HkpKSdM4552jq1KmaN2+e3nnnHVksFlksFi1atEiSdPfdd6tfv36KiopSr169dO+996qlpcW7vxkzZmj48OH6xz/+oby8PMXHx+uKK65QbW2t9zH19fW67rrrFBMTo4yMDD322GMH5Xr55Zc1atQoxcbGKj09XVdddZXKysq89y9atEgWi0UfffSRRo0aJbvdri+++KJT+wYAAAh2FN+D0OOPP677779f2dnZKikp0fLlyyVJ8+bNU1hYmL788kv97W9/U0lJicaNG6fhw4drxYoV+vDDD7V3715ddtll3n39/ve/10svvaRnnnlGGzZs0O23365rrrlGixcv9j6mqqpKX3zxhfeDeUlauHChCgsLtWDBAr333ntqaGjQmWeeqZiYGH3++edasmSJYmJidN5556m5uVmS9Nhjj+nFF1/UCy+8oCVLlqiiokLz588/ptfudrt1/vnna+nSpXr55Ze1ceNGPfTQQ7LZPLOR1q1bp4kTJ2rSpElau3atXn/9dS1ZskS//vWvvfuYPHmyduzYoU8//VRvvPGGnn766Q4nCMDGPTUqr2uWPcyqswakynIC+wqzWnVyUpuMtlZ9U+zSZ5v4XQNweIzxjPEwV5vb0L1vr5dhSJePytGZ/VM7/Vyb1aL9H/1FkrRlb50q6pu7KiYAAMHL3So1VChOdYoKNztMYCspKdGVV16pG264QYWFhVq0aJEmTZqkP/7xj7rssst03nnnqaSkRCUlJd6uWbGxsZo7d642btyoxx9/XM8995z+/Oc/d9jvtm3b9Pbbb+u9997Te++9p8WLF+uhhx7y3j916lR99tlnmj9/vj7++GMtWrRIK1eu7LCP5uZmPfDAA1qzZo3efvttFRUVafLkyQe9hrvuukuzZs1SYWGhhg4d2ql9AwAABDt6/QSh+Ph4xcbGymazKT093Xt7nz599Mgjj3h//sMf/qARI0Zo5syZ3ttefPFF5eTkaMuWLcrKytLs2bP16aef6tRTT5Uk9erVS0uWLNHf/vY3jRs3TpL0wQcfqKCgQDk5369eGR0dreeff97bivbFF1+U1WrV888/L4vFU6p86aWXlJCQoEWLFmnChAmaM2eOpk2bpksuuUSS9Ne//lUfffTRMb32Tz75RN98840KCwu9s/F69erlvf/RRx/VVVddpSlTpkiS+vbtqyeeeELjxo3TM888I6fTqX//+99atmyZxowZI0l64YUXNHDgwGPKgdDV3OrWV9s97eZH5/dQVMSJ/5mMizBUs+JdxY+5RDP+b4NO7Z101Pa1ALonxnjGeJjrzZW7tXlvreId4brnx8f+u9NStl0ZDrdKGq1aubNS5w5K64KUAAAEmZZGae96z1fdXknSReFS/T1xqmv5XNpRK6UXSJHxJgcNLCUlJWptbdWkSZPUs2dPSVJBQYEkyeFwyOVydThnkDwX4LbLy8vT7373O73++uu66667vLe73W7NnTtXsbGxkqRrr71WCxcu1J/+9CfV1dXphRde0N///ndvx6158+YpOzu7w3FuuOEG73avXr30xBNPaPTo0aqrq1NMTIz3vvvvv9+7n87uGwAAINhRfA8ho0aN6vDzypUr9dlnn3V409tu27Ztqq6uVlNTk/cNb7vm5maddNJJ3p9/2I62XUFBQYc1YFeuXKmtW7d637i3a2pq8h6rpKTEWwCQpLCwMI0aNeqgtrRHsnr1amVnZ3s/lP9P7Tl+2GbWMAy53W4VFRVpy5Yt3uO2GzBggBISEjqdAaFtxc4KNTS3KcERrmHZCT7bb/XS15Q//lLt3N+gl77cof83vrfP9g0g9DHGM8aj67laDT32yWZJ0m/O6qP445yO1z+uTSWNVm0qrdEpvXooNpJpfQCAbsowpJLVUtFiqbXp+9ut4Wppcyvc0qYY1Us7l0jOr6Ts0VLPUyVb55Z8CXXDhg3T2WefrYKCAk2cOFETJkzQz3/+cyUmJh72OW+88YbmzJmjrVu3qq6uTq2trYqLi+vwmLy8vA7v7TMyMrzdorZt26bm5uYO7+179Oih/v37d9jHt99+qxkzZmj16tWqqKiQ2+2WJDmdTg0aNMj7uB++N+/svgEAAIIdxfcQEh0d3eFnt9utiy66SA8//PBBj83IyND69eslSe+//76ysrI63G+32yVJLS0t+vDDDzVt2rSjHmvkyJGHXFs1JSWl06/BarUe9EH9D9emcjgcR3y+2+3WTTfdpNtuu+2g+3Jzc7V5s+cD1faZe8APNbW0afWuKknSaX2SZbP67vfEaG7U1QWxevKbav3t82265pRcPowH0GmM8Yzx6Hrvf1evvTUuZSc6dO2pPY97P0l2Q9kJDu2uatQqZ5XG9ev8/ycAAISM1iZpw9tS1Q7Pz9EpUsZJUnI/KSJa/1q4Wrf9+Q19Nu00DY2plKp3Sbu+kvYVSoMnSTGdX/olVNlsNi1YsEBLly7Vxx9/rCeffFLTp0/X119/fcjHL1u2TFdccYXuu+8+TZw4UfHx8XrttdcOWlc9PLzjZxEWi8VbPO/MxbP19fWaMGGCJkyYoJdfflkpKSlyOp2aOHGid1mqdj88tziWC3MBAACCmanF97y8PO3cufOg22+55Rb95S9/kWEYuu+++/Tss8+qsrJSY8aM0V/+8hcNHjzYhLTBZ8SIEXrzzTeVl5ensLCD/1MPGjRIdrtdTqfT2372P3322WdKSEjQ8OHDj3qs119/XampqQddUdsuIyNDy5Yt0xlnnCFJam1t1cqVKzVixAjvY1JSUlRSUuL9uaamRkVFRd6fhw4dqt27d2vLli2HnBk3YsQIbdiwQX369DlkhoEDB6q1tVUrVqzQ6NGjJUmbN29WVVXVEV8fuoc1u6vU0mYoOSZCvVOij/6EY3RGrkPvF7Vq+756vfTlDt12dl+fHwNA98AYfzDGeJwIS5hd726plyT9bkI/2cNObHmYkXmJ2r26UYUlNTqtd5LCbFZfxAQAIDg0Vknr/1dq2C9Zw6X8M6SskZLlh+OhRRWNhkqsWRo67Hxp/1Zp6wKpqUr69h/SgAuklAEmvYDAYbFYdNppp+m0007TH/7wB/Xs2VPz589XRESE2traOjz2yy+/VM+ePTV9+nTvbYf63PVI+vTpo/DwcC1btky5ubmSpMrKSm3ZssV7XrFp0yaVl5froYce8i5ftWLFCp/sGwAAIBSYWnxfvnx5hzeK69ev17nnnqtLL71UkvTII49o9uzZmjt3rvr166cHH3xQ5557rjZv3nxQ69OuUOjcF9THuPXWW/Xcc8/pyiuv1NSpU5WcnKytW7fqtdde03PPPafY2Fjdeeeduv322+V2u/WjH/1INTU1Wrp0qWJiYnT99dfr3XffPagd7aFcffXVevTRR3XxxRfr/vvvV3Z2tpxOp9566y1NnTpV2dnZ+u1vf6uHHnpIffv21cCBAzV79uyDPhA/66yzNHfuXF100UVKTEzUvffeK5vt+w8/x40bpzPOOEOXXHKJZs+erT59+mjTpk2yWCw677zzdPfdd+uUU07Rrbfeql/96leKjo5WYWGhFixYoCeffFL9+/fXeeedp1/96ld69tlnFRYWpilTphx1th1CX0ub2zvrfVTPHl0yc9JmtWjKOf102z+/1XNfbNf1p+Ydd0tbACfGH2N8Vx6HMZ4xHr4VXXCOalxu5fRw6KKhmSe8v9weUYqxh6nO1art5fXql9b15y4AAAQEV6205lXJVSNFxEoFP5di0o78HItFSu4rxWdLhe9IlTukje9IA9qktK6dgBPI5wVff/21Fi5cqAkTJig1NVVff/219u3bp4EDB6qpqUkfffSRNm/erKSkJMXHx6tPnz5yOp167bXXdPLJJ+v999/X/Pnzj+mYMTEx+uUvf6mpU6cqKSlJaWlpmj59uqzW7y+cyM3NVUREhJ588kndfPPNWr9+vR544AGf7BsAACAUmFp8/89WpQ899JB69+6tcePGyTAMzZkzR9OnT9ekSZMkSfPmzVNaWppeffVV3XTTTV2WKzk5WVFRDl0z819ddowfiopyKDk52ef7zczM1Jdffqm7775bEydOlMvlUs+ePXXeeed539g+8MADSk1N1axZs7R9+3YlJCRoxIgRuueeeyRJ7777rl588cVOvIYoff7557r77rs1adIk1dbWKisrS2effbZ3ltzvfvc7lZSUaPLkybJarbrhhhv0s5/9TNXV1d79TJs2Tdu3b9eFF16o+Ph4PfDAAx1mxUnSm2++qTvvvFNXXnml6uvr1adPHz300EOSPLPmFi9erOnTp+v000+XYRjq3bu3Lr/8cu/zX3rpJd14440aN26c0tLS9OCDD+ree+89sX9sBL31xdVqanEr3hGuvqkHr6HsKxcWZOgvn27V5r21+vtXO/QbZr8DfuXvMV7qmnGeMZ4xHr7jNqT4MZ7zjf86vZdPZqlbLRYNyojTNzsqtGFPDcV3AED30NIorX3dU3h3JErDrpLsxzAGhjukgsukLR9KpWulTf/nub0LCvDBcF4QFxenzz//XHPmzFFNTY169uypxx57TOeff75GjRqlRYsWadSoUaqrq9Nnn32miy++WLfffrt+/etfy+Vy6YILLtC9996rGTNmHFPORx99VHV1dfrJT36i2NhY/e53v+vwvj4lJUVz587VPffcoyeeeEIjRozQ//zP/3Tqwt6j7RsAACAUWIwAWXCnublZmZmZuuOOO3TPPfdo+/bt6t27t1atWqWTTjrJ+7iLL75YCQkJmjdv3iH343K55HK5vD8XFxdr0KBBqq6uPqhValNTk4qKipSfn6/IyMgO9zmdTpWXl/vwFR5ecnKyt91SIFm1apXOOuss7du376D1oLqbI/2uIPCtWrVKI0eO1B1/eUvZfQ8+aTcMQ/O+2qnqxhad1T9VBdnxHe5fufBdvfLwVF1337Mafuqxt0Lb/d0Gzb51krcF89vfFmvK66uVEmvXkrvPPOHWtggeNTU1io+PP+SYhGN3pH/PQBnjpcAc5xnjv8cYH7yONr531tLVhVq+P0xxdqu++f0ERYYf37j8n3mqG1s0d+kOSdIvTstTXGTn/l/7z/cNCB6M877FvydgjvbxbOVfb9WIflmdf6Lh9hTeq3ZKETHSSddKkfGHffgrn6zWNTP/pQ8fvFwTxw77j30Z0ncfSSWrPa3qCy6XEnse+2vZUqxJs97TO++8o/79+3NegCM60nkBYxIAAKGrK8Z5U2e+/9Dbb7+tqqoqTZ48WZJUWloqSUpL69iaKi0t7YjrFc2aNUv33XffCefJzc3t9m+IW1tb9eSTT3b7D+UR+pwVDapubFFEmFUDMrp+ZtoFQzP00L83qbSmSe+s3qPLRuV0+TEBfI8xnjEe+KFttZ6Z7hf0jTruwvuhxDvClZ3o0O5Kz9rvY/KTfLZvAAACzo4lnsK7Ndwze/0IhfejslikvhOl1iZp3yZpw1ueYn60b7tJcV4AAACArhAwi+q88MILOv/885WZ2XGNxf9cd9kwjCOuxTxt2jRVV1d7vzZu3NglebuD0aNH69prrzU7BtDl1hV7WpwNTI9VuA9azR5NuM2qyaflSZJe+KJIAdKABEA3whgPeJTVNqmi2SqjrUXn9ory+f4HZniumP5ub53P9w0AQMCo2C45l3q2+50nxaSe+D4tFmnAhVJcltTm8hTgW11Hfx4AAABgsoAovu/cuVOffPKJbrzxRu9t6enpkr6fAd+urKzsoNnwP2S32xUXF+f9io1lfUUAh1fX1Krt5fWSpIKsE7gy/xhdeXKuoiJs2ry3Vl9u3e+34wIAgO+t2+25AK9h81IlRPp+GZheydGyWqT99c2qrG/2+f4BADBda5O0+QPPduZJvl2f3RomDb5EioiVGis8a8Fz8ToAAAACXEAU31966SWlpqbqggsu8N6Wn5+v9PR0LViwwHtbc3OzFi9erLFjx5oRE0AI2rCnWoYhZSU4lBRj99tx46PCdcmIbEnSP79x+u24AADAw9Xapk2ltZKk2m8/6JJjRIbblJPomVH/3T5mvwMAQtC2T6XmOsmRKPU6y/f7j4iSBl3sWft9X6FnHXgAAAAggJlefHe73XrppZd0/fXXKyzs+yXoLRaLpkyZopkzZ2r+/Plav369Jk+erKioKF111VU+zUDLZxwNvyOhyTAMFR740H1IZpzfj3/laM/ach9tKNW+WtrnAV2Bv984Gn5Huq9NJbVqdRuKC3fLtXtDlx2nT2qMJGlbGcV3AECIqdwhla71bPf/sWQL75rjxGdL+eM829s+lRoqOv3U9vd6vOfD0fA7AgAAfMX04vsnn3wip9OpG2644aD77rrrLk2ZMkW33HKLRo0apeLiYn388cc+ayUfHu45KWhoaPDJ/hC62n9H2n9nEBpKa5pU3diicJtFvQ98MO5PgzLjNCwnQa1uQ2+s3O334wOhjDEencUY331tLKmRJOVFu7v0OL1SomWRVFbrUk1jS5ceCwAAv3G3Sd997NnOHCHF53Tt8bJHSwk9JXeLtOn/JKNz4/f+/fvldrs5L8BRcV4AAAB8JezoD+laEyZMOOyVhRaLRTNmzNCMGTO65Ng2m00JCQkqKyuTJEVFRclisXTJsRCcDMNQQ0ODysrKlJCQIJvN92uBwjztrWZ7p8Qo3GbOtUhXj87Vml1Vem25Uzed0UtWK3+DAF9gjMfRMMZ3b/vrXCqrdclqkXK7uPgeFRGmrASHdlc1auu+Oo3ITezS4wEA4BfFKzzrsIdHSflndP3xLBap/wXSihek2hLJuUzqefRlKevr6+V2uzkvwGFxXgAAAHzN9OK72dLT0yXJ+yYcOJSEhATv7wpCQ5vb0Ja9nuL7gHTfdNM4HhcOy9AD723Uzv0NWla0X2N7J5uWBQg1jPHoDMb47ql92Zm8pGjZbc1dfrzeqTHaXdWoovJ6iu8AgODXXCft/NKz3Wu8FBbpn+NGxkl9zpE2v+85fupAz1rznfDDC3OBQ+G8AAAA+Eq3L75bLBZlZGQoNTVVLS20gcTBwsPDueo1BO2sqFdTi1tRETblJEaZliMqIkwXDM3Qa8t3af6qYorvgA8xxuNoGOO7J7dhaFOpp+X8wIw4qbpSklRYWHhC+z3S83smed5r7KlqVHOrWxFhpq/+BQDA8dvxpdTWLMVmSGkF/j122hBp7wapaoe05UNp6BWeWfFHwHkBjobzAgAA4Evdvvjezmaz8SYL6Ea+21snSeqXGmt6q/efnZSl15bv0r/Xl+r+i4fIEcHfIsCXGOMB/NCuigbVu9pkD7MqLzlKW4r2SZKuueYan+y/rq7uoNsSHOGKd4SrurFFuysb1CslxifHAgDA7xoqpJLVnu1eZx218O1zFovUb6Kn/XzVTmnveim9cxcAcF4AAAAAf6D4DqDbaXMb2l5eL0nqk2b+h98n5/VQVoJDxVWNWlC4Vz8Zlml2JAAAQtaW9gvw0mIVZrWqsc4zC/6Cm6ar/9CRx73fwm8W69/zHldTU9NB91ksFvXsEaW1xdXasZ/iOwAgiO34QpIh9egtJeSYk8GRKPU8TSpaLG37VErq7Vl7HgAAAAgAFN8BdDu7KxvU3OppOZ8Z76e16Y7AarXoZydl6anPtmr+qt0U3wEA6CJtbkPb9rUX3zsWwJMyeyq77+Dj3vde57Yj3t8zyVN837m/XoZhyOLvmYIAAJyo2lJp34FlVvLHmZsle7RUtlGq3+cpwA+40Nw8AAAAwAEsNgig29la5vnQvXdKTMB88P2zEVmSpM+/K1d5ncvkNAAAhKZdFQ1ytV+Al+Dw67GzE6Nks1hU09SqqgbWmwUABCHnUs/31EFSTKq5Waw2qd95nu2966XKnebmAQAAAA6g+A6gW3EbhrbtO9ByPjVwWr72TolRQVa82tyGPlxfanYcAABC0payWkme9wBWP1+AFxFmVWaCp+POjv31fj02AAAnrK5MKt/i2c4da26WdnFZUuZJnu1tn0iG29w8AAAAgCi+A+hm9lQ1qrGlTfYwq7L8POPtaC4cmiFJen9ticlJAAAIPZ6W856id7/UWFMy5PbwrEe7u7LRlOMDAHDc2me9pwyQopPNzfJDeWdIYZGe9vN7vjU7DQAAAEDxHUD3sr3c86F7r5Ro2ayB0XK+3Y8LPMX3r4v2q6y2yeQ0AACEFmdFg5pb3YqOsCnjwAx0f8tOPFB8r2qU2zBMyQAAwDFr2C/t2+TZDpRZ7+3CHZ4CvCTt+EJq4QI3AAAAmIviO4BuZceB4nt+UrTJSQ6W0yNKw3IS5Dakj2g9DwCAT23fVydJ6pXi/5bz7VJj7YqwWdXc6ta+WpcpGQAAOGa7vvZ8T+pr/lrvh5I5XIpOkVqbpB2fm50GAAAA3RzFdwDdRl2LVNnQIqtFyk2KMjvOIV14YPb7e7SeBwDAZwzD8Ha/6Z1i3gV4VqvFu+47recBAEHBVSft3eDZzhljbpbDsVilPud6tveslur2mhoHAAAA3RvFdwDdRkmj509eZoJD9jCbyWkO7ccH1n3/ZkcFM+IAAPCR0pomNTS3KcJmVVaiw9QsOe2t5ysbTM0BAECnFK+QjDYpLluKzzY7zeEl5HrWo5chbf1EYnkXAAAAmITiO4Buo7TJ8ycvEFvOt8tKcGhodrwMQ/p0E1frAwDgC9v3eWa95yVFKcxq7ilQ9oHi/56qJrndFAYAAAGs1SXt+daznTPa3Cyd0etMyRomVe/6fo16AAAAwM8ovgPoFiwRDu1r8qzvmpccuMV3STpnYJokacHGMpOTAAAQGtqL771SYkxOIiXH2mUPs6q5za0yutwAAALZ3vVSm0ty9PCs9x7oIuOlnFM829s/k9XdYm4eAAAAdEt+L74//fTTys/PV2RkpEaOHKkvvvjiiI9/5ZVXNGzYMEVFRSkjI0O/+MUvtH//fj+lBRAqInsOkyGL4h3hSowKNzvOEbUX35ds3afG5jaT0wAAENwqG5pV0dAsq8Uz891sVotFWQme2e+7q2g9DwAIUIYhFa/0bGeNlCwWc/N0Vs4YyR4nuWqUVr/B7DQAAADohvxafH/99dc1ZcoUTZ8+Xd9++61OP/10nX/++XI6nYd8/JIlS3Tdddfpl7/8pTZs2KD//d//1fLly3XjjTf6MzaAEODIO0mS1DMpSpYA/9BgYEasshIcampxa8nWcrPjAAAQ1HaUe2a9ZyY4ZA+3mZzGo734XlLVZHISAAAOLba5RGqskGwRUtoQs+N0ni1c6n2WJCmtboPyEgL7/B8AAAChx6/F99mzZ+uXv/ylbrzxRg0cOFBz5sxRTk6OnnnmmUM+ftmyZcrLy9Ntt92m/Px8/ehHP9JNN92kFStW+DM2gBAQmX+g+N7D/BlvR2OxWHTuIM/s9082su47AAAnYud+z+zy/KTAWXYm80DxfU91owyDdd8BAIEnpX6zZyO9QAqzmxvmWCX3lxJyZZVb/3NupNlpAAAA0M34rfje3NyslStXasKECR1unzBhgpYuXXrI54wdO1a7d+/WBx98IMMwtHfvXr3xxhu64IILDnscl8ulmpoa71dtba1PXweA4FNa16rwxExZZCgr0WF2nE5pbz2/cNNetbn5UB4AgOPR0ubW7qpGSZ7uN4EiJdauMKtFTS1uVTawHi0AILDkJ1gU79rt+SFzhLlhjofFIvU+R4YsumRQuGL3rTQ7EQAAALoRvxXfy8vL1dbWprS0tA63p6WlqbS09JDPGTt2rF555RVdfvnlioiIUHp6uhISEvTkk08e9jizZs1SfHy892vQoEE+fR0Ags+avS5JUpLdkD0sMNrNHs2YXj0UGxmm8rpmrd5VZXYcAACC0u7KRrW5DcVGhqlHdITZcbxsVovS4jwz8fYcuDgAAIBA8f9OjpBFkhLzpagks+Mcn5hU7YvqJ0nKXv+U1NZqciAAAAB0F35tOy/poLWWDcM47PrLGzdu1G233aY//OEPWrlypT788EMVFRXp5ptvPuz+p02bpurqau/Xxo0bfZofQPBZs7dZkpQa6TY5SeeF26wa3z9VkvRJIa3nAQA4Hjv2e9Z7z0uKPuw5h1kyEw4U36spvgMAAoeltUm/POnABWtZI80Nc4JKYoepvMEtR+0OacULZscBAABAN+G34ntycrJsNttBs9zLysoOmg3fbtasWTrttNM0depUDR06VBMnTtTTTz+tF198USUlJYd8jt1uV1xcnPcrNjbW568FQPBocxtad2Dme1pkcLVvP2egp/i+gHXfAQA4ZoZhaEd5e/E9cFrOt/Ou+17VZHISAAC+16P4E/VwWOSyxUg9epkd54S0We36/aeezwP02Z+k+nJzAwEAAKBb8FvxPSIiQiNHjtSCBQs63L5gwQKNHTv2kM9paGiQ1doxos3maRltGMFVRANgjrW7q1TfYqitqU6JEcH1d2N8/1SFWS3aWlanogPFAwAA0DlVjS2qaWqVzWJRdmLgFd8z4j0z36sbW1TvohUuACAAGIZSiuZLkvZF9Zcsfm+Y6XPPrWpRQ1xvqala+vRBs+MAAACgG/Dru+g77rhDzz//vF588UUVFhbq9ttvl9Pp9LaRnzZtmq677jrv4y+66CK99dZbeuaZZ7R9+3Z9+eWXuu222zR69GhlZmb6MzqAILV0235JkmvnWgVYt9mjineEa0yvHpKkhbSeBwDgmDj3N0iSMhIiFREWeMUDe5hNyTGetr60ngcABITdyxVVs10NLYb2R/U2O41PuA1pV8Ftnh9WzpVK1piaBwAAAKHPr59CXX755ZozZ47uv/9+DR8+XJ9//rk++OAD9ezZU5JUUlIip9PpffzkyZM1e/ZsPfXUUxoyZIguvfRS9e/fX2+99ZY/YwMIYl8dKL43OdeanOT4nDPQsyzHx7SeBwDgmOyq9BTfc3sE3qz3dulxntnve6tdJicBAEDSynmSpNfXt6jNajc5jO/UJw2VhlwiyZD+fbdEN00AAAB0Ib9PAbnlllu0Y8cOuVwurVy5UmeccYb3vrlz52rRokUdHv+b3/xGGzZsUENDg/bs2aOXX35ZWVlZfk4NIBi5Wtu0YmeFpOAvvq/cWanqxhaT0wAAEBzcbkO7KjyzyXMCufh+oPV8aQ3rvgMATNZUI23wTHZ5/tsQPPc8934pPEpyfiWtf9PsNAAAAAhhgdd/EQB8ZM2uajW1uBVvt6ql3Hn0JwSgnB5R6p0SrTa3oaVby82OAxy3WbNmyWKxaMqUKd7bDMPQjBkzlJmZKYfDofHjx2vDhg3mhQQQMvbWNqm5zS17mFWpsYE7c887872mSW43s/AQnBjjgRCx7n+llgY1xvTU0l1tZqfxvfhs6Ud3eLY//r3nYgMAAACgC4SZHQAAusrSbZ5i9ZDUCAXnvHePcf1StW1fkd5cWqi0lpLj2kdycrJyc3N9nAzonOXLl+vZZ5/V0KFDO9z+yCOPaPbs2Zo7d6769eunBx98UOeee642b96s2NhYk9ICCAXOCk/L+ZzEKFktFpPTHF5idIQibFY1t7m1v75ZKQF8oQBwKIzxQAhZ5Wk5v7/nBZLWmZulq4z9jbTmValiu/TZTOn8h8xOBAAAgBBE8R1AyGpf770gNbg/yB6U6JkJ9+Eap164+ezj2ocjKkqbCgspwMPv6urqdPXVV+u5557Tgw8+6L3dMAzNmTNH06dP16RJkyRJ8+bNU1paml599VXddNNNZkUGEALaW84H8nrvkmS1WJQaZ9fuykbtrWmi+I6gwhgPhJA9q6WSNZItQvuzJ0gK0aJ0eKR0wWPSP34mffM3adgVUuZws1MBAAAgxFB8BxCSmlra9K2zSpJn5nswy4pokrvFpbC4FN345/mKizi2trR7ndv0ysNTVV5eTvEdfnfrrbfqggsu0DnnnNPhg/mioiKVlpZqwoQJ3tvsdrvGjRunpUuXHvKDeZfLJZfL5f25poZWkQAO1tLmVkl1+3rvDpPTHF16XKR2VzaqtKZJQ7LizY4DdJovx3iJcR4w1YFZ7xp4kdrsIT4W9T5LGvJzaf0b0ntTpBsXSlab2akAAAAQQii+AwhJq3ZWqrnNrfS4SGXEBPeJtD3MIteudXL0GqWmmAwN6plodiSgU1577TWtWrVKy5cvP+i+0tJSSVJaWlqH29PS0rRz585D7m/WrFm67777fB8UQEjZU9UotyHFRoYpISrwL8BLj/es+15a02RyEqDzfD3GS4zzgGma66W1/+vZHnG9VGVqGv+YOFP6boG051tp+QvSmP8yOxEAAABCiNXsAADQFb7ZUSFJGp3fQ5YAXuu1sxq3r5Ik7aioNzkJ0Dm7du3Sb3/7W7388suKjIw87OP+8/9PwzAO+//stGnTVF1d7f3atWuXTzMDCA3FVZ5Z71kJgT/rXZLS4jx/IyvqmtXc6jY5DXB0XTHGS4zzgGk2zJeaa6XEfCnvdLPT+EdsmnT2vZ7thfdLNSXm5gEAAEBIofgOICQtP1B8Pzm/h8lJfKOxaKUkaU9lk1ra+GAegW/lypUqKyvTyJEjFRYWprCwMC1evFhPPPGEwsLCvLPh2mfHtSsrKztoplw7u92uuLi4Dl8A8J+CrfgeYw9TjD1MhqSyWma/I/B1xRgvMc4Dpll5oOX8iOskazf6mHDUDVLWSM+FBx/dY3YaAAAAhJBu9K4aQHfR0ubWqp1VkqTReaFRfG+tKFaUzVCbYWh3ZaPZcYCjOvvss7Vu3TqtXr3a+zVq1ChdffXVWr16tXr16qX09HQtWLDA+5zm5mYtXrxYY8eONTE5gGDW2ubW3mrPmtFZicFRfJc8675LtJ5HcGCMB0JI+XfS7m8ki00afpXZafzLapMu/LNksUob3pK2fmJ2IgAAAIQI1nwHEHI27KlRY0ub4h3h6psao9V7zE7kG2kOt4rqbNq5v175ydFmxwGOKDY2VkOGDOlwW3R0tJKSkry3T5kyRTNnzlTfvn3Vt29fzZw5U1FRUbrqqm72wR8AnymtaVKbYSg6wqYER7jZcTotLd6urfvqVFrdpPTguWYA3RRjPBBCVr/q+d7nHCk23dwsZsgYJo25WVr2tPT+76RblknhDMQAAAA4MRTfAYScb4r2S5JOzushqzX413tvlx7pKb7v2N9gdhTAJ+666y41NjbqlltuUWVlpcaMGaOPP/5YsbGxZkcDEKSKK79vOX+ktaUDTfvM9701LonP/BECGOOBIOBuk9a85tnubrPef+jMe6QNb0uVO6TPH5XO/oPZiQAAABDkKL4DCDnfFFVKkkbnJ5qcxLdSIg1ZLVJ1Y4uqGpqVEBVhdiTgmCxatKjDzxaLRTNmzNCMGTNMyQMg9HjXew+ilvOSlBobKYukOlerGlvNTgMcO8Z4IAhtXyTV7pEiE6T+55udxjz2WOn8h6V/XSt9+bg0eJKUPuTozwMAAAAOgzXfAYQUt9vQip0Vkjwz30NJuFXKjPcUE3Yy+x0AgA7a3IZKqj1rpmclBFfxPSLMqh4xnovqKpqDZ8Y+ACCItbecL7hUCrObm8Vsg34iDbhQcrdK/3ebpysAAAAAcJyY+Q4gpGzdV6eqhhY5wm0akhVvdpwOCgsLT/h5PZOitLuqUTsrGjQsJ8FHyQAACH57a5rU6jbkCLepR3TwdYdJj4vU/rpmVTZzfTQAoIs1Vkmb3vNsd+eW8z/040elos+l4pXSN89Kp/w/sxMBAAAgSFF8BxBSvinyzHo/KTdB4bbA+PC6pmKfJOmaa645of3U1dUpt0+Uvty2X7srG9TmNmQLoTXtAQA4Ee0t5zMTIoNqvfd26XGR2rCnRhWu4MsOAAgyG+ZLrU1SykAp8ySz0wSGuEzp3Puk926XFj4gDbhASsg1OxUAAACCEMV3ACFl+Y7AaznfWFcjSbrgpunqP3TkMT+/8JvF+ve8x9XU1KSUGLsc4TY1trSptKYp6NrqAgDQVbzrvQfp2JgeHylJqmy2SJbAuIAQABCi2lvOD79KCsIL1rrMiMnS2v+VnEul9+6Qrv5f/n0AAABwzCi+Awgpyw/MfB+dHzjF93ZJmT2V3XfwMT9vr3Obd9tisSgn0aEtZXXaVdEQtAUGAAB8ye02VFLlWe89OzHK5DTHp0d0hMJtFrW0SeFJOWbHAQCEqvKt0u5vJItNGnqZ2WkCi9UqXfS49NfTpK0LpHVvSEMvNTsVAAAAggxTKgCEjN2VDdpT3aQwq0Un5SaYHafL5PTwFBWcFQ0mJwEAIDDsq3Opuc0te5hVSTHBt967JFktFqXGema/R6T3MTkNACBkrTkw673POVJsurlZAlFKP+mMuzzbH94t1e83Nw8AAACCDsV3ACGjfb33IVnxiooI3cYeuQeK76U1TXK1tpmcBgAA8xVXtq/37pA1iNvDpsbZJUkR6X1NTgIACElut7T2X57tYVeYmyWQnfZbKXWQ1LBf+ni62WkAAAAQZCi+AwgZ7eu9B2LLeV+Kc4Qr3hEuw/i+2AAAQHcW7Ou9t0s7MPPdnsHMdwBAF9i1TKreJUXESv3PNztN4AqLkC56QpJFWvNPaetCsxMBAAAgiFB8BxAy2me+n5wX2sV36fvZ77sqKL4DALo3w5D2hErxvX3me2ovtbQZJqcBAIScdf/r+T7oJ1J4cI+ZXS7nZGnMTZ7t96ZIzfWmxgEAAEDwoPgOICRU1Ddr2z7PyfConokmp+l6OT08H5Q4K1n3HQDQvdW2Sk2tboVZLUqJtZsd54TEO8IVbjFkCYvQrppWs+MAAEJJa7O0Yb5nu+BSc7MEi7N+L8VlS1VO6bOZZqcBAABAkDC9+F5cXKxrrrlGSUlJioqK0vDhw7Vy5Urv/YZhaMaMGcrMzJTD4dD48eO1YcMGExMDCETfOislSX1SY5QYHWFymq6Xk+iZ+V5R36y6Jj6cBwB0XxUuzylNapxdNmvwrvcuSRaLRYl2z4z3rRUtJqcBAISUbZ9KjZVSTJqUf4bZaYKDPVa6cLZne9nTUvEqc/MAAAAgKJhafK+srNRpp52m8PBw/fvf/9bGjRv12GOPKSEhwfuYRx55RLNnz9ZTTz2l5cuXKz09Xeeee65qa2vNCw4g4Kzc6Sm+j8hNMDeIn0SG27ytaXcx+x0A0I1VNHsK7hlxodE+NzHCU3zfVtlschIAQEhZ9y/P9yGXSFabuVmCSb+J0pCfS4Zb+r/bpDYujgMAAMCRmVp8f/jhh5WTk6OXXnpJo0ePVl5ens4++2z17t1bkmfW+5w5czR9+nRNmjRJQ4YM0bx589TQ0KBXX331kPt0uVyqqanxflGkB7qHVQdmvo/sBi3n27XPfndWUHwHAHRf+12e4nt6fKTJSXwjMcItiZnvAAAfctVJmz7wbBf83Nwswei8hyRHolS6TvrqKbPTAAAAIMCZWnx/9913NWrUKF166aVKTU3VSSedpOeee857f1FRkUpLSzVhwgTvbXa7XePGjdPSpUsPuc9Zs2YpPj7e+zVo0KAufx0AzNXa5taaXdWSpBG53af4ntvDU3zfVdEgwzBMTgMAgP9ZIhyqaTkw8z1kiu+eMd1Z3aqmljaT0wAAQsKm96XWRqlHbylzhNlpgk9MijTxwJrvix6S9m8zNw8AAAACmqnF9+3bt+uZZ55R37599dFHH+nmm2/Wbbfdpr///e+SpNLSUklSWlpah+elpaV57/tP06ZNU3V1tfdr48aNXfsiAJhuU2mtGlvaFBcZpt4pMWbH8ZuM+EiFWS2qb25TRT2taQEA3Y89o58ki+IiwxRtDzM7jk84bFJbfZXaDKmwpMbsOACAULDufz3fCy6VLBZzswSrYVdKvcZLrU3S//1W4gJ4AAAAHIapxXe3260RI0Zo5syZOumkk3TTTTfpV7/6lZ555pkOj7P8x4mBYRgH3dbObrcrLi7O+xUbG9tl+QEEhvb13k/KTZTV2n0+SAizWZWZ4FnfltbzAIDuyJ41UFLotJyXPDURV+l3kqS1u6tNTgMACHp1+6Rtn3q2Cy41N0sws1ikC+dIYQ5pxxfSty+bnQgAAAABytTie0ZGxkFt4QcOHCin0ylJSk9Pl6SDZrmXlZUdNBseQPfVvt57d2o5387ber6y0eQkAAD4X0Rmf0lSRrzD5CS+1UzxHQDgKxvflow2KfMkKbmP2WmCW4986cx7PNsfT5dq95qbBwAAAAHJ1OL7aaedps2bN3e4bcuWLerZs6ckKT8/X+np6VqwYIH3/ubmZi1evFhjx471a1YAgat95vvInt23+L67skFtbtreAQC6D8MwZD9QfA+lme+S1FziKb6vK64yNwgAIPit/Zfne8Fl5uYIFafcImUMk5qqpQ/vNjsNAAAAApCpCyPefvvtGjt2rGbOnKnLLrtM33zzjZ599lk9++yzkjzt5qdMmaKZM2eqb9++6tu3r2bOnKmoqChdddVVZkYHECDKapu0u7JRFos0LCfe7Dh+lxwTIUe4TY0tbSqtaVJWQmjN/AMA4HD21LbJ5oiT1WIoJcZudhyfai7dKknaWlaneldryKxnDwA4cU6nU+Xl5Z16bET9Hg3Z/Y0MWbXO6KvWVauO+pzCwsITjRjabGHST56Unj1T2jDfc1HDgB+bnQoAAAABxNRPcU4++WTNnz9f06ZN0/3336/8/HzNmTNHV199tfcxd911lxobG3XLLbeosrJSY8aM0ccff8xa7gAkSat2VkmS+qfFKjYy3NwwJrBYLMpJdGhLWZ12VTRQfAcAdBub9zdLkhIjDNmsFpPT+FZbfaV6OKyqaHRrw54ajc7vYXYkAEAAcDqdGjhwgBoaOrfs2D2nR+hPZ0Xqk23NmnDahGM6Vm1d3fFE7B4yhkljfy19+bj0/u+kXuOkiGizUwEAACBAmD6F4sILL9SFF1542PstFotmzJihGTNm+C8UgKDhXe+9G7acb5fTI0pbyurkrGjQKb2SzI4DAIBfbN7fIknqERGay6706RGub4pdWru7iuI7AECSVF5eroaGRr18z2UamJty5AcbhgaW/5/UWq2+I07Xyh91br33D77ZontfXKCmpiYfJA5h4/5b2vC2VLVTWvqUNJ4W9AAAAPAwvfgOACdiVft677ndt/jevu57aU2TXK1tsofZTE4EAEDX23Jg5nuS3W1ykq7RJ7G9+F5tdhQAQIAZmJuiEf2yjvygur1SabVksSlvyBjlhUV2at+Fzn0+SNgNRERJ5/xReuMGzwz4kZOl2DSzUwEAACAAWM0OAADHq7nVrbXFng+ku/PM9zhHuOId4TIMqbiyc+0HAQAIZnWuVu2qaZUk9bCH6sz3CEnSumKK7wCA47B3g+d7Uh+pk4V3HKPBk6SskVJLvbRoltlpAAAAECAovgMIWhv2VKu51a0e0RHKS4oyO46p2me/76qg+A4ACH1rdlXJbUit1XvlCNGGL70TwyVJReX1qm5sMTkNACCoGIZUVujZThtkbpZQZrFIEx70bK/6u7Rvs7l5AAAAEBAovgMIWisPtJwfkZsgi8VichpztRffnRUNJicBAKDrtS8749oTuh9yx9qtyunhkCStZ/Y7AOBYVDul5lrJZpd69DY7TWjrOVYacKFktEkL/mh2GgAAAAQAiu8Agta3zipJ0kndeL33dtmJng/nKxqaVdfUanIaAAC61re7qiRJruJN5gbpYkOzEyRJa3ZXmZoDABBkyjZ6vqf0l6xh5mbpDs6ZIVls0pZ/S0VfmJ0GAAAAJqP4DiBotc98H9mN13tvFxluU1qcXZK0q5LZ7wCA0GUYhta0F99LQnfmuyQNzYqXJK3bzcx3AEAnuVulfQcuTksdbG6W7iK5rzTqF57tBfd62v4DAACg2+LyVwBBaU9Vo0prmmSzWjQ0O97sOAEhJzFKe2tcclY0aGBGnNlxAADoEsVVjdpf3yybRWreu93sOF2qfeb7WorvAIDOqtgutbqkiBgpIcfsNAGhsLCwy4+R2ucaZa/+p7TnW2nLh1L/87v8mAAAAAhMFN8BBKVVTs+s90EZcYqK4E+Z5Fn3fcXOSjkrGmQYhiwWi9mRAADwufZCdG58mLa3tZicpmsNyfJcTFdc1aj9dS4lxdhNTgQACHjtLedTB0mW7t3wsqSiVhZJ11xzTZcfKyrKoT3/uFnxa1+QFs2S+p0ncU4OAADQLVGxAhCU2lvOj8hNMDdIAMmIj1SY1aKG5jZV1DfzAT0AICS1r3/ep0eEFpmapOvFRoarV0q0tu+r19riap3ZP9XsSACAQNbqkvZv9WynDjI3SwCoqmuSIempWybo1KF9u+w4hc59umbmv7Qj4yINK3xNKlnD7HcAAIBujOI7gKC0ylklSRrBeu9eYTarMhMcclY0yFnRQPEdABCS1u7yzHzv0yPc5CT+MSw7Qdv31WvdborvAICjKN/iWfM9KkmKSTM7TcDok5moEf2yuvw4bfZ4acx/SUv+LC16iNnvAAAA3VT37j8FICg1tbRpQ7Hng/cRuRTffyi3R5QkyVnRYHISAAB8z+02tL64exXfC7LiJUlrD8z4BwDgsDq0nKfoa4pTfyOFR0klq6Xti8xOAwAAABNQfAcQdNYVV6vVbSgl1q7sRIfZcQJKe/G9uKpRbW7D5DQAAPhW0f561bpaFRluVW5c92jiNSynvfhebXISAEBAa66TKnd4tmk5b57oJGnEdZ7tJX82NwsAAABMQfEdQNBpX+99ZG6iLFzN30FyTIQc4Ta1tBkqrW4yOw4AAD7VPvt7cGa8bNbu8R5gUEa8rBaprNbF2A4AOLyyTZIMKTZTctAhzlSn3ipZbFLRYql4ldlpAAAA4GcU3wEEnVUHiu8jeiaYGyQAWSwW5fTwdAOg9TwAINSsObDee3sr9u7AEWFTv7RYSbSeBwAcQdkGz/c0Zr2bLiFXKvi5Z/vLx83NAgAAAL+j+A4gqBiGoVXOKknSyJ5czX8orPsOAAhV7cXn9lbs3cXQbM/rXVdM63kAwCE0Vkq1JZIsUspAs9NAksbe5vle+H9S1S5zswAAAMCvKL4DCCq7KhpVXudSuM2iwZnd64P3zmovvu+taVJTS5vJaQAA8I3WNrc27KmRJA3NTjA3jJ8VHHi9a1j3HQBwKHsPzHpPzJMiok2NggPSh0h5p0tGm7T8ObPTAAAAwI8ovgMIKqucnpbzgzPjFRluMzlNYIqNDFdiVLgMSbsrG82OAwCAT2zZWydXq1ux9jDlJ3WvwsKw9pnvu6tkGIbJaQAAAcUwpLKNnu1UWs4HlFP+n+f7ynlSc725WQAAAOA3FN8BBJWVB9Z7p+X8kdF6HgAQatpbzhdkx8tqtZgbxs/6p8cq3GZRZUMLF9YBADqq2ys1VkjWMCm5n9lp8EP9zpMSekpNVdLaf5mdBgAAAH5C8R1AUGmf+T4il+L7kVB8BwCEmvaW692t5bwk2cNsGpAeJ0laS+t5AMAPtbecT+orhdnNzYKOrDZpzE2e7W+e83QpAAAAQMij+A4gaNS7WlVY4lnrdUTPBHPDBLjsxChZLFJ1Y4vqW81OAwDAiWuf+d7egr27GXrgda8trjI3CAAgcBhuaV+hZ5uW84Fp+FVSWKRUtkHavdzsNAAAAPADiu8Agsaa3VVyG1JmfKQy4h1mxwloEWFWpcdFSpLKmvhTDwAIbk0tbdpcWitJGpqTYG4Yk3iL77uY+Q4AOKDKKTXXeYq7PXqZnQaH4kiUBk/ybK94ydwsAAAA8AsqMgCCxqoD672PYL33TmlvPb+3qXutiwsACD2FJTVqdRtKjolQZnyk2XFM0d5uf31xtdxu2tYCACSVbfR8TxngaXGOwDTqF57vG96SGivNzQIAAIAuR/EdQNBY5aySxHrvndVefN/XZJUs/LkHAASvtT9Y791i6Z4XlfVNjZE9zKpaV6uK9tebHQcAYDZ3q7Rvs2eblvOBLftkKXWw1NokrXnd7DQAAADoYlRjAAQFwzC0ysnM92ORHhepCJtVzW6LItJoQQgACF7txfeCrO653rskhdmsGpwZJ0lat5vW8wDQ7e3fJrW5JHucFJ9jdhocicXy/ez31S+bmwUAAABdLszsAADQGdvL61XV0CJ7mFWDMuLMjhMUrFaLshMd2l5er8i8k8yOAwDAcduwp33me/ctvkuemf+rnFVau7taPz0py+w4AAAzlW3wfE8d6CnuwnSFhYWHvc/m7qsCa7ispetU+Nm/1Bjf55j3n5ycrNzc3BOJCAAAAD+g+A4gKLSv994rwab1a1cf03OPdAIc6nJ7RGl7eb0cecPNjgIAwHFpamnTd2V1kqQh3Xjmu/T9xQdrd1eZGwQAYCqbu9kz812i5XwAKKmolUXSNddcc8THvf5zhy4bHK4PH75Od3zkOubjREU5VFi4iQI8AABAgKP4DiAofL5xlyRp2Xv/1Mipc49rH3V1dT5MFBxykzzrvtuzBsnVapicBgCAY1dYUqM2t6HkGLtSY+1mxzHV0OwESdKGPTVqbXMrzMYqYgDQHSU0OSWjTYpKlqJTzY7T7VXVNcmQ9NQtE3Tq0L6HfVxcU7FU+al+PTZO4396iQyLrdPHKHTu0zUz/6Xy8nKK7wAAAAGO4juAoLCmuFaSdMopYzTosp8c03MLv1msf897XE1NTV0RLaAlOMIVZTPUoHBt2OfSqWYHAgDgGK3fUyNJGpIVJ0s3b6vbKzla0RE21Te3adu+evVPjzU7EgDABImNRZ6N1EG0nA8gfTITNaLfEZaFMTKkZV8rvLleJyU1Scn9/BcOAAAAfkPxHUDAq2lq0a7qVklSr6w0ZfcdfEzP3+vc1hWxgoLFYlFqpFs76m1as7fZ7DgAAByzDcWe9d6HZHbvlvOSZLVaNCQrXl8XVWjN7iqK7wDQDWXEWBTbXOr5gZbzwcVilVIHS7u/kfZuoPgOAAAQouhTCCDgrXZWyZDUUlmiyM53ZcMBqZGedvNr9x77mnIAAJhtXXvxvZuv995uWE6CJGnd7mpzgwAATHH5kHBZJCkuS3IkmJwGxyztwGSC/VulVs7RAQAAQhHFdwABb+XOSkmSq7jQ5CTBKTXSLUnaWd2qstru13ofABC8XK1t2rLXs/TMkKw4k9MEhoIDFyGs2V1lbhAAgCmuLgj3bKQeW0c4BIjoVCkqSTLapPItZqcBAABAF6DtPAC/cTqdKi8vP+bnLV6/X5Lk2rNJ0o98nCr02W2Sq3Sr7Ol9tOS7ck0akW12JAAAOuW7vXVqaTOUEBWurASH2XECwkm5CZKkjXtq1NjcJkcEbYEAoLuw1zk1ONMmQxZZUgaYHQfHw2LxLBew4wupbKOUXmB2IgAAAPgYxXcAfuF0OjVg4EA1NjQc2xMtVuX89jVZ7VFyFReqrq6uawKGuKbtK2VP76PFW/ZRfAcABI31P1jv3WKxmJwmMGQlOJQWZ9feGpfW7K7SKb2SzI4EAPCTHrsXSpJq7JmKj4gyOQ2OW3vxvXKH1Nwg8d8SAAAgpFB8B+AX5eXlamxo0NV3P6q03N6dfl51s0WflIbL6m5Vy76damqibfrxaCxapfixl+vzLfvU5jZks1LAAAAEvvV7PMX3wbSc97JYLBrVs4feX1eilTsrKb4DQHdhGEos9hTfKxz5ijc5Dk6AI1GKTZdqS6V9m6SsEWYnAgAAgA9RfAfgV2m5vZXdt/Nr01XsrpJK9ynO2iQZ7q4LFuJcezYpKtyiyoYWrS+u1rCcBLMjAQBwVOuKayR9v845PEb0TPQW3wEA3UTxSkXWF6u+2VC1nW5mQS9loKf4Xk7xHQAAINRYzQ4AAEdSUu2Z6R6rRpOTBDl3m4am2iVJi7fsMzkMAABH19LmVmGJp/g+JJPi+w+N6pkoSVrlrJTbbZicBgDgF2tflyTN39QitzXc5DA4YSn9Pd+rdknN9eZmAQAAgE9RfAcQ0NqL73EU30/YSekU3wEAwWPbvjo1t7oVaw9Tbg/WQv2hQZlxigy3qqqhRdvL68yOAwDoam0t0vo3JUn/WNtichj4RGSCp/W8DKn8O7PTAAAAwIcovgMIWPWuVlU3ej5YYOb7iRt+oPj+rbNS1Q18YAMACGzrD7ScH5QZJ6vVYnKawBJus2pYdoIk0XoeALqDrQulhv1qsSdq4fY2s9PAV5IHeL6XbzI3BwAAAHyK4juAgNU+6z0pJkJhYr33E5USbVPf1Bi5DWnJ1nKz4wAAcETri6slSUNY7/2QRh5oPb9iB8V3AAh5B1rOV2SdrTZWGwkd7a3nK3dKLUw4AAAACBUU3wEErJJqz8lnZrzD5CShY3z/FEnSos1lJicBAODI2ovvBRTfD6m9+L7SSfEdAEJaU420+QNJUkX2OSaHgU85EqXoVEmGtJ/W8wAAAKGC4juAgNU+8z0jPtLkJKFjXL9USZ513w2DKRMAgMDU5ja0scTTdn5IVpzJaQLTiFxP8X37vnpV1DebnAYA0GUK/09qbZKS+6sxvp/ZaeBryX093/dvNTcHAAAAfIbiO4CA1NrmVlmNSxLFd18alZcoR7hNZbUubSqtNTsOAACHVFRer4bmNjnCbcpPjjE7TkBKjI5Q75RoSdIq1n0HgNC19jXP96GXSRaLuVnge0kHiu8VRZK71dwsAAAA8AmK7wACUlmtS22GoagIm+Id4WbHCRmR4Tad2jtJkmf2OwAAgWjDHk/L+UGZcbJZKTQczqiePSRJKyi+A0Boqi6Wir7wbBdcam4WdI2YNCkiRnK3SFVOs9MAAADAByi+AwhIew6s954RHykLV/f71Lh+nnXfF2+m+A4ACEzt670PyaTl/JG0r/vOzHcACFHr35BkSLljpcSeZqdBV7BYpKTenm1azwMAAIQEiu8AAlJJVft67w6Tk4Se9uL7ip0VqnPR1g4AEHjWtRffs+JNThLYRuZ5iu9rdlepudVtchoAgM+t/Zfn+9DLzM2BrtWjj+f7/q2SYZibBQAAACfM1OL7jBkzZLFYOnylp6d77zcMQzNmzFBmZqYcDofGjx+vDRs2mJgYgD8YhqGSak/xPTOB9d59LS85Wj2TotTSZuirbfvNjgMAQAdut6ENxTWSKL4fTa/kaCVGhcvV6va26gcAhIjS9dLe9ZItQhr8U7PToCsl5knWMMlVI9XToQ4AACDYmT7zffDgwSopKfF+rVu3znvfI488otmzZ+upp57S8uXLlZ6ernPPPVe1tbUmJgbQ1aoaW9TY0iabxaKUWLvZcUKSt/X8ljKTkwAA0NGuygbVuloVEWZVn9QYs+MENIvF4m09v5LW8wAQWta+7vneb6LkSDQ3C7qWLVxKOLCsAK3nAQAAgp7pxfewsDClp6d7v1JSPAUhwzA0Z84cTZ8+XZMmTdKQIUM0b948NTQ06NVXXzU5NYCu1D7rPTXOrjCr6X+mQtL4/p6/tYs275NBWzsAQABZf2DW+8D0WIXbeB9wNCMOFN+X76gwOQkAwGfcbdK6NzzbQy83Nwv8I+lA6/mKbebmAAAAwAkz/dOs7777TpmZmcrPz9cVV1yh7du3S5KKiopUWlqqCRMmeB9rt9s1btw4LV269LD7c7lcqqmp8X4xSx4IPiVVjZKkTNZ77zKn9EpShM2q3ZWN2l5eb3YcAAC82td7H0zL+U4Zk58kSfq6qEJuNxfUAUBI2LFEqt0jRSZIfScc9eEIAe3F95piqZlzdAAAgGBmavF9zJgx+vvf/66PPvpIzz33nEpLSzV27Fjt379fpaWlkqS0tLQOz0lLS/PedyizZs1SfHy892vQoEFd+hoA+F77zPcM1nvvMlERYRqd30OStHgza8oBAAJH+9rlBRTfO2VodryiImyqamjR5r1ceAwAIaG95fzgn0lhLMXWLdhjpZgDn4FWbDc3CwAAAE6I34vvTz/9tPLz8xUZGanf//73Sk1NVUFBgc455xy9//77kqR58+Z5H9/c3Kzp06erZ8+estvteu2117Rnz57D7n/atGmqrq72fm3cuLHLXxMA33G1tGl/fbMkKT2O4ntX+n7dd4rvAIDAYBiG1h+Y+T4kk+J7Z4TbrBqV57mg7qtt+01OAwA4Yc310sZ3PNu0nO9e2me/s+47AABAUAvz58Fef/11TZkyRU8//bROO+00/e1vf9P555+vjRs3Kjc3V9HR0SooKNB3332nn/70p5KkyZMnq6mpSS+88IL69Omj6667TtHR0Yc9ht1ul93+/VXBNTU1Xf2yAPhQSY1n1nu8I1zRdr/+iep2xvVP0Z8+KNSy7fvV2NwmR4TN7EgAgG5uT3WTKhtaFGa1qF96jNlxTFVYWNjpx+baPe+fPly1TcOjKjvcl5ycrNzcXJ9mAwB0oY3vSM11Uo9eUu4pZqeBP/XoI+38UqosktytkpXPRAAAAIKRX9/FzZ49W7/85S914403SpLmzJmjjz76SM8884xmzZoll8ulwsJCnX766crPz1diYqK+/vprFRcXq0ePHmpubtbatWv18MMP+zM2AD8qqfJ8eJwZz6z3rtY3NUZZCQ4VVzXqq+3lOmtA2tGfBABAF2qf9d4vLVb2sO55UVhNhacjzTXXXNPp50Rk9FPGdbP11bZyjfztBEnfr/3uiIrSpsJCCvAAECy+fdnzffjVksVibhb4V2y6FBHt6X5QvVtKzDM7EQAAAI6D34rvzc3NWrlypf77v//be9udd96pQYMGaeHChfr666/14IMPqqamRtdff70sFov69++vFStW6MYbb9SSJUvU2NgowzD0s5/97LDHcblccrlc3p9ra1n3EAgme6obJUkZ8Q6Tk4Q+i8WiMwek6OVlTn26qYziOwDAdN6W81lxJicxT2Odp3PXBTdNV/+hIzv1HLch/d9uQ3LE6oY/v6WECE/xfa9zm155eKrKy8spvgNAMNi/zTPz2WKVhl1pdhr4m8UiJeZLe9d7Zr9TfAcAAAhKfiu+l5eXq62tTWlp3xd3du/erQULFqi2tlaTJk3SKaecomXLlqlnz56SpLi4OBmGoQ8++ECGYahfv37av3+//vu//1svvvjiIY8za9Ys3XfffX55TQB8y+02tPdA2/mMBGa++8NZA1I9xffCMhkXG7IwswIAYKL24ntBFuu9J2X2VHbfwZ1+fFZDsXbub1BzbIaycxO7MBkAoMusftXzvfdZUnyWuVlgjh8W33Wm2WkAAABwHKz+PuAPCzuvvfaa7r77bvXv31/FxcV68803NWjQIO/9hmEoPDxce/fulcvl0rp16/TUU09p7ty5amxsPOT+p02bpurqau/Xxo0bu/w1AfCN8jqXWtoMRdisSoqOMDtOtzC2d7Iiw63aU92kzXvpFAIAMNf6PZ5Z34Mpvh+znMQoSdKuigaTkwAAjou7TVrzT8/28KvNzQLztM92ryvztJ8HAABA0PFb8T05OVk2m02lpaUdbi8rK+swG/6HMjIylJWVpfj47z98GzhwoAzD0O7duw/5HLvdrri4OO9XbGys714EgC61p/rArPf4SGZg+0lkuE1jeydLkj7dVGZyGgBAd1ZW06R9tS5ZLdLA9O7bdv545fbwFN93Vzaq1e02OQ0A4Jht/0yqKZYcidKAC8xOA7NEREvRqZ7tqp3mZgEAAMBx8VvxPSIiQiNHjtSCBQs63L5gwQKNHTv2kM857bTTtGfPHtXV1Xlv27Jli6xWq7Kzs7s0LwD/21Pl6WiRmcB67/505gDPif2nhRTfAQDmWb/H03K+T2qMHBE2k9MEn+SYCEVF2NTqNlRS1WR2HADAsfr2Fc/3gkulMLu5WWCu9tnvFUWmxgAAAMDx8Wvb+TvuuEPPP/+8XnzxRRUWFur222+X0+nUzTffLMnTMv66667zPv6qq65SUlKSfvGLX2jjxo36/PPPNXXqVN1www1yOCjOAaHEMAwVe4vvrPfuT2cdKL6vclaqsr7Z5DQIFc8884yGDh3q7URz6qmn6t///rf3fsMwNGPGDGVmZsrhcGj8+PHasGGDiYkBmG3dbk/L+SGZtJw/HhaLxTv73UnreXQhxnigCzRUSJve82yfdI25WWC+xHzP98odkmGYGgUAAADHzq/F98svv1xz5szR/fffr+HDh+vzzz/XBx98oJ49e0qSSkpK5HQ6vY+PiYnRggULVFVVpVGjRunqq6/WRRddpCeeeMKfsQH4QXVjixqa22S1SOlxFN/9KSvBoQHpsXIb0uff7TM7DkJEdna2HnroIa1YsUIrVqzQWWedpYsvvtj74fsjjzyi2bNn66mnntLy5cuVnp6uc889V7W1tSYnB2CW9pnvrPd+/Ci+wx8Y44EusP5Nqa1ZSiuQMoaZnQZmi8+WLDapuVZq2G92GgAAAByjMH8f8JZbbtEtt9xyyPvmzp170G0DBgw4qFU9gNDTPus9LS5SYTa/XhcEeVrPbyqt1cLCMl08PMvsOAgBF110UYef//SnP+mZZ57RsmXLNGjQIM2ZM0fTp0/XpEmTJEnz5s1TWlqaXn31Vd10002H3KfL5ZLL5fL+XFNT03UvAIDfbSj2FN8LKL4ft/bie1mtSw3NrSanQajqijFeYpxHN2YY0sp5nu2TrjY3CwKDLVxKyPHMfK8skqKTzU4EAACAY0CFC0BA2HNgbVLWezfH2Qdazy/esk+tbW6T0yDUtLW16bXXXlN9fb1OPfVUFRUVqbS0VBMmTPA+xm63a9y4cVq6dOlh9zNr1izFx8d7v3JycvwRH4Af7K9zaU+1573AoMw4k9MEr2h7mJJjIiQx+x3+4asxXmKcRze2e4W0d50UFikNvdzsNAgUP2w9DwAAgKBC8R1AQGif+Z5F8d0UJ+UmKiEqXNWNLfp2V5XZcRAi1q1bp5iYGNntdt18882aP3++Bg0apNLSUklSWlpah8enpaV57zuUadOmqbq62vu1a9euLs0PwH/W7/HMcM1PjlaM3e/NuUJKzx7RkqSd+ym+o+v4eoyXGOfRja14wfN9yCVSVA9zsyBwJOZ5vlc5JXebqVEAAABwbPhkC4Dp6l2tqm5skSRlxrPeuxlsVovG9UvRO6v3aGFhmU7O40MfnLj+/ftr9erVqqqq0ptvvqnrr79eixcv9t5vsVg6PN4wjINu+yG73S673d5leQGYZ/2BlvNDaDl/wvKSo7TSWakd++s1KN3sNAhVvh7jJcZ5dFMNFdL6tzzbo24wNwsCS3SqFB4ltTRINcWSbGYnAgAAQCcx8x2A6fYcmPWeHBMhezgnlGY560Dr+c82lZmcBKEiIiJCffr00ahRozRr1iwNGzZMjz/+uNLTPdWg/5wBV1ZWdtBMOQDdw3rveu+0nD9RmfEO2cOsampxa7/ryMVO4HgxxgM+suafUptLSi+QskaanQaBxGL5fvY7recBAACCCjPfAZiOlvP+UVhYeMT7E1xuWS3S5r21+vCLb5Qa/f0QkZycrNzc3K6OiBBnGIZcLpfy8/OVnp6uBQsW6KSTTpIkNTc3a/HixXr44YdNTgnADOuY+e4zVqtFeUnR2ry3ViVNXGsN/2CMB46DYUgrXvRsj/qlp9gK/FBinlS2UaoskmJ6m50GAAAAnUTxHYDp9lQ1SZIyKb53iZqKfZKka6655qiPTbvqYUXmDNalv71Pdd9+4L3dERWlTYWFFODRaffcc4/OP/985eTkqLa2Vq+99poWLVqkDz/8UBaLRVOmTNHMmTPVt29f9e3bVzNnzlRUVJSuuuoqs6MD8LOqhmbtrvRciDc4k+K7L+QnHyi+N1LIge8xxgM+UvS5tH+rFBErFVxqdhoEosR8z/faEtmiXOZmAQAAQKdRfAdgKldrm/bVeU4imfneNRrraiRJF9w0Xf2HHrmV4eYaq9ZXSYN/crNOu/FGSdJe5za98vBUlZeXU3xHp+3du1fXXnutSkpKFB8fr6FDh+rDDz/UueeeK0m666671NjYqFtuuUWVlZUaM2aMPv74Y8XGxpqcHIC/rS/2jFM9k6IU7wg3OU1o6JkUJYtFqm2xKiyeVt/wLcZ4wEdWvOD5PuxyyR5jbhYEJnusFJUkNexXTDPLwwEAAAQLiu8ATNU+6z3eEa5oO3+SulJSZk9l9x18xMdE1rm0/mun9jXblNarr8JttKvF8XnhhReOeL/FYtGMGTM0Y8YM/wQCELBoOe97keE2ZcY7VFzVKEffMWbHQYhhjAd8oLZU2vS+Z3vkL8zNgsAWn3ug+F5qdhIAAAB0ElUVAKbac2C998yESJOTQJKSoiMUGxmmNrehXZUNZscBAHQD6/ccKL7Tct6neqdES5Ki+p1mchIAwEFW/V1yt0o5Y6T0IWanQSBL8HSfi3XtNTkIAAAAOoviOwBTFR8ovtNyPjBYLBblJ3k+rN9RTvEdAND11h+Y+V7AzHef6pPqaWFszx6oisY2k9MAALxamqRvnvNsn/wrc7Mg8CXkSJIcrZVKZM4CAABAUKD4DsA0rW1uldV41nvPpPgeMPKSPcX3ovJ6GYZhchoAQCirbmzRzv2ei72GZMWZnCa0xEaGq0eEWxaLVV/vbjI7DgCg3fo3pPoyKS5LGvxTs9Mg0EXESFFJskg6vSdL9QEAAAQDiu8ATLO3xqU2w1BUhE0JjnCz4+CAnESHwqwW1blaVV7XbHYcAEAI23Cg5XxOD4cSoiJMThN6sqLckqSlFN8BIDAYhvTVXzzbY26SbJwHoxMOtJ4/M89mchAAAAB0BsV3AKb5Yct5i8Vichq0C7NZldMjSpJUtL/e5DQAgFDW3nKe9d67RnvxfeO+Zu2rdZmcBgCg7Z9JZRul8GhpxPVmp0GwiPcU38fnMfMdAAAgGFB8B2CaPQeK77ScDzz5ye3rvlN8BwB0nXXFNZKkIaz33iWiwyTXni0yJP17fYnZcQAAS5/yfB9xreRIMDUKgsiBme9D06yyNdeYHAYAAABHQ/EdgCnchqGSak8L1CyK7wEnP8lTfC+pbpKrzeQwAICQ1T7zvYDie5epL1wsSXprVbHJSQCgm9u7Udq2ULJYpTE3m50GwSQiWo1h8bJaLIrZv9bsNAAAADgKiu8ATFFe61Jzm1sRNquSYljjNdDERIYpJdYuSSptZKgAAPhebVOLig50WGHme9epL1wsq0VavatK2/fVmR0HALqvZU97vg+4UOqRb24WBJ26iDRJUuz+1eYGAQAAwFFRUQFgivb13jMSImVlvfeA5J393sR/HwCA723Y42mbmpXgUI9oLsTrKu76Kg1L81xQ9/a3zH4HAFPUlUlr/+XZPvXX5mZBUKqNSJckxZSvNjcIAAAAjirM7AAAuqc9VbScD3T5ydH6ZkeF9jZaJSvDBQDAN5xOp8rLy/XhZs8s7Oxot1atWnXM+yksLPR1tJA1rqdD35a6NH91sW4/t58sXPgIAP617GmpzSVljZJyRpudBkGoLiJVkhRVs01qqJCiepicCAAAAIdDNQWA3xmG4Z35nknxPWClxdnlCLepsaVNkdmDzI4DAAgBTqdTAwYOVGNDg5Iu/J1iBp+pj/75vP512+vHvc+6OlqpH82YrEhFR9i0q6JRy3dUanQ+H9gDgN80VEjfPOfZPv13EhdA4Ti02hzaUNamwak2aeeX0sCLzI4EAACAw6D4DsDvqhpb1NjSJpvVorQ4u9lxcBgWi0X5ydHaWFIjRx9mZwAATlx5ebkaGxp09d2Pak3YANW2ShdccoXSr7n8mPdV+M1i/Xve42pqauqCpKHFHmbRBUMz9K8Vu/XaN06K7wDgT189JTXXSekFUv/zzU6DILZo54Hi+44lFN8BAAACGMV3AH63u9Iz6z09LlJhVqvJaXAk3uJ775PNjgIACCE9snurdrdn5t/A/v0UbT/205K9zm2+jhXSrhydq3+t2K331pXoDxcNUkJUhNmRACD0NVRIXz/r2R53N7PecUIW7WjVrSdHSEVfmB0FAAAAR3BcVa9evXpp//79B91eVVWlXr16nXAoAKFtd2WDJCk7kZbzgS63R5QsMhTeI0t7alvNjgM/YIwH4A/VzZ7iQ4w97LgK7zh2w3MSNDAjTs2tbr21qtjsODAJ4zzgZ8uelpprpbQhUv8LzE6DILd4R5tno2yD1FhpbhgAAAAc1nEV33fs2KG2traDbne5XCou5oMcAIdnGN/PfKf4HvgiwqxKiTQkSSv20Na3O2CMB+APlQeK76mxLD/jLxaLRVeNzpEk/fMbpwzDMDkRzMA4D/hRY6X09d882+Pukuj6hhO0r8FQU7RnLNeub8wNAwAAgMM6pmkm7777rnf7o48+Unx8vPfntrY2LVy4UHl5eT4LByD01LZKDc2e9d7T4yLNjoNOSI90q6zJqhUlLrOjoAsxxgPwpyqK76a4+KQszfxgk74rq9PXRRU6pVeS2ZHgJ4zzgAmW/VVy1Uipg6QBrM8N36hLKlBk/S7J+ZXUb6LZcQAAAHAIx1R8/+lPfyrJM2vi+uuv73BfeHi48vLy9Nhjj/ksHIDQs6/Jc7V/Rnykwmxc+R8MMhxura2SCvc1q7qxRfGOcLMjoQswxgPwp6qWA8V3LsTzq7jIcP30pCz98xunXvqyiOJ7N8I4D/hZY5W07BnPNrPe4UN1PQqU7PxA2vmV2VEAAABwGMdUfHe73ZKk/Px8LV++XMnJyV0SCkDoKnd5Pmyn5XzwiAmXWvbvkpJy9MV3+3Th0EyzI6ELMMYD8BdLuF01Lcx8N8sNp+Xpn9849fHGvXLub1BuUpTZkeAHjPOAny2ZLbmqpZSB0sCLzU6DEFLfo8CzsWeV1NIkhXMhIwAAQKA5puJ7u6KiIl/nANBNtM98z07gg95g0rD1G8Un5ejTwjKK7yGOMR5AV4tIzZdkUXSETdH24zodwQnomxar0/sm64vvyjXvqx2698JBZkeCHzHOA13H6XSqvLxcEQ2lGvTV07JK2pp/rWpWr/bZMQoLC322LwQnV3SmFJ0q1ZdJe76Vep5qdiQAAAD8h+P+tGvhwoVauHChysrKvFfRt3vxxRdPOBiA0BOenCuX26Iwq0Vp8cx0CyaN25YrfswlWrRln9rchmxWi9mR0IUY4wF0pYi0PpJoOW+mG36Ury++K9fry3fpt+f0VVwkS8p0J4zzgO85nU4NGDhQjQ0N+sfPIjVkaIQ+LWrV2ff9ukuOV1JR2yX7RRCwWDwF943vSM6lFN8BAAAC0HEV3++77z7df//9GjVqlDIyMmSxUIQBcHT2HE97tIyESIWx5l1QcRUXKjrcoor6Zq3eVaWRPRPNjoQuwhgPoKtFpPeWJKXQct404/qmqE9qjLaW1ekfX+3UrWf2MTsS/IRxHuga5eXlamxo0PT/vkPX2J+XJK0Z8aDuGJPn0+NsXrtS7//tT6qqa/LpfhFkctuL78vMTgIAAIBDOK7i+1//+lfNnTtX1157ra/zAAhhkT2HSqLlfFByt+mkdLuW7GrSp5v2UnwPYYzxALpa+8z3NIrvprFaLbr1zN66/fU1ev6L7Zo8No8lALoJxnmga02OWSK1SIUp50v9LlC2j/dfXl7u4z0iKOWe4vnu/FpyuyUmNwAAAASU43p31tzcrLFjx/o6C4AQ5jYMReYMkSRlJzpMToPjMTLT0x54YWGZyUnQlRjjAXQlV6uh8ORcSVJqLG3nzXTR0Ez1TIpSZUOLXvl6p9lx4CeM80DXuaBvmPq0bFKrJUJf5v4/s+MglKUVSBExkqta2ldodhoAAAD8h+Mqvt9444169dVXfZ0FQAjbVd0qW1S8bBZDaazxGpRGpNtltUibSmtVXNVodhx0EcZ4AF1pR1WLLFab7FZD0Xab2XG6tTCbVbeO93QhePbzIjW1tJmcCP7AOA90EXerHjnX09Hl28wrVBuZYXIghDRbmJR9smd751JzswAAAOAgx9VbsKmpSc8++6w++eQTDR06VOHh4R3unz17tk/CAQgd68ua9f/Zu/Mwqeoz/f/vU3vv+0ovbA00qwio4IYLJMYYE5OYjDGJk83EJeP4m3HGOJnB+WZ0kpk4JuPoJJNETYxJTOIWgwuKAorsOzTQQEND73tXd+1V5/dHAUrYej+93K/r6quKqnNO3aBwquo5n+cByHKb2G2aLTkSpbhtzCvNYOPhNlbubeSLl5RaHUkGgc7xIjKYKlvDAGS6Tc2aHgY+deE4fvRWJTXtfn67oZrbLp1gdSQZZDrPiwyOvIPPMS7HTpeRwoaiv7Y6jowFJQvh0Nvxue8Xfd3qNCIiIiLyIX0qvu/YsYMLLrgAgF27dp3ynL5EE5Ez2dUUBCDXHbM4ifTH1dPy4sX3igYV30cpneNFZDAdaI1fjJfh0vuB4cBpt/GtxZP4pxd38b+rDvFXF5fgdqgjwWim87zIIGg9RMG+pwF4OfnzhBzJFgeSMeHk3Pd11uYQERERkdP0qfj+9ttvD3QOERnFYjGT3U3xL9uzPabFaaQ/rp6Wy/df28t7B1vwhSIkuvp0GpFhTOd4ERlMB9riK98zXHo/MFx8dn4R/72ykvrOAH/YfIwvXKyL60YznedFBphpwit/iy0W4s1DETZfvIgiqzPJ2FA0H2wO6DwG7UchvdjqRCIiIiJynKomIjLodtd20hUyiQV9ZKhYO6JNyUtmXHoCNe1+1h5o4drpeVZHEhGREaLDH6bWG58rruL78OF22Ln9ikn86yt7ePztg3x2XjEuh83qWCIiI8OO5+DQO8RsLr75ipdLJ7dgr6sbtJdra28ftGPLCONKgoI5ULMZqt9X8V1ERERkGOlTFeyqq646Z0u6lStX9jmQiIw+aw40ARCo3omtbK7FaaQ/DMPgmvJcfvn+Ed7a26ji+yikc7yIDJadxzoACLfV4S7JsjiNfNgtF5fwxKqD1LT7eW7TUW7VaJlRS+d5kQHU3QKv3w/A/oJPcrDtZzQ8/zz25MxBe8loVysAPp9v0F5DRpCShR8U32ffbHUaERERETmuT8X3EzPiTgiHw2zbto1du3bx5S9/eSByicgo8m5lMwCBw1sBFd9HuqunxYvvK/c2YJozNR90lNE5XkQGy/Zj7QCE6vbDnIXWhpFTeJx27rpqMv/y8m7+e2Uln5lXhMep2e+jkc7zIgNoxXfB1wK509macBnwM66aO5FpU8sG7SU3bt3FO9shGAoO2mvICFJyCbz/mOa+i4iIiAwzfSq+/9d//dcZH1+2bBldXV39CiQio4s/FGXT4bb4/cNbLU4jA+GSiVkkOO00dAbZXdvJzHFpVkeSAaRzvIgMlu1H2wEI1lcCKr4PN5+/qJifrj5ETbufZ9Yd4WuXT7Q6kgwCnedFBsihVbDt14ABN/yI2OpKADKSPRRkpQ7ay6Ykugft2DIClRx/P9W4B/xtkJBhbR4RERERAWBAh/ndeuut/OIXvxjIQ4rICLfhcCuhaIzsRBuR1hqr48gA8DjtXFaWDcDKvY0Wp5GhonO8iPTXjuNt50N1lRYnkTNxO+x8+5rJADzxzkG6gxGLE8lQ0nlepBfCAXjlb+P3F3wVii+yNo+MXUnZkHW800L1emuziIiIiMhJA1p8f//99/F4PH3a9+GHH8YwDO65556Tj5mmybJlyygsLCQhIYHFixeze/fuAUorIkPh3cr4vPc5ebpCfzS5ZlouAG+p+D5m9OccLyLS0BmgvjOAzYBQwwGr48hZ3HRhEeOzEmnpDvHU2sNWx5EhpPO8SC+s+U9oPQjJ+XDNP1udRsa6kkvit9XvW5tDRERERE7qU9v5m2666ZRfm6ZJXV0dmzZt4rvf/W6vj7dx40Z++tOfMnv27FMe/8EPfsAjjzzCU089xZQpU/je977HkiVL2LdvHykpKX2JLiJDbM3xee8qvo8uVx0vvm8/2k6TN0hOiv77jhYDfY4XEYEPWs4XpzqoCmtO7XDltNu459op3PO7bfxk1UFuvaSUtASn1bFkAOk8L9JPjRXw7qPx+x/7AXg0gkssVrIQtv5Kc99FREREhpE+rXxPS0s75SczM5PFixezfPly/uVf/qVXx+rq6uILX/gC//d//0dGxgeziUzT5NFHH+WBBx7gpptuYubMmTz99NP4fD6effbZvsQWkSHW6A2wt94LwKxcl8VpZCDlpXqYdXzW+9v7tPp9NBnIc7yIyAnbj7UDMDlThdzh7oY5hUzJS6YzEOFnaw5ZHUcGmM7zIv0Qi8Gf/gZiYZj6MSj/hNWJRKD0+Nz32i3xkQgiIiIiYrk+rXx/8sknByzAnXfeyfXXX8+1117L9773vZOPV1VVUV9fz9KlS08+5na7ufLKK1m7di233377GY8XDAYJBj9YTeP1egcsq4j0znsH4qveZxSmkuaxW5xG+quiouKUX09Li7KzBl5cv5/JtqZz7pudnU1JSclgxpMBMpDneBGRE07Me1fxffiz2wzuXTKFbz6zhV+8W8Vti8aTlawON6OFzvMi/bDlKTi6HlzJ8LH/AMOwOpEIZEyA5DzoaogX4EsXWZ1IREREZMzrU/H9hM2bN1NRUYFhGEyfPp25c+f2av/f/va3bNmyhY0bN572XH19PQB5eXmnPJ6Xl8eRI0fOesyHH36YBx98sFc5RGRwnGg5f1lZNuC3Noz0WWdrvLB+6623nvK4e9w08m/9T9bsb+I3dy8FM3bWYyQkJrK3okIF+BGkv+d4EZETTNM82XZ+cqY64VjhLy+gO58c02RihoNDbREefO59brsglWAwiNvd/yK8LsgbHnSel7Gmurqa5ubmPu/vCLQwfeU/4QCOTvkyTQcbgQ86gFVVVfU/pEhfGEZ87vuel+Jz31V8FxEREbFcn4rvjY2NfP7zn+edd94hPT0d0zTp6Ojgqquu4re//S05OTnnPcbRo0f5m7/5G9544w08Hs9ZtzP+4kpi0zRPe+zD7r//fu69996Tv66pqWH69Ok9+F2JyEAyTZN3jxffL5+cA95qixNJX/m7OgG4/vYHmDp73snHYya8UmNCQgpf/uEfyXKbZ9y/ofogv/7+39Pc3Kwv20eAgTjHi4h82OEWH52BCC6HjdK0fl37K710tgvoesIzcR55n32QF3e38vjffJpoVxtw5nN9b+iCPGvpPC9jUXV1NeXl0/D5+n5B+O8+k8DsGU421kS55F8fJmY+fMbtwuFIn19DpM9KFsWL70feh8utDiMiIiIiffr26+6776azs5Pdu3dTXl4OwJ49e/jyl7/Mt7/9bX7zm9+c9xibN2+msbGRefM+KOREo1FWr17NY489xr59+4D4CviCgoKT2zQ2Np62Gv7D3G73KSsyOjs7e/37E5H+q2zsotEbxO2wMX98Bnt2qvg+0mUVllJUNuOUx8YH66hs7MKXkMecSVkWJZOBNBDneBGRD9txfN77jMJUHDa16B1KZ7uAridME1Y1xmjBzazb/o1tj32rT8f5MF2QZz2d52Usam5uxufz88x3bqa8pPcXmKQGjjG57W1MDJLnfIKNT2Sets3Plm/iiZfXE42q+C4WKLk4fntsA8RiYLNZm0dERERkjOtT8f21117jzTffPPlhHWD69On8z//8zykz2s/lmmuuYefOnac89td//ddMmzaNf/iHf2DixInk5+ezYsWKky3wQqEQq1at4vvf/35fYovIEFq9P77S6qIJmXicmvc+WpVmJVLZ2MWR1m4Wqvg+KgzEOV5E5MO2HW85P6coHQhaGWXMOtMFdD2xOMfHH7fU0J5UhD01t8/HkeFD53kZy8pLcrhwyrje7RQNwcaXADCKFlA+adYZNyvcsL+/8UT6Lm8WOJMg0AFNeyFPHUBFRERErNSn4nssFsPpdJ72uNPpJBY7+8zfD0tJSWHmzJmnPJaUlERWVtbJx++55x4eeughysrKKCsr46GHHiIxMZFbbrmlL7FFZAi9e+B4y/mybIuTyGAan5UEQENnEF8oQqJL7YRHuoE4x4uIfNiOYx0AzClOA7PxPFvLcFKUkUhxZgJHW/2kX/pXVseRAaDzvEgvVa2BYCd40mD8ZVanETkzuwOK5kHVaji6TsV3EREREYv1qQ/R1Vdfzd/8zd9QW1t78rGamhr+9m//lmuuuWbAwt13333cc8893HHHHcyfP5+amhreeOMNUlJSBuw1RGTgBcJR1h9qBeCyyZobOZoluR1kJ7sAqG71WZxGBsJQneNFZGwIRWLsqokX32cXpVsbRvpk4cR4Z5ukmVfji6mb0Uin87xIL3jroWZT/H7ZR8DusjaPyLkUXxK/rV5vbQ4RERER6Vvx/bHHHsPr9TJ+/HgmTZrE5MmTmTBhAl6vl//+7//uc5h33nmHRx999OSvDcNg2bJl1NXVEQgEWLVq1Wmr5UVk+Hn/UAv+cJT8VA/lBbpYZrQ7sfr9SIuK76PBYJ3jRWRsqqjrJBiJkZ7oZGJ2ktVxpA8K0hLIwIths3MkrPd1I53O8yI9ZMag8jXAhJxyyJxodSKRczsx9/3oOmtziIiIiEjf2s4XFxezZcsWVqxYwd69ezFNk+nTp3PttdcOdD4RGYFWVsRbyl5dnothGBankcFWmpXIpiNtHGnxYZqm/puPcDrHi8hA2lLdBsDc4nSdH0awUpppI4XGaAKd/jCpCae3LZeRQed5kR6q2x5f+W53w2R1hZARoGgBYEDbYfA2QEqe1YlERERExqxerXxfuXIl06dPp7OzE4AlS5Zw99138+1vf5sFCxYwY8YM1qxZMyhBRWRkME2TlXvjxfdry3MtTiNDoSAtAZfdhj8cpdEbtDqO9JHO8SIyGLZUtwNwYUmGtUGkX5IJ4D+8FTBOXlAhI4vO8yK9EPZB1ar4/fGXgyvZ2jwiPeFJg7zj3UK1+l1ERETEUr0qvj/66KN8/etfJzU19bTn0tLSuP3223nkkUcGLJyIjDx7673UtPvxOG0smpRtdRwZAnabQXFmAgCHW7otTiN9pXO8iAyGLUfihdoLS1V8H+k61/8RgN21nfhDUYvTSG/pPC/SC4fegUgAknJh3IVWpxHpuROt56tVfBcRERGxUq+K79u3b+ejH/3oWZ9funQpmzdv7ncoERm5Tqx6v3RSNh6n3eI0MlRKNfd9xNM5XkQGWmNngJp2P4YBc4rTrY4j/RQ4vI1kI0QkZrLjWLvVcaSXdJ4X6aHOGqjfEb9fthSMXn1tJmKt4kvityq+i4iIiFiqV58iGhoacDrPPt/P4XDQ1NTU71AiMnK9VdEAxOe9y9hRmpUIQH1HgEBYq+FGIp3jRWSgnWg5PzUvhWS3w9owMiCKnV0AbD/WQTgasziN9IbO8yI9YMag8o34/bxZkFZkbR6R3jqx8r1+B4R0YbyIiIiIVXpVfB83bhw7d+486/M7duygoKCg36FEZGRq6Qqy9Wg7AFdPU/F9LEn1OMlMcmEC1a36kD8S6RwvIgNt6/HZ4HM1733UyLEHSPU48Iej7KnttDqO9ILO8yI9ULcduhrA4YaJi61OI9J7acWQUgixCNSom4mIiIiIVXpVfP/Yxz7GP//zPxMIBE57zu/38y//8i98/OMfH7BwIjKyvLOvCdOE6QWpFKQlWB1HhtiJ1e9qPT8y6RwvIgNty/Hi+4Ul6dYGkQFjGHDh8YsptlS3EYuZFieSntJ5XuQ8oiE4/G78/vjLwZVkbR6RvjCMD1a/H1XreRERERGr9Kr/4z/90z/x/PPPM2XKFO666y6mTp2KYRhUVFTwP//zP0SjUR544IHByioiw9yJee/XqOX8mDQ+K4mt1e0caenGNE0Mw7A6kvSCzvEiMpBCkRg7jnUAcGGpVr6PJtMLU1lf1UpnIEJlYxdT81OsjiQ9oPO8yHkc2wjhbvCkQ8Fcq9OI9F3xJbD7Baheb3USERERkTGrV8X3vLw81q5dy7e+9S3uv/9+TDO+0sEwDD7ykY/w+OOPk5eXNyhBRWR4C0VirN4fnxN5Tbn+HRiLCtM8OGwG3aEozV0hclLcVkeSXtA5XkQGUkVdJ8FIjPREJxOztXpwNHHabcwuSmN9VSvbj7Wr+D5C6Dwvcg6hbjh6vFA54Uqw2a3NI9IfJ1a+H9sAsRjYetX0VEREREQGQK+K7wClpaUsX76ctrY2Dhw4gGmalJWVkZGhFS0iY9mmw614gxGyk13MHpdmdRyxgMNuoygjgcMtPo60dKv4PgLpHC8iA+VEy/m5xenqhDIKzRqXxsbDrdR1BGjsDJCb6rE6kvSAzvMiZ1G9Nt52PjkfcqZZnUakf/JmgTMJAh3QtBfypludSERERGTM6XXx/YSMjAwWLFgwkFlEZAR763jL+aum5mKz6Uv2sWp8VtLx4ruP+eMzrY4jfaRzvIj015bqduCD+eAyuiS5HUzOTWZ/Qxfbj3WwZLqK7yOJzvMiH+Jvg9qt8fsTF8dnZouMZHYHFM2HqlVQ/b6K7yIiIiIW6HPxXUTkBNM0WbGnAdC897GuNCsRgNoOP8FIFLdDLRtFRKxWXV1Nc3Nzv4+TnZ1NSUlJj7bdciS+8v1s896bm5uxJ9f1K097e3u/9pf+mVOUzv6GLvY1eLmsLJsEp875IjICVa0GMwYZEyBj/JC/fHfExlG/i4agg5aQA2/Eji9qIxozMAGHzcRtM0l2RElzROlIHI+7aAYhnIOerbK2lZz9NYN2/Kr6tkE79mhWUVFx3m0KnKUUAC3bX+WIfW6vX6M37/lERERE5HQqvotIv+2u7aS61YfHaeOKKTlWxxELpSe6SEtw0uEPU9PmZ2JOstWRRETGtOrqasrLp+Hz+ft9rMTEBCoq9p73y9jGzgA17X5sBswpTj/lubq6eMH9+eefx57cvw4pocYqAHw+X7+OI31TkOYhJ9lNU1eQitrOs15oISIybHnroel4IXPi4iF72UDUYK/XQ0VXAo3BcxfRQzGDUAy8ETt1ACnTyf/C9/kx8MZ7fq7M9rI4x8u8dB8umzkg+Zo7ugG4+/EVwIoBOea5NLZ3D/prjAZ1rV4M4NZbbz3vtksm2nnji0l07HydeV//Y69fq6fv+URERETkzFR8F5F+e21XPQCLp+SS6NI/K2NdSWYiO2s6qG71qfguImKx5uZmfD4/z3znZspL+n6BXEV1E7c+9BzNzc3n/SL2xLz3KXkpJLtPfV9wYrX6VXMnMm1qWZ/zAKxbG2ZNJQRDoX4dR/rGMAxmFaWxcm8ju2o7mFuSjqF2zSIyklSvjd/mTofkvEF/ua6IjU1tSezyJhA1P/j3MtcdpsATJtcVJtUZJckew2EzMYBwzCAQs9EVsdEacrCnrovWkB1Hag57uxLY25XATw7nkmyPsjjHyycK2rky24u7H4V4rz9+Xr32k59l9ozy/v62z2rTjgpW/+n3dPiCg/Yao0l7VwATeOyOpSycfe73ULZYCLPhOSZm2Nj+P39NxJ7Y49fpzXs+ERERETkzVclEpF9M02T5rvgqtutm5VucRoaDDxffRURkeCgvyeHCKeOG5LVOzns/x0rojGQPBVmp/Xqd1ARXv/aX/pual8KayibafGFq2wOMy0iwOpKISM90NULz/vj9kkWD+lLhGGxqT2Jze9LJonu2K8zMVD+Tk4IkOWLnOUL0+G0Q394t7Hzqcf79zhuZtvAjrGpJYXVzMs0hJ6/Up/NKfTqpjggfzevkEwXtLMzswt7H66Iys3MoKi3t2849UFnT/5E4Y9HkwoyevafryoHuRmZnRyFnaN4DioiIiEiciu8i0i+VjV0caurGZbdx9bTzz3vv64xXzXUdOYozEjCANl8YbyBsdRwRERliJ+e9l6gN+WjnctiYkpfC7tpOdtV2qPguIiNH9fvx25xpkJQ9aC9zzO9kRWManRE7AIWeEBdndFOcEKI/zUKSTB83FrZzY2E7MRN2dCQcL76nUR908VxNJs/VZJLtCvPJgnY+V9RKWbJWmI8paUXQ3Qgdx+L/n4uIiIjIkFHxXUT65dWd8Zbzl5dlk+I5+7y6/s541VzXkcPttJOX6qG+M0B1q480qwOJiMiQCYSj7DjWAcCC8Sq+jwUzC9PYXdtJZWMXV06J4nHarY4kInJuvpYPZr0P0qr3qAnvtyazuT0RMEh2RLkiy8vkpGC/iu5nYjPggnQ/F6T7+c7UOja0JfFyXTqvNqTRHHLysyM5/OxIDnPTuvlcURvX57eTct7V9jLipY6D2i3QWWN1EhEREZExR8V3Eekz0zT5045aAK6bVXDObfs741VzXUeWkszEk8X3WW6r04iIyFDZdrSdUDRGXqqbksyezxeVkSsv1U1WsouWrhD7G7zMLkq3OpKIyLkd2xC/zZoMyefv3tZb/qjB8oY0jvnjH4RmpPi5ItuLqx9z2HvKZsAlmd1cktnNg+U1rGpO4Xc1maxsSmVrRxJbO5J4cG8h1+e1c3NRKwvSfQN+MYAME2lF8duuBoiGwK5xPSIiIiJDRcV3EemzijovBxq7cDlsLJ2R16N9+jrjVXNdR5aSzEQ2HG7laKufmflWpxERkaGyoaoVgAXjMzH0bf6YYBgG0wtSWVPZzN56Fd9FZJgLdUP9rvj94osH/PABWyK/O5ZJR8SB04ixJLfTsnbvThtcm+vl2lwvjUEHL9Rm8LuaDA51e/hDbSZ/qM1kYmKQz45r5dPj2sh1RyzJKYPEnQquFAh5obMOMkqtTiQiIiIyZtisDiAiI9fL2+Or3q+emkvqOVrOy9iTn+bBaTfwh6N0hFV8EREZK04U3y+e0PsRMzJyTc1LwQDqOgK0+9SlSESGsZrNYEYhpRBSiwb00M7ciexOW0hHxEGqI8LN44bPnPVcd4TbJzTx1qX7+eNFB7h5XCuJ9iiHfG6+X1nAolXl3LGthLUtSQz++nwZEobxwer3zmPWZhEREREZY7TyXUR6rLq6mubmZgBipskfNjYBMCstwJYtW865b1VV1YBk8HZ2npwf31MnWt7L0LHbDMalJ3C4xUdDQMV3EZGxIByNsaW6DYCLJmRZnEaGUpLbQXFmItWtPvbWe7lkov77i8jwY4uF4zOwIb7qfQA7tDQ5csn/q68SsbnJcYW5saCdpEGcq360qZMt+/s2y9sAPu+CG4vsvNddwFveIvYFM1jekM7yhnSSUr9AyvwEIoa+Mhzx0sZBUwV0aO67iIiIyFDSO2kR6ZHq6mrKy6fh8/kBcI8rJ//W/yAW9HH3p2/CjPRslVM43LdWdoFwFIBNmzaxde+hXu0baowX/n0+X59eW/qmJDORwy0+GgNqsiIiMhbsru3EF4qSnuikLDfZ6jgyxKblp5wsvl88QWMHRGT4yfRXQSQAnnTILhuw4+7q9PBq2qew2TykhFv59PgIbvvgrB/3dXkB+MHv1/OD368fsOM6c8aTcsF1JM24im53NpnXfIMtZoRAY4gL031kuqID9loyhE50d+isAdMc0AtOREREROTsVHwXkR5pbm7G5/PzzHduprwkh580T+d1L1yT1crdj339vPv/bPkmnnh5PdFo34rvoUj8w/6cSbksWjC3V/uuWxtmTSUEQ2qDOpRKMhMBaA4YGA6XxWlERGSwbahqAWB+aSY2m77cHWsm5STjsDXS4Q9T3xmgIC3B6kgiIqfI8e2N3xk3D4yBuUC4qtvFlzZNJGRzEDi2h/kJR3HbLxiQY59JKBgA4JKP3siieXMG/Phhcw9v7u+kPWUCrtwJ7PY62O1NZEJikHnp3RR6wqrfjiTJuWBzQjQI3U3xX4uIiIjIoFPxXUR6pbwkh+mTinj/6DgAvjI1xIXZ4867X+GG/QPy+kkeJwVZqb3aJzVBhV8rZCa5SHLb6Q5GcY8rtzqOiIgMMs17H9tcDhuTcpPZV+9lb71XxXcRGVYWj7eTEOmIFyLzZw3IMZuCDr68eQKtYQfZ4Qa2/P5fsH/prwfk2OeTmplNUWnpoBw7e+drVPzhES677T5cEy/iYLebKl/8p8AdYmFmF8WJ4UF5bRlghg1SC6H9SHz1u4rvIiIiIkNCvYBFpNdWNKbSGXFQ6AmxKKvr3BtHQxDoIMMRoCjVwGX0beW7jDyGYZxc/e4Z37tuBSIiMrLEYiYbD5+Y967i+1g1LT8FgP0NXqKxwWm5LCLSF3dfdPyC7PyZ4PD0+3iBqMHXtoyn2u+mJCHI0o6XMUP+fh93OEkMtfDx/A6+VNzCrFQfdsOkLuji+bpM/libTl3AaXVE6Ym0463nO45Zm0NERERkDNHKdxHptT/UZgDw6cI27B9uOWfGoL0aWg9BZy34muMz9YA7C+DOv00Bfkuk5o/47Ul47em0OvJodeTS5silxZlPhz1zwFoAivVKMhOpqPPiGX+B1VFERGQQ7W/00uEPk+iyM6Owdx1qZPQoyUgk0WXHF4pypKWbiTnJVkcSEcHpb+TGqce//iqc1+/jmSbcv7uI7Z2JpDsjPDWvimerff0+7nCV4YpydY6XizO62diWxM7OBI753TxX42ZSUoArsrykOmNWx5SzOTn3XcV3ERERkaGi4ruI9EpLxM2a5viqpk8Xxle4EfbDsY1Qtx3C3afvZNgJRk0MM4rLbuAgTEq0nZRoO4Whw6dsGjJctDjyaXEW0OwsoMWRT7OzcJB/VzJYijPiK9/d+ZPpCEQtTiMiIoPlRMv5eaUZOOy6iG6sstkMpuSlsO1oO3vrvSq+i8iwkFX9KnabgdeVR0pSdr+P9/Mj2bxQl4HdMHl8zhEmJoUGIOXwl+SIsTjHy4Xp3WxoS2aP18PBbg9HfG4uyujiwnTfqRfny/CQWggYEOiAoBfcKVYnEhERERn1VHwXkV55p2scMQwuyuhifIIfjqyHo+vi7eUh3sIvewqkFUNyHnjSwOHmP555m+/+YgVf/vrXuHzuFBJiXaRFWsiMNJIZaSQj0kBmuAGXGaIgXE1BuPqU1715loutGYl0Zmwlpcuk1RlfLd9tSwVDn/CHqyS3gzRnjI6wjZ2NIa6yOpCIiAyK9ceL7xeNV8v5sa48P158P9TcTTASxe2wWx1JRMayWJTs6uUANCeW0d+y48a2RB7eXwDAP02tZVHWGS4+H+VSnTGuze3kgjQfbzenUBtwsbY1hQpvAtfmdFKYoHnww4rDDUk50N0Yn/ueM83qRCIiIiKjnorvItILBiu8xQB8NusIbHkKupviTyXnQskiyCoD29m/ZPWbLjodWXSSRYOr9NSjm1HSI81kR+rIDteRFa4jO1xPerSZDGeIqyc4gAPQceDkPkHDc7JtfavzRAv7PNod2cQMfdk7HOR6TDrCsL0haHUUEREZBKZpnlz5rnnvkpPiJiPRSZsvzKGmbsoLNIZARCx04C1c/kZafDHa80v6daiWkJ27t5cQNQ1uLGjjtpKWAQo5MmW7I3ymsI19XR7WtCTTFnbwh9oMFmR0c3FGNzZdIz98pBXFi+8dx1R8FxERERkCKr6LSI95JsylMZJIqj3EDTX/BaYPHAkw+RrIndHvFeimYafNmUebM4/KhAtOPu6Ihdi+8gUi+97m+ivncGGBk8xwI2nRZtxm4IOV8v4PjhU2nNQ7S6h1TaAjuZPVrn5Fk37I9cSo9NrZ3hDENE0MdSoQERlVjrT4aPIGcdltzClOtzqOWMwwDMryUthQ1UplY5eK7yJirc1PAfD09jCLJ/X94mzThL/fVUx90MXEpAAPTa9RAzbiXwFMSwkwITHIquYUKroS2NCWTLXPxUfyOkl3avTYsJBWBLVbNPddREREZIgM+UDGxx9/nAkTJuDxeJg3bx5r1qzp0X7vvfceDoeDCy64YHADishZpcz9GACf4U08pg8yJsCCr0PezEFt/R6xudjvT+Pp7WF+1jyHl7O+xlP53+Gxwh/wy9z7+FPmbbyX+jEqEuZR7ywmZLhxmmGKQwe5uOtNlhVvoPm+FP418dfMqn8ed6Rz0LLK6bLdJmYkTLMvxqHmsdeWUURktDux6n1OcRoep7rOCJTlxme9V7f4CEZUeBERi3Q1wv7XAPjZlv61Qv99TQYrm1Jx2WI8PqeaJEdsIBKOGm67ydK8Tj6a247LFqM+6OLZo5kc6HJbHU0AUsfFb70NH4wMFBEREZFBM6Qr33/3u99xzz338Pjjj3PppZfyk5/8hOuuu449e/ZQUnL29l8dHR186Utf4pprrqGhoWEIE4vICc2+KEmTF2ACt9hXQtFFMHExGEN+Dc9JUcNBi7OAFmfBqU+YMTIjTRSGqigIVZHZvptCVzcXUAUHH2bxof+kMvsathZ8noaUGdaEH0McNgjW7MFTOod3K5uZlJNsdSQRERlA66ribXcXaN67HJeV5DrZer6quZtp+Vr9LiIW2PkHMKN0p0+jonlDnw9zzO/kX/cWAvB3k+uZlhIYqISjztSUIAWeFt5oTKMm4OLPDeksDHtZkO5TpwAredLAnQJBL3TWQUbp+fcRERERkT4b0qrZI488wle/+lW+9rWvUV5ezqOPPkpxcTFPPPHEOfe7/fbbueWWW1i4cOEQJRWRv/Tupk2Yhp1LbLuZPHEyTLra0sL7ORk2Wp157Eq6hBUZf8U3Dl7NlP/u4iftl1LrGo/DDFPe9Bq37LiNGzZ/BfehN6mrqzvrT3t7u9W/oxHPf3gbAGsqm60NIiIiA8o0TdYeiBffL52cbXEaGS4Mw6AsNwWAyoYui9OIyJi147cAtBYv7fMhYibct6uIrqid+endfHW8Ps+cT6ozxk2FbcxJ8wHwfmsKrzemElGzAGulFsVv1XpeREREZNAN2cr3UCjE5s2b+cd//MdTHl+6dClr1649635PPvkkBw8e5JlnnuF73/veeV8nGAwSDAZP/trr9fY9tIgAENj5J/5UnwHADcn7oXSRxYl6JxCOUtka4+/eMfmH5JnMSSni9uLDfCa/hsmBnUyuu58/N+bxncrpHPEnnbZ/qLEKAJ/PN9TRR43A4a1w5ZdZd6iFSDSGwz5ML9wQEZFeOdjUTX1nAJfDxrzSDKvjyDAyOTeZDYdbOdIabz3vdmgkgYgMoca9ULcdbA5aC68Gzv990pn8qjqLta0pJNhj/OfMo9i1ertHbAYszvaS5YzwTnMK+7oS6Ajb+URBOwl20+p4Y1PaOGiqgI4aq5OIiIiIjHpDVnxvbm4mGo2Sl5d3yuN5eXnU19efcZ/Kykr+8R//kTVr1uBw9Czqww8/zIMPPtjvvCJyXN12XvjDr2jjy6SFGpiSMfJmtoWOzxqdMymXRQvmArANOGy2c3VkDQuiW7g+t4GluS286biSVfZLiRkffEG8bm2YNZUQDGk2Wl+FGg6R7DLoCkbYfqxDBRoRkVHivQPxFYALxmdo3rucIjvZRXqCk3Z/mMPNPqbmp1gdSUTGkh2/i99OXkLUndanQ1R1u3h4f3zE2f1T6hifpM+DvTUrzU+6K8Kf69OpD7r4Y00mnypsI8mhZfBD7uTK9xowY8O3k6GIiIjIKDDk77SMvxjyZJrmaY8BRKNRbrnlFh588EGmTJnS4+Pff//9dHR0nPzZs2dPvzOLjFmBTsznbuMXoWsAOPLun7DbRu6l/kkeJwVZqSd/ErJLeD//CzyTex9H3FNwEuG6yFvcE3uK8rTgye1SE1xWRx/5zBgzc+IXbqw9oFaNY8HDDz/MggULSElJITc3l09+8pPs27fvlG1M02TZsmUUFhaSkJDA4sWL2b17t0WJRaQv3j3+b7pazstfMgyDsrxkACob1Y1sNNE5Xoa9WAx2/j5+f87n+nQI04Tv7CkiELNxaaaXW4tbBjDg2FKcEObmca0k2aO0hB38vjaDzrAKv0MuORdsTogGoVufyUVEREQG05C9283OzsZut5+2yr2xsfG01fAQbxe/adMm7rrrLhwOBw6Hg3/9139l+/btOBwOVq5cecbXcbvdpKamnvxJSdEKC5E+MU145R5WNydTaRbhcUDn9jesTjUoWp15PJ/1TV7LuIWAkUB+uJovNP6QKb6tVkcbVWbnxS9ieO+gPuiPBatWreLOO+9k3bp1rFixgkgkwtKlS+nu7j65zQ9+8AMeeeQRHnvsMTZu3Eh+fj5LlizRyBiRESISjbHuYLwYcZmK73IGJ+a+H27xEdKw31FD53gZ9o68Bx1HwZ0GU67r0yFerk/n/dZk3LYY/z7jGCP4GvRhIdMV5bPj2kh1ROkIO/hDbSZBe6LVscYWwwap4+L3NfddREREZFANWdt5l8vFvHnzWLFiBZ/61KdOPr5ixQpuvPHG07ZPTU1l586dpzz2+OOPs3LlSv7whz8wYcKEQc8sMtpUV1fT3Nyzwmfm0RWM3/VH/jf6AAAXpgXZFxrFM88Ng4rEBVS7y/hY6zMUhQ5yfdsvyQsf5W1OnwMvvZfUdQxIZdPhVt7fsBm3o+ffYGVnZ1NSUjJ44WTAvfbaa6f8+sknnyQ3N5fNmzdzxRVXYJomjz76KA888AA33XQTAE8//TR5eXk8++yz3H777VbEFpFe2FHTgTcYIS3ByYzCvrX0ldEtO9lFWoKTDn+Ywy3dqMwyOugcL8Pejt/Gb2fcCE5Pr3f3Rmz82754u/m7JjZSnBgeyHRjVpozymfGtfJ8bQbtYQcHMy7BkXb6YhwZRGnjoP1wfO574YVWpxEREREZtYas+A5w77338sUvfpH58+ezcOFCfvrTn1JdXc03v/lNIN4yvqamhl/+8pfYbDZmzpx5yv65ubl4PJ7THheR86uurqa8fBo+n/+82+YmGey5I4mtnjLej83AjEZ47v/F/556u7oGO6qluu3p/CH7WyzqfJWLut5iftfb/FNRHm85rU42cnW2NgFwz1dvYdy3noTUHK7+3NcJHO55Z4GExET2VlSoAD+CdXR0AJCZmQlAVVUV9fX1LF269OQ2brebK6+8krVr157xi/lgMEgwGDz5687OzkFOLSJ/qaKi4uT93++Jr2Atz7KzfVvP/k2vqqoalFwyPBmGQVluMpuOtFHZ0MWcBKsTyWAYiHM86DwvAyTshz0vx+/P/nyfDvFfB/JoDDqZkBjkGxOaBjCcpDhifKawjRdqM2jBQ+7n/40u4xWrY40dJ+e+a+W7iIiIyGAa0uL75z73OVpaWvjXf/1X6urqmDlzJsuXL6e0tBSAuro6qqurhzKSyJjR3NyMz+fnme/cTHlJzjm3ndC2ioxANfeFPwPA1Wn1TPrsBXz3FysIBAJDEddSpmHnvbSP0+wsZGnbb7kopYHXb03kMUb/730w+LviX5xef/sDeAsyOdINl39tGbMyoj3av6H6IL/+/t/T3Nys4vsIZZom9957L5dddtnJC+hOjKH5y9EzeXl5HDly5IzHefjhh3nwwQcHN6yInFFda7zQfuutt558LO+vHsZTMos///yH/O7uV3t1PF8wMqD5ZPg6UXw/3NLNjEKr08hAG6hzPOg8LwNk33IIdkJaCZQs7PXuFV4PT1fHR6ksK6/BbTMHOuGYl+SI8anCNn55MBHS8/lD9JN8M9RAuqtnnw+lH1ILAQMCHRD0glujOkVEREQGw5AW3wHuuOMO7rjjjjM+99RTT51z32XLlrFs2bKBDyUyhpSX5HDhlHFn36DlANRVU2kW8VZ0DgYmD8zpZr0/Y+hCDhP7Ei+k057Jxxse57ISKIg+w4rwUvzOsfdnMRCyCksZV1rIkT0NtJNIUZkK6WPFXXfdxY4dO3j33XdPe84wTh0/YJrmaY+dcP/993Pvvfee/HVnZyfFxcUDG1ZEzqi9K34B2vW3P8DU2fOIxODlY05M4Jbbvk6y8+s9Os6Gt17h3ed/QTB89uK7YUZxmSEcZhADiJc9DExsBG0eooba0YwkOSluUj0OOgMRGgIamjzaDNQ5HnSelwGy47n47ezPgs3Wq11NE767ZxxR0+C6vHauzB7dXd+slOSIMbFtA7s8s2lOyea2LQn8en4VSY6Y1dFGN4cbknOgqxE6ayBnmtWJREREREalIS++i8gwFovAgTcB+JHzqxCEj+R2Mjk5yHqLo1mlzj2e+48s5J9y32FScj3pO7/B8zMeo8ut2XR9UZQZn/ba6A0SCEfxOO0WJ5LBdvfdd/Pyyy+zevVqioqKTj6en58PxFfHFRQUnHy8sbHxtJVyJ7jdbtxu9+AGFpFzyiospahsBodbujGP1ZLicTC1fPI5C2ofdnjnOi7It3FFYhWXdDaREm0jKdpJUqyTpKgXdyyAg3PP1o3gIGhL4KaJJnfckoDpeZWUY520JZTQmDyNLlcu9DCPDD7DMJiYk8y2o+3U+XtXCJPhbSDP8aDzvAwAfzsceCt+f/bner37i3XpbGpPItEe5btT6wY2m5zGFfPT+Nx3mfiVH7KtI4lvbivlFxdW4dSpYnClFsWL7x3HVHwXERERGSQqvovIB45thEA7e+1T+XPXFAD+ZnKDxaGsVxVM4/Infbx3ZyE5/sPcvPMbPDfrpyrA90Gy20FmkovW7hBH23yU5arN3WhlmiZ33303L7zwAu+88w4TJkw45fkJEyaQn5/PihUrmDt3LgChUIhVq1bx/e9/34rIItILR1t9AJRkJp698G7GyPJXUdi5nQLvDgo7d/K3edVwezLwHnjP/RoxDEwM4kc3sR1fA+8ggiPmJckNpWVOYDMc2Xxyv25nJg3J5dSlzOZwxiIak6aAoW/yrTQpJ+mD4rv+W4x4OsfLsLX/NYiFIacccqb2atdA1OA/KuMXjtw5sZHChHNfCCYDI9xczad9L/FC6mdZ05LCP1eM46HpNbqGbjClFUHtlnjxXUREREQGhYrvIhIX9MKRtQD8yPHXmBhcn9dOeYrmnANUtsa4q+HT/E/xa2QHa/nE9m/yv4UP021PO+++7e3tgx9wBCnOSIgX31v9Kr6PYnfeeSfPPvssL730EikpKSfnv6alpZGQkIBhGNxzzz089NBDlJWVUVZWxkMPPURiYiK33HKLxelF5HyqjxffizMST3ncMCMUdWxlcstKJre8Q3K4+bR9m7pjNNnziGZOpNOeSbc9jW57Kt22VAK2BMKGm7DNTRT7qSvYzRguM4g75sdtBti7cQ0N299myUeuZfa4ZLL8h8juPkRSuJWJbe8xse09Lq1+gm5nFoczFnIg6yqqMhZhGvoINNQK0xLwOG0EwjHcRTOsjiP9pHO8DFt7XorfTr+x17s+eSSb2oCLQk+Ir5aefu6SwVMQa+BHs4/yja2l/OZYFpOSgnxtvP4bDJrU42MIuxogGgK7y9o8IiIiIqOQvnkSkbgj70EszO6E+bzaVoKBqVXvxwXCUQCWr9/HVQfLeXV+G0Uc48add/GJzZfQGT337NlQYxUAPp9v0LOOBMWZiWw/1nFy1aSMTk888QQAixcvPuXxJ598kttuuw2A++67D7/fzx133EFbWxsXX3wxb7zxBikpuihDZDjzhSI0d4UAKM5MACDTV8Xs+j8ytel1EiPtJ7cN2zzUpcykNmU2talz+MPbO/nN/z7CDbd9iasmzevdCxs2QkYCIVsCXmC7L4c/bw3TuPgqaqfGCy32aIAcXyX53j2UdGyguH0jSeEWZjS+wozGV+h2ZlKR8zF2591Aa+LEgfjjkB6w2QwmZCdRUeclccolVseRftI5XoaloPeDlvPTP9GrXVtDdh6vygXg78rq8djNgU4n57Ekt5MHptbxvX2F/Nu+AkoTQyzJ7bQ61ujkSQN3SvzvTGctZIy3OpGIiIjIqKPiu4iArxXqtgPwKPHVKDfktzMlOWhlqmEjFIkX3+dMymXRgrn8Jjadb4We5ILUDt65ei8/d91K2Dj71eLr1oZZUwnBUGioIg9rRRkJGEC7P4w3ECbFc+6LF2RkMs3zf2lpGAbLli1j2bJlgx9IRAbM0VY/ANnJLiYF9nDRgV8wse29k8/7HWkcyFzMgeyrOJq2gKjtg3Nkp3lgULNF7R7qU2ZRnzKLbYWfwx4LMa5zKxNb1zC1+Q2Swq3Mr32G+bXPUJ02n41Ft7EZFVmGwqSc5HjxveySHp0jZPjSOV6Gpf2vQzQIWZMhd3qvdv3xwTy8ETvTU/x8sqB9cPLJeX21tJlD3W6ePZbFt3eU8PuLDjAzVZ34BkVqETRVQGeNiu8iIiIig8DSgXtPPPEEs2fPJjU1ldTUVBYuXMirr7568nnTNFm2bBmFhYUkJCSwePFidu/ebWFikVHq8GrAZGfy5axoy8eGybcnNVqdathJ8jgpyErFkTOJF3O+SdDwMNE8wld5nsLMJAqyUs/4k5qgNm4f5nbYyUv1AB8UcEREZOQ40toNwFXmBj6/86tMbHuPGDYOZF7J89N/xE8ueo03y/6JwxmXnlJ4t0LU5qI6/WLemfh3/N/85bw07T85kHklUcNOSccmPr37Lh50/YIbpzowzJilWUe7ksxE7IaJIy2Pw+0Rq+OIyGjz4ZbzvRgYfrjbxTNHswD4ztQ6bJo1bhnDgAfLa7g8y4s/auMbW8fTErJbHWt0SiuK32ruu4iIiMigsHTle1FREf/+7//O5MmTAXj66ae58cYb2bp1KzNmzOAHP/gBjzzyCE899RRTpkzhe9/7HkuWLGHfvn1qVycyULoaoWkvAP8V/QwAnyxsZ7JWvZ9Tk6uIF7O+zk0t/8vEYAVXdbzAyrRP9+qLnrGsODOB+s4A1W0+phemWh1HRER6yB6LUNPYArj4VOgVYnY7e3KvZ0PRbXQkFFsd75xiNgeHsq7kUNaVpATrmVfzDDMbXmQStbz4+UT2hX/EG1UGRz1T+3T89vb2gQ08yjjtNvI8JrV+g/U1AT5tdSARGRaqq6tpbu7ffG9bxM+s/W9gByqMKfi3bDnl+YqKirPu+x+V+URMgyuzO7ksq6tfOaT/nDZ4bM4RPrWujEM+N3duK+WZ+YdwWLp0aBQ6Mfe9sxbMGBj6AxYREREZSJYW32+44YZTfv1v//ZvPPHEE6xbt47p06fz6KOP8sADD3DTTTcB8eJ8Xl4ezz77LLfffvsZjxkMBgkGPygaer3ewfsNiIxAFdVNp/x6QtsqMoBVjstY2ZaDjRhX23awZf+p87ir6tsAqKxtJWd/Ta9ft7Z59M1rq3VP5NWMW7mh9SnmdL9Huz2bLSmLrY41IhRnJLLxcBtHW32YpomhixZERIa9OXk2Ph/7Iyujf0sKPrKzsvnlhAdpSxxvdbRe87rzeWfi37G++Ktkr/pnPmJ7n6nuGqbW/j2/rRvHsspy6kOeXh0z1FgFgM/nO8+WY1dhQoxav42NtWojLCLxwnt5+TR8vv51w7qp3MEfb07kUFuM6df81Vm383adWlzf1p7AnxvSsWFy/5T6fmWQgZPmjPGTuYf55LrJrGtL5uH9BXx3Wp3VsUaX5Fywu+KjGrqb478WERERkQEzbGa+R6NRfv/739Pd3c3ChQupqqqivr6epUuXntzG7XZz5ZVXsnbt2rMW3x9++GEefPDBoYotMmLU1cU/rN760HMnH5uRY2PXHckAfGX/pVAInTve5Ibv//isx7n78RXAij7n8IdGV5vRgwmzWZ36Ca7sfIkrOl+mw5HJwYTZVsca9grSPNhtBr5QlNbuEFnJbqsjiYjI2Zgmk42jrPtaEo9HSwGYlAZ/nvFDi4P1n9+ZwVMdC7j71yv4xVdncV36YT5fUMOnCppY7riWdfYFmD1cDbZubZg1lRAMhQY59ciVnxDDjEWpaoejrT6KMxOtjiQiFmpubsbn8/PMd26mvCSnz8cZ37YGAodJKprJ5v+dd9rzyzfs57u/WEEgcOqFP48cyAfgpsI2pqXooqDhpCw5yA9nHeWb28bz8yM5zE71c2Nhu9WxRg/DBimF0H4YOo+p+C4iIiIywCwvvu/cuZOFCxcSCARITk7mhRdeYPr06axduxaAvLy8U7bPy8vjyJEjZz3e/fffz7333nvy1zU1NUyfPn1wwouMICdaoV5/861MLZsIwK225cB+fhVZQrRwDgYxPjM3l6QL//m0/d9evZata97kko/eyKJ5c3r9+if2D0ai/fltDEtbkq8kPdrMnO73uK7tGX5vv5MGV6nVsYY1h91GYbqHo61+jrb5VXwXERmuzBgceJOLHbsBg1eCCwHIKZxgba4BVt9l8kTHZbSVfYrFHS9QGDrMpyLLudB+gDfSPk+XI+O8x0hNsHa+/UjgtkPw2B48JbNYsaeBr1w2uv4/EpG+KS/J4cIp4/q2cywCa2sByJsyn7zUwtM2+cvubwCb2hJZ3ZKCwzD5m0kNfXttGVQfzevkrokNPHYoj3/YXcTk5AAzUnWRxIBJGxcvvnccg8ILrU4jIiIiMqpYXnyfOnUq27Zto729nT/+8Y98+ctfZtWqVSef/8tWxOdrT+x2u3G7PyjidHaOvlbXIv2RlZtLUWkpaZFmLmioxDThp7bPAjA7NcDUnIIz7pecHp+Tl5qZTVFp7wvLJ/YflQyDt9M+RUqklYnBCj7R8guezb2Xbnua1cmGtZKMxHjxvdXHBcXpVscREZG/FA3Bnpeg9SCmCfe8m8ChBfH3CaVZo3PFcoOrhN9l380F3e9yeccrlAb386XGH/B2+k1UJMwHjUnpN1/l+3hKZvHGnnoV30Wk/1qr4ucrdyqknPmz7Jn814H4Qo/PjmulODE8WOmkn/52cgM7OxNY1ZzKHdtK+dPCSlKdMatjjQ6pRfHbjmPW5hAREREZhXrWQ3EQuVwuJk+ezPz583n44YeZM2cOP/rRj8jPj7f/qq8/de5WY2PjaavhRaT35natxsDkN/YbOBpKxmGYLMjotjrWiGUadpZnfolmRz7JsU4+3voUdnN0tdgfaCdazR5r8xOLmRanERGRU0SCsPM5aD0INgdrohfwi+Z455u8VDdJbsuv4R08ho1tyVfwTO7fUecsxW0G+Gjbsyxp/x12UwWa/vJXrgdgQ1Urbd1q0S8i/dS8L36bPaXHF0ita03ivdYUnEaMuyY2DmI46S+7AT+adZRxnhBH/G7+cXcRpj46DozUQsCAYCcEvVanERERERlVLC++/yXTNAkGg0yYMIH8/HxWrPhgtnQoFGLVqlUsWrTIwoQiI5875meGbz0x0+Cx8A0AzEnzkeTQFeT9EbZ5eDnrKwSMBApDh7mq/Y/om4Gzy0lx43bYCEVjNHjVPlBEZNiIhuKF945jYHfD7M9z1MwnYdICACZkJVkccGi0OXP5Xc7dvJdyHTEMZvrW87mmH5MSabM62ogW6WhgfLqDmAlv7VXRS0T6wYxBy8H4/ewpPdvFhEeOr3r/fFEr4xJ0UdVwl+6K8ticIziNGMsb0nm6OsvqSKODw/3BrPeOo9ZmERERERllLC2+f+c732HNmjUcPnyYnTt38sADD/DOO+/whS98AcMwuOeee3jooYd44YUX2LVrF7fddhuJiYnccsstVsYWGfFmda/FZYZ4zlhKbTgZlxFjXrpWvQ+EDkcOyzO/RAyDWb51zPattTrSsGUzDIoyEgA42uq3OI2IiAAQi8Lu56GzJv6l7OzPQ1oREdOGZ/wFAIzPHhvFd4h3ttmQupQXsm7Hb0siL3yMW5p+SHFgv9XRRrSLCj0AvLG7/jxbioicQ0cNRPzg8EBaUY92WduazIa2ZFy2GHdMPH0WvAxPc9P93D+1DoB/21fA9o4EixONEmnF8dv2amtziIiIiIwylhbfGxoa+OIXv8jUqVO55pprWL9+Pa+99hpLliwB4L777uOee+7hjjvuYP78+dTU1PDGG2+QkpJiZWyREc1OjAu61hA1DR4N3wTA3HQfCXat0B4oRzzTeC/1egAWtz/PjIQWixMNXydazx9t81mcREREME3Y92doOww2J8z6HKTG5+dWkYfNlYCLKLkpbmtzWqDaM5Vf59xLg7OIxFg3N7X8hBnd662ONWJdPC5efF9d2YQ/FLU4jYiMWC0H4reZk8A4/9dbH171fktRKwUerXofSf66pIWP5nYQNm3cub2UjrDd6kgjX3pJ/FYr30VEREQGlKXDGn/+85+f83nDMFi2bBnLli0bmkAiY8AC1yFSYh382lxCfTQFjy3G3HQVPgfapuSryQ3XMNW/lX8s2sSvU3o2f3CsKTlefK9rDxCOxnDah900FBGRsePIe9C4J17AmHHT8VmgcfvM+IrCTHsAo4czdYeKt7OTurq6Pu/f6e3ZnFOvI5Pf5Xyba9ueY7p/E0vbf0tytIP1KUt6PGdY4sanOxiXnkBNu581lU0snZFvdSQRGYlaKuO32WU92vytxiQ2JybhMqJcZu5gy/7ggEWpbe4csGPJmRkGfH/mUfa876Ha7+bvdhXx0wuO6BTcHydWvvtaINQNrrHT3UhERERkMFlafBeRoXddwk5Cpp1HI58FYF56N26bVr0POMPgjfTPkRluIIdaXvhcIv9OxOpUw056gpNkt4OuYITadj+lY2SOsIjIsNO0F468G78/5aOQOeHkU6YJ+8xxAGTZB65Q0V+BcHzF9KZNm9i691CfjxNqrAIgEjn/eTpqOHk94xa89nQu7nqTRd5XSY62szL9031+/bHIMAyWzsjjyfcO88aeBhXfRaT3fK3gb41fMJYx4ZybNnfER6z9qmkSnlJo3vhnlvz7Twcllj+kz3yDKc0Z4/ELqrlp3SRWNKbx8yPZfG18s9WxRi5nAiTlQHdTfPV7zjSrE4mIiIiMCiq+i4whl5fYGe9o4enIR2iKJZNojzInTaveB0vE5ublrK/w2Zrvs2Ac3B56jd3oy/kPMwyD4swEKuq8HG1T8V1ExBK+Ftj75/j9cQsgf/YpTx/yuWklFTMaJn0YFd9DkXjxfc6kXBYtmNvn46xc2c3mSojGetj+3DBYm3Y93fY0rup4ntm+90mMdfEOJX3OMBYtnZ7Pk+8d5q2KBiLRGA51vxGR3jix6j29BBznHofi9YdwFU7DUzoHgxifu7iExEv+eUDjvL16LVvXvEkwolEag21mqp/vTqvjuxXj+Pf9BVyY7uNCdfPru7TiePG9vVrFdxEREZEBouK7yBhy70IXQdPBj6PxAvBFGd049T3noOp0ZPH9mnn8v5L3WeraRqzxz1TkXm91rGGlJCMxXnxv1RcmIiJDLhqGPS9CLBwvYEy66rRN3mhIBSBQvRPHMFyhnORxUpCV2uf9Ez2uPu23Pfkyuu2pXNf6KyYHdnJ/USPL9b6qxxaMzyA90UmbL8ymI21cMjHL6kgiMpKcmPeeNblHm6ddEu/8Vp4SZEpuwYDHSU6vGPBjytndWtzC+rYkXqlP59s7Sli+cD+pzpjVsUam9BKo3aK57yIiIiIDSF8PiYwRieFWbpji4IXoZbSYySTZo8xI9Vsda0zY7svhwVXxlYLXHPx3Mn19b407GhUdn/ve6A2ebCEsIiJD5ODK+GonZyJM+0S8fe9feK0hDQDf/veHOt2wdyBhNi9lfZUITi5OaeAPNyfgQOeynnDYbVwzLQ+AN3Y3WJxGREaUsB86jsXv96D43mLPJrHsYjBN5qd3D3I4GQqGAQ9NP0ZRQohjfhff2VOEqWl6fXNi7nt3U/zvloiIiIj0m4rvImPEpM61YNj478inALgw3YfDsDjUGPK91SG2RSbgjAX4+N5/xBHVh9oTkt0OMhPjqw6PtenPRURkyLQchLqt8fvTbgB38mmbHPM72d6ZiIGp4vtZVHum8VLWVwnGbHxiqpN/SPgDtljY6lgjwtIZx4vve+oxVTURkZ5qPQSY8VnVnvTzbr4jcT4AmaE6Mly6QGq0SHXG+PHsauyGySv16fy+NsPqSCOTKwkSj3ef0ep3ERERkQGh4rvIWBANM7njPV6LXUQNObhtMWZq1fuQipnwQ/8n6XJmk+Wv4pqD/44uzf9AcWYCANVqPS8iMjTCftj/avz+uAWQOeGMm51Y9V5KAzFf+xCFG3mqPVP5f8cuwh82udhZyfX7voNhRqyONexdUZaDx2njWJufijqv1XFEZKQ4Me+9B6veD3e7OOQuA6DQf3AwU4kFLkz3ce/kegCWVRRysNttcaIR6sTq93YV30VEREQGgorvImPB/tdIiHTw36EbAJiT5sNlU+F3qHWYSSyf+m/EsDG9aTkzG16yOtKwUXy89fzRNhXfRUSGxIEVEOqChEyYcMVZNztRfJ9pVA9VshFrW3cON/zGR8i0M7n1Ha45oAvtzifBZefyshwgvvpdROS8YlForYrfzyo77+Y/OZyDadjwHdxIUlQX+YxG35zQxKJML76onW9vLyEYU4u/Xksvid926P2eiIiIyEBQ8V1kLNj0C9bEZrHXmIjNjHJBmgqcVqlJu5C1pd8C4Kqq/yS7e7/FiYaHovQEDKDdF8YbUKteEZFB1XoIGvcABkz7ONidZ9ysIeBgU3sSANONI0MYcOR6qyrKf/hvIoaNWY0vsaj6f62ONOwtna657yLSCx1HIRoEZxKkFJxz08aggz/UxFuRd77/3FCkEwvYDfivWUfJcEbY7U3gB/vzrY408pxY+d7ViC0WsjaLiIiIyCjgsDqAiAyy1io4uJLHow8AUBCpIcGuVmxW2jjuSxR2bmNi23t8fO/9PDvnaUKO0+fs/qW29nbq6up6/Xrt7e19SDm03E47eake6jsDHG31M73wzIUgERHpp2gYKl+P3x83H1ILz7rp643xVe9z07pJ64qPq+nrueiETu/oX3X4ZmsBU0vu4DPNj3HxsV9Q53ewNu3jPd5/JJy3B9I15XnYDNhT18nRVt/JbjjnU11dTXNz84BkyM7OpqSkZECOJSKDrOVA/DZrEhjnXuH81JFswqaNvHAtR2oqgKsGP59YIs8T4T9nHuWrWyfw8yM5XJbVxVU5H7znqKxtJWd/zaC9flV926Ade0i4U8CTDoF2kkONVqcRERERGfFUfBcZ7TY/xZbYZNbFZmBGwxSFjwLnn40ng8iw8XrZMr6w7VYyAtUsrvpP3ihbdtbN/f54wePtlStZvWFbr18u1Bhvy+jzDe+OB8WZCfHie5uP6YWpVscRERmdjrwHgQ5wp8KEy8+56avHW85/LL+D5s3dQN/PRSecOCdFIqNvHnogHAVg06ZNfG1vJhXjp/Ldyfv4RPNPefHtTbzYePYLHT5spJy3B0pmkosF4zNZX9XKij0NfOWyCefdp7q6mmnl5fgH6M8oITGRvRUVKsCLjASth+K3mZPOuZkvYvDro5kAzPJtYcNg5xLLXZPr5baSZp6qzubvdhXz6qL9NHfE37/c/fgKYMWgZ2hs7x701xg06SVQ305KSJ1oRERERPpLxXeR0SwSgq3P8JPIrQB0734HzyS7xaEEIOBM59Wp3+OzO29nRuOfOZy+iP05S8+4bTAYBGD+1HEsmDuz16+1bm2YNZUQDA3v9nHFGYlsPNzG0VYfpmlinGcli4iI9JK/DY5tjN+fvATsrrNu2hKys7413nL+o7kdPO2Pn0MunVHE7Fkz+hxh5cpuNldCNBbt8zGGq1Ak/nuaMymXRQvm4jXn827kVS6Lruf/Zu9gnmseR21F5z3OSDlvD6SlM/JZX9XKG3vqe1R8b25uxu/z8YV/+A/ySs5dgDufhuqD/Pr7f09zc7OK7yLDnb8d/K1g2CCj9Jyb/r4mk46Ig9KEICVNh4Ymn1juH6fUsb4tiQpvAvfuLGbq8fcv137ys8yeUT5or7tpRwWr//R7OnzBQXuNQZdWDPU7tPJdREREZACo+C4ymu39E0e7DFbE5gPQseGPMOlmi0PJCbWpF7Ch6K+55NjPuebgw9SlzMLrOfvcwpRENwVZvV8Rnppw9uLKcFKQ5sFuM+gORWntDpGVrPEIIiID6tA7YEYhYwJkl51z0zca0ohhMCvVR3Fi+OTjaYmuPp2LTkj0jIxzUn8keZwn/4w2mTdT2OplYmAPfx35Hc/m/i3d9vRz7j9SztsDaen0PP7fK3vYUNVKW3eIjKSe/RnklUyiqKzvF4OIyAjTejB+mzoOHJ6zbhY14edHsgH46vhmqqvNoUgnw4DHbvLfs6u5YV0Z77akEEyYB6wgMzuHotJzX7DRH5U1AzMGxVLp8QvQEsMtJI+9tyIiIiIiA8pmdQARGUSbn+JX0SXEsDE5OUyk5ZjVieQvrC/+GnXJM/FEu/ho5b9gmKNvJWBPOew2CtPjX6IdbfNbnEZEZHRJCjVA8z7AgElXn3f7Ey3nP5rXMcjJRjfTsLE844s0OwpIjnVyY8vPccTGzor2nirOTKS8IJWYCW/t1Yo7ETmLky3nJ55zsxWNqVT73aQ5InymsHUIgslwMjk5yL9MqwVgU9JCXHn965AyZnjSwJOGgcllJVqrJSIiItIfKr6LjFbt1fgOree30asAWJg1gtufjWIxm4NXp/w/QrZEijq3suDY01ZHslRxRiIAR1vHxpxbEZGhYABFnZvivyi4AJJyzrl9R9jO2tZkAK5T8b3fwjYPL2V9DZ8tibzwMT7S/iyYMatjDTsfmZEHwBu76y1OIiLDUiwC7dXx++cpvv/f4fh57taSFhIdWvU+Fn1uXCsfy2vHNOxk3/B3RPX1Z8+kx7sDXD1B4wpFRERE+kPvPkVGq22/4cXopXSSRGlWIlNSIlYnkrPoSChi5aT7AFhY/VNyuyosTmSdksx48f1Ym59YTF+UiYgMhC/MdpIUbo3PeB9/2Xm3X16fRsQ0mJbsZ2KSVmkPhE5HJn/K/ApR7Ezxb+ci75tWRxp2lk7PB2B1ZRP+0NjtBCQiZ9F+FGJhcCVDUu5ZN9vcnsjm9iRcRowvl7QMYUAZTgwD/m16DYnRLpxZxVQnDd6891HlRPF9vFa+i4iIiPSHiu8io1Eshrn11/wyugSAL15Sis2wOJOcU0XOx9ifdS02onyk8kHsY7QlbU6KG7fDRigao9Grbg0iIv1lRAI8fI07/ouSReBKOu8+L9alA/DJwvbBCzYG1bon8lb6ZwBY5H2NksA+ixMNL+UFKRRlJBAIx1hd2WR1HBEZbtpOtJyfEK+snsXPDsdnvX+ioJ1cty5AH8syXFGu8K4AoNFTSlW3Bpmf1/Hi+9wCG/ZQp8VhREREREYuFd9FRqPqtWxtc7LXLMXtsPHZecVWJ5LzMQxWTroPnzODbN9BLj76c6sTWcJmGBRlJABQ3abW8yIi/ZV36PcUpdoI2pOgaP55t6/xO1nfloyBySfy2wc/4BizO+kSdiZejIHJdW2/IjnabnWkYcMwjJOr39/Y3WBxGhEZdk7Oez/7/O5qn4vXG9IA+Pp4XcQjUBSupnPjiwCsaErFF9GqhHNyJxOwp2IzDJJbtludRkRERGTEUh8hkdFo27P8Jno1ANfPLiAt0WlxIOkJvzODtyb+Izfs+wcWHHuag5lX0pAy3epYQ644M5GDTd0cbfVx0fhMq+OIiIxc/jZyD/wOgNqUuUywnf+t/8vHV71fnNFNYUJ4MNONWW+n30Re+Bi54Rqub32a32ffSczQxzKApTPy+MV7Vby1t4FINIbDrmvFRcaCiupzF8pdkS5m+lowMdjR4ibaVnPG7X7WUk4MgwsSmuiuO8SWuvjjtc1awTuWta16mry51+AnhbeaUvl4fse5mieMeV53Ph5fJynNW62OIiIiIjJi6VsekdEm2EXHrtf5U/Q/ALjlohKLA0lvHMi+mn0tS5javIKlBx7k2Tm/sjrSkCvJiM99r+sIEInGLE4jIjKCvf84jkg3OxqiRPLHM6EHu7yklvODLmq4eCXzNm5p/CGFocNc0fEy76TfZHWsYWF+aQYZiU7afGE2Hm5j4aQsqyOJyCCqq4tXx2996LlzbveNeU5+8vEE3qsOc/mD/3fGbWzuJMbd8RQ2F7z+1I946fC207bxh9SGfkyKhpnUtY2K9Ms45POw2xtiZqrf6lTDlteVT45vPynNW6yOIiIiIjJiqfguMtrseYmXAnMJ4GZKXjLzSjOsTiS9tHLifRR1bCbbd4hLjv4fb5BidaQhlZ7oJNntoCsYobYjoPkoIiJ94WuFdU8AsOydIP90wfmXeFV4PeztSsBlxLgur32QA45tHY5sXs/4Aje2/py53Ws45p7EgYQ5VseynMNu45ryPP6w+Riv765X8V1klGtvbwfg+ptvZWrZxLNu99e2l4FDdI67gnsfuOiM2+yLFbArlkAaPm665RMYxidOPvf26rVsXfMmwUh0IOPLCJIU9bIoq4s1LSmsak5hnCdEhkv/P5yJ1x0fAZPgPQLeBkjJsziRiIiIyMij4rvICFJdXU1zc/M5tyl77yc8F70ZgMsKbGzdGm8VVlVVNej55Ny8nZ0nV3eczx8yb+fLDQ8z79gvyQl+fJCTDS+GYVCcmUBFnZfqVh/jrQ4kIjISvf8/EPLiS53Ei3u38k892OXF2nQArs7xkuZU55HBdihhJhuTr2ZB10qWtD1HvbOELocumvzIjPyTxfd//vh0bDb1BhYZ7bJycykqLT3jczYzwpS6GjChLf8SilzFp20TNeH1I9kAXJQTpjj11GMlp1cMfGgZceam+ajqdnMs4OL1xjRuHteKTjGni9rcbK2LMrfADofXwKzPWB1JREREZMRR8V1khKiurqa8fBo+39nbo01IN/jz3dPZZU7AjIZ58Csf51/8p863C4fVam+oBcLxK+o3bdrE1r2Herxf2qwCPplXxzeTVvA0EImMnf92xRmJVNR5OdrqY7zqECIivdPdAuv/F4C6qbdhcv6ZnTETXq5PB+CThW2DmU4+ZG3qxygOHiA/XM1H237NH7PvsDqS5S4vyybJZaeuI8C2Y+1cWKI3AiJj2bhQFS4zSLcthUbnuDNuc7DbTVfUToI9ypSUwBAnlJHCMGBpXgfPHM2iIehkS3si8zN8VscallYejsSL71WrVHwXERER6QMV30VGiObmZnw+P89852bKS3LOuE2Bdzs/b5sFwMKUFu77ry+efO5nyzfxxMvriUbHTgF3uAgdb284Z1IuixbM7fF+u8ypfDT4GBdn+/nGPCdVsbHTFq84Mz73vdEbJJRmcRgRkZHm/f+GUBfkz6Yj/9Ie7bK+LYm6gItUR4TF2d5BDignxAw7yzNv5dbG/6Q4dJD5XW/xJnarY1nK47RzTXkeL2+v5dWddSq+i4xx4wPxVetH3FPBOPNAqm0d8c8Os1L9OLSSWc4hxRHjyiwvK5rSWNeazISkIFlqP3+alVVR/r+FQNVqq6OIiIiIjEgqvouMMOUlOVw45QxX/Jsm4XV/4sXotwD4+lQ/F+Z+sF3hhv1DFVHOIsnjpCArtRd7pPJ+18e4quMF/v1aD9+sHTurOJLdDjITXbT6QjQF9A2aiEiPdTfD+p/G71/1Heju2b+hL9bGC5wfy+vAYzcHK52cQYcjh7fTPs1H2n/Dos7XmOJZxJ+tDmWxj83K5+XttSzfWc93PlaOYei9gMhYNT6wF4AqT/kZn28IOKgLuLBhMjv17F3iRE4oTwlQ2e3hsM/NG8fbz9t1mjnFmiMRTMOG0XYY2qshvcTqSCIiIiIjypkvGxaRkaf9CKv8E2kmjWxXmCu0am1U2J50GRXdqaR7DG7P2WZ1nCFVnJkAQGNApyoRkR57/zEId0PhXJjy0R7tEogaLG+Itxn5ZGH7IIaTs9mTuIB9CXOxEePvx20h2WV1ImstnppLostOTbufnTUdVscREYskR9rIjtQRw+CIZ+oZt9l+fNV7WXKAJEdsKOPJCGUYcE1OJ25bjMagk83tSVZHGna8IehOnxb/RdUaa8OIiIiIjECqaIiMMBXVTWzZX3PaT0vlel6IXgbAQs8xdh449fna5s7zHFmGI9Ow8cPqGURjJlelHKXk+MqPseBk63kV30VEeibQARt/Hr9/xX3xb5d74NWGNLwRO+M8IS7K6B7EgHJWhsFb6Z+l055BgcvHfyzxWJ3IUh6nnaum5QKwfGe9xWlExCrjg/sAqHeVErSdXiDtjtjY3xX/9/KCNM3ulp5LdsROjtlZ35pEU1CNQf9SV/bxkXlqPS8iIiLSa3p3KTJC1NXVAXDrQ8+d9lyKCw7+fzm8FYt/OPrpjx/lsYaDZzyOP6SZ7yPNfn8qP94Q4m8vcXN1+x/5Vd4/EDVG/z/fRekJGEBXxMCekmV1HBGRIVddXU1zc3OPt8+r/A3jgp34U0qp6M6FLVuoqKg4736/PZYJwOeKWrGp7aplgrYE3sj4Kz7T/DjfnO+itvvM7+XGio/NLODPO+p4dVcd//DRqWo9LzIGnZj3ftg97YzP7+pMIIpBnjtMvkefc0eqo02dbNlfMyjHPtcihKnJASq73BzyeVjRmMrnitR+/sO82XPJr/w1VK0C0+zxRZ0iIiIiouK7yIjR3t4OwPU338rUsomnPHexsYv38BOIuUnGz51f/cJpn4veXr2WrWveJBiJDlFiGUj//HaQL81PI4tmLuhaw+aUq6yONOjcTju5qW4aOoN4Si+wOo6IyJCqrq6mvHwaPl/P5te67XD4nmRItnH7L/fyq79bcMrz3q6uM+53qNvF+rZkbJh8dlxbv3NL/xx1l/FK63g+nnmYuxNe4bnINwg5kq2OZYnFU3PwOG0cafGxp66TGYVpVkcSkSFkM6OUBPcDcPgM896jJuzsjI+p0qr3kcnXFV95/oPfr+cHv18/qK91pkUIhgFX53ipPeqiKeRkY1sSl2SqA9AJXZmzwOEBbx007YXc0/8eioiIiMiZqfguMsJk5eZSVFp6ymOXNr3EP3d9GoDpGVGKM0tP2y85/fwr32T46grBUy0z+f/yNnGx9w32JM7Hb0+xOtagK81MoqEzSMKEuVZHEREZUs3Nzfh8fp75zs2Ul+Scd/ss337yO9YTsiVyz7e+wD1GfGTH8g37+e4vVhAIBM643++Or3pfnOOlwBMeuN+A9NmTjeVM4xCTM71cWfVfrCj7rtWRLJHkdrB4Si6v7a7n1Z31Kr6LjDEFocO4zQA+WzINzqLTnj/Q5aE7aifRHqUs+cznOBneQsH4f7dLPnoji+bNGZTXON8ihCRHjMXZnbzWmM7GtiQmJgXJdauLAoBpd0HpIji4Mv6j4ruIiIhIj6n4LjLCpUeaSAo2sjo2G4i3TpPRaUXneG4tqicvfIxLO5fzZsbnrI406EqzEtlwuBXP+LlEY6bVcUREhlx5SQ4XThl37o3MGGz4EwCuCYu4sKj45FMV1U1n3S0UM/hjbQYAny9q7X9YGRBB08Ffv+Rn1W1JzGx8mQNZV1GVeZnVsSxx3ax8Xttdz/Kddfx/S6eo9bzIGHKi5fwR91Q4fkHZh23riK96n53qV6vwES41M/u0BQYDpSeLEKYkB6nsDnCw28Objal8XmN4PjDp6uPF97dh4Z1WpxEREREZMU7/BCMiI8r07g28Fl1AGAfZrjCZLrWVH61MDN5J+xQAM33ryQkNzly84SQ/1YPTMLEnpnGoTSsyRUTOqGkfBNrBkQAFs3u828qmFJpDTnJcYa7KPvtMVBl671ZHeTl0MQDXHvg33BGvxYmscfW0XFwOG4eau9nXMDb/DETGqvHB4/Pez9Byvj7goD7owobJzNSejWcRORvDgKuyvbhtMZpCTrZ1JFodafiYdHX89vC7EAlam0VERERkBFHxXWQEM8wY0/2b+FNsEQBTtOp91Kt1T2RfwlwMTK7seBHM0b0a3GYzyPXEf49b6/VhX0TkNKYJR9fF74+bB3ZXj3f9zfGW858d14ZTnwqGnWeCi2n1lJAcbuayw49ZHccSKR4nV5TFxy4s31lvcRoRGSpJ0Q5yw7WYGBx2Tz3t+e3Hi6NTkgMkOWJDHU9GoSRHjMuy4hd5vd+aTEdYb4wAyJ0OyXkQ8UP1OqvTiIiIiIwYejcpMoIVB/fjj5i8H5sOqPg+VqxJ/TgRnBSHDjA5sNPqOIMuLyH+hZqK7yIiZ9B2GLoawOaMF997qMbvZHVzCgCfU8v5YSmEkzcnfweA2Q3PM65jq8WJrPGxWfkAvLqzzuIkIjJUSgN7Aah3FhOwJ5/yXHfExv4uDwBz0nxDnk1GrxkpAcZ5QkRMg7ebUkf7de49YxgfrH4/uNLaLCIiIiIjiIrvIiPYDN9GlkcvJoaNfHeINKeu+h8LvI5MNqUsBuCyjj9hM0f3qIE8T/z/68rWMO2+kMVpRESGmWMb47cFs8GZ0OPdnqvJxMRgUaaX0kT92zpc1aTNY2fujQBcc/Ah7LGx99/qmvI8nHaDysYuKtV6XmRMmHC85fwRz7TTntvZmUAMgwJ3iHxPZKijyShmGHBNTid2w+SI333yIo8xb+JV8dtDb1ubQ0RERGQEUfFdZIRyx/xM9u/k5ahazo9Fm5KvxmdLJiPazAzfeqvjDKpEB4SajhAz4d0DzVbHEREZPrqboO1Q/P64BT3eLWrC72syAPhcUdtgJJMBtGbCt+l2ZpLlP8yCY09ZHWfIpSU4uWxyNgCv7lLreZHRzjCjlAT2A1D1F/Peo2a8+A5a9S6DI8MV5aKMbgBWNacQiBoWJxoGJi6O39Zth259HhcRERHpCYfVAUSkb6b4t1IXS2OLOQUwKUtWS+6xJGzzsD5lCVd1vMAlna9TkTCfiK3nc35HGn/VZlw5paza18THZxdaHUdEZHg4til+mz0FEtLPuWllbSs5+2sA2OLLpjbgItkWIq+jgi3e83fOqW3u7G9a6aOgI5V3Jv4d1+/7Dhcde5L92UtoTZzQq2O0tbdTV9f3tu3t7e193ncgXDergLf3NbF8Zx3fvqbM0iwiMrgKQkfwmH78tiQanCWnPFfZ5cEXtZNkjzJZn39lkMxL72af10Nr2MF7LclckzvGu66k5EHeLGjYCYfegVmfsTqRiIiIyLCn4rvICDW9ewN/jl0EQJEnTLJDLefHmp1Ji7iwaxVp0Vbmdq9mY8q1VkcaNIFDm0m76CZWVzZhmiaGoRUIIjLGhXzQsCt+v+iis27W3BFfvXX34yuAFQBkf/J+kqZC7YZXWfjw//XqZf0htfi1wv6saynPWM7Etne59uBDPDfzp/H+uOfh9/sBeHvlSlZv2Nbn1w81VgHg81mz0nTp9Dy+YzPYW+/lUFOXJRlEZGiMD8bnvR9xT8U0Tm3WuK0jEYDZaX7s+jggg8RuwNU5nfyhNpNd3gRmpPo14mDSVfHi+8GVKr6LiIiI9ICK7yIjUEa4gcLwEV6P3gbAZLWcH5OihoP3Uz/KR9ueZb53JTuSFhG0JVoda1AEju3BbTdo6Ayyr8HLtPxUqyOJiFirdguYUUgpgNRxZ93M64/PCL/2k59l9oxyAqaD5dELMIGbLp5I2iX/3KOXe3v1WraueZNgJDoQ6aW3DIOVk/6Boi2bGde5jfKm5VTkXn/e3YLB+MrQ+VPHsWDuzD6//Lq1YdZUQjBkzcz59EQXCydlsaaymT/vqOPSDEtiiMgQGB+Iz3s//Bfz3usDDhqCTuyYzExRy3kZXOMSwkxL9rO3K4F3mlP53LjWnlzzNnpNuhrW/hgOvg2m2aMLAEVERETGMhXfRUagGb4NtJgpbDHjbTcnJKrl3li1N2Ee87xvkxOpY4H3Ld5Nu8HqSIMjGmZmrovNdUFW7WtS8V1ExrZYJF58h/iq9x58AZqZnUNRaSmb2hIxW23ku0PMKMrt8Usmp1f0Na0MEK87n/XFX+PyI//N5Yd/zKHMKwg6Unq0b0qim4Ksvp87UxOsH21zw+xC1lQ288qOOi69MtnqOCIyCBKjXvLCxwA44j61+H5i1fuU5ACJDnPIs8nYc1lWF4d8bhqCTnZ7E5iZ6rc6knVKFoLDA95aaNoHudPOv4+IiIjIGGY7/yYiMpzYiTHdt5G3ohcSw0aOK0yqUy3nxyrTsPFeanzl29yuNSRF260NNIguyHcDsGp/k8VJREQs1rAbwj5wp0LO1B7vZpqwy5sAMLa/QB7BthT+FS0J40kKt7Kw+idWxxlSH5mZj9NusK/By5GOsNVxRGQQlB5vOd/gLMJn/+Diou6IjcouDwBz0rXqXYZGkiPGJRnxUSfvtSTjj47h1d5OD5Quit8/uNLaLCIiIiIjgIrvIiPMbOdRkmJeXjUvBmBSkla9j3VVnunUuCbgIMzF3hVWxxk0c48X3zcebqU7OMZn7onI2GWacGxj/P64eWD0/O18TcBJR9iB04hRlqz3DyNRzObk7Yl/D8Ccut+T07XP4kRDJy3ByZVT4t0a3qvWyCWR0ehEy/kqT/kpj+/sTCCGQYEnRJ5bnwNk6MxJ85PlChOI2VjbOsa7rky6On578C1rc4iIiIiMACq+i4wwl3v24zddvBeNz+2cqOK7GMbJ1e8zu9eTHGmzONDgKEi2U5KZSDhq8t6BZqvjiIhYo+0w+JrB7oKCOb3adVdnfNX71JQALpta9o5UR9MvYl/2EmzEuPrQ98EcOx2QbphTAMC7R9W5QWS0McwYpYH4BUUfbjkfMePFd4AL0rTqXYaWzYCrsr1A/H1UQ3AMT++cfG389vC7ENLfRREREZFzUfFdZATJSzK4wFnN6thsQjhIcUTJdunKf4Ea9ySqXZOxE+WirtF5JbphGFw9Lb7i7a2KRovTiIhY5MSq9/zZ8dmbPRQxnBzojm8/I0WFy5Fu1fh7CNkSKPTuZHrjK1bHGTJLpueR4LRT3xXFlV9mdRwRGUD5oWoSTB8BI4E6V+nJxw90efBF7STbo+r6JpYYlxBmarIfMFjdnII5Vq9fzJkGacUQCcQL8CIiIiJyViq+i4wgt852YjdMXjavAGBSUgBjDI8dk1OtS/0IADO7143a1e/XlB8vvu9tJBYbq996iMiY1d0EbYcAA8bN79Wuze5CoqZBtiuslr2jQLc7l3UlXwfgsiP/gyvSZXGioZHocpx8L5BUfoXFaURkII0PxlvOH/FMwzTsQHzSytaORABmpfmw67OvWOTSrC4chkltwMWBbrfVcaxhGFC2NH6/8nVrs4iIiIgMcyq+i4wUpslX5jqJmDZWRWcBajkvp6pxT+boKF/9fvGELJLdDpq7guys6bA6jojI0Dq2KX6bXQYJ6b3atdFdDMDMVL8u3BslthZ8nlZPCUnhVi469qTVcYbMDXMKAUgsv3zsrj4UGYUmBPYAp857rw86aQzGL0CfmaquLWKdFEeMC9O7AXivJZnIWD3/nCy+v4FOwiIiIiJnZ2nx/eGHH2bBggWkpKSQm5vLJz/5Sfbt23fKNqZpsmzZMgoLC0lISGDx4sXs3r3bosQi1skKHGZ6jp11sWl0mW7cthjjPGGrY8kwc2L1+4xRuvrd5bBxxZRsAN6qaLA4jYjIEAp1Q8Ou+P2ii3q1q6tgCn5HKnbDZGpyYBDCiRViNierJ9wDwNza35DmP2ZtoCGyeGoOiU4DR0o2zUFdSSIyGqQZPvLC8X/DDn9o3vu2jvis96nJARLtKvSJteal+0iyR+mIONh+vCPDmDPhcrC7ob0amvdbnUZERERk2LK0+L5q1SruvPNO1q1bx4oVK4hEIixdupTu7u6T2/zgBz/gkUce4bHHHmPjxo3k5+ezZMkSvF6vhclFht6kzrUA/Dp8NQATEoPY9H2j/IVjx1e/O4iyYJSufr9mWh4Ab2ruu4iMJbVbwYxCSgGkjuvVrslz4hdmlSUF8Kh4MapUZVzG4fRLcJhhLj/8I6vjDAm3w87F4zwAHPOpkZvIaDDLFS+81ztL8NtTAOiK2DjQFf+7PifNZ1k2kRNcNpNFmfExLxvakvBFx+AXMq4kGH9Z/H7lG9ZmERERERnGLP224rXXXuO2225jxowZzJkzhyeffJLq6mo2b94MxFe9P/roozzwwAPcdNNNzJw5k6effhqfz8ezzz5rZXSRoRXqZnzXZkwT3jPVcl7ObbTPfl88NQfDgD11ndS2q/2kiIwBsQjUbonfL7qI3vSNDxnOk7Ox1bJ3FDIMVk+4hxh2ylrfoah9k9WJhsTlJfHVsMd8NmIxXVAiMtLNcR4FTm05v7MzgRgGhZ4Que6IVdFETlGeEiDHFSYUs7G+NdnqONb4cOt5ERERETmjYbVUoKMjPr83MzMTgKqqKurr61m6dOnJbdxuN1deeSVr16494zGCwSCdnZ0nf7RCXkaFPS/hjAV4s72QTiMFu2FSmhiyOpUMU/HV75NwEGV+19tWxxlwWcluLizJAOCtvVr9LiJjQMNuCPvAnQo5U3u16yH3FGyuBDzRLgo1rmZUakmcxI78mwC48vAjGGbU4kSDb1aui6ivg1DM4GibVsSKjGQOG8xw1gAfFN8jJuw83tb7Aq16l2HEMOCK7Pj3jDs7E2gN2S1OZIGyJfHbI2sh0GltFhEREZFhatgU303T5N577+Wyyy5j5syZANTX1wOQl5d3yrZ5eXknn/tLDz/8MGlpaSd/pk+fPrjBRYbC1mcAeKxxLgDFCSFcNq3ykbPbkBL/QDzTt45U++jrknBNeS6gue8iMgaYJtQcX808bh4YvXv7vs8zA4CcwNHeLJiXEeb9km8QsKeQ213JjIaXrY4z6Ow2A9/edwHY16CLrUVGsoVFdpJsIXy2JBqcxQBUdnnwx2wk26NMUsc3GWaKEsJMTAxgYrB2LK5+z5oEmZPinZkOvWN1GhEREZFhadgU3++66y527NjBb37zm9OeM/7im0LTNE977IT777+fjo6Okz979uwZlLwiQ6blIBx5jxgG2zwLAPQFhJxXtXsKDc4inGaYT2RWWR1nwF1bHr8oa+3BFnwhtaEUkdErJVQH3U1gd0HBnF7tu9froclZgBmNkBOsGaSEMhwEnOmsK/kaAIuqf4IjOvpHDHRXrAbgYGM3kVjM4jQi0lcfK3MAcMQ9DQwbpgnbjq96n53mw6YLx2QYWpTVhYHJwW4PdQGn1XGG3pT4qDu1nhcRERE5s2FRfL/77rt5+eWXefvttykqKjr5eH5+PsBpq9wbGxtPWw1/gtvtJjU19eRPSkrK4AUXGQrbfg3ADs/FGHlTwDSZkKjiu5yHYbAh5VoAPp5xmBSXxXkGWFluMsWZCYQiMd6tbLY6jojIoMnr2h2/kz8HHJ5e7fvbY/FRTr4D63GaGlcz2u3I/wwd7kKSwi1cWPus1XEGXfDYHhLsJqFojCMtakstMlKdKL5XeeJdC+uCThqDTuyGyczU0X8hkYxMWa4o5SkBAN5rSWbM9SU80Xq+ckW8S5OIiIiInMLS4rtpmtx11108//zzrFy5kgkTJpzy/IQJE8jPz2fFihUnHwuFQqxatYpFixYNdVyRoReLwrZ4N4gXbPEri1NjnSQ5tLpHzu+AZxatjlyS7WG+MW90Vd8Nw+CaafGLsN6q0Nx3ERmd5uTZSA3VAwYUze/VvoGowQu16QB0bX994MPJsBO1uXiv9A4A5tf8ioRwm8WJBptJUWL8PfH+erWeFxmJEsOtzM6zEzMNjrinArD9+Kr3qckBEuwq6snwdUlmF3bDpCbgwuvKsTrO0Cq9FJyJ0FUP9TutTiMiIiIy7DisfPE777yTZ599lpdeeomUlJSTK9zT0tJISEjAMAzuueceHnroIcrKyigrK+Ohhx4iMTGRW265xcroIkNj/+vgrYWETNaG4henZEWbAHV0kB4wbGxMvpqPtP+Wexe6uMscXe3Zry3P46m1h3lrbyOxmIlNPSlFZJT5/xbGL5xq9ZRyuLoL6Orxvqu7CuiIOPCE2wkc3gZcMigZZXjZl72EeTXPkNe9l4uP/pxXyLU60hlVVFQMyP7FiTEqvXYONXcTjsZw2odFYzcR6aFCX3xM4MFIDgF7Et6IjcouNwAXpKmjhQxvKY4Yc1J9bOlIoj55KjCGPo863DBxMexbDpWvQ8FsqxOJiIiIDCuWFt+feOIJABYvXnzK408++SS33XYbAPfddx9+v5877riDtrY2Lr74Yt544w21k5exYdPPAeic9WUOrYnPEcuONKPiu/TU3sR5XNj0IoUpAa7x7yDEp62ONGAumpBJittBc1eQrUfbmVeaYXUkEZEB03Z4F5+fGT/3L/nxLrbU7ejV/nmffwhPKdSvewXMGP7Q6LoAS87CsPHu+Lv49O67mF3/R/Lsw+uC5c7WJgBuvfXWATmeI+QlLSGPDn+YQ03dTM3Xe2SRkaSwexcA28PFAOzsSMDEYJwnRI5b5y0Z/uZndLPLm0DAmUrS9CutjjO0ypbGi+/7XoUr/t7qNCIiIiLDiqXFd7MHc4EMw2DZsmUsW7Zs8AOJDCetVXDgLQBWpX2SqFlDuOUoiR6tAJCeixkOXmiZxDfyd3OT+31+Z0YwDUv/6R8wLoeNq8tzeWlbLa/tqlPxXURGlYLql3DaDXZ0Z7H4K/ewuBf7ek03b0RnAyalCQF2AMFIdHCCyrBTnX4xh9MvYXz7Or6YvI5fWB3oQ/xdnQBcf/sDTJ09r8/Hqdiwilef/hHBYICpeSlsONzK/gaviu8iI0kkSL5vHwDbQ8WkxmBXZ7zl/BytepcRIsFuMi+9m/dbU0i7/Fai/N7qSENn6nXwyj1Qsxk66yC1wOpEIiIiIsPG6KjAiIxGm58CTJh0NSuq4xeq+CrXw6xMS2PJyPN6ewk3pe6kILGNsuaV7M9ZanWkAXPdzHxe2lbLq7vq+c7HyjGMMdTqT0RGr0AnkzvfBeDN6DyKSkt7tfu7LcnQDuMTQ8QSnYMQUIa7d0vvYnz7OhYn7OfCguHXij2rsJSishl93r+h+uDJ+1PyktlwuJXDLd0EwlE8TvtARBSRwVb9Pk4zSK03RnU0C0eXB3/MRrIjyqSkoNXpRHpsbpqPjU1OSM9nV2C61XGGTko+jJsPNZtg/6sw/ytWJxIREREZNobfNzEiApEgbP0VAKG5X+XtvY0A+CrXWZlKRqig6eDH60MALKh5CnrQdWSkuHJKLglOO8fa/Oyu7bQ6jojIwNjyS1yxAHuaouw43oq3p6ImVHg9AMxI8Q9GOhkBmpKnUpHzUQC+f60HGD3n/r+UlewmK8lFzISDTV1WxxGRnqpcAcCrByKYGGxtTwLihUybrqeVEcRpg9zu+EVh61wXEYyNof+Bp30sfrv3z9bmEBERERlmVHwXGY4q/gS+FkgpZL1zPt5ghGRHjFDtPquTyQj12IYQPtNFbncl49vWWh1nwCS47CyemgPA8p11FqcRERkA0TCsewKAH74fwqR3X+BWdbvxRe0k2qNM0MrBMW1tybcImzaunejgAs/oPkdOOd5ufn+Diu8iI0blGwAsr4zQas+kNezAZcR04ZiMSJn+o0S8LXhtKfzu2BjqVjjt4/HbqtUQ9FqbRURERGQYUfFdZDja+PP47bwvs2JvMwDTUsKM5lVLMrjaAvB66EIAFtQ8bXGagfXRmfkAvLarHnMUreoXkTFq94vQeQy/PZVf7wj3evc93gQAylMC2MfQwis5XaenkD/7ZgNwW8ZWMGMWJxo8U3KTATja6qM7GLE4jYicV2sVNO8nho03D0U45iwBYEaqH7dd7+dl5LERo+P93wHwP4dyCUTHyJuw7CmQOQmiITjwptVpRERERIYNFd9FhpvGCqheC4Ydc+4XWbGnAYDy1N5/AS/yYS+FLiZiOCnq3Eph53ar4wyYq6fl4rLbONTcrRVvIjKymSas/TEA+9KvJBjt3e5dERuHfS5ALecl7rmu+XQETCa62pjq32p1nEGTnugiL9WNCRxo1HsBkWHveJGuKWES/tQJtNszMTC5IM1ncTCRvuva8QYpMS8NQSe/PppldZyhYRgfaj2/3NosIiIiIsOIiu8iw82mX8Rvp17HLm8ydR0BEl12JiVrFY/0T6uZQkVu/IPx/JpfWpxm4KR4nFxelg3Aq7tGd1tdERnlqlZD/Q5wJlKZdnmvd9/r9WBiUOAJkeHqZeVeRqVOM4EfrI2PH1jY+TqGOXr/v5iSd6L1vNreigx7x1vO1ybOIHXBJwEoSw6S6hy9HTpkDIhGWBhaD8ATVTn4ImNk9fvU6+O3la/HxyeJiIiICA6rA4jIhwS9sP238fsLvsqKPfUAXFGWg9PWYmEwGS02F97KzIaXmdS6mkxfFa2JE6yONCCum1XAW3sbeW1XPfdcO8XqOCIifXN81TtzbyXUltyrXU0Tdh9vOa9V76ODt7OTurr+XVTW6fXy4/Uh7rsyjQyaKPdtYk/SxQOUcHiZkpvCmspmajsCeANhUjxOqyOJyJmEuuMXmwF73HNImj4RgLlp3VamEhkQM8IVVGRczlG/m2eOZvGNCc1WR+qzioqKnm1oOpjlSscZaKfyzafw5szr0W7Z2dmUlJT0I6GIiIjI8KXiu8hwsvUZCHZCVhlMWMwbf3oXgKUz8vBX7LU2m4wKbYnjOZh5JZNb32Feza9YUfbPVkcaEEvK83DYDPbWe6lq7mZCdpLVkUREeqd2a7wNr2GDS74Fr77fq93rAk7aww6cRoyy5OAghZShEAjHV6dv2rSJrXsP9etYocYq/CF4rq2cr2dv4xLv6+xNnEfMGH0fA5M9DsalJ1DT7qeyoYsLSzOsjiQiZ3LoHYgEIL2E1ztLMOxO0qLt5HvU6U1GPjsx7p7UyH27ivnfqly+UNxKkmNkdXSoa/ViALfeemuP9/nZJzx8da6LVx+9m795rWfvQxMTE6io2KsCvIiIiIxKo+9bF5GRKhaFdU/E71/yLarbAuyt92K3GVw9LZc/9/CiY5Hz2Vj0JSa3vkN506usLfkm3e5cqyP1W1qik4WTslhT2cyru+q4Y/FkqyOJiPTOmh/Gb2d9FjInAr0rvp9Y9V6WHMRlMwc4nAylUCRefJ8zKZdFC+b261grV3azuRL+1D6BW3IPkhZtY2b3enYkXzoQUYedKXnJ1LT72dfgVfFdZLg6Phe6e/INbFjrBqAoXA3kWRhKZODcVNDG44dyOexz81R1FndObLI6Uq+0dwUwgcfuWMrC2WU92ictcBTa3uH2RZlcduOn4rPgz6GiuolbH3qO5uZmFd9FRERkVFLxXWQIVVdX09x85rZjaXXvMqn9CBFnCjvNcl5+YxMA5VlODu3dRVVV1VBGlVGsPmUWx1LnUtS5lQvrfsua8d+2OtKAuG5mQbz4vrNexXcRGVkaK6DiT4ABl93b691DMYPKrngBY7pazo8aSR4nBVmp/TpGoscFQNB0sD5lCVd3PM/F3hXsTlpA1HANRMxhZXJuMu/sb6LRG6TNFyIjcfT9HkVGtFgU9r8GwB9iVxGIhQi31pLlbkbFdxktHDb4m0kN/O3OEn5alcOXSlpIGWGr3wEmF2Zw4ZRxPds4mgtr38Ud7ebCcU5I1t9nERERGdtsQ/2Cjz/+OBMmTMDj8TBv3jzWrFlz1m2ff/55lixZQk5ODqmpqSxcuJDXX399CNOKDJzq6mrKy6cxb968M/4c+/39APzH281cePGlPPGntQCs/s1jzJs3j+9+97sAhMNqxyf9t2nclwCYVf887ojX4jQDY+mMPOw2g501HRxu1sxIERlBTqx6L78Bcqf1evfKLjdh00a6M0KhJzzA4WS02JW0kE57OsmxDmZ3966zwkiR6HJQkpEIQGVDl8VpROQ0xzaCr5moO52f77UD0LnpRc69RlZk5PlEQTsTkwJ0RBz84nC21XEGn90JGRPi95v2WZtFREREZBgY0uL77373O+655x4eeOABtm7dyuWXX851111HdXX1GbdfvXo1S5YsYfny5WzevJmrrrqKG264ga1btw5lbJEB0dzcjM/n55nv3Mzm/73zlJ89//15rhzvwMTg4zfdwjv/cw+JJTMBeO62KWz+3zv51icuBiAaVfFd+q8qYxHNiRNxR7uZVf+81XEGRHaym0snx7/YeHl7rcVpRER6qOUg7Ppj/P4Vf9enQ+w53nJ+eor/fF0+ZQyLGg7WpywFYIH3TRyxns1kHWmm5KUAsL9hdFxcKDKq7P0zACuyv0x1q58Ee4zunW9ZHEpk4NkNuGdSAwA/P5JNR3jI1z4NvZyp8dtmFd9FREREhvTd3yOPPMJXv/pVvva1r1FeXs6jjz5KcXExTzzxxBm3f/TRR7nvvvtYsGABZWVlPPTQQ5SVlfGnP/3prK8RDAbp7Ow8+eP16ksXGV7KS3K4cMq4U37K7UcAMHLLmVVeRlPqVGIYTEv289EZmVw4ZRyF2f1rOypyCsN2cvX7hbW/wT5KvoC/cU4hAC9uq8E0NfPYaqtXr+aGG26gsLAQwzB48cUXT3neNE2WLVtGYWEhCQkJLF68mN27d1sTVsQq7/4XmDEoWwoFc3q9e1vITm3AhYFJeUpgEALKaLIn8SLa7Vkkxbq4oPtdq+MMikk5SdgNg5buEM1do+P9zXCkc7z0yb5XAfi/zgUAXJwZwozo76mMTtfnd1CWFKAz4uCpI2Ng9XvWZDDs4GuB7pE1515ERERkoA1Z8T0UCrF582aWLl16yuNLly5l7dq1PTpGLBbD6/WSmZl51m0efvhh0tLSTv5Mnz69X7lFBl2wKz7rFWBc/EuIFY3xQvvSvE6rUskYsC97KV5XLknhFsobX7U6zoBYOiMPt8PGoaZudtfq74/Vuru7mTNnDo899tgZn//BD37AI488wmOPPcbGjRvJz89nyZIlunBOxo72o7D9N/H7V/x9nw5xYtV7aWKI5BE4T1SGVsywsy71IwDM967EFRt9F2y4nXZKs+Kt57X6ffDoHC+91lwJLZVsMGewucmGy27j4iwV3mX0shvw7bG0+t3hgYzx8ftNey2NIiIiImI1x1C9UHNzM9FolLy8vFMez8vLo76+vkfH+OEPf0h3dzc333zzWbe5//77uffee0/+uqamRgV4Gd5qt8RXvKUWQWoBgajB6pZ4u8yluSoeyuCJ2ZxsLvz/2bvv8DiK84Hj372u3rssufdeMLYBm2aK6QndlAQSCCUYfqGHGkoCCQFCD6E306sBm2JjG/deZMtFzapWL6crupvfH2cLZMu2yulWOr2f57lHd3uzu+/u3mnmZnZmLmVG7r+ZUPgmW5LORGlGvcPqlAibmZOGJfHVpmI+W1/IyLQoAPLz8ykvL+/wduPj48nIyPBXmL3Gaaedxmmnndbqe0opnnzySe6++27OO+88AF5//XWSkpJ45513uOaaawIZqhD6WPokeJug33HQ56h2r+5VkFVnA2BERKOfgxPBalvIBCbVfUdcUxnj6hexYl9jfDAZkhzB7vIGskvrmdI/Dk3mY/A7yeNFu22fB8Bz5svBCb+dmE6kvVLnoIToWqcn1/D0Lgc7Gmy8lhfPTQPL9A6payUMhcpdvnnf+x6rdzRCCCGEELoJWOP7fgdWfCil2lQZ8u6773L//ffz2WefkZiYeMh0VqsVq9Xa/Lq2VhovRTfW5PQ1vgOkTwRgSUU4jR4DqTaXVKSLLrc56WyOLniZWEc+/St/Ylfc8XqH1GlnjU3lq03FfL6hiDtOG0bhngKGDhtGo93e4W2GhIayLStLGuD9KCcnh5KSkhYj4litVqZPn87PP//casW80+nE6fylh5Tk8aJHq8qFNa/7nh93W4c2kWe30OAxEmLw0i9Meg+KtlGageURpzKr6g0m1C9kffgxOA1heoflV/3iwzAZNGoa3ZTWOUmOtOkdUq/SkTweJJ8PetvmscWbycL6Phg0uOa4/iz5ZqPeUQnRpfb3fr9xYyYv5yVwZWY5UeYgHqkofhBkG8BeDg3lENYLhtsXQgghhGhFwBrf4+PjMRqNB/VyLysrO6g3/IHmzp3LVVddxQcffMBJJ53UlWEKEVhF66DJASGxED8YgAVlvp66JyfWIp10RFdzm8LYkHI+k/e8wqQ9b7ArdgY9/YM3Y0gCkTYTpbVOVuRUYKspp9Fu59LbHycpY0C7t1eav4u3/3Er5eXl0vjuR/vLA62NiJOXl9fqOo8++igPPPBAl8cmREAs/Ad43dD/eOjXsZ5BW/YNOT80ohFjz/7XLQIsO2QMR9WlktBUxIT6RfwcebreIfmV2Wigf0IY2aX1ZJfUSeN7gHUkjwfJ54NaQzkUrOC5phsAOGN0KplxYSzROSwhAmHWr3q/v5oXz5xg7v1uskFMv32937dB2DF6RySEEEIIoYuATThksViYMGECCxYsaLF8wYIFTJ069ZDrvfvuu1x55ZW88847zJo1q6vDFCJwPG7Ys8r3PONo0Ax4FHy/1zfk/Mky5LwIkPUpF9CkWUip30xa7Tq9w+k0q8nI6aNSAPh8fVHz8qSMAaQPGtHuR0ca7EXbtWdEnDvvvJOamprmR0FBU5GMSQAAgh1JREFUQSBCFML/yrbBxvd8z0+8p0ObsHs0chp8oz0Nl5FyRHtpBpbtG25+XP1P2DwNOgfkf4OTfGXq7LI6lFI6R9M7tXfUO8nng1j2N+R4k/jaOxmAP82Q8rXoPQwa3DRw/9zvCcE/93vCEN/f8u36xiGEEEIIoaOAlvhuueUWXn75ZV555RWysrK4+eabyc/P59prrwV8P7Yvv/zy5vTvvvsul19+Of/61784+uijKSkpoaSkhJqamkCGLUTXKNkI7gawRkLiCADWVIdS7jITaWpicky9zgGK3sJuiWNL0pkATCx8Q+do/OOssakAzNtUjNsjFe7dUXJyMkC7RsSxWq1ERka2eAjRI/34MCgvDD0D0iZ0aBPb6kLwopFkdRNv9fg5QNEb7LKNosychkU5mVC/UO9w/C4zLhSLyUCD00NRtUPvcHqVjuTxIPl8UNs2jxc9Z+DFwAlDExmWItdW9C6nJ9UwKMxBXZORV/OCfCj2uMGgGaBhL9gr9I5GCCGEEEIXAW18v/DCC3nyySd58MEHGTt2LD/99BPz5s0jMzMTgOLiYvLz85vTv/jiizQ1NXH99deTkpLS/LjpppsCGbYQ/uf1QMEK3/M+R4PBCMA3pb4h509KrMMc5DdDi+5lTeqlKDT6Vy0lrmGn3uF02uR+cSRFWql1NLGuROZB7o769etHcnJyixFxXC4XixYtOuyIOEL0eIVrIetzQIMT/tqhTSgFW2t9Q85Lr3fRYZrGsghf7/exDYuxeYLrxk+TwcDAhHAAtpfW6RxN7yJ5vGjBWU/xjrV85DkOgOuk17vohXpV73ezDWL6+p7v3aZrKEIIIYQQegl4ae+6664jNzcXp9PJmjVrOO6445rfe+2111i4cGHz64ULF6KUOujx2muvBTpsIfyrdDM4a8ESBimjAV9F+rf7Gt9PSZTRHURg1YT0YUfcCQBMLHxL52g6z2jQOGuMr/f7T3nSMKWX+vp61q9fz/r16wHIyclh/fr15Ofno2kac+bM4ZFHHuGTTz5h8+bNXHnllYSGhnLJJZfoG7gQXemHh3x/R18IicM6tIlSp4kKtwmjphgSLj16Rcftto2k1JyORTmZWP+j3uH43eAkX+P7zrJ6PF4ZCcefJI8XbbbjW152nogbE0f1jWFi31i9IxJCF72q93vCUN9faXwXQgghRC8VxLdaCtFNKS8ULPc9Tz8KDCYAttSFUOiwEGL0cly89M4Rgbc67TIAhpR/Q4Sz5Aipu7+zx6YBsLLIgcEWrnM0vdPq1asZN24c48aNA3zTz4wbN457770XgNtuu405c+Zw3XXXMXHiRAoLC5k/fz4RERF6hi1E18ldAru+9+X9M+7o8Ga21vl6vQ8Mc2A1SoOi6ARNY/m+3u9jGpYQEmS93/vEhBJiNtLo9rCnyq53OEFF8njRVns3fMPbnhMB+NPxA3WORgj99Kre73GDfjX0fKXe0QghhBBCBFwQl/SE6J5iHHnQWAUmG6SOa17+Talv3rsZ8bWESEW60EFpxAjyoyZiVB7GFb2rdzidNiI1kqHJETR5IXTYdL3D6ZVmzJhx2BFsNE3j/vvvp7i4GIfDwaJFixg5cqS+QQvRVbwe+OZO3/Pxl0Nsvw5txu2F7fU2AEbIkPPCD3bbRlBi7oNFuZhY/4Pe4fiVwaAxMFGGnu8KkseLNnE18NK2EBxYGZNkZsbgBL0jEkJXpyfVMDi8F/R+N4f8MvR82VZdQxFCCCGE0IM0vgsRQEYNUuo2+l6kTQSjpfm9b5qHnK/VIzQhAFiddjkAo0o+weru2dMfaJrG+RP7ABA+6iSdoxFC9Hrr34aSjWCNhBl3dXgzuxpsuLwGIk0e0kPcfgxQ9FqaxvLI/b3flxLqCa5G6iFJvp7Wu8oaaPJ4dY5GiN5l78YFvOmeAcCc08agaZq+AQmhM4MGNw3oJb3fE4f7/pZt8c2zKIQQQgjRi5j0DkCI3uSKsWZsnlowhUD6xOblO+ut7GywYda8HJ8gje9CP3nRR1MWNojEhh2MKfmIlX1+r3dInXLO2FQenbcVUgZR43KTrndAQojeyVED3z/oez79dgjveM+/LXW+Xu/DIhqRNgzhLznW4RSbM0hx5zOx/gd+ijpb75D8JjXaRrjVRL2zibxKOwMSZCoaIQLlxZ9ycDCYsZH1zBiSqHc4QnS5gr21rM0uPGyaJFVIH3MsBe4IHl5r5aKYnW3adk5JlT9CDJz4wWAw+0Z+rCuGyFS9IxJCCCGECBhpfBciQDSPi/unW30vMqb4hp3f59sy35DzU+PqiTJLjxyhI01jddrlnJ59D+OK32NN6iV6R9QpceFWJqbYWF7oILfBwAi9AxJC9E4/Pe6b8zJuIBz1xw5vpsZtYE+jFVAMlyHnhT9pGssiT+W8ipcY3bCU1eHHYzdG6h2VX2iaxqCkcNblV7O9pE4a34UIkLLKat4qywRgzow+0utdBDV7vW/UmMc+WMFjH6w4YvrQIetJOOdO3itJ4Z/3/xXlbGjzvsqq255WV0YLxA/yDTtftkUa34UQQgjRq0jjuxABkpD7KelRBlyGUCxp41u89+2+IedPlSHnRReqq62luLj4iOlK1UiONiUS6y4jfec7zK/u2VnFCf1CWF7ooKDBgMerMBqk4k8IEUAVu2D5C77npzwKJsvh0x/G1roQAPqEuIiUm/WEn+VZh1JkziTVncekuu9ZFH2u3iH5zeCkCNblV5NT3oDb48VsDOJhfoXoJl76cgkOrIw15zP96NP0DkeILuVyOgA4+tSzmTphzBHTKwXfeezU2sI55eYnGG48fG95gNUbs/jpiw+osTs7HW/AJI7Y1/ieBQNOBE3yXyGEEEL0Dj27RUWInsJRQ/KOtwEojhhNpuGXr15ho5mNtaFoKE6SxnfRBRxuDwCrV69m3bbdbVrH2yeBx4aUMTb/dRb+2BcAu93eVSF2qXHJVprqKyE8ltyKBunxJoQIrG/vBq8bBp4Mg2d2eDNKQda+xvcREQ5/RSfEL/b1fv9NxYuMbljG6ogTaDBG6R2VXyRFWIkKMVPT6CanvIHB++aBF0J0jbI6B29t8wAm5gyrRzNIg5voHSJj40nPzGxT2mPqXcwrDWWXlsL0dDNW4+HnRd9RWO6PEAMrpi+YQ8Fth6pciO2vd0RCCCGEEAEhje9CBMLPz2By1bKt3IM9eQC//ik2f9+Q85NiGkiwNukTnwhqriZf4/uYAYlMnTSuTeuY1RganDn0D7Vzy2Qzf8sGp8vVlWF2GaNBo2HLj0RN/g1bi2ql8V0IETjZ8yH7azCY4NRHO7Wp/EYLdU1GrAYvA8Kk8V10jXzrEIosfUl15TKp7nsWRp+nd0h+oWkagxLDWZ1XRXZpnTS+C9HFXvghG4fXxDhtB9OPma53OEJ0SwPDnMRZ3FS4zKyrCeXo2B4ynHx7GIyQMBSK1kLpFml8F0IIIUSvIbcfC9HV6vfCsmcBuPsH50HDbH29b8j5U6TXu+hiYTYzKXGRbXrEx8ezMeJYAC5LydU3cD+o37QAgJyKBhqccpOLECIAHLXw5c2+50f/yTfnZSdsqfX1eh8S7sAkJXjRVTSNZRGnAjCqYRlhnmp94/GjQUm+m+9yK+y4mmTaBiG6yp4qO2+tKADg5siFaOkTdY5IiO5J02ByjK/BfV1NKE5PkE6PljTC97c8Gzw984Z+IYQQQoj2kqo7IbrawkfA3UBD9BA+zmrZ6FfiMLGqKgyAU5Nq9IhOiENaH3YMbs3MoJAaju9r1DucTmmq2EOsxYtSsL2kTu9whBC9wXf3Q+0e33CbM+7q1KYaPRq7G6wAjIhs7HxsQhxGvnUwhZZ+mGhiUt33eofjNwnhVqJDzHi8it3l9XqHI0TQemJBNi4vTDVs5tixw0CGnBfikPb3fnd5DayrCdU7nK4RkQq2aN80TOU79I5GCCGEECIgZNh5IbpS8QZY/SoAhcOvBVaRlb+3+e0vavqi0BhmraS0IJfSw2yqqFx6xovAchjD2Rw6mXENS7htmpXndI4nKyurU+v1DfdSWWlgc1EN4zKi0bQg7VkghNBf7lJY/T/f87P+A5bOVaZuq7PhQSPB4iZRpqgRXU3TWBZ5Gr8tf45RDctYHX6C3hH5haZpDE6KYGVuJdml9QxNjtQ7JCGCzraSWj5ZVwjA7ab30Ea+pHNEQnRv+3u/zyuNZl1NKOOi7Eec+73H0TRf7/e8pVC25Zee8EIIIYQQQUwa34XoKkrBvNsABSN/ww53EgCzH3m/OUnyZf/CmgpLvpjLhHVftWmzjS6pdBeBszZ8BqPrl3DqQBNf15foEkNtpe+GldmzZ3dqOzGqFrMxjiq7m8LqRtJjgrRngRBCX+5G+PxG3/PxV0C/4zq1OQVsqfMNOS+93kWgFFgGsscygHTXLo6q/57PiNU7JL8YlBTOytxK8ioacLo9WM09e2QfIbqbx7/ZjlJwumEFY+IBGXJeiCPqFXO/J+5rfK/MAVcQHp8QQgghxAGk8V2IrrLpQyhYDuZQOPlBqr9cBMCsC2YzZFB/6pWVbz1DAMXlp03GdvqEw27ux59+Zt3i73A2eQIQvBA+taY4ltSmMj2qiHOty1jHNQGPobHeN+rDrGvuZsjow39PWpO1chFfv/4UTU4HQ5Ii2FxUy+bCWml8F0J0jYWPQuUuiEiBmX/r9ObqDBFUuMwYNcWQcIcfAhSiDTSNnyNP5YLyZxnZsJwE0wy9I/KL+HArsWEWKhtc7CpvYHiK9H4Xwl9W5Vby/bYyjHj5i2kujP6dr8erEOKwDuz9PjbKji3Yer+HxvqGn68rgtLNQLreEQkhhBBCdClpfBeiKzjrYcE9vufH3gJRv/ywiEtMJD0zk5VVoVAJGSEuBqamHXGT4dEdG3JbiM76qGIg06OKONa0lV2OImptqbrEEZeaSfqg9g9RV5q/q/n5yLQoNhfVsrOsnkaXhxCL9HgTQvhR/gr4+T++52f8G2xRnd5kicn3P3dgmCP4KmJFt1ZoHUi+ZSAZrp1cEL+T1/QOyE8GJ4azPKeS7NI6aXwXwk+UUvz9620AXGBcSH9DCYw6X9+ghOhBDuz9PiUYe7+njPY1vhdvgKgj14EJIYQQQvRkBr0DECIoLf4n1BVDTF+YcmOrSbLrbQAMll5sopvb7Yxi/q4mjJpifNE7eofTKUmRNhIjrHiUIqu4Vu9whBDBxF4JH10FygujLoAhp3V6k5rJSpnJN23NiAgZcl4E3vLIUwE4KTqfjKjg6ME6KCkCgIJKO41uGVFKCH9YsLWUNXlV2Ixe5pg+hPRJEDdA77CE6DH2934HWF8TisMTHHluCwnDwGCGxkrC3Hv1jkYIIYQQoktJ47sQ/la+E5Y963t+yqNgth2cxGmkwmXGgGJgmDPAAQrRfv9Y6vucjiz9FJu7Wt9gOmlkmq8n6qaiGpSSXqRCCD9QyjfPe00BxPSDWf/yy2ZDh0zDo5mINDWRHuL2yzaFaI9C6wDyrYMwa4q7j7XqHY5fxIZZiA+34FWwq6xe73CE6PGcTR4emecbpe334StI0qph9IX6BiVED7S/97vLa2BdTRBOkWayQsJQAOLsO3UORgghhBCia0njuxD+5PXCF38GjwsGnHjIXm87GnwN8n1DnVhlCFnRA/yQ42GnJxmz18m44rl6h9MpQ5IiMBs1qu1uCqulJ6kQwg9WvQzbvvT15jn/VbD5Zyjr8NEnAzAi0iHT5grdLIvw9X7/3VgzSVqVztH4x/7e79lldTpHIkTP99rSXHIr7CSEGbnO8V8wmGDEuXqHJUSP0yt6v6eMBiDGkUe4RedYhBBCCCG6kDS+C+FPa16FvKVgDoUznqC1mnIFbK/bP+S89HoXPcdHzqkAjC16D0tTz+0pZjEZGJLsq3TfVFijczRCiB6veCN8e5fv+cy/Qeo4v2y23GnAljEKlGKYDDkvdFRk7c/a+njMRo3zrUv1DscvBieGA7CnshGHjDwvRIeV1Tn4zw++Hqy3991FuOaAgSdBWLzOkQnRMw0McxK/r/f76uowvcPxv8h0CInFqJq4cIRZ72iEEEIIIbqMNL4L4S81e2DBfb7nJ97nm++9FfWGCGqaTJg0Rf8wme9d9Bw/Nw2jIqQfNk89Y3t47/dRqb6h53eW1WN3NekcjRCix2qshg+u9I14M/g0mHyt3za9psrXHSjWU0GEyeu37QrREe+UDwHgRPMGohr36BxN50WHWkiMsKKAQrv8JBaio/757XbqnU2MSY/ivPIXfQtHX6BvUEL0YJoGU2N9N7qvrwmlrinI8ihNg2Rf7/erxknjuxBCCCGCV5CV4oTQiVLwxRxw1UGfyXDUHw6ZtMyUBED/MCdm+QaKHkShsaLPVQBMKHoHc1ODzhF1XGKkjcQIK14FW4tr9Q5HCNETedy+hvfKXb5ePOc81+qINx3R5PGybl/je3JTsV+2KURnbGuM5ZudTRg1xeQ9/9M7HL8YvG/oeWl8F6JjNu6p5oM1vptx7p3kxVCTB5YI381oQogO6xvqIs3mwqM0lleG6x2O/yWPRKExpY8JW12u3tEIIYQQQnQJqWkQwh82vg87F4DRAmf9BwzG1tMZjJSakgEYEi5DyIqeJzv+JCptGdiaahlb8oHe4XTKqHRf7/dNe2rwKqVzNEKIHkUp+Pp22P0jmMPg4nchNNZvm1+4fS91TQY8DdXEecr9tl0hOuO+hb7pkoaVfU1UY4HO0XTeoH1Dz+91ahjDYnSORoieRSnFg19sRSk4Z2wqE0r2jYo1/CywhOobnBA9nKbBtDhf7/esOhsVrkPUL/VUlnBqrOkAxOXN0zkYIYQQQoiuIY3vQnRWXQl8c7vv+fTbIWHIIZOGDJiEW7MQavTQN9QVoACF8B+lGVm5r/f7+MK3MXvsOkfUcUOSIrCaDNQ6msit6Lm9+IUQOlj5Eqz+H6DBb/4LKaP9uvm5q30Nmw1bfsSA3BwkuoeVhR5WuQdiwMPRBT2/93tkiJnkSBugETpkmt7hCNGjfLq+kNV5VYSYjdx+fDps/tj3xrjL9A1MiCCRYnMzIMyBQuPniuDr/V4eOhCA2D3zwS3TMQohhBAi+EjjuxCd4fXCp3+CxirfvFXTbjps8vBRJwMwLMKBwT8j0woRcNsSZlJtSye0qZrRJR/pHU6HmY0GRqRGArCxoEbnaIQQPcaO7+CbO3zPT34Ahs7y6+aLaxr5YVsZAPUb5/t120J01rvO4wAYuvdrohvzdI6m8wYl+Ro0LMkDdY5EiJ6jqsHF377MAuCGEwaSkv85uO0QPwQyjtY5OiGCx7TYejQUu+02ChuDa370Wmsq+TVezK4a2PKJ3uEIIYQQQvidNL4L0RnLn4NdP4ApBH7zMhgP/YOo1q0RMmAiAMMjZMh50XMpzcSK9N8BMKHwLUyennun+qg039DzeZV2quwyGoUQ4ggKVsL7l4PywtjZMPXPft/Fuyvy8XgV/cLcuCt6/tDeIrjs9KayO+YYDHiDovf7sJRITk5xUTHvSb1DEaLHeGReFpUNLgYnhfOHY/rBmld9b0y40jdethDCL2IsHkZE+uqOllaEB9dYSJqB51fv+/298kXflE5CCCGEEEFEGt+F6KjiDfDd/b7npz562OHmAdZXW9AMRiI91cRaPF0fnxBdaFvC6dRYUwlzVzK65EO9w+mw6FALfeN881Ju3CO934UQh1G0Ht76LbgboP8MOOPffm9kcDV5eWelr8F9cpzcECS6p2UZfwRgyN5vibHn6htMJ4WYjUQGV2dCIbrUsl0VfLBmDwCPnjcKS9kGKNkERiuMuUjn6IQIPpNjGjBpimKnhRprst7h+NXLa914DWYoWgd7VusdjhBCCCGEX+na+P7TTz9x5plnkpqaiqZpfPrppy3eV0px//33k5qaSkhICDNmzGDLli36BCvEr7ka4MOrwOuGoWf47vI/DKUUayotACQ3FQcgQCG6ltdgYkWf3wMwac/rPXru9zHp0QBsLa7F7fHqG4wQonsqy4I3zwVnDWRMgYveAZPF77v5dksJ5fVOEiOsDI90+337QvhDWfgwdsUehwEvU/Nf1DscIUSAONwe7v5kEwCXTs5gQmYsrHnN9+bwsyE0Vr/ghAhS4SYvE6IbACiOGIbWBeVPvZTbFVVpJ/herHxJ32CEEEIIIfxM18b3hoYGxowZwzPPPNPq+4899hhPPPEEzzzzDKtWrSI5OZmTTz6Zurq6AEcqxAG+uQMqdkBEKpz1nyP2fFuTV0W5y4jX1UhCU1mAghSia2UlzKLK1ofQpmrGFb2ndzgdlhkXSlSIGVeTl20lkr8IIQ5QsQveOBsaKyF1PFzyPljCumRXby7zzaF98VEZGGXkXtGN/ZxxLQqNwRXfkVQnN0cL0Rs8v3AXu8sbSIiwctupQ8FZB5v2jYB1hJvRhRAdNyG6gXCTB7cxhMijztM7HL8q63eu78mWT6CuVN9ghBBCCCH8SNfG99NOO42HHnqI8847uPColOLJJ5/k7rvv5rzzzmPkyJG8/vrr2O123nnnnUNu0+l0Ultb2/yQhnrhd+vehrVvABqc92Kb7vB/f7VvCFn7tiWYkCHnRXDwGkzNQ89OKHwTa1OtzhF1jKZpjE73zf2+YU81SuabE0LsV7oFXj0d6kshaSTM/ghskV2yq20ltazMrcRo0Lj4qIwu2YcQ/lIeNoitiacDcGzuf2SuViGCXHZpHc8v3AXAfWcOJyrEDJs/8k3FEjcIMqfqHKEQwctsgGNifXWbkZN/S50WrnNE/tMYPQTSj/KNKrl/JA0hhBBCiCDQbed8z8nJoaSkhJkzZzYvs1qtTJ8+nZ9//vmQ6z366KNERUU1P4YPHx6IcEVvUbQOvrzZ93zGHdDvuCOu0uBs4suNvqHm6zcu6MrohAi47PiTKQ/tj81Tz4TCt/UOp8OGp0RiMmhU1LsoqnboHY4QojsoWAmvngb1JZA4Ai77pEuH1N3f633m8CSSo2xdth8h/GVZxrU0aRb61K6hb/Whf58JIXo2V5OXm+eux+XxcuLQRGaNSvG9sb+hbMIVRxwJTgjROYPDnYS6KjFYbPxknaZ3OP51lO+Gfla/Ah6ZdkkIIYQQwaHbNr6XlJQAkJSU1GJ5UlJS83utufPOO6mpqWl+bN26tUvjFL1IQznMvQw8Thh8Ghx3W5tW+3JjEXaXhziLB2ehfB5FcFGakZ8zrgVgXNG7hLirdI6oY2xmI0OTIwBf73chRC+38zvfUPOOGl9vnN99BeGJXba7OoebT9YVAnDZlMwu248Q/lRnTWZ9ygUAHJP7DJqS0Z2ECEb/+WEHW4pqiQk18+hvRqFpGhSt992YbrTAmEv0DlGIoKdpkFq3FaW8ZJmHsqY6VO+Q/Gf42RCe5LvhNetzvaMRQgghhPALk94BHIl2wB3USqmDlv2a1WrFarU2v66t7ZnDIItuxtMEH/4eagogdoBvuHnDke9dUUrxxr6ebBNjXazt6jiF0MGu2BmUhg0lqWEbE/e8zuJ+c/QOqUNGp0ezuaiWXXvrqXc2EW7t9lmkED1afn4+5eXlnd5OfHw8GRl+HKZ94/vw6XW+4S8HnAgXvtmuOd47clzzdjRgd3lIjzRhrc5j7dp8cnJy2hu5EAG3Mv1KRpZ+RoJ9J0P3fkNW4iy9QxJC+NEbXy3mmcU1gMap8dUs+PwjAKaUvE5/IDdkNEs//abD21+6dKl/AhWiFwhtqqVh03eEj57JA1mpfHL0TozBMOiEyQITfgeL/g7LX4AR58loGkIIIYTo8bpty0JycjLg6wGfkpLSvLysrOyg3vBCdLnv74ecRWAOg4veBltUm1Zbm1/NlqJarCYDE2NcvNS1UQqhD03j58xrOXfrHMaWfMja1EtosHZdD9GukhBhJTXKRlGNg82FNRzdP07vkIQIWvn5+QwbNhS7vbHT2woNDSEra1vnG+C9XvjxIVj8L9/rEefBuS/6KgTbqGPHpZF69fOY49LZ+NEzTLz7yxbvut1N7diWEIHlNEexKv1Kjs37D1PzXyA7/iQ8BuuRVxRCdHsLF//MnZ9lYY5No37Lj/z9H778MSVcI3dOOBg1zv/3QlYX/dDpfdXUd748IERvUPXTG8SOms7G2lDeLojj8owKvUPyj4m/hyX/hj0rIX8ZZE7VOyIhhBBCiE7pto3v/fr1Izk5mQULFjBu3DgAXC4XixYt4h//+IfO0YleZfWr8PN/fM/PeRYSh7V51TeX5QJw1phUQl2VXRCcEN1DbvRUCiPHkla7nqn5L7Jg0D16h9QhY/pEU1RTwqbCGib1jcVokDvuhegK5eXl2O2NvHXXBQzLSOjwdrLy9zL7kfcpLy/vXOO7sx4+uQa27Wv4njYHTrwXDMZ2baYjx7WiIZF/lKUTZnDz9tX9CTFcD8DL81bz/Ocr8Hik8V10b+tSLmBs8VwinSWML3qXVelX6h2SEMIPXlhegjk2DaO7gZl9jZhvuAWA34SswmLcwHZ3EsPPu5rhndjHqlVryFqxiEaXyz9BCxHkvA3VHOv8me9tx/NYdjIzE2tItgVBWTEiCcZeAmtehcVPSOO7EEIIIXo8XRvf6+vr2blzZ/PrnJwc1q9fT2xsLBkZGcyZM4dHHnmEQYMGMWjQIB555BFCQ0O55BKZU0wEyI4F8NX/+Z7PuBNGnNvmVffWOflqUzEAV0zty4aFm7siQiG6B01jceaNXLTpKkaUfcHa1IupCBuod1TtNiAhnDCLkQaXhx1ldQxNjtQ7JCGC2rCMBMYPTtM3iKo8mHsplGzyzV175tMw9uJObbI9x/XQigEAXJFZxbTByc3LU1dmdyoGIQLFY7SxNPN6Tt1xH0cVvMrWxDNosMTrHZYQohPmbylheYVvFIvhnh1MGj0EAJPXyckl74CC7UmnM7bviE7tJyevoNOxCtHbjHFvoiLpKNbXhHF/VhovjMvTOyT/mPZnWPs67FwAxRshZbTeEQkhhBBCdNiRJ63uQqtXr2bcuHHNPdtvueUWxo0bx7333gvAbbfdxpw5c7juuuuYOHEihYWFzJ8/n4iICD3DFr1F8QZ4/wpQHhhzCUy/vV2rv70iD7dHMS4jmpFpbRumXoierDhyNNlxJ6KhODb3ab3D6RCjQWNUuu/7ur6gGqWUzhEJIbrU9q/hxWN9De9hCXDFl51ueG+P1VWhrK0Ow6J5uTKzffPEC9GdZCWcSnH4SCxeO9PyntU7HCFEJxRU2vnLBxsAqF31KbGeX0ZwG25fTYiyU22MY7dtpF4hCtGrGVA8OqIQo6b4piyKBWVBcsN4bP9fOrwsfVLXUIQQQgghOkvXxvcZM2aglDro8dprrwGgaRr3338/xcXFOBwOFi1axMiR8gNPBEDNHnjnQnA3QL/j4MynQGv78NMOt4c3l/nuPv79tH5dFaUQ3c6SzOvxaCb6VS8jo3qF3uF0yKi0KIwGjdJaJyW1Dr3DEUJ0BY8bFtwL714EjhpImwB/+BEyJgc0jBdyfEPTn5daRaI1CIYMFb2XZmBhf99oUSPKviSpbovOAQkhOsLZ5OH6d9ZS62iiT0gTVQtf++VN5WV8/SIA1oUfh9J0rU4SolcbFuHg6sy9ANyXlUpDU5B8H4+52fd3yydQsUvfWIQQQgghOiFISmdC+FH9XnjzXKgrhoRhcMGbYLK0axOfrCukosFFWnQIp41MPvIKQgSJmpA+bEj+LQDH5j6Fpjw6R9R+oRYTQ5J8I6ysy6/WNxghhP9VF8DrZ8LSp3yvJ18Lv/sGovsENIyd9Va+2xuFhuIP/aTXu+j5SiJGsjVhFgAzcv4FyqtzREKI9nrkqyw27qkhOtTMRRkN4P3lxrB+jixiPHtxaDa2hAb2ZjUhxMHmDCwlPcRFkcPCP3ck6R2OfySPgoEn+8oQP/fM0fSEEEIIIUAa34VoyV7pa3gvz4bINLj0fQiJbtcmvF7FfxfvBuB30/piMsrXTPQuK/pchcMYTmLDDobt/VrvcDpkXEY0ADv31mOXzqhCBAelYMNceH4q5C8DSwSc/zqc9o9232TnDy/n+ubEPjmxlgFhzoDvX4iusCTzelyGEFLrNjF07zd6hyOEaIevNhbz+r7R2/59wViiLS2nXxpfvxCATWFTcRusgQ5PCHGAEKPioWF7AHgtP55llWE6R+Qnx97i+7v+Hagt1jcWIYQQQogOklZBIfZz1MLbv4XSTRCWCJd/DtEZ7d7MD9vK2L23gQiriQsnBbYXnRDdgcMczar03wEwNe95LLh1jqj94sOt9IkJQSnYVWfUOxwhRGfZK+GDK+GTP4KzFtInwTWLYMQ5uoRT5jTxcVEMANf03atLDEJ0hQZrAiv7+MoAx+Y9g9lj1zkiIURbbCmq4dYPffO8XzdjAMcPTWzxfpIrnwzXTjwYWB9+rB4hCiFaMSOhnovSKlBo/GVTH2rdQVDNmzkV+hwNHhcse0bvaIQQQgghOsSkdwBCdAsuu2/e18I1EBIDl38G8QMPSpafn095+aGHhlVK8Y/vKwA4sa+VHVs3Nb+Xk5Pj/7iFCKC62lqKi9t25/nX2nRGmOYS6ypjZtN3vNrFsXWFsRnRFFQ1klNvQDPb9A5HCNFRO7+DT6+H+hIwmGDGHTDtZjC2LAYfKY9vi6ysrDaleyUvHpcyMDG6gQkx0jgpgsva1EsYWfoZ0Y5CpuS/yE/9btY7JCHEYZTVOfjD66uxuzwcMzCeW04efFCao2u/BWB7yHjqjdEBjlAIcTh/HVrM0spwChqtPLgtlX+O2qN3SJ133F98nWNWvQxTrofIVL0jEkIIIYRoF2l8F8JZB+9cBHlLwRoJl30CScMPSpafn8+wYUOx2xsPuSlb5hiSLnoYr9vJf26YzVP26oPSuN0yhrXoWRxu37ztq1evZt223W1eLz+hD2+NKePckDUMjDVgt/esBqZ+cWFEhZipaXQTNvIEvcMRQrSXsw6+u99XaQcQPxjOewlSxx2UtC15fHvU1dcf8r1yp5E38uMA+FO/Mr/sT4juxGOw8kP/2zhv602MK3qP7fGnUBpxcNlaCKE/h9vDH95YQ1GNg/4JYTx76fiDpk3rbyqjv3MrXgwsj5ypU6RCiEMJN3l5YlQBF6wcwIdFsZycWMspSbV6h9U5A0+CjCm+qaIW/QPOfErviIQQQggh2kUa30XvZq/03U1buMY39+ulH7ZaKQ9QXl6O3d7IW3ddwLCMhFbT3Ft8FJsdMCuumD88cWmL916et5rnP1+BxyON76JncTX5Gt/HDEhk6qTWvx+tUort7hqGsJOnTrXygqtnzWmsaRrj+kSzMHsvkRPPxqvUkVcSQnQP2+bBvL9AbaHv9VHXwEn3gyW01eRtyePbYt7KbO55ZQEOh+OQaV7KTcTuMTIm0s4JCXUd3pcQ3VlezFSy4k9hWPm3nLTrYd4Z8zpKk5+eQnQnSilu/XAjGwqqiQ4188oVk4gKMR+U7uyQdQBkhU6gxtTxPFII0XUmxdi5pt9eXshJ5M4t6YyPzibB2oPrnjQNTrwPXj0V1r4JU25sdXRKIYQQQojuSmpARO9VVwpvngNlWyEkFi77+JAN7782LCOB8YPTDlq+uiqUzTlxmDTFX8c2khbSMk3qymx/RS6ELsJsZlLiItu1zs/u8+lf+ndOH2Rmnb3nfQeGpUSydEcZxKaxqczFRL0DEkIcXm0xfH0rZH3hex3TD858EvrPaNPqh8rj2yor//Dzt5c5Tc293ucMLEXTOrwrIbq9Rf1uoV/1MhIbshlf9C5r0i7TOyQhxK88sSCbLzYUYTJoPH/pBPrGhx2U5qg0I2MtBXgxsCLiZB2iFEK01c0DS1m4N4Jt9SH8ZXM6r47PxdCTy5qZU2DwqZD9Dfz4EJz/mt4RCSGEEEK0meHISYQIQlW5vjtoy7ZCeDL87us2Nbwfzr92JgPw29RK0kLcfghSiJ6v2pzIpxUDALjaNh+j59C9Qbsji8nA6BgPpXP/yuhEi97hCCEORXl9w8s/e5Sv4V0zwjE3w3XL2tzwHgjP7k7E4TUwLqqBGfHS610Et0ZLLD/1vQmAKfkvEuko1DkiIcR+Ly/ezX9+2AnAw+eOZMqAuFbT3TfdV/7NCp0ovd6F6OasBsVTo/OxGbwsKo/kmd2JeofUeSfcA2iw5RMoWqd3NEIIIYQQbSY930Wvkp+fT2P2Ivqv/CtmVzXO0GR2TP4Xrj122LP2sOtmZWX5/rbSq21TYyzLKsMx4eV4w0bWZh/cwFhU3sPn3BKig94rH8TR5mz6RNUwqfANlmf8Ue+Q2qVvuBdH7no06aIqRLc0PMHA4CV/hqotvgVpE+DMpyF5pL6BHSCnwcLbBb7GjVsHlUivd9ErbEk8k2Fl8+hTu4YTd/2dHzlJ75CE6PXmrsrnoa98v23/MnMwF07KaDVdXGMOpw4y41Ga9HoXoocYEuHkoeGF/GVzH/69M4nx0XaOiavXO6yOSx4Joy+AjXPh+wfhsk/0jkgIIYQQok2k8V30Gvn5+dxz7jBePM2I2aSxttjDme/uoKjuzHZtZ/Yj7x+0LOnSx7ClQ+XqLzn1Hy8ddv1GVw+ed0uIDnAqE7fMd/DB+aFM2vMa2fEnURnaX++whBA9nbeJlLr1rLsmDEvVFrCE++aGnHQVGIx6R3eQx3ck06Q0jo+vZWpcg97hCBEYmsZ3A+/ksnWX0Ld6Occb43hb75iE6MW+3FjEHR9vAuCa4/pz/fGHnkN5dOVXACx1DqLGFB+Q+IQQnffbtCpWV4XyXmEcf96QwVdTd5Bi68GjM864EzZ/BLt+gN2LoP90vSMSQgghhDgiaXwXvYNSmH7+N6+f6fvIV1vTMYw9hi/Gm9u8iZfnreb5z1dw0jnnM3rEsOblRd5olnkHY8TDRZP7EHL0va2u/+NPP7Nu8Xc4mzydOxYheqAPtzaxyj2QSeadzNzxN+aOfhmldb/GMSFED1GdB9nfkNJYBUaN6uSpRF/8X4hK1zuyVq2pDmVeaTQGFHcMLtY7HCECqjokk6WZ1zE990kuNc3nH7Ey85kQevhuaylz3luPUnDJ5AzuOG3ooUd2yllMqn0rTV7F541jSQtsqEKITrp/WBEba0PZWhfC9RsymDtpF+aemv3G9oMJv4NV/4Vv74ZrFnXLG22FEEIIIX5NGt9F8Gushs+uJ3XblwCUhg0jacKZjNXa98sjdWU2ALHxCaRnZgLgVfBDQRx4YVy0g0FxqYdcPzw6q2PxCxEknnOczgu2/5FSv5nxRe+wJu0yvUMSQvQ07kbY/SOUbATAZQjhoncr+OtbDzG+mza8exTcn+UrH/w2rYohEU6dIxIi8NamXky/qiVk1KzmrfNCeEHJzahCBNJn6wv5v/c30ORVnDUmlb+dPfLQDe9eL8z/KwAvrnGzt3+kNL4L0cPYjIrnx+ZxxrJBrK0O476sNB4eXthzpz2acSdseh9KN8Ga13wjXQkhhBBCdGPS+C6CW/FGeP9yqMrBazBz7We1XPvHiSS1s+H9UDbXhlDlNhFi8DIxWoaQFeJwKlQkP/Wdw8m7HmZq/ovsij2O6pBMvcMSQvQESsHeLNj5HbjtvmWp49jqGcwn2/7Lb7ZtozO1iVlZXXeD3Nw9sWyqDSXC5OHWQSVdth8hAqGutpbi4o6N3vBm1HX8uepPHJXmpFgtYDcn+Dk6IURr3lmRz92fbkIpOHdcGo//djRGw2HyzM0fQfF63AYbDyys4zSZLUqIbqVgby1rswvblPaGuAb+Xjqed/bEYbaXcXZU7hHXySmp6mSEXSAsDo7/K3x9K/zwNxhxLoTG6h2VEEIIIcQhSeO7CE5Kwbo3Yd6t0OSAqAyyx9zFf++7hGv9tAunV2N5VRgAk2PrsRqVn7YsRPDanHQ2gyu+I7N6BTN3/I33R70EfroZRggRpBqrYcd8qNrtex0aD4NPhah0CpdvQwNmz57tl13V1df7ZTv7VbmMPLYjGYBbBpaQYG3y6/aFCBSH29dTffXq1azbtrvD29lqTeDtY/dwpvqeD2o3UBQ5xl8hCiFa8dJPu3hk3jYAZh+dwYNnjcRwuIZ3lx2+fwCALTEz2Wt/KxBhCiHawF5fB8BjH6zgsQ9WtHm9iIlnE3viH3itYgiPv/wxjTuWtWm9supu1sFk4u9hzatQttX3f+rMp/SOSAghhBDikKTxXQRUfn4+5eXlHV4/Pj6ejIyMwydqqIAvb4KsL3yvB50C576AfVtuh/fbmhWVYTR6jESbmxgZ2ejXbQsRtDSNBQPu5vJ1F5FWt4FxxXNZl3qx3lEJIboj5YU9qyB3CXjdoBkhcyr0Obp5nsfqegcKeOa6mUwZPajDu5q3Mpt7XlmAw+HwU/A+f9ueQrXbxNDwRi7rU+HXbQsRSK4mX+P7mAGJTJ00rsPbWf6zm9fX7+aKsRZOzb6Hd8a8gcMc7acohRD7NXm8PDJvG68szQHgTzMGcNspQw491Px+S5+EmgKITGdb9AmANL4L0V24nL5y6tGnns3UCW2/eU0pWO8tZTdJpJx3B8cZtxGrHbphffXGLH764gNq7N1sqiSjCU7/J7x2um/o+TEXQ8bRekclhBBCCNEqaXwXAZOfn8+wYUOx2zveUB0aGkJW1rZDN8Bnz4fProeGMjCY4YS7YepNYDAAuR3e74EqXEY21IQCMCO+DmNPnTdLCB3U2VJY3PdGTtz9D47JfYY9kePZGz5E77CEEN1JXTFkfwP1pb7XUX18vd1D41pNPjA1hvGDOz4jbVb+3g6veyg/7o3g46JYNBSPjCjEJIN8iCAQZjOTEhfZ4fUjQyzc+KmDmaOTSHEWM2v7XXw84mmUJj9LhfCXmkY3N767jp+yfXnbbacO4boZA4+8YlUuLHnS9/yUh/Cst3dZjEKIjouMjSc9s33Tt6Up+LzYSV6jlRUM5YLUSqLM3lbT7ijseIeZLtd3Goy7zDfS5Rc3wTWLwWTROyohhBBCiINILYcImPLycuz2Rt666wKGZSS0e/2s/L3MfuR9ysvLD258b6yCBffB2td9rxOGwnkvQYr/h7JUwKLyCLxo9A91kBnq8vs+hAh2G5N/Q7+qpfSvWsKs7Xfx9pg3cJvC9A5LCKE3jwtyFkPhakCByQb9j4fk0Z2a0z3QGpWZB7b6bgb4XWY546OlAUOI/epc8LD9fJ6IfJOMmlUck/sMi/vN0TssIYJCTnkDV72+it17G7CZDTxxwVhOH5XStpW/uQs8Tuh7LAw/B9a/06WxCiECx6DBack1fFgYQ7nLzMdFMfw2rYoIU+sN8N3ayQ/C9q9h7zZY/C84/k69IxJCCCGEOIg0vouAG5aR0KneaS0oBZs/gm/u9PV2B5hyA5xwD5ht/tnHASosqRQ0WjFqiuPi/Ts3rBC9hqbx7aD7uHT9bGIc+Zy461G+Gfy3HtW4JoTws4pdsONbcNb6XicOhwEngqXn3ZjznmMSRU0W+oQ4+cvAEr3DEaLbyfcmMn/QfZyx/Q4mFr1NWfgwtiecondYQvRo324p4bYPN1LT6CYlysZ/L5/IyLSotq2c9QVs/woMJjjtMSmTCxGErAbF2SnVfFgYQ02TydcAn1pFWE9rgA+NhdMfgw9/D4v/CUNOg9SxekclhBBCCNGCDIApeq7KHHjrN/DRVb6G9/jBcOU8OOXhLmt4N9giyAsbBsBRMfVEmT1dsh8hegOHOZp5Qx7Gi5Fh5d8ysvQzvUMSQujBVQ9bP4XNH/ga3m1RMOoCGHZWj2x4Dx12HMubBmBA8cSoAkJNSu+QhOiWdsSfyMr0KwE4eeffSKjfrm9AQvRQjS4Pd32yiWveXENNo5txGdF8dsO0tje8O2ph3q2+51P/DEnDuy5YIYSuwk1ezkutIsLkodpt4pPiaBo9PfBmmxHnwfCzwdsEn/4JmrrZ/PRCCCGE6PWk8V30PN4mWPwEPHc07PoejFY4/q9w7RLf/E9dKOaEq2gyWImzuJkgQ8gK0WnFkWNYmvknAI7P+SdxDTt1jkgIESgaEGfPhlX/9Q0biQbpR8HEqyC2v97hdUiFCidu5nUA3DCgjEkxUlYQ4nB+zriWnOgpmL1Ozs66hQhHsd4hCdGjZBXXctYzS3hnRT4A1xzXn7l/nEJiRDtuRv/+Aagrhph+MP22LopUCNFdRJp9DfBhRg8VLjOfFsX0vAZ4TYNZT0BoPJRthe8e0DsiIYQQQogWpPFd9CinDjQxfOFVvgqCJgf0Ow6uWwbTbwWTtUv3nW/pS/iok0ApTkyow9jDfpsI0V2tTruMnJipmLxOztr2F2zuar1DEkJ0MVtdHouuDCWzZoWvp0p4Moy/AgacAEaL3uF1SKNH4x3vDAy2cAYYyvhz/1K9QxKi21Oaka+HPERFSD8iXGWct+UGQlyVeoclRLfncHt4Yv52znpmCTvK6kmIsPLWVZO58/RhWEztqObZvQhWvex7fuaTYA7pkniFEN1LtNnDealVhBi8lLnMfFgYS31TD6siDouHs5/1PV/+LGTP1zceIYQQQohfkTnfRc/QUM6Ayu/5+tJQqM/33d16ysMw+sKAzEdX5TKyOPwkAJIdOaTYet4wuEJ0W5qBbwY9wCUbriDaUcgZ227n4xHP4DWY9Y5MCOFvbgcseYKhP/0LQ6YJj2bC2H86pE0ATf8Kvx1FlSRkF7Z7PaXgqb2jKSEWT0MVf0j8CZNhcBdEKETwcZoi+WjEM1y46WpiHfmcu/UmPhz5PC5TuN6hCdEh+fn5lJeXd9n2N5Y6eWF1NSUNvnmaJ6VauW5iFKF1+axdm9/m7RjcDQxb+AeswN7MsyiojoS1a5vfz8nJ8XfoQohuJNbi4TdplXxaFEOl28QHhbGcm1qld1jtM+RUmHwtrHjBN/z8tYshMlXvqIQQQgghpPFddHPuRshbAoVriULh8iiqBl9E0vmP++aEDQCl4N6sNBqNYbgrCuhDNjAuIPsWordwmKP5bPgTXLjxKvrUruWkXQ8zf+B9Abm5RggRIDmL4cs5ULETA/DFdjd9jz2PUen6N1KX1zQAcONzC4AF7V4/aupFRB+bhvJ62Pvp33Ff1A/Q/7iE6CkarIl8POI/XLjpDyQ1bOPMbbfy6fAn8Ri6dmQrIfwtPz+focOG0Wj3/7Qjpqgkoo+7jLDhMwBoqqug6rsX+TD7Zz7swPZeOcvG2HEWdld5Gf3IWzS432o1nd3Z1PGghRDdWpzFw/lplXxcHEON28QHhTGkmSL0Dqt9TnoA8pZCySZ4/3K4ch6YeuZIWkIIIYQIHtL4LronbxMUrfMVoJscAFRb05n0z23MXXAtSQFqeAf4qCiGL0qi0ZSX8q/+jWHWSQHbtxC9SUXoAOYNfpizs/6PEWVf0WCOZ2nfG/QOSwjRWfZKWHAPrNtXqR+exO6hf+KsB25hzYzuMZJMXaMLgJPOOZ/RI4a1a908bxyrvQMAiCtdSf6eLdTYpceNEO1VHZLJJ8Of5rebryWjZjVnZf2FL4Y+RpNRhsEWPUd5eTmNdjuX3v44SRkD/LJNpwe21RrZVWdAoYFS1K79kskDkhh+0186tM1xjmXMrn0RLxpf97uba54cclCald9/yZKPX8HplsZ3IYJZpNnL+alVfFYczV6XmV0xR2PrP1HvsNrObIML3oCXZsCeVfDtnTDrX3pHJYQQQoheThrfRfeiFJRtgZyfwFnrWxaWAANOZPdeMzsrtwY0nN0NFu7N8lWgj7cvJ7c4G5DGdyG6Sm7sNL4beCczdz7EUYWv4zBHsYbA3WwjhPAjpWDTh/DNHWDfN/zuxN/DifdRnbVb39gOITY+gfTMzDanz2mwsLYkGoAJ0Q24S6tY10WxCdEblIUP5bNh/+LcrXPoW72c32y5gU+H/xunKVLv0IRol6SMAaQPGtGpbdhdTWwoqGF9YTUuj2+I+T6xIcRVbuWz714k8diXOrSPqMYCzt/guyFuZZ+rcGWcR3or6bZvXNOZ8IUQPUiYyctvUqv4oiSaQoeFxN/eyyrnUpTqIYPRxfaH8/4L71wAq16GxOEw6Sq9oxJCCCFELyaN76J7UAqqcmD3Qmgo8y2zhEPfYyF5lG8e2L2+OVizsrI6tIv2rtfo0bhhQyZ2j5GjY+oZvnc1H3doz0KI9tiSdDahrkqOyX+O43KfJs84k7f1DkoI0T5VufDlLbDre9/rhKFw5lOQcbSuYflTgd3MV6XReNEYHN7ItNh6FuodlBBBoDBqAh+NeJZzsm4mtW4j52+6hk9G/IcGS7zeoQkREDWNbtbmVbGluBaPVwGQGGFl6oA4MuPCWPP92iNs4dBMHgdnbL8dq6eBPZHjWN5HGqeEED5Wo+Lc1Cre2eykMjSDRbZj+b/NlTwyvBCbUekd3pENPgVO+Cv88BDMuxVi+sLAE/WOSgghhBC9lDS+C/3VlcDuH6E6z/faaPVVzqdNBKO5OVlxZR0aMHv27M7trr7+iGmUgru3prG1LoQ4SxNPjs7n5ewe8GNDiCCxKv1KTF4HR+95hcvM89kwxcJevYMSQhxZkwuWPQOLHoOmRl+ePv1WmHpTUM29mGe38GVJNB6l0T/UwczE2p7RK0iIHqI4cjTvj3yR32y5gQT7Ti7YdDWfDH+a6pAMvUMTokt4lSKvws7mwhpyyhvY/8szMcLKxL4xDEwIR+tsRqMUJ+16mMSGHTSYY/l68N9QmlQJCSF+YdQgrW4zO3/+mrgT/8DHRbHsqrfxnzH5ZIS69A7vyI79C1Tsgg3vwvtXwO++gpQxekclhBBCiF5IfmkJ/dgrIHcJ7N3XI10zQup4yJwC5tCDklfXO1DAM9fNZMroQe3e3byV2dzzygIcDscR075VEMfHRbEYUPxndB7JNpnnToiA0jSWZVyLAS9H7XmNf8608aX3K3ao43rIuHdC9EI7v4evb4OKnb7XfY+FM56E+IG6huVvuxqsfF0ShQeNvqFOTkuuwSj/loTwu4qwgcwd/TLnbbmBaEchF2+4gm8GP0hO7LF6hyaE39Q0utlWXMvmolrqnb/85syMC2ViZgxp0SGdb3TfZ3zR2wzb+w1ejHw15BHqrUl+2a4QIrhoQN2aL/jj1ETmR57JhtpQZi0bxCPD93BmSo3e4R2epvlG26rZA7mL4a3fwO+/hbgBekcmhBBCiF5GGt9FwFmbamHbWijdCvvv6U8cAf2OBVv0EdcfmBrD+MFp7d5vVn7b+s0uLg/n/m2+ed5vH1zC1LiGdu9LCOEHmsbSzOvZnZPPReYfOIMfycq+hwWD7sFjsOodnRBiv+oC+PYuyPrc9zosEWb+DUZfGFQ3yygF62pCWVwRDmgMDHNwapI0vAvRlWps6cwd9TJnbLuDtLoNnJN1C8v6/IG1SKOh6LnqHU1kl9WRXVpHaa2zebnNZGBYSiQjUiOJC/dvWXdAxY8cl/s0AIv6zaEwaoJfty+ECD59PfnMm7qDORszWF0dxo0bM1lSUcl9QwsJNXXjkSFNVrjobXjtDCjZCG+cA1d9C5GpekcmhBBCiF5EGt9FwFgainn5LBvD935Oc6N73EBfz7jw7lGBtrPeynUbMvEojfNSq/hjXxnoWgi9femZxoKvv+LFs8IYVv4t0Y5Cvhz6d+mtI4Tempyw7Fn46XFw20EzwFHXwPF3gi1K7+j8yqPgx70RbKnzjcwzMtLO8fF1GKThXYguZ7fE8+HI55me82/GlnzAlIL/YjUP4usQ+QKKnkEpRWmtk5zyBnIrGiir+6XBXQPSY0MYnhLJwIRwTEaD3/efXLeJ07LvQUOxIfk3rE+50O/7EEIEp/QQN+9N2sVTu5J4ZncicwtjWV4ZxqMj9nTvjiq2KJj9MbxyCoTEgMmmd0RCCCGE6GWk8V10vfKd8PNTjFj3NiPHWQAFsQMg8xiITNE7umZFjWYuX9OPuiYjE6MbeHTEnmDqsCdEj/bKOjeDzv4Dc4zvklK/mdnrL+WbwQ+QGzNN79CE6HXy83LxbPiA1G0vY7WXAFAfO4qCUTfRGDUAtu464jaysrK6Oky/afRozCuJZo/Dgobi2Lh6xkbZpYwgRAB5DWZ+HHAbpRHDOXHno4w37mDLdWG8XL+C4uLBndp2eXm5n6IU3V1+fn6XX+/9+VtjE2wvqSO3ooG8CjuNbk+LdKlRNgYnRTAwMZwwa9dVy8Q37ODcrXMwe53sjpnGj/3/ElSj0gghup7JAP83qJQpsfX83+Y+5DVauWT1AC5Kq+DOIcVEmb0d3nZX/yYwT3iU6ORM+oTGdul+hBBCCCEOJI3vousUroElT0LWF4BCA+bvaiLzqDMYMnKsvrEdoNJl5PI1/ShyWOgf6uTFcblYDd14GC0heqC62lqKi4vbvV51dTUAWdog3hn9OrO230lSwzbO3TqH9cnns7jvjTQZQ464narq6g7tXyrlhfhF6YqP2PvK75iQ4qu4L6z1cuf3Tt7cuBRY2u7tFVfW+TlC/ypsNPNNWRT1TUYsmpdTk2roF+bSOywheq2tiWewN3QQx629iYzwCu7mA+Yu/Znbt4+gusnSoW166isBOlRGED1Hfn4+Q4cNo9Fu75LtGyPisfUZiTVjFKl/eJF5RRYoKml+32I0kBkXSt/4MDJjQ7u0wX2/GHsu5225AVtTLUURo/lqyKMoTaqAhBAdMzWugfnTsnksO5k3C+J5rzCOH8ojuXtwMWelVLfrvp7iyjo0YPbs2V0W736hoSFkZW0jIyOjy/clhBBCCLGf/PIS/qUU7Pre1+ieu/iX5YNPZXvC6ZzywJWsOSZBt/BaU+kycunq/uxssJFic/HmxN3EWTxHXlEI0SaOfT19Vq9ezbptu9u9vqssBwC73U5NSDpzR7/MsblPM674fcaWfEC/qqX81O8mdsYe32pPnsbGRgB+/OEHflq5vt37l0p5IYCybfDdfSRlf0NSikajsvBj+BksTjiFhIFWbmnn5rZvXMNXLz5Mdb2jS8LtLI+CFZVhrKoOAzSizE2ckVRNvFXKB0LobW/4EP6890Im7/w3t02zcmFKIael1PC1+WTWGkajtPYN271t+w6+2PDLzX4iOJWXl9Not3Pp7Y+TlDGgU9tq8kKVS6PSpVHlNFDl0rB7DiyDKhIibPSJCaFffBgpUSEYAzhXSVzDTn6z5XrC3JWUhQ3m0+FPtulmVSGEOJwIk5e/DS/izJRq7tjch912KzdtyuDV/DjuGVLMhJi23eBUXe9AAc9cN5Mpowd1WbxZ+XuZ/cj7lJeXS+O7EEIIIQJKGt+FfzhqYMNcWP0K7N03bJTBBKPOh6l/hqThNKxdq2+MrSh3Gpm9uj/b6kOIt7h5c0IOaSFuvcMSIqi4mnyNVWMGJDJ10rh2r7/8ZzeLd4DT5ett6jFYWdj/VnbHHsfJO/5GlLOIM7fdTkHkeBb1u4W94UNarO90+ubVnDgkjUnjRrZ7/1IpL3q1siz46Z+w5WNQXpRm4LmVDspOe5roIVNI7OBmu/OIElUuI9+WRVHqNAMwPKKR6fF1WGREHCG6jSaM3PW9k8oBZ3Fnn3XENpVxofsTjjOvYWH0uZRY+rZ5W6XhMg9sb5KUMYD0QSPanN7rVVQ0uCitdVCy71FZ7+LAHEHTIDHCiqm2iHUfPsO5l/2RiUcd59/g2yi5bhPnbL2ZkKYaysIG89GIZ3GaInSJRQgRnI6KsTNvajb/y4vnud2JrK8J4zcrBzIruZpbBpYyIMzZpu0MTI1h/OC0Lo5WCCGEECLwpPFddE7Relj9P9j0Ibj33eFqDoUJV8LR10F0Hz2jO6zcBgtXrOlHXqOVRKubdybuZmB4234gCCHaL8xmJiUust3rRYa0PoxsfvRk3hj/PhP3vM7EorfpU7uWSzdcRlbC6axKv5zK0P4t0keEWju0f6mUF71SySZfo/vWz2B/E8PQM9iaej433H8ut8yKJFrP+LqAZrJQEDKI1QVxeNCwGrycmFDLICkbCNFtbXcl8FbirYyrX8TkugWkuPO5eO9TbAsZz/KIU6gyd/QWIdEbKaWoczRRUuvwNbbXOCirc9LkPfjmq3CriaRIK8mRNpKjbCRG2LCYDKz5fj0/71qFSfuDDkcAAyt+4LTsezF5nRSHj+CTEU/jNLW//CuEEEdiMyqu77+X89OqeGJHEnMLY/mqJJp5JVGcmVzNDQPKGCzlaCGEEEL0UtL4LtqvvsxXGb/+HSj6VW/2hKEw8SoYcyHYovSLrw1WVoVy7bq+VLpN9Alx8saEHJnDVYgeyG0MZVnmn9icdA7H5D3D0PL5DN/7FcP3fsWumGNZnX653iEK0XN4PZD9LSx/ruXUMcPOguNuhZTROLvhKDb+UGDOJOX3z1IUmgJAZqiTE+JriTR7dY5MCHEkHs3E6ogT2Ro6iWm1XzHSvpKhjWsZ3LiO7SHjWR4xk2pphBetcDZ5KK11+nq01/geje6DpxexGA0k/qqhPSnSRngA5mxvF+Xl6IKXObrgZTQUu2OOYd6Qh3EbQ/WOTAgR5BKtTfx9ZCGXZ1TwxM4kvtsbxeclMXxeEsOpiTX8vu9eJkXb2zUnvBBCCCFET9fNfjGKrpafn9+hoV4N7nqiixeTVLaYkOIVoPZVRhvMMPxsmHQVZExpdb7lA2Xl7233/gFySqo6tN6BvnMN4+NVA/AojVGRdl4Zn0uCtckv2xZC6KPOlsLXQx5mbeolTCp8nYEVCxlQtZgBVYuZEJdA5iQzVQa5616IVtXvhY1zYdXLUJXjW6YZYcQ5cOxfIGm4ruF1pQ01IfxzRzKLo0djBsyeRk5OdTIwzCkVhEL0MHZjJAtiLmZD2DEcXTefAY7NDGtcw5DGtWSHjGVt+HRKLZl6hyl04vEqzAn92F1nIGtrqW/4+IaDb742aBAfbiUp0tbc2B4TakbrxplCqKuCmTseoF/1MgDWpVzAon43ozSp7hFCBM7wSAcvj89jS62NZ3Yn8nVpNN+URfFNWRRDwxu5LKOCc1KqCTPJza1CCCGECH7ya6wXyc/PZ9iwodjtjW1KnxSmcfogE2cNMXHaQBNW068qHFLHw6jfwqgLIDyhTdsrLi4GYPYj77c79l8rq27o0Ho1KpTE8x/gA+cEAM5OqeLR4XsINckcrkIEi9KIEXw59DGiG/OYWPgWw8q+YqB5L8+eHoJLfURO5U62hYwjzzYEj9b6cPZC9AoeN+yYD+vehh3fgnffTWi2aJhwBUz6Q7eeOqazttXZeGJnEvPLfCP1aMpDzarPOWFAKIMGj9U3OCFEp5RZ+vB53FUkugo4uu5bBji2MLRxHUMb11Fk6cva8OnstI1CaUa9QxUBUlbnYPYnpaT+/j+sqwKobX4v0mYiOdJGUpSvsT0xworJaNAt1nZRioHlP3DirkcJbarGbbDy/YC7yEo8Xe/IhBC92IhIB8+PzSe7vpRX8+L5tDiGbfUh3L01nUe2p3BKUg1RqhS0HvK/VgghhBCiA6TxvRcpLy/Hbm/krbsuYFhGKw3mShHqriTKuYdIZyFh7ooWb1ercP75YzkXP/QhI447s937r66uBmDWBbMZMqj/4RO3YvXGLH764gNq7O3rveryaryRH8dT3qGE9LdgwsM9Q0u4PKNCerUJEaSqQzL5buDdLMm8HuN3DzLZvpAxyTCkcR1DGtfh0izk2Iaz0zaaHNsw3AaZ1130Ai477PoBsr6A7K/BUfPLe2kTYNxsGH0hWML0i7ELNXnhu72RvFUQx5KKCAAMKM5NrcK06TMe+/EjjAOu0zlKIYS/+BrhrybBVcj4+oUMaVxHqiuX1Mpc6g2RbAk9ii1hk/UOUwRAQrgVq0mj0V5PUmQIfVPifA3ukTbCutvw8W3UL1rjZvUKY7dnAVAWNohvBj1IRdhAnSMTQgifweFOHh1RyB2DS/igMIa3CuLItVv5uCgWOJm06ybwnqOM0HILk2MbsBikY4wQQgghgkfP/KUpOmVYRgLjB6eBUmCvgOp8qMn3/XXbWyaOSIbYgRA/mN2FLh5e/Bznhad1av9xiYmkZ7Z/yMcdhe0bLt/h0fi4KIYXchLIb7QC4CzazgMDt3FFZr92718I0fM4zNF8bx/LNS9+xU1/vJDLBzYw0LGRSE81QxrXM6RxPU2YyLMNIcc2nFzrMOpMMXqHLXTU0elZDhQfH09GRoa+8SgvIbU5RJSvJax0FZFVmzB6HM1vu60xVKafTEWfU3FE7ssXN28/7CazsnyV/OXl5RjDiw+ZzqsUbi+4PQqXR+FVvqF8fQ+N0up6MHR9MVQp2FpnY35ZFB8UxlDk8I14oaE4PamGmweWMjDcyUMbao+wpfYp2FvL2uzCTm2jqNy/MQnRndTV1jaPitURtXV1bU6715LGt7GXsthzJmMaljK64WfCvbVMrv/O94hIpWaI/CwOZpqm8c+T4znl2Fkc/8CLJIVGQpOb2so6/P2fdv8N510lzFXO5aZveOWGcCxk4dFMrE67nOV9rsZrMHfpvoUQoiOizB6u7lvOVZnlrKkO5dPiaD4uCMceHsuP7lh+XAMhWhNjQ/YyLrSckbYKkkyNfuks09FpL4UQQgghOktqGXoTbxPjkg3EN2yHrav3NbYfMIS70QIxfSFuIMT2B0v4L+9pnatEDoQmL6yvCeWLkmi+KI6m0u37iCdY3BzjXsWTb/2d5L+dr3OUQgg97HbFsSh6JovUOSS5CxjYuJFBjRuJ8exlgGMLAxxbACg3pZBrG0qObThFFrlRpzdp7/QshxMaGkJW1rZONcC3N55IK0xMNTI5zfeY0sdIYljL4Rxzq718nOXmk21N/FxQi1e9DLzc7tg+/vhjjOGxoBkwhsVgjIjFGBKJISQSY0gEmjXs8PPjWgaTeeunPNTUyOtLNZKtbtJCXKSHuEiz7X/uJtHqxtjOircyp4n1NaEsLg/n+72RzQ3uALHmJi5Mr+SSPhX0CXG3+7iPxF7vaxB87IMVPPbBCr9ss9HV5JftCNEdONweAFavXs26bbs7vB1XWQ4ATU1t/37YjZEsizyNFREnM8CxmZENK8h0bmeEpYiZA+RncbBz15QB6pf8q4vs/2za7fYjpGyfhPrtjC1+n6F7v8ZkcgMamxjMmrEPUxXa16/7EkKIrqBpMDHGzsQYO7FZy/jr18WEDJ5CyIBJNIbHssyewjJ7CgBNNWU48jfiLMzCWZSNuzwPVMfniu/MDX9CCCGEEB0htQzBSimoLYSi9bBnFexZxdg9q1l7TTjUrvwlncEEkWkQnQHRmRCRAobOzX/o9SoKquzsLm+guNpBcU0jRdUONueGkXTRI6y19WVjfigKMAIGTWHQwKwpLAYvFoPCbFBYDL+8tmiKGmsStswxFBuS2Fpro9FroNZtpMRhJtduYWtdCOuqQ6n3/BJ/qs3FVZnlXJReyacLd3aqsC6ECBKaRqklg1JLBksjZxHXVMIAx2b6OrJIceUS31RMfH0xE+t/xKlZ2RiegnmcGYun4cjbFj3aEadnaaOs/L3MfuR9ysvLO9X43mo8SmHyNmJrqsPWVNPiYfEeXNHv0Yzk2MN5fnEpkyZOYvDwMcwYpjGjgzG9+PV63tjYyKCpp6DFpFPlseLl0PM1mvBi1jwYNYVXaXjxPVxeDTQDjVoI2fWQXd/61A8mTZFi8zXIp4a4CDd6CTF6sRkVJk2x0jue2FMSealxKC+vSCDHbqHc1bLnn83g5dj4OmYl1XBacg3WLhzS0uX0jSxw9KlnM3XCmE5t68effmbd4u9wNnn8EZoQ3YJr3+d5zIBEpk4a1+Ht/PBDA2t2gMfb/u+HVzOxI2QsO0LGEtFUSVzOl7ywejG3X9LhcEQPsL9H+vHj+jN0yKAu28/yn90s3gFOl6tzG1KK2MZcBlQuYnD5fBIbdjS/le1N55o3t5Pxuz8yVhrehRA9kL3RQePu1Uwb3Y9RUTupIoxibzR7VSSVhGGKSiR81EmEjzoJACMeorETozUQq9UTpTUSjgODdvhy/fYdu/nq/be6fFQSIYQQQogDSeN7O3V2ONrODkPb6v69HqwNhYTW7iSkZgehNb6/ZldNi2QGoNqh0KLSiEru72twj0xt97Cv8+bNax52tskLxQ4je+xGihxGSh1GyhxG3Kq1bmpmbJmjqQPoSGez6AkkXTSBt4G3lx0mmbmJ6fF1nJNSzbFxdZgOaBPYUVRJQgeGgpXhX4XQV0eHqD3s0LSaRoU5hQpzCisjTsbqbSDTsZ1+jiz6OrcR6q1nkjWXSWeF8FVTVSeiFz1J8/QsgdLkhMZqaKyExqrmR+LOzfzjJCunRWwjtmEzuOrAWXf4G8lsURCRCpEpEJGKMSKFFT9s4oll7/PNrHTGD0lvV2heBVl1NhZXRLCkIpxlQ04maaiJSoB9bV42g5dkm5s4SxMx5iZizB6izB5sRu8he63/8PMa5r33Jn+9ehYnHTeVYqeZPY0WChvNFDZa2OOwUOIw06Q0ChqtFDRaodWvYDIRY0eypgmo9i3RUAwOdzAh2s5JibVMja3HZgzsHJKRsfEdmmLn18Kjs/wUjRDdT5jNTEpcZIfXD7VZjpyoDepMsSxunMCmskV+2Z7o/mLCbZ367B1JZEj7P5uaaiLMVU5MYwHJ9ZtJrttCSt1mwtwVzWmaNDO7YqezLvVivlyVy8LcW7ncn4ELIYQOYuMT6NM3kz7AaADsuL12ihwW9jRaKHWaKHWacXmNVBBBhYqAfcV6A4oYs4c4axNxliZizb6/UWYPhn2/Qcpr6vU5MCGEEEL0etL43g7+GI62w8PQKsWe7PVcevoxZIS5GBZvZFi8gaHxBgbFGbC0Urvd5FVk7fWyvNDDsgIPy/d42Fbu5cd/TmZ63yHtjn397mJM0Sn8/d3vsaQMxpo6GEviADTTwXPLqSYX7spCmmr34qkrp6l2L157DV5XI0cddwLjRwxAAzxKw4uvct/t1XApA26vhtOr+V57Dbi8vh5ye2saqK6uIT4+DosthBCjlwiTlySrm/QQF0MjHIyObGR4ZGOrlf3lNb5eqzc+twBY0O7j30+GfxUisDo7RG17hqZ1GsLIDh1Pduh4UF6S3Huw5S3CXLCC2oEBbIwV+lMKvE3gdYNn38PbBB7Xr5Y37Xu+77EvTXpNNS+cYSNz7SOwMxTcDmhqBHfjr57/elnrQ9OmA7dNs4IjFxy/fkcDWySExkNo3K8e8WBuvQd5exQ2mllWGc6SCt+jRU9yDZpqy0ixOBnbJ5IUm5tos6fdczJqgNdRR4K3gmPjW68U8ygodZjZ4/A1yBc7zDR6DL6H14DHC3uKSvhy0VquOXkI04Yk0T/MRb9QJ2EmGelGCCFE6zTlIcxbR7inhjBPDSHeBmxeOzavHau30fdXNWJUTRiVBwOe5r+a8uLVDHgx4tWM+/4a8Ox7PS29iqsvDCEx5EPiti1HafvSaUaUZsDodWP22DF7HVg8DYS79hLu3IuBg0dxaNLMFERPYlfsdLLjTsRpjtr3Tm5Az5cQQgSS2QCZoS4yQ30jiCgFVW4jpU4zJQ4zZU4TlS4TLmWgwm2iwt2yatuoKaJNHqItTbjM/QkbdRJu+WkghBBCiACTxvd26OxwtC2Goe3TZ19lvhNcdrBXgL0cGsr3Pa+AumKoLoCaAqguIL2pkcWXmWjtsnk0Iw5TDHZzLHZzDI2mWBrN0ag0ExPGwgSg/8ps7nllAQ6H46D1W1PlMrK+JpQNNSGsrwllmRpE2jW/PyidSbmI9NQS4a0jzFtPmLeeEOVAC1MQBqTYgD6sWlVG1sbFWMYPJj2k/b3/f8xdwxevPcdd153MDacd3+716xp9BfeTzjmf0SOGtX//MvyrELro7BC1HR6aVjNQaslgfeN43nhvIW+d0c7WRdGzrHmdEd89TOlfwokreQeKO/6/PhG4ZoIF9rTjRi/NALZoCIlpflQ44I0Pv+T8k6eSnp4O1kiwRoA13JfeT8qcJpZVhrOsIoxlleHkNVpbvB9q9DAltoFj4+rY/PP3/Ou/7zPt6usYHjnBbzG0xqhBaoib1BA3xLR+g8LbJet5e9n7nDDrQk5J6fyNB0IIIYKQs57jil5kxdVhDIp5h6iiRgx0zYgofSOAoWZgG1Rsa/N6Hs1InTWZsrBhFEeMpCRiJKVhQ/AYJW8TQvRumgaxFg+xFg/DInz1mUpBXZOBSpeJiv0Pt69RvklpvzTKW/oSd+qNaBxmNDwhhBBCiC4Q8Mb35557jscff5zi4mJGjBjBk08+ybHHHnvI9IsWLeKyyy6joKAATdNIT0/n7bffPuw6XSU+5zPuPtbCCTGFpBgrQXnA69nX423f3wOXqV+ej7K6mHV7BJFfngKfu6GdP/gVGkW1HiLjk4mIS23R281ojSRM0wg7zPpZ+XtbXe5RkG+3kF1vY0e9jex6KxtrQ8m1t6x8x+Dr0R6hGhgYbyHJ2kSyzU2UaX+Pt9B9j8RW95OTV9Cu4+0qsfEJHRoKVoZ/FUJfHR2i1l9D04qu096yQZdocmBtLCUxzODLu3/NYPI9jJZ9f82/LGvldUm1g2e/XM01188hve9AMNnAHLLvrw1MIb/6GwIh0WCNAkPLBvW8tWu55Q8fMf3c4aQn+mfkhbomA9vrbGzYd3Nda/m9UVOMjrQzNa6e4+LqGRdtx7JvnvSHFsv0C0IIIdquW+Tx5lDSGjbRJ80I+G7m8mCgwRhJvSGKRmM4DkMoTi0EhyHU99wQgkcz7evRbsKLAY9mQqFhwIthX094g/JgxINBeTHgYfuWLWxZs5xxJ53L0OEj0X6VTlMePJoFtzGk+VFvSaDOkozdEovSjIE9L0II0UNpGkSavUSaXfQNczUv9yqobzJQ5TZR5Tayo7iW7O3ZmMaM1zFaIYQQQvRGAW18nzt3LnPmzOG5555j2rRpvPjii5x22mls3bq11WHYc3JymDlzJk1NTTz44IMA3H///cycOZPt27d3au70jkja+S4PnWCD+k3QgWmDzEC0TQOv64B3NAiN9Q0XGxb/y/PwJIjuA1F9ILoP63eVMf6oKax54ZR2zUXrUb5e7IUqlpBBR/ODayirt6dQ5DCT02BlV4MVp7f1HnT9Qp2MjbIzNsrO2mWLefrl97jg93/k6KFd29tNCCFE79DeskGXGX422+rD+e3Fl/PefbMZObDPL43q7exlXpRdyEM//cy5/76Q9PGBrehp8kKV20Sxw0zRvuHav/NOIvGCkdxeP4Tq7w++TU9DMTzCwdS4eqbG1jMxpoEIGbZdCCFEJ3WbPN5gYEXSpfz9qRcZe+rFpA0dh90QgfLjKDL7fV9Tx1drF3Pm9Ik4Us/2+/aFEEIcmuFXjfKZAHnZLPvqCbj4Lb1DE0IIIUQvE9DG9yeeeIKrrrqKq6++GoAnn3ySb7/9lueff55HH330oPQvvPACBoOBP/7xj9xzzz0AFBYW8vrrrx9yna70deQF/LRhCZOGZxIZHgaaAbVv7jbfX6Ovgr7FMkNzusKKep76ZAU33HQzaX0ywWBCGcz70vmGM1YKFGrfX8AJlCpUKeTnu4kYfwaf1/Rl2e5onF4DTo/m+7tvnnSHx0Btk5Eat5Fqt+9vXZMRhQaMIPG8M5jr5KBp4qwGLwPDnAwOdzAo3MHwiEbGRjUSbfml91/B4lJfL34hhBDCT9pbNugquc4IfmjoS07M0Sx0DiKvMhbw5cVq30A1Cvblp74X+8evUQeky62DsJEn8kOOnZ3eguaE+/P3X29X7XtT/Wp7KN/SgvwGIiacxcfV/flpZ1Rzfu/w7M/3DdTuy++r3Eaq3SbqmlrrNZdKSD+o3reDRKub0ZGNjImyMzrKzpjIlvm9EEII4Q/dJY8H2B05hc+3P0v0yQlEG6OOvIIQQgghhBBCCNFBAWt8d7lcrFmzhjvuuKPF8pkzZ/Lzzz+3us7SpUtxOp3MnDmzedkpp5zCSy+9xNKlS1tdp7a2ltra2ubXhYWFzcs76297xrE3aQwfVgAVHdzIlDH8e2UDrNzaodWjj7ucV4qB4rak9ux7+Fia6qjbW0K/SC8Z4YpI7ERr9SRQQwwNaC4F+0aT3bHv8Wurt+0BICc3D4Oh/XMflxYVAVBUUMDK1WvbvX5BXi4AP28txPrVqnavr3f8sr5+6/fk2GV9/dfPz/flI3a7vdN5yf71a2pqOrWdYLG/bPDnP/+5xbmdMWMGixcvPuh8O51OnE5n8+v97/sjj/9yTS6P/VhE3OlzeKwA6NRMKfHEnjSQp5cUw5I2ZdiHFH3sbN4oAUqOlPKXPF9DEUYjUdiJ0hpoqChh3Zq1nDcikknpVmxNbmgAiiEb36OtOpuX7tfZPH2/FVvzAfhm9S7yqw4cWajt/HVcnf1/0xXbCtbtdMeYutt2umNM3W07IPl8V2lvHg9dm8/b7b7h5nfu3I3L5TxC6o4rKPB9ngq2b2KlzXqE1B2Tu3UDADs3r8HlaOySfQAU7PLNWV+Ql8tKS8fzxsPx53dZ7/3IsXTP/cixtJ+/yumH46+y95FIHi+EEEKIttifzyuljpCy7TTlz60dRlFREWlpaSxdupSpU6c2L3/kkUd4/fXX2b59+0Hr9O/fn5ycnBbr/Pzzz0ybNo3+/fuza9eug9aZMWMGixYt6roDEUIIIYQQQgghhBBCCCGEEEIIERR27dpF//79/bKtgA47D6BpLe9oVEodtOxw6+y/V+BQ63z++ect7masqqpi9OjRbN68maio3jm8XF1dHcOHD2fr1q1EREToHU6HdLdj6G7x9BSHOm9yPv2rq89nb7te/jxepRT19fWkpKRgMPh/ntH2qK2tpU+fPhQUFBAZGalLDMXFxQwdOpQFCxZw1FFHNS9//PHHmTt3LqtXr26R/sAecV6vl6qqKmJjY49YlghWtbW1jBgxAkDXaxks9n8moeX5fPzxx3nvvfdYs2bNQeuMGzeO2bNn83//93/Ny5YvX84pp5xCdnY2SUlJB61z4Ge5pqaGkSNHsmXLlqC7hr0pzwjEsfam8xkoB57Tzpzj7pTP6629eTwcOZ9vz7Xpzt+V7hxbewTD/zy5FoGxv7yal5dHdHT0YdPp/fukswJxDIf7/+qv8ur+4/j1Z6onlleD9bd8ZwTD90zIdQwWch2Dg1zH4FBTU0NGRgaxsbF+22bAGt/j4+MxGo2UlLQcN7WsrKzVSkmA1NRUcnNzW6xTVlaGpmmkpqa2uk5kZGSLD/n+53369Om1H/79NyOkpaX12HPQ3Y6hu8XTUxzqvMn59K+uPp+97XoF+/EemG8Gks1mw2g0UldXd9D/hJSUlDbFlZGR0ZUhdnu/vuFQz2sZLGw2GwaDAa/X2+J81tbWkpqa2ur5TUtLo7q6usV7DQ0NmEwm+vbti9lsPuJ+96+bnp4edNcw2P+H/logjrU3nc9AOfCcyjn2D3/k8dAyn2/PtenO17E7x9YewfA/T65FYOyPLzo6uk3xBUOZtiuP4XD/X/1dXv31Z6onlle7+3dDT8HwPRNyHYOFXMfgINcxOPjzBruA3apnsViYMGECCxYsaLF8wYIFLYah/7Vp06ZhtVpbrDN//nxsNhvTpk3r0niFEEII0bU6UjYQoitZLBbGjRt30PLDfSanTJly0Gd4/vz5TJw4sU0N70IIEYwkjxdCiK7Rkf+vUl4VQgghhAisgI6Tc8stt/Dyyy/zyiuvkJWVxc0330x+fj7XXnstAHfeeSeXX355c/prr70Wr9fLiy++yEMPPcRDDz3ESy+9hFKqeR0hhBBC9FxHKhsIEWjXX389AG+++Waby6t5eXnccsstZGVl8corr/C///2Pv/zlL7rEL4QQ3YXk8UII0TU6Ur8q5VUhhBBCiMAJ6JzvF154IRUVFTz44IMUFxczcuRI5s2bR2ZmJuCbtyg/P785fb9+/Zg/fz6zZ8/mnnvuAXzDx7/11lvN6xyJ1Wrlvvvuw2q1+v+AeohgOAfd7Ri6Wzw9xaHOm5xP/+rq89nbrlewHm93Oa4jlQ3E4VmtVu6+++7m56LzLrnkEj788EMee+wxbrnlljaVV+fNm8fNN9/Ms88+S2pqKk8//TS/+c1v2rzP7vJ97ArBfGwHCsSx9qbzGSgHnlM5x/7j7zy+PdemO1/H7hxbewTD/zy5FoHR1vi6+3G0RaCOoSP1q+0pr7Z2HD3x+vTEmLuanJPgINcxOMh1DA5yHYNDV1xHTSml/LY1IYQQQgghhBBCCCGEEEIIIYQQohcK6LDzQgghhBBCCCGEEEIIIYQQQgghRDCSxnchhBBCCCGEEEIIIYQQQgghhBCik6TxXQghhBBCCCGEEEIIIYQQQgghhOgkaXwXQgghhBBCCCGEEEIIIYQQQgghOikoGt+fe+45+vXrh81mY8KECSxevPiw6RctWsSECROw2Wz079+fF154IUCRdq1HH32USZMmERERQWJiIueccw7bt29vkUYpxf33309qaiohISHMmDGDLVu26BRxS+25jh9//DEnn3wyCQkJREZGMmXKFL799tsui+3RRx9F0zTmzJnTvKw7n0s9He46FhYWMnv2bOLi4ggNDWXs2LGsWbMGgKVLl2I0GklOTpZz2gZNTU389a9/pV+/foSEhNC/f38efPBBvF5vc5rOfEbb+3/V6XRy9913k5mZidVqZcCAAbzyyiudOsau9NNPP3HmmWeSmpqKpml8+umnLd5vy7lzOp3ceOONxMfHExYWxllnncWePXsCeBRH1t7r+PbbbzNmzBhCQ0NJSUnhd7/7HRUVFQGKVhzowM/poEGDjpjHX3nllVgsFjRNIyQkhHvvvVen6HuWQOTzwV5ePVweD8FTbgpEebWr8/je4Ej5/LPPPkt0dDSapmEwGBg3blyb8vlPPvkEk8nE2LFjA3cwvUx7ftNGR0djMBgwGAwMHz68W5VXn3/+eUaPHk1kZGTz9//rr78+6Bh60ne4q/LKri6v3n///Wia1uKRnJzs12MIlO6Y1x7p/+2B+vbte9D10DSN66+/Xrdj6Ihgyavr6uqYM2cOmZmZWCwWbDYbFoul+bt4uGPQq6zaW37Ld1R3rlsVbdfevHG/pUuXSlm1Gwn2utXeQupWe772llfBT+Uc1cO99957ymw2q//+979q69at6qabblJhYWEqLy+v1fS7d+9WoaGh6qabblJbt25V//3vf5XZbFYffvhhgCP3v1NOOUW9+uqravPmzWr9+vVq1qxZKiMjQ9XX1zen+fvf/64iIiLURx99pDZt2qQuvPBClZKSompra3WMvP3X8aabblL/+Mc/1MqVK1V2dra68847ldlsVmvXrvV7bCtXrlR9+/ZVo0ePVjfddFPz8u56LvV0uOtYWVmpMjMz1ZVXXqlWrFihcnJy1Hfffad27typqqurVf/+/dWgQYOUwWCQc9oGDz30kIqLi1NffvmlysnJUR988IEKDw9XTz75ZHOajn5G2/t9VEqps846S02ePFktWLBA5eTkqBUrVqilS5f67Xj9bd68eeruu+9WH330kQLUJ5980uL9tpy7a6+9VqWlpakFCxaotWvXquOPP16NGTNGNTU1BfhoWtfe67h48WJlMBjUU089pXbv3q0WL16sRowYoc4555wARy72O/BzeuONNx42j7/tttsUoGbNmqU+++wzNWnSJAWoN998U8ej6P4Ckc8He3n1cHn8fsFQbgpUebUr8/je4nD5/HvvvacMBoOy2WzqqaeeUpdeeqkyGo0qMTHxsPn8scceqywWizr55JPVmDFjAn9QvURbf9OGhIQoo9Go7r//fnXaaaepsLAwFRoa2m3Kq59//rn66quv1Pbt29X27dvVXXfdpcxms9q8eXPzMfSk73BX5ZWBKK/ed999asSIEaq4uLj5UVZW5rdjCJTumtce6XfVgVatWqVCQkLU1VdfrRYtWqRuvvlmBaj7779ft2PoiGDJqy+44AI1fPhwdd999ymTyaTOPPNMFR4erq666ioVFham7rjjjlaPYePGjbqVVXvDb/mO6s51q6LtOlInp5RqrludOXOmlFW7gd5Qt9obSN1qcGhvedVfdXI9vvH9qKOOUtdee22LZUOHDlV33HFHq+lvu+02NXTo0BbLrrnmGnX00Ud3WYx6KSsrU4BatGiRUkopr9erkpOT1d///vfmNA6HQ0VFRakXXnhBrzCVUu2/jq0ZPny4euCBB/waV11dnRo0aJBasGCBmj59enNFQ3c+l3o63HW8/fbb1THHHNPqehdeeKG6++67VXh4uEpJSWleLuf00GbNmqV+//vft1h23nnnqdmzZyulOvcZbe/38euvv1ZRUVGqoqKiI4eiuwMz3bacu+rqamU2m9V7773XnKawsFAZDAb1zTffBCz2w2nvdXz88cdV//79Wyx7+umnVXp6epfFKNruwM9pa3l8WFiYSkhIaE7jcDiUxWJR/fr1C3S4PUag8vlgL68eLo9XKnjKTYEqr3ZlHt8bHfj/c9KkSSo0NLTF+Rs8eLCyWq2HzefPOusspWmauvTSS6VCM4AO9Zs2PT29+fu4//OflJTUrcurMTEx6uWXX+5x3+GuzCsDUV697777Dvmd7UnXoifktW2pzDywjHPTTTepyMhINXny5G5xDG0VDHm13W5XRqNRffnlly2+i2PGjFF33323GjJkiAoLC2v1GGbOnNktyqrB+lu+o7pr3apon45exwsvvFD99a9/PWy+JwKnt9WtBiupWw0+HSmvKtWxck6PHnbe5XKxZs0aZs6c2WL5zJkz+fnnn1tdZ9myZQelP+WUU1i9ejVut7vLYtVDTU0NALGxsQDk5ORQUlLS4vitVivTp08/5PkKhI5cxwN5vV7q6uqaj9Vfrr/+embNmsVJJ53UYnl3PZd6OtJ1/Pzzz5k4cSLnn38+iYmJjBs3jv/+97+8+uqr7Nq1i8svv5z6+nrCw8Ob1+3t5/RwjjnmGL7//nuys7MB2LBhA0uWLOH0008HOv4Z7cj3cf+1feyxx0hLS2Pw4MH85S9/obGxsbOHqYu2nLs1a9bgdrtbpElNTWXkyJHd4vPakes4depU9uzZw7x581BKUVpayocffsisWbMCEbJop9by+IaGBk488cTmNFarlTFjxpCbmxt0ZRx/CUQ+3xvKq4fK4/cLhnJTIMurXZXHi1+uo91ub3H+Tj31VMLDww+Zz7/66qsUFRUxcuRICgoKdIm9tzrUb9qioqLm67P/8x8TE9Mty6sej4f33nuPhoYGpkyZ0uO+w12VVwayvLpjxw5SU1Pp168fF110Ebt37/bLMQRSsOS1vy7juFwu3nrrLc444wzWrFlDdnZ2jzgGCI68uqmpCY/Hg9FobPFdDAkJYcmSJRx99NE0NDS0egzr16/vlmXVYPgt31HduW5VtF1Hr+P+utX77ruvq0MUbSB1q8FB6lZ7L3/VyZn8HVgglZeX4/F4SEpKarE8KSmJkpKSVtcpKSlpNX1TUxPl5eWkpKR0WbyBpJTilltu4ZhjjmHkyJEAzeektePPy8sLeIz7deQ6Huhf//oXDQ0NXHDBBX6L67333mPt2rWsWrXqoPe667nU05GuY15eHs8//zy33HILd911FytXruTGG2/EYrGwevVqysvLATCbzQet31vP6eHcfvvt1NTUMHToUIxGIx6Ph4cffpiLL74Y6PhntCPfx927d7NkyRJsNhuffPIJ5eXlXHfddVRWVvbIuYnacu5KSkqwWCzExMQclKat/7e6Ukeu49SpU3n77be58MILcTgcNDU1cdZZZ/Gf//wnECGLdjhcHp+ZmdkibUpKCkqpoCrj+Eug8vneUF7dvXv3QXn8n//8Z6xWK5dffnlQlJsCWV7tqjxe+K7j/vl4f33+9n+/9p/bX+fzO3bs4I477mDx4sXccMMNMl9fAB0uv/N6vQddw61bt3ar8uqmTZuYMmUKDoeD8PBwPvnkE4YPH95cWdcTvsNdmVcGqrw6efJk3njjDQYPHkxpaSkPPfQQU6dOZcuWLT3q/2mw5LW/LuN8+umnVFdXc8EFF/DOO++wfft2oPsfAwRHXh0REcGUKVN48MEH8Xg8xMfH89Zbb7FixQoGDRrEkCFDgNaPob6+vluWVYPht3xHdde6VdE+HbmOvy6rmkw9uqknaEjdanCQutXey191ckHxH1nTtBavlVIHLTtS+taW92Q33HADGzduZMmSJQe9197zFSgdjevdd9/l/vvv57PPPiMxMdEvsRQUFHDTTTcxf/58bDbbIdN113Opp0OdE6/Xy8SJE3nkkUcAGD16NPfeey9hYWEMHjy4ufH9QHJOWzd37lzeeust3nnnHUaMGMH69euZM2cOqampXHHFFc3pOvoZbc96Xq8XTdN4++23iYqKAuCJJ57gt7/9Lc8++ywhISHtPbxuoSPnrrt9XttzDFu3buXPf/4z9957L6eccgrFxcXceuutXHvttfzvf/8LRLiijdqbx7e2vLfTI58P5vLqgXn8uHHj2LJlC88//zyXX355c7pgKDcForza1Xm88Pn1uTrU98vj8XDJJZfwwAMPMHjwYDnHAXa4/A4OvoaapnWr8uqQIUNYv3491dXVfPTRR1xxxRUsWrSo1fh/fQzdRaDyyq4ur5522mnNz0eNGsWUKVMYMGAAr7/+OkcffbRfjiEQgimv3R/P//73P0477TTi4+NbLO8JxxAsefWbb77J7NmzATj++OMZP348l1xyCWvXrm1O09YyaXcqqwbDb/mO6k51q6Lj2nodDyyriu5F6laDg9St9k7+KOf06GHn4+PjMRqNB91pUlZWdtCdCfslJye3mt5kMhEXF9dlsQbSjTfeyOeff86PP/5Ienp68/Lk5GSAdp2vQOjIddxv7ty5XHXVVbz//vsHDYPXGWvWrKGsrIwJEyZgMpkwmUwsWrSIp59+GpPJ1BxXdzuXejrSdUxJSWH48OHNy+vq6igrKyMnJweTycSxxx4L+DIpk8nEDz/80GJ90dKtt97KHXfcwUUXXcSoUaO47LLLuPnmm3n00UeBjn/fO/J9TElJIS0trblwCDBs2DCUUuzZs6dDx6entpy75ORkXC4XVVVVh0yjp45cx0cffZRp06Zx6623Mnr0aE455RSee+45XnnlFYqLiwMRtmiDI+Xxubm5LdKXlJSgaVrQlHH8JZD5fG8orx6Yx4MvH8jPzwe6bxm0PQJZXu2qPF74rqPB4PsJ/Ovzt//7dWA+X1BQwOrVq7nhhhswmUx89913rFmzhg0bNrQorwr/O1J+ZzAYDrqGRqOxW5VXLRYLAwcOZOLEiTz66KOMGTOGp556qsd8h7s6r9SrvBoWFsaoUaPYsWNHj7kWEDx57f4yTl5eHt999x1XX3118//g/Q1H3f0YIHjy6gEDBrBo0SIMBgMvvvgiK1euxO12069fP5xOJ9D6MYSHh3fLsmow/JbvqO5Ytyrar73Xsa6urkVZ1WQy8eCDD0pZVWdStxocpG619/JXnVyPbny3WCxMmDCBBQsWtFi+YMECpk6d2uo6U6ZMOSj9/PnzmThx4kHDXfc0SiluuOEGPv74Y3744Qf69evX4v1+/fqRnJzc4vhdLheLFi065PkKhI5cR/DdlXnllVfyzjvv+H3ejBNPPJFNmzaxfv365sfEiRO59NJLWb9+Pf379++W51JPR7qO06ZNax5GDiAyMpLZs2czZsyY5nMcGhpKfHw869evZ/Lkyb3+nB6O3W5vrjzez2g0Ng+l2tHve0e+j9OmTaOoqIj6+vrmZdnZ2RgMhhaVpT1FW87dhAkTMJvNLdIUFxezefPmbvF57ch1PNRnCn65u0/o66WXXjpsHh8WFtbix7XL5WLDhg307du3x5dx/C2Q+XxvKK8emMeDLx/YPw1Cdy2Dtkcgy6tdlceLX65jaGhoi/M3f/586uvrD8rnly9f3vy/YsGCBRgMBmbNmtXco3ny5Ml6HUrQautv2rS0tOZruP/zX11d3a3Lq0opnE5nj/kOd3VeqVd51el0kpWVRUpKSo+5FhA8ee3+Ms6rr75KYmIis2bNai7jDB48uEccAwRXXm2xWJg4cSJr1qyhqqqKb7/9lrPPPpsVK1YQFhbW6jGMHTu2W5ZVg+G3fEd1x7pV0X7tvY6RkZEH5dXXXnutlFV1JnWrwUHqVnsvv9XJqR7uvffeU2azWf3vf/9TW7duVXPmzFFhYWEqNzdXKaXUHXfcoS677LLm9Lt371ahoaHq5ptvVlu3blX/+9//lNlsVh9++KFeh+A3f/rTn1RUVJRauHChKi4ubn7Y7fbmNH//+99VVFSU+vjjj9WmTZvUxRdfrFJSUlRtba2Okbf/Or7zzjvKZDKpZ599tsWxVldXd1mM06dPVzfddFPz6+56LvV0uOu4cuVKZTAY1NixY9WOHTvU22+/rUJDQ9Vbb73VvP6JJ56oDAaDnNM2uOKKK1RaWpr68ssvVU5Ojvr4449VfHy8uu2225rTdPQz2t7vY11dnUpPT1e//e1v1ZYtW9SiRYvUoEGD1NVXX901B+8HdXV1at26dWrdunUKUE888YRat26dysvLU0q17dxde+21Kj09XX333Xdq7dq16oQTTlBjxoxRTU1Neh1WC+29jq+++qoymUzqueeeU7t27VJLlixREydOVEcddZReh9DrHfg5tdls6r///a9atWqVKi4uVjfccIO6+OKLm9PfdtttClBnnnmm+uyzz9TkyZMVoN58800dj6Ln6Mp8PtjLqytXrlQmk0k9/PDDh8zjg6HcFKjyalfm8b3F4fL59957TxkMBhUSEqKefvppNXv2bGU0GlViYqKqra1tvo6HyufvueceNWbMGL0PMWi19TdtaGioMhqN6oEHHlCzZs1SYWFhKjQ0tNuUV++88071008/qZycHLVx40Z11113KYPBoObPn998DD3xO+zvvDIQ5dX/+7//UwsXLlS7d+9Wy5cvV2eccYaKiIho3kdPuRbdNa890u+q1so4ISEhKiIiQl199dUHlXF6yvUIlrz6m2++UV9//bV6+umnldFoVH369FGjRo1SN954owoLC1O33367ioqKUueee64644wzmo9h48aNupVVe8Nv+Y7qCXWr4sjaex0PdN9990lZtRvoDXWrvYHUrQaHjpRX/VHO6fGN70op9eyzz6rMzExlsVjU+PHj1aJFi5rfu+KKK9T06dNbpF+4cKEaN26cslgsqm/fvur5558PcMRdA2j18eqrrzan8Xq96r777lPJycnKarWq4447Tm3atEm/oH+lPddx+vTprR7rFVdc0WXxHVjR0J3PpZ4Odx1POOEEFRYWpqxWqxo6dKh66aWXWqx77733qqSkJDmnbVBbW6tuuukmlZGRoWw2m+rfv7+6++67ldPpbE7Tmc9oe/+vZmVlqZNOOkmFhISo9PR0dcstt7SoJO1ufvzxx8P+D2nLuWtsbFQ33HCDio2NVSEhIeqMM85Q+fn5OhzNobX3Oj799NNq+PDhKiQkRKWkpKhLL71U7dmzJ8BRi/0O9Tn99WPIkCHN6b1er7riiiuUyWRSgLJareqvf/2rjkfQs3R1Ph/s5dUvvvhCjRw58pB5fLCUmwJRXu3qPL43OFI+/8wzz6ioqCgFKE3T1JgxY5rP3/7reKh8Xio0u1Z7ftNGRkY2X8Nhw4Z1q/Lq73//++b/FQkJCerEE09sbnj/9TH0tO9wV+SVXV1evfDCC1VKSooym80qNTVVnXfeeWrLli1+PYZA6Y557ZH+37Z2DR9//HEFKLPZfFAZp6dcj2DJq+fOnav69++vLBaLioyMVBEREcpsNjd/F/cfQ0hIiNI0rcUx6FVW7S2/5Tuqu9etirZpb974a1JW7T6CvW61t5C61Z6vI+VVf5RzNKVkvAMhhBBCCCGEEEIIIYQQQgghhBCiM3r0nO9CCCGEEEIIIYQQQgghhBBCCCFEdyCN70IIIYQQQgghhBBCCCGEEEIIIUQnSeO7EEIIIYQQQgghhBBCCCGEEEII0UnS+C6EEEIIIYQQQgghhBBCCCGEEEJ0kjS+CyGEEEIIIYQQQgghhBBCCCGEEJ0kje9CCCGEEEIIIYQQQgghhBBCCCFEJ0njuxBCCCGEEEIIIYQQQgghhBBCCNFJ0vguhBBCCCGEEEIIIYQQQgghhBBCdJI0vgshhBBCCCGEEEIIIYQQQgghhBCdJI3vQgghhBBCCCGEEEIIIYQQQgghRCdJ47sQol08Hg9er1fvMIQQQgjhZ5LHCyGEEMFL8nkhhBBCCCECQxrfhejhvvnmG4455hiio6OJi4vjjDPOYNeuXQBMmTKFO+64o0X6vXv3Yjab+fHHHwFwuVzcdtttpKWlERYWxuTJk1m4cGFz+tdee43o6Gi+/PJLhg8fjtVqJS8vj1WrVnHyyScTHx9PVFQU06dPZ+3atS32tW3bNo455hhsNhvDhw/nu+++Q9M0Pv300+Y0hYWFXHjhhcTExBAXF8fZZ59Nbm5ul5wrIYQQoieRPF4IIYQIXpLPCyGEEEIIEZyk8V2IHq6hoYFbbrmFVatW8f3332MwGDj33HPxer1ceumlvPvuuyilmtPPnTuXpKQkpk+fDsDvfvc7li5dynvvvcfGjRs5//zzOfXUU9mxY0fzOna7nUcffZSXX36ZLVu2kJiYSF1dHVdccQWLFy9m+fLlDBo0iNNPP526ujoAvF4v55xzDqGhoaxYsYKXXnqJu+++u0Xsdrud448/nvDwcH766SeWLFlCeHg4p556Ki6XKwBnTwghhOi+JI8XQgghgpfk80IIIYQQQgQpJYQIKmVlZQpQmzZtUmVlZcpkMqmffvqp+f0pU6aoW2+9VSml1M6dO5WmaaqwsLDFNk488UR15513KqWUevXVVxWg1q9ff9j9/n97d/di49rHAfzLyDaM0WCG1Iy8DSIaopQ540R5yaHVICmOTCgn+AMkpZRCOJCJE6OUl5RiSNIoTiY1mhpK3nJAoZh7Hzy1euZRu73Nfppm+Xxq1arfdd/X1XXyrfW772t9//69mDhxYnHt2rWiKIrixo0bxZgxY4rXr1+Xx9y+fbtIUnR2dhZFURRnz54t5s+fXwwMDJTHfPv2raiuri5u3br165sAABVIxgNA5ZLzAABQGbz5DiPcixcvsmXLlsyePTu1tbWZNWtWkqS/vz/19fVZu3ZtLl68mCTp6+vLw4cPUyqVkiRPnjxJURRpbm5OTU1N+XP37t3ycXdJMnbs2CxZsmTQvG/fvs3u3bvT3NycSZMmZdKkSfn8+XP6+/uTJM+fP09jY2OmT59evmblypWD7tHd3Z3e3t5MnDixPPfkyZPz9evXQfMDwO9IxgNA5ZLzAABQmcYM9wKAoVm/fn0aGxtz5syZzJgxIwMDA1m8eHH5qLdSqZT29vacOHEiHR0dWbRoUZYuXZrkP8fJVVVVpbu7O1VVVYPuW1NTU/5eXV2dUaNGDapv37497969y/HjxzNz5sz88ccfWbVqVXneoih+uuZ/DQwMZPny5eUfFP5bfX39P98MAKggMh4AKpecBwCAyqT5DiPYhw8f0tPTk1OnTqW1tTVJcv/+/UFjNm3alF27duXmzZvp6OhIW1tbudbS0pIfP37k7du35ev/rq6urpw8eTLr1q1Lkrx8+TLv378v1xcsWJD+/v68efMm06ZNS5I8fvx40D2WLVuWy5cvp6GhIbW1tf9ofgCoZDIeACqXnAcAgMrl2HkYwerq6jJlypScPn06vb29uXPnTvbt2zdozIQJE7Jx48YcPnw4PT092bJlS7nW3NycUqmUrVu35sqVK+nr68vjx49z5MiRXL9+/S/nnjt3bi5cuJCenp48evQopVIp1dXV5fratWszZ86cbNu2Lc+ePcuDBw9y8ODBJCk/RV8qlTJ16tRs3LgxXV1d6evry927d9Pe3p5Xr179W9sEACOOjAeAyiXnAQCgcmm+wwg2evToXLp0Kd3d3Vm8eHH27t2bo0eP/jSuVCrl6dOnaW1tTVNT06Da+fPns3Xr1uzfvz/z58/Phg0b8ujRozQ2Nv7l3OfOncvHjx/T0tKStra27NmzJw0NDeV6VVVVrl69ms+fP2fFihXZuXNnDh06lCQZN25ckmT8+PG5d+9empqasnnz5ixcuDA7duzIly9fPD0PwG9NxgNA5ZLzAABQuUYVRVEM9yKA38ODBw+yevXq9Pb2Zs6cOcO9HADgXyLjAaByyXkAAPj7NN+B/5vOzs7U1NRk3rx56e3tTXt7e+rq6n76LzsAYGSR8QBQueQ8AAD8ujHDvQCgcn369CkHDhzIy5cvM3Xq1KxZsybHjh0b7mUBAEMk4wGgcsl5AAD4dd58BwAAAAAAAIAhGj3cCwAAAAAAAACAkU7zHQAAAAAAAACGSPMdAAAAAAAAAIZI8x0AAAAAAAAAhkjzHQAAAAAAAACGSPMdAAAAAAAAAIZI8x0AAAAAAAAAhkjzHQAAAAAAAACG6E9wTo9aOxkc/AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2500x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,3,figsize=(25,6))\n",
    "plt.subplot(141)\n",
    "sns.histplot(data=df,x='average',kde=True,hue='lunch')\n",
    "plt.subplot(142)\n",
    "sns.histplot(data=df[df.gender=='female'],x='average',kde=True,hue='lunch')\n",
    "plt.subplot(143)\n",
    "sns.histplot(data=df[df.gender=='male'],x='average',kde=True,hue='lunch')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "569113e7",
   "metadata": {},
   "source": [
    "#####  Insights\n",
    "- Standard lunch helps perform well in exams.\n",
    "- Standard lunch helps perform well in exams be it a male or a female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0b6c697a",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interpret value `parental level of education` for parameter `hue`",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[14], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m3\u001b[39m,figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m25\u001b[39m,\u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39msubplot(\u001b[38;5;241m141\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m ax \u001b[38;5;241m=\u001b[39msns\u001b[38;5;241m.\u001b[39mhistplot(data\u001b[38;5;241m=\u001b[39mdf,x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maverage\u001b[39m\u001b[38;5;124m'\u001b[39m,kde\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparental level of education\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39msubplot(\u001b[38;5;241m142\u001b[39m)\n\u001b[1;32m      5\u001b[0m ax \u001b[38;5;241m=\u001b[39msns\u001b[38;5;241m.\u001b[39mhistplot(data\u001b[38;5;241m=\u001b[39mdf[df\u001b[38;5;241m.\u001b[39mgender\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmale\u001b[39m\u001b[38;5;124m'\u001b[39m],x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maverage\u001b[39m\u001b[38;5;124m'\u001b[39m,kde\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparental level of education\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/seaborn/distributions.py:1395\u001b[0m, in \u001b[0;36mhistplot\u001b[0;34m(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   1374\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhistplot\u001b[39m(\n\u001b[1;32m   1375\u001b[0m     data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m   1376\u001b[0m     \u001b[38;5;66;03m# Vector variables\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1392\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   1393\u001b[0m ):\n\u001b[0;32m-> 1395\u001b[0m     p \u001b[38;5;241m=\u001b[39m _DistributionPlotter(\n\u001b[1;32m   1396\u001b[0m         data\u001b[38;5;241m=\u001b[39mdata,\n\u001b[1;32m   1397\u001b[0m         variables\u001b[38;5;241m=\u001b[39m_DistributionPlotter\u001b[38;5;241m.\u001b[39mget_semantics(\u001b[38;5;28mlocals\u001b[39m())\n\u001b[1;32m   1398\u001b[0m     )\n\u001b[1;32m   1400\u001b[0m     p\u001b[38;5;241m.\u001b[39mmap_hue(palette\u001b[38;5;241m=\u001b[39mpalette, order\u001b[38;5;241m=\u001b[39mhue_order, norm\u001b[38;5;241m=\u001b[39mhue_norm)\n\u001b[1;32m   1402\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/seaborn/distributions.py:113\u001b[0m, in \u001b[0;36m_DistributionPlotter.__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m    108\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    109\u001b[0m     data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    110\u001b[0m     variables\u001b[38;5;241m=\u001b[39m{},\n\u001b[1;32m    111\u001b[0m ):\n\u001b[0;32m--> 113\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(data\u001b[38;5;241m=\u001b[39mdata, variables\u001b[38;5;241m=\u001b[39mvariables)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:640\u001b[0m, in \u001b[0;36mVectorPlotter.__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    635\u001b[0m \u001b[38;5;66;03m# var_ordered is relevant only for categorical axis variables, and may\u001b[39;00m\n\u001b[1;32m    636\u001b[0m \u001b[38;5;66;03m# be better handled by an internal axis information object that tracks\u001b[39;00m\n\u001b[1;32m    637\u001b[0m \u001b[38;5;66;03m# such information and is set up by the scale_* methods. The analogous\u001b[39;00m\n\u001b[1;32m    638\u001b[0m \u001b[38;5;66;03m# information for numeric axes would be information about log scales.\u001b[39;00m\n\u001b[1;32m    639\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_var_ordered \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m}  \u001b[38;5;66;03m# alt., used DefaultDict\u001b[39;00m\n\u001b[0;32m--> 640\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39massign_variables(data, variables)\n\u001b[1;32m    642\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var, \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_semantic_mappings\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m    643\u001b[0m \n\u001b[1;32m    644\u001b[0m     \u001b[38;5;66;03m# Create the mapping function\u001b[39;00m\n\u001b[1;32m    645\u001b[0m     map_func \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mmap, plotter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:701\u001b[0m, in \u001b[0;36mVectorPlotter.assign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    699\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    700\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_format \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlong\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 701\u001b[0m     plot_data, variables \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_assign_variables_longform(\n\u001b[1;32m    702\u001b[0m         data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mvariables,\n\u001b[1;32m    703\u001b[0m     )\n\u001b[1;32m    705\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mplot_data \u001b[38;5;241m=\u001b[39m plot_data\n\u001b[1;32m    706\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables \u001b[38;5;241m=\u001b[39m variables\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:938\u001b[0m, in \u001b[0;36mVectorPlotter._assign_variables_longform\u001b[0;34m(self, data, **kwargs)\u001b[0m\n\u001b[1;32m    933\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, (\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m)):\n\u001b[1;32m    934\u001b[0m \n\u001b[1;32m    935\u001b[0m     \u001b[38;5;66;03m# This looks like a column name but we don't know what it means!\u001b[39;00m\n\u001b[1;32m    937\u001b[0m     err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not interpret value `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` for parameter `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 938\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[1;32m    940\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    941\u001b[0m \n\u001b[1;32m    942\u001b[0m     \u001b[38;5;66;03m# Otherwise, assume the value is itself data\u001b[39;00m\n\u001b[1;32m    943\u001b[0m \n\u001b[1;32m    944\u001b[0m     \u001b[38;5;66;03m# Raise when data object is present and a vector can't matched\u001b[39;00m\n\u001b[1;32m    945\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, pd\u001b[38;5;241m.\u001b[39mDataFrame) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, pd\u001b[38;5;241m.\u001b[39mSeries):\n",
      "\u001b[0;31mValueError\u001b[0m: Could not interpret value `parental level of education` for parameter `hue`"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2500x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,3,figsize=(25,6))\n",
    "plt.subplot(141)\n",
    "ax =sns.histplot(data=df,x='average',kde=True,hue='parental level of education')\n",
    "plt.subplot(142)\n",
    "ax =sns.histplot(data=df[df.gender=='male'],x='average',kde=True,hue='parental level of education')\n",
    "plt.subplot(143)\n",
    "ax =sns.histplot(data=df[df.gender=='female'],x='average',kde=True,hue='parental level of education')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e7fd489",
   "metadata": {},
   "source": [
    "#####  Insights\n",
    "- In general parent's education don't help student perform well in exam.\n",
    "- 2nd plot shows that parent's whose education is of associate's degree or master's degree their male child tend to perform well in exam\n",
    "- 3rd plot we can see there is no effect of parent's education on female students."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b30cbd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplots(1,3,figsize=(25,6))\n",
    "plt.subplot(141)\n",
    "ax =sns.histplot(data=df,x='average',kde=True,hue='race/ethnicity')\n",
    "plt.subplot(142)\n",
    "ax =sns.histplot(data=df[df.gender=='female'],x='average',kde=True,hue='race/ethnicity')\n",
    "plt.subplot(143)\n",
    "ax =sns.histplot(data=df[df.gender=='male'],x='average',kde=True,hue='race/ethnicity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6180a334",
   "metadata": {},
   "source": [
    "#####  Insights\n",
    "- Students of group A and group B tends to perform poorly in exam.\n",
    "- Students of group A and group B tends to perform poorly in exam irrespective of whether they are male or female"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1f7eef3",
   "metadata": {},
   "source": [
    "#### 4.2 Maximumum score of students in all three subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14db115f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.figure(figsize=(18,8))\n",
    "plt.subplot(1, 4, 1)\n",
    "plt.title('MATH SCORES')\n",
    "sns.violinplot(y='math score',data=df,color='red',linewidth=3)\n",
    "plt.subplot(1, 4, 2)\n",
    "plt.title('READING SCORES')\n",
    "sns.violinplot(y='reading score',data=df,color='green',linewidth=3)\n",
    "plt.subplot(1, 4, 3)\n",
    "plt.title('WRITING SCORES')\n",
    "sns.violinplot(y='writing score',data=df,color='blue',linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66d1041f",
   "metadata": {},
   "source": [
    "#### Insights\n",
    "- From the above three plots its clearly visible that most of the students score in between 60-80 in Maths whereas in reading and writing most of them score from 50-80"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae77a33d",
   "metadata": {},
   "source": [
    "#### 4.3 Multivariate analysis using pieplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2ddf9ce3",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'race/ethnicity'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:3791\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3790\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3791\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3792\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mindex.pyx:152\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mindex.pyx:181\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'race/ethnicity'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 16\u001b[0m\n\u001b[1;32m     11\u001b[0m plt\u001b[38;5;241m.\u001b[39maxis(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moff\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     15\u001b[0m plt\u001b[38;5;241m.\u001b[39msubplot(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m---> 16\u001b[0m size \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrace/ethnicity\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue_counts()\n\u001b[1;32m     17\u001b[0m labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGroup C\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGroup D\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGroup B\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGroup E\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGroup A\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     18\u001b[0m color \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgreen\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcyan\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124morange\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/frame.py:3893\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3891\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   3892\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3893\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[1;32m   3894\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3895\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/base.py:3798\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3793\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3794\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3795\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3796\u001b[0m     ):\n\u001b[1;32m   3797\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3798\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3799\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3800\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3801\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3802\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3803\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'race/ethnicity'"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (30, 12)\n",
    "\n",
    "plt.subplot(1, 5, 1)\n",
    "size = df['gender'].value_counts()\n",
    "labels = 'Female', 'Male'\n",
    "color = ['red','green']\n",
    "\n",
    "\n",
    "plt.pie(size, colors = color, labels = labels,autopct = '.%2f%%')\n",
    "plt.title('Gender', fontsize = 20)\n",
    "plt.axis('off')\n",
    "\n",
    "\n",
    "\n",
    "plt.subplot(1, 5, 2)\n",
    "size = df['race/ethnicity'].value_counts()\n",
    "labels = 'Group C', 'Group D','Group B','Group E','Group A'\n",
    "color = ['red', 'green', 'blue', 'cyan','orange']\n",
    "\n",
    "plt.pie(size, colors = color,labels = labels,autopct = '.%2f%%')\n",
    "plt.title('Race/Ethnicity', fontsize = 20)\n",
    "plt.axis('off')\n",
    "\n",
    "\n",
    "\n",
    "plt.subplot(1, 5, 3)\n",
    "size = df['lunch'].value_counts()\n",
    "labels = 'Standard', 'Free'\n",
    "color = ['red','green']\n",
    "\n",
    "plt.pie(size, colors = color,labels = labels,autopct = '.%2f%%')\n",
    "plt.title('Lunch', fontsize = 20)\n",
    "plt.axis('off')\n",
    "\n",
    "\n",
    "plt.subplot(1, 5, 4)\n",
    "size = df['test preparation course'].value_counts()\n",
    "labels = 'None', 'Completed'\n",
    "color = ['red','green']\n",
    "\n",
    "plt.pie(size, colors = color,labels = labels,autopct = '.%2f%%')\n",
    "plt.title('Test Course', fontsize = 20)\n",
    "plt.axis('off')\n",
    "\n",
    "\n",
    "plt.subplot(1, 5, 5)\n",
    "size = df['parental level of education'].value_counts()\n",
    "labels = 'Some College', \"Associate's Degree\",'High School','Some High School',\"Bachelor's Degree\",\"Master's Degree\"\n",
    "color = ['red', 'green', 'blue', 'cyan','orange','grey']\n",
    "\n",
    "plt.pie(size, colors = color,labels = labels,autopct = '.%2f%%')\n",
    "plt.title('Parental Education', fontsize = 20)\n",
    "plt.axis('off')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2d686a",
   "metadata": {},
   "source": [
    "#####  Insights\n",
    "- Number of Male and Female students is almost equal\n",
    "- Number students are greatest in Group C\n",
    "- Number of students who have standard lunch are greater\n",
    "- Number of students who have not enrolled in any test preparation course is greater\n",
    "- Number of students whose parental education is \"Some College\" is greater followed closely by \"Associate's Degree\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab008237",
   "metadata": {},
   "source": [
    "#### 4.4 Feature Wise Visualization\n",
    "#### 4.4.1 GENDER COLUMN\n",
    "- How is distribution of Gender ?\n",
    "- Is gender has any impact on student's performance ?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a2c8f5",
   "metadata": {},
   "source": [
    "#### UNIVARIATE ANALYSIS ( How is distribution of Gender ? )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c435f53b",
   "metadata": {},
   "outputs": [],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(20,10))\n",
    "sns.countplot(x=df['gender'],data=df,palette ='bright',ax=ax[0],saturation=0.95)\n",
    "for container in ax[0].containers:\n",
    "    ax[0].bar_label(container,color='black',size=20)\n",
    "    \n",
    "plt.pie(x=df['gender'].value_counts(),labels=['Male','Female'],explode=[0,0.1],autopct='%1.1f%%',shadow=True,colors=['#ff4d4d','#ff8000'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfb8c9b2",
   "metadata": {},
   "source": [
    "#### Insights \n",
    "- Gender has balanced data with female students are 518 (48%) and male students are 482 (52%) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e440a3a5",
   "metadata": {},
   "source": [
    "#### BIVARIATE ANALYSIS ( Is gender has any impact on student's performance ? ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "526d49f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_group = df.groupby('gender').mean()\n",
    "gender_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b704f144",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "X = ['Total Average','Math Average']\n",
    "\n",
    "\n",
    "female_scores = [gender_group['average'][0], gender_group['math score'][0]]\n",
    "male_scores = [gender_group['average'][1], gender_group['math score'][1]]\n",
    "\n",
    "X_axis = np.arange(len(X))\n",
    "  \n",
    "plt.bar(X_axis - 0.2, male_scores, 0.4, label = 'Male')\n",
    "plt.bar(X_axis + 0.2, female_scores, 0.4, label = 'Female')\n",
    "  \n",
    "plt.xticks(X_axis, X)\n",
    "plt.ylabel(\"Marks\")\n",
    "plt.title(\"Total average v/s Math average marks of both the genders\", fontweight='bold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72fbab62",
   "metadata": {},
   "source": [
    "#### Insights \n",
    "- On an average females have a better overall score than men.\n",
    "- whereas males have scored higher in Maths."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a903c5c",
   "metadata": {},
   "source": [
    "#### 4.4.2 RACE/EHNICITY COLUMN\n",
    "- How is Group wise distribution ?\n",
    "- Is Race/Ehnicity has any impact on student's performance ?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69fe557f",
   "metadata": {},
   "source": [
    "#### UNIVARIATE ANALYSIS ( How is Group wise distribution ?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acbc5c8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(20,10))\n",
    "sns.countplot(x=df['race/ethnicity'],data=df,palette = 'bright',ax=ax[0],saturation=0.95)\n",
    "for container in ax[0].containers:\n",
    "    ax[0].bar_label(container,color='black',size=20)\n",
    "    \n",
    "plt.pie(x = df['race/ethnicity'].value_counts(),labels=df['race/ethnicity'].value_counts().index,explode=[0.1,0,0,0,0],autopct='%1.1f%%',shadow=True)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1762646a",
   "metadata": {},
   "source": [
    "#### Insights \n",
    "- Most of the student belonging from group C /group D.\n",
    "- Lowest number of students belong to groupA."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d3a3719",
   "metadata": {},
   "source": [
    "#### BIVARIATE ANALYSIS ( Is Race/Ehnicity has any impact on student's performance ? )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db261c61",
   "metadata": {},
   "outputs": [],
   "source": [
    "Group_data2=df.groupby('race/ethnicity')\n",
    "f,ax=plt.subplots(1,3,figsize=(20,8))\n",
    "sns.barplot(x=Group_data2['math score'].mean().index,y=Group_data2['math score'].mean().values,palette = 'mako',ax=ax[0])\n",
    "ax[0].set_title('Math score',color='#005ce6',size=20)\n",
    "\n",
    "for container in ax[0].containers:\n",
    "    ax[0].bar_label(container,color='black',size=15)\n",
    "\n",
    "sns.barplot(x=Group_data2['reading score'].mean().index,y=Group_data2['reading score'].mean().values,palette = 'flare',ax=ax[1])\n",
    "ax[1].set_title('Reading score',color='#005ce6',size=20)\n",
    "\n",
    "for container in ax[1].containers:\n",
    "    ax[1].bar_label(container,color='black',size=15)\n",
    "\n",
    "sns.barplot(x=Group_data2['writing score'].mean().index,y=Group_data2['writing score'].mean().values,palette = 'coolwarm',ax=ax[2])\n",
    "ax[2].set_title('Writing score',color='#005ce6',size=20)\n",
    "\n",
    "for container in ax[2].containers:\n",
    "    ax[2].bar_label(container,color='black',size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e292ddd",
   "metadata": {},
   "source": [
    "#### Insights \n",
    "- Group E students have scored the highest marks. \n",
    "- Group A students have scored the lowest marks. \n",
    "- Students from a lower Socioeconomic status have a lower avg in all course subjects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1409042e",
   "metadata": {},
   "source": [
    "#### 4.4.3 PARENTAL LEVEL OF EDUCATION COLUMN\n",
    "- What is educational background of student's parent ?\n",
    "- Is parental education has any impact on student's performance ?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38aca4fc",
   "metadata": {},
   "source": [
    "#### UNIVARIATE ANALYSIS ( What is educational background of student's parent ? )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c05ab987",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 9)\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.countplot(df['parental level of education'], palette = 'Blues')\n",
    "plt.title('Comparison of Parental Education', fontweight = 30, fontsize = 20)\n",
    "plt.xlabel('Degree')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d351e1d",
   "metadata": {},
   "source": [
    "#### Insights \n",
    "- Largest number of parents are from some college."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f38ab41",
   "metadata": {},
   "source": [
    "#### BIVARIATE ANALYSIS ( Is parental education has any impact on student's performance ? )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "122b2581",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby('parental level of education').agg('mean').plot(kind='barh',figsize=(10,10))\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b3999d",
   "metadata": {},
   "source": [
    "#### Insights \n",
    "- The score of student whose parents possess master and bachelor level education are higher than others."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "079f4f29",
   "metadata": {},
   "source": [
    "#### 4.4.4 LUNCH COLUMN \n",
    "- Which type of lunch is most common amoung students ?\n",
    "- What is the effect of lunch type on test results?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8584c755",
   "metadata": {},
   "source": [
    "#### UNIVARIATE ANALYSIS ( Which type of lunch is most common amoung students ? )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3a277e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 9)\n",
    "plt.style.use('seaborn-talk')\n",
    "sns.countplot(df['lunch'], palette = 'PuBu')\n",
    "plt.title('Comparison of different types of lunch', fontweight = 30, fontsize = 20)\n",
    "plt.xlabel('types of lunch')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "332b0c50",
   "metadata": {},
   "source": [
    "#### Insights \n",
    "- Students being served Standard lunch was more than free lunch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d75db26f",
   "metadata": {},
   "source": [
    "#### BIVARIATE ANALYSIS (  Is lunch type intake has any impact on student's performance ? )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "666babd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(20,8))\n",
    "sns.countplot(x=df['parental level of education'],data=df,palette = 'bright',hue='test preparation course',saturation=0.95,ax=ax[0])\n",
    "ax[0].set_title('Students vs test preparation course ',color='black',size=25)\n",
    "for container in ax[0].containers:\n",
    "    ax[0].bar_label(container,color='black',size=20)\n",
    "    \n",
    "sns.countplot(x=df['parental level of education'],data=df,palette = 'bright',hue='lunch',saturation=0.95,ax=ax[1])\n",
    "for container in ax[1].containers:\n",
    "    ax[1].bar_label(container,color='black',size=20)   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0677b04c",
   "metadata": {},
   "source": [
    "#### Insights \n",
    "- Students who get Standard Lunch tend to perform better than students who got free/reduced lunch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edd0ec29",
   "metadata": {},
   "source": [
    "#### 4.4.5 TEST PREPARATION COURSE COLUMN \n",
    "- Which type of lunch is most common amoung students ?\n",
    "- Is Test prepration course has any impact on student's performance ?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf8f65bd",
   "metadata": {},
   "source": [
    "#### BIVARIATE ANALYSIS ( Is Test prepration course has any impact on student's performance ? )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b08ed26",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.subplot(2,2,1)\n",
    "sns.barplot (x=df['lunch'], y=df['math score'], hue=df['test preparation course'])\n",
    "plt.subplot(2,2,2)\n",
    "sns.barplot (x=df['lunch'], y=df['reading score'], hue=df['test preparation course'])\n",
    "plt.subplot(2,2,3)\n",
    "sns.barplot (x=df['lunch'], y=df['writing score'], hue=df['test preparation course'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bab116e",
   "metadata": {},
   "source": [
    "#### Insights  \n",
    "- Students who have completed the Test Prepration Course have scores higher in all three categories than those who haven't taken the course"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4069d6e6",
   "metadata": {},
   "source": [
    "#### 4.4.6 CHECKING OUTLIERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62a813a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplots(1,4,figsize=(16,5))\n",
    "plt.subplot(141)\n",
    "sns.boxplot(df['math score'],color='skyblue')\n",
    "plt.subplot(142)\n",
    "sns.boxplot(df['reading score'],color='hotpink')\n",
    "plt.subplot(143)\n",
    "sns.boxplot(df['writing score'],color='yellow')\n",
    "plt.subplot(144)\n",
    "sns.boxplot(df['average'],color='lightgreen')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957e8bef",
   "metadata": {},
   "source": [
    "#### 4.4.7 MUTIVARIATE ANALYSIS USING PAIRPLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f09f746c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.pairplot(df,hue = 'gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7991322",
   "metadata": {},
   "source": [
    "#### Insights\n",
    "- From the above plot it is clear that all the scores increase linearly with each other."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e20716",
   "metadata": {},
   "source": [
    "### 5. Conclusions\n",
    "- Student's Performance is related with lunch, race, parental level education\n",
    "- Females lead in pass percentage and also are top-scorers\n",
    "- Student's Performance is not much related with test preparation course\n",
    "- Finishing preparation course is benefitial."
   ]
  }
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