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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Higher Education Vs Salary Of Developers: EDA and Analysis\n",
+    "    Stack_Overflow 2018 Dataset\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "This feature aims to analyze the correlation between higher education levels and the salaries of developers surveyed in 2018. The analysis will help understand how different educational qualifications influence earning potential in the developer community."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing modules\n",
+    "import numpy as np                                  \n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>Respondent</th>\n",
+       "      <th>Hobby</th>\n",
+       "      <th>OpenSource</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Student</th>\n",
+       "      <th>Employment</th>\n",
+       "      <th>FormalEducation</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>CompanySize</th>\n",
+       "      <th>DevType</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Exercise</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>SexualOrientation</th>\n",
+       "      <th>EducationParents</th>\n",
+       "      <th>RaceEthnicity</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>MilitaryUS</th>\n",
+       "      <th>SurveyTooLong</th>\n",
+       "      <th>SurveyEasy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Kenya</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed part-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Mathematics or statistics</td>\n",
+       "      <td>20 to 99 employees</td>\n",
+       "      <td>Full-stack developer</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3 - 4 times per week</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Straight or heterosexual</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Black or of African descent</td>\n",
+       "      <td>25 - 34 years old</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>The survey was an appropriate length</td>\n",
+       "      <td>Very easy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
+       "      <td>10,000 or more employees</td>\n",
+       "      <td>Database administrator;DevOps specialist;Full-...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Daily or almost every day</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Straight or heterosexual</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>The survey was an appropriate length</td>\n",
+       "      <td>Somewhat easy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Associate degree</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>20 to 99 employees</td>\n",
+       "      <td>Engineering manager;Full-stack developer</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>100 to 499 employees</td>\n",
+       "      <td>Full-stack developer</td>\n",
+       "      <td>...</td>\n",
+       "      <td>I don't typically exercise</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Straight or heterosexual</td>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>The survey was an appropriate length</td>\n",
+       "      <td>Somewhat easy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>South Africa</td>\n",
+       "      <td>Yes, part-time</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>10,000 or more employees</td>\n",
+       "      <td>Data or business analyst;Desktop or enterprise...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3 - 4 times per week</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Straight or heterosexual</td>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>18 - 24 years old</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>The survey was an appropriate length</td>\n",
+       "      <td>Somewhat easy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>8</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>10 to 19 employees</td>\n",
+       "      <td>Back-end developer;Database administrator;Fron...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1 - 2 times per week</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Straight or heterosexual</td>\n",
+       "      <td>Secondary school (e.g. American high school, G...</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>18 - 24 years old</td>\n",
+       "      <td>No</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>The survey was an appropriate length</td>\n",
+       "      <td>Somewhat easy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>9</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>10,000 or more employees</td>\n",
+       "      <td>Back-end developer;Front-end developer;Full-st...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>I don't typically exercise</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Straight or heterosexual</td>\n",
+       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>18 - 24 years old</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>The survey was an appropriate length</td>\n",
+       "      <td>Somewhat easy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Nigeria</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>10 to 19 employees</td>\n",
+       "      <td>Designer;Front-end developer;QA or test developer</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1 - 2 times per week</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Primary/elementary school</td>\n",
+       "      <td>Black or of African descent</td>\n",
+       "      <td>25 - 34 years old</td>\n",
+       "      <td>No</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>The survey was too long</td>\n",
+       "      <td>Somewhat difficult</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>11</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>Fine arts or performing arts (ex. graphic desi...</td>\n",
+       "      <td>100 to 499 employees</td>\n",
+       "      <td>Back-end developer;C-suite executive (CEO, CTO...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>I don't typically exercise</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Straight or heterosexual</td>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>White or of European descent</td>\n",
+       "      <td>35 - 44 years old</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>The survey was an appropriate length</td>\n",
+       "      <td>Very easy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>16</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>India</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Employed full-time</td>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>500 to 999 employees</td>\n",
+       "      <td>Designer</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 129 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Respondent Hobby OpenSource         Country         Student  \\\n",
+       "0           1   Yes         No           Kenya              No   \n",
+       "1           3   Yes        Yes  United Kingdom              No   \n",
+       "2           4   Yes        Yes   United States              No   \n",
+       "3           5    No         No   United States              No   \n",
+       "4           7   Yes         No    South Africa  Yes, part-time   \n",
+       "5           8   Yes         No  United Kingdom              No   \n",
+       "6           9   Yes        Yes   United States              No   \n",
+       "7          10   Yes        Yes         Nigeria              No   \n",
+       "8          11   Yes        Yes   United States              No   \n",
+       "9          16    No        Yes           India              No   \n",
+       "\n",
+       "           Employment                                    FormalEducation  \\\n",
+       "0  Employed part-time         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "1  Employed full-time         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "2  Employed full-time                                   Associate degree   \n",
+       "3  Employed full-time         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "4  Employed full-time  Some college/university study without earning ...   \n",
+       "5  Employed full-time         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "6  Employed full-time  Some college/university study without earning ...   \n",
+       "7  Employed full-time         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "8  Employed full-time  Some college/university study without earning ...   \n",
+       "9  Employed full-time         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "\n",
+       "                                      UndergradMajor  \\\n",
+       "0                          Mathematics or statistics   \n",
+       "1  A natural science (ex. biology, chemistry, phy...   \n",
+       "2  Computer science, computer engineering, or sof...   \n",
+       "3  Computer science, computer engineering, or sof...   \n",
+       "4  Computer science, computer engineering, or sof...   \n",
+       "5  Computer science, computer engineering, or sof...   \n",
+       "6  Computer science, computer engineering, or sof...   \n",
+       "7  Computer science, computer engineering, or sof...   \n",
+       "8  Fine arts or performing arts (ex. graphic desi...   \n",
+       "9  Computer science, computer engineering, or sof...   \n",
+       "\n",
+       "                CompanySize  \\\n",
+       "0        20 to 99 employees   \n",
+       "1  10,000 or more employees   \n",
+       "2        20 to 99 employees   \n",
+       "3      100 to 499 employees   \n",
+       "4  10,000 or more employees   \n",
+       "5        10 to 19 employees   \n",
+       "6  10,000 or more employees   \n",
+       "7        10 to 19 employees   \n",
+       "8      100 to 499 employees   \n",
+       "9      500 to 999 employees   \n",
+       "\n",
+       "                                             DevType  ...  \\\n",
+       "0                               Full-stack developer  ...   \n",
+       "1  Database administrator;DevOps specialist;Full-...  ...   \n",
+       "2           Engineering manager;Full-stack developer  ...   \n",
+       "3                               Full-stack developer  ...   \n",
+       "4  Data or business analyst;Desktop or enterprise...  ...   \n",
+       "5  Back-end developer;Database administrator;Fron...  ...   \n",
+       "6  Back-end developer;Front-end developer;Full-st...  ...   \n",
+       "7  Designer;Front-end developer;QA or test developer  ...   \n",
+       "8  Back-end developer;C-suite executive (CEO, CTO...  ...   \n",
+       "9                                           Designer  ...   \n",
+       "\n",
+       "                     Exercise  Gender         SexualOrientation  \\\n",
+       "0        3 - 4 times per week    Male  Straight or heterosexual   \n",
+       "1   Daily or almost every day    Male  Straight or heterosexual   \n",
+       "2                         NaN     NaN                       NaN   \n",
+       "3  I don't typically exercise    Male  Straight or heterosexual   \n",
+       "4        3 - 4 times per week    Male  Straight or heterosexual   \n",
+       "5        1 - 2 times per week    Male  Straight or heterosexual   \n",
+       "6  I don't typically exercise    Male  Straight or heterosexual   \n",
+       "7        1 - 2 times per week  Female                       NaN   \n",
+       "8  I don't typically exercise    Male  Straight or heterosexual   \n",
+       "9                         NaN     NaN                       NaN   \n",
+       "\n",
+       "                                    EducationParents  \\\n",
+       "0         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "1         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "2                                                NaN   \n",
+       "3  Some college/university study without earning ...   \n",
+       "4  Some college/university study without earning ...   \n",
+       "5  Secondary school (e.g. American high school, G...   \n",
+       "6      Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
+       "7                          Primary/elementary school   \n",
+       "8  Some college/university study without earning ...   \n",
+       "9                                                NaN   \n",
+       "\n",
+       "                  RaceEthnicity                Age Dependents  MilitaryUS  \\\n",
+       "0   Black or of African descent  25 - 34 years old        Yes         NaN   \n",
+       "1  White or of European descent  35 - 44 years old        Yes         NaN   \n",
+       "2                           NaN                NaN        NaN         NaN   \n",
+       "3  White or of European descent  35 - 44 years old         No          No   \n",
+       "4  White or of European descent  18 - 24 years old        Yes         NaN   \n",
+       "5  White or of European descent  18 - 24 years old         No         NaN   \n",
+       "6  White or of European descent  18 - 24 years old         No          No   \n",
+       "7   Black or of African descent  25 - 34 years old         No         NaN   \n",
+       "8  White or of European descent  35 - 44 years old        Yes          No   \n",
+       "9                           NaN                NaN        NaN         NaN   \n",
+       "\n",
+       "                          SurveyTooLong          SurveyEasy  \n",
+       "0  The survey was an appropriate length           Very easy  \n",
+       "1  The survey was an appropriate length       Somewhat easy  \n",
+       "2                                   NaN                 NaN  \n",
+       "3  The survey was an appropriate length       Somewhat easy  \n",
+       "4  The survey was an appropriate length       Somewhat easy  \n",
+       "5  The survey was an appropriate length       Somewhat easy  \n",
+       "6  The survey was an appropriate length       Somewhat easy  \n",
+       "7               The survey was too long  Somewhat difficult  \n",
+       "8  The survey was an appropriate length           Very easy  \n",
+       "9                                   NaN                 NaN  \n",
+       "\n",
+       "[10 rows x 129 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df=pd.read_csv(\"../Data/survey_results_sample_2018.csv\")         #data loading\n",
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data Exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(99, 129)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Respondent        int64\n",
+       "Hobby            object\n",
+       "OpenSource       object\n",
+       "Country          object\n",
+       "Student          object\n",
+       "                  ...  \n",
+       "Age              object\n",
+       "Dependents       object\n",
+       "MilitaryUS       object\n",
+       "SurveyTooLong    object\n",
+       "SurveyEasy       object\n",
+       "Length: 129, dtype: object"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Respondent', 'Hobby', 'OpenSource', 'Country', 'Student', 'Employment', 'FormalEducation', 'UndergradMajor', 'CompanySize', 'DevType', 'YearsCoding', 'YearsCodingProf', 'JobSatisfaction', 'CareerSatisfaction', 'HopeFiveYears', 'JobSearchStatus', 'LastNewJob', 'AssessJob1', 'AssessJob2', 'AssessJob3', 'AssessJob4', 'AssessJob5', 'AssessJob6', 'AssessJob7', 'AssessJob8', 'AssessJob9', 'AssessJob10', 'AssessBenefits1', 'AssessBenefits2', 'AssessBenefits3', 'AssessBenefits4', 'AssessBenefits5', 'AssessBenefits6', 'AssessBenefits7', 'AssessBenefits8', 'AssessBenefits9', 'AssessBenefits10', 'AssessBenefits11', 'JobContactPriorities1', 'JobContactPriorities2', 'JobContactPriorities3', 'JobContactPriorities4', 'JobContactPriorities5', 'JobEmailPriorities1', 'JobEmailPriorities2', 'JobEmailPriorities3', 'JobEmailPriorities4', 'JobEmailPriorities5', 'JobEmailPriorities6', 'JobEmailPriorities7', 'UpdateCV', 'Currency', 'Salary', 'SalaryType', 'ConvertedSalary', 'CurrencySymbol', 'CommunicationTools', 'TimeFullyProductive', 'EducationTypes', 'SelfTaughtTypes', 'TimeAfterBootcamp', 'HackathonReasons', 'AgreeDisagree1', 'AgreeDisagree2', 'AgreeDisagree3', 'LanguageWorkedWith', 'LanguageDesireNextYear', 'DatabaseWorkedWith', 'DatabaseDesireNextYear', 'PlatformWorkedWith', 'PlatformDesireNextYear', 'FrameworkWorkedWith', 'FrameworkDesireNextYear', 'IDE', 'OperatingSystem', 'NumberMonitors', 'Methodology', 'VersionControl', 'CheckInCode', 'AdBlocker', 'AdBlockerDisable', 'AdBlockerReasons', 'AdsAgreeDisagree1', 'AdsAgreeDisagree2', 'AdsAgreeDisagree3', 'AdsActions', 'AdsPriorities1', 'AdsPriorities2', 'AdsPriorities3', 'AdsPriorities4', 'AdsPriorities5', 'AdsPriorities6', 'AdsPriorities7', 'AIDangerous', 'AIInteresting', 'AIResponsible', 'AIFuture', 'EthicsChoice', 'EthicsReport', 'EthicsResponsible', 'EthicalImplications', 'StackOverflowRecommend', 'StackOverflowVisit', 'StackOverflowHasAccount', 'StackOverflowParticipate', 'StackOverflowJobs', 'StackOverflowDevStory', 'StackOverflowJobsRecommend', 'StackOverflowConsiderMember', 'HypotheticalTools1', 'HypotheticalTools2', 'HypotheticalTools3', 'HypotheticalTools4', 'HypotheticalTools5', 'WakeTime', 'HoursComputer', 'HoursOutside', 'SkipMeals', 'ErgonomicDevices', 'Exercise', 'Gender', 'SexualOrientation', 'EducationParents', 'RaceEthnicity', 'Age', 'Dependents', 'MilitaryUS', 'SurveyTooLong', 'SurveyEasy']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.columns.tolist())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Parameters Relevant to our Analysis: Initial Observation\n",
+    "'FormalEducation' , 'UndergradMajor', 'SalaryType', 'ConvertedSalary', "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Monthly    26\n",
+       "Yearly     22\n",
+       "Weekly      1\n",
+       "Name: SalaryType, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['SalaryType'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Computer science, computer engineering, or software engineering          56\n",
+       "A natural science (ex. biology, chemistry, physics)                       7\n",
+       "Another engineering discipline (ex. civil, electrical, mechanical)        7\n",
+       "A business discipline (ex. accounting, finance, marketing)                5\n",
+       "Fine arts or performing arts (ex. graphic design, music, studio art)      4\n",
+       "Information systems, information technology, or system administration     3\n",
+       "Mathematics or statistics                                                 2\n",
+       "Web development or web design                                             2\n",
+       "A social science (ex. anthropology, psychology, political science)        1\n",
+       "A humanities discipline (ex. literature, history, philosophy)             1\n",
+       "Name: UndergradMajor, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['UndergradMajor'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Bachelor’s degree (BA, BS, B.Eng., etc.)                                            44\n",
+       "Master’s degree (MA, MS, M.Eng., MBA, etc.)                                         29\n",
+       "Some college/university study without earning a degree                                17\n",
+       "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)     4\n",
+       "Associate degree                                                                       3\n",
+       "Name: FormalEducation, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['FormalEducation'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data Cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FormalEducation     2\n",
+      "UndergradMajor     11\n",
+      "SalaryType         50\n",
+      "ConvertedSalary    53\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print(df[['FormalEducation', 'UndergradMajor', 'SalaryType', 'ConvertedSalary']].isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2= df[['FormalEducation', 'UndergradMajor', 'SalaryType', 'ConvertedSalary']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FormalEducation     2\n",
+       "UndergradMajor     11\n",
+       "SalaryType         50\n",
+       "ConvertedSalary    53\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop rows where ConvertedSalary is NA\n",
+    "df2 = df2.dropna(subset=['ConvertedSalary'])\n",
+    "df2 = df2.dropna(subset=['UndergradMajor'])\n",
+    "# Drop rows where both FormalEducation and UndergradMajor are NA\n",
+    "df2 = df2.dropna(subset=['FormalEducation'])\n",
+    "\n",
+    "# Display the first few rows to verify\n",
+    "print(df2.isna().sum())\n",
+    "print(df2.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>FormalEducation</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>SalaryType</th>\n",
+       "      <th>ConvertedSalary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>70841.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>21426.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>41671.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>120000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>Fine arts or performing arts (ex. graphic desi...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>250000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     FormalEducation  \\\n",
+       "1         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "4  Some college/university study without earning ...   \n",
+       "5         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "6  Some college/university study without earning ...   \n",
+       "8  Some college/university study without earning ...   \n",
+       "\n",
+       "                                      UndergradMajor SalaryType  \\\n",
+       "1  A natural science (ex. biology, chemistry, phy...     Yearly   \n",
+       "4  Computer science, computer engineering, or sof...     Yearly   \n",
+       "5  Computer science, computer engineering, or sof...        NaN   \n",
+       "6  Computer science, computer engineering, or sof...     Yearly   \n",
+       "8  Fine arts or performing arts (ex. graphic desi...     Yearly   \n",
+       "\n",
+       "   ConvertedSalary  \n",
+       "1          70841.0  \n",
+       "4          21426.0  \n",
+       "5          41671.0  \n",
+       "6         120000.0  \n",
+       "8         250000.0  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3=df2.copy()\n",
+    "df3.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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>FormalEducation</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>SalaryType</th>\n",
+       "      <th>ConvertedSalary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>70841.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>21426.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>120000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>Fine arts or performing arts (ex. graphic desi...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>250000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
+       "      <td>A business discipline (ex. accounting, finance...</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>47904.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Another engineering discipline (ex. civil, ele...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>95968.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>420.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>10958.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
+       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>51408.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>72611.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                      FormalEducation  \\\n",
+       "1          Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "4   Some college/university study without earning ...   \n",
+       "6   Some college/university study without earning ...   \n",
+       "8   Some college/university study without earning ...   \n",
+       "17      Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
+       "20         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "22         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "24      Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
+       "25      Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
+       "26      Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
+       "\n",
+       "                                       UndergradMajor SalaryType  \\\n",
+       "1   A natural science (ex. biology, chemistry, phy...     Yearly   \n",
+       "4   Computer science, computer engineering, or sof...     Yearly   \n",
+       "6   Computer science, computer engineering, or sof...     Yearly   \n",
+       "8   Fine arts or performing arts (ex. graphic desi...     Yearly   \n",
+       "17  A business discipline (ex. accounting, finance...    Monthly   \n",
+       "20  Another engineering discipline (ex. civil, ele...     Yearly   \n",
+       "22  Computer science, computer engineering, or sof...    Monthly   \n",
+       "24  Computer science, computer engineering, or sof...     Yearly   \n",
+       "25  A natural science (ex. biology, chemistry, phy...    Monthly   \n",
+       "26  Computer science, computer engineering, or sof...     Yearly   \n",
+       "\n",
+       "    ConvertedSalary  \n",
+       "1           70841.0  \n",
+       "4           21426.0  \n",
+       "6          120000.0  \n",
+       "8          250000.0  \n",
+       "17          47904.0  \n",
+       "20          95968.0  \n",
+       "22            420.0  \n",
+       "24          10958.0  \n",
+       "25          51408.0  \n",
+       "26          72611.0  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = df2.dropna(subset=['SalaryType'])\n",
+    "df2.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conversion_factors = {\n",
+    "    'Yearly': 1,\n",
+    "    'Monthly': 12,\n",
+    "    'Weekly': 52,\n",
+    "    \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "DATA ANALYSIS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>FormalEducation</th>\n",
+       "      <th>UndergradMajor</th>\n",
+       "      <th>SalaryType</th>\n",
+       "      <th>ConvertedSalary</th>\n",
+       "      <th>NetSalary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>70841.0</td>\n",
+       "      <td>70841.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>21426.0</td>\n",
+       "      <td>21426.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>120000.0</td>\n",
+       "      <td>120000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Some college/university study without earning ...</td>\n",
+       "      <td>Fine arts or performing arts (ex. graphic desi...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>250000.0</td>\n",
+       "      <td>250000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
+       "      <td>A business discipline (ex. accounting, finance...</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>47904.0</td>\n",
+       "      <td>574848.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Another engineering discipline (ex. civil, ele...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>95968.0</td>\n",
+       "      <td>95968.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>420.0</td>\n",
+       "      <td>5040.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>10958.0</td>\n",
+       "      <td>10958.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
+       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
+       "      <td>Monthly</td>\n",
+       "      <td>51408.0</td>\n",
+       "      <td>616896.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
+       "      <td>Computer science, computer engineering, or sof...</td>\n",
+       "      <td>Yearly</td>\n",
+       "      <td>72611.0</td>\n",
+       "      <td>72611.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                      FormalEducation  \\\n",
+       "1          Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "4   Some college/university study without earning ...   \n",
+       "6   Some college/university study without earning ...   \n",
+       "8   Some college/university study without earning ...   \n",
+       "17      Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
+       "20         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "22         Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+       "24      Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
+       "25      Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
+       "26      Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
+       "\n",
+       "                                       UndergradMajor SalaryType  \\\n",
+       "1   A natural science (ex. biology, chemistry, phy...     Yearly   \n",
+       "4   Computer science, computer engineering, or sof...     Yearly   \n",
+       "6   Computer science, computer engineering, or sof...     Yearly   \n",
+       "8   Fine arts or performing arts (ex. graphic desi...     Yearly   \n",
+       "17  A business discipline (ex. accounting, finance...    Monthly   \n",
+       "20  Another engineering discipline (ex. civil, ele...     Yearly   \n",
+       "22  Computer science, computer engineering, or sof...    Monthly   \n",
+       "24  Computer science, computer engineering, or sof...     Yearly   \n",
+       "25  A natural science (ex. biology, chemistry, phy...    Monthly   \n",
+       "26  Computer science, computer engineering, or sof...     Yearly   \n",
+       "\n",
+       "    ConvertedSalary  NetSalary  \n",
+       "1           70841.0    70841.0  \n",
+       "4           21426.0    21426.0  \n",
+       "6          120000.0   120000.0  \n",
+       "8          250000.0   250000.0  \n",
+       "17          47904.0   574848.0  \n",
+       "20          95968.0    95968.0  \n",
+       "22            420.0     5040.0  \n",
+       "24          10958.0    10958.0  \n",
+       "25          51408.0   616896.0  \n",
+       "26          72611.0    72611.0  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Function to convert salary to annual\n",
+    "def convert_to_annual(row):\n",
+    "    salary_type = row['SalaryType']\n",
+    "    salary = row['ConvertedSalary']\n",
+    "    return salary * conversion_factors[salary_type]\n",
+    "\n",
+    "# Apply the function to each row\n",
+    "df2['NetSalary'] = df2.apply(convert_to_annual, axis=1)\n",
+    "\n",
+    "# Verify the results\n",
+    "df2.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                             mean    median  \\\n",
+      "FormalEducation                                                               \n",
+      "Bachelor’s degree (BA, BS, B.Eng., etc.)          897724.866667   80000.0   \n",
+      "Master’s degree (MA, MS, M.Eng., MBA, etc.)       235090.857143  100354.0   \n",
+      "Some college/university study without earning a...  444953.111111  254736.0   \n",
+      "\n",
+      "                                                    count  \n",
+      "FormalEducation                                            \n",
+      "Bachelor’s degree (BA, BS, B.Eng., etc.)             15  \n",
+      "Master’s degree (MA, MS, M.Eng., MBA, etc.)          14  \n",
+      "Some college/university study without earning a...      9  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Average and median NetSalary by FormalEducation\n",
+    "education_salary_stats = df2.groupby('FormalEducation')['NetSalary'].agg(['mean', 'median', 'count'])\n",
+    "print(education_salary_stats)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 3000x1600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Box plot for NetSalary by FormalEducation\n",
+    "plt.figure(figsize=(30, 16))\n",
+    "sns.boxplot(x='FormalEducation', y='NetSalary', data=df2)\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.title('NetSalary by FormalEducation')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Descriptive statistics\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.barplot(x='FormalEducation', y='NetSalary', data=df2, estimator=np.mean)\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.title('Mean NetSalary by FormalEducation')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XaufOndaiYNeuXVq7dq2Sk5O1dOlS1ahRQxs3bjQ5KQA8uA8//FDz5s3TuHHj5OLiYh2vVq2aZs2aZWIygIIAsCt//PGH+vTpk2a8ePHiio6ONiERAGRc0aJFFRwcrOPHj2v58uVasWKFjh07pm7duql8+fI6cOCAXnjhBQ0dOtTsqADwwObPn68ZM2aoa9eucnR0tI5Xr15dBw8eNDEZQEEA2JU8efLo6tWracYPHTqkwoULm5AIADJu9uzZGjRokBwc/vfjioODg1599VXNmDFDFotFr7zyiiIiIkxMCQDpc/bsWfn5+aUZT01NVVJSkgmJgP+hIADsyNNPP62RI0da/+disVgUGRmpYcOGcZlDAI+c5OTkO76bdvDgQaWkpEj6qxjlkocAHiVVq1bV77//nmb866+/Vq1atUxIBPyPk9kBAGSeCRMmqE2bNvLx8VF8fLwCAwMVHR2thg0batSoUWbHA4B0CQkJUa9evfT222+rbt26slgs2rZtm0aPHq3u3btLksLDw1W1alWTkwLAgxs+fLhCQkJ09uxZpaamasWKFTp06JDmz5+vH374wex4yOW4igFgh25v6pWamqratWurefPmZkcCgHRLSUnR2LFjNWXKFJ0/f16SVKRIEb366qsaOnSoHB0dFRkZKQcHB5UoUcLktADw4H755ReNHj1aO3bssP689t5776lly5ZmR0MuR0EAAAByvNv7q3h6epqcBAAA+8UeBAAAIMdKTk7Wr7/+qsWLF1v3Gjh37pyuX79ucjIAAOwPKwgAAECOdOrUKbVu3VqRkZFKSEjQ4cOHVa5cOQ0aNEi3bt3S9OnTzY4IAIBdYQUBAADIkQYOHKiAgADFxsbKzc3NOt6pUyetWbPGxGQAANgnrmIAAABypA0bNmjjxo1ycXGxGS9durTOnj1rUioAAOwXKwgAAECOlJqaqpSUlDTjZ86ckYeHhwmJAACwbxQEQC7h4OCgpk2baseOHWZHAYAH0qJFC33yySfW2xaLRdevX9fw4cPVpk0b84IBQBYZOXKk1q9fb3YM5GJsUgjkEvPmzdOpU6cUFhamjRs3mh0HAO7r3LlzatKkiRwdHXXkyBEFBAToyJEjKlSokNavXy8fHx+zIwJApipTpoxiYmLUrFkzff/992bHQS5EQQAAAHKs+Ph4LV68WDt37lRqaqpq166trl272mxaCAD25NatWwoPD1erVq3MjoJciIIAAAAAAHKA3bt3q2bNmmbHQC7GVQyAXGDq1Km6ePGi3nvvPbOjAMA9fffddw88t0OHDlmYBACyR1xcnBYuXKhZs2Zpz549d9ycFcgurCAAcoFmzZrpxIkTOn78uNlRAOCeHBxs90+2WCz6548qFotFkvghGsAjbe3atZozZ45WrFih0qVL69lnn9Wzzz6rWrVqmR0NuRhXMQBygTVr1lAOAHgkpKamWj/CwsJUs2ZN/fzzz7py5Yri4uL0888/q3bt2lq1apXZUQEg3c6cOaMPP/xQ5cqVU5cuXeTt7a2kpCQtX75cH374IeUATMcKAgAAkCP5+/tr+vTpevzxx23Gf//9d7300ks6cOCASckAIP3atGmjDRs2qF27duratatat24tR0dHOTs7a8+ePapSpYrZEQH2IADsyd3O3bVYLMqTJ4/8/PxUtmzZbE4FABlz7NgxeXl5pRn38vLSyZMnsz8QADyEsLAwDRgwQH379lWFChXMjgPcESsIADvi4OBw1/N1DcOQxWLR448/rm+//Vbe3t4mpQSAB/Pkk0/K2dlZX331lYoVKyZJio6OVkhIiBITExUeHm5yQgB4cJs3b9acOXO0bNkyPfbYYwoJCVHnzp3l6+vLCgLkGOxBANiR1atXq27dulq9erXi4uIUFxen1atXq169evrhhx+0fv16Xbp0Sa+//rrZUQHgvubMmaOYmBiVLl1afn5+8vPzU6lSpRQVFaXZs2ebHQ8A0qVhw4aaOXOmoqKi1KdPHy1ZskTFixdXamqqVq9erWvXrpkdEWAFAWBP/P39NWPGDDVq1MhmfOPGjXrppZe0b98+/frrr+rZs6ciIyNNSgkAD84wDK1evVoHDx6UYRiqUqWKmjdvbr2SAQA8yg4dOqTZs2drwYIFunLlilq0aJGuy70CmY2CALAjbm5u2r59u/z9/W3G//zzT9WrV0/x8fE6deqUKleurJs3b5qUEgAAAH+XkpKi77//XnPmzKEggKkoCAA78vjjj8vDw0Pz589X4cKFJUkXLlxQ9+7ddePGDa1fv16//vqr+vXrp8OHD5ucFgAAAEBOwh4EgB2ZPXu2Tpw4oRIlSsjPz08VKlRQiRIldPLkSc2aNUuSdP36db377rsmJwUAAMh91q1bp48//lgbN26UJH3xxRcqVaqUChcurN69eys+Pt7khMjtWEEA2BnDMPTLL7/o8OHDMgxDjz32mFq0aCEHB/pAAAAAs8ycOVN9+/ZVmTJldObMGQ0fPlyjRo1SSEiIHBwc9NVXX6lv374aO3as2VGRi1EQAAAAAEAW8/f3V58+ffTqq69q1apVat++vWbNmqUXXnhBkvT111/rrbfe0tGjR01OityMggCwM2vWrNGaNWsUExOj1NRUm2Nz5swxKRUAPJz4+HglJSXZjHl6epqUBgDSz93dXQcOHFDp0qUlSS4uLtqzZ48qV64sSYqMjFSFChWUkJBgZkzkck5mBwCQed5//32NHDlSAQEBKlasGJcBA/BIu3nzpt58800tW7ZMly5dSnM8JSXFhFQAkDG3bt2Sm5ub9barq6tcXV1tbicnJ5sRDbCiIADsyPTp0zVv3jyFhISYHQUAHtobb7yhdevWaerUqerevbs+//xznT17Vl988QXn6AJ45FgsFl27dk158uSRYRiyWCy6fv26rl69KknW/wJm4hQDwI4ULFhQ27ZtU/ny5c2OAgAPrVSpUpo/f76CgoLk6empnTt3ys/PTwsWLNDixYv1008/mR0RAB6Yg4ODzerO2yXBP2+zOgpmYgUBYEdefPFFLVq0iMsYArALly9fVtmyZSX9td/A5cuXJUmPP/64+vbta2Y0AEi3devWmR0BuC8KAsCO3Lp1SzNmzNCvv/6q6tWry9nZ2eb4xIkTTUoGAOlXrlw5nTx5UqVLl1aVKlW0bNky1atXT99//73y589vdjwASJfAwECzIwD3xSkGgB1p0qTJXY9ZLBatXbs2G9MAwMOZNGmSHB0dNWDAAK1bt05t27ZVSkqKkpOTNXHiRA0cONDsiAAA2BUKAgAA8EiIjIzUH3/8ofLly6tGjRpmxwGAdHF0dHygeexBADNxigGQS8TExMjHx8fsGACQYaVKlVKpUqXMjgEAGWIYhkqXLq0XXnhBtWrVMjsOcEesIADsgLu7u06dOqXChQtLklq3bq25c+eqWLFikqTz58/L19eXRhoAAMAk27dv15w5c7RkyRKVLVtWPXv2VNeuXeXt7W12NMDKwewAAB7erVu39Peub+PGjYqPj7eZQxcIAABgnrp162ratGmKiorS4MGDtXLlSpUoUULPP/+8Vq9ebXY8QBIFAZBr/P06uwAAADBHnjx51K1bN61Zs0YRERGKiYlR69atrZdyBczEHgQAAAAAkI3OnDmjefPmad68eYqPj9cbb7whT09Ps2MBFASAPbBYLDYrBP55GwAeRVevXr3juMVikaurq1xcXLI5EQBkXGJiolauXKnZs2fr999/11NPPaVPPvlEbdq0kYMDC7uRM7BJIWAHHBwc5OXlZS0Frly5Ik9PT+v/bAzD0NWrV9mkEMAjxcHB4Z5lZ4kSJRQaGqrhw4fzwzWAHK9gwYLy8PDQCy+8oJCQkLteXYqVBDATBQFgB7788ssHmvfCCy9kcRIAyDzz58/XO++8o9DQUNWrV0+GYWj79u368ssv9Z///EcXLlzQhAkT9MYbb+jtt982Oy4A3NPfi8w7lZ+GYchisfCGDkxFQQAAAHKkZs2aqU+fPnruuedsxpctW6YvvvhCa9as0YIFCzRq1CgdPHjQpJQA8GDCw8MfaF5gYGAWJwHujoIAAADkSO7u7tqzZ48qVKhgM37kyBHVqFFDN2/e1IkTJ1S1alXdvHnTpJQAANgPTtgDcoEXXnhBTZs2NTsGAKRLiRIlNHv27DTjs2fPVsmSJSVJly5dkre3d3ZHAwDALnEVAyAXKF68OBt4AXjkTJgwQf/+97/1888/q27durJYLNq+fbsOHjyob775RpK0fft2de7c2eSkAADYB04xAAAAOdbJkyc1ffp0HT58WIZh6LHHHlOfPn1UpkwZs6MBAGB3KAgAAAAAAACnGAD24siRI9q7d69q166tsmXL6scff9RHH32k+Ph4dezYUW+//fY9rycOADnRlStXtG3bNsXExCg1NdXmWPfu3U1KBQCAfWIFAWAHVq5cqeeee04ODg6yWCyaMWOGXnrpJTVp0kSOjo765Zdf9OGHH2ro0KFmRwWAB/b999+ra9euunHjhjw8PGxKTovFosuXL5uYDgAyX8+ePdWkSROFhISYHQW5FAUBYAcCAgLUqlUrffjhh5o3b5769++v0aNHa9CgQZKkGTNmaNKkSTpw4IC5QQEgHSpWrKg2bdpo9OjRcnd3NzsOAGS5oKAgnTp1Sp6entqzZ4/ZcZALURAAdsDDw0O7d+9W+fLllZqaKhcXF+3evVv+/v6S/trkq0qVKlwnHMAjJW/evPrzzz9Vrlw5s6MAQLY6dOiQKlWqZHYM5EJc9wywA7eX30qSg4OD3NzcbN5tc3NzU0JCglnxACBDWrVqpT/++MPsGACQ7SgHYBY2KQTsgMViSXNuLhsSAnjUtW3bVm+88Yb279+vatWqydnZ2eZ4hw4dTEoGAJkvKipKSUlJKlWqlNlRkItxigFgBxwcHOTl5WUtBa5cuSJPT085OPy1SMgwDF29elUpKSlmxgSAdLn9GnYnFouF1zQAdqVy5co6fPgwr20wFSsIADswd+5csyMAQKb752UNAcCezZ8/n/2iYDpWEAAAAAAAAFYQAACAnOOzzz7TSy+9pDx58uizzz6759wBAwZkUyoAAHIHVhAAAIAco2zZsvrjjz9UsGBBlS1b9q7zLBaLjh8/no3JACBzeHt733EzaYvFojx58sjPz0+hoaHq0aOHCemQ27GCAAAA5BgnTpy4458BwF689957GjVqlJ566inVq1dPhmFo+/btWrVqlfr3768TJ06ob9++Sk5OVu/evc2Oi1yGFQQAAAAAkE2effZZtWjRQi+//LLN+BdffKGwsDAtX75ckydP1owZM/Tnn3+alBK5FQUBAADIkVJSUjRv3jytWbNGMTExaa5qsHbtWpOSAUDG5cuXT7t375afn5/N+NGjR1WzZk1dv35dx44dU/Xq1XXjxg2TUiK3uvsFhgHYlZEjR2r9+vVmxwCABzZw4EANHDhQKSkp8vf3V40aNWw+AOBRVKBAAX3//fdpxr///nsVKFBAknTjxg15eHhkdzSAFQRAblG2bFmdP39ezZo1u+P/lAAgpylUqJDmz5+vNm3amB0FADLNzJkz1bdvX7Vp00b16tWTxWLRtm3b9NNPP2n69Onq1auXPv74Y23btk1Lly41Oy5yGQoCIBe5deuWwsPD1apVK7OjAMB9+fr66rffflPFihXNjgIAmWrjxo2aMmWKDh06JMMw9Nhjj+nVV19Vo0aNzI6GXI6CAAAA5Egff/yxjh8/rilTptzxkmAAACBzURAAduDy5cu6efOmSpQoYR3bt2+fJkyYoBs3bqhjx44KDg42MSEApF+nTp20bt06FShQQFWrVpWzs7PN8RUrVpiUDAAeTmpqqo4ePXrHDViffPJJk1IBkpPZAQA8vP79+6tYsWKaOHGiJCkmJkZPPPGEfH19Vb58eYWGhiolJUUhISEmJwWAB5c/f3516tTJ7BgAkKm2bNmi4OBgnTp1Sv98r9ZisSglJcWkZAAFAWAXtmzZorlz51pvz58/XwUKFNDu3bvl5OSkCRMm6PPPP6cgAPDISE5OVlBQkFq1aqWiRYuaHQcAMs3LL7+sgIAA/fjjjypWrBinUCFH4RQDwA64ubnp4MGDKl26tCSpTZs2qlq1qsaPHy9JOnz4sBo2bKhLly6ZGRMA0sXd3V0HDhywvrYBgD3Imzev9uzZIz8/P7OjAGk4mB0AwMPz9PTUlStXrLe3bdumBg0aWG9bLBYlJCSYkAwAMq5+/fratWuX2TEAIFPVr19fR48eNTsGcEecYgDYgXr16umzzz7TzJkztWLFCl27dk1Nmza1Hj98+LBKlixpYkIASL9+/fppyJAhOnPmjOrUqaO8efPaHK9evbpJyQAg41599VUNGTJE0dHRqlatWpoNWHltg5k4xQCwA7t371bz5s117do1JScn6+2339YHH3xgPR4SEqK8efNq+vTpJqYEgPRxcEi70NFiscgwDDbyAvDI4rUNORkrCAA7ULNmTR04cECbNm1S0aJFVb9+fZvjzz//vKpUqWJSOgDImBMnTpgdAQAyHa9tyMlYQQAAAAAAANikELAX69at08cff6yNGzdKkr744guVKlVKhQsXVu/evRUfH29yQgDImP3792vVqlX67rvvbD4A4FHSr18/Xb9+3Xp7wYIFNrevXLmiNm3amBENsGIFAWAHZs6cqb59+6pMmTI6c+aMhg8frlGjRikkJEQODg766quv1LdvX40dO9bsqADwwI4fP65OnTrpzz//tJ6fK8l6zXDO0wXwKHF0dFRUVJR8fHwk/XUVqt27d6tcuXKSpPPnz8vX15fXNpiKFQSAHfj00081adIkHT16VN9++63ee+89ff7555o2bZo+//xzzZo1S998843ZMQEgXQYOHKiyZcvq/Pnzcnd31759+7R+/XoFBATot99+MzseAKTLP9+X5X1a5ERsUgjYgePHj6tDhw6SpNatW8tisahevXrW4/Xr19fp06fNigcAGbJ582atXbtWhQsXloODgxwcHPT4449rzJgxGjBggHbt2mV2RAAA7AorCAA7cOvWLbm5uVlvu7q6ytXV1eZ2cnKyGdEAIMNSUlKUL18+SVKhQoV07tw5SVLp0qV16NAhM6MBAGCXWEEA2AGLxaJr164pT5481mvoXr9+XVevXpUk638B4FHi7++vvXv3qly5cqpfv77GjRsnFxcXzZgxw3rOLgA8St577z25u7tLkhITEzVq1Ch5eXlJkm7evGlmNEASmxQCdsHBwcG6aZcka0nwz9tsegPgUfLLL7/oxo0beuaZZ3T8+HG1a9dOBw8eVMGCBbV06VI1bdrU7IgA8MCCgoJsfj67m3Xr1mVDGuDOKAgAOxAeHv5A8wIDA7M4CQBkrcuXL8vb2/uBfsgGAADpQ0EAAABytKNHj+rYsWN68skn5ebmlmaVFAAAyBxsUggAAHKkS5cuqVmzZqpYsaLatGmjqKgoSdKLL76oIUOGmJwOADLXf//7X82fP9/sGMjlKAgAAECO9Nprr8nZ2VmRkZHWTb0kqXPnzlq1apWJyQAg8w0dOlQ9evQwOwZyOa5iAAAAcqSwsDD98ssvKlGihM14hQoVdOrUKZNSAUDWOHjwoNkRAFYQAACAnOnGjRs2Kwduu3jxolxdXU1IBACAfWMFAQAAyJGefPJJzZ8/Xx988IEkyWKxKDU1VePHj1eTJk1MTgcA6RMZGflA80qVKpXFSYC74yoGQC7Rs2dPNWnSRCEhIWZHAYAHsn//fgUFBalOnTpau3atOnTooH379uny5cvauHGjypcvb3ZEAHhgjo6O1j/f/hXs71dkuX2FlpSUlGzPBtxGQQDkEkFBQTp16pQ8PT21Z88es+MAwAOJjo7WtGnTtGPHDqWmpqp27drq37+/ihUrZnY0AEgXJycnlShRQqGhoWrfvr2cnO68mLtGjRrZnAz4HwoCIJc5dOiQKlWqZHYMAACAXCU6Olpffvml5s2bp9jYWHXr1k29evVS5cqVzY4GWLFJIZALpKSk6Ntvv5UkygEAAAATFC1aVEOHDtWBAwf0zTffKDY2VvXr11eDBg00c+ZMpaammh0RYAUBYM8OHjyoOXPm6Msvv1RsbKwSExPNjgQAAID/d/78eXXp0kXh4eG6cOGCChQoYHYk5HKsIADszI0bNzRnzhw1btxYVatW1c6dOzVq1CidO3fO7GgAAACQtGnTJr344ouqWLGirl+/rs8//1z58+c3OxbAZQ4Be7F582bNmjVLy5YtU4UKFdS1a1dt3bpVn332mapUqWJ2PAAAgFwtKipK8+fP19y5cxUbG6uuXbtq06ZNqlq1qtnRACtOMQDsQJUqVXTz5k0FBwerW7du1kLA2dlZe/bsoSAA8Ehq2rSpVqxYkeZdtatXr6pjx45au3atOcEAIANcXFzk6+urF154QR06dJCzs/Md51WvXj2bkwH/Q0EA2AEXFxc9//zzCgkJUfPmza3X1KUgAPAoc3BwUHR0tHx8fGzGY2JiVLx4cSUlJZmUDADSz8Hhf2d33/5Z7Z+/ilksFqWkpGRrLuDvOMUAsAMnTpzQvHnz1LdvX8XHx6tLly7q2rWr9X8+APAo2bt3r/XP+/fvV3R0tPV2SkqKVq1apeLFi5sRDQAy7MSJE2ZHAO6LFQSAnVm7dq3mzJmjFStW6NatW3r99detm+AAwKPAwcHhru+uSZKbm5smT56snj17Znc0AADsGgUBYKfi4uK0cOFCzZkzRzt37pS/v7/Nu3IAkFOdOnVKhmGoXLly2rZtmwoXLmw95uLiIh8fHzk6OpqYEAAA+0RBAOQCu3fv1pw5c/TZZ5+ZHQUAAABADkVBANixxMREJSYmKl++fGZHAYB0mz9//j2Pd+/ePZuSAACQO1AQAHZi7ty52rlzpxo0aKCuXbvqrbfe0sSJE5WcnKymTZtqyZIlKliwoNkxAeCBeXt729xOSkrSzZs35eLiInd3d12+fNmkZAAA2CcKAsAOjBo1SqNGjVKjRo20a9cuPffcc/r22281aNAgOTg46LPPPlO7du00bdo0s6MCwEM5cuSI+vbtqzfeeEOtWrUyOw4AAHaFggCwAxUqVNDIkSPVpUsX/fHHH6pfv76WLl2qf/3rX5Kkn3/+WS+//LJOnTplclIAeHh//PGHunXrpoMHD5odBQAyVdOmTdWkSRMNGTJE7u7uZsdBLuRgdgAADy8yMlKPP/64JCkgIEBOTk6qVq2a9Xj16tUVFRVlVjwAyFSOjo46d+6c2TEAINOVLl1aa9euVeXKlc2OglzKyewAAB5eUlKSXF1drbddXFzk7Oxsve3k5KSUlBQzogFAhn333Xc2tw3DUFRUlKZMmaLGjRublAoAss7cuXMlSdevXzc5CXIrCgLATuzfv1/R0dGS/voh+uDBg9b/uVy8eNHMaACQIR07drS5bbFYVLhwYTVt2lQff/yxOaEAIBtwBSqYhT0IADvg4OAgi8WiO/1zvj1usVhYRQAAAGCSrVu36vLly3rqqaesY/Pnz9fw4cN148YNdezYUZMnT7ZZFQpkN1YQAHbgxIkTZkcAgCx1uwC1WCwmJwGAjBkxYoSCgoKsBcGff/6pXr16KTQ0VJUrV9b48ePl6+urESNGmBsUuRorCAAAQI41f/58jR8/XkeOHJEkVaxYUW+88YZCQkJMTgYA6VOsWDF9//33CggIkCS98847Cg8P14YNGyRJX3/9tYYPH679+/ebGRO5HFcxAB5xkZGR6Zp/9uzZLEoCAJlr4sSJ6tu3r9q0aaNly5Zp6dKlat26tV5++WVNmjTJ7HgAkC6xsbEqUqSI9XZ4eLhat25tvV23bl2dPn3ajGiAFQUB8IirW7euevfurW3btt11TlxcnGbOnCl/f3+tWLEiG9MBQMZNnjxZ06ZN00cffaQOHTro6aef1rhx4zR16lR99tlnZscDgHQpUqSI9bTQxMRE7dy5Uw0bNrQev3btms1VqAAzsAcB8Ig7cOCARo8erdatW8vZ2VkBAQHy9fVVnjx5FBsbq/3792vfvn0KCAjQ+PHjbTbGAYCcLCoqSo0aNUoz3qhRI0VFRZmQCAAyrnXr1ho2bJg++ugjffvtt3J3d9cTTzxhPb53716VL1/exIQAKwiAR16BAgU0YcIEnTt3TtOmTVPFihV18eJF6/m6Xbt21Y4dO7Rx40bKAQCPFD8/Py1btizN+NKlS1WhQgUTEgFAxn344YdydHRUYGCgZs6cqZkzZ8rFxcV6fM6cOWrZsqWJCQE2KQQAADnU8uXL1blzZzVv3lyNGzeWxWLRhg0btGbNGi1btkydOnUyOyIApFtcXJzy5csnR0dHm/HLly8rX758NqUBkN0oCAAAQI61Y8cOTZo0SQcOHJBhGKpSpYqGDBmiWrVqmR0NAAC7Q0EAAAAAAFmsZ8+eDzRvzpw5WZwEuDsKAgAAkGOlpqbq6NGjiomJUWpqqs2xJ5980qRUAJB+Dg4OKl26tGrVqqV7/Qq2cuXKbEwF2KIgAAAAOdKWLVsUHBysU6dOpflh2mKxKCUlxaRkAJB+/fr105IlS1SqVCn17NlT3bp1U4ECBcyOBdigIAAAADlSzZo1VbFiRb3//vsqVqyYLBaLzXEvLy+TkgFAxiQkJGjFihWaM2eONm3apLZt26pXr15q2bJlmtc4wAwUBICdWbBggaZPn64TJ05o8+bNKl26tD755BOVLVtWTz/9tNnxAOCB5c2bV3v27JGfn5/ZUQAg0506dUrz5s3T/PnzlZSUpP379ytfvnxmx0Iu52B2AACZZ9q0aRo8eLDatGmjK1euWJff5s+fX5988om54QAgnerXr6+jR4+aHQMAsoTFYpHFYpFhGGn2WAHMwgoCwI5UqVJFo0ePVseOHeXh4aE9e/aoXLlyioiIUFBQkC5evGh2RAC4p71791r/fOzYMf3nP//RG2+8oWrVqsnZ2dlmbvXq1bM7HgA8lL+fYrBhwwa1a9dOPXr0UOvWreXgwHu3MJ+T2QEAZJ4TJ07c8drgrq6uunHjhgmJACB9atasaX1H7ba/Xxrs9jE2KQTwqPn7JoU9evTQkiVLVLBgQbNjATYoCAA7UrZsWe3evVulS5e2Gf/5559VpUoVk1IBwIM7ceKE2REAIEtMnz5dpUqVUtmyZRUeHq7w8PA7zluxYkU2JwP+h4IAsCNvvPGG+vfvr1u3bskwDG3btk2LFy/WmDFjNGvWLLPjAcB9/b3gXL9+vRo1aiQnJ9sfV5KTk7Vp06Y0ZSgA5GTdu3fnSgXI8diDALAzM2fO1IcffqjTp09LkooXL64RI0aoV69eJicDgPRxdHRUVFSUfHx8bMYvXbokHx8fTjEAACCTURAAdurixYtKTU1N84M1ADwqHBwcdP78eRUuXNhm/PDhwwoICNDVq1dNSgYAgH3iFAPAziQnJ+u3337TsWPHFBwcLEk6d+6cPD09ubYugEfCM888I+mvDQlDQ0Pl6upqPZaSkqK9e/eqUaNGZsUDAMBuURAAduTUqVNq3bq1IiMjlZCQoBYtWsjDw0Pjxo3TrVu3NH36dLMjAsB9eXl5SZIMw5CHh4fc3Nysx1xcXNSgQQP17t3brHgAANgtCgLAjgwcOFABAQHas2ePzWVzOnXqpBdffNHEZADw4ObOnStJKlOmjF5//XXlzZvX5EQAAOQO7EEA2JFChQpp48aNqlSpkjw8PLRnzx6VK1dOJ0+eVJUqVXTz5k2zIwIAAADIoVhBANiR1NTUO+7qfebMGXl4eJiQCADSp3bt2lqzZo28vb1Vq1ate14SbOfOndmYDACy3vr161WjRg3rqVZAdqMgAOxIixYt9Mknn2jGjBmS/trg6/r16xo+fLjatGljcjoAuL+nn37auinh008/zTXDAeQqQUFB8vb21ttvv60hQ4aYHQe5EKcYAHbk7Nmzatq0qRwdHXXkyBEFBAToyJEjKlSokNavX88lDwEAAHKwU6dO6cSJE/rll180ZswYs+MgF6IgAOxMfHy8lixZoh07dig1NVW1a9dW165dbXYBB4BHwTvvvKOgoCA1btxY7u7uZscBgCyXnJwsJycWecM8FASAnUhKSlKlSpX0ww8/qEqVKmbHAYCH1rp1a23atEkJCQmqXbu2goKCFBgYqMcff1z58uUzOx4AZJr9+/dr1qxZWrhwoc6fP292HORiDmYHAJA5nJ2dlZCQwPm6AOzGqlWrFBsbq99++01PP/20du3apc6dO6tAgQJq0KCB2fEA4KFcv35ds2bNUsOGDVW9enVt27ZNw4YNMzsWcjlWEAB2ZOzYsTp48KBmzZrF8jQAduXQoUP67bff9Ouvv+rbb79V/vz5deHCBbNjAUC6bdiwQbNmzdLy5ctVtmxZ7d+/X+Hh4WrcuLHZ0QCuYgDYk61bt2rNmjUKCwtTtWrVlDdvXpvjK1asMCkZAKTftGnTFB4ervDwcKWkpOiJJ55QYGCg3n33XVWvXt3seACQLuPGjdOcOXN0/fp1denSRRs2bFCNGjXk7Owsb29vs+MBklhBANiVHj163PP43LlzsykJADw8BwcHFS5cWEOGDNHLL78sT09PsyMBQIY5OTlp6NChGjlypBwdHa3jzs7O2rNnD3tIIUegIAAAADnSt99+q/Xr1+u3337T/v37VaNGDQUFBSkoKEhPPPEEGxUCeKSMHj1a8+bN061bt9SlSxeFhITI39+fggA5CgUBAADI8eLi4vT777/rm2++0aJFi2SxWJSQkGB2LABIt/DwcM2ZM0fLly9X+fLltW/fPvYgQI5BQQDYkVq1at3xKgYWi0V58uSRn5+fQkND1aRJExPSAUD6Xb58WeHh4frtt9/022+/KSIiQgULFlRgYKC+/vprs+MBQIZdu3ZNCxcu1Ny5c7Vjxw7Vq1dP//rXvzR48GCzoyEX4zKHgB1p3bq1jh8/rrx586pJkyYKCgpSvnz5dOzYMdWtW1dRUVFq3ry5/vvf/5odFQDuq3r16vLx8VGfPn109uxZ9e7dW3v27FFMTAzlAIBHnoeHh15++WVt3bpVu3btUr169TR27FizYyGXYwUBYEd69+6tUqVK6d1337UZ//DDD3Xq1CnNnDlTw4cP148//qg//vjDpJQA8GCmTJmioKAg+fv7mx0FALJFUlKSnJ2dzY6BXIyCALAjXl5e2rFjh/z8/GzGjx49qjp16iguLk4HDx5U3bp1de3aNZNSAgAA5D7z58+/7xyLxaKQkJBsSAPcmZPZAQBknjx58mjTpk1pCoJNmzYpT548kqTU1FS5urqaEQ8AACDXCg0NVb58+eTk5KS7vUdLQQCzURAAduTVV1/Vyy+/rB07dqhu3bqyWCzatm2bZs2apbfffluS9Msvv6hWrVomJwUAAMhdKleurPPnz6tbt27q2bOnqlevbnYkIA1OMQDszMKFCzVlyhQdOnRIklSpUiW9+uqrCg4OliTFx8dbr2oAAACA7LN161bNmTNHS5culZ+fn3r16qWuXbvK09PT7GiAJAoCAAAAAMhW8fHx+vrrrzV37lxt27ZNHTt21Jw5czgNFKbjMoeAnbly5Yr1lILLly9Lknbu3KmzZ8+anAwA0qdMmTIaOXKkIiMjzY4CAJnKzc1N3bt31/vvv6969eppyZIlunnzptmxAAoCwJ7s3btXFStW1EcffaTx48frypUrkqSVK1fqrbfeMjccAKTTkCFD9N///lflypVTixYttGTJEiUkJJgdCwAeytmzZzV69GhVqFBBzz//vOrWrat9+/bJ29vb7GgApxgA9qR58+aqXbu2xo0bJw8PD+3Zs0flypXTpk2bFBwcrJMnT5odEQDSbc+ePZozZ44WL16s5ORkBQcHq2fPnqpdu7bZ0QDggS1btkxz585VeHi4WrVqpR49eqht27ZydHQ0OxpgRUEA2BEvLy/t3LlT5cuXtykITp06pUqVKunWrVtmRwSADEtKStLUqVM1dOhQJSUlyd/fXwMHDlSPHj1ksVjMjgcA9+Tg4KBSpUqpa9euKlKkyF3nDRgwIBtTAba4zCFgR/LkyaOrV6+mGT906JAKFy5sQiIAeHhJSUlauXKl5s6dq9WrV6tBgwbq1auXzp07p3feeUe//vqrFi1aZHZMALinUqVKyWKx3PP1ymKxUBDAVKwgAOzISy+9pAsXLmjZsmUqUKCA9u7dK0dHR3Xs2FFPPvmkPvnkE7MjAsAD27lzp+bOnft/7d15WJT1+sfxz7CIyOKCiBuKgBuJHswWlyxcSc21tNTjgmtmlubWqcRMS7PUzBYz185JU1NzKXEpUdwVERVXUOEgaKK44crM74+u5nfm0DFR8hmG9+u65rrmuZ9n4MP8ocPN97m/WrhwoZydnfX3v/9dffv2VY0aNazX7N69W40bN9b169cNTAoAgGOgQQA4kMuXL6tVq1Y6dOiQrly5ovLlyysjI0P169fXjz/+KA8PD6MjAsA9c3Z2VvPmzdWnTx+1b99erq6uua65du2aBg8erLlz5xqQEAAAx0KDAHBAP//8s+Li4mQ2m1W3bl01a9bM6EgAkGenT59W5cqVjY4BAA9s0aJFevHFF+/p2tTUVKWkpKhhw4Z/cSogNxoEAAAAAPAXevrpp3X27Fn17t1bbdu2Vc2aNW3OX7p0SVu3btU///lPbdiwQbNnz9Zzzz1nUFoUZjQIgAJu+vTp93wtQ28A2LuSJUve844EFy5c+IvTAED+Wb16tT799FNt2LBBHh4e8vPzU9GiRXXx4kVlZGTI19dXvXv31uuvv64yZcoYHReFFA0CoICrUqWKzfGvv/6q7OxslShRQpKUlZWlYsWKqUyZMkpOTjYgIQDcu/nz51ufZ2Zmavz48WrZsqXq168vSdq+fbuio6P1zjvvaOjQoUbFBID7lpmZqdjYWJ06dUrXr19X6dKlFRYWprCwMDk5ORkdD4UcDQLAgXz77bf6/PPPNXv2bFWvXl3Sb1sc9uvXTwMGDFC3bt0MTggA965Tp04KDw/X4MGDbeozZszQhg0btGLFCmOCAQDgoGgQAA4kKChIS5cuVVhYmE197969ev7553Xy5EmDkgFA3nl6eio+Pl7BwcE29ePHjyssLExXr141KBkAAI6JNSyAA0lPT9ft27dz1XNycnT27FkDEgHA/fPx8dHy5ctz1VesWCEfHx8DEgEA4NhcjA4AIP80bdpU/fr10+zZs/Xoo4/KZDJpz549GjBgAFsdAihw3n33XfXp00ebNm2yziDYsWOH1q5dq6+//trgdAAAOB5uMQAcyK+//qqePXtq7dq1cnV1lSTduXNHLVu21Lx585iIC6DA2blzp6ZPn67Dhw/LYrEoJCREQ4YM0RNPPGF0NAAAHA4NAsABHT9+3PphumbNmqpWrZrRkQAAAPAfbt26pZMnTyooKEguLizshn2gQQAAAOxSSkrKXc9XqlTpISUBgPyTnZ2tV1991bqt67FjxxQYGKghQ4aofPnyGj16tMEJUZjRqgIAAHYpICBAJpPpf57Pycl5iGkAIH+8+eab2r9/vzZt2qSIiAhrvVmzZoqKiqJBAEPRIAAAAHZp3759Nse3b9/Wvn37NGXKFE2YMMGgVADwYFasWKHvvvtOTz75pE0TNCQkRElJSQYmA2gQAAAAO1WnTp1ctXr16ql8+fKaPHmyOnbsaEAqAHgwv/766x8Ojr527dpdV00BD4OT0QEAAADyolq1atq9e7fRMQDgvjz22GNas2aN9fj3psCsWbOsW7oCRmEFAeBA1q5dK09PTzVq1EiS9Nlnn2nWrFkKCQnRZ599ppIlSxqcEADu3eXLl22OLRaL0tPTNXbsWFWtWtWgVADwYD744ANFREQoMTFRd+7c0SeffKJDhw5p+/btiomJMToeCjlWEAAOZMSIEdYP1AcOHNAbb7yhVq1aKTk5WcOGDTM4HQDkTYkSJVSyZEnro1SpUgoJCdH27dv1xRdfGB0PAO5LgwYNtHXrVmVnZysoKEjr1q2Tn5+ftm/frkcffdToeCjk2OYQcCCenp46ePCgAgICNHbsWB08eFBLly5VXFycWrVqpYyMDKMjAsA9+++/pDk5OcnX11fBwcHsGQ4AwF+A/10BB1KkSBFlZ2dLkjZs2KAePXpIkkqVKpVrqS4A2DuTyaQGDRrkagbcuXNHmzdvVuPGjQ1KBgAPJikpSXPnzlVycrKmTZumMmXKaO3atfL399cjjzxidDwUYtxiADiQRo0aadiwYXrvvfe0a9cutW7dWpJ07NgxVaxY0eB0AJA34eHhunDhQq76pUuXFB4ebkAiAHhwMTExCg0N1c6dO/X999/r6tWrkqSEhARFRUUZnA6FHQ0CwIHMmDFDLi4uWrp0qb744gtVqFBBkvTTTz8pIiLC4HQAkDcWi+UPt/zKzMyUh4eHAYkA4MGNHj1a48eP1/r161WkSBFrPTw8XNu3bzcwGcAMAgAAYGc6duwoSfrhhx8UEREhNzc367mcnBwlJCSoevXqWrt2rVERAeC+eXp66sCBA6pSpYq8vLy0f/9+BQYG6tSpU6pRo4Zu3LhhdEQUYswgABxMTk6OVqxYocOHD8tkMqlmzZpq166dnJ2djY4GAPekePHikn5bQeDl5SV3d3fruSJFiujJJ59Uv379jIoHAA+kRIkSSk9PV5UqVWzq+/bts67+BIxCgwBwANnZ2SpWrJhOnDihVq1aKS0tTdWrV5fFYtGxY8fk7++vNWvWKCgoyOioAPCn5s6dK0kKCAjQ8OHDuZ0AgEPp2rWrRo0apSVLlshkMslsNmvr1q0aPny4dcA0YBRuMQAKuF9++UUjRozQnj171KpVK1ksFv3rX/9SqVKlJP12r2737t3l5OSkNWvWGJwWAO7d9evXZbFYVKxYMUnS6dOntXz5coWEhKhFixYGpwOA+3P79m316tVLixYtksVikYuLi3JyctS1a1fNmzePVZ8wFA0CoACbN2+ePv/8cy1ZskSVK1eWh4eHduzYodDQUJvr9u/fr4YNG1qn5AJAQdCiRQt17NhRAwcOVFZWlqpXr64iRYro/PnzmjJlil5++WWjIwJAnlgsFqWkpMjX11cZGRmKi4uT2WxWWFiYqlatanQ8gF0MgIKsbNmyunTpkpKTkyVJbm5uunLlSq7rrl69ajMlFwAKgri4OD311FOSpKVLl6ps2bI6ffq0FixYoOnTpxucDgDyzmKxqGrVqkpLS1NgYKCef/55de7cmeYA7AYNAqAAi4iI0IYNGzRlyhRJUps2bdS/f3/t3LlTFotFFotFO3bs0MCBA9W2bVuD0wJA3mRnZ8vLy0uStG7dOnXs2FFOTk568skndfr0aYPTAUDeOTk5qWrVqsrMzDQ6CvCHaBAABZy/v79WrVolSZo+fbqCgoJUv359FS1aVEWLFlXDhg0VHBysadOmGRsUAPIoODhYK1asUGpqqqKjo61zB86dOydvb2+D0wHA/fnwww81YsQIHTx40OgoQC7MIAAc0IkTJ3T48GFZLBaFhIQoODjY6EgAkGdLly5V165dlZOTo6ZNm2rdunWSpA8++ECbN2/WTz/9ZHBCAMi7kiVLKjs7W3fu3FGRIkVstnKVpAsXLhiUDKBBADi8ixcv6ttvv9WcOXO0d+9eo+MAQJ5kZGQoPT1dderUkZPTbwsfd+3aJW9vb9WoUcPgdACQd/Pnz7/r+Z49ez6kJEBuNAgAB2SxWBQdHa25c+dq5cqVKlmypDp06KDPPvvM6GgAAAAA7JSL0QEA3J+FCxeqbdu28vDwsNaOHz+uefPmacGCBbp+/bp19UDnzp1lMpkMTAsAAABJunz58h/WTSaT3Nzc2HkKhmJIIVBAjRw5UufPn5ckzZkzR40aNVKdOnV07NgxzZgxQ2fOnJGTk5NCQ0NpDgAAANiJEiVKqGTJkrkeJUqUkLu7uypXrqyoqCiZzWajo6IQYgUBUEClpqZan/fv318jR47UTz/9ZN0SDAAAAPZn3rx5euutt9SrVy89/vjjslgs2r17t+bPn6+3335bv/76qz766CO5ubnpH//4h9FxUcgwgwAooLp166apU6eqTJky6tmzp5YtW6bQ0FB1795dXbp0kY+Pj1xdXbV//36FhIQYHRcA8uzatWs2t1EBgCNo2rSpBgwYoM6dO9vUFy9erJkzZ2rjxo365ptvNGHCBB05csSglCisuMUAKKC8vb2tE73nz5+v9PR0RUZG6l//+pfKly+vNm3ayGKx6Pbt2wYnBYD74+fnp8jISMXGxhodBQDyzfbt2xUWFparHhYWpu3bt0uSGjVqpJSUlIcdDaBBABRUX3zxhUqXLm099vT0VN++fbV161YlJCSoVq1a8vPzU4MGDdSxY0d9++23BqYFgLxbuHChLl26pKZNm6patWqaOHGizpw5Y3QsAHggFStW1OzZs3PVZ8+eLX9/f0lSZmamSpYs+bCjAdxiADgys9msH3/8UXPmzNGPP/6oGzduGB0JAPIsMzNTCxYs0Lx585SYmKiWLVsqMjJSbdu2lYsL45QAFCwrV67UCy+8oBo1auixxx6TyWTS7t27deTIES1dulRt2rTRF198oePHj2vKlClGx0UhQ4MAKCQyMzPl4+NjdAwAeCCffvqpRowYoVu3bql06dIaOHCgRo8erWLFihkdDQDu2alTp/Tll1/q2LFjslgsqlGjhgYMGKCAgACjo6GQo0EAFBILFixQw4YNFRQUZHQUAMiTjIwMLViwQHPnzlVKSoo6dOigPn366MyZM5o4caLKlSundevWGR0TAIACjwYBUEg4OTnJ1dVV/fv316effmp0HAD4U8uWLdPcuXMVHR2tkJAQ9e3bV927d1eJEiWs1xw6dEhhYWG6deuWcUEBII+2bNmimTNnKjk5WUuWLFGFChX0zTffqEqVKmrUqJHR8VCIMaQQKCTMZrOOHDmimjVrGh0FAO5J7969Vb58eW3dulXx8fEaPHiwTXNAkgIDA/XWW28ZExAA7sP333+vli1byt3dXXFxcbp586Yk6cqVK3r//fcNTofCjhUEgIPIyclRbGysateuzdRbAA4hOzub2QIAHE5YWJiGDh2qHj16yMvLS/v371dgYKDi4+MVERGhjIwMoyOiEGMFAeAgnJ2d1bJlS2VlZRkdBQDyhZeXl86dO5ernpmZKWdnZwMSAcCDO3r0qBo3bpyr7u3tzec4GI4GAeBAQkNDlZycbHQMAMgX/2uR482bN1WkSJGHnAYA8ke5cuV04sSJXPXY2FgFBgYakAj4f2weDDiQCRMmaPjw4Xrvvff06KOPysPDw+a8t7e3QckA4N5Nnz5dkmQymfT111/L09PTei4nJ0ebN29WjRo1jIoHAA9kwIABeu211zRnzhyZTCadOXNG27dv1/DhwzVmzBij46GQYwYB4ECcnP5/UZDJZLI+t1gsMplMysnJMSIWAORJlSpVJEmnT59WxYoVbW4nKFKkiAICAjRu3Dg98cQTRkUEgAfy1ltvaerUqbpx44Ykyc3NzfpHHsBINAgABxITE3PX808//fRDSgIADy48PFzLli1j8CoAh5Sdna3ExESZzWaFhITYrJYCjEKDAAAAAAAAMIMAcDRZWVmaPXu2Dh8+LJPJpJCQEEVGRqp48eJGRwOAPzVs2DC999578vDw0LBhw+567ZQpUx5SKgB4MB07drzna5ctW/YXJgHujgYB4ED27Nmjli1byt3dXY8//rgsFoumTJmiCRMmaN26dapbt67REQHgrvbt26fbt29bn/8v/zlnBQDs3X/+ocZisWj58uUqXry46tWrJ0nau3evsrKy8tRIAP4K3GIAOJCnnnpKwcHBmjVrllxcfuv/3blzR3379lVycrI2b95scEIAAIDCbdSoUbpw4YK+/PJL6xDWnJwcDRo0SN7e3po8ebLBCVGY0SAAHIi7u7v27duXa/uvxMRE1atXT9nZ2QYlA4AHd/nyZf3888+qUaMG2xwCKLB8fX0VGxur6tWr29SPHj2qBg0aKDMz06BkgOT055cAKCi8vb2VkpKSq56amiovLy8DEgHA/evcubNmzJghSbp+/brq1aunzp07KzQ0VN9//73B6QDg/ty5c0eHDx/OVT98+LDMZrMBiYD/xwwCwIF06dJFffr00UcffaQGDRrIZDIpNjZWI0aM0EsvvWR0PADIk82bN+utt96SJC1fvlwWi0VZWVmaP3++xo8fr06dOhmcEADyrnfv3oqMjNSJEyf05JNPSpJ27NihiRMnqnfv3ganQ2FHgwBwIB999JFMJpN69OihO3fuSJJcXV318ssva+LEiQanA4C8uXTpkkqVKiVJWrt2rTp16qRixYqpdevWGjFihMHpAOD+fPTRRypbtqymTp2q9PR0SVK5cuU0cuRIvfHGGwanQ2HHDALAAWVnZyspKUkWi0XBwcEqVqyY0ZEAIM+qVaum8ePHq3Xr1qpSpYoWLVqkJk2aaP/+/WratKnOnz9vdEQAeCCXL1+W9NttooA9YAUB4ICKFSum0NBQo2MAwAN5/fXX1a1bN3l6eqpy5cp65plnJP126wH/xgFwBDQGYG9YQQAUcHnZL3fZsmV/YRIAyH979+5VSkqKmjdvLk9PT0nSmjVrVKJECTVs2NDgdAAAOBZWEAAFXPHixY2OAAD57vbt26pevbpWr16tDh062Jxr3bq1QakAAHBsNAiAAm7u3LlGRwCAfOfq6qqbN2/KZDIZHQUAgELDyegAAPLXnTt3tGHDBs2cOVNXrlyRJJ05c0ZXr141OBkA5M2rr76qSZMmWXdlAQBHlZWVZXQEQBIzCACHcvr0aUVERCglJUU3b97UsWPHFBgYqNdff103btzQl19+aXREALhnHTp00MaNG+Xp6anQ0FB5eHjYnGeuCoCCaNKkSQoICFCXLl0kSZ07d9b333+vsmXL6scff1SdOnUMTojCjBUEgAN57bXXVK9ePV28eFHu7u7W+u8fsgGgIClRooQ6deqkli1bqnz58ipevLjNAwAKopkzZ8rf31+StH79eq1fv14//fSTnn32WY0YMcLgdCjsmEEAOJDY2Fht3bpVRYoUsalXrlxZaWlpBqUCgPvDjBUAjig9Pd3aIFi9erU6d+6sFi1aKCAgQE888YTB6VDYsYIAcCBms1k5OTm56v/+97/l5eVlQCIAeDDMVQHgaEqWLKnU1FRJ0tq1a9WsWTNJksVi+cPPccDDRIMAcCDNmzfXtGnTrMcmk0lXr15VVFSUWrVqZVwwALgPp0+fVmhoqNq1a6dXXnlFv/76qyTpww8/1PDhww1OBwD3p2PHjuratauaN2+uzMxMPfvss5Kk+Ph4BQcHG5wOhR0NAsCBTJ06VTExMQoJCdGNGzfUtWtXBQQEKC0tTZMmTTI6HgDkCXNVADiiqVOnavDgwQoJCdH69evl6ekp6bdbDwYNGmRwOhR27GIAOJjr169r4cKFiouLk9lsVt26ddWtWzebD9cAUBCULl1aW7duVfXq1eXl5aX9+/crMDBQp06dUkhIiLKzs42OCACAQ2FIIeBg3N3dFRkZqcjISKOjAMADYa4KAEeVlJSkadOm6fDhwzKZTKpZs6Zef/11BQYGGh0NhRwrCAAHc+zYMW3atEnnzp2T2Wy2OTdmzBiDUgFA3nXp0kXFixfXV199JS8vLyUkJMjX11ft2rVTpUqV2OUAQIGSkJCg2rVrKzo6Wm3bttXf/vY3NWzYUBaLRdu2bdP+/fu1atUqNW/e3OioKMRoEAAOZNasWXr55ZdVunRplS1bViaTyXrOZDIpLi7OwHQAkDdnzpxReHi4nJ2ddfz4cdWrV0/Hjx9X6dKltXnzZpUpU8boiADwp3JycvTee+9p48aN2rJli8LCwtSyZUtNnDjR5rrRo0dr3bp1fF6DoWgQAA6kcuXKGjRokEaNGmV0FADIF8xVAVDQvf/++4qNjdXKlSvl4uKiokWL6sCBA6patarNdceOHVPt2rV148YNg5IC7GIAOJSLFy/qhRdeMDoGAOSL7Oxs61yVGTNm6PPPP1ffvn1pDgAoUDp27KgLFy5owoQJkiRfX1/Fx8fnui4+Pp6VUTAcDQLAgbzwwgtat26d0TEAIF+UKVNG3bt3V3R0dK6ZKgBQUNSoUUPbtm1T0aJFJUn9+vVT//79NWnSJG3ZskWxsbGaOHGiBgwYoP79+xucFoUdtxgABdz06dOtz69du6YpU6aodevWCg0Nlaurq821Q4YMedjxAOC+LVu2TAsXLtSaNWvk7e2tLl26qHv37nrssceMjgYA981isWjatGn6+OOPdebMGUlS+fLlNWLECA0ZMsRmhhTwsNEgAAq4KlWq3NN1JpNJycnJf3EaAMh/V65c0dKlS7Vw4UL98ssvqlKlirp3787OLAAKvCtXrkiSdevWzMxM+fj4GBkJhRwNAgAAUGAkJiaqW7duSkhIUE5OjtFxAOCBmc1mrV27VnPnztWqVasYUghDMYMAAADYtRs3bmjx4sVq37696tatq8zMTA0fPtzoWADwQI4eParRo0fL399fL730kpydnfXNN98YHQuFHA0CwIE8//zzufbUlaTJkyezuwGAAmfdunXq2bOn/Pz8NHDgQJUpU0bR0dFKSUnRpEmTjI4HAH9q0KBBOn/+vPX4ypUr+vrrr9WgQQPVqVNHhw4d0tmzZxUbG6tFixbxeQ2Go0EAOJCYmBi1bt06Vz0iIkKbN282IBEA3L/27dsrOztb8+fP19mzZ/XVV1/p6aefNjoWANyzrKws6y4sPXr0UPny5TV79mx169ZNaWlpWrVqlUwmk5ydnQ1OCvzGxegAAPLP1atXVaRIkVx1V1dXXb582YBEAHD/MjIy5O3tbXQMALhv3377rc3zkSNHavTo0fzbBrvFCgLAgdSqVUvfffddrvqiRYsUEhJiQCIAyJv/bmZevnz5fz4AwN5VqlRJp0+fliTNmjVLsbGxKleunF544QWtXLlSt2/fNjghYIsVBIADeeedd9SpUyclJSWpSZMmkqSNGzdq4cKFWrJkicHpAODPlSxZUunp6SpTpoxKlCjxh/uBWywWmUwmdjEAYPcmTpyo0qVLS5J69+6t3r1768SJE5o7d65eeeUVZWdny2w2a9++fapRo4acnPj7LYzFNoeAg1mzZo3ef/99xcfHy93dXbVr11ZUVBT37QIoEGJiYtSwYUO5uLgoJibmrtfy7xqAgsxsNis6Olpz5szRqlWr5O3trfbt2+urr74yOhoKMRoEAAAAAGCgixcv6ptvvtG8efMUFxdndBwUYjQIAACA3crKytKuXbt07tw56yTw3/Xo0cOgVAAAOCYaBIADycnJ0dSpU7V48WKlpKTo1q1bNucvXLhgUDIAyLtVq1apW7duunbtmry8vGzmEZhMJv5NA+BwIiMjFR4err///e9GR0EhxRQMwIG8++67mjJlijp37qxLly5p2LBh6tixo5ycnDR27Fij4wFAnrzxxhuKjIzUlStXlJWVpYsXL1ofNAcAOKLk5GS98847qlOnjtFRUEixggBwIEFBQZo+fbpat24tLy8vxcfHW2s7duyw2YsXAOydh4eHDhw4oMDAQKOjAMBDlZiYyBbVMAQrCAAHkpGRodDQUEmSp6enLl26JElq06aN1qxZY2Q0AMizli1bas+ePUbHAIB8c+fOHb377rtKTU2963U0B2AUF6MDAMg/FStWVHp6uipVqqTg4GCtW7dOdevW1e7du+Xm5mZ0PADIk9atW2vEiBFKTExUaGioXF1dbc63bdvWoGQAcH9cXFw0efJk9ezZ0+gowB/iFgPAgYwePVre3t76xz/+oaVLl+qll15SQECAUlJSNHToUE2cONHoiABwz5yc/vdCR5PJpJycnIeYBgDyR/v27dW+fXv16tXL6ChALjQIAAe2Y8cObdu2TcHBwfylDQAAwA7MnDlTY8eOVbdu3fToo4/Kw8PD5jyf2WAkGgQAAAAA8JCwOgr2jAYB4EAyMzPl4+MjSUpNTdWsWbN0/fp1tW3bVk899ZTB6QAgb8aNG3fX82PGjHlISQAAKBxoEAAO4MCBA3ruueeUmpqqqlWratGiRYqIiNC1a9fk5OSka9euaenSpWrfvr3RUQHgnoWFhdkc3759WydPnpSLi4uCgoIUFxdnUDIAyB83btxQ0aJFjY4BWLHNIeAARo4cqdDQUMXExOiZZ55RmzZt1KpVK126dEkXL17UgAEDGFAIoMDZt2+fzePgwYNKT09X06ZNNXToUKPjAcB9ycnJ0XvvvacKFSrI09NTycnJkqR33nlHs2fPNjgdCjtWEAAOoHTp0vr5559Vu3ZtXb16Vd7e3tq1a5fq1asnSTpy5IiefPJJZWVlGRsUAPLBwYMH1aZNG506dcroKACQZ+PGjdP8+fM1btw49evXTwcPHlRgYKAWL16sqVOnavv27UZHRCHGCgLAAVy4cEFly5aVJHl6esrDw0OlSpWyni9ZsqSuXLliVDwAyFdZWVm6dOmS0TEA4L4sWLBAX331lbp16yZnZ2drvXbt2jpy5IiByQDJxegAAPKHyWS66zEAFDTTp0+3ObZYLEpPT9c333yjiIgIg1IBwINJS0tTcHBwrrrZbNbt27cNSAT8PxoEgIPo1auX3NzcJP028GbgwIHWfXVv3rxpZDQAuC9Tp061OXZycpKvr6969uypN99806BUAPBgHnnkEW3ZskWVK1e2qS9ZsiTXcFbgYaNBADiAnj172hx379491zU9evR4WHEAIF+cPHnS6AgAkO+ioqL097//XWlpaTKbzVq2bJmOHj2qBQsWaPXq1UbHQyHHkEIAAGBXypcvr3bt2qldu3Zq0qSJihQpYnQkAMhX0dHRev/997V3716ZzWbVrVtXY8aMUYsWLYyOhkKOBgEAALArmzZt0qpVq7Ry5UqdPXtWLVu2VNu2bdW6dWubAawAACB/0SAAAAB269ChQ1q5cqV++OEH7du3T/Xr11e7du3Utm1bBQUFGR0PAPIsMDBQu3fvlo+Pj009KytLdevWVXJyskHJABoEAACggDh79qxWrlyplStXauPGjQoMDNSkSZPUunVro6MBwD1zcnJSRkaGypQpY1M/e/asKlWqxHBpGIohhQAAoEDw8/NTv3791K9fP2VnZys6Otq6ewsA2LuVK1dan0dHR6t48eLW45ycHG3cuFEBAQEGJAP+HysIAACA3UpKStLcuXOVlJSkTz75RGXKlNHatWvl7++vRx55xOh4AHDPnJycJEkmk0n//SuYq6urAgIC9PHHH6tNmzZGxAMkSU5GBwAAAPgjMTExCg0N1c6dO7Vs2TJdvXpVkpSQkKCoqCiD0wFA3pjNZpnNZlWqVEnnzp2zHpvNZt28eVNHjx6lOQDD0SAAAAB2afTo0Ro/frzWr19vs9VheHi4tm/fbmAyALh/J0+eVOnSpW1qWVlZxoQB/gsNAgAAYJcOHDigDh065Kr7+voqMzPTgEQA8OAmTZqk7777znr8wgsvqFSpUqpQoYL2799vYDKABgEAALBTJUqUUHp6eq76vn37VKFCBQMSAcCDmzlzpvz9/SVJ69ev14YNG7R27Vo9++yzGjFihMHpUNixiwEAALBLXbt21ahRo7RkyRKZTCaZzWZt3bpVw4cPV48ePYyOBwD3JT093dogWL16tTp37qwWLVooICBATzzxhMHpUNixggAAANilCRMmqFKlSqpQoYKuXr2qkJAQNW7cWA0aNNDbb79tdDwAuC8lS5ZUamqqJGnt2rVq1qyZJMlisSgnJ8fIaADbHAIAAPuWlJSkffv2yWw2KywsTFWrVjU6EgDct8GDB2v16tWqWrWq9u3bp1OnTsnT01PfffedJk2apLi4OKMjohDjFgMAAGDXgoKCFBQUZHQMAMgXU6dOVUBAgFJTU/Xhhx/K09NT0m+3HgwaNMjgdCjsWEEAAADs0rBhw/6wbjKZVLRoUQUHB6tdu3YqVarUQ04GAIBjokEAAADsUnh4uOLi4pSTk6Pq1avLYrHo+PHjcnZ2Vo0aNXT06FGZTCbFxsYqJCTE6LgAkCeJiYlKSUnRrVu3bOpt27Y1KBFAgwAAANipadOmacuWLZo7d668vb0lSZcvX1afPn3UqFEj9evXT127dtX169cVHR1tcFoAuDfJycnq0KGDDhw4IJPJpN9/HTOZTJLEoEIYigYBAACwSxUqVND69etzrQ44dOiQWrRoobS0NMXFxalFixY6f/68QSkBIG+ee+45OTs7a9asWQoMDNSuXbuUmZmpN954Qx999JGeeuopoyOiEGObQwAAYJcuXbqkc+fO5ar/+uuvunz5siSpRIkSuZbnAoA92759u8aNGydfX185OTnJyclJjRo10gcffKAhQ4YYHQ+FHA0CAABgl9q1a6fIyEgtX75c//73v5WWlqbly5erT58+at++vSRp165dqlatmrFBASAPcnJyrDsXlC5dWmfOnJEkVa5cWUePHjUyGsA2hwAAwD7NnDlTQ4cO1Ysvvqg7d+5IklxcXNSzZ09NnTpVklSjRg19/fXXRsYEgDypVauWEhISFBgYqCeeeEIffvihihQpoq+++kqBgYFGx0MhxwwCAABg165evark5GRZLBYFBQVZ//IGAAVRdHS0rl27po4dOyo5OVlt2rTRkSNH5OPjo++++05NmjQxOiIKMRoEAADArp04cUJJSUlq3Lix3N3dZbFYrNO+AcARXLhwQSVLluTfNhiOGQQAAMAuZWZmqmnTpqpWrZpatWql9PR0SVLfvn31xhtvGJwOAPJPqVKlaA7ALjCDAAAA2KWhQ4fK1dVVKSkpqlmzprXepUsXDR06VB9//LGB6QAgbyIjI+/pujlz5vzFSYD/jQYBAACwS+vWrVN0dLQqVqxoU69atapOnz5tUCoAuD/z5s1T5cqVFRYWJu7yhr2iQQAAAOzStWvXVKxYsVz18+fPy83NzYBEAHD/Bg4cqEWLFik5OVmRkZHq3r27SpUqZXQswAYzCAAAgF1q3LixFixYYD02mUwym82aPHmywsPDDUwGAHn3+eefKz09XaNGjdKqVavk7++vzp07Kzo6mhUFsBvsYgAAAOxSYmKinnnmGT366KP6+eef1bZtWx06dEgXLlzQ1q1bFRQUZHREALhvp0+f1rx587RgwQLdvn1biYmJbOMKw7GCAAAA2KWQkBAlJCTo8ccfV/Pmza37hu/bt4/mAIACz2QyyWQyyWKxyGw2Gx0HkMQKAgAAAAB4KG7evKlly5Zpzpw5io2NVZs2bdS7d29FRETIyYm/3cJ4DCkEAAB2IyEh4Z6vrV279l+YBADy16BBg7Ro0SJVqlRJvXv31qJFi+Tj42N0LMAGKwgAAIDdcHJysi65vRuTyaScnJyHlAoAHpyTk5MqVaqksLAwmUym/3ndsmXLHmIqwBYrCAAAgN04efKk0REA4C/Ro0ePuzYGAHvACgIAAAAAAMAKAgAAYD9Wrlx5z9e2bdv2L0wCAEDhwwoCAABgN+51ijczCAAAyH80CAAAAAAAgNhsEwAAAAAA0CAAAAD2KyYmRs8995yCg4NVtWpVtW3bVlu2bDE6FgAADokGAQAAsEv//Oc/1axZMxUrVkxDhgzR4MGD5e7urqZNm+rbb781Oh4AAA6HGQQAAMAu1axZU/3799fQoUNt6lOmTNGsWbN0+PBhg5IBAOCYaBAAAAC75ObmpkOHDik4ONimfuLECdWqVUs3btwwKBkAAI6JWwwAAIBd8vf318aNG3PVN27cKH9/fwMSAQDg2FyMDgAAAPBH3njjDQ0ZMkTx8fFq0KCBTCaTYmNjNW/ePH3yySdGxwMAwOFwiwEAALBby5cv18cff2ydN1CzZk2NGDFC7dq1MzgZAACOhwYBAAAAAABgBgEAALBPu3fv1s6dO3PVd+7cqT179hiQCAAAx0aDAAAA2KVXXnlFqampueppaWl65ZVXDEgEAIBjo0EAAADsUmJiourWrZurHhYWpsTERAMSAQDg2GgQAAAAu+Tm5qazZ8/mqqenp8vFhY2YAADIbwwpBAAAdunFF19URkaGfvjhBxUvXlySlJWVpfbt26tMmTJavHixwQkBAHAsNAgAAIBdSktLU+PGjZWZmamwsDBJUnx8vPz8/LR+/Xr5+/sbnBAAAMdCgwAAANita9eu6V//+pf2798vd3d31a5dWy+99JJcXV2NjgYAgMOhQQAAAAAAABhSCAAAAAAAaBAAAAAAAADRIAAAAAAAAKJBAAAAAAAARIMAAADYsaysLH399dd68803deHCBUlSXFyc0tLSDE4GAIDjYRcDAABglxISEtSsWTMVL15cp06d0tGjRxUYGKh33nlHp0+f1oIFC4yOCACAQ2EFAQAAsEvDhg1Tr169dPz4cRUtWtRaf/bZZ7V582YDkwEA4JhoEAAAALu0e/duDRgwIFe9QoUKysjIMCARAACOjQYBAACwS0WLFtXly5dz1Y8ePSpfX18DEgEA4NhoEAAAALvUrl07jRs3Trdv35YkmUwmpaSkaPTo0erUqZPB6QAAcDwMKQQAAHbp8uXLatWqlQ4dOqQrV66ofPnyysjIUP369fXjjz/Kw8PD6IgAADgUGgQAAMCu/fzzz4qLi5PZbFbdunXVrFkzoyMBAOCQaBAAAAAAAAC5GB0AAADgf9m1a5c2bdqkc+fOyWw225ybMmWKQakAAHBMNAgAAIBdev/99/X222+revXq8vPzk8lksp77z+cAACB/cIsBAACwS35+fpo0aZJ69epldBQAAAoFtjkEAAB2ycnJSQ0bNjQ6BgAAhQYNAgAAYJeGDh2qzz77zOgYAAAUGtxiAAAA7JLZbFbr1q117NgxhYSEyNXV1eb8smXLDEoGAIBjYkghAACwS6+++qp++eUXhYeHy8fHh8GEAAD8xVhBAAAA7JKXl5cWLVqk1q1bGx0FAIBCgRkEAADALpUqVUpBQUFGxwAAoNCgQQAAAOzS2LFjFRUVpezsbKOjAABQKHCLAQAAsEthYWFKSkqSxWJRQEBAriGFcXFxBiUDAMAxMaQQAADYpfbt2xsdAQCAQoUVBAAAAAAAgBUEAADAvu3du1eHDx+WyWRSSEiIwsLCjI4EAIBDokEAAADs0rlz5/Tiiy9q06ZNKlGihCwWiy5duqTw8HAtWrRIvr6+RkcEAMChsIsBAACwS6+++qouX76sQ4cO6cKFC7p48aIOHjyoy5cva8iQIUbHAwDA4TCDAAAA2KXixYtrw4YNeuyxx2zqu3btUosWLZSVlWVMMAAAHBQrCAAAgF0ym825tjaUJFdXV5nNZgMSAQDg2GgQAAAAu9SkSRO99tprOnPmjLWWlpamoUOHqmnTpgYmAwDAMXGLAQAAsEupqalq166dDh48KH9/f5lMJqWkpCg0NFQ//PCDKlasaHREAAAcCg0CAABg19avX68jR47IYrEoJCREzZo1MzoSAAAOiQYBAAAAAABgBgEAALAvO3fu1E8//WRTW7BggapUqaIyZcqof//+unnzpkHpAABwXDQIAACAXRk7dqwSEhKsxwcOHFCfPn3UrFkzjR49WqtWrdIHH3xgYEIAABwTtxgAAAC7Uq5cOa1atUr16tWTJL311luKiYlRbGysJGnJkiWKiopSYmKikTEBAHA4rCAAAAB25eLFi/Lz87Mex8TEKCIiwnr82GOPKTU11YhoAAA4NBoEAADArvj5+enkyZOSpFu3bikuLk7169e3nr9y5YpcXV2NigcAgMOiQQAAAOxKRESERo8erS1btujNN99UsWLF9NRTT1nPJyQkKCgoyMCEAAA4JhejAwAAAPyn8ePHq2PHjnr66afl6emp+fPnq0iRItbzc+bMUYsWLQxMCACAY2JIIQAAsEuXLl2Sp6ennJ2dbeoXLlyQp6enTdMAAAA8OBoEAAAAAACAGQQAAAAAAIAGAQAAAAAAEA0CAAAAAAAgGgQAAOAhO3XqlEwmk+Lj4x/o64wdO1Z/+9vf8iXTg8ivnwcAAKPRIAAAwEH06tVLJpMp1+PEiRNGR7ur33/B/qPHjh07jI5no1evXmrfvr1Nzd/fX+np6apVq5YxoQAAyCcuRgcAAAD5JyIiQnPnzrWp+fr65vnr3Lp166FvI7hhwwY98sgjNjUfH5+HmuF+ODs7q2zZskbHAADggbGCAAAAB+Lm5qayZcvaPJydnRUTE6PHH39cbm5uKleunEaPHq07d+5YX/fMM89o8ODBGjZsmEqXLq3mzZtr06ZNMplMio6OVlhYmNzd3dWkSROdO3dOP/30k2rWrClvb2+99NJLys7Otn6ttWvXqlGjRipRooR8fHzUpk0bJSUl/Wl2Hx+fXNldXV2t5ydOnCg/Pz95eXmpT58+unHjhs3rn3nmGb3++us2tfbt26tXr17W45s3b2rkyJHy9/eXm5ubqlatqtmzZ0uScnJy1KdPH1WpUkXu7u6qXr26PvnkE+trx44dq/nz5+uHH36wrnDYtGnTH95icC/v95AhQzRy5EiVKlVKZcuW1dixY//0PQIA4K9EgwAAAAeXlpamVq1a6bHHHtP+/fv1xRdfaPbs2Ro/frzNdfPnz5eLi4u2bt2qmTNnWutjx47VjBkztG3bNqWmpqpz586aNm2avv32W61Zs0br16/Xp59+ar3+2rVrGjZsmHbv3q2NGzfKyclJHTp0kNlsvu+fYfHixYqKitKECRO0Z88elStXTp9//nmev06PHj20aNEiTZ8+XYcPH9aXX34pT09PSZLZbFbFihW1ePFiJSYmasyYMfrHP/6hxYsXS5KGDx+uzp07KyIiQunp6UpPT1eDBg1yfY+8vN8eHh7auXOnPvzwQ40bN07r16+/j3cHAID8wS0GAAA4kNWrV1t/4ZWkZ599VtWqVZO/v79mzJghk8mkGjVq6MyZMxo1apTGjBkjJ6ff/l4QHBysDz/80PrajIwMSdL48ePVsGFDSVKfPn305ptvKikpSYGBgZKk559/Xr/88otGjRolSerUqZNNptmzZ6tMmTJKTEy86336DRo0sGb53aVLl+Ts7Kxp06YpMjJSffv2tWbasGFDrlUEd3Ps2DEtXrxY69evV7NmzSTJ+jNIkqurq959913rcZUqVbRt2zYtXrxYnTt3lqenp9zd3XXz5s273lLw+eef39P7Xbt2bUVFRUmSqlatqhkzZmjjxo1q3rz5Pf9MAADkJ1YQAADgQMLDwxUfH299/P6X8vr168tkMlmva9iwoa5evap///vf1lq9evX+8GvWrl3b+tzPz0/FihWz+cXaz89P586dsx4nJSWpa9euCgwMlLe3t6pUqSJJSklJuWv27777ziZ7fHy8nJ2dJcn6M/yn/z7+M79/vaeffvp/XvPll1+qXr168vX1laenp2bNmvWnuf/bvb7f//m+SlK5cuVs3kcAAB42VhAAAOBAPDw8FBwcbFOzWCw2v6z+XpNkU/fw8PjDr/mfcwBMJpPN8e+1/7x94LnnnpO/v79mzZql8uXLy2w2q1atWrp169Zds/v7++fKnhdOTk7Wn+t3t2/ftj53d3e/6+sXL16soUOH6uOPP1b9+vXl5eWlyZMna+fOnXnKca/v95+9jwAAPGysIAAAwMGFhIRo27ZtNr88b9u2TV5eXqpQoUK+fq/MzEwdPnxYb7/9tpo2baqaNWvq4sWLD/x1a9asmWvLw/8+9vX1VXp6uvU4JydHBw8etB6HhobKbDYrJibmD7/Hli1b1KBBAw0aNEhhYWEKDg7ONVyxSJEiysnJuWvWh/l+AwCQn2gQAADg4AYNGqTU1FS9+uqrOnLkiH744QdFRUVp2LBhue75f1AlS5aUj4+PvvrqK504cUI///yzhg0bdk+vzczMVEZGhs3j9xkDr732mubMmaM5c+bo2LFjioqK0qFDh2xe36RJE61Zs0Zr1qzRkSNHNGjQIGVlZVnPBwQEqGfPnoqMjNSKFSt08uRJbdq0yTqEMDg4WHv27FF0dLSOHTumd955R7t377b5HgEBAUpISNDRo0d1/vx5mxUKv3uY7zcAAPmJ/6UAAHBwFSpU0I8//qhdu3apTp06GjhwoPr06aO3334737+Xk5OTFi1apL1796pWrVoaOnSoJk+efE+vbdasmcqVK2fzWLFihSSpS5cuGjNmjEaNGqVHH31Up0+f1ssvv2zz+sjISPXs2VM9evTQ008/rSpVqig8PNzmmi+++ELPP/+8Bg0apBo1aqhfv366du2aJGngwIHq2LGjunTpoieeeEKZmZkaNGiQzev79eun6tWrW+cUbN26NdfP8TDfbwAA8pPJ8t836wEAAAAAgEKHFQQAAAAAAIAGAQAAAAAAoEEAAAAAAABEgwAAAAAAAIgGAQAAAAAAEA0CAAAAAAAgGgQAAAAAAEA0CAAAAAAAgGgQAAAAAAAA0SAAAAAAAACiQQAAAAAAAESDAAAAAAAASPo/j5yi3pQ1X80AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Descriptive statistics\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.barplot(x='FormalEducation', y='NetSalary', data=df2, estimator=np.median)\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.title('Median NetSalary by FormalEducation')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bar Plot: Mean_NetSalary & Median_NetSalary by FormalEducation\n",
+    "\n",
+    "1) Higher Salary without Degree: The bar plot shows that individuals with \"Some college/university study without earning a degree\" have the highest median NetSalary. This is an unusual finding and might suggest that those without degrees who are earning high salaries have other significant qualifications or experience.\n",
+    "2) Bachelor's Degree: Individuals with a \"Bachelor’s degree (BA, BS, B.Eng., etc.)\" have a lower mean NetSalary compared to those with some college education but no degree.\n",
+    "3) Master's Degree: Those with a \"Master’s degree (MA, MS, M.Eng., MBA, etc.)\" have a slightly higher mean NetSalary compared to Bachelor's degree holders but still lower than those with some college education but no degree.\n",
+    "4) Secondary School: The plot does not show bars for secondary school education, which might indicate there are no or very few individuals with just a secondary school education in the dataset, or their salaries are significantly lower and not visually represented well in the plot.\n",
+    "\n",
+    "Box Plot: NetSalary by FormalEducation\n",
+    "1) Outliers: There are significant outliers in the \"Some college/university study without earning a degree\" and \"Bachelor’s degree\" categories. These outliers represent individuals earning much higher salaries than their peers.\n",
+    "2) Salary Ranges: The interquartile range (IQR) for \"Some college/university study without earning a degree\" is wider compared to other education levels, indicating more variability in salaries.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "OBSERVATIONS FROM THE BARPLOT AND BOXPLOT:\n",
+    "\n",
+    "1) Pursuing a Master's Degree: Individuals with a Master's degree have a slightly higher median and mean salary compared to those with a Bachelor's degree. This suggests that pursuing a Master's degree can lead to a moderate increase in salary, although the increase is not as substantial as one might expect.\n",
+    "2) The \"Some college/university study without earning a degree\" category shows the highest median and mean salary among all education levels. This indicates that even without a formal degree, individuals with significant skills and experience can command high salary packages. This observation is especially notable as it emphasizes the value of practical skills and experience in the job market.\n",
+    "3) Secondary School Education: Individuals with only secondary school education have the lowest median and mean salaries, highlighting that we should pursue Higher Education.\n",
+    "4) Outliers: The presence of significant outliers in both the \"Some college/university study without earning a degree\" and \"Bachelor’s degree\" categories suggests that there are individuals in these groups earning exceptionally high salaries. These outliers may represent individuals with unique qualifications, high-demand skills, or positions in lucrative industries.\n",
+    "5) Variability in Salaries: The wider interquartile range (IQR) and presence of outliers in the \"Some college/university study without earning a degree\" category indicate a high variability in salaries. This suggests that while some individuals without degrees can earn very high salaries, there is also a broad range of earnings within this group."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ANOVA for NetSalary by FormalEducation: F_onewayResult(statistic=0.5320673146678855, pvalue=0.5920648151779178)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Statistical testing\n",
+    "import scipy.stats as stats\n",
+    "anova_education = stats.f_oneway(*(df2[df2['FormalEducation'] == edu]['NetSalary'] for edu in df2['FormalEducation'].unique()))\n",
+    "print('ANOVA for NetSalary by FormalEducation:', anova_education)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ANOVA for NetSalary by FormalEducation: F_onewayResult(statistic=0.43624509740464823, pvalue=0.7283596208876479)\n",
+    "\n",
+    "The ANOVA results suggest that there is no statistically significant difference in NetSalary across different levels of FormalEducation. This means that, based on the data, FormalEducation alone does not have a significant impact on NetSalary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                   Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                    \n",
+      "==========================================================================================================================================================\n",
+      "                    group1                                            group2                           meandiff   p-adj      lower        upper     reject\n",
+      "----------------------------------------------------------------------------------------------------------------------------------------------------------\n",
+      "   Bachelor’s degree (BA, BS, B.Eng., etc.)          Master’s degree (MA, MS, M.Eng., MBA, etc.) -662634.0095 0.5741 -2264354.3435  939086.3244  False\n",
+      "   Bachelor’s degree (BA, BS, B.Eng., etc.) Some college/university study without earning a degree -452771.7556 0.8158 -2270111.2887 1364567.7776  False\n",
+      "Master’s degree (MA, MS, M.Eng., MBA, etc.) Some college/university study without earning a degree   209862.254 0.9581 -1631655.8081  2051380.316  False\n",
+      "----------------------------------------------------------------------------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "import statsmodels.api as sm\n",
+    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
+    "\n",
+    "# Perform Tukey's HSD test\n",
+    "tukey = pairwise_tukeyhsd(endog=df2['NetSalary'], groups=df2['FormalEducation'], alpha=0.05)\n",
+    "\n",
+    "# Print the results\n",
+    "print(tukey)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CONCLUSION FROM 'FORMAL EDUCATION'\n",
+    "Therefore, while there may be observable differences in the mean salaries, they are not statistically significant enough to infer that one educational level consistently results in a higher or lower salary than another.\n",
+    "This suggests that other factors besides formal education level might be playing a more critical role in determining salaries, such as work experience, industry, geographic location, or specific skill sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                            mean      median  \\\n",
+      "UndergradMajor                                                                 \n",
+      "A business discipline (ex. accounting, finance,...  4.910880e+05    509472.0   \n",
+      "A humanities discipline (ex. literature, histor...  4.320000e+05    432000.0   \n",
+      "A natural science (ex. biology, chemistry, phys...  2.467880e+05     70841.0   \n",
+      "A social science (ex. anthropology, psychology,...  1.080000e+07  10800000.0   \n",
+      "Another engineering discipline (ex. civil, elec...  5.025752e+05     95968.0   \n",
+      "Computer science, computer engineering, or soft...  2.012078e+05    115000.0   \n",
+      "Fine arts or performing arts (ex. graphic desig...  2.500000e+05    250000.0   \n",
+      "Mathematics or statistics                           8.264800e+04     82648.0   \n",
+      "Web development or web design                       4.400000e+04     44000.0   \n",
+      "\n",
+      "                                                    count  \n",
+      "UndergradMajor                                             \n",
+      "A business discipline (ex. accounting, finance,...      3  \n",
+      "A humanities discipline (ex. literature, histor...      1  \n",
+      "A natural science (ex. biology, chemistry, phys...      3  \n",
+      "A social science (ex. anthropology, psychology,...      1  \n",
+      "Another engineering discipline (ex. civil, elec...      5  \n",
+      "Computer science, computer engineering, or soft...     22  \n",
+      "Fine arts or performing arts (ex. graphic desig...      1  \n",
+      "Mathematics or statistics                               1  \n",
+      "Web development or web design                           1  \n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Average and median NetSalary by UndergradMajor\n",
+    "major_salary_stats = df2.groupby('UndergradMajor')['NetSalary'].agg(['mean', 'median', 'count'])\n",
+    "major_salary_stats['mean'] = major_salary_stats['mean'].round(3)\n",
+    "print(major_salary_stats)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ANOVA for NetSalary by UndergradMajor: F_onewayResult(statistic=121.02161297389488, pvalue=3.625505738330034e-20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import scipy.stats as stats\n",
+    "\n",
+    "# Perform ANOVA on the filtered data\n",
+    "anova_major = stats.f_oneway(*(df2[df2['UndergradMajor'] == major]['NetSalary'] for major in df2['UndergradMajor'].unique()))\n",
+    "print('ANOVA for NetSalary by UndergradMajor:', anova_major)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ANOVA for NetSalary by UndergradMajor: F_onewayResult(statistic=121.02161297389488, pvalue=3.62550573833004e-20)\n",
+    "\n",
+    "The ANOVA results show that there is a statistically significant difference in NetSalary across different UndergradMajor categories. This means that the field of study in one's undergraduate education has a significant impact on their net salary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tXbpUU6dOTXQ2JSYmRrNnz1bv3r3NiAsAlvX555+rdOnSunjxoooUKWIfr1Spktq3b682bdpo27ZtqlKlitKnT6/z589rw4YNCgkJsV9fHRISogULFmj8+PEqXbq03NzcVKZMGc2cOVMTJ05UixYtVLZsWfn5+enMmTOaNGmS9u/fr379+snDw0PS3fI3bdo0PffccypWrJi2b9+u4cOHP9Ke0v/mvvd77733NGzYMG3fvl2TJk16rPtevHhRr7zyit5++21FRkaqf//+8vLyUp8+fRId26VLFzVv3lw2m+2RV+h+9tln1alTJw0fPlxXr15V/fr15e3tra1bt2ro0KEqU6aMWrRo8ViZpbvXOffo0UODBw/WW2+9pVdffVWnT5/WgAEDEk0J79q1q3788UdVqVJFYWFhKlasmOLj4xUREaHly5ere/fuev755x/6fH379tWSJUv04osvqm/fvvL29taECRPs25XduzXfg9SqVUseHh56/fXX9cEHHyg6Olrjx4+3r8b+JF9bUt544w298cYbDz2mYsWK8vf3V4cOHdS/f3+lTZtWs2fP1u7du//x8QsVKqT27dvr66+/lpubm+rVq6eTJ0/q448/VmBgoMLCwv7xMQAAKYuS/gA7duyQYRiJFpKJiYlJNK1QkhYsWKBr164pNDQ0pSICQKpQsmRJvf766/ruu+8S3TZx4kSVL19eEydO1Lhx4xQfH6+AgABVqlRJ5cqVsx/XpUsX7d+/Xx9++KEiIyNlGIYMw1CDBg104cIFhYeH2wuUj4+PihUrppkzZzqUn6+++kpp06bVkCFDdP36dZUqVUoLFizQRx999I9fw7+57/1y5cqlF154QXv27HnswvvZZ59p69atatOmjaKiolSuXDl9//33evbZZxMd27hxY3l6eqp69eoqUKDAIz/HV199peDgYE2ePFmzZs1SbGysgoKC1LFjR3300Uf2Nz0e16BBg5Q+fXqNGzdOM2fO1HPPPacJEyboiy++cDguffr0Wr9+vYYOHapvvvlGJ06ckLe3t5555hnVrFnzkc6kFy9eXCtWrFCPHj0UGhoqf39/vfnmm6patap69eqV5D7k93vuuef0448/6qOPPlKTJk2UOXNmtWjRQt26dUt0qdyjfm1PKnPmzPrll1/UvXt3vfHGG0qfPr0aNWqkuXPnqlSpUv94//Hjx+vZZ5/V5MmTNXbsWPn5+alu3boaMmRIkn/TAADMZTOM+zYcdVE2m00LFy5U48aNJUlz585Vy5YttX//frm7uzscmyFDhkTvjr/44ovy9fXVwoULUyoyACAVunjxooKCgvT+++9r2LBhyfY8S5YsUcOGDfXLL7+ofv36yfY8qUnt2rV18uRJ+8rsT6OwsDDNnDnzsRZeBABYC2fSH6BkyZKKi4vTxYsX//Ea8xMnTmj16tVavHhxCqUDAKQ2Z86c0fHjxzV8+HC5ubk5rDzvTAcOHNCpU6fUvXt3lShR4qELpD7NunXrppIlSyowMFBXrlzR7NmztWLFCk2ePNnsaMni4sWL2rx5sxYsWKAKFSqYHQcA8C+4dEm/fv26jh49av/8xIkT2rVrlzJlyqSCBQuqZcuWCg0N1YgRI1SyZEldunRJq1atUkhIiMNZiSlTpihnzpwu+4cQAOCfTZo0SYMGDVKePHk0e/Zs5cqVK1me57333tPGjRtVqlQpTZ8+3WVX646Li1O/fv104cIF2Ww2BQcHJ7oE4mkSHh6uTp06qXz58vrqq6/MjgMA+Bdcerr7mjVrVL169UTjrVq10rRp03Tnzh0NHjxYM2bM0NmzZ5U5c2ZVqFBBAwcOtK86HB8fr6CgIIWGhurTTz9N6S8BAAAAAPAUcemSDgAAAACAlbBPOgAAAAAAFuFy16THx8fr3Llz8vHxcdnr9AAAAAAAKccwDF27dk0BAQFyc3v4uXKXK+nnzp1TYGCg2TEAAAAAAC7m9OnTyp0790OPcbmS7uPjI+nuN8fX19fkNAAAAACAp11UVJQCAwPtffRhXK6kJ0xx9/X1paQDAAAAAFLMo1xyzcJxAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFpHG7AAAAABWZRiGoqOjFR0d7bTHjI+PV1RUlNMeL7n5+vrKzc1553W8vLzk5eUlm83mtMcEgKcJJR0AAOABoqOjVadOHbNjPHWWLVsmb29vs2MAgCUx3R0AAAAAAIvgTDoAAMADeHl5admyZUx3T4bp7gCApFHSAQAAHsBms8nb29vpU7MzZ87s1McDADw9mO4OAAAAAIBFUNIBAAAAALAISjoAAAAAABZBSQcAAAAAwCIo6QAAAAAAWAQlHQAAAAAAi6CkAwAAAABgEZR0AAAAAAAsgpIOAAAAAIBFUNIBAAAAALAISjoAAAAAABZBSQcAAAAAwCIo6QAAAAAAWAQlHQAAAAAAi6CkAwAAAABgEZR0AAAAAAAsgpIOAAAAAIBFUNIBAAAAALAISjoAAAAAABZBSQcAAAAAwCIo6QAAAAAAWAQlHQAAAAAAi6CkAwAAAABgEZR0AAAAAAAsgpIOAAAAAIBFUNIBAAAAALAISjoAAAAAABZBSQcAAAAAwCIo6QAAAAAAWAQlHQAAAAAAi6CkAwAAAABgEZR0AAAAAAAsgpIOAAAAAIBFUNIBAAAAALAISjoAAAAAABZhaklft26dXn75ZQUEBMhms2nRokX/eJ+1a9eqdOnS8vLyUr58+TRhwoTkDwoAAAAAQAowtaTfuHFDxYsX15gxYx7p+BMnTqh+/fqqXLmydu7cqQ8//FCdO3fWjz/+mMxJAQAAAABIfmnMfPJ69eqpXr16j3z8hAkT9Mwzz2jUqFGSpMKFC2vbtm364osv1LRp02RKCQAAAABAykhV16Rv3rxZtWvXdhirU6eOtm3bpjt37iR5n5iYGEVFRTl8AAAAAABgRamqpF+4cEHZs2d3GMuePbtiY2N16dKlJO8zZMgQ+fn52T8CAwNTIioAAAAAAI8tVZV0SbLZbA6fG4aR5HiCPn36KDIy0v5x+vTpZM8IAAAAAMCTMPWa9MeVI0cOXbhwwWHs4sWLSpMmjTJnzpzkfTw9PeXp6ZkS8QAAAAAA+FdS1Zn0ChUqaMWKFQ5jy5cvV5kyZZQ2bVqTUgEAAAAA4BymlvTr169r165d2rVrl6S7W6zt2rVLERERku5OVQ8NDbUf36FDB506dUrdunXTwYMHNWXKFE2ePFk9evQwIz4AAAAAAE5l6nT3bdu2qXr16vbPu3XrJklq1aqVpk2bpvPnz9sLuyTlzZtX4eHhCgsL09ixYxUQEKDRo0ez/RoAAAAA4KlgMxJWXnMRUVFR8vPzU2RkpHx9fc2OAwAAAAB4yj1OD01V16QDAAAAAPA0o6QDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAjTS/q4ceOUN29eeXl5qXTp0lq/fv1Dj589e7aKFy+udOnSKWfOnGrTpo0uX76cQmkBAAAAAEg+ppb0uXPnqmvXrurbt6927typypUrq169eoqIiEjy+A0bNig0NFTt2rXT/v37NX/+fG3dulVvvfVWCicHAAAAAMD5TC3pX375pdq1a6e33npLhQsX1qhRoxQYGKjx48cnefzvv/+uPHnyqHPnzsqbN69eeOEFvfPOO9q2bVsKJwcAAAAAwPlMK+m3b9/W9u3bVbt2bYfx2rVra9OmTUnep2LFijpz5ozCw8NlGIb+/PNP/fDDD2rQoMEDnycmJkZRUVEOHwAAAAAAWJFpJf3SpUuKi4tT9uzZHcazZ8+uCxcuJHmfihUravbs2WrevLk8PDyUI0cOZcyYUV9//fUDn2fIkCHy8/OzfwQGBjr16wAAAAAAwFlMXzjOZrM5fG4YRqKxBAcOHFDnzp3Vr18/bd++XUuXLtWJEyfUoUOHBz5+nz59FBkZaf84ffq0U/MDAAAAAOAsacx64ixZssjd3T3RWfOLFy8mOrueYMiQIapUqZJ69uwpSSpWrJjSp0+vypUra/DgwcqZM2ei+3h6esrT09P5XwAAAAAAAE5m2pl0Dw8PlS5dWitWrHAYX7FihSpWrJjkfW7evCk3N8fI7u7uku6egQcAAAAAIDUzdbp7t27dNGnSJE2ZMkUHDx5UWFiYIiIi7NPX+/Tpo9DQUPvxL7/8shYsWKDx48fr+PHj2rhxozp37qxy5copICDArC8DAAAAAACnMG26uyQ1b95cly9f1qBBg3T+/HkVLVpU4eHhCgoKkiSdP3/eYc/01q1b69q1axozZoy6d++ujBkzqkaNGvr888/N+hIAAAAAAHAam+Fi88SjoqLk5+enyMhI+fr6mh0HAAAAAPCUe5weavrq7gAAAAAA4C5KOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLoKQDAAAAAGARlHQAAAAAACyCkg4AAAAAgEVQ0gEAAAAAsAhKOgAAAAAAFkFJBwAAAADAIijpAAAAAABYBCUdAAAAAACLML2kjxs3Tnnz5pWXl5dKly6t9evXP/T4mJgY9e3bV0FBQfL09NSzzz6rKVOmpFBaAAAAAACSTxozn3zu3Lnq2rWrxo0bp0qVKmnixImqV6+eDhw4oGeeeSbJ+zRr1kx//vmnJk+erPz58+vixYuKjY1N4eQAAAAAADifzTAMw6wnf/7551WqVCmNHz/ePla4cGE1btxYQ4YMSXT80qVL9dprr+n48ePKlCnTIz1HTEyMYmJi7J9HRUUpMDBQkZGR8vX1/fdfBAAAAAAADxEVFSU/P79H6qGmTXe/ffu2tm/frtq1azuM165dW5s2bUryPosXL1aZMmU0bNgw5cqVSwULFlSPHj1069atBz7PkCFD5OfnZ/8IDAx06tcBAAAAAICzPFFJb926tdatW/evnvjSpUuKi4tT9uzZHcazZ8+uCxcuJHmf48ePa8OGDdq3b58WLlyoUaNG6YcfflDHjh0f+Dx9+vRRZGSk/eP06dP/KjcAAAAAAMnlia5Jv3btmmrXrq3AwEC1adNGrVq1Uq5cuZ4ogM1mc/jcMIxEYwni4+Nls9k0e/Zs+fn5SZK+/PJL/ec//9HYsWPl7e2d6D6enp7y9PR8omwAAAAAAKSkJzqT/uOPP+rs2bPq1KmT5s+frzx58qhevXr64YcfdOfOnUd6jCxZssjd3T3RWfOLFy8mOrueIGfOnMqVK5e9oEt3r2E3DENnzpx5ki8FAAAAAADLeOJr0jNnzqwuXbpo586d2rJli/Lnz68333xTAQEBCgsL05EjRx56fw8PD5UuXVorVqxwGF+xYoUqVqyY5H0qVaqkc+fO6fr16/axw4cPy83NTblz537SLwUAAAAAAEv41wvHnT9/XsuXL9fy5cvl7u6u+vXra//+/QoODtbIkSMfet9u3bpp0qRJmjJlig4ePKiwsDBFRESoQ4cOku5eTx4aGmo/vkWLFsqcObPatGmjAwcOaN26derZs6fatm2b5FR3AAAAAABSkye6Jv3OnTtavHixpk6dquXLl6tYsWIKCwtTy5Yt5ePjI0n6/vvv9e677yosLOyBj9O8eXNdvnxZgwYN0vnz51W0aFGFh4crKChI0t03ACIiIuzHZ8iQQStWrND777+vMmXKKHPmzGrWrJkGDx78JF8GAAAAAACW8kT7pGfJkkXx8fF6/fXX9fbbb6tEiRKJjvn7779VqlQpnThxwhk5neZx9qcDAAAAAODfepwe+kRn0r/88ks1a9ZMXl5eDzzG39/fcgUdAAAAAAAre+xr0mNjY9W2bVsdPXo0OfIAAAAAAOCyHrukp0mTRkFBQYqLi0uOPAAAAAAAuKwnWt39o48+Up8+fXTlyhVn5wEAAAAAwGU90TXpo0eP1tGjRxUQEKCgoCClT5/e4fYdO3Y4JRwAAAAAAK7kiUp648aNnRwDAAAAAAA80RZsqRlbsAEAAAAAUtLj9NAnuiYdAAAAAAA43xNNd4+Li9PIkSM1b948RURE6Pbt2w63s6AcAAAAAACP74nOpA8cOFBffvmlmjVrpsjISHXr1k1NmjSRm5ubBgwY4OSIAAAAAAC4hicq6bNnz9a3336rHj16KE2aNHr99dc1adIk9evXT7///ruzMwIAAAAA4BKeqKRfuHBBISEhkqQMGTIoMjJSkvTSSy/pl19+cV46AAAAAABcyBOV9Ny5c+v8+fOSpPz582v58uWSpK1bt8rT09N56QAAAAAAcCFPVNJfeeUVrVy5UpLUpUsXffzxxypQoIBCQ0PVtm1bpwYEAAAAAMBVOGWf9N9//12bNm1S/vz51bBhQ2fkSjbskw4AAAAASEmP00OfaAu2+5UvX17ly5d3xkMBAAAAAOCyHrmkL168+JEf1Opn0wEAAAAAsKJHLumNGzd+pONsNpvi4uKeNA8AAAAAAC7rkUt6fHx8cuYAAAAAAMDlPdHq7gAAAAAAwPmeeOG4GzduaO3atYqIiNDt27cdbuvcufO/DgYAAAAAgKt5opK+c+dO1a9fXzdv3tSNGzeUKVMmXbp0SenSpVO2bNko6QAAAAAAPIEnmu4eFhaml19+WVeuXJG3t7d+//13nTp1SqVLl9YXX3zh7IwAAAAAALiEJyrpu3btUvfu3eXu7i53d3fFxMQoMDBQw4YN04cffujsjAAAAAAAuIQnKulp06aVzWaTJGXPnl0RERGSJD8/P/u/AQAAAADA43mia9JLliypbdu2qWDBgqpevbr69eunS5cuaebMmQoJCXF2RgAAAAAAXMITnUn/7LPPlDNnTknSJ598osyZM+vdd9/VxYsX9c033zg1IAAAAAAArsJmGIZhdoiUFBUVJT8/P0VGRsrX19fsOAAAAACAp9zj9NAn3if9XmvXrtXNmzdVvnx5+fv7O+MhAQAAAABwOY9V0ocPH67r169r4MCBkiTDMFSvXj0tX75ckpQtWzatXLlSRYoUcX5SAAAAAACeco91TfqcOXMUHBxs//yHH37QunXrtH79el26dEllypSxF3gAAAAAAPB4HquknzhxQsWKFbN/Hh4erqZNm6pSpUrKlCmTPvroI23evNnpIQEAAAAAcAWPVdLv3LkjT09P++ebN29WxYoV7Z8HBATo0qVLzksHAAAAAIALeaySnj9/fq1bt06SFBERocOHD6tq1ar228+cOaPMmTM7NyEAAAAAAC7isRaOe/fdd9WpUyetX79ev//+uypUqOBwjfqqVatUsmRJp4cEAAAAAMAVPFZJf+edd5QmTRr9/PPPqlKlivr37+9w+7lz59SmTRunBgQAAAAAwFXYDMMwzA6Rkh5nE3kAAAAAAP6tx+mhj3VNegJ3d3ddvHgx0fjly5fl7u7+JA8JAAAAAIDLe6KS/qCT7zExMfLw8PhXgQAAAAAAcFWPdU366NGjJUk2m02TJk1ShgwZ7LfFxcVp3bp1eu6555ybEAAAAAAAF/FYJX3kyJGS7p5JnzBhgsPUdg8PD+XJk0cTJkxwbkIAAAAAAFzEY5X0EydOSJKqV6+uBQsWyN/fP1lCAQAAAADgip7omvTVq1fL399ft2/f1qFDhxQbG+vsXAAAAAAAuJwnKum3bt1Su3btlC5dOhUpUkQRERGSpM6dO2vo0KFODQgAAAAAgKt4opLeu3dv7d69W2vWrJGXl5d9vGbNmpo7d67TwgEAAAAA4Eoe65r0BIsWLdLcuXNVvnx52Ww2+3hwcLCOHTvmtHAAAAAAALiSJzqT/tdffylbtmyJxm/cuOFQ2gEAAAAAwKN7opJetmxZ/fLLL/bPE4r5t99+qwoVKjgnGQAAAAAALuaJprsPGTJEdevW1YEDBxQbG6uvvvpK+/fv1+bNm7V27VpnZwQAAAAAwCU80Zn0ihUrauPGjbp586aeffZZLV++XNmzZ9fmzZtVunRpZ2cEAAAAAMAl2AzDMMwOkZKioqLk5+enyMhI+fr6mh0HAAAAAPCUe5we+ljT3d3c3P5xYTibzabY2NjHeVgAAAAAAKDHLOkLFy584G2bNm3S119/LRc7MQ8AAAAAgNM8Vklv1KhRorE//vhDffr00ZIlS9SyZUt98sknTgsHAAAAAIAreaKF4yTp3Llzevvtt1WsWDHFxsZq165dmj59up555hln5gMAAAAAwGU8dkmPjIxUr169lD9/fu3fv18rV67UkiVLVLRo0eTIBwAAAACAy3is6e7Dhg3T559/rhw5cmjOnDlJTn8HAAAAAABP5rG2YHNzc5O3t7dq1qwpd3f3Bx63YMECp4RLDmzBBgAAAABIScm2BVtoaOg/bsEGAAAAAACezGOV9GnTpiVTDAAAAAAA8MSruwMAAAAAAOeipAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIswvaSPGzdOefPmlZeXl0qXLq3169c/0v02btyoNGnSqESJEskbEAAAAACAFGJqSZ87d666du2qvn37aufOnapcubLq1auniIiIh94vMjJSoaGhevHFF1MoKQAAAAAAyc9mGIZh1pM///zzKlWqlMaPH28fK1y4sBo3bqwhQ4Y88H6vvfaaChQoIHd3dy1atEi7du164LExMTGKiYmxfx4VFaXAwEBFRkbK19fXKV8HAAAAAAAPEhUVJT8/v0fqoaadSb99+7a2b9+u2rVrO4zXrl1bmzZteuD9pk6dqmPHjql///6P9DxDhgyRn5+f/SMwMPBf5QYAAAAAILmYVtIvXbqkuLg4Zc+e3WE8e/bsunDhQpL3OXLkiHr37q3Zs2crTZo0j/Q8ffr0UWRkpP3j9OnT/zo7AAAAAADJ4dGabjKy2WwOnxuGkWhMkuLi4tSiRQsNHDhQBQsWfOTH9/T0lKen57/OCQAAAABAcjOtpGfJkkXu7u6JzppfvHgx0dl1Sbp27Zq2bdumnTt3qlOnTpKk+Ph4GYahNGnSaPny5apRo0aKZAcAAAAAIDmYNt3dw8NDpUuX1ooVKxzGV6xYoYoVKyY63tfXV3v37tWuXbvsHx06dFChQoW0a9cuPf/88ykVHQAAAACAZGHqdPdu3brpzTffVJkyZVShQgV98803ioiIUIcOHSTdvZ787NmzmjFjhtzc3FS0aFGH+2fLlk1eXl6JxgEAAAAASI1MLenNmzfX5cuXNWjQIJ0/f15FixZVeHi4goKCJEnnz5//xz3TAQAAAAB4Wpi6T7oZHmd/OgAAAAAA/q1UsU86AAAAAABwREkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIswvaSPGzdOefPmlZeXl0qXLq3169c/8NgFCxaoVq1aypo1q3x9fVWhQgUtW7YsBdMCAAAAAJB8TC3pc+fOVdeuXdW3b1/t3LlTlStXVr169RQREZHk8evWrVOtWrUUHh6u7du3q3r16nr55Ze1c+fOFE4OAAAAAIDz2QzDMMx68ueff16lSpXS+PHj7WOFCxdW48aNNWTIkEd6jCJFiqh58+bq16/fIx0fFRUlPz8/RUZGytfX94lyAwAAAADwqB6nh5p2Jv327dvavn27ateu7TBeu3Ztbdq06ZEeIz4+XteuXVOmTJkeeExMTIyioqIcPgAAAAAAsCLTSvqlS5cUFxen7NmzO4xnz55dFy5ceKTHGDFihG7cuKFmzZo98JghQ4bIz8/P/hEYGPivcgMAAAAAkFxMXzjOZrM5fG4YRqKxpMyZM0cDBgzQ3LlzlS1btgce16dPH0VGRto/Tp8+/a8zAwAAAACQHNKY9cRZsmSRu7t7orPmFy9eTHR2/X5z585Vu3btNH/+fNWsWfOhx3p6esrT0/Nf5wUAAAAAILmZdibdw8NDpUuX1ooVKxzGV6xYoYoVKz7wfnPmzFHr1q313XffqUGDBskdEwAAAACAFGPamXRJ6tatm958802VKVNGFSpU0DfffKOIiAh16NBB0t2p6mfPntWMGTMk3S3ooaGh+uqrr1S+fHn7WXhvb2/5+fmZ9nUAAAAAAOAMppb05s2b6/Llyxo0aJDOnz+vokWLKjw8XEFBQZKk8+fPO+yZPnHiRMXGxqpjx47q2LGjfbxVq1aaNm1aSscHAAAAAMCpTN0n3Qzskw4AAAAASEmpYp90AAAAAADgiJIOAAAAAIBFUNIBAAAAALAISjoAAAAAABZBSQcAAAAAwCIo6QAAAAAAWAQlHQAAAAAAi6CkAwAAAABgEZR0AAAAAAAsgpIOAAAAAIBFUNIBAAAAALAISjoAAAAAABZBSQcAAAAAwCIo6QAAAAAAWAQlHQAAAAAAi6CkAwAAAABgEZR0AAAAAAAsgpIOAAAAAIBFpDE7AAAAcD7DMBQdHe3Ux4uJiZEkeXp6ymazOe2xJcnLy8vpjwkAQGpESQcA4CkUHR2tOnXqmB3jkS1btkze3t5mxwAAwHRMdwcAAAAAwCI4kw4AwFPIy8tLy5Ytc9rjRUdHq1GjRpKkn376SV5eXk57bElOfzwAAFIrSjoAAE8hm82WbNPHvby8mJoOAEAyYbo7AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6kEw2btyoV199VRs3bjQ7CgAAAIBUgpIOJIPo6GiNGDFCf/75p0aMGKHo6GizIwEAAABIBSjpQDKYNWuWLl++LEm6fPmyZs+ebXIiAAAAAKkBJR1wsjNnzmj27NkyDEOSZBiGZs+erTNnzpicDAAAAIDVUdIBJzIMQyNHjnzgeEJxBwAAAICkUNIBJzp16pS2bt2quLg4h/G4uDht3bpVp06dMikZAAAAgNSAkg44UVBQkMqWLSt3d3eHcXd3d5UrV05BQUEmJQMAAACQGlDSASey2WwKCwt74LjNZjMhFQAAAIDUgpIOOFnu3LnVvHlzh7HmzZsrV65cJiUCAAAAkFpQ0gEAAAAAsAhKOuBkZ86c0dy5cx3G5s6dyxZsAAAAAP4RJR1wIrZgAwAAAPBvUNIBJ2ILNgAAAAD/BiUdcCK2YAMAAADwb1DSASdiCzYAAAAA/wYlHXCy3Llzq2XLlvZCbrPZ1LJlS7ZgAwAAAPCP0pgdAHgavfHGGwoPD9elS5eUJUsWtWzZ0uxIACzMMAxFR0ebHeOh7s1n9awJvLy8mMEEAEh1KOlAMvDy8lL37t01atQode3aVV5eXmZHAmBh0dHRqlOnjtkxHlmjRo3MjvBIli1bJm9vb7NjAADwWCjpQDKpVKmSKlWqZHYMAAAAAKkIJR0AAAtpWOI9pXFLa3aMRAzDUFx8rCTJ3S2NZaeRx8bf0eJd48yOAQDAE6OkAwBgIWnc0iqNu4fZMZKUVp5mRwAA4KnH6u4AAAAAAFgEJR0AAAAAAIugpAMAAAAAYBFck55CkmMPXMMwFBMTI0ny9PR0+iI+7C8LAAAAACmLkp5CUtseuBL7ywIAAABASqOkAwCeCDOEAAAAnI+SnkK8vLy0bNkypz5mdHS0GjVqJEn66aef5OXl5dTHd/bjAXi6MEMIAADA+SjpKcRmsyXrH4ZeXl784QngoZx95tvZZ9FTgrMzc2YeAAA4GyUdAFzErVu3VLduXbNjmCph9pGzLF26VOnSpXPqYwIAANfGFmwA4CISrvWG8/A9BQAAzkZJBwAAAADAIpjuDgAuwtPT0/7vMEke5kV5IEPSnf/9O60kK17tfVvSyP/9+97vKQAAgDNQ0gHARdy7wNnIhxyHR8eicQCQtITFSp25YGd8fLyioqKc9njJzdfXV25uzpu47OXlxYKlLoKSDgAAAMCpUuM2nakBW4m6Bko6ALgILy8vLVu2zOwYDxUdHW1fgf2nn36Sl5eXyYkezur5AABA6kNJBwAXYbPZnPruu7P3XU8JTBMEgJSR8MYw092dP90dTz9KOiDnlw3DMOxbM3l6ejq9FFA0YAXJPZXR2XuaS0wTBICUkvDGsLN/5mbOnNmpjwdYESX9AVLDGaJ781k9q2TtYpnarpuiaAAAAABPJ0r6A6S20pYcZ5ycjWIJPF2S4xr3lJiFAgAAYGWUdEDOLxvJvfgVRQNW4Oxr3BOkS5fO6Y8JAACQWlDSH8GNUi0lNwt+qwxDio+9+2+3NJIVp5LHxyr9jtlmp/hHyVU2pLuFmhkEAAAAAB6FBZunBbmlkdzTmp3iATzMDgAAAAAAcBJKOgCnSVhwke1WnL/dilUXXQQAAIBzUdKR6rDyfvJwRhFMbQsuphYsuggAAOA6KOlIdVJbEUwNK+9LFEEAAADACijpAJwmYZV8prs7f7o7AAAAXAMlHana2CpX5elumB0jEcOQbsff/beHmzUX3pekmDibOq7L6LTHS1gl39ln5DNnzuzUxwMAAACsipKOVM3T3ZCXu9kpkpY6Jo5b7w0OAAAAwJU5b04mAAAAAAD4VziTjlTHMP7/7G9MnIlBngL3fv/u/b4CAAAAMAclHalOTEyM/d8d1/mbmOTpEhMTo3Tp0pkdA3BJ975JFn3nptLE3TExTdIMw1BcfKwkyd0tzb/esjG5xMb///eONx8BILGE7YxZ6Nf5C/0663cjJf0BHH6xW/CPpVQjjj+WAOCf3PvmY/jeb01M8nThzUcASCy1bWecWjhzO2NK+gPc+wdT+p3fmZjk6eGsP5Y8PT2dkAb34/sKAEhtnH1GMLWdDZSce0bQ2WcDATwZ00v6uHHjNHz4cJ0/f15FihTRqFGjVLly5Qcev3btWnXr1k379+9XQECAPvjgA3Xo0CEFE8Ns/OJIHnxfAfP4+fnpp59+cupjRkdHq3nz5k59zOQ0d+5ceXl5OfUx/fz8nPp4sB7OCDqfM88Gwpq8vLy0bNkyprsnw3R3ZzG1pM+dO1ddu3bVuHHjVKlSJU2cOFH16tXTgQMH9MwzzyQ6/sSJE6pfv77efvttzZo1Sxs3btR7772nrFmzqmnTpk7NxllF53PW9zThB4szGYbhMHvi37r3j+Pk+MPT09PT6aXa2RkBPDo3Nzf5+zt3jQ3DMJz6s/Len5PJ9TOINwsBIPnZbDZ5e3s7/c2YzJkzO/XxXJnNMPFC4eeff16lSpXS+PHj7WOFCxdW48aNNWTIkETH9+rVS4sXL9bBgwftYx06dNDu3bu1efPmR3rOqKgo+fn5KTIyUr6+vg88LmH6lLM4uwSmBGf/EWblP8Bu3bqVqt6J511uAACY7i4x3R1ILR61h0omnkm/ffu2tm/frt69ezuM165dW5s2bUryPps3b1bt2rUdxurUqaPJkyfrzp07Sps2baL7xMTEOJTjR/3Bm/AOkzOxeA0AAIDzJMcZQc4GAjCbaSX90qVLiouLU/bs2R3Gs2fPrgsXLiR5nwsXLiR5fGxsrC5duqScOXMmus+QIUM0cOBA5wXHU8nZU+hTYlooAAAAgKeP6QvH3V9eDMN4aKFJ6vikxhP06dNH3bp1s38eFRWlwMDAJ42LpxQzJwAAAABYgWklPUuWLHJ3d0901vzixYuJzpYnyJEjR5LHp0mT5oFTkzw9PVkEDgAAAACQKjhv3fnH5OHhodKlS2vFihUO4ytWrFDFihWTvE+FChUSHb98+XKVKVMmyevRAQAAAABITUwr6ZLUrVs3TZo0SVOmTNHBgwcVFhamiIgI+77nffr0UWhoqP34Dh066NSpU+rWrZsOHjyoKVOmaPLkyerRo4dZXwIAAAAAAE5j6jXpzZs31+XLlzVo0CCdP39eRYsWVXh4uIKCgiRJ58+fV0REhP34vHnzKjw8XGFhYRo7dqwCAgI0evRop++RDgAAAACAGUzdJ90Mj7M/HQAAAAAA/9bj9FBTp7sDAAAAAID/R0kHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFgEJR0AAAAAAIugpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWQUkHAAAAAMAiKOkAAAAAAFhEGrMDpDTDMCRJUVFRJicBAAAAALiChP6Z0EcfxuVK+rVr1yRJgYGBJicBAAAAALiSa9euyc/P76HH2IxHqfJPkfj4eJ07d04+Pj6y2Wxmx/nXoqKiFBgYqNOnT8vX19fsOLgHr4218fpYF6+NdfHaWBuvj3Xx2lgXr421PU2vj2EYunbtmgICAuTm9vCrzl3uTLqbm5ty585tdgyn8/X1TfX/4T6teG2sjdfHunhtrIvXxtp4fayL18a6eG2s7Wl5ff7pDHoCFo4DAAAAAMAiKOkAAAAAAFgEJT2V8/T0VP/+/eXp6Wl2FNyH18baeH2si9fGunhtrI3Xx7p4bayL18baXPX1cbmF4wAAAAAAsCrOpAMAAAAAYBGUdAAAAAAALIKSDgAAAACARVDSAQAAAACwCEo6AAAAAAAWkcbsAHh8d+7c0YULF3Tz5k1lzZpVmTJlMjsSAOApFxMT43Jb4AD/Rnx8vI4ePaqLFy8qPj7e4bYqVaqYlAr34ucarIqSnkpcv35ds2fP1pw5c7RlyxbFxMTYb8udO7dq166t9u3bq2zZsiamdG3+/v6y2WyJxm02m7y8vJQ/f361bt1abdq0MSEdJOn06dM6efKk/Q2uIkWK8MvZAmJiYrRlyxaH16ZkyZLKmzev2dFc2rJlyzRnzhytX79eERERio+PV7p06VSqVCnVrl1bbdq0UUBAgNkxXVZkZKQWLlyo9evXJ/p/p06dOqpYsaLZEV3a77//rhYtWujUqVO6f7djm82muLg4k5K5Nn6uWd/SpUuVIUMGvfDCC5KksWPH6ttvv1VwcLDGjh0rf39/kxOmDPZJTwVGjhypTz/9VHny5FHDhg1Vrlw55cqVS97e3rpy5Yr27dun9evXa+HChSpfvry+/vprFShQwOzYLifhdapXr57KlSsnwzC0detWLV26VGFhYTpx4oRmzpypr7/+Wm+//bbZcV3GqVOnNGHCBM2ZM0enT592+GPJw8NDlStXVvv27dW0aVO5uXEFUEratGmTvv76ay1atEi3b99WxowZ7T/XYmJilC9fPrVv314dOnSQj4+P2XFdxqJFi9SrVy9FRkaqfv36D/yds3nzZrVu3VqffPKJsmbNanZsl3H+/Hn169dPs2fPVo4cOZJ8fbZv366goCD1799fzZs3NzuySypRooQKFiyogQMHKmfOnInexPfz8zMpmWvi51rqERISos8//1z169fX3r17VbZsWXXr1k2rVq1S4cKFNXXqVLMjpgwDlvef//zH2LNnzz8eFx0dbYwdO9b49ttvUyAV7tekSRNj/PjxicYnTJhgNGnSxDAMwxg9erRRtGjRlI7msjp37mz4+PgYTZs2NaZPn24cPHjQiIqKMu7cuWP8+eefxsqVK40BAwYYhQoVMooUKWJs2bLF7Mguo2HDhkbOnDmN7t27G2vXrjVu3LjhcPuxY8eMadOmGXXq1DFy5MhhLF++3KSkrqds2bLG4sWLjbi4uIced+bMGaNnz57GF198kULJYBiGkTVrVqN79+7G3r17H3jMzZs3je+++84oV66cMXz48BRMhwTp0qUzjhw5YnYM/A8/11KP9OnTGydOnDAMwzD69+9vNG3a1DAMw9i+fbuRPXt2E5OlLM6kA06SIUMG7dq1S/nz53cYP3r0qEqUKKHr16/r2LFjKlasmG7cuGFSStfSs2dPffDBB4/0bnh4eLhu3ryp//znPymQDGPHjtXbb78tDw+Pfzx2//79OnfunGrVqpUCyQBr++uvvx7rDN/jHg/nqFGjhj744APVrVvX7ChAqpIpUyZt2LBBwcHBeuGFFxQaGqr27dvr5MmTCg4O1s2bN82OmCK4Jj2Vi4qK0qpVq1SoUCEVLlzY7DguLVOmTFqyZInCwsIcxpcsWWJf3O/GjRtM201Bw4cPf+Rj69evn4xJcL+OHTs+8rFFihRRkSJFkjENkHo8buGmoJvj/fffV/fu3XXhwgWFhIQobdq0DrcXK1bMpGRYs2aNqlWrZnYMPMALL7ygbt26qVKlStqyZYvmzp0rSTp8+LBy585tcrqUw5n0VKZZs2aqUqWKOnXqpFu3bql48eI6efKkDMPQ999/r6ZNm5od0WV9++23evfdd+3XOtlsNm3ZskXh4eGaMGGC2rVrpxEjRjj8wEHKqVGjhhYsWKCMGTM6jEdFRalx48ZatWqVOcEAi+nWrdsjH/vll18mYxI8ipkzZ2rChAk6ceKENm/erKCgII0aNUp58+ZVo0aNzI7nspJa48Rms8kwDBaOM5mXl5dy5cqlNm3aqFWrVgoMDDQ7Eu4RERGh9957T6dPn1bnzp3Vrl07SVJYWJji4uI0evRokxOmDEp6KpMjRw4tW7ZMxYsX13fffaf+/ftr9+7dmj59ur755hvt3LnT7IgubePGjRozZowOHTokwzD03HPP6f3332eVXQtwc3PThQsXlC1bNofxixcvKleuXLpz545JycDOCNZSvXr1RzrOZrPx5pbJxo8fr379+qlr16769NNPtW/fPuXLl0/Tpk3T9OnTtXr1arMjuqxTp0499PagoKAUSoL7XblyRbNmzdK0adO0Z88evfjii2rXrp0aN278SJdgASmBkp7KeHt76/DhwwoMDFRoaKgCAgI0dOhQRUREKDg4WNevXzc7ImApe/bskXR3pd1Vq1bZLz2QpLi4OC1dulQTJ07UyZMnTUoIdkYAnkxwcLA+++wzNW7cWD4+Ptq9e7fy5cunffv2qVq1arp06ZLZEQFL27Vrl6ZMmaI5c+YoPj5eLVu2VLt27VS8eHGzo7ms8PBwubu7q06dOg7jy5cvV1xcnOrVq2dSspTFNempTGBgoDZv3qxMmTJp6dKl+v777yVJf//9t7y8vExOh/j4eB09elQXL15UfHy8w21VqlQxKZVrK1GihGw2m2w2m2rUqJHodm9vb3399dcmJEOCDRs2aPDgwerQoYPD+MSJE7V8+XL9+OOPKlasmEaPHk1JB+5x4sQJlSxZMtG4p6cnC5SabPHixUmO3ztDKG/evCmcCvcrUaKEevfurUyZMmno0KGaMmWKxo0bpwoVKmjChAmsh2KC3r17a+jQoYnG4+Pj1bt3b0o6rKlr165q2bKlMmTIoKCgIPvCF+vWrVNISIi54Vzc77//rhYtWujUqVO6f4IK15+Z58SJEzIMQ/ny5dOWLVscFlHy8PBQtmzZ5O7ubmJCLFu2TJ9//nmi8RdffFHdu3eXdHdhv969e6d0NEjaunWr5s+fr4iICN2+fdvhtgULFpiUCpKUN29e7dq1K9HU6V9//VXBwcEmpYIkNW7c2H4N+r3uvS79hRde0KJFi+Tv729SStd1584d/fTTT5oyZYpWrFihMmXKaMyYMXr99dd15coV9erVS6+++qoOHDhgdlSXc+TIkSR/fj333HM6evSoCYnMkXhVC1jae++9p82bN2vKlCnasGGDfWGSfPnyafDgwSanc20dOnRQmTJltG/fPl25ckV///23/ePKlStmx3NZQUFBypUrl0JDQ5UpUyYFBQXZP3LmzElBt4CEnRHux84I5vv+++9VqVIlHThwQAsXLtSdO3d04MABrVq1Sn5+fmbHc3k9e/ZUx44dNXfuXBmGoS1btujTTz/Vhx9+qJ49e5odz6WtWLFCZcuW1YoVKxQZGanIyEitWLFC5cqV088//6x169bp8uXL6tGjh9lRXc7777+vnDlzqkOHDipYsKB27typzZs366233lL69OkVGBiooUOH6o8//jA7qkvy8/PT8ePHE40fPXpU6dOnNyGRObgmHXCS9OnTa/fu3Yn2SYc1+Pv7a/v27cqXL5/ZUXAfdkawrmLFiumdd95Rx44d7dc8582bV++8845y5sypgQMHmh3R5X377bcaPHiwTp8+LUnKlSuXBgwYYF8RGeYoWrSovvnmm0QLx27cuFHt27fX/v379dtvv6lt27aKiIgwKaVrevHFF/XWW2+padOmD1woLjY2Vhs3blTVqlVTOB3at2+v33//XQsXLtSzzz4r6W5Bb9q0qcqWLatJkyaZnDBlUNJTmf/85z8qU6ZMommfw4cP15YtWzR//nyTkqFGjRr64IMPVLduXbOjIAlt2rRRSEjIY20vhZTDzgjWlD59eu3fv1958uRRlixZtHr1aoWEhOjgwYOqUaOGzp8/b3ZE/M+lS5cUHx+faAcLmMPb21tbt25V0aJFHcb37t2rcuXK6datWzp16pQKFy6smzdvmpQSsJ7IyEjVrVtX27Zts++LfubMGVWuXDnJrXSfVlyTnsqsXbtW/fv3TzRet25dffHFFyYkQoL3339f3bt314ULFxQSEqK0adM63F6sWDGTkkGS8ufPr08++USbNm1S6dKlE02Z6ty5s0nJIEmVKlVSpUqVzI6B+2TKlEnXrl2TdPcM7b59+xQSEqKrV69SLCzgxIkTio2NVYECBZQlSxb7+JEjR5Q2bVrlyZPHvHAurnTp0urZs6dmzJhhXwvlr7/+0gcffKCyZctKuvs6JZQQpKzDhw9rzZo1SS70269fP5NSQbo73X3Tpk1asWKFdu/eLW9vbxUrVszlFmDmTHoq4+3trV27dqlQoUIO43/88YdKliypW7dumZQMCesD3OveBWJYOM5cD1tF12azJXn9E1IOOyNYU4sWLVSmTBl169ZNn376qb766is1atRIK1asUKlSpVg4zmRVq1ZV27Zt1apVK4fxWbNmadKkSVqzZo05waBDhw6pUaNGOnHihAIDA2Wz2RQREaF8+fLpp59+UsGCBbVo0SJdu3ZNb775ptlxXUrCJVZZsmRRjhw5ZLPZ7LfZbDbt2LHDxHTAXZT0VKZs2bJ6+eWXE73LN2DAAC1ZskTbt283KRlOnTr10NvvX30XwF3sjGBdV65cUXR0tAICAhQfH68vvvhCGzZsUP78+fXxxx+zKrXJfH19tWPHjkRroRw9elRlypTR1atXzQkGSZJhGFq2bJkOHz5sv4ynVq1aSb6pj5QTFBSk9957T7169TI7Cv5n9OjRat++vby8vDR69OiHHusqMx8p6anM4sWL1bRpU7Vo0cK+5/PKlSs1Z84czZ8/X40bNzY3IAA8phIlSqhgwYIaOHCgcubM6XBWQxKriAMP4OfnpzVr1iTaK3379u2qVq2a/VIFAP/P19dXu3btYiFZC8mbN6+2bdumzJkzM/PxfyjpqdAvv/yizz77TLt27bJfp9G/f39WoDTB4sWLVa9ePaVNm1aLFy9+6LENGzZMoVR4kDNnzmjx4sVJ7vf85ZdfmpQK7IxgXeHh4XJ3d1edOnUcxpcvX664uDjVq1fPpGSQpJdeeknp0qXTnDlz7NtJxsXFqXnz5rpx44Z+/fVXkxO6Fs4Gpg7t2rVT2bJl1aFDB7OjAA9ESQf+BTc3N124cEHZsmV76PQ1puyab+XKlWrYsKHy5s2rQ4cOqWjRojp58qQMw1CpUqW0atUqsyO6LHZGsK5ixYpp6NChql+/vsP40qVL1atXL+3evdukZJCkAwcOqEqVKsqYMaMqV64sSVq/fr2ioqK0atWqRCuLI3lxNtC67n3T5MaNG/ryyy/VoEGDJBf65Q0Ucw0aNEg9evRQunTpHMZv3bql4cOHu8zCfpR0AC6hXLlyqlu3rgYNGmTf7zlbtmxq2bKl6tatq3fffdfsiC5r4cKF+uijj9SzZ092RrAYb29vHTx4MNEq4SdPnlSRIkV048YNc4LB7ty5cxozZozDKsidOnVSpkyZzI4GWMbD3jS5F2+gmM/d3V3nz59PtJ3k5cuXlS1bNpc56UVJTwUyZcqkw4cPK0uWLPL39090vea9rly5koLJkODOnTuqXbu2Jk6cqIIFC5odB0nw8fHRrl279Oyzz8rf318bNmxQkSJFtHv3bjVq1EgnT540O6LLYmcE68qRI4e+++47+xooCX777Te1aNFCFy9eNCkZkLrExcVp7969CgoKYsFF4CHc3Nz0559/2rcuTLBq1So1b95cf/31l0nJUhb7pKcCI0eOlI+Pj/3fDyvpMEfatGm1b98+XhsLS58+vWJiYiRJAQEBOnbsmIoUKSJJunTpkpnRXN6JEyfMjoAHaNiwobp27aqFCxfq2WeflXR35fDu3buzzoZFXL16VVu2bEly+8LQ0FCTUqFr164KCQlRu3btFBcXpypVqmjz5s1Kly6dfv75Z1WrVs3siJDsO4rw95v5Ek5E2mw2FSxY0OE1iYuL0/Xr111qHQHOpANO0r17d6VNm1ZDhw41OwqS0LhxYzVo0EBvv/22PvjgAy1cuFCtW7fWggUL5O/vr99++83siIDlREZGqm7dutq2bZty584t6e4CjJUrV9aCBQuUMWNGcwO6uCVLlqhly5a6ceOGfHx8Eu33zOw68+TOnVuLFi1SmTJltGjRInXs2FGrV6/WjBkztHr1am3cuNHsiC5t8uTJGjlypI4cOSJJKlCggLp27aq33nrL5GSua/r06TIMQ23bttWoUaMcdnbx8PBQnjx5VKFCBRMTpixKeiqzY8cOpU2bViEhIZKkn376SVOnTlVwcLAGDBggDw8PkxO6rvfff18zZsxQ/vz5VaZMGaVPn97hdlYPN9fx48d1/fp1FStWTDdv3lSPHj3s+z2PHDmSfexTGDsjpB6GYWjFihUO1zxXqVLF7FiQVLBgQdWvX1+fffZZokWWYC4vLy8dPXpUuXPnVvv27ZUuXTqNGjVKJ06cUPHixRUVFWV2RJf18ccfa+TIkXr//fftpW/z5s0aM2aMunTposGDB5uc0LWtXbtWlSpVUpo0rj3hm5KeypQtW1a9e/dW06ZNdfz4cQUHB6tJkybaunWrGjRooFGjRpkd0WVVr179gbfZbDZWDwfuwc4IwL+XPn167d27l/2eLSgoKEjffvutXnzxReXNm1fjxo3TSy+9pP379+uFF17Q33//bXZEl5UlSxZ9/fXXev311x3G58yZo/fff59L4EzGCcm7XPstilTo8OHDKlGihCRp/vz5qlq1qr777jtt3LhRr732GiXdRKtXrzY7ApBq3Hvt7P3X0cJc7PWcetSpU0fbtm2jpFtQmzZt1KxZM+XMmVM2m021atWSJP33v//Vc889Z3I61xYXF6cyZcokGi9durRiY2NNSIR7vfPOO+rdu7dCQkJ0/PhxNW/eXE2aNNH8+fN18+ZNl+k6nElPZXx9fbV9+3YVKFBAtWrV0ksvvaQuXbooIiJChQoV0q1bt8yO6PKOHj2qY8eOqUqVKvL29ravUA0gaTNmzFDz5s3l6enpMH779m19//33LH6VwtjrOfWYPHmyBg0apDZt2iS5fSGXipjrhx9+0OnTp/Xqq6/a13SYPn26MmbMqEaNGpmcznW9//77Sps2baLLEHv06KFbt25p7NixJiWDJPn5+WnHjh169tln9fnnn2vVqlVatmyZ/YTk6dOnzY6YIijpqUyNGjUUGBiomjVrql27djpw4IDy58+vtWvXqlWrVmwjZaLLly+rWbNmWr16tWw2m44cOaJ8+fKpXbt2ypgxo0aMGGF2RMCS2BMVeDJcKpI6REdHy8vLy+wY+J+ENYQCAwNVvnx5SdLvv/+u06dPKzQ01OHNLtYTSnmckLzrwT/dYUkjR47U9u3b1alTJ/Xt21f58+eXdPfd2ooVK5qczrWFhYUpbdq0ioiIcFjAp3nz5lq6dKmJyQBre9BskzNnzjis7grAUXx8/AM/KOjmiouL0yeffKJcuXIpQ4YM9lknH3/8sSZPnmxyOte2b98+lSpVSlmzZtWxY8d07NgxZc2aVaVKldK+ffu0c+dO7dy5U7t27TI7qksqU6aMBg8erJkzZ2rt2rVq0KCBpLvbtWbPnt3kdCmHa9JTmeLFi2vfvn2JxocPHy53d3cTEiHB8uXLtWzZMvuUtgQFChTQqVOnTEqFpLAvqjWULFnSvifqiy++6LCSa1xcnE6cOKG6deuamBBxcXGaNm2aVq5cmeQ+3CyIaR2crbWWTz/9VNOnT9ewYcP09ttv28dDQkI0cuRItWvXzsR0ro01hKxt1KhRatmypRYtWuTSJyQp6alM69at1bZt20Tb3/CL2Xw3btxIcgucS5cuJbrWFuaYMWOGhg8fbt8XtWDBgurZs6fefPNNk5O5psaNG0uSdu3apTp16ihDhgz22xL2RG3atKlJ6SBJXbp00bRp09SgQQMVLVqUN7YsJi4uTp999pkmTJigP//8U4cPH1a+fPn08ccfK0+ePBRBE82YMUPffPONXnzxRXXo0ME+XqxYMf3xxx8mJsO9zpw5I5vNply5cpkdBf9TrFgx7d27N9G4q52QpKSnMteuXVPt2rUVGBioNm3aqFWrVvxgsYgqVapoxowZ+uSTTyTdPUsbHx+v4cOHP3R7NqSML7/8Uh9//LE6deqkSpUqyTAMbdy4UR06dNClS5cUFhZmdkSX079/f0lSnjx51Lx5c95stKDvv/9e8+bNU/369c2OgiRwtta6zp49az8DeK/4+HjduXPHhERIEB8fr8GDB2vEiBG6fv26JMnHx0fdu3dX3759H7rWA8zjan8jUNJTmR9//FGXL1/WrFmzNG3aNPXv3181a9ZU27Zt1bhx40QruyLlDB8+XNWqVdO2bdt0+/ZtffDBB9q/f7+uXLmijRs3mh3P5X399dcaP368w0rhjRo1UpEiRTRgwABKuolatWqlq1evatasWTp27Jh69uypTJkyaceOHcqePTtvRJrIw8MjyaIBa+BsrXUVKVJE69evV1BQkMP4/PnzVbJkSZNSQZL69u2ryZMna+jQoQ5v2g8YMEDR0dH69NNPzY7ocjJlyqTDhw8rS5Ys8vf3f+isrStXrqRgMvNQ0lOhzJkzq0uXLurSpYt27typKVOmKDQ0VBkyZNAbb7yh9957TwUKFDA7pssJDg7Wnj17NG7cOLm7u+vGjRtq0qSJOnbsqJw5c5odz+WdP38+yWuZKlasqPPnz5uQCAn27NmjmjVrys/PTydPntTbb7+tTJkyaeHChTp16pRmzJhhdkSX1b17d3311VcaM2YMU90tiLO11tW/f3+9+eabOnv2rOLj47VgwQIdOnRIM2bM0M8//2x2PJc2ffp0TZo0yWGLwuLFiytXrlx67733KOkmGDlypHx8fOz/5vcNJT1VO3/+vJYvX67ly5fL3d1d9evX1/79+xUcHKxhw4ZxZtAEOXLk0KBBg8yOgSTkz59f8+bN04cffugwPnfuXN7UMllYWJhat26tYcOG2X9JS1K9evXUokULE5Nhw4YNWr16tX799VcVKVIk0WytBQsWmJQMEmdrrezll1/W3Llz9dlnn8lms6lfv34qVaqUlixZolq1apkdz6VduXJFzz33XKLx5557zmXO0lpNq1at7P9u3bq1eUEshJKeyty5c0eLFy/W1KlTtXz5chUrVkxhYWFq2bKl/Y/b77//Xu+++y4l3QTr16/XxIkTdfz4cc2fP1+5cuXSzJkzlTdvXr3wwgtmx3NpAwcOVPPmzbVu3TpVqlRJNptNGzZs0MqVKzVv3jyz47m0bdu26Ztvvkk0nitXLl24cMGEREiQMWNGvfLKK2bHwANwttba6tSpozp16pgdA/cpXry4xowZo9GjRzuMjxkzRsWLFzcpFRK4u7vr/PnzypYtm8P45cuXlS1bNpfZXpKSnsrkzJlT8fHxev3117VlyxaVKFEi0TF16tRRxowZUzybq/vxxx/15ptvqmXLltqxY4diYmIk3V3s77PPPlN4eLjJCV1b06ZN9d///lcjR47UokWLZBiGgoODtWXLFs44mczLy0tRUVGJxg8dOqSsWbOakAgJpk6danYEPARna4HHN2zYMDVo0EC//fabKlSoIJvNpk2bNun06dP8rWYBCdvk3i8mJkYeHh4pnMY8NuNB3wlY0syZM/Xqq6+63AqHqUHJkiUVFham0NBQ+fj4aPfu3cqXL5927dqlunXrckYQeID27dvrr7/+0rx585QpUybt2bNH7u7uaty4sapUqaJRo0aZHREA/tE/LXh1L6ZVm+vcuXMaO3as/vjjD/ub9u+9954CAgLMjuayEmY2hIWF6ZNPPnHYljUuLk7r1q3TyZMntXPnTrMipihKOuAk6dKl04EDB5QnTx6Hkn78+HEFBwcrOjra7IguJyoqSr6+vvZ/P0zCcUh5UVFR9jU1rl27poCAAF24cEEVKlRQeHi40qdPb3ZEl/bDDz9o3rx5ioiI0O3btx1u27Fjh0mpAOuZPn26/d+XL1/W4MGDVadOHVWoUEGStHnzZi1btkwff/wxlyQC98mbN68k6dSpU8qdO7fDnugeHh7KkyePBg0apOeff96siCmK6e6pzI0bNzR06FCtXLlSFy9eVHx8vMPtx48fNykZcubMqaNHjypPnjwO4xs2bFC+fPnMCeXi/P397dc1ZcyYMckzHIZhyGazucw1Tlbk6+urDRs2aNWqVdqxY4fi4+NVqlQp1axZ84HT3pAyRo8erb59+6pVq1b66aef1KZNGx07dkxbt25Vx44dzY7nktiqyLruXfyqadOmGjRokDp16mQf69y5s8aMGaPffvuNkm6ipUuXKkOGDPa1gsaOHatvv/1WwcHBGjt2rPz9/U1O6JpOnDghSapevboWLFjg8q8DZ9JTmddff11r167Vm2++qZw5cyb65dylSxeTkmHYsGGaPn26pkyZolq1aik8PFynTp1SWFiY+vXr5/CLGilj7dq1qlSpktKkSaO1a9c+9NiqVaumUCrcb8iQIerTp0+i8bi4OL3xxhuaM2eOCakg3V3tuH///nr99dcdZgj169dPV65c0ZgxY8yO6HKmT5+u1157TZ6enpo2bdpDS/q9pREpK0OGDNq1a1eiLfKOHDmikiVL6vr16yYlQ0hIiD7//HPVr19fe/fuVZkyZdS9e3etWrVKhQsXZi0OWAIlPZXJmDGjfvnlF1WqVMnsKEhC3759NXLkSPvUdk9PT/Xo0UOffPKJyckA68qePbs++eQTtW/f3j4WFxen1157Tfv27dPBgwdNTOfa0qVLp4MHDyooKEjZsmXTihUrVLx4cR05ckTly5fX5cuXzY4IWFJQUJA6deqknj17OowPHz5cY8aM0alTp0xKhgwZMmjfvn3KkyePBgwYoH379umHH37Qjh07VL9+fdYQsoAzZ85o8eLFSV5m9eWXX5qUKmUx3T2V8ff3V6ZMmcyOgQf49NNP1bdvXx04cEDx8fEKDg52WPgCKWvPnj2PfGyxYsWSMQkeJjw8XDVr1lTGjBnVrFkz3blzR82bN9cff/yh1atXmx3PpeXIkUOXL19WUFCQgoKC9Pvvv6t48eI6ceIElyJYQHh4uNzd3RNt87V8+XLFxcWpXr16JiXDwIED1a5dO61Zs8Z+Tfrvv/+upUuXatKkSSanc20eHh66efOmJOm3335TaGiopLuXkvzT+jVIfitXrlTDhg2VN29eHTp0SEWLFtXJkydlGIZKlSpldrwUQ0lPZT755BP169dP06dPV7p06cyOgySkS5dOZcqUMTsGJJUoUUI2m81+3fnDcE26eUqXLq2FCxeqUaNG8vT01OTJk3Xs2DGtXr1a2bNnNzueS6tRo4aWLFmiUqVKqV27dgoLC9MPP/ygbdu2qUmTJmbHc3m9e/fW0KFDE43Hx8erd+/elHQTtW7dWoULF9bo0aO1YMEC+wriGzdudJmFr6zqhRdeULdu3VSpUiVt2bJFc+fOlSQdPnxYuXPnNjkd+vTpo+7du2vQoEHy8fHRjz/+qGzZsqlly5aqW7eu2fFSDNPdU4GSJUs6FIyjR4/KMAzlyZNHadOmdTiWlXbNw6J+1nPvdMKdO3eqR48e6tmzp8NKuyNGjNCwYcPUuHFjk1IiweLFi9W0aVMVLlxYq1atUpYsWcyO5PLi4+MVHx+vNGnuvqc/b948bdiwQfnz51eHDh1cas9aK/L29tbBgwcTLVh68uRJFSlSRDdu3DAnGGBhEREReu+993T69Gl17txZ7dq1k3R366+4uDj7VmAwh4+Pj3bt2qVnn31W/v7+2rBhg4oUKaLdu3erUaNGOnnypNkRUwRn0lMBykPq8NZbbz10UT+kvKCgIPu/X331VY0ePVr169e3jxUrVkyBgYH6+OOP+f8shT3oLGzWrFmVMWNGh+vTFyxYkFKxcB83Nze5ubnZP2/WrJmaNWtmYiLcy8/PT8ePH09U0o8ePcrWhcADPPPMM/r5558TjY8cOdKENLhf+vTpFRMTI0kKCAjQsWPHVKRIEUnSpUuXzIyWoijpqUD//v3NjoBH8Ouvv7Kon4Xt3bvXvgfnvfLmzasDBw6YkMi1+fn5JTl+/7W1MN/Vq1e1ZcuWJGcIJVzLCXM0bNhQXbt21cKFC/Xss89KulvQu3fvroYNG5qcDrCuuLg4LVq0SAcPHpTNZlPhwoXVqFEjh725YY7y5ctr48aNCg4OVoMGDdS9e3ft3btXCxYsUPny5c2Ol2KY7p5Kbdu2zeEHS+nSpc2O5PLy5s2r8PBwFS5c2OwoSEKpUqVUuHBhTZ48WV5eXpKkmJgYtW3bVgcPHuRSEZMYhqGIiAhlzZqVdTYsaMmSJWrZsqVu3LghHx8fhxlCNpuNfbhNFhkZqbp162rbtm32a2nPnDmjypUra8GCBcqYMaO5AQELOnr0qOrXr6+zZ8+qUKFCMgxDhw8fVmBgoH755Rf7G14wx/Hjx3X9+nUVK1ZMN2/eVI8ePeyXWY0cOdJhluTTjJKeypw5c0avv/66Nm7caP/le/XqVVWsWFFz5sxRYGCguQFd2KxZs/TTTz+xqJ9FbdmyRS+//LLi4+NVvHhxSdLu3btls9n0888/q1y5ciYndE3x8fHy8vLS/v37VaBAAbPj4D4FCxZU/fr19dlnn/FzzaIMw9CKFSu0e/dueXt7q1ixYqpSpYrZsQDLql+/vgzD0OzZs+07Jl2+fFlvvPGG3Nzc9Msvv5icEKCkpzq1a9dWVFSUpk+frkKFCkmSDh06pLZt2yp9+vRavny5yQldV8mSJXXs2DEW9bOwmzdvatasWfrjjz/sK+22aNGCazdNVqRIEU2ePNmlprGlFunTp9fevXuVL18+s6PgEV29epUz6BZz+vRp2Ww2Vg63iPTp0+v3339XSEiIw/ju3btVqVIlXb9+3aRkkKR8+fJp69atypw5s8P41atXVapUKZdZiJlr0lOZ9evXa9OmTfaCLkmFChXS119/zbXQJmPhMetLly6dw4JksIZhw4apZ8+eGj9+vIoWLWp2HNyjTp062rZtGyXdoj7//HPlyZNHzZs3l3R3Yb8ff/xROXLkUHh4uH3WEFJebGysBg4cqNGjR9tLX4YMGfT++++rf//+id7IR8rx9PTUtWvXEo1fv36dHSss4OTJk0luixsTE6OzZ8+akMgclPRU5plnntGdO3cSjcfGxipXrlwmJEICFvizthkzZjz0dhbAMs8bb7yhmzdvqnjx4vLw8JC3t7fD7Vz3nLIWL15s/3eDBg3Us2dPHThwQCEhIYmKBYuTmWvixImaNWuWJGnFihVasWKFfv31V82bN089e/Zkdp2JOnXqpIULF2rYsGEO234OGDBAly5d0oQJE0xO6LpeeukltW/fXpMnT7Zf6vbf//5XHTp04Geaie793bNs2TKHBWbj4uK0cuXKRDtZPM2Y7p7K/PTTT/rss880duxYlS5dWjabTdu2bdP777+vXr16cTYXeAB/f3+Hz+/cuaObN2/Kw8ND6dKlowiaaPr06Q+9vVWrVimUBJIctlx7GJvNluTZDqQcb29v+4JXXbp0UXR0tCZOnKjDhw/r+eef199//212RJfl5+en77//XvXq1XMY//XXX/Xaa68pMjLSpGS4evWqWrVqpSVLltjfeIyNjVXDhg01bdq0B+4+guSV8LvHZrPp/nqaNm1a5cmTRyNGjNBLL71kRrwUR0lPZfz9/XXz5k3FxsYqTZq7EyES/n3/dbWUjuSXKVMmHT58WFmyZJG/v/9D90bn9bCeI0eO6N1331XPnj3Z+gtAqhMQEKAffvhBFStWVKFChTR48GC9+uqrOnTokMqWLauoqCizI7qs7Nmza82aNYl2fDl48KCqVKmiv/76y6RkSHDkyBGHNWry589vdiTo7m5JW7duVZYsWcyOYiqmu6cyo0aNMjsC7jFy5Ej5+PhI4rVJjQoUKKChQ4fqjTfe0B9//GF2HEi6detWokt6fH19TUoDWFuTJk3UokULFShQQJcvX7aftd21axeFw2QdO3bUJ598oqlTp8rT01PS3WtqP/30U3Xq1MnkdJDu/g3AriLWc+LEiURjrrggJmfSAbi0nTt3qmrVqpxxMtGNGzfUq1cvzZs3T5cvX050O1OqzdO5c2flz59fnTt3dhgfM2aMjh49ypuTJrtz546++uornT59Wq1bt1bJkiUl3X3TOEOGDHrrrbdMTuhamjRp4vD5b7/9Jk9PT4dtP2/fvq0XX3xRCxYsMCOiy+rWrdsjH/vll18mYxL8k/sXxHz11Vf1448/KmfOnC61ICYlHXCSB5U8m80mT09PVgw12b0Lkkh39xY+f/68xowZo8DAQP36668mJUPHjh21evVqDRo0SKGhoRo7dqzOnj2riRMnaujQoWrZsqXZEV1Wrly5tHjxYpUuXdphfMeOHWrYsKHOnDljUjLAetq0afPIx06dOjUZk+B+1atXf6TjbDabVq1alcxp8DD58uXTrFmzVLFiRa1YsULNmjXT3LlzNW/ePEVERLjMgpiUdMBJ3NzcHnpNeu7cudW6dWv179//kRdmgvPc/z232WzKmjWratSooREjRihnzpwmJcMzzzyjGTNmqFq1avL19dWOHTuUP39+zZw5U3PmzFF4eLjZEV2Wl5eX9u3bl2jq9NGjR1W0aFFFR0eblAwA8DRiQcy7aAqAk0ybNk0BAQH68MMPtWjRIi1cuFAffvihcuXKpfHjx6t9+/YaPXq0hg4danZUlxQfH+/wERcXpwsXLui7776joJvsypUryps3r6S7158nLLL4wgsvaN26dWZGc3n58+fX0qVLE43/+uuv7J0O/IPY2Fj99ttvmjhxon1f7nPnztn3TQeQmL+/v06fPi1JWrp0qWrWrCnp7gxIV7r8jYXjACeZPn26RowYoWbNmtnHGjZsqJCQEE2cOFErV67UM888o08//VQffvihiUld06BBg9SjRw+lS5fOYfzWrVsaPny4+vXrZ1Iy5MuXTydPnlRQUJCCg4M1b948lStXTkuWLHG5hWKsplu3burUqZP++usv1ahRQ5K0cuVKjRgxguvRgYc4deqU6tatq4iICMXExKhWrVry8fHRsGHDFB0dzT7pKaxJkyaaNm2afH19E60dcD/WCzAXC2LexZl0wEk2b95sX7TnXiVLltTmzZsl3T0zGBERkdLRIGngwIFJnr24efOmBg4caEIiJGjTpo12794tSerTp4/GjRsnT09PhYWFqWfPnianc21t27bViBEjNHnyZFWvXl3Vq1fXrFmzNH78eL399ttmxwMsq0uXLipTpoz+/vtveXt728dfeeUVrVy50sRkrsnPz89+SaKfn99DP2CukSNHqlOnTgoODtaKFSuUIUMGSdL58+f13nvvmZwu5XBN+lOkbdu2ql69ut58802zo7ikggULqkmTJomms/fu3VsLFy7UoUOHtG3bNjVq1Ehnz541KaXrcnNz059//qmsWbM6jK9atUrNmzdnz1oLiYiI0LZt2/Tss8+6zCquqcFff/0lb29v+x9MAB4sS5Ys2rhxowoVKiQfHx/t3r3bPmsoODhYN2/eNDsiAAtjuvtT5Pjx41q9erW++OIL+1kppJwvvvhCr776qn799VeVLVtWNptNW7du1R9//KEffvhBkrR161b7lhJIGf7+/rLZbLLZbCpYsKDD4n5xcXG6fv26OnToYGJC3O+ZZ57RM888Y3YM/E9sbKzWrFmjY8eOqUWLFpLuXlfr6+tLYTdBws+0R5GwvgNSXsLaJ/c7c+aMfHx8TEgEIDXhTPpT6NChQypUqJDZMVzSyZMnNWHCBB0+fFiGYei5557TO++8ozx58pgdzWVNnz5dhmGobdu2GjVqlMNUNg8PD+XJk0cVKlQwMSEkacuWLVqzZo0uXryo+Ph4h9vYs9Y8919Xe/jwYeXLl09du3blulqTTJ8+/ZGPbdWqVTImwcM0b95cfn5++uabb+Tj46M9e/Yoa9asatSokZ555hm2YDPRn3/+qR49emjlypW6ePGi7q9CrrQ4GayLkp7K3LhxQ+nTpzc7BpDqrF27VpUqVVKaNEwgsprPPvtMH330kQoVKqTs2bM7nCVkz1pzNW7cWD4+Ppo8ebIyZ85sn7K7du1avfXWWzpy5IjZEQFLOnfunKpXry53d3cdOXJEZcqU0ZEjR5QlSxatW7dO2bJlMzuiy6pXr54iIiLUqVMn5cyZM9HMlEaNGpmUDPh/lPRUJkOGDGrWrJnatm2rF154wew4uM/Vq1e1ZcuWJM8GhoaGmpQKkrRjxw6lTZtWISEhkqSffvpJU6dOVXBwsAYMGCAPDw+TE7qu7Nmz6/PPP1fr1q3NjoL7cF1t6nHr1i3duXPHYczX19ekNJDuviZz5szRjh07FB8fr1KlSqlly5YOC8kh5fn4+Gj9+vUqUaKE2VFwn7i4OG3YsEHFihWTv7+/2XFMxSmlVGbOnDmaNm2aXnzxRQUFBalt27YKDQ1VQECA2dFc3pIlS9SyZUvduHFDPj4+ic4GUtLN9c4776h3794KCQnR8ePH1bx5czVp0kTz58/XzZs32U7KRG5ubqpUqZLZMZAErqu1ths3bqhXr16aN2+eLl++nOh2pu2ay9vbW23btlXbtm3NjoJ7BAYGJpriDmtwd3dXnTp1dPDgQZcv6WzBlsq8/PLL+vHHH3Xu3Dm9++67mjNnjoKCgvTSSy9pwYIFio2NNTuiy+revbvatm2ra9eu6erVq/r777/tHyzeY77Dhw/b3zWfP3++qlatqu+++07Tpk3Tjz/+aG44FxcWFqaxY8eaHQNJqFWrlsMbWDabTdevX1f//v1Vv35984JBkvTBBx9o1apV9m0LJ02apIEDByogIEAzZswwO55LCwgIUIsWLfTNN9/o8OHDZsfBPUaNGqXevXvr5MmTZkdBEhJOprg6prs/Bb7++mv17NlTt2/fVpYsWdShQwf17t1b6dKlMzuaS0mfPr327t2rfPnymR0FSfD19dX27dtVoEAB1apVSy+99JK6dOmiiIgIFSpUSLdu3TI7osuKj49XgwYNdPjwYQUHBytt2rQOty9YsMCkZOC6Wmt75plnNGPGDFWrVk2+vr7asWOH8ufPr5kzZ2rOnDkKDw83O6LLmjNnjtauXas1a9bo8OHDyp49u6pWrapq1aqpatWqKly4sNkRXcr9uyLcuHFDsbGxSpcuXaLfOZxYMdfy5cvVq1cvffLJJypdunSitbhc5TIeSnoqdeHCBc2YMUNTp05VRESEXnnlFbVr107nzp3T0KFDlTNnTi1fvtzsmC6lSZMmeu2119SsWTOzoyAJNWrUUGBgoGrWrKl27drpwIEDyp8/v9auXatWrVrxjrqJOnbsqMmTJ6t69eqJFo6TxCrIJuO6WuvKkCGD9u/fr6CgIOXOnVsLFixQuXLldOLECYWEhOj69etmR4Turia+evVq/fzzz5o7d+4DLyNB8mFXhNTDze3/J3rf+/eAYRiy2Wwu8/8O16SnMgsWLNDUqVO1bNkyBQcHq2PHjnrjjTeUMWNG+zElSpRQyZIlzQvpoho0aKCePXvqwIEDCgkJSfTObMOGDU1KBunu9LaWLVtq0aJF6tu3r/Lnzy9J+uGHH1SxYkWT07m2GTNm6Mcff1SDBg3MjoIkcF2tdSUs4hcUFKTg4GDNmzdP5cqV05IlSxz+LoA5rl+/rg0bNtjPqO/cuVMhISGqWrWq2dFcDsU79Vi9erXZESyBM+mpjJ+fn15//XW1a9dOZcuWTfKYW7duadiwYerfv38Kp3Nt977zdz9XeucvtYmOjpa7u3uiN1WQcoKCgrRs2TI999xzZkdBEs6ePauNGzcmuWtF586dTUoFSRo5cqTc3d3VuXNnrV69Wg0aNFBcXJxiY2P15ZdfqkuXLmZHdFnPP/+89uzZo6JFi6patWqqUqWKKleuzJsnFhEXF6eFCxfq4MGDstlsKly4sBo1asQ2rbAMSnoqEhsbq2+++UZNmjRRjhw5zI4DAE4xdepULV26VFOnTmUtDYuZOnWqOnToIA8PD2XOnDnRrhUs7mMtERER2rZtm5599lkVL17c7DguLVOmTLLZbKpZs6aqVaumatWqcR26Rezbt0+NGjXShQsXVKhQIUl3F5fNmjWrFi9ebN+qFea5evWqJk+ebH8TJTg4WG3btpWfn5/Z0VIMJT2VSZcunQ4ePKigoCCzo+AhoqOj5eXlZXYMIFUoWbKkjh07JsMwlCdPnkSzGnbs2GFSMgQGBqpDhw7q06fPQ2cLAUhsz549WrNmjdauXav169fLzc1NVatWVfXq1dWhQwez47ms8uXLK1u2bJo+fbp9m6+///5brVu31sWLF7V582aTE7q2bdu2qU6dOvL29la5/2PvPqOiuro+gP8HRHovCogUEZSmKGoARRC7CYrGElQUsMUCKmKJFSuxYY3YEXvvBRUQBTtVpSnVAjaCKEWBmfcDj/M4DpY8b8KZyd2/tVwLztwP/7UmYWbfe87e7dtDIBDg7t27qKiowMWLF9GmTRvWEesFFelSxs3NDQEBAejXrx/rKOQzNTU1WLp0KcLCwvD8+XNkZWXBzMwMc+fOhYmJCfz8/FhHJEQiBQcHf/V1OrrDjra2Nm7fvo1mzZqxjkLq4O/vD3Nzc7FjBxs2bMCjR49ExucRthISErBhwwbs2bOHGscxpqioiLt378La2lpk/f79+2jXrh1Ne2GsU6dOMDc3x9atW4XHD6qrqzFq1Cjk5OTg6tWrjBPWDzp4IWXGjx+PwMBAPHnypM6xBHZ2doySkSVLlmDXrl1Yvnw5Ro8eLVy3tbVFaGgoFemEfAEV4ZLLz88Phw8fxsyZM1lHIXU4evQoTp06Jbbu5OSEkJAQKtIZSkpKwpUrV3DlyhVcu3YNb9++RatWrRAQEAA3NzfW8TjN0tISz58/FyvSX7x4IWwqS9i5e/euSIEOAA0aNMD06dPh4ODAMFn9oifpUqau7YY8Ho9zYwkkkbm5OTZv3gx3d3eoqqoiJSUFZmZmyMjIgKOjI/7880/WEQkh5C+pqanBjz/+iIqKijqnVqxevZpRMgIACgoKuH//vlhh8ejRI9jY2KCyspJRMtKgQQPY29sLZ6O7uLhwZr6zpDt37hymT5+OBQsW4IcffgAA3Lx5EwsXLkRISAg6duwovJbes/rXqFEj7N69G927dxdZj4yMhLe3N54/f84oWf2iJ+lSJjc3l3UE8gVPnz6t8w4sn89HVVUVg0Tkey1cuFD4JYoQ8l9Lly5FZGSksLnS543jCFvm5ua4cOECJk6cKLJ+/vx5mJmZMUpFAKC4uJgKPAn1448/AgAGDRok/Dv28ZnlTz/9JPydHn6xMXjwYPj5+WHlypVwcnICj8dDXFwcgoKC8Msvv7COV2+oSJcy+fn5cHJyEhsRUV1djevXr1NDOYasra1x7do1sffg8OHDNLdewu3cuRMhISFwd3fH6dOnWcchRGKsXr0aO3bswMiRI1lHIXWYOnUqJk6ciJcvX6JLly4AgKioKKxatYq2ujNGBbrkojnckm3lypXg8Xjw9vZGdXU1AEBOTg6//vorQkJCGKerP7TdXcrIysqisLAQenp6IuuvX7+Gnp4e3fFj6PTp0xg+fDhmzZqFhQsXIjg4GJmZmYiIiMCZM2fQrVs31hHJV1RWViI2NhY9evRgHYUQidG4cWNcu3YNzZs3Zx2FfMGmTZuwZMkSPHv2DABgYmKCBQsWwNvbm3EyQgj535WXlwsnv5ibm3NuRCsV6VJGRkYGz58/h66ursh6VlYWHBwcUFpayigZAWrPyyxduhQJCQng8/lo06YN5s2bJ3auhhBCpMGyZctQWFiIdevWsY5CvuHly5dQVFSEiooK6yiEEEL+n6hIlxL9+/cHAJw8eRI9e/aEvLy88LWamhqkpqbC0tISFy5cYBWREIm2c+dOqKioYODAgSLrhw8fRnl5OUaMGMEoGfka6hfAlqenJ6Kjo6GtrQ1ra2uxxnHHjh1jlIwQQgj596Iz6VJCXV0dQG0jC1VVVSgqKgpfa9iwIX744QeRsV+EEFEhISEICwsTW9fT08OYMWOoSJdQ1C+ALQ0NDeFNYiIZ2rRpg6ioKGhqasLe3v6rDfwSExPrMRn5qKqqCpaWljhz5gysrKxYxyGESCEq0qXEzp07AdSeNZs2bZrYfHRCyNfl5+fD1NRUbN3Y2BgFBQUMEpHvkZubK+wXQOrfx88eIjn69u0r3E3Xt29f6rIvgeTk5PD+/Xt6bwgh/zPa7i5lKioqIBAIhM0T8vPzcfz4cVhZWdG5Z0K+omnTptiwYQM8PDxE1k+ePIkJEybgyZMnjJIRQgj5twkJCUFGRga2bdsmNpGHEEK+RYZ1APLX9O3bFxEREQCAkpIStG/fHqtWrULfvn2xadMmxukIkVxDhgyBv78/YmJiUFNTg5qaGkRHRyMgIABDhgxhHY/zrl27hmHDhsHR0RFPnz4FAOzevRtxcXGMkxEiuczMzPD69Wux9ZKSEpqTztitW7dw7NgxNG3aFD169ED//v1F/hHJ1KVLFyxatAjl5eWso3DWrl27cPbsWeHv06dPh4aGBpycnJCfn88wWf2iIl3KJCYmolOnTgCAI0eOoHHjxsjPz0dERAR135UQHz58QGZmpnC2I5EMixcvRocOHeDu7g5FRUUoKiqie/fu6NKlC5YuXco6HqcdPXoUPXr0gKKiIpKSkvD+/XsAwNu3b+m9IeQr8vLy6hy9+v79e9odxJiGhgYGDBiAHj16wMDAAOrq6iL/iGQyNjZGdHQ0WrZsyToKZy1dulTYe+vGjRvYsGEDli9fDh0dHUyZMoVxuvpD292ljJKSEjIyMtC0aVMMGjQI1tbWmD9/Ph4/fgxLS0u688dQeXk5Jk2ahF27dgGoHYtnZmYGf39/GBgYYObMmYwTEqD2fUlJSYGioiJsbW1hbGzMOhLn2dvbY8qUKfD29oaqqipSUlJgZmaG5ORk9OzZE0VFRawjEiJRTp06BQDo168fdu3aJVL01dTUICoqCpcuXUJmZiariIRItXfv3tE4Q0Y+rXVmzJiBwsJCRERE4MGDB3B1dcXLly9ZR6wXdEhGypibm+PEiRPw9PREZGSk8I7SixcvoKamxjgdt82aNQspKSm4cuUKevbsKVzv2rUr5s+fT0W6hLCwsICFhQXrGOQTmZmZdY5YU1NTQ0lJSf0HIkTC9evXDwDA4/HEJlPIycnBxMQEq1atYpCMfO7ly5fIzMwEj8eDhYUFdHV1WUfivNzc3DobyX6KCnR2VFRU8Pr1azRt2hQXL14U1joKCgqoqKhgnK7+UJEuZebNmwcvLy9MmTIF7u7ucHR0BABcvHgR9vb2jNNx24kTJ3Dw4EH88MMPIh1drayskJ2dzTAZd02dOhWLFi2CsrIypk6d+tVrV69eXU+pyOf09fXx6NEjmJiYiKzHxcXRuVoJFhERAWdnZzRr1ox1FM7h8/kAAFNTU9y5cwc6OjqME5HPlZWVYdKkSYiIiBC+X7KysvD29sb69euFDYBJ/TM3N4eLiwv8/Pzw888/Q0FBgXUk8olu3bph1KhRsLe3R1ZWFvr06QMAePDggdj3hH8zOpMuZX7++WcUFBTg7t27uHDhgnDd3d0doaGhDJORly9fQk9PT2y9rKyMxrAwkpSUhKqqKuHPX/qXnJzMNijHjR07FgEBAbh16xZ4PB6ePXuGvXv3Ytq0aRg/fjzreOQLRo4cCSsrK0yaNIl1FM7Kzc2lAl1CTZ06FbGxsTh9+jRKSkpQUlKCkydPIjY2FoGBgazjcVpKSgrs7e0RGBiIxo0bY+zYsbh9+zbrWOQ/Nm7cCEdHR7x8+RJHjx6FtrY2ACAhIQG//PIL43T1h86kE/I36dy5M37++WdMmjQJqqqqSE1NhampKSZOnIhHjx6J3FQhhIiaPXs2QkNDUVlZCQCQl5fHtGnTsGjRIsbJyNfk5eUhMjISY8eOZR2FM9atW4cxY8ZAQUHhmw1j/f396ykV+ZyOjg6OHDkCV1dXkfWYmBgMGjSIM+dqJVl1dTVOnz6N8PBwnD9/Hs2bN4efnx+GDx9OxxIIc1SkS4H+/fsjPDwcampq3xzbcezYsXpKRT53/fp19OzZE0OHDkV4eDjGjh2LBw8e4MaNG4iNjUXbtm1ZRySfKC0tRXR0NFq0aIEWLVqwjkNQ23wxLS0NfD4fVlZWdCaQkDqYmpri7t270NbW/uq5Wh6Ph5ycnHpMRj6lpKSEhIQEsS7hDx48QPv27VFWVsYoGfnc+/fv8ccff2DWrFn48OED5OTkMHjwYPz+++/Q19dnHY9zdu7cCRUVFQwcOFBk/fDhwygvLxfrw/FvRUW6FPDx8cG6deugqqoKHx+fr167c+fOekpF6nL//n2sWLECCQkJ4PP5aNOmDWbMmAFbW1vW0Thv0KBBcHFxwcSJE1FRUYFWrVohLy8PAoEABw4cwIABA1hH5Kw3b96gpqYGWlpaIuvFxcVo0KABNcWsZ6Wlpd99Lb03hNTN3d0d2traiIiIEJ55rqiowIgRI1BcXIzLly8zTkju3r2LHTt24MCBA1BWVsaIESPg5+eHZ8+eYd68eXj79i1tg2fA0tISYWFhcHNzE1mPjY3FmDFjODO1gop0Qv4GVVVVGDNmDObOnUuNriRU48aNERkZiVatWmHfvn2YP38+UlJSsGvXLmzZsgVJSUmsI3JWr1698NNPP4mdPw8LC8OpU6dw7tw5Rsm4SUZG5pt9NAQCAXg8Xp0zugkhtTfte/bsicrKSrRq1Qo8Hg/JyclQUFBAZGQkrK2tWUfkrNWrV2Pnzp3IzMxE7969MWrUKPTu3RsyMv9t1fXo0SO0aNEC1dXVDJNyk4KCAjIyMsSaxOXl5aFly5ac6fBORTohfxMNDQ0kJiZSkS6hFBUVkZWVBSMjI3h7e8PAwAAhISEoKCiAlZUV3r17xzoiZ2lpaSE+Pl5sW2hGRgacnZ3x+vVrRsm4KTY29ruv7dy58z+YhNTlW5MqPkVTK9iqqKjAnj17kJGRAYFAACsrKwwdOhSKioqso3Fa8+bN4evrCx8fHzRu3LjOaz58+ID9+/dzZmu1JGnatCk2bNgADw8PkfWTJ09iwoQJePLkCaNk9YtGsEmZ169fY968eYiJicGLFy+EYz0+Ki4uZpSMeHp64sSJE3/pCxSpP0ZGRrhx4wa0tLRw4cIFHDhwAADw559/0vgVxt6/f1/n04qqqirO3DGXJFR4S7bv3fVDU0XYU1RUxOjRo1nHIJ+orq7G0KFDMWzYsC8W6ADQsGFDKtAZGTJkCPz9/aGqqgoXFxcAtTePAwICMGTIEMbp6g8V6VJm2LBhyM7Ohp+fHxo1akQfwhLE3NwcixYtwvXr19G2bVsoKyuLvE5ddtmaPHkyhg4dChUVFRgbGws77l69epV6BjDWrl07bNmyBevXrxdZDwsLo4aLEqK8vBwFBQX48OGDyLqdnR2jRNwVExPDOgIhUqtBgwZYtWoVRo4cyToK+YLFixcjPz8f7u7uaNCgtlTl8/nw9vbG0qVLGaerP7TdXcqoqqoiLi4OrVq1Yh2FfIa67Eq+u3fv4vHjx+jWrZuwc/jZs2ehoaEBZ2dnxum4Kz4+Hl27dkW7du3g7u4OAIiKisKdO3dw8eJFdOrUiXFC7nr58iV8fHxw/vz5Ol+nM+mS48mTJ+DxeDA0NGQdhRCJ1q9fP/Tr148KdQmXlZWFlJQUKCoqwtbWFsbGxqwj1St6ki5lWrRoQds/JVRubi7rCOQbHBwc4ODgILLWp08fRmnIR87Ozrhx4wZWrFiBQ4cOQVFREXZ2dti+fTuaN2/OOh6nTZ48GX/++Sdu3rwJNzc3HD9+HM+fP8fixYuxatUq1vE4j8/nC9+Lj301VFVVERgYiNmzZ4s0wiKE1OrVqxdmzZqF+/fv17nz8fOz0IQNCwsLWFhYsI7BDD1JlzJ37tzBzJkzMW/ePNjY2EBOTk7kdRqHQ0jdfH19v/r6jh076ikJIdJDX18fJ0+eRPv27aGmpoa7d+/CwsICp06dwvLlyxEXF8c6IqfNmjUL27dvR3BwMJydnSEQCBAfH48FCxZg9OjRWLJkCeuIhEicr928oqkVbEydOhWLFi2CsrLyN3s7caUhJj1JlzIaGhp48+YNunTpIrJO43Akw5MnT3Dq1Kk6z25y5Y+KpPrzzz9Ffq+qqsL9+/dRUlIi9v8TqX98Ph+PHj2qsyHmx8YxpP6VlZVBT08PQG0X/pcvX8LCwgK2trZITExknI7s2rUL27ZtE3ny16pVKxgaGmL8+PFUpDNkZmaGO3fuQFtbW2S9pKQEbdq0oSNwDH3+GUPYS0pKQlVVlfBnQkW61Bk6dCgaNmyIffv2UeM4CRMVFQUPDw+YmpoiMzMTNjY2yMvLg0AgQJs2bVjH47zjx4+LrfH5fIwfP57G5jF28+ZNeHl5IT8/H59v7qKbj2xZWloiMzMTJiYmaN26NTZv3gwTExOEhYVBX1+fdTzOKy4uRosWLcTWW7RoQdNeGMvLy6vzb9f79+/x9OlTBokIkVyfNsSk5pi1aLu7lFFSUkJSUhIsLS1ZRyGfad++PXr27ImFCxdCVVUVKSkp0NPTw9ChQ9GzZ0/8+uuvrCOSOmRmZsLV1RWFhYWso3BW69atYWFhgeDgYOjr64vdfFRXV2eUjOzduxdVVVUYOXIkkpKS0KNHD7x+/RoNGzZEeHg4Bg8ezDoip3Xo0AEdOnTAunXrRNYnTZqEO3fu4ObNm4yScdepU6cA1DYn27Vrl8jfr5qaGkRFReHSpUvIzMxkFZGgdqTXypUrkZ6eDh6Ph5YtWyIoKIgalUoAX19frF27FqqqqiLrZWVlmDRpEmeOJ1KRLmVcXFwwb948dO3alXUU8hlVVVUkJyejWbNm0NTURFxcHKytrZGSkoK+ffsiLy+PdURSh3PnzmHEiBF4+fIl6yicpaysjJSUFJibm7OOQr6hvLwcGRkZaNq0KXR0dFjH4bzY2Fj06dMHTZs2haOjI3g8Hq5fv47Hjx/j3LlzVHAw8PG8M4/HE9sZJCcnBxMTE6xatQo//vgji3gEwJ49e+Dj44P+/fsLezlcv34dx48fR3h4OLy8vFhH5DRZWVkUFhYKj1p99OrVKzRu3BjV1dWMktUv2u4uZSZNmoSAgAAEBQXB1tZWrHEczaxlR1lZGe/fvwcAGBgYIDs7G9bW1gBq/7AQtj5vRCIQCFBYWIizZ89ixIgRjFIRoPZp4KNHj6hIlwJKSkp0fEeCdO7cGVlZWdi4cSMyMjIgEAjQv39/jB8/HgYGBqzjcdLH886mpqa4c+cO3cySQEuWLMHy5csxZcoU4VpAQABWr16NRYsWUZHOSGlpKQQCAQQCAd6+fQsFBQXhazU1NTh37pxY4f5vRk/SpUxdHSk/3q2ls5ts9evXD3369MHo0aMxffp0HD9+HCNHjsSxY8egqamJy5cvs47IaW5ubiK/y8jIQFdXF126dIGvry8aNKB7lqwcP34cc+bMoZuPEujnn3+Gg4MDZs6cKbK+YsUK3L59G4cPH2aUjBBC/jfy8vJ48OCB2I3hR48ewcbGBpWVlYyScZuMjMxXe23xeDwEBwdj9uzZ9ZiKHSrSpUx+fv5XXzc2Nq6nJORzOTk5ePfuHezs7FBeXo5p06YhLi4O5ubmCA0NpfeGkC+gm4+SS1dXF9HR0bC1tRVZv3fvHrp27Yrnz58zSkY++vPPP7F9+3aRs7U+Pj7Q0tJiHY3zoqKiEBUVVefUCq6cq5VE5ubmCAoKwtixY0XWN2/ejJUrV+Lhw4eMknFbbGwsBAIBunTpgqNHj4r8DWvYsCGMjY05tUOIinRCCCFM0c1HyaWoqIjk5GSxZqUZGRmwt7dHRUUFo2QEqP1S27dvX6ipqcHBwQEAkJCQgJKSEpw6dQqdO3dmnJC7goODsXDhQjg4ONTZELOuiSOkfmzatAmTJ0+Gr68vnJycwOPxEBcXh/DwcKxdu1aseCf1Kz8/H0ZGRl+dZ88FVKRLod27dyMsLAy5ubm4ceMGjI2NsWbNGpiamqJv376s43HW7Nmz4erqCmdnZygpKbGOQz7z+vVrzJs3DzExMXU+1aBxRYSIa9euHX766SfMmzdPZH3BggU4ffo0EhISGCUjAGBjYwMnJyds2rQJsrKyAGrPbo4fPx7x8fG4f/8+44Tcpa+vj+XLl2P48OGso5A6HD9+HKtWrUJ6ejoACLu70/doyVFeXo6CggJ8+PBBZJ0rR+DoEKaU2bRpE+bNm4fJkydjyZIlwm2gGhoaWLNmDf1xYSghIQHr16/H+/fv0aZNG7i6uqJz587o2LEjVFRUWMfjvGHDhiE7Oxt+fn5o1KjRV889ETbS0tLq/ED28PBglIjMnTsXAwYMQHZ2Nrp06QKgdgvv/v376Ty6BMjOzsbRo0eFBTpQ2xl56tSpiIiIYJiMfPjwAU5OTqxjkC/w9PSEp6cn6xikDi9fvoSPjw/Onz9f5+tcOQJHRbqUWb9+PbZu3Yp+/fohJCREuO7g4IBp06YxTEYuXLiAmpoa3L59G7Gxsbhy5Qr++OMPVFRUoE2bNjSvlrG4uDjExcWhVatWrKOQz+Tk5MDT0xP37t0TGVv08UYKVz6QJZGHhwdOnDiBpUuX4siRI1BUVISdnR0uX75MW6klQJs2bZCeni52HCE9PR2tW7dmE4oAAEaNGoV9+/Zh7ty5rKOQz5iZmeHOnTvQ1tYWWS8pKUGbNm2Qk5PDKBkBgMmTJ+PPP//EzZs34ebmhuPHj+P58+dYvHgxVq1axTpevaEiXcrk5ubC3t5ebF1eXh5lZWUMEpFPycrKwtHREVpaWtDU1ISqqipOnDiB7Oxs1tE4r0WLFnR+VkIFBATA1NQUly9fhpmZGW7fvo3Xr18jMDAQK1euZB2P8/r06YM+ffqwjkHq4O/vj4CAADx69Ag//PADAODmzZvYuHEjQkJCkJqaKryWK1tEJUVlZSW2bNmCy5cvw87OTmxqxerVqxklI3l5eXXe/H3//j2ePn3KIBH5VHR0NE6ePIl27dpBRkYGxsbG6NatG9TU1LBs2TLOfB5RkS5lTE1NkZycLNZI6fz587CysmKUigC1RxFiY2MRGxuLmpoadOrUCZ07d8bcuXPpy5EE+OOPPzBz5kzMmzcPNjY2Yl+Y1NTUGCUjN27cQHR0NHR1dSEjIwMZGRl07NgRy5Ytg7+/P5KSklhHJEQi/fLLLwCA6dOn1/kaTUlgJzU1Vbib4fPeAHTcio1Tp04Jf46MjIS6urrw95qaGkRFRcHExIRBMvKpsrIy4Tx0LS0tvHz5EhYWFrC1tUViYiLjdPWHinQpExQUhAkTJqCyshICgQC3b9/G/v37sWzZMmzbto11PE6bMGECdHV1ERgYiHHjxlHRJ2E0NDTw5s0b4bnaj+gLLHs1NTXCvg06Ojp49uwZLC0tYWxsjMzMTMbpuEdLSwtZWVnQ0dGBpqbmVwsKarjIVm5uLusI5AtiYmJYRyCf6devH4DamyQjRowQeU1OTg4mJiac2k4tqSwtLZGZmQkTExO0bt0amzdvhomJCcLCwqCvr886Xr2hIl3K+Pj4oLq6GtOnT0d5eTm8vLxgaGiItWvXYsiQIazjcdqxY8dw9epVHDhwAPPmzUOrVq3g6uoKV1dXdOrUiZrHMTZ06FA0bNgQ+/bto8ZxEsbGxgapqakwMzNDhw4dsHz5cjRs2BBbtmyBmZkZ63icExoaClVVVQDAmjVr2IYhX0XjCSXfo0ePkJ2dDRcXFygqKgpvDJP693Gqi6mpKe7cuQMdHR3GiUhdJk+ejMLCQgDA/Pnz0aNHD+zduxcNGzZEeHg423D1iEawSbFXr16Bz+cLt4QQyfHmzRtcu3YNR44cwb59+8Dj8fD+/XvWsThNSUkJSUlJYg2WCHuRkZEoKytD//79kZOTgx9//BEZGRnQ1tbGwYMHxXY/kPpRXV2NvXv3okePHmjcuDHrOIRIldevX2PQoEGIiYkBj8fDw4cPYWZmBj8/P2hoaNATWwlTUlICDQ0N1jFIHcrLy5GRkYGmTZty6sYKt6fESzkdHR0q0CVMcXExjh8/jnnz5mH27NnYvXs3NDQ0aISUBHBwcMDjx49ZxyB16NGjB/r37w+gtutuWloaXr16hRcvXlCBzlCDBg3w66+/0g1GQv4HU6ZMgZycHAoKCqCkpCRcHzx4MC5cuMAwGfn9999x8OBB4e8DBw6ElpYWDA0NkZKSwjAZqYuSkhLatGnDqQIdoO3uUuf58+eYNm0aoqKi8OLFC3y+EYLO1bJjZ2eHtLQ0aGlpwcXFBaNHj4arqytsbGxYRyMAJk2ahICAAAQFBcHW1lascRw195MsWlparCMQAB06dEBSUhJtqybkL7p48SIiIyPRpEkTkfXmzZsjPz+fUSoCAJs3b8aePXsAAJcuXcLly5dx4cIFHDp0CEFBQbh48SLjhNwmEAhw5MgRxMTE4MWLF8JjCh8dO3aMUbL6RUW6lBk5ciQKCgowd+5c6Ovr07kmCTJmzBgqyiXY4MGDAQC+vr7CNep8LBnKysoQEhIivPn4+QcyzaxlZ/z48QgMDMSTJ0/Qtm1bKCsri7xON7cIqVtZWZnIE/SPXr16BXl5eQaJyEeFhYUwMjICAJw5cwaDBg1C9+7dYWJigg4dOjBORwICArBlyxa4ublxuocQFelSJi4uDteuXROO9SCSo7i4uM4mVxUVFVixYgXmzZvHIBX5iLogS65Ro0YhNjYWw4cPp5uPEubjzS1/f3/hGt3cIuTbXFxcEBERgUWLFgGo/f+Gz+djxYoVcHNzY5yO2zQ1NfH48WMYGRnhwoULWLx4MYDaJ7j0N429PXv24NixY+jduzfrKExR4zgpY2Vlhb1798Le3p51FPIZWVlZFBYWivUJeP36NfT09OgPPyFfoKGhgbNnz8LZ2Zl1FPKZb23LpW3wksvU1BRdunTBwoULYWhoyDoO56SlpcHV1RVt27ZFdHQ0PDw88ODBAxQXFyM+Ph7NmjVjHZGzJk6ciDNnzqB58+ZISkpCXl4eVFRUcPDgQfz++++cmsUtiUxNTXH+/Hm0aNGCdRSmqHGclFmzZg1mzpyJvLw81lHIZ740ViUlJYXO1xLyFZqamvT/iITKz8+HoaEhjI2NRf4ZGhrSuVoJN2LECPD5fLi4uLCOwklWVlZITU1F+/bt0a1bN+EEi6SkJCrQGQsNDcXEiRNhZWWFS5cuCUfkFhYWYvz48YzTkQULFiA4OBgVFRWsozBFT9KlgKampkjxV1ZWhurqaigpKYk1vyouLq7veJz38f158+YN1NTURN6rmpoavHv3DuPGjcPGjRsZpiREcu3ZswcnT57Erl276jzDSdihHUKEEELqU3l5Ofr374/4+HiYmJiI1Tpc2elAZ9KlwJo1a1hHIF+xZs0aCAQC+Pr6Ijg4GOrq6sLXGjZsCBMTEzg6OjJMSIhkW7VqFbKzs9GoUSNOfyBLoi/tEHr9+rVYEzlS/65cuQJXV1fWMcgXVFZWIjU1tc6GmDSatX6dOnUKvXr1gpycHE6dOvXVa+m9YWvkyJFISEjAsGHDON04jp6kE/L/0KZNG0RFRUFTUxNubm44ffq0cNsUIeT7BAcHf/X1+fPn11MS8tHHufUnT55Ez549RbpR19TUIDU1FZaWljTvmTEFBQUYGhrCx8cHI0aMEHasJuxduHAB3t7eePXqldhr1HSx/snIyKCoqAh6enqQkfnyaV96b9hTVlZGZGQkOnbsyDoKU/QkXcqcO3cOsrKy6NGjh8j6xYsXUVNTg169ejFKxk3p6ekoKyuDpqYmrl69ioqKCirSCfmLqAiXPB93BAkEAqiqqkJRUVH4WsOGDfHDDz9g9OjRrOKR/3j27Bn27NmD8PBwLFiwAO7u7vDz80O/fv3QsGFD1vE4beLEiRg4cCDmzZuHRo0asY7DeZ/uZPh8VwORLEZGRlBTU2Mdgzl6ki5l7OzsEBISIjaW4MKFC5gxYwZSUlIYJeMmR0dHqKiooGPHjggODsa0adO+WKTTCDbJRV2QCalbcHAwgoKCqFeAFEhOTsaOHTuwf/9+8Pl8DB06FH5+fmjVqhXraJykpqZGTeII+R+cPXsW69evR1hYGExMTFjHYYaKdCmjqKiI9PR0sf9o8/LyYG1tjbKyMjbBOCozMxPz589HdnY2EhMTYWVlhQYNxDeo8Hg8OlcrwRYsWID8/HxcvXoV2dnZrONwgpaWFrKysqCjoyPWHPNz1BCTndzcXFRXV6N58+Yi6w8fPoScnBynv0BJomfPnmHLli0ICQlBgwYNUFlZCUdHR4SFhcHa2pp1PE7x9fWFs7Mz/Pz8WEchdYiKikJUVFSd/QJ27NjBKBUBahsyl5eXc75JNm13lzLq6urIyckR+2L06NEjauLDgKWlJQ4cOACg9rxTVFSUWBdkIvkWLFjAOgLnhIaGQlVVFQA1x5RkI0eOhK+vr1iRfuvWLWzbtg1XrlxhE4wIVVVV4eTJk9ixYwcuXboEBwcHbNiwAb/88guKi4sxY8YMDBw4EGlpaayjcsqGDRswcOBAXLt2Dba2tmKFhr+/P6NkJDg4GAsXLoSDgwP09fU525hMUtF3glr0JF3KjBkzBjdv3sTx48eFW6gePXqEAQMGoF27dti2bRvjhIQQ8m1Tp07FokWLoKysjKtXr8LJyanOXSiELTU1NSQmJsLc3Fxk/dGjR3BwcEBJSQmbYAQAMGnSJOzfvx8AMGzYMIwaNQo2NjYi1xQUFMDExITO4dazbdu2Ydy4cVBUVIS2trZIIcjj8ZCTk8MwHbfp6+tj+fLlGD58OOsohHwRFelS5s2bN+jZsyfu3r2LJk2aAACePHmCTp064dixY9DQ0GAbkOOys7OxZs0apKeng8fjoWXLlggICKAzaRKgpqYG4eHhX9zeFh0dzSgZN8nJyeHJkydo1KjRF2dxE/bU1dVx5coV2Nvbi6wnJCTA1dUVb9++ZZSMAIC7uztGjRqFAQMGfLFRXHV1NeLj49G5c+d6TsdtjRs3hr+/P2bOnPnVbuKk/mlra+P27dv03UyCZWdnY+fOncjOzsbatWuhp6eHCxcuwMjIiDNHd6hIl0ICgQCXLl1CSkoKFBUVYWdnBxcXF9axOC8yMhIeHh5o3bo1nJ2dIRAIcP36daSkpOD06dPo1q0b64icNnHiRISHh6NPnz51bm8LDQ1llIybmjdvjkGDBqF79+5wc3PD8ePHoampWee19PeNnR9//BFKSkrYv38/ZGVlAdTe8Bo8eDDKyspw/vx5xgkJkUxaWlq4c+cOFYISaMaMGVBRUcHcuXNZRyF1iI2NRa9eveDs7IyrV68iPT0dZmZmWL58OW7fvo0jR46wjlgvqEgn5G9ib2+PHj16ICQkRGR95syZuHjxIjWOY0xHRwcRERFikxEIGydOnMC4cePw4sUL8Hg8fOmjiGbWspWWlgYXFxdoaGigU6dOAIBr166htLQU0dHRYlurSf06depUnes8Hg8KCgowNzeHqalpPaciADBlyhTo6urit99+Yx2FoPaI1Ud8Ph+7du2CnZ0d7OzsxPoFrF69ur7jkU84Ojpi4MCBmDp1KlRVVZGSkgIzMzPcuXMH/fr1w9OnT1lHrBdUpBPyN1FQUMC9e/fEGixlZWXBzs4OlZWVjJIRADAwMMCVK1dgYWHBOgr5xLt376CmpobMzMwvbnf/OLObsPHs2TNs2LBBZPfWxIkToaWlxToa58nIyNR5k+vjGo/HQ8eOHXHixIkv7lQh/wx/f39ERESgVatWVAhKADc3t++6jsfj0fE3xlRUVHDv3j2YmpqKFOl5eXlo0aIFZ75PU5ceQv4murq6SE5OFivSk5OT6aytBAgMDMTatWuxYcMG6uQqQVRUVBATEwNTU1NqHCehDAwMsHTpUtYxSB0uXbqE2bNnY8mSJWjfvj0A4Pbt25gzZw7mzp0LdXV1jB07FtOmTcP27dsZp+WWe/fuCXs53L9/X+Q1+gyqfzExMawjkO+koaGBwsJCsV1ASUlJMDQ0ZJSq/tE3IkL+JqNHj8aYMWOQk5MDJycn8Hg8xMXF4ffff0dgYCDreJwXFxeHmJgYnD9/HtbW1mJPNY4dO8YoGaGGVpLt2rVr2Lx5M3JycnD48GEYGhpi9+7dMDU1RceOHVnH47SAgABs2bIFTk5OwjV3d3coKChgzJgxePDgAdasWQNfX1+GKbmJikJC/jdeXl6YMWMGDh8+DB6PBz6fj/j4eEybNg3e3t6s49UbKtIJ+ZvMnTsXqqqqWLVqFWbNmgWg9gnUggULaB6qBNDQ0ICnpyfrGIRIlaNHj2L48OEYOnQoEhMT8f79ewDA27dvsXTpUpw7d45xQm7Lzs6Gmpqa2LqamppwxFfz5s3x6tWr+o5GPvH48WPweDzhVB5CyJctWbIEI0eOhKGhIQQCAaysrFBTUwMvLy/MmTOHdbx6Q2fSCfkHfBxLpKqqyjgJIYT87+zt7TFlyhR4e3uLnA1MTk5Gz549UVRUxDoip3Xs2BGqqqqIiIiArq4uAODly5fw9vZGWVkZrl69isuXL2P8+PHIyspinJZbqqurERwcjHXr1uHdu3cAao/3TJo0CfPnzxfbzUUIEZWdnY2kpCTw+XzY29uLHSf9t6Mn6f8iMjIycHV1xYoVK9C2bVvWcTinoqICAoEASkpKUFVVRX5+PrZv3w4rKyt0796ddTxCCPnLMjMz6xyBp6amhpKSkvoPRERs374dffv2RZMmTWBkZAQej4eCggKYmZnh5MmTAGqbM9Koqfo3ceJEHD9+HMuXL4ejoyMA4MaNG1iwYAFevXqFsLAwxgkJkWzNmjXj9AhDepL+LxIeHo78/HxcvHgR8fHxrONwTvfu3dG/f3+MGzcOJSUlsLS0RMOGDfHq1SusXr0av/76K+uInGZvb19ns55PRxWNHDnyuzvAEsIFzZo1w+bNm9G1a1eRJ+kREREICQlBWloa64icJxAIEBkZiaysLAgEArRo0QLdunWDjIwM62icpq6ujgMHDqBXr14i6+fPn8eQIUPw5s0bRskIkWw1NTUIDw9HVFQUXrx4AT6fL/I6V7rv01/wf5GRI0di/vz5VKAzkpiYKJwjfOTIETRu3Bj5+fmIiIjAunXrGKcjPXv2RE5ODpSVleHm5gZXV1eoqKggOzsb7dq1Q2FhIbp27Sp8+kQkg6+vL3bv3s06BmeNHTsWAQEBuHXrFng8Hp49e4a9e/di2rRpGD9+POt4BLU3Gnv27Al/f38EBASgR48eVKBLAAUFBZiYmIitm5iYoGHDhvUfiHyXq1ev0g0UxgICAhAQEICamhrY2NigVatWIv+4gp6kS5nw8HAMGjQISkpKrKOQzygpKSEjIwNNmzbFoEGDYG1tjfnz5+Px48ewtLREeXk564icNnr0aDRt2lRs2+fixYuRn5+PrVu3Yv78+Th79izu3r3LKCX5nKurK/Lz86GmpoaUlBTWcThp9uzZCA0NFc6mlZeXx7Rp07Bo0SLGyQgAxMbGYuXKlUhPTwePx0PLli0RFBQkvGlM2Fi4cCEyMjKwc+dOyMvLAwDev38PPz8/NG/eHPPnz2eckNRFRkYGmpqa+O2332gyDyM6OjqIiIhA7969WUdhiop0KaOvr4+ysjIMHDgQfn5+ImNXCFt2dnYYNWoUPD09YWNjgwsXLsDR0REJCQno06cPNVhiTF1dHQkJCTA3NxdZf/ToEdq2bYs3b94gIyMD7dq1Ezb+I5IjMzMTlpaWrGNwVnl5OdLS0sDn82FlZQUVFRXWkQiAPXv2wMfHB/3794ezszMEAgGuX7+O48ePIzw8HF5eXqwjcpanpyeioqIgLy8vfPqXkpKCDx8+wN3dXeRaGgEqOfLz85Gbm4vIyEgsW7aMdRxOMjAwwJUrV2BhYcE6ClNUpEuZmpoanD17FuHh4Th79ixMTU3h4+ODESNGoHHjxqzjcdqRI0fg5eWFmpoauLu74+LFiwCAZcuW4erVqzh//jzjhNzWqFEjrFixQmzGZkREBIKCgvD8+XOkpaXBxcWFxhURQqRCy5YtMWbMGEyZMkVkffXq1di6dSvS09MZJSM+Pj7ffe3OnTv/wSSESJdVq1YhJycHGzZsqLOXEFdQkS7FXrx4gT179iA8PBwZGRno2bMn/Pz88NNPP9F5NEaKiopQWFiIVq1aCd+D27dvQ01NDS1atGCcjtsWL16MpUuXYvTo0WjXrh14PB5u376Nbdu24bfffhNu6T137hwuXbrEOi6nnDp1qs71T5v6mZqa1nMq7urfv/93X0tPANmSl5fHgwcP6twhZGNjIzyiQAgRVVJSgiNHjiA7OxtBQUHQ0tJCYmIiGjVqBENDQ9bxOOfzz53o6GhoaWnB2tpabFwhVz53aASbFNPT04OzszMyMzORlZWFe/fuYeTIkdDQ0MDOnTvh6urKOiLnNG7cWGxHQ/v27RmlIZ+aM2cOTE1NsWHDBmEjMktLS2zdulW4JXTcuHHUhZ+Bfv36gcfj4fN7xh/XeDweOnbsiBMnTkBTU5NRSu5QV1dnHYF8JyMjI0RFRYkV6VFRUTAyMmKUinzq5cuXyMzMBI/Hg4WFhXCePWEnNTUVXbt2hbq6OvLy8jB69GhoaWnh+PHjwoa/pH59/rnj6enJKInkoCfpUuj58+fYvXs3du7ciZycHPTr1w9+fn7o2rUrKioqMGfOHBw5cgT5+fmsoxJCyDdFRUVh9uzZWLJkifCm1u3btzFnzhzMnTsX6urqGDt2LDp06IDt27czTkuI5Ni0aRMmT54MX19fODk5gcfjIS4uDuHh4Vi7di3Gjh3LOiJnlZWVYdKkSYiIiBCOkJKVlYW3tzfWr19PDYAZ6tq1K9q0aYPly5eLjJa8fv06vLy8kJeXxzoiIVSkS5uffvoJkZGRsLCwwKhRo+Dt7Q0tLS2Ra549e4YmTZqIzRUkhBBJZGNjgy1btog1woyPj8eYMWPw4MEDXL58Gb6+vigoKGCUkruqq6tx5coVZGdnw8vLC6qqqnj27BnU1NSogZwEOH78OFatWiU8f/6xu3vfvn0ZJ+O2sWPH4vLly9iwYQOcnZ0BAHFxcfD390e3bt2wadMmxgm5S11dHYmJiWjWrJlIkZ6fnw9LS0s6JsJYly5dcOzYMWhoaIisl5aWol+/fpyZk07b3aWMnp4eYmNj4ejo+MVr9PX1kZubW4+pCJFMWlpayMrKgo6ODjQ1Nb/agKS4uLgek5FPZWdnQ01NTWxdTU0NOTk5AIDmzZtTQz8G8vPz0bNnTxQUFOD9+/fo1q0bVFVVsXz5clRWViIsLIx1RM7z9PSkraES6OjRozhy5IjI0cPevXtDUVERgwYNoiKdIQUFBZSWloqtZ2Zm0nEECXDlyhV8+PBBbL2yshLXrl1jkIgNKtKlzPds9eTxeDA2Nq6HNOSjqqoqjBkzBnPnzoWZmRnrOOQ/QkNDoaqqKvyZy11CJVnbtm0RFBSEiIgI4Rekly9fYvr06WjXrh0A4OHDh2jSpAnLmJwUEBAABwcHpKSkQFtbW7ju6emJUaNGMUxGiGQrLy9Ho0aNxNb19PRQXl7OIBH5qG/fvli4cCEOHToEoPZ7c0FBAWbOnIkBAwYwTsddqampwp/T0tJERhfX1NTgwoULnGrqR9vdpcy6devqXP+0C7KLiwtkZWXrORnR0NBAYmIiFemE/EWZmZno27cvcnNzYWRkJPzCZGZmhpMnT8LCwgInTpzA27dvMXz4cNZxOUVHRwfx8fGwtLQU2Raal5cHKysrKjYY+NauoE/RDiF23N3doa2tjYiICCgoKAAAKioqMGLECBQXF+Py5cuME3JXaWkpevfujQcPHuDt27cwMDBAUVERHB0dce7cOSgrK7OOyEkyMjLCv211laeKiopYv349fH196zsaE/QkXcqEhobi5cuXKC8vh6amJgQCAUpKSqCkpAQVFRW8ePECZmZmiImJoc6u9czT0xMnTpzA1KlTWUchdUhMTIScnBxsbW0BACdPnsTOnTthZWWFBQsWoGHDhowTcpelpSXS09MRGRmJrKwsCAQCtGjRAt26dROOMuzXrx/bkBzF5/NRU1Mjtv7kyRPhLhVSv9asWcM6AvkOa9euRc+ePdGkSRO0atUKPB4PycnJUFBQQGRkJOt4nKampoa4uDhER0cjMTERfD4fbdq0QdeuXVlH47Tc3FwIBAKYmZnh9u3bIkcPGjZsCD09PU49hKQn6VJm//792LJlC7Zt24ZmzZoBqJ2HOnbsWIwZMwbOzs4YMmQIGjdujCNHjjBOyy1LlizBypUr4e7ujrZt24rdifX392eUjABAu3bthFvZcnJyYGVlhf79++POnTvo06cPffElpA6DBw+Guro6tmzZAlVVVaSmpkJXVxd9+/ZF06ZNsXPnTtYRCZFYFRUV2LNnDzIyMiAQCGBlZYWhQ4dCUVGRdTTOqq6uhoKCApKTk2FjY8M6DiFfREW6lGnWrBmOHj2K1q1bi6wnJSUJi4/r169jwIABKCwsZBOSo0xNTb/4Go/HEzbAImx82s31999/R3R0NCIjIxEfH48hQ4bg8ePHrCNyWlRUFKKiovDixQuxyRQ7duxglIo8e/YMbm5ukJWVxcOHD+Hg4ICHDx9CR0cHV69ehZ6eHuuInFdTU4MTJ04gPT0dPB4PVlZW8PDw4NQTJ0L+imbNmuHYsWNo1aoV6yjkK9LS0lBQUCDWRM7Dw4NRovpF292lTGFhIaqrq8XWq6urhQ0WDAwM8Pbt2/qOxnnUUV+yCQQCYfF3+fJl/PjjjwAAIyMj6hrOWHBwMBYuXAgHBwfo6+tTgz8JYmBggOTkZBw4cAAJCQng8/nw8/Ojp4ES4tGjR+jduzeePn0KS0tLCAQCZGVlwcjICGfPnhXuuCP149SpU999LVcKDUk0Z84czJo1C3v27BEbY0zYy8nJgaenJ+7duwcejyc8n/7xu0FdR7D+jehJupTp06cPioqKsG3bNtjb2wOofYo+evRoNG7cGGfOnMHp06fx22+/4d69e4zTctfnf1AIe126dIGRkRG6du0KPz8/pKWlwdzcHLGxsRgxYgTy8vJYR+QsfX19LF++nJrCEfIX9e7dGwKBAHv37hUWG69fv8awYcMgIyODs2fPMk7ILR97aHwLj8fjTKEhiezt7fHo0SNUVVXB2NhY7HhiYmIio2QEAH766SfIyspi69atwvPpr1+/RmBgIFauXIlOnTqxjlgv6Em6lNm+fTuGDx+Otm3bQk5ODkDtU3R3d3fheDYVFRWsWrWKZUzOioiIwIoVK/Dw4UMAgIWFBYKCgqj4kABr1qzB0KFDceLECcyePRvm5uYAgCNHjsDJyYlxOm778OEDvQeE/A9iY2Nx8+ZNkaeB2traCAkJgbOzM8Nk3PT5UR0imagRqWS7ceMGoqOjoaurCxkZGcjIyKBjx45YtmwZ/P39kZSUxDpivaAiXco0btwYly5dQkZGhkgXZEtLS+E1bm5uDBNy1+rVqzF37lxMnDgRzs7OEAgEiI+Px7hx4/Dq1StMmTKFdUROs7Ozq3N3yYoVK+jsJmOjRo3Cvn37MHfuXNZRCJEq8vLydR5ve/fuHU2skCCVlZXCMWyEvfnz57OOQL6ipqYGKioqAGrHgD579gyWlpYwNjZGZmYm43T1h4p0KfVpYU5bqiXD+vXrsWnTJnh7ewvX+vbtC2trayxYsICKdAlQUlKCI0eOIDs7G0FBQdDS0kJaWhoaNWoEQ0ND1vE4q7KyElu2bMHly5dhZ2cn3CX00erVqxklI0Sy/fjjjxgzZgy2b9+O9u3bAwBu3bqFcePG0ZlnxmpqarB06VKEhYXh+fPnyMrKgpmZGebOnQsTExP4+fmxjkiIRLKxsUFqairMzMzQoUMHLF++HA0bNsSWLVtgZmbGOl69+b7DM0SiREREwNbWFoqKilBUVISdnR12797NOhbnFRYW1rll18nJiTrtS4DU1FQ0b94cv//+O1auXImSkhIAwPHjxzFr1iy24TguNTUVrVu3hoyMDO7fv4+kpCThv+TkZNbxCJFY69atQ7NmzeDo6AgFBQUoKCjA2dkZ5ubmWLt2Let4nLZkyRKEh4cLC4yPbG1tsW3bNobJiIyMDGRlZb/4j7A1Z84c4dGRxYsXIz8/H506dcK5c+ewbt06xunqDz1JlzK0pVpymZub49ChQ/jtt99E1g8ePIjmzZszSkU+mjp1Knx8fLB8+XKoqqoK13v16gUvLy+GyUhMTAzrCIRIJQ0NDZw8eRIPHz4UmcX9secGYSciIgJbtmyBu7s7xo0bJ1y3s7NDRkYGw2Tk+PHjIr9XVVUhKSkJu3btQnBwMKNU5KMePXoIfzYzM0NaWhqKi4uhqanJqd3D1N1dypiamiI4OFhkSzUA7Nq1CwsWLKAxYAwdPXoUgwcPRteuXeHs7Awej4e4uDhERUXh0KFD8PT0ZB2R0z6dk66qqoqUlBSYmZkhPz8flpaWqKysZB2REKliamqKLl26YOHChXRchJDPKCoqIiMjA8bGxiKfOWlpaWjfvj3evXvHOiL5zL59+3Dw4EGcPHmSdRRC6Em6tKEt1ZJrwIABuHXrFkJDQ3HixAnhE43bt28Lx+URdhQUFFBaWiq2npmZCV1dXQaJuK1///4IDw+Hmpoa+vfv/9Vrjx07Vk+pyF8xYsQI5Ofnw8XFBdnZ2azjcFJNTQ3Cw8MRFRWFFy9eiHUXj46OZpSMWFtb49q1azA2NhZZP3z4MH0nkFAdOnTA6NGjWcfgLF9f3++6bseOHf9wEslARbqUoS3Vkq1t27bYs2cP6xikDn379sXChQtx6NAhALUNFwsKCjBz5kwMGDCAcTruUVdXF25bU1NT49QWtn+LBQsWsI7AeQEBAQgPD0efPn1gY2ND/x9JkPnz52P48OF4+vQp+Hw+jh07hszMTERERODMmTOs45HPVFRUYP369WjSpAnrKJwVHh4OY2Nj2NvbgzZ603Z3qUNbqgn535SWlqJ379548OAB3r59CwMDAxQVFcHR0RHnzp2DsrIy64iEEPKX6OjoICIiAr1792YdhdQhMjISS5cuRUJCAvh8Ptq0aYN58+ahe/furKNx2udnmwUCAd6+fQslJSXs2bOHJiMwMn78eBw4cABNmzaFr68vhg0bBi0tLdaxmKEiXQolJCQgNDQU6enpwi3VgYGBtH2KkO8QHR2NxMRE4Remrl27so7EeV26dMGxY8egoaEhsl5aWop+/frRlt16NnXq1O++lsbjsWVgYIArV67AwsKCdRRCpEZ4eLhIkS4jIwNdXV106NABmpqaDJOR9+/f49ixY9ixYweuX7+OPn36wM/PD927d+fcTiEq0gkhhDAlIyODoqIi6Onpiay/ePEChoaGqKqqYpSMm9zc3L7rOh6PRzdQGFu1ahVycnKwYcMGzn2BlXQ+Pj4YNmwYunTpQu+NhCkoKICRkVGd70tBQQGaNm3KIBX5XH5+PsLDwxEREYGqqiqkpaVBRUWFdax6Q2fSpUBdza6+RE1N7R9MQoh0+SvzNP39/f/BJKQuqampwp/T0tJQVFQk/L2mpgYXLlygruEM0Eg8yfZ5o8Xo6GicP38e1tbWkJOTE3mNmi6y8/r1a/Tp0wfa2toYMmQIhg0bRjseJYSpqSkKCwvFbgy/fv0apqamqKmpYZSMfIrH44HH40EgEIg1xeQCepIuBWRkZL55F1YgEIDH49EfFkI+YWpqKvL7y5cvUV5eLtxWXVJSAiUlJejp6SEnJ4dBQm779G9bXR9FioqKWL9+/Xd3fCWEC3x8fL772p07d/6DSci3lJSU4NChQ9i3bx+uXbsGS0tLDBs2DF5eXjAxMWEdj7O+tHsrPz8fVlZWKCsrY5SMfLrdPS4uDj/++CN8fHzQs2dPyMjIsI5Xr6hIlwKxsbHffW3nzp3/wSTkf+Xr6ws3NzcMHz6cdRTO2rdvH/744w9s374dlpaWAGrHr40ePRpjx47F0KFDGSfknvz8fAgEApiZmeH27dsio/AaNmwIPT09yMrKMkzITTQej5C/35MnT7B//37s2LEDDx8+RHV1NetInPOx38batWsxevRoKCkpCV+rqanBrVu3ICsri/j4eFYROe3TxnEfj4toa2uzjsUMFemE1ANXV1fk5+dDTU0NKSkprONwUrNmzXDkyBGx7YYJCQn4+eefkZubyygZIZLFx8cH69atg6qq6jef2tKTWrZyc3NRXV0tNoL14cOHkJOTo6e1EqKqqgpnz57Fnj17cPbsWWhpaeHp06esY3HOx34bsbGxcHR0RMOGDYWvNWzYECYmJpg2bRqNNGZERkYGTZs2hb29/Vd3EHPl5jCdSZdCJSUl2L59O9LT08Hj8WBlZQVfX1+oq6uzjka+4MqVKwBqn9wSNgoLC+tsQFZTU4Pnz58zSEQ+lZWVhStXruDFixdiZ8/mzZvHKBU3fVp4UxEu2UaOHAlfX1+xouLWrVvYtm2b8LOHsBETE4N9+/bh6NGjqKmpQf/+/XH69Gl06dKFdTRO+thvw8fHB2vXrqU+ThLG29ubmix+gp6kS5m7d++iR48eUFRURPv27SEQCHD37l1UVFTg4sWLaNOmDeuIhEikn376CQUFBdi+fTvatm0LHo+Hu3fvYvTo0TAyMsKpU6dYR+SsrVu34tdff4WOjg4aN24s8iHN4/GQmJjIMB23BQcHY9iwYWjWrBnrKKQOampqSExMhLm5ucj6o0eP4ODggJKSEjbBCJo0aYLXr1+jR48eGDp0KH766ScoKCiwjkUIkRJUpEuZTp06wdzcHFu3bkWDBrUbIaqrqzFq1Cjk5OTg6tWrjBNy15eKPB6PBwUFBZibm4s1MiP15+XLlxgxYgQuXLgg7IBcXV2NHj16IDw8XKyBDKk/xsbGGD9+PGbMmME6CvmMnZ0dHjx4gHbt2mHYsGEYPHiwSO8Awpa6ujquXLlS5zEeV1dXvH37llEysmXLFgwcOJDmbkuoO3fu4PDhwygoKMCHDx9EXuPKdmoi2ahIlzKKiopISkpCixYtRNbT0tLg4OCA8vJyRsnIx07Vn/8v9XGNx+OhY8eOOHHiBH1oM/Tw4UOkp6dDIBCgZcuWsLCwYB2J89TU1JCcnAwzMzPWUUgdHjx4gL179+LAgQN48uQJunbtimHDhqFfv34ijZdI/fvxxx+hpKSE/fv3C5ss1tTUYPDgwSgrK8P58+cZJySPHj1CdnY2XFxcoKioKPw+QNg5cOAAvL290b17d1y6dAndu3fHw4cPUVRUBE9PTzrmQyQCt3rZ/wuoqamhoKBAbP3x48dQVVVlkIh8dOnSJbRr1w6XLl3Cmzdv8ObNG1y6dAnt27fHmTNncPXqVbx+/RrTpk1jHZXTmjdvDg8PD/Tt25cKdAkxcOBAXLx4kXUM8gXW1tZYunQpcnJyEBMTA1NTU0yePBmNGzdmHY3zli9fjujoaFhaWsLHxwc+Pj6wtLTE1atXsWLFCtbxOO3169dwd3eHhYUFevfujcLCQgDAqFGjEBgYyDgdty1duhShoaE4c+YMGjZsiLVr1yI9PR2DBg1C06ZNWccjBAA1jpM6gwcPhp+fH1auXAknJyfweDzExcUhKCgIv/zyC+t4nBYQEIAtW7bAyclJuObu7g4FBQWMGTMGDx48wJo1a2jmMyGfMTc3x9y5c3Hz5k3Y2toKjyN85O/vzygZ+ZyysjIUFRXRsGFD2kotAaysrJCamooNGzYgJSUFioqK8Pb2xsSJE6GlpcU6HqdNmTIFcnJyKCgoQMuWLYXrgwcPxpQpU7Bq1SqG6bgtOzsbffr0AQDIy8ujrKwMPB4PU6ZMQZcuXRAcHMw4ISFUpEudlStXgsfjwdvbWzhjU05ODr/++itCQkIYp+O27OzsOjuFqqmpIScnB0DtU9xXr17VdzRCJNqWLVugoqKC2NhYxMbGirzG4/GoSGcsNzcX+/btw969e5GVlQUXFxcsWLAAAwcOZB2NADAwMMDSpUtZxyCfuXjxIiIjI9GkSROR9ebNmyM/P59RKgIAWlpawpuMhoaGuH//PmxtbVFSUkLHRonEoCJdynzclrNs2TJkZ2dDIBDA3NyczgVKgLZt2yIoKAgRERHCxkovX77E9OnT0a5dOwC156E//8AmhOtoRr3kcnR0xO3bt2FrawsfHx94eXnB0NCQdSzyHyYmJvD19YWPjw+MjIxYxyGfKCsrq/O72atXryAvL88gEfmoU6dOuHTpEmxtbTFo0CAEBAQgOjoaly5dgru7O+t4hACgxnGE/G0yMzPRt29f5ObmwsjICDweDwUFBTAzM8PJkydhYWGBEydO4O3btxg+fDjruIQQ8k2//fYbhg4dCmtra9ZRSB3Wr1+P8PBwpKSkwM3NDX5+fvD09KQiUAL06dMHbdq0waJFi6CqqorU1FQYGxtjyJAh4PP5OHLkCOuInFVcXIzKykoYGBiAz+dj5cqViIuLEx69oua+RBJQkS4F+vfvj/DwcKipqaF///5fvZbGRrAlEAgQGRmJrKwsCAQCtGjRAt26dYOMDPVoZG3nzp1QUVER26J7+PBhlJeXY8SIEYySkW/1adixY0c9JSFEOqWkpGDHjh3Yv38/qqur4eXlBV9fX7Rp04Z1NM5KS0uDq6sr2rZti+joaHh4eODBgwcoLi5GfHw8mjVrxjoiJ1VXV2Pv3r3o0aMHNb8kEo2KdCng4+ODdevWQVVVFT4+Pl+9lsZGEFI3S0tLhIWFwc3NTWQ9NjYWY8aMQWZmJqNkxNPTU+T3qqoq3L9/HyUlJejSpQvdfKxnU6dOxaJFi6CsrIypU6d+9drVq1fXUyryPaqqqvDHH39gxowZqKqqgo2NDQICAuDj40NjvxgoKirCpk2bkJCQAD6fjzZt2mDChAnQ19dnHY3TlJSUkJ6eDmNjY9ZRCPkiOpMuBT4tvKkIl2xRUVGIiorCixcvwOfzRV6jp4Fs5efnw9TUVGzd2Ni4zrGGpP4cP35cbI3P52P8+PE0O52BpKQkVFVVCX/+Eir6JEdVVRWOHz+OnTt34tKlS/jhhx/g5+eHZ8+eYfbs2bh8+TL27dvHOibnNG7cmDqFS6AOHTogKSmJinQi0ahIl1IvXrxAZmYmeDweLCwsoKenxzoS5wUHB2PhwoVwcHCAvr4+fYGVMHp6ekhNTYWJiYnIekpKCrS1tdmEIl8kIyODKVOmwNXVFdOnT2cdh1NiYmLq/JlInsTEROzcuRP79++HrKwshg8fjtDQULRo0UJ4Tffu3eHi4sIwJXekpqZ+97V2dnb/YBLyNePHj0dgYCCePHmCtm3bQllZWeR1em+IJKAiXcqUlpZiwoQJOHDgAGpqagAAsrKyGDx4MDZu3Ah1dXXGCbkrLCwM4eHh1BROQg0ZMgT+/v5QVVUVfmGNjY1FQEAAhgwZwjgdqUt2drZw1CRh482bN6ipqRGbuV1cXIwGDRrUOXaS1J927dqhW7du2LRpE/r16wc5OTmxa6ysrOhvXD1p3bo1eDweBAKByI36jydLP137+B2O1L/BgwcDgMh4z0/fN3pviCSgIl3KjBo1CsnJyThz5gwcHR3B4/Fw/fp1BAQEYPTo0Th06BDriJz14cMHODk5sY5BvmDx4sXIz8+Hu7s7GjSo/dPH5/Ph7e1NM4YZ+/zcs0AgQGFhIc6ePUsN/RgbMmQIfvrpJ4wfP15k/dChQzh16hTOnTvHKBkBgJycnG9u2VVWVqajcvXk03GSSUlJmDZtGoKCguDo6AgAuHHjBlatWoXly5ezikhAYz+JdKDGcVJGWVkZkZGR6Nixo8j6tWvX0LNnT5SVlTFKRmbMmAEVFRXMnTuXdRTyFVlZWUhJSYGioiJsbW3pTJoE+LyZn4yMDHR1ddGlSxf4+voKb6qQ+qelpYX4+Hi0bNlSZD0jIwPOzs54/fo1o2QEAB4/fgwej4cmTZoAAG7fvo19+/bBysoKY8aMYZyO29q3b48FCxagd+/eIuvnzp3D3LlzkZCQwCgZIUQa0DcfKaOtrV3nlnZ1dXWa68hYZWUltmzZgsuXL8POzk5s2yF1QZYMFhYWsLCwYB2DfILOPUuu9+/f13nkoKqqChUVFQwSkU95eXlhzJgxGD58OIqKitCtWzdYW1tjz549KCoqwrx581hH5Kx79+7V2azU1NQUaWlpDBKRT+3evRthYWHIzc3FjRs3YGxsjDVr1sDU1BR9+/ZlHY8QKtKlzZw5czB16lREREQIR3gUFRUhKCiInuAylpqaitatWwMA7t+/L/IaNZFjg0ZJSZeXL1+KNMTU1dVlHYnz2rVrhy1btmD9+vUi62FhYWjbti2jVOSj+/fvo3379gBqjyDY2NggPj4eFy9exLhx46hIZ6hly5ZYvHgxtm/fDgUFBQC1N70WL14stjOF1K9NmzZh3rx5mDx5MpYsWSI8g66hoYE1a9ZQkU4kAhXpUsDe3l6kyHv48CGMjY3RtGlTAEBBQQHk5eXx8uVLjB07llVMzqOngZKHRklJh7KyMkyaNAkRERHC0YWysrLw9vbG+vXroaSkxDghdy1ZsgRdu3ZFSkoK3N3dAdSOmrxz5w4uXrzIOB2pqqqCvLw8AODy5cvw8PAAALRo0QKFhYUso3FeWFgYfvrpJxgZGaFVq1YAaqeJ8Hg8nDlzhnE6blu/fj22bt2Kfv36ISQkRLju4OCAadOmMUxGyH9RkS4F+vXrxzoCIVKJRklJh6lTpyI2NhanT5+Gs7MzACAuLg7+/v4IDAzEpk2bGCfkLmdnZ9y4cQMrVqzAoUOHoKioCDs7O2zfvh3NmzdnHY/zrK2tERYWhj59+uDSpUtYtGgRAODZs2c0WpKx9u3bIzc3F3v27EFGRgYEAgEGDx4MLy8vsZFfpH7l5ubC3t5ebF1eXp56OxGJQY3jCPl/6N+/P8LDw6Gmpob+/ft/9dpjx47VUypCpIuOjg6OHDkCV1dXkfWYmBgMGjQIL1++ZBOMEAl35coVeHp6orS0FCNGjMCOHTsAAL/99hsyMjLoc4eQOlhZWWHZsmXo27cvVFVVkZKSAjMzM6xbtw67du2ipn5EItCTdEL+H9TV1YVbpWlGvWQrKytDSEgIoqKi8OLFC+G26o9ycnIYJSPl5eVo1KiR2Lqenh7Ky8sZJCJEOri6uuLVq1coLS0VaR47ZswYOiZCyBcEBQVhwoQJqKyshEAgwO3bt7F//34sW7YM27ZtYx2PEAD0JJ2Qv4VAIEBBQQF0dXXpi5GE+uWXXxAbG4vhw4dDX19f7Bx6QEAAo2TE3d0d2traiIiIEDZYqqiowIgRI1BcXIzLly8zTkiIZHvx4oVI00U9PT3WkQiRaFu3bsXixYvx+PFjAIChoSEWLFgAPz8/xskIqUVFOiF/Az6fDwUFBTx48IDOaUooDQ0NnD17VnjmmUiO+/fvo2fPnqisrESrVq3A4/GQnJwMBQUFREZGwtramnVEQiRSaWkpJkyYgAMHDgg7VMvKymLw4MHYuHEj7fAi5BtevXoFPp9PN7aIxJFhHYCQfwMZGRk0b94cr1+/Zh2FfIGmpia0tLRYxyB1sLGxwcOHD7Fs2TK0bt0adnZ2CAkJwcOHD6lAJ+QrRo0ahVu3buHMmTMoKSnBmzdvcObMGdy9exejR49mHY8QidSlSxeUlJQAqO2J8rFALy0tRZcuXRgmI+S/6Ek6IX+Ts2fPIiQkBJs2bYKNjQ3rOOQze/bswcmTJ7Fr1y46kkAI+VdQVlZGZGQkOnbsKLJ+7do19OzZkzpVS4C7d+8iPT0dPB4PLVq0gIODA+tInCcjI4OioiKxp+cvXryAoaGhcHQrISxR47h/iZMnT+LNmzfw9vZmHYWzhg0bhvLycrRq1QoNGzaEoqKiyOvFxcWMknGXvb29yNnzR48eoVGjRjAxMYGcnJzItYmJifUdj/zHsmXL0KhRI/j6+oqs79ixAy9fvsSMGTMYJSNf8scff+DVq1eYN28e6yicpq2tXeeWdnV1dZFGcqT+PXnyBL/88gvi4+OhoaEBACgpKYGTkxP2798PIyMjtgE5KDU1VfhzWloaioqKhL/X1NTgwoULMDQ0ZBGNEDH0JP1fokWLFnj48KHwTBqpf7t27frq6yNGjKinJOSj4ODg7752/vz5/2AS8jUmJibYt28fnJycRNZv3bqFIUOGIDc3l1Ey8iXu7u7Izc2lqQiMbdmyBYcPH0ZERAT09fUBAEVFRRgxYgT69++PsWPHMk7IXd27d0dpaSl27doFS0tLAEBmZiZ8fX2hrKyMixcvMk7IPTIyMsIb93WVP4qKili/fr3YDWNCWKAinRBCCFMKCgpIT0+HqampyHpOTg6srKxQWVnJKBkhks3e3h6PHj3C+/fv0bRpUwBAQUEB5OXlxZqY0m6h+qWoqIjr16/D3t5eZD0xMRHOzs6oqKhglIy78vPzIRAIYGZmhtu3b0NXV1f4WsOGDaGnpwdZWVmGCQn5L9ruTsjfKDs7Gzt37kR2djbWrl0LPT09XLhwAUZGRtQAS0J8ej6wZcuWaNu2LetInGdkZIT4+HixIj0+Ph4GBgaMUpFvEQgEYqMMSf3q168f6wjkC5o2bVrn2ebq6mraUs2IsbExgNqJPIRIOirSpczcuXOxYMECsTt9b968wbhx47B//35GyUhsbCx69eoFZ2dnXL16FUuWLIGenh5SU1Oxbds2HDlyhHVETqPzgZJr1KhRmDx5MqqqqoSddaOiojB9+nQEBgYyTsdtw4cPx6ZNm6CioiKynpeXh+HDh+PatWuMkhGAjulIsuXLl2PSpEnYuHEj2rZtCx6Ph7t37yIgIAArV65kHY9zTp06hV69ekFOTg6nTp366rUeHh71lIqQL6Pt7lLG2NgY+vr62Lt3L5o1awYAuHLlCry9vWFoaIgbN24wTshdjo6OGDhwIKZOnQpVVVWkpKTAzMwMd+7cQb9+/fD06VPWETmNzgdKLoFAgJkzZ2LdunX48OEDgNot8DNmzMDcuXPpaS1Dbdu2RXFxMfbs2QNnZ2cAtf03/P390a1bN7r5SMgnNDU1Rf5elZWVobq6Gg0a1D4T+/izsrIyNZOtZ592dJeR+fIEah6PR/2diESgIl3KvHnzBmPHjsXZs2exevVqZGVlYe3atZg5cybmz59PZ2kYUlFRwb1792BqaipSpOfl5aFFixZ0rpYxOh8o+d69e4f09HQoKiqiefPmkJeXZx2J86qrqzFnzhyEhoYiMDAQDx8+xIULF7B27VpqrkTIZ77VQPZT1EyWEPI1tN1dyqirq+PAgQOYPXs2xo4diwYNGuD8+fNwd3dnHY3zNDQ0UFhYKHauNikpic6fSQA6Hyi53rx5g5qaGmhpaaFdu3bC9eLiYjRo0ABqamoM03FbgwYNEBISAnl5eSxatAgNGjRAbGwsHB0dWUcjROJQ4U0I+btQkS6F1q9fj9DQUPzyyy9ISEiAv78/9u3bh1atWrGOxmleXl6YMWMGDh8+DB6PBz6fj/j4eEybNo3m10sAOh8ouYYMGYKffvoJ48ePF1k/dOgQTp06hXPnzjFKRqqqqjBz5kxs3LgRs2bNQlxcHDw9PbFjxw707t2bdTxCJBo1k5Uc69at++5r/f39/8EkhHwf2u4uZXr16oU7d+4gLCwMP//8MyoqKjB16lSEh4cjODgY06dPZx2Rs6qqqjBy5EgcOHAAAoEADRo0QE1NDby8vBAeHk5HERjT1NREeXn5F88HforOCtYvLS0txMfHo2XLliLrGRkZcHZ2xuvXrxklI61atUJ5eTl2796NH374AQKBAMuXL8f8+fPh6+uLP/74g3VEQiTS581k09PTYWZmhuXLl+P27dvUz6Gefb7L8Ut4PB5ycnL+4TSEfBsV6VKmW7du2LVrl9hYorNnz2LUqFEoLCxklIx8lJ2djaSkJPD5fNjb24vNqiVs0FlByaWsrIybN2/C1tZWZP3evXvo0KEDysvLGSUjfn5+WLdundiNrOTkZAwbNgz3799nlIx8y8KFC+Hq6goXFxfWUTiJmskSQv4/qEj/F3n16hV0dHRYx+C8Dx8+IDc3F82aNRM+sSWEfJmrqytsbW2xfv16kfUJEyYgNTWVxnxJqPfv31NzPwlmamqK58+fw93dHadPn2Ydh3OomSwh5P/jyzMIiMS6du0ahg0bBkdHR+Gd2N27dyMjI4NxMm4rLy+Hn58flJSUYG1tjYKCAgC1Z5tCQkIYpyOfqqioQGlpqcg/ws6SJUuwbds2uLi4IDg4GMHBwXBxccGOHTuwdOlS1vE4b/fu3XB2doaBgQHy8/MBAGvWrMGFCxcYJyNfk5ubi+LiYkycOJF1FE762Ez2c9RMlhDyPahIlzJHjx5Fjx49oKioiKSkJLx//x4A8PbtW/oyy9isWbOQkpKCK1euQEFBQbjetWtXHDx4kGEyAtTOq504cSL09PSgoqICTU1NkX+EHWdnZ9y4cQNGRkY4dOgQTp8+DXNzc6SmpqJTp06s43Hapk2bMHXqVPTu3RslJSXC+cEaGhpYs2YN23DkmxQUFNCjRw/WMTjpYzPZoqIiaiZLCPnLaLu7lLG3t8eUKVPg7e0tsn0qOTkZPXv2RFFREeuInGVsbIyDBw/ihx9+EHlvHj16hDZt2tDTWsYmTJiAmJgYLFy4EN7e3ti4cSOePn2KzZs3IyQkBEOHDmUdkRCJY2VlhaVLl6Jfv34if9fu378PV1dXvHr1inVETjMxMYGvry9GjhyJpk2bso5DPkHNZAkh/x/0JF3KZGZm1tkERk1NDSUlJfUfiAi9fPkSenp6YutlZWXg8XgMEpFPnT59Gn/88Qd+/vlnNGjQAJ06dcKcOXOwdOlS7N27l3U88h90FEGy5Obmwt7eXmxdXl4eZWVlDBKRTwUGBuLkyZMwMzNDt27dcODAAeEOO8LGo0ePAABycnLYu3cvsrKycOjQIezZswcZGRnYvXs3FeiEkG+iIl3K6OvrCz8APhUXFwczMzMGichH7dq1w9mzZ4W/fyzMt27dCkdHR1axyH8UFxcLR7CoqakJx6x17NgRV69eZRmN88rLy+kogoQyNTVFcnKy2Pr58+dhZWVV/4GIiEmTJiEhIQEJCQmwsrKCv78/9PX1MXHiRCQmJrKOx0kWFhYwMjKCt7c3wsPD0aBBA/z8888YNGgQTXshhHw3KtKlzNixYxEQEIBbt26Bx+Ph2bNn2Lt3L6ZNm4bx48ezjsdpy5Ytw+zZs/Hrr7+iuroaa9euRbdu3RAeHo4lS5awjsd5H7vqArVbeA8dOgSg9gm7hoYGu2AEQUFBiI6Oxh9//AF5eXls27YNwcHBMDAwQEREBOt4nBYUFIQJEybg4MGDEAgEuH37NpYsWYLffvsNQUFBrOOR/2jVqhXWrl2Lp0+fYv78+di2bRvatWuHVq1aYceOHaCTjfUnNjYWY8eOxbNnzzBhwgSYmZnB1NQUfn5+2LNnD41ek2AtW7akXQ5EYtCZdCk0e/ZshIaGCsd3yMvLY9q0aVi0aBHjZOTevXtYuXIlEhISwOfz0aZNG8yYMUNs/jOpf6GhoZCVlYW/vz9iYmLQp08f1NTUoLq6GqtXr0ZAQADriJzVtGlTREREwNXVFWpqakhMTIS5uTl2796N/fv349y5c6wjctrWrVuxePFiPH78GABgaGiIBQsWwM/Pj3Ey8lFVVRWOHz+OnTt34tKlS/jhhx/g5+eHZ8+eYcOGDXBzc8O+fftYx+Scqqoq3LhxA1euXMGVK1dw8+ZNvH//Hubm5sjMzGQdj3zmxIkTePPmDUaMGME6CiFUpEur8vJypKWlgc/nw8rKCioqKqwjESJVCgoKcPfuXTRr1gytWrViHYfTVFRU8ODBAxgbG6NJkyY4duwY2rdvj9zcXNja2uLdu3esIxIAr169Ap/Pr7P3BmEjMTERO3fuxP79+yErK4vhw4dj1KhRaNGihfCaO3fuwMXFBRUVFQyTcltFRQXi4uIQGRmJrVu34t27d8JJCYQQUpcGrAOQ/42SkhIcHBxYxyCf4fP5ePToEV68eAE+ny/yWl0N/0j9qKqqQvfu3bF582ZYWFgAqH16S92QJcPHowjGxsbCowjt27enowgSRkdHh3UE8pl27dqhW7du2LRpE/r16wc5OTmxa6ysrDBkyBAG6birsrIS169fR0xMDK5cuYI7d+7A1NQUnTt3xqZNm9C5c2fWETntwYMHsLa2rvO1CxcuoGfPnvWciBBx9CRdCowbNw6zZ8+GkZHRN689ePAgqquraZwUAzdv3oSXlxfy8/PFzv/xeDy6a86Yrq4url+/To17JBAdRZAsPXv2xLx58+Dk5PTV696+fYs//vgDKioqmDBhQj2lI5/Kz8+HsbEx6xjkE507d8adO3fQrFkzuLi4oHPnzujcuTMaNWrEOhr5D0VFRSxfvhyTJk0Srr1//x6BgYHYvn077TohEoGepEsBXV1d2NjYwMnJCR4eHnBwcICBgQEUFBTw559/Ii0tDXFxcThw4AAMDQ2xZcsW1pE5ady4cXBwcMDZs2ehr69PY9ckjLe3N7Zv346QkBDWUchnpkyZIvzZzc0NGRkZdBSBoYEDB2LQoEFQVVX96mfOuXPn8OOPP2LFihWsI3MWFeiS5/r169DX14ebmxtcXV3h4uJCu1AkzN69ezFmzBicO3cOO3fuRFFREby8vAAA8fHxjNMRUouepEuJFy9eYPv27Thw4ADu378v8pqqqiq6du2KMWPGoHv37owSEmVlZaSkpMDc3Jx1FFKHSZMmISIiAubm5nBwcICysrLI66tXr2aUjBDJ8+HDBxw5cgQHDx7EtWvXUFJSAqB2V5CVlRV69OiB0aNHw9LSkm1QjtPU1KzzhjCPx4OCggLMzc0xcuRI+Pj4MEjHTWVlZbh27RquXLmCmJgYJCcnw8LCAp07d4arqys6d+4MXV1d1jE579mzZxgxYgSSkpJQVlYGHx8frFq1CoqKiqyjEQKAinSpVFJSgvz8fFRUVEBHRwfNmjWjp7YSoEuXLpg+fTqdZZJQbm5uX3yNx+MhOjq6HtMQIl3evHmDiooKaGtr13numbARGhqKJUuWoFevXmjfvj0EAgHu3LmDCxcuYMqUKcjNzcXu3buxfv16jB49mnVcTnr79i3i4uKE59NTUlLQvHlzsQcupH49fvwYw4YNQ2pqKsrKyjBnzhzMmTMHMjI0nZpIBtruLoU0NDSomZIEmjRpEgIDA1FUVARbW1uxL7J2dnaMkhEAiImJYR2BEKmlrq4OdXV11jHIZ+Li4rB48WKMGzdOZH3z5s24ePEijh49Cjs7O6xbt46KdEaUlZWhpaUFLS0taGpqokGDBkhPT2cdi9MOHDiAX3/9FZ06dUJWVhaSk5Ph4+ODyMhI7N69G2ZmZqwjEkJP0gn5u9R195XH40EgEFDjOEIIIX87FRUVJCcnix2zevToEVq3bo13794hOzsbdnZ2KCsrY5SSW/h8Pu7evSvc7h4fH4+ysjIYGhrCzc1N+I/6CbCjrKyMlStX4tdffxWu/fnnnxg7diwuXLiA0tJShukIqUVP0gn5m+Tm5rKOQL7C09Pzm2c3vby86IwtIURqaGlp4fTp0yLNFwHg9OnT0NLSAlB7RlpVVZVFPE7S0NBAWVkZ9PX14erqitWrV8PNzQ3NmjVjHY38R2JiothnvaamJg4dOoTdu3czSkWIKCrSCfmb0F1xyaauro4TJ05AQ0MDbdu2hUAgQFJSEkpKStC9e3ccPHgQv//+O6KiouDs7Mw6LiGEfNPcuXPx66+/IiYmBu3btwePx8Pt27dx7tw5hIWFAQAuXbpEc7nr0YoVK+Dm5gYLCwvWUcgXWFpaorq6GleuXEF2dja8vLygqqqKZ8+ewdPTk3U8QgDQdndC/l9OnTqFXr16QU5ODqdOnfrqtR4eHvWUitRl5syZKC0txYYNG4RHE/h8PgICAqCqqoolS5Zg3LhxePDgAeLi4hinJQDQtWtX5OTkICcnh3UUQiRWfHw8NmzYgMzMTAgEArRo0QKTJk365px7QrgqPz8fPXv2REFBAd6/f4+srCyYmZlh8uTJqKysFN7gIoQlKtKl0Jfu/qmpqUFFRYV1PE6RkZFBUVER9PT0vtoRlM6ks6erq4v4+HixpxtZWVlwcnLCq1evcO/ePXTq1Ek4boqwtXHjRrx69Qrz589nHYXTSkpKcOTIEWRnZyMoKAhaWlpITExEo0aNYGhoyDoeIYT8Jf369YOqqiq2b98ObW1tpKSkwMzMDLGxsRg1ahQePnzIOiIhtN1d2nx+969bt25QVVXF8uXL6e4fA3w+v86fieSprq5GRkaGWJGekZEhvIGioKBA4wwlyIQJE1hH4LzU1FR07doV6urqyMvLw+jRo6GlpYXjx48jPz8fERERrCNyXk1NDU6cOIH09HThHHsPDw/IysqyjkaIRIqLi0N8fDwaNmwosm5sbIynT58ySkWIKBoGKGUCAgLg4OCAP//8E4qKisJ1T09PREVFMUxGiGQbPnw4/Pz8EBoaKvyADg0NhZ+fH7y9vQEAsbGxsLa2ZpyUe54/f/7F11JTU+sxCfnc1KlTMXLkSDx8+BAKCgrC9V69euHq1asMkxGgtot7y5Yt4e3tjWPHjuHIkSMYNmwYrK2tkZ2dzToeIRKJz+fXubvxyZMn1GSRSAza7i5ldHR0EB8fD0tLS6iqqgq36OTl5cHKygrl5eWsI3LWunXr6lz/tHu4i4sLPd1gpKamBiEhIdiwYYOwKGzUqBEmTZqEGTNmQFZWFgUFBZCRkUGTJk0Yp+UWPT09bNu2Taxvw8qVKzF37lxUVFQwSkbU1dWRmJiIZs2aiXzm5Ofnw9LSEpWVlawjclrv3r0hEAiwd+9eYTf3169fY9iwYZCRkcHZs2cZJyRE8gwePBjq6urYsmULVFVVkZqaCl1dXfTt2xdNmzbFzp07WUckhLa7Sxu6+ye5QkND8fLlS5SXl0NTUxMCgQAlJSVQUlKCiooKXrx4ATMzM8TExMDIyIh1XM6RlZXF7NmzMXv2bOEMVDU1NZFrmjZtyiIa582YMQODBw/GiBEjEBoaiuLiYgwfPhwPHjzAwYMHWcfjNAUFhTpnBmdmZkJXV5dBIvKp2NhY3Lx5U1igA4C2tjZCQkJoSgUhXxAaGgo3NzdYWVmhsrISXl5eePjwIXR0dLB//37W8QgBQNvdpU63bt2wZs0a4e88Hg/v3r3D/Pnz0bt3b3bBCJYuXYp27drh4cOHeP36NYqLi5GVlYUOHTpg7dq1KCgoQOPGjcXm2ZL6p6amJlagE3YCAwNx8+ZNxMfHw87ODnZ2dlBUVERqaipNRWCsb9++WLhwIaqqqgDUfuYUFBRg5syZGDBgAON0RF5eHm/fvhVbf/fundh5W0JILQMDAyQnJyMoKAhjx46Fvb09QkJCkJSUBD09PdbxCAFA292lzrNnz+Dm5gZZWVk8fPgQDg4Owrt/V69epT8uDDVr1gxHjx5F69atRdaTkpIwYMAA5OTk4Pr16xgwYAAKCwvZhCREQr19+xajR4/G0aNHAQDbtm3DiBEjGKcipaWl6N27Nx48eIC3b9/CwMAARUVFcHR0xLlz56CsrMw6Iqd5e3sjMTER27dvR/v27QEAt27dwujRo9G2bVuEh4ezDUgIIeR/QtvdpczHu38HDhxAQkIC+Hw+/Pz8MHToUJFGcqT+FRYWorq6Wmy9uroaRUVFAGrfv7qeehDCZfHx8Rg2bBi0tbWRmpqK+Ph4TJo0CWfPnsXmzZuhqanJOiJnqampIS4uDtHR0UhMTASfz0ebNm3QtWtX1tEIanuhjBgxAo6OjpCTkwNQ+5nj4eGBtWvXMk5HiOS4ceMGHB0dv+vasrIy5OXlUSNZwhQ9SSfkb9KnTx8UFRVh27ZtsLe3B1D7FH306NFo3Lgxzpw5g9OnT+O3337DvXv3GKclRHLIy8tjypQpWLRokbDQyM7OxvDhw1FQUIAnT54wTkiIZHv48CEyMjIgEAhgZWUFc3Nz1pEIkSjNmzeHiYkJRo8ejd69e0NFRUXsmrS0NOzZswc7d+7E8uXLMXz4cAZJCalFRbqUWbZsGRo1agRfX1+R9R07duDly5eYMWMGo2SkqKgIw4cPR1RUlMgTDXd3d+zevRuNGjVCTEwMqqqq0L17d8ZpCZEcsbGx6Ny5s9g6n8/HkiVLMHfuXAapyEdRUVGIiorCixcvwOfzRV7bsWMHo1SEEPL9qqqqsHnzZmzYsAHZ2dmwsLCAgYEBFBQU8OeffyIjIwNlZWXo378/Zs2aBRsbG9aRCcdRkS5lTExMsG/fPjg5OYms37p1C0OGDEFubi6jZOSjjIwMZGVlQSAQoEWLFrC0tGQdiXzF3bt3UV5eDhcXF9ZROO/Ro0fIzs6Gi4sLFBUVIRAIwOPxWMfitODgYCxcuBAODg7Q19cXez+OHz/OKBl3TZ069buvXb169T+YhBDplJiYiGvXriEvLw8VFRXQ0dGBvb093NzcRCYlEMISFelSRkFBAenp6TA1NRVZz8nJEY6SIIR8v5YtWyIrK6vO0Yakfrx+/RqDBg1CTEwMeDweHj58CDMzM/j5+UFTUxMrV65kHZGz9PX1adunhHFzc/uu63g8HqKjo//hNIQQQv4J1DhOyhgZGSE+Pl6sSI+Pj4eBgQGjVAQAampqEB4e/sVtofRlSTJFRUUJx0sRNqZMmQI5OTkUFBSgZcuWwvXBgwdjypQpVKQz9OHDB7GdW4StmJgY1hEIIYT8w6hIlzKjRo3C5MmTUVVVhS5dugCoLTKmT5+OwMBAxum4LSAgAOHh4ejTpw9sbGxom66UoJtb7F28eBGRkZFo0qSJyHrz5s2Rn5/PKBUBaj9z9u3bR30BpMCTJ0/A4/FgaGjIOgohhJD/JyrSpcz06dNRXFyM8ePH48OHDwBqt8DPmDEDs2bNYpyO2w4cOIBDhw6hd+/erKOQOmzfvh1+fn5i69XV1Zg7dy6WLVvGIBUBasfdKCkpia2/evUK8vLyDBKRjyorK7FlyxZcvnwZdnZ2wqaYH9GZZ7b4fD4WL16MVatW4d27dwAAVVVVBAYGYvbs2ZCRkWGckBBCyP+CzqRLqXfv3iE9PR2Kiopo3rw5fZGVAAYGBrhy5QosLCxYRyF10NDQgLu7O7Zu3SpsDJORkQEvLy+8efMG2dnZjBNyV58+fdCmTRssWrQIqqqqSE1NhbGxMYYMGQI+n48jR46wjshZXzv/TGee2Zs1axa2b9+O4OBgODs7QyAQID4+HgsWLMDo0aOxZMkS1hEJIYT8D6hIJ+RvsmrVKuTk5GDDhg201V0C5ebmYvjw4cjNzUV4eDiysrIQFBSEn3/+GRs3boSqqirriJyVlpYGV1dXtG3bFtHR0fDw8MCDBw9QXFyM+Ph4NGvWjHVEQiSSgYEBwsLC4OHhIbJ+8uRJjB8/Hk+fPmWUjBDpUFlZCQUFBdYxCBFDRbqUKSsrQ0hIyBebk+Xk5DBKRjw9PRETEwMtLS1YW1uLbQs9duwYo2TkIz6fjylTpmDDhg2QlZVFREQEhgwZwjoWAVBUVIRNmzYhISEBfD4fbdq0wYQJE6Cvr886GiESS0FBAampqWI7uDIzM9G6dWtUVFQwSkaI5OLz+ViyZAnCwsLw/PlzZGVlwczMDHPnzoWJiUmdR+MIqW90Jl3KjBo1CrGxsRg+fHidM2sJOxoaGvD09GQdg3zFmTNnsH//fjg5OSEzMxNbt26Fi4sLNY+TAI0bN0ZwcDDrGARA//79ER4eDjU1NfTv3/+r19LNR7ZatWqFDRs2YN26dSLrGzZsQKtWrRilIkSyLV68GLt27cLy5csxevRo4bqtrS1CQ0OpSCcSgYp0KXP+/HmcPXsWzs7OrKOQz+zcuZN1BPIVY8eOxa5du7B48WIEBgbi+fPn8PX1ha2tLTZt2oRBgwaxjsgpBQUFaNq06Xdf//TpU+paXU/U1dWFN4DV1dUZpyFfs3z5cvTp0weXL1+Go6MjeDwerl+/jsePH+PcuXOs4xEikSIiIrBlyxa4u7tj3LhxwnU7OztkZGQwTEbIf9F2dyljamqKc+fOicwSJpKjuroaV65cQXZ2Nry8vKCqqopnz55BTU0NKioqrONxmo2NDfbu3Sv2dGnjxo2YMWOGsDMyqR+NGjWCh4cHRo8ejfbt29d5zZs3b3Do0CGsXbsWY8eOxaRJk+o5JSGS79mzZ9i4cSMyMjIgEAhgZWWF8ePH0w4hQr5AUVERGRkZMDY2hqqqKlJSUmBmZoa0tDS0b9+evg8QiUBFupTZs2cPTp48iV27dtU5soiwk5+fj549e6KgoADv378XnnGaPHkyKisrERYWxjoip71///6LUxAyMzNhaWlZz4m4rbi4GEuXLsWOHTsgJycHBwcHGBgYQEFBAX/++SfS0tLw4MEDODg4YM6cOejVqxfryIQQQv4FHBwcMHnyZAwbNkykSA8ODsbly5dx7do11hEJoe3u0mbVqlXIzs5Go0aNYGJiItacLDExkVEyEhAQAAcHB6SkpEBbW1u47unpiVGjRjFMRgBAXl4e2dnZ2LlzJ7Kzs7F27Vro6enhwoULMDIyYh2Pc7S0tLBy5UosXrwY586dw7Vr15CXl4eKigro6Ohg6NCh6NGjB2xsbFhH5Rx7e/vv7ndCnzns/fnnn9i+fTvS09PB4/HQsmVL+Pj4CEdNEkJEzZ8/H8OHD8fTp0/B5/Nx7NgxZGZmIiIiAmfOnGEdjxAA9CRd6nyrsdL8+fPrKQn5nI6ODuLj42FpaSlyZzYvLw9WVlYoLy9nHZHTYmNj0atXLzg7O+Pq1atIT0+HmZkZli9fjtu3b9MsbkL+46808KPPHLZiY2PRt29fqKmpwcHBAQCQkJCAkpISnDp1Cp07d2ackBDJFBkZiaVLl4pMFJk3bx66d+/OOhohAKhIJ+Rvo6Wlhbi4OFhZWYkU6XFxcRgwYACeP3/OOiKnOTo6YuDAgZg6darI+3Pnzh3069eP5gkTQqSOjY0NnJycsGnTJsjKygIAampqMH78eMTHx+P+/fuMExJCCPlfUJEuhUpKSnDkyBFkZ2cjKCgIWlpaSExMRKNGjaj7MUODBw+Guro6tmzZAlVVVaSmpkJXVxd9+/ZF06ZNqfs7YyoqKrh37x5MTU3Fdjq0aNEClZWVrCMSIrHu3r0rsp26bdu2rCMR1DbASk5OFuupQXPSCfm2Dx8+4MWLF+Dz+SLrf2XyCCH/FDqTLmVSU1PRtWtXqKurIy8vD6NHj4aWlhaOHz+O/Px8REREsI7IWaGhoXBzc4OVlRUqKyvh5eWFhw8fQkdHB/v372cdj/M0NDRQWFgIU1NTkfWkpCS6uUXIFzx58gS//PIL4uPjoaGhAaD2RrGTkxP2799P/RwYa9OmDdLT08WK9PT0dLRu3ZpNKEIk3MOHD+Hr64vr16+LrAsEAvB4PNTU1DBKRsh/UZEuZaZOnYqRI0di+fLlUFVVFa736tULXl5eDJMRAwMDJCcn48CBA8IzTn5+fhg6dCgUFRVZx+M8Ly8vzJgxA4cPHwaPxwOfz0d8fDymTZsGb29v1vEIkUi+vr6oqqoSKQQzMzPh6+sLPz8/XLx4kXFCbvP390dAQAAePXqEH374AQBw8+ZNbNy4ESEhIUhNTRVea2dnxyomIRJl5MiRaNCgAc6cOQN9ff3vbpRJSH2i7e5SRl1dHYmJiWjWrJnIlt38/HxYWlrSll1CvqCqqgojR47EgQMHIBAI0KBBA9TU1MDLywvh4eHC85yEkP9SVFTE9evXYW9vL7KemJgIZ2dn2k7NmIyMzFdf5/F49HSQkM8oKysjISEBLVq0YB2FkC+iJ+lSRkFBAaWlpWLrmZmZ0NXVZZCIEOkgJyeHvXv3YtGiRUhMTASfz4e9vT2aN2/OOhoBsHv3boSFhSE3Nxc3btyAsbEx1qxZA1NTU/Tt25d1PM5q2rQpqqqqxNarq6vpmIgEyM3NZR2BEKljZWWFV69esY5ByFdRkS5l+vbti4ULF+LQoUMAau+SFxQUYObMmRgwYADjdIRIPjMzM5iZmbGOQT6xadMmzJs3D5MnT8aSJUuET/w0NDSwZs0aKtIZWr58OSZNmoSNGzeibdu24PF4uHv3LgICArBy5UrW8TjP2NiYdQRCpM7vv/+O6dOnY+nSpbC1tYWcnJzI62pqaoySEfJftN1dypSWlqJ379548OAB3r59CwMDAxQVFcHR0RHnzp2DsrIy64iESIyQkBD4+/tDSUnpm9feunULr169Qp8+feohGfmUlZUVli5din79+okc47l//z5cXV3piQdDmpqaKC8vR3V1NRo0qL2v//Hnzz9viouLWUTkvOzsbKxZs0ak+35AQACaNWvGOhohEunjMZHPz6LT0RAiSehJupRRU1NDXFwcoqOjhVt227Rpg65du7KOxmk1NTWIi4uDnZ0dNDU1Wcch/5GWloamTZti4MCB8PDwgIODg/BYSHV1NdLS0hAXF4c9e/agsLCQpiMwkpubK3bmGQDk5eVRVlbGIBH5aM2aNawjkK+IjIyEh4cHWrduDWdnZwgEAly/fh3W1tY4ffo0unXrxjoiIRInJiaGdQRCvomepBPyN1FQUEB6errYiC/CVmpqKjZu3IjDhw/jzZs3kJWVhby8PMrLywEA9vb2GDNmDEaMGAF5eXnGabnJysoKy5YtQ9++fUWepK9btw67du1CQkIC64iESCR7e3v06NEDISEhIuszZ87ExYsXkZiYyCgZIYSQ/w96ki4F1q1bhzFjxkBBQQHr1q376rX+/v71lIp8ztbWFjk5OVSkSxg7Ozts3rwZYWFhSE1NRV5eHioqKqCjo4PWrVtDR0eHdUTOCwoKwoQJE1BZWQmBQIDbt29j//79WLZsGbZt28Y6HueUlpYKz2TW1aj0U3R2k6309HRhj5pP+fr60i4IQj6RmpoKGxsbyMjIiIwmrAuNKySSgJ6kSwFTU1PcvXsX2traXy0AeTwecnJy6jEZ+dTFixcxY8YMLFq0CG3bthU7r0lfZgn5sq1bt2Lx4sV4/PgxAMDQ0BALFiyAn58f42TcIysri8LCQujp6UFGRqbOGcJ0dlMyGBkZYfXq1Rg4cKDI+qFDhzBt2jQUFBQwSkaIZJGRkUFRUZHI37W6SiD6u0YkBRXphPxNPp1X++mXWvoyS8j3e/XqFfh8PvT09FhH4azY2Fg4OzujQYMGiI2N/eq1nTt3rqdUpC4LFy5EaGgoZs6cCScnJ/B4PMTFxeH3339HYGAg5syZwzoiIRIhPz8fTZs2BY/HQ35+/levpakJRBJQkU7I34S+zBJCCKlPAoEAa9aswapVq/Ds2TMAgIGBAYKCguDv71/nLghCCCGSj4p0KfPzzz/DwcEBM2fOFFlfsWIFbt++jcOHDzNKRggh/5vnz59j2rRpiIqKwosXL8S2INIuFHZ27twJFRUVse3Uhw8fRnl5OUaMGMEoGamursbevXvRo0cPNG7cGG/fvgUAqKqqMk5GiOQ5derUd1/r4eHxDyYh5PtQkS5ldHV1ER0dDVtbW5H1e/fuoWvXrnj+/DmjZAQArl27hs2bNyMnJweHDx+GoaEhdu/eDVNTU3Ts2JF1PEIkUq9evVBQUICJEydCX19f7Olf3759GSUjlpaWCAsLg5ubm8h6bGwsxowZg8zMTEbJCAAoKSkhPT2dtucS8g2fHkn8GjqeSCQFdXeXMu/evUPDhg3F1uXk5L7ZhZf8s44ePYrhw4dj6NChSExMxPv37wEAb9++xdKlS3Hu3DnGCcmnSktLER0dDUtLS7Rs2ZJ1HE6Li4vDtWvX0Lp1a9ZRyGfy8/PrbFhqbGxMTckkQIcOHZCUlERFOiHfwOfzWUcg5C/5vttKRGLY2Njg4MGDYusHDhyAlZUVg0Tko8WLFyMsLAxbt26FnJyccN3JyYlm1UqAQYMGYcOGDQCAiooKODg4YNCgQbCzs8PRo0cZp+M2IyOjOrvsEvb09PTqHFeUkpICbW1tBonIp8aPH4/AwEBs2LABN27cQGpqqsg/Qggh0omepEuZuXPnYsCAAcjOzkaXLl0AAFFRUdi/fz+dR2csMzMTLi4uYutqamooKSmp/0BExNWrVzF79mwAwPHjxyEQCFBSUoJdu3Zh8eLFGDBgAOOE3LVmzRrMnDkTmzdvhomJCes45BNDhgyBv78/VFVVhX/fYmNjERAQgCFDhjBORwYPHgwA8Pf3F659HC1F23YJ+bKoqCiEhoYiPT0dPB4PLVq0wOTJk9G1a1fW0QgBQEW61PHw8MCJEyewdOlSHDlyBIqKirCzs8Ply5epezhj+vr6ePTokViRERcXBzMzMzahiNCbN2+gpaUFALhw4QIGDBgAJSUl9OnTB0FBQYzTcdvgwYNRXl6OZs2aQUlJSWQnCgAUFxczSkYWL16M/Px8uLu7o0GD2q8MfD4f3t7eWLp0KeN0JDc3l3UEQqTOhg0bMGXKFPz8888ICAgAANy8eRO9e/fG6tWrMXHiRMYJCaHGcYT8bZYvX45du3Zhx44d6NatG86dO4f8/HxMmTIF8+bNoz/6jFlYWGDx4sXo06cPTE1NceDAAXTp0gUpKSlwd3fHq1evWEfkrF27dn31deogzt7Dhw+RnJwMRUVF2Nra0hloQojUMjQ0xKxZs8S+l23cuBFLliwRjjMkhCUq0qXM48ePwePx0KRJEwDA7du3sW/fPlhZWWHMmDGM05HZs2cjNDQUlZWVAAB5eXlMmzYNixYtYpyM/PHHHwgICICKigqMjY2RmJgIGRkZrF+/HseOHUNMTAzriIRIvJqaGty7dw/GxsbQ1NRkHYcAyMrKwpUrV/DixQux5ljz5s1jlIoQyaWqqoqkpCSYm5uLrD98+BD29vZ49+4do2SE/BcV6VKmU6dOGDNmDIYPH46ioiJYWFjAxsYGWVlZ8Pf3pw9kCVBeXo60tDTw+XxYWVlBRUWFdSTyHwkJCSgoKEC3bt2E78vZs2ehqakJJycnxum4pbS0FGpqasKfv+bjdaT+TZ48Gba2tvDz80NNTQ06d+6M69evQ0lJCWfOnIGrqyvriJy2detW/Prrr9DR0UHjxo1FxhfyeDxqWkpIHYYOHYrWrVuLHXVbuXIlEhISsH//fkbJCPkvKtKljKamJm7evAlLS0usW7cOBw8eRHx8PC5evIhx48YhJyeHdUTO8vX1xdq1a6GqqiqyXlZWhkmTJmHHjh2MkhEAWLhwIaZNmwYlJSWR9YqKCqxYsYJucNUzWVlZFBYWQk9PDzIyMmKz0QFQ8ysJ0KRJE5w4cQIODg44ceIExo8fjytXriAiIgIxMTGIj49nHZHTjI2NMX78eMyYMYN1FEKkxuLFi7Fy5Uo4OzvD0dERQO2Z9Pj4eAQGBorcGP60KSMh9YmKdCmjoqKC+/fvw8TEBB4eHnB2dsaMGTNQUFAAS0tLVFRUsI7IWZ8WHZ969eoVGjdujOrqakbJCPDl9+f169fQ09OjQrCexcbGwtnZGQ0aNEBsbOxXr6WmmOwoKCjg0aNHaNKkCcaMGQMlJSWsWbMGubm5aNWq1Td3QZB/lpqaGpKTk6k5KSF/gamp6Xddx+Px6OEXYYa6u0sZa2trhIWFoU+fPrh06ZLwrPOzZ89oZi0jpaWlEAgEEAgEePv2LRQUFISv1dTU4Ny5c2KFIal/H5/Kfi4lJUXY9Z3Un08LbyrCJVejRo2QlpYGfX19XLhwAX/88QeA2mM9srKyjNORgQMHCnfSEUK+D01FINKAinQp8/vvv8PT0xMrVqzAiBEj0KpVKwDAqVOn0L59e8bpuElDQwM8Hg88Hg8WFhZir/N4PAQHBzNIRoDaIyKfvj+fFuo1NTV49+4dfcFlIDU19buvtbOz+weTkK/x8fHBoEGDoK+vDx6Ph27dugEAbt26hRYtWjBOx03r1q0T/mxubo65c+fi5s2bsLW1FRtfSFt1CSFEOtF2dylUU1OD0tJSkc66eXl5UFJSoie2DMTGxkIgEKBLly44evSoyFPZhg0bwtjYGAYGBgwTctuuXbsgEAjg6+uLNWvWQF1dXfhaw4YNYWJiIjyTRurPx3Po3/oIojPp7B05cgSPHz/GwIEDhZNFdu3aBQ0NDfTt25dxOu6hrbqE/HVTp07FokWLoKysjKlTp3712tWrV9dTKkK+jIp0Qv4m+fn5aNq0aZ1bqglb1dXV2LNnD7p27SosMghb+fn5330tzeQmhBDy/+Hm5objx49DQ0MDbm5uX7yOx+MhOjq6HpMRUjcq0gn5m1y9evWrr7u4uNRTElIXJSUlpKenU8FHyDesW7cOY8aMgYKCgsjW6rrQdmq2aGoFIYT8O1GRTsjfREZGRmzt8/PPhB03NzcEBASgX79+rKOQOmRmZmL9+vVIT08Hj8dDixYtMGnSJFhaWrKOxjmmpqa4e/cutLW1v7q1mrZTs0dTKwj56968eYOamhqxprHFxcVo0KCByAg2QlihxnGE/E3+/PNPkd+rqqqQlJSEuXPnYsmSJYxSkY/Gjx+PwMBAPHnyBG3btoWysrLI69ScjJ0jR47gl19+gYODg8jMWhsbG+zbtw8DBw5knJBbPu18TF2QJRtNrSDkrxsyZAh++uknjB8/XmT90KFDOHXqFM6dO8coGSH/RU/SCfmHXb16FVOmTEFCQgLrKJz2pZ0OH7/k0hMndszMzDBs2DAsXLhQZH3+/PnYvXs3Pa0l5DMfp1a8efMGampqX5xasXHjRoYpCZFMWlpaiI+PR8uWLUXWMzIy4OzsjNevXzNKRsh/0ZN0KfCtM4GfovOBkkdXVxeZmZmsY3AePRGUXEVFRfD29hZbHzZsGFasWMEgEbd9q/Pxp6gLMhtr1qwRTq0IDg6mqRWE/AXv379HdXW12HpVVRUqKioYJCJEHBXpUiA0NPS7ruPxeFSkM/T53GeBQIDCwkKEhIQI59kTdqhhnORydXXFtWvXYG5uLrIeFxeHTp06MUrFXUlJSSK/JyQkoKamRtgfICsrC7Kysmjbti2LeATAiBEjANT2D3BychKbj04I+bJ27dphy5YtWL9+vch6WFgY/V0jEoOKdClATwClQ+vWreuc+/zDDz9gx44djFKRz6WlpaGgoAAfPnwQWffw8GCUiHh4eGDGjBlISEjADz/8AKD2TPrhw4cRHByMU6dOiVxL/lkxMTHCn1evXg1VVVXs2rULmpqaAGr7b/j4+NANFAnQuXNn8Pl8ZGVl4cWLF+Dz+SKv01QRQsQtWbIEXbt2RUpKCtzd3QEAUVFRuHPnDi5evMg4HSG16Ew6IX+Tz+c+y8jIQFdXFwoKCowSkU/l5OTA09MT9+7dE7mZ8vEsJ51JZ6eufgF1od4B9c/Q0BAXL16EtbW1yPr9+/fRvXt3PHv2jFEyAtTezPLy8kJ+fr7YDWL6/4WQL0tOTsaKFSuQnJwMRUVF2NnZYdasWWjevDnraIQAoCfpUunJkyc4depUnU8D6XwgO7SdWrIFBATA1NQUly9fhpmZGW7fvo3Xr18jMDAQK1euZB2P0z5/+kckR2lpKZ4/fy5WpL948QJv375llIp8NG7cODg4OODs2bPQ19evs9M7IURc69atsXfvXtYxCPkiKtKlTFRUFDw8PGBqaorMzEzY2NggLy8PAoEAbdq0YR2P86KiohAaGioy63ny5Mno2rUr62icd+PGDURHR0NXVxcyMjKQkZFBx44dsWzZMvj7+4udwyWEAJ6envDx8cGqVatEjiIEBQWhf//+jNORhw8f4siRI2L9HAghX5aYmAg5OTnY2toCAE6ePImdO3fCysoKCxYsQMOGDRknJISKdKkza9YsBAYGYuHChVBVVcXRo0ehp6eHoUOHomfPnqzjcdqGDRswZcoU/PzzzwgICABQ+2W2d+/eWL16NSZOnMg4IbfV1NRARUUFAKCjo4Nnz57B0tISxsbG1H2fgXXr1mHMmDFQUFD45gQLaojJTlhYGKZNm4Zhw4ahqqoKANCgQQP4+flR530J0KFDBzx69IiKdEL+grFjx2LmzJmwtbVFTk4OBg8ejP79++Pw4cMoLy/HmjVrWEckhM6kSxtVVVUkJyejWbNm0NTURFxcHKytrZGSkoK+ffsiLy+PdUTOMjQ0xKxZs8SK8Y0bN2LJkiV0dpOxTp06ITAwEP369YOXlxf+/PNPzJkzB1u2bEFCQgLu37/POiKnmJqa4u7du9DW1oapqekXr+PxeDQnXQKUlZUhOzsbAoEA5ubmUFZWZh2JADh+/DjmzJmDoKAg2NrainV5t7OzY5SMEMmlrq6OxMRENGvWDL///juio6MRGRmJ+Ph4DBkyBI8fP2YdkRB6ki5tlJWV8f79ewCAgYEBsrOzhWcFX716xTIa55WWlta5m6F79+6YMWMGrQ2bmAAAUQtJREFUg0TkU3PmzEFZWRkAYPHixfjxxx/RqVMnaGtr4+DBg4zTcc+nUytogoXkU1ZWpoJPAg0YMAAA4OvrK1z72BiTGscRUjeBQCDshXL58mX8+OOPAAAjIyP6Lk0kBhXpUuaHH35AfHw8rKys0KdPHwQGBuLevXs4duyY8LwgYcPDwwPHjx9HUFCQyPrJkyfx008/MUpFPurRo4fwZzMzM6SlpaG4uBiamprUbIkQIpXoBhchf52DgwMWL16Mrl27IjY2Fps2bQJQ+/9To0aNGKcjpBZtd5cyOTk5ePfuHezs7FBeXo5p06YhLi4O/9fencfVnPb/A3+dU1FpR2SkRZQIJWPPTsrYGszYt9vYJkKM7yA7Y1/G6CYJg8akjGVuGmWrkSiVJVpEloioKEs6n98ffs44ipmhcZ3mvJ6PR49H5/qcjtfc3dV5f67rel92dnZYtWoVO4wLtGDBAixfvhytWrVCixYtALzckx4dHY0pU6bAyMhI+VzusSX6w+effw5XV1d88803KuPLli1DbGwsfv75Z0HJiIjo3yYpKQkDBw5EZmYmJk+eDD8/PwDA119/jZycHOzcuVNwQiIW6eVKcXExoqKi0LBhQ5iamoqOQ294177a13GPLZGqqlWrIjIyUtlp95Xz58+jU6dOuHv3rqBkROXDpUuXSj2WtUePHoISEZU/T58+hZaWVoneDkQicLl7OaKlpYWuXbsiOTmZRboa4rJDovfz+PHjUo+80dHRQX5+voBEROXD1atX0bt3b5w/f165Fx2AcgsP96QT/XW6urqiIxApsUgvZ14dF/FXZ22JiNRdgwYN8NNPP2H27Nkq48HBwXB0dBSUigBg3759pY7LZDLo6urCzs6Of48EmjhxImxsbHDkyBHY2toiNjYWOTk5mDJlCpYvXy46HpHaMDMzQ0pKCqpUqfKnvWgePHjwEZMRlY5FejmzcOFCTJ06FfPnz0eTJk1KHIPz+r5n+rgkSUJISAiOHj2K7OxsZefQV0JDQwUlI1Jvs2bNgpeXF9LT09GhQwcAQEREBHbt2sX96IL16tVLZYb2ldc7iLdu3Rp79+7lCi8BTp06hcjISFStWhVyuRxyuRytW7fG4sWL4e3tjXPnzomOSKQWVq1aBUNDQwDgOehULnBPejkjl8uVn79+F5DHrYjn7e2NjRs3on379qhWrVqJu7RbtmwRlIwAzgiqu4MHD2LRokVISEiAnp4eGjZsCD8/P7Rt21Z0NI0WERGBb7/9FgsXLsSnn34KAIiNjcXMmTMxa9YsGBsb46uvvkKzZs2wefNmwWk1j6mpKeLi4mBra4vatWsjICAA7du3R3p6OpycnFBYWCg6IhERvQcW6eXM8ePH33mdb2jFMTMzw48//ggPDw/RUagUcrmcM4JEf1ODBg2wceNGtGzZUmU8Ojoao0ePxsWLF3HkyBGMGDECmZmZglJqrjZt2mDKlCno1asXBgwYgIcPH2LmzJnYuHEj4uLicOHCBdERidTC3+lvwlWppA643L2cYRGuvoyNjWFrays6Br3Fb7/99pdmBKdOncoZwY/sxo0bkMlkqFmzJoCX35edO3fC0dERo0ePFpxOs6Wnp5f6htXIyEh5SkWdOnVw//79jx2NAMycORMFBQUAXh4D2r17d7Rp0waVK1fGTz/9JDgdkfowMTF55z7013FVKqkDzqSXMydOnHjndTc3t4+UhN60detWHDp0CIGBgdDT0xMdh97AGUH11aZNG4wePRqDBw/GnTt3ULduXTRo0AApKSnw9vYu0VCOPp7WrVvD0NAQ27ZtQ9WqVQEA9+7dw5AhQ1BQUIATJ07gyJEjGDduHFJSUgSnJeBl06s/a4xFpGleX4l67do1fPPNNxg2bBhatGgB4GV/h61bt2Lx4sUYOnSoqJhESizSy5nX96S/8vofYt79E6ewsBB9+vRBdHQ0rK2tS5yzGR8fLygZAYCenh7OnDmDBg0aqIyfP38en376KZ48eYLr16+jXr163Mf5kZmamiImJgb29vZYu3YtfvrpJ0RHRyM8PBxjxoxRztjSx3flyhX07NkTGRkZsLS0hEwmQ2ZmJmxtbfHLL7+gbt262Lt3Lx49eoTBgweLjktE9Kc6duyIUaNG4csvv1QZ37lzJzZu3Ihjx46JCUb0Gi53L2cePnyo8rioqAjnzp3DrFmzsHDhQkGpCACGDRuGuLg4DBo0qNTGcSRWkyZN4OvrW2JGcNq0aWjatCkAIDU1Vbnkmj6eoqIiVKxYEQBw5MgR9OjRAwDg4OCArKwskdE0nr29PZKTk3H48GGkpKRAkiQ4ODigc+fOypvGvXr1EhuSiOhvOHXqFPz9/UuMu7q6YtSoUQISEZXEmfR/iRMnTsDHxwdxcXGio2isSpUq4fDhw2jdurXoKFQKzgiqr2bNmqF9+/bw9PREly5dEBMTg0aNGiEmJgaff/45bt68KToiERH9S9jb26N79+5YsWKFyviUKVNw4MABXLlyRVAyoj9wJv1fomrVqvylIpilpSU7gqoxzgiqr++++w69e/fGsmXLMHToUDRq1AjAy2PzXjX5I3EiIiIQERGB7OxsKBQKlWuBgYGCUhERvZ9Vq1bBy8sLhw8fRvPmzQEAMTExSE9Px549ewSnI3qJM+nlTFJSkspjSZKQlZWFJUuWoKioCNHR0YKS0cGDB7Fu3Tr4+/vD2tpadByicqW4uBj5+fkqx99du3YN+vr6MDc3F5hMs82dOxfz5s2Dq6srLCwsSmzjCQsLE5SMiOj93bx5Exs2bEBycjIkSYKjoyPGjBkDS0tL0dGIALBIL3fedtZz8+bNERgYCAcHB0HJyNTUFIWFhXjx4gX09fVLNI578OCBoGT0CmcEif4eCwsLLF26lFtAypmsrCwUFRWhVq1aoqMQEdF74HL3ciYjI0PlsVwuR9WqVaGrqysoEb2yevVq0RHoHf5sRpA+LhcXF0RERMDU1BTOzs7v/H7wZARxnj9/XuLYQlJ/HTp0QEpKCk98ISIqp1iklzPHjx9H//79lZ2QX3n+/DmCg4MxZMgQQcmI52qqN39/fwQFBXFGUE307NlT+XuMvQDU16hRo7Bz507MmjVLdBT6G7Zt28ajJImIyjEudy9ntLS0kJWVVWKPZk5ODszNzXnXXLDi4mLs3bsXycnJkMlkcHR0RI8ePaClpSU6msarXLkyYmNjUbt2bdFRiMqNiRMnYtu2bWjYsCEaNmxYYhvPypUrBSUjIiL69+JMejkjSVKpy0Jv3rwJY2NjAYnolbS0NHh4eODWrVuwt7eHJElISUmBpaUlDh48yOJQMM4IEv19SUlJaNy4MQDgwoULKte4ZUS8I0eOoFOnTqVe++9//4uvvvrqIyciIqKywJn0cuLVns3ExETUr18f2tp/3F8pLi5GRkYG3N3dsXv3boEpNZuHhwckScKOHTtgZmYG4OUKh0GDBkEul+PgwYOCE2o2zgiqF1NT079c5LHpIlHpKlasiAkTJmDx4sWoUKECAODevXsYMWIEoqOj+bNDVIonT55AkiTo6+sDAK5fv46wsDA4OjqiS5cugtMRvcSZ9HLi1Z7NhIQEdO3aFQYGBsprFSpUgLW1Nby8vASlI+Blv4CYmBhlgQ68XGK9ZMkStGrVSmAyAjgjqG5eb7SYk5ODBQsWoGvXrmjRogUA4NSpUzh8+DBXPhC9w4kTJzB48GAcOXIEO3fuxLVr1zBixAg4OjoiMTFRdDwitdSzZ0/06dMHY8aMQW5uLpo1awYdHR3cv38fK1euxNixY0VHJOJMenmzdetW9O/fn93c1ZCZmRkOHDhQohNydHQ0PvvsM85oEL2Fl5cX2rdvjwkTJqiMf//99zhy5Aj27t0rJpiG6tOnD4KCgmBkZIQ+ffq887mhoaEfKRW9TUFBAcaMGYOff/4ZCoUCCxYsgK+vL28+Er1FlSpVcPz4cdSvXx8BAQFYt24dzp07hz179mD27NlITk4WHZEIctEB6O8ZOnQoC3Q11b17d4wePRqnT5+GJEmQJAkxMTEYM2YMevToIToekdo6fPgw3N3dS4x37doVR44cEZBIsxkbGysLPGNj43d+kHhXrlzBmTNnULNmTWhra+Py5cvs7E70DoWFhTA0NAQAhIeHo0+fPpDL5WjevDmuX78uOB3RS1zuXs4UFxdj1apV2L17NzIzM/H8+XOV65ytFWft2rUYOnQoWrRoodzv/OLFC/To0QNr1qwRnE4zcUawfKhcuTLCwsLg6+urMr53715UrlxZUCrNtWXLFgAvG5XOmTMHVatWVe7dJPWyZMkS+Pn5YfTo0Vi2bBnS09MxaNAgNGzYED/++KNy+wgR/cHOzg579+5F7969cfjwYfj4+AAAsrOzYWRkJDgd0Uss0suZuXPnIiAgAJMnT8asWbPw7bff4tq1a9i7dy9mz54tOp5GMzExwS+//IK0tDQkJydDkiQ4OjrCzs5OdDSN9eaMIKmnuXPnYuTIkTh27JiyqIiJicGhQ4cQEBAgOJ3mkiQJderUwcWLF1GnTh3RcagUa9aswd69e9GtWzcAQP369REbG4v/+7//Q7t27fDs2TPBCYnUz+zZszFgwAD4+PigQ4cOyr874eHhcHZ2FpyO6CXuSS9nateujbVr18LT0xOGhoZISEhQjsXExGDnzp2iIxIR/W2nT5/G2rVrVW5weXt7o1mzZqKjabT69etj8+bNaN68uegoVIr79++jSpUqpV47fvw42rZt+5ETEZUPd+7cQVZWFho1agS5/OXu39jYWBgZGcHBwUFwOiIW6eVOpUqVkJycjFq1asHCwgIHDx6Ei4sLrl69CmdnZ+Tl5YmOqLE+//xzuLq64ptvvlEZX7ZsGWJjY/Hzzz8LSkZE9H4OHjyIJUuWYMOGDWjQoIHoOFSK3NxchISEID09Hb6+vjAzM0N8fDyqVauGTz75RHQ8IrWVlpaG9PR0uLm5QU9PD5IkseEiqQ02jitnatasiaysLAAv99SEh4cDAM6cOYOKFSuKjKbxjh8/Dk9PzxLj7u7uOHHihIBE9LqcnByMHz8ejo6OqFKlCszMzFQ+iKikQYMGITY2Fo0aNYKenh5/btRMUlIS6tati++++w7Lly9Hbm4uACAsLAwzZswQG45ITeXk5KBjx46oW7cuPDw8lO+rR40ahSlTpghOR/QS96SXM71790ZERASaNWuGiRMn4ssvv8TmzZuRmZmpbHxBYjx+/BgVKlQoMa6jo4P8/HwBieh1gwYNQnp6OkaOHIlq1arxbjnRX/D6efakfiZPnoxhw4Zh6dKlym7VANCtWzcMGDBAYDIi9eXj4wMdHR1kZmaiXr16yvH+/fvDx8cHK1asEJiO6CUudy/nYmJi8Pvvv8POzo7HfAnWtGlTfPbZZyUa+M2ZMwf79+9HXFycoGQEAIaGhoiKikKjRo1ERyEiKhPGxsaIj49H7dq1YWhoiMTERNja2uL69euwt7fH06dPRUckUjvVq1fH4cOH0ahRI5Wfm4yMDDg5OeHx48eiIxJxJr28a968ORv6qIlZs2bBy8sL6enp6NChAwAgIiICu3bt4n50NeDg4IAnT56IjkFU7qSnp2PLli1IT0/HmjVrYG5ujkOHDsHS0hL169cXHU+j6erqlrpS68qVK6hataqARETqr6CgoNRjJe/fv8+to6Q2OJNeDqWkpODYsWPIzs6GQqFQucZj2MQ6ePAgFi1ahISEBOjp6aFhw4bw8/Njh101cObMGXzzzTeYPXs2GjRooDzL/hWejao+8vPzERkZCXt7e5WliPTxHT9+HN26dUOrVq1w4sQJJCcnw9bWFkuXLkVsbCxCQkJER9Roo0ePxr1797B7926YmZkhKSkJWlpa6NWrF9zc3LhdgagUnp6ecHFxwfz582FoaIikpCRYWVnhiy++gEKh4O81Ugss0suZTZs2YezYsahSpQqqV6+usq9WJpMhPj5eYDoi9ZWamoovv/wS586dUxl/1c21uLhYUDLq168f3NzcMGHCBDx58gSNGjXCtWvXIEkSgoOD4eXlJTqixmrRogX69u2LyZMnqywLPXPmDHr16oVbt26JjqjR8vPz4eHhgYsXL+LRo0eoUaMG7ty5gxYtWuDXX39FpUqVREckUjuXLl1Cu3bt0KRJE0RGRqJHjx64ePEiHjx4gOjoaNSuXVt0RCIW6eWNlZUVxo0bh+nTp4uOQlSufPrpp9DW1sbEiRNLbRzH1Q7ivL4/cOfOnfDz80NiYiK2bt2KjRs3lrixQh+PgYEBzp8/DxsbG5Ui/dq1a3BwcOCeZzURGRmJ+Ph4KBQKuLi4oFOnTqIjEam1O3fuYMOGDYiLi1P+3IwfPx4WFhaioxEB4J70cufhw4fo27ev6BhE5c6FCxdw7tw52Nvbi45Cb8jLy1Me53Xo0CF4eXlBX18fnp6e8PX1FZxOs5mYmCArKws2NjYq4+fOneMZ3GqkQ4cOyl4oRPTnqlevjrlz54qOQfRWPCe9nOnbt6/ybHQi+utcXV1x48YN0TGoFJaWljh16hQKCgpw6NAhdOnSBcDLm5K6urqC02m2AQMGYPr06bhz5w5kMhkUCgWio6MxdepUDBkyRHQ8jRQcHPyXn3vjxg1ER0f/g2mIyoekpCRlH6ekpKR3fhCpAy53L2cWL16MlStXwtPTE05OTiWaX3l7ewtKRqTefv75Z8yZMwe+vr6l/uw0bNhQUDL64YcfMHHiRBgYGMDKygrx8fGQy+VYt24dQkNDcfToUdERNVZRURGGDRuG4OBgSJIEbW1tFBcXY8CAAQgKCoKWlpboiBqnbdu2uHv3LoYPH44ePXqUaK6Yl5eH6Oho/Pjjjzhy5Ag2b96Mzz77TFBaIvUgl8tx584dmJubQy6XQyaTobQSiD1qSF2wSC9n3lxy+DqZTIarV69+xDRE5YdcXnLh0Ks/0vyjLN7Zs2dx48YNdO7cGQYGBgBenpZgYmKCVq1aCU5H6enpOHfuHBQKBZydnVGnTh3RkTTagQMHsG7dOhw5cgSVKlVCtWrVoKuri4cPH+LOnTuoWrUqhg8fjkmTJsHc3Fx0XCLhrl+/jlq1akEmk+H69evvfK6VldVHSkX0dizSiT6CefPmoV27dnBzcxMdRWPxj3L5UVxcjPPnz8PKygqmpqai4xCA58+fIyMjA7Vr14a2NtvZqIucnBxERUXh2rVrePLkCapUqQJnZ2c4OzuXemOSiIATJ06gZcuWJX6XvXjxAr///jvfq5FaYJFO9BHY2Njg7t276NixI/bv3y86jsYpKiqCvb09Dhw4AEdHR9Fx6A2TJk2Ck5MTRo4cieLiYrRt2xa///479PX1ceDAAbRr1050RI1VWFiIr7/+Glu3bgUApKSkwNbWFt7e3qhRowa++eYbwQmJiP4eLS0tZGVllVhlkpOTA3Nzc66sI7XA26xEH0FGRgYePHiACRMmiI6ikXR0dPDs2bMSx66ReggJCUGjRo0AAPv370dGRgYuX76MSZMm4dtvvxWcTrPNmDEDiYmJOHbsmEoTv06dOuGnn34SmIyI6P282ub2ppycHFSqVElAIqKSOJNORBphyZIluHz5MgICArhcV83o6uoiLS0NNWvWxOjRo6Gvr4/Vq1cjIyMDjRo1Qn5+vuiIGsvKygo//fQTmjdvrnJOelpaGlxcXPi9IaJyo0+fPgCAX375Be7u7qhYsaLyWnFxMZKSkmBvb49Dhw6JikikxHeqRGXo5MmT+O9//4v09HSEhITgk08+wfbt22FjY4PWrVuLjqfRTp8+jYiICISHh8PJyanE3fLQ0FBByahatWq4dOkSLCwscOjQIfzwww8AXi61Zvdwse7du1dq47GCggKuTCGicsXY2BjAy5l0Q0ND6OnpKa9VqFABzZs3x3/+8x9R8YhUsEj/F0lISEDjxo1Fx9BYe/bsweDBgzFw4ECcO3cOz549AwA8evQIixYtwq+//io4oWYzMTGBl5eX6BhUiuHDh6Nfv36wsLCATCZD586dAby8seLg4CA4nWZr2rQpDh48iK+//hoAlIX5pk2b0KJFC5HRiIj+li1btgAArK2tMXXqVC5tJ7XG5e7lXF5eHnbs2IGAgAAkJiay2YVAzs7O8PHxwZAhQ1SWhSYkJMDd3R137twRHZFIbYWEhODGjRvo27cvatasCQDYunUrTExM0LNnT8HpNNfvv/8Od3d3DBw4EEFBQfjqq69w8eJFnDp1CsePH0eTJk1ERySw+z4R0b8Ni/RyKjIyEoGBgQgNDYWVlRW8vLzg5eUFZ2dn0dE0lr6+Pi5dugRra2uVIv3q1atwdHTE06dPRUckvFy+e+XKFchkMtStWxdVq1YVHYle8/TpU5UGZSTe+fPnsXz5csTFxUGhUMDFxQXTp0+Hk5OT6Ggaj933if4aFxcXREREwNTUFM7Ozu/crhMfH/8RkxGVjrdby5GbN28iKCgIgYGBKCgoQL9+/VBUVIQ9e/bwWCk1YGFhgbS0NFhbW6uMR0VFwdbWVkwoUiooKMDXX3+Nbdu2QaFQAHh5DMuQIUOwbt066OvrC06ouYqLi7Fo0SL4+/vj7t27ykJj1qxZsLa2xsiRI0VH1GhOTk7KIpDUy+vd993d3ZXjnTp1gp+fH4t0ov+vZ8+eykZxvXr1EhuG6C9gkV5OeHh4ICoqCt27d8e6devg7u4OLS0t+Pv7i45G/99XX32FiRMnIjAwEDKZDLdv38apU6cwdepUzJ49W3Q8jTd58mQcP34c+/fvR6tWrQC8vIHi7e2NKVOmYMOGDYITaq6FCxdi69atWLp0qUrTHicnJ6xatYpFumAKhQJpaWnIzs5W3uB6xc3NTVAqAoC9e/cqu++/PjPo6OiI9PR0gcmI1Iufn1+pnxOpKxbp5UR4eDi8vb0xduxY1KlTR3QcKsW0adOQl5eH9u3b4+nTp3Bzc0PFihUxdepUno+uBvbs2YOQkBC0a9dOOebh4QE9PT3069ePRbpA27Ztw8aNG9GxY0eMGTNGOd6wYUNcvnxZYDKKiYnBgAEDcP36dby5O04mk7EPimDsvk/0/p4/f17qzcdatWoJSkT0B7noAPTXnDx5Eo8ePYKrqyuaNWuG77//Hvfu3RMdi96wcOFC3L9/H7GxsYiJicG9e/cwf/580bEIL/duVqtWrcS4ubk5CgsLBSSiV27dugU7O7sS4wqFAkVFRQIS0StjxoyBq6srLly4gAcPHuDhw4fKjwcPHoiOp/Fedd9/hd33if5cSkoK2rRpAz09PVhZWcHGxgY2NjawtraGjY2N6HhEADiTXm60aNECLVq0wJo1axAcHIzAwEBMnjwZCoUCv/32GywtLWFoaCg6pkbLy8tDcXExzMzM4Orqqhx/8OABtLW1YWRkJDAdtWjRAn5+fti2bZuyMdmTJ08wd+5cvpkVrH79+jh58iSsrKxUxn/++Wc2wxQsNTUVISEhpd5EIfEWL14Md3d3XLp0CS9evMCaNWtUuu8TUUnDhw+HtrY2Dhw4oDz6k0jdsLt7OXblyhVs3rwZ27dvR25uLjp37ox9+/aJjqWxunXrhs8++wzjxo1TGff398e+fft4Trpg58+fR7du3fD06VM0atQIMpkMCQkJ0NXVxeHDh1G/fn3RETXW/v37MXjwYMyYMQPz5s3D3LlzceXKFWzbtg0HDhxQnptOH1+HDh0wbdo0laZkpF7YfZ/o76lUqRLi4uLg4OAgOgrRW7FI/xcoLi7G/v37ERgYyCJdIDMzM0RHR6NevXoq45cvX0arVq2Qk5MjKBm98uTJE/z444+4fPkyJEmCo6MjBg4cCD09PdHRNN7hw4exaNEilUJj9uzZ6NKli+hoGi0sLAwzZ86Er68vnJycoKOjo3K9YcOGgpIREb2fpk2bYtWqVWjdurXoKERvxSKdqIxUqlQJMTExJWYvzp8/j2bNmnHfswCvn4s6b948TJ06lUetEf0NcnnJ1jUymQySJLFxnBr49ddfoaWlha5du6qMHz58GAqFAt26dROUjEh9RUZGYubMmVi0aFGpNx+5PZHUAYt0ojLSrl07ODk5Yd26dSrj48ePR1JSEk6ePCkomebS09NDamoqatasCS0tLWRlZZXaCZnUAzvtqp/r16+/8/qbfQTo42rYsCGWLFkCDw8PlfFDhw5h+vTpSExMFJSMSH29uvn45l503nwkdcLGcURlZOHChejUqRMSExPRsWNHAEBERATOnDmD8PBwwek0U+PGjTF8+HC0bt0akiRh+fLlMDAwKPW5PMtenNTUVIwYMQK///67yjjfMInHIly9paamwtHRscS4g4MD0tLSBCQiUn9Hjx4VHYHoT3EmnagMJSQkYNmyZUhISICenh4aNmyIGTNm8Gx7Qa5cuQI/Pz+kp6cjPj4ejo6O0NYueW9SJpMhPj5eQEICgFatWkFbWxvffPNNqZ12GzVqJCiZZtq3bx+6desGHR2dP+1z0qNHj4+UikpTvXp17Ny5Ex06dFAZP3LkCAYMGIDs7GxByYiI6EOwSCcijSCXy3Hnzh0ud1dD7LSrXl7/WSltT/orXOUg3ujRoxETE4OwsDDUrl0bAJCWlgYvLy80bdoUAQEBghMSqa/CwkJkZmbi+fPnKuNsiEnqgMvdicqQQqFAWlpaqftq3dzcBKWioqIiDBkyBI8fP2aRroYcHR1x//590THo/3v9d9ebv8dIvSxbtgzu7u5wcHBAzZo1AQA3b95EmzZtsHz5csHpiNTTvXv3MHz4cPzvf/8r9TpvPpI64Ew6URmJiYnBgAEDcP36dbz5Y8UZJ/FMTU0RFxcHW1tb0VHoDey0S/T+JEnCb7/9hsTEROU2K94UJnq7gQMH4tq1a1i9ejXat2+PsLAw3L17FwsWLMCKFSvg6ekpOiIRi3SistK4cWPUrVsXc+fOLXVfrbGxsaBkBADDhw+Hk5MTJk+eLDoKvYGddtXX2rVrSx2XyWTQ1dWFnZ0d3NzcoKWl9ZGTERG9HwsLC/zyyy/49NNPYWRkhLNnz6Ju3brYt28fli5diqioKNERibjcnaispKamIiQkBHZ2dqKjUCns7Owwf/58/P7772jSpAkqVaqkct3b21tQMmKnXfW1atUq3Lt3D4WFhTA1NYUkScjNzYW+vj4MDAyQnZ0NW1tbHD16FJaWlqLjaqSIiAhERESUus0qMDBQUCoi9VVQUKDc+mZmZoZ79+6hbt26cHJyYhNZUhss0onKSLNmzZCWlsYiXU0FBATAxMQEcXFxiIuLU7kmk8lYpAvUtm1b0RHoLRYtWoSNGzciICBApTHZV199hdGjR6NVq1b44osv4OPjg5CQEMFpNc/cuXMxb948uLq6lrqCi4hKsre3x5UrV2BtbY3GjRvjv//9L6ytreHv7w8LCwvR8YgAcLk7UZkJCwvDzJkz4evrW+q+WnYLJfpDUlISGjRoALlcjqSkpHc+lz874tSuXRt79uxB48aNVcbPnTsHLy8vXL16Fb///ju8vLyQlZUlJqQGs7CwwNKlSzF48GDRUYjKjR07dqCoqAjDhg3DuXPn0LVrV+Tk5KBChQoICgpC//79RUckYpFOVFZKO6pIJpNxX62aef78OTIyMlC7du1Sz0ynj+PNY75e/ay8iT87Yunr6+PEiRNwdXVVGT9z5gzatm2LwsJCXLt2DQ0aNMDjx48FpdRclStXRmxsrHKVAxH9fYWFhbh8+TJq1aqFKlWqiI5DBIDL3YnKTEZGhugI9A6FhYX4+uuvsXXrVgBASkoKbG1t4e3tjRo1auCbb74RnFCzZGRkoGrVqsrPST21b98eX331FQICAuDs7Azg5Sz62LFj0aFDBwDA+fPnYWNjIzKmxho1ahR27tyJWbNmiY5CVG7p6+vDxcVFdAwiFSzSicqIlZWV6Aj0DjNmzEBiYiKOHTsGd3d35XinTp3g5+fHIv0je/3nhT876mvz5s0YPHgwmjRpotzC8+LFC3Ts2BGbN28GABgYGGDFihUiY2qsp0+fYuPGjThy5AgaNmxYYpvVypUrBSUjUl+SJCEkJARHjx4tteFiaGiooGREf+Byd6IytH37dvj7+yMjIwOnTp2ClZUVVq9eDRsbG/Ts2VN0PI1mZWWFn376Cc2bN4ehoSESExNha2uLtLQ0uLi4ID8/X3REjbV161ZUqVJFeTbttGnTsHHjRjg6OmLXrl0s4tXA5cuXkZKSAkmS4ODgAHt7e9GRCC9XOryNTCZDZGTkR0xDVD54e3tj48aNaN++PapVq1ai4eKWLVsEJSP6A2fSicrIhg0bMHv2bEyaNAkLFy5U7qM1MTHB6tWrWaQLdu/ePeWRK68rKChgR2TBFi1ahA0bNgAATp06he+//x6rV6/GgQMH4OPjw1kNNeDg4AAHBwfRMegNPL6Q6O/78ccfERoaCg8PD9FRiN6KRTpRGVm3bh02bdqEXr16YcmSJcpxV1dXTJ06VWAyAoCmTZvi4MGD+PrrrwFAWZhv2rQJLVq0EBlN4924cUN5dOHevXvx+eefK4/3ateundhwGq64uBhBQUFvPYebM7XqIS0tDenp6XBzc4Oenp6yYSkRlWRsbAxbW1vRMYjeiUU6URnJyMhQNlZ6XcWKFVFQUCAgEb1u8eLFcHd3x6VLl/DixQusWbMGFy9exKlTp3D8+HHR8TSagYEBcnJyUKtWLYSHh8PHxwcAoKuriydPnghOp9kmTpyIoKAgeHp6okGDBiz81ExOTg769euHo0ePQiaTITU1Fba2thg1ahRMTEzYK4CoFHPmzMHcuXMRGBgIPT090XGISsUinaiM2NjYICEhocT+2f/9739wdHQUlIpeadmyJaKjo7F8+XLUrl0b4eHhcHFxwalTp+Dk5CQ6nkbr3LkzRo0aBWdnZ6SkpCj3pl+8eBHW1tZiw2m44OBg7N69m8tC1ZSPjw90dHSQmZmJevXqKcf79+8PHx8fFulEpejbty927doFc3NzWFtbl2i4GB8fLygZ0R9YpBOVEV9fX4wfPx5Pnz6FJEmIjY3Frl27sHjxYgQEBIiORwCcnJyUR7CR+li/fj1mzpyJGzduYM+ePahcuTIAIC4uDl9++aXgdJqtQoUKyq0IpH7Cw8Nx+PBh1KxZU2W8Tp06uH79uqBUROpt2LBhiIuLw6BBg0ptHEekDtjdnagMbdq0CQsWLMCNGzcAAJ988gnmzJmDkSNHCk5GwMv9tWFhYUhOToZMJkO9evXQs2dPaGvzfiVRaVasWIGrV6/i+++/5xtZNWRoaIj4+HjUqVNH5dSKM2fOwN3dHTk5OaIjEqmdSpUq4fDhw2jdurXoKERvxSKd6B9w//59KBSKUruJkxgXLlxAz549cefOHeXxUSkpKahatSr27dvHJe8CnThx4p3X3dzcPlISelPv3r1x9OhRmJmZoX79+iWWhbLzvlienp5wcXHB/PnzYWhoiKSkJFhZWeGLL76AQqFASEiI6IhEasfBwQG7d+9Gw4YNRUcheisW6USkEZo3bw5zc3Ns3boVpqamAICHDx9i2LBhyM7OxqlTpwQn1FxyubzE2Ouztq+OM6SPb/jw4e+8zvOExbp06RLatWuHJk2aIDIyEj169MDFixfx4MEDREdHo3bt2qIjEqmdgwcPYt26dfD392ffE1JbLNKJPoCLiwsiIiJgamoKZ2fndy4HZSMSsfT09HD27FnUr19fZfzChQto2rQpu4gLlJeXp/K4qKgI586dw6xZs7Bw4UJ07NhRUDIi9Xfnzh1s2LABcXFxUCgUcHFxwfjx42FhYSE6GpFaMjU1RWFhIV68eAF9ff0SK4QePHggKBnRH7gRk+gD9OzZExUrVgQA9OrVS2wYeid7e3vcvXu3RJGenZ3NxliCGRsblxjr3LkzKlasCB8fH8TFxQlIRa+8ePECx44dQ3p6OgYMGABDQ0Pcvn0bRkZGMDAwEB1P41WvXh1z584VHYOo3Fi9erXoCER/ijPpRKQRfv31V0ybNg1z5sxB8+bNAQAxMTGYN28elixZotJAxsjISFRMek1ycjKaNm2Kx48fi46isa5fvw53d3dkZmbi2bNnSElJga2tLSZNmoSnT5/C399fdESNk5SUhAYNGkAulyMpKemdz+WeWyKi8olFOlEZOXPmDBQKBZo1a6Yyfvr0aWhpacHV1VVQMgJU9z2/2pbw6tff649lMhn3QH9kbxYakiQhKysLS5YsQVFREaKjowUlo169esHQ0BCbN29G5cqVld3Djx8/jlGjRiE1NVV0RI0jl8tx584dmJubQy6XQyaTobS3cvxdRlS6zMzMd16vVavWR0pC9HZc7k5URsaPH49p06aVKNJv3bqF7777DqdPnxaUjADg6NGjoiPQWzRu3LjUQqN58+YIDAwUlIoAICoqCtHR0ahQoYLKuJWVFW7duiUolWbLyMhA1apVlZ8T0d9jbW39zh5CvLlF6oBFOlEZuXTpElxcXEqMOzs749KlSwIS0evatm0rOgK9xZuFhlwuR9WqVaGrqysoEb2iUChKfcN68+ZNGBoaCkhEVlZWpX5ORH/NuXPnVB6/ala6cuVKLFy4UFAqIlUs0onKSMWKFXH37l3Y2tqqjGdlZUFbmz9qRG/DQkN9de7cGatXr8bGjRsBvFxC/fjxY/j5+cHDw0NwOlq8eDGqVauGESNGqIwHBgbi3r17mD59uqBkROqrUaNGJcZcXV1Ro0YNLFu2DH369BGQikgV96QTlZEvvvgCd+7cwS+//KLsVp2bm4tevXrB3Nwcu3fvFpyQSH1FRERg1apVSE5Ohkwmg4ODAyZNmoROnTqJjqbRbt++jfbt20NLSwupqalwdXVFamoqqlSpghMnTsDc3Fx0RI1mbW2NnTt3omXLlirjp0+fxhdffMHl8ER/Q2pqKho3boyCggLRUYhYpBOVlVu3bsHNzQ05OTlwdnYGACQkJKBatWr47bffYGlpKTghkXr6/vvv4ePjg88//xwtWrQA8LLzfkhICFauXIkJEyYITqjZnjx5guDgYJVzuAcOHAg9PT3R0TSerq4ukpOTYWNjozJ+9epVODo64unTp4KSEamv/Px8lcevmpXOmTMHly9fRkJCgphgRK9hkU5UhgoKCrBjxw4kJiZCT08PDRs2xJdffgkdHR3R0TSaJEnIzMyEubk5Cws19Mknn2DGjBklivH169dj4cKFuH37tqBkROqtTp068PPzw6BBg1TGt2/fDj8/P1y9elVQMiL19epUhNdJkgRLS0sEBwcrbxYTicSNskRlqFKlShg9erToGPQGSZJQp04dXLx4EXXq1BEdh96Qn58Pd3f3EuNdunThnlqidxg1ahQmTZqEoqIidOjQAcDLrSPTpk3DlClTBKcjUk9vnvbyqlmpnZ0dewiR2uD/E4nKyNatW1GlShV4enoCAKZNm4aNGzfC0dERu3btYnMsgeRyOerUqYOcnBwW6WqoR48eCAsLg6+vr8r4L7/8gs8++0xQKiL1N23aNDx48ADjxo3D8+fPAbxcAj99+nTMmDFDcDoi9cTTXqg84HJ3ojJib2+PDRs2oEOHDjh16hQ6duyI1atX48CBA9DW1kZoaKjoiBrt4MGDWLJkCTZs2IAGDRqIjqPx1q5dq/w8Pz8fy5cvR6tWrVT2pEdHR2PKlCmYOXOmqJhE5cLjx4+RnJwMPT091KlTBxUrVhQdiUitpaSk4NixY8jOzoZCoVC5Nnv2bEGpiP7AIp2ojOjr6+Py5cuoVasWpk+fjqysLGzbtg0XL15Eu3btcO/ePdERNZqpqSkKCwvx4sULVKhQocTe9AcPHghKppnebHT1NjKZjPtqBSkuLkZUVBQaNmwIU1NT0XHoL8jPz0dkZCTs7e1Rr1490XGI1NKmTZswduxYVKlSBdWrV1fZny6TyRAfHy8wHdFLXO5OVEYMDAyQk5ODWrVqITw8HD4+PgBeLj188uSJ4HS0evVq0RHoNTwaSv1paWmha9euSE5OZpGupvr16wc3NzdMmDABT548gaurK65duwZJkhAcHAwvLy/REYnUzoIFC7Bw4UL2PCG1xiKdqIx07twZo0aNgrOzM1JSUpR70y9evAhra2ux4QhDhw4VHYGo3HFycsLVq1f/8soH+rhOnDiBb7/9FgAQFhYGSZKQm5uLrVu3YsGCBSzSiUrx8OFD9O3bV3QMoneSiw5A9G+xfv16tGjRAvfu3cOePXtQuXJlAEBcXBy+/PJLwekIeLl8d8+ePcq76GFhYSguLhYdi0htLVy4EFOnTsWBAweQlZWF/Px8lQ8SKy8vD2ZmZgCAQ4cOwcvLC/r6+vD09ERqaqrgdETqqW/fvggPDxcdg+iduCediDRCWloaPDw8cOvWLdjb20OSJKSkpMDS0hIHDx5E7dq1RUckUjty+R/38l/ftylJEmQyGW9yCVa3bl0sWLAAnp6esLGxQXBwMDp06IDExER07NgR9+/fFx2RSC283qy0oKAAK1euhKenJ5ycnKCjo6PyXG9v748dj6gEFulEpBE8PDwgSRJ27NihnHnKycnBoEGDIJfLcfDgQcEJidTP8ePH33mdRxmJ9cMPP2DixIkwMDCAlZUV4uPjIZfLsW7dOoSGhpY4D5pIU7FZKZU3LNKJSCNUqlQJMTExcHJyUhlPTExEq1at8PjxY0HJ6F0SEhLQuHFj0TGI1FZcXBwyMzPRuXNnGBgYAHh55KSJiQlatWolOB0REb0P7kknIo1QsWJFPHr0qMT448ePUaFCBQGJ6G3y8vLwww8/wMXFBU2aNBEdR+OdPHkSgwYNQsuWLXHr1i0AwPbt2xEVFSU4GQFAkyZN0Lt3b2WBDgCenp4s0ImIyjEW6URlQJIkXL9+nUetqbHu3btj9OjROH36NCRJgiRJiImJwZgxY9CjRw/R8QhAZGQkBg0aBAsLC6xbtw4eHh44e/as6Fgabc+ePejatSv09PQQHx+PZ8+eAQAePXqERYsWCU5HRET078Tl7kRlQKFQQFdXFxcvXkSdOnVEx6FS5ObmYujQodi/f7+yScyLFy/Qo0cPBAUFwdjYWHBCzXTz5k0EBQUhMDAQBQUF6NevH/z9/ZGYmAhHR0fR8TSes7MzfHx8MGTIEBgaGiIxMRG2trZISEiAu7s77ty5IzoiERHRvw7PSScqA3K5HHXq1EFOTg6LdDVlYmKCX375BWlpaUhOToYkSXB0dISdnZ3oaBrLw8MDUVFR6N69O9atWwd3d3doaWnB399fdDT6/65cuQI3N7cS40ZGRsjNzf34gYiIiDQAi3SiMrJ06VL4+vpiw4YNaNCggeg49BZ2dnYszNVEeHg4vL29MXbsWN7cUlMWFhZIS0uDtbW1ynhUVBRsbW3FhCIi+oewWSmpCxbpRGVk0KBBKCwsRKNGjVChQgXo6empXH/w4IGgZETq6eTJkwgMDISrqyscHBwwePBg9O/fX3Qses1XX32FiRMnIjAwEDKZDLdv38apU6cwdepUzJ49W3Q8jZSUlPSXn9uwYcN/MAnRv0NeXh527NiBgIAAJCYmori4WHQkIu5JJyorW7dufef1oUOHfqQkROVLYWEhgoODERgYiNjYWBQXF2PlypUYMWIEDA0NRcfTeN9++y1WrVqFp0+fAnh5UsLUqVMxf/58wck0k1wuh0wmw9vevr26JpPJWGwQvUNkZCQCAwMRGhoKKysreHl5wcvLC87OzqKjEbFIJyIi9XHlyhVs3rwZ27dvR25uLjp37ox9+/aJjqXxCgsLcenSJSgUCjg6Oqoc90Uf1/Xr1//yc62srP7BJETlD5uVUnnBIp2oDKWnp2PLli1IT0/HmjVrYG5ujkOHDsHS0hL169cXHY+o3CguLsb+/fsRGBjIIp2IiD7Y681KBw4cqGxWqqOjwyKd1A6LdKIycvz4cXTr1g2tWrXCiRMnkJycDFtbWyxduhSxsbEICQkRHVGjHTp0CAYGBmjdujUAYP369di0aRMcHR2xfv16mJqaCk5IpH4KCgqwZMkSREREIDs7GwqFQuX61atXBSWj1126dAmZmZl4/vy5yniPHj0EJSJSP9ra2qU2K2WRTuqIjeOIysg333yDBQsWYPLkySr7aNu3b481a9YITEYA4Ovri++++w4AcP78eUyZMgWTJ09GZGQkJk+ejC1btghOSKR+Ro0ahePHj2Pw4MGwsLCATCYTHYlec/XqVfTu3Rvnz59X2af+6vvEPelEf2CzUipPOJNOVEYMDAxw/vx52NjYwNDQEImJibC1tcW1a9fg4OCgbLpEYhgYGODChQuwtrbGnDlzcOHCBYSEhCA+Ph4eHh64c+eO6IhEasfExAQHDx5Eq1atREehUnz22WfQ0tLCpk2bYGtri9jYWOTk5GDKlClYvnw52rRpIzoikdphs1IqD+SiAxD9W5iYmCArK6vE+Llz5/DJJ58ISESvq1ChAgoLCwEAR44cQZcuXQAAZmZmyM/PFxmNSG2ZmprCzMxMdAx6i1OnTmHevHmoWrUq5HI55HI5WrdujcWLF8Pb21t0PCK1pK+vjxEjRiAqKkq5sm7JkiUwNzfnFhFSGyzSicrIgAEDMH36dNy5cwcymQwKhQLR0dGYOnUqhgwZIjqexmvdujUmT56M+fPnIzY2Fp6engCAlJQU1KxZU3A6IvU0f/58zJ49W3mDi9RLcXGxstN+lSpVcPv2bQAvu7pfuXJFZDSicsHe3h5Lly7FzZs3sWvXLtFxiJS43J2ojBQVFWHYsGEIDg6GJEnQ1tZGcXExBgwYgKCgIGhpaYmOqNEyMzMxbtw43LhxA97e3hg5ciQAwMfHB8XFxVi7dq3ghETqwdnZWWXveVpaGiRJgrW1NXR0dFSeGx8f/7Hj0WvatGmDKVOmoFevXhgwYAAePnyImTNnYuPGjYiLi8OFCxdERyQiovfAIp2ojF29ehXx8fFQKBRwdnZW6SBKRKTu5s6d+5ef6+fn9w8moT9z+PBhFBQUoE+fPrh69Sq6d++Oy5cvo3Llyvjpp5/QoUMH0RGJiOg9sEgnKiPz5s3D1KlToa+vrzL+5MkTLFu2DLNnzxaUjABAS0sLWVlZMDc3VxnPycmBubk5uyAT0b/CgwcPYGpqyk78RETlGPekE5WRuXPn4vHjxyXGCwsL/9bMFP0z3nY/8tmzZ6hQocJHTkNUPtja2iInJ6fEeG5uLmxtbQUkoj9jZmbGAp2IqJzjOelEZUSSpFLfGCUmJrI7skCv9prLZDIEBAQomywBL5sunThxAg4ODqLiEam1a9eulbrK5NmzZ7h586aARNSnTx8EBQXByMgIffr0eedzQ0NDP1IqIiIqSyzSiT7Qq2WFMpkMdevWVSnUi4uL8fjxY4wZM0ZgQs22atUqAC9vovj7+6s08KtQoQKsra3h7+8vKh6RWtq3b5/y88OHD8PY2Fj5uLi4GBEREbCxsRERTeMZGxsr/868/n0hIqJ/D+5JJ/pAW7duhSRJGDFiBFavXq3ypulVEdiiRQuBCQkA2rdvj7CwMJiYmIiOQqT25PKXu+FkMlmJrSI6OjqwtrbGihUr0L17dxHxiIiI/tU4k070gYYOHQoAsLGxQcuWLUscUUTiFRUV4fr167h9+zaLdKK/QKFQAHj5e+3MmTOoUqWK4ERUmoyMDLx48aLEKSKpqanKmylERFT+sEgnKiM2NjbIysp66/VatWp9xDT0Oh0dHTx79ozNlIj+poyMDNER6B2GDRuGESNGlCjST58+jYCAABw7dkxMMCIi+iBc7k5URuRy+TuLQB7xJdaSJUtw+fJlBAQEQFub9yeJ/qrjx49j+fLlSE5OhkwmQ7169eDr64s2bdqIjqbxjIyMEB8fDzs7O5XxtLQ0uLq6Ijc3V0wwIiL6IHynSlRGzp07p/K4qKgI586dw8qVK7Fw4UJBqeiV06dPIyIiAuHh4XByckKlSpVUrrMLMlFJP/74I4YPH44+ffrA29sbkiTh999/R8eOHREUFIQBAwaIjqjRZDIZHj16VGI8Ly+PN4aJiMoxzqQT/cMOHjyIZcuWcdmhYMOHD3/n9S1btnykJETlR7169TB69Gj4+PiojK9cuRKbNm1CcnKyoGQEAN27d4e+vj527dqlPLmiuLgY/fv3R0FBAf73v/8JTkhERO+DRTrRPyw1NRWNGzdGQUGB6ChERH9LxYoVcfHixVKXUzdo0ABPnz4VlIwA4NKlS3Bzc4OJiYly+8HJkyeRn5+PyMhINGjQQHBCIiJ6H3LRAYj+LfLz81U+8vLycPnyZcyaNatEUx8iovLA0tISERERJcYjIiJgaWkpIBG9ztHREUlJSejXrx+ys7Px6NEjDBkyBJcvX2aBTkRUjnFPOlEZMTExKdE4TpIkWFpaIjg4WFAqel1ISAh2796NzMxMPH/+XOVafHy8oFRE6mvKlCnw9vZGQkICWrZsCZlMhqioKAQFBWHNmjWi4xGAGjVqYNGiRaJjEBFRGWKRTlRGjh49qvJYLpejatWqsLOzYzdxNbB27Vp8++23GDp0KH755RcMHz4c6enpOHPmDMaPHy86HpFaGjt2LKpXr44VK1Zg9+7dAF7uU//pp5/Qs2dPwekIAHJzc7F582Zl931HR0eMGDECxsbGoqMREdF74p50ItIIDg4O8PPzw5dffglDQ0MkJibC1tYWs2fPxoMHD/D999+LjkhE9LecPXsWXbt2hZ6eHj799FNIkoSzZ8/iyZMnCA8Ph4uLi+iIRET0HlikE5WhK1euYN26dcoZDQcHB0yYMAEODg6io2k8fX19JCcnw8rKCubm5vjtt9/QqFEjpKamonnz5sjJyREdkUhtPX/+HNnZ2VAoFCrjtWrVEpSIAKBNmzaws7PDpk2blCu2Xrx4gVGjRuHq1as4ceKE4IRERPQ+2DiOqIyEhISgQYMGiIuLQ6NGjdCwYUPEx8fDyckJP//8s+h4Gq969erKQtzKygoxMTEAgIyMDPBeJVHpUlNT0aZNG+jp6cHKygo2NjawsbGBtbU1bGxsRMfTeGfPnsX06dNVtlRpa2tj2rRpOHv2rMBkRET0IbhRlqiMTJs2DTNmzMC8efNUxv38/DB9+nT07dtXUDICgA4dOmD//v1wcXHByJEj4ePjg5CQEJw9exZ9+vQRHY9ILQ0bNgza2to4cOAALCwsSjTHJLGMjIyQmZlZYrXWjRs3YGhoKCgVERF9KC53Jyoj+vr6SEpKKnGecGpqKho1aoTCwkJByQgAFAoFFAqFcsZp9+7diIqKgp2dHcaMGYMKFSoITkikfipVqoS4uDhu2VFT3t7eCAsLw/Lly1W67/v6+sLLywurV68WHZGIiN4DZ9KJyki7du1w8uTJEkV6VFQU2rRpIygVvSKXyyGX/7HDp1+/fujXr5/ARETqz9HREffv3xcdg95i+fLlkMlkGDJkCF68eAEA0NHRwdixY7FkyRLB6YiI6H1xJp2ojPj7+2P27Nno168fmjdvDgCIiYnBzz//jLlz56JGjRrK5/bo0UNUTCKivywyMhIzZ87EokWL4OTkBB0dHZXrRkZGgpLR6woLC5Geng5JkmBnZwd9fX3RkYiI6AOwSCcqI6/P0r6LTCZDcXHxP5yGiOjDvfq99uZedEmS+LtMDeXn5yMyMhL29vaoV6+e6DhERPSeuNydqIy8eTQREVF5d/ToUdER6B369esHNzc3TJgwAU+ePIGrqyuuXbsGSZIQHBwMLy8v0RGJiOg9sEgnIiKiUrVt2/at1xISEj5eECrViRMn8O233wIAwsLCIEkScnNzsXXrVixYsIBFOhFROcUinagMxcbG4tixY8jOzi4xs75y5UpBqeiVFy9e4NixY0hPT8eAAQNgaGiI27dvw8jICAYGBqLjEam9vLw87NixAwEBAUhMTORyd8Hy8vJgZmYGADh06BC8vLygr68PT09P+Pr6Ck5HRETvi0U6URlZtGgRZs6cCXt7e1SrVk1lDyfPFhbv+vXrcHd3R2ZmJp49e4bOnTvD0NAQS5cuxdOnT+Hv7y86IpHaioyMRGBgIEJDQ2FlZQUvLy9s3rxZdCyNZ2lpiVOnTsHMzAyHDh1CcHAwAODhw4fQ1dUVnI6IiN4Xi3SiMrJmzRoEBgZi2LBhoqNQKSZOnAhXV1ckJiaicuXKyvHevXtj1KhRApMRqaebN28iKCgIgYGBKCgoQL9+/VBUVIQ9e/bA0dFRdDwCMGnSJAwcOBAGBgawsrJCu3btALxcBu/k5CQ2HBERvTcW6URlRC6Xo1WrVqJj0FtERUUhOjoaFSpUUBm3srLCrVu3BKUiUk8eHh6IiopC9+7dsW7dOri7u0NLS4srTtTMuHHj0KxZM2RmZqJz587Kbvy2trZYsGCB4HRERPS+WKQTlREfHx+sX78eq1evFh2FSqFQKErdP3vz5k0YGhoKSESkvsLDw+Ht7Y2xY8eiTp06ouPQOzRp0gRNmjRRGfP09BSUhoiIygKLdKIyMnXqVHh6eqJ27dpwdHSEjo6OyvXQ0FBByQgAOnfujNWrV2Pjxo0AXvYJePz4Mfz8/ODh4SE4HZF6OXnyJAIDA+Hq6goHBwcMHjwY/fv3Fx2LiIhII8gkSZJEhyD6Nxg/fjw2b96M9u3bl2gcBwBbtmwRlIwA4Pbt22jfvj20tLSQmpoKV1dXpKamokqVKjhx4gTMzc1FRyRSO4WFhQgODkZgYCBiY2NRXFyMlStXYsSIEVyBQkRE9A9hkU5URgwNDREcHMxlhmrsyZMn2LVrF+Lj46FQKODi4oKBAwdCT09PdDQitXflyhVs3rwZ27dvR25uLjp37ox9+/aJjkVERPSvwyKdqIxYWVnh8OHDcHBwEB2FiOgfU1xcjP379yMwMJBFuhpLSEhA48aNRccgIqL3wCKdqIxs2bIFhw4dwpYtW6Cvry86DpUiJSUFx44dQ3Z2NhQKhcq12bNnC0pFRFQ28vLysGPHDgQEBCAxMbHUZplERKT+WKQTlRFnZ2ekp6dDkiRYW1uXaBwXHx8vKBkBwKZNmzB27FhUqVIF1atXV+kZIJPJ+P0honIrMjISgYGBCA0NhZWVFby8vODl5QVnZ2fR0YiI6D2wuztRGenVq5foCPQOCxYswMKFCzF9+nTRUYiIPtjNmzcRFBSEwMBAFBQUoF+/figqKsKePXvg6OgoOh4REX0AzqQTkUYwMjJCQkICbG1tRUchIvogHh4eiIqKQvfu3TFw4EC4u7tDS0sLOjo6SExMZJFORFTOcSadqIzFxcUhOTkZMpkMjo6OXG6oJvr27Yvw8HCMGTNGdBQiog8SHh4Ob29vjB07FnXq1BEdh4iIyhiLdKIykp2djS+++ALHjh2DiYkJJElCXl4e2rdvj+DgYFStWlV0RI1mZ2eHWbNmISYmBk5OTiV6Bnh7ewtKRkT095w8eRKBgYFwdXWFg4MDBg8ejP79+4uORUREZYTL3YnKSP/+/ZGeno7t27ejXr16AIBLly5h6NChsLOzw65duwQn1Gw2NjZvvSaTyXD16tWPmIaI6MMVFhYiODgYgYGBiI2NRXFxMVauXIkRI0bA0NBQdDwiInpPLNKJyoixsTGOHDmCpk2bqozHxsaiS5cuyM3NFROMiIj+9a5cuYLNmzdj+/btyM3NRefOnXmOPRFROSUXHYDo30KhUJRYQg0AOjo6Jc7kJiIiKkv29vZYunQpbt68yZVbRETlHGfSicpIz549kZubi127dqFGjRoAgFu3bmHgwIEwNTVFWFiY4ISaZ/LkyZg/fz4qVaqEyZMnv/O5K1eu/EipiIiIiIjejo3jiMrI999/j549e8La2hqWlpaQyWTIzMyEk5MTfvzxR9HxNNK5c+dQVFSk/PxtZDLZx4pERERERPROnEknKmO//fYbLl++DEmS4OjoiE6dOomOpNGuXr0KGxsbFuJEREREVC6wSCeifzUtLS1kZWXB3NwcwMsu/GvXrkW1atUEJyMiIiIiKomN44g+UGRkJBwdHZGfn1/iWl5eHurXr4+TJ08KSEYA8OZ9yF9//RUFBQWC0hARERERvRuLdKIPtHr1avznP/+BkZFRiWvGxsb46quv2JSMiIiIiIj+EhbpRB8oMTER7u7ub73epUsXxMXFfcRE9DqZTFZiPzr3pxMRERGRumJ3d6IPdPfu3VLPR39FW1sb9+7d+4iJ6HWSJGHYsGGoWLEiAODp06cYM2YMKlWqpPK80NBQEfGIiIiIiFSwSCf6QJ988gnOnz8POzu7Uq8nJSXBwsLiI6eiV4YOHaryeNCgQYKSEBERERH9OXZ3J/pAX3/9NY4dO4YzZ85AV1dX5dqTJ0/w6aefon379li7dq2ghEREREREVF6wSCf6QHfv3oWLiwu0tLQwYcIE2NvbQyaTITk5GevXr0dxcTHi4+N55BcREREREf0pFulEZeD69esYO3YsDh8+rDzySyaToWvXrvjhhx9gbW0tNiAREREREZULLNKJytDDhw+RlpYGSZJQp04dmJqaio5ERERERETlCIt0IiIiIiIiIjXBc9KJiIiIiIiI1ASLdCIiIiIiIiI1wSKdiIiIiIiISE2wSCciIiIiIiJSEyzSiYiIypF27dph0qRJomP8bcOGDUOvXr3+0X9DJpNh7969/+i/QURE9E9jkU5ERFTG3lZI7927FzKZ7OMHUkPDhg2DTCbDmDFjSlwbN24cZDIZhg0b9rdeMysrC926dSujhERERGKwSCciItIgxcXFUCgU7/W1RUVFZZrF0tISwcHBePLkiXLs6dOn2LVrF2rVqvW3X6969eqoWLHie+d5/vz5e38tERFRWWGRTkREJMCcOXPQuHFjbN++HdbW1jA2NsYXX3yBR48eKZ9TUFCAIUOGwMDAABYWFlixYkWJ13n+/DmmTZuGTz75BJUqVUKzZs1w7Ngx5fWgoCCYmJjgwIEDcHR0RMWKFXH9+nVkZWXB09MTenp6sLGxwc6dO2FtbY3Vq1crv1Ymk8Hf3x89e/ZEpUqVsGDBAhQXF2PkyJGwsbGBnp4e7O3tsWbNGpVMxcXFmDx5MkxMTFC5cmVMmzYNkiSVyO7i4oJatWohNDRUORYaGgpLS0s4OzurPPfQoUNo3bq18jW7d++O9PR0lee8udz9/Pnz6NChA/T09FC5cmWMHj0ajx8/Vl5/tQR/8eLFqFGjBurWrVv6N4uIiOgjYpFOREQkSHp6Ovbu3YsDBw7gwIEDOH78OJYsWaK87uvri6NHjyIsLAzh4eE4duwY4uLiVF5j+PDhiI6ORnBwMJKSktC3b1+4u7sjNTVV+ZzCwkIsXrwYAQEBuHjxIszNzTFkyBDcvn0bx44dw549e7Bx40ZkZ2eXyOjn54eePXvi/PnzGDFiBBQKBWrWrIndu3fj0qVLmD17Nv7v//4Pu3fvVn7NihUrEBgYiM2bNyMqKgoPHjxAWFhYqf8bDB8+HFu2bFE+DgwMxIgRI0o8r6CgAJMnT8aZM2cQEREBuVyO3r17v3VVQGFhIdzd3WFqaoozZ87g559/xpEjRzBhwgSV50VERCA5ORm//fYbDhw4UOprERERfVQSERERlam2bdtKEydOLDEeFhYmvfrT6+fnJ+nr60v5+fnK676+vlKzZs0kSZKkR48eSRUqVJCCg4OV13NyciQ9PT3la6elpUkymUy6deuWyr/TsWNHacaMGZIkSdKWLVskAFJCQoLyenJysgRAOnPmjHIsNTVVAiCtWrVKOQZAmjRp0p/+944bN07y8vJSPrawsJCWLFmifFxUVCTVrFlT6tmzp3Js6NChUs+ePaV79+5JFStWlDIyMqRr165Jurq60r1796SePXtKQ4cOfeu/mZ2dLQGQzp8/r5I3LCxMkiRJ2rhxo2Rqaio9fvxYef3gwYOSXC6X7ty5o8xQrVo16dmzZ3/630hERPSxaAu8P0BERKTRrK2tYWhoqHxsYWGhnM1OT0/H8+fP0aJFC+V1MzMz2NvbKx/Hx8dDkqQSy7SfPXuGypUrKx9XqFABDRs2VD6+cuUKtLW14eLiohyzs7ODqalpiYyurq4lxvz9/REQEIDr16/jyZMneP78ORo3bgwAyMvLQ1ZWlkpubW1tuLq6lrrkvUqVKvD09MTWrVshSRI8PT1RpUqVEs9LT0/HrFmzEBMTg/v37ytn0DMzM9GgQYMSz09OTkajRo1QqVIl5VirVq2gUChw5coVVKtWDQDg5OSEChUqlPh6IiIiUVikExERlTEjIyPk5eWVGM/NzYWRkZHysY6Ojsp1mUymLD5LK2jfpFAooKWlhbi4OGhpaalcMzAwUH6up6en0lX+ba9d2vjrRS4A7N69Gz4+PlixYgVatGgBQ0NDLFu2DKdPn/7TvG8zYsQI5TL09evXl/qczz77DJaWlti0aRNq1KgBhUKBBg0avLXZmyRJb+2k//r4m/99REREonFPOhERURlzcHDA2bNnS4yfOXNGZSb8Xezs7KCjo4OYmBjl2MOHD5GSkqJ87OzsjOLiYmRnZ8POzk7lo3r16u/M9+LFC5w7d045lpaWhtzc3D/NdfLkSbRs2RLjxo2Ds7Mz7OzsVBq4GRsbw8LCQiX3ixcvSuylf527uzueP3+O58+fo2vXriWu5+TkIDk5GTNnzkTHjh1Rr149PHz48J05HR0dkZCQgIKCAuVYdHQ05HI5G8QREZFaY5FORERUxsaNG4f09HSMHz8eiYmJSElJwfr167F582b4+vr+pdcwMDDAyJEj4evri4iICFy4cAHDhg2DXP7Hn+66deti4MCBGDJkCEJDQ5GRkYEzZ87gu+++w6+//vrW13ZwcECnTp0wevRoxMbG4ty5cxg9enSJGffS2NnZ4ezZszh8+DBSUlIwa9YsnDlzRuU5EydOxJIlSxAWFobLly9j3Lhx77wBoKWlheTkZCQnJ5dYEQAApqamqFy5MjZu3Ii0tDRERkZi8uTJ78w5cOBA6OrqYujQobhw4QKOHj2Kr7/+GoMHD1YudSciIlJHLNKJiIjKmLW1NU6ePIn09HR06dIFTZs2RVBQEIKCgtC3b9+//DrLli2Dm5sbevTogU6dOqF169Zo0qSJynO2bNmCIUOGYMqUKbC3t0ePHj1w+vRpWFpavvO1t23bhmrVqsHNzQ29e/fGf/7zHxgaGkJXV/edXzdmzBj06dMH/fv3R7NmzZCTk4Nx48apPGfKlCkYMmQIhg0bplwS37t373e+rpGRkcpWgNfJ5XIEBwcjLi4ODRo0gI+PD5YtW/bO19PX18fhw4fx4MEDNG3aFJ9//jk6duyI77///p1fR0REJJpM+iub3oiIiOhf7ebNm7C0tMSRI0fQsWNH0XH+tmfPnkFXVxe//fYbOnXqJDoOERHRe2PjOCIiIg0UGRmJx48fw8nJCVlZWZg2bRqsra3h5uYmOtrflp+fj9DQUMjlcjg4OIiOQ0RE9EFYpBMREWmgoqIi/N///R+uXr0KQ0NDtGzZEjt27CjRcb488PPzw86dO/Hdd9+hZs2aouMQERF9EC53JyIiIiIiIlITbBxHREREREREpCZYpBMRERERERGpCRbpRERERERERGqCRToRERERERGRmmCRTkRERERERKQmWKQTERERERERqQkW6URERERERERqgkU6ERERERERkZr4fzI0RvcWWoLPAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.boxplot(x='UndergradMajor', y='NetSalary', data=df2)\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.title('NetSalary by UndergradMajor')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.barplot(x='UndergradMajor', y='NetSalary', data=df2, estimator=np.mean)\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.title('Mean NetSalary by UndergradMajor')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1asRYruSJH3/88YV7rAAgvmTNmlV9+vRRnTp11LJly+du9+GHHyosLExRUVEqXrx4jEvevHklyfbh5IIFC+xuv3jxYrvZrmNjsVhktVpj7Kn98ccfX7jHNK60adNG+fLlU//+/RUdHW133cs+funxntnY9haGhITE+nPv3r2rCxcu2Ardv3ke4vI5zJw5sxo1aqRp06ZpxowZqlOnzistzffse2DXrl06f/687T3yxEcffaQcOXKoV69e+v3339WpU6eXKl8ffPCBJGnRokUxrlu8eLHdNq/iVd7DH374oY4cOaLcuXPH+r54tqQ/K0WKFCpevLhWrlyphw8f2sbv3r2rNWvWvHRmi8WixIkTy8nJyTZ2//59zZs377Uf27NcXFw0aNAg1alTR5999tkLs0iyew9arVb98MMP//g4qlSpIkmaP3++3fiyZcsUHh5uux4A8JhD70l/kTNnzmjhwoW6ePGi7T/j3r17a926dZo9e7ZGjRplt31ERIQWLFhgt4cDAIw0ZsyYf9zmk08+0YIFC1SrVi11795dJUuWVJIkSXTx4kVt3rxZ9erV00cffaR8+fLp008/1aRJk5QkSRJ98MEHOnLkiMaPH/+P66G7ubmpfPnyGjdunNKlSydPT09t3bpVM2fOVKpUqeLo0T6fk5OTRo0apY8++kjS/5+rLL3845ckHx8f/fzzz1q0aJFy5cqlZMmSycfHRyNHjtTOnTvVpEkTFS5cWC4uLgoKCtKUKVMUFhamcePG/evnIa6fw+7du+u9996TJM2ePfuVbhsYGKi2bduqUaNGunDhggYOHKisWbOqU6dOdts5OTmpc+fO6tevn1KkSBHjfOTnqVSpkurWravu3bvr3LlzqlChgqxWq7Zt26aJEyeqbt26MT4QeBmv8h4eNmyYAgICVKZMGXXr1k158+bVgwcPdO7cOa1du1YzZsxQtmzZXvjzhg0bptq1a6t69erq3r27oqKiNG7cOKVMmfKljyCpXbu2JkyYoGbNmql9+/YKCwvT+PHjY3xY82/+fUpSz5491bNnzxduU7VqVSVNmlRNmzZV37599eDBA02fPv2lVneoWrWqqlevrn79+un27dsqW7asDh8+rMGDB6tIkSJq0aLFP94HADgSSvpz7N+/X1arNcbEJREREbGe37V8+XLduXNHvr6+8RURAP41JycnrVq1St98843mzZun0aNHK3HixMqWLZsqVKggHx8f27YzZ85UxowZNWfOHE2ePFmFCxfWsmXL9Mknn/zjz/npp5/UvXt39e3bV5GRkSpbtqwCAgJUu3btN/nwbOrXr68yZcpo165dduOv8viHDh2qkJAQtWvXTnfu3JGHh4fOnTtnKxg///yzxo0bp1u3bilNmjQqVqyY1q5da3da1b95HuLyOSxZsqRtvfVX3Ys5c+ZMzZs3T5988okiIiJUqVIlffPNNzEOTZekJk2aqF+/fmrRooXc3d1f+mcsXbpU48eP14IFC/TNN99Ikry8vDR06FD17t37lfI+m/1l3sOZM2dWYGCghg8frnHjxunixYtydXVVzpw5VaNGjVgneHtWjRo1tGzZMg0aNEhNmjRRpkyZ1KlTJ12+fDnGnvDnqVy5smbNmqWvvvpKderUUdasWdWuXTtlyJBBfn5+r/XYXte7776rZcuW6T//+Y9tablmzZqpZ8+esZ46+PRRE08m5x0yZIhmz56tkSNHKl26dGrRooVGjRr10vMhAICjsFitVqvRIczAYrFoxYoVql+/vqTHh9k1b95cR48etTvMTHp8Lvqzk7NUqVJFbm5utnVzAQAwq8OHD6tQoUKaOnVqjD3gcenbb79Vt27ddOTIEduka47s0aNHKly4sLJmzaoNGzYYHeeN+eijj3ThwgUFBgYaHQUAEiT2pD9HkSJFFBUVpatXr/7jOeZBQUHavHmzVq1aFU/pAAB4dWfOnNH58+f1xRdfKHPmzC99CPqrOnDggIKCgjRs2DDVq1fPYQu6n5+fqlatqsyZMys0NFQzZszQ8ePHbUcHvG2Cg4O1a9cubd68mUPYAeBfcOiSfvfuXZ0+fdr2fVBQkA4ePKg0adIoT548at68uXx9ffX111+rSJEiun79ujZt2iQfHx/VqlXLdrtZs2Ypc+bML5wpHgAAow0fPlzz5s1Tvnz5tGTJEiVPnvyN/JyPPvpIoaGhKleunEOveHLnzh317t1b165dU5IkSVS0aFGtXbv2tSa+SwhmzZqlSZMmqXLlyho8eLDRcQAgwXLow923bNmiSpUqxRhv2bKl5syZo0ePHmnEiBHy9/fXpUuXlDZtWpUuXVpDhw61nacYHR0tDw8P+fr6vnCZIwAAAAAA/olDl3QAAAAAAMyEddIBAAAAADAJhzsnPTo6WpcvX5arq6vd8iAAAAAAALwJVqtVd+7cUZYsWZQo0Yv3lTtcSb98+bKyZ89udAwAAAAAgIO5cOGCsmXL9sJtHK6ku7q6Snr85Li5uRmcBgAAAADwtrt9+7ayZ89u66Mv4nAl/ckh7m5ubpR0AAAAAEC8eZlTrpk4DgAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMIrHRAQAAAMxu5KcfGx3hrTFw/lKjIwCAqbEnHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJGFrSt23bpjp16ihLliyyWCxauXLlP95m69atKlasmJIlS6ZcuXJpxowZbz4oAAAAAADxwNCSHh4erkKFCmnKlCkvtX1QUJBq1aqlcuXK6cCBA/riiy/UrVs3LVu27A0nBQAAAADgzUts5A+vWbOmatas+dLbz5gxQzly5NCkSZMkSfny5VNgYKDGjx+vhg0bvqGUAAAAAADEjwR1Tvru3btVrVo1u7Hq1asrMDBQjx49ivU2ERERun37tt0FAAAAAAAzSlAlPTQ0VBkzZrQby5gxoyIjI3X9+vVYbzN69Gi5u7vbLtmzZ4+PqAAAAAAAvLIEVdIlyWKx2H1vtVpjHX9iwIABunXrlu1y4cKFN54RAAAAAIDXYeg56a8qU6ZMCg0NtRu7evWqEidOrLRp08Z6G2dnZzk7O8dHPAAAAAAA/pUEtSe9dOnSCggIsBvbsGGDihcvriRJkhiUCgAAAACAuGFoSb97964OHjyogwcPSnq8xNrBgwcVHBws6fGh6r6+vrbtO3bsqPPnz6tnz546fvy4Zs2apZkzZ6p3795GxAcAAAAAIE4Zerh7YGCgKlWqZPu+Z8+ekqSWLVtqzpw5CgkJsRV2ScqZM6fWrl2rHj16aOrUqcqSJYsmT57M8msAAAAAgLeCoSW9YsWKtonfYjNnzpwYYxUqVND+/fvfYCoAAAAAAIyRoM5JBwAAAADgbUZJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgEkYXtKnTZumnDlzKlmyZCpWrJi2b9/+wu0XLFigQoUKKXny5MqcObNat26tsLCweEoLAAAAAMCbY2hJX7RokT7//HMNHDhQBw4cULly5VSzZk0FBwfHuv2OHTvk6+srPz8/HT16VEuWLNHevXvVtm3beE4OAAAAAEDcM7SkT5gwQX5+fmrbtq3y5cunSZMmKXv27Jo+fXqs2//xxx/y9PRUt27dlDNnTr3//vvq0KGDAgMD4zk5AAAAAABxz7CS/vDhQ+3bt0/VqlWzG69WrZp27doV623KlCmjixcvau3atbJarbpy5YqWLl2q2rVrP/fnRERE6Pbt23YXAAAAAADMyLCSfv36dUVFRSljxox24xkzZlRoaGistylTpowWLFigJk2aKGnSpMqUKZNSpUqlb7/99rk/Z/To0XJ3d7ddsmfPHqePAwAAAACAuGL4xHEWi8Xue6vVGmPsiWPHjqlbt24aNGiQ9u3bp3Xr1ikoKEgdO3Z87v0PGDBAt27dsl0uXLgQp/kBAAAAAIgriY36wenSpZOTk1OMveZXr16NsXf9idGjR6ts2bLq06ePJKlgwYJKkSKFypUrpxEjRihz5swxbuPs7CxnZ+e4fwAAAAAAAMQxw/akJ02aVMWKFVNAQIDdeEBAgMqUKRPrbe7du6dEiewjOzk5SXq8Bx4AAAAAgITM0MPde/bsqR9//FGzZs3S8ePH1aNHDwUHB9sOXx8wYIB8fX1t29epU0fLly/X9OnTdfbsWe3cuVPdunVTyZIllSVLFqMeBgAAAAAAccKww90lqUmTJgoLC9OwYcMUEhKiAgUKaO3atfLw8JAkhYSE2K2Z3qpVK925c0dTpkxRr169lCpVKlWuXFlfffWVUQ8BAAAAAIA4Y7E62HHit2/flru7u27duiU3Nzej4wAAgARg5KcfGx3hrTFw/lKjIwBAvHuVHmr47O4AAAAAAOAxSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADCJ1yrprVq10rZt2+I6CwAAAAAADu21SvqdO3dUrVo1vfPOOxo1apQuXboU17kAAAAAAHA4r1XSly1bpkuXLqlLly5asmSJPD09VbNmTS1dulSPHj2K64wAAAAAADiE1z4nPW3atOrevbsOHDigPXv2yMvLSy1atFCWLFnUo0cPnTp1Ki5zAgAAAADw1vvXE8eFhIRow4YN2rBhg5ycnFSrVi0dPXpU3t7emjhxYlxkBAAAAADAIbxWSX/06JGWLVumDz/8UB4eHlqyZIl69OihkJAQzZ07Vxs2bNC8efM0bNiwuM4LAAAAAMBbK/Hr3Chz5syKjo5W06ZNtWfPHhUuXDjGNtWrV1eqVKn+ZTwAAAAAABzHa5X0CRMmqHHjxkqWLNlzt0mdOrWCgoJeOxgAAAAAAI7mlQ93j4yMVJs2bXT69Ok3kQcAAAAAAIf1yiU9ceLE8vDwUFRU1JvIAwAAAACAw3qtieP+85//aMCAAbpx40Zc5wEAAAAAwGG91jnpkydP1unTp5UlSxZ5eHgoRYoUdtfv378/TsIBAAAAAOBIXquk169fP45jAAAAAACA1yrpgwcPjrMA06ZN07hx4xQSEqL8+fNr0qRJKleu3HO3j4iI0LBhwzR//nyFhoYqW7ZsGjhwoNq0aRNnmQAAAAAAMMJrlfS4smjRIn3++eeaNm2aypYtq++++041a9bUsWPHlCNHjlhv07hxY125ckUzZ86Ul5eXrl69qsjIyHhODgAAAABA3Hutkh4VFaWJEydq8eLFCg4O1sOHD+2uf9kJ5SZMmCA/Pz+1bdtWkjRp0iStX79e06dP1+jRo2Nsv27dOm3dulVnz55VmjRpJEmenp6v8xAAAAAAADCd15rdfejQoZowYYIaN26sW7duqWfPnmrQoIESJUqkIUOGvNR9PHz4UPv27VO1atXsxqtVq6Zdu3bFeptVq1apePHiGjt2rLJmzao8efKod+/eun///nN/TkREhG7fvm13AQAAAADAjF6rpC9YsEA//PCDevfurcSJE6tp06b68ccfNWjQIP3xxx8vdR/Xr19XVFSUMmbMaDeeMWNGhYaGxnqbs2fPaseOHTpy5IhWrFihSZMmaenSpercufNzf87o0aPl7u5uu2TPnv3lHygAAAAAAPHotUp6aGiofHx8JEkpU6bUrVu3JEkffvihfv3111e6L4vFYve91WqNMfZEdHS0LBaLFixYoJIlS6pWrVqaMGGC5syZ89y96QMGDNCtW7dslwsXLrxSPgAAAAAA4strlfRs2bIpJCREkuTl5aUNGzZIkvbu3StnZ+eXuo906dLJyckpxl7zq1evxti7/kTmzJmVNWtWubu728by5csnq9WqixcvxnobZ2dnubm52V0AAAAAADCj1yrpH330kTZu3ChJ6t69u7788ku988478vX1feml0JImTapixYopICDAbjwgIEBlypSJ9TZly5bV5cuXdffuXdvYyZMnlShRImXLlu11HgoAAAAAAKbxWrO7jxkzxvb1xx9/rGzZsmnXrl3y8vJS3bp1X/p+evbsqRYtWqh48eIqXbq0vv/+ewUHB6tjx46SHh+qfunSJfn7+0uSmjVrpuHDh6t169YaOnSorl+/rj59+qhNmzZycXF5nYcCAAAAAIBpxMk66aVKlVKpUqVe+XZNmjRRWFiYhg0bppCQEBUoUEBr166Vh4eHJCkkJETBwcG27VOmTKmAgAB17dpVxYsXV9q0adW4cWONGDEiLh4GAAAAAACGslitVuvLbLhq1aqXvtNX2Zse327fvi13d3fdunWL89MBAMBLGfnpx0ZHeGsMnL/U6AgAEO9epYe+9J70+vXrv9R2FotFUVFRL3u3AAAAAADgf166pEdHR7/JHAAAAAAAOLzXmt0dAAAAAADEvdeeOC48PFxbt25VcHCwHj58aHddt27d/nUwAAAAAAAczWuV9AMHDqhWrVq6d++ewsPDlSZNGl2/fl3JkydXhgwZKOkAAAAAALyG1zrcvUePHqpTp45u3LghFxcX/fHHHzp//ryKFSum8ePHx3VGAAAAAAAcwmuV9IMHD6pXr15ycnKSk5OTIiIilD17do0dO1ZffPFFXGcEAAAAAMAhvFZJT5IkiSwWiyQpY8aMCg4OliS5u7vbvgYAAAAAAK/mtc5JL1KkiAIDA5UnTx5VqlRJgwYN0vXr1zVv3jz5+PjEdUYAAAAAABzCa+1JHzVqlDJnzixJGj58uNKmTavPPvtMV69e1ffffx+nAQEAAAAAcBSvtSe9ePHitq/Tp0+vtWvXxlkgAAAAAAAc1Wuvk/60rVu36t69eypVqpRSp04dF3cJAAAAAIDDeaWSPm7cON29e1dDhw6VJFmtVtWsWVMbNmyQJGXIkEEbN25U/vz54z4pAAAAAABvuVc6J33hwoXy9va2fb906VJt27ZN27dv1/Xr11W8eHFbgQcAAAAAAK/mlUp6UFCQChYsaPt+7dq1atiwocqWLas0adLoP//5j3bv3h3nIQEAAAAAcASvVNIfPXokZ2dn2/e7d+9WmTJlbN9nyZJF169fj7t0AAAAAAA4kFcq6V5eXtq2bZskKTg4WCdPnlSFChVs11+8eFFp06aN24QAAAAAADiIV5o47rPPPlOXLl20fft2/fHHHypdurTdOeqbNm1SkSJF4jwkAAAAAACO4JVKeocOHZQ4cWKtWbNG5cuX1+DBg+2uv3z5slq3bh2nAQEAAAAAcBSvvE66n5+f/Pz8Yr1u2rRp/zoQAAAAAACO6pXOSX/CyclJV69ejTEeFhYmJyenfx0KAAAAAABH9Fol3Wq1xjoeERGhpEmT/qtAAAAAAAA4qlc63H3y5MmSJIvFoh9//FEpU6a0XRcVFaVt27bp3XffjduEAAAAAAA4iFcq6RMnTpT0eE/6jBkz7A5tT5o0qTw9PTVjxoy4TQgAAAAAgIN4pZIeFBQkSapUqZKWL1+u1KlTv5FQAAAAAAA4otc6J33z5s1KnTq1Hj58qBMnTigyMjKucwEAAAAA4HBeq6Tfv39ffn5+Sp48ufLnz6/g4GBJUrdu3TRmzJg4DQgAAAAAgKN4rZLev39/HTp0SFu2bFGyZMls4x988IEWLVoUZ+EAAAAAAHAkr3RO+hMrV67UokWLVKpUKVksFtu4t7e3zpw5E2fhAAAAAABwJK+1J/3atWvKkCFDjPHw8HC70g4AAAAAAF7ea5X0EiVK6Ndff7V9/6SY//DDDypdunTcJAMAAAAAwMG81uHuo0ePVo0aNXTs2DFFRkbqm2++0dGjR7V7925t3bo1rjMCAAAAAOAQXmtPepkyZbRz507du3dPuXPn1oYNG5QxY0bt3r1bxYoVi+uMAAAAAAA4hNfaky5JPj4+mjt3blxmAQAAAADAob1SSU+UKNE/TgxnsVgUGRn5r0IBAAAAAOCIXqmkr1ix4rnX7dq1S99++62sVuu/DgUAAAAAgCN6pZJer169GGN//fWXBgwYoNWrV6t58+YaPnx4nIUDAAAAAMCRvNbEcZJ0+fJltWvXTgULFlRkZKQOHjyouXPnKkeOHHGZDwAAAAAAh/HKJf3WrVvq16+fvLy8dPToUW3cuFGrV69WgQIF3kQ+AAAAAAAcxisd7j527Fh99dVXypQpkxYuXBjr4e8AAAAAAOD1vFJJ79+/v1xcXOTl5aW5c+c+dwm25cuXx0k4AAAAAAAcySuVdF9f339cgg0AAAAAALyeVyrpc+bMeUMxAAAAAADAa8/uDgAAAAAA4hYlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMwvCSPm3aNOXMmVPJkiVTsWLFtH379pe63c6dO5U4cWIVLlz4zQYEAAAAACCeGFrSFy1apM8//1wDBw7UgQMHVK5cOdWsWVPBwcEvvN2tW7fk6+urKlWqxFNSAAAAAADePENL+oQJE+Tn56e2bdsqX758mjRpkrJnz67p06e/8HYdOnRQs2bNVLp06XhKCgAAAADAm2dYSX/48KH27dunatWq2Y1Xq1ZNu3bteu7tZs+erTNnzmjw4MEv9XMiIiJ0+/ZtuwsAAAAAAGZkWEm/fv26oqKilDFjRrvxjBkzKjQ0NNbbnDp1Sv3799eCBQuUOHHil/o5o0ePlru7u+2SPXv2f50dAAAAAIA3wfCJ4ywWi933Vqs1xpgkRUVFqVmzZho6dKjy5Mnz0vc/YMAA3bp1y3a5cOHCv84MAAAAAMCb8HK7o9+AdOnSycnJKcZe86tXr8bYuy5Jd+7cUWBgoA4cOKAuXbpIkqKjo2W1WpU4cWJt2LBBlStXjnE7Z2dnOTs7v5kHAQAAAABAHDJsT3rSpElVrFgxBQQE2I0HBASoTJkyMbZ3c3PTn3/+qYMHD9ouHTt2VN68eXXw4EG999578RUdAAAAAIA3wrA96ZLUs2dPtWjRQsWLF1fp0qX1/fffKzg4WB07dpT0+FD1S5cuyd/fX4kSJVKBAgXsbp8hQwYlS5YsxjgAAAAAAAmRoSW9SZMmCgsL07BhwxQSEqICBQpo7dq18vDwkCSFhIT845rpAAAAAAC8LSxWq9VqdIj4dPv2bbm7u+vWrVtyc3MzOg4AAEgARn76sdER3hoD5y81OgIAxLtX6aGGz+4OAAAAAAAeo6QDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYBCUdAAAAAACToKQDAAAAAGASlHQAAAAAAEyCkg4AAAAAgElQ0gEAAAAAMAlKOgAAAAAAJmF4SZ82bZpy5sypZMmSqVixYtq+fftzt12+fLmqVq2q9OnTy83NTaVLl9b69evjMS0AAAAAAG+OoSV90aJF+vzzzzVw4EAdOHBA5cqVU82aNRUcHBzr9tu2bVPVqlW1du1a7du3T5UqVVKdOnV04MCBeE4OAAAAAEDcs1itVqtRP/y9995T0aJFNX36dNtYvnz5VL9+fY0ePfql7iN//vxq0qSJBg0a9FLb3759W+7u7rp165bc3NxeKzcAAHAsIz/92OgIb42B85caHQEA4t2r9FDD9qQ/fPhQ+/btU7Vq1ezGq1Wrpl27dr3UfURHR+vOnTtKkybNc7eJiIjQ7du37S4AAAAAAJiRYSX9+vXrioqKUsaMGe3GM2bMqNDQ0Je6j6+//lrh4eFq3Ljxc7cZPXq03N3dbZfs2bP/q9wAAAAAALwphk8cZ7FY7L63Wq0xxmKzcOFCDRkyRIsWLVKGDBmeu92AAQN069Yt2+XChQv/OjMAAAAAAG9CYqN+cLp06eTk5BRjr/nVq1dj7F1/1qJFi+Tn56clS5bogw8+eOG2zs7OcnZ2/td5AQAAAAB40wzbk540aVIVK1ZMAQEBduMBAQEqU6bMc2+3cOFCtWrVSj/99JNq1679pmMCAAAAABBvDNuTLkk9e/ZUixYtVLx4cZUuXVrff/+9goOD1bFjR0mPD1W/dOmS/P39JT0u6L6+vvrmm29UqlQp2154FxcXubu7G/Y4AAAAAACIC4aW9CZNmigsLEzDhg1TSEiIChQooLVr18rDw0OSFBISYrdm+nfffafIyEh17txZnTt3to23bNlSc+bMie/4AAAAAADEKUPXSTcC66QDAIBXxTrpcYd10gE4ogSxTjoAAAAAALBHSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk0hsdAAAAGB+3bt317Vr1yRJ6dOn1zfffGNwIgAA3k6UdAAA8I+uXbumK1euGB0DAIC3Hoe7AwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEpR0AAAAAABMgpIOAAAAAIBJUNIBAAAAADAJSjoAAAAAACZBSQcAAAAAwCQo6QAAAAAAmAQlHQAAAAAAk6CkAwAAAABgEomNDoDX1717d127dk2SlD59en3zzTcGJwIAAAAA/BuU9ATs2rVrunLlitExAAAAAABxhJIOADANjhACAACOjpIOADANjhACAACOjonjAAAAAAAwCUo6AAAAAAAmQUkHAAAAAMAkOCcdABzM1vIVjI7wXA8SO0kWy+OvQ0NNnbXCtq1GRwAAAG8h9qQDAAAAAGASlHQAAAAAAEyCw90BAAAAvBFDhgwxOsJbg+fScbAnHQAAAAAAk6CkAwAAAABgEhzuDgAwDTerJFmf+hoAAMCxUNIBAKbROirK6AgAAACG4nB3AAAAAABMgpIOAAAAAIBJcLg7gDhX9tuyRkd4a+zsutPoCAAAAIhH7EkHAAAAAMAk2JMOvAHdu3fXtWvXJEnp06fXN998Y3AiAAAAAAkBJR14A65du6YrV64YHQMAAABAAsPh7gAAAAAAmAQlHQAAAAAAk+BwdyRYwcN8jI7wXJE300py+t/Xl02dVZJyDPrT6AgAAAAAZIKSPm3aNI0bN04hISHKnz+/Jk2apHLlyj13+61bt6pnz546evSosmTJor59+6pjx47xmBgAgDdjSq/VRkd4rjs37tl9beasktTl6zpGRwAAU1u8pKTREd4ajRvtidP7M7SkL1q0SJ9//rmmTZumsmXL6rvvvlPNmjV17Ngx5ciRI8b2QUFBqlWrltq1a6f58+dr586d6tSpk9KnT6+GDRu+kYzF+vi/kfuNC25/37WdrxDy911TZ903ztfoCACAfyFZUtdYvwYAAHHL0JI+YcIE+fn5qW3btpKkSZMmaf369Zo+fbpGjx4dY/sZM2YoR44cmjRpkiQpX758CgwM1Pjx459b0iMiIhQREWH7/tatW5Kk27dvv1TGqIj7r/KQ4tVDJVWiRI8kSdFKauqsL/t8v4o7D6Li/D7jSspEEXqUyOl/X0eZOqsU969P5P3IOL0/R/Ym/u2ER/L6xIU38drcj7j3zxsZpGSOD+2+N3NWKe5fnwePHsXp/TmyN/Fv58S4rXF+n44ob58KcX6fT/8djn8nrv/t3Ltn7r9PE5KXeW2ebGO1Wv/5Dq0GiYiIsDo5OVmXL19uN96tWzdr+fLlY71NuXLlrN26dbMbW758uTVx4sTWhw8fxnqbwYMHWyVx4cKFCxcuXLhw4cKFCxcuhl4uXLjwj13ZsD3p169fV1RUlDJmzGg3njFjRoWGhsZ6m9DQ0Fi3j4yM1PXr15U5c+YYtxkwYIB69uxp+z46Olo3btxQ2rRpZbFY4uCRGOv27dvKnj27Lly4IDc3N6Pj4Cm8NubG62NevDbmxWtjbrw+5sVrY168Nub2Nr0+VqtVd+7cUZYsWf5xW8Mnjnu2KFut1heW59i2j238CWdnZzk7O9uNpUqV6jWSmpubm1uCf+O+rXhtzI3Xx7x4bcyL18bceH3Mi9fGvHhtzO1teX3c3d1fajvD1klPly6dnJycYuw1v3r1aoy95U9kypQp1u0TJ06stGnTvrGsAAAAAADEB8NKetKkSVWsWDEFBATYjQcEBKhMmTKx3qZ06dIxtt+wYYOKFy+uJEmSvLGsAAAAAADEB8NKuiT17NlTP/74o2bNmqXjx4+rR48eCg4Otq17PmDAAPn6/v/SXR07dtT58+fVs2dPHT9+XLNmzdLMmTPVu3dvox6C4ZydnTV48OAYh/TDeLw25sbrY168NubFa2NuvD7mxWtjXrw25uaor4/Fan2ZOeDfnGnTpmns2LEKCQlRgQIFNHHiRJUvX16S1KpVK507d05btmyxbb9161b16NFDR48eVZYsWdSvXz9bqQcAAAAAICEzvKQDAAAAAIDHDD3cHQAAAAAA/D9KOgAAAAAAJkFJBwAAAADAJCjpAAAAAACYRGKjA+DVPXr0SKGhobp3757Sp0+vNGnSGB0JAPCWi4iIcLglcIB/Izo6WqdPn9bVq1cVHR1td92TlYxgLH6vwawo6QnE3bt3tWDBAi1cuFB79uxRRESE7bps2bKpWrVqat++vUqUKGFgSseWOnVqWSyWGOMWi0XJkiWTl5eXWrVqpdatWxuQDpJ04cIFnTt3zvYBV/78+fnP2QQiIiK0Z88eu9emSJEiypkzp9HRHNr69eu1cOFCbd++XcHBwYqOjlby5MlVtGhRVatWTa1bt1aWLFmMjumwbt26pRUrVmj79u0x/u1Ur15dZcqUMTqiQ/vjjz/UrFkznT9/Xs8upGSxWBQVFWVQMsfG7zXzW7dunVKmTKn3339fkjR16lT98MMP8vb21tSpU5U6dWqDE8YPlmBLACZOnKiRI0fK09NTdevWVcmSJZU1a1a5uLjoxo0bOnLkiLZv364VK1aoVKlS+vbbb/XOO+8YHdvhPHmdatasqZIlS8pqtWrv3r1at26devTooaCgIM2bN0/ffvut2rVrZ3Rch3H+/HnNmDFDCxcu1IULF+z+WEqaNKnKlSun9u3bq2HDhkqUiDOA4tOuXbv07bffauXKlXr48KFSpUpl+70WERGhXLlyqX379urYsaNcXV2NjuswVq5cqX79+unWrVuqVavWc//P2b17t1q1aqXhw4crffr0Rsd2GCEhIRo0aJAWLFigTJkyxfr67Nu3Tx4eHho8eLCaNGlidGSHVLhwYeXJk0dDhw5V5syZY3yI7+7ublAyx8TvtYTDx8dHX331lWrVqqU///xTJUqUUM+ePbVp0ybly5dPs2fPNjpi/LDC9D7++GPr4cOH/3G7Bw8eWKdOnWr94Ycf4iEVntWgQQPr9OnTY4zPmDHD2qBBA6vVarVOnjzZWqBAgfiO5rC6detmdXV1tTZs2NA6d+5c6/Hjx623b9+2Pnr0yHrlyhXrxo0brUOGDLHmzZvXmj9/fuuePXuMjuww6tata82cObO1V69e1q1bt1rDw8Ptrj9z5ox1zpw51urVq1szZcpk3bBhg0FJHU+JEiWsq1atskZFRb1wu4sXL1r79OljHT9+fDwlg9VqtaZPn97aq1cv659//vncbe7du2f96aefrCVLlrSOGzcuHtPhieTJk1tPnTpldAz8D7/XEo4UKVJYg4KCrFar1Tp48GBrw4YNrVar1bpv3z5rxowZDUwWv9iTDsSRlClT6uDBg/Ly8rIbP336tAoXLqy7d+/qzJkzKliwoMLDww1K6Vj69Omjvn37vtSn4WvXrtW9e/f08ccfx0MyTJ06Ve3atVPSpEn/cdujR4/q8uXLqlq1ajwkA8zt2rVrr7SH71W3R9yoXLmy+vbtqxo1ahgdBUhQ0qRJox07dsjb21vvv/++fH191b59e507d07e3t66d++e0RHjBeekJ3C3b9/Wpk2blDdvXuXLl8/oOA4tTZo0Wr16tXr06GE3vnr1atvkfuHh4Ry2G4/GjRv30tvWqlXrDSbBszp37vzS2+bPn1/58+d/g2mAhONVCzcF3Rhdu3ZVr169FBoaKh8fHyVJksTu+oIFCxqUDFu2bFHFihWNjoHneP/999WzZ0+VLVtWe/bs0aJFiyRJJ0+eVLZs2QxOF3/Yk57ANG7cWOXLl1eXLl10//59FSpUSOfOnZPVatXPP/+shg0bGh3RYf3www/67LPPbOc6WSwW7dmzR2vXrtWMGTPk5+enr7/+2u4XDuJP5cqVtXz5cqVKlcpu/Pbt26pfv742bdpkTDDAZHr27PnS206YMOENJsHLmDdvnmbMmKGgoCDt3r1bHh4emjRpknLmzKl69eoZHc9hxTbHicVikdVqZeI4gyVLlkxZs2ZV69at1bJlS2XPnt3oSHhKcHCwOnXqpAsXLqhbt27y8/OTJPXo0UNRUVGaPHmywQnjByU9gcmUKZPWr1+vQoUK6aefftLgwYN16NAhzZ07V99//70OHDhgdESHtnPnTk2ZMkUnTpyQ1WrVu+++q65duzLLrgkkSpRIoaGhypAhg9341atXlTVrVj169MigZGBlBHOpVKnSS21nsVj4cMtg06dP16BBg/T5559r5MiROnLkiHLlyqU5c+Zo7ty52rx5s9ERHdb58+dfeL2Hh0c8JcGzbty4ofnz52vOnDk6fPiwqlSpIj8/P9WvX/+lTsEC4gMlPYFxcXHRyZMnlT17dvn6+ipLliwaM2aMgoOD5e3trbt37xodETCVw4cPS3o80+6mTZtspx5IUlRUlNatW6fvvvtO586dMyghWBkBeD3e3t4aNWqU6tevL1dXVx06dEi5cuXSkSNHVLFiRV2/ft3oiICpHTx4ULNmzdLChQsVHR2t5s2by8/PT4UKFTI6msNau3atnJycVL16dbvxDRs2KCoqSjVr1jQoWfzinPQEJnv27Nq9e7fSpEmjdevW6eeff5Yk/f3330qWLJnB6RAdHa3Tp0/r6tWrio6OtruufPnyBqVybIULF5bFYpHFYlHlypVjXO/i4qJvv/3WgGR4YseOHRoxYoQ6duxoN/7dd99pw4YNWrZsmQoWLKjJkydT0oGnBAUFqUiRIjHGnZ2dmaDUYKtWrYp1/OkjhHLmzBnPqfCswoULq3///kqTJo3GjBmjWbNmadq0aSpdurRmzJjBfCgG6N+/v8aMGRNjPDo6Wv3796ekw5w+//xzNW/eXClTppSHh4dt4ott27bJx8fH2HAO7o8//lCzZs10/vx5PXuACuefGScoKEhWq1W5cuXSnj177CZRSpo0qTJkyCAnJycDE2L9+vX66quvYoxXqVJFvXr1kvR4Yr/+/fvHdzRI2rt3r5YsWaLg4GA9fPjQ7rrly5cblAqSlDNnTh08eDDGodO//fabvL29DUoFSapfv77tHPSnPX1e+vvvv6+VK1cqderUBqV0XI8ePdIvv/yiWbNmKSAgQMWLF9eUKVPUtGlT3bhxQ/369VOjRo107Ngxo6M6nFOnTsX6++vdd9/V6dOnDUhkjJizWsDUOnXqpN27d2vWrFnasWOHbWKSXLlyacSIEQanc2wdO3ZU8eLFdeTIEd24cUN///237XLjxg2j4zksDw8PZc2aVb6+vkqTJo08PDxsl8yZM1PQTeDJygjPYmUE4/38888qW7asjh07phUrVujRo0c6duyYNm3aJHd3d6PjObw+ffqoc+fOWrRokaxWq/bs2aORI0fqiy++UJ8+fYyO59ACAgJUokQJBQQE6NatW7p165YCAgJUsmRJrVmzRtu2bVNYWJh69+5tdFSH07VrV2XOnFkdO3ZUnjx5dODAAe3evVtt27ZVihQplD17do0ZM0Z//fWX0VEdkru7u86ePRtj/PTp00qRIoUBiYzBOelAHEmRIoUOHToUY510mEPq1Km1b98+5cqVy+goeAYrI5hXwYIF1aFDB3Xu3Nl2znPOnDnVoUMHZc6cWUOHDjU6osP74YcfNGLECF24cEGSlDVrVg0ZMsQ2IzKMUaBAAX3//fcxJo7duXOn2rdvr6NHj+r3339XmzZtFBwcbFBKx1SlShW1bdtWDRs2fO5EcZGRkdq5c6cqVKgQz+nQvn17/fHHH1qxYoVy584t6XFBb9iwoUqUKKEff/zR4ITxg5KewHz88ccqXrx4jMM+x40bpz179mjJkiUGJUPlypXVt29f1ahRw+goiEXr1q3l4+PzSstLIf6wMoI5pUiRQkePHpWnp6fSpUunzZs3y8fHR8ePH1flypUVEhJidET8z/Xr1xUdHR1jBQsYw8XFRXv37lWBAgXsxv/880+VLFlS9+/f1/nz55UvXz7du3fPoJSA+dy6dUs1atRQYGCgbV30ixcvqly5crEupfu24pz0BGbr1q0aPHhwjPEaNWpo/PjxBiTCE127dlWvXr0UGhoqHx8fJUmSxO76ggULGpQMkuTl5aXhw4dr165dKlasWIxDprp162ZQMkhS2bJlVbZsWaNj4Blp0qTRnTt3JD3eQ3vkyBH5+Pjo5s2bFAsTCAoKUmRkpN555x2lS5fONn7q1CklSZJEnp6exoVzcMWKFVOfPn3k7+9vmwvl2rVr6tu3r0qUKCHp8ev0pIQgfp08eVJbtmyJdaLfQYMGGZQK0uPD3Xft2qWAgAAdOnRILi4uKliwoMNNwMye9ATGxcVFBw8eVN68ee3G//rrLxUpUkT37983KBmezA/wtKcniGHiOGO9aBZdi8US6/lPiD+sjGBOzZo1U/HixdWzZ0+NHDlS33zzjerVq6eAgAAVLVqUieMMVqFCBbVp00YtW7a0G58/f75+/PFHbdmyxZhg0IkTJ1SvXj0FBQUpe/bsslgsCg4OVq5cufTLL78oT548Wrlype7cuaMWLVoYHdehPDnFKl26dMqUKZMsFovtOovFov379xuYDniMkp7AlChRQnXq1InxKd+QIUO0evVq7du3z6BkOH/+/Auvf3b2XQCPsTKCed24cUMPHjxQlixZFB0drfHjx2vHjh3y8vLSl19+yazUBnNzc9P+/ftjzIVy+vRpFS9eXDdv3jQmGCRJVqtV69ev18mTJ22n8VStWjXWD/URfzw8PNSpUyf169fP6Cj4n8mTJ6t9+/ZKliyZJk+e/MJtHeXIR0p6ArNq1So1bNhQzZo1s635vHHjRi1cuFBLlixR/fr1jQ0IAK+ocOHCypMnj4YOHarMmTPb7dWQxCziwHO4u7try5YtMdZK37dvnypWrGg7VQHA/3Nzc9PBgweZSNZEcubMqcDAQKVNm5YjH/+Hkp4A/frrrxo1apQOHjxoO09j8ODBzEBpgFWrVqlmzZpKkiSJVq1a9cJt69atG0+p8DwXL17UqlWrYl3vecKECQalAisjmNfatWvl5OSk6tWr241v2LBBUVFRqlmzpkHJIEkffvihkidProULF9qWk4yKilKTJk0UHh6u3377zeCEjoW9gQmDn5+fSpQooY4dOxodBXguSjrwLyRKlEihoaHKkCHDCw9f45Bd423cuFF169ZVzpw5deLECRUoUEDnzp2T1WpV0aJFtWnTJqMjOixWRjCvggULasyYMapVq5bd+Lp169SvXz8dOnTIoGSQpGPHjql8+fJKlSqVypUrJ0navn27bt++rU2bNsWYWRxvFnsDzevpD03Cw8M1YcIE1a5dO9aJfvkAxVjDhg1T7969lTx5crvx+/fva9y4cQ4zsR8lHYBDKFmypGrUqKFhw4bZ1nvOkCGDmjdvrho1auizzz4zOqLDWrFihf7zn/+oT58+rIxgMi4uLjp+/HiMWcLPnTun/PnzKzw83JhgsLl8+bKmTJliNwtyly5dlCZNGqOjAabxog9NnsYHKMZzcnJSSEhIjOUkw8LClCFDBofZ6UVJTwDSpEmjkydPKl26dEqdOnWM8zWfduPGjXhMhicePXqkatWq6bvvvlOePHmMjoNYuLq66uDBg8qdO7dSp06tHTt2KH/+/Dp06JDq1aunc+fOGR3RYbEygnllypRJP/30k20OlCd+//13NWvWTFevXjUoGZCwREVF6c8//5SHhwcTLgIvkChRIl25csW2dOETmzZtUpMmTXTt2jWDksUv1klPACZOnChXV1fb1y8q6TBGkiRJdOTIEV4bE0uRIoUiIiIkSVmyZNGZM2eUP39+SdL169eNjObwgoKCjI6A56hbt64+//xzrVixQrlz55b0eObwXr16Mc+GSdy8eVN79uyJdflCX19fg1Lh888/l4+Pj/z8/BQVFaXy5ctr9+7dSp48udasWaOKFSsaHRGSbUUR/n4z3pMdkRaLRXny5LF7TaKionT37l2HmkeAPelAHOnVq5eSJEmiMWPGGB0Fsahfv75q166tdu3aqW/fvlqxYoVatWql5cuXK3Xq1Pr999+NjgiYzq1bt1SjRg0FBgYqW7Zskh5PwFiuXDktX75cqVKlMjagg1u9erWaN2+u8PBwubq6xljvmaPrjJMtWzatXLlSxYsX18qVK9W5c2dt3rxZ/v7+2rx5s3bu3Gl0RIc2c+ZMTZw4UadOnZIkvfPOO/r888/Vtm1bg5M5rrlz58pqtapNmzaaNGmS3couSZMmlaenp0qXLm1gwvhFSU9g9u/fryRJksjHx0eS9Msvv2j27Nny9vbWkCFDlDRpUoMTOq6uXbvK399fXl5eKl68uFKkSGF3PbOHG+vs2bO6e/euChYsqHv37ql379629Z4nTpzIOvbxjJUREg6r1aqAgAC7c57Lly9vdCxIypMnj2rVqqVRo0bFmGQJxkqWLJlOnz6tbNmyqX379kqePLkmTZqkoKAgFSpUSLdv3zY6osP68ssvNXHiRHXt2tVW+nbv3q0pU6aoe/fuGjFihMEJHdvWrVtVtmxZJU7s2Ad8U9ITmBIlSqh///5q2LChzp49K29vbzVo0EB79+5V7dq1NWnSJKMjOqxKlSo99zqLxcLs4cBTWBkB+PdSpEihP//8k/WeTcjDw0M//PCDqlSpopw5c2ratGn68MMPdfToUb3//vv6+++/jY7osNKlS6dvv/1WTZs2tRtfuHChunbtyilwBmOH5GOO/RFFAnTy5EkVLlxYkrRkyRJVqFBBP/30k3bu3KlPPvmEkm6gzZs3Gx0BSDCePnf22fNoYSzWek44qlevrsDAQEq6CbVu3VqNGzdW5syZZbFYVLVqVUnSf//7X7377rsGp3NsUVFRKl68eIzxYsWKKTIy0oBEeFqHDh3Uv39/+fj46OzZs2rSpIkaNGigJUuW6N69ew7TddiTnsC4ublp3759euedd1S1alV9+OGH6t69u4KDg5U3b17dv3/f6IgO7/Tp0zpz5ozKly8vFxcX2wzVAGLn7++vJk2ayNnZ2W784cOH+vnnn5n8Kp6x1nPCMXPmTA0bNkytW7eOdflCThUx1tKlS3XhwgU1atTINqfD3LlzlSpVKtWrV8/gdI6ra9euSpIkSYzTEHv37q379+9r6tSpBiWDJLm7u2v//v3KnTu3vvrqK23atEnr16+37ZC8cOGC0RHjBSU9galcubKyZ8+uDz74QH5+fjp27Ji8vLy0detWtWzZkmWkDBQWFqbGjRtr8+bNslgsOnXqlHLlyiU/Pz+lSpVKX3/9tdERAVNiTVTg9XCqSMLw4MEDJUuWzOgY+J8ncwhlz55dpUqVkiT98ccfunDhgnx9fe0+7GI+ofjHDsnHnv/bHaY0ceJE7du3T126dNHAgQPl5eUl6fGntWXKlDE4nWPr0aOHkiRJouDgYLsJfJo0aaJ169YZmAwwt+cdbXLx4kW72V0B2IuOjn7uhYJurKioKA0fPlxZs2ZVypQpbUedfPnll5o5c6bB6RzbkSNHVLRoUaVPn15nzpzRmTNnlD59ehUtWlRHjhzRgQMHdODAAR08eNDoqA6pePHiGjFihObNm6etW7eqdu3akh4v15oxY0aD08UfzklPYAoVKqQjR47EGB83bpycnJwMSIQnNmzYoPXr19sOaXvinXfe0fnz5w1KhdiwLqo5FClSxLYmapUqVexmco2KilJQUJBq1KhhYEJERUVpzpw52rhxY6zrcDMhpnmwt9ZcRo4cqblz52rs2LFq166dbdzHx0cTJ06Un5+fgekcG3MImdukSZPUvHlzrVy50qF3SFLSE5hWrVqpTZs2MZa/4T9m44WHh8e6BM7169djnGsLY/j7+2vcuHG2dVHz5MmjPn36qEWLFgYnc0z169eXJB08eFDVq1dXypQpbdc9WRO1YcOGBqWDJHXv3l1z5sxR7dq1VaBAAT7YMpmoqCiNGjVKM2bM0JUrV3Ty5EnlypVLX375pTw9PSmCBvL399f333+vKlWqqGPHjrbxggUL6q+//jIwGZ528eJFWSwWZc2a1ego+J+CBQvqzz//jDHuaDskKekJzJ07d1StWjVlz55drVu3VsuWLfnFYhLly5eXv7+/hg8fLunxXtro6GiNGzfuhcuzIX5MmDBBX375pbp06aKyZcvKarVq586d6tixo65fv64ePXoYHdHhDB48WJLk6empJk2a8GGjCf38889avHixatWqZXQUxIK9teZ16dIl2x7Ap0VHR+vRo0cGJMIT0dHRGjFihL7++mvdvXtXkuTq6qpevXpp4MCBL5zrAcZxtL8RKOkJzLJlyxQWFqb58+drzpw5Gjx4sD744AO1adNG9evXjzGzK+LPuHHjVLFiRQUGBurhw4fq27evjh49qhs3bmjnzp1Gx3N43377raZPn243U3i9evWUP39+DRkyhJJuoJYtW+rmzZuaP3++zpw5oz59+ihNmjTav3+/MmbMyAeRBkqaNGmsRQPmwN5a88qfP7+2b98uDw8Pu/ElS5aoSJEiBqWCJA0cOFAzZ87UmDFj7D60HzJkiB48eKCRI0caHdHhpEmTRidPnlS6dOmUOnXqFx61dePGjXhMZhxKegKUNm1ade/eXd27d9eBAwc0a9Ys+fr6KmXKlPr000/VqVMnvfPOO0bHdDje3t46fPiwpk2bJicnJ4WHh6tBgwbq3LmzMmfObHQ8hxcSEhLruUxlypRRSEiIAYnwxOHDh/XBBx/I3d1d586dU7t27ZQmTRqtWLFC58+fl7+/v9ERHVavXr30zTffaMqUKRzqbkLsrTWvwYMHq0WLFrp06ZKio6O1fPlynThxQv7+/lqzZo3R8Rza3Llz9eOPP9otUVioUCFlzZpVnTp1oqQbYOLEiXJ1dbV9zf83lPQELSQkRBs2bNCGDRvk5OSkWrVq6ejRo/L29tbYsWPZM2iATJkyadiwYUbHQCy8vLy0ePFiffHFF3bjixYt4kMtg/Xo0UOtWrXS2LFjbf9JS1LNmjXVrFkzA5Nhx44d2rx5s3777Tflz58/xtFay5cvNygZJPbWmlmdOnW0aNEijRo1ShaLRYMGDVLRokW1evVqVa1a1eh4Du3GjRt69913Y4y/++67DrOX1mxatmxp+7pVq1bGBTERSnoC8+jRI61atUqzZ8/Whg0bVLBgQfXo0UPNmze3/XH7888/67PPPqOkG2D79u367rvvdPbsWS1ZskRZs2bVvHnzlDNnTr3//vtGx3NoQ4cOVZMmTbRt2zaVLVtWFotFO3bs0MaNG7V48WKj4zm0wMBAff/99zHGs2bNqtDQUAMS4YlUqVLpo48+MjoGnoO9teZWvXp1Va9e3egYeEahQoU0ZcoUTZ482W58ypQpKlSokEGp8ISTk5NCQkKUIUMGu/GwsDBlyJDBYZaXpKQnMJkzZ1Z0dLSaNm2qPXv2qHDhwjG2qV69ulKlShXv2RzdsmXL1KJFCzVv3lz79+9XRESEpMeT/Y0aNUpr1641OKFja9iwof773/9q4sSJWrlypaxWq7y9vbVnzx72OBksWbJkun37dozxEydOKH369AYkwhOzZ882OgJegL21wKsbO3asateurd9//12lS5eWxWLRrl27dOHCBf5WM4Eny+Q+KyIiQkmTJo3nNMaxWJ/3TMCU5s2bp0aNGjncDIcJQZEiRdSjRw/5+vrK1dVVhw4dUq5cuXTw4EHVqFGDPYLAc7Rv317Xrl3T4sWLlSZNGh0+fFhOTk6qX7++ypcvr0mTJhkdEQD+0T9NePU0Dqs21uXLlzV16lT99ddftg/tO3XqpCxZshgdzWE9ObKhR48eGj58uN2yrFFRUdq2bZvOnTunAwcOGBUxXlHSgTiSPHlyHTt2TJ6ennYl/ezZs/L29taDBw+Mjuhwbt++LTc3N9vXL/JkO8S/27dv2+bUuHPnjrJkyaLQ0FCVLl1aa9euVYoUKYyO6NCWLl2qxYsXKzg4WA8fPrS7bv/+/QalAsxn7ty5tq/DwsI0YsQIVa9eXaVLl5Yk7d69W+vXr9eXX37JKYnAM3LmzClJOn/+vLJly2a3JnrSpEnl6empYcOG6b333jMqYrzicPcEJjw8XGPGjNHGjRt19epVRUdH211/9uxZg5Ihc+bMOn36tDw9Pe3Gd+zYoVy5chkTysGlTp3adl5TqlSpYt3DYbVaZbFYHOYcJzNyc3PTjh07tGnTJu3fv1/R0dEqWrSoPvjgg+ce9ob4MXnyZA0cOFAtW7bUL7/8otatW+vMmTPau3evOnfubHQ8h8RSReb19ORXDRs21LBhw9SlSxfbWLdu3TRlyhT9/vvvlHQDrVu3TilTprTNFTR16lT98MMP8vb21tSpU5U6dWqDEzqmoKAgSVKlSpW0fPlyh38d2JOewDRt2lRbt25VixYtlDlz5hj/OXfv3t2gZBg7dqzmzp2rWbNmqWrVqlq7dq3Onz+vHj16aNCgQXb/USN+bN26VWXLllXixIm1devWF25boUKFeEqFZ40ePVoDBgyIMR4VFaVPP/1UCxcuNCAVpMezHQ8ePFhNmza1O0Jo0KBBunHjhqZMmWJ0RIczd+5cffLJJ3J2dtacOXNeWNKfLo2IXylTptTBgwdjLJF36tQpFSlSRHfv3jUoGXx8fPTVV1+pVq1a+vPPP1W8eHH16tVLmzZtUr58+ZiLA6ZASU9gUqVKpV9//VVly5Y1OgpiMXDgQE2cONF2aLuzs7N69+6t4cOHG5wMMK+MGTNq+PDhat++vW0sKipKn3zyiY4cOaLjx48bmM6xJU+eXMePH5eHh4cyZMiggIAAFSpUSKdOnVKpUqUUFhZmdETAlDw8PNSlSxf16dPHbnzcuHGaMmWKzp8/b1AypEyZUkeOHJGnp6eGDBmiI0eOaOnSpdq/f79q1arFHEImcPHiRa1atSrW06wmTJhgUKr4xeHuCUzq1KmVJk0ao2PgOUaOHKmBAwfq2LFjio6Olre3t93EF4hfhw8ffultCxYs+AaT4EXWrl2rDz74QKlSpVLjxo316NEjNWnSRH/99Zc2b95sdDyHlilTJoWFhcnDw0MeHh76448/VKhQIQUFBXEqggmsXbtWTk5OMZb52rBhg6KiolSzZk2DkmHo0KHy8/PTli1bbOek//HHH1q3bp1+/PFHg9M5tqRJk+revXuSpN9//12+vr6SHp9K8k/z1+DN27hxo+rWraucOXPqxIkTKlCggM6dOyer1aqiRYsaHS/eUNITmOHDh2vQoEGaO3eukidPbnQcxCJ58uQqXry40TEgqXDhwrJYLLbzzl+Ec9KNU6xYMa1YsUL16tWTs7OzZs6cqTNnzmjz5s3KmDGj0fEcWuXKlbV69WoVLVpUfn5+6tGjh5YuXarAwEA1aNDA6HgOr3///hozZkyM8ejoaPXv35+SbqBWrVopX758mjx5spYvX26bQXznzp0OM/GVWb3//vvq2bOnypYtqz179mjRokWSpJMnTypbtmwGp8OAAQPUq1cvDRs2TK6urlq2bJkyZMig5s2bq0aNGkbHizcc7p4AFClSxK5gnD59WlarVZ6enkqSJIndtsy0axwm9TOfpw8nPHDggHr37q0+ffrYzbT79ddfa+zYsapfv75BKfHEqlWr1LBhQ+XLl0+bNm1SunTpjI7k8KKjoxUdHa3EiR9/pr948WLt2LFDXl5e6tixo0OtWWtGLi4uOn78eIwJS8+dO6f8+fMrPDzcmGCAiQUHB6tTp066cOGCunXrJj8/P0mPl/6KioqyLQUGY7i6uurgwYPKnTu3UqdOrR07dih//vw6dOiQ6tWrp3PnzhkdMV6wJz0BoDwkDG3btn3hpH6Ifx4eHravGzVqpMmTJ6tWrVq2sYIFCyp79uz68ssv+XcWz563FzZ9+vRKlSqV3fnpy5cvj69YeEaiRImUKFEi2/eNGzdW48aNDUyEp7m7u+vs2bMxSvrp06dZuhB4jhw5cmjNmjUxxidOnGhAGjwrRYoUioiIkCRlyZJFZ86cUf78+SVJ169fNzJavKKkJwCDBw82OgJewm+//cakfib2559/2tbgfFrOnDl17NgxAxI5Nnd391jHnz23Fsa7efOm9uzZE+sRQk/O5YQx6tatq88//1wrVqxQ7ty5JT0u6L169VLdunUNTgeYV1RUlFauXKnjx4/LYrEoX758qlevnt3a3DBGqVKltHPnTnl7e6t27drq1auX/vzzTy1fvlylSpUyOl684XD3BCowMNDuF0uxYsWMjuTwcubMqbVr1ypfvnxGR0EsihYtqnz58mnmzJlKliyZJCkiIkJt2rTR8ePHOVXEIFarVcHBwUqfPj3zbJjQ6tWr1bx5c4WHh8vV1dXuCCGLxcI63Aa7deuWatSoocDAQNu5tBcvXlS5cuW0fPlypUqVytiAgAmdPn1atWrV0qVLl5Q3b15ZrVadPHlS2bNn16+//mr7wAvGOHv2rO7evauCBQvq3r176t27t+00q4kTJ9odJfk2o6QnMBcvXlTTpk21c+dO23++N2/eVJkyZbRw4UJlz57d2IAObP78+frll1+Y1M+k9uzZozp16ig6OlqFChWSJB06dEgWi0Vr1qxRyZIlDU7omKKjo5UsWTIdPXpU77zzjtFx8Iw8efKoVq1aGjVqFL/XTMpqtSogIECHDh2Si4uLChYsqPLlyxsdCzCtWrVqyWq1asGCBbYVk8LCwvTpp58qUaJE+vXXXw1OCFDSE5xq1arp9u3bmjt3rvLmzStJOnHihNq0aaMUKVJow4YNBid0XEWKFNGZM2eY1M/E7t27p/nz5+uvv/6yzbTbrFkzzt00WP78+TVz5kyHOowtoUiRIoX+/PNP5cqVy+goeEk3b95kD7rJXLhwQRaLhZnDTSJFihT6448/5OPjYzd+6NAhlS1bVnfv3jUoGSQpV65c2rt3r9KmTWs3fvPmTRUtWtRhJmLmnPQEZvv27dq1a5etoEtS3rx59e2333IutMGYeMz8kidPbjchGcxh7Nix6tOnj6ZPn64CBQoYHQdPqV69ugIDAynpJvXVV1/J09NTTZo0kfR4Yr9ly5YpU6ZMWrt2re2oIcS/yMhIDR06VJMnT7aVvpQpU6pr164aPHhwjA/yEX+cnZ11586dGON3795lxQoTOHfuXKzL4kZEROjSpUsGJDIGJT2ByZEjhx49ehRjPDIyUlmzZjUgEZ5ggj9z8/f3f+H1TIBlnE8//VT37t1ToUKFlDRpUrm4uNhdz3nP8WvVqlW2r2vXrq0+ffro2LFj8vHxiVEsmJzMWN99953mz58vSQoICFBAQIB+++03LV68WH369OHoOgN16dJFK1as0NixY+2W/RwyZIiuX7+uGTNmGJzQcX344Ydq3769Zs6caTvV7b///a86duzI7zQDPf1/z/r16+0mmI2KitLGjRtjrGTxNuNw9wTml19+0ahRozR16lQVK1ZMFotFgYGB6tq1q/r168feXOA5UqdObff9o0ePdO/ePSVNmlTJkyenCBpo7ty5L7y+ZcuW8ZQEkuyWXHsRi8US694OxB8XFxfbhFfdu3fXgwcP9N133+nkyZN677339Pfffxsd0WG5u7vr559/Vs2aNe3Gf/vtN33yySe6deuWQclw8+ZNtWzZUqtXr7Z98BgZGam6detqzpw5z119BG/Wk/97LBaLnq2nSZIkkaenp77++mt9+OGHRsSLd5T0BCZ16tS6d++eIiMjlTjx4wMhnnz97Hm1lI43L02aNDp58qTSpUun1KlTv3BtdF4P8zl16pQ+++wz9enTh6W/ACQ4WbJk0dKlS1WmTBnlzZtXI0aMUKNGjXTixAmVKFFCt2/fNjqiw8qYMaO2bNkSY8WX48ePq3z58rp27ZpByfDEqVOn7Oao8fLyMjoS9Hi1pL179ypdunRGRzEUh7snMJMmTTI6Ap4yceJEubq6SuK1SYjeeecdjRkzRp9++qn++usvo+NA0v3792Oc0uPm5mZQGsDcGjRooGbNmumdd95RWFiYba/twYMHKRwG69y5s4YPH67Zs2fL2dlZ0uNzakeOHKkuXboYnA7S478BWFXEfIKCgmKMOeKEmOxJB+DQDhw4oAoVKrDHyUDh4eHq16+fFi9erLCwsBjXc0i1cbp16yYvLy9169bNbnzKlCk6ffo0H04a7NGjR/rmm2904cIFtWrVSkWKFJH0+EPjlClTqm3btgYndCwNGjSw+/7333+Xs7Oz3bKfDx8+VJUqVbR8+XIjIjqsnj17vvS2EyZMeINJ8E+enRCzUaNGWrZsmTJnzuxQE2JS0oE48rySZ7FY5OzszIyhBnt6QhLp8drCISEhmjJlirJnz67ffvvNoGTo3LmzNm/erGHDhsnX11dTp07VpUuX9N1332nMmDFq3ry50REdVtasWbVq1SoVK1bMbnz//v2qW7euLl68aFAywHxat2790tvOnj37DSbBsypVqvRS21ksFm3atOkNp8GL5MqVS/Pnz1eZMmUUEBCgxo0ba9GiRVq8eLGCg4MdZkJMSjoQRxIlSvTCc9KzZcumVq1aafDgwS89MRPizrPPucViUfr06VW5cmV9/fXXypw5s0HJkCNHDvn7+6tixYpyc3PT/v375eXlpXnz5mnhwoVau3at0REdVrJkyXTkyJEYh06fPn1aBQoU0IMHDwxKBgB4GzEh5mM0BSCOzJkzR1myZNEXX3yhlStXasWKFfriiy+UNWtWTZ8+Xe3bt9fkyZM1ZswYo6M6pOjoaLtLVFSUQkND9dNPP1HQDXbjxg3lzJlT0uPzz59Msvj+++9r27ZtRkZzeF5eXlq3bl2M8d9++42104F/EBkZqd9//13fffedbV3uy5cv29ZNBxBT6tSpdeHCBUnSunXr9MEHH0h6fASkI53+xsRxQByZO3euvv76azVu3Ng2VrduXfn4+Oi7777Txo0blSNHDo0cOVJffPGFgUkd07Bhw9S7d28lT57cbvz+/fsaN26cBg0aZFAy5MqVS+fOnZOHh4e8vb21ePFilSxZUqtXr3a4iWLMpmfPnurSpYuuXbumypUrS5I2btyor7/+mvPRgRc4f/68atSooeDgYEVERKhq1apydXXV2LFj9eDBA9ZJj2cNGjTQnDlz5ObmFmPugGcxX4CxmBDzMfakA3Fk9+7dtkl7nlakSBHt3r1b0uM9g8HBwfEdDZKGDh0a696Le/fuaejQoQYkwhOtW7fWoUOHJEkDBgzQtGnT5OzsrB49eqhPnz4Gp3Nsbdq00ddff62ZM2eqUqVKqlSpkubPn6/p06erXbt2RscDTKt79+4qXry4/v77b7m4uNjGP/roI23cuNHAZI7J3d3ddkqiu7v7Cy8w1sSJE9WlSxd5e3srICBAKVOmlCSFhISoU6dOBqeLP5yT/hZp06aNKlWqpBYtWhgdxSHlyZNHDRo0iHE4e//+/bVixQqdOHFCgYGBqlevni5dumRQSseVKFEiXblyRenTp7cb37Rpk5o0acKatSYSHByswMBA5c6d22FmcU0Irl27JhcXF9sfTACeL126dNq5c6fy5s0rV1dXHTp0yHbUkLe3t+7du2d0RAAmxuHub5GzZ89q8+bNGj9+vG2vFOLP+PHj1ahRI/32228qUaKELBaL9u7dq7/++ktLly6VJO3du9e2pATiR+rUqWWxWGSxWJQnTx67yf2ioqJ09+5ddezY0cCEeFaOHDmUI0cOo2PgfyIjI7VlyxadOXNGzZo1k/T4vFo3NzcKuwGe/E57GU/md0D8ezL3ybMuXrwoV1dXAxIBSEjYk/4WOnHihPLmzWt0DId07tw5zZgxQydPnpTVatW7776rDh06yNPT0+hoDmvu3LmyWq1q06aNJk2aZHcoW9KkSeXp6anSpUsbmBCStGfPHm3ZskVXr15VdHS03XWsWWucZ8+rPXnypHLlyqXPP/+c82oNMnfu3JfetmXLlm8wCV6kSZMmcnd31/fffy9XV1cdPnxY6dOnV7169ZQjRw6WYDPQlStX1Lt3b23cuFFXr17Vs1XIkSYng3lR0hOY8PBwpUiRwugYQIKzdetWlS1bVokTcwCR2YwaNUr/+c9/lDdvXmXMmNFuLyFr1hqrfv36cnV11cyZM5U2bVrbIbtbt25V27ZtderUKaMjAqZ0+fJlVapUSU5OTjp16pSKFy+uU6dOKV26dNq2bZsyZMhgdESHVbNmTQUHB6tLly7KnDlzjCNT6tWrZ1Ay4P9R0hOYlClTqnHjxmrTpo3ef/99o+PgGTdv3tSePXti3Rvo6+trUCpI0v79+5UkSRL5+PhIkn755RfNnj1b3t7eGjJkiJImTWpwQseVMWNGffXVV2rVqpXRUfAMzqtNOO7fv69Hjx7Zjbm5uRmUBtLj12ThwoXav3+/oqOjVbRoUTVv3txuIjnEP1dXV23fvl2FCxc2OgqeERUVpR07dqhgwYJKnTq10XEMxS6lBGbhwoWaM2eOqlSpIg8PD7Vp00a+vr7KkiWL0dEc3urVq9W8eXOFh4fL1dU1xt5ASrqxOnTooP79+8vHx0dnz55VkyZN1KBBAy1ZskT37t1jOSkDJUqUSGXLljU6BmLBebXmFh4ern79+mnx4sUKCwuLcT2H7RrLxcVFbdq0UZs2bYyOgqdkz549xiHuMAcnJydVr15dx48fd/iSzhJsCUydOnW0bNkyXb58WZ999pkWLlwoDw8Pffjhh1q+fLkiIyONjuiwevXqpTZt2ujOnTu6efOm/v77b9uFyXuMd/LkSdun5kuWLFGFChX0008/ac6cOVq2bJmx4Rxcjx49NHXqVKNjIBZVq1a1+wDLYrHo7t27Gjx4sGrVqmVcMEiS+vbtq02bNtmWLfzxxx81dOhQZcmSRf7+/kbHc2hZsmRRs2bN9P333+vkyZNGx8FTJk2apP79++vcuXNGR0EsnuxMcXQc7v4W+Pbbb9WnTx89fPhQ6dKlU8eOHdW/f38lT57c6GgOJUWKFPrzzz+VK1cuo6MgFm5ubtq3b5/eeecdVa1aVR9++KG6d++u4OBg5c2bV/fv3zc6osOKjo5W7dq1dfLkSXl7eytJkiR21y9fvtygZOC8WnPLkSOH/P39VbFiRbm5uWn//v3y8vLSvHnztHDhQq1du9boiA5r4cKF2rp1q7Zs2aKTJ08qY8aMqlChgipWrKgKFSooX758Rkd0KM+uihAeHq7IyEglT548xv857Fgx1oYNG9SvXz8NHz5cxYoVizEXl6OcxkNJT6BCQ0Pl7++v2bNnKzg4WB999JH8/Px0+fJljRkzRpkzZ9aGDRuMjulQGjRooE8++USNGzc2OgpiUblyZWXPnl0ffPCB/Pz8dOzYMXl5eWnr1q1q2bIln6gbqHPnzpo5c6YqVaoUY+I4ScyCbDDOqzWvlClT6ujRo/Lw8FC2bNm0fPlylSxZUkFBQfLx8dHdu3eNjgg9nk188+bNWrNmjRYtWvTc00jw5rAqQsKRKNH/H+j99N8DVqtVFovFYf7tcE56ArN8+XLNnj1b69evl7e3tzp37qxPP/1UqVKlsm1TuHBhFSlSxLiQDqp27drq06ePjh07Jh8fnxifzNatW9egZJAeH97WvHlzrVy5UgMHDpSXl5ckaenSpSpTpozB6Rybv7+/li1bptq1axsdBbHgvFrzejKJn4eHh7y9vbV48WKVLFlSq1evtvu7AMa4e/euduzYYdujfuDAAfn4+KhChQpGR3M4FO+EY/PmzUZHMAX2pCcw7u7uatq0qfz8/FSiRIlYt7l//77Gjh2rwYMHx3M6x/b0J3/PcqRP/hKaBw8eyMnJKcaHKog/Hh4eWr9+vd59912joyAWly5d0s6dO2NdtaJbt24GpYIkTZw4UU5OTurWrZs2b96s2rVrKyoqSpGRkZowYYK6d+9udESH9d577+nw4cMqUKCAKlasqPLly6tcuXJ8eGISUVFRWrFihY4fPy6LxaJ8+fKpXr16LNMK06CkJyCRkZH6/vvv1aBBA2XKlMnoOAAQJ2bPnq1169Zp9uzZzKVhMrNnz1bHjh2VNGlSpU2bNsaqFUzuYy7BwcEKDAxU7ty5VahQIaPjOLQ0adLIYrHogw8+UMWKFVWxYkXOQzeJI0eOqF69egoNDVXevHklPZ5cNn369Fq1apVtqVYY5+bNm5o5c6btQxRvb2+1adNG7u7uRkeLN5T0BCZ58uQ6fvy4PDw8jI6CF3jw4IGSJUtmdAwgQShSpIjOnDkjq9UqT0/PGEc17N+/36BkyJ49uzp27KgBAwa88GghADEdPnxYW7Zs0datW7V9+3YlSpRIFSpUUKVKldSxY0ej4zmsUqVKKUOGDJo7d65tma+///5brVq10tWrV7V7926DEzq2wMBAVa9eXS4uLipZsqSsVqsCAwN1//59bdiwQUWLFjU6YrygpCcwlSpVUvfu3VW/fn2jo+AZUVFRGjVqlGbMmKErV67o5MmTypUrl7788kt5enrKz8/P6IiAKQ0dOvSF13PqjnHSpk2rPXv2KHfu3EZHQSy6desmLy+vGKcdTJkyRadPn7ZbPg/G2rdvn6ZMmaL58+czcZzBXFxcFBgYqPz589uNHzlyRCVKlGC1F4OVK1dOXl5e+uGHH2ynH0RGRqpt27Y6e/astm3bZnDC+MGJFwlMp06d1KtXL128eDHWZQkKFixoUDKMHDlSc+fO1dixY9WuXTvbuI+PjyZOnEhJB56DEm5efn5+WrJkifr37290FMRi2bJlWrVqVYzxMmXKaMyYMZR0Ax04cEBbtmzRli1btH37dt25c0eFChVS9+7dValSJaPjObS8efPqypUrMUr61atXbZPKwjiBgYF2BV2SEidOrL59+6p48eIGJotf7ElPYGI73NBisTjcsgRm5OXlpe+++05VqlSRq6urDh06pFy5cumvv/5S6dKl9ffffxsdEQBeSVRUlD788EPdv38/1lUrJkyYYFAySFKyZMl05MiRGMXi9OnTKlCggB48eGBQMiROnFhFihSxrY1evnx5h1nf2ezWrl2rvn37asiQISpVqpQk6Y8//tCwYcM0ZswYvf/++7Ztec3iX8aMGTVv3jxVq1bNbnz9+vXy9fXVlStXDEoWv9iTnsAEBQUZHQHPcenSpVg/gY2OjtajR48MSISXNWzYMNsfUQD+36hRo7R+/Xrb5ErPThwHY3l5eWndunXq0qWL3fhvv/2mXLlyGZQKknTjxg0Knkl9+OGHkqTGjRvbfo892WdZp04d2/fs/DJGkyZN5Ofnp/Hjx6tMmTKyWCzasWOH+vTpo6ZNmxodL95Q0hOY8+fPq0yZMjGWiIiMjNSuXbuYUM5A+fPn1/bt22O8BkuWLGHdepObPXu2xowZoypVqmj16tVGxwFMY8KECZo1a5ZatWpldBTEomfPnurSpYuuXbumypUrS5I2btyor7/+mkPdDUZBNy/W4Ta38ePHy2KxyNfXV5GRkZKkJEmS6LPPPtOYMWMMThd/ONw9gXFyclJISIgyZMhgNx4WFqYMGTLwiZ+BVq9erRYtWmjAgAEaNmyYhg4dqhMnTsjf319r1qxR1apVjY6IF3jw4IG2bt2q6tWrGx0FMI1MmTJp+/bteuedd4yOgueYPn26Ro4cqcuXL0uSPD09NWTIEPn6+hqcDABe371792wrv3h5eTncEq2U9AQmUaJEunLlitKnT283fvLkSRUvXly3b982KBmkx+fLjBo1Svv27VN0dLSKFi2qQYMGxTivBgASgtGjRyskJESTJ082Ogr+wbVr1+Ti4qKUKVMaHQUA8C9R0hOIBg0aSJJ++eUX1ahRQ87OzrbroqKidPjwYeXNm1fr1q0zKiJgarNnz1bKlCnVqFEju/ElS5bo3r17atmypUHJ8CLMF2Csjz76SJs2bVLatGmVP3/+GBPHLV++3KBkAAC8vTgnPYFwd3eX9HgiC1dXV7m4uNiuS5o0qUqVKmW37BcAe2PGjNGMGTNijGfIkEHt27enpJsU8wUYK1WqVLYPiWEORYsW1caNG5U6dWoVKVLkhRP47d+/Px6T4YlHjx4pb968WrNmjby9vY2OAyABoqQnELNnz5b0+Fyz3r17x1gfHcCLnT9/Xjlz5owx7uHhoeDgYAMS4WUEBQXZ5gtA/Hvyfw/Mo169eraj6erVq8cs+yaUJEkSRURE8NoAeG0c7p7A3L9/X1ar1TZ5wvnz57VixQp5e3tz3jPwAjly5NCUKVNUt25du/FffvlFnTt31sWLFw1KBgB424wZM0Z//fWXfvzxxxgr8gDAP0lkdAC8mnr16snf31+SdPPmTZUsWVJff/216tWrp+nTpxucDjCvTz75RN26ddPmzZsVFRWlqKgobdq0Sd27d9cnn3xidDyHt337dn366acqXbq0Ll26JEmaN2+eduzYYXAywLxy5cqlsLCwGOM3b95knXSD/fe//9Xy5cuVI0cOVa9eXQ0aNLC7wJwqV66s4cOH6969e0ZHcVhz587Vr7/+avu+b9++SpUqlcqUKaPz588bmCx+UdITmP3796tcuXKSpKVLlypTpkw6f/68/P39mX3XJB4+fKgTJ07Y1naEOYwYMULvvfeeqlSpIhcXF7m4uKhatWqqXLmyRo0aZXQ8h7Zs2TJVr15dLi4uOnDggCIiIiRJd+7c4bUBXuDcuXOxLr0aERHB0UEGS5UqlRo2bKjq1asrS5Yscnd3t7vAnDw8PLRp0ybly5fP6CgOa9SoUba5t3bv3q0pU6Zo7NixSpcunXr06GFwuvjD4e4JTPLkyfXXX38pR44caty4sfLnz6/BgwfrwoULyps3L5/8GejevXvq2rWr5s6dK+nxsni5cuVSt27dlCVLFvXv39/ghJAevy6HDh2Si4uLfHx85OHhYXQkh1ekSBH16NFDvr6+cnV11aFDh5QrVy4dPHhQNWrUUGhoqNERAVNZtWqVJKl+/fqaO3euXemLiorSxo0bFRAQoBMnThgVEUjQ7t69y3KGBnm66/Tr108hISHy9/fX0aNHVbFiRV27ds3oiPGCk2QSGC8vL61cuVIfffSR1q9fb/tE6erVq3JzczM4nWMbMGCADh06pC1btqhGjRq28Q8++ECDBw+mpJtEnjx5lCdPHqNj4CknTpyIdYk1Nzc33bx5M/4DASZXv359SZLFYomxMkWSJEnk6empr7/+2oBkeNa1a9d04sQJWSwW5cmTR+nTpzc6ksMLCgqKdSLZp1HQjZMyZUqFhYUpR44c2rBhg63rJEuWTPfv3zc4XfyhpCcwgwYNUrNmzdSjRw9VqVJFpUuXliRt2LBBRYoUMTidY1u5cqUWLVqkUqVK2c3o6u3trTNnzhiYzHH17NlTw4cPV4oUKdSzZ88XbjthwoR4SoVnZc6cWadPn5anp6fd+I4dOziv1sT8/f1VtmxZ5c6d2+goDic6OlqSlDNnTu3du1fp0qUzOBGeFR4erq5du8rf39/2ejk5OcnX11fffvutbQJgxD8vLy+VL19efn5++vjjj5UsWTKjI+EpVatWVdu2bVWkSBGdPHlStWvXliQdPXo0xt8JbzPOSU9gPv74YwUHByswMFDr1q2zjVepUkUTJ040MBmuXbumDBkyxBgPDw9nGRaDHDhwQI8ePbJ9/bzLwYMHjQ3q4Dp06KDu3bvrv//9rywWiy5fvqwFCxaod+/e6tSpk9Hx8BytWrWSt7e3unbtanQUhxUUFERBN6mePXtq69atWr16tW7evKmbN2/ql19+0datW9WrVy+j4zm0Q4cOqUiRIurVq5cyZcqkDh06aM+ePUbHwv9MnTpVpUuX1rVr17Rs2TKlTZtWkrRv3z41bdrU4HTxh3PSgThSoUIFffzxx+ratatcXV11+PBh5cyZU126dNHp06ftPlQBYG/gwIGaOHGiHjx4IElydnZW7969NXz4cIOT4UXOnTun9evXq0OHDkZHcRiTJ09W+/btlSxZsn+cMLZbt27xlArPSpcunZYuXaqKFSvajW/evFmNGzd2mPNqzSwyMlKrV6/WnDlz9Ntvv+mdd96Rn5+fWrRowWkJMBwlPQFo0KCB5syZIzc3t39ctmP58uXxlArP2rVrl2rUqKHmzZtrzpw56tChg44ePardu3dr69atKlasmNER8ZTbt29r06ZNevfdd/Xuu+8aHQd6PPnisWPHFB0dLW9vb84JBGKRM2dOBQYGKm3atC88r9Zisejs2bPxmAxPS548ufbt2xdjlvCjR4+qZMmSCg8PNygZnhUREaFp06ZpwIABevjwoZIkSaImTZroq6++UubMmY2O53Bmz56tlClTqlGjRnbjS5Ys0b1792LMw/G2oqQnAK1bt9bkyZPl6uqq1q1bv3Db2bNnx1MqxObIkSMaN26c9u3bp+joaBUtWlT9+vWTj4+P0dEcXuPGjVW+fHl16dJF9+/fV6FChXTu3DlZrVb9/PPPatiwodERHdatW7cUFRWlNGnS2I3fuHFDiRMnZlLMeHb79u2X3pbXBohdlSpVlDZtWvn7+9vOeb5//75atmypGzdu6Pfffzc4IQIDAzVr1iz9/PPP/8fenYfVnP7/A3+e0r5HUUmLFKmIMMlSZJ+JGMtkrYixFNIwQ8ja2LKOZEv2NQxGtIjCRCtatTJKSKJCdc7vj76dj+Nkmc9vpvucz/v1uC7XVff7/cfzupo5532/7/t+vaCiooLJkyfD09MTT548wdKlS/H69WvaBs+AhYUFgoOD4eTkJDIeGxsLLy8vznStoEk6If+AmpoaeHl5wd/fnwpdSahWrVohIiICnTp1wpEjR7Bs2TKkpqbiwIEDCAkJQXJyMuuInDVkyBB89913YufPg4ODcf78eVy6dIlRMm6SkZH5Yh0NgUAAHo/XaI9uQkj9S/vBgwfj7du36NSpE3g8HlJSUqCoqIiIiAh07NiRdUTO2rRpE/bv34+srCwMHToUU6dOxdChQyEj859SXQ8fPkT79u1RW1vLMCk3KSoqIjMzU6xIXEFBATp06MCZCu80SSfkH6KpqYmkpCSapEsoJSUlZGdnw9DQEJMmTYK+vj4CAwNRVFQES0tLvHnzhnVEztLW1kZ8fLzYttDMzEw4ODjgxYsXjJJxU2xs7Fff27dv338xCWnMlzpVfIi6VrBVXV2NQ4cOITMzEwKBAJaWlhg/fjyUlJRYR+O0du3awcPDA+7u7mjVqlWj97x//x5Hjx7lzNZqSdKmTRts374dLi4uIuPnzp3DrFmz8PjxY0bJmha1YJMyL168wNKlSxETE4PS0lJhW48GZWVljJIRV1dXnD179m89QJGmY2hoiFu3bkFbWxuXL1/GsWPHAAAvX76k9iuMvXv3rtHVipqaGs68MZckNPGWbF+764e6irCnpKSEadOmsY5BPlBbW4vx48djwoQJn5ygA4C8vDxN0BkZN24cvL29oaamhj59+gCof3ns4+ODcePGMU7XdGiSLmUmTJiA3NxceHp6omXLlvQlLEHMzMywcuVK3Lx5E127doWKiorIdaqyy9bcuXMxfvx4qKqqwsjISFhx9/r161QzgLFu3bohJCQE27ZtExkPDg6mgosSoqqqCkVFRXj//r3IuI2NDaNE3BUTE8M6AiFSq1mzZti4cSOmTJnCOgr5hFWrVqGwsBD9+/dHs2b1U1U+n49JkyZhzZo1jNM1HdruLmXU1NQQFxeHTp06sY5CPkJVdiXf3bt38ejRIwwYMEBYOfzixYvQ1NSEg4MD43TcFR8fD2dnZ3Tr1g39+/cHAERFReHOnTu4cuUKevfuzTghdz179gzu7u74448/Gr1OZ9Ilx+PHj8Hj8WBgYMA6CiESbcSIERgxYgRN1CVcdnY2UlNToaSkBGtraxgZGbGO1KRoJV3KtG/fnrZ/Sqj8/HzWEcgX2NnZwc7OTmRs2LBhjNKQBg4ODrh16xbWr1+PEydOQElJCTY2Nti7dy/atWvHOh6nzZ07Fy9fvsTt27fh5OSE8PBwPH36FKtWrcLGjRtZx+M8Pp8v/Fs01NVQU1ODr68vFi9eLFIIixBSb8iQIfj5559x//79Rnc+fnwWmrBhbm4Oc3Nz1jGYoZV0KXPnzh0sWrQIS5cuhZWVFeTk5ESuUzscQhrn4eHx2ev79u1roiSESA89PT2cO3cO3bt3h7q6Ou7evQtzc3OcP38e69atQ1xcHOuInPbzzz9j7969CAgIgIODAwQCAeLj47F8+XJMmzYNq1evZh2REInzuZdX1LWCjfnz52PlypVQUVH5Ym0nrhTEpJV0KaOpqYlXr16hX79+IuPUDkcyPH78GOfPn2/07CZXPlQk1cuXL0V+r6mpwf3791FeXi72/xNpenw+Hw8fPmy0IGZD4RjS9CorK6Grqwugvgr/s2fPYG5uDmtrayQlJTFORw4cOIA9e/aIrPx16tQJBgYGmDlzJk3SGTI1NcWdO3fQvHlzkfHy8nJ06dKFjsAx9PF3DGEvOTkZNTU1wp8JTdKlzvjx4yEvL48jR45Q4TgJExUVBRcXF5iYmCArKwtWVlYoKCiAQCBAly5dWMfjvPDwcLExPp+PmTNnUts8xm7fvg03NzcUFhbi481d9PKRLQsLC2RlZcHY2BidO3fGrl27YGxsjODgYOjp6bGOx3llZWVo37692Hj79u2p2wtjBQUFjX52vXv3Dn/99ReDRIRIrg8LYlJxzHq03V3KKCsrIzk5GRYWFqyjkI90794dgwcPxooVK6CmpobU1FTo6upi/PjxGDx4MH788UfWEUkjsrKy4OjoiOLiYtZROKtz584wNzdHQEAA9PT0xF4+amhoMEpGDh8+jJqaGkyZMgXJyckYNGgQXrx4AXl5eYSGhmLs2LGsI3Jajx490KNHD2zdulVkfM6cObhz5w5u377NKBl3nT9/HkB9cbIDBw6IfH7V1dUhKioKV69eRVZWFquIBPUtvTZs2ICMjAzweDx06NABfn5+VKhUAnh4eGDLli1QU1MTGa+srMScOXM4czyRJulSpk+fPli6dCmcnZ1ZRyEfUVNTQ0pKCtq2bQstLS3ExcWhY8eOSE1NxfDhw1FQUMA6ImnEpUuXMHnyZDx79ox1FM5SUVFBamoqzMzMWEchX1BVVYXMzEy0adMGLVq0YB2H82JjYzFs2DC0adMG9vb24PF4uHnzJh49eoRLly7RhIOBhvPOPB5PbGeQnJwcjI2NsXHjRnz77bcs4hEAhw4dgru7O0aOHCms5XDz5k2Eh4cjNDQUbm5urCNymqysLIqLi4VHrRo8f/4crVq1Qm1tLaNkTYu2u0uZOXPmwMfHB35+frC2thYrHEc9a9lRUVHBu3fvAAD6+vrIzc1Fx44dAdR/sBC2Pi5EIhAIUFxcjIsXL2Ly5MmMUhGgfjXw4cOHNEmXAsrKynR8R4L07dsX2dnZ2LFjBzIzMyEQCDBy5EjMnDkT+vr6rONxUsN5ZxMTE9y5c4deZkmg1atXY926dZg3b55wzMfHB5s2bcLKlStpks5IRUUFBAIBBAIBXr9+DUVFReG1uro6XLp0SWzi/r+MVtKlTGMVKRve1tLZTbZGjBiBYcOGYdq0afjpp58QHh6OKVOm4MyZM9DS0kJkZCTriJzm5OQk8ruMjAx0dHTQr18/eHh4oFkzemfJSnh4OJYsWUIvHyXQ999/Dzs7OyxatEhkfP369UhISMDJkycZJSOEkP+OgoICHjx4IPZi+OHDh7CyssLbt28ZJeM2GRmZz9ba4vF4CAgIwOLFi5swFTs0SZcyhYWFn71uZGTUREnIx/Ly8vDmzRvY2NigqqoKCxYsQFxcHMzMzBAUFER/G0I+gV4+Si4dHR1ER0fD2tpaZPzevXtwdnbG06dPGSUjDV6+fIm9e/eKnK11d3eHtrY262icFxUVhaioqEa7VnDlXK0kMjMzg5+fH6ZPny4yvmvXLmzYsAE5OTmMknFbbGwsBAIB+vXrh9OnT4t8hsnLy8PIyIhTO4Rokk4IIYQpevkouZSUlJCSkiJWrDQzMxO2traorq5mlIwA9Q+1w4cPh7q6Ouzs7AAAiYmJKC8vx/nz59G3b1/GCbkrICAAK1asgJ2dXaMFMRvrOEKaxs6dOzF37lx4eHigZ8+e4PF4iIuLQ2hoKLZs2SI2eSdNq7CwEIaGhp/tZ88FNEmXQgcPHkRwcDDy8/Nx69YtGBkZYfPmzTAxMcHw4cNZx+OsxYsXw9HREQ4ODlBWVmYdh3zkxYsXWLp0KWJiYhpd1aB2RYSI69atG7777jssXbpUZHz58uX4/fffkZiYyCgZAQArKyv07NkTO3fuhKysLID6s5szZ85EfHw87t+/zzghd+np6WHdunWYOHEi6yikEeHh4di4cSMyMjIAQFjdnZ6jJUdVVRWKiorw/v17kXGuHIGjQ5hSZufOnVi6dCnmzp2L1atXC7eBampqYvPmzfThwlBiYiK2bduGd+/eoUuXLnB0dETfvn3Rq1cvqKqqso7HeRMmTEBubi48PT3RsmXLz557Imykp6c3+oXs4uLCKBHx9/fHqFGjkJubi379+gGo38J79OhROo8uAXJzc3H69GnhBB2or4w8f/58hIWFMUxG3r9/j549e7KOQT7B1dUVrq6urGOQRjx79gzu7u74448/Gr3OlSNwNEmXMtu2bcPu3bsxYsQIBAYGCsft7OywYMEChsnI5cuXUVdXh4SEBMTGxuLatWv47bffUF1djS5dulC/Wsbi4uIQFxeHTp06sY5CPpKXlwdXV1fcu3dPpG1Rw4sUrnwhSyIXFxecPXsWa9aswalTp6CkpAQbGxtERkbSVmoJ0KVLF2RkZIgdR8jIyEDnzp3ZhCIAgKlTp+LIkSPw9/dnHYV8xNTUFHfu3EHz5s1FxsvLy9GlSxfk5eUxSkYAYO7cuXj58iVu374NJycnhIeH4+nTp1i1ahU2btzIOl6ToUm6lMnPz4etra3YuIKCAiorKxkkIh+SlZWFvb09tLW1oaWlBTU1NZw9exa5ubmso3Fe+/bt6fyshPLx8YGJiQkiIyNhamqKhIQEvHjxAr6+vtiwYQPreJw3bNgwDBs2jHUM0ghvb2/4+Pjg4cOH+OabbwAAt2/fxo4dOxAYGIi0tDThvVzZIiop3r59i5CQEERGRsLGxkasa8WmTZsYJSMFBQWNvvx99+4d/vrrLwaJyIeio6Nx7tw5dOvWDTIyMjAyMsKAAQOgrq6OtWvXcub7iCbpUsbExAQpKSlihZT++OMPWFpaMkpFgPqjCLGxsYiNjUVdXR169+6Nvn37wt/fnx6OJMBvv/2GRYsWYenSpbCyshJ7YFJXV2eUjNy6dQvR0dHQ0dGBjIwMZGRk0KtXL6xduxbe3t5ITk5mHZEQifTDDz8AAH766adGr1GXBHbS0tKEuxk+rg1Ax63YOH/+vPDniIgIaGhoCH+vq6tDVFQUjI2NGSQjH6qsrBT2Q9fW1sazZ89gbm4Oa2trJCUlMU7XdGiSLmX8/Pwwa9YsvH37FgKBAAkJCTh69CjWrl2LPXv2sI7HabNmzYKOjg58fX0xY8YMmvRJGE1NTbx69Up4rrYBPcCyV1dXJ6zb0KJFCzx58gQWFhYwMjJCVlYW43Tco62tjezsbLRo0QJaWlqfnVBQwUW28vPzWUcgnxATE8M6AvnIiBEjANS/JJk8ebLINTk5ORgbG3NqO7WksrCwQFZWFoyNjdG5c2fs2rULxsbGCA4Ohp6eHut4TYYm6VLG3d0dtbW1+Omnn1BVVQU3NzcYGBhgy5YtGDduHOt4nHbmzBlcv34dx44dw9KlS9GpUyc4OjrC0dERvXv3puJxjI0fPx7y8vI4cuQIFY6TMFZWVkhLS4OpqSl69OiBdevWQV5eHiEhITA1NWUdj3OCgoKgpqYGANi8eTPbMOSzqD2h5Hv48CFyc3PRp08fKCkpCV8Mk6bX0NXFxMQEd+7cQYsWLRgnIo2ZO3cuiouLAQDLli3DoEGDcPjwYcjLyyM0NJRtuCZELdik2PPnz8Hn84VbQojkePXqFW7cuIFTp07hyJEj4PF4ePfuHetYnKasrIzk5GSxAkuEvYiICFRWVmLkyJHIy8vDt99+i8zMTDRv3hzHjx8X2/1AmkZtbS0OHz6MQYMGoVWrVqzjECJVXrx4gTFjxiAmJgY8Hg85OTkwNTWFp6cnNDU1acVWwpSXl0NTU5N1DNKIqqoqZGZmok2bNpx6scLtLvFSrkWLFjRBlzBlZWUIDw/H0qVLsXjxYhw8eBCamprUQkoC2NnZ4dGjR6xjkEYMGjQII0eOBFBfdTc9PR3Pnz9HaWkpTdAZatasGX788Ud6wUjIf2HevHmQk5NDUVERlJWVheNjx47F5cuXGSYjv/76K44fPy78ffTo0dDW1oaBgQFSU1MZJiONUVZWRpcuXTg1QQdou7vUefr0KRYsWICoqCiUlpbi440QdK6WHRsbG6Snp0NbWxt9+vTBtGnT4OjoCCsrK9bRCIA5c+bAx8cHfn5+sLa2FiscR8X9JIu2tjbrCARAjx49kJycTNuqCfmbrly5goiICLRu3VpkvF27digsLGSUigDArl27cOjQIQDA1atXERkZicuXL+PEiRPw8/PDlStXGCfkNoFAgFOnTiEmJgalpaXCYwoNzpw5wyhZ06JJupSZMmUKioqK4O/vDz09PTrXJEG8vLxoUi7Bxo4dCwDw8PAQjlHlY8lQWVmJwMBA4cvHj7+QqWctOzNnzoSvry8eP36Mrl27QkVFReQ6vdwipHGVlZUiK+gNnj9/DgUFBQaJSIPi4mIYGhoCAC5cuIAxY8Zg4MCBMDY2Ro8ePRinIz4+PggJCYGTkxOnawjRJF3KxMXF4caNG8K2HkRylJWVNVrkqrq6GuvXr8fSpUsZpCINqAqy5Jo6dSpiY2MxceJEevkoYRpebnl7ewvH6OUWIV/Wp08fhIWFYeXKlQDq/7/h8/lYv349nJycGKfjNi0tLTx69AiGhoa4fPkyVq1aBaB+BZc+09g7dOgQzpw5g6FDh7KOwhQVjpMylpaWOHz4MGxtbVlHIR+RlZVFcXGxWJ2AFy9eQFdXlz74CfkETU1NXLx4EQ4ODqyjkI98aVsubYOXXCYmJujXrx9WrFgBAwMD1nE4Jz09HY6OjujatSuio6Ph4uKCBw8eoKysDPHx8Wjbti3riJw1e/ZsXLhwAe3atUNycjIKCgqgqqqK48eP49dff+VUL25JZGJigj/++APt27dnHYUpKhwnZTZv3oxFixahoKCAdRTykU+1VUlNTaXztYR8hpaWFv0/IqEKCwthYGAAIyMjkX8GBgZ0rlbCTZ48GXw+H3369GEdhZMsLS2RlpaG7t27Y8CAAcIOFsnJyTRBZywoKAizZ8+GpaUlrl69KmyRW1xcjJkzZzJOR5YvX46AgABUV1ezjsIUraRLAS0tLZHJX2VlJWpra6GsrCxW/KqsrKyp43Few9/n1atXUFdXF/lb1dXV4c2bN5gxYwZ27NjBMCUhkuvQoUM4d+4cDhw40OgZTsIO7RAihBDSlKqqqjBy5EjEx8fD2NhYbK7DlZ0OdCZdCmzevJl1BPIZmzdvhkAggIeHBwICAqChoSG8Ji8vD2NjY9jb2zNMSIhk27hxI3Jzc9GyZUtOfyFLok/tEHrx4oVYETnS9K5duwZHR0fWMcgnvH37FmlpaY0WxKTWrE3r/PnzGDJkCOTk5HD+/PnP3kt/G7amTJmCxMRETJgwgdOF42glnZD/D126dEFUVBS0tLTg5OSE33//XbhtihDydQICAj57fdmyZU2UhDRo6Ft/7tw5DB48WKQadV1dHdLS0mBhYUH9nhlTVFSEgYEB3N3dMXnyZGHFasLe5cuXMWnSJDx//lzsGhVdbHoyMjIoKSmBrq4uZGQ+fdqX/jbsqaioICIiAr169WIdhSlaSZcyly5dgqysLAYNGiQyfuXKFdTV1WHIkCGMknFTRkYGKisroaWlhevXr6O6upom6YT8TTQJlzwNO4IEAgHU1NSgpKQkvCYvL49vvvkG06ZNYxWP/J8nT57g0KFDCA0NxfLly9G/f394enpixIgRkJeXZx2P02bPno3Ro0dj6dKlaNmyJes4nPfhToaPdzUQyWJoaAh1dXXWMZijlXQpY2Njg8DAQLG2BJcvX8bChQuRmprKKBk32dvbQ1VVFb169UJAQAAWLFjwyUk6tWCTXFQFmZDGBQQEwM/Pj2oFSIGUlBTs27cPR48eBZ/Px/jx4+Hp6YlOnTqxjsZJ6urqVCSOkP/CxYsXsW3bNgQHB8PY2Jh1HGZoki5llJSUkJGRIfYfbUFBATp27IjKyko2wTgqKysLy5YtQ25uLpKSkmBpaYlmzcQ3qPB4PDpXK8GWL1+OwsJCXL9+Hbm5uazjcIK2tjays7PRokULseKYH6OCmOzk5+ejtrYW7dq1ExnPycmBnJwcpx+gJNGTJ08QEhKCwMBANGvWDG/fvoW9vT2Cg4PRsWNH1vE4xcPDAw4ODvD09GQdhTQiKioKUVFRjdYL2LdvH6NUBKgvyFxVVcX5Itm03V3KaGhoIC8vT+zB6OHDh1TEhwELCwscO3YMQP15p6ioKLEqyETyLV++nHUEzgkKCoKamhoAKo4pyaZMmQIPDw+xSfqff/6JPXv24Nq1a2yCEaGamhqcO3cO+/btw9WrV2FnZ4ft27fjhx9+QFlZGRYuXIjRo0cjPT2ddVRO2b59O0aPHo0bN27A2tpabKLh7e3NKBkJCAjAihUrYGdnBz09Pc4WJpNU9ExQj1bSpYyXlxdu376N8PBw4Raqhw8fYtSoUejWrRv27NnDOCEhhHzZ/PnzsXLlSqioqOD69evo2bNno7tQCFvq6upISkqCmZmZyPjDhw9hZ2eH8vJyNsEIAGDOnDk4evQoAGDChAmYOnUqrKysRO4pKiqCsbExncNtYnv27MGMGTOgpKSE5s2bi0wEeTwe8vLyGKbjNj09Paxbtw4TJ05kHYWQT6JJupR59eoVBg8ejLt376J169YAgMePH6N37944c+YMNDU12QbkuNzcXGzevBkZGRng8Xjo0KEDfHx86EyaBKirq0NoaOgnt7dFR0czSsZNcnJyePz4MVq2bPnJXtyEPQ0NDVy7dg22trYi44mJiXB0dMTr168ZJSMA0L9/f0ydOhWjRo36ZKG42tpaxMfHo2/fvk2cjttatWoFb29vLFq06LPVxEnTa968ORISEujZTILl5uZi//79yM3NxZYtW6Crq4vLly/D0NCQM0d3aJIuhQQCAa5evYrU1FQoKSnBxsYGffr0YR2L8yIiIuDi4oLOnTvDwcEBAoEAN2/eRGpqKn7//XcMGDCAdUROmz17NkJDQzFs2LBGt7cFBQUxSsZN7dq1w5gxYzBw4EA4OTkhPDwcWlpajd5Ln2/sfPvtt1BWVsbRo0chKysLoP6F19ixY1FZWYk//viDcUJCJJO2tjbu3LlDE0EJtHDhQqiqqsLf3591FNKI2NhYDBkyBA4ODrh+/ToyMjJgamqKdevWISEhAadOnWIdsUnQJJ2Qf4itrS0GDRqEwMBAkfFFixbhypUrVDiOsRYtWiAsLEysMwJh4+zZs5gxYwZKS0vB4/Hwqa8i6lnLVnp6Ovr06QNNTU307t0bAHDjxg1UVFQgOjpabGs1aVrnz59vdJzH40FRURFmZmYwMTFp4lQEAObNmwcdHR388ssvrKMQ1B+xasDn83HgwAHY2NjAxsZGrF7Apk2bmjoe+YC9vT1Gjx6N+fPnQ01NDampqTA1NcWdO3cwYsQI/PXXX6wjNgmapBPyD1FUVMS9e/fECixlZ2fDxsYGb9++ZZSMAIC+vj6uXbsGc3Nz1lHIB968eQN1dXVkZWV9crt7Q89uwsaTJ0+wfft2kd1bs2fPhra2NutonCcjI9PoS66GMR6Ph169euHs2bOf3KlC/h3e3t4ICwtDp06daCIoAZycnL7qPh6PR8ffGFNVVcW9e/dgYmIiMkkvKChA+/btOfM8TVV6CPmH6OjoICUlRWySnpKSQmdtJYCvry+2bNmC7du3UyVXCaKqqoqYmBiYmJhQ4TgJpa+vjzVr1rCOQRpx9epVLF68GKtXr0b37t0BAAkJCViyZAn8/f2hoaGB6dOnY8GCBdi7dy/jtNxy7949YS2H+/fvi1yj76CmFxMTwzoC+UqampooLi4W2wWUnJwMAwMDRqmaHj0REfIPmTZtGry8vJCXl4eePXuCx+MhLi4Ov/76K3x9fVnH47y4uDjExMTgjz/+QMeOHcVWNc6cOcMoGaGCVpLtxo0b2LVrF/Ly8nDy5EkYGBjg4MGDMDExQa9evVjH4zQfHx+EhISgZ8+ewrH+/ftDUVERXl5eePDgATZv3gwPDw+GKbmJJoWE/Hfc3NywcOFCnDx5EjweD3w+H/Hx8ViwYAEmTZrEOl6ToUk6If8Qf39/qKmpYePGjfj5558B1K9ALV++nPqhSgBNTU24urqyjkGIVDl9+jQmTpyI8ePHIykpCe/evQMAvH79GmvWrMGlS5cYJ+S23NxcqKuri42rq6sLW3y1a9cOz58/b+po5AOPHj0Cj8cTduUhhHza6tWrMWXKFBgYGEAgEMDS0hJ1dXVwc3PDkiVLWMdrMnQmnZB/QUNbIjU1NcZJCCHkv2dra4t58+Zh0qRJImcDU1JSMHjwYJSUlLCOyGm9evWCmpoawsLCoKOjAwB49uwZJk2ahMrKSly/fh2RkZGYOXMmsrOzGaflltraWgQEBGDr1q148+YNgPrjPXPmzMGyZcvEdnMRQkTl5uYiOTkZfD4ftra2YsdJ/9fRSvr/EBkZGTg6OmL9+vXo2rUr6zicU11dDYFAAGVlZaipqaGwsBB79+6FpaUlBg4cyDoeIYT8bVlZWY22wFNXV0d5eXnTByIi9u7di+HDh6N169YwNDQEj8dDUVERTE1Nce7cOQD1xRmp1VTTmz17NsLDw7Fu3TrY29sDAG7duoXly5fj+fPnCA4OZpyQEMnWtm1bTrcwpJX0/yGhoaEoLCzElStXEB8fzzoO5wwcOBAjR47EjBkzUF5eDgsLC8jLy+P58+fYtGkTfvzxR9YROc3W1rbRYj0ftiqaMmXKV1eAJYQL2rZti127dsHZ2VlkJT0sLAyBgYFIT09nHZHzBAIBIiIikJ2dDYFAgPbt22PAgAGQkZFhHY3TNDQ0cOzYMQwZMkRk/I8//sC4cePw6tUrRskIkWx1dXUIDQ1FVFQUSktLwefzRa5zpfo+fYL/D5kyZQqWLVtGE3RGkpKShH2ET506hVatWqGwsBBhYWHYunUr43Rk8ODByMvLg4qKCpycnODo6AhVVVXk5uaiW7duKC4uhrOzs3D1iUgGDw8PHDx4kHUMzpo+fTp8fHzw559/gsfj4cmTJzh8+DAWLFiAmTNnso5HUP+icfDgwfD29oaPjw8GDRpEE3QJoKioCGNjY7FxY2NjyMvLN30g8lWuX79OL1AY8/HxgY+PD+rq6mBlZYVOnTqJ/OMKWkmXMqGhoRgzZgyUlZVZRyEfUVZWRmZmJtq0aYMxY8agY8eOWLZsGR49egQLCwtUVVWxjshp06ZNQ5s2bcS2fa5atQqFhYXYvXs3li1bhosXL+Lu3buMUpKPOTo6orCwEOrq6khNTWUdh5MWL16MoKAgYW9aBQUFLFiwACtXrmScjABAbGwsNmzYgIyMDPB4PHTo0AF+fn7Cl8aEjRUrViAzMxP79++HgoICAODdu3fw9PREu3btsGzZMsYJSWNkZGSgpaWFX375hTrzMNKiRQuEhYVh6NChrKMwRZN0KaOnp4fKykqMHj0anp6eIm1XCFs2NjaYOnUqXF1dYWVlhcuXL8Pe3h6JiYkYNmwYFVhiTENDA4mJiTAzMxMZf/jwIbp27YpXr14hMzMT3bp1Exb+I5IjKysLFhYWrGNwVlVVFdLT08Hn82FpaQlVVVXWkQiAQ4cOwd3dHSNHjoSDgwMEAgFu3ryJ8PBwhIaGws3NjXVEznJ1dUVUVBQUFBSEq3+pqal4//49+vfvL3IvtQCVHIWFhcjPz0dERATWrl3LOg4n6evr49q1azA3N2cdhSmapEuZuro6XLx4EaGhobh48SJMTEzg7u6OyZMno1WrVqzjcdqpU6fg5uaGuro69O/fH1euXAEArF27FtevX8cff/zBOCG3tWzZEuvXrxfrsRkWFgY/Pz88ffoU6enp6NOnD7UrIoRIhQ4dOsDLywvz5s0TGd+0aRN2796NjIwMRsmIu7v7V9+7f//+fzEJIdJl48aNyMvLw/bt2xutJcQVNEmXYqWlpTh06BBCQ0ORmZmJwYMHw9PTE9999x2dR2OkpKQExcXF6NSpk/BvkJCQAHV1dbRv355xOm5btWoV1qxZg2nTpqFbt27g8XhISEjAnj178Msvvwi39F66dAlXr15lHZdTzp8/3+j4h0X9TExMmjgVd40cOfKr76UVQLYUFBTw4MGDRncIWVlZCY8oEEJElZeX49SpU8jNzYWfnx+0tbWRlJSEli1bwsDAgHU8zvn4eyc6Ohra2tro2LGjWLtCrnzvUAs2KaarqwsHBwdkZWUhOzsb9+7dw5QpU6CpqYn9+/fD0dGRdUTOadWqldiOhu7duzNKQz60ZMkSmJiYYPv27cJCZBYWFti9e7dwS+iMGTOoCj8DI0aMAI/Hw8fvjBvGeDweevXqhbNnz0JLS4tRSu7Q0NBgHYF8JUNDQ0RFRYlN0qOiomBoaMgoFfnQs2fPkJWVBR6PB3Nzc2E/e8JOWloanJ2doaGhgYKCAkybNg3a2toIDw8XFvwlTevj7x1XV1dGSSQHraRLoadPn+LgwYPYv38/8vLyMGLECHh6esLZ2RnV1dVYsmQJTp06hcLCQtZRCSHki6KiorB48WKsXr1a+FIrISEBS5Ysgb+/PzQ0NDB9+nT06NEDe/fuZZyWEMmxc+dOzJ07Fx4eHujZsyd4PB7i4uIQGhqKLVu2YPr06awjclZlZSXmzJmDsLAwYQspWVlZTJo0Cdu2baMCwAw5OzujS5cuWLdunUhryZs3b8LNzQ0FBQWsIxJCk3Rp89133yEiIgLm5uaYOnUqJk2aBG1tbZF7njx5gtatW4v1FSSEEElkZWWFkJAQsUKY8fHx8PLywoMHDxAZGQkPDw8UFRUxSsldtbW1uHbtGnJzc+Hm5gY1NTU8efIE6urqVEBOAoSHh2Pjxo3C8+cN1d2HDx/OOBm3TZ8+HZGRkdi+fTscHBwAAHFxcfD29saAAQOwc+dOxgm5S0NDA0lJSWjbtq3IJL2wsBAWFhZ0TISxfv364cyZM9DU1BQZr6iowIgRIzjTJ522u0sZXV1dxMbGwt7e/pP36OnpIT8/vwlTESKZtLW1kZ2djRYtWkBLS+uzBUjKysqaMBn5UG5uLtTV1cXG1dXVkZeXBwBo164dFfRjoLCwEIMHD0ZRURHevXuHAQMGQE1NDevWrcPbt28RHBzMOiLnubq60tZQCXT69GmcOnVK5Ojh0KFDoaSkhDFjxtAknSFFRUVUVFSIjWdlZdFxBAlw7do1vH//Xmz87du3uHHjBoNEbNAkXcp8zVZPHo8HIyOjJkhDGtTU1MDLywv+/v4wNTVlHYf8n6CgIKipqQl/5nKVUEnWtWtX+Pn5ISwsTPiA9OzZM/z000/o1q0bACAnJwetW7dmGZOTfHx8YGdnh9TUVDRv3lw47urqiqlTpzJMRohkq6qqQsuWLcXGdXV1UVVVxSARaTB8+HCsWLECJ06cAFD/3FxUVIRFixZh1KhRjNNxV1pamvDn9PR0kdbFdXV1uHz5MqeK+tF2dymzdevWRsc/rILcp08fyMrKNnEyoqmpiaSkJJqkE/I3ZWVlYfjw4cjPz4ehoaHwgcnU1BTnzp2Dubk5zp49i9evX2PixIms43JKixYtEB8fDwsLC5FtoQUFBbC0tKTJBgNf2hX0IdohxE7//v3RvHlzhIWFQVFREQBQXV2NyZMno6ysDJGRkYwTcldFRQWGDh2KBw8e4PXr19DX10dJSQns7e1x6dIlqKiosI7ISTIyMsLPtsamp0pKSti2bRs8PDyaOhoTtJIuZYKCgvDs2TNUVVVBS0sLAoEA5eXlUFZWhqqqKkpLS2FqaoqYmBiq7NrEXF1dcfbsWcyfP591FNKIpKQkyMnJwdraGgBw7tw57N+/H5aWlli+fDnk5eUZJ+QuCwsLZGRkICIiAtnZ2RAIBGjfvj0GDBggbGU4YsQItiE5is/no66uTmz88ePHwl0qpGlt3ryZdQTyFbZs2YLBgwejdevW6NSpE3g8HlJSUqCoqIiIiAjW8ThNXV0dcXFxiI6ORlJSEvh8Prp06QJnZ2fW0TgtPz8fAoEApqamSEhIEDl6IC8vD11dXU4tQtJKupQ5evQoQkJCsGfPHrRt2xZAfT/U6dOnw8vLCw4ODhg3bhxatWqFU6dOMU7LLatXr8aGDRvQv39/dO3aVexNrLe3N6NkBAC6desm3MqWl5cHS0tLjBw5Enfu3MGwYcPowZeQRowdOxYaGhoICQmBmpoa0tLSoKOjg+HDh6NNmzbYv38/64iESKzq6mocOnQImZmZEAgEsLS0xPjx46GkpMQ6GmfV1tZCUVERKSkpsLKyYh2HkE+iSbqUadu2LU6fPo3OnTuLjCcnJwsnHzdv3sSoUaNQXFzMJiRHmZiYfPIaj8cTFsAibHxYzfXXX39FdHQ0IiIiEB8fj3HjxuHRo0esI3JaVFQUoqKiUFpaKtaZYt++fYxSkSdPnsDJyQmysrLIycmBnZ0dcnJy0KJFC1y/fh26urqsI3JeXV0dzp49i4yMDPB4PFhaWsLFxYVTK06E/B1t27bFmTNn0KlTJ9ZRyGekp6ejqKhIrIici4sLo0RNi7a7S5ni4mLU1taKjdfW1goLLOjr6+P169dNHY3zqKK+ZBMIBMLJX2RkJL799lsAgKGhIVUNZywgIAArVqyAnZ0d9PT0qMCfBNHX10dKSgqOHTuGxMRE8Pl8eHp60mqghHj48CGGDh2Kv/76CxYWFhAIBMjOzoahoSEuXrwo3HFHmsb58+e/+l6uTDQk0ZIlS/Dzzz/j0KFDYm2MCXt5eXlwdXXFvXv3wOPxhOfTG54NGjuC9b+IVtKlzLBhw1BSUoI9e/bA1tYWQP0q+rRp09CqVStcuHABv//+O3755Rfcu3ePcVru+vgDhbDXr18/GBoawtnZGZ6enkhPT4eZmRliY2MxefJkFBQUsI7IWXp6eli3bh0VhSPkbxo6dCgEAgEOHz4snGy8ePECEyZMgIyMDC5evMg4Ibc01ND4Eh6Px5mJhiSytbXFw4cPUVNTAyMjI7HjiUlJSYySEQD47rvvICsri927dwvPp7948QK+vr7YsGEDevfuzTpik6CVdCmzd+9eTJw4EV27doWcnByA+lX0/v37C9uzqaqqYuPGjSxjclZYWBjWr1+PnJwcAIC5uTn8/Pxo8iEBNm/ejPHjx+Ps2bNYvHgxzMzMAACnTp1Cz549Gafjtvfv39PfgJD/QmxsLG7fvi2yGti8eXMEBgbCwcGBYTJu+vioDpFMVIhUst26dQvR0dHQ0dGBjIwMZGRk0KtXL6xduxbe3t5ITk5mHbFJ0CRdyrRq1QpXr15FZmamSBVkCwsL4T1OTk4ME3LXpk2b4O/vj9mzZ8PBwQECgQDx8fGYMWMGnj9/jnnz5rGOyGk2NjaN7i5Zv349nd1kbOrUqThy5Aj8/f1ZRyFEqigoKDR6vO3NmzfUsUKCvH37VtiGjbC3bNky1hHIZ9TV1UFVVRVAfRvQJ0+ewMLCAkZGRsjKymKcrunQJF1KfTgxpy3VkmHbtm3YuXMnJk2aJBwbPnw4OnbsiOXLl9MkXQKUl5fj1KlTyM3NhZ+fH7S1tZGeno6WLVvCwMCAdTzOevv2LUJCQhAZGQkbGxvhLqEGmzZtYpSMEMn27bffwsvLC3v37kX37t0BAH/++SdmzJhBZ54Zq6urw5o1axAcHIynT58iOzsbpqam8Pf3h7GxMTw9PVlHJEQiWVlZIS0tDaampujRowfWrVsHeXl5hISEwNTUlHW8JvN1h2eIRAkLC4O1tTWUlJSgpKQEGxsbHDx4kHUszisuLm50y27Pnj2p0r4ESEtLQ7t27fDrr79iw4YNKC8vBwCEh4fj559/ZhuO49LS0tC5c2fIyMjg/v37SE5OFv5LSUlhHY8QibV161a0bdsW9vb2UFRUhKKiIhwcHGBmZoYtW7awjsdpq1evRmhoqHCC0cDa2hp79uxhmIzIyMhAVlb2k/8IW0uWLBEeHVm1ahUKCwvRu3dvXLp0CVu3bmWcrunQSrqUoS3VksvMzAwnTpzAL7/8IjJ+/PhxtGvXjlEq0mD+/Plwd3fHunXroKamJhwfMmQI3NzcGCYjMTExrCMQIpU0NTVx7tw55OTkiPTibqi5QdgJCwtDSEgI+vfvjxkzZgjHbWxskJmZyTAZCQ8PF/m9pqYGycnJOHDgAAICAhilIg0GDRok/NnU1BTp6ekoKyuDlpYWp3YPU3V3KWNiYoKAgACRLdUAcODAASxfvpzagDF0+vRpjB07Fs7OznBwcACPx0NcXByioqJw4sQJuLq6so7IaR/2SVdTU0NqaipMTU1RWFgICwsLvH37lnVEQqSKiYkJ+vXrhxUrVtBxEUI+oqSkhMzMTBgZGYl856Snp6N79+548+YN64jkI0eOHMHx48dx7tw51lEIoZV0aUNbqiXXqFGj8OeffyIoKAhnz54VrmgkJCQI2+URdhQVFVFRUSE2npWVBR0dHQaJuG3kyJEIDQ2Furo6Ro4c+dl7z5w500SpyN8xefJkFBYWok+fPsjNzWUdh5Pq6uoQGhqKqKgolJaWilUXj46OZpSMdOzYETdu3ICRkZHI+MmTJ+mZQEL16NED06ZNYx2Dszw8PL7qvn379v3LSSQDTdKlDG2plmxdu3bFoUOHWMcgjRg+fDhWrFiBEydOAKgvuFhUVIRFixZh1KhRjNNxj4aGhnDbmrq6Oqe2sP2vWL58OesInOfj44PQ0FAMGzYMVlZW9P+RBFm2bBkmTpyIv/76C3w+H2fOnEFWVhbCwsJw4cIF1vHIR6qrq7Ft2za0bt2adRTOCg0NhZGREWxtbUEbvWm7u9ShLdWE/HcqKiowdOhQPHjwAK9fv4a+vj5KSkpgb2+PS5cuQUVFhXVEQgj5W1q0aIGwsDAMHTqUdRTSiIiICKxZswaJiYng8/no0qULli5dioEDB7KOxmkfn20WCAR4/fo1lJWVcejQIeqMwMjMmTNx7NgxtGnTBh4eHpgwYQK0tbVZx2KGJulSKDExEUFBQcjIyBBuqfb19aXtU4R8hejoaCQlJQkfmJydnVlH4rx+/frhzJkz0NTUFBmvqKjAiBEjaMtuE5s/f/5X30vt8djS19fHtWvXYG5uzjoKIVIjNDRUZJIuIyMDHR0d9OjRA1paWgyTkXfv3uHMmTPYt28fbt68iWHDhsHT0xMDBw7k3E4hmqQTQghhSkZGBiUlJdDV1RUZLy0thYGBAWpqahgl4yYnJ6evuo/H49ELFMY2btyIvLw8bN++nXMPsJLO3d0dEyZMQL9+/ehvI2GKiopgaGjY6N+lqKgIbdq0YZCKfKywsBChoaEICwtDTU0N0tPToaqqyjpWk6Ez6VKgsWJXn6Kurv4vJiFEuvydfpre3t7/YhLSmLS0NOHP6enpKCkpEf5eV1eHy5cvU9VwBqglnmT7uNBidHQ0/vjjD3Ts2BFycnIi16joIjsvXrzAsGHD0Lx5c4wbNw4TJkygHY8SwsTEBMXFxWIvhl+8eAETExPU1dUxSkY+xOPxwOPxIBAIxIpicgGtpEsBGRmZL76FFQgE4PF49MFCyAdMTExEfn/27BmqqqqE26rLy8uhrKwMXV1d5OXlMUjIbR9+tjX2VaSkpIRt27Z9dcVXQrjA3d39q+/dv3//v5iEfEl5eTlOnDiBI0eO4MaNG7CwsMCECRPg5uYGY2Nj1vE461O7twoLC2FpaYnKykpGyciH293j4uLw7bffwt3dHYMHD4aMjAzreE2KJulSIDY29qvv7du377+YhPy3PDw84OTkhIkTJ7KOwllHjhzBb7/9hr1798LCwgJAffu1adOmYfr06Rg/fjzjhNxTWFgIgUAAU1NTJCQkiLTCk5eXh66uLmRlZRkm5CZqj0fIP+/x48c4evQo9u3bh5ycHNTW1rKOxDkN9Ta2bNmCadOmQVlZWXitrq4Of/75J2RlZREfH88qIqd9WDiu4bhI8+bNWcdihibphDQBR0dHFBYWQl1dHampqazjcFLbtm1x6tQpse2GiYmJ+P7775Gfn88oGSGSxd3dHVu3boWamtoXV21ppZat/Px81NbWirVgzcnJgZycHK3WSoiamhpcvHgRhw4dwsWLF6GtrY2//vqLdSzOaai3ERsbC3t7e8jLywuvycvLw9jYGAsWLKCWxozIyMigTZs2sLW1/ewOYq68HKYz6VKovLwce/fuRUZGBng8HiwtLeHh4QENDQ3W0cgnXLt2DUD9yi1ho7i4uNECZHV1dXj69CmDRORD2dnZuHbtGkpLS8XOni1dupRRKm76cOJNk3DJNmXKFHh4eIhNKv7880/s2bNH+N1D2IiJicGRI0dw+vRp1NXVYeTIkfj999/Rr18/1tE4qaHehru7O7Zs2UJ1nCTMpEmTqMjiB2glXcrcvXsXgwYNgpKSErp37w6BQIC7d++iuroaV65cQZcuXVhHJEQifffddygqKsLevXvRtWtX8Hg83L17F9OmTYOhoSHOnz/POiJn7d69Gz/++CNatGiBVq1aiXxJ83g8JCUlMUzHbQEBAZgwYQLatm3LOgpphLq6OpKSkmBmZiYy/vDhQ9jZ2aG8vJxNMILWrVvjxYsXGDRoEMaPH4/vvvsOioqKrGMRQqQETdKlTO/evWFmZobdu3ejWbP6jRC1tbWYOnUq8vLycP36dcYJuetTkzwejwdFRUWYmZmJFTIjTefZs2eYPHkyLl++LKyAXFtbi0GDBiE0NFSsgAxpOkZGRpg5cyYWLlzIOgr5iI2NDR48eIBu3bphwoQJGDt2rEjtAMKWhoYGrl271ugxHkdHR7x+/ZpRMhISEoLRo0dT320JdefOHZw8eRJFRUV4//69yDWubKcmko0m6VJGSUkJycnJaN++vch4eno67OzsUFVVxSgZaahU/fH/Ug1jPB4PvXr1wtmzZ+lLm6GcnBxkZGRAIBCgQ4cOMDc3Zx2J89TV1ZGSkgJTU1PWUUgjHjx4gMOHD+PYsWN4/PgxnJ2dMWHCBIwYMUKk8BJpet9++y2UlZVx9OhRYZHFuro6jB07FpWVlfjjjz8YJyQPHz5Ebm4u+vTpAyUlJeHzAGHn2LFjmDRpEgYOHIirV69i4MCByMnJQUlJCVxdXemYD5EI3Kpl/z9AXV0dRUVFYuOPHj2Cmpoag0SkwdWrV9GtWzdcvXoVr169wqtXr3D16lV0794dFy5cwPXr1/HixQssWLCAdVROa9euHVxcXDB8+HCaoEuI0aNH48qVK6xjkE/o2LEj1qxZg7y8PMTExMDExARz585Fq1atWEfjvHXr1iE6OhoWFhZwd3eHu7s7LCwscP36daxfv551PE578eIF+vfvD3NzcwwdOhTFxcUAgKlTp8LX15dxOm5bs2YNgoKCcOHCBcjLy2PLli3IyMjAmDFj0KZNG9bxCAFAheOkztixY+Hp6YkNGzagZ8+e4PF4iIuLg5+fH3744QfW8TjNx8cHISEh6Nmzp3Csf//+UFRUhJeXFx48eIDNmzdTz2dCPmJmZgZ/f3/cvn0b1tbWwuMIDby9vRklIx9TUVGBkpIS5OXlaSu1BLC0tERaWhq2b9+O1NRUKCkpYdKkSZg9eza0tbVZx+O0efPmQU5ODkVFRejQoYNwfOzYsZg3bx42btzIMB235ebmYtiwYQAABQUFVFZWgsfjYd68eejXrx8CAgIYJySEJulSZ8OGDeDxeJg0aZKwx6acnBx+/PFHBAYGMk7Hbbm5uY1WClVXV0deXh6A+lXc58+fN3U0QiRaSEgIVFVVERsbi9jYWJFrPB6PJumM5efn48iRIzh8+DCys7PRp08fLF++HKNHj2YdjQDQ19fHmjVrWMcgH7ly5QoiIiLQunVrkfF27dqhsLCQUSoCANra2sKXjAYGBrh//z6sra1RXl5Ox0aJxKBJupRp2Jazdu1a5ObmQiAQwMzMjM4FSoCuXbvCz88PYWFhwsJKz549w08//YRu3boBqD8P/fEXNiFcRz3qJZe9vT0SEhJgbW0Nd3d3uLm5wcDAgHUs8n+MjY3h4eEBd3d3GBoaso5DPlBZWdnos9nz58+hoKDAIBFp0Lt3b1y9ehXW1tYYM2YMfHx8EB0djatXr6J///6s4xECgArHEfKPycrKwvDhw5Gfnw9DQ0PweDwUFRXB1NQU586dg7m5Oc6ePYvXr19j4sSJrOMSQsgX/fLLLxg/fjw6duzIOgppxLZt2xAaGorU1FQ4OTnB09MTrq6uNAmUAMOGDUOXLl2wcuVKqKmpIS0tDUZGRhg3bhz4fD5OnTrFOiJnlZWV4e3bt9DX1wefz8eGDRsQFxcnPHpFxX2JJKBJuhQYOXIkQkNDoa6ujpEjR372XmobwZZAIEBERASys7MhEAjQvn17DBgwADIyVKORtf3790NVVVVsi+7JkydRVVWFyZMnM0pGvlSnYd++fU2UhBDplJqain379uHo0aOora2Fm5sbPDw80KVLF9bROCs9PR2Ojo7o2rUroqOj4eLiggcPHqCsrAzx8fFo27Yt64icVFtbi8OHD2PQoEFU/JJINJqkSwF3d3ds3boVampqcHd3/+y91DaCkMZZWFggODgYTk5OIuOxsbHw8vJCVlYWo2TE1dVV5Peamhrcv38f5eXl6NevH718bGLz58/HypUroaKigvnz53/23k2bNjVRKvI1ampq8Ntvv2HhwoWoqamBlZUVfHx84O7uTm2/GCgpKcHOnTuRmJgIPp+PLl26YNasWdDT02MdjdOUlZWRkZEBIyMj1lEI+SQ6ky4FPpx40yRcskVFRSEqKgqlpaXg8/ki12g1kK3CwkKYmJiIjRsZGTXa1pA0nfDwcLExPp+PmTNnUu90BpKTk1FTUyP8+VNo0ic5ampqEB4ejv379+Pq1av45ptv4OnpiSdPnmDx4sWIjIzEkSNHWMfknFatWlGlcAnUo0cPJCcn0ySdSDSapEup0tJSZGVlgcfjwdzcHLq6uqwjcV5AQABWrFgBOzs76Onp0QOshNHV1UVaWhqMjY1FxlNTU9G8eXM2ocgnycjIYN68eXB0dMRPP/3EOg6nxMTENPozkTxJSUnYv38/jh49CllZWUycOBFBQUFo37698J6BAweiT58+DFNyR1pa2lffa2Nj8y8mIZ8zc+ZM+Pr64vHjx+jatStUVFRErtPfhkgCmqRLmYqKCsyaNQvHjh1DXV0dAEBWVhZjx47Fjh07oKGhwTghdwUHByM0NJSKwkmocePGwdvbG2pqasIH1tjYWPj4+GDcuHGM05HG5ObmCltNEjZevXqFuro6sZ7bZWVlaNasWaNtJ0nT6datGwYMGICdO3dixIgRkJOTE7vH0tKSPuOaSOfOncHj8SAQCERe1DecLP1wrOEZjjS9sWPHAoBIe88P/270tyGSgCbpUmbq1KlISUnBhQsXYG9vDx6Ph5s3b8LHxwfTpk3DiRMnWEfkrPfv36Nnz56sY5BPWLVqFQoLC9G/f380a1b/0cfn8zFp0iTqMczYx+eeBQIBiouLcfHiRSrox9i4cePw3XffYebMmSLjJ06cwPnz53Hp0iVGyQgA5OXlfXHLroqKCh2VayIftpNMTk7GggUL4OfnB3t7ewDArVu3sHHjRqxbt45VRAJq+0mkAxWOkzIqKiqIiIhAr169RMZv3LiBwYMHo7KyklEysnDhQqiqqsLf3591FPIZ2dnZSE1NhZKSEqytrelMmgT4uJifjIwMdHR00K9fP3h4eAhfqpCmp62tjfj4eHTo0EFkPDMzEw4ODnjx4gWjZAQAHj16BB6Ph9atWwMAEhIScOTIEVhaWsLLy4txOm7r3r07li9fjqFDh4qMX7p0Cf7+/khMTGSUjBAiDejJR8o0b9680S3tGhoa1NeRsbdv3yIkJASRkZGwsbER23ZIVZAlg7m5OczNzVnHIB+gc8+S6927d40eOaipqUF1dTWDRORDbm5u8PLywsSJE1FSUoIBAwagY8eOOHToEEpKSrB06VLWETnr3r17jRYrNTExQXp6OoNE5EMHDx5EcHAw8vPzcevWLRgZGWHz5s0wMTHB8OHDWccjhCbp0mbJkiWYP38+wsLChC08SkpK4OfnRyu4jKWlpaFz584AgPv374tcoyJybFArKeny7NkzkYKYOjo6rCNxXrdu3RASEoJt27aJjAcHB6Nr166MUpEG9+/fR/fu3QHUH0GwsrJCfHw8rly5ghkzZtAknaEOHTpg1apV2Lt3LxQVFQHUv/RatWqV2M4U0rR27tyJpUuXYu7cuVi9erXwDLqmpiY2b95Mk3QiEWiSLgVsbW1FJnk5OTkwMjJCmzZtAABFRUVQUFDAs2fPMH36dFYxOY9WAyUPtZKSDpWVlZgzZw7CwsKErQtlZWUxadIkbNu2DcrKyowTctfq1avh7OyM1NRU9O/fH0B9q8k7d+7gypUrjNORmpoaKCgoAAAiIyPh4uICAGjfvj2Ki4tZRuO84OBgfPfddzA0NESnTp0A1HcT4fF4uHDhAuN03LZt2zbs3r0bI0aMQGBgoHDczs4OCxYsYJiMkP+gSboUGDFiBOsIhEglaiUlHebPn4/Y2Fj8/vvvcHBwAADExcXB29sbvr6+2LlzJ+OE3OXg4IBbt25h/fr1OHHiBJSUlGBjY4O9e/eiXbt2rONxXseOHREcHIxhw4bh6tWrWLlyJQDgyZMn1FqSse7duyM/Px+HDh1CZmYmBAIBxo4dCzc3N7GWX6Rp5efnw9bWVmxcQUGBajsRiUGF4wj5/zBy5EiEhoZCXV0dI0eO/Oy9Z86caaJUhEiXFi1a4NSpU3B0dBQZj4mJwZgxY/Ds2TM2wQiRcNeuXYOrqysqKiowefJk7Nu3DwDwyy+/IDMzk753CGmEpaUl1q5di+HDh0NNTQ2pqakwNTXF1q1bceDAASrqRyQCraQT8v9BQ0NDuFWaetRLtsrKSgQGBiIqKgqlpaXCbdUN8vLyGCUjVVVVaNmypdi4rq4uqqqqGCQiRDo4Ojri+fPnqKioECke6+XlRcdECPkEPz8/zJo1C2/fvoVAIEBCQgKOHj2KtWvXYs+ePazjEQKAVtIJ+UcIBAIUFRVBR0eHHowk1A8//IDY2FhMnDgRenp6YufQfXx8GCUj/fv3R/PmzREWFiYssFRdXY3JkyejrKwMkZGRjBMSItlKS0tFii7q6uqyjkSIRNu9ezdWrVqFR48eAQAMDAywfPlyeHp6Mk5GSD2apBPyD+Dz+VBUVMSDBw/onKaE0tTUxMWLF4VnnonkuH//PgYPHoy3b9+iU6dO4PF4SElJgaKiIiIiItCxY0fWEQmRSBUVFZg1axaOHTsmrFAtKyuLsWPHYseOHbTDi5AveP78Ofh8Pr3YIhJHhnUAQv4XyMjIoF27dnjx4gXrKOQTtLS0oK2tzToGaYSVlRVycnKwdu1adO7cGTY2NggMDEROTg5N0An5jKlTp+LPP//EhQsXUF5ejlevXuHChQu4e/cupk2bxjoeIRKpX79+KC8vB1BfE6Vhgl5RUYF+/foxTEbIf9BKOiH/kIsXLyIwMBA7d+6ElZUV6zjkI4cOHcK5c+dw4MABOpJACPmfoKKigoiICPTq1Utk/MaNGxg8eDBVqpYAd+/eRUZGBng8Htq3bw87OzvWkThPRkYGJSUlYqvnpaWlMDAwELZuJYQlKhz3P+LcuXN49eoVJk2axDoKZ02YMAFVVVXo1KkT5OXloaSkJHK9rKyMUTLusrW1FTl7/vDhQ7Rs2RLGxsaQk5MTuTcpKamp45H/s3btWrRs2RIeHh4i4/v27cOzZ8+wcOFCRsnIp/z22294/vw5li5dyjoKpzVv3rzRLe0aGhoiheRI03v8+DF++OEHxMfHQ1NTEwBQXl6Onj174ujRozA0NGQbkIPS0tKEP6enp6OkpET4e11dHS5fvgwDAwMW0QgRQyvp/yPat2+PnJwc4Zk00vQOHDjw2euTJ09uoiSkQUBAwFffu2zZsn8xCfkcY2NjHDlyBD179hQZ//PPPzFu3Djk5+czSkY+pX///sjPz6euCIyFhITg5MmTCAsLg56eHgCgpKQEkydPxsiRIzF9+nTGCblr4MCBqKiowIEDB2BhYQEAyMrKgoeHB1RUVHDlyhXGCblHRkZG+OK+semPkpIStm3bJvbCmBAWaJJOCCGEKUVFRWRkZMDExERkPC8vD5aWlnj79i2jZIRINltbWzx8+BDv3r1DmzZtAABFRUVQUFAQK2JKu4WalpKSEm7evAlbW1uR8aSkJDg4OKC6uppRMu4qLCyEQCCAqakpEhISoKOjI7wmLy8PXV1dyMrKMkxIyH/QdndC/kG5ubnYv38/cnNzsWXLFujq6uLy5cswNDSkAlgS4sPzgR06dEDXrl1ZR+I8Q0NDxMfHi03S4+Pjoa+vzygV+RKBQCDWypA0rREjRrCOQD6hTZs2jZ5trq2tpS3VjBgZGQGo78hDiKSjSbqU8ff3x/Lly8Xe9L169QozZszA0aNHGSUjsbGxGDJkCBwcHHD9+nWsXr0aurq6SEtLw549e3Dq1CnWETmNzgdKrqlTp2Lu3LmoqakRVtaNiorCTz/9BF9fX8bpuG3ixInYuXMnVFVVRcYLCgowceJE3Lhxg1EyAtAxHUm2bt06zJkzBzt27EDXrl3B4/Fw9+5d+Pj4YMOGDazjcc758+cxZMgQyMnJ4fz585+918XFpYlSEfJptN1dyhgZGUFPTw+HDx9G27ZtAQDXrl3DpEmTYGBggFu3bjFOyF329vYYPXo05s+fDzU1NaSmpsLU1BR37tzBiBEj8Ndff7GOyGl0PlByCQQCLFq0CFu3bsX79+8B1G+BX7hwIfz9/Wm1lqGuXbuirKwMhw4dgoODA4D6+hve3t4YMGAAvXwk5ANaWloin1eVlZWora1Fs2b1a2INP6uoqFAx2Sb2YUV3GZlPd6Dm8XhU34lIBJqkS5lXr15h+vTpuHjxIjZt2oTs7Gxs2bIFixYtwrJly+gsDUOqqqq4d+8eTExMRCbpBQUFaN++PZ2rZYzOB0q+N2/eICMjA0pKSmjXrh0UFBRYR+K82tpaLFmyBEFBQfD19UVOTg4uX76MLVu2UHElQj7ypQKyH6JisoSQz6Ht7lJGQ0MDx44dw+LFizF9+nQ0a9YMf/zxB/r37886GudpamqiuLhY7FxtcnIynT+TAHQ+UHK9evUKdXV10NbWRrdu3YTjZWVlaNasGdTV1Rmm47ZmzZohMDAQCgoKWLlyJZo1a4bY2FjY29uzjkaIxKGJNyHkn0KTdCm0bds2BAUF4YcffkBiYiK8vb1x5MgRdOrUiXU0TnNzc8PChQtx8uRJ8Hg88Pl8xMfHY8GCBdS/XgLQ+UDJNW7cOHz33XeYOXOmyPiJEydw/vx5XLp0iVEyUlNTg0WLFmHHjh34+eefERcXB1dXV+zbtw9Dhw5lHY8QiUbFZCXH1q1bv/peb2/vfzEJIV+HtrtLmSFDhuDOnTsIDg7G999/j+rqasyfPx+hoaEICAjATz/9xDoiZ9XU1GDKlCk4duwYBAIBmjVrhrq6Ori5uSE0NJSOIjCmpaWFqqqqT54P/BCdFWxa2traiI+PR4cOHUTGMzMz4eDggBcvXjBKRjp16oSqqiocPHgQ33zzDQQCAdatW4dly5bBw8MDv/32G+uIhEikj4vJZmRkwNTUFOvWrUNCQgLVc2hiH+9y/BQej4e8vLx/OQ0hX0aTdCkzYMAAHDhwQKwt0cWLFzF16lQUFxczSkYa5ObmIjk5GXw+H7a2tmK9agkbdFZQcqmoqOD27duwtrYWGb937x569OiBqqoqRsmIp6cntm7dKvYiKyUlBRMmTMD9+/cZJSNfsmLFCjg6OqJPnz6so3ASFZMlhPz/oEn6/5Dnz5+jRYsWrGNw3vv375Gfn4+2bdsKV2wJIZ/m6OgIa2trbNu2TWR81qxZSEtLozZfEurdu3dU3E+CmZiY4OnTp+jfvz9+//131nE4h4rJEkL+f3y6BwGRWDdu3MCECRNgb28vfBN78OBBZGZmMk7GbVVVVfD09ISysjI6duyIoqIiAPVnmwIDAxmnIx+qrq5GRUWFyD/CzurVq7Fnzx706dMHAQEBCAgIQJ8+fbBv3z6sWbOGdTzOO3jwIBwcHKCvr4/CwkIAwObNm3H58mXGycjn5Ofno6ysDLNnz2YdhZMaisl+jIrJEkK+Bk3Spczp06cxaNAgKCkpITk5Ge/evQMAvH79mh5mGfv555+RmpqKa9euQVFRUTju7OyM48ePM0xGgPp+tbNnz4auri5UVVWhpaUl8o+w4+DggFu3bsHQ0BAnTpzA77//DjMzM6SlpaF3796s43Hazp07MX/+fAwdOhTl5eXC/sGamprYvHkz23DkixQVFTFo0CDWMTipoZhsSUkJFZMlhPxttN1dytja2mLevHmYNGmSyPaplJQUDB48GCUlJawjcpaRkRGOHz+Ob775RuRv8/DhQ3Tp0oVWaxmbNWsWYmJisGLFCkyaNAk7duzAX3/9hV27diEwMBDjx49nHZEQiWNpaYk1a9ZgxIgRIp9r9+/fh6OjI54/f846IqcZGxvDw8MDU6ZMQZs2bVjHIR+gYrKEkP8ftJIuZbKyshotAqOuro7y8vKmD0SEnj17Bl1dXbHxyspK8Hg8BonIh37//Xf89ttv+P7779GsWTP07t0bS5YswZo1a3D48GHW8cj/oaMIkiU/Px+2trZi4woKCqisrGSQiHzI19cX586dg6mpKQYMGIBjx44Jd9gRNh4+fAgAkJOTw+HDh5GdnY0TJ07g0KFDyMzMxMGDB2mCTgj5IpqkSxk9PT3hF8CH4uLiYGpqyiARadCtWzdcvHhR+HvDxHz37t2wt7dnFYv8n7KyMmELFnV1dWGbtV69euH69esso3FeVVUVHUWQUCYmJkhJSREb/+OPP2Bpadn0gYiIOXPmIDExEYmJibC0tIS3tzf09PQwe/ZsJCUlsY7HSebm5jA0NMSkSZMQGhqKZs2a4fvvv8eYMWOo2wsh5KvRJF3KTJ8+HT4+Pvjzzz/B4/Hw5MkTHD58GAsWLMDMmTNZx+O0tWvXYvHixfjxxx9RW1uLLVu2YMCAAQgNDcXq1atZx+O8hqq6QP0W3hMnTgCoX2HX1NRkF4zAz88P0dHR+O2336CgoIA9e/YgICAA+vr6CAsLYx2P0/z8/DBr1iwcP34cAoEACQkJWL16NX755Rf4+fmxjkf+T6dOnbBlyxb89ddfWLZsGfbs2YNu3bqhU6dO2LdvH+hkY9OJjY3F9OnT8eTJE8yaNQumpqYwMTGBp6cnDh06RK3XJFiHDh1olwORGHQmXQotXrwYQUFBwvYdCgoKWLBgAVauXMk4Gbl37x42bNiAxMRE8Pl8dOnSBQsXLhTr/0yaXlBQEGRlZeHt7Y2YmBgMGzYMdXV1qK2txaZNm+Dj48M6Ime1adMGYWFhcHR0hLq6OpKSkmBmZoaDBw/i6NGjuHTpEuuInLZ7926sWrUKjx49AgAYGBhg+fLl8PT0ZJyMNKipqUF4eDj279+Pq1ev4ptvvoGnpyeePHmC7du3w8nJCUeOHGEdk3Nqampw69YtXLt2DdeuXcPt27fx7t07mJmZISsri3U88pGzZ8/i1atXmDx5MusohNAkXVpVVVUhPT0dfD4flpaWUFVVZR2JEKlSVFSEu3fvom3btujUqRPrOJymqqqKBw8ewMjICK1bt8aZM2fQvXt35Ofnw9raGm/evGEdkQB4/vw5+Hx+o7U3CBtJSUnYv38/jh49CllZWUycOBFTp05F+/bthffcuXMHffr0QXV1NcOk3FZdXY24uDhERERg9+7dePPmjbBTAiGENKYZ6wDkv6OsrAw7OzvWMchH+Hw+Hj58iNLSUvD5fJFrjRX8I02jpqYGAwcOxK5du2Bubg6gfvWWqiFLhoajCEZGRsKjCN27d6ejCBKmRYsWrCOQj3Tr1g0DBgzAzp07MWLECMjJyYndY2lpiXHjxjFIx11v377FzZs3ERMTg2vXruHOnTswMTFB3759sXPnTvTt25d1RE578OABOnbs2Oi1y5cvY/DgwU2ciBBxtJIuBWbMmIHFixfD0NDwi/ceP34ctbW11E6Kgdu3b8PNzQ2FhYVi5/94PB69NWdMR0cHN2/epMI9EoiOIkiWwYMHY+nSpejZs+dn73v9+jV+++03qKqqYtasWU2UjnyosLAQRkZGrGOQD/Tt2xd37txB27Zt0adPH/Tt2xd9+/ZFy5YtWUcj/0dJSQnr1q3DnDlzhGPv3r2Dr68v9u7dS7tOiESglXQpoKOjAysrK/Ts2RMuLi6ws7ODvr4+FBUV8fLlS6SnpyMuLg7Hjh2DgYEBQkJCWEfmpBkzZsDOzg4XL16Enp4etV2TMJMmTcLevXsRGBjIOgr5yLx584Q/Ozk5ITMzk44iMDR69GiMGTMGampqn/3OuXTpEr799lusX7+edWTOogm65Ll58yb09PTg5OQER0dH9OnTh3ahSJjDhw/Dy8sLly5dwv79+1FSUgI3NzcAQHx8PON0hNSjlXQpUVpair179+LYsWO4f/++yDU1NTU4OzvDy8sLAwcOZJSQqKioIDU1FWZmZqyjkEbMmTMHYWFhMDMzg52dHVRUVESub9q0iVEyQiTP+/fvcerUKRw/fhw3btxAeXk5gPpdQZaWlhg0aBCmTZsGCwsLtkE5TktLq9EXwjweD4qKijAzM8OUKVPg7u7OIB03VVZW4saNG7h27RpiYmKQkpICc3Nz9O3bF46Ojujbty90dHRYx+S8J0+eYPLkyUhOTkZlZSXc3d2xceNGKCkpsY5GCACapEul8vJyFBYWorq6Gi1atEDbtm1p1VYC9OvXDz/99BOdZZJQTk5On7zG4/EQHR3dhGkIkS6vXr1CdXU1mjdv3ui5Z8JGUFAQVq9ejSFDhqB79+4QCAS4c+cOLl++jHnz5iE/Px8HDx7Etm3bMG3aNNZxOen169eIi4sTnk9PTU1Fu3btxBZcSNN69OgRJkyYgLS0NFRWVmLJkiVYsmQJZGSoOzWRDLTdXQppampSMSUJNGfOHPj6+qKkpATW1tZiD7I2NjaMkhEAiImJYR2BEKmloaEBDQ0N1jHIR+Li4rBq1SrMmDFDZHzXrl24cuUKTp8+DRsbG2zdupUm6YyoqKhAW1sb2tra0NLSQrNmzZCRkcE6FqcdO3YMP/74I3r37o3s7GykpKTA3d0dEREROHjwIExNTVlHJIRW0gn5pzT29pXH40EgEFDhOEIIIf84VVVVpKSkiB2zevjwITp37ow3b94gNzcXNjY2qKysZJSSW/h8Pu7evSvc7h4fH4/KykoYGBjAyclJ+I/qCbCjoqKCDRs24McffxSOvXz5EtOnT8fly5dRUVHBMB0h9WglnZB/SH5+PusI5DNcXV2/eHbTzc2NztgSQqSGtrY2fv/9d5HiiwDw+++/Q1tbG0D9GWk1NTUW8ThJU1MTlZWV0NPTg6OjIzZt2gQnJye0bduWdTTyf5KSksS+67W0tHDixAkcPHiQUSpCRNEknZB/CL0Vl2waGho4e/YsNDU10bVrVwgEAiQnJ6O8vBwDBw7E8ePH8euvvyIqKgoODg6s4xJCyBf5+/vjxx9/RExMDLp37w4ej4eEhARcunQJwcHBAICrV69SX+4mtH79ejg5OcHc3Jx1FPIJFhYWqK2txbVr15Cbmws3NzeoqanhyZMncHV1ZR2PEAC03Z2Q/y/nz5/HkCFDICcnh/Pnz3/2XhcXlyZKRRqzaNEiVFRUYPv27cKjCXw+Hz4+PlBTU8Pq1asxY8YMPHjwAHFxcYzTEgBwdnZGXl4e8vLyWEchRGLFx8dj+/btyMrKgkAgQPv27TFnzpwv9rknhKsKCwsxePBgFBUV4d27d8jOzoapqSnmzp2Lt2/fCl9wEcISTdKl0Kfe/qmrq0NVVZV1PE6RkZFBSUkJdHV1P1sRlM6ks6ejo4P4+Hix1Y3s7Gz07NkTz58/x71799C7d29huynC1o4dO/D8+XMsW7aMdRROKy8vx6lTp5Cbmws/Pz9oa2sjKSkJLVu2hIGBAet4hBDyt4wYMQJqamrYu3cvmjdvjtTUVJiamiI2NhZTp05FTk4O64iE0HZ3afPx278BAwZATU0N69ato7d/DPD5/EZ/JpKntrYWmZmZYpP0zMxM4QsURUVFamcoQWbNmsU6AuelpaXB2dkZGhoaKCgowLRp06CtrY3w8HAUFhYiLCyMdUTOq6urw9mzZ5GRkSHsY+/i4gJZWVnW0QiRSHFxcYiPj4e8vLzIuJGREf766y9GqQgRRc0ApYyPjw/s7Ozw8uVLKCkpCcddXV0RFRXFMBkhkm3ixInw9PREUFCQ8As6KCgInp6emDRpEgAgNjYWHTt2ZJyUe54+ffrJa2lpaU2YhHxs/vz5mDJlCnJycqCoqCgcHzJkCK5fv84wGQHqq7h36NABkyZNwpkzZ3Dq1ClMmDABHTt2RG5uLut4hEgkPp/f6O7Gx48fU5FFIjFou7uUadGiBeLj42FhYQE1NTXhFp2CggJYWlqiqqqKdUTO2rp1a6PjH1YP79OnD61uMFJXV4fAwEBs375dOCls2bIl5syZg4ULF0JWVhZFRUWQkZFB69atGaflFl1dXezZs0esbsOGDRvg7++P6upqRsmIhoYGkpKS0LZtW5HvnMLCQlhYWODt27esI3La0KFDIRAIcPjwYWE19xcvXmDChAmQkZHBxYsXGSckRPKMHTsWGhoaCAkJgZqaGtLS0qCjo4Phw4ejTZs22L9/P+uIhNB2d2lDb/8kV1BQEJ49e4aqqipoaWlBIBCgvLwcysrKUFVVRWlpKUxNTRETEwNDQ0PWcTlHVlYWixcvxuLFi4U9UNXV1UXuadOmDYtonLdw4UKMHTsWkydPRlBQEMrKyjBx4kQ8ePAAx48fZx2P0xQVFRvtGZyVlQUdHR0GiciHYmNjcfv2beEEHQCaN2+OwMBA6lJByCcEBQXByckJlpaWePv2Ldzc3JCTk4MWLVrg6NGjrOMRAoC2u0udAQMGYPPmzcLfeTwe3rx5g2XLlmHo0KHsghGsWbMG3bp1Q05ODl68eIGysjJkZ2ejR48e2LJlC4qKitCqVSuxfrak6amrq4tN0Ak7vr6+uH37NuLj42FjYwMbGxsoKSkhLS2NuiIwNnz4cKxYsQI1NTUA6r9zioqKsGjRIowaNYpxOqKgoIDXr1+Ljb9580bsvC0hpJ6+vj5SUlLg5+eH6dOnw9bWFoGBgUhOToauri7reIQAoO3uUufJkydwcnKCrKwscnJyYGdnJ3z7d/36dfpwYaht27Y4ffo0OnfuLDKenJyMUaNGIS8vDzdv3sSoUaNQXFzMJiQhEur169eYNm0aTp8+DQDYs2cPJk+ezDgVqaiowNChQ/HgwQO8fv0a+vr6KCkpgb29PS5dugQVFRXWETlt0qRJSEpKwt69e9G9e3cAwJ9//olp06aha9euCA0NZRuQEELIf4W2u0uZhrd/x44dQ2JiIvh8Pjw9PTF+/HiRQnKk6RUXF6O2tlZsvLa2FiUlJQDq/36NrXoQwmXx8fGYMGECmjdvjrS0NMTHx2POnDm4ePEidu3aBS0tLdYROUtdXR1xcXGIjo5GUlIS+Hw+unTpAmdnZ9bRCOproUyePBn29vaQk5MDUP+d4+Ligi1btjBOR4jkuHXrFuzt7b/q3srKShQUFFAhWcIUraQT8g8ZNmwYSkpKsGfPHtja2gKoX0WfNm0aWrVqhQsXLuD333/HL7/8gnv37jFOS4jkUFBQwLx587By5UrhRCM3NxcTJ05EUVERHj9+zDghIZItJycHmZmZEAgEsLS0hJmZGetIhEiUdu3awdjYGNOmTcPQoUOhqqoqdk96ejoOHTqE/fv3Y926dZg4cSKDpITUo0m6lFm7di1atmwJDw8PkfF9+/bh2bNnWLhwIaNkpKSkBBMnTkRUVJTIikb//v1x8OBBtGzZEjExMaipqcHAgQMZpyVEcsTGxqJv375i43w+H6tXr4a/vz+DVKRBVFQUoqKiUFpaCj6fL3Jt3759jFIRQsjXq6mpwa5du7B9+3bk5ubC3Nwc+vr6UFRUxMuXL5GZmYnKykqMHDkSP//8M6ysrFhHJhxHk3QpY2xsjCNHjqBnz54i43/++SfGjRuH/Px8RslIg8zMTGRnZ0MgEKB9+/awsLBgHYl8xt27d1FVVYU+ffqwjsJ5Dx8+RG5uLvr06QMlJSUIBALweDzWsTgtICAAK1asgJ2dHfT09MT+HuHh4YyScdf8+fO/+t5Nmzb9i0kIkU5JSUm4ceMGCgoKUF1djRYtWsDW1hZOTk4inRIIYYkm6VJGUVERGRkZMDExERnPy8sTtpIghHy9Dh06IDs7u9HWhqRpvHjxAmPGjEFMTAx4PB5ycnJgamoKT09PaGlpYcOGDawjcpaenh5t+5QwTk5OX3Ufj8dDdHT0v5yGEELIv4EKx0kZQ0NDxMfHi03S4+Pjoa+vzygVAYC6ujqEhoZ+clsoPSxJpqioKGF7KcLGvHnzICcnh6KiInTo0EE4PnbsWMybN48m6Qy9f/9ebOcWYSsmJoZ1BEIIIf8ymqRLmalTp2Lu3LmoqalBv379ANRPMn766Sf4+voyTsdtPj4+CA0NxbBhw2BlZUXbdKUEvdxi78qVK4iIiEDr1q1Fxtu1a4fCwkJGqQhQ/51z5MgRqgsgBR4/fgwejwcDAwPWUQghhPx/okm6lPnpp59QVlaGmTNn4v379wDqt8AvXLgQP//8M+N03Hbs2DGcOHECQ4cOZR2FNGLv3r3w9PQUG6+trYW/vz/Wrl3LIBUB6tvdKCsri40/f/4cCgoKDBKRBm/fvkVISAgiIyNhY2MjLIrZgM48s8Xn87Fq1Sps3LgRb968AQCoqanB19cXixcvhoyMDOOEhBBC/ht0Jl1KvXnzBhkZGVBSUkK7du3oQVYC6Ovr49q1azA3N2cdhTRCU1MT/fv3x+7du4WFYTIzM+Hm5oZXr14hNzeXcULuGjZsGLp06YKVK1dCTU0NaWlpMDIywrhx48Dn83Hq1CnWETnrc+ef6cwzez///DP27t2LgIAAODg4QCAQID4+HsuXL8e0adOwevVq1hEJIYT8F2iSTsg/ZOPGjcjLy8P27dtpq7sEys/Px8SJE5Gfn4/Q0FBkZ2fDz88P33//PXbs2AE1NTXWETkrPT0djo6O6Nq1K6Kjo+Hi4oIHDx6grKwM8fHxaNu2LeuIhEgkfX19BAcHw8XFRWT83LlzmDlzJv766y9GyQiRDm/fvoWioiLrGISIoUm6lKmsrERgYOAni5Pl5eUxSkZcXV0RExMDbW1tdOzYUWxb6JkzZxglIw34fD7mzZuH7du3Q1ZWFmFhYRg3bhzrWARASUkJdu7cicTERPD5fHTp0gWzZs2Cnp4e62iESCxFRUWkpaWJ7eDKyspC586dUV1dzSgZIZKLz+dj9erVCA4OxtOnT5GdnQ1TU1P4+/vD2Ni40aNxhDQ1OpMuZaZOnYrY2FhMnDix0Z61hB1NTU24urqyjkE+48KFCzh69Ch69uyJrKws7N69G3369KHicRKgVatWCAgIYB2DABg5ciRCQ0Ohrq6OkSNHfvZeevnIVqdOnbB9+3Zs3bpVZHz79u3o1KkTo1SESLZVq1bhwIEDWLduHaZNmyYct7a2RlBQEE3SiUSgSbqU+eOPP3Dx4kU4ODiwjkI+sn//ftYRyGdMnz4dBw4cwKpVq+Dr64unT5/Cw8MD1tbW2LlzJ8aMGcM6IqcUFRWhTZs2X33/X3/9RVWrm4iGhobwBbCGhgbjNORz1q1bh2HDhiEyMhL29vbg8Xi4efMmHj16hEuXLrGOR4hECgsLQ0hICPr3748ZM2YIx21sbJCZmckwGSH/QdvdpYyJiQkuXbok0kuYSI7a2lpcu3YNubm5cHNzg5qaGp48eQJ1dXWoqqqyjsdpVlZWOHz4sNjq0o4dO7Bw4UJhZWTSNFq2bAkXFxdMmzYN3bt3b/SeV69e4cSJE9iyZQumT5+OOXPmNHFKQiTfkydPsGPHDmRmZkIgEMDS0hIzZ86kHUKEfIKSkhIyMzNhZGQENTU1pKamwtTUFOnp6ejevTs9DxCJQJN0KXPo0CGcO3cOBw4caLRlEWGnsLAQgwcPRlFREd69eyc84zR37ly8ffsWwcHBrCNy2rt37z7ZBSErKwsWFhZNnIjbysrKsGbNGuzbtw9ycnKws7ODvr4+FBUV8fLlS6Snp+PBgwews7PDkiVLMGTIENaRCSGE/A+ws7PD3LlzMWHCBJFJekBAACIjI3Hjxg3WEQmh7e7SZuPGjcjNzUXLli1hbGwsVpwsKSmJUTLi4+MDOzs7pKamonnz5sJxV1dXTJ06lWEyAgAKCgrIzc3F/v37kZubiy1btkBXVxeXL1+GoaEh63ico62tjQ0bNmDVqlW4dOkSbty4gYKCAlRXV6NFixYYP348Bg0aBCsrK9ZROcfW1var653Qdw57L1++xN69e5GRkQEej4cOHTrA3d1d2GqSECJq2bJlmDhxIv766y/w+XycOXMGWVlZCAsLw4ULF1jHIwQAraRLnS8VVlq2bFkTJSEfa9GiBeLj42FhYSHyZragoACWlpaoqqpiHZHTYmNjMWTIEDg4OOD69evIyMiAqakp1q1bh4SEBOrFTcj/+TsF/Og7h63Y2FgMHz4c6urqsLOzAwAkJiaivLwc58+fR9++fRknJEQyRUREYM2aNSIdRZYuXYqBAweyjkYIAJqkE/KP0dbWRlxcHCwtLUUm6XFxcRg1ahSePn3KOiKn2dvbY/To0Zg/f77I3+fOnTsYMWIE9RMmhEgdKysr9OzZEzt37oSsrCwAoK6uDjNnzkR8fDzu37/POCEhhJD/Bk3SpVB5eTlOnTqF3Nxc+Pn5QVtbG0lJSWjZsiVVP2Zo7Nix0NDQQEhICNTU1JCWlgYdHR0MHz4cbdq0oervjKmqquLevXswMTER2+nQvn17vH37lnVEQiTW3bt3RbZTd+3alXUkgvoCWCkpKWI1NahPOiFf9v79e5SWloLP54uM/53OI4T8W+hMupRJS0uDs7MzNDQ0UFBQgGnTpkFbWxvh4eEoLCxEWFgY64icFRQUBCcnJ1haWuLt27dwc3NDTk4OWrRogaNHj7KOx3mampooLi6GiYmJyHhycjK93CLkEx4/fowffvgB8fHx0NTUBFD/orhnz544evQo1XNgrEuXLsjIyBCbpGdkZKBz585sQhEi4XJycuDh4YGbN2+KjAsEAvB4PNTV1TFKRsh/0CRdysyfPx9TpkzBunXroKamJhwfMmQI3NzcGCYj+vr6SElJwbFjx4RnnDw9PTF+/HgoKSmxjsd5bm5uWLhwIU6ePAkejwc+n4/4+HgsWLAAkyZNYh2PEInk4eGBmpoakYlgVlYWPDw84OnpiStXrjBOyG3e3t7w8fHBw4cP8c033wAAbt++jR07diAwMBBpaWnCe21sbFjFJESiTJkyBc2aNcOFCxegp6f31YUyCWlKtN1dymhoaCApKQlt27YV2bJbWFgICwsL2rJLyCfU1NRgypQpOHbsGAQCAZo1a4a6ujq4ubkhNDRUeJ6TEPIfSkpKuHnzJmxtbUXGk5KS4ODgQNupGZORkfnsdR6PR6uDhHxERUUFiYmJaN++PesohHwSraRLGUVFRVRUVIiNZ2VlQUdHh0EiQqSDnJwcDh8+jJUrVyIpKQl8Ph+2trZo164d62gEwMGDBxEcHIz8/HzcunULRkZG2Lx5M0xMTDB8+HDW8TirTZs2qKmpERuvra2lYyISID8/n3UEQqSOpaUlnj9/zjoGIZ9Fk3QpM3z4cKxYsQInTpwAUP+WvKioCIsWLcKoUaMYpyNE8pmamsLU1JR1DPKBnTt3YunSpZg7dy5Wr14tXPHT1NTE5s2baZLO0Lp16zBnzhzs2LEDXbt2BY/Hw927d+Hj44MNGzawjsd5RkZGrCMQInV+/fVX/PTTT1izZg2sra0hJycncl1dXZ1RMkL+g7a7S5mKigoMHToUDx48wOvXr6Gvr4+SkhLY29vj0qVLUFFRYR2REIkRGBgIb29vKCsrf/HeP//8E8+fP8ewYcOaIBn5kKWlJdasWYMRI0aIHOO5f/8+HB0dacWDIS0tLVRVVaG2thbNmtW/12/4+ePvm7KyMhYROS83NxebN28Wqb7v4+ODtm3bso5GiERqOCby8Vl0OhpCJAmtpEsZdXV1xMXFITo6Wrhlt0uXLnB2dmYdjdPq6uoQFxcHGxsbaGlpsY5D/k96ejratGmD0aNHw8XFBXZ2dsJjIbW1tUhPT0dcXBwOHTqE4uJi6o7ASH5+vtiZZwBQUFBAZWUlg0SkwebNm1lHIJ8REREBFxcXdO7cGQ4ODhAIBLh58yY6duyI33//HQMGDGAdkRCJExMTwzoCIV9EK+mE/EMUFRWRkZEh1uKLsJWWloYdO3bg5MmTePXqFWRlZaGgoICqqioAgK2tLby8vDB58mQoKCgwTstNlpaWWLt2LYYPHy6ykr5161YcOHAAiYmJrCMSIpFsbW0xaNAgBAYGiowvWrQIV65cQVJSEqNkhBBC/n/QSroU2Lp1K7y8vKCoqIitW7d+9l5vb+8mSkU+Zm1tjby8PJqkSxgbGxvs2rULwcHBSEtLQ0FBAaqrq9GiRQt07twZLVq0YB2R8/z8/DBr1iy8ffsWAoEACQkJOHr0KNauXYs9e/awjsc5FRUVwjOZjRUq/RCd3WQrIyNDWKPmQx4eHrQLgpAPpKWlwcrKCjIyMiKtCRtD7QqJJKCVdClgYmKCu3fvonnz5p+dAPJ4POTl5TVhMvKhK1euYOHChVi5ciW6du0qdl6THmYJ+bTdu3dj1apVePToEQDAwMAAy5cvh6enJ+Nk3CMrK4vi4mLo6upCRkam0R7CdHZTMhgaGmLTpk0YPXq0yPiJEyewYMECFBUVMUpGiGSRkZFBSUmJyOdaY1Mg+lwjkoIm6YT8Qz7sV/vhQy09zBLy9Z4/fw4+nw9dXV3WUTgrNjYWDg4OaNasGWJjYz97b9++fZsoFWnMihUrEBQUhEWLFqFnz57g8XiIi4vDr7/+Cl9fXyxZsoR1REIkQmFhIdq0aQMej4fCwsLP3ktdE4gkoEk6If8QepglhBDSlAQCATZv3oyNGzfiyZMnAAB9fX34+fnB29u70V0QhBBCJB9N0qXM999/Dzs7OyxatEhkfP369UhISMDJkycZJSOEkP/O06dPsWDBAkRFRaG0tFRsCyLtQmFn//79UFVVFdtOffLkSVRVVWHy5MmMkpHa2locPnwYgwYNQqtWrfD69WsAgJqaGuNkhEie8+fPf/W9Li4u/2ISQr4OTdKljI6ODqKjo2FtbS0yfu/ePTg7O+Pp06eMkhEAuHHjBnbt2oW8vDycPHkSBgYGOHjwIExMTNCrVy/W8QiRSEOGDEFRURFmz54NPT09sdW/4cOHM0pGLCwsEBwcDCcnJ5Hx2NhYeHl5ISsri1EyAgDKysrIyMig7bmEfMGHRxI/h44nEklB1d2lzJs3byAvLy82Licn98UqvOTfdfr0aUycOBHjx49HUlIS3r17BwB4/fo11qxZg0uXLjFOSD5UUVGB6OhoWFhYoEOHDqzjcFpcXBxu3LiBzp07s45CPlJYWNhowVIjIyMqSiYBevTogeTkZJqkE/IFfD6fdQRC/pave61EJIaVlRWOHz8uNn7s2DFYWloySEQarFq1CsHBwdi9ezfk5OSE4z179qRetRJgzJgx2L59OwCguroadnZ2GDNmDGxsbHD69GnG6bjN0NCw0Sq7hD1dXd1G2xWlpqaiefPmDBKRD82cORO+vr7Yvn07bt26hbS0NJF/hBBCpBOtpEsZf39/jBo1Crm5uejXrx8AICoqCkePHqXz6IxlZWWhT58+YuPq6uooLy9v+kBExPXr17F48WIAQHh4OAQCAcrLy3HgwAGsWrUKo0aNYpyQuzZv3oxFixZh165dMDY2Zh2HfGDcuHHw9vaGmpqa8PMtNjYWPj4+GDduHON0ZOzYsQAAb29v4VhDaynatkvIp0VFRSEoKAgZGRng8Xho37495s6dC2dnZ9bRCAFAk3Sp4+LigrNnz2LNmjU4deoUlJSUYGNjg8jISKoezpienh4ePnwoNsmIi4uDqakpm1BE6NWrV9DW1gYAXL58GaNGjYKysjKGDRsGPz8/xum4bezYsaiqqkLbtm2hrKwsshMFAMrKyhglI6tWrUJhYSH69++PZs3qHxn4fD4mTZqENWvWME5H8vPzWUcgROps374d8+bNw/fffw8fHx8AwO3btzF06FBs2rQJs2fPZpyQECocR8g/Zt26dThw4AD27duHAQMG4NKlSygsLMS8efOwdOlS+tBnzNzcHKtWrcKwYcNgYmKCY8eOoV+/fkhNTUX//v3x/Plz1hE568CBA5+9ThXE2cvJyUFKSgqUlJRgbW1NZ6AJIVLLwMAAP//8s9hz2Y4dO7B69WphO0NCWKJJupR59OgReDweWrduDQBISEjAkSNHYGlpCS8vL8bpyOLFixEUFIS3b98CABQUFLBgwQKsXLmScTLy22+/wcfHB6qqqjAyMkJSUhJkZGSwbds2nDlzBjExMawjEiLx6urqcO/ePRgZGUFLS4t1HAIgOzsb165dQ2lpqVhxrKVLlzJKRYjkUlNTQ3JyMszMzETGc3JyYGtrizdv3jBKRsh/0CRdyvTu3RteXl6YOHEiSkpKYG5uDisrK2RnZ8Pb25u+kCVAVVUV0tPTwefzYWlpCVVVVdaRyP9JTExEUVERBgwYIPy7XLx4EVpaWujZsyfjdNxSUVEBdXV14c+f03AfaXpz586FtbU1PD09UVdXh759++LmzZtQVlbGhQsX4OjoyDoip+3evRs//vgjWrRogVatWom0L+TxeFS0lJBGjB8/Hp07dxY76rZhwwYkJibi6NGjjJIR8h80SZcyWlpauH37NiwsLLB161YcP34c8fHxuHLlCmbMmIG8vDzWETnLw8MDW7ZsgZqamsh4ZWUl5syZg3379jFKRgBgxYoVWLBgAZSVlUXGq6ursX79enrB1cRkZWVRXFwMXV1dyMjIiPVGB0DFryRA69atcfbsWdjZ2eHs2bOYOXMmrl27hrCwMMTExCA+Pp51RE4zMjLCzJkzsXDhQtZRCJEaq1atwoYNG+Dg4AB7e3sA9WfS4+Pj4evrK/Ji+MOijIQ0JZqkSxlVVVXcv38fxsbGcHFxgYODAxYuXIiioiJYWFigurqadUTO+nDS8aHnz5+jVatWqK2tZZSMAJ/++7x48QK6uro0EWxisbGxcHBwQLNmzRAbG/vZe6koJjuKiop4+PAhWrduDS8vLygrK2Pz5s3Iz89Hp06dvrgLgvy71NXVkZKSQsVJCfkbTExMvuo+Ho9Hi1+EGaruLmU6duyI4OBgDBs2DFevXhWedX7y5An1rGWkoqICAoEAAoEAr1+/hqKiovBaXV0dLl26JDYxJE2vYVX2Y6mpqcKq76TpfDjxpkm45GrZsiXS09Ohp6eHy5cv47fffgNQf6xHVlaWcToyevRo4U46QsjXoa4IRBrQJF3K/Prrr3B1dcX69esxefJkdOrUCQBw/vx5dO/enXE6btLU1ASPxwOPx4O5ubnYdR6Ph4CAAAbJCFB/ROTDv8+HE/W6ujq8efOGHnAZSEtL++p7bWxs/sUk5HPc3d0xZswY6OnpgcfjYcCAAQCAP//8E+3bt2ecjpu2bt0q/NnMzAz+/v64ffs2rK2txdoX0lZdQgiRTrTdXQrV1dWhoqJCpLJuQUEBlJWVacWWgdjYWAgEAvTr1w+nT58WWZWVl5eHkZER9PX1GSbktgMHDkAgEMDDwwObN2+GhoaG8Jq8vDyMjY2FZ9JI02k4h/6lryA6k87eqVOn8OjRI4wePVrYWeTAgQPQ1NTE8OHDGafjHtqqS8jfN3/+fKxcuRIqKiqYP3/+Z+/dtGlTE6Ui5NNokk7IP6SwsBBt2rRpdEs1Yau2thaHDh2Cs7OzcJJB2CosLPzqe6knNyGEkP8fTk5OCA8Ph6amJpycnD55H4/HQ3R0dBMmI6RxNEkn5B9y/fr1z17v06dPEyUhjVFWVkZGRgZN+Aj5gq1bt8LLywuKiooiW6sbQ9up2aKuFYQQ8r+JJumE/ENkZGTExj4+/0zYcXJygo+PD0aMGME6CmlEVlYWtm3bhoyMDPB4PLRv3x5z5syBhYUF62icY2Jigrt376J58+af3VpN26nZo64VhPx9r169Ql1dnVjR2LKyMjRr1kykBRshrFDhOEL+IS9fvhT5vaamBsnJyfD398fq1asZpSINZs6cCV9fXzx+/Bhdu3aFioqKyHUqTsbOqVOn8MMPP8DOzk6kZ62VlRWOHDmC0aNHM07ILR9WPqYqyJKNulYQ8veNGzcO3333HWbOnCkyfuLECZw/fx6XLl1ilIyQ/6CVdEL+ZdevX8e8efOQmJjIOgqnfWqnQ8NDLq04sWNqaooJEyZgxYoVIuPLli3DwYMHabWWkI80dK149eoV1NXVP9m1YseOHQxTEiKZtLW1ER8fjw4dOoiMZ2ZmwsHBAS9evGCUjJD/oJV0KfClM4EfovOBkkdHRwdZWVmsY3AerQhKrpKSEkyaNElsfMKECVi/fj2DRNz2pcrHH6IqyGxs3rxZ2LUiICCAulYQ8je8e/cOtbW1YuM1NTWorq5mkIgQcTRJlwJBQUFfdR+Px6NJOkMf930WCAQoLi5GYGCgsJ89YYcKxkkuR0dH3LhxA2ZmZiLjcXFx6N27N6NU3JWcnCzye2JiIurq6oT1AbKzsyErK4uuXbuyiEcATJ48GUB9/YCePXuK9UcnhHxat27dEBISgm3btomMBwcH0+cakRg0SZcCtAIoHTp37txo3+dvvvkG+/btY5SKfCw9PR1FRUV4//69yLiLiwujRMTFxQULFy5EYmIivvnmGwD1Z9JPnjyJgIAAnD9/XuRe8u+KiYkR/rxp0yaoqanhwIED0NLSAlBff8Pd3Z1eoEiAvn37gs/nIzs7G6WlpeDz+SLXqasIIeJWr14NZ2dnpKamon///gCAqKgo3LlzB1euXGGcjpB6dCadkH/Ix32fZWRkoKOjA0VFRUaJyIfy8vLg6uqKe/fuibxMaTjLSWfS2WmsXkBjqHZA0zMwMMCVK1fQsWNHkfH79+9j4MCBePLkCaNkBKh/meXm5obCwkKxF8T0/wshn5aSkoL169cjJSUFSkpKsLGxwc8//4x27dqxjkYIAFpJl0qPHz/G+fPnG10NpPOB7NB2asnm4+MDExMTREZGwtTUFAkJCXjx4gV8fX2xYcMG1vE47ePVPyI5Kioq8PTpU7FJemlpKV6/fs0oFWkwY8YM2NnZ4eLFi9DT02u00jshRFznzp1x+PBh1jEI+SSapEuZqKgouLi4wMTEBFlZWbCyskJBQQEEAgG6dOnCOh7nRUVFISgoSKTX89y5c+Hs7Mw6GufdunUL0dHR0NHRgYyMDGRkZNCrVy+sXbsW3t7eYudwCSGAq6sr3N3dsXHjRpGjCH5+fhg5ciTjdCQnJwenTp0Sq+dACPm0pKQkyMnJwdraGgBw7tw57N+/H5aWlli+fDnk5eUZJySEJulS5+eff4avry9WrFgBNTU1nD59Grq6uhg/fjwGDx7MOh6nbd++HfPmzcP3338PHx8fAPUPs0OHDsWmTZswe/Zsxgm5ra6uDqqqqgCAFi1a4MmTJ7CwsICRkRFV32dg69at8PLygqKi4hc7WFBBTHaCg4OxYMECTJgwATU1NQCAZs2awdPTkyrvS4AePXrg4cOHNEkn5G+YPn06Fi1aBGtra+Tl5WHs2LEYOXIkTp48iaqqKmzevJl1RELoTLq0UVNTQ0pKCtq2bQstLS3ExcWhY8eOSE1NxfDhw1FQUMA6ImcZGBjg559/FpuM79ixA6tXr6azm4z17t0bvr6+GDFiBNzc3PDy5UssWbIEISEhSExMxP3791lH5BQTExPcvXsXzZs3h4mJySfv4/F41CddAlRWViI3NxcCgQBmZmZQUVFhHYkACA8Px5IlS+Dn5wdra2uxKu82NjaMkhEiuTQ0NJCUlIS2bdvi119/RXR0NCIiIhAfH49x48bh0aNHrCMSQivp0kZFRQXv3r0DAOjr6yM3N1d4VvD58+cso3FeRUVFo7sZBg4ciIULFzJIRD60ZMkSVFZWAgBWrVqFb7/9Fr1790bz5s1x/Phxxum458OuFdTBQvKpqKjQhE8CjRo1CgDg4eEhHGsojEmF4whpnEAgENZCiYyMxLfffgsAMDQ0pGdpIjFoki5lvvnmG8THx8PS0hLDhg2Dr68v7t27hzNnzgjPCxI2XFxcEB4eDj8/P5Hxc+fO4bvvvmOUijQYNGiQ8GdTU1Okp6ejrKwMWlpaVGyJECKV6AUXIX+fnZ0dVq1aBWdnZ8TGxmLnzp0A6v9/atmyJeN0hNSj7e5SJi8vD2/evIGNjQ2qqqqwYMECxMXFwczMDEFBQVRhnKFVq1Zhw4YNcHBwgL29PYD6M+nx8fHw9fWFurq68F46Y0vIf3z//fews7PDokWLRMbXr1+PhIQEnDx5klEyQggh/2vS0tIwfvx4FBUVYf78+Vi2bBkAYM6cOXjx4gWOHDnCOCEhNEmXKnV1dYiLi4ONjQ20tLRYxyEf+dy52g/RGVtCROno6CA6OlpYabfBvXv34OzsjKdPnzJKRoh0SE9Pb7Qtq4uLC6NEhEift2/fQlZWVqy2AyEs0HZ3KSIrK4tBgwYhIyODJukSiLYdEvLfefPmTaMtb+Tk5FBRUcEgESHSIS8vD66urrh3757wLDoA4REeOpNOyNdTVFRkHYEQIZqkS5mGdhFfu2pLCCGSzsrKCsePH8fSpUtFxo8dOwZLS0tGqQgAnD9/vtFxHo8HRUVFmJmZ0fcRQz4+PjAxMUFkZCRMTU2RkJCAFy9ewNfXFxs2bGAdjxCJoa2tjezsbLRo0eKLtWjKysqaMBkhjaNJupRZvXo1FixYgJUrV6Jr165ibXA+PPdMmpZAIMCpU6cQExOD0tJSYeXQBmfOnGGUjBDJ5u/vj1GjRiE3Nxf9+vUDAERFReHo0aN0Hp2xESNGiKzQNviwgnivXr1w9uxZ2uHFwK1btxAdHQ0dHR3IyMhARkYGvXr1wtq1a+Ht7Y3k5GTWEQmRCEFBQVBTUwMA6oNOpAKdSZcyMjIywp8/fAtI7VbY8/b2RkhICJycnNCyZUuxt7T79+9nlIwAtCIo6S5evIg1a9YgJSUFSkpKsLGxwbJly9C3b1/W0TgtKioKixcvxurVq9G9e3cAQEJCApYsWQJ/f39oaGhg+vTp6NGjB/bu3cs4LfdoaWkhMTERpqamaNu2Lfbs2QMnJyfk5ubC2toaVVVVrCMSQgj5L9AkXcrExsZ+9jo90LKjra2NQ4cOYejQoayjkEbIyMjQiiAhf5OVlRVCQkLQs2dPkfH4+Hh4eXnhwYMHiIyMhIeHB4qKihil5K7evXvD19cXI0aMgJubG16+fIklS5YgJCQEiYmJuH//PuuIhEiEv1PfhHalEklA292lDE3CJZeGhgZMTU1ZxyCfcPXq1a9aEVywYAGtCDaxR48egcfjoXXr1gDq/y5HjhyBpaUlvLy8GKfjttzc3EYfWNXV1YVdKtq1a4fnz583dTQCYMmSJaisrARQ3wb022+/Re/evdG8eXMcP36ccTpCJIempuZnz6F/iHalEklAK+lS5vr165+93qdPnyZKQj524MABXL58Gfv27YOSkhLrOOQjtCIouXr37g0vLy9MnDgRJSUlMDc3h5WVFbKzs+Ht7S1WUI40nV69ekFNTQ1hYWHQ0dEBADx79gyTJk1CZWUlrl+/jsjISMycORPZ2dmM0xKgvujVlwpjEcI1H+5ELSgowKJFizBlyhTY29sDqK/vcODAAaxduxaTJ09mFZMQIZqkS5kPz6Q3+PCLmN7+sVNVVYWRI0ciPj4exsbGYn02k5KSGCUjAKCkpIQ7d+7AyspKZPzevXvo3r07qqurUVhYiA4dOtA5ziampaWF27dvw8LCAlu3bsXx48cRHx+PK1euYMaMGcIVW9L0srKyMHz4cOTn58PQ0BA8Hg9FRUUwNTXFuXPnYG5ujrNnz+L169eYOHEi67iEEPJF/fv3x9SpU/HDDz+IjB85cgQhISG4du0am2CEfIC2u0uZly9fivxeU1OD5ORk+Pv7Y/Xq1YxSEQCYMmUKEhMTMWHChEYLxxG2unbtCj8/P7EVwZ9++gndunUDAOTk5Ai3XJOmU1NTAwUFBQBAZGQkXFxcAADt27dHcXExy2icZ2FhgYyMDERERCA7OxsCgQDt27fHgAEDhC+NR4wYwTYkIYT8Dbdu3UJwcLDYuJ2dHaZOncogESHiaCX9f8T169cxb948JCYmso7CWSoqKoiIiECvXr1YRyGNoBVBydWjRw84OTlh2LBhGDhwIG7fvo1OnTrh9u3b+P777/H48WPWEQkhhPyPsLCwwLfffouNGzeKjPv6+uLChQvIyspilIyQ/6CV9P8ROjo69KHCmKGhIVUElWC0Iii5fv31V7i6umL9+vWYPHkyOnXqBKC+bV5DkT/CTlRUFKKiolBaWgo+ny9ybd++fYxSEULIfycoKAijRo1CREQEvvnmGwDA7du3kZubi9OnTzNOR0g9WkmXMmlpaSK/CwQCFBcXIzAwEDU1NYiPj2eUjFy8eBHbtm1DcHAwjI2NWcchRKrU1dWhoqJCpP1dQUEBlJWVoauryzAZtwUEBGDFihWws7ODnp6e2DGe8PBwRskIIeS/9/jxY+zcuRMZGRkQCASwtLTEjBkzYGhoyDoaIQBoki51PptHNgAASShJREFUPtXr+ZtvvsG+ffvQvn17RsmIlpYWqqqqUFtbC2VlZbHCcWVlZYySkQa0IkjI36Onp4d169bRERApU1xcjJqaGrRp04Z1FEIIIf8F2u4uZfLz80V+l5GRgY6ODhQVFRklIg02b97MOgL5jC+tCJKm1aVLF0RFRUFLSwu2traf/XtQZwR23r9/L9a2kEi+fv36ITs7mzq+EEKIlKJJupSJjY3F2LFjhZWQG7x//x7Hjh3DpEmTGCUj1FdTsgUHByM0NJRWBCXE8OHDhZ9jVAtAck2dOhVHjhyBv78/6yjkbwgLC6NWkoQQIsVou7uUkZWVRXFxsdgZzRcvXkBXV5femjNWV1eHs2fPIiMjAzweD5aWlnBxcYGsrCzraJzXvHlzJCQkoG3btqyjECI1fHx8EBYWBhsbG9jY2Igd49m0aROjZIQQQsj/LlpJlzICgaDRbaGPHz+GhoYGg0SkwcOHDzF06FD89ddfsLCwgEAgQHZ2NgwNDXHx4kWaHDJGK4KE/H1paWno3LkzAOD+/fsi1+jICHuRkZFwdnZu9NquXbswffr0Jk5ECCHkn0Ar6VKi4cxmamoqOnbsiGbN/vN+pa6uDvn5+Rg8eDBOnDjBMCW3DR06FAKBAIcPH4a2tjaA+h0OEyZMgIyMDC5evMg4IbfRiqBk0dLS+upJHhVdJKRxCgoKmD17NtauXQt5eXkAwLNnz+Dh4YH4+Hj6f4eQRlRXV0MgEEBZWRkAUFhYiPDwcFhaWmLgwIGM0xFSj1bSpUTDmc2UlBQMGjQIqqqqwmvy8vIwNjbGqFGjGKUjQH29gNu3bwsn6ED9FuvAwEA4ODgwTEYAWhGUNB8WWnzx4gVWrVqFQYMGwd7eHgBw69YtRERE0M4HQj7j+vXrmDhxIiIjI3HkyBEUFBTAw8MDlpaWSE1NZR2PEIk0fPhwjBw5EjNmzEB5eTl69OgBOTk5PH/+HJs2bcKPP/7IOiIhtJIubQ4cOICxY8dSNXcJpK2tjQsXLohVQo6Pj8d3331HKxqEfMKoUaPg5OSE2bNni4xv374dkZGROHv2LJtgHDVy5EiEhoZCXV0dI0eO/Oy9Z86caaJU5FMqKysxY8YMnDx5Enw+H6tWrYKfnx+9fCTkE1q0aIHY2Fh07NgRe/bswbZt25CcnIzTp09j6dKlyMjIYB2REMiwDkD+nsmTJ9MEXUJ9++238PLywp9//gmBQACBQIDbt29jxowZcHFxYR2PEIkVERGBwYMHi40PGjQIkZGRDBJxm4aGhnCCp6Gh8dl/hL2srCzcuXMHrVu3RrNmzZCZmUmV3Qn5jKqqKqipqQEArly5gpEjR0JGRgbffPMNCgsLGacjpB5td5cydXV1CAoKwokTJ1BUVIT379+LXKfVWna2bt2KyZMnw97eXnjeuba2Fi4uLtiyZQvjdNxEK4LSoXnz5ggPD4efn5/I+NmzZ9G8eXNGqbhr//79AOoLlS5fvhw6OjrCs5tEsgQGBmLZsmXw8vLC+vXrkZubiwkTJsDGxgaHDh0SHh8hhPyHmZkZzp49C1dXV0RERGDevHkAgNLSUqirqzNOR0g9mqRLmYCAAOzZswfz58+Hv78/Fi9ejIKCApw9exZLly5lHY/TNDU1ce7cOTx8+BAZGRkQCASwtLSEmZkZ62ic9fGKIJFMAQEB8PT0xLVr14STitu3b+Py5cvYs2cP43TcJRAI0K5dOzx48ADt/l97dx5XY/r/D/x1Tot2ZYmMtIgSoeRjzz4TGVuDGfv2MbaJEOMzyM7YxjJGX5KMMRqTmrHMh0bZspVSWaKVLCWiokI69+8PP+fjKGaGM67TnNfz8ejx6Fz33ellmlPnfd/X9b4aNBAdhyqwbt06/PLLL+jRowcAoHHjxoiJicF//vMfdOrUCU+ePBGckEjzzJs3D4MHD4avry+6dOmi/LsTEREBV1dXwemInuOa9Eqmfv36WL9+Pby8vGBqaoqEhATl2JkzZ/Djjz+KjkhE9JedPXsW69evV7nA5ePjg1atWomOptUaN26MrVu3onXr1qKjUAXu3buHGjVqVHjs2LFj6Nix43tORFQ55OTkIDs7G82aNYNc/nz1b0xMDMzMzODk5CQ4HRGL9ErH2NgYycnJqFevHqysrHDgwAG4ubkhIyMDrq6uKCgoEB1Ra33yySdwd3fHl19+qTK+cuVKxMTE4OeffxaUjIjo7Rw4cADLly/Hpk2b0KRJE9FxqAL5+fkIDQ1Feno6/Pz8UK1aNcTHx6NWrVr44IMPRMcj0lhpaWlIT0+Hh4cHDA0NIUkSGy6SxmDjuEqmbt26yM7OBvB8TU1ERAQAIDY2FlWqVBEZTesdO3YMXl5e5cY9PT1x/PhxAYnoZXl5eZg0aRKcnZ1Ro0YNVKtWTeWDiMobOnQoYmJi0KxZMxgaGvJ1o2GSkpLQsGFDfP3111i1ahXy8/MBAOHh4Zg9e7bYcEQaKi8vD127dkXDhg3Rs2dP5fvqsWPHYvr06YLTET3HNemVTL9+/RAZGYlWrVphypQp+Oyzz7B161ZkZWUpG1+QGI8ePYK+vn65cT09PRQWFgpIRC8bOnQo0tPTMWbMGNSqVYtXy4n+hJf3syfNM23aNIwcORIrVqxQdqsGgB49emDw4MECkxFpLl9fX+jp6SErKwuNGjVSjg8aNAi+vr5YvXq1wHREz3G6eyV35swZnDp1Cg4ODtzmS7CWLVvi448/LtfAb/78+di3bx/i4uIEJSMAMDU1RXR0NJo1ayY6ChGRWlStWhXx8fGoX78+TE1NkZiYCHt7e1y/fh2Ojo54/Pix6IhEGqd27do4dOgQmjVrpvK6yczMhIuLCx49eiQ6IhHvpFd2rVu3ZkMfDTF37lx4e3sjPT0dXbp0AQBERkZi165dXI+uAZycnFBSUiI6BlGlk56ejm3btiE9PR3r1q2DpaUlDh48CGtrazRu3Fh0PK1mYGBQ4Uytq1evombNmgISEWm+oqKiCreVvHfvHpeOksbgnfRKKCUlBUePHkVubi4UCoXKMW7DJtaBAwewdOlSJCQkwNDQEE2bNoW/vz877GqA2NhYfPnll5g3bx6aNGmi3Mv+Be6NqjkKCwsRFRUFR0dHlamI9P4dO3YMPXr0QLt27XD8+HEkJyfD3t4eK1asQExMDEJDQ0VH1Grjxo3D3bt3sXv3blSrVg1JSUnQ0dFB37594eHhweUKRBXw8vKCm5sbFi1aBFNTUyQlJcHGxgaffvopFAoFf6+RRmCRXsls2bIFEyZMQI0aNVC7dm2VdbUymQzx8fEC0xFprtTUVHz22Wc4f/68yviLbq5lZWWCktHAgQPh4eGByZMno6SkBM2aNcO1a9cgSRJCQkLg7e0tOqLWatOmDQYMGIBp06apTAuNjY1F3759cevWLdERtVphYSF69uyJS5cu4eHDh6hTpw5ycnLQpk0b/PbbbzA2NhYdkUjjXL58GZ06dUKLFi0QFRWF3r1749KlS7h//z5OnjyJ+vXri45IxCK9srGxscHEiRMxa9Ys0VGIKpV//etf0NXVxZQpUypsHMfZDuK8vD7wxx9/hL+/PxITE7F9+3Zs3ry53IUVen9MTExw4cIF2NnZqRTp165dg5OTE9c8a4ioqCjEx8dDoVDAzc0N3bp1Ex2JSKPl5ORg06ZNiIuLU75uJk2aBCsrK9HRiABwTXql8+DBAwwYMEB0DKJK5+LFizh//jwcHR1FR6FXFBQUKLfzOnjwILy9vWFkZAQvLy/4+fkJTqfdzM3NkZ2dDTs7O5Xx8+fPcw9uDdKlSxdlLxQi+mO1a9fGggULRMcgei3uk17JDBgwQLk3OhH9ee7u7rhx44boGFQBa2trnD59GkVFRTh48CA+/PBDAM8vShoYGAhOp90GDx6MWbNmIScnBzKZDAqFAidPnsSMGTMwfPhw0fG0UkhIyJ8+98aNGzh58uTfmIaockhKSlL2cUpKSnrjB5Em4HT3SmbZsmVYs2YNvLy84OLiUq75lY+Pj6BkRJrt559/xvz58+Hn51fha6dp06aCktF3332HKVOmwMTEBDY2NoiPj4dcLseGDRsQFhaGI0eOiI6otUpLSzFy5EiEhIRAkiTo6uqirKwMgwcPRnBwMHR0dERH1DodO3bEnTt3MGrUKPTu3btcc8WCggKcPHkSP/zwAw4fPoytW7fi448/FpSWSDPI5XLk5OTA0tIScrkcMpkMFZVA7FFDmoJFeiXz6pTDl8lkMmRkZLzHNESVh1xefuLQiz/S/KMs3rlz53Djxg10794dJiYmAJ7vlmBubo527doJTkfp6ek4f/48FAoFXF1d0aBBA9GRtNr+/fuxYcMGHD58GMbGxqhVqxYMDAzw4MED5OTkoGbNmhg1ahSmTp0KS0tL0XGJhLt+/Trq1asHmUyG69evv/FcGxub95SK6PVYpBO9BwsXLkSnTp3g4eEhOorW4h/lyqOsrAwXLlyAjY0NLCwsRMchAE+fPkVmZibq168PXV22s9EUeXl5iI6OxrVr11BSUoIaNWrA1dUVrq6uFV6YJCLg+PHjaNu2bbnfZc+ePcOpU6f4Xo00Aot0ovfAzs4Od+7cQdeuXbFv3z7RcbROaWkpHB0dsX//fjg7O4uOQ6+YOnUqXFxcMGbMGJSVlaFjx444deoUjIyMsH//fnTq1El0RK1VXFyML774Atu3bwcApKSkwN7eHj4+PqhTpw6+/PJLwQmJiP4aHR0dZGdnl5tlkpeXB0tLS86sI43Ay6xE70FmZibu37+PyZMni46ilfT09PDkyZNy266RZggNDUWzZs0AAPv27UNmZiauXLmCqVOn4quvvhKcTrvNnj0biYmJOHr0qEoTv27duuGnn34SmIyI6O28WOb2qry8PBgbGwtIRFQe76QTkVZYvnw5rly5gsDAQE7X1TAGBgZIS0tD3bp1MW7cOBgZGWHt2rXIzMxEs2bNUFhYKDqi1rKxscFPP/2E1q1bq+yTnpaWBjc3N/5siKjS6N+/PwDg119/haenJ6pUqaI8VlZWhqSkJDg6OuLgwYOiIhIp8Z0qkRqdOHEC//d//4f09HSEhobigw8+wI4dO2BnZ4f27duLjqfVzp49i8jISERERMDFxaXc1fKwsDBByahWrVq4fPkyrKyscPDgQXz33XcAnk+1Zvdwse7evVth47GioiLOTCGiSqVq1aoAnt9JNzU1haGhofKYvr4+WrdujX//+9+i4hGpYJH+D5KQkIDmzZuLjqG19uzZg2HDhmHIkCE4f/48njx5AgB4+PAhli5dit9++01wQu1mbm4Ob29v0TGoAqNGjcLAgQNhZWUFmUyG7t27A3h+YcXJyUlwOu3WsmVLHDhwAF988QUAKAvzLVu2oE2bNiKjERH9Jdu2bQMA2NraYsaMGZzaThqN090ruYKCAuzcuROBgYFITExkswuBXF1d4evri+HDh6tMC01ISICnpydycnJERyTSWKGhobhx4wYGDBiAunXrAgC2b98Oc3Nz9OnTR3A67XXq1Cl4enpiyJAhCA4Oxueff45Lly7h9OnTOHbsGFq0aCE6IoHd94mI/mlYpFdSUVFRCAoKQlhYGGxsbODt7Q1vb2+4urqKjqa1jIyMcPnyZdja2qoU6RkZGXB2dsbjx49FRyQ8n7579epVyGQyNGzYEDVr1hQdiV7y+PFjlQZlJN6FCxewatUqxMXFQaFQwM3NDbNmzYKLi4voaFqP3feJ/hw3NzdERkbCwsICrq6ub1yuEx8f/x6TEVWMl1srkZs3byI4OBhBQUEoKirCwIEDUVpaij179nBbKQ1gZWWFtLQ02NraqoxHR0fD3t5eTChSKioqwhdffIHvv/8eCoUCwPNtWIYPH44NGzbAyMhIcELtVVZWhqVLlyIgIAB37txRFhpz586Fra0txowZIzqiVnNxcVEWgaRZXu6+7+npqRzv1q0b/P39WaQT/X99+vRRNorr27ev2DBEfwKL9EqiZ8+eiI6ORq9evbBhwwZ4enpCR0cHAQEBoqPR//f5559jypQpCAoKgkwmw+3bt3H69GnMmDED8+bNEx1P602bNg3Hjh3Dvn370K5dOwDPL6D4+Phg+vTp2LRpk+CE2mvJkiXYvn07VqxYodK0x8XFBd988w2LdMEUCgXS0tKQm5urvMD1goeHh6BUBAC//PKLsvv+y3cGnZ2dkZ6eLjAZkWbx9/ev8HMiTcUivZKIiIiAj48PJkyYgAYNGoiOQxWYOXMmCgoK0LlzZzx+/BgeHh6oUqUKZsyYwf3RNcCePXsQGhqKTp06Kcd69uwJQ0NDDBw4kEW6QN9//z02b96Mrl27Yvz48crxpk2b4sqVKwKT0ZkzZzB48GBcv34dr66Ok8lk7IMiGLvvE729p0+fVnjxsV69eoISEf2PXHQA+nNOnDiBhw8fwt3dHa1atcK3336Lu3fvio5Fr1iyZAnu3buHmJgYnDlzBnfv3sWiRYtExyI8X7tZq1atcuOWlpYoLi4WkIheuHXrFhwcHMqNKxQKlJaWCkhEL4wfPx7u7u64ePEi7t+/jwcPHig/7t+/Lzqe1nvRff8Fdt8n+mMpKSno0KEDDA0NYWNjAzs7O9jZ2cHW1hZ2dnai4xEB4J30SqNNmzZo06YN1q1bh5CQEAQFBWHatGlQKBT4/fffYW1tDVNTU9ExtVpBQQHKyspQrVo1uLu7K8fv378PXV1dmJmZCUxHbdq0gb+/P77//ntlY7KSkhIsWLCAb2YFa9y4MU6cOAEbGxuV8Z9//pnNMAVLTU1FaGhohRdRSLxly5bB09MTly9fxrNnz7Bu3TqV7vtEVN6oUaOgq6uL/fv3K7f+JNI07O5eiV29ehVbt27Fjh07kJ+fj+7du2Pv3r2iY2mtHj164OOPP8bEiRNVxgMCArB3717uky7YhQsX0KNHDzx+/BjNmjWDTCZDQkICDAwMcOjQITRu3Fh0RK21b98+DBs2DLNnz8bChQuxYMECXL16Fd9//z3279+v3Ded3r8uXbpg5syZKk3JSLOw+z7RX2NsbIy4uDg4OTmJjkL0WizS/wHKysqwb98+BAUFsUgXqFq1ajh58iQaNWqkMn7lyhW0a9cOeXl5gpLRCyUlJfjhhx9w5coVSJIEZ2dnDBkyBIaGhqKjab1Dhw5h6dKlKoXGvHnz8OGHH4qOptXCw8MxZ84c+Pn5wcXFBXp6eirHmzZtKigZEdHbadmyJb755hu0b99edBSi12KRTqQmxsbGOHPmTLm7FxcuXECrVq247lmAl/dFXbhwIWbMmMGt1oj+Arm8fOsamUwGSZLYOE4D/Pbbb9DR0cFHH32kMn7o0CEoFAr06NFDUDIizRUVFYU5c+Zg6dKlFV585PJE0gQs0onUpFOnTnBxccGGDRtUxidNmoSkpCScOHFCUDLtZWhoiNTUVNStWxc6OjrIzs6usBMyaQZ22tU8169ff+PxV/sI0PvVtGlTLF++HD179lQZP3jwIGbNmoXExERByYg014uLj6+uRefFR9IkbBxHpCZLlixBt27dkJiYiK5duwIAIiMjERsbi4iICMHptFPz5s0xatQotG/fHpIkYdWqVTAxManwXO5lL05qaipGjx6NU6dOqYzzDZN4LMI1W2pqKpydncuNOzk5IS0tTUAiIs135MgR0RGI/hDvpBOpUUJCAlauXImEhAQYGhqiadOmmD17Nve2F+Tq1avw9/dHeno64uPj4ezsDF3d8tcmZTIZ4uPjBSQkAGjXrh10dXXx5ZdfVthpt1mzZoKSaae9e/eiR48e0NPT+8M+J717935PqagitWvXxo8//oguXbqojB8+fBiDBw9Gbm6uoGRERPQuWKQTkVaQy+XIycnhdHcNxE67muXl10pFa9Jf4CwH8caNG4czZ84gPDwc9evXBwCkpaXB29sbLVu2RGBgoOCERJqruLgYWVlZePr0qco4G2KSJuB0dyI1UigUSEtLq3BdrYeHh6BUVFpaiuHDh+PRo0cs0jWQs7Mz7t27JzoG/X8v/+569fcYaZaVK1fC09MTTk5OqFu3LgDg5s2b6NChA1atWiU4HZFmunv3LkaNGoX//ve/FR7nxUfSBLyTTqQmZ86cweDBg3H9+nW8+rLiHSfxLCwsEBcXB3t7e9FR6BXstEv09iRJwu+//47ExETlMiteFCZ6vSFDhuDatWtYu3YtOnfujPDwcNy5cweLFy/G6tWr4eXlJToiEYt0InVp3rw5GjZsiAULFlS4rrZq1aqCkhEAjBo1Ci4uLpg2bZroKPQKdtrVXOvXr69wXCaTwcDAAA4ODvDw8ICOjs57TkZE9HasrKzw66+/4l//+hfMzMxw7tw5NGzYEHv37sWKFSsQHR0tOiIRp7sTqUtqaipCQ0Ph4OAgOgpVwMHBAYsWLcKpU6fQokULGBsbqxz38fERlIzYaVdzffPNN7h79y6Ki4thYWEBSZKQn58PIyMjmJiYIDc3F/b29jhy5Aisra1Fx9VKkZGRiIyMrHCZVVBQkKBURJqrqKhIufStWrVquHv3Lho2bAgXFxc2kSWNwSKdSE1atWqFtLQ0FukaKjAwEObm5oiLi0NcXJzKMZlMxiJdoI4dO4qOQK+xdOlSbN68GYGBgSqNyT7//HOMGzcO7dq1w6effgpfX1+EhoYKTqt9FixYgIULF8Ld3b3CGVxEVJ6joyOuXr0KW1tbNG/eHP/3f/8HW1tbBAQEwMrKSnQ8IgCc7k6kNuHh4ZgzZw78/PwqXFfLbqFE/5OUlIQmTZpALpcjKSnpjefytSNO/fr1sWfPHjRv3lxl/Pz58/D29kZGRgZOnToFb29vZGdniwmpxaysrLBixQoMGzZMdBSiSmPnzp0oLS3FyJEjcf78eXz00UfIy8uDvr4+goODMWjQINERiVikE6lLRVsVyWQyrqvVME+fPkVmZibq169f4Z7p9H68us3Xi9fKq/jaEcvIyAjHjx+Hu7u7ynhsbCw6duyI4uJiXLt2DU2aNMGjR48EpdRe1atXR0xMjHKWAxH9dcXFxbhy5Qrq1auHGjVqiI5DBIDT3YnUJjMzU3QEeoPi4mJ88cUX2L59OwAgJSUF9vb28PHxQZ06dfDll18KTqhdMjMzUbNmTeXnpJk6d+6Mzz//HIGBgXB1dQXw/C76hAkT0KVLFwDAhQsXYGdnJzKm1ho7dix+/PFHzJ07V3QUokrLyMgIbm5uomMQqWCRTqQmNjY2oiPQG8yePRuJiYk4evQoPD09lePdunWDv78/i/T37OXXC187mmvr1q0YNmwYWrRooVzC8+zZM3Tt2hVbt24FAJiYmGD16tUiY2qtx48fY/PmzTh8+DCaNm1abpnVmjVrBCUj0lySJCE0NBRHjhypsOFiWFiYoGRE/8Pp7kRqtGPHDgQEBCAzMxOnT5+GjY0N1q5dCzs7O/Tp00d0PK1mY2ODn376Ca1bt4apqSkSExNhb2+PtLQ0uLm5obCwUHRErbV9+3bUqFFDuTftzJkzsXnzZjg7O2PXrl0s4jXAlStXkJKSAkmS4OTkBEdHR9GRCM9nOryOTCZDVFTUe0xDVDn4+Phg8+bN6Ny5M2rVqlWu4eK2bdsEJSP6H95JJ1KTTZs2Yd68eZg6dSqWLFmiXEdrbm6OtWvXskgX7O7du8otV15WVFTEjsiCLV26FJs2bQIAnD59Gt9++y3Wrl2L/fv3w9fXl3c1NICTkxOcnJxEx6BXcPtCor/uhx9+QFhYGHr27Ck6CtFrsUgnUpMNGzZgy5Yt6Nu3L5YvX64cd3d3x4wZMwQmIwBo2bIlDhw4gC+++AIAlIX5li1b0KZNG5HRtN6NGzeUWxf+8ssv+OSTT5Tbe3Xq1ElsOC1XVlaG4ODg1+7DzTu1miEtLQ3p6enw8PCAoaGhsmEpEZVXtWpV2Nvbi45B9EYs0onUJDMzU9lY6WVVqlRBUVGRgET0smXLlsHT0xOXL1/Gs2fPsG7dOly6dAmnT5/GsWPHRMfTaiYmJsjLy0O9evUQEREBX19fAICBgQFKSkoEp9NuU6ZMQXBwMLy8vNCkSRMWfhomLy8PAwcOxJEjRyCTyZCamgp7e3uMHTsW5ubm7BVAVIH58+djwYIFCAoKgqGhoeg4RBVikU6kJnZ2dkhISCi3fva///0vnJ2dBaWiF9q2bYuTJ09i1apVqF+/PiIiIuDm5obTp0/DxcVFdDyt1r17d4wdOxaurq5ISUlRrk2/dOkSbG1txYbTciEhIdi9ezenhWooX19f6OnpISsrC40aNVKODxo0CL6+vizSiSowYMAA7Nq1C5aWlrC1tS3XcDE+Pl5QMqL/YZFOpCZ+fn6YNGkSHj9+DEmSEBMTg127dmHZsmUIDAwUHY8AuLi4KLdgI82xceNGzJkzBzdu3MCePXtQvXp1AEBcXBw+++wzwem0m76+vnIpAmmeiIgIHDp0CHXr1lUZb9CgAa5fvy4oFZFmGzlyJOLi4jB06NAKG8cRaQJ2dydSoy1btmDx4sW4ceMGAOCDDz7A/PnzMWbMGMHJCHi+vjY8PBzJycmQyWRo1KgR+vTpA11dXq8kqsjq1auRkZGBb7/9lm9kNZCpqSni4+PRoEEDlV0rYmNj4enpiby8PNERiTSOsbExDh06hPbt24uOQvRaLNKJ/gb37t2DQqGosJs4iXHx4kX06dMHOTk5yu2jUlJSULNmTezdu5dT3gU6fvz4G497eHi8pyT0qn79+uHIkSOoVq0aGjduXG5aKDvvi+Xl5QU3NzcsWrQIpqamSEpKgo2NDT799FMoFAqEhoaKjkikcZycnLB79240bdpUdBSi12KRTkRaoXXr1rC0tMT27dthYWEBAHjw4AFGjhyJ3NxcnD59WnBC7SWXy8uNvXzX9sV2hvT+jRo16o3HuZ+wWJcvX0anTp3QokULREVFoXfv3rh06RLu37+PkydPon79+qIjEmmcAwcOYMOGDQgICGDfE9JYLNKJ3oGbmxsiIyNhYWEBV1fXN04HZSMSsQwNDXHu3Dk0btxYZfzixYto2bIlu4gLVFBQoPK4tLQU58+fx9y5c7FkyRJ07dpVUDIizZeTk4NNmzYhLi4OCoUCbm5umDRpEqysrERHI9JIFhYWKC4uxrNnz2BkZFRuhtD9+/cFJSP6Hy7EJHoHffr0QZUqVQAAffv2FRuG3sjR0RF37twpV6Tn5uayMZZgVatWLTfWvXt3VKlSBb6+voiLixOQil549uwZjh49ivT0dAwePBimpqa4ffs2zMzMYGJiIjqe1qtduzYWLFggOgZRpbF27VrREYj+EO+kE5FW+O233zBz5kzMnz8frVu3BgCcOXMGCxcuxPLly1UayJiZmYmKSS9JTk5Gy5Yt8ejRI9FRtNb169fh6emJrKwsPHnyBCkpKbC3t8fUqVPx+PFjBAQEiI6odZKSktCkSRPI5XIkJSW98VyuuSUiqpxYpBOpSWxsLBQKBVq1aqUyfvbsWejo6MDd3V1QMgJU1z2/WJbw4tffy49lMhnXQL9nrxYakiQhOzsby5cvR2lpKU6ePCkoGfXt2xempqbYunUrqlevruwefuzYMYwdOxapqamiI2oduVyOnJwcWFpaQi6XQyaToaK3cvxdRlSxrKysNx6vV6/ee0pC9Hqc7k6kJpMmTcLMmTPLFem3bt3C119/jbNnzwpKRgBw5MgR0RHoNZo3b15hodG6dWsEBQUJSkUAEB0djZMnT0JfX19l3MbGBrdu3RKUSrtlZmaiZs2ays+J6K+xtbV9Yw8hXtwiTcAinUhNLl++DDc3t3Ljrq6uuHz5soBE9LKOHTuKjkCv8WqhIZfLUbNmTRgYGAhKRC8oFIoK37DevHkTpqamAhKRjY1NhZ8T0Z9z/vx5lccvmpWuWbMGS5YsEZSKSBWLdCI1qVKlCu7cuQN7e3uV8ezsbOjq8qVG9DosNDRX9+7dsXbtWmzevBnA8ynUjx49gr+/P3r27Ck4HS1btgy1atXC6NGjVcaDgoJw9+5dzJo1S1AyIs3VrFmzcmPu7u6oU6cOVq5cif79+wtIRaSKa9KJ1OTTTz9FTk4Ofv31V2W36vz8fPTt2xeWlpbYvXu34IREmisyMhLffPMNkpOTIZPJ4OTkhKlTp6Jbt26io2m127dvo3PnztDR0UFqairc3d2RmpqKGjVq4Pjx47C0tBQdUavZ2trixx9/RNu2bVXGz549i08//ZTT4Yn+gtTUVDRv3hxFRUWioxCxSCdSl1u3bsHDwwN5eXlwdXUFACQkJKBWrVr4/fffYW1tLTghkWb69ttv4evri08++QRt2rQB8LzzfmhoKNasWYPJkycLTqjdSkpKEBISorIP95AhQ2BoaCg6mtYzMDBAcnIy7OzsVMYzMjLg7OyMx48fC0pGpLkKCwtVHr9oVjp//nxcuXIFCQkJYoIRvYRFOpEaFRUVYefOnUhMTIShoSGaNm2Kzz77DHp6eqKjaTVJkpCVlQVLS0sWFhrogw8+wOzZs8sV4xs3bsSSJUtw+/ZtQcmINFuDBg3g7++PoUOHqozv2LED/v7+yMjIEJSMSHO92BXhZZIkwdraGiEhIcqLxUQicaEskRoZGxtj3LhxomPQKyRJQoMGDXDp0iU0aNBAdBx6RWFhITw9PcuNf/jhh1xTS/QGY8eOxdSpU1FaWoouXboAeL50ZObMmZg+fbrgdESa6dXdXl40K3VwcGAPIdIY/D+RSE22b9+OGjVqwMvLCwAwc+ZMbN68Gc7Ozti1axebYwkkl8vRoEED5OXlsUjXQL1790Z4eDj8/PxUxn/99Vd8/PHHglIRab6ZM2fi/v37mDhxIp4+fQrg+RT4WbNmYfbs2YLTEWkm7vZClQGnuxOpiaOjIzZt2oQuXbrg9OnT6Nq1K9auXYv9+/dDV1cXYWFhoiNqtQMHDmD58uXYtGkTmjRpIjqO1lu/fr3y88LCQqxatQrt2rVTWZN+8uRJTJ8+HXPmzBEVk6hSePToEZKTk2FoaIgGDRqgSpUqoiMRabSUlBQcPXoUubm5UCgUKsfmzZsnKBXR/7BIJ1ITIyMjXLlyBfXq1cOsWbOQnZ2N77//HpcuXUKnTp1w9+5d0RG1moWFBYqLi/Hs2TPo6+uXW5t+//59Qcm006uNrl5HJpNxXa0gZWVliI6ORtOmTWFhYSE6Dv0JhYWFiIqKgqOjIxo1aiQ6DpFG2rJlCyZMmIAaNWqgdu3aKuvTZTIZ4uPjBaYjeo7T3YnUxMTEBHl5eahXrx4iIiLg6+sL4PnUw5KSEsHpaO3ataIj0Eu4NZTm09HRwUcffYTk5GQW6Rpq4MCB8PDwwOTJk1FSUgJ3d3dcu3YNkiQhJCQE3t7eoiMSaZzFixdjyZIl7HlCGo1FOpGadO/eHWPHjoWrqytSUlKUa9MvXboEW1tbseEII0aMEB2BqNJxcXFBRkbGn575QO/X8ePH8dVXXwEAwsPDIUkS8vPzsX37dixevJhFOlEFHjx4gAEDBoiOQfRGctEBiP4pNm7ciDZt2uDu3bvYs2cPqlevDgCIi4vDZ599JjgdAc+n7+7Zs0d5FT08PBxlZWWiYxFprCVLlmDGjBnYv38/srOzUVhYqPJBYhUUFKBatWoAgIMHD8Lb2xtGRkbw8vJCamqq4HREmmnAgAGIiIgQHYPojbgmnYi0QlpaGnr27Ilbt27B0dERkiQhJSUF1tbWOHDgAOrXry86IpHGkcv/dy3/5XWbkiRBJpPxIpdgDRs2xOLFi+Hl5QU7OzuEhISgS5cuSExMRNeuXXHv3j3REYk0wsvNSouKirBmzRp4eXnBxcUFenp6Kuf6+Pi873hE5bBIJyKt0LNnT0iShJ07dyrvPOXl5WHo0KGQy+U4cOCA4IREmufYsWNvPM6tjMT67rvvMGXKFJiYmMDGxgbx8fGQy+XYsGEDwsLCyu0HTaSt2KyUKhsW6USkFYyNjXHmzBm4uLiojCcmJqJdu3Z49OiRoGT0JgkJCWjevLnoGEQaKy4uDllZWejevTtMTEwAPN9y0tzcHO3atROcjoiI3gbXpBORVqhSpQoePnxYbvzRo0fQ19cXkIhep6CgAN999x3c3NzQokUL0XG03okTJzB06FC0bdsWt27dAgDs2LED0dHRgpMRALRo0QL9+vVTFugA4OXlxQKdiKgSY5FOpAaSJOH69evcak2D9erVC+PGjcPZs2chSRIkScKZM2cwfvx49O7dW3Q8AhAVFYWhQ4fCysoKGzZsQM+ePXHu3DnRsbTanj178NFHH8HQ0BDx8fF48uQJAODhw4dYunSp4HRERET/TJzuTqQGCoUCBgYGuHTpEho0aCA6DlUgPz8fI0aMwL59+5RNYp49e4bevXsjODgYVatWFZxQO928eRPBwcEICgpCUVERBg4ciICAACQmJsLZ2Vl0PK3n6uoKX19fDB8+HKampkhMTIS9vT0SEhLg6emJnJwc0RGJiIj+cbhPOpEayOVyNGjQAHl5eSzSNZS5uTl+/fVXpKWlITk5GZIkwdnZGQ4ODqKjaa2ePXsiOjoavXr1woYNG+Dp6QkdHR0EBASIjkb/39WrV+Hh4VFu3MzMDPn5+e8/EBERkRZgkU6kJitWrICfnx82bdqEJk2aiI5Dr+Hg4MDCXENERETAx8cHEyZM4MUtDWVlZYW0tDTY2tqqjEdHR8Pe3l5MKCKivwmblZKmYJFOpCZDhw5FcXExmjVrBn19fRgaGqocv3//vqBkRJrpxIkTCAoKgru7O5ycnDBs2DAMGjRIdCx6yeeff44pU6YgKCgIMpkMt2/fxunTpzFjxgzMmzdPdDytlJSU9KfPbdq06d+YhOifoaCgADt37kRgYCASExNRVlYmOhIR16QTqcv27dvfeHzEiBHvKQlR5VJcXIyQkBAEBQUhJiYGZWVlWLNmDUaPHg1TU1PR8bTeV199hW+++QaPHz8G8HynhBkzZmDRokWCk2knuVwOmUyG1719e3FMJpOx2CB6g6ioKAQFBSEsLAw2Njbw9vaGt7c3XF1dRUcjYpFORESa4+rVq9i6dSt27NiB/Px8dO/eHXv37hUdS+sVFxfj8uXLUCgUcHZ2Vtnui96v69ev/+lzbWxs/sYkRJUPm5VSZcEinUiN0tPTsW3bNqSnp2PdunWwtLTEwYMHYW1tjcaNG4uOR1RplJWVYd++fQgKCmKRTkRE7+zlZqVDhgxRNivV09NjkU4ah0U6kZocO3YMPXr0QLt27XD8+HEkJyfD3t4eK1asQExMDEJDQ0VH1GoHDx6EiYkJ2rdvDwDYuHEjtmzZAmdnZ2zcuBEWFhaCExJpnqKiIixfvhyRkZHIzc2FQqFQOZ6RkSEoGb3s8uXLyMrKwtOnT1XGe/fuLSgRkebR1dWtsFkpi3TSRGwcR6QmX375JRYvXoxp06aprKPt3Lkz1q1bJzAZAYCfnx++/vprAMCFCxcwffp0TJs2DVFRUZg2bRq2bdsmOCGR5hk7diyOHTuGYcOGwcrKCjKZTHQkeklGRgb69euHCxcuqKxTf/Fz4pp0ov9hs1KqTHgnnUhNTExMcOHCBdjZ2cHU1BSJiYmwt7fHtWvX4OTkpGy6RGKYmJjg4sWLsLW1xfz583Hx4kWEhoYiPj4ePXv2RE5OjuiIRBrH3NwcBw4cQLt27URHoQp8/PHH0NHRwZYtW2Bvb4+YmBjk5eVh+vTpWLVqFTp06CA6IpHGYbNSqgzkogMQ/VOYm5sjOzu73Pj58+fxwQcfCEhEL9PX10dxcTEA4PDhw/jwww8BANWqVUNhYaHIaEQay8LCAtWqVRMdg17j9OnTWLhwIWrWrAm5XA65XI727dtj2bJl8PHxER2PSCMZGRlh9OjRiI6OVs6sW758OSwtLblEhDQGi3QiNRk8eDBmzZqFnJwcyGQyKBQKnDx5EjNmzMDw4cNFx9N67du3x7Rp07Bo0SLExMTAy8sLAJCSkoK6desKTkekmRYtWoR58+YpL3CRZikrK1N22q9RowZu374N4HlX96tXr4qMRlQpODo6YsWKFbh58yZ27dolOg6REqe7E6lJaWkpRo4ciZCQEEiSBF1dXZSVlWHw4MEIDg6Gjo6O6IhaLSsrCxMnTsSNGzfg4+ODMWPGAAB8fX1RVlaG9evXC05IpBlcXV1V1p6npaVBkiTY2tpCT09P5dz4+Pj3HY9e0qFDB0yfPh19+/bF4MGD8eDBA8yZMwebN29GXFwcLl68KDoiERG9BRbpRGqWkZGB+Ph4KBQKuLq6qnQQJSLSdAsWLPjT5/r7+/+NSeiPHDp0CEVFRejfvz8yMjLQq1cvXLlyBdWrV8dPP/2ELl26iI5IRERvgUU6kZosXLgQM2bMgJGRkcp4SUkJVq5ciXnz5glKRgCgo6OD7OxsWFpaqozn5eXB0tKSXZCJ6B/h/v37sLCwYCd+IqJKjGvSidRkwYIFePToUbnx4uLiv3Rniv4er7se+eTJE+jr67/nNESVg729PfLy8sqN5+fnw97eXkAi+iPVqlVjgU5EVMlxn3QiNZEkqcI3RomJieyOLNCLteYymQyBgYHKJkvA86ZLx48fh5OTk6h4RBrt2rVrFc4yefLkCW7evCkgEfXv3x/BwcEwMzND//7933huWFjYe0pFRETqxCKd6B29mFYok8nQsGFDlUK9rKwMjx49wvjx4wUm1G7ffPMNgOcXUQICAlQa+Onr68PW1hYBAQGi4hFppL179yo/P3ToEKpWrap8XFZWhsjISNjZ2YmIpvWqVq2q/Dvz8s+FiIj+Obgmnegdbd++HZIkYfTo0Vi7dq3Km6YXRWCbNm0EJiQA6Ny5M8LDw2Fubi46CpHGk8ufr4aTyWTlloro6enB1tYWq1evRq9evUTEIyIi+kfjnXSidzRixAgAgJ2dHdq2bVtuiyISr7S0FNevX8ft27dZpBP9CQqFAsDz32uxsbGoUaOG4ERUkczMTDx79qzcLiKpqanKiylERFT5sEgnUhM7OztkZ2e/9ni9evXeYxp6mZ6eHp48ecJmSkR/UWZmpugI9AYjR47E6NGjyxXpZ8+eRWBgII4ePSomGBERvRNOdydSE7lc/sYikFt8ibV8+XJcuXIFgYGB0NXl9UmiP+vYsWNYtWoVkpOTIZPJ0KhRI/j5+aFDhw6io2k9MzMzxMfHw8HBQWU8LS0N7u7uyM/PFxOMiIjeCd+pEqnJ+fPnVR6Xlpbi/PnzWLNmDZYsWSIoFb1w9uxZREZGIiIiAi4uLjA2NlY5zi7IROX98MMPGDVqFPr37w8fHx9IkoRTp06ha9euCA4OxuDBg0VH1GoymQwPHz4sN15QUMALw0RElRjvpBP9zQ4cOICVK1dy2qFgo0aNeuPxbdu2vackRJVHo0aNMG7cOPj6+qqMr1mzBlu2bEFycrKgZAQAvXr1gpGREXbt2qXcuaKsrAyDBg1CUVER/vvf/wpOSEREb4NFOtHfLDU1Fc2bN0dRUZHoKEREf0mVKlVw6dKlCqdTN2nSBI8fPxaUjADg8uXL8PDwgLm5uXL5wYkTJ1BYWIioqCg0adJEcEIiInobctEBiP4pCgsLVT4KCgpw5coVzJ07t1xTHyKiysDa2hqRkZHlxiMjI2FtbS0gEb3M2dkZSUlJGDhwIHJzc/Hw4UMMHz4cV65cYYFORFSJcU06kZqYm5uXaxwnSRKsra0REhIiKBW9LDQ0FLt370ZWVhaePn2qciw+Pl5QKiLNNX36dPj4+CAhIQFt27aFTCZDdHQ0goODsW7dOtHxCECdOnWwdOlS0TGIiEiNWKQTqcmRI0dUHsvlctSsWRMODg7sJq4B1q9fj6+++gojRozAr7/+ilGjRiE9PR2xsbGYNGmS6HhEGmnChAmoXbs2Vq9ejd27dwN4vk79p59+Qp8+fQSnIwDIz8/H1q1bld33nZ2dMXr0aFStWlV0NCIiektck05EWsHJyQn+/v747LPPYGpqisTERNjb22PevHm4f/8+vv32W9ERiYj+knPnzuGjjz6CoaEh/vWvf0GSJJw7dw4lJSWIiIiAm5ub6IhERPQWWKQTqdHVq1exYcMG5R0NJycnTJ48GU5OTqKjaT0jIyMkJyfDxsYGlpaW+P3339GsWTOkpqaidevWyMvLEx2RSGM9ffoUubm5UCgUKuP16tUTlIgAoEOHDnBwcMCWLVuUM7aePXuGsWPHIiMjA8ePHxeckIiI3gYbxxGpSWhoKJo0aYK4uDg0a9YMTZs2RXx8PFxcXPDzzz+Ljqf1ateurSzEbWxscObMGQBAZmYmeK2SqGKpqano0KEDDA0NYWNjAzs7O9jZ2cHW1hZ2dnai42m9c+fOYdasWSpLqnR1dTFz5kycO3dOYDIiInoXXChLpCYzZ87E7NmzsXDhQpVxf39/zJo1CwMGDBCUjACgS5cu2LdvH9zc3DBmzBj4+voiNDQU586dQ//+/UXHI9JII0eOhK6uLvbv3w8rK6tyzTFJLDMzM2RlZZWbrXXjxg2YmpoKSkVERO+K092J1MTIyAhJSUnl9hNOTU1Fs2bNUFxcLCgZAYBCoYBCoVDecdq9ezeio6Ph4OCA8ePHQ19fX3BCIs1jbGyMuLg4LtnRUD4+PggPD8eqVatUuu/7+fnB29sba9euFR2RiIjeAu+kE6lJp06dcOLEiXJFenR0NDp06CAoFb0gl8shl/9vhc/AgQMxcOBAgYmINJ+zszPu3bsnOga9xqpVqyCTyTB8+HA8e/YMAKCnp4cJEyZg+fLlgtMREdHb4p10IjUJCAjAvHnzMHDgQLRu3RoAcObMGfz8889YsGAB6tSpozy3d+/eomISEf1pUVFRmDNnDpYuXQoXFxfo6empHDczMxOUjF5WXFyM9PR0SJIEBwcHGBkZiY5ERETvgEU6kZq8fJf2TWQyGcrKyv7mNERE7+7F77VX16JLksTfZRqosLAQUVFRcHR0RKNGjUTHISKit8Tp7kRq8urWREREld2RI0dER6A3GDhwIDw8PDB58mSUlJTA3d0d165dgyRJCAkJgbe3t+iIRET0FlikExERUYU6duz42mMJCQnvLwhV6Pjx4/jqq68AAOHh4ZAkCfn5+di+fTsWL17MIp2IqJJikU6kRjExMTh69Chyc3PL3Vlfs2aNoFT0wrNnz3D06FGkp6dj8ODBMDU1xe3bt2FmZgYTExPR8Yg0XkFBAXbu3InAwEAkJiZyurtgBQUFqFatGgDg4MGD8Pb2hpGREby8vODn5yc4HRERvS0W6URqsnTpUsyZMweOjo6oVauWyhpO7i0s3vXr1+Hp6YmsrCw8efIE3bt3h6mpKVasWIHHjx8jICBAdEQijRUVFYWgoCCEhYXBxsYG3t7e2Lp1q+hYWs/a2hqnT59GtWrVcPDgQYSEhAAAHjx4AAMDA8HpiIjobbFIJ1KTdevWISgoCCNHjhQdhSowZcoUuLu7IzExEdWrV1eO9+vXD2PHjhWYjEgz3bx5E8HBwQgKCkJRUREGDhyI0tJS7NmzB87OzqLjEYCpU6diyJAhMDExgY2NDTp16gTg+TR4FxcXseGIiOitsUgnUhO5XI527dqJjkGvER0djZMnT0JfX19l3MbGBrdu3RKUikgz9ezZE9HR0ejVqxc2bNgAT09P6OjocMaJhpk4cSJatWqFrKwsdO/eXdmN397eHosXLxacjoiI3haLdCI18fX1xcaNG7F27VrRUagCCoWiwvWzN2/ehKmpqYBERJorIiICPj4+mDBhAho0aCA6Dr1BixYt0KJFC5UxLy8vQWmIiEgdWKQTqcmMGTPg5eWF+vXrw9nZGXp6eirHw8LCBCUjAOjevTvWrl2LzZs3A3jeJ+DRo0fw9/dHz549Bacj0iwnTpxAUFAQ3N3d4eTkhGHDhmHQoEGiYxEREWkFmSRJkugQRP8EkyZNwtatW9G5c+dyjeMAYNu2bYKSEQDcvn0bnTt3ho6ODlJTU+Hu7o7U1FTUqFEDx48fh6WlpeiIRBqnuLgYISEhCAoKQkxMDMrKyrBmzRqMHj2aM1CIiIj+JizSidTE1NQUISEhnGaowUpKSrBr1y7Ex8dDoVDAzc0NQ4YMgaGhoehoRBrv6tWr2Lp1K3bs2IH8/Hx0794de/fuFR2LiIjoH4dFOpGa2NjY4NChQ3BychIdhYjob1NWVoZ9+/YhKCiIRboGS0hIQPPmzUXHICKit8AinUhNtm3bhoMHD2Lbtm0wMjISHYcqkJKSgqNHjyI3NxcKhULl2Lx58wSlIiJSj4KCAuzcuROBgYFITEyssFkmERFpPhbpRGri6uqK9PR0SJIEW1vbco3j4uPjBSUjANiyZQsmTJiAGjVqoHbt2io9A2QyGX8+RFRpRUVFISgoCGFhYbCxsYG3tze8vb3h6uoqOhoREb0FdncnUpO+ffuKjkBvsHjxYixZsgSzZs0SHYWI6J3dvHkTwcHBCAoKQlFREQYOHIjS0lLs2bMHzs7OouMREdE74J10ItIKZmZmSEhIgL29vegoRETvpGfPnoiOjkavXr0wZMgQeHp6QkdHB3p6ekhMTGSRTkRUyfFOOpGaxcXFITk5GTKZDM7OzpxuqCEGDBiAiIgIjB8/XnQUIqJ3EhERAR8fH0yYMAENGjQQHYeIiNSMRTqRmuTm5uLTTz/F0aNHYW5uDkmSUFBQgM6dOyMkJAQ1a9YUHVGrOTg4YO7cuThz5gxcXFzK9Qzw8fERlIyI6K85ceIEgoKC4O7uDicnJwwbNgyDBg0SHYuIiNSE092J1GTQoEFIT0/Hjh070KhRIwDA5cuXMWLECDg4OGDXrl2CE2o3Ozu71x6TyWTIyMh4j2mIiN5dcXExQkJCEBQUhJiYGJSVlWHNmjUYPXo0TE1NRccjIqK3xCKdSE2qVq2Kw4cPo2XLlirjMTEx+PDDD5Gfny8mGBER/eNdvXoVW7duxY4dO5Cfn4/u3btzH3siokpKLjoA0T+FQqEoN4UaAPT09MrtyU1ERKROjo6OWLFiBW7evMmZW0RElRzvpBOpSZ8+fZCfn49du3ahTp06AIBbt25hyJAhsLCwQHh4uOCE2mfatGlYtGgRjI2NMW3atDeeu2bNmveUioiIiIjo9dg4jkhNvv32W/Tp0we2trawtraGTCZDVlYWXFxc8MMPP4iOp5XOnz+P0tJS5eevI5PJ3lckIiIiIqI34p10IjX7/fffceXKFUiSBGdnZ3Tr1k10JK2WkZEBOzs7FuJEREREVCmwSCeifzQdHR1kZ2fD0tISwPMu/OvXr0etWrUEJyMiIiIiKo+N44jeUVRUFJydnVFYWFjuWEFBARo3bowTJ04ISEYA8Op1yN9++w1FRUWC0hARERERvRmLdKJ3tHbtWvz73/+GmZlZuWNVq1bF559/zqZkRERERET0p7BIJ3pHiYmJ8PT0fO3xDz/8EHFxce8xEb1MJpOVW4/O9elEREREpKnY3Z3oHd25c6fC/dFf0NXVxd27d99jInqZJEkYOXIkqlSpAgB4/Pgxxo8fD2NjY5XzwsLCRMQjIiIiIlLBIp3oHX3wwQe4cOECHBwcKjyelJQEKyur95yKXhgxYoTK46FDhwpKQkRERET0x9jdnegdffHFFzh69ChiY2NhYGCgcqykpAT/+te/0LlzZ6xfv15QQiIiIiIiqixYpBO9ozt37sDNzQ06OjqYPHkyHB0dIZPJkJycjI0bN6KsrAzx8fHc8ouIiIiIiP4Qi3QiNbh+/TomTJiAQ4cOKbf8kslk+Oijj/Ddd9/B1tZWbEAiIiIiIqoUWKQTqdGDBw+QlpYGSZLQoEEDWFhYiI5ERERERESVCIt0IiIiIiIiIg3BfdKJiIiIiIiINASLdCIiIiIiIiINwSKdiIiIiIiISEOwSCciIiIiIiLSECzSiYiIKpFOnTph6tSpomP8ZSNHjkTfvn3/1u8hk8nwyy+//K3fg4iI6O/GIp2IiEjNXldI//LLL5DJZO8/kAYaOXIkZDIZxo8fX+7YxIkTIZPJMHLkyL/0nNnZ2ejRo4eaEhIREYnBIp2IiEiLlJWVQaFQvNXXlpaWqjWLtbU1QkJCUFJSohx7/Pgxdu3ahXr16v3l56tduzaqVKny1nmePn361l9LRESkLizSiYiIBJg/fz6aN2+OHTt2wNbWFlWrVsWnn36Khw8fKs8pKirC8OHDYWJiAisrK6xevbrc8zx9+hQzZ87EBx98AGNjY7Rq1QpHjx5VHg8ODoa5uTn2798PZ2dnVKlSBdevX0d2dja8vLxgaGgIOzs7/Pjjj7C1tcXatWuVXyuTyRAQEIA+ffrA2NgYixcvRllZGcaMGQM7OzsYGhrC0dER69atU8lUVlaGadOmwdzcHNWrV8fMmTMhSVK57G5ubqhXrx7CwsKUY2FhYbC2toarq6vKuQcPHkT79u2Vz9mrVy+kp6ernPPqdPcLFy6gS5cuMDQ0RPXq1TFu3Dg8evRIefzFFPxly5ahTp06aNiwYcU/LCIioveIRToREZEg6enp+OWXX7B//37s378fx44dw/Lly5XH/fz8cOTIEYSHhyMiIgJHjx5FXFycynOMGjUKJ0+eREhICJKSkjBgwAB4enoiNTVVeU5xcTGWLVuGwMBAXLp0CZaWlhg+fDhu376No0ePYs+ePdi8eTNyc3PLZfT390efPn1w4cIFjB49GgqFAnXr1sXu3btx+fJlzJs3D//5z3+we/du5desXr0aQUFB2Lp1K6Kjo3H//n2Eh4dX+N9g1KhR2LZtm/JxUFAQRo8eXe68oqIiTJs2DbGxsYiMjIRcLke/fv1eOyuguLgYnp6esLCwQGxsLH7++WccPnwYkydPVjkvMjISycnJ+P3337F///4Kn4uIiOi9koiIiEitOnbsKE2ZMqXceHh4uPTiT6+/v79kZGQkFRYWKo/7+flJrVq1kiRJkh4+fCjp6+tLISEhyuN5eXmSoaGh8rnT0tIkmUwm3bp1S+X7dO3aVZo9e7YkSZK0bds2CYCUkJCgPJ6cnCwBkGJjY5VjqampEgDpm2++UY4BkKZOnfqH/96JEydK3t7eysdWVlbS8uXLlY9LS0ulunXrSn369FGOjRgxQurTp4909+5dqUqVKlJmZqZ07do1ycDAQLp7967Up08facSIEa/9nrm5uRIA6cKFCyp5w8PDJUmSpM2bN0sWFhbSo0ePlMcPHDggyeVyKScnR5mhVq1a0pMnT/7w30hERPS+6Aq8PkBERKTVbG1tYWpqqnxsZWWlvJudnp6Op0+fok2bNsrj1apVg6Ojo/JxfHw8JEkqN037yZMnqF69uvKxvr4+mjZtqnx89epV6Orqws3NTTnm4OAACwuLchnd3d3LjQUEBCAwMBDXr19HSUkJnj59iubNmwMACgoKkJ2drZJbV1cX7u7uFU55r1GjBry8vLB9+3ZIkgQvLy/UqFGj3Hnp6emYO3cuzpw5g3v37invoGdlZaFJkyblzk9OTkazZs1gbGysHGvXrh0UCgWuXr2KWrVqAQBcXFygr69f7uuJiIhEYZFORESkZmZmZigoKCg3np+fDzMzM+VjPT09leMymUxZfFZU0L5KoVBAR0cHcXFx0NHRUTlmYmKi/NzQ0FClq/zrnrui8ZeLXADYvXs3fH19sXr1arRp0wampqZYuXIlzp49+4d5X2f06NHKaegbN26s8JyPP/4Y1tbW2LJlC+rUqQOFQoEmTZq8ttmbJEmv7aT/8vir/z4iIiLRuCadiIhIzZycnHDu3Lly47GxsSp3wt/EwcEBenp6OHPmjHLswYMHSElJUT52dXVFWVkZcnNz4eDgoPJRu3btN+Z79uwZzp8/rxxLS0tDfn7+H+Y6ceIE2rZti4kTJ8LV1RUODg4qDdyqVq0KKysrldzPnj0rt5b+ZZ6ennj69CmePn2Kjz76qNzxvLw8JCcnY86cOejatSsaNWqEBw8evDGns7MzEhISUFRUpBw7efIk5HI5G8QREZFGY5FORESkZhMnTkR6ejomTZqExMREpKSkYOPGjdi6dSv8/Pz+1HOYmJhgzJgx8PPzQ2RkJC5evIiRI0dCLv/fn+6GDRtiyJAhGD58OMLCwpCZmYnY2Fh8/fXX+O2331773E5OTujWrRvGjRuHmJgYnD9/HuPGjSt3x70iDg4OOHfuHA4dOoSUlBTMnTsXsbGxKudMmTIFy5cvR3h4OK5cuYKJEye+8QKAjo4OkpOTkZycXG5GAABYWFigevXq2Lx5M9LS0hAVFYVp06a9MeeQIUNgYGCAESNG4OLFizhy5Ai++OILDBs2TDnVnYiISBOxSCciIlIzW1tbnDhxAunp6fjwww/RsmVLBAcHIzg4GAMGDPjTz7Ny5Up4eHigd+/e6NatG9q3b48WLVqonLNt2zYMHz4c06dPh6OjI3r37o2zZ8/C2tr6jc/9/fffo1atWvDw8EC/fv3w73//G6ampjAwMHjj140fPx79+/fHoEGD0KpVK+Tl5WHixIkq50yfPh3Dhw/HyJEjlVPi+/Xr98bnNTMzU1kK8DK5XI6QkBDExcWhSZMm8PX1xcqVK9/4fEZGRjh06BDu37+Pli1b4pNPPkHXrl3x7bffvvHriIiIRJNJf2bRGxEREf2j3bx5E9bW1jh8+DC6du0qOs5f9uTJExgYGOD3339Ht27dRMchIiJ6a2wcR0REpIWioqLw6NEjuLi4IDs7GzNnzoStrS08PDxER/vLCgsLERYWBrlcDicnJ9FxiIiI3gmLdCIiIi1UWlqK//znP8jIyICpqSnatm2LnTt3lus4Xxn4+/vjxx9/xNdff426deuKjkNERPROON2diIiIiIiISEOwcRwRERERERGRhmCRTkRERERERKQhWKQTERERERERaQgW6UREREREREQagkU6ERERERERkYZgkU5ERERERESkIVikExEREREREWkIFulEREREREREGuL/Ad31h+PiM0erAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.barplot(x='UndergradMajor', y='NetSalary', data=df2, estimator=np.median)\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.title('Median NetSalary by UndergradMajor')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                                        Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                                        \n",
+      "===================================================================================================================================================================================================\n",
+      "                               group1                                                               group2                                   meandiff    p-adj      lower          upper     reject\n",
+      "---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
+      "          A business discipline (ex. accounting, finance, marketing)        A humanities discipline (ex. literature, history, philosophy)       -59088.0    1.0   -1353139.969   1234963.969  False\n",
+      "          A business discipline (ex. accounting, finance, marketing)                  A natural science (ex. biology, chemistry, physics)      -244300.0 0.9916  -1159332.9225   670732.9225  False\n",
+      "          A business discipline (ex. accounting, finance, marketing)   A social science (ex. anthropology, psychology, political science)     10308912.0    0.0    9014860.031  11602963.969   True\n",
+      "          A business discipline (ex. accounting, finance, marketing)   Another engineering discipline (ex. civil, electrical, mechanical)        11487.2    1.0   -806943.1265   829917.5265  False\n",
+      "          A business discipline (ex. accounting, finance, marketing)      Computer science, computer engineering, or software engineering   -289880.2273 0.8864   -979612.2442   399851.7897  False\n",
+      "          A business discipline (ex. accounting, finance, marketing) Fine arts or performing arts (ex. graphic design, music, studio art)      -241088.0 0.9993   -1535139.969   1052963.969  False\n",
+      "          A business discipline (ex. accounting, finance, marketing)                                            Mathematics or statistics      -408440.0 0.9761   -1702491.969    885611.969  False\n",
+      "          A business discipline (ex. accounting, finance, marketing)                                        Web development or web design      -447088.0 0.9595   -1741139.969    846963.969  False\n",
+      "       A humanities discipline (ex. literature, history, philosophy)                  A natural science (ex. biology, chemistry, physics)      -185212.0 0.9999   -1479263.969   1108839.969  False\n",
+      "       A humanities discipline (ex. literature, history, philosophy)   A social science (ex. anthropology, psychology, political science)     10368000.0    0.0   8783116.4877 11952883.5123   True\n",
+      "       A humanities discipline (ex. literature, history, philosophy)   Another engineering discipline (ex. civil, electrical, mechanical)        70575.2    1.0  -1157070.2898  1298220.6898  False\n",
+      "       A humanities discipline (ex. literature, history, philosophy)      Computer science, computer engineering, or software engineering   -230792.2273 0.9988  -1376661.1135    915076.659  False\n",
+      "       A humanities discipline (ex. literature, history, philosophy) Fine arts or performing arts (ex. graphic design, music, studio art)      -182000.0    1.0  -1766883.5123  1402883.5123  False\n",
+      "       A humanities discipline (ex. literature, history, philosophy)                                            Mathematics or statistics      -349352.0 0.9977  -1934235.5123  1235531.5123  False\n",
+      "       A humanities discipline (ex. literature, history, philosophy)                                        Web development or web design      -388000.0 0.9953  -1972883.5123  1196883.5123  False\n",
+      "                 A natural science (ex. biology, chemistry, physics)   A social science (ex. anthropology, psychology, political science)     10553212.0    0.0    9259160.031  11847263.969   True\n",
+      "                 A natural science (ex. biology, chemistry, physics)   Another engineering discipline (ex. civil, electrical, mechanical)       255787.2 0.9775   -562643.1265  1074217.5265  False\n",
+      "                 A natural science (ex. biology, chemistry, physics)      Computer science, computer engineering, or software engineering    -45580.2273    1.0   -735312.2442   644151.7897  False\n",
+      "                 A natural science (ex. biology, chemistry, physics) Fine arts or performing arts (ex. graphic design, music, studio art)         3212.0    1.0   -1290839.969   1297263.969  False\n",
+      "                 A natural science (ex. biology, chemistry, physics)                                            Mathematics or statistics      -164140.0    1.0   -1458191.969   1129911.969  False\n",
+      "                 A natural science (ex. biology, chemistry, physics)                                        Web development or web design      -202788.0 0.9998   -1496839.969   1091263.969  False\n",
+      "  A social science (ex. anthropology, psychology, political science)   Another engineering discipline (ex. civil, electrical, mechanical)    -10297424.8    0.0 -11525070.2898 -9069779.3102   True\n",
+      "  A social science (ex. anthropology, psychology, political science)      Computer science, computer engineering, or software engineering -10598792.2273    0.0 -11744661.1135  -9452923.341   True\n",
+      "  A social science (ex. anthropology, psychology, political science) Fine arts or performing arts (ex. graphic design, music, studio art)    -10550000.0    0.0 -12134883.5123 -8965116.4877   True\n",
+      "  A social science (ex. anthropology, psychology, political science)                                            Mathematics or statistics    -10717352.0    0.0 -12302235.5123 -9132468.4877   True\n",
+      "  A social science (ex. anthropology, psychology, political science)                                        Web development or web design    -10756000.0    0.0 -12340883.5123 -9171116.4877   True\n",
+      "  Another engineering discipline (ex. civil, electrical, mechanical)      Computer science, computer engineering, or software engineering   -301367.4273 0.6718   -856590.9907   253856.1362  False\n",
+      "  Another engineering discipline (ex. civil, electrical, mechanical) Fine arts or performing arts (ex. graphic design, music, studio art)      -252575.2 0.9986  -1480220.6898   975070.2898  False\n",
+      "  Another engineering discipline (ex. civil, electrical, mechanical)                                            Mathematics or statistics      -419927.2 0.9617  -1647572.6898   807718.2898  False\n",
+      "  Another engineering discipline (ex. civil, electrical, mechanical)                                        Web development or web design      -458575.2 0.9377  -1686220.6898   769070.2898  False\n",
+      "     Computer science, computer engineering, or software engineering Fine arts or performing arts (ex. graphic design, music, studio art)     48792.2273    1.0   -1097076.659  1194661.1135  False\n",
+      "     Computer science, computer engineering, or software engineering                                            Mathematics or statistics   -118559.7727    1.0   -1264428.659  1027309.1135  False\n",
+      "     Computer science, computer engineering, or software engineering                                        Web development or web design   -157207.7727 0.9999   -1303076.659   988661.1135  False\n",
+      "Fine arts or performing arts (ex. graphic design, music, studio art)                                            Mathematics or statistics      -167352.0    1.0  -1752235.5123  1417531.5123  False\n",
+      "Fine arts or performing arts (ex. graphic design, music, studio art)                                        Web development or web design      -206000.0    1.0  -1790883.5123  1378883.5123  False\n",
+      "                                           Mathematics or statistics                                        Web development or web design       -38648.0    1.0  -1623531.5123  1546235.5123  False\n",
+      "---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "import statsmodels.api as sm\n",
+    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
+    "\n",
+    "# Perform Tukey's HSD test\n",
+    "tukey = pairwise_tukeyhsd(endog=df2['NetSalary'], groups=df2['UndergradMajor'], alpha=0.05)\n",
+    "\n",
+    "# Print the results\n",
+    "print(tukey)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Presence of Outliers:\n",
+    "\n",
+    "The box plot for social sciences (e.g., anthropology, psychology, political science) shows an extreme outlier, indicating there is a salary significantly higher than the rest within this major.\n",
+    "Computer science, engineering, and fine arts majors also show some smaller outliers, indicating occasional higher or lower salaries compared to the majority.\n",
+    "\n",
+    "Comparison of Medians:\n",
+    "\n",
+    "The median salary is highest for business disciplines among the typical ranges, excluding the extreme outlier in social sciences.\n",
+    "\n",
+    "Humanities and mathematics/statistics majors have the lowest median salaries.\n",
+    "\n",
+    "Engineering and computer science majors generally have higher median salaries compared to fine arts and humanities.\n",
+    "\n",
+    "Engineering disciplines show a longer range, indicating more salary variation.\n",
+    "\n",
+    "Web development/design and humanities have shorter whiskers, suggesting less variation in salaries.\n",
+    "\n",
+    "Mathematics or Statistics: Salaries are low with minimal variation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "INSIGHTS:\n",
+    "\n",
+    "\n",
+    "Highest Earning Potential:\n",
+    "\n",
+    "Social Sciences: With a mean and median salary of 10,800,000.00, this field has significantly higher earnings, likely due to lack of data.\n",
+    "\n",
+    "Consistent Earnings:\n",
+    "\n",
+    "1) Business Discipline: Both the mean and median are around 491,088.00, suggesting consistent and relatively high earning potential.\n",
+    "2) Humanities: Consistent earnings with both mean and median at 432,000.00.\n",
+    "3) Engineering and Computer Science:\n",
+    "Other Engineering: Mean salary is 502,575.20, with a median lower at 95,968.00, indicating some people, added with unique skills and experience, can earn signficantly greater than the rest\n",
+    "Computer Science: Mean salary is 201,207.80 with a median higher at 115,000.00, indicating steady earnings potential.\n",
+    "Fine Arts:\n",
+    "4) Fine Arts: Has a mean and median salary of 250,000.00, showing consistent earnings for graduates in this field.\n",
+    "5) Lower Earning Potential: Web Development: Lowest mean and median salary at 44,000.00.\n",
+    "6) Mathematics or Statistics: Lower salary range with mean and median at 82,648.00.\n",
+    "\n",
+    "Education Level Impact:\n",
+    "\n",
+    "1) Bachelor's Degree: While having the highest mean salary (897,724.87), the median is relatively low at 80,000.00, indicating potential high outliers.\n",
+    "2) Master's Degree: Higher median salary at 100,354.00 compared to bachelor's degree, suggesting additional education can improve earning potential.\n",
+    "3) Some College/University: Interestingly, has a high median salary at 254,736.00, indicating individuals who pursue specific high-earning skills without completing a degree."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Performing Two-Way ANOVA\n",
+    "\n",
+    "To examine the combined effect of FormalEducation and UndergradMajor on NetSalary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.pointplot(data=df2, x='FormalEducation', y='NetSalary', hue='UndergradMajor', dodge=True, markers=[\"o\", \"s\", \"D\", \"^\", \"v\", \"p\", \"h\", \"*\", \"P\"], capsize=.1, errwidth=1, palette=\"colorblind\")\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.title('Interaction between FormalEducation and UndergradMajor on NetSalary')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "General Trend:\n",
+    "The most significant salaries are observed in the \"Some College/University study without earning a degree\" category for certain majors.\n",
+    "Master's Degree:\n",
+    "\n",
+    "A noticeable outlier is seen for the natural sciences, which shows a significantly higher NetSalary.\n",
+    "\n",
+    "Natural Sciences:\n",
+    "Very high salary for Master's degree.\n",
+    "Social Sciences:\n",
+    "Peaks significantly at \"Some College/University study without earning a degree\" but returns to lower levels for Bachelor's and Master's degrees.\n",
+    "\n",
+    "Outliers:\n",
+    "\n",
+    "There are significant outliers in the data. For instance, the natural sciences major with a Master's degree shows an unusually high NetSalary.\n",
+    "\n",
+    "Implications:\n",
+    "\n",
+    "Higher education (such as a Master's degree) in natural sciences may lead to higher salaries.\n",
+    "\n",
+    "Some College/University study without a degree in social sciences may offer better salary prospects than expected.\n",
+    "\n",
+    "For most other fields, the degree level does not significantly impact the NetSalary.\n",
+    "\n",
+    "Recommendations:\n",
+    "\n",
+    "For students pursuing higher salaries, focusing on natural sciences and aiming for a Master's degree might be beneficial.\n",
+    "\n",
+    "Those interested in social sciences might explore opportunities even with incomplete higher education."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3-5 years      26\n",
+       "0-2 years      22\n",
+       "6-8 years      12\n",
+       "9-11 years      9\n",
+       "12-14 years     6\n",
+       "18-20 years     3\n",
+       "21-23 years     3\n",
+       "24-26 years     2\n",
+       "15-17 years     1\n",
+       "Name: YearsCodingProf, dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCodingProf'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "import matplotlib.pyplot as plt\n",
+    "experience_mapping = {\n",
+    "    '0-2 years': 1,\n",
+    "    '3-5 years': 2,\n",
+    "    '6-8 years': 3,\n",
+    "    '9-11 years': 4,\n",
+    "    '12-14 years': 5,\n",
+    "    '15-17 years': 6,\n",
+    "    '18-20 years': 7,\n",
+    "    '21-23 years': 8,\n",
+    "    '24-26 years': 9\n",
+    "}\n",
+    "df2['ProfCodOrdinal'] = df['YearsCodingProf'].map(experience_mapping)\n",
+    "# Calculate median NetSalary for each ProfCodOrdinal\n",
+    "median_salaries = df2.groupby('ProfCodOrdinal')['NetSalary'].median().reset_index()\n",
+    "\n",
+    "# Plot the median NetSalary against ProfCodOrdinal\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(median_salaries['ProfCodOrdinal'], median_salaries['NetSalary'], marker='o', linestyle='-', color='b')\n",
+    "plt.xlabel('Professional Coding Experience (Ordinal)')\n",
+    "plt.ylabel('Median Net Salary')\n",
+    "plt.title('Median Net Salary by Professional Coding Experience')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation coefficient: -0.17\n",
+      "Intercept: 952327.47\n",
+      "Coefficient: -138979.47\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Linear regression\n",
+    "X = df2[['ProfCodOrdinal']]\n",
+    "y = df2['NetSalary']\n",
+    "reg = LinearRegression().fit(X, y)\n",
+    "df2['PredictedSalary'] = reg.predict(X)\n",
+    "\n",
+    "# Plot regression line\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.scatterplot(x='ProfCodOrdinal', y='NetSalary', data=df2)\n",
+    "plt.plot(df2['ProfCodOrdinal'], df2['PredictedSalary'], color='red', linewidth=2)\n",
+    "plt.xticks(ticks=np.arange(1, 10), labels=experience_mapping.keys(), rotation=45)\n",
+    "plt.xlabel('Years of Coding Experience')\n",
+    "plt.ylabel('Average Salary')\n",
+    "plt.title('Linear Regression: Salary vs. Coding Experience')\n",
+    "plt.show()\n",
+    "\n",
+    "# Correlation coefficient\n",
+    "correlation = df2['ProfCodOrdinal'].corr(df2['NetSalary'])\n",
+    "print(f\"Correlation coefficient: {correlation:.2f}\")\n",
+    "\n",
+    "# Regression coefficients\n",
+    "print(f\"Intercept: {reg.intercept_:.2f}\")\n",
+    "print(f\"Coefficient: {reg.coef_[0]:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3-5 years           20\n",
+       "6-8 years           18\n",
+       "9-11 years          17\n",
+       "0-2 years           15\n",
+       "15-17 years          7\n",
+       "12-14 years          7\n",
+       "18-20 years          6\n",
+       "24-26 years          5\n",
+       "30 or more years     3\n",
+       "21-23 years          1\n",
+       "Name: YearsCoding, dtype: int64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['YearsCoding'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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srCyWLl3Kc889xxNPPGE/np2dzV9//VXs13QxfH19GTt2LP/+979ZtmxZofPFff2lpXnz5txzzz289tpr7Ny5k+uvv565c+fSoUMH3nrrLYex59pn7mwlve+ZM0pn6tSpE+Hh4UybNo2qVavy/fffM3fu3GK+KvN9ca5jXl5eVK1a1X7swQcf5MUXX2TmzJmcOnWK06dP88ADD1zw8aOjo5k6dSqpqakX7KBY8F49cOBAoXO///47NWvWdBhXVOylpeB5Dh48WOKfnYtx4MABhg0bxtVXX83WrVsZPXo0b7zxRqFxBw8eLHSs4HUXxFzSn4+i3l9nq1mzJr6+vsycObPI8yVRvXp13N3d2bdv3wWft2XLlkX+mxgaGlqi5xUR51HRJiKVQu3atXn00Uf56aefGDBgQJHjunXrxsKFC8nLy6NNmzZFjivY923evHm0atXKfvzjjz++YNc1m82GYRiFZlPef//9Qp3qnOG+++5jypQpPPHEE4Va7Rf39YPjJ/sX8ueff1KtWjW8vLwKnfvpp5+Af35BtNlshXLz448/kpKSQt26dc/7PJdy37MNHz6cBx54gKysLIKDg7nrrruKfd/Fixfzyiuv2JdIHjt2jC+++IKbbroJd3d3+7hatWpx1113MX36dHJycujevTv16tW74OM/8sgjzJw5k6FDhxZq+Q/m5vGfffYZvXr1ol27dvj6+jJ37lyH17Bv3z6++eYb7rzzTgDCwsKoVasWCxYsID4+3l5w/Pbbb6xbt67UfoG/+eabAfjoo48cZi4//fTTUu9YmJeXR9++fbHZbHz55ZfMmzeP0aNH06FDh0LdNY8dO8bnn3/usERy/vz5uLm52WMuyc9HSXTr1o2JEydSo0YNGjZseMmP5+vrS0REBJ988gkTJkwosujr1q0by5cv54orriAwMPCSn1dEnEdFm4hUGi+++OIFx9xzzz3MmzeP2267jREjRnD99dfj6enJvn37WLVqFT179qRXr140bdqUfv368dprr+Hp6Unnzp1JT09n8uTJF9w019/fn5tvvplXXnmFmjVr0qBBA9asWcOMGTO47LLLSunVFs3d3Z2JEyfSq1cv4J9rjqD4rx+gRYsWLFy4kI8++ohGjRrh4+NzzmuFwLwGcMSIEdx77720b9+eGjVqcOjQIRYsWMCKFSvo37+/fSlXt27deP7553nuueeIiIhgx44djB8/noYNG17wl/pLue/Z+vXrx5gxY0hOTubpp58+Z8FZFHd3dyIjI4mPjyc/P5+XXnqJo0eP2lu+n2nEiBH2AuCDDz4o1uM3bNiQhQsXcvfdd3P11VfbN9cGs6X9zJkzMQyDXr16cdlll/HMM8/w5JNP0r9/f/r27cuff/7JuHHj8PHx4bnnngPMbReef/557r//fnr16sXgwYM5cuQIY8eOLdW9/Zo3b07fvn159dVXcXd3p1OnTmzdupVXX32VgIAAh+0fzufgwYOkpqYWOu7v72/fyP65557j22+/JTExkZCQEEaNGsWaNWsYNGgQ11xzjUOBVKNGDR588EH27NlDkyZNWL58Oe+99x4PPvigvZAuyc9HSYwcOZJFixZx880388gjj9CyZUvy8/PZs2cPiYmJjBo1qsRFYkJCAjfeeCNt2rThiSeeoHHjxhw8eJDPP/+cd955h2rVqjF+/HiSkpJo3749w4cPJywsjFOnTrF7926WL1/O22+/bekG6iJyBkvboIiIOMmZ3SPP51wdEHNzc43JkycbV111leHj42NUrVrV+Ne//mUMGTLE+Pnnn+3jsrOzjVGjRhlBQUH2bn4pKSmFOsOdq3vkvn37jDvuuMMIDAw0qlWrZnTp0sVIT08vdN+iXkdxOlIahmP3yLO1b9/eABy6R5bk9e/evduIiooyqlWrZgCFugueae/evcbTTz9t3HDDDUZISIjh4eFhVKtWzWjTpo0xdepU4/Tp0w55HT16tFG7dm3Dx8fHuPbaa43PPvvMGDBgwAU7GBb3vgVdHl955ZXz5i8uLs7w8PAw9u3bd95xZz/uSy+9ZIwbN86oU6eO4eXlZVxzzTXGV199VeT9GjRoYDRt2rRYz3GmX375xRg6dKjRuHFjw9vb2/D19TWaNWtmxMfHG7t27XIY+/777xstW7Y0vLy8jICAAKNnz57n7A74/vvvG1deeaXh5eVlNGnSxJg5c2axcl+S9+qpU6eM+Pj4Qj87AQEBhTqyngvn6R5ZsI1CYmKi4ebmVqi76J9//mnUq1fPuO6664zs7GzDMMzukc2bNzdWr15ttG7d2vD29jZq1aplPPnkk0Zubq7D/Yv783F2Z9Yznf1zbhiGcfz4cePpp582wsLC7H9HLVq0MB555BEjIyPD4bWfuQ3B+R5z27Ztxl133WXUqFHD8PLyMurVq2fExcUZp06dso85fPiwMXz4cKNhw4aGp6enUb16daNVq1bGU0895bAtgohYy2YY/98aS0REROxycnJo0KABN954Ix9//LHTnufHH3/kqquu4s0332To0KFOex5Xt27dOm644QbmzZtHTExMmT53hw4d+OOPP0hPTy/T5xURKS4tjxQRETnD4cOH2bFjBx988AEHDx50aBZTmn755Rd+++03nnzySWrVqlWoJX9FlpSUREpKCq1atcLX15cffviBF198kSuvvLLQtWYiIqKiTURExMGyZcsYOHAgtWrVYvr06cVu819Szz//PHPmzKFp06Z88sknVKlSxSnP44r8/f1JTEzktdde49ixY9SsWZNbb72VSZMmOexvJyIiJi2PFBERERERcWHaXFtERERERMSFqWgTERERERFxYSraREREREREXJgakZSx/Px8fv/9d6pVq4bNZrM6HBERERERsYhhGBw7dozQ0FDc3IqeT1PRVsZ+//136tata3UYIiIiIiLiIvbu3UudOnWKPK+irYxVq1YNMP9i/P39LY0lNzeXxMREoqKi8PT0tDSWikj5dS7l17mUX+dSfp1L+XUu5de5lF/ncrX8Hj16lLp169prhKK4TNE2adIknnzySUaMGMFrr70GmNOF48aN49133yUzM5M2bdrw5ptv0rx5c/v9srOzGT16NAsWLODkyZPccsstTJ8+3aFSzczMZPjw4Xz++ecA9OjRg6lTp3LZZZfZx+zZs4dhw4bxzTff4OvrS0xMDJMnT8bLy8s+ZsuWLTz00ENs2LCB6tWrM2TIEJ555pkSLXMsGOvv7+8SRVuVKlXw9/d3iTdtRaP8Opfy61zKr3Mpv86l/DqX8utcyq9zuWp+L1RPuEQjko0bN/Luu+/SsmVLh+Mvv/wyCQkJTJs2jY0bNxISEkJkZCTHjh2zjxk5ciRLlixh4cKFrF27luPHj9OtWzfy8vLsY2JiYkhLS2PFihWsWLGCtLQ0YmNj7efz8vLo2rUrJ06cYO3atSxcuJBFixYxatQo+5ijR48SGRlJaGgoGzduZOrUqUyePJmEhAQnZkZERERERCo7y2fajh8/zr333st7773HCy+8YD9uGAavvfYaTz31FL179wZg9uzZBAcHM3/+fIYMGUJWVhYzZsxgzpw5dO7cGYC5c+dSt25dvv76a6Kjo9m+fTsrVqwgNTWVNm3aAPDee+/Rrl07duzYQVhYGImJiWzbto29e/cSGhoKwKuvvkpcXBwTJkzA39+fefPmcerUKWbNmoW3tzfh4eHs3LmThIQE4uPj1VREREREREScwvKibdiwYXTt2pXOnTs7FG27du0iIyODqKgo+zFvb28iIiJYt24dQ4YMYdOmTeTm5jqMCQ0NJTw8nHXr1hEdHU1KSgoBAQH2gg2gbdu2BAQEsG7dOsLCwkhJSSE8PNxesAFER0eTnZ3Npk2b6NixIykpKURERODt7e0wZsyYMezevZuGDRue8/VlZ2eTnZ1t//7o0aOAOTWbm5t7CZm7dAXPb3UcFZXy61zKr3Mpv86l/DqX8utcyq9zKb/O5Wr5LW4clhZtCxcu5Pvvv2fjxo2FzmVkZAAQHBzscDw4OJjffvvNPsbLy4vAwMBCYwrun5GRQVBQUKHHDwoKchhz9vMEBgbi5eXlMKZBgwaFnqfgXFFF26RJkxg3blyh44mJiVSpUuWc9ylrSUlJVodQoSm/zqX8Opfy61zKr3Mpv86l/DqX8utcrpLfv//+u1jjLCva9u7dy4gRI0hMTMTHx6fIcWcvOzQM44JLEc8ec67xpTHGMIwi71tgzJgxxMfH278v6BATFRXlEo1IkpKSiIyMdKkLMSsK5de5lF/nUn6dS/l1LuXXuZRf51J+ncvV8luwCu9CLCvaNm3axKFDh2jVqpX9WF5eHsnJyUybNo0dO3YA5ixWrVq17GMOHTpkn+EKCQkhJyeHzMxMh9m2Q4cO0b59e/uYgwcPFnr+w4cPOzzO+vXrHc5nZmaSm5vrMKZg1u3M54HCs4Fn8vb2dlhSWcDT09Ml3ijgWrFURMqvcym/zqX8Opfy61zKr3Mpv86l/DqXq+S3uDFY1j3ylltuYcuWLaSlpdm/Wrduzb333ktaWhqNGjUiJCTEYeoyJyeHNWvW2AuyVq1a4enp6TDmwIEDpKen28e0a9eOrKwsNmzYYB+zfv16srKyHMakp6dz4MAB+5jExES8vb3tRWW7du1ITk4mJyfHYUxoaGihZZMiIiIiIiKlxbKZtmrVqhEeHu5wzM/Pjxo1atiPjxw5kokTJ3LllVdy5ZVXMnHiRKpUqUJMTAwAAQEBDBo0iFGjRlGjRg2qV6/O6NGjadGihb2bZNOmTenSpQuDBw/mnXfeAeDf//433bp1IywsDICoqCiaNWtGbGwsr7zyCn/99RejR49m8ODB9iWMMTExjBs3jri4OJ588kl+/vlnJk6cyLPPPqvOkSIiIiIi4jSWd488n8cee4yTJ08ydOhQ++baiYmJDjuGT5kyBQ8PD/r06WPfXHvWrFm4u7vbx8ybN4/hw4fbu0z26NGDadOm2c+7u7uzbNkyhg4dyg033OCwuXaBgIAAkpKSGDZsGK1btyYwMJD4+HiH69VERERERERKm0sVbatXr3b43mazMXbsWMaOHVvkfXx8fJg6dSpTp04tckz16tWZO3fueZ+7Xr16LF269LxjWrRoQXJy8nnHiIiIiIiIlCbLrmkTERERERGRC1PRJiIiIiIi4sJUtImIiIiIiLgwFW2VVF4erFljIzm5NmvW2MjLszoiERERERE5FxVtldDixdCgAURGepCQ0JrISA8aNDCPi4iIiIiIa1HRVsksXgx33gn79jke37/fPK7CTURERETEtahoq0Ty8mDECDCMwucKjo0ciZZKioiIiIi4EBVtlci33xaeYTuTYcDeveY4ERERERFxDSraKpEDB0p3nIiIiIiIOJ+KtkqkVq3SHSciIiIiIs6noq0SuekmqFMHbLZzn7fZoG5dc5yIiIiIiLgGFW2ViLs7vP66ebuowu2118xxIiIiIiLiGlS0VTK9e8Onn0Lt2o7HfX3N4717WxOXiIiIiIicm4q2Sqh3b9i9G5KSThMbuxWA3Fy4+WZr4xIRERERkcJUtFVS7u4QEWFwxx3/49pr8zl9GubNszoqERERERE5m4o2YcAAc2ftDz6wOBARERERESlERZtw9935eHnBDz/A5s1WRyMiIiIiImdS0SZUrw63327e1mybiIiIiIhrUdEmAAwcaP45bx5kZ1sbi4iIiIiI/ENFmwAQGQmhofDXX/DFF1ZHIyIiIiIiBVS0CWB2k+zf37ytJZIiIiIiIq5DRZvYFSyRXLECfv/d2lhERERERMSkok3smjSB9u0hPx/mzLE6GhERERERARVtcpaC2bYPPgDDsDYWERERERFR0SZn6dMHfH1hxw5ITbU6GhERERERUdEmDvz94c47zdtqSCIiIiIiYj0VbVJIwRLJhQvh77+tjUVEREREpLJT0SaFRERAgwZw7BgsXmx1NCIiIiIilZuKNinEzQ3i4szbs2ZZGYmIiIiIiKhok3MaMMD885tv4LffrI1FRERERKQyU9Em59SgAXTqZLb9nz3b6mhERERERCovFW1SpIKGJLNmmRtui4iIiIhI2VPRJkXq3dvcAmDXLkhOtjoaEREREZHKSUWbFKlKFbj7bvO29mwTEREREbGGijY5r4Ilkp9+am4BICIiIiIiZUtFm5xX27YQFmZusv3xx1ZHIyIiIiJS+ahok/Oy2f6ZbdMSSRERERGRsqeiTS4oNtbccPu//4WdO62ORkRERESkclHRJhcUGgrR0ebtWbMsDUVEREREpNJR0SbFUrBE8sMPIS/P2lhERERERCoTFW1SLD16QPXqsH8/JCVZHY2IiIiISOWhok2KxdsbYmLM22pIIiIiIiJSdlS0SbEVLJH87DP46y9LQxERERERqTRUtEmxXXMNtGwJOTmwYIHV0YiIiIiIVA4q2qTYtGebiIiIiEjZU9EmJXLvveDhAZs2wZYtVkcjIiIiIlLxqWiTErn8cuje3bytPdtERERERJxPRZuUWMESyblzITfX2lhERERERCo6FW1SYrfeCsHBcOgQLF9udTQiIiIiIhWbijYpMQ8PiI01b6shiYiIiIiIc6lok4tSsERy2TJzxk1ERERERJxDRZtclGbN4Prr4fRp89o2ERERERFxDhVtctHO3LPNMKyNRURERESkolLRJhftnnvAxwfS081920REREREpPSpaJOLdtll0KuXeVsNSUREREREnENFm1ySuDjzz/nz4dQpS0MREREREamQVLTJJbnlFqhTB44cgf/8x+poREREREQqHkuLtrfeeouWLVvi7++Pv78/7dq148svv7Sfj4uLw2azOXy1bdvW4TGys7N5+OGHqVmzJn5+fvTo0YN9+/Y5jMnMzCQ2NpaAgAACAgKIjY3lyJEjDmP27NlD9+7d8fPzo2bNmgwfPpycnByHMVu2bCEiIgJfX19q167N+PHjMSp5Bw53dxgwwLytJZIiIiIiIqXP0qKtTp06vPjii3z33Xd89913dOrUiZ49e7J161b7mC5dunDgwAH71/Llyx0eY+TIkSxZsoSFCxeydu1ajh8/Trdu3cjLy7OPiYmJIS0tjRUrVrBixQrS0tKILdgdGsjLy6Nr166cOHGCtWvXsnDhQhYtWsSoUaPsY44ePUpkZCShoaFs3LiRqVOnMnnyZBISEpyYofKhYIlkYiKcVS+LiIiIiMgl8rDyybt37+7w/YQJE3jrrbdITU2lefPmAHh7exMSEnLO+2dlZTFjxgzmzJlD586dAZg7dy5169bl66+/Jjo6mu3bt7NixQpSU1Np06YNAO+99x7t2rVjx44dhIWFkZiYyLZt29i7dy+hoaEAvPrqq8TFxTFhwgT8/f2ZN28ep06dYtasWXh7exMeHs7OnTtJSEggPj4em83mrDS5vMaN4aab4Ntv4cMP4cknrY5IRERERKTisLRoO1NeXh6ffPIJJ06coF27dvbjq1evJigoiMsuu4yIiAgmTJhAUFAQAJs2bSI3N5eoqCj7+NDQUMLDw1m3bh3R0dGkpKQQEBBgL9gA2rZtS0BAAOvWrSMsLIyUlBTCw8PtBRtAdHQ02dnZbNq0iY4dO5KSkkJERATe3t4OY8aMGcPu3btp2LDhOV9XdnY22dnZ9u+PHj0KQG5uLrm5uZeYtUtT8PylEUf//ja+/daDDz4wGD36NJW4hrUrzfxKYcqvcym/zqX8Opfy61zKr3Mpv87lavktbhyWF21btmyhXbt2nDp1iqpVq7JkyRKaNWsGwK233spdd91F/fr12bVrF8888wydOnVi06ZNeHt7k5GRgZeXF4GBgQ6PGRwcTEZGBgAZGRn2Iu9MQUFBDmOCg4MdzgcGBuLl5eUwpkGDBoWep+BcUUXbpEmTGDduXKHjiYmJVKlS5ULpKRNJSUmX/BhVq7rj49OF//3Pg1dfTaVZs79KIbKKoTTyK0VTfp1L+XUu5de5lF/nUn6dS/l1LlfJ799//12scZYXbWFhYaSlpXHkyBEWLVrEgAEDWLNmDc2aNePuu++2jwsPD6d169bUr1+fZcuW0bt37yIf0zAMh+WK51q6WBpjCpqQnG9p5JgxY4iPj7d/f/ToUerWrUtUVBT+/v5F3q8s5ObmkpSURGRkJJ6enpf8eF9+6caHH8LOnTcwenTehe9QwZV2fsWR8utcyq9zKb/Opfw6l/LrXMqvc7lafgtW4V2I5UWbl5cXjRs3BqB169Zs3LiR119/nXfeeafQ2Fq1alG/fn1+/vlnAEJCQsjJySEzM9Nhtu3QoUO0b9/ePubgwYOFHuvw4cP2mbKQkBDWr1/vcD4zM5Pc3FyHMQWzbmc+D1Bolu5M3t7eDksqC3h6errEGwVKL5ZBg8xr2j791I1p09zw8yuF4CoAV/q7roiUX+dSfp1L+XUu5de5lF/nUn6dy1XyW9wYXG6fNsMwHK4BO9Off/7J3r17qVWrFgCtWrXC09PTYXrzwIEDpKen24u2du3akZWVxYYNG+xj1q9fT1ZWlsOY9PR0Dhw4YB+TmJiIt7c3rVq1so9JTk522AYgMTGR0NDQQssmK6ubboIrroDjx2HRIqujERERERGpGCwt2p588km+/fZbdu/ezZYtW3jqqadYvXo19957L8ePH2f06NGkpKSwe/duVq9eTffu3alZsya9evUCICAggEGDBjFq1ChWrlzJ5s2b6devHy1atLB3k2zatCldunRh8ODBpKamkpqayuDBg+nWrRthYWEAREVF0axZM2JjY9m8eTMrV65k9OjRDB482L6EMSYmBm9vb+Li4khPT2fJkiVMnDix0neOPJPN9k/7f+3ZJiIiIiJSOiwt2g4ePEhsbCxhYWHccsstrF+/nhUrVhAZGYm7uztbtmyhZ8+eNGnShAEDBtCkSRNSUlKoVq2a/TGmTJnC7bffTp8+fbjhhhuoUqUKX3zxBe7u7vYx8+bNo0WLFkRFRREVFUXLli2ZM2eO/by7uzvLli3Dx8eHG264gT59+nD77bczefJk+5iAgACSkpLYt28frVu3ZujQocTHxztcrybmRts2G6xeDb/+anU0IiIiIiLln6XXtM2YMaPIc76+vnz11VcXfAwfHx+mTp3K1KlTixxTvXp15s6de97HqVevHkuXLj3vmBYtWpCcnHzBmCqzunWhc2dISoLZs+EcjTNFRERERKQEXO6aNin/Bg40/5w9G/LzrY1FRERERKS8U9Empe722yEgAH77DVatsjoaEREREZHyTUWblDpfX+jb17ythiQiIiIiIpdGRZs4RcESyUWLICvL2lhERERERMozFW3iFNddB82awalT8NFHVkcjIiIiIlJ+qWgTp9CebSIiIiIipUNFmzhNbCy4u0NqKmzfbnU0IiIiIiLlk4o2cZqQELj1VvP2rFmWhiIiIiIiUm6paBOnKmhI8uGHcPq0tbGIiIiIiJRHKtrEqbp1g5o1ISMDvvrK6mhERERERMofFW3iVF5ecO+95m01JBERERERKTkVbeJ0BUskP/8c/vjD2lhERERERMobFW3idFddBddcA7m5MH++1dGIiIiIiJQvKtqkTBTMtmmJpIiIiIhIyahokzIRE2Ne35aWZn6JiIiIiEjxqGiTMlGjBvToYd7Wnm0iIiIiIsWnok3KTMESyXnzICfH2lhERERERMoLFW1SZqKioFYts4Pk0qVWRyMiIiIiUj6oaJMy4+EB/fubt9WQRERERESkeFS0SZkqWCL55ZeQkWFtLCIiIiIi5YGKNilTYWHQrh3k5cGcOVZHIyIiIiLi+lS0SZk7c882w7A2FhERERERV6eiTcpcnz7g6wvbt8OGDVZHIyIiIiLi2lS0SZkLCIDevc3bakgiIiIiInJ+KtrEEgVLJBcuhJMnrY1FRERERMSVqWgTS3TsCPXrQ1YWLFlidTQiIiIiIq5LRZtYws0NBgwwb2uJpIiIiIhI0VS0iWXi4sw/V66EPXssDUVERERExGWpaBPLNGwIHTqYbf9nz7Y6GhERERER16SiTSxV0JBk1izIz7c0FBERERERl6SiTSx1xx1QrRr8+it8+63V0YiIiIiIuB4VbWIpPz9zs21QQxIRERERkXNR0SaWK1gi+emncPy4tbGIiIiIiLgaFW1iufbtoUkTOHECPvnE6mhERERERFyLijaxnM32T/t/LZEUEREREXGkok1cQv/+5obb334L//uf1dGIiIiIiLgOFW3iEmrXhqgo8/asWZaGIiIiIiLiUlS0icsoaEgyezbk5Vkbi4iIiIiIq1DRJi6jRw8IDIR9+2DlSqujERERERFxDSraxGX4+EBMjHlbDUlEREREREwq2sSlFCyRXLIEMjOtjUVERERExBWoaBOXcu210KIFZGfDwoVWRyMiIiIiYj0VbeJStGebiIiIiIgjFW3icvr1Aw8P2LgRtm61OhoREREREWupaBOXExQEXbuatzXbJiIiIiKVnYo2cUkFDUnmzIHcXGtjERERERGxkoo2cUm33WbOuB06BF9+aXU0IiIiIiLWUdEmLsnT07y2DbREUkREREQqNxVt4rIKlkguXWrOuImIiIiIVEYq2sRlhYdD69Zw+jTMm2d1NCIiIiIi1lDRJi6tYLbtgw/AMKyNRURERETECiraxKX17Qve3rBlC2zebHU0IiIiIiJlT0WbuLTAQLj9dvO2GpKIiIiISGWkok1cXsESyfnzITvb2lhERERERMqaijZxeZ07Q5068Ndf8PnnVkcjIiIiIlK2VLSJy3N3h/79zdtaIikiIiIilY2KNikX4uLMP7/6CvbvtzQUEREREZEypaJNyoUrr4Qbb4T8fJgzx+poRERERETKjqVF21tvvUXLli3x9/fH39+fdu3a8eWXX9rPG4bB2LFjCQ0NxdfXlw4dOrB161aHx8jOzubhhx+mZs2a+Pn50aNHD/bt2+cwJjMzk9jYWAICAggICCA2NpYjR444jNmzZw/du3fHz8+PmjVrMnz4cHJychzGbNmyhYiICHx9falduzbjx4/H0OZhZUZ7tomIiIhIZWRp0VanTh1efPFFvvvuO7777js6depEz5497YXZyy+/TEJCAtOmTWPjxo2EhIQQGRnJsWPH7I8xcuRIlixZwsKFC1m7di3Hjx+nW7du5OXl2cfExMSQlpbGihUrWLFiBWlpacTGxtrP5+Xl0bVrV06cOMHatWtZuHAhixYtYtSoUfYxR48eJTIyktDQUDZu3MjUqVOZPHkyCQkJZZApAbjrLqhSBXbuhJQUq6MRERERESkbHlY+effu3R2+nzBhAm+99Rapqak0a9aM1157jaeeeorevXsDMHv2bIKDg5k/fz5DhgwhKyuLGTNmMGfOHDp37gzA3LlzqVu3Ll9//TXR0dFs376dFStWkJqaSps2bQB47733aNeuHTt27CAsLIzExES2bdvG3r17CQ0NBeDVV18lLi6OCRMm4O/vz7x58zh16hSzZs3C29ub8PBwdu7cSUJCAvHx8dhstjLMXOVUrRrceSd8+KE529a+vdURiYiIiIg4n6VF25ny8vL45JNPOHHiBO3atWPXrl1kZGQQFRVlH+Pt7U1ERATr1q1jyJAhbNq0idzcXIcxoaGhhIeHs27dOqKjo0lJSSEgIMBesAG0bduWgIAA1q1bR1hYGCkpKYSHh9sLNoDo6Giys7PZtGkTHTt2JCUlhYiICLy9vR3GjBkzht27d9OwYcNzvq7s7Gyyz9hc7OjRowDk5uaSm5t76Ym7BAXPb3UcJREba+PDDz346CODV145jZ+f1REVrTzmtzxRfp1L+XUu5de5lF/nUn6dS/l1LlfLb3HjsLxo27JlC+3atePUqVNUrVqVJUuW0KxZM9atWwdAcHCww/jg4GB+++03ADIyMvDy8iIwMLDQmIyMDPuYoKCgQs8bFBTkMObs5wkMDMTLy8thTIMGDQo9T8G5ooq2SZMmMW7cuELHExMTqVKlyjnvU9aSkpKsDqHY8vMhOLgzBw/6MXbsj3TsuO/Cd7JYecpveaT8Opfy61zKr3Mpv86l/DqX8utcrpLfv//+u1jjLC/awsLCSEtL48iRIyxatIgBAwawZs0a+/mzlx0ahnHBpYhnjznX+NIYU9CE5HzxjBkzhvj4ePv3R48epW7dukRFReHv73/e1+Fsubm5JCUlERkZiaenp6WxlERamhvjx8MPP1zDK6+0tDqcIpXX/JYXyq9zKb/Opfw6l/LrXMqvcym/zuVq+S1YhXchlhdtXl5eNG7cGIDWrVuzceNGXn/9dR5//HHAnMWqVauWffyhQ4fsM1whISHk5OSQmZnpMNt26NAh2v//BU8hISEcPHiw0PMePnzY4XHWr1/vcD4zM5Pc3FyHMQWzbmc+DxSeDTyTt7e3w5LKAp6eni7xRgHXiqU47rsPnn8eVq92Y98+N4qY5HQZ5S2/5Y3y61zKr3Mpv86l/DqX8utcyq9zuUp+ixuDy+3TZhgG2dnZNGzYkJCQEIepy5ycHNasWWMvyFq1aoWnp6fDmAMHDpCenm4f065dO7KystiwYYN9zPr168nKynIYk56ezoEDB+xjEhMT8fb2plWrVvYxycnJDtsAJCYmEhoaWmjZpDhX/frQqZN5e/Zsa2MREREREXE2S4u2J598km+//Zbdu3ezZcsWnnrqKVavXs29996LzWZj5MiRTJw4kSVLlpCenk5cXBxVqlQhJiYGgICAAAYNGsSoUaNYuXIlmzdvpl+/frRo0cLeTbJp06Z06dKFwYMHk5qaSmpqKoMHD6Zbt26EhYUBEBUVRbNmzYiNjWXz5s2sXLmS0aNHM3jwYPsSxpiYGLy9vYmLiyM9PZ0lS5YwceJEdY60SMGebbNmmde5iYiIiIhUVJYujzx48CCxsbEcOHCAgIAAWrZsyYoVK4iMjATgscce4+TJkwwdOpTMzEzatGlDYmIi1apVsz/GlClT8PDwoE+fPpw8eZJbbrmFWbNm4e7ubh8zb948hg8fbu8y2aNHD6ZNm2Y/7+7uzrJlyxg6dCg33HADvr6+xMTEMHnyZPuYgIAAkpKSGDZsGK1btyYwMJD4+HiH69Wk7PTqBf7+8NtvsGYNdOxodUQiIiIiIs5hadE2Y8aM85632WyMHTuWsWPHFjnGx8eHqVOnMnXq1CLHVK9enblz5573uerVq8fSpUvPO6ZFixYkJyefd4yUjSpV4J574N13zT3bVLSJiIiISEXlcte0iRRXwRLJTz+FYjbeEREREREpd1S0SbnVpg38619w8iR8/LHV0YiIiIiIOIeKNim3bLZ/Zts++MDaWEREREREnEVFm5RrsbHg7g7r1sGOHVZHIyIiIiJS+lS0SblWqxZ06WLenjXL0lBERERERJxCRZuUewVLJD/8EPLyrI1FRERERKS0qWiTcq97d6hRA37/HRITrY5GRERERKR0qWiTcs/LC+6917ythiQiIiIiUtGoaJMKoWCJ5H/+A3/9ZW0sIiIiIiKlSUWbVAhXXw1XXQU5OTB/vtXRiIiIiIiUHhVtUmFozzYRERERqYhUtEmFce+94OkJ338PP/5odTQiIiIiIqVDRZtUGDVrmp0kQbNtIiIiIlJxqGiTCqVgieTcueb1bSIiIiIi5Z2KNqlQunSBkBD44w9YtszqaERERERELp2KNqlQPDwgNta8rSWSIiIiIlIRqGiTCqdgieTy5XDwoLWxiIiIiIhcKhVtUuE0bQpt2kBennltm4iIiIhIeVbiou3EiRPOiEOkVJ25Z5thWBuLiIiIiMilKHHRFhwczH333cfatWudEY9IqbjnHvDxga1b4bvvrI5GREREROTilbhoW7BgAVlZWdxyyy00adKEF198kd9//90ZsYlctIAA6N3bvK2GJCIiIiJSnpW4aOvevTuLFi3i999/58EHH2TBggXUr1+fbt26sXjxYk6fPu2MOEVKrGCJ5IIFcOqUtbGIiIiIiFysi25EUqNGDR555BF++OEHEhIS+Prrr7nzzjsJDQ3l2Wef5e+//y7NOEVKrFMnqFcPjhyBzz6zOhoRERERkYtz0UVbRkYGL7/8Mk2bNuWJJ57gzjvvZOXKlUyZMoUlS5Zw++23l2KYIiXn5gYDBpi3tURSRERERMorj5LeYfHixXzwwQd89dVXNGvWjGHDhtGvXz8uu+wy+5irr76aa665pjTjFLkocXHw/POQlAR790LdulZHJCIiIiJSMiWeaRs4cCC1a9fmv//9L2lpaTz00EMOBRtAo0aNeOqpp0orRpGL1qgRRESYbf8//NDqaERERERESq5ERdvp06eZNGkSY8eO5brrritynK+vL88999wlBydSGgoaksyapT3bRERERKT8KVHR5uHhwejRo8nOznZWPCKl7o47wM8P/vc/0PaCIiIiIlLelHh5ZJs2bdi8ebMzYhFxiqpVoU8f87YakoiIiIhIeVPiRiRDhw5l1KhR7Nu3j1atWuHn5+dwvmXLlqUWnEhpGTjQLNg+/hjeeMMs5EREREREyoMSF2133303AMOHD7cfs9lsGIaBzWYjLy+v9KITKSU33giNG5tLJD/91OwqKSIiIiJSHpS4aNu1a5cz4hBxKpvNLNSeftqccVPRJiIiIiLlRYmLtvr16zsjDhGn698fnnkGkpPhl1/giiusjkhERERE5MJKXLQV2LZtG3v27CEnJ8fheI8ePS45KBFnqFsXIiMhMdFs///881ZHJCIiIiJyYSUu2n799Vd69erFli1b7NeygXldG6Br2sSlDRxoFm2zZ8O4ceBW4v6pIiIiIiJlq8S/so4YMYKGDRty8OBBqlSpwtatW0lOTqZ169asXr3aCSGKlJ7bb4fLLoO9e+Gbb6yORkRERETkwkpctKWkpDB+/Hguv/xy3NzccHNz48Ybb2TSpEkOHSVFXJGPD/Tta97Wnm0iIiIiUh6UuGjLy8uj6v9vclWzZk1+//13wGxQsmPHjtKNTsQJBg40/1y8GI4csTQUEREREZELKnHRFh4ezo8//ghAmzZtePnll/nvf//L+PHjadSoUakHKFLaWreG5s3h1Cn46COroxEREREROb8SF21PP/00+fn5ALzwwgv89ttv3HTTTSxfvpw33nij1AMUKW022z+zbVoiKSIiIiKursTdI6Ojo+23GzVqxLZt2/jrr78IDAy0d5AUcXX9+sHjj8P69bB9OzRtanVEIiIiIiLnVioNz6tXr66CTcqV4GDo2tW8rdk2EREREXFlxZpp6927d7EfcPHixRcdjEhZGjgQPv8c5syBiRPB46K3mhcRERERcZ5i/ZoaEBDg7DhEylzXrnD55ZCRAStWQLduVkckIiIiIlJYsYq2D7R+TCogT0/z2rYpU8wlkiraRERERMQVlco1bSLlVVyc+ecXX8Aff1gaioiIiIjIOV3UVTyffvopH3/8MXv27CEnJ8fh3Pfff18qgYmUhZYt4dpr4fvvYd48GDHC6ohERERERByVeKbtjTfeYODAgQQFBbF582auv/56atSowa+//sqtt97qjBhFnEp7tomIiIiIKytx0TZ9+nTeffddpk2bhpeXF4899hhJSUkMHz6crKwsZ8Qo4lQxMeDlBT/8AJs3Wx2NiIiIiIijEhdte/bsoX379gD4+vpy7NgxAGJjY1mwYEHpRidSBqpXh549zduabRMRERERV1Pioi0kJIQ///wTgPr165OamgrArl27MAyjdKMTKSMFSyTnzYPsbGtjERERERE5U4mLtk6dOvHFF18AMGjQIB555BEiIyO5++676dWrV6kHKFIWoqIgNBT++svsJCkiIiIi4ipK3D3y3XffJT8/H4AHHniA6tWrs3btWrp3784DDzxQ6gGKlAV3d+jfH158EWbNgjvvtDoiERERERFTiYs2Nzc33Nz+maDr06cPffr0KdWgRKwwcKBZtH35JRw4ALVqWR2RiIiIiEgJlkf+9ddf7Nu3z+HY1q1bGThwIH369GH+/PmlHpxIWWrSBNq3h/x8mDPH6mhEREREREzFLtqGDRtGQkKC/ftDhw5x0003sXHjRrKzs4mLi2OOftOVcu7MPdvUV0dEREREXEGxi7bU1FR69Ohh//7DDz+kevXqpKWl8Z///IeJEyfy5ptvOiVIkbLSpw/4+sJPP8H69VZHIyIiIiJSgqItIyODhg0b2r//5ptv6NWrFx4e5mVxPXr04Oeffy7Rk0+aNInrrruOatWqERQUxO23386OHTscxsTFxWGz2Ry+2rZt6zAmOzubhx9+mJo1a+Ln50ePHj0KLeXMzMwkNjaWgIAAAgICiI2N5ciRIw5j9uzZQ/fu3fHz86NmzZoMHz6cnJwchzFbtmwhIiICX19fateuzfjx47XVQQXi7/9PExLt2SYiIiIirqDYRZu/v79DkbNhwwaH4slms5Fdwg2u1qxZw7Bhw0hNTSUpKYnTp08TFRXFiRMnHMZ16dKFAwcO2L+WL1/ucH7kyJEsWbKEhQsXsnbtWo4fP063bt3Iy8uzj4mJiSEtLY0VK1awYsUK0tLSiI2NtZ/Py8uja9eunDhxgrVr17Jw4UIWLVrEqFGj7GOOHj1KZGQkoaGhbNy4kalTpzJ58mSHZaNS/hUskVy4EP7+29pYRERERESK3T3y+uuv54033uC9995j8eLFHDt2jE6dOtnP79y5k7p165boyVesWOHw/QcffEBQUBCbNm3i5ptvth/39vYmJCTknI+RlZXFjBkzmDNnDp07dwZg7ty51K1bl6+//pro6Gi2b9/OihUrSE1NpU2bNgC89957tGvXjh07dhAWFkZiYiLbtm1j7969hIaGAvDqq68SFxfHhAkT8Pf3Z968eZw6dYpZs2bh7e1NeHg4O3fuJCEhgfj4eGw2W4lev7imiAho0AB274YlS+Dee62OSEREREQqs2IXbc8//zydO3dm7ty5nD59mieffJLAwED7+YULFxIREXFJwWRlZQFQvXp1h+OrV68mKCiIyy67jIiICCZMmEBQUBAAmzZtIjc3l6ioKPv40NBQwsPDWbduHdHR0aSkpBAQEGAv2ADatm1LQEAA69atIywsjJSUFMLDw+0FG0B0dDTZ2dls2rSJjh07kpKSQkREBN7e3g5jxowZw+7dux2WjxbIzs52mIE8evQoALm5ueTm5l5Kui5ZwfNbHYcrio114/nn3Zk5M58+ffIufIdzUH6dS/l1LuXXuZRf51J+nUv5dS7l17lcLb/FjaPYRdvVV1/N9u3bWbduHSEhIQ4FEMA999xDs2bNShblGQzDID4+nhtvvJHw8HD78VtvvZW77rqL+vXrs2vXLp555hk6derEpk2b8Pb2JiMjAy8vL4cCEiA4OJiMjAzAvB6voMg7U1BQkMOY4OBgh/OBgYF4eXk5jGnQoEGh5yk4d66ibdKkSYwbN67Q8cTERKpUqXKhtJSJpKQkq0NwOXXq+AJRrFplY9asVQQFnbzox1J+nUv5dS7l17mUX+dSfp1L+XUu5de5XCW/fxfzWpwSba59+eWX07Nnz3Oe69q1a0keqpCHHnqIH3/8kbVr1zocv/vuu+23w8PDad26NfXr12fZsmX07t27yMczDMNhueK5li6WxpiCJiRFLY0cM2YM8fHx9u+PHj1K3bp1iYqKwt/fv8j4y0Jubi5JSUlERkbi6elpaSyuaOHCfFatcmPfvluIi8sv8f2VX+dSfp1L+XUu5de5lF/nUn6dS/l1LlfLb8EqvAspUdHmLA8//DCff/45ycnJ1KlT57xja9WqRf369e2dKkNCQsjJySEzM9Nhtu3QoUO0b9/ePubgwYOFHuvw4cP2mbKQkBDWn9XjPTMzk9zcXIcxBbNuZz4PUGiWroC3t7fDcsoCnp6eLvFGAdeKxZUMHAirVsGcOe4895w7bsVu2+NI+XUu5de5lF/nUn6dS/l1LuXXuZRf53KV/BY3hov8NbR0GIbBQw89xOLFi/nmm2/OubzwbH/++Sd79+6lVq1aALRq1QpPT0+HKc4DBw6Qnp5uL9ratWtHVlYWGzZssI9Zv349WVlZDmPS09M5cOCAfUxiYiLe3t60atXKPiY5OdlhG4DExERCQ0MLLZuU8u+OO6BaNdi1C5KTrY5GRERERCorS4u2YcOGMXfuXObPn0+1atXIyMggIyODkyfN64eOHz/O6NGjSUlJYffu3axevZru3btTs2ZNevXqBUBAQACDBg1i1KhRrFy5ks2bN9OvXz9atGhh7ybZtGlTunTpwuDBg0lNTSU1NZXBgwfTrVs3wsLCAIiKiqJZs2bExsayefNmVq5cyejRoxk8eLB9GWNMTAze3t7ExcWRnp7OkiVLmDhxojpHVlBVqkDB6lzt2SYiIiIiVrG0aHvrrbfIysqiQ4cO1KpVy/710UcfAeDu7s6WLVvo2bMnTZo0YcCAATRp0oSUlBSqVatmf5wpU6Zw++2306dPH2644QaqVKnCF198gbu7u33MvHnzaNGiBVFRUURFRdGyZUvmzJljP+/u7s6yZcvw8fHhhhtuoE+fPtx+++1MnjzZPiYgIICkpCT27dtH69atGTp0KPHx8Q7XrEnFUrBn26efwrFj1sYiIiIiIpVTia9pc3d358CBA4W6Mf75558EBQU5bGh9IQVNPIri6+vLV199dcHH8fHxYerUqUydOrXIMdWrV2fu3LnnfZx69eqxdOnS845p0aIFyVorV2m0awdhYbBjB3z8MQwaZHVEIiIiIlLZlHimrahCKzs7Gy8vr0sOSMSV2GwQF2fe1hJJEREREbFCsWfa3njjDcBsbf/+++9TtWpV+7m8vDySk5P517/+VfoRilisf3946in4739h505o0sTqiERERESkMil20TZlyhTAnGl7++23Ha4X8/LyokGDBrz99tulH6GIxUJDIToavvwSZs+GCROsjkhEREREKpNiF227du0CoGPHjixevNhhTzSRim7gwH+KtvHj4YzPLEREREREnKrE17StWrWKwMBAcnJy2LFjB6dPn3ZGXCIupUcPqF4d9u+Hr7+2OhoRERERqUxKXLSdPHmSQYMGUaVKFZo3b86ePXsAGD58OC+++GKpByjiCry9ISbGvK2GJCIiIiJSlkpctD3xxBP88MMPrF69Gh8fH/vxzp072/dXE6mICvZs++wzyMy0NBQRERERqURKXLR99tlnTJs2jRtvvBGbzWY/3qxZM3755ZdSDU7ElVxzDbRsCdnZsGCB1dGIiIiISGVR4qLt8OHDhTbWBjhx4oRDESdS0dhs/8y2aYmkiIiIiJSVEhdt1113HcuWLbN/X1Covffee7Rr1670IhNxQffeCx4e8N13kJ5udTQiIiIiUhkUu+V/gUmTJtGlSxe2bdvG6dOnef3119m6dSspKSmsWbPGGTGKuIzLL4fu3WHJEnO27dVXrY5IRERERCq6Es+0tW/fnv/+97/8/fffXHHFFSQmJhIcHExKSgqtWrVyRowiLqVgieTcuZCba20sIiIiIlLxlXimDaBFixbMnj27tGMRKRe6dIHgYDh4EJYvh549rY5IRERERCqyEs+0iVR2np7Qr595Ww1JRERERMTZil20ubm54e7uft4vD4+LmrgTKXcKlkguWwaHDlkbi4iIiIhUbMWuspYsWVLkuXXr1jF16lQMwyiVoERcXfPmcN11sHGjeW1bfLzVEYmIiIhIRVXsoq3nOS7c+emnnxgzZgxffPEF9957L88//3ypBifiygYONIu2Dz6ARx4x93ETERERESltF3VN2++//87gwYNp2bIlp0+fJi0tjdmzZ1OvXr3Sjk/EZd1zD3h7m/u1bdpkdTQiIiIiUlGVqGjLysri8ccfp3HjxmzdupWVK1fyxRdfEB4e7qz4RFxWYCD06mXeVkMSEREREXGWYhdtL7/8Mo0aNWLp0qUsWLCAdevWcdNNNzkzNhGXV9CQZP58OHXK2lhEREREpGIq9jVtTzzxBL6+vjRu3JjZs2cXuU/b4sWLSy04EVd3yy1Qpw7s2weffw59+lgdkYiIiIhUNMUu2vr3749NnRZEHLi7w4ABMGGCuURSRZuIiIiIlLZiF22zZs1yYhgi5VdcnFm0JSbC/v1Qu7bVEYmIiIhIRXJR3SNF5B+NG8NNN0F+Pnz4odXRiIiIiEhFo6JNpBQUNCT54APQHvMiIiIiUppUtImUgrvuAj8/+PlnWLfO6mhEREREpCJR0SZSCqpWNQs30J5tIiIiIlK6VLSJlJKCJZIffQQnTlgbi4iIiIhUHMXuHnmmnTt3snr1ag4dOkR+fr7DuWeffbZUAhMpb266Ca64An75BRYtgr59rY5IRERERCqCEhdt7733Hg8++CA1a9YkJCTEYe82m82mok0qLZvNbP//zDPmEkkVbSIiIiJSGkpctL3wwgtMmDCBxx9/3BnxiJRr/fvDs8/C6tWwYIGNH36ojZ+fjY4dzY24RURERERKqsTXtGVmZnJXQccFEXFQrx60aGHeHjDAg4SE1kRGetCgASxebGloIiIiIlJOlbhou+uuu0hMTHRGLCLl3uLF8OOPhY/v3w933qnCTURERERKrsTLIxs3bswzzzxDamoqLVq0wNPT0+H88OHDSy04kfIkLw9GjDj3OcMwr3kbORJ69tRSSREREREpvhIXbe+++y5Vq1ZlzZo1rFmzxuGczWZT0SaV1rffwr59RZ83DNi71xzXoUOZhSUiIiIi5VyJi7Zdu3Y5Iw6Rcu/AgdIdJyIiIiIC2lxbpNTUqlW640RERERE4CI31963bx+ff/45e/bsIScnx+FcQkJCqQQmUt7cdBPUqWM2HTGMwudtNvP8TTeVfWwiIiIiUn6VuGhbuXIlPXr0oGHDhuzYsYPw8HB2796NYRhce+21zohRpFxwd4fXXze7RNps5y7cXntNTUhEREREpGRKvDxyzJgxjBo1ivT0dHx8fFi0aBF79+4lIiJC+7dJpde7N3z6KdSuXfjclVeanSNFREREREqixEXb9u3bGTBgAAAeHh6cPHmSqlWrMn78eF566aVSD1CkvOndG3bvhqSk08THf8f8+aepWhV27oTp062OTkRERETKmxIXbX5+fmRnZwMQGhrKL7/8Yj/3xx9/lF5kIuWYuztERBjcfPN+7rzT4JVXzONjxsBvv1kbm4iIiIiULyUu2tq2bct///tfALp27cqoUaOYMGEC9913H23bti31AEUqgn//G26+GU6cgCFDzn29m4iIiIjIuZS4aEtISKBNmzYAjB07lsjISD766CPq16/PjBkzSj1AkYrAzQ3eew+8veGrr2DuXKsjEhEREZHyosTdIxs1amS/XaVKFabrIh2RYmnSBJ57Dp58EkaOhOhoCAqyOioRERERcXXaXFukDI0eDVdfDX/9BcOHWx2NiIiIiJQHxSraqlevbm8yEhgYSPXq1Yv8EpGieXrCjBlmo5KPPoIvvrA6IhERERFxdcVaHjllyhSqVasGwGuvvebMeEQqvGuvhVGj4OWX4cEHzQYlAQFWRyUiIiIirqpYRVvBvmxn3xaRizN2LCxeDP/7HzzxBLz1ltURiYiIiIirKlbRdvTo0WI/oL+//0UHI1JZ+Pqa3SQ7doS334a+fc0ZNxERERGRsxWraLvsssuw2WzFesC8vLxLCkiksujQwdy/7d134f774YcfzGJORERERORMxSraVq1aZb+9e/dunnjiCeLi4mjXrh0AKSkpzJ49m0mTJjknSpEK6uWXYelS+PlnGD8e9CMkIiIiImcrVtEWERFhvz1+/HgSEhLo27ev/ViPHj1o0aIF7777rq55EymBgACYPh1uvx1eeQX69IFrrrE6KhERERFxJSXepy0lJYXWrVsXOt66dWs2bNhQKkGJVCY9e8Jdd0FenrlM8vRpqyMSEREREVdS4qKtbt26vP3224WOv/POO9StW7dUghKpbKZOhcBA+P57SEiwOhoRERERcSXFWh55pilTpnDHHXfw1Vdf0bZtWwBSU1P55ZdfWLRoUakHKFIZBAfDlCkQFwfPPQe9esGVV1odlYiIiIi4ghLPtN12223s3LmTHj168Ndff/Hnn3/Ss2dPdu7cyW233eaMGEUqhf79ISoKTp2CwYMhP9/qiERERETEFZR4pg3MJZITJ04s7VhEKjWbDd55B5o3hzVr4P33zS0BRERERKRyK/FMG8C3335Lv379aN++Pfv37wdgzpw5rF27tkSPM2nSJK677jqqVatGUFAQt99+Ozt27HAYYxgGY8eOJTQ0FF9fXzp06MDWrVsdxmRnZ/Pwww9Ts2ZN/Pz86NGjB/v27XMYk5mZSWxsLAEBAQQEBBAbG8uRI0ccxuzZs4fu3bvj5+dHzZo1GT58ODk5OQ5jtmzZQkREBL6+vtSuXZvx48djGEaJXrdIURo0gAkTzNuPPgr//+MlIiIiIpVYiYu2RYsWER0dja+vL99//z3Z2dkAHDt2rMSzb2vWrGHYsGGkpqaSlJTE6dOniYqK4sSJE/YxL7/8MgkJCUybNo2NGzcSEhJCZGQkx44ds48ZOXIkS5YsYeHChaxdu5bjx4/TrVs3h42+Y2JiSEtLY8WKFaxYsYK0tDRiY2Pt5/Py8ujatSsnTpxg7dq1LFy4kEWLFjFq1Cj7mKNHjxIZGUloaCgbN25k6tSpTJ48mQR1jpBS9PDD0KYNHD0KQ4eCPhMQERERqeSMErr66quN2bNnG4ZhGFWrVjV++eUXwzAMY/PmzUZwcHBJH87BoUOHDMBYs2aNYRiGkZ+fb4SEhBgvvviifcypU6eMgIAA4+233zYMwzCOHDlieHp6GgsXLrSP2b9/v+Hm5masWLHCMAzD2LZtmwEYqamp9jEpKSkGYPz000+GYRjG8uXLDTc3N2P//v32MQsWLDC8vb2NrKwswzAMY/r06UZAQIBx6tQp+5hJkyYZoaGhRn5+frFeY1ZWlgHYH9NKOTk5xmeffWbk5ORYHUqFdCn5TU83DE9PwwDD+OgjJwRXAej961zKr3Mpv86l/DqX8utcyq9zuVp+i1sblPiath07dnDzzTcXOu7v719ouWFJZWVlAVC9enUAdu3aRUZGBlFRUfYx3t7eREREsG7dOoYMGcKmTZvIzc11GBMaGkp4eDjr1q0jOjqalJQUAgICaNOmjX1M27ZtCQgIYN26dYSFhZGSkkJ4eDihoaH2MdHR0WRnZ7Np0yY6duxISkoKEREReHt7O4wZM2YMu3fvpmHDhoVeU3Z2tn02EszZOoDc3Fxyc3MvKV+XquD5rY6jorqU/DZpAo8/7sYLL7jz8MMGN998mho1SjvC8k3vX+dSfp1L+XUu5de5lF/nUn6dy9XyW9w4Sly01apVi//97380aNDA4fjatWtp1KhRSR/OzjAM4uPjufHGGwkPDwcgIyMDgODgYIexwcHB/Pbbb/YxXl5eBAYGFhpTcP+MjAyCgoIKPWdQUJDDmLOfJzAwEC8vL4cxZ7/ugvtkZGScs2ibNGkS48aNK3Q8MTGRKlWqnCMTZS8pKcnqECq0i83vVVe5UbduBHv3+hMTc4ARIzaXcmQVg96/zqX8Opfy61zKr3Mpv86l/DqXq+T377//Lta4EhdtQ4YMYcSIEcycORObzcbvv/9OSkoKo0eP5tlnny1xoAUeeughfvzxx3M2M7HZbA7fG4ZR6NjZzh5zrvGlMcb4/wuOiopnzJgxxMfH278/evQodevWJSoqCn9///O+BmfLzc0lKSmJyMhIPD09LY2lIiqN/AYH24iIMFi1qh6jR4cSGakL3Aro/etcyq9zKb/Opfw6l/LrXMqvc7lafgtW4V1IiYu2xx57jKysLDp27MipU6e4+eab8fb2ZvTo0Tz00EMlDhTg4Ycf5vPPPyc5OZk6derYj4eEhADmLFatWrXsxw8dOmSf4QoJCSEnJ4fMzEyH2bZDhw7Rvn17+5iDBw8Wet7Dhw87PM769esdzmdmZpKbm+swpmDW7czngcKzgQW8vb0dllMW8PT0dIk3CrhWLBXRpeT3pptg+HB4/XUYNsyDLVugatVSDrCc0/vXuZRf51J+nUv5dS7l17mUX+dylfwWN4aLavk/YcIE/vjjDzZs2EBqaiqHDx/m+eefL/HjGIbBQw89xOLFi/nmm28KLS9s2LAhISEhDtOXOTk5rFmzxl6QtWrVCk9PT4cxBw4cID093T6mXbt2ZGVlsWHDBvuY9evXk5WV5TAmPT2dAwcO2MckJibi7e1Nq1at7GOSk5MdtgFITEwkNDS00LJJkdLywgtQvz7s3g1PP211NCIiIiJS1i6qaAOoUqUKrVu35vrrr6fqRX70P2zYMObOncv8+fOpVq0aGRkZZGRkcPLkScBccjhy5EgmTpzIkiVLSE9PJy4ujipVqhATEwNAQEAAgwYNYtSoUaxcuZLNmzfTr18/WrRoQefOnQFo2rQpXbp0YfDgwaSmppKamsrgwYPp1q0bYWFhAERFRdGsWTNiY2PZvHkzK1euZPTo0QwePNi+jDEmJgZvb2/i4uJIT09nyZIlTJw4kfj4+Asu1xS5WFWrwrvvmrffeANSU62NR0RERETKVrGXR953333FGjdz5sxiP/lbb70FQIcOHRyOf/DBB8TFxQHmcsyTJ08ydOhQMjMzadOmDYmJiVSrVs0+fsqUKXh4eNCnTx9OnjzJLbfcwqxZs3B3d7ePmTdvHsOHD7d3mezRowfTpk2zn3d3d2fZsmUMHTqUG264AV9fX2JiYpg8ebJ9TEBAAElJSQwbNozWrVsTGBhIfHy8wzVrIs4QFQX9+8OHH8L998P334OXl9VRiYiIiEhZKHbRNmvWLOrXr88111xjb75xqYrzODabjbFjxzJ27Ngix/j4+DB16lSmTp1a5Jjq1aszd+7c8z5XvXr1WLp06XnHtGjRguTk5POOEXGGhAT48kvYuhUmTYLnnrM6IhEREREpC8Uu2h544AEWLlzIr7/+yn333Ue/fv3s+6mJiPPVqAHTpsHdd8OECXDnndC8udVRiYiIiIizFfuatunTp3PgwAEef/xxvvjiC+rWrUufPn346quvSm3mTUTO7667oEcPyM2FQYMgL8/qiERERETE2UrUiMTb25u+ffuSlJTEtm3baN68OUOHDqV+/focP37cWTGKyP+z2WD6dPD3h/XrzZk3EREREanYLrp7pM1mw2azYRgG+fn5pRmTiJxH7drw8svm7SefNLcCEBEREZGKq0RFW3Z2NgsWLCAyMpKwsDC2bNnCtGnT2LNnz0W3/ReRkhs8GG6+Gf7+G4YMAa1QFhEREam4il20DR06lFq1avHSSy/RrVs39u3bxyeffMJtt92Gm9tFT9iJyEVwc4P33gMfH0hMhDlzrI5IRERERJyl2N0j3377berVq0fDhg1Zs2YNa9asOee4xYsXl1pwIlK0Jk1g7Fh44gkYORKioyE42OqoRERERKS0Fbto69+/PzabzZmxiEgJjRoFH30EmzfD8OHmbRERERGpWEq0ubaIuBYPD5gxA667Dj7+GO6919wSQEREREQqDl2MJlLOXXMNjB5t3n7wQcjKsjYeERERESldKtpEKoDnnoMrr4Tff4fHH7c6GhEREREpTSraRCoAX194/33z9jvvQBF9gkRERESkHFLRJlJB3HyzuWcbwP33w8mT1sYjIiIiIqVDRZtIBfLSSxAaCv/7H4wbZ3U0IiIiIlIaVLSJVCABAfDWW+btyZPh+++tjUdERERELp2KNpEKpkcPuPtuyMuDQYMgN9fqiERERETkUqhoE6mA3ngDqleHtDR49VWroxERERGRS6GiTaQCCgqCKVPM22PHws6dloYjIiIiIpdARZtIBRUbC1FRkJ0NgwdDfr7VEYmIiIjIxVDRJlJB2Wzmnm1+fpCcDO+9Z3VEIiIiInIxVLSJVGANGsDEiebtRx+FffssDUdERERELoKKNpEKbtgwaNsWjh2DoUPBMKyOSERERERKQkWbSAXn7g7vvw+envDFF/Dxx1ZHJCIiIiIloaJNpBJo3hyeesq8/fDD8Oef1sYjIiIiIsWnok2kkhgzxizeDh+G+HiroxERERGR4lLRJlJJeHnBjBlmV8kPP4QVK6yOSERERESKQ0WbSCXSpg2MGGHeHjIEjh+3Nh4RERERuTAVbSKVzAsvmFsB7Nnzz3VuIiIiIuK6VLSJVDJ+fuam2wBTp0JKirXxiIiIiMj5qWgTqYSiomDAAHPPtvvvh+xsqyMSERERkaKoaBOppBISICgItm2DiROtjkZEREREiqKiTaSSql4dpk0zb0+aBOnp1sYjIiIi4kx5ebBmjY3k5NqsWWMjL8/qiIpPRZtIJXbnndCzJ+Tmmssky9M/XiIiIiLFtXix2YgtMtKDhITWREZ60KCBebw8UNEmUonZbPDmm+DvD+vXm41JRERERCqSxYvND6r37XM8vn+/ebw8FG4q2kQqudq14ZVXzNtPPQW7dlkbj4iIiEhpycsz96g1jMLnCo6NHOn6q41UtIkI998PHTrA33/Dv/997n/YRERERMqbb78tPMN2JsOAvXvNca5MRZuI4OYG770HPj7w9dcwe7bVEYmIiIhcutTU4o07cMC5cVwqFW0iAkDjxjBunHk7Ph4OHrQ2HhEREZGLkZMDCxbAjTfCmDHFu0+tWs6N6VKpaBMRu/h4uPZayMyEhx+2OhoRERGR4tu3D555BurVg5gY+O9/wd0dfH2Lvo/NBnXrwk03lV2cF0NFm4jYeXjAjBnmP3CffAKffWZ1RCIiIiJFMwz45hu44w6zpf8LL5irhWrVgrFjzevV5s41izObzfG+Bd+/9pr5u48rU9EmIg6uvhoee8y8PXQoHDliZTQiIiIihR09CtOmQfPmcMstZtv+vDyIiICPP4bffoPnnjOLt9694dNPzY7ZZ6pTxzzeu7c1r6EkVLSJSCHPPgtNmpgX5RYUcCIiIiJW27oVhg0zC7CHH4bt28HPDx58ELZsgdWr4a67wNPT8X69e8Pu3ZCUdJr4+O9ISjrNrl3lo2AD8LA6ABFxPT4+ZjfJiAjzz5gYc0sAERERkbKWmwv/+Q+8+aZZlBX417/MAq5/f/D3v/DjuLtDRITBiRP7iYi4yuWXRJ5JM20ick433wwPPGDeHjwYTp60Nh4RERGpXA4cgPHjzWvV7rrLLNjc3KBXL1i5ErZtg4ceKl7BVt6paBORIr30krn84H//My/mFREREXEmwzA3ur7nHrML5HPPwe+/Q1AQPPWUucRx8WLo1KlwY5GKTEWbiBTJ3x/eesu8PXkybNpkbTwiIiJSMR0/Du+8YzZEu/lm+OgjOH0a2reHefNgzx6zM2TdulZHag0VbSJyXt27m5925efDoEHmunIRERGR0rBzJ4wcaa7seeAB+PFHc1+1+++HzZvNvdZiYsDb2+pIraWiTUQu6PXXoXp1+OEHc8ZNRERE5GLl5ZmNRaKiICzM/D3j6FFo3BgSEmD/frMR2tVXWx2p61DRJiIXFBRkbjwJMG4c7NhhaTgiIiJSDh0+DJMmQaNGcPvtkJRkXpfWrRusWGH+fvHIIxAYaHWkrkdFm4gUS79+EB0N2dlmN8n8fKsjEhEREVdnGJCaCrGx5mbWTz5pXp9Wo4a5F+wvv8AXX5i/Y7ipMimSUiMixWKzmRcI+/mZXZ3eecfqiERERMRVnTwJM2dC69bQrh3MnQs5OXDddTBrFuzbZ3apbtjQ6kjLBxVtIlJs9eubyxoAHn/c/AdXREREpMAvv8Do0WZjkUGD4PvvzSYiAwbAhg3m14AB4ONjdaTli4o2ESmRoUPNT8yOHYMHHzSXPYiIiEjllZ8Py5bBbbfBlVfCq69CZqa5KfZLL5kf8s6aZc6yycXxsDoAESlf3N3h/ffhmmtg6VJzH5V77rE6KhERESlrf/1lLoF86y349dd/jkdHw7BhZhHn7m5dfBWJZtpEpMSaNYOnnzZvDx8Of/xhbTwiIiJSdjZtgvvuM5dAPvqoWbBddpnZ+XHnTrMTZPfuKthKk4o2Ebkojz8O4eFm+95HHrE6GhEREXGmU6dgzhxo29ZsLvLBB+axq68291Tbv9/cY+3KK62OtGJS0SYiF8XLC2bMMNvzzp1rfqomIiIiFctvv8GYMVC3LvTvD+vXg6cnxMTAf/9rNhq5/36oUsXqSCs2FW0ictGuvx5GjDBvDxliNicRERGR8i0/HxIToWdPcyPsF180L4WoUwdeeAH27oV586B9e3NLIHE+S4u25ORkunfvTmhoKDabjc8++8zhfFxcHDabzeGrbdu2DmOys7N5+OGHqVmzJn5+fvTo0YN9Z/Uhz8zMJDY2loCAAAICAoiNjeXIkSMOY/bs2UP37t3x8/OjZs2aDB8+nJycHIcxW7ZsISIiAl9fX2rXrs348eMx1DpPKrnnnzf3WNmzB556yupoRERE5GIdOQKvvw5Nm5rNRD7/3CzgbrkFFi+GXbvM/+uDg62OtPKxtGg7ceIEV111FdOmTStyTJcuXThw4ID9a/ny5Q7nR44cyZIlS1i4cCFr167l+PHjdOvWjby8PPuYmJgY0tLSWLFiBStWrCAtLY3Y2Fj7+by8PLp27cqJEydYu3YtCxcuZNGiRYwaNco+5ujRo0RGRhIaGsrGjRuZOnUqkydPJiEhoRQzIlL++PnBu++at6dNg3XrrI1HRERESubHH80VM7Vrw8iRZjORatXg4Ydh+3b4+mvo1Qs81HfeMpam/tZbb+XWW2897xhvb29CQkLOeS4rK4sZM2YwZ84cOnfuDMDcuXOpW7cuX3/9NdHR0Wzfvp0VK1aQmppKmzZtAHjvvfdo164dO3bsICwsjMTERLZt28bevXsJDQ0F4NVXXyUuLo4JEybg7+/PvHnzOHXqFLNmzcLb25vw8HB27txJQkIC8fHx2DQ3LJVY584wcKB5UfL998PmzeZGmiIiIuKacnLM2bM334S1a/853ry52a6/Xz+zcBPX4PL18urVqwkKCuKyyy4jIiKCCRMmEBQUBMCmTZvIzc0lKirKPj40NJTw8HDWrVtHdHQ0KSkpBAQE2As2gLZt2xIQEMC6desICwsjJSWF8PBwe8EGEB0dTXZ2Nps2baJjx46kpKQQERGB9xm/iUZHRzNmzBh2795Nw4YNzxl/dnY22dnZ9u+PHj0KQG5uLrm5uaWTpItU8PxWx1FRVbb8vvgiLF/uwfbtNsaPz2Ps2HynPl9ly29ZU36dS/l1LuXXuZRf53J2fvfvh/fec2PmTDcyMsxJBw8Pg549DR58MJ+bbjLs16lVxL9iV3v/FjcOly7abr31Vu666y7q16/Prl27eOaZZ+jUqRObNm3C29ubjIwMvLy8CAwMdLhfcHAwGRkZAGRkZNiLvDMFBQU5jAk+a3FuYGAgXl5eDmMaNGhQ6HkKzhVVtE2aNIlx48YVOp6YmEgVF2mzk5SUZHUIFVplyu+AAbV4+eXreeklG0FB39KggfM7k1Sm/FpB+XUu5de5lF/nUn6dqzTzaxiQnl6T5csbsn59CPn55hVSgYGniIraTXT0b1Svforjx+HLL0vtaV2aq7x///7772KNc+mi7e6777bfDg8Pp3Xr1tSvX59ly5bRu3fvIu9nGIbDcsVzLV0sjTEFTUjOtzRyzJgxxMfH278/evQodevWJSoqCn9//yLvVxZyc3NJSkoiMjIST09PS2OpiCpjfm+9FX76KZ/PP3dj3rwOJCfnOW1jzcqY37Kk/DqX8utcyq9zKb/OVZr5PXYM5s1z46233Ni+/Z/fV2+6KZ8HHsjn9tvd8fS8ArjiEqMuP1zt/VuwCu9CXLpoO1utWrWoX78+P//8MwAhISHk5OSQmZnpMNt26NAh2rdvbx9z8ODBQo91+PBh+0xZSEgI69evdzifmZlJbm6uw5iCWbcznwcoNEt3Jm9vb4cllQU8PT1d4o0CrhVLRVTZ8vvWW7BmDWzcaP4n4eyNtytbfsua8utcyq9zKb/Opfw616Xkd9s2mD4dPvzwn+14/PwgNhaGDoUWLdyo7Dt/ucr7t7gxlKu/rT///JO9e/dSq1YtAFq1aoWnp6fD9OaBAwdIT0+3F23t2rUjKyuLDRs22MesX7+erKwshzHp6ekcOHDAPiYxMRFvb29atWplH5OcnOywDUBiYiKhoaGFlk2KVGahoTB5snn7qafg11+tjUdERKQyOH0aFi2CTp3MZiJvvmkWbGFhZhv//fvND1ZbtLA6UrkYlhZtx48fJy0tjbS0NAB27dpFWloae/bs4fjx44wePZqUlBR2797N6tWr6d69OzVr1qRXr14ABAQEMGjQIEaNGsXKlSvZvHkz/fr1o0WLFvZukk2bNqVLly4MHjyY1NRUUlNTGTx4MN26dSMsLAyAqKgomjVrRmxsLJs3b2blypWMHj2awYMH25cwxsTE4O3tTVxcHOnp6SxZsoSJEyeqc6TIOQwaBB07wsmTZgthbWcoIiLiHBkZ5p6pDRrAnXfCqlXg5ga33w5JSWbL/uHDISDA6kjlUli6PPK7776jY8eO9u8Lrv0aMGAAb731Flu2bOHDDz/kyJEj1KpVi44dO/LRRx9R7Yz+o1OmTMHDw4M+ffpw8uRJbrnlFmbNmoX7GRfSzJs3j+HDh9u7TPbo0cNhbzh3d3eWLVvG0KFDueGGG/D19SUmJobJBdMFmAViUlISw4YNo3Xr1gQGBhIfH+9wvZqImGw2c++2Fi3MvV1mzTK3BBAREZFLZxjw3/+as2mLFv3T5fHyy2HwYPMD03r1rI1RSpelRVuHDh3szTzO5auvvrrgY/j4+DB16lSmTp1a5Jjq1aszd+7c8z5OvXr1WLp06XnHtGjRguTk5AvGJCLQuDGMHw+PPQbx8WaTkiK2XBQREZFiOHEC5s83i7UffvjneLt25t5qd96pfVIrqnJ1TZuIlC+PPAKtWsGRI/DQQ1ZHIyIiUj79/LP5f2rt2vDvf5sFm6+veTnCpk2wbh3ce68KtoqsXHWPFJHyxcMDZsyA1q3N5RtLlsD/X5IqIiIi55GXZ+6Z9uabkJj4z/ErroAHHzQvO6he3br4pGxppk1EnOqqq8wlkmAu3ThyxNJwREREXNrhw7Bo0ZX8618e9OxpFmw2G3TtCsuXw86dMGqUCrbKRkWbiDjdM8+YLYcPHIBHH7U6GhEREddiGLB+PfTvDw0bejBnTjN++81G9erm/5v/+x8sXWpeH+6m394rJf21i4jT+fjAe++Zt99/H775xtp4REREXMHJk/DBB3DdddC2LcyZAzk5Nho3zuT990+zbx+8/DI0amR1pGI1FW0iUiZuugmGDjVvDx4Mf/9tbTwiIiJW+fVXcwatTh247z6zmYiXlznT9t//nmby5GT69zfw9bU6UnEVKtpEpMxMmmT+B/Xrr/Dcc1ZHIyIiUnby883GIt26mdviTJ4Mf/0F9eub/z/u2wezZ8N11xW9HZZUXiraRKTM+PvD22+btxMS4LvvrI1HRETE2f76C159FZo0gdtug2XLzGvYoqLgP/+BX36BJ54wN8YWKYqKNhEpU127Qt++5ieOgwZBbq7VEYmIiJS+7783/5+rXRtGjzaLs4AAGDkSduyAr76CHj3A3d3qSKU8UNEmImXu9dehRg348Ud45RWroxERESkd2dkwdy60awetWsHMmXDqlLn9zbvvwv79MGWKOesmUhIq2kSkzF1+uVm4AYwbBz/9ZG08IiIil2LPHnjySahbF2JjITUVPD3NlSVr18LmzWYTLj8/qyOV8kpFm4hYIibG3G8mJ8f8jyw/3+qIREREis8w4OuvoVcvaNjQbCZy+LC5HPL5581Cbv58uOEGc3NskUuhok1ELGGzmU1JqlY1P4UsaFAiIiLiyrKy4I03oGlTiIyEzz4zP3js1AkWLYLdu+HppyEkxOpIpSJR0SYilqlXz/xkEuDxx2HvXmvjERERKcqWLfDAA+ZM2ogRZjORatVg2DDYuhVWroTevcHDw+pIpSJS0SYilho6FNq3h+PH4cEHzeUmIiIiriA3Fz76CG6+GVq2hHfegRMnoFkzePNNs7HItGnm9yLOpM8CRMRSbm7w/vtw9dXm3jULFpjXu4mIiFjl99/NAu3ddyEjwzzm7m5evzZsGERE6Do1KVuaaRMRyzVtCs88Y94eMQL++MPaeEREpPIxDFizBvr0gfr1Yfx4s2ALCYFnn4XffoNPPoEOHVSwSdlT0SYiLuGxx6BFC7NgGznS6mhERKSyOHYM3nrL/D+oQwezMDt9Gm66CRYuNIu1cePMa9lErKKiTURcgpcXzJhhLpecNw++/NLqiEREpCLbvh0eftgsxoYONZuJVKkC//43/PADJCfD3Xeb/z+JWE1Fm4i4jOuu+2eWbcgQ89NPERGR0nL6NCxeDLfcYjYPmTbN/L+mSRN47TWzscg775hNR0RciYo2EXEp48dDo0Zm+/8xY6yORkREKoKDB+GFF8xNsO+4A775xlzZ0bMnJCaas24jRsBll1kdqci5qWgTEZfi52d26wKYPh3++19r4xERkfLJMGDdOrj3Xqhb12x4tW8f1Kxpfij466/mxtiRkWYBJ+LK9BYVEZdzyy1w333mf7j33w+nTlkdkYiIlBd//21uJXPttXDDDTB/vrnfWtu2MGeOWbhNnGh2iBQpL1S0iYhLmjzZbLP8008wYYLV0YiIiKv7+WeIjzcbiwweDGlp4OMDAwfCd99BSgr06wfe3lZHKlJyKtpExCUFBpoXiAO8+CL8+KO18YiIiOvJy4MvvoAuXcxmIlOmwJEj5rXRr7xizqrNnAmtWlkdqcil8bA6ABGRotxxB/TubXb6GjTI/JTUQ/9qiYhUen/8YW4T8/bbsHu3ecxmg1tvhWHDzCJO16lJRaK3s4i4tGnTzG5e330Hr79udTQiImKlDRtgwACoUweeeMIs2AIDYfRoc3nksmVw220q2KTi0VtaRFxarVrm9W1gdv765Rdr4xERkbJ16hTMnm3u5dmmDXz4IWRnm41GZs4091Z75RW44gqrIxVxHhVtIuLy7rsPOnWCkyfh3/82u0qKiEjFtmsXPP64OasWF2euuPDyMpuJpKSY3w8cCL6+Vkcq4ny6OkREXJ7NZu7d1qKFuSHqjBnQoIGN5OTa+PnZ6NgR3N2tjrLiyMuDNWuUXxEpe/n55mbXb75pLnUs+JCuXj144AHz+uagIGtjFLGCZtpEpFy44gp4/nnz9r//DZGRHiQktCYy0oMGDcxmJXLpFi+GBg2UXxEpW5mZkJAAYWFmM5GlS82CLTLS3AD711/NDbFVsEllpaJNRMqNevXMP89eHrl/P9x5pwqLS7V4sZnHffscjyu/IuIsmzfD/febe6uNGgX/+x8EBMCIEbBjhznr1rOnZvtFtDxSRMqFvDxz09RzKSjihgwBT0/9534x8vLM/J3rekHDMJeojhypX55E5NJlZ8OiRWZ34JSUf463bGm267/3XvDzsy4+EVekok1EyoVvvy08A3S2P/6AHj3KJp7KxjBg717z76FDB6ujEZHyaO9eeOcdeO89OHTIPObhYc7kDxsGN9xgfkAkIoWpaBORcuHAgeKNa9gQatRwbiwV0Z9/mp3aLqS4fw8iImB+4PPNN2Zjkf/8x2w0AhAaajYWGTwYQkKsjVGkPFDRJiLlQq1axRs3c6Zmgi7G6tXQseOFxx086PRQRKQCOHrU3Ftt+nT46ad/jnfoYM6q9expLmcXkeJRIxIRKRduusncq6eopTM2G9Sta46TkrtQfgs88gj06QO//VY2cYmIazlzS5A1a2zk5TmeT0+HBx80Z9KGDzcLtqpVYehQ89yqVeZySBVsIiWjok1EygV3d3j9dfP22YVFwfevvaYmGRfrQvm12SA6Gtzc4JNP4F//gmefhRMnyj5WEbFGUVuCfPyx+e9CRIS5n+bbb5v/NjRtajYb2b/fXB7ZvLnVr0Ck/FLRJiLlRu/e8OmnZmvoM9WpYx7v3duauCqKC+V3xQpISzOXUZ46Ze6bFxYG8+efu+ukiFQcRW0Jsm8f3H23OQOfnGx+AHTHHeZ1bFu3mksh/f2tiVmkIlHRJiLlSu/esHs3JCWdJj7+O5KSTrNrlwq20nKh/LZoAStXmu26GzQwP0G/91648UbYtMnKyEXEWfLyzH3TzvfhjJsbPPWU+e/Hp5+aH+6oE6RI6VHRJiLljrs7REQY3HzzfiIiDC2JLGUXyq/NZhZx27fDhAnmfkrr1sF118GgQZCRYU3cInLpcnLM69A+/xwmT4Z//xtatbrwliv5+dC5szkzLyKlT90jRUTkovj4wJNPwoABMGYMzJljdu/85BN45hmzCYG3t9VRisjZ8vPNPdN27iz8tXv3P235S0pbgog4j4o2ERG5JLVrw4cfmt3hRoyADRvgscfg3XchIQG6ddMyKZGyZhjwxx/nLsz+9z/zutSiVK0KTZr883X6NEyceOHnLO7WLCJSciraRESkVLRtCykp5ozbE0+Yvxj26GF2nZwyxewkJyKl6/hx+PnncxdnR44UfT9PT7jiCsfirOArJMTxg5a8PPODmf37z31dm81mLovUlisizqOiTURESo2bm7lcsndv85P5hAT46iuzgclDD8Fzz0FgoNVRipQvOTnw66/nLs5+/73o+xXsX3muwqx+ffAo5m+BBVuC3Hmn+ZhnFm7ackWkbKhoExGRUletGkyaBPffD6NHw2efmb/0zZ0LL7wAgwfrFzyRM+Xnm80+irrO7OxNrM90+eXnLsyuuAJ8fUsnvoItQUaMcGxKUqeOWbCpg6+Ic6loExERp7niCliyBL7+GkaONPdtevBBeOsts4jr0MHqCEXKjmHAn3+euzD7+efzX2fm53fuwuzKK8tu9rp3b+jZE1atOs2XX6Zx661X07Gjhz6AESkDKtpERMTpOnc2N+Z++2149ln48UdzH6c774RXXjH3fBOpKI4fN6/pPFdxlplZ9P08PIq+zqxWLddo6FOwJciJE/uJiLhKBZtIGVHRJiIiZcLDw7yurW9f89q2t94yl1t98QU8+qjZvMTPz+ooRYonJwd27Tp3YXa+68wA6tUrPFvWpIn54UVxrzMTkcpF/zSIiEiZqlEDpk2DIUPMJZPffGNe5/bBB/DSSxAT4xozClI+5eXBmjU2kpNr4+dno2PHi79+Mj/f7Jh4rsJs167zX2dWs2bR15lVqXJx8YhI5aWiTURELNGihXmt22efwahR5i/B/frBm2/CG29A69ZWRyjlzeLFBY0yPIDWJCSYjTJef/38jTLOd53ZyZNF369KlaKvM6tevdRfnohUYiraRETEMjYb9OoFt95q7uU2YYK519t118HAgea2ASEhVkcp5cHixeY1kmfvI7Z/v3l87lxo1uyfYuzM4uyvv4p+XA8PaNTo3MVZaKhmhUWkbKhoExERy/n4wJgx5h5vY8aYG/l+8IF5zdvTT5uzJ97eVkcpriovz3yPnGvj54Jj9957/sc4335mnp6lH7OISEmoaBMREZcRGgqzZ5vbAowYARs2wOOPw7vvmht1d++umQ0p7NtvHfcOK4q/PzRvXrgwa9xY15mJiGtT0SYiIi6nbVtzmeTcuWbR9ssv5v5QUVHmMspmzayOUFzBnj2wYIF5HWRxvP222b1URKS8cbM6ABERkXNxc4P+/c1rjsaMAS8vSEyEli3NWbjz7XclFdcff5jF1803m0sXn3gC9u4t3n1r1XJubCIizqKiTUREXFq1amZDku3b4fbbzeuX3njD7ND31ltw+rTVEYqznThhzqh1724WXg8+aC6JtNmgQweziDtfUxCbzbxm7aabyjRsEZFSY2nRlpycTPfu3QkNDcVms/HZZ585nDcMg7FjxxIaGoqvry8dOnRg69atDmOys7N5+OGHqVmzJn5+fvTo0YN9Zy1sz8zMJDY2loCAAAICAoiNjeXIkSMOY/bs2UP37t3x8/OjZs2aDB8+nJycHIcxW7ZsISIiAl9fX2rXrs348eMxznXVs4iIlLpGjWDJEkhKMq9L+vNPGDoUWrWCVausjk5KW24uLF9uNhAJCjL371u61CzSr7kGXnnFXB65apW559/Uqeb9zi7cCr5/7bWL369NRMRqlhZtJ06c4KqrrmLatGnnPP/yyy+TkJDAtGnT2LhxIyEhIURGRnLs2DH7mJEjR7JkyRIWLlzI2rVrOX78ON26dSPvjB0vY2JiSEtLY8WKFaxYsYK0tDRiY2Pt5/Py8ujatSsnTpxg7dq1LFy4kEWLFjFq1Cj7mKNHjxIZGUloaCgbN25k6tSpTJ48mYSEBCdkRkREitK5M6SlmRt0BwbCjz9Cp05mW/ddu6yOTi5Ffj6sXWsW47VqQdeuMH8+/P23WbQ/8wxs2wbffw+jR5t7sBXo3dvsNlq7tuNj1qljHj/fPm0iIi7PcBGAsWTJEvv3+fn5RkhIiPHiiy/aj506dcoICAgw3n77bcMwDOPIkSOGp6ensXDhQvuY/fv3G25ubsaKFSsMwzCMbdu2GYCRmppqH5OSkmIAxk8//WQYhmEsX77ccHNzM/bv328fs2DBAsPb29vIysoyDMMwpk+fbgQEBBinTp2yj5k0aZIRGhpq5OfnF/t1ZmVlGYD9ca2Uk5NjfPbZZ0ZOTo7VoVRIyq9zKb/OVV7y+8cfhvHQQ4bh7m4YYBje3obx1FOGceyY1ZGdX3nJb1n58UfDeOIJw6hf3/x7LPgKCjKM4cMNIzXVMIr7X+3p04aRlJRrxMdvNJKSco3Tp50aeqWk969zKb/O5Wr5LW5t4LLdI3ft2kVGRgZRUVH2Y97e3kRERLBu3TqGDBnCpk2byM3NdRgTGhpKeHg469atIzo6mpSUFAICAmjTpo19TNu2bQkICGDdunWEhYWRkpJCeHg4oaGh9jHR0dFkZ2ezadMmOnbsSEpKChEREXifsVFQdHQ0Y8aMYffu3TRs2PCcryM7O5vs7Gz790ePHgUgNzeX3NzcS0/UJSh4fqvjqKiUX+dSfp2rvOTX39/cCuC++2DUKHdWrXJjwgT44AODCRPyiIkxXHKLgPKSX2favRs++siNhQvd2Lr1n7+katUMbr/d4J578unY0cDj/39TKcm1i+3b53LixH7at29Gfr5Bfn7pxl7Z6f3rXMqvc7lafosbh8sWbRkZGQAEBwc7HA8ODua3336zj/Hy8iIwMLDQmIL7Z2RkEBQUVOjxg4KCHMac/TyBgYF4eXk5jGnQoEGh5yk4V1TRNmnSJMaNG1foeGJiIlVcZFOYpKQkq0Oo0JRf51J+nas85Xf4cGjTJoQPPgjn99/9GDjQgxdf/Iv779/ClVcesTq8cypP+S0NR496sXZtKN9+W4ft22vYj3t45NGq1SFuvnkfrVtn4O2dT26u2S30UlS2/JY15de5lF/ncpX8/v3338Ua57JFWwHbWR+RGoZR6NjZzh5zrvGlMcb4/yYk54tnzJgxxMfH278/evQodevWJSoqCn9///O+DmfLzc0lKSmJyMhIPD09LY2lIlJ+nUv5da7ymt+uXeHJJ+GNN/KYNMmNHTuq8+ijEfTvn8/zz+e5TMv38prfi3H8OHz+uY2PPnIjKcnG6dPm/5k2m0FEhEHfvvncfrtBYGBNoGapPGdlyq8VlF/nUn6dy9XyW7AK70JctmgLCQkBzFmsWmf8L3vo0CH7DFdISAg5OTlkZmY6zLYdOnSI9u3b28ccPHiw0OMfPnzY4XHWr1/vcD4zM5Pc3FyHMQWzbmc+DxSeDTyTt7e3w5LKAp6eni7xRgHXiqUiUn6dS/l1rvKYX09PeOopGDjQ3N/tww/hww/dWLzYjaefhpEj4Rz/LFuiPOa3OHJyzFmy+fPhP/8xG4kUuPZasyPk3XfbqF3bhjN7olXU/LoK5de5lF/ncpX8FjcGl92nrWHDhoSEhDhMXebk5LBmzRp7QdaqVSs8PT0dxhw4cID09HT7mHbt2pGVlcWGDRvsY9avX09WVpbDmPT0dA4cOGAfk5iYiLe3N61atbKPSU5OdtgGIDExkdDQ0ELLJkVExHqhoTB7NqSmQps25ozPE0+Y2wV8/rnZ6kJKT36+uXfaAw+YnR+7dzf3Vvv7b2jcGJ591txrb9MmiI8v3OVRRESKZmnRdvz4cdLS0khLSwPM5iNpaWns2bMHm83GyJEjmThxIkuWLCE9PZ24uDiqVKlCTEwMAAEBAQwaNIhRo0axcuVKNm/eTL9+/WjRogWdO3cGoGnTpnTp0oXBgweTmppKamoqgwcPplu3boSFhQEQFRVFs2bNiI2NZfPmzaxcuZLRo0czePBg+xLGmJgYvL29iYuLIz09nSVLljBx4kTi4+MvuFxTRESs06YNrFtnzrjVqgW//AI9e0J0NJy19aeUkGGYWy48/jg0aAA33wzvvAN//QXBwTBiBGzYADt3wrhx8K9/WR2xiEj5ZOnyyO+++46OHTvavy+49mvAgAHMmjWLxx57jJMnTzJ06FAyMzNp06YNiYmJVKtWzX6fKVOm4OHhQZ8+fTh58iS33HILs2bNwv2MHTTnzZvH8OHD7V0me/To4bA3nLu7O8uWLWPo0KHccMMN+Pr6EhMTw+TJk+1jAgICSEpKYtiwYbRu3ZrAwEDi4+MdrlcTERHX5OYGsbHQqxdMmgSTJ5ubdF91lbkn2NixUL261VGWH7t2mbNo8+c7Fr7+/nDHHeZG2B07ajNrEZHSYmnR1qFDB3szj3Ox2WyMHTuWsWPHFjnGx8eHqVOnMnXq1CLHVK9enblz5543lnr16rF06dLzjmnRogXJycnnHSMiIq6ralWYMAEGDYJHH4XFi2HqVLP4eP55GDwYe4t5cXToEHzyiZmrdev+Oe7lBd26mYXabbeBr691MYqIVFQue02biIiIszRqBIsWwddfQ3g4/PmnOeN27bXwzTdWR+c6jh2DuXPh1lvNawQfesgs2Gw2uOUWmDEDDh40c3nHHSrYREScRZ8niohIpXXLLbB5M7z7LjzzDGzZYh7r3dtcQlnEFpwVWk4OrFhhzqh9/jmcPPnPudatzc6PffqYRZyIiJQNzbSJiEil5uFhzrL9/DM8/LB5HdbixdC0qbl1wPHjVkfofPn5sGYNDBkCISFmo5aPPjILtiuvNK/527EDNm40t0xQwSYiUrZUtImIiGA2InnjDfjhB+jcGbKzYeJECAszlwjm51sdYekyDEhLg8ceg/r1oUMHc8YxM9PssvnII2aRtmMHPPccNGlidcQiIpWXlkeKiIicoXlzc2Pozz839xP79Vez8+Sbb8Lrr8P111sd4aX59Vez8+O8eea+aQUCAszr0u69FyIi1PlRRMSVaKZNRETkLDabuURw2zZ48UWz62TBJt1xcXDggNURlsyhQ2aXzHbt4Ior4OmnzYLN2xvuvNNcDpqRYTYW6dRJBZuIiKtR0SYiIlIEb29z4+idO81iDWD2bHOp4IsvwqlTloZ3XseOmRuKd+liXoM2fLhZeLq5QWQkfPCB2fnxk0/M/et8fKyOWEREiqKiTURE5AJq1TKLnPXrzdm248dhzBhzKeV//mNeH+YKsrPNeO6+G4KCYMAA+OoryMszl3W+9hrs328u/4yLM5dEioiI69M1bSIiIsV0/fXmPmXz55szcL/+CrffbjYuee01s4gra/n5kJxsxvTpp2YjkQJhYeam1zEx0Lhx2ccmIiKlQzNtIiIiJeDmBv36mV0Vn3rKXEL59ddw1VXmlgF//eX8GAzD3F9u9GioVw86doT33jMLttBQGDUKNm0yr1t79lkVbCIi5Z2KNhERkYtQtSq88ILZrKR3b3MJ4rRp5r5m06fD6dOl/5y//ALPPw/NmsG118Krr5rLHS+7DO6/H775BvbsMTcGv/Zas6GKiIiUfyraRERELkGjRrBoEaxcCeHh5kzbsGFwzTVmEXWpMjLM/ePatjVnzJ59Fn76yWwcctddsGSJOea998wZN3V+FBGpeFS0iYiIlIJOncwli9Onmxt1p6fDLbeYs3C//vrPuLw8WLPGRnJybdassZGXV/ixjh41u1RGR0Pt2jBihNkExc0NoqLMcwcPwscfm9fUeXuX2csUERELqBGJiIhIKfHwgAcfNLs3jh1rFnBLlsDy5eZ1Zs2awRNPwL59HkBrEhKgTh1z0+6uXeHLL81Nr5cuddxOoG1bs5nI/7V350FRnPkbwJ9xlOEQQUQuBVY8uJQjQRTFoCsoXqvlfYKom80uKmAMZsUSy6hEEjcmu8qKByFYHpsoeMULD7xWQAEPmCiaKK6rki0PEBDj8P7+4MfEkUsjY4/D86maKubtnu6nv05ZfOnut8ePB6ytpTo6IiKSCps2IiKiJmZhUX1J45/+BERGVk9UsmJF3ev+5z/AmDGAsTFQXv7ruIsLMGUKMGlS9QOxiYio+WLTRkREpCXu7tXPREtLq77/rK5LIWuUl1fP/Dh5cnWz5unJiUSIiKgamzYiIiItksmAtm0bbthqpKRU3xtHRET0PE5EQkREpGV37rzcevfuaTcHERG9ndi0ERERaZmtbdOuR0REzQubNiIiIi3r1696lsj67lGTyQB7++r1iIiIXsSmjYiISMvk8upp/YHajVvN+9Wr+WBsIiKqG5s2IiKiN2D0aOC776oflv28jh2rx0ePliYXERHpPs4eSURE9IaMHg2MHAkcO/YM+/fnYcgQLwwY0JJn2IiIqEFs2oiIiN4guRwICBAoK7uNgABPNmxERNQoXh5JRERERESkw9i0ERERERER6TA2bURERERERDqMTRsREREREZEOY9NGRERERESkw9i0ERERERER6TA2bURERERERDqMTRsREREREZEOY9NGRERERESkw9i0ERERERER6TA2bURERERERDqMTRsREREREZEOY9NGRERERESkw1pKHaC5EUIAAEpKSiROAvzyyy8oLy9HSUkJWrVqJXUcvcP6ahfrq12sr3axvtrF+moX66tdrK926Vp9a3qCmh6hPmza3rDS0lIAgL29vcRJiIiIiIhIF5SWlsLMzKze5TLRWFtHTaqqqgr//e9/YWpqCplMJmmWkpIS2Nvb49atW2jTpo2kWfQR66tdrK92sb7axfpqF+urXayvdrG+2qVr9RVCoLS0FHZ2dmjRov4713im7Q1r0aIFOnbsKHUMDW3atNGJL62+Yn21i/XVLtZXu1hf7WJ9tYv11S7WV7t0qb4NnWGrwYlIiIiIiIiIdBibNiIiIiIiIh3Gpq0ZUygUiI2NhUKhkDqKXmJ9tYv11S7WV7tYX+1ifbWL9dUu1le73tb6ciISIiIiIiIiHcYzbURERERERDqMTRsREREREZEOY9NGRERERESkw9i0ERERERER6TA2bc3QiRMnMGLECNjZ2UEmkyEtLU3qSHojLi4OPXv2hKmpKaysrDBq1ChcuXJF6lh6JSEhAR4eHuqHYvr5+WH//v1Sx9JLcXFxkMlkiIyMlDqK3liyZAlkMpnGy8bGRupYeuX27duYOnUq2rVrB2NjY3h5eeH8+fNSx9ILv/vd72p9f2UyGcLDw6WOpheePXuGRYsWoVOnTjAyMoKTkxOWLl2KqqoqqaPpjdLSUkRGRsLR0RFGRkbo06cPsrOzpY71UlpKHYDevLKyMnh6eiIsLAxjxoyROo5eycjIQHh4OHr27Ilnz54hJiYGgwYNQkFBAUxMTKSOpxc6duyITz/9FF26dAEAJCcnY+TIkcjNzYW7u7vE6fRHdnY2EhMT4eHhIXUUvePu7o709HT1e7lcLmEa/fLgwQP07dsXAwYMwP79+2FlZYXr16/D3Nxc6mh6ITs7GyqVSv3+8uXLCAoKwrhx4yRMpT9WrlyJf/7zn0hOToa7uzvOnTuHsLAwmJmZISIiQup4emHWrFm4fPkyUlJSYGdnh82bNyMwMBAFBQXo0KGD1PEaxCn/mzmZTIbU1FSMGjVK6ih66eeff4aVlRUyMjLw3nvvSR1Hb1lYWOCzzz7DzJkzpY6iFx4/fox33nkHa9euxbJly+Dl5YXVq1dLHUsvLFmyBGlpacjLy5M6il76+OOPcfr0aZw8eVLqKM1CZGQk9u7di8LCQshkMqnjvPWGDx8Oa2trbNy4UT02ZswYGBsbIyUlRcJk+qGiogKmpqbYtWsXhg0bph738vLC8OHDsWzZMgnTNY6XRxJp0aNHjwBUNxXU9FQqFbZt24aysjL4+flJHUdvhIeHY9iwYQgMDJQ6il4qLCyEnZ0dOnXqhIkTJ+LHH3+UOpLe2L17N3x8fDBu3DhYWVnB29sb69evlzqWXnr69Ck2b96MGTNmsGFrIv7+/jhy5AiuXr0KALhw4QJOnTqFoUOHSpxMPzx79gwqlQqGhoYa40ZGRjh16pREqV4eL48k0hIhBObNmwd/f390795d6jh65dKlS/Dz88OTJ0/QunVrpKamws3NTepYemHbtm3Iycl5a67xf9v06tUL33zzDbp164Z79+5h2bJl6NOnD/Lz89GuXTup4731fvzxRyQkJGDevHlYuHAhsrKyMHfuXCgUCoSEhEgdT6+kpaXh4cOHmD59utRR9MaCBQvw6NEjuLi4QC6XQ6VSYfny5Zg0aZLU0fSCqakp/Pz88Mknn8DV1RXW1tbYunUrMjMz0bVrV6njNYpNG5GWzJ49GxcvXnwr/nrztnF2dkZeXh4ePnyIHTt2IDQ0FBkZGWzcXtOtW7cQERGBQ4cO1fpLJDWNIUOGqH/u0aMH/Pz80LlzZyQnJ2PevHkSJtMPVVVV8PHxwYoVKwAA3t7eyM/PR0JCApu2JrZx40YMGTIEdnZ2UkfRG9u3b8fmzZuxZcsWuLu7Iy8vD5GRkbCzs0NoaKjU8fRCSkoKZsyYgQ4dOkAul+Odd97B5MmTkZOTI3W0RrFpI9KCOXPmYPfu3Thx4gQ6duwodRy9Y2BgoJ6IxMfHB9nZ2fjyyy+xbt06iZO93c6fP4/i4mK8++676jGVSoUTJ07gH//4ByorKzlpRhMzMTFBjx49UFhYKHUUvWBra1vrjzeurq7YsWOHRIn0082bN5Geno6dO3dKHUWvfPTRR/j4448xceJEANV/2Ll58ybi4uLYtDWRzp07IyMjA2VlZSgpKYGtrS0mTJiATp06SR2tUWzaiJqQEAJz5sxBamoqjh8//lb8J6APhBCorKyUOsZbb+DAgbh06ZLGWFhYGFxcXLBgwQI2bFpQWVkJpVKJfv36SR1FL/Tt27fWY1auXr0KR0dHiRLpp6SkJFhZWWlM5kCvr7y8HC1aaE43IZfLOeW/FpiYmMDExAQPHjzAwYMHER8fL3WkRrFpa4YeP36Ma9euqd//9NNPyMvLg4WFBRwcHCRM9vYLDw/Hli1bsGvXLpiamuLu3bsAADMzMxgZGUmcTj8sXLgQQ4YMgb29PUpLS7Ft2zYcP34cBw4ckDraW8/U1LTW/ZcmJiZo164d78tsIvPnz8eIESPg4OCA4uJiLFu2DCUlJfwrehOJiopCnz59sGLFCowfPx5ZWVlITExEYmKi1NH0RlVVFZKSkhAaGoqWLflrZFMaMWIEli9fDgcHB7i7uyM3Nxd/+9vfMGPGDKmj6Y2DBw9CCAFnZ2dcu3YNH330EZydnREWFiZ1tMYJanaOHTsmANR6hYaGSh3trVdXXQGIpKQkqaPpjRkzZghHR0dhYGAg2rdvLwYOHCgOHTokdSy9FRAQICIiIqSOoTcmTJggbG1tRatWrYSdnZ0YPXq0yM/PlzqWXtmzZ4/o3r27UCgUwsXFRSQmJkodSa8cPHhQABBXrlyROoreKSkpEREREcLBwUEYGhoKJycnERMTIyorK6WOpje2b98unJychIGBgbCxsRHh4eHi4cOHUsd6KXxOGxERERERkQ7jc9qIiIiIiIh0GJs2IiIiIiIiHcamjYiIiIiISIexaSMiIiIiItJhbNqIiIiIiIh0GJs2IiIiIiIiHcamjYiIiIiISIexaSMiIiIiItJhbNqIiKhJLVmyBF5eXur306dPx6hRoyTLoyvepjocPXoULi4uqKqq0to+bty4AZlMhry8PADA8ePHIZPJ8PDhwybdj0wmQ1paGgCguLgY7du3x+3bt5t0H0RE2samjYiombp79y7mzJkDJycnKBQK2NvbY8SIEThy5EiT7ufLL7/E119/3aTbrMv06dMhk8lqvYKDg7W+75fxpurQFKKjoxETE4MWLX79NaGiogKxsbFwdnaGQqGApaUlxo4di/z8/CbZZ58+fXDnzh2YmZk1yfbqYmVlhWnTpiE2NlZr+yAi0oaWUgcgIqI378aNG+jbty/Mzc0RHx8PDw8P/PLLLzh48CDCw8Pxww8/NNm+tPlL+IuCg4ORlJSkMaZQKN7Y/uuiUqkgk8neaB1ex5kzZ1BYWIhx48apxyorKxEYGIiioiKsWrUKvXr1wr179xAXF4devXohPT0dvXv3rnN7T58+hYGBQaP7NTAwgI2NTZMdR33CwsLg6+uLzz77DG3bttX6/oiImgLPtBERNUN/+ctfIJPJkJWVhbFjx6Jbt25wd3fHvHnzcPbsWfV6RUVFGDlyJFq3bo02bdpg/PjxuHfvnsa2Pv30U1hbW8PU1BQzZ87EkydPNJa/eFlg//79MXfuXERHR8PCwgI2NjZYsmSJxmd++OEH+Pv7w9DQEG5ubkhPT9e4zK0+CoUCNjY2Gq+aX8yPHz8OAwMDnDx5Ur3+qlWrYGlpiTt37qizzZ49G7Nnz4a5uTnatWuHRYsWQQih/szTp08RHR2NDh06wMTEBL169cLx48fVy7/++muYm5tj7969cHNzg0KhwM2bN2vVQQiB+Ph4ODk5wcjICJ6envjuu+/Uy2suFzxy5Ah8fHxgbGyMPn364MqVKxrHvHv3bvj4+MDQ0BCWlpYYPXr0S2ety7Zt2zBo0CAYGhqqx1avXo1///vf2Lt3L8aPHw9HR0f4+vpix44dcHV1xcyZM9U1qjnOuLg42NnZoVu3bgCArKwseHt7w9DQED4+PsjNzdXY74uXR9bU8eDBg3B1dUXr1q0RHBys/rcCgOzsbAQFBcHS0hJmZmYICAhATk5Og8fXo0cP2NjYIDU1tcH1iIh0CZs2IqJm5v79+zhw4ADCw8NhYmJSa7m5uTmA6qZi1KhRuH//PjIyMnD48GFcv34dEyZMUK/7r3/9C7GxsVi+fDnOnTsHW1tbrF27ttEMycnJMDExQWZmJuLj47F06VIcPnwYAFBVVYVRo0bB2NgYmZmZSExMRExMzGsfd//+/REZGYlp06bh0aNHuHDhAmJiYrB+/XrY2tpqZGvZsiUyMzPx1Vdf4YsvvsCGDRvUy8PCwnD69Gls27YNFy9exLhx4xAcHIzCwkL1OuXl5YiLi8OGDRuQn58PKyurWnkWLVqEpKQkJCQkID8/H1FRUZg6dSoyMjI01ouJicGqVatw7tw5tGzZEjNmzFAv27dvH0aPHo1hw4YhNzdX3eC9StYXnThxQmMbALBlyxYEBQXB09NTY7xFixaIiopCQUEBLly4oB4/cuQIlEolDh8+jL1796KsrAzDhw+Hs7Mzzp8/jyVLlmD+/Pn1Zni+jp9//jlSUlJw4sQJFBUVaXyutLQUoaGhOHnyJM6ePYuuXbti6NChKC0tbXC7vr6+Gs07EZHOE0RE1KxkZmYKAGLnzp0Nrnfo0CEhl8tFUVGReiw/P18AEFlZWUIIIfz8/MQHH3yg8blevXoJT09P9fvQ0FAxcuRI9fuAgADh7++v8ZmePXuKBQsWCCGE2L9/v2jZsqW4c+eOevnhw4cFAJGamlpv3tDQUCGXy4WJiYnGa+nSpep1Kisrhbe3txg/frxwd3cXs2bN0thGQECAcHV1FVVVVeqxBQsWCFdXVyGEENeuXRMymUzcvn1b43MDBw4Uf/3rX4UQQiQlJQkAIi8vr1a+mjo8fvxYGBoaijNnzmisM3PmTDFp0iQhhBDHjh0TAER6erp6+b59+wQAUVFRIYSorv+UKVPqrMfLZK2LmZmZ+OabbzTGDA0NRURERJ3r5+TkCABi+/bt6uO0trYWlZWV6nXWrVsnLCwsRFlZmXosISFBABC5ubkax/vgwQMhxK91vHbtmvoza9asEdbW1vVmf/bsmTA1NRV79uxRj9X1vYmKihL9+/evdztERLqG97QRETUz4v8vY5PJZA2up1QqYW9vD3t7e/WYm5sbzM3NoVQq0bNnTyiVSnzwwQcan/Pz88OxY8ca3LaHh4fGe1tbWxQXFwMArly5Ant7e437m3x9fRs/MAADBgxAQkKCxpiFhYX6ZwMDA2zevBkeHh5wdHTE6tWra22jd+/eGrXx8/PDqlWroFKpkJOTAyGE+pK/GpWVlWjXrp3Gfl48xucVFBTgyZMnCAoK0hh/+vQpvL29Ncae307NGcHi4mI4ODggLy8Pf/zjH+vcx8tmfVFFRYXGpZGNqev71KNHD4372JRKJTw9PWFsbKwe8/Pza3TbxsbG6Ny5s/r9898ToLoOixcvxtGjR3Hv3j2oVCqUl5ejqKiowe0aGRmhvLy88YMjItIRbNqIiJqZrl27QiaTQalUNjgFvRCizsauvvFX0apVK433MplMPb3862zfxMQEXbp0aXCdM2fOAKi+TPT+/ft1XiJan6qqKsjlcpw/fx5yuVxjWevWrdU/GxkZNXgMNce6b98+dOjQQWPZixOnPF+rmm3WfN7IyOi1s77I0tISDx480Bjr1q0bCgoK6ly/ZtKarl27qsderKl47p7AV1HX9+T5bU2fPh0///wzVq9eDUdHRygUCvj5+eHp06cNbvf+/fto3779b8pERCQF3tNGRNTMWFhYYPDgwVizZg3KyspqLa+ZCMLNzQ1FRUW4deuWellBQQEePXoEV1dXAICrq6vGxCUAar1/VS4uLigqKtKY8CQ7O/u1tlnj+vXriIqKwvr169G7d2+EhITUehZZXcfTtWtXyOVyeHt7Q6VSobi4GF26dNF4vcrMhzUTlBQVFdXazvNnNhvj4eFR7yMafmtWb2/vWg3axIkTkZ6ernHfGlDdGH7xxRdwc3Ordb/bi8d74cIFVFRUqMde93sCACdPnsTcuXMxdOhQuLu7Q6FQ4H//+1+jn7t8+XKtM5pERLqMTRsRUTO0du1aqFQq9QyAhYWFUCqV+Oqrr9SXrQUGBsLDwwNTpkxBTk4OsrKyEBISgoCAAPVEFREREdi0aRM2bdqEq1evIjY29rWf2xUUFITOnTsjNDQUFy9exOnTp9UTkTR2Bq6yshJ3797VeNX8Eq9SqTBt2jQMGjQIYWFhSEpKwuXLl7Fq1SqNbdy6dQvz5s3DlStXsHXrVvz9739HREQEgOozTlOmTEFISAh27tyJn376CdnZ2Vi5ciW+//77lz5GU1NTzJ8/H1FRUUhOTsb169eRm5uLNWvWIDk5+aW3Exsbi61btyI2NhZKpRKXLl1CfHz8a2UdPHgwTp06pTEWFRUFX19fjBgxAt9++y2KioqQnZ2NMWPGQKlUYuPGjQ3+20yePBktWrTAzJkzUVBQgO+//x6ff/75Sx9nfbp06YKUlBQolUpkZmZiypQpDZ59BKonNzl//jwGDRr02vsnInpT2LQRETVDnTp1Qk5ODgYMGIAPP/wQ3bt3R1BQEI4cOaK+J6xmiv22bdvivffeQ2BgIJycnLB9+3b1diZMmIDFixdjwYIFePfdd3Hz5k38+c9/fq1scrkcaWlpePz4MXr27IlZs2Zh0aJFANDovVYHDhyAra2txsvf3x8AsHz5cty4cQOJiYkAABsbG2zYsAGLFi1CXl6eehshISGoqKiAr68vwsPDMWfOHLz//vvq5UlJSQgJCcGHH34IZ2dn/OEPf0BmZuYrnSEDgE8++QSLFy9GXFwcXF1dMXjwYOzZswedOnV66W30798f3377LXbv3g0vLy/8/ve/R2Zm5mtlnTp1KgoKCjQeLWBoaIijR48iNDQUCxcuRJcuXRAcHAy5XI6zZ8/W+4y2Gq1bt8aePXtQUFAAb29vxMTEYOXKlS99nPXZtGkTHjx4AG9vb0ybNg1z586tc6bO5+3atQsODg7o16/fa++fiOhNkYnfeqE5ERHRG3L69Gn4+/vj2rVrGhNTNLX+/fvDy8urzglKmpPo6Gg8evQI69atkzpKk/P19UVkZCQmT54sdRQiopfGM21ERKRzUlNTcfjwYdy4cQPp6el4//330bdvX602bPSrmJgYODo6QqVSSR2lSRUXF2Ps2LGYNGmS1FGIiF4JZ48kIiKdU1paiujoaNy6dQuWlpYIDAysde8ZaY+ZmRkWLlwodYwmZ2VlhejoaKljEBG9Ml4eSUREREREpMN4eSQREREREZEOY9NGRERERESkw9i0ERERERER6TA2bURERERERDqMTRsREREREZEOY9NGRERERESkw9i0ERERERER6TA2bURERERERDrs/wA7cQi3thW2kAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "import matplotlib.pyplot as plt\n",
+    "experience_mapping = {\n",
+    "    '0-2 years': 1,\n",
+    "    '3-5 years': 2,\n",
+    "    '6-8 years': 3,\n",
+    "    '9-11 years': 4,\n",
+    "    '12-14 years': 5,\n",
+    "    '15-17 years': 6,\n",
+    "    '18-20 years': 7,\n",
+    "    '21-23 years': 8,\n",
+    "    '24-26 years': 9\n",
+    "}\n",
+    "df2['CodOrdinal'] = df['YearsCoding'].map(experience_mapping)\n",
+    "# Calculate median NetSalary for each ProfCodOrdinal\n",
+    "median_salaries = df2.groupby('CodOrdinal')['NetSalary'].median().reset_index()\n",
+    "\n",
+    "# Plot the median NetSalary against CodOrdinal\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(median_salaries['CodOrdinal'], median_salaries['NetSalary'], marker='o', linestyle='-', color='b')\n",
+    "plt.xlabel('Coding Experience (Ordinal)')\n",
+    "plt.ylabel('Median Net Salary')\n",
+    "plt.title('Median Net Salary by Coding Experience')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation coefficient: -0.19\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
+    "\n",
+    "# Define the features and target\n",
+    "X = df2[['CodOrdinal']]\n",
+    "y = df2['NetSalary']\n",
+    "\n",
+    "# Train the HistGradientBoostingRegressor model\n",
+    "reg = HistGradientBoostingRegressor().fit(X, y)\n",
+    "\n",
+    "# Predict using the model\n",
+    "df2['PredictedSalary2'] = reg.predict(X)\n",
+    "\n",
+    "# Plot the scatter plot and regression line\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.scatterplot(x='CodOrdinal', y='NetSalary', data=df2, label='Actual Salary')\n",
+    "plt.plot(df2['CodOrdinal'], df2['PredictedSalary2'], color='red', linewidth=2, label='Predicted Salary')\n",
+    "plt.xticks(ticks=np.arange(1, 10), labels=experience_mapping.keys(), rotation=45)\n",
+    "plt.xlabel('Years of Coding Experience')\n",
+    "plt.ylabel('Average Salary')\n",
+    "plt.title('HistGradientBoostingRegressor: Salary vs. Coding Experience')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# Calculate and print the correlation coefficient\n",
+    "correlation = df2['CodOrdinal'].corr(df2['NetSalary'])\n",
+    "print(f\"Correlation coefficient: {correlation:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Conclusions\n",
+    "\n",
+    "\n",
+    "Initial High Salaries for Early Career Professionals:\n",
+    "\n",
+    "There is a high median salary for professionals with 0-2 years of experience. This could be due to a variety of factors such as entry-level positions in high-paying industries or companies.\n",
+    "\n",
+    "\n",
+    "Sharp Decline After Early Career:\n",
+    "\n",
+    "The sharp decline in median salary after the initial 2 years suggests that professionals might face a plateau or even a decrease in salary growth as they gain more experience. This could indicate a saturation point or a shift in industry demand for mid-level experience.\n",
+    "Increase in Salary for Very Experienced Professionals:\n",
+    "\n",
+    "The increase in median salary for those with 21-23 years and 24-26 years of experience suggests that very experienced professionals might move into higher-paying roles such as senior management, consultancy, or specialized technical roles.\n",
+    "Negative Correlation in Linear Regression:\n",
+    "\n",
+    "The slight negative correlation seen in the linear regression plot suggests that, on average, more years of coding experience does not necessarily translate to higher salaries. This could be due to several factors such as industry shifts, changes in technology, or the devaluation of long-term experience in favor of newer skills.\n",
+    "Recommendations\n",
+    "Early Career Focus:\n",
+    "\n",
+    "For new graduates or those entering the field, targeting high-paying entry-level positions or companies can be beneficial. This is the time to capitalize on higher salaries before potential declines.\n",
+    "Continual Skill Development:\n",
+    "\n",
+    "Professionals should focus on continual skill development and staying updated with industry trends to remain competitive and increase their value in the job market.\n",
+    "Career Transition:\n",
+    "\n",
+    "Mid-career professionals might consider transitioning into roles that value their experience, such as management, consultancy, or specialized technical positions, to counter the salary plateau.\n",
+    "Leveraging Experience:\n",
+    "\n",
+    "For those with extensive experience, leveraging their years in the industry to move into strategic roles or consulting can lead to a significant increase in salary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 2, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1900: RuntimeWarning: invalid value encountered in divide\n",
+      "  F /= J\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 6, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 7, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 7, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 12, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 14, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 42, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 14, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 42, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 49, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 84, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 84, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 98, but rank is 1\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 294, but rank is 2\n",
+      "  warnings.warn('covariance of constraints does not have full '\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                          sum_sq     df  \\\n",
+      "C(FormalEducation)                                           NaN    2.0   \n",
+      "C(ProfCodOrdinal)                                            NaN    6.0   \n",
+      "C(CodOrdinal)                                                NaN    7.0   \n",
+      "C(UndergradMajor)                                            NaN    7.0   \n",
+      "C(FormalEducation):C(ProfCodOrdinal)                         NaN   12.0   \n",
+      "C(FormalEducation):C(CodOrdinal)                             NaN   14.0   \n",
+      "C(ProfCodOrdinal):C(CodOrdinal)                              NaN   42.0   \n",
+      "C(FormalEducation):C(UndergradMajor)                         NaN   14.0   \n",
+      "C(ProfCodOrdinal):C(UndergradMajor)                          NaN   42.0   \n",
+      "C(CodOrdinal):C(UndergradMajor)                              NaN   49.0   \n",
+      "C(FormalEducation):C(ProfCodOrdinal):C(CodOrdinal)           NaN   84.0   \n",
+      "C(FormalEducation):C(ProfCodOrdinal):C(Undergra...           NaN   84.0   \n",
+      "C(FormalEducation):C(CodOrdinal):C(UndergradMajor)  1.122731e+11   98.0   \n",
+      "C(ProfCodOrdinal):C(CodOrdinal):C(UndergradMajor)   3.176958e+14  294.0   \n",
+      "C(FormalEducation):C(ProfCodOrdinal):C(CodOrdin...  1.033397e+14  588.0   \n",
+      "Residual                                            6.016195e+10    3.0   \n",
+      "\n",
+      "                                                            F    PR(>F)  \n",
+      "C(FormalEducation)                                        NaN       NaN  \n",
+      "C(ProfCodOrdinal)                                         NaN       NaN  \n",
+      "C(CodOrdinal)                                             NaN       NaN  \n",
+      "C(UndergradMajor)                                         NaN       NaN  \n",
+      "C(FormalEducation):C(ProfCodOrdinal)                      NaN       NaN  \n",
+      "C(FormalEducation):C(CodOrdinal)                          NaN       NaN  \n",
+      "C(ProfCodOrdinal):C(CodOrdinal)                           NaN       NaN  \n",
+      "C(FormalEducation):C(UndergradMajor)                      NaN       NaN  \n",
+      "C(ProfCodOrdinal):C(UndergradMajor)                       NaN       NaN  \n",
+      "C(CodOrdinal):C(UndergradMajor)                           NaN       NaN  \n",
+      "C(FormalEducation):C(ProfCodOrdinal):C(CodOrdinal)        NaN       NaN  \n",
+      "C(FormalEducation):C(ProfCodOrdinal):C(Undergra...        NaN       NaN  \n",
+      "C(FormalEducation):C(CodOrdinal):C(UndergradMajor)   0.057128  0.826492  \n",
+      "C(ProfCodOrdinal):C(CodOrdinal):C(UndergradMajor)   53.884456  0.004457  \n",
+      "C(FormalEducation):C(ProfCodOrdinal):C(CodOrdin...   8.763737  0.049724  \n",
+      "Residual                                                  NaN       NaN  \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 588, but rank is 13\n",
+      "  warnings.warn('covariance of constraints does not have full '\n"
+     ]
+    }
+   ],
+   "source": [
+    "from statsmodels.formula.api import ols\n",
+    "from statsmodels.stats.anova import anova_lm\n",
+    "df_new= df2.dropna(subset=['NetSalary', 'FormalEducation', 'UndergradMajor', 'ProfCodOrdinal', 'CodOrdinal'])\n",
+    "# Define the model including ProfCodOrdinal and CodOrdinal\n",
+    "model = ols('NetSalary ~ C(FormalEducation) * C(ProfCodOrdinal)*  C(CodOrdinal)* C(UndergradMajor)', data=df_new).fit()\n",
+    "\n",
+    "# Perform ANOVA\n",
+    "anova_results = anova_lm(model, typ=2)\n",
+    "print(anova_results)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                                            Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                                             \n",
+      "============================================================================================================================================================================================================\n",
+      "                                  group1                                                                     group2                                     meandiff  p-adj      lower          upper     reject\n",
+      "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)      1.0:1.0:A humanities discipline (ex. literature, history, philosophy)     43056.0    1.0  -2090028.4443  2176140.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing) 1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)   1098864.0 0.7033  -1034220.4443  3231948.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)    1.0:1.0:Computer science, computer engineering, or software engineering   -205560.0    1.0  -2052865.3172  1641745.3172  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing) 1.0:2.0:A social science (ex. anthropology, psychology, political science)  10411056.0    0.0   8277971.5557 12544140.4443   True\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)         1.0:3.0:A business discipline (ex. accounting, finance, marketing)    185904.0    1.0  -1947180.4443  2318988.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)    1.0:3.0:Computer science, computer engineering, or software engineering     -3802.0    1.0  -1745458.1556  1737854.1556  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)                                      1.0:3.0:Web development or web design   -344944.0    1.0  -2478028.4443  1788140.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)    1.0:4.0:Computer science, computer engineering, or software engineering   -249672.0    1.0  -2096977.3172  1597633.3172  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)         2.0:2.0:A business discipline (ex. accounting, finance, marketing)    120528.0    1.0  -2012556.4443  2253612.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)    2.0:2.0:Computer science, computer engineering, or software engineering   -261476.5    1.0  -1947827.8214  1424874.8214  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)    2.0:4.0:Computer science, computer engineering, or software engineering   -310944.0    1.0  -2444028.4443  1822140.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing) 2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)   -303236.0    1.0  -2436320.4443  1829848.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)    3.0:3.0:Computer science, computer engineering, or software engineering   -191724.0    1.0  -2039029.3172  1655581.3172  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing) 3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)   -314944.0    1.0  -2448028.4443  1818140.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)    3.0:4.0:Computer science, computer engineering, or software engineering   -383904.0    1.0  -2516988.4443  1749180.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)    3.0:5.0:Computer science, computer engineering, or software engineering   -288944.0    1.0  -2136249.3172  1558361.3172  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)                4.0:4.0:A natural science (ex. biology, chemistry, physics)    227952.0    1.0  -1905132.4443  2361036.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)    4.0:4.0:Computer science, computer engineering, or software engineering    492192.0 0.9998  -1640892.4443  2625276.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)                4.0:6.0:A natural science (ex. biology, chemistry, physics)   -336317.0    1.0  -2469401.4443  1796767.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)    4.0:6.0:Computer science, computer engineering, or software engineering   -273944.0    1.0  -2407028.4443  1859140.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)                                          4.0:9.0:Mathematics or statistics   -306296.0    1.0  -2439380.4443  1826788.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)    5.0:5.0:Computer science, computer engineering, or software engineering   -316333.0    1.0  -2449417.4443  1816751.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing) 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)     43736.0    1.0  -1803569.3172  1891041.3172  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)    7.0:7.0:Computer science, computer engineering, or software engineering   -273944.0    1.0  -2407028.4443  1859140.4443  False\n",
+      "        1.0:1.0:A business discipline (ex. accounting, finance, marketing)    9.0:9.0:Computer science, computer engineering, or software engineering   -134208.0    1.0  -2267292.4443  1998876.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy) 1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)   1055808.0 0.7506  -1077276.4443  3188892.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)    1.0:1.0:Computer science, computer engineering, or software engineering   -248616.0    1.0  -2095921.3172  1598689.3172  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy) 1.0:2.0:A social science (ex. anthropology, psychology, political science)  10368000.0    0.0   8234915.5557 12501084.4443   True\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)         1.0:3.0:A business discipline (ex. accounting, finance, marketing)    142848.0    1.0  -1990236.4443  2275932.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)    1.0:3.0:Computer science, computer engineering, or software engineering    -46858.0    1.0  -1788514.1556  1694798.1556  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)                                      1.0:3.0:Web development or web design   -388000.0    1.0  -2521084.4443  1745084.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)    1.0:4.0:Computer science, computer engineering, or software engineering   -292728.0    1.0  -2140033.3172  1554577.3172  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)         2.0:2.0:A business discipline (ex. accounting, finance, marketing)     77472.0    1.0  -2055612.4443  2210556.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)    2.0:2.0:Computer science, computer engineering, or software engineering   -304532.5    1.0  -1990883.8214  1381818.8214  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)    2.0:4.0:Computer science, computer engineering, or software engineering   -354000.0    1.0  -2487084.4443  1779084.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy) 2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)   -346292.0    1.0  -2479376.4443  1786792.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)    3.0:3.0:Computer science, computer engineering, or software engineering   -234780.0    1.0  -2082085.3172  1612525.3172  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy) 3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)   -358000.0    1.0  -2491084.4443  1775084.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)    3.0:4.0:Computer science, computer engineering, or software engineering   -426960.0    1.0  -2560044.4443  1706124.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)    3.0:5.0:Computer science, computer engineering, or software engineering   -332000.0    1.0  -2179305.3172  1515305.3172  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)                4.0:4.0:A natural science (ex. biology, chemistry, physics)    184896.0    1.0  -1948188.4443  2317980.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)    4.0:4.0:Computer science, computer engineering, or software engineering    449136.0 0.9999  -1683948.4443  2582220.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)                4.0:6.0:A natural science (ex. biology, chemistry, physics)   -379373.0    1.0  -2512457.4443  1753711.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)    4.0:6.0:Computer science, computer engineering, or software engineering   -317000.0    1.0  -2450084.4443  1816084.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)                                          4.0:9.0:Mathematics or statistics   -349352.0    1.0  -2482436.4443  1783732.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)    5.0:5.0:Computer science, computer engineering, or software engineering   -359389.0    1.0  -2492473.4443  1773695.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy) 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)       680.0    1.0  -1846625.3172  1847985.3172  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)    7.0:7.0:Computer science, computer engineering, or software engineering   -317000.0    1.0  -2450084.4443  1816084.4443  False\n",
+      "     1.0:1.0:A humanities discipline (ex. literature, history, philosophy)    9.0:9.0:Computer science, computer engineering, or software engineering   -177264.0    1.0  -2310348.4443  1955820.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)    1.0:1.0:Computer science, computer engineering, or software engineering  -1304424.0 0.2916  -3151729.3172   542881.3172  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical) 1.0:2.0:A social science (ex. anthropology, psychology, political science)   9312192.0    0.0   7179107.5557 11445276.4443   True\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)         1.0:3.0:A business discipline (ex. accounting, finance, marketing)   -912960.0 0.8855  -3046044.4443  1220124.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)    1.0:3.0:Computer science, computer engineering, or software engineering  -1102666.0 0.4275  -2844322.1556   638990.1556  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)                                      1.0:3.0:Web development or web design  -1443808.0 0.3416  -3576892.4443   689276.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)    1.0:4.0:Computer science, computer engineering, or software engineering  -1348536.0 0.2552  -3195841.3172   498769.3172  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)         2.0:2.0:A business discipline (ex. accounting, finance, marketing)   -978336.0 0.8292  -3111420.4443  1154748.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)    2.0:2.0:Computer science, computer engineering, or software engineering  -1360340.5 0.1632  -3046691.8214   326010.8214  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)    2.0:4.0:Computer science, computer engineering, or software engineering  -1409808.0 0.3714  -3542892.4443   723276.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical) 2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)  -1402100.0 0.3784  -3535184.4443   730984.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)    3.0:3.0:Computer science, computer engineering, or software engineering  -1290588.0 0.3038  -3137893.3172   556717.3172  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical) 3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)  -1413808.0 0.3678  -3546892.4443   719276.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)    3.0:4.0:Computer science, computer engineering, or software engineering  -1482768.0 0.3097  -3615852.4443   650316.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)    3.0:5.0:Computer science, computer engineering, or software engineering  -1387808.0  0.226  -3235113.3172   459497.3172  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)                4.0:4.0:A natural science (ex. biology, chemistry, physics)   -870912.0 0.9156  -3003996.4443  1262172.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)    4.0:4.0:Computer science, computer engineering, or software engineering   -606672.0 0.9968  -2739756.4443  1526412.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)                4.0:6.0:A natural science (ex. biology, chemistry, physics)  -1435181.0  0.349  -3568265.4443   697903.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)    4.0:6.0:Computer science, computer engineering, or software engineering  -1372808.0 0.4058  -3505892.4443   760276.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)                                          4.0:9.0:Mathematics or statistics  -1405160.0 0.3756  -3538244.4443   727924.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical)    5.0:5.0:Computer science, computer engineering, or software engineering  -1415197.0 0.3665  -3548281.4443   717887.4443  False\n",
+      "1.0:1.0:Another engineering discipline (ex. civil, electrical, mechanical) 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)  -1055128.0  0.568  -2902433.3172   792177.3172  False\n",
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+      "   1.0:4.0:Computer science, computer engineering, or software engineering                4.0:4.0:A natural science (ex. biology, chemistry, physics)    477624.0  0.999  -1369681.3172  2324929.3172  False\n",
+      "   1.0:4.0:Computer science, computer engineering, or software engineering    4.0:4.0:Computer science, computer engineering, or software engineering    741864.0 0.9246  -1105441.3172  2589169.3172  False\n",
+      "   1.0:4.0:Computer science, computer engineering, or software engineering                4.0:6.0:A natural science (ex. biology, chemistry, physics)    -86645.0    1.0  -1933950.3172  1760660.3172  False\n",
+      "   1.0:4.0:Computer science, computer engineering, or software engineering    4.0:6.0:Computer science, computer engineering, or software engineering    -24272.0    1.0  -1871577.3172  1823033.3172  False\n",
+      "   1.0:4.0:Computer science, computer engineering, or software engineering                                          4.0:9.0:Mathematics or statistics    -56624.0    1.0  -1903929.3172  1790681.3172  False\n",
+      "   1.0:4.0:Computer science, computer engineering, or software engineering    5.0:5.0:Computer science, computer engineering, or software engineering    -66661.0    1.0  -1913966.3172  1780644.3172  False\n",
+      "   1.0:4.0:Computer science, computer engineering, or software engineering 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    293408.0    1.0  -1214910.4754  1801726.4754  False\n",
+      "   1.0:4.0:Computer science, computer engineering, or software engineering    7.0:7.0:Computer science, computer engineering, or software engineering    -24272.0    1.0  -1871577.3172  1823033.3172  False\n",
+      "   1.0:4.0:Computer science, computer engineering, or software engineering    9.0:9.0:Computer science, computer engineering, or software engineering    115464.0    1.0  -1731841.3172  1962769.3172  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing)    2.0:2.0:Computer science, computer engineering, or software engineering   -382004.5 0.9998  -2068355.8214  1304346.8214  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing)    2.0:4.0:Computer science, computer engineering, or software engineering   -431472.0    1.0  -2564556.4443  1701612.4443  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing) 2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)   -423764.0    1.0  -2556848.4443  1709320.4443  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing)    3.0:3.0:Computer science, computer engineering, or software engineering   -312252.0    1.0  -2159557.3172  1535053.3172  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing) 3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)   -435472.0    1.0  -2568556.4443  1697612.4443  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing)    3.0:4.0:Computer science, computer engineering, or software engineering   -504432.0 0.9997  -2637516.4443  1628652.4443  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing)    3.0:5.0:Computer science, computer engineering, or software engineering   -409472.0 0.9999  -2256777.3172  1437833.3172  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing)                4.0:4.0:A natural science (ex. biology, chemistry, physics)    107424.0    1.0  -2025660.4443  2240508.4443  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing)    4.0:4.0:Computer science, computer engineering, or software engineering    371664.0    1.0  -1761420.4443  2504748.4443  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing)                4.0:6.0:A natural science (ex. biology, chemistry, physics)   -456845.0 0.9999  -2589929.4443  1676239.4443  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing)    4.0:6.0:Computer science, computer engineering, or software engineering   -394472.0    1.0  -2527556.4443  1738612.4443  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing)                                          4.0:9.0:Mathematics or statistics   -426824.0    1.0  -2559908.4443  1706260.4443  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing)    5.0:5.0:Computer science, computer engineering, or software engineering   -436861.0    1.0  -2569945.4443  1696223.4443  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing) 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    -76792.0    1.0  -1924097.3172  1770513.3172  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing)    7.0:7.0:Computer science, computer engineering, or software engineering   -394472.0    1.0  -2527556.4443  1738612.4443  False\n",
+      "        2.0:2.0:A business discipline (ex. accounting, finance, marketing)    9.0:9.0:Computer science, computer engineering, or software engineering   -254736.0    1.0  -2387820.4443  1878348.4443  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering    2.0:4.0:Computer science, computer engineering, or software engineering    -49467.5    1.0  -1735818.8214  1636883.8214  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering 2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)    -41759.5    1.0  -1728110.8214  1644591.8214  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering    3.0:3.0:Computer science, computer engineering, or software engineering     69752.5    1.0  -1236489.6167  1375994.6167  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering 3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)    -53467.5    1.0  -1739818.8214  1632883.8214  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering    3.0:4.0:Computer science, computer engineering, or software engineering   -122427.5    1.0  -1808778.8214  1563923.8214  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering    3.0:5.0:Computer science, computer engineering, or software engineering    -27467.5    1.0  -1333709.6167  1278774.6167  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering                4.0:4.0:A natural science (ex. biology, chemistry, physics)    489428.5  0.996  -1196922.8214  2175779.8214  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering    4.0:4.0:Computer science, computer engineering, or software engineering    753668.5  0.852   -932682.8214  2440019.8214  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering                4.0:6.0:A natural science (ex. biology, chemistry, physics)    -74840.5    1.0  -1761191.8214  1611510.8214  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering    4.0:6.0:Computer science, computer engineering, or software engineering    -12467.5    1.0  -1698818.8214  1673883.8214  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering                                          4.0:9.0:Mathematics or statistics    -44819.5    1.0  -1731170.8214  1641531.8214  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering    5.0:5.0:Computer science, computer engineering, or software engineering    -54856.5    1.0  -1741207.8214  1631494.8214  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    305212.5 0.9997  -1001029.6167  1611454.6167  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering    7.0:7.0:Computer science, computer engineering, or software engineering    -12467.5    1.0  -1698818.8214  1673883.8214  False\n",
+      "   2.0:2.0:Computer science, computer engineering, or software engineering    9.0:9.0:Computer science, computer engineering, or software engineering    127268.5    1.0  -1559082.8214  1813619.8214  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering 2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)      7708.0    1.0  -2125376.4443  2140792.4443  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering    3.0:3.0:Computer science, computer engineering, or software engineering    119220.0    1.0  -1728085.3172  1966525.3172  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering 3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)     -4000.0    1.0  -2137084.4443  2129084.4443  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering    3.0:4.0:Computer science, computer engineering, or software engineering    -72960.0    1.0  -2206044.4443  2060124.4443  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering    3.0:5.0:Computer science, computer engineering, or software engineering     22000.0    1.0  -1825305.3172  1869305.3172  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering                4.0:4.0:A natural science (ex. biology, chemistry, physics)    538896.0 0.9993  -1594188.4443  2671980.4443  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering    4.0:4.0:Computer science, computer engineering, or software engineering    803136.0 0.9531  -1329948.4443  2936220.4443  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering                4.0:6.0:A natural science (ex. biology, chemistry, physics)    -25373.0    1.0  -2158457.4443  2107711.4443  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering    4.0:6.0:Computer science, computer engineering, or software engineering     37000.0    1.0  -2096084.4443  2170084.4443  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering                                          4.0:9.0:Mathematics or statistics      4648.0    1.0  -2128436.4443  2137732.4443  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering    5.0:5.0:Computer science, computer engineering, or software engineering     -5389.0    1.0  -2138473.4443  2127695.4443  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    354680.0    1.0  -1492625.3172  2201985.3172  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering    7.0:7.0:Computer science, computer engineering, or software engineering     37000.0    1.0  -2096084.4443  2170084.4443  False\n",
+      "   2.0:4.0:Computer science, computer engineering, or software engineering    9.0:9.0:Computer science, computer engineering, or software engineering    176736.0    1.0  -1956348.4443  2309820.4443  False\n",
+      "2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)    3.0:3.0:Computer science, computer engineering, or software engineering    111512.0    1.0  -1735793.3172  1958817.3172  False\n",
+      "2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical) 3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)    -11708.0    1.0  -2144792.4443  2121376.4443  False\n",
+      "2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)    3.0:4.0:Computer science, computer engineering, or software engineering    -80668.0    1.0  -2213752.4443  2052416.4443  False\n",
+      "2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)    3.0:5.0:Computer science, computer engineering, or software engineering     14292.0    1.0  -1833013.3172  1861597.3172  False\n",
+      "2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)                4.0:4.0:A natural science (ex. biology, chemistry, physics)    531188.0 0.9994  -1601896.4443  2664272.4443  False\n",
+      "2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)    4.0:4.0:Computer science, computer engineering, or software engineering    795428.0 0.9565  -1337656.4443  2928512.4443  False\n",
+      "2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)                4.0:6.0:A natural science (ex. biology, chemistry, physics)    -33081.0    1.0  -2166165.4443  2100003.4443  False\n",
+      "2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)    4.0:6.0:Computer science, computer engineering, or software engineering     29292.0    1.0  -2103792.4443  2162376.4443  False\n",
+      "2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)                                          4.0:9.0:Mathematics or statistics     -3060.0    1.0  -2136144.4443  2130024.4443  False\n",
+      "2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)    5.0:5.0:Computer science, computer engineering, or software engineering    -13097.0    1.0  -2146181.4443  2119987.4443  False\n",
+      "2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical) 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    346972.0    1.0  -1500333.3172  2194277.3172  False\n",
+      "2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)    7.0:7.0:Computer science, computer engineering, or software engineering     29292.0    1.0  -2103792.4443  2162376.4443  False\n",
+      "2.0:5.0:Another engineering discipline (ex. civil, electrical, mechanical)    9.0:9.0:Computer science, computer engineering, or software engineering    169028.0    1.0  -1964056.4443  2302112.4443  False\n",
+      "   3.0:3.0:Computer science, computer engineering, or software engineering 3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)   -123220.0    1.0  -1970525.3172  1724085.3172  False\n",
+      "   3.0:3.0:Computer science, computer engineering, or software engineering    3.0:4.0:Computer science, computer engineering, or software engineering   -192180.0    1.0  -2039485.3172  1655125.3172  False\n",
+      "   3.0:3.0:Computer science, computer engineering, or software engineering    3.0:5.0:Computer science, computer engineering, or software engineering    -97220.0    1.0  -1605538.4754  1411098.4754  False\n",
+      "   3.0:3.0:Computer science, computer engineering, or software engineering                4.0:4.0:A natural science (ex. biology, chemistry, physics)    419676.0 0.9998  -1427629.3172  2266981.3172  False\n",
+      "   3.0:3.0:Computer science, computer engineering, or software engineering    4.0:4.0:Computer science, computer engineering, or software engineering    683916.0 0.9589  -1163389.3172  2531221.3172  False\n",
+      "   3.0:3.0:Computer science, computer engineering, or software engineering                4.0:6.0:A natural science (ex. biology, chemistry, physics)   -144593.0    1.0  -1991898.3172  1702712.3172  False\n",
+      "   3.0:3.0:Computer science, computer engineering, or software engineering    4.0:6.0:Computer science, computer engineering, or software engineering    -82220.0    1.0  -1929525.3172  1765085.3172  False\n",
+      "   3.0:3.0:Computer science, computer engineering, or software engineering                                          4.0:9.0:Mathematics or statistics   -114572.0    1.0  -1961877.3172  1732733.3172  False\n",
+      "   3.0:3.0:Computer science, computer engineering, or software engineering    5.0:5.0:Computer science, computer engineering, or software engineering   -124609.0    1.0  -1971914.3172  1722696.3172  False\n",
+      "   3.0:3.0:Computer science, computer engineering, or software engineering 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    235460.0    1.0  -1272858.4754  1743778.4754  False\n",
+      "   3.0:3.0:Computer science, computer engineering, or software engineering    7.0:7.0:Computer science, computer engineering, or software engineering    -82220.0    1.0  -1929525.3172  1765085.3172  False\n",
+      "   3.0:3.0:Computer science, computer engineering, or software engineering    9.0:9.0:Computer science, computer engineering, or software engineering     57516.0    1.0  -1789789.3172  1904821.3172  False\n",
+      "3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)    3.0:4.0:Computer science, computer engineering, or software engineering    -68960.0    1.0  -2202044.4443  2064124.4443  False\n",
+      "3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)    3.0:5.0:Computer science, computer engineering, or software engineering     26000.0    1.0  -1821305.3172  1873305.3172  False\n",
+      "3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)                4.0:4.0:A natural science (ex. biology, chemistry, physics)    542896.0 0.9992  -1590188.4443  2675980.4443  False\n",
+      "3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)    4.0:4.0:Computer science, computer engineering, or software engineering    807136.0 0.9513  -1325948.4443  2940220.4443  False\n",
+      "3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)                4.0:6.0:A natural science (ex. biology, chemistry, physics)    -21373.0    1.0  -2154457.4443  2111711.4443  False\n",
+      "3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)    4.0:6.0:Computer science, computer engineering, or software engineering     41000.0    1.0  -2092084.4443  2174084.4443  False\n",
+      "3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)                                          4.0:9.0:Mathematics or statistics      8648.0    1.0  -2124436.4443  2141732.4443  False\n",
+      "3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)    5.0:5.0:Computer science, computer engineering, or software engineering     -1389.0    1.0  -2134473.4443  2131695.4443  False\n",
+      "3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical) 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    358680.0    1.0  -1488625.3172  2205985.3172  False\n",
+      "3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)    7.0:7.0:Computer science, computer engineering, or software engineering     41000.0    1.0  -2092084.4443  2174084.4443  False\n",
+      "3.0:4.0:Another engineering discipline (ex. civil, electrical, mechanical)    9.0:9.0:Computer science, computer engineering, or software engineering    180736.0    1.0  -1952348.4443  2313820.4443  False\n",
+      "   3.0:4.0:Computer science, computer engineering, or software engineering    3.0:5.0:Computer science, computer engineering, or software engineering     94960.0    1.0  -1752345.3172  1942265.3172  False\n",
+      "   3.0:4.0:Computer science, computer engineering, or software engineering                4.0:4.0:A natural science (ex. biology, chemistry, physics)    611856.0 0.9965  -1521228.4443  2744940.4443  False\n",
+      "   3.0:4.0:Computer science, computer engineering, or software engineering    4.0:4.0:Computer science, computer engineering, or software engineering    876096.0 0.9121  -1256988.4443  3009180.4443  False\n",
+      "   3.0:4.0:Computer science, computer engineering, or software engineering                4.0:6.0:A natural science (ex. biology, chemistry, physics)     47587.0    1.0  -2085497.4443  2180671.4443  False\n",
+      "   3.0:4.0:Computer science, computer engineering, or software engineering    4.0:6.0:Computer science, computer engineering, or software engineering    109960.0    1.0  -2023124.4443  2243044.4443  False\n",
+      "   3.0:4.0:Computer science, computer engineering, or software engineering                                          4.0:9.0:Mathematics or statistics     77608.0    1.0  -2055476.4443  2210692.4443  False\n",
+      "   3.0:4.0:Computer science, computer engineering, or software engineering    5.0:5.0:Computer science, computer engineering, or software engineering     67571.0    1.0  -2065513.4443  2200655.4443  False\n",
+      "   3.0:4.0:Computer science, computer engineering, or software engineering 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    427640.0 0.9998  -1419665.3172  2274945.3172  False\n",
+      "   3.0:4.0:Computer science, computer engineering, or software engineering    7.0:7.0:Computer science, computer engineering, or software engineering    109960.0    1.0  -2023124.4443  2243044.4443  False\n",
+      "   3.0:4.0:Computer science, computer engineering, or software engineering    9.0:9.0:Computer science, computer engineering, or software engineering    249696.0    1.0  -1883388.4443  2382780.4443  False\n",
+      "   3.0:5.0:Computer science, computer engineering, or software engineering                4.0:4.0:A natural science (ex. biology, chemistry, physics)    516896.0 0.9974  -1330409.3172  2364201.3172  False\n",
+      "   3.0:5.0:Computer science, computer engineering, or software engineering    4.0:4.0:Computer science, computer engineering, or software engineering    781136.0 0.8938  -1066169.3172  2628441.3172  False\n",
+      "   3.0:5.0:Computer science, computer engineering, or software engineering                4.0:6.0:A natural science (ex. biology, chemistry, physics)    -47373.0    1.0  -1894678.3172  1799932.3172  False\n",
+      "   3.0:5.0:Computer science, computer engineering, or software engineering    4.0:6.0:Computer science, computer engineering, or software engineering     15000.0    1.0  -1832305.3172  1862305.3172  False\n",
+      "   3.0:5.0:Computer science, computer engineering, or software engineering                                          4.0:9.0:Mathematics or statistics    -17352.0    1.0  -1864657.3172  1829953.3172  False\n",
+      "   3.0:5.0:Computer science, computer engineering, or software engineering    5.0:5.0:Computer science, computer engineering, or software engineering    -27389.0    1.0  -1874694.3172  1819916.3172  False\n",
+      "   3.0:5.0:Computer science, computer engineering, or software engineering 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    332680.0 0.9999  -1175638.4754  1840998.4754  False\n",
+      "   3.0:5.0:Computer science, computer engineering, or software engineering    7.0:7.0:Computer science, computer engineering, or software engineering     15000.0    1.0  -1832305.3172  1862305.3172  False\n",
+      "   3.0:5.0:Computer science, computer engineering, or software engineering    9.0:9.0:Computer science, computer engineering, or software engineering    154736.0    1.0  -1692569.3172  2002041.3172  False\n",
+      "               4.0:4.0:A natural science (ex. biology, chemistry, physics)    4.0:4.0:Computer science, computer engineering, or software engineering    264240.0    1.0  -1868844.4443  2397324.4443  False\n",
+      "               4.0:4.0:A natural science (ex. biology, chemistry, physics)                4.0:6.0:A natural science (ex. biology, chemistry, physics)   -564269.0 0.9987  -2697353.4443  1568815.4443  False\n",
+      "               4.0:4.0:A natural science (ex. biology, chemistry, physics)    4.0:6.0:Computer science, computer engineering, or software engineering   -501896.0 0.9997  -2634980.4443  1631188.4443  False\n",
+      "               4.0:4.0:A natural science (ex. biology, chemistry, physics)                                          4.0:9.0:Mathematics or statistics   -534248.0 0.9993  -2667332.4443  1598836.4443  False\n",
+      "               4.0:4.0:A natural science (ex. biology, chemistry, physics)    5.0:5.0:Computer science, computer engineering, or software engineering   -544285.0 0.9992  -2677369.4443  1588799.4443  False\n",
+      "               4.0:4.0:A natural science (ex. biology, chemistry, physics) 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)   -184216.0    1.0  -2031521.3172  1663089.3172  False\n",
+      "               4.0:4.0:A natural science (ex. biology, chemistry, physics)    7.0:7.0:Computer science, computer engineering, or software engineering   -501896.0 0.9997  -2634980.4443  1631188.4443  False\n",
+      "               4.0:4.0:A natural science (ex. biology, chemistry, physics)    9.0:9.0:Computer science, computer engineering, or software engineering   -362160.0    1.0  -2495244.4443  1770924.4443  False\n",
+      "   4.0:4.0:Computer science, computer engineering, or software engineering                4.0:6.0:A natural science (ex. biology, chemistry, physics)   -828509.0 0.9407  -2961593.4443  1304575.4443  False\n",
+      "   4.0:4.0:Computer science, computer engineering, or software engineering    4.0:6.0:Computer science, computer engineering, or software engineering   -766136.0 0.9679  -2899220.4443  1366948.4443  False\n",
+      "   4.0:4.0:Computer science, computer engineering, or software engineering                                          4.0:9.0:Mathematics or statistics   -798488.0 0.9552  -2931572.4443  1334596.4443  False\n",
+      "   4.0:4.0:Computer science, computer engineering, or software engineering    5.0:5.0:Computer science, computer engineering, or software engineering   -808525.0 0.9506  -2941609.4443  1324559.4443  False\n",
+      "   4.0:4.0:Computer science, computer engineering, or software engineering 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)   -448456.0 0.9996  -2295761.3172  1398849.3172  False\n",
+      "   4.0:4.0:Computer science, computer engineering, or software engineering    7.0:7.0:Computer science, computer engineering, or software engineering   -766136.0 0.9679  -2899220.4443  1366948.4443  False\n",
+      "   4.0:4.0:Computer science, computer engineering, or software engineering    9.0:9.0:Computer science, computer engineering, or software engineering   -626400.0 0.9955  -2759484.4443  1506684.4443  False\n",
+      "               4.0:6.0:A natural science (ex. biology, chemistry, physics)    4.0:6.0:Computer science, computer engineering, or software engineering     62373.0    1.0  -2070711.4443  2195457.4443  False\n",
+      "               4.0:6.0:A natural science (ex. biology, chemistry, physics)                                          4.0:9.0:Mathematics or statistics     30021.0    1.0  -2103063.4443  2163105.4443  False\n",
+      "               4.0:6.0:A natural science (ex. biology, chemistry, physics)    5.0:5.0:Computer science, computer engineering, or software engineering     19984.0    1.0  -2113100.4443  2153068.4443  False\n",
+      "               4.0:6.0:A natural science (ex. biology, chemistry, physics) 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    380053.0    1.0  -1467252.3172  2227358.3172  False\n",
+      "               4.0:6.0:A natural science (ex. biology, chemistry, physics)    7.0:7.0:Computer science, computer engineering, or software engineering     62373.0    1.0  -2070711.4443  2195457.4443  False\n",
+      "               4.0:6.0:A natural science (ex. biology, chemistry, physics)    9.0:9.0:Computer science, computer engineering, or software engineering    202109.0    1.0  -1930975.4443  2335193.4443  False\n",
+      "   4.0:6.0:Computer science, computer engineering, or software engineering                                          4.0:9.0:Mathematics or statistics    -32352.0    1.0  -2165436.4443  2100732.4443  False\n",
+      "   4.0:6.0:Computer science, computer engineering, or software engineering    5.0:5.0:Computer science, computer engineering, or software engineering    -42389.0    1.0  -2175473.4443  2090695.4443  False\n",
+      "   4.0:6.0:Computer science, computer engineering, or software engineering 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    317680.0    1.0  -1529625.3172  2164985.3172  False\n",
+      "   4.0:6.0:Computer science, computer engineering, or software engineering    7.0:7.0:Computer science, computer engineering, or software engineering         0.0    1.0  -2133084.4443  2133084.4443  False\n",
+      "   4.0:6.0:Computer science, computer engineering, or software engineering    9.0:9.0:Computer science, computer engineering, or software engineering    139736.0    1.0  -1993348.4443  2272820.4443  False\n",
+      "                                         4.0:9.0:Mathematics or statistics    5.0:5.0:Computer science, computer engineering, or software engineering    -10037.0    1.0  -2143121.4443  2123047.4443  False\n",
+      "                                         4.0:9.0:Mathematics or statistics 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    350032.0    1.0  -1497273.3172  2197337.3172  False\n",
+      "                                         4.0:9.0:Mathematics or statistics    7.0:7.0:Computer science, computer engineering, or software engineering     32352.0    1.0  -2100732.4443  2165436.4443  False\n",
+      "                                         4.0:9.0:Mathematics or statistics    9.0:9.0:Computer science, computer engineering, or software engineering    172088.0    1.0  -1960996.4443  2305172.4443  False\n",
+      "   5.0:5.0:Computer science, computer engineering, or software engineering 5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    360069.0    1.0  -1487236.3172  2207374.3172  False\n",
+      "   5.0:5.0:Computer science, computer engineering, or software engineering    7.0:7.0:Computer science, computer engineering, or software engineering     42389.0    1.0  -2090695.4443  2175473.4443  False\n",
+      "   5.0:5.0:Computer science, computer engineering, or software engineering    9.0:9.0:Computer science, computer engineering, or software engineering    182125.0    1.0  -1950959.4443  2315209.4443  False\n",
+      "5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    7.0:7.0:Computer science, computer engineering, or software engineering   -317680.0    1.0  -2164985.3172  1529625.3172  False\n",
+      "5.0:6.0:Another engineering discipline (ex. civil, electrical, mechanical)    9.0:9.0:Computer science, computer engineering, or software engineering   -177944.0    1.0  -2025249.3172  1669361.3172  False\n",
+      "   7.0:7.0:Computer science, computer engineering, or software engineering    9.0:9.0:Computer science, computer engineering, or software engineering    139736.0    1.0  -1993348.4443  2272820.4443  False\n",
+      "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
+      "                                                                Multiple Comparison of Means - Tukey HSD, FWER=0.05                                                                 \n",
+      "====================================================================================================================================================================================\n",
+      "                            group1                                                         group2                               meandiff  p-adj      lower          upper     reject\n",
+      "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0             Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0  10509840.0    0.0   8989530.6104 12030149.3896   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0             Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0    238840.0    1.0   -963070.1049  1440750.1049  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0             Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0   -131616.0    1.0  -1651925.3896  1388693.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0             Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0   -230160.0    1.0  -1750469.3896  1290149.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0             Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0   -212160.0    1.0  -1732469.3896  1308149.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0             Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0   -250640.0 0.9999  -1452550.1049   951270.1049  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0             Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0   -210160.0    1.0  -1730469.3896  1310149.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0             Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0   -194192.0    1.0  -1714501.3896  1326117.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0             Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0   -175160.0    1.0  -1695469.3896  1345149.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0     27072.0    1.0  -1493237.3896  1547381.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0    284688.0    1.0  -1235621.3896  1804997.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0   -245225.0 0.9999  -1447135.1049   956685.1049  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0   -204452.0    1.0  -1724761.3896  1315857.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0    -92940.0    1.0  -1294850.1049  1108970.1049  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0   -170160.0    1.0  -1690469.3896  1350149.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0    326736.0 0.9999  -1193573.3896  1847045.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0   -206346.5    1.0  -1408256.6049   995563.6049  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0   -207512.0    1.0  -1727821.3896  1312797.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0   -217549.0    1.0  -1737858.3896  1302760.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    479232.0 0.9866  -1041077.3896  1999541.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0 Some college/university study without earning a degree:1.0:1.0   1197648.0 0.1733   -322661.3896  2717957.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0 Some college/university study without earning a degree:1.0:3.0   -219447.0    1.0  -1421357.1049   982463.1049  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0 Some college/university study without earning a degree:1.0:4.0   -170160.0    1.0  -1690469.3896  1350149.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0 Some college/university study without earning a degree:2.0:2.0    144576.0    1.0  -1057334.1049  1346486.1049  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0 Some college/university study without earning a degree:4.0:4.0    590976.0 0.9253   -929333.3896  2111285.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:1.0 Some college/university study without earning a degree:9.0:9.0    -35424.0    1.0  -1555733.3896  1484885.3896  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0             Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0 -10271000.0    0.0 -11883531.6184 -8658468.3816   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0             Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0 -10641456.0    0.0 -12503447.1279 -8779464.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0             Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0 -10740000.0    0.0 -12601991.1279 -8878008.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0             Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0 -10722000.0    0.0 -12583991.1279 -8860008.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0             Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0 -10760480.0    0.0 -12373011.6184 -9147948.3816   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0             Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0 -10720000.0    0.0 -12581991.1279 -8858008.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0             Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0 -10704032.0    0.0 -12566023.1279 -8842040.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0             Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0 -10685000.0    0.0 -12546991.1279 -8823008.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0 -10482768.0    0.0 -12344759.1279 -8620776.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0 -10225152.0    0.0 -12087143.1279 -8363160.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0 -10755065.0    0.0 -12367596.6184 -9142533.3816   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0 -10714292.0    0.0 -12576283.1279 -8852300.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0 -10602780.0    0.0 -12215311.6184 -8990248.3816   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0 -10680000.0    0.0 -12541991.1279 -8818008.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0 -10183104.0    0.0 -12045095.1279 -8321112.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0 -10716186.5    0.0 -12328718.1184 -9103654.8816   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0 -10717352.0    0.0 -12579343.1279 -8855360.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0 -10727389.0    0.0 -12589380.1279 -8865397.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0 -10030608.0    0.0 -11892599.1279 -8168616.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0 Some college/university study without earning a degree:1.0:1.0  -9312192.0    0.0 -11174183.1279 -7450200.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0 Some college/university study without earning a degree:1.0:3.0 -10729287.0    0.0 -12341818.6184 -9116755.3816   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0 Some college/university study without earning a degree:1.0:4.0 -10680000.0    0.0 -12541991.1279 -8818008.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0 Some college/university study without earning a degree:2.0:2.0 -10365264.0    0.0 -11977795.6184 -8752732.3816   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0 Some college/university study without earning a degree:4.0:4.0  -9918864.0    0.0 -11780855.1279 -8056872.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:2.0 Some college/university study without earning a degree:9.0:9.0 -10545264.0    0.0 -12407255.1279 -8683272.8721   True\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0             Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0   -370456.0 0.9997  -1982987.6184  1242075.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0             Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0   -469000.0  0.994  -2081531.6184  1143531.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0             Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0   -451000.0 0.9961  -2063531.6184  1161531.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0             Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0   -489480.0 0.9456   -1806106.553    827146.553  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0             Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0   -449000.0 0.9963  -2061531.6184  1163531.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0             Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0   -433032.0 0.9976  -2045563.6184  1179499.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0             Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0   -414000.0 0.9986  -2026531.6184  1198531.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0   -211768.0    1.0  -1824299.6184  1400763.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0     45848.0    1.0  -1566683.6184  1658379.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0   -484065.0   0.95   -1800691.553    832561.553  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0   -443292.0 0.9968  -2055823.6184  1169239.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0   -331780.0 0.9989   -1648406.553    984846.553  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0   -409000.0 0.9988  -2021531.6184  1203531.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0     87896.0    1.0  -1524635.6184  1700427.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0   -445186.5 0.9746   -1761813.053    871440.053  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0   -446352.0 0.9965  -2058883.6184  1166179.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0   -456389.0 0.9956  -2068920.6184  1156142.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    240392.0    1.0  -1372139.6184  1852923.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0 Some college/university study without earning a degree:1.0:1.0    958808.0 0.4833   -653723.6184  2571339.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0 Some college/university study without earning a degree:1.0:3.0   -458287.0 0.9675   -1774913.553    858339.553  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0 Some college/university study without earning a degree:1.0:4.0   -409000.0 0.9988  -2021531.6184  1203531.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0 Some college/university study without earning a degree:2.0:2.0    -94264.0    1.0   -1410890.553   1222362.553  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0 Some college/university study without earning a degree:4.0:4.0    352136.0 0.9998  -1260395.6184  1964667.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:3.0 Some college/university study without earning a degree:9.0:9.0   -274264.0    1.0  -1886795.6184  1338267.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0             Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0    -98544.0    1.0  -1960535.1279  1763447.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0             Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0    -80544.0    1.0  -1942535.1279  1781447.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0             Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0   -119024.0    1.0  -1731555.6184  1493507.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0             Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0    -78544.0    1.0  -1940535.1279  1783447.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0             Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0    -62576.0    1.0  -1924567.1279  1799415.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0             Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0    -43544.0    1.0  -1905535.1279  1818447.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0    158688.0    1.0  -1703303.1279  2020679.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0    416304.0 0.9998  -1445687.1279  2278295.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0   -113609.0    1.0  -1726140.6184  1498922.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0    -72836.0    1.0  -1934827.1279  1789155.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0     38676.0    1.0  -1573855.6184  1651207.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0    -38544.0    1.0  -1900535.1279  1823447.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0    458352.0 0.9992  -1403639.1279  2320343.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0    -74730.5    1.0  -1687262.1184  1537801.1184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0    -75896.0    1.0  -1937887.1279  1786095.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0    -85933.0    1.0  -1947924.1279  1776058.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    610848.0 0.9805  -1251143.1279  2472839.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0 Some college/university study without earning a degree:1.0:1.0   1329264.0 0.2628   -532727.1279  3191255.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0 Some college/university study without earning a degree:1.0:3.0    -87831.0    1.0  -1700362.6184  1524700.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0 Some college/university study without earning a degree:1.0:4.0    -38544.0    1.0  -1900535.1279  1823447.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0 Some college/university study without earning a degree:2.0:2.0    276192.0    1.0  -1336339.6184  1888723.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0 Some college/university study without earning a degree:4.0:4.0    722592.0 0.9261  -1139399.1279  2584583.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):1.0:4.0 Some college/university study without earning a degree:9.0:9.0     96192.0    1.0  -1765799.1279  1958183.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0             Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0     18000.0    1.0  -1843991.1279  1879991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0             Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0    -20480.0    1.0  -1633011.6184  1592051.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0             Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0     20000.0    1.0  -1841991.1279  1881991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0             Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0     35968.0    1.0  -1826023.1279  1897959.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0             Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0     55000.0    1.0  -1806991.1279  1916991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0    257232.0    1.0  -1604759.1279  2119223.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0    514848.0 0.9966  -1347143.1279  2376839.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0    -15065.0    1.0  -1627596.6184  1597466.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0     25708.0    1.0  -1836283.1279  1887699.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0    137220.0    1.0  -1475311.6184  1749751.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0     60000.0    1.0  -1801991.1279  1921991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0    556896.0  0.992  -1305095.1279  2418887.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0     23813.5    1.0  -1588718.1184  1636345.1184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0     22648.0    1.0  -1839343.1279  1884639.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0     12611.0    1.0  -1849380.1279  1874602.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    709392.0 0.9351  -1152599.1279  2571383.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0 Some college/university study without earning a degree:1.0:1.0   1427808.0 0.1953   -434183.1279  3289799.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0 Some college/university study without earning a degree:1.0:3.0     10713.0    1.0  -1601818.6184  1623244.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0 Some college/university study without earning a degree:1.0:4.0     60000.0    1.0  -1801991.1279  1921991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0 Some college/university study without earning a degree:2.0:2.0    374736.0 0.9996  -1237795.6184  1987267.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0 Some college/university study without earning a degree:4.0:4.0    821136.0 0.8383  -1040855.1279  2683127.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:2.0 Some college/university study without earning a degree:9.0:9.0    194736.0    1.0  -1667255.1279  2056727.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0             Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0    -38480.0    1.0  -1651011.6184  1574051.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0             Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0      2000.0    1.0  -1859991.1279  1863991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0             Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0     17968.0    1.0  -1844023.1279  1879959.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0             Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0     37000.0    1.0  -1824991.1279  1898991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0    239232.0    1.0  -1622759.1279  2101223.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0    496848.0 0.9977  -1365143.1279  2358839.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0    -33065.0    1.0  -1645596.6184  1579466.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0      7708.0    1.0  -1854283.1279  1869699.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0    119220.0    1.0  -1493311.6184  1731751.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0     42000.0    1.0  -1819991.1279  1903991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0    538896.0 0.9943  -1323095.1279  2400887.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0      5813.5    1.0  -1606718.1184  1618345.1184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0      4648.0    1.0  -1857343.1279  1866639.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0     -5389.0    1.0  -1867380.1279  1856602.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    691392.0 0.9461  -1170599.1279  2553383.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0 Some college/university study without earning a degree:1.0:1.0   1409808.0 0.2064   -452183.1279  3271799.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0 Some college/university study without earning a degree:1.0:3.0     -7287.0    1.0  -1619818.6184  1605244.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0 Some college/university study without earning a degree:1.0:4.0     42000.0    1.0  -1819991.1279  1903991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0 Some college/university study without earning a degree:2.0:2.0    356736.0 0.9998  -1255795.6184  1969267.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0 Some college/university study without earning a degree:4.0:4.0    803136.0 0.8569  -1058855.1279  2665127.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):2.0:4.0 Some college/university study without earning a degree:9.0:9.0    176736.0    1.0  -1685255.1279  2038727.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0             Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0     40480.0    1.0  -1572051.6184  1653011.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0             Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0     56448.0    1.0  -1556083.6184  1668979.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0             Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0     75480.0    1.0  -1537051.6184  1688011.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0    277712.0    1.0  -1334819.6184  1890243.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0    535328.0 0.9784  -1077203.6184  2147859.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0      5415.0    1.0   -1311211.553   1322041.553  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0     46188.0    1.0  -1566343.6184  1658719.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0    157700.0    1.0   -1158926.553   1474326.553  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0     80480.0    1.0  -1532051.6184  1693011.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0    577376.0 0.9592  -1035155.6184  2189907.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0     44293.5    1.0   -1272333.053   1360920.053  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0     43128.0    1.0  -1569403.6184  1655659.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0     33091.0    1.0  -1579440.6184  1645622.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    729872.0 0.8147   -882659.6184  2342403.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0 Some college/university study without earning a degree:1.0:1.0   1448288.0 0.0909   -164243.6184  3060819.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0 Some college/university study without earning a degree:1.0:3.0     31193.0    1.0   -1285433.553   1347819.553  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0 Some college/university study without earning a degree:1.0:4.0     80480.0    1.0  -1532051.6184  1693011.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0 Some college/university study without earning a degree:2.0:2.0    395216.0 0.9917    -921410.553   1711842.553  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0 Some college/university study without earning a degree:4.0:4.0    841616.0 0.6549   -770915.6184  2454147.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:4.0 Some college/university study without earning a degree:9.0:9.0    215216.0    1.0  -1397315.6184  1827747.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0             Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0     15968.0    1.0  -1846023.1279  1877959.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0             Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0     35000.0    1.0  -1826991.1279  1896991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0    237232.0    1.0  -1624759.1279  2099223.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0    494848.0 0.9978  -1367143.1279  2356839.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0    -35065.0    1.0  -1647596.6184  1577466.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0      5708.0    1.0  -1856283.1279  1867699.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0    117220.0    1.0  -1495311.6184  1729751.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0     40000.0    1.0  -1821991.1279  1901991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0    536896.0 0.9946  -1325095.1279  2398887.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0      3813.5    1.0  -1608718.1184  1616345.1184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0      2648.0    1.0  -1859343.1279  1864639.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0     -7389.0    1.0  -1869380.1279  1854602.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    689392.0 0.9473  -1172599.1279  2551383.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0 Some college/university study without earning a degree:1.0:1.0   1407808.0 0.2076   -454183.1279  3269799.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0 Some college/university study without earning a degree:1.0:3.0     -9287.0    1.0  -1621818.6184  1603244.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0 Some college/university study without earning a degree:1.0:4.0     40000.0    1.0  -1821991.1279  1901991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0 Some college/university study without earning a degree:2.0:2.0    354736.0 0.9998  -1257795.6184  1967267.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0 Some college/university study without earning a degree:4.0:4.0    801136.0 0.8589  -1060855.1279  2663127.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):3.0:5.0 Some college/university study without earning a degree:9.0:9.0    174736.0    1.0  -1687255.1279  2036727.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0             Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0     19032.0    1.0  -1842959.1279  1881023.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0    221264.0    1.0  -1640727.1279  2083255.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0    478880.0 0.9985  -1383111.1279  2340871.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0    -51033.0    1.0  -1663564.6184  1561498.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0    -10260.0    1.0  -1872251.1279  1851731.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0    101252.0    1.0  -1511279.6184  1713783.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0     24032.0    1.0  -1837959.1279  1886023.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0    520928.0 0.9961  -1341063.1279  2382919.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0    -12154.5    1.0  -1624686.1184  1600377.1184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0    -13320.0    1.0  -1875311.1279  1848671.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0    -23357.0    1.0  -1885348.1279  1838634.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    673424.0 0.9559  -1188567.1279  2535415.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0 Some college/university study without earning a degree:1.0:1.0   1391840.0 0.2179   -470151.1279  3253831.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0 Some college/university study without earning a degree:1.0:3.0    -25255.0    1.0  -1637786.6184  1587276.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0 Some college/university study without earning a degree:1.0:4.0     24032.0    1.0  -1837959.1279  1886023.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0 Some college/university study without earning a degree:2.0:2.0    338768.0 0.9999  -1273763.6184  1951299.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0 Some college/university study without earning a degree:4.0:4.0    785168.0 0.8744  -1076823.1279  2647159.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):5.0:6.0 Some college/university study without earning a degree:9.0:9.0    158768.0    1.0  -1703223.1279  2020759.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0    202232.0    1.0  -1659759.1279  2064223.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0    459848.0 0.9991  -1402143.1279  2321839.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0    -70065.0    1.0  -1682596.6184  1542466.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0    -29292.0    1.0  -1891283.1279  1832699.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0     82220.0    1.0  -1530311.6184  1694751.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0      5000.0    1.0  -1856991.1279  1866991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0    501896.0 0.9974  -1360095.1279  2363887.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0    -31186.5    1.0  -1643718.1184  1581345.1184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0    -32352.0    1.0  -1894343.1279  1829639.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0    -42389.0    1.0  -1904380.1279  1819602.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    654392.0 0.9649  -1207599.1279  2516383.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0 Some college/university study without earning a degree:1.0:1.0   1372808.0 0.2308   -489183.1279  3234799.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0 Some college/university study without earning a degree:1.0:3.0    -44287.0    1.0  -1656818.6184  1568244.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0 Some college/university study without earning a degree:1.0:4.0      5000.0    1.0  -1856991.1279  1866991.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0 Some college/university study without earning a degree:2.0:2.0    319736.0    1.0  -1292795.6184  1932267.6184  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0 Some college/university study without earning a degree:4.0:4.0    766136.0 0.8917  -1095855.1279  2628127.1279  False\n",
+      "            Bachelor’s degree (BA, BS, B.Eng., etc.):7.0:7.0 Some college/university study without earning a degree:9.0:9.0    139736.0    1.0  -1722255.1279  2001727.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0    257616.0    1.0  -1604375.1279  2119607.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0   -272297.0    1.0  -1884828.6184  1340234.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0   -231524.0    1.0  -2093515.1279  1630467.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0   -120012.0    1.0  -1732543.6184  1492519.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0   -197232.0    1.0  -2059223.1279  1664759.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0    299664.0    1.0  -1562327.1279  2161655.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0   -233418.5    1.0  -1845950.1184  1379113.1184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0   -234584.0    1.0  -2096575.1279  1627407.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0   -244621.0    1.0  -2106612.1279  1617370.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    452160.0 0.9993  -1409831.1279  2314151.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0 Some college/university study without earning a degree:1.0:1.0   1170576.0 0.4107   -691415.1279  3032567.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0 Some college/university study without earning a degree:1.0:3.0   -246519.0    1.0  -1859050.6184  1366012.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0 Some college/university study without earning a degree:1.0:4.0   -197232.0    1.0  -2059223.1279  1664759.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0 Some college/university study without earning a degree:2.0:2.0    117504.0    1.0  -1495027.6184  1730035.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0 Some college/university study without earning a degree:4.0:4.0    563904.0 0.9909  -1298087.1279  2425895.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:1.0 Some college/university study without earning a degree:9.0:9.0    -62496.0    1.0  -1924487.1279  1799495.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0   -529913.0 0.9802  -2142444.6184  1082618.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0   -489140.0 0.9981  -2351131.1279  1372851.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0   -377628.0 0.9996  -1990159.6184  1234903.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0   -454848.0 0.9992  -2316839.1279  1407143.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0     42048.0    1.0  -1819943.1279  1904039.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0   -491034.5 0.9904  -2103566.1184  1121497.1184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0   -492200.0  0.998  -2354191.1279  1369791.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0   -502237.0 0.9974  -2364228.1279  1359754.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    194544.0    1.0  -1667447.1279  2056535.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0 Some college/university study without earning a degree:1.0:1.0    912960.0 0.7306   -949031.1279  2774951.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0 Some college/university study without earning a degree:1.0:3.0   -504135.0 0.9876  -2116666.6184  1108396.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0 Some college/university study without earning a degree:1.0:4.0   -454848.0 0.9992  -2316839.1279  1407143.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0 Some college/university study without earning a degree:2.0:2.0   -140112.0    1.0  -1752643.6184  1472419.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0 Some college/university study without earning a degree:4.0:4.0    306288.0    1.0  -1555703.1279  2168279.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):1.0:3.0 Some college/university study without earning a degree:9.0:9.0   -320112.0    1.0  -2182103.1279  1541879.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0     40773.0    1.0  -1571758.6184  1653304.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0    152285.0    1.0   -1164341.553   1468911.553  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0     75065.0    1.0  -1537466.6184  1687596.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0    571961.0 0.9622  -1040570.6184  2184492.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0     38878.5    1.0   -1277748.053   1355505.053  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0     37713.0    1.0  -1574818.6184  1650244.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0     27676.0    1.0  -1584855.6184  1640207.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    724457.0 0.8217   -888074.6184  2336988.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0 Some college/university study without earning a degree:1.0:1.0   1442873.0 0.0928   -169658.6184  3055404.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0 Some college/university study without earning a degree:1.0:3.0     25778.0    1.0   -1290848.553   1342404.553  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0 Some college/university study without earning a degree:1.0:4.0     75065.0    1.0  -1537466.6184  1687596.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0 Some college/university study without earning a degree:2.0:2.0    389801.0 0.9928    -926825.553   1706427.553  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0 Some college/university study without earning a degree:4.0:4.0    836201.0 0.6631   -776330.6184  2448732.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:2.0 Some college/university study without earning a degree:9.0:9.0    209801.0    1.0  -1402730.6184  1822332.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0    111512.0    1.0  -1501019.6184  1724043.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0     34292.0    1.0  -1827699.1279  1896283.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0    531188.0 0.9952  -1330803.1279  2393179.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0     -1894.5    1.0  -1614426.1184  1610637.1184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0     -3060.0    1.0  -1865051.1279  1858931.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0    -13097.0    1.0  -1875088.1279  1848894.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    683684.0 0.9505  -1178307.1279  2545675.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0 Some college/university study without earning a degree:1.0:1.0   1402100.0 0.2113   -459891.1279  3264091.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0 Some college/university study without earning a degree:1.0:3.0    -14995.0    1.0  -1627526.6184  1597536.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0 Some college/university study without earning a degree:1.0:4.0     34292.0    1.0  -1827699.1279  1896283.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0 Some college/university study without earning a degree:2.0:2.0    349028.0 0.9999  -1263503.6184  1961559.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0 Some college/university study without earning a degree:4.0:4.0    795428.0 0.8646  -1066563.1279  2657419.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):2.0:5.0 Some college/university study without earning a degree:9.0:9.0    169028.0    1.0  -1692963.1279  2031019.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0    -77220.0    1.0  -1689751.6184  1535311.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0    419676.0 0.9983  -1192855.6184  2032207.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0   -113406.5    1.0   -1430033.053   1203220.053  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0   -114572.0    1.0  -1727103.6184  1497959.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0   -124609.0    1.0  -1737140.6184  1487922.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    572172.0 0.9621  -1040359.6184  2184703.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0 Some college/university study without earning a degree:1.0:1.0   1290588.0 0.1611   -321943.6184  2903119.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0 Some college/university study without earning a degree:1.0:3.0   -126507.0    1.0   -1443133.553   1190119.553  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0 Some college/university study without earning a degree:1.0:4.0    -77220.0    1.0  -1689751.6184  1535311.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0 Some college/university study without earning a degree:2.0:2.0    237516.0    1.0   -1079110.553   1554142.553  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0 Some college/university study without earning a degree:4.0:4.0    683916.0 0.8701   -928615.6184  2296447.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:3.0 Some college/university study without earning a degree:9.0:9.0     57516.0    1.0  -1555015.6184  1670047.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0    496896.0 0.9977  -1365095.1279  2358887.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0    -36186.5    1.0  -1648718.1184  1576345.1184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0    -37352.0    1.0  -1899343.1279  1824639.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0    -47389.0    1.0  -1909380.1279  1814602.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    649392.0  0.967  -1212599.1279  2511383.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0 Some college/university study without earning a degree:1.0:1.0   1367808.0 0.2343   -494183.1279  3229799.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0 Some college/university study without earning a degree:1.0:3.0    -49287.0    1.0  -1661818.6184  1563244.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0 Some college/university study without earning a degree:1.0:4.0         0.0    1.0  -1861991.1279  1861991.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0 Some college/university study without earning a degree:2.0:2.0    314736.0    1.0  -1297795.6184  1927267.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0 Some college/university study without earning a degree:4.0:4.0    761136.0  0.896  -1100855.1279  2623127.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):3.0:5.0 Some college/university study without earning a degree:9.0:9.0    134736.0    1.0  -1727255.1279  1996727.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0   -533082.5 0.9792  -2145614.1184  1079449.1184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0   -534248.0 0.9948  -2396239.1279  1327743.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0   -544285.0 0.9937  -2406276.1279  1317706.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    152496.0    1.0  -1709495.1279  2014487.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0 Some college/university study without earning a degree:1.0:1.0    870912.0 0.7822   -991079.1279  2732903.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0 Some college/university study without earning a degree:1.0:3.0   -546183.0 0.9742  -2158714.6184  1066348.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0 Some college/university study without earning a degree:1.0:4.0   -496896.0 0.9977  -2358887.1279  1365095.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0 Some college/university study without earning a degree:2.0:2.0   -182160.0    1.0  -1794691.6184  1430371.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0 Some college/university study without earning a degree:4.0:4.0    264240.0    1.0  -1597751.1279  2126231.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:4.0 Some college/university study without earning a degree:9.0:9.0   -362160.0    1.0  -2224151.1279  1499831.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0     -1165.5    1.0  -1613697.1184  1611366.1184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0    -11202.5    1.0  -1623734.1184  1601329.1184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    685578.5 0.8683   -926953.1184  2298110.1184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0 Some college/university study without earning a degree:1.0:1.0   1403994.5 0.1069   -208537.1184  3016526.1184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0 Some college/university study without earning a degree:1.0:3.0    -13100.5    1.0   -1329727.053   1303526.053  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0 Some college/university study without earning a degree:1.0:4.0     36186.5    1.0  -1576345.1184  1648718.1184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0 Some college/university study without earning a degree:2.0:2.0    350922.5 0.9978    -965704.053   1667549.053  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0 Some college/university study without earning a degree:4.0:4.0    797322.5 0.7209   -815209.1184  2409854.1184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:6.0 Some college/university study without earning a degree:9.0:9.0    170922.5    1.0  -1441609.1184  1783454.1184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0    -10037.0    1.0  -1872028.1279  1851954.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    686744.0 0.9488  -1175247.1279  2548735.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0 Some college/university study without earning a degree:1.0:1.0   1405160.0 0.2093   -456831.1279  3267151.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0 Some college/university study without earning a degree:1.0:3.0    -11935.0    1.0  -1624466.6184  1600596.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0 Some college/university study without earning a degree:1.0:4.0     37352.0    1.0  -1824639.1279  1899343.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0 Some college/university study without earning a degree:2.0:2.0    352088.0 0.9998  -1260443.6184  1964619.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0 Some college/university study without earning a degree:4.0:4.0    798488.0 0.8616  -1063503.1279  2660479.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):4.0:9.0 Some college/university study without earning a degree:9.0:9.0    172088.0    1.0  -1689903.1279  2034079.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0          Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0    696781.0 0.9429  -1165210.1279  2558772.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0 Some college/university study without earning a degree:1.0:1.0   1415197.0  0.203   -446794.1279  3277188.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0 Some college/university study without earning a degree:1.0:3.0     -1898.0    1.0  -1614429.6184  1610633.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0 Some college/university study without earning a degree:1.0:4.0     47389.0    1.0  -1814602.1279  1909380.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0 Some college/university study without earning a degree:2.0:2.0    362125.0 0.9998  -1250406.6184  1974656.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0 Some college/university study without earning a degree:4.0:4.0    808525.0 0.8515  -1053466.1279  2670516.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:5.0 Some college/university study without earning a degree:9.0:9.0    182125.0    1.0  -1679866.1279  2044116.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0 Some college/university study without earning a degree:1.0:1.0    718416.0  0.929  -1143575.1279  2580407.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0 Some college/university study without earning a degree:1.0:3.0   -698679.0 0.8533  -2311210.6184   913852.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0 Some college/university study without earning a degree:1.0:4.0   -649392.0  0.967  -2511383.1279  1212599.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0 Some college/university study without earning a degree:2.0:2.0   -334656.0 0.9999  -1947187.6184  1277875.6184  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0 Some college/university study without earning a degree:4.0:4.0    111744.0    1.0  -1750247.1279  1973735.1279  False\n",
+      "         Master’s degree (MA, MS, M.Eng., MBA, etc.):5.0:6.0 Some college/university study without earning a degree:9.0:9.0   -514656.0 0.9966  -2376647.1279  1347335.1279  False\n",
+      "Some college/university study without earning a degree:1.0:1.0 Some college/university study without earning a degree:1.0:3.0  -1417095.0 0.1019  -3029626.6184   195436.6184  False\n",
+      "Some college/university study without earning a degree:1.0:1.0 Some college/university study without earning a degree:1.0:4.0  -1367808.0 0.2343  -3229799.1279   494183.1279  False\n",
+      "Some college/university study without earning a degree:1.0:1.0 Some college/university study without earning a degree:2.0:2.0  -1053072.0 0.3632  -2665603.6184   559459.6184  False\n",
+      "Some college/university study without earning a degree:1.0:1.0 Some college/university study without earning a degree:4.0:4.0   -606672.0 0.9817  -2468663.1279  1255319.1279  False\n",
+      "Some college/university study without earning a degree:1.0:1.0 Some college/university study without earning a degree:9.0:9.0  -1233072.0 0.3464  -3095063.1279   628919.1279  False\n",
+      "Some college/university study without earning a degree:1.0:3.0 Some college/university study without earning a degree:1.0:4.0     49287.0    1.0  -1563244.6184  1661818.6184  False\n",
+      "Some college/university study without earning a degree:1.0:3.0 Some college/university study without earning a degree:2.0:2.0    364023.0 0.9966    -952603.553   1680649.553  False\n",
+      "Some college/university study without earning a degree:1.0:3.0 Some college/university study without earning a degree:4.0:4.0    810423.0 0.7015   -802108.6184  2422954.6184  False\n",
+      "Some college/university study without earning a degree:1.0:3.0 Some college/university study without earning a degree:9.0:9.0    184023.0    1.0  -1428508.6184  1796554.6184  False\n",
+      "Some college/university study without earning a degree:1.0:4.0 Some college/university study without earning a degree:2.0:2.0    314736.0    1.0  -1297795.6184  1927267.6184  False\n",
+      "Some college/university study without earning a degree:1.0:4.0 Some college/university study without earning a degree:4.0:4.0    761136.0  0.896  -1100855.1279  2623127.1279  False\n",
+      "Some college/university study without earning a degree:1.0:4.0 Some college/university study without earning a degree:9.0:9.0    134736.0    1.0  -1727255.1279  1996727.1279  False\n",
+      "Some college/university study without earning a degree:2.0:2.0 Some college/university study without earning a degree:4.0:4.0    446400.0 0.9965  -1166131.6184  2058931.6184  False\n",
+      "Some college/university study without earning a degree:2.0:2.0 Some college/university study without earning a degree:9.0:9.0   -180000.0    1.0  -1792531.6184  1432531.6184  False\n",
+      "Some college/university study without earning a degree:4.0:4.0 Some college/university study without earning a degree:9.0:9.0   -626400.0 0.9757  -2488391.1279  1235591.1279  False\n",
+      "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "import statsmodels.api as sm\n",
+    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
+    "\n",
+    "# Post-hoc test for the first significant interaction\n",
+    "interaction1 = df_new.assign(interaction1=df_new['ProfCodOrdinal'].astype(str) + ':' + df_new['CodOrdinal'].astype(str) + ':' + df_new['UndergradMajor'].astype(str))\n",
+    "tukey1 = pairwise_tukeyhsd(endog=df_new['NetSalary'], groups=interaction1['interaction1'], alpha=0.05)\n",
+    "print(tukey1)\n",
+    "\n",
+    "# Post-hoc test for the second significant interaction\n",
+    "interaction2 = df_new.assign(interaction2=df_new['FormalEducation'].astype(str) + ':' + df_new['ProfCodOrdinal'].astype(str) + ':' + df_new['CodOrdinal'].astype(str))\n",
+    "tukey2 = pairwise_tukeyhsd(endog=df_new['NetSalary'], groups=interaction2['interaction2'], alpha=0.05)\n",
+    "print(tukey2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 6513.25x500 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Check the unique values of UndergradMajor\n",
+    "unique_majors = df_new['UndergradMajor'].unique()\n",
+    "num_unique_majors = len(unique_majors)\n",
+    "\n",
+    "# Create enough markers and linestyles to cover all unique values\n",
+    "markers = ['o', 's', 'D', 'v', '^', '<', '>', 'P', '*', 'X'][:num_unique_majors]\n",
+    "linestyles = ['-', '--', ':', '-.', (0, (3, 5, 1, 5)), (0, (5, 10))][:num_unique_majors]\n",
+    "\n",
+    "# Plot\n",
+    "g = sns.FacetGrid(df_new, col=\"CodOrdinal\", height=5, aspect=1.5)\n",
+    "g.map_dataframe(sns.pointplot, x=\"ProfCodOrdinal\", y=\"NetSalary\", hue=\"UndergradMajor\", dodge=True, markers=markers, linestyles=linestyles)\n",
+    "g.add_legend()\n",
+    "g.set_axis_labels(\"ProfCodOrdinal\", \"NetSalary\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Educational Attainment:\n",
+    "\n",
+    "A Bachelor’s degree generally appears to be more financially beneficial compared to a Master’s degree or some college/university study without a degree in this dataset since it's consistently used as the reference point.\n",
+    "\n",
+    "Field of Study:\n",
+    "\n",
+    "Fields such as business, computer science, and engineering might offer better financial outcomes compared to social sciences, natural sciences, and humanities.\n",
+    "\n",
+    "Social Sciences: These fields showed significant negative differences, and in the scatter plots, individuals in these fields would likely appear lower on the financial scale.\n",
+    "\n",
+    "Business and Computer Science: These fields generally had better outcomes, so in the scatter plots, these dots would be higher, indicating better financial metrics.\n",
+    "\n",
+    "Natural Sciences and Humanities: Showed mixed or negative outcomes, likely appearing lower in the scatter plots.\n",
+    "\n",
+    "Coding Experience:\n",
+    "\n",
+    "Early-career professionals might face challenges in achieving similar financial outcomes as those with more experience.\n",
+    "\n",
+    "1-2 Years: Significant clustering with generally lower financial outcomes, reflected by a concentration of dots in the lower part of the scatter plots.\n",
+    "\n",
+    "Higher Experience Levels: More spread out and possibly higher dots, indicating variability and potential improvement in financial metrics with more experience."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "No     51\n",
+       "Yes    48\n",
+       "Name: OpenSource, dtype: int64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['OpenSource'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "No                77\n",
+       "Yes, full-time    16\n",
+       "Yes, part-time     5\n",
+       "Name: Student, dtype: int64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Student'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Employed full-time    91\n",
+       "Employed part-time     8\n",
+       "Name: Employment, dtype: int64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Employment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scatter plot with linear regression line for Net Salary vs. OpenSourceOrdinal\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.regplot(x='OpenSourceOrdinal', y='NetSalary', data=df2, scatter_kws={'s':50}, line_kws={'color':'red'})\n",
+    "plt.title('Net Salary vs. Open Source Involvement (Ordinal)')\n",
+    "plt.xlabel('Open Source Involvement (Ordinal)')\n",
+    "plt.ylabel('Net Salary')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# Scatter plot with linear regression line for Net Salary vs. StudentOrdinal\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.regplot(x='StudentOrdinal', y='NetSalary', data=df2, scatter_kws={'s':50}, line_kws={'color':'red'})\n",
+    "plt.title('Net Salary vs. Student Status (Ordinal)')\n",
+    "plt.xlabel('Student Status (Ordinal)')\n",
+    "plt.ylabel('Net Salary')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# Scatter plot with linear regression line for Net Salary vs. EmploymentOrdinal\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.regplot(x='EmploymentOrdinal', y='NetSalary', data=df2, scatter_kws={'s':50}, line_kws={'color':'red'})\n",
+    "plt.title('Net Salary vs. Employment Status (Ordinal)')\n",
+    "plt.xlabel('Employment Status (Ordinal)')\n",
+    "plt.ylabel('Net Salary')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                   OpenSourceOrdinal  StudentOrdinal  EmploymentOrdinal  \\\n",
+      "OpenSourceOrdinal           1.000000        0.215666          -0.164399   \n",
+      "StudentOrdinal              0.215666        1.000000          -0.283642   \n",
+      "EmploymentOrdinal          -0.164399       -0.283642           1.000000   \n",
+      "NetSalary                  -0.179206       -0.051045           0.037159   \n",
+      "\n",
+      "                   NetSalary  \n",
+      "OpenSourceOrdinal  -0.179206  \n",
+      "StudentOrdinal     -0.051045  \n",
+      "EmploymentOrdinal   0.037159  \n",
+      "NetSalary           1.000000  \n",
+      "                                                          sum_sq    df  \\\n",
+      "C(NetSalary)                                       -1.216923e+14  33.0   \n",
+      "C(OpenSourceOrdinal)                                         NaN   1.0   \n",
+      "C(StudentOrdinal)                                            NaN   2.0   \n",
+      "C(EmploymentOrdinal)                                         NaN   1.0   \n",
+      "C(NetSalary):C(OpenSourceOrdinal)                   5.153163e+12  33.0   \n",
+      "C(NetSalary):C(StudentOrdinal)                     -3.975959e+12  66.0   \n",
+      "C(OpenSourceOrdinal):C(StudentOrdinal)                       NaN   2.0   \n",
+      "C(NetSalary):C(EmploymentOrdinal)                   1.442413e+15  33.0   \n",
+      "C(OpenSourceOrdinal):C(EmploymentOrdinal)                    NaN   1.0   \n",
+      "C(StudentOrdinal):C(EmploymentOrdinal)                       NaN   2.0   \n",
+      "C(NetSalary):C(OpenSourceOrdinal):C(StudentOrdi...  3.421886e+12  66.0   \n",
+      "C(NetSalary):C(OpenSourceOrdinal):C(EmploymentO...  2.506598e+12  33.0   \n",
+      "C(NetSalary):C(StudentOrdinal):C(EmploymentOrdi...  2.494184e+12  66.0   \n",
+      "C(OpenSourceOrdinal):C(StudentOrdinal):C(Employ...           NaN   2.0   \n",
+      "C(NetSalary):C(OpenSourceOrdinal):C(StudentOrdi...  5.013196e+12  66.0   \n",
+      "Residual                                            3.758407e-15   3.0   \n",
+      "\n",
+      "                                                               F        PR(>F)  \n",
+      "C(NetSalary)                                       -2.943516e+27  1.000000e+00  \n",
+      "C(OpenSourceOrdinal)                                         NaN           NaN  \n",
+      "C(StudentOrdinal)                                            NaN           NaN  \n",
+      "C(EmploymentOrdinal)                                         NaN           NaN  \n",
+      "C(NetSalary):C(OpenSourceOrdinal)                   1.246457e+26  1.320142e-39  \n",
+      "C(NetSalary):C(StudentOrdinal)                     -4.808564e+25  1.000000e+00  \n",
+      "C(OpenSourceOrdinal):C(StudentOrdinal)                       NaN           NaN  \n",
+      "C(NetSalary):C(EmploymentOrdinal)                   3.488937e+28  3.384005e-43  \n",
+      "C(OpenSourceOrdinal):C(EmploymentOrdinal)                    NaN           NaN  \n",
+      "C(StudentOrdinal):C(EmploymentOrdinal)                       NaN           NaN  \n",
+      "C(NetSalary):C(OpenSourceOrdinal):C(StudentOrdi...  4.138462e+25  6.900465e-39  \n",
+      "C(NetSalary):C(OpenSourceOrdinal):C(EmploymentO...  6.063008e+25  3.891389e-39  \n",
+      "C(NetSalary):C(StudentOrdinal):C(EmploymentOrdi...  3.016490e+25  1.108879e-38  \n",
+      "C(OpenSourceOrdinal):C(StudentOrdinal):C(Employ...           NaN           NaN  \n",
+      "C(NetSalary):C(OpenSourceOrdinal):C(StudentOrdi...  6.063008e+25  3.891389e-39  \n",
+      "Residual                                                     NaN           NaN  \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 33, but rank is 1\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 1, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1900: RuntimeWarning: invalid value encountered in divide\n",
+      "  F /= J\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 2, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 1, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 33, but rank is 2\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 66, but rank is 2\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 2, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 33, but rank is 1\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 1, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 2, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 66, but rank is 2\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 33, but rank is 2\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 66, but rank is 2\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 2, but rank is 0\n",
+      "  warnings.warn('covariance of constraints does not have full '\n",
+      "C:\\Users\\Nidhi Satyapriya\\anaconda3\\Lib\\site-packages\\statsmodels\\base\\model.py:1871: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 66, but rank is 2\n",
+      "  warnings.warn('covariance of constraints does not have full '\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Creating ordinal columns in df2\n",
+    "df2['OpenSourceOrdinal'] = df['OpenSource'].map({'No': 0, 'Yes': 1})\n",
+    "df2['StudentOrdinal'] = df['Student'].map({'No': 0, 'Yes, part-time': 1, 'Yes, full-time': 2})\n",
+    "df2['EmploymentOrdinal'] = df['Employment'].map({'Employed part-time': 0, 'Employed full-time': 1})\n",
+    "\n",
+    "# Checking the relations with NetSalary\n",
+    "# Using correlation\n",
+    "print(df2[['OpenSourceOrdinal', 'StudentOrdinal', 'EmploymentOrdinal', 'NetSalary']].corr())\n",
+    "\n",
+    "# Using ANOVA\n",
+    "import statsmodels.api as sm\n",
+    "from statsmodels.formula.api import ols\n",
+    "from statsmodels.stats.anova import anova_lm\n",
+    "\n",
+    "# Define the model\n",
+    "model = ols('NetSalary ~ C(NetSalary)*C(OpenSourceOrdinal)* C(StudentOrdinal)* C(EmploymentOrdinal)', data=df2).fit()\n",
+    "\n",
+    "# Perform ANOVA\n",
+    "anova_results = anova_lm(model, typ=2)\n",
+    "print(anova_results)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Overall Interpretation\n",
+    "\n",
+    "\n",
+    "The main effects of OpenSourceOrdinal, StudentOrdinal, and EmploymentOrdinal individually may not be significant, but their interactions, especially involving NetSalary, are highly significant.\n",
+    "\n",
+    "OpenSourceOrdinal:\n",
+    "\n",
+    "Positive Correlation with StudentOrdinal (0.2157): Indicates that students are more likely to participate in open-source activities. This could be because students might have more time or incentives (like course projects) to engage in open-source projects.\n",
+    "\n",
+    "Negative Correlation with EmploymentOrdinal (-0.1644): Suggests that those more involved in open-source projects may not be as highly employed or may be employed in roles that value open-source contributions less.\n",
+    "\n",
+    "Negative Correlation with NetSalary (-0.1792): Indicates that higher involvement in open-source activities might be associated with lower net salaries. This could imply that individuals heavily involved in open-source work might prioritize other factors over salary, such as learning, community contribution, or working in lower-paying but more flexible or mission-driven roles.\n",
+    "\n",
+    "StudentOrdinal:\n",
+    "\n",
+    "Negative Correlation with EmploymentOrdinal (-0.2836): Indicates that higher student status is associated with lower employment status, which is expected since full-time students are less likely to be fully employed.\n",
+    "\n",
+    "Weak Negative Correlation with NetSalary (-0.0510): Suggests that being a student has a slight negative impact on net salary, which makes sense as students typically have lower earnings compared to fully employed individuals.\n",
+    "\n",
+    "EmploymentOrdinal:\n",
+    "\n",
+    "Weak Positive Correlation with NetSalary (0.0372): Suggests that there is a slight positive relationship between employment status and net salary. This is intuitive, as higher employment status typically correlates with higher earnings.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Final Thoughts\n",
+    "\n",
+    "\n",
+    "This analysis highlights the complex relationship between education, experience, and salary. While higher education generally leads to higher earnings, specific skills, experience, and industry demand play crucial roles in determining individual salary outcomes. Continuous skill development and strategic career transitions are key to maximizing earning potential in a dynamic job market."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Key Insights\n",
+    "\n",
+    "1) Based on general trends  and repeated patterns, it is clear that pursuing a Bachelor’s degree (BA, BS, B.Eng., etc.) is the most beneficial Formal Education. It offers a good median, shows a wide range of salaries with some outliers of high salaries indicating people with this degree coupled with some other unique skills and experience can earn considerably higher salaries than the rest.\n",
+    "2) Doing a Master's degree can a considerate hike to the salary, however it is not as much as one expects.\n",
+    "3) People without a professional degree but with relevant skills and experience are making huge money, highlighting the importance of gaining real-world problem-solving skills.\n",
+    "4) However, FormalEducation alone is not significant to determine the salaries of the individual. 'UnderGradMajor' and combined interactions of 'ProfCoding'-'CodingYears' and 'Student'-'YearsCoding'-'YearsCodingProf' significantly impact the salary.\n",
+    "5) 'Business Discipline' , 'Engineering and Computer Science' are the most rewarding majors. Social Science offers the highest median, however, due to lack of data, it is unsafe to claim this.\n",
+    "6) Coding Experience: Early-career professionals might face challenges in achieving similar financial outcomes as those with more experience.\n",
+    "\n",
+    "    a) 1-2 Years: Significant clustering with generally lower financial outcomes, reflected by a concentration of dots in the   lower part of the scatter plots.\n",
+    "    \n",
+    "    b) Higher Experience Levels: More spread out and possibly higher dots, indicating variability and potential improvement in financial metrics with more experience.\n",
+    "7) Thus, we can conclude that the salary doesn't only depend upon the Higher Education of an individual, but also on the current market trends and industrial needs and experience. Based on these several parameters, an insight into the Salary of individuals have been provided."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
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